diff --git a/build/package.json b/build/package.json
index a570de9..73ebb2f 100644
--- a/build/package.json
+++ b/build/package.json
@@ -1,6 +1,6 @@
 {
     "name": "@vladmandic/face-api",
-    "version": "0.7.4",
+    "version": "0.8.1",
     "description": "JavaScript module for Face Detection and Face Recognition Using Tensorflow/JS",
     "main": "dist/face-api.cjs",
     "module": "dist/face-api.esm.js",
diff --git a/build/src/faceFeatureExtractor/FaceFeatureExtractor.js b/build/src/faceFeatureExtractor/FaceFeatureExtractor.js
index ab01e38..c4551a6 100644
--- a/build/src/faceFeatureExtractor/FaceFeatureExtractor.js
+++ b/build/src/faceFeatureExtractor/FaceFeatureExtractor.js
@@ -15,7 +15,7 @@ export class FaceFeatureExtractor extends NeuralNetwork {
             throw new Error('FaceFeatureExtractor - load model before inference');
         }
         return tf.tidy(() => {
-            const batchTensor = input.toBatchTensor(112, true);
+            const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
             const meanRgb = [122.782, 117.001, 104.298];
             const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255));
             let out = denseBlock4(normalized, params.dense0, true);
diff --git a/build/src/faceFeatureExtractor/FaceFeatureExtractor.js.map b/build/src/faceFeatureExtractor/FaceFeatureExtractor.js.map
index 03286b3..5aa3c9c 100644
--- a/build/src/faceFeatureExtractor/FaceFeatureExtractor.js.map
+++ b/build/src/faceFeatureExtractor/FaceFeatureExtractor.js.map
@@ -1 +1 @@
-{"version":3,"file":"FaceFeatureExtractor.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/FaceFeatureExtractor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,WAAW,EAAE,MAAM,cAAc,CAAC;AAC3C,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAChD,OAAO,EAAE,0BAA0B,EAAE,MAAM,8BAA8B,CAAC;AAG1E,MAAM,OAAO,oBAAqB,SAAQ,aAAyC;IAEjF;QACE,KAAK,CAAC,sBAAsB,CAAC,CAAA;IAC/B,CAAC;IAEM,YAAY,CAAC,KAAe;QAEjC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,oDAAoD,CAAC,CAAA;SACtE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;YAClD,MAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,MAAM,UAAU,GAAG,SAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,WAAW,CAAC,UAAU,EAAE,MAAM,CAAC,MAAM,EAAE,IAAI,CAAC,CAAA;YACtD,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;YAE9C,OAAO,GAAG,CAAA;QACZ,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAES,mBAAmB;QAC3B,OAAO,8BAA8B,CAAA;IACvC,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,0BAA0B,CAAC,SAAS,CAAC,CAAA;IAC9C,CAAC;IAES,aAAa,CAAC,OAAqB;QAC3C,OAAO,aAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;CACF"}
\ No newline at end of file
+{"version":3,"file":"FaceFeatureExtractor.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/FaceFeatureExtractor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,WAAW,EAAE,MAAM,cAAc,CAAC;AAC3C,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAChD,OAAO,EAAE,0BAA0B,EAAE,MAAM,8BAA8B,CAAC;AAG1E,MAAM,OAAO,oBAAqB,SAAQ,aAAyC;IAEjF;QACE,KAAK,CAAC,sBAAsB,CAAC,CAAA;IAC/B,CAAC;IAEM,YAAY,CAAC,KAAe;QAEjC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,oDAAoD,CAAC,CAAA;SACtE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,WAAW,GAAG,EAAE,CAAC,IAAI,CAAC,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,EAAE,SAAS,CAAC,CAAC;YACvE,MAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,MAAM,UAAU,GAAG,SAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,WAAW,CAAC,UAAU,EAAE,MAAM,CAAC,MAAM,EAAE,IAAI,CAAC,CAAA;YACtD,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;YAE9C,OAAO,GAAG,CAAA;QACZ,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAES,mBAAmB;QAC3B,OAAO,8BAA8B,CAAA;IACvC,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,0BAA0B,CAAC,SAAS,CAAC,CAAA;IAC9C,CAAC;IAES,aAAa,CAAC,OAAqB;QAC3C,OAAO,aAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js b/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js
index cd59a41..b43da07 100644
--- a/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js
+++ b/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js
@@ -15,7 +15,7 @@ export class TinyFaceFeatureExtractor extends NeuralNetwork {
             throw new Error('TinyFaceFeatureExtractor - load model before inference');
         }
         return tf.tidy(() => {
-            const batchTensor = input.toBatchTensor(112, true);
+            const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
             const meanRgb = [122.782, 117.001, 104.298];
             const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255));
             let out = denseBlock3(normalized, params.dense0, true);
diff --git a/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js.map b/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js.map
index 44dca6e..e56255a 100644
--- a/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js.map
+++ b/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js.map
@@ -1 +1 @@
-{"version":3,"file":"TinyFaceFeatureExtractor.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,WAAW,EAAE,MAAM,cAAc,CAAC;AAC3C,OAAO,EAAE,8BAA8B,EAAE,MAAM,kCAAkC,CAAC;AAClF,OAAO,EAAE,iBAAiB,EAAE,MAAM,qBAAqB,CAAC;AAGxD,MAAM,OAAO,wBAAyB,SAAQ,aAA6C;IAEzF;QACE,KAAK,CAAC,0BAA0B,CAAC,CAAA;IACnC,CAAC;IAEM,YAAY,CAAC,KAAe;QAEjC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,wDAAwD,CAAC,CAAA;SAC1E;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;YAClD,MAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,MAAM,UAAU,GAAG,SAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,WAAW,CAAC,UAAU,EAAE,MAAM,CAAC,MAAM,EAAE,IAAI,CAAC,CAAA;YACtD,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;YAEhD,OAAO,GAAG,CAAA;QACZ,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAES,mBAAmB;QAC3B,OAAO,mCAAmC,CAAA;IAC5C,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,8BAA8B,CAAC,SAAS,CAAC,CAAA;IAClD,CAAC;IAES,aAAa,CAAC,OAAqB;QAC3C,OAAO,iBAAiB,CAAC,OAAO,CAAC,CAAA;IACnC,CAAC;CACF"}
\ No newline at end of file
+{"version":3,"file":"TinyFaceFeatureExtractor.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,WAAW,EAAE,MAAM,cAAc,CAAC;AAC3C,OAAO,EAAE,8BAA8B,EAAE,MAAM,kCAAkC,CAAC;AAClF,OAAO,EAAE,iBAAiB,EAAE,MAAM,qBAAqB,CAAC;AAGxD,MAAM,OAAO,wBAAyB,SAAQ,aAA6C;IAEzF;QACE,KAAK,CAAC,0BAA0B,CAAC,CAAA;IACnC,CAAC;IAEM,YAAY,CAAC,KAAe;QAEjC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,wDAAwD,CAAC,CAAA;SAC1E;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,WAAW,GAAG,EAAE,CAAC,IAAI,CAAC,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,EAAE,SAAS,CAAC,CAAC;YACvE,MAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,MAAM,UAAU,GAAG,SAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,WAAW,CAAC,UAAU,EAAE,MAAM,CAAC,MAAM,EAAE,IAAI,CAAC,CAAA;YACtD,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;YAEhD,OAAO,GAAG,CAAA;QACZ,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAES,mBAAmB;QAC3B,OAAO,mCAAmC,CAAA;IAC5C,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,8BAA8B,CAAC,SAAS,CAAC,CAAA;IAClD,CAAC;IAES,aAAa,CAAC,OAAqB;QAC3C,OAAO,iBAAiB,CAAC,OAAO,CAAC,CAAA;IACnC,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/faceLandmarkNet/FaceLandmark68NetBase.js b/build/src/faceLandmarkNet/FaceLandmark68NetBase.js
index db71f0a..fa494c6 100644
--- a/build/src/faceLandmarkNet/FaceLandmark68NetBase.js
+++ b/build/src/faceLandmarkNet/FaceLandmark68NetBase.js
@@ -15,10 +15,7 @@ export class FaceLandmark68NetBase extends FaceProcessor {
         });
         const batchSize = inputDimensions.length;
         return tf.tidy(() => {
-            const createInterleavedTensor = (fillX, fillY) => tf.stack([
-                tf.fill([68], fillX),
-                tf.fill([68], fillY)
-            ], 1).as2D(1, 136).as1D();
+            const createInterleavedTensor = (fillX, fillY) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D();
             const getPadding = (batchIdx, cond) => {
                 const { width, height } = inputDimensions[batchIdx];
                 return cond(width, height) ? Math.abs(width - height) / 2 : 0;
@@ -26,7 +23,7 @@ export class FaceLandmark68NetBase extends FaceProcessor {
             const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h);
             const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w);
             const landmarkTensors = output
-                .mul(tf.fill([batchSize, 136], inputSize))
+                .mul(tf.fill([batchSize, 136], inputSize, 'float32'))
                 .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx)))))
                 .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
             return landmarkTensors;
diff --git a/build/src/faceLandmarkNet/FaceLandmark68NetBase.js.map b/build/src/faceLandmarkNet/FaceLandmark68NetBase.js.map
index aa67b20..7f353bd 100644
--- a/build/src/faceLandmarkNet/FaceLandmark68NetBase.js.map
+++ b/build/src/faceLandmarkNet/FaceLandmark68NetBase.js.map
@@ -1 +1 @@
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\ No newline at end of file
+{"version":3,"file":"FaceLandmark68NetBase.js","sourceRoot":"","sources":["../../../src/faceLandmarkNet/FaceLandmark68NetBase.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAe,KAAK,EAAE,MAAM,YAAY,CAAC;AAChD,OAAO,EAAE,eAAe,EAAE,MAAM,4BAA4B,CAAC;AAC7D,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AAEzD,OAAO,EAAE,aAAa,EAAE,MAAM,gCAAgC,CAAC;AAC/D,OAAO,EAAE,MAAM,EAAE,MAAM,UAAU,CAAC;AAElC,MAAM,OAAgB,qBAGpB,SAAQ,aAA+B;IAEhC,WAAW,CAAC,MAAmB,EAAE,SAAiB,EAAE,kBAAiC;QAE1F,MAAM,eAAe,GAAG,kBAAkB,CAAC,GAAG,CAAC,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,EAAE,EAAE;YACnE,MAAM,KAAK,GAAG,SAAS,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,EAAE,KAAK,CAAC,CAAA;YACjD,OAAO;gBACL,KAAK,EAAE,KAAK,GAAG,KAAK;gBACpB,MAAM,EAAE,MAAM,GAAG,KAAK;aACvB,CAAA;QACH,CAAC,CAAC,CAAA;QAEF,MAAM,SAAS,GAAG,eAAe,CAAC,MAAM,CAAA;QAExC,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,uBAAuB,GAAG,CAAC,KAAa,EAAE,KAAa,EAAE,EAAE,CAC/D,EAAE,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,SAAS,CAAC,EAAE,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,SAAS,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,IAAI,EAAE,CAAA;YAErG,MAAM,UAAU,GAAG,CAAC,QAAgB,EAAE,IAAuC,EAAU,EAAE;gBACvF,MAAM,EAAE,KAAK,EAAE,MAAM,EAAE,GAAG,eAAe,CAAC,QAAQ,CAAC,CAAA;gBACnD,OAAO,IAAI,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,KAAK,GAAG,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;YAC/D,CAAC,CAAA;YACD,MAAM,WAAW,GAAG,CAAC,QAAgB,EAAE,EAAE,CAAC,UAAU,CAAC,QAAQ,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,GAAG,CAAC,CAAC,CAAA;YAC/E,MAAM,WAAW,GAAG,CAAC,QAAgB,EAAE,EAAE,CAAC,UAAU,CAAC,QAAQ,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,GAAG,CAAC,CAAC,CAAA;YAE/E,MAAM,eAAe,GAAG,MAAM;iBAC3B,GAAG,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,SAAS,EAAE,GAAG,CAAC,EAAE,SAAS,EAAE,SAAS,CAAC,CAAC;iBACpD,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,EAAE,QAAQ,EAAE,EAAE,CACzD,uBAAuB,CACrB,WAAW,CAAC,QAAQ,CAAC,EACrB,WAAW,CAAC,QAAQ,CAAC,CACtB,CACF,CAAC,CAAC;iBACF,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,EAAE,QAAQ,EAAE,EAAE,CACzD,uBAAuB,CACrB,eAAe,CAAC,QAAQ,CAAC,CAAC,KAAK,EAC/B,eAAe,CAAC,QAAQ,CAAC,CAAC,MAAM,CACjC,CACF,CAAC,CAAC,CAAA;YAEL,OAAO,eAA8B,CAAA;QACvC,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,YAAY,CAAC,KAAe;QACjC,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,GAAG,GAAG,IAAI,CAAC,MAAM,CAAC,KAAK,CAAC,CAAA;YAC9B,OAAO,IAAI,CAAC,WAAW,CACrB,GAAG,EACH,KAAK,CAAC,SAAmB,EACzB,KAAK,CAAC,eAAe,CAAC,GAAG,CAAC,CAAC,CAAC,MAAM,EAAE,KAAK,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,MAAM,EAAE,KAAK,EAAE,CAAC,CAAC,CACpE,CAAA;QACH,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAEM,KAAK,CAAC,eAAe,CAAC,KAAgB;QAC3C,MAAM,QAAQ,GAAG,MAAM,UAAU,CAAC,KAAK,CAAC,CAAA;QACxC,MAAM,eAAe,GAAG,EAAE,CAAC,IAAI,CAC7B,GAAG,EAAE,CAAC,EAAE,CAAC,OAAO,CAAC,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,CAC9C,CAAA;QAED,MAAM,iBAAiB,GAAG,MAAM,OAAO,CAAC,GAAG,CAAC,eAAe,CAAC,GAAG,CAC7D,KAAK,EAAE,cAAc,EAAE,QAAQ,EAAE,EAAE;YACjC,MAAM,cAAc,GAAG,KAAK,CAAC,IAAI,CAAC,MAAM,cAAc,CAAC,IAAI,EAAE,CAAC,CAAA;YAC9D,MAAM,OAAO,GAAG,cAAc,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAA;YAC1D,MAAM,OAAO,GAAG,cAAc,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAA;YAE3D,OAAO,IAAI,eAAe,CACxB,KAAK,CAAC,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,IAAI,KAAK,CAAC,OAAO,CAAC,CAAC,CAAW,EAAE,OAAO,CAAC,CAAC,CAAW,CAAC,CAAC,EACtF;gBACE,MAAM,EAAE,QAAQ,CAAC,cAAc,CAAC,QAAQ,CAAC;gBACzC,KAAK,EAAG,QAAQ,CAAC,aAAa,CAAC,QAAQ,CAAC;aACzC,CACF,CAAA;QACH,CAAC,CACF,CAAC,CAAA;QAEF,eAAe,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,OAAO,EAAE,CAAC,CAAA;QAEzC,OAAO,QAAQ,CAAC,YAAY,CAAC,CAAC,CAAC,iBAAsC,CAAC,CAAC,CAAC,iBAAiB,CAAC,CAAC,CAAoB,CAAC;IAClH,CAAC;IAES,wBAAwB;QAChC,OAAO,GAAG,CAAA;IACZ,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/ops/normalize.js b/build/src/ops/normalize.js
index 155051c..2f94096 100644
--- a/build/src/ops/normalize.js
+++ b/build/src/ops/normalize.js
@@ -2,9 +2,9 @@ import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
 export function normalize(x, meanRgb) {
     return tf.tidy(() => {
         const [r, g, b] = meanRgb;
-        const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r);
-        const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g);
-        const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b);
+        const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');
+        const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');
+        const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');
         const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);
         return tf.sub(x, avg_rgb);
     });
diff --git a/build/src/ops/normalize.js.map b/build/src/ops/normalize.js.map
index c9811f4..c197d3f 100644
--- a/build/src/ops/normalize.js.map
+++ b/build/src/ops/normalize.js.map
@@ -1 +1 @@
-{"version":3,"file":"normalize.js","sourceRoot":"","sources":["../../../src/ops/normalize.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,MAAM,UAAU,SAAS,CAAC,CAAc,EAAE,OAAiB;IACzD,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,OAAO,CAAA;QACzB,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;QACrD,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;QACrD,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;QACrD,MAAM,OAAO,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,KAAK,EAAE,KAAK,EAAE,KAAK,CAAC,EAAE,CAAC,CAAC,CAAA;QAEnD,OAAO,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;IAC3B,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
+{"version":3,"file":"normalize.js","sourceRoot":"","sources":["../../../src/ops/normalize.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,MAAM,UAAU,SAAS,CAAC,CAAc,EAAE,OAAiB;IACzD,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,OAAO,CAAA;QACzB,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,SAAS,CAAC,CAAA;QAChE,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,SAAS,CAAC,CAAA;QAChE,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,SAAS,CAAC,CAAA;QAChE,MAAM,OAAO,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,KAAK,EAAE,KAAK,EAAE,KAAK,CAAC,EAAE,CAAC,CAAC,CAAA;QAEnD,OAAO,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;IAC3B,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/ops/padToSquare.js b/build/src/ops/padToSquare.js
index 3f01e61..f544b9f 100644
--- a/build/src/ops/padToSquare.js
+++ b/build/src/ops/padToSquare.js
@@ -19,7 +19,7 @@ export function padToSquare(imgTensor, isCenterImage = false) {
         const createPaddingTensor = (paddingAmount) => {
             const paddingTensorShape = imgTensor.shape.slice();
             paddingTensorShape[paddingAxis] = paddingAmount;
-            return tf.fill(paddingTensorShape, 0);
+            return tf.fill(paddingTensorShape, 0, 'float32');
         };
         const paddingTensorAppend = createPaddingTensor(paddingAmount);
         const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
diff --git a/build/src/ops/padToSquare.js.map b/build/src/ops/padToSquare.js.map
index c2f85ef..184bd74 100644
--- a/build/src/ops/padToSquare.js.map
+++ b/build/src/ops/padToSquare.js.map
@@ -1 +1 @@
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\ No newline at end of file
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diff --git a/build/src/xception/TinyXception.js b/build/src/xception/TinyXception.js
index 9cb3ecb..3a6ce2e 100644
--- a/build/src/xception/TinyXception.js
+++ b/build/src/xception/TinyXception.js
@@ -35,7 +35,7 @@ export class TinyXception extends NeuralNetwork {
             throw new Error('TinyXception - load model before inference');
         }
         return tf.tidy(() => {
-            const batchTensor = input.toBatchTensor(112, true);
+            const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
             const meanRgb = [122.782, 117.001, 104.298];
             const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256));
             let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));
diff --git a/build/src/xception/TinyXception.js.map b/build/src/xception/TinyXception.js.map
index a08eb62..d7d1e30 100644
--- a/build/src/xception/TinyXception.js.map
+++ b/build/src/xception/TinyXception.js.map
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/dist/face-api.cjs b/dist/face-api.cjs
index d5a95e5..2043fa4 100644
--- a/dist/face-api.cjs
+++ b/dist/face-api.cjs
@@ -1,39 +1,39 @@
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tk=Object.freeze({__proto__:null,shuffle:Ly,clamp:Hl,nearestLargerEven:Sy,sum:iT,randUniform:KD,distSquared:XD,assert:k,assertShapesMatch:dt,assertNonNull:wo,flatten:Ji,sizeFromShape:we,isScalarShape:JD,arraysEqual:ot,isInt:Ut,tanh:ZD,sizeToSquarishShape:Sd,createShuffledIndices:QD,rightPad:Lo,repeatedTry:Iy,inferFromImplicitShape:Id,parseAxisParam:ft,squeezeShape:Rr,getTypedArrayFromDType:wn,getArrayFromDType:So,checkConversionForErrors:rT,isValidDtype:oT,hasEncodingLoss:xy,isTypedArray:Ln,bytesPerElement:Ty,bytesFromStringArray:aT,isString:Or,isBoolean:cT,isNumber:xd,inferDtype:Oa,isFunction:Er,nearestDivisor:Td,computeStrides:Ot,createScalarValue:lT,toTypedArray:Dr,toNestedArray:Ls,makeOnesTypedArray:Ay,makeZerosTypedArray:Ea,makeZerosNestedTypedArray:vy,now:qn,assertNonNegativeIntegerDimensions:Ny,fetch:uT,encodeString:Cy,decodeString:Yl,locToIndex:ti,indexToLoc:Da});class nk{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new ik)}profileKernel(e,t,n){let 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Buffer shape=${this.shape}`;throw new Error(i)}t++}let n=e[e.length-1];for(let s=0;s<e.length-1;++s)n+=this.strides[s]*e[s];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];const t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return vi().makeTensor(this.values,this.shape,this.dtype)}}let vi=null,Ea=null,mT=null;function lk(e){vi=e}function hk(e){Ea=e}function uk(e){mT=e}class Q{constructor(e,t,n,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=we(e),this.strides=Ot(e),this.dataId=n,this.id=s,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const e=await this.data();return Ea.buffer(this.shape,this.dtype,e)}bufferSync(){return Ea.buffer(this.shape,this.dtype,this.dataSync())}async array(){const e=await this.data();return Ls(this.shape,e)}arraySync(){return Ls(this.shape,this.dataSync())}async data(){this.throwIfDisposed();const e=vi().read(this.dataId);if(this.dtype==="string"){const t=await e;try{return t.map(n=>Yl(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();const e=vi().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Yl(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();const e=await vi().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){if(this.isDisposed)return;vi().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Ea.print(this,e)}clone(){return this.throwIfDisposed(),Ea.clone(this)}toString(e=!1){const t=this.dataSync();return ak(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Ea.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),vi().makeVariable(this,e,t,n)}}Object.defineProperty(Q,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});class Xl extends Q{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!ot(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);vi().disposeTensor(this),this.dataId=e.dataId,vi().incRef(this,null)}dispose(){vi().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(Xl,Symbol.hasInstance,{value:e=>e instanceof Q&&e.assign!=null&&e.assign instanceof Function});(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(r.Rank||(r.Rank={}));var Oy;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(Oy||(Oy={}));var Ey;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(Ey||(Ey={}));var Dy;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(Dy||(Dy={}));var ky;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(ky||(ky={}));const dk={float32:Dy,int32:Oy,bool:Ey,complex64:ky};function Cn(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return dk[e][t]}function vd(e){return Cn(e,"int32")}function Bt(e,t){if(e.dtype===t.dtype)return[e,t];const n=Cn(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function fT(e,t){k(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function Nd(e,t){return t.some(n=>n.id===e.id)}function Zi(e){const t=[],n=new Set;return gT(e,t,n),t}function gT(e,t,n){if(e==null)return;if(e instanceof Q){t.push(e);return}if(!pk(e))return;const s=e;for(const i in s){const o=s[i];n.has(o)||(n.add(o),gT(o,t,n))}}function pk(e){return Array.isArray(e)||typeof e=="object"}var mk=Object.freeze({__proto__:null,makeTypesMatch:Bt,assertTypesMatch:fT,isTensorInList:Nd,getTensorsInContainer:Zi});class yT{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class Jl{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new yT}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],s=await this.initializeBackend(n).success;if(s){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){const{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;const{success:t,asyncInit:n}=this.initializeBackend(e),s=n?await t:t;if(!s)return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new nk(this.backendInstance),!0}setupRegisteredKernels(){const e=wd(this.backendName);e.forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){const t=wd(e);t.forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){const t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{const n=t.factory();if(n&&!(n instanceof f)&&typeof n.then=="function"){const s=++this.pendingBackendInitId,i=n.then(o=>s<this.pendingBackendInitId?!1:(this.registry[e]=o,this.pendingBackendInit=null,!0)).catch(o=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(o.stack||o.message)),!1));return this.pendingBackendInit=i,{success:i,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],{success:s,asyncInit:i}=this.initializeBackend(n);if(i||s)return{name:n,asyncInit:i}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),s=n.backend,i=this.readSync(t);s.disposeData(t),n.backend=e,e.move(t,i,n.shape,n.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{const s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return Jl.nextTensorId++}nextVariableId(){return Jl.nextVariableId++}clone(e){const t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),n={x:e},s=o=>({x:()=>{const a="float32",c={x:o},u={dtype:a};return V.runKernelFunc(p=>p.cast(o,a),c,null,ul,u)}}),i=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,i,{}),t}runKernel(e,t,n,s,i){const o=null,a=null;return this.runKernelFunc(o,t,a,e,n,s,i)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const s=this.backend.numDataIds();let i=0;n.forEach(c=>{i+=c.dtype==="complex64"?3:1});const o=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=s-t-i-o;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${e}'`)}runKernelFunc(e,t,n,s,i,o,a){let c,u=[];const p=this.isTapeOn();s==null&&(s=this.state.activeScope!=null?this.state.activeScope.name:"");const m=this.state.numBytes,y=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let b;const w=yy(s,this.backendName);let I;if(w!=null)b=()=>{const v=this.backend.numDataIds();I=w.kernelFunc({inputs:t,attrs:i,backend:this.backend});const N=Array.isArray(I)?I:[I];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,v,N);const E=N.map(({dataId:D,shape:F,dtype:_})=>this.makeTensorFromDataId(D,F,_));if(p){let D=this.getTensorsForGradient(s,t,E);if(D==null){a==null&&(a=[]);const F=E.filter((_,B)=>a[B]);D=(o||[]).slice().concat(F)}u=this.saveTensorsForBackwardMode(D)}return E};else{const v=N=>{if(!p)return;u=N.map(E=>this.keep(this.clone(E)))};b=()=>{const N=this.backend.numDataIds();I=this.tidy(()=>e(this.backend,v));const E=Array.isArray(I)?I:[I];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,N,E),E}}let T;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?c=b():(T=this.profiler.profileKernel(s,t,()=>b()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(T),c=T.outputs)}),p&&this.addTapeNode(s,t,c,n,u,i),this.state.profiling&&this.state.activeProfile.kernels.push({name:s,bytesAdded:this.state.numBytes-m,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-y,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(v=>t[v]!=null?t[v].shape:null),outputShapes:c.map(v=>v.shape),kernelTimeMs:T.timeMs,extraInfo:T.extraInfo}),Array.isArray(I)?c:c[0]}saveTensorsForBackwardMode(e){const t=e.map(n=>this.keep(this.clone(n)));return t}getTensorsForGradient(e,t,n){const s=by(e);if(s!=null){const i=s.inputsToSave||[],o=s.outputsToSave||[];let a;s.saveAllInputs?(k(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(t).map(u=>t[u])):a=i.map(u=>t[u]);const c=n.filter((u,p)=>o[p]);return a.concat(c)}return null}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let i=e;n==="string"&&Or(e[0])&&(i=e.map(c=>Cy(c)));const o=s.write(i,t,n),a=new Q(t,n,o,this.nextTensorId());if(this.incRef(a,s),n==="string"){const c=this.state.tensorInfo.get(o),u=aT(i);this.state.numBytes+=u-c.bytes,c.bytes=u}return a}makeTensorFromDataId(e,t,n,s){n=n||"float32";const i=new Q(t,n,e,this.nextTensorId());return this.incRef(i,s),i}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));const i=new Xl(e,t,n,this.nextTensorId());if(this.state.registeredVariables[i.name]!=null)throw new Error(`Variable with name ${i.name} was already registered`);return this.state.registeredVariables[i.name]=i,this.incRef(i,this.backend),i}incRef(e,t){const n=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,n===0){this.state.numDataBuffers++;let s=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(s=e.size*Ty(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:s,refCount:0}),this.state.numBytes+=s}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof Xl||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;const t=this.state.tensorInfo.get(e.dataId),n=t.refCount;n<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):this.state.tensorInfo.get(e.dataId).refCount--}disposeVariables(){for(const e in this.state.registeredVariables){const t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){const e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;const t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(const s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,i,o){const a={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:i},c=by(e);c!=null&&(s=c.gradFunc),s!=null&&(a.gradient=u=>(u=u.map((p,m)=>{if(p==null){const y=n[m],b=Ra(y.size,y.dtype);return this.makeTensor(b,y.shape,y.dtype)}return p}),s(u.length>1?u:u[0],i,o))),this.state.activeTape.push(a)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){const t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){const t=Zi(e),n=new Set(t.map(i=>i.id));for(let i=0;i<this.state.activeScope.track.length;i++){const o=this.state.activeScope.track[i];!o.kept&&!n.has(o.id)&&o.dispose()}const s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(i=>{!i.kept&&i.scopeId===s.id&&this.track(i)})}gradients(e,t,n,s=!1){if(k(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);const i=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));k(i instanceof Q,()=>"The result y returned by f() must be a tensor.");const o=rk(this.state.activeTape,t,i);if(!s&&o.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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Please use tf.complex(real, imag).");if(!Ln(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){Ny(t);const i=we(t),o=we(n);k(i===o,()=>`Based on the provided shape, [${t}], the tensor should have ${i} values but has ${o}`);for(let a=0;a<n.length;++a){const c=n[a],u=a===n.length-1?c!==we(t.slice(a)):!0;k(n[a]===t[a]||!u,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!Ln(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=s!=="string"?Dr(e,s):Ji(e,[],!0),V.makeTensor(e,t,s)}function en(e,t,n){const s=Ni(e,n);return Fr(e,t,s,n)}const _y={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8};const Cd=4;async function Wy(e,t){const n=[],s=[],i=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);for(let a=0;a<i.length;++a){const c=i[a],u=Array.isArray(e)?e[a].tensor:e[c];if(u.dtype!=="float32"&&u.dtype!=="int32"&&u.dtype!=="bool"&&u.dtype!=="string"&&u.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${c}': ${u.dtype}`);const p={name:c,shape:u.shape,dtype:u.dtype};if(u.dtype==="string"){const m=new Promise(async y=>{const b=await u.bytes(),w=b.reduce((v,N)=>v+N.length,0)+Cd*b.length,I=new Uint8Array(w);let T=0;for(let v=0;v<b.length;v++){const N=b[v],E=new Uint8Array(new Uint32Array([N.length]).buffer);I.set(E,T),T+=Cd,I.set(N,T),T+=N.length}y(I)});s.push(m)}else s.push(u.data());t!=null&&(p.group=t),n.push(p)}const o=await Promise.all(s);return{data:Lk(o),specs:n}}function Rd(e,t){const n={};let s,i=0;for(const o of t){const a=o.name,c=o.dtype,u=o.shape,p=we(u);let m;if("quantization"in o){const y=o.quantization;if(y.dtype==="uint8"||y.dtype==="uint16"){if(!("min"in y&&"scale"in y))throw new Error(`Weight ${o.name} with quantization ${y.dtype} doesn't have corresponding metadata min and scale.`)}else if(y.dtype==="float16"){if(c!=="float32")throw new Error(`Weight ${o.name} is quantized with ${y.dtype} which only supports weights of type float32 not ${c}.`)}else throw new Error(`Weight ${o.name} has unknown quantization dtype ${y.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);const b=_y[y.dtype],w=e.slice(i,i+p*b),I=y.dtype==="uint8"?new Uint8Array(w):new Uint16Array(w);if(c==="float32")if(y.dtype==="uint8"||y.dtype==="uint16"){m=new Float32Array(I.length);for(let T=0;T<I.length;T++){const v=I[T];m[T]=v*y.scale+y.min}}else if(y.dtype==="float16")s===void 0&&(s=vk()),m=s(I);else throw new Error(`Unsupported quantization type ${y.dtype} for weight type float32.`);else if(c==="int32"){if(y.dtype!=="uint8"&&y.dtype!=="uint16")throw new Error(`Unsupported quantization type ${y.dtype} for weight type int32.`);m=new Int32Array(I.length);for(let T=0;T<I.length;T++){const v=I[T];m[T]=Math.round(v*y.scale+y.min)}}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=p*b}else if(c==="string"){const y=we(o.shape);m=[];for(let b=0;b<y;b++){const w=new Uint32Array(e.slice(i,i+Cd))[0];i+=Cd;const I=new Uint8Array(e.slice(i,i+w));m.push(I),i+=w}}else{const y=_y[c],b=e.slice(i,i+p*y);if(c==="float32")m=new Float32Array(b);else if(c==="int32")m=new Int32Array(b);else if(c==="bool")m=new Uint8Array(b);else if(c==="complex64"){m=new Float32Array(b);const w=new Float32Array(m.length/2),I=new Float32Array(m.length/2);for(let N=0;N<w.length;N++)w[N]=m[N*2],I[N]=m[N*2+1];const T=en(w,u,"float32"),v=en(I,u,"float32");n[a]=Ci(T,v)}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=p*y}c!=="complex64"&&(n[a]=en(m,u,c))}return n}function Lk(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0;const n=[];e.forEach(o=>{if(t+=o.byteLength,n.push(o.byteLength===o.buffer.byteLength?o:new o.constructor(o)),!(o instanceof Float32Array||o instanceof Int32Array||o instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${o.constructor.name}`)});const s=new Uint8Array(t);let i=0;return n.forEach(o=>{s.set(new Uint8Array(o.buffer),i),i+=o.byteLength}),s.buffer}const $y=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function xT(e){return $y?Buffer.byteLength(e):new Blob([e]).size}function Sk(e){if($y)return Buffer.from(e).toString("base64");const t=new Uint8Array(e);let n="";for(let s=0,i=t.length;s<i;s++)n+=String.fromCharCode(t[s]);return btoa(n)}function Ik(e){if($y){const s=Buffer.from(e,"base64");return s.buffer.slice(s.byteOffset,s.byteOffset+s.byteLength)}const t=atob(e),n=new Uint8Array(t.length);for(let s=0;s<t.length;++s)n.set([t.charCodeAt(s)],s);return n.buffer}function Od(e){if(e.length===1)return e[0];let t=0;e.forEach(i=>{t+=i.byteLength});const n=new Uint8Array(t);let s=0;return e.forEach(i=>{n.set(new Uint8Array(i),s),s+=i.byteLength}),n.buffer}function TT(e){const t="/";for(e=e.trim();e.endsWith(t);)e=e.slice(0,e.length-1);const n=e.split(t);return n[n.length-1]}function Ql(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:e.modelTopology==null?0:xT(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:xT(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function xk(){const e=n=>{let s=n<<13,i=0;for(;(s&8388608)===0;)i-=8388608,s<<=1;return s&=~8388608,i+=947912704,s|i},t=new Uint32Array(2048);t[0]=0;for(let n=1;n<1024;n++)t[n]=e(n);for(let n=1024;n<2048;n++)t[n]=939524096+(n-1024<<13);return t}function Tk(){const e=new Uint32Array(64);e[0]=0,e[31]=1199570944,e[32]=2147483648,e[63]=3347054592;for(let t=1;t<31;t++)e[t]=t<<23;for(let t=33;t<63;t++)e[t]=2147483648+(t-32<<23);return e}function Ak(){const e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function vk(){const e=xk(),t=Tk(),n=Ak();return s=>{const i=new ArrayBuffer(4*s.length),o=new Uint32Array(i);for(let a=0;a<s.length;a++){const c=s[a],u=e[n[c>>10]+(c&1023)]+t[c>>10];o[a]=u}return new Float32Array(i)}}class Xt{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Xt.instance==null&&(Xt.instance=new Xt),Xt.instance}static registerSaveRouter(e){Xt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Xt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Xt.getHandlers(e,"save")}static getLoadHandlers(e,t){return Xt.getHandlers(e,"load",t)}static getHandlers(e,t,n){const s=[],i=t==="load"?Xt.getInstance().loadRouters:Xt.getInstance().saveRouters;return i.forEach(o=>{const a=o(e,n);a!==null&&s.push(a)}),s}}const Nk=e=>Xt.registerSaveRouter(e),Ck=e=>Xt.registerLoadRouter(e),Uy=e=>Xt.getSaveHandlers(e),By=(e,t)=>Xt.getLoadHandlers(e,t);const Ed="tensorflowjs",My=1,Lo="models_store",_r="model_info_store";async function NQ(){const e=Py();return new Promise((t,n)=>{const s=e.deleteDatabase(Ed);s.onsuccess=()=>t(),s.onerror=i=>n(i)})}function Py(){if(!C().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");const e=typeof window=="undefined"?self:window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(t==null)throw new Error("The current browser does not appear to support IndexedDB.");return t}function zy(e){const t=e.result;t.createObjectStore(Lo,{keyPath:"modelPath"}),t.createObjectStore(_r,{keyPath:"modelPath"})}class So{constructor(e){if(this.indexedDB=Py(),e==null||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,e)}async load(){return this.databaseAction(this.modelPath)}databaseAction(e,t){return new Promise((n,s)=>{const i=this.indexedDB.open(Ed,My);i.onupgradeneeded=()=>zy(i),i.onsuccess=()=>{const o=i.result;if(t==null){const a=o.transaction(Lo,"readonly"),c=a.objectStore(Lo),u=c.get(this.modelPath);u.onsuccess=()=>{if(u.result==null)return o.close(),s(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(u.result.modelArtifacts)},u.onerror=p=>(o.close(),s(u.error)),a.oncomplete=()=>o.close()}else{const a=Ql(t),c=o.transaction(_r,"readwrite");let u=c.objectStore(_r);const p=u.put({modelPath:this.modelPath,modelArtifactsInfo:a});let m;p.onsuccess=()=>{m=o.transaction(Lo,"readwrite");const y=m.objectStore(Lo),b=y.put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:a});b.onsuccess=()=>n({modelArtifactsInfo:a}),b.onerror=w=>{u=c.objectStore(_r);const I=u.delete(this.modelPath);I.onsuccess=()=>(o.close(),s(b.error)),I.onerror=T=>(o.close(),s(b.error))}},p.onerror=y=>(o.close(),s(p.error)),c.oncomplete=()=>{m==null?o.close():m.oncomplete=()=>o.close()}}},i.onerror=o=>s(i.error)})}}So.URL_SCHEME="indexeddb://";const AT=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(So.URL_SCHEME))?Rk(e.slice(So.URL_SCHEME.length)):null;Xt.registerSaveRouter(AT),Xt.registerLoadRouter(AT);function Rk(e){return new So(e)}function Ok(e){return e.startsWith(So.URL_SCHEME)?e.slice(So.URL_SCHEME.length):e}class Ek{constructor(){this.indexedDB=Py()}async listModels(){return new Promise((e,t)=>{const n=this.indexedDB.open(Ed,My);n.onupgradeneeded=()=>zy(n),n.onsuccess=()=>{const s=n.result,i=s.transaction(_r,"readonly"),o=i.objectStore(_r),a=o.getAll();a.onsuccess=()=>{const c={};for(const u of a.result)c[u.modelPath]=u.modelArtifactsInfo;e(c)},a.onerror=c=>(s.close(),t(a.error)),i.oncomplete=()=>s.close()},n.onerror=s=>t(n.error)})}async removeModel(e){return e=Ok(e),new Promise((t,n)=>{const s=this.indexedDB.open(Ed,My);s.onupgradeneeded=()=>zy(s),s.onsuccess=()=>{const i=s.result,o=i.transaction(_r,"readwrite"),a=o.objectStore(_r),c=a.get(e);let u;c.onsuccess=()=>{if(c.result==null)return i.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{const p=a.delete(e),m=()=>{u=i.transaction(Lo,"readwrite");const y=u.objectStore(Lo),b=y.delete(e);b.onsuccess=()=>t(c.result.modelArtifactsInfo),b.onerror=w=>n(c.error)};p.onsuccess=m,p.onerror=y=>(m(),i.close(),n(c.error))}},c.onerror=p=>(i.close(),n(c.error)),o.oncomplete=()=>{u==null?i.close():u.oncomplete=()=>i.close()}},s.onerror=i=>n(s.error)})}}const Ri="/",Io="tensorflowjs_models",vT="info",Dk="model_topology",kk="weight_specs",Fk="weight_data",_k="model_metadata";function CQ(){if(!C().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("purgeLocalStorageModels() cannot proceed because local storage is unavailable in the current environment.");const e=window.localStorage,t=[];for(let n=0;n<e.length;++n){const s=e.key(n),i=Io+Ri;if(s.startsWith(i)&&s.length>i.length){e.removeItem(s);const o=CT(s);t.indexOf(o)===-1&&t.push(o)}}return t}function NT(e){return{info:[Io,e,vT].join(Ri),topology:[Io,e,Dk].join(Ri),weightSpecs:[Io,e,kk].join(Ri),weightData:[Io,e,Fk].join(Ri),modelMetadata:[Io,e,_k].join(Ri)}}function CT(e){const t=e.split(Ri);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(Ri)}function Wk(e){return e.startsWith(xo.URL_SCHEME)?e.slice(xo.URL_SCHEME.length):e}class xo{constructor(e){if(!C().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=NT(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{const t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),s=Ql(e);try{return this.LS.setItem(this.keys.info,JSON.stringify(s)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,Sk(e.weightData)),this.LS.setItem(this.keys.modelMetadata,JSON.stringify({format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,userDefinedMetadata:e.userDefinedMetadata})),{modelArtifactsInfo:s}}catch(i){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${s.modelTopologyBytes}, weightSpecsBytes=${s.weightSpecsBytes}, weightDataBytes=${s.weightDataBytes}.`)}}}async load(){const e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(e.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");const t={},n=JSON.parse(this.LS.getItem(this.keys.topology));if(n==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);t.modelTopology=n;const s=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(s==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=s;const i=this.LS.getItem(this.keys.modelMetadata);if(i!=null){const a=JSON.parse(i);t.format=a.format,t.generatedBy=a.generatedBy,t.convertedBy=a.convertedBy,t.userDefinedMetadata=a.userDefinedMetadata}const o=this.LS.getItem(this.keys.weightData);if(o==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=Ik(o),t}}xo.URL_SCHEME="localstorage://";const RT=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(xo.URL_SCHEME))?$k(e.slice(xo.URL_SCHEME.length)):null;Xt.registerSaveRouter(RT),Xt.registerLoadRouter(RT);function $k(e){return new xo(e)}class Uk{constructor(){k(C().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),k(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){const e={},t=Io+Ri,n=Ri+vT;for(let s=0;s<this.LS.length;++s){const i=this.LS.key(s);if(i.startsWith(t)&&i.endsWith(n)){const o=CT(i);e[o]=JSON.parse(this.LS.getItem(i))}}return e}async removeModel(e){e=Wk(e);const t=NT(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);const n=JSON.parse(this.LS.getItem(t.info));return this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),n}}const Da="://";class Ss{constructor(){this.managers={}}static getInstance(){return Ss.instance==null&&(Ss.instance=new Ss),Ss.instance}static registerManager(e,t){k(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(Da)&&(e=e.slice(0,e.indexOf(Da))),k(e.length>0,()=>"scheme must not be an empty string.");const n=Ss.getInstance();k(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),n.managers[e]=t}static getManager(e){const t=this.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}}function Dd(e){if(e.indexOf(Da)===-1)throw new Error(`The url string provided does not contain a scheme. 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s){const o=n+Da+i;t[o]=s[i]}}return t}async function Mk(e){const t=Dd(e),n=Ss.getManager(t.scheme);return n.removeModel(t.path)}async function Pk(e,t){const n=!1;return OT(e,t,n)}async function zk(e,t){const n=!0;return OT(e,t,n)}class Gk{fetch(e,t){return fetch(e,t)}now(){return performance.now()}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${t}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(e)}decode(e,t){return new TextDecoder(t).decode(e)}}if(C().get("IS_BROWSER")){C().setPlatform("browser",new Gk);try{Ss.registerManager(xo.URL_SCHEME,new Uk)}catch(e){}try{Ss.registerManager(So.URL_SCHEME,new Ek)}catch(e){}}const Vk={importFetch:()=>i2()};let ka;function RQ(){ka=null}function OQ(e){ka=e}function EQ(){return ka}class Hk{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return C().global.fetch!=null?C().global.fetch(e,t):(ka==null&&(ka=Vk.importFetch()),ka(e,t))}now(){const e=process.hrtime();return e[0]*1e3+e[1]/1e6}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${t}`);return this.textEncoder.encode(e)}decode(e,t){return e.length===0?"":new this.util.TextDecoder(t).decode(e)}}C().get("IS_NODE")&&C().setPlatform("node",new Hk);function Qe(e,t="float32",n){return t=t||"float32",Ny(e),new kr(e,t,n)}function Yk(e,t){const n=W(e,"x","cast");if(!oT(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");const s={x:n},i={dtype:t};return V.runKernelFunc(o=>o.cast(n,t),s,null,ul,i)}const ve=P({cast_:Yk});function qk(e){const t=W(e,"x","clone",null),n=()=>V.makeTensorFromDataId(t.dataId,t.shape,t.dtype),s={x:t};return V.runKernelFunc(n,s,null,Sl)}const Wr=P({clone_:qk});function ET(e,t=!1){console.log(e.toString(t))}bT();const jk={buffer:Qe,cast:ve,clone:Wr,print:ET};hk(jk);const Kk="model",Xk=".json",Jk=".weights.bin";function DT(e){return new Promise(t=>setTimeout(t)).then(e)}class Fa{constructor(e){if(!C().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(Fa.URL_SCHEME)&&(e=e.slice(Fa.URL_SCHEME.length)),(e==null||e.length===0)&&(e=Kk),this.modelTopologyFileName=e+Xk,this.weightDataFileName=e+Jk}async save(e){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");const t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{const n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],s={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n},i=window.URL.createObjectURL(new Blob([JSON.stringify(s)],{type:"application/json"})),o=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(o.download=this.modelTopologyFileName,o.href=i,await DT(()=>o.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){const a=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;a.download=this.weightDataFileName,a.href=t,await DT(()=>a.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Ql(e)}}}}Fa.URL_SCHEME="downloads://";class Zk{constructor(e){if(e==null||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.files=e}async load(){const e=this.files[0],t=this.files.slice(1);return new Promise((n,s)=>{const i=new FileReader;i.onload=o=>{const a=JSON.parse(o.target.result),c=a.modelTopology;if(c==null){s(new Error(`modelTopology field is missing from file ${e.name}`));return}t.length===0&&n({modelTopology:c});const u=a.weightsManifest;if(u==null){s(new Error(`weightManifest field is missing from file ${e.name}`));return}let p;try{p=this.checkManifestAndWeightFiles(u,t)}catch(w){s(w);return}const m=[],y=[],b=[];u.forEach(w=>{w.paths.forEach(I=>{y.push(I),b.push(null)}),m.push(...w.weights)}),u.forEach(w=>{w.paths.forEach(I=>{const T=new FileReader;T.onload=v=>{const N=v.target.result,E=y.indexOf(I);b[E]=N,b.indexOf(null)===-1&&n({modelTopology:c,weightSpecs:m,weightData:Od(b),format:a.format,generatedBy:a.generatedBy,convertedBy:a.convertedBy,userDefinedMetadata:a.userDefinedMetadata})},T.onerror=v=>s(`Failed to weights data from file of path '${I}'.`),T.readAsArrayBuffer(p[I])})})},i.onerror=o=>s(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),i.readAsText(e)})}checkManifestAndWeightFiles(e,t){const n=[],s=t.map(o=>TT(o.name)),i={};for(const o of e)o.paths.forEach(a=>{const c=TT(a);if(n.indexOf(c)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${c}'`);if(n.push(c),s.indexOf(c)===-1)throw new Error(`Weight file with basename '${c}' is not provided.`);i[a]=t[s.indexOf(c)]});if(n.length!==t.length)throw new Error(`Mismatch in the number of files in weights manifest (${n.length}) and the number of weight files provided (${t.length}).`);return i}}const Qk=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(Fa.URL_SCHEME))?eF(e.slice(Fa.URL_SCHEME.length)):null;Xt.registerSaveRouter(Qk);function eF(e="model"){return new Fa(e)}function tF(e){return new Zk(e)}function kT(e,t,n,s){a(e),n=n==null?0:n,s=s==null?1:s,c(n,s);let i=0;const o=u=>(u.then(p=>{const m=n+ ++i/e.length*(s-n);return t(m),p}),u);function a(u){k(u!=null&&Array.isArray(u)&&u.length>0,()=>"promises must be a none empty array")}function c(u,p){k(u>=0&&u<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${u}`),k(p>=0&&p<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${p}`),k(p>=u,()=>`startFraction must be no more than endFraction, but got startFraction ${u} and endFraction ${p}`)}return Promise.all(e.map(o))}async function FT(e,t){t==null&&(t={});const n=t.fetchFunc==null?C().platform.fetch:t.fetchFunc,s=e.map(y=>n(y,t.requestInit,{isBinary:!0})),i=0,o=.5,a=t.onProgress==null?await Promise.all(s):await kT(s,t.onProgress,i,o),c=a.map(y=>y.arrayBuffer()),u=.5,p=1,m=t.onProgress==null?await Promise.all(c):await kT(c,t.onProgress,u,p);return m}async function _T(e,t="",n,s){const i=a=>FT(a,{requestInit:s}),o=WT(i);return o(e,t,n)}function WT(e){return async(t,n="",s)=>{const i=t.map(()=>!1),o={},a=s!=null?s.map(()=>!1):[],c=[];if(t.forEach((w,I)=>{let T=0;w.weights.forEach(v=>{const N="quantization"in v?v.quantization.dtype:v.dtype,E=_y[N]*we(v.shape),D=()=>{i[I]=!0,o[I]==null&&(o[I]=[]),o[I].push({manifestEntry:v,groupOffset:T,sizeBytes:E})};s!=null?s.forEach((F,_)=>{F===v.name&&(D(),a[_]=!0)}):D(),c.push(v.name),T+=E})}),!a.every(w=>w)){const w=s.filter((I,T)=>!a[T]);throw new Error(`Could not find weights in manifest with names: ${w.join(", ")}. 
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Buffer shape=${this.shape}`;throw new Error(i)}t++}let n=e[e.length-1];for(let s=0;s<e.length-1;++s)n+=this.strides[s]*e[s];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];const t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return vi().makeTensor(this.values,this.shape,this.dtype)}}let vi=null,ka=null,mT=null;function lk(e){vi=e}function hk(e){ka=e}function uk(e){mT=e}class Q{constructor(e,t,n,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=we(e),this.strides=Ot(e),this.dataId=n,this.id=s,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const e=await this.data();return ka.buffer(this.shape,this.dtype,e)}bufferSync(){return ka.buffer(this.shape,this.dtype,this.dataSync())}async array(){const e=await this.data();return Ls(this.shape,e)}arraySync(){return Ls(this.shape,this.dataSync())}async data(){this.throwIfDisposed();const e=vi().read(this.dataId);if(this.dtype==="string"){const t=await e;try{return t.map(n=>Yl(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();const e=vi().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Yl(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();const e=await vi().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){if(this.isDisposed)return;vi().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return ka.print(this,e)}clone(){return this.throwIfDisposed(),ka.clone(this)}toString(e=!1){const t=this.dataSync();return ak(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),ka.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),vi().makeVariable(this,e,t,n)}}Object.defineProperty(Q,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});class Xl extends Q{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!ot(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);vi().disposeTensor(this),this.dataId=e.dataId,vi().incRef(this,null)}dispose(){vi().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(Xl,Symbol.hasInstance,{value:e=>e instanceof Q&&e.assign!=null&&e.assign instanceof Function});(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(r.Rank||(r.Rank={}));var Oy;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(Oy||(Oy={}));var Ey;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(Ey||(Ey={}));var Dy;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(Dy||(Dy={}));var ky;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(ky||(ky={}));const dk={float32:Dy,int32:Oy,bool:Ey,complex64:ky};function Cn(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return dk[e][t]}function vd(e){return Cn(e,"int32")}function Bt(e,t){if(e.dtype===t.dtype)return[e,t];const n=Cn(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function fT(e,t){k(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function Nd(e,t){return t.some(n=>n.id===e.id)}function Zi(e){const t=[],n=new Set;return gT(e,t,n),t}function gT(e,t,n){if(e==null)return;if(e instanceof Q){t.push(e);return}if(!pk(e))return;const s=e;for(const i in s){const o=s[i];n.has(o)||(n.add(o),gT(o,t,n))}}function pk(e){return Array.isArray(e)||typeof e=="object"}var mk=Object.freeze({__proto__:null,makeTypesMatch:Bt,assertTypesMatch:fT,isTensorInList:Nd,getTensorsInContainer:Zi});class yT{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class Jl{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new yT}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],s=await this.initializeBackend(n).success;if(s){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){const{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;const{success:t,asyncInit:n}=this.initializeBackend(e),s=n?await t:t;if(!s)return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new nk(this.backendInstance),!0}setupRegisteredKernels(){const e=wd(this.backendName);e.forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){const t=wd(e);t.forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){const t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{const n=t.factory();if(n&&!(n instanceof f)&&typeof n.then=="function"){const s=++this.pendingBackendInitId,i=n.then(o=>s<this.pendingBackendInitId?!1:(this.registry[e]=o,this.pendingBackendInit=null,!0)).catch(o=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(o.stack||o.message)),!1));return this.pendingBackendInit=i,{success:i,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],{success:s,asyncInit:i}=this.initializeBackend(n);if(i||s)return{name:n,asyncInit:i}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),s=n.backend,i=this.readSync(t);s.disposeData(t),n.backend=e,e.move(t,i,n.shape,n.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{const s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return Jl.nextTensorId++}nextVariableId(){return Jl.nextVariableId++}clone(e){const t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),n={x:e},s=o=>({x:()=>{const a="float32",c={x:o},u={dtype:a};return V.runKernelFunc(p=>p.cast(o,a),c,null,ul,u)}}),i=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,i,{}),t}runKernel(e,t,n,s,i){const o=null,a=null;return this.runKernelFunc(o,t,a,e,n,s,i)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const s=this.backend.numDataIds();let i=0;n.forEach(c=>{i+=c.dtype==="complex64"?3:1});const o=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=s-t-i-o;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${e}'`)}runKernelFunc(e,t,n,s,i,o,a){let c,u=[];const p=this.isTapeOn();s==null&&(s=this.state.activeScope!=null?this.state.activeScope.name:"");const m=this.state.numBytes,y=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let b;const w=yy(s,this.backendName);let I;if(w!=null)b=()=>{const v=this.backend.numDataIds();I=w.kernelFunc({inputs:t,attrs:i,backend:this.backend});const N=Array.isArray(I)?I:[I];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,v,N);const E=N.map(({dataId:D,shape:F,dtype:_})=>this.makeTensorFromDataId(D,F,_));if(p){let D=this.getTensorsForGradient(s,t,E);if(D==null){a==null&&(a=[]);const F=E.filter((_,B)=>a[B]);D=(o||[]).slice().concat(F)}u=this.saveTensorsForBackwardMode(D)}return E};else{const v=N=>{if(!p)return;u=N.map(E=>this.keep(this.clone(E)))};b=()=>{const N=this.backend.numDataIds();I=this.tidy(()=>e(this.backend,v));const E=Array.isArray(I)?I:[I];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,N,E),E}}let T;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?c=b():(T=this.profiler.profileKernel(s,t,()=>b()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(T),c=T.outputs)}),p&&this.addTapeNode(s,t,c,n,u,i),this.state.profiling&&this.state.activeProfile.kernels.push({name:s,bytesAdded:this.state.numBytes-m,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-y,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(v=>t[v]!=null?t[v].shape:null),outputShapes:c.map(v=>v.shape),kernelTimeMs:T.timeMs,extraInfo:T.extraInfo}),Array.isArray(I)?c:c[0]}saveTensorsForBackwardMode(e){const t=e.map(n=>this.keep(this.clone(n)));return t}getTensorsForGradient(e,t,n){const s=by(e);if(s!=null){const i=s.inputsToSave||[],o=s.outputsToSave||[];let a;s.saveAllInputs?(k(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(t).map(u=>t[u])):a=i.map(u=>t[u]);const c=n.filter((u,p)=>o[p]);return a.concat(c)}return null}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let i=e;n==="string"&&Or(e[0])&&(i=e.map(c=>Cy(c)));const o=s.write(i,t,n),a=new Q(t,n,o,this.nextTensorId());if(this.incRef(a,s),n==="string"){const c=this.state.tensorInfo.get(o),u=aT(i);this.state.numBytes+=u-c.bytes,c.bytes=u}return a}makeTensorFromDataId(e,t,n,s){n=n||"float32";const i=new Q(t,n,e,this.nextTensorId());return this.incRef(i,s),i}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));const i=new Xl(e,t,n,this.nextTensorId());if(this.state.registeredVariables[i.name]!=null)throw new Error(`Variable with name ${i.name} was already registered`);return this.state.registeredVariables[i.name]=i,this.incRef(i,this.backend),i}incRef(e,t){const n=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,n===0){this.state.numDataBuffers++;let s=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(s=e.size*Ty(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:s,refCount:0}),this.state.numBytes+=s}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof Xl||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;const t=this.state.tensorInfo.get(e.dataId),n=t.refCount;n<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):this.state.tensorInfo.get(e.dataId).refCount--}disposeVariables(){for(const e in this.state.registeredVariables){const t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){const e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;const t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(const s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,i,o){const a={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:i},c=by(e);c!=null&&(s=c.gradFunc),s!=null&&(a.gradient=u=>(u=u.map((p,m)=>{if(p==null){const y=n[m],b=Ea(y.size,y.dtype);return this.makeTensor(b,y.shape,y.dtype)}return p}),s(u.length>1?u:u[0],i,o))),this.state.activeTape.push(a)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){const t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){const t=Zi(e),n=new Set(t.map(i=>i.id));for(let i=0;i<this.state.activeScope.track.length;i++){const o=this.state.activeScope.track[i];!o.kept&&!n.has(o.id)&&o.dispose()}const s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(i=>{!i.kept&&i.scopeId===s.id&&this.track(i)})}gradients(e,t,n,s=!1){if(k(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);const i=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));k(i instanceof Q,()=>"The result y returned by f() must be a tensor.");const o=rk(this.state.activeTape,t,i);if(!s&&o.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);const b=_y[y.dtype],w=e.slice(i,i+p*b),I=y.dtype==="uint8"?new Uint8Array(w):new Uint16Array(w);if(c==="float32")if(y.dtype==="uint8"||y.dtype==="uint16"){m=new Float32Array(I.length);for(let T=0;T<I.length;T++){const v=I[T];m[T]=v*y.scale+y.min}}else if(y.dtype==="float16")s===void 0&&(s=vk()),m=s(I);else throw new Error(`Unsupported quantization type ${y.dtype} for weight type float32.`);else if(c==="int32"){if(y.dtype!=="uint8"&&y.dtype!=="uint16")throw new Error(`Unsupported quantization type ${y.dtype} for weight type int32.`);m=new Int32Array(I.length);for(let T=0;T<I.length;T++){const v=I[T];m[T]=Math.round(v*y.scale+y.min)}}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=p*b}else if(c==="string"){const y=we(o.shape);m=[];for(let b=0;b<y;b++){const w=new Uint32Array(e.slice(i,i+Cd))[0];i+=Cd;const I=new Uint8Array(e.slice(i,i+w));m.push(I),i+=w}}else{const y=_y[c],b=e.slice(i,i+p*y);if(c==="float32")m=new Float32Array(b);else if(c==="int32")m=new Int32Array(b);else if(c==="bool")m=new Uint8Array(b);else if(c==="complex64"){m=new Float32Array(b);const w=new Float32Array(m.length/2),I=new Float32Array(m.length/2);for(let N=0;N<w.length;N++)w[N]=m[N*2],I[N]=m[N*2+1];const T=en(w,u,"float32"),v=en(I,u,"float32");n[a]=Ci(T,v)}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=p*y}c!=="complex64"&&(n[a]=en(m,u,c))}return n}function Lk(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0;const n=[];e.forEach(o=>{if(t+=o.byteLength,n.push(o.byteLength===o.buffer.byteLength?o:new o.constructor(o)),!(o instanceof Float32Array||o instanceof Int32Array||o instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${o.constructor.name}`)});const s=new Uint8Array(t);let i=0;return n.forEach(o=>{s.set(new Uint8Array(o.buffer),i),i+=o.byteLength}),s.buffer}const $y=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function xT(e){return $y?Buffer.byteLength(e):new Blob([e]).size}function Sk(e){if($y)return Buffer.from(e).toString("base64");const t=new Uint8Array(e);let n="";for(let s=0,i=t.length;s<i;s++)n+=String.fromCharCode(t[s]);return btoa(n)}function Ik(e){if($y){const s=Buffer.from(e,"base64");return s.buffer.slice(s.byteOffset,s.byteOffset+s.byteLength)}const t=atob(e),n=new Uint8Array(t.length);for(let s=0;s<t.length;++s)n.set([t.charCodeAt(s)],s);return n.buffer}function Od(e){if(e.length===1)return e[0];let t=0;e.forEach(i=>{t+=i.byteLength});const n=new Uint8Array(t);let s=0;return e.forEach(i=>{n.set(new Uint8Array(i),s),s+=i.byteLength}),n.buffer}function TT(e){const t="/";for(e=e.trim();e.endsWith(t);)e=e.slice(0,e.length-1);const n=e.split(t);return n[n.length-1]}function Ql(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received 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c=s[a],u=e[n[c>>10]+(c&1023)]+t[c>>10];o[a]=u}return new Float32Array(i)}}class Xt{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Xt.instance==null&&(Xt.instance=new Xt),Xt.instance}static registerSaveRouter(e){Xt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Xt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Xt.getHandlers(e,"save")}static getLoadHandlers(e,t){return Xt.getHandlers(e,"load",t)}static getHandlers(e,t,n){const s=[],i=t==="load"?Xt.getInstance().loadRouters:Xt.getInstance().saveRouters;return i.forEach(o=>{const a=o(e,n);a!==null&&s.push(a)}),s}}const Nk=e=>Xt.registerSaveRouter(e),Ck=e=>Xt.registerLoadRouter(e),Uy=e=>Xt.getSaveHandlers(e),By=(e,t)=>Xt.getLoadHandlers(e,t);const Ed="tensorflowjs",My=1,Io="models_store",_r="model_info_store";async function NQ(){const e=Py();return new Promise((t,n)=>{const s=e.deleteDatabase(Ed);s.onsuccess=()=>t(),s.onerror=i=>n(i)})}function Py(){if(!C().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");const e=typeof window=="undefined"?self:window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(t==null)throw new Error("The current browser does not appear to support IndexedDB.");return t}function zy(e){const t=e.result;t.createObjectStore(Io,{keyPath:"modelPath"}),t.createObjectStore(_r,{keyPath:"modelPath"})}class xo{constructor(e){if(this.indexedDB=Py(),e==null||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,e)}async load(){return this.databaseAction(this.modelPath)}databaseAction(e,t){return new Promise((n,s)=>{const i=this.indexedDB.open(Ed,My);i.onupgradeneeded=()=>zy(i),i.onsuccess=()=>{const o=i.result;if(t==null){const a=o.transaction(Io,"readonly"),c=a.objectStore(Io),u=c.get(this.modelPath);u.onsuccess=()=>{if(u.result==null)return o.close(),s(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(u.result.modelArtifacts)},u.onerror=p=>(o.close(),s(u.error)),a.oncomplete=()=>o.close()}else{const a=Ql(t),c=o.transaction(_r,"readwrite");let u=c.objectStore(_r);const p=u.put({modelPath:this.modelPath,modelArtifactsInfo:a});let m;p.onsuccess=()=>{m=o.transaction(Io,"readwrite");const y=m.objectStore(Io),b=y.put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:a});b.onsuccess=()=>n({modelArtifactsInfo:a}),b.onerror=w=>{u=c.objectStore(_r);const I=u.delete(this.modelPath);I.onsuccess=()=>(o.close(),s(b.error)),I.onerror=T=>(o.close(),s(b.error))}},p.onerror=y=>(o.close(),s(p.error)),c.oncomplete=()=>{m==null?o.close():m.oncomplete=()=>o.close()}}},i.onerror=o=>s(i.error)})}}xo.URL_SCHEME="indexeddb://";const AT=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(xo.URL_SCHEME))?Rk(e.slice(xo.URL_SCHEME.length)):null;Xt.registerSaveRouter(AT),Xt.registerLoadRouter(AT);function Rk(e){return new xo(e)}function Ok(e){return e.startsWith(xo.URL_SCHEME)?e.slice(xo.URL_SCHEME.length):e}class Ek{constructor(){this.indexedDB=Py()}async listModels(){return new Promise((e,t)=>{const n=this.indexedDB.open(Ed,My);n.onupgradeneeded=()=>zy(n),n.onsuccess=()=>{const s=n.result,i=s.transaction(_r,"readonly"),o=i.objectStore(_r),a=o.getAll();a.onsuccess=()=>{const c={};for(const u of a.result)c[u.modelPath]=u.modelArtifactsInfo;e(c)},a.onerror=c=>(s.close(),t(a.error)),i.oncomplete=()=>s.close()},n.onerror=s=>t(n.error)})}async removeModel(e){return e=Ok(e),new Promise((t,n)=>{const s=this.indexedDB.open(Ed,My);s.onupgradeneeded=()=>zy(s),s.onsuccess=()=>{const i=s.result,o=i.transaction(_r,"readwrite"),a=o.objectStore(_r),c=a.get(e);let u;c.onsuccess=()=>{if(c.result==null)return i.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{const p=a.delete(e),m=()=>{u=i.transaction(Io,"readwrite");const y=u.objectStore(Io),b=y.delete(e);b.onsuccess=()=>t(c.result.modelArtifactsInfo),b.onerror=w=>n(c.error)};p.onsuccess=m,p.onerror=y=>(m(),i.close(),n(c.error))}},c.onerror=p=>(i.close(),n(c.error)),o.oncomplete=()=>{u==null?i.close():u.oncomplete=()=>i.close()}},s.onerror=i=>n(s.error)})}}const Ri="/",To="tensorflowjs_models",vT="info",Dk="model_topology",kk="weight_specs",Fk="weight_data",_k="model_metadata";function CQ(){if(!C().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("purgeLocalStorageModels() cannot proceed because local storage is unavailable in the current environment.");const e=window.localStorage,t=[];for(let n=0;n<e.length;++n){const s=e.key(n),i=To+Ri;if(s.startsWith(i)&&s.length>i.length){e.removeItem(s);const o=CT(s);t.indexOf(o)===-1&&t.push(o)}}return t}function NT(e){return{info:[To,e,vT].join(Ri),topology:[To,e,Dk].join(Ri),weightSpecs:[To,e,kk].join(Ri),weightData:[To,e,Fk].join(Ri),modelMetadata:[To,e,_k].join(Ri)}}function CT(e){const t=e.split(Ri);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(Ri)}function Wk(e){return e.startsWith(Ao.URL_SCHEME)?e.slice(Ao.URL_SCHEME.length):e}class Ao{constructor(e){if(!C().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or 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is a possible cause of this failure: modelTopologyBytes=${s.modelTopologyBytes}, weightSpecsBytes=${s.weightSpecsBytes}, weightDataBytes=${s.weightDataBytes}.`)}}}async load(){const e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(e.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");const t={},n=JSON.parse(this.LS.getItem(this.keys.topology));if(n==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);t.modelTopology=n;const s=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(s==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=s;const i=this.LS.getItem(this.keys.modelMetadata);if(i!=null){const a=JSON.parse(i);t.format=a.format,t.generatedBy=a.generatedBy,t.convertedBy=a.convertedBy,t.userDefinedMetadata=a.userDefinedMetadata}const o=this.LS.getItem(this.keys.weightData);if(o==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=Ik(o),t}}Ao.URL_SCHEME="localstorage://";const RT=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(Ao.URL_SCHEME))?$k(e.slice(Ao.URL_SCHEME.length)):null;Xt.registerSaveRouter(RT),Xt.registerLoadRouter(RT);function $k(e){return new Ao(e)}class Uk{constructor(){k(C().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),k(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){const e={},t=To+Ri,n=Ri+vT;for(let s=0;s<this.LS.length;++s){const i=this.LS.key(s);if(i.startsWith(t)&&i.endsWith(n)){const o=CT(i);e[o]=JSON.parse(this.LS.getItem(i))}}return e}async removeModel(e){e=Wk(e);const t=NT(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);const n=JSON.parse(this.LS.getItem(t.info));return this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),n}}const Fa="://";class Ss{constructor(){this.managers={}}static getInstance(){return Ss.instance==null&&(Ss.instance=new Ss),Ss.instance}static registerManager(e,t){k(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(Fa)&&(e=e.slice(0,e.indexOf(Fa))),k(e.length>0,()=>"scheme must not be an empty string.");const n=Ss.getInstance();k(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),n.managers[e]=t}static getManager(e){const t=this.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}}function Dd(e){if(e.indexOf(Fa)===-1)throw new Error(`The url string provided does not contain a scheme. 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s){const o=n+Fa+i;t[o]=s[i]}}return t}async function Mk(e){const t=Dd(e),n=Ss.getManager(t.scheme);return n.removeModel(t.path)}async function Pk(e,t){const n=!1;return OT(e,t,n)}async function zk(e,t){const n=!0;return OT(e,t,n)}class Gk{fetch(e,t){return fetch(e,t)}now(){return performance.now()}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${t}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(e)}decode(e,t){return new TextDecoder(t).decode(e)}}if(C().get("IS_BROWSER")){C().setPlatform("browser",new Gk);try{Ss.registerManager(Ao.URL_SCHEME,new Uk)}catch(e){}try{Ss.registerManager(xo.URL_SCHEME,new Ek)}catch(e){}}const Vk={importFetch:()=>i2()};let _a;function RQ(){_a=null}function OQ(e){_a=e}function EQ(){return _a}class Hk{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return C().global.fetch!=null?C().global.fetch(e,t):(_a==null&&(_a=Vk.importFetch()),_a(e,t))}now(){const e=process.hrtime();return e[0]*1e3+e[1]/1e6}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${t}`);return this.textEncoder.encode(e)}decode(e,t){return e.length===0?"":new this.util.TextDecoder(t).decode(e)}}C().get("IS_NODE")&&C().setPlatform("node",new Hk);function Qe(e,t="float32",n){return t=t||"float32",Ny(e),new kr(e,t,n)}function Yk(e,t){const n=W(e,"x","cast");if(!oT(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");const s={x:n},i={dtype:t};return V.runKernelFunc(o=>o.cast(n,t),s,null,ul,i)}const ve=P({cast_:Yk});function qk(e){const t=W(e,"x","clone",null),n=()=>V.makeTensorFromDataId(t.dataId,t.shape,t.dtype),s={x:t};return V.runKernelFunc(n,s,null,Sl)}const Wr=P({clone_:qk});function ET(e,t=!1){console.log(e.toString(t))}bT();const jk={buffer:Qe,cast:ve,clone:Wr,print:ET};hk(jk);const Kk="model",Xk=".json",Jk=".weights.bin";function DT(e){return new Promise(t=>setTimeout(t)).then(e)}class Wa{constructor(e){if(!C().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(Wa.URL_SCHEME)&&(e=e.slice(Wa.URL_SCHEME.length)),(e==null||e.length===0)&&(e=Kk),this.modelTopologyFileName=e+Xk,this.weightDataFileName=e+Jk}async save(e){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");const t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{const n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],s={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n},i=window.URL.createObjectURL(new Blob([JSON.stringify(s)],{type:"application/json"})),o=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(o.download=this.modelTopologyFileName,o.href=i,await DT(()=>o.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){const a=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;a.download=this.weightDataFileName,a.href=t,await DT(()=>a.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Ql(e)}}}}Wa.URL_SCHEME="downloads://";class Zk{constructor(e){if(e==null||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.files=e}async load(){const e=this.files[0],t=this.files.slice(1);return new Promise((n,s)=>{const i=new FileReader;i.onload=o=>{const a=JSON.parse(o.target.result),c=a.modelTopology;if(c==null){s(new Error(`modelTopology field is missing from file ${e.name}`));return}t.length===0&&n({modelTopology:c});const u=a.weightsManifest;if(u==null){s(new Error(`weightManifest field is missing from file ${e.name}`));return}let p;try{p=this.checkManifestAndWeightFiles(u,t)}catch(w){s(w);return}const m=[],y=[],b=[];u.forEach(w=>{w.paths.forEach(I=>{y.push(I),b.push(null)}),m.push(...w.weights)}),u.forEach(w=>{w.paths.forEach(I=>{const T=new FileReader;T.onload=v=>{const N=v.target.result,E=y.indexOf(I);b[E]=N,b.indexOf(null)===-1&&n({modelTopology:c,weightSpecs:m,weightData:Od(b),format:a.format,generatedBy:a.generatedBy,convertedBy:a.convertedBy,userDefinedMetadata:a.userDefinedMetadata})},T.onerror=v=>s(`Failed to weights data from file of path '${I}'.`),T.readAsArrayBuffer(p[I])})})},i.onerror=o=>s(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),i.readAsText(e)})}checkManifestAndWeightFiles(e,t){const n=[],s=t.map(o=>TT(o.name)),i={};for(const o of e)o.paths.forEach(a=>{const c=TT(a);if(n.indexOf(c)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${c}'`);if(n.push(c),s.indexOf(c)===-1)throw new Error(`Weight file with basename '${c}' is not provided.`);i[a]=t[s.indexOf(c)]});if(n.length!==t.length)throw new Error(`Mismatch in the number of files in weights manifest (${n.length}) and the number of weight files provided (${t.length}).`);return i}}const Qk=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(Wa.URL_SCHEME))?eF(e.slice(Wa.URL_SCHEME.length)):null;Xt.registerSaveRouter(Qk);function eF(e="model"){return new Wa(e)}function tF(e){return new Zk(e)}function kT(e,t,n,s){a(e),n=n==null?0:n,s=s==null?1:s,c(n,s);let i=0;const o=u=>(u.then(p=>{const m=n+ ++i/e.length*(s-n);return t(m),p}),u);function a(u){k(u!=null&&Array.isArray(u)&&u.length>0,()=>"promises must be a none empty array")}function c(u,p){k(u>=0&&u<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${u}`),k(p>=0&&p<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${p}`),k(p>=u,()=>`startFraction must be no more than endFraction, but got startFraction ${u} and endFraction ${p}`)}return Promise.all(e.map(o))}async function FT(e,t){t==null&&(t={});const n=t.fetchFunc==null?C().platform.fetch:t.fetchFunc,s=e.map(y=>n(y,t.requestInit,{isBinary:!0})),i=0,o=.5,a=t.onProgress==null?await Promise.all(s):await kT(s,t.onProgress,i,o),c=a.map(y=>y.arrayBuffer()),u=.5,p=1,m=t.onProgress==null?await Promise.all(c):await kT(c,t.onProgress,u,p);return m}async function _T(e,t="",n,s){const i=a=>FT(a,{requestInit:s}),o=WT(i);return o(e,t,n)}function WT(e){return async(t,n="",s)=>{const i=t.map(()=>!1),o={},a=s!=null?s.map(()=>!1):[],c=[];if(t.forEach((w,I)=>{let T=0;w.weights.forEach(v=>{const N="quantization"in v?v.quantization.dtype:v.dtype,E=_y[N]*we(v.shape),D=()=>{i[I]=!0,o[I]==null&&(o[I]=[]),o[I].push({manifestEntry:v,groupOffset:T,sizeBytes:E})};s!=null?s.forEach((F,_)=>{F===v.name&&(D(),a[_]=!0)}):D(),c.push(v.name),T+=E})}),!a.every(w=>w)){const w=s.filter((I,T)=>!a[T]);throw new Error(`Could not find weights in manifest with names: ${w.join(", ")}. 
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ah=P({batchToSpaceND_:g_});function y_(e){let t;return e.rank===0||e.rank===1?t=K(e,[1,1,1,e.size]):e.rank===2?t=K(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=K(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function b_(e,t,n,s,i,o){o==null&&(o=.001);const a=W(e,"x","batchNorm"),c=W(t,"mean","batchNorm"),u=W(n,"variance","batchNorm");let p;i!=null&&(p=W(i,"scale","batchNorm"));let m;s!=null&&(m=W(s,"offset","batchNorm")),k(c.rank===u.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k(m==null||c.rank===m.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k(p==null||c.rank===p.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");const y=y_(a),b=(v,N)=>(N([y,c,u,p]),v.batchNorm(y,Vd(c),Vd(u),Vd(m),Vd(p),o)),w={x:y,scale:p,offset:m,mean:c,variance:u},I={varianceEpsilon:o},T=V.runKernelFunc(b,w,null,Ll,I);return K(T,a.shape)}function Vd(e){return 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a=W(e,"x","batchNorm"),c=W(t,"mean","batchNorm"),u=W(n,"variance","batchNorm");let p;i!=null&&(p=W(i,"scale","batchNorm"));let m;return s!=null&&(m=W(s,"offset","batchNorm")),k(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),k(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${c.rank}.`),k(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`),p!=null&&k(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${p.rank}.`),m!=null&&k(m.rank===3||m.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${m.rank}.`),Ro(a,c,u,m,p,o)}const sA=P({batchNorm3d_:L_});function S_(e,t,n,s,i,o){const a=W(e,"x","batchNorm"),c=W(t,"mean","batchNorm"),u=W(n,"variance","batchNorm");let p;i!=null&&(p=W(i,"scale","batchNorm"));let m;return s!=null&&(m=W(s,"offset","batchNorm")),k(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),k(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${c.rank}.`),k(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),p!=null&&k(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${p.rank}.`),m!=null&&k(m.rank===4||m.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${m.rank}.`),Ro(a,c,u,m,p,o)}const iA=P({batchNorm4d_:S_});function I_(e,t){let n=W(e,"broadcastTo","x");const s=n.shape;if(t.some(m=>!(m>0)||m%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){const m=n.shape.slice();for(;m.length<t.length;)m.unshift(1);n=K(n,m)}const i=n.shape,o=Array.from(t);for(let m=t.length-1;m>=0;m--)if(i[m]===t[m])o[m]=1;else 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c=W(e,"x","conv2d"),u=W(t,"filter","conv2d");let p=c,m=!1;c.rank===3&&(m=!0,p=K(c,[1,c.shape[0],c.shape[1],c.shape[2]])),k(p.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${p.rank}.`),k(u.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`),a!=null&&k(Ut(s),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${s}.`);const y=i==="NHWC"?p.shape[3]:p.shape[1];k(y===u.shape[2],()=>`Error in conv2d: depth of input (${y}) must match input depth for filter ${u.shape[2]}.`),k(on(n,o),()=>`Error in conv2D: Either strides or dilations must be 1. 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c=W(e,"a","fused matMul"),u=W(t,"b","fused matMul");[c,u]=Bt(c,u);const p=n?c.shape[c.rank-2]:c.shape[c.rank-1],m=s?u.shape[u.rank-1]:u.shape[u.rank-2],y=n?c.shape[c.rank-1]:c.shape[c.rank-2],b=s?u.shape[u.rank-2]:u.shape[u.rank-1],w=c.shape.slice(0,-2),I=u.shape.slice(0,-2),T=we(w),v=we(I);k(c.rank>=2&&u.rank>=2&&c.rank===u.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${c.rank} and ${u.rank}.`),k(ot(w,I),()=>`Error in fused matMul: outer dimensions (${w}) and (${I}) of Tensors with shapes ${c.shape} and ${u.shape} must match.`),k(p===m,()=>`Error in fused matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${c.shape} and ${u.shape} and transposeA=${n} and transposeB=${s} must match.`);const N=c.shape.slice(0,-2).concat([y,b]),E=n?K(c,[T,p,y]):K(c,[T,y,p]),D=s?K(u,[v,b,m]):K(u,[v,m,b]);let F;i!=null&&(F=W(i,"bias","fused matMul"),[F]=Bt(F,c),nt(N,F.shape));let _;a!=null&&(_=W(a,"prelu weights","fused matMul"));const B=(J,oe)=>{const[ce,ue,he,pe]=oe,le=mp(K(J,he.shape),he,o);let ye,me;if(!n&&!s?(ye=at(le,ue,!1,!0),me=at(ce,le,!0,!1)):!n&&s?(ye=at(le,ue,!1,!1),me=at(le,ce,!0,!1)):n&&!s?(ye=at(ue,le,!1,!0),me=at(ce,le,!1,!1)):(ye=at(ue,le,!0,!0),me=at(le,ce,!0,!0)),i!=null){const Ie=fp(pe,le);return[ye,me,Ie]}else return[ye,me]},U=J=>{const oe=J.fusedBatchMatMul({a:E,b:D,transposeA:n,transposeB:s,bias:F,activation:o,preluActivationWeights:_});return oe},Y={a:E,b:D,bias:F,preluActivationWeights:_},q={transposeA:n,transposeB:s,activation:o};if(i==null){const J=Di((oe,ce,ue)=>{const he=V.runKernelFunc(U,Y,null,my,q);return ue([oe,ce,he]),{value:K(he,N),gradFunc:B}});return J(E,D)}else{const J=Di((oe,ce,ue,he)=>{const pe=V.runKernelFunc(U,Y,null,my,q);return he([oe,ce,pe,ue]),{value:K(pe,N),gradFunc:B}});return J(E,D,F)}}const bp=P({fusedMatMul_:iB});var rB=Object.freeze({__proto__:null,conv2d:Hb,depthwiseConv2d:YA,matMul:bp});function oB(e){return Gb(e,.54,.46)}const aB=P({hammingWindow_:oB});function 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UB(e,t,n){k(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),k(n%1===0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);const s=W(e,"a","bandPart");k(s.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${s.rank}.`);const i=s.shape,[o,a]=s.shape.slice(-2);if(!(t<=o))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${o}).`);if(!(n<=a))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${a}).`);t<0&&(t=o),n<0&&(n=a);const c=K(yh(0,o,1,"int32"),[-1,1]),u=yh(0,a,1,"int32"),p=Ce(c,u),m=Bs(Mr(p,Ne(+t,"int32")),tr(p,Ne(-n,"int32"))),y=ct([o,a],s.dtype);return K(as(_i(K(s,[-1,o,a])).map(b=>$n(m,b,y))),i)}const BB=P({bandPart_:UB});function MB(e){let t;if(Array.isArray(e)){t=!1,k(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");const i=e[0].shape[0];for(let o=1;o<e.length;++o)k(e[o].shape[0]===i,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[o].shape[0]} vs. ${i})`)}else t=!0,e=os(e,e.shape[0],0).map(i=>zr(i,[0]));k(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);const n=[],s=e;for(let i=0;i<e.length;++i)n.push(V.tidy(()=>{let o=s[i];if(i>0)for(let a=0;a<i;++a){const c=X(Ue(X(n[a],o)),n[a]);o=Ce(o,c)}return _e(o,pp(o,"euclidean"))}));return t?as(n,0):n}const PB=P({gramSchmidt_:MB});function zB(e,t=!1){if(k(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return ZA(e,t);{const n=e.shape.slice(0,e.shape.length-2).reduce((u,p)=>u*p),s=_i(K(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),i=[],o=[];s.forEach(u=>{const[p,m]=ZA(u,t);i.push(p),o.push(m)});const a=K(as(i,0),e.shape),c=K(as(o,0),e.shape);return[a,c]}}function ZA(e,t=!1){return V.tidy(()=>{k(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);const n=e.shape[0],s=e.shape[1];let i=Kd(n),o=Wr(e);const a=Gr([[1]],[1,1]);let c=Wr(a);const u=n>=s?s:n;for(let p=0;p<u;++p){const m=o,y=c,b=i;[c,o,i]=V.tidy(()=>{const w=st(o,[p,p],[n-p,1]),I=pp(w),T=st(o,[p,p],[1,1]),v=$n(Ts(T,0),Gr([[-1]]),Gr([[1]])),N=Ce(T,X(v,I)),E=_e(w,N);E.shape[0]===1?c=Wr(a):c=Mt([a,st(E,[1,0],[E.shape[0]-1,E.shape[1]])],0);const D=Pt(_e(at(v,N),I)),F=st(o,[p,0],[n-p,s]),_=X(D,c),B=Pe(c);if(p===0)o=Ce(F,at(_,at(B,F)));else{const q=Ce(F,at(_,at(B,F)));o=Mt([st(o,[0,0],[p,s]),q],0)}const U=Pe(_),Y=st(i,[0,p],[n,i.shape[1]-p]);if(p===0)i=Ce(Y,at(at(Y,c),U));else{const q=Ce(Y,at(at(Y,c),U));i=Mt([st(i,[0,0],[n,p]),q],1)}return[c,o,i]}),qe([m,y,b])}return!t&&n>s&&(i=st(i,[0,0],[n,s]),o=st(o,[0,0],[s,s])),[i,o]})}const GB=P({qr_:zB});(function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(r.Reduction||(r.Reduction={}));function VB(e,t,n=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const s=W(e,"losses","computeWeightedLoss");let i=null;t!=null&&(i=W(t,"weights","computeWeightedLoss"));const o=i==null?s:X(s,i);if(n===r.Reduction.NONE)return o;if(n===r.Reduction.SUM)return Ue(o);if(n===r.Reduction.MEAN){if(i==null)return zt(o);{const a=s.size/i.size,c=_e(Ue(o),Ue(i));return a>1?_e(c,Ne(a)):c}}if(n===r.Reduction.SUM_BY_NONZERO_WEIGHTS){if(i==null)return _e(Ue(o),Ne(s.size));{const a=X(i,si(s.shape)),c=ve(Ue(Pr(a,Ne(0))),"float32");return _e(Ue(o),c)}}throw Error(`Unknown reduction: ${n}`)}const nr=P({computeWeightedLoss_:VB});function HB(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=W(e,"labels","absoluteDifference"),o=W(t,"predictions","absoluteDifference");let a=null;n!=null&&(a=W(n,"weights","absoluteDifference")),dt(i.shape,o.shape,"Error in absoluteDifference: ");const c=sn(Ce(i,o));return nr(c,a,s)}const YB=P({absoluteDifference_:HB});function qB(e,t,n,s,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","cosineDistance"),a=W(t,"predictions","cosineDistance");let c=null;s!=null&&(c=W(s,"weights","cosineDistance")),dt(o.shape,a.shape,"Error in cosineDistance: ");const u=Ne(1),p=Ce(u,Ue(X(o,a),n,!0));return nr(p,c,i)}const jB=P({cosineDistance_:qB});function KB(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let i=W(e,"labels","hingeLoss");const o=W(t,"predictions","hingeLoss");let a=null;n!=null&&(a=W(n,"weights","hingeLoss")),dt(i.shape,o.shape,"Error in hingeLoss: ");const c=Ne(1);i=Ce(X(Ne(2),i),c);const u=Fi(Ce(c,X(i,o)));return nr(u,a,s)}const XB=P({hingeLoss_:KB});function JB(e,t,n,s=1,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","huberLoss"),a=W(t,"predictions","huberLoss");let c=null;n!=null&&(c=W(n,"weights","huberLoss")),dt(o.shape,a.shape,"Error in huberLoss: ");const u=Ne(s),p=sn(Ce(a,o)),m=Eo(p,u),y=Ce(p,m),b=be(X(Ne(.5),Lt(m)),X(u,y));return nr(b,c,i)}const ZB=P({huberLoss_:JB});function QB(e,t,n,s=1e-7,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","logLoss"),a=W(t,"predictions","logLoss");let c=null;n!=null&&(c=W(n,"weights","logLoss")),dt(o.shape,a.shape,"Error in logLoss: ");const u=Ne(1),p=Ne(s),m=Pt(X(o,is(be(a,p)))),y=X(Ce(u,o),is(be(Ce(u,a),p))),b=Ce(m,y);return nr(b,c,i)}const eM=P({logLoss_:QB});function tM(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=W(e,"labels","meanSquaredError"),o=W(t,"predictions","meanSquaredError");let a=null;n!=null&&(a=W(n,"weights","meanSquaredError")),dt(i.shape,o.shape,"Error in meanSquaredError: ");const c=Sh(i,o);return nr(c,a,s)}const nM=P({meanSquaredError_:tM});function sM(e,t){const n=W(e,"labels","sigmoidCrossEntropyWithLogits"),s=W(t,"logits","sigmoidCrossEntropyWithLogits");dt(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");const i=Fi(s),o=X(s,n),a=Jd(xs(Pt(sn(s))));return be(Ce(i,o),a)}function iM(e,t,n,s=0,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=W(e,"multiClassLabels","sigmoidCrossEntropy");const a=W(t,"logits","sigmoidCrossEntropy");let c=null;if(n!=null&&(c=W(n,"weights","sigmoidCrossEntropy")),dt(o.shape,a.shape,"Error in sigmoidCrossEntropy: "),s>0){const p=Ne(s),m=Ne(1),y=Ne(.5);o=be(X(o,Ce(m,p)),X(y,p))}const u=sM(o,a);return nr(u,c,i)}const rM=P({sigmoidCrossEntropy_:iM});function oM(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);const s=Di((i,o,a)=>{const c=!0,u=vb(o,[n],c),p=Ce(ve(o,"float32"),u);a([i,p]);const m=Pt(X(p,i)),y=Ue(m,[n]),b=(w,I)=>{const[T,v]=I,N=En(w.shape,[n]);return[X(K(w,N),Ce(ve(T,"float32"),xs(v))),X(K(w,N),Ce(xs(v),ve(T,"float32")))]};return{value:y,gradFunc:b}});return s(e,t)}function aM(e,t,n,s=0,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=W(e,"onehotLabels","softmaxCrossEntropy");const a=W(t,"logits","softmaxCrossEntropy");let c=null;if(n!=null&&(c=W(n,"weights","softmaxCrossEntropy")),dt(o.shape,a.shape,"Error in softmaxCrossEntropy: "),s>0){const p=Ne(s),m=Ne(1),y=Ne(o.shape[1]);o=be(X(o,Ce(m,p)),_e(p,y))}const u=oM(o,a);return nr(u,c,i)}const cM=P({softmaxCrossEntropy_:aM});const lM={fft:wh,ifft:Ya,rfft:Lh,irfft:hp},hM={hammingWindow:aB,hannWindow:qA,frame:jA,stft:uB},Vr={flipLeftRight:fB,resizeNearestNeighbor:JA,resizeBilinear:XA,rotateWithOffset:yB,cropAndResize:pB,nonMaxSuppression:wB,nonMaxSuppressionAsync:NB,nonMaxSuppressionWithScore:RB,nonMaxSuppressionWithScoreAsync:EB,nonMaxSuppressionPadded:kB,nonMaxSuppressionPaddedAsync:_B},QA={bandPart:BB,gramSchmidt:PB,qr:GB},uM={absoluteDifference:YB,computeWeightedLoss:nr,cosineDistance:jB,hingeLoss:XB,huberLoss:ZB,logLoss:eM,meanSquaredError:nM,sigmoidCrossEntropy:rM,softmaxCrossEntropy:cM};class sr extends Ao{minimize(e,t=!1,n){const{value:s,grads:i}=this.computeGradients(e,n);if(n!=null){const o=n.map(a=>({name:a.name,tensor:i[a.name]}));this.applyGradients(o)}else this.applyGradients(i);return qe(i),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Ab(e,t)}dispose(){this.iterations_!=null&&qe(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ne(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(sr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});class xh extends sr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n],o=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:ee(()=>et(i).variable(o))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:ee(()=>et(i).variable(o))});const a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;const c=this.accumulatedGrads[s].variable,u=this.accumulatedUpdates[s].variable;ee(()=>{const p=be(X(c,this.rho),X(Lt(a),1-this.rho)),m=X(_e(Sn(be(u,this.epsilon)),Sn(be(c,this.epsilon))),a),y=be(X(u,this.rho),X(Lt(m),1-this.rho));c.assign(p),u.assign(y);const b=be(X(m,-this.learningRate),i);i.assign(b)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(qe(this.accumulatedGrads.map(e=>e.variable)),qe(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}xh.className="Adadelta",ge(xh);class Th extends sr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n];if(this.accumulatedGrads[s]==null){const c=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:ee(()=>hh(i.shape,this.initialAccumulatorValue).variable(c))}}const o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;const a=this.accumulatedGrads[s].variable;ee(()=>{const c=be(a,Lt(o));a.assign(c);const u=be(X(_e(o,Sn(be(c,V.backend.epsilon()))),-this.learningRate),i);i.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&qe(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}Th.className="Adagrad",ge(Th);class Ah extends sr{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],ee(()=>{this.accBeta1=Ne(t).variable(),this.accBeta2=Ne(n).variable()}),s==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);ee(()=>{const n=Ce(1,this.accBeta1),s=Ce(1,this.accBeta2);t.forEach((i,o)=>{const a=V.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:ee(()=>et(a).variable(c))}),this.accumulatedSecondMoment[o]==null&&(this.accumulatedSecondMoment[o]={originalName:`${i}/v`,variable:ee(()=>et(a).variable(c))});const u=Array.isArray(e)?e[o].tensor:e[i];if(u==null)return;const p=this.accumulatedFirstMoment[o].variable,m=this.accumulatedSecondMoment[o].variable,y=be(X(p,this.beta1),X(u,1-this.beta1)),b=be(X(m,this.beta2),X(Lt(u),1-this.beta2)),w=_e(y,n),I=_e(b,s);p.assign(y),m.assign(b);const T=be(X(_e(w,be(Sn(I),this.epsilon)),-this.learningRate),a);a.assign(T)}),this.accBeta1.assign(X(this.accBeta1,this.beta1)),this.accBeta2.assign(X(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&qe(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&qe(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),ee(()=>{this.accBeta1.assign(ii(this.beta1,this.iterations_+1)),this.accBeta2.assign(ii(this.beta2,this.iterations_+1))});const t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}}Ah.className="Adam",ge(Ah);class vh extends sr{constructor(e,t,n,s=null,i=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=i,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],ee(()=>{this.iteration=Ne(0).variable(),this.accBeta1=Ne(t).variable()}),s==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);ee(()=>{const n=Ce(1,this.accBeta1),s=_e(-this.learningRate,be(X(this.iteration,this.decay),1));t.forEach((i,o)=>{const a=V.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:et(a).variable(c)}),this.accumulatedWeightedInfNorm[o]==null&&(this.accumulatedWeightedInfNorm[o]={originalName:`${i}/v`,variable:et(a).variable(c)});const u=Array.isArray(e)?e[o].tensor:e[i];if(u==null)return;const p=this.accumulatedFirstMoment[o].variable,m=this.accumulatedWeightedInfNorm[o].variable,y=be(X(p,this.beta1),X(u,1-this.beta1)),b=X(m,this.beta2),w=sn(u),I=Us(b,w);p.assign(y),m.assign(I);const T=be(X(_e(s,n),_e(y,be(I,this.epsilon))),a);a.assign(T)}),this.iteration.assign(be(this.iteration,1)),this.accBeta1.assign(X(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&qe(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&qe(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}}vh.className="Adamax",ge(vh);class Xa extends sr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;const o=V.registeredVariables[n];ee(()=>{const a=be(X(this.c,i),o);o.assign(a)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Rn(Ne(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}Xa.className="SGD",ge(Xa);class Nh extends Xa{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Ne(this.momentum)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n];if(this.accumulations[s]==null){const c=!1;this.accumulations[s]={originalName:`${n}/momentum`,variable:ee(()=>et(i).variable(c))}}const o=this.accumulations[s].variable,a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;ee(()=>{let c;const u=be(X(this.m,o),a);this.useNesterov?c=be(X(this.c,be(a,X(u,this.m))),i):c=be(X(this.c,u),i),o.assign(u),i.assign(c)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&qe(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}Nh.className="Momentum",ge(Nh);class Ch extends sr{constructor(e,t=.9,n=0,s=null,i=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=i,s==null&&(this.epsilon=V.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n],o=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:ee(()=>et(i).variable(o))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:ee(()=>et(i).variable(o))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:ee(()=>et(i).variable(o))});const a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;const c=this.accumulatedMeanSquares[s].variable,u=this.accumulatedMoments[s].variable;ee(()=>{const p=be(X(c,this.decay),X(Lt(a),1-this.decay));if(this.centered){const m=this.accumulatedMeanGrads[s].variable,y=be(X(m,this.decay),X(a,1-this.decay)),b=_e(X(a,this.learningRate),Sn(Ce(p,be(Lt(y),this.epsilon)))),w=be(X(u,this.momentum),b);c.assign(p),m.assign(y),u.assign(w);const I=Ce(i,w);i.assign(I)}else{const m=be(X(c,this.decay),X(Lt(a),1-this.decay)),y=be(X(u,this.momentum),_e(X(a,this.learningRate),Sn(be(m,this.epsilon))));c.assign(m),u.assign(y);const b=Ce(i,y);i.assign(b)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&qe(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&qe(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&qe(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}}Ch.className="RMSProp",ge(Ch);class $o{static sgd(e){return new Xa(e)}static momentum(e,t,n=!1){return new Nh(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,i=!1){return new Ch(e,t,n,s,i)}static 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mM=Object.freeze({__proto__:null,nonMaxSuppressionV3Impl:wp,nonMaxSuppressionV4Impl:Lp,nonMaxSuppressionV5Impl:Sp,split:rw,tile:ow,topkImpl:aw,whereImpl:dp});const fM={kernelName:fe,inputsToSave:["x"],gradFunc:(e,t)=>{const[n]=t;return{x:()=>X(e,qa(ve(n,"float32"),-1))}}};const gM={kernelName:de,inputsToSave:["x"],gradFunc:(e,t)=>{const[n]=t;return{x:()=>{const s=Lt(ve(n,"float32")),i=Sn(Ce(Ne(1),s));return Pt(_e(e,i))}}}};const yM={kernelName:Ae,inputsToSave:["x"],gradFunc:(e,t)=>{const[n]=t;return{x:()=>{const s=Sn(Ce(Lt(ve(n,"float32")),1));return _e(e,s)}}}};const bM={kernelName:xe,inputsToSave:["a","b"],gradFunc:(e,t)=>{const[n,s]=t,i=nt(n.shape,s.shape),o=()=>{let c=e;const u=on(n.shape,i);return u.length>0&&(c=Ue(c,u)),K(c,n.shape)},a=()=>{let c=e;const u=on(s.shape,i);return u.length>0&&(c=Ue(c,u)),K(c,s.shape)};return{a:o,b:a}}};const wM={kernelName:Me,saveAllInputs:!0,gradFunc:(e,t)=>{const n={};return t.forEach((s,i)=>{n[i]=()=>e.clone()}),n}};const 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vM={kernelName:Qs,inputsToSave:["x"],gradFunc:(e,t)=>{const[n]=t;return{x:()=>_e(e,Ce(Ne(1),Lt(ve(n,"float32"))))}}};function NM(e,t,n,s,i=[1,1,1],o,a){const c=W(e,"dy","avgPool3dBackprop"),u=W(t,"input","avgPool3dBackprop");let p=c,m=u,y=!1;u.rank===4&&(y=!0,p=K(c,[1,c.shape[0],c.shape[1],c.shape[2],c.shape[3]]),m=K(u,[1,u.shape[0],u.shape[1],u.shape[2],u.shape[3]])),k(p.rank===5,()=>`Error in avgPool3dBackprop: dy must be rank 5 but got rank ${p.rank}.`),k(m.rank===5,()=>`Error in avgPool3dBackprop: input must be rank 5 but got rank ${m.rank}.`),k(rn(s,i),()=>`Error in avgPool3dBackprop: Either strides or dilations must be 1. 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c=W(e,"a","fused matMul"),u=W(t,"b","fused matMul");[c,u]=Bt(c,u);const p=n?c.shape[c.rank-2]:c.shape[c.rank-1],m=s?u.shape[u.rank-1]:u.shape[u.rank-2],y=n?c.shape[c.rank-1]:c.shape[c.rank-2],b=s?u.shape[u.rank-2]:u.shape[u.rank-1],w=c.shape.slice(0,-2),I=u.shape.slice(0,-2),T=we(w),v=we(I);k(c.rank>=2&&u.rank>=2&&c.rank===u.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${c.rank} and ${u.rank}.`),k(ot(w,I),()=>`Error in fused matMul: outer dimensions (${w}) and (${I}) of Tensors with shapes ${c.shape} and ${u.shape} must match.`),k(p===m,()=>`Error in fused matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${c.shape} and ${u.shape} and transposeA=${n} and transposeB=${s} must match.`);const N=c.shape.slice(0,-2).concat([y,b]),E=n?K(c,[T,p,y]):K(c,[T,y,p]),D=s?K(u,[v,b,m]):K(u,[v,m,b]);let F;i!=null&&(F=W(i,"bias","fused matMul"),[F]=Bt(F,c),nt(N,F.shape));let _;a!=null&&(_=W(a,"prelu weights","fused matMul"));const B=(J,oe)=>{const[ce,ue,he,pe]=oe,le=mp(K(J,he.shape),he,o);let ye,me;if(!n&&!s?(ye=at(le,ue,!1,!0),me=at(ce,le,!0,!1)):!n&&s?(ye=at(le,ue,!1,!1),me=at(le,ce,!0,!1)):n&&!s?(ye=at(ue,le,!1,!0),me=at(ce,le,!1,!1)):(ye=at(ue,le,!0,!0),me=at(le,ce,!0,!0)),i!=null){const Ie=fp(pe,le);return[ye,me,Ie]}else return[ye,me]},U=J=>{const oe=J.fusedBatchMatMul({a:E,b:D,transposeA:n,transposeB:s,bias:F,activation:o,preluActivationWeights:_});return oe},Y={a:E,b:D,bias:F,preluActivationWeights:_},q={transposeA:n,transposeB:s,activation:o};if(i==null){const J=Di((oe,ce,ue)=>{const he=V.runKernelFunc(U,Y,null,my,q);return ue([oe,ce,he]),{value:K(he,N),gradFunc:B}});return J(E,D)}else{const J=Di((oe,ce,ue,he)=>{const pe=V.runKernelFunc(U,Y,null,my,q);return he([oe,ce,pe,ue]),{value:K(pe,N),gradFunc:B}});return J(E,D,F)}}const bp=P({fusedMatMul_:iB});var rB=Object.freeze({__proto__:null,conv2d:Hb,depthwiseConv2d:YA,matMul:bp});function oB(e){return Gb(e,.54,.46)}const aB=P({hammingWindow_:oB});function 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UB(e,t,n){k(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),k(n%1===0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);const s=W(e,"a","bandPart");k(s.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${s.rank}.`);const i=s.shape,[o,a]=s.shape.slice(-2);if(!(t<=o))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${o}).`);if(!(n<=a))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${a}).`);t<0&&(t=o),n<0&&(n=a);const c=K(yh(0,o,1,"int32"),[-1,1]),u=yh(0,a,1,"int32"),p=Ce(c,u),m=Bs(Mr(p,Ne(+t,"int32")),tr(p,Ne(-n,"int32"))),y=ct([o,a],s.dtype);return K(as(_i(K(s,[-1,o,a])).map(b=>$n(m,b,y))),i)}const BB=P({bandPart_:UB});function MB(e){let t;if(Array.isArray(e)){t=!1,k(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");const i=e[0].shape[0];for(let o=1;o<e.length;++o)k(e[o].shape[0]===i,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[o].shape[0]} vs. ${i})`)}else t=!0,e=os(e,e.shape[0],0).map(i=>zr(i,[0]));k(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);const n=[],s=e;for(let i=0;i<e.length;++i)n.push(V.tidy(()=>{let o=s[i];if(i>0)for(let a=0;a<i;++a){const c=X(Ue(X(n[a],o)),n[a]);o=Ce(o,c)}return _e(o,pp(o,"euclidean"))}));return t?as(n,0):n}const PB=P({gramSchmidt_:MB});function zB(e,t=!1){if(k(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return ZA(e,t);{const n=e.shape.slice(0,e.shape.length-2).reduce((u,p)=>u*p),s=_i(K(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),i=[],o=[];s.forEach(u=>{const[p,m]=ZA(u,t);i.push(p),o.push(m)});const a=K(as(i,0),e.shape),c=K(as(o,0),e.shape);return[a,c]}}function ZA(e,t=!1){return V.tidy(()=>{k(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);const n=e.shape[0],s=e.shape[1];let i=Kd(n),o=Wr(e);const a=Gr([[1]],[1,1]);let c=Wr(a);const u=n>=s?s:n;for(let p=0;p<u;++p){const m=o,y=c,b=i;[c,o,i]=V.tidy(()=>{const w=st(o,[p,p],[n-p,1]),I=pp(w),T=st(o,[p,p],[1,1]),v=$n(Ts(T,0),Gr([[-1]]),Gr([[1]])),N=Ce(T,X(v,I)),E=_e(w,N);E.shape[0]===1?c=Wr(a):c=Mt([a,st(E,[1,0],[E.shape[0]-1,E.shape[1]])],0);const D=Pt(_e(at(v,N),I)),F=st(o,[p,0],[n-p,s]),_=X(D,c),B=Pe(c);if(p===0)o=Ce(F,at(_,at(B,F)));else{const q=Ce(F,at(_,at(B,F)));o=Mt([st(o,[0,0],[p,s]),q],0)}const U=Pe(_),Y=st(i,[0,p],[n,i.shape[1]-p]);if(p===0)i=Ce(Y,at(at(Y,c),U));else{const q=Ce(Y,at(at(Y,c),U));i=Mt([st(i,[0,0],[n,p]),q],1)}return[c,o,i]}),qe([m,y,b])}return!t&&n>s&&(i=st(i,[0,0],[n,s]),o=st(o,[0,0],[s,s])),[i,o]})}const GB=P({qr_:zB});(function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(r.Reduction||(r.Reduction={}));function VB(e,t,n=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const s=W(e,"losses","computeWeightedLoss");let i=null;t!=null&&(i=W(t,"weights","computeWeightedLoss"));const o=i==null?s:X(s,i);if(n===r.Reduction.NONE)return o;if(n===r.Reduction.SUM)return Ue(o);if(n===r.Reduction.MEAN){if(i==null)return zt(o);{const a=s.size/i.size,c=_e(Ue(o),Ue(i));return a>1?_e(c,Ne(a)):c}}if(n===r.Reduction.SUM_BY_NONZERO_WEIGHTS){if(i==null)return _e(Ue(o),Ne(s.size));{const a=X(i,si(s.shape)),c=ve(Ue(Pr(a,Ne(0))),"float32");return _e(Ue(o),c)}}throw Error(`Unknown reduction: ${n}`)}const nr=P({computeWeightedLoss_:VB});function HB(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=W(e,"labels","absoluteDifference"),o=W(t,"predictions","absoluteDifference");let a=null;n!=null&&(a=W(n,"weights","absoluteDifference")),dt(i.shape,o.shape,"Error in absoluteDifference: ");const c=rn(Ce(i,o));return nr(c,a,s)}const YB=P({absoluteDifference_:HB});function qB(e,t,n,s,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","cosineDistance"),a=W(t,"predictions","cosineDistance");let c=null;s!=null&&(c=W(s,"weights","cosineDistance")),dt(o.shape,a.shape,"Error in cosineDistance: ");const u=Ne(1),p=Ce(u,Ue(X(o,a),n,!0));return nr(p,c,i)}const jB=P({cosineDistance_:qB});function KB(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let i=W(e,"labels","hingeLoss");const o=W(t,"predictions","hingeLoss");let a=null;n!=null&&(a=W(n,"weights","hingeLoss")),dt(i.shape,o.shape,"Error in hingeLoss: ");const c=Ne(1);i=Ce(X(Ne(2),i),c);const u=Fi(Ce(c,X(i,o)));return nr(u,a,s)}const XB=P({hingeLoss_:KB});function JB(e,t,n,s=1,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","huberLoss"),a=W(t,"predictions","huberLoss");let c=null;n!=null&&(c=W(n,"weights","huberLoss")),dt(o.shape,a.shape,"Error in huberLoss: ");const u=Ne(s),p=rn(Ce(a,o)),m=ko(p,u),y=Ce(p,m),b=be(X(Ne(.5),Lt(m)),X(u,y));return nr(b,c,i)}const ZB=P({huberLoss_:JB});function QB(e,t,n,s=1e-7,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","logLoss"),a=W(t,"predictions","logLoss");let c=null;n!=null&&(c=W(n,"weights","logLoss")),dt(o.shape,a.shape,"Error in logLoss: ");const u=Ne(1),p=Ne(s),m=Pt(X(o,is(be(a,p)))),y=X(Ce(u,o),is(be(Ce(u,a),p))),b=Ce(m,y);return nr(b,c,i)}const eM=P({logLoss_:QB});function tM(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=W(e,"labels","meanSquaredError"),o=W(t,"predictions","meanSquaredError");let a=null;n!=null&&(a=W(n,"weights","meanSquaredError")),dt(i.shape,o.shape,"Error in meanSquaredError: ");const c=Sh(i,o);return nr(c,a,s)}const nM=P({meanSquaredError_:tM});function sM(e,t){const n=W(e,"labels","sigmoidCrossEntropyWithLogits"),s=W(t,"logits","sigmoidCrossEntropyWithLogits");dt(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");const i=Fi(s),o=X(s,n),a=Jd(xs(Pt(rn(s))));return be(Ce(i,o),a)}function iM(e,t,n,s=0,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=W(e,"multiClassLabels","sigmoidCrossEntropy");const a=W(t,"logits","sigmoidCrossEntropy");let c=null;if(n!=null&&(c=W(n,"weights","sigmoidCrossEntropy")),dt(o.shape,a.shape,"Error in sigmoidCrossEntropy: "),s>0){const p=Ne(s),m=Ne(1),y=Ne(.5);o=be(X(o,Ce(m,p)),X(y,p))}const u=sM(o,a);return nr(u,c,i)}const rM=P({sigmoidCrossEntropy_:iM});function oM(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);const s=Di((i,o,a)=>{const c=!0,u=vb(o,[n],c),p=Ce(ve(o,"float32"),u);a([i,p]);const m=Pt(X(p,i)),y=Ue(m,[n]),b=(w,I)=>{const[T,v]=I,N=En(w.shape,[n]);return[X(K(w,N),Ce(ve(T,"float32"),xs(v))),X(K(w,N),Ce(xs(v),ve(T,"float32")))]};return{value:y,gradFunc:b}});return s(e,t)}function aM(e,t,n,s=0,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=W(e,"onehotLabels","softmaxCrossEntropy");const a=W(t,"logits","softmaxCrossEntropy");let c=null;if(n!=null&&(c=W(n,"weights","softmaxCrossEntropy")),dt(o.shape,a.shape,"Error in softmaxCrossEntropy: "),s>0){const p=Ne(s),m=Ne(1),y=Ne(o.shape[1]);o=be(X(o,Ce(m,p)),_e(p,y))}const u=oM(o,a);return nr(u,c,i)}const cM=P({softmaxCrossEntropy_:aM});const lM={fft:wh,ifft:ja,rfft:Lh,irfft:hp},hM={hammingWindow:aB,hannWindow:qA,frame:jA,stft:uB},Vr={flipLeftRight:fB,resizeNearestNeighbor:JA,resizeBilinear:XA,rotateWithOffset:yB,cropAndResize:pB,nonMaxSuppression:wB,nonMaxSuppressionAsync:NB,nonMaxSuppressionWithScore:RB,nonMaxSuppressionWithScoreAsync:EB,nonMaxSuppressionPadded:kB,nonMaxSuppressionPaddedAsync:_B},QA={bandPart:BB,gramSchmidt:PB,qr:GB},uM={absoluteDifference:YB,computeWeightedLoss:nr,cosineDistance:jB,hingeLoss:XB,huberLoss:ZB,logLoss:eM,meanSquaredError:nM,sigmoidCrossEntropy:rM,softmaxCrossEntropy:cM};class sr extends No{minimize(e,t=!1,n){const{value:s,grads:i}=this.computeGradients(e,n);if(n!=null){const o=n.map(a=>({name:a.name,tensor:i[a.name]}));this.applyGradients(o)}else this.applyGradients(i);return qe(i),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Ab(e,t)}dispose(){this.iterations_!=null&&qe(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ne(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(sr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});class xh extends sr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n],o=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:ee(()=>et(i).variable(o))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:ee(()=>et(i).variable(o))});const a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;const c=this.accumulatedGrads[s].variable,u=this.accumulatedUpdates[s].variable;ee(()=>{const p=be(X(c,this.rho),X(Lt(a),1-this.rho)),m=X(_e(Sn(be(u,this.epsilon)),Sn(be(c,this.epsilon))),a),y=be(X(u,this.rho),X(Lt(m),1-this.rho));c.assign(p),u.assign(y);const b=be(X(m,-this.learningRate),i);i.assign(b)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(qe(this.accumulatedGrads.map(e=>e.variable)),qe(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}xh.className="Adadelta",ge(xh);class Th extends sr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n];if(this.accumulatedGrads[s]==null){const c=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:ee(()=>hh(i.shape,this.initialAccumulatorValue).variable(c))}}const o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;const a=this.accumulatedGrads[s].variable;ee(()=>{const c=be(a,Lt(o));a.assign(c);const u=be(X(_e(o,Sn(be(c,V.backend.epsilon()))),-this.learningRate),i);i.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&qe(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}Th.className="Adagrad",ge(Th);class Ah extends sr{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],ee(()=>{this.accBeta1=Ne(t).variable(),this.accBeta2=Ne(n).variable()}),s==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);ee(()=>{const n=Ce(1,this.accBeta1),s=Ce(1,this.accBeta2);t.forEach((i,o)=>{const a=V.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:ee(()=>et(a).variable(c))}),this.accumulatedSecondMoment[o]==null&&(this.accumulatedSecondMoment[o]={originalName:`${i}/v`,variable:ee(()=>et(a).variable(c))});const u=Array.isArray(e)?e[o].tensor:e[i];if(u==null)return;const p=this.accumulatedFirstMoment[o].variable,m=this.accumulatedSecondMoment[o].variable,y=be(X(p,this.beta1),X(u,1-this.beta1)),b=be(X(m,this.beta2),X(Lt(u),1-this.beta2)),w=_e(y,n),I=_e(b,s);p.assign(y),m.assign(b);const T=be(X(_e(w,be(Sn(I),this.epsilon)),-this.learningRate),a);a.assign(T)}),this.accBeta1.assign(X(this.accBeta1,this.beta1)),this.accBeta2.assign(X(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&qe(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&qe(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),ee(()=>{this.accBeta1.assign(ii(this.beta1,this.iterations_+1)),this.accBeta2.assign(ii(this.beta2,this.iterations_+1))});const t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}}Ah.className="Adam",ge(Ah);class vh extends sr{constructor(e,t,n,s=null,i=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=i,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],ee(()=>{this.iteration=Ne(0).variable(),this.accBeta1=Ne(t).variable()}),s==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);ee(()=>{const n=Ce(1,this.accBeta1),s=_e(-this.learningRate,be(X(this.iteration,this.decay),1));t.forEach((i,o)=>{const a=V.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:et(a).variable(c)}),this.accumulatedWeightedInfNorm[o]==null&&(this.accumulatedWeightedInfNorm[o]={originalName:`${i}/v`,variable:et(a).variable(c)});const u=Array.isArray(e)?e[o].tensor:e[i];if(u==null)return;const p=this.accumulatedFirstMoment[o].variable,m=this.accumulatedWeightedInfNorm[o].variable,y=be(X(p,this.beta1),X(u,1-this.beta1)),b=X(m,this.beta2),w=rn(u),I=Us(b,w);p.assign(y),m.assign(I);const T=be(X(_e(s,n),_e(y,be(I,this.epsilon))),a);a.assign(T)}),this.iteration.assign(be(this.iteration,1)),this.accBeta1.assign(X(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&qe(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&qe(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}}vh.className="Adamax",ge(vh);class Za extends sr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;const o=V.registeredVariables[n];ee(()=>{const a=be(X(this.c,i),o);o.assign(a)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Rn(Ne(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}Za.className="SGD",ge(Za);class Nh extends Za{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Ne(this.momentum)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n];if(this.accumulations[s]==null){const c=!1;this.accumulations[s]={originalName:`${n}/momentum`,variable:ee(()=>et(i).variable(c))}}const o=this.accumulations[s].variable,a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;ee(()=>{let c;const u=be(X(this.m,o),a);this.useNesterov?c=be(X(this.c,be(a,X(u,this.m))),i):c=be(X(this.c,u),i),o.assign(u),i.assign(c)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&qe(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}Nh.className="Momentum",ge(Nh);class Ch extends sr{constructor(e,t=.9,n=0,s=null,i=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=i,s==null&&(this.epsilon=V.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n],o=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:ee(()=>et(i).variable(o))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:ee(()=>et(i).variable(o))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:ee(()=>et(i).variable(o))});const a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;const c=this.accumulatedMeanSquares[s].variable,u=this.accumulatedMoments[s].variable;ee(()=>{const p=be(X(c,this.decay),X(Lt(a),1-this.decay));if(this.centered){const m=this.accumulatedMeanGrads[s].variable,y=be(X(m,this.decay),X(a,1-this.decay)),b=_e(X(a,this.learningRate),Sn(Ce(p,be(Lt(y),this.epsilon)))),w=be(X(u,this.momentum),b);c.assign(p),m.assign(y),u.assign(w);const I=Ce(i,w);i.assign(I)}else{const m=be(X(c,this.decay),X(Lt(a),1-this.decay)),y=be(X(u,this.momentum),_e(X(a,this.learningRate),Sn(be(m,this.epsilon))));c.assign(m),u.assign(y);const b=Ce(i,y);i.assign(b)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&qe(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&qe(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&qe(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}}Ch.className="RMSProp",ge(Ch);class Bo{static sgd(e){return new Za(e)}static momentum(e,t,n=!1){return new Nh(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,i=!1){return new Ch(e,t,n,s,i)}static adam(e=.001,t=.9,n=.999,s=null){return new Ah(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new xh(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,i=0){return new vh(e,t,n,s,i)}static adagrad(e,t=.1){return new Th(e,t)}}const Mo={sgd:Bo.sgd,momentum:Bo.momentum,adadelta:Bo.adadelta,adagrad:Bo.adagrad,rmsprop:Bo.rmsprop,adamax:Bo.adamax,adam:Bo.adam};const dM=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function Ip(){return new Promise(e=>dM(()=>e()))}function qb(e,t,n){const s=n*(typeof e=="number"?e:e[0]),i=t*(typeof e=="number"?e:e[1]);return[s,i]}function Rh(e,t,n,s=!0){let i=[];if(s)i=i.concat(t.slice(0)),i.push(e[0]/n),i=i.concat(e.slice(1));else{i=i.concat(e[0]);const o=t.length;for(let a=0;a<o;++a)i=i.concat([e[a+1]/t[a],t[a]]);i=i.concat(e.slice(o+1))}return i}function Oh(e,t,n=!0){const s=[];if(n){s.push(t);for(let i=t+1;i<e;++i)i<=2*t?(s.push(i),s.push(i-(t+1))):s.push(i)}else{const 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iw=Object.freeze({__proto__:null,slice_util:KT,segment_util:oW,castTensor:ov,reshapeTensor:av,linspaceImpl:sw,upcastType:Cn,axesAreInnerMostDims:ib,combineLocations:tA,computeOutAndReduceShapes:On,expandShapeToKeepDim:En,assertAxesAreInnerMostDims:ss,getAxesPermutation:_n,getUndoAxesPermutation:eh,getInnerMostAxes:Is,getBroadcastDims:Eo,getReductionAxes:an,assertAndGetBroadcastShape:nt,assertParamsConsistent:mb,computeOutShape:Ur,computeDilation2DInfo:zd,computePool2DInfo:Wn,computePool3DInfo:sh,computeConv2DInfo:Oi,computeConv3DInfo:ih,computeDefaultPad:ub,tupleValuesAreOne:$r,eitherStridesOrDilationsAreOne:on,convertConv2DDataFormat:rh,getFusedDyActivation:mp,getFusedBiasGradient:fp,applyActivation:gp,shouldFuse:yp,PARALLELIZE_THRESHOLD:xb,computeOptimalWindowSize:uh,getImageCenter:qb,getReshaped:Rh,getPermuted:Oh,getReshapedPermuted:Eh,getSliceBeginCoords:jb,getSliceSize:Kb,prepareAndValidate:Fd,validateUpdateShape:qy,validateInput:jy,calculateShapes:Ua,SELU_SCALEALPHA:xp,SELU_SCALE:Tp,ERF_P:Xb,ERF_A1:Jb,ERF_A2:Zb,ERF_A3:Qb,ERF_A4:ew,ERF_A5:tw,warn:Qa,log:pM,mergeRealAndImagArrays:ir,splitRealAndImagArrays:ev,complexWithEvenIndex:tv,complexWithOddIndex:nv,getComplexWithIndex:nw,assignToTypedArray:sv,exponents:iv,exponent:rv,prepareSplitSize:IA});function 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mM=Object.freeze({__proto__:null,nonMaxSuppressionV3Impl:wp,nonMaxSuppressionV4Impl:Lp,nonMaxSuppressionV5Impl:Sp,split:rw,tile:ow,topkImpl:aw,whereImpl:dp});const fM={kernelName:fe,inputsToSave:["x"],gradFunc:(e,t)=>{const[n]=t;return{x:()=>X(e,Ka(ve(n,"float32"),-1))}}};const gM={kernelName:de,inputsToSave:["x"],gradFunc:(e,t)=>{const[n]=t;return{x:()=>{const s=Lt(ve(n,"float32")),i=Sn(Ce(Ne(1),s));return Pt(_e(e,i))}}}};const yM={kernelName:Ae,inputsToSave:["x"],gradFunc:(e,t)=>{const[n]=t;return{x:()=>{const s=Sn(Ce(Lt(ve(n,"float32")),1));return _e(e,s)}}}};const bM={kernelName:xe,inputsToSave:["a","b"],gradFunc:(e,t)=>{const[n,s]=t,i=nt(n.shape,s.shape),o=()=>{let c=e;const u=an(n.shape,i);return u.length>0&&(c=Ue(c,u)),K(c,n.shape)},a=()=>{let c=e;const u=an(s.shape,i);return u.length>0&&(c=Ue(c,u)),K(c,s.shape)};return{a:o,b:a}}};const wM={kernelName:Me,saveAllInputs:!0,gradFunc:(e,t)=>{const n={};return t.forEach((s,i)=>{n[i]=()=>e.clone()}),n}};const LM={kernelName:$t,inputsToSave:["x"],gradFunc:(e,t)=>{const[n]=t;return{x:()=>et(n)}}};const SM={kernelName:Kt,inputsToSave:["x"],gradFunc:(e,t)=>{const[n]=t;return{x:()=>et(n)}}};const IM={kernelName:Fn,inputsToSave:["x"],gradFunc:(e,t)=>{const[n]=t;return{x:()=>_e(e,Sn(Ce(Ne(1),Lt(ve(n,"float32")))))}}};const xM={kernelName:vn,inputsToSave:["x"],gradFunc:(e,t)=>{const[n]=t;return{x:()=>{const s=Sn(be(Ne(1),Lt(ve(n,"float32"))));return _e(e,s)}}}};const TM={kernelName:Ai,inputsToSave:["a","b"],gradFunc:(e,t)=>{const[n,s]=t,i=nt(n.shape,s.shape),o=()=>{const c=be(Lt(n),Lt(s));let u=X(e,_e(s,c));const p=an(n.shape,i);return p.length>0&&(u=Ue(u,p)),K(u,n.shape)},a=()=>{const c=be(Lt(n),Lt(s));let u=Pt(X(e,_e(n,c)));const p=an(s.shape,i);return p.length>0&&(u=Ue(u,p)),K(u,s.shape)};return{a:o,b:a}}};const AM={kernelName:Nn,inputsToSave:["x"],gradFunc:(e,t)=>{const[n]=t;return{x:()=>_e(e,be(Lt(ve(n,"float32")),1))}}};const vM={kernelName:Qs,inputsToSave:["x"],gradFunc:(e,t)=>{const[n]=t;return{x:()=>_e(e,Ce(Ne(1),Lt(ve(n,"float32"))))}}};function NM(e,t,n,s,i=[1,1,1],o,a){const c=W(e,"dy","avgPool3dBackprop"),u=W(t,"input","avgPool3dBackprop");let p=c,m=u,y=!1;u.rank===4&&(y=!0,p=K(c,[1,c.shape[0],c.shape[1],c.shape[2],c.shape[3]]),m=K(u,[1,u.shape[0],u.shape[1],u.shape[2],u.shape[3]])),k(p.rank===5,()=>`Error in avgPool3dBackprop: dy must be rank 5 but got rank ${p.rank}.`),k(m.rank===5,()=>`Error in avgPool3dBackprop: input must be rank 5 but got rank ${m.rank}.`),k(on(s,i),()=>`Error in avgPool3dBackprop: Either strides or dilations must be 1. 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Ps{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new Ge("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return ee(()=>{if(e.length<2)throw new Ge("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);const n=e[0]>e[1]?[e[1],e[0]]:e,s=Rp(n,0,1,"float32");let i=QA.gramSchmidt(s);return e[0]>e[1]&&(i=i.transpose()),X(this.gain,i)})}getConfig(){return{gain:this.gain,seed:this.seed}}}vw.className="Orthogonal",ge(vw);const Ov={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function Ev(e,t={}){return Dh(e,Ws.getMap().classNameMap,t,"initializer")}function Vt(e){return cw(e)}function Ft(e){if(typeof e=="string"){const t=e in Ov?Ov[e]:e;if(t==="GlorotNormal")return new Dp;if(t==="GlorotUniform")return new Ep;if(t==="HeNormal")return new kp;if(t==="HeUniform")return new Fp;if(t==="LeCunNormal")return new _p;if(t==="LeCunUniform")return new Wp;{const n={};return n.className=t,n.config={},Ev(n)}}else return e instanceof Ps?e:Ev(e)}function Mz(){return new Lw}function Pz(){return new Op}function zz(e){return new Sw(e)}function Gz(e){return new Iw(e)}function Vz(e){return new xw(e)}function Hz(e){return new Tw(e)}function Yz(e){return new Aw(e)}function qz(e){return new Zn(e)}function jz(e){return new Ep(e)}function Kz(e){return new Dp(e)}function Xz(e){return new kp(e)}function Jz(e){return new Fp(e)}function Zz(e){return new _p(e)}function Qz(e){return new Wp(e)}function e3(e){return new vw(e)}var t3=Object.freeze({__proto__:null,zeros:Mz,ones:Pz,constant:zz,randomUniform:Gz,randomNormal:Vz,truncatedNormal:Hz,identity:Yz,varianceScaling:qz,glorotUniform:jz,glorotNormal:Kz,heNormal:Xz,heUniform:Jz,leCunNormal:Zz,leCunUniform:Qz,orthogonal:e3});let n3=0;function Dv(){return n3++}const $p={};function Up(e=""){return e in $p||($p[e]=0),$p[e]+=1,e+$p[e].toString()}function Nw(e){return Array.isArray(e)&&Array.isArray(e[0])}function Bp(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function Xe(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new j(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function 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lee(e,t){return e.write(be(e.read(),t))}function hee(e,t){return e.write(Ce(e.read(),t))}function Cw(e){return e.map(t=>t.read())}function Rw(e){e.forEach(t=>{const n=t[0];n.write(t[1])})}function uee(e,t){const n=t.map(i=>i.read()),s=Ab(e,n);return t.map(i=>s.grads[i.name])}class fn{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}}class li{constructor(e,t,n,s,i,o,a){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=i,this.outputTensorIndex=a,this.id=Dv(),o!=null&&(this.originalName=xv(o),this.name=Tv(this.originalName)),this.rank=t.length}}let i3=0;class Pp{constructor(e,t){this.callArgs=t,this.id=i3++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(const n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){const e=[];for(const t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}}let r3=0;class lt extends Ao{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=r3++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){const n=this.getClassName();t=or(n)+"_"+Up(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let i=null;e.batchSize!=null&&(i=e.batchSize),n=[i].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new oi(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new j(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Jn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Jn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new rr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new rr(`Layer ${this.name} is not connected, no input to return.`);return Jn(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new rr(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new rr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Jn(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=Nt(e),this.inputSpec==null||this.inputSpec.length===0)return;const t=Nt(this.inputSpec);if(e.length!==t.length)throw new j(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){const s=e[n],i=t[n];if(i==null)continue;const o=s.rank;if(i.ndim!=null&&o!==i.ndim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${i.ndim}, found ndim=${o}`);if(i.maxNDim!=null&&o>i.maxNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${i.maxNDim}, found ndim=${o}`);if(i.minNDim!=null&&o<i.minNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${i.minNDim}, found ndim=${o}.`);if(i.dtype!=null&&s.dtype!==i.dtype)throw new j(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${i.dtype}, found dtype=${s.dtype}.`);if(i.axes){const a=s.shape;for(const c in i.axes){const u=Number(c),p=i.axes[c],m=u>=0?a[u]:a[a.length+u];if(p!=null&&[p,null].indexOf(m)===-1)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${p} but got shape ${a}.`)}}if(i.shape!=null)for(let a=0;a<i.shape.length;++a){const c=i.shape[a],u=s.shape[a];if(c!=null&&u!=null&&c!==u)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected shape=${i.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();const n=Nt(e);let s=!0;for(const o of n)if(!(o instanceof li)){s=!1;break}let i=!0;for(const o of n)if(o instanceof li){i=!1;break}if(s===i)throw new j("Arguments to apply() must be all SymbolicTensors or all Tensors");return Po(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);const o=[];for(const a of Nt(e))o.push(a.shape);this.build(Jn(o)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&i&&(this._refCount=1)}if(this.assertInputCompatibility(e),i){let o=this.call(e,t);const a=Nt(o),c=[];for(let u of a)n.indexOf(u)!==-1&&(u=u.clone()),c.push(u);if(o=Jn(c),this.activityRegularizer!=null)throw new Ge("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}else{const o=o3(e),a=this.computeOutputShape(o);let c;const u=a3(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?o[0]:o),a!=null&&a.length>0&&Array.isArray(a[0])?c=a.map((p,m)=>new li(u,p,this,Nt(e),t,this.name,m)):c=new li(u,a,this,Nt(e),t,this.name),this.addInboundNode(e,c,null,null,o,a,t),this._refCount++,this.activityRegularizer!=null)throw new Ge("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return c}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape==null)return;if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new rr(`The layer ${this.name} has never been called and thus has no defined output shape.`);const e=[];for(const t of this.inboundNodes){const n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){const t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new rr(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new oi(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Mp(this.weights)}build(e){this.built=!0}getWeights(e=!1){return Cw(e?this.trainableWeights:this.weights)}setWeights(e){ee(()=>{const t=this.weights;if(t.length!==e.length)throw new j(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;const n=[],s=Cw(t);for(let i=0;i<s.length;++i){const o=s[i],a=t[i],c=e[i];if(!ot(o.shape,c.shape))throw new j(`Layer weight shape ${o.shape} not compatible with provided weight shape ${c.shape}`);n.push([a,c])}Rw(n)})}addWeight(e,t,n,s,i,o,a){if(this._addedWeightNames.indexOf(e)!==-1)throw new j(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=Ft("zeros"));const c=s.apply(t,n),u=new ci(c,n,e,o,a);return c.dispose(),i!=null&&this.addLoss(()=>i.apply(u.read())),o==null&&(o=!0),o?this._trainableWeights.push(u):this._nonTrainableWeights.push(u),u}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){if(e==null||Array.isArray(e)&&e.length===0)return;e=Nt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e)}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,i,o,a=null){const c=Nt(e);t=Nt(t),n=Nt(n),s=Nt(s),i=Bp(i),o=Bp(o);const u=[],p=[],m=[];for(const y of c)u.push(y.sourceLayer),p.push(y.nodeIndex),m.push(y.tensorIndex);new Pp({outboundLayer:this,inboundLayers:u,nodeIndices:p,tensorIndices:m,inputTensors:c,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:i,outputShapes:o},a);for(let y=0;y<t.length;y++)t[y].sourceLayer=this,t[y].nodeIndex=this.inboundNodes.length-1,t[y].tensorIndex=y}getConfig(){const e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount===0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}}function o3(e){e=Nt(e);const t=[];for(const n of e)t.push(n.shape);return Jn(t)}function a3(e){return"float32"}function Fv(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{const s=t.inboundNodes[n];if(s.inboundLayers.length===0)return s.inputTensors;{const i=[];for(let o=0;o<s.inboundLayers.length;o++){const a=s.inputTensors[o],c=s.inboundLayers[o],u=s.nodeIndices[o],p=Fv(a,c,u);for(const m of p)i.indexOf(m)===-1&&i.push(m)}return i}}}class tc extends lt{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Up("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new j("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new j("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new j("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");const n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];const s=new li(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new Pp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new j(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}}tc.className="InputLayer",ge(tc);function _v(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. 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y,b;if(Uw[m]==null){const I=F3(a,t);y=I.sorted,b=I.recipientCounts,Uw[m]=y,Jv[m]=b}y=Uw[m],b={},i||Object.assign(b,Jv[m]);const w=new Go(t);for(let I=0;I<y.length;++I){if(s!=null){const q=Bd().numTensors;q>s.maxNumTensors&&(s.maxNumTensors=q),q<s.minNumTensors&&(s.minNumTensors=q)}const T=y[I],v=T.sourceLayer;if(v instanceof tc)continue;const N=[],E=[],D=[];let F=!1;for(const q of T.inputs){const J=w.getValue(q),oe=w.getMask(q);N.push(J),E.push(oe),oe!=null&&(F=!0),i||(b[q.name]--,b[q.name]===0&&!t.hasKey(q)&&c.indexOf(q.name)===-1&&!J.isDisposed&&q.sourceLayer.stateful!==!0&&D.push(J))}F&&(n=n||{},n.mask=E[0]);const _=Nt(v.apply(N,n));let B=null;v.supportsMasking&&(B=v.computeMask(N,E));const U=W3(T),Y=Array.isArray(U)?U:[U];for(let q=0;q<Y.length;++q){w.hasKey(Y[q])||w.add(Y[q],_[q],Array.isArray(B)?B[0]:B);const J=c.indexOf(Y[q].name);J!==-1&&(u[J]=_[q])}i||qe(D)}return w.disposeMasks(),o?u:u[0]}function F3(e,t){k(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],s={};if(e.length===1){const i=Zv(e[0],t);n=i.sorted,s=i.recipientMap}else{const i=new Set;for(const o of e){const{sorted:a,recipientMap:c}=Zv(o,t);for(const u of a)i.has(u.name)||(n.push(u),i.add(u.name));for(const u in c)s[u]==null&&(s[u]=new Set),c[u].forEach(p=>s[u].add(p))}}return{sorted:n,recipientCounts:_3(s)}}function _3(e){const t={};for(const n in e)t[n]=e[n].size;return t}function Zv(e,t){const n=new Set,s=[],i={};for(const c of t.names())n.add(c);const o=[],a=[];for(o.push(e);o.length>0;){const c=o[o.length-1];if(n.has(c.name)){o.pop();continue}const u=a[a.length-1]===o.length-1;if(c.inputs.length===0||u)o.pop(),s.push(c),n.add(c.name),u&&a.pop();else{a.push(o.length-1);for(const p of c.inputs){if(i[p.name]==null&&(i[p.name]=new Set),i[p.name].add(c.name),n.has(p.name))continue;o.push(p)}}}return{sorted:s,recipientMap:i}}function W3(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(const i of e.sourceLayer.inboundNodes[s].outputTensors)if(i.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}class Ui extends lt{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){const N=this.getClassName().toLowerCase();this.name=Up(N)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],Hr(this.inputs).length!==this.inputs.length)throw new j(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(N=>N.name)}`);Hr(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(N=>N.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(const N of this.outputs){const E=N.sourceLayer,D=N.nodeIndex,F=N.tensorIndex;this.outputLayers.push(E),this.outputLayersNodeIndices.push(D),this.outputLayersTensorIndices.push(F)}for(const N of this.inputs){const E=N.sourceLayer,D=N.nodeIndex,F=N.tensorIndex;vs(D===0,"input layer has >1 nodes"),vs(F===0,"input layer has >1 tensors"),this.inputLayers.push(E),this.inputLayersNodeIndices.push(D),this.inputLayersTensorIndices.push(F)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let N=0;N<this.inputLayers.length;N++){const E=this.inputLayers[N];if(!(E instanceof tc))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${N} (0-based) originates from layer type ${E.getClassName()}.`);this.inputNames.push(E.name),this.feedInputShapes.push(E.batchInputShape),this.feedInputNames.push(E.name)}for(const N of this.outputLayers)this.outputNames.push(N.name);this.internalInputShapes=this.inputs.map(N=>N.shape),this.internalOutputShapes=this.outputs.map(N=>N.shape);const t={},n={},s={},i={},o={},a=[],c=(N,E,D,F,_,B)=>{(F==null||_==null||B==null)&&(F=N.sourceLayer,_=N.nodeIndex,B=N.tensorIndex);const U=F.inboundNodes[_];if(D.indexOf(U)!==-1)throw new oi(`The tensor ${N.name} at layer "${F.name}" is part of a cycle.`);if(E.indexOf(U)!==-1)return;this.containerNodes.add(Ui.nodeKey(F,_)),F.id in o||(o[F.id]=Object.keys(o).length),D.indexOf(U)===-1&&D.push(U);const Y=U.inboundLayers.length;for(let q=0;q<Y;q++){const J=U.inputTensors[q],oe=U.inboundLayers[q],ce=U.nodeIndices[q],ue=U.tensorIndices[q];c(J,E,D,oe,ce,ue)}for(E.push(U);D.indexOf(U)>=0;)D.splice(D.indexOf(U),1);a.push(U)},u=[],p=[];for(const N of this.outputs)c(N,u,p);const m=a.slice().reverse();for(const N of m){n[N.id]=N,N.id in t||(t[N.id]=0);let E=t[N.id];const D=s[N.outboundLayer.id]==null?0:s[N.outboundLayer.id];E=Math.max(E,D),s[N.outboundLayer.id]=E,i[N.outboundLayer.id]=N.outboundLayer,t[N.id]=E;for(let F=0;F<N.inboundLayers.length;F++){const _=N.inboundLayers[F],B=N.nodeIndices[F],U=_.inboundNodes[B],Y=t[U.id]==null?0:t[U.id];t[U.id]=Math.max(E+1,Y),n[U.id]=U}}const y={};for(const N in t){const E=t[N];E in y||(y[E]=[]),y[E].push(n[N])}const b={};for(const N in s){const E=s[N];E in b||(b[E]=[]),b[E].push(i[N])}let w=Object.keys(b).map(N=>parseInt(N,10)).sort(vp);this.layers=[];for(const N of w){const E=b[N];E.sort((D,F)=>{const _=o[D.id],B=o[F.id];return _<B?-1:_>B?1:0});for(const D of E)D instanceof Ui&&this.internalContainerRefs.push(D),this.layers.push(D)}this.layersByDepth=b,w=Object.keys(y).map(N=>parseInt(N,10)).sort(vp);const I=this.inputs.slice(),T=[];for(const N of w)for(const E of y[N]){const D=E.outboundLayer;if(D!=null){for(const F of E.inputTensors)if(I.indexOf(F)===-1)throw new oi(`Graph disconnected: cannot obtain value for tensor ${F} at layer "${D.name}". The following previous layers were accessed without issue: ${T}`);for(const F of E.outputTensors)I.push(F);T.push(D.name)}}this.nodesByDepth=y;const v=this.layers.map(N=>N.name);for(const N of v){const E=v.filter(D=>D===N).length;if(E!==1)throw new oi(`The name "${N}" is used ${E} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(v))}this.outboundNodes=[],this.inboundNodes=[],new Pp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(N=>null),outputMasks:this.outputs.map(N=>null),inputShapes:this.inputs.map(N=>N.shape),outputShapes:this.outputs.map(N=>N.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();const e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(const t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(const t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new j("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(const t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){const n={};let s=0;for(const o of this.layers)for(const a of o.weights){if(n[a.originalName]!=null)throw new j(`Duplicate weight name: ${a.originalName}`);n[a.originalName]=a,s++}const i=[];for(const o in e){let a=o;if(n[o]==null){const c=o.split("/"),u=c.slice(0,-2).concat([c[c.length-1]]);a=u.join("/")}if(n[a]!=null)i.push([n[a],e[o]]);else if(t)throw new j(`Provided weight data has no target variable: ${o}`);delete n[a]}if(t){const o=[];for(const a in n)o.push(a);if(o.length>0)throw new j(`${o.length} of ${s} weights are not set: ${o}`)}Rw(i)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${Xp}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){const n=$w(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return ee(()=>{e=Nt(e);const n=new Go;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Ph(this.outputs,n,t)})}computeMask(e,t){return ee(()=>{e=Nt(e);let n;return t==null?n=Bo(null,e.length):n=Nt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){const t=Bp(e);if(t.length!==this.inputLayers.length)throw new j(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);const n={};for(let a=0;a<t.length;a++){const c=this.inputLayers[a],u=t[a],p=c.name+"_0_0";n[p]=u}const s=Object.keys(this.nodesByDepth).map(a=>parseInt(a,10)).sort(vp);if(s.length>1)for(const a of s){const c=this.nodesByDepth[a];for(const u of c){const p=u.outboundLayer;if(this.inputLayers.map(I=>I.id).indexOf(p.id)!==-1)continue;const m=[];for(let I=0;I<u.inboundLayers.length;I++){const T=u.inboundLayers[I],v=u.nodeIndices[I],N=u.tensorIndices[I],E=`${T.name}_${v}_${N}`,D=n[E];m.push(D)}const y=p.computeOutputShape(Jn(m)),b=Bp(y),w=p.inboundNodes.indexOf(u);for(let I=0;I<b.length;I++){const T=`${p.name}_${w}_${I}`;n[T]=b[I]}}}const i=[],o=[];for(let a=0;a<this.outputLayers.length;a++){const c=this.outputLayers[a],u=this.outputLayersNodeIndices[a],p=this.outputLayersTensorIndices[a],m=`${c.name}_${u}_${p}`;o.push(m)}for(let a=0;a<o.length;a++){const c=o[a];vs(c in n),i.push(n[c])}return Jn(i)}runInternalGraph(e,t){t==null&&(t=Bo(null,e.length));const n={};for(let c=0;c<this.inputs.length;++c){const u=this.inputs[c],p=e[c],m=t[c];n[u.id]=[p,m]}const s=Object.keys(this.nodesByDepth).map(c=>parseInt(c,10)).sort(vp);for(const c of s){const u=this.nodesByDepth[c];for(const p of u){const m=p.outboundLayer,y=p.inputTensors,b=p.outputTensors,w=new Array;for(const I of y)I.id in n&&w.push(n[I.id]);if(w.length===y.length){let I={},T,v,N,E;if(p.callArgs!=null&&(I=p.callArgs),w.length===1){const[D,F]=w[0];I.mask==null&&(I.mask=F),N=Nt(m.call(D,I)),E=Nt(m.computeMask(D,F)),T=[D],v=[F]}else T=w.map(D=>D[0]),v=w.map(D=>D[1]),I.mask==null&&(I.mask=v),N=Nt(m.call(T,I)),E=Nt(m.computeMask(T,v));if(m.activityRegularizer)throw new Ge("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let D=0;D<b.length;++D){const F=b[D],_=N[D],B=E[D];n[F.id]=[_,B]}}}}const i=[],o=[],a=[];for(const c of this.outputs){vs(c.id in n,`Could not compute output ${c.name} : ${c.id}`);const[u,p]=n[c.id];a.push(u.shape),i.push(u),o.push(p)}return[i,o,a]}buildNodeConversionMap(e){const t={};let n;for(const s of this.layers){n=s instanceof Ui?1:0;for(let i=0;i<s.inboundNodes.length;i++){const o=Ui.nodeKey(s,i);this.containerNodes.has(o)&&(t[o]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new j(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new j("Provide either a layer name or layer index");for(const n of this.layers)if(n.name===e)return n;throw new j(`No such layer: ${e}`)}calculateLosses(){return ee(()=>{const e=[];for(const t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){const s=Ui.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){const e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(const o of this.layers){const a=o.getClassName(),c=o.getConfig(),u=[];for(let m=0;m<o.inboundNodes.length;m++){const y=o.inboundNodes[m],b=Ui.nodeKey(o,m);let w={};if(this.containerNodes.has(b)){if(y.callArgs)try{JSON.stringify(y.callArgs),w=y.callArgs}catch(I){console.warn(`Layer ${o.name} was passed non-serializable keyword arguments: ${y.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),w={}}if(y.inboundLayers.length>0){const I=[];for(let T=0;T<y.inboundLayers.length;T++){const v=y.inboundLayers[T],N=y.nodeIndices[T],E=y.tensorIndices[T],D=Ui.nodeKey(v,N);let F=t[D];F==null&&(F=0),I.push([v.name,F,E,w])}u.push(I)}}}const p={};p.name=o.name,p.className=a,p.config=c,p.inboundNodes=u,n.push(p)}e.layers=n;const s=[];for(let o=0;o<this.inputLayers.length;o++){const a=this.inputLayers[o],c=this.inputLayersNodeIndices[o],u=Ui.nodeKey(a,c);if(!this.containerNodes.has(u))continue;let p=t[u];p==null&&(p=0);const m=this.inputLayersTensorIndices[o];s.push([a.name,p,m])}e.inputLayers=s;const i=[];for(let o=0;o<this.outputLayers.length;o++){const a=this.outputLayers[o],c=this.outputLayersNodeIndices[o],u=Ui.nodeKey(a,c);if(!this.containerNodes.has(u))continue;let p=t[u];p==null&&(p=0);const m=this.outputLayersTensorIndices[o];i.push([a.name,p,m])}return e.outputLayers=i,e}static fromConfig(e,t,n={},s=!1){const i={},o={};function a(T,v){T.name in o?o[T.name].push(v):o[T.name]=[v]}function c(T,v){const N=[];let E;for(const D of v){const F=D[0],_=D[1],B=D[2];if(E=D[3]==null?{}:D[3],!(F in i)){a(T,v);return}const U=i[F];if(U.inboundNodes.length<=_){a(T,v);return}const Y=U.inboundNodes[_];N.push(Y.outputTensors[B])}N.length>0&&T.apply(Jn(N),E)}function u(T){const v=T.name,N=hi(T,t.customObjects!=null?t.customObjects:{});N.setFastWeightInitDuringBuild(s),i[v]=N;const E=T.inboundNodes;E.forEach(D=>{if(!(D instanceof Array))throw new j(`Corrupted configuration, expected array for nodeData: ${D}`);a(N,D)})}const p=t.name,m=t.layers;for(const T of m)u(T);for(;!pz(o);)for(const T of m){const v=i[T.name];if(v.name in o){const N=o[v.name];delete o[v.name];for(const E of N)c(v,E)}}const y=[],b=[],w=t.inputLayers;for(const T of w){const v=T[0],N=T[1],E=T[2];vs(v in i);const D=i[v],F=D.inboundNodes[N].outputTensors;y.push(F[E])}const I=t.outputLayers;for(const T of I){const v=T[0],N=T[1],E=T[2];vs(v in i);const D=i[v],F=D.inboundNodes[N].outputTensors;b.push(F[E])}return new e({inputs:y,outputs:b,name:p})}get stateful(){if(this._stateful)throw new j("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(const e of this.layers)if(e.stateful)return!0;return!1}resetStates(){ee(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}}function Qv(e,t,n){const s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(i=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){const i=[];return t.forEach(o=>{o in e?i.push(e[o]):i.push(null)}),i}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function eN(e,t){return Qv(e,t,"classWeight")}function Aee(e,t){return Qv(e,t,"sampleWeight")}async function tN(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){const i=ee(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){const c=1;return e.argMax(c)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),o=Array.from(await i.data());qe(i);const a=[];return o.forEach(c=>{if(n[c]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${c} exists in the data but not in classWeight`);a.push(n[c])}),rs(a,"float32")}else return null}function $3(e,t){return X(e,t)}const U3=32;function nN(e,t){let n,s;const i=t;n=i.xs,s=i.ys,k(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);const o=sN("input",e.inputNames,n),a=sN("output",e.outputNames,s),c=o[0].shape[0];k(o.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${o.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),k(a.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${a.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<o.length;u++)k(o[u].shape[0]===c,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${o[u].shape[0]}; expected  ${c} based on input ${e.inputNames[0]}.`);for(let u=0;u<a.length;u++)k(a[u].shape[0]===c,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${a[u].shape[0]}; expected  ${c} based on input ${e.inputNames[0]}.`);return{xs:o,ys:a}}function sN(e,t,n){if(n instanceof Q)return[n];if(Array.isArray(n))return k(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{const s=[];for(const i of t){if(n[i]==null)throw new j(`The feature data generated by the dataset lacks the required ${e} key '${i}'.`);s.push(n[i])}return s}}function B3(e){if(e.length===3)throw new Ge("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function M3(e,t,n){const s=n.batchesPerEpoch!=null;if(k(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),k(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),k(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. 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Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(e,t,n={}){const s=n.batchSize==null?32:n.batchSize;Bw(s);const i=!0,o=this.standardizeUserDataXY(e,t,i,s);try{const a=o[0].concat(o[1]);this.makeTestFunction();const c=this.testFunction,u=this.testLoop(c,a,s,n.verbose,n.steps);return Jn(u)}finally{Vo(o[0],e),Vo(o[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),G3(this,e,t)}checkNumSamples(e,t,n,s="steps"){let i;if(n!=null){if(i=null,t!=null)throw new j(`If ${s} is set, batchSize must be null or undefined.Got batchSize = ${t}`)}else if(e!=null)Array.isArray(e)?i=e[0].shape[0]:i=e.shape[0];else throw new j(`Either the input data should have a defined shape, or ${s} shoud be specified.`);return i}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new j("`outputs` is an empty Array, which is not allowed.");const n=Array.isArray(t),s=n?t:[t],i=this.retrieveSymbolicTensors(s),o=new Go;if(e instanceof Q&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new j(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let c=0;c<this.inputs.length;++c)o.add(this.inputs[c],e[c])}else for(const c of this.inputs){const u=e[c.name];if(u==null)throw new j(`No value is provided for the model's input ${c.name}`);o.add(c,u)}const a=Ph(i,o);return n?a:a[0]}retrieveSymbolicTensors(e){const t=Bo(null,e.length);let n=e.length;for(const s of this.layers){const i=Array.isArray(s.output)?s.output:[s.output],o=i.map(a=>a.name);for(let a=0;a<e.length;++a){const c=o.indexOf(e[a]);if(c!==-1&&(t[a]=i[c],n--),n===0)break}if(n===0)break}if(n>0){const s=[];throw t.forEach((i,o)=>{i==null&&s.push(e[o])}),new j(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(s)}`)}return t}predictLoop(e,t=32,n=!1){return ee(()=>{const s=this.checkNumSamples(e);if(n)throw new Ge("Verbose predictLoop() is not implemented yet.");const i=Pw(s,t),o=this.outputs.map(a=>[]);for(let a=0;a<i.length;++a){const c=ee(()=>{const u=i[a][0],p=i[a][1],m=zh(e,u,p),y=[];if(Array.isArray(m))for(let w=0;w<m.length;++w)y.push({key:this.inputs[w],value:m[w]});else y.push({key:this.inputs[0],value:m});const b=new Go(y);return Ph(this.outputs,b)});c.forEach((u,p)=>o[p].push(u))}return Jn(o.map(a=>Mt(a,0)))})}predict(e,t={}){const n=rN(e);cN(n,this.inputNames,this.feedInputShapes,!1);try{const s=t.batchSize==null?32:t.batchSize;return Bw(s),this.predictLoop(n,s)}finally{Vo(n,e)}}predictOnBatch(e){cN(e,this.inputNames,this.feedInputShapes,!0);const t=(Array.isArray(e)?e[0]:e).shape[0];return this.predictLoop(e,t)}standardizeUserDataXY(e,t,n=!0,s){if(this.optimizer_==null)throw new oi("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");const i=[];for(let o=0;o<this.feedOutputShapes.length;++o){const a=this.feedOutputShapes[o],c=this.feedLossFns[o];c===Gp?i.push(a.slice(0,a.length-1).concat([1])):i.push(a)}if(e=aN(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=aN(t,this.feedOutputNames,i,!1,"target"),q3(e,t,null),j3(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new j(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,i=!0,o){const[a,c]=this.standardizeUserDataXY(e,t,i,o);if(n!=null)throw new Error("sample weight is not supported yet.");let u=null;if(s!=null){const p=eN(s,this.outputNames);u=[];for(let m=0;m<p.length;++m)u.push(await tN(c[m],null,p[m]))}return[a,c,u]}testLoop(e,t,n,s=0,i){return ee(()=>{const o=this.checkNumSamples(t,n,i,"steps"),a=[];if(s>0)throw new Ge("Verbose mode is not implemented yet.");if(i!=null)throw new Ge("steps mode in testLoop() is not implemented yet");{const c=Pw(o,n),u=rs(ai(0,o));for(let p=0;p<c.length;++p){const m=c[p][0],y=c[p][1],b=zo(u,m,y-m),w=Mw(t,b),I=e(w);if(p===0)for(let T=0;T<I.length;++T)a.push(Ne(0));for(let T=0;T<I.length;++T){const v=I[T];a[T]=be(a[T],X(y-m,v))}}for(let p=0;p<a.length;++p)a[p]=_e(a[p],o)}return a})}getDedupedMetricsNames(){const e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){const s=e[n];let i=s;if(gv(e,s)>1){const o=gv(e.slice(0,n),s);i+=`_${o}`}t.push(i)}return t}makeTrainFunction(){return e=>{const t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),i=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),o=[],a=()=>{const m=[];for(let I=0;I<this.inputs.length;++I)m.push({key:this.inputs[I],value:n[I]});const y=new Go(m),b=Ph(this.outputs,y,{training:!0});let w;for(let I=0;I<this.lossFunctions.length;++I){const T=this.lossFunctions[I];let v=T(s[I],b[I]);i[I]!=null&&(v=$3(v,i[I]));const N=zt(v);t.push(N),I===0?w=v:w=be(w,v)}for(let I=0;I<this.metricsTensors.length;++I){let T;if(this.outputs.length>1&&I<this.outputs.length)T=t[I];else{const v=this.metricsTensors[I][0],N=this.metricsTensors[I][1];T=zt(v(s[N],b[N]))}Rn(T),o.push(T)}return w=zt(w),this.calculateLosses().forEach(I=>{w=be(w,I)}),w},c=this.collectedTrainableWeights.map(m=>m.read()),u=!0,p=this.optimizer_.minimize(a,u,c);return[p].concat(o)}}makeTestFunction(){this.testFunction=e=>ee(()=>{const t=[];let n;const s=e.slice(0,this.inputs.length),i=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),o=[];for(let u=0;u<this.inputs.length;++u)o.push({key:this.inputs[u],value:s[u]});const a=new Go(o),c=Ph(this.outputs,a);for(let u=0;u<this.lossFunctions.length;++u){const p=this.lossFunctions[u],m=zt(p(i[u],c[u]));u===0?n=m:n=be(n,m),t.push(n)}for(let u=0;u<this.metricsTensors.length;++u){const p=this.metricsTensors[u][0],m=this.metricsTensors[u][1],y=zt(p(i[m],c[m]));t.push(y)}return t})}async fit(e,t,n={}){return H3(this,e,t,n)}async fitDataset(e,t){return M3(this,e,t)}async trainOnBatch(e,t){const n=await this.standardizeUserData(e,t),s=n[0],i=n[1],o=this.makeTrainFunction(),a=o(s.concat(i)),c=[];for(const u of a){const p=await u.data();c.push(p[0])}return qe(a),Jn(c)}getNamedWeights(e){const t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,i=this.getWeights(n);for(let o=0;o<s.length;++o){if(n&&!s[o].trainable)continue;t.push({name:s[o].originalName,tensor:i[o]})}return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){const e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){const t=Bd().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Bd().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=or(this.loss);else if(Array.isArray(this.loss)){for(const t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>or(t))}else{const t=Object.keys(this.loss);e={};const n=this.loss;for(const s of t)if(typeof n[s]=="string")e[s]=or(n[s]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[or(jp(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>or(jp(e)));{const e={};for(const t in this.metrics)e[t]=or(jp(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");const t=Mh(e.optimizer_config),n=hi(t);let s;if(typeof e.loss=="string")s=Mo(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(o=>Mo(o));else if(e.loss!=null){s={};for(const o in e.loss)s[o]=Mo(e.loss[o])}let i;if(Array.isArray(e.metrics))i=e.metrics.map(o=>Mo(o));else if(e.metrics!=null){i={};for(const o in e.metrics)i[o]=Mo(e.metrics[o])}this.compile({loss:s,metrics:i,optimizer:n})}async save(e,t){if(typeof e=="string"){const u=Uy(e);if(u.length===0)throw new j(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new j(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new j("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const n=await Wy(this.getNamedWeights(t)),s=!1,i=null,o=this.toJSON(i,s),a={modelTopology:o,format:X3,generatedBy:`TensorFlow.js tfjs-layers v${Xp}`,convertedBy:null},c=t==null?!1:t.includeOptimizer;if(c&&this.optimizer!=null){a.trainingConfig=this.getTrainingConfig();const u="optimizer",{data:p,specs:m}=await Wy(await this.optimizer.getWeights(),u);n.specs.push(...m),n.data=Od([n.data,p])}if(this.userDefinedMetadata!=null){const u=!0;Kv(this.userDefinedMetadata,this.name,u),a.userDefinedMetadata=this.userDefinedMetadata}return a.weightData=n.data,a.weightSpecs=n.specs,e.save(a)}setUserDefinedMetadata(e){Kv(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}}cr.className="Model",ge(cr);class lN extends cr{}lN.className="Functional",ge(lN);async function J3(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);const s=Mh(n),i=hi(s,t);if(e.weightsManifest!=null){const o=await _T(e.weightsManifest,e.pathPrefix,i.weights.map(c=>c.originalName)),a={};for(const c of i.weights)a[c.originalName]=o[c.originalName];i.loadWeights(a),qe(o)}return i}async function Z3(e,t){if(t==null&&(t={}),typeof e=="string"){const n=By(e,t);if(n.length===0)n.push(kd(e,t));else if(n.length>1)throw new j(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Q3(e,void 0,t)}async function Q3(e,t,n){if(n==null&&(n={}),e.load==null)throw new j("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const s=await e.load();let i=s.modelTopology;i.model_config!=null&&(i=i.model_config);const o=n.strict==null?!0:n.strict,a=s.weightData!=null&&s.weightSpecs!=null&&o,c=hi(Mh(i),t,a),u=s.trainingConfig;if(u!=null&&c.loadTrainingConfig(u),s.userDefinedMetadata!=null&&c.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new j("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");const{modelWeights:p,optimizerWeights:m}=eG(s.weightData,s.weightSpecs);c.loadWeights(p,o),c.optimizer!=null&&m.length>0&&await c.optimizer.setWeights(m),qe(p),qe(m.map(y=>y.tensor))}return c}function eG(e,t){const n=Rd(e,t),s={},i=[];return t.forEach(o=>{o.group==="optimizer"?i.push({name:o.name,tensor:n[o.name]}):s[o.name]=n[o.name]}),{modelWeights:s,optimizerWeights:i}}class ic extends cr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Up("sequential_"),e.layers!=null)for(const t of e.layers)this.add(t)}checkShape(e){const t=e.inboundNodes[0].outputTensors[0].shape;if(t.some(n=>n<0))throw new j(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){const t=e instanceof ic||e instanceof cr;let n;if(t){if(n=e,n.outputs.length!==1)throw new j("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new j("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new j("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");const s=_v({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new j(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new j("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=Fv(this.outputs[0])}this.inboundNodes=[],new Pp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Bo(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{const s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{const e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(It(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new cr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new oi("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new oi("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new oi("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new oi("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let i,o={};if(t instanceof Array){if(!(t[0].className!=null)||t[0].className==="Merge")throw new j("Legacy serialization format not supported yet.");i=t}else k(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),i=t.layers,delete t.layers,o=t;const a=new e(o);if(!(a instanceof ic))throw new Ge(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(const c of i){const u=void 0,p=hi(c,u,s);s&&p.setFastWeightInitDuringBuild(!0),a.add(p)}return a}set stopTraining(e){if(this.model==null)throw new j("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new j("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){const e=[];for(const t of this.layers){const n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}}ic.className="Sequential",ge(ic);function tG(e){return new cr(e)}function nG(e){return new ic(e)}function sG(e,t){return t==null&&(t={}),Z3(e,t)}function hN(e){return _v(e)}function iG(e,t){zs.registerCallbackConstructor(e,t)}class cs extends Ao{getConfig(){return{}}}class uN extends cs{apply(e,t=1){return Dz(e,t)}}uN.className="elu",ge(uN);class dN extends cs{apply(e){return rp(e)}}dN.className="selu",ge(dN);class pN extends cs{apply(e){return Fi(e)}}pN.className="relu",ge(pN);class mN extends cs{apply(e){return ee(()=>Eo(6,Fi(e)))}}mN.className="relu6",ge(mN);class fN extends cs{apply(e){return e}}fN.className="linear",ge(fN);class gN extends cs{apply(e){return Ei(e)}}gN.className="sigmoid",ge(gN);class yN extends cs{apply(e){return Fz(e)}}yN.className="hardSigmoid",ge(yN);class bN extends cs{apply(e){return za(e)}}bN.className="softplus",ge(bN);class wN extends cs{apply(e){return kz(e)}}wN.className="softsign",ge(wN);class LN extends cs{apply(e){return Ua(e)}}LN.className="tanh",ge(LN);class Gw extends cs{apply(e,t=-1){return Wo(e,t)}}Gw.className="softmax",ge(Gw);class SN extends cs{apply(e,t=-1){return Qd(e,t)}}SN.className="logSoftmax",ge(SN);class IN extends cs{apply(e,t=1){return ee(()=>Ei(e.mul(t)).mul(e))}}IN.className="swish",ge(IN);function Xr(e){return e.getClassName()}function Vw(e,t={}){return Dh(e,Ws.getMap().classNameMap,t,"activation")}function Jr(e){if(e==null){const t={};return t.className="linear",t.config={},Vw(t)}if(typeof e=="string"){const t={};return t.className=e,t.config={},Vw(t)}else return e instanceof cs?e:Vw(e)}function Hw(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}class xN extends Ao{}class Gh extends xN{constructor(e){super();Hw(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return ee(()=>{let t=ct([1]);return this.hasL1&&(t=be(t,Ue(X(this.l1,sn(e))))),this.hasL2&&(t=be(t,Ue(X(this.l2,$h(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}Gh.className="L1L2",ge(Gh);function rG(e){return Hw(e),new Gh({l1:e!=null?e.l1:null,l2:0})}function oG(e){return Hw(e),new Gh({l2:e!=null?e.l2:null,l1:0})}const TN={l1l2:"L1L2"};function xt(e){return cw(e)}function AN(e,t={}){return Dh(e,Ws.getMap().classNameMap,t,"regularizer")}function _t(e){if(e==null)return null;if(typeof e=="string"){const t=e in TN?TN[e]:e,n={className:t,config:{}};return AN(n)}else return e instanceof xN?e:AN(e)}class Yw extends lt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Xe(e);let n=Fi(e);return this.maxValue!=null&&(n=jn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}Yw.className="ReLU",ge(Yw);class qw extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=Xe(e);return Xd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}qw.className="LeakyReLU",ge(qw);class jw extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Ft(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=_t(e.alphaRegularizer),this.alphaConstraint=ln(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new j(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=It(e);const t=e.slice(1);if(this.sharedAxes!=null)for(const s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new fn({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Xe(e),gh(e,this.alpha.read())}getConfig(){const e={alphaInitializer:Vt(this.alphaInitializer),alphaRegularizer:xt(this.alphaRegularizer),alphaConstraint:cn(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}}jw.className="PReLU",ge(jw);class Kw extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ge(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=Xe(e);return Oo(n)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}Kw.className="ELU",ge(Kw);class Xw extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){const n=Xe(e);return n.mul(_h(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){const e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}}Xw.className="ThresholdedReLU",ge(Xw);class Jw extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Gw().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){const n=Xe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}Jw.className="Softmax",ge(Jw);function rc(e,t,n){if(typeof e=="number")return Bo(e,t);if(e.length!==t)throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){const i=e[s];if(!vz(i))throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${i}`)}return e}function ui(e,t,n,s,i=1){if(e==null)return e;const o=t+(t-1)*(i-1);let a;return n==="same"?a=e:a=e-o+1,Math.floor((a+s-1)/s)}function Jp(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+qr([n-t,0]);else if(s==="same")e=e*t;else throw new j(`Unsupport padding mode: ${s}.`);return e}function Zw(e,t){return ee(()=>(Gt(t),t==="channelsFirst"?Pe(e,[0,2,3,1]):e))}function vN(e,t){return ee(()=>(Gt(t),t==="channelsFirst"?Pe(e,[0,2,3,4,1]):e))}function NN(e,t,n,s=1,i="valid",o,a=1){return ee(()=>{if(o==null&&(o=ri()),Gt(o),e.shape.length!==3)throw new j(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new j(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new j(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(o==="channelsFirst"&&(e=Pe(e,[0,2,1])),i==="causal")throw new Ge("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let c=Hd(e,t,s,i==="same"?"same":"valid","NWC",a);return n!=null&&(c=$i(c,n)),c})}function vee(e,t,n=1,s="valid",i,o=1){return ee(()=>(Gt(i),NN(e,t,null,n,s,i,o)))}function Nee(e,t,n=[1,1],s="valid",i,o){return ee(()=>(Gt(i),Qw(e,t,null,n,s,i,o)))}function Qw(e,t,n,s=[1,1],i="valid",o,a,c=null){return ee(()=>{if(o==null&&(o=ri()),Gt(o),e.rank!==3&&e.rank!==4)throw new j(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new j(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=Zw(e,o);if(i==="causal")throw new Ge("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=Hb({x:u,filter:t,strides:s,pad:i==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:n,activation:c}),o==="channelsFirst"&&(u=Pe(u,[0,3,1,2])),u})}function Cee(e,t,n=[1,1,1],s="valid",i,o){return ee(()=>(Gt(i),CN(e,t,null,n,s,i,o)))}function CN(e,t,n,s=[1,1,1],i="valid",o,a){return ee(()=>{if(o==null&&(o=ri()),Gt(o),e.rank!==4&&e.rank!==5)throw new j(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new j(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let c=vN(e,o);if(i==="causal")throw new Ge("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return c=yb(c,t,s,i==="same"?"same":"valid","NDHWC",a),n!=null&&(c=$i(c,n)),o==="channelsFirst"&&(c=Pe(c,[0,4,1,2,3])),c})}class eL extends lt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",eL.verifyArgs(t),this.rank=e,mn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ge(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=rc(t.kernelSize,e,"kernelSize"),this.strides=rc(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Ns(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Gt(this.dataFormat),this.activation=Jr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Ft(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=ln(t.biasConstraint),this.biasRegularizer=_t(t.biasRegularizer),this.activityRegularizer=_t(t.activityRegularizer),this.dilationRate=rc(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new j(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new j(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new j(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(vs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!hw(e.kernelSize,"number",1,3))throw new j(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){const e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Xr(this.activation),useBias:this.useBias,biasInitializer:Vt(this.biasInitializer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),biasConstraint:cn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class Vh extends eL{constructor(e,t){super(e,t);this.kernel=null,Vh.verifyArgs(t),this.filters=t.filters,mn(this.filters,"filters"),this.kernelInitializer=Ft(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=ln(t.kernelConstraint),this.kernelRegularizer=_t(t.kernelRegularizer)}build(e){e=It(e);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[t]}`);const n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return ee(()=>{e=Xe(e);let n;const s=this.bias==null?null:this.bias.read(),i=bv(this.activation.getClassName());if(i!=null&&this.rank===2)n=Qw(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,i);else{if(this.rank===1)n=NN(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Qw(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=CN(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ge("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=It(e);const t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let i=0;i<n.length;++i){const o=ui(n[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);t.push(o)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){const e={filters:this.filters,kernelInitializer:Vt(this.kernelInitializer),kernelRegularizer:xt(this.kernelRegularizer),kernelConstraint:cn(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new j(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}}class Hh extends Vh{constructor(e){super(2,e);Hh.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!hw(e.kernelSize,"number",1,2))throw new j(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}Hh.className="Conv2D",ge(Hh);class Zp extends Vh{constructor(e){super(3,e);Zp.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new j(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}Zp.className="Conv3D",ge(Zp);class tL extends Hh{constructor(e){super(e);if(this.inputSpec=[new fn({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new j(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=It(e),e.length!==4)throw new j("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new fn({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return ee(()=>{let n=Xe(e);if(n.shape.length!==4)throw new j(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);const s=n.shape,i=s[0];let o,a;this.dataFormat==="channelsFirst"?(o=2,a=3):(o=1,a=2);const c=s[o],u=s[a],p=this.kernelSize[0],m=this.kernelSize[1],y=this.strides[0],b=this.strides[1],w=Jp(c,y,p,this.padding),I=Jp(u,b,m,this.padding),T=[i,w,I,this.filters];this.dataFormat!=="channelsLast"&&(n=Pe(n,[0,2,3,1]));let v=Yd(n,this.kernel.read(),T,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=Pe(v,[0,3,1,2])),this.bias!=null&&(v=$i(v,this.bias.read(),this.dataFormat)),this.activation!=null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=It(e);const t=e.slice();let n,s,i;this.dataFormat==="channelsFirst"?(n=1,s=2,i=3):(n=3,s=1,i=2);const o=this.kernelSize[0],a=this.kernelSize[1],c=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Jp(t[s],c,o,this.padding),t[i]=Jp(t[i],u,a,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}tL.className="Conv2DTranspose",ge(tL);class RN extends Vh{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new j("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new j("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new j(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Ft(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=_t(t.depthwiseRegularizer),this.depthwiseConstraint=ln(t.depthwiseConstraint),this.pointwiseInitializer=Ft(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=_t(t.pointwiseRegularizer),this.pointwiseConstraint=ln(t.pointwiseConstraint)}build(e){if(e=It(e),e.length<this.rank+2)throw new j(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);const n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),i=[];for(let a=0;a<this.rank;++a)i.push(1);i.push(n*this.depthMultiplier,this.filters);const o=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,o,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",i,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,o,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,o,this.biasConstraint):this.bias=null,this.inputSpec=[new fn({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return ee(()=>{e=Xe(e);let n;if(this.rank===1)throw new Ge("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Pe(e,[0,2,3,1])),n=Fb(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=$i(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Pe(n,[0,3,1,2])),n})}getConfig(){const e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Vt(this.depthwiseInitializer),e.pointwiseInitializer=Vt(this.pointwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.pointwiseRegularizer=xt(this.pointwiseRegularizer),e.depthwiseConstraint=cn(this.depthwiseConstraint),e.pointwiseConstraint=cn(this.pointwiseConstraint),e}}RN.className="SeparableConv";class nL extends RN{constructor(e){super(2,e)}}nL.className="SeparableConv2D",ge(nL);class Qp extends Vh{constructor(e){super(1,e);Qp.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){const e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!hw(e.kernelSize,"number",1,1))throw new j(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}Qp.className="Conv1D",ge(Qp);class sL extends lt{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return ee(()=>{if(e=Xe(e),this.dataFormat==="channelsLast"){const n=Cp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Cp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{const n=Cp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Cp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){const e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}sL.className="Cropping2D",ge(sL);class iL extends lt{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){const t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{const t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return ee(()=>{let n=Xe(e);const s=n.shape;if(this.dataFormat==="channelsFirst"){n=Pe(n,[0,2,3,1]);const i=this.size[0]*s[2],o=this.size[1]*s[3],a=n.resizeNearestNeighbor([i,o]);return Pe(a,[0,3,1,2])}else{const i=this.size[0]*s[1],o=this.size[1]*s[2];return n.resizeNearestNeighbor([i,o])}})}getConfig(){const e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}iL.className="UpSampling2D",ge(iL);function aG(e,t,n=[1,1],s="valid",i,o){return ee(()=>{i==null&&(i=ri()),Gt(i);let a=Zw(e,i);if(e.rank!==4)throw new j(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new j(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return a=Co(a,t,n,s==="same"?"same":"valid","NHWC",o),i==="channelsFirst"&&(a=Pe(a,[0,3,1,2])),a})}class rL extends eL{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Ft(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=ln(e.depthwiseConstraint),this.depthwiseRegularizer=_t(e.depthwiseRegularizer)}build(e){if(e=It(e),e.length<4)throw new j(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);const n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{e=Xe(e);let n=aG(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=$i(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=It(e);const t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,i=ui(t,this.kernelSize[0],this.padding,this.strides[0]),o=ui(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,i,o]:[e[0],i,o,s]}getConfig(){const e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Vt(this.depthwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.depthwiseConstraint=cn(this.depthwiseRegularizer),e}}rL.className="DepthwiseConv2D",ge(rL);function ON(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new j("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function i(o){return o==null||Array.isArray(o)?o:[o]}return t=i(t),n=i(n),{inputs:e,initialState:t,constants:n}}function EN(e,t,n,s=!1,i,o,a=!1,c=!1){return ee(()=>{const u=t.shape.length;if(u<3)throw new j(`Input should be at least 3D, but is ${u}D.`);const p=[1,0].concat(ai(2,u));if(t=Pe(t,p),o!=null)throw new Ge("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),i!=null&&(i=i.asType("bool").asType("float32"),i.rank===u-1&&(i=Kn(i,-1)),i=Pe(i,p)),s&&(t=As(t,0),i!=null&&(i=As(i,0)));const m=[];let y,b=n;const w=t.shape[0],I=_i(t);let T;i!=null&&(T=_i(i));for(let N=0;N<w;++N){const E=I[N],D=ee(()=>e(E,b));if(i==null)y=D[0],b=D[1];else{const F=ee(()=>{const _=T[N],B=Dn(_).sub(_),U=D[0].mul(_).add(b[0].mul(B)),Y=b.map((q,J)=>D[1][J].mul(_).add(q.mul(B)));return{output:U,newStates:Y}});y=F.output,b=F.newStates}c&&m.push(y)}let v;if(c){const N=1;v=as(m,N)}return[y,v,b]})}class Bi extends lt{constructor(e){super(e);let t;if(e.cell==null)throw new j("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new nm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new j("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new fn({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ai(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Nw(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);const n=t[0];let s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){const i=[];for(const o of t)i.push([e[0],o]);return[s].concat(i)}else return s}computeMask(e,t){return ee(()=>{Array.isArray(t)&&(t=t[0]);const n=this.returnSequences?t:null;if(this.returnState){const s=this.states.map(i=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){const t=null;if(this.numConstants!=null)throw new Ge("Constants support is not implemented in RNN yet.");Nw(e)&&(e=e[0]),e=e;const n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new fn({shape:[n,null,...s]});const i=[e[0]].concat(e.slice(2));if(t!=null)throw new Ge("Constants support is not implemented in RNN yet.");this.cell.build(i);let o;if(Array.isArray(this.cell.stateSize)?o=this.cell.stateSize:o=[this.cell.stateSize],this.stateSpec!=null){if(!ot(this.stateSpec.map(a=>a.shape[a.shape.length-1]),o))throw new j(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=o.map(a=>new fn({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){ee(()=>{if(!this.stateful)throw new rr("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape[0];if(n==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>ct([n,s])):this.states_=[ct([n,this.cell.stateSize])];else if(e==null)qe(this.states_),this.keptStates!=null&&(qe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>ct([n,s])):this.states_[0]=ct([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):qe(this.states_);for(let s=0;s<this.states_.length;++s){const i=e[s],o=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,a=[n,o];if(!ot(i.shape,a))throw new j(`State ${s} is incompatible with layer ${this.name}: expected shape=${a}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Rn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=ON(e,n,s,this.numConstants);e=i.inputs,n=i.initialState,s=i.constants;let o=[],a=[];if(n!=null){t.initialState=n,o=o.concat(n),this.stateSpec=[];for(const u of n)this.stateSpec.push(new fn({shape:u.shape}));a=a.concat(this.stateSpec)}s!=null&&(t.constants=s,o=o.concat(s),this.numConstants=s.length);const c=o[0]instanceof li;if(c){const u=[e].concat(o),p=this.inputSpec.concat(a),m=this.inputSpec;this.inputSpec=p;const y=super.apply(u,t);return this.inputSpec=m,y}else return super.apply(e,t)}call(e,t){return ee(()=>{const n=t==null?null:t.mask,s=t==null?null:t.training;let i=t==null?null:t.initialState;e=Xe(e),i==null&&(this.stateful?i=this.states_:i=this.getInitialState(e));const o=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(i.length!==o)throw new j(`RNN Layer has ${o} state(s) but was passed ${i.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const a={training:s},c=(w,I)=>{const T=this.cell.call([w].concat(I),a);return[T[0],T.slice(1)]},u=EN(c,e,i,this.goBackwards,n,null,this.unroll,this.returnSequences),p=u[0],m=u[1],y=u[2];this.stateful&&this.resetStates(y,s);const b=this.returnSequences?m:p;return this.returnState?[b].concat(y):b})}getInitialState(e){return ee(()=>{let t=ct(e.shape);return t=Ue(t,[1,2]),t=Wh(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?bw(t,[1,n]):t):this.cell.stateSize>1?[bw(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){const e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);const n=this.cell.getConfig();return this.getClassName()===Bi.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){const s=t.cell,i=hi(s,n);return new e(Object.assign(t,{cell:i}))}}Bi.className="RNN",ge(Bi);class oc extends lt{}class em extends oc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,mn(this.units,"units"),this.activation=Jr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=ln(e.kernelConstraint),this.recurrentConstraint=ln(e.recurrentConstraint),this.biasConstraint=ln(e.biasConstraint),this.dropout=ec([1,qr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ec([1,qr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=It(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{if(e=e,e.length!==2)throw new j(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];const s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Zr({ones:()=>Dn(e),rate:this.dropout,training:s})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Zr({ones:()=>Dn(n),rate:this.recurrentDropout,training:s}));let i;const o=this.dropoutMask,a=this.recurrentDropoutMask;o!=null?i=Wi(X(e,o),this.kernel.read()):i=Wi(e,this.kernel.read()),this.bias!=null&&(i=$i(i,this.bias.read())),a!=null&&(n=X(n,a));let c=be(i,Wi(n,this.recurrentKernel.read()));return this.activation!=null&&(c=this.activation.apply(c)),[c,c]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Xr(this.activation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),recurrentInitializer:Vt(this.recurrentInitializer),biasInitializer:Vt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:cn(this.kernelConstraint),recurrentConstraint:cn(this.recurrentConstraint),biasConstraint:cn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}}em.className="SimpleRNNCell",ge(em);class oL extends Bi{constructor(e){e.cell=new em(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(qe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(qe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return new e(t)}}oL.className="SimpleRNN",ge(oL);class tm extends oc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new j("GRUCell does not support reset_after parameter set to true.");this.units=e.units,mn(this.units,"units"),this.activation=Jr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Jr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=ln(e.kernelConstraint),this.recurrentConstraint=ln(e.recurrentConstraint),this.biasConstraint=ln(e.biasConstraint),this.dropout=ec([1,qr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ec([1,qr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=It(e);const t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{if(e=e,e.length!==2)throw new j(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training==null?!1:t.training;let s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Zr({ones:()=>Dn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Zr({ones:()=>Dn(s),rate:this.recurrentDropout,training:n,count:3}));const i=this.dropoutMask,o=this.recurrentDropoutMask;let a,c,u;0<this.dropout&&this.dropout<1&&(e=X(e,i[0]));let p=Wi(e,this.kernel.read());this.useBias&&(p=$i(p,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,o[0]));const m=this.recurrentKernel.read(),[y,b]=os(m,[2*this.units,this.units],m.rank-1),w=Wi(s,y),[I,T,v]=os(p,3,p.rank-1),[N,E]=os(w,2,w.rank-1);a=this.recurrentActivation.apply(be(I,N)),c=this.recurrentActivation.apply(be(T,E));const D=Wi(X(c,s),b);u=this.activation.apply(be(v,D));const F=be(X(a,s),X(be(1,Pt(a)),u));return[F,F]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Xr(this.activation),recurrentActivation:Xr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),recurrentInitializer:Vt(this.recurrentInitializer),biasInitializer:Vt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:cn(this.kernelConstraint),recurrentConstraint:cn(this.recurrentConstraint),biasConstraint:cn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}}tm.className="GRUCell",ge(tm);class aL extends Bi{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new tm(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(qe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(qe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}aL.className="GRU",ge(aL);class Yh extends oc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,mn(this.units,"units"),this.activation=Jr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Jr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=ln(e.kernelConstraint),this.recurrentConstraint=ln(e.recurrentConstraint),this.biasConstraint=ln(e.biasConstraint),this.dropout=ec([1,qr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ec([1,qr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=It(e);const n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){const i=this.biasInitializer,o=this.units;s=new(t=class extends Ps{apply(c,u){const p=i.apply([o]),m=new Op().apply([o]),y=i.apply([o*2]);return Nv(Nv(p,m),y)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return ee(()=>{const n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new j(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1];const i=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Zr({ones:()=>Dn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Zr({ones:()=>Dn(s),rate:this.recurrentDropout,training:n,count:4}));const o=this.dropoutMask,a=this.recurrentDropoutMask;let c,u,p,m;0<this.dropout&&this.dropout<1&&(e=X(e,o[0]));let y=Wi(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,a[0])),y=be(y,Wi(s,this.recurrentKernel.read())),this.useBias&&(y=$i(y,this.bias.read()));const[b,w,I,T]=os(y,4,y.rank-1);c=this.recurrentActivation.apply(b),u=this.recurrentActivation.apply(w),p=be(X(u,i),X(c,this.activation.apply(I))),m=this.recurrentActivation.apply(T);const v=X(m,this.activation.apply(p));return[v,v,p]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Xr(this.activation),recurrentActivation:Xr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),recurrentInitializer:Vt(this.recurrentInitializer),biasInitializer:Vt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:cn(this.kernelConstraint),recurrentConstraint:cn(this.recurrentConstraint),biasConstraint:cn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}}Yh.className="LSTMCell",ge(Yh);class cL extends Bi{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Yh(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(qe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(qe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}cL.className="LSTM",ge(cL);class nm extends oc{constructor(e){super(e);this.cells=e.cells}get stateSize(){const e=[];for(const t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return ee(()=>{e=e;let n=e.slice(1);const s=[];for(const a of this.cells.slice().reverse())Array.isArray(a.stateSize)?s.push(n.splice(0,a.stateSize.length)):s.push(n.splice(0,1));s.reverse();const i=[];let o;for(let a=0;a<this.cells.length;++a){const c=this.cells[a];n=s[a],a===0?o=[e[0]].concat(n):o=[o[0]].concat(n),o=c.call(o,t),i.push(o.slice(1))}n=[];for(const a of i.slice().reverse())n.push(...a);return[o[0]].concat(n)})}build(e){Nw(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Po(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){const e=super.getConfig(),t=i=>({className:i.getClassName(),config:i.getConfig()}),n=this.cells.map(t),s={cells:n};return Object.assign({},e,s)}static fromConfig(e,t,n={}){const s=[];for(const i of t.cells)s.push(hi(i,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];const e=[];for(const t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){const e=[];for(const t of this.cells)e.push(...t.weights);return Cw(e)}setWeights(e){const t=[];for(const n of this.cells){const s=n.weights.length,i=e.splice(s);for(let o=0;o<n.weights.length;++o)t.push([n.weights[o],i[o]])}Rw(t)}}nm.className="StackedRNNCells",ge(nm);function Zr(e){const{ones:t,rate:n,training:s=!1,count:i=1}=e,o=()=>Rv(t(),n),a=()=>Uh(o,t,s);if(!i||i<=1)return Rn(a().clone());const c=Array(i).fill(void 0).map(a);return c.map(u=>Rn(u.clone()))}var cG=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,s=Object.getOwnPropertySymbols(e);i<s.length;i++)t.indexOf(s[i])<0&&Object.prototype.propertyIsEnumerable.call(e,s[i])&&(n[s[i]]=e[s[i]]);return n};class Ree extends oc{}class DN extends Bi{constructor(e){if(e.unroll)throw new Ge("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ge("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new fn({ndim:5})]}call(e,t){return ee(()=>{if(this.cell.dropoutMask!=null&&(qe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(qe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new j("ConvRNN2D cell does not support constants");const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return ee(()=>{const{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),i=[s[0],...s.slice(2)],o=ct(i);return Array.isArray(t)?Array(t.length).fill(o):[o]})}resetStates(e,t=!1){ee(()=>{if(!this.stateful)throw new rr("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),i=[s[0],...s.slice(2)],o=n[0];if(o==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ct(i)):this.states_=[ct(i)];else if(e==null)qe(this.states_),this.keptStates!=null&&(qe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ct(i)):this.states_[0]=ct(i);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):qe(this.states_);for(let a=0;a<this.states_.length;++a){const c=e[a],u=i;if(!ot(c.shape,u))throw new j(`State ${a} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${c.shape}`);this.states_[a]=c}}this.states_=this.states_.map(a=>Rn(a.clone()))})}computeSingleOutputShape(e){const{dataFormat:t,filters:n,kernelSize:s,padding:i,strides:o,dilationRate:a}=this.cell,c=t==="channelsFirst",u=e[c?3:2],p=e[c?4:3],m=ui(u,s[0],i,o[0],a[0]),y=ui(p,s[1],i,o[1],a[1]),b=[...e.slice(0,2),...c?[n,m,y]:[m,y,n]];return b}}DN.className="ConvRNN2D";class sm extends Yh{constructor(e){const{filters:t,kernelSize:n,strides:s,padding:i,dataFormat:o,dilationRate:a}=e;super(Object.assign({},e,{units:t}));this.filters=t,mn(this.filters,"filters"),this.kernelSize=rc(n,2,"kernelSize"),this.kernelSize.forEach(c=>mn(c,"kernelSize")),this.strides=rc(s||1,2,"strides"),this.strides.forEach(c=>mn(c,"strides")),this.padding=i||"valid",Ns(this.padding),this.dataFormat=o||"channelsLast",Gt(this.dataFormat),this.dilationRate=rc(a||1,2,"dilationRate"),this.dilationRate.forEach(c=>mn(c,"dilationRate"))}build(e){var t;e=It(e);const n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[n]}`);const s=e[n],i=4,o=this.kernelSize.concat([s,this.filters*i]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const a=this.kernelSize.concat([this.filters,this.filters*i]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let c;if(this.unitForgetBias){const u=this.biasInitializer,p=this.filters;c=new(t=class extends Ps{apply(y,b){const w=u.apply([p]),I=si([p]),T=u.apply([p*2]);return yw([w,I,T])}},t.className="CustomInit",t)}else c=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*i],null,c,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return ee(()=>{if(e.length!==3)throw new j(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training||!1,s=e[0],i=e[1],o=e[2],a=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Zr({ones:()=>Dn(s),rate:this.dropout,training:n,count:a}));const c=this.dropoutMask,u=(Ie,Se,Ee)=>!Se||!Se[Ee]?Ie:X(Se[Ee],Ie);let p=u(s,c,0),m=u(s,c,1),y=u(s,c,2),b=u(s,c,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Zr({ones:()=>Dn(i),rate:this.recurrentDropout,training:n,count:a}));const w=this.recurrentDropoutMask;let I=u(i,w,0),T=u(i,w,1),v=u(i,w,2),N=u(i,w,3);const E=3,[D,F,_,B]=os(this.kernel.read(),a,E),[U,Y,q,J]=this.useBias?os(this.bias.read(),a):[null,null,null,null];p=this.inputConv(p,D,U,this.padding),m=this.inputConv(m,F,Y,this.padding),y=this.inputConv(y,_,q,this.padding),b=this.inputConv(b,B,J,this.padding);const[oe,ce,ue,he]=os(this.recurrentKernel.read(),a,E);I=this.recurrentConv(I,oe),T=this.recurrentConv(T,ce),v=this.recurrentConv(v,ue),N=this.recurrentConv(N,he);const pe=this.recurrentActivation.apply(be(p,I)),le=this.recurrentActivation.apply(be(m,T)),ye=be(X(le,o),X(pe,this.activation.apply(be(y,v)))),me=X(this.recurrentActivation.apply(be(b,N)),this.activation.apply(ye));return[me,me,ye]})}getConfig(){const e=super.getConfig(),{units:t}=e,n=cG(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){const i=er(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?$i(i,n,this.dataFormat):i}recurrentConv(e,t){const n=1;return er(e,t,n,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}sm.className="ConvLSTM2DCell",ge(sm);class lL extends DN{constructor(e){const t=new sm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}}lL.className="ConvLSTM2D",ge(lL);class im extends lt{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;const t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e);if(0<this.rate&&this.rate<1){const s=t.training==null?!1:t.training,i=this.getNoiseShape(n),o=Uh(()=>Rv(n,this.rate,i,this.seed),()=>n,s);return o}return e})}getConfig(){const e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}}im.className="Dropout",ge(im);class hL extends im{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}hL.className="SpatialDropout1D",ge(hL);class uL extends lt{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,mn(this.units,"units"),this.activation=Jr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=ln(e.kernelConstraint),this.biasConstraint=ln(e.biasConstraint),this.kernelRegularizer=_t(e.kernelRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=It(e);const t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=It(e);const t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e),s=bv(this.activation.getClassName());let i;return s!=null?i=Wi(n,this.kernel.read(),s,this.bias?this.bias.read():null):(i=Wi(n,this.kernel.read()),this.bias!=null&&(i=$i(i,this.bias.read())),this.activation!=null&&(i=this.activation.apply(i))),i})}getConfig(){const e={units:this.units,activation:Xr(this.activation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),biasInitializer:Vt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:cn(this.kernelConstraint),biasConstraint:cn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}uL.className="Dense",ge(uL);class dL extends lt{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=It(e);for(const t of e.slice(1))if(t==null)throw new j(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Yr(e,1)]}call(e,t){return ee(()=>{this.invokeCallHook(e,t);let n=Xe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){const s=[0];for(let i=2;i<n.rank;++i)s.push(i);s.push(1),n=n.transpose(s)}return Ez(n)})}getConfig(){const e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}dL.className="Flatten",ge(dL);class pL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.activation=Jr(e.activation)}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e);return this.activation.apply(n)})}getConfig(){const e={activation:Xr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}pL.className="Activation",ge(pL);class mL extends lt{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return ee(()=>(e=Xe(e),Rz(e,this.n)))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}mL.className="RepeatVector",ge(mL);class fL extends lt{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){const n="Total size of new array must be unchanged.",s=t.slice();let i=1,o=null;for(let c=0;c<s.length;++c){const u=s[c];if(this.isUnknown(u))if(o===null)o=c;else throw new j("Can only specifiy one unknown dimension.");else i*=u}const a=Yr(e);if(o!==null){if(i===0||a%i!==0)throw new j(n);s[o]=a/i}else if(a!==i)throw new j(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e),s=n.shape,i=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return n.reshape(i)})}getConfig(){const e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}}fL.className="Reshape",ge(fL);class gL extends lt{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);const t=ai(1,e.dims.length+1);if(!ot(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new fn({ndim:this.dims.length+1})]}computeOutputShape(e){e=It(e);const t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Pe(Xe(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}gL.className="Permute",ge(gL);class yL extends lt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){const n=Xe(e),s=-1;return th(Pr(n,this.maskValue),s)}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e),s=-1,i=!0,o=th(Pr(n,this.maskValue),s,i),a=n.mul(o.asType(n.dtype));return a})}}yL.className="Masking",ge(yL);class bL extends lt{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(Nt(e.inputLength))}this.inputDim=e.inputDim,mn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,mn(this.outputDim,"outputDim"),this.embeddingsInitializer=Ft(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=_t(e.embeddingsRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.embeddingsConstraint=ln(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return ee(()=>this.maskZero?(e=Xe(e),Pr(e,et(e))):null)}computeOutputShape(e){if(e=It(e),this.inputLength==null)return[...e,this.outputDim];const t=Nt(this.inputLength);if(t.length!==e.length-1)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){const i=t[s],o=e[s+1];if(i!=null&&o!=null&&i!==o)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);i==null&&(t[n]=o),n++}}return[e[0],...t,this.outputDim]}call(e,t){return ee(()=>{this.invokeCallHook(e,t);let n=Xe(e);n.dtype!=="int32"&&(n=_h(n,"int32"));const s=Cv(this.embeddings.read(),n.as1D());return s.reshape(It(this.computeOutputShape(n.shape)))})}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Vt(this.embeddingsInitializer),embeddingsRegularizer:xt(this.embeddingsRegularizer),activityRegularizer:xt(this.activityRegularizer),embeddingsConstraint:cn(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}bL.className="Embedding",ge(bL);class Ho extends lt{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Ge}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;const n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){const i=e[e.length-t.length+s],o=t[s];if(i==null||o==null||i<0||o<0)n.push(null);else if(i===1)n.push(o);else if(o===1)n.push(i);else{if(i!==o)throw new j("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(i)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[It(e)]),e=e,e.length<2)throw new j(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(const i of e)i!=null&&i[0]!==null&&t.push(i[0]);if(t=Hr(t),t.length>1)throw new j(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let i=1;i<e.length;++i){const o=e[i]==null?null:e[i].slice(1);n=this.computeElementwiseOpOutputShape(n,o)}const s=e.map(i=>i.length);e.indexOf(null)===-1&&Hr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return ee(()=>{if(e=e,this.reshapeRequired){const n=[],s=e.map(i=>i.rank);if(s.indexOf(null)===-1){const i=qr(s);for(let o of e){const a=o.rank;for(let c=0;c<i-a;++c)o=Wh(o,1);n.push(o)}return this.mergeFunction(n)}else{let i=!1;for(const c of e){const u=c.rank;if(u==null){const p=c.shape,m=p[0],y=p.slice(1).concat([m]);let b=c.reshape([m].concat(Yr(p.slice(1))));b=Pe(b,[1,0]),b=b.reshape(y),n.push(b),i=!0}else if(u>1){const p=ai(1,u).concat([0]);n.push(Pe(c,p)),i=!0}else n.push(c)}let o=this.mergeFunction(n);const a=o.rank;if(i){if(a==null){const c=o.shape,u=c.length,p=c[u-1],m=[p].concat(c.slice(0,c.length-1));o=Pe(o.reshape([-1,p]),[1,0]).reshape(m)}else if(a>1){const c=[a-1].concat(ai(0,a-1));o=Pe(o,c)}}return o}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){const i=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,i)}let n=[];for(const s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=Hr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return ee(()=>{if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an Array");if(!Array.isArray(e))throw new j("`inputs` should be an Array");if(t.length!==e.length)throw new j(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Kn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Bs(n,t[s]);return n})}}class qh extends Ho{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return t})}}qh.className="Add",ge(qh);function Oee(e){if(Array.isArray(e)){const t=new qh({});return t.apply(e)}else return new qh(e)}class jh extends Ho{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=X(t,e[n]);return t})}}jh.className="Multiply",ge(jh);function Eee(e){if(Array.isArray(e)){const t=new jh({});return t.apply(e)}else return new jh(e)}class Kh extends Ho{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return X(1/e.length,t)})}}Kh.className="Average",ge(Kh);function Dee(e){if(Array.isArray(e)){const t=new Kh({});return t.apply(e)}else return new Kh(e)}class Xh extends Ho{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Us(t,e[n]);return t})}}Xh.className="Maximum",ge(Xh);function kee(e){if(Array.isArray(e)){const t=new Xh({});return t.apply(e)}else return new Xh(e)}class Jh extends Ho{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Eo(t,e[n]);return t})}}Jh.className="Minimum",ge(Jh);function Fee(e){if(Array.isArray(e)){const t=new Jh({});return t.apply(e)}else return new Jh(e)}class Zh extends Ho{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new j("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(const s of e)if(s!=null){t=!1;break}if(t)return;const n=[];for(let s=0;s<e.length;++s){const i=e[s].slice();i.splice(this.axis,1);let o=!1;for(const a of n)if(ot(a,i)){o=!0;break}o||n.push(i)}if(n.length>1)throw new j("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return ee(()=>yw(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new j("A `Concatenate` layer should be called on a list of inputs.");const t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(const i of t.slice(1)){if(n[s]==null||i[s]==null){n[s]=null;break}n[s]+=i[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new j("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new j(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return ee(()=>{let n=!0;if(t.forEach(o=>{if(o!=null){n=!1;return}}),n)return null;const s=[];for(let o=0;o<e.length;++o)t[o]==null?s.push(Dn(e[o]).asType("bool")):t[o].rank<e[o].rank?s.push(Kn(t[o],-1)):s.push(t[o]);const i=Mt(s,this.axis);return Pd(i,-1,!1)})}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}Zh.className="Concatenate",ge(Zh);function _ee(e){if(Array.isArray(e)){const t=new Zh({});return t.apply(e)}else return new Zh(e)}function Qh(e,t){for(;e<0;)e+=t;return e}function lG(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Ge("batchDot is not implemented for tensors of 4D or higher rank yet");if(k(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Ge("batchDot is not implemented for complex64-type Tensors yet.");const s=e.shape.length,i=t.shape.length;n==null&&(n=[s-1,i-2]);const o=n;return ee(()=>{let a;if(s>i){a=s-i;const u=[];for(let p=0;p<a;++p)u.push(1);t=t.reshape(t.shape.concat(u))}else if(i>s){a=i-s;const u=[];for(let p=0;p<a;++p)u.push(1);e=e.reshape(e.shape.concat(u))}else a=0;let c;if(e.shape.length===2&&t.shape.length===2)o[0]===o[1]?c=e.mul(t).sum(o[0]):c=e.transpose([1,0]).mul(t).sum(o[1]);else{const u=o[0]!==e.shape.length-1,p=o[1]===t.shape.length-1;c=e.matMul(t,u,p)}if(a>0){let u;s>i?u=s+i-3:u=s-1;const p=[];for(let m=u;m<u+a;++m)p.push(m);c=c.squeeze(p)}return c.shape.length===1&&(c=c.expandDims(1)),c})}class wL extends Ho{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Ge("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new j(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new j(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((i,o)=>Qh(i,e[o].shape.length)):s=[Qh(this.axes,t.shape.length),Qh(this.axes,n.shape.length)],this.normalize&&(t=zp(t,s[0]),n=zp(n,s[1])),lG(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Qh(this.axes,e.length),Qh(this.axes,t.length)],n}computeOutputShape(e){k(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Ge("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);const i=t.concat(n);return i.length===1&&i.push(1),i}computeMask(e,t){return null}getConfig(){const e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}}wL.className="Dot",ge(wL);class LL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e),s=()=>Rp(n.shape,0,this.stddev).add(n),i=Uh(s,()=>n,t.training||!1);return i})}}LL.className="GaussianNoise",ge(LL);class SL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e);if(this.rate>0&&this.rate<1){const s=()=>{const i=Math.sqrt(this.rate/(1-this.rate));return n.mul(Rp(n.shape,1,i))};return Uh(s,()=>n,t.training||!1)}return n})}}SL.className="GaussianDropout",ge(SL);class IL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Xe(e).shape}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return ee(()=>{if(this.rate<1&&this.rate>0){const n=this._getNoiseShape(e),s=()=>{const i=Xe(e),o=1.6732632423543772,a=1.0507009873554805,c=-o*a;let u=tr(_o(n),this.rate);u=_h(u,"float32");const p=((1-this.rate)*(1+this.rate*c**2))**-.5,m=-p*c*this.rate,y=i.mul(u).add(u.add(-1).mul(c));return y.mul(p).add(m)};return Uh(s,()=>Xe(e),t.training||!1)}return e})}}IL.className="AlphaDropout",ge(IL);function eu(e,t,n,s,i,o=.001){let a;if(e.rank===2)a=nA(e,t,n,s,i,o);else if(e.rank===3)a=sA(e,t,n,s,i,o);else if(e.rank===4)a=iA(e,t,n,s,i,o);else throw new Ge(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return a}function hG(e,t,n,s,i=.001){return ee(()=>{const o=np(e,s),a=o.mean,c=o.variance,u=eu(e,a,c,n,t,i);return[u,a,c]})}function uG(e,t,n,s,i=.001){return ee(()=>{const o=np(e,s),a=o.mean,c=o.variance,u=[];for(const I of ai(0,e.rank))s.indexOf(I)!==-1?u.push(1):u.push(e.shape[I]);const p=a.reshape(u),m=c.reshape(u),y=t==null?null:t.reshape(u),b=n==null?null:n.reshape(u),w=eu(e,p,m,b,y,i);return[w,a,c]})}function dG(e,t,n,s,i=.001){return ot(s.slice().sort(),ai(0,e.rank-1))?hG(e,t,n,s,i):uG(e,t,n,s,i)}class xL extends lt{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ft(e.betaInitializer||"zeros"),this.gammaInitializer=Ft(e.gammaInitializer||"ones"),this.movingMeanInitializer=Ft(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Ft(e.movingVarianceInitializer||"ones"),this.betaConstraint=ln(e.betaConstraint),this.gammaConstraint=ln(e.gammaConstraint),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer)}build(e){e=It(e);const t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new j(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new fn({ndim:e.length,axes:{[t]:n}})];const s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return ee(()=>{const n=t.training==null?!1:t.training,s=Xe(e),i=s.shape,o=i.length,a=ai(0,o),c=this.axis>=0?this.axis:this.axis+o;a.splice(c,1);const u=Bo(1,o);u[c]=i[c];const p=a.slice();p.sort();const m=!ot(p,ai(0,o).slice(0,o-1)),y=()=>{if(m){const N=this.movingMean.read().reshape(u),E=this.movingVariance.read().reshape(u),D=this.center?this.beta.read().reshape(u):null,F=this.scale?this.gamma.read().reshape(u):null;return eu(s,N,E,D,F,this.epsilon)}else return eu(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return y();const[b,w,I]=dG(s,this.gamma.read(),this.beta.read(),a,this.epsilon),T=(N,E,D)=>{ee(()=>{const F=1-D,_=N.read(),B=_.sub(E).mul(F);N.write(_.sub(B))})},v=()=>{T(this.movingMean,w,this.momentum),T(this.movingVariance,I,this.momentum)};return v(),b})}getConfig(){const e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Vt(this.betaInitializer),gammaInitializer:Vt(this.gammaInitializer),movingMeanInitializer:Vt(this.movingMeanInitializer),movingVarianceInitializer:Vt(this.movingVarianceInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer),betaConstraint:cn(this.betaConstraint),gammaConstraint:cn(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}xL.className="BatchNormalization",ge(xL);class TL extends lt{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(const t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ft(e.betaInitializer||"zeros"),this.gammaInitializer=Ft(e.gammaInitializer||"ones"),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=It(e);const t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i<this.axis.length;++i)this.axis[i]<0&&(this.axis[i]+=t);for(const i of this.axis)if(i<0||i>=t)throw new Error(`Invalid axis: ${i}`);if(this.axis.length!==Hr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);const n=this.axis.map(i=>e[i]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){const n=Xe(e),s=n.shape,i=s.length;return ee(()=>{const o=!0;let{mean:a,variance:c}=np(n,this.axis,o);const u=Bo(1,i);for(const I of this.axis)u[I]=s[I];const p=I=>I!=null&&I.shape.length!==i&&this.axis!==[i-1]?I.reshape(u):I;let m=p(this.gamma.read()),y=p(this.beta.read());const b=[],w=[];for(let I=0;I<i;++I)this.axis.indexOf(I)!==-1?(b.push(s[I]),w.push(1)):(b.push(1),w.push(s[I]));return a=a.tile(b),c=c.tile(b),m=m.tile(w),y=y.tile(w),eu(n,a,c,y,m,this.epsilon)})}getConfig(){const e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Vt(this.betaInitializer),gammaInitializer:Vt(this.gammaInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}}TL.className="LayerNormalization",ge(TL);function Wee(e,t){return ee(()=>{if(e.rank!==3)throw new j(`temporalPadding expects input tensor to be 3-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[1,1]),t.length!==2)throw new j(`temporalPadding expects input padding pattern to be a length-2 array, but received a length-${t.length} array.`);const n=[[0,0],t,[0,0]];return ki(e,n)})}function pG(e,t,n){return ee(()=>{if(e.rank!==4)throw new j(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new j("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ri()),n!=="channelsLast"&&n!=="channelsFirst")throw new j(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],ki(e,s)})}class AL extends lt{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ri():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new j(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new j(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new j(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new fn({ndim:4})]}computeOutputShape(e){e=It(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return ee(()=>pG(Xe(e),this.padding,this.dataFormat))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}AL.className="ZeroPadding2D",ge(AL);function rm(e,t,n,s,i,o){return ee(()=>{Gt(i),Sv(o),Ns(s),n==null&&(n=[1,1]),s==null&&(s="valid"),i==null&&(i=ri()),o==null&&(o="max"),e=Zw(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=mh(e,t,n,c):a=oh(e,t,n,c),i==="channelsFirst"&&(a=Pe(a,[0,3,1,2])),a})}function kN(e,t,n,s,i,o){return ee(()=>{Gt(i),Sv(o),Ns(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),i==null&&(i=ri()),o==null&&(o="max"),e=vN(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=Nb(e,t,n,c):a=pb(e,t,n,c),i==="channelsFirst"&&(a=Pe(a,[0,4,1,2,3])),a})}class FN extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new j(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(mn(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new j(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);mn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Ns(this.padding),this.inputSpec=[new fn({ndim:3})]}computeOutputShape(e){e=It(e);const t=ui(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return ee(()=>{this.invokeCallHook(e,t),e=Wh(Xe(e),2);const n=this.poolingFunction(Xe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return zr(n,[2])})}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class vL extends FN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ns(s),rm(e,t,n,s,i,"max")}}vL.className="MaxPooling1D",ge(vL);class NL extends FN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ns(s),rm(e,t,n,s,i,"avg")}}NL.className="AveragePooling1D",ge(NL);class _N extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new j(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];mn(this.poolSize,"poolSize"),mn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),Ns(this.padding),this.inputSpec=[new fn({ndim:4})]}computeOutputShape(e){e=It(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ui(t,this.poolSize[0],this.padding,this.strides[0]),n=ui(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return ee(()=>(this.invokeCallHook(e,t),this.poolingFunction(Xe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class CL extends _N{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ns(s),rm(e,t,n,s,i,"max")}}CL.className="MaxPooling2D",ge(CL);class RL extends _N{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ns(s),rm(e,t,n,s,i,"avg")}}RL.className="AveragePooling2D",ge(RL);class WN extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new j(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];mn(this.poolSize,"poolSize"),mn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),Ns(this.padding),this.inputSpec=[new fn({ndim:5})]}computeOutputShape(e){e=It(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=ui(t,this.poolSize[0],this.padding,this.strides[0]),n=ui(n,this.poolSize[1],this.padding,this.strides[1]),s=ui(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return ee(()=>(this.invokeCallHook(e,t),this.poolingFunction(Xe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class OL extends WN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ns(s),kN(e,t,n,s,i,"max")}}OL.className="MaxPooling3D",ge(OL);class EL extends WN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ns(s),kN(e,t,n,s,i,"avg")}}EL.className="AveragePooling3D",ge(EL);class $N extends lt{constructor(e){super(e);this.inputSpec=[new fn({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ge}}class DL extends $N{constructor(e){super(e||{})}call(e,t){return ee(()=>{const n=Xe(e);return zt(n,1)})}}DL.className="GlobalAveragePooling1D",ge(DL);class kL extends $N{constructor(e){super(e||{})}call(e,t){return ee(()=>{const n=Xe(e);return Xn(n,1)})}}kL.className="GlobalMaxPooling1D",ge(kL);class UN extends lt{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),this.inputSpec=[new fn({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ge}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class FL extends UN{call(e,t){return ee(()=>{const n=Xe(e);return this.dataFormat==="channelsLast"?zt(n,[1,2]):zt(n,[2,3])})}}FL.className="GlobalAveragePooling2D",ge(FL);class _L extends UN{call(e,t){return ee(()=>{const n=Xe(e);return this.dataFormat==="channelsLast"?Xn(n,[1,2]):Xn(n,[2,3])})}}_L.className="GlobalMaxPooling2D",ge(_L);class BN extends lt{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){const e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){const s=t.layer,i=hi(s,n);delete t.layer;const o={layer:i};return Object.assign(o,t),new e(o)}}class WL extends BN{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=It(e),e.length<3)throw new j(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];const t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=It(e);const t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return ee(()=>{e=Xe(e);const n=(o,a)=>{const c=Xe(this.layer.call(o,t));return[c,[]]},s=EN(n,e,[],!1,null,null,!1,!0),i=s[1];return i})}}WL.className="TimeDistributed",ge(WL);function mG(e){Za(xz,"BidirectionalMergeMode",e)}const fG="concat";class $L extends BN{constructor(e){super(e);const t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=hi(n),t.goBackwards=!(t.goBackwards===!0);const s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=hi(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?fG:e.mergeMode,mG(this.mergeMode),e.weights)throw new Ge("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){const t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,i;return this.returnState&&(i=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(i).concat(i.slice()):[n].concat(i).concat(i.slice()):Jn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=ON(e,n,s,this.numConstants);if(e=i.inputs,n=i.initialState,s=i.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);const o=[],a=[];if(n!=null){const u=n.length;if(u%2>0)throw new j("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,o.push(...n);const p=n.map(m=>new fn({shape:m.shape}));this.forwardLayer.stateSpec=p.slice(0,u/2),this.backwardLayer.stateSpec=p.slice(u/2),a.push(...p)}if(s!=null)throw new Ge("Support for constants in Bidirectional layers is not implemented yet.");const c=o[0]instanceof li;for(const u of o)if(u instanceof li!==c)throw new j("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(c){const u=[e].concat(o),p=this.inputSpec.concat(a),m=this.inputSpec;this.inputSpec=p;const y=super.apply(u,t);return this.inputSpec=m,y}else return super.apply(e,t)}call(e,t){return ee(()=>{const n=t.initialState;let s,i;if(n==null)s=this.forwardLayer.call(e,t),i=this.backwardLayer.call(e,t);else{const c=n.slice(0,n.length/2),u=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:c})),i=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let o;this.returnState&&(Array.isArray(s)&&(o=s.slice(1).concat(i.slice(1))),s=s[0],i=i[0]),this.returnSequences&&(i=As(i,1));let a;return this.mergeMode==="concat"?a=yw([s,i]):this.mergeMode==="sum"?a=be(s,i):this.mergeMode==="ave"?a=X(.5,be(s,i)):this.mergeMode==="mul"?a=X(s,i):this.mergeMode==null&&(a=[s,i]),this.returnState?this.mergeMode==null?a.concat(o):[a].concat(o):a})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Po(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Po(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){const s=this.forwardLayer.states,i=s.map(o=>null);return Array.isArray(n)?n.concat(i).concat(i):[n].concat(i).concat(i)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){const e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){const n=hi(t.layer);if(delete t.layer,t.numConstants!=null)throw new Ge("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const s=t;return s.layer=n,new e(s)}}$L.className="Bidirectional",ge($L);function gG(e){return new tc(e)}function yG(e){return new Kw(e)}function bG(e){return new Yw(e)}function wG(e){return new qw(e)}function LG(e){return new jw(e)}function SG(e){return new Jw(e)}function IG(e){return new Xw(e)}function xG(e){return new Qp(e)}function TG(e){return new Hh(e)}function AG(e){return new tL(e)}function 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aH=Object.freeze({__proto__:null,json:oH});const 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dH=[{tfOpName:"PlaceholderWithDefault",category:"graph",inputs:[{start:0,name:"default",type:"tensor"}],attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Placeholder",category:"graph",attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Const",category:"graph"},{tfOpName:"Identity",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IdentityN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Snapshot",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Rank",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Size",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Shape",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"ShapeN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Print",category:"graph",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"data",type:"tensors"}],attrs:[{tfName:"message",name:"message",type:"string"},{tfName:"first_n",name:"firstN",type:"number",notSupported:!0},{tfName:"summarize",name:"summarize",type:"number",defaultValue:3}]},{tfOpName:"NoOp",category:"graph",inputs:[]},{tfOpName:"StopGradient",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"FakeQuantWithMinMaxVars",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"min",name:"min",type:"number"},{tfName:"max",name:"max",type:"number"}]}];var pH=Object.freeze({__proto__:null,json:dH});const mH=[{tfOpName:"ResizeBilinear",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ResizeNearestNeighbor",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"CropAndResize",category:"image",inputs:[{start:0,name:"image",type:"tensor"},{start:1,name:"boxes",type:"tensor"},{start:2,name:"boxInd",type:"tensor"},{start:3,name:"cropSize",type:"number[]"}],attrs:[{tfName:"method",name:"method",type:"string"},{tfName:"extrapolation_value",name:"extrapolationValue",type:"number"}]}];var fH=Object.freeze({__proto__:null,json:mH});const 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yH=Object.freeze({__proto__:null,json:gH});const 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wH=Object.freeze({__proto__:null,json:bH});const LH=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}];var SH=Object.freeze({__proto__:null,json:LH});const 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xH=Object.freeze({__proto__:null,json:IH});const TH=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool",notSupported:!0}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}];var AH=Object.freeze({__proto__:null,json:TH});const vH=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}];var NH=Object.freeze({__proto__:null,json:vH});const CH=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}];var 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Ov={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function Ev(e,t={}){return Dh(e,Ws.getMap().classNameMap,t,"initializer")}function Vt(e){return cw(e)}function Ft(e){if(typeof e=="string"){const t=e in Ov?Ov[e]:e;if(t==="GlorotNormal")return new Dp;if(t==="GlorotUniform")return new Ep;if(t==="HeNormal")return new kp;if(t==="HeUniform")return new Fp;if(t==="LeCunNormal")return new _p;if(t==="LeCunUniform")return new Wp;{const n={};return n.className=t,n.config={},Ev(n)}}else return e instanceof Ps?e:Ev(e)}function Mz(){return new Lw}function Pz(){return new Op}function zz(e){return new Sw(e)}function Gz(e){return new Iw(e)}function Vz(e){return new xw(e)}function Hz(e){return new Tw(e)}function Yz(e){return new Aw(e)}function qz(e){return new Zn(e)}function jz(e){return new Ep(e)}function Kz(e){return new Dp(e)}function Xz(e){return new kp(e)}function Jz(e){return new Fp(e)}function Zz(e){return new _p(e)}function Qz(e){return new Wp(e)}function e3(e){return new vw(e)}var t3=Object.freeze({__proto__:null,zeros:Mz,ones:Pz,constant:zz,randomUniform:Gz,randomNormal:Vz,truncatedNormal:Hz,identity:Yz,varianceScaling:qz,glorotUniform:jz,glorotNormal:Kz,heNormal:Xz,heUniform:Jz,leCunNormal:Zz,leCunUniform:Qz,orthogonal:e3});let n3=0;function Dv(){return n3++}const $p={};function Up(e=""){return e in $p||($p[e]=0),$p[e]+=1,e+$p[e].toString()}function Nw(e){return Array.isArray(e)&&Array.isArray(e[0])}function Bp(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function Xe(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new j(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function 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trainable(e){this.trainable_=e,this.val.trainable=e}}function s3(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function QQ(e,t,n,s){return new ci(e,t,n,!0,s)}function eee(e,t,n){return new ci(ct(e),t,n)}function tee(e,t,n){return new ci(et(e),t,n)}function nee(e,t,n){const s=si(e);return new ci(s,t,n)}function see(e,t,n){const s=Dn(e);return new ci(s,t,n)}function iee(e,t,n){return new ci(Kd(e),t,n)}function ree(e,t,n,s,i,o="randomUniform"){return new ci($o(e,t,n,s),s,o)}function oee(e,t=0,n=1,s,i,o="truncatedNormal"){if(s=s||"float32",s!=="float32"&&s!=="int32")throw new Ge(`randomNormal does not support dType ${s}.`);return new ci(Ih(e,t,n,s,i),s,o)}function aee(e,t=0,n=1,s,i,o="randomNormal"){if(s=s||"float32",s!=="float32"&&s!=="int32")throw new Ge(`randomNormalVariable does not support dType ${s}.`);return new ci(Ob(e,t,n,s,i),s,o)}function cee(e,t){return e.write(t)}function lee(e,t){return e.write(be(e.read(),t))}function hee(e,t){return e.write(Ce(e.read(),t))}function Cw(e){return e.map(t=>t.read())}function Rw(e){e.forEach(t=>{const n=t[0];n.write(t[1])})}function uee(e,t){const n=t.map(i=>i.read()),s=Ab(e,n);return t.map(i=>s.grads[i.name])}class fn{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}}class li{constructor(e,t,n,s,i,o,a){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=i,this.outputTensorIndex=a,this.id=Dv(),o!=null&&(this.originalName=xv(o),this.name=Tv(this.originalName)),this.rank=t.length}}let i3=0;class Pp{constructor(e,t){this.callArgs=t,this.id=i3++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(const n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){const e=[];for(const t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}}let r3=0;class lt extends No{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=r3++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){const n=this.getClassName();t=or(n)+"_"+Up(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let i=null;e.batchSize!=null&&(i=e.batchSize),n=[i].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new oi(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new j(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Jn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Jn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new rr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new rr(`Layer ${this.name} is not connected, no input to return.`);return Jn(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new rr(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new rr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Jn(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=Nt(e),this.inputSpec==null||this.inputSpec.length===0)return;const t=Nt(this.inputSpec);if(e.length!==t.length)throw new j(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){const s=e[n],i=t[n];if(i==null)continue;const o=s.rank;if(i.ndim!=null&&o!==i.ndim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${i.ndim}, found ndim=${o}`);if(i.maxNDim!=null&&o>i.maxNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${i.maxNDim}, found ndim=${o}`);if(i.minNDim!=null&&o<i.minNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${i.minNDim}, found ndim=${o}.`);if(i.dtype!=null&&s.dtype!==i.dtype)throw new j(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${i.dtype}, found dtype=${s.dtype}.`);if(i.axes){const a=s.shape;for(const c in i.axes){const u=Number(c),p=i.axes[c],m=u>=0?a[u]:a[a.length+u];if(p!=null&&[p,null].indexOf(m)===-1)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${p} but got shape ${a}.`)}}if(i.shape!=null)for(let a=0;a<i.shape.length;++a){const c=i.shape[a],u=s.shape[a];if(c!=null&&u!=null&&c!==u)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected shape=${i.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();const n=Nt(e);let s=!0;for(const o of n)if(!(o instanceof li)){s=!1;break}let i=!0;for(const o of n)if(o instanceof li){i=!1;break}if(s===i)throw new j("Arguments to apply() must be all SymbolicTensors or all Tensors");return Go(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);const o=[];for(const a of Nt(e))o.push(a.shape);this.build(Jn(o)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&i&&(this._refCount=1)}if(this.assertInputCompatibility(e),i){let o=this.call(e,t);const a=Nt(o),c=[];for(let u of a)n.indexOf(u)!==-1&&(u=u.clone()),c.push(u);if(o=Jn(c),this.activityRegularizer!=null)throw new Ge("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}else{const o=o3(e),a=this.computeOutputShape(o);let c;const u=a3(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?o[0]:o),a!=null&&a.length>0&&Array.isArray(a[0])?c=a.map((p,m)=>new li(u,p,this,Nt(e),t,this.name,m)):c=new li(u,a,this,Nt(e),t,this.name),this.addInboundNode(e,c,null,null,o,a,t),this._refCount++,this.activityRegularizer!=null)throw new Ge("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return c}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape==null)return;if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new rr(`The layer ${this.name} has never been called and thus has no defined output shape.`);const e=[];for(const t of this.inboundNodes){const n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){const t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new rr(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new oi(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Mp(this.weights)}build(e){this.built=!0}getWeights(e=!1){return Cw(e?this.trainableWeights:this.weights)}setWeights(e){ee(()=>{const t=this.weights;if(t.length!==e.length)throw new j(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;const n=[],s=Cw(t);for(let i=0;i<s.length;++i){const o=s[i],a=t[i],c=e[i];if(!ot(o.shape,c.shape))throw new j(`Layer weight shape ${o.shape} not compatible with provided weight shape ${c.shape}`);n.push([a,c])}Rw(n)})}addWeight(e,t,n,s,i,o,a){if(this._addedWeightNames.indexOf(e)!==-1)throw new j(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=Ft("zeros"));const c=s.apply(t,n),u=new ci(c,n,e,o,a);return c.dispose(),i!=null&&this.addLoss(()=>i.apply(u.read())),o==null&&(o=!0),o?this._trainableWeights.push(u):this._nonTrainableWeights.push(u),u}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){if(e==null||Array.isArray(e)&&e.length===0)return;e=Nt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e)}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,i,o,a=null){const c=Nt(e);t=Nt(t),n=Nt(n),s=Nt(s),i=Bp(i),o=Bp(o);const u=[],p=[],m=[];for(const y of c)u.push(y.sourceLayer),p.push(y.nodeIndex),m.push(y.tensorIndex);new Pp({outboundLayer:this,inboundLayers:u,nodeIndices:p,tensorIndices:m,inputTensors:c,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:i,outputShapes:o},a);for(let y=0;y<t.length;y++)t[y].sourceLayer=this,t[y].nodeIndex=this.inboundNodes.length-1,t[y].tensorIndex=y}getConfig(){const e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount===0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}}function o3(e){e=Nt(e);const t=[];for(const n of e)t.push(n.shape);return Jn(t)}function a3(e){return"float32"}function Fv(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{const s=t.inboundNodes[n];if(s.inboundLayers.length===0)return s.inputTensors;{const i=[];for(let o=0;o<s.inboundLayers.length;o++){const a=s.inputTensors[o],c=s.inboundLayers[o],u=s.nodeIndices[o],p=Fv(a,c,u);for(const m of p)i.indexOf(m)===-1&&i.push(m)}return i}}}class sc extends lt{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Up("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new j("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new j("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new j("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");const n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];const s=new li(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new Pp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new j(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}}sc.className="InputLayer",ge(sc);function _v(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. 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n=[],s={};if(e.length===1){const i=Zv(e[0],t);n=i.sorted,s=i.recipientMap}else{const i=new Set;for(const o of e){const{sorted:a,recipientMap:c}=Zv(o,t);for(const u of a)i.has(u.name)||(n.push(u),i.add(u.name));for(const u in c)s[u]==null&&(s[u]=new Set),c[u].forEach(p=>s[u].add(p))}}return{sorted:n,recipientCounts:_3(s)}}function _3(e){const t={};for(const n in e)t[n]=e[n].size;return t}function Zv(e,t){const n=new Set,s=[],i={};for(const c of t.names())n.add(c);const o=[],a=[];for(o.push(e);o.length>0;){const c=o[o.length-1];if(n.has(c.name)){o.pop();continue}const u=a[a.length-1]===o.length-1;if(c.inputs.length===0||u)o.pop(),s.push(c),n.add(c.name),u&&a.pop();else{a.push(o.length-1);for(const p of c.inputs){if(i[p.name]==null&&(i[p.name]=new Set),i[p.name].add(c.name),n.has(p.name))continue;o.push(p)}}}return{sorted:s,recipientMap:i}}function W3(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(const i of e.sourceLayer.inboundNodes[s].outputTensors)if(i.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}class Ui extends lt{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){const N=this.getClassName().toLowerCase();this.name=Up(N)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],Hr(this.inputs).length!==this.inputs.length)throw new j(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(N=>N.name)}`);Hr(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(N=>N.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(const N of this.outputs){const E=N.sourceLayer,D=N.nodeIndex,F=N.tensorIndex;this.outputLayers.push(E),this.outputLayersNodeIndices.push(D),this.outputLayersTensorIndices.push(F)}for(const N of this.inputs){const E=N.sourceLayer,D=N.nodeIndex,F=N.tensorIndex;vs(D===0,"input layer has >1 nodes"),vs(F===0,"input layer has >1 tensors"),this.inputLayers.push(E),this.inputLayersNodeIndices.push(D),this.inputLayersTensorIndices.push(F)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let N=0;N<this.inputLayers.length;N++){const E=this.inputLayers[N];if(!(E instanceof sc))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${N} (0-based) originates from layer type ${E.getClassName()}.`);this.inputNames.push(E.name),this.feedInputShapes.push(E.batchInputShape),this.feedInputNames.push(E.name)}for(const N of this.outputLayers)this.outputNames.push(N.name);this.internalInputShapes=this.inputs.map(N=>N.shape),this.internalOutputShapes=this.outputs.map(N=>N.shape);const t={},n={},s={},i={},o={},a=[],c=(N,E,D,F,_,B)=>{(F==null||_==null||B==null)&&(F=N.sourceLayer,_=N.nodeIndex,B=N.tensorIndex);const U=F.inboundNodes[_];if(D.indexOf(U)!==-1)throw new oi(`The tensor ${N.name} at layer "${F.name}" is part of a cycle.`);if(E.indexOf(U)!==-1)return;this.containerNodes.add(Ui.nodeKey(F,_)),F.id in o||(o[F.id]=Object.keys(o).length),D.indexOf(U)===-1&&D.push(U);const Y=U.inboundLayers.length;for(let q=0;q<Y;q++){const J=U.inputTensors[q],oe=U.inboundLayers[q],ce=U.nodeIndices[q],ue=U.tensorIndices[q];c(J,E,D,oe,ce,ue)}for(E.push(U);D.indexOf(U)>=0;)D.splice(D.indexOf(U),1);a.push(U)},u=[],p=[];for(const N of this.outputs)c(N,u,p);const m=a.slice().reverse();for(const N of m){n[N.id]=N,N.id in t||(t[N.id]=0);let E=t[N.id];const D=s[N.outboundLayer.id]==null?0:s[N.outboundLayer.id];E=Math.max(E,D),s[N.outboundLayer.id]=E,i[N.outboundLayer.id]=N.outboundLayer,t[N.id]=E;for(let F=0;F<N.inboundLayers.length;F++){const _=N.inboundLayers[F],B=N.nodeIndices[F],U=_.inboundNodes[B],Y=t[U.id]==null?0:t[U.id];t[U.id]=Math.max(E+1,Y),n[U.id]=U}}const y={};for(const N in t){const E=t[N];E in y||(y[E]=[]),y[E].push(n[N])}const b={};for(const N in s){const E=s[N];E in b||(b[E]=[]),b[E].push(i[N])}let w=Object.keys(b).map(N=>parseInt(N,10)).sort(vp);this.layers=[];for(const N of w){const E=b[N];E.sort((D,F)=>{const _=o[D.id],B=o[F.id];return _<B?-1:_>B?1:0});for(const D of E)D instanceof Ui&&this.internalContainerRefs.push(D),this.layers.push(D)}this.layersByDepth=b,w=Object.keys(y).map(N=>parseInt(N,10)).sort(vp);const I=this.inputs.slice(),T=[];for(const N of w)for(const E of y[N]){const D=E.outboundLayer;if(D!=null){for(const F of E.inputTensors)if(I.indexOf(F)===-1)throw new oi(`Graph disconnected: cannot obtain value for tensor ${F} at layer "${D.name}". The following previous layers were accessed without issue: ${T}`);for(const F of E.outputTensors)I.push(F);T.push(D.name)}}this.nodesByDepth=y;const v=this.layers.map(N=>N.name);for(const N of v){const E=v.filter(D=>D===N).length;if(E!==1)throw new oi(`The name "${N}" is used ${E} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(v))}this.outboundNodes=[],this.inboundNodes=[],new Pp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(N=>null),outputMasks:this.outputs.map(N=>null),inputShapes:this.inputs.map(N=>N.shape),outputShapes:this.outputs.map(N=>N.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();const e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(const t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(const t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new j("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(const t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){const n={};let s=0;for(const o of this.layers)for(const a of o.weights){if(n[a.originalName]!=null)throw new j(`Duplicate weight name: ${a.originalName}`);n[a.originalName]=a,s++}const i=[];for(const o in e){let a=o;if(n[o]==null){const c=o.split("/"),u=c.slice(0,-2).concat([c[c.length-1]]);a=u.join("/")}if(n[a]!=null)i.push([n[a],e[o]]);else if(t)throw new j(`Provided weight data has no target variable: ${o}`);delete n[a]}if(t){const o=[];for(const a in n)o.push(a);if(o.length>0)throw new j(`${o.length} of ${s} weights are not set: ${o}`)}Rw(i)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${Xp}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){const n=$w(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return ee(()=>{e=Nt(e);const n=new Ho;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Ph(this.outputs,n,t)})}computeMask(e,t){return ee(()=>{e=Nt(e);let n;return t==null?n=Po(null,e.length):n=Nt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){const t=Bp(e);if(t.length!==this.inputLayers.length)throw new j(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);const n={};for(let a=0;a<t.length;a++){const c=this.inputLayers[a],u=t[a],p=c.name+"_0_0";n[p]=u}const s=Object.keys(this.nodesByDepth).map(a=>parseInt(a,10)).sort(vp);if(s.length>1)for(const a of s){const c=this.nodesByDepth[a];for(const u of c){const p=u.outboundLayer;if(this.inputLayers.map(I=>I.id).indexOf(p.id)!==-1)continue;const m=[];for(let I=0;I<u.inboundLayers.length;I++){const T=u.inboundLayers[I],v=u.nodeIndices[I],N=u.tensorIndices[I],E=`${T.name}_${v}_${N}`,D=n[E];m.push(D)}const y=p.computeOutputShape(Jn(m)),b=Bp(y),w=p.inboundNodes.indexOf(u);for(let I=0;I<b.length;I++){const T=`${p.name}_${w}_${I}`;n[T]=b[I]}}}const i=[],o=[];for(let a=0;a<this.outputLayers.length;a++){const c=this.outputLayers[a],u=this.outputLayersNodeIndices[a],p=this.outputLayersTensorIndices[a],m=`${c.name}_${u}_${p}`;o.push(m)}for(let a=0;a<o.length;a++){const c=o[a];vs(c in n),i.push(n[c])}return Jn(i)}runInternalGraph(e,t){t==null&&(t=Po(null,e.length));const n={};for(let c=0;c<this.inputs.length;++c){const u=this.inputs[c],p=e[c],m=t[c];n[u.id]=[p,m]}const s=Object.keys(this.nodesByDepth).map(c=>parseInt(c,10)).sort(vp);for(const c of s){const u=this.nodesByDepth[c];for(const p of u){const m=p.outboundLayer,y=p.inputTensors,b=p.outputTensors,w=new Array;for(const I of y)I.id in n&&w.push(n[I.id]);if(w.length===y.length){let I={},T,v,N,E;if(p.callArgs!=null&&(I=p.callArgs),w.length===1){const[D,F]=w[0];I.mask==null&&(I.mask=F),N=Nt(m.call(D,I)),E=Nt(m.computeMask(D,F)),T=[D],v=[F]}else T=w.map(D=>D[0]),v=w.map(D=>D[1]),I.mask==null&&(I.mask=v),N=Nt(m.call(T,I)),E=Nt(m.computeMask(T,v));if(m.activityRegularizer)throw new Ge("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let D=0;D<b.length;++D){const F=b[D],_=N[D],B=E[D];n[F.id]=[_,B]}}}}const i=[],o=[],a=[];for(const c of this.outputs){vs(c.id in n,`Could not compute output ${c.name} : ${c.id}`);const[u,p]=n[c.id];a.push(u.shape),i.push(u),o.push(p)}return[i,o,a]}buildNodeConversionMap(e){const t={};let n;for(const s of this.layers){n=s instanceof Ui?1:0;for(let i=0;i<s.inboundNodes.length;i++){const o=Ui.nodeKey(s,i);this.containerNodes.has(o)&&(t[o]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new j(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new j("Provide either a layer name or layer index");for(const n of this.layers)if(n.name===e)return n;throw new j(`No such layer: ${e}`)}calculateLosses(){return ee(()=>{const e=[];for(const t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){const s=Ui.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){const e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(const o of this.layers){const a=o.getClassName(),c=o.getConfig(),u=[];for(let m=0;m<o.inboundNodes.length;m++){const y=o.inboundNodes[m],b=Ui.nodeKey(o,m);let w={};if(this.containerNodes.has(b)){if(y.callArgs)try{JSON.stringify(y.callArgs),w=y.callArgs}catch(I){console.warn(`Layer ${o.name} was passed non-serializable keyword arguments: ${y.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),w={}}if(y.inboundLayers.length>0){const I=[];for(let T=0;T<y.inboundLayers.length;T++){const v=y.inboundLayers[T],N=y.nodeIndices[T],E=y.tensorIndices[T],D=Ui.nodeKey(v,N);let F=t[D];F==null&&(F=0),I.push([v.name,F,E,w])}u.push(I)}}}const p={};p.name=o.name,p.className=a,p.config=c,p.inboundNodes=u,n.push(p)}e.layers=n;const s=[];for(let o=0;o<this.inputLayers.length;o++){const a=this.inputLayers[o],c=this.inputLayersNodeIndices[o],u=Ui.nodeKey(a,c);if(!this.containerNodes.has(u))continue;let p=t[u];p==null&&(p=0);const m=this.inputLayersTensorIndices[o];s.push([a.name,p,m])}e.inputLayers=s;const i=[];for(let o=0;o<this.outputLayers.length;o++){const a=this.outputLayers[o],c=this.outputLayersNodeIndices[o],u=Ui.nodeKey(a,c);if(!this.containerNodes.has(u))continue;let p=t[u];p==null&&(p=0);const m=this.outputLayersTensorIndices[o];i.push([a.name,p,m])}return e.outputLayers=i,e}static fromConfig(e,t,n={},s=!1){const i={},o={};function a(T,v){T.name in o?o[T.name].push(v):o[T.name]=[v]}function c(T,v){const N=[];let E;for(const D of v){const F=D[0],_=D[1],B=D[2];if(E=D[3]==null?{}:D[3],!(F in i)){a(T,v);return}const U=i[F];if(U.inboundNodes.length<=_){a(T,v);return}const Y=U.inboundNodes[_];N.push(Y.outputTensors[B])}N.length>0&&T.apply(Jn(N),E)}function u(T){const v=T.name,N=hi(T,t.customObjects!=null?t.customObjects:{});N.setFastWeightInitDuringBuild(s),i[v]=N;const E=T.inboundNodes;E.forEach(D=>{if(!(D instanceof Array))throw new j(`Corrupted configuration, expected array for nodeData: ${D}`);a(N,D)})}const p=t.name,m=t.layers;for(const T of m)u(T);for(;!pz(o);)for(const T of m){const v=i[T.name];if(v.name in o){const N=o[v.name];delete o[v.name];for(const E of N)c(v,E)}}const y=[],b=[],w=t.inputLayers;for(const T of w){const v=T[0],N=T[1],E=T[2];vs(v in i);const D=i[v],F=D.inboundNodes[N].outputTensors;y.push(F[E])}const I=t.outputLayers;for(const T of I){const v=T[0],N=T[1],E=T[2];vs(v in i);const D=i[v],F=D.inboundNodes[N].outputTensors;b.push(F[E])}return new e({inputs:y,outputs:b,name:p})}get stateful(){if(this._stateful)throw new j("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(const e of this.layers)if(e.stateful)return!0;return!1}resetStates(){ee(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}}function Qv(e,t,n){const s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(i=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){const i=[];return t.forEach(o=>{o in e?i.push(e[o]):i.push(null)}),i}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function eN(e,t){return Qv(e,t,"classWeight")}function Aee(e,t){return Qv(e,t,"sampleWeight")}async function tN(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){const i=ee(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){const c=1;return e.argMax(c)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),o=Array.from(await i.data());qe(i);const a=[];return o.forEach(c=>{if(n[c]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${c} exists in the data but not in classWeight`);a.push(n[c])}),rs(a,"float32")}else return null}function $3(e,t){return X(e,t)}const U3=32;function nN(e,t){let n,s;const i=t;n=i.xs,s=i.ys,k(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);const o=sN("input",e.inputNames,n),a=sN("output",e.outputNames,s),c=o[0].shape[0];k(o.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${o.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),k(a.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${a.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<o.length;u++)k(o[u].shape[0]===c,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${o[u].shape[0]}; expected  ${c} based on input ${e.inputNames[0]}.`);for(let u=0;u<a.length;u++)k(a[u].shape[0]===c,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${a[u].shape[0]}; expected  ${c} based on input ${e.inputNames[0]}.`);return{xs:o,ys:a}}function sN(e,t,n){if(n instanceof Q)return[n];if(Array.isArray(n))return k(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{const s=[];for(const i of t){if(n[i]==null)throw new j(`The feature data generated by the dataset lacks the required ${e} key '${i}'.`);s.push(n[i])}return s}}function B3(e){if(e.length===3)throw new Ge("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function M3(e,t,n){const s=n.batchesPerEpoch!=null;if(k(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),k(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),k(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{const i=n.validationData!=null;let o,a;if(i)if(iN(n.validationData))k(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{const v=B3(n.validationData);o=v.xs,a=v.ys}const c=e.makeTrainFunction(),u=e.getDedupedMetricsNames();let p;i?p=u.slice().concat(u.map(v=>"val_"+v)):p=u.slice();const m=Pv(n.callbacks,n.yieldEvery),y=n.verbose==null?1:n.verbose,{callbackList:b,history:w}=zv(m,y,n.epochs,null,null,P3(t,n),null,i,p);b.setModel(e),e.history=w,await b.onTrainBegin(),e.stopTraining_=!1;let I=n.initialEpoch==null?0:n.initialEpoch,T=await t.iterator();for(;I<n.epochs;){const v={};await b.onEpochBegin(I);let N=0,E=0;for(s||(T=await t.iterator());s?N<n.batchesPerEpoch:!0;){const D=await T.next();if(s&&D.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${N} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). 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Use LayersModel.compile(modelCompileArgs).");const i=[];for(let o=0;o<this.feedOutputShapes.length;++o){const a=this.feedOutputShapes[o],c=this.feedLossFns[o];c===Gp?i.push(a.slice(0,a.length-1).concat([1])):i.push(a)}if(e=aN(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=aN(t,this.feedOutputNames,i,!1,"target"),q3(e,t,null),j3(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new j(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,i=!0,o){const[a,c]=this.standardizeUserDataXY(e,t,i,o);if(n!=null)throw new Error("sample weight is not supported yet.");let u=null;if(s!=null){const p=eN(s,this.outputNames);u=[];for(let m=0;m<p.length;++m)u.push(await tN(c[m],null,p[m]))}return[a,c,u]}testLoop(e,t,n,s=0,i){return ee(()=>{const o=this.checkNumSamples(t,n,i,"steps"),a=[];if(s>0)throw new Ge("Verbose mode is not implemented yet.");if(i!=null)throw new Ge("steps mode in testLoop() is not implemented yet");{const c=Pw(o,n),u=rs(ai(0,o));for(let p=0;p<c.length;++p){const m=c[p][0],y=c[p][1],b=Vo(u,m,y-m),w=Mw(t,b),I=e(w);if(p===0)for(let T=0;T<I.length;++T)a.push(Ne(0));for(let T=0;T<I.length;++T){const v=I[T];a[T]=be(a[T],X(y-m,v))}}for(let p=0;p<a.length;++p)a[p]=_e(a[p],o)}return a})}getDedupedMetricsNames(){const e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){const s=e[n];let i=s;if(gv(e,s)>1){const o=gv(e.slice(0,n),s);i+=`_${o}`}t.push(i)}return t}makeTrainFunction(){return e=>{const t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),i=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),o=[],a=()=>{const m=[];for(let I=0;I<this.inputs.length;++I)m.push({key:this.inputs[I],value:n[I]});const y=new Ho(m),b=Ph(this.outputs,y,{training:!0});let w;for(let I=0;I<this.lossFunctions.length;++I){const T=this.lossFunctions[I];let v=T(s[I],b[I]);i[I]!=null&&(v=$3(v,i[I]));const N=zt(v);t.push(N),I===0?w=v:w=be(w,v)}for(let I=0;I<this.metricsTensors.length;++I){let T;if(this.outputs.length>1&&I<this.outputs.length)T=t[I];else{const v=this.metricsTensors[I][0],N=this.metricsTensors[I][1];T=zt(v(s[N],b[N]))}Rn(T),o.push(T)}return w=zt(w),this.calculateLosses().forEach(I=>{w=be(w,I)}),w},c=this.collectedTrainableWeights.map(m=>m.read()),u=!0,p=this.optimizer_.minimize(a,u,c);return[p].concat(o)}}makeTestFunction(){this.testFunction=e=>ee(()=>{const t=[];let n;const s=e.slice(0,this.inputs.length),i=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),o=[];for(let u=0;u<this.inputs.length;++u)o.push({key:this.inputs[u],value:s[u]});const a=new Ho(o),c=Ph(this.outputs,a);for(let u=0;u<this.lossFunctions.length;++u){const p=this.lossFunctions[u],m=zt(p(i[u],c[u]));u===0?n=m:n=be(n,m),t.push(n)}for(let u=0;u<this.metricsTensors.length;++u){const p=this.metricsTensors[u][0],m=this.metricsTensors[u][1],y=zt(p(i[m],c[m]));t.push(y)}return t})}async fit(e,t,n={}){return H3(this,e,t,n)}async fitDataset(e,t){return M3(this,e,t)}async trainOnBatch(e,t){const n=await this.standardizeUserData(e,t),s=n[0],i=n[1],o=this.makeTrainFunction(),a=o(s.concat(i)),c=[];for(const u of a){const p=await u.data();c.push(p[0])}return qe(a),Jn(c)}getNamedWeights(e){const t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,i=this.getWeights(n);for(let o=0;o<s.length;++o){if(n&&!s[o].trainable)continue;t.push({name:s[o].originalName,tensor:i[o]})}return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){const e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){const t=Bd().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Bd().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=or(this.loss);else if(Array.isArray(this.loss)){for(const t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>or(t))}else{const t=Object.keys(this.loss);e={};const n=this.loss;for(const s of t)if(typeof n[s]=="string")e[s]=or(n[s]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[or(jp(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>or(jp(e)));{const e={};for(const t in this.metrics)e[t]=or(jp(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");const t=Mh(e.optimizer_config),n=hi(t);let s;if(typeof e.loss=="string")s=zo(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(o=>zo(o));else if(e.loss!=null){s={};for(const o in e.loss)s[o]=zo(e.loss[o])}let i;if(Array.isArray(e.metrics))i=e.metrics.map(o=>zo(o));else if(e.metrics!=null){i={};for(const o in e.metrics)i[o]=zo(e.metrics[o])}this.compile({loss:s,metrics:i,optimizer:n})}async save(e,t){if(typeof e=="string"){const u=Uy(e);if(u.length===0)throw new j(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new j(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new j("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const n=await Wy(this.getNamedWeights(t)),s=!1,i=null,o=this.toJSON(i,s),a={modelTopology:o,format:X3,generatedBy:`TensorFlow.js tfjs-layers v${Xp}`,convertedBy:null},c=t==null?!1:t.includeOptimizer;if(c&&this.optimizer!=null){a.trainingConfig=this.getTrainingConfig();const u="optimizer",{data:p,specs:m}=await Wy(await this.optimizer.getWeights(),u);n.specs.push(...m),n.data=Od([n.data,p])}if(this.userDefinedMetadata!=null){const u=!0;Kv(this.userDefinedMetadata,this.name,u),a.userDefinedMetadata=this.userDefinedMetadata}return a.weightData=n.data,a.weightSpecs=n.specs,e.save(a)}setUserDefinedMetadata(e){Kv(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}}cr.className="Model",ge(cr);class lN extends cr{}lN.className="Functional",ge(lN);async function J3(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);const s=Mh(n),i=hi(s,t);if(e.weightsManifest!=null){const o=await _T(e.weightsManifest,e.pathPrefix,i.weights.map(c=>c.originalName)),a={};for(const c of i.weights)a[c.originalName]=o[c.originalName];i.loadWeights(a),qe(o)}return i}async function Z3(e,t){if(t==null&&(t={}),typeof e=="string"){const n=By(e,t);if(n.length===0)n.push(kd(e,t));else if(n.length>1)throw new j(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Q3(e,void 0,t)}async function Q3(e,t,n){if(n==null&&(n={}),e.load==null)throw new j("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const s=await e.load();let i=s.modelTopology;i.model_config!=null&&(i=i.model_config);const o=n.strict==null?!0:n.strict,a=s.weightData!=null&&s.weightSpecs!=null&&o,c=hi(Mh(i),t,a),u=s.trainingConfig;if(u!=null&&c.loadTrainingConfig(u),s.userDefinedMetadata!=null&&c.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new j("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");const{modelWeights:p,optimizerWeights:m}=eG(s.weightData,s.weightSpecs);c.loadWeights(p,o),c.optimizer!=null&&m.length>0&&await c.optimizer.setWeights(m),qe(p),qe(m.map(y=>y.tensor))}return c}function eG(e,t){const n=Rd(e,t),s={},i=[];return t.forEach(o=>{o.group==="optimizer"?i.push({name:o.name,tensor:n[o.name]}):s[o.name]=n[o.name]}),{modelWeights:s,optimizerWeights:i}}class oc extends cr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Up("sequential_"),e.layers!=null)for(const t of e.layers)this.add(t)}checkShape(e){const t=e.inboundNodes[0].outputTensors[0].shape;if(t.some(n=>n<0))throw new j(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){const t=e instanceof oc||e instanceof cr;let n;if(t){if(n=e,n.outputs.length!==1)throw new j("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new j("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new j("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");const s=_v({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new j(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new j("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=Fv(this.outputs[0])}this.inboundNodes=[],new Pp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Po(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{const s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{const e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(It(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new cr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new oi("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new oi("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new oi("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new oi("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let i,o={};if(t instanceof Array){if(!(t[0].className!=null)||t[0].className==="Merge")throw new j("Legacy serialization format not supported yet.");i=t}else k(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),i=t.layers,delete t.layers,o=t;const a=new e(o);if(!(a instanceof oc))throw new Ge(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(const c of i){const u=void 0,p=hi(c,u,s);s&&p.setFastWeightInitDuringBuild(!0),a.add(p)}return a}set stopTraining(e){if(this.model==null)throw new j("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new j("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){const e=[];for(const t of this.layers){const n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}}oc.className="Sequential",ge(oc);function tG(e){return new cr(e)}function nG(e){return new oc(e)}function sG(e,t){return t==null&&(t={}),Z3(e,t)}function hN(e){return _v(e)}function iG(e,t){zs.registerCallbackConstructor(e,t)}class cs extends No{getConfig(){return{}}}class uN extends cs{apply(e,t=1){return Dz(e,t)}}uN.className="elu",ge(uN);class dN extends cs{apply(e){return rp(e)}}dN.className="selu",ge(dN);class pN extends cs{apply(e){return Fi(e)}}pN.className="relu",ge(pN);class mN extends cs{apply(e){return ee(()=>ko(6,Fi(e)))}}mN.className="relu6",ge(mN);class fN extends cs{apply(e){return e}}fN.className="linear",ge(fN);class gN extends cs{apply(e){return Ei(e)}}gN.className="sigmoid",ge(gN);class yN extends cs{apply(e){return Fz(e)}}yN.className="hardSigmoid",ge(yN);class bN extends cs{apply(e){return Va(e)}}bN.className="softplus",ge(bN);class wN extends cs{apply(e){return kz(e)}}wN.className="softsign",ge(wN);class LN extends cs{apply(e){return Ma(e)}}LN.className="tanh",ge(LN);class Gw extends cs{apply(e,t=-1){return Uo(e,t)}}Gw.className="softmax",ge(Gw);class SN extends cs{apply(e,t=-1){return Qd(e,t)}}SN.className="logSoftmax",ge(SN);class IN extends cs{apply(e,t=1){return ee(()=>Ei(e.mul(t)).mul(e))}}IN.className="swish",ge(IN);function Xr(e){return e.getClassName()}function Vw(e,t={}){return Dh(e,Ws.getMap().classNameMap,t,"activation")}function Jr(e){if(e==null){const t={};return t.className="linear",t.config={},Vw(t)}if(typeof e=="string"){const t={};return t.className=e,t.config={},Vw(t)}else return e instanceof cs?e:Vw(e)}function Hw(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}class xN extends No{}class Gh extends xN{constructor(e){super();Hw(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return ee(()=>{let t=ct([1]);return this.hasL1&&(t=be(t,Ue(X(this.l1,rn(e))))),this.hasL2&&(t=be(t,Ue(X(this.l2,$h(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}Gh.className="L1L2",ge(Gh);function rG(e){return Hw(e),new Gh({l1:e!=null?e.l1:null,l2:0})}function oG(e){return Hw(e),new Gh({l2:e!=null?e.l2:null,l1:0})}const TN={l1l2:"L1L2"};function xt(e){return cw(e)}function AN(e,t={}){return Dh(e,Ws.getMap().classNameMap,t,"regularizer")}function _t(e){if(e==null)return null;if(typeof e=="string"){const t=e in TN?TN[e]:e,n={className:t,config:{}};return AN(n)}else return e instanceof xN?e:AN(e)}class Yw extends lt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Xe(e);let n=Fi(e);return this.maxValue!=null&&(n=jn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}Yw.className="ReLU",ge(Yw);class qw extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=Xe(e);return Xd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}qw.className="LeakyReLU",ge(qw);class jw extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Ft(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=_t(e.alphaRegularizer),this.alphaConstraint=hn(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new j(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=It(e);const t=e.slice(1);if(this.sharedAxes!=null)for(const s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new fn({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Xe(e),gh(e,this.alpha.read())}getConfig(){const e={alphaInitializer:Vt(this.alphaInitializer),alphaRegularizer:xt(this.alphaRegularizer),alphaConstraint:ln(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}}jw.className="PReLU",ge(jw);class Kw extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ge(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=Xe(e);return Do(n)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}Kw.className="ELU",ge(Kw);class Xw extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){const n=Xe(e);return n.mul(_h(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){const e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}}Xw.className="ThresholdedReLU",ge(Xw);class Jw extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Gw().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){const n=Xe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}Jw.className="Softmax",ge(Jw);function ac(e,t,n){if(typeof e=="number")return Po(e,t);if(e.length!==t)throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){const i=e[s];if(!vz(i))throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${i}`)}return e}function ui(e,t,n,s,i=1){if(e==null)return e;const o=t+(t-1)*(i-1);let a;return n==="same"?a=e:a=e-o+1,Math.floor((a+s-1)/s)}function Jp(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+qr([n-t,0]);else if(s==="same")e=e*t;else throw new j(`Unsupport padding mode: ${s}.`);return e}function Zw(e,t){return ee(()=>(Gt(t),t==="channelsFirst"?Pe(e,[0,2,3,1]):e))}function vN(e,t){return ee(()=>(Gt(t),t==="channelsFirst"?Pe(e,[0,2,3,4,1]):e))}function NN(e,t,n,s=1,i="valid",o,a=1){return ee(()=>{if(o==null&&(o=ri()),Gt(o),e.shape.length!==3)throw new j(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new j(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new j(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(o==="channelsFirst"&&(e=Pe(e,[0,2,1])),i==="causal")throw new Ge("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let c=Hd(e,t,s,i==="same"?"same":"valid","NWC",a);return n!=null&&(c=$i(c,n)),c})}function vee(e,t,n=1,s="valid",i,o=1){return ee(()=>(Gt(i),NN(e,t,null,n,s,i,o)))}function Nee(e,t,n=[1,1],s="valid",i,o){return ee(()=>(Gt(i),Qw(e,t,null,n,s,i,o)))}function Qw(e,t,n,s=[1,1],i="valid",o,a,c=null){return ee(()=>{if(o==null&&(o=ri()),Gt(o),e.rank!==3&&e.rank!==4)throw new j(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new j(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=Zw(e,o);if(i==="causal")throw new Ge("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=Hb({x:u,filter:t,strides:s,pad:i==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:n,activation:c}),o==="channelsFirst"&&(u=Pe(u,[0,3,1,2])),u})}function Cee(e,t,n=[1,1,1],s="valid",i,o){return ee(()=>(Gt(i),CN(e,t,null,n,s,i,o)))}function CN(e,t,n,s=[1,1,1],i="valid",o,a){return ee(()=>{if(o==null&&(o=ri()),Gt(o),e.rank!==4&&e.rank!==5)throw new j(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new j(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let c=vN(e,o);if(i==="causal")throw new Ge("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return c=yb(c,t,s,i==="same"?"same":"valid","NDHWC",a),n!=null&&(c=$i(c,n)),o==="channelsFirst"&&(c=Pe(c,[0,4,1,2,3])),c})}class eL extends lt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",eL.verifyArgs(t),this.rank=e,mn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ge(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=ac(t.kernelSize,e,"kernelSize"),this.strides=ac(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Ns(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Gt(this.dataFormat),this.activation=Jr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Ft(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=hn(t.biasConstraint),this.biasRegularizer=_t(t.biasRegularizer),this.activityRegularizer=_t(t.activityRegularizer),this.dilationRate=ac(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new j(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new j(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new j(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(vs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!hw(e.kernelSize,"number",1,3))throw new j(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){const e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Xr(this.activation),useBias:this.useBias,biasInitializer:Vt(this.biasInitializer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),biasConstraint:ln(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class Vh extends eL{constructor(e,t){super(e,t);this.kernel=null,Vh.verifyArgs(t),this.filters=t.filters,mn(this.filters,"filters"),this.kernelInitializer=Ft(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=hn(t.kernelConstraint),this.kernelRegularizer=_t(t.kernelRegularizer)}build(e){e=It(e);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[t]}`);const n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return ee(()=>{e=Xe(e);let n;const s=this.bias==null?null:this.bias.read(),i=bv(this.activation.getClassName());if(i!=null&&this.rank===2)n=Qw(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,i);else{if(this.rank===1)n=NN(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Qw(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=CN(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ge("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=It(e);const t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let i=0;i<n.length;++i){const o=ui(n[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);t.push(o)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){const e={filters:this.filters,kernelInitializer:Vt(this.kernelInitializer),kernelRegularizer:xt(this.kernelRegularizer),kernelConstraint:ln(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new j(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}}class Hh extends Vh{constructor(e){super(2,e);Hh.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!hw(e.kernelSize,"number",1,2))throw new j(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}Hh.className="Conv2D",ge(Hh);class Zp extends Vh{constructor(e){super(3,e);Zp.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new j(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}Zp.className="Conv3D",ge(Zp);class tL extends Hh{constructor(e){super(e);if(this.inputSpec=[new fn({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new j(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=It(e),e.length!==4)throw new j("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new fn({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return ee(()=>{let n=Xe(e);if(n.shape.length!==4)throw new j(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);const s=n.shape,i=s[0];let o,a;this.dataFormat==="channelsFirst"?(o=2,a=3):(o=1,a=2);const c=s[o],u=s[a],p=this.kernelSize[0],m=this.kernelSize[1],y=this.strides[0],b=this.strides[1],w=Jp(c,y,p,this.padding),I=Jp(u,b,m,this.padding),T=[i,w,I,this.filters];this.dataFormat!=="channelsLast"&&(n=Pe(n,[0,2,3,1]));let v=Yd(n,this.kernel.read(),T,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=Pe(v,[0,3,1,2])),this.bias!=null&&(v=$i(v,this.bias.read(),this.dataFormat)),this.activation!=null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=It(e);const t=e.slice();let n,s,i;this.dataFormat==="channelsFirst"?(n=1,s=2,i=3):(n=3,s=1,i=2);const o=this.kernelSize[0],a=this.kernelSize[1],c=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Jp(t[s],c,o,this.padding),t[i]=Jp(t[i],u,a,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}tL.className="Conv2DTranspose",ge(tL);class RN extends Vh{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new j("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new j("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new j(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Ft(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=_t(t.depthwiseRegularizer),this.depthwiseConstraint=hn(t.depthwiseConstraint),this.pointwiseInitializer=Ft(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=_t(t.pointwiseRegularizer),this.pointwiseConstraint=hn(t.pointwiseConstraint)}build(e){if(e=It(e),e.length<this.rank+2)throw new j(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);const n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),i=[];for(let a=0;a<this.rank;++a)i.push(1);i.push(n*this.depthMultiplier,this.filters);const o=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,o,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",i,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,o,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,o,this.biasConstraint):this.bias=null,this.inputSpec=[new fn({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return ee(()=>{e=Xe(e);let n;if(this.rank===1)throw new Ge("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Pe(e,[0,2,3,1])),n=Fb(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=$i(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Pe(n,[0,3,1,2])),n})}getConfig(){const e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Vt(this.depthwiseInitializer),e.pointwiseInitializer=Vt(this.pointwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.pointwiseRegularizer=xt(this.pointwiseRegularizer),e.depthwiseConstraint=ln(this.depthwiseConstraint),e.pointwiseConstraint=ln(this.pointwiseConstraint),e}}RN.className="SeparableConv";class nL extends RN{constructor(e){super(2,e)}}nL.className="SeparableConv2D",ge(nL);class Qp extends Vh{constructor(e){super(1,e);Qp.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){const e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!hw(e.kernelSize,"number",1,1))throw new j(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}Qp.className="Conv1D",ge(Qp);class sL extends lt{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return ee(()=>{if(e=Xe(e),this.dataFormat==="channelsLast"){const n=Cp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Cp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{const n=Cp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Cp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){const e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}sL.className="Cropping2D",ge(sL);class iL extends lt{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){const t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{const t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return ee(()=>{let n=Xe(e);const s=n.shape;if(this.dataFormat==="channelsFirst"){n=Pe(n,[0,2,3,1]);const i=this.size[0]*s[2],o=this.size[1]*s[3],a=n.resizeNearestNeighbor([i,o]);return Pe(a,[0,3,1,2])}else{const i=this.size[0]*s[1],o=this.size[1]*s[2];return n.resizeNearestNeighbor([i,o])}})}getConfig(){const e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}iL.className="UpSampling2D",ge(iL);function aG(e,t,n=[1,1],s="valid",i,o){return ee(()=>{i==null&&(i=ri()),Gt(i);let a=Zw(e,i);if(e.rank!==4)throw new j(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new j(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return a=Oo(a,t,n,s==="same"?"same":"valid","NHWC",o),i==="channelsFirst"&&(a=Pe(a,[0,3,1,2])),a})}class rL extends eL{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Ft(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=hn(e.depthwiseConstraint),this.depthwiseRegularizer=_t(e.depthwiseRegularizer)}build(e){if(e=It(e),e.length<4)throw new j(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);const n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{e=Xe(e);let n=aG(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=$i(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=It(e);const t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,i=ui(t,this.kernelSize[0],this.padding,this.strides[0]),o=ui(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,i,o]:[e[0],i,o,s]}getConfig(){const e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Vt(this.depthwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.depthwiseConstraint=ln(this.depthwiseRegularizer),e}}rL.className="DepthwiseConv2D",ge(rL);function ON(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new j("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function i(o){return o==null||Array.isArray(o)?o:[o]}return t=i(t),n=i(n),{inputs:e,initialState:t,constants:n}}function EN(e,t,n,s=!1,i,o,a=!1,c=!1){return ee(()=>{const u=t.shape.length;if(u<3)throw new j(`Input should be at least 3D, but is ${u}D.`);const p=[1,0].concat(ai(2,u));if(t=Pe(t,p),o!=null)throw new Ge("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),i!=null&&(i=i.asType("bool").asType("float32"),i.rank===u-1&&(i=Kn(i,-1)),i=Pe(i,p)),s&&(t=As(t,0),i!=null&&(i=As(i,0)));const m=[];let y,b=n;const w=t.shape[0],I=_i(t);let T;i!=null&&(T=_i(i));for(let N=0;N<w;++N){const E=I[N],D=ee(()=>e(E,b));if(i==null)y=D[0],b=D[1];else{const F=ee(()=>{const _=T[N],B=Dn(_).sub(_),U=D[0].mul(_).add(b[0].mul(B)),Y=b.map((q,J)=>D[1][J].mul(_).add(q.mul(B)));return{output:U,newStates:Y}});y=F.output,b=F.newStates}c&&m.push(y)}let v;if(c){const N=1;v=as(m,N)}return[y,v,b]})}class Bi extends lt{constructor(e){super(e);let t;if(e.cell==null)throw new j("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new nm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new j("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new fn({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ai(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Nw(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);const n=t[0];let s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){const i=[];for(const o of t)i.push([e[0],o]);return[s].concat(i)}else return s}computeMask(e,t){return ee(()=>{Array.isArray(t)&&(t=t[0]);const n=this.returnSequences?t:null;if(this.returnState){const s=this.states.map(i=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){const t=null;if(this.numConstants!=null)throw new Ge("Constants support is not implemented in RNN yet.");Nw(e)&&(e=e[0]),e=e;const n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new fn({shape:[n,null,...s]});const i=[e[0]].concat(e.slice(2));if(t!=null)throw new Ge("Constants support is not implemented in RNN yet.");this.cell.build(i);let o;if(Array.isArray(this.cell.stateSize)?o=this.cell.stateSize:o=[this.cell.stateSize],this.stateSpec!=null){if(!ot(this.stateSpec.map(a=>a.shape[a.shape.length-1]),o))throw new j(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=o.map(a=>new fn({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){ee(()=>{if(!this.stateful)throw new rr("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape[0];if(n==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>ct([n,s])):this.states_=[ct([n,this.cell.stateSize])];else if(e==null)qe(this.states_),this.keptStates!=null&&(qe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>ct([n,s])):this.states_[0]=ct([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):qe(this.states_);for(let s=0;s<this.states_.length;++s){const i=e[s],o=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,a=[n,o];if(!ot(i.shape,a))throw new j(`State ${s} is incompatible with layer ${this.name}: expected shape=${a}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Rn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=ON(e,n,s,this.numConstants);e=i.inputs,n=i.initialState,s=i.constants;let o=[],a=[];if(n!=null){t.initialState=n,o=o.concat(n),this.stateSpec=[];for(const u of n)this.stateSpec.push(new fn({shape:u.shape}));a=a.concat(this.stateSpec)}s!=null&&(t.constants=s,o=o.concat(s),this.numConstants=s.length);const c=o[0]instanceof li;if(c){const u=[e].concat(o),p=this.inputSpec.concat(a),m=this.inputSpec;this.inputSpec=p;const y=super.apply(u,t);return this.inputSpec=m,y}else return super.apply(e,t)}call(e,t){return ee(()=>{const n=t==null?null:t.mask,s=t==null?null:t.training;let i=t==null?null:t.initialState;e=Xe(e),i==null&&(this.stateful?i=this.states_:i=this.getInitialState(e));const o=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(i.length!==o)throw new j(`RNN Layer has ${o} state(s) but was passed ${i.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const a={training:s},c=(w,I)=>{const T=this.cell.call([w].concat(I),a);return[T[0],T.slice(1)]},u=EN(c,e,i,this.goBackwards,n,null,this.unroll,this.returnSequences),p=u[0],m=u[1],y=u[2];this.stateful&&this.resetStates(y,s);const b=this.returnSequences?m:p;return this.returnState?[b].concat(y):b})}getInitialState(e){return ee(()=>{let t=ct(e.shape);return t=Ue(t,[1,2]),t=Wh(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?bw(t,[1,n]):t):this.cell.stateSize>1?[bw(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){const e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);const n=this.cell.getConfig();return this.getClassName()===Bi.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){const s=t.cell,i=hi(s,n);return new e(Object.assign(t,{cell:i}))}}Bi.className="RNN",ge(Bi);class cc extends lt{}class em extends cc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,mn(this.units,"units"),this.activation=Jr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=hn(e.kernelConstraint),this.recurrentConstraint=hn(e.recurrentConstraint),this.biasConstraint=hn(e.biasConstraint),this.dropout=nc([1,qr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nc([1,qr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=It(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{if(e=e,e.length!==2)throw new j(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];const s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Zr({ones:()=>Dn(e),rate:this.dropout,training:s})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Zr({ones:()=>Dn(n),rate:this.recurrentDropout,training:s}));let i;const o=this.dropoutMask,a=this.recurrentDropoutMask;o!=null?i=Wi(X(e,o),this.kernel.read()):i=Wi(e,this.kernel.read()),this.bias!=null&&(i=$i(i,this.bias.read())),a!=null&&(n=X(n,a));let c=be(i,Wi(n,this.recurrentKernel.read()));return this.activation!=null&&(c=this.activation.apply(c)),[c,c]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Xr(this.activation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),recurrentInitializer:Vt(this.recurrentInitializer),biasInitializer:Vt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),recurrentConstraint:ln(this.recurrentConstraint),biasConstraint:ln(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}}em.className="SimpleRNNCell",ge(em);class oL extends Bi{constructor(e){e.cell=new em(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(qe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(qe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return new e(t)}}oL.className="SimpleRNN",ge(oL);class tm extends cc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new j("GRUCell does not support reset_after parameter set to true.");this.units=e.units,mn(this.units,"units"),this.activation=Jr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Jr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=hn(e.kernelConstraint),this.recurrentConstraint=hn(e.recurrentConstraint),this.biasConstraint=hn(e.biasConstraint),this.dropout=nc([1,qr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nc([1,qr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=It(e);const t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{if(e=e,e.length!==2)throw new j(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training==null?!1:t.training;let s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Zr({ones:()=>Dn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Zr({ones:()=>Dn(s),rate:this.recurrentDropout,training:n,count:3}));const i=this.dropoutMask,o=this.recurrentDropoutMask;let a,c,u;0<this.dropout&&this.dropout<1&&(e=X(e,i[0]));let p=Wi(e,this.kernel.read());this.useBias&&(p=$i(p,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,o[0]));const m=this.recurrentKernel.read(),[y,b]=os(m,[2*this.units,this.units],m.rank-1),w=Wi(s,y),[I,T,v]=os(p,3,p.rank-1),[N,E]=os(w,2,w.rank-1);a=this.recurrentActivation.apply(be(I,N)),c=this.recurrentActivation.apply(be(T,E));const D=Wi(X(c,s),b);u=this.activation.apply(be(v,D));const F=be(X(a,s),X(be(1,Pt(a)),u));return[F,F]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Xr(this.activation),recurrentActivation:Xr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),recurrentInitializer:Vt(this.recurrentInitializer),biasInitializer:Vt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),recurrentConstraint:ln(this.recurrentConstraint),biasConstraint:ln(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}}tm.className="GRUCell",ge(tm);class aL extends Bi{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new tm(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(qe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(qe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}aL.className="GRU",ge(aL);class Yh extends cc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,mn(this.units,"units"),this.activation=Jr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Jr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=hn(e.kernelConstraint),this.recurrentConstraint=hn(e.recurrentConstraint),this.biasConstraint=hn(e.biasConstraint),this.dropout=nc([1,qr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=nc([1,qr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=It(e);const n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){const i=this.biasInitializer,o=this.units;s=new(t=class extends Ps{apply(c,u){const p=i.apply([o]),m=new Op().apply([o]),y=i.apply([o*2]);return Nv(Nv(p,m),y)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return ee(()=>{const n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new j(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1];const i=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Zr({ones:()=>Dn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Zr({ones:()=>Dn(s),rate:this.recurrentDropout,training:n,count:4}));const o=this.dropoutMask,a=this.recurrentDropoutMask;let c,u,p,m;0<this.dropout&&this.dropout<1&&(e=X(e,o[0]));let y=Wi(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,a[0])),y=be(y,Wi(s,this.recurrentKernel.read())),this.useBias&&(y=$i(y,this.bias.read()));const[b,w,I,T]=os(y,4,y.rank-1);c=this.recurrentActivation.apply(b),u=this.recurrentActivation.apply(w),p=be(X(u,i),X(c,this.activation.apply(I))),m=this.recurrentActivation.apply(T);const v=X(m,this.activation.apply(p));return[v,v,p]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Xr(this.activation),recurrentActivation:Xr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),recurrentInitializer:Vt(this.recurrentInitializer),biasInitializer:Vt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),recurrentConstraint:ln(this.recurrentConstraint),biasConstraint:ln(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}}Yh.className="LSTMCell",ge(Yh);class cL extends Bi{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Yh(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(qe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(qe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}cL.className="LSTM",ge(cL);class nm extends cc{constructor(e){super(e);this.cells=e.cells}get stateSize(){const e=[];for(const t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return ee(()=>{e=e;let n=e.slice(1);const s=[];for(const a of this.cells.slice().reverse())Array.isArray(a.stateSize)?s.push(n.splice(0,a.stateSize.length)):s.push(n.splice(0,1));s.reverse();const i=[];let o;for(let a=0;a<this.cells.length;++a){const c=this.cells[a];n=s[a],a===0?o=[e[0]].concat(n):o=[o[0]].concat(n),o=c.call(o,t),i.push(o.slice(1))}n=[];for(const a of i.slice().reverse())n.push(...a);return[o[0]].concat(n)})}build(e){Nw(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Go(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){const e=super.getConfig(),t=i=>({className:i.getClassName(),config:i.getConfig()}),n=this.cells.map(t),s={cells:n};return Object.assign({},e,s)}static fromConfig(e,t,n={}){const s=[];for(const i of t.cells)s.push(hi(i,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];const e=[];for(const t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){const e=[];for(const t of this.cells)e.push(...t.weights);return Cw(e)}setWeights(e){const t=[];for(const n of this.cells){const s=n.weights.length,i=e.splice(s);for(let o=0;o<n.weights.length;++o)t.push([n.weights[o],i[o]])}Rw(t)}}nm.className="StackedRNNCells",ge(nm);function Zr(e){const{ones:t,rate:n,training:s=!1,count:i=1}=e,o=()=>Rv(t(),n),a=()=>Uh(o,t,s);if(!i||i<=1)return Rn(a().clone());const c=Array(i).fill(void 0).map(a);return c.map(u=>Rn(u.clone()))}var cG=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,s=Object.getOwnPropertySymbols(e);i<s.length;i++)t.indexOf(s[i])<0&&Object.prototype.propertyIsEnumerable.call(e,s[i])&&(n[s[i]]=e[s[i]]);return n};class Ree extends cc{}class DN extends Bi{constructor(e){if(e.unroll)throw new Ge("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ge("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new fn({ndim:5})]}call(e,t){return ee(()=>{if(this.cell.dropoutMask!=null&&(qe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(qe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new j("ConvRNN2D cell does not support constants");const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return ee(()=>{const{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),i=[s[0],...s.slice(2)],o=ct(i);return Array.isArray(t)?Array(t.length).fill(o):[o]})}resetStates(e,t=!1){ee(()=>{if(!this.stateful)throw new rr("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),i=[s[0],...s.slice(2)],o=n[0];if(o==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ct(i)):this.states_=[ct(i)];else if(e==null)qe(this.states_),this.keptStates!=null&&(qe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ct(i)):this.states_[0]=ct(i);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):qe(this.states_);for(let a=0;a<this.states_.length;++a){const c=e[a],u=i;if(!ot(c.shape,u))throw new j(`State ${a} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${c.shape}`);this.states_[a]=c}}this.states_=this.states_.map(a=>Rn(a.clone()))})}computeSingleOutputShape(e){const{dataFormat:t,filters:n,kernelSize:s,padding:i,strides:o,dilationRate:a}=this.cell,c=t==="channelsFirst",u=e[c?3:2],p=e[c?4:3],m=ui(u,s[0],i,o[0],a[0]),y=ui(p,s[1],i,o[1],a[1]),b=[...e.slice(0,2),...c?[n,m,y]:[m,y,n]];return b}}DN.className="ConvRNN2D";class sm extends Yh{constructor(e){const{filters:t,kernelSize:n,strides:s,padding:i,dataFormat:o,dilationRate:a}=e;super(Object.assign({},e,{units:t}));this.filters=t,mn(this.filters,"filters"),this.kernelSize=ac(n,2,"kernelSize"),this.kernelSize.forEach(c=>mn(c,"kernelSize")),this.strides=ac(s||1,2,"strides"),this.strides.forEach(c=>mn(c,"strides")),this.padding=i||"valid",Ns(this.padding),this.dataFormat=o||"channelsLast",Gt(this.dataFormat),this.dilationRate=ac(a||1,2,"dilationRate"),this.dilationRate.forEach(c=>mn(c,"dilationRate"))}build(e){var t;e=It(e);const n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[n]}`);const s=e[n],i=4,o=this.kernelSize.concat([s,this.filters*i]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const a=this.kernelSize.concat([this.filters,this.filters*i]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let c;if(this.unitForgetBias){const u=this.biasInitializer,p=this.filters;c=new(t=class extends Ps{apply(y,b){const w=u.apply([p]),I=si([p]),T=u.apply([p*2]);return yw([w,I,T])}},t.className="CustomInit",t)}else c=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*i],null,c,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return ee(()=>{if(e.length!==3)throw new j(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training||!1,s=e[0],i=e[1],o=e[2],a=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Zr({ones:()=>Dn(s),rate:this.dropout,training:n,count:a}));const c=this.dropoutMask,u=(Ie,Se,Ee)=>!Se||!Se[Ee]?Ie:X(Se[Ee],Ie);let p=u(s,c,0),m=u(s,c,1),y=u(s,c,2),b=u(s,c,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Zr({ones:()=>Dn(i),rate:this.recurrentDropout,training:n,count:a}));const w=this.recurrentDropoutMask;let I=u(i,w,0),T=u(i,w,1),v=u(i,w,2),N=u(i,w,3);const E=3,[D,F,_,B]=os(this.kernel.read(),a,E),[U,Y,q,J]=this.useBias?os(this.bias.read(),a):[null,null,null,null];p=this.inputConv(p,D,U,this.padding),m=this.inputConv(m,F,Y,this.padding),y=this.inputConv(y,_,q,this.padding),b=this.inputConv(b,B,J,this.padding);const[oe,ce,ue,he]=os(this.recurrentKernel.read(),a,E);I=this.recurrentConv(I,oe),T=this.recurrentConv(T,ce),v=this.recurrentConv(v,ue),N=this.recurrentConv(N,he);const pe=this.recurrentActivation.apply(be(p,I)),le=this.recurrentActivation.apply(be(m,T)),ye=be(X(le,o),X(pe,this.activation.apply(be(y,v)))),me=X(this.recurrentActivation.apply(be(b,N)),this.activation.apply(ye));return[me,me,ye]})}getConfig(){const e=super.getConfig(),{units:t}=e,n=cG(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){const i=er(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?$i(i,n,this.dataFormat):i}recurrentConv(e,t){const n=1;return er(e,t,n,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}sm.className="ConvLSTM2DCell",ge(sm);class lL extends DN{constructor(e){const t=new sm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}}lL.className="ConvLSTM2D",ge(lL);class im extends lt{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;const t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e);if(0<this.rate&&this.rate<1){const s=t.training==null?!1:t.training,i=this.getNoiseShape(n),o=Uh(()=>Rv(n,this.rate,i,this.seed),()=>n,s);return o}return e})}getConfig(){const e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}}im.className="Dropout",ge(im);class hL extends im{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}hL.className="SpatialDropout1D",ge(hL);class uL extends lt{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,mn(this.units,"units"),this.activation=Jr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=hn(e.kernelConstraint),this.biasConstraint=hn(e.biasConstraint),this.kernelRegularizer=_t(e.kernelRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=It(e);const t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=It(e);const t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e),s=bv(this.activation.getClassName());let i;return s!=null?i=Wi(n,this.kernel.read(),s,this.bias?this.bias.read():null):(i=Wi(n,this.kernel.read()),this.bias!=null&&(i=$i(i,this.bias.read())),this.activation!=null&&(i=this.activation.apply(i))),i})}getConfig(){const e={units:this.units,activation:Xr(this.activation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),biasInitializer:Vt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),biasConstraint:ln(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}uL.className="Dense",ge(uL);class dL extends lt{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=It(e);for(const t of e.slice(1))if(t==null)throw new j(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Yr(e,1)]}call(e,t){return ee(()=>{this.invokeCallHook(e,t);let n=Xe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){const s=[0];for(let i=2;i<n.rank;++i)s.push(i);s.push(1),n=n.transpose(s)}return Ez(n)})}getConfig(){const e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}dL.className="Flatten",ge(dL);class pL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.activation=Jr(e.activation)}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e);return this.activation.apply(n)})}getConfig(){const e={activation:Xr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}pL.className="Activation",ge(pL);class mL extends lt{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return ee(()=>(e=Xe(e),Rz(e,this.n)))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}mL.className="RepeatVector",ge(mL);class fL extends lt{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){const n="Total size of new array must be unchanged.",s=t.slice();let i=1,o=null;for(let c=0;c<s.length;++c){const u=s[c];if(this.isUnknown(u))if(o===null)o=c;else throw new j("Can only specifiy one unknown dimension.");else i*=u}const a=Yr(e);if(o!==null){if(i===0||a%i!==0)throw new j(n);s[o]=a/i}else if(a!==i)throw new j(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e),s=n.shape,i=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return n.reshape(i)})}getConfig(){const e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}}fL.className="Reshape",ge(fL);class gL extends lt{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);const t=ai(1,e.dims.length+1);if(!ot(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new fn({ndim:this.dims.length+1})]}computeOutputShape(e){e=It(e);const t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Pe(Xe(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}gL.className="Permute",ge(gL);class yL extends lt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){const n=Xe(e),s=-1;return th(Pr(n,this.maskValue),s)}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e),s=-1,i=!0,o=th(Pr(n,this.maskValue),s,i),a=n.mul(o.asType(n.dtype));return a})}}yL.className="Masking",ge(yL);class bL extends lt{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(Nt(e.inputLength))}this.inputDim=e.inputDim,mn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,mn(this.outputDim,"outputDim"),this.embeddingsInitializer=Ft(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=_t(e.embeddingsRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.embeddingsConstraint=hn(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return ee(()=>this.maskZero?(e=Xe(e),Pr(e,et(e))):null)}computeOutputShape(e){if(e=It(e),this.inputLength==null)return[...e,this.outputDim];const t=Nt(this.inputLength);if(t.length!==e.length-1)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){const i=t[s],o=e[s+1];if(i!=null&&o!=null&&i!==o)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);i==null&&(t[n]=o),n++}}return[e[0],...t,this.outputDim]}call(e,t){return ee(()=>{this.invokeCallHook(e,t);let n=Xe(e);n.dtype!=="int32"&&(n=_h(n,"int32"));const s=Cv(this.embeddings.read(),n.as1D());return s.reshape(It(this.computeOutputShape(n.shape)))})}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Vt(this.embeddingsInitializer),embeddingsRegularizer:xt(this.embeddingsRegularizer),activityRegularizer:xt(this.activityRegularizer),embeddingsConstraint:ln(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}bL.className="Embedding",ge(bL);class qo extends lt{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Ge}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;const n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){const i=e[e.length-t.length+s],o=t[s];if(i==null||o==null||i<0||o<0)n.push(null);else if(i===1)n.push(o);else if(o===1)n.push(i);else{if(i!==o)throw new j("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(i)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[It(e)]),e=e,e.length<2)throw new j(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(const i of e)i!=null&&i[0]!==null&&t.push(i[0]);if(t=Hr(t),t.length>1)throw new j(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let i=1;i<e.length;++i){const o=e[i]==null?null:e[i].slice(1);n=this.computeElementwiseOpOutputShape(n,o)}const s=e.map(i=>i.length);e.indexOf(null)===-1&&Hr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return ee(()=>{if(e=e,this.reshapeRequired){const n=[],s=e.map(i=>i.rank);if(s.indexOf(null)===-1){const i=qr(s);for(let o of e){const a=o.rank;for(let c=0;c<i-a;++c)o=Wh(o,1);n.push(o)}return this.mergeFunction(n)}else{let i=!1;for(const c of e){const u=c.rank;if(u==null){const p=c.shape,m=p[0],y=p.slice(1).concat([m]);let b=c.reshape([m].concat(Yr(p.slice(1))));b=Pe(b,[1,0]),b=b.reshape(y),n.push(b),i=!0}else if(u>1){const p=ai(1,u).concat([0]);n.push(Pe(c,p)),i=!0}else n.push(c)}let o=this.mergeFunction(n);const a=o.rank;if(i){if(a==null){const c=o.shape,u=c.length,p=c[u-1],m=[p].concat(c.slice(0,c.length-1));o=Pe(o.reshape([-1,p]),[1,0]).reshape(m)}else if(a>1){const c=[a-1].concat(ai(0,a-1));o=Pe(o,c)}}return o}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){const i=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,i)}let n=[];for(const s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=Hr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return ee(()=>{if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an Array");if(!Array.isArray(e))throw new j("`inputs` should be an Array");if(t.length!==e.length)throw new j(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Kn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Bs(n,t[s]);return n})}}class qh extends qo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return t})}}qh.className="Add",ge(qh);function Oee(e){if(Array.isArray(e)){const t=new qh({});return t.apply(e)}else return new qh(e)}class jh extends qo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=X(t,e[n]);return t})}}jh.className="Multiply",ge(jh);function Eee(e){if(Array.isArray(e)){const t=new jh({});return t.apply(e)}else return new jh(e)}class Kh extends qo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return X(1/e.length,t)})}}Kh.className="Average",ge(Kh);function Dee(e){if(Array.isArray(e)){const t=new Kh({});return t.apply(e)}else return new Kh(e)}class Xh extends qo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Us(t,e[n]);return t})}}Xh.className="Maximum",ge(Xh);function kee(e){if(Array.isArray(e)){const t=new Xh({});return t.apply(e)}else return new Xh(e)}class Jh extends qo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=ko(t,e[n]);return t})}}Jh.className="Minimum",ge(Jh);function Fee(e){if(Array.isArray(e)){const t=new Jh({});return t.apply(e)}else return new Jh(e)}class Zh extends qo{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new j("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(const s of e)if(s!=null){t=!1;break}if(t)return;const n=[];for(let s=0;s<e.length;++s){const i=e[s].slice();i.splice(this.axis,1);let o=!1;for(const a of n)if(ot(a,i)){o=!0;break}o||n.push(i)}if(n.length>1)throw new j("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return ee(()=>yw(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new j("A `Concatenate` layer should be called on a list of inputs.");const t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(const i of t.slice(1)){if(n[s]==null||i[s]==null){n[s]=null;break}n[s]+=i[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new j("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new j(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return ee(()=>{let n=!0;if(t.forEach(o=>{if(o!=null){n=!1;return}}),n)return null;const s=[];for(let o=0;o<e.length;++o)t[o]==null?s.push(Dn(e[o]).asType("bool")):t[o].rank<e[o].rank?s.push(Kn(t[o],-1)):s.push(t[o]);const i=Mt(s,this.axis);return Pd(i,-1,!1)})}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}Zh.className="Concatenate",ge(Zh);function _ee(e){if(Array.isArray(e)){const t=new Zh({});return t.apply(e)}else return new Zh(e)}function Qh(e,t){for(;e<0;)e+=t;return e}function lG(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Ge("batchDot is not implemented for tensors of 4D or higher rank yet");if(k(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Ge("batchDot is not implemented for complex64-type Tensors yet.");const s=e.shape.length,i=t.shape.length;n==null&&(n=[s-1,i-2]);const o=n;return ee(()=>{let a;if(s>i){a=s-i;const u=[];for(let p=0;p<a;++p)u.push(1);t=t.reshape(t.shape.concat(u))}else if(i>s){a=i-s;const u=[];for(let p=0;p<a;++p)u.push(1);e=e.reshape(e.shape.concat(u))}else a=0;let c;if(e.shape.length===2&&t.shape.length===2)o[0]===o[1]?c=e.mul(t).sum(o[0]):c=e.transpose([1,0]).mul(t).sum(o[1]);else{const u=o[0]!==e.shape.length-1,p=o[1]===t.shape.length-1;c=e.matMul(t,u,p)}if(a>0){let u;s>i?u=s+i-3:u=s-1;const p=[];for(let m=u;m<u+a;++m)p.push(m);c=c.squeeze(p)}return c.shape.length===1&&(c=c.expandDims(1)),c})}class wL extends qo{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Ge("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new j(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new j(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((i,o)=>Qh(i,e[o].shape.length)):s=[Qh(this.axes,t.shape.length),Qh(this.axes,n.shape.length)],this.normalize&&(t=zp(t,s[0]),n=zp(n,s[1])),lG(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Qh(this.axes,e.length),Qh(this.axes,t.length)],n}computeOutputShape(e){k(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Ge("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);const i=t.concat(n);return i.length===1&&i.push(1),i}computeMask(e,t){return null}getConfig(){const e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}}wL.className="Dot",ge(wL);class LL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e),s=()=>Rp(n.shape,0,this.stddev).add(n),i=Uh(s,()=>n,t.training||!1);return i})}}LL.className="GaussianNoise",ge(LL);class SL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Xe(e);if(this.rate>0&&this.rate<1){const s=()=>{const i=Math.sqrt(this.rate/(1-this.rate));return n.mul(Rp(n.shape,1,i))};return Uh(s,()=>n,t.training||!1)}return n})}}SL.className="GaussianDropout",ge(SL);class IL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Xe(e).shape}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return ee(()=>{if(this.rate<1&&this.rate>0){const n=this._getNoiseShape(e),s=()=>{const i=Xe(e),o=1.6732632423543772,a=1.0507009873554805,c=-o*a;let u=tr($o(n),this.rate);u=_h(u,"float32");const p=((1-this.rate)*(1+this.rate*c**2))**-.5,m=-p*c*this.rate,y=i.mul(u).add(u.add(-1).mul(c));return y.mul(p).add(m)};return Uh(s,()=>Xe(e),t.training||!1)}return e})}}IL.className="AlphaDropout",ge(IL);function eu(e,t,n,s,i,o=.001){let a;if(e.rank===2)a=nA(e,t,n,s,i,o);else if(e.rank===3)a=sA(e,t,n,s,i,o);else if(e.rank===4)a=iA(e,t,n,s,i,o);else throw new Ge(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return a}function hG(e,t,n,s,i=.001){return ee(()=>{const o=np(e,s),a=o.mean,c=o.variance,u=eu(e,a,c,n,t,i);return[u,a,c]})}function uG(e,t,n,s,i=.001){return ee(()=>{const o=np(e,s),a=o.mean,c=o.variance,u=[];for(const I of ai(0,e.rank))s.indexOf(I)!==-1?u.push(1):u.push(e.shape[I]);const p=a.reshape(u),m=c.reshape(u),y=t==null?null:t.reshape(u),b=n==null?null:n.reshape(u),w=eu(e,p,m,b,y,i);return[w,a,c]})}function dG(e,t,n,s,i=.001){return ot(s.slice().sort(),ai(0,e.rank-1))?hG(e,t,n,s,i):uG(e,t,n,s,i)}class xL extends lt{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ft(e.betaInitializer||"zeros"),this.gammaInitializer=Ft(e.gammaInitializer||"ones"),this.movingMeanInitializer=Ft(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Ft(e.movingVarianceInitializer||"ones"),this.betaConstraint=hn(e.betaConstraint),this.gammaConstraint=hn(e.gammaConstraint),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer)}build(e){e=It(e);const t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new j(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new fn({ndim:e.length,axes:{[t]:n}})];const s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return ee(()=>{const n=t.training==null?!1:t.training,s=Xe(e),i=s.shape,o=i.length,a=ai(0,o),c=this.axis>=0?this.axis:this.axis+o;a.splice(c,1);const u=Po(1,o);u[c]=i[c];const p=a.slice();p.sort();const m=!ot(p,ai(0,o).slice(0,o-1)),y=()=>{if(m){const N=this.movingMean.read().reshape(u),E=this.movingVariance.read().reshape(u),D=this.center?this.beta.read().reshape(u):null,F=this.scale?this.gamma.read().reshape(u):null;return eu(s,N,E,D,F,this.epsilon)}else return eu(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return y();const[b,w,I]=dG(s,this.gamma.read(),this.beta.read(),a,this.epsilon),T=(N,E,D)=>{ee(()=>{const F=1-D,_=N.read(),B=_.sub(E).mul(F);N.write(_.sub(B))})},v=()=>{T(this.movingMean,w,this.momentum),T(this.movingVariance,I,this.momentum)};return v(),b})}getConfig(){const e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Vt(this.betaInitializer),gammaInitializer:Vt(this.gammaInitializer),movingMeanInitializer:Vt(this.movingMeanInitializer),movingVarianceInitializer:Vt(this.movingVarianceInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer),betaConstraint:ln(this.betaConstraint),gammaConstraint:ln(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}xL.className="BatchNormalization",ge(xL);class TL extends lt{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(const t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ft(e.betaInitializer||"zeros"),this.gammaInitializer=Ft(e.gammaInitializer||"ones"),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=It(e);const t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i<this.axis.length;++i)this.axis[i]<0&&(this.axis[i]+=t);for(const i of this.axis)if(i<0||i>=t)throw new Error(`Invalid axis: ${i}`);if(this.axis.length!==Hr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);const n=this.axis.map(i=>e[i]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){const n=Xe(e),s=n.shape,i=s.length;return ee(()=>{const o=!0;let{mean:a,variance:c}=np(n,this.axis,o);const u=Po(1,i);for(const I of this.axis)u[I]=s[I];const p=I=>I!=null&&I.shape.length!==i&&this.axis!==[i-1]?I.reshape(u):I;let m=p(this.gamma.read()),y=p(this.beta.read());const b=[],w=[];for(let I=0;I<i;++I)this.axis.indexOf(I)!==-1?(b.push(s[I]),w.push(1)):(b.push(1),w.push(s[I]));return a=a.tile(b),c=c.tile(b),m=m.tile(w),y=y.tile(w),eu(n,a,c,y,m,this.epsilon)})}getConfig(){const e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Vt(this.betaInitializer),gammaInitializer:Vt(this.gammaInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}}TL.className="LayerNormalization",ge(TL);function Wee(e,t){return ee(()=>{if(e.rank!==3)throw new j(`temporalPadding expects input tensor to be 3-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[1,1]),t.length!==2)throw new j(`temporalPadding expects input padding pattern to be a length-2 array, but received a length-${t.length} array.`);const n=[[0,0],t,[0,0]];return ki(e,n)})}function pG(e,t,n){return ee(()=>{if(e.rank!==4)throw new j(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new j("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ri()),n!=="channelsLast"&&n!=="channelsFirst")throw new j(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],ki(e,s)})}class AL extends lt{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ri():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new j(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new j(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new j(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new fn({ndim:4})]}computeOutputShape(e){e=It(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return ee(()=>pG(Xe(e),this.padding,this.dataFormat))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}AL.className="ZeroPadding2D",ge(AL);function rm(e,t,n,s,i,o){return ee(()=>{Gt(i),Sv(o),Ns(s),n==null&&(n=[1,1]),s==null&&(s="valid"),i==null&&(i=ri()),o==null&&(o="max"),e=Zw(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=mh(e,t,n,c):a=oh(e,t,n,c),i==="channelsFirst"&&(a=Pe(a,[0,3,1,2])),a})}function kN(e,t,n,s,i,o){return ee(()=>{Gt(i),Sv(o),Ns(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),i==null&&(i=ri()),o==null&&(o="max"),e=vN(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=Nb(e,t,n,c):a=pb(e,t,n,c),i==="channelsFirst"&&(a=Pe(a,[0,4,1,2,3])),a})}class FN extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new j(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(mn(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new j(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);mn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Ns(this.padding),this.inputSpec=[new fn({ndim:3})]}computeOutputShape(e){e=It(e);const t=ui(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return ee(()=>{this.invokeCallHook(e,t),e=Wh(Xe(e),2);const n=this.poolingFunction(Xe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return zr(n,[2])})}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class vL extends FN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ns(s),rm(e,t,n,s,i,"max")}}vL.className="MaxPooling1D",ge(vL);class NL extends FN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ns(s),rm(e,t,n,s,i,"avg")}}NL.className="AveragePooling1D",ge(NL);class _N extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new j(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];mn(this.poolSize,"poolSize"),mn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),Ns(this.padding),this.inputSpec=[new fn({ndim:4})]}computeOutputShape(e){e=It(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ui(t,this.poolSize[0],this.padding,this.strides[0]),n=ui(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return ee(()=>(this.invokeCallHook(e,t),this.poolingFunction(Xe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class CL extends _N{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ns(s),rm(e,t,n,s,i,"max")}}CL.className="MaxPooling2D",ge(CL);class RL extends _N{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ns(s),rm(e,t,n,s,i,"avg")}}RL.className="AveragePooling2D",ge(RL);class WN extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new j(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];mn(this.poolSize,"poolSize"),mn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),Ns(this.padding),this.inputSpec=[new fn({ndim:5})]}computeOutputShape(e){e=It(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=ui(t,this.poolSize[0],this.padding,this.strides[0]),n=ui(n,this.poolSize[1],this.padding,this.strides[1]),s=ui(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return ee(()=>(this.invokeCallHook(e,t),this.poolingFunction(Xe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class OL extends WN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ns(s),kN(e,t,n,s,i,"max")}}OL.className="MaxPooling3D",ge(OL);class EL extends WN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ns(s),kN(e,t,n,s,i,"avg")}}EL.className="AveragePooling3D",ge(EL);class $N extends lt{constructor(e){super(e);this.inputSpec=[new fn({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ge}}class DL extends $N{constructor(e){super(e||{})}call(e,t){return ee(()=>{const n=Xe(e);return zt(n,1)})}}DL.className="GlobalAveragePooling1D",ge(DL);class kL extends $N{constructor(e){super(e||{})}call(e,t){return ee(()=>{const n=Xe(e);return Xn(n,1)})}}kL.className="GlobalMaxPooling1D",ge(kL);class UN extends lt{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),this.inputSpec=[new fn({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ge}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class FL extends UN{call(e,t){return ee(()=>{const n=Xe(e);return this.dataFormat==="channelsLast"?zt(n,[1,2]):zt(n,[2,3])})}}FL.className="GlobalAveragePooling2D",ge(FL);class _L extends UN{call(e,t){return ee(()=>{const n=Xe(e);return this.dataFormat==="channelsLast"?Xn(n,[1,2]):Xn(n,[2,3])})}}_L.className="GlobalMaxPooling2D",ge(_L);class BN extends lt{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){const e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){const s=t.layer,i=hi(s,n);delete t.layer;const o={layer:i};return Object.assign(o,t),new e(o)}}class WL extends BN{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=It(e),e.length<3)throw new j(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];const t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=It(e);const t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return ee(()=>{e=Xe(e);const n=(o,a)=>{const c=Xe(this.layer.call(o,t));return[c,[]]},s=EN(n,e,[],!1,null,null,!1,!0),i=s[1];return i})}}WL.className="TimeDistributed",ge(WL);function mG(e){ec(xz,"BidirectionalMergeMode",e)}const fG="concat";class $L extends BN{constructor(e){super(e);const t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=hi(n),t.goBackwards=!(t.goBackwards===!0);const s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=hi(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?fG:e.mergeMode,mG(this.mergeMode),e.weights)throw new Ge("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){const t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,i;return this.returnState&&(i=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(i).concat(i.slice()):[n].concat(i).concat(i.slice()):Jn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=ON(e,n,s,this.numConstants);if(e=i.inputs,n=i.initialState,s=i.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);const o=[],a=[];if(n!=null){const u=n.length;if(u%2>0)throw new j("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,o.push(...n);const p=n.map(m=>new fn({shape:m.shape}));this.forwardLayer.stateSpec=p.slice(0,u/2),this.backwardLayer.stateSpec=p.slice(u/2),a.push(...p)}if(s!=null)throw new Ge("Support for constants in Bidirectional layers is not implemented yet.");const c=o[0]instanceof li;for(const u of o)if(u instanceof li!==c)throw new j("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(c){const u=[e].concat(o),p=this.inputSpec.concat(a),m=this.inputSpec;this.inputSpec=p;const y=super.apply(u,t);return this.inputSpec=m,y}else return super.apply(e,t)}call(e,t){return ee(()=>{const n=t.initialState;let s,i;if(n==null)s=this.forwardLayer.call(e,t),i=this.backwardLayer.call(e,t);else{const c=n.slice(0,n.length/2),u=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:c})),i=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let o;this.returnState&&(Array.isArray(s)&&(o=s.slice(1).concat(i.slice(1))),s=s[0],i=i[0]),this.returnSequences&&(i=As(i,1));let a;return this.mergeMode==="concat"?a=yw([s,i]):this.mergeMode==="sum"?a=be(s,i):this.mergeMode==="ave"?a=X(.5,be(s,i)):this.mergeMode==="mul"?a=X(s,i):this.mergeMode==null&&(a=[s,i]),this.returnState?this.mergeMode==null?a.concat(o):[a].concat(o):a})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Go(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Go(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){const s=this.forwardLayer.states,i=s.map(o=>null);return Array.isArray(n)?n.concat(i).concat(i):[n].concat(i).concat(i)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){const e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){const n=hi(t.layer);if(delete t.layer,t.numConstants!=null)throw new Ge("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const s=t;return s.layer=n,new e(s)}}$L.className="Bidirectional",ge($L);function gG(e){return new sc(e)}function yG(e){return new Kw(e)}function bG(e){return new Yw(e)}function wG(e){return new qw(e)}function LG(e){return new jw(e)}function SG(e){return new Jw(e)}function IG(e){return new Xw(e)}function xG(e){return new Qp(e)}function TG(e){return new Hh(e)}function AG(e){return new tL(e)}function 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aH=Object.freeze({__proto__:null,json:oH});const 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dH=[{tfOpName:"PlaceholderWithDefault",category:"graph",inputs:[{start:0,name:"default",type:"tensor"}],attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Placeholder",category:"graph",attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Const",category:"graph"},{tfOpName:"Identity",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IdentityN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Snapshot",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Rank",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Size",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Shape",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"ShapeN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Print",category:"graph",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"data",type:"tensors"}],attrs:[{tfName:"message",name:"message",type:"string"},{tfName:"first_n",name:"firstN",type:"number",notSupported:!0},{tfName:"summarize",name:"summarize",type:"number",defaultValue:3}]},{tfOpName:"NoOp",category:"graph",inputs:[]},{tfOpName:"StopGradient",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"FakeQuantWithMinMaxVars",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"min",name:"min",type:"number"},{tfName:"max",name:"max",type:"number"}]}];var pH=Object.freeze({__proto__:null,json:dH});const mH=[{tfOpName:"ResizeBilinear",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ResizeNearestNeighbor",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"CropAndResize",category:"image",inputs:[{start:0,name:"image",type:"tensor"},{start:1,name:"boxes",type:"tensor"},{start:2,name:"boxInd",type:"tensor"},{start:3,name:"cropSize",type:"number[]"}],attrs:[{tfName:"method",name:"method",type:"string"},{tfName:"extrapolation_value",name:"extrapolationValue",type:"number"}]}];var fH=Object.freeze({__proto__:null,json:mH});const 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yH=Object.freeze({__proto__:null,json:gH});const 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wH=Object.freeze({__proto__:null,json:bH});const LH=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}];var SH=Object.freeze({__proto__:null,json:LH});const 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xH=Object.freeze({__proto__:null,json:IH});const TH=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool",notSupported:!0}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}];var AH=Object.freeze({__proto__:null,json:TH});const vH=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}];var NH=Object.freeze({__proto__:null,json:vH});const CH=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}];var 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zL(this.node.rawAttrs,e,t);if(n.b!=null)return GL(this.node.rawAttrs,e,t);if(n.shape!=null)return jL(this.node.rawAttrs,e,t);if(n.type!=null)return YL(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return KL(this.node.rawAttrs,e,t);if(n.list.s!=null)return XL(this.node.rawAttrs,e,t);if(n.list.shape!=null)return JL(this.node.rawAttrs,e,t);if(n.list.b!=null)return ZL(this.node.rawAttrs,e,t);if(n.list.type!=null)return qL(this.node.rawAttrs,e,t)}return t}}const DH=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[be(R("a",e,t,n),R("b",e,t,n))];case"AddN":return[eA(R("tensors",e,t,n))];case"FloorMod":case"Mod":return[tp(R("a",e,t,n),R("b",e,t,n))];case"Mul":return[X(R("a",e,t,n),R("b",e,t,n))];case"RealDiv":case"Div":return[_e(R("a",e,t,n),R("b",e,t,n))];case"DivNoNan":return[Lb(R("a",e,t,n),R("b",e,t,n))];case"FloorDiv":return[Md(R("a",e,t,n),R("b",e,t,n))];case"Sub":return[Ce(R("a",e,t,n),R("b",e,t,n))];case"Minimum":return[ko(R("a",e,t,n),R("b",e,t,n))];case"Maximum":return[Us(R("a",e,t,n),R("b",e,t,n))];case"Pow":return[ii(R("a",e,t,n),R("b",e,t,n))];case"SquaredDifference":return[Sh(R("a",e,t,n),R("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Uee="arithmetic";const kH=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[rn(R("x",e,t,n))];case"Acos":return[nb(R("x",e,t,n))];case"Acosh":return[sb(R("x",e,t,n))];case"Asin":return[ob(R("x",e,t,n))];case"Asinh":return[ab(R("x",e,t,n))];case"Atan":return[cb(R("x",e,t,n))];case"Atan2":return[lb(R("x",e,t,n),R("y",e,t,n))];case"Atanh":return[hb(R("x",e,t,n))];case"Ceil":return[fb(R("x",e,t,n))];case"Complex":return[Ci(R("real",e,t,n),R("imag",e,t,n))];case"Cos":return[lh(R("x",e,t,n))];case"Cosh":return[qd(R("x",e,t,n))];case"Elu":return[Do(R("x",e,t,n))];case"Erf":return[Sb(R("x",e,t,n))];case"Exp":return[xs(R("x",e,t,n))];case"Expm1":return[Ib(R("x",e,t,n))];case"Floor":return[Pa(R("x",e,t,n))];case"Log":return[is(R("x",e,t,n))];case"Log1p":return[Jd(R("x",e,t,n))];case"Imag":return[Ga(R("x",e,t,n))];case"Neg":return[Pt(R("x",e,t,n))];case"Reciprocal":return[Eb(R("x",e,t,n))];case"Real":return[Fo(R("x",e,t,n))];case"Relu":return[Fi(R("x",e,t,n))];case"Round":return[kb(R("x",e,t,n))];case"Selu":return[rp(R("x",e,t,n))];case"Sigmoid":return[Ei(R("x",e,t,n))];case"Sin":return[op(R("x",e,t,n))];case"Sign":return[_b(R("x",e,t,n))];case"Sinh":return[ap(R("x",e,t,n))];case"Softplus":return[Va(R("x",e,t,n))];case"Sqrt":return[Sn(R("x",e,t,n))];case"Square":return[Lt(R("x",e,t,n))];case"Tanh":return[Ma(R("x",e,t,n))];case"Tan":return[Ub(R("x",e,t,n))];case"Relu6":case"ClipByValue":return[jn(R("x",e,t,n),R("clipValueMin",e,t,n),R("clipValueMax",e,t,n))];case"Rsqrt":return[ip(Qn(e.inputNames[0],t,n))];case"Prod":return[sp(R("x",e,t,n),R("axes",e,t,n))];case"LeakyRelu":return[Xd(R("x",e,t,n),R("alpha",e,t,n))];case"Prelu":return[gh(R("x",e,t,n),R("alpha",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Bee="basic_math";function Gs(e,t,n=""){k(FH(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function FH(e,t){if(e.length!==t.length)return!1;for(let n=0;n<e.length;n++)if(e[n]!==-1&&t[n]!==-1&&e[n]!==t[n])return!1;return!0}class _H{constructor(e,t,n,s,i,o,a){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=s,this.identicalElementShapes=i,this.dynamicSize=o,this.clearAfterRead=a,this.tensors=[],this.closed_=!1,this.idTensor=Ne(0),Rn(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);const t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);const n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
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         ${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);const i=n===0?0:t.size/n,o=[];ee(()=>{t=K(t,[1,n,i]);for(let c=0;c<e.length;++c){const u=c===0?0:s[c-1],p=[0,u,0],m=[1,e[c],i];o[c]=K(st(t,p,m),this.elementShape)}return o});const a=[];for(let c=0;c<e.length;c++)a[c]=c;this.writeMany(a,o)}}class tu{constructor(e,t,n,s=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(i=>{if(n!==i.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${i.dtype}`);Gs(t,i.shape,"TensorList shape mismatch: "),Rn(i)}),this.idTensor=Ne(0),this.maxNumElements=s,Rn(this.idTensor)}get id(){return this.idTensor.id}copy(){return new tu([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);return Gs(e,this.elementShape,"TensorList shape mismatch: "),ee(()=>{const s=this.tensors.map(i=>K(i,e));return as(s,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");const n=this.tensors.pop();return Gs(n.shape,e,"TensorList shape mismatch: "),K(n,e)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Gs(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Rn(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);return Gs(this.tensors[e].shape,t,"TensorList shape mismatch: "),this.tensors[e]}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);Gs(this.elementShape,t.shape,"TensorList shape mismatch: "),Rn(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);return Gs(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size()),e.length===0?en([],[0].concat(this.elementShape)):ee(()=>{const s=e.map(i=>K(this.tensors[i],n));return as(s,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);return Gs(this.elementShape,t,"TensorList shape mismatch: "),this.size()===0?en([],[0].concat(this.elementShape)):ee(()=>{const n=this.tensors.map(s=>K(s,t));return Mt(n,0)})}}function WH(e,t,n){const s=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);const i=e.shape.slice(1);Gs(i,t,"TensorList shape mismatch: ");const o=_i(e);return new tu(o,t,s)}function $H(e,t,n){return new tu([],e,t,n)}function UH(e,t,n,s){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);const i=Math.max(...t);if(s!=null&&s!==-1&&i>=s)throw new Error(`Max index must be < array size (${i}  vs. ${s})`);const o=new tu([],n,e.dtype,s),a=_i(e,0);return t.forEach((c,u)=>{o.setItem(c,a[u])}),o}function BH(e,t,n){let s=0;const i=t.map(u=>(s+=u,s));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
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n.functionMap[s].executeFunctionAsync(p,n.tensorArrayMap,n.tensorListMap);const y=p.map(w=>w.id);m.forEach(w=>{!w.kept&&c.indexOf(w.id)===-1&&y.indexOf(w.id)===-1&&w.dispose()});const b=await n.functionMap[i].executeFunctionAsync(p,n.tensorArrayMap,n.tensorListMap);u=await b[0].data(),b.forEach(w=>{!w.kept&&c.indexOf(w.id)===-1&&y.indexOf(w.id)===-1&&w.dispose()})}return p}case"LoopCond":{const s=R("pred",e,t,n);return[hr(s)]}case"Switch":{const s=R("pred",e,t,n);let i=R("data",e,t,n);return i.kept||(i=hr(i)),(await s.data())[0]?[void 0,i]:[i,void 0]}case"Merge":{const s=e.inputNames.find(i=>Qn(i,t,n)!==void 0);if(s){const i=Qn(s,t,n);return[hr(i)]}return}case"Enter":{const s=R("frameName",e,t,n),i=R("tensor",e,t,n);return n.enterFrame(s),[hr(i)]}case"Exit":{const s=R("tensor",e,t,n);return n.exitFrame(),[hr(s)]}case"NextIteration":{const s=R("tensor",e,t,n);return n.nextIteration(),[hr(s)]}case"TensorArrayV3":{const s=R("size",e,t,n),i=R("dtype",e,t,n),o=R("elementShape",e,t,n),a=R("dynamicSize",e,t,n),c=R("clearAfterRead",e,t,n),u=R("identicalElementShapes",e,t,n),p=R("name",e,t,n),m=new _H(p,i,s,o,u,a,c);return n.addTensorArray(m),[m.idTensor,Ne(1)]}case"TensorArrayWriteV3":{const s=R("tensorArrayId",e,t,n),i=R("index",e,t,n),o=R("tensor",e,t,n),a=n.getTensorArray(s.id);return a.write(i,o),[a.idTensor]}case"TensorArrayReadV3":{const s=R("tensorArrayId",e,t,n),i=R("index",e,t,n),o=n.getTensorArray(s.id);return[o.read(i)]}case"TensorArrayGatherV3":{const s=R("tensorArrayId",e,t,n),i=R("indices",e,t,n),o=R("dtype",e,t,n),a=n.getTensorArray(s.id);return[a.gather(i,o)]}case"TensorArrayScatterV3":{const s=R("tensorArrayId",e,t,n),i=R("indices",e,t,n),o=R("tensor",e,t,n),a=n.getTensorArray(s.id);return a.scatter(i,o),[a.idTensor]}case"TensorArrayConcatV3":{const s=R("tensorArrayId",e,t,n),i=n.getTensorArray(s.id),o=R("dtype",e,t,n);return[i.concat(o)]}case"TensorArraySplitV3":{const 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qH=(e,t,n)=>{switch(e.op){case"Equal":return[ni(R("a",e,t,n),R("b",e,t,n))];case"NotEqual":return[Pr(R("a",e,t,n),R("b",e,t,n))];case"Greater":return[Ts(R("a",e,t,n),R("b",e,t,n))];case"GreaterEqual":return[tr(R("a",e,t,n),R("b",e,t,n))];case"Less":return[dh(R("a",e,t,n),R("b",e,t,n))];case"LessEqual":return[Mr(R("a",e,t,n),R("b",e,t,n))];case"LogicalAnd":return[Bs(R("a",e,t,n),R("b",e,t,n))];case"LogicalNot":return[ph(R("a",e,t,n))];case"LogicalOr":return[ep(R("a",e,t,n),R("b",e,t,n))];case"Select":case"SelectV2":return[$n(R("condition",e,t,n),R("a",e,t,n),R("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},qee="logical";const jH=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[at(R("a",e,t,n),R("b",e,t,n),R("transposeA",e,t,n),R("transposeB",e,t,n))];case"Transpose":return[Pe(R("x",e,t,n),R("perm",e,t,n))];case"_FusedMatMul":const[s,i]=R("fusedOps",e,t,n),o=s==="biasadd",a=i==="prelu",c=R("numArgs",e,t,n);if(o){if(a&&c!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&c!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}const[u,p]=R("args",e,t,n);return[bp({a:R("a",e,t,n),b:R("b",e,t,n),transposeA:R("transposeA",e,t,n),transposeB:R("transposeB",e,t,n),bias:u,activation:i,preluActivationWeights:p})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},jee="matrices";const KH=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[No(R("x",e,t,n),R("mean",e,t,n),R("variance",e,t,n),R("offset",e,t,n),R("scale",e,t,n),R("epsilon",e,t,n))];case"FusedBatchNormV3":return[No(R("x",e,t,n),R("mean",e,t,n),R("variance",e,t,n),R("offset",e,t,n),R("scale",e,t,n),R("epsilon",e,t,n))];case"LRN":return[Tb(R("x",e,t,n),R("radius",e,t,n),R("bias",e,t,n),R("alpha",e,t,n),R("beta",e,t,n))];case"Softmax":return[Wo(R("x",e,t,n))];case"LogSoftmax":return[Qd(R("x",e,t,n))];case"SparseToDense":return[zb(R("sparseIndices",e,t,n),R("outputShape",e,t,n),R("sparseValues",e,t,n),R("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Kee="normalization";const XH=(e,t,n)=>{switch(e.op){case"Max":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[Xn(R("x",e,t,n),s,i)]}case"Mean":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[zt(R("x",e,t,n),s,i)]}case"Min":{const 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s=R("axis",e,t,n);return[zr(R("x",e,t,n),s)]}case"Reshape":return[K(R("x",e,t,n),R("shape",e,t,n))];case"PadV2":case"Pad":return[ki(R("x",e,t,n),R("padding",e,t,n),R("constantValue",e,t,n))];case"SpaceToBatchND":{const s=R("blockShape",e,t,n),i=R("paddings",e,t,n);return[fh(R("x",e,t,n),s,i)]}case"BatchToSpaceND":{const s=R("blockShape",e,t,n),i=R("crops",e,t,n);return[ah(R("x",e,t,n),s,i)]}case"DepthToSpace":{const s=R("blockSize",e,t,n),i=R("dataFormat",e,t,n).toUpperCase();return[bb(R("x",e,t,n),s,i)]}case"BroadcastTo":return[ch(R("x",e,t,n),R("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Qee="transformation";function e0(e,t,n){const s=((i,o,a)=>{switch(i.category){case"arithmetic":return ee(()=>DH(i,o,a));case"basic_math":return ee(()=>kH(i,o,a));case"control":return MH(i,o,a);case"convolution":return ee(()=>PH(i,o,a));case"creation":return ee(()=>zH(i,o,a));case"dynamic":return GH(i,o,a);case"evaluation":return ee(()=>VH(i,o,a));case"image":return ee(()=>YH(i,o,a));case"graph":return ee(()=>HH(i,o,a));case"logical":return ee(()=>qH(i,o,a));case"matrices":return ee(()=>jH(i,o,a));case"normalization":return ee(()=>KH(i,o,a));case"reduction":return ee(()=>XH(i,o,a));case"slice_join":return ee(()=>JH(i,o,a));case"spectral":return ee(()=>ZH(i,o,a));case"transformation":return ee(()=>QH(i,o,a));case"custom":const c=jN(i.op);if(c&&c.customExecutor)return c.customExecutor(new EH(i,o,a));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return s instanceof Promise?s.then(i=>[].concat(i)):[].concat(s)}class t0{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){const e=[];for(let t=0;t<this.contexts.length-1;t++){const n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;const e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(const t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(const t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}}function n0(e,t,n,s){const i=new Set,o=[];let a=null,c=null;const u=new Set,p=Object.keys(e).map(b=>ls(b)[0]);let m=[];s!=null&&(m=s.map(b=>ls(b.name)[0]));const y=[...t];for(;y.length>0;){const b=y.pop();if((s0(b)||sY(b))&&(a==null&&(a=b,c=a.children.map(w=>w.name).filter(w=>i.has(w)))),i.add(b.name),n[b.name]!=null)continue;if(p.indexOf(b.name)!==-1)continue;if(m.indexOf(b.name)!==-1)continue;if(b.inputs.length===0){o.push(b.name);continue}b.inputs.forEach(w=>{if(u.has(w.name))return;u.add(w.name),y.push(w)})}return{inputs:e,outputs:t,usedNodes:i,missingInputs:o,dynamicNode:a,syncInputs:c}}function eY(e,t,n){const{usedNodes:s,inputs:i}=n,o=[],a=Object.keys(i).map(m=>ls(m)[0]).map(m=>e.nodes[m]),c=e.initNodes;a.forEach(m=>{s.has(m.name)&&o.push(m)}),e.weights.forEach(m=>{s.has(m.name)&&o.push(m)}),c!=null&&c.forEach(m=>{s.has(m.name)&&o.push(m)});const u=new Set,p=[];for(;o.length>0;){const m=o.pop();u.add(m.name),t[m.name]||p.push(m),m.children.forEach(y=>{!u.has(y.name)&&s.has(y.name)&&y.inputs.every(b=>u.has(b.name))&&o.push(y)})}return p}const tY=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],nY=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"];function s0(e){return tY.indexOf(e.op)>=0}function sY(e){return nY.indexOf(e.op)>=0}class eS{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new eS(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){const t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{const t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){const n=e.map(i=>i.name).sort(),s=t.map(i=>i.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){const n=n0(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:i,syncInputs:o}=n;if(i!=null)throw new Error(`This execution contains the node '${i.name}', which has the dynamic op '${i.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${o}]`);if(s.length>0){const a=t.map(u=>u.name),c=Object.keys(e);throw new Error(`Cannot compute the outputs [${a}] from the provided inputs [${c}]. Missing the following inputs: [${s}]`)}return eY(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);const n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);const s=n.map(m=>this.graph.nodes[ls(m)[0]]),i=t.map(m=>ls(m)[0]);let o=i.map(m=>this.graph.nodes[m]);o.length===0&&(o=this._outputs);const a=this.getCompilationKey(s,o);let c=this.compiledMap.get(a);c==null&&(c=this.compile(e,o),this.compiledMap.set(a,c));const u={},p={};return ee(()=>{const m=new t0(this.weightMap,u,p,this.functionExecutorMap),y=Object.assign({},this.weightMap);Object.keys(e).forEach(I=>{const[T,v]=ls(I),N=[];N[v]=e[I],y[T]=N});const b=this.getFrozenTensorIds(y),w={};for(let I=0;I<c.length;I++){const T=c[I];if(!y[T.name]){const v=e0(T,y,m);if(v instanceof Promise)throw new Error(`The execution of the op '${T.op}' returned a promise. Please use model.executeAsync() instead.`);y[T.name]=v,this.checkTensorForDisposal(T.name,T,y,m,b,i,w)}}return this.parent==null&&m.dispose(b),t.map(I=>Qn(I,y,m))})}getFrozenTensorIds(e){const t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,i,o,a){if(t.category==="control"||o.indexOf(e)!==-1)return;n[e].forEach(c=>{c!=null&&(a[c.id]=(a[c.id]||0)+t.children.length)}),t.inputs.forEach(c=>{if(c.category!=="control"){const u=JV(c.name,n,s);u!=null&&u.forEach(p=>{if(p&&!i.has(p.id)){const m=a[p.id];m===1?(p.dispose(),delete a[p.id]):m!=null&&a[p.id]--}})}})}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,s={},i={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));const o=new t0(this.weightMap,s,i,this.functionExecutorMap),a=await this.executeWithControlFlow(e,o,t,n),c=t.map(y=>Qn(y,a,o)),u=c.map(y=>y.id),p=Object.keys(e).map(y=>e[y].id),m=new Set([...u,...p,...this.weightIds]);return Object.keys(a).forEach(y=>{const b=a[y];b.forEach(w=>{w&&!w.isDisposed&&!m.has(w.id)&&w.dispose()})}),this.parent==null&&o.dispose(m),c}async executeFunctionAsync(e,t,n){const s=e.reduce((i,o,a)=>(i[this.inputs[a].name]=o,i),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){const i=Object.keys(e),o=i.map(E=>this.graph.nodes[ls(E)[0]]),a=n.map(E=>ls(E)[0]),c=a.map(E=>this.graph.nodes[E]),{usedNodes:u,missingInputs:p,dynamicNode:m,syncInputs:y}=n0(e,c,this.weightMap),b=[...o,...this.graph.weights].map(E=>({node:E,contexts:t.currentContext})),w=Object.assign({},this.weightMap);Object.keys(e).forEach(E=>{const[D,F]=ls(E),_=[];_[F]=e[E],w[D]=_});const I={},T=this.getFrozenTensorIds(w),v={};for(;b.length>0;){const E=this.processStack(o,b,t,w,v,T,a,I,u);await Promise.all(E)}m==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");const N=c.filter(E=>!s0(E)&&!Qn(E.name,w,t)).map(E=>E.name);if(N.length>0){let E="";throw m!=null&&(E=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${y}]`),new Error(`Cannot compute the outputs [${N}] from the provided inputs [${i}]. Consider providing the following inputs: [${p}]. ${E}`)}return w}processStack(e,t,n,s,i,o,a,c,u){const p=[];for(;t.length>0;){const m=t.pop();n.currentContext=m.contexts;let y="";if(m.node.op==="Enter"&&R("isConstant",m.node,s,n)&&([y]=lr(m.node.name,n)),e.indexOf(m.node)===-1){const b=e0(m.node,s,n);y||([y]=lr(m.node.name,n));const w=n.currentContext;b instanceof Promise?p.push(b.then(I=>(s[y]=I,n.currentContext=w,this.checkTensorForDisposal(y,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,u),I))):(s[y]=b,this.checkTensorForDisposal(y,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,u))}else this.processChildNodes(m.node,t,n,s,i,u)}return p}processChildNodes(e,t,n,s,i,o){e.children.forEach(a=>{const[c]=lr(a.name,n);if(i[c]||!o.has(a.name))return;a.op==="Merge"?a.inputNames.some(u=>!!Qn(u,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a})):a.inputNames.every(u=>!!Qn(u,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a}))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{const n=e[t],[s]=ls(t),i=this.graph.nodes[s];if(i.attrParams.shape&&i.attrParams.shape.value){const o=i.attrParams.shape.value,a=o.length===n.shape.length&&n.shape.every((c,u)=>o[u]===-1||o[u]===c);k(a,()=>`The shape of dict['${i.name}'] provided in model.execute(dict) must be [${o}], but was [${n.shape}]`)}i.attrParams.dtype&&i.attrParams.dtype.value&&k(n.dtype===i.attrParams.dtype.value,()=>`The dtype of dict['${i.name}'] provided in model.execute(dict) must be ${i.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){const t={};for(const n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){const s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){const t=Object.keys(e).filter(n=>{const[s]=ls(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{if(this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null){const n=this._signature.outputs[t];return n.name}return t},{})}checkOutputs(e){e.forEach(t=>{const[n]=ls(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}}const iY="?tfjs-format=file",rY="model.json";class i0{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={})}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){const e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=kd(e,this.loadOptions);else{const t=By(e,this.loadOptions);if(t.length===0)t.push(kd(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;const t=this.artifacts.modelTopology;let n={};this.artifacts.userDefinedMetadata!=null&&(n=this.artifacts.userDefinedMetadata.signature),this.version=`${t.versions.producer}.${t.versions.minConsumer}`;const s=Rd(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new eS(KN.Instance.transformGraph(t,n)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),e.modelInitializer!=null){const i=KN.Instance.transformGraph(e.modelInitializer);this.initializer=new eS(i),this.initializer.weightMap=this.executor.weightMap,this.initializer.execute({},[])}return!0}async save(e,t){if(typeof e=="string"){const n=Uy(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Q)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose()}}async function oY(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&(e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${rY}${iY}`));const n=new i0(e,t);return await n.load(),n}const r0="2.6.0";function aY(e,t){return lm(e,t)}function lm(e,t,n=new Map,s=new Set){if(e==null)return null;if(s.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(i.recurse)if(ac(e)){const o=Array.isArray(e)?[]:{};s.add(e);for(const a in e){const c=e[a],u=lm(c,t,n,s);o[a]=u}return s.delete(e),o}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,i.value),i.value}function cY(e,t=a0){return o0(e,t)}function o0(e,t,n=new Set){const s=e[0];if(n.has(s))throw new Error("Circular references are not supported.");const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(i.recurse)if(ac(s)){const o=Array.isArray(s)?[]:{};n.add(s);for(const a in s){const c=e.map(p=>p[a]),u=o0(c,t,n);o[a]=u}return n.delete(s),o}else throw new Error(`Can't recurse into non-iterable type: ${s}`);else return i.value}function a0(e){return e===null?null:ac(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function c0(e,t){const n=new Map;lm(e,t,n);for(const i of Array.from(n.keys())){const o=n.get(i);if(o instanceof Promise){const a=await o;n.set(i,a)}}const s=lm(e,t,n);return s}function ac(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Q))}function lY(e){return e==null||hY(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Q||Ln(e)}function hY(e){return e===null||typeof e!="object"&&typeof e!="function"}function uY(e){return aY(e,dY)}function dY(e){return e instanceof Q?{value:e.clone(),recurse:!1}:ac(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class l0{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(const t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);const e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}}class tS extends l0{constructor(){super(tS.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){const e=this.capacity*2,t=new Array(e),n=this.length();for(let s=0;s<n;s++)t[s]=this.get(this.wrap(this.begin+s));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}}tS.INITIAL_CAPACITY=32;function h0(e){return new mY(e)}function ete(e){let t=e;return nu(()=>({value:t++,done:!1}))}function nu(e){return new fY(e)}function u0(e,t){return new p0(e,t)}function tte(e,t,n){return u0(nu(e).take(t),n)}function pY(e,t=Qr.FAIL){return new TY(e,t)}class gn{async toArray(){const e=[];let t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){const e=this.prefetch(100),t=[];let n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new IY(this,e)}filter(e){return new LY(this,e)}map(e){return new SY(this,e)}mapAsync(e){return new d0(this,e)}serialMapAsync(e){return new d0(this,e).serial()}flatmap(e){return new xY(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new wY(this,e,t)}columnMajorBatch(e,t=!0,n=a0){const s=this.rowMajorBatch(e,t);return s.map(i=>cY(i,n))}concatenate(e,t){return new p0(h0([this,e]),t)}take(e){return e<0||e==null?this:new bY(this,e)}skip(e){return e<0||e==null?this:new yY(this,e)}prefetch(e){return new m0(this,e)}shuffle(e,t){return new AY(this,e,t)}serial(){return new gY(this)}}class mY extends gn{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};const e=this.items[this.trav];return this.trav++,{value:uY(e),done:!1}}}class fY extends gn{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}}class gY extends gn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class yY extends gn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){const e=await this.upstream.next();if(e.done)return e;qe(e.value)}return this.upstream.next()}}class bY extends gn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class wY extends gn{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){const e=[];for(;e.length<this.batchSize;){const t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class LY extends gn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){const e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;qe(e.value)}}}class SY extends gn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=Zi(e.value),n=this.transform(e.value),s=Zi(n);for(const i of t)Nd(i,s)||i.dispose();return{value:n,done:!1}}}class IY extends gn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}}class d0 extends gn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=Zi(e.value),n=await this.transform(e.value),s=Zi(n);for(const i of t)Nd(i,s)||i.dispose();return{value:n,done:!1}}}class nS extends gn{constructor(){super();this.outputQueue=new tS,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class xY extends nS{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=Zi(e.value),n=this.transform(e.value),s=Zi(n);this.outputQueue.pushAll(n);for(const i of t)Nd(i,s)||i.dispose();return!0}}class p0 extends gn{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){const e="TODO: fill in upstream of chained summaries";return`${e} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){const n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}var Qr;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Qr||(Qr={}));class TY extends gn{constructor(e,t=Qr.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){const e="TODO: fill in upstream of zip summaries";return`{${e}} -> Zip`}async nextState(e){await e;let t=0,n=0;function s(o){if(o instanceof gn){const a=o.next();return{value:a.then(c=>(t++,c.done&&n++,c.value)),recurse:!1}}else return{value:null,recurse:!0}}const i=await c0(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Qr.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Qr.SHORTEST:return{value:null,done:!0};case Qr.LONGEST:default:}return this.count++,{value:i,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class m0 extends gn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new l0(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){const e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}}class AY extends m0{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Ha(n||qn().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){const e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}}class cc{constructor(){this.size=null}batch(e,t=!0){const n=this;k(e>0,()=>`batchSize needs to be positive, but it is
-      ${e}`);let s;return this.size===Infinity||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),hs(async()=>(await n.iterator()).columnMajorBatch(e,t,CY),s)}concatenate(e){const t=this;let n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,hs(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){const t=this;let n;return this.size===Infinity?n=Infinity:n=null,hs(async()=>(await t.iterator()).filter(s=>ee(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return hs(async()=>(await t.iterator()).map(n=>ee(()=>e(n))),this.size)}mapAsync(e){const t=this;return hs(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const t=this;return hs(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){const t=this;let n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,hs(async()=>{const s=nu(async()=>({value:await t.iterator(),done:!1}));return u0(s.take(e))},n)}skip(e){const t=this;let n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,hs(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const s=this,i=Ha(t||qn().toString());return hs(async()=>{let o=i.int32();return n&&(o+=i.int32()),(await s.iterator()).shuffle(e,o.toString())},this.size)}take(e){const t=this;let n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,hs(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}}cc.MAX_BUFFER_SIZE=1e4;function hs(e,t=null){return new class extends cc{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function vY(e){return hs(async()=>h0(e),e.length)}function NY(e){if(!ac(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(const n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return hs(async()=>{const n=await c0(e,s=>{if(s instanceof cc)return{value:s.iterator(),recurse:!1};if(ac(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return pY(n,Qr.SHORTEST)},t)}function CY(e){if(e===null)return null;const t=e[0];if(lY(t)){const n=RY(e);return{value:n,recurse:!1}}return{value:null,recurse:!0}}function RY(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Q?as(e):en(e)}class f0 extends cc{constructor(e){super();this.input=e}async iterator(){const e=await this.input.iterator(),t=e.decodeUTF8(),n=t.split(`
-`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s));return n}}const hm='"',su=Symbol("out"),g0=Symbol("field"),um=Symbol("quote"),sS=Symbol("quoteafterquote"),y0=Symbol("quoteinquote");class b0 extends cc{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new f0(e),t||(t={}),this.hasHeader=!(t.hasHeader===!1),this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){const e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&k(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);const t=this.fullColumnNames.reduce((s,i)=>(s[i]=s[i]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(k(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs)for(const s of Object.keys(this.columnConfigs)){const i=this.fullColumnNames.indexOf(s);if(i===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){const e=await this.base.iterator(),t=await e.next();if(t.done)throw new Error("No data was found for CSV parsing.");const n=t.value,s=this.parseRow(n,!1);return s}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){const t=this.parseRow(e),n={},s={};for(let i=0;i<this.fullColumnNames.length;i++){const o=this.fullColumnNames[i],a=this.columnConfigs?this.columnConfigs[o]:null;if(this.configuredColumnsOnly&&!a)continue;{const c=t[i];let u=null;if(c==="")if(a&&a.default!==void 0)u=a.default;else{if(a&&(a.required||a.isLabel))throw new Error(`Required column ${o} is empty in this line: ${e}`);u=void 0}else{const p=Number(c);if(isNaN(p))a&&a.dtype==="bool"?u=this.getBoolean(c):u=c;else if(!a||!a.dtype)u=p;else switch(a.dtype){case"float32":u=p;break;case"int32":u=Math.floor(p);break;case"bool":u=this.getBoolean(c);break;default:u=p}}a&&a.isLabel?s[o]=u:n[o]=u}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){const n=[];let s=0;const i=e.length;let o=su;for(let a=0;a<i;a++)switch(o){case su:switch(e.charAt(a)){case hm:s=a+1,o=um;break;case this.delimiter:if(s=a+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),o=su;break;default:o=g0,s=a;break}break;case g0:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a)),o=su,s=a+1;break;default:}break;case um:switch(e.charAt(a)){case hm:o=sS;break;default:}break;case sS:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a-1)),o=su,s=a+1;break;case hm:o=um;break;default:o=y0;break}break;case y0:switch(e.charAt(a)){case hm:o=um;break;default:}break;default:}if(o===sS?n.push(e.substring(s,i-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}}class w0 extends gn{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;const t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=!(e.includeSpectrogram===!1),this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(C().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");const t=new w0(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");const e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);const t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize);return}async next(){if(this.isClosed)return{value:null,done:!0};let e,t;const n=await this.getAudioData();if(this.includeSpectrogram){const s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){const s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){const e=[],t=[];let n=0;return new Promise(s=>{const i=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(i),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){const t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,i)=>n.set(s,i*t)),n}getTensorFromAudioDataArray(e,t){const n=new Float32Array(we(t));return n.set(e,n.length-e.length),en(n,t)}}class L0 extends gn{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=rs([0],"int32"),this.webcamConfig.centerCrop){const n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,i=(1-n)/2,o=(1-s)/2,a=i+n,c=s+o;this.cropBox=Gr([o,i,c,a],[1,4])}else this.cropBox=Gr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(C().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}const n=new L0(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=BT(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return ee(()=>{const t=e.toFloat().expandDims(0);let n;n=Vr.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");const s=n.shape;return n.reshape(s.slice(1))})}async capture(){return(await this.next()).value}stop(){const e=this.stream.getTracks();e.forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class S0{}class I0 extends gn{split(e){return new OY(this,e)}}class OY extends I0{constructor(e,t){super();this.upstream=e,this.impl=new EY(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class EY extends nS{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){const e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);const t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(const n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}}class DY extends gn{decodeUTF8(){return new kY(this)}}class kY extends I0{constructor(e){super();this.upstream=e,this.impl=new FY(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class FY extends nS{constructor(e){super();if(this.upstream=e,C().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{const{StringDecoder:t}=require("string_decoder");this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){const e=await this.upstream.next();let t;if(e.done)return!1;t=e.value;let n;return C().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}}class x0 extends DY{constructor(e,t={}){super();this.file=e,this.options=t,k(e instanceof Uint8Array||(C().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};const e=new Promise((t,n)=>{const s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{const i=new FileReader;i.onload=a=>{let c=i.result;if(c instanceof ArrayBuffer&&(c=new Uint8Array(c)),!(c instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(c)},i.onabort=a=>n(new Error("Aborted")),i.onerror=a=>n(new Error(a.type));const o=this.file.slice(this.offset,s);i.readAsArrayBuffer(o)}this.offset=s});return{value:await e,done:!1}}}async function _Y(e,t={}){let n,s;typeof e=="string"?n=e:(n=e.url,s=WY(e));const i=await uT(n,s);if(i.ok){const o=new Uint8Array(await i.arrayBuffer());return new x0(o,t)}else throw new Error(i.statusText)}const WY=e=>{const t={method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity};return t};function T0(e){return typeof e=="string"&&e.substr(0,7)==="file://"}class A0 extends S0{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(T0(this.input)&&C().get("IS_NODE")){const e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new x0(this.input,this.options)}}class v0 extends S0{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return T0(this.url)?new A0(this.url,this.fileOptions).iterator():_Y(this.url,this.fileOptions)}}function $Y(e,t={}){return new b0(new v0(e),t)}function UY(e){const t=nu(e);return hs(async()=>t)}function BY(e){return hs(async()=>{const t=await e();return nu(()=>t.next())})}async function MY(e,t){return L0.create(e,t)}async function PY(e){return w0.create(e)}const N0="2.6.0";var zY=Object.freeze({__proto__:null,array:vY,Dataset:cc,zip:NY,CSVDataset:b0,TextLineDataset:f0,csv:$Y,func:UY,generator:BY,microphone:PY,webcam:MY,FileDataSource:A0,URLDataSource:v0,version_data:N0});function Te(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}const GY=wp,VY=rw,HY=ow,YY=aw,qY=dp;function iS(e,t,n,s){if(n==="linear")return e.linear(t);if(n==="relu")return e.relu(t);if(n==="elu")return Oo(t);if(n==="relu6")return e.relu6(t);if(n==="prelu")return e.prelu(t,s);throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}class jY extends f{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new d(this,$s())}write(e,t,n){this.firstUse&&(this.firstUse=!1,C().get("IS_NODE")&&Ja(`
+        ${s}, and tensor's shape is: ${e.shape}`);const o=s===0?0:e.size/s,a=ee(()=>{const u=[];e=K(e,[1,s,o]);for(let p=0;p<t.length;++p){const m=p===0?0:i[p-1],y=[0,m,0],b=[1,t[p],o];u[p]=K(st(e,y,b),n)}return e.dispose(),u}),c=new tu([],n,e.dtype,t.length);for(let u=0;u<a.length;u++)c.setItem(u,a[u]);return c}const MH=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{const s=R("thenBranch",e,t,n),i=R("elseBranch",e,t,n),o=R("cond",e,t,n),a=R("args",e,t,n),c=await o.data();return c[0]?n.functionMap[s].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap):n.functionMap[i].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{const s=R("body",e,t,n),i=R("cond",e,t,n),o=R("args",e,t,n),a=await n.functionMap[i].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap),c=o.map(m=>m.id);let u=await a[0].data();a.forEach(m=>{!m.kept&&c.indexOf(m.id)===-1&&m.dispose()});let p=o;for(;u[0];){const m=p;p=await n.functionMap[s].executeFunctionAsync(p,n.tensorArrayMap,n.tensorListMap);const y=p.map(w=>w.id);m.forEach(w=>{!w.kept&&c.indexOf(w.id)===-1&&y.indexOf(w.id)===-1&&w.dispose()});const b=await n.functionMap[i].executeFunctionAsync(p,n.tensorArrayMap,n.tensorListMap);u=await b[0].data(),b.forEach(w=>{!w.kept&&c.indexOf(w.id)===-1&&y.indexOf(w.id)===-1&&w.dispose()})}return p}case"LoopCond":{const s=R("pred",e,t,n);return[hr(s)]}case"Switch":{const s=R("pred",e,t,n);let i=R("data",e,t,n);return i.kept||(i=hr(i)),(await s.data())[0]?[void 0,i]:[i,void 0]}case"Merge":{const s=e.inputNames.find(i=>Qn(i,t,n)!==void 0);if(s){const i=Qn(s,t,n);return[hr(i)]}return}case"Enter":{const s=R("frameName",e,t,n),i=R("tensor",e,t,n);return n.enterFrame(s),[hr(i)]}case"Exit":{const s=R("tensor",e,t,n);return n.exitFrame(),[hr(s)]}case"NextIteration":{const s=R("tensor",e,t,n);return n.nextIteration(),[hr(s)]}case"TensorArrayV3":{const s=R("size",e,t,n),i=R("dtype",e,t,n),o=R("elementShape",e,t,n),a=R("dynamicSize",e,t,n),c=R("clearAfterRead",e,t,n),u=R("identicalElementShapes",e,t,n),p=R("name",e,t,n),m=new _H(p,i,s,o,u,a,c);return n.addTensorArray(m),[m.idTensor,Ne(1)]}case"TensorArrayWriteV3":{const s=R("tensorArrayId",e,t,n),i=R("index",e,t,n),o=R("tensor",e,t,n),a=n.getTensorArray(s.id);return a.write(i,o),[a.idTensor]}case"TensorArrayReadV3":{const s=R("tensorArrayId",e,t,n),i=R("index",e,t,n),o=n.getTensorArray(s.id);return[o.read(i)]}case"TensorArrayGatherV3":{const s=R("tensorArrayId",e,t,n),i=R("indices",e,t,n),o=R("dtype",e,t,n),a=n.getTensorArray(s.id);return[a.gather(i,o)]}case"TensorArrayScatterV3":{const s=R("tensorArrayId",e,t,n),i=R("indices",e,t,n),o=R("tensor",e,t,n),a=n.getTensorArray(s.id);return a.scatter(i,o),[a.idTensor]}case"TensorArrayConcatV3":{const s=R("tensorArrayId",e,t,n),i=n.getTensorArray(s.id),o=R("dtype",e,t,n);return[i.concat(o)]}case"TensorArraySplitV3":{const s=R("tensorArrayId",e,t,n),i=R("tensor",e,t,n),o=R("lengths",e,t,n),a=n.getTensorArray(s.id);return a.split(o,i),[a.idTensor]}case"TensorArraySizeV3":{const s=R("tensorArrayId",e,t,n),i=n.getTensorArray(s.id);return[Ne(i.size(),"int32")]}case"TensorArrayCloseV3":{const s=R("tensorArrayId",e,t,n),i=n.getTensorArray(s.id);return i.clearAndClose(),[i.idTensor]}case"TensorListSetItem":{const s=R("tensorListId",e,t,n),i=R("index",e,t,n),o=R("tensor",e,t,n),a=n.getTensorList(s.id);return a.setItem(i,o),[a.idTensor]}case"TensorListGetItem":{const s=R("tensorListId",e,t,n),i=R("index",e,t,n),o=R("elementShape",e,t,n),a=R("elementDType",e,t,n),c=n.getTensorList(s.id);return[c.getItem(i,o,a)]}case"TensorListScatterV2":case"TensorListScatter":{const s=R("indices",e,t,n),i=R("tensor",e,t,n),o=R("elementShape",e,t,n),a=R("numElements",e,t,n),c=UH(i,s,o,a);return n.addTensorList(c),[c.idTensor]}case"TensorListReserve":{const s=R("elementShape",e,t,n),i=R("elementDType",e,t,n),o=R("numElements",e,t,n),a=$H(s,i,o);return n.addTensorList(a),[a.idTensor]}case"TensorListGather":{const s=R("tensorListId",e,t,n),i=R("indices",e,t,n),o=R("elementShape",e,t,n),a=R("elementDType",e,t,n),c=n.getTensorList(s.id);return[c.gather(i,a,o)]}case"TensorListStack":{const s=R("tensorListId",e,t,n),i=R("elementShape",e,t,n),o=R("elementDType",e,t,n),a=R("numElements",e,t,n),c=n.getTensorList(s.id);return[c.stack(i,o,a)]}case"TensorListFromTensor":{const s=R("tensor",e,t,n),i=R("elementShape",e,t,n),o=R("elementDType",e,t,n),a=WH(s,i,o);return n.addTensorList(a),[a.idTensor]}case"TensorListConcat":{const s=R("tensorListId",e,t,n),i=n.getTensorList(s.id),o=R("dtype",e,t,n),a=R("elementShape",e,t,n);return[i.concat(o,a)]}case"TensorListPushBack":{const s=R("tensorListId",e,t,n),i=R("tensor",e,t,n),o=n.getTensorList(s.id);return o.pushBack(i),[o.idTensor]}case"TensorListPopBack":{const s=R("tensorListId",e,t,n),i=R("elementShape",e,t,n),o=R("elementDType",e,t,n),a=n.getTensorList(s.id);return[a.popBack(i,o)]}case"TensorListSplit":{const s=R("tensor",e,t,n),i=R("elementShape",e,t,n),o=R("lengths",e,t,n),a=BH(s,o,i);return n.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Mee="control";function QN(e,t,n){const[s,i]=R("fusedOps",e,t,n),o=s==="biasadd",a=i==="prelu",c=s==="fusedbatchnorm",u=R("numArgs",e,t,n);if(o){if(a&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(c)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");const p=R("strides",e,t,n),m=cm(e,t,n),y=R("dataFormat",e,t,n).toUpperCase(),b=R("dilations",e,t,n),[w,I]=R("args",e,t,n);return{stride:p,pad:m,dataFormat:y,dilations:b,biasArg:w,preluArg:I,activationFunc:i}}const PH=(e,t,n)=>{switch(e.op){case"Conv1D":{const s=R("stride",e,t,n),i=R("pad",e,t,n),o=R("dataFormat",e,t,n).toUpperCase(),a=R("dilation",e,t,n);return[Hd(R("x",e,t,n),R("filter",e,t,n),s,i,o,a)]}case"Conv2D":{const s=R("strides",e,t,n),i=cm(e,t,n),o=R("dataFormat",e,t,n).toUpperCase(),a=R("dilations",e,t,n);return[er(R("x",e,t,n),R("filter",e,t,n),[s[1],s[2]],i,o,[a[1],a[2]])]}case"_FusedConv2D":{const{stride:s,pad:i,dataFormat:o,dilations:a,biasArg:c,preluArg:u,activationFunc:p}=QN(e,t,n);return[Hb({x:R("x",e,t,n),filter:R("filter",e,t,n),strides:[s[1],s[2]],pad:i,dataFormat:o,dilations:[a[1],a[2]],bias:c,activation:p,preluActivationWeights:u})]}case"FusedDepthwiseConv2dNative":{const{stride:s,pad:i,dataFormat:o,dilations:a,biasArg:c,preluArg:u,activationFunc:p}=QN(e,t,n);return[YA({x:R("x",e,t,n),filter:R("filter",e,t,n),strides:[s[1],s[2]],pad:i,dataFormat:o,dilations:[a[1],a[2]],bias:c,activation:p,preluActivationWeights:u})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{const s=R("outputShape",e,t,n),i=R("strides",e,t,n),o=cm(e,t,n);return[Yd(R("x",e,t,n),R("filter",e,t,n),s,[i[1],i[2]],o)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{const s=R("strides",e,t,n),i=cm(e,t,n),o=R("dilations",e,t,n),a=R("dataFormat",e,t,n).toUpperCase();return[Oo(R("input",e,t,n),R("filter",e,t,n),[s[1],s[2]],i,a,[o[1],o[2]])]}case"Conv3D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("dataFormat",e,t,n).toUpperCase(),a=R("dilations",e,t,n);return[yb(R("x",e,t,n),R("filter",e,t,n),[s[1],s[2],s[3]],i,o,[a[1],a[2],a[3]])]}case"AvgPool":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[oh(R("x",e,t,n),[o[1],o[2]],[s[1],s[2]],i)]}case"MaxPool":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[mh(R("x",e,t,n),[o[1],o[2]],[s[1],s[2]],i)]}case"MaxPoolWithArgmax":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n),a=R("includeBatchInIndex",e,t,n),{result:c,indexes:u}=bA(R("x",e,t,n),[o[1],o[2]],[s[1],s[2]],i,a);return[c,u]}case"AvgPool3D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[pb(R("x",e,t,n),[o[1],o[2],o[3]],[s[1],s[2],s[3]],i)]}case"MaxPool3D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[Nb(R("x",e,t,n),[o[1],o[2],o[3]],[s[1],s[2],s[3]],i)]}case"Dilation2D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("dilations",e,t,n),a=s[1],c=s[2],u=o[1],p=o[2];return[wb(R("x",e,t,n),R("filter",e,t,n),[a,c],i,[u,p],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Pee="convolution";const zH=(e,t,n)=>{switch(e.op){case"Fill":{const s=R("shape",e,t,n),i=R("dtype",e,t,n),o=R("value",e,t,n);return[hh(s,o,i)]}case"LinSpace":{const s=R("start",e,t,n),i=R("stop",e,t,n),o=R("num",e,t,n);return[fA(s,i,o)]}case"Multinomial":{const s=R("logits",e,t,n),i=R("numSamples",e,t,n),o=R("seed",e,t,n);return[wA(s,i,o)]}case"OneHot":{const s=R("indices",e,t,n),i=R("depth",e,t,n),o=R("onValue",e,t,n),a=R("offValue",e,t,n);return[vo(s,i,o,a)]}case"Ones":return[si(R("shape",e,t,n),R("dtype",e,t,n))];case"OnesLike":return[Dn(R("x",e,t,n))];case"RandomUniform":return[$o(R("shape",e,t,n),R("minval",e,t,n),R("maxval",e,t,n),R("dtype",e,t,n))];case"Range":{const s=R("start",e,t,n),i=R("stop",e,t,n),o=R("step",e,t,n);return[yh(s,i,o,R("dtype",e,t,n))]}case"TruncatedNormal":{const s=R("shape",e,t,n),i=R("mean",e,t,n),o=R("stdDev",e,t,n),a=R("seed",e,t,n);return[Ih(s,i,o,R("dtype",e,t,n),a)]}case"Zeros":return[ct(R("shape",e,t,n),R("dtype",e,t,n))];case"ZerosLike":return[et(R("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},zee="creation";function QL(e,t,n){const s=R("boxes",e,t,n),i=R("scores",e,t,n),o=R("maxOutputSize",e,t,n),a=R("iouThreshold",e,t,n),c=R("scoreThreshold",e,t,n),u=R("softNmsSigma",e,t,n);return{boxes:s,scores:i,maxOutputSize:o,iouThreshold:a,scoreThreshold:c,softNmsSigma:u}}const GH=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{const{boxes:s,scores:i,maxOutputSize:o,iouThreshold:a,scoreThreshold:c,softNmsSigma:u}=QL(e,t,n),p=await Vr.nonMaxSuppressionWithScoreAsync(s,i,o,a,c,u);return[p.selectedIndices,p.selectedScores]}case"NonMaxSuppressionV4":{const{boxes:s,scores:i,maxOutputSize:o,iouThreshold:a,scoreThreshold:c}=QL(e,t,n),u=R("padToMaxOutputSize",e,t,n),p=await Vr.nonMaxSuppressionPaddedAsync(s,i,o,a,c,u);return[p.selectedIndices,p.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{const{boxes:s,scores:i,maxOutputSize:o,iouThreshold:a,scoreThreshold:c}=QL(e,t,n);return[await Vr.nonMaxSuppressionAsync(s,i,o,a,c)]}case"Where":{const s=ve(R("condition",e,t,n),"bool"),i=[await Pb(s)];return s.dispose(),i}case"ListDiff":return SA(R("x",e,t,n),R("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},Gee="dynamic";const VH=(e,t,n)=>{switch(e.op){case"TopKV2":{const s=R("x",e,t,n),i=R("k",e,t,n),o=R("sorted",e,t,n),a=Bb(s,i,o);return[a.values,a.indices]}case"Unique":{const s=R("x",e,t,n),i=up(s);return[i.values,i.indices]}case"UniqueV2":{const s=R("x",e,t,n),i=R("axis",e,t,n),o=up(s,i);return[o.values,o.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Vee="evaluation";const HH=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":const s=R("default",e,t,n);return[Qn(e.name,t,n)||s];case"Placeholder":return[Qn(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{const p=R("x",e,t,n);return[hr(p)]}case"IdentityN":return R("x",e,t,n).map(p=>hr(p));case"Snapshot":const i=R("x",e,t,n);return[hr(i)];case"Shape":return[rs(R("x",e,t,n).shape,"int32")];case"ShapeN":return R("x",e,t,n).map(p=>rs(p.shape));case"Size":return[Ne(R("x",e,t,n).size,"int32")];case"Rank":return[Ne(R("x",e,t,n).rank,"int32")];case"NoOp":return[Ne(1)];case"Print":const o=R("x",e,t,n),a=R("data",e,t,n),c=R("message",e,t,n),u=R("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(c);for(let p=0;p<a.length;p++)console.log(Array.prototype.slice.call(a[p].dataSync()).slice(0,u));return[o];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Hee="graph";const YH=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{const s=R("images",e,t,n),i=R("size",e,t,n),o=R("alignCorners",e,t,n);return[Vr.resizeBilinear(s,[i[0],i[1]],o)]}case"ResizeNearestNeighbor":{const s=R("images",e,t,n),i=R("size",e,t,n),o=R("alignCorners",e,t,n);return[Vr.resizeNearestNeighbor(s,[i[0],i[1]],o)]}case"CropAndResize":{const s=R("image",e,t,n),i=R("boxes",e,t,n),o=R("boxInd",e,t,n),a=R("cropSize",e,t,n),c=R("method",e,t,n),u=R("extrapolationValue",e,t,n);return[Vr.cropAndResize(s,i,o,a,c,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Yee="image";const qH=(e,t,n)=>{switch(e.op){case"Equal":return[ni(R("a",e,t,n),R("b",e,t,n))];case"NotEqual":return[Pr(R("a",e,t,n),R("b",e,t,n))];case"Greater":return[Ts(R("a",e,t,n),R("b",e,t,n))];case"GreaterEqual":return[tr(R("a",e,t,n),R("b",e,t,n))];case"Less":return[dh(R("a",e,t,n),R("b",e,t,n))];case"LessEqual":return[Mr(R("a",e,t,n),R("b",e,t,n))];case"LogicalAnd":return[Bs(R("a",e,t,n),R("b",e,t,n))];case"LogicalNot":return[ph(R("a",e,t,n))];case"LogicalOr":return[ep(R("a",e,t,n),R("b",e,t,n))];case"Select":case"SelectV2":return[$n(R("condition",e,t,n),R("a",e,t,n),R("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},qee="logical";const jH=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[at(R("a",e,t,n),R("b",e,t,n),R("transposeA",e,t,n),R("transposeB",e,t,n))];case"Transpose":return[Pe(R("x",e,t,n),R("perm",e,t,n))];case"_FusedMatMul":const[s,i]=R("fusedOps",e,t,n),o=s==="biasadd",a=i==="prelu",c=R("numArgs",e,t,n);if(o){if(a&&c!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&c!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}const[u,p]=R("args",e,t,n);return[bp({a:R("a",e,t,n),b:R("b",e,t,n),transposeA:R("transposeA",e,t,n),transposeB:R("transposeB",e,t,n),bias:u,activation:i,preluActivationWeights:p})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},jee="matrices";const KH=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Ro(R("x",e,t,n),R("mean",e,t,n),R("variance",e,t,n),R("offset",e,t,n),R("scale",e,t,n),R("epsilon",e,t,n))];case"FusedBatchNormV3":return[Ro(R("x",e,t,n),R("mean",e,t,n),R("variance",e,t,n),R("offset",e,t,n),R("scale",e,t,n),R("epsilon",e,t,n))];case"LRN":return[Tb(R("x",e,t,n),R("radius",e,t,n),R("bias",e,t,n),R("alpha",e,t,n),R("beta",e,t,n))];case"Softmax":return[Uo(R("x",e,t,n))];case"LogSoftmax":return[Qd(R("x",e,t,n))];case"SparseToDense":return[zb(R("sparseIndices",e,t,n),R("outputShape",e,t,n),R("sparseValues",e,t,n),R("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Kee="normalization";const XH=(e,t,n)=>{switch(e.op){case"Max":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[Xn(R("x",e,t,n),s,i)]}case"Mean":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[zt(R("x",e,t,n),s,i)]}case"Min":{const 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ee(()=>VH(i,o,a));case"image":return ee(()=>YH(i,o,a));case"graph":return ee(()=>HH(i,o,a));case"logical":return ee(()=>qH(i,o,a));case"matrices":return ee(()=>jH(i,o,a));case"normalization":return ee(()=>KH(i,o,a));case"reduction":return ee(()=>XH(i,o,a));case"slice_join":return ee(()=>JH(i,o,a));case"spectral":return ee(()=>ZH(i,o,a));case"transformation":return ee(()=>QH(i,o,a));case"custom":const c=jN(i.op);if(c&&c.customExecutor)return c.customExecutor(new EH(i,o,a));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. 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this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(const t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(const t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}}function n0(e,t,n,s){const i=new Set,o=[];let a=null,c=null;const u=new Set,p=Object.keys(e).map(b=>ls(b)[0]);let m=[];s!=null&&(m=s.map(b=>ls(b.name)[0]));const y=[...t];for(;y.length>0;){const b=y.pop();if((s0(b)||sY(b))&&(a==null&&(a=b,c=a.children.map(w=>w.name).filter(w=>i.has(w)))),i.add(b.name),n[b.name]!=null)continue;if(p.indexOf(b.name)!==-1)continue;if(m.indexOf(b.name)!==-1)continue;if(b.inputs.length===0){o.push(b.name);continue}b.inputs.forEach(w=>{if(u.has(w.name))return;u.add(w.name),y.push(w)})}return{inputs:e,outputs:t,usedNodes:i,missingInputs:o,dynamicNode:a,syncInputs:c}}function eY(e,t,n){const{usedNodes:s,inputs:i}=n,o=[],a=Object.keys(i).map(m=>ls(m)[0]).map(m=>e.nodes[m]),c=e.initNodes;a.forEach(m=>{s.has(m.name)&&o.push(m)}),e.weights.forEach(m=>{s.has(m.name)&&o.push(m)}),c!=null&&c.forEach(m=>{s.has(m.name)&&o.push(m)});const u=new Set,p=[];for(;o.length>0;){const m=o.pop();u.add(m.name),t[m.name]||p.push(m),m.children.forEach(y=>{!u.has(y.name)&&s.has(y.name)&&y.inputs.every(b=>u.has(b.name))&&o.push(y)})}return p}const tY=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],nY=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"];function s0(e){return tY.indexOf(e.op)>=0}function sY(e){return nY.indexOf(e.op)>=0}class eS{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new eS(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){const t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{const t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){const n=e.map(i=>i.name).sort(),s=t.map(i=>i.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){const n=n0(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:i,syncInputs:o}=n;if(i!=null)throw new Error(`This execution contains the node '${i.name}', which has the dynamic op '${i.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${o}]`);if(s.length>0){const a=t.map(u=>u.name),c=Object.keys(e);throw new Error(`Cannot compute the outputs [${a}] from the provided inputs [${c}]. Missing the following inputs: [${s}]`)}return eY(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);const n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);const s=n.map(m=>this.graph.nodes[ls(m)[0]]),i=t.map(m=>ls(m)[0]);let o=i.map(m=>this.graph.nodes[m]);o.length===0&&(o=this._outputs);const a=this.getCompilationKey(s,o);let c=this.compiledMap.get(a);c==null&&(c=this.compile(e,o),this.compiledMap.set(a,c));const u={},p={};return ee(()=>{const m=new t0(this.weightMap,u,p,this.functionExecutorMap),y=Object.assign({},this.weightMap);Object.keys(e).forEach(I=>{const[T,v]=ls(I),N=[];N[v]=e[I],y[T]=N});const b=this.getFrozenTensorIds(y),w={};for(let I=0;I<c.length;I++){const T=c[I];if(!y[T.name]){const v=e0(T,y,m);if(v instanceof Promise)throw new Error(`The execution of the op '${T.op}' returned a promise. Please use model.executeAsync() instead.`);y[T.name]=v,this.checkTensorForDisposal(T.name,T,y,m,b,i,w)}}return this.parent==null&&m.dispose(b),t.map(I=>Qn(I,y,m))})}getFrozenTensorIds(e){const t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,i,o,a){if(t.category==="control"||o.indexOf(e)!==-1)return;n[e].forEach(c=>{c!=null&&(a[c.id]=(a[c.id]||0)+t.children.length)}),t.inputs.forEach(c=>{if(c.category!=="control"){const u=JV(c.name,n,s);u!=null&&u.forEach(p=>{if(p&&!i.has(p.id)){const m=a[p.id];m===1?(p.dispose(),delete a[p.id]):m!=null&&a[p.id]--}})}})}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,s={},i={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));const o=new t0(this.weightMap,s,i,this.functionExecutorMap),a=await this.executeWithControlFlow(e,o,t,n),c=t.map(y=>Qn(y,a,o)),u=c.map(y=>y.id),p=Object.keys(e).map(y=>e[y].id),m=new Set([...u,...p,...this.weightIds]);return Object.keys(a).forEach(y=>{const b=a[y];b.forEach(w=>{w&&!w.isDisposed&&!m.has(w.id)&&w.dispose()})}),this.parent==null&&o.dispose(m),c}async executeFunctionAsync(e,t,n){const s=e.reduce((i,o,a)=>(i[this.inputs[a].name]=o,i),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){const i=Object.keys(e),o=i.map(E=>this.graph.nodes[ls(E)[0]]),a=n.map(E=>ls(E)[0]),c=a.map(E=>this.graph.nodes[E]),{usedNodes:u,missingInputs:p,dynamicNode:m,syncInputs:y}=n0(e,c,this.weightMap),b=[...o,...this.graph.weights].map(E=>({node:E,contexts:t.currentContext})),w=Object.assign({},this.weightMap);Object.keys(e).forEach(E=>{const[D,F]=ls(E),_=[];_[F]=e[E],w[D]=_});const I={},T=this.getFrozenTensorIds(w),v={};for(;b.length>0;){const E=this.processStack(o,b,t,w,v,T,a,I,u);await Promise.all(E)}m==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");const N=c.filter(E=>!s0(E)&&!Qn(E.name,w,t)).map(E=>E.name);if(N.length>0){let E="";throw m!=null&&(E=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${y}]`),new Error(`Cannot compute the outputs [${N}] from the provided inputs [${i}]. Consider providing the following inputs: [${p}]. ${E}`)}return w}processStack(e,t,n,s,i,o,a,c,u){const p=[];for(;t.length>0;){const m=t.pop();n.currentContext=m.contexts;let y="";if(m.node.op==="Enter"&&R("isConstant",m.node,s,n)&&([y]=lr(m.node.name,n)),e.indexOf(m.node)===-1){const b=e0(m.node,s,n);y||([y]=lr(m.node.name,n));const w=n.currentContext;b instanceof Promise?p.push(b.then(I=>(s[y]=I,n.currentContext=w,this.checkTensorForDisposal(y,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,u),I))):(s[y]=b,this.checkTensorForDisposal(y,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,u))}else this.processChildNodes(m.node,t,n,s,i,u)}return p}processChildNodes(e,t,n,s,i,o){e.children.forEach(a=>{const[c]=lr(a.name,n);if(i[c]||!o.has(a.name))return;a.op==="Merge"?a.inputNames.some(u=>!!Qn(u,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a})):a.inputNames.every(u=>!!Qn(u,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a}))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{const n=e[t],[s]=ls(t),i=this.graph.nodes[s];if(i.attrParams.shape&&i.attrParams.shape.value){const o=i.attrParams.shape.value,a=o.length===n.shape.length&&n.shape.every((c,u)=>o[u]===-1||o[u]===c);k(a,()=>`The shape of dict['${i.name}'] provided in model.execute(dict) must be [${o}], but was [${n.shape}]`)}i.attrParams.dtype&&i.attrParams.dtype.value&&k(n.dtype===i.attrParams.dtype.value,()=>`The dtype of dict['${i.name}'] provided in model.execute(dict) must be ${i.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){const t={};for(const n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){const s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){const t=Object.keys(e).filter(n=>{const[s]=ls(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{if(this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null){const n=this._signature.outputs[t];return n.name}return t},{})}checkOutputs(e){e.forEach(t=>{const[n]=ls(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}}const iY="?tfjs-format=file",rY="model.json";class i0{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={})}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){const e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=kd(e,this.loadOptions);else{const t=By(e,this.loadOptions);if(t.length===0)t.push(kd(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;const t=this.artifacts.modelTopology;let n={};this.artifacts.userDefinedMetadata!=null&&(n=this.artifacts.userDefinedMetadata.signature),this.version=`${t.versions.producer}.${t.versions.minConsumer}`;const s=Rd(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new eS(KN.Instance.transformGraph(t,n)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),e.modelInitializer!=null){const i=KN.Instance.transformGraph(e.modelInitializer);this.initializer=new eS(i),this.initializer.weightMap=this.executor.weightMap,this.initializer.execute({},[])}return!0}async save(e,t){if(typeof e=="string"){const n=Uy(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Q)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose()}}async function oY(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&(e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${rY}${iY}`));const n=new i0(e,t);return await n.load(),n}const r0="2.6.0";function aY(e,t){return lm(e,t)}function lm(e,t,n=new Map,s=new Set){if(e==null)return null;if(s.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(i.recurse)if(lc(e)){const o=Array.isArray(e)?[]:{};s.add(e);for(const a in e){const c=e[a],u=lm(c,t,n,s);o[a]=u}return s.delete(e),o}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,i.value),i.value}function cY(e,t=a0){return o0(e,t)}function o0(e,t,n=new Set){const s=e[0];if(n.has(s))throw new Error("Circular references are not supported.");const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(i.recurse)if(lc(s)){const o=Array.isArray(s)?[]:{};n.add(s);for(const a in s){const c=e.map(p=>p[a]),u=o0(c,t,n);o[a]=u}return n.delete(s),o}else throw new Error(`Can't recurse into non-iterable type: ${s}`);else return i.value}function a0(e){return e===null?null:lc(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function c0(e,t){const n=new Map;lm(e,t,n);for(const i of Array.from(n.keys())){const o=n.get(i);if(o instanceof Promise){const a=await o;n.set(i,a)}}const s=lm(e,t,n);return s}function lc(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Q))}function lY(e){return e==null||hY(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Q||Ln(e)}function hY(e){return e===null||typeof e!="object"&&typeof e!="function"}function uY(e){return aY(e,dY)}function dY(e){return e instanceof Q?{value:e.clone(),recurse:!1}:lc(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class l0{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(const t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);const e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}}class tS extends l0{constructor(){super(tS.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){const e=this.capacity*2,t=new Array(e),n=this.length();for(let s=0;s<n;s++)t[s]=this.get(this.wrap(this.begin+s));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}}tS.INITIAL_CAPACITY=32;function h0(e){return new mY(e)}function ete(e){let t=e;return nu(()=>({value:t++,done:!1}))}function nu(e){return new fY(e)}function u0(e,t){return new p0(e,t)}function tte(e,t,n){return u0(nu(e).take(t),n)}function pY(e,t=Qr.FAIL){return new TY(e,t)}class gn{async toArray(){const e=[];let t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){const e=this.prefetch(100),t=[];let n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new IY(this,e)}filter(e){return new LY(this,e)}map(e){return new SY(this,e)}mapAsync(e){return new d0(this,e)}serialMapAsync(e){return new d0(this,e).serial()}flatmap(e){return new xY(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new wY(this,e,t)}columnMajorBatch(e,t=!0,n=a0){const s=this.rowMajorBatch(e,t);return s.map(i=>cY(i,n))}concatenate(e,t){return new p0(h0([this,e]),t)}take(e){return e<0||e==null?this:new bY(this,e)}skip(e){return e<0||e==null?this:new yY(this,e)}prefetch(e){return new m0(this,e)}shuffle(e,t){return new AY(this,e,t)}serial(){return new gY(this)}}class mY extends gn{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};const e=this.items[this.trav];return this.trav++,{value:uY(e),done:!1}}}class fY extends gn{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}}class gY extends gn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class yY extends gn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){const e=await this.upstream.next();if(e.done)return e;qe(e.value)}return this.upstream.next()}}class bY extends gn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class wY extends gn{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){const e=[];for(;e.length<this.batchSize;){const t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class LY extends gn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){const e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;qe(e.value)}}}class SY extends gn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=Zi(e.value),n=this.transform(e.value),s=Zi(n);for(const i of t)Nd(i,s)||i.dispose();return{value:n,done:!1}}}class IY extends gn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}}class d0 extends gn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=Zi(e.value),n=await this.transform(e.value),s=Zi(n);for(const i of t)Nd(i,s)||i.dispose();return{value:n,done:!1}}}class nS extends gn{constructor(){super();this.outputQueue=new tS,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class xY extends nS{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=Zi(e.value),n=this.transform(e.value),s=Zi(n);this.outputQueue.pushAll(n);for(const i of t)Nd(i,s)||i.dispose();return!0}}class p0 extends gn{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){const e="TODO: fill in upstream of chained summaries";return`${e} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){const n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}var Qr;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Qr||(Qr={}));class TY extends gn{constructor(e,t=Qr.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){const e="TODO: fill in upstream of zip summaries";return`{${e}} -> Zip`}async nextState(e){await e;let t=0,n=0;function s(o){if(o instanceof gn){const a=o.next();return{value:a.then(c=>(t++,c.done&&n++,c.value)),recurse:!1}}else return{value:null,recurse:!0}}const i=await c0(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Qr.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Qr.SHORTEST:return{value:null,done:!0};case Qr.LONGEST:default:}return this.count++,{value:i,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class m0 extends gn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new l0(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){const e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}}class AY extends m0{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=qa(n||qn().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){const e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}}class hc{constructor(){this.size=null}batch(e,t=!0){const n=this;k(e>0,()=>`batchSize needs to be positive, but it is
+      ${e}`);let s;return this.size===Infinity||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),hs(async()=>(await n.iterator()).columnMajorBatch(e,t,CY),s)}concatenate(e){const t=this;let n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,hs(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){const t=this;let n;return this.size===Infinity?n=Infinity:n=null,hs(async()=>(await t.iterator()).filter(s=>ee(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return hs(async()=>(await t.iterator()).map(n=>ee(()=>e(n))),this.size)}mapAsync(e){const t=this;return hs(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const t=this;return hs(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){const t=this;let n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,hs(async()=>{const s=nu(async()=>({value:await t.iterator(),done:!1}));return u0(s.take(e))},n)}skip(e){const t=this;let n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,hs(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const s=this,i=qa(t||qn().toString());return hs(async()=>{let o=i.int32();return n&&(o+=i.int32()),(await s.iterator()).shuffle(e,o.toString())},this.size)}take(e){const t=this;let n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,hs(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}}hc.MAX_BUFFER_SIZE=1e4;function hs(e,t=null){return new class extends hc{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function vY(e){return hs(async()=>h0(e),e.length)}function NY(e){if(!lc(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(const n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return hs(async()=>{const n=await c0(e,s=>{if(s instanceof hc)return{value:s.iterator(),recurse:!1};if(lc(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return pY(n,Qr.SHORTEST)},t)}function CY(e){if(e===null)return null;const t=e[0];if(lY(t)){const n=RY(e);return{value:n,recurse:!1}}return{value:null,recurse:!0}}function RY(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Q?as(e):en(e)}class f0 extends hc{constructor(e){super();this.input=e}async iterator(){const e=await this.input.iterator(),t=e.decodeUTF8(),n=t.split(`
+`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s));return n}}const hm='"',su=Symbol("out"),g0=Symbol("field"),um=Symbol("quote"),sS=Symbol("quoteafterquote"),y0=Symbol("quoteinquote");class b0 extends hc{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new f0(e),t||(t={}),this.hasHeader=!(t.hasHeader===!1),this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){const e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&k(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);const t=this.fullColumnNames.reduce((s,i)=>(s[i]=s[i]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(k(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs)for(const s of Object.keys(this.columnConfigs)){const i=this.fullColumnNames.indexOf(s);if(i===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){const e=await this.base.iterator(),t=await e.next();if(t.done)throw new Error("No data was found for CSV parsing.");const n=t.value,s=this.parseRow(n,!1);return s}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){const t=this.parseRow(e),n={},s={};for(let i=0;i<this.fullColumnNames.length;i++){const o=this.fullColumnNames[i],a=this.columnConfigs?this.columnConfigs[o]:null;if(this.configuredColumnsOnly&&!a)continue;{const c=t[i];let u=null;if(c==="")if(a&&a.default!==void 0)u=a.default;else{if(a&&(a.required||a.isLabel))throw new Error(`Required column ${o} is empty in this line: ${e}`);u=void 0}else{const p=Number(c);if(isNaN(p))a&&a.dtype==="bool"?u=this.getBoolean(c):u=c;else if(!a||!a.dtype)u=p;else switch(a.dtype){case"float32":u=p;break;case"int32":u=Math.floor(p);break;case"bool":u=this.getBoolean(c);break;default:u=p}}a&&a.isLabel?s[o]=u:n[o]=u}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){const n=[];let s=0;const i=e.length;let o=su;for(let a=0;a<i;a++)switch(o){case su:switch(e.charAt(a)){case hm:s=a+1,o=um;break;case this.delimiter:if(s=a+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),o=su;break;default:o=g0,s=a;break}break;case g0:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a)),o=su,s=a+1;break;default:}break;case um:switch(e.charAt(a)){case hm:o=sS;break;default:}break;case sS:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a-1)),o=su,s=a+1;break;case hm:o=um;break;default:o=y0;break}break;case y0:switch(e.charAt(a)){case hm:o=um;break;default:}break;default:}if(o===sS?n.push(e.substring(s,i-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}}class w0 extends gn{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;const t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=!(e.includeSpectrogram===!1),this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(C().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");const t=new w0(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");const e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);const t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize);return}async next(){if(this.isClosed)return{value:null,done:!0};let e,t;const n=await this.getAudioData();if(this.includeSpectrogram){const s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){const s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){const e=[],t=[];let n=0;return new Promise(s=>{const i=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(i),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){const t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,i)=>n.set(s,i*t)),n}getTensorFromAudioDataArray(e,t){const n=new Float32Array(we(t));return n.set(e,n.length-e.length),en(n,t)}}class L0 extends gn{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=rs([0],"int32"),this.webcamConfig.centerCrop){const n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,i=(1-n)/2,o=(1-s)/2,a=i+n,c=s+o;this.cropBox=Gr([o,i,c,a],[1,4])}else this.cropBox=Gr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(C().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}const n=new L0(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=BT(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return ee(()=>{const t=e.toFloat().expandDims(0);let n;n=Vr.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");const s=n.shape;return n.reshape(s.slice(1))})}async capture(){return(await this.next()).value}stop(){const e=this.stream.getTracks();e.forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class S0{}class I0 extends gn{split(e){return new OY(this,e)}}class OY extends I0{constructor(e,t){super();this.upstream=e,this.impl=new EY(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class EY extends nS{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){const e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);const t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(const n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}}class DY extends gn{decodeUTF8(){return new kY(this)}}class kY extends I0{constructor(e){super();this.upstream=e,this.impl=new FY(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class FY extends nS{constructor(e){super();if(this.upstream=e,C().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{const{StringDecoder:t}=require("string_decoder");this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){const e=await this.upstream.next();let t;if(e.done)return!1;t=e.value;let n;return C().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}}class x0 extends DY{constructor(e,t={}){super();this.file=e,this.options=t,k(e instanceof Uint8Array||(C().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};const e=new Promise((t,n)=>{const s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{const i=new FileReader;i.onload=a=>{let c=i.result;if(c instanceof ArrayBuffer&&(c=new Uint8Array(c)),!(c instanceof Uint8Array))return n(new 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 ============================
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Bn=Ht*F-$e,mi=ue*ye+he*un+pe*Ht,Rs=c*(y-1-ds)+u*(b-1-Bn)+p*me;for(let ps=0;ps<v;++ps){const Hs=o[mi+le*ps],mu=a[Rs+ps];bt+=Hs*mu}}}const Jt=q*ye+J*Ie+oe*Oe+ce*me;i[Jt]=bt}}return s.toTensor()}conv3dDerInput(e,t,n){const s=Qe(n.inShape,"float32"),i=s.values,[o,a,c,u]=s.strides,p=this.readSync(e.dataId),[m,y,b,w]=e.strides,I=this.readSync(t.dataId),[T,v,N,E]=t.strides,{batchSize:D,filterDepth:F,filterHeight:_,filterWidth:B,inChannels:U,inDepth:Y,inHeight:q,inWidth:J,outChannels:oe,outDepth:ce,outHeight:ue,outWidth:he,strideDepth:pe,strideHeight:le,strideWidth:ye}=n,me=F-1-n.padInfo.front,Ie=_-1-n.padInfo.top,Se=B-1-n.padInfo.left;for(let Ee=0;Ee<D;++Ee)for(let We=0;We<U;++We)for(let Oe=0;Oe<Y;++Oe){const $e=Oe-me,He=Math.max(0,Math.ceil($e/pe)),tt=Math.min(ce,(F+$e)/pe);for(let bt=0;bt<q;++bt){const Jt=bt-Ie,un=Math.max(0,Math.ceil(Jt/le)),ds=Math.min(ue,(_+Jt)/le);for(let Ht=0;Ht<J;++Ht){const Bn=Ht-Se,mi=Math.max(0,Math.ceil(Bn/ye)),Rs=Math.min(he,(B+Bn)/ye);let ps=0;for(let Hs=He;Hs<tt;++Hs){const mu=Hs*pe-$e;for(let Sc=un;Sc<ds;++Sc){const Ic=Sc*le-Jt;for(let xc=mi;xc<Rs;++xc){const vS=xc*ye-Bn,NS=m*Ee+y*Hs+b*Sc+w*xc,pX=T*(F-1-mu)+v*(_-1-Ic)+N*(B-1-vS)+E*We;for(let vm=0;vm<oe;++vm){const mX=p[NS+vm],fX=I[pX+vm];ps+=mX*fX}}}}i[o*Ee+a*Oe+c*bt+u*Ht+We]=ps}}}return s.toTensor()}conv2dDerFilter(e,t,n){Te([e,t],"conv2dDerFilter");const s=n.strideHeight,i=n.strideWidth,o=n.filterHeight,a=n.filterWidth,c=n.dataFormat==="channelsLast",u=Qe(n.filterShape,"float32"),p=n.padInfo.left,m=n.padInfo.top,y=this.bufferSync(e),b=this.bufferSync(t);for(let w=0;w<o;++w){const I=Math.max(0,Math.ceil((m-w)/s)),T=Math.min(n.outHeight,(n.inHeight+m-w)/s);for(let v=0;v<a;++v){const N=Math.max(0,Math.ceil((p-v)/i)),E=Math.min(n.outWidth,(n.inWidth+p-v)/i);for(let D=0;D<n.inChannels;++D)for(let F=0;F<n.outChannels;++F){let _=0;for(let B=0;B<n.batchSize;++B)for(let U=I;U<T;++U){const Y=w+U*s-m;for(let q=N;q<E;++q){const J=v+q*i-p;c?_+=y.get(B,Y,J,D)*b.get(B,U,q,F):_+=y.get(B,D,Y,J)*b.get(B,F,U,q)}}u.set(_,w,v,D,F)}}}return u.toTensor()}conv3dDerFilter(e,t,n){const s=n.strideDepth,i=n.strideHeight,o=n.strideWidth,a=n.filterDepth,c=n.filterHeight,u=n.filterWidth,p=Qe(n.filterShape,"float32"),m=p.values,[y,b,w,I]=p.strides,T=this.readSync(t.dataId),[v,N,E,D]=t.strides,F=this.readSync(e.dataId),[_,B,U,Y]=e.strides,q=n.padInfo.front,J=n.padInfo.left,oe=n.padInfo.top;for(let ce=0;ce<a;++ce){const ue=Math.max(0,Math.ceil((q-ce)/s)),he=Math.min(n.outDepth,(n.inDepth+q-ce)/s),pe=ce*y;for(let le=0;le<c;++le){const ye=Math.max(0,Math.ceil((oe-le)/i)),me=Math.min(n.outHeight,(n.inHeight+oe-le)/i),Ie=le*b+pe;for(let Se=0;Se<u;++Se){const Ee=Math.max(0,Math.ceil((J-Se)/o)),We=Math.min(n.outWidth,(n.inWidth+J-Se)/o),Oe=Se*w+Ie;for(let $e=0;$e<n.inChannels;++$e){const He=$e*I+Oe;for(let tt=0;tt<n.outChannels;++tt){let bt=0;for(let Jt=0;Jt<n.batchSize;++Jt){const un=Jt*_,ds=Jt*v;for(let Ht=ue;Ht<he;++Ht){const 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q=E+Y*m.strides[2],J=Y*n.strideWidth-u;for(let oe=0;oe<i;++oe){const ce=J+oe*a;if(ce<0||ce>=n.inWidth)continue;const ue=B+oe*t.strides[1],he=U+ce*n.inChannels;let pe=q,le=ue;for(let ye=0;ye<n.inChannels;++ye){const me=y[he+ye];for(let Ie=0;Ie<p;++Ie)w[pe+Ie]+=me*b[le+Ie];pe+=p,le+=p}}}}}}return m.toTensor()}depthwiseConv2DDerInput(e,t,n){Te([e,t],"depthwiseConv2DDerInput");const s=Qe(n.inShape,"float32"),i=s.values,[o,a,c]=s.strides,u=this.readSync(e.dataId),[p,m,y]=e.strides,b=this.readSync(t.dataId),[w,I,T]=t.strides,{batchSize:v,filterHeight:N,filterWidth:E,inChannels:D,inHeight:F,inWidth:_,outChannels:B,outHeight:U,outWidth:Y,strideHeight:q,strideWidth:J}=n,oe=N-1-n.padInfo.top,ce=E-1-n.padInfo.left,ue=B/D;for(let he=0;he<v;++he)for(let pe=0;pe<D;++pe)for(let le=0;le<F;++le){const ye=le-oe,me=Math.max(0,Math.ceil(ye/q)),Ie=Math.min(U,(N+ye)/q);for(let Se=0;Se<_;++Se){const Ee=Se-ce,We=Math.max(0,Math.ceil(Ee/J)),Oe=Math.min(Y,(E+Ee)/J);let $e=0;for(let He=me;He<Ie;++He){const tt=He*q-ye;for(let bt=We;bt<Oe;++bt){const Jt=bt*J-Ee,un=p*he+m*He+y*bt,ds=w*(N-1-tt)+I*(E-1-Jt)+T*pe;for(let Ht=0;Ht<ue;++Ht){const Bn=pe*ue+Ht,mi=u[un+Bn],Rs=b[ds+Ht];$e+=mi*Rs}}}i[o*he+a*le+c*Se+pe]=$e}}return s.toTensor()}depthwiseConv2DDerFilter(e,t,n){Te([e,t],"depthwiseConv2DDerFilter");const s=n.strideHeight,i=n.strideWidth,o=n.filterHeight,a=n.filterWidth,c=Qe(n.filterShape,"float32"),u=n.padInfo.left,p=n.padInfo.top,m=n.outChannels/n.inChannels,y=this.bufferSync(e),b=this.bufferSync(t);for(let w=0;w<o;++w){const I=Math.max(0,Math.ceil((p-w)/s)),T=Math.min(n.outHeight,(n.inHeight+p-w)/s);for(let v=0;v<a;++v){const N=Math.max(0,Math.ceil((u-v)/i)),E=Math.min(n.outWidth,(n.inWidth+u-v)/i);for(let D=0;D<n.outChannels;++D){const F=Math.trunc(D/m),_=D%m;let B=0;for(let U=0;U<n.batchSize;++U)for(let Y=I;Y<T;++Y){const q=w+Y*s-p;for(let J=N;J<E;++J){const oe=v+J*i-u;B+=y.get(U,q,oe,F)*b.get(U,Y,J,D)}}c.set(B,w,v,F,_)}}}return c.toTensor()}tile(e,t){return 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s=t.strideDepth,i=t.strideHeight,o=t.strideWidth,a=t.dilationDepth,c=t.dilationHeight,u=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,y=t.effectiveFilterWidth,b=t.padInfo.front,w=t.padInfo.top,I=t.padInfo.left,T=n==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,v=this.readSync(e.dataId),N=Qe(t.outShape,e.dtype),E=N.values,D=t.outShape[1]*t.outShape[2]*t.outShape[3]*t.outShape[4],F=t.outShape[2]*t.outShape[3]*t.outShape[4],_=t.outShape[3]*t.outShape[4],B=t.outShape[4];for(let U=0;U<t.batchSize;++U){const Y=U*D,q=U*e.strides[0];for(let J=0;J<t.inChannels;++J)for(let oe=0;oe<t.outDepth;++oe){const ce=oe*s-b;let ue=ce;for(;ue<0;)ue+=a;const he=Math.min(t.inDepth,p+ce),pe=Y+oe*F;for(let le=0;le<t.outHeight;++le){const ye=le*i-w;let me=ye;for(;me<0;)me+=c;const Ie=Math.min(t.inHeight,m+ye),Se=pe+le*_;for(let Ee=0;Ee<t.outWidth;++Ee){const We=Ee*o-I;let Oe=We;for(;Oe<0;)Oe+=u;const $e=Math.min(t.inWidth,y+We),He=Se+Ee*B;let tt=T,bt=0,Jt=0;for(let ds=ue;ds<he;ds+=a){const Ht=q+ds*e.strides[1];for(let Bn=me;Bn<Ie;Bn+=c){const mi=Ht+Bn*e.strides[2];for(let Rs=Oe;Rs<$e;Rs+=u){const ps=mi+Rs*e.strides[3],Hs=v[ps+J];if(n==="max"&&Hs>tt?tt=Hs:n==="avg"&&(bt+=Hs,Jt++),isNaN(tt))break}if(isNaN(tt))break}if(isNaN(tt))break}const un=He+J;E[un]=n==="avg"?bt/Jt:tt}}}}return N.toTensor()}avgPool3d(e,t){return Te(e,"avgPool3d"),this.pool3d(e,t,"avg").toFloat()}avgPool3dBackprop(e,t,n){Te([e,t],"avgPool3dBackprop");const s=n.strideDepth,i=n.strideHeight,o=n.strideWidth,a=n.filterDepth,c=n.filterHeight,u=n.filterWidth,p=n.dilationDepth,m=n.dilationHeight,y=n.dilationWidth,b=n.effectiveFilterDepth,w=n.effectiveFilterHeight,I=n.effectiveFilterWidth,T=b-1-n.padInfo.front,v=I-1-n.padInfo.left,N=w-1-n.padInfo.top,E=Qe(t.shape,"float32"),D=1/(a*c*u),F=this.bufferSync(e);for(let _=0;_<n.batchSize;++_)for(let B=0;B<n.inChannels;++B)for(let U=0;U<n.inDepth;++U)for(let Y=0;Y<n.inHeight;++Y)for(let q=0;q<n.inWidth;++q){const J=U-T,oe=Y-N,ce=q-v;let ue=0;for(let he=0;he<b;he+=p){const pe=(J+he)/s;if(pe<0||pe>=n.outDepth||Math.floor(pe)!==pe)continue;for(let le=0;le<w;le+=m){const ye=(oe+le)/i;if(ye<0||ye>=n.outHeight||Math.floor(ye)!==ye)continue;for(let me=0;me<I;me+=y){const Ie=(ce+me)/o;if(Ie<0||Ie>=n.outWidth||Math.floor(Ie)!==Ie)continue;const Se=F.get(_,pe,ye,Ie,B);ue+=Se}}}E.set(ue*D,_,U,Y,q,B)}return E.toTensor()}maxPool3d(e,t){return Te(e,"maxPool3d"),this.pool3d(e,t,"max").toFloat()}maxPool3dPositions(e,t){const n=Qe(t.outShape,"int32"),s=t.strideDepth,i=t.strideHeight,o=t.strideWidth,a=t.dilationDepth,c=t.dilationHeight,u=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,y=t.effectiveFilterWidth,b=t.padInfo.front,w=t.padInfo.top,I=t.padInfo.left,T=this.bufferSync(e);for(let v=0;v<t.batchSize;++v)for(let N=0;N<t.inChannels;++N)for(let E=0;E<t.outDepth;++E){const D=E*s-b;let F=D;for(;F<0;)F+=a;const _=Math.min(t.inDepth,p+D);for(let B=0;B<t.outHeight;++B){const U=B*i-w;let Y=U;for(;Y<0;)Y+=c;const q=Math.min(t.inHeight,m+U);for(let J=0;J<t.outWidth;++J){const oe=J*o-I;let ce=oe;for(;ce<0;)ce+=u;const ue=Math.min(t.inWidth,y+oe);let he=Number.NEGATIVE_INFINITY,pe=-1;for(let le=F;le<_;le+=a){const ye=le-D;for(let me=Y;me<q;me+=c){const Ie=me-U;for(let Se=ce;Se<ue;Se+=u){const Ee=Se-oe,We=T.get(v,le,me,Se,N);We>=he&&(he=We,pe=ye*m*y+Ie*m+Ee)}}}n.set(pe,v,E,B,J,N)}}}return n.toTensor()}maxPool3dBackprop(e,t,n,s){Te([t,n],"maxPool3dBackprop");const i=this.maxPool3dPositions(t,s),o=s.strideDepth,a=s.strideHeight,c=s.strideWidth,u=s.dilationDepth,p=s.dilationHeight,m=s.dilationWidth,y=s.effectiveFilterDepth,b=s.effectiveFilterHeight,w=s.effectiveFilterWidth,I=y-1-s.padInfo.front,T=w-1-s.padInfo.left,v=b-1-s.padInfo.top,N=Qe(t.shape,"float32"),E=this.bufferSync(i),D=this.bufferSync(e);for(let F=0;F<s.batchSize;++F)for(let _=0;_<s.inChannels;++_)for(let B=0;B<s.inDepth;++B)for(let U=0;U<s.inHeight;++U)for(let Y=0;Y<s.inWidth;++Y){const q=B-I,J=U-v,oe=Y-T;let ce=0;for(let ue=0;ue<y;ue+=u){const he=(q+ue)/o;if(he<0||he>=s.outDepth||Math.floor(he)!==he)continue;for(let pe=0;pe<b;pe+=p){const le=(J+pe)/a;if(le<0||le>=s.outHeight||Math.floor(le)!==le)continue;for(let ye=0;ye<w;ye+=m){const me=(oe+ye)/c;if(me<0||me>=s.outWidth||Math.floor(me)!==me)continue;const Ie=y*b*w-1-E.get(F,he,le,me,_),Se=ue*b*w+pe*w+ye,Ee=Ie===Se?1:0;if(Ee===0)continue;const We=D.get(F,he,le,me,_);ce+=We*Ee}}}N.set(ce,F,B,U,Y,_)}return N.toTensor()}resizeBilinear(e,t,n,s){Te(e,"resizeBilinear");const[i,o,a,c]=e.shape,u=this.readSync(e.dataId),p=new Float32Array(we([i,t,n,c])),m=[s&&t>1?o-1:o,s&&n>1?a-1:a],y=[s&&t>1?t-1:t,s&&n>1?n-1:n];let b=0;const w=m[0]/y[0],I=m[1]/y[1];for(let T=0;T<i;T++)for(let v=0;v<t;v++){const N=w*v,E=Math.floor(N),D=N-E,F=Math.min(o-1,Math.ceil(N)),_=T*e.strides[0]+E*e.strides[1],B=T*e.strides[0]+F*e.strides[1];for(let U=0;U<n;U++){const Y=I*U,q=Math.floor(Y),J=Y-q,oe=Math.min(a-1,Math.ceil(Y)),ce=_+q*e.strides[2],ue=B+q*e.strides[2],he=_+oe*e.strides[2],pe=B+oe*e.strides[2];for(let le=0;le<c;le++){const ye=u[ce+le],me=u[ue+le],Ie=u[he+le],Se=u[pe+le],Ee=ye+(Ie-ye)*J,We=me+(Se-me)*J,Oe=Ee+(We-Ee)*D;p[b++]=Oe}}}return en(p,[i,t,n,c])}resizeBilinearBackprop(e,t,n){Te([e,t],"resizeBilinearBackprop");const[s,i,o,a]=t.shape,[,c,u]=e.shape,p=new Float32Array(s*i*o*a),m=[n&&c>1?i-1:i,n&&u>1?o-1:o],y=[n&&c>1?c-1:c,n&&u>1?u-1:u],b=m[0]/y[0],w=m[1]/y[1],I=this.readSync(e.dataId);let T=0;for(let v=0;v<s;v++){const N=v*t.strides[0];for(let E=0;E<c;E++){const D=E*b,F=Math.floor(D),_=Math.min(Math.ceil(D),i-1),B=N+F*t.strides[1],U=N+_*t.strides[1],Y=D-F,q=1-Y;for(let J=0;J<u;J++){const oe=J*w,ce=Math.floor(oe),ue=Math.min(Math.ceil(oe),o-1),he=oe-ce,pe=1-he,le=B+ce*t.strides[2],ye=B+ue*t.strides[2],me=U+ce*t.strides[2],Ie=U+ue*t.strides[2],Se=q*pe,Ee=q*he,We=Y*pe,Oe=Y*he;for(let $e=0;$e<a;$e++){const 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Kq={kernelName:nd,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s,filter:i}=e,{strides:o,pad:a,dilations:c}=n,u=t,p=u.data.get(s.dataId).values,m=s.shape.length,y=u.data.get(i.dataId).values,b=i.shape.length,{batchSize:w,inHeight:I,inWidth:T,inChannels:v,outHeight:N,outWidth:E,padInfo:D,strideHeight:F,strideWidth:_,filterHeight:B,filterWidth:U,dilationHeight:Y,dilationWidth:q,outShape:J}=zd(s.shape,i.shape,o,a,"NHWC",c),oe=we(J),ce=J.length,ue=wo(s.dtype,oe);for(let pe=0;pe<w;++pe)for(let le=0;le<N;++le){const ye=le*F-D.top;for(let me=0;me<E;++me){const Ie=me*_-D.left;for(let Se=0;Se<v;++Se){let Ee=Number.MIN_SAFE_INTEGER;for(let Oe=0;Oe<B;++Oe){const $e=ye+Oe*Y;if($e>=0&&$e<I)for(let He=0;He<U;++He){const tt=Ie+He*q;if(tt>=0&&tt<T){const bt=ti([pe,$e,tt,Se],m,Ot(s.shape)),Jt=ti([Oe,He,Se],b,Ot(i.shape)),un=p[bt]+y[Jt];un>Ee&&(Ee=un)}}}const We=ti([pe,le,me,Se],ce,Ot(J));ue[We]=Ee}}}const 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bt=0;bt<n.outChannels;++bt)T[me+bt]+=tt*I[$e+bt];$e+=n.outChannels}}}}}}}}return b.toTensor()}conv2dDerInput(e,t,n){Te([e,t],"conv2dDerInput");const s=Qe(n.inShape,"float32"),i=s.values,o=this.readSync(e.dataId),a=this.readSync(t.dataId),[c,u,p]=t.strides,{batchSize:m,filterHeight:y,filterWidth:b,inChannels:w,inHeight:I,inWidth:T,outChannels:v,outHeight:N,outWidth:E,strideHeight:D,strideWidth:F,dataFormat:_}=n,B=y-1-n.padInfo.top,U=b-1-n.padInfo.left,Y=_==="channelsLast",q=s.strides[0],J=Y?s.strides[1]:s.strides[2],oe=Y?s.strides[2]:1,ce=Y?1:s.strides[1],ue=e.strides[0],he=Y?e.strides[1]:e.strides[2],pe=Y?e.strides[2]:1,le=Y?1:e.strides[1];for(let ye=0;ye<m;++ye)for(let me=0;me<w;++me)for(let Ie=0;Ie<I;++Ie){const Se=Ie-B,Ee=Math.max(0,Math.ceil(Se/D)),We=Math.min(N,(y+Se)/D);for(let Oe=0;Oe<T;++Oe){const $e=Oe-U,He=Math.max(0,Math.ceil($e/F)),tt=Math.min(E,(b+$e)/F);let bt=0;for(let dn=Ee;dn<We;++dn){const ds=dn*D-Se;for(let Ht=He;Ht<tt;++Ht){const Bn=Ht*F-$e,mi=ue*ye+he*dn+pe*Ht,Rs=c*(y-1-ds)+u*(b-1-Bn)+p*me;for(let ps=0;ps<v;++ps){const Hs=o[mi+le*ps],mu=a[Rs+ps];bt+=Hs*mu}}}const Jt=q*ye+J*Ie+oe*Oe+ce*me;i[Jt]=bt}}return s.toTensor()}conv3dDerInput(e,t,n){const s=Qe(n.inShape,"float32"),i=s.values,[o,a,c,u]=s.strides,p=this.readSync(e.dataId),[m,y,b,w]=e.strides,I=this.readSync(t.dataId),[T,v,N,E]=t.strides,{batchSize:D,filterDepth:F,filterHeight:_,filterWidth:B,inChannels:U,inDepth:Y,inHeight:q,inWidth:J,outChannels:oe,outDepth:ce,outHeight:ue,outWidth:he,strideDepth:pe,strideHeight:le,strideWidth:ye}=n,me=F-1-n.padInfo.front,Ie=_-1-n.padInfo.top,Se=B-1-n.padInfo.left;for(let Ee=0;Ee<D;++Ee)for(let We=0;We<U;++We)for(let Oe=0;Oe<Y;++Oe){const $e=Oe-me,He=Math.max(0,Math.ceil($e/pe)),tt=Math.min(ce,(F+$e)/pe);for(let bt=0;bt<q;++bt){const Jt=bt-Ie,dn=Math.max(0,Math.ceil(Jt/le)),ds=Math.min(ue,(_+Jt)/le);for(let Ht=0;Ht<J;++Ht){const Bn=Ht-Se,mi=Math.max(0,Math.ceil(Bn/ye)),Rs=Math.min(he,(B+Bn)/ye);let ps=0;for(let Hs=He;Hs<tt;++Hs){const mu=Hs*pe-$e;for(let xc=dn;xc<ds;++xc){const Tc=xc*le-Jt;for(let Ac=mi;Ac<Rs;++Ac){const vS=Ac*ye-Bn,NS=m*Ee+y*Hs+b*xc+w*Ac,pX=T*(F-1-mu)+v*(_-1-Tc)+N*(B-1-vS)+E*We;for(let vm=0;vm<oe;++vm){const mX=p[NS+vm],fX=I[pX+vm];ps+=mX*fX}}}}i[o*Ee+a*Oe+c*bt+u*Ht+We]=ps}}}return s.toTensor()}conv2dDerFilter(e,t,n){Te([e,t],"conv2dDerFilter");const s=n.strideHeight,i=n.strideWidth,o=n.filterHeight,a=n.filterWidth,c=n.dataFormat==="channelsLast",u=Qe(n.filterShape,"float32"),p=n.padInfo.left,m=n.padInfo.top,y=this.bufferSync(e),b=this.bufferSync(t);for(let w=0;w<o;++w){const I=Math.max(0,Math.ceil((m-w)/s)),T=Math.min(n.outHeight,(n.inHeight+m-w)/s);for(let v=0;v<a;++v){const N=Math.max(0,Math.ceil((p-v)/i)),E=Math.min(n.outWidth,(n.inWidth+p-v)/i);for(let D=0;D<n.inChannels;++D)for(let F=0;F<n.outChannels;++F){let _=0;for(let B=0;B<n.batchSize;++B)for(let U=I;U<T;++U){const Y=w+U*s-m;for(let q=N;q<E;++q){const J=v+q*i-p;c?_+=y.get(B,Y,J,D)*b.get(B,U,q,F):_+=y.get(B,D,Y,J)*b.get(B,F,U,q)}}u.set(_,w,v,D,F)}}}return u.toTensor()}conv3dDerFilter(e,t,n){const s=n.strideDepth,i=n.strideHeight,o=n.strideWidth,a=n.filterDepth,c=n.filterHeight,u=n.filterWidth,p=Qe(n.filterShape,"float32"),m=p.values,[y,b,w,I]=p.strides,T=this.readSync(t.dataId),[v,N,E,D]=t.strides,F=this.readSync(e.dataId),[_,B,U,Y]=e.strides,q=n.padInfo.front,J=n.padInfo.left,oe=n.padInfo.top;for(let ce=0;ce<a;++ce){const ue=Math.max(0,Math.ceil((q-ce)/s)),he=Math.min(n.outDepth,(n.inDepth+q-ce)/s),pe=ce*y;for(let le=0;le<c;++le){const ye=Math.max(0,Math.ceil((oe-le)/i)),me=Math.min(n.outHeight,(n.inHeight+oe-le)/i),Ie=le*b+pe;for(let Se=0;Se<u;++Se){const Ee=Math.max(0,Math.ceil((J-Se)/o)),We=Math.min(n.outWidth,(n.inWidth+J-Se)/o),Oe=Se*w+Ie;for(let $e=0;$e<n.inChannels;++$e){const He=$e*I+Oe;for(let tt=0;tt<n.outChannels;++tt){let bt=0;for(let Jt=0;Jt<n.batchSize;++Jt){const dn=Jt*_,ds=Jt*v;for(let Ht=ue;Ht<he;++Ht){const Bn=ce+Ht*s-q,mi=Bn*B+dn,Rs=Ht*N+ds;for(let ps=ye;ps<me;++ps){const Hs=le+ps*i-oe,mu=Hs*U+mi,xc=ps*E+Rs;for(let Tc=Ee;Tc<We;++Tc){const Ac=Se+Tc*o-J,vS=Ac*Y+mu,NS=Tc*D+xc;bt+=F[vS+$e]*T[NS+tt]}}}}m[He+tt]=bt}}}}}return p.toTensor()}fusedDepthwiseConv2D({input:e,filter:t,convInfo:n,bias:s,activation:i,preluActivationWeights:o}){let a=this.depthwiseConv2D(e,t,n);return s&&(a=be(a,s)),i&&(a=iS(this,a,i,o)),a}depthwiseConv2D(e,t,n){Te([e,t],"depthwiseConv2D");const s=n.filterHeight,i=n.filterWidth,o=n.dilationHeight,a=n.dilationWidth,c=n.padInfo.left,u=n.padInfo.top,p=n.outChannels/n.inChannels,m=Qe(n.outShape,e.dtype),y=this.readSync(e.dataId),b=this.readSync(t.dataId),w=m.values;for(let I=0;I<n.batchSize;++I){const T=I*e.strides[0],v=I*m.strides[0];for(let N=0;N<n.outHeight;++N){const E=v+N*m.strides[1],D=N*n.strideHeight-c;for(let F=0;F<s;++F){const _=D+F*o;if(_<0||_>=n.inHeight)continue;const B=F*t.strides[0],U=T+_*e.strides[1];for(let Y=0;Y<n.outWidth;++Y){const q=E+Y*m.strides[2],J=Y*n.strideWidth-u;for(let oe=0;oe<i;++oe){const ce=J+oe*a;if(ce<0||ce>=n.inWidth)continue;const ue=B+oe*t.strides[1],he=U+ce*n.inChannels;let pe=q,le=ue;for(let ye=0;ye<n.inChannels;++ye){const me=y[he+ye];for(let Ie=0;Ie<p;++Ie)w[pe+Ie]+=me*b[le+Ie];pe+=p,le+=p}}}}}}return m.toTensor()}depthwiseConv2DDerInput(e,t,n){Te([e,t],"depthwiseConv2DDerInput");const s=Qe(n.inShape,"float32"),i=s.values,[o,a,c]=s.strides,u=this.readSync(e.dataId),[p,m,y]=e.strides,b=this.readSync(t.dataId),[w,I,T]=t.strides,{batchSize:v,filterHeight:N,filterWidth:E,inChannels:D,inHeight:F,inWidth:_,outChannels:B,outHeight:U,outWidth:Y,strideHeight:q,strideWidth:J}=n,oe=N-1-n.padInfo.top,ce=E-1-n.padInfo.left,ue=B/D;for(let he=0;he<v;++he)for(let pe=0;pe<D;++pe)for(let le=0;le<F;++le){const ye=le-oe,me=Math.max(0,Math.ceil(ye/q)),Ie=Math.min(U,(N+ye)/q);for(let Se=0;Se<_;++Se){const Ee=Se-ce,We=Math.max(0,Math.ceil(Ee/J)),Oe=Math.min(Y,(E+Ee)/J);let $e=0;for(let He=me;He<Ie;++He){const tt=He*q-ye;for(let bt=We;bt<Oe;++bt){const Jt=bt*J-Ee,dn=p*he+m*He+y*bt,ds=w*(N-1-tt)+I*(E-1-Jt)+T*pe;for(let Ht=0;Ht<ue;++Ht){const Bn=pe*ue+Ht,mi=u[dn+Bn],Rs=b[ds+Ht];$e+=mi*Rs}}}i[o*he+a*le+c*Se+pe]=$e}}return s.toTensor()}depthwiseConv2DDerFilter(e,t,n){Te([e,t],"depthwiseConv2DDerFilter");const s=n.strideHeight,i=n.strideWidth,o=n.filterHeight,a=n.filterWidth,c=Qe(n.filterShape,"float32"),u=n.padInfo.left,p=n.padInfo.top,m=n.outChannels/n.inChannels,y=this.bufferSync(e),b=this.bufferSync(t);for(let w=0;w<o;++w){const I=Math.max(0,Math.ceil((p-w)/s)),T=Math.min(n.outHeight,(n.inHeight+p-w)/s);for(let v=0;v<a;++v){const N=Math.max(0,Math.ceil((u-v)/i)),E=Math.min(n.outWidth,(n.inWidth+u-v)/i);for(let D=0;D<n.outChannels;++D){const F=Math.trunc(D/m),_=D%m;let B=0;for(let U=0;U<n.batchSize;++U)for(let Y=I;Y<T;++Y){const q=w+Y*s-p;for(let J=N;J<E;++J){const oe=v+J*i-u;B+=y.get(U,q,oe,F)*b.get(U,Y,J,D)}}c.set(B,w,v,F,_)}}}return c.toTensor()}tile(e,t){return Te(e,"tile"),HY(this.bufferSync(e),t)}gather(e,t,n){Te([e,t],"gather");const s=e.shape.slice(),i=this.readSync(t.dataId);s[n]=i.length;const o=Qe(s,e.dtype),a=this.bufferSync(e);for(let c=0;c<o.size;++c){const u=o.indexToLoc(c),p=u.slice();p[n]=i[u[n]];const m=a.locToIndex(p);o.values[c]=a.values[m]}return o.toTensor()}batchToSpaceND(e,t,n){Te([e],"batchToSpaceND");const s=t.reduce((p,m)=>p*m),i=Rh(e.shape,t,s),o=Oh(i.length,t.length),a=Eh(e.shape,t,s),c=jb(n,t.length),u=Kb(a,n,t.length);return Pe(e.reshape(i),o).reshape(a).slice(c,u)}pool3d(e,t,n){Te(e,"pool3d");const s=t.strideDepth,i=t.strideHeight,o=t.strideWidth,a=t.dilationDepth,c=t.dilationHeight,u=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,y=t.effectiveFilterWidth,b=t.padInfo.front,w=t.padInfo.top,I=t.padInfo.left,T=n==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,v=this.readSync(e.dataId),N=Qe(t.outShape,e.dtype),E=N.values,D=t.outShape[1]*t.outShape[2]*t.outShape[3]*t.outShape[4],F=t.outShape[2]*t.outShape[3]*t.outShape[4],_=t.outShape[3]*t.outShape[4],B=t.outShape[4];for(let U=0;U<t.batchSize;++U){const Y=U*D,q=U*e.strides[0];for(let J=0;J<t.inChannels;++J)for(let oe=0;oe<t.outDepth;++oe){const ce=oe*s-b;let ue=ce;for(;ue<0;)ue+=a;const he=Math.min(t.inDepth,p+ce),pe=Y+oe*F;for(let le=0;le<t.outHeight;++le){const ye=le*i-w;let me=ye;for(;me<0;)me+=c;const Ie=Math.min(t.inHeight,m+ye),Se=pe+le*_;for(let Ee=0;Ee<t.outWidth;++Ee){const We=Ee*o-I;let Oe=We;for(;Oe<0;)Oe+=u;const $e=Math.min(t.inWidth,y+We),He=Se+Ee*B;let tt=T,bt=0,Jt=0;for(let ds=ue;ds<he;ds+=a){const Ht=q+ds*e.strides[1];for(let Bn=me;Bn<Ie;Bn+=c){const mi=Ht+Bn*e.strides[2];for(let Rs=Oe;Rs<$e;Rs+=u){const ps=mi+Rs*e.strides[3],Hs=v[ps+J];if(n==="max"&&Hs>tt?tt=Hs:n==="avg"&&(bt+=Hs,Jt++),isNaN(tt))break}if(isNaN(tt))break}if(isNaN(tt))break}const dn=He+J;E[dn]=n==="avg"?bt/Jt:tt}}}}return N.toTensor()}avgPool3d(e,t){return Te(e,"avgPool3d"),this.pool3d(e,t,"avg").toFloat()}avgPool3dBackprop(e,t,n){Te([e,t],"avgPool3dBackprop");const s=n.strideDepth,i=n.strideHeight,o=n.strideWidth,a=n.filterDepth,c=n.filterHeight,u=n.filterWidth,p=n.dilationDepth,m=n.dilationHeight,y=n.dilationWidth,b=n.effectiveFilterDepth,w=n.effectiveFilterHeight,I=n.effectiveFilterWidth,T=b-1-n.padInfo.front,v=I-1-n.padInfo.left,N=w-1-n.padInfo.top,E=Qe(t.shape,"float32"),D=1/(a*c*u),F=this.bufferSync(e);for(let _=0;_<n.batchSize;++_)for(let B=0;B<n.inChannels;++B)for(let U=0;U<n.inDepth;++U)for(let Y=0;Y<n.inHeight;++Y)for(let q=0;q<n.inWidth;++q){const J=U-T,oe=Y-N,ce=q-v;let ue=0;for(let he=0;he<b;he+=p){const pe=(J+he)/s;if(pe<0||pe>=n.outDepth||Math.floor(pe)!==pe)continue;for(let le=0;le<w;le+=m){const ye=(oe+le)/i;if(ye<0||ye>=n.outHeight||Math.floor(ye)!==ye)continue;for(let me=0;me<I;me+=y){const Ie=(ce+me)/o;if(Ie<0||Ie>=n.outWidth||Math.floor(Ie)!==Ie)continue;const Se=F.get(_,pe,ye,Ie,B);ue+=Se}}}E.set(ue*D,_,U,Y,q,B)}return E.toTensor()}maxPool3d(e,t){return Te(e,"maxPool3d"),this.pool3d(e,t,"max").toFloat()}maxPool3dPositions(e,t){const n=Qe(t.outShape,"int32"),s=t.strideDepth,i=t.strideHeight,o=t.strideWidth,a=t.dilationDepth,c=t.dilationHeight,u=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,y=t.effectiveFilterWidth,b=t.padInfo.front,w=t.padInfo.top,I=t.padInfo.left,T=this.bufferSync(e);for(let v=0;v<t.batchSize;++v)for(let N=0;N<t.inChannels;++N)for(let E=0;E<t.outDepth;++E){const D=E*s-b;let F=D;for(;F<0;)F+=a;const _=Math.min(t.inDepth,p+D);for(let B=0;B<t.outHeight;++B){const U=B*i-w;let Y=U;for(;Y<0;)Y+=c;const q=Math.min(t.inHeight,m+U);for(let J=0;J<t.outWidth;++J){const oe=J*o-I;let ce=oe;for(;ce<0;)ce+=u;const ue=Math.min(t.inWidth,y+oe);let he=Number.NEGATIVE_INFINITY,pe=-1;for(let le=F;le<_;le+=a){const ye=le-D;for(let me=Y;me<q;me+=c){const Ie=me-U;for(let Se=ce;Se<ue;Se+=u){const Ee=Se-oe,We=T.get(v,le,me,Se,N);We>=he&&(he=We,pe=ye*m*y+Ie*m+Ee)}}}n.set(pe,v,E,B,J,N)}}}return n.toTensor()}maxPool3dBackprop(e,t,n,s){Te([t,n],"maxPool3dBackprop");const i=this.maxPool3dPositions(t,s),o=s.strideDepth,a=s.strideHeight,c=s.strideWidth,u=s.dilationDepth,p=s.dilationHeight,m=s.dilationWidth,y=s.effectiveFilterDepth,b=s.effectiveFilterHeight,w=s.effectiveFilterWidth,I=y-1-s.padInfo.front,T=w-1-s.padInfo.left,v=b-1-s.padInfo.top,N=Qe(t.shape,"float32"),E=this.bufferSync(i),D=this.bufferSync(e);for(let F=0;F<s.batchSize;++F)for(let _=0;_<s.inChannels;++_)for(let B=0;B<s.inDepth;++B)for(let U=0;U<s.inHeight;++U)for(let Y=0;Y<s.inWidth;++Y){const q=B-I,J=U-v,oe=Y-T;let ce=0;for(let ue=0;ue<y;ue+=u){const he=(q+ue)/o;if(he<0||he>=s.outDepth||Math.floor(he)!==he)continue;for(let pe=0;pe<b;pe+=p){const le=(J+pe)/a;if(le<0||le>=s.outHeight||Math.floor(le)!==le)continue;for(let ye=0;ye<w;ye+=m){const me=(oe+ye)/c;if(me<0||me>=s.outWidth||Math.floor(me)!==me)continue;const Ie=y*b*w-1-E.get(F,he,le,me,_),Se=ue*b*w+pe*w+ye,Ee=Ie===Se?1:0;if(Ee===0)continue;const We=D.get(F,he,le,me,_);ce+=We*Ee}}}N.set(ce,F,B,U,Y,_)}return N.toTensor()}resizeBilinear(e,t,n,s){Te(e,"resizeBilinear");const[i,o,a,c]=e.shape,u=this.readSync(e.dataId),p=new Float32Array(we([i,t,n,c])),m=[s&&t>1?o-1:o,s&&n>1?a-1:a],y=[s&&t>1?t-1:t,s&&n>1?n-1:n];let b=0;const w=m[0]/y[0],I=m[1]/y[1];for(let T=0;T<i;T++)for(let v=0;v<t;v++){const N=w*v,E=Math.floor(N),D=N-E,F=Math.min(o-1,Math.ceil(N)),_=T*e.strides[0]+E*e.strides[1],B=T*e.strides[0]+F*e.strides[1];for(let U=0;U<n;U++){const 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QY={kernelName:ey,backendName:"cpu",kernelFunc:iu};function ru(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{dtype:o}=s;if(o==="complex64"){if(i.dtype==="complex64")return uc({inputs:{x:i},backend:n});const a=ct(i.shape),c=ru({inputs:{x:i},backend:n,attrs:{dtype:"float32"}}),u=pi({inputs:{real:c,imag:a},backend:n});return a.dispose(),n.disposeIntermediateTensorInfo(c),u}if(i.dtype==="complex64"){const a=iu({inputs:{input:i},backend:n}),c=ru({inputs:{x:a},backend:n,attrs:{dtype:o}});return n.disposeIntermediateTensorInfo(a),c}if(!xy(i.dtype,o)){const a=uc({inputs:{x:i},backend:n});return{dataId:a.dataId,shape:a.shape,dtype:o}}if(o==="int32"){const a=n.data.get(i.dataId).values,c=Int32Array.from(a);return n.makeTensorInfo(i.shape,"int32",c)}if(o==="bool"){const a=n.data.get(i.dataId).values,c=Dr([0],i.dtype),[u,p]=jo((m,y)=>m!==y?1:0)(i.shape,[],a,c,"bool");return n.makeTensorInfo(p,"bool",u)}throw new Error(`Error in Cast: failed to cast ${i.dtype} to ${o}`)}const eq={kernelName:ul,backendName:"cpu",kernelFunc:ru};function dc(e,t,n,s){return n==null?({inputs:i,backend:o})=>{const{a,b:c}=i,u=o;Te([a,c],e);const p=u.data.get(a.dataId).values,m=u.data.get(c.dataId).values,y=s||a.dtype,[b,w]=t(a.shape,c.shape,p,m,y);return u.makeTensorInfo(w,y,b)}:({inputs:i,backend:o})=>{const{a,b:c}=i,u=o;if(a.dtype==="complex64"||c.dtype==="complex64"){const p=ru({inputs:{x:a},backend:u,attrs:{dtype:"complex64"}}),m=u.data.get(p.dataId),y=m.complexTensorInfos.real,b=m.complexTensorInfos.imag,w=u.data.get(y.dataId).values,I=u.data.get(b.dataId).values,T=ru({inputs:{x:c},backend:u,attrs:{dtype:"complex64"}}),v=u.data.get(T.dataId),N=v.complexTensorInfos.real,E=v.complexTensorInfos.imag,D=u.data.get(N.dataId).values,F=u.data.get(E.dataId).values,[_,B,U]=n(a.shape,c.shape,w,I,D,F),Y=u.makeTensorInfo(U,"float32",_),q=u.makeTensorInfo(U,"float32",B),J=pi({inputs:{real:Y,imag:q},backend:u});return 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Kq={kernelName:nd,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s,filter:i}=e,{strides:o,pad:a,dilations:c}=n,u=t,p=u.data.get(s.dataId).values,m=s.shape.length,y=u.data.get(i.dataId).values,b=i.shape.length,{batchSize:w,inHeight:I,inWidth:T,inChannels:v,outHeight:N,outWidth:E,padInfo:D,strideHeight:F,strideWidth:_,filterHeight:B,filterWidth:U,dilationHeight:Y,dilationWidth:q,outShape:J}=zd(s.shape,i.shape,o,a,"NHWC",c),oe=we(J),ce=J.length,ue=So(s.dtype,oe);for(let pe=0;pe<w;++pe)for(let le=0;le<N;++le){const ye=le*F-D.top;for(let me=0;me<E;++me){const Ie=me*_-D.left;for(let Se=0;Se<v;++Se){let Ee=Number.MIN_SAFE_INTEGER;for(let Oe=0;Oe<B;++Oe){const $e=ye+Oe*Y;if($e>=0&&$e<I)for(let He=0;He<U;++He){const tt=Ie+He*q;if(tt>=0&&tt<T){const bt=ti([pe,$e,tt,Se],m,Ot(s.shape)),Jt=ti([Oe,He,Se],b,Ot(i.shape)),dn=p[bt]+y[Jt];dn>Ee&&(Ee=dn)}}}const We=ti([pe,le,me,Se],ce,Ot(J));ue[We]=Ee}}}const he=u.write(Dr(ue,s.dtype),J,s.dtype);return{dataId:he,shape:J,dtype:s.dtype}}};const Xq={kernelName:id,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s,filter:i,dy:o}=e,{strides:a,pad:c,dilations:u}=n,p=t,m=Ls(s.shape,p.data.get(s.dataId).values),y=Ls(i.shape,p.data.get(i.dataId).values),{batchSize:b,inHeight:w,inWidth:I,inChannels:T,outHeight:v,outWidth:N,padInfo:E,strideHeight:D,strideWidth:F,filterHeight:_,filterWidth:B,dilationHeight:U,dilationWidth:Y,outShape:q}=zd(s.shape,i.shape,a,c,"NHWC",u);k(o.rank===q.length,()=>`Error in ${id}, dy must have the same rank as output ${q.length}, but got ${o.rank}`);const J=Ls(q,p.data.get(o.dataId).values),oe=vy(i.shape,i.dtype);for(let ue=0;ue<b;++ue)for(let he=0;he<v;++he){const pe=he*D-E.top;for(let le=0;le<N;++le){const ye=le*F-E.left;for(let me=0;me<T;++me){let Ie=Number.MIN_SAFE_INTEGER,Se=0,Ee=0;for(let We=0;We<_;++We){const Oe=pe+We*U;if(Oe>=0&&Oe<w)for(let $e=0;$e<B;++$e){const He=ye+$e*Y;if(He>=0&&He<I){const tt=m[ue][Oe][He][me]+y[We][$e][me];tt>Ie&&(Ie=tt,Se=We,Ee=$e)}}}oe[Se][Ee][me]+=J[ue][he][le][me]}}}const ce=p.write(Dr(oe,s.dtype),i.shape,i.dtype);return{dataId:ce,shape:i.shape,dtype:i.dtype}}};const Jq={kernelName:sd,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s,filter:i,dy:o}=e,{strides:a,pad:c,dilations:u}=n,p=t,m=Ls(s.shape,p.data.get(s.dataId).values),y=Ls(i.shape,p.data.get(i.dataId).values),{batchSize:b,inHeight:w,inWidth:I,inChannels:T,outHeight:v,outWidth:N,padInfo:E,strideHeight:D,strideWidth:F,filterHeight:_,filterWidth:B,dilationHeight:U,dilationWidth:Y,outShape:q}=zd(s.shape,i.shape,a,c,"NHWC",u);k(o.rank===q.length,()=>`Error in ${sd}, dy must have the same rank as output ${q.length}, but got ${o.rank}`);const J=Ls(q,p.data.get(o.dataId).values),oe=vy(s.shape,s.dtype);for(let ue=0;ue<b;++ue)for(let he=0;he<v;++he){const pe=he*D-E.top;for(let le=0;le<N;++le){const ye=le*F-E.left;for(let me=0;me<T;++me){let Ie=Number.MIN_SAFE_INTEGER,Se=pe<0?0:pe,Ee=ye<0?0:ye;for(let We=0;We<_;++We){const Oe=pe+We*U;if(Oe>=0&&Oe<w)for(let $e=0;$e<B;++$e){const He=ye+$e*Y;if(He>=0&&He<I){const tt=m[ue][Oe][He][me]+y[We][$e][me];tt>Ie&&(Ie=tt,Se=Oe,Ee=He)}}}oe[ue][Se][Ee][me]+=J[ue][he][le][me]}}}const ce=p.write(Dr(oe,s.dtype),s.shape,s.dtype);return{dataId:ce,shape:s.shape,dtype:s.dtype}}};const Zq=jo((e,t)=>e/t),Qq=dc(Aa,Zq),lS={kernelName:Aa,backendName:"cpu",kernelFunc:Qq};const e4=Tt(fl,e=>e>=0?e:Math.exp(e)-1),t4={kernelName:fl,backendName:"cpu",kernelFunc:e4};const n4=Xb,s4=Jb,i4=Zb,r4=Qb,o4=ew,a4=tw,c4=Tt(gl,e=>{const t=Math.sign(e),n=Math.abs(e),s=1/(1+n4*n);return t*(1-((((a4*s+o4)*s+r4)*s+i4)*s+s4)*s*Math.exp(-n*n))}),l4={kernelName:gl,backendName:"cpu",kernelFunc:c4};function H0(e,t,n){const s=e.shape,i=s[0],o=s[1],a=n.data.get(e.dataId),c=a.complexTensorInfos.real,u=a.complexTensorInfos.imag,p=[i,o],m=we(p),y=wn("float32",m),b=wn("float32",m);for(let v=0;v<i;v++){const N=oS({inputs:{x:c},backend:n,attrs:{begin:[v,0],size:[1,o]}}),E=oS({inputs:{x:u},backend:n,attrs:{begin:[v,0],size:[1,o]}}),D=pi({inputs:{real:N,imag:E},backend:n}),{real:F,imag:_}=h4(D,t,n),B=ir(F,_);for(let U=0;U<o;U++){const Y=nw(B,U);y[v*o+U]=Y.real,b[v*o+U]=Y.imag}n.disposeIntermediateTensorInfo(N),n.disposeIntermediateTensorInfo(E),n.disposeIntermediateTensorInfo(D)}const w=n.makeTensorInfo(p,"float32",y),I=n.makeTensorInfo(p,"float32",b),T=pi({inputs:{real:w,imag:I},backend:n});return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(I),T}function h4(e,t,n){const s=we(e.shape),i=n.data.get(e.dataId),o=n.data.get(i.complexTensorInfos.real.dataId).values,a=n.data.get(i.complexTensorInfos.imag.dataId).values;if(u4(s)){const c=hS(o,a,s,t,n),u=[e.shape[0],e.shape[1]];if(t){const p=n.makeTensorInfo(u,"float32",c.real),m=n.makeTensorInfo(u,"float32",c.imag),y=n.makeTensorInfo([],"float32",lT(s,"float32")),b=uc({inputs:{x:y},backend:n}),w=lS.kernelFunc({inputs:{a:p,b:y},backend:n}),I=lS.kernelFunc({inputs:{a:m,b},backend:n}),T=n.data.get(w.dataId).values,v=n.data.get(I.dataId).values;return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(I),{real:T,imag:v}}return c}else{const c=ir(o,a),u=d4(c,s,t);return ev(u)}}function u4(e){return(e&e-1)===0}function hS(e,t,n,s,i){if(n===1)return{real:e,imag:t};const o=ir(e,t),a=n/2,c=tv(o),u=c.real,p=c.imag,m=[u.length],y=i.makeTensorInfo(m,"float32",u),b=i.makeTensorInfo(m,"float32",p),w=pi({inputs:{real:y,imag:b},backend:i}),I=nv(o),T=I.real,v=I.imag,N=[T.length],E=i.makeTensorInfo(N,"float32",T),D=i.makeTensorInfo(N,"float32",v),F=pi({inputs:{real:E,imag:D},backend:i}),_=hS(u,p,a,s,i),B=_.real,U=_.imag,Y=[B.length],q=i.makeTensorInfo(Y,"float32",B),J=i.makeTensorInfo(Y,"float32",U),oe=pi({inputs:{real:q,imag:J},backend:i}),ce=hS(T,v,a,s,i),ue=ce.real,he=ce.imag,pe=[ue.length],le=i.makeTensorInfo(pe,"float32",ue),ye=i.makeTensorInfo(pe,"float32",he),me=pi({inputs:{real:le,imag:ye},backend:i}),Ie=iv(n,s),Se=[Ie.real.length],Ee=i.makeTensorInfo(Se,"float32",Ie.real),We=i.makeTensorInfo(Se,"float32",Ie.imag),Oe=pi({inputs:{real:Ee,imag:We},backend:i}),$e=U0({inputs:{a:Oe,b:me},backend:i}),He=O0({inputs:{a:oe,b:$e},backend:i}),tt=z0({inputs:{a:oe,b:$e},backend:i}),bt=iu({inputs:{input:He},backend:i}),Jt=iu({inputs:{input:tt},backend:i}),dn=dm({inputs:{input:He},backend:i}),ds=dm({inputs:{input:tt},backend:i}),Ht=ou({inputs:[bt,Jt],backend:i,attrs:{axis:0}}),Bn=ou({inputs:[dn,ds],backend:i,attrs:{axis:0}}),mi=i.data.get(Ht.dataId).values,Rs=i.data.get(Bn.dataId).values;return i.disposeIntermediateTensorInfo(y),i.disposeIntermediateTensorInfo(b),i.disposeIntermediateTensorInfo(w),i.disposeIntermediateTensorInfo(E),i.disposeIntermediateTensorInfo(D),i.disposeIntermediateTensorInfo(F),i.disposeIntermediateTensorInfo(q),i.disposeIntermediateTensorInfo(J),i.disposeIntermediateTensorInfo(oe),i.disposeIntermediateTensorInfo(le),i.disposeIntermediateTensorInfo(ye),i.disposeIntermediateTensorInfo(me),i.disposeIntermediateTensorInfo(Ee),i.disposeIntermediateTensorInfo(We),i.disposeIntermediateTensorInfo(Oe),i.disposeIntermediateTensorInfo($e),i.disposeIntermediateTensorInfo(He),i.disposeIntermediateTensorInfo(tt),i.disposeIntermediateTensorInfo(bt),i.disposeIntermediateTensorInfo(dn),i.disposeIntermediateTensorInfo(Jt),i.disposeIntermediateTensorInfo(ds),i.disposeIntermediateTensorInfo(Ht),i.disposeIntermediateTensorInfo(Bn),{real:mi,imag:Rs}}function d4(e,t,n){const s=new Float32Array(t*2);for(let i=0;i<t;i++){let o=0,a=0;for(let c=0;c<t;c++){const 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e.getUniformLocation(t,n)}function Jj(e,t,n,s){Re(e,()=>jj(e,t,s)),Re(e,()=>e.uniform1i(n,s))}function ate(e){Re(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Re(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),Re(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function mS(e,t,n){Re(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),Re(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function X0(e,t){Re(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),Re(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function mm(e){const t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+Zj(e,t))}function Zj(e,t){switch(t){case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case e.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${t}`}}function ur(e,t,n){const s=Re(e,()=>t());if(s==null)throw new Error(n);return s}function J0(e,t){const n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,s=t+e.TEXTURE0;if(s<e.TEXTURE0||s>n){const i=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${i}.`)}}function gc(e,t=2){return we(e.slice(0,e.length-t))}function yc(e){if(e.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[e.length>1?e[e.length-2]:1,e[e.length-1]]}function fS(e){let t=[1,1,1];const n=e.length===0||e.length===1&&e[0]===1;return n||(t=[gc(e),...yc(e)]),t}function Qj(e,t=!1){let n=C().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(t&&(n=n*2,e=e.map((i,o)=>o>=e.length-2?Sy(e[o]):e[o]),e.length===1&&(e=[2,e[0]])),e.length!==2){const i=Rr(e);e=i.newShape}let s=we(e);if(e.length<=1&&s<=n)return[1,s];if(e.length===2&&e[0]<=n&&e[1]<=n)return e;if(e.length===3&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){const i=gc(e);let o=2,a=2;return e.length&&([o,a]=yc(e)),s=i*(o/2)*(a/2),Sd(s).map(c=>c*2)}return Sd(s)}function fm(e){return e%2===0}function gm(e,t){if(e=e.slice(-2),t=t.slice(-2),ot(e,t))return!0;if(!e.length||!t.length)return!0;if(e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){const n=e.slice(-1)[0],s=t.slice(-1)[0];if(n===s)return!0;if(fm(n)&&fm(s)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&fm(e[0])&&fm(t[0])}let ym,bm;function eK(e){if(ym==null){const t=Mi(e);ym=t.getParameter(t.MAX_TEXTURE_SIZE)}return ym}function cte(){ym=null}function lte(){bm=null}function tK(e){if(bm==null){const t=Mi(e);bm=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,bm)}function nK(e){if(e===0)return 0;let t;const n=Mi(e);return Vs(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:Vs(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function Vs(e,t){const n=e.getExtension(t);return n!=null}function Z0(e){try{const t=Mi(e);if(t!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function sK(e){if(e===0)return!1;const t=Mi(e);if(e===1){if(!Vs(t,"OES_texture_float"))return!1}else if(!Vs(t,"EXT_color_buffer_float"))return!1;const n=gS(t);return n}function iK(e){if(e===0)return!1;const t=Mi(e);if(e===1){if(!Vs(t,"OES_texture_float"))return!1;if(!Vs(t,"WEBGL_color_buffer_float"))return!1}else{if(Vs(t,"EXT_color_buffer_float"))return gS(t);const s="EXT_color_buffer_half_float";if(Vs(t,s)){const i=t.getExtension(s);return rK(t,i)}return!1}const n=gS(t);return n}function gS(e){const t=dS(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);const s=1,i=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,s,i,0,t.textureFormatFloat,t.textureTypeFloat,null);const o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);const a=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(o),a}function rK(e,t){const n=dS(e,t),s=e.createTexture();e.bindTexture(e.TEXTURE_2D,s);const i=1,o=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,i,o,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);const a=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,a),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);const c=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(a),c}function oK(e){if(e!==2)return!1;const t=Mi(e),n=t.fenceSync!=null;return n}function hu(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}const Ve=C();Ve.registerFlag("HAS_WEBGL",()=>Ve.getNumber("WEBGL_VERSION")>0),Ve.registerFlag("WEBGL_VERSION",()=>Z0(2)?2:Z0(1)?1:0),Ve.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1),Ve.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ve.get("WEBGL_VERSION")===2),Ve.registerFlag("WEBGL_CPU_FORWARD",()=>!0),Ve.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1),Ve.registerFlag("WEBGL_PACK",()=>Ve.getBool("HAS_WEBGL")),Ve.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ve.getBool("WEBGL_PACK")),Ve.registerFlag("WEBGL_PACK_CLIP",()=>Ve.getBool("WEBGL_PACK")),Ve.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1),Ve.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ve.getBool("WEBGL_PACK")),Ve.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ve.getBool("WEBGL_PACK")),Ve.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ve.getBool("WEBGL_PACK")),Ve.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ve.getBool("WEBGL_PACK")),Ve.registerFlag("WEBGL_PACK_REDUCE",()=>Ve.getBool("WEBGL_PACK")),Ve.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ve.getBool("WEBGL_PACK")),Ve.registerFlag("WEBGL_CONV_IM2COL",()=>Ve.getBool("WEBGL_PACK")),Ve.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>eK(Ve.getNumber("WEBGL_VERSION"))),Ve.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>tK(Ve.getNumber("WEBGL_VERSION"))),Ve.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{const e=Ve.getNumber("WEBGL_VERSION");return e===0?0:nK(e)}),Ve.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ve.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!wT()),Ve.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>sK(Ve.getNumber("WEBGL_VERSION"))),Ve.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ve.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ve.getBool("WEBGL_RENDER_FLOAT32_CAPABLE")),Ve.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>iK(Ve.getNumber("WEBGL_VERSION"))),Ve.registerFlag("WEBGL_FENCE_API_ENABLED",()=>oK(Ve.getNumber("WEBGL_VERSION"))),Ve.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>{const e=Ve.getBool("WEBGL_RENDER_FLOAT32_ENABLED");return e?4:0}),Ve.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});const{simpleAbsImpl:aK,addImpl:cK,ceilImpl:lK,expImpl:hK,expm1Impl:uK,floorImpl:dK,logImpl:pK,maxImpl:mK,multiplyImpl:fK,rsqrtImpl:gK,sliceImpl:yK,subImpl:bK,transposeImpl:Q0,uniqueImpl:wK}=Lq;class LK{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((i,o)=>`T${o}`);const n=[];this.variableNames.forEach(i=>{n.push(`float v${i} = get${i}AtOutCoords();`)});const s=this.variableNames.map(i=>`v${i}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -114,7 +114,7 @@ Hi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed thi
       ivec4 round(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:i,output:o,defineOutput:a,defineSpecialNaN:c,defineSpecialInf:u,defineRound:p}}function jo(e,t,n="index"){const s=Ot(t);return s.map((i,o)=>{const a=`int ${e[o]} = ${n} / ${i}`,c=o===s.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${i}`:`index -= ${e[o]} * ${i}`;return`${a}; ${c};`}).join("")}function wm(e){return e.length===1?`${e[0]}`:`vec${e.length}(${e.join(",")})`}function hte(e,t){if(e.length!==t.length)throw new Error(`Vectors to be dotted must be of the same length -got ${e.length} and ${t.length}`);const n=[],s=Math.floor(e.length/4),i=e.length%4;for(let o=0;o<s;o++){const a=e.slice(o*4,o*4+4),c=t.slice(o*4,o*4+4);n.push(`${wm(a)}, ${wm(c)}`)}if(i!==0){let o=e.slice(s*4),a=t.slice(s*4);o.length===1&&(o=o.map(c=>`float(${c})`),a=a.map(c=>`float(${c})`)),n.push(`${wm(o)}, ${wm(a)}`)}return n.map((o,a)=>`dot(${o})`).join("+")}function yS(e){const t=Ot(e).map(n=>n.toString());return`
+    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:i,output:o,defineOutput:a,defineSpecialNaN:c,defineSpecialInf:u,defineRound:p}}function Xo(e,t,n="index"){const s=Ot(t);return s.map((i,o)=>{const a=`int ${e[o]} = ${n} / ${i}`,c=o===s.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${i}`:`index -= ${e[o]} * ${i}`;return`${a}; ${c};`}).join("")}function wm(e){return e.length===1?`${e[0]}`:`vec${e.length}(${e.join(",")})`}function hte(e,t){if(e.length!==t.length)throw new Error(`Vectors to be dotted must be of the same length -got ${e.length} and ${t.length}`);const n=[],s=Math.floor(e.length/4),i=e.length%4;for(let o=0;o<s;o++){const a=e.slice(o*4,o*4+4),c=t.slice(o*4,o*4+4);n.push(`${wm(a)}, ${wm(c)}`)}if(i!==0){let o=e.slice(s*4),a=t.slice(s*4);o.length===1&&(o=o.map(c=>`float(${c})`),a=a.map(c=>`float(${c})`)),n.push(`${wm(o)}, ${wm(a)}`)}return n.map((o,a)=>`dot(${o})`).join("+")}function yS(e){const t=Ot(e).map(n=>n.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
   }
@@ -160,7 +160,7 @@ Hi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed thi
 `;const{getBroadcastDims:nC}=iw;function TK(e,t,n,s){const i=[];e.forEach(I=>{const T=we(I.shapeInfo.logicalShape);I.shapeInfo.isUniform?i.push(`uniform float ${I.name}${T>1?`[${T}]`:""};`):(i.push(`uniform sampler2D ${I.name};`),i.push(`uniform int offset${I.name};`))});const o=i.join(`
 `),a=e.map(I=>AK(I,t,s)).join(`
 `),c=t.texShape,u=Un(),p=CK(u);let m,y,b=EK(u);t.isPacked?(m=vK(t.logicalShape,c),y=OK(u)):(m=NK(t.logicalShape,c),y=RK(u)),s&&(b+=_K);const w=[b,p,y,o,m,a,n].join(`
-`);return w}function gc(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return qK(e);case 1:return KK(e);case 2:return JK(e);case 3:return QK(e);case 4:return t5(e);case 5:return n5(e);case 6:return s5(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function sC(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return YK(e);case 1:return jK(e);case 2:return XK(e);case 3:return ZK(e);default:return e5(e)}}function AK(e,t,n=!1){let s="";n?s+=sC(e):s+=gc(e);const i=e.shapeInfo.logicalShape,o=t.logicalShape;return i.length<=o.length&&(n?s+=i5(e,t):s+=r5(e,t)),s}function vK(e,t){switch(e.length){case 0:return iC();case 1:return WK(e,t);case 2:return VK(e,t);case 3:return UK(e,t);default:return MK(e,t)}}function NK(e,t){switch(e.length){case 0:return iC();case 1:return $K(e,t);case 2:return HK(e,t);case 3:return BK(e,t);case 4:return PK(e,t);case 5:return zK(e,t);case 6:return GK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function CK(e){return`
+`);return w}function bc(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return qK(e);case 1:return KK(e);case 2:return JK(e);case 3:return QK(e);case 4:return t5(e);case 5:return n5(e);case 6:return s5(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function sC(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return YK(e);case 1:return jK(e);case 2:return XK(e);case 3:return ZK(e);default:return e5(e)}}function AK(e,t,n=!1){let s="";n?s+=sC(e):s+=bc(e);const i=e.shapeInfo.logicalShape,o=t.logicalShape;return i.length<=o.length&&(n?s+=i5(e,t):s+=r5(e,t)),s}function vK(e,t){switch(e.length){case 0:return iC();case 1:return WK(e,t);case 2:return VK(e,t);case 3:return UK(e,t);default:return MK(e,t)}}function NK(e,t){switch(e.length){case 0:return iC();case 1:return $K(e,t);case 2:return HK(e,t);case 3:return BK(e,t);case 4:return PK(e,t);case 5:return zK(e,t);case 6:return GK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function CK(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
@@ -316,7 +316,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec3(b, r, c);
     }
-  `}function BK(e,t){const n=jo(["r","c","d"],e);return`
+  `}function BK(e,t){const n=Xo(["r","c","d"],e);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${t[0]}, ${t[1]}));
@@ -343,7 +343,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec${e.length}(${c});
     }
-  `}function PK(e,t){const n=jo(["r","c","d","d2"],e);return`
+  `}function PK(e,t){const n=Xo(["r","c","d","d2"],e);return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -351,7 +351,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec4(r, c, d, d2);
     }
-  `}function zK(e,t){const n=jo(["r","c","d","d2","d3"],e);return`
+  `}function zK(e,t){const n=Xo(["r","c","d","d2","d3"],e);return`
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                              ${t[1]}));
@@ -363,7 +363,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function GK(e,t){const n=jo(["r","c","d","d2","d3","d4"],e);return`
+  `}function GK(e,t){const n=Xo(["r","c","d","d2","d3","d4"],e);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -416,7 +416,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       int c = index - r * ${e[1]};
       return ivec2(r, c);
     }
-  `}function Ko(e){return`offset${e}`}function YK(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Un();return`
+  `}function Jo(e){return`offset${e}`}function YK(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Un();return`
     vec4 ${n}() {
       return ${s.texture2D}(${t}, halfCR);
     }
@@ -424,7 +424,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       float ${n}() {
         return sampleTexture(${t}, halfCR);
       }
-    `;const[o,a]=e.shapeInfo.texShape,c=Ko(t);return`
+    `;const[o,a]=e.shapeInfo.texShape,c=Jo(t);return`
     float ${n}() {
       vec2 uv = uvFromFlat(${o}, ${a}, ${c});
       return sampleTexture(${t}, uv);
@@ -437,13 +437,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     }
   `}function KK(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
       float ${n}(int index) {
-        ${yc(e)}
+        ${wc(e)}
       }
     `;const s=e.shapeInfo.texShape,i=s[0],o=s[1];if(o===1&&i===1)return`
       float ${n}(int index) {
         return sampleTexture(${t}, halfCR);
       }
-    `;const a=Ko(t);return o===1?`
+    `;const a=Jo(t);return o===1?`
       float ${n}(int index) {
         vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${i}.0);
         return sampleTexture(${t}, uv);
@@ -474,17 +474,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${b}.0, ${y}.0);
       return sampleTexture(${n}, uv);
     }
-  `}const{newShape:o,keptDims:a}=Rr(t),c=o;if(c.length<t.length){const y=bc(e,c),b=["row","col"];return`
-      ${gc(y)}
+  `}const{newShape:o,keptDims:a}=Rr(t),c=o;if(c.length<t.length){const y=Lc(e,c),b=["row","col"];return`
+      ${bc(y)}
       float ${s}(int row, int col) {
-        return ${s}(${wc(b,a)});
+        return ${s}(${Sc(b,a)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col) {
         int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
-        ${yc(e)}
+        ${wc(e)}
       }
-    `;const u=i[0],p=i[1],m=Ko(n);return p===1?`
+    `;const u=i[0],p=i[1],m=Jo(n);return p===1?`
     float ${s}(int row, int col) {
       float index = dot(vec3(row, col, ${m}), vec3(${t[1]}, 1, 1));
       vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
@@ -503,10 +503,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     vec2 uv = uvFromFlat(${u}, ${p}, index);
     return sampleTexture(${n}, uv);
   }
-`}function ZK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=e.shapeInfo.texShape,o=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)];if(t[0]===1){const y=t.slice(1),b=[1,2],w=bc(e,y),I=["b","row","col"];return`
+`}function ZK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=e.shapeInfo.texShape,o=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)];if(t[0]===1){const y=t.slice(1),b=[1,2],w=Lc(e,y),I=["b","row","col"];return`
         ${sC(w)}
         vec4 ${s}(int b, int row, int col) {
-          return ${s}(${wc(I,b)});
+          return ${s}(${Sc(I,b)});
         }
       `}const a=o[0],c=o[1],u=Math.ceil(t[2]/2),p=u*Math.ceil(t[1]/2),m=Un();return`
     vec4 ${s}(int b, int row, int col) {
@@ -514,16 +514,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${a}, ${c}, ${p}, ${u}, b, row, col);
       return ${m.texture2D}(${n}, uv);
     }
-  `}function QK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[1]*t[2],o=t[2],{newShape:a,keptDims:c}=Rr(t),u=a;if(u.length<t.length){const I=bc(e,u),T=["row","col","depth"];return`
-        ${gc(I)}
+  `}function QK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[1]*t[2],o=t[2],{newShape:a,keptDims:c}=Rr(t),u=a;if(u.length<t.length){const I=Lc(e,u),T=["row","col","depth"];return`
+        ${bc(I)}
         float ${s}(int row, int col, int depth) {
-          return ${s}(${wc(T,c)});
+          return ${s}(${Sc(T,c)});
         }
       `}if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
                           vec3(${i}, ${o}, 1)));
-        ${yc(e)}
+        ${wc(e)}
       }
     `;const p=e.shapeInfo.texShape,m=p[0],y=p[1],b=e.shapeInfo.flatOffset;if(y===i&&b==null)return`
         float ${s}(int row, int col, int depth) {
@@ -540,7 +540,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${y}.0, ${m}.0);
       return sampleTexture(${n}, uv);
     }
-  `;const w=Ko(n);return`
+  `;const w=Jo(n);return`
       float ${s}(int row, int col, int depth) {
         // Explicitly use integer operations as dot() only works on floats.
         int index = row * ${i} + col * ${o} + depth + ${w};
@@ -555,16 +555,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${u}, ${c});
       return ${w.texture2D}(${s}, uv);
     }
-  `}function t5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[3],o=t[2]*i,a=t[1]*o,{newShape:c,keptDims:u}=Rr(t);if(c.length<t.length){const I=bc(e,c),T=["row","col","depth","depth2"];return`
-      ${gc(I)}
+  `}function t5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[3],o=t[2]*i,a=t[1]*o,{newShape:c,keptDims:u}=Rr(t);if(c.length<t.length){const I=Lc(e,c),T=["row","col","depth","depth2"];return`
+      ${bc(I)}
       float ${s}(int row, int col, int depth, int depth2) {
-        return ${s}(${wc(T,u)});
+        return ${s}(${Sc(T,u)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
                           vec4(${a}, ${o}, ${i}, 1)));
-        ${yc(e)}
+        ${wc(e)}
       }
     `;const p=e.shapeInfo.flatOffset,m=e.shapeInfo.texShape,y=m[0],b=m[1];if(b===a&&p==null)return`
       float ${s}(int row, int col, int depth, int depth2) {
@@ -585,7 +585,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${b}.0, ${y}.0);
         return sampleTexture(${n}, uv);
       }
-    `;const w=Ko(n);return`
+    `;const w=Jo(n);return`
     float ${s}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${a} + col * ${o} +
@@ -593,10 +593,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${y}, ${b}, index + ${w});
       return sampleTexture(${n}, uv);
     }
-  `}function n5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[4],o=t[3]*i,a=t[2]*o,c=t[1]*a,{newShape:u,keptDims:p}=Rr(t);if(u.length<t.length){const T=bc(e,u),v=["row","col","depth","depth2","depth3"];return`
-      ${gc(T)}
+  `}function n5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[4],o=t[3]*i,a=t[2]*o,c=t[1]*a,{newShape:u,keptDims:p}=Rr(t);if(u.length<t.length){const T=Lc(e,u),v=["row","col","depth","depth2","depth3"];return`
+      ${bc(T)}
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
-        return ${s}(${wc(v,p)});
+        return ${s}(${Sc(v,p)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
@@ -604,7 +604,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           vec4(row, col, depth, depth2),
           vec4(${c}, ${a}, ${o}, ${i})) +
           depth3;
-        ${yc(e)}
+        ${wc(e)}
       }
     `;const m=e.shapeInfo.flatOffset,y=e.shapeInfo.texShape,b=y[0],w=y[1];if(w===c&&m==null)return`
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
@@ -626,7 +626,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${w}.0, ${b}.0);
         return sampleTexture(${n}, uv);
       }
-    `;const I=Ko(n);return`
+    `;const I=Jo(n);return`
     float ${s}(int row, int col, int depth, int depth2, int depth3) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${c} + col * ${a} + depth * ${o} +
@@ -634,11 +634,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${b}, ${w}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function s5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:i,keptDims:o}=Rr(t);if(i.length<t.length){const v=bc(e,i),N=["row","col","depth","depth2","depth3","depth4"];return`
-      ${gc(v)}
+  `}function s5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:i,keptDims:o}=Rr(t);if(i.length<t.length){const v=Lc(e,i),N=["row","col","depth","depth2","depth3","depth4"];return`
+      ${bc(v)}
       float ${s}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${s}(${wc(N,o)});
+        return ${s}(${Sc(N,o)});
       }
     `}const a=t[5],c=t[4]*a,u=t[3]*c,p=t[2]*u,m=t[1]*p;if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth,
@@ -649,7 +649,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           dot(
             vec2(depth3, depth4),
             vec2(${a}, 1)));
-        ${yc(e)}
+        ${wc(e)}
       }
     `;const y=e.shapeInfo.flatOffset,b=e.shapeInfo.texShape,w=b[0],I=b[1];if(I===m&&y==null)return`
       float ${s}(int row, int col, int depth,
@@ -675,7 +675,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${I}.0, ${w}.0);
         return sampleTexture(${n}, uv);
       }
-    `;const T=Ko(n);return`
+    `;const T=Jo(n);return`
     float ${s}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
       // Explicitly use integer operations as dot() only works on floats.
@@ -684,7 +684,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${w}, ${I}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function yc(e){const t=e.name,n=we(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
+  `}function wc(e){const t=e.name,n=we(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
     for (int i = 0; i < ${n}; i++) {
       if (i == index) {
         return ${t}[i];
@@ -715,7 +715,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${b}
       return get${s}(${I});
     }
-  `}function Et(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function bc(e,t){const n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function wc(e,t){return t.map(n=>e[n]).join(", ")}class o5{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);const i=e[e.length-1],o=Math.ceil(i/t);this.outputShape=e.slice(0,-1),o>1&&this.outputShape.push(o),s||this.variableNames.push("bestIndicesA");const a=this.outputShape,c=a.length,u=Et(c),p=us("coords",c);let m,y;if(o===1){y=c+1;const U=Et(y);m=`
+  `}function Et(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Lc(e,t){const n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Sc(e,t){return t.map(n=>e[n]).join(", ")}class o5{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);const i=e[e.length-1],o=Math.ceil(i/t);this.outputShape=e.slice(0,-1),o>1&&this.outputShape.push(o),s||this.variableNames.push("bestIndicesA");const a=this.outputShape,c=a.length,u=Et(c),p=us("coords",c);let m,y;if(o===1){y=c+1;const U=Et(y);m=`
         ${U} sourceLocR = ${U}(${p.join()}, 0);
         ++${p[c-1]};
         ${U} sourceLocG = ${U}(${p.join()}, 0);
@@ -915,7 +915,7 @@ return (round(mod(b, 2.0)) != 1) ?
 `,L5=aC+`
   return min(a, b);
 `,S5=`if (b == 0.0) return NAN;
-  return mod(a, b);`,I5="return (b >= 1.0) ? a : a * (b + 1.0);",lC="return (a < 0.) ? b * a : a;";class hn{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=nt(t,n),this.userCode=`
+  return mod(a, b);`,I5="return (b >= 1.0) ? a : a * (b + 1.0);",lC="return (a < 0.) ? b * a : a;";class un{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=nt(t,n),this.userCode=`
       float binaryOperation(float a, float b) {
         ${e}
       }
@@ -1900,7 +1900,7 @@ return (round(mod(b, 2.0)) != 1) ?
       }
     `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}}function fC(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function gC(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}class X5{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=au.DENSE;const t=lu(e),n=Un();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${jo(["r","c","d"],e)}
+        ${Xo(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -1921,7 +1921,7 @@ return (round(mod(b, 2.0)) != 1) ?
       }
     `}}class J5{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=au.DENSE;const t=lu(e),n=Un();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${jo(["r","c","d"],e)}
+        ${Xo(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -2118,7 +2118,7 @@ return (round(mod(b, 2.0)) != 1) ?
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return $j(e,n)}function l8(e){const t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return Gj(e,t)}function h8(e){const t=new Uint16Array([0,1,2,2,1,3]);return Vj(e,t)}function uu(e,t,n,s,i,o){Yj(t,n);const a=Hj(e),c=e.TEXTURE_2D;return Re(e,()=>e.bindTexture(c,a)),Re(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Re(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Re(e,()=>e.texParameteri(c,e.TEXTURE_MIN_FILTER,e.NEAREST)),Re(e,()=>e.texParameteri(c,e.TEXTURE_MAG_FILTER,e.NEAREST)),Re(e,()=>e.texImage2D(c,0,s,t,n,0,i,o,null)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null)),a}function wC(e){return e.internalFormatFloat}function u8(e,t,n,s){const[i,o]=cu(t,n);return uu(e,i,o,wC(s),s.textureFormatFloat,e.FLOAT)}function LC(e){return e.internalFormatHalfFloat}function d8(e,t,n,s){const[i,o]=cu(t,n);return uu(e,i,o,LC(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function SC(e){return e.downloadTextureFormat}function p8(e,t,n,s){const[i,o]=cu(t,n);return uu(e,i,o,SC(s),e.RGBA,e.UNSIGNED_BYTE)}function IC(e){return e.internalFormatPackedFloat}function m8(e,t,n,s){const[i,o]=pc(t,n);return uu(e,i,o,IC(s),e.RGBA,e.FLOAT)}function xC(e){return e.internalFormatPackedHalfFloat}function f8(e,t,n,s){const[i,o]=pc(t,n);return uu(e,i,o,xC(s),e.RGBA,s.textureTypeHalfFloat)}function g8(e,t,n){const s=0,i=3*4,o=3*4+2*4;Re(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n));const a=K0(e,t,"clipSpacePos",n,3,o,s);return a&&K0(e,t,"uv",n,2,o,i)}function y8(e,t,n,s,i,o){Re(e,()=>e.bindTexture(e.TEXTURE_2D,t));let a,c,u;i instanceof Uint8Array?(a=new Uint8Array(n*s*4),c=e.UNSIGNED_BYTE,u=e.RGBA):(a=new Float32Array(n*s*4),c=e.FLOAT,u=o.internalFormatPackedFloat),a.set(i),Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,u,n,s,0,e.RGBA,c,a)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function b8(e,t,n){Re(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function w8(e,t,n,s){const i=e.createBuffer();Re(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,i));const o=4,a=4,c=o*a*t*n;return Re(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,c,e.STREAM_READ)),Re(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Re(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),i}function L8(e,t,n){const s=e,i=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,i),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),i}function S8(e,t,n,s){const[i,o]=cu(t,n),a=4,c=new Uint8Array(Oj(t*n,a));return Re(e,()=>e.readPixels(0,0,i,o,s.downloadTextureFormat,e.UNSIGNED_BYTE,c)),new Float32Array(c.buffer)}function I8(e,t,n,s,i,o,a,c){const u=e,p=new Float32Array(Ej(o,a));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,p),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),p}function x8(e,t,n){const s=new Float32Array(t*n*4);return Re(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}class T8{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];const t=C().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Nj(t,e)):this.gl=Mi(t);let n="WEBGL_color_buffer_float";const s="EXT_color_buffer_half_float";if(C().getNumber("WEBGL_VERSION")===1){const i="OES_texture_float",o="OES_texture_half_float";if(this.textureFloatExtension=pm(this.gl,i),Vs(this.gl,o))this.textureHalfFloatExtension=pm(this.gl,o);else if(C().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Vs(this.gl,s))this.colorBufferHalfFloatExtension=pm(this.gl,s);else if(C().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Vs(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Vs(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=l8(this.gl),this.indexBuffer=h8(this.gl),this.framebuffer=qj(this.gl),this.textureConfig=dS(this.gl,this.textureHalfFloatExtension)}get debug(){return C().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");const e=this.gl;Re(e,()=>e.finish()),Re(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Re(e,()=>e.deleteFramebuffer(this.framebuffer)),Re(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Re(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Re(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),u8(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),d8(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),p8(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),b8(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),y8(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),f8(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),m8(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(X0(this.gl,this.framebuffer),this.outputTexture=null),Re(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>S8(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,i,o){return I8(this.gl,e,t,n,s,i,o,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return L8(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);const s=w8(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){const e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(C().getBool("WEBGL_FENCE_API_ENABLED")){const s=e,i=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{const o=s.clientWaitSync(i,0,0);return o===s.ALREADY_SIGNALED||o===s.CONDITION_SATISFIED},t=i}else C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>x8(this.gl,t,n))}createProgram(e){this.throwIfDisposed();const t=this.gl,n=Uj(t,e),s=c8(t),i=Pj(t);return Re(t,()=>t.attachShader(i,s)),Re(t,()=>t.attachShader(i,n)),zj(t,i),this.debug&&pS(t,i),this.vertexAttrsAreBound||(this.setProgram(i),this.vertexAttrsAreBound=g8(t,this.program,this.vertexBuffer)),i}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Re(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&pS(this.gl,this.program),Re(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?Kj(this.gl,e,t):Xj(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Re(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),Jj(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();const[s,i]=pc(t,n);this.setOutputMatrixTextureDriver(e,s,i)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&pS(this.gl,this.program),mm(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;this.debug&&this.debugValidate(),Re(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Re(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=pm(this.gl,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const n=this.gl,s=this.getQueryTimerExtensionWebGL2(),i=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,i),i}const e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}const e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await Iy(()=>this.disposed||this.isQueryAvailable(e,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){const n=this.gl,s=n.getQueryParameter(e,n.QUERY_RESULT);return s/1e6}else{const n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT);return s/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){const n=this.gl,s=this.getQueryTimerExtensionWebGL2(),i=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),i&&!this.disjoint}else{const n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){const e=A8(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){const{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;Iy(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),mS(this.gl,e,this.framebuffer),this.debug&&mm(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(mS(this.gl,this.outputTexture,this.framebuffer),this.debug&&mm(this.gl)):X0(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);const n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();const s=this.gl;mS(s,e,this.framebuffer),this.debug&&mm(s),this.outputTexture=e,Re(s,()=>s.viewport(0,0,t,n)),Re(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Re(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}}function A8(e){let t=0;for(;t<e.length;++t){const n=e[t]();if(!n)break}return t-1}function v8(e,t,n,s){const i=t.userCode,o=n.map((w,I)=>{const T={logicalShape:w.shape,texShape:w.isUniform?null:w.texData.texShape,isUniform:w.isUniform,isPacked:w.isUniform?!1:w.texData.isPacked,flatOffset:null};return w.texData!=null&&w.texData.slice!=null&&w.texData.slice.flatOffset>0&&(T.flatOffset=w.texData.slice.flatOffset),{name:t.variableNames[I],shapeInfo:T}}),a=o.map(w=>w.shapeInfo),c={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},u=TK(o,c,i,t.packedInputs),p=e.createProgram(u);let m=null;const y=e.getUniformLocation(p,"NAN",!1);C().getNumber("WEBGL_VERSION")===1&&(m=e.getUniformLocation(p,"INFINITY",!1));const b={};for(let w=0;w<t.variableNames.length;w++){const I=t.variableNames[w],T=!1;b[I]=e.getUniformLocation(p,I,T),b[`offset${I}`]=e.getUniformLocation(p,`offset${I}`,T)}return{program:t,source:u,webGLProgram:p,uniformLocations:b,inShapeInfos:a,outShapeInfo:c,infLoc:m,nanLoc:y}}function TC(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{const i=n.logicalShape,o=t[s],a=o.shape;if(!ot(i,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${i} and ${a} must match`);if(n.isUniform&&o.isUniform)return;const c=n.texShape,u=o.isUniform?null:o.texData.texShape;if(!ot(c,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${c} and ${u} must match`)})}function N8(e,t,n,s,i){TC(t.inShapeInfos,n),TC([t.outShapeInfo],[s]);const o=s.texData.texture,a=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(o,a[0],a[1]):e.setOutputMatrixTexture(o,a[0],a[1]),e.setProgram(t.webGLProgram),C().getNumber("WEBGL_VERSION")===1&&(t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity)),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((c,u)=>{const p=t.program.variableNames[u],m=t.uniformLocations[p],y=t.uniformLocations[`offset${p}`];if(m==null)return;if(c.isUniform){if(we(c.shape)<2)e.gl.uniform1f(m,c.uniformValues[0]);else{let b=c.uniformValues;b instanceof Float32Array||(b=new Float32Array(b)),e.gl.uniform1fv(m,b)}return}c.texData.slice!=null&&y!=null&&e.gl.uniform1i(y,c.texData.slice.flatOffset),e.setInputMatrixTexture(c.texData.texture,m,u)}),i!=null&&i(e,t.webGLProgram),e.executeProgram()}function C8(e,t,n){let s="";t.concat(n).forEach(a=>{const c=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0,u=a.isUniform?"uniform":a.texData.texShape;s+=`${a.shape}_${u}_${c}`});const i=e.userCode;let o=e.constructor.name;return o+="_"+s+"_"+i,o}class R8{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;const{filterWidth:s,inChannels:i,strideWidth:o,strideHeight:a,padInfo:c,outWidth:u,dilationWidth:p,dilationHeight:m,dataFormat:y}=n,{left:b,top:w}=c,I=i*s,T=Un(),v=y==="channelsLast",N=v?0:1,E=v?1:2;let D="";for(let F=0;F<=1;F++)for(let _=0;_<=1;_++)D+=`
+    }`;return $j(e,n)}function l8(e){const t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return Gj(e,t)}function h8(e){const t=new Uint16Array([0,1,2,2,1,3]);return Vj(e,t)}function uu(e,t,n,s,i,o){Yj(t,n);const a=Hj(e),c=e.TEXTURE_2D;return Re(e,()=>e.bindTexture(c,a)),Re(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Re(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Re(e,()=>e.texParameteri(c,e.TEXTURE_MIN_FILTER,e.NEAREST)),Re(e,()=>e.texParameteri(c,e.TEXTURE_MAG_FILTER,e.NEAREST)),Re(e,()=>e.texImage2D(c,0,s,t,n,0,i,o,null)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null)),a}function wC(e){return e.internalFormatFloat}function u8(e,t,n,s){const[i,o]=cu(t,n);return uu(e,i,o,wC(s),s.textureFormatFloat,e.FLOAT)}function LC(e){return e.internalFormatHalfFloat}function d8(e,t,n,s){const[i,o]=cu(t,n);return uu(e,i,o,LC(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function SC(e){return e.downloadTextureFormat}function p8(e,t,n,s){const[i,o]=cu(t,n);return uu(e,i,o,SC(s),e.RGBA,e.UNSIGNED_BYTE)}function IC(e){return e.internalFormatPackedFloat}function m8(e,t,n,s){const[i,o]=fc(t,n);return uu(e,i,o,IC(s),e.RGBA,e.FLOAT)}function xC(e){return e.internalFormatPackedHalfFloat}function f8(e,t,n,s){const[i,o]=fc(t,n);return uu(e,i,o,xC(s),e.RGBA,s.textureTypeHalfFloat)}function g8(e,t,n){const s=0,i=3*4,o=3*4+2*4;Re(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n));const a=K0(e,t,"clipSpacePos",n,3,o,s);return a&&K0(e,t,"uv",n,2,o,i)}function y8(e,t,n,s,i,o){Re(e,()=>e.bindTexture(e.TEXTURE_2D,t));let a,c,u;i instanceof Uint8Array?(a=new Uint8Array(n*s*4),c=e.UNSIGNED_BYTE,u=e.RGBA):(a=new Float32Array(n*s*4),c=e.FLOAT,u=o.internalFormatPackedFloat),a.set(i),Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,u,n,s,0,e.RGBA,c,a)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function b8(e,t,n){Re(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function w8(e,t,n,s){const i=e.createBuffer();Re(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,i));const o=4,a=4,c=o*a*t*n;return Re(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,c,e.STREAM_READ)),Re(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Re(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),i}function L8(e,t,n){const s=e,i=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,i),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),i}function S8(e,t,n,s){const[i,o]=cu(t,n),a=4,c=new Uint8Array(Oj(t*n,a));return Re(e,()=>e.readPixels(0,0,i,o,s.downloadTextureFormat,e.UNSIGNED_BYTE,c)),new Float32Array(c.buffer)}function I8(e,t,n,s,i,o,a,c){const u=e,p=new Float32Array(Ej(o,a));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,p),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),p}function x8(e,t,n){const s=new Float32Array(t*n*4);return Re(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}class T8{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];const t=C().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Nj(t,e)):this.gl=Mi(t);let n="WEBGL_color_buffer_float";const s="EXT_color_buffer_half_float";if(C().getNumber("WEBGL_VERSION")===1){const i="OES_texture_float",o="OES_texture_half_float";if(this.textureFloatExtension=pm(this.gl,i),Vs(this.gl,o))this.textureHalfFloatExtension=pm(this.gl,o);else if(C().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Vs(this.gl,s))this.colorBufferHalfFloatExtension=pm(this.gl,s);else if(C().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Vs(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Vs(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=l8(this.gl),this.indexBuffer=h8(this.gl),this.framebuffer=qj(this.gl),this.textureConfig=dS(this.gl,this.textureHalfFloatExtension)}get debug(){return C().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");const e=this.gl;Re(e,()=>e.finish()),Re(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Re(e,()=>e.deleteFramebuffer(this.framebuffer)),Re(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Re(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Re(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),u8(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),d8(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),p8(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),b8(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),y8(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),f8(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),m8(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(X0(this.gl,this.framebuffer),this.outputTexture=null),Re(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>S8(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,i,o){return I8(this.gl,e,t,n,s,i,o,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return L8(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);const s=w8(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){const e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(C().getBool("WEBGL_FENCE_API_ENABLED")){const s=e,i=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{const o=s.clientWaitSync(i,0,0);return o===s.ALREADY_SIGNALED||o===s.CONDITION_SATISFIED},t=i}else C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>x8(this.gl,t,n))}createProgram(e){this.throwIfDisposed();const t=this.gl,n=Uj(t,e),s=c8(t),i=Pj(t);return Re(t,()=>t.attachShader(i,s)),Re(t,()=>t.attachShader(i,n)),zj(t,i),this.debug&&pS(t,i),this.vertexAttrsAreBound||(this.setProgram(i),this.vertexAttrsAreBound=g8(t,this.program,this.vertexBuffer)),i}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Re(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&pS(this.gl,this.program),Re(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?Kj(this.gl,e,t):Xj(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Re(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),Jj(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();const[s,i]=fc(t,n);this.setOutputMatrixTextureDriver(e,s,i)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&pS(this.gl,this.program),mm(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;this.debug&&this.debugValidate(),Re(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Re(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=pm(this.gl,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const n=this.gl,s=this.getQueryTimerExtensionWebGL2(),i=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,i),i}const e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}const e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await Iy(()=>this.disposed||this.isQueryAvailable(e,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){const n=this.gl,s=n.getQueryParameter(e,n.QUERY_RESULT);return s/1e6}else{const n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT);return s/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){const n=this.gl,s=this.getQueryTimerExtensionWebGL2(),i=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),i&&!this.disjoint}else{const n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){const e=A8(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){const{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;Iy(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),mS(this.gl,e,this.framebuffer),this.debug&&mm(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(mS(this.gl,this.outputTexture,this.framebuffer),this.debug&&mm(this.gl)):X0(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);const n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();const s=this.gl;mS(s,e,this.framebuffer),this.debug&&mm(s),this.outputTexture=e,Re(s,()=>s.viewport(0,0,t,n)),Re(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Re(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}}function A8(e){let t=0;for(;t<e.length;++t){const n=e[t]();if(!n)break}return t-1}function v8(e,t,n,s){const i=t.userCode,o=n.map((w,I)=>{const T={logicalShape:w.shape,texShape:w.isUniform?null:w.texData.texShape,isUniform:w.isUniform,isPacked:w.isUniform?!1:w.texData.isPacked,flatOffset:null};return w.texData!=null&&w.texData.slice!=null&&w.texData.slice.flatOffset>0&&(T.flatOffset=w.texData.slice.flatOffset),{name:t.variableNames[I],shapeInfo:T}}),a=o.map(w=>w.shapeInfo),c={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},u=TK(o,c,i,t.packedInputs),p=e.createProgram(u);let m=null;const y=e.getUniformLocation(p,"NAN",!1);C().getNumber("WEBGL_VERSION")===1&&(m=e.getUniformLocation(p,"INFINITY",!1));const b={};for(let w=0;w<t.variableNames.length;w++){const I=t.variableNames[w],T=!1;b[I]=e.getUniformLocation(p,I,T),b[`offset${I}`]=e.getUniformLocation(p,`offset${I}`,T)}return{program:t,source:u,webGLProgram:p,uniformLocations:b,inShapeInfos:a,outShapeInfo:c,infLoc:m,nanLoc:y}}function TC(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{const i=n.logicalShape,o=t[s],a=o.shape;if(!ot(i,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${i} and ${a} must match`);if(n.isUniform&&o.isUniform)return;const c=n.texShape,u=o.isUniform?null:o.texData.texShape;if(!ot(c,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${c} and ${u} must match`)})}function N8(e,t,n,s,i){TC(t.inShapeInfos,n),TC([t.outShapeInfo],[s]);const o=s.texData.texture,a=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(o,a[0],a[1]):e.setOutputMatrixTexture(o,a[0],a[1]),e.setProgram(t.webGLProgram),C().getNumber("WEBGL_VERSION")===1&&(t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity)),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((c,u)=>{const p=t.program.variableNames[u],m=t.uniformLocations[p],y=t.uniformLocations[`offset${p}`];if(m==null)return;if(c.isUniform){if(we(c.shape)<2)e.gl.uniform1f(m,c.uniformValues[0]);else{let b=c.uniformValues;b instanceof Float32Array||(b=new Float32Array(b)),e.gl.uniform1fv(m,b)}return}c.texData.slice!=null&&y!=null&&e.gl.uniform1i(y,c.texData.slice.flatOffset),e.setInputMatrixTexture(c.texData.texture,m,u)}),i!=null&&i(e,t.webGLProgram),e.executeProgram()}function C8(e,t,n){let s="";t.concat(n).forEach(a=>{const c=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0,u=a.isUniform?"uniform":a.texData.texShape;s+=`${a.shape}_${u}_${c}`});const i=e.userCode;let o=e.constructor.name;return o+="_"+s+"_"+i,o}class R8{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;const{filterWidth:s,inChannels:i,strideWidth:o,strideHeight:a,padInfo:c,outWidth:u,dilationWidth:p,dilationHeight:m,dataFormat:y}=n,{left:b,top:w}=c,I=i*s,T=Un(),v=y==="channelsLast",N=v?0:1,E=v?1:2;let D="";for(let F=0;F<=1;F++)for(let _=0;_<=1;_++)D+=`
           blockIndex = rc.y + ${_};
           pos = rc.x + ${F};
 
@@ -2966,7 +2966,7 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(result);
       }
-    `}}function V8(e){const t=jo(["r","c","d"],e);return`
+    `}}function V8(e){const t=Xo(["r","c","d"],e);return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
       ${t}
       return ivec3(r, c, d);
@@ -3478,7 +3478,7 @@ return (round(mod(b, 2.0)) != 1) ?
         ${o} coords = getOutputCoords();
         setOutput(getX(${a}));
       }
-    `}}class i6{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){const s=RC(t,n),i=OC(e,s,n);i in this.freeTextures||(this.freeTextures[i]=[]),i in this.usedTextures||(this.usedTextures[i]=[]);const o=CC(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[i].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=o,this.log();const c=this.freeTextures[i].shift();return this.usedTextures[i].push(c),c}let a;return s===In.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===In.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===In.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===In.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===In.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[i].push(a),this.numUsedTextures++,this._numBytesAllocated+=o,this.log(),a}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;const i=RC(n,s),o=OC(t,i,s);o in this.freeTextures||(this.freeTextures[o]=[]);const a=CC(t,i,this.gpgpu.gl,this.gpgpu.textureConfig,s),c=C().get("WEBGL_DELETE_TEXTURE_THRESHOLD");c!==-1&&this._numBytesAllocated>c?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=a):(this.freeTextures[o].push(e),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;const u=this.usedTextures[o],p=u.indexOf(e);if(p<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(p,1),this.log()}log(){if(!this.logEnabled)return;const e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);const t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures==null)return;for(const e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(const e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}function r6(e,t){const n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function CC(e,t,n,s,i){const o=o6(t,s);let a;if(i){const[u,p]=pc(e[0],e[1]);a=u*p}else{const[u,p]=cu(e[0],e[1]);a=u*p}const c=r6(n,o);return a*c}function o6(e,t){switch(e){case In.PACKED_2X2_FLOAT32:return IC(t);case In.PACKED_2X2_FLOAT16:return xC(t);case In.UNPACKED_FLOAT32:return wC(t);case In.UNPACKED_FLOAT16:return LC(t);case In.PACKED_4X1_UNSIGNED_BYTE:return SC(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function a6(e){return C().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?In.PACKED_2X2_FLOAT32:In.UNPACKED_FLOAT32:e?In.PACKED_2X2_FLOAT16:In.UNPACKED_FLOAT16}function RC(e,t){if(e===Cs.UPLOAD)return In.PACKED_2X2_FLOAT32;if(e===Cs.RENDER||e==null)return a6(t);if(e===Cs.DOWNLOAD||e===Cs.PIXELS)return In.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function OC(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}class c6{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let o=0;o<n.length;o++)n[o]=e[o]*t[o];this.outputShape=n,this.rank=n.length;const s=Et(this.rank),i=l6(e);this.userCode=`
+    `}}class i6{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){const s=RC(t,n),i=OC(e,s,n);i in this.freeTextures||(this.freeTextures[i]=[]),i in this.usedTextures||(this.usedTextures[i]=[]);const o=CC(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[i].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=o,this.log();const c=this.freeTextures[i].shift();return this.usedTextures[i].push(c),c}let a;return s===In.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===In.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===In.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===In.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===In.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[i].push(a),this.numUsedTextures++,this._numBytesAllocated+=o,this.log(),a}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;const i=RC(n,s),o=OC(t,i,s);o in this.freeTextures||(this.freeTextures[o]=[]);const a=CC(t,i,this.gpgpu.gl,this.gpgpu.textureConfig,s),c=C().get("WEBGL_DELETE_TEXTURE_THRESHOLD");c!==-1&&this._numBytesAllocated>c?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=a):(this.freeTextures[o].push(e),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;const u=this.usedTextures[o],p=u.indexOf(e);if(p<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(p,1),this.log()}log(){if(!this.logEnabled)return;const e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);const t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures==null)return;for(const e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(const e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}function r6(e,t){const n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function CC(e,t,n,s,i){const o=o6(t,s);let a;if(i){const[u,p]=fc(e[0],e[1]);a=u*p}else{const[u,p]=cu(e[0],e[1]);a=u*p}const c=r6(n,o);return a*c}function o6(e,t){switch(e){case In.PACKED_2X2_FLOAT32:return IC(t);case In.PACKED_2X2_FLOAT16:return xC(t);case In.UNPACKED_FLOAT32:return wC(t);case In.UNPACKED_FLOAT16:return LC(t);case In.PACKED_4X1_UNSIGNED_BYTE:return SC(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function a6(e){return C().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?In.PACKED_2X2_FLOAT32:In.UNPACKED_FLOAT32:e?In.PACKED_2X2_FLOAT16:In.UNPACKED_FLOAT16}function RC(e,t){if(e===Cs.UPLOAD)return In.PACKED_2X2_FLOAT32;if(e===Cs.RENDER||e==null)return a6(t);if(e===Cs.DOWNLOAD||e===Cs.PIXELS)return In.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function OC(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}class c6{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let o=0;o<n.length;o++)n[o]=e[o]*t[o];this.outputShape=n,this.rank=n.length;const s=Et(this.rank),i=l6(e);this.userCode=`
       void main() {
         ${s} resRC = getOutputCoords();
         setOutput(getA(${i}));
@@ -3641,7 +3641,7 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(getChannel(packedInput, ${a}));
       }
-    `}}const{segment_util:GC}=iw,M6=rw,P6=ow,z6=aw,G6=dp,V6=1e-7,H6=1e-4,xm={};function Y6(e){return e in xm||(xm[e]={}),xm[e]}function Tm(e,t=!1){if(e==="linear")return t?$6:h6;if(e==="relu")return t?MC:DC;if(e==="elu")return t?zC:FC;if(e==="relu6")return t?PC:kC;if(e==="prelu")return t?hC:lC;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}const q6=128,j6=600;function K6(){return C().global.screen==null?1024:C().global.screen.height*C().global.screen.width*window.devicePixelRatio*j6/1024/1024}const VC=1e3;class X6 extends f{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!C().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){const t=Mi(C().getNumber("WEBGL_VERSION"));this.binaryCache=Y6(C().getNumber("WEBGL_VERSION")),this.gpgpu=new T8(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new i6(this.gpgpu),this.numMBBeforeWarning=K6(),this.texData=new d(this,$s())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((C().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||C().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");const s={};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:Cs.UPLOAD,refCount:1}),s}incRef(e){const t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){const t=this.texData.get(e);t.refCount--}}move(e,t,n,s){if(C().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:Cs.UPLOAD,refCount:1})}disposeIntermediateTensorInfo(e){const t=e.dataId;if(this.texData.has(t)){const n=this.texData.get(t);n.refCount--,n.refCount<1&&this.disposeData(t)}}readSync(e){const t=this.texData.get(e),{values:n,dtype:s,complexTensors:i,slice:o,shape:a,isPacked:c}=t;if(o!=null){let y;c?y=new pu(a,Im):y=new it(a,Im);const b=this.runWebGLProgram(y,[{dataId:e,shape:a,dtype:s}],s),w=this.readSync(b.dataId);return this.disposeIntermediateTensorInfo(b),w}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;const u=this.activeTimers!=null;let p;u&&(p=qn());let m;if(s==="complex64"){const y=i.real.dataSync(),b=i.imag.dataSync();m=ir(y,b)}else m=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=qn()-p),this.convertAndCacheOnCPU(e,m)}async read(e){if(this.pendingRead.has(e)){const w=this.pendingRead.get(e);return new Promise(I=>w.push(I))}const t=this.texData.get(e),{values:n,shape:s,slice:i,dtype:o,complexTensors:a,isPacked:c}=t;if(i!=null){let w;c?w=new pu(s,Im):w=new it(s,Im);const I=this.runWebGLProgram(w,[{dataId:e,shape:s,dtype:o}],o),T=this.read(I.dataId);return this.disposeIntermediateTensorInfo(I),T}if(n!=null)return this.convertAndCacheOnCPU(e);if(!C().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&C().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,p;if(o!=="complex64"&&C().get("WEBGL_BUFFER_SUPPORTED")){p=this.decode(e);const w=this.texData.get(p.dataId);u=this.gpgpu.createBufferFromTexture(w.texture,...lu(s))}this.pendingRead.set(e,[]),o!=="complex64"&&await this.gpgpu.createAndWaitForFence();let m;if(o==="complex64"){const w=await Promise.all([a.real.data(),a.imag.data()]),I=w[0],T=w[1];m=ir(I,T)}else if(u==null)m=this.getValuesFromTexture(e);else{const w=we(s);m=this.gpgpu.downloadFloat32MatrixFromBuffer(u,w)}p!=null&&this.disposeIntermediateTensorInfo(p);const y=this.convertAndCacheOnCPU(e,m),b=this.pendingRead.get(e);return this.pendingRead.delete(e),b.forEach(w=>w(y)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),y}checkNumericalProblems(e){if(e==null)return;for(let t=0;t<e.length;t++){const n=e[t];if(!_j(n))throw C().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){const{shape:t,dtype:n,isPacked:s}=this.texData.get(e),i=we(t);if(C().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const y=this.decode(e),b=this.texData.get(y.dataId),w=this.gpgpu.downloadMatrixFromPackedTexture(b.texture,...lu(t)).subarray(0,i);return this.disposeIntermediateTensorInfo(y),w}const o=C().getBool("WEBGL_PACK")&&s===!0,a=o?fS(t):t,c=o?new t8(a):new e8(a),u=this.runWebGLProgram(c,[{shape:a,dtype:n,dataId:e}],"float32"),p=this.texData.get(u.dataId),m=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(p.texture,p.texShape[0],p.texShape[1]).subarray(0,i);return this.disposeIntermediateTensorInfo(u),m}async time(e){const t=this.activeTimers,n=[];let s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();const i=Ji(this.activeTimers.map(c=>c.query)).filter(c=>c!=null),o=Ji(this.activeTimers.map(c=>c.name)).filter(c=>c!=null);this.activeTimers=t,s&&(this.programTimersStack=null);const a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const c=await Promise.all(i);a.kernelMs=iT(c),a.getExtraProfileInfo=()=>c.map((u,p)=>({name:o[p],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:qn(),endMs:null}}endTimer(e){return C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=qn(),e)}async getQueryTime(e){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);const t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;this.releaseGPUData(e);const{complexTensors:t}=this.texData.get(e);t!=null&&(t.real.dispose(),t.imag.dispose()),this.texData.delete(e)}releaseGPUData(e){const{texture:t,dtype:n,texShape:s,usage:i,isPacked:o,slice:a}=this.texData.get(e),c=a&&a.origDataId||e,u=this.dataRefCount.get(c);u>1?this.dataRefCount.set(c,u-1):(this.dataRefCount.delete(c),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,i,o)));const p=this.texData.get(e);p.texture=null,p.texShape=null,p.isPacked=!1,p.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return C().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=$s().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=q6){const n=this.getCPUBackend();return!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(s=>this.texData.get(s.dataId).texture==null&&we(s.shape)<t)}getGPGPUContext(){return this.gpgpu}complex(e,t){const n=this.makeOutput(e.shape,"complex64"),s=this.texData.get(n.dataId);return s.complexTensors={real:$s().keep(e.clone()),imag:$s().keep(t.clone())},n}real(e){const t=this.texData.get(e.dataId);return t.complexTensors.real.clone()}imag(e){const t=this.texData.get(e.dataId);return t.complexTensors.imag.clone()}slice(e,t,n){if(this.shouldExecuteOnCPU([e])){const o=yK(this.texData.get(e.dataId).values,t,n,e.shape,e.dtype);return this.makeOutput(n,e.dtype,o)}if(we(n)===0)return en([],n,e.dtype);const{isPacked:s}=this.texData.get(e.dataId),i=Xy(e.shape,t,n);if(s||!i){const o=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new n6(n):new e6(n),a=o.getCustomSetupFunc(t);return this.compileAndRun(o,[e],null,a)}return this.uploadToGPU(e.dataId),this.shallowSlice(e,t,n)}shallowSlice(e,t,n){const s=this.texData.get(e.dataId),i=this.makeOutput(n,e.dtype),o=this.texData.get(i.dataId);Object.assign(o,s),o.shape=n,o.dtype=e.dtype;let a=Jy(t,e.strides);s.slice&&(a+=s.slice.flatOffset),o.slice={flatOffset:a,origDataId:s.slice&&s.slice.origDataId||e.dataId};const c=this.dataRefCount.get(o.slice.origDataId)||1;return this.dataRefCount.set(o.slice.origDataId,c+1),i}stridedSlice(e,t,n,s){const i=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.stridedSlice(e,t,n,s));if(i)return i;const o=Wd(t,n,s);if(o.some(c=>c===0))return en([],o);const a=new s6(t,s,o);return this.compileAndRun(a,[e])}reverse(e,t){const n=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new J8(e.shape,t):new X8(e.shape,t);return this.compileAndRun(n,[e])}concat(e,t){if(e[0].dtype==="complex64"){const a=e.map(u=>Do(u)),c=e.map(u=>Pa(u));return Ci(this.concat(a,t),this.concat(c,t))}if(e.length===1)return e[0];if(e.length>C().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){const a=Math.floor(e.length/2),c=this.concat(e.slice(0,a),t),u=this.concat(e.slice(a),t);return this.concat([c,u],t)}if(C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].rank>1){const a=new P5(e.map(c=>c.shape),t);return this.compileAndRun(a,e)}const n=Ur(e.map(a=>a.shape),t),s=e.map(a=>a.as2D(-1,we(a.shape.slice(t)))),i=new M5(s.map(a=>a.shape)),o=this.compileAndRun(i,s);return o.reshape(n)}neg(e){const t=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.neg(e));if(t)return t;if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,_C,e.dtype);const n=new it(e.shape,_C);return this.compileAndRun(n,[e])}batchMatMul(e,t,n,s){const i=n?e.shape[2]:e.shape[1],o=s?t.shape[1]:t.shape[2],a=n?e.shape[1]:e.shape[2],[c,,]=e.shape;if((i===1||o===1)&&a>VC){n&&(e=Pe(e,[0,2,1])),s&&(t=Pe(t,[0,2,1]));const m=o===1?e:e.as3D(c,a,1),y=o===1?2:1,b=o===1?t.as3D(c,1,a):t;return this.multiply(m,b).sum(y,!0)}const u=Cn(e.dtype,t.dtype),p=new LS(e.shape,[c,i,o],n,s);return this.compileAndRun(p,[e,t],u)}fusedBatchMatMul({a:e,b:t,transposeA:n,transposeB:s,bias:i,activation:o,preluActivationWeights:a}){const c=n?e.shape[2]:e.shape[1],u=s?t.shape[1]:t.shape[2],[p,,]=e.shape,m=Cn(e.dtype,t.dtype),y=i!=null,b=a!=null,w=o?Tm(o,!0):null,I=new LS(e.shape,[p,c,u],n,s,y,w,b),T=[e,t];return i&&T.push(i),a&&T.push(a),this.compileAndRun(I,T,m)}multiply(e,t){if(e.dtype==="complex64"){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),a=new oC(rC.REAL,e.shape,t.shape),c=new oC(rC.IMAG,e.shape,t.shape),u=[this.makeComplexComponentTensorInfo(e,i.complexTensors.real),this.makeComplexComponentTensorInfo(e,i.complexTensors.imag),this.makeComplexComponentTensorInfo(t,o.complexTensors.real),this.makeComplexComponentTensorInfo(t,o.complexTensors.imag)],p=this.compileAndRun(a,u),m=this.compileAndRun(c,u),y=this.complex(p,m);return p.dispose(),m.dispose(),y}const n=Cn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=fK(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,cC,e.dtype);const s=new hn(cC,e.shape,t.shape);return this.compileAndRun(s,[e,t],e.dtype)}localResponseNormalization4D(e,t,n,s,i){const o=C().getBool("WEBGL_PACK_NORMALIZATION")?new D8(e.shape,t,n,s,i):new O8(e.shape,t,n,s,i);return this.compileAndRun(o,[e])}LRNGrad(e,t,n,s,i,o,a){const c=new E8(t.shape,s,i,o,a);return this.compileAndRun(c,[t,n,e])}tile(e,t){if(e.dtype==="string"){const s=this.readSync(e.dataId),i=s.map(a=>Yl(a)),o=Qe(e.shape,e.dtype,i);return P6(o,t)}const n=new c6(e.shape,t);return this.compileAndRun(n,[e])}pad(e,t,n){const s=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new G8(e.shape,t,n):new z8(e.shape,t,n);return this.compileAndRun(s,[e])}gather(e,t,n){const s=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.gather(e,t,n));if(s)return s;const i=new r8(e.shape,t.size,n);return this.compileAndRun(i,[e,t])}batchToSpaceND(e,t,n){k(e.rank<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");const s=t.reduce((p,m)=>p*m),i=Rh(e.shape,t,s),o=Oh(i.length,t.length),a=Eh(e.shape,t,s),c=jb(n,t.length),u=Kb(a,n,t.length);return Pe(e.reshape(i),o).reshape(a).slice(c,u)}spaceToBatchND(e,t,n){k(e.rank<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");const s=t.reduce((m,y)=>m*y),i=[[0,0]];i.push(...n);for(let m=1+t.length;m<e.shape.length;++m)i.push([0,0]);const o=e.pad(i),a=Rh(o.shape,t,s,!1),c=Oh(a.length,t.length,!1),u=Eh(o.shape,t,s,!1),p=Pe(o.reshape(a),c);return K(p,u)}reduce(e,t,n){const s=e.shape[0],i=e.shape[1],o=uh(i),a=Math.ceil(i/o),c={windowSize:o,inSize:i,batchSize:s,outSize:a},u=new AC(c,t),p=this.compileAndRun(u,[e],n);return p.shape[1]===1?p:this.reduce(p,t,n)}argReduce(e,t,n=null){let s=e.shape[0],i=e.shape[1];n!=null&&(s=n.shape[0],i=n.shape[1]);const o=uh(i),a={windowSize:o,inSize:i,batchSize:s,outSize:Math.ceil(i/o)},c=new IK(a,t,n==null),u=[e];n!=null&&u.push(n);const p=this.compileAndRun(c,u,"int32");return p.shape[1]===1?p:this.argReduce(e,t,p)}argReducePacked(e,t,n=null){const s=n!=null?n.shape:e.shape,i=s[s.length-1],o=uh(i),a=new o5(s,o,t,n==null),c=n==null?[e]:[e,n],u=this.compileAndRun(a,c,"int32");return u.rank===e.rank?this.argReducePacked(e,t,u):u}sum(e,t){ss("sum",t,e.rank);const[n,s]=On(e.shape,t),i=we(s),o=e.as2D(-1,i),a=vd(e.dtype);return this.reduce(o,"sum",a).reshape(n)}prod(e,t){const n=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.prod(e,t));if(n)return n;const[s,i]=On(e.shape,t),o=we(i),a=e.as2D(-1,o),c=vd(e.dtype);return this.reduce(a,"prod",c).reshape(s)}unsortedSegmentSum(e,t,n){let s=0;const i=_n([s],e.rank);let o=e;i!=null&&(o=Pe(e,i),s=Is(1,e.rank)[0]);const a=GC.computeOutShape(o.shape,s,n),c=we([o.shape[s]]),u=o.as2D(-1,c),p=vd(e.dtype);let m=this.segOpCompute(u,"unsortedSegmentSum",t,p,n).reshape(a);return i!=null&&(m=Pe(m,eh(i))),m}segOpCompute(e,t,n,s,i){const o=e.shape[0],a=e.shape[1],c=GC.segOpComputeOptimalWindowSize(a,i),u={windowSize:c,inSize:a,batchSize:o,numSegments:i},p=new Z8(u,t),m=this.compileAndRun(p,[e,n],s);return m.shape[1]===i?m:(n=yh(0,i).tile([a/c]),this.segOpCompute(m,t,n,s,i))}argMinMaxReduce(e,t,n){const s=[t];if(ss("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.rank),!C().getBool("WEBGL_PACK_REDUCE")||e.rank<=2){const[i,o]=On(e.shape,s),a=we(o),c=e.as2D(-1,a);return this.argReduce(c,n).reshape(i)}return this.argReducePacked(e,n)}argMin(e,t){return this.argMinMaxReduce(e,t,"min")}argMax(e,t){return this.argMinMaxReduce(e,t,"max")}cumsum(e,t,n,s){if(t!==e.rank-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${e.rank-1} but got axis=${t}`);const i=e.shape[t];let o=e;for(let a=0;a<=Math.ceil(Math.log2(i))-1;a++){const c=new mC(e.shape,!1,s),u=c.getCustomSetupFunc(a),p=o;o=this.compileAndRun(c,[o],o.dtype,u),p.dispose()}if(n){const a=new mC(e.shape,n,s),c=o;o=this.compileAndRun(a,[o]),c.dispose()}return o}equal(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,v5,"bool");const n=new hn(u5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}notEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,N5,"bool");const n=new hn(d5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}less(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.less(e,t));if(n)return n;if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,C5,"bool");const s=new hn(p5,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}lessEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,R5,"bool");const n=new hn(m5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}greater(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.greater(e,t));if(n)return n;if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,O5,"bool");const s=new hn(f5,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}greaterEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,E5,"bool");const n=new hn(g5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}logicalNot(e){const t=new it(e.shape,_6);return this.compileAndRun(t,[e])}logicalAnd(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,D5,"bool");const n=new hn(y5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}logicalOr(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,k5,"bool");const n=new hn(b5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}select(e,t,n){const s=new Q8(e.rank,t.shape,t.rank);return this.compileAndRun(s,[e,t,n],Cn(t.dtype,n.dtype))}where(e){Ja("tf.where() in webgl locks the UI thread. 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this.reduce(o,"all",o.dtype).reshape(n)}any(e,t){ss("any",t,e.rank);const[n,s]=On(e.shape,t),i=we(s),o=e.as2D(-1,i);return this.reduce(o,"any",o.dtype).reshape(n)}floorDiv(e,t){const n=l5,s="int32";if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,x5,s);const i=new hn(n,e.shape,t.shape);return this.compileAndRun(i,[e,t],s)}add(e,t){if(e.dtype==="complex64"&&t.dtype==="complex64")return this.complexSeparableBinaryOp(e,t,bS);const n=Cn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=cK(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,bS,n);const s=new hn(bS,e.shape,t.shape);return this.compileAndRun(s,[e,t],n)}packedUnaryOp(e,t,n){const s=new pu(e.shape,t);return this.compileAndRun(s,[e],n)}packedBinaryOp(e,t,n,s,i=!1){const o=new to(n,e.shape,t.shape,i);return 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this.complexSeparableBinaryOp(e,t,wS);const n=Cn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=bK(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,wS,e.dtype);const s=new hn(wS,e.shape,t.shape);return this.compileAndRun(s,[e,t],n)}pow(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS"),s=n?new to(T5,e.shape,t.shape):new hn(h5,e.shape,t.shape),i=Cn(e.dtype,t.dtype);return this.compileAndRun(s,[e,t],i)}ceil(e){if(this.shouldExecuteOnCPU([e])){const n=lK(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,WC,e.dtype);const t=new it(e.shape,WC);return this.compileAndRun(t,[e])}floor(e){if(this.shouldExecuteOnCPU([e])){const n=dK(this.texData.get(e.dataId).values,e.dtype);return 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this.compileAndRun(t,[e])}relu(e){let t;return C().getBool("WEBGL_PACK")?t=new pu(e.shape,MC):t=new it(e.shape,DC),this.compileAndRun(t,[e])}relu6(e){let t;return C().getBool("WEBGL_PACK")?t=new pu(e.shape,PC):t=new it(e.shape,kC),this.compileAndRun(t,[e])}prelu(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new to(hC,e.shape,t.shape):new hn(lC,e.shape,t.shape);return this.compileAndRun(n,[e,t])}elu(e){if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,zC,e.dtype);const t=new it(e.shape,FC);return this.compileAndRun(t,[e])}eluDer(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new to(A5,e.shape,t.shape):new hn(I5,e.shape,t.shape);return this.compileAndRun(n,[e,t])}selu(e){const t=new it(e.shape,u6);return this.compileAndRun(t,[e])}int(e){const t=new it(e.shape,W6);return this.compileAndRun(t,[e],"int32")}clip(e,t,n){let s;C().getBool("WEBGL_PACK_CLIP")?s=new U5(e.shape):s=new $5(e.shape);const i=s.getCustomSetupFunc(t,n);return 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K(q,n.outShape)}const v=y?a[0]*a[1]*(a[2]+1):a[0]*a[2]*(a[3]+1),N={dataId:e.dataId,shape:[1,v,n.inChannels],dtype:e.dtype},E=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k(gm(c.shape,N.shape),()=>`packed reshape ${c.shape} to ${N.shape} isn't free`);const D=K(t,[1,n.inChannels,n.outChannels]),F=this.fusedBatchMatMul({a:N,b:D,transposeA:b,transposeB:w,bias:s,activation:i,preluActivationWeights:o}),_=this.texData.get(F.dataId);return k(_.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=E,_.shape=n.outShape,$s().makeTensorFromDataId(F.dataId,n.outShape,F.dtype)}conv2dWithIm2Row(e,t,n,s,i,o){const{filterWidth:a,filterHeight:c,inChannels:u,outWidth:p,outHeight:m,dataFormat:y}=n,b=y==="channelsLast",w=a*c*u,I=m*p,T=[w,I],v=!0,N=!1,E=e.squeeze([0]),D=t.reshape([1,w,-1]),F=new R8(T,E.shape,n),_=this.compileAndRun(F,[E]).reshape([1,T[0],T[1]]),B=s!=null,U=o!=null,Y=i?Tm(i,!0):null,q=new LS(_.shape,[1,I,n.outChannels],v,N,B,Y,U),J=[_,D];s&&J.push(s),U&&J.push(o);const oe=this.compileAndRun(q,J);return b?oe.reshape([1,m,p,n.outChannels]):oe.reshape([1,n.outChannels,m,p])}fusedConv2d({input:e,filter:t,convInfo:n,bias:s,activation:i,preluActivationWeights:o}){if(n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return this.conv2dByMatMul(e,t,n,s,i,o);if(C().getBool("WEBGL_CONV_IM2COL")&&e.shape[0]===1)return this.conv2dWithIm2Row(e,t,n,s,i,o);const a=s!=null,c=o!=null,u=i?Tm(i,!1):null,p=new uC(n,a,u,c),m=[e,t];return s&&m.push(s),o&&m.push(o),this.compileAndRun(p,m)}conv2d(e,t,n){if(n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return this.conv2dByMatMul(e,t,n);if(C().getBool("WEBGL_CONV_IM2COL")&&e.shape[0]===1)return 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bC(yC.IMAG,e.shape,t),o=[this.makeComplexComponentTensorInfo(e,n.complexTensors.real),this.makeComplexComponentTensorInfo(e,n.complexTensors.imag)],a=this.compileAndRun(s,o),c=this.compileAndRun(i,o),u=this.complex(a,c).as2D(e.shape[0],e.shape[1]);return a.dispose(),c.dispose(),u}gatherND(e,t){const n=t.shape,s=n[n.length-1],[i,o,a,c]=Fd(e,t),u=t.reshape([o,s]),p=e.reshape([e.size/a,a]),m=new a8(s,c,[o,a]),y=this.compileAndRun(m,[p,u]);return y.reshape(i)}fill(e,t,n){if(n=n||Ca(t),n==="string"){const s=wo(n,we(e));return s.fill(t),$s().makeTensor(s,e,n,this)}else{const s=new i8(e,t),i=s.getCustomSetupFunc(t);return this.compileAndRun(s,[],n,i)}}onesLike(e){if(e.dtype==="string")throw new Error("onesLike is not supported under string dtype");return this.fill(e.shape,1,e.dtype)}zerosLike(e){return this.fill(e.shape,e.dtype==="string"?"":0,e.dtype)}linspace(e,t,n){return sw(e,t,n)}makeTensorInfo(e,t,n){const s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){const{dataId:s}=this.makeTensorInfo(e,t,n);return $s().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){const t=new B6(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new $8(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){const n=[mc(e.shape),...fc(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},i=[mc(t),...fc(t)],o=new vC(i,n),a=!0,c=this.runWebGLProgram(o,[s],e.dtype,null,a);return{dataId:c.dataId,shape:t,dtype:c.dtype}}decode(e){const t=this.texData.get(e),{isPacked:n,shape:s,dtype:i}=t,o=fS(s);let a;n?a=new J5(o):a=new X5(o);const c=!0,u=this.runWebGLProgram(a,[{shape:o,dtype:i,dataId:e}],i,null,c);return{dtype:i,shape:s,dataId:u.dataId}}runWebGLProgram(e,t,n,s,i=!1){const o=this.makeTensorInfo(e.outputShape,n),a=this.texData.get(o.dataId);if(e.packedOutput&&(a.isPacked=!0),e.outPackingScheme===au.DENSE){const I=lu(e.outputShape);a.texShape=I.map(T=>T*2)}if(e.outTexUsage!=null&&(a.usage=e.outTexUsage),we(o.shape)===0)return a.values=wn(o.dtype,0),o;const c=[],u=t.map(I=>{if(I.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let T=this.texData.get(I.dataId);if(T.texture==null){if(!e.packedInputs&&we(I.shape)<=C().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:I.shape,texData:null,isUniform:!0,uniformValues:T.values};e.packedInputs&&(T.isPacked=!0,T.shape=I.shape)}else if(!!T.isPacked!==!!e.packedInputs)I=T.isPacked?this.unpackTensor(I):this.packTensor(I),c.push(I),T=this.texData.get(I.dataId);else if(T.isPacked&&!gm(T.shape,I.shape)){const v=I,N=I.shape;I.shape=T.shape,I=this.packedReshape(I,N),c.push(I),T=this.texData.get(I.dataId),v.shape=N}return this.uploadToGPU(I.dataId),{shape:I.shape,texData:T,isUniform:!1}});this.uploadToGPU(o.dataId);const p={shape:o.shape,texData:a,isUniform:!1},m=C8(e,u,p),y=this.getAndSaveBinary(m,()=>v8(this.gpgpu,e,u,p)),b=this.activeTimers!=null;let w;if(b&&(w=this.startTimer()),N8(this.gpgpu,y,u,p,s),c.forEach(I=>this.disposeIntermediateTensorInfo(I)),b&&(w=this.endTimer(w),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(w)})),!C().getBool("WEBGL_LAZILY_UNPACK")&&a.isPacked&&i===!1){const I=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),I}return o}compileAndRun(e,t,n,s,i=!1){n=n||t[0].dtype;const o=this.runWebGLProgram(e,t,n,s,i);return $s().makeTensorFromDataId(o.dataId,o.shape,o.dtype)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){if(this.disposed)return;if(!C().getBool("IS_TEST")){const e=Object.keys(this.binaryCache);e.forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]})}this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=ee(()=>{if(!C().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=C().getBool("DEBUG");C().set("DEBUG",!1);const t=this.abs(Ne(1e-8)).dataSync()[0];if(C().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?V6:H6}uploadToGPU(e){const t=this.texData.get(e),{shape:n,dtype:s,values:i,texture:o,usage:a,isPacked:c}=t;if(o!=null)return;const u=this.activeTimers!=null;let p;u&&(p=qn());let m=t.texShape;if(m==null&&(m=Qj(n,c),t.texShape=m),i!=null){const y=fS(n);let b,w=m[1],I=m[0];const T=i instanceof Uint8Array;c?([w,I]=pc(m[0],m[1]),b=new s8(y,[I,w],T)):b=new n8(y,[I,w],T);const v=this.makeTensorInfo([I,w],s);T?this.texData.get(v.dataId).usage=Cs.PIXELS:this.texData.get(v.dataId).usage=Cs.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(v.dataId),w,I,i);const N=!0,E=this.runWebGLProgram(b,[v],s,null,N),D=this.texData.get(E.dataId);t.texture=D.texture,t.texShape=D.texShape,t.isPacked=D.isPacked,t.usage=D.usage,this.disposeIntermediateTensorInfo(v),this.texData.delete(E.dataId),t.values=null,u&&(this.uploadWaitMs+=qn()-p)}else{const y=this.acquireTexture(m,a,s,c);t.texture=y}}convertAndCacheOnCPU(e,t){const n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=J6(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){const i=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${i} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*Ty(t)}tryRunOnCpuOrThrow(e,t){if(this.shouldExecuteOnCPU(e))try{return t()}catch(n){if(C().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}}function J6(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){const n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}const Z6="2.6.0";function Q6(){C().set("WEBGL_FORCE_F16_TEXTURES",!0)}Fy()&&tb("webgl",()=>new X6,2);const dte={forceHalfFloat:Q6},HC="if (isnan(x)) return x;",e7=`
+    `}}const{segment_util:GC}=iw,M6=rw,P6=ow,z6=aw,G6=dp,V6=1e-7,H6=1e-4,xm={};function Y6(e){return e in xm||(xm[e]={}),xm[e]}function Tm(e,t=!1){if(e==="linear")return t?$6:h6;if(e==="relu")return t?MC:DC;if(e==="elu")return t?zC:FC;if(e==="relu6")return t?PC:kC;if(e==="prelu")return t?hC:lC;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}const q6=128,j6=600;function K6(){return C().global.screen==null?1024:C().global.screen.height*C().global.screen.width*window.devicePixelRatio*j6/1024/1024}const VC=1e3;class X6 extends f{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!C().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){const t=Mi(C().getNumber("WEBGL_VERSION"));this.binaryCache=Y6(C().getNumber("WEBGL_VERSION")),this.gpgpu=new T8(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new i6(this.gpgpu),this.numMBBeforeWarning=K6(),this.texData=new d(this,$s())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((C().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||C().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");const s={};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:Cs.UPLOAD,refCount:1}),s}incRef(e){const t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){const t=this.texData.get(e);t.refCount--}}move(e,t,n,s){if(C().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:Cs.UPLOAD,refCount:1})}disposeIntermediateTensorInfo(e){const t=e.dataId;if(this.texData.has(t)){const n=this.texData.get(t);n.refCount--,n.refCount<1&&this.disposeData(t)}}readSync(e){const t=this.texData.get(e),{values:n,dtype:s,complexTensors:i,slice:o,shape:a,isPacked:c}=t;if(o!=null){let y;c?y=new pu(a,Im):y=new it(a,Im);const b=this.runWebGLProgram(y,[{dataId:e,shape:a,dtype:s}],s),w=this.readSync(b.dataId);return this.disposeIntermediateTensorInfo(b),w}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;const u=this.activeTimers!=null;let p;u&&(p=qn());let m;if(s==="complex64"){const y=i.real.dataSync(),b=i.imag.dataSync();m=ir(y,b)}else m=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=qn()-p),this.convertAndCacheOnCPU(e,m)}async read(e){if(this.pendingRead.has(e)){const w=this.pendingRead.get(e);return new Promise(I=>w.push(I))}const t=this.texData.get(e),{values:n,shape:s,slice:i,dtype:o,complexTensors:a,isPacked:c}=t;if(i!=null){let w;c?w=new pu(s,Im):w=new it(s,Im);const I=this.runWebGLProgram(w,[{dataId:e,shape:s,dtype:o}],o),T=this.read(I.dataId);return this.disposeIntermediateTensorInfo(I),T}if(n!=null)return this.convertAndCacheOnCPU(e);if(!C().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&C().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,p;if(o!=="complex64"&&C().get("WEBGL_BUFFER_SUPPORTED")){p=this.decode(e);const w=this.texData.get(p.dataId);u=this.gpgpu.createBufferFromTexture(w.texture,...lu(s))}this.pendingRead.set(e,[]),o!=="complex64"&&await this.gpgpu.createAndWaitForFence();let m;if(o==="complex64"){const w=await Promise.all([a.real.data(),a.imag.data()]),I=w[0],T=w[1];m=ir(I,T)}else if(u==null)m=this.getValuesFromTexture(e);else{const w=we(s);m=this.gpgpu.downloadFloat32MatrixFromBuffer(u,w)}p!=null&&this.disposeIntermediateTensorInfo(p);const y=this.convertAndCacheOnCPU(e,m),b=this.pendingRead.get(e);return this.pendingRead.delete(e),b.forEach(w=>w(y)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),y}checkNumericalProblems(e){if(e==null)return;for(let t=0;t<e.length;t++){const n=e[t];if(!_j(n))throw C().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){const{shape:t,dtype:n,isPacked:s}=this.texData.get(e),i=we(t);if(C().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const y=this.decode(e),b=this.texData.get(y.dataId),w=this.gpgpu.downloadMatrixFromPackedTexture(b.texture,...lu(t)).subarray(0,i);return this.disposeIntermediateTensorInfo(y),w}const o=C().getBool("WEBGL_PACK")&&s===!0,a=o?fS(t):t,c=o?new t8(a):new e8(a),u=this.runWebGLProgram(c,[{shape:a,dtype:n,dataId:e}],"float32"),p=this.texData.get(u.dataId),m=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(p.texture,p.texShape[0],p.texShape[1]).subarray(0,i);return this.disposeIntermediateTensorInfo(u),m}async time(e){const t=this.activeTimers,n=[];let s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();const i=Ji(this.activeTimers.map(c=>c.query)).filter(c=>c!=null),o=Ji(this.activeTimers.map(c=>c.name)).filter(c=>c!=null);this.activeTimers=t,s&&(this.programTimersStack=null);const a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const c=await Promise.all(i);a.kernelMs=iT(c),a.getExtraProfileInfo=()=>c.map((u,p)=>({name:o[p],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:qn(),endMs:null}}endTimer(e){return C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=qn(),e)}async getQueryTime(e){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);const t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;this.releaseGPUData(e);const{complexTensors:t}=this.texData.get(e);t!=null&&(t.real.dispose(),t.imag.dispose()),this.texData.delete(e)}releaseGPUData(e){const{texture:t,dtype:n,texShape:s,usage:i,isPacked:o,slice:a}=this.texData.get(e),c=a&&a.origDataId||e,u=this.dataRefCount.get(c);u>1?this.dataRefCount.set(c,u-1):(this.dataRefCount.delete(c),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,i,o)));const p=this.texData.get(e);p.texture=null,p.texShape=null,p.isPacked=!1,p.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return C().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=$s().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=q6){const n=this.getCPUBackend();return!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(s=>this.texData.get(s.dataId).texture==null&&we(s.shape)<t)}getGPGPUContext(){return this.gpgpu}complex(e,t){const n=this.makeOutput(e.shape,"complex64"),s=this.texData.get(n.dataId);return s.complexTensors={real:$s().keep(e.clone()),imag:$s().keep(t.clone())},n}real(e){const t=this.texData.get(e.dataId);return t.complexTensors.real.clone()}imag(e){const t=this.texData.get(e.dataId);return t.complexTensors.imag.clone()}slice(e,t,n){if(this.shouldExecuteOnCPU([e])){const o=yK(this.texData.get(e.dataId).values,t,n,e.shape,e.dtype);return this.makeOutput(n,e.dtype,o)}if(we(n)===0)return en([],n,e.dtype);const{isPacked:s}=this.texData.get(e.dataId),i=Xy(e.shape,t,n);if(s||!i){const o=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new n6(n):new e6(n),a=o.getCustomSetupFunc(t);return this.compileAndRun(o,[e],null,a)}return this.uploadToGPU(e.dataId),this.shallowSlice(e,t,n)}shallowSlice(e,t,n){const s=this.texData.get(e.dataId),i=this.makeOutput(n,e.dtype),o=this.texData.get(i.dataId);Object.assign(o,s),o.shape=n,o.dtype=e.dtype;let a=Jy(t,e.strides);s.slice&&(a+=s.slice.flatOffset),o.slice={flatOffset:a,origDataId:s.slice&&s.slice.origDataId||e.dataId};const c=this.dataRefCount.get(o.slice.origDataId)||1;return this.dataRefCount.set(o.slice.origDataId,c+1),i}stridedSlice(e,t,n,s){const i=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.stridedSlice(e,t,n,s));if(i)return i;const o=Wd(t,n,s);if(o.some(c=>c===0))return en([],o);const a=new s6(t,s,o);return this.compileAndRun(a,[e])}reverse(e,t){const n=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new J8(e.shape,t):new X8(e.shape,t);return this.compileAndRun(n,[e])}concat(e,t){if(e[0].dtype==="complex64"){const a=e.map(u=>Fo(u)),c=e.map(u=>Ga(u));return Ci(this.concat(a,t),this.concat(c,t))}if(e.length===1)return e[0];if(e.length>C().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){const a=Math.floor(e.length/2),c=this.concat(e.slice(0,a),t),u=this.concat(e.slice(a),t);return this.concat([c,u],t)}if(C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].rank>1){const a=new P5(e.map(c=>c.shape),t);return this.compileAndRun(a,e)}const n=Ur(e.map(a=>a.shape),t),s=e.map(a=>a.as2D(-1,we(a.shape.slice(t)))),i=new M5(s.map(a=>a.shape)),o=this.compileAndRun(i,s);return o.reshape(n)}neg(e){const t=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.neg(e));if(t)return t;if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,_C,e.dtype);const n=new it(e.shape,_C);return this.compileAndRun(n,[e])}batchMatMul(e,t,n,s){const i=n?e.shape[2]:e.shape[1],o=s?t.shape[1]:t.shape[2],a=n?e.shape[1]:e.shape[2],[c,,]=e.shape;if((i===1||o===1)&&a>VC){n&&(e=Pe(e,[0,2,1])),s&&(t=Pe(t,[0,2,1]));const m=o===1?e:e.as3D(c,a,1),y=o===1?2:1,b=o===1?t.as3D(c,1,a):t;return this.multiply(m,b).sum(y,!0)}const u=Cn(e.dtype,t.dtype),p=new LS(e.shape,[c,i,o],n,s);return this.compileAndRun(p,[e,t],u)}fusedBatchMatMul({a:e,b:t,transposeA:n,transposeB:s,bias:i,activation:o,preluActivationWeights:a}){const c=n?e.shape[2]:e.shape[1],u=s?t.shape[1]:t.shape[2],[p,,]=e.shape,m=Cn(e.dtype,t.dtype),y=i!=null,b=a!=null,w=o?Tm(o,!0):null,I=new LS(e.shape,[p,c,u],n,s,y,w,b),T=[e,t];return i&&T.push(i),a&&T.push(a),this.compileAndRun(I,T,m)}multiply(e,t){if(e.dtype==="complex64"){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),a=new oC(rC.REAL,e.shape,t.shape),c=new oC(rC.IMAG,e.shape,t.shape),u=[this.makeComplexComponentTensorInfo(e,i.complexTensors.real),this.makeComplexComponentTensorInfo(e,i.complexTensors.imag),this.makeComplexComponentTensorInfo(t,o.complexTensors.real),this.makeComplexComponentTensorInfo(t,o.complexTensors.imag)],p=this.compileAndRun(a,u),m=this.compileAndRun(c,u),y=this.complex(p,m);return p.dispose(),m.dispose(),y}const 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a=0;a<=Math.ceil(Math.log2(i))-1;a++){const c=new mC(e.shape,!1,s),u=c.getCustomSetupFunc(a),p=o;o=this.compileAndRun(c,[o],o.dtype,u),p.dispose()}if(n){const a=new mC(e.shape,n,s),c=o;o=this.compileAndRun(a,[o]),c.dispose()}return o}equal(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,v5,"bool");const n=new un(u5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}notEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,N5,"bool");const n=new un(d5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}less(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.less(e,t));if(n)return n;if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,C5,"bool");const s=new un(p5,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}lessEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,R5,"bool");const n=new un(m5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}greater(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.greater(e,t));if(n)return n;if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,O5,"bool");const s=new un(f5,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}greaterEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,E5,"bool");const n=new un(g5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}logicalNot(e){const t=new it(e.shape,_6);return this.compileAndRun(t,[e])}logicalAnd(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,D5,"bool");const n=new un(y5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}logicalOr(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,k5,"bool");const n=new un(b5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}select(e,t,n){const s=new Q8(e.rank,t.shape,t.rank);return this.compileAndRun(s,[e,t,n],Cn(t.dtype,n.dtype))}where(e){Qa("tf.where() in webgl locks the UI thread. 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this.compileAndRun(o,[e,t],s)}complexSeparableBinaryOp(e,t,n){const s=this.texData.get(e.dataId),i=this.texData.get(t.dataId),[o,a]=[[s.complexTensors.real,i.complexTensors.real],[s.complexTensors.imag,i.complexTensors.imag]].map(u=>{const[p,m]=u,y=this.makeComplexComponentTensorInfo(e,p),b=this.makeComplexComponentTensorInfo(t,m),w=new un(n,e.shape,t.shape);return this.compileAndRun(w,[y,b],Cn(p.dtype,m.dtype))}),c=this.complex(o,a);return o.dispose(),a.dispose(),c}makeComplexComponentTensorInfo(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}addN(e){if(e.length===1)return e[0];if(e.length>C().get("WEBGL_MAX_TEXTURES_IN_SHADER")){const o=Math.floor(e.length/2),a=this.addN(e.slice(0,o)),c=this.addN(e.slice(o));return this.addN([a,c])}const t=e.map(o=>o.dtype).reduce((o,a)=>Cn(o,a)),n=e.map(o=>o.shape),s=C().getBool("WEBGL_PACK"),i=s?new SK(e[0].shape,n):new LK(e[0].shape,n);return this.compileAndRun(i,e,t)}subtract(e,t){if(e.dtype==="complex64"&&t.dtype==="complex64")return this.complexSeparableBinaryOp(e,t,wS);const n=Cn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=bK(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,wS,e.dtype);const s=new un(wS,e.shape,t.shape);return this.compileAndRun(s,[e,t],n)}pow(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS"),s=n?new to(T5,e.shape,t.shape):new un(h5,e.shape,t.shape),i=Cn(e.dtype,t.dtype);return this.compileAndRun(s,[e,t],i)}ceil(e){if(this.shouldExecuteOnCPU([e])){const n=lK(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,WC,e.dtype);const t=new it(e.shape,WC);return this.compileAndRun(t,[e])}floor(e){if(this.shouldExecuteOnCPU([e])){const n=dK(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,$C,e.dtype);const t=new it(e.shape,$C);return this.compileAndRun(t,[e])}sign(e){const t=new it(e.shape,p6);return this.compileAndRun(t,[e])}isNaN(e){const t=new it(e.shape,m6);return this.compileAndRun(t,[e],"bool")}isInf(e){const t=new it(e.shape,f6);return this.compileAndRun(t,[e],"bool")}isFinite(e){const t=new it(e.shape,g6);return this.compileAndRun(t,[e],"bool")}round(e){const t=new it(e.shape,y6);return this.compileAndRun(t,[e])}exp(e){if(this.shouldExecuteOnCPU([e])){const n=hK(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,UC,e.dtype);const t=new it(e.shape,UC);return this.compileAndRun(t,[e])}expm1(e){if(this.shouldExecuteOnCPU([e])){const n=uK(this.texData.get(e.dataId).values,e.dtype);return 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this.compileAndRun(t,[e])}relu(e){let t;return C().getBool("WEBGL_PACK")?t=new pu(e.shape,MC):t=new it(e.shape,DC),this.compileAndRun(t,[e])}relu6(e){let t;return C().getBool("WEBGL_PACK")?t=new pu(e.shape,PC):t=new it(e.shape,kC),this.compileAndRun(t,[e])}prelu(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new to(hC,e.shape,t.shape):new un(lC,e.shape,t.shape);return this.compileAndRun(n,[e,t])}elu(e){if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,zC,e.dtype);const t=new it(e.shape,FC);return this.compileAndRun(t,[e])}eluDer(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new to(A5,e.shape,t.shape):new un(I5,e.shape,t.shape);return this.compileAndRun(n,[e,t])}selu(e){const t=new it(e.shape,u6);return this.compileAndRun(t,[e])}int(e){const t=new it(e.shape,W6);return this.compileAndRun(t,[e],"int32")}clip(e,t,n){let s;C().getBool("WEBGL_PACK_CLIP")?s=new U5(e.shape):s=new $5(e.shape);const i=s.getCustomSetupFunc(t,n);return 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K(q,n.outShape)}const v=y?a[0]*a[1]*(a[2]+1):a[0]*a[2]*(a[3]+1),N={dataId:e.dataId,shape:[1,v,n.inChannels],dtype:e.dtype},E=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k(gm(c.shape,N.shape),()=>`packed reshape ${c.shape} to ${N.shape} isn't free`);const D=K(t,[1,n.inChannels,n.outChannels]),F=this.fusedBatchMatMul({a:N,b:D,transposeA:b,transposeB:w,bias:s,activation:i,preluActivationWeights:o}),_=this.texData.get(F.dataId);return k(_.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=E,_.shape=n.outShape,$s().makeTensorFromDataId(F.dataId,n.outShape,F.dtype)}conv2dWithIm2Row(e,t,n,s,i,o){const{filterWidth:a,filterHeight:c,inChannels:u,outWidth:p,outHeight:m,dataFormat:y}=n,b=y==="channelsLast",w=a*c*u,I=m*p,T=[w,I],v=!0,N=!1,E=e.squeeze([0]),D=t.reshape([1,w,-1]),F=new R8(T,E.shape,n),_=this.compileAndRun(F,[E]).reshape([1,T[0],T[1]]),B=s!=null,U=o!=null,Y=i?Tm(i,!0):null,q=new 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s=new j5(n);return this.compileAndRun(s,[e,t])}conv3dDerInput(e,t,n){const s=new H5(n);return this.compileAndRun(s,[e,t])}conv3dDerFilter(e,t,n){const s=new V5(n);return this.compileAndRun(s,[e,t])}cast(e,t){return ov(e,t,this)}unstack(e,t){const n=e.shape[t],s=new Array(e.rank-1);let i=0;for(let u=0;u<e.rank;u++)u!==t&&(s[i++]=e.shape[u]);const o=new Array(e.rank).fill(0),a=e.shape.slice();a[t]=1;const c=new Array(n);for(let u=0;u<c.length;u++)o[t]=u,c[u]=this.slice(e,o,a).reshape(s);return c}avgPool3d(e,t){const n=new SS(t,"avg",!1);return this.compileAndRun(n,[e],"float32")}avgPool3dBackprop(e,t,n){const s=new c5(n);return this.compileAndRun(s,[e],t.dtype)}maxPool3d(e,t){const n=new SS(t,"max",!1);return this.compileAndRun(n,[e],"float32")}maxPool3dBackprop(e,t,n,s){const i=!0,o=new SS(s,"max",i),a=this.compileAndRun(o,[t]),c=new F8(s),u=this.compileAndRun(c,[e,a],t.dtype);return a.dispose(),u}resizeBilinear(e,t,n,s){const i=C().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new q8(e.shape,t,n,s):new Y8(e.shape,t,n,s);return this.compileAndRun(i,[e],"float32")}resizeBilinearBackprop(e,t,n){const s=new H8(e,t,n);return this.compileAndRun(s,[e])}resizeNearestNeighbor(e,t,n,s){const i=new K8(e.shape,t,n,s);return this.compileAndRun(i,[e])}resizeNearestNeighborBackprop(e,t,n){const s=new j8(e,t,n);return this.compileAndRun(s,[e])}multinomial(e,t,n,s){const i=t?e:Uo(e),o=i.shape[0],a=i.shape[1],c=new _8(o,a,n),u=c.getCustomSetupFunc(s);return this.compileAndRun(c,[i],"int32",u)}oneHot(e,t,n,s){const i=new W8(e.size,t,n,s);return this.compileAndRun(i,[e])}diag(e){const t=new Q5(e.size);return this.compileAndRun(t,[e])}cropAndResize(e,t,n,s,i,o){const a=new K5(e.shape,t.shape,s,i,o);return this.compileAndRun(a,[e,t,n],"float32")}depthToSpace(e,t,n){k(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`);const 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I=lu(e.outputShape);a.texShape=I.map(T=>T*2)}if(e.outTexUsage!=null&&(a.usage=e.outTexUsage),we(o.shape)===0)return a.values=wn(o.dtype,0),o;const c=[],u=t.map(I=>{if(I.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let T=this.texData.get(I.dataId);if(T.texture==null){if(!e.packedInputs&&we(I.shape)<=C().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:I.shape,texData:null,isUniform:!0,uniformValues:T.values};e.packedInputs&&(T.isPacked=!0,T.shape=I.shape)}else if(!!T.isPacked!==!!e.packedInputs)I=T.isPacked?this.unpackTensor(I):this.packTensor(I),c.push(I),T=this.texData.get(I.dataId);else if(T.isPacked&&!gm(T.shape,I.shape)){const v=I,N=I.shape;I.shape=T.shape,I=this.packedReshape(I,N),c.push(I),T=this.texData.get(I.dataId),v.shape=N}return this.uploadToGPU(I.dataId),{shape:I.shape,texData:T,isUniform:!1}});this.uploadToGPU(o.dataId);const p={shape:o.shape,texData:a,isUniform:!1},m=C8(e,u,p),y=this.getAndSaveBinary(m,()=>v8(this.gpgpu,e,u,p)),b=this.activeTimers!=null;let w;if(b&&(w=this.startTimer()),N8(this.gpgpu,y,u,p,s),c.forEach(I=>this.disposeIntermediateTensorInfo(I)),b&&(w=this.endTimer(w),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(w)})),!C().getBool("WEBGL_LAZILY_UNPACK")&&a.isPacked&&i===!1){const I=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),I}return o}compileAndRun(e,t,n,s,i=!1){n=n||t[0].dtype;const o=this.runWebGLProgram(e,t,n,s,i);return $s().makeTensorFromDataId(o.dataId,o.shape,o.dtype)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){if(this.disposed)return;if(!C().getBool("IS_TEST")){const e=Object.keys(this.binaryCache);e.forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]})}this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=ee(()=>{if(!C().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=C().getBool("DEBUG");C().set("DEBUG",!1);const t=this.abs(Ne(1e-8)).dataSync()[0];if(C().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?V6:H6}uploadToGPU(e){const t=this.texData.get(e),{shape:n,dtype:s,values:i,texture:o,usage:a,isPacked:c}=t;if(o!=null)return;const u=this.activeTimers!=null;let p;u&&(p=qn());let m=t.texShape;if(m==null&&(m=Qj(n,c),t.texShape=m),i!=null){const y=fS(n);let b,w=m[1],I=m[0];const T=i instanceof Uint8Array;c?([w,I]=fc(m[0],m[1]),b=new s8(y,[I,w],T)):b=new n8(y,[I,w],T);const v=this.makeTensorInfo([I,w],s);T?this.texData.get(v.dataId).usage=Cs.PIXELS:this.texData.get(v.dataId).usage=Cs.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(v.dataId),w,I,i);const N=!0,E=this.runWebGLProgram(b,[v],s,null,N),D=this.texData.get(E.dataId);t.texture=D.texture,t.texShape=D.texShape,t.isPacked=D.isPacked,t.usage=D.usage,this.disposeIntermediateTensorInfo(v),this.texData.delete(E.dataId),t.values=null,u&&(this.uploadWaitMs+=qn()-p)}else{const y=this.acquireTexture(m,a,s,c);t.texture=y}}convertAndCacheOnCPU(e,t){const n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=J6(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){const i=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${i} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*Ty(t)}tryRunOnCpuOrThrow(e,t){if(this.shouldExecuteOnCPU(e))try{return t()}catch(n){if(C().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}}function J6(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){const n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}const Z6="2.6.0";function Q6(){C().set("WEBGL_FORCE_F16_TEXTURES",!0)}Fy()&&tb("webgl",()=>new X6,2);const dte={forceHalfFloat:Q6},HC="if (isnan(x)) return x;",e7=`
   if (isnan(a)) return a;
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@@ -3649,14 +3649,14 @@ return (round(mod(b, 2.0)) != 1) ?
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;function Am(e){return({inputs:t,backend:n})=>{const{x:s}=t,i=n,o=new it(s.shape,e);return i.runWebGLProgram(o,[s],s.dtype)}}function xS(e,t,n,s){return({inputs:i,backend:o})=>{const{a,b:c}=i,u=o,p=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new to(t,a.shape,c.shape,!!n):new hn(e,a.shape,c.shape),m=s||a.dtype,y=u.runWebGLProgram(p,[a,c],m);return y}}const n7=e7+`
+`;function Am(e){return({inputs:t,backend:n})=>{const{x:s}=t,i=n,o=new it(s.shape,e);return i.runWebGLProgram(o,[s],s.dtype)}}function xS(e,t,n,s){return({inputs:i,backend:o})=>{const{a,b:c}=i,u=o,p=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new to(t,a.shape,c.shape,!!n):new un(e,a.shape,c.shape),m=s||a.dtype,y=u.runWebGLProgram(p,[a,c],m);return y}}const n7=e7+`
   return atan(a, b);
 `,s7=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+t7+`
   return result;
-`,i7=xS(n7,s7),r7={kernelName:Ai,backendName:"webgl",kernelFunc:i7};function TS(e){const{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}const o7={kernelName:Sl,backendName:"webgl",kernelFunc:TS};function a7(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;hu(i,"avgPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:u}=s,p=1;k(rn(a,p),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${p}'`);const m=Wn(i.shape,o,a,p,c,u);if(m.filterWidth===1&&m.filterHeight===1&&ot(m.inShape,m.outShape))return TS({inputs:{x:i},backend:n});const y=new du(m,"avg",!1);return n.runWebGLProgram(y,[i],"float32")}const c7={kernelName:ei,backendName:"webgl",kernelFunc:a7};function l7(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o}=t,a=o;hu([i,o],"avgPoolBackprop");const{filterSize:c,strides:u,pad:p}=s,m=Wn(a.shape,c,u,1,p),y=new a5(m);return n.runWebGLProgram(y,[i],a.dtype)}const h7={kernelName:Sa,backendName:"webgl",kernelFunc:l7};class u7{constructor(e,t,n,s,i,o){this.outputShape=[],this.variableNames=["x","mean","variance"],nt(e,t),nt(e,n);let a="0.0";s!=null&&(nt(e,s),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let c="1.0";i!=null&&(nt(e,i),this.variableNames.push("scale"),c="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+`,i7=xS(n7,s7),r7={kernelName:Ai,backendName:"webgl",kernelFunc:i7};function TS(e){const{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}const o7={kernelName:Sl,backendName:"webgl",kernelFunc:TS};function a7(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;hu(i,"avgPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:u}=s,p=1;k(on(a,p),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${p}'`);const m=Wn(i.shape,o,a,p,c,u);if(m.filterWidth===1&&m.filterHeight===1&&ot(m.inShape,m.outShape))return TS({inputs:{x:i},backend:n});const y=new du(m,"avg",!1);return n.runWebGLProgram(y,[i],"float32")}const c7={kernelName:ei,backendName:"webgl",kernelFunc:a7};function l7(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o}=t,a=o;hu([i,o],"avgPoolBackprop");const{filterSize:c,strides:u,pad:p}=s,m=Wn(a.shape,c,u,1,p),y=new a5(m);return n.runWebGLProgram(y,[i],a.dtype)}const h7={kernelName:xa,backendName:"webgl",kernelFunc:l7};class u7{constructor(e,t,n,s,i,o){this.outputShape=[],this.variableNames=["x","mean","variance"],nt(e,t),nt(e,n);let a="0.0";s!=null&&(nt(e,s),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let c="1.0";i!=null&&(nt(e,i),this.variableNames.push("scale"),c="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -3681,7 +3681,7 @@ return (round(mod(b, 2.0)) != 1) ?
       }
     `}}const p7=({inputs:e,backend:t,attrs:n})=>{const{x:s,mean:i,variance:o,offset:a,scale:c}=e;k(i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k(c==null||i.shape.length===c.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=n;u==null&&(u=.001);const p=[s,i,o];let m=null;a!=null&&(m=a.shape,p.push(a));let y=null;c!=null&&(y=c.shape,p.push(c));const b=C().getBool("WEBGL_PACK_NORMALIZATION")?new d7(s.shape,i.shape,o.shape,m,y,u):new u7(s.shape,i.shape,o.shape,m,y,u),w=t.runWebGLProgram(b,p,p[0].dtype);return w},m7={kernelName:Ll,backendName:"webgl",kernelFunc:p7};const f7=HC+`
   return cos(x);
-`,g7=Am(f7),y7={kernelName:Ia,backendName:"webgl",kernelFunc:g7};const b7=`
+`,g7=Am(f7),y7={kernelName:Ta,backendName:"webgl",kernelFunc:g7};const b7=`
 if (a == b) {
   return 1.0;
 };
@@ -3703,7 +3703,7 @@ return a / b;`,w7=`
   }
 
   return result;
-`,L7=xS(b7,w7,!0),S7={kernelName:xa,backendName:"webgl",kernelFunc:L7};class I7{constructor(e){this.variableNames=["Image"],this.outputShape=[];const t=e[2];this.outputShape=e,this.userCode=`
+`,L7=xS(b7,w7,!0),S7={kernelName:Aa,backendName:"webgl",kernelFunc:L7};class I7{constructor(e){this.variableNames=["Image"],this.outputShape=[];const t=e[2];this.outputShape=e,this.userCode=`
         void main() {
           ivec4 coords = getOutputCoords();
           int x = coords[2];
@@ -3773,7 +3773,7 @@ return a / b;`,w7=`
 
         ${t.output} = result;
       }
-    `}}const v7={kernelName:yd,backendName:"webgl",kernelFunc:N7};let Lc;function N7(e){const{inputs:t,backend:n,attrs:s}=e;let{pixels:i}=t;const{numChannels:o}=s,a=typeof HTMLVideoElement!="undefined"&&i instanceof HTMLVideoElement,c=typeof HTMLImageElement!="undefined"&&i instanceof HTMLImageElement,[u,p]=a?[i.videoWidth,i.videoHeight]:[i.width,i.height],m=[p,u],y=[p,u,o];(c||a)&&(Lc==null&&(Lc=document.createElement("canvas").getContext("2d")),Lc.canvas.width=u,Lc.canvas.height=p,Lc.drawImage(i,0,0,u,p),i=Lc.canvas);const b=n.makeTensorInfo(m,"int32");n.texData.get(b.dataId).usage=Cs.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(b.dataId),i);const w=C().getBool("WEBGL_PACK")?new A7(y):new T7(y),I=n.runWebGLProgram(w,[b],"int32");return n.disposeData(b.dataId),I}function C7(e){const t=[];for(;t.length===0||t[t.length-1].outSize!==1;){const n=t.length?t[t.length-1].outSize:e[1],s=uh(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function R7(e,t,n,s){const i=C7(e.shape);let o=e;for(let a=0;a<i.length;a++){const{inSize:c,windowSize:u,outSize:p}=i[a],m=new AC({windowSize:u,inSize:c,batchSize:e.shape[0],outSize:p},n),y=o;o=s.runWebGLProgram(m,[o],t),y.dataId!==e.dataId&&s.disposeData(y.dataId)}return o}function O7(e,t,n){const s=[mc(e.shape),...fc(e.shape)],i={dtype:e.dtype,shape:s,dataId:e.dataId},o=[mc(t),...fc(t)],a=new vC(o,s),c=!0,u=n.runWebGLProgram(a,[i],e.dtype,null,c);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function AS(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{shape:o}=s,a=n,c=we(i.shape),u=Id(o,c),p=we(u);k(c===p,()=>`The new shape (${u}) has ${p} elements and the old shape (${i.shape}) has ${c} elements. The new shape and old shape must have the same number of elements.`);const m=a.texData.get(i.dataId);return m.isPacked&&!gm(i.shape,u)&&!(m.texture!==null&&gm(m.shape,u))?O7(i,u,a):(a.incRef(i.dataId),{dataId:i.dataId,shape:u,dtype:i.dtype})}const E7={kernelName:El,backendName:"webgl",kernelFunc:AS};function D7(e,t,n,s){const i=we(t),o=we(e.shape),a=o/i,c=AS({inputs:{x:e},attrs:{shape:[a,i]},backend:s}),u=R7(c,e.dtype,"max",s),p=AS({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(c),s.disposeIntermediateTensorInfo(u),p}class k7{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let o=0;o<n.length;o++)n[o]=e[t[o]];this.outputShape=n,this.rank=n.length;const s=Et(this.rank),i=F7(t);this.userCode=`
+    `}}const v7={kernelName:yd,backendName:"webgl",kernelFunc:N7};let Ic;function N7(e){const{inputs:t,backend:n,attrs:s}=e;let{pixels:i}=t;const{numChannels:o}=s,a=typeof HTMLVideoElement!="undefined"&&i instanceof HTMLVideoElement,c=typeof HTMLImageElement!="undefined"&&i instanceof HTMLImageElement,[u,p]=a?[i.videoWidth,i.videoHeight]:[i.width,i.height],m=[p,u],y=[p,u,o];(c||a)&&(Ic==null&&(Ic=document.createElement("canvas").getContext("2d")),Ic.canvas.width=u,Ic.canvas.height=p,Ic.drawImage(i,0,0,u,p),i=Ic.canvas);const b=n.makeTensorInfo(m,"int32");n.texData.get(b.dataId).usage=Cs.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(b.dataId),i);const w=C().getBool("WEBGL_PACK")?new A7(y):new T7(y),I=n.runWebGLProgram(w,[b],"int32");return n.disposeData(b.dataId),I}function C7(e){const t=[];for(;t.length===0||t[t.length-1].outSize!==1;){const n=t.length?t[t.length-1].outSize:e[1],s=uh(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function R7(e,t,n,s){const i=C7(e.shape);let o=e;for(let a=0;a<i.length;a++){const{inSize:c,windowSize:u,outSize:p}=i[a],m=new AC({windowSize:u,inSize:c,batchSize:e.shape[0],outSize:p},n),y=o;o=s.runWebGLProgram(m,[o],t),y.dataId!==e.dataId&&s.disposeData(y.dataId)}return o}function O7(e,t,n){const s=[gc(e.shape),...yc(e.shape)],i={dtype:e.dtype,shape:s,dataId:e.dataId},o=[gc(t),...yc(t)],a=new vC(o,s),c=!0,u=n.runWebGLProgram(a,[i],e.dtype,null,c);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function AS(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{shape:o}=s,a=n,c=we(i.shape),u=Id(o,c),p=we(u);k(c===p,()=>`The new shape (${u}) has ${p} elements and the old shape (${i.shape}) has ${c} elements. The new shape and old shape must have the same number of elements.`);const m=a.texData.get(i.dataId);return m.isPacked&&!gm(i.shape,u)&&!(m.texture!==null&&gm(m.shape,u))?O7(i,u,a):(a.incRef(i.dataId),{dataId:i.dataId,shape:u,dtype:i.dtype})}const E7={kernelName:El,backendName:"webgl",kernelFunc:AS};function D7(e,t,n,s){const i=we(t),o=we(e.shape),a=o/i,c=AS({inputs:{x:e},attrs:{shape:[a,i]},backend:s}),u=R7(c,e.dtype,"max",s),p=AS({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(c),s.disposeIntermediateTensorInfo(u),p}class k7{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let o=0;o<n.length;o++)n[o]=e[t[o]];this.outputShape=n,this.rank=n.length;const s=Et(this.rank),i=F7(t);this.userCode=`
     void main() {
       ${s} resRC = getOutputCoords();
       setOutput(getA(${i}));
@@ -3795,7 +3795,7 @@ return a / b;`,w7=`
       }
       setOutput(result);
     }
-    `}}function YC(e,t,n){const s=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new _7(e.shape,t):new k7(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}const W7={kernelName:Nl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{reductionIndices:i,keepDims:o}=t,a=n,c=s.shape.length,u=ft(i,s.shape);let p=u;const m=_n(p,c),y=m!=null,b=a.shouldExecuteOnCPU([s]);let w=s;if(y){if(b){const E=a.texData.get(w.dataId),D=E.values,F=new Array(c);for(let U=0;U<F.length;U++)F[U]=s.shape[m[U]];const _=Q0(D,s.shape,s.dtype,m,F);w=a.makeTensorInfo(F,s.dtype);const B=a.texData.get(w.dataId);B.values=_}else w=YC(s,m,a);p=Is(p.length,c)}ss("max",p,c);const[I,T]=On(w.shape,p);let v=I;o&&(v=En(I,u));let N;if(b){const E=a.texData.get(w.dataId),D=E.values,F=mK(D,we(T),v,s.dtype);N=a.makeTensorInfo(v,s.dtype);const _=a.texData.get(N.dataId);_.values=F}else N=D7(w,T,v,a);return y&&a.disposeIntermediateTensorInfo(w),N}};function $7(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;hu(i,"maxPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:u}=s,p=1;k(rn(a,p),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${p}'`);const m=Wn(i.shape,o,a,p,c,u);if(m.filterWidth===1&&m.filterHeight===1&&ot(m.inShape,m.outShape))return TS({inputs:{x:i},backend:n});const y=new du(m,"max",!1);return n.runWebGLProgram(y,[i],i.dtype)}const U7={kernelName:Cl,backendName:"webgl",kernelFunc:$7};function B7(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o,output:a}=t,c=o;hu([o,a],"maxPoolBackprop");const{filterSize:u,strides:p,pad:m,dimRoundingMode:y}=s,b=Wn(c.shape,u,p,1,m,y),w=!0,I=new du(b,"max",w),T=n.runWebGLProgram(I,[c],c.dtype),v=new k8(b),N=n.runWebGLProgram(v,[i,T],c.dtype);return n.disposeIntermediateTensorInfo(T),N}const M7={kernelName:ad,backendName:"webgl",kernelFunc:B7};function P7(e,t,n,s){let i=new du(n,"max",!1);const o=s.runWebGLProgram(i,[e],"float32");i=new du(n,"max",!0,!0,t);const a=s.runWebGLProgram(i,[e],"float32");return[o,a]}const z7={kernelName:cd,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{filterSize:i,strides:o,pad:a,includeBatchInIndex:c}=t,u=n;k(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);const p=[1,1];k(rn(o,p),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${p}'`);const m=Wn(s.shape,i,o,p,a),[y,b]=P7(s,c,m,u);return[y,b]}};const G7={kernelName:Kg,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Ja("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c}=n,u=t,p=u.readSync(s.dataId),m=u.readSync(i.dataId),y=o,b=a,w=c;return wp(p,m,y,b,w)}};const V7=Lp,H7={kernelName:hd,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Ja("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,padToMaxOutputSize:u}=n,p=t,m=p.readSync(s.dataId),y=p.readSync(i.dataId),{selectedIndices:b,validOutputs:w}=V7(m,y,o,a,c,u);return[b,w]}};const Y7=Sp,q7={kernelName:ud,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Ja("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,softNmsSigma:u}=n,p=t,m=p.readSync(s.dataId),y=p.readSync(i.dataId),b=o,w=a,I=c,T=u,{selectedIndices:v,selectedScores:N}=Y7(m,y,b,w,I,T);return[v,N]}};class j7{constructor(e,t,n,s){this.variableNames=["Image"],this.outputShape=[];const i=e[1],o=e[2],a=Math.sin(t).toFixed(3),c=Math.cos(t).toFixed(3);this.outputShape=e;const[u,p]=qb(s,i,o),m=u.toFixed(3),y=p.toFixed(3);let b="";typeof n=="number"?b=`float outputValue = ${n.toFixed(2)};`:b=`
+    `}}function YC(e,t,n){const s=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new _7(e.shape,t):new k7(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}const W7={kernelName:Nl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{reductionIndices:i,keepDims:o}=t,a=n,c=s.shape.length,u=ft(i,s.shape);let p=u;const m=_n(p,c),y=m!=null,b=a.shouldExecuteOnCPU([s]);let w=s;if(y){if(b){const E=a.texData.get(w.dataId),D=E.values,F=new Array(c);for(let U=0;U<F.length;U++)F[U]=s.shape[m[U]];const _=Q0(D,s.shape,s.dtype,m,F);w=a.makeTensorInfo(F,s.dtype);const B=a.texData.get(w.dataId);B.values=_}else w=YC(s,m,a);p=Is(p.length,c)}ss("max",p,c);const[I,T]=On(w.shape,p);let v=I;o&&(v=En(I,u));let N;if(b){const E=a.texData.get(w.dataId),D=E.values,F=mK(D,we(T),v,s.dtype);N=a.makeTensorInfo(v,s.dtype);const _=a.texData.get(N.dataId);_.values=F}else N=D7(w,T,v,a);return y&&a.disposeIntermediateTensorInfo(w),N}};function $7(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;hu(i,"maxPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:u}=s,p=1;k(on(a,p),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${p}'`);const m=Wn(i.shape,o,a,p,c,u);if(m.filterWidth===1&&m.filterHeight===1&&ot(m.inShape,m.outShape))return TS({inputs:{x:i},backend:n});const y=new du(m,"max",!1);return n.runWebGLProgram(y,[i],i.dtype)}const U7={kernelName:Cl,backendName:"webgl",kernelFunc:$7};function B7(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o,output:a}=t,c=o;hu([o,a],"maxPoolBackprop");const{filterSize:u,strides:p,pad:m,dimRoundingMode:y}=s,b=Wn(c.shape,u,p,1,m,y),w=!0,I=new du(b,"max",w),T=n.runWebGLProgram(I,[c],c.dtype),v=new k8(b),N=n.runWebGLProgram(v,[i,T],c.dtype);return n.disposeIntermediateTensorInfo(T),N}const M7={kernelName:ad,backendName:"webgl",kernelFunc:B7};function P7(e,t,n,s){let i=new du(n,"max",!1);const o=s.runWebGLProgram(i,[e],"float32");i=new du(n,"max",!0,!0,t);const a=s.runWebGLProgram(i,[e],"float32");return[o,a]}const z7={kernelName:cd,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{filterSize:i,strides:o,pad:a,includeBatchInIndex:c}=t,u=n;k(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);const p=[1,1];k(on(o,p),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${p}'`);const m=Wn(s.shape,i,o,p,a),[y,b]=P7(s,c,m,u);return[y,b]}};const G7={kernelName:Kg,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Qa("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c}=n,u=t,p=u.readSync(s.dataId),m=u.readSync(i.dataId),y=o,b=a,w=c;return wp(p,m,y,b,w)}};const V7=Lp,H7={kernelName:hd,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Qa("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,padToMaxOutputSize:u}=n,p=t,m=p.readSync(s.dataId),y=p.readSync(i.dataId),{selectedIndices:b,validOutputs:w}=V7(m,y,o,a,c,u);return[b,w]}};const Y7=Sp,q7={kernelName:ud,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Qa("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,softNmsSigma:u}=n,p=t,m=p.readSync(s.dataId),y=p.readSync(i.dataId),b=o,w=a,I=c,T=u,{selectedIndices:v,selectedScores:N}=Y7(m,y,b,w,I,T);return[v,N]}};class j7{constructor(e,t,n,s){this.variableNames=["Image"],this.outputShape=[];const i=e[1],o=e[2],a=Math.sin(t).toFixed(3),c=Math.cos(t).toFixed(3);this.outputShape=e;const[u,p]=qb(s,i,o),m=u.toFixed(3),y=p.toFixed(3);let b="";typeof n=="number"?b=`float outputValue = ${n.toFixed(2)};`:b=`
         vec3 fill = vec3(${n.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         void main() {
@@ -3814,12 +3814,12 @@ return a / b;`,w7=`
         }
     `}}const K7={kernelName:bd,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{image:s}=e,{radians:i,fillValue:o,center:a}=t,c=n,u=new j7(s.shape,i,o,a),p=c.runWebGLProgram(u,[s],s.dtype);return p}};const X7=HC+`
   return sin(x);
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Cc(this.score,this.classScore,this.className,this.relativeBox,{width:r,height:l})}}class Yt extends Cc{constructor(r,l,h){super(r,r,"",l,h)}forSize(r,l){const{score:h,relativeBox:d,imageDims:f}=super.forSize(r,l);return new Yt(h,d,f)}}function zS(r,l,h=!0){const d=Math.max(0,Math.min(r.right,l.right)-Math.max(r.left,l.left)),f=Math.max(0,Math.min(r.bottom,l.bottom)-Math.max(r.top,l.top)),g=d*f;return h?g/(r.area+l.area-g):g/Math.min(r.area,l.area)}function GS(r){const l=r.map(L=>L.x),h=r.map(L=>L.y),d=l.reduce((L,x)=>x<L?x:L,Infinity),f=h.reduce((L,x)=>x<L?x:L,Infinity),g=l.reduce((L,x)=>L<x?x:L,0),S=h.reduce((L,x)=>L<x?x:L,0);return new wu(d,f,g,S)}function VS(r,l,h,d=!0){let f=l.map((S,L)=>({score:S,boxIndex:L})).sort((S,L)=>S.score-L.score).map(S=>S.boxIndex);const g=[];for(;f.length>0;){const S=f.pop();g.push(S);const L=f,x=[];for(let A=0;A<L.length;A++){const O=L[A],C=r[S],$=r[O];x.push(zS(C,$,d))}f=f.filter((A,O)=>x[O]<=h)}return g}const Gi=Ye(Je());function yi(r,l){return 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Ze(d,f)).add(h))}get shift(){return new Ze(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(r=>r.sub(this._shift).div(new Ze(this.imageWidth,this.imageHeight)))}forSize(r,l){return new this.constructor(this.relativePositions,{width:r,height:l})}shiftBy(r,l){return new this.constructor(this.relativePositions,this._imgDims,new Ze(r,l))}shiftByPoint(r){return this.shiftBy(r.x,r.y)}align(r,l={}){if(r){const f=r instanceof Yt?r.box.floor():new Ct(r);return this.shiftBy(f.x,f.y).align(null,l)}const{useDlibAlignment:h,minBoxPadding:d}=Object.assign({},{useDlibAlignment:!1,minBoxPadding:.2},l);return h?this.alignDlib():this.alignMinBbox(d)}alignDlib(){const r=this.getRefPointsForAlignment(),[l,h,d]=r,f=O=>d.sub(O).magnitude(),g=(f(l)+f(h))/2,S=Math.floor(g/UX),L=Qo(r),x=Math.floor(Math.max(0,L.x-WX*S)),A=Math.floor(Math.max(0,L.y-$X*S));return new Su(x,A,Math.min(S,this.imageWidth+x),Math.min(S,this.imageHeight+A))}alignMinBbox(r){const l=GS(this.positions);return l.pad(l.width*r,l.height*r)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}}class BX extends qs{getRefPointsForAlignment(){const r=this.positions;return[r[0],r[1],Qo([r[3],r[4]])]}}class Iu extends qs{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Qo)}}class km{constructor(r,l){this._label=r,this._distance=l}get label(){return this._label}get distance(){return this._distance}toString(r=!0){return`${this.label}${r?` (${Zo(this.distance)})`:""}`}}class Fm extends Ct{static assertIsValidLabeledBox(r,l){if(Ct.assertIsValidBox(r,l),!gi(r.label))throw new Error(`${l} - expected property label (${r.label}) to be a number`)}constructor(r,l){super(r);this._label=l}get label(){return this._label}}class ea{constructor(r,l){if(!(typeof r=="string"))throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(l)||l.some(h=>!(h instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=r,this._descriptors=l}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(r=>Array.from(r))}}static fromJSON(r){const l=r.descriptors.map(h=>new Float32Array(h));return new ea(r.label,l)}}class MX extends Fm{static assertIsValidPredictedBox(r,l){if(Fm.assertIsValidLabeledBox(r,l),!Nc(r.score)||!Nc(r.classScore))throw new Error(`${l} - expected properties score (${r.score}) and (${r.classScore}) to be a number between [0, 1]`)}constructor(r,l,h,d){super(r,l);this._score=h,this._classScore=d}get score(){return this._score}get classScore(){return this._classScore}}function Vi(r){return r.detection instanceof Yt}function ta(r,l){const h={detection:l};return Object.assign({},r,h)}function YS(){const r=window.fetch||function(){throw new Error("fetch - missing fetch implementation for browser environment")},l=function(){throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:r,readFile:l}}function _m(r){let l="";if(!r)try{r=require("fs")}catch(d){l=d.toString()}const h=r?function(d){return new Promise((f,g)=>{r.readFile(d,function(S,L){return S?g(S):f(L)})})}:function(){throw new Error(`readFile - failed to require fs in nodejs environment with error: ${l}`)};return{readFile:h}}function qS(){const r=global.Canvas||global.HTMLCanvasElement,l=global.Image||global.HTMLImageElement,h=function(){if(r)return new r;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},d=function(){if(l)return new l;throw new Error("createImageElement - missing Image implementation for nodejs environment")},f=global.fetch||function(){throw new Error("fetch - missing fetch implementation for nodejs environment")},g=_m();return{Canvas:r||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:l||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:h,createImageElement:d,fetch:f,...g}}function jS(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}const KS=Ye(a2());let yn;function PX(){if(!yn)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return yn}function XS(r){yn=r}function JS(){if(jS())return XS(YS());if(KS.isNodejs())return XS(qS())}function zX(r){if(yn||JS(),!yn)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");const{Canvas:l=yn.Canvas,Image:h=yn.Image}=r;yn.Canvas=l,yn.Image=h,yn.createCanvasElement=r.createCanvasElement||(()=>new l),yn.createImageElement=r.createImageElement||(()=>new h),yn.ImageData=r.ImageData||yn.ImageData,yn.Video=r.Video||yn.Video,yn.fetch=r.fetch||yn.fetch,yn.readFile=r.readFile||yn.readFile}const gt={getEnv:PX,setEnv:XS,initialize:JS,createBrowserEnv:YS,createFileSystem:_m,createNodejsEnv:qS,monkeyPatch:zX,isBrowser:jS,isNodejs:KS.isNodejs};JS();function na(r){return!gt.isNodejs()&&typeof r=="string"?document.getElementById(r):r}function es(r){const{Canvas:l,CanvasRenderingContext2D:h}=gt.getEnv();if(r instanceof h)return r;const d=na(r);if(!(d instanceof l))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");const f=d.getContext("2d");if(!f)throw new Error("resolveContext2d - canvas 2d context is null");return f}var Hi;(function(r){r.TOP_LEFT="TOP_LEFT",r.TOP_RIGHT="TOP_RIGHT",r.BOTTOM_LEFT="BOTTOM_LEFT",r.BOTTOM_RIGHT="BOTTOM_RIGHT"})(Hi||(Hi={}));class Wm{constructor(r={}){const{anchorPosition:l,backgroundColor:h,fontColor:d,fontSize:f,fontStyle:g,padding:S}=r;this.anchorPosition=l||Hi.TOP_LEFT,this.backgroundColor=h||"rgba(0, 0, 0, 0.5)",this.fontColor=d||"rgba(255, 255, 255, 1)",this.fontSize=f||14,this.fontStyle=g||"Georgia",this.padding=S||4}}class Rc{constructor(r,l,h={}){this.text=typeof r=="string"?[r]:r instanceof Rc?r.text:r,this.anchor=l,this.options=new Wm(h)}measureWidth(r){const{padding:l}=this.options;return this.text.map(h=>r.measureText(h).width).reduce((h,d)=>h<d?d:h,0)+2*l}measureHeight(){const{fontSize:r,padding:l}=this.options;return this.text.length*r+2*l}getUpperLeft(r,l){const{anchorPosition:h}=this.options,d=h===Hi.BOTTOM_RIGHT||h===Hi.TOP_RIGHT,f=h===Hi.BOTTOM_LEFT||h===Hi.BOTTOM_RIGHT,g=this.measureWidth(r),S=this.measureHeight(),L=d?this.anchor.x-g:this.anchor.x,x=f?this.anchor.y-S:this.anchor.y;if(l){const{width:A,height:O}=l,C=Math.max(Math.min(L,A-g),0),$=Math.max(Math.min(x,O-S),0);return{x:C,y:$}}return{x:L,y:x}}draw(r){const l=na(r),h=es(l),{backgroundColor:d,fontColor:f,fontSize:g,fontStyle:S,padding:L}=this.options;h.font=`${g}px ${S}`;const x=this.measureWidth(h),A=this.measureHeight();h.fillStyle=d;const O=this.getUpperLeft(h,l);h.fillRect(O.x,O.y,x,A),h.fillStyle=f,this.text.forEach((C,$)=>{const z=L+O.x,ne=L+O.y+($+1)*g;h.fillText(C,z,ne)})}}class l2{constructor(r={}){const{boxColor:l,lineWidth:h,label:d,drawLabelOptions:f}=r;this.boxColor=l||"rgba(0, 0, 255, 1)",this.lineWidth=h||2,this.label=d;const g={anchorPosition:Hi.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Wm(Object.assign({},g,f))}}class ZS{constructor(r,l={}){this.box=new Ct(r),this.options=new l2(l)}draw(r){const l=es(r),{boxColor:h,lineWidth:d}=this.options,{x:f,y:g,width:S,height:L}=this.box;l.strokeStyle=h,l.lineWidth=d,l.strokeRect(f,g,S,L);const{label:x}=this.options;x&&new Rc([x],{x:f-d/2,y:g},this.options.drawLabelOptions).draw(r)}}function GX(r,l){const h=Array.isArray(l)?l:[l];h.forEach(d=>{const f=d instanceof Yt?d.score:Vi(d)?d.detection.score:void 0,g=d instanceof Yt?d.box:Vi(d)?d.detection.box:new Ct(d),S=f?`${Zo(f)}`:void 0;new ZS(g,{label:S}).draw(r)})}function xu(r){const{Image:l,Video:h}=gt.getEnv();return r instanceof l&&r.complete||r instanceof h&&r.readyState>=3}function QS(r){return new Promise((l,h)=>{if(r instanceof gt.getEnv().Canvas||xu(r))return l(null);function d(g){if(!g.currentTarget)return;g.currentTarget.removeEventListener("load",d),g.currentTarget.removeEventListener("error",f),l(g)}function 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r=this.getRefPointsForAlignment(),[l,h,d]=r,f=O=>d.sub(O).magnitude(),g=(f(l)+f(h))/2,S=Math.floor(g/UX),L=ta(r),x=Math.floor(Math.max(0,L.x-WX*S)),A=Math.floor(Math.max(0,L.y-$X*S));return new Su(x,A,Math.min(S,this.imageWidth+x),Math.min(S,this.imageHeight+A))}alignMinBbox(r){const l=GS(this.positions);return l.pad(l.width*r,l.height*r)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}}class BX extends qs{getRefPointsForAlignment(){const r=this.positions;return[r[0],r[1],ta([r[3],r[4]])]}}class Iu extends qs{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(ta)}}class km{constructor(r,l){this._label=r,this._distance=l}get label(){return this._label}get distance(){return this._distance}toString(r=!0){return`${this.label}${r?` (${ea(this.distance)})`:""}`}}class Fm extends Ct{static assertIsValidLabeledBox(r,l){if(Ct.assertIsValidBox(r,l),!gi(r.label))throw new Error(`${l} - expected property label (${r.label}) to be a number`)}constructor(r,l){super(r);this._label=l}get label(){return this._label}}class na{constructor(r,l){if(!(typeof r=="string"))throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(l)||l.some(h=>!(h instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=r,this._descriptors=l}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(r=>Array.from(r))}}static fromJSON(r){const l=r.descriptors.map(h=>new Float32Array(h));return new na(r.label,l)}}class MX extends Fm{static assertIsValidPredictedBox(r,l){if(Fm.assertIsValidLabeledBox(r,l),!Rc(r.score)||!Rc(r.classScore))throw new Error(`${l} - expected properties score (${r.score}) and (${r.classScore}) to be a number between [0, 1]`)}constructor(r,l,h,d){super(r,l);this._score=h,this._classScore=d}get score(){return this._score}get classScore(){return this._classScore}}function Vi(r){return r.detection instanceof Yt}function sa(r,l){const h={detection:l};return Object.assign({},r,h)}function YS(){const r=window.fetch||function(){throw new Error("fetch - missing fetch implementation for browser environment")},l=function(){throw new Error("readFile - filesystem not available for browser 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Hi;(function(r){r.TOP_LEFT="TOP_LEFT",r.TOP_RIGHT="TOP_RIGHT",r.BOTTOM_LEFT="BOTTOM_LEFT",r.BOTTOM_RIGHT="BOTTOM_RIGHT"})(Hi||(Hi={}));class Wm{constructor(r={}){const{anchorPosition:l,backgroundColor:h,fontColor:d,fontSize:f,fontStyle:g,padding:S}=r;this.anchorPosition=l||Hi.TOP_LEFT,this.backgroundColor=h||"rgba(0, 0, 0, 0.5)",this.fontColor=d||"rgba(255, 255, 255, 1)",this.fontSize=f||14,this.fontStyle=g||"Georgia",this.padding=S||4}}class Ec{constructor(r,l,h={}){this.text=typeof r=="string"?[r]:r instanceof Ec?r.text:r,this.anchor=l,this.options=new Wm(h)}measureWidth(r){const{padding:l}=this.options;return this.text.map(h=>r.measureText(h).width).reduce((h,d)=>h<d?d:h,0)+2*l}measureHeight(){const{fontSize:r,padding:l}=this.options;return this.text.length*r+2*l}getUpperLeft(r,l){const{anchorPosition:h}=this.options,d=h===Hi.BOTTOM_RIGHT||h===Hi.TOP_RIGHT,f=h===Hi.BOTTOM_LEFT||h===Hi.BOTTOM_RIGHT,g=this.measureWidth(r),S=this.measureHeight(),L=d?this.anchor.x-g:this.anchor.x,x=f?this.anchor.y-S:this.anchor.y;if(l){const{width:A,height:O}=l,C=Math.max(Math.min(L,A-g),0),$=Math.max(Math.min(x,O-S),0);return{x:C,y:$}}return{x:L,y:x}}draw(r){const l=ia(r),h=es(l),{backgroundColor:d,fontColor:f,fontSize:g,fontStyle:S,padding:L}=this.options;h.font=`${g}px ${S}`;const x=this.measureWidth(h),A=this.measureHeight();h.fillStyle=d;const O=this.getUpperLeft(h,l);h.fillRect(O.x,O.y,x,A),h.fillStyle=f,this.text.forEach((C,$)=>{const z=L+O.x,ne=L+O.y+($+1)*g;h.fillText(C,z,ne)})}}class l2{constructor(r={}){const{boxColor:l,lineWidth:h,label:d,drawLabelOptions:f}=r;this.boxColor=l||"rgba(0, 0, 255, 1)",this.lineWidth=h||2,this.label=d;const g={anchorPosition:Hi.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new 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To get the original bytes, call tensor.bytes().")}return r}async bytes(){this.throwIfDisposed();const r=await Yi().read(this.dataId);return this.dtype==="string"?r:new Uint8Array(r.buffer)}dispose(){if(this.isDisposed)return;Yi().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(r=!1){return Wc.print(this,r)}clone(){return this.throwIfDisposed(),Wc.clone(this)}toString(r=!1){const l=this.dataSync();return ZR(l,this.shape,this.dtype,r)}cast(r){return this.throwIfDisposed(),Wc.cast(this,r)}variable(r=!0,l,h){return this.throwIfDisposed(),Yi().makeVariable(this,r,l,h)}}Object.defineProperty(Tn,Symbol.hasInstance,{value:r=>!!r&&r.data!=null&&r.dataSync!=null&&r.throwIfDisposed!=null});class Pf extends Tn{constructor(r,l,h,d){super(r.shape,r.dtype,r.dataId,d);this.trainable=l,this.name=h}assign(r){if(r.dtype!==this.dtype)throw new Error(`dtype of the new value (${r.dtype}) and previous value (${this.dtype}) must match`);if(!ia(r.shape,this.shape))throw new Error(`shape of the new value (${r.shape}) and previous value (${this.shape}) must match`);Yi().disposeTensor(this),this.dataId=r.dataId,Yi().incRef(this,null)}dispose(){Yi().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(Pf,Symbol.hasInstance,{value:r=>r instanceof Tn&&r.assign!=null&&r.assign instanceof Function});var iO;(function(r){r.R0="R0",r.R1="R1",r.R2="R2",r.R3="R3",r.R4="R4",r.R5="R5",r.R6="R6"})(iO||(iO={}));var mI;(function(r){r.float32="float32",r.int32="int32",r.bool="int32",r.complex64="complex64"})(mI||(mI={}));var fI;(function(r){r.float32="float32",r.int32="int32",r.bool="bool",r.complex64="complex64"})(fI||(fI={}));var gI;(function(r){r.float32="float32",r.int32="float32",r.bool="float32",r.complex64="complex64"})(gI||(gI={}));var yI;(function(r){r.float32="complex64",r.int32="complex64",r.bool="complex64",r.complex64="complex64"})(yI||(yI={}));const rJ={float32:gI,int32:mI,bool:fI,complex64:yI};function rO(r,l){if(r==="string"||l==="string"){if(r==="string"&&l==="string")return"string";throw new Error(`Can not upcast ${r} with ${l}`)}return rJ[r][l]}function mt(r,l){if(r.dtype===l.dtype)return[r,l];const h=rO(r.dtype,l.dtype);return[r.cast(h),l.cast(h)]}function zf(r){const l=[],h=new Set;return oO(r,l,h),l}function oO(r,l,h){if(r==null)return;if(r instanceof Tn){l.push(r);return}if(!oJ(r))return;const d=r;for(const f in d){const g=d[f];h.has(g)||(h.add(g),oO(g,l,h))}}function oJ(r){return Array.isArray(r)||typeof r=="object"}class aO{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const r in this.registeredVariables)this.registeredVariables[r].dispose()}}class Eu{constructor(r){this.ENV=r,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new aO}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const r=this.getSortedBackends();for(let l=0;l<r.length;l++){const h=r[l],d=await this.initializeBackend(h).success;if(d){await this.setBackend(h);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){const{name:r,asyncInit:l}=this.initializeBackendsAndReturnBest();if(l)throw new Error(`The highest priority backend '${r}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(r)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(r){if(!(r in this.registry))if(r in this.registryFactory){const{asyncInit:l}=this.initializeBackend(r);if(l)return null}else return null;return this.registry[r]}findBackendFactory(r){return r in this.registryFactory?this.registryFactory[r].factory:null}registerBackend(r,l,h=1){return r in this.registryFactory?(console.warn(`${r} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[r]={factory:l,priority:h},!0)}async setBackend(r){if(this.registryFactory[r]==null)throw new Error(`Backend name '${r}' not found in registry`);if(this.backendName=r,this.registry[r]==null){this.backendInstance=null;const{success:l,asyncInit:h}=this.initializeBackend(r),d=h?await l:l;if(!d)return!1}return this.backendInstance=this.registry[r],this.setupRegisteredKernels(),this.profiler=new jR(this.backendInstance),!0}setupRegisteredKernels(){const r=cI(this.backendName);r.forEach(l=>{l.setupFunc!=null&&l.setupFunc(this.backendInstance)})}disposeRegisteredKernels(r){const l=cI(r);l.forEach(h=>{h.disposeFunc!=null&&h.disposeFunc(this.registry[r])})}initializeBackend(r){const l=this.registryFactory[r];if(l==null)throw new Error(`Cannot initialize backend ${r}, no registration found.`);try{const h=l.factory();if(h&&!(h instanceof h2)&&typeof h.then=="function"){const d=++this.pendingBackendInitId,f=h.then(g=>d<this.pendingBackendInitId?!1:(this.registry[r]=g,this.pendingBackendInit=null,!0)).catch(g=>(d<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${r} failed`),console.warn(g.stack||g.message)),!1));return this.pendingBackendInit=f,{success:f,asyncInit:!0}}else return this.registry[r]=h,{success:!0,asyncInit:!1}}catch(h){return console.warn(`Initialization of backend ${r} failed`),console.warn(h.stack||h.message),{success:!1,asyncInit:!1}}}removeBackend(r){if(!(r in this.registryFactory))throw new Error(`${r} backend not found in registry`);this.backendName===r&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,r in this.registry&&(this.disposeRegisteredKernels(r),this.registry[r].dispose(),delete this.registry[r]),delete this.registryFactory[r],this.backendName===r&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((r,l)=>this.registryFactory[l].priority-this.registryFactory[r].priority)}initializeBackendsAndReturnBest(){const r=this.getSortedBackends();for(let l=0;l<r.length;l++){const h=r[l],{success:d,asyncInit:f}=this.initializeBackend(h);if(f||d)return{name:h,asyncInit:f}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(r,l){const h=this.state.tensorInfo.get(l),d=h.backend,f=this.readSync(l);d.disposeData(l),h.backend=r,r.move(l,f,h.shape,h.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(r,l){let h=null;if(l==null){if(typeof r!="function")throw new Error("Please provide a function to tidy()");l=r}else{if(typeof r!="string"&&!(r instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof l!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");h=r}let d;return this.scopedRun(()=>this.startScope(h),()=>this.endScope(d),()=>(d=l(),d instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),d))}scopedRun(r,l,h){r();try{const d=h();return l(),d}catch(d){throw l(),d}}nextTensorId(){return Eu.nextTensorId++}nextVariableId(){return Eu.nextVariableId++}clone(r){const l=this.makeTensorFromDataId(r.dataId,r.shape,r.dtype),h={x:r},d=g=>({x:()=>{const S="float32",L={x:g},x={dtype:S};return H.runKernelFunc(A=>A.cast(g,S),L,null,Dc,x)}}),f=[];return this.addTapeNode(this.state.activeScope.name,h,[l],d,f,{}),l}runKernel(r,l,h,d,f){const g=null,S=null;return this.runKernelFunc(g,l,S,r,h,d,f)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(r,l,h){const d=this.backend.numDataIds();let f=0;h.forEach(L=>{f+=L.dtype==="complex64"?3:1});const g=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],S=d-l-f-g;if(S>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${S} data ids) after running '${r}'`)}runKernelFunc(r,l,h,d,f,g,S){let L,x=[];const A=this.isTapeOn();d==null&&(d=this.state.activeScope!=null?this.state.activeScope.name:"");const O=this.state.numBytes,C=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let $;const z=Wf(d,this.backendName);let ne;if(z!=null)$=()=>{const se=this.backend.numDataIds();ne=z.kernelFunc({inputs:l,attrs:f,backend:this.backend});const fe=Array.isArray(ne)?ne:[ne];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,se,fe);const de=fe.map(({dataId:Ae,shape:xe,dtype:Me})=>this.makeTensorFromDataId(Ae,xe,Me));if(A){let Ae=this.getTensorsForGradient(d,l,de);if(Ae==null){S==null&&(S=[]);const xe=de.filter((Me,Ke)=>S[Ke]);Ae=(g||[]).slice().concat(xe)}x=this.saveTensorsForBackwardMode(Ae)}return de};else{const se=fe=>{if(!A)return;x=fe.map(de=>this.keep(this.clone(de)))};$=()=>{const fe=this.backend.numDataIds();ne=this.tidy(()=>r(this.backend,se));const de=Array.isArray(ne)?ne:[ne];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,fe,de),de}}let te;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?L=$():(te=this.profiler.profileKernel(d,l,()=>$()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(te),L=te.outputs)}),A&&this.addTapeNode(d,l,L,h,x,f),this.state.profiling&&this.state.activeProfile.kernels.push({name:d,bytesAdded:this.state.numBytes-O,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-C,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(se=>l[se]!=null?l[se].shape:null),outputShapes:L.map(se=>se.shape),kernelTimeMs:te.timeMs,extraInfo:te.extraInfo}),Array.isArray(ne)?L:L[0]}saveTensorsForBackwardMode(r){const l=r.map(h=>this.keep(this.clone(h)));return l}getTensorsForGradient(r,l,h){const d=aI(r);if(d!=null){const f=d.inputsToSave||[],g=d.outputsToSave||[];let S;d.saveAllInputs?(Z(Array.isArray(l),()=>"saveAllInputs is true, expected inputs to be an array."),S=Object.keys(l).map(x=>l[x])):S=f.map(x=>l[x]);const L=h.filter((x,A)=>g[A]);return S.concat(L)}return null}makeTensor(r,l,h,d){if(r==null)throw new Error("Values passed to engine.makeTensor() are null");h=h||"float32",d=d||this.backend;let f=r;h==="string"&&Au(r[0])&&(f=r.map(L=>qR(L)));const g=d.write(f,l,h),S=new Tn(l,h,g,this.nextTensorId());if(this.incRef(S,d),h==="string"){const L=this.state.tensorInfo.get(g),x=HR(f);this.state.numBytes+=x-L.bytes,L.bytes=x}return S}makeTensorFromDataId(r,l,h,d){h=h||"float32";const f=new Tn(l,h,r,this.nextTensorId());return this.incRef(f,d),f}makeVariable(r,l=!0,h,d){h=h||this.nextVariableId().toString(),d!=null&&d!==r.dtype&&(r=r.cast(d));const f=new Pf(r,l,h,this.nextTensorId());if(this.state.registeredVariables[f.name]!=null)throw new Error(`Variable with name ${f.name} was already registered`);return this.state.registeredVariables[f.name]=f,this.incRef(f,this.backend),f}incRef(r,l){const h=this.state.tensorInfo.has(r.dataId)?this.state.tensorInfo.get(r.dataId).refCount:0;if(this.state.numTensors++,r.dtype==="string"&&this.state.numStringTensors++,h===0){this.state.numDataBuffers++;let d=0;r.dtype!=="complex64"&&r.dtype!=="string"&&(d=r.size*VR(r.dtype)),this.state.tensorInfo.set(r.dataId,{backend:l||this.backend,dtype:r.dtype,shape:r.shape,bytes:d,refCount:0}),this.state.numBytes+=d}this.state.tensorInfo.get(r.dataId).refCount++,r instanceof Pf||this.track(r)}disposeTensor(r){if(!this.state.tensorInfo.has(r.dataId))return;this.state.numTensors--,r.dtype==="string"&&this.state.numStringTensors--;const l=this.state.tensorInfo.get(r.dataId),h=l.refCount;h<=1?(r.dtype!=="complex64"&&(this.state.numBytes-=l.bytes),this.state.numDataBuffers--,l.backend.disposeData(r.dataId),this.state.tensorInfo.delete(r.dataId)):this.state.tensorInfo.get(r.dataId).refCount--}disposeVariables(){for(const r in this.state.registeredVariables){const l=this.state.registeredVariables[r];this.disposeVariable(l)}}disposeVariable(r){this.disposeTensor(r),this.state.registeredVariables[r.name]!=null&&delete this.state.registeredVariables[r.name]}memory(){const r=this.backend.memory();return r.numTensors=this.state.numTensors,r.numDataBuffers=this.state.numDataBuffers,r.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(r.unreliable=!0,r.reasons==null&&(r.reasons=[]),r.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),r}async profile(r){this.state.profiling=!0;const l=this.state.numBytes,h=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await r(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-l,this.state.activeProfile.newTensors=this.state.numTensors-h;for(const d of this.state.activeProfile.kernels)d.kernelTimeMs=await d.kernelTimeMs,d.extraInfo=await d.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(r,l,h,d,f,g){const S={id:this.state.nextTapeNodeId++,kernelName:r,inputs:l,outputs:h,saved:f},L=aI(r);L!=null&&(d=L.gradFunc),d!=null&&(S.gradient=x=>(x=x.map((A,O)=>{if(A==null){const C=h[O],$=ra(C.size,C.dtype);return this.makeTensor($,C.shape,C.dtype)}return A}),d(x.length>1?x:x[0],f,g))),this.state.activeTape.push(S)}keep(r){return r.kept=!0,r}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(r){const l={track:[],name:"unnamed scope",id:this.state.nextScopeId++};r&&(l.name=r),this.state.scopeStack.push(l),this.state.activeScope=l}endScope(r){const l=zf(r),h=new Set(l.map(f=>f.id));for(let f=0;f<this.state.activeScope.track.length;f++){const g=this.state.activeScope.track[f];!g.kept&&!h.has(g.id)&&g.dispose()}const d=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],l.forEach(f=>{!f.kept&&f.scopeId===d.id&&this.track(f)})}gradients(r,l,h,d=!1){if(Z(l.length>0,()=>"gradients() received an empty list of xs."),h!=null&&h.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${h.dtype}'`);const f=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",r));Z(f instanceof Tn,()=>"The result y returned by f() must be a tensor.");const g=KR(this.state.activeTape,l,f);if(!d&&g.length===0&&l.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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Error(`param tensor has already been disposed for path ${l.path}`);l.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:r})=>Array.from(r.dataSync())).reduce((r,l)=>r.concat(l)))}async load(r){if(r instanceof Float32Array){this.extractWeights(r);return}await this.loadFromUri(r)}async loadFromUri(r){if(r&&typeof r!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);const l=await rx(r,this.getDefaultModelName());this.loadFromWeightMap(l)}async loadFromDisk(r){if(r&&typeof r!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);const{readFile:l}=gt.getEnv(),{manifestUri:h,modelBaseUri:d}=rg(r,this.getDefaultModelName()),f=x=>Promise.all(x.map(A=>l(A).then(O=>O.buffer))),g=xr.io.weightsLoaderFactory(f),S=JSON.parse((await l(h)).toString()),L=await g(S,d);this.loadFromWeightMap(L)}loadFromWeightMap(r){const{paramMappings:l,params:h}=this.extractParamsFromWeigthMap(r);this._paramMappings=l,this._params=h}extractWeights(r){const{paramMappings:l,params:h}=this.extractParams(r);this._paramMappings=l,this._params=h}traversePropertyPath(r){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");const l=r.split("/").reduce((f,g)=>{if(!f.nextObj.hasOwnProperty(g))throw new Error(`traversePropertyPath - object does not have property ${g}, for path ${r}`);return{obj:f.nextObj,objProp:g,nextObj:f.nextObj[g]}},{nextObj:this.params}),{obj:h,objProp:d}=l;if(!h||!d||!(h[d]instanceof xr.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${r}`);return{obj:h,objProp:d}}}const Yc=Ye(Je());function ns(r,l,h){return Yc.tidy(()=>{let d=Yc.separableConv2d(r,l.depthwise_filter,l.pointwise_filter,h,"same");return d=Yc.add(d,l.bias),d})}const Dt=Ye(Je());function og(r,l,h=!1){return Dt.tidy(()=>{const d=Dt.relu(h?Dt.add(Dt.conv2d(r,l.conv0.filters,[2,2],"same"),l.conv0.bias):ns(r,l.conv0,[2,2])),f=ns(d,l.conv1,[1,1]),g=Dt.relu(Dt.add(d,f)),S=ns(g,l.conv2,[1,1]);return Dt.relu(Dt.add(d,Dt.add(f,S)))})}function Mu(r,l,h=!1,d=!0){return Dt.tidy(()=>{const f=Dt.relu(h?Dt.add(Dt.conv2d(r,l.conv0.filters,d?[2,2]:[1,1],"same"),l.conv0.bias):ns(r,l.conv0,d?[2,2]:[1,1])),g=ns(f,l.conv1,[1,1]),S=Dt.relu(Dt.add(f,g)),L=ns(S,l.conv2,[1,1]),x=Dt.relu(Dt.add(f,Dt.add(g,L))),A=ns(x,l.conv3,[1,1]);return Dt.relu(Dt.add(f,Dt.add(g,Dt.add(L,A))))})}const mo=Ye(Je());function ma(r,l,h="same",d=!1){return mo.tidy(()=>{const f=mo.add(mo.conv2d(r,l.filters,[1,1],h),l.bias);return d?mo.relu(f):f})}function Vn(r,l){Object.keys(r).forEach(h=>{l.some(d=>d.originalPath===h)||r[h].dispose()})}const ag=Ye(Je());function qc(r,l){return function(h,d,f,g){const S=ag.tensor4d(r(h*d*f*f),[f,f,h,d]),L=ag.tensor1d(r(d));return l.push({paramPath:`${g}/filters`},{paramPath:`${g}/bias`}),{filters:S,bias:L}}}const cg=Ye(Je());function lg(r,l){return function(h,d,f){const g=cg.tensor2d(r(h*d),[h,d]),S=cg.tensor1d(r(d));return l.push({paramPath:`${f}/weights`},{paramPath:`${f}/bias`}),{weights:g,bias:S}}}class ox{constructor(r,l,h){this.depthwise_filter=r;this.pointwise_filter=l;this.bias=h}}const Pu=Ye(Je());function jc(r,l){return function(h,d,f){const g=Pu.tensor4d(r(3*3*h),[3,3,h,1]),S=Pu.tensor4d(r(h*d),[1,1,h,d]),L=Pu.tensor1d(r(d));return l.push({paramPath:`${f}/depthwise_filter`},{paramPath:`${f}/pointwise_filter`},{paramPath:`${f}/bias`}),new ox(g,S,L)}}function Kc(r){return function(l){const h=r(`${l}/depthwise_filter`,4),d=r(`${l}/pointwise_filter`,4),f=r(`${l}/bias`,1);return new ox(h,d,f)}}function ys(r,l){return function(h,d,f){const g=r[h];if(!Jo(g,d))throw new Error(`expected weightMap[${h}] to be a Tensor${d}D, instead have ${g}`);return l.push({originalPath:h,paramPath:f||h}),g}}function Hn(r){let l=r;function h(f){const g=l.slice(0,f);return l=l.slice(f),g}function d(){return l}return{extractWeights:h,getRemainingWeights:d}}function hg(r,l){const h=qc(r,l),d=jc(r,l);function f(S,L,x,A=!1){const O=A?h(S,L,3,`${x}/conv0`):d(S,L,`${x}/conv0`),C=d(L,L,`${x}/conv1`),$=d(L,L,`${x}/conv2`);return{conv0:O,conv1:C,conv2:$}}function g(S,L,x,A=!1){const{conv0:O,conv1:C,conv2:$}=f(S,L,x,A),z=d(L,L,`${x}/conv3`);return{conv0:O,conv1:C,conv2:$,conv3:z}}return{extractDenseBlock3Params:f,extractDenseBlock4Params:g}}function JE(r){const l=[],{extractWeights:h,getRemainingWeights:d}=Hn(r),{extractDenseBlock4Params:f}=hg(h,l),g=f(3,32,"dense0",!0),S=f(32,64,"dense1"),L=f(64,128,"dense2"),x=f(128,256,"dense3");if(d().length!==0)throw new Error(`weights remaing after extract: ${d().length}`);return{paramMappings:l,params:{dense0:g,dense1:S,dense2:L,dense3:x}}}function ug(r){return function(l){const h=r(`${l}/filters`,4),d=r(`${l}/bias`,1);return{filters:h,bias:d}}}function dg(r,l){const h=ys(r,l),d=ug(h),f=Kc(h);function g(L,x=!1){const A=x?d(`${L}/conv0`):f(`${L}/conv0`),O=f(`${L}/conv1`),C=f(`${L}/conv2`);return{conv0:A,conv1:O,conv2:C}}function S(L,x=!1){const A=x?d(`${L}/conv0`):f(`${L}/conv0`),O=f(`${L}/conv1`),C=f(`${L}/conv2`),$=f(`${L}/conv3`);return{conv0:A,conv1:O,conv2:C,conv3:$}}return{extractDenseBlock3Params:g,extractDenseBlock4Params:S}}function ZE(r){const l=[],{extractDenseBlock4Params:h}=dg(r,l),d={dense0:h("dense0",!0),dense1:h("dense1"),dense2:h("dense2"),dense3:h("dense3")};return Vn(r,l),{params:d,paramMappings:l}}const Xc=Ye(Je());class pg extends kn{constructor(){super("FaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceFeatureExtractor - load model before inference");return Xc.tidy(()=>{const h=r.toBatchTensor(112,!0),d=[122.782,117.001,104.298],f=yi(h,d).div(Xc.scalar(255));let g=Mu(f,l.dense0,!0);return g=Mu(g,l.dense1),g=Mu(g,l.dense2),g=Mu(g,l.dense3),g=Xc.avgPool(g,[7,7],[2,2],"valid"),g})}async forward(r){return this.forwardInput(await Rt(r))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(r){return ZE(r)}extractParams(r){return JE(r)}}const Jc=Ye(Je());function zu(r,l){return Jc.tidy(()=>Jc.add(Jc.matMul(r,l.weights),l.bias))}function QE(r,l,h){const d=[],{extractWeights:f,getRemainingWeights:g}=Hn(r),S=lg(f,d),L=S(l,h,"fc");if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{paramMappings:d,params:{fc:L}}}function eD(r){const l=[],h=ys(r,l);function d(g){const S=h(`${g}/weights`,2),L=h(`${g}/bias`,1);return{weights:S,bias:L}}const f={fc:d("fc")};return Vn(r,l),{params:f,paramMappings:l}}function mg(r){const l={},h={};return Object.keys(r).forEach(d=>{const f=d.startsWith("fc")?h:l;f[d]=r[d]}),{featureExtractorMap:l,classifierMap:h}}const tD=Ye(Je());class fg extends kn{constructor(r,l){super(r);this._faceFeatureExtractor=l}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return tD.tidy(()=>{const h=r instanceof po?this.faceFeatureExtractor.forwardInput(r):r;return zu(h.as2D(h.shape[0],-1),l.fc)})}dispose(r=!0){this.faceFeatureExtractor.dispose(r),super.dispose(r)}loadClassifierParams(r){const{params:l,paramMappings:h}=this.extractClassifierParams(r);this._params=l,this._paramMappings=h}extractClassifierParams(r){return QE(r,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:h}=mg(r);return this.faceFeatureExtractor.loadFromWeightMap(l),eD(h)}extractParams(r){const l=this.getClassifierChannelsIn(),h=this.getClassifierChannelsOut(),d=h*l+h,f=r.slice(0,r.length-d),g=r.slice(r.length-d);return this.faceFeatureExtractor.extractWeights(f),this.extractClassifierParams(g)}}const ax=["neutral","happy","sad","angry","fearful","disgusted","surprised"];class fa{constructor(r){if(r.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${r.length}`);ax.forEach((l,h)=>{this[l]=r[h]})}asSortedArray(){return ax.map(r=>({expression:r,probability:this[r]})).sort((r,l)=>l.probability-r.probability)}}const Zc=Ye(Je());class cx extends fg{constructor(r=new pg){super("FaceExpressionNet",r)}forwardInput(r){return Zc.tidy(()=>Zc.softmax(this.runNet(r)))}async forward(r){return this.forwardInput(await Rt(r))}async predictExpressions(r){const l=await Rt(r),h=await this.forwardInput(l),d=await Promise.all(Zc.unstack(h).map(async g=>{const S=await g.data();return g.dispose(),S}));h.dispose();const f=d.map(g=>new fa(g));return l.isBatchInput?f:f[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}}function lx(r){return r.expressions instanceof fa}function gg(r,l){const h={expressions:l};return Object.assign({},r,h)}function kZ(r,l,h=.1,d){const f=Array.isArray(l)?l:[l];f.forEach(g=>{const S=g instanceof fa?g:lx(g)?g.expressions:void 0;if(!S)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");const L=S.asSortedArray(),x=L.filter(C=>C.probability>h),A=Vi(g)?g.detection.box.bottomLeft:d||new Ze(0,0),O=new Rc(x.map(C=>`${C.expression} (${Zo(C.probability)})`),A);O.draw(r)})}function ga(r){return Vi(r)&&r.landmarks instanceof qs&&r.unshiftedLandmarks instanceof qs&&r.alignedRect instanceof Yt}function Qc(r,l){const{box:h}=r.detection,d=l.shiftBy(h.x,h.y),f=d.align(),{imageDims:g}=r.detection,S=new Yt(r.detection.score,f.rescale(g.reverse()),g),L={landmarks:d,unshiftedLandmarks:l,alignedRect:S};return Object.assign({},r,L)}class nD{constructor(r={}){const{drawLines:l=!0,drawPoints:h=!0,lineWidth:d,lineColor:f,pointSize:g,pointColor:S}=r;this.drawLines=l,this.drawPoints=h,this.lineWidth=d||1,this.pointSize=g||2,this.lineColor=f||"rgba(0, 255, 255, 1)",this.pointColor=S||"rgba(255, 0, 255, 1)"}}class sD{constructor(r,l={}){this.faceLandmarks=r,this.options=new nD(l)}draw(r){const l=es(r),{drawLines:h,drawPoints:d,lineWidth:f,lineColor:g,pointSize:S,pointColor:L}=this.options;if(h&&this.faceLandmarks instanceof Iu&&(l.strokeStyle=g,l.lineWidth=f,gr(l,this.faceLandmarks.getJawOutline()),gr(l,this.faceLandmarks.getLeftEyeBrow()),gr(l,this.faceLandmarks.getRightEyeBrow()),gr(l,this.faceLandmarks.getNose()),gr(l,this.faceLandmarks.getLeftEye(),!0),gr(l,this.faceLandmarks.getRightEye(),!0),gr(l,this.faceLandmarks.getMouth(),!0)),d){l.strokeStyle=L,l.fillStyle=L;const x=A=>{l.beginPath(),l.arc(A.x,A.y,S,0,2*Math.PI),l.fill()};this.faceLandmarks.positions.forEach(x)}}}function FZ(r,l){const h=Array.isArray(l)?l:[l];h.forEach(d=>{const f=d instanceof qs?d:ga(d)?d.landmarks:void 0;if(!f)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new sD(f).draw(r)})}const hx={};Tc(hx,{AnchorPosition:()=>Hi,DrawBox:()=>ZS,DrawBoxOptions:()=>l2,DrawFaceLandmarks:()=>sD,DrawFaceLandmarksOptions:()=>nD,DrawTextField:()=>Rc,DrawTextFieldOptions:()=>Wm,drawContour:()=>gr,drawDetections:()=>GX,drawFaceExpressions:()=>kZ,drawFaceLandmarks:()=>FZ});function _Z(r,l){const h=qc(r,l),d=jc(r,l);function f(S,L,x){const A=d(S,L,`${x}/separable_conv0`),O=d(L,L,`${x}/separable_conv1`),C=h(S,L,1,`${x}/expansion_conv`);return{separable_conv0:A,separable_conv1:O,expansion_conv:C}}function g(S,L){const x=d(S,S,`${L}/separable_conv0`),A=d(S,S,`${L}/separable_conv1`),O=d(S,S,`${L}/separable_conv2`);return{separable_conv0:x,separable_conv1:A,separable_conv2:O}}return{extractConvParams:h,extractSeparableConvParams:d,extractReductionBlockParams:f,extractMainBlockParams:g}}function iD(r,l){const h=[],{extractWeights:d,getRemainingWeights:f}=Hn(r),{extractConvParams:g,extractSeparableConvParams:S,extractReductionBlockParams:L,extractMainBlockParams:x}=_Z(d,h),A=g(3,32,3,"entry_flow/conv_in"),O=L(32,64,"entry_flow/reduction_block_0"),C=L(64,128,"entry_flow/reduction_block_1"),$={conv_in:A,reduction_block_0:O,reduction_block_1:C},z={};zi(l,0,1).forEach(fe=>{z[`main_block_${fe}`]=x(128,`middle_flow/main_block_${fe}`)});const ne=L(128,256,"exit_flow/reduction_block"),te=S(256,512,"exit_flow/separable_conv"),se={reduction_block:ne,separable_conv:te};if(f().length!==0)throw new Error(`weights remaing after extract: ${f().length}`);return{paramMappings:h,params:{entry_flow:$,middle_flow:z,exit_flow:se}}}function WZ(r,l){const h=ys(r,l),d=ug(h),f=Kc(h);function g(L){const x=f(`${L}/separable_conv0`),A=f(`${L}/separable_conv1`),O=d(`${L}/expansion_conv`);return{separable_conv0:x,separable_conv1:A,expansion_conv:O}}function S(L){const x=f(`${L}/separable_conv0`),A=f(`${L}/separable_conv1`),O=f(`${L}/separable_conv2`);return{separable_conv0:x,separable_conv1:A,separable_conv2:O}}return{extractConvParams:d,extractSeparableConvParams:f,extractReductionBlockParams:g,extractMainBlockParams:S}}function rD(r,l){const h=[],{extractConvParams:d,extractSeparableConvParams:f,extractReductionBlockParams:g,extractMainBlockParams:S}=WZ(r,h),L=d("entry_flow/conv_in"),x=g("entry_flow/reduction_block_0"),A=g("entry_flow/reduction_block_1"),O={conv_in:L,reduction_block_0:x,reduction_block_1:A},C={};zi(l,0,1).forEach(te=>{C[`main_block_${te}`]=S(`middle_flow/main_block_${te}`)});const $=g("exit_flow/reduction_block"),z=f("exit_flow/separable_conv"),ne={reduction_block:$,separable_conv:z};return Vn(r,h),{params:{entry_flow:O,middle_flow:C,exit_flow:ne},paramMappings:h}}const pn=Ye(Je());function oD(r,l,h){return pn.add(pn.conv2d(r,l.filters,h,"same"),l.bias)}function ux(r,l,h=!0){let d=h?pn.relu(r):r;return d=ns(d,l.separable_conv0,[1,1]),d=ns(pn.relu(d),l.separable_conv1,[1,1]),d=pn.maxPool(d,[3,3],[2,2],"same"),d=pn.add(d,oD(r,l.expansion_conv,[2,2])),d}function $Z(r,l){let h=ns(pn.relu(r),l.separable_conv0,[1,1]);return h=ns(pn.relu(h),l.separable_conv1,[1,1]),h=ns(pn.relu(h),l.separable_conv2,[1,1]),h=pn.add(h,r),h}class aD extends kn{constructor(r){super("TinyXception");this._numMainBlocks=r}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyXception - load model before inference");return pn.tidy(()=>{const h=r.toBatchTensor(112,!0),d=[122.782,117.001,104.298],f=yi(h,d).div(pn.scalar(256));let g=pn.relu(oD(f,l.entry_flow.conv_in,[2,2]));return g=ux(g,l.entry_flow.reduction_block_0,!1),g=ux(g,l.entry_flow.reduction_block_1),zi(this._numMainBlocks,0,1).forEach(S=>{g=$Z(g,l.middle_flow[`main_block_${S}`])}),g=ux(g,l.exit_flow.reduction_block),g=pn.relu(ns(g,l.exit_flow.separable_conv,[1,1])),g})}async forward(r){return this.forwardInput(await Rt(r))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(r){return rD(r,this._numMainBlocks)}extractParams(r){return iD(r,this._numMainBlocks)}}function cD(r){const l=[],{extractWeights:h,getRemainingWeights:d}=Hn(r),f=lg(h,l),g=f(512,1,"fc/age"),S=f(512,2,"fc/gender");if(d().length!==0)throw new Error(`weights remaing after extract: ${d().length}`);return{paramMappings:l,params:{fc:{age:g,gender:S}}}}function lD(r){const l=[],h=ys(r,l);function d(g){const S=h(`${g}/weights`,2),L=h(`${g}/bias`,1);return{weights:S,bias:L}}const f={fc:{age:d("fc/age"),gender:d("fc/gender")}};return Vn(r,l),{params:f,paramMappings:l}}var Tr;(function(r){r.FEMALE="female",r.MALE="male"})(Tr||(Tr={}));const Xi=Ye(Je());class dx extends kn{constructor(r=new aD(2)){super("AgeGenderNet");this._faceFeatureExtractor=r}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return Xi.tidy(()=>{const h=r instanceof po?this.faceFeatureExtractor.forwardInput(r):r,d=Xi.avgPool(h,[7,7],[2,2],"valid").as2D(h.shape[0],-1),f=zu(d,l.fc.age).as1D(),g=zu(d,l.fc.gender);return{age:f,gender:g}})}forwardInput(r){return Xi.tidy(()=>{const{age:l,gender:h}=this.runNet(r);return{age:l,gender:Xi.softmax(h)}})}async forward(r){return this.forwardInput(await Rt(r))}async predictAgeAndGender(r){const l=await Rt(r),h=await this.forwardInput(l),d=Xi.unstack(h.age),f=Xi.unstack(h.gender),g=d.map((L,x)=>({ageTensor:L,genderTensor:f[x]})),S=await Promise.all(g.map(async({ageTensor:L,genderTensor:x})=>{const A=(await L.data())[0],O=(await x.data())[0],C=O>.5,$=C?Tr.MALE:Tr.FEMALE,z=C?O:1-O;return L.dispose(),x.dispose(),{age:A,gender:$,genderProbability:z}}));return h.age.dispose(),h.gender.dispose(),l.isBatchInput?S:S[0]}getDefaultModelName(){return"age_gender_model"}dispose(r=!0){this.faceFeatureExtractor.dispose(r),super.dispose(r)}loadClassifierParams(r){const{params:l,paramMappings:h}=this.extractClassifierParams(r);this._params=l,this._paramMappings=h}extractClassifierParams(r){return cD(r)}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:h}=mg(r);return this.faceFeatureExtractor.loadFromWeightMap(l),lD(h)}extractParams(r){const l=512*1+1+(512*2+2),h=r.slice(0,r.length-l),d=r.slice(r.length-l);return this.faceFeatureExtractor.extractWeights(h),this.extractClassifierParams(d)}}const bs=Ye(Je());class yg extends fg{postProcess(r,l,h){const d=h.map(({width:g,height:S})=>{const L=l/Math.max(S,g);return{width:g*L,height:S*L}}),f=d.length;return bs.tidy(()=>{const g=(O,C)=>bs.stack([bs.fill([68],O),bs.fill([68],C)],1).as2D(1,136).as1D(),S=(O,C)=>{const{width:$,height:z}=d[O];return C($,z)?Math.abs($-z)/2:0},L=O=>S(O,(C,$)=>C<$),x=O=>S(O,(C,$)=>$<C),A=r.mul(bs.fill([f,136],l)).sub(bs.stack(Array.from(Array(f),(O,C)=>g(L(C),x(C))))).div(bs.stack(Array.from(Array(f),(O,C)=>g(d[C].width,d[C].height))));return A})}forwardInput(r){return bs.tidy(()=>{const l=this.runNet(r);return this.postProcess(l,r.inputSize,r.inputDimensions.map(([h,d])=>({height:h,width:d})))})}async forward(r){return this.forwardInput(await Rt(r))}async detectLandmarks(r){const l=await Rt(r),h=bs.tidy(()=>bs.unstack(this.forwardInput(l))),d=await Promise.all(h.map(async(f,g)=>{const S=Array.from(await f.data()),L=S.filter((A,O)=>Em(O)),x=S.filter((A,O)=>!Em(O));return new Iu(Array(68).fill(0).map((A,O)=>new Ze(L[O],x[O])),{height:l.getInputHeight(g),width:l.getInputWidth(g)})}));return h.forEach(f=>f.dispose()),l.isBatchInput?d:d[0]}getClassifierChannelsOut(){return 136}}class Gu extends yg{constructor(r=new pg){super("FaceLandmark68Net",r)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}}function hD(r){const l=[],{extractDenseBlock3Params:h}=dg(r,l),d={dense0:h("dense0",!0),dense1:h("dense1"),dense2:h("dense2")};return Vn(r,l),{params:d,paramMappings:l}}function uD(r){const l=[],{extractWeights:h,getRemainingWeights:d}=Hn(r),{extractDenseBlock3Params:f}=hg(h,l),g=f(3,32,"dense0",!0),S=f(32,64,"dense1"),L=f(64,128,"dense2");if(d().length!==0)throw new Error(`weights remaing after extract: ${d().length}`);return{paramMappings:l,params:{dense0:g,dense1:S,dense2:L}}}const el=Ye(Je());class dD extends kn{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyFaceFeatureExtractor - load model before inference");return el.tidy(()=>{const h=r.toBatchTensor(112,!0),d=[122.782,117.001,104.298],f=yi(h,d).div(el.scalar(255));let g=og(f,l.dense0,!0);return g=og(g,l.dense1),g=og(g,l.dense2),g=el.avgPool(g,[14,14],[2,2],"valid"),g})}async forward(r){return this.forwardInput(await Rt(r))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(r){return hD(r)}extractParams(r){return uD(r)}}class px extends yg{constructor(r=new dD){super("FaceLandmark68TinyNet",r)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}}class UZ extends Gu{}const bg=Ye(Je());function pD(r,l){return bg.add(bg.mul(r,l.weights),l.biases)}const tl=Ye(Je());function mx(r,l,h,d,f="same"){const{filters:g,bias:S}=l.conv;let L=tl.conv2d(r,g,h,f);return L=tl.add(L,S),L=pD(L,l.scale),d?tl.relu(L):L}function mD(r,l){return mx(r,l,[1,1],!0)}function fx(r,l){return mx(r,l,[1,1],!1)}function wg(r,l){return mx(r,l,[2,2],!0,"valid")}const ws=Ye(Je());function BZ(r,l){function h(L,x,A){const O=r(L),C=O.length/(x*A*A);if(MS(C))throw new Error(`depth has to be an integer: ${C}, weights.length: ${O.length}, numFilters: ${x}, filterSize: ${A}`);return ws.tidy(()=>ws.transpose(ws.tensor4d(O,[x,C,A,A]),[2,3,1,0]))}function d(L,x,A,O){const C=h(L,x,A),$=ws.tensor1d(r(x));return l.push({paramPath:`${O}/filters`},{paramPath:`${O}/bias`}),{filters:C,bias:$}}function f(L,x){const A=ws.tensor1d(r(L)),O=ws.tensor1d(r(L));return l.push({paramPath:`${x}/weights`},{paramPath:`${x}/biases`}),{weights:A,biases:O}}function g(L,x,A,O){const C=d(L,x,A,`${O}/conv`),$=f(x,`${O}/scale`);return{conv:C,scale:$}}function S(L,x,A,O,C=!1){const $=g((C?.5:1)*L,x,A,`${O}/conv1`),z=g(L,x,A,`${O}/conv2`);return{conv1:$,conv2:z}}return{extractConvLayerParams:g,extractResidualLayerParams:S}}function fD(r){const{extractWeights:l,getRemainingWeights:h}=Hn(r),d=[],{extractConvLayerParams:f,extractResidualLayerParams:g}=BZ(l,d),S=f(4704,32,7,"conv32_down"),L=g(9216,32,3,"conv32_1"),x=g(9216,32,3,"conv32_2"),A=g(9216,32,3,"conv32_3"),O=g(36864,64,3,"conv64_down",!0),C=g(36864,64,3,"conv64_1"),$=g(36864,64,3,"conv64_2"),z=g(36864,64,3,"conv64_3"),ne=g(147456,128,3,"conv128_down",!0),te=g(147456,128,3,"conv128_1"),se=g(147456,128,3,"conv128_2"),fe=g(589824,256,3,"conv256_down",!0),de=g(589824,256,3,"conv256_1"),Ae=g(589824,256,3,"conv256_2"),xe=g(589824,256,3,"conv256_down_out"),Me=ws.tidy(()=>ws.transpose(ws.tensor2d(l(256*128),[128,256]),[1,0]));if(d.push({paramPath:"fc"}),h().length!==0)throw new Error(`weights remaing after extract: ${h().length}`);const Ke={conv32_down:S,conv32_1:L,conv32_2:x,conv32_3:A,conv64_down:O,conv64_1:C,conv64_2:$,conv64_3:z,conv128_down:ne,conv128_1:te,conv128_2:se,conv256_down:fe,conv256_1:de,conv256_2:Ae,conv256_down_out:xe,fc:Me};return{params:Ke,paramMappings:d}}function MZ(r,l){const h=ys(r,l);function d(S){const L=h(`${S}/scale/weights`,1),x=h(`${S}/scale/biases`,1);return{weights:L,biases:x}}function f(S){const L=h(`${S}/conv/filters`,4),x=h(`${S}/conv/bias`,1),A=d(S);return{conv:{filters:L,bias:x},scale:A}}function g(S){return{conv1:f(`${S}/conv1`),conv2:f(`${S}/conv2`)}}return{extractConvLayerParams:f,extractResidualLayerParams:g}}function gD(r){const l=[],{extractConvLayerParams:h,extractResidualLayerParams:d}=MZ(r,l),f=h("conv32_down"),g=d("conv32_1"),S=d("conv32_2"),L=d("conv32_3"),x=d("conv64_down"),A=d("conv64_1"),O=d("conv64_2"),C=d("conv64_3"),$=d("conv128_down"),z=d("conv128_1"),ne=d("conv128_2"),te=d("conv256_down"),se=d("conv256_1"),fe=d("conv256_2"),de=d("conv256_down_out"),Ae=r.fc;if(l.push({originalPath:"fc",paramPath:"fc"}),!BS(Ae))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${Ae}`);const xe={conv32_down:f,conv32_1:g,conv32_2:S,conv32_3:L,conv64_down:x,conv64_1:A,conv64_2:O,conv64_3:C,conv128_down:$,conv128_1:z,conv128_2:ne,conv256_down:te,conv256_1:se,conv256_2:fe,conv256_down_out:de,fc:Ae};return Vn(r,l),{params:xe,paramMappings:l}}const Yn=Ye(Je());function Li(r,l){let h=mD(r,l.conv1);return h=fx(h,l.conv2),h=Yn.add(h,r),h=Yn.relu(h),h}function Vu(r,l){let h=wg(r,l.conv1);h=fx(h,l.conv2);let d=Yn.avgPool(r,2,2,"valid");const f=Yn.zeros(d.shape),g=d.shape[3]!==h.shape[3],S=d.shape[1]!==h.shape[1]||d.shape[2]!==h.shape[2];if(S){const L=[...h.shape];L[1]=1;const x=Yn.zeros(L);h=Yn.concat([h,x],1);const A=[...h.shape];A[2]=1;const O=Yn.zeros(A);h=Yn.concat([h,O],2)}return d=g?Yn.concat([d,f],3):d,h=Yn.add(d,h),h=Yn.relu(h),h}const _s=Ye(Je());class Hu extends kn{constructor(){super("FaceRecognitionNet")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceRecognitionNet - load model before inference");return _s.tidy(()=>{const h=_s.cast(r.toBatchTensor(150,!0),"float32"),d=[122.782,117.001,104.298],f=yi(h,d).div(_s.scalar(256));let g=wg(f,l.conv32_down);g=_s.maxPool(g,3,2,"valid"),g=Li(g,l.conv32_1),g=Li(g,l.conv32_2),g=Li(g,l.conv32_3),g=Vu(g,l.conv64_down),g=Li(g,l.conv64_1),g=Li(g,l.conv64_2),g=Li(g,l.conv64_3),g=Vu(g,l.conv128_down),g=Li(g,l.conv128_1),g=Li(g,l.conv128_2),g=Vu(g,l.conv256_down),g=Li(g,l.conv256_1),g=Li(g,l.conv256_2),g=Vu(g,l.conv256_down_out);const S=g.mean([1,2]),L=_s.matMul(S,l.fc);return L})}async forward(r){return this.forwardInput(await Rt(r))}async computeFaceDescriptor(r){const l=await Rt(r),h=_s.tidy(()=>_s.unstack(this.forwardInput(l))),d=await Promise.all(h.map(f=>f.data()));return h.forEach(f=>f.dispose()),l.isBatchInput?d:d[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(r){return gD(r)}extractParams(r){return fD(r)}}function PZ(r){const l=new Hu;return l.extractWeights(r),l}function Lg(r,l){const h={descriptor:l};return Object.assign({},r,h)}function zZ(r){return typeof r.age=="number"}function Sg(r,l){const h={age:l};return Object.assign({},r,h)}function GZ(r){return(r.gender===Tr.MALE||r.gender===Tr.FEMALE)&&Nc(r.genderProbability)}function Ig(r,l,h){const d={gender:l,genderProbability:h};return Object.assign({},r,d)}const Si=Ye(Je());function VZ(r,l){function h(x,A){const O=Si.tensor4d(r(3*3*x),[3,3,x,1]),C=Si.tensor1d(r(x)),$=Si.tensor1d(r(x)),z=Si.tensor1d(r(x)),ne=Si.tensor1d(r(x));return l.push({paramPath:`${A}/filters`},{paramPath:`${A}/batch_norm_scale`},{paramPath:`${A}/batch_norm_offset`},{paramPath:`${A}/batch_norm_mean`},{paramPath:`${A}/batch_norm_variance`}),{filters:O,batch_norm_scale:C,batch_norm_offset:$,batch_norm_mean:z,batch_norm_variance:ne}}function d(x,A,O,C,$){const z=Si.tensor4d(r(x*A*O*O),[O,O,x,A]),ne=Si.tensor1d(r(A));return l.push({paramPath:`${C}/filters`},{paramPath:`${C}/${$?"batch_norm_offset":"bias"}`}),{filters:z,bias:ne}}function f(x,A,O,C){const{filters:$,bias:z}=d(x,A,O,C,!0);return{filters:$,batch_norm_offset:z}}function g(x,A,O){const C=h(x,`${O}/depthwise_conv`),$=f(x,A,1,`${O}/pointwise_conv`);return{depthwise_conv:C,pointwise_conv:$}}function S(){const x=f(3,32,3,"mobilenetv1/conv_0"),A=g(32,64,"mobilenetv1/conv_1"),O=g(64,128,"mobilenetv1/conv_2"),C=g(128,128,"mobilenetv1/conv_3"),$=g(128,256,"mobilenetv1/conv_4"),z=g(256,256,"mobilenetv1/conv_5"),ne=g(256,512,"mobilenetv1/conv_6"),te=g(512,512,"mobilenetv1/conv_7"),se=g(512,512,"mobilenetv1/conv_8"),fe=g(512,512,"mobilenetv1/conv_9"),de=g(512,512,"mobilenetv1/conv_10"),Ae=g(512,512,"mobilenetv1/conv_11"),xe=g(512,1024,"mobilenetv1/conv_12"),Me=g(1024,1024,"mobilenetv1/conv_13");return{conv_0:x,conv_1:A,conv_2:O,conv_3:C,conv_4:$,conv_5:z,conv_6:ne,conv_7:te,conv_8:se,conv_9:fe,conv_10:de,conv_11:Ae,conv_12:xe,conv_13:Me}}function L(){const x=f(1024,256,1,"prediction_layer/conv_0"),A=f(256,512,3,"prediction_layer/conv_1"),O=f(512,128,1,"prediction_layer/conv_2"),C=f(128,256,3,"prediction_layer/conv_3"),$=f(256,128,1,"prediction_layer/conv_4"),z=f(128,256,3,"prediction_layer/conv_5"),ne=f(256,64,1,"prediction_layer/conv_6"),te=f(64,128,3,"prediction_layer/conv_7"),se=d(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),fe=d(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),de=d(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),Ae=d(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),xe=d(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),Me=d(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),Ke=d(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),wt=d(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),$t=d(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),Kt=d(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),Fn=d(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),vn=d(128,18,1,"prediction_layer/box_predictor_5/class_predictor"),Nn={box_encoding_predictor:se,class_predictor:fe},Qs={box_encoding_predictor:de,class_predictor:Ae},Ai={box_encoding_predictor:xe,class_predictor:Me},ei={box_encoding_predictor:Ke,class_predictor:wt},Sa={box_encoding_predictor:$t,class_predictor:Kt},hl={box_encoding_predictor:Fn,class_predictor:vn};return{conv_0:x,conv_1:A,conv_2:O,conv_3:C,conv_4:$,conv_5:z,conv_6:ne,conv_7:te,box_predictor_0:Nn,box_predictor_1:Qs,box_predictor_2:Ai,box_predictor_3:ei,box_predictor_4:Sa,box_predictor_5:hl}}return{extractMobilenetV1Params:S,extractPredictionLayerParams:L}}function 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O=`mobilenetv1/conv_${A}`,C=`MobilenetV1/Conv2d_${A}_depthwise`,$=`${O}/depthwise_conv`,z=`${O}/pointwise_conv`,ne=h(`${C}/depthwise_weights`,4,`${$}/filters`),te=h(`${C}/BatchNorm/gamma`,1,`${$}/batch_norm_scale`),se=h(`${C}/BatchNorm/beta`,1,`${$}/batch_norm_offset`),fe=h(`${C}/BatchNorm/moving_mean`,1,`${$}/batch_norm_mean`),de=h(`${C}/BatchNorm/moving_variance`,1,`${$}/batch_norm_variance`);return{depthwise_conv:{filters:ne,batch_norm_scale:te,batch_norm_offset:se,batch_norm_mean:fe,batch_norm_variance:de},pointwise_conv:d("MobilenetV1",A,z)}}function g(){return{conv_0:d("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:f(1),conv_2:f(2),conv_3:f(3),conv_4:f(4),conv_5:f(5),conv_6:f(6),conv_7:f(7),conv_8:f(8),conv_9:f(9),conv_10:f(10),conv_11:f(11),conv_12:f(12),conv_13:f(13)}}function S(A,O){const C=h(`${A}/weights`,4,`${O}/filters`),$=h(`${A}/biases`,1,`${O}/bias`);return{filters:C,bias:$}}function L(A){const O=S(`Prediction/BoxPredictor_${A}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${A}/box_encoding_predictor`),C=S(`Prediction/BoxPredictor_${A}/ClassPredictor`,`prediction_layer/box_predictor_${A}/class_predictor`);return{box_encoding_predictor:O,class_predictor:C}}function x(){return{conv_0:d("Prediction",0,"prediction_layer/conv_0"),conv_1:d("Prediction",1,"prediction_layer/conv_1"),conv_2:d("Prediction",2,"prediction_layer/conv_2"),conv_3:d("Prediction",3,"prediction_layer/conv_3"),conv_4:d("Prediction",4,"prediction_layer/conv_4"),conv_5:d("Prediction",5,"prediction_layer/conv_5"),conv_6:d("Prediction",6,"prediction_layer/conv_6"),conv_7:d("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:L(0),box_predictor_1:L(1),box_predictor_2:L(2),box_predictor_3:L(3),box_predictor_4:L(4),box_predictor_5:L(5)}}return{extractMobilenetV1Params:g,extractPredictionLayerParams:x}}function bD(r){const 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d=r.arraySync(),f=Math.min(d[l][0],d[l][2]),g=Math.min(d[l][1],d[l][3]),S=Math.max(d[l][0],d[l][2]),L=Math.max(d[l][1],d[l][3]),x=Math.min(d[h][0],d[h][2]),A=Math.min(d[h][1],d[h][3]),O=Math.max(d[h][0],d[h][2]),C=Math.max(d[h][1],d[h][3]),$=(S-f)*(L-g),z=(O-x)*(C-A);if($<=0||z<=0)return 0;const ne=Math.max(f,x),te=Math.max(g,A),se=Math.min(S,O),fe=Math.min(L,C),de=Math.max(se-ne,0)*Math.max(fe-te,0);return de/($+z-de)}const De=Ye(Je());function XZ(r){const l=De.unstack(De.transpose(r,[1,0])),h=[De.sub(l[2],l[0]),De.sub(l[3],l[1])],d=[De.add(l[0],De.div(h[0],De.scalar(2))),De.add(l[1],De.div(h[1],De.scalar(2)))];return{sizes:h,centers:d}}function JZ(r,l){const{sizes:h,centers:d}=XZ(r),f=De.unstack(De.transpose(l,[1,0])),g=De.div(De.mul(De.exp(De.div(f[2],De.scalar(5))),h[0]),De.scalar(2)),S=De.add(De.mul(De.div(f[0],De.scalar(10)),h[0]),d[0]),L=De.div(De.mul(De.exp(De.div(f[3],De.scalar(5))),h[1]),De.scalar(2)),x=De.add(De.mul(De.div(f[1],De.scalar(10)),h[1]),d[1]);return De.transpose(De.stack([De.sub(S,g),De.sub(x,L),De.add(S,g),De.add(x,L)]),[1,0])}function SD(r,l,h){return De.tidy(()=>{const d=r.shape[0];let f=JZ(De.reshape(De.tile(h.extra_dim,[d,1,1]),[-1,4]),De.reshape(r,[-1,4]));f=De.reshape(f,[d,f.shape[0]/d,4]);const g=De.sigmoid(De.slice(l,[0,0,1],[-1,-1,-1]));let S=De.slice(g,[0,0,0],[-1,-1,1]);S=De.reshape(S,[d,S.shape[1]]);const L=De.unstack(f),x=De.unstack(S);return{boxes:L,scores:x}})}const Yu=Ye(Je());function ya(r,l){return Yu.tidy(()=>{const h=r.shape[0],d=Yu.reshape(ma(r,l.box_encoding_predictor),[h,-1,1,4]),f=Yu.reshape(ma(r,l.class_predictor),[h,-1,3]);return{boxPredictionEncoding:d,classPrediction:f}})}const qu=Ye(Je());function ID(r,l,h){return qu.tidy(()=>{const d=Xs(r,h.conv_0,[1,1]),f=Xs(d,h.conv_1,[2,2]),g=Xs(f,h.conv_2,[1,1]),S=Xs(g,h.conv_3,[2,2]),L=Xs(S,h.conv_4,[1,1]),x=Xs(L,h.conv_5,[2,2]),A=Xs(x,h.conv_6,[1,1]),O=Xs(A,h.conv_7,[2,2]),C=ya(l,h.box_predictor_0),$=ya(r,h.box_predictor_1),z=ya(f,h.box_predictor_2),ne=ya(S,h.box_predictor_3),te=ya(x,h.box_predictor_4),se=ya(O,h.box_predictor_5),fe=qu.concat([C.boxPredictionEncoding,$.boxPredictionEncoding,z.boxPredictionEncoding,ne.boxPredictionEncoding,te.boxPredictionEncoding,se.boxPredictionEncoding],1),de=qu.concat([C.classPrediction,$.classPrediction,z.classPrediction,ne.classPrediction,te.classPrediction,se.classPrediction],1);return{boxPredictions:fe,classPredictions:de}})}class Ii{constructor({minConfidence:r,maxResults:l}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=r||.5,this._maxResults=l||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}}const xi=Ye(Je());class nl extends kn{constructor(){super("SsdMobilenetv1")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("SsdMobilenetv1 - load model before inference");return xi.tidy(()=>{const h=xi.cast(r.toBatchTensor(512,!1),"float32"),d=xi.sub(xi.mul(h,xi.scalar(.007843137718737125)),xi.scalar(1)),f=wD(d,l.mobilenetv1),{boxPredictions:g,classPredictions:S}=ID(f.out,f.conv11,l.prediction_layer);return SD(g,S,l.output_layer)})}async forward(r){return this.forwardInput(await Rt(r))}async locateFaces(r,l={}){const{maxResults:h,minConfidence:d}=new Ii(l),f=await Rt(r),{boxes:g,scores:S}=this.forwardInput(f),L=g[0],x=S[0];for(let de=1;de<g.length;de++)g[de].dispose(),S[de].dispose();const A=Array.from(await x.data()),O=.5,C=LD(L,A,h,O,d),$=f.getReshapedInputDimensions(0),z=f.inputSize,ne=z/$.width,te=z/$.height,se=L.arraySync(),fe=C.map(de=>{const[Ae,xe]=[Math.max(0,se[de][0]),Math.min(1,se[de][2])].map(wt=>wt*te),[Me,Ke]=[Math.max(0,se[de][1]),Math.min(1,se[de][3])].map(wt=>wt*ne);return new Yt(A[de],new Su(Me,Ae,Ke-Me,xe-Ae),{height:f.getInputHeight(0),width:f.getInputWidth(0)})});return L.dispose(),x.dispose(),fe}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(r){return bD(r)}extractParams(r){return yD(r)}}function xD(r){const l=new nl;return l.extractWeights(r),l}function ZZ(r){return xD(r)}class QZ extends nl{}const TD=.4,AD=[new Ze(.738768,.874946),new Ze(2.42204,2.65704),new Ze(4.30971,7.04493),new Ze(10.246,4.59428),new Ze(12.6868,11.8741)],vD=[new Ze(1.603231,2.094468),new Ze(6.041143,7.080126),new Ze(2.882459,3.518061),new Ze(4.266906,5.178857),new Ze(9.041765,10.66308)],ND=[117.001,114.697,97.404],CD="tiny_yolov2_model",RD="tiny_yolov2_separable_conv_model";const xg=r=>typeof r=="number";function gx(r){if(!r)throw new Error(`invalid config: ${r}`);if(typeof r.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${r.withSeparableConvs}`);if(!xg(r.iouThreshold)||r.iouThreshold<0||r.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${r.iouThreshold}`);if(!Array.isArray(r.classes)||!r.classes.length||!r.classes.every(l=>typeof l=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(r.classes)}`);if(!Array.isArray(r.anchors)||!r.anchors.length||!r.anchors.map(l=>l||{}).every(l=>xg(l.x)&&xg(l.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(r.anchors)}`);if(r.meanRgb&&(!Array.isArray(r.meanRgb)||r.meanRgb.length!==3||!r.meanRgb.every(xg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(r.meanRgb)}`)}const Js=Ye(Je());function sl(r){return Js.tidy(()=>{const l=Js.mul(r,Js.scalar(.10000000149011612));return Js.add(Js.relu(Js.sub(r,l)),l)})}const Zs=Ye(Je());function vr(r,l){return Zs.tidy(()=>{let h=Zs.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return h=Zs.conv2d(h,l.conv.filters,[1,1],"valid"),h=Zs.sub(h,l.bn.sub),h=Zs.mul(h,l.bn.truediv),h=Zs.add(h,l.conv.bias),sl(h)})}const go=Ye(Je());function Nr(r,l){return go.tidy(()=>{let h=go.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return h=go.separableConv2d(h,l.depthwise_filter,l.pointwise_filter,[1,1],"valid"),h=go.add(h,l.bias),sl(h)})}const yx=Ye(Je());function eQ(r,l){const h=qc(r,l);function d(S,L){const x=yx.tensor1d(r(S)),A=yx.tensor1d(r(S));return l.push({paramPath:`${L}/sub`},{paramPath:`${L}/truediv`}),{sub:x,truediv:A}}function f(S,L,x){const A=h(S,L,3,`${x}/conv`),O=d(L,`${x}/bn`);return{conv:A,bn:O}}const g=jc(r,l);return{extractConvParams:h,extractConvWithBatchNormParams:f,extractSeparableConvParams:g}}function OD(r,l,h,d){const{extractWeights:f,getRemainingWeights:g}=Hn(r),S=[],{extractConvParams:L,extractConvWithBatchNormParams:x,extractSeparableConvParams:A}=eQ(f,S);let O;if(l.withSeparableConvs){const[C,$,z,ne,te,se,fe,de,Ae]=d,xe=l.isFirstLayerConv2d?L(C,$,3,"conv0"):A(C,$,"conv0"),Me=A($,z,"conv1"),Ke=A(z,ne,"conv2"),wt=A(ne,te,"conv3"),$t=A(te,se,"conv4"),Kt=A(se,fe,"conv5"),Fn=de?A(fe,de,"conv6"):void 0,vn=Ae?A(de,Ae,"conv7"):void 0,Nn=L(Ae||de||fe,5*h,1,"conv8");O={conv0:xe,conv1:Me,conv2:Ke,conv3:wt,conv4:$t,conv5:Kt,conv6:Fn,conv7:vn,conv8:Nn}}else{const[C,$,z,ne,te,se,fe,de,Ae]=d,xe=x(C,$,"conv0"),Me=x($,z,"conv1"),Ke=x(z,ne,"conv2"),wt=x(ne,te,"conv3"),$t=x(te,se,"conv4"),Kt=x(se,fe,"conv5"),Fn=x(fe,de,"conv6"),vn=x(de,Ae,"conv7"),Nn=L(Ae,5*h,1,"conv8");O={conv0:xe,conv1:Me,conv2:Ke,conv3:wt,conv4:$t,conv5:Kt,conv6:Fn,conv7:vn,conv8:Nn}}if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{params:O,paramMappings:S}}function tQ(r,l){const h=ys(r,l);function d(L){const x=h(`${L}/sub`,1),A=h(`${L}/truediv`,1);return{sub:x,truediv:A}}function f(L){const x=h(`${L}/filters`,4),A=h(`${L}/bias`,1);return{filters:x,bias:A}}function g(L){const x=f(`${L}/conv`),A=d(`${L}/bn`);return{conv:x,bn:A}}const S=Kc(h);return{extractConvParams:f,extractConvWithBatchNormParams:g,extractSeparableConvParams:S}}function ED(r,l){const h=[],{extractConvParams:d,extractConvWithBatchNormParams:f,extractSeparableConvParams:g}=tQ(r,h);let S;if(l.withSeparableConvs){const L=l.filterSizes&&l.filterSizes.length||9;S={conv0:l.isFirstLayerConv2d?d("conv0"):g("conv0"),conv1:g("conv1"),conv2:g("conv2"),conv3:g("conv3"),conv4:g("conv4"),conv5:g("conv5"),conv6:L>7?g("conv6"):void 0,conv7:L>8?g("conv7"):void 0,conv8:d("conv8")}}else S={conv0:f("conv0"),conv1:f("conv1"),conv2:f("conv2"),conv3:f("conv3"),conv4:f("conv4"),conv5:f("conv5"),conv6:f("conv6"),conv7:f("conv7"),conv8:d("conv8")};return Vn(r,h),{params:S,paramMappings:h}}var bx;(function(r){r[r.XS=224]="XS",r[r.SM=320]="SM",r[r.MD=416]="MD",r[r.LG=608]="LG"})(bx||(bx={}));class Cr{constructor({inputSize:r,scoreThreshold:l}={}){this._name="TinyYolov2Options";if(this._inputSize=r||416,this._scoreThreshold=l||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}}const kt=Ye(Je());class il extends kn{constructor(r){super("TinyYolov2");gx(r),this._config=r}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(r,l){let h=vr(r,l.conv0);return h=kt.maxPool(h,[2,2],[2,2],"same"),h=vr(h,l.conv1),h=kt.maxPool(h,[2,2],[2,2],"same"),h=vr(h,l.conv2),h=kt.maxPool(h,[2,2],[2,2],"same"),h=vr(h,l.conv3),h=kt.maxPool(h,[2,2],[2,2],"same"),h=vr(h,l.conv4),h=kt.maxPool(h,[2,2],[2,2],"same"),h=vr(h,l.conv5),h=kt.maxPool(h,[2,2],[1,1],"same"),h=vr(h,l.conv6),h=vr(h,l.conv7),ma(h,l.conv8,"valid",!1)}runMobilenet(r,l){let h=this.config.isFirstLayerConv2d?sl(ma(r,l.conv0,"valid",!1)):Nr(r,l.conv0);return h=kt.maxPool(h,[2,2],[2,2],"same"),h=Nr(h,l.conv1),h=kt.maxPool(h,[2,2],[2,2],"same"),h=Nr(h,l.conv2),h=kt.maxPool(h,[2,2],[2,2],"same"),h=Nr(h,l.conv3),h=kt.maxPool(h,[2,2],[2,2],"same"),h=Nr(h,l.conv4),h=kt.maxPool(h,[2,2],[2,2],"same"),h=Nr(h,l.conv5),h=kt.maxPool(h,[2,2],[1,1],"same"),h=l.conv6?Nr(h,l.conv6):h,h=l.conv7?Nr(h,l.conv7):h,ma(h,l.conv8,"valid",!1)}forwardInput(r,l){const{params:h}=this;if(!h)throw new Error("TinyYolov2 - load model before inference");return kt.tidy(()=>{let d=kt.cast(r.toBatchTensor(l,!1),"float32");return d=this.config.meanRgb?yi(d,this.config.meanRgb):d,d=d.div(kt.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(d,h):this.runTinyYolov2(d,h)})}async forward(r,l){return await this.forwardInput(await Rt(r),l)}async detect(r,l={}){const{inputSize:h,scoreThreshold:d}=new Cr(l),f=await Rt(r),g=await this.forwardInput(f,h),S=kt.tidy(()=>kt.unstack(g)[0].expandDims()),L={width:f.getInputWidth(0),height:f.getInputHeight(0)},x=await this.extractBoxes(S,f.getReshapedInputDimensions(0),d);g.dispose(),S.dispose();const A=x.map(te=>te.box),O=x.map(te=>te.score),C=x.map(te=>te.classScore),$=x.map(te=>this.config.classes[te.label]),z=VS(A.map(te=>te.rescale(h)),O,this.config.iouThreshold,!0),ne=z.map(te=>new Cc(O[te],C[te],$[te],A[te],L));return ne}getDefaultModelName(){return""}extractParamsFromWeigthMap(r){return ED(r,this.config)}extractParams(r){const l=this.config.filterSizes||il.DEFAULT_FILTER_SIZES,h=l?l.length:void 0;if(h!==7&&h!==8&&h!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${h} filterSizes in config`);return OD(r,this.config,this.boxEncodingSize,l)}async extractBoxes(r,l,h){const{width:d,height:f}=l,g=Math.max(d,f),S=g/d,L=g/f,x=r.shape[1],A=this.config.anchors.length,[O,C,$]=kt.tidy(()=>{const se=r.reshape([x,x,A,this.boxEncodingSize]),fe=se.slice([0,0,0,0],[x,x,A,4]),de=se.slice([0,0,0,4],[x,x,A,1]),Ae=this.withClassScores?kt.softmax(se.slice([0,0,0,5],[x,x,A,this.config.classes.length]),3):kt.scalar(0);return[fe,de,Ae]}),z=[],ne=await C.array(),te=await O.array();for(let se=0;se<x;se++)for(let fe=0;fe<x;fe++)for(let de=0;de<A;de++){const Ae=Lu(ne[se][fe][de][0]);if(!h||Ae>h){const xe=(fe+Lu(te[se][fe][de][0]))/x*S,Me=(se+Lu(te[se][fe][de][1]))/x*L,Ke=Math.exp(te[se][fe][de][2])*this.config.anchors[de].x/x*S,wt=Math.exp(te[se][fe][de][3])*this.config.anchors[de].y/x*L,$t=xe-Ke/2,Kt=Me-wt/2,Fn={row:se,col:fe,anchor:de},{classScore:vn,label:Nn}=this.withClassScores?await this.extractPredictedClass($,Fn):{classScore:1,label:0};z.push({box:new wu($t,Kt,$t+Ke,Kt+wt),score:Ae,classScore:Ae*vn,label:Nn,...Fn})}}return O.dispose(),C.dispose(),$.dispose(),z}async extractPredictedClass(r,l){const{row:h,col:d,anchor:f}=l,g=await r.array();return Array(this.config.classes.length).fill(0).map((S,L)=>g[h][d][f][L]).map((S,L)=>({classScore:S,label:L})).reduce((S,L)=>S.classScore>L.classScore?S:L)}}il.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];class ju extends il{constructor(r=!0){const l=Object.assign({},{withSeparableConvs:r,iouThreshold:TD,classes:["face"]},r?{anchors:vD,meanRgb:ND}:{anchors:AD,withClassScores:!0});super(l)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(r,l){const h=await this.detect(r,l);return h.map(d=>new Yt(d.score,d.relativeBox,{width:d.imageWidth,height:d.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?RD:CD}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}function nQ(r,l=!0){const h=new ju(l);return h.extractWeights(r),h}class wx extends Cr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}}class Ti{async then(r){return r(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}}const Lx=Ye(Je());async function ba(r,l,h,d,f=({alignedRect:g})=>g){const g=r.map(x=>ga(x)?f(x):x.detection),S=d||(l instanceof Lx.Tensor?await Hc(l,g):await Vc(l,g)),L=await h(S);return S.forEach(x=>x instanceof Lx.Tensor&&x.dispose()),L}async function rl(r,l,h,d,f){return ba([r],l,async g=>h(g[0]),d,f)}const DD=.4,kD=[new Ze(1.603231,2.094468),new Ze(6.041143,7.080126),new Ze(2.882459,3.518061),new Ze(4.266906,5.178857),new Ze(9.041765,10.66308)],FD=[117.001,114.697,97.404];class Ku extends il{constructor(){const r={withSeparableConvs:!0,iouThreshold:DD,classes:["face"],anchors:kD,meanRgb:FD,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(r)}get anchors(){return this.config.anchors}async locateFaces(r,l){const h=await this.detect(r,l);return h.map(d=>new Yt(d.score,d.relativeBox,{width:d.imageWidth,height:d.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}const pt={ssdMobilenetv1:new nl,tinyFaceDetector:new Ku,tinyYolov2:new ju,faceLandmark68Net:new Gu,faceLandmark68TinyNet:new px,faceRecognitionNet:new Hu,faceExpressionNet:new cx,ageGenderNet:new dx},_D=(r,l)=>pt.ssdMobilenetv1.locateFaces(r,l),sQ=(r,l)=>pt.tinyFaceDetector.locateFaces(r,l),iQ=(r,l)=>pt.tinyYolov2.locateFaces(r,l),WD=r=>pt.faceLandmark68Net.detectLandmarks(r),rQ=r=>pt.faceLandmark68TinyNet.detectLandmarks(r),oQ=r=>pt.faceRecognitionNet.computeFaceDescriptor(r),aQ=r=>pt.faceExpressionNet.predictExpressions(r),cQ=r=>pt.ageGenderNet.predictAgeAndGender(r),$D=r=>pt.ssdMobilenetv1.load(r),lQ=r=>pt.tinyFaceDetector.load(r),hQ=r=>pt.tinyYolov2.load(r),uQ=r=>pt.faceLandmark68Net.load(r),dQ=r=>pt.faceLandmark68TinyNet.load(r),pQ=r=>pt.faceRecognitionNet.load(r),mQ=r=>pt.faceExpressionNet.load(r),fQ=r=>pt.ageGenderNet.load(r),gQ=$D,yQ=_D,bQ=WD;class UD extends Ti{constructor(r,l,h){super();this.parentTask=r;this.input=l;this.extractedFaces=h}}class Zu extends UD{async run(){const r=await this.parentTask,l=await ba(r,this.input,async h=>await Promise.all(h.map(d=>pt.faceExpressionNet.predictExpressions(d))),this.extractedFaces);return r.map((h,d)=>gg(h,l[d]))}withAgeAndGender(){return new Xu(this,this.input)}}class Qu extends UD{async run(){const r=await this.parentTask;if(!r)return;const l=await rl(r,this.input,h=>pt.faceExpressionNet.predictExpressions(h),this.extractedFaces);return gg(r,l)}withAgeAndGender(){return new Ju(this,this.input)}}class cl extends Zu{withAgeAndGender(){return new ol(this,this.input)}withFaceDescriptors(){return new wa(this,this.input)}}class ll extends Qu{withAgeAndGender(){return new al(this,this.input)}withFaceDescriptor(){return new La(this,this.input)}}class BD extends Ti{constructor(r,l,h){super();this.parentTask=r;this.input=l;this.extractedFaces=h}}class Xu extends BD{async run(){const r=await this.parentTask,l=await ba(r,this.input,async h=>await Promise.all(h.map(d=>pt.ageGenderNet.predictAgeAndGender(d))),this.extractedFaces);return r.map((h,d)=>{const{age:f,gender:g,genderProbability:S}=l[d];return Sg(Ig(h,g,S),f)})}withFaceExpressions(){return new Zu(this,this.input)}}class Ju extends BD{async run(){const r=await this.parentTask;if(!r)return;const{age:l,gender:h,genderProbability:d}=await rl(r,this.input,f=>pt.ageGenderNet.predictAgeAndGender(f),this.extractedFaces);return Sg(Ig(r,h,d),l)}withFaceExpressions(){return new Qu(this,this.input)}}class ol extends Xu{withFaceExpressions(){return new cl(this,this.input)}withFaceDescriptors(){return new wa(this,this.input)}}class al extends Ju{withFaceExpressions(){return new ll(this,this.input)}withFaceDescriptor(){return new La(this,this.input)}}class Sx extends Ti{constructor(r,l){super();this.parentTask=r;this.input=l}}class wa extends Sx{async run(){const r=await this.parentTask,l=await ba(r,this.input,h=>Promise.all(h.map(d=>pt.faceRecognitionNet.computeFaceDescriptor(d))),null,h=>h.landmarks.align(null,{useDlibAlignment:!0}));return l.map((h,d)=>Lg(r[d],h))}withFaceExpressions(){return new cl(this,this.input)}withAgeAndGender(){return new ol(this,this.input)}}class La extends Sx{async run(){const r=await this.parentTask;if(!r)return;const l=await rl(r,this.input,h=>pt.faceRecognitionNet.computeFaceDescriptor(h),null,h=>h.landmarks.align(null,{useDlibAlignment:!0}));return Lg(r,l)}withFaceExpressions(){return new ll(this,this.input)}withAgeAndGender(){return new al(this,this.input)}}const ed=Ye(Je());class Ix extends Ti{constructor(r,l,h){super();this.parentTask=r;this.input=l;this.useTinyLandmarkNet=h}get landmarkNet(){return this.useTinyLandmarkNet?pt.faceLandmark68TinyNet:pt.faceLandmark68Net}}class xx extends Ix{async run(){const r=await this.parentTask,l=r.map(f=>f.detection),h=this.input instanceof ed.Tensor?await Hc(this.input,l):await Vc(this.input,l),d=await Promise.all(h.map(f=>this.landmarkNet.detectLandmarks(f)));return h.forEach(f=>f instanceof ed.Tensor&&f.dispose()),r.map((f,g)=>Qc(f,d[g]))}withFaceExpressions(){return new cl(this,this.input)}withAgeAndGender(){return new ol(this,this.input)}withFaceDescriptors(){return new wa(this,this.input)}}class Tx extends Ix{async run(){const r=await this.parentTask;if(!r)return;const{detection:l}=r,h=this.input instanceof ed.Tensor?await Hc(this.input,[l]):await Vc(this.input,[l]),d=await this.landmarkNet.detectLandmarks(h[0]);return h.forEach(f=>f instanceof ed.Tensor&&f.dispose()),Qc(r,d)}withFaceExpressions(){return new ll(this,this.input)}withAgeAndGender(){return new al(this,this.input)}withFaceDescriptor(){return new La(this,this.input)}}class Ax extends Ti{constructor(r,l=new Ii){super();this.input=r;this.options=l}}class Tg extends Ax{async run(){const{input:r,options:l}=this,h=l instanceof wx?d=>pt.tinyFaceDetector.locateFaces(d,l):l instanceof Ii?d=>pt.ssdMobilenetv1.locateFaces(d,l):l instanceof Cr?d=>pt.tinyYolov2.locateFaces(d,l):null;if(!h)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return h(r)}runAndExtendWithFaceDetections(){return new Promise(async r=>{const l=await this.run();return r(l.map(h=>ta({},h)))})}withFaceLandmarks(r=!1){return new xx(this.runAndExtendWithFaceDetections(),this.input,r)}withFaceExpressions(){return new Zu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Xu(this.runAndExtendWithFaceDetections(),this.input)}}class vx extends Ax{async run(){const r=await new Tg(this.input,this.options);let l=r[0];return r.forEach(h=>{h.score>l.score&&(l=h)}),l}runAndExtendWithFaceDetection(){return new Promise(async r=>{const l=await this.run();return r(l?ta({},l):void 0)})}withFaceLandmarks(r=!1){return new Tx(this.runAndExtendWithFaceDetection(),this.input,r)}withFaceExpressions(){return new Qu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Ju(this.runAndExtendWithFaceDetection(),this.input)}}function wQ(r,l=new Ii){return new vx(r,l)}function Ag(r,l=new Ii){return new Tg(r,l)}async function MD(r,l){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await Ag(r,new Ii(l?{minConfidence:l}:{})).withFaceLandmarks().withFaceDescriptors()}async function LQ(r,l={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await Ag(r,new Cr(l)).withFaceLandmarks().withFaceDescriptors()}const SQ=MD;function Nx(r,l){if(r.length!==l.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");const h=Array.from(r),d=Array.from(l);return Math.sqrt(h.map((f,g)=>f-d[g]).reduce((f,g)=>f+Math.pow(g,2),0))}class PD{constructor(r,l=.6){this._distanceThreshold=l;const h=Array.isArray(r)?r:[r];if(!h.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let d=1;const f=()=>`person ${d++}`;this._labeledDescriptors=h.map(g=>{if(g instanceof ea)return g;if(g instanceof Float32Array)return new ea(f(),[g]);if(g.descriptor&&g.descriptor instanceof Float32Array)return new ea(f(),[g.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(r,l){return l.map(h=>Nx(h,r)).reduce((h,d)=>h+d,0)/(l.length||1)}matchDescriptor(r){return this.labeledDescriptors.map(({descriptors:l,label:h})=>new km(h,this.computeMeanDistance(r,l))).reduce((l,h)=>l.distance<h.distance?l:h)}findBestMatch(r){const l=this.matchDescriptor(r);return l.distance<this.distanceThreshold?l:new km("unknown",l.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(r=>r.toJSON())}}static fromJSON(r){const l=r.labeledDescriptors.map(h=>ea.fromJSON(h));return new PD(l,r.distanceThreshold)}}function IQ(r){const l=new Ku;return l.extractWeights(r),l}function zD(r,l){const{width:h,height:d}=new ms(l.width,l.height);if(h<=0||d<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:h,height:d})}`);if(Array.isArray(r))return r.map(f=>zD(f,{width:h,height:d}));if(ga(r)){const f=r.detection.forSize(h,d),g=r.unshiftedLandmarks.forSize(f.box.width,f.box.height);return Qc(ta(r,f),g)}return Vi(r)?ta(r,r.detection.forSize(h,d)):r instanceof qs||r instanceof Yt?r.forSize(h,d):r}var 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xQ=Ye(Je()),TQ=typeof process!="undefined",AQ=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",vQ={faceapi:GD,node:TQ,browser:AQ};
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Error(`param tensor has already been disposed for path ${l.path}`);l.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:r})=>Array.from(r.dataSync())).reduce((r,l)=>r.concat(l)))}async load(r){if(r instanceof Float32Array){this.extractWeights(r);return}await this.loadFromUri(r)}async loadFromUri(r){if(r&&typeof r!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);const l=await rx(r,this.getDefaultModelName());this.loadFromWeightMap(l)}async loadFromDisk(r){if(r&&typeof r!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);const{readFile:l}=gt.getEnv(),{manifestUri:h,modelBaseUri:d}=rg(r,this.getDefaultModelName()),f=x=>Promise.all(x.map(A=>l(A).then(O=>O.buffer))),g=xr.io.weightsLoaderFactory(f),S=JSON.parse((await l(h)).toString()),L=await g(S,d);this.loadFromWeightMap(L)}loadFromWeightMap(r){const{paramMappings:l,params:h}=this.extractParamsFromWeigthMap(r);this._paramMappings=l,this._params=h}extractWeights(r){const{paramMappings:l,params:h}=this.extractParams(r);this._paramMappings=l,this._params=h}traversePropertyPath(r){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");const l=r.split("/").reduce((f,g)=>{if(!f.nextObj.hasOwnProperty(g))throw new Error(`traversePropertyPath - object does not have property ${g}, for path ${r}`);return{obj:f.nextObj,objProp:g,nextObj:f.nextObj[g]}},{nextObj:this.params}),{obj:h,objProp:d}=l;if(!h||!d||!(h[d]instanceof xr.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${r}`);return{obj:h,objProp:d}}}const jc=Ye(Je());function ns(r,l,h){return jc.tidy(()=>{let d=jc.separableConv2d(r,l.depthwise_filter,l.pointwise_filter,h,"same");return d=jc.add(d,l.bias),d})}const Dt=Ye(Je());function og(r,l,h=!1){return Dt.tidy(()=>{const d=Dt.relu(h?Dt.add(Dt.conv2d(r,l.conv0.filters,[2,2],"same"),l.conv0.bias):ns(r,l.conv0,[2,2])),f=ns(d,l.conv1,[1,1]),g=Dt.relu(Dt.add(d,f)),S=ns(g,l.conv2,[1,1]);return Dt.relu(Dt.add(d,Dt.add(f,S)))})}function Mu(r,l,h=!1,d=!0){return Dt.tidy(()=>{const f=Dt.relu(h?Dt.add(Dt.conv2d(r,l.conv0.filters,d?[2,2]:[1,1],"same"),l.conv0.bias):ns(r,l.conv0,d?[2,2]:[1,1])),g=ns(f,l.conv1,[1,1]),S=Dt.relu(Dt.add(f,g)),L=ns(S,l.conv2,[1,1]),x=Dt.relu(Dt.add(f,Dt.add(g,L))),A=ns(x,l.conv3,[1,1]);return Dt.relu(Dt.add(f,Dt.add(g,Dt.add(L,A))))})}const mo=Ye(Je());function ga(r,l,h="same",d=!1){return mo.tidy(()=>{const f=mo.add(mo.conv2d(r,l.filters,[1,1],h),l.bias);return d?mo.relu(f):f})}function Vn(r,l){Object.keys(r).forEach(h=>{l.some(d=>d.originalPath===h)||r[h].dispose()})}const ag=Ye(Je());function Kc(r,l){return function(h,d,f,g){const S=ag.tensor4d(r(h*d*f*f),[f,f,h,d]),L=ag.tensor1d(r(d));return l.push({paramPath:`${g}/filters`},{paramPath:`${g}/bias`}),{filters:S,bias:L}}}const cg=Ye(Je());function lg(r,l){return function(h,d,f){const g=cg.tensor2d(r(h*d),[h,d]),S=cg.tensor1d(r(d));return l.push({paramPath:`${f}/weights`},{paramPath:`${f}/bias`}),{weights:g,bias:S}}}class ox{constructor(r,l,h){this.depthwise_filter=r;this.pointwise_filter=l;this.bias=h}}const Pu=Ye(Je());function Xc(r,l){return function(h,d,f){const g=Pu.tensor4d(r(3*3*h),[3,3,h,1]),S=Pu.tensor4d(r(h*d),[1,1,h,d]),L=Pu.tensor1d(r(d));return l.push({paramPath:`${f}/depthwise_filter`},{paramPath:`${f}/pointwise_filter`},{paramPath:`${f}/bias`}),new ox(g,S,L)}}function Jc(r){return function(l){const h=r(`${l}/depthwise_filter`,4),d=r(`${l}/pointwise_filter`,4),f=r(`${l}/bias`,1);return new ox(h,d,f)}}function ys(r,l){return function(h,d,f){const g=r[h];if(!Qo(g,d))throw new Error(`expected weightMap[${h}] to be a Tensor${d}D, instead have ${g}`);return l.push({originalPath:h,paramPath:f||h}),g}}function Hn(r){let l=r;function h(f){const g=l.slice(0,f);return l=l.slice(f),g}function d(){return l}return{extractWeights:h,getRemainingWeights:d}}function hg(r,l){const h=Kc(r,l),d=Xc(r,l);function f(S,L,x,A=!1){const O=A?h(S,L,3,`${x}/conv0`):d(S,L,`${x}/conv0`),C=d(L,L,`${x}/conv1`),$=d(L,L,`${x}/conv2`);return{conv0:O,conv1:C,conv2:$}}function g(S,L,x,A=!1){const{conv0:O,conv1:C,conv2:$}=f(S,L,x,A),z=d(L,L,`${x}/conv3`);return{conv0:O,conv1:C,conv2:$,conv3:z}}return{extractDenseBlock3Params:f,extractDenseBlock4Params:g}}function JE(r){const l=[],{extractWeights:h,getRemainingWeights:d}=Hn(r),{extractDenseBlock4Params:f}=hg(h,l),g=f(3,32,"dense0",!0),S=f(32,64,"dense1"),L=f(64,128,"dense2"),x=f(128,256,"dense3");if(d().length!==0)throw new Error(`weights remaing after extract: ${d().length}`);return{paramMappings:l,params:{dense0:g,dense1:S,dense2:L,dense3:x}}}function ug(r){return function(l){const h=r(`${l}/filters`,4),d=r(`${l}/bias`,1);return{filters:h,bias:d}}}function dg(r,l){const h=ys(r,l),d=ug(h),f=Jc(h);function g(L,x=!1){const A=x?d(`${L}/conv0`):f(`${L}/conv0`),O=f(`${L}/conv1`),C=f(`${L}/conv2`);return{conv0:A,conv1:O,conv2:C}}function S(L,x=!1){const A=x?d(`${L}/conv0`):f(`${L}/conv0`),O=f(`${L}/conv1`),C=f(`${L}/conv2`),$=f(`${L}/conv3`);return{conv0:A,conv1:O,conv2:C,conv3:$}}return{extractDenseBlock3Params:g,extractDenseBlock4Params:S}}function ZE(r){const l=[],{extractDenseBlock4Params:h}=dg(r,l),d={dense0:h("dense0",!0),dense1:h("dense1"),dense2:h("dense2"),dense3:h("dense3")};return Vn(r,l),{params:d,paramMappings:l}}const fo=Ye(Je());class pg extends kn{constructor(){super("FaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceFeatureExtractor - load model before inference");return fo.tidy(()=>{const h=fo.cast(r.toBatchTensor(112,!0),"float32"),d=[122.782,117.001,104.298],f=yi(h,d).div(fo.scalar(255));let g=Mu(f,l.dense0,!0);return g=Mu(g,l.dense1),g=Mu(g,l.dense2),g=Mu(g,l.dense3),g=fo.avgPool(g,[7,7],[2,2],"valid"),g})}async forward(r){return this.forwardInput(await Rt(r))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(r){return ZE(r)}extractParams(r){return JE(r)}}const Zc=Ye(Je());function zu(r,l){return Zc.tidy(()=>Zc.add(Zc.matMul(r,l.weights),l.bias))}function QE(r,l,h){const d=[],{extractWeights:f,getRemainingWeights:g}=Hn(r),S=lg(f,d),L=S(l,h,"fc");if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{paramMappings:d,params:{fc:L}}}function eD(r){const l=[],h=ys(r,l);function d(g){const S=h(`${g}/weights`,2),L=h(`${g}/bias`,1);return{weights:S,bias:L}}const f={fc:d("fc")};return Vn(r,l),{params:f,paramMappings:l}}function mg(r){const l={},h={};return Object.keys(r).forEach(d=>{const f=d.startsWith("fc")?h:l;f[d]=r[d]}),{featureExtractorMap:l,classifierMap:h}}const tD=Ye(Je());class fg extends kn{constructor(r,l){super(r);this._faceFeatureExtractor=l}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return tD.tidy(()=>{const h=r instanceof po?this.faceFeatureExtractor.forwardInput(r):r;return zu(h.as2D(h.shape[0],-1),l.fc)})}dispose(r=!0){this.faceFeatureExtractor.dispose(r),super.dispose(r)}loadClassifierParams(r){const{params:l,paramMappings:h}=this.extractClassifierParams(r);this._params=l,this._paramMappings=h}extractClassifierParams(r){return QE(r,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:h}=mg(r);return this.faceFeatureExtractor.loadFromWeightMap(l),eD(h)}extractParams(r){const l=this.getClassifierChannelsIn(),h=this.getClassifierChannelsOut(),d=h*l+h,f=r.slice(0,r.length-d),g=r.slice(r.length-d);return this.faceFeatureExtractor.extractWeights(f),this.extractClassifierParams(g)}}const ax=["neutral","happy","sad","angry","fearful","disgusted","surprised"];class ya{constructor(r){if(r.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${r.length}`);ax.forEach((l,h)=>{this[l]=r[h]})}asSortedArray(){return ax.map(r=>({expression:r,probability:this[r]})).sort((r,l)=>l.probability-r.probability)}}const Qc=Ye(Je());class cx extends fg{constructor(r=new pg){super("FaceExpressionNet",r)}forwardInput(r){return Qc.tidy(()=>Qc.softmax(this.runNet(r)))}async forward(r){return this.forwardInput(await Rt(r))}async predictExpressions(r){const l=await Rt(r),h=await this.forwardInput(l),d=await Promise.all(Qc.unstack(h).map(async g=>{const S=await g.data();return g.dispose(),S}));h.dispose();const f=d.map(g=>new ya(g));return l.isBatchInput?f:f[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}}function lx(r){return r.expressions instanceof ya}function gg(r,l){const h={expressions:l};return Object.assign({},r,h)}function kZ(r,l,h=.1,d){const f=Array.isArray(l)?l:[l];f.forEach(g=>{const S=g instanceof ya?g:lx(g)?g.expressions:void 0;if(!S)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");const L=S.asSortedArray(),x=L.filter(C=>C.probability>h),A=Vi(g)?g.detection.box.bottomLeft:d||new Ze(0,0),O=new Ec(x.map(C=>`${C.expression} (${ea(C.probability)})`),A);O.draw(r)})}function ba(r){return Vi(r)&&r.landmarks instanceof qs&&r.unshiftedLandmarks instanceof qs&&r.alignedRect instanceof Yt}function el(r,l){const{box:h}=r.detection,d=l.shiftBy(h.x,h.y),f=d.align(),{imageDims:g}=r.detection,S=new Yt(r.detection.score,f.rescale(g.reverse()),g),L={landmarks:d,unshiftedLandmarks:l,alignedRect:S};return Object.assign({},r,L)}class nD{constructor(r={}){const{drawLines:l=!0,drawPoints:h=!0,lineWidth:d,lineColor:f,pointSize:g,pointColor:S}=r;this.drawLines=l,this.drawPoints=h,this.lineWidth=d||1,this.pointSize=g||2,this.lineColor=f||"rgba(0, 255, 255, 1)",this.pointColor=S||"rgba(255, 0, 255, 1)"}}class sD{constructor(r,l={}){this.faceLandmarks=r,this.options=new nD(l)}draw(r){const l=es(r),{drawLines:h,drawPoints:d,lineWidth:f,lineColor:g,pointSize:S,pointColor:L}=this.options;if(h&&this.faceLandmarks instanceof Iu&&(l.strokeStyle=g,l.lineWidth=f,gr(l,this.faceLandmarks.getJawOutline()),gr(l,this.faceLandmarks.getLeftEyeBrow()),gr(l,this.faceLandmarks.getRightEyeBrow()),gr(l,this.faceLandmarks.getNose()),gr(l,this.faceLandmarks.getLeftEye(),!0),gr(l,this.faceLandmarks.getRightEye(),!0),gr(l,this.faceLandmarks.getMouth(),!0)),d){l.strokeStyle=L,l.fillStyle=L;const x=A=>{l.beginPath(),l.arc(A.x,A.y,S,0,2*Math.PI),l.fill()};this.faceLandmarks.positions.forEach(x)}}}function FZ(r,l){const h=Array.isArray(l)?l:[l];h.forEach(d=>{const f=d instanceof qs?d:ba(d)?d.landmarks:void 0;if(!f)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new sD(f).draw(r)})}const hx={};vc(hx,{AnchorPosition:()=>Hi,DrawBox:()=>ZS,DrawBoxOptions:()=>l2,DrawFaceLandmarks:()=>sD,DrawFaceLandmarksOptions:()=>nD,DrawTextField:()=>Ec,DrawTextFieldOptions:()=>Wm,drawContour:()=>gr,drawDetections:()=>GX,drawFaceExpressions:()=>kZ,drawFaceLandmarks:()=>FZ});function _Z(r,l){const h=Kc(r,l),d=Xc(r,l);function f(S,L,x){const A=d(S,L,`${x}/separable_conv0`),O=d(L,L,`${x}/separable_conv1`),C=h(S,L,1,`${x}/expansion_conv`);return{separable_conv0:A,separable_conv1:O,expansion_conv:C}}function g(S,L){const x=d(S,S,`${L}/separable_conv0`),A=d(S,S,`${L}/separable_conv1`),O=d(S,S,`${L}/separable_conv2`);return{separable_conv0:x,separable_conv1:A,separable_conv2:O}}return{extractConvParams:h,extractSeparableConvParams:d,extractReductionBlockParams:f,extractMainBlockParams:g}}function iD(r,l){const h=[],{extractWeights:d,getRemainingWeights:f}=Hn(r),{extractConvParams:g,extractSeparableConvParams:S,extractReductionBlockParams:L,extractMainBlockParams:x}=_Z(d,h),A=g(3,32,3,"entry_flow/conv_in"),O=L(32,64,"entry_flow/reduction_block_0"),C=L(64,128,"entry_flow/reduction_block_1"),$={conv_in:A,reduction_block_0:O,reduction_block_1:C},z={};zi(l,0,1).forEach(fe=>{z[`main_block_${fe}`]=x(128,`middle_flow/main_block_${fe}`)});const ne=L(128,256,"exit_flow/reduction_block"),te=S(256,512,"exit_flow/separable_conv"),se={reduction_block:ne,separable_conv:te};if(f().length!==0)throw new Error(`weights remaing after extract: ${f().length}`);return{paramMappings:h,params:{entry_flow:$,middle_flow:z,exit_flow:se}}}function WZ(r,l){const h=ys(r,l),d=ug(h),f=Jc(h);function g(L){const x=f(`${L}/separable_conv0`),A=f(`${L}/separable_conv1`),O=d(`${L}/expansion_conv`);return{separable_conv0:x,separable_conv1:A,expansion_conv:O}}function S(L){const x=f(`${L}/separable_conv0`),A=f(`${L}/separable_conv1`),O=f(`${L}/separable_conv2`);return{separable_conv0:x,separable_conv1:A,separable_conv2:O}}return{extractConvParams:d,extractSeparableConvParams:f,extractReductionBlockParams:g,extractMainBlockParams:S}}function rD(r,l){const h=[],{extractConvParams:d,extractSeparableConvParams:f,extractReductionBlockParams:g,extractMainBlockParams:S}=WZ(r,h),L=d("entry_flow/conv_in"),x=g("entry_flow/reduction_block_0"),A=g("entry_flow/reduction_block_1"),O={conv_in:L,reduction_block_0:x,reduction_block_1:A},C={};zi(l,0,1).forEach(te=>{C[`main_block_${te}`]=S(`middle_flow/main_block_${te}`)});const $=g("exit_flow/reduction_block"),z=f("exit_flow/separable_conv"),ne={reduction_block:$,separable_conv:z};return Vn(r,h),{params:{entry_flow:O,middle_flow:C,exit_flow:ne},paramMappings:h}}const tn=Ye(Je());function oD(r,l,h){return tn.add(tn.conv2d(r,l.filters,h,"same"),l.bias)}function ux(r,l,h=!0){let d=h?tn.relu(r):r;return d=ns(d,l.separable_conv0,[1,1]),d=ns(tn.relu(d),l.separable_conv1,[1,1]),d=tn.maxPool(d,[3,3],[2,2],"same"),d=tn.add(d,oD(r,l.expansion_conv,[2,2])),d}function $Z(r,l){let h=ns(tn.relu(r),l.separable_conv0,[1,1]);return h=ns(tn.relu(h),l.separable_conv1,[1,1]),h=ns(tn.relu(h),l.separable_conv2,[1,1]),h=tn.add(h,r),h}class aD extends kn{constructor(r){super("TinyXception");this._numMainBlocks=r}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyXception - load model before inference");return tn.tidy(()=>{const h=tn.cast(r.toBatchTensor(112,!0),"float32"),d=[122.782,117.001,104.298],f=yi(h,d).div(tn.scalar(256));let g=tn.relu(oD(f,l.entry_flow.conv_in,[2,2]));return g=ux(g,l.entry_flow.reduction_block_0,!1),g=ux(g,l.entry_flow.reduction_block_1),zi(this._numMainBlocks,0,1).forEach(S=>{g=$Z(g,l.middle_flow[`main_block_${S}`])}),g=ux(g,l.exit_flow.reduction_block),g=tn.relu(ns(g,l.exit_flow.separable_conv,[1,1])),g})}async forward(r){return this.forwardInput(await Rt(r))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(r){return rD(r,this._numMainBlocks)}extractParams(r){return iD(r,this._numMainBlocks)}}function cD(r){const l=[],{extractWeights:h,getRemainingWeights:d}=Hn(r),f=lg(h,l),g=f(512,1,"fc/age"),S=f(512,2,"fc/gender");if(d().length!==0)throw new Error(`weights remaing after extract: ${d().length}`);return{paramMappings:l,params:{fc:{age:g,gender:S}}}}function lD(r){const l=[],h=ys(r,l);function d(g){const S=h(`${g}/weights`,2),L=h(`${g}/bias`,1);return{weights:S,bias:L}}const f={fc:{age:d("fc/age"),gender:d("fc/gender")}};return Vn(r,l),{params:f,paramMappings:l}}var Tr;(function(r){r.FEMALE="female",r.MALE="male"})(Tr||(Tr={}));const Xi=Ye(Je());class dx extends kn{constructor(r=new aD(2)){super("AgeGenderNet");this._faceFeatureExtractor=r}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return Xi.tidy(()=>{const h=r instanceof po?this.faceFeatureExtractor.forwardInput(r):r,d=Xi.avgPool(h,[7,7],[2,2],"valid").as2D(h.shape[0],-1),f=zu(d,l.fc.age).as1D(),g=zu(d,l.fc.gender);return{age:f,gender:g}})}forwardInput(r){return Xi.tidy(()=>{const{age:l,gender:h}=this.runNet(r);return{age:l,gender:Xi.softmax(h)}})}async forward(r){return this.forwardInput(await Rt(r))}async predictAgeAndGender(r){const l=await Rt(r),h=await this.forwardInput(l),d=Xi.unstack(h.age),f=Xi.unstack(h.gender),g=d.map((L,x)=>({ageTensor:L,genderTensor:f[x]})),S=await Promise.all(g.map(async({ageTensor:L,genderTensor:x})=>{const A=(await L.data())[0],O=(await x.data())[0],C=O>.5,$=C?Tr.MALE:Tr.FEMALE,z=C?O:1-O;return L.dispose(),x.dispose(),{age:A,gender:$,genderProbability:z}}));return h.age.dispose(),h.gender.dispose(),l.isBatchInput?S:S[0]}getDefaultModelName(){return"age_gender_model"}dispose(r=!0){this.faceFeatureExtractor.dispose(r),super.dispose(r)}loadClassifierParams(r){const{params:l,paramMappings:h}=this.extractClassifierParams(r);this._params=l,this._paramMappings=h}extractClassifierParams(r){return cD(r)}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:h}=mg(r);return this.faceFeatureExtractor.loadFromWeightMap(l),lD(h)}extractParams(r){const l=512*1+1+(512*2+2),h=r.slice(0,r.length-l),d=r.slice(r.length-l);return this.faceFeatureExtractor.extractWeights(h),this.extractClassifierParams(d)}}const bs=Ye(Je());class yg extends fg{postProcess(r,l,h){const d=h.map(({width:g,height:S})=>{const L=l/Math.max(S,g);return{width:g*L,height:S*L}}),f=d.length;return bs.tidy(()=>{const g=(O,C)=>bs.stack([bs.fill([68],O,"float32"),bs.fill([68],C,"float32")],1).as2D(1,136).as1D(),S=(O,C)=>{const{width:$,height:z}=d[O];return C($,z)?Math.abs($-z)/2:0},L=O=>S(O,(C,$)=>C<$),x=O=>S(O,(C,$)=>$<C),A=r.mul(bs.fill([f,136],l,"float32")).sub(bs.stack(Array.from(Array(f),(O,C)=>g(L(C),x(C))))).div(bs.stack(Array.from(Array(f),(O,C)=>g(d[C].width,d[C].height))));return A})}forwardInput(r){return bs.tidy(()=>{const l=this.runNet(r);return this.postProcess(l,r.inputSize,r.inputDimensions.map(([h,d])=>({height:h,width:d})))})}async forward(r){return this.forwardInput(await Rt(r))}async detectLandmarks(r){const l=await Rt(r),h=bs.tidy(()=>bs.unstack(this.forwardInput(l))),d=await Promise.all(h.map(async(f,g)=>{const S=Array.from(await f.data()),L=S.filter((A,O)=>Em(O)),x=S.filter((A,O)=>!Em(O));return new Iu(Array(68).fill(0).map((A,O)=>new Ze(L[O],x[O])),{height:l.getInputHeight(g),width:l.getInputWidth(g)})}));return h.forEach(f=>f.dispose()),l.isBatchInput?d:d[0]}getClassifierChannelsOut(){return 136}}class Gu extends yg{constructor(r=new pg){super("FaceLandmark68Net",r)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}}function hD(r){const l=[],{extractDenseBlock3Params:h}=dg(r,l),d={dense0:h("dense0",!0),dense1:h("dense1"),dense2:h("dense2")};return Vn(r,l),{params:d,paramMappings:l}}function uD(r){const l=[],{extractWeights:h,getRemainingWeights:d}=Hn(r),{extractDenseBlock3Params:f}=hg(h,l),g=f(3,32,"dense0",!0),S=f(32,64,"dense1"),L=f(64,128,"dense2");if(d().length!==0)throw new Error(`weights remaing after extract: ${d().length}`);return{paramMappings:l,params:{dense0:g,dense1:S,dense2:L}}}const go=Ye(Je());class dD extends kn{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyFaceFeatureExtractor - load model before inference");return go.tidy(()=>{const h=go.cast(r.toBatchTensor(112,!0),"float32"),d=[122.782,117.001,104.298],f=yi(h,d).div(go.scalar(255));let g=og(f,l.dense0,!0);return g=og(g,l.dense1),g=og(g,l.dense2),g=go.avgPool(g,[14,14],[2,2],"valid"),g})}async forward(r){return this.forwardInput(await Rt(r))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(r){return hD(r)}extractParams(r){return uD(r)}}class px extends yg{constructor(r=new dD){super("FaceLandmark68TinyNet",r)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}}class UZ extends Gu{}const bg=Ye(Je());function pD(r,l){return bg.add(bg.mul(r,l.weights),l.biases)}const tl=Ye(Je());function mx(r,l,h,d,f="same"){const{filters:g,bias:S}=l.conv;let L=tl.conv2d(r,g,h,f);return L=tl.add(L,S),L=pD(L,l.scale),d?tl.relu(L):L}function mD(r,l){return mx(r,l,[1,1],!0)}function fx(r,l){return mx(r,l,[1,1],!1)}function wg(r,l){return mx(r,l,[2,2],!0,"valid")}const ws=Ye(Je());function BZ(r,l){function h(L,x,A){const O=r(L),C=O.length/(x*A*A);if(MS(C))throw new Error(`depth has to be an integer: ${C}, weights.length: ${O.length}, numFilters: ${x}, filterSize: ${A}`);return ws.tidy(()=>ws.transpose(ws.tensor4d(O,[x,C,A,A]),[2,3,1,0]))}function d(L,x,A,O){const C=h(L,x,A),$=ws.tensor1d(r(x));return l.push({paramPath:`${O}/filters`},{paramPath:`${O}/bias`}),{filters:C,bias:$}}function f(L,x){const A=ws.tensor1d(r(L)),O=ws.tensor1d(r(L));return l.push({paramPath:`${x}/weights`},{paramPath:`${x}/biases`}),{weights:A,biases:O}}function g(L,x,A,O){const C=d(L,x,A,`${O}/conv`),$=f(x,`${O}/scale`);return{conv:C,scale:$}}function S(L,x,A,O,C=!1){const $=g((C?.5:1)*L,x,A,`${O}/conv1`),z=g(L,x,A,`${O}/conv2`);return{conv1:$,conv2:z}}return{extractConvLayerParams:g,extractResidualLayerParams:S}}function fD(r){const{extractWeights:l,getRemainingWeights:h}=Hn(r),d=[],{extractConvLayerParams:f,extractResidualLayerParams:g}=BZ(l,d),S=f(4704,32,7,"conv32_down"),L=g(9216,32,3,"conv32_1"),x=g(9216,32,3,"conv32_2"),A=g(9216,32,3,"conv32_3"),O=g(36864,64,3,"conv64_down",!0),C=g(36864,64,3,"conv64_1"),$=g(36864,64,3,"conv64_2"),z=g(36864,64,3,"conv64_3"),ne=g(147456,128,3,"conv128_down",!0),te=g(147456,128,3,"conv128_1"),se=g(147456,128,3,"conv128_2"),fe=g(589824,256,3,"conv256_down",!0),de=g(589824,256,3,"conv256_1"),Ae=g(589824,256,3,"conv256_2"),xe=g(589824,256,3,"conv256_down_out"),Me=ws.tidy(()=>ws.transpose(ws.tensor2d(l(256*128),[128,256]),[1,0]));if(d.push({paramPath:"fc"}),h().length!==0)throw new Error(`weights remaing after extract: ${h().length}`);const Ke={conv32_down:S,conv32_1:L,conv32_2:x,conv32_3:A,conv64_down:O,conv64_1:C,conv64_2:$,conv64_3:z,conv128_down:ne,conv128_1:te,conv128_2:se,conv256_down:fe,conv256_1:de,conv256_2:Ae,conv256_down_out:xe,fc:Me};return{params:Ke,paramMappings:d}}function MZ(r,l){const h=ys(r,l);function d(S){const L=h(`${S}/scale/weights`,1),x=h(`${S}/scale/biases`,1);return{weights:L,biases:x}}function f(S){const L=h(`${S}/conv/filters`,4),x=h(`${S}/conv/bias`,1),A=d(S);return{conv:{filters:L,bias:x},scale:A}}function g(S){return{conv1:f(`${S}/conv1`),conv2:f(`${S}/conv2`)}}return{extractConvLayerParams:f,extractResidualLayerParams:g}}function gD(r){const l=[],{extractConvLayerParams:h,extractResidualLayerParams:d}=MZ(r,l),f=h("conv32_down"),g=d("conv32_1"),S=d("conv32_2"),L=d("conv32_3"),x=d("conv64_down"),A=d("conv64_1"),O=d("conv64_2"),C=d("conv64_3"),$=d("conv128_down"),z=d("conv128_1"),ne=d("conv128_2"),te=d("conv256_down"),se=d("conv256_1"),fe=d("conv256_2"),de=d("conv256_down_out"),Ae=r.fc;if(l.push({originalPath:"fc",paramPath:"fc"}),!BS(Ae))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${Ae}`);const xe={conv32_down:f,conv32_1:g,conv32_2:S,conv32_3:L,conv64_down:x,conv64_1:A,conv64_2:O,conv64_3:C,conv128_down:$,conv128_1:z,conv128_2:ne,conv256_down:te,conv256_1:se,conv256_2:fe,conv256_down_out:de,fc:Ae};return Vn(r,l),{params:xe,paramMappings:l}}const Yn=Ye(Je());function Li(r,l){let h=mD(r,l.conv1);return h=fx(h,l.conv2),h=Yn.add(h,r),h=Yn.relu(h),h}function Vu(r,l){let h=wg(r,l.conv1);h=fx(h,l.conv2);let d=Yn.avgPool(r,2,2,"valid");const f=Yn.zeros(d.shape),g=d.shape[3]!==h.shape[3],S=d.shape[1]!==h.shape[1]||d.shape[2]!==h.shape[2];if(S){const L=[...h.shape];L[1]=1;const x=Yn.zeros(L);h=Yn.concat([h,x],1);const A=[...h.shape];A[2]=1;const O=Yn.zeros(A);h=Yn.concat([h,O],2)}return d=g?Yn.concat([d,f],3):d,h=Yn.add(d,h),h=Yn.relu(h),h}const _s=Ye(Je());class Hu extends kn{constructor(){super("FaceRecognitionNet")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceRecognitionNet - load model before inference");return _s.tidy(()=>{const h=_s.cast(r.toBatchTensor(150,!0),"float32"),d=[122.782,117.001,104.298],f=yi(h,d).div(_s.scalar(256));let g=wg(f,l.conv32_down);g=_s.maxPool(g,3,2,"valid"),g=Li(g,l.conv32_1),g=Li(g,l.conv32_2),g=Li(g,l.conv32_3),g=Vu(g,l.conv64_down),g=Li(g,l.conv64_1),g=Li(g,l.conv64_2),g=Li(g,l.conv64_3),g=Vu(g,l.conv128_down),g=Li(g,l.conv128_1),g=Li(g,l.conv128_2),g=Vu(g,l.conv256_down),g=Li(g,l.conv256_1),g=Li(g,l.conv256_2),g=Vu(g,l.conv256_down_out);const S=g.mean([1,2]),L=_s.matMul(S,l.fc);return L})}async forward(r){return this.forwardInput(await Rt(r))}async computeFaceDescriptor(r){const l=await Rt(r),h=_s.tidy(()=>_s.unstack(this.forwardInput(l))),d=await Promise.all(h.map(f=>f.data()));return h.forEach(f=>f.dispose()),l.isBatchInput?d:d[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(r){return gD(r)}extractParams(r){return fD(r)}}function PZ(r){const l=new Hu;return l.extractWeights(r),l}function Lg(r,l){const h={descriptor:l};return Object.assign({},r,h)}function zZ(r){return typeof r.age=="number"}function Sg(r,l){const h={age:l};return Object.assign({},r,h)}function GZ(r){return(r.gender===Tr.MALE||r.gender===Tr.FEMALE)&&Rc(r.genderProbability)}function Ig(r,l,h){const d={gender:l,genderProbability:h};return Object.assign({},r,d)}const Si=Ye(Je());function VZ(r,l){function h(x,A){const 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d=r.arraySync(),f=Math.min(d[l][0],d[l][2]),g=Math.min(d[l][1],d[l][3]),S=Math.max(d[l][0],d[l][2]),L=Math.max(d[l][1],d[l][3]),x=Math.min(d[h][0],d[h][2]),A=Math.min(d[h][1],d[h][3]),O=Math.max(d[h][0],d[h][2]),C=Math.max(d[h][1],d[h][3]),$=(S-f)*(L-g),z=(O-x)*(C-A);if($<=0||z<=0)return 0;const ne=Math.max(f,x),te=Math.max(g,A),se=Math.min(S,O),fe=Math.min(L,C),de=Math.max(se-ne,0)*Math.max(fe-te,0);return de/($+z-de)}const De=Ye(Je());function XZ(r){const l=De.unstack(De.transpose(r,[1,0])),h=[De.sub(l[2],l[0]),De.sub(l[3],l[1])],d=[De.add(l[0],De.div(h[0],De.scalar(2))),De.add(l[1],De.div(h[1],De.scalar(2)))];return{sizes:h,centers:d}}function JZ(r,l){const{sizes:h,centers:d}=XZ(r),f=De.unstack(De.transpose(l,[1,0])),g=De.div(De.mul(De.exp(De.div(f[2],De.scalar(5))),h[0]),De.scalar(2)),S=De.add(De.mul(De.div(f[0],De.scalar(10)),h[0]),d[0]),L=De.div(De.mul(De.exp(De.div(f[3],De.scalar(5))),h[1]),De.scalar(2)),x=De.add(De.mul(De.div(f[1],De.scalar(10)),h[1]),d[1]);return De.transpose(De.stack([De.sub(S,g),De.sub(x,L),De.add(S,g),De.add(x,L)]),[1,0])}function SD(r,l,h){return De.tidy(()=>{const d=r.shape[0];let f=JZ(De.reshape(De.tile(h.extra_dim,[d,1,1]),[-1,4]),De.reshape(r,[-1,4]));f=De.reshape(f,[d,f.shape[0]/d,4]);const g=De.sigmoid(De.slice(l,[0,0,1],[-1,-1,-1]));let S=De.slice(g,[0,0,0],[-1,-1,1]);S=De.reshape(S,[d,S.shape[1]]);const L=De.unstack(f),x=De.unstack(S);return{boxes:L,scores:x}})}const Yu=Ye(Je());function wa(r,l){return Yu.tidy(()=>{const h=r.shape[0],d=Yu.reshape(ga(r,l.box_encoding_predictor),[h,-1,1,4]),f=Yu.reshape(ga(r,l.class_predictor),[h,-1,3]);return{boxPredictionEncoding:d,classPrediction:f}})}const qu=Ye(Je());function ID(r,l,h){return qu.tidy(()=>{const d=Xs(r,h.conv_0,[1,1]),f=Xs(d,h.conv_1,[2,2]),g=Xs(f,h.conv_2,[1,1]),S=Xs(g,h.conv_3,[2,2]),L=Xs(S,h.conv_4,[1,1]),x=Xs(L,h.conv_5,[2,2]),A=Xs(x,h.conv_6,[1,1]),O=Xs(A,h.conv_7,[2,2]),C=wa(l,h.box_predictor_0),$=wa(r,h.box_predictor_1),z=wa(f,h.box_predictor_2),ne=wa(S,h.box_predictor_3),te=wa(x,h.box_predictor_4),se=wa(O,h.box_predictor_5),fe=qu.concat([C.boxPredictionEncoding,$.boxPredictionEncoding,z.boxPredictionEncoding,ne.boxPredictionEncoding,te.boxPredictionEncoding,se.boxPredictionEncoding],1),de=qu.concat([C.classPrediction,$.classPrediction,z.classPrediction,ne.classPrediction,te.classPrediction,se.classPrediction],1);return{boxPredictions:fe,classPredictions:de}})}class Ii{constructor({minConfidence:r,maxResults:l}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=r||.5,this._maxResults=l||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}}const xi=Ye(Je());class nl extends kn{constructor(){super("SsdMobilenetv1")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("SsdMobilenetv1 - load model before inference");return xi.tidy(()=>{const h=xi.cast(r.toBatchTensor(512,!1),"float32"),d=xi.sub(xi.mul(h,xi.scalar(.007843137718737125)),xi.scalar(1)),f=wD(d,l.mobilenetv1),{boxPredictions:g,classPredictions:S}=ID(f.out,f.conv11,l.prediction_layer);return SD(g,S,l.output_layer)})}async forward(r){return this.forwardInput(await Rt(r))}async locateFaces(r,l={}){const{maxResults:h,minConfidence:d}=new Ii(l),f=await Rt(r),{boxes:g,scores:S}=this.forwardInput(f),L=g[0],x=S[0];for(let de=1;de<g.length;de++)g[de].dispose(),S[de].dispose();const A=Array.from(await x.data()),O=.5,C=LD(L,A,h,O,d),$=f.getReshapedInputDimensions(0),z=f.inputSize,ne=z/$.width,te=z/$.height,se=L.arraySync(),fe=C.map(de=>{const[Ae,xe]=[Math.max(0,se[de][0]),Math.min(1,se[de][2])].map(wt=>wt*te),[Me,Ke]=[Math.max(0,se[de][1]),Math.min(1,se[de][3])].map(wt=>wt*ne);return new Yt(A[de],new Su(Me,Ae,Ke-Me,xe-Ae),{height:f.getInputHeight(0),width:f.getInputWidth(0)})});return L.dispose(),x.dispose(),fe}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(r){return bD(r)}extractParams(r){return yD(r)}}function xD(r){const l=new nl;return l.extractWeights(r),l}function ZZ(r){return xD(r)}class QZ extends nl{}const TD=.4,AD=[new Ze(.738768,.874946),new Ze(2.42204,2.65704),new Ze(4.30971,7.04493),new Ze(10.246,4.59428),new Ze(12.6868,11.8741)],vD=[new Ze(1.603231,2.094468),new Ze(6.041143,7.080126),new Ze(2.882459,3.518061),new Ze(4.266906,5.178857),new Ze(9.041765,10.66308)],ND=[117.001,114.697,97.404],CD="tiny_yolov2_model",RD="tiny_yolov2_separable_conv_model";const xg=r=>typeof r=="number";function gx(r){if(!r)throw new Error(`invalid config: ${r}`);if(typeof r.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${r.withSeparableConvs}`);if(!xg(r.iouThreshold)||r.iouThreshold<0||r.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${r.iouThreshold}`);if(!Array.isArray(r.classes)||!r.classes.length||!r.classes.every(l=>typeof l=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(r.classes)}`);if(!Array.isArray(r.anchors)||!r.anchors.length||!r.anchors.map(l=>l||{}).every(l=>xg(l.x)&&xg(l.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(r.anchors)}`);if(r.meanRgb&&(!Array.isArray(r.meanRgb)||r.meanRgb.length!==3||!r.meanRgb.every(xg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(r.meanRgb)}`)}const Js=Ye(Je());function sl(r){return Js.tidy(()=>{const l=Js.mul(r,Js.scalar(.10000000149011612));return Js.add(Js.relu(Js.sub(r,l)),l)})}const Zs=Ye(Je());function vr(r,l){return Zs.tidy(()=>{let h=Zs.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return h=Zs.conv2d(h,l.conv.filters,[1,1],"valid"),h=Zs.sub(h,l.bn.sub),h=Zs.mul(h,l.bn.truediv),h=Zs.add(h,l.conv.bias),sl(h)})}const bo=Ye(Je());function Nr(r,l){return bo.tidy(()=>{let h=bo.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return h=bo.separableConv2d(h,l.depthwise_filter,l.pointwise_filter,[1,1],"valid"),h=bo.add(h,l.bias),sl(h)})}const yx=Ye(Je());function eQ(r,l){const h=Kc(r,l);function d(S,L){const x=yx.tensor1d(r(S)),A=yx.tensor1d(r(S));return l.push({paramPath:`${L}/sub`},{paramPath:`${L}/truediv`}),{sub:x,truediv:A}}function f(S,L,x){const A=h(S,L,3,`${x}/conv`),O=d(L,`${x}/bn`);return{conv:A,bn:O}}const g=Xc(r,l);return{extractConvParams:h,extractConvWithBatchNormParams:f,extractSeparableConvParams:g}}function OD(r,l,h,d){const{extractWeights:f,getRemainingWeights:g}=Hn(r),S=[],{extractConvParams:L,extractConvWithBatchNormParams:x,extractSeparableConvParams:A}=eQ(f,S);let O;if(l.withSeparableConvs){const[C,$,z,ne,te,se,fe,de,Ae]=d,xe=l.isFirstLayerConv2d?L(C,$,3,"conv0"):A(C,$,"conv0"),Me=A($,z,"conv1"),Ke=A(z,ne,"conv2"),wt=A(ne,te,"conv3"),$t=A(te,se,"conv4"),Kt=A(se,fe,"conv5"),Fn=de?A(fe,de,"conv6"):void 0,vn=Ae?A(de,Ae,"conv7"):void 0,Nn=L(Ae||de||fe,5*h,1,"conv8");O={conv0:xe,conv1:Me,conv2:Ke,conv3:wt,conv4:$t,conv5:Kt,conv6:Fn,conv7:vn,conv8:Nn}}else{const[C,$,z,ne,te,se,fe,de,Ae]=d,xe=x(C,$,"conv0"),Me=x($,z,"conv1"),Ke=x(z,ne,"conv2"),wt=x(ne,te,"conv3"),$t=x(te,se,"conv4"),Kt=x(se,fe,"conv5"),Fn=x(fe,de,"conv6"),vn=x(de,Ae,"conv7"),Nn=L(Ae,5*h,1,"conv8");O={conv0:xe,conv1:Me,conv2:Ke,conv3:wt,conv4:$t,conv5:Kt,conv6:Fn,conv7:vn,conv8:Nn}}if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{params:O,paramMappings:S}}function tQ(r,l){const h=ys(r,l);function d(L){const x=h(`${L}/sub`,1),A=h(`${L}/truediv`,1);return{sub:x,truediv:A}}function f(L){const x=h(`${L}/filters`,4),A=h(`${L}/bias`,1);return{filters:x,bias:A}}function g(L){const x=f(`${L}/conv`),A=d(`${L}/bn`);return{conv:x,bn:A}}const S=Jc(h);return{extractConvParams:f,extractConvWithBatchNormParams:g,extractSeparableConvParams:S}}function ED(r,l){const h=[],{extractConvParams:d,extractConvWithBatchNormParams:f,extractSeparableConvParams:g}=tQ(r,h);let S;if(l.withSeparableConvs){const L=l.filterSizes&&l.filterSizes.length||9;S={conv0:l.isFirstLayerConv2d?d("conv0"):g("conv0"),conv1:g("conv1"),conv2:g("conv2"),conv3:g("conv3"),conv4:g("conv4"),conv5:g("conv5"),conv6:L>7?g("conv6"):void 0,conv7:L>8?g("conv7"):void 0,conv8:d("conv8")}}else S={conv0:f("conv0"),conv1:f("conv1"),conv2:f("conv2"),conv3:f("conv3"),conv4:f("conv4"),conv5:f("conv5"),conv6:f("conv6"),conv7:f("conv7"),conv8:d("conv8")};return Vn(r,h),{params:S,paramMappings:h}}var bx;(function(r){r[r.XS=224]="XS",r[r.SM=320]="SM",r[r.MD=416]="MD",r[r.LG=608]="LG"})(bx||(bx={}));class Cr{constructor({inputSize:r,scoreThreshold:l}={}){this._name="TinyYolov2Options";if(this._inputSize=r||416,this._scoreThreshold=l||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}}const kt=Ye(Je());class il extends kn{constructor(r){super("TinyYolov2");gx(r),this._config=r}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(r,l){let h=vr(r,l.conv0);return h=kt.maxPool(h,[2,2],[2,2],"same"),h=vr(h,l.conv1),h=kt.maxPool(h,[2,2],[2,2],"same"),h=vr(h,l.conv2),h=kt.maxPool(h,[2,2],[2,2],"same"),h=vr(h,l.conv3),h=kt.maxPool(h,[2,2],[2,2],"same"),h=vr(h,l.conv4),h=kt.maxPool(h,[2,2],[2,2],"same"),h=vr(h,l.conv5),h=kt.maxPool(h,[2,2],[1,1],"same"),h=vr(h,l.conv6),h=vr(h,l.conv7),ga(h,l.conv8,"valid",!1)}runMobilenet(r,l){let h=this.config.isFirstLayerConv2d?sl(ga(r,l.conv0,"valid",!1)):Nr(r,l.conv0);return h=kt.maxPool(h,[2,2],[2,2],"same"),h=Nr(h,l.conv1),h=kt.maxPool(h,[2,2],[2,2],"same"),h=Nr(h,l.conv2),h=kt.maxPool(h,[2,2],[2,2],"same"),h=Nr(h,l.conv3),h=kt.maxPool(h,[2,2],[2,2],"same"),h=Nr(h,l.conv4),h=kt.maxPool(h,[2,2],[2,2],"same"),h=Nr(h,l.conv5),h=kt.maxPool(h,[2,2],[1,1],"same"),h=l.conv6?Nr(h,l.conv6):h,h=l.conv7?Nr(h,l.conv7):h,ga(h,l.conv8,"valid",!1)}forwardInput(r,l){const{params:h}=this;if(!h)throw new Error("TinyYolov2 - load model before inference");return kt.tidy(()=>{let d=kt.cast(r.toBatchTensor(l,!1),"float32");return d=this.config.meanRgb?yi(d,this.config.meanRgb):d,d=d.div(kt.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(d,h):this.runTinyYolov2(d,h)})}async forward(r,l){return await this.forwardInput(await Rt(r),l)}async detect(r,l={}){const{inputSize:h,scoreThreshold:d}=new Cr(l),f=await Rt(r),g=await this.forwardInput(f,h),S=kt.tidy(()=>kt.unstack(g)[0].expandDims()),L={width:f.getInputWidth(0),height:f.getInputHeight(0)},x=await this.extractBoxes(S,f.getReshapedInputDimensions(0),d);g.dispose(),S.dispose();const A=x.map(te=>te.box),O=x.map(te=>te.score),C=x.map(te=>te.classScore),$=x.map(te=>this.config.classes[te.label]),z=VS(A.map(te=>te.rescale(h)),O,this.config.iouThreshold,!0),ne=z.map(te=>new Oc(O[te],C[te],$[te],A[te],L));return ne}getDefaultModelName(){return""}extractParamsFromWeigthMap(r){return ED(r,this.config)}extractParams(r){const l=this.config.filterSizes||il.DEFAULT_FILTER_SIZES,h=l?l.length:void 0;if(h!==7&&h!==8&&h!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${h} filterSizes in config`);return OD(r,this.config,this.boxEncodingSize,l)}async extractBoxes(r,l,h){const{width:d,height:f}=l,g=Math.max(d,f),S=g/d,L=g/f,x=r.shape[1],A=this.config.anchors.length,[O,C,$]=kt.tidy(()=>{const se=r.reshape([x,x,A,this.boxEncodingSize]),fe=se.slice([0,0,0,0],[x,x,A,4]),de=se.slice([0,0,0,4],[x,x,A,1]),Ae=this.withClassScores?kt.softmax(se.slice([0,0,0,5],[x,x,A,this.config.classes.length]),3):kt.scalar(0);return[fe,de,Ae]}),z=[],ne=await C.array(),te=await O.array();for(let se=0;se<x;se++)for(let fe=0;fe<x;fe++)for(let de=0;de<A;de++){const Ae=Lu(ne[se][fe][de][0]);if(!h||Ae>h){const xe=(fe+Lu(te[se][fe][de][0]))/x*S,Me=(se+Lu(te[se][fe][de][1]))/x*L,Ke=Math.exp(te[se][fe][de][2])*this.config.anchors[de].x/x*S,wt=Math.exp(te[se][fe][de][3])*this.config.anchors[de].y/x*L,$t=xe-Ke/2,Kt=Me-wt/2,Fn={row:se,col:fe,anchor:de},{classScore:vn,label:Nn}=this.withClassScores?await this.extractPredictedClass($,Fn):{classScore:1,label:0};z.push({box:new wu($t,Kt,$t+Ke,Kt+wt),score:Ae,classScore:Ae*vn,label:Nn,...Fn})}}return O.dispose(),C.dispose(),$.dispose(),z}async extractPredictedClass(r,l){const{row:h,col:d,anchor:f}=l,g=await r.array();return Array(this.config.classes.length).fill(0).map((S,L)=>g[h][d][f][L]).map((S,L)=>({classScore:S,label:L})).reduce((S,L)=>S.classScore>L.classScore?S:L)}}il.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];class ju extends il{constructor(r=!0){const l=Object.assign({},{withSeparableConvs:r,iouThreshold:TD,classes:["face"]},r?{anchors:vD,meanRgb:ND}:{anchors:AD,withClassScores:!0});super(l)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(r,l){const h=await this.detect(r,l);return h.map(d=>new Yt(d.score,d.relativeBox,{width:d.imageWidth,height:d.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?RD:CD}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}function nQ(r,l=!0){const h=new ju(l);return h.extractWeights(r),h}class wx extends Cr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}}class Ti{async then(r){return r(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}}const Lx=Ye(Je());async function La(r,l,h,d,f=({alignedRect:g})=>g){const g=r.map(x=>ba(x)?f(x):x.detection),S=d||(l instanceof Lx.Tensor?await qc(l,g):await Yc(l,g)),L=await h(S);return S.forEach(x=>x instanceof Lx.Tensor&&x.dispose()),L}async function rl(r,l,h,d,f){return La([r],l,async g=>h(g[0]),d,f)}const DD=.4,kD=[new Ze(1.603231,2.094468),new Ze(6.041143,7.080126),new Ze(2.882459,3.518061),new Ze(4.266906,5.178857),new Ze(9.041765,10.66308)],FD=[117.001,114.697,97.404];class Ku extends il{constructor(){const r={withSeparableConvs:!0,iouThreshold:DD,classes:["face"],anchors:kD,meanRgb:FD,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(r)}get anchors(){return this.config.anchors}async locateFaces(r,l){const h=await this.detect(r,l);return h.map(d=>new Yt(d.score,d.relativeBox,{width:d.imageWidth,height:d.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}const pt={ssdMobilenetv1:new nl,tinyFaceDetector:new Ku,tinyYolov2:new ju,faceLandmark68Net:new Gu,faceLandmark68TinyNet:new px,faceRecognitionNet:new Hu,faceExpressionNet:new cx,ageGenderNet:new dx},_D=(r,l)=>pt.ssdMobilenetv1.locateFaces(r,l),sQ=(r,l)=>pt.tinyFaceDetector.locateFaces(r,l),iQ=(r,l)=>pt.tinyYolov2.locateFaces(r,l),WD=r=>pt.faceLandmark68Net.detectLandmarks(r),rQ=r=>pt.faceLandmark68TinyNet.detectLandmarks(r),oQ=r=>pt.faceRecognitionNet.computeFaceDescriptor(r),aQ=r=>pt.faceExpressionNet.predictExpressions(r),cQ=r=>pt.ageGenderNet.predictAgeAndGender(r),$D=r=>pt.ssdMobilenetv1.load(r),lQ=r=>pt.tinyFaceDetector.load(r),hQ=r=>pt.tinyYolov2.load(r),uQ=r=>pt.faceLandmark68Net.load(r),dQ=r=>pt.faceLandmark68TinyNet.load(r),pQ=r=>pt.faceRecognitionNet.load(r),mQ=r=>pt.faceExpressionNet.load(r),fQ=r=>pt.ageGenderNet.load(r),gQ=$D,yQ=_D,bQ=WD;class UD extends Ti{constructor(r,l,h){super();this.parentTask=r;this.input=l;this.extractedFaces=h}}class Zu extends UD{async run(){const r=await this.parentTask,l=await La(r,this.input,async h=>await Promise.all(h.map(d=>pt.faceExpressionNet.predictExpressions(d))),this.extractedFaces);return r.map((h,d)=>gg(h,l[d]))}withAgeAndGender(){return new Xu(this,this.input)}}class Qu extends UD{async run(){const r=await this.parentTask;if(!r)return;const l=await rl(r,this.input,h=>pt.faceExpressionNet.predictExpressions(h),this.extractedFaces);return gg(r,l)}withAgeAndGender(){return new Ju(this,this.input)}}class cl extends Zu{withAgeAndGender(){return new ol(this,this.input)}withFaceDescriptors(){return new Sa(this,this.input)}}class ll extends Qu{withAgeAndGender(){return new al(this,this.input)}withFaceDescriptor(){return new Ia(this,this.input)}}class BD extends Ti{constructor(r,l,h){super();this.parentTask=r;this.input=l;this.extractedFaces=h}}class Xu extends BD{async run(){const r=await this.parentTask,l=await La(r,this.input,async h=>await Promise.all(h.map(d=>pt.ageGenderNet.predictAgeAndGender(d))),this.extractedFaces);return r.map((h,d)=>{const{age:f,gender:g,genderProbability:S}=l[d];return Sg(Ig(h,g,S),f)})}withFaceExpressions(){return new Zu(this,this.input)}}class Ju extends BD{async run(){const r=await this.parentTask;if(!r)return;const{age:l,gender:h,genderProbability:d}=await rl(r,this.input,f=>pt.ageGenderNet.predictAgeAndGender(f),this.extractedFaces);return Sg(Ig(r,h,d),l)}withFaceExpressions(){return new Qu(this,this.input)}}class ol extends Xu{withFaceExpressions(){return new cl(this,this.input)}withFaceDescriptors(){return new Sa(this,this.input)}}class al extends Ju{withFaceExpressions(){return new ll(this,this.input)}withFaceDescriptor(){return new Ia(this,this.input)}}class Sx extends Ti{constructor(r,l){super();this.parentTask=r;this.input=l}}class Sa extends Sx{async run(){const r=await this.parentTask,l=await La(r,this.input,h=>Promise.all(h.map(d=>pt.faceRecognitionNet.computeFaceDescriptor(d))),null,h=>h.landmarks.align(null,{useDlibAlignment:!0}));return l.map((h,d)=>Lg(r[d],h))}withFaceExpressions(){return new cl(this,this.input)}withAgeAndGender(){return new ol(this,this.input)}}class Ia extends Sx{async run(){const r=await this.parentTask;if(!r)return;const l=await rl(r,this.input,h=>pt.faceRecognitionNet.computeFaceDescriptor(h),null,h=>h.landmarks.align(null,{useDlibAlignment:!0}));return Lg(r,l)}withFaceExpressions(){return new ll(this,this.input)}withAgeAndGender(){return new al(this,this.input)}}const ed=Ye(Je());class Ix extends Ti{constructor(r,l,h){super();this.parentTask=r;this.input=l;this.useTinyLandmarkNet=h}get landmarkNet(){return this.useTinyLandmarkNet?pt.faceLandmark68TinyNet:pt.faceLandmark68Net}}class xx extends Ix{async run(){const r=await this.parentTask,l=r.map(f=>f.detection),h=this.input instanceof ed.Tensor?await qc(this.input,l):await Yc(this.input,l),d=await Promise.all(h.map(f=>this.landmarkNet.detectLandmarks(f)));return h.forEach(f=>f instanceof ed.Tensor&&f.dispose()),r.map((f,g)=>el(f,d[g]))}withFaceExpressions(){return new cl(this,this.input)}withAgeAndGender(){return new ol(this,this.input)}withFaceDescriptors(){return new Sa(this,this.input)}}class Tx extends Ix{async run(){const r=await this.parentTask;if(!r)return;const{detection:l}=r,h=this.input instanceof ed.Tensor?await qc(this.input,[l]):await Yc(this.input,[l]),d=await this.landmarkNet.detectLandmarks(h[0]);return h.forEach(f=>f instanceof ed.Tensor&&f.dispose()),el(r,d)}withFaceExpressions(){return new ll(this,this.input)}withAgeAndGender(){return new al(this,this.input)}withFaceDescriptor(){return new Ia(this,this.input)}}class Ax extends Ti{constructor(r,l=new Ii){super();this.input=r;this.options=l}}class Tg extends Ax{async run(){const{input:r,options:l}=this,h=l instanceof wx?d=>pt.tinyFaceDetector.locateFaces(d,l):l instanceof Ii?d=>pt.ssdMobilenetv1.locateFaces(d,l):l instanceof Cr?d=>pt.tinyYolov2.locateFaces(d,l):null;if(!h)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return h(r)}runAndExtendWithFaceDetections(){return new Promise(async r=>{const l=await this.run();return r(l.map(h=>sa({},h)))})}withFaceLandmarks(r=!1){return new xx(this.runAndExtendWithFaceDetections(),this.input,r)}withFaceExpressions(){return new Zu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Xu(this.runAndExtendWithFaceDetections(),this.input)}}class vx extends Ax{async run(){const r=await new Tg(this.input,this.options);let l=r[0];return r.forEach(h=>{h.score>l.score&&(l=h)}),l}runAndExtendWithFaceDetection(){return new Promise(async r=>{const l=await this.run();return r(l?sa({},l):void 0)})}withFaceLandmarks(r=!1){return new Tx(this.runAndExtendWithFaceDetection(),this.input,r)}withFaceExpressions(){return new Qu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Ju(this.runAndExtendWithFaceDetection(),this.input)}}function wQ(r,l=new Ii){return new vx(r,l)}function Ag(r,l=new Ii){return new Tg(r,l)}async function MD(r,l){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await Ag(r,new Ii(l?{minConfidence:l}:{})).withFaceLandmarks().withFaceDescriptors()}async function LQ(r,l={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await Ag(r,new Cr(l)).withFaceLandmarks().withFaceDescriptors()}const SQ=MD;function Nx(r,l){if(r.length!==l.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");const h=Array.from(r),d=Array.from(l);return Math.sqrt(h.map((f,g)=>f-d[g]).reduce((f,g)=>f+Math.pow(g,2),0))}class PD{constructor(r,l=.6){this._distanceThreshold=l;const h=Array.isArray(r)?r:[r];if(!h.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let d=1;const f=()=>`person ${d++}`;this._labeledDescriptors=h.map(g=>{if(g instanceof na)return g;if(g instanceof Float32Array)return new na(f(),[g]);if(g.descriptor&&g.descriptor instanceof Float32Array)return new na(f(),[g.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(r,l){return l.map(h=>Nx(h,r)).reduce((h,d)=>h+d,0)/(l.length||1)}matchDescriptor(r){return this.labeledDescriptors.map(({descriptors:l,label:h})=>new km(h,this.computeMeanDistance(r,l))).reduce((l,h)=>l.distance<h.distance?l:h)}findBestMatch(r){const l=this.matchDescriptor(r);return l.distance<this.distanceThreshold?l:new km("unknown",l.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(r=>r.toJSON())}}static fromJSON(r){const l=r.labeledDescriptors.map(h=>na.fromJSON(h));return new PD(l,r.distanceThreshold)}}function IQ(r){const l=new Ku;return l.extractWeights(r),l}function zD(r,l){const{width:h,height:d}=new ms(l.width,l.height);if(h<=0||d<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:h,height:d})}`);if(Array.isArray(r))return r.map(f=>zD(f,{width:h,height:d}));if(ba(r)){const f=r.detection.forSize(h,d),g=r.unshiftedLandmarks.forSize(f.box.width,f.box.height);return el(sa(r,f),g)}return Vi(r)?sa(r,r.detection.forSize(h,d)):r instanceof qs||r instanceof Yt?r.forSize(h,d):r}var GD="0.8.1";vc(exports,{AgeGenderNet:()=>dx,BoundingBox:()=>wu,Box:()=>Ct,ComposableTask:()=>Ti,ComputeAllFaceDescriptorsTask:()=>Sa,ComputeFaceDescriptorsTaskBase:()=>Sx,ComputeSingleFaceDescriptorTask:()=>Ia,DetectAllFaceLandmarksTask:()=>xx,DetectAllFacesTask:()=>Tg,DetectFaceLandmarksTaskBase:()=>Ix,DetectFacesTaskBase:()=>Ax,DetectSingleFaceLandmarksTask:()=>Tx,DetectSingleFaceTask:()=>vx,Dimensions:()=>ms,FACE_EXPRESSION_LABELS:()=>ax,FaceDetection:()=>Yt,FaceDetectionNet:()=>QZ,FaceExpressionNet:()=>cx,FaceExpressions:()=>ya,FaceLandmark68Net:()=>Gu,FaceLandmark68TinyNet:()=>px,FaceLandmarkNet:()=>UZ,FaceLandmarks:()=>qs,FaceLandmarks5:()=>BX,FaceLandmarks68:()=>Iu,FaceMatch:()=>km,FaceMatcher:()=>PD,FaceRecognitionNet:()=>Hu,Gender:()=>Tr,LabeledBox:()=>Fm,LabeledFaceDescriptors:()=>na,NetInput:()=>po,NeuralNetwork:()=>kn,ObjectDetection:()=>Oc,Point:()=>Ze,PredictedBox:()=>MX,Rect:()=>Su,SsdMobilenetv1:()=>nl,SsdMobilenetv1Options:()=>Ii,TinyFaceDetector:()=>Ku,TinyFaceDetectorOptions:()=>wx,TinyYolov2:()=>ju,TinyYolov2Options:()=>Cr,TinyYolov2SizeType:()=>bx,allFaces:()=>SQ,allFacesSsdMobilenetv1:()=>MD,allFacesTinyYolov2:()=>LQ,awaitMediaLoaded:()=>QS,bufferToImage:()=>eI,computeFaceDescriptor:()=>oQ,createCanvas:()=>Dc,createCanvasFromMedia:()=>Tu,createFaceDetectionNet:()=>ZZ,createFaceRecognitionNet:()=>PZ,createSsdMobilenetv1:()=>xD,createTinyFaceDetector:()=>IQ,createTinyYolov2:()=>nQ,detectAllFaces:()=>Ag,detectFaceLandmarks:()=>WD,detectFaceLandmarksTiny:()=>rQ,detectLandmarks:()=>bQ,detectSingleFace:()=>wQ,draw:()=>hx,env:()=>gt,euclideanDistance:()=>Nx,extendWithAge:()=>Sg,extendWithFaceDescriptor:()=>Lg,extendWithFaceDetection:()=>sa,extendWithFaceExpressions:()=>gg,extendWithFaceLandmarks:()=>el,extendWithGender:()=>Ig,extractFaceTensors:()=>qc,extractFaces:()=>Yc,fetchImage:()=>OZ,fetchJson:()=>ix,fetchNetWeights:()=>EZ,fetchOrThrow:()=>fa,getContext2dOrThrow:()=>es,getMediaDimensions:()=>ra,imageTensorToCanvas:()=>tI,imageToSquare:()=>sx,inverseSigmoid:()=>_X,iou:()=>zS,isMediaElement:()=>Um,isMediaLoaded:()=>xu,isWithAge:()=>zZ,isWithFaceDetection:()=>Vi,isWithFaceExpressions:()=>lx,isWithFaceLandmarks:()=>ba,isWithGender:()=>GZ,loadAgeGenderModel:()=>fQ,loadFaceDetectionModel:()=>gQ,loadFaceExpressionModel:()=>mQ,loadFaceLandmarkModel:()=>uQ,loadFaceLandmarkTinyModel:()=>dQ,loadFaceRecognitionModel:()=>pQ,loadSsdMobilenetv1Model:()=>$D,loadTinyFaceDetectorModel:()=>lQ,loadTinyYolov2Model:()=>hQ,loadWeightMap:()=>rx,locateFaces:()=>yQ,matchDimensions:()=>DZ,minBbox:()=>GS,nets:()=>pt,nonMaxSuppression:()=>VS,normalize:()=>yi,padToSquare:()=>HS,predictAgeAndGender:()=>cQ,recognizeFaceExpressions:()=>aQ,resizeResults:()=>zD,resolveInput:()=>ia,shuffleArray:()=>FX,sigmoid:()=>Lu,ssdMobilenetv1:()=>_D,tf:()=>xQ,tinyFaceDetector:()=>sQ,tinyYolov2:()=>iQ,toNetInput:()=>Rt,utils:()=>US,validateConfig:()=>gx,version:()=>vQ});const xQ=Ye(Je()),TQ=typeof process!="undefined",AQ=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",vQ={faceapi:GD,node:TQ,browser:AQ};
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/face-api.cjs.map b/dist/face-api.cjs.map
index 3bc7f40..61ce4c3 100644
--- a/dist/face-api.cjs.map
+++ b/dist/face-api.cjs.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["node_modules/node-fetch/lib/index.mjs", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/backends/backend.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/environment.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/global_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/kernel_names.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/profiler.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/tape.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/tensor_format.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/tensor.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/types.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/tensor_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/engine.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/device_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/flags.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/operation.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/complex.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/types.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/model_management.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/platforms/platform_browser.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/platforms/platform_node.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/cast.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/clone.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/print.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/base_side_effects.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/progress.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/http.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/io.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/reshape.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/mat_mul.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/one_hot.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/math.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/tensor3d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/browser.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/serialization.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/test_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/version.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/globals.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/add.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/floorDiv.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/div.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/mul.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/abs.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/acos.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/acosh.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/add_n.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/all.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/any.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/arg_max.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/arg_min.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/asin.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/asinh.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/atan.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/atan2.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/atanh.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/concat.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/sigmoid.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/slice.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/tanh.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/basic_lstm_cell.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/batch_to_space_nd.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm2d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm3d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm4d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_to.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/ceil.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/clip_by_value.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/concat_1d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/concat_2d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/concat_3d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/concat_4d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/conv2d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/conv1d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_input.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose.js", 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properties are enumerable.\nObject.defineProperties(Body.prototype, {\n\tbody: { enumerable: true },\n\tbodyUsed: { enumerable: true },\n\tarrayBuffer: { enumerable: true },\n\tblob: { enumerable: true },\n\tjson: { enumerable: true },\n\ttext: { enumerable: true }\n});\n\nBody.mixIn = function (proto) {\n\tfor (const name of Object.getOwnPropertyNames(Body.prototype)) {\n\t\t// istanbul ignore else: future proof\n\t\tif (!(name in proto)) {\n\t\t\tconst desc = Object.getOwnPropertyDescriptor(Body.prototype, name);\n\t\t\tObject.defineProperty(proto, name, desc);\n\t\t}\n\t}\n};\n\n/**\n * Consume and convert an entire Body to a Buffer.\n *\n * Ref: https://fetch.spec.whatwg.org/#concept-body-consume-body\n *\n * @return  Promise\n */\nfunction consumeBody() {\n\tvar _this4 = this;\n\n\tif (this[INTERNALS].disturbed) {\n\t\treturn Body.Promise.reject(new TypeError(`body used already for: ${this.url}`));\n\t}\n\n\tthis[INTERNALS].disturbed = true;\n\n\tif (this[INTERNALS].error) {\n\t\treturn Body.Promise.reject(this[INTERNALS].error);\n\t}\n\n\tlet body = this.body;\n\n\t// body is null\n\tif (body === null) {\n\t\treturn Body.Promise.resolve(Buffer.alloc(0));\n\t}\n\n\t// body is blob\n\tif (isBlob(body)) {\n\t\tbody = body.stream();\n\t}\n\n\t// body is buffer\n\tif (Buffer.isBuffer(body)) {\n\t\treturn Body.Promise.resolve(body);\n\t}\n\n\t// istanbul ignore if: should never happen\n\tif (!(body instanceof Stream)) {\n\t\treturn Body.Promise.resolve(Buffer.alloc(0));\n\t}\n\n\t// body is stream\n\t// get ready to actually consume the body\n\tlet accum = [];\n\tlet accumBytes = 0;\n\tlet abort = false;\n\n\treturn new Body.Promise(function (resolve, reject) {\n\t\tlet resTimeout;\n\n\t\t// allow timeout on slow response body\n\t\tif (_this4.timeout) {\n\t\t\tresTimeout = setTimeout(function () {\n\t\t\t\tabort = true;\n\t\t\t\treject(new FetchError(`Response timeout while trying to fetch ${_this4.url} (over ${_this4.timeout}ms)`, 'body-timeout'));\n\t\t\t}, _this4.timeout);\n\t\t}\n\n\t\t// handle stream errors\n\t\tbody.on('error', function (err) {\n\t\t\tif (err.name === 'AbortError') {\n\t\t\t\t// if the request was aborted, reject with this Error\n\t\t\t\tabort = true;\n\t\t\t\treject(err);\n\t\t\t} else {\n\t\t\t\t// other errors, such as incorrect content-encoding\n\t\t\t\treject(new FetchError(`Invalid response body while trying to fetch ${_this4.url}: ${err.message}`, 'system', err));\n\t\t\t}\n\t\t});\n\n\t\tbody.on('data', function (chunk) {\n\t\t\tif (abort || chunk === null) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tif (_this4.size && accumBytes + chunk.length > _this4.size) {\n\t\t\t\tabort = true;\n\t\t\t\treject(new FetchError(`content size at ${_this4.url} over limit: ${_this4.size}`, 'max-size'));\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\taccumBytes += chunk.length;\n\t\t\taccum.push(chunk);\n\t\t});\n\n\t\tbody.on('end', function () {\n\t\t\tif (abort) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tclearTimeout(resTimeout);\n\n\t\t\ttry {\n\t\t\t\tresolve(Buffer.concat(accum, accumBytes));\n\t\t\t} catch (err) {\n\t\t\t\t// handle streams that have accumulated too much data (issue #414)\n\t\t\t\treject(new FetchError(`Could not create Buffer from response body for ${_this4.url}: ${err.message}`, 'system', err));\n\t\t\t}\n\t\t});\n\t});\n}\n\n/**\n * Detect buffer encoding and convert to target encoding\n * ref: http://www.w3.org/TR/2011/WD-html5-20110113/parsing.html#determining-the-character-encoding\n *\n * @param   Buffer  buffer    Incoming buffer\n * @param   String  encoding  Target encoding\n * @return  String\n */\nfunction convertBody(buffer, headers) {\n\tif (typeof convert !== 'function') {\n\t\tthrow new Error('The package `encoding` must be installed to use the textConverted() function');\n\t}\n\n\tconst ct = headers.get('content-type');\n\tlet charset = 'utf-8';\n\tlet res, str;\n\n\t// header\n\tif (ct) {\n\t\tres = /charset=([^;]*)/i.exec(ct);\n\t}\n\n\t// no charset in content type, peek at response body for at most 1024 bytes\n\tstr = buffer.slice(0, 1024).toString();\n\n\t// html5\n\tif (!res && str) {\n\t\tres = /<meta.+?charset=(['\"])(.+?)\\1/i.exec(str);\n\t}\n\n\t// html4\n\tif (!res && str) {\n\t\tres = /<meta[\\s]+?http-equiv=(['\"])content-type\\1[\\s]+?content=(['\"])(.+?)\\2/i.exec(str);\n\t\tif (!res) {\n\t\t\tres = /<meta[\\s]+?content=(['\"])(.+?)\\1[\\s]+?http-equiv=(['\"])content-type\\3/i.exec(str);\n\t\t\tif (res) {\n\t\t\t\tres.pop(); // drop last quote\n\t\t\t}\n\t\t}\n\n\t\tif (res) {\n\t\t\tres = /charset=(.*)/i.exec(res.pop());\n\t\t}\n\t}\n\n\t// xml\n\tif (!res && str) {\n\t\tres = /<\\?xml.+?encoding=(['\"])(.+?)\\1/i.exec(str);\n\t}\n\n\t// found charset\n\tif (res) {\n\t\tcharset = res.pop();\n\n\t\t// prevent decode issues when sites use incorrect encoding\n\t\t// ref: https://hsivonen.fi/encoding-menu/\n\t\tif (charset === 'gb2312' || charset === 'gbk') {\n\t\t\tcharset = 'gb18030';\n\t\t}\n\t}\n\n\t// turn raw buffers into a single utf-8 buffer\n\treturn convert(buffer, 'UTF-8', charset).toString();\n}\n\n/**\n * Detect a URLSearchParams object\n * ref: https://github.com/bitinn/node-fetch/issues/296#issuecomment-307598143\n *\n * @param   Object  obj     Object to detect by type or brand\n * @return  String\n */\nfunction isURLSearchParams(obj) {\n\t// Duck-typing as a necessary condition.\n\tif (typeof obj !== 'object' || typeof obj.append !== 'function' || typeof obj.delete !== 'function' || typeof obj.get !== 'function' || typeof obj.getAll !== 'function' || typeof obj.has !== 'function' || typeof obj.set !== 'function') {\n\t\treturn false;\n\t}\n\n\t// Brand-checking and more duck-typing as optional condition.\n\treturn obj.constructor.name === 'URLSearchParams' || Object.prototype.toString.call(obj) === '[object URLSearchParams]' || typeof obj.sort === 'function';\n}\n\n/**\n * Check if `obj` is a W3C `Blob` object (which `File` inherits from)\n * @param  {*} obj\n * @return {boolean}\n */\nfunction isBlob(obj) {\n\treturn typeof obj === 'object' && typeof obj.arrayBuffer === 'function' && typeof obj.type === 'string' && typeof obj.stream === 'function' && typeof obj.constructor === 'function' && typeof obj.constructor.name === 'string' && /^(Blob|File)$/.test(obj.constructor.name) && /^(Blob|File)$/.test(obj[Symbol.toStringTag]);\n}\n\n/**\n * Clone body given Res/Req instance\n *\n * @param   Mixed  instance  Response or Request instance\n * @return  Mixed\n */\nfunction clone(instance) {\n\tlet p1, p2;\n\tlet body = instance.body;\n\n\t// don't allow cloning a used body\n\tif (instance.bodyUsed) {\n\t\tthrow new Error('cannot clone body after it is used');\n\t}\n\n\t// check that body is a stream and not form-data object\n\t// note: we can't clone the form-data object without having it as a dependency\n\tif (body instanceof Stream && typeof body.getBoundary !== 'function') {\n\t\t// tee instance body\n\t\tp1 = new PassThrough();\n\t\tp2 = new PassThrough();\n\t\tbody.pipe(p1);\n\t\tbody.pipe(p2);\n\t\t// set instance body to teed body and return the other teed body\n\t\tinstance[INTERNALS].body = p1;\n\t\tbody = p2;\n\t}\n\n\treturn body;\n}\n\n/**\n * Performs the operation \"extract a `Content-Type` value from |object|\" as\n * specified in the specification:\n * https://fetch.spec.whatwg.org/#concept-bodyinit-extract\n *\n * This function assumes that instance.body is present.\n *\n * @param   Mixed  instance  Any options.body input\n */\nfunction extractContentType(body) {\n\tif (body === null) {\n\t\t// body is null\n\t\treturn null;\n\t} else if (typeof body === 'string') {\n\t\t// body is string\n\t\treturn 'text/plain;charset=UTF-8';\n\t} else if (isURLSearchParams(body)) {\n\t\t// body is a URLSearchParams\n\t\treturn 'application/x-www-form-urlencoded;charset=UTF-8';\n\t} else if (isBlob(body)) {\n\t\t// body is blob\n\t\treturn body.type || null;\n\t} else if (Buffer.isBuffer(body)) {\n\t\t// body is buffer\n\t\treturn null;\n\t} else if (Object.prototype.toString.call(body) === '[object ArrayBuffer]') {\n\t\t// body is ArrayBuffer\n\t\treturn null;\n\t} else if (ArrayBuffer.isView(body)) {\n\t\t// body is ArrayBufferView\n\t\treturn null;\n\t} else if (typeof body.getBoundary === 'function') {\n\t\t// detect form data input from form-data module\n\t\treturn `multipart/form-data;boundary=${body.getBoundary()}`;\n\t} else if (body instanceof Stream) {\n\t\t// body is stream\n\t\t// can't really do much about this\n\t\treturn null;\n\t} else {\n\t\t// Body constructor defaults other things to string\n\t\treturn 'text/plain;charset=UTF-8';\n\t}\n}\n\n/**\n * The Fetch Standard treats this as if \"total bytes\" is a property on the body.\n * For us, we have to explicitly get it with a function.\n *\n * ref: https://fetch.spec.whatwg.org/#concept-body-total-bytes\n *\n * @param   Body    instance   Instance of Body\n * @return  Number?            Number of bytes, or null if not possible\n */\nfunction getTotalBytes(instance) {\n\tconst body = instance.body;\n\n\n\tif (body === null) {\n\t\t// body is null\n\t\treturn 0;\n\t} else if (isBlob(body)) {\n\t\treturn body.size;\n\t} else if (Buffer.isBuffer(body)) {\n\t\t// body is buffer\n\t\treturn body.length;\n\t} else if (body && typeof body.getLengthSync === 'function') {\n\t\t// detect form data input from form-data module\n\t\tif (body._lengthRetrievers && body._lengthRetrievers.length == 0 || // 1.x\n\t\tbody.hasKnownLength && body.hasKnownLength()) {\n\t\t\t// 2.x\n\t\t\treturn body.getLengthSync();\n\t\t}\n\t\treturn null;\n\t} else {\n\t\t// body is stream\n\t\treturn null;\n\t}\n}\n\n/**\n * Write a Body to a Node.js WritableStream (e.g. http.Request) object.\n *\n * @param   Body    instance   Instance of Body\n * @return  Void\n */\nfunction writeToStream(dest, instance) {\n\tconst body = instance.body;\n\n\n\tif (body === null) {\n\t\t// body is null\n\t\tdest.end();\n\t} else if (isBlob(body)) {\n\t\tbody.stream().pipe(dest);\n\t} else if (Buffer.isBuffer(body)) {\n\t\t// body is buffer\n\t\tdest.write(body);\n\t\tdest.end();\n\t} else {\n\t\t// body is stream\n\t\tbody.pipe(dest);\n\t}\n}\n\n// expose Promise\nBody.Promise = global.Promise;\n\n/**\n * headers.js\n *\n * Headers class offers convenient helpers\n */\n\nconst invalidTokenRegex = /[^\\^_`a-zA-Z\\-0-9!#$%&'*+.|~]/;\nconst invalidHeaderCharRegex = /[^\\t\\x20-\\x7e\\x80-\\xff]/;\n\nfunction validateName(name) {\n\tname = `${name}`;\n\tif (invalidTokenRegex.test(name) || name === '') {\n\t\tthrow new TypeError(`${name} is not a legal HTTP header name`);\n\t}\n}\n\nfunction validateValue(value) {\n\tvalue = `${value}`;\n\tif (invalidHeaderCharRegex.test(value)) {\n\t\tthrow new TypeError(`${value} is not a legal HTTP header value`);\n\t}\n}\n\n/**\n * Find the key in the map object given a header name.\n *\n * Returns undefined if not found.\n *\n * @param   String  name  Header name\n * @return  String|Undefined\n */\nfunction find(map, name) {\n\tname = name.toLowerCase();\n\tfor (const key in map) {\n\t\tif (key.toLowerCase() === name) {\n\t\t\treturn key;\n\t\t}\n\t}\n\treturn undefined;\n}\n\nconst MAP = Symbol('map');\nclass Headers {\n\t/**\n  * Headers class\n  *\n  * @param   Object  headers  Response headers\n  * @return  Void\n  */\n\tconstructor() {\n\t\tlet init = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : undefined;\n\n\t\tthis[MAP] = Object.create(null);\n\n\t\tif (init instanceof Headers) {\n\t\t\tconst rawHeaders = init.raw();\n\t\t\tconst headerNames = Object.keys(rawHeaders);\n\n\t\t\tfor (const headerName of headerNames) {\n\t\t\t\tfor (const value of rawHeaders[headerName]) {\n\t\t\t\t\tthis.append(headerName, value);\n\t\t\t\t}\n\t\t\t}\n\n\t\t\treturn;\n\t\t}\n\n\t\t// We don't worry about converting prop to ByteString here as append()\n\t\t// will handle it.\n\t\tif (init == null) ; else if (typeof init === 'object') {\n\t\t\tconst method = init[Symbol.iterator];\n\t\t\tif (method != null) {\n\t\t\t\tif (typeof method !== 'function') {\n\t\t\t\t\tthrow new TypeError('Header pairs must be iterable');\n\t\t\t\t}\n\n\t\t\t\t// sequence<sequence<ByteString>>\n\t\t\t\t// Note: per spec we have to first exhaust the lists then process them\n\t\t\t\tconst pairs = [];\n\t\t\t\tfor (const pair of init) {\n\t\t\t\t\tif (typeof pair !== 'object' || typeof pair[Symbol.iterator] !== 'function') {\n\t\t\t\t\t\tthrow new TypeError('Each header pair must be iterable');\n\t\t\t\t\t}\n\t\t\t\t\tpairs.push(Array.from(pair));\n\t\t\t\t}\n\n\t\t\t\tfor (const pair of pairs) {\n\t\t\t\t\tif (pair.length !== 2) {\n\t\t\t\t\t\tthrow new TypeError('Each header pair must be a name/value tuple');\n\t\t\t\t\t}\n\t\t\t\t\tthis.append(pair[0], pair[1]);\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\t// record<ByteString, ByteString>\n\t\t\t\tfor (const key of Object.keys(init)) {\n\t\t\t\t\tconst value = init[key];\n\t\t\t\t\tthis.append(key, value);\n\t\t\t\t}\n\t\t\t}\n\t\t} else {\n\t\t\tthrow new TypeError('Provided initializer must be an object');\n\t\t}\n\t}\n\n\t/**\n  * Return combined header value given name\n  *\n  * @param   String  name  Header name\n  * @return  Mixed\n  */\n\tget(name) {\n\t\tname = `${name}`;\n\t\tvalidateName(name);\n\t\tconst key = find(this[MAP], name);\n\t\tif (key === undefined) {\n\t\t\treturn null;\n\t\t}\n\n\t\treturn this[MAP][key].join(', ');\n\t}\n\n\t/**\n  * Iterate over all headers\n  *\n  * @param   Function  callback  Executed for each item with parameters (value, name, thisArg)\n  * @param   Boolean   thisArg   `this` context for callback function\n  * @return  Void\n  */\n\tforEach(callback) {\n\t\tlet thisArg = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : undefined;\n\n\t\tlet pairs = getHeaders(this);\n\t\tlet i = 0;\n\t\twhile (i < pairs.length) {\n\t\t\tvar _pairs$i = pairs[i];\n\t\t\tconst name = _pairs$i[0],\n\t\t\t      value = _pairs$i[1];\n\n\t\t\tcallback.call(thisArg, value, name, this);\n\t\t\tpairs = getHeaders(this);\n\t\t\ti++;\n\t\t}\n\t}\n\n\t/**\n  * Overwrite header values given name\n  *\n  * @param   String  name   Header name\n  * @param   String  value  Header value\n  * @return  Void\n  */\n\tset(name, value) {\n\t\tname = `${name}`;\n\t\tvalue = `${value}`;\n\t\tvalidateName(name);\n\t\tvalidateValue(value);\n\t\tconst key = find(this[MAP], name);\n\t\tthis[MAP][key !== undefined ? key : name] = [value];\n\t}\n\n\t/**\n  * Append a value onto existing header\n  *\n  * @param   String  name   Header name\n  * @param   String  value  Header value\n  * @return  Void\n  */\n\tappend(name, value) {\n\t\tname = `${name}`;\n\t\tvalue = `${value}`;\n\t\tvalidateName(name);\n\t\tvalidateValue(value);\n\t\tconst key = find(this[MAP], name);\n\t\tif (key !== undefined) {\n\t\t\tthis[MAP][key].push(value);\n\t\t} else {\n\t\t\tthis[MAP][name] = [value];\n\t\t}\n\t}\n\n\t/**\n  * Check for header name existence\n  *\n  * @param   String   name  Header name\n  * @return  Boolean\n  */\n\thas(name) {\n\t\tname = `${name}`;\n\t\tvalidateName(name);\n\t\treturn find(this[MAP], name) !== undefined;\n\t}\n\n\t/**\n  * Delete all header values given name\n  *\n  * @param   String  name  Header name\n  * @return  Void\n  */\n\tdelete(name) {\n\t\tname = `${name}`;\n\t\tvalidateName(name);\n\t\tconst key = find(this[MAP], name);\n\t\tif (key !== undefined) {\n\t\t\tdelete this[MAP][key];\n\t\t}\n\t}\n\n\t/**\n  * Return raw headers (non-spec api)\n  *\n  * @return  Object\n  */\n\traw() {\n\t\treturn this[MAP];\n\t}\n\n\t/**\n  * Get an iterator on keys.\n  *\n  * @return  Iterator\n  */\n\tkeys() {\n\t\treturn createHeadersIterator(this, 'key');\n\t}\n\n\t/**\n  * Get an iterator on values.\n  *\n  * @return  Iterator\n  */\n\tvalues() {\n\t\treturn createHeadersIterator(this, 'value');\n\t}\n\n\t/**\n  * Get an iterator on entries.\n  *\n  * This is the default iterator of the Headers object.\n  *\n  * @return  Iterator\n  */\n\t[Symbol.iterator]() {\n\t\treturn createHeadersIterator(this, 'key+value');\n\t}\n}\nHeaders.prototype.entries = Headers.prototype[Symbol.iterator];\n\nObject.defineProperty(Headers.prototype, Symbol.toStringTag, {\n\tvalue: 'Headers',\n\twritable: false,\n\tenumerable: false,\n\tconfigurable: true\n});\n\nObject.defineProperties(Headers.prototype, {\n\tget: { enumerable: true },\n\tforEach: { enumerable: true },\n\tset: { enumerable: true },\n\tappend: { enumerable: true },\n\thas: { enumerable: true },\n\tdelete: { enumerable: true },\n\tkeys: { enumerable: true },\n\tvalues: { enumerable: true },\n\tentries: { enumerable: true }\n});\n\nfunction getHeaders(headers) {\n\tlet kind = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : 'key+value';\n\n\tconst keys = Object.keys(headers[MAP]).sort();\n\treturn keys.map(kind === 'key' ? function (k) {\n\t\treturn k.toLowerCase();\n\t} : kind === 'value' ? function (k) {\n\t\treturn headers[MAP][k].join(', ');\n\t} : function (k) {\n\t\treturn [k.toLowerCase(), headers[MAP][k].join(', ')];\n\t});\n}\n\nconst INTERNAL = Symbol('internal');\n\nfunction createHeadersIterator(target, kind) {\n\tconst iterator = Object.create(HeadersIteratorPrototype);\n\titerator[INTERNAL] = {\n\t\ttarget,\n\t\tkind,\n\t\tindex: 0\n\t};\n\treturn iterator;\n}\n\nconst HeadersIteratorPrototype = Object.setPrototypeOf({\n\tnext() {\n\t\t// istanbul ignore if\n\t\tif (!this || Object.getPrototypeOf(this) !== HeadersIteratorPrototype) {\n\t\t\tthrow new TypeError('Value of `this` is not a HeadersIterator');\n\t\t}\n\n\t\tvar _INTERNAL = this[INTERNAL];\n\t\tconst target = _INTERNAL.target,\n\t\t      kind = _INTERNAL.kind,\n\t\t      index = _INTERNAL.index;\n\n\t\tconst values = getHeaders(target, kind);\n\t\tconst len = values.length;\n\t\tif (index >= len) {\n\t\t\treturn {\n\t\t\t\tvalue: undefined,\n\t\t\t\tdone: true\n\t\t\t};\n\t\t}\n\n\t\tthis[INTERNAL].index = index + 1;\n\n\t\treturn {\n\t\t\tvalue: values[index],\n\t\t\tdone: false\n\t\t};\n\t}\n}, Object.getPrototypeOf(Object.getPrototypeOf([][Symbol.iterator]())));\n\nObject.defineProperty(HeadersIteratorPrototype, Symbol.toStringTag, {\n\tvalue: 'HeadersIterator',\n\twritable: false,\n\tenumerable: false,\n\tconfigurable: true\n});\n\n/**\n * Export the Headers object in a form that Node.js can consume.\n *\n * @param   Headers  headers\n * @return  Object\n */\nfunction exportNodeCompatibleHeaders(headers) {\n\tconst obj = Object.assign({ __proto__: null }, headers[MAP]);\n\n\t// http.request() only supports string as Host header. This hack makes\n\t// specifying custom Host header possible.\n\tconst hostHeaderKey = find(headers[MAP], 'Host');\n\tif (hostHeaderKey !== undefined) {\n\t\tobj[hostHeaderKey] = obj[hostHeaderKey][0];\n\t}\n\n\treturn obj;\n}\n\n/**\n * Create a Headers object from an object of headers, ignoring those that do\n * not conform to HTTP grammar productions.\n *\n * @param   Object  obj  Object of headers\n * @return  Headers\n */\nfunction createHeadersLenient(obj) {\n\tconst headers = new Headers();\n\tfor (const name of Object.keys(obj)) {\n\t\tif (invalidTokenRegex.test(name)) {\n\t\t\tcontinue;\n\t\t}\n\t\tif (Array.isArray(obj[name])) {\n\t\t\tfor (const val of obj[name]) {\n\t\t\t\tif (invalidHeaderCharRegex.test(val)) {\n\t\t\t\t\tcontinue;\n\t\t\t\t}\n\t\t\t\tif (headers[MAP][name] === undefined) {\n\t\t\t\t\theaders[MAP][name] = [val];\n\t\t\t\t} else {\n\t\t\t\t\theaders[MAP][name].push(val);\n\t\t\t\t}\n\t\t\t}\n\t\t} else if (!invalidHeaderCharRegex.test(obj[name])) {\n\t\t\theaders[MAP][name] = [obj[name]];\n\t\t}\n\t}\n\treturn headers;\n}\n\nconst INTERNALS$1 = Symbol('Response internals');\n\n// fix an issue where \"STATUS_CODES\" aren't a named export for node <10\nconst STATUS_CODES = http.STATUS_CODES;\n\n/**\n * Response class\n *\n * @param   Stream  body  Readable stream\n * @param   Object  opts  Response options\n * @return  Void\n */\nclass Response {\n\tconstructor() {\n\t\tlet body = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null;\n\t\tlet opts = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n\t\tBody.call(this, body, opts);\n\n\t\tconst status = opts.status || 200;\n\t\tconst headers = new Headers(opts.headers);\n\n\t\tif (body != null && !headers.has('Content-Type')) {\n\t\t\tconst contentType = extractContentType(body);\n\t\t\tif (contentType) {\n\t\t\t\theaders.append('Content-Type', contentType);\n\t\t\t}\n\t\t}\n\n\t\tthis[INTERNALS$1] = {\n\t\t\turl: opts.url,\n\t\t\tstatus,\n\t\t\tstatusText: opts.statusText || STATUS_CODES[status],\n\t\t\theaders,\n\t\t\tcounter: opts.counter\n\t\t};\n\t}\n\n\tget url() {\n\t\treturn this[INTERNALS$1].url || '';\n\t}\n\n\tget status() {\n\t\treturn this[INTERNALS$1].status;\n\t}\n\n\t/**\n  * Convenience property representing if the request ended normally\n  */\n\tget ok() {\n\t\treturn this[INTERNALS$1].status >= 200 && this[INTERNALS$1].status < 300;\n\t}\n\n\tget redirected() {\n\t\treturn this[INTERNALS$1].counter > 0;\n\t}\n\n\tget statusText() {\n\t\treturn this[INTERNALS$1].statusText;\n\t}\n\n\tget headers() {\n\t\treturn this[INTERNALS$1].headers;\n\t}\n\n\t/**\n  * Clone this response\n  *\n  * @return  Response\n  */\n\tclone() {\n\t\treturn new Response(clone(this), {\n\t\t\turl: this.url,\n\t\t\tstatus: this.status,\n\t\t\tstatusText: this.statusText,\n\t\t\theaders: this.headers,\n\t\t\tok: this.ok,\n\t\t\tredirected: this.redirected\n\t\t});\n\t}\n}\n\nBody.mixIn(Response.prototype);\n\nObject.defineProperties(Response.prototype, {\n\turl: { enumerable: true },\n\tstatus: { enumerable: true },\n\tok: { enumerable: true },\n\tredirected: { enumerable: true },\n\tstatusText: { enumerable: true },\n\theaders: { enumerable: true },\n\tclone: { enumerable: true }\n});\n\nObject.defineProperty(Response.prototype, Symbol.toStringTag, {\n\tvalue: 'Response',\n\twritable: false,\n\tenumerable: false,\n\tconfigurable: true\n});\n\nconst INTERNALS$2 = Symbol('Request internals');\n\n// fix an issue where \"format\", \"parse\" aren't a named export for node <10\nconst parse_url = Url.parse;\nconst format_url = Url.format;\n\nconst streamDestructionSupported = 'destroy' in Stream.Readable.prototype;\n\n/**\n * Check if a value is an instance of Request.\n *\n * @param   Mixed   input\n * @return  Boolean\n */\nfunction isRequest(input) {\n\treturn typeof input === 'object' && typeof input[INTERNALS$2] === 'object';\n}\n\nfunction isAbortSignal(signal) {\n\tconst proto = signal && typeof signal === 'object' && Object.getPrototypeOf(signal);\n\treturn !!(proto && proto.constructor.name === 'AbortSignal');\n}\n\n/**\n * Request class\n *\n * @param   Mixed   input  Url or Request instance\n * @param   Object  init   Custom options\n * @return  Void\n */\nclass Request {\n\tconstructor(input) {\n\t\tlet init = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n\t\tlet parsedURL;\n\n\t\t// normalize input\n\t\tif (!isRequest(input)) {\n\t\t\tif (input && input.href) {\n\t\t\t\t// in order to support Node.js' Url objects; though WHATWG's URL objects\n\t\t\t\t// will fall into this branch also (since their `toString()` will return\n\t\t\t\t// `href` property anyway)\n\t\t\t\tparsedURL = parse_url(input.href);\n\t\t\t} else {\n\t\t\t\t// coerce input to a string before attempting to parse\n\t\t\t\tparsedURL = parse_url(`${input}`);\n\t\t\t}\n\t\t\tinput = {};\n\t\t} else {\n\t\t\tparsedURL = parse_url(input.url);\n\t\t}\n\n\t\tlet method = init.method || input.method || 'GET';\n\t\tmethod = method.toUpperCase();\n\n\t\tif ((init.body != null || isRequest(input) && input.body !== null) && (method === 'GET' || method === 'HEAD')) {\n\t\t\tthrow new TypeError('Request with GET/HEAD method cannot have body');\n\t\t}\n\n\t\tlet inputBody = init.body != null ? init.body : isRequest(input) && input.body !== null ? clone(input) : null;\n\n\t\tBody.call(this, inputBody, {\n\t\t\ttimeout: init.timeout || input.timeout || 0,\n\t\t\tsize: init.size || input.size || 0\n\t\t});\n\n\t\tconst headers = new Headers(init.headers || input.headers || {});\n\n\t\tif (inputBody != null && !headers.has('Content-Type')) {\n\t\t\tconst contentType = extractContentType(inputBody);\n\t\t\tif (contentType) {\n\t\t\t\theaders.append('Content-Type', contentType);\n\t\t\t}\n\t\t}\n\n\t\tlet signal = isRequest(input) ? input.signal : null;\n\t\tif ('signal' in init) signal = init.signal;\n\n\t\tif (signal != null && !isAbortSignal(signal)) {\n\t\t\tthrow new TypeError('Expected signal to be an instanceof AbortSignal');\n\t\t}\n\n\t\tthis[INTERNALS$2] = {\n\t\t\tmethod,\n\t\t\tredirect: init.redirect || input.redirect || 'follow',\n\t\t\theaders,\n\t\t\tparsedURL,\n\t\t\tsignal\n\t\t};\n\n\t\t// node-fetch-only options\n\t\tthis.follow = init.follow !== undefined ? init.follow : input.follow !== undefined ? input.follow : 20;\n\t\tthis.compress = init.compress !== undefined ? init.compress : input.compress !== undefined ? input.compress : true;\n\t\tthis.counter = init.counter || input.counter || 0;\n\t\tthis.agent = init.agent || input.agent;\n\t}\n\n\tget method() {\n\t\treturn this[INTERNALS$2].method;\n\t}\n\n\tget url() {\n\t\treturn format_url(this[INTERNALS$2].parsedURL);\n\t}\n\n\tget headers() {\n\t\treturn this[INTERNALS$2].headers;\n\t}\n\n\tget redirect() {\n\t\treturn this[INTERNALS$2].redirect;\n\t}\n\n\tget signal() {\n\t\treturn this[INTERNALS$2].signal;\n\t}\n\n\t/**\n  * Clone this request\n  *\n  * @return  Request\n  */\n\tclone() {\n\t\treturn new Request(this);\n\t}\n}\n\nBody.mixIn(Request.prototype);\n\nObject.defineProperty(Request.prototype, Symbol.toStringTag, {\n\tvalue: 'Request',\n\twritable: false,\n\tenumerable: false,\n\tconfigurable: true\n});\n\nObject.defineProperties(Request.prototype, {\n\tmethod: { enumerable: true },\n\turl: { enumerable: true },\n\theaders: { enumerable: true },\n\tredirect: { enumerable: true },\n\tclone: { enumerable: true },\n\tsignal: { enumerable: true }\n});\n\n/**\n * Convert a Request to Node.js http request options.\n *\n * @param   Request  A Request instance\n * @return  Object   The options object to be passed to http.request\n */\nfunction getNodeRequestOptions(request) {\n\tconst parsedURL = request[INTERNALS$2].parsedURL;\n\tconst headers = new Headers(request[INTERNALS$2].headers);\n\n\t// fetch step 1.3\n\tif (!headers.has('Accept')) {\n\t\theaders.set('Accept', '*/*');\n\t}\n\n\t// Basic fetch\n\tif (!parsedURL.protocol || !parsedURL.hostname) {\n\t\tthrow new TypeError('Only absolute URLs are supported');\n\t}\n\n\tif (!/^https?:$/.test(parsedURL.protocol)) {\n\t\tthrow new TypeError('Only HTTP(S) protocols are supported');\n\t}\n\n\tif (request.signal && request.body instanceof Stream.Readable && !streamDestructionSupported) {\n\t\tthrow new Error('Cancellation of streamed requests with AbortSignal is not supported in node < 8');\n\t}\n\n\t// HTTP-network-or-cache fetch steps 2.4-2.7\n\tlet contentLengthValue = null;\n\tif (request.body == null && /^(POST|PUT)$/i.test(request.method)) {\n\t\tcontentLengthValue = '0';\n\t}\n\tif (request.body != null) {\n\t\tconst totalBytes = getTotalBytes(request);\n\t\tif (typeof totalBytes === 'number') {\n\t\t\tcontentLengthValue = String(totalBytes);\n\t\t}\n\t}\n\tif (contentLengthValue) {\n\t\theaders.set('Content-Length', contentLengthValue);\n\t}\n\n\t// HTTP-network-or-cache fetch step 2.11\n\tif (!headers.has('User-Agent')) {\n\t\theaders.set('User-Agent', 'node-fetch/1.0 (+https://github.com/bitinn/node-fetch)');\n\t}\n\n\t// HTTP-network-or-cache fetch step 2.15\n\tif (request.compress && !headers.has('Accept-Encoding')) {\n\t\theaders.set('Accept-Encoding', 'gzip,deflate');\n\t}\n\n\tlet agent = request.agent;\n\tif (typeof agent === 'function') {\n\t\tagent = agent(parsedURL);\n\t}\n\n\tif (!headers.has('Connection') && !agent) {\n\t\theaders.set('Connection', 'close');\n\t}\n\n\t// HTTP-network fetch step 4.2\n\t// chunked encoding is handled by Node.js\n\n\treturn Object.assign({}, parsedURL, {\n\t\tmethod: request.method,\n\t\theaders: exportNodeCompatibleHeaders(headers),\n\t\tagent\n\t});\n}\n\n/**\n * abort-error.js\n *\n * AbortError interface for cancelled requests\n */\n\n/**\n * Create AbortError instance\n *\n * @param   String      message      Error message for human\n * @return  AbortError\n */\nfunction AbortError(message) {\n  Error.call(this, message);\n\n  this.type = 'aborted';\n  this.message = message;\n\n  // hide custom error implementation details from end-users\n  Error.captureStackTrace(this, this.constructor);\n}\n\nAbortError.prototype = Object.create(Error.prototype);\nAbortError.prototype.constructor = AbortError;\nAbortError.prototype.name = 'AbortError';\n\n// fix an issue where \"PassThrough\", \"resolve\" aren't a named export for node <10\nconst PassThrough$1 = Stream.PassThrough;\nconst resolve_url = Url.resolve;\n\n/**\n * Fetch function\n *\n * @param   Mixed    url   Absolute url or Request instance\n * @param   Object   opts  Fetch options\n * @return  Promise\n */\nfunction fetch(url, opts) {\n\n\t// allow custom promise\n\tif (!fetch.Promise) {\n\t\tthrow new Error('native promise missing, set fetch.Promise to your favorite alternative');\n\t}\n\n\tBody.Promise = fetch.Promise;\n\n\t// wrap http.request into fetch\n\treturn new fetch.Promise(function (resolve, reject) {\n\t\t// build request object\n\t\tconst request = new Request(url, opts);\n\t\tconst options = getNodeRequestOptions(request);\n\n\t\tconst send = (options.protocol === 'https:' ? https : http).request;\n\t\tconst signal = request.signal;\n\n\t\tlet response = null;\n\n\t\tconst abort = function abort() {\n\t\t\tlet error = new AbortError('The user aborted a request.');\n\t\t\treject(error);\n\t\t\tif (request.body && request.body instanceof Stream.Readable) {\n\t\t\t\trequest.body.destroy(error);\n\t\t\t}\n\t\t\tif (!response || !response.body) return;\n\t\t\tresponse.body.emit('error', error);\n\t\t};\n\n\t\tif (signal && signal.aborted) {\n\t\t\tabort();\n\t\t\treturn;\n\t\t}\n\n\t\tconst abortAndFinalize = function abortAndFinalize() {\n\t\t\tabort();\n\t\t\tfinalize();\n\t\t};\n\n\t\t// send request\n\t\tconst req = send(options);\n\t\tlet reqTimeout;\n\n\t\tif (signal) {\n\t\t\tsignal.addEventListener('abort', abortAndFinalize);\n\t\t}\n\n\t\tfunction finalize() {\n\t\t\treq.abort();\n\t\t\tif (signal) signal.removeEventListener('abort', abortAndFinalize);\n\t\t\tclearTimeout(reqTimeout);\n\t\t}\n\n\t\tif (request.timeout) {\n\t\t\treq.once('socket', function (socket) {\n\t\t\t\treqTimeout = setTimeout(function () {\n\t\t\t\t\treject(new FetchError(`network timeout at: ${request.url}`, 'request-timeout'));\n\t\t\t\t\tfinalize();\n\t\t\t\t}, request.timeout);\n\t\t\t});\n\t\t}\n\n\t\treq.on('error', function (err) {\n\t\t\treject(new FetchError(`request to ${request.url} failed, reason: ${err.message}`, 'system', err));\n\t\t\tfinalize();\n\t\t});\n\n\t\treq.on('response', function (res) {\n\t\t\tclearTimeout(reqTimeout);\n\n\t\t\tconst headers = createHeadersLenient(res.headers);\n\n\t\t\t// HTTP fetch step 5\n\t\t\tif (fetch.isRedirect(res.statusCode)) {\n\t\t\t\t// HTTP fetch step 5.2\n\t\t\t\tconst location = headers.get('Location');\n\n\t\t\t\t// HTTP fetch step 5.3\n\t\t\t\tconst locationURL = location === null ? null : resolve_url(request.url, location);\n\n\t\t\t\t// HTTP fetch step 5.5\n\t\t\t\tswitch (request.redirect) {\n\t\t\t\t\tcase 'error':\n\t\t\t\t\t\treject(new FetchError(`uri requested responds with a redirect, redirect mode is set to error: ${request.url}`, 'no-redirect'));\n\t\t\t\t\t\tfinalize();\n\t\t\t\t\t\treturn;\n\t\t\t\t\tcase 'manual':\n\t\t\t\t\t\t// node-fetch-specific step: make manual redirect a bit easier to use by setting the Location header value to the resolved URL.\n\t\t\t\t\t\tif (locationURL !== null) {\n\t\t\t\t\t\t\t// handle corrupted header\n\t\t\t\t\t\t\ttry {\n\t\t\t\t\t\t\t\theaders.set('Location', locationURL);\n\t\t\t\t\t\t\t} catch (err) {\n\t\t\t\t\t\t\t\t// istanbul ignore next: nodejs server prevent invalid response headers, we can't test this through normal request\n\t\t\t\t\t\t\t\treject(err);\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tcase 'follow':\n\t\t\t\t\t\t// HTTP-redirect fetch step 2\n\t\t\t\t\t\tif (locationURL === null) {\n\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// HTTP-redirect fetch step 5\n\t\t\t\t\t\tif (request.counter >= request.follow) {\n\t\t\t\t\t\t\treject(new FetchError(`maximum redirect reached at: ${request.url}`, 'max-redirect'));\n\t\t\t\t\t\t\tfinalize();\n\t\t\t\t\t\t\treturn;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// HTTP-redirect fetch step 6 (counter increment)\n\t\t\t\t\t\t// Create a new Request object.\n\t\t\t\t\t\tconst requestOpts = {\n\t\t\t\t\t\t\theaders: new Headers(request.headers),\n\t\t\t\t\t\t\tfollow: request.follow,\n\t\t\t\t\t\t\tcounter: request.counter + 1,\n\t\t\t\t\t\t\tagent: request.agent,\n\t\t\t\t\t\t\tcompress: request.compress,\n\t\t\t\t\t\t\tmethod: request.method,\n\t\t\t\t\t\t\tbody: request.body,\n\t\t\t\t\t\t\tsignal: request.signal,\n\t\t\t\t\t\t\ttimeout: request.timeout,\n\t\t\t\t\t\t\tsize: request.size\n\t\t\t\t\t\t};\n\n\t\t\t\t\t\t// HTTP-redirect fetch step 9\n\t\t\t\t\t\tif (res.statusCode !== 303 && request.body && getTotalBytes(request) === null) {\n\t\t\t\t\t\t\treject(new FetchError('Cannot follow redirect with body being a readable stream', 'unsupported-redirect'));\n\t\t\t\t\t\t\tfinalize();\n\t\t\t\t\t\t\treturn;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// HTTP-redirect fetch step 11\n\t\t\t\t\t\tif (res.statusCode === 303 || (res.statusCode === 301 || res.statusCode === 302) && request.method === 'POST') {\n\t\t\t\t\t\t\trequestOpts.method = 'GET';\n\t\t\t\t\t\t\trequestOpts.body = undefined;\n\t\t\t\t\t\t\trequestOpts.headers.delete('content-length');\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// HTTP-redirect fetch step 15\n\t\t\t\t\t\tresolve(fetch(new Request(locationURL, requestOpts)));\n\t\t\t\t\t\tfinalize();\n\t\t\t\t\t\treturn;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// prepare response\n\t\t\tres.once('end', function () {\n\t\t\t\tif (signal) signal.removeEventListener('abort', abortAndFinalize);\n\t\t\t});\n\t\t\tlet body = res.pipe(new PassThrough$1());\n\n\t\t\tconst response_options = {\n\t\t\t\turl: request.url,\n\t\t\t\tstatus: res.statusCode,\n\t\t\t\tstatusText: res.statusMessage,\n\t\t\t\theaders: headers,\n\t\t\t\tsize: request.size,\n\t\t\t\ttimeout: request.timeout,\n\t\t\t\tcounter: request.counter\n\t\t\t};\n\n\t\t\t// HTTP-network fetch step 12.1.1.3\n\t\t\tconst codings = headers.get('Content-Encoding');\n\n\t\t\t// HTTP-network fetch step 12.1.1.4: handle content codings\n\n\t\t\t// in following scenarios we ignore compression support\n\t\t\t// 1. compression support is disabled\n\t\t\t// 2. HEAD request\n\t\t\t// 3. no Content-Encoding header\n\t\t\t// 4. no content response (204)\n\t\t\t// 5. content not modified response (304)\n\t\t\tif (!request.compress || request.method === 'HEAD' || codings === null || res.statusCode === 204 || res.statusCode === 304) {\n\t\t\t\tresponse = new Response(body, response_options);\n\t\t\t\tresolve(response);\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// For Node v6+\n\t\t\t// Be less strict when decoding compressed responses, since sometimes\n\t\t\t// servers send slightly invalid responses that are still accepted\n\t\t\t// by common browsers.\n\t\t\t// Always using Z_SYNC_FLUSH is what cURL does.\n\t\t\tconst zlibOptions = {\n\t\t\t\tflush: zlib.Z_SYNC_FLUSH,\n\t\t\t\tfinishFlush: zlib.Z_SYNC_FLUSH\n\t\t\t};\n\n\t\t\t// for gzip\n\t\t\tif (codings == 'gzip' || codings == 'x-gzip') {\n\t\t\t\tbody = body.pipe(zlib.createGunzip(zlibOptions));\n\t\t\t\tresponse = new Response(body, response_options);\n\t\t\t\tresolve(response);\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// for deflate\n\t\t\tif (codings == 'deflate' || codings == 'x-deflate') {\n\t\t\t\t// handle the infamous raw deflate response from old servers\n\t\t\t\t// a hack for old IIS and Apache servers\n\t\t\t\tconst raw = res.pipe(new PassThrough$1());\n\t\t\t\traw.once('data', function (chunk) {\n\t\t\t\t\t// see http://stackoverflow.com/questions/37519828\n\t\t\t\t\tif ((chunk[0] & 0x0F) === 0x08) {\n\t\t\t\t\t\tbody = body.pipe(zlib.createInflate());\n\t\t\t\t\t} else {\n\t\t\t\t\t\tbody = body.pipe(zlib.createInflateRaw());\n\t\t\t\t\t}\n\t\t\t\t\tresponse = new Response(body, response_options);\n\t\t\t\t\tresolve(response);\n\t\t\t\t});\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// for br\n\t\t\tif (codings == 'br' && typeof zlib.createBrotliDecompress === 'function') {\n\t\t\t\tbody = body.pipe(zlib.createBrotliDecompress());\n\t\t\t\tresponse = new Response(body, response_options);\n\t\t\t\tresolve(response);\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// otherwise, use response as-is\n\t\t\tresponse = new Response(body, response_options);\n\t\t\tresolve(response);\n\t\t});\n\n\t\twriteToStream(req, request);\n\t});\n}\n/**\n * Redirect code matching\n *\n * @param   Number   code  Status code\n * @return  Boolean\n */\nfetch.isRedirect = function (code) {\n\treturn code === 301 || code === 302 || code === 303 || code === 307 || code === 308;\n};\n\n// expose Promise\nfetch.Promise = global.Promise;\n\nexport default fetch;\nexport { Headers, Request, Response, FetchError };\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage {\n    constructor(backend, dataMover) {\n        this.backend = backend;\n        this.dataMover = dataMover;\n        this.data = new WeakMap();\n        this.dataIdsCount = 0;\n    }\n    get(dataId) {\n        if (!this.data.has(dataId)) {\n            this.dataMover.moveData(this.backend, dataId);\n        }\n        return this.data.get(dataId);\n    }\n    set(dataId, value) {\n        this.dataIdsCount++;\n        this.data.set(dataId, value);\n    }\n    has(dataId) {\n        return this.data.has(dataId);\n    }\n    delete(dataId) {\n        this.dataIdsCount--;\n        return this.data.delete(dataId);\n    }\n    numDataIds() {\n        return this.dataIdsCount;\n    }\n}\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend {\n    time(f) {\n        return notYetImplemented('time');\n    }\n    read(dataId) {\n        return notYetImplemented('read');\n    }\n    readSync(dataId) {\n        return notYetImplemented('readSync');\n    }\n    numDataIds() {\n        return notYetImplemented('numDataIds');\n    }\n    disposeData(dataId) {\n        return notYetImplemented('disposeData');\n    }\n    write(values, shape, dtype) {\n        return notYetImplemented('write');\n    }\n    move(dataId, values, shape, dtype) {\n        return notYetImplemented('move');\n    }\n    memory() {\n        return notYetImplemented('memory');\n    }\n    /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n    floatPrecision() {\n        return notYetImplemented('floatPrecision');\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        return notYetImplemented('batchMatMul');\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedBatchMatMul');\n    }\n    slice(x, begin, size) {\n        return notYetImplemented('slice');\n    }\n    stridedSlice(x, begin, end, strides) {\n        return notYetImplemented('stridedSlice');\n    }\n    unstack(x, axis) {\n        return notYetImplemented('unstack');\n    }\n    reverse(a, axis) {\n        return notYetImplemented('reverse');\n    }\n    concat(tensors, axis) {\n        return notYetImplemented('concat');\n    }\n    neg(a) {\n        return notYetImplemented('neg');\n    }\n    add(a, b) {\n        return notYetImplemented('add');\n    }\n    addN(tensors) {\n        return notYetImplemented('addN');\n    }\n    subtract(a, b) {\n        return notYetImplemented('subtract');\n    }\n    multiply(a, b) {\n        return notYetImplemented('multiply');\n    }\n    realDivide(a, b) {\n        return notYetImplemented('realDivide');\n    }\n    floorDiv(a, b) {\n        return notYetImplemented('floorDiv');\n    }\n    sum(x, axes) {\n        return notYetImplemented('sum');\n    }\n    prod(x, axes) {\n        return notYetImplemented('prod');\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        return notYetImplemented('unsortedSegmentSum');\n    }\n    argMin(x, axis) {\n        return notYetImplemented('argMin');\n    }\n    argMax(x, axis) {\n        return notYetImplemented('argMax');\n    }\n    equal(a, b) {\n        return notYetImplemented('equal');\n    }\n    notEqual(a, b) {\n        return notYetImplemented('notEqual');\n    }\n    less(a, b) {\n        return notYetImplemented('less');\n    }\n    lessEqual(a, b) {\n        return notYetImplemented('lessEqual');\n    }\n    greater(a, b) {\n        return notYetImplemented('greater');\n    }\n    greaterEqual(a, b) {\n        return notYetImplemented('greaterEqual');\n    }\n    logicalNot(a) {\n        return notYetImplemented('logicalNot');\n    }\n    logicalAnd(a, b) {\n        return notYetImplemented('logicalAnd');\n    }\n    logicalOr(a, b) {\n        return notYetImplemented('logicalOr');\n    }\n    where(condition) {\n        return notYetImplemented('where');\n    }\n    select(condition, a, b) {\n        return notYetImplemented('select');\n    }\n    topk(x, k, sorted) {\n        return notYetImplemented('topk');\n    }\n    min(x, axes) {\n        return notYetImplemented('min');\n    }\n    minimum(a, b) {\n        return notYetImplemented('minimum');\n    }\n    mod(a, b) {\n        return notYetImplemented('mod');\n    }\n    max(x, axes) {\n        return notYetImplemented('max');\n    }\n    maximum(a, b) {\n        return notYetImplemented('maximum');\n    }\n    all(x, axes) {\n        return notYetImplemented('all');\n    }\n    any(x, axes) {\n        return notYetImplemented('any');\n    }\n    squaredDifference(a, b) {\n        return notYetImplemented('squaredDifference');\n    }\n    ceil(x) {\n        return notYetImplemented('ceil');\n    }\n    floor(x) {\n        return notYetImplemented('floor');\n    }\n    round(x) {\n        return notYetImplemented('round');\n    }\n    sign(x) {\n        return notYetImplemented('sign');\n    }\n    isNaN(x) {\n        return notYetImplemented('isNaN');\n    }\n    isInf(x) {\n        return notYetImplemented('isInf');\n    }\n    isFinite(x) {\n        return notYetImplemented('isFinite');\n    }\n    pow(a, b) {\n        return notYetImplemented('pow');\n    }\n    exp(x) {\n        return notYetImplemented('exp');\n    }\n    expm1(x) {\n        return notYetImplemented('expm1');\n    }\n    softmax(x, dim) {\n        return notYetImplemented('softmax');\n    }\n    log(x) {\n        return notYetImplemented('log');\n    }\n    log1p(x) {\n        return notYetImplemented('log1p');\n    }\n    sqrt(x) {\n        return notYetImplemented('sqrt');\n    }\n    rsqrt(x) {\n        return notYetImplemented('rsqrt');\n    }\n    square(x) {\n        return notYetImplemented('square');\n    }\n    reciprocal(x) {\n        return notYetImplemented('reciprocal');\n    }\n    relu(x) {\n        return notYetImplemented('relu');\n    }\n    relu6(x) {\n        return notYetImplemented('relu6');\n    }\n    prelu(x, a) {\n        return notYetImplemented('prelu');\n    }\n    elu(x) {\n        return notYetImplemented('elu');\n    }\n    eluDer(dy, y) {\n        return notYetImplemented('eluDer');\n    }\n    selu(x) {\n        return notYetImplemented('selu');\n    }\n    int(x) {\n        return notYetImplemented('int');\n    }\n    clip(x, min, max) {\n        return notYetImplemented('clip');\n    }\n    abs(x) {\n        return notYetImplemented('abs');\n    }\n    complexAbs(x) {\n        return notYetImplemented('complexAbs');\n    }\n    sigmoid(x) {\n        return notYetImplemented('sigmoid');\n    }\n    softplus(x) {\n        return notYetImplemented('softplus');\n    }\n    sin(x) {\n        return notYetImplemented('sin');\n    }\n    cos(x) {\n        return notYetImplemented('cos');\n    }\n    tan(x) {\n        return notYetImplemented('tan');\n    }\n    asin(x) {\n        return notYetImplemented('asin');\n    }\n    acos(x) {\n        return notYetImplemented('acos');\n    }\n    atan(x) {\n        return notYetImplemented('atan');\n    }\n    atan2(a, b) {\n        return notYetImplemented('atan2');\n    }\n    sinh(x) {\n        return notYetImplemented('sinh');\n    }\n    cosh(x) {\n        return notYetImplemented('cosh');\n    }\n    tanh(x) {\n        return notYetImplemented('tanh');\n    }\n    asinh(x) {\n        return notYetImplemented('asinh');\n    }\n    acosh(x) {\n        return notYetImplemented('acosh');\n    }\n    atanh(x) {\n        return notYetImplemented('atanh');\n    }\n    erf(x) {\n        return notYetImplemented('erf');\n    }\n    step(x, alpha) {\n        return notYetImplemented('step');\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedConv2d');\n    }\n    conv2d(x, filter, convInfo) {\n        return notYetImplemented('conv2d');\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        return notYetImplemented('conv2dDerInput');\n    }\n    conv2dDerFilter(x, dY, convInfo) {\n        return notYetImplemented('conv2dDerFilter');\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedDepthwiseConv2D');\n    }\n    depthwiseConv2D(input, filter, convInfo) {\n        return notYetImplemented('depthwiseConv2D');\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        return notYetImplemented('depthwiseConv2DDerInput');\n    }\n    depthwiseConv2DDerFilter(x, dY, convInfo) {\n        return notYetImplemented('depthwiseConv2DDerFilter');\n    }\n    conv3d(x, filter, convInfo) {\n        return notYetImplemented('conv3d');\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        return notYetImplemented('conv3dDerInput');\n    }\n    conv3dDerFilter(x, dY, convInfo) {\n        return notYetImplemented('conv3dDerFilter');\n    }\n    maxPool(x, convInfo) {\n        return notYetImplemented('maxPool');\n    }\n    maxPoolBackprop(dy, x, y, convInfo) {\n        return notYetImplemented('maxPoolBackprop');\n    }\n    avgPool(x, convInfo) {\n        return notYetImplemented('avgPool');\n    }\n    avgPoolBackprop(dy, x, convInfo) {\n        return notYetImplemented('avgPoolBackprop');\n    }\n    avgPool3d(x, convInfo) {\n        return notYetImplemented('avgPool3d');\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        return notYetImplemented('avgPool3dBackprop');\n    }\n    maxPool3d(x, convInfo) {\n        return notYetImplemented('maxPool3d');\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        return notYetImplemented('maxPool3dBackprop');\n    }\n    reshape(x, shape) {\n        return notYetImplemented('reshape');\n    }\n    cast(x, dtype) {\n        return notYetImplemented('cast');\n    }\n    tile(x, reps) {\n        return notYetImplemented('tile');\n    }\n    pad(x, paddings, constantValue) {\n        return notYetImplemented('pad');\n    }\n    transpose(x, perm) {\n        return notYetImplemented('transpose');\n    }\n    gather(x, indices, axis) {\n        return notYetImplemented('gather');\n    }\n    gatherND(x, indices) {\n        return notYetImplemented('gatherND');\n    }\n    scatterND(indices, updates, shape) {\n        return notYetImplemented('scatterND');\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        return notYetImplemented('batchToSpaceND');\n    }\n    spaceToBatchND(x, blockShape, paddings) {\n        return notYetImplemented('spaceToBatchND');\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        return notYetImplemented('resizeBilinear');\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        return notYetImplemented('resizeBilinearBackprop');\n    }\n    resizeNearestNeighbor(x, newHEight, newWidth, alignCorners) {\n        return notYetImplemented('resizeNearestNeighbor');\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        return notYetImplemented('resizeNearestNeighborBackprop');\n    }\n    batchNorm(x, mean, variance, offset, scale, varianceEpsilon) {\n        return notYetImplemented('batchNorm');\n    }\n    localResponseNormalization4D(x, radius, bias, alpha, beta) {\n        return notYetImplemented('localResponseNormalization4D');\n    }\n    LRNGrad(dy, inputImage, outputImage, radius, bias, alpha, beta) {\n        return notYetImplemented('LRNGrad');\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        return notYetImplemented('multinomial');\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        return notYetImplemented('oneHot');\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        return notYetImplemented('cumsum');\n    }\n    nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n        return notYetImplemented('nonMaxSuppression');\n    }\n    fft(x) {\n        return notYetImplemented('fft');\n    }\n    ifft(x) {\n        return notYetImplemented('ifft');\n    }\n    complex(real, imag) {\n        return notYetImplemented('complex');\n    }\n    real(input) {\n        return notYetImplemented('real');\n    }\n    imag(input) {\n        return notYetImplemented('imag');\n    }\n    cropAndResize(image, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        return notYetImplemented('cropAndResize');\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        return notYetImplemented('depthToSpace');\n    }\n    // Aligns with the \"SplitV\" kernel in TensorFlow.\n    split(value, sizeSplits, axis) {\n        return notYetImplemented('split');\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        return notYetImplemented('sparseToDense');\n    }\n    diag(x) {\n        return notYetImplemented('diag');\n    }\n    fill(shape, value, dtype) {\n        return notYetImplemented('fill');\n    }\n    onesLike(x) {\n        return notYetImplemented('onesLike');\n    }\n    zerosLike(x) {\n        return notYetImplemented('zerosLike');\n    }\n    linspace(start, stop, num) {\n        return notYetImplemented('linspace');\n    }\n    dispose() {\n        return notYetImplemented('dispose');\n    }\n}\nfunction notYetImplemented(kernelName) {\n    throw new Error(`'${kernelName}' not yet implemented or not found in the registry. ` +\n        `This kernel may not be supported by the tfjs backend you have chosen`);\n}\n//# sourceMappingURL=backend.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.\nconst TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';\n/**\n * The environment contains evaluated flags as well as the registered platform.\n * This is always used as a global singleton and can be retrieved with\n * `tf.env()`.\n *\n * @doc {heading: 'Environment'}\n */\nexport class Environment {\n    // tslint:disable-next-line: no-any\n    constructor(global) {\n        this.global = global;\n        this.flags = {};\n        this.flagRegistry = {};\n        this.urlFlags = {};\n        this.populateURLFlags();\n    }\n    setPlatform(platformName, platform) {\n        if (this.platform != null) {\n            console.warn(`Platform ${this.platformName} has already been set. ` +\n                `Overwriting the platform with ${platform}.`);\n        }\n        this.platformName = platformName;\n        this.platform = platform;\n    }\n    registerFlag(flagName, evaluationFn, setHook) {\n        this.flagRegistry[flagName] = { evaluationFn, setHook };\n        // Override the flag value from the URL. This has to happen here because the\n        // environment is initialized before flags get registered.\n        if (this.urlFlags[flagName] != null) {\n            const flagValue = this.urlFlags[flagName];\n            console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);\n            this.set(flagName, flagValue);\n        }\n    }\n    async getAsync(flagName) {\n        if (flagName in this.flags) {\n            return this.flags[flagName];\n        }\n        this.flags[flagName] = await this.evaluateFlag(flagName);\n        return this.flags[flagName];\n    }\n    get(flagName) {\n        if (flagName in this.flags) {\n            return this.flags[flagName];\n        }\n        const flagValue = this.evaluateFlag(flagName);\n        if (flagValue instanceof Promise) {\n            throw new Error(`Flag ${flagName} cannot be synchronously evaluated. ` +\n                `Please use getAsync() instead.`);\n        }\n        this.flags[flagName] = flagValue;\n        return this.flags[flagName];\n    }\n    getNumber(flagName) {\n        return this.get(flagName);\n    }\n    getBool(flagName) {\n        return this.get(flagName);\n    }\n    getFlags() {\n        return this.flags;\n    }\n    // For backwards compatibility.\n    get features() {\n        return this.flags;\n    }\n    set(flagName, value) {\n        if (this.flagRegistry[flagName] == null) {\n            throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);\n        }\n        this.flags[flagName] = value;\n        if (this.flagRegistry[flagName].setHook != null) {\n            this.flagRegistry[flagName].setHook(value);\n        }\n    }\n    evaluateFlag(flagName) {\n        if (this.flagRegistry[flagName] == null) {\n            throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);\n        }\n        return this.flagRegistry[flagName].evaluationFn();\n    }\n    setFlags(flags) {\n        this.flags = Object.assign({}, flags);\n    }\n    reset() {\n        this.flags = {};\n        this.urlFlags = {};\n        this.populateURLFlags();\n    }\n    populateURLFlags() {\n        if (typeof this.global === 'undefined' ||\n            typeof this.global.location === 'undefined' ||\n            typeof this.global.location.search === 'undefined') {\n            return;\n        }\n        const urlParams = getQueryParams(this.global.location.search);\n        if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n            const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');\n            keyValues.forEach(keyValue => {\n                const [key, value] = keyValue.split(':');\n                this.urlFlags[key] = parseValue(key, value);\n            });\n        }\n    }\n}\nexport function getQueryParams(queryString) {\n    const params = {};\n    queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n        decodeParam(params, t[0], t[1]);\n        return t.join('=');\n    });\n    return params;\n}\nfunction decodeParam(params, name, value) {\n    params[decodeURIComponent(name)] = decodeURIComponent(value || '');\n}\nfunction parseValue(flagName, value) {\n    value = value.toLowerCase();\n    if (value === 'true' || value === 'false') {\n        return value === 'true';\n    }\n    else if (`${+value}` === value) {\n        return +value;\n    }\n    throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);\n}\n/**\n * Returns the current environment (a global singleton).\n *\n * The environment object contains the evaluated feature values as well as the\n * active platform.\n *\n * @doc {heading: 'Environment'}\n */\nexport function env() {\n    return ENV;\n}\nexport let ENV = null;\nexport function setEnvironmentGlobal(environment) {\n    ENV = environment;\n}\n//# sourceMappingURL=environment.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Note that the identifier globalNameSpace is scoped to this module, but will\n// always resolve to the same global object regardless of how the module is\n// resolved.\n// tslint:disable-next-line:no-any\nlet globalNameSpace;\n// tslint:disable-next-line:no-any\nexport function getGlobalNamespace() {\n    if (globalNameSpace == null) {\n        // tslint:disable-next-line:no-any\n        let ns;\n        if (typeof (window) !== 'undefined') {\n            ns = window;\n        }\n        else if (typeof (global) !== 'undefined') {\n            ns = global;\n        }\n        else if (typeof (process) !== 'undefined') {\n            ns = process;\n        }\n        else if (typeof (self) !== 'undefined') {\n            ns = self;\n        }\n        else {\n            throw new Error('Could not find a global object');\n        }\n        globalNameSpace = ns;\n    }\n    return globalNameSpace;\n}\n// tslint:disable-next-line:no-any\nfunction getGlobalMap() {\n    const ns = getGlobalNamespace();\n    if (ns._tfGlobals == null) {\n        ns._tfGlobals = new Map();\n    }\n    return ns._tfGlobals;\n}\n/**\n * Returns a globally accessible 'singleton' object.\n *\n * @param key the name of the object\n * @param init a function to initialize to initialize this object\n *             the first time it is fetched.\n */\nexport function getGlobal(key, init) {\n    const globalMap = getGlobalMap();\n    if (globalMap.has(key)) {\n        return globalMap.get(key);\n    }\n    else {\n        const singleton = init();\n        globalMap.set(key, singleton);\n        return globalMap.get(key);\n    }\n}\n//# sourceMappingURL=global_util.js.map", "export const Abs = 'Abs';\nexport const Acos = 'Acos';\nexport const Acosh = 'Acosh';\nexport const Add = 'Add';\nexport const AddN = 'AddN';\nexport const All = 'All';\nexport const Any = 'Any';\nexport const ArgMax = 'ArgMax';\nexport const ArgMin = 'ArgMin';\nexport const Asin = 'Asin';\nexport const Asinh = 'Asinh';\nexport const Atan = 'Atan';\nexport const Atanh = 'Atanh';\nexport const Atan2 = 'Atan2';\nexport const AvgPool = 'AvgPool';\nexport const AvgPoolBackprop = 'AvgPoolBackprop';\nexport const AvgPool3D = 'AvgPool3D';\nexport const AvgPool3DBackprop = 'AvgPool3DBackprop';\nexport const BatchMatMul = 'BatchMatMul';\nexport const BatchToSpaceND = 'BatchToSpaceND';\nexport const BroadcastTo = 'BroadcastTo';\nexport const Cast = 'Cast';\nexport const Ceil = 'Ceil';\nexport const ClipByValue = 'ClipByValue';\nexport const Complex = 'Complex';\nexport const Concat = 'Concat';\nexport const Conv2D = 'Conv2D';\nexport const Conv2DBackpropFilter = 'Conv2DBackpropFilter';\nexport const Conv2DBackpropInput = 'Conv2DBackpropInput';\nexport const Conv3D = 'Conv3D';\nexport const Conv3DBackpropFilterV2 = 'Conv3DBackpropFilterV2';\nexport const Conv3DBackpropInputV2 = 'Conv3DBackpropInputV2';\nexport const Cos = 'Cos';\nexport const Cosh = 'Cosh';\nexport const Cumsum = 'Cumsum';\nexport const CropAndResize = 'CropAndResize';\nexport const DepthToSpace = 'DepthToSpace';\nexport const DepthwiseConv2dNative = 'DepthwiseConv2dNative';\nexport const DepthwiseConv2dNativeBackpropFilter = 'DepthwiseConv2dNativeBackpropFilter';\nexport const DepthwiseConv2dNativeBackpropInput = 'DepthwiseConv2dNativeBackpropInput';\nexport const Diag = 'Diag';\nexport const Dilation2D = 'Dilation2D';\nexport const Dilation2DBackpropInput = 'Dilation2DBackpropInput';\nexport const Dilation2DBackpropFilter = 'Dilation2DBackpropFilter';\nexport const Div = 'Div';\nexport const Elu = 'Elu';\nexport const EluGrad = 'EluGrad';\nexport const Erf = 'Erf';\nexport const Equal = 'Equal';\nexport const Exp = 'Exp';\nexport const Expm1 = 'Expm1';\nexport const FFT = 'FFT';\nexport const Fill = 'Fill';\nexport const FlipLeftRight = 'FlipLeftRight';\nexport const Floor = 'Floor';\nexport const FloorDiv = 'FloorDiv';\nexport const FusedBatchNorm = 'FusedBatchNorm';\nexport const GatherV2 = 'GatherV2';\nexport const GatherNd = 'GatherNd';\nexport const Greater = 'Greater';\nexport const GreaterEqual = 'GreaterEqual';\nexport const Identity = 'Identity';\nexport const IFFT = 'IFFT';\nexport const Imag = 'Imag';\nexport const IsFinite = 'IsFinite';\nexport const IsInf = 'IsInf';\nexport const IsNan = 'IsNan';\nexport const Less = 'Less';\nexport const LessEqual = 'LessEqual';\nexport const LinSpace = 'LinSpace';\nexport const Log = 'Log';\nexport const Log1p = 'Log1p';\nexport const LogicalAnd = 'LogicalAnd';\nexport const LogicalNot = 'LogicalNot';\nexport const LogicalOr = 'LogicalOr';\nexport const LogSoftmax = 'LogSoftmax';\nexport const LRN = 'LRN';\nexport const LRNBackprop = 'LRNBackprop';\nexport const Max = 'Max';\nexport const Maximum = 'Maximum';\nexport const MaxPool = 'MaxPool';\nexport const MaxPoolBackprop = 'MaxPoolBackprop';\nexport const MaxPool3D = 'MaxPool3D';\nexport const MaxPool3DBackprop = 'MaxPool3DBackprop';\nexport const MaxPoolWithArgmax = 'MaxPoolWithArgmax';\nexport const Mean = 'Mean';\nexport const Min = 'Min';\nexport const Minimum = 'Minimum';\nexport const Mod = 'Mod';\nexport const Multiply = 'Multiply';\nexport const Negate = 'Negate';\nexport const NotEqual = 'NotEqual';\nexport const NonMaxSuppressionV3 = 'NonMaxSuppressionV3';\nexport const NonMaxSuppressionV4 = 'NonMaxSuppressionV4';\nexport const NonMaxSuppressionV5 = 'NonMaxSuppressionV5';\nexport const OnesLike = 'OnesLike';\nexport const OneHot = 'OneHot';\nexport const PadV2 = 'PadV2';\nexport const Pool = 'Pool';\nexport const Pow = 'Pow';\nexport const Prelu = 'Prelu';\nexport const Prod = 'Prod';\nexport const Range = 'Range';\nexport const Real = 'Real';\nexport const Reciprocal = 'Reciprocal';\nexport const Relu = 'Relu';\nexport const Reshape = 'Reshape';\nexport const ResizeNearestNeighbor = 'ResizeNearestNeighbor';\nexport const ResizeNearestNeighborGrad = 'ResizeNearestNeighborGrad';\nexport const ResizeBilinear = 'ResizeBilinear';\nexport const ResizeBilinearGrad = 'ResizeBilinearGrad';\nexport const Relu6 = 'Relu6';\nexport const Reverse = 'Reverse';\nexport const Round = 'Round';\nexport const Rsqrt = 'Rsqrt';\nexport const ScatterNd = 'ScatterNd';\nexport const SelectV2 = 'SelectV2';\nexport const Selu = 'Selu';\nexport const Slice = 'Slice';\nexport const Sin = 'Sin';\nexport const Sinh = 'Sinh';\nexport const Sign = 'Sign';\nexport const Sigmoid = 'Sigmoid';\nexport const Softplus = 'Softplus';\nexport const Sqrt = 'Sqrt';\nexport const Sum = 'Sum';\nexport const SpaceToBatchND = 'SpaceToBatchND';\nexport const SplitV = 'SplitV';\nexport const Softmax = 'Softmax';\nexport const SquaredDifference = 'SquaredDifference';\nexport const Square = 'Square';\nexport const Sub = 'Sub';\nexport const SparseToDense = 'SparseToDense';\nexport const StridedSlice = 'StridedSlice';\nexport const Tan = 'Tan';\nexport const Tanh = 'Tanh';\nexport const Tile = 'Tile';\nexport const TopK = 'TopK';\nexport const Transpose = 'Transpose';\nexport const Unique = 'Unique';\nexport const Unpack = 'Unpack';\nexport const UnsortedSegmentSum = 'UnsortedSegmentSum';\nexport const ZerosLike = 'ZerosLike';\n/**\n * TensorFlow.js-only kernels\n */\nexport const Step = 'Step';\nexport const FromPixels = 'FromPixels';\nexport const RotateWithOffset = 'RotateWithOffset';\nexport const _FusedMatMul = '_FusedMatMul';\nexport const FusedConv2D = 'FusedConv2D';\nexport const FusedDepthwiseConv2D = 'FusedDepthwiseConv2D';\n//# sourceMappingURL=kernel_names.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nimport { getGlobal } from './global_util';\nconst kernelRegistry = getGlobal('kernelRegistry', () => new Map());\nconst gradRegistry = getGlobal('gradRegistry', () => new Map());\n/**\n * Returns the kernel function (code) associated with the provided names.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n */\nexport function getKernel(kernelName, backendName) {\n    const key = makeKey(kernelName, backendName);\n    return kernelRegistry.get(key);\n}\n/**\n * Returns the registered gradient info associated with the provided kernel.\n * @param kernelName The official TF kernel name.\n */\nexport function getGradient(kernelName) {\n    return gradRegistry.get(kernelName);\n}\nexport function getKernelsForBackend(backendName) {\n    const it = kernelRegistry.entries();\n    const result = [];\n    while (true) {\n        const { done, value } = it.next();\n        if (done) {\n            break;\n        }\n        const [key, config] = value;\n        const [backend,] = key.split('_');\n        if (backend === backendName) {\n            result.push(config);\n        }\n    }\n    return result;\n}\n/**\n * Registers the function (forward pass) for the kernel in a global registry.\n *\n * @param config A config object with the following properties:\n * - `kernelName` The official name of the kernel.\n * - `backendName` The official name of the backend.\n * - `kernelFunc` The function to run during the forward pass of the kernel.\n * - `setupFunc` Optional. Gets called once, after the backend initializes.\n * - `disposeFunc` Optional. Gets called once, right before the backend is\n * disposed.\n */\nexport function registerKernel(config) {\n    const { kernelName, backendName } = config;\n    const key = makeKey(kernelName, backendName);\n    if (kernelRegistry.has(key)) {\n        console.warn(`The kernel '${kernelName}' for backend ` +\n            `'${backendName}' is already registered`);\n    }\n    kernelRegistry.set(key, config);\n}\n/**\n * Registers a gradient function for a given kernel in the global registry,\n * to be used during the back-propagation of that kernel.\n *\n * @param config An object with the following properties:\n * - `kernelName` The name of the kernel that the gradient function is for.\n * - `gradFunc` The function to run during back-propagation.\n */\nexport function registerGradient(config) {\n    const { kernelName } = config;\n    if (gradRegistry.has(kernelName)) {\n        // TODO (yassogba) after 3.0 assess whether we need to keep this gated\n        // to debug mode.\n        if (env().getBool('DEBUG')) {\n            console.warn(`Overriding the gradient for '${kernelName}'`);\n        }\n    }\n    gradRegistry.set(kernelName, config);\n}\n/**\n * Removes the kernel function from the registry.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n *\n */\nexport function unregisterKernel(kernelName, backendName) {\n    const key = makeKey(kernelName, backendName);\n    if (!kernelRegistry.has(key)) {\n        throw new Error(`The kernel '${kernelName}' for backend ` +\n            `'${backendName}' is not registered`);\n    }\n    kernelRegistry.delete(key);\n}\n/** Removes the registered gradient from the global registry. */\nexport function unregisterGradient(kernelName) {\n    if (!gradRegistry.has(kernelName)) {\n        throw new Error(`The gradient '${kernelName}' for backend is not registered`);\n    }\n    gradRegistry.delete(kernelName);\n}\n/**\n * Finds kernels that have already been registered to a backend and re-registers\n * them for a new backend. Useful for registering custom backends.\n * @param registeredBackendName Already registered backend.\n * @param newBackendName New backend.\n */\nexport function copyRegisteredKernels(registeredBackendName, newBackendName) {\n    const kernels = getKernelsForBackend(registeredBackendName);\n    kernels.forEach(kernelConfig => {\n        const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName });\n        registerKernel(newKernelConfig);\n    });\n}\nfunction makeKey(kernelName, backendName) {\n    return `${backendName}_${kernelName}`;\n}\n//# sourceMappingURL=kernel_registry.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\n/**\n * Shuffles the array in-place using Fisher-Yates algorithm.\n *\n * ```js\n * const a = [1, 2, 3, 4, 5];\n * tf.util.shuffle(a);\n * console.log(a);\n * ```\n *\n * @param array The array to shuffle in-place.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\n// tslint:disable-next-line:no-any\nexport function shuffle(array) {\n    let counter = array.length;\n    let temp = 0;\n    let index = 0;\n    // While there are elements in the array\n    while (counter > 0) {\n        // Pick a random index\n        index = (Math.random() * counter) | 0;\n        // Decrease counter by 1\n        counter--;\n        // And swap the last element with it\n        temp = array[counter];\n        array[counter] = array[index];\n        array[index] = temp;\n    }\n}\n/** Clamps a value to a specified range. */\nexport function clamp(min, x, max) {\n    return Math.max(min, Math.min(x, max));\n}\nexport function nearestLargerEven(val) {\n    return val % 2 === 0 ? val : val + 1;\n}\nexport function sum(arr) {\n    let sum = 0;\n    for (let i = 0; i < arr.length; i++) {\n        sum += arr[i];\n    }\n    return sum;\n}\n/**\n * Returns a sample from a uniform [a, b) distribution.\n *\n * @param a The minimum support (inclusive).\n * @param b The maximum support (exclusive).\n * @return A pseudorandom number on the half-open interval [a,b).\n */\nexport function randUniform(a, b) {\n    const r = Math.random();\n    return (b * r) + (1 - r) * a;\n}\n/** Returns the squared Euclidean distance between two vectors. */\nexport function distSquared(a, b) {\n    let result = 0;\n    for (let i = 0; i < a.length; i++) {\n        const diff = Number(a[i]) - Number(b[i]);\n        result += diff * diff;\n    }\n    return result;\n}\n/**\n * Asserts that the expression is true. Otherwise throws an error with the\n * provided message.\n *\n * ```js\n * const x = 2;\n * tf.util.assert(x === 2, 'x is not 2');\n * ```\n *\n * @param expr The expression to assert (as a boolean).\n * @param msg A function that returns the message to report when throwing an\n *     error. We use a function for performance reasons.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function assert(expr, msg) {\n    if (!expr) {\n        throw new Error(typeof msg === 'string' ? msg : msg());\n    }\n}\nexport function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = '') {\n    assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nexport function assertNonNull(a) {\n    assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);\n}\n// NOTE: We explicitly type out what T extends instead of any so that\n// util.flatten on a nested array of number doesn't try to infer T as a\n// number[][], causing us to explicitly type util.flatten<number>().\n/**\n *  Flattens an arbitrarily nested array.\n *\n * ```js\n * const a = [[1, 2], [3, 4], [5, [6, [7]]]];\n * const flat = tf.util.flatten(a);\n * console.log(flat);\n * ```\n *\n *  @param arr The nested array to flatten.\n *  @param result The destination array which holds the elements.\n *  @param skipTypedArray If true, avoids flattening the typed arrays. Defaults\n *      to false.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function flatten(arr, result = [], skipTypedArray = false) {\n    if (result == null) {\n        result = [];\n    }\n    if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {\n        for (let i = 0; i < arr.length; ++i) {\n            flatten(arr[i], result, skipTypedArray);\n        }\n    }\n    else {\n        result.push(arr);\n    }\n    return result;\n}\n/**\n * Returns the size (number of elements) of the tensor given its shape.\n *\n * ```js\n * const shape = [3, 4, 2];\n * const size = tf.util.sizeFromShape(shape);\n * console.log(size);\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function sizeFromShape(shape) {\n    if (shape.length === 0) {\n        // Scalar.\n        return 1;\n    }\n    let size = shape[0];\n    for (let i = 1; i < shape.length; i++) {\n        size *= shape[i];\n    }\n    return size;\n}\nexport function isScalarShape(shape) {\n    return shape.length === 0;\n}\nexport function arraysEqual(n1, n2) {\n    if (n1 === n2) {\n        return true;\n    }\n    if (n1 == null || n2 == null) {\n        return false;\n    }\n    if (n1.length !== n2.length) {\n        return false;\n    }\n    for (let i = 0; i < n1.length; i++) {\n        if (n1[i] !== n2[i]) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function isInt(a) {\n    return a % 1 === 0;\n}\nexport function tanh(x) {\n    // tslint:disable-next-line:no-any\n    if (Math.tanh != null) {\n        // tslint:disable-next-line:no-any\n        return Math.tanh(x);\n    }\n    if (x === Infinity) {\n        return 1;\n    }\n    else if (x === -Infinity) {\n        return -1;\n    }\n    else {\n        const e2x = Math.exp(2 * x);\n        return (e2x - 1) / (e2x + 1);\n    }\n}\nexport function sizeToSquarishShape(size) {\n    const width = Math.ceil(Math.sqrt(size));\n    return [width, Math.ceil(size / width)];\n}\n/**\n * Creates a new array with randomized indicies to a given quantity.\n *\n * ```js\n * const randomTen = tf.util.createShuffledIndices(10);\n * console.log(randomTen);\n * ```\n *\n * @param number Quantity of how many shuffled indicies to create.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function createShuffledIndices(n) {\n    const shuffledIndices = new Uint32Array(n);\n    for (let i = 0; i < n; ++i) {\n        shuffledIndices[i] = i;\n    }\n    shuffle(shuffledIndices);\n    return shuffledIndices;\n}\nexport function rightPad(a, size) {\n    if (size <= a.length) {\n        return a;\n    }\n    return a + ' '.repeat(size - a.length);\n}\nexport function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {\n    return new Promise((resolve, reject) => {\n        let tryCount = 0;\n        const tryFn = () => {\n            if (checkFn()) {\n                resolve();\n                return;\n            }\n            tryCount++;\n            const nextBackoff = delayFn(tryCount);\n            if (maxCounter != null && tryCount >= maxCounter) {\n                reject();\n                return;\n            }\n            setTimeout(tryFn, nextBackoff);\n        };\n        tryFn();\n    });\n}\n/**\n * Given the full size of the array and a shape that may contain -1 as the\n * implicit dimension, returns the inferred shape where -1 is replaced.\n * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].\n *\n * @param shape The shape, which may contain -1 in some dimension.\n * @param size The full size (number of elements) of the array.\n * @return The inferred shape where -1 is replaced with the inferred size.\n */\nexport function inferFromImplicitShape(shape, size) {\n    let shapeProd = 1;\n    let implicitIdx = -1;\n    for (let i = 0; i < shape.length; ++i) {\n        if (shape[i] >= 0) {\n            shapeProd *= shape[i];\n        }\n        else if (shape[i] === -1) {\n            if (implicitIdx !== -1) {\n                throw Error(`Shapes can only have 1 implicit size. ` +\n                    `Found -1 at dim ${implicitIdx} and dim ${i}`);\n            }\n            implicitIdx = i;\n        }\n        else if (shape[i] < 0) {\n            throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n        }\n    }\n    if (implicitIdx === -1) {\n        if (size > 0 && size !== shapeProd) {\n            throw Error(`Size(${size}) must match the product of shape ${shape}`);\n        }\n        return shape;\n    }\n    if (shapeProd === 0) {\n        throw Error(`Cannot infer the missing size in [${shape}] when ` +\n            `there are 0 elements`);\n    }\n    if (size % shapeProd !== 0) {\n        throw Error(`The implicit shape can't be a fractional number. ` +\n            `Got ${size} / ${shapeProd}`);\n    }\n    const newShape = shape.slice();\n    newShape[implicitIdx] = size / shapeProd;\n    return newShape;\n}\nexport function parseAxisParam(axis, shape) {\n    const rank = shape.length;\n    // Normalize input\n    axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n    // Check for valid range\n    assert(axis.every(ax => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but ` +\n        `got axis ${axis}`);\n    // Check for only integers\n    assert(axis.every(ax => isInt(ax)), () => `All values in axis param must be integers but ` +\n        `got axis ${axis}`);\n    // Handle negative axis.\n    return axis.map(a => a < 0 ? rank + a : a);\n}\n/** Reduces the shape by removing all dimensions of shape 1. */\nexport function squeezeShape(shape, axis) {\n    const newShape = [];\n    const keptDims = [];\n    const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;\n    const axes = (axis == null || isEmptyArray) ?\n        null :\n        parseAxisParam(axis, shape).sort();\n    let j = 0;\n    for (let i = 0; i < shape.length; ++i) {\n        if (axes != null) {\n            if (axes[j] === i && shape[i] !== 1) {\n                throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n            }\n            if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {\n                newShape.push(shape[i]);\n                keptDims.push(i);\n            }\n            if (axes[j] <= i) {\n                j++;\n            }\n        }\n        if (shape[i] !== 1) {\n            newShape.push(shape[i]);\n            keptDims.push(i);\n        }\n    }\n    return { newShape, keptDims };\n}\nexport function getTypedArrayFromDType(dtype, size) {\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    return values;\n}\nexport function getArrayFromDType(dtype, size) {\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else if (dtype === 'string') {\n        values = new Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    return values;\n}\nexport function checkConversionForErrors(vals, dtype) {\n    for (let i = 0; i < vals.length; i++) {\n        const num = vals[i];\n        if (isNaN(num) || !isFinite(num)) {\n            throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n        }\n    }\n}\n/** Returns true if the dtype is valid. */\nexport function isValidDtype(dtype) {\n    return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' ||\n        dtype === 'int32' || dtype === 'string';\n}\n/**\n * Returns true if the new type can't encode the old type without loss of\n * precision.\n */\nexport function hasEncodingLoss(oldType, newType) {\n    if (newType === 'complex64') {\n        return false;\n    }\n    if (newType === 'float32' && oldType !== 'complex64') {\n        return false;\n    }\n    if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {\n        return false;\n    }\n    if (newType === 'bool' && oldType === 'bool') {\n        return false;\n    }\n    return true;\n}\nexport function isTypedArray(a) {\n    return a instanceof Float32Array || a instanceof Int32Array ||\n        a instanceof Uint8Array;\n}\nexport function bytesPerElement(dtype) {\n    if (dtype === 'float32' || dtype === 'int32') {\n        return 4;\n    }\n    else if (dtype === 'complex64') {\n        return 8;\n    }\n    else if (dtype === 'bool') {\n        return 1;\n    }\n    else {\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n}\n/**\n * Returns the approximate number of bytes allocated in the string array - 2\n * bytes per character. Computing the exact bytes for a native string in JS is\n * not possible since it depends on the encoding of the html page that serves\n * the website.\n */\nexport function bytesFromStringArray(arr) {\n    if (arr == null) {\n        return 0;\n    }\n    let bytes = 0;\n    arr.forEach(x => bytes += x.length);\n    return bytes;\n}\n/** Returns true if the value is a string. */\nexport function isString(value) {\n    return typeof value === 'string' || value instanceof String;\n}\nexport function isBoolean(value) {\n    return typeof value === 'boolean';\n}\nexport function isNumber(value) {\n    return typeof value === 'number';\n}\nexport function inferDtype(values) {\n    if (Array.isArray(values)) {\n        return inferDtype(values[0]);\n    }\n    if (values instanceof Float32Array) {\n        return 'float32';\n    }\n    else if (values instanceof Int32Array || values instanceof Uint8Array) {\n        return 'int32';\n    }\n    else if (isNumber(values)) {\n        return 'float32';\n    }\n    else if (isString(values)) {\n        return 'string';\n    }\n    else if (isBoolean(values)) {\n        return 'bool';\n    }\n    return 'float32';\n}\nexport function isFunction(f) {\n    return !!(f && f.constructor && f.call && f.apply);\n}\nexport function nearestDivisor(size, start) {\n    for (let i = start; i < size; ++i) {\n        if (size % i === 0) {\n            return i;\n        }\n    }\n    return size;\n}\nexport function computeStrides(shape) {\n    const rank = shape.length;\n    if (rank < 2) {\n        return [];\n    }\n    // Last dimension has implicit stride of 1, thus having D-1 (instead of D)\n    // strides.\n    const strides = new Array(rank - 1);\n    strides[rank - 2] = shape[rank - 1];\n    for (let i = rank - 3; i >= 0; --i) {\n        strides[i] = strides[i + 1] * shape[i + 1];\n    }\n    return strides;\n}\n/**\n * Create typed array for scalar value. Used for storing in `DataStorage`.\n */\nexport function createScalarValue(value, dtype) {\n    if (dtype === 'string') {\n        return encodeString(value);\n    }\n    return toTypedArray([value], dtype);\n}\nexport function toTypedArray(a, dtype) {\n    if (dtype === 'string') {\n        throw new Error('Cannot convert a string[] to a TypedArray');\n    }\n    if (Array.isArray(a)) {\n        a = flatten(a);\n    }\n    if (env().getBool('DEBUG')) {\n        checkConversionForErrors(a, dtype);\n    }\n    if (noConversionNeeded(a, dtype)) {\n        return a;\n    }\n    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n        return new Float32Array(a);\n    }\n    else if (dtype === 'int32') {\n        return new Int32Array(a);\n    }\n    else if (dtype === 'bool') {\n        const bool = new Uint8Array(a.length);\n        for (let i = 0; i < bool.length; ++i) {\n            if (Math.round(a[i]) !== 0) {\n                bool[i] = 1;\n            }\n        }\n        return bool;\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\nfunction createNestedArray(offset, shape, a) {\n    const ret = new Array();\n    if (shape.length === 1) {\n        const d = shape[0];\n        for (let i = 0; i < d; i++) {\n            ret[i] = a[offset + i];\n        }\n    }\n    else {\n        const d = shape[0];\n        const rest = shape.slice(1);\n        const len = rest.reduce((acc, c) => acc * c);\n        for (let i = 0; i < d; i++) {\n            ret[i] = createNestedArray(offset + i * len, rest, a);\n        }\n    }\n    return ret;\n}\n// Provide a nested array of TypedArray in given shape.\nexport function toNestedArray(shape, a) {\n    if (shape.length === 0) {\n        // Scalar type should return a single number.\n        return a[0];\n    }\n    const size = shape.reduce((acc, c) => acc * c);\n    if (size === 0) {\n        // A tensor with shape zero should be turned into empty list.\n        return [];\n    }\n    if (size !== a.length) {\n        throw new Error(`[${shape}] does not match the input size ${a.length}.`);\n    }\n    return createNestedArray(0, shape, a);\n}\nfunction noConversionNeeded(a, dtype) {\n    return (a instanceof Float32Array && dtype === 'float32') ||\n        (a instanceof Int32Array && dtype === 'int32') ||\n        (a instanceof Uint8Array && dtype === 'bool');\n}\nexport function makeOnesTypedArray(size, dtype) {\n    const array = makeZerosTypedArray(size, dtype);\n    for (let i = 0; i < array.length; i++) {\n        array[i] = 1;\n    }\n    return array;\n}\nexport function makeZerosTypedArray(size, dtype) {\n    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n        return new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        return new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        return new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\n/**\n * Make nested `TypedArray` filled with zeros.\n * @param shape The shape information for the nested array.\n * @param dtype dtype of the array element.\n */\nexport function makeZerosNestedTypedArray(shape, dtype) {\n    const size = shape.reduce((prev, curr) => prev * curr, 1);\n    if (dtype == null || dtype === 'float32') {\n        return toNestedArray(shape, new Float32Array(size));\n    }\n    else if (dtype === 'int32') {\n        return toNestedArray(shape, new Int32Array(size));\n    }\n    else if (dtype === 'bool') {\n        return toNestedArray(shape, new Uint8Array(size));\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\n/**\n * Returns the current high-resolution time in milliseconds relative to an\n * arbitrary time in the past. It works across different platforms (node.js,\n * browsers).\n *\n * ```js\n * console.log(tf.util.now());\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function now() {\n    return env().platform.now();\n}\nexport function assertNonNegativeIntegerDimensions(shape) {\n    shape.forEach(dimSize => {\n        assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got ` +\n            `shape [${shape}].`);\n    });\n}\n/**\n * Returns a platform-specific implementation of\n * [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n *\n * If `fetch` is defined on the global object (`window`, `process`, etc.),\n * `tf.util.fetch` returns that function.\n *\n * If not, `tf.util.fetch` returns a platform-specific solution.\n *\n * ```js\n * const resource = await tf.util.fetch('https://unpkg.com/@tensorflow/tfjs');\n * // handle response\n * ```\n *\n * @doc {heading: 'Util'}\n */\nexport function fetch(path, requestInits) {\n    return env().platform.fetch(path, requestInits);\n}\n/**\n * Encodes the provided string into bytes using the provided encoding scheme.\n *\n * @param s The string to encode.\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function encodeString(s, encoding = 'utf-8') {\n    encoding = encoding || 'utf-8';\n    return env().platform.encode(s, encoding);\n}\n/**\n * Decodes the provided bytes into a string using the provided encoding scheme.\n * @param bytes The bytes to decode.\n *\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function decodeString(bytes, encoding = 'utf-8') {\n    encoding = encoding || 'utf-8';\n    return env().platform.decode(bytes, encoding);\n}\n/**\n * Computes flat index for a given location (multidimentionsal index) in a\n * Tensor/multidimensional array.\n *\n * @param locs Location in the tensor.\n * @param rank Rank of the tensor.\n * @param strides Tensor strides.\n */\nexport function locToIndex(locs, rank, strides) {\n    if (rank === 0) {\n        return 0;\n    }\n    else if (rank === 1) {\n        return locs[0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n        index += strides[i] * locs[i];\n    }\n    return index;\n}\n/**\n * Computes the location (multidimensional index) in a tensor/multidimentional\n * array for a given flat index.\n *\n * @param index Index in flat array.\n * @param rank Rank of tensor.\n * @param strides Strides of tensor.\n */\nexport function indexToLoc(index, rank, strides) {\n    if (rank === 0) {\n        return [];\n    }\n    else if (rank === 1) {\n        return [index];\n    }\n    const locs = new Array(rank);\n    for (let i = 0; i < locs.length - 1; ++i) {\n        locs[i] = Math.floor(index / strides[i]);\n        index -= locs[i] * strides[i];\n    }\n    locs[locs.length - 1] = index;\n    return locs;\n}\n//# sourceMappingURL=util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from './util';\nexport class Profiler {\n    constructor(backendTimer, logger) {\n        this.backendTimer = backendTimer;\n        this.logger = logger;\n        if (logger == null) {\n            this.logger = new Logger();\n        }\n    }\n    profileKernel(kernelName, inputs, f) {\n        let outputs;\n        const holdResultWrapperFn = () => {\n            outputs = f();\n        };\n        const timer = this.backendTimer.time(holdResultWrapperFn);\n        for (let i = 0; i < outputs.length; i++) {\n            const output = outputs[i];\n            // Dangling promise here because we don't want to propagate up\n            // asynchronicity.\n            output.data().then(tensorVals => {\n                checkComputationForErrors(tensorVals, output.dtype, kernelName);\n            });\n        }\n        const kernelProfile = {\n            kernelName,\n            outputs,\n            inputs,\n            timeMs: timer.then(timing => timing.kernelMs),\n            extraInfo: timer.then(timing => timing.getExtraProfileInfo != null ?\n                timing.getExtraProfileInfo() :\n                '')\n        };\n        return kernelProfile;\n    }\n    logKernelProfile(kernelProfile) {\n        const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile;\n        outputs.forEach(result => {\n            Promise.all([result.data(), timeMs, extraInfo]).then(valueContainer => {\n                this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);\n            });\n        });\n    }\n}\nexport function checkComputationForErrors(vals, dtype, kernelName) {\n    if (dtype !== 'float32') {\n        // Only floating point computations will generate NaN values\n        return false;\n    }\n    for (let i = 0; i < vals.length; i++) {\n        const num = vals[i];\n        if (isNaN(num) || !isFinite(num)) {\n            // Throwing custom exception so behavior is testable.\n            console.warn(`Found ${num} in the result of '${kernelName}'`);\n            return true;\n        }\n    }\n    return false;\n}\nexport class Logger {\n    logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {\n        const time = typeof timeMs === 'number' ? util.rightPad(`${timeMs}ms`, 9) :\n            timeMs['error'];\n        const paddedName = util.rightPad(name, 25);\n        const rank = result.rank;\n        const size = result.size;\n        const shape = util.rightPad(result.shape.toString(), 14);\n        let inputShapesDescription = '';\n        for (const name in inputs) {\n            const input = inputs[name];\n            if (input != null) {\n                // The input might be a non-tensor (e.g HTMLImageElement), in which case\n                // we claim the output shape as input shape.\n                const inputShape = input.shape || result.shape;\n                const inputRank = inputShape.length;\n                inputShapesDescription +=\n                    `${name}: ${inputRank}D ${inputRank > 0 ? inputShape : ''} `;\n            }\n        }\n        console.log(`%c${paddedName}\\t%c${time}\\t%c${rank}D ${shape}\\t%c${size}\\t%c${inputShapesDescription}\\t%c${extraInfo}`, 'font-weight:bold', 'color:red', 'color:blue', 'color: orange', 'color: green', 'color: steelblue');\n    }\n}\n//# sourceMappingURL=profiler.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from './util';\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nexport function getFilteredNodesXToY(tape, xs, y) {\n    // Forward pass to compute all the nodes and Tensors that are transitively a\n    // function of x.\n    const tensorsFromX = {};\n    const nodesFromX = {};\n    for (let i = 0; i < xs.length; i++) {\n        tensorsFromX[xs[i].id] = true;\n    }\n    for (let i = 0; i < tape.length; i++) {\n        const node = tape[i];\n        const nodeInputs = node.inputs;\n        for (const inputName in nodeInputs) {\n            const input = nodeInputs[inputName];\n            let anyInputFromX = false;\n            for (let j = 0; j < xs.length; j++) {\n                if (tensorsFromX[input.id]) {\n                    node.outputs.forEach(output => tensorsFromX[output.id] = true);\n                    anyInputFromX = true;\n                    nodesFromX[node.id] = true;\n                    break;\n                }\n            }\n            if (anyInputFromX) {\n                break;\n            }\n        }\n    }\n    // Backward pass to find all of the nodes and Tensors that lead to y.\n    const tensorsLeadToY = {};\n    tensorsLeadToY[y.id] = true;\n    const nodesToY = {};\n    for (let i = tape.length - 1; i >= 0; i--) {\n        const node = tape[i];\n        const nodeInputs = node.inputs;\n        // If any of the outputs lead to y, mark all of the inputs as leading to y.\n        for (let j = 0; j < node.outputs.length; j++) {\n            if (tensorsLeadToY[node.outputs[j].id]) {\n                for (const inputName in nodeInputs) {\n                    tensorsLeadToY[nodeInputs[inputName].id] = true;\n                    nodesToY[node.id] = true;\n                }\n                break;\n            }\n        }\n    }\n    // Return the paths that come from x and lead to y.\n    const filteredTape = [];\n    for (let i = 0; i < tape.length; i++) {\n        const node = tape[i];\n        if (nodesFromX[node.id] && nodesToY[node.id]) {\n            // Prune the inputs from the node that aren't a function of x.\n            const prunedInputs = {};\n            for (const inputName in node.inputs) {\n                const nodeInput = node.inputs[inputName];\n                if (tensorsFromX[nodeInput.id]) {\n                    prunedInputs[inputName] = nodeInput;\n                }\n            }\n            // Copy the node and overwrite inputsAndArgs to the pruned version.\n            const prunedNode = Object.assign({}, node);\n            prunedNode.inputs = prunedInputs;\n            prunedNode.outputs = node.outputs;\n            filteredTape.push(prunedNode);\n        }\n    }\n    return filteredTape;\n}\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nexport function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy, add) {\n    // Walk the tape backward and keep a map of Tensor to its gradient.\n    for (let i = filteredTape.length - 1; i >= 0; i--) {\n        const node = filteredTape[i];\n        const dys = [];\n        node.outputs.forEach(o => {\n            const gradTensor = tensorAccumulatedGradientMap[o.id];\n            if (gradTensor != null) {\n                dys.push(gradTensor);\n            }\n            else {\n                // This particular output is not in the back-propagation subgraph, so it\n                // does not affect the final output, thus we put null for its dy.\n                dys.push(null);\n            }\n        });\n        if (node.gradient == null) {\n            throw new Error(`Cannot compute gradient: gradient function not found ` +\n                `for ${node.kernelName}.`);\n        }\n        // Backprop dy through this node and accumulate gradients over the inputs.\n        const inputGradients = node.gradient(dys);\n        for (const inputName in node.inputs) {\n            if (!(inputName in inputGradients)) {\n                throw new Error(`Cannot backprop through input ${inputName}. ` +\n                    `Available gradients found: ${Object.keys(inputGradients)}.`);\n            }\n            // Call the gradient function.\n            const dx = tidy(() => inputGradients[inputName]());\n            if (dx.dtype !== 'float32') {\n                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n                    `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n            }\n            const x = node.inputs[inputName];\n            if (!util.arraysEqual(dx.shape, x.shape)) {\n                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n                    `'${inputName}' has shape '${dx.shape}', which does not match ` +\n                    `the shape of the input '${x.shape}'`);\n            }\n            if (tensorAccumulatedGradientMap[x.id] == null) {\n                tensorAccumulatedGradientMap[x.id] = dx;\n            }\n            else {\n                const curGradient = tensorAccumulatedGradientMap[x.id];\n                tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);\n                curGradient.dispose();\n            }\n        }\n    }\n}\n//# sourceMappingURL=tape.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { computeStrides, isString, rightPad, sizeFromShape } from './util';\n// Maximum number of values before we decide to show ellipsis.\nconst FORMAT_LIMIT_NUM_VALS = 20;\n// Number of first and last values to show when displaying a, b,...,y, z.\nconst FORMAT_NUM_FIRST_LAST_VALS = 3;\n// Number of significant digits to show.\nconst FORMAT_NUM_SIG_DIGITS = 7;\nexport function tensorToString(vals, shape, dtype, verbose) {\n    const strides = computeStrides(shape);\n    const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n    const rank = shape.length;\n    const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n    const lines = ['Tensor'];\n    if (verbose) {\n        lines.push(`  dtype: ${dtype}`);\n        lines.push(`  rank: ${rank}`);\n        lines.push(`  shape: [${shape}]`);\n        lines.push(`  values:`);\n    }\n    lines.push(valsLines.map(l => '    ' + l).join('\\n'));\n    return lines.join('\\n');\n}\nfunction computeMaxSizePerColumn(vals, shape, dtype, strides) {\n    const n = sizeFromShape(shape);\n    const numCols = strides[strides.length - 1];\n    const padPerCol = new Array(numCols).fill(0);\n    const rank = shape.length;\n    const valuesOrTuples = dtype === 'complex64' ? createComplexTuples(vals) : vals;\n    if (rank > 1) {\n        for (let row = 0; row < n / numCols; row++) {\n            const offset = row * numCols;\n            for (let j = 0; j < numCols; j++) {\n                padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);\n            }\n        }\n    }\n    return padPerCol;\n}\nfunction valToString(val, pad, dtype) {\n    let valStr;\n    if (Array.isArray(val)) {\n        valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +\n            `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n    }\n    else if (isString(val)) {\n        valStr = `'${val}'`;\n    }\n    else if (dtype === 'bool') {\n        valStr = boolNumToString(val);\n    }\n    else {\n        valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n    }\n    return rightPad(valStr, pad);\n}\nfunction boolNumToString(v) {\n    return v === 0 ? 'false' : 'true';\n}\nfunction subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {\n    const storagePerElement = dtype === 'complex64' ? 2 : 1;\n    const size = shape[0];\n    const rank = shape.length;\n    if (rank === 0) {\n        if (dtype === 'complex64') {\n            const complexTuple = createComplexTuples(vals);\n            return [valToString(complexTuple[0], 0, dtype)];\n        }\n        if (dtype === 'bool') {\n            return [boolNumToString(vals[0])];\n        }\n        return [vals[0].toString()];\n    }\n    if (rank === 1) {\n        if (size > FORMAT_LIMIT_NUM_VALS) {\n            const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n            let firstVals = Array.from(vals.slice(0, firstValsSize));\n            let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));\n            if (dtype === 'complex64') {\n                firstVals = createComplexTuples(firstVals);\n                lastVals = createComplexTuples(lastVals);\n            }\n            return [\n                '[' +\n                    firstVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n                        .join(', ') +\n                    ', ..., ' +\n                    lastVals\n                        .map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype))\n                        .join(', ') +\n                    ']'\n            ];\n        }\n        const displayVals = dtype === 'complex64' ? createComplexTuples(vals) :\n            Array.from(vals);\n        return [\n            '[' +\n                displayVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n                    .join(', ') +\n                ']'\n        ];\n    }\n    // The array is rank 2 or more.\n    const subshape = shape.slice(1);\n    const substrides = strides.slice(1);\n    const stride = strides[0] * storagePerElement;\n    const lines = [];\n    if (size > FORMAT_LIMIT_NUM_VALS) {\n        for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false /* isLast */));\n        }\n        lines.push('...');\n        for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n        }\n    }\n    else {\n        for (let i = 0; i < size; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n        }\n    }\n    const sep = rank === 2 ? ',' : '';\n    lines[0] = '[' + lines[0] + sep;\n    for (let i = 1; i < lines.length - 1; i++) {\n        lines[i] = ' ' + lines[i] + sep;\n    }\n    let newLineSep = ',\\n';\n    for (let i = 2; i < rank; i++) {\n        newLineSep += '\\n';\n    }\n    lines[lines.length - 1] =\n        ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);\n    return lines;\n}\nfunction createComplexTuples(vals) {\n    const complexTuples = [];\n    for (let i = 0; i < vals.length; i += 2) {\n        complexTuples.push([vals[i], vals[i + 1]]);\n    }\n    return complexTuples;\n}\n//# sourceMappingURL=tensor_format.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensorToString } from './tensor_format';\nimport * as util from './util';\nimport { computeStrides, toNestedArray } from './util';\n/**\n * A mutable object, similar to `tf.Tensor`, that allows users to set values\n * at locations before converting to an immutable `tf.Tensor`.\n *\n * See `tf.buffer` for creating a tensor buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class TensorBuffer {\n    constructor(shape, dtype, values) {\n        this.dtype = dtype;\n        this.shape = shape.slice();\n        this.size = util.sizeFromShape(shape);\n        if (values != null) {\n            const n = values.length;\n            util.assert(n === this.size, () => `Length of values '${n}' does not match the size ` +\n                `inferred by the shape '${this.size}'.`);\n        }\n        if (dtype === 'complex64') {\n            throw new Error(`complex64 dtype TensorBuffers are not supported. Please create ` +\n                `a TensorBuffer for the real and imaginary parts separately and ` +\n                `call tf.complex(real, imag).`);\n        }\n        this.values = values || util.getArrayFromDType(dtype, this.size);\n        this.strides = computeStrides(shape);\n    }\n    /**\n     * Sets a value in the buffer at a given location.\n     *\n     * @param value The value to set.\n     * @param locs  The location indices.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    set(value, ...locs) {\n        if (locs.length === 0) {\n            locs = [0];\n        }\n        util.assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must ` +\n            `match the rank (${this.rank})`);\n        const index = this.locToIndex(locs);\n        this.values[index] = value;\n    }\n    /**\n     * Returns the value in the buffer at the provided location.\n     *\n     * @param locs The location indices.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    get(...locs) {\n        if (locs.length === 0) {\n            locs = [0];\n        }\n        let i = 0;\n        for (const loc of locs) {\n            if (loc < 0 || loc >= this.shape[i]) {\n                const msg = `Requested out of range element at ${locs}. ` +\n                    `  Buffer shape=${this.shape}`;\n                throw new Error(msg);\n            }\n            i++;\n        }\n        let index = locs[locs.length - 1];\n        for (let i = 0; i < locs.length - 1; ++i) {\n            index += this.strides[i] * locs[i];\n        }\n        return this.values[index];\n    }\n    locToIndex(locs) {\n        if (this.rank === 0) {\n            return 0;\n        }\n        else if (this.rank === 1) {\n            return locs[0];\n        }\n        let index = locs[locs.length - 1];\n        for (let i = 0; i < locs.length - 1; ++i) {\n            index += this.strides[i] * locs[i];\n        }\n        return index;\n    }\n    indexToLoc(index) {\n        if (this.rank === 0) {\n            return [];\n        }\n        else if (this.rank === 1) {\n            return [index];\n        }\n        const locs = new Array(this.shape.length);\n        for (let i = 0; i < locs.length - 1; ++i) {\n            locs[i] = Math.floor(index / this.strides[i]);\n            index -= locs[i] * this.strides[i];\n        }\n        locs[locs.length - 1] = index;\n        return locs;\n    }\n    get rank() {\n        return this.shape.length;\n    }\n    /**\n     * Creates an immutable `tf.Tensor` object from the buffer.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    toTensor() {\n        return trackerFn().makeTensor(this.values, this.shape, this.dtype);\n    }\n}\n// For tracking tensor creation and disposal.\nlet trackerFn = null;\n// Used by chaining methods to call into ops.\nlet opHandler = null;\n// Used to warn about deprecated methods.\nlet deprecationWarningFn = null;\n// This here so that we can use this method on dev branches and keep the\n// functionality at master.\n// tslint:disable-next-line:no-unused-expression\n[deprecationWarningFn];\n/**\n * An external consumer can register itself as the tensor tracker. This way\n * the Tensor class can notify the tracker for every tensor created and\n * disposed.\n */\nexport function setTensorTracker(fn) {\n    trackerFn = fn;\n}\n/**\n * An external consumer can register itself as the op handler. This way the\n * Tensor class can have chaining methods that call into ops via the op\n * handler.\n */\nexport function setOpHandler(handler) {\n    opHandler = handler;\n}\n/**\n * Sets the deprecation warning function to be used by this file. This way the\n * Tensor class can be a leaf but still use the environment.\n */\nexport function setDeprecationWarningFn(fn) {\n    deprecationWarningFn = fn;\n}\n/**\n * A `tf.Tensor` object represents an immutable, multidimensional array of\n * numbers that has a shape and a data type.\n *\n * See `tf.tensor` for details on how to create a `tf.Tensor`.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Tensor {\n    constructor(shape, dtype, dataId, id) {\n        /** Whether this tensor has been globally kept. */\n        this.kept = false;\n        this.isDisposedInternal = false;\n        this.shape = shape.slice();\n        this.dtype = dtype || 'float32';\n        this.size = util.sizeFromShape(shape);\n        this.strides = computeStrides(shape);\n        this.dataId = dataId;\n        this.id = id;\n        this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher');\n    }\n    get rank() {\n        return this.shape.length;\n    }\n    /**\n     * Returns a promise of `tf.TensorBuffer` that holds the underlying data.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async buffer() {\n        const vals = await this.data();\n        return opHandler.buffer(this.shape, this.dtype, vals);\n    }\n    /**\n     * Returns a `tf.TensorBuffer` that holds the underlying data.\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    bufferSync() {\n        return opHandler.buffer(this.shape, this.dtype, this.dataSync());\n    }\n    /**\n     * Returns the tensor data as a nested array. The transfer of data is done\n     * asynchronously.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async array() {\n        const vals = await this.data();\n        return toNestedArray(this.shape, vals);\n    }\n    /**\n     * Returns the tensor data as a nested array. The transfer of data is done\n     * synchronously.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    arraySync() {\n        return toNestedArray(this.shape, this.dataSync());\n    }\n    /**\n     * Asynchronously downloads the values from the `tf.Tensor`. Returns a\n     * promise of `TypedArray` that resolves when the computation has finished.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async data() {\n        this.throwIfDisposed();\n        const data = trackerFn().read(this.dataId);\n        if (this.dtype === 'string') {\n            const bytes = await data;\n            try {\n                return bytes.map(b => util.decodeString(b));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode the string bytes into utf-8. ' +\n                    'To get the original bytes, call tensor.bytes().');\n            }\n        }\n        return data;\n    }\n    /**\n     * Synchronously downloads the values from the `tf.Tensor`. This blocks the\n     * UI thread until the values are ready, which can cause performance issues.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    dataSync() {\n        this.throwIfDisposed();\n        const data = trackerFn().readSync(this.dataId);\n        if (this.dtype === 'string') {\n            try {\n                return data.map(b => util.decodeString(b));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode the string bytes into utf-8. ' +\n                    'To get the original bytes, call tensor.bytes().');\n            }\n        }\n        return data;\n    }\n    /** Returns the underlying bytes of the tensor's data. */\n    async bytes() {\n        this.throwIfDisposed();\n        const data = await trackerFn().read(this.dataId);\n        if (this.dtype === 'string') {\n            return data;\n        }\n        else {\n            return new Uint8Array(data.buffer);\n        }\n    }\n    /**\n     * Disposes `tf.Tensor` from memory.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    dispose() {\n        if (this.isDisposed) {\n            return;\n        }\n        trackerFn().disposeTensor(this);\n        this.isDisposedInternal = true;\n    }\n    get isDisposed() {\n        return this.isDisposedInternal;\n    }\n    throwIfDisposed() {\n        if (this.isDisposed) {\n            throw new Error(`Tensor is disposed.`);\n        }\n    }\n    /**\n     * Prints the `tf.Tensor`. See `tf.print` for details.\n     *\n     * @param verbose Whether to print verbose information about the tensor,\n     *    including dtype and size.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    print(verbose = false) {\n        return opHandler.print(this, verbose);\n    }\n    /**\n     * Returns a copy of the tensor. See `tf.clone` for details.\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    clone() {\n        this.throwIfDisposed();\n        return opHandler.clone(this);\n    }\n    /**\n     * Returns a human-readable description of the tensor. Useful for logging.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    toString(verbose = false) {\n        const vals = this.dataSync();\n        return tensorToString(vals, this.shape, this.dtype, verbose);\n    }\n    cast(dtype) {\n        this.throwIfDisposed();\n        return opHandler.cast(this, dtype);\n    }\n    variable(trainable = true, name, dtype) {\n        this.throwIfDisposed();\n        return trackerFn().makeVariable(this, trainable, name, dtype);\n    }\n}\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n    value: (instance) => {\n        // Implementation note: we should use properties of the object that will be\n        // defined before the constructor body has finished executing (methods).\n        // This is because when this code is transpiled by babel, babel will call\n        // classCallCheck before the constructor body is run.\n        // See https://github.com/tensorflow/tfjs/issues/3384 for backstory.\n        return !!instance && instance.data != null && instance.dataSync != null &&\n            instance.throwIfDisposed != null;\n    }\n});\n/**\n * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Variable extends Tensor {\n    constructor(initialValue, trainable, name, tensorId) {\n        super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);\n        this.trainable = trainable;\n        this.name = name;\n    }\n    /**\n     * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n     * the same shape and dtype as the old `tf.Tensor`.\n     *\n     * @param newValue New tensor to be assigned to this variable.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    assign(newValue) {\n        if (newValue.dtype !== this.dtype) {\n            throw new Error(`dtype of the new value (${newValue.dtype}) and ` +\n                `previous value (${this.dtype}) must match`);\n        }\n        if (!util.arraysEqual(newValue.shape, this.shape)) {\n            throw new Error(`shape of the new value (${newValue.shape}) and ` +\n                `previous value (${this.shape}) must match`);\n        }\n        trackerFn().disposeTensor(this);\n        this.dataId = newValue.dataId;\n        trackerFn().incRef(this, null /* backend */);\n    }\n    dispose() {\n        trackerFn().disposeVariable(this);\n        this.isDisposedInternal = true;\n    }\n}\nObject.defineProperty(Variable, Symbol.hasInstance, {\n    value: (instance) => {\n        return instance instanceof Tensor && instance.assign != null &&\n            instance.assign instanceof Function;\n    }\n});\n//# sourceMappingURL=tensor.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Rank;\n(function (Rank) {\n    Rank[\"R0\"] = \"R0\";\n    Rank[\"R1\"] = \"R1\";\n    Rank[\"R2\"] = \"R2\";\n    Rank[\"R3\"] = \"R3\";\n    Rank[\"R4\"] = \"R4\";\n    Rank[\"R5\"] = \"R5\";\n    Rank[\"R6\"] = \"R6\";\n})(Rank || (Rank = {}));\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nvar UpcastInt32AndMap;\n(function (UpcastInt32AndMap) {\n    UpcastInt32AndMap[\"float32\"] = \"float32\";\n    UpcastInt32AndMap[\"int32\"] = \"int32\";\n    UpcastInt32AndMap[\"bool\"] = \"int32\";\n    UpcastInt32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));\nvar UpcastBoolAndMap;\n(function (UpcastBoolAndMap) {\n    UpcastBoolAndMap[\"float32\"] = \"float32\";\n    UpcastBoolAndMap[\"int32\"] = \"int32\";\n    UpcastBoolAndMap[\"bool\"] = \"bool\";\n    UpcastBoolAndMap[\"complex64\"] = \"complex64\";\n})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));\nvar UpcastFloat32AndMap;\n(function (UpcastFloat32AndMap) {\n    UpcastFloat32AndMap[\"float32\"] = \"float32\";\n    UpcastFloat32AndMap[\"int32\"] = \"float32\";\n    UpcastFloat32AndMap[\"bool\"] = \"float32\";\n    UpcastFloat32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));\nvar UpcastComplex64AndMap;\n(function (UpcastComplex64AndMap) {\n    UpcastComplex64AndMap[\"float32\"] = \"complex64\";\n    UpcastComplex64AndMap[\"int32\"] = \"complex64\";\n    UpcastComplex64AndMap[\"bool\"] = \"complex64\";\n    UpcastComplex64AndMap[\"complex64\"] = \"complex64\";\n})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));\nconst upcastTypeMap = {\n    'float32': UpcastFloat32AndMap,\n    'int32': UpcastInt32AndMap,\n    'bool': UpcastBoolAndMap,\n    'complex64': UpcastComplex64AndMap\n};\nexport function upcastType(typeA, typeB) {\n    if (typeA === 'string' || typeB === 'string') {\n        if (typeA === 'string' && typeB === 'string') {\n            return 'string';\n        }\n        throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n    }\n    return upcastTypeMap[typeA][typeB];\n}\n/** Returns the output type after summation. */\nexport function sumOutType(type) {\n    return upcastType(type, 'int32');\n}\n//# sourceMappingURL=types.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from './tensor';\nimport { upcastType } from './types';\nimport { assert } from './util';\nexport function makeTypesMatch(a, b) {\n    if (a.dtype === b.dtype) {\n        return [a, b];\n    }\n    const dtype = upcastType(a.dtype, b.dtype);\n    return [a.cast(dtype), b.cast(dtype)];\n}\nexport function assertTypesMatch(a, b) {\n    assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and` +\n        ` second(${b.dtype}) input must match`);\n}\nexport function isTensorInList(tensor, tensorList) {\n    return tensorList.some(x => x.id === tensor.id);\n}\n/**\n * Extracts any `Tensor`s found within the provided object.\n *\n * @param container an object that may be a `Tensor` or may directly contain\n *   `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it\n *   is safe to pass any object here, except that `Promise`s are not\n *   supported.\n * @returns An array of `Tensors` found within the passed object. If the\n *   argument is simply a `Tensor', a list containing that `Tensor` is\n *   returned. If the object is not a `Tensor` or does not\n *   contain `Tensors`, an empty list is returned.\n */\nexport function getTensorsInContainer(result) {\n    const list = [];\n    const seen = new Set();\n    walkTensorContainer(result, list, seen);\n    return list;\n}\nfunction walkTensorContainer(container, list, seen) {\n    if (container == null) {\n        return;\n    }\n    if (container instanceof Tensor) {\n        list.push(container);\n        return;\n    }\n    if (!isIterable(container)) {\n        return;\n    }\n    // Iteration over keys works also for arrays.\n    const iterable = container;\n    for (const k in iterable) {\n        const val = iterable[k];\n        if (!seen.has(val)) {\n            seen.add(val);\n            walkTensorContainer(val, list, seen);\n        }\n    }\n}\n// tslint:disable-next-line:no-any\nfunction isIterable(obj) {\n    return Array.isArray(obj) || typeof obj === 'object';\n}\n//# sourceMappingURL=tensor_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { KernelBackend } from './backends/backend';\nimport { Environment, setEnvironmentGlobal } from './environment';\nimport { getGlobalNamespace } from './global_util';\nimport { Add, Cast } from './kernel_names';\nimport { getGradient, getKernel, getKernelsForBackend } from './kernel_registry';\nimport { Profiler } from './profiler';\nimport { backpropagateGradients, getFilteredNodesXToY } from './tape';\nimport { setTensorTracker, Tensor, Variable } from './tensor';\nimport { getTensorsInContainer } from './tensor_util';\nimport * as util from './util';\nimport { bytesFromStringArray, makeOnesTypedArray, now, sizeFromShape } from './util';\nclass EngineState {\n    constructor() {\n        // Public since optimizers will use it.\n        this.registeredVariables = {};\n        this.nextTapeNodeId = 0;\n        this.numBytes = 0;\n        this.numTensors = 0;\n        this.numStringTensors = 0;\n        this.numDataBuffers = 0;\n        // Number of nested tf.grad() statements when computing higher-order\n        // gradients. E.g. `1` for first-order gradients and `2` for second-order\n        // gradients. Used to track if the tape should be removed after a backprop.\n        this.gradientDepth = 0;\n        // Number of nested kernel calls. When kernel depth is greater than 1, we turn\n        // off the tape.\n        this.kernelDepth = 0;\n        this.scopeStack = [];\n        /**\n         * Keeps track of the number of data moves during a kernel execution. We\n         * maintain a stack since kernels can call other kernels, recursively.\n         */\n        this.numDataMovesStack = [];\n        this.nextScopeId = 0;\n        this.tensorInfo = new WeakMap();\n        this.profiling = false;\n        this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null };\n    }\n    dispose() {\n        for (const variableName in this.registeredVariables) {\n            this.registeredVariables[variableName].dispose();\n        }\n    }\n}\nexport class Engine {\n    constructor(ENV) {\n        this.ENV = ENV;\n        this.registry = {};\n        this.registryFactory = {};\n        this.pendingBackendInitId = 0;\n        this.state = new EngineState();\n    }\n    async ready() {\n        if (this.pendingBackendInit != null) {\n            return this.pendingBackendInit.then(() => { });\n        }\n        if (this.backendInstance != null) {\n            return;\n        }\n        const sortedBackends = this.getSortedBackends();\n        for (let i = 0; i < sortedBackends.length; i++) {\n            const backendName = sortedBackends[i];\n            const success = await this.initializeBackend(backendName).success;\n            if (success) {\n                await this.setBackend(backendName);\n                return;\n            }\n        }\n        throw new Error(`Could not initialize any backends, all backend initializations ` +\n            `failed.`);\n    }\n    get backend() {\n        if (this.pendingBackendInit != null) {\n            throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make ` +\n                `sure to await tf.ready() or await tf.setBackend() before calling ` +\n                `other methods`);\n        }\n        if (this.backendInstance == null) {\n            const { name, asyncInit } = this.initializeBackendsAndReturnBest();\n            if (asyncInit) {\n                throw new Error(`The highest priority backend '${name}' has not yet been ` +\n                    `initialized. Make sure to await tf.ready() or ` +\n                    `await tf.setBackend() before calling other methods`);\n            }\n            this.setBackend(name);\n        }\n        return this.backendInstance;\n    }\n    backendNames() {\n        return Object.keys(this.registryFactory);\n    }\n    findBackend(backendName) {\n        if (!(backendName in this.registry)) {\n            // If the backend hasn't been initialized but we have a registry entry for\n            // it, initialize it and return it.\n            if (backendName in this.registryFactory) {\n                const { asyncInit } = this.initializeBackend(backendName);\n                if (asyncInit) {\n                    // Backend is not ready yet.\n                    return null;\n                }\n            }\n            else {\n                return null;\n            }\n        }\n        return this.registry[backendName];\n    }\n    findBackendFactory(backendName) {\n        if (!(backendName in this.registryFactory)) {\n            return null;\n        }\n        return this.registryFactory[backendName].factory;\n    }\n    registerBackend(backendName, factory, priority = 1) {\n        if (backendName in this.registryFactory) {\n            console.warn(`${backendName} backend was already registered. ` +\n                `Reusing existing backend factory.`);\n            return false;\n        }\n        this.registryFactory[backendName] = { factory, priority };\n        return true;\n    }\n    async setBackend(backendName) {\n        if (this.registryFactory[backendName] == null) {\n            throw new Error(`Backend name '${backendName}' not found in registry`);\n        }\n        this.backendName = backendName;\n        if (this.registry[backendName] == null) {\n            this.backendInstance = null;\n            const { success, asyncInit } = this.initializeBackend(backendName);\n            const result = asyncInit ? await success : success;\n            if (!result) {\n                return false;\n            }\n        }\n        this.backendInstance = this.registry[backendName];\n        this.setupRegisteredKernels();\n        // Reset the profiler.\n        this.profiler = new Profiler(this.backendInstance);\n        return true;\n    }\n    setupRegisteredKernels() {\n        const kernels = getKernelsForBackend(this.backendName);\n        kernels.forEach(kernel => {\n            if (kernel.setupFunc != null) {\n                kernel.setupFunc(this.backendInstance);\n            }\n        });\n    }\n    disposeRegisteredKernels(backendName) {\n        const kernels = getKernelsForBackend(backendName);\n        kernels.forEach(kernel => {\n            if (kernel.disposeFunc != null) {\n                kernel.disposeFunc(this.registry[backendName]);\n            }\n        });\n    }\n    /**\n     * Initializes a backend by looking up the backend name in the factory\n     * registry and calling the factory method. Returns a boolean representing\n     * whether the initialization of the backend suceeded. Throws an error if\n     * there is no backend in the factory registry.\n     */\n    initializeBackend(backendName) {\n        const registryFactoryEntry = this.registryFactory[backendName];\n        if (registryFactoryEntry == null) {\n            throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);\n        }\n        try {\n            const backend = registryFactoryEntry.factory();\n            /* Test if the factory returns a promise.\n            Done in a more liberal way than\n            previous 'Promise.resolve(backend)===backend'\n            as we needed to account for custom Promise\n            implementations (e.g. Angular) */\n            if (backend && !(backend instanceof KernelBackend)\n                && typeof backend.then === 'function') {\n                const promiseId = ++this.pendingBackendInitId;\n                const success = backend\n                    .then(backendInstance => {\n                    // Outdated promise. Another backend was set in the meantime.\n                    if (promiseId < this.pendingBackendInitId) {\n                        return false;\n                    }\n                    this.registry[backendName] = backendInstance;\n                    this.pendingBackendInit = null;\n                    return true;\n                })\n                    .catch(err => {\n                    // Outdated promise. Another backend was set in the meantime.\n                    if (promiseId < this.pendingBackendInitId) {\n                        return false;\n                    }\n                    this.pendingBackendInit = null;\n                    console.warn(`Initialization of backend ${backendName} failed`);\n                    console.warn(err.stack || err.message);\n                    return false;\n                });\n                this.pendingBackendInit = success;\n                return { success, asyncInit: true };\n            }\n            else {\n                this.registry[backendName] = backend;\n                return { success: true, asyncInit: false };\n            }\n        }\n        catch (err) {\n            console.warn(`Initialization of backend ${backendName} failed`);\n            console.warn(err.stack || err.message);\n            return { success: false, asyncInit: false };\n        }\n    }\n    removeBackend(backendName) {\n        if (!(backendName in this.registryFactory)) {\n            throw new Error(`${backendName} backend not found in registry`);\n        }\n        if (this.backendName === backendName && this.pendingBackendInit != null) {\n            // There is a pending promise of the backend we want to remove. Make it\n            // obsolete.\n            this.pendingBackendInitId++;\n        }\n        if (backendName in this.registry) {\n            this.disposeRegisteredKernels(backendName);\n            this.registry[backendName].dispose();\n            delete this.registry[backendName];\n        }\n        delete this.registryFactory[backendName];\n        // Unset the backend if it is active.\n        if (this.backendName === backendName) {\n            this.pendingBackendInit = null;\n            this.backendName = null;\n            this.backendInstance = null;\n        }\n    }\n    getSortedBackends() {\n        if (Object.keys(this.registryFactory).length === 0) {\n            throw new Error('No backend found in registry.');\n        }\n        return Object.keys(this.registryFactory).sort((a, b) => {\n            // Highest priority comes first.\n            return this.registryFactory[b].priority -\n                this.registryFactory[a].priority;\n        });\n    }\n    initializeBackendsAndReturnBest() {\n        const sortedBackends = this.getSortedBackends();\n        for (let i = 0; i < sortedBackends.length; i++) {\n            const backendName = sortedBackends[i];\n            const { success, asyncInit } = this.initializeBackend(backendName);\n            if (asyncInit || success) {\n                return { name: backendName, asyncInit };\n            }\n        }\n        throw new Error(`Could not initialize any backends, all backend initializations ` +\n            `failed.`);\n    }\n    moveData(backend, dataId) {\n        const info = this.state.tensorInfo.get(dataId);\n        const srcBackend = info.backend;\n        const values = this.readSync(dataId);\n        // Delete the tensor from the old backend and move it to the new\n        // backend.\n        srcBackend.disposeData(dataId);\n        info.backend = backend;\n        backend.move(dataId, values, info.shape, info.dtype);\n        if (this.shouldCheckForMemLeaks()) {\n            // Track the number of moves during a kernel execution to correctly\n            // detect memory leaks.\n            this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;\n        }\n    }\n    tidy(nameOrFn, fn) {\n        let name = null;\n        if (fn == null) {\n            // Called with only 1 argument.\n            if (typeof nameOrFn !== 'function') {\n                throw new Error('Please provide a function to tidy()');\n            }\n            fn = nameOrFn;\n        }\n        else {\n            // Called with 2 arguments.\n            if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {\n                throw new Error('When calling with two arguments, the first argument ' +\n                    'to tidy() must be a string');\n            }\n            if (typeof fn !== 'function') {\n                throw new Error('When calling with two arguments, the 2nd argument ' +\n                    'to tidy() must be a function');\n            }\n            name = nameOrFn;\n            // TODO(nsthorat,smilkov): Do operation logging and performance\n            // profiling.\n        }\n        let result;\n        return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {\n            result = fn();\n            if (result instanceof Promise) {\n                console.error('Cannot return a Promise inside of tidy.');\n            }\n            return result;\n        });\n    }\n    scopedRun(start, end, f) {\n        start();\n        try {\n            const res = f();\n            end();\n            return res;\n        }\n        catch (ex) {\n            end();\n            throw ex;\n        }\n    }\n    nextTensorId() {\n        return Engine.nextTensorId++;\n    }\n    nextVariableId() {\n        return Engine.nextVariableId++;\n    }\n    /**\n     * This method is called instead of the public-facing tensor.clone() when\n     * saving a tensor for backwards pass. It makes sure to add the clone\n     * operation to the tape regardless of being called inside a kernel\n     * execution.\n     *\n     * This method will go away once all kernels are modularized since we won't\n     * need to turn off the tape inside runKernel().\n     */\n    clone(x) {\n        const y = this.makeTensorFromDataId(x.dataId, x.shape, x.dtype);\n        const inputs = { x };\n        const grad = (dy) => ({\n            x: () => {\n                const dtype = 'float32';\n                const gradInputs = { x: dy };\n                const attrs = { dtype };\n                return ENGINE.runKernelFunc(backend => backend.cast(dy, dtype), gradInputs, null /* grad */, Cast, attrs);\n            }\n        });\n        const saved = [];\n        this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {});\n        return y;\n    }\n    /**\n     * Execute a kernel with the given name and return the output tensor.\n     *\n     * @param kernelName The name of the kernel to execute.\n     * @param inputs A map of input names to tensors.\n     * @param attrs A map of attribute names to their values. An attribute is a\n     *     primitive (non-tensor) input to the kernel.\n     * @param inputsToSave A list of tensors, inputs to save for the backprop\n     *     computation.\n     * @param outputsToSave A list of booleans, specifying which output to save\n     *     for the backprop computation. These are booleans since the output\n     * tensors are not visible to the user.\n     */\n    runKernel(kernelName, inputs, attrs, inputsToSave, outputsToSave) {\n        const forwardFunc = null;\n        const backwardsFunc = null;\n        // Call runKernel as a stop-gap until we modularize all kernels.\n        // Once we modularize all kernels, we will remove the existing\n        // `runKernelFunc`.\n        return this.runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave);\n    }\n    shouldCheckForMemLeaks() {\n        return this.ENV.getBool('IS_TEST');\n    }\n    checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {\n        const numDataIdsAfter = this.backend.numDataIds();\n        // Count the number of data ids associated with the result of the kernel.\n        let numOutputDataIds = 0;\n        outInfos.forEach(info => {\n            // Complex numbers allocate 3 data ids, one for 'real', one for\n            // 'imaginary', and one for the container that holds the former two.\n            numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1);\n        });\n        // Account for the number of moves during kernel execution. A \"data move\"\n        // can happen in the middle of a kernel execution, placing a new (key,value)\n        // pair in the data storage. Since data moves have net zero effect (we\n        // always remove the data from the old backend), we have to cancel them out\n        // when detecting memory leaks.\n        const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];\n        const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;\n        if (dataIdsLeaked > 0) {\n            throw new Error(`Backend '${this.backendName}' has an internal memory leak ` +\n                `(${dataIdsLeaked} data ids) after running '${kernelName}'`);\n        }\n    }\n    /**\n     * @deprecated Use `runKernel` for newly added kernels. Keep using this method\n     *     only for kernels that are not yet fully modularized.\n     */\n    runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave) {\n        let outputs;\n        let saved = [];\n        const isTapeOn = this.isTapeOn();\n        if (kernelName == null) {\n            kernelName =\n                this.state.activeScope != null ? this.state.activeScope.name : '';\n        }\n        const startingBytecount = this.state.numBytes;\n        const startingNumTensors = this.state.numTensors;\n        if (this.shouldCheckForMemLeaks()) {\n            this.state.numDataMovesStack.push(0);\n        }\n        let kernelFunc;\n        const kernel = getKernel(kernelName, this.backendName);\n        let out;\n        if (kernel != null) {\n            kernelFunc = () => {\n                const numDataIdsBefore = this.backend.numDataIds();\n                out = kernel.kernelFunc({ inputs, attrs, backend: this.backend });\n                const outInfos = Array.isArray(out) ? out : [out];\n                if (this.shouldCheckForMemLeaks()) {\n                    this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);\n                }\n                const outTensors = outInfos.map(({ dataId, shape, dtype }) => this.makeTensorFromDataId(dataId, shape, dtype));\n                // Save the inputs and outputs.\n                // Do not save unless we are recording to the tape. Otherwise it would\n                // cause a mem leak since we would never run backprop, which disposes\n                // the kept tensors.\n                if (isTapeOn) {\n                    let tensorsToSave = this.getTensorsForGradient(kernelName, inputs, outTensors);\n                    if (tensorsToSave == null) {\n                        // Fallback for ops that call runKernelFunc and pass in\n                        // inputsToSave and outputsToSave. Currently this is the set of ops\n                        // with kernel support in the WASM backend. Once those ops and\n                        // respective gradients are modularised we can remove this path.\n                        if (outputsToSave == null) {\n                            outputsToSave = [];\n                        }\n                        const outsToSave = outTensors.filter((_, i) => outputsToSave[i]);\n                        tensorsToSave = (inputsToSave || []).slice().concat(outsToSave);\n                    }\n                    saved = this.saveTensorsForBackwardMode(tensorsToSave);\n                }\n                return outTensors;\n            };\n        }\n        else {\n            const saveFunc = (tensors) => {\n                // Do not save unless we are recording to the tape. Otherwise it would\n                // cause a mem leak since we would never run backprop, which disposes\n                // the kept tensors.\n                if (!isTapeOn) {\n                    return;\n                }\n                saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n            };\n            kernelFunc = () => {\n                const numDataIdsBefore = this.backend.numDataIds();\n                out = this.tidy(() => forwardFunc(this.backend, saveFunc));\n                const outs = (Array.isArray(out) ? out : [out]);\n                if (this.shouldCheckForMemLeaks()) {\n                    this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outs);\n                }\n                return outs;\n            };\n        }\n        // Stop recording to a tape when running a kernel.\n        let kernelProfile;\n        this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {\n            if (!this.ENV.getBool('DEBUG') && !this.state.profiling) {\n                outputs = kernelFunc();\n            }\n            else {\n                kernelProfile = this.profiler.profileKernel(kernelName, inputs, () => kernelFunc());\n                if (this.ENV.getBool('DEBUG')) {\n                    this.profiler.logKernelProfile(kernelProfile);\n                }\n                outputs = kernelProfile.outputs;\n            }\n        });\n        if (isTapeOn) {\n            this.addTapeNode(kernelName, inputs, outputs, backwardsFunc, saved, attrs);\n        }\n        if (this.state.profiling) {\n            this.state.activeProfile.kernels.push({\n                name: kernelName,\n                bytesAdded: this.state.numBytes - startingBytecount,\n                totalBytesSnapshot: this.state.numBytes,\n                tensorsAdded: this.state.numTensors - startingNumTensors,\n                totalTensorsSnapshot: this.state.numTensors,\n                inputShapes: Object.keys(inputs).map(key => inputs[key] != null ? inputs[key].shape : null),\n                outputShapes: outputs.map(item => item.shape),\n                kernelTimeMs: kernelProfile.timeMs,\n                extraInfo: kernelProfile.extraInfo\n            });\n        }\n        return (Array.isArray(out) ? outputs : outputs[0]);\n    }\n    /**\n     * Saves tensors used in forward mode for use in backward mode.\n     *\n     * @param tensors the list of tensors to save.\n     */\n    saveTensorsForBackwardMode(tensors) {\n        const saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n        return saved;\n    }\n    /**\n     * Returns a list of tensors to save for a given gradient calculation.\n     *\n     * Returns undefined if their is no registered gradient for this kernel in the\n     * gradient registry.\n     *\n     * @param kernelName name of kernel to look up gradient for.\n     * @param inputs a map of input tensors.\n     * @param outputs an array of output tensors from forward mode of kernel.\n     */\n    getTensorsForGradient(kernelName, inputs, outputs) {\n        const gradConfig = getGradient(kernelName);\n        if (gradConfig != null) {\n            const inputsToSave = gradConfig.inputsToSave || [];\n            const outputsToSave = gradConfig.outputsToSave || [];\n            // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs\n            // specified in inputsToSave will be saved.\n            let inputTensorsToSave;\n            if (gradConfig.saveAllInputs) {\n                util.assert(Array.isArray(inputs), () => 'saveAllInputs is true, expected inputs to be an array.');\n                inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);\n            }\n            else {\n                inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);\n            }\n            const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);\n            return inputTensorsToSave.concat(outputTensorsToSave);\n        }\n        // TODO(yassogba) throw exception here once all runkernelFunc calls with\n        // inputsToSave/outputsToSave are removed\n        return null;\n    }\n    /**\n     * Internal method used by public APIs for tensor creation. Makes a new\n     * tensor with the provided shape, dtype and values. It always\n     * creates a new data id and writes the values to the underlying backend.\n     */\n    makeTensor(values, shape, dtype, backend) {\n        if (values == null) {\n            throw new Error('Values passed to engine.makeTensor() are null');\n        }\n        dtype = dtype || 'float32';\n        backend = backend || this.backend;\n        let backendVals = values;\n        if (dtype === 'string' && util.isString(values[0])) {\n            backendVals = values.map(d => util.encodeString(d));\n        }\n        const dataId = backend.write(backendVals, shape, dtype);\n        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n        this.incRef(t, backend);\n        // Count bytes for string tensors.\n        if (dtype === 'string') {\n            const info = this.state.tensorInfo.get(dataId);\n            const newBytes = bytesFromStringArray(backendVals);\n            this.state.numBytes += newBytes - info.bytes;\n            info.bytes = newBytes;\n        }\n        return t;\n    }\n    /**\n     * Internal method used by backends. Makes a new tensor\n     * that is a wrapper around an existing data id. It doesn't create\n     * a new data id, only increments the ref count used in memory tracking.\n     */\n    makeTensorFromDataId(dataId, shape, dtype, backend) {\n        dtype = dtype || 'float32';\n        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n        this.incRef(t, backend);\n        return t;\n    }\n    makeVariable(initialValue, trainable = true, name, dtype) {\n        name = name || this.nextVariableId().toString();\n        if (dtype != null && dtype !== initialValue.dtype) {\n            initialValue = initialValue.cast(dtype);\n        }\n        const v = new Variable(initialValue, trainable, name, this.nextTensorId());\n        if (this.state.registeredVariables[v.name] != null) {\n            throw new Error(`Variable with name ${v.name} was already registered`);\n        }\n        this.state.registeredVariables[v.name] = v;\n        this.incRef(v, this.backend);\n        return v;\n    }\n    incRef(a, backend) {\n        const refCount = this.state.tensorInfo.has(a.dataId) ?\n            this.state.tensorInfo.get(a.dataId).refCount :\n            0;\n        this.state.numTensors++;\n        if (a.dtype === 'string') {\n            this.state.numStringTensors++;\n        }\n        if (refCount === 0) {\n            this.state.numDataBuffers++;\n            // Bytes for complex numbers are counted by their components. Bytes for\n            // string tensors are counted when writing values.\n            let bytes = 0;\n            if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n                bytes = a.size * util.bytesPerElement(a.dtype);\n            }\n            this.state.tensorInfo.set(a.dataId, {\n                backend: backend || this.backend,\n                dtype: a.dtype,\n                shape: a.shape,\n                bytes,\n                refCount: 0\n            });\n            this.state.numBytes += bytes;\n        }\n        this.state.tensorInfo.get(a.dataId).refCount++;\n        if (!(a instanceof Variable)) {\n            this.track(a);\n        }\n    }\n    disposeTensor(a) {\n        if (!this.state.tensorInfo.has(a.dataId)) {\n            return;\n        }\n        this.state.numTensors--;\n        if (a.dtype === 'string') {\n            this.state.numStringTensors--;\n        }\n        const info = this.state.tensorInfo.get(a.dataId);\n        const refCount = info.refCount;\n        if (refCount <= 1) {\n            // Don't count bytes for complex numbers as they are counted by their\n            // components.\n            if (a.dtype !== 'complex64') {\n                this.state.numBytes -= info.bytes;\n            }\n            this.state.numDataBuffers--;\n            info.backend.disposeData(a.dataId);\n            this.state.tensorInfo.delete(a.dataId);\n        }\n        else {\n            this.state.tensorInfo.get(a.dataId).refCount--;\n        }\n        // TODO(nsthorat): Construct an error and save the stack trace for\n        // debugging when in debug mode. Creating a stack trace is too expensive\n        // to do unconditionally.\n    }\n    disposeVariables() {\n        for (const varName in this.state.registeredVariables) {\n            const v = this.state.registeredVariables[varName];\n            this.disposeVariable(v);\n        }\n    }\n    disposeVariable(v) {\n        this.disposeTensor(v);\n        if (this.state.registeredVariables[v.name] != null) {\n            delete this.state.registeredVariables[v.name];\n        }\n    }\n    memory() {\n        const info = this.backend.memory();\n        info.numTensors = this.state.numTensors;\n        info.numDataBuffers = this.state.numDataBuffers;\n        info.numBytes = this.state.numBytes;\n        if (this.state.numStringTensors > 0) {\n            info.unreliable = true;\n            if (info.reasons == null) {\n                info.reasons = [];\n            }\n            info.reasons.push('Memory usage by string tensors is approximate ' +\n                '(2 bytes per character)');\n        }\n        return info;\n    }\n    async profile(query) {\n        this.state.profiling = true;\n        const startBytes = this.state.numBytes;\n        const startNumTensors = this.state.numTensors;\n        this.state.activeProfile.kernels = [];\n        this.state.activeProfile.result = await query();\n        this.state.profiling = false;\n        this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map(d => d.totalBytesSnapshot));\n        this.state.activeProfile.newBytes = this.state.numBytes - startBytes;\n        this.state.activeProfile.newTensors =\n            this.state.numTensors - startNumTensors;\n        for (const kernel of this.state.activeProfile.kernels) {\n            kernel.kernelTimeMs = await kernel.kernelTimeMs;\n            kernel.extraInfo = await kernel.extraInfo;\n        }\n        return this.state.activeProfile;\n    }\n    isTapeOn() {\n        return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;\n    }\n    addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {\n        const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved };\n        const gradConfig = getGradient(kernelName);\n        if (gradConfig != null) {\n            gradientsFunc = gradConfig.gradFunc;\n        }\n        if (gradientsFunc != null) {\n            tapeNode.gradient = (dys) => {\n                // TODO(smilkov): To optimize back-prop, pass dys that are not used in\n                // the backprop graph to the user as null instead of zeros\n                dys = dys.map((dy, i) => {\n                    if (dy == null) {\n                        const output = outputs[i];\n                        const vals = util.makeZerosTypedArray(output.size, output.dtype);\n                        return this.makeTensor(vals, output.shape, output.dtype);\n                    }\n                    return dy;\n                });\n                // Grad functions of ops with single outputs expect a dy, while ops\n                // with multiple outputs expect dys (array of dy).\n                return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);\n            };\n        }\n        this.state.activeTape.push(tapeNode);\n    }\n    keep(result) {\n        result.kept = true;\n        return result;\n    }\n    startTape() {\n        if (this.state.gradientDepth === 0) {\n            this.state.activeTape = [];\n        }\n        this.state.gradientDepth++;\n    }\n    endTape() {\n        this.state.gradientDepth--;\n    }\n    /**\n     * Start a scope. Use this with endScope() to achieve the same functionality\n     * as scope() without the need for a function closure.\n     */\n    startScope(name) {\n        const scopeInfo = {\n            track: [],\n            name: 'unnamed scope',\n            id: this.state.nextScopeId++\n        };\n        if (name) {\n            scopeInfo.name = name;\n        }\n        this.state.scopeStack.push(scopeInfo);\n        this.state.activeScope = scopeInfo;\n    }\n    /**\n     * End a scope. Use this with startScope() to achieve the same functionality\n     * as scope() without the need for a function closure.\n     */\n    endScope(result) {\n        const tensorsToTrackInParent = getTensorsInContainer(result);\n        const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map(t => t.id));\n        // Dispose the arrays tracked in this scope.\n        for (let i = 0; i < this.state.activeScope.track.length; i++) {\n            const tensor = this.state.activeScope.track[i];\n            if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) {\n                tensor.dispose();\n            }\n        }\n        const oldScope = this.state.scopeStack.pop();\n        this.state.activeScope = this.state.scopeStack.length === 0 ?\n            null :\n            this.state.scopeStack[this.state.scopeStack.length - 1];\n        // Track the current result in the parent scope.\n        tensorsToTrackInParent.forEach(tensor => {\n            // Only track the tensor if was allocated in the inner scope and is not\n            // globally kept.\n            if (!tensor.kept && tensor.scopeId === oldScope.id) {\n                this.track(tensor);\n            }\n        });\n    }\n    /**\n     * Returns gradients of `f` with respect to each of the `xs`. The gradients\n     * returned are of the same length as `xs`, but some might be null if `f`\n     * was not a function of that `x`. It also takes optional dy to multiply the\n     * gradient, which defaults to `1`.\n     */\n    gradients(f, xs, dy, allowNoGradients = false) {\n        util.assert(xs.length > 0, () => 'gradients() received an empty list of xs.');\n        if (dy != null && dy.dtype !== 'float32') {\n            throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n        }\n        const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy('forward', f));\n        util.assert(y instanceof Tensor, () => 'The result y returned by f() must be a tensor.');\n        // Filter out the nodes that don't connect x => y.\n        const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);\n        if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n            throw new Error('Cannot compute gradient of y=f(x) with respect to x. Make sure ' +\n                'that the f you passed encloses all operations that lead from x ' +\n                'to y.');\n        }\n        return this.tidy('backward', () => {\n            const accumulatedGradientMap = {};\n            accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;\n            // Backprop gradients through the filtered nodes.\n            backpropagateGradients(accumulatedGradientMap, filteredTape, \n            // Pass the tidy function to avoid circular dep with `tape.ts`.\n            f => this.tidy(f), \n            // Pass an add function to avoide a circular dep with `tape.ts`.\n            add);\n            const grads = xs.map(x => accumulatedGradientMap[x.id]);\n            if (this.state.gradientDepth === 0) {\n                // This means that we are not computing higher-order gradients\n                // and can clean up the tape.\n                this.state.activeTape.forEach(node => {\n                    for (const tensor of node.saved) {\n                        tensor.dispose();\n                    }\n                });\n                this.state.activeTape = null;\n            }\n            return { value: y, grads };\n        });\n    }\n    customGrad(f) {\n        util.assert(util.isFunction(f), () => 'The f passed in customGrad(f) must be a function.');\n        return (...inputs) => {\n            util.assert(inputs.every(t => t instanceof Tensor), () => 'The args passed in customGrad(f)(x1, x2,...) must all be ' +\n                'tensors');\n            let res;\n            const inputMap = {};\n            inputs.forEach((input, i) => {\n                inputMap[i] = input;\n            });\n            return this.runKernelFunc((_, save) => {\n                res = f(...[...inputs, save]);\n                util.assert(res.value instanceof Tensor, () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.value` is a tensor');\n                util.assert(util.isFunction(res.gradFunc), () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function.');\n                return res.value;\n            }, inputMap, (dy, saved) => {\n                const gradRes = res.gradFunc(dy, saved);\n                const grads = Array.isArray(gradRes) ? gradRes : [gradRes];\n                util.assert(grads.length === inputs.length, () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'the same number of tensors as inputs passed to f(...).');\n                util.assert(grads.every(t => t instanceof Tensor), () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'a list of only tensors.');\n                const gradMap = {};\n                grads.forEach((grad, i) => {\n                    gradMap[i] = () => grad;\n                });\n                return gradMap;\n            });\n        };\n    }\n    readSync(dataId) {\n        // Route the read to the correct backend.\n        const info = this.state.tensorInfo.get(dataId);\n        return info.backend.readSync(dataId);\n    }\n    read(dataId) {\n        // Route the read to the correct backend.\n        const info = this.state.tensorInfo.get(dataId);\n        return info.backend.read(dataId);\n    }\n    async time(query) {\n        const start = now();\n        const timingInfo = await this.backend.time(query);\n        timingInfo.wallMs = now() - start;\n        return timingInfo;\n    }\n    /**\n     * Tracks a Tensor in the current scope to be automatically cleaned up\n     * when the current scope ends, and returns the value.\n     *\n     * @param result The Tensor to track in the current scope.\n     */\n    track(result) {\n        if (this.state.activeScope != null) {\n            result.scopeId = this.state.activeScope.id;\n            this.state.activeScope.track.push(result);\n        }\n        return result;\n    }\n    get registeredVariables() {\n        return this.state.registeredVariables;\n    }\n    /**\n     * Resets the engine state. Removes all backends but does not remove\n     * registered backend factories.\n     */\n    reset() {\n        // Make any pending promise obsolete.\n        this.pendingBackendInitId++;\n        this.state.dispose();\n        this.ENV.reset();\n        this.state = new EngineState();\n        for (const backendName in this.registry) {\n            this.disposeRegisteredKernels(backendName);\n            this.registry[backendName].dispose();\n            delete this.registry[backendName];\n        }\n        this.backendName = null;\n        this.backendInstance = null;\n        this.pendingBackendInit = null;\n    }\n}\nEngine.nextTensorId = 0;\nEngine.nextVariableId = 0;\nfunction ones(shape) {\n    const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');\n    return ENGINE.makeTensor(values, shape, 'float32');\n}\nexport function getOrMakeEngine() {\n    const ns = getGlobalNamespace();\n    if (ns._tfengine == null) {\n        const environment = new Environment(ns);\n        ns._tfengine = new Engine(environment);\n    }\n    setEnvironmentGlobal(ns._tfengine.ENV);\n    // Tell the current tensor interface that the global engine is responsible\n    // for tracking.\n    setTensorTracker(() => ns._tfengine);\n    return ns._tfengine;\n}\nexport const ENGINE = getOrMakeEngine();\n/**\n * A implementation of the add op for use within engine and tape.\n *\n * This allows us to avoid a circular dependency between add.ts and engine.\n * It is exported to be available in tape tests.\n */\nexport function add(a, b) {\n    // We duplicate Add here to avoid a circular dependency with add.ts.\n    const inputs = { a, b };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.add(a, b);\n        save([a, b]);\n        return res;\n    }, inputs, null /* gradient */, Add);\n}\n//# sourceMappingURL=engine.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line:no-any\nfunction _isNavigatorDefined() {\n    return typeof navigator !== 'undefined' && navigator != null;\n}\nexport function isMobile() {\n    if (_isNavigatorDefined()) {\n        // tslint:disable-next-line:no-any\n        const a = navigator.userAgent || navigator.vendor || window.opera;\n        // tslint:disable-next-line:max-line-length\n        return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n            .test(a) ||\n            // tslint:disable-next-line:max-line-length\n            /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i\n                .test(a.substr(0, 4));\n    }\n    return false;\n}\nexport function isBrowser() {\n    return (typeof window !== 'undefined' && window.document != null) ||\n        //@ts-ignore\n        (typeof WorkerGlobalScope !== 'undefined');\n}\n//# sourceMappingURL=device_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './engine';\nimport * as device_util from './device_util';\nimport { env } from './environment';\nconst ENV = env();\n/**\n * This file contains environment-related flag registrations.\n */\n/** Whether to enable debug mode. */\nENV.registerFlag('DEBUG', () => false, debugValue => {\n    if (debugValue) {\n        console.warn('Debugging mode is ON. The output of every math call will ' +\n            'be downloaded to CPU and checked for NaNs. ' +\n            'This significantly impacts performance.');\n    }\n});\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_BROWSER', () => device_util.isBrowser());\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_NODE', () => (typeof process !== 'undefined') &&\n    (typeof process.versions !== 'undefined') &&\n    (typeof process.versions.node !== 'undefined'));\n/** Whether this browser is Chrome. */\nENV.registerFlag('IS_CHROME', () => typeof navigator !== 'undefined' && navigator != null &&\n    navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&\n    /Google Inc/.test(navigator.vendor));\n/**\n * True when the environment is \"production\" where we disable safety checks\n * to gain performance.\n */\nENV.registerFlag('PROD', () => false);\n/**\n * Whether to do sanity checks when inferring a shape from user-provided\n * values, used when creating a new tensor.\n */\nENV.registerFlag('TENSORLIKE_CHECK_SHAPE_CONSISTENCY', () => ENV.getBool('DEBUG'));\n/** Whether deprecation warnings are enabled. */\nENV.registerFlag('DEPRECATION_WARNINGS_ENABLED', () => true);\n/** True if running unit tests. */\nENV.registerFlag('IS_TEST', () => false);\n//# sourceMappingURL=flags.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { env } from './environment';\nimport { Tensor } from './tensor';\nimport { assert, flatten, inferDtype, isTypedArray, toTypedArray } from './util';\nexport function inferShape(val, dtype) {\n    let firstElem = val;\n    if (isTypedArray(val)) {\n        return dtype === 'string' ? [] : [val.length];\n    }\n    if (!Array.isArray(val)) {\n        return []; // Scalar.\n    }\n    const shape = [];\n    while (Array.isArray(firstElem) ||\n        isTypedArray(firstElem) && dtype !== 'string') {\n        shape.push(firstElem.length);\n        firstElem = firstElem[0];\n    }\n    if (Array.isArray(val) &&\n        env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {\n        deepAssertShapeConsistency(val, shape, []);\n    }\n    return shape;\n}\nfunction deepAssertShapeConsistency(val, shape, indices) {\n    indices = indices || [];\n    if (!(Array.isArray(val)) && !isTypedArray(val)) {\n        assert(shape.length === 0, () => `Element arr[${indices.join('][')}] is a primitive, ` +\n            `but should be an array/TypedArray of ${shape[0]} elements`);\n        return;\n    }\n    assert(shape.length > 0, () => `Element arr[${indices.join('][')}] should be a primitive, ` +\n        `but is an array of ${val.length} elements`);\n    assert(val.length === shape[0], () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +\n        `elements, but has ${val.length} elements`);\n    const subShape = shape.slice(1);\n    for (let i = 0; i < val.length; ++i) {\n        deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n    }\n}\nfunction assertDtype(expectedDtype, actualDType, argName, functionName) {\n    if (expectedDtype == null) {\n        return;\n    }\n    if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||\n        expectedDtype === 'numeric' && actualDType === 'string') {\n        throw new Error(`Argument '${argName}' passed to '${functionName}' must ` +\n            `be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n    }\n}\nexport function convertToTensor(x, argName, functionName, parseAsDtype = 'numeric') {\n    if (x instanceof Tensor) {\n        assertDtype(parseAsDtype, x.dtype, argName, functionName);\n        return x;\n    }\n    let inferredDtype = inferDtype(x);\n    // If the user expects a bool/int/float, use that info to update the\n    // inferredDtype when it is not a string.\n    if (inferredDtype !== 'string' &&\n        ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {\n        inferredDtype = parseAsDtype;\n    }\n    assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n    if ((x == null) ||\n        (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&\n            typeof x !== 'boolean' && typeof x !== 'string')) {\n        const type = x == null ? 'null' : x.constructor.name;\n        throw new Error(`Argument '${argName}' passed to '${functionName}' must be a ` +\n            `Tensor or TensorLike, but got '${type}'`);\n    }\n    const inferredShape = inferShape(x, inferredDtype);\n    if (!isTypedArray(x) && !Array.isArray(x)) {\n        x = [x];\n    }\n    const skipTypedArray = true;\n    const values = inferredDtype !== 'string' ?\n        toTypedArray(x, inferredDtype) :\n        flatten(x, [], skipTypedArray);\n    return ENGINE.makeTensor(values, inferredShape, inferredDtype);\n}\nexport function convertToTensorArray(arg, argName, functionName, parseAsDtype = 'numeric') {\n    if (!Array.isArray(arg)) {\n        throw new Error(`Argument ${argName} passed to ${functionName} must be a ` +\n            '`Tensor[]` or `TensorLike[]`');\n    }\n    const tensors = arg;\n    return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName), parseAsDtype);\n}\n//# sourceMappingURL=tensor_util_env.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nexport const OP_SCOPE_SUFFIX = '__op';\n/**\n * Used for wrapping functions that perform math operations on\n * Tensors. The function will be wrapped in a named scope that cleans all\n * memory usage after the function is done.\n */\nexport function op(f) {\n    const keys = Object.keys(f);\n    if (keys.length !== 1) {\n        throw new Error(`Please provide an object with a single key ` +\n            `(operation name) mapping to a function. Got an object with ` +\n            `${keys.length} keys.`);\n    }\n    let opName = keys[0];\n    const fn = f[opName];\n    // Strip the underscore from the end of the function name.\n    if (opName.endsWith('_')) {\n        opName = opName.substring(0, opName.length - 1);\n    }\n    // add an __op suffix to distinguish ops from kernels in tf.profile\n    opName = opName + OP_SCOPE_SUFFIX;\n    // tslint:disable-next-line:no-any\n    const f2 = (...args) => {\n        ENGINE.startScope(opName);\n        try {\n            const result = fn(...args);\n            if (result instanceof Promise) {\n                console.error('Cannot return a Promise inside of tidy.');\n            }\n            ENGINE.endScope(result);\n            return result;\n        }\n        catch (ex) {\n            ENGINE.endScope(null);\n            throw ex;\n        }\n    };\n    Object.defineProperty(f2, 'name', { value: opName, configurable: true });\n    // tslint:disable-next-line:no-any\n    return f2;\n}\n//# sourceMappingURL=operation.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Complex } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction complex_(real, imag) {\n    const $real = convertToTensor(real, 'real', 'complex');\n    const $imag = convertToTensor(imag, 'imag', 'complex');\n    util.assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n        `must match in call to tf.complex().`);\n    const forward = (backend) => {\n        return backend.complex($real, $imag);\n    };\n    const inputs = { real: $real, imag: $imag };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Complex);\n}\nexport const complex = op({ complex_ });\n//# sourceMappingURL=complex.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { assert, assertNonNegativeIntegerDimensions, flatten, inferDtype, isTypedArray, sizeFromShape, toTypedArray } from '../util';\n/** This is shared code across all tensor creation methods. */\nexport function makeTensor(values, shape, inferredShape, dtype) {\n    if (dtype == null) {\n        dtype = inferDtype(values);\n    }\n    if (dtype === 'complex64') {\n        throw new Error(`Cannot construct a complex64 tensor directly. ` +\n            `Please use tf.complex(real, imag).`);\n    }\n    if (!isTypedArray(values) && !Array.isArray(values) &&\n        typeof values !== 'number' && typeof values !== 'boolean' &&\n        typeof values !== 'string') {\n        throw new Error('values passed to tensor(values) must be a number/boolean/string or ' +\n            'an array of numbers/booleans/strings, or a TypedArray');\n    }\n    if (shape != null) {\n        assertNonNegativeIntegerDimensions(shape);\n        const providedSize = sizeFromShape(shape);\n        const inferredSize = sizeFromShape(inferredShape);\n        assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ` +\n            `${providedSize} values but has ${inferredSize}`);\n        for (let i = 0; i < inferredShape.length; ++i) {\n            const inferred = inferredShape[i];\n            const flatDimsDontMatch = i === inferredShape.length - 1 ?\n                inferred !== sizeFromShape(shape.slice(i)) :\n                true;\n            assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape ` +\n                `(${inferredShape}) does not match the provided ` +\n                `shape (${shape}). `);\n        }\n    }\n    if (!isTypedArray(values) && !Array.isArray(values)) {\n        values = [values];\n    }\n    shape = shape || inferredShape;\n    values = dtype !== 'string' ?\n        toTypedArray(values, dtype) :\n        flatten(values, [], true);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=tensor_ops_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`. If the values are strings,\n *     they will be encoded as utf-8 and kept as `Uint8Array[]`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor(values, shape, dtype) {\n    const inferredShape = inferShape(values, dtype);\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/* Type definitions for exporting and importing of models. */\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP = {\n    'float32': 4,\n    'float16': 2,\n    'int32': 4,\n    'uint16': 2,\n    'uint8': 1,\n    'bool': 1,\n    'complex64': 8\n};\n//# sourceMappingURL=types.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../ops/complex';\nimport { tensor } from '../ops/tensor';\nimport { sizeFromShape } from '../util';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/** Number of bytes reserved for the length of the string. (32bit integer). */\nconst NUM_BYTES_STRING_LENGTH = 4;\n/**\n * Encode a map from names to weight values as an ArrayBuffer, along with an\n * `Array` of `WeightsManifestEntry` as specification of the encoded weights.\n *\n * This function does not perform sharding.\n *\n * This function is the reverse of `decodeWeights`.\n *\n * @param tensors A map (\"dict\") from names to tensors.\n * @param group Group to which the weights belong (optional).\n * @returns A `Promise` of\n *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s\n *     concatenated.\n *   - An `Array` of `WeightManifestEntry`s, carrying information including\n *     tensor names, `dtype`s and shapes.\n * @throws Error: on unsupported tensor `dtype`.\n */\nexport async function encodeWeights(tensors, group) {\n    // TODO(adarob, cais): Support quantization.\n    const specs = [];\n    const dataPromises = [];\n    const names = Array.isArray(tensors) ?\n        tensors.map(tensor => tensor.name) :\n        Object.keys(tensors);\n    for (let i = 0; i < names.length; ++i) {\n        const name = names[i];\n        const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];\n        if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool' &&\n            t.dtype !== 'string' && t.dtype !== 'complex64') {\n            throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n        }\n        const spec = { name, shape: t.shape, dtype: t.dtype };\n        if (t.dtype === 'string') {\n            const utf8bytes = new Promise(async (resolve) => {\n                const vals = await t.bytes();\n                const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) +\n                    NUM_BYTES_STRING_LENGTH * vals.length;\n                const bytes = new Uint8Array(totalNumBytes);\n                let offset = 0;\n                for (let i = 0; i < vals.length; i++) {\n                    const val = vals[i];\n                    const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);\n                    bytes.set(bytesOfLength, offset);\n                    offset += NUM_BYTES_STRING_LENGTH;\n                    bytes.set(val, offset);\n                    offset += val.length;\n                }\n                resolve(bytes);\n            });\n            dataPromises.push(utf8bytes);\n        }\n        else {\n            dataPromises.push(t.data());\n        }\n        if (group != null) {\n            spec.group = group;\n        }\n        specs.push(spec);\n    }\n    const tensorValues = await Promise.all(dataPromises);\n    return { data: concatenateTypedArrays(tensorValues), specs };\n}\n/**\n * Decode flat ArrayBuffer as weights.\n *\n * This function does not handle sharding.\n *\n * This function is the reverse of `encodeWeights`.\n *\n * @param buffer A flat ArrayBuffer carrying the binary values of the tensors\n *   concatenated in the order specified in `specs`.\n * @param specs Specifications of the names, dtypes and shapes of the tensors\n *   whose value are encoded by `buffer`.\n * @return A map from tensor name to tensor value, with the names corresponding\n *   to names in `specs`.\n * @throws Error, if any of the tensors has unsupported dtype.\n */\nexport function decodeWeights(buffer, specs) {\n    // TODO(adarob, cais): Support quantization.\n    const out = {};\n    let float16Decode;\n    let offset = 0;\n    for (const spec of specs) {\n        const name = spec.name;\n        const dtype = spec.dtype;\n        const shape = spec.shape;\n        const size = sizeFromShape(shape);\n        let values;\n        if ('quantization' in spec) {\n            const quantization = spec.quantization;\n            if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n                if (!('min' in quantization && 'scale' in quantization)) {\n                    throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} ` +\n                        `doesn't have corresponding metadata min and scale.`);\n                }\n            }\n            else if (quantization.dtype === 'float16') {\n                if (dtype !== 'float32') {\n                    throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} ` +\n                        `which only supports weights of type float32 not ${dtype}.`);\n                }\n            }\n            else {\n                throw new Error(`Weight ${spec.name} has unknown ` +\n                    `quantization dtype ${quantization.dtype}. ` +\n                    `Supported quantization dtypes are: ` +\n                    `'uint8', 'uint16', and 'float16'.`);\n            }\n            const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n            const byteBuffer = buffer.slice(offset, offset + size * quantizationSizeFactor);\n            const quantizedArray = (quantization.dtype === 'uint8') ?\n                new Uint8Array(byteBuffer) :\n                new Uint16Array(byteBuffer);\n            if (dtype === 'float32') {\n                if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n                    values = new Float32Array(quantizedArray.length);\n                    for (let i = 0; i < quantizedArray.length; i++) {\n                        const v = quantizedArray[i];\n                        values[i] = v * quantization.scale + quantization.min;\n                    }\n                }\n                else if (quantization.dtype === 'float16') {\n                    if (float16Decode === undefined) {\n                        float16Decode = getFloat16Decoder();\n                    }\n                    values = float16Decode(quantizedArray);\n                }\n                else {\n                    throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n                        `for weight type float32.`);\n                }\n            }\n            else if (dtype === 'int32') {\n                if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {\n                    throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n                        `for weight type int32.`);\n                }\n                values = new Int32Array(quantizedArray.length);\n                for (let i = 0; i < quantizedArray.length; i++) {\n                    const v = quantizedArray[i];\n                    values[i] = Math.round(v * quantization.scale + quantization.min);\n                }\n            }\n            else {\n                throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n            }\n            offset += size * quantizationSizeFactor;\n        }\n        else if (dtype === 'string') {\n            const size = sizeFromShape(spec.shape);\n            values = [];\n            for (let i = 0; i < size; i++) {\n                const byteLength = new Uint32Array(buffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];\n                offset += NUM_BYTES_STRING_LENGTH;\n                const bytes = new Uint8Array(buffer.slice(offset, offset + byteLength));\n                values.push(bytes);\n                offset += byteLength;\n            }\n        }\n        else {\n            const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n            const byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);\n            if (dtype === 'float32') {\n                values = new Float32Array(byteBuffer);\n            }\n            else if (dtype === 'int32') {\n                values = new Int32Array(byteBuffer);\n            }\n            else if (dtype === 'bool') {\n                values = new Uint8Array(byteBuffer);\n            }\n            else if (dtype === 'complex64') {\n                values = new Float32Array(byteBuffer);\n                const real = new Float32Array(values.length / 2);\n                const image = new Float32Array(values.length / 2);\n                for (let i = 0; i < real.length; i++) {\n                    real[i] = values[i * 2];\n                    image[i] = values[i * 2 + 1];\n                }\n                const realTensor = tensor(real, shape, 'float32');\n                const imageTensor = tensor(image, shape, 'float32');\n                out[name] = complex(realTensor, imageTensor);\n            }\n            else {\n                throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n            }\n            offset += size * dtypeFactor;\n        }\n        if (dtype !== 'complex64') {\n            out[name] = tensor(values, shape, dtype);\n        }\n    }\n    return out;\n}\n/**\n * Concatenate TypedArrays into an ArrayBuffer.\n */\nexport function concatenateTypedArrays(xs) {\n    // TODO(adarob, cais): Support quantization.\n    if (xs === null) {\n        throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n    }\n    let totalByteLength = 0;\n    // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'\n    // can have a different byte length from that of the `TypedArray` itself,\n    // for example, when the `TypedArray` is created from an offset in an\n    // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match\n    // the `TypedArray` in byte length. If an element of `xs` does not show\n    // this property, a new `TypedArray` that satisfy this property will be\n    // constructed and pushed into `normalizedXs`.\n    const normalizedXs = [];\n    xs.forEach((x) => {\n        totalByteLength += x.byteLength;\n        // tslint:disable:no-any\n        normalizedXs.push(x.byteLength === x.buffer.byteLength ? x :\n            new x.constructor(x));\n        if (!(x instanceof Float32Array || x instanceof Int32Array ||\n            x instanceof Uint8Array)) {\n            throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n        }\n        // tslint:enable:no-any\n    });\n    const y = new Uint8Array(totalByteLength);\n    let offset = 0;\n    normalizedXs.forEach((x) => {\n        y.set(new Uint8Array(x.buffer), offset);\n        offset += x.byteLength;\n    });\n    return y.buffer;\n}\n// Use Buffer on Node.js instead of Blob/atob/btoa\nconst useNodeBuffer = typeof Buffer !== 'undefined' &&\n    (typeof Blob === 'undefined' || typeof atob === 'undefined' ||\n        typeof btoa === 'undefined');\n/**\n * Calculate the byte length of a JavaScript string.\n *\n * Note that a JavaScript string can contain wide characters, therefore the\n * length of the string is not necessarily equal to the byte length.\n *\n * @param str Input string.\n * @returns Byte length.\n */\nexport function stringByteLength(str) {\n    if (useNodeBuffer) {\n        return Buffer.byteLength(str);\n    }\n    return new Blob([str]).size;\n}\n/**\n * Encode an ArrayBuffer as a base64 encoded string.\n *\n * @param buffer `ArrayBuffer` to be converted.\n * @returns A string that base64-encodes `buffer`.\n */\nexport function arrayBufferToBase64String(buffer) {\n    if (useNodeBuffer) {\n        return Buffer.from(buffer).toString('base64');\n    }\n    const buf = new Uint8Array(buffer);\n    let s = '';\n    for (let i = 0, l = buf.length; i < l; i++) {\n        s += String.fromCharCode(buf[i]);\n    }\n    return btoa(s);\n}\n/**\n * Decode a base64 string as an ArrayBuffer.\n *\n * @param str Base64 string.\n * @returns Decoded `ArrayBuffer`.\n */\nexport function base64StringToArrayBuffer(str) {\n    if (useNodeBuffer) {\n        const buf = Buffer.from(str, 'base64');\n        return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n    }\n    const s = atob(str);\n    const buffer = new Uint8Array(s.length);\n    for (let i = 0; i < s.length; ++i) {\n        buffer.set([s.charCodeAt(i)], i);\n    }\n    return buffer.buffer;\n}\n/**\n * Concatenate a number of ArrayBuffers into one.\n *\n * @param buffers A number of array buffers to concatenate.\n * @returns Result of concatenating `buffers` in order.\n */\nexport function concatenateArrayBuffers(buffers) {\n    if (buffers.length === 1) {\n        return buffers[0];\n    }\n    let totalByteLength = 0;\n    buffers.forEach((buffer) => {\n        totalByteLength += buffer.byteLength;\n    });\n    const temp = new Uint8Array(totalByteLength);\n    let offset = 0;\n    buffers.forEach((buffer) => {\n        temp.set(new Uint8Array(buffer), offset);\n        offset += buffer.byteLength;\n    });\n    return temp.buffer;\n}\n/**\n * Get the basename of a path.\n *\n * Behaves in a way analogous to Linux's basename command.\n *\n * @param path\n */\nexport function basename(path) {\n    const SEPARATOR = '/';\n    path = path.trim();\n    while (path.endsWith(SEPARATOR)) {\n        path = path.slice(0, path.length - 1);\n    }\n    const items = path.split(SEPARATOR);\n    return items[items.length - 1];\n}\n/**\n * Populate ModelArtifactsInfo fields for a model with JSON topology.\n * @param modelArtifacts\n * @returns A ModelArtifactsInfo object.\n */\nexport function getModelArtifactsInfoForJSON(modelArtifacts) {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n        throw new Error('Expected JSON model topology, received ArrayBuffer.');\n    }\n    return {\n        dateSaved: new Date(),\n        modelTopologyType: 'JSON',\n        modelTopologyBytes: modelArtifacts.modelTopology == null ?\n            0 :\n            stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n        weightSpecsBytes: modelArtifacts.weightSpecs == null ?\n            0 :\n            stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n        weightDataBytes: modelArtifacts.weightData == null ?\n            0 :\n            modelArtifacts.weightData.byteLength,\n    };\n}\n/**\n * Computes mantisa table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 2048 mantissa lookup values.\n */\nfunction computeFloat16MantisaTable() {\n    const convertMantissa = (i) => {\n        let m = i << 13;\n        let e = 0;\n        while ((m & 0x00800000) === 0) {\n            e -= 0x00800000;\n            m <<= 1;\n        }\n        m &= ~0x00800000;\n        e += 0x38800000;\n        return m | e;\n    };\n    const mantisaTable = new Uint32Array(2048);\n    mantisaTable[0] = 0;\n    for (let i = 1; i < 1024; i++) {\n        mantisaTable[i] = convertMantissa(i);\n    }\n    for (let i = 1024; i < 2048; i++) {\n        mantisaTable[i] = 0x38000000 + ((i - 1024) << 13);\n    }\n    return mantisaTable;\n}\n/**\n * Computes exponent table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 64 exponent lookup values.\n */\nfunction computeFloat16ExponentTable() {\n    const exponentTable = new Uint32Array(64);\n    exponentTable[0] = 0;\n    exponentTable[31] = 0x47800000;\n    exponentTable[32] = 0x80000000;\n    exponentTable[63] = 0xc7800000;\n    for (let i = 1; i < 31; i++) {\n        exponentTable[i] = i << 23;\n    }\n    for (let i = 33; i < 63; i++) {\n        exponentTable[i] = 0x80000000 + ((i - 32) << 23);\n    }\n    return exponentTable;\n}\n/**\n * Computes offset table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 6d offset values.\n */\nfunction computeFloat16OffsetTable() {\n    const offsetTable = new Uint32Array(64);\n    for (let i = 0; i < 64; i++) {\n        offsetTable[i] = 1024;\n    }\n    offsetTable[0] = offsetTable[32] = 0;\n    return offsetTable;\n}\n/**\n * Retrieve a Float16 decoder which will decode a ByteArray of Float16 values\n * to a Float32Array.\n *\n * @returns Function (buffer: Uint16Array) => Float32Array which decodes\n *          the Uint16Array of Float16 bytes to a Float32Array.\n */\nexport function getFloat16Decoder() {\n    // Algorithm is based off of\n    // http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n    // Cache lookup tables\n    const mantisaTable = computeFloat16MantisaTable();\n    const exponentTable = computeFloat16ExponentTable();\n    const offsetTable = computeFloat16OffsetTable();\n    return (quantizedArray) => {\n        const buffer = new ArrayBuffer(4 * quantizedArray.length);\n        const bufferUint32View = new Uint32Array(buffer);\n        for (let index = 0; index < quantizedArray.length; index++) {\n            const float16Bits = quantizedArray[index];\n            const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 0x3ff)] +\n                exponentTable[float16Bits >> 10];\n            bufferUint32View[index] = float32Bits;\n        }\n        return new Float32Array(buffer);\n    };\n}\n//# sourceMappingURL=io_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class IORouterRegistry {\n    constructor() {\n        this.saveRouters = [];\n        this.loadRouters = [];\n    }\n    static getInstance() {\n        if (IORouterRegistry.instance == null) {\n            IORouterRegistry.instance = new IORouterRegistry();\n        }\n        return IORouterRegistry.instance;\n    }\n    /**\n     * Register a save-handler router.\n     *\n     * @param saveRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `save` method defined or `null`.\n     */\n    static registerSaveRouter(saveRouter) {\n        IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n    }\n    /**\n     * Register a load-handler router.\n     *\n     * @param loadRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `load` method defined or `null`.\n     */\n    static registerLoadRouter(loadRouter) {\n        IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n    }\n    /**\n     * Look up IOHandler for saving, given a URL-like string.\n     *\n     * @param url\n     * @returns If only one match is found, an instance of IOHandler with the\n     * `save` method defined. If no match is found, `null`.\n     * @throws Error, if more than one match is found.\n     */\n    static getSaveHandlers(url) {\n        return IORouterRegistry.getHandlers(url, 'save');\n    }\n    /**\n     * Look up IOHandler for loading, given a URL-like string.\n     *\n     * @param url\n     * @param loadOptions Optional, custom load options.\n     * @returns All valid handlers for `url`, given the currently registered\n     *   handler routers.\n     */\n    static getLoadHandlers(url, loadOptions) {\n        return IORouterRegistry.getHandlers(url, 'load', loadOptions);\n    }\n    static getHandlers(url, handlerType, loadOptions) {\n        const validHandlers = [];\n        const routers = handlerType === 'load' ?\n            IORouterRegistry.getInstance().loadRouters :\n            IORouterRegistry.getInstance().saveRouters;\n        routers.forEach(router => {\n            const handler = router(url, loadOptions);\n            if (handler !== null) {\n                validHandlers.push(handler);\n            }\n        });\n        return validHandlers;\n    }\n}\nexport const registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);\nexport const registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);\nexport const getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);\nexport const getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);\n//# sourceMappingURL=router_registry.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DATABASE_NAME = 'tensorflowjs';\nconst DATABASE_VERSION = 1;\n// Model data and ModelArtifactsInfo (metadata) are stored in two separate\n// stores for efficient access of the list of stored models and their metadata.\n// 1. The object store for model data: topology, weights and weight manifests.\nconst MODEL_STORE_NAME = 'models_store';\n// 2. The object store for ModelArtifactsInfo, including meta-information such\n//    as the type of topology (JSON vs binary), byte size of the topology, byte\n//    size of the weights, etc.\nconst INFO_STORE_NAME = 'model_info_store';\n/**\n * Delete the entire database for tensorflow.js, including the models store.\n */\nexport async function deleteDatabase() {\n    const idbFactory = getIndexedDBFactory();\n    return new Promise((resolve, reject) => {\n        const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);\n        deleteRequest.onsuccess = () => resolve();\n        deleteRequest.onerror = error => reject(error);\n    });\n}\nfunction getIndexedDBFactory() {\n    if (!env().getBool('IS_BROWSER')) {\n        // TODO(cais): Add more info about what IOHandler subtypes are available.\n        //   Maybe point to a doc page on the web and/or automatically determine\n        //   the available IOHandlers and print them in the error message.\n        throw new Error('Failed to obtain IndexedDB factory because the current environment' +\n            'is not a web browser.');\n    }\n    // tslint:disable-next-line:no-any\n    const theWindow = typeof window === 'undefined' ? self : window;\n    const factory = theWindow.indexedDB || theWindow.mozIndexedDB ||\n        theWindow.webkitIndexedDB || theWindow.msIndexedDB ||\n        theWindow.shimIndexedDB;\n    if (factory == null) {\n        throw new Error('The current browser does not appear to support IndexedDB.');\n    }\n    return factory;\n}\nfunction setUpDatabase(openRequest) {\n    const db = openRequest.result;\n    db.createObjectStore(MODEL_STORE_NAME, { keyPath: 'modelPath' });\n    db.createObjectStore(INFO_STORE_NAME, { keyPath: 'modelPath' });\n}\n/**\n * IOHandler subclass: Browser IndexedDB.\n *\n * See the doc string of `browserIndexedDB` for more details.\n */\nexport class BrowserIndexedDB {\n    constructor(modelPath) {\n        this.indexedDB = getIndexedDBFactory();\n        if (modelPath == null || !modelPath) {\n            throw new Error('For IndexedDB, modelPath must not be null, undefined or empty.');\n        }\n        this.modelPath = modelPath;\n    }\n    async save(modelArtifacts) {\n        // TODO(cais): Support saving GraphDef models.\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        return this.databaseAction(this.modelPath, modelArtifacts);\n    }\n    async load() {\n        return this.databaseAction(this.modelPath);\n    }\n    /**\n     * Perform database action to put model artifacts into or read model artifacts\n     * from IndexedDB object store.\n     *\n     * Whether the action is put or get depends on whether `modelArtifacts` is\n     * specified. If it is specified, the action will be put; otherwise the action\n     * will be get.\n     *\n     * @param modelPath A unique string path for the model.\n     * @param modelArtifacts If specified, it will be the model artifacts to be\n     *   stored in IndexedDB.\n     * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`\n     *   of `ModelArtifacts`, if the action is get.\n     */\n    databaseAction(modelPath, modelArtifacts) {\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                if (modelArtifacts == null) {\n                    // Read model out from object store.\n                    const modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');\n                    const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                    const getRequest = modelStore.get(this.modelPath);\n                    getRequest.onsuccess = () => {\n                        if (getRequest.result == null) {\n                            db.close();\n                            return reject(new Error(`Cannot find model with path '${this.modelPath}' ` +\n                                `in IndexedDB.`));\n                        }\n                        else {\n                            resolve(getRequest.result.modelArtifacts);\n                        }\n                    };\n                    getRequest.onerror = error => {\n                        db.close();\n                        return reject(getRequest.error);\n                    };\n                    modelTx.oncomplete = () => db.close();\n                }\n                else {\n                    // Put model into object store.\n                    const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n                    // First, put ModelArtifactsInfo into info store.\n                    const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n                    let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                    const putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo });\n                    let modelTx;\n                    putInfoRequest.onsuccess = () => {\n                        // Second, put model data into model store.\n                        modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n                        const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                        const putModelRequest = modelStore.put({\n                            modelPath: this.modelPath,\n                            modelArtifacts,\n                            modelArtifactsInfo\n                        });\n                        putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo });\n                        putModelRequest.onerror = error => {\n                            // If the put-model request fails, roll back the info entry as\n                            // well.\n                            infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                            const deleteInfoRequest = infoStore.delete(this.modelPath);\n                            deleteInfoRequest.onsuccess = () => {\n                                db.close();\n                                return reject(putModelRequest.error);\n                            };\n                            deleteInfoRequest.onerror = error => {\n                                db.close();\n                                return reject(putModelRequest.error);\n                            };\n                        };\n                    };\n                    putInfoRequest.onerror = error => {\n                        db.close();\n                        return reject(putInfoRequest.error);\n                    };\n                    infoTx.oncomplete = () => {\n                        if (modelTx == null) {\n                            db.close();\n                        }\n                        else {\n                            modelTx.oncomplete = () => db.close();\n                        }\n                    };\n                }\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n}\nBrowserIndexedDB.URL_SCHEME = 'indexeddb://';\nexport const indexedDBRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n            return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\n/**\n * Creates a browser IndexedDB IOHandler for saving and loading models.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save('indexeddb://MyModel'));\n * console.log(saveResult);\n * ```\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),\n *   which can be used with, e.g., `tf.Model.save`.\n */\nexport function browserIndexedDB(modelPath) {\n    return new BrowserIndexedDB(modelPath);\n}\nfunction maybeStripScheme(key) {\n    return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?\n        key.slice(BrowserIndexedDB.URL_SCHEME.length) :\n        key;\n}\nexport class BrowserIndexedDBManager {\n    constructor() {\n        this.indexedDB = getIndexedDBFactory();\n    }\n    async listModels() {\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                const tx = db.transaction(INFO_STORE_NAME, 'readonly');\n                const store = tx.objectStore(INFO_STORE_NAME);\n                // tslint:disable:max-line-length\n                // Need to cast `store` as `any` here because TypeScript's DOM\n                // library does not have the `getAll()` method even though the\n                // method is supported in the latest version of most mainstream\n                // browsers:\n                // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll\n                // tslint:enable:max-line-length\n                // tslint:disable-next-line:no-any\n                const getAllInfoRequest = store.getAll();\n                getAllInfoRequest.onsuccess = () => {\n                    const out = {};\n                    for (const item of getAllInfoRequest.result) {\n                        out[item.modelPath] = item.modelArtifactsInfo;\n                    }\n                    resolve(out);\n                };\n                getAllInfoRequest.onerror = error => {\n                    db.close();\n                    return reject(getAllInfoRequest.error);\n                };\n                tx.oncomplete = () => db.close();\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n    async removeModel(path) {\n        path = maybeStripScheme(path);\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n                const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                const getInfoRequest = infoStore.get(path);\n                let modelTx;\n                getInfoRequest.onsuccess = () => {\n                    if (getInfoRequest.result == null) {\n                        db.close();\n                        return reject(new Error(`Cannot find model with path '${path}' ` +\n                            `in IndexedDB.`));\n                    }\n                    else {\n                        // First, delete the entry in the info store.\n                        const deleteInfoRequest = infoStore.delete(path);\n                        const deleteModelData = () => {\n                            // Second, delete the entry in the model store.\n                            modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n                            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                            const deleteModelRequest = modelStore.delete(path);\n                            deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo);\n                            deleteModelRequest.onerror = error => reject(getInfoRequest.error);\n                        };\n                        // Proceed with deleting model data regardless of whether deletion\n                        // of info data succeeds or not.\n                        deleteInfoRequest.onsuccess = deleteModelData;\n                        deleteInfoRequest.onerror = error => {\n                            deleteModelData();\n                            db.close();\n                            return reject(getInfoRequest.error);\n                        };\n                    }\n                };\n                getInfoRequest.onerror = error => {\n                    db.close();\n                    return reject(getInfoRequest.error);\n                };\n                infoTx.oncomplete = () => {\n                    if (modelTx == null) {\n                        db.close();\n                    }\n                    else {\n                        modelTx.oncomplete = () => db.close();\n                    }\n                };\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n}\n//# sourceMappingURL=indexed_db.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { arrayBufferToBase64String, base64StringToArrayBuffer, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst PATH_SEPARATOR = '/';\nconst PATH_PREFIX = 'tensorflowjs_models';\nconst INFO_SUFFIX = 'info';\nconst MODEL_TOPOLOGY_SUFFIX = 'model_topology';\nconst WEIGHT_SPECS_SUFFIX = 'weight_specs';\nconst WEIGHT_DATA_SUFFIX = 'weight_data';\nconst MODEL_METADATA_SUFFIX = 'model_metadata';\n/**\n * Purge all tensorflow.js-saved model artifacts from local storage.\n *\n * @returns Paths of the models purged.\n */\nexport function purgeLocalStorageArtifacts() {\n    if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n        typeof window.localStorage === 'undefined') {\n        throw new Error('purgeLocalStorageModels() cannot proceed because local storage is ' +\n            'unavailable in the current environment.');\n    }\n    const LS = window.localStorage;\n    const purgedModelPaths = [];\n    for (let i = 0; i < LS.length; ++i) {\n        const key = LS.key(i);\n        const prefix = PATH_PREFIX + PATH_SEPARATOR;\n        if (key.startsWith(prefix) && key.length > prefix.length) {\n            LS.removeItem(key);\n            const modelName = getModelPathFromKey(key);\n            if (purgedModelPaths.indexOf(modelName) === -1) {\n                purgedModelPaths.push(modelName);\n            }\n        }\n    }\n    return purgedModelPaths;\n}\nfunction getModelKeys(path) {\n    return {\n        info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n        topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n        weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n        weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),\n        modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)\n    };\n}\n/**\n * Get model path from a local-storage key.\n *\n * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'\n *\n * @param key\n */\nfunction getModelPathFromKey(key) {\n    const items = key.split(PATH_SEPARATOR);\n    if (items.length < 3) {\n        throw new Error(`Invalid key format: ${key}`);\n    }\n    return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\nfunction maybeStripScheme(key) {\n    return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?\n        key.slice(BrowserLocalStorage.URL_SCHEME.length) :\n        key;\n}\n/**\n * IOHandler subclass: Browser Local Storage.\n *\n * See the doc string to `browserLocalStorage` for more details.\n */\nexport class BrowserLocalStorage {\n    constructor(modelPath) {\n        if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n            typeof window.localStorage === 'undefined') {\n            // TODO(cais): Add more info about what IOHandler subtypes are\n            // available.\n            //   Maybe point to a doc page on the web and/or automatically determine\n            //   the available IOHandlers and print them in the error message.\n            throw new Error('The current environment does not support local storage.');\n        }\n        this.LS = window.localStorage;\n        if (modelPath == null || !modelPath) {\n            throw new Error('For local storage, modelPath must not be null, undefined or empty.');\n        }\n        this.modelPath = modelPath;\n        this.keys = getModelKeys(this.modelPath);\n    }\n    /**\n     * Save model artifacts to browser local storage.\n     *\n     * See the documentation to `browserLocalStorage` for details on the saved\n     * artifacts.\n     *\n     * @param modelArtifacts The model artifacts to be stored.\n     * @returns An instance of SaveResult.\n     */\n    async save(modelArtifacts) {\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        else {\n            const topology = JSON.stringify(modelArtifacts.modelTopology);\n            const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n            const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n            try {\n                this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n                this.LS.setItem(this.keys.topology, topology);\n                this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n                this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData));\n                this.LS.setItem(this.keys.modelMetadata, JSON.stringify({\n                    format: modelArtifacts.format,\n                    generatedBy: modelArtifacts.generatedBy,\n                    convertedBy: modelArtifacts.convertedBy,\n                    userDefinedMetadata: modelArtifacts.userDefinedMetadata\n                }));\n                return { modelArtifactsInfo };\n            }\n            catch (err) {\n                // If saving failed, clean up all items saved so far.\n                this.LS.removeItem(this.keys.info);\n                this.LS.removeItem(this.keys.topology);\n                this.LS.removeItem(this.keys.weightSpecs);\n                this.LS.removeItem(this.keys.weightData);\n                this.LS.removeItem(this.keys.modelMetadata);\n                throw new Error(`Failed to save model '${this.modelPath}' to local storage: ` +\n                    `size quota being exceeded is a possible cause of this failure: ` +\n                    `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` +\n                    `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` +\n                    `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n            }\n        }\n    }\n    /**\n     * Load a model from local storage.\n     *\n     * See the documentation to `browserLocalStorage` for details on the saved\n     * artifacts.\n     *\n     * @returns The loaded model (if loading succeeds).\n     */\n    async load() {\n        const info = JSON.parse(this.LS.getItem(this.keys.info));\n        if (info == null) {\n            throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);\n        }\n        if (info.modelTopologyType !== 'JSON') {\n            throw new Error('BrowserLocalStorage does not support loading non-JSON model ' +\n                'topology yet.');\n        }\n        const out = {};\n        // Load topology.\n        const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n        if (topology == null) {\n            throw new Error(`In local storage, the topology of model '${this.modelPath}' ` +\n                `is missing.`);\n        }\n        out.modelTopology = topology;\n        // Load weight specs.\n        const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n        if (weightSpecs == null) {\n            throw new Error(`In local storage, the weight specs of model '${this.modelPath}' ` +\n                `are missing.`);\n        }\n        out.weightSpecs = weightSpecs;\n        // Load meta-data fields.\n        const metadataString = this.LS.getItem(this.keys.modelMetadata);\n        if (metadataString != null) {\n            const metadata = JSON.parse(metadataString);\n            out.format = metadata['format'];\n            out.generatedBy = metadata['generatedBy'];\n            out.convertedBy = metadata['convertedBy'];\n            out.userDefinedMetadata = metadata['userDefinedMetadata'];\n        }\n        // Load weight data.\n        const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n        if (weightDataBase64 == null) {\n            throw new Error(`In local storage, the binary weight values of model ` +\n                `'${this.modelPath}' are missing.`);\n        }\n        out.weightData = base64StringToArrayBuffer(weightDataBase64);\n        return out;\n    }\n}\nBrowserLocalStorage.URL_SCHEME = 'localstorage://';\nexport const localStorageRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n            return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\n/**\n * Factory function for local storage IOHandler.\n *\n * This `IOHandler` supports both `save` and `load`.\n *\n * For each model's saved artifacts, four items are saved to local storage.\n *   - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the\n *     model, such as date saved, type of the topology, size in bytes, etc.\n *   - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-\n *     style models, this is a stringized JSON.\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the\n *     model, can be used to decode the saved binary weight values (see\n *     item below).\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary\n *     weight values, stored as a base64-encoded string.\n *\n * Saving may throw an `Error` if the total size of the artifacts exceed the\n * browser-specific quota.\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `IOHandler`, which can be used with, e.g.,\n *   `tf.Model.save`.\n */\nexport function browserLocalStorage(modelPath) {\n    return new BrowserLocalStorage(modelPath);\n}\nexport class BrowserLocalStorageManager {\n    constructor() {\n        assert(env().getBool('IS_BROWSER'), () => 'Current environment is not a web browser');\n        assert(typeof window === 'undefined' ||\n            typeof window.localStorage !== 'undefined', () => 'Current browser does not appear to support localStorage');\n        this.LS = window.localStorage;\n    }\n    async listModels() {\n        const out = {};\n        const prefix = PATH_PREFIX + PATH_SEPARATOR;\n        const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n        for (let i = 0; i < this.LS.length; ++i) {\n            const key = this.LS.key(i);\n            if (key.startsWith(prefix) && key.endsWith(suffix)) {\n                const modelPath = getModelPathFromKey(key);\n                out[modelPath] = JSON.parse(this.LS.getItem(key));\n            }\n        }\n        return out;\n    }\n    async removeModel(path) {\n        path = maybeStripScheme(path);\n        const keys = getModelKeys(path);\n        if (this.LS.getItem(keys.info) == null) {\n            throw new Error(`Cannot find model at path '${path}'`);\n        }\n        const info = JSON.parse(this.LS.getItem(keys.info));\n        this.LS.removeItem(keys.info);\n        this.LS.removeItem(keys.topology);\n        this.LS.removeItem(keys.weightSpecs);\n        this.LS.removeItem(keys.weightData);\n        return info;\n    }\n}\n//# sourceMappingURL=local_storage.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Classes and functions for model management across multiple storage mediums.\n *\n * Supported client actions:\n * - Listing models on all registered storage mediums.\n * - Remove model by URL from any registered storage mediums, by using URL\n *   string.\n * - Moving or copying model from one path to another in the same medium or from\n *   one medium to another, by using URL strings.\n */\nimport { assert } from '../util';\nimport { IORouterRegistry } from './router_registry';\nconst URL_SCHEME_SUFFIX = '://';\nexport class ModelStoreManagerRegistry {\n    constructor() {\n        this.managers = {};\n    }\n    static getInstance() {\n        if (ModelStoreManagerRegistry.instance == null) {\n            ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n        }\n        return ModelStoreManagerRegistry.instance;\n    }\n    /**\n     * Register a save-handler router.\n     *\n     * @param saveRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `save` method defined or `null`.\n     */\n    static registerManager(scheme, manager) {\n        assert(scheme != null, () => 'scheme must not be undefined or null.');\n        if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n            scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n        }\n        assert(scheme.length > 0, () => 'scheme must not be an empty string.');\n        const registry = ModelStoreManagerRegistry.getInstance();\n        assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);\n        registry.managers[scheme] = manager;\n    }\n    static getManager(scheme) {\n        const manager = this.getInstance().managers[scheme];\n        if (manager == null) {\n            throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n        }\n        return manager;\n    }\n    static getSchemes() {\n        return Object.keys(this.getInstance().managers);\n    }\n}\n/**\n * Helper method for parsing a URL string into a scheme and a path.\n *\n * @param url E.g., 'localstorage://my-model'\n * @returns A dictionary with two fields: scheme and path.\n *   Scheme: e.g., 'localstorage' in the example above.\n *   Path: e.g., 'my-model' in the example above.\n */\nfunction parseURL(url) {\n    if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n        throw new Error(`The url string provided does not contain a scheme. ` +\n            `Supported schemes are: ` +\n            `${ModelStoreManagerRegistry.getSchemes().join(',')}`);\n    }\n    return {\n        scheme: url.split(URL_SCHEME_SUFFIX)[0],\n        path: url.split(URL_SCHEME_SUFFIX)[1],\n    };\n}\nasync function cloneModelInternal(sourceURL, destURL, deleteSource = false) {\n    assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);\n    const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n    assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);\n    assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n        `load handlers for source URL ${sourceURL}.`);\n    const loadHandler = loadHandlers[0];\n    const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n    assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination ` +\n        `URL ${destURL}.`);\n    assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n        `save handlers for destination URL ${destURL}.`);\n    const saveHandler = saveHandlers[0];\n    const sourceScheme = parseURL(sourceURL).scheme;\n    const sourcePath = parseURL(sourceURL).path;\n    const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n    const modelArtifacts = await loadHandler.load();\n    // If moving within the same storage medium, remove the old model as soon as\n    // the loading is done. Without doing this, it is possible that the combined\n    // size of the two models will cause the cloning to fail.\n    if (deleteSource && sameMedium) {\n        await ModelStoreManagerRegistry.getManager(sourceScheme)\n            .removeModel(sourcePath);\n    }\n    const saveResult = await saveHandler.save(modelArtifacts);\n    // If moving between mediums, the deletion is done after the save succeeds.\n    // This guards against the case in which saving to the destination medium\n    // fails.\n    if (deleteSource && !sameMedium) {\n        await ModelStoreManagerRegistry.getManager(sourceScheme)\n            .removeModel(sourcePath);\n    }\n    return saveResult.modelArtifactsInfo;\n}\n/**\n * List all models stored in registered storage mediums.\n *\n * For a web browser environment, the registered mediums are Local Storage and\n * IndexedDB.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @returns A `Promise` of a dictionary mapping URLs of existing models to\n * their model artifacts info. URLs include medium-specific schemes, e.g.,\n *   'indexeddb://my/model/1'. Model artifacts info include type of the\n * model's topology, byte sizes of the topology, weights, etc.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function listModels() {\n    const schemes = ModelStoreManagerRegistry.getSchemes();\n    const out = {};\n    for (const scheme of schemes) {\n        const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();\n        for (const path in schemeOut) {\n            const url = scheme + URL_SCHEME_SUFFIX + path;\n            out[url] = schemeOut[path];\n        }\n    }\n    return out;\n}\n/**\n * Remove a model specified by URL from a reigstered storage medium.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @param url A URL to a stored model, with a scheme prefix, e.g.,\n *   'localstorage://my-model-1', 'indexeddb://my/model/2'.\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n *   is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function removeModel(url) {\n    const schemeAndPath = parseURL(url);\n    const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n    return manager.removeModel(schemeAndPath.path);\n}\n/**\n * Copy a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Copying within a storage medium, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Copying between two storage mediums, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Copy the model, from Local Storage to IndexedDB.\n * await tf.io.copyModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove both models.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of copying.\n * @param destURL Destination URL of copying.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function copyModel(sourceURL, destURL) {\n    const deleteSource = false;\n    return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n/**\n * Move a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Moving within a storage medium, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Moving between two storage mediums, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Move the model, from Local Storage to IndexedDB.\n * await tf.io.moveModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove the moved model.\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of moving.\n * @param destURL Destination URL of moving.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function moveModel(sourceURL, destURL) {\n    const deleteSource = true;\n    return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\nexport { moveModel, copyModel, removeModel, listModels };\n//# sourceMappingURL=model_management.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { BrowserIndexedDB, BrowserIndexedDBManager } from '../io/indexed_db';\nimport { BrowserLocalStorage, BrowserLocalStorageManager } from '../io/local_storage';\nimport { ModelStoreManagerRegistry } from '../io/model_management';\nexport class PlatformBrowser {\n    fetch(path, init) {\n        return fetch(path, init);\n    }\n    now() {\n        return performance.now();\n    }\n    encode(text, encoding) {\n        if (encoding !== 'utf-8' && encoding !== 'utf8') {\n            throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);\n        }\n        if (this.textEncoder == null) {\n            this.textEncoder = new TextEncoder();\n        }\n        return this.textEncoder.encode(text);\n    }\n    decode(bytes, encoding) {\n        return new TextDecoder(encoding).decode(bytes);\n    }\n}\nif (env().get('IS_BROWSER')) {\n    env().setPlatform('browser', new PlatformBrowser());\n    // Register LocalStorage IOHandler\n    try {\n        ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n    }\n    catch (err) {\n    }\n    // Register IndexedDB IOHandler\n    try {\n        ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n    }\n    catch (err) {\n    }\n}\n//# sourceMappingURL=platform_browser.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\n// We are wrapping this within an object so it can be stubbed by Jasmine.\nexport const getNodeFetch = {\n    // tslint:disable-next-line:no-require-imports\n    importFetch: () => require('node-fetch')\n};\nlet systemFetch;\n// These getters and setters are for testing so we don't export a mutable\n// variable.\nexport function resetSystemFetch() {\n    systemFetch = null;\n}\nexport function setSystemFetch(fetchFn) {\n    systemFetch = fetchFn;\n}\nexport function getSystemFetch() {\n    return systemFetch;\n}\nexport class PlatformNode {\n    constructor() {\n        // tslint:disable-next-line:no-require-imports\n        this.util = require('util');\n        // According to the spec, the built-in encoder can do only UTF-8 encoding.\n        // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder\n        this.textEncoder = new this.util.TextEncoder();\n    }\n    fetch(path, requestInits) {\n        if (env().global.fetch != null) {\n            return env().global.fetch(path, requestInits);\n        }\n        if (systemFetch == null) {\n            systemFetch = getNodeFetch.importFetch();\n        }\n        return systemFetch(path, requestInits);\n    }\n    now() {\n        const time = process.hrtime();\n        return time[0] * 1000 + time[1] / 1000000;\n    }\n    encode(text, encoding) {\n        if (encoding !== 'utf-8' && encoding !== 'utf8') {\n            throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);\n        }\n        return this.textEncoder.encode(text);\n    }\n    decode(bytes, encoding) {\n        if (bytes.length === 0) {\n            return '';\n        }\n        return new this.util.TextDecoder(encoding).decode(bytes);\n    }\n}\nif (env().get('IS_NODE')) {\n    env().setPlatform('node', new PlatformNode());\n}\n//# sourceMappingURL=platform_node.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport * as util from '../util';\n/**\n * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.\n *\n * The values are stored in CPU as `TypedArray`. Fill the buffer using\n * `buffer.set()`, or by modifying directly `buffer.values`.\n *\n * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with\n * those values.\n *\n * ```js\n * // Create a buffer and set values at particular indices.\n * const buffer = tf.buffer([2, 2]);\n * buffer.set(3, 0, 0);\n * buffer.set(5, 1, 0);\n *\n * // Convert the buffer back to a tensor.\n * buffer.toTensor().print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The dtype of the buffer. Defaults to 'float32'.\n * @param values The values of the buffer as `TypedArray`. Defaults to\n * zeros.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function buffer(shape, dtype = 'float32', values) {\n    dtype = dtype || 'float32';\n    util.assertNonNegativeIntegerDimensions(shape);\n    return new TensorBuffer(shape, dtype, values);\n}\n//# sourceMappingURL=buffer.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cast } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Casts a `tf.Tensor` to a new dtype.\n *\n * ```js\n * const x = tf.tensor1d([1.5, 2.5, 3]);\n * tf.cast(x, 'int32').print();\n * ```\n * @param x The input tensor to be casted.\n * @param dtype The dtype to cast the input tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction cast_(x, dtype) {\n    const $x = convertToTensor(x, 'x', 'cast');\n    // Sanity checks.\n    if (!util.isValidDtype(dtype)) {\n        throw new Error(`Failed to cast to unknown dtype ${dtype}`);\n    }\n    if (dtype === 'string' && $x.dtype !== 'string' ||\n        dtype !== 'string' && $x.dtype === 'string') {\n        throw new Error('Only strings can be casted to strings');\n    }\n    const inputs = { x: $x };\n    const attrs = { dtype };\n    return ENGINE.runKernelFunc(backend => backend.cast($x, dtype), inputs, null /* grad */, Cast, attrs);\n}\nexport const cast = op({ cast_ });\n//# sourceMappingURL=cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Identity } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction clone_(x) {\n    const $x = convertToTensor(x, 'x', 'clone', null);\n    const forward = () => ENGINE.makeTensorFromDataId($x.dataId, $x.shape, $x.dtype);\n    const inputs = { x: $x };\n    // Note this op is called tf.identity in python. Hence the kernel name used\n    // here.\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Identity);\n}\nexport const clone = op({ clone_ });\n//# sourceMappingURL=clone.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function print(x, verbose = false) {\n    console.log(x.toString(verbose));\n}\n//# sourceMappingURL=print.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Required side effectful code for tfjs-core\n// Set up Engine and ENV\nimport { getOrMakeEngine } from './engine';\ngetOrMakeEngine();\n// Register backend-agnostic flags.\nimport './flags';\n// Register platforms\nimport './platforms/platform_browser';\nimport './platforms/platform_node';\n// Set up OpHandler\nimport { buffer } from './ops/buffer';\nimport { cast } from './ops/cast';\nimport { clone } from './ops/clone';\nimport { print } from './ops/print';\nimport { setOpHandler } from './tensor';\nconst opHandler = {\n    buffer,\n    cast,\n    clone,\n    print\n};\nsetOpHandler(opHandler);\n//# sourceMappingURL=base_side_effects.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandlers related to files, such as browser-triggered file downloads,\n * user-selected files in browser.\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { basename, concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DEFAULT_FILE_NAME_PREFIX = 'model';\nconst DEFAULT_JSON_EXTENSION_NAME = '.json';\nconst DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';\nfunction defer(f) {\n    return new Promise(resolve => setTimeout(resolve)).then(f);\n}\nexport class BrowserDownloads {\n    constructor(fileNamePrefix) {\n        if (!env().getBool('IS_BROWSER')) {\n            // TODO(cais): Provide info on what IOHandlers are available under the\n            //   current environment.\n            throw new Error('browserDownloads() cannot proceed because the current environment ' +\n                'is not a browser.');\n        }\n        if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n            fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n        }\n        if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n            fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n        }\n        this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n        this.weightDataFileName =\n            fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n    }\n    async save(modelArtifacts) {\n        if (typeof (document) === 'undefined') {\n            throw new Error('Browser downloads are not supported in ' +\n                'this environment since `document` is not present');\n        }\n        const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: 'application/octet-stream' }));\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserDownloads.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        else {\n            const weightsManifest = [{\n                    paths: ['./' + this.weightDataFileName],\n                    weights: modelArtifacts.weightSpecs\n                }];\n            const modelTopologyAndWeightManifest = {\n                modelTopology: modelArtifacts.modelTopology,\n                format: modelArtifacts.format,\n                generatedBy: modelArtifacts.generatedBy,\n                convertedBy: modelArtifacts.convertedBy,\n                weightsManifest\n            };\n            const modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: 'application/json' }));\n            // If anchor elements are not provided, create them without attaching them\n            // to parents, so that the downloaded file names can be controlled.\n            const jsonAnchor = this.jsonAnchor == null ? document.createElement('a') :\n                this.jsonAnchor;\n            jsonAnchor.download = this.modelTopologyFileName;\n            jsonAnchor.href = modelTopologyAndWeightManifestURL;\n            // Trigger downloads by evoking a click event on the download anchors.\n            // When multiple downloads are started synchronously, Firefox will only\n            // save the last one.\n            await defer(() => jsonAnchor.dispatchEvent(new MouseEvent('click')));\n            if (modelArtifacts.weightData != null) {\n                const weightDataAnchor = this.weightDataAnchor == null ?\n                    document.createElement('a') :\n                    this.weightDataAnchor;\n                weightDataAnchor.download = this.weightDataFileName;\n                weightDataAnchor.href = weightsURL;\n                await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent('click')));\n            }\n            return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) };\n        }\n    }\n}\nBrowserDownloads.URL_SCHEME = 'downloads://';\nclass BrowserFiles {\n    constructor(files) {\n        if (files == null || files.length < 1) {\n            throw new Error(`When calling browserFiles, at least 1 file is required, ` +\n                `but received ${files}`);\n        }\n        this.files = files;\n    }\n    async load() {\n        const jsonFile = this.files[0];\n        const weightFiles = this.files.slice(1);\n        return new Promise((resolve, reject) => {\n            const jsonReader = new FileReader();\n            jsonReader.onload = (event) => {\n                // tslint:disable-next-line:no-any\n                const modelJSON = JSON.parse(event.target.result);\n                const modelTopology = modelJSON.modelTopology;\n                if (modelTopology == null) {\n                    reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));\n                    return;\n                }\n                if (weightFiles.length === 0) {\n                    resolve({ modelTopology });\n                }\n                const weightsManifest = modelJSON.weightsManifest;\n                if (weightsManifest == null) {\n                    reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));\n                    return;\n                }\n                let pathToFile;\n                try {\n                    pathToFile =\n                        this.checkManifestAndWeightFiles(weightsManifest, weightFiles);\n                }\n                catch (err) {\n                    reject(err);\n                    return;\n                }\n                const weightSpecs = [];\n                const paths = [];\n                const perFileBuffers = [];\n                weightsManifest.forEach(weightsGroup => {\n                    weightsGroup.paths.forEach(path => {\n                        paths.push(path);\n                        perFileBuffers.push(null);\n                    });\n                    weightSpecs.push(...weightsGroup.weights);\n                });\n                weightsManifest.forEach(weightsGroup => {\n                    weightsGroup.paths.forEach(path => {\n                        const weightFileReader = new FileReader();\n                        weightFileReader.onload = (event) => {\n                            // tslint:disable-next-line:no-any\n                            const weightData = event.target.result;\n                            const index = paths.indexOf(path);\n                            perFileBuffers[index] = weightData;\n                            if (perFileBuffers.indexOf(null) === -1) {\n                                resolve({\n                                    modelTopology,\n                                    weightSpecs,\n                                    weightData: concatenateArrayBuffers(perFileBuffers),\n                                    format: modelJSON.format,\n                                    generatedBy: modelJSON.generatedBy,\n                                    convertedBy: modelJSON.convertedBy,\n                                    userDefinedMetadata: modelJSON.userDefinedMetadata\n                                });\n                            }\n                        };\n                        weightFileReader.onerror = error => reject(`Failed to weights data from file of path '${path}'.`);\n                        weightFileReader.readAsArrayBuffer(pathToFile[path]);\n                    });\n                });\n            };\n            jsonReader.onerror = error => reject(`Failed to read model topology and weights manifest JSON ` +\n                `from file '${jsonFile.name}'. BrowserFiles supports loading ` +\n                `Keras-style tf.Model artifacts only.`);\n            jsonReader.readAsText(jsonFile);\n        });\n    }\n    /**\n     * Check the compatibility between weights manifest and weight files.\n     */\n    checkManifestAndWeightFiles(manifest, files) {\n        const basenames = [];\n        const fileNames = files.map(file => basename(file.name));\n        const pathToFile = {};\n        for (const group of manifest) {\n            group.paths.forEach(path => {\n                const pathBasename = basename(path);\n                if (basenames.indexOf(pathBasename) !== -1) {\n                    throw new Error(`Duplicate file basename found in weights manifest: ` +\n                        `'${pathBasename}'`);\n                }\n                basenames.push(pathBasename);\n                if (fileNames.indexOf(pathBasename) === -1) {\n                    throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);\n                }\n                else {\n                    pathToFile[path] = files[fileNames.indexOf(pathBasename)];\n                }\n            });\n        }\n        if (basenames.length !== files.length) {\n            throw new Error(`Mismatch in the number of files in weights manifest ` +\n                `(${basenames.length}) and the number of weight files provided ` +\n                `(${files.length}).`);\n        }\n        return pathToFile;\n    }\n}\nexport const browserDownloadsRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n            return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\n/**\n * Creates an IOHandler that triggers file downloads from the browser.\n *\n * The returned `IOHandler` instance can be used as model exporting methods such\n * as `tf.Model.save` and supports only saving.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * const saveResult = await model.save('downloads://mymodel');\n * // This will trigger downloading of two files:\n * //   'mymodel.json' and 'mymodel.weights.bin'.\n * console.log(saveResult);\n * ```\n *\n * @param fileNamePrefix Prefix name of the files to be downloaded. For use with\n *   `tf.Model`, `fileNamePrefix` should follow either of the following two\n *   formats:\n *   1. `null` or `undefined`, in which case the default file\n *      names will be used:\n *      - 'model.json' for the JSON file containing the model topology and\n *        weights manifest.\n *      - 'model.weights.bin' for the binary file containing the binary weight\n *        values.\n *   2. A single string or an Array of a single string, as the file name prefix.\n *      For example, if `'foo'` is provided, the downloaded JSON\n *      file and binary weights file will be named 'foo.json' and\n *      'foo.weights.bin', respectively.\n * @param config Additional configuration for triggering downloads.\n * @returns An instance of `BrowserDownloads` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserDownloads(fileNamePrefix = 'model') {\n    return new BrowserDownloads(fileNamePrefix);\n}\n/**\n * Creates an IOHandler that loads model artifacts from user-selected files.\n *\n * This method can be used for loading from files such as user-selected files\n * in the browser.\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * // Note: This code snippet won't run properly without the actual file input\n * //   elements in the HTML DOM.\n *\n * // Suppose there are two HTML file input (`<input type=\"file\" ...>`)\n * // elements.\n * const uploadJSONInput = document.getElementById('upload-json');\n * const uploadWeightsInput = document.getElementById('upload-weights');\n * const model = await tf.loadLayersModel(tf.io.browserFiles(\n *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));\n * ```\n *\n * @param files `File`s to load from. Currently, this function supports only\n *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for\n *   which an `Array` of `File`s is expected (in that order):\n *   - A JSON file containing the model topology and weight manifest.\n *   - Optionally, One or more binary files containing the binary weights.\n *     These files must have names that match the paths in the `weightsManifest`\n *     contained by the aforementioned JSON file, or errors will be thrown\n *     during loading. These weights files have the same format as the ones\n *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`\n *     Python PIP package. If no weights files are provided, only the model\n *     topology will be loaded from the JSON file above.\n * @returns An instance of `Files` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserFiles(files) {\n    return new BrowserFiles(files);\n}\n//# sourceMappingURL=browser_files.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../util';\n/**\n * Monitor Promise.all progress, fire onProgress callback function.\n *\n * @param promises Promise list going to be monitored\n * @param onProgress Callback function. Fired when a promise resolved.\n * @param startFraction Optional fraction start. Default to 0.\n * @param endFraction Optional fraction end. Default to 1.\n */\nexport function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {\n    checkPromises(promises);\n    startFraction = startFraction == null ? 0 : startFraction;\n    endFraction = endFraction == null ? 1 : endFraction;\n    checkFraction(startFraction, endFraction);\n    let resolvedPromise = 0;\n    const registerMonitor = (promise) => {\n        promise.then(value => {\n            const fraction = startFraction +\n                ++resolvedPromise / promises.length * (endFraction - startFraction);\n            // pass fraction as parameter to callback function.\n            onProgress(fraction);\n            return value;\n        });\n        return promise;\n    };\n    function checkPromises(promises) {\n        assert(promises != null && Array.isArray(promises) && promises.length > 0, () => 'promises must be a none empty array');\n    }\n    function checkFraction(startFraction, endFraction) {\n        assert(startFraction >= 0 && startFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n            `got startFraction ${startFraction}`);\n        assert(endFraction >= 0 && endFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n            `got endFraction ${endFraction}`);\n        assert(endFraction >= startFraction, () => `startFraction must be no more than endFraction, but ` +\n            `got startFraction ${startFraction} and endFraction ` +\n            `${endFraction}`);\n    }\n    return Promise.all(promises.map(registerMonitor));\n}\n//# sourceMappingURL=progress.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\nimport * as util from '../util';\nimport { decodeWeights } from './io_utils';\nimport { monitorPromisesProgress } from './progress';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/**\n * Reads binary weights data from a number of URLs.\n *\n * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.\n * @param requestOptions RequestInit (options) for the HTTP requests.\n * @param fetchFunc Optional overriding value for the `window.fetch` function.\n * @param onProgress Optional, progress callback function, fired periodically\n *   before the load is completed.\n * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same\n *   length as `fetchURLs`.\n */\nexport async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {\n    if (loadOptions == null) {\n        loadOptions = {};\n    }\n    const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch :\n        loadOptions.fetchFunc;\n    // Create the requests for all of the weights in parallel.\n    const requests = fetchURLs.map(fetchURL => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true }));\n    const fetchStartFraction = 0;\n    const fetchEndFraction = 0.5;\n    const responses = loadOptions.onProgress == null ?\n        await Promise.all(requests) :\n        await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);\n    const bufferPromises = responses.map(response => response.arrayBuffer());\n    const bufferStartFraction = 0.5;\n    const bufferEndFraction = 1;\n    const buffers = loadOptions.onProgress == null ?\n        await Promise.all(bufferPromises) :\n        await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);\n    return buffers;\n}\n/**\n * Reads a weights manifest JSON configuration, fetches the weights and\n * returns them as `Tensor`s.\n *\n * @param manifest The weights manifest JSON.\n * @param filePathPrefix The path prefix for filenames given in the manifest.\n *     Defaults to the empty string.\n * @param weightNames The names of the weights to be fetched.\n */\nexport async function loadWeights(manifest, filePathPrefix = '', weightNames, requestInit) {\n    // TODO(nsthorat): Groups are currently fetched atomically. If you need a\n    // single weight from a group, the whole group will be fetched. At a future\n    // date, we should support fetching only the individual shards within a\n    // group that are needed to reconstruct the requested weight.\n    // TODO(cais): Use `decodeWeights` for implementation.\n    const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit });\n    const loadWeights = weightsLoaderFactory(fetchWeights);\n    return loadWeights(manifest, filePathPrefix, weightNames);\n}\n/**\n * Creates a function, which reads a weights manifest JSON configuration,\n * fetches the weight files using the specified function and returns them as\n * `Tensor`s.\n *\n * ```js\n * // example for creating a nodejs weight loader, which reads the weight files\n * // from disk using fs.readFileSync\n *\n * import * as fs from 'fs'\n *\n * const fetchWeightsFromDisk = (filePaths: string[]) =>\n *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)\n *\n * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n *\n * const manifest = JSON.parse(\n *   fs.readFileSync('./my_model-weights_manifest').toString()\n * )\n * const weightMap = await loadWeights(manifest, './')\n * ```\n * @param fetchWeightsFunction The function used for fetching the weight files.\n * @returns Weight loading function.\n */\nexport function weightsLoaderFactory(fetchWeightsFunction) {\n    return async (manifest, filePathPrefix = '', weightNames) => {\n        // Collect all the groups, weights, and their relative offsets to be\n        // fetched.\n        const groupIndicesToFetchMap = manifest.map(() => false);\n        const groupWeightsToFetch = {};\n        const weightsFound = weightNames != null ? weightNames.map(() => false) : [];\n        const allManifestWeightNames = [];\n        manifest.forEach((manifestGroupConfig, groupIndex) => {\n            let groupOffset = 0;\n            manifestGroupConfig.weights.forEach(weightsEntry => {\n                const rawDtype = ('quantization' in weightsEntry) ?\n                    weightsEntry.quantization.dtype :\n                    weightsEntry.dtype;\n                const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *\n                    util.sizeFromShape(weightsEntry.shape);\n                const enqueueWeightsForFetchingFn = () => {\n                    groupIndicesToFetchMap[groupIndex] = true;\n                    if (groupWeightsToFetch[groupIndex] == null) {\n                        groupWeightsToFetch[groupIndex] = [];\n                    }\n                    groupWeightsToFetch[groupIndex].push({\n                        manifestEntry: weightsEntry,\n                        groupOffset,\n                        sizeBytes: weightsBytes\n                    });\n                };\n                if (weightNames != null) {\n                    weightNames.forEach((weightName, weightIndex) => {\n                        if (weightName === weightsEntry.name) {\n                            enqueueWeightsForFetchingFn();\n                            weightsFound[weightIndex] = true;\n                        }\n                    });\n                }\n                else {\n                    enqueueWeightsForFetchingFn();\n                }\n                allManifestWeightNames.push(weightsEntry.name);\n                groupOffset += weightsBytes;\n            });\n        });\n        if (!weightsFound.every(found => found)) {\n            const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n            throw new Error(`Could not find weights in manifest with names: ` +\n                `${weightsNotFound.join(', ')}. \\n` +\n                `Manifest JSON has weights with names: ` +\n                `${allManifestWeightNames.join(', ')}.`);\n        }\n        // Convert the one-hot boolean groupId => shouldFetch map to a list of group\n        // IDs.\n        const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n            if (shouldFetch) {\n                accumulator.push(i);\n            }\n            return accumulator;\n        }, []);\n        const fetchUrls = [];\n        groupIndicesToFetch.forEach(i => {\n            manifest[i].paths.forEach(filepath => {\n                const fetchUrl = filePathPrefix +\n                    (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;\n                fetchUrls.push(fetchUrl);\n            });\n        });\n        const buffers = await fetchWeightsFunction(fetchUrls);\n        const weightsTensorMap = {};\n        let bufferIndexOffset = 0;\n        groupIndicesToFetch.forEach(i => {\n            const numBuffers = manifest[i].paths.length;\n            let groupBytes = 0;\n            for (let i = 0; i < numBuffers; i++) {\n                groupBytes += buffers[bufferIndexOffset + i].byteLength;\n            }\n            // Create a buffer for the whole group.\n            const groupBuffer = new ArrayBuffer(groupBytes);\n            const groupByteBuffer = new Uint8Array(groupBuffer);\n            let groupBufferOffset = 0;\n            for (let i = 0; i < numBuffers; i++) {\n                const buffer = new Uint8Array(buffers[bufferIndexOffset + i]);\n                groupByteBuffer.set(buffer, groupBufferOffset);\n                groupBufferOffset += buffer.byteLength;\n            }\n            const weightsEntries = groupWeightsToFetch[i];\n            weightsEntries.forEach(weightsEntry => {\n                const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);\n                const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n                for (const name in nameToTensorMap) {\n                    weightsTensorMap[name] = nameToTensorMap[name];\n                }\n            });\n            bufferIndexOffset += numBuffers;\n        });\n        return weightsTensorMap;\n    };\n}\n//# sourceMappingURL=weights_loader.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandler implementations based on HTTP requests in the web browser.\n *\n * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n */\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nimport { loadWeightsAsArrayBuffer } from './weights_loader';\nconst OCTET_STREAM_MIME_TYPE = 'application/octet-stream';\nconst JSON_TYPE = 'application/json';\nexport class HTTPRequest {\n    constructor(path, loadOptions) {\n        this.DEFAULT_METHOD = 'POST';\n        if (loadOptions == null) {\n            loadOptions = {};\n        }\n        this.weightPathPrefix = loadOptions.weightPathPrefix;\n        this.onProgress = loadOptions.onProgress;\n        this.weightUrlConverter = loadOptions.weightUrlConverter;\n        if (loadOptions.fetchFunc != null) {\n            assert(typeof loadOptions.fetchFunc === 'function', () => 'Must pass a function that matches the signature of ' +\n                '`fetch` (see ' +\n                'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');\n            this.fetch = loadOptions.fetchFunc;\n        }\n        else {\n            this.fetch = env().platform.fetch;\n        }\n        assert(path != null && path.length > 0, () => 'URL path for http must not be null, undefined or ' +\n            'empty.');\n        if (Array.isArray(path)) {\n            assert(path.length === 2, () => 'URL paths for http must have a length of 2, ' +\n                `(actual length is ${path.length}).`);\n        }\n        this.path = path;\n        if (loadOptions.requestInit != null &&\n            loadOptions.requestInit.body != null) {\n            throw new Error('requestInit is expected to have no pre-existing body, but has one.');\n        }\n        this.requestInit = loadOptions.requestInit || {};\n    }\n    async save(modelArtifacts) {\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserHTTPRequest.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        const init = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);\n        init.body = new FormData();\n        const weightsManifest = [{\n                paths: ['./model.weights.bin'],\n                weights: modelArtifacts.weightSpecs,\n            }];\n        const modelTopologyAndWeightManifest = {\n            modelTopology: modelArtifacts.modelTopology,\n            format: modelArtifacts.format,\n            generatedBy: modelArtifacts.generatedBy,\n            convertedBy: modelArtifacts.convertedBy,\n            userDefinedMetadata: modelArtifacts.userDefinedMetadata,\n            weightsManifest\n        };\n        init.body.append('model.json', new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), 'model.json');\n        if (modelArtifacts.weightData != null) {\n            init.body.append('model.weights.bin', new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), 'model.weights.bin');\n        }\n        const response = await this.fetch(this.path, init);\n        if (response.ok) {\n            return {\n                modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n                responses: [response],\n            };\n        }\n        else {\n            throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ` +\n                `${response.status}.`);\n        }\n    }\n    /**\n     * Load model artifacts via HTTP request(s).\n     *\n     * See the documentation to `tf.io.http` for details on the saved\n     * artifacts.\n     *\n     * @returns The loaded model artifacts (if loading succeeds).\n     */\n    async load() {\n        const modelConfigRequest = await this.fetch(this.path, this.requestInit);\n        if (!modelConfigRequest.ok) {\n            throw new Error(`Request to ${this.path} failed with status code ` +\n                `${modelConfigRequest.status}. Please verify this URL points to ` +\n                `the model JSON of the model to load.`);\n        }\n        let modelConfig;\n        try {\n            modelConfig = await modelConfigRequest.json();\n        }\n        catch (e) {\n            let message = `Failed to parse model JSON of response from ${this.path}.`;\n            // TODO(nsthorat): Remove this after some time when we're comfortable that\n            // .pb files are mostly gone.\n            if (this.path.endsWith('.pb')) {\n                message += ' Your path contains a .pb file extension. ' +\n                    'Support for .pb models have been removed in TensorFlow.js 1.0 ' +\n                    'in favor of .json models. You can re-convert your Python ' +\n                    'TensorFlow model using the TensorFlow.js 1.0 conversion scripts ' +\n                    'or you can convert your.pb models with the \\'pb2json\\'' +\n                    'NPM script in the tensorflow/tfjs-converter repository.';\n            }\n            else {\n                message += ' Please make sure the server is serving valid ' +\n                    'JSON for this request.';\n            }\n            throw new Error(message);\n        }\n        const modelTopology = modelConfig.modelTopology;\n        const weightsManifest = modelConfig.weightsManifest;\n        const generatedBy = modelConfig.generatedBy;\n        const convertedBy = modelConfig.convertedBy;\n        const format = modelConfig.format;\n        const userDefinedMetadata = modelConfig.userDefinedMetadata;\n        // We do not allow both modelTopology and weightsManifest to be missing.\n        if (modelTopology == null && weightsManifest == null) {\n            throw new Error(`The JSON from HTTP path ${this.path} contains neither model ` +\n                `topology or manifest for weights.`);\n        }\n        let weightSpecs;\n        let weightData;\n        if (weightsManifest != null) {\n            const results = await this.loadWeights(weightsManifest);\n            [weightSpecs, weightData] = results;\n        }\n        const artifacts = {\n            modelTopology,\n            weightSpecs,\n            weightData,\n            userDefinedMetadata,\n            generatedBy,\n            convertedBy,\n            format\n        };\n        const initializer = modelConfig.modelInitializer;\n        if (initializer) {\n            artifacts.modelInitializer = initializer;\n        }\n        return artifacts;\n    }\n    async loadWeights(weightsManifest) {\n        const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n        const [prefix, suffix] = parseUrl(weightPath);\n        const pathPrefix = this.weightPathPrefix || prefix;\n        const weightSpecs = [];\n        for (const entry of weightsManifest) {\n            weightSpecs.push(...entry.weights);\n        }\n        const fetchURLs = [];\n        const urlPromises = [];\n        for (const weightsGroup of weightsManifest) {\n            for (const path of weightsGroup.paths) {\n                if (this.weightUrlConverter != null) {\n                    urlPromises.push(this.weightUrlConverter(path));\n                }\n                else {\n                    fetchURLs.push(pathPrefix + path + suffix);\n                }\n            }\n        }\n        if (this.weightUrlConverter) {\n            fetchURLs.push(...await Promise.all(urlPromises));\n        }\n        const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {\n            requestInit: this.requestInit,\n            fetchFunc: this.fetch,\n            onProgress: this.onProgress\n        });\n        return [weightSpecs, concatenateArrayBuffers(buffers)];\n    }\n}\nHTTPRequest.URL_SCHEME_REGEX = /^https?:\\/\\//;\n/**\n * Extract the prefix and suffix of the url, where the prefix is the path before\n * the last file, and suffix is the search params after the last file.\n * ```\n * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'\n * [prefix, suffix] = parseUrl(url)\n * // prefix = 'http://tfhub.dev/model/1/'\n * // suffix = '?tfjs-format=file'\n * ```\n * @param url the model url to be parsed.\n */\nexport function parseUrl(url) {\n    const lastSlash = url.lastIndexOf('/');\n    const lastSearchParam = url.lastIndexOf('?');\n    const prefix = url.substring(0, lastSlash);\n    const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';\n    return [prefix + '/', suffix];\n}\nexport function isHTTPScheme(url) {\n    return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;\n}\nexport const httpRouter = (url, loadOptions) => {\n    if (typeof fetch === 'undefined' &&\n        (loadOptions == null || loadOptions.fetchFunc == null)) {\n        // `http` uses `fetch` or `node-fetch`, if one wants to use it in\n        // an environment that is not the browser or node they have to setup a\n        // global fetch polyfill.\n        return null;\n    }\n    else {\n        let isHTTP = true;\n        if (Array.isArray(url)) {\n            isHTTP = url.every(urlItem => isHTTPScheme(urlItem));\n        }\n        else {\n            isHTTP = isHTTPScheme(url);\n        }\n        if (isHTTP) {\n            return http(url, loadOptions);\n        }\n    }\n    return null;\n};\nIORouterRegistry.registerSaveRouter(httpRouter);\nIORouterRegistry.registerLoadRouter(httpRouter);\n/**\n * Creates an IOHandler subtype that sends model artifacts to HTTP server.\n *\n * An HTTP request of the `multipart/form-data` mime type will be sent to the\n * `path` URL. The form data includes artifacts that represent the topology\n * and/or weights of the model. In the case of Keras-style `tf.Model`, two\n * blobs (files) exist in form-data:\n *   - A JSON file consisting of `modelTopology` and `weightsManifest`.\n *   - A binary weights file consisting of the concatenated weight values.\n * These files are in the same format as the one generated by\n * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).\n *\n * The following code snippet exemplifies the client-side code that uses this\n * function:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save(tf.io.http(\n *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));\n * console.log(saveResult);\n * ```\n *\n * If the default `POST` method is to be used, without any custom parameters\n * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:\n *\n * ```js\n * const saveResult = await model.save('http://model-server:5000/upload');\n * ```\n *\n * The following GitHub Gist\n * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864\n * implements a server based on [flask](https://github.com/pallets/flask) that\n * can receive the request. Upon receiving the model artifacts via the requst,\n * this particular server reconsistutes instances of [Keras\n * Models](https://keras.io/models/model/) in memory.\n *\n *\n * @param path A URL path to the model.\n *   Can be an absolute HTTP path (e.g.,\n *   'http://localhost:8000/model-upload)') or a relative path (e.g.,\n *   './model-upload').\n * @param requestInit Request configurations to be used when sending\n *    HTTP request to server using `fetch`. It can contain fields such as\n *    `method`, `credentials`, `headers`, `mode`, etc. See\n *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request\n *    for more information. `requestInit` must not have a body, because the\n * body will be set by TensorFlow.js. File blobs representing the model\n * topology (filename: 'model.json') and the weights of the model (filename:\n * 'model.weights.bin') will be appended to the body. If `requestInit` has a\n * `body`, an Error will be thrown.\n * @param loadOptions Optional configuration for the loading. It includes the\n *   following fields:\n *   - weightPathPrefix Optional, this specifies the path prefix for weight\n *     files, by default this is calculated from the path param.\n *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,\n *     the `fetch` from node-fetch can be used here.\n *   - onProgress Optional, progress callback function, fired periodically\n *     before the load is completed.\n * @returns An instance of `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function http(path, loadOptions) {\n    return new HTTPRequest(path, loadOptions);\n}\n/**\n * Deprecated. Use `tf.io.http`.\n * @param path\n * @param loadOptions\n */\nexport function browserHTTPRequest(path, loadOptions) {\n    return http(path, loadOptions);\n}\n//# sourceMappingURL=http.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nclass PassthroughLoader {\n    constructor(modelArtifacts) {\n        this.modelArtifacts = modelArtifacts;\n    }\n    async load() {\n        return this.modelArtifacts;\n    }\n}\nclass PassthroughSaver {\n    constructor(saveHandler) {\n        this.saveHandler = saveHandler;\n    }\n    async save(modelArtifacts) {\n        return this.saveHandler(modelArtifacts);\n    }\n}\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadLayersModel(tf.io.fromMemory(\n *     modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelArtifacts a object containing model topology (i.e., parsed from\n *   the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n *   names, shapes, types, and quantization of the weight data.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n *   concatenated in the order described by the weightSpecs.\n * @param trainingConfig Model training configuration. Optional.\n *\n * @returns A passthrough `IOHandler` that simply loads the provided data.\n */\nexport function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {\n    if (arguments.length === 1) {\n        const isModelArtifacts = modelArtifacts.modelTopology != null ||\n            modelArtifacts.weightSpecs != null;\n        if (isModelArtifacts) {\n            return new PassthroughLoader(modelArtifacts);\n        }\n        else {\n            // Legacy support: with only modelTopology.\n            // TODO(cais): Remove this deprecated API.\n            console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n                'The argument should be of type ModelArtifacts. ' +\n                'The multi-argument signature of tf.io.fromMemory() has been ' +\n                'deprecated and will be removed in a future release.');\n            return new PassthroughLoader({ modelTopology: modelArtifacts });\n        }\n    }\n    else {\n        // Legacy support.\n        // TODO(cais): Remove this deprecated API.\n        console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n            'The argument should be of type ModelArtifacts. ' +\n            'The multi-argument signature of tf.io.fromMemory() has been ' +\n            'deprecated and will be removed in a future release.');\n        return new PassthroughLoader({\n            modelTopology: modelArtifacts,\n            weightSpecs,\n            weightData,\n            trainingConfig\n        });\n    }\n}\n/**\n * Creates an IOHandler that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n *   // ... do something with the artifacts ...\n *   return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n *     `SaveResult`.\n */\nexport function withSaveHandler(saveHandler) {\n    return new PassthroughSaver(saveHandler);\n}\n//# sourceMappingURL=passthrough.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Importing local_storage and indexed_db is necessary for the routers to be\n// registered.\nimport './indexed_db';\nimport './local_storage';\nimport { browserFiles } from './browser_files';\nimport { browserHTTPRequest, http, isHTTPScheme } from './http';\nimport { concatenateArrayBuffers, decodeWeights, encodeWeights, getModelArtifactsInfoForJSON } from './io_utils';\nimport { fromMemory, withSaveHandler } from './passthrough';\nimport { getLoadHandlers, getSaveHandlers, registerLoadRouter, registerSaveRouter } from './router_registry';\nimport { loadWeights, weightsLoaderFactory } from './weights_loader';\nexport { copyModel, listModels, moveModel, removeModel } from './model_management';\nexport { browserFiles, browserHTTPRequest, concatenateArrayBuffers, decodeWeights, encodeWeights, fromMemory, getLoadHandlers, getModelArtifactsInfoForJSON, getSaveHandlers, http, isHTTPScheme, loadWeights, registerLoadRouter, registerSaveRouter, weightsLoaderFactory, withSaveHandler };\n//# sourceMappingURL=io.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reshape } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Reshapes a `tf.Tensor` to a given shape.\n *\n * Given an input tensor, returns a new tensor with the same values as the\n * input tensor with shape `shape`.\n *\n * If one component of shape is the special value -1, the size of that\n * dimension is computed so that the total size remains constant. In\n * particular, a shape of [-1] flattens into 1-D. At most one component of\n * shape can be -1.\n *\n * If shape is 1-D or higher, then the operation returns a tensor with shape\n * shape filled with the values of tensor. In this case, the number of\n * elements implied by shape must be the same as the number of elements in\n * tensor.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.reshape([2, 2]).print();\n * ```\n *\n * @param x The input tensor to be reshaped.\n * @param shape An array of integers defining the output tensor shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction reshape_(x, shape) {\n    const $x = convertToTensor(x, 'x', 'reshape', null);\n    const inputs = { x: $x };\n    const attrs = { shape };\n    const forward = (backend, save) => {\n        shape = util.inferFromImplicitShape(shape, $x.size);\n        util.assert($x.size === util.sizeFromShape(shape), () => 'new shape and old shape must have the same number of elements.');\n        save([$x]);\n        return backend.reshape($x, shape);\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Reshape, attrs);\n}\nexport const reshape = op({ reshape_ });\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchMatMul } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the dot product of two matrices, A * B. These must be matrices.\n *\n * ```js\n * const a = tf.tensor2d([1, 2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.matMul(b).print();  // or tf.matMul(a, b)\n * ```\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction matMul_(a, b, transposeA = false, transposeB = false) {\n    let $a = convertToTensor(a, 'a', 'matMul');\n    let $b = convertToTensor(b, 'b', 'matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    util.assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in matMul: inputs must have the same rank of at least 2, ` +\n        `got ranks ${$a.rank} and ${$b.rank}.`);\n    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n    const outerDimsA = $a.shape.slice(0, -2);\n    const outerDimsB = $b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n    util.assert(util.arraysEqual(outerDimsA, outerDimsB), () => `Error in matMul: outer dimensions (${outerDimsA}) and (` +\n        `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} must match.`);\n    util.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (` +\n        `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} and transposeA=${transposeA}` +\n        ` and transposeB=${transposeB} must match.`);\n    const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n    const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n        reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n    const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n        reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n    const forward = (backend, save) => {\n        save([a3D, b3D]);\n        return backend.batchMatMul(a3D, b3D, transposeA, transposeB);\n    };\n    const inputs = { a: a3D, b: b3D };\n    const attrs = { transposeA, transposeB };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, BatchMatMul, attrs);\n    return reshape(res, outShape);\n}\nexport const matMul = op({ matMul_ });\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OneHot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank\n * `R+1` with the last axis of size `depth`.\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor` of indices with dtype `int32`.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n *     not match the location.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction oneHot_(indices, depth, onValue = 1, offValue = 0) {\n    if (depth < 2) {\n        throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n    }\n    const $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');\n    const outShape = [...$indices.shape, depth];\n    const forward = (backend, save) => {\n        save([$indices]);\n        return reshape(backend.oneHot(reshape($indices, [$indices.size]), depth, onValue, offValue), outShape);\n    };\n    const inputs = { indices: $indices };\n    const attrs = { depth, onValue, offValue };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, OneHot, attrs);\n}\nexport const oneHot = op({ oneHot_ });\n//# sourceMappingURL=one_hot.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Transpose } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print();  // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction transpose_(x, perm) {\n    const $x = convertToTensor(x, 'x', 'transpose');\n    if (perm == null) {\n        perm = $x.shape.map((s, i) => i).reverse();\n    }\n    util.assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n        `must match length of perm ${perm}.`);\n    perm.forEach(axis => {\n        util.assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n            ` but got ${perm}`);\n    });\n    if ($x.rank <= 1) {\n        return $x.clone();\n    }\n    const inputs = { x: $x };\n    const attrs = { perm };\n    return ENGINE.runKernelFunc(backend => backend.transpose($x, perm), inputs, null /* gradient */, Transpose, attrs);\n}\nexport const transpose = op({ transpose_ });\n//# sourceMappingURL=transpose.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { matMul } from './mat_mul';\nimport { oneHot } from './one_hot';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Computes the confusion matrix from true labels and predicted labels.\n *\n * ```js\n * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n * const numClasses = 3;\n * const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n * out.print();\n * // Expected output matrix:\n * // [[2, 0, 0],\n * //  [0, 1, 1],\n * //  [0, 0, 1]]\n * ```\n *\n * @param labels The target labels, assumed to be 0-based integers\n *   for the classes. The shape is `[numExamples]`, where\n *   `numExamples` is the number of examples included.\n * @param predictions The predicted classes, assumed to be\n *   0-based integers for the classes. Must have the same shape as `labels`.\n * @param numClasses Number of all classes, as an integer.\n *   Its value must be larger than the largest element in `labels` and\n *   `predictions`.\n * @returns The confusion matrix as a int32-type 2D tensor. The value at\n *   row `r` and column `c` is the number of times examples of actual class\n *   `r` were predicted as class `c`.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nexport function confusionMatrix_(labels, predictions, numClasses) {\n    const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');\n    const $predictions = convertToTensor(predictions, 'predictions', 'confusionMatrix');\n    util.assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, ` +\n        `but got ${numClasses}`);\n    util.assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n    util.assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, ` +\n        `but got ${$predictions.rank}`);\n    util.assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ` +\n        `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +\n        `Labels and predictions should have the same number of elements.`);\n    util.assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ` +\n        `${numClasses}`);\n    // TODO(cais): In the future, if oneHot supports tensors inputs for\n    //   `numClasses`, `confusionMatrix` can make `numClasses` optional.\n    const oneHotLabels = oneHot(cast($labels, 'int32'), numClasses);\n    const oneHotPredictions = oneHot(cast($predictions, 'int32'), numClasses);\n    const oneHotLabelsT = transpose(oneHotLabels);\n    return cast(matMul(oneHotLabelsT, oneHotPredictions), 'int32');\n}\nexport const confusionMatrix = op({ confusionMatrix_ });\n//# sourceMappingURL=confusion_matrix.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Exports under the tf.math.* namespace.\n */\nimport { confusionMatrix } from './ops/confusion_matrix';\nexport { confusionMatrix };\n//# sourceMappingURL=math.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor3d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided,  it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor3d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 3) {\n        throw new Error('tensor3d() requires shape to have three numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n        throw new Error('tensor3d() requires values to be number[][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor3d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor3d.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FromPixels } from '../kernel_names';\nimport { getKernel } from '../kernel_registry';\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { tensor3d } from './tensor3d';\nlet fromPixels2DContext;\n/**\n * Creates a `tf.Tensor` from an image.\n *\n * ```js\n * const image = new ImageData(1, 1);\n * image.data[0] = 100;\n * image.data[1] = 150;\n * image.data[2] = 200;\n * image.data[3] = 255;\n *\n * tf.browser.fromPixels(image).print();\n * ```\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel. You can also pass in an image\n * object with following attributes:\n * `{data: Uint8Array; width: number; height: number}`\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n *\n * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}\n */\nfunction fromPixels_(pixels, numChannels = 3) {\n    // Sanity checks.\n    if (numChannels > 4) {\n        throw new Error('Cannot construct Tensor with more than 4 channels from pixels.');\n    }\n    if (pixels == null) {\n        throw new Error('pixels passed to tf.browser.fromPixels() can not be null');\n    }\n    let isPixelData = false;\n    let isImageData = false;\n    let isVideo = false;\n    let isImage = false;\n    let isCanvasLike = false;\n    if (pixels.data instanceof Uint8Array) {\n        isPixelData = true;\n    }\n    else if (typeof (ImageData) !== 'undefined' && pixels instanceof ImageData) {\n        isImageData = true;\n    }\n    else if (typeof (HTMLVideoElement) !== 'undefined' &&\n        pixels instanceof HTMLVideoElement) {\n        isVideo = true;\n    }\n    else if (typeof (HTMLImageElement) !== 'undefined' &&\n        pixels instanceof HTMLImageElement) {\n        isImage = true;\n        // tslint:disable-next-line: no-any\n    }\n    else if (pixels.getContext != null) {\n        isCanvasLike = true;\n    }\n    else {\n        throw new Error('pixels passed to tf.browser.fromPixels() must be either an ' +\n            `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData ` +\n            `in browser, or OffscreenCanvas, ImageData in webworker` +\n            ` or {data: Uint32Array, width: number, height: number}, ` +\n            `but was ${pixels.constructor.name}`);\n    }\n    if (isVideo) {\n        const HAVE_CURRENT_DATA_READY_STATE = 2;\n        if (isVideo &&\n            pixels.readyState <\n                HAVE_CURRENT_DATA_READY_STATE) {\n            throw new Error('The video element has not loaded data yet. Please wait for ' +\n                '`loadeddata` event on the <video> element.');\n        }\n    }\n    // If the current backend has 'FromPixels' registered, it has a more\n    // efficient way of handling pixel uploads, so we call that.\n    const kernel = getKernel(FromPixels, ENGINE.backendName);\n    if (kernel != null) {\n        const inputs = { pixels };\n        const attrs = { numChannels };\n        return ENGINE.runKernel(FromPixels, inputs, attrs);\n    }\n    const [width, height] = isVideo ?\n        [\n            pixels.videoWidth,\n            pixels.videoHeight\n        ] :\n        [pixels.width, pixels.height];\n    let vals;\n    if (isCanvasLike) {\n        vals =\n            // tslint:disable-next-line:no-any\n            pixels.getContext('2d').getImageData(0, 0, width, height).data;\n    }\n    else if (isImageData || isPixelData) {\n        vals = pixels.data;\n    }\n    else if (isImage || isVideo) {\n        if (fromPixels2DContext == null) {\n            fromPixels2DContext = document.createElement('canvas').getContext('2d');\n        }\n        fromPixels2DContext.canvas.width = width;\n        fromPixels2DContext.canvas.height = height;\n        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n        vals = fromPixels2DContext.getImageData(0, 0, width, height).data;\n    }\n    let values;\n    if (numChannels === 4) {\n        values = new Int32Array(vals);\n    }\n    else {\n        const numPixels = width * height;\n        values = new Int32Array(numPixels * numChannels);\n        for (let i = 0; i < numPixels; i++) {\n            for (let channel = 0; channel < numChannels; ++channel) {\n                values[i * numChannels + channel] = vals[i * 4 + channel];\n            }\n        }\n    }\n    const outShape = [height, width, numChannels];\n    return tensor3d(values, outShape, 'int32');\n}\n/**\n * Draws a `tf.Tensor` of pixel values to a byte array or optionally a\n * canvas.\n *\n * When the dtype of the input is 'float32', we assume values in the range\n * [0-1]. Otherwise, when input is 'int32', we assume values in the range\n * [0-255].\n *\n * Returns a promise that resolves when the canvas has been drawn to.\n *\n * @param img A rank-2 or rank-3 tensor. If rank-2, draws grayscale. If\n *     rank-3, must have depth of 1, 3 or 4. When depth of 1, draws\n * grayscale. When depth of 3, we draw with the first three components of\n * the depth dimension corresponding to r, g, b and alpha = 1. When depth of\n * 4, all four components of the depth dimension correspond to r, g, b, a.\n * @param canvas The canvas to draw to.\n *\n * @doc {heading: 'Browser', namespace: 'browser'}\n */\nexport async function toPixels(img, canvas) {\n    let $img = convertToTensor(img, 'img', 'toPixels');\n    if (!(img instanceof Tensor)) {\n        // Assume int32 if user passed a native array.\n        const originalImgTensor = $img;\n        $img = cast(originalImgTensor, 'int32');\n        originalImgTensor.dispose();\n    }\n    if ($img.rank !== 2 && $img.rank !== 3) {\n        throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);\n    }\n    const [height, width] = $img.shape.slice(0, 2);\n    const depth = $img.rank === 2 ? 1 : $img.shape[2];\n    if (depth > 4 || depth === 2) {\n        throw new Error(`toPixels only supports depth of size ` +\n            `1, 3 or 4 but got ${depth}`);\n    }\n    if ($img.dtype !== 'float32' && $img.dtype !== 'int32') {\n        throw new Error(`Unsupported type for toPixels: ${$img.dtype}.` +\n            ` Please use float32 or int32 tensors.`);\n    }\n    const data = await $img.data();\n    const multiplier = $img.dtype === 'float32' ? 255 : 1;\n    const bytes = new Uint8ClampedArray(width * height * 4);\n    for (let i = 0; i < height * width; ++i) {\n        const rgba = [0, 0, 0, 255];\n        for (let d = 0; d < depth; d++) {\n            const value = data[i * depth + d];\n            if ($img.dtype === 'float32') {\n                if (value < 0 || value > 1) {\n                    throw new Error(`Tensor values for a float32 Tensor must be in the ` +\n                        `range [0 - 1] but encountered ${value}.`);\n                }\n            }\n            else if ($img.dtype === 'int32') {\n                if (value < 0 || value > 255) {\n                    throw new Error(`Tensor values for a int32 Tensor must be in the ` +\n                        `range [0 - 255] but encountered ${value}.`);\n                }\n            }\n            if (depth === 1) {\n                rgba[0] = value * multiplier;\n                rgba[1] = value * multiplier;\n                rgba[2] = value * multiplier;\n            }\n            else {\n                rgba[d] = value * multiplier;\n            }\n        }\n        const j = i * 4;\n        bytes[j + 0] = Math.round(rgba[0]);\n        bytes[j + 1] = Math.round(rgba[1]);\n        bytes[j + 2] = Math.round(rgba[2]);\n        bytes[j + 3] = Math.round(rgba[3]);\n    }\n    if (canvas != null) {\n        canvas.width = width;\n        canvas.height = height;\n        const ctx = canvas.getContext('2d');\n        const imageData = new ImageData(bytes, width, height);\n        ctx.putImageData(imageData, 0, 0);\n    }\n    if ($img !== img) {\n        $img.dispose();\n    }\n    return bytes;\n}\nexport const fromPixels = op({ fromPixels_ });\n//# sourceMappingURL=browser.js.map", "import { computeStrides } from '../util';\n/**\n * Validate gather nd inputs.\n *\n * @param tensor The tensor contains the source values.\n * @param indices The tensor contains the indices to slice the source.\n *\n * @returns [resultShape, numUpdates, sliceSize, strides]\n */\nexport function prepareAndValidate(tensor, indices) {\n    if (tensor.rank < 1) {\n        throw new Error('tf.gatherND() expects the input to be rank 1 or higher,' +\n            ` but the rank was ${tensor.rank}.`);\n    }\n    if (indices.rank < 1) {\n        throw new Error('tf.gatherND() expects the indices to be rank 1 or higher,' +\n            ` but the rank was ${indices.rank}.`);\n    }\n    if (indices.dtype !== 'int32') {\n        throw new Error('tf.gatherND() expects the indices to be int32 type,' +\n            ` but the dtype was ${indices.dtype}.`);\n    }\n    if (indices.shape[indices.rank - 1] > tensor.rank) {\n        throw new Error('index innermost dimension length must be <= tensor rank; saw: ' +\n            `${indices.shape[indices.rank - 1]} vs. ${tensor.rank}`);\n    }\n    if (tensor.size === 0) {\n        throw new Error('Requested more than 0 entries, but input is empty.' +\n            ` Input shape: ${tensor.shape}.`);\n    }\n    const indicesShape = indices.shape;\n    const sliceRank = indicesShape[indicesShape.length - 1];\n    // The result shape is\n    //   indices.shape[:-1] + params.shape[indices.shape[-1]:]\n    let nResult = 1;\n    for (let i = 0; i < indicesShape.length - 1; ++i) {\n        nResult *= indicesShape[i];\n    }\n    const inputShape = tensor.shape;\n    const resultShape = indicesShape.slice();\n    resultShape.pop();\n    let sliceSize = 1;\n    for (let i = sliceRank; i < tensor.rank; ++i) {\n        sliceSize *= inputShape[i];\n        resultShape.push(inputShape[i]);\n    }\n    const strides = [...computeStrides(tensor.shape).map(stride => stride / sliceSize),\n        1].slice(0, sliceRank);\n    return [resultShape, nResult, sliceSize, strides];\n}\n//# sourceMappingURL=gather_nd_util.js.map", "import { computeStrides, sizeFromShape } from '../util';\n/**\n * Check whether updates.shape = indices.shape[:batchDim] +\n * shape[sliceDim:]\n *\n * @param x The input tensor.\n */\nexport function validateUpdateShape(shape, indices, updates) {\n    const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n    const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;\n    const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +\n        `shape[sliceDim:], got updates.shape: ${updates.shape}` +\n        `, indices.shape: ${indices.shape}, shape: ${shape}` +\n        `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n    if (updates.rank < batchDim) {\n        throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n    }\n    if (shape.length < sliceDim + (updates.rank - batchDim)) {\n        throw new Error(shapeError +\n            ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n    }\n    if (updates.rank !== batchDim + shape.length - sliceDim) {\n        throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n    }\n    for (let d = 0; d < batchDim; ++d) {\n        if (updates.shape[d] !== indices.shape[d]) {\n            throw new Error(shapeError +\n                ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);\n        }\n    }\n    for (let d = 0; d < updates.rank - batchDim; ++d) {\n        if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n            throw new Error(shapeError +\n                ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);\n        }\n    }\n}\n/**\n * Validate scatter nd inputs.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n */\nexport function validateInput(updates, indices, shape) {\n    if (indices.rank < 1) {\n        throw new Error('tf.scatterND() expects the indices to be rank 1 or higher,' +\n            ` but the rank was ${indices.rank}.`);\n    }\n    if (updates.rank < 1) {\n        throw new Error('tf.scatterND() expects the updates to be rank 1 or higher,' +\n            ` but the rank was ${updates.rank}.`);\n    }\n    if (indices.dtype !== 'int32') {\n        throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);\n    }\n    if (shape.length < 1) {\n        throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);\n    }\n    if (shape.length === 0) {\n        if (indices.size === 0) {\n            throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);\n        }\n        if (updates.size === 0) {\n            throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);\n        }\n    }\n    validateUpdateShape(shape, indices, updates);\n}\n/**\n * Calculate the shape information for the output.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n *\n * @returns ScatterShapeInfo\n */\nexport function calculateShapes(updates, indices, shape) {\n    // Calculate the number of dimensions in indices\n    const indicesRank = indices.shape.length;\n    const sliceRank = (indicesRank > 1) ? indices.shape[indicesRank - 1] : 1;\n    // Calculate the number of elements that make up each slice of our updated\n    // tensor. This allows us to work with flattened tensors and copy over whole\n    // slices at a time.\n    const totalNd = shape.length;\n    let sliceSize = 1;\n    for (let i = sliceRank; i < totalNd; ++i) {\n        sliceSize *= shape[i];\n    }\n    const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;\n    const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;\n    const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];\n    const outputSize = sizeFromShape(shape);\n    return { sliceRank, numUpdates, sliceSize, strides, outputSize };\n}\n//# sourceMappingURL=scatter_nd_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nexport function assertParamsValid(input, begin, size) {\n    const inputRank = input.shape.length;\n    util.assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must ` +\n        `match the rank of the array (${inputRank}).`);\n    util.assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must ` +\n        `match the rank of the array (${inputRank}).`);\n    for (let i = 0; i < inputRank; ++i) {\n        util.assert(begin[i] + size[i] <= input.shape[i], () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] ` +\n            `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${input.shape[i]})`);\n    }\n}\n/** Converts a binary mask to an array of axes. Used in stridedSlice(). */\nexport function maskToAxes(mask) {\n    const axes = [];\n    let axis = 0;\n    while (mask > 0) {\n        if (mask & 1) {\n            axes.push(axis);\n        }\n        mask /= 2;\n        axis++;\n    }\n    return axes;\n}\n/** Computes the output shape given the strided slice params. */\nexport function computeOutShape(begin, end, strides) {\n    const size = [];\n    for (let axis = 0; axis < begin.length; axis++) {\n        size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);\n    }\n    return size;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stride value. Otherwise, insert.\nexport function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {\n    const newStrides = [...strides];\n    for (let i = newStrides.length; i < inputShape.length; i++) {\n        newStrides.push(1);\n    }\n    for (let i = 0; i < numElidedAxes; i++) {\n        if (i === 0) {\n            newStrides[ellipsisInsertionIndex] = 1;\n        }\n        else {\n            newStrides.splice(ellipsisInsertionIndex, 0 /* num elements to delete */, 1 /* element to add */);\n            newStrides.pop();\n        }\n    }\n    return newStrides;\n}\nfunction unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {\n    if (normalizedAxis <= ellipsisInsertionIndex) {\n        return normalizedAxis;\n    }\n    return normalizedAxis - (numElidedAxes - 1);\n}\nfunction getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {\n    const elidedAxes = [];\n    for (let i = 0; i < numElidedAxes; i++) {\n        elidedAxes.push(ellipsisInsertionIndex + i);\n    }\n    return elidedAxes;\n}\n// Normalize the start, end and strides.\nexport function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {\n    const inputRank = inputShape.length;\n    let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);\n    if (ellipsisAxes.length && numInterpolatedAxes > 0) {\n        const fullIndex = ellipsisAxes[0];\n        // The ellipsis applies to the masked index as well as any dimensions\n        // that are interpolated.\n        const numElidedAxes = numInterpolatedAxes + 1;\n        normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);\n        normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);\n        normalizedStrides =\n            stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);\n    }\n    else {\n        for (let axis = 0; axis < inputRank; axis++) {\n            normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);\n            normalizedEnd[axis] =\n                stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);\n            normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);\n        }\n    }\n    return {\n        begin: normalizedBegin,\n        end: normalizedEnd,\n        strides: normalizedStrides\n    };\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current start value. Otherwise, insert.\nexport function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {\n    const newIndices = [...inputShape];\n    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n    for (let axis = 0; axis < newIndices.length; axis++) {\n        if (elidedAxes.indexOf(axis) > -1) {\n            newIndices[axis] = 0;\n        }\n        else {\n            const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n            let originalValue = originalBegin[originalAxis];\n            if (beginMask & 1 << originalAxis) {\n                originalValue = 0;\n            }\n            newIndices[axis] = originalValue;\n        }\n    }\n    return newIndices;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stop value. Otherwise, insert.\nexport function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {\n    const newIndices = [...inputShape];\n    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n    for (let axis = 0; axis < newIndices.length; axis++) {\n        if (elidedAxes.indexOf(axis) > -1) {\n            newIndices[axis] = Number.MAX_SAFE_INTEGER;\n        }\n        else {\n            const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n            let originalValue = originalEnd[originalAxis];\n            if (endMask & 1 << originalAxis) {\n                originalValue = Number.MAX_SAFE_INTEGER;\n            }\n            newIndices[axis] = originalValue;\n        }\n    }\n    for (let i = 0; i < newIndices.length; i++) {\n        // Handle negative indices\n        const axisSize = inputShape[i];\n        if (newIndices[i] < 0) {\n            newIndices[i] += axisSize;\n        }\n        newIndices[i] = util.clamp(0, newIndices[i], inputShape[i]);\n    }\n    return newIndices;\n}\nexport function stridesForAxis(strides, axis, ellipsisMask) {\n    let stride = strides[axis];\n    if (ellipsisMask & (1 << axis) || stride == null) {\n        stride = 1;\n    }\n    return stride;\n}\nexport function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {\n    // Begin with the specified index\n    let start = startIndices[axis];\n    const stride = strides[axis] || 1;\n    // Check the axis bit from right of masked axes, or the begin index is not set\n    // for the axis.\n    if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {\n        if (stride > 0) {\n            // Forward iteration - use the first element. These values will get\n            // clamped below (Note: We could have set them to 0 and axis_size-1, but\n            // use lowest() and max() to maintain symmetry with StopForAxis())\n            start = Number.MIN_SAFE_INTEGER;\n        }\n        else {\n            // Backward iteration - use the last element.\n            start = Number.MAX_SAFE_INTEGER;\n        }\n    }\n    // Handle negative indices\n    const axisSize = inputShape[axis];\n    if (start < 0) {\n        start += axisSize;\n    }\n    // Clamping\n    start = util.clamp(0, start, axisSize - 1);\n    return start;\n}\nexport function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {\n    // Begin with the specified index\n    let stop = stopIndices[axis];\n    const stride = strides[axis] || 1;\n    // Check the axis bit from right of masked axes, or if the stop index is not\n    // set for this axis.\n    if (endMask & (1 << axis) || ellipsisMask & (1 << axis) || stop == null) {\n        if (stride > 0) {\n            // Forward iteration - use the last element. These values will get\n            // clamped below\n            stop = Number.MAX_SAFE_INTEGER;\n        }\n        else {\n            // Backward iteration - use the first element.\n            stop = Number.MIN_SAFE_INTEGER;\n        }\n    }\n    // Handle negative indices\n    const axisSize = inputShape[axis];\n    if (stop < 0) {\n        stop += axisSize;\n    }\n    // Clamping\n    // Because the end index points one past the last element, we need slightly\n    // different clamping ranges depending on the direction.\n    if (stride > 0) {\n        // Forward iteration\n        stop = util.clamp(0, stop, axisSize);\n    }\n    else {\n        // Backward iteration\n        stop = util.clamp(-1, stop, axisSize - 1);\n    }\n    return stop;\n}\n/**\n * Returns true if the slice occupies a continous set of elements in the\n * 'flat' space.\n */\nexport function isSliceContinous(shape, begin, size) {\n    // Index of the first axis that has size > 1.\n    let firstNonOneAxis = size.length;\n    for (let i = 0; i < size.length; i++) {\n        if (size[i] > 1) {\n            firstNonOneAxis = i;\n            break;\n        }\n    }\n    for (let i = firstNonOneAxis + 1; i < size.length; i++) {\n        if (begin[i] > 0 || size[i] !== shape[i]) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function computeFlatOffset(begin, strides) {\n    let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;\n    for (let i = 0; i < begin.length - 1; i++) {\n        flatOffset += begin[i] * strides[i];\n    }\n    return flatOffset;\n}\nexport function parseSliceParams(x, begin, size) {\n    // The following logic allows for more ergonomic calls.\n    let begin_;\n    const xRank = x.shape.length;\n    if (typeof begin === 'number') {\n        begin_ = [begin, ...new Array(xRank - 1).fill(0)];\n    }\n    else if (begin.length < xRank) {\n        begin_ = begin.concat(new Array(xRank - begin.length).fill(0));\n    }\n    else {\n        begin_ = begin.slice();\n    }\n    begin_.forEach(d => {\n        util.assert(d !== -1, () => 'slice() does not support negative begin indexing.');\n    });\n    let size_;\n    if (size == null) {\n        size_ = new Array(xRank).fill(-1);\n    }\n    else if (typeof size === 'number') {\n        size_ = [size, ...new Array(xRank - 1).fill(-1)];\n    }\n    else if (size.length < xRank) {\n        size_ = size.concat(new Array(xRank - size.length).fill(-1));\n    }\n    else {\n        size_ = size;\n    }\n    size_ = size_.map((d, i) => {\n        if (d >= 0) {\n            return d;\n        }\n        else {\n            util.assert(d === -1, () => `Negative size values should be exactly -1 but got ` +\n                `${d} for the slice() size at index ${i}.`);\n            return x.shape[i] - begin_[i];\n        }\n    });\n    return [begin_, size_];\n}\n//# sourceMappingURL=slice_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from './util';\n/**\n * Serializable defines the serialization contract.\n *\n * TFJS requires serializable classes to return their className when asked\n * to avoid issues with minification.\n */\nexport class Serializable {\n    /**\n     * Return the class name for this class to use in serialization contexts.\n     *\n     * Generally speaking this will be the same thing that constructor.name\n     * would have returned.  However, the class name needs to be robust\n     * against minification for serialization/deserialization to work properly.\n     *\n     * There's also places such as initializers.VarianceScaling, where\n     * implementation details between different languages led to different\n     * class hierarchies and a non-leaf node is used for serialization purposes.\n     */\n    getClassName() {\n        return this.constructor\n            .className;\n    }\n    /**\n     * Creates an instance of T from a ConfigDict.\n     *\n     * This works for most descendants of serializable.  A few need to\n     * provide special handling.\n     * @param cls A Constructor for the class to instantiate.\n     * @param config The Configuration for the object.\n     */\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/**\n * Maps string keys to class constructors.\n *\n * Used during (de)serialization from the cross-language JSON format, which\n * requires the class name in the serialization format matches the class\n * names as used in Python, should it exist.\n */\nexport class SerializationMap {\n    constructor() {\n        this.classNameMap = {};\n    }\n    /**\n     * Returns the singleton instance of the map.\n     */\n    static getMap() {\n        if (SerializationMap.instance == null) {\n            SerializationMap.instance = new SerializationMap();\n        }\n        return SerializationMap.instance;\n    }\n    /**\n     * Registers the class as serializable.\n     */\n    static register(cls) {\n        SerializationMap.getMap().classNameMap[cls.className] =\n            [cls, cls.fromConfig];\n    }\n}\n/**\n * Register a class with the serialization map of TensorFlow.js.\n *\n * This is often used for registering custom Layers, so they can be\n * serialized and deserialized.\n *\n * Example:\n *\n * ```js\n * class MyCustomLayer extends tf.layers.Layer {\n *   static className = 'MyCustomLayer';\n *\n *   constructor(config) {\n *     super(config);\n *   }\n * }\n * tf.serialization.registerClass(MyCustomLayer);\n * ```\n *\n * @param cls The class to be registered. It must have a public static member\n *   called `className` defined and the value must be a non-empty string.\n *\n * @doc {heading: 'Models', subheading: 'Serialization', ignoreCI: true}\n */\nexport function registerClass(cls) {\n    assert(cls.className != null, () => `Class being registered does not have the static className ` +\n        `property defined.`);\n    assert(typeof cls.className === 'string', () => `className is required to be a string, but got type ` +\n        typeof cls.className);\n    assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, ` +\n        `which is disallowed.`);\n    SerializationMap.register(cls);\n}\n//# sourceMappingURL=serialization.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { inferShape } from './tensor_util_env';\nimport { arraysEqual, flatten, isString, isTypedArray } from './util';\nconst TEST_EPSILON_FLOAT32 = 1e-3;\nexport const TEST_EPSILON_FLOAT16 = 1e-1;\nexport function expectArraysClose(actual, expected, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon));\n}\nexport function testEpsilon() {\n    return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :\n        TEST_EPSILON_FLOAT16;\n}\nfunction expectArraysPredicate(actual, expected, predicate) {\n    let checkClassType = true;\n    if (isTypedArray(actual) || isTypedArray(expected)) {\n        checkClassType = false;\n    }\n    if (isTypedArray(actual) && isTypedArray(expected)) {\n        checkClassType = true;\n    }\n    if (checkClassType) {\n        const aType = actual.constructor.name;\n        const bType = expected.constructor.name;\n        if (aType !== bType) {\n            throw new Error(`Arrays are of different type. Actual: ${aType}. ` +\n                `Expected: ${bType}`);\n        }\n    }\n    if (Array.isArray(actual) && Array.isArray(expected)) {\n        const actualShape = inferShape(actual);\n        const expectedShape = inferShape(expected);\n        if (!arraysEqual(actualShape, expectedShape)) {\n            throw new Error(`Arrays have different shapes. ` +\n                `Actual: [${actualShape}]. Expected: [${expectedShape}]`);\n        }\n    }\n    const actualFlat = isTypedArray(actual) ? actual : flatten(actual);\n    const expectedFlat = isTypedArray(expected) ?\n        expected :\n        flatten(expected);\n    if (actualFlat.length !== expectedFlat.length) {\n        throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs ` +\n            `expected: ${expectedFlat.length}.\\n` +\n            `Actual:   ${actualFlat}.\\n` +\n            `Expected: ${expectedFlat}.`);\n    }\n    for (let i = 0; i < expectedFlat.length; ++i) {\n        const a = actualFlat[i];\n        const e = expectedFlat[i];\n        if (!predicate(a, e)) {\n            throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\\n` +\n                `Actual:   ${actualFlat}.\\n` +\n                `Expected: ${expectedFlat}.`);\n        }\n    }\n}\nexport function expectPromiseToFail(fn, done) {\n    fn().then(() => done.fail(), () => done());\n}\nexport function expectArraysEqual(actual, expected) {\n    const exp = typeof expected === 'string' || typeof expected === 'number' ||\n        typeof expected === 'boolean' ?\n        [expected] :\n        expected;\n    if (isString(actual) || isString(actual[0]) ||\n        isString(expected) || isString(expected[0])) {\n        // tslint:disable-next-line: triple-equals\n        return expectArraysPredicate(actual, exp, (a, b) => a == b);\n    }\n    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));\n}\nexport function expectNumbersClose(a, e, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    if (!areClose(a, e, epsilon)) {\n        throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n    }\n}\nfunction areClose(a, e, epsilon) {\n    if (!isFinite(a) && !isFinite(e)) {\n        return true;\n    }\n    if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {\n        return false;\n    }\n    return true;\n}\nexport function expectValuesInRange(actual, low, high) {\n    for (let i = 0; i < actual.length; i++) {\n        if (actual[i] < low || actual[i] > high) {\n            throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);\n        }\n    }\n}\nexport function expectArrayBuffersEqual(actual, expected) {\n    // Safari & Jasmine don't like comparing ArrayBuffers directly. Wrapping in\n    // a Float32Array solves this issue.\n    expect(new Float32Array(actual)).toEqual(new Float32Array(expected));\n}\n//# sourceMappingURL=test_util.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { env } from './environment';\nimport { setDeprecationWarningFn } from './tensor';\nimport { getTensorsInContainer } from './tensor_util';\n/**\n * Enables production mode which disables correctness checks in favor of\n * performance.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableProdMode() {\n    env().set('PROD', true);\n}\n/**\n * Enables debug mode which will log information about all executed kernels:\n * the elapsed time of the kernel execution, as well as the rank, shape, and\n * size of the output tensor.\n *\n * Debug mode will significantly slow down your application as it will\n * download the result of every operation to the CPU. This should not be used in\n * production. Debug mode does not affect the timing information of the kernel\n * execution as we do not measure download time in the kernel execution time.\n *\n * See also: `tf.profile`, `tf.memory`.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableDebugMode() {\n    env().set('DEBUG', true);\n}\n/** Globally disables deprecation warnings */\nexport function disableDeprecationWarnings() {\n    env().set('DEPRECATION_WARNINGS_ENABLED', false);\n    console.warn(`TensorFlow.js deprecation warnings have been disabled.`);\n}\n/** Warn users about deprecated functionality. */\nexport function deprecationWarn(msg) {\n    if (env().getBool('DEPRECATION_WARNINGS_ENABLED')) {\n        console.warn(msg + ' You can disable deprecation warnings with ' +\n            'tf.disableDeprecationWarnings().');\n    }\n}\nsetDeprecationWarningFn(deprecationWarn);\n/**\n * Dispose all variables kept in backend engine.\n *\n * @doc {heading: 'Environment'}\n */\nexport function disposeVariables() {\n    ENGINE.disposeVariables();\n}\n/**\n * It returns the global engine that keeps track of all tensors and backends.\n *\n * @doc {heading: 'Environment'}\n */\nexport function engine() {\n    return ENGINE;\n}\n/**\n * Returns memory info at the current time in the program. The result is an\n * object with the following properties:\n *\n * - `numBytes`: Number of bytes allocated (undisposed) at this time.\n * - `numTensors`: Number of unique tensors allocated.\n * - `numDataBuffers`: Number of unique data buffers allocated\n *   (undisposed) at this time, which is \u2264 the number of tensors\n *   (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same\n *   data buffer with `a`).\n * - `unreliable`: True if the memory usage is unreliable. See `reasons` when\n *    `unreliable` is true.\n * - `reasons`: `string[]`, reasons why the memory is unreliable, present if\n *    `unreliable` is true.\n *\n * WebGL Properties:\n * - `numBytesInGPU`: Number of bytes allocated (undisposed) in the GPU only at\n *     this time.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function memory() {\n    return ENGINE.memory();\n}\n/**\n * Executes the provided function `f()` and returns a promise that resolves\n * with information about the function's memory use:\n * - `newBytes`: the number of new bytes allocated\n * - `newTensors`: the number of new tensors created\n * - `peakBytes`: the peak number of bytes allocated\n * - `kernels`: an array of objects for each kernel involved that reports\n * their input and output shapes, number of bytes used, and number of new\n * tensors created.\n *\n * ```js\n * const profile = await tf.profile(() => {\n *   const x = tf.tensor1d([1, 2, 3]);\n *   let x2 = x.square();\n *   x2.dispose();\n *   x2 = x.square();\n *   x2.dispose();\n *   return x;\n * });\n *\n * console.log(`newBytes: ${profile.newBytes}`);\n * console.log(`newTensors: ${profile.newTensors}`);\n * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>\n * k.totalBytesSnapshot)}`);\n * ```\n *\n *\n * @doc {heading: 'Performance', subheading: 'Profile'}\n */\nexport function profile(f) {\n    return ENGINE.profile(f);\n}\n/**\n * Executes the provided function `fn` and after it is executed, cleans up all\n * intermediate tensors allocated by `fn` except those returned by `fn`.\n * `fn` must not return a Promise (async functions not allowed). The returned\n * result can be a complex object.\n *\n * Using this method helps avoid memory leaks. In general, wrap calls to\n * operations in `tf.tidy` for automatic memory cleanup.\n *\n * NOTE: Variables do *not* get cleaned up when inside a tidy(). If you want to\n * dispose variables, please use `tf.disposeVariables` or call dispose()\n * directly on variables.\n *\n * ```js\n * // y = 2 ^ 2 + 1\n * const y = tf.tidy(() => {\n *   // a, b, and one will be cleaned up when the tidy ends.\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *   const b = a.square();\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * y.print();\n * ```\n *\n * @param nameOrFn The name of the closure, or the function to execute.\n *     If a name is provided, the 2nd argument should be the function.\n *     If debug mode is on, the timing and the memory usage of the function\n *     will be tracked and displayed on the console using the provided name.\n * @param fn The function to execute.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function tidy(nameOrFn, fn) {\n    return ENGINE.tidy(nameOrFn, fn);\n}\n/**\n * Disposes any `tf.Tensor`s found within the provided object.\n *\n * @param container an object that may be a `tf.Tensor` or may directly\n *     contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If\n *     the object is not a `tf.Tensor` or does not contain `Tensors`, nothing\n *     happens. In general it is safe to pass any object here, except that\n *     `Promise`s are not supported.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function dispose(container) {\n    const tensors = getTensorsInContainer(container);\n    tensors.forEach(tensor => tensor.dispose());\n}\n/**\n * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed\n * automatically.\n *\n * ```js\n * let b;\n * const y = tf.tidy(() => {\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *\n *   // b will not be cleaned up by the tidy. a and one will be cleaned up\n *   // when the tidy ends.\n *   b = tf.keep(a.square());\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * console.log('y:');\n * y.print();\n * console.log('b:');\n * b.print();\n * ```\n *\n * @param result The tensor to keep from being disposed.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function keep(result) {\n    return ENGINE.keep(result);\n}\n/**\n * Executes `f()` and returns a promise that resolves with timing\n * information.\n *\n * The result is an object with the following properties:\n *\n * - `wallMs`: Wall execution time.\n * - `kernelMs`: Kernel execution time, ignoring data transfer. If using the\n * WebGL backend and the query timer extension is not available, this will\n * return an error object.\n * - On `WebGL` The following additional properties exist:\n *   - `uploadWaitMs`: CPU blocking time on texture uploads.\n *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).\n *\n * ```js\n * const x = tf.randomNormal([20, 20]);\n * const time = await tf.time(() => x.matMul(x));\n *\n * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);\n * ```\n *\n * @param f The function to execute and time.\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nexport function time(f) {\n    return ENGINE.time(f);\n}\n/**\n * Sets the backend (cpu, webgl, wasm, etc) responsible for creating tensors and\n * executing operations on those tensors. Returns a promise that resolves\n * to a boolean if the backend initialization was successful.\n *\n * Note this disposes the current backend, if any, as well as any tensors\n * associated with it. A new backend is initialized, even if it is of the\n * same type as the previous one.\n *\n * @param backendName The name of the backend. Currently supports\n *     `'webgl'|'cpu'` in the browser, `'tensorflow'` under node.js\n *     (requires tfjs-node), and `'wasm'` (requires tfjs-backend-wasm).\n *\n * @doc {heading: 'Backends'}\n */\nexport function setBackend(backendName) {\n    return ENGINE.setBackend(backendName);\n}\n/**\n * Returns a promise that resolves when the currently selected backend (or the\n * highest priority one) has initialized. Await this promise when you are using\n * a backend that has async initialization.\n *\n * @doc {heading: 'Backends'}\n */\nexport function ready() {\n    return ENGINE.ready();\n}\n/**\n * Returns the current backend name (cpu, webgl, etc). The backend is\n * responsible for creating tensors and executing operations on those tensors.\n *\n * @doc {heading: 'Backends'}\n */\nexport function getBackend() {\n    return ENGINE.backendName;\n}\n/**\n * Removes a backend and the registered factory.\n *\n * @doc {heading: 'Backends'}\n */\nexport function removeBackend(name) {\n    ENGINE.removeBackend(name);\n}\n/**\n * Finds the backend registered under the provided name. Returns null if the\n * name is not in the registry, or the registration hasn't finished yet.\n */\nexport function findBackend(name) {\n    return ENGINE.findBackend(name);\n}\n/**\n * Finds the backend factory registered under the provided name. Returns a\n * function that produces a new backend when called. Returns null if the name\n * is not in the registry.\n */\nexport function findBackendFactory(name) {\n    return ENGINE.findBackendFactory(name);\n}\n/**\n * Registers a global backend. The registration should happen when importing\n * a module file (e.g. when importing `backend_webgl.ts`), and is used for\n * modular builds (e.g. custom tfjs bundle with only webgl support).\n *\n * @param factory The backend factory function. When called, it should\n * return a backend instance, or a promise of an instance.\n * @param priority The priority of the backend (higher = more important).\n *     In case multiple backends are registered, the priority is used to find\n *     the best backend. Defaults to 1.\n * @return False if there is already a registered backend under this name, true\n *     if not.\n *\n * @doc {heading: 'Backends'}\n */\nexport function registerBackend(name, factory, priority = 1) {\n    return ENGINE.registerBackend(name, factory, priority);\n}\n/**\n * Gets the current backend. If no backends have been initialized, this will\n * attempt to initialize the best backend. Will throw an error if the highest\n * priority backend has async initialization, in which case, you should call\n * 'await tf.ready()' before running other code.\n *\n * @doc {heading: 'Backends'}\n */\nexport function backend() {\n    return ENGINE.backend;\n}\n/**\n * Sets the global platform.\n *\n * @param platformName The name of this platform.\n * @param platform A platform implementation.\n */\nexport function setPlatform(platformName, platform) {\n    env().setPlatform(platformName, platform);\n}\n//# sourceMappingURL=globals.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Add } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n *\n * ```js\n * // Broadcast add a with b.\n * const a = tf.scalar(5);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n * @param a The first `tf.Tensor` to add.\n * @param b The second `tf.Tensor` to add. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction add_(a, b) {\n    let $a = convertToTensor(a, 'a', 'add');\n    let $b = convertToTensor(b, 'b', 'add');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.add($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Add);\n}\nexport const add = op({ add_ });\n//# sourceMappingURL=add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FloorDiv } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n * The result is rounded with floor function.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.floorDiv(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.floorDiv(b).print();  // or tf.floorDiv(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction floorDiv_(a, b) {\n    let $a = convertToTensor(a, 'a', 'floorDiv');\n    let $b = convertToTensor(b, 'b', 'floorDiv');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.floorDiv($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, FloorDiv);\n}\nexport const floorDiv = op({ floorDiv_ });\n//# sourceMappingURL=floorDiv.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Div } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { floorDiv } from './floorDiv';\nimport { op } from './operation';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction div_(a, b) {\n    let $a = convertToTensor(a, 'a', 'div');\n    let $b = convertToTensor(b, 'b', 'div');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'int32' && $b.dtype === 'int32') {\n        return floorDiv($a, $b);\n    }\n    const forward = (backend, save) => {\n        const res = backend.realDivide($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = {};\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Div, attrs);\n}\nexport const div = op({ div_ });\n//# sourceMappingURL=div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Multiply } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.\n *\n * We also expose `tf.mulStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([2, 3, 4, 5]);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n *\n * ```js\n * // Broadcast mul a with b.\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.scalar(5);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n * @param a The first tensor to multiply.\n * @param b The second tensor to multiply. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mul_(a, b) {\n    let $a = convertToTensor(a, 'a', 'mul');\n    let $b = convertToTensor(b, 'b', 'mul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.multiply($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Multiply);\n}\nexport const mul = op({ mul_ });\n//# sourceMappingURL=mul.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Abs } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes absolute value element-wise: `abs(x)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.abs().print();  // or tf.abs(x)\n * ```\n * @param x The input `tf.Tensor`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction abs_(x) {\n    const $x = convertToTensor(x, 'x', 'abs');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'complex64') {\n            return backend.complexAbs($x);\n        }\n        return backend.abs($x);\n    }, inputs, null /* grad */, Abs);\n}\nexport const abs = op({ abs_ });\n//# sourceMappingURL=abs.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.acos().print();  // or tf.acos(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acos_(x) {\n    const $x = convertToTensor(x, 'x', 'acos');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.acos($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Acos);\n}\nexport const acos = op({ acos_ });\n//# sourceMappingURL=acos.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:\n * `acosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([10, 1, 3, 5.7]);\n *\n * x.acosh().print();  // or tf.acosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acosh_(x) {\n    const $x = convertToTensor(x, 'x', 'acosh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.acosh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Acosh);\n}\nexport const acosh = op({ acosh_ });\n//# sourceMappingURL=acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AddN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n *\n * tf.addN([a, b, c]).print();\n * ```\n * @param tensors A list of tensors with the same shape and dtype.\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction addN_(tensors) {\n    util.assert(Array.isArray(tensors), () => 'The argument passed to tf.addN() must be a list of tensors');\n    util.assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ` +\n        `${tensors.length}`);\n    const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));\n    const firstTensor = $tensors[0];\n    $tensors.forEach(t => {\n        if (t.dtype !== firstTensor.dtype) {\n            throw new Error('All tensors passed to tf.addN() must have the same dtype');\n        }\n    });\n    $tensors.forEach(t => {\n        if (!util.arraysEqual(t.shape, firstTensor.shape)) {\n            throw new Error('All tensors passed to tf.addN() must have the same shape');\n        }\n    });\n    const forward = (backend, save) => {\n        const res = backend.addN($tensors);\n        save($tensors);\n        return res;\n    };\n    const inputs = $tensors;\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, AddN);\n}\nexport const addN = op({ addN_ });\n//# sourceMappingURL=add_n.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n * Returns true if the axis specifies the inner most dimensions of the\n * array.\n */\nexport function axesAreInnerMostDims(axes, rank) {\n    for (let i = 0; i < axes.length; ++i) {\n        if (axes[axes.length - i - 1] !== rank - 1 - i) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function combineLocations(outputLoc, reduceLoc, axes) {\n    const rank = outputLoc.length + reduceLoc.length;\n    const loc = [];\n    let outIdx = 0;\n    let reduceIdx = 0;\n    for (let dim = 0; dim < rank; dim++) {\n        if (axes.indexOf(dim) === -1) {\n            loc.push(outputLoc[outIdx++]);\n        }\n        else {\n            loc.push(reduceLoc[reduceIdx++]);\n        }\n    }\n    return loc;\n}\nexport function computeOutAndReduceShapes(aShape, axes) {\n    const outShape = [];\n    const rank = aShape.length;\n    for (let dim = 0; dim < rank; dim++) {\n        if (axes.indexOf(dim) === -1) {\n            outShape.push(aShape[dim]);\n        }\n    }\n    const reduceShape = axes.map(dim => aShape[dim]);\n    return [outShape, reduceShape];\n}\nexport function expandShapeToKeepDim(shape, axes) {\n    const reduceSubShape = axes.map(x => 1);\n    return combineLocations(shape, reduceSubShape, axes);\n}\nexport function assertAxesAreInnerMostDims(msg, axes, rank) {\n    util.assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. ` +\n        `Got axes ${axes} and rank-${rank} input.`);\n}\n/**\n * Returns the axes permutation to be used with `tf.transpose`, if such\n * permutation is necessary. Otherwise it returns null. This method is used by\n * operations that operate only on inner-most axes.\n */\nexport function getAxesPermutation(axes, rank) {\n    if (axesAreInnerMostDims(axes, rank)) {\n        return null;\n    }\n    const result = [];\n    for (let i = 0; i < rank; ++i) {\n        if (axes.indexOf(i) === -1) {\n            result.push(i);\n        }\n    }\n    axes.forEach(axis => result.push(axis));\n    return result;\n}\n/** Returns the axes permutation that undoes the original permutation. */\nexport function getUndoAxesPermutation(axes) {\n    return axes.map((axis, i) => [i, axis])\n        .sort((a, b) => a[1] - b[1])\n        .map(x => x[0]);\n}\nexport function getInnerMostAxes(numAxes, rank) {\n    const res = [];\n    for (let i = rank - numAxes; i < rank; ++i) {\n        res.push(i);\n    }\n    return res;\n}\n//# sourceMappingURL=axis_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { All } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the logical and of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.all().print();  // or tf.all(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.all(axis).print();  // or tf.all(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction all_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'all', 'bool');\n    const forward = (backend) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = getInnerMostAxes(axes.length, $x.rank);\n        }\n        const res = backend.all($x, axes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(res.shape, origAxes);\n            return reshape(res, newShape);\n        }\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, All, attrs);\n}\nexport const all = op({ all_ });\n//# sourceMappingURL=all.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Any } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the logical or of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.any().print();  // or tf.any(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.any(axis).print();  // or tf.any(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction any_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'any', 'bool');\n    const forward = (backend) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = getInnerMostAxes(axes.length, $x.rank);\n        }\n        const res = backend.any($x, axes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(res.shape, origAxes);\n            return reshape(res, newShape);\n        }\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Any, attrs);\n}\n// tslint:disable-next-line:variable-name\nexport const any = op({ any_ });\n//# sourceMappingURL=any.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Returns the indices of the maximum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMax().print();  // or tf.argMax(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMax(axis).print();  // or tf.argMax(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMax_(x, axis = 0) {\n    let $x = convertToTensor(x, 'x', 'argMax');\n    const forward = (backend, save) => {\n        save([$x]);\n        let axes = util.parseAxisParam(axis, $x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        return backend.argMax($x, axes[0]);\n    };\n    const inputs = { x: $x };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ArgMax, attrs);\n}\nexport const argMax = op({ argMax_ });\n//# sourceMappingURL=arg_max.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Returns the indices of the minimum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMin().print();  // or tf.argMin(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMin(axis).print();  // or tf.argMin(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMin_(x, axis = 0) {\n    let $x = convertToTensor(x, 'x', 'argMin');\n    const forward = (backend, save) => {\n        save([$x]);\n        if (axis == null) {\n            axis = 0;\n        }\n        let axes = util.parseAxisParam(axis, $x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        return backend.argMin($x, axes[0]);\n    };\n    const inputs = { x: $x };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ArgMin, attrs);\n}\nexport const argMin = op({ argMin_ });\n//# sourceMappingURL=arg_min.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asin().print();  // or tf.asin(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asin_(x) {\n    const $x = convertToTensor(x, 'x', 'asin');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.asin($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Asin);\n}\nexport const asin = op({ asin_ });\n//# sourceMappingURL=asin.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:\n * `asinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asinh().print();  // or tf.asinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asinh_(x) {\n    const $x = convertToTensor(x, 'x', 'asinh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.asinh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Asinh);\n}\nexport const asinh = op({ asinh_ });\n//# sourceMappingURL=asinh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.atan().print();  // or tf.atan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan_(x) {\n    const $x = convertToTensor(x, 'x', 'atan');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.atan($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Atan);\n}\nexport const atan = op({ atan_ });\n//# sourceMappingURL=atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan2 } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);\n * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);\n *\n * tf.atan2(a, b).print()\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan2_(a, b) {\n    let $a = convertToTensor(a, 'a', 'atan2');\n    let $b = convertToTensor(b, 'b', 'atan2');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.atan2($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Atan2);\n}\nexport const atan2 = op({ atan2_ });\n//# sourceMappingURL=atan2.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:\n * `atanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.atanh().print();  // or tf.atanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atanh_(x) {\n    const $x = convertToTensor(x, 'x', 'atanh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.atanh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Atanh);\n}\nexport const atanh = op({ atanh_ });\n//# sourceMappingURL=atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n *\n * @param inputShape Input tensor shape is of the following dimensions:\n *     `[batch, height, width, inChannels]`.\n * @param filterShape The filter shape is of the following dimensions:\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat The data format of the input and output data.\n *     Defaults to 'NHWC'.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`.\n *     Defaults to `[1, 1]`. If `dilations` is a single number, then\n *     `dilationHeight == dilationWidth`.\n */\nexport function computeDilation2DInfo(inputShape, filterShape, strides, pad, dataFormat = 'NHWC', dilations) {\n    // `computerConv2DInfo` require filterShape to be in the dimension of:\n    // `[filterHeight, filterWidth, depth, outDepth]`, dilation2d doesn't have\n    // outDepth, it should have the same depth as the input.\n    // Input shape: [batch, height, width, inChannels]\n    const inputChannels = inputShape[3];\n    const $filterShape = [...filterShape, inputChannels];\n    const $dataFormat = convertConv2DDataFormat(dataFormat);\n    return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad, null /* roundingMode */, null /* depthWise */, $dataFormat);\n}\nexport function computePool2DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'channelsLast') {\n    const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n    let filterShape;\n    if (dataFormat === 'channelsLast') {\n        filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n    }\n    else if (dataFormat === 'channelsFirst') {\n        filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    return computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, false, dataFormat);\n}\n/**\n * Computes the information for a forward pass of a pooling3D operation.\n */\nexport function computePool3DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'NDHWC') {\n    const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);\n    let filterShape;\n    let $dataFormat;\n    if (dataFormat === 'NDHWC') {\n        $dataFormat = 'channelsLast';\n        filterShape =\n            [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];\n    }\n    else if (dataFormat === 'NCDHW') {\n        $dataFormat = 'channelsFirst';\n        filterShape =\n            [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    return computeConv3DInfo(inShape, filterShape, strides, dilations, pad, false, $dataFormat, roundingMode);\n}\n/**\n * Computes the information for a forward pass of a convolution/pooling\n * operation.\n */\nexport function computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, depthwise = false, dataFormat = 'channelsLast') {\n    let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n    if (dataFormat === 'channelsLast') {\n        [batchSize, inHeight, inWidth, inChannels] = inShape;\n    }\n    else if (dataFormat === 'channelsFirst') {\n        [batchSize, inChannels, inHeight, inWidth] = inShape;\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    const [filterHeight, filterWidth, , filterChannels] = filterShape;\n    const [strideHeight, strideWidth] = parseTupleParam(strides);\n    const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n    const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);\n    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n    let outShape;\n    if (dataFormat === 'channelsFirst') {\n        outShape = [batchSize, outChannels, outHeight, outWidth];\n    }\n    else if (dataFormat === 'channelsLast') {\n        outShape = [batchSize, outHeight, outWidth, outChannels];\n    }\n    return {\n        batchSize,\n        dataFormat,\n        inHeight,\n        inWidth,\n        inChannels,\n        outHeight,\n        outWidth,\n        outChannels,\n        padInfo,\n        strideHeight,\n        strideWidth,\n        filterHeight,\n        filterWidth,\n        effectiveFilterHeight,\n        effectiveFilterWidth,\n        dilationHeight,\n        dilationWidth,\n        inShape,\n        outShape,\n        filterShape\n    };\n}\n/**\n * Computes the information for a forward pass of a 3D convolution/pooling\n * operation.\n */\nexport function computeConv3DInfo(inShape, filterShape, strides, dilations, pad, depthwise = false, dataFormat = 'channelsLast', roundingMode) {\n    let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];\n    if (dataFormat === 'channelsLast') {\n        [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n    }\n    else if (dataFormat === 'channelsFirst') {\n        [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;\n    const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n    const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);\n    const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);\n    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n    const { padInfo, outDepth, outHeight, outWidth } = get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);\n    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n    let outShape;\n    if (dataFormat === 'channelsFirst') {\n        outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n    }\n    else if (dataFormat === 'channelsLast') {\n        outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n    }\n    return {\n        batchSize,\n        dataFormat,\n        inDepth,\n        inHeight,\n        inWidth,\n        inChannels,\n        outDepth,\n        outHeight,\n        outWidth,\n        outChannels,\n        padInfo,\n        strideDepth,\n        strideHeight,\n        strideWidth,\n        filterDepth,\n        filterHeight,\n        filterWidth,\n        effectiveFilterDepth,\n        effectiveFilterHeight,\n        effectiveFilterWidth,\n        dilationDepth,\n        dilationHeight,\n        dilationWidth,\n        inShape,\n        outShape,\n        filterShape\n    };\n}\nfunction computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {\n    if (zeroPad == null) {\n        zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n    }\n    const inputRows = inShape[0];\n    const inputCols = inShape[1];\n    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    return [outputRows, outputCols];\n}\nfunction computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {\n    if (zeroPad == null) {\n        zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n    }\n    const inputDepth = inShape[0];\n    const inputRows = inShape[1];\n    const inputCols = inShape[2];\n    const outputDepths = conditionalRound((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputDepths), () => `The output # of depths (${outputDepths}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    return [outputDepths, outputRows, outputCols, outChannels];\n}\nexport function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {\n    const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n    return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\nfunction parseTupleParam(param) {\n    if (typeof param === 'number') {\n        return [param, param, param];\n    }\n    if (param.length === 2) {\n        return [param[0], param[1], 1];\n    }\n    return param;\n}\nfunction parse3TupleParam(param) {\n    return typeof param === 'number' ? [param, param, param] : param;\n}\n/* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d\n * Atrous convolution is equivalent to standard convolution with upsampled\n * filters with effective_filter_height =\n * filter_height + (filter_height - 1) * (dilation - 1)\n * and effective_filter_width =\n * filter_width + (filter_width - 1) * (dilation - 1),\n * produced by inserting dilation - 1 zeros along consecutive elements across\n * the filters' spatial dimensions.\n * When there is a dilation, this converts a filter dimension to the\n * effective filter dimension, so it can be used in a standard convolution.\n */\nfunction getEffectiveFilterSize(filterSize, dilation) {\n    if (dilation <= 1) {\n        return filterSize;\n    }\n    return filterSize + (filterSize - 1) * (dilation - 1);\n}\nfunction getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {\n    let padInfo;\n    let outHeight;\n    let outWidth;\n    if (typeof pad === 'number') {\n        const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n        padInfo = { top: pad, bottom: pad, left: pad, right: pad, type: padType };\n        const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad, roundingMode);\n        outHeight = outShape[0];\n        outWidth = outShape[1];\n    }\n    else if (pad === 'same') {\n        outHeight = Math.ceil(inHeight / strideHeight);\n        outWidth = Math.ceil(inWidth / strideWidth);\n        const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);\n        const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);\n        const top = Math.floor(padAlongHeight / 2);\n        const bottom = padAlongHeight - top;\n        const left = Math.floor(padAlongWidth / 2);\n        const right = padAlongWidth - left;\n        padInfo = { top, bottom, left, right, type: 'SAME' };\n    }\n    else if (pad === 'valid') {\n        padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: 'VALID' };\n        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n    }\n    else if (typeof pad === 'object') {\n        const top = dataFormat === 'channelsLast' ? pad[1][0] : pad[2][0];\n        const bottom = dataFormat === 'channelsLast' ? pad[1][1] : pad[2][1];\n        const left = dataFormat === 'channelsLast' ? pad[2][0] : pad[3][0];\n        const right = dataFormat === 'channelsLast' ? pad[2][1] : pad[3][1];\n        const padType = (top === 0 && bottom === 0 && left === 0 && right === 0) ?\n            'VALID' :\n            'EXPLICIT';\n        padInfo = { top, bottom, left, right, type: padType };\n        outHeight = conditionalRound((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);\n        outWidth = conditionalRound((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);\n    }\n    else {\n        throw Error(`Unknown padding parameter: ${pad}`);\n    }\n    return { padInfo, outHeight, outWidth };\n}\nfunction get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {\n    let padInfo;\n    let outDepth;\n    let outHeight;\n    let outWidth;\n    if (typeof pad === 'number') {\n        const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n        padInfo = {\n            top: pad,\n            bottom: pad,\n            left: pad,\n            right: pad,\n            front: pad,\n            back: pad,\n            type: padType\n        };\n        const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad, roundingMode);\n        outDepth = outShape[0];\n        outHeight = outShape[1];\n        outWidth = outShape[2];\n    }\n    else if (pad === 'same') {\n        outDepth = Math.ceil(inDepth / strideDepth);\n        outHeight = Math.ceil(inHeight / strideHeight);\n        outWidth = Math.ceil(inWidth / strideWidth);\n        const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n        const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;\n        const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n        const front = Math.floor(padAlongDepth / 2);\n        const back = padAlongDepth - front;\n        const top = Math.floor(padAlongHeight / 2);\n        const bottom = padAlongHeight - top;\n        const left = Math.floor(padAlongWidth / 2);\n        const right = padAlongWidth - left;\n        padInfo = { top, bottom, left, right, front, back, type: 'SAME' };\n    }\n    else if (pad === 'valid') {\n        padInfo = {\n            top: 0,\n            bottom: 0,\n            left: 0,\n            right: 0,\n            front: 0,\n            back: 0,\n            type: 'VALID'\n        };\n        outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);\n        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n    }\n    else {\n        throw Error(`Unknown padding parameter: ${pad}`);\n    }\n    return { padInfo, outDepth, outHeight, outWidth };\n}\n/**\n * Rounds a value depending on the rounding mode\n * @param value\n * @param roundingMode\n */\nfunction conditionalRound(value, roundingMode) {\n    if (!roundingMode) {\n        return value;\n    }\n    switch (roundingMode) {\n        case 'round':\n            // used for Caffe Conv\n            return Math.round(value);\n        case 'ceil':\n            // used for Caffe Pool\n            return Math.ceil(value);\n        case 'floor':\n            return Math.floor(value);\n        default:\n            throw new Error(`Unknown roundingMode ${roundingMode}`);\n    }\n}\nexport function tupleValuesAreOne(param) {\n    const [dimA, dimB, dimC] = parseTupleParam(param);\n    return dimA === 1 && dimB === 1 && dimC === 1;\n}\nexport function eitherStridesOrDilationsAreOne(strides, dilations) {\n    return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n/**\n * Convert Conv2D dataFormat from 'NHWC'|'NCHW' to\n *    'channelsLast'|'channelsFirst'\n * @param dataFormat in 'NHWC'|'NCHW' mode\n * @return dataFormat in 'channelsLast'|'channelsFirst' mode\n * @throws unknown dataFormat\n */\nexport function convertConv2DDataFormat(dataFormat) {\n    if (dataFormat === 'NHWC') {\n        return 'channelsLast';\n    }\n    else if (dataFormat === 'NCHW') {\n        return 'channelsFirst';\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n}\n//# sourceMappingURL=conv_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction avgPool_(x, filterSize, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'avgPool', 'float32');\n    const dilations = 1;\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in avgPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        save([x4D]);\n        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n            util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n            return x4D.clone();\n        }\n        return backend.avgPool(x4D, convInfo);\n    };\n    const inputs = { x: x4D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool, attrs);\n    res = cast(res, $x.dtype);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const avgPool = op({ avgPool_ });\n//# sourceMappingURL=avg_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { deprecationWarn } from '../globals';\nimport { AvgPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D average pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.avgPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates:\n *     `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction avgPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat = 'NDHWC', dilations) {\n    if (dilations == null) {\n        dilations = [1, 1, 1];\n    }\n    else {\n        deprecationWarn('dilations is deprecated, this field will be gone in ' +\n            'v3.0.0.');\n    }\n    const $x = convertToTensor(x, 'x', 'avgPool3d', 'float32');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in avgPool3d: Only NDHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool3d: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in avgPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1, 1];\n        }\n        const convInfo = conv_util.computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);\n        save([x5D]);\n        return backend.avgPool3d(x5D, convInfo);\n    };\n    const inputs = { x: x5D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat, dilations };\n    let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool3D, attrs);\n    res = cast(res, x5D.dtype);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const avgPool3d = op({ avgPool3d_ });\n//# sourceMappingURL=avg_pool_3d.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nexport function assertParamsConsistent(shapes, axis) {\n    const rank = shapes[0].length;\n    shapes.forEach((shape, i) => {\n        util.assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same ` +\n            `as the rank of the rest (${rank})`);\n    });\n    util.assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);\n    const firstShape = shapes[0];\n    shapes.forEach((shape, i) => {\n        for (let r = 0; r < rank; r++) {\n            util.assert((r === axis) || (shape[r] === firstShape[r]), () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) ` +\n                `does not match the shape of the rest (${firstShape}) ` +\n                `along the non-concatenated axis ${i}.`);\n        }\n    });\n}\nexport function computeOutShape(shapes, axis) {\n    const outputShape = shapes[0].slice();\n    for (let i = 1; i < shapes.length; i++) {\n        outputShape[axis] += shapes[i][axis];\n    }\n    return outputShape;\n}\n//# sourceMappingURL=concat_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Concat } from '../kernel_names';\nimport { convertToTensorArray } from '../tensor_util_env';\nimport { assert, parseAxisParam, sizeFromShape } from '../util';\nimport { assertParamsConsistent, computeOutShape } from './concat_util';\nimport { op } from './operation';\nimport { tensor } from './tensor';\n/**\n * Concatenates a list of `tf.Tensor`s along a given axis.\n *\n * The tensors ranks and types must match, and their sizes must match in all\n * dimensions except `axis`.\n *\n * Also available are stricter rank-specific methods that assert that\n * `tensors` are of the given rank:\n *   - `tf.concat1d`\n *   - `tf.concat2d`\n *   - `tf.concat3d`\n *   - `tf.concat4d`\n *\n * Except `tf.concat1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * a.concat(b).print();  // or a.concat(b)\n * ```\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.concat([a, b, c]).print();\n * ```\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [10, 20]]);\n * const b = tf.tensor2d([[3, 4], [30, 40]]);\n * const axis = 1;\n * tf.concat([a, b], axis).print();\n * ```\n * @param tensors A list of tensors to concatenate.\n * @param axis The axis to concate along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction concat_(tensors, axis = 0) {\n    assert(tensors.length >= 1, () => 'Pass at least one tensor to concat');\n    let $tensors = convertToTensorArray(tensors, 'tensors', 'concat');\n    if ($tensors[0].dtype === 'complex64') {\n        $tensors.forEach(tensor => {\n            if (tensor.dtype !== 'complex64') {\n                throw new Error(`Cannot concatenate complex64 tensors with a tensor\n          with dtype ${tensor.dtype}. `);\n            }\n        });\n    }\n    const forward = (backend, save) => {\n        const $axis = parseAxisParam(axis, $tensors[0].shape)[0];\n        const outShape = computeOutShape($tensors.map(t => t.shape), $axis);\n        if (sizeFromShape(outShape) === 0) {\n            return tensor([], outShape);\n        }\n        // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n        $tensors = $tensors.filter(t => t.size > 0);\n        if ($tensors.length === 1) {\n            return $tensors[0];\n        }\n        const shapes = $tensors.map(t => t.shape);\n        assertParamsConsistent(shapes, $axis);\n        const res = backend.concat($tensors, $axis);\n        save($tensors);\n        return res;\n    };\n    const inputs = $tensors;\n    const attr = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Concat, attr);\n}\nexport const concat = op({ concat_ });\n//# sourceMappingURL=concat.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sigmoid } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sigmoid element-wise, `1 / (1 + exp(-x))`\n *\n * ```js\n * const x = tf.tensor1d([0, -1, 2, -3]);\n *\n * x.sigmoid().print();  // or tf.sigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sigmoid_(x) {\n    const $x = convertToTensor(x, 'x', 'sigmoid');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sigmoid($x);\n        save([res]);\n        return res;\n    }, inputs, null /* grad */, Sigmoid);\n}\nexport const sigmoid = op({ sigmoid_ });\n//# sourceMappingURL=sigmoid.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Slice } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport * as slice_util from './slice_util';\n/**\n * Extracts a slice from a `tf.Tensor` starting at coordinates `begin`\n * and is of size `size`.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `x` is of the given rank:\n *   - `tf.slice1d`\n *   - `tf.slice2d`\n *   - `tf.slice3d`\n *   - `tf.slice4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.slice([1], [2]).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * x.slice([1, 0], [1, 2]).print();\n * ```\n * @param x The input `tf.Tensor` to slice from.\n * @param begin The coordinates to start the slice from. The length can be\n *     less than the rank of x - the rest of the axes will have implicit 0 as\n *     start. Can also be a single number, in which case it specifies the\n *     first axis.\n * @param size The size of the slice. The length can be less than the rank of\n *     x - the rest of the axes will have implicit -1. A value of -1 requests\n *     the rest of the dimensions in the axis. Can also be a single number,\n *     in which case it specifies the size of the first axis.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction slice_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice');\n    if ($x.rank === 0) {\n        throw new Error('Slicing scalar is not possible');\n    }\n    const forward = (backend, save) => {\n        const [begin_, size_] = slice_util.parseSliceParams($x, begin, size);\n        slice_util.assertParamsValid($x, begin_, size_);\n        save([$x]);\n        return backend.slice($x, begin_, size_);\n    };\n    const inputs = { x: $x };\n    const attrs = { begin, size };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Slice, attrs);\n}\nexport const slice = op({ slice_ });\n//# sourceMappingURL=slice.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, 70]);\n *\n * x.tanh().print();  // or tf.tanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tanh_(x) {\n    const $x = convertToTensor(x, 'x', 'tanh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const y = backend.tanh($x);\n        save([y]);\n        return y;\n    }, inputs, null /* grad */, Tanh);\n}\nexport const tanh = op({ tanh_ });\n//# sourceMappingURL=tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { add } from './add';\nimport { concat } from './concat';\nimport { matMul } from './mat_mul';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { slice } from './slice';\nimport { tanh } from './tanh';\n/**\n * Computes the next state and output of a BasicLSTMCell.\n *\n * Returns `[newC, newH]`.\n *\n * Derived from tf.contrib.rnn.BasicLSTMCell.\n *\n * @param forgetBias Forget bias for the cell.\n * @param lstmKernel The weights for the cell.\n * @param lstmBias The bias for the cell.\n * @param data The input to the cell.\n * @param c Previous cell state.\n * @param h Previous cell output.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {\n    const $forgetBias = convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');\n    const $lstmKernel = convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');\n    const $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');\n    const $data = convertToTensor(data, 'data', 'basicLSTMCell');\n    const $c = convertToTensor(c, 'c', 'basicLSTMCell');\n    const $h = convertToTensor(h, 'h', 'basicLSTMCell');\n    const combined = concat([$data, $h], 1);\n    const weighted = matMul(combined, $lstmKernel);\n    const res = add(weighted, $lstmBias);\n    // i = input_gate, j = new_input, f = forget_gate, o = output_gate\n    const batchSize = res.shape[0];\n    const sliceCols = res.shape[1] / 4;\n    const sliceSize = [batchSize, sliceCols];\n    const i = slice(res, [0, 0], sliceSize);\n    const j = slice(res, [0, sliceCols], sliceSize);\n    const f = slice(res, [0, sliceCols * 2], sliceSize);\n    const o = slice(res, [0, sliceCols * 3], sliceSize);\n    const newC = add(mul(sigmoid(i), tanh(j)), mul($c, sigmoid(add($forgetBias, f))));\n    const newH = mul(tanh(newC), sigmoid(o));\n    return [newC, newH];\n}\nexport const basicLSTMCell = op({ basicLSTMCell_ });\n//# sourceMappingURL=basic_lstm_cell.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchToSpaceND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation reshapes the \"batch\" dimension 0 into `M + 1` dimensions of\n * shape `blockShape + [batch]`, interleaves these blocks back into the grid\n * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with\n * the same rank as the input. The spatial dimensions of this intermediate\n * result are then optionally cropped according to `crops` to produce the\n * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise\n * description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n * const blockShape = [2, 2];\n * const crops = [[0, 0], [0, 0]];\n *\n * x.batchToSpaceND(blockShape, crops).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param crops A 2-D array.  Must have shape `[M, 2]`, all values must be >= 0.\n * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input\n * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required\n * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,\n * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,\n * x.shape[N-1]]`\n *\n * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /\n * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`\n * according to `crops` to produce the output of shape: `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],\n * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -\n * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction batchToSpaceND_(x, blockShape, crops) {\n    const $x = convertToTensor(x, 'x', 'batchToSpaceND');\n    const prod = blockShape.reduce((a, b) => a * b);\n    util.assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);\n    util.assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);\n    util.assert($x.shape[0] % prod === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of ` +\n        `the elements of blockShape ${blockShape.join(' * ')} === ${prod}`);\n    const forward = backend => {\n        return backend.batchToSpaceND($x, blockShape, crops);\n    };\n    const inputs = { x: $x };\n    const attrs = { blockShape, crops };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, BatchToSpaceND, attrs);\n}\nexport const batchToSpaceND = op({ batchToSpaceND_ });\n//# sourceMappingURL=batch_to_space_nd.js.map", "import { reshape } from './reshape';\nexport function xAs4D(x) {\n    let x4D;\n    if (x.rank === 0 || x.rank === 1) {\n        x4D = reshape(x, [1, 1, 1, x.size]);\n    }\n    else if (x.rank === 2) {\n        x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n    }\n    else if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    else {\n        x4D = x;\n    }\n    return x4D;\n}\n//# sourceMappingURL=batchnorm_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FusedBatchNorm } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { xAs4D } from './batchnorm_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Batch normalization.\n *\n * As described in\n * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).\n *\n * Mean, variance, scale, and offset can be of two shapes:\n *   - The same shape as the input.\n *   - In the common case, the depth dimension is the last dimension of x, so\n *     the values would be an `tf.Tensor1D` of shape [depth].\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that parameters passed are of given rank\n *   - `tf.batchNorm2d`\n *   - `tf.batchNorm3d`\n *   - `tf.batchNorm4d`\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction batchNorm_(x, mean, variance, offset, scale, varianceEpsilon) {\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($mean.rank === $variance.rank, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert($offset == null || $mean.rank === $offset.rank, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert($scale == null || $mean.rank === $scale.rank, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    const x4D = xAs4D($x);\n    const forward = (backend, save) => {\n        save([x4D, $mean, $variance, $scale]);\n        return backend.batchNorm(x4D, as1DOr4D($mean), as1DOr4D($variance), as1DOr4D($offset), as1DOr4D($scale), varianceEpsilon);\n    };\n    const inputs = {\n        x: x4D,\n        scale: $scale,\n        offset: $offset,\n        mean: $mean,\n        variance: $variance\n    };\n    const attrs = { varianceEpsilon };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, FusedBatchNorm, attrs);\n    return reshape(res, $x.shape);\n}\nfunction as1DOr4D(x) {\n    if (x == null) {\n        return null;\n    }\n    if (x.rank === 0) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [x.size]);\n    }\n    else if (x.rank === 1) {\n        return x;\n    }\n    else if (x.rank === 2) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n    }\n    else if (x.rank === 3) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    return x;\n}\nexport const batchNorm = op({ batchNorm_ });\n//# sourceMappingURL=batchnorm.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 2D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm2d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm2d = op({ batchNorm2d_ });\n//# sourceMappingURL=batchnorm2d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 3D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm3d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm3d = op({ batchNorm3d_ });\n//# sourceMappingURL=batchnorm3d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 4D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm4d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm4d = op({ batchNorm4d_ });\n//# sourceMappingURL=batchnorm4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BroadcastTo } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { clone } from './clone';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Broadcast an array to a compatible shape NumPy-style.\n *\n * The tensor's shape is compared to the broadcast shape from end to beginning.\n * Ones are prepended to the tensor's shape until is has the same length as\n * the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is\n * already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then\n * the input tensor is tiled N times along that axis (using tf.tile).\n *\n * @param input The tensor that is to be broadcasted.\n * @param shape The input is to be broadcast to this shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction broadcastTo_(x, shape) {\n    let input = convertToTensor(x, 'broadcastTo', 'x');\n    const xShape = input.shape;\n    if (shape.some(d => !(d > 0) || d % 1 !== 0)) {\n        throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);\n    }\n    if (shape.length < input.rank) {\n        throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input.rank}.`);\n    }\n    if (shape.length > input.rank) {\n        const newShape = input.shape.slice();\n        while (newShape.length < shape.length) {\n            newShape.unshift(1);\n        }\n        input = reshape(input, newShape);\n    }\n    const inputShape = input.shape;\n    const reps = Array.from(shape);\n    for (let i = shape.length - 1; i >= 0; i--) {\n        if (inputShape[i] === shape[i]) {\n            reps[i] = 1;\n        }\n        else if (input.shape[i] !== 1) {\n            throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);\n        }\n    }\n    const axes = reps.map((n, i) => n > 1 ? i : -1).filter(i => i >= 0);\n    if (axes.length === 0) {\n        return clone(input);\n    }\n    const forward = (backend) => backend.tile(input, reps);\n    const inputs = { x: input };\n    const attrs = { shape, inputShape };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, BroadcastTo, attrs);\n}\nexport const broadcastTo = op({ broadcastTo_ });\n//# sourceMappingURL=broadcast_to.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Ceil } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.ceil().print();  // or tf.ceil(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction ceil_(x) {\n    const $x = convertToTensor(x, 'x', 'ceil');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.ceil($x), inputs, null /* grad */, Ceil);\n}\nexport const ceil = op({ ceil_ });\n//# sourceMappingURL=ceil.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ClipByValue } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)\n * ```\n * @param x The input tensor.\n * @param clipValueMin Lower-bound of range to be clipped to.\n * @param clipValueMax Upper-bound of range to be clipped to.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction clipByValue_(x, clipValueMin, clipValueMax) {\n    const $x = convertToTensor(x, 'x', 'clipByValue');\n    util.assert((clipValueMin <= clipValueMax), () => `Error in clip: min (${clipValueMin}) must be ` +\n        `less than or equal to max (${clipValueMax}).`);\n    const inputs = { x: $x };\n    const attrs = { clipValueMin, clipValueMax };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.clip($x, clipValueMin, clipValueMax);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, ClipByValue, attrs);\n}\nexport const clipByValue = op({ clipByValue_ });\n//# sourceMappingURL=clip_by_value.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(3) = |r1, g1, b1|\n * B: shape(2) = |r2, g2|\n * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @return The concatenated array.\n */\nfunction concat1d_(tensors) {\n    return concat(tensors, 0 /* axis */);\n}\nexport const concat1d = op({ concat1d_ });\n//# sourceMappingURL=concat_1d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *\n * B: shape(2, 3) = | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * C = tf.concat2d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *                  | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * if axis = 1:\n * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |\n *                   | r2, g2, b2, r4, g4, b4 |\n *\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concatenate along.\n * @return The concatenated array.\n */\nfunction concat2d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat2d = op({ concat2d_ });\n//# sourceMappingURL=concat_2d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor3D`s along an axis.\n * See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *\n * B: shape(2, 1, 3) = | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * C = tf.concat3d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *                     | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * if axis = 1:\n * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |\n *                     | r2, g2, b2, r4, g4, b4 |\n *\n * if axis = 2:\n * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |\n *                      | r2, g2, b2, r4, g4, b4 |\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat3d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat3d = op({ concat3d_ });\n//# sourceMappingURL=concat_3d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor4D`s along an axis.\n * See `concat` for details.\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat4d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat4d = op({ concat4d_ });\n//# sourceMappingURL=concat_4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 2D convolution over the input x.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv2d');\n    const $filter = convertToTensor(filter, 'filter', 'conv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n    util.assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match ` +\n        `input depth for filter ${$filter.shape[2]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const forward = (backend, save) => {\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        const res = backend.conv2d(x4D, $filter, convInfo);\n        save([x4D, $filter]);\n        return res;\n    };\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv2D, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const conv2d = op({ conv2d_ });\n//# sourceMappingURL=conv2d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 1D convolution over the input x.\n *\n * @param x The input tensor, of rank 3 or rank 2, of shape\n *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.\n * @param filter The filter, rank 3, of shape\n *     `[filterWidth, inDepth, outDepth]`.\n * @param stride The number of entries by which the filter is moved right at\n *     each step.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from \"NWC\", \"NCW\". Defaults to \"NWC\",\n *     the data is stored in the order of [batch, in_width, in_channels]. Only\n *     \"NWC\" is currently supported.\n * @param dilation The dilation rate in which we sample input values in\n *     atrous convolution. Defaults to `1`. If it is greater than 1, then\n *     stride must be `1`.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv1d_(x, filter, stride, pad, dataFormat = 'NWC', dilation = 1, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv1d');\n    const $filter = convertToTensor(filter, 'filter', 'conv1d');\n    let x3D = $x;\n    let reshapedTo3D = false;\n    if ($x.rank === 2) {\n        reshapedTo3D = true;\n        x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);\n    }\n    util.assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n    util.assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv1d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    util.assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +\n        `input depth for filter ${$filter.shape[1]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(stride, dilation), () => 'Error in conv1D: Either stride or dilation must be 1. ' +\n        `Got stride ${stride} and dilation '${dilation}'`);\n    util.assert(dataFormat === 'NWC', () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);\n    const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);\n    const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);\n    const strides = [1, stride];\n    const dilations = [1, dilation];\n    const conv2dDataFormat = 'NHWC';\n    const res = conv2d(input4D, filter4D, strides, pad, conv2dDataFormat, dilations, dimRoundingMode);\n    if (reshapedTo3D) {\n        return reshape(res, [res.shape[2], res.shape[3]]);\n    }\n    return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);\n}\nexport const conv1d = op({ conv1d_ });\n//# sourceMappingURL=conv1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropInput } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 2D convolution.\n *\n * @param xShape The shape of the input: [batch, height, width, inDepth].\n * If length of 3, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 4 or rank 3 of shape\n *   `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction conv2DBackpropInput_(xShape, dy, filter, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n    util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n        `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n    let xShape4D = xShape;\n    let dy4D = dy;\n    let reshapedTo4D = false;\n    if (dy.rank === 3) {\n        reshapedTo4D = true;\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n        xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n    }\n    util.assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ` +\n        `${xShape4D.length}.`);\n    util.assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got ` +\n        `rank ${dy4D.rank}`);\n    util.assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got ` +\n        `rank ${filter.rank}`);\n    const inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1];\n    const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n    util.assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must ` +\n        `match input depth for filter ${filter.shape[2]}.`);\n    util.assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must ` +\n        `match output depth for filter ${filter.shape[3]}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2dDerInput: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const dilations = 1;\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(xShape4D, filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        const res = backend.conv2dDerInput(dy4D, filter, convInfo);\n        save([dy4D, filter]);\n        return res;\n    };\n    const inputs = { dy: dy4D, filter };\n    const attrs = { strides, pad, dataFormat, dimRoundingMode, inputShape: xShape4D };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv2DBackpropInput, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const conv2DBackpropInput = op({ conv2DBackpropInput_ });\n//# sourceMappingURL=conv2d_backprop_input.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport { conv2DBackpropInput } from './conv2d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 2D convolution of an image, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 4 or rank 3, of shape\n *   `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 4 or rank 3:\n *     `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.\n * @param strides The strides of the original convolution:\n *     `[strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n * @param dimRoundingMode The rounding mode used when computing output\n *    dimensions if pad is a number. If none is provided, it will not round\n *    and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2dTranspose_(x, filter, outputShape, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv2dTranspose');\n    const $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');\n    return conv2DBackpropInput(outputShape, $x, $filter, strides, pad, 'NHWC', dimRoundingMode);\n}\nexport const conv2dTranspose = op({ conv2dTranspose_ });\n//# sourceMappingURL=conv2d_transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { eitherStridesOrDilationsAreOne } from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 3D convolution over the input x.\n *\n * @param x The input tensor, of rank 5 or rank 4, of shape\n *     `[batch, depth, height, width, channels]`. If rank 4,\n * batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.\n *      inChannels must match between input and filter.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationDepth, dilationHeight,\n *     dilationWidth]` in which we sample input values across the height\n *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.\n *     If `dilations` is a single number, then\n *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater\n *     than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3d_(x, filter, strides, pad, dataFormat = 'NDHWC', dilations = [1, 1, 1]) {\n    const $x = convertToTensor(x, 'x', 'conv3d');\n    const $filter = convertToTensor(filter, 'filter', 'conv3d');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n    util.assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +\n        `input depth for filter ${$filter.shape[3]}.`);\n    util.assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv3D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computeConv3DInfo(x5D.shape, $filter.shape, strides, dilations, pad);\n        const res = backend.conv3d(x5D, $filter, convInfo);\n        save([x5D, $filter]);\n        return res;\n    };\n    const inputs = { x: x5D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv3D, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const conv3d = op({ conv3d_ });\n//# sourceMappingURL=conv3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3DBackpropInputV2 } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 3D convolution.\n *\n * @param xShape The shape of the input: [batch, depth, height, width,\n * in_channels]. If length of 4, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 5 or rank 4 of shape\n *   `[batch, outDepth, outHeight, outWidth, in_channels]`.\n * If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n */\nfunction conv3DBackpropInput_(xShape, dy, filter, strides, pad) {\n    util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n        `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n    let xShape5D = xShape;\n    let dy5D = dy;\n    let reshapedTo5D = false;\n    if (dy.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n        xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n    }\n    const inDepth = xShape5D[4];\n    const outDepth = dy5D.shape[4];\n    util.assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ` +\n        `${xShape5D.length}.`);\n    util.assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got ` +\n        `rank ${dy5D.rank}`);\n    util.assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got ` +\n        `rank ${filter.rank}`);\n    util.assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must ` +\n        `match input depth for filter ${filter.shape[3]}.`);\n    util.assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must ` +\n        `match output depth for filter ${filter.shape[4]}.`);\n    const forward = backend => {\n        const dilations = 1;\n        const convInfo = conv_util.computeConv3DInfo(xShape5D, filter.shape, strides, dilations, pad);\n        return backend.conv3dDerInput(dy5D, filter, convInfo);\n    };\n    const inputs = { dy: dy5D };\n    const attrs = { pad };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropInputV2, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const conv3DBackpropInput = op({ conv3DBackpropInput_ });\n//# sourceMappingURL=conv3d_backprop_input.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport { conv3DBackpropInput } from './conv3d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 3D convolution of a volume, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 5 or rank 4, of shape\n *   `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[depth, filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 5 or rank 4:\n *     `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is\n *    assumed.\n * @param strides The strides of the original convolution:\n *     `[strideDepth, strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3dTranspose_(x, filter, outputShape, strides, pad) {\n    const $x = convertToTensor(x, 'x', 'conv3dTranspose');\n    const $filter = convertToTensor(filter, 'filter', 'conv3dTranspose');\n    return conv3DBackpropInput(outputShape, $x, $filter, strides, pad);\n}\nexport const conv3dTranspose = op({ conv3dTranspose_ });\n//# sourceMappingURL=conv3d_transpose.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.cos().print();  // or tf.cos(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cos_(x) {\n    const $x = convertToTensor(x, 'x', 'cos');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.cos($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Cos);\n}\nexport const cos = op({ cos_ });\n//# sourceMappingURL=cos.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.cosh().print();  // or tf.cosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cosh_(x) {\n    const $x = convertToTensor(x, 'x', 'cosh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.cosh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Cosh);\n}\nexport const cosh = op({ cosh_ });\n//# sourceMappingURL=cosh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cumsum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { getAxesPermutation, getInnerMostAxes, getUndoAxesPermutation } from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Computes the cumulative sum of a `tf.Tensor` along `axis`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4]);\n * x.cumsum().print();\n * ```\n * ```js\n * const x = tf.tensor([[1, 2], [3, 4]]);\n * x.cumsum().print();\n * ```\n *\n * @param x The input tensor to be summed.\n * @param axis The axis along which to sum. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative sum. Optional.\n *     Defaults to false. If set to true then the sum of each tensor entry\n *     does not include its own value, but only the values previous to it\n *     along the specified axis.\n * @param reverse Whether to sum in the opposite direction. Optional.\n *     Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Scan'}\n */\nfunction cumsum_(x, axis = 0, exclusive = false, reverse = false) {\n    const $x = convertToTensor(x, 'x', 'cumsum');\n    const forward = (backend, save) => {\n        const permutation = getAxesPermutation([axis], $x.rank);\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n        }\n        const permutedAxis = getInnerMostAxes(1, $x.rank)[0];\n        let value = backend.cumsum(permutedX, permutedAxis, exclusive, reverse);\n        save([$x]);\n        if (permutation != null) {\n            const reversePermutation = getUndoAxesPermutation(permutation);\n            value = transpose(value, reversePermutation);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, exclusive, reverse };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Cumsum, attrs);\n}\nexport const cumsum = op({ cumsum_ });\n//# sourceMappingURL=cumsum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthToSpace } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Rearranges data from depth into blocks of spatial data. More specifically,\n * this op outputs a copy of the input tensor where values from the `depth`\n * dimension are moved in spatial blocks to the `height` and `width` dimensions.\n * The attr `blockSize` indicates the input block size and how the data is\n * moved.\n *\n *  - Chunks of data of size `blockSize * blockSize` from depth are rearranged\n * into non-overlapping blocks of size `blockSize x blockSize`\n *\n *  - The width the output tensor is `inputWidth * blockSize`, whereas the\n * height is `inputHeight * blockSize`\n *\n *  - The Y, X coordinates within each block of the output image are determined\n * by the high order component of the input channel index\n *\n *  - The depth of the input tensor must be divisible by `blockSize *\n * blockSize`\n *\n * The `dataFormat` attr specifies the layout of the input and output tensors\n * with the following options: \"NHWC\": [ `batch, height, width, channels` ]\n * \"NCHW\": [ `batch, channels, height, width` ]\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);\n * const blockSize = 2;\n * const dataFormat = \"NHWC\";\n *\n * tf.depthToSpace(x, blockSize, dataFormat).print();\n * ```\n *\n * @param x The input tensor of rank 4\n * @param blockSIze  An `int` that is `>= 2`. The size of the spatial block\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to \"NHWC\"\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction depthToSpace_(x, blockSize, dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'depthToSpace');\n    const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];\n    const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];\n    const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];\n    util.assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputHeight} and ${blockSize}  for depthToSpace with input shape\n    ${$x.shape}`);\n    util.assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputWidth} and ${blockSize} for depthToSpace with input shape\n        ${$x.shape}`);\n    util.assert((inputDepth % (blockSize * blockSize) === 0), () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);\n    const forward = backend => backend.depthToSpace($x, blockSize, dataFormat);\n    const inputs = { x: $x };\n    const attrs = { blockSize, dataFormat };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, DepthToSpace, attrs);\n}\nexport const depthToSpace = op({ depthToSpace_ });\n//# sourceMappingURL=depth_to_space.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNative } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Depthwise 2D convolution.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction depthwiseConv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n    const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels ` +\n        `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n        `filter ${$filter.shape[2]}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1];\n        }\n        util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +\n            `1. Got strides ${strides} and dilations '${dilations}'`);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n        const res = backend.depthwiseConv2D(x4D, $filter, convInfo);\n        save([x4D, $filter]);\n        return res;\n    };\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, DepthwiseConv2dNative, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const depthwiseConv2d = op({ depthwiseConv2d_ });\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Diag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Returns a diagonal tensor with a given diagonal values.\n *\n * Given a diagonal, this operation returns a tensor with the diagonal and\n * everything else padded with zeros.\n *\n * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor\n * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * tf.diag(x).print()\n * ```\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4, 5, 6, 6, 8], [4, 2])\n *\n * tf.diag(x).print()\n * ```\n * @param x The input tensor.\n */\nfunction diag_(x) {\n    const $x = convertToTensor(x, 'x', 'diag');\n    const forward = backend => {\n        const flat = reshape($x, [$x.size]);\n        const result = backend.diag(flat);\n        const outShape = [...x.shape, ...x.shape];\n        return reshape(result, outShape);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Diag);\n}\nexport const diag = op({ diag_ });\n//# sourceMappingURL=diag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Dilation2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the grayscale dilation over the input `x`.\n *\n * @param x The input tensor, rank 3 or rank 4 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter tensor, rank 3, of shape\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat Specify the data format of the input and output data.\n *      Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the\n *      default format \"NHWC\", the data is stored in the order of: [batch,\n *      height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations`\n *     is a single number, then `dilationHeight == dilationWidth`. If it is\n *     greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction dilation2d_(x, filter, strides, pad, dilations = [1, 1], dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'dilation2d');\n    const $filter = convertToTensor(filter, 'filter', 'dilation2d');\n    util.assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ` +\n        `${$x.rank}.`);\n    util.assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(dataFormat === 'NHWC', () => `Error in dilation2d: Only NHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n        reshapedTo4D = true;\n    }\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dilations };\n    const res = ENGINE.runKernel(Dilation2D, inputs, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const dilation2d = op({ dilation2d_ });\n//# sourceMappingURL=dilation2d.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the dimensions in the input shape that are broadcasted to\n * produce the provided output shape.\n *\n * The returned dimensions are 0-indexed and sorted. An example:\n * inShape = [4, 1, 3]\n * outShape = [5, 4, 3, 3]\n * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.\n */\nexport function getBroadcastDims(inShape, outShape) {\n    const inRank = inShape.length;\n    const dims = [];\n    for (let i = 0; i < inRank; i++) {\n        const dim = inRank - 1 - i;\n        const a = inShape[dim] || 1;\n        const b = outShape[outShape.length - 1 - i] || 1;\n        if (b > 1 && a === 1) {\n            dims.unshift(dim);\n        }\n    }\n    return dims;\n}\n/**\n * Returns the axes in the output space that should be reduced to produce\n * the input space.\n */\nexport function getReductionAxes(inShape, outShape) {\n    const result = [];\n    for (let i = 0; i < outShape.length; i++) {\n        const inDim = inShape[inShape.length - i - 1];\n        const outAxis = outShape.length - i - 1;\n        const outDim = outShape[outAxis];\n        if (inDim == null || (inDim === 1 && outDim > 1)) {\n            result.unshift(outAxis);\n        }\n    }\n    return result;\n}\nexport function assertAndGetBroadcastShape(shapeA, shapeB) {\n    const result = [];\n    const l = Math.max(shapeA.length, shapeB.length);\n    for (let i = 0; i < l; i++) {\n        let a = shapeA[shapeA.length - i - 1];\n        if (a == null) {\n            a = 1;\n        }\n        let b = shapeB[shapeB.length - i - 1];\n        if (b == null) {\n            b = 1;\n        }\n        if (a === 1) {\n            result.unshift(b);\n        }\n        else if (b === 1) {\n            result.unshift(a);\n        }\n        else if (a !== b) {\n            const errMsg = `Operands could not be broadcast together with shapes ` +\n                `${shapeA} and ${shapeB}.`;\n            throw Error(errMsg);\n        }\n        else {\n            result.unshift(a);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=broadcast_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Equal } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a == b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.equal(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction equal_(a, b) {\n    let $a = convertToTensor(a, 'a', 'equal');\n    let $b = convertToTensor(b, 'b', 'equal');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.equal($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null, Equal);\n}\nexport const equal = op({ equal_ });\n//# sourceMappingURL=equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SelectV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, assertShapesMatch } from '../util';\nimport { broadcastTo } from './broadcast_to';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the elements, either `a` or `b` depending on the `condition`.\n *\n * If the condition is true, select from `a`, otherwise select from `b`.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const a = tf.tensor1d([1 , 2, 3]);\n * const b = tf.tensor1d([-1, -2, -3]);\n *\n * a.where(cond, b).print();\n * ```\n *\n * @param condition The input condition. Must be of dtype bool.\n * @param a If `condition` is rank 1, `a` may have a higher rank but\n *     its first dimension must match the size of `condition`.\n * @param b A tensor with the same dtype as `a` and with shape that is\n *     compatible with `a`.\n * @return A tensor with same dtype as `a` and `b`, and shape that is\n *     broadcastable from `a` and `b`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction where_(condition, a, b) {\n    const $a = convertToTensor(a, 'a', 'where');\n    const $b = convertToTensor(b, 'b', 'where');\n    const $condition = convertToTensor(condition, 'condition', 'where', 'bool');\n    // TODO: move this logic to forward function when the broadcastTo op is\n    // implemented in WASM.\n    // Find the broadcastable shape for $a and $b.\n    const broadcastShape = assertAndGetBroadcastShape($a.shape, $b.shape);\n    const $broadcastedA = broadcastTo($a, broadcastShape);\n    const $broadcastedB = broadcastTo($b, broadcastShape);\n    if ($condition.rank === 1) {\n        // If condition rank is 1, then the first dimension must match the size of\n        // condition.\n        assert($condition.shape[0] === $a.shape[0], () => 'The first dimension of `a` must match the size of `condition`.');\n    }\n    if ($condition.rank !== 1) {\n        // A must have the same shape as condition.\n        assertShapesMatch($condition.shape, $broadcastedB.shape, 'Error in where: ');\n    }\n    const forward = (backend, save) => {\n        const res = backend.select($condition, $broadcastedA, $broadcastedB);\n        save([$condition]);\n        return res;\n    };\n    const inputs = {\n        condition: $condition,\n        t: $broadcastedA,\n        e: $broadcastedB\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, SelectV2);\n}\nexport const where = op({ where_ });\n//# sourceMappingURL=where.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ZerosLike } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.zerosLike(x).print();\n * ```\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction zerosLike_(x) {\n    const $x = convertToTensor(x, 'x', 'zerosLike');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.zerosLike($x), inputs, null /* grad */, ZerosLike);\n}\nexport const zerosLike = op({ zerosLike_ });\n//# sourceMappingURL=zeros_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { div } from './div';\nimport { equal } from './equal';\nimport { op } from './operation';\nimport { where } from './where';\nimport { zerosLike } from './zeros_like';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0\n * if denominator is 0.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n * const c = tf.tensor1d([0, 0, 0, 0]);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n * const c = tf.scalar(0);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction divNoNan_(a, b) {\n    // TODO: Make this into its own kernel.\n    let $a = convertToTensor(a, 'a', 'div');\n    let $b = convertToTensor(b, 'b', 'div');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const divResult = div($a, $b);\n    const zeros = zerosLike(divResult);\n    const bEqualsZero = equal($b, zeros);\n    return where(bEqualsZero, zeros, divResult);\n}\nexport const divNoNan = op({ divNoNan_ });\n//# sourceMappingURL=div_no_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor2d([[1, 2], [3, 4]]);\n * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n *\n * a.dot(b).print();  // or tf.dot(a, b)\n * b.dot(a).print();\n * b.dot(c).print();\n * ```\n * @param t1 The first tensor in the dot operation.\n * @param t2 The second tensor in the dot operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction dot_(t1, t2) {\n    const $t1 = convertToTensor(t1, 't1', 'dot');\n    const $t2 = convertToTensor(t2, 't2', 'dot');\n    util.assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ` +\n        `${$t1.rank} and ${$t2.rank}.`);\n    const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);\n    const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);\n    util.assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ` +\n        `${t1Inner} and ${t2Inner}.`);\n    if ($t1.rank === 1 && $t2.rank === 1) {\n        const t12D = reshape($t1, [1, -1]);\n        const t22D = reshape($t2, [-1, 1]);\n        const t1t2 = matMul(t12D, t22D);\n        return reshape(t1t2, []);\n    }\n    else if ($t1.rank === 1 && $t2.rank === 2) {\n        const t12D = reshape($t1, [1, -1]);\n        const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n        const t1t2 = matMul(t12D, t22D);\n        return reshape(t1t2, [t1t2.size]);\n    }\n    else if ($t1.rank === 2 && $t2.rank === 1) {\n        const t22D = reshape($t2, [-1, 1]);\n        const t1t2 = matMul($t1, t22D);\n        return reshape(t1t2, [t1t2.size]);\n    }\n    else {\n        const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n        const t1t2 = matMul($t1, t22D);\n        return t1t2;\n    }\n}\nexport const dot = op({ dot_ });\n//# sourceMappingURL=dot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Elu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential linear element-wise: `x > 0 ? e ^ x - 1 : 0`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 1, -3, 2]);\n *\n * x.elu().print();  // or tf.elu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction elu_(x) {\n    const $x = convertToTensor(x, 'x', 'elu');\n    const forward = (backend, save) => {\n        const y = backend.elu($x);\n        save([y]);\n        return y;\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Elu);\n}\nexport const elu = op({ elu_ });\n//# sourceMappingURL=elu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Erf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes gause error function of the input `tf.Tensor` element-wise:\n * `erf(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.erf().print(); // or tf.erf(x);\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction erf_(x) {\n    let $x = convertToTensor(x, 'x', 'erf');\n    util.assert($x.dtype === 'int32' || $x.dtype === 'float32', () => 'Input dtype must be `int32` or `float32`.');\n    if ($x.dtype === 'int32') {\n        $x = cast($x, 'float32');\n    }\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.erf($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Erf);\n}\nexport const erf = op({ erf_ });\n//# sourceMappingURL=erf.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Exp } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.exp().print();  // or tf.exp(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction exp_(x) {\n    const $x = convertToTensor(x, 'x', 'exp');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.exp($x);\n        save([res]);\n        return res;\n    }, inputs, null /* grad */, Exp);\n}\nexport const exp = op({ exp_ });\n//# sourceMappingURL=exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n * into the tensor's shape.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const axis = 1;\n * x.expandDims(axis).print();\n * ```\n *\n * @param x The input tensor whose dimensions to be expanded.\n * @param axis The dimension index at which to insert shape of `1`. Defaults\n *     to 0 (the first dimension).\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction expandDims_(x, axis = 0) {\n    const parseAs = null;\n    const $x = convertToTensor(x, 'x', 'expandDims', parseAs);\n    util.assert(axis <= $x.rank, () => 'Axis must be <= rank of the tensor');\n    const newShape = $x.shape.slice();\n    if (axis < 0) {\n        // Negative value is counted from the tail of rank.\n        util.assert(-($x.rank + 1) <= axis, () => `Axis must be in the interval [${-($x.rank + 1)}, ${$x.rank}]`);\n        axis = $x.rank + axis + 1;\n    }\n    newShape.splice(axis, 0, 1);\n    return reshape($x, newShape);\n}\nexport const expandDims = op({ expandDims_ });\n//# sourceMappingURL=expand_dims.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Expm1 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential of the input `tf.Tensor` minus one element-wise.\n * `e ^ x - 1`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.expm1().print();  // or tf.expm1(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction expm1_(x) {\n    const $x = convertToTensor(x, 'x', 'expm1');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.expm1($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Expm1);\n}\nexport const expm1 = op({ expm1_ });\n//# sourceMappingURL=expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tile } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Construct a tensor by repeating it the number of times given by reps.\n *\n * This operation creates a new tensor by replicating `input` `reps`\n * times. The output tensor's i'th dimension has `input.shape[i] *\n * reps[i]` elements, and the values of `input` are replicated\n * `reps[i]` times along the i'th dimension. For example, tiling\n * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n *\n * a.tile([2]).print();    // or a.tile([2])\n * ```\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.tile([1, 2]).print();  // or a.tile([1, 2])\n * ```\n * @param x The tensor to tile.\n * @param reps Determines the number of replications per dimension.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction tile_(x, reps) {\n    const parseAs = null;\n    const $x = convertToTensor(x, 'x', 'tile', parseAs);\n    util.assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n        `must match length of reps ${reps}.`);\n    const forward = (backend, save) => {\n        const res = backend.tile($x, reps);\n        save([$x]);\n        return res;\n    };\n    const inputsToSave = [$x];\n    const inputs = { x: $x };\n    const attrs = { reps };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Tile, attrs, inputsToSave);\n}\nexport const tile = op({ tile_ });\n//# sourceMappingURL=tile.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { expandDims } from './expand_dims';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { tile } from './tile';\n/**\n * Create an identity matrix.\n *\n * @param numRows Number of rows.\n * @param numColumns Number of columns. Defaults to `numRows`.\n * @param batchShape If provided, will add the batch shape to the beginning\n *   of the shape of the returned `tf.Tensor` by repeating the identity\n *   matrix.\n * @param dtype Data type.\n * @returns Identity matrix of the specified size and data type, possibly\n *   with batch repetition if `batchShape` is specified.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction eye_(numRows, numColumns, batchShape, dtype = 'float32') {\n    if (numColumns == null) {\n        numColumns = numRows;\n    }\n    const buff = buffer([numRows, numColumns], dtype);\n    const n = numRows <= numColumns ? numRows : numColumns;\n    for (let i = 0; i < n; ++i) {\n        buff.set(1, i, i);\n    }\n    const out = reshape(buff.toTensor(), [numRows, numColumns]);\n    if (batchShape == null) {\n        return out;\n    }\n    else {\n        if (batchShape.length === 1) {\n            return tile(expandDims(out, 0), [batchShape[0], 1, 1]);\n        }\n        else if (batchShape.length === 2) {\n            // tslint:disable-next-line:no-unnecessary-type-assertion\n            return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);\n        }\n        else if (batchShape.length === 3) {\n            // tslint:disable-next-line:no-unnecessary-type-assertion\n            return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [\n                batchShape[0], batchShape[1], batchShape[2], 1, 1\n            ]);\n        }\n        else {\n            throw new Error(`eye() currently supports only 1D and 2D ` +\n                // tslint:disable-next-line:no-any\n                `batchShapes, but received ${batchShape.length}D.`);\n        }\n    }\n}\nexport const eye = op({ eye_ });\n//# sourceMappingURL=eye.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Fill } from '../kernel_names';\n/**\n * Creates a `tf.Tensor` filled with a scalar value.\n *\n * ```js\n * tf.fill([2, 2], 4).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param value The scalar value to fill the tensor with.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction fill(shape, value, dtype) {\n    const attrs = { shape, value, dtype };\n    return ENGINE.runKernelFunc(backend => backend.fill(shape, value, dtype), {}, null, Fill, attrs);\n}\nexport { fill };\n//# sourceMappingURL=fill.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Floor } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.floor().print();  // or tf.floor(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction floor_(x) {\n    const $x = convertToTensor(x, 'x', 'floor');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.floor($x), inputs, null /* grad */, Floor);\n}\nexport const floor = op({ floor_ });\n//# sourceMappingURL=floor.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inputs of size above this threshold will be parallelized by calling multiple\n * shader programs.\n */\nimport { nearestDivisor } from '../util';\nexport const PARALLELIZE_THRESHOLD = 30;\nexport function computeOptimalWindowSize(inSize) {\n    if (inSize <= PARALLELIZE_THRESHOLD) {\n        return inSize;\n    }\n    return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n}\n//# sourceMappingURL=reduce_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nearestDivisor } from '../util';\nimport { PARALLELIZE_THRESHOLD } from './reduce_util';\nexport function segOpComputeOptimalWindowSize(inSize, numSegments) {\n    let done = false;\n    let res;\n    if (inSize <= PARALLELIZE_THRESHOLD) {\n        res = inSize;\n        done = true;\n    }\n    else {\n        res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n    }\n    while (!done) {\n        if (res > numSegments || res === inSize) {\n            done = true;\n        }\n        else {\n            res = nearestDivisor(inSize, res + 1);\n        }\n    }\n    return res;\n}\nexport function computeOutShape(aShape, axis, numSegments) {\n    const outShape = [];\n    const rank = aShape.length;\n    for (let dim = 0; dim < rank; dim++) {\n        if (dim !== axis) {\n            outShape.push(aShape[dim]);\n        }\n        else {\n            outShape.push(numSegments);\n        }\n    }\n    return outShape;\n}\nexport function collectGatherOpShapeInfo(x, indices, axis) {\n    const dimSize = x.shape[axis];\n    const outputShape = [];\n    let batchSize = 1;\n    let sliceSize = 1;\n    for (let i = 0; i < axis; i++) {\n        outputShape.push(x.shape[i]);\n        batchSize *= x.shape[i];\n    }\n    for (let i = 0; i < indices.rank; i++) {\n        outputShape.push(indices.shape[i]);\n    }\n    for (let i = axis + 1; i < x.rank; i++) {\n        outputShape.push(x.shape[i]);\n        sliceSize *= x.shape[i];\n    }\n    return { batchSize, sliceSize, dimSize, outputShape };\n}\n//# sourceMappingURL=segment_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { collectGatherOpShapeInfo } from './segment_util';\n/**\n * Gather slices from tensor `x`'s axis `axis` according to `indices`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const indices = tf.tensor1d([1, 3, 3], 'int32');\n *\n * x.gather(indices).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const indices = tf.tensor1d([1, 1, 0], 'int32');\n *\n * x.gather(indices).print();\n * ```\n * @param x The input tensor whose slices to be gathered.\n * @param indices The indices of the values to extract.\n * @param axis The axis over which to select values. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction gather_(x, indices, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'gather');\n    const $indices = convertToTensor(indices, 'indices', 'gather', 'int32');\n    const inputs = { x: $x, indices: $indices };\n    const attrs = { axis };\n    const forward = (backend, save) => {\n        const parsedAxis = parseAxisParam(axis, $x.shape)[0];\n        const shapeInfo = collectGatherOpShapeInfo($x, $indices, parsedAxis);\n        const res = backend.gather($x, reshape($indices, [$indices.size]), parsedAxis);\n        save([$x, $indices]);\n        return reshape(res, shapeInfo.outputShape);\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, GatherV2, attrs);\n}\nexport const gather = op({ gather_ });\n//# sourceMappingURL=gather.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Greater } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a > b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greater(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greater_(a, b) {\n    let $a = convertToTensor(a, 'a', 'greater');\n    let $b = convertToTensor(b, 'b', 'greater');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.greater($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Greater);\n}\nexport const greater = op({ greater_ });\n//# sourceMappingURL=greater.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GreaterEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a >= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greaterEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greaterEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'greaterEqual');\n    let $b = convertToTensor(b, 'b', 'greaterEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.greaterEqual($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, GreaterEqual);\n}\nexport const greaterEqual = op({ greaterEqual_ });\n//# sourceMappingURL=greater_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Imag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the imaginary part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the imaginary part of each element in input considered as a complex number.\n * If input is real, a tensor of all zeros is returned.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.imag(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction imag_(input) {\n    const $input = convertToTensor(input, 'input', 'imag');\n    const forward = (backend) => {\n        return backend.imag($input);\n    };\n    const inputs = { input: $input };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Imag);\n}\nexport const imag = op({ imag_ });\n//# sourceMappingURL=imag.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsFinite } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are finite.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isFinite().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isFinite_(x) {\n    const $x = convertToTensor(x, 'x', 'isFinite');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.isFinite($x), inputs, null /* grad */, IsFinite);\n}\nexport const isFinite = op({ isFinite_ });\n//# sourceMappingURL=is_finite.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsInf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are Infinity or -Infinity.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isInf().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isInf_(x) {\n    const $x = convertToTensor(x, 'x', 'isInf');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.isInf($x), inputs, null /* grad */, IsInf);\n}\nexport const isInf = op({ isInf_ });\n//# sourceMappingURL=is_inf.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsNan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * RReturns which elements of x are NaN.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isNaN().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isNaN_(x) {\n    const $x = convertToTensor(x, 'x', 'isNaN');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.isNaN($x), inputs, null /* grad */, IsNan);\n}\nexport const isNaN = op({ isNaN_ });\n//# sourceMappingURL=is_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Maximum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.maximumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * ```js\n * // Broadcast maximum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction maximum_(a, b) {\n    let $a = convertToTensor(a, 'a', 'maximum');\n    let $b = convertToTensor(b, 'b', 'maximum');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'bool') {\n        $a = cast($a, 'int32');\n        $b = cast($b, 'int32');\n    }\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.maximum($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Maximum);\n}\nexport const maximum = op({ maximum_ });\n//# sourceMappingURL=maximum.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { isTypedArray } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.scalar` as it makes the code more readable.\n *\n * ```js\n * tf.scalar(3.14).print();\n * ```\n *\n * @param value The value of the scalar.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function scalar(value, dtype) {\n    if (((isTypedArray(value) && dtype !== 'string') || Array.isArray(value)) &&\n        dtype !== 'complex64') {\n        throw new Error('Error creating a new Scalar: value must be a primitive ' +\n            '(number|boolean|string)');\n    }\n    if (dtype === 'string' && isTypedArray(value) &&\n        !(value instanceof Uint8Array)) {\n        throw new Error('When making a scalar from encoded string, ' +\n            'the value must be `Uint8Array`.');\n    }\n    const shape = [];\n    const inferredShape = [];\n    return makeTensor(value, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=scalar.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { maximum } from './maximum';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { scalar } from './scalar';\n/**\n * Computes leaky rectified linear element-wise.\n *\n * See\n * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](\n *     http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.leakyRelu(0.1).print();  // or tf.leakyRelu(x, 0.1)\n * ```\n * @param x The input tensor.\n * @param alpha The scaling factor for negative values, defaults to 0.2.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction leakyRelu_(x, alpha = 0.2) {\n    const $x = convertToTensor(x, 'x', 'leakyRelu');\n    return maximum(mul(scalar(alpha), $x), $x);\n}\nexport const leakyRelu = op({ leakyRelu_ });\n//# sourceMappingURL=leaky_relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Less } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a < b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.less(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction less_(a, b) {\n    let $a = convertToTensor(a, 'a', 'less');\n    let $b = convertToTensor(b, 'b', 'less');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.less($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Less);\n}\nexport const less = op({ less_ });\n//# sourceMappingURL=less.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LessEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a <= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.lessEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction lessEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'lessEqual');\n    let $b = convertToTensor(b, 'b', 'lessEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.lessEqual($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LessEqual);\n}\nexport const lessEqual = op({ lessEqual_ });\n//# sourceMappingURL=less_equal.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LinSpace } from '../kernel_names';\n/**\n * Return an evenly spaced sequence of numbers over the given interval.\n *\n * ```js\n * tf.linspace(0, 9, 10).print();\n * ```\n * @param start The start value of the sequence.\n * @param stop The end value of the sequence.\n * @param num The number of values to generate.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function linspace(start, stop, num) {\n    if (num <= 0) {\n        throw new Error('The number of values should be positive.');\n    }\n    const attrs = { start, stop, num };\n    return ENGINE.runKernelFunc(backend => backend.linspace(start, stop, num), {} /* inputs */, null /* grad */, LinSpace, attrs);\n}\n//# sourceMappingURL=linspace.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LRN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Normalizes the activation of a local neighborhood across or within\n * channels.\n *\n * @param x The input tensor. The 4-D input tensor is treated as a 3-D array\n *     of 1D vectors (along the last dimension), and each vector is\n *     normalized independently.\n * @param depthRadius The number of adjacent channels in the 1D normalization\n *     window.\n * @param bias A constant bias term for the basis.\n * @param alpha A scale factor, usually positive.\n * @param beta An exponent.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n    const $x = convertToTensor(x, 'x', 'localResponseNormalization');\n    util.assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${$x.rank}.`);\n    util.assert(util.isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an ` +\n        `integer but got depthRadius ${depthRadius}.`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    const forward = (backend, save) => {\n        const y = backend.localResponseNormalization4D(x4D, depthRadius, bias, alpha, beta);\n        save([x4D, y]);\n        return y;\n    };\n    const inputs = { x: x4D };\n    const attrs = { depthRadius, bias, alpha, beta };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, LRN, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    else {\n        return res;\n    }\n}\nexport const localResponseNormalization = op({ localResponseNormalization_ });\n//# sourceMappingURL=local_response_normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E]);\n *\n * x.log().print();  // or tf.log(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log_(x) {\n    const $x = convertToTensor(x, 'x', 'log');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.log($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Log);\n}\nexport const log = op({ log_ });\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log1p } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` plus one\n * element-wise: `ln(1 + x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E - 1]);\n *\n * x.log1p().print();  // or tf.log1p(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log1p_(x) {\n    const $x = convertToTensor(x, 'x', 'log1p');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.log1p($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Log1p);\n}\nexport const log1p = op({ log1p_ });\n//# sourceMappingURL=log1p.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { Tensor, Variable } from './tensor';\nimport { convertToTensor, convertToTensorArray } from './tensor_util_env';\nimport * as util from './util';\n/**\n * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the\n * gradient of `f(x)` with respect to `x`.\n *\n * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to\n * `x` is computed instead. `f(x)` must take a single tensor `x` and return a\n * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.grad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * g(x).print();\n * ```\n *\n * ```js\n * // f(x) = x ^ 3\n * const f = x => x.pow(tf.scalar(3, 'int32'));\n * // f'(x) = 3x ^ 2\n * const g = tf.grad(f);\n * // f''(x) = 6x\n * const gg = tf.grad(g);\n *\n * const x = tf.tensor1d([2, 3]);\n * gg(x).print();\n * ```\n *\n * @param f The function f(x), to compute gradient for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grad(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in grad(f) must be a function');\n    return (x, dy) => {\n        // x can be of any dtype, thus null as the last argument.\n        const $x = convertToTensor(x, 'x', 'tf.grad', null);\n        const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grad') : null;\n        return ENGINE.tidy(() => {\n            const { value, grads } = ENGINE.gradients(() => f($x), [$x], $dy);\n            if ($dy != null) {\n                util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grad(f)(x, dy) must match the shape ' +\n                    'returned by f(x)');\n            }\n            checkGrads(grads);\n            return grads[0];\n        });\n    };\n}\n/**\n * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,\n * which gives an array of gradients of `f()` with respect to each input\n * [`x1`,`x2`,...].\n *\n * If `dy` is passed when calling `g()`, the gradient of\n * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.\n * The provided `f` must take one or more tensors and return a single tensor\n * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df / da = b, df / db = a\n * const g = tf.grads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const [da, db] = g([a, b]);\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @param f The function `f(x1, x2,...)` to compute gradients for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grads(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in grads(f) must be a function');\n    return (args, dy) => {\n        util.assert(Array.isArray(args), () => 'The args passed in grads(f)(args) must be an array ' +\n            'of `Tensor`s or `TensorLike`s');\n        // args can be of any dtype, thus null as the last argument.\n        const $args = convertToTensorArray(args, 'args', 'tf.grads', null);\n        const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grads') : null;\n        return ENGINE.tidy(() => {\n            const { value, grads } = ENGINE.gradients(() => f(...$args), $args, $dy);\n            if ($dy != null) {\n                util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grads(f)([x1,...], dy) must ' +\n                    'match the shape returned by f([x1,...])');\n            }\n            checkGrads(grads);\n            return grads;\n        });\n    };\n}\n/**\n * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.valueAndGrad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * const {value, grad} = g(x);\n *\n * console.log('value');\n * value.print();\n * console.log('grad');\n * grad.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrad(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in valueAndGrad(f) must be a function');\n    return (x, dy) => {\n        util.assert(x instanceof Tensor, () => 'The x passed in valueAndGrad(f)(x) must be a tensor');\n        util.assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrad(f)(x, dy) must be a tensor');\n        const { grads, value } = ENGINE.gradients(() => f(x), [x], dy);\n        checkGrads(grads);\n        return { grad: grads[0], value };\n    };\n}\n/**\n * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df/da = b, df/db = a\n * const g = tf.valueAndGrads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const {value, grads} = g([a, b]);\n *\n * const [da, db] = grads;\n *\n * console.log('value');\n * value.print();\n *\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrads(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in valueAndGrads(f) must be a function');\n    return (args, dy) => {\n        util.assert(Array.isArray(args) && args.every(arg => arg instanceof Tensor), () => 'The args passed in valueAndGrads(f)(args) must be array of ' +\n            'tensors');\n        util.assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrads(f)(args, dy) must be a tensor');\n        const res = ENGINE.gradients(() => f(...args), args, dy);\n        if (dy != null) {\n            util.assertShapesMatch(res.value.shape, dy.shape, 'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +\n                'match the shape returned by f([x1,...])');\n        }\n        checkGrads(res.grads);\n        return res;\n    };\n}\n/**\n * Computes and returns the gradient of f(x) with respect to the list of\n * trainable variables provided by `varList`. If no list is provided, it\n * defaults to all trainable variables.\n *\n * ```js\n * const a = tf.variable(tf.tensor1d([3, 4]));\n * const b = tf.variable(tf.tensor1d([5, 6]));\n * const x = tf.tensor1d([1, 2]);\n *\n * // f(a, b) = a * x ^ 2 + b * x\n * const f = () => a.mul(x.square()).add(b.mul(x)).sum();\n * // df/da = x ^ 2, df/db = x\n * const {value, grads} = tf.variableGrads(f);\n *\n * Object.keys(grads).forEach(varName => grads[varName].print());\n * ```\n *\n * @param f The function to execute. f() should return a scalar.\n * @param varList The list of variables to compute the gradients with respect\n *     to. Defaults to all trainable variables.\n * @returns An object with the following keys and values:\n *   - `value`: The value of the function `f`.\n *   - `grads`: A map from the names of the variables to the gradients.\n *     If the `varList` argument is provided explicitly and contains a subset of\n *     non-trainable variables, this map in the return value will contain keys\n *     that map the names of the non-trainable variables to `null`.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction variableGrads(f, varList) {\n    util.assert(util.isFunction(f), () => 'The f passed in variableGrads(f) must be a function');\n    util.assert(varList == null ||\n        Array.isArray(varList) && varList.every(v => v instanceof Variable), () => 'The varList passed in variableGrads(f, varList) must be an array ' +\n        'of variables');\n    const specifiedVarList = varList != null;\n    if (!specifiedVarList) {\n        // Get all of the trainable variables.\n        varList = [];\n        for (const varName in ENGINE.registeredVariables) {\n            varList.push(ENGINE.registeredVariables[varName]);\n        }\n    }\n    const specifiedNonTrainable = specifiedVarList ? varList.filter(variable => !variable.trainable) : null;\n    // Prune non-trainable variables.\n    const originalVarCount = varList.length;\n    varList = varList.filter(variable => variable.trainable);\n    util.assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to ` +\n        `be trainable, but none of the ${originalVarCount} variables is ` +\n        `trainable.`);\n    const allowNoGradients = true;\n    const { value, grads } = ENGINE.gradients(f, varList, null, allowNoGradients);\n    util.assert(grads.some(g => g != null), () => 'Cannot find a connection between any variable and the result of ' +\n        'the loss function y=f(x). Please make sure the operations that ' +\n        'use variables are inside the function f passed to minimize().');\n    util.assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it ` +\n        `returned a rank-${value.rank} tensor`);\n    const namedGrads = {};\n    varList.forEach((v, i) => {\n        if (grads[i] != null) {\n            namedGrads[v.name] = grads[i];\n        }\n    });\n    if (specifiedNonTrainable != null) {\n        // If varList is explicitly provided and contains non-trainable values,\n        // add them to the returned gradients with `null` values.\n        specifiedNonTrainable.forEach(v => namedGrads[v.name] = null);\n    }\n    return { value, grads: namedGrads };\n}\n/**\n * Overrides the gradient computation of a function `f`.\n *\n * Takes a function\n * `f(...inputs, save) => {value: Tensor, gradFunc: (dy, saved) => Tensor[]}`\n * and returns another function `g(...inputs)` which takes the same inputs as\n * `f`. When called, `g` returns `f().value`. In backward mode, custom gradients\n * with respect to each input of `f` are computed using `f().gradFunc`.\n *\n * The `save` function passsed to `f` should be used for saving tensors needed\n * in the gradient. And the `saved` passed to the `gradFunc` is a\n * `NamedTensorMap`, which contains those saved tensor.\n *\n * ```js\n * const customOp = tf.customGrad((x, save) => {\n *   // Save x to make sure it's available later for the gradient.\n *   save([x]);\n *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);\n *   return {\n *     value: x.square(),\n *     // Note `saved.x` which points to the `x` we saved earlier.\n *     gradFunc: (dy, saved) => [dy.mul(saved[0].abs())]\n *   };\n * });\n *\n * const x = tf.tensor1d([-1, -2, 3]);\n * const dx = tf.grad(x => customOp(x));\n *\n * console.log(`f(x):`);\n * customOp(x).print();\n * console.log(`f'(x):`);\n * dx(x).print();\n * ```\n *\n * @param f The function to evaluate in forward mode, which should return\n *     `{value: Tensor, gradFunc: (dy, saved) => Tensor[]}`, where `gradFunc`\n *     returns the custom gradients of `f` with respect to its inputs.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction customGrad(f) {\n    return ENGINE.customGrad(f);\n}\nfunction checkGrads(grads) {\n    const numNullGradients = grads.filter(g => g == null).length;\n    if (numNullGradients > 0) {\n        throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.`);\n    }\n}\nexport { customGrad, variableGrads, valueAndGrad, valueAndGrads, grad, grads, };\n//# sourceMappingURL=gradients.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Negate } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes `-1 * x` element-wise.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);\n *\n * x.neg().print();  // or tf.neg(x)\n * ```\n *\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction neg_(x) {\n    const $x = convertToTensor(x, 'x', 'neg');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.neg($x), inputs, null /* grad */, Negate);\n}\nexport const neg = op({ neg_ });\n//# sourceMappingURL=neg.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softplus } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.softplus().print();  // or tf.softplus(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction softplus_(x) {\n    const $x = convertToTensor(x, 'x', 'softplus');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.softplus($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Softplus);\n}\nexport const softplus = op({ softplus_ });\n//# sourceMappingURL=softplus.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../gradients';\nimport { convertToTensor } from '../tensor_util_env';\nimport { mul } from './mul';\nimport { neg } from './neg';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { softplus } from './softplus';\n/**\n * Computes log sigmoid of the input `tf.Tensor` element-wise:\n * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.logSigmoid().print();  // or tf.logSigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction logSigmoid_(x) {\n    const $x = convertToTensor(x, 'x', 'logSigmoid');\n    // Use a custom gradient to maintain previous implementation.\n    // There is no LogSigmoid kernel in TF so we can't use engine.runKernel\n    // directly\n    const customOp = customGrad((x) => {\n        // TODO(yassogba) we can remove the chained softplus call here only\n        // after backends have modualrized softplus at which point we can call\n        // engine runKernel(..., Sotfplus, ...) directly.\n        const value = neg(softplus(neg(x)));\n        const gradFunc = (dy) => {\n            const derX = mul(dy, sigmoid(neg(x)));\n            return derX;\n        };\n        return { value, gradFunc };\n    });\n    return customOp($x);\n}\nexport const logSigmoid = op({ logSigmoid_ });\n//# sourceMappingURL=log_sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Max } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the maximum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.max().print();  // or tf.max(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.max(axis).print();  // or tf.max(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction max_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'max');\n    const forward = (backend, save) => {\n        const origAxes = util.parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let maxInput = $x;\n        if (permutedAxes != null) {\n            maxInput = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, maxInput.rank);\n        }\n        const y = backend.max(maxInput, axes);\n        if (permutedAxes != null) {\n            maxInput.dispose();\n        }\n        let res = y;\n        if (keepDims) {\n            const expandedShape = axis_util.expandShapeToKeepDim(res.shape, util.parseAxisParam(axis, $x.shape));\n            res = reshape(res, expandedShape);\n            y.dispose();\n        }\n        save([$x, res]);\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { reductionIndices: axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Max, attrs);\n}\nexport const max = op({ max_ });\n//# sourceMappingURL=max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sub } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n *\n * ```js\n * // Broadcast subtract a with b.\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.scalar(5);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n * @param a The first `tf.Tensor` to subtract from.\n * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction sub_(a, b) {\n    let $a = convertToTensor(a, 'a', 'sub');\n    let $b = convertToTensor(b, 'b', 'sub');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.subtract($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Sub);\n}\nexport const sub = op({ sub_ });\n//# sourceMappingURL=sub.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the sum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If axes has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.sum().print();  // or tf.sum(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.sum(axis).print();  // or tf.sum(x, axis)\n * ```\n *\n * @param x The input tensor to compute the sum over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction sum_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'sum');\n    if ($x.dtype === 'bool') {\n        $x = cast($x, 'int32');\n    }\n    const forward = (backend, save) => {\n        save([$x]);\n        const axes = parseAxisParam(axis, $x.shape);\n        const permutation = getAxesPermutation(axes, $x.rank);\n        let reductionAxes = axes;\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n        }\n        let value = backend.sum(permutedX, reductionAxes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(value.shape, axes);\n            value = reshape(value, newShape);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Sum, attrs);\n}\nexport const sum = op({ sum_ });\n//# sourceMappingURL=sum.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogSoftmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { op } from './operation';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log softmax.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param axis The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction logSoftmax_(logits, axis = -1) {\n    const $logits = convertToTensor(logits, 'logits', 'logSoftmax');\n    if (axis === -1) {\n        axis = $logits.rank - 1;\n    }\n    if (axis !== $logits.rank - 1) {\n        throw Error('Log Softmax along a non-last dimension is not yet supported. ' +\n            `Logits was rank ${$logits.rank} and axis was ${axis}`);\n    }\n    const forward = (backend, save) => {\n        const keepDims = true;\n        const xMax = max(logits, axis, true);\n        const shifted = sub(logits, xMax);\n        const value = sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis, keepDims)));\n        save([value]);\n        return value;\n    };\n    const inputs = { logits: $logits };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LogSoftmax, attrs);\n}\nexport const logSoftmax = op({ logSoftmax_ });\n//# sourceMappingURL=log_softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { add } from './add';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log(sum(exp(elements across the reduction dimensions)).\n *\n * Reduces the input along the dimensions given in `axis`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.logSumExp().print();  // or tf.logSumExp(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.logSumExp(axis).print();  // or tf.logSumExp(a, axis)\n * ```\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. If null (the default),\n *     reduces all dimensions.\n * @param keepDims If true, retains reduced dimensions with length\n *     of 1. Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction logSumExp_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'logSumExp');\n    const axes = parseAxisParam(axis, $x.shape);\n    const xMax = max($x, axes, true /* keepDims */);\n    const a = sub($x, xMax);\n    const b = exp(a);\n    const c = sum(b, axes);\n    const d = log(c);\n    const res = add(reshape(xMax, d.shape), d);\n    if (keepDims) {\n        const newShape = expandShapeToKeepDim(res.shape, axes);\n        return reshape(res, newShape);\n    }\n    return res;\n}\nexport const logSumExp = op({ logSumExp_ });\n//# sourceMappingURL=log_sum_exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalAnd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a AND b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalAnd(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalAnd_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(backend => backend.logicalAnd($a, $b), inputs, null /* grad */, LogicalAnd);\n}\nexport const logicalAnd = op({ logicalAnd_ });\n//# sourceMappingURL=logical_and.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalNot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the truth value of `NOT x` element-wise.\n *\n * ```js\n * const a = tf.tensor1d([false, true], 'bool');\n *\n * a.logicalNot().print();\n * ```\n *\n * @param x The input tensor. Must be of dtype 'bool'.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalNot_(x) {\n    const $x = convertToTensor(x, 'x', 'logicalNot', 'bool');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.logicalNot($x), inputs, null /* grad */, LogicalNot);\n}\nexport const logicalNot = op({ logicalNot_ });\n//# sourceMappingURL=logical_not.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalOr } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a OR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalOr(b).print();\n * ```\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalOr_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalOr', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalOr', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(backend => backend.logicalOr($a, $b), inputs, null /* grad */, LogicalOr);\n}\nexport const logicalOr = op({ logicalOr_ });\n//# sourceMappingURL=logical_or.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { logicalAnd } from './logical_and';\nimport { logicalNot } from './logical_not';\nimport { logicalOr } from './logical_or';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalXor(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalXor_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalXor', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalXor', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    // x ^ y = (x | y) & ~(x & y)\n    return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));\n}\nexport const logicalXor = op({ logicalXor_ });\n//# sourceMappingURL=logical_xor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction maxPool_(x, filterSize, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'maxPool');\n    const dilations = 1;\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        let y;\n        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n            util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n            y = x4D.clone();\n        }\n        else {\n            y = backend.maxPool(x4D, convInfo);\n        }\n        save([x4D, y]);\n        return y;\n    };\n    const inputs = { x: x4D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const maxPool = op({ maxPool_ });\n//# sourceMappingURL=max_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { deprecationWarn } from '../globals';\nimport { MaxPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D max pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.maxPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPool3d_(x, filterSize = [1, 1, 1], strides, pad, dimRoundingMode, dataFormat = 'NDHWC', dilations) {\n    if (dilations == null) {\n        dilations = [1, 1, 1];\n    }\n    else {\n        deprecationWarn('dilations is deprecated, this field will be gone in ' +\n            'v3.0.0.');\n    }\n    const $x = convertToTensor(x, 'x', 'maxPool3d');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in maxPool3d: Only NDHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool3d: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1, 1];\n        }\n        const convInfo = conv_util.computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);\n        const y = backend.maxPool3d(x5D, convInfo);\n        save([x5D, y]);\n        return y;\n    };\n    const inputs = { x: x5D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat, dilations };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool3D, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const maxPool3d = op({ maxPool3d_ });\n//# sourceMappingURL=max_pool_3d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPoolWithArgmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the 2D max pooling of an image with Argmax index.\n * The indices in argmax are flattened, so that a maximum value at position `[b,\n * y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if\n * include_batch_in_index is False; `((b * height + y) * width + x) * channels\n * +c` if include_batch_in_index is True.\n *\n * The indices returned are always in `[0, height) x [0, width)` before\n * flattening.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param includeBatchIndex Defaults to False. Whether to include batch\n *    dimension in flattened index of argmax.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPoolWithArgmax_(x, filterSize, strides, pad, includeBatchInIndex = false) {\n    const $x = convertToTensor(x, 'x', 'maxPoolWithArgmax');\n    const inputs = { x: $x };\n    const attrs = { filterSize, strides, pad, includeBatchInIndex };\n    const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);\n    return { result: result[0], indexes: result[1] };\n}\nexport const maxPoolWithArgmax = op({ maxPoolWithArgmax_ });\n//# sourceMappingURL=max_pool_with_argmax.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeZerosTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\n/**\n * Creates a `tf.Tensor` with all elements set to 0.\n *\n * ```js\n * tf.zeros([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Can\n *     be 'float32', 'int32' or 'bool'. Defaults to 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function zeros(shape, dtype = 'float32') {\n    if (dtype === 'complex64') {\n        const real = zeros(shape, 'float32');\n        const imag = zeros(shape, 'float32');\n        return complex(real, imag);\n    }\n    const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=zeros.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeOnesTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\nimport { zeros } from './zeros';\n/**\n * Creates a `tf.Tensor` with all elements set to 1.\n *\n * ```js\n * tf.ones([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n *     'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function ones(shape, dtype = 'float32') {\n    if (dtype === 'complex64') {\n        const real = ones(shape, 'float32');\n        const imag = zeros(shape, 'float32');\n        return complex(real, imag);\n    }\n    const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=ones.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../gradients';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam, sizeFromShape } from '../util';\nimport { computeOutAndReduceShapes } from './axis_util';\nimport { cast } from './cast';\nimport { div } from './div';\nimport { mul } from './mul';\nimport { ones } from './ones';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { scalar } from './scalar';\nimport { sum } from './sum';\n/**\n * Computes the mean of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is\n * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with\n * a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.mean().print();  // or tf.mean(a)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.mean(axis).print();  // or tf.mean(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction mean_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'mean');\n    const axes = parseAxisParam(axis, $x.shape);\n    const shapes = computeOutAndReduceShapes($x.shape, axes);\n    const reduceShape = shapes[1];\n    const reduceSize = sizeFromShape(reduceShape);\n    // Use a custom gradient to bypass 2 gradient backprops since mean is used\n    // extremely often.\n    const customOp = customGrad((x) => {\n        const reduceSizeScalar = scalar(reduceSize);\n        // Cast if needed.\n        const xReduce = reduceSizeScalar.dtype === x.dtype ?\n            x :\n            cast(x, reduceSizeScalar.dtype);\n        const res = div(xReduce, reduceSizeScalar);\n        const value = sum(res, axis, keepDims);\n        const gradFunc = (dy) => {\n            const expandedDyShape = x.shape.slice();\n            axes.forEach(axis => {\n                expandedDyShape[axis] = 1;\n            });\n            const expandedDy = reshape(dy, expandedDyShape);\n            const derX = div(mul(expandedDy, ones(x.shape, 'float32')), reduceSize);\n            return derX;\n        };\n        return { value, gradFunc };\n    });\n    return customOp($x);\n}\nexport const mean = op({ mean_ });\n//# sourceMappingURL=mean.js.map", "import { ENGINE } from '../engine';\nimport { Min } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the minimum value from the input.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axes`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axes` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.min().print();  // or tf.min(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.min(axis).print();  // or tf.min(x, axis)\n * ```\n *\n * @param x The input Tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction min_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'min');\n    const forward = (backend, save) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let minInput = $x;\n        if (permutedAxes != null) {\n            minInput = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        const y = backend.min(minInput, axes);\n        if (permutedAxes != null) {\n            minInput.dispose();\n        }\n        let res = y;\n        if (keepDims) {\n            const expandedShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n            res = reshape(y, expandedShape);\n            y.dispose();\n        }\n        save([$x, res]);\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Min, attrs);\n}\nexport const min = op({ min_ });\n//# sourceMappingURL=min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Minimum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `minimumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * ```js\n * // Broadcast minimum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction minimum_(a, b) {\n    let $a = convertToTensor(a, 'a', 'minimum');\n    let $b = convertToTensor(b, 'b', 'minimum');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'bool') {\n        $a = cast($a, 'int32');\n        $b = cast($b, 'int32');\n    }\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.minimum($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Minimum);\n}\nexport const minimum = op({ minimum_ });\n//# sourceMappingURL=minimum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Mod } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the mod of a and b element-wise.\n * `floor(x / y) * y + mod(x, y) = x`\n * Supports broadcasting.\n *\n * We also expose `tf.modStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * ```js\n * // Broadcast a mod b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mod_(a, b) {\n    let $a = convertToTensor(a, 'a', 'mod');\n    let $b = convertToTensor(b, 'b', 'mod');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.mod($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Mod);\n}\nexport const mod = op({ mod_ });\n//# sourceMappingURL=mod.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes square of `x` element-wise: `x ^ 2`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);\n *\n * x.square().print();  // or tf.square(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction square_(x) {\n    const $x = convertToTensor(x, 'x', 'square');\n    const attrs = {};\n    const inputsToSave = [$x];\n    const outputsToSave = [];\n    return ENGINE.runKernelFunc((backend, save) => {\n        save([$x]);\n        return backend.square($x);\n    }, { x: $x }, null /* grad */, 'Square', attrs, inputsToSave, outputsToSave);\n}\nexport const square = op({ square_ });\n//# sourceMappingURL=square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { cast } from './cast';\nimport { mean } from './mean';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { square } from './square';\nimport { sub } from './sub';\n/**\n * Calculates the mean and variance of `x`. The mean and variance are\n * calculated by aggregating the contents of `x` across `axes`. If `x` is\n * 1-D and `axes = [0]` this is just the mean and variance of a vector.\n *\n * @param x The input tensor.\n * @param axis The dimension(s) along with to compute mean and\n *     variance. By default it reduces all dimensions.\n * @param keepDims If true, the moments have the same dimensionality as the\n *     input.\n * @return An object with two keys: `mean` and `variance`.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction moments_(x, axis = null, keepDims = false) {\n    x = convertToTensor(x, 'x', 'moments');\n    const axes = parseAxisParam(axis, x.shape);\n    const xMean = mean(x, axes, keepDims);\n    let keepDimsShape = xMean.shape;\n    if (!keepDims) {\n        keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);\n    }\n    const devSquared = square(sub(cast(x, 'float32'), reshape(xMean, keepDimsShape)));\n    const variance = mean(devSquared, axes, keepDims);\n    return { mean: xMean, variance };\n}\nexport const moments = op({ moments_ });\n//# sourceMappingURL=moments.js.map", "import { convertToTensor, convertToTensorArray } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the next states and outputs of a stack of LSTMCells.\n *\n * Each cell output is used as input to the next cell.\n *\n * Returns `[cellState, cellOutput]`.\n *\n * Derived from tf.contrib.rn.MultiRNNCell.\n *\n * @param lstmCells Array of LSTMCell functions.\n * @param data The input to the cell.\n * @param c Array of previous cell states.\n * @param h Array of previous cell outputs.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction multiRNNCell_(lstmCells, data, c, h) {\n    const $data = convertToTensor(data, 'data', 'multiRNNCell');\n    const $c = convertToTensorArray(c, 'c', 'multiRNNCell');\n    const $h = convertToTensorArray(h, 'h', 'multiRNNCell');\n    let input = $data;\n    const newStates = [];\n    for (let i = 0; i < lstmCells.length; i++) {\n        const output = lstmCells[i](input, $c[i], $h[i]);\n        newStates.push(output[0]);\n        newStates.push(output[1]);\n        input = output[1];\n    }\n    const newC = [];\n    const newH = [];\n    for (let i = 0; i < newStates.length; i += 2) {\n        newC.push(newStates[i]);\n        newH.push(newStates[i + 1]);\n    }\n    return [newC, newH];\n}\nexport const multiRNNCell = op({ multiRNNCell_ });\n//# sourceMappingURL=multi_rnn_cell.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Creates a `tf.Tensor` with values drawn from a multinomial distribution.\n *\n * ```js\n * const probs = tf.tensor([.75, .25]);\n * tf.multinomial(probs, 3).print();\n * ```\n *\n * @param logits 1D array with unnormalized log-probabilities, or\n *     2D array of shape `[batchSize, numOutcomes]`. See the `normalized`\n *     parameter.\n * @param numSamples Number of samples to draw for each row slice.\n * @param seed The seed number.\n * @param normalized Whether the provided `logits` are normalized true\n *     probabilities (sum to 1). Defaults to false.\n * @return 1D array of shape `[numSamples]`, or 2D array of shape\n *     `[batchSize, numSamples]`, depending on the rank of the input.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction multinomial_(logits, numSamples, seed, normalized = false) {\n    const $logits = convertToTensor(logits, 'logits', 'multinomial');\n    const numOutcomes = $logits.size;\n    const origRank = $logits.rank;\n    if (numOutcomes < 2) {\n        throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ` +\n            `${numOutcomes}.`);\n    }\n    if (origRank > 2) {\n        throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n    }\n    seed = seed || Math.random();\n    const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;\n    const res = ENGINE.runKernelFunc(backend => backend.multinomial(logits2D, normalized, numSamples, seed), { logits2D });\n    // tslint:disable-next-line:no-unnecessary-type-assertion\n    return origRank === 1 ? reshape(res, [res.size]) : res;\n}\nexport const multinomial = op({ multinomial_ });\n//# sourceMappingURL=multinomial.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { NotEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a != b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([0, 2, 3]);\n *\n * a.notEqual(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction notEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'notEqual');\n    let $b = convertToTensor(b, 'b', 'notEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend) => backend.notEqual($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, NotEqual);\n}\nexport const notEqual = op({ notEqual_ });\n//# sourceMappingURL=not_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Real } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the real part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the real part of each element in input considered as a complex number.\n *\n * If the input is real, it simply makes a clone.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.real(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction real_(input) {\n    const $input = convertToTensor(input, 'input', 'real');\n    const forward = (backend) => {\n        return backend.real($input);\n    };\n    const inputs = { input: $input };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Real);\n}\nexport const real = op({ real_ });\n//# sourceMappingURL=real.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OnesLike } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { complex } from './complex';\nimport { imag } from './imag';\nimport { op } from './operation';\nimport { real } from './real';\nimport { zerosLike } from './zeros_like';\n/**\n * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.onesLike(x).print();\n * ```\n * @param x A tensor.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction onesLike_(x) {\n    const $x = convertToTensor(x, 'x', 'onesLike');\n    const forward = (backend, save) => {\n        if ($x.dtype === 'complex64') {\n            const r = onesLike(real($x));\n            const i = zerosLike(imag($x));\n            return complex(r, i);\n        }\n        return backend.onesLike($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, OnesLike);\n}\nexport const onesLike = op({ onesLike_ });\n//# sourceMappingURL=ones_like.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the outer product of two vectors, `v1` and `v2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([3, 4, 5]);\n *\n * tf.outerProduct(a, b).print();\n * ```\n * @param v1 The first vector in the outer product operation.\n * @param v2 The second vector in the outer product operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction outerProduct_(v1, v2) {\n    const $v1 = convertToTensor(v1, 'v1', 'outerProduct');\n    const $v2 = convertToTensor(v2, 'v2', 'outerProduct');\n    util.assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ` +\n        `${$v1.rank} and ${$v2.rank}.`);\n    const v12D = reshape($v1, [-1, 1]);\n    const v22D = reshape($v2, [1, -1]);\n    return matMul(v12D, v22D);\n}\nexport const outerProduct = op({ outerProduct_ });\n//# sourceMappingURL=outer_product.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { PadV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Pads a `tf.Tensor` with a given value and paddings.\n *\n * This operation currently only implements the `CONSTANT` mode.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `paddings` is of given length.\n *   - `tf.pad1d`\n *   - `tf.pad2d`\n *   - `tf.pad3d`\n *   - `tf.pad4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.pad([[1, 2]]).print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * @param constantValue The pad value to use. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction pad_(x, paddings, constantValue = 0) {\n    const $x = convertToTensor(x, 'x', 'pad');\n    if ($x.rank === 0) {\n        throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');\n    }\n    const forward = (backend, save) => {\n        save([$x]);\n        return backend.pad($x, paddings, constantValue);\n    };\n    const attrs = { paddings, constantValue };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, PadV2, attrs);\n}\nexport const pad = op({ pad_ });\n//# sourceMappingURL=pad.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.\n */\nfunction pad1d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 2, () => 'Invalid number of paddings. Must be length of 2.');\n    return pad(x, [paddings], constantValue);\n}\nexport const pad1d = op({ pad1d_ });\n//# sourceMappingURL=pad1d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.\n */\nfunction pad2d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 2 && paddings[0].length === 2 &&\n        paddings[1].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad2d = op({ pad2d_ });\n//# sourceMappingURL=pad2d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.\n */\nfunction pad3d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 3 && paddings[0].length === 2 &&\n        paddings[1].length === 2 && paddings[2].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad3d = op({ pad3d_ });\n//# sourceMappingURL=pad3d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.\n */\nfunction pad4d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 4 && paddings[0].length === 2 &&\n        paddings[1].length === 2 && paddings[2].length === 2 &&\n        paddings[3].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad4d = op({ pad4d_ });\n//# sourceMappingURL=pad4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SpaceToBatchND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation divides \"spatial\" dimensions `[1, ..., M]` of the input into\n * a grid of blocks of shape `blockShape`, and interleaves these blocks with\n * the \"batch\" dimension (0) such that in the output, the spatial\n * dimensions `[1, ..., M]` correspond to the position within the grid,\n * and the batch dimension combines both the position within a spatial block\n * and the original batch position. Prior to division into blocks,\n * the spatial dimensions of the input are optionally zero padded\n * according to `paddings`. See below for a precise description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n * const blockShape = [2, 2];\n * const paddings = [[0, 0], [0, 0]];\n *\n * x.spaceToBatchND(blockShape, paddings).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=\n *     0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad\n * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It\n * is required that\n * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input\n * according to `paddings` to produce `padded` of shape paddedShape.\n *\n * 2. Reshape `padded` to `reshapedPadded` of shape:\n * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`\n *\n * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`\n * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the\n * batch dimension, producing an output tensor of shape:\n * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction spaceToBatchND_(x, blockShape, paddings) {\n    const $x = convertToTensor(x, 'x', 'spaceToBatchND');\n    util.assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);\n    util.assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);\n    util.assert($x.shape.reduce((a, b, i) => {\n        if (i > 0 && i <= blockShape.length) {\n            return a &&\n                ((b + paddings[i - 1][0] + paddings[i - 1][1]) %\n                    blockShape[i - 1] ===\n                    0);\n        }\n        return a;\n    }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);\n    const forward = backend => backend.spaceToBatchND($x, blockShape, paddings);\n    const inputs = { x: $x };\n    const attrs = { blockShape, paddings };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, SpaceToBatchND, attrs);\n}\nexport const spaceToBatchND = op({ spaceToBatchND_ });\n//# sourceMappingURL=space_to_batch_nd.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { avgPool } from './avg_pool';\nimport { batchToSpaceND } from './batch_to_space_nd';\nimport * as conv_util from './conv_util';\nimport { maxPool } from './max_pool';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { spaceToBatchND } from './space_to_batch_nd';\n/**\n * Performs an N-D pooling operation\n *\n * @param input The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param windowShape The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param poolingType The type of pooling, either 'max' or 'avg'.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction pool_(input, windowShape, poolingType, pad, dilations, strides) {\n    if (dilations == null) {\n        dilations = [1, 1];\n    }\n    if (strides == null) {\n        strides = 1;\n    }\n    if (pad === 0) {\n        pad = 'valid';\n    }\n    const $x = convertToTensor(input, 'x', 'maxPool');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in pool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = conv_util.computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad);\n    const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];\n    // The following implementation does batchToSpace(pool(spaceToBatch(x)))\n    // whenever dilation > 1 since the TF kernels do not support dilation > 1.\n    // tslint:disable-next-line:max-line-length\n    // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037\n    let basePadding;\n    if (pad === 'same') {\n        basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);\n    }\n    else {\n        basePadding = [[0, 0], [0, 0]];\n    }\n    const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n    const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n    const convertedPad = isDilationOne ? pad : 'valid';\n    const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n    const forwardOp = poolingType === 'avg' ?\n        () => avgPool(convertedX, windowShape, strides, convertedPad) :\n        () => maxPool(convertedX, windowShape, strides, convertedPad);\n    const y = forwardOp();\n    const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\n// Helper function to compute crops and paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184\nfunction requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {\n    const padStart = basePadding.map(b => b[0]);\n    const origPadEnd = basePadding.map(b => b[1]);\n    const fullInputShape = inputShape.concat(padStart, origPadEnd);\n    const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n    const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n    const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n    const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n    return [paddings, crops];\n}\n// Helper function to compute base paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524\nfunction withSpaceToBatchBasePaddings(filterShape, dilation) {\n    // Spatial dimensions of the filters and the upsampled filters in which we\n    // introduce (rate - 1) zeros between consecutive filter values.\n    const dilatedFilterShape = filterShape.map((s, i) => {\n        return s + (s - 1) * (dilation[i] - 1);\n    });\n    const padExtraShape = dilatedFilterShape.map(s => s - 1);\n    // When padding is odd, we pad more at end, following the same\n    // convention as conv2d.\n    const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));\n    const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n    return padExtraShape.map((_, i) => {\n        return [padExtraStart[i], padExtraEnd[i]];\n    });\n}\nexport const pool = op({ pool_ });\n//# sourceMappingURL=pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Pow } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the power of one `tf.Tensor` to another. Supports broadcasting.\n *\n * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for\n * corresponding elements in x and y. The result's dtype will be the upcasted\n * type of the `base` and `exp` dtypes.\n *\n * ```js\n * const a = tf.tensor([[2, 3], [4, 5]])\n * const b = tf.tensor([[1, 2], [3, 0]]).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n *\n * ```js\n * const a = tf.tensor([[1, 2], [3, 4]])\n * const b = tf.tensor(2).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n * We also expose `powStrict` which has the same signature as this op and\n * asserts that `base` and `exp` are the same shape (does not broadcast).\n *\n * @param base The base `tf.Tensor` to pow element-wise.\n * @param exp The exponent `tf.Tensor` to pow element-wise.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction pow_(base, exp) {\n    let $base = convertToTensor(base, 'base', 'pow');\n    let $exp = convertToTensor(exp, 'exp', 'pow');\n    [$base, $exp] = makeTypesMatch($base, $exp);\n    const inputs = { a: $base, b: $exp };\n    const forward = (backend, save) => {\n        const y = backend.pow($base, $exp);\n        save([$base, $exp, y]);\n        return y;\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Pow);\n}\nexport const pow = op({ pow_ });\n//# sourceMappingURL=pow.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prelu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes leaky rectified linear element-wise with parametric alphas.\n *\n * `x < 0 ? alpha * x : f(x) = x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n * const alpha = tf.scalar(0.1);\n *\n * x.prelu(alpha).print();  // or tf.prelu(x, alpha)\n * ```\n * @param x The input tensor.\n * @param alpha Scaling factor for negative values.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction prelu_(x, alpha) {\n    const $x = convertToTensor(x, 'x', 'prelu');\n    const $alpha = convertToTensor(alpha, 'alpha', 'prelu');\n    const forward = (backend, save) => {\n        const res = backend.prelu($x, $alpha);\n        save([$x, $alpha]);\n        return res;\n    };\n    const inputs = { x: $x, alpha: $alpha };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Prelu);\n}\nexport const prelu = op({ prelu_ });\n//# sourceMappingURL=prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prod } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the product of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.prod().print();  // or tf.prod(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.prod(axis).print();  // or tf.prod(x, axis)\n * ```\n *\n * @param x The input tensor to compute the product over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction prod_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'prod');\n    const forward = (backend) => {\n        if ($x.dtype === 'bool') {\n            $x = cast($x, 'int32');\n        }\n        const axes = parseAxisParam(axis, $x.shape);\n        const permutation = getAxesPermutation(axes, $x.rank);\n        let reductionAxes = axes;\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n        }\n        let value = backend.prod(permutedX, reductionAxes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(value.shape, axes);\n            value = reshape(value, newShape);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Prod, attrs);\n}\nexport const prod = op({ prod_ });\n//# sourceMappingURL=prod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { sizeFromShape } from '../util';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with values sampled from a random number generator\n * function defined by the user.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param randFunction A random number generator function which is called\n * for each element in the output tensor.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\nfunction rand_(shape, randFunction, dtype) {\n    const size = sizeFromShape(shape);\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    for (let i = 0; i < size; i++) {\n        values[i] = randFunction();\n    }\n    return ENGINE.makeTensor(values, shape, dtype);\n}\nexport const rand = op({ rand_ });\n//# sourceMappingURL=rand.js.map", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as seedrandom from 'seedrandom';\nimport { expectNumbersClose, testEpsilon } from '../test_util';\n// https://en.wikipedia.org/wiki/Marsaglia_polar_method\nexport class MPRandGauss {\n    constructor(mean, stdDeviation, dtype, truncated, seed) {\n        this.mean = mean;\n        this.stdDev = stdDeviation;\n        this.dtype = dtype;\n        this.nextVal = NaN;\n        this.truncated = truncated;\n        if (this.truncated) {\n            this.upper = this.mean + this.stdDev * 2;\n            this.lower = this.mean - this.stdDev * 2;\n        }\n        const seedValue = seed ? seed : Math.random();\n        this.random = seedrandom.alea(seedValue.toString());\n    }\n    /** Returns next sample from a Gaussian distribution. */\n    nextValue() {\n        if (!isNaN(this.nextVal)) {\n            const value = this.nextVal;\n            this.nextVal = NaN;\n            return value;\n        }\n        let resultX, resultY;\n        let isValid = false;\n        while (!isValid) {\n            let v1, v2, s;\n            do {\n                v1 = 2 * this.random() - 1;\n                v2 = 2 * this.random() - 1;\n                s = v1 * v1 + v2 * v2;\n            } while (s >= 1 || s === 0);\n            const mul = Math.sqrt(-2.0 * Math.log(s) / s);\n            resultX = this.mean + this.stdDev * v1 * mul;\n            resultY = this.mean + this.stdDev * v2 * mul;\n            if (!this.truncated || this.isValidTruncated(resultX)) {\n                isValid = true;\n            }\n        }\n        if (!this.truncated || this.isValidTruncated(resultY)) {\n            this.nextVal = this.convertValue(resultY);\n        }\n        return this.convertValue(resultX);\n    }\n    /** Handles proper rounding for non-floating-point numbers. */\n    convertValue(value) {\n        if (this.dtype == null || this.dtype === 'float32') {\n            return value;\n        }\n        return Math.round(value);\n    }\n    /** Returns true if less than 2-standard-deviations from the mean. */\n    isValidTruncated(value) {\n        return value <= this.upper && value >= this.lower;\n    }\n}\n// Marsaglia, George, and Wai Wan Tsang. 2000. \"A Simple Method for Generating\n// Gamma Variables.\"\nexport class RandGamma {\n    constructor(alpha, beta, dtype, seed) {\n        this.alpha = alpha;\n        this.beta = 1 / beta; // convert rate to scale parameter\n        this.dtype = dtype;\n        const seedValue = seed ? seed : Math.random();\n        this.randu = seedrandom.alea(seedValue.toString());\n        this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());\n        if (alpha < 1) {\n            this.d = alpha + (2 / 3);\n        }\n        else {\n            this.d = alpha - (1 / 3);\n        }\n        this.c = 1 / Math.sqrt(9 * this.d);\n    }\n    /** Returns next sample from a gamma distribution. */\n    nextValue() {\n        let x2, v0, v1, x, u, v;\n        while (true) {\n            do {\n                x = this.randn.nextValue();\n                v = 1 + (this.c * x);\n            } while (v <= 0);\n            v *= v * v;\n            x2 = x * x;\n            v0 = 1 - (0.331 * x2 * x2);\n            v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v)));\n            u = this.randu();\n            if (u < v0 || Math.log(u) < v1) {\n                break;\n            }\n        }\n        v = (1 / this.beta) * this.d * v;\n        if (this.alpha < 1) {\n            v *= Math.pow(this.randu(), 1 / this.alpha);\n        }\n        return this.convertValue(v);\n    }\n    /** Handles proper rounding for non-floating-point numbers. */\n    convertValue(value) {\n        if (this.dtype === 'float32') {\n            return value;\n        }\n        return Math.round(value);\n    }\n}\nexport class UniformRandom {\n    constructor(min = 0, max = 1, dtype, seed) {\n        /** Handles proper rounding for non floating point numbers. */\n        this.canReturnFloat = () => (this.dtype == null || this.dtype === 'float32');\n        this.min = min;\n        this.range = max - min;\n        this.dtype = dtype;\n        if (seed == null) {\n            seed = Math.random();\n        }\n        if (typeof seed === 'number') {\n            seed = seed.toString();\n        }\n        if (!this.canReturnFloat() && this.range <= 1) {\n            throw new Error(`The difference between ${min} - ${max} <= 1 and dtype is not float`);\n        }\n        this.random = seedrandom.alea(seed);\n    }\n    convertValue(value) {\n        if (this.canReturnFloat()) {\n            return value;\n        }\n        return Math.round(value);\n    }\n    nextValue() {\n        return this.convertValue(this.min + this.range * this.random());\n    }\n}\nexport function jarqueBeraNormalityTest(values) {\n    // https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test\n    const n = values.length;\n    const s = skewness(values);\n    const k = kurtosis(values);\n    const jb = n / 6 * (Math.pow(s, 2) + 0.25 * Math.pow(k - 3, 2));\n    // JB test requires 2-degress of freedom from Chi-Square @ 0.95:\n    // http://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm\n    const CHI_SQUARE_2DEG = 5.991;\n    if (jb > CHI_SQUARE_2DEG) {\n        throw new Error(`Invalid p-value for JB: ${jb}`);\n    }\n}\nexport function expectArrayInMeanStdRange(actual, expectedMean, expectedStdDev, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    const actualMean = mean(actual);\n    expectNumbersClose(actualMean, expectedMean, epsilon);\n    expectNumbersClose(standardDeviation(actual, actualMean), expectedStdDev, epsilon);\n}\nfunction mean(values) {\n    let sum = 0;\n    for (let i = 0; i < values.length; i++) {\n        sum += values[i];\n    }\n    return sum / values.length;\n}\nfunction standardDeviation(values, mean) {\n    let squareDiffSum = 0;\n    for (let i = 0; i < values.length; i++) {\n        const diff = values[i] - mean;\n        squareDiffSum += diff * diff;\n    }\n    return Math.sqrt(squareDiffSum / values.length);\n}\nfunction kurtosis(values) {\n    // https://en.wikipedia.org/wiki/Kurtosis\n    const valuesMean = mean(values);\n    const n = values.length;\n    let sum2 = 0;\n    let sum4 = 0;\n    for (let i = 0; i < n; i++) {\n        const v = values[i] - valuesMean;\n        sum2 += Math.pow(v, 2);\n        sum4 += Math.pow(v, 4);\n    }\n    return (1 / n) * sum4 / Math.pow((1 / n) * sum2, 2);\n}\nfunction skewness(values) {\n    // https://en.wikipedia.org/wiki/Skewness\n    const valuesMean = mean(values);\n    const n = values.length;\n    let sum2 = 0;\n    let sum3 = 0;\n    for (let i = 0; i < n; i++) {\n        const v = values[i] - valuesMean;\n        sum2 += Math.pow(v, 2);\n        sum3 += Math.pow(v, 3);\n    }\n    return (1 / n) * sum3 / Math.pow((1 / (n - 1)) * sum2, 3 / 2);\n}\n//# sourceMappingURL=rand_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { RandGamma } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a gamma distribution.\n *\n * ```js\n * tf.randomGamma([2, 2], 1).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param alpha The shape parameter of the gamma distribution.\n * @param beta The inverse scale parameter of the gamma distribution. Defaults\n *     to 1.\n * @param dtype The data type of the output. Defaults to float32.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomGamma_(shape, alpha, beta = 1, dtype = 'float32', seed) {\n    if (beta == null) {\n        beta = 1;\n    }\n    if (dtype == null) {\n        dtype = 'float32';\n    }\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new Error(`Unsupported data type ${dtype}`);\n    }\n    const rgamma = new RandGamma(alpha, beta, dtype, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = rgamma.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomGamma = op({ randomGamma_ });\n//# sourceMappingURL=random_gamma.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a normal distribution.\n *\n * ```js\n * tf.randomNormal([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n    if (dtype != null && dtype === 'bool') {\n        throw new Error(`Unsupported data type ${dtype}`);\n    }\n    const randGauss = new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = randGauss.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomNormal = op({ randomNormal_ });\n//# sourceMappingURL=random_normal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { UniformRandom } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a uniform distribution.\n *\n * The generated values follow a uniform distribution in the range [minval,\n * maxval). The lower bound minval is included in the range, while the upper\n * bound maxval is excluded.\n *\n * ```js\n * tf.randomUniform([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param minval The lower bound on the range of random values to generate.\n *   Defaults to 0.\n * @param maxval The upper bound on the range of random values to generate.\n *   Defaults to 1.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomUniform_(shape, minval = 0, maxval = 1, dtype = 'float32', seed) {\n    const res = buffer(shape, dtype);\n    const random = new UniformRandom(minval, maxval, null, seed);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = random.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomUniform = op({ randomUniform_ });\n//# sourceMappingURL=random_uniform.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor1d` as it makes the code more readable.\n *\n * ```js\n * tf.tensor1d([1, 2, 3]).print();\n * ```\n *\n * @param values The values of the tensor. Can be array of numbers,\n *     or a `TypedArray`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor1d(values, dtype) {\n    assertNonNull(values);\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 1) {\n        throw new Error('tensor1d() requires values to be a flat/TypedArray');\n    }\n    const shape = null;\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor1d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Range } from '../kernel_names';\nimport { makeZerosTypedArray } from '../util';\nimport { tensor1d } from './tensor1d';\nimport { zeros } from './zeros';\n/**\n * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.\n *\n * The tensor is a is half-open interval meaning it includes start, but\n * excludes stop. Decrementing ranges and negative step values are also\n * supported.sv\n *\n *\n * ```js\n * tf.range(0, 9, 2).print();\n * ```\n *\n * @param start An integer start value\n * @param stop An integer stop value\n * @param step An integer increment (will default to 1 or -1)\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function range(start, stop, step = 1, dtype = 'float32') {\n    if (step === 0) {\n        throw new Error('Cannot have a step of zero');\n    }\n    const forward = () => {\n        const sameStartStop = start === stop;\n        const increasingRangeNegativeStep = start < stop && step < 0;\n        const decreasingRangePositiveStep = stop < start && step > 1;\n        if (sameStartStop || increasingRangeNegativeStep ||\n            decreasingRangePositiveStep) {\n            return zeros([0], dtype);\n        }\n        const numElements = Math.abs(Math.ceil((stop - start) / step));\n        const values = makeZerosTypedArray(numElements, dtype);\n        if (stop < start && step === 1) {\n            // Auto adjust the step's sign if it hasn't been set\n            // (or was set to 1)\n            step = -1;\n        }\n        values[0] = start;\n        for (let i = 1; i < values.length; i++) {\n            values[i] = values[i - 1] + step;\n        }\n        return tensor1d(values, dtype);\n    };\n    const attrs = { start, stop, step, dtype };\n    return ENGINE.runKernelFunc(forward, {} /* inputs */, null /* grad */, Range, attrs);\n}\n//# sourceMappingURL=range.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reciprocal } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of x element-wise: `1 / x`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, 2]);\n *\n * x.reciprocal().print();  // or tf.reciprocal(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction reciprocal_(x) {\n    const $x = convertToTensor(x, 'x', 'reciprocal');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.reciprocal($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Reciprocal);\n}\nexport const reciprocal = op({ reciprocal_ });\n//# sourceMappingURL=reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { cast } from './cast';\n/**\n * Computes rectified linear element-wise: `max(x, 0)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.relu().print();  // or tf.relu(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu_(x) {\n    const $x = convertToTensor(x, 'x', 'relu');\n    const forward = (backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'bool') {\n            return cast($x, 'int32');\n        }\n        return backend.relu($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Relu);\n}\nexport const relu = op({ relu_ });\n//# sourceMappingURL=relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu6 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes rectified linear 6 element-wise: `min(max(x, 0), 6)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 8]);\n *\n * x.relu6().print();  // or tf.relu6(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu6_(x) {\n    const $x = convertToTensor(x, 'x', 'relu6');\n    const forward = (backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'bool') {\n            return cast($x, 'int32');\n        }\n        return backend.relu6($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Relu6);\n}\nexport const relu6 = op({ relu6_ });\n//# sourceMappingURL=relu6.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reverse } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { clone } from './clone';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Reverses a `tf.Tensor` along a specified axis.\n *\n * Also available are stricter rank-specific methods that assert that `x` is\n * of the given rank:\n *   - `tf.reverse1d`\n *   - `tf.reverse2d`\n *   - `tf.reverse3d`\n *   - `tf.reverse4d`\n *\n * Except `tf.reverse1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.reverse().print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.reverse(axis).print();\n * ```\n * @param x The input tensor to be reversed.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction reverse_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    const forward = (backend) => {\n        const axes = parseAxisParam(axis, $x.shape);\n        if ($x.rank === 0) {\n            return clone($x);\n        }\n        const res = backend.reverse($x, axes);\n        return reshape(res, $x.shape);\n    };\n    const inputs = { x: $x };\n    const attrs = { dims: axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Reverse, attrs);\n}\nexport const reverse = op({ reverse_ });\n//# sourceMappingURL=reverse.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor1D`.\n *\n * @param x The input tensor.\n */\nfunction reverse1d_(x) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);\n    return reverse($x, 0);\n}\nexport const reverse1d = op({ reverse1d_ });\n//# sourceMappingURL=reverse_1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor2D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse2d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse2d = op({ reverse2d_ });\n//# sourceMappingURL=reverse_2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor3D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse3d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse3d = op({ reverse3d_ });\n//# sourceMappingURL=reverse_3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor4D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse4d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse4d = op({ reverse4d_ });\n//# sourceMappingURL=reverse_4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Round } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes round of input `tf.Tensor` element-wise: `round(x)`.\n * It implements banker's rounding.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.round().print();  // or tf.round(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction round_(x) {\n    const $x = convertToTensor(x, 'x', 'round');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.round($x), inputs, null /* grad */, Round);\n}\nexport const round = op({ round_ });\n//# sourceMappingURL=round.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Rsqrt } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of square root of the input `tf.Tensor` element-wise:\n * `y = 1 / sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.rsqrt().print();  // or tf.rsqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction rsqrt_(x) {\n    const $x = convertToTensor(x, 'x', 'rsqrt');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.rsqrt($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Rsqrt);\n}\nexport const rsqrt = op({ rsqrt_ });\n//# sourceMappingURL=rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Selu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes scaled exponential linear element-wise.\n *\n * `x < 0 ? scale * alpha * (exp(x) - 1) : x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.selu().print();  // or tf.selu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction selu_(x) {\n    const $x = convertToTensor(x, 'x', 'selu');\n    const forward = (backend, save) => {\n        const res = backend.selu($x);\n        save([$x]);\n        return res;\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Selu);\n}\nexport const selu = op({ selu_ });\n//# sourceMappingURL=selu.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport { depthwiseConv2d } from './depthwise_conv2d';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * 2-D convolution with separable filters.\n *\n * Performs a depthwise convolution that acts separately on channels followed\n * by a pointwise convolution that mixes channels. Note that this is\n * separability between dimensions [1, 2] and 3, not spatial separability\n * between dimensions 1 and 2.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is\n *     the filter used in the first step.\n * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape\n *     `[1, 1, inChannels * channelMultiplier, outChannels]`. This is\n *     the filter used in the second step.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation = [1, 1], dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'separableConv2d');\n    const $depthwiseFilter = convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');\n    const $pointwiseFilter = convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    if (dataFormat === 'NCHW') {\n        throw new Error('separableConv2d currently does not support dataFormat NCHW; only ' +\n            'NHWC is supported');\n    }\n    util.assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but ` +\n        `got rank ${$depthwiseFilter.rank}.`);\n    util.assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but ` +\n        `got rank ${$depthwiseFilter.rank}.`);\n    util.assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter ` +\n        ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n    util.assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise ` +\n        `filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n    const inChannels = $depthwiseFilter.shape[2];\n    const channelMultiplier = $depthwiseFilter.shape[3];\n    util.assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter ` +\n        `must be ${inChannels * channelMultiplier}, ` +\n        `but got ${$pointwiseFilter.shape[2]}.`);\n    const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);\n    const pointwiseStride = 1;\n    const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const separableConv2d = op({ separableConv2d_ });\n//# sourceMappingURL=separable_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\n/**\n * Computes the difference between two lists of numbers.\n *\n * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`\n * that represents all values that are in `x` but not in `y`. The returned\n * Tensor `out` is sorted in the same order that the numbers appear in `x`\n * (duplicates are preserved). This operation also returns a Tensor indices that\n * represents the position of each out element in `x`. In other words:\n *\n * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`\n *\n * ```js\n * const x = [1, 2, 3, 4, 5, 6];\n * const y = [1, 3, 5];\n *\n * const [out, indices] = await tf.setdiff1dAsync(x, y);\n * out.print(); // [2, 4, 6]\n * indices.print(); // [1, 3, 5]\n * ```\n *\n * @param x 1-D Tensor. Values to keep.\n * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the\n *     output.\n * @returns Promise of Tensor tuple [out, indices].\n *  out: Tensor with the same type as x.\n *  indices: A Tensor of type int32.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nasync function setdiff1dAsync_(x, y) {\n    const $x = convertToTensor(x, 'x', 'setdiff1d');\n    const $y = convertToTensor(y, 'y', 'setdiff1d');\n    util.assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);\n    util.assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);\n    util.assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);\n    const xVals = await $x.data();\n    const yVals = await $y.data();\n    const ySet = new Set(yVals);\n    let outputSize = 0;\n    for (let i = 0; i < xVals.length; i++) {\n        if (!ySet.has(xVals[i])) {\n            outputSize++;\n        }\n    }\n    const buffer = new TensorBuffer([outputSize], $x.dtype);\n    const indices = new TensorBuffer([outputSize], 'int32');\n    for (let i = 0, p = 0; i < xVals.length; i++) {\n        if (!ySet.has(xVals[i])) {\n            buffer.values[p] = xVals[i];\n            indices.values[p] = i;\n            p++;\n        }\n    }\n    return [buffer.toTensor(), indices.toTensor()];\n}\nexport const setdiff1dAsync = setdiff1dAsync_;\n//# sourceMappingURL=setdiff1d_async.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sign } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns an element-wise indication of the sign of a number.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);\n *\n * x.sign().print();  // or tf.sign(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sign_(x) {\n    const $x = convertToTensor(x, 'x', 'sign');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.sign($x), inputs, null /* grad */, Sign);\n}\nexport const sign = op({ sign_ });\n//# sourceMappingURL=sign.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sin of the input Tensor element-wise: `sin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.sin().print();  // or tf.sin(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sin_(x) {\n    const $x = convertToTensor(x, 'x', 'sin');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sin($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sin);\n}\nexport const sin = op({ sin_ });\n//# sourceMappingURL=sin.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.sinh().print();  // or tf.sinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sinh_(x) {\n    const $x = convertToTensor(x, 'x', 'sinh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sinh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sinh);\n}\nexport const sinh = op({ sinh_ });\n//# sourceMappingURL=sinh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 1D slice from 1D array starting at coordinates `begin` and is\n * of length `size`. See `slice` for details.\n */\nfunction slice1d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice1d');\n    util.assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, [begin], [size]);\n}\nexport const slice1d = op({ slice1d_ });\n//# sourceMappingURL=slice1d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 2D slice from a 2D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice2d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice2d');\n    util.assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice2d = op({ slice2d_ });\n//# sourceMappingURL=slice2d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 3D slice from a 3D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice3d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice3d');\n    util.assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice3d = op({ slice3d_ });\n//# sourceMappingURL=slice3d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 4D slice from a 4D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice4d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice4d');\n    util.assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice4d = op({ slice4d_ });\n//# sourceMappingURL=slice4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the softmax normalized vector given the logits.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction softmax_(logits, dim = -1) {\n    const $logits = convertToTensor(logits, 'logits', 'softmax', 'float32');\n    if (dim === -1) {\n        dim = $logits.rank - 1;\n    }\n    if (dim !== $logits.rank - 1) {\n        throw Error('Softmax along a non-last dimension is not yet supported. ' +\n            `Logits was rank ${$logits.rank} and dim was ${dim}`);\n    }\n    const inputs = { logits: $logits };\n    const attrs = { dim };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const y = backend.softmax($logits, dim);\n        save([y]);\n        return y;\n    }, inputs, null /* grad */, Softmax, attrs);\n}\nexport const softmax = op({ softmax_ });\n//# sourceMappingURL=softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\n/**\n * Fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the inner-most\n * dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.fft().print();  // tf.spectral.fft(x).print();\n * ```\n * @param input The complex input to compute an fft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction fft_(input) {\n    assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.fft() must be complex64 ` +\n        `but got ${input.dtype}.`);\n    const inputs = { input };\n    return ENGINE.runKernelFunc(backend => {\n        // Collapse all outer dimensions to a single batch dimension.\n        const innerDimensionSize = input.shape[input.shape.length - 1];\n        const batch = input.size / innerDimensionSize;\n        const input2D = input.as2D(batch, innerDimensionSize);\n        const result = backend.fft(input2D);\n        return result.reshape(input.shape);\n    }, inputs, null /* gradient */, FFT);\n}\nexport const fft = op({ fft_ });\n//# sourceMappingURL=fft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { IFFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Inverse fast Fourier transform.\n *\n * Computes the inverse 1-dimensional discrete Fourier transform over the\n * inner-most dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.ifft().print();  // tf.spectral.ifft(x).print();\n * ```\n * @param input The complex input to compute an ifft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction ifft_(input) {\n    assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.ifft() must be complex64 ` +\n        `but got ${input.dtype}.`);\n    const inputs = { input };\n    return ENGINE.runKernelFunc(backend => {\n        // Collapse all outer dimensions to a single batch dimension.\n        const innerDimensionSize = input.shape[input.shape.length - 1];\n        const batch = input.size / innerDimensionSize;\n        const input2D = reshape(input, [batch, innerDimensionSize]);\n        const result = backend.ifft(input2D);\n        return reshape(result, input.shape);\n    }, inputs, null /* gradient */, IFFT);\n}\nexport const ifft = op({ ifft_ });\n//# sourceMappingURL=ifft.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { reverse } from '../reverse';\nimport { scalar } from '../scalar';\nimport { slice } from '../slice';\nimport { ifft } from './ifft';\n/**\n * Inversed real value input fast Fourier transform.\n *\n * Computes the 1-dimensional inversed discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([0, 0, 0]);\n * const x = tf.complex(real, imag);\n *\n * x.irfft().print();\n * ```\n * @param input The real value input to compute an irfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction irfft_(input) {\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n    let ret;\n    if (innerDimensionSize <= 2) {\n        const complexInput = reshape(input, [batch, innerDimensionSize]);\n        ret = ifft(complexInput);\n    }\n    else {\n        // The length of unique components of the DFT of a real-valued signal\n        // is 2 * (input_len - 1)\n        const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n        const realInput = reshape(real(input), [batch, innerDimensionSize]);\n        const imagInput = reshape(imag(input), [batch, innerDimensionSize]);\n        const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);\n        const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));\n        const r = concat([realInput, realConjugate], 1);\n        const i = concat([imagInput, imagConjugate], 1);\n        const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);\n        ret = ifft(complexInput);\n    }\n    ret = real(ret);\n    // reshape the result if the input is 3D tensor.\n    if (input.rank === 3 && input.shape[0] !== 0) {\n        const temp = ret;\n        const batch = input.shape[0];\n        ret = reshape(ret, [batch, ret.shape[0] / batch, ret.shape[1]]);\n        temp.dispose();\n    }\n    return ret;\n}\nexport const irfft = op({ irfft_ });\n//# sourceMappingURL=irfft.js.map", "import { assert } from '../util';\n/**\n * Prepare the split size array. When the input is a number, the axis is evenly\n * divided among the split size. When the input contains the negative value, the\n * rest of the axis is allocated toward that.\n */\nexport function prepareSplitSize(x, numOrSizeSplits, axis = 0) {\n    let splitSizes = [];\n    if (typeof (numOrSizeSplits) === 'number') {\n        assert(x.shape[axis] % numOrSizeSplits === 0, () => 'Number of splits must evenly divide the axis.');\n        splitSizes =\n            new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);\n    }\n    else {\n        const numOfNegs = numOrSizeSplits.reduce((count, value) => {\n            if (value === -1) {\n                count += 1;\n            }\n            return count;\n        }, 0);\n        assert(numOfNegs <= 1, () => 'There should be only one negative value in split array.');\n        const negIndex = numOrSizeSplits.indexOf(-1);\n        // Allow the number of split array to be -1, which indicates the rest\n        // of dimension is allocated to that split.\n        if (negIndex !== -1) {\n            const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);\n            numOrSizeSplits[negIndex] = x.shape[axis] - total;\n        }\n        assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => 'The sum of sizes must match the size of the axis dimension.');\n        splitSizes = numOrSizeSplits;\n    }\n    return splitSizes;\n}\n//# sourceMappingURL=split_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SplitV } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { op } from './operation';\nimport { prepareSplitSize } from './split_util';\n/**\n * Splits a `tf.Tensor` into sub tensors.\n *\n * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`\n * into `numOrSizeSplits` smaller tensors.\n * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.\n *\n * If `numOrSizeSplits` is a number array, splits `x` into\n * `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the\n * same size as `x` except along dimension `axis` where the size is\n * `numOrSizeSplits[i]`.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);\n * const [a, b] = tf.split(x, 2, 1);\n * a.print();\n * b.print();\n *\n * const [c, d, e] = tf.split(x, [1, 2, 1], 1);\n * c.print();\n * d.print();\n * e.print();\n * ```\n *\n * @param x The input tensor to split.\n * @param numOrSizeSplits Either an integer indicating the number of\n * splits along the axis or an array of integers containing the sizes of\n * each output tensor along the axis. If a number then it must evenly divide\n * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.\n * Can contain one -1 indicating that dimension is to be inferred.\n * @param axis The dimension along which to split. Defaults to 0 (the first\n * dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction split_(x, numOrSizeSplits, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'split');\n    const forward = (backend, _) => {\n        const $axis = parseAxisParam(axis, $x.shape)[0];\n        const splitSizes = prepareSplitSize($x, numOrSizeSplits, $axis);\n        return backend.split($x, splitSizes, $axis);\n    };\n    const inputs = { x: $x };\n    const attr = { numOrSizeSplits, axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, SplitV, attr);\n}\nexport const split = op({ split_ });\n//# sourceMappingURL=split.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../../util';\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { split } from '../split';\nimport { zeros } from '../zeros';\nimport { zerosLike } from '../zeros_like';\nimport { fft } from './fft';\n/**\n * Real value input fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * real.rfft().print();\n * ```\n * @param input The real value input to compute an rfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction rfft_(input, fftLength) {\n    assert(input.dtype === 'float32', () => `The dtype for rfft() must be real value but got ${input.dtype}`);\n    let innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n    let adjustedInput;\n    if (fftLength != null && fftLength < innerDimensionSize) {\n        // Need to crop\n        const begin = input.shape.map(v => 0);\n        const size = input.shape.map(v => v);\n        size[input.shape.length - 1] = fftLength;\n        adjustedInput = slice(input, begin, size);\n        innerDimensionSize = fftLength;\n    }\n    else if (fftLength != null && fftLength > innerDimensionSize) {\n        // Need to pad with zeros\n        const zerosShape = input.shape.map(v => v);\n        zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;\n        adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);\n        innerDimensionSize = fftLength;\n    }\n    else {\n        adjustedInput = input;\n    }\n    // Complement the input with zero imaginary numbers.\n    const zerosInput = zerosLike(adjustedInput);\n    const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);\n    const ret = fft(complexInput);\n    // Exclude complex conjugations. These conjugations are put symmetrically.\n    const half = Math.floor(innerDimensionSize / 2) + 1;\n    const realValues = real(ret);\n    const imagValues = imag(ret);\n    const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);\n    const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);\n    const outputShape = adjustedInput.shape.slice();\n    outputShape[adjustedInput.shape.length - 1] = half;\n    return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);\n}\nexport const rfft = op({ rfft_ });\n//# sourceMappingURL=rfft.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sqrt } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.sqrt().print();  // or tf.sqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sqrt_(x) {\n    const $x = convertToTensor(x, 'x', 'sqrt');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sqrt($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sqrt);\n}\nexport const sqrt = op({ sqrt_ });\n//# sourceMappingURL=sqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SquaredDifference } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns (a - b) * (a - b) element-wise.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * ```js\n * // Broadcast squared difference  a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction squaredDifference_(a, b) {\n    let $a = convertToTensor(a, 'a', 'squaredDifference');\n    let $b = convertToTensor(b, 'b', 'squaredDifference');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.squaredDifference($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = {};\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, SquaredDifference, attrs);\n}\nexport const squaredDifference = op({ squaredDifference_ });\n//# sourceMappingURL=squared_difference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { squeezeShape } from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Removes dimensions of size 1 from the shape of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);\n * x.squeeze().print();\n * ```\n *\n * @param x The input tensor to be squeezed.\n * @param axis An optional list of numbers. If specified, only\n *     squeezes the dimensions listed. The dimension index starts at 0. It\n * is an error to squeeze a dimension that is not 1.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction squeeze_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'squeeze');\n    return reshape($x, squeezeShape($x.shape, axis).newShape);\n}\nexport const squeeze = op({ squeeze_ });\n//# sourceMappingURL=squeeze.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensorArray } from '../tensor_util_env';\nimport * as util from '../util';\nimport { concat } from './concat';\nimport { expandDims } from './expand_dims';\nimport { op } from './operation';\n/**\n * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.stack([a, b, c]).print();\n * ```\n *\n * @param tensors A list of tensor objects with the same shape and dtype.\n * @param axis The axis to stack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction stack_(tensors, axis = 0) {\n    const $tensors = convertToTensorArray(tensors, 'tensors', 'stack');\n    util.assert($tensors.length >= 1, () => 'Pass at least one tensor to tf.stack');\n    if ($tensors.length === 1) {\n        return expandDims($tensors[0], axis);\n    }\n    const rank = $tensors[0].rank;\n    const shape = $tensors[0].shape;\n    const dtype = $tensors[0].dtype;\n    util.assert(axis <= rank, () => 'Axis must be <= rank of the tensor');\n    $tensors.forEach(t => {\n        util.assertShapesMatch(shape, t.shape, 'All tensors passed to stack must have matching shapes');\n        util.assert(dtype === t.dtype, () => 'All tensors passed to stack must have matching dtypes');\n    });\n    const expandedTensors = $tensors.map(t => expandDims(t, axis));\n    // Stack exists in the TensorFlow C++ API\n    // (https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/stack) but not\n    // in\n    // https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/core/ops/ops.pbtxt.\n    // Therefore we are treating it like a high-level op rather than\n    // creating a dedicated stack kernel.\n    return concat(expandedTensors, axis);\n}\nexport const stack = op({ stack_ });\n//# sourceMappingURL=stack.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Step } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha * x`\n *\n * ```js\n * const x = tf.tensor1d([0, 2, -1, -3]);\n *\n * x.step(.5).print();  // or tf.step(x, .5)\n * ```\n * @param x The input tensor.\n * @param alpha The gradient when input is negative.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction step_(x, alpha = 0.0) {\n    const $x = convertToTensor(x, 'x', 'step');\n    const inputs = { x: $x };\n    const attrs = { alpha };\n    return ENGINE.runKernelFunc(backend => backend.step($x, alpha), inputs, null /* grad */, Step, attrs);\n}\nexport const step = op({ step_ });\n//# sourceMappingURL=step.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { StridedSlice } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { slice } from './slice';\nimport { computeOutShape, getNormalizedAxes, maskToAxes } from './slice_util';\n/**\n * Extracts a strided slice of a tensor.\n *\n * Roughly speaking, this op extracts a slice of size (end-begin)/stride from\n * the given input tensor (x). Starting at the location specified by begin the\n * slice continues by adding stride to the index until all dimensions are not\n * less than end. Note that a stride can be negative, which causes a reverse\n * slice.\n *\n * ```js\n * const t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],\n *    [3, 2, 3]);\n * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print()  // [[[3, 3, 3]]]\n * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print()  // [[[3, 3, 3],\n *                                                     // [4, 4, 4]]]\n * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],\n *                                                     // [3, 3, 3]]]\n * ```\n *\n * @param x The tensor to stride slice.\n * @param begin The coordinates to start the slice from.\n * @param end: The coordinates to end the slice at.\n * @param strides: The size of the slice.\n * @param beginMask: If the ith bit of beginMask is set, begin[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param endMask: If the ith bit of endMask is set, end[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param shrinkAxisMask: a bitmask where bit i implies that\n * the ith specification should shrink the dimensionality. begin and end must\n * imply a slice of size 1 in the dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {\n    let $x = convertToTensor(x, 'x', 'stridedSlice');\n    const forward = (backend) => {\n        if (strides == null) {\n            strides = new Array(begin.length);\n        }\n        const ellipsisAxes = maskToAxes(ellipsisMask);\n        if (ellipsisAxes.length > 1) {\n            throw new Error('Multiple ellipses in slice is not allowed.');\n        }\n        if (ellipsisMask !== 0 && newAxisMask !== 0) {\n            throw new Error('Using both ellipsisMask and newAxisMask is not yet supported.');\n        }\n        if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {\n            throw new Error('Using both ellipsisMask and shrinkAxisMask is not yet supported.');\n        }\n        const numInterpolatedAxes = $x.rank - begin.length;\n        // Expand the dims of x based on the newAxisMask.\n        const expandAxes = maskToAxes(newAxisMask);\n        const newShape = $x.shape.slice();\n        expandAxes.forEach(axis => {\n            begin[axis] = 0;\n            end[axis] = 1;\n            newShape.splice(axis, 0, 1);\n        });\n        $x = reshape($x, newShape);\n        const { begin: normalizedBegin, end: normalizedEnd, strides: normalizedStrides } = getNormalizedAxes($x.shape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask);\n        begin = normalizedBegin;\n        end = normalizedEnd;\n        strides = normalizedStrides;\n        const shrinkAxes = maskToAxes(shrinkAxisMask);\n        // Adjust the ends based on the shrink mask.\n        shrinkAxes.forEach(axis => {\n            end[axis] = begin[axis] + 1;\n            strides[axis] = 1;\n        });\n        // Figure out the output shape.\n        const size = computeOutShape(begin, end, strides);\n        // Remove the axes based on shrinkMask.\n        const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);\n        const nonStrided = strides.every(v => v === 1);\n        if (nonStrided) {\n            return reshape(slice($x, begin, size), outShape);\n        }\n        const res = backend.stridedSlice($x, begin, end, strides);\n        return reshape(res, outShape);\n    };\n    const inputs = { x: $x };\n    const attrs = {\n        begin,\n        end,\n        strides,\n        beginMask,\n        endMask,\n        ellipsisMask,\n        newAxisMask,\n        shrinkAxisMask\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, StridedSlice, attrs);\n}\nexport const stridedSlice = op({ stridedSlice_ });\n//# sourceMappingURL=strided_slice.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.tan().print();  // or tf.tan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tan_(x) {\n    const $x = convertToTensor(x, 'x', 'tan');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.tan($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Tan);\n}\nexport const tan = op({ tan_ });\n//# sourceMappingURL=tan.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor2d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor2d([[1, 2], [3, 4]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor2d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 2) {\n        throw new Error('tensor2d() requires shape to have two numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n        throw new Error('tensor2d() requires values to be number[][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor2d() requires shape to be provided when `values` ' +\n            'are a flat/TypedArray');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor2d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor4d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor4d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 4) {\n        throw new Error('tensor4d() requires shape to have four numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n        throw new Error('tensor4d() requires values to be number[][][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor4d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor5d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor5d([[[[[1], [2]], [[3], [4]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor5d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 5) {\n        throw new Error('tensor5d() requires shape to have five numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n        throw new Error('tensor5d() requires values to be ' +\n            'number[][][][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor5d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor5d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor6d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor6d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 6) {\n        throw new Error('tensor6d() requires shape to have six numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n        throw new Error('tensor6d() requires values to be number[][][][][][] or ' +\n            'flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor6d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    shape = shape ||\n        inferredShape;\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor6d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { TopK } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Finds the values and indices of the `k` largest entries along the last\n * dimension.\n *\n * If the input is a vector (rank=1), finds the k largest entries in the vector\n * and outputs their values and indices as vectors. Thus values[j] is the j-th\n * largest entry in input, and its index is indices[j].\n * For higher rank inputs, computes the top k entries along the last dimension.\n *\n * If two elements are equal, the lower-index element appears first.\n *\n * ```js\n * const a = tf.tensor2d([[1, 5], [4, 3]]);\n * const {values, indices} = tf.topk(a);\n * values.print();\n * indices.print();\n * ```\n * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.\n * @param k Number of top elements to look for along the last dimension.\n * @param sorted If true, the resulting `k` elements will be sorted by the\n *     values in descending order.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction topk_(x, k = 1, sorted = true) {\n    const $x = convertToTensor(x, 'x', 'topk');\n    if ($x.rank === 0) {\n        throw new Error('topk() expects the input to be of rank 1 or higher');\n    }\n    const lastDim = $x.shape[$x.shape.length - 1];\n    if (k > lastDim) {\n        throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) ` +\n            `but got ${k}`);\n    }\n    const inputs = { x: $x };\n    const attrs = { k, sorted };\n    const [values, indices] = ENGINE.runKernelFunc(b => b.topk($x, k, sorted), inputs, null /* grad */, TopK, attrs);\n    return { values, indices };\n}\nexport const topk = op({ topk_ });\n//# sourceMappingURL=topk.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a truncated normal\n * distribution.\n *\n * ```js\n * tf.truncatedNormal([2, 2]).print();\n * ```\n *\n * The generated values follow a normal distribution with specified mean and\n * standard deviation, except that values whose magnitude is more than 2\n * standard deviations from the mean are dropped and re-picked.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output tensor.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction truncatedNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n    if (dtype != null && dtype === 'bool') {\n        throw new Error(`Unsupported data type $ { dtype }`);\n    }\n    const randGauss = new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = randGauss.nextValue();\n    }\n    return res.toTensor();\n}\nexport const truncatedNormal = op({ truncatedNormal_ });\n//# sourceMappingURL=truncated_normal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unique } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert } from '../util';\nimport { op } from './operation';\n/**\n * Finds unique elements along an axis of a tensor.\n *\n * It returns a tensor `values` containing all of the unique elements along the\n * `axis` of the given tensor `x` in the same order that they occur along the\n * `axis` in `x`; `x` does not need to be sorted. It also returns a tensor\n * `indices` the same size as the number of the elements in `x` along the `axis`\n * dimension. It contains the index in the unique output `values`.\n *\n * ```js\n * // A 1-D tensor\n * const a = tf.tensor1d([1, 1, 2, 4, 4, 4, 7, 8, 8]);\n * const {values, indices} = tf.unique(a);\n * values.print();   // [1, 2, 4, 7, 8,]\n * indices.print();  // [0, 0, 1, 2, 2, 2, 3, 4, 4]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=0\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 0)\n * values.print();   // [[1, 0, 0],\n *                   //  [2, 0, 0]]\n * indices.print();  // [0, 0, 1]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=1\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 1)\n * values.print();   // [[1, 0],\n *                   //  [1, 0],\n *                   //  [2, 0]]\n * indices.print();  // [0, 1, 1]\n * ```\n * @param x A tensor (int32, string, bool).\n * @param axis The axis of the tensor to find the unique elements.\n * @returns [uniqueElements, indices] (see above for details)\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction unique_(x, axis = 0) {\n    // x can be of any dtype, thus null as the last argument.\n    const $x = convertToTensor(x, 'x', 'unique', null);\n    assert($x.rank > 0, () => 'The input tensor must be at least 1D');\n    const inputs = { x: $x };\n    const attrs = { axis };\n    const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs);\n    return { values, indices };\n}\nexport const unique = op({ unique_ });\n//# sourceMappingURL=unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { UnsortedSegmentSum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, isInt } from '../util';\nimport { op } from './operation';\n/**\n * Computes the sum along segments of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');\n * const numSegments = 3;\n *\n * x.unsortedSegmentSum(segmentIds, numSegments).print()\n * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)\n * ```\n * @param x The `tf.Tensor` that will be summed along its segments.\n * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s\n * dimension along the `axis`.  Maps each element of `x` to a segment.\n * @param numSegments The number of distinct `segmentIds`.\n *\n * @doc {heading: 'Operations', subheading: 'Segment'}\n */\nfunction unsortedSegmentSum_(x, segmentIds, numSegments) {\n    const $x = convertToTensor(x, 'x', 'unsortedSegmentSum');\n    const $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');\n    assert(isInt(numSegments), () => 'numSegments must be of dtype int');\n    const inputs = { x: $x, segmentIds: $segmentIds };\n    const attrs = { numSegments };\n    const forward = (backend, save) => {\n        const res = backend.unsortedSegmentSum($x, $segmentIds, numSegments);\n        save([$segmentIds]);\n        return res;\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, UnsortedSegmentSum, attrs);\n}\nexport const unsortedSegmentSum = op({ unsortedSegmentSum_ });\n//# sourceMappingURL=unsorted_segment_sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unpack } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * tf.unstack(a).forEach(tensor => tensor.print());\n * ```\n *\n * @param x A tensor object.\n * @param axis The axis to unstack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction unstack_(x, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'unstack');\n    util.assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);\n    if (axis < 0) {\n        axis += $x.shape.length;\n    }\n    const inputs = { value: $x };\n    const attrs = { axis };\n    const forward = (backend) => backend.unstack($x, axis);\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Unpack, attrs);\n}\nexport const unstack = op({ unstack_ });\n//# sourceMappingURL=unstack.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\n/**\n * Creates a new variable with the provided initial value.\n * ```js\n * const x = tf.variable(tf.tensor([1, 2, 3]));\n * x.assign(tf.tensor([4, 5, 6]));\n *\n * x.print();\n * ```\n *\n * @param initialValue Initial value for the tensor.\n * @param trainable If true, optimizers are allowed to update it.\n * @param name Name of the variable. Defaults to a unique id.\n * @param dtype If set, initialValue will be converted to the given type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function variable(initialValue, trainable = true, name, dtype) {\n    return ENGINE.makeVariable(initialValue, trainable, name, dtype);\n}\n//# sourceMappingURL=variable.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the Where kernel shared between cpu and webgl */\nimport { buffer } from '../ops/buffer';\nexport function whereImpl(condShape, condVals) {\n    const indices = [];\n    for (let i = 0; i < condVals.length; i++) {\n        if (condVals[i]) {\n            indices.push(i);\n        }\n    }\n    const inBuffer = buffer(condShape, 'int32');\n    const out = buffer([indices.length, condShape.length], 'int32');\n    for (let i = 0; i < indices.length; i++) {\n        const loc = inBuffer.indexToLoc(indices[i]);\n        const offset = i * condShape.length;\n        out.values.set(loc, offset);\n    }\n    return out.toTensor();\n}\n//# sourceMappingURL=where_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { whereImpl } from '../backends/where_impl';\nimport { convertToTensor } from '../tensor_util_env';\n/**\n * Returns the coordinates of true elements of condition.\n *\n * The coordinates are returned in a 2-D tensor where the first dimension (rows)\n * represents the number of true elements, and the second dimension (columns)\n * represents the coordinates of the true elements. Keep in mind, the shape of\n * the output tensor can vary depending on how many true values there are in\n * input. Indices are output in row-major order. The resulting tensor has the\n * shape `[numTrueElems, condition.rank]`.\n *\n * This is analogous to calling the python `tf.where(cond)` without an x or y.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const result = await tf.whereAsync(cond);\n * result.print();\n * ```\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nasync function whereAsync_(condition) {\n    const $condition = convertToTensor(condition, 'condition', 'whereAsync', 'bool');\n    const vals = await $condition.data();\n    const res = whereImpl($condition.shape, vals);\n    if (condition !== $condition) {\n        $condition.dispose();\n    }\n    return res;\n}\nexport const whereAsync = whereAsync_;\n//# sourceMappingURL=where_async.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { gather } from './gather';\nimport { reshape } from './reshape';\nimport { squeeze } from './squeeze';\nimport { whereAsync } from './where_async';\n/**\n * Apply boolean mask to tensor.\n *\n * ```js\n * const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);\n * const mask = tf.tensor1d([1, 0, 1], 'bool');\n * const result = await tf.booleanMaskAsync(tensor, mask);\n * result.print();\n * ```\n *\n * @param tensor N-D tensor.\n * @param mask K-D boolean tensor, K <= N and K must be known statically.\n * @param axis A 0-D int Tensor representing the axis in tensor to mask from.\n *     By default, axis is 0 which will mask from the first dimension.\n *     Otherwise K + axis <= N.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nasync function booleanMaskAsync_(tensor, mask, axis) {\n    const $tensor = convertToTensor(tensor, 'tensor', 'boolMask');\n    const $mask = convertToTensor(mask, 'mask', 'boolMask', 'bool');\n    const axisFrom = axis == null ? 0 : axis;\n    const maskDim = $mask.rank;\n    const tensorShape = $tensor.shape;\n    util.assert(maskDim > 0, () => 'mask cannot be scalar');\n    util.assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);\n    let leadingSize = 1;\n    for (let i = axisFrom; i < axisFrom + maskDim; i++) {\n        leadingSize *= tensorShape[i];\n    }\n    const targetTensorShape = tensorShape.slice(0, axisFrom)\n        .concat([leadingSize], tensorShape.slice(axisFrom + maskDim));\n    const reshapedTensor = reshape($tensor, targetTensorShape);\n    const reshapedMask = reshape($mask, [-1]);\n    const positivePositions = await whereAsync(reshapedMask);\n    const indices = squeeze(positivePositions, [1]);\n    const res = gather(reshapedTensor, indices, axisFrom);\n    // Ensure no memory leak.\n    if (tensor !== $tensor) {\n        $tensor.dispose();\n    }\n    if (mask !== $mask) {\n        $mask.dispose();\n    }\n    indices.dispose();\n    reshapedTensor.dispose();\n    reshapedMask.dispose();\n    positivePositions.dispose();\n    return res;\n}\nexport const booleanMaskAsync = booleanMaskAsync_;\n//# sourceMappingURL=boolean_mask.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../globals';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertShapesMatch } from '../util';\nimport { equal } from './equal';\nimport { greater } from './greater';\nimport { greaterEqual } from './greater_equal';\nimport { less } from './less';\nimport { lessEqual } from './less_equal';\nimport { notEqual } from './not_equal';\nimport { op } from './operation';\n/**\n * @deprecated\n * Strict version of `tf.notEqual` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction notEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'notEqualStrict');\n    const $b = convertToTensor(b, 'b', 'notEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in notEqualStrict: ');\n    return notEqual($a, $b);\n}\n/**\n * @deprecated\n * Strict version of `tf.less` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction lessStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'lessStrict');\n    const $b = convertToTensor(b, 'b', 'lessStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in lessStrict: ');\n    return less($a, $b);\n}\nfunction equalStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'equalStrict');\n    const $b = convertToTensor(b, 'b', 'equalStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in equalStrict: ');\n    return equal($a, $b);\n}\nfunction lessEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'lessEqualStrict');\n    const $b = convertToTensor(b, 'b', 'lessEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in lessEqualStrict: ');\n    return lessEqual($a, $b);\n}\nfunction greaterStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'greaterStrict');\n    const $b = convertToTensor(b, 'b', 'greaterStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in greaterStrict: ');\n    return greater($a, $b);\n}\nfunction greaterEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'greaterEqualStrict');\n    const $b = convertToTensor(b, 'b', 'greaterEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in greaterEqualStrict: ');\n    return greaterEqual($a, $b);\n}\nexport const equalStrict = op({ equalStrict_ });\nexport const greaterEqualStrict = op({ greaterEqualStrict_ });\nexport const greaterStrict = op({ greaterStrict_ });\nexport const lessEqualStrict = op({ lessEqualStrict_ });\nexport const lessStrict = op({ lessStrict_ });\nexport const notEqualStrict = op({ notEqualStrict_ });\n//# sourceMappingURL=compare.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../globals';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { maximum } from './maximum';\nimport { minimum } from './minimum';\nimport { mod } from './mod';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { squaredDifference } from './squared_difference';\nimport { sub } from './sub';\n/**\n * @deprecated\n * Adds two `tf.Tensor`s element-wise, A + B.\n *\n * Inputs must be the same shape. For broadcasting support, use add() instead.\n *\n * @param a The first Tensor to add element-wise.\n * @param b The second Tensor to add element-wise.\n */\nfunction addStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'addStrict');\n    const $b = convertToTensor(b, 'b', 'addStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in addStrict: ');\n    return add($a, $b);\n}\n/**\n * @deprecated\n * Subtracts two `tf.Tensor`s element-wise, A - B. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.sub` instead.\n *\n * @param a The first Tensor to subtract element-wise.\n * @param b The second Tensor to subtract element-wise.\n */\nfunction subStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'subStrict');\n    const $b = convertToTensor(b, 'b', 'subStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in subStrict: ');\n    return sub($a, $b);\n}\n/**\n * @deprecated\n * Computes the power of one `tf.Tensor` to another. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.pow` instead.\n *\n * @param base The base tensor to pow element-wise.\n * @param exp The exponent tensor to pow element-wise.\n */\nfunction powStrict_(base, exp) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    util.assertShapesMatch(base.shape, exp.shape, 'Error in powStrict: ');\n    return pow(base, exp);\n}\n/**\n * @deprecated\n * Multiplies two `tf.Tensor`s element-wise, A * B.\n *\n * Inputs must be the same shape. For broadcasting support, use `tf.mul`.\n *\n * @param a The first tensor to multiply.\n * @param b The first tensor to multiply. Must have the same\n *    dtype as `a`.\n */\nfunction mulStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'mul');\n    const $b = convertToTensor(b, 'b', 'mul');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in multiplyStrict: ');\n    return mul($a, $b);\n}\n/**\n * @deprecated\n * Divides two `tf.Tensor`s element-wise, A / B. Inputs must\n * be the same shape.\n *\n * @param a The first tensor as the numerator for element-wise division.\n * @param b The second tensor as the denominator for element-wise division.\n */\nfunction divStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'div');\n    const $b = convertToTensor(b, 'b', 'div');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in divideStrict: ');\n    return div($a, $b);\n}\n/**\n * @deprecated\n * Returns the mod of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use mod().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction modStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'modStrict');\n    const $b = convertToTensor(b, 'b', 'modStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in modStrict: ');\n    return mod($a, $b);\n}\n/**\n * @deprecated\n * Returns the min of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use minimum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction minimumStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'minimumStrict');\n    const $b = convertToTensor(b, 'b', 'minimumStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in minimumStrict: ');\n    return minimum($a, $b);\n}\n/**\n * @deprecated\n * Returns the max of a and b (`a > b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use maximum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction maximumStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'maximumStrict');\n    const $b = convertToTensor(b, 'b', 'maximumStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in maximumStrict: ');\n    return maximum($a, $b);\n}\n/**\n * @deprecated\n * Returns (a - b) * (a - b) element-wise.\n *\n * Inputs must be the same shape. For broadcasting support, use\n * `tf.squaredDifference` instead.\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\nfunction squaredDifferenceStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'squaredDifferenceStrict');\n    const $b = convertToTensor(b, 'b', 'squaredDifferenceStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in squaredDifferenceStrict: ');\n    return squaredDifference($a, $b);\n}\nexport const addStrict = op({ addStrict_ });\nexport const divStrict = op({ divStrict_ });\nexport const maximumStrict = op({ maximumStrict_ });\nexport const minimumStrict = op({ minimumStrict_ });\nexport const modStrict = op({ modStrict_ });\nexport const mulStrict = op({ mulStrict_ });\nexport const powStrict = op({ powStrict_ });\nexport const squaredDifferenceStrict = op({ squaredDifferenceStrict_ });\nexport const subStrict = op({ subStrict_ });\n//# sourceMappingURL=binary_ops.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { abs } from './abs';\nimport * as axis_util from './axis_util';\nimport { max } from './max';\nimport { min } from './min';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { reshape } from './reshape';\nimport { scalar } from './scalar';\nimport { sqrt } from './sqrt';\nimport { square } from './square';\nimport { sum } from './sum';\n/**\n * Computes the norm of scalar, vectors, and matrices.\n * This function can compute several different vector norms (the 1-norm, the\n * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)\n * and matrix norms (Frobenius, 1-norm, and inf-norm).\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.norm().print();  // or tf.norm(x)\n * ```\n *\n * @param x The input array.\n * @param ord Optional. Order of the norm. Supported norm types are\n * following:\n *\n *  | ord        | norm for matrices         | norm for vectors\n *  |------------|---------------------------|---------------------\n *  |'euclidean' |Frobenius norm             |2-norm\n *  |'fro'       |Frobenius norm\t           |\n *  |Infinity    |max(sum(abs(x), axis=1))   |max(abs(x))\n *  |-Infinity   |min(sum(abs(x), axis=1))   |min(abs(x))\n *  |1           |max(sum(abs(x), axis=0))   |sum(abs(x))\n *  |2           |                           |sum(abs(x)^2)^1/2*\n *\n * @param axis Optional. If axis is null (the default), the input is\n * considered a vector and a single vector norm is computed over the entire\n * set of values in the Tensor, i.e. norm(x, ord) is equivalent\n * to norm(x.reshape([-1]), ord). If axis is a integer, the input\n * is considered a batch of vectors, and axis determines the axis in x\n * over which to compute vector norms. If axis is a 2-tuple of integer it is\n * considered a batch of matrices and axis determines the axes in NDArray\n * over which to compute a matrix norm.\n * @param keepDims Optional. If true, the norm have the same dimensionality\n * as the input.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction norm_(x, ord = 'euclidean', axis = null, keepDims = false) {\n    x = convertToTensor(x, 'x', 'norm');\n    const norm = normImpl(x, ord, axis);\n    let keepDimsShape = norm.shape;\n    if (keepDims) {\n        const axes = parseAxisParam(axis, x.shape);\n        keepDimsShape = axis_util.expandShapeToKeepDim(norm.shape, axes);\n    }\n    return reshape(norm, keepDimsShape);\n}\nfunction normImpl(x, p, axis = null) {\n    if (x.rank === 0) {\n        return abs(x);\n    }\n    // consider vector when no axis is specified\n    if (x.rank !== 1 && axis === null) {\n        return normImpl(reshape(x, [-1]), p, axis);\n    }\n    // vector\n    if (x.rank === 1 || typeof axis === 'number' ||\n        Array.isArray(axis) && axis.length === 1) {\n        if (p === 1) {\n            return sum(abs(x), axis);\n        }\n        if (p === Infinity) {\n            return max(abs(x), axis);\n        }\n        if (p === -Infinity) {\n            return min(abs(x), axis);\n        }\n        if (p === 'euclidean' || p === 2) {\n            // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2\n            return sqrt(sum(pow(abs(x), scalar(2, 'int32')), axis));\n        }\n        throw new Error(`Error in norm: invalid ord value: ${p}`);\n    }\n    // matrix (assumption axis[0] < axis[1])\n    if (Array.isArray(axis) && axis.length === 2) {\n        if (p === 1) {\n            return max(sum(abs(x), axis[0]), axis[1] - 1);\n        }\n        if (p === Infinity) {\n            return max(sum(abs(x), axis[1]), axis[0]);\n        }\n        if (p === -Infinity) {\n            return min(sum(abs(x), axis[1]), axis[0]);\n        }\n        if (p === 'fro' || p === 'euclidean') {\n            // norm(x) = sqrt(sum(pow(x, 2)))\n            return sqrt(sum(square(x), axis));\n        }\n        throw new Error(`Error in norm: invalid ord value: ${p}`);\n    }\n    throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\nexport const norm = op({ norm_ });\n//# sourceMappingURL=norm.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assertTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { scalar } from './scalar';\nimport { sub } from './sub';\n/**\n * Compute the moving average of a variable.\n *\n * Without zeroDebias, the moving average operation is defined by:\n *   `v += delta`\n * where\n *   `delta = (1 - decay) * (x - v)`\n *\n * With zeroDebias (default), the `delta` term is scaled to debias the\n * effect of the (assumed) zero-initialization of `v`.\n *   `delta /= (1 - decay ^ step)`\n *\n * For more details on the zero-debiasing algorithm, see:\n *   https://arxiv.org/abs/1412.6980\n *\n * Note that this function is completely stateless and does not keep track of\n * step count. The step count needs to be maintained by the caller and passed\n * in as `step`.\n *\n * @param v The current moving average value.\n * @param x New input value, must have the same shape and dtype as `v`.\n * @param decay The decay factor. Typical values are 0.95 and 0.99.\n * @param step Step count.\n * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).\n * @returns The new moving average value.\n *\n * @doc {heading: 'Operations', subheading: 'Moving Average'}\n */\nfunction movingAverage_(v, x, decay, step, zeroDebias = true) {\n    const $v = convertToTensor(v, 'v', 'movingAverage');\n    const $x = convertToTensor(x, 'x', 'movingAverage');\n    const $decay = convertToTensor(decay, 'decay', 'movingAverage');\n    assertTypesMatch($v, $x);\n    util.assert(util.arraysEqual($v.shape, $x.shape), () => 'Shape mismatch in v and x');\n    const one = scalar(1);\n    const oneMinusDecay = sub(one, $decay);\n    let update = mul(sub($x, $v), oneMinusDecay);\n    if (zeroDebias) {\n        util.assert(step != null, () => 'When using zeroDebias: true, step is required.');\n        const $step = convertToTensor(step, 'step', 'movingAverage');\n        update = div(update, sub(one, pow($decay, $step)));\n    }\n    return add($v, update);\n}\nexport const movingAverage = op({ movingAverage_ });\n//# sourceMappingURL=moving_average.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ScatterNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport * as scatter_nd_util from './scatter_nd_util';\n/**\n * Creates a new tensor by applying sparse updates to individual\n * values or slices within a zero tensor of the given shape tensor according to\n * indices. This operator is the inverse of the `tf.gatherND` operator which\n * extracts values or slices from a given tensor.\n *\n * ```js\n * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32');\n * const updates = tf.tensor1d([9, 10, 11, 12]);\n * const shape = [8];\n * tf.scatterND(indices, updates, shape).print() //[0, 11, 0, 10, 9, 0, 0, 12]\n * ```\n *\n * @param indices The tensor contains the indices into the output tensor.\n * @param updates The tensor contains the value for the indices.\n * @param shape: The shape of the output tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction scatterND_(indices, updates, shape) {\n    const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');\n    const $updates = convertToTensor(updates, 'updates', 'scatterND');\n    scatter_nd_util.validateInput($updates, $indices, shape);\n    const forward = (backend) => {\n        return backend.scatterND($indices, $updates, shape);\n    };\n    const inputs = { indices: $indices, updates: $updates };\n    const attrs = { shape };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ScatterNd, attrs);\n}\nexport const scatterND = op({ scatterND_ });\n//# sourceMappingURL=scatter_nd.js.map", "/**\n * Validate sparseToDense inputs.\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape number[]. Shape of the dense output tensor.\n * @param validateIndices boolean. indice validation is not supported, error\n * will be thrown if it is set.\n */\nexport function validateInput(sparseIndices, sparseValues, outputShape, defaultValues) {\n    if (sparseIndices.dtype !== 'int32') {\n        throw new Error('tf.sparseToDense() expects the indices to be int32 type,' +\n            ` but the dtype was ${sparseIndices.dtype}.`);\n    }\n    if (sparseIndices.rank > 2) {\n        throw new Error('sparseIndices should be a scalar, vector, or matrix,' +\n            ` but got shape ${sparseIndices.shape}.`);\n    }\n    const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n    const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n    if (outputShape.length !== numDims) {\n        throw new Error('outputShape has incorrect number of elements:,' +\n            ` ${outputShape.length}, should be: ${numDims}.`);\n    }\n    const numValues = sparseValues.size;\n    if (!(sparseValues.rank === 0 ||\n        sparseValues.rank === 1 && numValues === numElems)) {\n        throw new Error('sparseValues has incorrect shape ' +\n            `${sparseValues.shape}, should be [] or [${numElems}]`);\n    }\n    if (sparseValues.dtype !== defaultValues.dtype) {\n        throw new Error('sparseValues.dtype must match defaultValues.dtype');\n    }\n}\n//# sourceMappingURL=sparse_to_dense_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SparseToDense } from '../kernel_names';\nimport * as sparse_to_dense from '../ops/sparse_to_dense_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Converts a sparse representation into a dense tensor.\n *\n * Builds an array dense with shape outputShape such that:\n *\n * // If sparseIndices is scalar\n * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)\n *\n * // If sparseIndices is a vector, then for each i\n * dense[sparseIndices[i]] = sparseValues[i]\n *\n * // If sparseIndices is an n by d matrix, then for each i in [0, n)\n * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]\n * All other values in dense are set to defaultValue. If sparseValues is a\n * scalar, all sparse indices are set to this single value.\n *\n * If indices are repeated the final value is summed over all values for those\n * indices.\n *\n * ```js\n * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');\n * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');\n * const shape = [8];\n * tf.sparseToDense(indices, values, shape).print();\n * ```\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape Shape of the dense output tensor. the type is inferred.\n * @param defaultValue Scalar. Value to set for indices not specified in\n * sparseIndices. Defaults to zero.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {\n    const $sparseIndices = convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');\n    const $sparseValues = convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');\n    const $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);\n    sparse_to_dense.validateInput($sparseIndices, $sparseValues, outputShape, $defaultValue);\n    const inputs = {\n        sparseIndices: $sparseIndices,\n        sparseValues: $sparseValues,\n        defaultValue: $defaultValue\n    };\n    const attrs = { outputShape };\n    return ENGINE.runKernelFunc(backend => backend.sparseToDense($sparseIndices, $sparseValues, outputShape, $defaultValue), inputs, null /* grad */, SparseToDense, attrs);\n}\nexport const sparseToDense = op({ sparseToDense_ });\n//# sourceMappingURL=sparse_to_dense.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Gather slices from input tensor into a Tensor with shape specified by\n * `indices`.\n *\n * `indices` is an K-dimensional integer tensor, best thought of as a\n * (K-1)-dimensional tensor of indices into input, where each element defines a\n * slice of input:\n * output[\\\\(i_0, ..., i_{K-2}\\\\)] = input[indices[\\\\(i_0, ..., i_{K-2}\\\\)]]\n *\n * Whereas in `tf.gather`, `indices` defines slices into the first dimension of\n * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions\n * of input, where N = indices.shape[-1].\n *\n * The last dimension of indices can be at most the rank of input:\n * indices.shape[-1] <= input.rank\n *\n * The last dimension of `indices` corresponds to elements\n * (if indices.shape[-1] == input.rank) or slices\n * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of\n * input.\n * The output tensor has shape\n * indices.shape[:-1] + input.shape[indices.shape[-1]:]\n *\n * Note that on CPU, if an out of bound index is found, an error is returned. On\n * GPU, if an out of bound index is found, a 0 is stored in the corresponding\n * output value.\n *\n * ```js\n * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');\n * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);\n * tf.gatherND(input, indices).print() // [10, 11]\n * ```\n *\n * @param x The tensor from which to gather values.\n * @param indices Index tensor, must be of type int32.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction gatherND_(x, indices) {\n    const $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');\n    const $x = convertToTensor(x, 'x', 'gatherND');\n    const forward = (backend) => {\n        return backend.gatherND($x, $indices);\n    };\n    const inputs = { params: $x, indices: $indices };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, GatherNd);\n}\nexport const gatherND = op({ gatherND_ });\n//# sourceMappingURL=gather_nd.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n * Normalize noise shape based on provided tensor and noise shape.\n *\n * @param x Tensor.\n * @param noiseShape The shape for the randomly generated keep/drop flags, as\n *   an array of numbers. Optional.\n * @returns Normalized noise shape.\n */\nexport function getNoiseShape(x, noiseShape) {\n    if (noiseShape == null) {\n        return x.shape.slice();\n    }\n    if (util.arraysEqual(x.shape, noiseShape)) {\n        return noiseShape;\n    }\n    if (x.shape.length === noiseShape.length) {\n        const newDimension = [];\n        for (let i = 0; i < x.shape.length; i++) {\n            if (noiseShape[i] == null && x.shape[i] != null) {\n                newDimension.push(x.shape[i]);\n            }\n            else {\n                newDimension.push(noiseShape[i]);\n            }\n        }\n        return newDimension;\n    }\n    return noiseShape;\n}\n//# sourceMappingURL=dropout_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { getNoiseShape } from './dropout_util';\nimport { floor } from './floor';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { randomUniform } from './random_uniform';\n/**\n * Computes dropout.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 2, 1]);\n * const rate = 0.75;\n * const output = tf.dropout(x, rate);\n * output.print();\n * ```\n *\n * @param x A floating point Tensor or TensorLike.\n * @param rate A float in the range [0, 1). The probability that each element\n *   of x is discarded.\n * @param noiseShape An array of numbers of type int32, representing the\n * shape for randomly generated keep/drop flags. If the noiseShape has null\n * value, it will be automatically replaced with the x's relative dimension\n * size. Optional.\n * @param seed Used to create random seeds. Optional.\n * @returns A Tensor of the same shape of x.\n *\n * @doc {heading: 'Operations', subheading: 'Dropout'}\n */\nfunction dropout_(x, rate, noiseShape, seed) {\n    const $x = convertToTensor(x, 'x', 'dropout');\n    util.assert($x.dtype === 'float32', () => `x has to be a floating point tensor since it's going to be ` +\n        `scaled, but got a ${$x.dtype} tensor instead.`);\n    util.assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);\n    if (rate === 0) {\n        return x instanceof Tensor ? $x.clone() : $x;\n    }\n    const $noiseShape = getNoiseShape($x, noiseShape);\n    const keepProb = 1 - rate;\n    const multiplier = div(floor(add(randomUniform($noiseShape, 0, 1, 'float32', seed), keepProb)), keepProb);\n    return mul($x, multiplier);\n}\nexport const dropout = op({ dropout_ });\n//# sourceMappingURL=dropout.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensor1d } from './tensor1d';\nexport function enclosingPowerOfTwo(value) {\n    // Return 2**N for integer N such that 2**N >= value.\n    return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2.0))));\n}\nexport function cosineWindow(windowLength, a, b) {\n    const even = 1 - windowLength % 2;\n    const newValues = new Float32Array(windowLength);\n    for (let i = 0; i < windowLength; ++i) {\n        const cosArg = (2.0 * Math.PI * i) / (windowLength + even - 1);\n        newValues[i] = a - b * Math.cos(cosArg);\n    }\n    return tensor1d(newValues, 'float32');\n}\n//# sourceMappingURL=signal_ops_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, assertShapesMatch, getTypedArrayFromDType } from '../util';\nimport { tensor } from './tensor';\n/**\n * Returns whether the targets are in the top K predictions.\n *\n * ```js\n * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);\n * const targets = tf.tensor1d([2, 0]);\n * const precision = await tf.inTopKAsync(predictions, targets);\n * precision.print();\n * ```\n * @param predictions 2-D or higher `tf.Tensor` with last dimension being\n *     at least `k`.\n * @param targets 1-D or higher `tf.Tensor`.\n * @param k Optional Number of top elements to look at for computing precision,\n *     default to 1.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nasync function inTopKAsync_(predictions, targets, k = 1) {\n    const $predictions = convertToTensor(predictions, 'predictions', 'inTopK');\n    const $targets = convertToTensor(targets, 'targets', 'inTopK');\n    assert($predictions.rank > 1, () => 'inTopK() expects the predictions to be of rank 2 or higher, ' +\n        `but got ${$predictions.rank}`);\n    assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than ` +\n        `targets rank, but got predictions rank ` +\n        `${$predictions.rank} and targets rank ${$targets.rank}`);\n    assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, ` +\n        'except the last dimension.');\n    const lastDim = $predictions.shape[$predictions.shape.length - 1];\n    assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last ` +\n        `dimension (${lastDim}), but got ${k}`);\n    const predictionsVals = await $predictions.data();\n    const targetsVals = await $targets.data();\n    // Reshape predictionsVals into a 2d tensor [batch, lastDim]\n    // and look up topK along lastDim.\n    const [batch, size] = [predictionsVals.length / lastDim, lastDim];\n    const precision = getTypedArrayFromDType('bool', batch);\n    for (let b = 0; b < batch; b++) {\n        const offset = b * size;\n        const vals = predictionsVals.subarray(offset, offset + size);\n        const valAndInd = [];\n        for (let i = 0; i < vals.length; i++) {\n            valAndInd.push({ value: vals[i], index: i });\n        }\n        valAndInd.sort((a, b) => b.value - a.value);\n        precision[b] = 0;\n        for (let i = 0; i < k; i++) {\n            if (valAndInd[i].index === targetsVals[b]) {\n                precision[b] = 1;\n                break;\n            }\n        }\n    }\n    if (predictions !== $predictions) {\n        $predictions.dispose();\n    }\n    if (targets !== $targets) {\n        $targets.dispose();\n    }\n    // Output precision has the same shape as targets.\n    return tensor(precision, $targets.shape, 'bool');\n}\nexport const inTopKAsync = inTopKAsync_;\n//# sourceMappingURL=in_top_k.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropFilter } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the filter of a 2D convolution.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.\n * @param dy The dy image, of rank 4 or rank 3, of shape\n *     [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.\n * @param filterShape The shape of the filter, length 4,\n *     [filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction conv2DBackpropFilter_(x, dy, filterShape, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n    let x4D = x;\n    if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    let dy4D = dy;\n    if (dy4D.rank === 3) {\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ` +\n        `${x4D.shape}.`);\n    util.assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ` +\n        `${dy4D.shape}.`);\n    util.assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ` +\n        `${filterShape}.`);\n    const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n    const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n    util.assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must ` +\n        `match input depth in filter (${filterShape[2]}.`);\n    util.assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must ` +\n        `match output depth for filter (${filterShape[3]}).`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2dDerFilter: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const dilations = 1;\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, filterShape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        return backend.conv2dDerFilter(x4D, dy4D, convInfo);\n    };\n    const inputs = { x: x4D, dy: dy4D };\n    const attrs = { strides, pad, dataFormat, dimRoundingMode };\n    return ENGINE.runKernelFunc(forward, inputs, null, Conv2DBackpropFilter, attrs);\n}\nexport const conv2DBackpropFilter = op({ conv2DBackpropFilter_ });\n//# sourceMappingURL=conv2d_backprop_filter.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as broadcast_util from './broadcast_util';\nimport { elu } from './elu';\nimport { mul } from './mul';\nimport { prelu } from './prelu';\nimport { relu } from './relu';\nimport { relu6 } from './relu6';\nimport { reshape } from './reshape';\nimport { step } from './step';\nimport { sum } from './sum';\n// Returns gradient for fused activation.\nexport function getFusedDyActivation(dy, y, activation) {\n    if (activation == null || activation === 'linear') {\n        return dy;\n    }\n    if (activation === 'relu') {\n        return mul(dy, step(y));\n    }\n    throw new Error(`Cannot compute gradient for fused activation ${activation}.`);\n}\n// Returns gradient for fused bias.\nexport function getFusedBiasGradient(bias, dyActivation) {\n    let res = dyActivation;\n    const reduceAxes = broadcast_util.getReductionAxes(bias.shape, dyActivation.shape);\n    if (reduceAxes.length > 0) {\n        res = sum(res, reduceAxes);\n    }\n    return reshape(res, bias.shape);\n}\nexport function applyActivation(x, activation, preluActivationWeights) {\n    if (activation === 'linear') {\n        return x;\n    }\n    else if (activation === 'relu') {\n        return relu(x);\n    }\n    else if (activation === 'elu') {\n        return elu(x);\n    }\n    else if (activation === 'relu6') {\n        return relu6(x);\n    }\n    else if (activation === 'prelu') {\n        return prelu(x, preluActivationWeights);\n    }\n    throw new Error(`Unknown fused activation ${activation}.`);\n}\n// Whether we should call fused ops.\nexport const shouldFuse = (gradientDepth, activation) => {\n    const gradientMode = gradientDepth > 0;\n    return !gradientMode || activation === 'linear';\n};\n//# sourceMappingURL=fused_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { conv2d as unfusedConv2d } from '../conv2d';\nimport { conv2DBackpropFilter } from '../conv2d_backprop_filter';\nimport { conv2DBackpropInput } from '../conv2d_backprop_input';\nimport * as conv_util from '../conv_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes a 2D convolution over the input x, optionally fused with adding a\n * bias and applying an activation.\n *\n * ```js\n * const inputDepth = 2;\n * const inShape = [2, 2, 2, inputDepth];\n * const outputDepth = 2;\n * const fSize = 1;\n * const pad = 0;\n * const strides = 1;\n *\n * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n * 16], inShape);\n * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,\n * outputDepth]);\n *\n * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',\n * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();\n * ```\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid` output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`) to be\n *     applied\n *      after biasAdd.\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights }) {\n    activation = activation || 'linear';\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    const $x = convertToTensor(x, 'x', 'conv2d');\n    const $filter = convertToTensor(filter, 'filter', 'conv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ` +\n        `${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in fused conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match ` +\n        `input depth for filter ${$filter.shape[2]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    util.assert(dataFormat === 'NHWC', () => `Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);\n    const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n        [$bias] = makeTypesMatch($bias, $x);\n        broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused conv2d');\n    }\n    const grad = (dy, saved) => {\n        const [$filter, x4D, y, $bias] = saved;\n        const dyActivation = getFusedDyActivation(dy, y, activation);\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused conv2D: ' +\n            `dilation rates greater than 1 ` +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n        const xDer = conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad);\n        const filterDer = conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad);\n        const der = [xDer, filterDer];\n        if ($bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            der.push(biasDer);\n        }\n        return der;\n    };\n    const forward = (backend) => {\n        const res = backend.fusedConv2d({\n            input: x4D,\n            filter: $filter,\n            convInfo,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return res;\n    };\n    const inputs = {\n        x: x4D,\n        filter: $filter,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((x4D, filter, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedConv2D, attrs);\n            save([filter, x4D, res]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOp(x4D, $filter);\n    }\n    else {\n        const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedConv2D, attrs);\n            save([filter, x4D, res, bias]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOpWithBias(x4D, $filter, $bias);\n    }\n}\nexport const conv2d = op({ fusedConv2d_ });\n//# sourceMappingURL=conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropFilter } from '../kernel_names';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, convInfo) {\n    let x4D = x;\n    if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    let dy4D = dy;\n    if (dy4D.rank === 3) {\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    const forward = backend => backend.depthwiseConv2DDerFilter(x4D, dy4D, convInfo);\n    const inputs = { x: x4D, dy: dy4D };\n    return ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropFilter);\n}\nexport const depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ });\n//# sourceMappingURL=depthwise_conv2d_native_backprop_filter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropInput } from '../kernel_names';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, convInfo) {\n    let dy4D = dy;\n    let reshapedTo4D = false;\n    if (dy.rank === 3) {\n        reshapedTo4D = true;\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    const forward = backend => backend.depthwiseConv2DDerInput(dy4D, filter, convInfo);\n    const inputs = { dy: dy4D };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropInput);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ });\n//# sourceMappingURL=depthwise_conv2d_native_backprop_input.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedDepthwiseConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport * as conv_util from '../conv_util';\nimport { depthwiseConv2d as unfusedDepthwiseConv2d } from '../depthwise_conv2d';\nimport { depthwiseConv2dNativeBackpropFilter } from '../depthwise_conv2d_native_backprop_filter';\nimport { depthwiseConv2dNativeBackpropInput } from '../depthwise_conv2d_native_backprop_input';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes depthwise 2D convolution, optionally fused with adding a\n * bias and applying an activation.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`).\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedDepthwiseConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights }) {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedDepthwiseConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n    const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, ` +\n        `but got rank ${$filter.rank}.`);\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels ` +\n        `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n        `filter ${$filter.shape[2]}.`);\n    if (dilations == null) {\n        dilations = [1, 1];\n    }\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in fused depthwiseConv2d: Either strides or dilations must ' +\n        `be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in fused depthwiseConv2d: pad must be an integer when ` +\n            `using dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n        [$bias] = makeTypesMatch($bias, $x);\n        broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');\n    }\n    const grad = (dy, saved) => {\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused depthwiseConv2d: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${dilations}'`);\n        const [$filter, x4D, y, bias] = saved;\n        const dyActivation = getFusedDyActivation(dy, y, activation);\n        const xDer = depthwiseConv2dNativeBackpropInput(x4D.shape, dyActivation, $filter, convInfo);\n        const filterDer = depthwiseConv2dNativeBackpropFilter(x4D, dyActivation, $filter.shape, convInfo);\n        if (bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            return [xDer, filterDer, biasDer];\n        }\n        return [xDer, filterDer];\n    };\n    const forward = (backend) => {\n        const res = backend.fusedDepthwiseConv2D({\n            input: x4D,\n            filter: $filter,\n            convInfo,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return res;\n    };\n    const inputs = {\n        x: x4D,\n        filter: $filter,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((x4D, filter, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedDepthwiseConv2D, attrs);\n            save([filter, x4D, res]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOp(x4D, $filter);\n    }\n    else {\n        const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedDepthwiseConv2D, attrs);\n            save([filter, x4D, res, bias]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOpWithBias(x4D, $filter, $bias);\n    }\n}\nexport const depthwiseConv2d = op({ fusedDepthwiseConv2d_ });\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { _FusedMatMul } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { matMul as unfusedMatMul } from '../mat_mul';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes the dot product of two matrices with optional activation and bias.\n *\n * ```js\n * const a = tf.tensor2d([-1, -2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const bias = tf.tensor2d([1, 2], [1, 2]);\n *\n * tf.fused.matMul({a, b, bias, activation: 'relu'}).print();\n * ```\n *\n * @param obj An object with the following properties:\n * - `a` First matrix in dot product operation.\n * - `b` Second matrix in dot product operation.\n * - `transposeA` If true, `a` is transposed before multiplication.\n * - `transposeB` If true, `b` is transposed before multiplication.\n * - `bias` Matrix to be added to the result.\n * - `activation` Name of activation kernel (defaults to `linear`).\n * - `preluActivationWeights` Tensor of prelu weights.\n */\nfunction fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation = 'linear', preluActivationWeights }) {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedMatMul(a, b, transposeA, transposeB);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    let $a = convertToTensor(a, 'a', 'fused matMul');\n    let $b = convertToTensor(b, 'b', 'fused matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n    const outerDimsA = $a.shape.slice(0, -2);\n    const outerDimsB = $b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n    util.assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in fused matMul: inputs must have the same rank of at least ` +\n        `2, got ranks ${$a.rank} and ${$b.rank}.`);\n    util.assert(util.arraysEqual(outerDimsA, outerDimsB), () => `Error in fused matMul: outer dimensions (${outerDimsA}) and (` +\n        `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} must match.`);\n    util.assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (` +\n        `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} and transposeA=${transposeA}` +\n        ` and transposeB=${transposeB} must match.`);\n    const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n    const a3D = transposeA ?\n        reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n        reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n    const b3D = transposeB ?\n        reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n        reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused matMul');\n        [$bias] = makeTypesMatch($bias, $a);\n        broadcast_util.assertAndGetBroadcastShape(outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused matMul');\n    }\n    const grad = (dy, saved) => {\n        const [a3D, b3D, y, $bias] = saved;\n        // we reshape dy because the result of the forward is not\n        // necessarily going to be a 3d tensor due to a reshape done at the end of\n        // the customOp.\n        const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation);\n        let aDer;\n        let bDer;\n        if (!transposeA && !transposeB) {\n            aDer = unfusedMatMul(dyActivation, b3D, false, true);\n            bDer = unfusedMatMul(a3D, dyActivation, true, false);\n        }\n        else if (!transposeA && transposeB) {\n            aDer = unfusedMatMul(dyActivation, b3D, false, false);\n            bDer = unfusedMatMul(dyActivation, a3D, true, false);\n        }\n        else if (transposeA && !transposeB) {\n            aDer = unfusedMatMul(b3D, dyActivation, false, true);\n            bDer = unfusedMatMul(a3D, dyActivation, false, false);\n        }\n        else {\n            aDer = unfusedMatMul(b3D, dyActivation, true, true);\n            bDer = unfusedMatMul(dyActivation, a3D, true, true);\n        }\n        if (bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            return [aDer, bDer, biasDer];\n        }\n        else {\n            return [aDer, bDer];\n        }\n    };\n    const forward = (backend) => {\n        const y = backend.fusedBatchMatMul({\n            a: a3D,\n            b: b3D,\n            transposeA,\n            transposeB,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return y;\n    };\n    const inputs = {\n        a: a3D,\n        b: b3D,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { transposeA, transposeB, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((a3D, b3D, save) => {\n            const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, _FusedMatMul, attrs);\n            save([a3D, b3D, res]);\n            return { value: reshape(res, outShape), gradFunc: grad };\n        });\n        return customOp(a3D, b3D);\n    }\n    else {\n        const customOpWithBias = customGrad((a3D, b3D, $bias, save) => {\n            const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, _FusedMatMul, attrs);\n            save([a3D, b3D, res, $bias]);\n            return { value: reshape(res, outShape), gradFunc: grad };\n        });\n        return customOpWithBias(a3D, b3D, $bias);\n    }\n}\nexport const matMul = op({ fusedMatMul_ });\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2d } from './fused/conv2d';\nimport { depthwiseConv2d } from './fused/depthwise_conv2d';\nimport { matMul } from './fused/mat_mul';\nexport { conv2d, depthwiseConv2d, matMul };\n//# sourceMappingURL=fused_ops.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a hamming window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hammingWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hammingWindow_(windowLength) {\n    return cosineWindow(windowLength, 0.54, 0.46);\n}\nexport const hammingWindow = op({ hammingWindow_ });\n//# sourceMappingURL=hamming_window.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a Hann window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hannWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hannWindow_(windowLength) {\n    return cosineWindow(windowLength, 0.5, 0.5);\n}\nexport const hannWindow = op({ hannWindow_ });\n//# sourceMappingURL=hann_window.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../concat';\nimport { fill } from '../fill';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { tensor2d } from '../tensor2d';\n/**\n * Expands input into frames of frameLength.\n * Slides a window size with frameStep.\n *\n * ```js\n * tf.signal.frame([1, 2, 3], 2, 1).print();\n * ```\n * @param signal The input tensor to be expanded\n * @param frameLength Length of each frame\n * @param frameStep The frame hop size in samples.\n * @param padEnd Whether to pad the end of signal with padValue.\n * @param padValue An number to use where the input signal does\n *     not exist when padEnd is True.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction frame_(signal, frameLength, frameStep, padEnd = false, padValue = 0) {\n    let start = 0;\n    const output = [];\n    while (start + frameLength <= signal.size) {\n        output.push(slice(signal, start, frameLength));\n        start += frameStep;\n    }\n    if (padEnd) {\n        while (start < signal.size) {\n            const padLen = (start + frameLength) - signal.size;\n            const pad = concat([\n                slice(signal, start, frameLength - padLen), fill([padLen], padValue)\n            ]);\n            output.push(pad);\n            start += frameStep;\n        }\n    }\n    if (output.length === 0) {\n        return tensor2d([], [0, frameLength]);\n    }\n    return reshape(concat(output), [output.length, frameLength]);\n}\nexport const frame = op({ frame_ });\n//# sourceMappingURL=frame.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../concat';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { enclosingPowerOfTwo } from '../signal_ops_util';\nimport { slice } from '../slice';\nimport { rfft } from '../spectral/rfft';\nimport { frame } from './frame';\nimport { hannWindow } from './hann_window';\n/**\n * Computes the Short-time Fourier Transform of signals\n * See: https://en.wikipedia.org/wiki/Short-time_Fourier_transform\n *\n * ```js\n * const input = tf.tensor1d([1, 1, 1, 1, 1])\n * tf.signal.stft(input, 3, 1).print();\n * ```\n * @param signal 1-dimensional real value tensor.\n * @param frameLength The window length of samples.\n * @param frameStep The number of samples to step.\n * @param fftLength The size of the FFT to apply.\n * @param windowFn A callable that takes a window length and returns 1-d tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction stft_(signal, frameLength, frameStep, fftLength, windowFn = hannWindow) {\n    if (fftLength == null) {\n        fftLength = enclosingPowerOfTwo(frameLength);\n    }\n    const framedSignal = frame(signal, frameLength, frameStep);\n    const windowedSignal = mul(framedSignal, windowFn(frameLength));\n    const output = [];\n    for (let i = 0; i < framedSignal.shape[0]; i++) {\n        output.push(rfft(slice(windowedSignal, [i, 0], [1, frameLength]), fftLength));\n    }\n    return concat(output);\n}\nexport const stft = op({ stft_ });\n//# sourceMappingURL=stft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { CropAndResize } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Extracts crops from the input image tensor and resizes them using bilinear\n * sampling or nearest neighbor sampling (possibly with aspect ratio change)\n * to a common output size specified by crop_size.\n *\n * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,\n *     where imageHeight and imageWidth must be positive, specifying the\n *     batch of images from which to take crops\n * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized\n *     coordinates of the box in the boxInd[i]'th image in the batch\n * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range\n *     `[0, batch)` that specifies the image that the `i`-th box refers to.\n * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`\n *     specifying the size to which all crops are resized to.\n * @param method Optional string from `'bilinear' | 'nearest'`,\n *     defaults to bilinear, which specifies the sampling method for resizing\n * @param extrapolationValue A threshold for deciding when to remove boxes based\n *     on score. Defaults to 0.\n * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction cropAndResize_(image, boxes, boxInd, cropSize, method, extrapolationValue) {\n    const $image = convertToTensor(image, 'image', 'cropAndResize');\n    const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');\n    const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');\n    method = method || 'bilinear';\n    extrapolationValue = extrapolationValue || 0;\n    const numBoxes = $boxes.shape[0];\n    util.assert($image.rank === 4, () => 'Error in cropAndResize: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    util.assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +\n        `but had shape ${$boxes.shape}.`);\n    util.assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +\n        `but had shape ${$boxes.shape}.`);\n    util.assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got ` +\n        `length ${cropSize.length}.`);\n    util.assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);\n    util.assert(method === 'bilinear' || method === 'nearest', () => `method must be bilinear or nearest, but was ${method}`);\n    const forward = (backend) => backend.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);\n    const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };\n    const attrs = { method, extrapolationValue, cropSize };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, CropAndResize, attrs);\n    return res;\n}\nexport const cropAndResize = op({ cropAndResize_ });\n//# sourceMappingURL=crop_and_resize.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FlipLeftRight } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Flips the image left to right. Currently available in the CPU, WebGL, and\n * WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction flipLeftRight_(image) {\n    const $image = convertToTensor(image, 'image', 'flipLeftRight', 'float32');\n    util.assert($image.rank === 4, () => 'Error in flipLeftRight: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    const inputs = { image: $image };\n    const res = ENGINE.runKernel(FlipLeftRight, inputs, {});\n    return res;\n}\nexport const flipLeftRight = op({ flipLeftRight_ });\n//# sourceMappingURL=flip_left_right.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { RotateWithOffset } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Rotates the input image tensor counter-clockwise with an optional offset\n * center of rotation. Currently available in the CPU, WebGL, and WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n * @param radians The amount of rotation.\n * @param fillValue The value to fill in the empty space leftover\n *     after rotation. Can be either a single grayscale value (0-255), or an\n *     array of three numbers `[red, green, blue]` specifying the red, green,\n *     and blue channels. Defaults to `0` (black).\n * @param center The center of rotation. Can be either a single value (0-1), or\n *     an array of two numbers `[centerX, centerY]`. Defaults to `0.5` (rotates\n *     the image around its center).\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction rotateWithOffset_(image, radians, fillValue = 0, center = 0.5) {\n    const $image = convertToTensor(image, 'image', 'rotateWithOffset', 'float32');\n    util.assert($image.rank === 4, () => 'Error in rotateWithOffset: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    const inputs = { image: $image };\n    const attrs = { radians, fillValue, center };\n    const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);\n    return res;\n}\nexport const rotateWithOffset = op({ rotateWithOffset_ });\n//# sourceMappingURL=rotate_with_offset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nfunction nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n    if (iouThreshold == null) {\n        iouThreshold = 0.5;\n    }\n    if (scoreThreshold == null) {\n        scoreThreshold = Number.NEGATIVE_INFINITY;\n    }\n    if (softNmsSigma == null) {\n        softNmsSigma = 0.0;\n    }\n    const numBoxes = boxes.shape[0];\n    maxOutputSize = Math.min(maxOutputSize, numBoxes);\n    util.assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n    util.assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n    util.assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n    util.assert(scores.rank === 1, () => 'scores must be a 1D tensor');\n    util.assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, ` +\n        `but was ${scores.shape[0]}`);\n    util.assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);\n    return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n}\nexport { nonMaxSuppSanityCheck };\n//# sourceMappingURL=nonmax_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV3 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\nfunction nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n    maxOutputSize = inputs.maxOutputSize;\n    iouThreshold = inputs.iouThreshold;\n    scoreThreshold = inputs.scoreThreshold;\n    const attrs = { maxOutputSize, iouThreshold, scoreThreshold };\n    return ENGINE.runKernelFunc(b => b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold), { boxes: $boxes, scores: $scores }, null /* grad */, NonMaxSuppressionV3, attrs);\n}\nexport const nonMaxSuppression = op({ nonMaxSuppression_ });\n//# sourceMappingURL=non_max_suppression.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inserts a value into a sorted array. This method allows duplicate, meaning it\n * allows inserting duplicate value, in which case, the element will be inserted\n * at the lowest index of the value.\n * @param arr The array to modify.\n * @param element The element to insert.\n * @param comparator Optional. If no comparator is specified, elements are\n * compared using array_util.defaultComparator, which is suitable for Strings\n * and Numbers in ascending arrays. If the array contains multiple instances of\n * the target value, the left-most instance will be returned. To provide a\n * comparator, it should take 2 arguments to compare and return a negative,\n * zero, or a positive number.\n */\nexport function binaryInsert(arr, element, comparator) {\n    const index = binarySearch(arr, element, comparator);\n    const insertionPoint = index < 0 ? -(index + 1) : index;\n    arr.splice(insertionPoint, 0, element);\n}\n/**\n * Searches the array for the target using binary search, returns the index\n * of the found element, or position to insert if element not found. If no\n * comparator is specified, elements are compared using array_\n * util.defaultComparator, which is suitable for Strings and Numbers in\n * ascending arrays. If the array contains multiple instances of the target\n * value, the left-most instance will be returned.\n * @param arr The array to be searched in.\n * @param target The target to be searched for.\n * @param comparator Should take 2 arguments to compare and return a negative,\n *    zero, or a positive number.\n * @return Lowest index of the target value if found, otherwise the insertion\n *    point where the target should be inserted, in the form of\n *    (-insertionPoint - 1).\n */\nexport function binarySearch(arr, target, comparator) {\n    return binarySearch_(arr, target, comparator || defaultComparator);\n}\n/**\n * Compares its two arguments for order.\n * @param a The first element to be compared.\n * @param b The second element to be compared.\n * @return A negative number, zero, or a positive number as the first\n *     argument is less than, equal to, or greater than the second.\n */\nfunction defaultComparator(a, b) {\n    return a > b ? 1 : a < b ? -1 : 0;\n}\nfunction binarySearch_(arr, target, comparator) {\n    let left = 0;\n    let right = arr.length;\n    let middle = 0;\n    let found = false;\n    while (left < right) {\n        middle = left + ((right - left) >>> 1);\n        const compareResult = comparator(target, arr[middle]);\n        if (compareResult > 0) {\n            left = middle + 1;\n        }\n        else {\n            right = middle;\n            // If compareResult is 0, the value is found. We record it is found,\n            // and then keep looking because there may be duplicate.\n            found = !compareResult;\n        }\n    }\n    return found ? left : -left - 1;\n}\n//# sourceMappingURL=array_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Implementation of the NonMaxSuppression kernel shared between webgl and cpu.\n */\nimport { scalar } from '../ops/scalar';\nimport { tensor1d } from '../ops/tensor1d';\nimport { binaryInsert } from './array_util';\nexport function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */)\n        .selectedIndices;\n}\nexport function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */, false /* returnScoresTensor */, padToMaxOutputSize /* padToMaxOutputSize */, true\n    /* returnValidOutputs */ );\n}\nexport function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true /* returnScoresTensor */);\n}\nfunction nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {\n    // The list is sorted in ascending order, so that we can always pop the\n    // candidate with the largest score in O(1) time.\n    const candidates = [];\n    for (let i = 0; i < scores.length; i++) {\n        if (scores[i] > scoreThreshold) {\n            candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 });\n        }\n    }\n    candidates.sort(ascendingComparator);\n    // If softNmsSigma is 0, the outcome of this algorithm is exactly same as\n    // before.\n    const scale = softNmsSigma > 0 ? (-0.5 / softNmsSigma) : 0.0;\n    const selectedIndices = [];\n    const selectedScores = [];\n    while (selectedIndices.length < maxOutputSize && candidates.length > 0) {\n        const candidate = candidates.pop();\n        const { score: originalScore, boxIndex, suppressBeginIndex } = candidate;\n        if (originalScore < scoreThreshold) {\n            break;\n        }\n        // Overlapping boxes are likely to have similar scores, therefore we\n        // iterate through the previously selected boxes backwards in order to\n        // see if candidate's score should be suppressed. We use\n        // suppressBeginIndex to track and ensure a candidate can be suppressed\n        // by a selected box no more than once. Also, if the overlap exceeds\n        // iouThreshold, we simply ignore the candidate.\n        let ignoreCandidate = false;\n        for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {\n            const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);\n            if (iou >= iouThreshold) {\n                ignoreCandidate = true;\n                break;\n            }\n            candidate.score =\n                candidate.score * suppressWeight(iouThreshold, scale, iou);\n            if (candidate.score <= scoreThreshold) {\n                break;\n            }\n        }\n        // At this point, if `candidate.score` has not dropped below\n        // `scoreThreshold`, then we know that we went through all of the\n        // previous selections and can safely update `suppressBeginIndex` to the\n        // end of the selected array. Then we can re-insert the candidate with\n        // the updated score and suppressBeginIndex back in the candidate list.\n        // If on the other hand, `candidate.score` has dropped below the score\n        // threshold, we will not add it back to the candidates list.\n        candidate.suppressBeginIndex = selectedIndices.length;\n        if (!ignoreCandidate) {\n            // Candidate has passed all the tests, and is not suppressed, so\n            // select the candidate.\n            if (candidate.score === originalScore) {\n                selectedIndices.push(boxIndex);\n                selectedScores.push(candidate.score);\n            }\n            else if (candidate.score > scoreThreshold) {\n                // Candidate's score is suppressed but is still high enough to be\n                // considered, so add back to the candidates list.\n                binaryInsert(candidates, candidate, ascendingComparator);\n            }\n        }\n    }\n    // NonMaxSuppressionV4 feature: padding output to maxOutputSize.\n    const validOutputs = selectedIndices.length;\n    const elemsToPad = maxOutputSize - validOutputs;\n    if (padToMaxOutputSize && elemsToPad > 0) {\n        selectedIndices.push(...new Array(elemsToPad).fill(0));\n        selectedScores.push(...new Array(elemsToPad).fill(0.0));\n    }\n    const result = { selectedIndices: tensor1d(selectedIndices, 'int32') };\n    if (returnScoresTensor) {\n        result['selectedScores'] = tensor1d(selectedScores, 'float32');\n    }\n    if (returnValidOutputs) {\n        result['validOutputs'] = scalar(validOutputs, 'int32');\n    }\n    return result;\n}\nfunction intersectionOverUnion(boxes, i, j) {\n    const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n    const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n    const yminI = Math.min(iCoord[0], iCoord[2]);\n    const xminI = Math.min(iCoord[1], iCoord[3]);\n    const ymaxI = Math.max(iCoord[0], iCoord[2]);\n    const xmaxI = Math.max(iCoord[1], iCoord[3]);\n    const yminJ = Math.min(jCoord[0], jCoord[2]);\n    const xminJ = Math.min(jCoord[1], jCoord[3]);\n    const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n    const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n    const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n    const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n    if (areaI <= 0 || areaJ <= 0) {\n        return 0.0;\n    }\n    const intersectionYmin = Math.max(yminI, yminJ);\n    const intersectionXmin = Math.max(xminI, xminJ);\n    const intersectionYmax = Math.min(ymaxI, ymaxJ);\n    const intersectionXmax = Math.min(xmaxI, xmaxJ);\n    const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *\n        Math.max(intersectionXmax - intersectionXmin, 0.0);\n    return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n// A Gaussian penalty function, this method always returns values in [0, 1].\n// The weight is a function of similarity, the more overlap two boxes are, the\n// smaller the weight is, meaning highly overlapping boxe will be significantly\n// penalized. On the other hand, a non-overlapping box will not be penalized.\nfunction suppressWeight(iouThreshold, scale, iou) {\n    const weight = Math.exp(scale * iou * iou);\n    return iou <= iouThreshold ? weight : 0.0;\n}\nfunction ascendingComparator(c1, c2) {\n    // For objects with same scores, we make the object with the larger index go\n    // first. In an array that pops from the end, this means that the object with\n    // the smaller index will be popped first. This ensures the same output as\n    // the TensorFlow python version.\n    return (c1.score - c2.score) ||\n        ((c1.score === c2.score) && (c2.boxIndex - c1.boxIndex));\n}\n//# sourceMappingURL=non_max_suppression_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV3Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This is the async version of `nonMaxSuppression`\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n    maxOutputSize = inputs.maxOutputSize;\n    iouThreshold = inputs.iouThreshold;\n    scoreThreshold = inputs.scoreThreshold;\n    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n    const boxesVals = boxesAndScores[0];\n    const scoresVals = boxesAndScores[1];\n    // We call a cpu based impl directly with the typedarray data  here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\n//# sourceMappingURL=non_max_suppression_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV5 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    maxOutputSize = params.maxOutputSize;\n    iouThreshold = params.iouThreshold;\n    scoreThreshold = params.scoreThreshold;\n    softNmsSigma = params.softNmsSigma;\n    const inputs = { boxes: $boxes, scores: $scores };\n    const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n    const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);\n    return { selectedIndices: result[0], selectedScores: result[1] };\n}\nexport const nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ });\n//# sourceMappingURL=non_max_suppression_with_score.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV5Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    maxOutputSize = params.maxOutputSize;\n    iouThreshold = params.iouThreshold;\n    scoreThreshold = params.scoreThreshold;\n    softNmsSigma = params.softNmsSigma;\n    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n    const boxesVals = boxesAndScores[0];\n    const scoresVals = boxesAndScores[1];\n    // We call a cpu based impl directly with the typedarray data  here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;\n//# sourceMappingURL=non_max_suppression_with_score_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV4 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n    const $maxOutputSize = params.maxOutputSize;\n    const $iouThreshold = params.iouThreshold;\n    const $scoreThreshold = params.scoreThreshold;\n    const inputs = { boxes: $boxes, scores: $scores };\n    const attrs = {\n        maxOutputSize: $maxOutputSize,\n        iouThreshold: $iouThreshold,\n        scoreThreshold: $scoreThreshold,\n        padToMaxOutputSize\n    };\n    const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);\n    return { selectedIndices: result[0], validOutputs: result[1] };\n}\nexport const nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ });\n//# sourceMappingURL=non_max_suppression_padded.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV4Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n    const $maxOutputSize = params.maxOutputSize;\n    const $iouThreshold = params.iouThreshold;\n    const $scoreThreshold = params.scoreThreshold;\n    const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);\n    // We call a cpu based impl directly with the typedarray data here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;\n//# sourceMappingURL=non_max_suppression_padded_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeBilinear } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Bilinear resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeBilinear_(images, size, alignCorners = false) {\n    const $images = convertToTensor(images, 'images', 'resizeBilinear');\n    util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got ` +\n        `rank ${$images.rank}.`);\n    util.assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ` +\n        `${size}.`);\n    let batchImages = $images;\n    let reshapedTo4D = false;\n    if ($images.rank === 3) {\n        reshapedTo4D = true;\n        batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n    }\n    const [newHeight, newWidth] = size;\n    const forward = (backend, save) => {\n        save([batchImages]);\n        return backend.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);\n    };\n    const inputs = { images: batchImages };\n    const attrs = { alignCorners, size };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, ResizeBilinear, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const resizeBilinear = op({ resizeBilinear_ });\n//# sourceMappingURL=resize_bilinear.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeNearestNeighbor } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * NearestNeighbor resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeNearestNeighbor_(images, size, alignCorners = false) {\n    const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');\n    util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` +\n        `rank ${$images.rank}.`);\n    util.assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ` +\n        `${size}.`);\n    util.assert($images.dtype === 'float32' || $images.dtype === 'int32', () => '`images` must have `int32` or `float32` as dtype');\n    let batchImages = $images;\n    let reshapedTo4D = false;\n    if ($images.rank === 3) {\n        reshapedTo4D = true;\n        batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n    }\n    const [newHeight, newWidth] = size;\n    const inputs = { images: batchImages };\n    const attrs = { alignCorners, size };\n    const forward = (backend, save) => {\n        save([batchImages]);\n        return backend.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);\n    };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, ResizeNearestNeighbor, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const resizeNearestNeighbor = op({ resizeNearestNeighbor_ });\n//# sourceMappingURL=resize_nearest_neighbor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assert } from '../../util';\nimport { greaterEqual } from '../greater_equal';\nimport { lessEqual } from '../less_equal';\nimport { logicalAnd } from '../logical_and';\nimport { op } from '../operation';\nimport { range } from '../range';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\nimport { zeros } from '../zeros';\n/**\n * Copy a tensor setting everything outside a central band in each innermost\n * matrix to zero.\n *\n * The band part is computed as follows: Assume input has `k` dimensions\n * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where\n * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.\n * The indicator function\n * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower))`\n * `&& (num_upper < 0 || (n-m) <= num_upper)`\n *\n * ```js\n * const x = tf.tensor2d([[ 0,  1,  2, 3],\n *                        [-1,  0,  1, 2],\n *                        [-2, -1,  0, 1],\n *                        [-3, -2, -1, 0]]);\n * let y = tf.linalg.bandPart(x, 1, -1);\n * y.print(); // [[ 0,  1,  2, 3],\n *            //  [-1,  0,  1, 2],\n *            //  [ 0, -1,  0, 1],\n *            //  [ 0, 0 , -1, 0]]\n * let z = tf.linalg.bandPart(x, 2, 1);\n * z.print(); // [[ 0,  1,  0, 0],\n *            //  [-1,  0,  1, 0],\n *            //  [-2, -1,  0, 1],\n *            //  [ 0, -2, -1, 0]]\n * ```\n *\n * @param x Rank `k` tensor\n * @param numLower Number of subdiagonals to keep.\n *   If negative, keep entire lower triangle.\n * @param numUpper Number of subdiagonals to keep.\n *   If negative, keep entire upper triangle.\n * @returns Rank `k` tensor of the same shape as input.\n *   The extracted banded tensor.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction bandPart_(a, numLower, numUpper) {\n    assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);\n    assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);\n    const $a = convertToTensor(a, 'a', 'bandPart');\n    assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);\n    const shape = $a.shape;\n    const [M, N] = $a.shape.slice(-2);\n    if (!(numLower <= M)) {\n        throw new Error(`bandPart(): numLower (${numLower})` +\n            ` must not be greater than the number of rows (${M}).`);\n    }\n    if (!(numUpper <= N)) {\n        throw new Error(`bandPart(): numUpper (${numUpper})` +\n            ` must not be greater than the number of columns (${N}).`);\n    }\n    if (numLower < 0) {\n        numLower = M;\n    }\n    if (numUpper < 0) {\n        numUpper = N;\n    }\n    const i = reshape(range(0, M, 1, 'int32'), [-1, 1]);\n    const j = range(0, N, 1, 'int32');\n    const ij = sub(i, j);\n    const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, 'int32')), greaterEqual(ij, scalar(-numUpper, 'int32')));\n    const zero = zeros([M, N], $a.dtype);\n    return reshape(stack(unstack(reshape($a, [-1, M, N]))\n        .map(mat => where(inBand, mat, zero))), shape);\n}\nexport const bandPart = op({ bandPart_ });\n//# sourceMappingURL=band_part.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { assert } from '../../util';\nimport { div } from '../div';\nimport { mul } from '../mul';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { split } from '../split';\nimport { squeeze } from '../squeeze';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\n/**\n * Gram-Schmidt orthogonalization.\n *\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * let y = tf.linalg.gramSchmidt(x);\n * y.print();\n * console.log('Othogonalized:');\n * y.dot(y.transpose()).print();  // should be nearly the identity matrix.\n * console.log('First row direction maintained:');\n * const data = await y.array();\n * console.log(data[0][1] / data[0][0]);  // should be nearly 2.\n * ```\n *\n * @param xs The vectors to be orthogonalized, in one of the two following\n *   formats:\n *   - An Array of `tf.Tensor1D`.\n *   - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows\n *     of `xs`.\n *   In each case, all the vectors must have the same length and the length\n *   must be greater than or equal to the number of vectors.\n * @returns The orthogonalized and normalized vectors or matrix.\n *   Orthogonalization means that the vectors or the rows of the matrix\n *   are orthogonal (zero inner products). Normalization means that each\n *   vector or each row of the matrix has an L2 norm that equals `1`.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction gramSchmidt_(xs) {\n    let inputIsTensor2D;\n    if (Array.isArray(xs)) {\n        inputIsTensor2D = false;\n        assert(xs != null && xs.length > 0, () => 'Gram-Schmidt process: input must not be null, undefined, or ' +\n            'empty');\n        const dim = xs[0].shape[0];\n        for (let i = 1; i < xs.length; ++i) {\n            assert(xs[i].shape[0] === dim, () => 'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +\n                `(${xs[i].shape[0]} vs. ${dim})`);\n        }\n    }\n    else {\n        inputIsTensor2D = true;\n        xs = split(xs, xs.shape[0], 0).map(x => squeeze(x, [0]));\n    }\n    assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds ` +\n        `number of dimensions (${xs[0].shape[0]}).`);\n    const ys = [];\n    const xs1d = xs;\n    for (let i = 0; i < xs.length; ++i) {\n        ys.push(ENGINE.tidy(() => {\n            let x = xs1d[i];\n            if (i > 0) {\n                for (let j = 0; j < i; ++j) {\n                    const proj = mul(sum(mul(ys[j], x)), ys[j]);\n                    x = sub(x, proj);\n                }\n            }\n            return div(x, norm(x, 'euclidean'));\n        }));\n    }\n    if (inputIsTensor2D) {\n        return stack(ys, 0);\n    }\n    else {\n        return ys;\n    }\n}\nexport const gramSchmidt = op({ gramSchmidt_ });\n//# sourceMappingURL=gram_schmidt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { dispose } from '../../globals';\nimport { assert } from '../../util';\nimport { clone } from '../clone';\nimport { concat } from '../concat';\nimport { div } from '../div';\nimport { eye } from '../eye';\nimport { greater } from '../greater';\nimport { matMul } from '../mat_mul';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { tensor2d } from '../tensor2d';\nimport { transpose } from '../transpose';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\n/**\n * Compute QR decomposition of m-by-n matrix using Householder transformation.\n *\n * Implementation based on\n *   [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]\n * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [3, 4]]);\n * let [q, r] = tf.linalg.qr(a);\n * console.log('Q');\n * q.print();\n * console.log('R');\n * r.print();\n * console.log('Orthogonalized');\n * q.dot(q.transpose()).print()  // should be nearly the identity matrix.\n * console.log('Reconstructed');\n * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];\n * ```\n *\n * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose\n *   it has the shape `[..., M, N]`.\n * @param fullMatrices An optional boolean parameter. Defaults to `false`.\n *   If `true`, compute full-sized `Q`. If `false` (the default),\n *   compute only the leading N columns of `Q` and `R`.\n * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,\n *   i.e., its columns all have unit norm and are mutually orthogonal.\n *   If `M >= N`,\n *     If `fullMatrices` is `false` (default),\n *       - `Q` has a shape of `[..., M, N]`,\n *       - `R` has a shape of `[..., N, N]`.\n *     If `fullMatrices` is `true` (default),\n *       - `Q` has a shape of `[..., M, M]`,\n *       - `R` has a shape of `[..., M, N]`.\n *   If `M < N`,\n *     - `Q` has a shape of `[..., M, M]`,\n *     - `R` has a shape of `[..., M, N]`.\n * @throws If the rank of `x` is less than 2.\n *\n * @doc {heading:'Operations',\n *       subheading:'Linear Algebra',\n *       namespace:'linalg'}\n */\nfunction qr_(x, fullMatrices = false) {\n    assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);\n    if (x.rank === 2) {\n        return qr2d(x, fullMatrices);\n    }\n    else {\n        // Rank > 2.\n        // TODO(cais): Below we split the input into individual 2D tensors,\n        //   perform QR decomposition on them and then stack the results back\n        //   together. We should explore whether this can be parallelized.\n        const outerDimsProd = x.shape.slice(0, x.shape.length - 2)\n            .reduce((value, prev) => value * prev);\n        const x2ds = unstack(reshape(x, [\n            outerDimsProd, x.shape[x.shape.length - 2],\n            x.shape[x.shape.length - 1]\n        ]), 0);\n        const q2ds = [];\n        const r2ds = [];\n        x2ds.forEach(x2d => {\n            const [q2d, r2d] = qr2d(x2d, fullMatrices);\n            q2ds.push(q2d);\n            r2ds.push(r2d);\n        });\n        const q = reshape(stack(q2ds, 0), x.shape);\n        const r = reshape(stack(r2ds, 0), x.shape);\n        return [q, r];\n    }\n}\nfunction qr2d(x, fullMatrices = false) {\n    return ENGINE.tidy(() => {\n        assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);\n        const m = x.shape[0];\n        const n = x.shape[1];\n        let q = eye(m); // Orthogonal transform so far.\n        let r = clone(x); // Transformed matrix so far.\n        const one2D = tensor2d([[1]], [1, 1]);\n        let w = clone(one2D);\n        const iters = m >= n ? n : m;\n        for (let j = 0; j < iters; ++j) {\n            // This tidy within the for-loop ensures we clean up temporary\n            // tensors as soon as they are no longer needed.\n            const rTemp = r;\n            const wTemp = w;\n            const qTemp = q;\n            [w, r, q] = ENGINE.tidy(() => {\n                // Find H = I - tau * w * w', to put zeros below R(j, j).\n                const rjEnd1 = slice(r, [j, j], [m - j, 1]);\n                const normX = norm(rjEnd1);\n                const rjj = slice(r, [j, j], [1, 1]);\n                // The sign() function returns 0 on 0, which causes division by zero.\n                const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));\n                const u1 = sub(rjj, mul(s, normX));\n                const wPre = div(rjEnd1, u1);\n                if (wPre.shape[0] === 1) {\n                    w = clone(one2D);\n                }\n                else {\n                    w = concat([\n                        one2D,\n                        slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])\n                    ], 0);\n                }\n                const tau = neg(div(matMul(s, u1), normX));\n                // -- R := HR, Q := QH.\n                const rjEndAll = slice(r, [j, 0], [m - j, n]);\n                const tauTimesW = mul(tau, w);\n                const wT = transpose(w);\n                if (j === 0) {\n                    r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n                }\n                else {\n                    const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n                    r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);\n                }\n                const tawTimesWT = transpose(tauTimesW);\n                const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);\n                if (j === 0) {\n                    q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n                }\n                else {\n                    const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n                    q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);\n                }\n                return [w, r, q];\n            });\n            dispose([rTemp, wTemp, qTemp]);\n        }\n        if (!fullMatrices && m > n) {\n            q = slice(q, [0, 0], [m, n]);\n            r = slice(r, [0, 0], [n, n]);\n        }\n        return [q, r];\n    });\n}\nexport const qr = op({ qr_ });\n//# sourceMappingURL=qr.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Reduction;\n(function (Reduction) {\n    Reduction[Reduction[\"NONE\"] = 0] = \"NONE\";\n    Reduction[Reduction[\"MEAN\"] = 1] = \"MEAN\";\n    Reduction[Reduction[\"SUM\"] = 2] = \"SUM\";\n    Reduction[Reduction[\"SUM_BY_NONZERO_WEIGHTS\"] = 3] = \"SUM_BY_NONZERO_WEIGHTS\";\n})(Reduction || (Reduction = {}));\n//# sourceMappingURL=loss_ops_utils.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { Reduction } from '../loss_ops_utils';\nimport { mean } from '../mean';\nimport { mul } from '../mul';\nimport { notEqual } from '../not_equal';\nimport { ones } from '../ones';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sum } from '../sum';\n/**\n * Computes the weighted loss between two tensors.\n *\n * @param losses Tensor of shape `[batch_size, d1, ... dN]`.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `losses`, and must be broadcastable to `losses` (i.e., all\n *    dimensions must be either `1`, or the same as the corresponding\n *    `losses` dimension).\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction computeWeightedLoss_(losses, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');\n    }\n    const weightedLoss = ($weights == null) ? $losses : mul($losses, $weights);\n    if (reduction === Reduction.NONE) {\n        return weightedLoss;\n    }\n    if (reduction === Reduction.SUM) {\n        return sum(weightedLoss);\n    }\n    if (reduction === Reduction.MEAN) {\n        if ($weights == null) {\n            return mean(weightedLoss);\n        }\n        else {\n            const broadcastFactor = $losses.size / $weights.size;\n            const result = div(sum(weightedLoss), sum($weights));\n            return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) :\n                result;\n        }\n    }\n    if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n        if ($weights == null) {\n            return div(sum(weightedLoss), scalar($losses.size));\n        }\n        else {\n            const broadcastedWeights = mul($weights, ones($losses.shape));\n            const numNonZeros = cast(sum(notEqual(broadcastedWeights, scalar(0))), 'float32');\n            return div(sum(weightedLoss), numNonZeros);\n        }\n    }\n    throw Error(`Unknown reduction: ${reduction}`);\n}\nexport const computeWeightedLoss = op({ computeWeightedLoss_ });\n//# sourceMappingURL=compute_weighted_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the absolute difference loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'absoluteDifference');\n    const $predictions = convertToTensor(predictions, 'predictions', 'absoluteDifference');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'absoluteDifference');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in absoluteDifference: ');\n    const losses = abs(sub($labels, $predictions));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const absoluteDifference = op({ absoluteDifference_ });\n//# sourceMappingURL=absolute_difference.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the cosine distance loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param axis The dimension along which the cosine distance is computed.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'cosineDistance');\n    const $predictions = convertToTensor(predictions, 'predictions', 'cosineDistance');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'cosineDistance');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in cosineDistance: ');\n    const one = scalar(1);\n    const losses = sub(one, sum(mul($labels, $predictions), axis, true));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const cosineDistance = op({ cosineDistance_ });\n//# sourceMappingURL=cosine_distance.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the Hinge loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $labels = convertToTensor(labels, 'labels', 'hingeLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'hingeLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');\n    const one = scalar(1);\n    // Convert binary labels to (-1, 1)\n    $labels = sub(mul(scalar(2), $labels), one);\n    const losses = relu(sub(one, mul($labels, $predictions)));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const hingeLoss = op({ hingeLoss_ });\n//# sourceMappingURL=hinge_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { Reduction } from '../loss_ops_utils';\nimport { minimum } from '../minimum';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { square } from '../square';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the huber loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param delta Point where huber loss changes from quadratic to linear.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`.\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction huberLoss_(labels, predictions, weights, delta = 1.0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'huberLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'huberLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');\n    const deltaScalar = scalar(delta);\n    const error = abs(sub($predictions, $labels));\n    const quadratic = minimum(error, deltaScalar);\n    const linear = sub(error, quadratic);\n    const losses = add(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const huberLoss = op({ huberLoss_ });\n//# sourceMappingURL=huber_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { log } from '../log';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the log loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param epsilon A small increment to avoid taking log of zero\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction logLoss_(labels, predictions, weights, epsilon = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'logLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'logLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'logLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');\n    const one = scalar(1);\n    const epsilonScalar = scalar(epsilon);\n    const l1 = neg(mul($labels, log(add($predictions, epsilonScalar))));\n    const l2 = mul(sub(one, $labels), log(add(sub(one, $predictions), epsilonScalar)));\n    const losses = sub(l1, l2);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const logLoss = op({ logLoss_ });\n//# sourceMappingURL=log_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { squaredDifference } from '../squared_difference';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the mean squared error between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'meanSquaredError');\n    const $predictions = convertToTensor(predictions, 'predictions', 'meanSquaredError');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'meanSquaredError');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in meanSquaredError: ');\n    const losses = squaredDifference($labels, $predictions);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const meanSquaredError = op({ meanSquaredError_ });\n//# sourceMappingURL=mean_squared_error.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { exp } from '../exp';\nimport { log1p } from '../log1p';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\nfunction sigmoidCrossEntropyWithLogits_(labels, logits) {\n    const $labels = convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');\n    const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');\n    assertShapesMatch($labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');\n    /**\n     * Implementation Details:\n     *\n     * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n     *     z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n     *   = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n     *   = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n     *   = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n     *   = (1 - z) * x + log(1 + exp(-x))\n     *   = x - x * z + log(1 + exp(-x))\n     *\n     *   For x < 0, to avoid overflow in exp(-x), we reformulate the above\n     *     x - x * z + log(1 + exp(-x))\n     *   = log(exp(x)) - x * z + log(1 + exp(-x))\n     *   = - x * z + log(1 + exp(x))\n     *\n     * Hence, to ensure stability and avoid overflow, the implementation uses\n     * this equivalent formulation:\n     *     max(x, 0) - x * z + log(1 + exp(-abs(x)))\n     */\n    const maxOutput = relu($logits);\n    const outputXTarget = mul($logits, $labels);\n    const sigmoidOutput = log1p(exp(neg(abs($logits))));\n    return add(sub(maxOutput, outputXTarget), sigmoidOutput);\n}\n/**\n * Computes the sigmoid cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)\n *                         + 0.5 * labelSmoothing\n *\n * @param multiClassLabels The ground truth output tensor of shape\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $multiClassLabels = convertToTensor(multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');\n    const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');\n    }\n    assertShapesMatch($multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');\n    if (labelSmoothing > 0) {\n        const labelSmoothingScalar = scalar(labelSmoothing);\n        const one = scalar(1);\n        const half = scalar(0.5);\n        $multiClassLabels =\n            add(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));\n    }\n    const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ });\n//# sourceMappingURL=sigmoid_cross_entropy.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../../gradients';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { expandShapeToKeepDim } from '../axis_util';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { exp } from '../exp';\nimport { logSumExp } from '../log_sum_exp';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes softmax cross entropy between logits and labels.\n *\n * Measures the probability error in discrete classification tasks in which\n * the classes are mutually exclusive (each entry is in exactly one class).\n * For example, each CIFAR-10 image is labeled with one and only one label: an\n * image can be a dog or a truck, but not both.\n *\n * `NOTE`: While the classes are mutually exclusive, their probabilities need\n * not be. All that is required is that each row of labels is a valid\n * probability distribution. If they are not, the computation of the gradient\n * will be incorrect.\n *\n * `WARNING`: This op expects unscaled logits, since it performs a softmax on\n * logits internally for efficiency. Do not call this op with the output of\n * softmax, as it will produce incorrect results.\n *\n * logits and labels must have the same shape, e.g. [batch_size, num_classes]\n * and the same dtype.\n * @param labels The labels array.\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\nfunction softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {\n    if (dim === -1) {\n        dim = logits.rank - 1;\n    }\n    if (dim !== logits.rank - 1) {\n        throw Error(`Softmax cross entropy along a non-last dimension is not yet ` +\n            `supported. Labels / logits was rank ${logits.rank} ` +\n            `and dim was ${dim}`);\n    }\n    // Use a custom gradient for numerical stability.\n    const customOp = customGrad((labels, logits, save) => {\n        // Reference:\n        //   1. http://cs231n.github.io/linear-classify/#softmax\n        //   2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/\n        const keepDims = true;\n        const lse = logSumExp(logits, [dim], keepDims);\n        const logResult = sub(cast(logits, 'float32'), lse);\n        save([labels, logResult]);\n        const costVector = neg(mul(logResult, labels));\n        const value = sum(costVector, [dim]);\n        const gradFunc = (dy, saved) => {\n            const [labels, logResult] = saved;\n            const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n            return [\n                mul(reshape(dy, dyShape), sub(cast(labels, 'float32'), exp(logResult))),\n                mul(reshape(dy, dyShape), sub(exp(logResult), cast(labels, 'float32'))),\n            ];\n        };\n        return { value, gradFunc };\n    });\n    return customOp(labels, logits);\n}\n/**\n * Computes the softmax cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newOnehotLabels = onehotLabels * (1 - labelSmoothing)\n *                         + labelSmoothing / numClasses\n *\n * @param onehotLabels One hot encoded labels\n *    [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or 1, and must be\n *    broadcastable to `loss`  of shape [batch_size]\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $onehotLabels = convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');\n    const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');\n    }\n    assertShapesMatch($onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');\n    if (labelSmoothing > 0) {\n        const labelSmoothingScalar = scalar(labelSmoothing);\n        const one = scalar(1);\n        const numClasses = scalar($onehotLabels.shape[1]);\n        $onehotLabels =\n            add(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));\n    }\n    const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const softmaxCrossEntropy = op({ softmaxCrossEntropy_ });\n//# sourceMappingURL=softmax_cross_entropy.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Modularized ops.\nexport { abs } from './abs';\nexport { acos } from './acos';\nexport { acosh } from './acosh';\nexport { add } from './add';\nexport { addN } from './add_n';\nexport { all } from './all';\nexport { any } from './any';\nexport { argMax } from './arg_max';\nexport { argMin } from './arg_min';\nexport { asin } from './asin';\nexport { asinh } from './asinh';\nexport { atan } from './atan';\nexport { atan2 } from './atan2';\nexport { atanh } from './atanh';\nexport { avgPool } from './avg_pool';\nexport { avgPool3d } from './avg_pool_3d';\nexport { basicLSTMCell } from './basic_lstm_cell';\nexport { batchToSpaceND } from './batch_to_space_nd';\nexport { batchNorm } from './batchnorm';\nexport { batchNorm2d } from './batchnorm2d';\nexport { batchNorm3d } from './batchnorm3d';\nexport { batchNorm4d } from './batchnorm4d';\nexport { broadcastTo } from './broadcast_to';\nexport { buffer } from './buffer';\nexport { cast } from './cast';\nexport { ceil } from './ceil';\nexport { clipByValue } from './clip_by_value';\nexport { clone } from './clone';\nexport { complex } from './complex';\nexport { concat } from './concat';\nexport { concat1d } from './concat_1d';\nexport { concat2d } from './concat_2d';\nexport { concat3d } from './concat_3d';\nexport { concat4d } from './concat_4d';\nexport { conv1d } from './conv1d';\nexport { conv2d } from './conv2d';\nexport { conv2dTranspose } from './conv2d_transpose';\nexport { conv3d } from './conv3d';\nexport { conv3dTranspose } from './conv3d_transpose';\nexport { cos } from './cos';\nexport { cosh } from './cosh';\nexport { cumsum } from './cumsum';\nexport { depthToSpace } from './depth_to_space';\nexport { depthwiseConv2d } from './depthwise_conv2d';\nexport { diag } from './diag';\nexport { dilation2d } from './dilation2d';\nexport { div } from './div';\nexport { divNoNan } from './div_no_nan';\nexport { dot } from './dot';\nexport { elu } from './elu';\nexport { equal } from './equal';\nexport { erf } from './erf';\nexport { exp } from './exp';\nexport { expandDims } from './expand_dims';\nexport { expm1 } from './expm1';\nexport { eye } from './eye';\nexport { fill } from './fill';\nexport { floor } from './floor';\nexport { floorDiv } from './floorDiv';\nexport { gather } from './gather';\nexport { greater } from './greater';\nexport { greaterEqual } from './greater_equal';\nexport { imag } from './imag';\nexport { isFinite } from './is_finite';\nexport { isInf } from './is_inf';\nexport { isNaN } from './is_nan';\nexport { leakyRelu } from './leaky_relu';\nexport { less } from './less';\nexport { lessEqual } from './less_equal';\nexport { linspace } from './linspace';\nexport { localResponseNormalization } from './local_response_normalization';\nexport { log } from './log';\nexport { log1p } from './log1p';\nexport { logSigmoid } from './log_sigmoid';\nexport { logSoftmax } from './log_softmax';\nexport { logSumExp } from './log_sum_exp';\nexport { logicalAnd } from './logical_and';\nexport { logicalNot } from './logical_not';\nexport { logicalOr } from './logical_or';\nexport { logicalXor } from './logical_xor';\nexport { matMul } from './mat_mul';\nexport { max } from './max';\nexport { maxPool } from './max_pool';\nexport { maxPool3d } from './max_pool_3d';\nexport { maxPoolWithArgmax } from './max_pool_with_argmax';\nexport { maximum } from './maximum';\nexport { mean } from './mean';\nexport { min } from './min';\nexport { minimum } from './minimum';\nexport { mod } from './mod';\nexport { moments } from './moments';\nexport { mul } from './mul';\nexport { multiRNNCell } from './multi_rnn_cell';\nexport { multinomial } from './multinomial';\nexport { neg } from './neg';\nexport { notEqual } from './not_equal';\nexport { oneHot } from './one_hot';\nexport { ones } from './ones';\nexport { onesLike } from './ones_like';\nexport { outerProduct } from './outer_product';\nexport { pad } from './pad';\nexport { pad1d } from './pad1d';\nexport { pad2d } from './pad2d';\nexport { pad3d } from './pad3d';\nexport { pad4d } from './pad4d';\nexport { pool } from './pool';\nexport { pow } from './pow';\nexport { prelu } from './prelu';\nexport { print } from './print';\nexport { prod } from './prod';\nexport { rand } from './rand';\nexport { randomGamma } from './random_gamma';\nexport { randomNormal } from './random_normal';\nexport { randomUniform } from './random_uniform';\nexport { range } from './range';\nexport { real } from './real';\nexport { reciprocal } from './reciprocal';\nexport { relu } from './relu';\nexport { relu6 } from './relu6';\nexport { reshape } from './reshape';\nexport { reverse } from './reverse';\nexport { reverse1d } from './reverse_1d';\nexport { reverse2d } from './reverse_2d';\nexport { reverse3d } from './reverse_3d';\nexport { reverse4d } from './reverse_4d';\nexport { round } from './round';\nexport { rsqrt } from './rsqrt';\nexport { scalar } from './scalar';\nexport { selu } from './selu';\nexport { separableConv2d } from './separable_conv2d';\nexport { setdiff1dAsync } from './setdiff1d_async';\nexport { sigmoid } from './sigmoid';\nexport { sign } from './sign';\nexport { sin } from './sin';\nexport { sinh } from './sinh';\nexport { slice } from './slice';\nexport { slice1d } from './slice1d';\nexport { slice2d } from './slice2d';\nexport { slice3d } from './slice3d';\nexport { slice4d } from './slice4d';\nexport { softmax } from './softmax';\nexport { softplus } from './softplus';\nexport { spaceToBatchND } from './space_to_batch_nd';\nexport { fft } from './spectral/fft';\nexport { ifft } from './spectral/ifft';\nexport { irfft } from './spectral/irfft';\nexport { rfft } from './spectral/rfft';\nexport { split } from './split';\nexport { sqrt } from './sqrt';\nexport { square } from './square';\nexport { squaredDifference } from './squared_difference';\nexport { squeeze } from './squeeze';\nexport { stack } from './stack';\nexport { step } from './step';\nexport { stridedSlice } from './strided_slice';\nexport { sub } from './sub';\nexport { sum } from './sum';\nexport { tan } from './tan';\nexport { tanh } from './tanh';\nexport { tensor } from './tensor';\nexport { tensor1d } from './tensor1d';\nexport { tensor2d } from './tensor2d';\nexport { tensor3d } from './tensor3d';\nexport { tensor4d } from './tensor4d';\nexport { tensor5d } from './tensor5d';\nexport { tensor6d } from './tensor6d';\nexport { tile } from './tile';\nexport { topk } from './topk';\nexport { truncatedNormal } from './truncated_normal';\nexport { unique } from './unique';\nexport { unsortedSegmentSum } from './unsorted_segment_sum';\nexport { unstack } from './unstack';\nexport { variable } from './variable';\nexport { where } from './where';\nexport { whereAsync } from './where_async';\nexport { zeros } from './zeros';\nexport { zerosLike } from './zeros_like';\nexport * from './boolean_mask';\nexport * from './compare';\nexport * from './binary_ops';\nexport * from './transpose';\nexport * from './norm';\nexport * from './moving_average';\nexport * from './scatter_nd';\nexport * from './sparse_to_dense';\nexport * from './gather_nd';\nexport * from './dropout';\nexport * from './signal_ops_util';\nexport * from './in_top_k';\nexport { op, OP_SCOPE_SUFFIX } from './operation';\nimport { rfft } from './spectral/rfft';\nimport { fft } from './spectral/fft';\nimport { ifft } from './spectral/ifft';\nimport { irfft } from './spectral/irfft';\nconst spectral = {\n    fft,\n    ifft,\n    rfft,\n    irfft\n};\nimport * as fused from './fused_ops';\nimport { hammingWindow } from './signal/hamming_window';\nimport { hannWindow } from './signal/hann_window';\nimport { frame } from './signal/frame';\nimport { stft } from './signal/stft';\nconst signal = {\n    hammingWindow,\n    hannWindow,\n    frame,\n    stft,\n};\n// Image Ops namespace\nimport { cropAndResize } from './image/crop_and_resize';\nimport { flipLeftRight } from './image/flip_left_right';\nimport { rotateWithOffset } from './image/rotate_with_offset';\nimport { nonMaxSuppression } from './image/non_max_suppression';\nimport { nonMaxSuppressionAsync } from './image/non_max_suppression_async';\nimport { nonMaxSuppressionWithScore } from './image/non_max_suppression_with_score';\nimport { nonMaxSuppressionWithScoreAsync } from './image/non_max_suppression_with_score_async';\nimport { nonMaxSuppressionPadded } from './image/non_max_suppression_padded';\nimport { nonMaxSuppressionPaddedAsync } from './image/non_max_suppression_padded_async';\nimport { resizeBilinear } from './image/resize_bilinear';\nimport { resizeNearestNeighbor } from './image/resize_nearest_neighbor';\nconst image = {\n    flipLeftRight,\n    resizeNearestNeighbor,\n    resizeBilinear,\n    rotateWithOffset,\n    cropAndResize,\n    nonMaxSuppression,\n    nonMaxSuppressionAsync,\n    nonMaxSuppressionWithScore,\n    nonMaxSuppressionWithScoreAsync,\n    nonMaxSuppressionPadded,\n    nonMaxSuppressionPaddedAsync\n};\n// linalg namespace\nimport { bandPart } from './linalg/band_part';\nimport { gramSchmidt } from './linalg/gram_schmidt';\nimport { qr } from './linalg/qr';\nconst linalg = {\n    bandPart,\n    gramSchmidt,\n    qr\n};\n// losses namespace;\nimport { absoluteDifference } from './losses/absolute_difference';\nimport { computeWeightedLoss } from './losses/compute_weighted_loss';\nimport { cosineDistance } from './losses/cosine_distance';\nimport { hingeLoss } from './losses/hinge_loss';\nimport { huberLoss } from './losses/huber_loss';\nimport { logLoss } from './losses/log_loss';\nimport { meanSquaredError } from './losses/mean_squared_error';\nimport { sigmoidCrossEntropy } from './losses/sigmoid_cross_entropy';\nimport { softmaxCrossEntropy } from './losses/softmax_cross_entropy';\nconst losses = {\n    absoluteDifference,\n    computeWeightedLoss,\n    cosineDistance,\n    hingeLoss,\n    huberLoss,\n    logLoss,\n    meanSquaredError,\n    sigmoidCrossEntropy,\n    softmaxCrossEntropy\n};\n// Second level exports.\nexport { image, linalg, losses, spectral, fused, signal };\n//# sourceMappingURL=ops.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dispose } from '../globals';\nimport { variableGrads } from '../gradients';\nimport { scalar } from '../ops/ops';\nimport { Serializable } from '../serialization';\n/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */\nexport class Optimizer extends Serializable {\n    /**\n     * Executes `f()` and minimizes the scalar output of `f()` by computing\n     * gradients of y with respect to the list of trainable variables provided by\n     * `varList`. If no list is provided, it defaults to all trainable variables.\n     *\n     * @param f The function to execute and whose output to minimize.\n     * @param returnCost Whether to return the scalar cost value produced by\n     * executing `f()`.\n     * @param varList An optional list of variables to update. If specified, only\n     * the trainable variables in varList will be updated by minimize. Defaults to\n     * all trainable variables.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers'}\n     */\n    minimize(f, returnCost = false, varList) {\n        const { value, grads } = this.computeGradients(f, varList);\n        if (varList != null) {\n            const gradArray = varList.map(v => ({ name: v.name, tensor: grads[v.name] }));\n            this.applyGradients(gradArray);\n        }\n        else {\n            this.applyGradients(grads);\n        }\n        // Dispose gradients.\n        dispose(grads);\n        if (returnCost) {\n            return value;\n        }\n        else {\n            value.dispose();\n            return null;\n        }\n    }\n    /**\n     * The number of iterations that this optimizer instance has been invoked for.\n     */\n    get iterations() {\n        if (this.iterations_ == null) {\n            this.iterations_ = 0;\n        }\n        return this.iterations_;\n    }\n    incrementIterations() {\n        this.iterations_ = this.iterations + 1;\n    }\n    /**\n     * Executes f() and computes the gradient of the scalar output of f() with\n     * respect to the list of trainable variables provided by `varList`. If no\n     * list is provided, it defaults to all trainable variables.\n     *\n     * @param f The function to execute and whose output to use for computing\n     * gradients with respect to variables.\n     * @param varList An optional list of variables to compute gradients with\n     * respect to. If specified, only the trainable variables in varList will have\n     * gradients computed with respect to. Defaults to all trainable variables.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers'}\n     */\n    computeGradients(f, varList) {\n        return variableGrads(f, varList);\n    }\n    /**\n     * Dispose the variables (if any) owned by this optimizer instance.\n     */\n    dispose() {\n        if (this.iterations_ != null) {\n            dispose(this.iterations_);\n        }\n    }\n    async saveIterations() {\n        if (this.iterations_ == null) {\n            this.iterations_ = 0;\n        }\n        return {\n            name: 'iter',\n            // TODO(cais): Use 'int64' type when available.\n            tensor: scalar(this.iterations_, 'int32')\n        };\n    }\n    async getWeights() {\n        throw new Error('getWeights() is not implemented for this optimizer yet.');\n    }\n    async setWeights(weightValues) {\n        throw new Error(`setWeights() is not implemented for this optimizer class ` +\n            `${this.getClassName()}`);\n    }\n    /**\n     * Extract the first element of the weight values and set it\n     * as the iterations counter variable of this instance of optimizer.\n     *\n     * @param weightValues\n     * @returns Weight values with the first element consumed and excluded.\n     */\n    async extractIterations(weightValues) {\n        this.iterations_ = (await weightValues[0].tensor.data())[0];\n        return weightValues.slice(1);\n    }\n}\nObject.defineProperty(Optimizer, Symbol.hasInstance, {\n    value: (instance) => {\n        return instance.minimize != null && instance.computeGradients != null &&\n            instance.applyGradients != null;\n    }\n});\n//# sourceMappingURL=optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/ops';\nimport { square } from '../ops/square';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class AdadeltaOptimizer extends Optimizer {\n    constructor(learningRate, rho, epsilon = null) {\n        super();\n        this.learningRate = learningRate;\n        this.rho = rho;\n        this.epsilon = epsilon;\n        this.accumulatedGrads = [];\n        this.accumulatedUpdates = [];\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            const trainable = false;\n            if (this.accumulatedGrads[i] == null) {\n                this.accumulatedGrads[i] = {\n                    originalName: `${name}/accum_grad`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedUpdates[i] == null) {\n                this.accumulatedUpdates[i] = {\n                    originalName: `${name}/accum_var`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedGrad = this.accumulatedGrads[i].variable;\n            const accumulatedUpdate = this.accumulatedUpdates[i].variable;\n            tidy(() => {\n                const newAccumulatedGrad = add(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));\n                const updates = mul(div(sqrt(add(accumulatedUpdate, this.epsilon)), sqrt(add(accumulatedGrad, this.epsilon))), gradient);\n                const newAccumulatedUpdate = add(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));\n                accumulatedGrad.assign(newAccumulatedGrad);\n                accumulatedUpdate.assign(newAccumulatedUpdate);\n                const newValue = add(mul(updates, -this.learningRate), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedUpdates != null) {\n            dispose(this.accumulatedGrads.map(v => v.variable));\n            dispose(this.accumulatedUpdates.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const variableCount = weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedGrads =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedUpdates =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'rho': this.rho,\n            'epsilon': this.epsilon\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['rho'], config['epsilon']);\n    }\n}\n/** @nocollapse */\nAdadeltaOptimizer.className = 'Adadelta'; // Name matters for Python compatibility.\nregisterClass(AdadeltaOptimizer);\n//# sourceMappingURL=adadelta_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { fill } from '../ops/fill';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class AdagradOptimizer extends Optimizer {\n    constructor(learningRate, initialAccumulatorValue = 0.1) {\n        super();\n        this.learningRate = learningRate;\n        this.initialAccumulatorValue = initialAccumulatorValue;\n        this.accumulatedGrads = [];\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            if (this.accumulatedGrads[i] == null) {\n                const trainable = false;\n                this.accumulatedGrads[i] = {\n                    originalName: `${name}/accumulator`,\n                    variable: tidy(() => fill(value.shape, this.initialAccumulatorValue)\n                        .variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedGrad = this.accumulatedGrads[i].variable;\n            tidy(() => {\n                const newAccumulatedGrad = add(accumulatedGrad, square(gradient));\n                accumulatedGrad.assign(newAccumulatedGrad);\n                const newValue = add(mul(div(gradient, sqrt(add(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedGrads != null) {\n            dispose(this.accumulatedGrads.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        return [await this.saveIterations()].concat(this.accumulatedGrads.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const trainable = false;\n        this.accumulatedGrads = weightValues.map(v => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'initialAccumulatorValue': this.initialAccumulatorValue,\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['initialAccumulatorValue']);\n    }\n}\n/** @nocollapse */\nAdagradOptimizer.className = 'Adagrad'; // Note: Name matters for Python compatibility.\nregisterClass(AdagradOptimizer);\n//# sourceMappingURL=adagrad_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { pow } from '../ops/pow';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\nexport class AdamOptimizer extends Optimizer {\n    constructor(learningRate, beta1, beta2, epsilon = null) {\n        super();\n        this.learningRate = learningRate;\n        this.beta1 = beta1;\n        this.beta2 = beta2;\n        this.epsilon = epsilon;\n        this.accumulatedFirstMoment = [];\n        this.accumulatedSecondMoment = [];\n        tidy(() => {\n            // accB* will be updated by batch.\n            this.accBeta1 = scalar(beta1).variable();\n            this.accBeta2 = scalar(beta2).variable();\n        });\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const varNames = Array.isArray(variableGradients) ?\n            variableGradients.map(v => v.name) :\n            Object.keys(variableGradients);\n        tidy(() => {\n            const oneMinusAccBeta1 = sub(1, this.accBeta1);\n            const oneMinusAccBeta2 = sub(1, this.accBeta2);\n            varNames.forEach((name, i) => {\n                const value = ENGINE.registeredVariables[name];\n                const trainable = false;\n                if (this.accumulatedFirstMoment[i] == null) {\n                    this.accumulatedFirstMoment[i] = {\n                        originalName: `${name}/m`,\n                        variable: tidy(() => zerosLike(value).variable(trainable))\n                    };\n                }\n                if (this.accumulatedSecondMoment[i] == null) {\n                    this.accumulatedSecondMoment[i] = {\n                        originalName: `${name}/v`,\n                        variable: tidy(() => zerosLike(value).variable(trainable))\n                    };\n                }\n                const gradient = Array.isArray(variableGradients) ?\n                    variableGradients[i].tensor :\n                    variableGradients[name];\n                if (gradient == null) {\n                    return;\n                }\n                const firstMoment = this.accumulatedFirstMoment[i].variable;\n                const secondMoment = this.accumulatedSecondMoment[i].variable;\n                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n                const newSecondMoment = add(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));\n                const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);\n                const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);\n                firstMoment.assign(newFirstMoment);\n                secondMoment.assign(newSecondMoment);\n                const newValue = add(mul(div(biasCorrectedFirstMoment, add(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);\n                value.assign(newValue);\n            });\n            this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n            this.accBeta2.assign(mul(this.accBeta2, this.beta2));\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.accBeta1.dispose();\n        this.accBeta2.dispose();\n        if (this.accumulatedFirstMoment != null) {\n            dispose(this.accumulatedFirstMoment.map(v => v.variable));\n        }\n        if (this.accumulatedSecondMoment != null) {\n            dispose(this.accumulatedSecondMoment.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        tidy(() => {\n            this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));\n            this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));\n        });\n        const variableCount = weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedFirstMoment =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedSecondMoment =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'beta1': this.beta1,\n            'beta2': this.beta2,\n            'epsilon': this.epsilon,\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon']);\n    }\n}\n/** @nocollapse */\nAdamOptimizer.className = 'Adam'; // Note: Name matters for Python compatibility.\nregisterClass(AdamOptimizer);\n//# sourceMappingURL=adam_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { abs } from '../ops/abs';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { maximum } from '../ops/maximum';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\nexport class AdamaxOptimizer extends Optimizer {\n    constructor(learningRate, beta1, beta2, epsilon = null, decay = 0.0) {\n        super();\n        this.learningRate = learningRate;\n        this.beta1 = beta1;\n        this.beta2 = beta2;\n        this.epsilon = epsilon;\n        this.decay = decay;\n        this.accumulatedFirstMoment = [];\n        this.accumulatedWeightedInfNorm = [];\n        tidy(() => {\n            this.iteration = scalar(0).variable();\n            this.accBeta1 = scalar(beta1).variable();\n        });\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        tidy(() => {\n            const oneMinusAccBeta1 = sub(1, this.accBeta1);\n            const lr = div(-this.learningRate, add(mul(this.iteration, this.decay), 1));\n            variableNames.forEach((name, i) => {\n                const value = ENGINE.registeredVariables[name];\n                const trainable = false;\n                if (this.accumulatedFirstMoment[i] == null) {\n                    this.accumulatedFirstMoment[i] = {\n                        originalName: `${name}/m`,\n                        variable: zerosLike(value).variable(trainable)\n                    };\n                }\n                if (this.accumulatedWeightedInfNorm[i] == null) {\n                    this.accumulatedWeightedInfNorm[i] = {\n                        originalName: `${name}/v`,\n                        variable: zerosLike(value).variable(trainable)\n                    };\n                }\n                const gradient = Array.isArray(variableGradients) ?\n                    variableGradients[i].tensor :\n                    variableGradients[name];\n                if (gradient == null) {\n                    return;\n                }\n                const firstMoment = this.accumulatedFirstMoment[i].variable;\n                const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;\n                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n                const ut0 = mul(weightedInfNorm, this.beta2);\n                const ut1 = abs(gradient);\n                const newWeightedInfNorm = maximum(ut0, ut1);\n                firstMoment.assign(newFirstMoment);\n                weightedInfNorm.assign(newWeightedInfNorm);\n                const newValue = add(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add(newWeightedInfNorm, this.epsilon))), value);\n                value.assign(newValue);\n            });\n            this.iteration.assign(add(this.iteration, 1));\n            this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.accBeta1.dispose();\n        this.iteration.dispose();\n        if (this.accumulatedFirstMoment != null) {\n            dispose(this.accumulatedFirstMoment.map(v => v.variable));\n        }\n        if (this.accumulatedWeightedInfNorm != null) {\n            dispose(this.accumulatedWeightedInfNorm.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        throw new Error('getWeights() is not implemented for Adamax yet.');\n    }\n    async setWeights(weightValues) {\n        throw new Error('setWeights() is not implemented for Adamax yet.');\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'beta1': this.beta1,\n            'beta2': this.beta2,\n            'epsilon': this.epsilon,\n            'decay': this.decay\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon'], config['decay']);\n    }\n}\n/** @nocollapse */\nAdamaxOptimizer.className = 'Adamax'; // Note: Name matters for Python compatbility.\nregisterClass(AdamaxOptimizer);\n//# sourceMappingURL=adamax_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { keep, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class SGDOptimizer extends Optimizer {\n    constructor(learningRate) {\n        super();\n        this.learningRate = learningRate;\n        this.setLearningRate(learningRate);\n    }\n    applyGradients(variableGradients) {\n        const varNames = Array.isArray(variableGradients) ?\n            variableGradients.map(v => v.name) :\n            Object.keys(variableGradients);\n        varNames.forEach((name, i) => {\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const value = ENGINE.registeredVariables[name];\n            tidy(() => {\n                const newValue = add(mul(this.c, gradient), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    /**\n     * Sets the learning rate of the optimizer.\n     */\n    setLearningRate(learningRate) {\n        this.learningRate = learningRate;\n        if (this.c != null) {\n            this.c.dispose();\n        }\n        this.c = keep(scalar(-learningRate));\n    }\n    dispose() {\n        this.c.dispose();\n    }\n    async getWeights() {\n        return [await this.saveIterations()];\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        if (weightValues.length !== 0) {\n            throw new Error('SGD optimizer does not have settable weights.');\n        }\n    }\n    getConfig() {\n        return { 'learningRate': this.learningRate };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate']);\n    }\n}\n/** @nocollapse */\nSGDOptimizer.className = 'SGD'; // Note: Name matters for Python compatibility.\nregisterClass(SGDOptimizer);\n//# sourceMappingURL=sgd_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { SGDOptimizer } from './sgd_optimizer';\n/** @doclink Optimizer */\nexport class MomentumOptimizer extends SGDOptimizer {\n    constructor(learningRate, momentum, useNesterov = false) {\n        super(learningRate);\n        this.learningRate = learningRate;\n        this.momentum = momentum;\n        this.useNesterov = useNesterov;\n        this.accumulations = [];\n        this.m = scalar(this.momentum);\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            if (this.accumulations[i] == null) {\n                const trainable = false;\n                this.accumulations[i] = {\n                    originalName: `${name}/momentum`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const accumulation = this.accumulations[i].variable;\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            tidy(() => {\n                let newValue;\n                const newAccumulation = add(mul(this.m, accumulation), gradient);\n                if (this.useNesterov) {\n                    newValue = add(mul(this.c, add(gradient, mul(newAccumulation, this.m))), value);\n                }\n                else {\n                    newValue = add(mul(this.c, newAccumulation), value);\n                }\n                accumulation.assign(newAccumulation);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.m.dispose();\n        if (this.accumulations != null) {\n            dispose(this.accumulations.map(v => v.variable));\n        }\n    }\n    /**\n     * Sets the momentum of the optimizer.\n     *\n     * @param momentum\n     */\n    setMomentum(momentum) {\n        this.momentum = momentum;\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        return [await this.saveIterations()].concat(this.accumulations.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const trainable = false;\n        this.accumulations = weightValues.map(v => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'momentum': this.momentum,\n            'useNesterov': this.useNesterov\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['momentum'], config['useNesterov']);\n    }\n}\n/** @nocollapse */\nMomentumOptimizer.className = 'Momentum'; // Name matters for Python compatibility.\nregisterClass(MomentumOptimizer);\n//# sourceMappingURL=momentum_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class RMSPropOptimizer extends Optimizer {\n    constructor(learningRate, decay = 0.9, momentum = 0.0, epsilon = null, centered = false) {\n        super();\n        this.learningRate = learningRate;\n        this.decay = decay;\n        this.momentum = momentum;\n        this.epsilon = epsilon;\n        this.accumulatedMeanSquares = [];\n        this.accumulatedMoments = [];\n        this.accumulatedMeanGrads = [];\n        this.centered = centered;\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n        if (learningRate == null) {\n            throw new Error(`learningRate for RMSPropOptimizer must be defined.`);\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            const trainable = false;\n            if (this.accumulatedMeanSquares[i] == null) {\n                this.accumulatedMeanSquares[i] = {\n                    originalName: `${name}/rms`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedMoments[i] == null) {\n                this.accumulatedMoments[i] = {\n                    originalName: `${name}/momentum`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedMeanGrads[i] == null && this.centered) {\n                this.accumulatedMeanGrads[i] = {\n                    originalName: `${name}/mg`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;\n            const accumulatedMoments = this.accumulatedMoments[i].variable;\n            tidy(() => {\n                const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n                if (this.centered) {\n                    const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;\n                    // Centered gradient\n                    const newAccumulatedMeanGrad = add(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));\n                    const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add(square(newAccumulatedMeanGrad), this.epsilon))));\n                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), gradContribution);\n                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n                    accumulatedMeanGrad.assign(newAccumulatedMeanGrad);\n                    accumulatedMoments.assign(newAccumulatedMoments);\n                    const newValue = sub(value, newAccumulatedMoments);\n                    value.assign(newValue);\n                }\n                else {\n                    // Plain gradient\n                    const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add(newAccumulatedMeanSquare, this.epsilon))));\n                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n                    accumulatedMoments.assign(newAccumulatedMoments);\n                    const newValue = sub(value, newAccumulatedMoments);\n                    value.assign(newValue);\n                }\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedMeanSquares != null) {\n            dispose(this.accumulatedMeanSquares.map(v => v.variable));\n        }\n        if (this.accumulatedMeanGrads != null && this.centered) {\n            dispose(this.accumulatedMeanGrads.map(v => v.variable));\n        }\n        if (this.accumulatedMoments != null) {\n            dispose(this.accumulatedMoments.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];\n        if (this.centered) {\n            variables.push(...this.accumulatedMeanGrads);\n        }\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedMeanSquares =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedMoments =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        if (this.centered) {\n            this.accumulatedMeanGrads =\n                weightValues.slice(variableCount * 2, variableCount * 3)\n                    .map(v => ({\n                    originalName: v.name,\n                    variable: v.tensor.variable(trainable)\n                }));\n        }\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'decay': this.decay,\n            'momentum': this.momentum,\n            'epsilon': this.epsilon,\n            'centered': this.centered\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['decay'], config['momentum'], config['epsilon'], config['centered']);\n    }\n}\n/** @nocollapse */\nRMSPropOptimizer.className = 'RMSProp'; // Note: Name matters for Python compatibility.\nregisterClass(RMSPropOptimizer);\n//# sourceMappingURL=rmsprop_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AdadeltaOptimizer } from './adadelta_optimizer';\nimport { AdagradOptimizer } from './adagrad_optimizer';\nimport { AdamOptimizer } from './adam_optimizer';\nimport { AdamaxOptimizer } from './adamax_optimizer';\nimport { MomentumOptimizer } from './momentum_optimizer';\nimport { RMSPropOptimizer } from './rmsprop_optimizer';\nimport { SGDOptimizer } from './sgd_optimizer';\nexport class OptimizerConstructors {\n    /**\n     * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.\n     *\n     * ```js\n     * // Fit a quadratic function by learning the coefficients a, b, c.\n     * const xs = tf.tensor1d([0, 1, 2, 3]);\n     * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);\n     *\n     * const a = tf.scalar(Math.random()).variable();\n     * const b = tf.scalar(Math.random()).variable();\n     * const c = tf.scalar(Math.random()).variable();\n     *\n     * // y = a * x^2 + b * x + c.\n     * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);\n     * const loss = (pred, label) => pred.sub(label).square().mean();\n     *\n     * const learningRate = 0.01;\n     * const optimizer = tf.train.sgd(learningRate);\n     *\n     * // Train the model.\n     * for (let i = 0; i < 10; i++) {\n     *   optimizer.minimize(() => loss(f(xs), ys));\n     * }\n     *\n     * // Make predictions.\n     * console.log(\n     *     `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);\n     * const preds = f(xs).dataSync();\n     * preds.forEach((pred, i) => {\n     *   console.log(`x: ${i}, pred: ${pred}`);\n     * });\n     * ```\n     *\n     * @param learningRate The learning rate to use for the SGD algorithm.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static sgd(learningRate) {\n        return new SGDOptimizer(learningRate);\n    }\n    /**\n     * Constructs a `tf.MomentumOptimizer` that uses momentum gradient\n     * descent.\n     *\n     * See\n     * [http://proceedings.mlr.press/v28/sutskever13.pdf](\n     * http://proceedings.mlr.press/v28/sutskever13.pdf)\n     *\n     * @param learningRate The learning rate to use for the Momentum gradient\n     * descent algorithm.\n     * @param momentum The momentum to use for the momentum gradient descent\n     * algorithm.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static momentum(learningRate, momentum, useNesterov = false) {\n        return new MomentumOptimizer(learningRate, momentum, useNesterov);\n    }\n    /**\n     * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient\n     * descent. This implementation uses plain momentum and is not centered\n     * version of RMSProp.\n     *\n     * See\n     * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](\n     * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\n     *\n     * @param learningRate The learning rate to use for the RMSProp gradient\n     * descent algorithm.\n     * @param decay The discounting factor for the history/coming gradient.\n     * @param momentum The momentum to use for the RMSProp gradient descent\n     * algorithm.\n     * @param epsilon Small value to avoid zero denominator.\n     * @param centered If true, gradients are normalized by the estimated\n     * variance of the gradient.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static rmsprop(learningRate, decay = .9, momentum = 0.0, epsilon = null, centered = false) {\n        return new RMSPropOptimizer(learningRate, decay, momentum, epsilon, centered);\n    }\n    /**\n     * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.\n     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n     *\n     * @param learningRate The learning rate to use for the Adam gradient\n     * descent algorithm.\n     * @param beta1 The exponential decay rate for the 1st moment estimates.\n     * @param beta2 The exponential decay rate for the 2nd moment estimates.\n     * @param epsilon A small constant for numerical stability.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adam(learningRate = 0.001, beta1 = 0.9, beta2 = 0.999, epsilon = null) {\n        return new AdamOptimizer(learningRate, beta1, beta2, epsilon);\n    }\n    /**\n     * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.\n     * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)\n     *\n     * @param learningRate The learning rate to use for the Adadelta gradient\n     * descent algorithm.\n     * @param rho The learning rate decay over each update.\n     * @param epsilon A constant epsilon used to better condition the grad\n     * update.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adadelta(learningRate = .001, rho = .95, epsilon = null) {\n        return new AdadeltaOptimizer(learningRate, rho, epsilon);\n    }\n    /**\n     * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.\n     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n     *\n     * @param learningRate The learning rate to use for the Adamax gradient\n     * descent algorithm.\n     * @param beta1 The exponential decay rate for the 1st moment estimates.\n     * @param beta2 The exponential decay rate for the 2nd moment estimates.\n     * @param epsilon A small constant for numerical stability.\n     * @param decay The learning rate decay over each update.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adamax(learningRate = 0.002, beta1 = 0.9, beta2 = 0.999, epsilon = null, decay = 0.0) {\n        return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);\n    }\n    /**\n     * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.\n     * See\n     * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](\n     * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\n     * or\n     * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](\n     * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)\n     *\n     * @param learningRate The learning rate to use for the Adagrad gradient\n     * descent algorithm.\n     * @param initialAccumulatorValue Starting value for the accumulators, must be\n     * positive.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adagrad(learningRate, initialAccumulatorValue = 0.1) {\n        return new AdagradOptimizer(learningRate, initialAccumulatorValue);\n    }\n}\n//# sourceMappingURL=optimizer_constructors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// So typings can propagate.\nimport { AdadeltaOptimizer } from './optimizers/adadelta_optimizer';\nimport { AdagradOptimizer } from './optimizers/adagrad_optimizer';\nimport { AdamOptimizer } from './optimizers/adam_optimizer';\nimport { AdamaxOptimizer } from './optimizers/adamax_optimizer';\nimport { MomentumOptimizer } from './optimizers/momentum_optimizer';\nimport { OptimizerConstructors } from './optimizers/optimizer_constructors';\nimport { RMSPropOptimizer } from './optimizers/rmsprop_optimizer';\nimport { SGDOptimizer } from './optimizers/sgd_optimizer';\n// tslint:disable-next-line:no-unused-expression\n[MomentumOptimizer, SGDOptimizer, AdadeltaOptimizer, AdagradOptimizer,\n    RMSPropOptimizer, AdamaxOptimizer, AdamOptimizer];\nexport const train = {\n    sgd: OptimizerConstructors.sgd,\n    momentum: OptimizerConstructors.momentum,\n    adadelta: OptimizerConstructors.adadelta,\n    adagrad: OptimizerConstructors.adagrad,\n    rmsprop: OptimizerConstructors.rmsprop,\n    adamax: OptimizerConstructors.adamax,\n    adam: OptimizerConstructors.adam\n};\n//# sourceMappingURL=train.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst delayCallback = (() => {\n    if (typeof requestAnimationFrame !== 'undefined') {\n        return requestAnimationFrame;\n    }\n    else if (typeof setImmediate !== 'undefined') {\n        return setImmediate;\n    }\n    return (f) => f(); // no delays\n})();\n/**\n * Returns a promise that resolve when a requestAnimationFrame has completed.\n *\n * On Node.js this uses setImmediate instead of requestAnimationFrame.\n *\n * This is simply a sugar method so that users can do the following:\n * `await tf.nextFrame();`\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nfunction nextFrame() {\n    return new Promise(resolve => delayCallback(() => resolve()));\n}\nexport { nextFrame };\n//# sourceMappingURL=browser_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Returns the image center in pixels.\nexport function getImageCenter(center, imageHeight, imageWidth) {\n    const centerX = imageWidth * (typeof center === 'number' ? center : center[0]);\n    const centerY = imageHeight * (typeof center === 'number' ? center : center[1]);\n    return [centerX, centerY];\n}\n//# sourceMappingURL=rotate_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Gets the new shape of the input Tensor after it's been reshaped\n * to:\n * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),\n * inputShape[1], ..., inputShape[N-1]]\n *\n * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshaped(inputShape, blockShape, prod, batchToSpace = true) {\n    let reshaped = [];\n    if (batchToSpace) {\n        reshaped = reshaped.concat(blockShape.slice(0));\n        reshaped.push(inputShape[0] / prod);\n        reshaped = reshaped.concat(inputShape.slice(1));\n    }\n    else {\n        reshaped = reshaped.concat(inputShape[0]);\n        const spatialLength = blockShape.length;\n        for (let i = 0; i < spatialLength; ++i) {\n            reshaped =\n                reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);\n        }\n        reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n    }\n    return reshaped;\n}\n/**\n * Gets the permutation that will transpose the dimensions of the\n * reshaped tensor to shape:\n *\n * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,\n * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {\n    const permuted = [];\n    if (batchToSpace) {\n        permuted.push(blockShapeRank);\n        for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {\n            if (i <= 2 * blockShapeRank) {\n                permuted.push(i);\n                permuted.push(i - (blockShapeRank + 1));\n            }\n            else {\n                permuted.push(i);\n            }\n        }\n    }\n    else {\n        const permutedBeforeBatch = [];\n        const permutedAfterBatch = [];\n        for (let i = 1; i < reshapedRank; ++i) {\n            if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {\n                permutedAfterBatch.push(i);\n            }\n            else {\n                permutedBeforeBatch.push(i);\n            }\n        }\n        permuted.push(...permutedBeforeBatch);\n        permuted.push(0);\n        permuted.push(...permutedAfterBatch);\n    }\n    return permuted;\n}\n/**\n * Gets the shape of the reshaped and permuted input Tensor before any cropping\n * is applied.  The new shape will be:\n *\n * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,\n * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshapedPermuted(inputShape, blockShape, prod, batchToSpace = true) {\n    const reshapedPermuted = [];\n    if (batchToSpace) {\n        reshapedPermuted.push(inputShape[0] / prod);\n    }\n    else {\n        reshapedPermuted.push(inputShape[0] * prod);\n    }\n    for (let i = 1; i < inputShape.length; ++i) {\n        if (i <= blockShape.length) {\n            if (batchToSpace) {\n                reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);\n            }\n            else {\n                reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);\n            }\n        }\n        else {\n            reshapedPermuted.push(inputShape[i]);\n        }\n    }\n    return reshapedPermuted;\n}\n/**\n * Converts the crops argument into the beginning coordinates of a slice\n * operation.\n */\nexport function getSliceBeginCoords(crops, blockShape) {\n    const sliceBeginCoords = [0];\n    for (let i = 0; i < blockShape; ++i) {\n        sliceBeginCoords.push(crops[i][0]);\n    }\n    return sliceBeginCoords;\n}\n/**\n * Converts the crops argument into the size of a slice operation.  When\n * combined with getSliceBeginCoords this function allows the reshaped and\n * permuted Tensor to be cropped to its final output shape of:\n *\n * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,\n * inputShape[M] * blockShape[M-1] -crops[M-1,0] -\n * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getSliceSize(uncroppedShape, crops, blockShape) {\n    const sliceSize = uncroppedShape.slice(0, 1);\n    for (let i = 0; i < blockShape; ++i) {\n        sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);\n    }\n    return sliceSize;\n}\n//# sourceMappingURL=array_ops_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const SELU_SCALEALPHA = 1.7580993408473768599402175208123;\nexport const SELU_SCALE = 1.0507009873554804934193349852946;\n//# sourceMappingURL=selu_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const ERF_P = 0.3275911;\nexport const ERF_A1 = 0.254829592;\nexport const ERF_A2 = -0.284496736;\nexport const ERF_A3 = 1.421413741;\nexport const ERF_A4 = -1.453152027;\nexport const ERF_A5 = 1.061405429;\n//# sourceMappingURL=erf_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nexport function warn(...msg) {\n    if (!env().getBool('IS_TEST')) {\n        console.warn(...msg);\n    }\n}\nexport function log(...msg) {\n    if (!env().getBool('IS_TEST')) {\n        console.log(...msg);\n    }\n}\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Merges real and imaginary Float32Arrays into a single complex Float32Array.\n *\n * The memory layout is interleaved as follows:\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n * complex: [r0, i0, r1, i1, r2, i2]\n *\n * This is the inverse of splitRealAndImagArrays.\n *\n * @param real The real values of the complex tensor values.\n * @param imag The imag values of the complex tensor values.\n * @returns A complex tensor as a Float32Array with merged values.\n */\nexport function mergeRealAndImagArrays(real, imag) {\n    if (real.length !== imag.length) {\n        throw new Error(`Cannot merge real and imag arrays of different lengths. real:` +\n            `${real.length}, imag: ${imag.length}.`);\n    }\n    const result = new Float32Array(real.length * 2);\n    for (let i = 0; i < result.length; i += 2) {\n        result[i] = real[i / 2];\n        result[i + 1] = imag[i / 2];\n    }\n    return result;\n}\n/**\n * Splits a complex Float32Array into real and imag parts.\n *\n * The memory layout is interleaved as follows:\n * complex: [r0, i0, r1, i1, r2, i2]\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n *\n * This is the inverse of mergeRealAndImagArrays.\n *\n * @param complex The complex tensor values.\n * @returns An object with real and imag Float32Array components of the complex\n *     tensor.\n */\nexport function splitRealAndImagArrays(complex) {\n    const real = new Float32Array(complex.length / 2);\n    const imag = new Float32Array(complex.length / 2);\n    for (let i = 0; i < complex.length; i += 2) {\n        real[i / 2] = complex[i];\n        imag[i / 2] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Extracts even indexed complex values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithEvenIndex(complex) {\n    const len = Math.ceil(complex.length / 4);\n    const real = new Float32Array(len);\n    const imag = new Float32Array(len);\n    for (let i = 0; i < complex.length; i += 4) {\n        real[Math.floor(i / 4)] = complex[i];\n        imag[Math.floor(i / 4)] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Extracts odd indexed comple values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithOddIndex(complex) {\n    const len = Math.floor(complex.length / 4);\n    const real = new Float32Array(len);\n    const imag = new Float32Array(len);\n    for (let i = 2; i < complex.length; i += 4) {\n        real[Math.floor(i / 4)] = complex[i];\n        imag[Math.floor(i / 4)] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Get the map representing a complex value in the given array.\n * @param complex The complex tensor values.\n * @param index An index of the target complex value.\n */\nexport function getComplexWithIndex(complex, index) {\n    const real = complex[index * 2];\n    const imag = complex[index * 2 + 1];\n    return { real, imag };\n}\n/**\n * Insert a given complex value into the TypedArray.\n * @param data The array in which the complex value is inserted.\n * @param c The complex value to be inserted.\n * @param index An index of the target complex value.\n */\nexport function assignToTypedArray(data, real, imag, index) {\n    data[index * 2] = real;\n    data[index * 2 + 1] = imag;\n}\n/**\n * Make the list of exponent terms used by FFT.\n */\nexport function exponents(n, inverse) {\n    const real = new Float32Array(n / 2);\n    const imag = new Float32Array(n / 2);\n    for (let i = 0; i < Math.ceil(n / 2); i++) {\n        const x = (inverse ? 2 : -2) * Math.PI * (i / n);\n        real[i] = Math.cos(x);\n        imag[i] = Math.sin(x);\n    }\n    return { real, imag };\n}\n/**\n * Make the exponent term used by FFT.\n */\nexport function exponent(k, n, inverse) {\n    const x = (inverse ? 2 : -2) * Math.PI * (k / n);\n    const real = Math.cos(x);\n    const imag = Math.sin(x);\n    return { real, imag };\n}\n//# sourceMappingURL=complex_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { cast } from '../ops/cast';\nimport { scalar } from '../ops/scalar';\nimport { tensor1d } from '../ops/tensor1d';\nimport { zeros } from '../ops/zeros';\nimport { hasEncodingLoss, makeZerosTypedArray } from '../util';\n// Utilities needed by backend consumers of tf-core.\nexport * from '../ops/axis_util';\nexport * from '../ops/broadcast_util';\nexport * from '../ops/concat_util';\nexport * from '../ops/conv_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/reduce_util';\nimport * as slice_util from '../ops/slice_util';\nexport { slice_util };\nexport { upcastType } from '../types';\nexport * from '../ops/rotate_util';\nexport * from '../ops/array_ops_util';\nexport * from '../ops/gather_nd_util';\nexport * from '../ops/scatter_nd_util';\nexport * from '../ops/selu_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/erf_util';\nexport * from '../log';\nexport * from '../backends/complex_util';\nexport * from '../ops/split_util';\nimport * as segment_util from '../ops/segment_util';\nexport { segment_util };\nexport function castTensor(x, dtype, backend) {\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return x.clone();\n        }\n        const zerosTensor = zeros(x.shape);\n        const floatX = cast(x, 'float32');\n        const result = backend.complex(floatX, zerosTensor);\n        zerosTensor.dispose();\n        floatX.dispose();\n        return result;\n    }\n    if (!hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        return ENGINE.makeTensorFromDataId(x.dataId, x.shape, dtype);\n    }\n    if (x.dtype === 'complex64') {\n        const real = backend.real(x);\n        const result = cast(real, dtype);\n        real.dispose();\n        return result;\n    }\n    if (dtype === 'int32') {\n        return backend.int(x);\n    }\n    else if (dtype === 'bool') {\n        const zero = scalar(0, x.dtype);\n        const result = backend.notEqual(x, zero);\n        zero.dispose();\n        return result;\n    }\n    else {\n        throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n    }\n}\nexport function reshapeTensor(x, shape) {\n    return ENGINE.makeTensorFromDataId(x.dataId, shape, x.dtype);\n}\nexport function linspaceImpl(start, stop, num) {\n    const step = (stop - start) / (num - 1);\n    const values = makeZerosTypedArray(num, 'float32');\n    values[0] = start;\n    for (let i = 1; i < values.length; i++) {\n        values[i] = values[i - 1] + step;\n    }\n    return tensor1d(values, 'float32');\n}\n//# sourceMappingURL=backend_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { slice } from '../ops/slice';\n// TODO(annxingyuan): Use this helper in WASM Split kernel once intermediate\n// kernels have been modularized in WebGL and CPU\n// https://github.com/tensorflow/tfjs/issues/2822.\n/** Shared implementation of the split kernel across WebGL and CPU. */\nexport function split(x, sizeSplits, axis) {\n    const begin = new Array(x.rank).fill(0);\n    const size = x.shape.slice();\n    return sizeSplits.map(s => {\n        const sliceSize = [...size];\n        sliceSize[axis] = s;\n        const sliceT = slice(x, begin, sliceSize);\n        begin[axis] += s;\n        return sliceT;\n    });\n}\n//# sourceMappingURL=split_shared.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * An implementation of the tile kernel shared between webgl and cpu for string\n * tensors only.\n */\nimport { buffer } from '../ops/buffer';\nexport function tile(xBuf, reps) {\n    const newShape = new Array(xBuf.rank);\n    for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = xBuf.shape[i] * reps[i];\n    }\n    const result = buffer(newShape, xBuf.dtype);\n    for (let i = 0; i < result.values.length; ++i) {\n        const newLoc = result.indexToLoc(i);\n        const originalLoc = new Array(xBuf.rank);\n        for (let j = 0; j < originalLoc.length; j++) {\n            originalLoc[j] = newLoc[j] % xBuf.shape[j];\n        }\n        const originalIndex = xBuf.locToIndex(originalLoc);\n        result.values[i] = xBuf.values[originalIndex];\n    }\n    return result.toTensor();\n}\n//# sourceMappingURL=tile_impl.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the TopK kernel shared between webgl and cpu. */\nimport { tensor } from '../ops/tensor';\nimport { getTypedArrayFromDType } from '../util';\nexport function topkImpl(x, xShape, xDtype, k, sorted) {\n    // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.\n    const lastDim = xShape[xShape.length - 1];\n    const [batch, size] = [x.length / lastDim, lastDim];\n    const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);\n    const allTopKIndices = getTypedArrayFromDType('int32', batch * k);\n    for (let b = 0; b < batch; b++) {\n        const offset = b * size;\n        const vals = x.subarray(offset, offset + size);\n        const valAndInd = [];\n        for (let i = 0; i < vals.length; i++) {\n            valAndInd.push({ value: vals[i], index: i });\n        }\n        valAndInd.sort((a, b) => b.value - a.value);\n        const outOffset = b * k;\n        const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n        const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n        for (let i = 0; i < k; i++) {\n            topKVals[i] = valAndInd[i].value;\n            topKIndices[i] = valAndInd[i].index;\n        }\n    }\n    // Reshape back to the original input shape, except that the last\n    // dimension is k.\n    const outputShape = xShape.slice();\n    outputShape[outputShape.length - 1] = k;\n    return [\n        tensor(allTopKVals, outputShape, xDtype),\n        tensor(allTopKIndices, outputShape, 'int32')\n    ];\n}\n//# sourceMappingURL=topk_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { nonMaxSuppressionV3Impl, nonMaxSuppressionV4Impl, nonMaxSuppressionV5Impl } from './non_max_suppression_impl';\nexport { split } from './split_shared';\nexport { tile } from './tile_impl';\nexport { topkImpl } from './topk_impl';\nexport { whereImpl } from './where_impl';\n//# sourceMappingURL=kernel_impls.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// base.ts is tfjs-core without auto registration of things like flags,\n// gradients, chained ops or the opHandler. See base_side_effects.ts for parts\n// tfjs core that are required side effects.\n/**\n * @fileoverview\n * @suppress {partialAlias} Optimization disabled due to passing the module\n * object into a function below:\n *\n *   import * as ops from './ops/ops';\n *   setOpHandler(ops);\n */\n// Serialization.\nimport * as io from './io/io';\nimport * as math from './math';\nimport * as browser from './ops/browser';\nimport * as gather_util from './ops/gather_nd_util';\nimport * as scatter_util from './ops/scatter_nd_util';\nimport * as slice_util from './ops/slice_util';\nimport * as serialization from './serialization';\nimport * as tensor_util from './tensor_util';\nimport * as test_util from './test_util';\nimport * as util from './util';\nimport { version } from './version';\n// Optimizers.\nexport { AdadeltaOptimizer } from './optimizers/adadelta_optimizer';\nexport { AdagradOptimizer } from './optimizers/adagrad_optimizer';\nexport { AdamOptimizer } from './optimizers/adam_optimizer';\nexport { AdamaxOptimizer } from './optimizers/adamax_optimizer';\nexport { MomentumOptimizer } from './optimizers/momentum_optimizer';\nexport { Optimizer } from './optimizers/optimizer';\nexport { RMSPropOptimizer } from './optimizers/rmsprop_optimizer';\nexport { SGDOptimizer } from './optimizers/sgd_optimizer';\nexport { Tensor, TensorBuffer, Variable } from './tensor';\nexport { Rank, sumOutType, upcastType } from './types';\nexport * from './ops/ops';\nexport { Reduction } from './ops/loss_ops_utils';\nexport * from './train';\nexport * from './globals';\nexport * from './kernel_registry';\nexport { customGrad, grad, grads, valueAndGrad, valueAndGrads, variableGrads } from './gradients';\nexport { Environment, env, ENV } from './environment';\nexport { version as version_core };\n// Top-level method exports.\nexport { nextFrame } from './browser_util';\n// Second level exports.\nimport * as backend_util from './backends/backend_util';\nimport * as device_util from './device_util';\nexport { browser, io, math, serialization, test_util, util, backend_util, tensor_util, slice_util, gather_util, scatter_util, device_util };\nimport * as kernel_impls from './backends/kernel_impls';\nexport { kernel_impls };\n// Backend specific.\nexport { KernelBackend, DataStorage } from './backends/backend';\n// Export all kernel names / info.\nexport * from './kernel_names';\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const absGradConfig = {\n    kernelName: Abs,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, step(cast(x, 'float32'), -1)) };\n    }\n};\n//# sourceMappingURL=Abs_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acos } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { neg } from '../ops/neg';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const acosGradConfig = {\n    kernelName: Acos,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = square(cast(x, 'float32'));\n                const b = sqrt(sub(scalar(1), a));\n                return neg(div(dy, b));\n            }\n        };\n    }\n};\n//# sourceMappingURL=Acos_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acosh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const acoshGradConfig = {\n    kernelName: Acosh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = sqrt(sub(square(cast(x, 'float32')), 1));\n                return div(dy, a);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Acosh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const addGradConfig = {\n    kernelName: Add,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Add_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AddN } from '../kernel_names';\nexport const addNGradConfig = {\n    kernelName: AddN,\n    saveAllInputs: true,\n    gradFunc: (dy, saved) => {\n        const ders = {};\n        saved.forEach((_, i) => {\n            ders[i] = () => dy.clone();\n        });\n        return ders;\n    }\n};\n//# sourceMappingURL=AddN_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ArgMax } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const argMaxGradConfig = {\n    kernelName: ArgMax,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => zerosLike(x) };\n    }\n};\n//# sourceMappingURL=ArgMax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ArgMin } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const argMinGradConfig = {\n    kernelName: ArgMin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => zerosLike(x) };\n    }\n};\n//# sourceMappingURL=ArgMin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asin } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const asinGradConfig = {\n    kernelName: Asin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, 'float32'))))) };\n    }\n};\n//# sourceMappingURL=Asin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asinh } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nexport const asinhGradConfig = {\n    kernelName: Asinh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = sqrt(add(scalar(1), square(cast(x, 'float32'))));\n                return div(dy, a);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Asinh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan2 } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const atan2GradConfig = {\n    kernelName: Atan2,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const d = add(square(a), square(b));\n            let res = mul(dy, div(b, d));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            const d = add(square(a), square(b));\n            let res = neg(mul(dy, div(a, d)));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Atan2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nexport const atanGradConfig = {\n    kernelName: Atan,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, add(square(cast(x, 'float32')), 1)) };\n    }\n};\n//# sourceMappingURL=Atan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atanh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { scalar } from '../ops/scalar';\nexport const atanhGradConfig = {\n    kernelName: Atanh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, sub(scalar(1), square(cast(x, 'float32')))) };\n    }\n};\n//# sourceMappingURL=Atanh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool3DBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of a 3d avg pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0. The dilation\n *     rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction avgPool3dBackprop_(dy, input, filterSize, strides, dilations = [1, 1, 1], pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'avgPool3dBackprop');\n    const $input = convertToTensor(input, 'input', 'avgPool3dBackprop');\n    let dy5D = $dy;\n    let input5D = $input;\n    let reshapedTo5D = false;\n    if ($input.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n        input5D = reshape($input, [\n            1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n        ]);\n    }\n    util.assert(dy5D.rank === 5, () => `Error in avgPool3dBackprop: dy must be rank 5 but got rank ` +\n        `${dy5D.rank}.`);\n    util.assert(input5D.rank === 5, () => `Error in avgPool3dBackprop: input must be rank 5 but got rank ` +\n        `${input5D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool3dBackprop: Either strides or dilations ' +\n        `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n        return backend.avgPool3dBackprop(dy5D, input5D, convInfo);\n    };\n    const inputs = { dy: dy5D, input: input5D };\n    const attrs = { filterSize, strides, dilations, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool3DBackprop, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const avgPool3dBackprop = op({ avgPool3dBackprop_ });\n//# sourceMappingURL=avg_pool_3d_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool3D } from '../kernel_names';\nimport { avgPool3dBackprop } from '../ops/avg_pool_3d_backprop';\nexport const avgPool3DGradConfig = {\n    kernelName: AvgPool3D,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { filterSize, strides, dilations, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1, 1] : dilations;\n        return {\n            x: () => avgPool3dBackprop(dy, x, filterSize, strides, $dilations, pad, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=AvgPool3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPoolBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of an 2D avg pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The input image, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction avgPoolBackprop_(dy, input, filterSize, strides, pad) {\n    const $dy = convertToTensor(dy, 'dy', 'avgPoolBackprop');\n    const $input = convertToTensor(input, 'input', 'avgPoolBackprop');\n    util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n    let input4D = $input;\n    let dy4D = $dy;\n    let reshapedTo4D = false;\n    if ($input.rank === 3) {\n        reshapedTo4D = true;\n        input4D =\n            reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);\n        dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);\n    }\n    util.assert(dy4D.rank === 4, () => `Error in avgPoolBackprop: dy must be rank 4 but got rank ` +\n        `${dy4D.rank}.`);\n    util.assert(input4D.rank === 4, () => `Error in avgPoolBackprop: input must be rank 4 but got rank ` +\n        `${input4D.rank}.`);\n    const forward = backend => {\n        const convInfo = conv_util.computePool2DInfo(input4D.shape, filterSize, strides, 1 /* dilations */, pad);\n        return backend.avgPoolBackprop(dy4D, input4D, convInfo);\n    };\n    const inputs = { dy: dy4D, input: input4D };\n    const attrs = { filterSize, strides, pad };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, AvgPoolBackprop, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const avgPoolBackprop = op({ avgPoolBackprop_ });\n//# sourceMappingURL=avg_pool_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool } from '../kernel_names';\nimport { avgPoolBackprop } from '../ops/avg_pool_backprop';\nexport const avgPoolGradConfig = {\n    kernelName: AvgPool,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { filterSize, strides, pad } = attrs;\n        return {\n            x: () => avgPoolBackprop(dy, x, filterSize, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=AvgPool_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchMatMul } from '../kernel_names';\nimport { matMul } from '../ops/mat_mul';\nexport const batchMatMulGradConfig = {\n    kernelName: BatchMatMul,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved, attrs) => {\n        const [a, b] = saved;\n        const { transposeA, transposeB } = attrs;\n        if (!transposeA && !transposeB) {\n            return {\n                a: () => matMul(dy, b, false, true),\n                b: () => matMul(a, dy, true, false)\n            };\n        }\n        else if (!transposeA && transposeB) {\n            return {\n                a: () => matMul(dy, b, false, false),\n                b: () => matMul(dy, a, true, false)\n            };\n        }\n        else if (transposeA && !transposeB) {\n            return {\n                a: () => matMul(b, dy, false, true),\n                b: () => matMul(a, dy, false, false)\n            };\n        }\n        else {\n            return {\n                a: () => matMul(b, dy, true, true),\n                b: () => matMul(dy, a, true, true)\n            };\n        }\n    }\n};\n//# sourceMappingURL=BatchMatMul_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchToSpaceND } from '../kernel_names';\nimport { spaceToBatchND } from '../ops/space_to_batch_nd';\nexport const batchToSpaceNDGradConfig = {\n    kernelName: BatchToSpaceND,\n    gradFunc: (dy, saved, attrs) => {\n        const { blockShape, crops } = attrs;\n        return { x: () => spaceToBatchND(dy, blockShape, crops) };\n    }\n};\n//# sourceMappingURL=BatchToSpaceND_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BroadcastTo } from '../kernel_names';\nimport { sum } from '../ops/sum';\nexport const broadcastToGradConfig = {\n    kernelName: BroadcastTo,\n    gradFunc: (dy, saved, attrs) => {\n        const broadCastToAttrs = attrs;\n        const inputShape = broadCastToAttrs.inputShape;\n        const outputShape = broadCastToAttrs.shape;\n        const reps = Array.from(outputShape);\n        for (let i = inputShape.length - 1; i >= 0; i--) {\n            if (inputShape[i] === outputShape[i]) {\n                reps[i] = 1;\n            }\n            else if (inputShape[i] !== 1) {\n                throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);\n            }\n        }\n        const axes = [];\n        for (let i = 0; i < reps.length; i++) {\n            if (reps[i] > 1) {\n                axes.push(i);\n            }\n        }\n        return { x: () => sum(dy, axes, true /* keepDims */) };\n    }\n};\n//# sourceMappingURL=BroadcastTo_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cast } from '../kernel_names';\nexport const castGradConfig = {\n    kernelName: Cast,\n    gradFunc: (dy) => {\n        return { x: () => dy.clone() };\n    }\n};\n//# sourceMappingURL=Cast_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const ceilGradConfig = {\n    kernelName: Ceil,\n    gradFunc: (dy) => {\n        // TODO(manrajgrover): Return null for gradients when backprop supports it.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Ceil_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ClipByValue } from '../kernel_names';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { lessEqual } from '../ops/less_equal';\nimport { logicalAnd } from '../ops/logical_and';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const clipByValueGradConfig = {\n    kernelName: ClipByValue,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { clipValueMin, clipValueMax } = attrs;\n        return {\n            x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy)),\n        };\n    }\n};\n//# sourceMappingURL=ClipByValue_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Concat } from '../kernel_names';\nimport { split } from '../ops/split';\nimport { parseAxisParam } from '../util';\nexport const concatGradConfig = {\n    kernelName: Concat,\n    saveAllInputs: true,\n    gradFunc: (dy, saved, attrs) => {\n        const shapes = saved.map(t => t.shape);\n        const { axis } = attrs;\n        const $axis = parseAxisParam(axis, saved[0].shape)[0];\n        const sizeSplits = shapes.map(s => s[$axis]);\n        const derTensors = split(dy, sizeSplits, $axis);\n        return derTensors.map(t => () => t);\n    }\n};\n//# sourceMappingURL=Concat_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv2D } from '../kernel_names';\nimport { conv2DBackpropFilter } from '../ops/conv2d_backprop_filter';\nimport { conv2DBackpropInput } from '../ops/conv2d_backprop_input';\nimport * as conv_util from '../ops/conv_util';\nimport * as util from '../util';\nexport const conv2DGradConfig = {\n    kernelName: Conv2D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x4D, $filter] = saved;\n        const { dilations, strides, pad, dataFormat } = attrs;\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of conv2D: dilation rates greater than 1 ' +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n        return {\n            x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad, dataFormat),\n            filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad, dataFormat)\n        };\n    }\n};\n//# sourceMappingURL=Conv2D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv2DBackpropInput } from '../kernel_names';\nimport { conv2d } from '../ops/conv2d';\nimport { conv2DBackpropFilter } from '../ops/conv2d_backprop_filter';\nexport const conv2DBackpropInputGradConfig = {\n    kernelName: Conv2DBackpropInput,\n    inputsToSave: ['dy', 'filter'],\n    gradFunc: (ddx, saved, attrs) => {\n        const [dy, filter] = saved;\n        const { strides, pad, dataFormat, dimRoundingMode } = attrs;\n        return {\n            dy: () => conv2d(ddx, filter, strides, pad, dataFormat, 1 /* dilations */, dimRoundingMode),\n            filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad, dataFormat, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=Conv2DBackpropInput_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3DBackpropFilterV2 } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the filter of a 3D convolution.\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     [batch, depth, height, width, inChannels]. If rank 4, batch of 1 is\n *     assumed.\n * @param dy The dy image, of rank 5 or rank 4, of shape\n *     [batch, depth, height, width, outDepth]. If rank 4, batch of 1 is\n *     assumed.\n * @param filterShape The shape of the filter, length 5,\n *     [filterDepth, filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideDepth, strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction conv3DBackpropFilter_(x, dy, filterShape, strides, pad) {\n    let x5D = x;\n    if (x.rank === 4) {\n        x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);\n    }\n    let dy5D = dy;\n    if (dy5D.rank === 4) {\n        dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ` +\n        `${x5D.shape}.`);\n    util.assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ` +\n        `${dy5D.shape}.`);\n    util.assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ` +\n        `${filterShape}.`);\n    util.assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must ` +\n        `match input depth in filter (${filterShape[3]}.`);\n    util.assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must ` +\n        `match output depth for filter (${filterShape[4]}).`);\n    const forward = backend => {\n        const dilations = 1;\n        const convInfo = conv_util.computeConv3DInfo(x5D.shape, filterShape, strides, dilations, pad);\n        return backend.conv3dDerFilter(x5D, dy5D, convInfo);\n    };\n    const inputs = { x: x5D, y: dy5D };\n    const attrs = { strides, pad };\n    return ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropFilterV2, attrs);\n}\nexport const conv3DBackpropFilter = op({ conv3DBackpropFilter_ });\n//# sourceMappingURL=conv3d_backprop_filter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv3D } from '../kernel_names';\nimport { conv3DBackpropFilter } from '../ops/conv3d_backprop_filter';\nimport { conv3DBackpropInput } from '../ops/conv3d_backprop_input';\nimport { tupleValuesAreOne } from '../ops/conv_util';\nimport * as util from '../util';\nexport const conv3DGradConfig = {\n    kernelName: Conv3D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const { dilations, strides, pad } = attrs;\n        util.assert(tupleValuesAreOne(dilations), () => 'Error in gradient of conv3D: dilation rates greater than 1 are ' +\n            `not yet supported in gradients. Got dilations '${dilations}'`);\n        const [x5D, $filter] = saved;\n        return {\n            x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad),\n            filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=Conv3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { sin } from '../ops/sin';\nexport const cosGradConfig = {\n    kernelName: Cos,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(neg(sin(cast(x, 'float32'))), dy) };\n    }\n};\n//# sourceMappingURL=Cos_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cosh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { sinh } from '../ops/sinh';\nexport const coshGradConfig = {\n    kernelName: Cosh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(sinh(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Cosh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cumsum } from '../kernel_names';\nimport { getAxesPermutation } from '../ops/axis_util';\nimport { cumsum } from '../ops/cumsum';\nimport { transpose } from '../ops/transpose';\nexport const cumsumGradConfig = {\n    kernelName: Cumsum,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { axis, exclusive, reverse } = attrs;\n        return {\n            x: () => {\n                const permutation = getAxesPermutation([axis], x.rank);\n                let out = cumsum(dy, axis, exclusive, !reverse);\n                if (permutation != null) {\n                    out = transpose(out, permutation);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Cumsum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { DepthwiseConv2dNative } from '../kernel_names';\nimport * as conv_util from '../ops/conv_util';\nimport { depthwiseConv2dNativeBackpropFilter } from '../ops/depthwise_conv2d_native_backprop_filter';\nimport { depthwiseConv2dNativeBackpropInput } from '../ops/depthwise_conv2d_native_backprop_input';\nimport * as util from '../util';\nexport const depthwiseConv2dNativeGradConfig = {\n    kernelName: DepthwiseConv2dNative,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const { dilations, strides, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1] : dilations;\n        util.assert(conv_util.tupleValuesAreOne($dilations), () => 'Error in gradient of depthwiseConv2dNative: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${$dilations}'`);\n        const [x, filter] = saved;\n        util.assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be ` +\n            `rank 4, but got rank ${x.rank}.`);\n        util.assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be ` +\n            `rank 4, but got rank ${filter.rank}.`);\n        util.assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input ` +\n            `channels (${x.shape[3]}) must match the inChannels dimension ` +\n            `in filter ${filter.shape[2]}.`);\n        util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, $dilations), () => 'Error in gradient of depthwiseConv2d: Either strides or ' +\n            `dilations must be  1. Got strides ${strides} and dilations ` +\n            `'${$dilations}'.`);\n        if (dimRoundingMode != null) {\n            util.assert(util.isInt(pad), () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n                `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n        }\n        const convInfo = conv_util.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad, dimRoundingMode, true /* depthwise */);\n        return {\n            x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, convInfo),\n            filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, convInfo),\n        };\n    }\n};\n//# sourceMappingURL=DepthwiseConv2dNative_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput } from '../kernel_names';\nexport const dilation2dGradConfig = {\n    kernelName: Dilation2D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, filter] = saved;\n        const inputInputs = { x, filter, dy };\n        const filterInputs = { x, filter, dy };\n        return {\n            x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),\n            filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)\n        };\n    }\n};\n//# sourceMappingURL=Dilation2D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const divGradConfig = {\n    kernelName: Div,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = div(dy, cast(b, 'float32'));\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            let res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = reshape(sum(res, reduceAxes), b.shape);\n            }\n            const tmp = square(b);\n            return neg(div(res, cast(tmp, 'float32')));\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Div_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Elu, EluGrad } from '../kernel_names';\nexport const eluGradConfig = {\n    kernelName: Elu,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        const backPropKernelFunc = (backend) => {\n            return backend.eluDer(dy, y);\n        };\n        const inputs = { dy, y };\n        return {\n            x: () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* grad */, EluGrad)\n        };\n    }\n};\n//# sourceMappingURL=Elu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Erf } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { square } from '../ops/square';\nexport const erfGradConfig = {\n    kernelName: Erf,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));\n        return { x: () => mul(dy, a) };\n    }\n};\n//# sourceMappingURL=Erf_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '../kernel_names';\nimport { mul } from '../ops/mul';\nexport const expGradConfig = {\n    kernelName: Exp,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(dy, y) };\n    }\n};\n//# sourceMappingURL=Exp_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nexport const expm1GradConfig = {\n    kernelName: Expm1,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, exp(x)) };\n    }\n};\n//# sourceMappingURL=Expm1_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const floorGradConfig = {\n    kernelName: Floor,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Floor_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FloorDiv } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const floorDivGradConfig = {\n    kernelName: FloorDiv,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = div(dy, cast(b, 'float32'));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            let res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = reshape(sum(res, reduceAxes), b.shape);\n            }\n            const tmp = square(b);\n            return neg(div(res, cast(tmp, 'float32')));\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=FloorDiv_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedBatchNorm } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { getReductionAxes } from '../ops/broadcast_util';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { rsqrt } from '../ops/rsqrt';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nimport { tile } from '../ops/tile';\nexport const fusedBatchNormGradConfig = {\n    kernelName: FusedBatchNorm,\n    inputsToSave: ['x', 'mean', 'variance', 'scale'],\n    gradFunc: (dy, saved, attrs) => {\n        const { varianceEpsilon } = attrs;\n        const [x, mean, variance, scale] = saved;\n        const scaleValue = scale == null ? scalar(1) : scale;\n        const reductionAxes = getReductionAxes(mean.shape, x.shape);\n        const tileShape = [];\n        if (mean.rank === 1) {\n            for (let i = 0; i < x.shape.length - 1; ++i) {\n                tileShape.push(x.shape[i]);\n            }\n            tileShape.push(1);\n        }\n        const xMinusMean = sub(x, mean);\n        const dyTimesScaleValue = mul(dy, scaleValue);\n        const oneOverSqrtVariance = rsqrt(add(variance, scalar(varianceEpsilon)));\n        const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));\n        const derX = () => {\n            if (mean.rank === 1) {\n                return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean.shape[0]]), tileShape)), scaleValue), x.shape);\n            }\n            else {\n                return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);\n            }\n        };\n        const derMean = () => {\n            let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);\n            if (mean.rank === 1) {\n                meanDer = sum(meanDer, reductionAxes);\n            }\n            return reshape(meanDer, mean.shape);\n        };\n        const derVariance = () => {\n            let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);\n            if (mean.rank === 1) {\n                varianceDer = sum(varianceDer, reductionAxes);\n            }\n            return reshape(varianceDer, mean.shape);\n        };\n        const derScale = () => {\n            const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);\n            let scaleDer = mul(dy, xMinusMean2TimesRsqrt);\n            if (mean.rank === 1) {\n                scaleDer = sum(scaleDer, reductionAxes);\n            }\n            return reshape(scaleDer, mean.shape);\n        };\n        const derOffset = () => {\n            let offsetDer = dy;\n            if (mean.rank === 1) {\n                offsetDer = sum(offsetDer, reductionAxes);\n            }\n            return reshape(offsetDer, mean.shape);\n        };\n        return {\n            x: derX,\n            mean: derMean,\n            variance: derVariance,\n            scale: derScale,\n            offset: derOffset\n        };\n    }\n};\n//# sourceMappingURL=FusedBatchNorm_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GatherV2 } from '../kernel_names';\nimport { getUndoAxesPermutation } from '../ops/axis_util';\nimport { reshape } from '../ops/reshape';\nimport { transpose } from '../ops/transpose';\nimport { unsortedSegmentSum } from '../ops/unsorted_segment_sum';\nimport { parseAxisParam } from '../util';\nexport const gatherGradConfig = {\n    kernelName: GatherV2,\n    inputsToSave: ['x', 'indices'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, indices] = saved;\n        const { axis } = attrs;\n        const parsedAxis = parseAxisParam(axis, x.shape)[0];\n        const derX = () => {\n            const paramsShape = x.shape;\n            const indicesSize = indices.size;\n            const outerShape = paramsShape.slice(0, parsedAxis);\n            const outerDims = outerShape.length;\n            const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);\n            const innerDims = innerShape.length;\n            const outerAxesIndices = arrayRange(0, outerDims);\n            const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);\n            const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);\n            const values = reshape(dy, valuesShape);\n            const reshapedIndices = reshape(indices, [indicesSize]);\n            const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);\n            const valuesTranspose = transpose(values, transposeDims);\n            let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);\n            const invertTransposeDims = getUndoAxesPermutation(transposeDims);\n            paramsGrad = transpose(paramsGrad, invertTransposeDims);\n            return paramsGrad;\n        };\n        return { x: derX, indices: () => indices };\n    }\n};\nfunction arrayRange(start, stop) {\n    const result = [];\n    for (let i = start; i < stop; ++i) {\n        result.push(i);\n    }\n    return result;\n}\nfunction arrayConcat(arrays) {\n    const result = [];\n    for (let i = 0; i < arrays.length; ++i) {\n        for (let j = 0; j < arrays[i].length; ++j) {\n            result.push(arrays[i][j]);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=GatherV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GreaterEqual } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const greaterEqualGradConfig = {\n    kernelName: GreaterEqual,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        return { a: () => zerosLike(a), b: () => zerosLike(b) };\n    }\n};\n//# sourceMappingURL=GreaterEqual_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '../kernel_names';\nimport { cast } from '../ops/cast';\nexport const identityGradConfig = {\n    kernelName: Identity,\n    gradFunc: (dy) => {\n        return { x: () => cast(dy, 'float32') };\n    }\n};\n//# sourceMappingURL=Identity_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsFinite } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isFiniteGradConfig = {\n    kernelName: IsFinite,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsFinite_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsInf } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isInfGradConfig = {\n    kernelName: IsInf,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsInf_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsNan } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isNanGradConfig = {\n    kernelName: IsNan,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsNan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log1p } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nexport const log1pGradConfig = {\n    kernelName: Log1p,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, add(x, 1)) };\n    }\n};\n//# sourceMappingURL=Log1p_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nexport const logGradConfig = {\n    kernelName: Log,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, cast(x, 'float32')) };\n    }\n};\n//# sourceMappingURL=Log_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LogSoftmax } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nexport const logSoftmaxGradConfig = {\n    kernelName: LogSoftmax,\n    inputsToSave: [],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [value] = saved;\n        const { axis } = attrs;\n        return {\n            logits: () => {\n                const keepDims = true;\n                const softmax = exp(value);\n                return sub(dy, mul(sum(dy, axis, keepDims), softmax));\n            }\n        };\n    }\n};\n//# sourceMappingURL=LogSoftmax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LRNBackprop } from '../kernel_names';\nimport { op } from './operation';\nfunction localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n    const forward = backend => backend.LRNGrad(dy, x, y, depthRadius, bias, alpha, beta);\n    const inputs = { x, y, dy };\n    const attrs = { depthRadius, bias, alpha, beta };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LRNBackprop, attrs);\n}\nexport const localResponseNormalizationBackprop = op({ localResponseNormalizationBackprop_ });\n//# sourceMappingURL=local_response_normalization_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LRN } from '../kernel_names';\nimport { localResponseNormalizationBackprop } from '../ops/local_response_normalization_backprop';\nexport const lrnGradConfig = {\n    kernelName: LRN,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { depthRadius, bias, alpha, beta } = attrs;\n        return {\n            x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)\n        };\n    }\n};\n//# sourceMappingURL=LRN_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as axis_util from '../ops/axis_util';\nimport { cast } from '../ops/cast';\nimport { equal } from '../ops/equal';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { transpose } from '../ops/transpose';\n/**\n * Gradient helper function for the min and max operations.\n */\nexport function gradForMinAndMax(dy, y, xOrig, origAxes, permutedAxes) {\n    if (y.rank < xOrig.rank) {\n        y = reshape(y, axis_util.expandShapeToKeepDim(y.shape, origAxes));\n    }\n    if (dy.rank < xOrig.rank) {\n        dy = reshape(dy, axis_util.expandShapeToKeepDim(dy.shape, origAxes));\n    }\n    return {\n        x: () => {\n            const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));\n            return permutedAxes == null ? dx : transpose(dx, permutedAxes);\n        }\n    };\n}\n//# sourceMappingURL=min_max_grad_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nimport * as util from '../util';\nimport { gradForMinAndMax } from './min_max_grad_util';\nexport const maxGradConfig = {\n    kernelName: Max,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const maxAttrs = attrs;\n        const { reductionIndices } = maxAttrs;\n        const [x, y] = saved;\n        const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(origAxes, x.rank);\n        const maxGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);\n        return {\n            x: () => {\n                let out = maxGrad['x']();\n                if (permutedAxes != null) {\n                    out = transpose(out);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Max_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Maximum } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { less } from '../ops/less';\nimport { mul } from '../ops/mul';\nexport const maximumGradConfig = {\n    kernelName: Maximum,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const derA = () => mul(dy, cast(greaterEqual(a, b), 'float32'));\n        const derB = () => mul(dy, cast(less(a, b), 'float32'));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Maximum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool3DBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of a 3d max pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank 4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param output The original output image, of rank 5 of shape\n *     [batchSize, outDepth, outHeight, outWidth, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPool3dBackprop_(dy, input, output, filterSize, strides, dilations = [1, 1, 1], pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'maxPool3dBackprop');\n    const $input = convertToTensor(input, 'input', 'maxPool3dBackprop');\n    const $output = convertToTensor(output, 'output', 'maxPool3dBackprop');\n    let dy5D = $dy;\n    let input5D = $input;\n    let output5D = $output;\n    let reshapedTo5D = false;\n    if ($input.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n        input5D = reshape($input, [\n            1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n        ]);\n        output5D = reshape($output, [\n            1, $output.shape[0], $output.shape[1], $output.shape[2], $output.shape[3]\n        ]);\n    }\n    util.assert(dy5D.rank === 5, () => `Error in maxPool3dBackprop: dy must be rank 5 but got rank ` +\n        `${dy5D.rank}.`);\n    util.assert(input5D.rank === 5, () => `Error in maxPool3dBackprop: input must be rank 5 but got rank ` +\n        `${input5D.rank}.`);\n    util.assert(output5D.rank === 5, () => `Error in maxPool3dBackprop: output must be rank 5 but got rank ` +\n        `${output5D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool3dBackprop: Either strides or dilations ' +\n        `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n        return backend.maxPool3dBackprop(dy5D, input5D, output5D, convInfo);\n    };\n    const inputs = { dy: dy5D, input: input5D, output: output5D };\n    const attrs = { filterSize, strides, dilations, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool3DBackprop, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const maxPool3dBackprop = op({ maxPool3dBackprop_ });\n//# sourceMappingURL=max_pool_3d_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPool3D } from '../kernel_names';\nimport { maxPool3dBackprop } from '../ops/max_pool_3d_backprop';\nexport const maxPool3DGradConfig = {\n    kernelName: MaxPool3D,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { filterSize, strides, dilations, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1, 1] : dilations;\n        return {\n            x: () => maxPool3dBackprop(dy, x, y, filterSize, strides, $dilations, pad, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=MaxPool3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPoolBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\n/**\n * Computes the backprop of a 2D max pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The original input image, of rank 4, of shape\n *     [batchSize, height, width, channels].\n * @param output The original output image, of rank 4, of shape\n *     [batchSize, outHeight, outWidth, channels].\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPoolBackprop_(dy, input, output, filterSize, strides, pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'maxPoolBackprop');\n    const $input = convertToTensor(input, 'input', 'maxPoolBackprop');\n    const $output = convertToTensor(output, 'output', 'maxPoolBackprop');\n    util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy ` +\n        `(${$dy.rank})`);\n    util.assert($dy.rank === 4, () => `Error in maxPoolBackprop: dy must be rank 4 but got rank ` +\n        `${$dy.rank}.`);\n    util.assert($input.rank === 4, () => `Error in maxPoolBackprop: input must be rank 4 but got rank ` +\n        `${$input.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPoolBackprop: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool2DInfo($input.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        return backend.maxPoolBackprop($dy, $input, $output, convInfo);\n    };\n    const inputs = { dy: $dy, input: $input, output: $output };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    return ENGINE.runKernelFunc(forward, inputs, null, MaxPoolBackprop, attrs);\n}\nexport const maxPoolBackprop = op({ maxPoolBackprop_ });\n//# sourceMappingURL=max_pool_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPool } from '../kernel_names';\nimport { maxPoolBackprop } from '../ops/max_pool_backprop';\nexport const maxPoolGradConfig = {\n    kernelName: MaxPool,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { filterSize, strides, pad } = attrs;\n        return {\n            x: () => maxPoolBackprop(dy, x, y, filterSize, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=MaxPool_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Min } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nimport * as util from '../util';\nimport { gradForMinAndMax } from './min_max_grad_util';\nexport const minGradConfig = {\n    kernelName: Min,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const minAttrs = attrs;\n        const { axis } = minAttrs;\n        const [x, y] = saved;\n        const origAxes = util.parseAxisParam(axis, x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(origAxes, x.rank);\n        const minGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);\n        return {\n            x: () => {\n                let out = minGrad['x']();\n                if (permutedAxes != null) {\n                    out = transpose(out);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Min_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Minimum } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { greater } from '../ops/greater';\nimport { lessEqual } from '../ops/less_equal';\nimport { mul } from '../ops/mul';\nexport const minimumGradConfig = {\n    kernelName: Minimum,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const derA = () => mul(dy, cast(lessEqual(a, b), 'float32'));\n        const derB = () => mul(dy, cast(greater(a, b), 'float32'));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Minimum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Mod } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { div } from '../ops/div';\nimport { floor } from '../ops/floor';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const modGradConfig = {\n    kernelName: Mod,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(dy, reduceAxes), a.shape);\n            }\n            return dy;\n        };\n        const derB = () => {\n            const res = mul(dy, neg(floor(div(a, b))));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), b.shape);\n            }\n            return res;\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Mod_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const multiplyGradConfig = {\n    kernelName: Multiply,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = mul(dy, cast(b, 'float32'));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            const res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), b.shape);\n            }\n            return res;\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Multiply_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Negate } from '../kernel_names';\nimport { neg } from '../ops/neg';\nexport const negateGradConfig = {\n    kernelName: Negate,\n    gradFunc: (dy) => {\n        return { x: () => neg(dy) };\n    }\n};\n//# sourceMappingURL=Negate_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { OneHot } from '../kernel_names';\nimport { zeros } from '../ops/zeros';\nexport const oneHotGradConfig = {\n    kernelName: OneHot,\n    inputsToSave: ['indices'],\n    gradFunc: (dy, saved) => {\n        const indices = saved[0];\n        return { indices: () => zeros(indices.shape, 'float32') };\n    }\n};\n//# sourceMappingURL=OneHot_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { OnesLike } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const onesLikeGradConfig = {\n    kernelName: OnesLike,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=OnesLike_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { PadV2 } from '../kernel_names';\nimport { slice } from '../ops/slice';\nexport const padV2GradConfig = {\n    kernelName: PadV2,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        // Pad introduces values around the original tensor, so the gradient\n        // slices the original shape out of the gradient.\n        const x = saved[0];\n        const { paddings } = attrs;\n        const begin = paddings.map(p => p[0]);\n        return { x: () => slice(dy, begin, x.shape) };\n    }\n};\n//# sourceMappingURL=PadV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Pow } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { greater } from '../ops/greater';\nimport { log } from '../ops/log';\nimport { mul } from '../ops/mul';\nimport { pow } from '../ops/pow';\nimport { reshape } from '../ops/reshape';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const powGradConfig = {\n    kernelName: Pow,\n    inputsToSave: ['a', 'b'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [a, b, y] = saved;\n        const base = a;\n        const exp = b;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(base.shape, exp.shape);\n        const derBase = () => {\n            const expFloat = cast(exp, 'float32');\n            let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));\n            const reduceAxes = broadcast_util.getReductionAxes(base.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, base.shape);\n        };\n        const derExp = () => {\n            const condition = greater(base, 0);\n            const logBase = where(condition, log(base), zerosLike(base));\n            let res = mul(dy, mul(y, logBase));\n            const reduceAxes = broadcast_util.getReductionAxes(exp.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, exp.shape);\n        };\n        return { a: derBase, b: derExp };\n    }\n};\n//# sourceMappingURL=Pow_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Prelu } from '../kernel_names';\nimport { getReductionAxes } from '../ops/broadcast_util';\nimport { greater } from '../ops/greater';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const preluGradConfig = {\n    kernelName: Prelu,\n    inputsToSave: ['x', 'alpha'],\n    gradFunc: (dy, saved) => {\n        const [x, alpha] = saved;\n        const mask = greater(x, 0);\n        return {\n            x: () => where(mask, dy, mul(dy, alpha)),\n            alpha: () => {\n                let res = where(mask, zerosLike(dy), mul(dy, x));\n                const reduceAxes = getReductionAxes(alpha.shape, dy.shape);\n                if (reduceAxes.length > 0) {\n                    res = sum(res, reduceAxes);\n                }\n                return reshape(res, alpha.shape);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Prelu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reciprocal } from '../kernel_names';\nimport { div } from '../ops/div';\nimport { neg } from '../ops/neg';\nimport { square } from '../ops/square';\nexport const reciprocalGradConfig = {\n    kernelName: Reciprocal,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, neg(square(x))) };\n    }\n};\n//# sourceMappingURL=Reciprocal_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu6 } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { lessEqual } from '../ops/less_equal';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const relu6GradConfig = {\n    kernelName: Relu6,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        const mask = mul(lessEqual(x, 6), step(x));\n        return { x: () => mul(dy, cast(mask, 'float32')) };\n    }\n};\n//# sourceMappingURL=Relu6_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const reluGradConfig = {\n    kernelName: Relu,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, cast(step(x), 'float32')) };\n    }\n};\n//# sourceMappingURL=Relu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape } from '../kernel_names';\nimport { reshape } from '../ops/reshape';\nexport const reshapeGradConfig = {\n    kernelName: Reshape,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => reshape(dy, x.shape) };\n    }\n};\n//# sourceMappingURL=Reshape_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ResizeBilinear, ResizeBilinearGrad } from '../kernel_names';\nexport const resizeBilinearGradConfig = {\n    kernelName: ResizeBilinear,\n    inputsToSave: ['images'],\n    gradFunc: (dy, saved, attrs) => {\n        const [images] = saved;\n        const backPropKernelFunc = (backend) => {\n            const { alignCorners } = attrs;\n            return backend.resizeBilinearBackprop(dy, images, alignCorners);\n        };\n        const inputs = { images };\n        const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* gradient */, ResizeBilinearGrad, attrs);\n        return { images: imagesDer };\n    }\n};\n//# sourceMappingURL=ResizeBilinear_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ResizeNearestNeighbor, ResizeNearestNeighborGrad } from '../kernel_names';\nexport const resizeNearestNeighborGradConfig = {\n    kernelName: ResizeNearestNeighbor,\n    inputsToSave: ['images'],\n    gradFunc: (dy, saved, attrs) => {\n        const [images] = saved;\n        const backPropKernelFunc = (backend) => {\n            const { alignCorners } = attrs;\n            return backend.resizeNearestNeighborBackprop(dy, images, alignCorners);\n        };\n        const inputs = { images };\n        const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* gradient */, ResizeNearestNeighborGrad, attrs);\n        return { images: imagesDer };\n    }\n};\n//# sourceMappingURL=ResizeNearestNeighbor_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reverse } from '../kernel_names';\nimport { reverse } from '../ops/reverse';\nimport { parseAxisParam } from '../util';\nexport const reverseGradConfig = {\n    kernelName: Reverse,\n    gradFunc: (dy, saved, attrs) => {\n        const { dims } = attrs;\n        const axes = parseAxisParam(dims, dy.shape);\n        return { x: () => reverse(dy, axes) };\n    }\n};\n//# sourceMappingURL=Reverse_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Round } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const roundGradConfig = {\n    kernelName: Round,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Round_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '../kernel_names';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { pow } from '../ops/pow';\nexport const rsqrtGradConfig = {\n    kernelName: Rsqrt,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => neg(div(dy, mul(pow(x, 1.5), 2))) };\n    }\n};\n//# sourceMappingURL=Rsqrt_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SelectV2 } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { logicalNot } from '../ops/logical_not';\nimport { mul } from '../ops/mul';\nimport { zerosLike } from '../ops/zeros_like';\nexport const selectV2PoolGradConfig = {\n    kernelName: SelectV2,\n    inputsToSave: ['condition'],\n    gradFunc: (dy, saved) => {\n        const [condition] = saved;\n        return {\n            // TODO(julianoks): Return null for condition gradient\n            // when backprop supports it.\n            condition: () => cast(zerosLike(condition), 'float32'),\n            t: () => mul(dy, cast(condition, dy.dtype)),\n            e: () => mul(dy, cast(logicalNot(condition), dy.dtype))\n        };\n    }\n};\n//# sourceMappingURL=SelectV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Selu } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { exp } from '../ops/exp';\nimport { greater } from '../ops/greater';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { SELU_SCALE, SELU_SCALEALPHA } from '../ops/selu_util';\nimport { where } from '../ops/where';\nexport const seluGradConfig = {\n    kernelName: Selu,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const mask = greater(x, scalar(0));\n                const scaleAlpha = scalar(SELU_SCALEALPHA);\n                const scale = scalar(SELU_SCALE);\n                const greaterThanZeroDer = mul(dy, scale);\n                const lessEqualZeroDer = mul(mul(dy, scaleAlpha), exp(cast(x, 'float32')));\n                return where(mask, greaterThanZeroDer, lessEqualZeroDer);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Selu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sigmoid } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nexport const sigmoidGradConfig = {\n    kernelName: Sigmoid,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(dy, mul(y, sub(scalar(1), y))) };\n    }\n};\n//# sourceMappingURL=Sigmoid_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sign } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const signGradConfig = {\n    kernelName: Sign,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Sign_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { cos } from '../ops/cos';\nimport { mul } from '../ops/mul';\nexport const sinGradConfig = {\n    kernelName: Sin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(cos(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Sin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sinh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { cosh } from '../ops/cosh';\nimport { mul } from '../ops/mul';\nexport const sinhGradConfig = {\n    kernelName: Sinh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(cosh(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Sinh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Slice } from '../kernel_names';\nimport { pad } from '../ops/pad';\nimport { parseSliceParams } from '../ops/slice_util';\nexport const sliceGradConfig = {\n    kernelName: Slice,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { begin, size } = attrs;\n        const inputShape = x.shape;\n        const [begin_, size_] = parseSliceParams(x, begin, size);\n        // Create an Nx2 padding where the first column represents how many\n        // zeros are prepended (at start) for each dimension, and the second\n        // column indicates how many zeros are appended (at end).\n        // The number of zeros to append is the shape of the input\n        // elementwise-subtracted by both the begin vector and sizes vector.\n        const paddings = [];\n        for (let i = 0; i < dy.rank; i++) {\n            paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);\n        }\n        return { x: () => pad(dy, paddings) };\n    }\n};\n//# sourceMappingURL=Slice_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softmax } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nexport const softmaxGradConfig = {\n    kernelName: Softmax,\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [y] = saved;\n        const { dim } = attrs;\n        const keepDims = true;\n        const dyTimesY = mul(dy, y);\n        return {\n            logits: () => sub(dyTimesY, mul(sum(dyTimesY, [dim], keepDims), y))\n        };\n    }\n};\n//# sourceMappingURL=Softmax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softplus } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { sigmoid } from '../ops/sigmoid';\nexport const softplusGradConfig = {\n    kernelName: Softplus,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, sigmoid(x)) };\n    }\n};\n//# sourceMappingURL=Softplus_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SpaceToBatchND } from '../kernel_names';\nimport { batchToSpaceND } from '../ops/batch_to_space_nd';\nexport const spaceToBatchNDGradConfig = {\n    kernelName: SpaceToBatchND,\n    gradFunc: (dy, saved, attrs) => {\n        const { blockShape, paddings } = attrs;\n        return { x: () => batchToSpaceND(dy, blockShape, paddings) };\n    }\n};\n//# sourceMappingURL=SpaceToBatchND_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SplitV } from '../kernel_names';\nimport { concat } from '../ops/concat';\nexport const splitVGradConfig = {\n    kernelName: SplitV,\n    gradFunc: (dy, saved, attrs) => {\n        const { axis } = attrs;\n        return { x: () => concat(dy, axis) };\n    }\n};\n//# sourceMappingURL=SplitV_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sqrt } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nexport const sqrtGradConfig = {\n    kernelName: Sqrt,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, mul(sqrt(cast(x, 'float32')), 2)) };\n    }\n};\n//# sourceMappingURL=Sqrt_grad.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nexport const squareGradConfig = {\n    kernelName: Square,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, mul(cast(x, 'float32'), 2)) };\n    }\n};\n//# sourceMappingURL=Square_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nexport const squaredDifferenceGradConfig = {\n    kernelName: SquaredDifference,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const two = scalar(2);\n        const derA = () => mul(dy, mul(two, sub(a, b)));\n        const derB = () => mul(dy, mul(two, sub(b, a)));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=SquaredDifference_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Step } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const stepGradConfig = {\n    kernelName: Step,\n    gradFunc: (dy) => {\n        // TODO(manrajgrover): Return null for gradients when backprop supports\n        // it.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Step_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const subGradConfig = {\n    kernelName: Sub,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(neg(res), b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Sub_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sum } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { ones } from '../ops/ones';\nimport { reshape } from '../ops/reshape';\nimport { parseAxisParam } from '../util';\nexport const sumGradConfig = {\n    kernelName: Sum,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const expandedDyShape = x.shape.slice();\n        const { axis } = attrs;\n        const axes = parseAxisParam(axis, x.shape);\n        axes.forEach(axis => {\n            expandedDyShape[axis] = 1;\n        });\n        const expandedDy = reshape(dy, expandedDyShape);\n        const derX = mul(expandedDy, ones(x.shape, 'float32'));\n        return { x: () => derX };\n    }\n};\n//# sourceMappingURL=Sum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '../kernel_names';\nimport { cos } from '../ops/cos';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nexport const tanGradConfig = {\n    kernelName: Tan,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, square(cos(x))) };\n    }\n};\n//# sourceMappingURL=Tan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tanh } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const tanhGradConfig = {\n    kernelName: Tanh,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(sub(scalar(1), square(y)), dy) };\n    }\n};\n//# sourceMappingURL=Tanh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tile } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { slice } from '../ops/slice';\nimport { zerosLike } from '../ops/zeros_like';\nexport const tileGradConfig = {\n    kernelName: Tile,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { reps } = attrs;\n        const derX = () => {\n            let xGrad = zerosLike(x);\n            // TODO(cais): Maybe reduce memory footprint by avoiding repeated\n            // slicing.\n            if (x.rank === 1) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    xGrad = add(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));\n                }\n            }\n            else if (x.rank === 2) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        xGrad = add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [\n                            x.shape[0], x.shape[1]\n                        ]));\n                    }\n                }\n            }\n            else if (x.rank === 3) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        for (let k = 0; k < reps[2]; ++k) {\n                            xGrad =\n                                add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));\n                        }\n                    }\n                }\n            }\n            else if (x.rank === 4) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        for (let k = 0; k < reps[2]; ++k) {\n                            for (let l = 0; l < reps[3]; ++l) {\n                                xGrad =\n                                    add(xGrad, slice(dy, [\n                                        i * x.shape[0], j * x.shape[1], k * x.shape[2],\n                                        l * x.shape[3]\n                                    ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));\n                            }\n                        }\n                    }\n                }\n            }\n            else {\n                throw new Error(`Gradient for tile operation is not implemented for rank-` +\n                    `${x.rank} tensors yet.`);\n            }\n            return xGrad;\n        };\n        return { x: derX };\n    },\n};\n//# sourceMappingURL=Tile_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nexport const transposeGradConfig = {\n    kernelName: Transpose,\n    gradFunc: (dy, saved, attrs) => {\n        const transposeAttrs = attrs;\n        const { perm } = transposeAttrs;\n        const undoPerm = axis_util.getUndoAxesPermutation(perm);\n        return { x: () => transpose(dy, undoPerm) };\n    }\n};\n//# sourceMappingURL=Transpose_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unpack } from '../kernel_names';\nimport { stack } from '../ops/stack';\nexport const unpackGradConfig = {\n    kernelName: Unpack,\n    gradFunc: (dy, saved, attrs) => {\n        const unpackAttrs = attrs;\n        const { axis } = unpackAttrs;\n        return { value: () => stack(dy, axis) };\n    }\n};\n//# sourceMappingURL=Unpack_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { UnsortedSegmentSum } from '../kernel_names';\nimport { expandDims } from '../ops/expand_dims';\nimport { gather } from '../ops/gather';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { logicalAnd } from '../ops/logical_and';\nimport { maximum } from '../ops/maximum';\nimport { ones } from '../ops/ones';\nimport { scalar } from '../ops/scalar';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const unsortedSegmentSumGradConfig = {\n    kernelName: UnsortedSegmentSum,\n    inputsToSave: ['segmentIds'],\n    gradFunc: (dy, saved) => {\n        const [segmentIds] = saved;\n        const derX = () => {\n            return gatherDropNegatives(dy, segmentIds);\n        };\n        return { x: derX };\n    }\n};\nfunction gatherDropNegatives(x, indices) {\n    // Helper function for unsorted segment ops. Gathers params for\n    // positive segment ids and gathers 0 for inputs with negative segment id.\n    // Mirrors _GatherDropNegatives from tensorflow/python/ops/math_grad.py\n    const zeroClippedIndices = maximum(indices, zerosLike(indices));\n    const gathered = gather(x, zeroClippedIndices);\n    let isPositive = greaterEqual(indices, scalar(0, 'int32'));\n    const numIters = gathered.rank - isPositive.rank;\n    for (let i = 0; i < numIters; ++i) {\n        isPositive = expandDims(isPositive, i + 1);\n    }\n    isPositive = logicalAnd(isPositive, ones(gathered.shape, 'bool'));\n    const zeroSlice = zerosLike(gathered);\n    return where(isPositive, gathered, zeroSlice);\n}\n//# sourceMappingURL=UnsortedSegmentSum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ZerosLike } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const zerosLikeGradConfig = {\n    kernelName: ZerosLike,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=ZerosLike_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { absGradConfig } from './gradients/Abs_grad';\nimport { acosGradConfig } from './gradients/Acos_grad';\nimport { acoshGradConfig } from './gradients/Acosh_grad';\nimport { addGradConfig } from './gradients/Add_grad';\nimport { addNGradConfig } from './gradients/AddN_grad';\nimport { argMaxGradConfig } from './gradients/ArgMax_grad';\nimport { argMinGradConfig } from './gradients/ArgMin_grad';\nimport { asinGradConfig } from './gradients/Asin_grad';\nimport { asinhGradConfig } from './gradients/Asinh_grad';\nimport { atan2GradConfig } from './gradients/Atan2_grad';\nimport { atanGradConfig } from './gradients/Atan_grad';\nimport { atanhGradConfig } from './gradients/Atanh_grad';\nimport { avgPool3DGradConfig } from './gradients/AvgPool3D_grad';\nimport { avgPoolGradConfig } from './gradients/AvgPool_grad';\nimport { batchMatMulGradConfig } from './gradients/BatchMatMul_grad';\nimport { batchToSpaceNDGradConfig } from './gradients/BatchToSpaceND_grad';\nimport { broadcastToGradConfig } from './gradients/BroadcastTo_grad';\nimport { castGradConfig } from './gradients/Cast_grad';\nimport { ceilGradConfig } from './gradients/Ceil_grad';\nimport { clipByValueGradConfig } from './gradients/ClipByValue_grad';\nimport { concatGradConfig } from './gradients/Concat_grad';\nimport { conv2DGradConfig } from './gradients/Conv2D_grad';\nimport { conv2DBackpropInputGradConfig } from './gradients/Conv2DBackpropInput_grad';\nimport { conv3DGradConfig } from './gradients/Conv3D_grad';\nimport { cosGradConfig } from './gradients/Cos_grad';\nimport { coshGradConfig } from './gradients/Cosh_grad';\nimport { cumsumGradConfig } from './gradients/Cumsum_grad';\nimport { depthwiseConv2dNativeGradConfig } from './gradients/DepthwiseConv2dNative_grad';\nimport { dilation2dGradConfig } from './gradients/Dilation2D_grad';\nimport { divGradConfig } from './gradients/Div_grad';\nimport { eluGradConfig } from './gradients/Elu_grad';\nimport { erfGradConfig } from './gradients/Erf_grad';\nimport { expGradConfig } from './gradients/Exp_grad';\nimport { expm1GradConfig } from './gradients/Expm1_grad';\nimport { floorGradConfig } from './gradients/Floor_grad';\nimport { floorDivGradConfig } from './gradients/FloorDiv_grad';\nimport { fusedBatchNormGradConfig } from './gradients/FusedBatchNorm_grad';\nimport { gatherGradConfig } from './gradients/GatherV2_grad';\nimport { greaterEqualGradConfig } from './gradients/GreaterEqual_grad';\nimport { identityGradConfig } from './gradients/Identity_grad';\nimport { isFiniteGradConfig } from './gradients/IsFinite_grad';\nimport { isInfGradConfig } from './gradients/IsInf_grad';\nimport { isNanGradConfig } from './gradients/IsNan_grad';\nimport { log1pGradConfig } from './gradients/Log1p_grad';\nimport { logGradConfig } from './gradients/Log_grad';\nimport { logSoftmaxGradConfig } from './gradients/LogSoftmax_grad';\nimport { lrnGradConfig } from './gradients/LRN_grad';\nimport { maxGradConfig } from './gradients/Max_grad';\nimport { maximumGradConfig } from './gradients/Maximum_grad';\nimport { maxPool3DGradConfig } from './gradients/MaxPool3D_grad';\nimport { maxPoolGradConfig } from './gradients/MaxPool_grad';\nimport { minGradConfig } from './gradients/Min_grad';\nimport { minimumGradConfig } from './gradients/Minimum_grad';\nimport { modGradConfig } from './gradients/Mod_grad';\nimport { multiplyGradConfig } from './gradients/Multiply_grad';\nimport { negateGradConfig } from './gradients/Negate_grad';\nimport { oneHotGradConfig } from './gradients/OneHot_grad';\nimport { onesLikeGradConfig } from './gradients/OnesLike_grad';\nimport { padV2GradConfig } from './gradients/PadV2_grad';\nimport { powGradConfig } from './gradients/Pow_grad';\nimport { preluGradConfig } from './gradients/Prelu_grad';\nimport { reciprocalGradConfig } from './gradients/Reciprocal_grad';\nimport { relu6GradConfig } from './gradients/Relu6_grad';\nimport { reluGradConfig } from './gradients/Relu_grad';\nimport { reshapeGradConfig } from './gradients/Reshape_grad';\nimport { resizeBilinearGradConfig } from './gradients/ResizeBilinear_grad';\nimport { resizeNearestNeighborGradConfig } from './gradients/ResizeNearestNeighbor_grad';\nimport { reverseGradConfig } from './gradients/Reverse_grad';\nimport { roundGradConfig } from './gradients/Round_grad';\nimport { rsqrtGradConfig } from './gradients/Rsqrt_grad';\nimport { selectV2PoolGradConfig } from './gradients/SelectV2_grad';\nimport { seluGradConfig } from './gradients/Selu_grad';\nimport { sigmoidGradConfig } from './gradients/Sigmoid_grad';\nimport { signGradConfig } from './gradients/Sign_grad';\nimport { sinGradConfig } from './gradients/Sin_grad';\nimport { sinhGradConfig } from './gradients/Sinh_grad';\nimport { sliceGradConfig } from './gradients/Slice_grad';\nimport { softmaxGradConfig } from './gradients/Softmax_grad';\nimport { softplusGradConfig } from './gradients/Softplus_grad';\nimport { spaceToBatchNDGradConfig } from './gradients/SpaceToBatchND_grad';\nimport { splitVGradConfig } from './gradients/SplitV_grad';\nimport { sqrtGradConfig } from './gradients/Sqrt_grad';\nimport { squareGradConfig } from './gradients/Square_grad';\nimport { squaredDifferenceGradConfig } from './gradients/SquaredDifference_grad';\nimport { stepGradConfig } from './gradients/Step_grad';\nimport { subGradConfig } from './gradients/Sub_grad';\nimport { sumGradConfig } from './gradients/Sum_grad';\nimport { tanGradConfig } from './gradients/Tan_grad';\nimport { tanhGradConfig } from './gradients/Tanh_grad';\nimport { tileGradConfig } from './gradients/Tile_grad';\nimport { transposeGradConfig } from './gradients/Transpose_grad';\nimport { unpackGradConfig } from './gradients/Unpack_grad';\nimport { unsortedSegmentSumGradConfig } from './gradients/UnsortedSegmentSum_grad';\nimport { zerosLikeGradConfig } from './gradients/ZerosLike_grad';\nimport { registerGradient } from './kernel_registry';\n// Export all kernel configs here so that the package can auto register them\nconst gradConfigs = [\n    absGradConfig,\n    acosGradConfig,\n    acoshGradConfig,\n    addGradConfig,\n    addNGradConfig,\n    argMaxGradConfig,\n    argMinGradConfig,\n    asinGradConfig,\n    asinhGradConfig,\n    atan2GradConfig,\n    atanGradConfig,\n    atanhGradConfig,\n    avgPool3DGradConfig,\n    avgPoolGradConfig,\n    batchMatMulGradConfig,\n    batchToSpaceNDGradConfig,\n    broadcastToGradConfig,\n    castGradConfig,\n    ceilGradConfig,\n    clipByValueGradConfig,\n    concatGradConfig,\n    conv2DBackpropInputGradConfig,\n    conv2DGradConfig,\n    conv3DGradConfig,\n    cosGradConfig,\n    coshGradConfig,\n    cumsumGradConfig,\n    depthwiseConv2dNativeGradConfig,\n    dilation2dGradConfig,\n    divGradConfig,\n    eluGradConfig,\n    erfGradConfig,\n    expGradConfig,\n    expm1GradConfig,\n    floorDivGradConfig,\n    floorGradConfig,\n    fusedBatchNormGradConfig,\n    gatherGradConfig,\n    greaterEqualGradConfig,\n    identityGradConfig,\n    isFiniteGradConfig,\n    isInfGradConfig,\n    isNanGradConfig,\n    log1pGradConfig,\n    logGradConfig,\n    logSoftmaxGradConfig,\n    lrnGradConfig,\n    maxGradConfig,\n    maxGradConfig,\n    maximumGradConfig,\n    maxPool3DGradConfig,\n    maxPoolGradConfig,\n    minGradConfig,\n    minimumGradConfig,\n    modGradConfig,\n    multiplyGradConfig,\n    negateGradConfig,\n    oneHotGradConfig,\n    onesLikeGradConfig,\n    padV2GradConfig,\n    padV2GradConfig,\n    powGradConfig,\n    preluGradConfig,\n    reciprocalGradConfig,\n    relu6GradConfig,\n    reluGradConfig,\n    reshapeGradConfig,\n    resizeBilinearGradConfig,\n    resizeNearestNeighborGradConfig,\n    reverseGradConfig,\n    roundGradConfig,\n    rsqrtGradConfig,\n    selectV2PoolGradConfig,\n    seluGradConfig,\n    sigmoidGradConfig,\n    signGradConfig,\n    sinGradConfig,\n    sinhGradConfig,\n    sliceGradConfig,\n    softmaxGradConfig,\n    softplusGradConfig,\n    spaceToBatchNDGradConfig,\n    spaceToBatchNDGradConfig,\n    splitVGradConfig,\n    splitVGradConfig,\n    sqrtGradConfig,\n    squaredDifferenceGradConfig,\n    squareGradConfig,\n    stepGradConfig,\n    subGradConfig,\n    sumGradConfig,\n    tanGradConfig,\n    tanhGradConfig,\n    tileGradConfig,\n    transposeGradConfig,\n    unpackGradConfig,\n    unsortedSegmentSumGradConfig,\n    zerosLikeGradConfig\n];\nfor (const gradientConfig of gradConfigs) {\n    registerGradient(gradientConfig);\n}\n//# sourceMappingURL=register_all_gradients.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { abs } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.abs = function () {\n    this.throwIfDisposed();\n    return abs(this);\n};\n//# sourceMappingURL=abs.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { acos } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.acos = function () {\n    this.throwIfDisposed();\n    return acos(this);\n};\n//# sourceMappingURL=acos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { acosh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.acosh = function () {\n    this.throwIfDisposed();\n    return acosh(this);\n};\n//# sourceMappingURL=acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { addStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.addStrict = function (x) {\n    this.throwIfDisposed();\n    return addStrict(this, x);\n};\n//# sourceMappingURL=add_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { add } from '../../ops/add';\nimport { Tensor } from '../../tensor';\nTensor.prototype.add = function (b) {\n    this.throwIfDisposed();\n    return add(this, b);\n};\n//# sourceMappingURL=add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { all } from '../../ops/all';\nimport { Tensor } from '../../tensor';\nTensor.prototype.all = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return all(this, axis, keepDims);\n};\n//# sourceMappingURL=all.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { any } from '../../ops/any';\nimport { Tensor } from '../../tensor';\nTensor.prototype.any = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return any(this, axis, keepDims);\n};\n//# sourceMappingURL=any.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { argMax } from '../../ops/arg_max';\nimport { Tensor } from '../../tensor';\nTensor.prototype.argMax = function (axis) {\n    this.throwIfDisposed();\n    return argMax(this, axis);\n};\n//# sourceMappingURL=arg_max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { argMin } from '../../ops/arg_min';\nimport { Tensor } from '../../tensor';\nTensor.prototype.argMin = function (axis) {\n    this.throwIfDisposed();\n    return argMin(this, axis);\n};\n//# sourceMappingURL=arg_min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\nimport { assert } from '../../util';\n/** Converts a size-1 `tf.Tensor` to a `tf.Scalar`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asScalar = function () {\n    this.throwIfDisposed();\n    assert(this.size === 1, () => 'The array must have only 1 element.');\n    return reshape(this, []);\n};\n//# sourceMappingURL=as_scalar.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * Casts a `tf.Tensor` to a specified dtype.\n *\n * @param dtype Data-type to cast the tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asType = function (dtype) {\n    this.throwIfDisposed();\n    return cast(this, dtype);\n};\n//# sourceMappingURL=as_type.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/** Converts a `tf.Tensor` to a `tf.Tensor1D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as1D = function () {\n    this.throwIfDisposed();\n    return reshape(this, [this.size]);\n};\n//# sourceMappingURL=as1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor2D`.\n *\n * @param rows Number of rows in `tf.Tensor2D`.\n * @param columns Number of columns in `tf.Tensor2D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as2D = function (rows, columns) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns]);\n};\n//# sourceMappingURL=as2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor3D`.\n *\n * @param rows Number of rows in `tf.Tensor3D`.\n * @param columns Number of columns in `tf.Tensor3D`.\n * @param depth Depth of `tf.Tensor3D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as3D = function (rows, columns, depth) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth]);\n};\n//# sourceMappingURL=as3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor4D`.\n *\n * @param rows Number of rows in `tf.Tensor4D`.\n * @param columns Number of columns in `tf.Tensor4D`.\n * @param depth Depth of `tf.Tensor4D`.\n * @param depth2 4th dimension of `tf.Tensor4D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as4D = function (rows, columns, depth, depth2) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth, depth2]);\n};\n//# sourceMappingURL=as4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor5D`.\n *\n * @param rows Number of rows in `tf.Tensor5D`.\n * @param columns Number of columns in `tf.Tensor5D`.\n * @param depth Depth of `tf.Tensor5D`.\n * @param depth2 4th dimension of `tf.Tensor5D`.\n * @param depth3 5th dimension of 'tf.Tensor5D'\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as5D = function (rows, columns, depth, depth2, depth3) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth, depth2, depth3]);\n};\n//# sourceMappingURL=as5d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { asin } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.asin = function () {\n    this.throwIfDisposed();\n    return asin(this);\n};\n//# sourceMappingURL=asin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { asinh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.asinh = function () {\n    this.throwIfDisposed();\n    return asinh(this);\n};\n//# sourceMappingURL=asinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { atan } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atan = function () {\n    this.throwIfDisposed();\n    return atan(this);\n};\n//# sourceMappingURL=atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { atan2 } from '../../ops/atan2';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atan2 = function (b) {\n    this.throwIfDisposed();\n    return atan2(this, b);\n};\n//# sourceMappingURL=atan2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { atanh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atanh = function () {\n    this.throwIfDisposed();\n    return atanh(this);\n};\n//# sourceMappingURL=atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { avgPool } from '../../ops/avg_pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.avgPool = function (filterSize, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return avgPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=avg_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { batchToSpaceND } from '../../ops/batch_to_space_nd';\nimport { Tensor } from '../../tensor';\nTensor.prototype.batchToSpaceND = function (blockShape, crops) {\n    this.throwIfDisposed();\n    return batchToSpaceND(this, blockShape, crops);\n};\n//# sourceMappingURL=batch_to_space_nd.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { batchNorm } from '../../ops/batchnorm';\nimport { Tensor } from '../../tensor';\nTensor.prototype.batchNorm = function (mean, variance, offset, scale, varianceEpsilon) {\n    this.throwIfDisposed();\n    return batchNorm(this, mean, variance, offset, scale, varianceEpsilon);\n};\n//# sourceMappingURL=batchnorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { broadcastTo } from '../../ops/broadcast_to';\nimport { Tensor } from '../../tensor';\nTensor.prototype.broadcastTo = function (shape) {\n    this.throwIfDisposed();\n    return broadcastTo(this, shape);\n};\n//# sourceMappingURL=broadcast_to.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cast = function (dtype) {\n    this.throwIfDisposed();\n    return cast(this, dtype);\n};\n//# sourceMappingURL=cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { ceil } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.ceil = function () {\n    this.throwIfDisposed();\n    return ceil(this);\n};\n//# sourceMappingURL=ceil.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { clipByValue } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.clipByValue = function (min, max) {\n    this.throwIfDisposed();\n    return clipByValue(this, min, max);\n};\n//# sourceMappingURL=clip_by_value.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../../ops/concat';\nimport { Tensor } from '../../tensor';\nTensor.prototype.concat = function (x, axis) {\n    this.throwIfDisposed();\n    if (x instanceof Tensor) {\n        x = [x];\n    }\n    return concat([this, ...x], axis);\n};\n//# sourceMappingURL=concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv1d } from '../../ops/conv1d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv1d = function (filter, stride, pad, dataFormat, dilation, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv1d(this, filter, stride, pad, dataFormat, dilation, dimRoundingMode);\n};\n//# sourceMappingURL=conv1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2dTranspose } from '../../ops/conv2d_transpose';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv2dTranspose = function (filter, outputShape, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv2dTranspose(this, filter, outputShape, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=conv2d_transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2d } from '../../ops/conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cos } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cos = function () {\n    this.throwIfDisposed();\n    return cos(this);\n};\n//# sourceMappingURL=cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cosh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cosh = function () {\n    this.throwIfDisposed();\n    return cosh(this);\n};\n//# sourceMappingURL=cosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { cumsum } from '../../ops/cumsum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cumsum = function (axis, exclusive, reverse) {\n    this.throwIfDisposed();\n    return cumsum(this, axis, exclusive, reverse);\n};\n//# sourceMappingURL=cumsum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { depthToSpace } from '../../ops/depth_to_space';\nimport { Tensor } from '../../tensor';\nTensor.prototype.depthToSpace = function (blockSize, dataFormat) {\n    this.throwIfDisposed();\n    return depthToSpace(this, blockSize, dataFormat);\n};\n//# sourceMappingURL=depth_to_space.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../../globals';\nimport { depthwiseConv2d } from '../../ops/depthwise_conv2d';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated Use `depthwiseConv2d` instead.\n */\nTensor.prototype.depthwiseConv2D = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    deprecationWarn('depthwiseConv2D is deprecated, use depthwiseConv2d instead');\n    this.throwIfDisposed();\n    return depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=depthwise_conv2D_deprecated.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { depthwiseConv2d } from '../../ops/depthwise_conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.depthwiseConv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    this.throwIfDisposed();\n    return depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dilation2d } from '../../ops/dilation2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.dilation2d = function (filter, strides, pad, dilations, dataFormat) {\n    this.throwIfDisposed();\n    return dilation2d(this, filter, strides, pad, dilations, dataFormat);\n};\n//# sourceMappingURL=dilation2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { divNoNan } from '../../ops/div_no_nan';\nimport { Tensor } from '../../tensor';\nTensor.prototype.divNoNan = function (b) {\n    this.throwIfDisposed();\n    return divNoNan(this, b);\n};\n//# sourceMappingURL=div_no_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { divStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.divStrict = function (x) {\n    this.throwIfDisposed();\n    return divStrict(this, x);\n};\n//# sourceMappingURL=div_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { div } from '../../ops/div';\nimport { Tensor } from '../../tensor';\nTensor.prototype.div = function (b) {\n    this.throwIfDisposed();\n    return div(this, b);\n};\n//# sourceMappingURL=div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dot } from '../../ops/dot';\nimport { Tensor } from '../../tensor';\nTensor.prototype.dot = function (b) {\n    this.throwIfDisposed();\n    return dot(this, b);\n};\n//# sourceMappingURL=dot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { elu } from '../../ops/elu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.elu = function () {\n    this.throwIfDisposed();\n    return elu(this);\n};\n//# sourceMappingURL=elu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { equalStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.equalStrict = function (x) {\n    this.throwIfDisposed();\n    return equalStrict(this, x);\n};\n//# sourceMappingURL=equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { equal } from '../../ops/equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.equal = function (b) {\n    this.throwIfDisposed();\n    return equal(this, b);\n};\n//# sourceMappingURL=equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { erf } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.erf = function () {\n    this.throwIfDisposed();\n    return erf(this);\n};\n//# sourceMappingURL=erf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { exp } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.exp = function () {\n    this.throwIfDisposed();\n    return exp(this);\n};\n//# sourceMappingURL=exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { expandDims } from '../../ops/expand_dims';\nimport { Tensor } from '../../tensor';\nTensor.prototype.expandDims = function (axis) {\n    this.throwIfDisposed();\n    return expandDims(this, axis);\n};\n//# sourceMappingURL=expand_dims.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { expm1 } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.expm1 = function () {\n    this.throwIfDisposed();\n    return expm1(this);\n};\n//# sourceMappingURL=expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { fft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.fft = function () {\n    this.throwIfDisposed();\n    return fft(this);\n};\n//# sourceMappingURL=fft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/** Flatten a Tensor to a 1D array.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.flatten = function () {\n    this.throwIfDisposed();\n    return reshape(this, [this.size]);\n};\n//# sourceMappingURL=flatten.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { floor } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.floor = function () {\n    this.throwIfDisposed();\n    return floor(this);\n};\n//# sourceMappingURL=floor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { floorDiv } from '../../ops/floorDiv';\nimport { Tensor } from '../../tensor';\nTensor.prototype.floorDiv = function (b) {\n    this.throwIfDisposed();\n    return floorDiv(this, b);\n};\n//# sourceMappingURL=floorDiv.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { gather } from '../../ops/gather';\nimport { Tensor } from '../../tensor';\nTensor.prototype.gather = function (indices, axis) {\n    this.throwIfDisposed();\n    return gather(this, indices, axis);\n};\n//# sourceMappingURL=gather.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { greaterEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return greaterEqualStrict(this, x);\n};\n//# sourceMappingURL=greater_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { greaterEqual } from '../../ops/greater_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.greaterEqual = function (b) {\n    this.throwIfDisposed();\n    return greaterEqual(this, b);\n};\n//# sourceMappingURL=greater_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { greaterStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterStrict = function (x) {\n    this.throwIfDisposed();\n    return greaterStrict(this, x);\n};\n//# sourceMappingURL=greater_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { greater } from '../../ops/greater';\nimport { Tensor } from '../../tensor';\nTensor.prototype.greater = function (b) {\n    this.throwIfDisposed();\n    return greater(this, b);\n};\n//# sourceMappingURL=greater.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { ifft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.ifft = function () {\n    this.throwIfDisposed();\n    return ifft(this);\n};\n//# sourceMappingURL=ifft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { irfft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.irfft = function () {\n    this.throwIfDisposed();\n    return irfft(this);\n};\n//# sourceMappingURL=irfft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isFinite } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isFinite = function () {\n    this.throwIfDisposed();\n    return isFinite(this);\n};\n//# sourceMappingURL=is_finite.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isInf } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isInf = function () {\n    this.throwIfDisposed();\n    return isInf(this);\n};\n//# sourceMappingURL=is_inf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isNaN } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isNaN = function () {\n    this.throwIfDisposed();\n    return isNaN(this);\n};\n//# sourceMappingURL=is_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { leakyRelu } from '../../ops/leaky_relu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.leakyRelu = function (alpha) {\n    this.throwIfDisposed();\n    return leakyRelu(this, alpha);\n};\n//# sourceMappingURL=leaky_relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { lessEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.lessEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return lessEqualStrict(this, x);\n};\n//# sourceMappingURL=less_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { lessEqual } from '../../ops/less_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.lessEqual = function (b) {\n    this.throwIfDisposed();\n    return lessEqual(this, b);\n};\n//# sourceMappingURL=less_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { lessStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.lessStrict = function (x) {\n    this.throwIfDisposed();\n    return lessStrict(this, x);\n};\n//# sourceMappingURL=less_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { less } from '../../ops/less';\nimport { Tensor } from '../../tensor';\nTensor.prototype.less = function (b) {\n    this.throwIfDisposed();\n    return less(this, b);\n};\n//# sourceMappingURL=less.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { localResponseNormalization } from '../../ops/local_response_normalization';\nimport { Tensor } from '../../tensor';\nTensor.prototype.localResponseNormalization = function (depthRadius, bias, alpha, beta) {\n    this.throwIfDisposed();\n    return localResponseNormalization(this, depthRadius, bias, alpha, beta);\n};\n//# sourceMappingURL=local_response_normalization.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { logSigmoid } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSigmoid = function () {\n    this.throwIfDisposed();\n    return logSigmoid(this);\n};\n//# sourceMappingURL=log_sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { logSoftmax } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSoftmax = function (axis) {\n    this.throwIfDisposed();\n    return logSoftmax(this, axis);\n};\n//# sourceMappingURL=log_softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logSumExp } from '../../ops/log_sum_exp';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSumExp = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return logSumExp(this, axis, keepDims);\n};\n//# sourceMappingURL=log_sum_exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { log } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.log = function () {\n    this.throwIfDisposed();\n    return log(this);\n};\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { log1p } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.log1p = function () {\n    this.throwIfDisposed();\n    return log1p(this);\n};\n//# sourceMappingURL=log1p.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalAnd } from '../../ops/logical_and';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalAnd = function (b) {\n    this.throwIfDisposed();\n    return logicalAnd(this, b);\n};\n//# sourceMappingURL=logical_and.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalNot } from '../../ops/logical_not';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalNot = function () {\n    this.throwIfDisposed();\n    return logicalNot(this);\n};\n//# sourceMappingURL=logical_not.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalOr } from '../../ops/logical_or';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalOr = function (b) {\n    this.throwIfDisposed();\n    return logicalOr(this, b);\n};\n//# sourceMappingURL=logical_or.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalXor } from '../../ops/logical_xor';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalXor = function (b) {\n    this.throwIfDisposed();\n    return logicalXor(this, b);\n};\n//# sourceMappingURL=logical_xor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { matMul } from '../../ops/mat_mul';\nimport { Tensor } from '../../tensor';\nTensor.prototype.matMul = function (b, transposeA, transposeB) {\n    this.throwIfDisposed();\n    return matMul(this, b, transposeA, transposeB);\n};\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { maxPool } from '../../ops/max_pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.maxPool = function (filterSize, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return maxPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=max_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { max } from '../../ops/max';\nimport { Tensor } from '../../tensor';\nTensor.prototype.max = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return max(this, axis, keepDims);\n};\n//# sourceMappingURL=max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { maximumStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.maximumStrict = function (x) {\n    this.throwIfDisposed();\n    return maximumStrict(this, x);\n};\n//# sourceMappingURL=maximum_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { maximum } from '../../ops/maximum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.maximum = function (b) {\n    this.throwIfDisposed();\n    return maximum(this, b);\n};\n//# sourceMappingURL=maximum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mean } from '../../ops/mean';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mean = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return mean(this, axis, keepDims);\n};\n//# sourceMappingURL=mean.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { min } from '../../ops/min';\nimport { Tensor } from '../../tensor';\nTensor.prototype.min = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return min(this, axis, keepDims);\n};\n//# sourceMappingURL=min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { minimumStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.minimumStrict = function (x) {\n    this.throwIfDisposed();\n    return minimumStrict(this, x);\n};\n//# sourceMappingURL=minimum_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { minimum } from '../../ops/minimum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.minimum = function (b) {\n    this.throwIfDisposed();\n    return minimum(this, b);\n};\n//# sourceMappingURL=minimum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { modStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.modStrict = function (x) {\n    this.throwIfDisposed();\n    return modStrict(this, x);\n};\n//# sourceMappingURL=mod_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mod } from '../../ops/mod';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mod = function (b) {\n    this.throwIfDisposed();\n    return mod(this, b);\n};\n//# sourceMappingURL=mod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { mulStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.mulStrict = function (x) {\n    this.throwIfDisposed();\n    return mulStrict(this, x);\n};\n//# sourceMappingURL=mul_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mul } from '../../ops/mul';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mul = function (b) {\n    this.throwIfDisposed();\n    return mul(this, b);\n};\n//# sourceMappingURL=mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { neg } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.neg = function () {\n    this.throwIfDisposed();\n    return neg(this);\n};\n//# sourceMappingURL=neg.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { norm } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.norm = function (ord, axis, keepDims) {\n    this.throwIfDisposed();\n    return norm(this, ord, axis, keepDims);\n};\n//# sourceMappingURL=norm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { notEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.notEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return notEqualStrict(this, x);\n};\n//# sourceMappingURL=not_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { notEqual } from '../../ops/not_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.notEqual = function (b) {\n    this.throwIfDisposed();\n    return notEqual(this, b);\n};\n//# sourceMappingURL=not_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { oneHot } from '../../ops/one_hot';\nimport { Tensor } from '../../tensor';\nTensor.prototype.oneHot = function (depth, onValue = 1, offValue = 0) {\n    this.throwIfDisposed();\n    return oneHot(this, depth, onValue, offValue);\n};\n//# sourceMappingURL=one_hot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { onesLike } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.onesLike = function () {\n    this.throwIfDisposed();\n    return onesLike(this);\n};\n//# sourceMappingURL=ones_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pad } from '../../ops/pad';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pad = function (paddings, constantValue) {\n    this.throwIfDisposed();\n    return pad(this, paddings, constantValue);\n};\n//# sourceMappingURL=pad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pool } from '../../ops/pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pool = function (windowShape, poolingType, padding, dilationRate, strides) {\n    this.throwIfDisposed();\n    return pool(this, windowShape, poolingType, padding, dilationRate, strides);\n};\n//# sourceMappingURL=pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { powStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.powStrict = function (exp) {\n    this.throwIfDisposed();\n    return powStrict(this, exp);\n};\n//# sourceMappingURL=pow_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pow } from '../../ops/pow';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pow = function (exp) {\n    this.throwIfDisposed();\n    return pow(this, exp);\n};\n//# sourceMappingURL=pow.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { prelu } from '../../ops/prelu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.prelu = function (alpha) {\n    this.throwIfDisposed();\n    return prelu(this, alpha);\n};\n//# sourceMappingURL=prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { prod } from '../../ops/prod';\nimport { Tensor } from '../../tensor';\nTensor.prototype.prod = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return prod(this, axis, keepDims);\n};\n//# sourceMappingURL=prod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { reciprocal } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reciprocal = function () {\n    this.throwIfDisposed();\n    return reciprocal(this);\n};\n//# sourceMappingURL=reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { relu } from '../../ops/relu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.relu = function () {\n    this.throwIfDisposed();\n    return relu(this);\n};\n//# sourceMappingURL=relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { relu6 } from '../../ops/relu6';\nimport { Tensor } from '../../tensor';\nTensor.prototype.relu6 = function () {\n    this.throwIfDisposed();\n    return relu6(this);\n};\n//# sourceMappingURL=relu6.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Reshapes the tensor into the shape of the provided tensor.\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.reshapeAs = function (x) {\n    this.throwIfDisposed();\n    return reshape(this, x.shape);\n};\n//# sourceMappingURL=reshape_as.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reshape = function (shape) {\n    this.throwIfDisposed();\n    return reshape(this, shape);\n};\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { resizeBilinear } from '../../ops/image/resize_bilinear';\nimport { Tensor } from '../../tensor';\nTensor.prototype.resizeBilinear = function (newShape2D, alignCorners) {\n    this.throwIfDisposed();\n    return resizeBilinear(this, newShape2D, alignCorners);\n};\n//# sourceMappingURL=resize_bilinear.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { resizeNearestNeighbor } from '../../ops/image/resize_nearest_neighbor';\nimport { Tensor } from '../../tensor';\nTensor.prototype.resizeNearestNeighbor = function (newShape2D, alignCorners) {\n    this.throwIfDisposed();\n    return resizeNearestNeighbor(this, newShape2D, alignCorners);\n};\n//# sourceMappingURL=resize_nearest_neighbor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reverse } from '../../ops/reverse';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reverse = function (axis) {\n    this.throwIfDisposed();\n    return reverse(this, axis);\n};\n//# sourceMappingURL=reverse.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { rfft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.rfft = function () {\n    this.throwIfDisposed();\n    return rfft(this);\n};\n//# sourceMappingURL=rfft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { round } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.round = function () {\n    this.throwIfDisposed();\n    return round(this);\n};\n//# sourceMappingURL=round.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { rsqrt } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.rsqrt = function () {\n    this.throwIfDisposed();\n    return rsqrt(this);\n};\n//# sourceMappingURL=rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { selu } from '../../ops/selu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.selu = function () {\n    this.throwIfDisposed();\n    return selu(this);\n};\n//# sourceMappingURL=selu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { separableConv2d } from '../../ops/separable_conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.separableConv2d = function (depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat) {\n    this.throwIfDisposed();\n    return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat);\n};\n//# sourceMappingURL=separable_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sigmoid } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sigmoid = function () {\n    this.throwIfDisposed();\n    return sigmoid(this);\n};\n//# sourceMappingURL=sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sign } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sign = function () {\n    this.throwIfDisposed();\n    return sign(this);\n};\n//# sourceMappingURL=sign.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sin } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sin = function () {\n    this.throwIfDisposed();\n    return sin(this);\n};\n//# sourceMappingURL=sin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sinh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sinh = function () {\n    this.throwIfDisposed();\n    return sinh(this);\n};\n//# sourceMappingURL=sinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { slice } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.slice = function (begin, size) {\n    this.throwIfDisposed();\n    return slice(this, begin, size);\n};\n//# sourceMappingURL=slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { softmax } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.softmax = function (dim) {\n    this.throwIfDisposed();\n    return softmax(this, dim);\n};\n//# sourceMappingURL=softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { softplus } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.softplus = function () {\n    this.throwIfDisposed();\n    return softplus(this);\n};\n//# sourceMappingURL=softplus.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { spaceToBatchND } from '../../ops/space_to_batch_nd';\nimport { Tensor } from '../../tensor';\nTensor.prototype.spaceToBatchND = function (blockShape, paddings) {\n    this.throwIfDisposed();\n    return spaceToBatchND(this, blockShape, paddings);\n};\n//# sourceMappingURL=space_to_batch_nd.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { split } from '../../ops/split';\nimport { Tensor } from '../../tensor';\nTensor.prototype.split = function (numOrSizeSplits, axis) {\n    this.throwIfDisposed();\n    return split(this, numOrSizeSplits, axis);\n};\n//# sourceMappingURL=split.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sqrt } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sqrt = function () {\n    this.throwIfDisposed();\n    return sqrt(this);\n};\n//# sourceMappingURL=sqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { square } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.square = function () {\n    this.throwIfDisposed();\n    return square(this);\n};\n//# sourceMappingURL=square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { squaredDifference } from '../../ops/squared_difference';\nimport { Tensor } from '../../tensor';\nTensor.prototype.squaredDifference = function (b) {\n    this.throwIfDisposed();\n    return squaredDifference(this, b);\n};\n//# sourceMappingURL=squared_difference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { squaredDifferenceStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.squaredDifferenceStrict = function (x) {\n    this.throwIfDisposed();\n    return squaredDifferenceStrict(this, x);\n};\n//# sourceMappingURL=squared_difference_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { squeeze } from '../../ops/squeeze';\nimport { Tensor } from '../../tensor';\nTensor.prototype.squeeze = function (axis) {\n    this.throwIfDisposed();\n    return squeeze(this, axis);\n};\n//# sourceMappingURL=squeeze.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { stack } from '../../ops/stack';\nimport { Tensor } from '../../tensor';\nTensor.prototype.stack = function (x, axis) {\n    this.throwIfDisposed();\n    const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];\n    return stack(tensorsToBeStacked, axis);\n};\n//# sourceMappingURL=stack.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { step } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.step = function (alpha) {\n    this.throwIfDisposed();\n    return step(this, alpha);\n};\n//# sourceMappingURL=step.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { stridedSlice } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.stridedSlice = function (begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n    this.throwIfDisposed();\n    return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n};\n//# sourceMappingURL=strided_slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { subStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.subStrict = function (x) {\n    this.throwIfDisposed();\n    return subStrict(this, x);\n};\n//# sourceMappingURL=sub_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { sub } from '../../ops/sub';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sub = function (b) {\n    this.throwIfDisposed();\n    return sub(this, b);\n};\n//# sourceMappingURL=sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { sum } from '../../ops/sum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sum = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return sum(this, axis, keepDims);\n};\n//# sourceMappingURL=sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { tan } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tan = function () {\n    this.throwIfDisposed();\n    return tan(this);\n};\n//# sourceMappingURL=tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { tanh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tanh = function () {\n    this.throwIfDisposed();\n    return tanh(this);\n};\n//# sourceMappingURL=tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tile } from '../../ops/tile';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tile = function (reps) {\n    this.throwIfDisposed();\n    return tile(this, reps);\n};\n//# sourceMappingURL=tile.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `bool`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toBool = function () {\n    this.throwIfDisposed();\n    return cast(this, 'bool');\n};\n//# sourceMappingURL=to_bool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `float32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toFloat = function () {\n    this.throwIfDisposed();\n    return cast(this, 'float32');\n};\n//# sourceMappingURL=to_float.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `int32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toInt = function () {\n    this.throwIfDisposed();\n    return cast(this, 'int32');\n};\n//# sourceMappingURL=to_int.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { topk } from '../../ops/topk';\nimport { Tensor } from '../../tensor';\nTensor.prototype.topk = function (k, sorted) {\n    this.throwIfDisposed();\n    return topk(this, k, sorted);\n};\n//# sourceMappingURL=topk.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { transpose } from '../../ops/transpose';\nimport { Tensor } from '../../tensor';\nTensor.prototype.transpose = function (perm) {\n    this.throwIfDisposed();\n    return transpose(this, perm);\n};\n//# sourceMappingURL=transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unique } from '../../ops/unique';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unique = function (axis) {\n    this.throwIfDisposed();\n    return unique(this, axis);\n};\n//# sourceMappingURL=unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unsortedSegmentSum } from '../../ops/unsorted_segment_sum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unsortedSegmentSum = function (segmentIds, numSegments) {\n    this.throwIfDisposed();\n    return unsortedSegmentSum(this, segmentIds, numSegments);\n};\n//# sourceMappingURL=unsorted_segment_sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unstack } from '../../ops/unstack';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unstack = function (axis) {\n    this.throwIfDisposed();\n    return unstack(this, axis);\n};\n//# sourceMappingURL=unstack.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { where } from '../../ops/where';\nimport { Tensor } from '../../tensor';\nTensor.prototype.where = function (condition, x) {\n    this.throwIfDisposed();\n    return where(condition, this, x);\n};\n//# sourceMappingURL=where.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { zerosLike } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.zerosLike = function () {\n    this.throwIfDisposed();\n    return zerosLike(this);\n};\n//# sourceMappingURL=zeros_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './abs';\nimport './acos';\nimport './acosh';\nimport './add_strict';\nimport './add';\nimport './all';\nimport './any';\nimport './arg_max';\nimport './arg_min';\nimport './as_scalar';\nimport './as_type';\nimport './as1d';\nimport './as2d';\nimport './as3d';\nimport './as4d';\nimport './as5d';\nimport './asin';\nimport './asinh';\nimport './atan';\nimport './atan2';\nimport './atanh';\nimport './avg_pool';\nimport './batch_to_space_nd';\nimport './batchnorm';\nimport './broadcast_to';\nimport './cast';\nimport './ceil';\nimport './clip_by_value';\nimport './concat';\nimport './conv1d';\nimport './conv2d_transpose';\nimport './conv2d';\nimport './cos';\nimport './cosh';\nimport './cumsum';\nimport './depth_to_space';\nimport './depthwise_conv2D_deprecated';\nimport './depthwise_conv2d';\nimport './dilation2d';\nimport './div_no_nan';\nimport './div_strict';\nimport './div';\nimport './dot';\nimport './elu';\nimport './equal_strict';\nimport './equal';\nimport './erf';\nimport './exp';\nimport './expand_dims';\nimport './expm1';\nimport './fft';\nimport './flatten';\nimport './floor';\nimport './floorDiv';\nimport './gather';\nimport './greater_equal_strict';\nimport './greater_equal';\nimport './greater_strict';\nimport './greater';\nimport './ifft';\nimport './irfft';\nimport './is_finite';\nimport './is_inf';\nimport './is_nan';\nimport './leaky_relu';\nimport './less_equal_strict';\nimport './less_equal';\nimport './less_strict';\nimport './less';\nimport './local_response_normalization';\nimport './log_sigmoid';\nimport './log_softmax';\nimport './log_sum_exp';\nimport './log';\nimport './log1p';\nimport './logical_and';\nimport './logical_not';\nimport './logical_or';\nimport './logical_xor';\nimport './mat_mul';\nimport './max_pool';\nimport './max';\nimport './maximum_strict';\nimport './maximum';\nimport './mean';\nimport './min';\nimport './minimum_strict';\nimport './minimum';\nimport './mod_strict';\nimport './mod';\nimport './mul_strict';\nimport './mul';\nimport './neg';\nimport './norm';\nimport './not_equal_strict';\nimport './not_equal';\nimport './one_hot';\nimport './ones_like';\nimport './pad';\nimport './pool';\nimport './pow_strict';\nimport './pow';\nimport './prelu';\nimport './prod';\nimport './reciprocal';\nimport './relu';\nimport './relu6';\nimport './reshape_as';\nimport './reshape';\nimport './resize_bilinear';\nimport './resize_nearest_neighbor';\nimport './reverse';\nimport './rfft';\nimport './round';\nimport './rsqrt';\nimport './selu';\nimport './separable_conv2d';\nimport './sigmoid';\nimport './sign';\nimport './sin';\nimport './sinh';\nimport './slice';\nimport './softmax';\nimport './softplus';\nimport './space_to_batch_nd';\nimport './split';\nimport './sqrt';\nimport './square';\nimport './squared_difference';\nimport './squared_difference_strict';\nimport './squeeze';\nimport './stack';\nimport './step';\nimport './strided_slice';\nimport './sub_strict';\nimport './sub';\nimport './sum';\nimport './tan';\nimport './tanh';\nimport './tile';\nimport './to_bool';\nimport './to_float';\nimport './to_int';\nimport './topk';\nimport './transpose';\nimport './unique';\nimport './unsorted_segment_sum';\nimport './unstack';\nimport './where';\nimport './zeros_like';\n//# sourceMappingURL=register_all_chained_ops.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Required side effectful code.\nimport './base_side_effects';\n// All exports from this package should be in base.\nexport * from './base';\n// Register all the gradients.\nimport './register_all_gradients';\n// Import all op chainers and add type info to Tensor.\nimport './public/chained_ops/register_all_chained_ops';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { backend } from '@tensorflow/tfjs-core';\nlet _epsilon;\n/**\n * Returns the value of the fuzz factor used in numeric expressions.\n */\nexport function epsilon() {\n    if (_epsilon == null) {\n        _epsilon = backend().epsilon();\n    }\n    return _epsilon;\n}\n/**\n * Sets the value of the fuzz factor used in numeric expressions.\n * @param e New value of epsilon.\n */\nexport function setEpsilon(e) {\n    _epsilon = e;\n}\n/**\n * Returns the default image data format convention.\n */\nexport function imageDataFormat() {\n    return 'channelsLast';\n}\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Explicit error types.\n *\n * See the following link for more information about why the code includes\n * calls to setPrototypeOf:\n *\n * https://github.com/Microsoft/TypeScript-wiki/blob/master/Breaking-Changes.md#extending-built-ins-like-error-array-and-map-may-no-longer-work\n */\n// tslint:enable\n/**\n * Equivalent of Python's AttributeError.\n */\nexport class AttributeError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, AttributeError.prototype);\n    }\n}\n/**\n * Equivalent of Python's RuntimeError.\n */\nexport class RuntimeError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, RuntimeError.prototype);\n    }\n}\n/**\n * Equivalent of Python's ValueError.\n */\nexport class ValueError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, ValueError.prototype);\n    }\n}\n/**\n * Equivalent of Python's NotImplementedError.\n */\nexport class NotImplementedError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, NotImplementedError.prototype);\n    }\n}\n/**\n * Equivalent of Python's AssertionError.\n */\nexport class AssertionError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, AssertionError.prototype);\n    }\n}\n/**\n * Equivalent of Python's IndexError.\n */\nexport class IndexError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, IndexError.prototype);\n    }\n}\n//# sourceMappingURL=errors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: utils/generic_utils.py */\nimport { util } from '@tensorflow/tfjs-core';\nimport { AssertionError, ValueError } from '../errors';\n// tslint:enable\n/**\n * If `value` is an Array, equivalent to Python's `value * numValues`.\n * If `value` is not an Array, equivalent to Python's `[value] * numValues`\n */\n// tslint:disable-next-line:no-any\nexport function pyListRepeat(value, numValues) {\n    if (Array.isArray(value)) {\n        // tslint:disable-next-line:no-any\n        let newArray = [];\n        for (let i = 0; i < numValues; i++) {\n            newArray = newArray.concat(value);\n        }\n        return newArray;\n    }\n    else {\n        const newArray = new Array(numValues);\n        newArray.fill(value);\n        return newArray;\n    }\n}\nexport function assert(val, message) {\n    if (!val) {\n        throw new AssertionError(message);\n    }\n}\n/**\n * Count the number of elements of the `array` that are equal to `reference`.\n */\nexport function count(array, refernce) {\n    let counter = 0;\n    for (const item of array) {\n        if (item === refernce) {\n            counter++;\n        }\n    }\n    return counter;\n}\n/**\n * If an array is of length 1, just return the first element. Otherwise, return\n * the full array.\n * @param tensors\n */\nexport function singletonOrArray(xs) {\n    if (xs.length === 1) {\n        return xs[0];\n    }\n    return xs;\n}\n/**\n * Normalizes a list/tensor into a list.\n *\n * If a tensor is passed, we return\n * a list of size 1 containing the tensor.\n *\n * @param x target object to be normalized.\n */\n// tslint:disable-next-line:no-any\nexport function toList(x) {\n    if (Array.isArray(x)) {\n        return x;\n    }\n    return [x];\n}\n/**\n * Generate a UID for a list\n */\n// tslint:disable-next-line:no-any\nexport function objectListUid(objs) {\n    const objectList = toList(objs);\n    let retVal = '';\n    for (const obj of objectList) {\n        if (obj.id == null) {\n            throw new ValueError(`Object ${obj} passed to objectListUid without an id`);\n        }\n        if (retVal !== '') {\n            retVal = retVal + ', ';\n        }\n        retVal = `${retVal}${Math.abs(obj.id)}`;\n    }\n    return retVal;\n}\n/**\n * Converts string to snake-case.\n * @param name\n */\nexport function toSnakeCase(name) {\n    const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, '$1_$2');\n    const insecure = intermediate.replace(/([a-z])([A-Z])/g, '$1_$2').toLowerCase();\n    /*\n     If the class is private the name starts with \"_\" which is not secure\n     for creating scopes. We prefix the name with \"private\" in this case.\n     */\n    if (insecure[0] !== '_') {\n        return insecure;\n    }\n    return 'private' + insecure;\n}\nexport function toCamelCase(identifier) {\n    // quick return for empty string or single character strings\n    if (identifier.length <= 1) {\n        return identifier;\n    }\n    // Check for the underscore indicating snake_case\n    if (identifier.indexOf('_') === -1) {\n        return identifier;\n    }\n    return identifier.replace(/[_]+(\\w|$)/g, (m, p1) => p1.toUpperCase());\n}\n// tslint:disable-next-line:no-any\nlet _GLOBAL_CUSTOM_OBJECTS = {};\nexport function serializeKerasObject(instance) {\n    if (instance === null || instance === undefined) {\n        return null;\n    }\n    const dict = {};\n    dict['className'] = instance.getClassName();\n    dict['config'] = instance.getConfig();\n    return dict;\n}\n/**\n * Replace ndarray-style scalar objects in serialization objects with numbers.\n *\n * Background: In some versions of tf.keras, certain scalar values in the HDF5\n * model save file can be serialized as: `{'type': 'ndarray', 'value': num}`,\n * where in `num` is a plain number. This method converts such serialization\n * to a `number`.\n *\n * @param config The keras-format serialization object to be processed\n *   (in place).\n */\nfunction convertNDArrayScalarsInConfig(config) {\n    if (config == null || typeof config !== 'object') {\n        return;\n    }\n    else if (Array.isArray(config)) {\n        config.forEach(configItem => convertNDArrayScalarsInConfig(configItem));\n    }\n    else {\n        const fields = Object.keys(config);\n        for (const field of fields) {\n            const value = config[field];\n            if (value != null && typeof value === 'object') {\n                if (!Array.isArray(value) && value['type'] === 'ndarray' &&\n                    typeof value['value'] === 'number') {\n                    config[field] = value['value'];\n                }\n                else {\n                    convertNDArrayScalarsInConfig(value);\n                }\n            }\n        }\n    }\n}\n/**\n * Deserialize a saved Keras Object\n * @param identifier either a string ID or a saved Keras dictionary\n * @param moduleObjects a list of Python class names to object constructors\n * @param customObjects a list of Python class names to object constructors\n * @param printableModuleName debug text for the object being reconstituted\n * @param fastWeightInit Optional flag to use fast weight initialization\n *   during deserialization. This is applicable to cases in which\n *   the initialization will be immediately overwritten by loaded weight\n *   values. Default: `false`.\n * @returns a TensorFlow.js Layers object\n */\n// tslint:disable:no-any\nexport function deserializeKerasObject(identifier, moduleObjects = {}, customObjects = {}, printableModuleName = 'object', fastWeightInit = false) {\n    // tslint:enable\n    if (typeof identifier === 'string') {\n        const functionName = identifier;\n        let fn;\n        if (functionName in customObjects) {\n            fn = customObjects[functionName];\n        }\n        else if (functionName in _GLOBAL_CUSTOM_OBJECTS) {\n            fn = _GLOBAL_CUSTOM_OBJECTS[functionName];\n        }\n        else {\n            fn = moduleObjects[functionName];\n            if (fn == null) {\n                throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. ` +\n                    `This may be due to one of the following reasons:\\n` +\n                    `1. The ${printableModuleName} is defined in Python, in which ` +\n                    `case it needs to be ported to TensorFlow.js or your JavaScript ` +\n                    `code.\\n` +\n                    `2. The custom ${printableModuleName} is defined in JavaScript, ` +\n                    `but is not registered properly with ` +\n                    `tf.serialization.registerClass().`);\n                // TODO(cais): Add link to tutorial page on custom layers.\n            }\n        }\n        return fn;\n    }\n    else {\n        // In this case we are dealing with a Keras config dictionary.\n        const config = identifier;\n        if (config['className'] == null || config['config'] == null) {\n            throw new ValueError(`${printableModuleName}: Improper config format: ` +\n                `${JSON.stringify(config)}.\\n` +\n                `'className' and 'config' must set.`);\n        }\n        const className = config['className'];\n        let cls, fromConfig;\n        if (className in customObjects) {\n            [cls, fromConfig] = customObjects[className];\n        }\n        else if (className in _GLOBAL_CUSTOM_OBJECTS) {\n            [cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS['className'];\n        }\n        else if (className in moduleObjects) {\n            [cls, fromConfig] = moduleObjects[className];\n        }\n        if (cls == null) {\n            throw new ValueError(`Unknown ${printableModuleName}: ${className}. ` +\n                `This may be due to one of the following reasons:\\n` +\n                `1. The ${printableModuleName} is defined in Python, in which ` +\n                `case it needs to be ported to TensorFlow.js or your JavaScript ` +\n                `code.\\n` +\n                `2. The custom ${printableModuleName} is defined in JavaScript, ` +\n                `but is not registered properly with ` +\n                `tf.serialization.registerClass().`);\n            // TODO(cais): Add link to tutorial page on custom layers.\n        }\n        if (fromConfig != null) {\n            // Porting notes: Instead of checking to see whether fromConfig accepts\n            // customObjects, we create a customObjects dictionary and tack it on to\n            // config['config'] as config['config'].customObjects. Objects can use it,\n            // if they want.\n            // tslint:disable-next-line:no-any\n            const customObjectsCombined = {};\n            for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) {\n                customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key];\n            }\n            for (const key of Object.keys(customObjects)) {\n                customObjectsCombined[key] = customObjects[key];\n            }\n            // Add the customObjects to config\n            const nestedConfig = config['config'];\n            nestedConfig['customObjects'] = customObjectsCombined;\n            const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n            for (const key of Object.keys(customObjects)) {\n                _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n            }\n            convertNDArrayScalarsInConfig(config['config']);\n            const returnObj = fromConfig(cls, config['config'], customObjects, fastWeightInit);\n            _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n            return returnObj;\n        }\n        else {\n            // Then `cls` may be a function returning a class.\n            // In this case by convention `config` holds\n            // the kwargs of the function.\n            const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n            for (const key of Object.keys(customObjects)) {\n                _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n            }\n            // In python this is **config['config'], for tfjs-layers we require\n            // classes that use this fall-through construction method to take\n            // a config interface that mimics the expansion of named parameters.\n            const returnObj = new cls(config['config']);\n            _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n            return returnObj;\n        }\n    }\n}\n/**\n * Compares two numbers for sorting.\n * @param a\n * @param b\n */\nexport function numberCompare(a, b) {\n    return (a < b) ? -1 : ((a > b) ? 1 : 0);\n}\n/**\n * Comparison of two numbers for reverse sorting.\n * @param a\n * @param b\n */\nexport function reverseNumberCompare(a, b) {\n    return -1 * numberCompare(a, b);\n}\n/**\n * Convert a string into the corresponding DType.\n * @param dtype\n * @returns An instance of DType.\n */\nexport function stringToDType(dtype) {\n    switch (dtype) {\n        case 'float32':\n            return 'float32';\n        default:\n            throw new ValueError(`Invalid dtype: ${dtype}`);\n    }\n}\n/**\n * Test the element-by-element equality of two Arrays of strings.\n * @param xs First array of strings.\n * @param ys Second array of strings.\n * @returns Wether the two arrays are all equal, element by element.\n */\nexport function stringsEqual(xs, ys) {\n    if (xs == null || ys == null) {\n        return xs === ys;\n    }\n    if (xs.length !== ys.length) {\n        return false;\n    }\n    for (let i = 0; i < xs.length; ++i) {\n        if (xs[i] !== ys[i]) {\n            return false;\n        }\n    }\n    return true;\n}\n/**\n * Get the unique elements of an array.\n * @param xs Array.\n * @returns An Array consisting of the unique elements in `xs`.\n */\nexport function unique(xs) {\n    if (xs == null) {\n        return xs;\n    }\n    const out = [];\n    // TODO(cais): Maybe improve performance by sorting.\n    for (const x of xs) {\n        if (out.indexOf(x) === -1) {\n            out.push(x);\n        }\n    }\n    return out;\n}\n/**\n * Determine if an Object is empty (i.e., does not have own properties).\n * @param obj Object\n * @returns Whether the Object is empty.\n * @throws ValueError: If object is `null` or `undefined`.\n */\nexport function isObjectEmpty(obj) {\n    if (obj == null) {\n        throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);\n    }\n    for (const key in obj) {\n        if (obj.hasOwnProperty(key)) {\n            return false;\n        }\n    }\n    return true;\n}\n/**\n * Helper function used to build type union/enum run-time checkers.\n * @param values The list of allowed values.\n * @param label A string name for the type\n * @param value The value to test.\n * @throws ValueError: If the value is not in values nor `undefined`/`null`.\n */\nexport function checkStringTypeUnionValue(values, label, value) {\n    if (value == null) {\n        return;\n    }\n    if (values.indexOf(value) < 0) {\n        throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`);\n    }\n}\n/**\n * Helper function for verifying the types of inputs.\n *\n * Ensures that the elements of `x` are all of type `expectedType`.\n * Also verifies that the length of `x` is within bounds.\n *\n * @param x Object to test.\n * @param expectedType The string expected type of all of the elements in the\n * Array.\n * @param minLength Return false if x.length is less than this.\n * @param maxLength Return false if x.length is greater than this.\n * @returns true if and only if `x` is an `Array<expectedType>` with\n * length >= `minLength` and <= `maxLength`.\n */\n// tslint:disable:no-any\nexport function checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) {\n    assert(minLength >= 0);\n    assert(maxLength >= minLength);\n    return (Array.isArray(x) && x.length >= minLength && x.length <= maxLength &&\n        x.every(e => typeof e === expectedType));\n}\n// tslint:enable:no-any\n/**\n * Assert that a value or an array of value are positive integer.\n *\n * @param value The value being asserted on. May be a single number or an array\n *   of numbers.\n * @param name Name of the value, used to make the error message.\n */\nexport function assertPositiveInteger(value, name) {\n    if (Array.isArray(value)) {\n        util.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`);\n        value.forEach((v, i) => assertPositiveInteger(v, `element ${i + 1} of ${name}`));\n    }\n    else {\n        util.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ` +\n            `${formatAsFriendlyString(value)}.`);\n    }\n}\n/**\n * Format a value into a display-friendly, human-readable fashion.\n *\n * - `null` is formatted as `'null'`\n * - Strings are formated with flanking pair of quotes.\n * - Arrays are formatted with flanking pair of square brackets.\n *\n * @param value The value to display.\n * @return Formatted string.\n */\n// tslint:disable-next-line:no-any\nexport function formatAsFriendlyString(value) {\n    if (value === null) {\n        return 'null';\n    }\n    else if (Array.isArray(value)) {\n        return '[' + value.map(v => formatAsFriendlyString(v)).join(',') + ']';\n    }\n    else if (typeof value === 'string') {\n        return `\"${value}\"`;\n    }\n    else {\n        return `${value}`;\n    }\n}\n/**\n * Returns a function `f2` (decorator) which wraps the original function\n * `f`. `f2` guarantees that `f` can be called at most once\n * every `waitMs` ms. If `f2` is called more often, it will return\n * the last returned result of `f`.\n *\n * @param f The original function `f` to wrap.\n * @param waitMs The time between two consecutive calls to `f` in ms.\n */\nexport function debounce(f, waitMs) {\n    let lastTime = util.now();\n    let lastResult;\n    const f2 = (...args) => {\n        const now = util.now();\n        if (now - lastTime < waitMs) {\n            return lastResult;\n        }\n        lastTime = now;\n        lastResult = f(...args);\n        return lastResult;\n    };\n    return f2;\n}\n/**\n * Returns the fusable activation given a layers identifier.\n *\n * @param activationName The layers identifier string.\n * @return The name of the fusable activation.\n */\nexport function mapActivationToFusedKernel(activationName) {\n    if (activationName === 'relu') {\n        return 'relu';\n    }\n    if (activationName === 'linear') {\n        return 'linear';\n    }\n    if (activationName === 'elu') {\n        return 'elu';\n    }\n    return null;\n}\n/**\n * Returns the cartesian product of sets of values.\n * This works the same as itertools.product in Python.\n *\n * Example:\n *\n * filters = [128, 256, 512]\n * paddings = ['same', 'valid']\n *\n * product = [ [128, 'same'], [128, 'valid'], [256, 'same'], [256, 'valid'],\n * [512, 'same'], [512, 'valid']]\n *\n * @param arrayOfValues List/array of values.\n * @return The cartesian product.\n */\nexport function getCartesianProductOfValues(...arrayOfValues) {\n    assert(arrayOfValues.length > 0, 'arrayOfValues is empty');\n    for (const values of arrayOfValues) {\n        assert(Array.isArray(values), 'one of the values is not an array');\n        assert(values.length > 0, 'one of the values is empty');\n    }\n    return arrayOfValues.reduce((products, values) => {\n        if (products.length === 0) {\n            return values.map(value => [value]);\n        }\n        return values\n            .map(value => {\n            return products.map((prevValue) => [...prevValue, value]);\n        })\n            .reduce((flattenedProduct, unflattenedProduct) => {\n            return flattenedProduct.concat(unflattenedProduct);\n        }, []);\n    }, []);\n}\n//# sourceMappingURL=generic_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/contraints.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport { deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\n/**\n * Helper function used by many of the Constraints to find the L2Norms.\n */\nfunction calcL2Norms(w, axis) {\n    return tidy(() => tfc.sqrt(tfc.sum(tfc.mul(w, w), axis, true)));\n}\n/**\n * Base class for functions that impose constraints on weight values\n *\n * @doc {\n *   heading: 'Constraints',\n *   subheading: 'Classes',\n *   namespace: 'constraints'\n * }\n */\nexport class Constraint extends serialization.Serializable {\n    getConfig() {\n        return {};\n    }\n}\nexport class MaxNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultMaxValue = 2;\n        this.defaultAxis = 0;\n        this.maxValue =\n            args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => {\n            const norms = calcL2Norms(w, this.axis);\n            const desired = tfc.clipByValue(norms, 0, this.maxValue);\n            return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));\n        });\n    }\n    getConfig() {\n        return { maxValue: this.maxValue, axis: this.axis };\n    }\n}\n/** @nocollapse */\nMaxNorm.className = 'MaxNorm';\nserialization.registerClass(MaxNorm);\nexport class UnitNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultAxis = 0;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => tfc.div(w, tfc.add(epsilon(), calcL2Norms(w, this.axis))));\n    }\n    getConfig() {\n        return { axis: this.axis };\n    }\n}\n/** @nocollapse */\nUnitNorm.className = 'UnitNorm';\nserialization.registerClass(UnitNorm);\nexport class NonNeg extends Constraint {\n    apply(w) {\n        return tfc.relu(w);\n    }\n}\n/** @nocollapse */\nNonNeg.className = 'NonNeg';\nserialization.registerClass(NonNeg);\nexport class MinMaxNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultMinValue = 0.0;\n        this.defaultMaxValue = 1.0;\n        this.defaultRate = 1.0;\n        this.defaultAxis = 0;\n        this.minValue =\n            args.minValue != null ? args.minValue : this.defaultMinValue;\n        this.maxValue =\n            args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n        this.rate = args.rate != null ? args.rate : this.defaultRate;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => {\n            const norms = calcL2Norms(w, this.axis);\n            const desired = tfc.add(tfc.mul(this.rate, tfc.clipByValue(norms, this.minValue, this.maxValue)), tfc.mul(1.0 - this.rate, norms));\n            return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));\n        });\n    }\n    getConfig() {\n        return {\n            minValue: this.minValue,\n            maxValue: this.maxValue,\n            rate: this.rate,\n            axis: this.axis\n        };\n    }\n}\n/** @nocollapse */\nMinMaxNorm.className = 'MinMaxNorm';\nserialization.registerClass(MinMaxNorm);\n// Maps the JavaScript-like identifier keys to the corresponding registry\n// symbols.\nexport const CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'maxNorm': 'MaxNorm',\n    'minMaxNorm': 'MinMaxNorm',\n    'nonNeg': 'NonNeg',\n    'unitNorm': 'UnitNorm'\n};\nexport function serializeConstraint(constraint) {\n    return serializeKerasObject(constraint);\n}\nexport function deserializeConstraint(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'constraint');\n}\nexport function getConstraint(identifier) {\n    if (identifier == null) {\n        return null;\n    }\n    if (typeof identifier === 'string') {\n        const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        const config = { className, config: {} };\n        return deserializeConstraint(config);\n    }\n    else if (identifier instanceof Constraint) {\n        return identifier;\n    }\n    else {\n        return deserializeConstraint(identifier);\n    }\n}\n//# sourceMappingURL=constraints.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// tslint:disable-next-line:max-line-length\nimport { MaxNorm, MinMaxNorm, NonNeg, UnitNorm } from './constraints';\n/**\n * MaxNorm weight constraint.\n *\n * Constrains the weights incident to each hidden unit\n * to have a norm less than or equal to a desired value.\n *\n * References\n *       - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting\n * Srivastava, Hinton, et al.\n * 2014](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)\n *\n * @doc {heading: 'Constraints',namespace: 'constraints'}\n */\nexport function maxNorm(args) {\n    return new MaxNorm(args);\n}\n/**\n * Constrains the weights incident to each hidden unit to have unit norm.\n *\n * @doc {heading: 'Constraints', namespace: 'constraints'}\n */\nexport function unitNorm(args) {\n    return new UnitNorm(args);\n}\n/**\n * Constains the weight to be non-negative.\n *\n * @doc {heading: 'Constraints', namespace: 'constraints'}\n */\nexport function nonNeg() {\n    return new NonNeg();\n}\n/** @doc {heading: 'Constraints', namespace: 'constraints'} */\nexport function minMaxNorm(config) {\n    return new MinMaxNorm(config);\n}\n//# sourceMappingURL=exports_constraints.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport const VALID_DATA_FORMAT_VALUES = ['channelsFirst', 'channelsLast'];\nexport const VALID_PADDING_MODE_VALUES = ['valid', 'same', 'causal'];\nexport const VALID_POOL_MODE_VALUES = ['max', 'avg'];\nexport const VALID_BIDIRECTIONAL_MERGE_MODES = ['sum', 'mul', 'concat', 'ave'];\nexport const VALID_SAMPLE_WEIGHT_MODES = ['temporal'];\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Common functions for TensorFlow.js Layers.\n */\nimport { VALID_DATA_FORMAT_VALUES, VALID_PADDING_MODE_VALUES, VALID_POOL_MODE_VALUES } from './keras_format/common';\nimport { checkStringTypeUnionValue } from './utils/generic_utils';\n// A map from the requested scoped name of a Tensor to the number of Tensors\n// wanting that name so far.  This allows enforcing name uniqueness by appending\n// an incrementing index, e.g. scope/name, scope/name_1, scope/name_2, etc.\nconst nameMap = new Map();\nexport function checkDataFormat(value) {\n    checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, 'DataFormat', value);\n}\nexport function checkPaddingMode(value) {\n    checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, 'PaddingMode', value);\n}\nexport function checkPoolMode(value) {\n    checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, 'PoolMode', value);\n}\nconst _nameScopeStack = [];\nconst _nameScopeDivider = '/';\n/**\n * Enter namescope, which can be nested.\n */\nexport function nameScope(name, fn) {\n    _nameScopeStack.push(name);\n    try {\n        const val = fn();\n        _nameScopeStack.pop();\n        return val;\n    }\n    catch (e) {\n        _nameScopeStack.pop();\n        throw e;\n    }\n}\n/**\n * Get the current namescope as a flat, concatenated string.\n */\nfunction currentNameScopePrefix() {\n    if (_nameScopeStack.length === 0) {\n        return '';\n    }\n    else {\n        return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider;\n    }\n}\n/**\n * Get the name a Tensor (or Variable) would have if not uniqueified.\n * @param tensorName\n * @return Scoped name string.\n */\nexport function getScopedTensorName(tensorName) {\n    if (!isValidTensorName(tensorName)) {\n        throw new Error('Not a valid tensor name: \\'' + tensorName + '\\'');\n    }\n    return currentNameScopePrefix() + tensorName;\n}\n/**\n * Get unique names for Tensors and Variables.\n * @param scopedName The fully-qualified name of the Tensor, i.e. as produced by\n *  `getScopedTensorName()`.\n * @return A unique version of the given fully scoped name.\n *   If this is the first time that the scoped name is seen in this session,\n *   then the given `scopedName` is returned unaltered.  If the same name is\n *   seen again (producing a collision), an incrementing suffix is added to the\n *   end of the name, so it takes the form 'scope/name_1', 'scope/name_2', etc.\n */\nexport function getUniqueTensorName(scopedName) {\n    if (!isValidTensorName(scopedName)) {\n        throw new Error('Not a valid tensor name: \\'' + scopedName + '\\'');\n    }\n    if (!nameMap.has(scopedName)) {\n        nameMap.set(scopedName, 0);\n    }\n    const index = nameMap.get(scopedName);\n    nameMap.set(scopedName, nameMap.get(scopedName) + 1);\n    if (index > 0) {\n        const result = `${scopedName}_${index}`;\n        // Mark the composed name as used in case someone wants\n        // to call getUniqueTensorName(\"name_1\").\n        nameMap.set(result, 1);\n        return result;\n    }\n    else {\n        return scopedName;\n    }\n}\nconst tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\\._\\/]*$/);\n/**\n * Determine whether a string is a valid tensor name.\n * @param name\n * @returns A Boolean indicating whether `name` is a valid tensor name.\n */\nexport function isValidTensorName(name) {\n    return !!name.match(tensorNameRegex);\n}\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Math utility functions.\n *\n * This file contains some frequently used math function that operates on\n * number[] or Float32Array and return a number. Many of these functions are\n * not-so-thick wrappers around TF.js Core functions. But they offer the\n * convenience of\n * 1) not having to convert the inputs into Tensors,\n * 2) not having to convert the returned Tensors to numbers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { scalar, tensor1d } from '@tensorflow/tfjs-core';\nimport { ValueError } from '../errors';\n/**\n * Determine if a number is an integer.\n */\nexport function isInteger(x) {\n    return x === parseInt(x.toString(), 10);\n}\n/**\n * Calculate the product of an array of numbers.\n * @param array The array to calculate the product over.\n * @param begin Beginning index, inclusive.\n * @param end Ending index, exclusive.\n * @return The product.\n */\nexport function arrayProd(array, begin, end) {\n    if (begin == null) {\n        begin = 0;\n    }\n    if (end == null) {\n        end = array.length;\n    }\n    let prod = 1;\n    for (let i = begin; i < end; ++i) {\n        prod *= array[i];\n    }\n    return prod;\n}\n/**\n * A helper function transforms the two input types to an instance of Tensor1D,\n * so the return value can be fed directly into various TF.js Core functions.\n * @param array\n */\nfunction toArray1D(array) {\n    array = Array.isArray(array) ? new Float32Array(array) : array;\n    return tensor1d(array);\n}\n/**\n * Compute minimum value.\n * @param array\n * @return minimum value.\n */\nexport function min(array) {\n    return tfc.min(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute maximum value.\n * @param array\n * @return maximum value\n */\nexport function max(array) {\n    return tfc.max(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute sum of array.\n * @param array\n * @return The sum.\n */\nexport function sum(array) {\n    return tfc.sum(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute mean of array.\n * @param array\n * @return The mean.\n */\nexport function mean(array) {\n    return sum(array) / array.length;\n}\n/**\n * Compute variance of array.\n * @param array\n * @return The variance.\n */\nexport function variance(array) {\n    const demeaned = tfc.sub(toArray1D(array), scalar(mean(array)));\n    const sumSquare = tfc.sum(tfc.mul(demeaned, demeaned)).dataSync()[0];\n    return sumSquare / array.length;\n}\n/**\n * Compute median of array.\n * @param array\n * @return The median value.\n */\nexport function median(array) {\n    const arraySorted = array.slice().sort((a, b) => a - b);\n    const lowIdx = Math.floor((arraySorted.length - 1) / 2);\n    const highIdx = Math.ceil((arraySorted.length - 1) / 2);\n    if (lowIdx === highIdx) {\n        return arraySorted[lowIdx];\n    }\n    return (arraySorted[lowIdx] + arraySorted[highIdx]) / 2;\n}\n/**\n * Generate an array of integers in [begin, end).\n * @param begin Beginning integer, inclusive.\n * @param end Ending integer, exclusive.\n * @returns Range array.\n * @throws ValueError, iff `end` < `begin`.\n */\nexport function range(begin, end) {\n    if (end < begin) {\n        throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);\n    }\n    const out = [];\n    for (let i = begin; i < end; ++i) {\n        out.push(i);\n    }\n    return out;\n}\n//# sourceMappingURL=math_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * deeplearn.js backend.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { onesLike as coreOnesLike, scalar, tensor1d, tidy, where, zerosLike as coreZerosLike } from '@tensorflow/tfjs-core';\nimport { checkDataFormat } from '../common';\nimport { NotImplementedError, ValueError } from '../errors';\nimport * as math_utils from '../utils/math_utils';\nimport { imageDataFormat } from './common';\n// tslint:enable\n/* Setting and getting backend from deeplearn.js. */\n// Default deeplearn.js backend is WebGL (GPU).\nlet backend = 'webgl';\nexport function setBackend(requestedBackend) {\n    tfc.setBackend(requestedBackend);\n    backend = requestedBackend;\n}\nexport function getBackend() {\n    return backend;\n}\n/**\n * Indicates whether the backend is operating symbolically.\n *\n * This function will be used to determine how to interpret user code. If\n * it returns true, calls to the backend construct a symbolic graph; if\n * it returns false, calls to the backend execute immediately.\n */\nexport function isBackendSymbolic() {\n    return false;\n}\n/**\n * Get the number of elements in a Tensor.\n * @param x The Tensor.\n * @return Number of elements in `x`.\n */\nexport function countParams(x) {\n    const shape = x.shape;\n    if (shape.length > 0) {\n        return shape.reduce((a, b) => a * b);\n    }\n    else {\n        // Scalar.\n        return 1;\n    }\n}\n/**\n * Casts a tensor to a different dtype and returns it.\n * @param x Input tensor.\n * @param dtype String: 'float32'|'int32'|'bool'.\n * @returns Tensor of the specified `dtype`.\n */\nexport function cast(x, dtype) {\n    return x.asType(dtype);\n}\n/**\n * Adds a 1-sized dimension at index \"axis\".\n * @param x Input tensor.\n * @param axis Position where to add the new axis.\n * @returns Result of the dimension expansion.\n */\nexport function expandDims(x, axis = -1) {\n    const outShape = x.shape.slice();\n    if (axis < 0) {\n        axis = outShape.length + axis + 1;\n    }\n    outShape.splice(axis, 0, 1);\n    return x.reshape(outShape);\n}\n/**\n * Repeats a 2D tensor.\n *\n * If `x` has shape `[samples, dim]` and `n` is 2, for example, the output\n * will have shape `[samples, 2, dim]`.\n *\n * @param x Input tensor.\n * @param n Integer, number of times to repeat.\n * @returns The result of the repeat operation.\n * @throws ValueError: If input tensor is not 2D.\n */\nexport function repeat(x, n) {\n    return tidy(() => {\n        if (x.shape.length !== 2) {\n            throw new ValueError(`repeat() expects a rank-2 tensor, but received a ` +\n                `rank-${x.shape.length} tensor.`);\n        }\n        const y = expandDims(x, 1);\n        return tile(y, [1, n, 1]);\n    });\n}\n/**\n * Flatten a Tensor into 1D.\n * @param x Input tensor.\n * @return The result of the flattening `x`.\n */\nexport function flatten(x) {\n    const newShape = [math_utils.arrayProd(x.shape)];\n    return x.reshape(newShape);\n}\n/**\n * Turn a nD tensor into a 2D tensor with same 0th dimension.\n * In other words, it flattens each data samples of a batch.\n *\n * @param x The tensor to flatten. The rank of this tensor is required to be 2\n *   or higher.\n * @return The result of the flattening.\n */\nexport function batchFlatten(x) {\n    if (x.rank <= 1) {\n        throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);\n    }\n    const newShape = [x.shape[0], math_utils.arrayProd(x.shape, 1)];\n    return x.reshape(newShape);\n}\n/**\n * Do slicing along the first axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size size of the slice along the first axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongFirstAxis(array, start, size) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                return tfc.slice2d(array, [start, 0], [size, array.shape[1]]);\n            case 3:\n                return tfc.slice3d(array, [start, 0, 0], [size, array.shape[1], array.shape[2]]);\n            case 4:\n                return tfc.slice4d(array, [start, 0, 0, 0], [size, array.shape[1], array.shape[2], array.shape[3]]);\n            case 5:\n                return tfc.slice(array, [start, 0, 0, 0, 0], [\n                    size, array.shape[1], array.shape[2], array.shape[3], array.shape[4]\n                ]);\n            case 6:\n                return tfc.slice(array, [start, 0, 0, 0, 0, 0], [\n                    size, array.shape[1], array.shape[2], array.shape[3], array.shape[4],\n                    array.shape[5]\n                ]);\n            default:\n                throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Do slicing along the last axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size size of the slice along the last axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongLastAxis(array, start, size) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                return tfc.slice2d(array, [0, start], [array.shape[0], size]);\n            case 3:\n                return tfc.slice3d(array, [0, 0, start], [array.shape[0], array.shape[1], size]);\n            case 4:\n                return tfc.slice4d(array, [0, 0, 0, start], [array.shape[0], array.shape[1], array.shape[2], size]);\n            default:\n                throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Do slicing along the sepcified axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size of the slice along the chosen axis.\n * @param choose an axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongAxis(array, start, size, axis) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            case 3:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return tfc.slice3d(array, [0, start, 0], [array.shape[0], size, array.shape[2]]);\n                    case 3:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            case 4:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return tfc.slice4d(array, [0, start, 0, 0], [array.shape[0], size, array.shape[2], array.shape[3]]);\n                    case 3:\n                        return tfc.slice4d(array, [0, 0, start, 0], [array.shape[0], array.shape[1], size, array.shape[3]]);\n                    case 4:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            default:\n                throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Concatenates a list of tensors alongside the specified axis.\n * @param tensors `Array` of tensors to concatenate.\n * @param axis Concatenation axis.\n * @returns The result of the concatenation.\n */\nexport function concatenate(tensors, axis = -1) {\n    let rank;\n    if (axis < 0) {\n        rank = tensors[0].rank;\n        if (rank !== 0) {\n            axis = rank;\n        }\n        else {\n            axis = 0;\n        }\n    }\n    if (axis === tensors[0].rank) {\n        // Porting Note: This is necessary because tfc.concat() requires axis to be\n        //   in the interval [-rank, rank).\n        axis = -1;\n    }\n    // Porting Note: Sparse concat is not supported yet.\n    return tfc.concat(tensors, axis);\n}\n/**\n * Concatenate two arrays along the first dimension.\n * @param a The 1st `tf.Tensor` to concatenate.\n * @param b The 2nd `tf.Tensor` to concatenate.\n * @returns Result of the concatenation.\n * @throws ValueError: If `a` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function concatAlongFirstAxis(a, b) {\n    switch (a.rank) {\n        case 1:\n            return tfc.concat1d([a, b]);\n        case 2:\n            return tfc.concat2d([a, b], 0);\n        case 3:\n            return tfc.concat3d([a, b], 0);\n        case 4:\n            return tfc.concat4d([a, b], 0);\n        default:\n            throw new ValueError(`concatAlongFirstAxis() received an unsupported ` +\n                `tensor rank: ${a.rank}`);\n    }\n}\n/**\n * Creates a tensor by tiling `x` by `n`.\n * @param x A tensor.\n * @param n An Array of integers or a single integer. If an Array, the length\n *   must be the same as the number of dimensions in `x`. If a single integer,\n *   it will be treated as an Array of length 1.\n */\nexport function tile(x, n) {\n    if (!Array.isArray(n)) {\n        n = [n];\n    }\n    if (x.rank !== n.length) {\n        throw new ValueError(`The length of input n (${n.length}) does not match ` +\n            `the number of dimensions in input x (${x.rank})`);\n    }\n    return tfc.tile(x, n);\n}\n/* Creation of random tensors. */\n/**\n * Get a tensor with normal distribution of values.\n *\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @return The normal tensor.\n */\nexport function randomNormal(shape, mean = 0.0, stddev = 1.0, dtype, seed) {\n    return tfc.randomNormal(shape, mean, stddev, dtype, seed);\n}\n/* Linear Algebra */\n/**\n * Multiply two tensors and returns the result as a tensor.\n *\n * For 2D tensors, this is equivalent to matrix multiplication (matMul).\n * For tensors of higher ranks, it follows the Theano behavior,\n * (e.g. `(2, 3) * (4, 3, 5) -> (2, 4, 5)`).  From the Theano documentation:\n *\n * For N dimensions it is a sum product over the last axis of x and the\n * second-to-last of y:\n *\n * @param a A tensor of at least rank 2.\n * @param b A tensor of at least rank 2.\n * @param activation (optional) A string identifying the activation\n *   function.\n * @return Result of the dot operation.\n */\nexport function dot(a, b, activation, bias) {\n    if ((a.rank < 2) || (b.rank < 2)) {\n        throw new NotImplementedError(`dot requires both inputs to be rank >= 2` +\n            ` but got x shape = ${a.shape} and y shape = ${b.shape}`);\n    }\n    if (b.rank >= 3) {\n        const xLastDim = a.shape.slice(-1)[0];\n        const ySecondLastDim = b.shape.slice(-2)[0];\n        if (xLastDim !== ySecondLastDim) {\n            throw new NotImplementedError(`If rank y >= 3, then the second last dim` +\n                ` of y must equal the last dim of x but got x shape = ${a.shape} and ` +\n                ` y shape = ${b.shape}`);\n        }\n    }\n    // Handle basic 2D x 2D case.\n    if ((a.rank === 2) && (b.rank === 2)) {\n        const transposeA = false;\n        const transposeB = false;\n        // tfc.fused.matMul only fuses certain activation functions. Unsupported\n        // activation functions are treated as 'linear' activations, which is\n        // equivalent to a no-op.\n        return tfc.fused.matMul({\n            a,\n            b: b,\n            transposeA,\n            transposeB,\n            bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n            activation\n        });\n    }\n    else {\n        // Reshape x into the analogous 2D Tensor.\n        const aFirstDims = a.shape.slice(); // Holds all but the last dim of x.\n        const aLastDim = aFirstDims.pop();\n        a = a.reshape([-1, aLastDim]);\n        // Reshape y into the analogous 2D Tensor, and keep track of the\n        // required dimensions to reproduce the output shape.\n        const bShape = b.shape.slice();\n        const bLastDim = bShape.pop();\n        const ySecondLastDim = bShape.pop();\n        const yOtherDims = [...bShape, bLastDim];\n        // permutation should be like [r-2, 0, 1, 2, ... r-4, r-3, r-1]\n        // where r is the rank of y.\n        const perm = Array.from({ length: b.rank }, (_, i) => {\n            if (i === 0) {\n                return b.rank - 2;\n            }\n            else if (i <= b.rank - 2) {\n                return i - 1;\n            }\n            return i;\n        });\n        b = b.transpose(perm).reshape([ySecondLastDim, -1]);\n        // Multiply x and y as 2D Tensors, and then reshape back to original.\n        const outputShape = [...aFirstDims, ...yOtherDims];\n        const transposeA = false;\n        const transposeB = false;\n        return tfc.fused\n            .matMul({\n            a,\n            b,\n            transposeA,\n            transposeB,\n            bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n            activation\n        })\n            .reshape(outputShape);\n    }\n}\n/**\n * Compute the sign Tensor of an input Tensor.\n *\n * Elements of the input `tf.Tensor` that are === 0 are mapped to 0.\n * Elements of the input `tf.Tensor` that are > 0 are mapped to 1.\n * Elements of the input `tf.Tensor` that are < 0 are mapped to -1.\n *\n * @param x Input `tf.Tensor`.\n * @return The sign `tf.Tensor`.\n */\nexport function sign(x) {\n    // TODO(cais): Move to the core.\n    return tidy(() => {\n        const zerosLikeX = coreZerosLike(x);\n        const onesLikeX = coreOnesLike(x);\n        return where(tfc.equal(x, zerosLikeX), zerosLikeX, where(tfc.greater(x, coreZerosLike(x)), onesLikeX, tfc.mul(-1, onesLikeX)));\n    });\n}\n/**\n * Computes the one-hot representation of an integer tensor.\n * @param indices nD integer tensor of shape\n *   `(batch_size, dim1, dim2, ... dim(n-1))`\n * @param numClasses Integer, number of classes to consider.\n * @returns (n + 1)D one hot representation of the input\n *   with shape `(batch_size, dim1, dim2, ... dim(n-1), num_classes)`\n */\nexport function oneHot(indices, numClasses) {\n    return tidy(() => {\n        if (indices.rank !== 1) {\n            throw new Error('Only 1D one-hot tensors are supported in the ' +\n                'deeplearn backend, at present.');\n        }\n        indices = indices.toInt();\n        return tfc.oneHot(indices, numClasses).toFloat();\n    });\n}\n/* Elementary math functions. */\n/**\n * Retrieves the elements of indices `indices` in the tensor `reference`.\n * @param reference A tensor.\n * @param indices An integer tensor of indices or an `Array` of integers.\n * @param axis Axis along which to perform the gather operation.\n * @returns The result of the gathering as a tensor.\n */\nexport function gather(reference, indices, axis) {\n    return tidy(() => {\n        if (Array.isArray(indices)) {\n            indices = tensor1d(indices, 'int32');\n        }\n        else {\n            indices = indices.toInt();\n        }\n        return tfc.gather(reference, indices, axis);\n    });\n}\n/**\n * Element-wise square.\n * @param x Input tensor.\n * @return element-wise x^2\n */\nexport function square(x) {\n    return tfc.mul(x, x);\n}\n/**\n * Element-wise exponentiation.\n *\n * Porting Note: In PyKeras, `a` (the exponent) is a Python integer, which\n *   takes advatnage of the backend's (e.g., TensorFlow's) automatic\n * conversion to tensor. Here we allow `a` to be either a number or a tensor.\n *\n * @param x The base tensor.\n * @param a The exponent, tensor or number. If a number, it is rounded to the\n *   nearest integer and converted to a tensor.\n * @returns A tensor of the same shape as `x`.\n */\nexport function pow(x, a) {\n    return tidy(() => {\n        if (typeof (a) === 'number') {\n            a = scalar(Math.round(a), 'int32');\n        }\n        if (a.dtype !== 'int32') {\n            throw new NotImplementedError(`Non-int32 dtype (${a.dtype}) is not supported by pow() yet`);\n        }\n        return tfc.pow(x, a);\n    });\n}\n/**\n * Reshapes bias tensor according to rank of x.\n */\nfunction reshapeBias(xRank, bias, dataFormat) {\n    const biasShape = bias.shape;\n    if (bias.rank !== 1 && bias.rank !== xRank) {\n        throw new ValueError(`Unexpected bias dimensions: ${bias.rank}` +\n            `; expected it to be 1 or ${xRank}`);\n    }\n    if (xRank === 5) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1, 1, 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, 1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank === 4) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1, 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[2], biasShape[0], biasShape[1]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank === 3) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[1], biasShape[0]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank < 3) {\n        return bias;\n    }\n    throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);\n}\n/* Neural-network operations. */\n/**\n * Add a bias to a tensor.\n *\n * @param x The tensor to add the bias to.\n * @param bias The bias to add to `x`. Must be 1D or the same rank as `x`.\n * @return Result of the bias adding.\n * @throws ValueError: If the rank of `bias` is incorrect.\n */\nexport function biasAdd(x, bias, dataFormat) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        return x.add(reshapeBias(x.rank, bias, dataFormat));\n    });\n}\n/**\n * Exponential linear unit (ELU).\n * @param x A tensor or variable to compute the activation function for.\n * @param alpha: A scalar, a scaling factor for the negative section.\n * @return Output of the ELU operation.\n */\nexport function elu(x, alpha = 1) {\n    // TODO(cais): Add support for alpha values other than 1.\n    if (alpha !== 1) {\n        throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented ` +\n            `yet.`);\n    }\n    return tfc.elu(x);\n}\n/**\n * Softsign of a tensor.\n *\n * Defined as x / (abs(x) + 1), element-wise.\n *\n * @param x: Input.\n * @returns Output.\n */\nexport function softsign(x) {\n    return tidy(() => tfc.div(x, tfc.abs(x).add(1)));\n}\n/**\n * Sets entries in `x` to zero at random, while scaling the entire tensor.\n *\n * @param x input tensor.\n * @param level fraction of the entries in the tensor that will be set to 0.\n * @param noiseShape shape of randomly generated keep/drop flags, must be\n *   broadcastable to the shape of `x`. Optional.\n * @param seed random seed to ensure determinism. Optional.\n * @returns Result of the dropout operation.\n */\nexport function dropout(x, level, noiseShape, seed) {\n    return tidy(() => tfc.dropout(x, level, noiseShape, seed));\n}\n/**\n * Element-wise, segment-wise linear approximation of sigmoid.\n *\n * Returns `0.` if `x < -2.5`, `1.` if `x > 2.5`.\n * In `-2.5 <= x <= 2.5`, returns `0.2 * x + 0.5`.\n *\n * @param x Input tensor.\n * @returns Output tensor.\n */\nexport function hardSigmoid(x) {\n    return tidy(() => {\n        const y = tfc.add(.5, tfc.mul(.2, x));\n        return tfc.clipByValue(y, 0, 1);\n    });\n}\n/**\n * Invoke `x` in the training phase, and `alt` otherwise.\n *\n * Porting Note: We do not create placeholder tensors for the `training`\n * boolean flag here, because there is no such thing in the TF.js imperative\n * backend.\n *\n * @param x The function to invoke iff `training` is `true`.\n * @param alt The function to invoke iff `training` is `false`.\n * @param training Boolean flag for whether training phase is active.\n * @returns The return value of `x()` if `training` is `true`, or the return\n *   value of `alt()` if `training` is `false`.\n */\nexport function inTrainPhase(x, alt, training = false) {\n    return training ? x() : alt();\n}\n//# sourceMappingURL=tfjs_backend.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport const VALID_FAN_MODE_VALUES = ['fanIn', 'fanOut', 'fanAvg'];\nexport const VALID_DISTRIBUTION_VALUES = ['normal', 'uniform', 'truncatedNormal'];\n// We can't easily extract a string[] from the string union type, but we can\n// recapitulate the list, enforcing at compile time that the values are valid\n// and that we have the right number of them.\n/**\n * A string array of valid Initializer class names.\n *\n * This is guaranteed to match the `InitializerClassName` union type.\n */\nexport const initializerClassNames = [\n    'Zeros', 'Ones', 'Constant', 'RandomNormal', 'RandomUniform',\n    'TruncatedNormal', 'VarianceScaling', 'Orthogonal', 'Identity'\n];\n//# sourceMappingURL=initializer_config.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { eye, linalg, mul, ones, randomUniform, scalar, serialization, tidy, truncatedNormal, zeros } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { checkDataFormat } from './common';\nimport { NotImplementedError, ValueError } from './errors';\nimport { VALID_DISTRIBUTION_VALUES, VALID_FAN_MODE_VALUES } from './keras_format/initializer_config';\nimport { checkStringTypeUnionValue, deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\nimport { arrayProd } from './utils/math_utils';\nexport function checkFanMode(value) {\n    checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, 'FanMode', value);\n}\nexport function checkDistribution(value) {\n    checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, 'Distribution', value);\n}\n/**\n * Initializer base class.\n *\n * @doc {\n *   heading: 'Initializers', subheading: 'Classes', namespace: 'initializers'}\n */\nexport class Initializer extends serialization.Serializable {\n    fromConfigUsesCustomObjects() {\n        return false;\n    }\n    getConfig() {\n        return {};\n    }\n}\nexport class Zeros extends Initializer {\n    apply(shape, dtype) {\n        return zeros(shape, dtype);\n    }\n}\n/** @nocollapse */\nZeros.className = 'Zeros';\nserialization.registerClass(Zeros);\nexport class Ones extends Initializer {\n    apply(shape, dtype) {\n        return ones(shape, dtype);\n    }\n}\n/** @nocollapse */\nOnes.className = 'Ones';\nserialization.registerClass(Ones);\nexport class Constant extends Initializer {\n    constructor(args) {\n        super();\n        if (typeof args !== 'object') {\n            throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);\n        }\n        if (args.value === undefined) {\n            throw new ValueError(`config must have value set but got ${args}`);\n        }\n        this.value = args.value;\n    }\n    apply(shape, dtype) {\n        return tidy(() => mul(scalar(this.value), ones(shape, dtype)));\n    }\n    getConfig() {\n        return {\n            value: this.value,\n        };\n    }\n}\n/** @nocollapse */\nConstant.className = 'Constant';\nserialization.registerClass(Constant);\nexport class RandomUniform extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MINVAL = -0.05;\n        this.DEFAULT_MAXVAL = 0.05;\n        this.minval = args.minval || this.DEFAULT_MINVAL;\n        this.maxval = args.maxval || this.DEFAULT_MAXVAL;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        return randomUniform(shape, this.minval, this.maxval, dtype);\n    }\n    getConfig() {\n        return { minval: this.minval, maxval: this.maxval, seed: this.seed };\n    }\n}\n/** @nocollapse */\nRandomUniform.className = 'RandomUniform';\nserialization.registerClass(RandomUniform);\nexport class RandomNormal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MEAN = 0.;\n        this.DEFAULT_STDDEV = 0.05;\n        this.mean = args.mean || this.DEFAULT_MEAN;\n        this.stddev = args.stddev || this.DEFAULT_STDDEV;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        dtype = dtype || 'float32';\n        if (dtype !== 'float32' && dtype !== 'int32') {\n            throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n        }\n        return K.randomNormal(shape, this.mean, this.stddev, dtype, this.seed);\n    }\n    getConfig() {\n        return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n    }\n}\n/** @nocollapse */\nRandomNormal.className = 'RandomNormal';\nserialization.registerClass(RandomNormal);\nexport class TruncatedNormal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MEAN = 0.;\n        this.DEFAULT_STDDEV = 0.05;\n        this.mean = args.mean || this.DEFAULT_MEAN;\n        this.stddev = args.stddev || this.DEFAULT_STDDEV;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        dtype = dtype || 'float32';\n        if (dtype !== 'float32' && dtype !== 'int32') {\n            throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);\n        }\n        return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed);\n    }\n    getConfig() {\n        return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n    }\n}\n/** @nocollapse */\nTruncatedNormal.className = 'TruncatedNormal';\nserialization.registerClass(TruncatedNormal);\nexport class Identity extends Initializer {\n    constructor(args) {\n        super();\n        this.gain = args.gain != null ? args.gain : 1.0;\n    }\n    apply(shape, dtype) {\n        return tidy(() => {\n            if (shape.length !== 2 || shape[0] !== shape[1]) {\n                throw new ValueError('Identity matrix initializer can only be used for' +\n                    ' 2D square matrices.');\n            }\n            else {\n                return mul(this.gain, eye(shape[0]));\n            }\n        });\n    }\n    getConfig() {\n        return { gain: this.gain };\n    }\n}\n/** @nocollapse */\nIdentity.className = 'Identity';\nserialization.registerClass(Identity);\n/**\n * Computes the number of input and output units for a weight shape.\n * @param shape Shape of weight.\n * @param dataFormat data format to use for convolution kernels.\n *   Note that all kernels in Keras are standardized on the\n *   CHANNEL_LAST ordering (even when inputs are set to CHANNEL_FIRST).\n * @return An length-2 array: fanIn, fanOut.\n */\nfunction computeFans(shape, dataFormat = 'channelsLast') {\n    let fanIn;\n    let fanOut;\n    checkDataFormat(dataFormat);\n    if (shape.length === 2) {\n        fanIn = shape[0];\n        fanOut = shape[1];\n    }\n    else if ([3, 4, 5].indexOf(shape.length) !== -1) {\n        if (dataFormat === 'channelsFirst') {\n            const receptiveFieldSize = arrayProd(shape, 2);\n            fanIn = shape[1] * receptiveFieldSize;\n            fanOut = shape[0] * receptiveFieldSize;\n        }\n        else if (dataFormat === 'channelsLast') {\n            const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2);\n            fanIn = shape[shape.length - 2] * receptiveFieldSize;\n            fanOut = shape[shape.length - 1] * receptiveFieldSize;\n        }\n    }\n    else {\n        const shapeProd = arrayProd(shape);\n        fanIn = Math.sqrt(shapeProd);\n        fanOut = Math.sqrt(shapeProd);\n    }\n    return [fanIn, fanOut];\n}\nexport class VarianceScaling extends Initializer {\n    /**\n     * Constructor of VarianceScaling.\n     * @throws ValueError for invalid value in scale.\n     */\n    constructor(args) {\n        super();\n        if (args.scale < 0.0) {\n            throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);\n        }\n        this.scale = args.scale == null ? 1.0 : args.scale;\n        this.mode = args.mode == null ? 'fanIn' : args.mode;\n        checkFanMode(this.mode);\n        this.distribution =\n            args.distribution == null ? 'normal' : args.distribution;\n        checkDistribution(this.distribution);\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        const fans = computeFans(shape);\n        const fanIn = fans[0];\n        const fanOut = fans[1];\n        let scale = this.scale;\n        if (this.mode === 'fanIn') {\n            scale /= Math.max(1, fanIn);\n        }\n        else if (this.mode === 'fanOut') {\n            scale /= Math.max(1, fanOut);\n        }\n        else {\n            scale /= Math.max(1, (fanIn + fanOut) / 2);\n        }\n        if (this.distribution === 'normal') {\n            const stddev = Math.sqrt(scale);\n            dtype = dtype || 'float32';\n            if (dtype !== 'float32' && dtype !== 'int32') {\n                throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);\n            }\n            return truncatedNormal(shape, 0, stddev, dtype, this.seed);\n        }\n        else {\n            const limit = Math.sqrt(3 * scale);\n            return randomUniform(shape, -limit, limit, dtype);\n        }\n    }\n    getConfig() {\n        return {\n            scale: this.scale,\n            mode: this.mode,\n            distribution: this.distribution,\n            seed: this.seed\n        };\n    }\n}\n/** @nocollapse */\nVarianceScaling.className = 'VarianceScaling';\nserialization.registerClass(VarianceScaling);\nexport class GlorotUniform extends VarianceScaling {\n    /**\n     * Constructor of GlorotUniform\n     * @param scale\n     * @param mode\n     * @param distribution\n     * @param seed\n     */\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanAvg',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, GlorotUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nGlorotUniform.className = 'GlorotUniform';\nserialization.registerClass(GlorotUniform);\nexport class GlorotNormal extends VarianceScaling {\n    /**\n     * Constructor of GlorotNormal.\n     * @param scale\n     * @param mode\n     * @param distribution\n     * @param seed\n     */\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanAvg',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, GlorotNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nGlorotNormal.className = 'GlorotNormal';\nserialization.registerClass(GlorotNormal);\nexport class HeNormal extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 2.0,\n            mode: 'fanIn',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, HeNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nHeNormal.className = 'HeNormal';\nserialization.registerClass(HeNormal);\nexport class HeUniform extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 2.0,\n            mode: 'fanIn',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, HeUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nHeUniform.className = 'HeUniform';\nserialization.registerClass(HeUniform);\nexport class LeCunNormal extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanIn',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, LeCunNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nLeCunNormal.className = 'LeCunNormal';\nserialization.registerClass(LeCunNormal);\nexport class LeCunUniform extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanIn',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, LeCunUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nLeCunUniform.className = 'LeCunNormal';\nserialization.registerClass(LeCunUniform);\nexport class Orthogonal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_GAIN = 1;\n        this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain;\n        this.seed = args.seed;\n        if (this.seed != null) {\n            throw new NotImplementedError('Random seed is not implemented for Orthogonal Initializer yet.');\n        }\n    }\n    apply(shape, dtype) {\n        return tidy(() => {\n            if (shape.length < 2) {\n                throw new NotImplementedError('Shape must be at least 2D.');\n            }\n            if (shape[0] * shape[1] > 2000) {\n                console.warn(`Orthogonal initializer is being called on a matrix with more ` +\n                    `than 2000 (${shape[0] * shape[1]}) elements: ` +\n                    `Slowness may result.`);\n            }\n            // TODO(cais): Add seed support.\n            const normalizedShape = shape[0] > shape[1] ? [shape[1], shape[0]] : shape;\n            const a = K.randomNormal(normalizedShape, 0, 1, 'float32');\n            let q = linalg.gramSchmidt(a);\n            if (shape[0] > shape[1]) {\n                q = q.transpose();\n            }\n            return mul(this.gain, q);\n        });\n    }\n    getConfig() {\n        return {\n            gain: this.gain,\n            seed: this.seed,\n        };\n    }\n}\n/** @nocollapse */\nOrthogonal.className = 'Orthogonal';\nserialization.registerClass(Orthogonal);\n// Maps the JavaScript-like identifier keys to the corresponding registry\n// symbols.\nexport const INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'constant': 'Constant',\n    'glorotNormal': 'GlorotNormal',\n    'glorotUniform': 'GlorotUniform',\n    'heNormal': 'HeNormal',\n    'heUniform': 'HeUniform',\n    'identity': 'Identity',\n    'leCunNormal': 'LeCunNormal',\n    'leCunUniform': 'LeCunUniform',\n    'ones': 'Ones',\n    'orthogonal': 'Orthogonal',\n    'randomNormal': 'RandomNormal',\n    'randomUniform': 'RandomUniform',\n    'truncatedNormal': 'TruncatedNormal',\n    'varianceScaling': 'VarianceScaling',\n    'zeros': 'Zeros'\n};\nfunction deserializeInitializer(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'initializer');\n}\nexport function serializeInitializer(initializer) {\n    return serializeKerasObject(initializer);\n}\nexport function getInitializer(identifier) {\n    if (typeof identifier === 'string') {\n        const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        /* We have four 'helper' classes for common initializers that\n        all get serialized as 'VarianceScaling' and shouldn't go through\n        the deserializeInitializer pathway. */\n        if (className === 'GlorotNormal') {\n            return new GlorotNormal();\n        }\n        else if (className === 'GlorotUniform') {\n            return new GlorotUniform();\n        }\n        else if (className === 'HeNormal') {\n            return new HeNormal();\n        }\n        else if (className === 'HeUniform') {\n            return new HeUniform();\n        }\n        else if (className === 'LeCunNormal') {\n            return new LeCunNormal();\n        }\n        else if (className === 'LeCunUniform') {\n            return new LeCunUniform();\n        }\n        else {\n            const config = {};\n            config['className'] = className;\n            config['config'] = {};\n            return deserializeInitializer(config);\n        }\n    }\n    else if (identifier instanceof Initializer) {\n        return identifier;\n    }\n    else {\n        return deserializeInitializer(identifier);\n    }\n}\n//# sourceMappingURL=initializers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// tslint:disable-next-line:max-line-length\nimport { Constant, GlorotNormal, GlorotUniform, HeNormal, HeUniform, Identity, LeCunNormal, LeCunUniform, Ones, Orthogonal, RandomNormal, RandomUniform, TruncatedNormal, VarianceScaling, Zeros } from './initializers';\n/**\n * Initializer that generates tensors initialized to 0.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function zeros() {\n    return new Zeros();\n}\n/**\n * Initializer that generates tensors initialized to 1.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function ones() {\n    return new Ones();\n}\n/**\n * Initializer that generates values initialized to some constant.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function constant(args) {\n    return new Constant(args);\n}\n/**\n * Initializer that generates random values initialized to a uniform\n * distribution.\n *\n * Values will be distributed uniformly between the configured minval and\n * maxval.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function randomUniform(args) {\n    return new RandomUniform(args);\n}\n/**\n * Initializer that generates random values initialized to a normal\n * distribution.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function randomNormal(args) {\n    return new RandomNormal(args);\n}\n/**\n * Initializer that generates random values initialized to a truncated normal.\n * distribution.\n *\n * These values are similar to values from a `RandomNormal` except that values\n * more than two standard deviations from the mean are discarded and re-drawn.\n * This is the recommended initializer for neural network weights and filters.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function truncatedNormal(args) {\n    return new TruncatedNormal(args);\n}\n/**\n * Initializer that generates the identity matrix.\n * Only use for square 2D matrices.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function identity(args) {\n    return new Identity(args);\n}\n/**\n * Initializer capable of adapting its scale to the shape of weights.\n * With distribution=NORMAL, samples are drawn from a truncated normal\n * distribution centered on zero, with `stddev = sqrt(scale / n)` where n is:\n *   - number of input units in the weight tensor, if mode = FAN_IN.\n *   - number of output units, if mode = FAN_OUT.\n *   - average of the numbers of input and output units, if mode = FAN_AVG.\n * With distribution=UNIFORM,\n * samples are drawn from a uniform distribution\n * within [-limit, limit], with `limit = sqrt(3 * scale / n)`.\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function varianceScaling(config) {\n    return new VarianceScaling(config);\n}\n/**\n * Glorot uniform initializer, also called Xavier uniform initializer.\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotUniform(args) {\n    return new GlorotUniform(args);\n}\n/**\n * Glorot normal initializer, also called Xavier normal initializer.\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor.\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotNormal(args) {\n    return new GlorotNormal(args);\n}\n/**\n * He normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function heNormal(args) {\n    return new HeNormal(args);\n}\n/**\n * He uniform initializer.\n *\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / fan_in)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function heUniform(args) {\n    return new HeUniform(args);\n}\n/**\n * LeCun normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(1 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * References:\n *   [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n *   [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunNormal(args) {\n    return new LeCunNormal(args);\n}\n/**\n * LeCun uniform initializer.\n *\n * It draws samples from a uniform distribution in the interval\n * `[-limit, limit]` with `limit = sqrt(3 / fanIn)`,\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunUniform(args) {\n    return new LeCunUniform(args);\n}\n/**\n * Initializer that generates a random orthogonal matrix.\n *\n * Reference:\n * [Saxe et al., http://arxiv.org/abs/1312.6120](http://arxiv.org/abs/1312.6120)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function orthogonal(args) {\n    return new Orthogonal(args);\n}\n//# sourceMappingURL=exports_initializers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Utilities related to persistent state in the backend.\n */\n/**\n * An ID to track `tf.SymbolicTensor`s and derived classes.\n * Required in different places in engine/topology.ts to identify unique\n * tensors.\n */\nlet _nextUniqueTensorId = 0;\nexport function getNextUniqueTensorId() {\n    return _nextUniqueTensorId++;\n}\nconst _uidPrefixes = {};\n/**\n * Provides a unique UID given a string prefix.\n *\n * @param prefix\n */\nexport function getUid(prefix = '') {\n    if (!(prefix in _uidPrefixes)) {\n        _uidPrefixes[prefix] = 0;\n    }\n    _uidPrefixes[prefix] += 1;\n    return prefix + _uidPrefixes[prefix].toString();\n}\n//# sourceMappingURL=state.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { ValueError } from '../errors';\n// tslint:enable\n/**\n * Determine whether the input is an Array of Shapes.\n */\nexport function isArrayOfShapes(x) {\n    return Array.isArray(x) && Array.isArray(x[0]);\n}\n/**\n * Special case of normalizing shapes to lists.\n *\n * @param x A shape or list of shapes to normalize into a list of Shapes.\n * @return A list of Shapes.\n */\nexport function normalizeShapeList(x) {\n    if (x.length === 0) {\n        return [];\n    }\n    if (!Array.isArray(x[0])) {\n        return [x];\n    }\n    return x;\n}\n/**\n * Helper function to obtain exactly one Tensor.\n * @param xs: A single `tf.Tensor` or an `Array` of `tf.Tensor`s.\n * @return A single `tf.Tensor`. If `xs` is an `Array`, return the first one.\n * @throws ValueError: If `xs` is an `Array` and its length is not 1.\n */\nexport function getExactlyOneTensor(xs) {\n    let x;\n    if (Array.isArray(xs)) {\n        if (xs.length !== 1) {\n            throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);\n        }\n        x = xs[0];\n    }\n    else {\n        x = xs;\n    }\n    return x;\n}\n/**\n * Helper function to obtain exactly on instance of Shape.\n *\n * @param shapes Input single `Shape` or Array of `Shape`s.\n * @returns If input is a single `Shape`, return it unchanged. If the input is\n *   an `Array` containing exactly one instance of `Shape`, return the instance.\n *   Otherwise, throw a `ValueError`.\n * @throws ValueError: If input is an `Array` of `Shape`s, and its length is not\n *   1.\n */\nexport function getExactlyOneShape(shapes) {\n    if (Array.isArray(shapes) && Array.isArray(shapes[0])) {\n        if (shapes.length === 1) {\n            shapes = shapes;\n            return shapes[0];\n        }\n        else {\n            throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`);\n        }\n    }\n    else {\n        return shapes;\n    }\n}\n//# sourceMappingURL=types_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Count the elements in an Array of LayerVariables.\n *\n * @param weights: The LayerVariables of which the constituent numbers are to\n *   be counted.\n * @returns A count of the elements in all the LayerVariables\n */\nexport function countParamsInWeights(weights) {\n    let count = 0;\n    for (const weight of weights) {\n        if (weight.shape.length === 0) {\n            count += 1;\n        }\n        else {\n            count += weight.shape.reduce((a, b) => a * b);\n        }\n    }\n    return count;\n}\n//# sourceMappingURL=variable_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { variableGrads } from '@tensorflow/tfjs-core';\nimport { getNextUniqueTensorId } from './backend/state';\nimport { getScopedTensorName, getUniqueTensorName } from './common';\nimport { NotImplementedError } from './errors';\nconst DEFAULT_VARIABLE_NAME_PREFIX = 'Variable';\n/**\n * A `tf.layers.LayerVariable` is similar to a `tf.Tensor` in that it has a\n * dtype and shape, but its value is mutable.  The value is itself represented\n * as a`tf.Tensor`, and can be read with the `read()` method and updated with\n * the `write()` method.\n */\nexport class LayerVariable {\n    /**\n     * Construct Variable from a `tf.Tensor`.\n     *\n     * If not explicitly named, the Variable will be given a name with the\n     * prefix 'Variable'. Variable names are unique. In the case of name\n     * collision, suffixies '_<num>' will be added to the name.\n     *\n     * @param val Initial value of the Variable.\n     * @param name Name of the variable. If `null` or `undefined` is provided, it\n     *   will default a name with the prefix 'Variable'.\n     * @param constraint Optional, projection function to be applied to the\n     * variable after optimize updates\n     * @throws ValueError if `name` is `null` or `undefined`.\n     */\n    constructor(val, dtype = 'float32', name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true, constraint = null) {\n        this.dtype = dtype == null ? 'float32' : dtype;\n        this.shape = val.shape;\n        this.id = getNextUniqueTensorId();\n        name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name;\n        this.originalName = getScopedTensorName(name);\n        this.name = getUniqueTensorName(this.originalName);\n        this.trainable_ = trainable;\n        this.constraint = constraint;\n        this.val = tfc.variable(val, this.trainable_, this.name, this.dtype);\n    }\n    /**\n     * Get a snapshot of the Variable's value.\n     *\n     * The returned value is a snapshot of the Variable's value at the time of\n     * the invocation. Future mutations in the value of the tensor will only\n     * be reflected by future calls to this method.\n     */\n    read() {\n        this.assertNotDisposed();\n        return this.val;\n    }\n    /**\n     * Update the value of the Variable.\n     *\n     * @param newVal: The new value to update to. Must be consistent with the\n     *   dtype and shape of the Variable.\n     * @return This Variable.\n     */\n    write(newVal) {\n        // TODO(cais): Once  TF.js Core supports Tensor.dtype, check dtype match.\n        this.assertNotDisposed();\n        checkShapesMatch(this.val, newVal);\n        // Skip updating if this is the exact same tensor.\n        if (this.val.id !== newVal.id) {\n            this.val.assign(newVal);\n            if (this.constraint != null) {\n                this.val.assign(this.constraint.apply(this.val));\n            }\n        }\n        return this;\n    }\n    /**\n     * Dispose this LayersVariable instance from memory.\n     */\n    dispose() {\n        this.assertNotDisposed();\n        this.val.dispose();\n    }\n    assertNotDisposed() {\n        if (this.val.isDisposed) {\n            throw new Error(`LayersVariable ${this.name} is already disposed.`);\n        }\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this.trainable_ = trainable;\n        this.val.trainable = trainable;\n    }\n}\nfunction checkShapesMatch(x, y) {\n    if (x.shape.toString() !== y.shape.toString()) {\n        throw new Error('Shape mismatch: ' + JSON.stringify(x.shape) + ' vs. ' +\n            JSON.stringify(y.shape));\n    }\n}\n/**\n * Create a Variable.\n * @param x The initial value of the `Variable`.\n * @param dtype optional, the type of the variable.\n * @param name optional, the name of the variable, default provided by\n * Variable.\n * @param constraint optional, a constraint to be applied after every update.\n * @return The newly instantiated `Variable`.\n */\nexport function variable(x, dtype, name, constraint) {\n    return new LayerVariable(x, dtype, name, true, constraint);\n}\n/**\n * Instantiates an all-zeros Variable and returns it.\n *\n * @param shape Shape of the tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return An all-zero Variable.\n */\nexport function zerosVariable(shape, dtype, name) {\n    // TODO(cais): Implement logic for dtype.\n    return new LayerVariable(tfc.zeros(shape), dtype, name);\n}\n/**\n * Instantiates an all-zeros tensor of the same shape as another tensor.\n *\n * @param x The other tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return A newly instantiated Variable.\n */\nexport function zerosLike(x, dtype, name) {\n    return new LayerVariable(tfc.zerosLike(x), dtype, name);\n}\n/**\n * Instantiates an all-ones tensor and returns it.\n *\n * @param shape Shape of the tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return An all-ones Variable.\n */\nexport function onesVariable(shape, dtype, name) {\n    // TODO(cais): Implement logic for dtype.\n    const allocated = tfc.ones(shape);\n    return new LayerVariable(allocated, dtype, name);\n}\n/**\n * Instantiates an all-ones tensor of the same shape as another tensor.\n *\n * @param x The other tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return A newly instantiated Variable.\n */\nexport function onesLike(x, dtype, name) {\n    const allocated = tfc.onesLike(x);\n    return new LayerVariable(allocated, dtype, name);\n}\n/**\n * Instantiate an identity matrix and returns it, as a Variable\n *\n * @param size Number of rows/columns.\n * @param dtype Data type of returned Variable.\n * @param name Name of returned Variable.\n * @return A Variable, an identity matrix.\n */\nexport function eyeVariable(size, dtype, name) {\n    return new LayerVariable(tfc.eye(size), dtype, name);\n}\n/**\n * Get a Variable with uniform distribution of values.\n * @param shape Shape of the tensor.\n * @param minval Lower bound of the uniform distribution.\n * @param maxval Upper bound of the uniform distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The uniform-random Variable.\n */\nexport function randomUniformVariable(shape, minval, maxval, dtype, seed, name = 'randomUniform') {\n    return new LayerVariable(tfc.randomUniform(shape, minval, maxval, dtype), dtype, name);\n}\n/**\n * Get a Variable with truncated-normal distribution of values.\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The truncated-normal-random Variable.\n */\nexport function truncatedNormalVariable(shape, mean = 0.0, stddev = 1.0, dtype, seed, name = 'truncatedNormal') {\n    // TODO(cais): Implement logic for dtype and seed once they are supported\n    // by deeplearn.js.\n    dtype = dtype || 'float32';\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n    }\n    return new LayerVariable(tfc.truncatedNormal(shape, mean, stddev, dtype, seed), dtype, name);\n}\n/**\n * Get a Variable with normal distribution of values.\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The truncated-normal-random Variable.\n */\nexport function randomNormalVariable(shape, mean = 0.0, stddev = 1.0, dtype, seed, name = 'randomNormal') {\n    dtype = dtype || 'float32';\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new NotImplementedError(`randomNormalVariable does not support dType ${dtype}.`);\n    }\n    return new LayerVariable(tfc.randomNormal(shape, mean, stddev, dtype, seed), dtype, name);\n}\n/**\n * Update the value of a Variable.\n * @param x The Variable to be updated.\n * @param xNew The new value to update to.\n * @return The Variable updated.\n */\nexport function update(x, xNew) {\n    return x.write(xNew);\n}\n/**\n * Update the value of a Variable by adding an increment.\n * @param x The Variable to be updated.\n * @param increment The incrment to add to `x`.\n * @return The Variable updated.\n */\nexport function updateAdd(x, increment) {\n    return x.write(tfc.add(x.read(), increment));\n}\n/**\n * Update the value of a Variable by subtracting a decrement.\n * @param x The Variable to be updated.\n * @param decrement The decrement to subtract from `x`.\n * @return The Variable updated.\n */\nexport function updateSub(x, decrement) {\n    return x.write(tfc.sub(x.read(), decrement));\n}\n/**\n * Get the values of an array of Variables.\n *\n * @param tensors An `Array` of `Variable`s to get the values of.\n * @return The values of the inputs, as an `Array` of`tf.Tensor`s.\n */\nexport function batchGetValue(xs) {\n    return xs.map(x => x.read());\n}\n/**\n * Update the value of multiple Variables at once.\n *\n * @param variablesAndValues An `Array`, each element is of type\n *   [Variable, Tensor]. The first item is the\n *   `Variable` of which the value is to be updated. The second item\n *   carries the new value.\n */\nexport function batchSetValue(variablesAndValues) {\n    variablesAndValues.forEach(variableAndValue => {\n        const variable = variableAndValue[0];\n        variable.write(variableAndValue[1]);\n    });\n}\n/**\n * Returns the gradients of `variables` w.r.t. the return value of `lossFn`.\n * @param lossFn A function which returns a Scalar to be used as the function\n *   value (i.e., numerator) for differentiation.\n * @param variables List of variables to be used as the independent variables\n *   (i.e., denominator) for differentiation.\n * @returns An Array of gradients tensors.\n */\nexport function gradients(lossFn, variables) {\n    // TODO(cais): The return type signature can be simplified if deeplearn makes\n    //   the corresponding type public.\n    const variableList = variables.map(variable => variable.read());\n    const valudAndGrads = variableGrads(lossFn, variableList);\n    return variables.map(variable => valudAndGrads.grads[variable.name]);\n}\n//# sourceMappingURL=variables.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/engine/topology.py */\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getNextUniqueTensorId, getUid } from '../backend/state';\nimport { getScopedTensorName, getUniqueTensorName, nameScope } from '../common';\nimport { AttributeError, NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { getInitializer } from '../initializers';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as types_utils from '../utils/types_utils';\nimport * as variable_utils from '../utils/variable_utils';\nimport { batchGetValue, batchSetValue, LayerVariable } from '../variables';\n/**\n * Specifies the ndim, dtype and shape of every input to a layer.\n *\n * Every layer should expose (if appropriate) an `inputSpec` attribute:\n * a list of instances of InputSpec (one per input tensor).\n *\n * A null entry in a shape is compatible with any dimension,\n * a null shape is compatible with any shape.\n */\nexport class InputSpec {\n    constructor(args) {\n        this.dtype = args.dtype;\n        this.shape = args.shape;\n        /*\n          TODO(michaelterry): Could throw error if ndim and shape are both defined\n            (then backport).\n        */\n        if (args.shape != null) {\n            this.ndim = args.shape.length;\n        }\n        else {\n            this.ndim = args.ndim;\n        }\n        this.maxNDim = args.maxNDim;\n        this.minNDim = args.minNDim;\n        this.axes = args.axes || {};\n    }\n}\n/**\n * `tf.SymbolicTensor` is a placeholder for a Tensor without any concrete value.\n *\n * They are most often encountered when building a graph of `Layer`s for a\n * a `tf.LayersModel` and the input data's shape, but not values are known.\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\nexport class SymbolicTensor {\n    /**\n     *\n     * @param dtype\n     * @param shape\n     * @param sourceLayer The Layer that produced this symbolic tensor.\n     * @param inputs The inputs passed to sourceLayer's __call__() method.\n     * @param nodeIndex\n     * @param tensorIndex\n     * @param callArgs The keyword arguments passed to the __call__() method.\n     * @param name\n     * @param outputTensorIndex The index of this tensor in the list of outputs\n     *   returned by apply().\n     */\n    constructor(dtype, shape, sourceLayer, inputs, callArgs, name, outputTensorIndex) {\n        this.dtype = dtype;\n        this.shape = shape;\n        this.sourceLayer = sourceLayer;\n        this.inputs = inputs;\n        this.callArgs = callArgs;\n        this.outputTensorIndex = outputTensorIndex;\n        this.id = getNextUniqueTensorId();\n        if (name != null) {\n            this.originalName = getScopedTensorName(name);\n            this.name = getUniqueTensorName(this.originalName);\n        }\n        this.rank = shape.length;\n    }\n}\nlet _nextNodeID = 0;\n/**\n * A `Node` describes the connectivity between two layers.\n *\n * Each time a layer is connected to some new input,\n * a node is added to `layer.inboundNodes`.\n *\n * Each time the output of a layer is used by another layer,\n * a node is added to `layer.outboundNodes`.\n *\n * `nodeIndices` and `tensorIndices` are basically fine-grained coordinates\n * describing the origin of the `inputTensors`, verifying the following:\n *\n * `inputTensors[i] ==\n * inboundLayers[i].inboundNodes[nodeIndices[i]].outputTensors[\n *   tensorIndices[i]]`\n *\n * A node from layer A to layer B is added to:\n *     A.outboundNodes\n *     B.inboundNodes\n */\nexport class Node {\n    constructor(args, \n    // TODO(michaelterry): Define actual type for this.\n    callArgs) {\n        this.callArgs = callArgs;\n        this.id = _nextNodeID++;\n        /*\n          Layer instance (NOT a list).\n          this is the layer that takes a list of input tensors\n          and turns them into a list of output tensors.\n          the current node will be added to\n          the inboundNodes of outboundLayer.\n        */\n        this.outboundLayer = args.outboundLayer;\n        /*\n            The following 3 properties describe where\n            the input tensors come from: which layers,\n            and for each layer, which node and which\n            tensor output of each node.\n        */\n        // List of layer instances.\n        this.inboundLayers = args.inboundLayers;\n        // List of integers, 1:1 mapping with inboundLayers.\n        this.nodeIndices = args.nodeIndices;\n        // List of integers, 1:1 mapping with inboundLayers.\n        this.tensorIndices = args.tensorIndices;\n        /*\n            Following 2 properties:\n            tensor inputs and outputs of outboundLayer.\n        */\n        // List of tensors. 1:1 mapping with inboundLayers.\n        this.inputTensors = args.inputTensors;\n        // List of tensors, created by outboundLayer.call().\n        this.outputTensors = args.outputTensors;\n        /*\n            Following 2 properties: input and output masks.\n            List of tensors, 1:1 mapping with inputTensor.\n        */\n        this.inputMasks = args.inputMasks;\n        // List of tensors, created by outboundLayer.computeMask().\n        this.outputMasks = args.outputMasks;\n        // Following 2 properties: input and output shapes.\n        // List of shape tuples, shapes of inputTensors.\n        this.inputShapes = args.inputShapes;\n        // List of shape tuples, shapes of outputTensors.\n        this.outputShapes = args.outputShapes;\n        // Add nodes to all layers involved.\n        for (const layer of args.inboundLayers) {\n            if (layer != null) {\n                layer.outboundNodes.push(this);\n            }\n        }\n        args.outboundLayer.inboundNodes.push(this);\n    }\n    getConfig() {\n        const inboundNames = [];\n        for (const layer of this.inboundLayers) {\n            if (layer != null) {\n                inboundNames.push(layer.name);\n            }\n            else {\n                inboundNames.push(null);\n            }\n        }\n        return {\n            outboundLayer: this.outboundLayer ? this.outboundLayer.name : null,\n            inboundLayers: inboundNames,\n            nodeIndices: this.nodeIndices,\n            tensorIndices: this.tensorIndices\n        };\n    }\n}\nlet _nextLayerID = 0;\n/**\n * A layer is a grouping of operations and weights that can be composed to\n * create a `tf.LayersModel`.\n *\n * Layers are constructed by using the functions under the\n * [tf.layers](#Layers-Basic) namespace.\n *\n * @doc {heading: 'Layers', subheading: 'Classes', namespace: 'layers'}\n */\nexport class Layer extends serialization.Serializable {\n    constructor(args = {}) {\n        super();\n        this._callHook = null;\n        this._addedWeightNames = [];\n        // Porting Notes: PyKeras does not have this property in this base Layer\n        //   class. Instead lets Layer subclass set it dynamically and checks the\n        //   value with `hasattr`. In tfjs-layers, we let this be a member of this\n        //   base class.\n        this._stateful = false;\n        this.id = _nextLayerID++;\n        this.activityRegularizer = null;\n        this.inputSpec = null;\n        this.supportsMasking = false;\n        // These properties will be set upon call of this.build()\n        this._trainableWeights = [];\n        this._nonTrainableWeights = [];\n        this._losses = [];\n        this._updates = [];\n        this._built = false;\n        /*\n          These lists will be filled via successive calls\n          to this.addInboundNode().\n         */\n        this.inboundNodes = [];\n        this.outboundNodes = [];\n        let name = args.name;\n        if (!name) {\n            const prefix = this.getClassName();\n            name = generic_utils.toSnakeCase(prefix) + '_' + getUid(prefix);\n        }\n        this.name = name;\n        this.trainable_ = args.trainable == null ? true : args.trainable;\n        if (args.inputShape != null || args.batchInputShape != null) {\n            /*\n              In this case we will later create an input layer\n              to insert before the current layer\n             */\n            let batchInputShape;\n            if (args.batchInputShape != null) {\n                batchInputShape = args.batchInputShape;\n            }\n            else if (args.inputShape != null) {\n                let batchSize = null;\n                if (args.batchSize != null) {\n                    batchSize = args.batchSize;\n                }\n                batchInputShape = [batchSize].concat(args.inputShape);\n            }\n            this.batchInputShape = batchInputShape;\n            // Set dtype.\n            let dtype = args.dtype;\n            if (dtype == null) {\n                dtype = args.inputDType;\n            }\n            if (dtype == null) {\n                dtype = 'float32';\n            }\n            this.dtype = dtype;\n        }\n        if (args.weights != null) {\n            this.initialWeights = args.weights;\n        }\n        else {\n            this.initialWeights = null;\n        }\n        // The value of `_refCount` is initialized to null. When the layer is used\n        // in a symbolic way for the first time, it will be set to 1.\n        this._refCount = null;\n        this.fastWeightInitDuringBuild = false;\n    }\n    /**\n     * Converts a layer and its index to a unique (immutable type) name.\n     * This function is used internally with `this.containerNodes`.\n     * @param layer The layer.\n     * @param nodeIndex The layer's position (e.g. via enumerate) in a list of\n     *   nodes.\n     *\n     * @returns The unique name.\n     */\n    static nodeKey(layer, nodeIndex) {\n        return layer.name + '_ib-' + nodeIndex.toString();\n    }\n    /**\n     * Returns this.inboundNode at index nodeIndex.\n     *\n     * Porting note: This is a replacement for _get_node_attribute_at_index()\n     * @param nodeIndex\n     * @param attrName The name of the attribute related to request for this node.\n     */\n    getNodeAtIndex(nodeIndex, attrName) {\n        if (this.inboundNodes.length === 0) {\n            throw new RuntimeError('The layer has never been called ' +\n                `and thus has no defined ${attrName}.`);\n        }\n        if (this.inboundNodes.length <= nodeIndex) {\n            throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, ` +\n                `but the layer has only ${this.inboundNodes.length} inbound nodes.`);\n        }\n        return this.inboundNodes[nodeIndex];\n    }\n    /**\n     * Retrieves the input tensor(s) of a layer at a given node.\n     *\n     * @param nodeIndex Integer, index of the node from which to retrieve the\n     *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n     *   was called.\n     *\n     * @return A tensor (or list of tensors if the layer has multiple inputs).\n     */\n    getInputAt(nodeIndex) {\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(nodeIndex, 'input').inputTensors);\n    }\n    /**\n     * Retrieves the output tensor(s) of a layer at a given node.\n     *\n     * @param nodeIndex Integer, index of the node from which to retrieve the\n     *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n     *   was called.\n     *\n     * @return A tensor (or list of tensors if the layer has multiple outputs).\n     */\n    getOutputAt(nodeIndex) {\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(nodeIndex, 'output').outputTensors);\n    }\n    // Properties\n    /**\n     * Retrieves the input tensor(s) of a layer.\n     *\n     * Only applicable if the layer has exactly one inbound node,\n     * i.e. if it is connected to one incoming layer.\n     *\n     * @return Input tensor or list of input tensors.\n     *\n     * @exception AttributeError if the layer is connected to more than one\n     *   incoming layers.\n     */\n    get input() {\n        if (this.inboundNodes.length > 1) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has multiple inbound nodes, ' +\n                'hence the notion of \"layer input\" ' +\n                'is ill-defined. ' +\n                'Use `getInputAt(nodeIndex)` instead.');\n        }\n        else if (this.inboundNodes.length === 0) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' is not connected, no input to return.');\n        }\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(0, 'input').inputTensors);\n    }\n    /**\n     * Retrieves the output tensor(s) of a layer.\n     *\n     * Only applicable if the layer has exactly one inbound node,\n     * i.e. if it is connected to one incoming layer.\n     *\n     * @return Output tensor or list of output tensors.\n     *\n     * @exception AttributeError if the layer is connected to more than one\n     *   incoming layers.\n     */\n    get output() {\n        if (this.inboundNodes.length === 0) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has no inbound nodes.');\n        }\n        if (this.inboundNodes.length > 1) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has multiple inbound nodes, ' +\n                'hence the notion of \"layer output\" ' +\n                'is ill-defined. ' +\n                'Use `getOutputAt(nodeIndex)` instead.');\n        }\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(0, 'output').outputTensors);\n    }\n    get losses() {\n        return this._losses;\n    }\n    /**\n     * Retrieves the Layer's current loss values.\n     *\n     * Used for regularizers during training.\n     */\n    calculateLosses() {\n        // Porting Node: This is an augmentation to Layer.loss in PyKeras.\n        //   In PyKeras, Layer.loss returns symbolic tensors. Here a concrete\n        //   Tensor (specifically Scalar) values are returned. This is due to the\n        //   imperative backend.\n        return this.losses.map(lossFn => lossFn());\n    }\n    get updates() {\n        return this._updates;\n    }\n    get built() {\n        return this._built;\n    }\n    set built(built) {\n        this._built = built;\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this._trainableWeights.forEach(w => w.trainable = trainable);\n        this.trainable_ = trainable;\n    }\n    get trainableWeights() {\n        if (this.trainable_) {\n            return this._trainableWeights.filter(w => w.trainable);\n        }\n        else {\n            return [];\n        }\n    }\n    set trainableWeights(weights) {\n        this._trainableWeights = weights;\n    }\n    get nonTrainableWeights() {\n        if (this.trainable) {\n            return this._trainableWeights.filter(w => !w.trainable)\n                .concat(this._nonTrainableWeights);\n        }\n        else {\n            return this._trainableWeights.concat(this._nonTrainableWeights);\n        }\n    }\n    set nonTrainableWeights(weights) {\n        this._nonTrainableWeights = weights;\n    }\n    /**\n     * The concatenation of the lists trainableWeights and nonTrainableWeights\n     * (in this order).\n     */\n    get weights() {\n        return this.trainableWeights.concat(this.nonTrainableWeights);\n    }\n    get stateful() {\n        return this._stateful;\n    }\n    /**\n     * Reset the states of the layer.\n     *\n     * This method of the base Layer class is essentially a no-op.\n     * Subclasses that are stateful (e.g., stateful RNNs) should override this\n     * method.\n     */\n    resetStates() {\n        if (!this.stateful) {\n            throw new Error('Cannot call the resetStates() method of a non-stateful Layer ' +\n                'object.');\n        }\n    }\n    /**\n     * Checks compatibility between the layer and provided inputs.\n     *\n     * This checks that the tensor(s) `input`\n     * verify the input assumptions of the layer\n     * (if any). If not, exceptions are raised.\n     *\n     * @param inputs Input tensor or list of input tensors.\n     *\n     * @exception ValueError in case of mismatch between\n     *   the provided inputs and the expectations of the layer.\n     */\n    assertInputCompatibility(inputs) {\n        inputs = generic_utils.toList(inputs);\n        if (this.inputSpec == null || this.inputSpec.length === 0) {\n            return;\n        }\n        const inputSpec = generic_utils.toList(this.inputSpec);\n        if (inputs.length !== inputSpec.length) {\n            throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, ` +\n                `but it received ${inputs.length} input tensors. ` +\n                `Input received: ${inputs}`);\n        }\n        for (let inputIndex = 0; inputIndex < inputs.length; inputIndex++) {\n            const x = inputs[inputIndex];\n            const spec = inputSpec[inputIndex];\n            if (spec == null) {\n                continue;\n            }\n            // Check ndim.\n            const ndim = x.rank;\n            if (spec.ndim != null) {\n                if (ndim !== spec.ndim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: ` +\n                        `expected ndim=${spec.ndim}, found ndim=${ndim}`);\n                }\n            }\n            if (spec.maxNDim != null) {\n                if (ndim > spec.maxNDim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}` +\n                        `: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);\n                }\n            }\n            if (spec.minNDim != null) {\n                if (ndim < spec.minNDim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}` +\n                        `: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);\n                }\n            }\n            // Check dtype.\n            if (spec.dtype != null) {\n                if (x.dtype !== spec.dtype) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} ` +\n                        `: expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);\n                }\n            }\n            // Check specific shape axes.\n            if (spec.axes) {\n                const xShape = x.shape;\n                for (const key in spec.axes) {\n                    const axis = Number(key);\n                    const value = spec.axes[key];\n                    // Perform Python-style slicing in case axis < 0;\n                    // TODO(cais): Use https://github.com/alvivi/typescript-underscore to\n                    // ensure type safety through Underscore calls.\n                    const xShapeAtAxis = axis >= 0 ? xShape[axis] : xShape[xShape.length + axis];\n                    if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) {\n                        throw new ValueError(`Input ${inputIndex} is incompatible with layer ` +\n                            `${this.name}: expected axis ${axis} of input shape to ` +\n                            `have value ${value} but got shape ${xShape}.`);\n                    }\n                }\n            }\n            // Check shape.\n            if (spec.shape != null) {\n                for (let i = 0; i < spec.shape.length; ++i) {\n                    const specDim = spec.shape[i];\n                    const dim = x.shape[i];\n                    if (specDim != null && dim != null) {\n                        if (specDim !== dim) {\n                            throw new ValueError(`Input ${inputIndex} is incompatible with layer ` +\n                                `${this.name}: expected shape=${spec.shape}, ` +\n                                `found shape=${x.shape}.`);\n                        }\n                    }\n                }\n            }\n        }\n    }\n    /**\n     * This is where the layer's logic lives.\n     *\n     * @param inputs Input tensor, or list/tuple of input tensors.\n     * @param kwargs Additional keyword arguments.\n     *\n     * @return A tensor or list/tuple of tensors.\n     */\n    call(inputs, kwargs) {\n        return inputs;\n    }\n    invokeCallHook(inputs, kwargs) {\n        if (this._callHook != null) {\n            this._callHook(inputs, kwargs);\n        }\n    }\n    /**\n     * Set call hook.\n     * This is currently used for testing only.\n     * @param callHook\n     */\n    setCallHook(callHook) {\n        this._callHook = callHook;\n    }\n    /**\n     * Clear call hook.\n     * This is currently used for testing only.\n     */\n    clearCallHook() {\n        this._callHook = null;\n    }\n    /**\n     * Builds or executes a `Layer's logic.\n     *\n     * When called with `tf.Tensor`(s), execute the `Layer`s computation and\n     * return Tensor(s). For example:\n     *\n     * ```js\n     * const denseLayer = tf.layers.dense({\n     *   units: 1,\n     *   kernelInitializer: 'zeros',\n     *   useBias: false\n     * });\n     *\n     * // Invoke the layer's apply() method with a `tf.Tensor` (with concrete\n     * // numeric values).\n     * const input = tf.ones([2, 2]);\n     * const output = denseLayer.apply(input);\n     *\n     * // The output's value is expected to be [[0], [0]], due to the fact that\n     * // the dense layer has a kernel initialized to all-zeros and does not have\n     * // a bias.\n     * output.print();\n     * ```\n     *\n     * When called with `tf.SymbolicTensor`(s), this will prepare the layer for\n     * future execution.  This entails internal book-keeping on shapes of\n     * expected Tensors, wiring layers together, and initializing weights.\n     *\n     * Calling `apply` with `tf.SymbolicTensor`s are typically used during the\n     * building of non-`tf.Sequential` models. For example:\n     *\n     * ```js\n     * const flattenLayer = tf.layers.flatten();\n     * const denseLayer = tf.layers.dense({units: 1});\n     *\n     * // Use tf.layers.input() to obtain a SymbolicTensor as input to apply().\n     * const input = tf.input({shape: [2, 2]});\n     * const output1 = flattenLayer.apply(input);\n     *\n     * // output1.shape is [null, 4]. The first dimension is the undetermined\n     * // batch size. The second dimension comes from flattening the [2, 2]\n     * // shape.\n     * console.log(JSON.stringify(output1.shape));\n     *\n     * // The output SymbolicTensor of the flatten layer can be used to call\n     * // the apply() of the dense layer:\n     * const output2 = denseLayer.apply(output1);\n     *\n     * // output2.shape is [null, 1]. The first dimension is the undetermined\n     * // batch size. The second dimension matches the number of units of the\n     * // dense layer.\n     * console.log(JSON.stringify(output2.shape));\n     *\n     * // The input and output and be used to construct a model that consists\n     * // of the flatten and dense layers.\n     * const model = tf.model({inputs: input, outputs: output2});\n     * ```\n     *\n     * @param inputs a `tf.Tensor` or `tf.SymbolicTensor` or an Array of them.\n     * @param kwargs Additional keyword arguments to be passed to `call()`.\n     *\n     * @return Output of the layer's `call` method.\n     *\n     * @exception ValueError error in case the layer is missing shape information\n     *   for its `build` call.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    // Porting Note: This is a replacement for __call__() in Python.\n    apply(inputs, kwargs) {\n        kwargs = kwargs || {};\n        this.assertNotDisposed();\n        // Ensure inputs are all the same type.\n        const inputsList = generic_utils.toList(inputs);\n        let allAreSymbolic = true;\n        for (const input of inputsList) {\n            if (!(input instanceof SymbolicTensor)) {\n                allAreSymbolic = false;\n                break;\n            }\n        }\n        let noneAreSymbolic = true;\n        for (const input of inputsList) {\n            if (input instanceof SymbolicTensor) {\n                noneAreSymbolic = false;\n                break;\n            }\n        }\n        if (allAreSymbolic === noneAreSymbolic) {\n            throw new ValueError('Arguments to apply() must be all ' +\n                'SymbolicTensors or all Tensors');\n        }\n        // TODO(michaelterry): nameScope() may not be necessary.\n        return nameScope(this.name, () => {\n            // Handle laying building (weight creating, input spec locking).\n            if (!this.built) {\n                /*\n                  Throw exceptions in case the input is not compatible\n                  with the inputSpec specified in the layer constructor.\n                 */\n                this.assertInputCompatibility(inputs);\n                // Collect input shapes to build layer.\n                const inputShapes = [];\n                for (const xElem of generic_utils.toList(inputs)) {\n                    inputShapes.push(xElem.shape);\n                }\n                this.build(generic_utils.singletonOrArray(inputShapes));\n                this.built = true;\n                // Load weights that were specified at layer instantiation.\n                if (this.initialWeights) {\n                    this.setWeights(this.initialWeights);\n                }\n                if (this._refCount === null && noneAreSymbolic) {\n                    // The first use of this layer is a non-symbolic call, set ref count\n                    // to 1 so the Layer can be properly disposed if its dispose() method\n                    // is called.\n                    this._refCount = 1;\n                }\n            }\n            /*\n              Throw exceptions in case the input is not compatible\n              with the inputSpec set at build time.\n            */\n            this.assertInputCompatibility(inputs);\n            // Handle mask propagation.\n            // TODO(michaelterry): Mask propagation not currently implemented.\n            // Actually call the layer, collecting output(s), mask(s), and shape(s).\n            if (noneAreSymbolic) {\n                let output = this.call(inputs, kwargs);\n                // TODO(michaelterry): Compute the outputMask\n                // If the layer returns tensors from its inputs, unmodified,\n                // we copy them to avoid loss of tensor metadata.\n                const outputList = generic_utils.toList(output);\n                const outputListCopy = [];\n                // TODO(michaelterry): This copying may not be necessary given our eager\n                // backend.\n                for (let x of outputList) {\n                    if (inputsList.indexOf(x) !== -1) {\n                        x = x.clone();\n                    }\n                    outputListCopy.push(x);\n                }\n                output = generic_utils.singletonOrArray(outputListCopy);\n                if (this.activityRegularizer != null) {\n                    throw new NotImplementedError('Layer invocation in the presence of activity ' +\n                        'regularizer(s) is not supported yet.');\n                }\n                // TODO(michaelterry): Call addInboundNode()?\n                return output;\n            }\n            else {\n                const inputShape = collectInputShape(inputs);\n                const outputShape = this.computeOutputShape(inputShape);\n                let output;\n                const outputDType = guessOutputDType(inputs);\n                this.warnOnIncompatibleInputShape(Array.isArray(inputs) ? inputShape[0] :\n                    inputShape);\n                if (outputShape != null && outputShape.length > 0 &&\n                    Array.isArray(outputShape[0])) {\n                    // We have multiple output shapes. Create multiple output tensors.\n                    output = outputShape\n                        .map((shape, index) => new SymbolicTensor(outputDType, shape, this, generic_utils.toList(inputs), kwargs, this.name, index));\n                }\n                else {\n                    output = new SymbolicTensor(outputDType, outputShape, this, generic_utils.toList(inputs), kwargs, this.name);\n                }\n                /*\n                  Add an inbound node to the layer, so that it keeps track\n                  of the call and of all new variables created during the call.\n                  This also updates the layer history of the output tensor(s).\n                  If the input tensor(s) had no previous history,\n                  this does nothing.\n                */\n                this.addInboundNode(inputs, output, null, null, inputShape, outputShape, kwargs);\n                this._refCount++;\n                if (this.activityRegularizer != null) {\n                    throw new NotImplementedError('Layer invocation in the presence of activity ' +\n                        'regularizer(s) is not supported yet.');\n                }\n                return output;\n            }\n        });\n    }\n    /**\n     * Check compatibility between input shape and this layer's batchInputShape.\n     *\n     * Print warning if any incompatibility is found.\n     *\n     * @param inputShape Input shape to be checked.\n     */\n    warnOnIncompatibleInputShape(inputShape) {\n        if (this.batchInputShape == null) {\n            return;\n        }\n        else if (inputShape.length !== this.batchInputShape.length) {\n            console.warn(`The rank of the input tensor provided (shape: ` +\n                `${JSON.stringify(inputShape)}) does not match that of the ` +\n                `batchInputShape (${JSON.stringify(this.batchInputShape)}) ` +\n                `of the layer ${this.name}`);\n        }\n        else {\n            let dimMismatch = false;\n            this.batchInputShape.forEach((dimension, i) => {\n                if (dimension != null && inputShape[i] != null &&\n                    inputShape[i] !== dimension) {\n                    dimMismatch = true;\n                }\n            });\n            if (dimMismatch) {\n                console.warn(`The shape of the input tensor ` +\n                    `(${JSON.stringify(inputShape)}) does not ` +\n                    `match the expectation of layer ${this.name}: ` +\n                    `${JSON.stringify(this.batchInputShape)}`);\n            }\n        }\n    }\n    /**\n     * Retrieves the output shape(s) of a layer.\n     *\n     * Only applicable if the layer has only one inbound node, or if all inbound\n     * nodes have the same output shape.\n     *\n     * @returns Output shape or shapes.\n     * @throws AttributeError: if the layer is connected to more than one incoming\n     *   nodes.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    get outputShape() {\n        if (this.inboundNodes == null || this.inboundNodes.length === 0) {\n            throw new AttributeError(`The layer ${this.name} has never been called and thus has no ` +\n                `defined output shape.`);\n        }\n        const allOutputShapes = [];\n        for (const node of this.inboundNodes) {\n            const shapeString = JSON.stringify(node.outputShapes);\n            if (allOutputShapes.indexOf(shapeString) === -1) {\n                allOutputShapes.push(shapeString);\n            }\n        }\n        if (allOutputShapes.length === 1) {\n            const outputShapes = this.inboundNodes[0].outputShapes;\n            if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) &&\n                outputShapes.length === 1) {\n                return outputShapes[0];\n            }\n            else {\n                return outputShapes;\n            }\n        }\n        else {\n            throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different ` +\n                `output shapes. Hence the notion of \"output shape\" is ill-defined ` +\n                `for the layer.`);\n            // TODO(cais): Implement getOutputShapeAt().\n        }\n    }\n    /**\n     * Counts the total number of numbers (e.g., float32, int32) in the\n     * weights.\n     *\n     * @returns An integer count.\n     * @throws RuntimeError: If the layer is not built yet (in which case its\n     *   weights are not defined yet.)\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    countParams() {\n        if (!this.built) {\n            throw new RuntimeError(`You tried to call countParams() on ${this.name}, ` +\n                `but the layer is not built yet. Build it first by calling ` +\n                `build(batchInputShape).`);\n        }\n        return variable_utils.countParamsInWeights(this.weights);\n    }\n    /**\n     * Creates the layer weights.\n     *\n     * Must be implemented on all layers that have weights.\n     *\n     * Called when apply() is called to construct the weights.\n     *\n     * @param inputShape A `Shape` or array of `Shape` (unused).\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    build(inputShape) {\n        this.built = true;\n    }\n    /**\n     * Returns the current values of the weights of the layer.\n     *\n     * @param trainableOnly Whether to get the values of only trainable weights.\n     * @returns Weight values as an `Array` of `tf.Tensor`s.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    getWeights(trainableOnly = false) {\n        return batchGetValue(trainableOnly ? this.trainableWeights : this.weights);\n    }\n    /**\n     * Sets the weights of the layer, from Tensors.\n     *\n     * @param weights a list of Tensors. The number of arrays and their shape\n     *   must match number of the dimensions of the weights of the layer (i.e.\n     *   it should match the output of `getWeights`).\n     *\n     * @exception ValueError If the provided weights list does not match the\n     *   layer's specifications.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    setWeights(weights) {\n        tidy(() => {\n            const params = this.weights;\n            if (params.length !== weights.length) {\n                // TODO(cais): Restore the following and use `providedWeights`, instead\n                // of `weights` in the error message, once the deeplearn.js bug is\n                // fixed: https://github.com/PAIR-code/deeplearnjs/issues/498 const\n                // providedWeights = JSON.stringify(weights).substr(0, 50);\n                throw new ValueError(`You called setWeights(weights) on layer \"${this.name}\" ` +\n                    `with a weight list of length ${weights.length}, ` +\n                    `but the layer was expecting ${params.length} weights. ` +\n                    `Provided weights: ${weights}...`);\n            }\n            if (params.length === 0) {\n                return;\n            }\n            const weightValueTuples = [];\n            const paramValues = batchGetValue(params);\n            for (let i = 0; i < paramValues.length; ++i) {\n                const pv = paramValues[i];\n                const p = params[i];\n                const w = weights[i];\n                if (!util.arraysEqual(pv.shape, w.shape)) {\n                    throw new ValueError(`Layer weight shape ${pv.shape} ` +\n                        `not compatible with provided weight shape ${w.shape}`);\n                }\n                weightValueTuples.push([p, w]);\n            }\n            batchSetValue(weightValueTuples);\n        });\n    }\n    /**\n     * Adds a weight variable to the layer.\n     *\n     * @param name Name of the new weight variable.\n     * @param shape The shape of the weight.\n     * @param dtype The dtype of the weight.\n     * @param initializer An initializer instance.\n     * @param regularizer A regularizer instance.\n     * @param trainable Whether the weight should be trained via backprop or not\n     *   (assuming that the layer itself is also trainable).\n     * @param constraint An optional trainable.\n     * @return The created weight variable.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint) {\n        // Reject duplicate weight names.\n        if (this._addedWeightNames.indexOf(name) !== -1) {\n            throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);\n        }\n        this._addedWeightNames.push(name);\n        if (dtype == null) {\n            dtype = 'float32';\n        }\n        if (this.fastWeightInitDuringBuild) {\n            initializer = getInitializer('zeros');\n        }\n        const initValue = initializer.apply(shape, dtype);\n        const weight = new LayerVariable(initValue, dtype, name, trainable, constraint);\n        initValue.dispose();\n        // Request backend not to dispose the weights of the model on scope() exit.\n        if (regularizer != null) {\n            this.addLoss(() => regularizer.apply(weight.read()));\n        }\n        if (trainable == null) {\n            trainable = true;\n        }\n        if (trainable) {\n            this._trainableWeights.push(weight);\n        }\n        else {\n            this._nonTrainableWeights.push(weight);\n        }\n        return weight;\n    }\n    /**\n     * Set the fast-weight-initialization flag.\n     *\n     * In cases where the initialized weight values will be immediately\n     * overwritten by loaded weight values during model loading, setting\n     * the flag to `true` saves unnecessary calls to potentially expensive\n     * initializers and speeds up the loading process.\n     *\n     * @param value Target value of the flag.\n     */\n    setFastWeightInitDuringBuild(value) {\n        this.fastWeightInitDuringBuild = value;\n    }\n    /**\n     * Add losses to the layer.\n     *\n     * The loss may potentionally be conditional on some inputs tensors,\n     * for instance activity losses are conditional on the layer's inputs.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    addLoss(losses) {\n        if (losses == null || Array.isArray(losses) && losses.length === 0) {\n            return;\n        }\n        // Update this.losses\n        losses = generic_utils.toList(losses);\n        if (this._losses !== undefined && this._losses !== null) {\n            this.losses.push(...losses);\n        }\n    }\n    /**\n     * Computes the output shape of the layer.\n     *\n     * Assumes that the layer will be built to match that input shape provided.\n     *\n     * @param inputShape A shape (tuple of integers) or a list of shape tuples\n     *   (one per output tensor of the layer). Shape tuples can include null for\n     *   free dimensions, instead of an integer.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    /**\n     * Computes an output mask tensor.\n     *\n     * @param inputs Tensor or list of tensors.\n     * @param mask Tensor or list of tensors.\n     *\n     * @return null or a tensor (or list of tensors, one per output tensor of the\n     * layer).\n     */\n    computeMask(inputs, mask) {\n        if (!this.supportsMasking) {\n            if (mask != null) {\n                if (Array.isArray(mask)) {\n                    mask.forEach(maskElement => {\n                        if (maskElement != null) {\n                            throw new TypeError(`Layer ${this.name} does not support masking, ` +\n                                'but was passed an inputMask.');\n                        }\n                    });\n                }\n                else {\n                    throw new TypeError(`Layer ${this.name} does not support masking, ` +\n                        'but was passed an inputMask.');\n                }\n            }\n            // masking not explicitly supported: return null as mask\n            return null;\n        }\n        // if masking is explictly supported, by default\n        // carry over the input mask\n        return mask;\n    }\n    /**\n     * Internal method to create an inbound node for the layer.\n     *\n     * @param inputTensors List of input tensors.\n     * @param outputTensors List of output tensors.\n     * @param inputMasks List of input masks (a mask can be a tensor, or null).\n     * @param outputMasks List of output masks (a mask can be a tensor, or null).\n     * @param inputShapes List of input shape tuples.\n     * @param outputShapes List of output shape tuples.\n     * @param kwargs Dictionary of keyword arguments that were passed to the\n     *   `call` method of the layer at the call that created the node.\n     */\n    addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) {\n        const inputTensorList = generic_utils.toList(inputTensors);\n        outputTensors = generic_utils.toList(outputTensors);\n        inputMasks = generic_utils.toList(inputMasks);\n        outputMasks = generic_utils.toList(outputMasks);\n        inputShapes = types_utils.normalizeShapeList(inputShapes);\n        outputShapes = types_utils.normalizeShapeList(outputShapes);\n        // Collect input tensor(s) coordinates.\n        const inboundLayers = [];\n        const nodeIndices = [];\n        const tensorIndices = [];\n        for (const x of inputTensorList) {\n            /*\n             * TODO(michaelterry): Keras adds this value to tensors; it's not\n             * clear whether we'll use this or not.\n             */\n            inboundLayers.push(x.sourceLayer);\n            nodeIndices.push(x.nodeIndex);\n            tensorIndices.push(x.tensorIndex);\n        }\n        // Create node, add it to inbound nodes.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers,\n            nodeIndices,\n            tensorIndices,\n            inputTensors: inputTensorList,\n            outputTensors,\n            inputMasks,\n            outputMasks,\n            inputShapes,\n            outputShapes\n        }, kwargs);\n        // Update tensor history\n        for (let i = 0; i < outputTensors.length; i++) {\n            // TODO(michaelterry: _uses_learning_phase not tracked.\n            outputTensors[i].sourceLayer = this;\n            outputTensors[i].nodeIndex = this.inboundNodes.length - 1;\n            outputTensors[i].tensorIndex = i;\n        }\n    }\n    /**\n     * Returns the config of the layer.\n     *\n     * A layer config is a TS dictionary (serializable)\n     * containing the configuration of a layer.\n     * The same layer can be reinstantiated later\n     * (without its trained weights) from this configuration.\n     *\n     * The config of a layer does not include connectivity\n     * information, nor the layer class name.  These are handled\n     * by 'Container' (one layer of abstraction above).\n     *\n     * Porting Note: The TS dictionary follows TS naming standrds for\n     * keys, and uses tfjs-layers type-safe Enums.  Serialization methods\n     * should use a helper function to convert to the pythonic storage\n     * standard. (see serialization_utils.convertTsToPythonic)\n     *\n     * @returns TS dictionary of configuration.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    getConfig() {\n        const config = { name: this.name, trainable: this.trainable };\n        if (this.batchInputShape != null) {\n            config['batchInputShape'] = this.batchInputShape;\n        }\n        if (this.dtype != null) {\n            config['dtype'] = this.dtype;\n        }\n        return config;\n    }\n    /**\n     * Dispose the weight variables that this Layer instance holds.\n     *\n     * @returns {number} Number of disposed variables.\n     */\n    disposeWeights() {\n        this.weights.forEach(weight => weight.dispose());\n        return this.weights.length;\n    }\n    assertNotDisposed() {\n        if (this._refCount === 0) {\n            throw new Error(`Layer '${this.name}' is already disposed.`);\n        }\n    }\n    /**\n     * Attempt to dispose layer's weights.\n     *\n     * This method decrease the reference count of the Layer object by 1.\n     *\n     * A Layer is reference-counted. Its reference count is incremented by 1\n     * the first item its `apply()` method is called and when it becomes a part\n     * of a new `Node` (through calling the `apply()`) method on a\n     * `tf.SymbolicTensor`).\n     *\n     * If the reference count of a Layer becomes 0, all the weights will be\n     * disposed and the underlying memory (e.g., the textures allocated in WebGL)\n     * will be freed.\n     *\n     * Note: If the reference count is greater than 0 after the decrement, the\n     * weights of the Layer will *not* be disposed.\n     *\n     * After a Layer is disposed, it cannot be used in calls such as `apply()`,\n     * `getWeights()` or `setWeights()` anymore.\n     *\n     * @returns A DisposeResult Object with the following fields:\n     *   - refCountAfterDispose: The reference count of the Container after this\n     *     `dispose()` call.\n     *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n     *     during this `dispose()` call.\n     * @throws {Error} If the layer is not built yet, or if the layer has already\n     *   been disposed.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    dispose() {\n        if (!this.built) {\n            throw new Error(`Cannot dispose Layer ${this.name} because it has not been ` +\n                `built yet.`);\n        }\n        if (this._refCount === null) {\n            throw new Error(`Cannot dispose Layer ${this.name} because it has not been used ` +\n                `yet.`);\n        }\n        this.assertNotDisposed();\n        let numDisposedVariables = 0;\n        if (--this._refCount === 0) {\n            numDisposedVariables = this.disposeWeights();\n        }\n        return { refCountAfterDispose: this._refCount, numDisposedVariables };\n    }\n}\n/**\n * Collects the input shape(s) of a list of `tf.Tensor`s or\n * `tf.SymbolicTensor`s.\n *\n * TODO(michaelterry): Update PyKeras docs (backport).\n *\n * @param inputTensors List of input tensors (or single input tensor).\n *\n * @return List of shape tuples (or single tuple), one tuple per input.\n */\nfunction collectInputShape(inputTensors) {\n    inputTensors =\n        generic_utils.toList(inputTensors);\n    const shapes = [];\n    for (const x of inputTensors) {\n        shapes.push(x.shape);\n    }\n    return generic_utils.singletonOrArray(shapes);\n}\n/**\n * Guesses output dtype based on inputs.\n *\n * At present, just returns 'float32' for any input.\n *\n * @param inputTensors List of input tensors (or single input tensor).\n *\n * @return The guessed DType. At present, always returns 'float32'.\n */\nfunction guessOutputDType(inputTensors) {\n    return 'float32';\n}\n/**\n * Returns the list of input tensors necessary to compute `tensor`.\n *\n * Output will always be a list of tensors (potentially with 1 element).\n *\n * @param tensor The tensor to start from.\n * @param layer Origin layer of the tensor.\n * @param nodeIndex Origin node index of the tensor.\n *\n * @return Array of input tensors.\n */\nexport function getSourceInputs(tensor, layer, nodeIndex) {\n    if (layer == null || (nodeIndex != null && nodeIndex > 0)) {\n        layer = tensor.sourceLayer;\n        nodeIndex = tensor.nodeIndex;\n    }\n    if (layer.inboundNodes.length === 0) {\n        return [tensor];\n    }\n    else {\n        const node = layer.inboundNodes[nodeIndex];\n        if (node.inboundLayers.length === 0) {\n            return node.inputTensors;\n        }\n        else {\n            const sourceTensors = [];\n            for (let i = 0; i < node.inboundLayers.length; i++) {\n                const x = node.inputTensors[i];\n                const layer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const previousSources = getSourceInputs(x, layer, nodeIndex);\n                // Avoid input redundancy.\n                for (const x of previousSources) {\n                    if (sourceTensors.indexOf(x) === -1) {\n                        sourceTensors.push(x);\n                    }\n                }\n            }\n            return sourceTensors;\n        }\n    }\n}\n//# sourceMappingURL=topology.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { serialization } from '@tensorflow/tfjs-core';\nimport { getUid } from '../backend/state';\nimport { ValueError } from '../errors';\nimport { Layer, Node, SymbolicTensor } from './topology';\nexport class InputLayer extends Layer {\n    constructor(args) {\n        super({\n            dtype: args.dtype,\n            name: args.name != null ? args.name : getUid('input').toString()\n        });\n        // Normalize config.batchSize and config.sparse\n        if (args.batchSize == null) {\n            args.batchSize = null;\n        }\n        if (args.sparse == null) {\n            args.sparse = false;\n        }\n        this.trainable = false;\n        this.built = true;\n        this.sparse = args.sparse;\n        if (args.inputShape != null && args.batchInputShape != null) {\n            throw new ValueError('Only provide the inputShape OR ' +\n                'batchInputShape argument to inputLayer, not both at the same time.');\n        }\n        let batchInputShape = args.batchInputShape;\n        if (batchInputShape == null) {\n            if (args.inputShape == null) {\n                throw new ValueError('An InputLayer should be passed either a ' +\n                    '`batchInputShape` or an `inputShape`.');\n            }\n            else {\n                batchInputShape = [args.batchSize].concat(args.inputShape);\n            }\n        }\n        else {\n            // TODO(michaelterry): Backport to PyKeras\n            if (args.batchSize != null) {\n                throw new ValueError('Cannot specify batchSize if batchInputShape is ' +\n                    'specified when creating an InputLayer.');\n            }\n        }\n        const dtype = args.dtype || 'float32';\n        this.batchInputShape = batchInputShape;\n        this.dtype = dtype;\n        // TODO(michaelterry): Backport this to PyKeras?\n        this.inputSpec = [{ shape: batchInputShape }];\n        const inputTensor = new SymbolicTensor(this.dtype, this.batchInputShape, this, [], {}, this.name);\n        inputTensor.nodeIndex = 0;\n        inputTensor.tensorIndex = 0;\n        // Create an input node to add to this.outboundNode.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers: [],\n            nodeIndices: [],\n            tensorIndices: [],\n            inputTensors: [inputTensor],\n            outputTensors: [inputTensor],\n            inputMasks: [null],\n            outputMasks: [null],\n            inputShapes: [batchInputShape],\n            outputShapes: [batchInputShape]\n        });\n    }\n    apply(inputs, kwargs) {\n        throw new ValueError('Cannot pass any input to an ' +\n            `InputLayer's apply() method. InputLayer name: ${this.name}`);\n    }\n    dispose() {\n        // dispose() for InputLayer is overridden as no-op.\n        return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 };\n    }\n    getConfig() {\n        return {\n            batchInputShape: this.batchInputShape,\n            dtype: this.dtype,\n            sparse: this.sparse,\n            name: this.name\n        };\n    }\n}\n/** @nocollapse */\nInputLayer.className = 'InputLayer';\nserialization.registerClass(InputLayer);\nexport function Input(config) {\n    if (config.batchShape == null && config.shape == null) {\n        throw new Error('Please provide to Input either a `shape`' +\n            ' or a `batchShape` argument. Note that ' +\n            '`shape` does not include the batch ' +\n            'dimension.');\n    }\n    if (config.batchShape != null && config.shape != null) {\n        // TODO(michaelterry): Backport to PyKeras.\n        throw new ValueError('Please provide either a `shape` or `batchShape` ' +\n            'argument to Input, but not both.');\n    }\n    let batchShape = config.batchShape;\n    if (config.shape != null && batchShape == null) {\n        batchShape = [null].concat(config.shape);\n    }\n    let dtype = config.dtype;\n    if (dtype == null) {\n        dtype = 'float32';\n    }\n    const inputLayer = new InputLayer({\n        batchInputShape: batchShape,\n        name: config.name,\n        dtype,\n        sparse: config.sparse\n    });\n    const outputs = inputLayer.inboundNodes[0].outputTensors;\n    return outputs[0];\n}\n//# sourceMappingURL=input_layer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { dispose } from '@tensorflow/tfjs-core';\n/**\n * Turn any Scalar values in a Logs object into actual number values.\n *\n * @param logs The `Logs` object to be resolved in place.\n */\nexport async function resolveScalarsInLogs(logs) {\n    if (logs == null) {\n        return;\n    }\n    const promises = [];\n    const keys = [];\n    const scalarsToDispose = [];\n    for (const key in logs) {\n        const value = logs[key];\n        if (typeof value !== 'number') {\n            const valueScalar = value;\n            promises.push(valueScalar.data());\n            keys.push(key);\n            scalarsToDispose.push(valueScalar);\n        }\n    }\n    if (promises.length > 0) {\n        const values = await Promise.all(promises);\n        for (let i = 0; i < values.length; ++i) {\n            logs[keys[i]] = values[i][0];\n        }\n        // Dispose the original scalar tensors.\n        dispose(scalarsToDispose);\n    }\n}\n/**\n * Dispose all Tensors in an UnresolvedLogs object.\n *\n * @param logs An `UnresolvedLogs` object potentially containing `tf.Tensor`s in\n *   places where the values can be `tf.Tensor` or `number`.\n */\nexport function disposeTensorsInLogs(logs) {\n    if (logs == null) {\n        return;\n    }\n    for (const key in logs) {\n        const value = logs[key];\n        if (typeof value !== 'number') {\n            value.dispose();\n        }\n    }\n}\n//# sourceMappingURL=logs.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/callbacks.py */\nimport { add, div, keep, mul, nextFrame, tidy, util } from '@tensorflow/tfjs-core';\nimport { ValueError } from './errors';\nimport { resolveScalarsInLogs } from './logs';\nimport * as generic_utils from './utils/generic_utils';\n/** Verbosity logging level when fitting a model. */\nexport var ModelLoggingVerbosity;\n(function (ModelLoggingVerbosity) {\n    ModelLoggingVerbosity[ModelLoggingVerbosity[\"SILENT\"] = 0] = \"SILENT\";\n    ModelLoggingVerbosity[ModelLoggingVerbosity[\"VERBOSE\"] = 1] = \"VERBOSE\";\n})(ModelLoggingVerbosity || (ModelLoggingVerbosity = {}));\n/** How often to yield to the main thread when training (in ms). */\nexport const DEFAULT_YIELD_EVERY_MS = 125;\n/**\n * Abstract base class used to build new callbacks.\n *\n * The `logs` dictionary that callback methods take as argument will contain\n * keys for quantities relevant to the current batch or epoch.\n *\n * Currently, the `.fit()` method of the `Sequential` model class\n * will include the following quantities in the `logs` that\n * it passes to its callbacks:\n *\n * onEpochEnd: Logs include `acc` and `loss`, and optionally include `valLoss`\n *   (if validation is enabled in `fit`), and `valAcc` (if validation and\n *   accuracy monitoring are enabled).\n * onBatchBegin: Logs include `size`, the number of samples in the current\n *   batch.\n * onBatchEnd: Logs include `loss`, and optionally `acc` (if accuracy monitoring\n *   is enabled).\n */\nexport class BaseCallback {\n    constructor() {\n        // TODO(michaelterry): This type is a best guess.\n        this.validationData = null;\n    }\n    setParams(params) {\n        this.params = params;\n    }\n    async onEpochBegin(epoch, logs) { }\n    async onEpochEnd(epoch, logs) { }\n    async onBatchBegin(batch, logs) { }\n    async onBatchEnd(batch, logs) { }\n    async onTrainBegin(logs) { }\n    async onTrainEnd(logs) { }\n    // LayersModel needs to call Callback.setModel(), but cannot actually depend\n    // on Callback because that creates a cyclic dependency.  Providing this no-op\n    // method on BaseCallback breaks the cycle: this way LayersModel can depend on\n    // BaseCallback but not on Callback.  The argument is typed as `Container`\n    // (the superclass of LayersModel) to avoid recapitulating the cycle. Callback\n    // overrides this method and enforces that the argument is really a\n    // LayersModel.\n    setModel(model) {\n        // Do nothing. Use Callback instead of BaseCallback to track the model.\n    }\n}\n/**\n * Container abstracting a list of callbacks.\n */\nexport class CallbackList {\n    // TODO(cais): When the need arises, uncomment the following lines and\n    // implement the queue for time values.\n    // private deltaTBatch: number;\n    // private deltaTsBatchBegin: Array<number>;\n    // private deltaTsBatchEnd: Array<number>;\n    /**\n     * Constructor of CallbackList.\n     * @param callbacks Array of `Callback` instances.\n     * @param queueLength Queue length for keeping running statistics over\n     *   callback execution time.\n     */\n    constructor(callbacks, queueLength = 10) {\n        // TODO(cais): Make use of queueLength when implementing the queue for time\n        // values.\n        if (callbacks == null) {\n            callbacks = [];\n        }\n        this.callbacks = callbacks;\n        this.queueLength = queueLength;\n    }\n    append(callback) {\n        this.callbacks.push(callback);\n    }\n    setParams(params) {\n        for (const callback of this.callbacks) {\n            callback.setParams(params);\n        }\n    }\n    setModel(model) {\n        for (const callback of this.callbacks) {\n            callback.setModel(model);\n        }\n    }\n    /**\n     * Called at the start of an epoch.\n     * @param epoch Index of epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onEpochBegin(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onEpochBegin(epoch, logs);\n        }\n    }\n    /**\n     * Called at the end of an epoch.\n     * @param epoch Index of epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onEpochEnd(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onEpochEnd(epoch, logs);\n        }\n    }\n    /**\n     * Called  right before processing a batch.\n     * @param batch Index of batch within the current epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onBatchBegin(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onBatchBegin(batch, logs);\n        }\n    }\n    /**\n     * Called at the end of a batch.\n     * @param batch Index of batch within the current epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onBatchEnd(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onBatchEnd(batch, logs);\n        }\n    }\n    /**\n     * Called at the beginning of training.\n     * @param logs Dictionary of logs.\n     */\n    async onTrainBegin(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onTrainBegin(logs);\n        }\n    }\n    /**\n     * Called at the end of training.\n     * @param logs Dictionary of logs.\n     */\n    async onTrainEnd(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onTrainEnd(logs);\n        }\n    }\n}\n/**\n * Callback that accumulates epoch averages of metrics.\n *\n * This callback is automatically applied to every LayersModel.\n */\nexport class BaseLogger extends BaseCallback {\n    constructor() {\n        super();\n    }\n    async onEpochBegin(epoch) {\n        this.seen = 0;\n        this.totals = {};\n    }\n    async onBatchEnd(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        const batchSize = logs['size'] == null ? 0 : logs['size'];\n        this.seen += batchSize;\n        for (const key in logs) {\n            const value = logs[key];\n            if (typeof value === 'number') {\n                if (!this.totals.hasOwnProperty(key)) {\n                    this.totals[key] = 0;\n                }\n                this.totals[key] = this.totals[key] + value * batchSize;\n            }\n            else {\n                let oldTotalsToDispose;\n                if (key in this.totals) {\n                    oldTotalsToDispose = this.totals[key];\n                }\n                else {\n                    this.totals[key] = 0;\n                }\n                const total = tidy(() => add((this.totals[key]), mul(value, batchSize)));\n                this.totals[key] = total;\n                if (oldTotalsToDispose != null) {\n                    oldTotalsToDispose.dispose();\n                }\n            }\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        if (logs != null) {\n            for (const key of this.params['metrics']) {\n                if (this.totals[key] == null) {\n                    continue;\n                }\n                if (typeof this.totals[key] === 'number') {\n                    logs[key] = this.totals[key] / this.seen;\n                }\n                else {\n                    tidy(() => {\n                        const log = mul(div(1, this.seen), this.totals[key]);\n                        logs[key] = log;\n                        this.totals[key].dispose();\n                        keep(logs[key]);\n                    });\n                }\n            }\n        }\n    }\n}\n/**\n * Callback that records events into a `History` object. This callback is\n * automatically applied to every TF.js Layers model. The `History` object\n * gets returned by the `fit` method of models.\n */\nexport class History extends BaseCallback {\n    async onTrainBegin(logs) {\n        this.epoch = [];\n        this.history = {};\n    }\n    async onEpochEnd(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        this.epoch.push(epoch);\n        for (const key in logs) {\n            if (this.history[key] == null) {\n                this.history[key] = [];\n            }\n            this.history[key].push(logs[key]);\n        }\n    }\n    /**\n     * Await the values of all losses and metrics.\n     */\n    async syncData() {\n        const promises = [];\n        const keys = [];\n        const indices = [];\n        for (const key in this.history) {\n            const valueArray = this.history[key];\n            for (let i = 0; i < valueArray.length; ++i) {\n                if (typeof valueArray[i] !== 'number') {\n                    const valueScalar = valueArray[i];\n                    promises.push(valueScalar.data());\n                    keys.push(key);\n                    indices.push(i);\n                }\n            }\n        }\n        const values = await Promise.all(promises);\n        for (let n = 0; n < values.length; ++n) {\n            const tensorToDispose = this.history[keys[n]][indices[n]];\n            tensorToDispose.dispose();\n            this.history[keys[n]][indices[n]] = values[n][0];\n        }\n    }\n}\n/**\n * Custom callback for training.\n */\nexport class CustomCallback extends BaseCallback {\n    constructor(args, yieldEvery) {\n        super();\n        this.currentEpoch = 0;\n        this.yieldEvery = yieldEvery || 'auto';\n        if (this.yieldEvery === 'auto') {\n            this.yieldEvery = DEFAULT_YIELD_EVERY_MS;\n        }\n        if (this.yieldEvery === 'never' && args.onYield != null) {\n            throw new Error('yieldEvery is `never` but you provided an `onYield` callback. ' +\n                'Either change `yieldEvery` or remove the callback');\n        }\n        if (util.isNumber(this.yieldEvery)) {\n            // Decorate `maybeWait` so it will be called at most once every\n            // `yieldEvery` ms.\n            this.maybeWait = generic_utils.debounce(this.maybeWait.bind(this), this.yieldEvery);\n        }\n        this.trainBegin = args.onTrainBegin;\n        this.trainEnd = args.onTrainEnd;\n        this.epochBegin = args.onEpochBegin;\n        this.epochEnd = args.onEpochEnd;\n        this.batchBegin = args.onBatchBegin;\n        this.batchEnd = args.onBatchEnd;\n        this.yield = args.onYield;\n    }\n    async maybeWait(epoch, batch, logs) {\n        const ps = [];\n        if (this.yield != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.yield(epoch, batch, logs));\n        }\n        ps.push(nextFrame());\n        await Promise.all(ps);\n    }\n    async onEpochBegin(epoch, logs) {\n        this.currentEpoch = epoch;\n        if (this.epochBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.epochBegin(epoch, logs);\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        const ps = [];\n        if (this.epochEnd != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.epochEnd(epoch, logs));\n        }\n        if (this.yieldEvery === 'epoch') {\n            ps.push(nextFrame());\n        }\n        await Promise.all(ps);\n    }\n    async onBatchBegin(batch, logs) {\n        if (this.batchBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.batchBegin(batch, logs);\n        }\n    }\n    async onBatchEnd(batch, logs) {\n        const ps = [];\n        if (this.batchEnd != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.batchEnd(batch, logs));\n        }\n        if (this.yieldEvery === 'batch') {\n            ps.push(nextFrame());\n        }\n        else if (util.isNumber(this.yieldEvery)) {\n            ps.push(this.maybeWait(this.currentEpoch, batch, logs));\n        }\n        await Promise.all(ps);\n    }\n    async onTrainBegin(logs) {\n        if (this.trainBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.trainBegin(logs);\n        }\n    }\n    async onTrainEnd(logs) {\n        if (this.trainEnd != null) {\n            await resolveScalarsInLogs(logs);\n            await this.trainEnd(logs);\n        }\n    }\n}\n/**\n * Standardize callbacks or configurations of them to an Array of callbacks.\n */\nexport function standardizeCallbacks(callbacks, yieldEvery) {\n    if (callbacks == null) {\n        callbacks = {};\n    }\n    if (callbacks instanceof BaseCallback) {\n        return [callbacks];\n    }\n    if (Array.isArray(callbacks) && callbacks[0] instanceof BaseCallback) {\n        return callbacks;\n    }\n    // Convert custom callback configs to custom callback objects.\n    const callbackConfigs = generic_utils.toList(callbacks);\n    return callbackConfigs.map(callbackConfig => new CustomCallback(callbackConfig, yieldEvery));\n}\n/**\n * A global registry for callback constructors to be used during\n * LayersModel.fit().\n */\nexport class CallbackConstructorRegistry {\n    /**\n     * Blocks public access to constructor.\n     */\n    constructor() { }\n    /**\n     * Register a tf.LayersModel.fit() callback constructor.\n     *\n     * The registered callback constructor will be used to instantiate\n     * callbacks for every tf.LayersModel.fit() call afterwards.\n     *\n     * @param verbosityLevel Level of verbosity at which the `callbackConstructor`\n     *   is to be reigstered.\n     * @param callbackConstructor A no-arg constructor for `tf.Callback`.\n     * @throws Error, if the same callbackConstructor has been registered before,\n     *   either at the same or a different `verbosityLevel`.\n     */\n    static registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n        util.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, ` +\n            `but got ${verbosityLevel}`);\n        CallbackConstructorRegistry.checkForDuplicate(callbackConstructor);\n        if (CallbackConstructorRegistry.constructors[verbosityLevel] == null) {\n            CallbackConstructorRegistry.constructors[verbosityLevel] = [];\n        }\n        CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor);\n    }\n    static checkForDuplicate(callbackConstructor) {\n        for (const levelName in CallbackConstructorRegistry.constructors) {\n            const constructors = CallbackConstructorRegistry.constructors[+levelName];\n            constructors.forEach(ctor => {\n                if (ctor === callbackConstructor) {\n                    throw new ValueError('Duplicate callback constructor.');\n                }\n            });\n        }\n    }\n    /**\n     * Clear all registered callback constructors.\n     */\n    static clear() {\n        CallbackConstructorRegistry.constructors = {};\n    }\n    /**\n     * Create callbacks using the registered callback constructors.\n     *\n     * Given `verbosityLevel`, all constructors registered at that level or above\n     * will be called and the instantiated callbacks will be used.\n     *\n     * @param verbosityLevel: Level of verbosity.\n     */\n    static createCallbacks(verbosityLevel) {\n        const constructors = [];\n        for (const levelName in CallbackConstructorRegistry.constructors) {\n            const level = +levelName;\n            if (verbosityLevel >= level) {\n                constructors.push(...CallbackConstructorRegistry.constructors[level]);\n            }\n        }\n        return constructors.map(ctor => new ctor());\n    }\n}\nCallbackConstructorRegistry.constructors = {};\nexport function configureCallbacks(callbacks, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) {\n    const history = new History();\n    const actualCallbacks = [\n        new BaseLogger(), ...CallbackConstructorRegistry.createCallbacks(verbose)\n    ];\n    if (callbacks != null) {\n        actualCallbacks.push(...callbacks);\n    }\n    actualCallbacks.push(history);\n    const callbackList = new CallbackList(actualCallbacks);\n    // TODO(cais): Figure out when this LayersModel instance can have a\n    // dynamically\n    //   set property called 'callback_model' as in PyKeras.\n    callbackList.setParams({\n        epochs,\n        initialEpoch,\n        samples: numTrainSamples,\n        steps: stepsPerEpoch,\n        batchSize,\n        verbose,\n        doValidation,\n        metrics: callbackMetrics,\n    });\n    return { callbackList, history };\n}\n//# sourceMappingURL=base_callbacks.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source layers/__init__.py */\nimport { serialization } from '@tensorflow/tfjs-core';\nimport { deserializeKerasObject } from '../utils/generic_utils';\n/**\n * Instantiate a layer from a config dictionary.\n * @param config dict of the form {class_name: str, config: dict}\n * @param customObjects dict mapping class names (or function names)\n *   of custom (non-Keras) objects to class/functions\n * @param fastWeightInit Optional flag to use fast weight initialization\n *   during deserialization. This is applicable to cases in which\n *   the initialization will be immediately overwritten by loaded weight\n *   values. Default: `false`.\n * @returns Layer instance (may be LayersModel, Sequential, Layer...)\n */\nexport function deserialize(config, customObjects = {}, fastWeightInit = false) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'layer', fastWeightInit);\n}\n//# sourceMappingURL=serialization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source: losses.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { tidy, util } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport * as K from './backend/tfjs_backend';\nimport { ValueError } from './errors';\n/**\n * Normalizes a tensor wrt the L2 norm alongside the specified axis.\n * @param x\n * @param axis Axis along which to perform normalization.\n */\nexport function l2Normalize(x, axis) {\n    return tidy(() => {\n        if (x.dtype !== 'float32') {\n            x = x.asType('float32');\n        }\n        const squareSum = tfc.sum(K.square(x), axis, true);\n        const epsilonTensor = tfc.fill(squareSum.shape, epsilon());\n        const norm = tfc.sqrt(tfc.maximum(squareSum, epsilonTensor));\n        return tfc.div(x, norm);\n    });\n}\nexport function meanSquaredError(yTrue, yPred) {\n    return tidy(() => tfc.mean(K.square(tfc.sub(yPred, yTrue)), -1));\n}\nexport function meanAbsoluteError(yTrue, yPred) {\n    return tidy(() => tfc.mean(tfc.abs(tfc.sub(yPred, yTrue)), -1));\n}\nexport function meanAbsolutePercentageError(yTrue, yPred) {\n    return tidy(() => {\n        const diff = tfc.sub(yTrue, yPred);\n        const clippedTrue = tfc.clipByValue(tfc.abs(yTrue), epsilon(), Number.MAX_VALUE);\n        const absResult = tfc.abs(tfc.div(diff, clippedTrue));\n        return tfc.mul(100, tfc.mean(absResult, -1));\n    });\n}\nexport function meanSquaredLogarithmicError(yTrue, yPred) {\n    return tidy(() => {\n        const clippedPred = tfc.clipByValue(yPred, epsilon(), Number.MAX_VALUE);\n        const firstLog = tfc.log(tfc.add(1, clippedPred));\n        const clippedTrue = tfc.clipByValue(yTrue, epsilon(), Number.MAX_VALUE);\n        const secondLog = tfc.log(tfc.add(1, clippedTrue));\n        return tfc.mean(K.square(tfc.sub(firstLog, secondLog)), -1);\n    });\n}\nexport function squaredHinge(yTrue, yPred) {\n    return tidy(() => {\n        const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n        return tfc.mean(K.square(maxResult), -1);\n    });\n}\nexport function hinge(yTrue, yPred) {\n    return tidy(() => {\n        const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n        return tfc.mean(maxResult, -1);\n    });\n}\nexport function categoricalHinge(yTrue, yPred) {\n    return tidy(() => {\n        const pos = tfc.sum(tfc.mul(yTrue, yPred), -1);\n        const neg = tfc.max(tfc.mul(tfc.sub(1, yTrue), yPred), -1);\n        return tfc.maximum(0, tfc.add(1, tfc.sub(neg, pos)));\n    });\n}\n/**\n * Logarithm of the hyperbolic cosine of the prediction error.\n *\n * `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and\n * to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly\n * like the mean squared error, but will not be so strongly affected by the\n * occasional wildly incorrect prediction.\n */\nexport function logcosh(yTrue, yPred) {\n    return tidy(() => {\n        const log2 = Math.log(2);\n        const predictionDiff = tfc.sub(yPred, yTrue);\n        const logcoshResult = tfc.sub(tfc.add(predictionDiff, tfc.softplus(tfc.mul(-2, predictionDiff))), log2);\n        return tfc.mean(logcoshResult, -1);\n    });\n}\nexport function categoricalCrossentropy(target, output, fromLogits = false) {\n    return tidy(() => {\n        if (fromLogits) {\n            output = tfc.softmax(output);\n        }\n        else {\n            // scale preds so that the class probabilities of each sample sum to 1.\n            const outputSum = tfc.sum(output, output.shape.length - 1, true);\n            output = tfc.div(output, outputSum);\n        }\n        output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n        return tfc.neg(tfc.sum(tfc.mul(target.toFloat(), tfc.log(output)), output.shape.length - 1));\n    });\n}\n/**\n * Categorical crossentropy with integer targets.\n *\n * @param target An integer tensor.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n */\nexport function sparseCategoricalCrossentropy(target, output, fromLogits = false) {\n    return tidy(() => {\n        const flatTarget = tfc.floor(K.flatten(target)).toInt();\n        output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n        const outputShape = output.shape;\n        const oneHotTarget = tfc.oneHot(flatTarget, outputShape[outputShape.length - 1])\n            .reshape(outputShape);\n        return categoricalCrossentropy(oneHotTarget, output, fromLogits);\n    });\n}\n/**\n * From TensorFlow's implementation in nn_impl.py:\n *\n * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n *      z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n *    = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n *    = (1 - z) * x + log(1 + exp(-x))\n *    = x - x * z + log(1 + exp(-x))\n * For x < 0, to avoid overflow in exp(-x), we reformulate the above\n *      x - x * z + log(1 + exp(-x))\n *    = log(exp(x)) - x * z + log(1 + exp(-x))\n *    = - x * z + log(1 + exp(x))\n * Hence, to ensure stability and avoid overflow, the implementation uses this\n * equivalent formulation\n *    max(x, 0) - x * z + log(1 + exp(-abs(x)))\n *\n * @param labels The labels.\n * @param logits The logits.\n */\nexport function sigmoidCrossEntropyWithLogits(labels, logits) {\n    if (!util.arraysEqual(labels.shape, logits.shape)) {\n        throw new ValueError(`logits and labels must have the same shape, but got shapes ` +\n            `${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);\n    }\n    return tidy(() => {\n        // The logistic loss formula from above is\n        //   x - x * z + log(1 + exp(-x))\n        // For x < 0, a more numerically stable formula is\n        //   -x * z + log(1 + exp(x))\n        // Note that these two expressions can be combined into the following:\n        //   max(x, 0) - x * z + log(1 + exp(-abs(x)))\n        const reluLogits = logits.relu();\n        const negAbsLogits = logits.abs().neg();\n        return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p());\n    });\n}\nexport function binaryCrossentropy(yTrue, yPred) {\n    return tidy(() => {\n        let y;\n        y = tfc.clipByValue(yPred, epsilon(), 1 - epsilon());\n        y = tfc.log(tfc.div(y, tfc.sub(1, y)));\n        return tfc.mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);\n    });\n}\nexport function kullbackLeiblerDivergence(yTrue, yPred) {\n    return tidy(() => {\n        const clippedTrue = tfc.clipByValue(yTrue, epsilon(), 1);\n        const clippedPred = tfc.clipByValue(yPred, epsilon(), 1);\n        return tfc.sum(tfc.mul(yTrue, tfc.log(tfc.div(clippedTrue, clippedPred))), -1);\n    });\n}\nexport function poisson(yTrue, yPred) {\n    return tidy(() => {\n        const logPred = tfc.log(tfc.add(epsilon(), yPred));\n        return tfc.mean(tfc.sub(yPred, tfc.mul(yTrue, logPred)), -1);\n    });\n}\nexport function cosineProximity(yTrue, yPred) {\n    return tidy(() => {\n        const trueNormalized = l2Normalize(yTrue, -1);\n        const predNormalized = l2Normalize(yPred, -1);\n        const trueXPred = tfc.mul(trueNormalized, predNormalized);\n        return tfc.neg(tfc.sum(trueXPred, -1));\n    });\n}\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const msle = meanSquaredLogarithmicError;\nexport const MSLE = meanSquaredLogarithmicError;\nexport const kld = kullbackLeiblerDivergence;\nexport const KLD = kullbackLeiblerDivergence;\nexport const cosine = cosineProximity;\n// TODO(michaelterry): Add deserialize() function.\nexport const lossesMap = {\n    meanSquaredError,\n    meanAbsoluteError,\n    meanAbsolutePercentageError,\n    meanSquaredLogarithmicError,\n    squaredHinge,\n    hinge,\n    categoricalHinge,\n    logcosh,\n    categoricalCrossentropy,\n    sparseCategoricalCrossentropy,\n    binaryCrossentropy,\n    kullbackLeiblerDivergence,\n    poisson,\n    cosineProximity\n};\n// Porting note: This diverges from the PyKeras implementation and may need to\n// change based on (de)serialization requirements.\nexport function get(identifierOrFn) {\n    if (typeof identifierOrFn === 'string') {\n        if (identifierOrFn in lossesMap) {\n            return lossesMap[identifierOrFn];\n        }\n        let errMsg = `Unknown loss ${identifierOrFn}`;\n        if (identifierOrFn.toLowerCase().includes('softmaxcrossentropy')) {\n            errMsg = `Unknown loss ${identifierOrFn}. ` +\n                'Use \"categoricalCrossentropy\" as the string name for ' +\n                'tf.losses.softmaxCrossEntropy';\n        }\n        throw new ValueError(errMsg);\n    }\n    else {\n        return identifierOrFn;\n    }\n}\n//# sourceMappingURL=losses.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Built-in metrics.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { tidy } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { NotImplementedError, ValueError } from './errors';\nimport { categoricalCrossentropy as categoricalCrossentropyLoss, cosineProximity, meanAbsoluteError, meanAbsolutePercentageError, meanSquaredError, sparseCategoricalCrossentropy as sparseCategoricalCrossentropyLoss } from './losses';\nimport { binaryCrossentropy as lossBinaryCrossentropy } from './losses';\nimport { lossesMap } from './losses';\nimport * as util from './utils/generic_utils';\nexport function binaryAccuracy(yTrue, yPred) {\n    return tidy(() => {\n        const threshold = tfc.mul(.5, tfc.onesLike(yPred));\n        const yPredThresholded = K.cast(tfc.greater(yPred, threshold), yTrue.dtype);\n        return tfc.mean(tfc.equal(yTrue, yPredThresholded), -1);\n    });\n}\nexport function categoricalAccuracy(yTrue, yPred) {\n    return tidy(() => K.cast(tfc.equal(tfc.argMax(yTrue, -1), tfc.argMax(yPred, -1)), 'float32'));\n}\nfunction truePositives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(1), yPred.equal(1)).sum().cast('float32');\n    });\n}\nfunction falseNegatives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(1), yPred.equal(0)).sum().cast('float32');\n    });\n}\nfunction falsePositives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(0), yPred.equal(1)).sum().cast('float32');\n    });\n}\nexport function precision(yTrue, yPred) {\n    return tidy(() => {\n        const tp = truePositives(yTrue, yPred);\n        const fp = falsePositives(yTrue, yPred);\n        const denominator = tp.add(fp);\n        return tfc.where(tfc.greater(denominator, 0), tp.div(denominator), 0)\n            .cast('float32');\n    });\n}\nexport function recall(yTrue, yPred) {\n    return tidy(() => {\n        const tp = truePositives(yTrue, yPred);\n        const fn = falseNegatives(yTrue, yPred);\n        const denominator = tp.add(fn);\n        return tfc.where(tfc.greater(denominator, 0), tp.div(denominator), 0)\n            .cast('float32');\n    });\n}\nexport function binaryCrossentropy(yTrue, yPred) {\n    return lossBinaryCrossentropy(yTrue, yPred);\n}\nexport function sparseCategoricalAccuracy(yTrue, yPred) {\n    if (yTrue.rank === yPred.rank) {\n        yTrue = yTrue.squeeze([yTrue.rank - 1]);\n    }\n    yPred = yPred.argMax(-1);\n    if (yPred.dtype !== yTrue.dtype) {\n        yPred = yPred.asType(yTrue.dtype);\n    }\n    return tfc.equal(yTrue, yPred).asType('float32');\n}\nexport function topKCategoricalAccuracy(yTrue, yPred) {\n    throw new NotImplementedError();\n}\nexport function sparseTopKCategoricalAccuracy(yTrue, yPred) {\n    throw new NotImplementedError();\n}\n// Aliases.\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const categoricalCrossentropy = categoricalCrossentropyLoss;\nexport const cosine = cosineProximity;\nexport const sparseCategoricalCrossentropy = sparseCategoricalCrossentropyLoss;\n// TODO(cais, nielsene): Add serialize().\nexport const metricsMap = {\n    binaryAccuracy,\n    categoricalAccuracy,\n    precision,\n    categoricalCrossentropy,\n    sparseCategoricalCrossentropy,\n    mse,\n    MSE,\n    mae,\n    MAE,\n    mape,\n    MAPE,\n    cosine\n};\nexport function get(identifier) {\n    if (typeof identifier === 'string' && identifier in metricsMap) {\n        return metricsMap[identifier];\n    }\n    else if (typeof identifier !== 'string' && identifier != null) {\n        return identifier;\n    }\n    else {\n        throw new ValueError(`Unknown metric ${identifier}`);\n    }\n}\n/**\n * Get the shortcut function name.\n *\n * If the fn name is a string,\n *   directly return the string name.\n * If the function is included in metricsMap or lossesMap,\n *   return key of the map.\n *   - If the function relative to multiple keys,\n *     return the first found key as the function name.\n *   - If the function exists in both lossesMap and metricsMap,\n *     search lossesMap first.\n * If the function is not included in metricsMap or lossesMap,\n *   return the function name.\n *\n * @param fn loss function, metric function, or short cut name.\n * @returns Loss or Metric name in string.\n */\nexport function getLossOrMetricName(fn) {\n    util.assert(fn !== null, `Unknown LossOrMetricFn ${fn}`);\n    if (typeof fn === 'string') {\n        return fn;\n    }\n    else {\n        let fnName;\n        for (const key of Object.keys(lossesMap)) {\n            if (lossesMap[key] === fn) {\n                fnName = key;\n                break;\n            }\n        }\n        if (fnName !== undefined) {\n            return fnName;\n        }\n        for (const key of Object.keys(metricsMap)) {\n            if (metricsMap[key] === fn) {\n                fnName = key;\n                break;\n            }\n        }\n        if (fnName !== undefined) {\n            return fnName;\n        }\n        return fn.name;\n    }\n}\n//# sourceMappingURL=metrics.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Optimizers.\n */\nimport { train } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport { ValueError } from './errors';\n// Add (de)serialize()\n// Porting note: This diverges from the PyKeras implementation and may need to\n// change based on (de)serialization requirements.\nexport function getOptimizer(identifier) {\n    const optimizerMap = {\n        'Adagrad': () => train.adagrad(0.01),\n        'Adadelta': () => train.adadelta(1, 0.95, epsilon()),\n        'Adam': () => train.adam(0.001, 0.9, 0.999, epsilon()),\n        'Adamax': () => train.adamax(0.002, 0.9, 0.999, epsilon(), 0),\n        'RMSProp': () => train.rmsprop(0.001, 0.9, 0, epsilon()),\n        'SGD': () => train.sgd(0.01)\n    };\n    optimizerMap['adagrad'] = optimizerMap['Adagrad'];\n    optimizerMap['adadelta'] = optimizerMap['Adadelta'];\n    optimizerMap['adam'] = optimizerMap['Adam'];\n    optimizerMap['adamax'] = optimizerMap['Adamax'];\n    optimizerMap['rmsprop'] = optimizerMap['RMSProp'];\n    optimizerMap['sgd'] = optimizerMap['SGD'];\n    if (identifier in optimizerMap) {\n        return optimizerMap[identifier]();\n    }\n    throw new ValueError(`Unknown Optimizer ${identifier}`);\n}\n//# sourceMappingURL=optimizers.js.map", "/**\n * @license\n * Copyright 2019 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/** Utility functions related to user-defined metadata. */\n// Maximum recommended serialized size for user-defined metadata.\n// Beyond this limit, a warning message will be printed during model loading and\n// saving.\nexport const MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024;\n/**\n * Check validity of user-defined metadata.\n *\n * @param userDefinedMetadata\n * @param modelName Name of the model that the user-defined metadata belongs to.\n *   Used during construction of error messages.\n * @param checkSize Whether to check the size of the metadata is under\n *   recommended limit. Default: `false`. If `true`, will try stringify the\n *   JSON object and print a console warning if the serialzied size is above the\n *   limit.\n * @throws Error if `userDefinedMetadata` is not a plain JSON object.\n */\nexport function checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) {\n    if (userDefinedMetadata == null ||\n        typeof userDefinedMetadata !== 'object' ||\n        Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype ||\n        !plainObjectCheck(userDefinedMetadata)) {\n        throw new Error('User-defined metadata is expected to be a JSON object, but is not.');\n    }\n    if (checkSize) {\n        const out = JSON.stringify(userDefinedMetadata);\n        if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) {\n            console.warn(`User-defined metadata of model \"${modelName}\" is too large in ` +\n                `size (length=${out.length} when serialized). It is not ` +\n                `recommended to store such large objects in user-defined metadata. ` +\n                `Please make sure its serialized length is <= ` +\n                `${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`);\n        }\n    }\n}\n/**\n * Check if an input is plain JSON object or any valid subfield of it.\n *\n * @param x The input to be checked.\n * @param assertObject Whether to assert `x` is a JSON object, i.e., reject\n *   cases of arrays and primitives.\n * @return Returns `true` if and only if `x` is a plain JSON object,\n *   a JSON-valid primitive including string, number, boolean and null,\n *   or an array of the said types.\n */\n// tslint:disable-next-line:no-any\nexport function plainObjectCheck(x) {\n    if (x === null) {\n        // Note: typeof `null` is 'object', and `null` is valid in JSON.\n        return true;\n    }\n    else if (typeof x === 'object') {\n        if (Object.getPrototypeOf(x) === Object.prototype) {\n            // `x` is a JavaScript object and its prototype is Object.\n            const keys = Object.keys(x);\n            for (const key of keys) {\n                if (typeof key !== 'string') {\n                    // JSON keys must be strings.\n                    return false;\n                }\n                if (!plainObjectCheck(x[key])) { // Recursive call.\n                    return false;\n                }\n            }\n            return true;\n        }\n        else {\n            // `x` is a JavaScript object but its prototype is not Object.\n            if (Array.isArray(x)) {\n                // `x` is a JavaScript array.\n                for (const item of x) {\n                    if (!plainObjectCheck(item)) { // Recursive call.\n                        return false;\n                    }\n                }\n                return true;\n            }\n            else {\n                // `x` is a JavaScript object and its prototype is not Object,\n                // and it's not an Array. I.e., it's a complex object such as\n                // `Error` and `Date`.\n                return false;\n            }\n        }\n    }\n    else {\n        // `x` is not a JavaScript object or `null`.\n        const xType = typeof x;\n        return xType === 'string' || xType === 'number' || xType === 'boolean';\n    }\n}\n//# sourceMappingURL=user_defined_metadata.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { countParamsInWeights } from './variable_utils';\n/**\n * Print the summary of a LayersModel object.\n *\n * @param model tf.LayersModel instance.\n * @param lineLength Total length of printed lines. Set this to adapt to the\n *   display to different terminal or console sizes.\n * @param positions Relative or absolute positions of log elements in each\n *   line. Each number corresponds to right-most (i.e., ending) position of a\n *   column.\n *   If not provided, defaults to `[0.45, 0.85, 1]` for sequential-like\n *   models and `[0.33, 0.55, 0.67, 1]` for non-sequential like models.\n * @param printFn Print function to use.\n *   It will be called on each line of the summary. You can provide a custom\n *   function in order to capture the string summary. Defaults to `console.log`.\n */\nexport function printSummary(model, lineLength, positions, \n// tslint:disable-next-line:no-any\nprintFn = console.log) {\n    const sequentialLike = isModelSequentialLike(model);\n    // Header names for different log elements.\n    const toDisplay = ['Layer (type)', 'Output shape', 'Param #'];\n    if (sequentialLike) {\n        lineLength = lineLength || 65;\n        positions = positions || [0.45, 0.85, 1];\n    }\n    else {\n        lineLength = lineLength || 98;\n        positions = positions || [0.33, 0.55, 0.67, 1];\n        // Header names for different log elements.\n    }\n    if (positions[positions.length - 1] <= 1) {\n        // `positions` is relative. Convert it to absolute positioning.\n        positions = positions.map(p => Math.floor(lineLength * p));\n    }\n    let relevantNodes;\n    if (!sequentialLike) {\n        toDisplay.push('Receives inputs');\n        relevantNodes = [];\n        for (const depth in model.nodesByDepth) {\n            relevantNodes.push(...model.nodesByDepth[depth]);\n        }\n    }\n    printFn('_'.repeat(lineLength));\n    printRow(toDisplay, positions, printFn);\n    printFn('='.repeat(lineLength));\n    const layers = model.layers;\n    for (let i = 0; i < layers.length; ++i) {\n        if (sequentialLike) {\n            printLayerSummary(layers[i], positions, printFn);\n        }\n        else {\n            printLayerSummaryWithConnections(layers[i], positions, relevantNodes, printFn);\n        }\n        printFn((i === layers.length - 1 ? '=' : '_').repeat(lineLength));\n    }\n    // tslint:disable-next-line:no-any\n    model.checkTrainableWeightsConsistency();\n    const trainableCount = countTrainableParams(model);\n    const nonTrainableCount = countParamsInWeights(model.nonTrainableWeights);\n    printFn(`Total params: ${trainableCount + nonTrainableCount}`);\n    printFn(`Trainable params: ${trainableCount}`);\n    printFn(`Non-trainable params: ${nonTrainableCount}`);\n    printFn('_'.repeat(lineLength));\n}\nfunction countTrainableParams(model) {\n    let trainableCount;\n    // tslint:disable:no-any\n    if (model.collectedTrainableWeights != null) {\n        trainableCount =\n            countParamsInWeights(model.collectedTrainableWeights);\n    }\n    else {\n        trainableCount = countParamsInWeights(model.trainableWeights);\n    }\n    // tslint:enable:no-any\n    return trainableCount;\n}\nfunction isModelSequentialLike(model) {\n    let sequentialLike = true;\n    const nodesByDepth = [];\n    const nodes = [];\n    for (const depth in model.nodesByDepth) {\n        nodesByDepth.push(model.nodesByDepth[depth]);\n    }\n    for (const depthNodes of nodesByDepth) {\n        if (depthNodes.length > 1 ||\n            depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) {\n            sequentialLike = false;\n            break;\n        }\n        nodes.push(...depthNodes);\n    }\n    if (sequentialLike) {\n        // Search for shared layers.\n        for (const layer of model.layers) {\n            let flag = false;\n            for (const node of layer.inboundNodes) {\n                if (nodes.indexOf(node) !== -1) {\n                    if (flag) {\n                        sequentialLike = false;\n                        break;\n                    }\n                    else {\n                        flag = true;\n                    }\n                }\n            }\n            if (!sequentialLike) {\n                break;\n            }\n        }\n    }\n    return sequentialLike;\n}\nfunction printRow(fields, positions, \n// tslint:disable-next-line:no-any\nprintFn = console.log) {\n    let line = '';\n    for (let i = 0; i < fields.length; ++i) {\n        if (i > 0) {\n            line = line.slice(0, line.length - 1) + ' ';\n        }\n        line += fields[i];\n        line = line.slice(0, positions[i]);\n        line += ' '.repeat(positions[i] - line.length);\n    }\n    printFn(line);\n}\n/**\n * Prints a summary for a single Layer, without connectivity information.\n *\n * @param layer: Layer instance to print.\n */\nfunction printLayerSummary(layer, positions, \n// tslint:disable-next-line:no-any\nprintFn) {\n    let outputShape;\n    try {\n        outputShape = JSON.stringify(layer.outputShape);\n    }\n    catch (err) {\n        outputShape = 'multiple';\n    }\n    const name = layer.name;\n    const className = layer.getClassName();\n    const fields = [`${name} (${className})`, outputShape, layer.countParams().toString()];\n    printRow(fields, positions, printFn);\n}\n/**\n * Prints a summary for a single Layer, with connectivity information.\n */\nfunction printLayerSummaryWithConnections(layer, positions, relevantNodes, \n// tslint:disable-next-line:no-any\nprintFn) {\n    let outputShape;\n    try {\n        outputShape = JSON.stringify(layer.outputShape);\n    }\n    catch (err) {\n        outputShape = 'multiple';\n    }\n    const connections = [];\n    for (const node of layer.inboundNodes) {\n        if (relevantNodes != null && relevantNodes.length > 0 &&\n            relevantNodes.indexOf(node) === -1) {\n            continue;\n        }\n        for (let i = 0; i < node.inboundLayers.length; ++i) {\n            const inboundLayer = node.inboundLayers[i].name;\n            const inboundLayerIndex = node.nodeIndices[i];\n            const inboundTensorIndex = node.tensorIndices[i];\n            connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);\n        }\n    }\n    const name = layer.name;\n    const className = layer.getClassName();\n    const firstConnection = connections.length === 0 ? '' : connections[0];\n    const fields = [\n        `${name} (${className})`, outputShape, layer.countParams().toString(),\n        firstConnection\n    ];\n    printRow(fields, positions, printFn);\n    for (let i = 1; i < connections.length; ++i) {\n        printRow(['', '', '', connections[i]], positions, printFn);\n    }\n}\n//# sourceMappingURL=layer_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as generic_utils from '../utils/generic_utils';\n// tslint:enable\n/**\n * Test whether a value in an array is the name of a LayersModel or Layer.\n * @param key The key name that the value is found under. Note that the key\n *   may not be at the level immediately above the value, if the value is in a\n *   nested array.\n * @param index Index of the value in the Array that it is found in.\n * @param value The value object.\n * @returns A boolean indicating whether value is a name.\n */\nfunction isArrayItemInputOrOutputName(key, index, value) {\n    return (key === 'inboundNodes' || key === 'outputLayers' ||\n        key === 'inputLayers') &&\n        index === 0 && typeof value === 'string';\n}\n/**\n * Convert a Pythonic config object to TypeScript config object.\n * @param pythonicConfig The config object to convert.\n * @param key Optional key name of the object being converted.\n * @returns Result of the conversion.\n */\nexport function convertPythonicToTs(pythonicConfig, key) {\n    if (pythonicConfig === null) {\n        return null;\n    }\n    else if (typeof pythonicConfig === 'string') {\n        return generic_utils.toCamelCase(pythonicConfig);\n    }\n    else if ((typeof pythonicConfig === 'number') ||\n        (typeof pythonicConfig === 'boolean')) {\n        return pythonicConfig;\n    }\n    else if (pythonicConfig instanceof Array) {\n        const tsArray = [];\n        const arrayLength = pythonicConfig.length;\n        for (let i = 0; i < arrayLength; ++i) {\n            const item = pythonicConfig[i];\n            if (isArrayItemInputOrOutputName(key, i, item)) {\n                tsArray.push(item);\n            }\n            else {\n                tsArray.push(convertPythonicToTs(item, key));\n            }\n        }\n        return tsArray;\n    }\n    else {\n        const tsDict = {};\n        for (const pythonicKey of Object.keys(pythonicConfig)) {\n            const pythonicValue = pythonicConfig[pythonicKey];\n            if (pythonicKey === 'name' && typeof pythonicValue === 'string') {\n                // Special case the 'name' key with a string value. Name values, such as\n                // the names of LayersModel and Layer instances, should not undergo the\n                // camel-case conversion.\n                tsDict[pythonicKey] = pythonicValue;\n            }\n            else {\n                const tsKey = generic_utils.toCamelCase(pythonicKey);\n                tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey);\n            }\n        }\n        return tsDict;\n    }\n}\n/**\n * Convert a TypeScript config object to Python config object.\n * @param tsConfig The config object to convert.\n * @param key Optional key name of the object being converted.\n * @returns Result of the conversion.\n */\nexport function convertTsToPythonic(tsConfig, key) {\n    if (tsConfig === null || tsConfig === undefined) {\n        return null;\n    }\n    else if (typeof tsConfig === 'string') {\n        return generic_utils.toSnakeCase(tsConfig);\n    }\n    else if ((typeof tsConfig === 'number') || (typeof tsConfig === 'boolean')) {\n        return tsConfig;\n    }\n    else if (tsConfig instanceof Array) {\n        const pyArray = [];\n        const arrayLength = tsConfig.length;\n        for (let i = 0; i < arrayLength; ++i) {\n            const item = tsConfig[i];\n            if (isArrayItemInputOrOutputName(key, i, item)) {\n                pyArray.push(item);\n            }\n            else {\n                pyArray.push(convertTsToPythonic(item, key));\n            }\n        }\n        return pyArray;\n    }\n    else {\n        const pyDict = {};\n        for (const tsKey of Object.keys(tsConfig)) {\n            const tsValue = tsConfig[tsKey];\n            const pyKey = generic_utils.toSnakeCase(tsKey);\n            if ((tsKey === 'name' || tsKey === 'className') &&\n                typeof tsValue === 'string') {\n                // Special case the 'name' key with a string value. Name values, such as\n                // the names of LayersModel and Layer instances, should not undergo the\n                // snake-case conversion.\n                pyDict[pyKey] = tsValue;\n            }\n            else {\n                pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey);\n            }\n        }\n        return pyDict;\n    }\n}\n//# sourceMappingURL=serialization_utils.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Executor: Evaluates SymbolicTensor based on feeds.\n */\nimport { cast, dispose, memory, util } from '@tensorflow/tfjs-core';\nimport { ValueError } from '../errors';\nimport { toList } from '../utils/generic_utils';\nimport { InputLayer } from './input_layer';\nimport { SymbolicTensor } from './topology';\n/**\n * Helper function to check the dtype and shape compatibility of a feed value.\n */\nfunction assertFeedCompatibility(key, val) {\n    // Check dtype compatibility.\n    if (key.dtype == null || key.dtype === val.dtype) {\n        //  a.  If types match, return val tensor as is.\n        return val;\n    }\n    try {\n        //  b. Attempt to convert to expected type.\n        return cast(val, key.dtype);\n    }\n    catch (err) {\n        //  c. If conversion fails, return helpful error.\n        throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype ` +\n            `of the key '${key.name}' (${key.dtype}).`);\n    }\n}\n/**\n * FeedDict: A mapping from unique SymbolicTensors to feed values for them.\n * A feed value is a concrete value represented as an `Tensor`.\n */\nexport class FeedDict {\n    /**\n     * Constructor, optionally does copy-construction.\n     * @param feeds An Array of `Feed`s, or another `FeedDict`, in which case\n     *   copy-construction will be performed.\n     */\n    constructor(feeds) {\n        this.id2Value = {};\n        this.id2Mask = {};\n        this.name2Id = {};\n        if (feeds instanceof FeedDict) {\n            for (const id in feeds.id2Value) {\n                this.id2Value[id] = feeds.id2Value[id];\n                if (id in feeds.id2Mask) {\n                    this.id2Mask[id] = feeds.id2Mask[id];\n                }\n            }\n        }\n        else {\n            if (feeds == null) {\n                return;\n            }\n            for (const feed of feeds) {\n                this.add(feed.key, feed.value);\n            }\n        }\n    }\n    /**\n     * Add a key-value pair to the FeedDict.\n     *\n     * @param key The key of the feed.\n     * @param value The value of the tensor feed.\n     * @param mask The value of the mask feed (optional).\n     * @returns This `FeedDict`.\n     * @throws ValueError: If the key `SymbolicTensor` already exists in the\n     *   `FeedDict`.\n     */\n    add(key, value, mask) {\n        if (this.id2Value[key.id] == null) {\n            this.id2Value[key.id] = assertFeedCompatibility(key, value);\n            this.name2Id[key.name] = key.id;\n            if (mask != null) {\n                this.id2Mask[key.id] = mask;\n            }\n        }\n        else {\n            throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);\n        }\n        return this;\n    }\n    /**\n     * Add a Feed to the FeedDict.\n     * @param feed The new `Feed` to add.\n     * @returns This `FeedDict`.\n     */\n    addFeed(feed) {\n        this.add(feed.key, feed.value);\n    }\n    /**\n     * Probe whether a key already exists in the FeedDict.\n     * @param key\n     */\n    hasKey(key) {\n        return this.id2Value[key.id] != null;\n    }\n    /**\n     * Get all the SymbolicTensor available in this FeedDict.\n     */\n    names() {\n        return Object.keys(this.name2Id);\n    }\n    /**\n     * Get the feed value for given key.\n     * @param key The SymbolicTensor, or its name (as a string), of which the\n     *     value is sought.\n     * @returns If `key` exists, the corresponding feed value.\n     * @throws ValueError: If `key` does not exist in this `FeedDict`.\n     */\n    getValue(key) {\n        if (key instanceof SymbolicTensor) {\n            if (this.id2Value[key.id] == null) {\n                throw new ValueError(`Nonexistent key: ${key.name}`);\n            }\n            else {\n                return this.id2Value[key.id];\n            }\n        }\n        else {\n            const id = this.name2Id[key];\n            if (id == null) {\n                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n            }\n            return this.id2Value[id];\n        }\n    }\n    /**\n     * Get the feed mask for given key.\n     * @param key The SymbolicTensor, or its name (as a string), of which the\n     *     value is sought.\n     * @returns If `key` exists, the corresponding feed mask.\n     * @throws ValueError: If `key` does not exist in this `FeedDict`.\n     */\n    getMask(key) {\n        if (key instanceof SymbolicTensor) {\n            if (this.id2Value[key.id] == null) {\n                throw new ValueError(`Nonexistent key: ${key.name}`);\n            }\n            else {\n                return this.id2Mask[key.id];\n            }\n        }\n        else {\n            const id = this.name2Id[key];\n            if (id == null) {\n                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n            }\n            return this.id2Mask[id];\n        }\n    }\n    /** Dispose all mask Tensors held by this object. */\n    disposeMasks() {\n        if (this.id2Mask != null) {\n            dispose(this.id2Mask);\n        }\n    }\n}\n// Cache for topologically sorted SymbolicTensors for given execution\n// targets (i.e., fetches).\nconst cachedSorted = {};\n// Cache for recipient count maps for given execution targets (i.e., fetches).\nconst cachedRecipientCounts = {};\n/**\n * Execute a SymbolicTensor by using concrete feed values.\n *\n * A `SymbolicTensor` object is a node in a computation graph of TF.js\n * Layers. The object is backed by a source layer and input\n * `SymbolicTensor`s to the source layer. This method evaluates\n * the `call()` method of the source layer, using concrete values of the\n * inputs obtained from either\n * * `feedDict`, if the input key exists in `feedDict`, or else,\n * * a recursive call to `execute()` itself.\n *\n * @param x: The `SymbolicTensor` to execute.\n * @param feedDict: The feed values, as base condition of the recursion.\n *   execution.\n * @param kwargs: Optional keyword arguments.\n * @param probe: A probe object (of interface `ExecutionProbe`) used for\n *   testing memory footprint of `execute` calls.\n * @returns Result of the execution.\n * @throws ValueError: If any `SymbolicTensor`s from `InputLayer`s\n *   encountered during the execution lacks a feed value in `feedDict`.\n */\nexport function execute(fetches, feedDict, kwargs, probe) {\n    const training = kwargs == null ? false : kwargs['training'];\n    const arrayFetches = Array.isArray(fetches);\n    const fetchArray = arrayFetches ? fetches : [fetches];\n    const outputNames = fetchArray.map(t => t.name);\n    const finalOutputs = [];\n    const feedNames = feedDict.names();\n    for (const outputName of outputNames) {\n        if (feedNames.indexOf(outputName) !== -1) {\n            finalOutputs.push(feedDict.getValue(outputName));\n        }\n        else {\n            finalOutputs.push(null);\n        }\n    }\n    if (probe != null) {\n        // For optional probing of memory footprint during execution.\n        probe.maxNumTensors = -Infinity;\n        probe.minNumTensors = Infinity;\n    }\n    // Check cache.\n    const fetchAndFeedKey = outputNames.join(',') + '|' + feedDict.names().join(',');\n    let sorted;\n    let recipientCounts;\n    if (cachedSorted[fetchAndFeedKey] == null) {\n        // Cache doesn't contain the desired combination of fetches. Compute\n        // topological sort for the combination for the first time.\n        const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);\n        sorted = out.sorted;\n        recipientCounts = out.recipientCounts;\n        // Store results in cache for future use.\n        cachedSorted[fetchAndFeedKey] = sorted;\n        cachedRecipientCounts[fetchAndFeedKey] = recipientCounts;\n    }\n    sorted = cachedSorted[fetchAndFeedKey];\n    recipientCounts = {};\n    if (!training) {\n        Object.assign(recipientCounts, cachedRecipientCounts[fetchAndFeedKey]);\n    }\n    const internalFeedDict = new FeedDict(feedDict);\n    // Start iterative execution on the topologically-sorted SymbolicTensors.\n    for (let i = 0; i < sorted.length; ++i) {\n        if (probe != null) {\n            // For optional probing of memory usage during execution.\n            const numTensors = memory().numTensors;\n            if (numTensors > probe.maxNumTensors) {\n                probe.maxNumTensors = numTensors;\n            }\n            if (numTensors < probe.minNumTensors) {\n                probe.minNumTensors = numTensors;\n            }\n        }\n        const symbolic = sorted[i];\n        const srcLayer = symbolic.sourceLayer;\n        if (srcLayer instanceof InputLayer) {\n            continue;\n        }\n        const inputValues = [];\n        const inputMasks = [];\n        const tensorsToDispose = [];\n        let maskExists = false;\n        for (const input of symbolic.inputs) {\n            const value = internalFeedDict.getValue(input);\n            const mask = internalFeedDict.getMask(input);\n            inputValues.push(value);\n            inputMasks.push(mask);\n            if (mask != null) {\n                maskExists = true;\n            }\n            if (!training) {\n                recipientCounts[input.name]--;\n                if (recipientCounts[input.name] === 0 && !feedDict.hasKey(input) &&\n                    outputNames.indexOf(input.name) === -1 && !value.isDisposed &&\n                    input.sourceLayer.stateful !== true) {\n                    tensorsToDispose.push(value);\n                }\n            }\n        }\n        if (maskExists) {\n            kwargs = kwargs || {};\n            kwargs['mask'] = inputMasks[0];\n        }\n        const outputTensors = toList(srcLayer.apply(inputValues, kwargs));\n        let outputMask = null;\n        if (srcLayer.supportsMasking) {\n            outputMask = srcLayer.computeMask(inputValues, inputMasks);\n        }\n        const layerOutputs = getNodeOutputs(symbolic);\n        const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];\n        for (let i = 0; i < outputSymbolicTensors.length; ++i) {\n            if (!internalFeedDict.hasKey(outputSymbolicTensors[i])) {\n                internalFeedDict.add(outputSymbolicTensors[i], outputTensors[i], Array.isArray(outputMask) ? outputMask[0] : outputMask);\n            }\n            const index = outputNames.indexOf(outputSymbolicTensors[i].name);\n            if (index !== -1) {\n                finalOutputs[index] = outputTensors[i];\n            }\n        }\n        if (!training) {\n            // Clean up Tensors that are no longer needed.\n            dispose(tensorsToDispose);\n        }\n    }\n    // NOTE(cais): Unlike intermediate tensors, we don't discard mask\n    // tensors as we go, because these tensors are sometimes passed over a\n    // series of mutliple layers, i.e., not obeying the immediate input\n    // relations in the graph. If this becomes a memory-usage concern,\n    // we can improve this in the future.\n    internalFeedDict.disposeMasks();\n    return arrayFetches ? finalOutputs : finalOutputs[0];\n}\n/**\n * Sort the `SymbolicTensor`s topologically, for an array of fetches.\n *\n * This function calls getTopologicalSortAndRecipientCountsForOneFetch and\n * merges their results.\n *\n * @param fetch The array of fetches requested. Must be a non-empty array.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientCounts: Recipient counts for all SymbolicTensors in `sorted`.\n */\nfunction getTopologicalSortAndRecipientCounts(fetches, feedDict) {\n    util.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`);\n    let finalSorted = [];\n    let finalRecipientMap = {};\n    if (fetches.length === 1) {\n        // Special-casing 1 fetch for efficiency.\n        const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);\n        finalSorted = out.sorted;\n        finalRecipientMap = out.recipientMap;\n    }\n    else {\n        const visited = new Set();\n        for (const fetch of fetches) {\n            const { sorted, recipientMap } = getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict);\n            // Merge sorted SymbolicTensor Arrays.\n            for (const symbolicTensor of sorted) {\n                if (!visited.has(symbolicTensor.name)) {\n                    finalSorted.push(symbolicTensor);\n                    visited.add(symbolicTensor.name);\n                }\n            }\n            // Merge recipient maps.\n            for (const name in recipientMap) {\n                if (finalRecipientMap[name] == null) {\n                    finalRecipientMap[name] = new Set();\n                }\n                recipientMap[name].forEach(recipient => finalRecipientMap[name].add(recipient));\n            }\n        }\n    }\n    return {\n        sorted: finalSorted,\n        recipientCounts: recipientMap2Counts(finalRecipientMap)\n    };\n}\nfunction recipientMap2Counts(recipientMap) {\n    const recipientCounts = {};\n    for (const name in recipientMap) {\n        recipientCounts[name] = recipientMap[name].size;\n    }\n    return recipientCounts;\n}\n/**\n * Sort the `SymbolicTensor`s topologically, for a single fetch.\n *\n * This helper function processes the upstream SymbolicTensors of a single\n * fetch.\n *\n * @param fetch The single fetch requested.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientMap: Recipient names for all SymbolicTensors in `sorted`.\n */\nexport function getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict) {\n    const visited = new Set();\n    const sorted = [];\n    const recipientMap = {};\n    // Put keys of the feedDict into visited first, so they don't have to be\n    // walked. This is needed in case where there are feeds for intermediate\n    // SymbolicTensors of the graph.\n    for (const key of feedDict.names()) {\n        visited.add(key);\n    }\n    const stack = [];\n    const marks = [];\n    // Initial population of stack and marks.\n    stack.push(fetch);\n    while (stack.length > 0) {\n        const top = stack[stack.length - 1];\n        if (visited.has(top.name)) {\n            stack.pop();\n            continue;\n        }\n        const topIsMarked = marks[marks.length - 1] === stack.length - 1;\n        if (top.inputs.length === 0 || topIsMarked) {\n            // Input SymbolicTensor or all children have been visited.\n            stack.pop();\n            sorted.push(top);\n            visited.add(top.name);\n            if (topIsMarked) {\n                marks.pop();\n            }\n        }\n        else {\n            // A non-input SymbolicTensor whose upstream SymbolicTensors haven't\n            // been visited yet. Push them onto the stack.\n            marks.push(stack.length - 1);\n            for (const input of top.inputs) {\n                // Increment the recipient count. Note that this needs to happen\n                // regardless of whether the SymbolicTensor has been visited before.\n                if (recipientMap[input.name] == null) {\n                    recipientMap[input.name] = new Set();\n                }\n                recipientMap[input.name].add(top.name);\n                if (visited.has(input.name)) {\n                    continue; // Avoid repeated visits to the same SymbolicTensor.\n                }\n                stack.push(input);\n            }\n        }\n    }\n    return { sorted, recipientMap };\n}\n/**\n * Get the symbolic output tensors of the node to which a given fetch belongs.\n * @param fetch The fetched symbolic tensor.\n * @returns The Array of symbolic tensors output by the node to which `fetch`\n *   belongs.\n */\nfunction getNodeOutputs(fetch) {\n    let layerOutputs;\n    if (fetch.sourceLayer.inboundNodes.length === 1) {\n        layerOutputs = fetch.sourceLayer.output;\n    }\n    else {\n        let nodeIndex = null;\n        for (let i = 0; i < fetch.sourceLayer.inboundNodes.length; ++i) {\n            for (const outputTensor of fetch.sourceLayer.inboundNodes[i]\n                .outputTensors) {\n                if (outputTensor.id === fetch.id) {\n                    nodeIndex = i;\n                    break;\n                }\n            }\n        }\n        layerOutputs = fetch.sourceLayer.getOutputAt(nodeIndex);\n    }\n    return layerOutputs;\n}\n//# sourceMappingURL=executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/engine/topology.py */\nimport { tidy } from '@tensorflow/tfjs-core';\nimport { getUid } from '../backend/state';\nimport { NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { deserialize as deserializeLayer } from '../layers/serialization';\nimport * as generic_utils from '../utils/generic_utils';\nimport { convertTsToPythonic } from '../utils/serialization_utils';\nimport * as types_utils from '../utils/types_utils';\nimport { batchSetValue } from '../variables';\nimport { version as layersVersion } from '../version';\nimport { execute, FeedDict } from './executor';\nimport { InputLayer } from './input_layer';\nimport { Layer, Node } from './topology';\n/**\n * A Container is a directed acyclic graph of layers.\n *\n * It is the topological form of a \"model\". A LayersModel\n * is simply a Container with added training routines.\n *\n */\nexport class Container extends Layer {\n    constructor(args) {\n        // No args passed to super's constructor.\n        super({});\n        this.containerNodes = new Set();\n        this.name = args.name;\n        if (this.name == null) {\n            const prefix = this.getClassName().toLowerCase();\n            this.name = getUid(prefix);\n        }\n        this.supportsMasking = false;\n        this.trainable_ = true;\n        // TODO(michaelterry): Initialize perInputLosses/Updates here.\n        // Container-specific properties.\n        if (Array.isArray(args.inputs)) {\n            this.inputs = args.inputs.slice();\n        }\n        else {\n            this.inputs = [args.inputs];\n        }\n        if (Array.isArray(args.outputs)) {\n            this.outputs = args.outputs.slice();\n        }\n        else {\n            this.outputs = [args.outputs];\n        }\n        // Check for redundancy in inputs.\n        if (generic_utils.unique(this.inputs).length !== this.inputs.length) {\n            throw new ValueError('The list of inputs passed to the model is ' +\n                'redundant. All inputs should only appear once. Found: ' +\n                `${this.inputs.map(x => x.name)}`);\n        }\n        // Check for redundancy in outputs.\n        if (generic_utils.unique(this.outputs).length !== this.outputs.length) {\n            console.warn('The list of outputs passed to the model is redundant. ' +\n                'All outputs should only appear once. Found: ' +\n                `${this.outputs.map(x => x.name)}`);\n        }\n        /*\n          List of initial layers (1 to 1 mapping with this.inputs, hence the same\n          layer might appear twice)\n        */\n        this.inputLayers = [];\n        this.inputLayersNodeIndices = [];\n        this.inputLayersTensorIndices = [];\n        /*\n          List of layers (1 to 1 mapping with this.outputs, hence the same layer\n          might appear twice)\n        */\n        this.outputLayers = [];\n        this.outputLayersNodeIndices = [];\n        this.outputLayersTensorIndices = [];\n        /*\n          All layers in order of horizontal graph traversal. Entries are unique.\n          Includes input and output layers.\n        */\n        this.layers = [];\n        /*\n          References to container layers that were constructed internally. We need\n          these to properly dispose of tensors from nested containers.\n        */\n        this.internalContainerRefs = [];\n        // TODO(michaelterry): Determine if caching still needed with eager\n        // backend.\n        /*\n          This is for performance optimization when calling the Container on new\n          inputs. Every time the Container is called on a set on input tensors,\n          we compute the output tensors, output masks and output shapes in one pass,\n          then cache them here. When one of these outputs is queried later,\n          we retrieve it from there instead of recomputing it.\n        */\n        // this.outputTensorCache = {};\n        // this.outputShapeCache = {};\n        // Build this.outputLayers:\n        for (const x of this.outputs) {\n            const layer = x.sourceLayer;\n            const nodeIndex = x.nodeIndex;\n            const tensorIndex = x.tensorIndex;\n            this.outputLayers.push(layer);\n            this.outputLayersNodeIndices.push(nodeIndex);\n            this.outputLayersTensorIndices.push(tensorIndex);\n        }\n        // TODO(michaelterry): Add output mask cache code.\n        // Build this.inputLayers:\n        for (const x of this.inputs) {\n            const layer = x.sourceLayer;\n            const nodeIndex = x.nodeIndex;\n            const tensorIndex = x.tensorIndex;\n            /*\n              It's supposed to be an input layer, so only one node\n              and one tensor output.\n            */\n            generic_utils.assert(nodeIndex === 0, 'input layer has >1 nodes');\n            generic_utils.assert(tensorIndex === 0, 'input layer has >1 tensors');\n            this.inputLayers.push(layer);\n            this.inputLayersNodeIndices.push(nodeIndex);\n            this.inputLayersTensorIndices.push(tensorIndex);\n        }\n        // Build this.inputNames and this.outputNames.\n        this.inputNames = [];\n        this.outputNames = [];\n        this.feedInputShapes = [];\n        this.feedInputNames = [];\n        this.feedOutputNames = [];\n        for (let i = 0; i < this.inputLayers.length; i++) {\n            const layer = this.inputLayers[i];\n            // Check that layer is an InputLayer.\n            if (!(layer instanceof InputLayer)) {\n                throw new TypeError('Input layers to a LayersModel must be InputLayer objects. ' +\n                    `Received inputs: ${args.inputs}. ` +\n                    `Input ${i} (0-based) originates ` +\n                    `from layer type ${layer.getClassName()}.`);\n            }\n            this.inputNames.push(layer.name);\n            this.feedInputShapes.push(layer.batchInputShape);\n            this.feedInputNames.push(layer.name);\n        }\n        for (const layer of this.outputLayers) {\n            this.outputNames.push(layer.name);\n        }\n        this.internalInputShapes = this.inputs.map(x => x.shape);\n        this.internalOutputShapes = this.outputs.map(x => x.shape);\n        /*\n          Container_nodes: set of nodes included in the graph (not all nodes\n          included in the layers are relevant to the current graph).\n        */\n        // ids of all nodes relevant to the Container:\n        const nodesDepths = {};\n        // To recover nodes from their ID.\n        const nodeIDToNode = {};\n        const layersDepths = {};\n        // To layers from their ID.\n        const layerIDToLayer = {};\n        const layerIndices = {};\n        const nodesInDecreasingDepth = [];\n        /**\n         * Builds a map of the graph of layers.\n         *\n         * This recursively updates the map `layerIndices`,\n         * the list `nodesInDecreasingDepth` and the set `containerNodes`.\n         *\n         * @param tensor Some tensor in a graph.\n         * @param finishedNodes Set of nodes whose subgraphs have been traversed\n         *         completely. Useful to prevent duplicated work.\n         * @param nodesInProgress Set of nodes that are currently active on the\n         *         recursion stack. Useful to detect cycles.\n         * @param layer Layer from which `tensor` comes from. If not provided,\n         *   will be obtained from tensor.sourceLayer.\n         * @param nodeIndex Node index from which `tensor` comes from.\n         * @param tensorIndex TensorIndex from which `tensor` comes from.\n         *\n         * @exception RuntimeError if a cycle is detected.\n         */\n        const buildMapOfGraph = (tensor, finishedNodes, nodesInProgress, layer, nodeIndex, tensorIndex) => {\n            if (layer == null || nodeIndex == null || tensorIndex == null) {\n                layer = tensor.sourceLayer;\n                nodeIndex = tensor.nodeIndex;\n                tensorIndex = tensor.tensorIndex;\n            }\n            const node = layer.inboundNodes[nodeIndex];\n            // Prevent cycles.\n            if (nodesInProgress.indexOf(node) !== -1) {\n                throw new RuntimeError(`The tensor ${tensor.name} at layer \"${layer.name}\" ` +\n                    'is part of a cycle.');\n            }\n            // Don't repeat work for shared subgraphs\n            if (finishedNodes.indexOf(node) !== -1) {\n                return;\n            }\n            // Update containerNodes.\n            this.containerNodes.add(Container.nodeKey(layer, nodeIndex));\n            // Store the traversal order for layer sorting.\n            if (!(layer.id in layerIndices)) {\n                layerIndices[layer.id] = Object.keys(layerIndices).length;\n            }\n            if (nodesInProgress.indexOf(node) === -1) {\n                nodesInProgress.push(node);\n            }\n            // Propagate to all previous tensors connected to this node.\n            const numInboundLayers = node.inboundLayers.length;\n            for (let i = 0; i < numInboundLayers; i++) {\n                const x = node.inputTensors[i];\n                const layer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const tensorIndex = node.tensorIndices[i];\n                buildMapOfGraph(x, finishedNodes, nodesInProgress, layer, nodeIndex, tensorIndex);\n            }\n            finishedNodes.push(node);\n            while (nodesInProgress.indexOf(node) >= 0) {\n                nodesInProgress.splice(nodesInProgress.indexOf(node), 1);\n            }\n            nodesInDecreasingDepth.push(node);\n        };\n        const finishedNodes = [];\n        const nodesInProgress = [];\n        for (const x of this.outputs) {\n            buildMapOfGraph(x, finishedNodes, nodesInProgress);\n        }\n        const reversedNodesInDecreasingDepth = nodesInDecreasingDepth.slice().reverse();\n        for (const node of reversedNodesInDecreasingDepth) {\n            nodeIDToNode[node.id] = node;\n            // If the depth is not set, the node has no outbound nodes (depth 0).\n            if (!(node.id in nodesDepths)) {\n                nodesDepths[node.id] = 0;\n            }\n            let depth = nodesDepths[node.id];\n            // Update the depth of the corresponding layer\n            const previousDepth = (layersDepths[node.outboundLayer.id] == null ?\n                0 :\n                layersDepths[node.outboundLayer.id]);\n            /*\n              If we've seen this layer before at a higher depth, we should use that\n              depth instead of the node depth.  This is necessary for shared layers\n              that have inputs at different depth levels in the graph.\n            */\n            depth = Math.max(depth, previousDepth);\n            layersDepths[node.outboundLayer.id] = depth;\n            layerIDToLayer[node.outboundLayer.id] = node.outboundLayer;\n            nodesDepths[node.id] = depth;\n            // Update the depth of inbound nodes.\n            for (let i = 0; i < node.inboundLayers.length; i++) {\n                const inboundLayer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const inboundNode = inboundLayer.inboundNodes[nodeIndex];\n                const previousDepth = (nodesDepths[inboundNode.id] == null ? 0 :\n                    nodesDepths[inboundNode.id]);\n                nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth);\n                nodeIDToNode[inboundNode.id] = inboundNode;\n            }\n        }\n        // Build a dict {depth: list of nodes with this depth}\n        const nodesByDepth = {};\n        for (const nodeID in nodesDepths) {\n            const depth = nodesDepths[nodeID];\n            if (!(depth in nodesByDepth)) {\n                nodesByDepth[depth] = [];\n            }\n            nodesByDepth[depth].push(nodeIDToNode[nodeID]);\n        }\n        // Build a dict {depth: list of layers with this depth}\n        const layersByDepth = {};\n        for (const layerID in layersDepths) {\n            const depth = layersDepths[layerID];\n            if (!(depth in layersByDepth)) {\n                layersByDepth[depth] = [];\n            }\n            layersByDepth[depth].push(layerIDToLayer[layerID]);\n        }\n        // Get sorted list of layer depths.\n        let depthKeys = Object.keys(layersByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Set this.layers and this.layersByDepth.\n        this.layers = [];\n        for (const depth of depthKeys) {\n            const layersForDepth = layersByDepth[depth];\n            // Container.layers needs to have a deterministic order:\n            // here we order them by traversal order.\n            layersForDepth.sort((a, b) => {\n                const aIndex = layerIndices[a.id];\n                const bIndex = layerIndices[b.id];\n                if (aIndex < bIndex) {\n                    return -1;\n                }\n                if (aIndex > bIndex) {\n                    return 1;\n                }\n                return 0;\n            });\n            for (const layer of layersForDepth) {\n                if (layer instanceof Container) {\n                    this.internalContainerRefs.push(layer);\n                }\n                this.layers.push(layer);\n            }\n        }\n        this.layersByDepth = layersByDepth;\n        // Get sorted list of node depths;\n        depthKeys = Object.keys(nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Check that all tensors required are computable.\n        // computable_tensors: all tensors in the graph\n        // that can be computed from the inputs provided.\n        const computableTensors = this.inputs.slice();\n        // To provide a better error msg.\n        const layersWithCompleteInput = [];\n        for (const depth of depthKeys) {\n            for (const node of nodesByDepth[depth]) {\n                const layer = node.outboundLayer;\n                if (layer != null) {\n                    for (const x of node.inputTensors) {\n                        if (computableTensors.indexOf(x) === -1) {\n                            throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x}` +\n                                ` at layer \"${layer.name}\". ` +\n                                'The following previous layers were accessed without ' +\n                                `issue: ${layersWithCompleteInput}`);\n                        }\n                    }\n                    for (const x of node.outputTensors) {\n                        computableTensors.push(x);\n                    }\n                    layersWithCompleteInput.push(layer.name);\n                }\n            }\n        }\n        // Set this.containerNodes and this.nodesByDepth.\n        this.nodesByDepth = nodesByDepth;\n        // Ensure name unicity, which will be crucial for serialization\n        // (since serialized nodes refer to layers by their name).\n        const allNames = this.layers.map(x => x.name);\n        for (const name of allNames) {\n            const numOccurrences = allNames.filter(x => x === name).length;\n            if (numOccurrences !== 1) {\n                throw new RuntimeError(`The name \"${name}\" is used ${numOccurrences} times ` +\n                    'in the model. All layer names should be unique. Layer names: ' +\n                    JSON.stringify(allNames));\n            }\n        }\n        // Layer parameters.\n        // The new container starts with a single inbound node\n        // for its inputs, and no outbound nodes.\n        // Will be appended to by future calls to apply().\n        this.outboundNodes = [];\n        // Will be appended to below, and by future calls to apply().\n        this.inboundNodes = [];\n        // Create the node linking internal inputs to internal outputs.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers: [],\n            nodeIndices: [],\n            tensorIndices: [],\n            inputTensors: this.inputs,\n            outputTensors: this.outputs,\n            inputMasks: this.inputs.map(x => null),\n            outputMasks: this.outputs.map(x => null),\n            inputShapes: this.inputs.map(x => x.shape),\n            outputShapes: this.outputs.map(x => x.shape)\n        });\n        this.built = true;\n        this._refCount = 1; // The ref count of a container always start at 1.\n    }\n    assertNotDisposed() {\n        if (this._refCount === 0) {\n            throw new Error(`Container '${this.name}' is already disposed.`);\n        }\n    }\n    /**\n     * Attempt to dispose a LayersModel's weights.\n     *\n     * This method decrease the reference count of the LayersModel object by 1.\n     *\n     * A LayersModel is reference-counted. Its reference count is incremented by 1\n     * when it is first constructed and when it is used as a Layer of another\n     * LayersModel.\n     *\n     * If the reference count of a LayersModel becomes 0, the `dispose` method of\n     * all its constituent `Layer`s will be called.\n     *\n     * Note: If the reference count is greater than 0 after the decrement, the\n     * `dispose` method of its constituent `Layer`s will *not* be called.\n     *\n     * After a LayersModel is disposed, it cannot be used in calls such as\n     * 'predict`, `evaluate` or `fit` anymore.\n     *\n     * @returns A DisposeResult Object with the following fields:\n     *   - refCountAfterDispose: The reference count of the LayersModel after this\n     *     `dispose()` call.\n     *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n     *     during this `dispose()` call.\n     * @throws {Error} If the layer is not built yet, or if the LayersModel has\n     *   already been disposed.\n     */\n    dispose() {\n        this.assertNotDisposed();\n        const result = { refCountAfterDispose: null, numDisposedVariables: 0 };\n        if (--this._refCount === 0) {\n            for (const layer of this.layers) {\n                result.numDisposedVariables += layer.dispose().numDisposedVariables;\n            }\n            // Call dispose on each internally created container layer again to ensure\n            // their refCounts hit zero and their tensors are subsequently deleted.\n            for (const container of this.internalContainerRefs) {\n                result.numDisposedVariables += container.dispose().numDisposedVariables;\n            }\n        }\n        result.refCountAfterDispose = this._refCount;\n        return result;\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this.layers.forEach(layer => {\n            // tslint:disable-next-line:no-any\n            layer._trainableWeights\n                .forEach(w => w.trainable = trainable);\n        });\n        this.trainable_ = trainable;\n    }\n    get trainableWeights() {\n        // Porting Note: This check below is to prevent errors where the\n        //   _trainableWeights inherited from the parent class (Layer) gets\n        //   inadvertently used.\n        if (this._trainableWeights.length > 0) {\n            throw new ValueError('Container instance unexpectedly contains _trainableWeights.' +\n                'The trainable weights of a Container are a union of the ' +\n                'trainable weights of its consituent Layers. Its own ' +\n                '_trainableWeights must remain an empty Array.');\n        }\n        if (!this.trainable) {\n            return [];\n        }\n        let weights = [];\n        for (const layer of this.layers) {\n            weights = weights.concat(layer.trainableWeights);\n        }\n        return weights;\n    }\n    get nonTrainableWeights() {\n        const weights = [];\n        for (const layer of this.layers) {\n            weights.push(...layer.nonTrainableWeights);\n        }\n        if (!this.trainable) {\n            const trainableWeights = [];\n            for (const layer of this.layers) {\n                trainableWeights.push(...layer.trainableWeights);\n            }\n            return trainableWeights.concat(weights);\n        }\n        return weights;\n    }\n    get weights() {\n        return this.trainableWeights.concat(this.nonTrainableWeights);\n    }\n    /**\n     * Loads all layer weights from a JSON object.\n     *\n     * Porting Note: HDF5 weight files cannot be directly loaded in JavaScript /\n     *   TypeScript. The utility script at `scripts/pykeras.py` offers means\n     *   to convert them into JSON strings compatible with this method.\n     * Porting Note: TensorFlow.js Layers supports only loading by name currently.\n     *\n     * @param weights A JSON mapping weight names to weight values as nested\n     *   arrays of numbers, or a `NamedTensorMap`, i.e., a JSON mapping weight\n     *   names to `tf.Tensor` objects.\n     * @param strict Require that the provided weights exactly match those\n     *   required by the container.  Default: `true`.  Passing `false` means that\n     *   extra weights and missing weights will be silently ignored.\n     */\n    loadWeights(weights, strict = true) {\n        const nameToWeight = {};\n        let totalWeightsCount = 0;\n        for (const layer of this.layers) {\n            for (const weight of layer.weights) {\n                if (nameToWeight[weight.originalName] != null) {\n                    throw new ValueError(`Duplicate weight name: ${weight.originalName}`);\n                }\n                nameToWeight[weight.originalName] = weight;\n                totalWeightsCount++;\n            }\n        }\n        const weightValueTuples = [];\n        for (const name in weights) {\n            // TF 2.2.0 added cell name to the weight name in the format of\n            // layer_name/cell_name/weight_name, we need to remove\n            // the inner cell name.\n            let validatedName = name;\n            if (nameToWeight[name] == null) {\n                const tokens = name.split('/');\n                const shortenNameArray = tokens.slice(0, -2).concat([tokens[tokens.length - 1]]);\n                validatedName = shortenNameArray.join('/');\n            }\n            if (nameToWeight[validatedName] != null) {\n                weightValueTuples.push([nameToWeight[validatedName], weights[name]]);\n            }\n            else if (strict) {\n                throw new ValueError(`Provided weight data has no target variable: ${name}`);\n            }\n            delete nameToWeight[validatedName];\n        }\n        if (strict) {\n            // Check that all weights are set.\n            const unsetNames = [];\n            for (const name in nameToWeight) {\n                unsetNames.push(name);\n            }\n            if (unsetNames.length > 0) {\n                throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ` +\n                    `${unsetNames}`);\n            }\n        }\n        batchSetValue(weightValueTuples);\n    }\n    /**\n     * Util shared between different serialization methods.\n     * @returns LayersModel config with Keras version information added.\n     */\n    updatedConfig() {\n        const theConfig = this.getConfig();\n        const modelConfig = {};\n        modelConfig['className'] = this.getClassName();\n        modelConfig['config'] = theConfig;\n        modelConfig['kerasVersion'] = `tfjs-layers ${layersVersion}`;\n        // TODO(nielsene): Replace something like K.backend() once\n        // possible.\n        modelConfig['backend'] = 'TensorFlow.js';\n        return modelConfig;\n    }\n    /**\n     * Returns a JSON string containing the network configuration.\n     *\n     * To load a network from a JSON save file, use\n     * models.modelFromJSON(jsonString);\n     * @param extraJsonArgs Unused in tfjs-layers, maintained for PyKeras\n     * @param returnString Whether the return value should be stringified\n     *    (default: `true`).\n     * @returns a JSON string if `returnString` (default), or a JSON object if\n     *   `!returnString`.\n     */\n    // tslint:disable-next-line:no-any\n    toJSON(unused, returnString = true) {\n        const modelConfig = convertTsToPythonic(this.updatedConfig());\n        return returnString ? JSON.stringify(modelConfig) : modelConfig;\n    }\n    /**\n     * Call the model on new inputs.\n     *\n     * In this case `call` just reapplies all ops in the graph to the new inputs\n     * (e.g. build a new computational graph from the provided inputs).\n     *\n     * @param inputs A tensor or list of tensors.\n     * @param mask A mask or list of masks. A mask can be either a tensor or null\n     *   (no mask).\n     *\n     * @return A tensor if there is a single output, or a list of tensors if there\n     *   are more than one outputs.\n     */\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = generic_utils.toList(inputs);\n            const feedDict = new FeedDict();\n            for (let i = 0; i < this.inputs.length; ++i) {\n                feedDict.add(this.inputs[i], inputs[i]);\n            }\n            return execute(this.outputs, feedDict, kwargs);\n        });\n    }\n    /**\n     * Computes an output mask tensor.\n     *\n     * @param inputs Tensor or list of tensors.\n     * @param mask Tensor or list of tensors.\n     *\n     * @return null or a tensor (or list of tensors, one per output tensor of the\n     * layer).\n     */\n    computeMask(inputs, mask) {\n        return tidy(() => {\n            inputs = generic_utils.toList(inputs);\n            let masks;\n            if (mask == null) {\n                masks = generic_utils.pyListRepeat(null, inputs.length);\n            }\n            else {\n                masks = generic_utils.toList(mask);\n            }\n            // TODO(michaelterry): Add support for mask caching.\n            return this.runInternalGraph(inputs, masks)[1];\n        });\n    }\n    /**\n     * Computes the output shape of the layer.\n     *\n     * Assumes that the layer will be built to match that input shape provided.\n     *\n     * @param inputShape A shape (tuple of integers) or a list of shape tuples\n     *   (one per output tensor of the layer). Shape tuples can include null for\n     *   free dimensions, instead of an integer.\n     */\n    computeOutputShape(inputShape) {\n        const inputShapes = types_utils.normalizeShapeList(inputShape);\n        if (inputShapes.length !== this.inputLayers.length) {\n            throw new ValueError(`Invalid inputShape argument ${inputShape}: ` +\n                `model has ${this.inputLayers.length} tensor inputs.`);\n        }\n        // TODO(michaelterry): Add caching\n        const layersToOutputShapes = {};\n        for (let i = 0; i < inputShapes.length; i++) {\n            const layer = this.inputLayers[i];\n            const inputShape = inputShapes[i];\n            // It's an input layer: computeOutputShape is identity,\n            // and there is only one node and one tensor output.\n            const shapeKey = layer.name + '_0_0';\n            layersToOutputShapes[shapeKey] = inputShape;\n        }\n        const depthKeys = Object.keys(this.nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Iterate over nodes, by depth level.\n        if (depthKeys.length > 1) {\n            for (const depth of depthKeys) {\n                const nodes = this.nodesByDepth[depth];\n                for (const node of nodes) {\n                    // This is always a single layer, never a list.\n                    const layer = node.outboundLayer;\n                    if (this.inputLayers.map(x => x.id).indexOf(layer.id) !== -1) {\n                        // We've already covered the input layers a few lines above.\n                        continue;\n                    }\n                    // Potentially redundant list, same size of node.inputTensors.\n                    const inputShapes = [];\n                    for (let j = 0; j < node.inboundLayers.length; j++) {\n                        const inboundLayer = node.inboundLayers[j];\n                        const nodeIndex = node.nodeIndices[j];\n                        const tensorIndex = node.tensorIndices[j];\n                        const shapeKey = `${inboundLayer.name}_${nodeIndex}_${tensorIndex}`;\n                        const inputShape = layersToOutputShapes[shapeKey];\n                        inputShapes.push(inputShape);\n                    }\n                    const outputShape = layer.computeOutputShape(generic_utils.singletonOrArray(inputShapes));\n                    const outputShapes = types_utils.normalizeShapeList(outputShape);\n                    const nodeIndex = layer.inboundNodes.indexOf(node);\n                    for (let j = 0; j < outputShapes.length; j++) {\n                        const shapeKey = `${layer.name}_${nodeIndex}_${j}`;\n                        layersToOutputShapes[shapeKey] = outputShapes[j];\n                    }\n                }\n            }\n        }\n        // Read final output shapes from layersToOutputShapes.\n        const outputShapes = [];\n        const outputShapeKeys = [];\n        for (let i = 0; i < this.outputLayers.length; i++) {\n            const layer = this.outputLayers[i];\n            const nodeIndex = this.outputLayersNodeIndices[i];\n            const tensorIndex = this.outputLayersTensorIndices[i];\n            const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;\n            outputShapeKeys.push(shapeKey);\n        }\n        for (let i = 0; i < outputShapeKeys.length; i++) {\n            const key = outputShapeKeys[i];\n            generic_utils.assert(key in layersToOutputShapes);\n            outputShapes.push(layersToOutputShapes[key]);\n        }\n        // TODO(michaelterry): Update cache\n        return generic_utils.singletonOrArray(outputShapes);\n    }\n    /**\n     * Computes output tensors for new inputs.\n     *\n     * Note:\n     *   - Expects `inputs` to be a list (potentially with 1 element).\n     *\n     * @param inputs List of tensors\n     * @param masks List of masks (tensors or null).\n     * @return Three lists: outputTensors, outputMasks, outputShapes\n     */\n    runInternalGraph(inputs, masks) {\n        if (masks == null) {\n            masks = generic_utils.pyListRepeat(null, inputs.length);\n        }\n        // Dictionary mapping reference tensors to tuples\n        // (computed tensor, compute mask)\n        // we assume a 1:1 mapping from tensor to mask\n        // TODO: raise exception when a `.computeMask()` call\n        // does not return a list the same size as `call`\n        const tensorMap = {};\n        for (let i = 0; i < this.inputs.length; ++i) {\n            const x = this.inputs[i];\n            const y = inputs[i];\n            const mask = masks[i];\n            tensorMap[x.id] = [y, mask];\n        }\n        const depthKeys = Object.keys(this.nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        for (const depth of depthKeys) {\n            const nodes = this.nodesByDepth[depth];\n            for (const node of nodes) {\n                // This is always a single layer, never a list.\n                const layer = node.outboundLayer;\n                const referenceInputTensors = node.inputTensors;\n                const referenceOutputTensors = node.outputTensors;\n                // If all previous input tensors are available in tensorMap,\n                // then call node.inboundLayer on them.\n                // List of tuples [input, mask]:\n                const computedData = new Array();\n                for (const x of referenceInputTensors) {\n                    if (x.id in tensorMap) {\n                        computedData.push(tensorMap[x.id]);\n                    }\n                }\n                if (computedData.length === referenceInputTensors.length) {\n                    // TODO(michaelterry): Add K.name_scope here, if we need it.\n                    let kwargs = {};\n                    let computedTensors;\n                    let computedMasks;\n                    let outputTensors;\n                    let outputMasks;\n                    // call layer\n                    if (node.callArgs != null) {\n                        kwargs = node.callArgs;\n                    }\n                    if (computedData.length === 1) {\n                        const [computedTensor, computedMask] = computedData[0];\n                        if (kwargs['mask'] == null) {\n                            kwargs['mask'] = computedMask;\n                        }\n                        outputTensors =\n                            generic_utils.toList(layer.call(computedTensor, kwargs));\n                        outputMasks = generic_utils.toList(layer.computeMask(computedTensor, computedMask));\n                        computedTensors = [computedTensor];\n                        computedMasks = [computedMask];\n                    }\n                    else {\n                        computedTensors = computedData.map(x => x[0]);\n                        computedMasks = computedData.map(x => x[1]);\n                        if (kwargs['mask'] == null) {\n                            kwargs['mask'] = computedMasks;\n                        }\n                        outputTensors =\n                            generic_utils.toList(layer.call(computedTensors, kwargs));\n                        outputMasks = generic_utils.toList(layer.computeMask(computedTensors, computedMasks));\n                    }\n                    if (layer.activityRegularizer) {\n                        throw new NotImplementedError('LayersModel invocation with concrete Tensor value(s) in the ' +\n                            'presence of activity regularizer(s) is not supported yet.');\n                    }\n                    // TODO(michaelterry): Add model updates and losses\n                    // Update tensor map.\n                    for (let i = 0; i < referenceOutputTensors.length; ++i) {\n                        const x = referenceOutputTensors[i];\n                        const y = outputTensors[i];\n                        const mask = outputMasks[i];\n                        tensorMap[x.id] = [y, mask];\n                    }\n                }\n            }\n        }\n        const outputTensors = [];\n        const outputMasks = [];\n        const outputShapes = [];\n        for (const x of this.outputs) {\n            generic_utils.assert(x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`);\n            const [tensor, mask] = tensorMap[x.id];\n            outputShapes.push(tensor.shape);\n            outputTensors.push(tensor);\n            outputMasks.push(mask);\n        }\n        // TODO(michaelterry): Add support for caches.\n        return [outputTensors, outputMasks, outputShapes];\n    }\n    /**\n     * Builds a map of internal node keys to node ordering.\n     * Used in serializaion a node orderings may change as unused nodes are\n     * dropped. Porting Note:  This helper method was pulled out of getConfig to\n     * improve readability.\n     * @param layers An array of Layers in the model.\n     * @returns Map of Node Keys to index order within the layer.\n     */\n    buildNodeConversionMap(layers) {\n        const nodeConversionMap = {};\n        let keptNodes;\n        for (const layer of this.layers) {\n            keptNodes = layer instanceof Container ? 1 : 0;\n            for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n                const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n                if (this.containerNodes.has(nodeKey)) {\n                    // i.e. we mark it to be saved\n                    nodeConversionMap[nodeKey] = keptNodes;\n                    keptNodes += 1;\n                }\n            }\n        }\n        return nodeConversionMap;\n    }\n    /**\n     * Retrieves a layer based on either its name (unique) or index.\n     *\n     * Indices are based on order of horizontal graph traversal (bottom-up).\n     *\n     * If both `name` and `index` are specified, `index` takes precedence.\n     *\n     * @param name Name of layer.\n     * @param index Index of layer.\n     * @returns A Layer instance.\n     * @throws ValueError: In case of invalid layer name or index.\n     *\n     * @doc {\n     *    heading: 'Layers',\n     *    subheading: 'Classes',\n     *    namespace: 'layers',\n     *    subclasses: ['LayersModel']\n     * }\n     */\n    getLayer(name, index) {\n        if (index != null) {\n            if (this.layers.length <= index) {\n                throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only ` +\n                    `has ${this.layers.length} layer(s).`);\n            }\n            else {\n                return this.layers[index];\n            }\n        }\n        else {\n            if (name == null) {\n                throw new ValueError('Provide either a layer name or layer index');\n            }\n        }\n        for (const layer of this.layers) {\n            if (layer.name === name) {\n                return layer;\n            }\n        }\n        throw new ValueError(`No such layer: ${name}`);\n    }\n    /**\n     * Retrieves the Container's current loss values.\n     *\n     * Used for regularizers during training.\n     */\n    calculateLosses() {\n        // Porting Node: This is an augmentation to Container.loss in PyKeras.\n        //   In PyKeras, Container.loss returns symbolic tensors. Here a concrete\n        //   Tensor (specifically Scalar) values are returned. This is due to the\n        //   imperative backend.\n        return tidy(() => {\n            const losses = [];\n            for (const layer of this.layers) {\n                for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length; ++nodeIndex) {\n                    const nodeKey = Container.nodeKey(layer, nodeIndex);\n                    if (this.containerNodes.has(nodeKey)) {\n                        losses.push(...layer.calculateLosses());\n                    }\n                }\n            }\n            // TODO(cais): Add any unconditional model-level losses?\n            return losses;\n        });\n    }\n    getConfig() {\n        const config = { name: this.name };\n        // Build a map from layer unique name (self._node_key)\n        // to the index of the nodes that are saved in the config.\n        // Only nodes in container_nodes are saved.\n        const nodeConversionMap = this.buildNodeConversionMap(this.layers);\n        // Serialize and save the layers in layerConfigs\n        const layerConfigs = [];\n        for (const layer of this.layers) {\n            const layerClassName = layer.getClassName();\n            const layerConfig = layer.getConfig();\n            const filteredInboundNodes = [];\n            for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n                const node = layer.inboundNodes[originalNodeIndex];\n                const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n                let kwargs = {};\n                if (this.containerNodes.has(nodeKey)) {\n                    // The node is relevant to the model:\n                    // add to filteredInboundNodes.\n                    if (node.callArgs) {\n                        try {\n                            JSON.stringify(node.callArgs);\n                            kwargs = node.callArgs;\n                        }\n                        catch (err) {\n                            console.warn(`Layer ${layer.name} was passed ` +\n                                `non-serializable keyword arguments: ` +\n                                `${node.callArgs}. They will not be included ` +\n                                `in the serialized model (and thus will be ` +\n                                `missing at deserialization time).`);\n                            kwargs = {};\n                        }\n                    }\n                    if (node.inboundLayers.length > 0) {\n                        const nodeData = [];\n                        for (let i = 0; i < node.inboundLayers.length; i++) {\n                            const inboundLayer = node.inboundLayers[i];\n                            const nodeIndex = node.nodeIndices[i];\n                            const tensorIndex = node.tensorIndices[i];\n                            const nodeKey = Container.nodeKey(inboundLayer, nodeIndex);\n                            let newNodeIndex = nodeConversionMap[nodeKey];\n                            if (newNodeIndex == null) {\n                                newNodeIndex = 0;\n                            }\n                            nodeData.push([inboundLayer.name, newNodeIndex, tensorIndex, kwargs]);\n                        }\n                        filteredInboundNodes.push(nodeData);\n                    }\n                }\n            }\n            const dict = {};\n            dict['name'] = layer.name;\n            dict['className'] = layerClassName;\n            dict['config'] = layerConfig;\n            dict['inboundNodes'] = filteredInboundNodes;\n            layerConfigs.push(dict);\n        }\n        config['layers'] = layerConfigs;\n        // Gather info about inputs and outputs\n        const modelInputs = [];\n        for (let i = 0; i < this.inputLayers.length; i++) {\n            const layer = this.inputLayers[i];\n            const nodeIndex = this.inputLayersNodeIndices[i];\n            const nodeKey = Container.nodeKey(layer, nodeIndex);\n            if (!this.containerNodes.has(nodeKey)) {\n                continue;\n            }\n            let newNodeIndex = nodeConversionMap[nodeKey];\n            if (newNodeIndex === null || newNodeIndex === undefined) {\n                newNodeIndex = 0;\n            }\n            const tensorIndex = this.inputLayersTensorIndices[i];\n            modelInputs.push([layer.name, newNodeIndex, tensorIndex]);\n        }\n        config['inputLayers'] = modelInputs;\n        const modelOutputs = [];\n        for (let i = 0; i < this.outputLayers.length; i++) {\n            const layer = this.outputLayers[i];\n            const nodeIndex = this.outputLayersNodeIndices[i];\n            const nodeKey = Container.nodeKey(layer, nodeIndex);\n            if (!this.containerNodes.has(nodeKey)) {\n                continue;\n            }\n            let newNodeIndex = nodeConversionMap[nodeKey];\n            if (newNodeIndex === null || newNodeIndex === undefined) {\n                newNodeIndex = 0;\n            }\n            const tensorIndex = this.outputLayersTensorIndices[i];\n            modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);\n        }\n        config['outputLayers'] = modelOutputs;\n        return config;\n    }\n    /**\n     * Instantiates a LayersModel from its config (output of `get_config()`).\n     * @param cls the class to create\n     * @param config LayersModel config dictionary.\n     * @param customObjects An optional dictionary of custom objects.\n     * @param fastWeightInit Optional flag to use fast weight initialization\n     *   during deserialization. This is applicable to cases in which\n     *   the initialization will be immediately overwritten by loaded weight\n     *   values. Default: `false`.\n     * @returns A LayersModel instance.\n     * @throws ValueError: In case of improperly formatted config dict.\n     */\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n        // Layer instances created during\n        // the graph reconstruction process\n        const createdLayers = {};\n        // Dictionary mapping layer instances to\n        // node data that specifies a layer call.\n        // It acts as a queue that maintains any unprocessed\n        // layer call until it becomes possible to process it\n        // (i.e. until the input tensors to the call all exist).\n        const unprocessedNodes = {};\n        function addUnprocessedNode(layer, nodeData) {\n            if (!(layer.name in unprocessedNodes)) {\n                unprocessedNodes[layer.name] = [nodeData];\n            }\n            else {\n                unprocessedNodes[layer.name].push(nodeData);\n            }\n        }\n        function processNode(layer, nodeData) {\n            const inputTensors = [];\n            let kwargs;\n            for (const inputData of nodeData) {\n                const inboundLayerName = inputData[0];\n                const inboundNodeIndex = inputData[1];\n                const inboundTensorIndex = inputData[2];\n                kwargs = inputData[3] == null ?\n                    {} :\n                    inputData[3];\n                if (!(inboundLayerName in createdLayers)) {\n                    addUnprocessedNode(layer, nodeData);\n                    return;\n                }\n                const inboundLayer = createdLayers[inboundLayerName];\n                if (inboundLayer.inboundNodes.length <= inboundNodeIndex) {\n                    addUnprocessedNode(layer, nodeData);\n                    return;\n                }\n                const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex];\n                inputTensors.push(inboundNode.outputTensors[inboundTensorIndex]);\n            }\n            // Call layer on its inputs, thus creating the node\n            // and building the layer if needed.\n            // Note: This has Eager vs Graph Implications.\n            if (inputTensors.length > 0) {\n                layer.apply(generic_utils.singletonOrArray(inputTensors), kwargs); // was ** kwargs\n            }\n        }\n        /**\n         * Deserialize a layer, then call it on appropriate inputs.\n         * @param layerData: layer config dict.\n         * @throws ValueError: In case of improperly formatted `layer_data`\n         * dict.\n         */\n        function processLayer(layerData) {\n            const layerName = layerData['name'];\n            // Instantiate layer.\n            const layer = deserializeLayer(layerData, config['customObjects'] != null ?\n                config['customObjects'] :\n                {});\n            layer.setFastWeightInitDuringBuild(fastWeightInit);\n            createdLayers[layerName] = layer;\n            // Gather layer inputs.\n            const inboundNodesData = layerData['inboundNodes'];\n            inboundNodesData.forEach(nodeData => {\n                if (!(nodeData instanceof Array)) {\n                    throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);\n                }\n                // We don't process nodes (i.e. make layer calls)\n                // on the fly because the inbound node may not yet exist,\n                // in case of layer shared at different topological depths\n                // (e.g.a model such as A(B(A(B(x)))))\n                addUnprocessedNode(layer, nodeData);\n            });\n        }\n        // First, we create all layers and enqueue nodes to be processed.\n        const name = config['name'];\n        const layersFromConfig = config['layers'];\n        for (const layerData of layersFromConfig) {\n            processLayer(layerData);\n        }\n        // Then we process nodes in order of layer depth.\n        // Nodes that cannot yet be processed(if the inbound node\n        // does not yet exist) are re - enqueued, and the process\n        // is repeated until all nodes are processed.\n        while (!generic_utils.isObjectEmpty(unprocessedNodes)) {\n            for (const layerData of layersFromConfig) {\n                const layer = createdLayers[layerData['name']];\n                if (layer.name in unprocessedNodes) {\n                    const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name];\n                    delete unprocessedNodes[layer.name];\n                    for (const nodeData of currentUnprocessedNodesForLayer) {\n                        processNode(layer, nodeData);\n                    }\n                }\n            }\n        }\n        const inputTensors = [];\n        const outputTensors = [];\n        const inputLayersFromConfig = config['inputLayers'];\n        for (const layerData of inputLayersFromConfig) {\n            const layerName = layerData[0];\n            const nodeIndex = layerData[1];\n            const tensorIndex = layerData[2];\n            generic_utils.assert(layerName in createdLayers);\n            const layer = createdLayers[layerName];\n            const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n            inputTensors.push(layerOutputTensors[tensorIndex]);\n        }\n        const outputLayersFromConfig = config['outputLayers'];\n        for (const layerData of outputLayersFromConfig) {\n            const layerName = layerData[0];\n            const nodeIndex = layerData[1];\n            const tensorIndex = layerData[2];\n            generic_utils.assert(layerName in createdLayers);\n            const layer = createdLayers[layerName];\n            const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n            outputTensors.push(layerOutputTensors[tensorIndex]);\n        }\n        return new cls({ inputs: inputTensors, outputs: outputTensors, name });\n    }\n    /**\n     * Determine whether the container is stateful.\n     *\n     * Porting Note: this is the equivalent of the stateful @property of\n     *   the Container class in PyKeras.\n     */\n    get stateful() {\n        // Porting Note: This check is to prevent inadvertent setting of the\n        //   _stateful property of the Container instance.\n        if (this._stateful) {\n            throw new ValueError('Container instance unexpectedly has _stateful = true. The ' +\n                'statefulness of a Container is determined by the Layers it ' +\n                'contains. Its _stateful property must remain the default false.');\n        }\n        for (const layer of this.layers) {\n            if (layer.stateful) {\n                return true;\n            }\n        }\n        return false;\n    }\n    /**\n     * Reset the state of all stateful constituent layers (if any).\n     *\n     * Examples of stateful layers include RNN layers whose `stateful` property\n     * is set as `true`.\n     */\n    resetStates() {\n        tidy(() => {\n            this.layers.forEach(layer => {\n                // tslint:disable:no-any\n                if (layer.stateful) {\n                    layer.resetStates();\n                }\n                // tslint:enable:no-any\n            });\n        });\n    }\n}\n//# sourceMappingURL=container.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { dispose, mul, tensor1d, tidy } from '@tensorflow/tfjs-core';\nfunction standardizeSampleOrClassWeights(xWeight, outputNames, weightType) {\n    const numOutputs = outputNames.length;\n    if (xWeight == null || (Array.isArray(xWeight) && xWeight.length === 0)) {\n        return outputNames.map(name => null);\n    }\n    if (numOutputs === 1) {\n        if (Array.isArray(xWeight) && xWeight.length === 1) {\n            return xWeight;\n        }\n        else if (typeof xWeight === 'object' && outputNames[0] in xWeight) {\n            return [xWeight[outputNames[0]]];\n        }\n        else {\n            return [xWeight];\n        }\n    }\n    if (Array.isArray(xWeight)) {\n        if (xWeight.length !== numOutputs) {\n            throw new Error(`Provided ${weightType} is an array of ${xWeight.length} ` +\n                `element(s), but the model has ${numOutputs} outputs. ` +\n                `Make sure a set of weights is provided for each model output.`);\n        }\n        return xWeight;\n    }\n    else if (typeof xWeight === 'object' && Object.keys(xWeight).length > 0 &&\n        typeof xWeight[Object.keys(xWeight)[0]] ===\n            'object') {\n        const output = [];\n        outputNames.forEach(outputName => {\n            if (outputName in xWeight) {\n                output.push(xWeight[outputName]);\n            }\n            else {\n                output.push(null);\n            }\n        });\n        return output;\n    }\n    else {\n        throw new Error(`The model has multiple (${numOutputs}) outputs, ` +\n            `so ${weightType} must be either an array with ` +\n            `${numOutputs} elements or an object with ${outputNames} keys. ` +\n            `Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);\n    }\n}\n/**\n * Standardize class weighting objects.\n *\n * This function takes a single class-weighting object, an array of them,\n * or a map from output name to class-weighting object. It compares it to the\n * output name(s) of the model, base on which it outputs an array of\n * class-weighting objects of which the length matches the number of outputs.\n *\n * @param classWeight Input class-weighting object(s).\n * @param outputNames All output name(s) of the model.\n * @return An array of class-weighting objects. The length of the array matches\n *   the model's number of outputs.\n */\nexport function standardizeClassWeights(classWeight, outputNames) {\n    return standardizeSampleOrClassWeights(classWeight, outputNames, 'classWeight');\n}\nexport function standardizeSampleWeights(classWeight, outputNames) {\n    return standardizeSampleOrClassWeights(classWeight, outputNames, 'sampleWeight');\n}\n/**\n * Standardize by-sample and/or by-class weights for training.\n *\n * Note that this function operates on one model output at a time. For a model\n * with multiple outputs, you must call this function multiple times.\n *\n * @param y The target tensor that the by-sample and/or by-class weight is for.\n *     The values of y are assumed to encode the classes, either directly\n *     as an integer index, or as one-hot encoding.\n * @param sampleWeight By-sample weights.\n * @param classWeight By-class weights: an object mapping class indices\n *     (integers) to a weight (float) to apply to the model's loss for the\n *     samples from this class during training. This can be useful to tell the\n *     model to \"pay more attention\" to samples from an under-represented class.\n * @param sampleWeightMode The mode for the sample weights.\n * @return A Promise of weight tensor, of which the size of the first dimension\n *     matches that of `y`.\n */\nexport async function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) {\n    if (sampleWeight != null || sampleWeightMode != null) {\n        // TODO(cais): Once 'temporal' mode is implemented, document it in the doc\n        // string.\n        throw new Error('Support sampleWeight is not implemented yet');\n    }\n    if (classWeight != null) {\n        // Apply class weights per sample.\n        const yClasses = tidy(() => {\n            if (y.shape.length === 1) {\n                // Assume class indices.\n                return y.clone();\n            }\n            else if (y.shape.length === 2) {\n                if (y.shape[1] > 1) {\n                    // Assume one-hot encoding of classes.\n                    const axis = 1;\n                    return y.argMax(axis);\n                }\n                else if (y.shape[1] === 1) {\n                    // Class index.\n                    return y.reshape([y.shape[0]]);\n                }\n                else {\n                    throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) ` +\n                        `during handling of class weights. The size is expected to be ` +\n                        `>= 1.`);\n                }\n            }\n            else {\n                throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during ` +\n                    `handling of class weights. The rank is expected to be 1 or 2.`);\n            }\n        });\n        const yClassIndices = Array.from(await yClasses.data());\n        dispose(yClasses);\n        const classSampleWeight = [];\n        yClassIndices.forEach(classIndex => {\n            if (classWeight[classIndex] == null) {\n                throw new Error(`classWeight must contain all classes in the training data. ` +\n                    `The class ${classIndex} exists in the data but not in ` +\n                    `classWeight`);\n            }\n            else {\n                classSampleWeight.push(classWeight[classIndex]);\n            }\n        });\n        return tensor1d(classSampleWeight, 'float32');\n    }\n    else {\n        return null;\n    }\n}\n/**\n * Apply per-sample weights on the loss values from a number of samples.\n *\n * @param losses Loss tensor of shape `[batchSize]`.\n * @param sampleWeights Per-sample weight tensor of shape `[batchSize]`.\n * @returns Tensor of the same shape as`losses`.\n */\nexport function computeWeightedLoss(losses, sampleWeights) {\n    return mul(losses, sampleWeights);\n}\n//# sourceMappingURL=training_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Interfaces and methods for training models using TensorFlow.js datasets.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { scalar } from '@tensorflow/tfjs-core';\nimport { configureCallbacks, standardizeCallbacks } from '../base_callbacks';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { disposeTensorsInLogs } from '../logs';\nimport { singletonOrArray, toList } from '../utils/generic_utils';\nimport { standardizeClassWeights, standardizeWeights } from './training_utils';\n// Default batch size used during tensor-based validation.\nconst DEFAULT_VALIDATION_BATCH_SIZE = 32;\n/**\n * Standardize the output of a dataset iterator for use by\n * LayersModel.fitDataset().\n *\n * @param model: A `tf.LayersModel` object.\n * @param iteratorOut The output of a dataset iterator. It is required to be\n *   an object of the form `{xs: TensorOrArrayOrMap, ys:\n * TensorOrArrayOrMap}`, where `TensorOrArrayOrMap` is a single `tf.Tensor`,\n * a `tf.Tensor[]`, or a flat map from string names to `tf.Tensor`s.\n * @returns A flat array of `tf.Tensor` objects: the input `tf.Tensor`s\n *   followed by the target `tf.Tensor`s.  When `tf.Tensor`s are provided\n *   as a map, the order in the resulting array is taken from the `inputNames`\n *   and `outputNames` of the model.\n */\nfunction standardizeDataIteratorOutput(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, iteratorOut) {\n    let xs;\n    let ys;\n    const iteratorOutObj = iteratorOut;\n    xs = iteratorOutObj['xs'];\n    ys = iteratorOutObj['ys'];\n    tfc.util.assert(xs != null && ys != null, () => 'A Dataset iterator for fitDataset() is expected to generate ' +\n        'objects of the form `{xs: xVal, ys: yVal}`, where the two ' +\n        'values may be `tf.Tensor`, an array of Tensors, or a map of ' +\n        'string to Tensor.  The provided Dataset instead generates ' +\n        `${iteratorOut}`);\n    const flattenedXs = flattenTensorOrArrayOrMap('input', model.inputNames, xs);\n    const flattenedYs = flattenTensorOrArrayOrMap('output', model.outputNames, ys);\n    const batchSize = flattenedXs[0].shape[0];\n    tfc.util.assert(flattenedXs.length === model.inputs.length, () => `LayersModel has ${model.inputs.length} inputs, but the dataset ` +\n        `provides ${flattenedXs.length} inputs.  (Expected input keys: ` +\n        `${JSON.stringify(model.inputNames)})`);\n    tfc.util.assert(flattenedYs.length === model.outputs.length, () => `LayersModel has ${model.outputs.length} outputs, but the dataset ` +\n        `provides ${flattenedYs.length} outputs.  (Expected output keys: ` +\n        `${JSON.stringify(model.outputNames)})`);\n    for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {\n        tfc.util.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ` +\n            `${model.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; ` +\n            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);\n    }\n    for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {\n        tfc.util.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ` +\n            `${model.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; ` +\n            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);\n    }\n    return { xs: flattenedXs, ys: flattenedYs };\n}\nfunction flattenTensorOrArrayOrMap(inputOrOutput, names, values) {\n    if (values instanceof tfc.Tensor) {\n        return [values];\n    }\n    else if (Array.isArray(values)) {\n        tfc.util.assert(values.length === names.length, () => `Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`);\n        return values;\n    }\n    else {\n        const result = [];\n        // Check that all the required keys are available.\n        for (const name of names) {\n            if (values[name] == null) {\n                throw new ValueError(`The feature data generated by the dataset lacks the required ` +\n                    `${inputOrOutput} key '${name}'.`);\n            }\n            result.push(values[name]);\n        }\n        return result;\n    }\n}\nfunction standardizeTensorValidationData(data) {\n    if (data.length === 3) {\n        throw new NotImplementedError('Validation with sample weights is not implemented yet.');\n    }\n    return { xs: data[0], ys: data[1] };\n}\nexport async function fitDataset(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, dataset, args) {\n    const hasBatchesPerEpoch = args.batchesPerEpoch != null;\n    tfc.util.assert(model.optimizer != null, () => 'You must compile a model before training/testing. Use ' +\n        'LayersModel.compile(modelCompileConfig).');\n    tfc.util.assert(args != null, () => `For fitDataset(), the 2nd argument (config) is required, ` +\n        `but it is not provided in this call.`);\n    tfc.util.assert(args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs), () => `For fitDataset(), config.epochs is expected to be a positive ` +\n        `integer, but got ${args.epochs}`);\n    tfc.util.assert(!hasBatchesPerEpoch ||\n        (args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch)), () => `For fitDataset(), config.batchesPerEpoch is expected to be a ` +\n        `positive integer if specified, but got ${args.batchesPerEpoch}`);\n    tfc.util.assert(\n    // tslint:disable-next-line:no-any\n    args['validationSplit'] == null, () => '`validationSplit` is not supported by `fitDataset()`. ' +\n        'Use validationData instead.');\n    if (model.isTraining) {\n        throw new Error('Cannot start training because another fit() call is ongoing.');\n    }\n    model.isTraining = true;\n    try {\n        const doValidation = args.validationData != null;\n        let valXs;\n        let valYs;\n        if (doValidation) {\n            if (isDatasetObject(args.validationData)) {\n                tfc.util.assert(args.validationBatches == null ||\n                    (args.validationBatches > 0 &&\n                        Number.isInteger(args.validationBatches)), () => `For fitDataset() with dataset-based validation, ` +\n                    `config.validationBatches is expected not to be provided, ` +\n                    `or to be a positive integer, ` +\n                    `but got ${args.validationBatches}`);\n            }\n            else {\n                const validationData = standardizeTensorValidationData(args.validationData);\n                valXs = validationData.xs;\n                valYs = validationData.ys;\n            }\n        }\n        const trainFunction = model.makeTrainFunction();\n        const outLabels = model.getDedupedMetricsNames();\n        let callbackMetrics;\n        if (doValidation) {\n            callbackMetrics =\n                outLabels.slice().concat(outLabels.map(n => 'val_' + n));\n        }\n        else {\n            callbackMetrics = outLabels.slice();\n        }\n        const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);\n        const verbose = args.verbose == null ? 1 : args.verbose;\n        const { callbackList, history } = configureCallbacks(callbacks, verbose, args.epochs, null, null, getStepsPerEpoch(dataset, args), null, // Batch size determined by the dataset itself.\n        doValidation, callbackMetrics);\n        callbackList.setModel(model);\n        model.history = history;\n        await callbackList.onTrainBegin();\n        model.stopTraining_ = false;\n        let epoch = args.initialEpoch == null ? 0 : args.initialEpoch;\n        let dataIterator = await dataset.iterator();\n        while (epoch < args.epochs) {\n            const epochLogs = {};\n            await callbackList.onEpochBegin(epoch);\n            let stepsDone = 0;\n            let batchIndex = 0;\n            if (!hasBatchesPerEpoch) {\n                dataIterator = await dataset.iterator();\n            }\n            while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) {\n                const iteratorOut = await dataIterator.next();\n                // If `batchesPerEpoch` is specified, the dataset should not be\n                // exhausted until all epoches are done.\n                if (hasBatchesPerEpoch && iteratorOut.done) {\n                    console.warn('You provided `batchesPerEpoch` as ' +\n                        `${args.batchesPerEpoch}, ` +\n                        'but your dataset iterator ran out of data after ' +\n                        `${stepsDone} batches; ` +\n                        'interrupting training. Make sure that your ' +\n                        'dataset can generate at least `batchesPerEpoch * epochs` ' +\n                        'batches (in this case, ' +\n                        `${args.batchesPerEpoch * args.epochs} batches). ` +\n                        'You may need to use the repeat() function when building ' +\n                        'your dataset.');\n                    break;\n                }\n                if (iteratorOut.value != null) {\n                    const { xs, ys } = standardizeDataIteratorOutput(model, iteratorOut.value);\n                    const batchLogs = {};\n                    batchLogs['batch'] = batchIndex;\n                    batchLogs['size'] = xs[0].shape[0];\n                    await callbackList.onBatchBegin(batchIndex, batchLogs);\n                    const sampleWeights = [];\n                    if (args.classWeight != null) {\n                        const standardClassWeights = standardizeClassWeights(args.classWeight, model.outputNames);\n                        for (let i = 0; i < standardClassWeights.length; ++i) {\n                            sampleWeights.push(await standardizeWeights(ys[i], null, standardClassWeights[i]));\n                        }\n                    }\n                    // Train on batch.\n                    const ins = xs.concat(ys).concat(sampleWeights);\n                    const outs = trainFunction(ins);\n                    tfc.dispose(ins);\n                    for (let i = 0; i < outLabels.length; ++i) {\n                        const label = outLabels[i];\n                        const out = outs[i];\n                        batchLogs[label] = out;\n                        tfc.keep(out);\n                    }\n                    await callbackList.onBatchEnd(batchIndex, batchLogs);\n                    disposeTensorsInLogs(batchLogs);\n                    batchIndex++;\n                    stepsDone++;\n                }\n                if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch :\n                    iteratorOut.done) {\n                    // Epoch finished. Perform validation.\n                    if (doValidation) {\n                        let valOuts;\n                        if (isDatasetObject(args.validationData)) {\n                            valOuts = toList(await model.evaluateDataset(args.validationData, { batches: args.validationBatches }));\n                        }\n                        else {\n                            valOuts = toList(model.evaluate(valXs, valYs, {\n                                batchSize: args.validationBatchSize == null ?\n                                    DEFAULT_VALIDATION_BATCH_SIZE :\n                                    args.validationBatchSize,\n                                verbose: 0\n                            }));\n                        }\n                        for (let i = 0; i < model.metricsNames.length; ++i) {\n                            epochLogs[`val_${model.metricsNames[i]}`] = valOuts[i];\n                        }\n                    }\n                    // Call `break` to exit one epoch lopp after validation is done. If\n                    // config.batchesPerEpoch is specified, an epoch while loop will\n                    // stop when `stepsDone >= config.batchesPerEpoch`. When\n                    // config.batchesPerEpoch is not provided, the following `break` is\n                    // required to exit the while lopp after dataset is exhausted.\n                    break;\n                }\n                if (model.stopTraining_) {\n                    break;\n                }\n            }\n            await callbackList.onEpochEnd(epoch, epochLogs);\n            epoch++;\n            if (model.stopTraining_) {\n                break;\n            }\n        }\n        await callbackList.onTrainEnd();\n        await model.history.syncData();\n        return model.history;\n    }\n    finally {\n        model.isTraining = false;\n    }\n}\n/** Helper function that determines number of steps (batches) per epoch. */\nfunction getStepsPerEpoch(dataset, args) {\n    // Attempt to determine # of batches in an epoch.\n    let stepsPerEpoch = null;\n    if (args.batchesPerEpoch != null) {\n        stepsPerEpoch = args.batchesPerEpoch;\n    }\n    else if (Number.isFinite(dataset.size)) {\n        stepsPerEpoch = dataset.size;\n    }\n    return stepsPerEpoch;\n}\n// Check if provided object is a Dataset object by checking its .iterator\n// element.\nfunction isDatasetObject(dataset) {\n    return (typeof dataset.iterator === 'function');\n}\n// Check if provided object is a LazyIterator object by checking it's .next\n// element.\nfunction isLazyIteratorObject(iterator) {\n    return (typeof iterator.next === 'function');\n}\nexport async function evaluateDataset(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, dataset, args) {\n    args = args || {};\n    const hasBatches = args.batches != null;\n    const f = model.testFunction;\n    let outs = [];\n    if (args.verbose > 0) {\n        throw new NotImplementedError('Verbose mode is not implemented yet.');\n    }\n    tfc.util.assert(!hasBatches || (args.batches > 0 && Number.isInteger(args.batches)), () => 'Test loop expects `batches` to be a positive integer, but ' +\n        `received ${JSON.stringify(args.batches)}`);\n    const dataIterator = isLazyIteratorObject(dataset) ?\n        dataset :\n        await dataset.iterator();\n    // Keeps track of number of examples used in this evaluation.\n    let numExamples = 0;\n    let batch = 0;\n    while (hasBatches ? batch < args.batches : true) {\n        const iteratorOut = await dataIterator.next();\n        outs = tfc.tidy(() => {\n            if (iteratorOut.value) {\n                // TODO(cais): Once real dataset is available, use\n                //   `map(x => standardizeDataIteratorOutput(model, x).map(f)`.\n                const { xs, ys } = standardizeDataIteratorOutput(model, iteratorOut.value);\n                const xsAndYs = xs.concat(ys);\n                const batchOuts = tfc.tidy(() => f(xsAndYs));\n                tfc.dispose(xsAndYs);\n                if (batch === 0) {\n                    for (let i = 0; i < batchOuts.length; ++i) {\n                        outs.push(scalar(0));\n                    }\n                }\n                const batchSize = xsAndYs[0].shape[0];\n                for (let i = 0; i < batchOuts.length; ++i) {\n                    const batchOut = batchOuts[i];\n                    const oldScalar = outs[i];\n                    outs[i] =\n                        tfc.tidy(() => tfc.add(outs[i], tfc.mul(batchSize, batchOut)));\n                    if (batch > 0) {\n                        tfc.dispose(oldScalar);\n                    }\n                }\n                tfc.dispose(batchOuts);\n                numExamples += batchSize;\n                ++batch;\n            }\n            return outs;\n        });\n        if (iteratorOut.done) {\n            if (hasBatches) {\n                console.warn('Your dataset iterator ran out of data during evaluateDataset(). ' +\n                    'Interrupting evalution. Make sure that your ' +\n                    'dataset can generate at least `batches` ' +\n                    `batches (in this case, ${args.batches} batches). ` +\n                    'You may need to use the repeat() function when building ' +\n                    'your dataset.');\n            }\n            break;\n        }\n    }\n    for (let i = 0; i < outs.length; ++i) {\n        const oldScalar = outs[i];\n        outs[i] = tfc.div(outs[i], numExamples);\n        tfc.dispose(oldScalar);\n    }\n    return singletonOrArray(outs);\n}\n//# sourceMappingURL=training_dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Interfaces and methods for training models using tf.Tensor objects.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { Tensor, tensor1d, util } from '@tensorflow/tfjs-core';\nimport { expandDims, gather, sliceAlongFirstAxis } from '../backend/tfjs_backend';\nimport { configureCallbacks, standardizeCallbacks } from '../base_callbacks';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { disposeTensorsInLogs } from '../logs';\nimport { range } from '../utils/math_utils';\nexport function checkBatchSize(batchSize) {\n    tfc.util.assert(batchSize > 0 && Number.isInteger(batchSize), () => `batchSize is required to be a positive integer, but got ${batchSize}`);\n}\n/**\n * Slice a Tensor or an Array of Tensors, by start and stop indices.\n *\n * Porting Note: The `_slice_arrays` function in PyKeras is covered by this\n *   function and `sliceArraysByIndices()` together.\n *\n * @param arrays: the input.\n * @param start: the starting index (inclusive).\n * @param stop: the stopping index (exclusive).\n * @returns The result of the slicing. If `arrays` is an `Array` of\n *   `tf.Tensor`s, the slicing will be applied to all elements of the `Array`\n *   in the same way.\n */\nexport function sliceArrays(arrays, start, stop) {\n    if (arrays == null) {\n        return [null];\n    }\n    else if (Array.isArray(arrays)) {\n        return arrays.map(array => sliceAlongFirstAxis(array, start, stop - start));\n    }\n    else { // Tensor.\n        return sliceAlongFirstAxis(arrays, start, stop - start);\n    }\n}\n/**\n * Slice a Tensor or an Array of Tensors, by random-order indices.\n *\n * Porting Note: The `_slice_arrays` function in PyKeras is covered by this\n *   function and `sliceArrays()` together.\n *\n * @param arrays The input `tf.Tensor` or `Array` of `tf.Tensor`s to slice.\n *   If an `Array` of `tf.Tensor`s, all `tf.Tensor`s will be sliced in the\n *   same fashion.\n * @param indices The indices to use for slicing along the first (batch)\n *   dimension.\n * @returns Result(s) of the slicing.\n */\nexport function sliceArraysByIndices(arrays, indices) {\n    return tfc.tidy(() => {\n        if (arrays == null) {\n            return null;\n        }\n        else if (Array.isArray(arrays)) {\n            return arrays.map(array => sliceArraysByIndices(array, indices));\n        }\n        else {\n            // TODO(cais): indices should be a pre-constructed Tensor1D to avoid\n            //   tensor1d() calls.\n            return gather(arrays, indices.dtype === 'int32' ? indices : indices.toInt());\n        }\n    });\n}\n/**\n * Returns a list of batch indices (tuples of indices).\n * @param size: Integer, total size of the data to slice into batches.\n * @param batchSize: Integer, batch size.\n * @returns An Array of [batchStart, batchEnd] tuples. batchStart is\n *   inclusive; batchEnd is exclusive. I.e., each batch consists of indices x\n *   that satisfy batchStart <= x < batchEnd.\n */\nexport function makeBatches(size, batchSize) {\n    const output = [];\n    let batchStart = 0;\n    let batchEnd = null;\n    while (batchStart < size) {\n        batchEnd = batchStart + batchSize;\n        if (batchEnd >= size) {\n            batchEnd = size;\n        }\n        output.push([batchStart, batchEnd]);\n        batchStart = batchEnd;\n    }\n    return output;\n}\n/**\n * Abstract fit function for `f(ins)`.\n * @param f A Function returning a list of tensors. For training, this\n *   function is expected to perform the updates to the variables.\n * @param ins List of tensors to be fed to `f`.\n * @param outLabels List of strings, display names of the outputs of `f`.\n * @param batchSize Integer batch size or `== null` if unknown. Default : 32.\n * @param epochs Number of times to iterate over the data. Default : 1.\n * @param verbose Verbosity mode: 0, 1, or 2. Default: 1.\n * @param callbacks List of callbacks to be called during training.\n * @param valF Function to call for validation.\n * @param valIns List of tensors to be fed to `valF`.\n * @param shuffle Whether to shuffle the data at the beginning of every\n * epoch. Default : true.\n * @param callbackMetrics List of strings, the display names of the metrics\n *   passed to the callbacks. They should be the concatenation of the\n *   display names of the outputs of `f` and the list of display names\n *   of the outputs of `valF`.\n * @param initialEpoch Epoch at which to start training (useful for\n *   resuming a previous training run). Default : 0.\n * @param stepsPerEpoch Total number of steps (batches on samples) before\n *   declaring one epoch finished and starting the next epoch. Ignored with\n *   the default value of `undefined` or `null`.\n * @param validationSteps Number of steps to run validation for (only if\n *   doing validation from data tensors). Not applicable for tfjs-layers.\n * @returns A `History` object.\n */\nasync function fitLoop(\n// Type `model` as `any` here to avoid circular dependency w/ training.ts.\n// tslint:disable-next-line:no-any\nmodel, f, ins, outLabels, batchSize, epochs, verbose, callbacks, valF, valIns, shuffle, callbackMetrics, initialEpoch, stepsPerEpoch, validationSteps) {\n    if (batchSize == null) {\n        batchSize = 32;\n    }\n    if (epochs == null) {\n        epochs = 1;\n    }\n    if (shuffle == null) {\n        shuffle = true;\n    }\n    if (initialEpoch == null) {\n        initialEpoch = 0;\n    }\n    // TODO(cais): Change const to let below when implementing validation.\n    let doValidation = false;\n    if (valF != null && valIns != null) {\n        doValidation = true;\n        // TODO(cais): verbose message.\n    }\n    if (validationSteps != null) {\n        doValidation = true;\n        if (stepsPerEpoch == null) {\n            throw new ValueError('Can only use `validationSteps` when doing step-wise training, ' +\n                'i.e., `stepsPerEpoch` must be set.');\n        }\n    }\n    const numTrainSamples = model.checkNumSamples(ins, batchSize, stepsPerEpoch, 'steps_per_epoch');\n    let indexArray;\n    if (numTrainSamples != null) {\n        indexArray = range(0, numTrainSamples);\n    }\n    if (verbose == null) {\n        verbose = 1;\n    }\n    const { callbackList, history } = configureCallbacks(callbacks, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics);\n    callbackList.setModel(model);\n    model.history = history;\n    await callbackList.onTrainBegin();\n    model.stopTraining_ = false;\n    // TODO(cais): Take care of callbacks.validation_data as in PyKeras.\n    // TODO(cais): Pre-convert feeds for performance as in PyKeras.\n    for (let epoch = initialEpoch; epoch < epochs; ++epoch) {\n        await callbackList.onEpochBegin(epoch);\n        const epochLogs = {};\n        if (stepsPerEpoch != null) {\n            throw new NotImplementedError('stepsPerEpoch mode is not implemented yet.');\n        }\n        else {\n            if (shuffle === 'batch') {\n                throw new NotImplementedError('batch shuffling is not implemneted yet');\n            }\n            else if (shuffle) {\n                util.shuffle(indexArray);\n            }\n            // Convert the potentially shuffled indices to Tensor1D, to avoid the\n            // cost of repeated creation of Array1Ds later on.\n            const epochIndexArray1D = tensor1d(indexArray);\n            const batches = makeBatches(numTrainSamples, batchSize);\n            for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                const batchLogs = {};\n                await callbackList.onBatchBegin(batchIndex, batchLogs);\n                tfc.tidy(() => {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    const batchIds = sliceAlongFirstAxis(epochIndexArray1D, batchStart, batchEnd - batchStart);\n                    batchLogs['batch'] = batchIndex;\n                    batchLogs['size'] = batchEnd - batchStart;\n                    // TODO(cais): In ins, train flag can be a number, instead of an\n                    //   Tensor? Do we need to handle this in tfjs-layers?\n                    const insBatch = sliceArraysByIndices(ins, batchIds);\n                    const outs = f(insBatch);\n                    for (let i = 0; i < outLabels.length; ++i) {\n                        const label = outLabels[i];\n                        const out = outs[i];\n                        batchLogs[label] = out;\n                        tfc.keep(out);\n                        // TODO(cais): Use scope() to avoid ownership.\n                    }\n                    if (batchIndex === batches.length - 1) { // Last batch.\n                        if (doValidation) {\n                            const valOuts = model.testLoop(valF, valIns, batchSize);\n                            // Porting Notes: In tfjs-layers, valOuts is always an Array.\n                            for (let i = 0; i < outLabels.length; ++i) {\n                                const label = outLabels[i];\n                                const out = valOuts[i];\n                                tfc.keep(out);\n                                // TODO(cais): Use scope() to avoid ownership.\n                                epochLogs['val_' + label] = out;\n                            }\n                        }\n                    }\n                });\n                await callbackList.onBatchEnd(batchIndex, batchLogs);\n                disposeTensorsInLogs(batchLogs);\n                if (model.stopTraining_) {\n                    break;\n                }\n                // TODO(cais): return outs as list of Tensor.\n            }\n            epochIndexArray1D.dispose();\n        }\n        // TODO(cais): Run validation at the end of the epoch.\n        await callbackList.onEpochEnd(epoch, epochLogs);\n        if (model.stopTraining_) {\n            break;\n        }\n    }\n    await callbackList.onTrainEnd();\n    await model.history.syncData();\n    return model.history;\n}\nexport async function fitTensors(\n// Type `model` as `any` here to avoid circular dependency w/ training.ts.\n// tslint:disable-next-line:no-any\nmodel, x, y, args = {}) {\n    if (model.isTraining) {\n        throw new Error('Cannot start training because another fit() call is ongoing.');\n    }\n    model.isTraining = true;\n    let inputs;\n    let targets;\n    let inputValX;\n    let inputValY;\n    let valX;\n    let valY;\n    let sampleWeights;\n    try {\n        const batchSize = args.batchSize == null ? 32 : args.batchSize;\n        checkBatchSize(batchSize);\n        // Validate user data.\n        // TODO(cais): Support sampleWeight.\n        const checkBatchAxis = false;\n        const standardizedOuts = await model.standardizeUserData(x, y, args.sampleWeight, args.classWeight, checkBatchAxis, batchSize);\n        inputs = standardizedOuts[0];\n        targets = standardizedOuts[1];\n        sampleWeights = standardizedOuts[2];\n        // Prepare validation data.\n        let doValidation = false;\n        let valIns;\n        if (args.validationData != null && args.validationData.length > 0) {\n            doValidation = true;\n            if (args.validationData.length === 2) {\n                // config.validationData consists of valX and valY.\n                inputValX = args.validationData[0];\n                inputValY = args.validationData[1];\n            }\n            else if (args.validationData.length === 3) {\n                throw new NotImplementedError('validationData including sample weights is not supported yet.');\n            }\n            else {\n                throw new ValueError(`When passing validation data, it must contain 2 (valX, valY) ` +\n                    `or 3 (valX, valY, valSampleWeight) items; ` +\n                    `${args.validationData} is invalid.`);\n            }\n            const checkBatchAxis = true;\n            const valStandardized = await model.standardizeUserData(inputValX, inputValY, null, /** Unused sample weights. */ null, /** Unused class weights. */ checkBatchAxis, batchSize);\n            valX = valStandardized[0];\n            valY = valStandardized[1];\n            valIns = valX.concat(valY);\n            // TODO(cais): Add useLearningPhase data properly.\n        }\n        else if (args.validationSplit != null && args.validationSplit > 0 &&\n            args.validationSplit < 1) {\n            doValidation = true;\n            // Porting Note: In tfjs-layers, inputs[0] is always a Tensor.\n            const splitAt = Math.floor(inputs[0].shape[0] * (1 - args.validationSplit));\n            const originalBatchSize = inputs[0].shape[0];\n            valX = sliceArrays(inputs, splitAt, originalBatchSize);\n            inputs = sliceArrays(inputs, 0, splitAt);\n            valY = sliceArrays(targets, splitAt, originalBatchSize);\n            targets = sliceArrays(targets, 0, splitAt);\n            // TODO(cais): Once sampleWeights becomes available, slice it to get\n            //   valSampleWeights.\n            valIns = valX.concat(valY);\n            // TODO(cais): Add useLearningPhase data properly.\n        }\n        else if (args.validationSteps != null) {\n            doValidation = true;\n            // TODO(cais): Add useLearningPhase.\n        }\n        const ins = inputs.concat(targets).concat(sampleWeights);\n        model.checkTrainableWeightsConsistency();\n        // TODO(cais): Handle use_learning_phase and learning_phase?\n        // Porting Note: Here we see a key deviation of tfjs-layers from\n        // Keras.\n        //  Due to the imperative nature of tfjs-layers' backend (tfjs-core),\n        //  we do not construct symbolic computation graphs to embody the\n        //  training process. Instead, we define a function that performs the\n        //  training action. In PyKeras, the data (inputs and targets) are fed\n        //  through graph placeholders. In tfjs-layers, the data are fed as\n        //  function arguments. Since the function are defined below in the\n        //  scope, we don't have equivalents of PyKeras's\n        //  `_make_train_funciton`.\n        const trainFunction = model.makeTrainFunction();\n        const outLabels = model.getDedupedMetricsNames();\n        let valFunction;\n        let callbackMetrics;\n        if (doValidation) {\n            model.makeTestFunction();\n            valFunction = model.testFunction;\n            callbackMetrics =\n                outLabels.slice().concat(outLabels.map(n => 'val_' + n));\n        }\n        else {\n            valFunction = null;\n            valIns = [];\n            callbackMetrics = outLabels.slice();\n        }\n        const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);\n        const out = await fitLoop(model, trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null);\n        return out;\n    }\n    finally {\n        model.isTraining = false;\n        // Memory clean up.\n        disposeNewTensors(inputs, x);\n        disposeNewTensors(targets, y);\n        disposeNewTensors(valX, inputValX);\n        disposeNewTensors(valY, inputValY);\n        if (sampleWeights != null) {\n            tfc.dispose(sampleWeights);\n        }\n    }\n    // TODO(cais): Add value to outLabels.\n}\n/**\n * Ensure tensors all have a rank of at least 2.\n *\n * If a tensor has a rank of 1, it is dimension-expanded to rank 2.\n * If any tensor has a rank of 0 (i.e., is a scalar), an error will be thrown.\n */\nexport function ensureTensorsRank2OrHigher(tensors) {\n    const outs = [];\n    if (tensors instanceof Tensor) {\n        tensors = [tensors];\n    }\n    // Make Tensors at least 2D.\n    for (let i = 0; i < tensors.length; ++i) {\n        const tensor = tensors[i];\n        if (tensor.rank === 1) {\n            outs.push(expandDims(tensor, 1));\n        }\n        else if (tensor.rank === 0) {\n            throw new Error('Expected tensor to be at least 1D, but received a 0D tensor ' +\n                '(scalar).');\n        }\n        else {\n            outs.push(tensor);\n        }\n    }\n    return outs;\n}\n/**\n * Compare a set of tensors with a reference (old) set, discard the ones\n * in the new set that are not present in the reference set.\n *\n * This method is used for memory clenaup during calls such as\n * LayersModel.fit().\n *\n * @param tensors New set which may contain Tensors not present in\n *   `refTensors`.\n * @param refTensors Reference Tensor set.\n */\n// TODO(cais, kangyizhang): Deduplicate with tfjs-data.\nexport function disposeNewTensors(tensors, refTensors) {\n    if (tensors == null) {\n        return;\n    }\n    const oldTensorIds = [];\n    if (refTensors instanceof Tensor) {\n        oldTensorIds.push(refTensors.id);\n    }\n    else if (Array.isArray(refTensors)) {\n        refTensors.forEach(t => oldTensorIds.push(t.id));\n    }\n    else if (refTensors != null) {\n        // `oldTensors` is a map from string name to Tensor.\n        for (const name in refTensors) {\n            const oldTensor = refTensors[name];\n            oldTensorIds.push(oldTensor.id);\n        }\n    }\n    const tensorsToDispose = [];\n    if (tensors instanceof Tensor) {\n        if (oldTensorIds.indexOf(tensors.id) === -1) {\n            tensorsToDispose.push(tensors);\n        }\n    }\n    else if (Array.isArray(tensors)) {\n        tensors.forEach(t => {\n            if (oldTensorIds.indexOf(t.id) === -1) {\n                tensorsToDispose.push(t);\n            }\n        });\n    }\n    else if (tensors != null) {\n        // `oldTensors` is a map from string name to Tensor.\n        for (const name in tensors) {\n            const tensor = tensors[name];\n            if (oldTensorIds.indexOf(tensor.id) === -1) {\n                tensorsToDispose.push(tensor);\n            }\n        }\n    }\n    tensorsToDispose.forEach(t => {\n        if (!t.isDisposed) {\n            t.dispose();\n        }\n    });\n}\n//# sourceMappingURL=training_tensors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source: engine/training.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { io, Optimizer, scalar, serialization, Tensor, tensor1d, util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { deserialize } from '../layers/serialization';\nimport * as losses from '../losses';\nimport * as Metrics from '../metrics';\nimport * as optimizers from '../optimizers';\nimport { checkUserDefinedMetadata } from '../user_defined_metadata';\nimport { count, pyListRepeat, singletonOrArray, toCamelCase, toSnakeCase, unique } from '../utils/generic_utils';\nimport { printSummary } from '../utils/layer_utils';\nimport { range } from '../utils/math_utils';\nimport { convertPythonicToTs } from '../utils/serialization_utils';\nimport { version } from '../version';\nimport { Container } from './container';\nimport { execute, FeedDict } from './executor';\nimport { evaluateDataset, fitDataset } from './training_dataset';\nimport { checkBatchSize, disposeNewTensors, ensureTensorsRank2OrHigher, fitTensors, makeBatches, sliceArrays, sliceArraysByIndices } from './training_tensors';\nimport { computeWeightedLoss, standardizeClassWeights, standardizeWeights } from './training_utils';\n/**\n * Helper function for polymorphic input data: 1. singleton Tensor.\n */\nexport function isDataTensor(x) {\n    return x instanceof Tensor;\n}\n/**\n * Helper function for polymorphic input data: 2. Array of Tensor.\n */\nexport function isDataArray(x) {\n    return Array.isArray(x);\n}\n/**\n * Helper function for polymorphic input data: 3. \"dict\" of Tensor.\n */\nexport function isDataDict(x) {\n    return !isDataTensor(x) && !isDataArray(x);\n}\n/**\n * Normalizes inputs and targets provided by users.\n * @param data User-provided input data (polymorphic).\n * @param names An Array of expected Tensor names.\n * @param shapes Optional Array of expected Tensor shapes.\n * @param checkBatchAxis Whether to check that the batch axis of the arrays\n *   match  the expected value found in `shapes`.\n * @param exceptionPrefix String prefix used for exception formatting.\n * @returns List of standardized input Tensors (one Tensor per model input).\n * @throws ValueError: in case of improperly formatted user data.\n */\nexport function standardizeInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = '') {\n    if (names == null || names.length === 0) {\n        // Check for the case where the model expected no data, but some data got\n        // sent.\n        if (data != null) {\n            let gotUnexpectedData = false;\n            if (isDataArray(data) && data.length > 0) {\n                gotUnexpectedData = true;\n            }\n            else if (isDataDict(data)) {\n                for (const key in data) {\n                    if (data.hasOwnProperty(key)) {\n                        gotUnexpectedData = true;\n                        break;\n                    }\n                }\n            }\n            else {\n                // `data` is a singleton Tensor in this case.\n                gotUnexpectedData = true;\n            }\n            if (gotUnexpectedData) {\n                throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, ` +\n                    `but got ${data}`);\n            }\n        }\n        return [];\n    }\n    if (data == null) {\n        return names.map(name => null);\n    }\n    let arrays;\n    if (isDataDict(data)) {\n        data = data;\n        arrays = [];\n        for (const name of names) {\n            if (data[name] == null) {\n                throw new ValueError(`No data provided for \"${name}\". Need data for each key in: ` +\n                    `${names}`);\n            }\n            arrays.push(data[name]);\n        }\n    }\n    else if (isDataArray(data)) {\n        data = data;\n        if (data.length !== names.length) {\n            throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of ` +\n                `Tensors that you are passing to your model is not the size the ` +\n                `model expected. Expected to see ${names.length} Tensor(s), but ` +\n                `instead got the following list of Tensor(s): ${data}`);\n        }\n        arrays = data;\n    }\n    else {\n        data = data;\n        if (names.length > 1) {\n            throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), ` +\n                `but only received one Tensor. Found: Tensor with shape ${data.shape}`);\n        }\n        arrays = [data];\n    }\n    arrays = ensureTensorsRank2OrHigher(arrays);\n    // Check shape compatibility.\n    if (shapes != null) {\n        for (let i = 0; i < names.length; ++i) {\n            if (shapes[i] == null) {\n                continue;\n            }\n            const array = arrays[i];\n            if (array.shape.length !== shapes[i].length) {\n                throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                    `to have ${shapes[i].length} dimension(s). but got array with ` +\n                    `shape ${array.shape}`);\n            }\n            for (let j = 0; j < shapes[i].length; ++j) {\n                if (j === 0 && !checkBatchAxis) {\n                    // Skip the first (batch) axis.\n                    continue;\n                }\n                const dim = array.shape[j];\n                const refDim = shapes[i][j];\n                if (refDim != null && refDim >= 0 && dim !== refDim) {\n                    throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                        `to have shape [${shapes[i]}], but got array with shape ` +\n                        `[${array.shape}].`);\n                }\n            }\n        }\n    }\n    return arrays;\n}\n/**\n * User input validation for Tensors.\n * @param inputs `Array` of `tf.Tensor`s for inputs.\n * @param targets `Array` of `tf.Tensor`s for targets.\n * @param weights Optional `Array` of `tf.Tensor`s for sample weights.\n * @throws ValueError: in case of incorrectly formatted data.\n */\nexport function checkArrayLengths(inputs, targets, weights) {\n    const setX = unique(inputs.map(input => input.shape[0]));\n    setX.sort();\n    const setY = unique(targets.map(target => target.shape[0]));\n    setY.sort();\n    // TODO(cais): Check `weights` as well.\n    if (setX.length > 1) {\n        throw new ValueError(`All input Tensors (x) should have the same number of samples. ` +\n            `Got array shapes: ` +\n            `${JSON.stringify(inputs.map(input => input.shape))}`);\n    }\n    if (setY.length > 1) {\n        throw new ValueError(`All target Tensors (y) should have the same number of samples. ` +\n            `Got array shapes: ` +\n            `${JSON.stringify(targets.map(target => target.shape))}`);\n    }\n    if (setX.length > 0 && setY.length > 0 && !util.arraysEqual(setX, setY)) {\n        throw new ValueError(`Input Tensors should have the same number of samples as target ` +\n            `Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target ` +\n            `sample(s).`);\n    }\n}\n/**\n * Validation on the compatibility of targes and loss functions.\n *\n * This helps prevent users from using loss functions incorrectly.\n *\n * @param targets `Array` of `tf.Tensor`s of targets.\n * @param lossFns `Array` of loss functions.\n * @param outputShapes `Array` of shapes of model outputs.\n */\nfunction checkLossAndTargetCompatibility(targets, lossFns, outputShapes) {\n    // TODO(cais): Dedicated test coverage?\n    const keyLosses = [\n        losses.meanSquaredError, losses.binaryCrossentropy,\n        losses.categoricalCrossentropy\n    ];\n    for (let i = 0; i < targets.length; ++i) {\n        const y = targets[i];\n        const loss = lossFns[i];\n        const shape = outputShapes[i];\n        if (loss == null) {\n            continue;\n        }\n        if (loss === losses.categoricalCrossentropy) {\n            if (y.shape[y.shape.length - 1] === 1) {\n                throw new ValueError(`You are passing a target array of shape ${y.shape} while using ` +\n                    `a loss 'categorical_crossentropy'. 'categorical_crossentropy'` +\n                    `expects targets to be binary matrices (1s and 0s) of shape ` +\n                    `[samples, classes].`);\n                // TODO(cais): Example code in error message.\n            }\n        }\n        if (keyLosses.indexOf(loss) !== -1) {\n            const slicedYShape = y.shape.slice(1);\n            const slicedShape = shape.slice(1);\n            for (let j = 0; j < slicedYShape.length; ++j) {\n                const targetDim = slicedYShape[j];\n                const outDim = slicedShape[j];\n                if (outDim != null && targetDim !== outDim) {\n                    throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an ` +\n                        `output of shape ${shape}, while using a loss function that ` +\n                        `expects targets to have the same shape as the output.`);\n                }\n            }\n        }\n    }\n}\n/**\n * Check inputs provided by the user.\n *\n * Porting Note: This corresponds to _standardize_input_data() in Python\n *   Keras. Because of the strong typing in TF.js, we do not need to convert\n *   the data. Specifically:\n *   1) in PyKeras, `data` can be `DataFrame` instances from pandas, for\n *      example. We don't need to worry about that here because there is no\n *      widely popular javascript/typesdcript equivalent of pandas (so far).\n *      If one becomes available in the future, we can add support.\n *   2) in PyKeras, inputs can be Python dict. But here we are stipulating\n * that the data is either a single `tf.Tensor` or an Array of `tf.Tensor`s. We\n * may add support for `Object` data inputs in the future when the need\n * arises.\n *\n * Instead, we perform basic checks for number of parameters and shapes.\n *\n * @param data: The input data.\n * @param names: Name for the inputs, from the model.\n * @param shapes: Expected shapes for the input data, from the model.\n * @param checkBatchAxis: Whether the size along the batch axis (i.e., the\n *   first dimension) will be checked for matching.\n * @param exceptionPrefix: Execption prefix message, used in generating error\n *   messages.\n * @throws ValueError: on incorrect number of inputs or mismatches in shapes.\n */\nfunction checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = '') {\n    let arrays;\n    if (Array.isArray(data)) {\n        if (data.length !== names.length) {\n            throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of ` +\n                `Tensors that you are passing to your model is not the size the ` +\n                `the model expected. Expected to see ${names.length} Tensor(s),` +\n                ` but instead got ${data.length} Tensors(s).`);\n        }\n        arrays = data;\n    }\n    else {\n        if (names.length > 1) {\n            throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, ` +\n                `but only received one Tensor. Found: array with shape ` +\n                `${JSON.stringify(data.shape)}.`);\n        }\n        arrays = [data];\n    }\n    if (shapes != null) {\n        for (let i = 0; i < names.length; ++i) {\n            if (shapes[i] == null) {\n                continue;\n            }\n            const array = arrays[i];\n            if (array.shape.length !== shapes[i].length) {\n                throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                    `to have ${shapes[i].length} dimension(s), but got array with ` +\n                    `shape ${JSON.stringify(array.shape)}`);\n            }\n            for (let j = 0; j < shapes[i].length; ++j) {\n                if (j === 0 && !checkBatchAxis) {\n                    continue;\n                }\n                const dim = array.shape[j];\n                const refDim = shapes[i][j];\n                if (refDim != null) {\n                    if (refDim !== dim) {\n                        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ` +\n                            `${names[i]} to have shape ${JSON.stringify(shapes[i])} but ` +\n                            `got array with shape ${JSON.stringify(array.shape)}.`);\n                    }\n                }\n            }\n        }\n    }\n}\n/**\n * Maps metric functions to model outputs.\n * @param metrics An shortcut strings name, metric function, `Array` or dict\n *   (`Object`) of metric functions.\n * @param outputNames An `Array` of the names of model outputs.\n * @returns An `Array` (one entry per model output) of `Array` of metric\n *   functions. For instance, if the model has 2 outputs, and for the first\n *   output we want to compute `binaryAccuracy` and `binaryCrossentropy`,\n *   and just `binaryAccuracy` for the second output, the `Array` would look\n *   like:\n *     `[[binaryAccuracy, binaryCrossentropy],  [binaryAccuracy]]`\n * @throws TypeError: incompatible metrics format.\n */\nexport function collectMetrics(metrics, outputNames) {\n    if (metrics == null || Array.isArray(metrics) && metrics.length === 0) {\n        return outputNames.map(name => []);\n    }\n    let wrappedMetrics;\n    if (typeof metrics === 'string' || typeof metrics === 'function') {\n        wrappedMetrics = [metrics];\n    }\n    else if (Array.isArray(metrics) || typeof metrics === 'object') {\n        wrappedMetrics = metrics;\n    }\n    else {\n        throw new TypeError('Type of metrics argument not understood. Expected an string,' +\n            `function, Array, or Object, found: ${metrics}`);\n    }\n    if (Array.isArray(wrappedMetrics)) {\n        // We then apply all metrics to all outputs.\n        return outputNames.map(name => wrappedMetrics);\n    }\n    else {\n        // In this case, metrics is a dict.\n        const nestedMetrics = [];\n        for (const name of outputNames) {\n            let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : [];\n            if (!Array.isArray(outputMetrics)) {\n                outputMetrics = [outputMetrics];\n            }\n            nestedMetrics.push(outputMetrics);\n        }\n        return nestedMetrics;\n    }\n}\nconst LAYERS_MODEL_FORMAT_NAME = 'layers-model';\n/**\n * A `tf.LayersModel` is a directed, acyclic graph of `tf.Layer`s plus methods\n * for training, evaluation, prediction and saving.\n *\n * `tf.LayersModel` is the basic unit of training, inference and evaluation in\n * TensorFlow.js. To create a `tf.LayersModel`, use `tf.LayersModel`.\n *\n * See also:\n *   `tf.Sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class LayersModel extends Container {\n    constructor(args) {\n        super(args);\n        this.isTraining = false;\n    }\n    /**\n     * Print a text summary of the model's layers.\n     *\n     * The summary includes\n     * - Name and type of all layers that comprise the model.\n     * - Output shape(s) of the layers\n     * - Number of weight parameters of each layer\n     * - If the model has non-sequential-like topology, the inputs each layer\n     *   receives\n     * - The total number of trainable and non-trainable parameters of the model.\n     *\n     * ```js\n     * const input1 = tf.input({shape: [10]});\n     * const input2 = tf.input({shape: [20]});\n     * const dense1 = tf.layers.dense({units: 4}).apply(input1);\n     * const dense2 = tf.layers.dense({units: 8}).apply(input2);\n     * const concat = tf.layers.concatenate().apply([dense1, dense2]);\n     * const output =\n     *     tf.layers.dense({units: 3, activation: 'softmax'}).apply(concat);\n     *\n     * const model = tf.model({inputs: [input1, input2], outputs: output});\n     * model.summary();\n     * ```\n     *\n     * @param lineLength Custom line length, in number of characters.\n     * @param positions Custom widths of each of the columns, as either\n     *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n     *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n     *   right-most (i.e., ending) position of a column.\n     * @param printFn Custom print function. Can be used to replace the default\n     *   `console.log`. For example, you can use `x => {}` to mute the printed\n     *   messages in the console.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    summary(lineLength, positions, printFn = console.log) {\n        if (!this.built) {\n            throw new ValueError(`This model has never been called, thus its weights have not been ` +\n                `created yet. So no summary can be displayed. Build the model ` +\n                `first (e.g., by calling it on some test data).`);\n        }\n        printSummary(this, lineLength, positions, printFn);\n    }\n    /**\n     * Configures and prepares the model for training and evaluation.  Compiling\n     * outfits the model with an optimizer, loss, and/or metrics.  Calling `fit`\n     * or `evaluate` on an un-compiled model will throw an error.\n     *\n     * @param args a `ModelCompileArgs` specifying the loss, optimizer, and\n     * metrics to be used for fitting and evaluating this model.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    compile(args) {\n        if (args.loss == null) {\n            args.loss = [];\n        }\n        this.loss = args.loss;\n        if (typeof args.optimizer === 'string') {\n            this.optimizer_ = optimizers.getOptimizer(args.optimizer);\n            this.isOptimizerOwned = true;\n        }\n        else {\n            if (!(args.optimizer instanceof Optimizer)) {\n                throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`);\n            }\n            this.optimizer_ = args.optimizer;\n            this.isOptimizerOwned = false;\n        }\n        // TODO(cais): Add lossWeights.\n        // TODO(cais): Add sampleWeightMode.\n        // Prepare loss functions.\n        let lossFunctions = [];\n        if (!Array.isArray(args.loss) && typeof args.loss !== 'string' &&\n            typeof args.loss !== 'function') {\n            args.loss = args.loss;\n            for (const name in args.loss) {\n                if (this.outputNames.indexOf(name) === -1) {\n                    throw new ValueError(`Unknown entry in loss dictionary: \"${name}\". ` +\n                        `Only expected the following keys: ${this.outputNames}`);\n                }\n            }\n            for (const name of this.outputNames) {\n                if (args.loss[name] == null) {\n                    console.warn(`Output \"${name}\" is missing from loss dictionary. We assume ` +\n                        `this was done on purpose, and we will not be expecting data ` +\n                        `to be passed to ${name} during training`);\n                }\n                lossFunctions.push(losses.get(args.loss[name]));\n            }\n        }\n        else if (Array.isArray(args.loss)) {\n            if (args.loss.length !== this.outputs.length) {\n                throw new ValueError(`When passing an Array as loss, it should have one entry per ` +\n                    `model output. The model has ${this.outputs.length} output(s), ` +\n                    `but you passed loss=${args.loss}.`);\n            }\n            const theLosses = args.loss;\n            lossFunctions = theLosses.map(l => losses.get(l));\n        }\n        else {\n            const lossFunction = losses.get(args.loss);\n            this.outputs.forEach(_ => {\n                lossFunctions.push(lossFunction);\n            });\n        }\n        this.lossFunctions = lossFunctions;\n        this.feedOutputNames = [];\n        this.feedOutputShapes = [];\n        this.feedLossFns = [];\n        for (let i = 0; i < this.outputs.length; ++i) {\n            // TODO(cais): Logic for skipping target(s).\n            const shape = this.internalOutputShapes[i];\n            const name = this.outputNames[i];\n            this.feedOutputNames.push(name);\n            this.feedOutputShapes.push(shape);\n            this.feedLossFns.push(this.lossFunctions[i]);\n        }\n        // TODO(cais): Add logic for output masks.\n        // TODO(cais): Add logic for sample weights.\n        const skipTargetIndices = [];\n        // Prepare metrics.\n        this.metrics = args.metrics;\n        // TODO(cais): Add weightedMetrics.\n        this.metricsNames = ['loss'];\n        this.metricsTensors = [];\n        // Compute total loss.\n        // Porting Note: In PyKeras, metrics_tensors are symbolic tensor objects.\n        //   Here, metricsTensors are TypeScript functions. This difference is due\n        //   to the difference in symbolic/imperative property of the backends.\n        nameScope('loss', () => {\n            for (let i = 0; i < this.outputs.length; ++i) {\n                if (skipTargetIndices.indexOf(i) !== -1) {\n                    continue;\n                }\n                // TODO(cais): Add weightedLoss, sampleWeight and mask.\n                //   The following line should be weightedLoss\n                const weightedLoss = this.lossFunctions[i];\n                if (this.outputs.length > 1) {\n                    this.metricsTensors.push([weightedLoss, i]);\n                    this.metricsNames.push(this.outputNames[i] + '_loss');\n                }\n            }\n            // Porting Note: Due to the imperative nature of the backend, we calculate\n            //   the regularizer penalties in the totalLossFunction, instead of here.\n        });\n        const nestedMetrics = collectMetrics(args.metrics, this.outputNames);\n        // TODO(cais): Add nestedWeightedMetrics.\n        /**\n         * Helper function used in loop below.\n         */\n        const appendMetric = (outputIndex, metricName, metricTensor) => {\n            if (this.outputNames.length > 1) {\n                metricName = this.outputNames[outputIndex] + '_' + metricName;\n            }\n            this.metricsNames.push(metricName);\n            this.metricsTensors.push([metricTensor, outputIndex]);\n        };\n        nameScope('metric', () => {\n            for (let i = 0; i < this.outputs.length; ++i) {\n                if (skipTargetIndices.indexOf(i) !== -1) {\n                    continue;\n                }\n                const outputMetrics = nestedMetrics[i];\n                // TODO(cais): Add weights and outputWeightedMetrics.\n                // TODO(cais): Add optional arg `weights` to the following function.\n                const handleMetrics = (metrics) => {\n                    const metricNamePrefix = '';\n                    let metricName;\n                    let accFn;\n                    let weightedMetricFn;\n                    //  TODO(cais): Use 'weights_' for weighted metrics.\n                    for (const metric of metrics) {\n                        if (typeof metric === 'string' &&\n                            ['accuracy', 'acc', 'crossentropy', 'ce'].indexOf(metric) !==\n                                -1) {\n                            const outputShape = this.internalOutputShapes[i];\n                            if (outputShape[outputShape.length - 1] === 1 ||\n                                this.lossFunctions[i] === losses.binaryCrossentropy) {\n                                // case: binary accuracy/crossentropy.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.binaryAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.binaryCrossentropy;\n                                }\n                            }\n                            else if (this.lossFunctions[i] ===\n                                losses.sparseCategoricalCrossentropy) {\n                                // case: categorical accuracy / crossentropy with sparse\n                                // targets.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.sparseCategoricalAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.sparseCategoricalCrossentropy;\n                                }\n                            }\n                            else {\n                                // case: categorical accuracy / crossentropy.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.categoricalAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.categoricalCrossentropy;\n                                }\n                            }\n                            let suffix;\n                            if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                suffix = 'acc';\n                            }\n                            else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                suffix = 'ce';\n                            }\n                            // TODO(cais): Add weighting actually.\n                            weightedMetricFn = accFn;\n                            metricName = metricNamePrefix + suffix;\n                        }\n                        else {\n                            const metricFn = Metrics.get(metric);\n                            // TODO(cais): Add weighting actually.\n                            weightedMetricFn = metricFn;\n                            metricName =\n                                metricNamePrefix + Metrics.getLossOrMetricName(metric);\n                        }\n                        // TODO(cais): Add weighting and masking to metricResult.\n                        let metricResult;\n                        nameScope(metricName, () => {\n                            metricResult = weightedMetricFn;\n                        });\n                        appendMetric(i, metricName, metricResult);\n                    }\n                };\n                handleMetrics(outputMetrics);\n                // TODO(cais): Call handleMetrics with weights.\n            }\n        });\n        // Porting Notes: Given the imperative backend of tfjs-core,\n        //   there is no need for constructing the symbolic graph and placeholders.\n        this.collectedTrainableWeights = this.trainableWeights;\n    }\n    /**\n     * Check trainable weights count consistency.\n     *\n     * This will raise a warning if `this.trainableWeights` and\n     * `this.collectedTrainableWeights` are inconsistent (i.e., have different\n     * numbers of parameters).\n     * Inconsistency will typically arise when one modifies `model.trainable`\n     * without calling `model.compile()` again.\n     */\n    checkTrainableWeightsConsistency() {\n        if (this.collectedTrainableWeights == null) {\n            return;\n        }\n        if (this.trainableWeights.length !==\n            this.collectedTrainableWeights.length) {\n            console.warn('Discrepancy between trainableweights and collected trainable ' +\n                'weights. Did you set `model.trainable` without calling ' +\n                '`model.compile()` afterwards?');\n        }\n    }\n    /**\n     * Returns the loss value & metrics values for the model in test mode.\n     *\n     * Loss and metrics are specified during `compile()`, which needs to happen\n     * before calls to `evaluate()`.\n     *\n     * Computation is done in batches.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const result = model.evaluate(\n     *     tf.ones([8, 10]), tf.ones([8, 1]), {batchSize: 4});\n     * result.print();\n     * ```\n     *\n     * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple inputs.\n     * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple outputs.\n     * @param args A `ModelEvaluateArgs`, containing optional fields.\n     *\n     * @return `Scalar` test loss (if the model has a single output and no\n     *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n     *   and/or metrics). The attribute `model.metricsNames`\n     *   will give you the display labels for the scalar outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    evaluate(x, y, args = {}) {\n        const batchSize = args.batchSize == null ? 32 : args.batchSize;\n        checkBatchSize(batchSize);\n        // TODO(cais): Standardize `config.sampleWeights` as well.\n        // Validate user data.\n        const checkBatchAxis = true;\n        const standardizedOuts = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n        try {\n            // TODO(cais): If uses `useLearningPhase`, set the corresponding element\n            // of the input to 0.\n            const ins = standardizedOuts[0].concat(standardizedOuts[1]);\n            this.makeTestFunction();\n            const f = this.testFunction;\n            const testOuts = this.testLoop(f, ins, batchSize, args.verbose, args.steps);\n            return singletonOrArray(testOuts);\n        }\n        finally {\n            disposeNewTensors(standardizedOuts[0], x);\n            disposeNewTensors(standardizedOuts[1], y);\n        }\n    }\n    // TODO(cais): Add code snippet below once real dataset objects are\n    //   available.\n    /**\n     * Evaluate model using a dataset object.\n     *\n     * Note: Unlike `evaluate()`, this method is asynchronous (`async`);\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for evaluation. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs. Of the two items in the array, the\n     *   first is the input feature(s) and the second is the output target(s).\n     * @param args A configuration object for the dataset-based evaluation.\n     * @returns Loss and metric values as an Array of `Scalar` objects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async evaluateDataset(dataset, args) {\n        this.makeTestFunction();\n        return evaluateDataset(this, dataset, args);\n    }\n    /**\n     * Get number of samples provided for training, evaluation or prediction.\n     *\n     * @param ins Input `tf.Tensor`.\n     * @param batchSize Integer batch size, optional.\n     * @param steps Total number of steps (batches of samples) before\n     * declaring loop finished. Optional.\n     * @param stepsName The public API's parameter name for `steps`.\n     * @returns Number of samples provided.\n     */\n    checkNumSamples(ins, batchSize, steps, stepsName = 'steps') {\n        let numSamples;\n        if (steps != null) {\n            numSamples = null;\n            if (batchSize != null) {\n                throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.` +\n                    `Got batchSize = ${batchSize}`);\n            }\n        }\n        else if (ins != null) {\n            if (Array.isArray(ins)) {\n                numSamples = ins[0].shape[0];\n            }\n            else {\n                numSamples = ins.shape[0];\n            }\n        }\n        else {\n            throw new ValueError(`Either the input data should have a defined shape, or ` +\n                `${stepsName} shoud be specified.`);\n        }\n        return numSamples;\n    }\n    /**\n     * Execute internal tensors of the model with input data feed.\n     * @param inputs Input data feed. Must match the inputs of the model.\n     * @param outputs Names of the output tensors to be fetched. Must match\n     *   names of the SymbolicTensors that belong to the graph.\n     * @returns Fetched values for `outputs`.\n     */\n    execute(inputs, outputs) {\n        if (Array.isArray(outputs) && outputs.length === 0) {\n            throw new ValueError('`outputs` is an empty Array, which is not allowed.');\n        }\n        const outputsIsArray = Array.isArray(outputs);\n        const outputNames = (outputsIsArray ? outputs : [outputs]);\n        const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames);\n        // Format the input into a FeedDict.\n        const feedDict = new FeedDict();\n        if (inputs instanceof Tensor) {\n            inputs = [inputs];\n        }\n        if (Array.isArray(inputs)) {\n            if (inputs.length !== this.inputs.length) {\n                throw new ValueError(`The number of inputs provided (${inputs.length}) ` +\n                    `does not match the number of inputs of this model ` +\n                    `(${this.inputs.length}).`);\n            }\n            for (let i = 0; i < this.inputs.length; ++i) {\n                feedDict.add(this.inputs[i], inputs[i]);\n            }\n        }\n        else {\n            for (const input of this.inputs) {\n                const tensorValue = inputs[input.name];\n                if (tensorValue == null) {\n                    throw new ValueError(`No value is provided for the model's input ${input.name}`);\n                }\n                feedDict.add(input, tensorValue);\n            }\n        }\n        // Run execution.\n        const executeOutputs = execute(outputSymbolicTensors, feedDict);\n        return outputsIsArray ? executeOutputs : executeOutputs[0];\n    }\n    /**\n     * Retrieve the model's internal symbolic tensors from symbolic-tensor names.\n     */\n    retrieveSymbolicTensors(symbolicTensorNames) {\n        const outputSymbolicTensors = pyListRepeat(null, symbolicTensorNames.length);\n        let outputsRemaining = symbolicTensorNames.length;\n        for (const layer of this.layers) {\n            const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output];\n            const layerOutputNames = layerOutputs.map(output => output.name);\n            for (let i = 0; i < symbolicTensorNames.length; ++i) {\n                const index = layerOutputNames.indexOf(symbolicTensorNames[i]);\n                if (index !== -1) {\n                    outputSymbolicTensors[i] = layerOutputs[index];\n                    outputsRemaining--;\n                }\n                if (outputsRemaining === 0) {\n                    break;\n                }\n            }\n            if (outputsRemaining === 0) {\n                break;\n            }\n        }\n        if (outputsRemaining > 0) {\n            const remainingNames = [];\n            outputSymbolicTensors.forEach((tensor, i) => {\n                if (tensor == null) {\n                    remainingNames.push(symbolicTensorNames[i]);\n                }\n            });\n            throw new ValueError(`Cannot find SymbolicTensors for output name(s): ` +\n                `${JSON.stringify(remainingNames)}`);\n        }\n        return outputSymbolicTensors;\n    }\n    /**\n     * Helper method to loop over some data in batches.\n     *\n     * Porting Note: Not using the functional approach in the Python equivalent\n     *   due to the imperative backend.\n     * Porting Note: Does not support step mode currently.\n     *\n     * @param ins: input data\n     * @param batchSize: integer batch size.\n     * @param verbose: verbosity model\n     * @returns: Predictions as `tf.Tensor` (if a single output) or an `Array` of\n     *   `tf.Tensor` (if multipe outputs).\n     */\n    predictLoop(ins, batchSize = 32, verbose = false) {\n        return tfc.tidy(() => {\n            const numSamples = this.checkNumSamples(ins);\n            if (verbose) {\n                throw new NotImplementedError('Verbose predictLoop() is not implemented yet.');\n            }\n            // Sample-based predictions.\n            // Porting Note: Tensor currently does not support sliced assignments as\n            //   in numpy, e.g., x[1:3] = y. Therefore we use concatenation while\n            //   iterating over the batches.\n            const batches = makeBatches(numSamples, batchSize);\n            const outsBatches = this.outputs.map(output => []);\n            // TODO(cais): Can the scope() be pushed down inside the for loop?\n            for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                const batchOuts = tfc.tidy(() => {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    // TODO(cais): Take care of the case of the last element is a flag for\n                    //   training/test.\n                    const insBatch = sliceArrays(ins, batchStart, batchEnd);\n                    // Construct the feeds for execute();\n                    const feeds = [];\n                    if (Array.isArray(insBatch)) {\n                        for (let i = 0; i < insBatch.length; ++i) {\n                            feeds.push({ key: this.inputs[i], value: insBatch[i] });\n                        }\n                    }\n                    else {\n                        feeds.push({ key: this.inputs[0], value: insBatch });\n                    }\n                    const feedDict = new FeedDict(feeds);\n                    return execute(this.outputs, feedDict);\n                });\n                batchOuts.forEach((batchOut, i) => outsBatches[i].push(batchOut));\n            }\n            return singletonOrArray(outsBatches.map(batches => tfc.concat(batches, 0)));\n        });\n    }\n    /**\n     * Generates output predictions for the input samples.\n     *\n     * Computation is done in batches.\n     *\n     * Note: the \"step\" mode of predict() is currently not supported.\n     *   This is because the TensorFlow.js core backend is imperative only.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predict(tf.ones([8, 10]), {batchSize: 4}).print();\n     * ```\n     *\n     * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n     *   the model has multiple inputs.\n     * @param args A `ModelPredictArgs` object containing optional fields.\n     *\n     * @return Prediction results as a `tf.Tensor`(s).\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and the model's expectations, or in case a stateful model receives a\n     *   number of samples that is not a multiple of the batch size.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(x, args = {}) {\n        const xsRank2OrHigher = ensureTensorsRank2OrHigher(x);\n        checkInputData(xsRank2OrHigher, this.inputNames, this.feedInputShapes, false);\n        try {\n            // TODO(cais): Take care of stateful models.\n            //   if (this.stateful) ...\n            // TODO(cais): Take care of the learning_phase boolean flag.\n            //   if (this.useLearningPhase) ...\n            const batchSize = args.batchSize == null ? 32 : args.batchSize;\n            checkBatchSize(batchSize);\n            return this.predictLoop(xsRank2OrHigher, batchSize);\n        }\n        finally {\n            disposeNewTensors(xsRank2OrHigher, x);\n        }\n    }\n    /**\n     * Returns predictions for a single batch of samples.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predictOnBatch(tf.ones([8, 10])).print();\n     * ```\n     * @param x: Input samples, as a Tensor (for models with exactly one\n     *   input) or an array of Tensors (for models with more than one input).\n     * @return Tensor(s) of predictions\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predictOnBatch(x) {\n        checkInputData(x, this.inputNames, this.feedInputShapes, true);\n        // TODO(cais): Take care of the learning_phase boolean flag.\n        //   if (this.useLearningPhase) ...\n        const batchSize = (Array.isArray(x) ? x[0] : x).shape[0];\n        return this.predictLoop(x, batchSize);\n    }\n    standardizeUserDataXY(x, y, checkBatchAxis = true, batchSize) {\n        // TODO(cais): Add sampleWeight, classWeight\n        if (this.optimizer_ == null) {\n            throw new RuntimeError('You must compile a model before training/testing. Use ' +\n                'LayersModel.compile(modelCompileArgs).');\n        }\n        const outputShapes = [];\n        for (let i = 0; i < this.feedOutputShapes.length; ++i) {\n            const outputShape = this.feedOutputShapes[i];\n            const lossFn = this.feedLossFns[i];\n            if (lossFn === losses.sparseCategoricalCrossentropy) {\n                outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1]));\n            }\n            else {\n                // Porting Note: Because of strong typing `lossFn` must be a function.\n                outputShapes.push(outputShape);\n            }\n        }\n        x = standardizeInputData(x, this.feedInputNames, this.feedInputShapes, false, 'input');\n        y = standardizeInputData(y, this.feedOutputNames, outputShapes, false, 'target');\n        // TODO(cais): Standardize sampleWeights & classWeights.\n        checkArrayLengths(x, y, null);\n        // TODO(cais): Check sampleWeights as well.\n        checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes);\n        if (this.stateful && batchSize != null && batchSize > 0) {\n            if (x[0].shape[0] % batchSize !== 0) {\n                throw new ValueError(`In a stateful network, you should only pass inputs with a ` +\n                    `number of samples that is divisible by the batch size ` +\n                    `${batchSize}. Found: ${x[0].shape[0]} sample(s).`);\n            }\n        }\n        return [x, y];\n    }\n    async standardizeUserData(x, y, sampleWeight, classWeight, checkBatchAxis = true, batchSize) {\n        const [standardXs, standardYs] = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n        // TODO(cais): Handle sampleWeights.\n        if (sampleWeight != null) {\n            throw new Error('sample weight is not supported yet.');\n        }\n        let standardSampleWeights = null;\n        if (classWeight != null) {\n            const classWeights = standardizeClassWeights(classWeight, this.outputNames);\n            standardSampleWeights = [];\n            for (let i = 0; i < classWeights.length; ++i) {\n                standardSampleWeights.push(await standardizeWeights(standardYs[i], null, classWeights[i]));\n            }\n        }\n        // TODO(cais): Deal with the case of model.stateful == true.\n        return [standardXs, standardYs, standardSampleWeights];\n    }\n    /**\n     * Loop over some test data in batches.\n     * @param f A Function returning a list of tensors.\n     * @param ins Array of tensors to be fed to `f`.\n     * @param batchSize Integer batch size or `null` / `undefined`.\n     * @param verbose verbosity mode.\n     * @param steps Total number of steps (batches of samples) before\n     * declaring test finished. Ignored with the default value of `null` /\n     * `undefined`.\n     * @returns Array of Scalars.\n     */\n    testLoop(f, ins, batchSize, verbose = 0, steps) {\n        return tfc.tidy(() => {\n            const numSamples = this.checkNumSamples(ins, batchSize, steps, 'steps');\n            const outs = [];\n            if (verbose > 0) {\n                throw new NotImplementedError('Verbose mode is not implemented yet.');\n            }\n            // TODO(cais): Use `indicesForConversionToDense' to prevent slow down.\n            if (steps != null) {\n                throw new NotImplementedError('steps mode in testLoop() is not implemented yet');\n            }\n            else {\n                const batches = makeBatches(numSamples, batchSize);\n                const indexArray = tensor1d(range(0, numSamples));\n                for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    const batchIds = K.sliceAlongFirstAxis(indexArray, batchStart, batchEnd - batchStart);\n                    // TODO(cais): In ins, train flag can be a number, instead of an\n                    //   Tensor? Do we need to handle this in tfjs-layers?\n                    const insBatch = sliceArraysByIndices(ins, batchIds);\n                    const batchOuts = f(insBatch);\n                    if (batchIndex === 0) {\n                        for (let i = 0; i < batchOuts.length; ++i) {\n                            outs.push(scalar(0));\n                        }\n                    }\n                    for (let i = 0; i < batchOuts.length; ++i) {\n                        const batchOut = batchOuts[i];\n                        outs[i] =\n                            tfc.add(outs[i], tfc.mul(batchEnd - batchStart, batchOut));\n                    }\n                }\n                for (let i = 0; i < outs.length; ++i) {\n                    outs[i] = tfc.div(outs[i], numSamples);\n                }\n            }\n            return outs;\n        });\n    }\n    getDedupedMetricsNames() {\n        const outLabels = this.metricsNames;\n        // Rename duplicated metrics names (can happen with an output layer\n        // shared among multiple dataflows).\n        const dedupedOutLabels = [];\n        for (let i = 0; i < outLabels.length; ++i) {\n            const label = outLabels[i];\n            let newLabel = label;\n            if (count(outLabels, label) > 1) {\n                const dupIndex = count(outLabels.slice(0, i), label);\n                newLabel += `_${dupIndex}`;\n            }\n            dedupedOutLabels.push(newLabel);\n        }\n        return dedupedOutLabels;\n    }\n    /**\n     * Creates a function that performs the following actions:\n     *\n     * 1. computes the losses\n     * 2. sums them to get the total loss\n     * 3. call the optimizer computes the gradients of the LayersModel's\n     *    trainable weights w.r.t. the total loss and update the variables\n     * 4. calculates the metrics\n     * 5. returns the values of the losses and metrics.\n     */\n    makeTrainFunction() {\n        return (data) => {\n            const lossValues = [];\n            const inputs = data.slice(0, this.inputs.length);\n            const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n            const sampleWeights = data.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2);\n            const metricsValues = [];\n            // Create a function that computes the total loss based on the\n            // inputs. This function is used for obtaining gradients through\n            // backprop.\n            const totalLossFunction = () => {\n                const feeds = [];\n                for (let i = 0; i < this.inputs.length; ++i) {\n                    feeds.push({ key: this.inputs[i], value: inputs[i] });\n                }\n                const feedDict = new FeedDict(feeds);\n                const outputs = execute(this.outputs, feedDict, { 'training': true });\n                // TODO(cais): Take care of the case of multiple outputs from a\n                //   single layer?\n                let totalLoss;\n                for (let i = 0; i < this.lossFunctions.length; ++i) {\n                    const lossFunction = this.lossFunctions[i];\n                    let loss = lossFunction(targets[i], outputs[i]);\n                    if (sampleWeights[i] != null) {\n                        loss = computeWeightedLoss(loss, sampleWeights[i]);\n                    }\n                    // TODO(cais): push Scalar instead.\n                    const meanLoss = tfc.mean(loss);\n                    // TODO(cais): Use a scope() instead, to avoid ownership.\n                    lossValues.push(meanLoss);\n                    if (i === 0) {\n                        totalLoss = loss;\n                    }\n                    else {\n                        totalLoss = tfc.add(totalLoss, loss);\n                    }\n                }\n                // Compute the metrics.\n                // TODO(cais): These should probably be calculated outside\n                //   totalLossFunction to benefit speed?\n                for (let i = 0; i < this.metricsTensors.length; ++i) {\n                    let weightedMetric;\n                    if (this.outputs.length > 1 && i < this.outputs.length) {\n                        weightedMetric = lossValues[i];\n                    }\n                    else {\n                        const metric = this.metricsTensors[i][0];\n                        const outputIndex = this.metricsTensors[i][1];\n                        weightedMetric =\n                            tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));\n                    }\n                    tfc.keep(weightedMetric);\n                    // TODO(cais): Use a scope() instead, to avoid ownership.\n                    metricsValues.push(weightedMetric);\n                }\n                totalLoss = tfc.mean(totalLoss);\n                // Add regularizer penalties.\n                this.calculateLosses().forEach(regularizerLoss => {\n                    totalLoss = tfc.add(totalLoss, regularizerLoss);\n                });\n                return totalLoss;\n            };\n            const variables = this.collectedTrainableWeights.map(param => param.read());\n            const returnCost = true;\n            const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables);\n            return [totalLossValue].concat(metricsValues);\n        };\n    }\n    /**\n     * Create a function which, when invoked with an array of `tf.Tensor`s as a\n     * batch of inputs, returns the prespecified loss and metrics of the model\n     * under the batch of input data.\n     */\n    makeTestFunction() {\n        this.testFunction = (data) => {\n            return tfc.tidy(() => {\n                const valOutputs = [];\n                let totalLoss;\n                const inputs = data.slice(0, this.inputs.length);\n                const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n                const feeds = [];\n                for (let i = 0; i < this.inputs.length; ++i) {\n                    feeds.push({ key: this.inputs[i], value: inputs[i] });\n                }\n                const feedDict = new FeedDict(feeds);\n                const outputs = execute(this.outputs, feedDict);\n                // Compute total loss.\n                for (let i = 0; i < this.lossFunctions.length; ++i) {\n                    const lossFunction = this.lossFunctions[i];\n                    // TODO(cais): Add sample weighting and replace the simple\n                    // averaging.\n                    const loss = tfc.mean(lossFunction(targets[i], outputs[i]));\n                    if (i === 0) {\n                        totalLoss = loss;\n                    }\n                    else {\n                        totalLoss = tfc.add(totalLoss, loss);\n                    }\n                    valOutputs.push(totalLoss);\n                }\n                // Compute the metrics.\n                for (let i = 0; i < this.metricsTensors.length; ++i) {\n                    const metric = this.metricsTensors[i][0];\n                    const outputIndex = this.metricsTensors[i][1];\n                    // TODO(cais): Replace K.mean() with a proper weighting function.\n                    const meanMetric = tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));\n                    valOutputs.push(meanMetric);\n                }\n                return valOutputs;\n            });\n        };\n    }\n    /**\n     * Trains the model for a fixed number of epochs (iterations on a\n     * dataset).\n     *\n     * ```js\n     * const model = tf.sequential({\n     *     layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * for (let i = 1; i < 5 ; ++i) {\n     *   const h = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *       batchSize: 4,\n     *       epochs: 3\n     *   });\n     *   console.log(\"Loss after Epoch \" + i + \" : \" + h.history.loss[0]);\n     * }\n     * ```\n     *\n     * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n     * model has multiple inputs. If all inputs in the model are named, you\n     * can also pass a dictionary mapping input names to `tf.Tensor`s.\n     * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n     * the model has multiple outputs. If all outputs in the model are named,\n     * you can also pass a dictionary mapping output names to `tf.Tensor`s.\n     * @param args A `ModelFitArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @exception ValueError In case of mismatch between the provided input\n     * data and what the model expects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fit(x, y, args = {}) {\n        return fitTensors(this, x, y, args);\n    }\n    // TODO(cais): Add code snippet below when it's possible to instantiate\n    //   actual dataset objects.\n    /**\n     * Trains the model using a dataset object.\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for training. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs.\n     *   Of the two items in the array, the first is the input feature(s) and\n     *   the second is the output target(s).\n     * @param args A `ModelFitDatasetArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fitDataset(dataset, args) {\n        return fitDataset(this, dataset, args);\n    }\n    /**\n     * Runs a single gradient update on a single batch of data.\n     *\n     * This method differs from `fit()` and `fitDataset()` in the following\n     * regards:\n     *   - It operates on exactly one batch of data.\n     *   - It returns only the loss and matric values, instead of\n     *     returning the batch-by-batch loss and metric values.\n     *   - It doesn't support fine-grained options such as verbosity and\n     *     callbacks.\n     *\n     * @param x Input data. It could be one of the following:\n     *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n     *     multiple inputs).\n     *   - An Object mapping input names to corresponding `tf.Tensor` (if the\n     *     model has named inputs).\n     * @param y Target darta. It could be either a `tf.Tensor` a multiple\n     *   `tf.Tensor`s. It should be consistent with `x`.\n     * @returns Training loss or losses (in case the model has\n     *   multiple outputs), along with metrics (if any), as numbers.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async trainOnBatch(x, y) {\n        // TODO(cais): Support sampleWeight and classWeight.\n        // TODO(cais): Support Dataset objects.\n        const standardizeOut = await this.standardizeUserData(x, y);\n        const inputs = standardizeOut[0];\n        const targets = standardizeOut[1];\n        const trainFunction = this.makeTrainFunction();\n        const losses = trainFunction(inputs.concat(targets));\n        const lossValues = [];\n        for (const loss of losses) {\n            const v = await loss.data();\n            lossValues.push(v[0]);\n        }\n        tfc.dispose(losses);\n        return singletonOrArray(lossValues);\n    }\n    /**\n     * Extract weight values of the model.\n     *\n     * @param config: An instance of `io.SaveConfig`, which specifies\n     * model-saving options such as whether only trainable weights are to be\n     * saved.\n     * @returns A `NamedTensorMap` mapping original weight names (i.e.,\n     *   non-uniqueified weight names) to their values.\n     */\n    getNamedWeights(config) {\n        const namedWeights = [];\n        const trainableOnly = config != null && config.trainableOnly;\n        const weights = trainableOnly ? this.trainableWeights : this.weights;\n        const weightValues = this.getWeights(trainableOnly);\n        for (let i = 0; i < weights.length; ++i) {\n            if (trainableOnly && !weights[i].trainable) {\n                // Optionally skip non-trainable weights.\n                continue;\n            }\n            namedWeights.push({ name: weights[i].originalName, tensor: weightValues[i] });\n        }\n        return namedWeights;\n    }\n    /**\n     * Setter used for force stopping of LayersModel.fit() (i.e., training).\n     *\n     * Example:\n     *\n     * ```js\n     * const input = tf.input({shape: [10]});\n     * const output = tf.layers.dense({units: 1}).apply(input);\n     * const model = tf.model({inputs: [input], outputs: [output]});\n     * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n     * const xs = tf.ones([8, 10]);\n     * const ys = tf.zeros([8, 1]);\n     *\n     * const history = await model.fit(xs, ys, {\n     *   epochs: 10,\n     *   callbacks: {\n     *     onEpochEnd: async (epoch, logs) => {\n     *       if (epoch === 2) {\n     *         model.stopTraining = true;\n     *       }\n     *     }\n     *   }\n     * });\n     *\n     * // There should be only 3 values in the loss array, instead of 10\n     * values,\n     * // due to the stopping after 3 epochs.\n     * console.log(history.history.loss);\n     * ```\n     */\n    set stopTraining(stop) {\n        this.stopTraining_ = stop;\n    }\n    get stopTraining() {\n        return this.stopTraining_;\n    }\n    get optimizer() {\n        return this.optimizer_;\n    }\n    set optimizer(optimizer) {\n        if (this.optimizer_ !== optimizer) {\n            this.optimizer_ = optimizer;\n            this.isOptimizerOwned = false;\n        }\n    }\n    dispose() {\n        const result = super.dispose();\n        if (result.refCountAfterDispose === 0 && this.optimizer != null &&\n            this.isOptimizerOwned) {\n            const numTensorsBeforeOptmizerDisposal = tfc.memory().numTensors;\n            this.optimizer_.dispose();\n            result.numDisposedVariables +=\n                numTensorsBeforeOptmizerDisposal - tfc.memory().numTensors;\n        }\n        return result;\n    }\n    getLossIdentifiers() {\n        let lossNames;\n        if (typeof this.loss === 'string') {\n            lossNames = toSnakeCase(this.loss);\n        }\n        else if (Array.isArray(this.loss)) {\n            for (const loss of this.loss) {\n                if (typeof loss !== 'string') {\n                    throw new Error('Serialization of non-string loss is not supported.');\n                }\n            }\n            lossNames = this.loss.map(name => toSnakeCase(name));\n        }\n        else {\n            const outputNames = Object.keys(this.loss);\n            lossNames = {};\n            const losses = this.loss;\n            for (const outputName of outputNames) {\n                if (typeof losses[outputName] === 'string') {\n                    lossNames[outputName] =\n                        toSnakeCase(losses[outputName]);\n                }\n                else {\n                    throw new Error('Serialization of non-string loss is not supported.');\n                }\n            }\n        }\n        return lossNames;\n    }\n    getMetricIdentifiers() {\n        if (typeof this.metrics === 'string' ||\n            typeof this.metrics === 'function') {\n            return [toSnakeCase(Metrics.getLossOrMetricName(this.metrics))];\n        }\n        else if (Array.isArray(this.metrics)) {\n            return this.metrics.map(metric => toSnakeCase(Metrics.getLossOrMetricName(metric)));\n        }\n        else {\n            const metricsIdentifiers = {};\n            for (const key in this.metrics) {\n                metricsIdentifiers[key] =\n                    toSnakeCase(Metrics.getLossOrMetricName(this.metrics[key]));\n            }\n            return metricsIdentifiers;\n        }\n    }\n    getTrainingConfig() {\n        return {\n            loss: this.getLossIdentifiers(),\n            metrics: this.getMetricIdentifiers(),\n            optimizer_config: {\n                class_name: this.optimizer.getClassName(),\n                config: this.optimizer.getConfig()\n            }\n        };\n        // TODO(cais): Add weight_metrics when they are supported.\n        // TODO(cais): Add sample_weight_mode when it's supported.\n        // TODO(cais): Add loss_weights when it's supported.\n    }\n    loadTrainingConfig(trainingConfig) {\n        if (trainingConfig.weighted_metrics != null) {\n            throw new Error('Loading weight_metrics is not supported yet.');\n        }\n        if (trainingConfig.loss_weights != null) {\n            throw new Error('Loading loss_weights is not supported yet.');\n        }\n        if (trainingConfig.sample_weight_mode != null) {\n            throw new Error('Loading sample_weight_mode is not supported yet.');\n        }\n        const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config);\n        const optimizer = deserialize(tsConfig);\n        let loss;\n        if (typeof trainingConfig.loss === 'string') {\n            loss = toCamelCase(trainingConfig.loss);\n        }\n        else if (Array.isArray(trainingConfig.loss)) {\n            loss = trainingConfig.loss.map(lossEntry => toCamelCase(lossEntry));\n        }\n        else if (trainingConfig.loss != null) {\n            loss = {};\n            for (const key in trainingConfig.loss) {\n                loss[key] = toCamelCase(trainingConfig.loss[key]);\n            }\n        }\n        let metrics;\n        if (Array.isArray(trainingConfig.metrics)) {\n            metrics = trainingConfig.metrics.map(metric => toCamelCase(metric));\n        }\n        else if (trainingConfig.metrics != null) {\n            metrics = {};\n            for (const key in trainingConfig.metrics) {\n                metrics[key] = toCamelCase(trainingConfig.metrics[key]);\n            }\n        }\n        this.compile({ loss, metrics, optimizer });\n    }\n    /**\n     * Save the configuration and/or weights of the LayersModel.\n     *\n     * An `IOHandler` is an object that has a `save` method of the proper\n     * signature defined. The `save` method manages the storing or\n     * transmission of serialized data (\"artifacts\") that represent the\n     * model's topology and weights onto or via a specific medium, such as\n     * file downloads, local storage, IndexedDB in the web browser and HTTP\n     * requests to a server. TensorFlow.js provides `IOHandler`\n     * implementations for a number of frequently used saving mediums, such as\n     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n     * for more details.\n     *\n     * This method also allows you to refer to certain types of `IOHandler`s\n     * as URL-like string shortcuts, such as 'localstorage://' and\n     * 'indexeddb://'.\n     *\n     * Example 1: Save `model`'s topology and weights to browser [local\n     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n     * then load it back.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * console.log('Prediction from original model:');\n     * model.predict(tf.ones([1, 3])).print();\n     *\n     * const saveResults = await model.save('localstorage://my-model-1');\n     *\n     * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * loadedModel.predict(tf.ones([1, 3])).print();\n     * ```\n     *\n     * Example 2. Saving `model`'s topology and weights to browser\n     * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n     * then load it back.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * console.log('Prediction from original model:');\n     * model.predict(tf.ones([1, 3])).print();\n     *\n     * const saveResults = await model.save('indexeddb://my-model-1');\n     *\n     * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * loadedModel.predict(tf.ones([1, 3])).print();\n     * ```\n     *\n     * Example 3. Saving `model`'s topology and weights as two files\n     * (`my-model-1.json` and `my-model-1.weights.bin`) downloaded from\n     * browser.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * const saveResults = await model.save('downloads://my-model-1');\n     * ```\n     *\n     * Example 4. Send  `model`'s topology and weights to an HTTP server.\n     * See the documentation of `tf.io.http` for more details\n     * including specifying request parameters and implementation of the\n     * server.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * const saveResults = await model.save('http://my-server/model/upload');\n     * ```\n     *\n     * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n     * scheme-based string shortcut for `IOHandler`.\n     * @param config Options for saving the model.\n     * @returns A `Promise` of `SaveResult`, which summarizes the result of\n     * the saving, such as byte sizes of the saved artifacts for the model's\n     *   topology and weight values.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async save(handlerOrURL, config) {\n        if (typeof handlerOrURL === 'string') {\n            const handlers = io.getSaveHandlers(handlerOrURL);\n            if (handlers.length === 0) {\n                throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n            }\n            else if (handlers.length > 1) {\n                throw new ValueError(`Found more than one (${handlers.length}) save handlers for ` +\n                    `URL '${handlerOrURL}'`);\n            }\n            handlerOrURL = handlers[0];\n        }\n        if (handlerOrURL.save == null) {\n            throw new ValueError('LayersModel.save() cannot proceed because the IOHandler ' +\n                'provided does not have the `save` attribute defined.');\n        }\n        const weightDataAndSpecs = await io.encodeWeights(this.getNamedWeights(config));\n        const returnString = false;\n        const unusedArg = null;\n        const modelConfig = this.toJSON(unusedArg, returnString);\n        const modelArtifacts = {\n            modelTopology: modelConfig,\n            format: LAYERS_MODEL_FORMAT_NAME,\n            generatedBy: `TensorFlow.js tfjs-layers v${version}`,\n            convertedBy: null,\n        };\n        const includeOptimizer = config == null ? false : config.includeOptimizer;\n        if (includeOptimizer && this.optimizer != null) {\n            modelArtifacts.trainingConfig = this.getTrainingConfig();\n            const weightType = 'optimizer';\n            const { data: optimizerWeightData, specs: optimizerWeightSpecs } = await io.encodeWeights(await this.optimizer.getWeights(), weightType);\n            weightDataAndSpecs.specs.push(...optimizerWeightSpecs);\n            weightDataAndSpecs.data = io.concatenateArrayBuffers([weightDataAndSpecs.data, optimizerWeightData]);\n        }\n        if (this.userDefinedMetadata != null) {\n            // Check serialized size of user-defined metadata.\n            const checkSize = true;\n            checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize);\n            modelArtifacts.userDefinedMetadata = this.userDefinedMetadata;\n        }\n        modelArtifacts.weightData = weightDataAndSpecs.data;\n        modelArtifacts.weightSpecs = weightDataAndSpecs.specs;\n        return handlerOrURL.save(modelArtifacts);\n    }\n    /**\n     * Set user-defined metadata.\n     *\n     * The set metadata will be serialized together with the topology\n     * and weights of the model during `save()` calls.\n     *\n     * @param setUserDefinedMetadata\n     */\n    setUserDefinedMetadata(userDefinedMetadata) {\n        checkUserDefinedMetadata(userDefinedMetadata, this.name);\n        this.userDefinedMetadata = userDefinedMetadata;\n    }\n    /**\n     * Get user-defined metadata.\n     *\n     * The metadata is supplied via one of the two routes:\n     *   1. By calling `setUserDefinedMetadata()`.\n     *   2. Loaded during model loading (if the model is constructed\n     *      via `tf.loadLayersModel()`.)\n     *\n     * If no user-defined metadata is available from either of the\n     * two routes, this function will return `undefined`.\n     */\n    getUserDefinedMetadata() {\n        return this.userDefinedMetadata;\n    }\n}\n// The class name is 'Model' rather than 'LayersModel' for backwards\n// compatibility since this class name shows up in the serialization format.\n/** @nocollapse */\nLayersModel.className = 'Model';\nserialization.registerClass(LayersModel);\n/**\n * A `tf.Functional` is an alias to `tf.LayersModel`.\n *\n * See also:\n *   `tf.LayersModel`, `tf.Sequential`, `tf.loadLayersModel`.\n */\n/** @doc {heading: 'Models', subheading: 'Classes'} */\nexport class Functional extends LayersModel {\n}\nFunctional.className = 'Functional';\nserialization.registerClass(Functional);\n//# sourceMappingURL=training.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source keras/models.py */\nimport { dispose, io, serialization, util } from '@tensorflow/tfjs-core';\nimport { getUid } from './backend/state';\nimport { Input } from './engine/input_layer';\nimport { getSourceInputs, Node } from './engine/topology';\nimport { LayersModel } from './engine/training';\nimport { NotImplementedError, RuntimeError, ValueError } from './errors';\nimport { deserialize } from './layers/serialization';\nimport * as generic_utils from './utils/generic_utils';\nimport { convertPythonicToTs } from './utils/serialization_utils';\nimport { getExactlyOneShape } from './utils/types_utils';\n/**\n * Parses a JSON model configuration file and returns a model instance.\n *\n * ```js\n * // This example shows how to serialize a model using `toJSON()` and\n * // deserialize it as another model using `tf.models.modelFromJSON()`.\n * // Note: this example serializes and deserializes only the topology\n * // of the model; the weights of the loaded model will be different\n * // from those of the the original model, due to random weight\n * // initialization.\n * // To load the topology and weights of a model, use `tf.loadLayersModel()`.\n * const model1 = tf.sequential();\n * model1.add(tf.layers.repeatVector({inputShape: [2], n: 4}));\n * // Serialize `model1` as a JSON object.\n * const model1JSON = model1.toJSON(null, false);\n * model1.summary();\n *\n * const model2 = await tf.models.modelFromJSON(model1JSON);\n * model2.summary();\n * ```\n *\n *  @param modelAndWeightsConfig JSON object or string encoding a model and\n *       weights configuration. It can also be only the topology JSON of the\n *       model, in which case the weights will not be loaded.\n *  @param custom_objects Optional dictionary mapping names\n *       (strings) to custom classes or functions to be\n *       considered during deserialization.\n * @returns A TensorFlow.js Layers `tf.LayersModel` instance (uncompiled).\n */\nexport async function modelFromJSON(modelAndWeightsConfig, customObjects) {\n    if (!('modelTopology' in modelAndWeightsConfig)) {\n        modelAndWeightsConfig = { modelTopology: modelAndWeightsConfig };\n    }\n    modelAndWeightsConfig = modelAndWeightsConfig;\n    let modelTopology = modelAndWeightsConfig.modelTopology;\n    if (modelTopology['model_config'] != null) {\n        // If the model-topology JSON contains a 'model_config' field, then it is\n        // a full model JSON (e.g., from `keras.Model.save()`), which contains\n        // not only the model's architecture in its 'model_config' field, but\n        // additional information such as the model's optimizer. We use only the\n        // 'model_config' field currently.\n        modelTopology = modelTopology['model_config'];\n    }\n    const tsConfig = convertPythonicToTs(modelTopology);\n    const model = deserialize(tsConfig, customObjects);\n    if (modelAndWeightsConfig.weightsManifest != null) {\n        // Load the weight values keyed by the original tensor names in the model\n        // file that was loaded.  These should match the keys of the weight\n        // manifest.\n        const weightValues = await io.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model.weights.map(weight => weight.originalName));\n        // Map the weights to the unique tensor names generated during model loading\n        const uniqueWeightValues = {};\n        for (const weight of model.weights) {\n            uniqueWeightValues[weight.originalName] =\n                weightValues[weight.originalName];\n        }\n        model.loadWeights(uniqueWeightValues);\n        // Dispose temporary weight values.\n        dispose(weightValues);\n    }\n    return model;\n}\n/**\n * Load a model, including its topology and optionally weights.  See the\n * Tutorial named \"How to import a Keras Model\" for usage examples.\n *\n * Example 1: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 2. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Load a model from user-selected files from HTML\n * [file input\n * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).\n *\n * ```js\n * // Note: this code snippet will not work without the HTML elements in the\n * //   page\n * const jsonUpload = document.getElementById('json-upload');\n * const weightsUpload = document.getElementById('weights-upload');\n *\n * const model = await tf.loadLayersModel(\n *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));\n * ```\n *\n * Example 4. Load a model from an HTTP server.\n *\n * ```js\n * const model = await\n *     tf.loadLayersModel('https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');\n * model.summary();\n * ```\n *\n * @param pathOrIOHandler Can be either of the two formats\n *   1. A string path to the `ModelAndWeightsConfig` JSON describing\n *      the model in the canonical TensorFlow.js format. This path will be\n *      interpreted as a relative HTTP path, to which `fetch` will be used to\n *      request the model topology and weight manifest JSON.\n *      The content of the JSON file is assumed to be a JSON object with the\n *      following fields and values:\n *      - 'modelTopology': A JSON object that can be either of:\n *        1. a model architecture JSON consistent with the format of the return\n *            value of `keras.Model.to_json()`\n *        2. a full model JSON in the format of `keras.models.save_model()`.\n *      - 'weightsManifest': A TensorFlow.js weights manifest.\n *      See the Python converter function `save_model()` for more details.\n *      It is also assumed that model weights can be accessed from relative\n *      paths described by the `paths` fields in weights manifest.\n *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`\n *      method.\n * @param options Optional configuration arguments for the model loading,\n *   including:\n *   - `strict`: Require that the provided weights exactly match those required\n *     by the layers.  Default true.  Passing false means that both extra\n *     weights and missing weights will be silently ignored.\n *   - `onProgress`: A progress callback of the form:\n *     `(fraction: number) => void`. This callback can be used to monitor the\n *     model-loading process.\n * @returns A `Promise` of `tf.LayersModel`, with the topology and weights\n *     loaded.\n */\nexport async function loadLayersModelInternal(pathOrIOHandler, options) {\n    if (options == null) {\n        options = {};\n    }\n    if (typeof pathOrIOHandler === 'string') {\n        const handlers = io.getLoadHandlers(pathOrIOHandler, options);\n        if (handlers.length === 0) {\n            // For backward compatibility: if no load handler can be found,\n            // assume it is a relative http path.\n            // TODO(cais): Reformat the args into a single `LoadOptions` once the core\n            // is refactored.\n            handlers.push(io.browserHTTPRequest(pathOrIOHandler, options));\n        }\n        else if (handlers.length > 1) {\n            throw new ValueError(`Found more than one (${handlers.length}) load handlers for ` +\n                `URL '${pathOrIOHandler}'`);\n        }\n        pathOrIOHandler = handlers[0];\n    }\n    return loadLayersModelFromIOHandler(pathOrIOHandler, undefined, options);\n}\n/**\n * Load a model and optionally its weights, using an IOHandler object.\n *\n * @param handler The instance of `IOHandler` to be used during the model\n *   loading.\n * @param customObjects Any optional custom objects to be used during model\n *   loading.\n * @param strict Whether the weight loading will be done in strict mode.\n *   Default: `true`.\n */\nexport async function loadLayersModelFromIOHandler(handler, customObjects, options) {\n    if (options == null) {\n        options = {};\n    }\n    if (handler.load == null) {\n        throw new ValueError('Cannot proceed with model loading because the IOHandler provided ' +\n            'does not have the `load` method implemented.');\n    }\n    const artifacts = await handler.load();\n    let modelTopology = artifacts.modelTopology;\n    if (modelTopology['model_config'] != null) {\n        modelTopology = modelTopology['model_config'];\n    }\n    const strict = options.strict == null ? true : options.strict;\n    // If weights are provided and the weight-loading mode is strict, use\n    // fast weight initialization. This skips costly initializers such as\n    // 'orthogonal' and saves unnecessary computation in cases where\n    // the initialized weight values will immediately be overwritten by\n    // loaded weight values.\n    const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict;\n    const model = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit);\n    const trainingConfig = artifacts.trainingConfig;\n    if (trainingConfig != null) {\n        model.loadTrainingConfig(trainingConfig);\n    }\n    if (artifacts.userDefinedMetadata != null) {\n        model.setUserDefinedMetadata(artifacts.userDefinedMetadata);\n    }\n    // If weightData is present, load the weights into the model.\n    if (artifacts.weightData != null) {\n        // Loading weights requires weightSpecs.\n        if (artifacts.weightSpecs == null) {\n            throw new ValueError('LayersModel artifacts contains weight data, but not weight specs. ' +\n                'Therefore loading of weights cannot proceed.');\n        }\n        const { modelWeights, optimizerWeights } = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs);\n        model.loadWeights(modelWeights, strict);\n        if (model.optimizer != null && optimizerWeights.length > 0) {\n            await model.optimizer.setWeights(optimizerWeights);\n        }\n        // Dispose temporary weight values.\n        dispose(modelWeights);\n        dispose(optimizerWeights.map(w => w.tensor));\n    }\n    return model;\n}\nfunction decodeModelAndOptimizerWeights(buffer, specs) {\n    const name2Tensor = io.decodeWeights(buffer, specs);\n    const modelWeights = {};\n    const optimizerWeights = [];\n    specs.forEach(spec => {\n        if (spec.group === 'optimizer') {\n            optimizerWeights.push({ name: spec.name, tensor: name2Tensor[spec.name] });\n        }\n        else {\n            modelWeights[spec.name] = name2Tensor[spec.name];\n        }\n    });\n    return { modelWeights, optimizerWeights };\n}\n/**\n * A model with a stack of layers, feeding linearly from one to the next.\n *\n * `tf.sequential` is a factory function that creates an instance of\n * `tf.Sequential`.\n *\n * ```js\n *  // Define a model for linear regression.\n *  const model = tf.sequential();\n *  model.add(tf.layers.dense({units: 1, inputShape: [1]}));\n *\n *  // Prepare the model for training: Specify the loss and the optimizer.\n *  model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n *\n *  // Generate some synthetic data for training.\n *  const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);\n *  const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);\n *\n *  // Train the model using the data then do inference on a data point the\n *  // model hasn't seen:\n *  await model.fit(xs, ys);\n *  model.predict(tf.tensor2d([5], [1, 1])).print();\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class Sequential extends LayersModel {\n    constructor(args) {\n        super({ inputs: [], outputs: [] });\n        args = args || {};\n        this.trainable = true;\n        this.built = false;\n        // Set model name.\n        this.name = (args.name != null) ? args.name : getUid('sequential_');\n        // Add to the model any layers passed to the constructor.\n        if (args.layers != null) {\n            for (const layer of args.layers) {\n                this.add(layer);\n            }\n        }\n    }\n    // Helper function to Sequential.add  Throws if the new output shape will be\n    // invalid.\n    checkShape(layer) {\n        const shape = layer.inboundNodes[0].outputTensors[0].shape;\n        if (shape.some(x => x < 0)) {\n            throw new ValueError('Negative dimension size caused by adding layer ' +\n                `${layer.name} with input shape [` +\n                `${layer.inboundNodes[0].inputTensors[0].shape}]`);\n        }\n    }\n    /**\n     * Adds a layer instance on top of the layer stack.\n     *\n     * ```js\n     *  const model = tf.sequential();\n     *  model.add(tf.layers.dense({units: 8, inputShape: [1]}));\n     *  model.add(tf.layers.dense({units: 4, activation: 'relu6'}));\n     *  model.add(tf.layers.dense({units: 1, activation: 'relu6'}));\n     *  // Note that the untrained model is random at this point.\n     *  model.predict(tf.randomNormal([10, 1])).print();\n     * ```\n     * @param layer Layer instance.\n     *\n     * @exception ValueError In case the `layer` argument does not know its\n     * input shape.\n     * @exception ValueError In case the `layer` argument has multiple output\n     *   tensors, or is already connected somewhere else (forbidden in\n     *   `Sequential` models).\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    add(layer) {\n        const isLayerModelInstance = layer instanceof Sequential || layer instanceof LayersModel;\n        let modelLayer;\n        if (isLayerModelInstance) {\n            modelLayer = layer;\n            if (modelLayer.outputs.length !== 1) {\n                throw new ValueError('All layers in a Sequential model ' +\n                    'should have a single output tensor. ' +\n                    'For multi-output layers, ' +\n                    'use the functional API.');\n            }\n            if (modelLayer.inputs.length !== 1) {\n                throw new ValueError('All layers in a Sequential model ' +\n                    'should have a single input tensor. ' +\n                    'For multi-input layers, ' +\n                    'use the functional API.');\n            }\n        }\n        if (this.outputs.length === 0) {\n            // first layer in model: check that it is an input layer\n            if (layer.inboundNodes.length === 0) {\n                // create an input layer\n                if (layer.batchInputShape == null) {\n                    throw new ValueError('The first layer in a Sequential model must ' +\n                        'get an `inputShape` or `batchInputShape` argument.');\n                }\n                // Instantiate the input layer.\n                const x = Input({\n                    batchShape: layer.batchInputShape,\n                    dtype: layer.dtype,\n                    name: layer.name + '_input'\n                });\n                // This will build the current layer and create the node connecting\n                // the current layer to the input layer we just created.\n                layer.apply(x);\n            }\n            if (isLayerModelInstance) {\n                this.outputs = modelLayer.outputs;\n                this.inputs = modelLayer.inputs;\n            }\n            else {\n                if (layer.inboundNodes.length !== 1) {\n                    throw new ValueError('A layer added to a Sequential model must not already be ' +\n                        `connected somewhere else. LayersModel received layer ${layer.name} ` +\n                        `which has ${layer.inboundNodes.length} pre-existing inbound ` +\n                        'connections.');\n                }\n                if (layer.inboundNodes[0].outputTensors.length !== 1) {\n                    throw new ValueError('All layers in a Sequential model ' +\n                        'should have a single output tensor. ' +\n                        'For multi-output layers, ' +\n                        'use the functional API.');\n                }\n                this.checkShape(layer);\n                this.outputs = [layer.inboundNodes[0].outputTensors[0]];\n                this.inputs = getSourceInputs(this.outputs[0]);\n            }\n            this.inboundNodes = [];\n            // We create an input node, which we will keep updated\n            // as we add more layers.\n            // (This call has side effects.)\n            // tslint:disable-next-line:no-unused-expression\n            new Node({\n                outboundLayer: this,\n                inboundLayers: [],\n                nodeIndices: [],\n                tensorIndices: [],\n                inputTensors: this.inputs,\n                outputTensors: this.outputs,\n                // no model-level masking for now\n                inputMasks: generic_utils.pyListRepeat(null, this.inputs.length),\n                outputMasks: [null],\n                inputShapes: this.inputs.map(x => x.shape),\n                outputShapes: this.outputs[0].shape\n            });\n        }\n        else {\n            const outputTensor = layer.apply(this.outputs[0]);\n            if (Array.isArray(outputTensor)) {\n                throw new TypeError('All layers in a Sequential model ' +\n                    'should have a single output tensor. ' +\n                    'For multi-output layers, ' +\n                    'use the functional API.');\n            }\n            this.checkShape(layer);\n            this.outputs = [outputTensor];\n            // update self.inbound_nodes\n            this.inboundNodes[0].outputTensors = this.outputs;\n            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n        }\n        this.layers.push(layer);\n        this.built = false;\n    }\n    /**\n     * Removes the last layer in the model.\n     *\n     * @exception TypeError if there are no layers in the model.\n     */\n    pop() {\n        if (this.layers.length === 0) {\n            throw new TypeError('There are no layers in the model.');\n        }\n        this.layers.pop();\n        if (this.layers.length === 0) {\n            this.outputs = [];\n            this.inboundNodes = [];\n            this.outboundNodes = [];\n        }\n        else {\n            const lastLayerIndex = this.layers.length - 1;\n            this.layers[lastLayerIndex].outboundNodes = [];\n            this.outputs = [this.layers[lastLayerIndex].output];\n            // update self.inbound_nodes\n            this.inboundNodes[0].outputTensors = this.outputs;\n            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n        }\n    }\n    call(inputs, kwargs) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.call(inputs, kwargs);\n    }\n    build(inputShape) {\n        // Call `getExactlyOneShape` without using its return value,\n        // to verify that exactly one input shape is provided.\n        getExactlyOneShape(inputShape);\n        if (this.inputs.length === 0 || this.outputs.length === 0) {\n            throw new TypeError('Sequential model cannot be built: model is empty.' +\n                ' Add some layers first.');\n        }\n        // actually create the model\n        this.model = new LayersModel({\n            inputs: this.inputs,\n            outputs: this.outputs[0],\n            name: this.name + '_model'\n        });\n        this.model.trainable = this.trainable;\n        // mirror model attributes\n        this.supportsMasking = this.model.supportsMasking;\n        // TODO(michaelterry): Add caches\n        this.inputLayers = this.model.inputLayers;\n        this.inputLayersNodeIndices = this.model.inputLayersNodeIndices;\n        this.inputLayersTensorIndices = this.model.inputLayersTensorIndices;\n        this.outputLayers = this.model.outputLayers;\n        this.outputLayersNodeIndices = this.model.outputLayersNodeIndices;\n        this.outputLayersTensorIndices = this.model.outputLayersTensorIndices;\n        this.nodesByDepth = this.model.nodesByDepth;\n        this.containerNodes = this.model.containerNodes;\n        this.outputNames = this.model.outputNames;\n        this.inputNames = this.model.inputNames;\n        // TODO(michaelterry): Add feedInputNames, feedInputs, if needed.\n        // TODO(michaelterry): Add callbackModel if needed.\n        this.built = true;\n    }\n    countParams() {\n        if (!this.built) {\n            this.build();\n        }\n        return super.countParams();\n    }\n    /**\n     * Print a text summary of the Sequential model's layers.\n     *\n     * The summary includes\n     * - Name and type of all layers that comprise the model.\n     * - Output shape(s) of the layers\n     * - Number of weight parameters of each layer\n     * - The total number of trainable and non-trainable parameters of the\n     * model.\n     *\n     * ```js\n     * const model = tf.sequential();\n     * model.add(\n     *     tf.layers.dense({units: 100, inputShape: [10], activation: 'relu'}));\n     * model.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));\n     *\n     * model.summary();\n     * ```\n     *\n     * @param lineLength Custom line length, in number of characters.\n     * @param positions Custom widths of each of the columns, as either\n     *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n     *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n     *   right-most (i.e., ending) position of a column.\n     * @param printFn Custom print function. Can be used to replace the default\n     *   `console.log`. For example, you can use `x => {}` to mute the printed\n     *   messages in the console.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    summary(lineLength, positions, printFn = console.log) {\n        if (!this.built) {\n            this.build();\n        }\n        super.summary(lineLength, positions, printFn);\n    }\n    /**\n     * Sets the weights of the model.\n     *\n     * @param weights Should be a list of Tensors with shapes and types matching\n     *   the output of `model.getWeights()`.\n     */\n    setWeights(weights) {\n        if (this.model == null) {\n            this.build();\n        }\n        this.model.setWeights(weights);\n    }\n    /**\n     * Returns the loss value & metrics values for the model in test mode.\n     *\n     * Loss and metrics are specified during `compile()`, which needs to happen\n     * before calls to `evaluate()`.\n     *\n     * Computation is done in batches.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const result = model.evaluate(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *   batchSize: 4,\n     * });\n     * result.print();\n     * ```\n     *\n     * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple inputs.\n     * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple outputs.\n     * @param args A `ModelEvaluateConfig`, containing optional fields.\n     *\n     * @return `Scalar` test loss (if the model has a single output and no\n     *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n     *   and/or metrics). The attribute `model.metricsNames`\n     *   will give you the display labels for the scalar outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    evaluate(x, y, args = {}) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before being used.');\n        }\n        return this.model.evaluate(x, y, args);\n    }\n    // TODO(cais): Add code snippet below once real dataset objects are\n    //   available.\n    /**\n     * Evaluate model using a dataset object.\n     *\n     * Note: Unlike `evaluate()`, this method is asynchronous (`async`);\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for evaluation. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs. Of the two items in the array, the\n     *   first is the input feature(s) and the second is the output target(s).\n     * @param args A configuration object for the dataset-based evaluation.\n     * @returns Loss and metric values as an Array of `Scalar` objects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async evaluateDataset(dataset, args) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before being used.');\n        }\n        return this.model.evaluateDataset(dataset, args);\n    }\n    /**\n     * Generates output predictions for the input samples.\n     *\n     * Computation is done in batches.\n     *\n     * Note: the \"step\" mode of predict() is currently not supported.\n     *   This is because the TensorFow.js core backend is imperative only.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predict(tf.ones([2, 10])).print();\n     * ```\n     *\n     * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n     *   the model has multiple inputs.\n     * @param conifg A `ModelPredictConfig` object containing optional fields.\n     *\n     * @return `tf.Tensor`(s) of predictions.\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and the model's expectations, or in case a stateful model receives a\n     *   number of samples that is not a multiple of the batch size.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(x, args = {}) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.predict(x, args);\n    }\n    /**\n     * Returns predictions for a single batch of samples.\n     *\n     * @param x: Input samples, as a Tensor, or list of Tensors (if the model\n     *   has multiple inputs).\n     * @return Tensor(s) of predictions\n     */\n    predictOnBatch(x) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.predictOnBatch(x);\n    }\n    /**\n     * See `LayersModel.compile`.\n     *\n     * @param args\n     */\n    compile(args) {\n        this.build();\n        this.model.compile(args);\n        this.optimizer_ = this.model.optimizer;\n        // tslint:disable-next-line:no-any\n        this.isOptimizerOwned = this.model.isOptimizerOwned;\n        this.loss = this.model.loss;\n        this.metrics = this.model.metrics;\n        // TODO(cais): Add this.lossWeights, this.sampleWeightMode,\n        //   this.weightedMetrics, this.targets.\n        this.metricsTensors = this.model.metricsTensors;\n        this.metricsNames = this.model.metricsNames;\n        // TODO(cais): Add sampleWeights.\n    }\n    get optimizer() {\n        return this.model == null ? undefined : this.model.optimizer;\n    }\n    set optimizer(optimizer) {\n        this.model.optimizer = optimizer;\n    }\n    /**\n     * Trains the model for a fixed number of epochs (iterations on a dataset).\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const history = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *   batchSize: 4,\n     *   epochs: 3\n     * });\n     * console.log(history.history.loss[0]);\n     * ```\n     *\n     * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n     * model has multiple inputs. If all inputs in the model are named, you can\n     * also pass a dictionary mapping input names to `tf.Tensor`s.\n     * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n     * the model has multiple outputs. If all outputs in the model are named, you\n     *  can also pass a dictionary mapping output names to `tf.Tensor`s.\n     * @param args  A `ModelFitConfig`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and what the model expects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fit(x, y, args = {}) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before ' +\n                'being used.');\n        }\n        return this.model.fit(x, y, args);\n    }\n    /**\n     * Trains the model using a dataset object.\n     *\n     * ```js\n     * const xArray = [\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     * ];\n     * const yArray = [1, 1, 1, 1];\n     * // Create a dataset from the JavaScript array.\n     * const xDataset = tf.data.array(xArray);\n     * const yDataset = tf.data.array(yArray);\n     * // Zip combines the `x` and `y` Datasets into a single Dataset, the\n     * // iterator of which will return an object containing of two tensors,\n     * // corresponding to `x` and `y`.  The call to `batch(4)` will bundle\n     * // four such samples into a single object, with the same keys now pointing\n     * // to tensors that hold 4 examples, organized along the batch dimension.\n     * // The call to `shuffle(4)` causes each iteration through the dataset to\n     * // happen in a different order.  The size of the shuffle window is 4.\n     * const xyDataset = tf.data.zip({xs: xDataset, ys: yDataset})\n     *     .batch(4)\n     *     .shuffle(4);\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [9]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const history = await model.fitDataset(xyDataset, {\n     *   epochs: 4,\n     *   callbacks: {onEpochEnd: (epoch, logs) => console.log(logs.loss)}\n     * });\n     * ```\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected to\n     *   generate a dataset iterator object, the `next()` method of which is\n     *   expected to produce data batches for evaluation. The return value of the\n     *   `next()` call ought to contain a boolean `done` field and a `value`\n     *   field.\n     *\n     *   The `value` field is expected to be an object of with fields\n     *   `xs` and `ys`, which point to the feature tensor and the target tensor,\n     *   respectively. This case is for models with exactly one input and one\n     *   output (e.g.. a sequential model). For example:\n     *   ```js\n     *   {value: {xs: xsTensor, ys: ysTensor}, done: false}\n     *   ```\n     *\n     *   If the model has multiple inputs, the `xs` field of `value` should\n     *   be an object mapping input names to their respective feature tensors.\n     *   For example:\n     *   ```js\n     *   {\n     *     value: {\n     *       xs: {\n     *         input_1: xsTensor1,\n     *         input_2: xsTensor2\n     *       },\n     *       ys: ysTensor\n     *     },\n     *     done: false\n     *   }\n     *   ```\n     *   If the model has multiple outputs, the `ys` field of `value` should\n     *   be an object mapping output names to their respective target tensors.\n     *   For example:\n     *   ```js\n     *   {\n     *     value: {\n     *       xs: xsTensor,\n     *       ys: {\n     *         output_1: ysTensor1,\n     *         output_2: ysTensor2\n     *       },\n     *     },\n     *     done: false\n     *   }\n     *   ```\n     * @param args A `ModelFitDatasetArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async fitDataset(dataset, args) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before ' +\n                'being used.');\n        }\n        return this.model.fitDataset(dataset, args);\n    }\n    /**\n     * Runs a single gradient update on a single batch of data.\n     *\n     * This method differs from `fit()` and `fitDataset()` in the following\n     * regards:\n     *   - It operates on exactly one batch of data.\n     *   - It returns only the loss and matric values, instead of\n     *     returning the batch-by-batch loss and metric values.\n     *   - It doesn't support fine-grained options such as verbosity and\n     *     callbacks.\n     *\n     * @param x Input data. It could be one of the following:\n     *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n     *     multiple inputs).\n     *   - An Object mapping input names to corresponding `tf.Tensor` (if the\n     *     model has named inputs).\n     * @param y Target darta. It could be either a `tf.Tensor` a multiple\n     *   `tf.Tensor`s. It should be consistent with `x`.\n     * @returns Training loss or losses (in case the model has\n     *   multiple outputs), along with metrics (if any), as numbers.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async trainOnBatch(x, y) {\n        return this.model.trainOnBatch(x, y);\n    }\n    /* See parent class for JsDoc */\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n        let configArray;\n        let extraModelConfig = {};\n        if (config instanceof Array) {\n            if (!(config[0].className != null) ||\n                config[0]['className'] === 'Merge') {\n                throw new ValueError('Legacy serialization format not supported yet.');\n            }\n            configArray = config;\n        }\n        else {\n            util.assert(config['layers'] != null, () => `When the config data for a Sequential model is not an Array, ` +\n                `it must be an Object that contains the 'layers' field.`);\n            configArray = config['layers'];\n            delete config['layers'];\n            extraModelConfig = config;\n        }\n        const model = new cls(extraModelConfig);\n        if (!(model instanceof Sequential)) {\n            throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model}`);\n        }\n        for (const conf of configArray) {\n            const customObjects = undefined;\n            const layer = deserialize(conf, customObjects, fastWeightInit);\n            if (fastWeightInit) {\n                layer.setFastWeightInitDuringBuild(true);\n            }\n            model.add(layer);\n        }\n        return model;\n    }\n    /**\n     * Setter used for force stopping of LayersModel.fit() (i.e., training).\n     *\n     * Example:\n     *\n     * ```js\n     * const model = tf.sequential();\n     * model.add(tf.layers.dense({units: 1, inputShape: [10]}));\n     * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n     * const xs = tf.ones([8, 10]);\n     * const ys = tf.zeros([8, 1]);\n     *\n     * const history = await model.fit(xs, ys, {\n     *   epochs: 10,\n     *   callbacks: {\n     *     onEpochEnd: async (epoch, logs) => {\n     *       if (epoch === 2) {\n     *         model.stopTraining = true;\n     *       }\n     *     }\n     *   }\n     * });\n     *\n     * // There should be only 3 values in the loss array, instead of 10 values,\n     * // due to the stopping after 3 epochs.\n     * console.log(history.history.loss);\n     * ```\n     */\n    set stopTraining(stop) {\n        // TODO(cais): When refactoring to remove the composition pattern happens,\n        // remove this method overriding.\n        if (this.model == null) {\n            throw new ValueError('Cannot set the stopTraining property of a sequential model before ' +\n                'it is compiled.');\n        }\n        this.model.stopTraining = stop;\n    }\n    get stopTraining() {\n        if (this.model == null) {\n            throw new ValueError('Cannot get the stopTraining property of a sequential model before ' +\n                'it is compiled.');\n        }\n        return this.model.stopTraining;\n    }\n    // TODO(cais): Override get trainableWeights() here\n    // tslint:disable-next-line:no-any\n    getConfig() {\n        // NOTE(cais): We override the return type of getConfig() to `any` here,\n        //   because the `Sequential` class is a special case among `Container`\n        //   subtypes in that its getConfig() method returns an Array (not a\n        //   dict).\n        const layers = [];\n        for (const layer of this.layers) {\n            const dict = {};\n            dict['className'] = layer.getClassName();\n            dict['config'] = layer.getConfig();\n            layers.push(dict);\n        }\n        return { name: this.name, layers };\n    }\n}\n/** @nocollapse */\nSequential.className = 'Sequential';\nserialization.registerClass(Sequential);\n//# sourceMappingURL=models.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { CallbackConstructorRegistry } from './base_callbacks';\nimport { Input, } from './engine/input_layer';\nimport { LayersModel } from './engine/training';\nimport { loadLayersModelInternal, Sequential } from './models';\n// TODO(cais): Add doc string to all the public static functions in this\n//   class; include exectuable JavaScript code snippets where applicable\n//   (b/74074458).\n// LayersModel and related factory methods.\n/**\n * A model is a data structure that consists of `Layers` and defines inputs\n * and outputs.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.model` is more generic, supporting an arbitrary graph (without\n * cycles) of layers. `tf.sequential` is less generic and supports only a linear\n * stack of layers.\n *\n * When creating a `tf.LayersModel`, specify its input(s) and output(s). Layers\n * are used to wire input(s) to output(s).\n *\n * For example, the following code snippet defines a model consisting of\n * two `dense` layers, with 10 and 4 units, respectively.\n *\n * ```js\n * // Define input, which has a size of 5 (not including batch dimension).\n * const input = tf.input({shape: [5]});\n *\n * // First dense layer uses relu activation.\n * const denseLayer1 = tf.layers.dense({units: 10, activation: 'relu'});\n * // Second dense layer uses softmax activation.\n * const denseLayer2 = tf.layers.dense({units: 4, activation: 'softmax'});\n *\n * // Obtain the output symbolic tensor by applying the layers on the input.\n * const output = denseLayer2.apply(denseLayer1.apply(input));\n *\n * // Create the model based on the inputs.\n * const model = tf.model({inputs: input, outputs: output});\n *\n * // The model can be used for training, evaluation and prediction.\n * // For example, the following line runs prediction with the model on\n * // some fake data.\n * model.predict(tf.ones([2, 5])).print();\n * ```\n * See also:\n *   `tf.sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function model(args) {\n    return new LayersModel(args);\n}\n/**\n * Creates a `tf.Sequential` model.  A sequential model is any model where the\n * outputs of one layer are the inputs to the next layer, i.e. the model\n * topology is a simple 'stack' of layers, with no branching or skipping.\n *\n * This means that the first layer passed to a `tf.Sequential` model should have\n * a defined input shape. What that means is that it should have received an\n * `inputShape` or `batchInputShape` argument, or for some type of layers\n * (recurrent, Dense...) an `inputDim` argument.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.sequential` is less generic, supporting only a linear stack of layers.\n * `tf.model` is more generic and supports an arbitrary graph (without\n * cycles) of layers.\n *\n * Examples:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have an input shape defined.\n * model.add(tf.layers.dense({units: 32, inputShape: [50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output, which equals\n * // `[null, 4]`. The 1st dimension is the undetermined batch dimension; the\n * // 2nd is the output size of the model's last layer.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * It is also possible to specify a batch size (with potentially undetermined\n * batch dimension, denoted by \"null\") for the first layer using the\n * `batchInputShape` key. The following example is equivalent to the above:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have a defined input shape\n * model.add(tf.layers.dense({units: 32, batchInputShape: [null, 50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * You can also use an `Array` of already-constructed `Layer`s to create\n * a `tf.Sequential` model:\n *\n * ```js\n * const model = tf.sequential({\n *   layers: [tf.layers.dense({units: 32, inputShape: [50]}),\n *            tf.layers.dense({units: 4})]\n * });\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function sequential(config) {\n    return new Sequential(config);\n}\n/**\n * Load a model composed of Layer objects, including its topology and optionally\n * weights. See the Tutorial named \"How to import a Keras Model\" for usage\n * examples.\n *\n * This method is applicable to:\n *\n * 1. Models created with the `tf.layers.*`, `tf.sequential`, and\n * `tf.model` APIs of TensorFlow.js and later saved with the\n * `tf.LayersModel.save` method.\n * 2. Models converted from Keras or TensorFlow tf.keras using the\n * [tensorflowjs_converter](https://github.com/tensorflow/tfjs/tree/master/tfjs-converter).\n *\n * This mode is *not* applicable to TensorFlow `SavedModel`s or their converted\n * forms. For those models, use `tf.loadGraphModel`.\n *\n * Example 1. Load a model from an HTTP server.\n *\n * ```js\n * const model = await tf.loadLayersModel(\n *     'https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');\n * model.summary();\n * ```\n *\n * Example 2: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 4. Load a model from user-selected files from HTML\n * [file input\n * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).\n *\n * ```js\n * // Note: this code snippet will not work without the HTML elements in the\n * //   page\n * const jsonUpload = document.getElementById('json-upload');\n * const weightsUpload = document.getElementById('weights-upload');\n *\n * const model = await tf.loadLayersModel(\n *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));\n * ```\n *\n * @param pathOrIOHandler Can be either of the two formats\n *   1. A string path to the `ModelAndWeightsConfig` JSON describing\n *      the model in the canonical TensorFlow.js format. For file://\n *      (tfjs-node-only), http:// and https:// schemas, the path can be\n *      either absolute or relative.\n *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`\n *      method.\n * @param options Optional configuration arguments for the model loading,\n *   including:\n *   - `strict`: Require that the provided weights exactly match those required\n *     by the layers.  Default true.  Passing false means that both extra\n *     weights and missing weights will be silently ignored.\n *   - `onProgress`: A function of the signature `(fraction: number) => void',\n *     that can be used as the progress callback for the model loading.\n * @returns A `Promise` of `tf.LayersModel`, with the topology and weights\n *     loaded.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport function loadLayersModel(pathOrIOHandler, options) {\n    if (options == null) {\n        options = {};\n    }\n    return loadLayersModelInternal(pathOrIOHandler, options);\n}\n/**\n * Used to instantiate an input to a model as a `tf.SymbolicTensor`.\n *\n * Users should call the `input` factory function for\n * consistency with other generator functions.\n *\n * Example:\n *\n * ```js\n * // Defines a simple logistic regression model with 32 dimensional input\n * // and 3 dimensional output.\n * const x = tf.input({shape: [32]});\n * const y = tf.layers.dense({units: 3, activation: 'softmax'}).apply(x);\n * const model = tf.model({inputs: x, outputs: y});\n * model.predict(tf.ones([2, 32])).print();\n * ```\n *\n * Note: `input` is only necessary when using `model`. When using\n * `sequential`, specify `inputShape` for the first layer or use `inputLayer`\n * as the first layer.\n *\n * @doc {heading: 'Models', subheading: 'Inputs'}\n */\nexport function input(config) {\n    return Input(config);\n}\nexport function registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n    CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);\n}\n//# sourceMappingURL=exports.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// Layer activation functions\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { deserializeKerasObject } from './utils/generic_utils';\n/**\n * Base class for Activations.\n *\n * Special note: due to cross-language compatibility reasons, the\n * static readonly className field in this family of classes must be set to\n * the initialLowerCamelCase name of the activation.\n */\nexport class Activation extends serialization.Serializable {\n    getConfig() {\n        return {};\n    }\n}\n/**\n * Exponential linear unit (ELU).\n * Reference: https://arxiv.org/abs/1511.07289\n */\nexport class Elu extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x: Input.\n     * @param alpha: Scaling factor the negative section.\n     * @return Output of the ELU activation.\n     */\n    apply(x, alpha = 1) {\n        return K.elu(x, alpha);\n    }\n}\n/** @nocollapse */\nElu.className = 'elu';\nserialization.registerClass(Elu);\n/**\n * Scaled Exponential Linear Unit. (Klambauer et al., 2017).\n * Reference: Self-Normalizing Neural Networks, https://arxiv.org/abs/1706.02515\n * Notes:\n *   - To be used together with the initialization \"lecunNormal\".\n *   - To be used together with the dropout variant \"AlphaDropout\".\n */\nexport class Selu extends Activation {\n    apply(x) {\n        return tfc.selu(x);\n    }\n}\n/** @nocollapse */\nSelu.className = 'selu';\nserialization.registerClass(Selu);\n/**\n *  Rectified linear unit\n */\nexport class Relu extends Activation {\n    apply(x) {\n        return tfc.relu(x);\n    }\n}\n/** @nocollapse */\nRelu.className = 'relu';\nserialization.registerClass(Relu);\n/**\n * Rectified linear unit activation maxing out at 6.0.\n */\nexport class Relu6 extends Activation {\n    apply(x) {\n        return tidy(() => tfc.minimum(6.0, tfc.relu(x)));\n    }\n}\n/** @nocollapse */\nRelu6.className = 'relu6';\nserialization.registerClass(Relu6);\n//* Linear activation (no-op) */\nexport class Linear extends Activation {\n    apply(x) {\n        return x;\n    }\n}\n/** @nocollapse */\nLinear.className = 'linear';\nserialization.registerClass(Linear);\n/**\n * Sigmoid activation function.\n */\nexport class Sigmoid extends Activation {\n    apply(x) {\n        return tfc.sigmoid(x);\n    }\n}\n/** @nocollapse */\nSigmoid.className = 'sigmoid';\nserialization.registerClass(Sigmoid);\n/**\n * Segment-wise linear approximation of sigmoid.\n */\nexport class HardSigmoid extends Activation {\n    apply(x) {\n        return K.hardSigmoid(x);\n    }\n}\n/** @nocollapse */\nHardSigmoid.className = 'hardSigmoid';\nserialization.registerClass(HardSigmoid);\n/**\n * Softplus activation function.\n */\nexport class Softplus extends Activation {\n    apply(x) {\n        return tfc.softplus(x);\n    }\n}\n/** @nocollapse */\nSoftplus.className = 'softplus';\nserialization.registerClass(Softplus);\n/**\n * Softsign activation function.\n */\nexport class Softsign extends Activation {\n    apply(x) {\n        return K.softsign(x);\n    }\n}\n/** @nocollapse */\nSoftsign.className = 'softsign';\nserialization.registerClass(Softsign);\n/**\n * Hyperbolic tangent function.\n */\nexport class Tanh extends Activation {\n    apply(x) {\n        return tfc.tanh(x);\n    }\n}\n/** @nocollapse */\nTanh.className = 'tanh';\nserialization.registerClass(Tanh);\n/**\n * Softmax activation function\n */\nexport class Softmax extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x Tensor.\n     * @param axis Integer, axis along which the softmax normalization is applied.\n     * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n     * an error.\n     *\n     * @returns a Tensor of the same shape as x\n     *\n     * @throws ValueError: In case `dim(x) < 2`.\n     */\n    apply(x, axis = (-1)) {\n        return tfc.softmax(x, axis);\n    }\n}\n/** @nocollapse */\nSoftmax.className = 'softmax';\nserialization.registerClass(Softmax);\n/**\n * Log softmax activation function\n */\nexport class LogSoftmax extends Activation {\n    /**\n     * Calculate the activation function of log softmax:\n     * log( exp(x_i) / sum(exp(x)) )\n     *\n     * @param x Tensor.\n     * @param axis Integer, axis along which the softmax normalization is applied.\n     * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n     * an error.\n     *\n     * @returns a Tensor of the same shape as x\n     *\n     * @throws ValueError: In case `dim(x) < 2`.\n     */\n    apply(x, axis = (-1)) {\n        return tfc.logSoftmax(x, axis);\n    }\n}\n/** @nocollapse */\nLogSoftmax.className = 'logSoftmax';\nserialization.registerClass(LogSoftmax);\n/**\n * Swish activation function\n */\nexport class Swish extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x Tensor.\n     * @param alpha Scaling factor for the sigmoid function.\n     * @returns a Tensor of the same shape as x\n     */\n    apply(x, alpha = 1) {\n        return tidy(() => tfc.sigmoid(x.mul(alpha)).mul(x));\n    }\n}\n/** @nocollapse */\nSwish.className = 'swish';\nserialization.registerClass(Swish);\nexport function serializeActivation(activation) {\n    return activation.getClassName();\n}\nexport function deserializeActivation(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'activation');\n}\nexport function getActivation(identifier) {\n    if (identifier == null) {\n        const config = {};\n        config['className'] = 'linear';\n        config['config'] = {};\n        return deserializeActivation(config);\n    }\n    if (typeof identifier === 'string') {\n        const config = {};\n        config['className'] = identifier;\n        config['config'] = {};\n        return deserializeActivation(config);\n    }\n    else if (identifier instanceof Activation) {\n        return identifier;\n    }\n    else {\n        return deserializeActivation(identifier);\n    }\n}\n//# sourceMappingURL=activations.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* original source: keras/regularizers.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { abs, add, serialization, sum, tidy, zeros } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\nfunction assertObjectArgs(args) {\n    if (args != null && typeof args !== 'object') {\n        throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an ` +\n            `object, but received: ${args}`);\n    }\n}\n/**\n * Regularizer base class.\n */\nexport class Regularizer extends serialization.Serializable {\n}\nexport class L1L2 extends Regularizer {\n    constructor(args) {\n        super();\n        assertObjectArgs(args);\n        this.l1 = args == null || args.l1 == null ? 0.01 : args.l1;\n        this.l2 = args == null || args.l2 == null ? 0.01 : args.l2;\n        this.hasL1 = this.l1 !== 0;\n        this.hasL2 = this.l2 !== 0;\n    }\n    /**\n     * Porting note: Renamed from __call__.\n     * @param x Variable of which to calculate the regularization score.\n     */\n    apply(x) {\n        return tidy(() => {\n            let regularization = zeros([1]);\n            if (this.hasL1) {\n                regularization = add(regularization, sum(tfc.mul(this.l1, abs(x))));\n            }\n            if (this.hasL2) {\n                regularization =\n                    add(regularization, sum(tfc.mul(this.l2, K.square(x))));\n            }\n            return regularization.asScalar();\n        });\n    }\n    getConfig() {\n        return { 'l1': this.l1, 'l2': this.l2 };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls({ l1: config['l1'], l2: config['l2'] });\n    }\n}\n/** @nocollapse */\nL1L2.className = 'L1L2';\nserialization.registerClass(L1L2);\nexport function l1(args) {\n    assertObjectArgs(args);\n    return new L1L2({ l1: args != null ? args.l1 : null, l2: 0 });\n}\nexport function l2(args) {\n    assertObjectArgs(args);\n    return new L1L2({ l2: args != null ? args.l2 : null, l1: 0 });\n}\n// Maps the JavaScript-like identifier keys to the corresponding keras symbols.\nexport const REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'l1l2': 'L1L2'\n};\nexport function serializeRegularizer(constraint) {\n    return serializeKerasObject(constraint);\n}\nexport function deserializeRegularizer(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'regularizer');\n}\nexport function getRegularizer(identifier) {\n    if (identifier == null) {\n        return null;\n    }\n    if (typeof identifier === 'string') {\n        const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        const config = { className, config: {} };\n        return deserializeRegularizer(config);\n    }\n    else if (identifier instanceof Regularizer) {\n        return identifier;\n    }\n    else {\n        return deserializeRegularizer(identifier);\n    }\n}\n//# sourceMappingURL=regularizers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n *  Advanced activation layers.\n */\nimport { clipByValue, elu, leakyRelu, prelu, relu, serialization } from '@tensorflow/tfjs-core';\nimport { Softmax as softmaxActivation } from '../activations';\nimport { cast } from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class ReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.supportsMasking = true;\n        if (args != null) {\n            this.maxValue = args.maxValue;\n        }\n    }\n    call(inputs, kwargs) {\n        inputs = getExactlyOneTensor(inputs);\n        let output = relu(inputs);\n        if (this.maxValue != null) {\n            output = clipByValue(output, 0, this.maxValue);\n        }\n        return output;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { maxValue: this.maxValue };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nReLU.className = 'ReLU';\nserialization.registerClass(ReLU);\nexport class LeakyReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA = 0.3;\n        if (args == null) {\n            args = {};\n        }\n        this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return leakyRelu(x, this.alpha);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { alpha: this.alpha };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nLeakyReLU.className = 'LeakyReLU';\nserialization.registerClass(LeakyReLU);\nexport class PReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA_INITIALIZER = 'zeros';\n        if (args == null) {\n            args = {};\n        }\n        this.supportsMasking = true;\n        this.alphaInitializer =\n            getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER);\n        this.alphaRegularizer = getRegularizer(args.alphaRegularizer);\n        this.alphaConstraint = getConstraint(args.alphaConstraint);\n        if (args.sharedAxes == null) {\n            this.sharedAxes = null;\n        }\n        else if (Array.isArray(args.sharedAxes)) {\n            this.sharedAxes = args.sharedAxes;\n        }\n        else if (typeof args.sharedAxes === 'number') {\n            this.sharedAxes = [args.sharedAxes];\n        }\n        else {\n            throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, ` +\n                `but got ${args.sharedAxes}`);\n        }\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const paramShape = inputShape.slice(1);\n        if (this.sharedAxes != null) {\n            for (const i of this.sharedAxes) {\n                paramShape[i - 1] = 1;\n            }\n        }\n        this.alpha = this.addWeight('alpha', paramShape, 'float32', this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);\n        // Set input spec.\n        const axes = {};\n        if (this.sharedAxes != null) {\n            for (let i = 1; i < inputShape.length; ++i) {\n                axes[i] = inputShape[i];\n            }\n        }\n        this.inputSpec = [new InputSpec({\n                ndim: inputShape.length,\n                axes,\n            })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        inputs = getExactlyOneTensor(inputs);\n        return prelu(inputs, this.alpha.read());\n    }\n    getConfig() {\n        const config = {\n            alphaInitializer: serializeInitializer(this.alphaInitializer),\n            alphaRegularizer: serializeRegularizer(this.alphaRegularizer),\n            alphaConstraint: serializeConstraint(this.alphaConstraint),\n            sharedAxes: this.sharedAxes\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nPReLU.className = 'PReLU';\nserialization.registerClass(PReLU);\nexport class ELU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) {\n            throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ` +\n                `ELU layer yet.`);\n        }\n        this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return elu(x);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { alpha: this.alpha };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nELU.className = 'ELU';\nserialization.registerClass(ELU);\nexport class ThresholdedReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_THETA = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return x.mul(cast(x.greater(this.theta), 'float32'));\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { theta: this.theta };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nThresholdedReLU.className = 'ThresholdedReLU';\nserialization.registerClass(ThresholdedReLU);\nexport class Softmax extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_AXIS = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        this.softmax = new softmaxActivation().apply;\n        this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return this.softmax(x, this.axis);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { axis: this.axis };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nSoftmax.className = 'Softmax';\nserialization.registerClass(Softmax);\n//# sourceMappingURL=advanced_activations.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { ValueError } from '../errors';\nimport { pyListRepeat } from './generic_utils';\nimport { isInteger, max } from './math_utils';\n/**\n * Transforms a single number of array of numbers into an array of numbers.\n * @param value\n * @param n: The size of the tuple to be returned.\n * @param name: Name of the parameter, used for generating error messages.\n * @returns An array of numbers.\n */\nexport function normalizeArray(value, n, name) {\n    if (typeof value === 'number') {\n        return pyListRepeat(value, n);\n    }\n    else {\n        if (value.length !== n) {\n            throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers.` +\n                ` Received: ${value.length} elements.`);\n        }\n        for (let i = 0; i < n; ++i) {\n            const singleValue = value[i];\n            if (!isInteger(singleValue)) {\n                throw new ValueError(`The ${name} argument must be an integer or tuple of ${n}` +\n                    ` integers. Received: ${JSON.stringify(value)} including a` +\n                    ` non-integer number ${singleValue}`);\n            }\n        }\n        return value;\n    }\n}\n/**\n * Determines output length of a convolution given input length.\n * @param inputLength\n * @param filterSize\n * @param padding\n * @param stride\n * @param dilation: dilation rate.\n */\nexport function convOutputLength(inputLength, filterSize, padding, stride, dilation = 1) {\n    if (inputLength == null) {\n        return inputLength;\n    }\n    const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1);\n    let outputLength;\n    if (padding === 'same') {\n        outputLength = inputLength;\n    }\n    else { // VALID\n        outputLength = inputLength - dilatedFilterSize + 1;\n    }\n    return Math.floor((outputLength + stride - 1) / stride);\n}\nexport function deconvLength(dimSize, strideSize, kernelSize, padding) {\n    if (dimSize == null) {\n        return null;\n    }\n    if (padding === 'valid') {\n        dimSize = dimSize * strideSize + max([kernelSize - strideSize, 0]);\n    }\n    else if (padding === 'same') {\n        dimSize = dimSize * strideSize;\n    }\n    else {\n        throw new ValueError(`Unsupport padding mode: ${padding}.`);\n    }\n    return dimSize;\n}\n//# sourceMappingURL=conv_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Convolutional Layers\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { convOutputLength, deconvLength, normalizeArray } from '../utils/conv_utils';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Transpose and cast the input before the conv2d.\n * @param x Input image tensor.\n * @param dataFormat\n */\nexport function preprocessConv2DInput(x, dataFormat) {\n    // TODO(cais): Cast type to float32 if not.\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        if (dataFormat === 'channelsFirst') {\n            return tfc.transpose(x, [0, 2, 3, 1]); // NCHW -> NHWC.\n        }\n        else {\n            return x;\n        }\n    });\n}\n/**\n * Transpose and cast the input before the conv3d.\n * @param x Input image tensor.\n * @param dataFormat\n */\nexport function preprocessConv3DInput(x, dataFormat) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        if (dataFormat === 'channelsFirst') {\n            return tfc.transpose(x, [0, 2, 3, 4, 1]); // NCDHW -> NDHWC.\n        }\n        else {\n            return x;\n        }\n    });\n}\n/**\n * 1D-convolution with bias added.\n *\n * Porting Note: This function does not exist in the Python Keras backend.\n *   It is exactly the same as `conv2d`, except the added `bias`.\n *\n * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.\n * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.\n * @param bias Bias, rank-3, of shape `[outDepth]`.\n * @param strides\n * @param padding Padding mode.\n * @param dataFormat Data format.\n * @param dilationRate\n * @returns The result of the 1D convolution.\n * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.\n */\nexport function conv1dWithBias(x, kernel, bias, strides = 1, padding = 'valid', dataFormat, dilationRate = 1) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        // Check the ranks of x, kernel and bias.\n        if (x.shape.length !== 3) {\n            throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ` +\n                `${x.shape.length} instead.`);\n        }\n        if (kernel.shape.length !== 3) {\n            throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ` +\n                `${kernel.shape.length} instead`);\n        }\n        if (bias != null && bias.shape.length !== 1) {\n            throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ` +\n                `${kernel.shape.length} instead`);\n        }\n        // TODO(cais): Support CAUSAL padding mode.\n        if (dataFormat === 'channelsFirst') {\n            x = tfc.transpose(x, [0, 2, 1]); // NCW -> NWC.\n        }\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv1dWithBias is not ' +\n                'implemented yet.');\n        }\n        let y = tfc.conv1d(x, kernel, strides, padding === 'same' ? 'same' : 'valid', 'NWC', dilationRate);\n        if (bias != null) {\n            y = K.biasAdd(y, bias);\n        }\n        return y;\n    });\n}\n/**\n * 1D-convolution.\n *\n * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.\n * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.s\n * @param strides\n * @param padding Padding mode.\n * @param dataFormat Data format.\n * @param dilationRate\n * @returns The result of the 1D convolution.\n * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.\n */\nexport function conv1d(x, kernel, strides = 1, padding = 'valid', dataFormat, dilationRate = 1) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv1dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 2D Convolution\n * @param x\n * @param kernel kernel of the convolution.\n * @param strides strides array.\n * @param padding padding mode. Default to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param dilationRate dilation rate array.\n * @returns Result of the 2D pooling.\n */\nexport function conv2d(x, kernel, strides = [1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv2dWithBiasActivation(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 2D Convolution with an added bias and optional activation.\n * Note: This function does not exist in the Python Keras Backend. This function\n * is exactly the same as `conv2d`, except the added `bias`.\n */\nexport function conv2dWithBiasActivation(x, kernel, bias, strides = [1, 1], padding = 'valid', dataFormat, dilationRate, activation = null) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        if (x.rank !== 3 && x.rank !== 4) {\n            throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, ` +\n                `but received ${x.rank}.`);\n        }\n        if (kernel.rank !== 3 && kernel.rank !== 4) {\n            throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, ` +\n                `but received ${x.rank}.`);\n        }\n        let y = preprocessConv2DInput(x, dataFormat);\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv1dWithBias is not ' +\n                'implemented yet.');\n        }\n        y = tfc.fused.conv2d({\n            x: y,\n            filter: kernel,\n            strides: strides,\n            pad: padding === 'same' ? 'same' : 'valid',\n            dilations: dilationRate,\n            dataFormat: 'NHWC',\n            bias,\n            activation\n        });\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]);\n        }\n        return y;\n    });\n}\n/**\n * 3D Convolution.\n * @param x\n * @param kernel kernel of the convolution.\n * @param strides strides array.\n * @param padding padding mode. Default to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param dilationRate dilation rate array.\n * @returns Result of the 3D convolution.\n */\nexport function conv3d(x, kernel, strides = [1, 1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv3dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 3D Convolution with an added bias.\n * Note: This function does not exist in the Python Keras Backend. This function\n * is exactly the same as `conv3d`, except the added `bias`.\n */\nexport function conv3dWithBias(x, kernel, bias, strides = [1, 1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        if (x.rank !== 4 && x.rank !== 5) {\n            throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ` +\n                `${x.rank}.`);\n        }\n        if (kernel.rank !== 4 && kernel.rank !== 5) {\n            throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ` +\n                `${x.rank}.`);\n        }\n        let y = preprocessConv3DInput(x, dataFormat);\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv3dWithBias is not ' +\n                'implemented yet.');\n        }\n        y = tfc.conv3d(y, kernel, strides, padding === 'same' ? 'same' : 'valid', 'NDHWC', dilationRate);\n        if (bias != null) {\n            y = K.biasAdd(y, bias);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 4, 1, 2, 3]);\n        }\n        return y;\n    });\n}\n/**\n * Abstract convolution layer.\n */\nexport class BaseConv extends Layer {\n    constructor(rank, args) {\n        super(args);\n        this.bias = null;\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        BaseConv.verifyArgs(args);\n        this.rank = rank;\n        generic_utils.assertPositiveInteger(this.rank, 'rank');\n        if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) {\n            throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is ` +\n                `not implemented yet.`);\n        }\n        this.kernelSize = normalizeArray(args.kernelSize, rank, 'kernelSize');\n        this.strides = normalizeArray(args.strides == null ? 1 : args.strides, rank, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        checkPaddingMode(this.padding);\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        this.activation = getActivation(args.activation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.dilationRate = normalizeArray(args.dilationRate == null ? 1 : args.dilationRate, rank, 'dilationRate');\n        if (this.rank === 1 &&\n            (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) {\n            throw new ValueError(`dilationRate must be a number or an array of a single number ` +\n                `for 1D convolution, but received ` +\n                `${JSON.stringify(this.dilationRate)}`);\n        }\n        else if (this.rank === 2) {\n            if (typeof this.dilationRate === 'number') {\n                this.dilationRate = [this.dilationRate, this.dilationRate];\n            }\n            else if (this.dilationRate.length !== 2) {\n                throw new ValueError(`dilationRate must be a number or array of two numbers for 2D ` +\n                    `convolution, but received ${JSON.stringify(this.dilationRate)}`);\n            }\n        }\n        else if (this.rank === 3) {\n            if (typeof this.dilationRate === 'number') {\n                this.dilationRate =\n                    [this.dilationRate, this.dilationRate, this.dilationRate];\n            }\n            else if (this.dilationRate.length !== 3) {\n                throw new ValueError(`dilationRate must be a number or array of three numbers for 3D ` +\n                    `convolution, but received ${JSON.stringify(this.dilationRate)}`);\n            }\n        }\n    }\n    static verifyArgs(args) {\n        // Check config.kernelSize type and shape.\n        generic_utils.assert('kernelSize' in args, `required key 'kernelSize' not in config`);\n        if (typeof args.kernelSize !== 'number' &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 3)) {\n            throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with ` +\n                `length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n    getConfig() {\n        const config = {\n            kernelSize: this.kernelSize,\n            strides: this.strides,\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n            dilationRate: this.dilationRate,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            biasConstraint: serializeConstraint(this.biasConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/**\n * Abstract nD convolution layer.  Ancestor of convolution layers which reduce\n * across channels, i.e., Conv1D and Conv2D, but not DepthwiseConv2D.\n */\nexport class Conv extends BaseConv {\n    constructor(rank, args) {\n        super(rank, args);\n        this.kernel = null;\n        Conv.verifyArgs(args);\n        this.filters = args.filters;\n        generic_utils.assertPositiveInteger(this.filters, 'filters');\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError(`The channel dimension of the input should be defined. ` +\n                `Found ${inputShape[channelAxis]}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const kernelShape = this.kernelSize.concat([inputDim, this.filters]);\n        this.kernel = this.addWeight('kernel', kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        this.inputSpec = [{ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } }];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let outputs;\n            const biasValue = this.bias == null ? null : this.bias.read();\n            const fusedActivationName = generic_utils.mapActivationToFusedKernel(this.activation.getClassName());\n            if (fusedActivationName != null && this.rank === 2) {\n                outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate, fusedActivationName);\n            }\n            else {\n                if (this.rank === 1) {\n                    outputs = conv1dWithBias(inputs, this.kernel.read(), biasValue, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);\n                }\n                else if (this.rank === 2) {\n                    // TODO(cais): Move up to constructor.\n                    outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n                }\n                else if (this.rank === 3) {\n                    outputs = conv3dWithBias(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n                }\n                else {\n                    throw new NotImplementedError('convolutions greater than 3D are not implemented yet.');\n                }\n                if (this.activation != null) {\n                    outputs = this.activation.apply(outputs);\n                }\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const newSpace = [];\n        const space = (this.dataFormat === 'channelsLast') ?\n            inputShape.slice(1, inputShape.length - 1) :\n            inputShape.slice(2);\n        for (let i = 0; i < space.length; ++i) {\n            const newDim = convOutputLength(space[i], this.kernelSize[i], this.padding, this.strides[i], typeof this.dilationRate === 'number' ? this.dilationRate :\n                this.dilationRate[i]);\n            newSpace.push(newDim);\n        }\n        let outputShape = [inputShape[0]];\n        if (this.dataFormat === 'channelsLast') {\n            outputShape = outputShape.concat(newSpace);\n            outputShape.push(this.filters);\n        }\n        else {\n            outputShape.push(this.filters);\n            outputShape = outputShape.concat(newSpace);\n        }\n        return outputShape;\n    }\n    getConfig() {\n        const config = {\n            filters: this.filters,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    static verifyArgs(args) {\n        // Check config.filters type, shape, and value.\n        if (!('filters' in args) || typeof args.filters !== 'number' ||\n            args.filters < 1) {\n            throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 ` +\n                `but got ${JSON.stringify(args.filters)}`);\n        }\n    }\n}\nexport class Conv2D extends Conv {\n    constructor(args) {\n        super(2, args);\n        Conv2D.verifyArgs(args);\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if ((typeof args.kernelSize !== 'number') &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 2)) {\n            throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with ` +\n                `length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n}\n/** @nocollapse */\nConv2D.className = 'Conv2D';\nserialization.registerClass(Conv2D);\nexport class Conv3D extends Conv {\n    constructor(args) {\n        super(3, args);\n        Conv3D.verifyArgs(args);\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if (typeof args.kernelSize !== 'number') {\n            if (!(Array.isArray(args.kernelSize) &&\n                (args.kernelSize.length === 1 || args.kernelSize.length === 3))) {\n                throw new ValueError(`Conv3D expects config.kernelSize to be number or` +\n                    ` [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`);\n            }\n        }\n    }\n}\n/** @nocollapse */\nConv3D.className = 'Conv3D';\nserialization.registerClass(Conv3D);\nexport class Conv2DTranspose extends Conv2D {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n        if (this.padding !== 'same' && this.padding !== 'valid') {\n            throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' ` +\n                `and 'valid', but received padding mode ${this.padding}`);\n        }\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length !== 4) {\n            throw new ValueError('Input should have rank 4; Received input shape: ' +\n                JSON.stringify(inputShape));\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError('The channel dimension of the inputs should be defined. ' +\n                'Found `None`.');\n        }\n        const inputDim = inputShape[channelAxis];\n        const kernelShape = this.kernelSize.concat([this.filters, inputDim]);\n        this.kernel = this.addWeight('kernel', kernelShape, 'float32', this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], 'float32', this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        // Set input spec.\n        this.inputSpec =\n            [new InputSpec({ ndim: 4, axes: { [channelAxis]: inputDim } })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            let input = getExactlyOneTensor(inputs);\n            if (input.shape.length !== 4) {\n                throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but ` +\n                    `received a tensor of rank-${input.shape.length}`);\n            }\n            const inputShape = input.shape;\n            const batchSize = inputShape[0];\n            let hAxis;\n            let wAxis;\n            if (this.dataFormat === 'channelsFirst') {\n                hAxis = 2;\n                wAxis = 3;\n            }\n            else {\n                hAxis = 1;\n                wAxis = 2;\n            }\n            const height = inputShape[hAxis];\n            const width = inputShape[wAxis];\n            const kernelH = this.kernelSize[0];\n            const kernelW = this.kernelSize[1];\n            const strideH = this.strides[0];\n            const strideW = this.strides[1];\n            // Infer the dynamic output shape.\n            const outHeight = deconvLength(height, strideH, kernelH, this.padding);\n            const outWidth = deconvLength(width, strideW, kernelW, this.padding);\n            // Porting Note: We don't branch based on `this.dataFormat` here,\n            // because\n            //   the tjfs-core function `conv2dTranspose` called below always\n            //   assumes channelsLast.\n            const outputShape = [batchSize, outHeight, outWidth, this.filters];\n            if (this.dataFormat !== 'channelsLast') {\n                input = tfc.transpose(input, [0, 2, 3, 1]);\n            }\n            let outputs = tfc.conv2dTranspose(input, this.kernel.read(), outputShape, this.strides, this.padding);\n            if (this.dataFormat !== 'channelsLast') {\n                outputs = tfc.transpose(outputs, [0, 3, 1, 2]);\n            }\n            if (this.bias != null) {\n                outputs =\n                    K.biasAdd(outputs, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                outputs = this.activation.apply(outputs);\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        let channelAxis;\n        let heightAxis;\n        let widthAxis;\n        if (this.dataFormat === 'channelsFirst') {\n            channelAxis = 1;\n            heightAxis = 2;\n            widthAxis = 3;\n        }\n        else {\n            channelAxis = 3;\n            heightAxis = 1;\n            widthAxis = 2;\n        }\n        const kernelH = this.kernelSize[0];\n        const kernelW = this.kernelSize[1];\n        const strideH = this.strides[0];\n        const strideW = this.strides[1];\n        outputShape[channelAxis] = this.filters;\n        outputShape[heightAxis] =\n            deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);\n        outputShape[widthAxis] =\n            deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);\n        return outputShape;\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['dilationRate'];\n        return config;\n    }\n}\n/** @nocollapse */\nConv2DTranspose.className = 'Conv2DTranspose';\nserialization.registerClass(Conv2DTranspose);\nexport class SeparableConv extends Conv {\n    constructor(rank, config) {\n        super(rank, config);\n        this.DEFAULT_DEPTHWISE_INITIALIZER = 'glorotUniform';\n        this.DEFAULT_POINTWISE_INITIALIZER = 'glorotUniform';\n        this.depthwiseKernel = null;\n        this.pointwiseKernel = null;\n        if (config.filters == null) {\n            throw new ValueError('The `filters` configuration field is required by SeparableConv, ' +\n                'but is unspecified.');\n        }\n        if (config.kernelInitializer != null || config.kernelRegularizer != null ||\n            config.kernelConstraint != null) {\n            throw new ValueError('Fields kernelInitializer, kernelRegularizer and kernelConstraint ' +\n                'are invalid for SeparableConv2D. Use depthwiseInitializer, ' +\n                'depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, ' +\n                'pointwiseRegularizer and pointwiseConstraint instead.');\n        }\n        if (config.padding != null && config.padding !== 'same' &&\n            config.padding !== 'valid') {\n            throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: ` +\n                `'same' and 'valid', but received ${JSON.stringify(config.padding)}`);\n        }\n        this.depthMultiplier =\n            config.depthMultiplier == null ? 1 : config.depthMultiplier;\n        this.depthwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER);\n        this.depthwiseRegularizer = getRegularizer(config.depthwiseRegularizer);\n        this.depthwiseConstraint = getConstraint(config.depthwiseConstraint);\n        this.pointwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER);\n        this.pointwiseRegularizer = getRegularizer(config.pointwiseRegularizer);\n        this.pointwiseConstraint = getConstraint(config.pointwiseConstraint);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < this.rank + 2) {\n            throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ` +\n                `${this.rank + 2}, but received input shape: ` +\n                `${JSON.stringify(inputShape)}`);\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n            throw new ValueError(`The channel dimension of the inputs should be defined, ` +\n                `but found ${JSON.stringify(inputShape[channelAxis])}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]);\n        const pointwiseKernelShape = [];\n        for (let i = 0; i < this.rank; ++i) {\n            pointwiseKernelShape.push(1);\n        }\n        pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);\n        const trainable = true;\n        this.depthwiseKernel = this.addWeight('depthwise_kernel', depthwiseKernelShape, 'float32', this.depthwiseInitializer, this.depthwiseRegularizer, trainable, this.depthwiseConstraint);\n        this.pointwiseKernel = this.addWeight('pointwise_kernel', pointwiseKernelShape, 'float32', this.pointwiseInitializer, this.pointwiseRegularizer, trainable, this.pointwiseConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], 'float32', this.biasInitializer, this.biasRegularizer, trainable, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.inputSpec =\n            [new InputSpec({ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let output;\n            if (this.rank === 1) {\n                throw new NotImplementedError('1D separable convolution is not implemented yet.');\n            }\n            else if (this.rank === 2) {\n                if (this.dataFormat === 'channelsFirst') {\n                    inputs = tfc.transpose(inputs, [0, 2, 3, 1]); // NCHW -> NHWC.\n                }\n                output = tfc.separableConv2d(inputs, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, 'NHWC');\n            }\n            if (this.useBias) {\n                output = K.biasAdd(output, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                output = this.activation.apply(output);\n            }\n            if (this.dataFormat === 'channelsFirst') {\n                output = tfc.transpose(output, [0, 3, 1, 2]); // NHWC -> NCHW.\n            }\n            return output;\n        });\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        delete config['kernelInitializer'];\n        delete config['kernelRegularizer'];\n        delete config['kernelConstraint'];\n        config['depthwiseInitializer'] =\n            serializeInitializer(this.depthwiseInitializer);\n        config['pointwiseInitializer'] =\n            serializeInitializer(this.pointwiseInitializer);\n        config['depthwiseRegularizer'] =\n            serializeRegularizer(this.depthwiseRegularizer);\n        config['pointwiseRegularizer'] =\n            serializeRegularizer(this.pointwiseRegularizer);\n        config['depthwiseConstraint'] =\n            serializeConstraint(this.depthwiseConstraint);\n        config['pointwiseConstraint'] =\n            serializeConstraint(this.pointwiseConstraint);\n        return config;\n    }\n}\n/** @nocollapse */\nSeparableConv.className = 'SeparableConv';\nexport class SeparableConv2D extends SeparableConv {\n    constructor(args) {\n        super(2, args);\n    }\n}\n/** @nocollapse */\nSeparableConv2D.className = 'SeparableConv2D';\nserialization.registerClass(SeparableConv2D);\nexport class Conv1D extends Conv {\n    constructor(args) {\n        super(1, args);\n        Conv1D.verifyArgs(args);\n        this.inputSpec = [{ ndim: 3 }];\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        delete config['dataFormat'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if (typeof args.kernelSize !== 'number' &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 1)) {\n            throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with ` +\n                `length 1, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n}\n/** @nocollapse */\nConv1D.className = 'Conv1D';\nserialization.registerClass(Conv1D);\nexport class Cropping2D extends Layer {\n    constructor(args) {\n        super(args);\n        if (typeof args.cropping === 'number') {\n            this.cropping =\n                [[args.cropping, args.cropping], [args.cropping, args.cropping]];\n        }\n        else if (typeof args.cropping[0] === 'number') {\n            this.cropping = [\n                [args.cropping[0], args.cropping[0]],\n                [args.cropping[1], args.cropping[1]]\n            ];\n        }\n        else {\n            this.cropping = args.cropping;\n        }\n        this.dataFormat =\n            args.dataFormat === undefined ? 'channelsLast' : args.dataFormat;\n        this.inputSpec = [{ ndim: 4 }];\n    }\n    computeOutputShape(inputShape) {\n        if (this.dataFormat === 'channelsFirst') {\n            return [\n                inputShape[0], inputShape[1],\n                inputShape[2] - this.cropping[0][0] - this.cropping[0][1],\n                inputShape[3] - this.cropping[1][0] - this.cropping[1][1]\n            ];\n        }\n        else {\n            return [\n                inputShape[0],\n                inputShape[1] - this.cropping[0][0] - this.cropping[0][1],\n                inputShape[2] - this.cropping[1][0] - this.cropping[1][1], inputShape[3]\n            ];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                const hSliced = K.sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);\n                return K.sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);\n            }\n            else {\n                const hSliced = K.sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);\n                return K.sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);\n            }\n        });\n    }\n    getConfig() {\n        const config = { cropping: this.cropping, dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nCropping2D.className = 'Cropping2D';\nserialization.registerClass(Cropping2D);\nexport class UpSampling2D extends Layer {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_SIZE = [2, 2];\n        this.inputSpec = [{ ndim: 4 }];\n        this.size = args.size == null ? this.DEFAULT_SIZE : args.size;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n    }\n    computeOutputShape(inputShape) {\n        if (this.dataFormat === 'channelsFirst') {\n            const height = inputShape[2] == null ? null : this.size[0] * inputShape[2];\n            const width = inputShape[3] == null ? null : this.size[1] * inputShape[3];\n            return [inputShape[0], inputShape[1], height, width];\n        }\n        else {\n            const height = inputShape[1] == null ? null : this.size[0] * inputShape[1];\n            const width = inputShape[2] == null ? null : this.size[1] * inputShape[2];\n            return [inputShape[0], height, width, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            let input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            if (this.dataFormat === 'channelsFirst') {\n                input = tfc.transpose(input, [0, 2, 3, 1]);\n                const height = this.size[0] * inputShape[2];\n                const width = this.size[1] * inputShape[3];\n                const resized = input.resizeNearestNeighbor([height, width]);\n                return tfc.transpose(resized, [0, 3, 1, 2]);\n            }\n            else {\n                const height = this.size[0] * inputShape[1];\n                const width = this.size[1] * inputShape[2];\n                return input.resizeNearestNeighbor([height, width]);\n            }\n        });\n    }\n    getConfig() {\n        const config = { size: this.size, dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nUpSampling2D.className = 'UpSampling2D';\nserialization.registerClass(UpSampling2D);\n//# sourceMappingURL=convolutional.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Depthwise Convolutional Layers\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { convOutputLength } from '../utils/conv_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { BaseConv, preprocessConv2DInput } from './convolutional';\n/**\n * 2D convolution with separable filters.\n * @param x Input tensor.\n * @param depthwiseKernel Convolution kernel for depthwise convolution.\n * @param strides Strides (Array of two integers).\n * @param padding Padding model.\n * @param dataFormat Data format.\n * @param dilationRate Array of two integers, dilation rates for the separable\n *   convolution.\n * @returns Output tensor.\n * @throws ValueError If depthwiseKernel is not a 4D array.\n */\nexport function depthwiseConv2d(x, depthwiseKernel, strides = [1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        let y = preprocessConv2DInput(x, dataFormat);\n        if (x.rank !== 4) {\n            throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ` +\n                `${x.rank}-D`);\n        }\n        if (depthwiseKernel.rank !== 4) {\n            throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ` +\n                `${depthwiseKernel.rank}-D`);\n        }\n        y = tfc.depthwiseConv2d(y, depthwiseKernel, strides, padding === 'same' ? 'same' : 'valid', 'NHWC', dilationRate);\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]);\n        }\n        return y;\n    });\n}\nexport class DepthwiseConv2D extends BaseConv {\n    constructor(args) {\n        super(2, args);\n        this.depthwiseKernel = null;\n        this.depthMultiplier =\n            args.depthMultiplier == null ? 1 : args.depthMultiplier;\n        this.depthwiseInitializer = getInitializer(args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.depthwiseConstraint = getConstraint(args.depthwiseConstraint);\n        this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < 4) {\n            throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. ` +\n                `Received input shape: ${JSON.stringify(inputShape)}.`);\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : 3;\n        if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n            throw new ValueError('The channel dimension of the inputs to DepthwiseConv2D should ' +\n                `be defined, but is not (${inputShape[channelAxis]}).`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const depthwiseKernelShape = [\n            this.kernelSize[0], this.kernelSize[1], inputDim, this.depthMultiplier\n        ];\n        this.depthwiseKernel = this.addWeight('depthwise_kernel', depthwiseKernelShape, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [inputDim * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let outputs = depthwiseConv2d(inputs, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);\n            // TODO(cais): Add support for dilation.\n            if (this.useBias) {\n                outputs = K.biasAdd(outputs, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                outputs = this.activation.apply(outputs);\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const rows = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        const cols = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        const outFilters = this.dataFormat === 'channelsFirst' ?\n            inputShape[1] * this.depthMultiplier :\n            inputShape[3] * this.depthMultiplier;\n        const outRows = convOutputLength(rows, this.kernelSize[0], this.padding, this.strides[0]);\n        const outCols = convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], outFilters, outRows, outCols];\n        }\n        else {\n            // In this case, assume 'channelsLast'.\n            return [inputShape[0], outRows, outCols, outFilters];\n        }\n    }\n    getConfig() {\n        const config = super.getConfig();\n        config['depthMultiplier'] = this.depthMultiplier;\n        config['depthwiseInitializer'] =\n            serializeInitializer(this.depthwiseInitializer);\n        config['depthwiseRegularizer'] =\n            serializeRegularizer(this.depthwiseRegularizer);\n        config['depthwiseConstraint'] =\n            serializeConstraint(this.depthwiseRegularizer);\n        return config;\n    }\n}\n/** @nocollapse */\nDepthwiseConv2D.className = 'DepthwiseConv2D';\nserialization.registerClass(DepthwiseConv2D);\n//# sourceMappingURL=convolutional_depthwise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Recurrent Neural Network Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, SymbolicTensor } from '../engine/topology';\nimport { Layer } from '../engine/topology';\nimport { AttributeError, NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, Initializer, Ones, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor, isArrayOfShapes } from '../utils/types_utils';\nimport { batchGetValue, batchSetValue } from '../variables';\nimport { deserialize } from './serialization';\n/**\n * Standardize `apply()` args to a single list of tensor inputs.\n *\n * When running a model loaded from file, the input tensors `initialState` and\n * `constants` are passed to `RNN.apply()` as part of `inputs` instead of the\n * dedicated kwargs fields. `inputs` consists of\n * `[inputs, initialState0, initialState1, ..., constant0, constant1]` in this\n * case.\n * This method makes sure that arguments are\n * separated and that `initialState` and `constants` are `Array`s of tensors\n * (or None).\n *\n * @param inputs Tensor or `Array` of  tensors.\n * @param initialState Tensor or `Array` of tensors or `null`/`undefined`.\n * @param constants Tensor or `Array` of tensors or `null`/`undefined`.\n * @returns An object consisting of\n *   inputs: A tensor.\n *   initialState: `Array` of tensors or `null`.\n *   constants: `Array` of tensors or `null`.\n * @throws ValueError, if `inputs` is an `Array` but either `initialState` or\n *   `constants` is provided.\n */\nexport function standardizeArgs(inputs, initialState, constants, numConstants) {\n    if (Array.isArray(inputs)) {\n        if (initialState != null || constants != null) {\n            throw new ValueError('When inputs is an array, neither initialState or constants ' +\n                'should be provided');\n        }\n        if (numConstants != null) {\n            constants = inputs.slice(inputs.length - numConstants, inputs.length);\n            inputs = inputs.slice(0, inputs.length - numConstants);\n        }\n        if (inputs.length > 1) {\n            initialState = inputs.slice(1, inputs.length);\n        }\n        inputs = inputs[0];\n    }\n    function toListOrNull(x) {\n        if (x == null || Array.isArray(x)) {\n            return x;\n        }\n        else {\n            return [x];\n        }\n    }\n    initialState = toListOrNull(initialState);\n    constants = toListOrNull(constants);\n    return { inputs, initialState, constants };\n}\n/**\n * Iterates over the time dimension of a tensor.\n *\n * @param stepFunction RNN step function.\n *   Parameters:\n *     inputs: tensor with shape `[samples, ...]` (no time dimension),\n *       representing input for the batch of samples at a certain time step.\n *     states: an Array of tensors.\n *   Returns:\n *     outputs: tensor with shape `[samples, outputDim]` (no time dimension).\n *     newStates: list of tensors, same length and shapes as `states`. The first\n *       state in the list must be the output tensor at the previous timestep.\n * @param inputs Tensor of temporal data of shape `[samples, time, ...]` (at\n *   least 3D).\n * @param initialStates Tensor with shape `[samples, outputDim]` (no time\n *   dimension), containing the initial values of the states used in the step\n *   function.\n * @param goBackwards If `true`, do the iteration over the time dimension in\n *   reverse order and return the reversed sequence.\n * @param mask Binary tensor with shape `[sample, time, 1]`, with a zero for\n *   every element that is masked.\n * @param constants An Array of constant values passed at each step.\n * @param unroll Whether to unroll the RNN or to use a symbolic loop. *Not*\n *   applicable to this imperative deeplearn.js backend. Its value is ignored.\n * @param needPerStepOutputs Whether the per-step outputs are to be\n *   concatenated into a single tensor and returned (as the second return\n *   value). Default: `false`. This arg is included so that the relatively\n *   expensive concatenation of the stepwise outputs can be omitted unless\n *   the stepwise outputs need to be kept (e.g., for an LSTM layer of which\n *   `returnSequence` is `true`.)\n * @returns An Array: `[lastOutput, outputs, newStates]`.\n *   lastOutput: the lastest output of the RNN, of shape `[samples, ...]`.\n *   outputs: tensor with shape `[samples, time, ...]` where each entry\n *     `output[s, t]` is the output of the step function at time `t` for sample\n *     `s`. This return value is provided if and only if the\n *     `needPerStepOutputs` is set as `true`. If it is set as `false`, this\n *     return value will be `undefined`.\n *   newStates: Array of tensors, latest states returned by the step function,\n *      of shape `(samples, ...)`.\n * @throws ValueError If input dimension is less than 3.\n *\n * TODO(nielsene): This needs to be tidy-ed.\n */\nexport function rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, constants, unroll = false, needPerStepOutputs = false) {\n    return tfc.tidy(() => {\n        const ndim = inputs.shape.length;\n        if (ndim < 3) {\n            throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);\n        }\n        // Transpose to time-major, i.e., from [batch, time, ...] to [time, batch,\n        // ...].\n        const axes = [1, 0].concat(math_utils.range(2, ndim));\n        inputs = tfc.transpose(inputs, axes);\n        if (constants != null) {\n            throw new NotImplementedError('The rnn() functoin of the deeplearn.js backend does not support ' +\n                'constants yet.');\n        }\n        // Porting Note: the unroll option is ignored by the imperative backend.\n        if (unroll) {\n            console.warn('Backend rnn(): the unroll = true option is not applicable to the ' +\n                'imperative deeplearn.js backend.');\n        }\n        if (mask != null) {\n            mask = mask.asType('bool').asType('float32');\n            if (mask.rank === ndim - 1) {\n                mask = tfc.expandDims(mask, -1);\n            }\n            mask = tfc.transpose(mask, axes);\n        }\n        if (goBackwards) {\n            inputs = tfc.reverse(inputs, 0);\n            if (mask != null) {\n                mask = tfc.reverse(mask, 0);\n            }\n        }\n        // Porting Note: PyKeras with TensorFlow backend uses a symbolic loop\n        //   (tf.while_loop). But for the imperative deeplearn.js backend, we just\n        //   use the usual TypeScript control flow to iterate over the time steps in\n        //   the inputs.\n        // Porting Note: PyKeras patches a \"_use_learning_phase\" attribute to\n        // outputs.\n        //   This is not idiomatic in TypeScript. The info regarding whether we are\n        //   in a learning (i.e., training) phase for RNN is passed in a different\n        //   way.\n        const perStepOutputs = [];\n        let lastOutput;\n        let states = initialStates;\n        const timeSteps = inputs.shape[0];\n        const perStepInputs = tfc.unstack(inputs);\n        let perStepMasks;\n        if (mask != null) {\n            perStepMasks = tfc.unstack(mask);\n        }\n        for (let t = 0; t < timeSteps; ++t) {\n            const currentInput = perStepInputs[t];\n            const stepOutputs = tfc.tidy(() => stepFunction(currentInput, states));\n            if (mask == null) {\n                lastOutput = stepOutputs[0];\n                states = stepOutputs[1];\n            }\n            else {\n                const maskedOutputs = tfc.tidy(() => {\n                    const stepMask = perStepMasks[t];\n                    const negStepMask = tfc.onesLike(stepMask).sub(stepMask);\n                    // TODO(cais): Would tfc.where() be better for performance?\n                    const output = stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask));\n                    const newStates = states.map((state, i) => {\n                        return stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask));\n                    });\n                    return { output, newStates };\n                });\n                lastOutput = maskedOutputs.output;\n                states = maskedOutputs.newStates;\n            }\n            if (needPerStepOutputs) {\n                perStepOutputs.push(lastOutput);\n            }\n        }\n        let outputs;\n        if (needPerStepOutputs) {\n            const axis = 1;\n            outputs = tfc.stack(perStepOutputs, axis);\n        }\n        return [lastOutput, outputs, states];\n    });\n}\nexport class RNN extends Layer {\n    constructor(args) {\n        super(args);\n        let cell;\n        if (args.cell == null) {\n            throw new ValueError('cell property is missing for the constructor of RNN.');\n        }\n        else if (Array.isArray(args.cell)) {\n            cell = new StackedRNNCells({ cells: args.cell });\n        }\n        else {\n            cell = args.cell;\n        }\n        if (cell.stateSize == null) {\n            throw new ValueError('The RNN cell should have an attribute `stateSize` (tuple of ' +\n                'integers, one integer per RNN state).');\n        }\n        this.cell = cell;\n        this.returnSequences =\n            args.returnSequences == null ? false : args.returnSequences;\n        this.returnState = args.returnState == null ? false : args.returnState;\n        this.goBackwards = args.goBackwards == null ? false : args.goBackwards;\n        this._stateful = args.stateful == null ? false : args.stateful;\n        this.unroll = args.unroll == null ? false : args.unroll;\n        this.supportsMasking = true;\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n        this.stateSpec = null;\n        this.states_ = null;\n        // TODO(cais): Add constantsSpec and numConstants.\n        this.numConstants = null;\n        // TODO(cais): Look into the use of initial_state in the kwargs of the\n        //   constructor.\n        this.keptStates = [];\n    }\n    // Porting Note: This is the equivalent of `RNN.states` property getter in\n    //   PyKeras.\n    getStates() {\n        if (this.states_ == null) {\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            return math_utils.range(0, numStates).map(x => null);\n        }\n        else {\n            return this.states_;\n        }\n    }\n    // Porting Note: This is the equivalent of the `RNN.states` property setter in\n    //   PyKeras.\n    setStates(states) {\n        this.states_ = states;\n    }\n    computeOutputShape(inputShape) {\n        if (isArrayOfShapes(inputShape)) {\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        // TODO(cais): Remove the casting once stacked RNN cells become supported.\n        let stateSize = this.cell.stateSize;\n        if (!Array.isArray(stateSize)) {\n            stateSize = [stateSize];\n        }\n        const outputDim = stateSize[0];\n        let outputShape;\n        if (this.returnSequences) {\n            outputShape = [inputShape[0], inputShape[1], outputDim];\n        }\n        else {\n            outputShape = [inputShape[0], outputDim];\n        }\n        if (this.returnState) {\n            const stateShape = [];\n            for (const dim of stateSize) {\n                stateShape.push([inputShape[0], dim]);\n            }\n            return [outputShape].concat(stateShape);\n        }\n        else {\n            return outputShape;\n        }\n    }\n    computeMask(inputs, mask) {\n        return tfc.tidy(() => {\n            if (Array.isArray(mask)) {\n                mask = mask[0];\n            }\n            const outputMask = this.returnSequences ? mask : null;\n            if (this.returnState) {\n                const stateMask = this.states.map(s => null);\n                return [outputMask].concat(stateMask);\n            }\n            else {\n                return outputMask;\n            }\n        });\n    }\n    /**\n     * Get the current state tensors of the RNN.\n     *\n     * If the state hasn't been set, return an array of `null`s of the correct\n     * length.\n     */\n    get states() {\n        if (this.states_ == null) {\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            const output = [];\n            for (let i = 0; i < numStates; ++i) {\n                output.push(null);\n            }\n            return output;\n        }\n        else {\n            return this.states_;\n        }\n    }\n    set states(s) {\n        this.states_ = s;\n    }\n    build(inputShape) {\n        // Note inputShape will be an Array of Shapes of initial states and\n        // constants if these are passed in apply().\n        const constantShape = null;\n        if (this.numConstants != null) {\n            throw new NotImplementedError('Constants support is not implemented in RNN yet.');\n        }\n        if (isArrayOfShapes(inputShape)) {\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        const batchSize = this.stateful ? inputShape[0] : null;\n        const inputDim = inputShape.slice(2);\n        this.inputSpec[0] = new InputSpec({ shape: [batchSize, null, ...inputDim] });\n        // Allow cell (if RNNCell Layer) to build before we set or validate\n        // stateSpec.\n        const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        if (constantShape != null) {\n            throw new NotImplementedError('Constants support is not implemented in RNN yet.');\n        }\n        else {\n            this.cell.build(stepInputShape);\n        }\n        // Set or validate stateSpec.\n        let stateSize;\n        if (Array.isArray(this.cell.stateSize)) {\n            stateSize = this.cell.stateSize;\n        }\n        else {\n            stateSize = [this.cell.stateSize];\n        }\n        if (this.stateSpec != null) {\n            if (!util.arraysEqual(this.stateSpec.map(spec => spec.shape[spec.shape.length - 1]), stateSize)) {\n                throw new ValueError(`An initialState was passed that is not compatible with ` +\n                    `cell.stateSize. Received stateSpec=${this.stateSpec}; ` +\n                    `However cell.stateSize is ${this.cell.stateSize}`);\n            }\n        }\n        else {\n            this.stateSpec =\n                stateSize.map(dim => new InputSpec({ shape: [null, dim] }));\n        }\n        if (this.stateful) {\n            this.resetStates();\n        }\n    }\n    /**\n     * Reset the state tensors of the RNN.\n     *\n     * If the `states` argument is `undefined` or `null`, will set the\n     * state tensor(s) of the RNN to all-zero tensors of the appropriate\n     * shape(s).\n     *\n     * If `states` is provided, will set the state tensors of the RNN to its\n     * value.\n     *\n     * @param states Optional externally-provided initial states.\n     * @param training Whether this call is done during training. For stateful\n     *   RNNs, this affects whether the old states are kept or discarded. In\n     *   particular, if `training` is `true`, the old states will be kept so\n     *   that subsequent backpropgataion through time (BPTT) may work properly.\n     *   Else, the old states will be discarded.\n     */\n    resetStates(states, training = false) {\n        tidy(() => {\n            if (!this.stateful) {\n                throw new AttributeError('Cannot call resetStates() on an RNN Layer that is not stateful.');\n            }\n            const batchSize = this.inputSpec[0].shape[0];\n            if (batchSize == null) {\n                throw new ValueError('If an RNN is stateful, it needs to know its batch size. Specify ' +\n                    'the batch size of your input tensors: \\n' +\n                    '- If using a Sequential model, specify the batch size by ' +\n                    'passing a `batchInputShape` option to your first layer.\\n' +\n                    '- If using the functional API, specify the batch size by ' +\n                    'passing a `batchShape` option to your Input layer.');\n            }\n            // Initialize state if null.\n            if (this.states_ == null) {\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ =\n                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n                }\n                else {\n                    this.states_ = [tfc.zeros([batchSize, this.cell.stateSize])];\n                }\n            }\n            else if (states == null) {\n                // Dispose old state tensors.\n                tfc.dispose(this.states_);\n                // For stateful RNNs, fully dispose kept old states.\n                if (this.keptStates != null) {\n                    tfc.dispose(this.keptStates);\n                    this.keptStates = [];\n                }\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ =\n                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n                }\n                else {\n                    this.states_[0] = tfc.zeros([batchSize, this.cell.stateSize]);\n                }\n            }\n            else {\n                if (!Array.isArray(states)) {\n                    states = [states];\n                }\n                if (states.length !== this.states_.length) {\n                    throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), ` +\n                        `but it received ${states.length} state value(s). Input ` +\n                        `received: ${states}`);\n                }\n                if (training === true) {\n                    // Store old state tensors for complete disposal later, i.e., during\n                    // the next no-arg call to this method. We do not dispose the old\n                    // states immediately because that BPTT (among other things) require\n                    // them.\n                    this.keptStates.push(this.states_.slice());\n                }\n                else {\n                    tfc.dispose(this.states_);\n                }\n                for (let index = 0; index < this.states_.length; ++index) {\n                    const value = states[index];\n                    const dim = Array.isArray(this.cell.stateSize) ?\n                        this.cell.stateSize[index] :\n                        this.cell.stateSize;\n                    const expectedShape = [batchSize, dim];\n                    if (!util.arraysEqual(value.shape, expectedShape)) {\n                        throw new ValueError(`State ${index} is incompatible with layer ${this.name}: ` +\n                            `expected shape=${expectedShape}, received shape=${value.shape}`);\n                    }\n                    this.states_[index] = value;\n                }\n            }\n            this.states_ = this.states_.map(state => tfc.keep(state.clone()));\n        });\n    }\n    apply(inputs, kwargs) {\n        // TODO(cais): Figure out whether initialState is in kwargs or inputs.\n        let initialState = kwargs == null ? null : kwargs['initialState'];\n        let constants = kwargs == null ? null : kwargs['constants'];\n        if (kwargs == null) {\n            kwargs = {};\n        }\n        const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n        inputs = standardized.inputs;\n        initialState = standardized.initialState;\n        constants = standardized.constants;\n        // If any of `initial_state` or `constants` are specified and are\n        // `tf.SymbolicTensor`s, then add them to the inputs and temporarily modify\n        // the input_spec to include them.\n        let additionalInputs = [];\n        let additionalSpecs = [];\n        if (initialState != null) {\n            kwargs['initialState'] = initialState;\n            additionalInputs = additionalInputs.concat(initialState);\n            this.stateSpec = [];\n            for (const state of initialState) {\n                this.stateSpec.push(new InputSpec({ shape: state.shape }));\n            }\n            // TODO(cais): Use the following instead.\n            // this.stateSpec = initialState.map(state => new InputSpec({shape:\n            // state.shape}));\n            additionalSpecs = additionalSpecs.concat(this.stateSpec);\n        }\n        if (constants != null) {\n            kwargs['constants'] = constants;\n            additionalInputs = additionalInputs.concat(constants);\n            // TODO(cais): Add this.constantsSpec.\n            this.numConstants = constants.length;\n        }\n        const isTensor = additionalInputs[0] instanceof SymbolicTensor;\n        if (isTensor) {\n            // Compute full input spec, including state and constants.\n            const fullInput = [inputs].concat(additionalInputs);\n            const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n            // Perform the call with temporarily replaced inputSpec.\n            const originalInputSpec = this.inputSpec;\n            this.inputSpec = fullInputSpec;\n            const output = super.apply(fullInput, kwargs);\n            this.inputSpec = originalInputSpec;\n            return output;\n        }\n        else {\n            return super.apply(inputs, kwargs);\n        }\n    }\n    // tslint:disable-next-line:no-any\n    call(inputs, kwargs) {\n        // Input shape: `[samples, time (padded with zeros), input_dim]`.\n        // Note that the .build() method of subclasses **must** define\n        // this.inputSpec and this.stateSpec owith complete input shapes.\n        return tidy(() => {\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            let initialState = kwargs == null ? null : kwargs['initialState'];\n            inputs = getExactlyOneTensor(inputs);\n            if (initialState == null) {\n                if (this.stateful) {\n                    initialState = this.states_;\n                }\n                else {\n                    initialState = this.getInitialState(inputs);\n                }\n            }\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            if (initialState.length !== numStates) {\n                throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ` +\n                    `${initialState.length} initial state(s).`);\n            }\n            if (this.unroll) {\n                console.warn('Ignoring unroll = true for RNN layer, due to imperative backend.');\n            }\n            const cellCallKwargs = { training };\n            // TODO(cais): Add support for constants.\n            const step = (inputs, states) => {\n                // `inputs` and `states` are concatenated to form a single `Array` of\n                // `tf.Tensor`s as the input to `cell.call()`.\n                const outputs = this.cell.call([inputs].concat(states), cellCallKwargs);\n                // Marshall the return value into output and new states.\n                return [outputs[0], outputs.slice(1)];\n            };\n            // TODO(cais): Add support for constants.\n            const rnnOutputs = rnn(step, inputs, initialState, this.goBackwards, mask, null, this.unroll, this.returnSequences);\n            const lastOutput = rnnOutputs[0];\n            const outputs = rnnOutputs[1];\n            const states = rnnOutputs[2];\n            if (this.stateful) {\n                this.resetStates(states, training);\n            }\n            const output = this.returnSequences ? outputs : lastOutput;\n            // TODO(cais): Porperty set learning phase flag.\n            if (this.returnState) {\n                return [output].concat(states);\n            }\n            else {\n                return output;\n            }\n        });\n    }\n    getInitialState(inputs) {\n        return tidy(() => {\n            // Build an all-zero tensor of shape [samples, outputDim].\n            // [Samples, timeSteps, inputDim].\n            let initialState = tfc.zeros(inputs.shape);\n            // [Samples].\n            initialState = tfc.sum(initialState, [1, 2]);\n            initialState = K.expandDims(initialState); // [Samples, 1].\n            if (Array.isArray(this.cell.stateSize)) {\n                return this.cell.stateSize.map(dim => dim > 1 ? K.tile(initialState, [1, dim]) : initialState);\n            }\n            else {\n                return this.cell.stateSize > 1 ?\n                    [K.tile(initialState, [1, this.cell.stateSize])] :\n                    [initialState];\n            }\n        });\n    }\n    get trainableWeights() {\n        if (!this.trainable) {\n            return [];\n        }\n        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n        return this.cell.trainableWeights;\n    }\n    get nonTrainableWeights() {\n        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n        if (!this.trainable) {\n            return this.cell.weights;\n        }\n        return this.cell.nonTrainableWeights;\n    }\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.cell != null) {\n            this.cell.setFastWeightInitDuringBuild(value);\n        }\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            returnSequences: this.returnSequences,\n            returnState: this.returnState,\n            goBackwards: this.goBackwards,\n            stateful: this.stateful,\n            unroll: this.unroll,\n        };\n        if (this.numConstants != null) {\n            config['numConstants'] = this.numConstants;\n        }\n        const cellConfig = this.cell.getConfig();\n        if (this.getClassName() === RNN.className) {\n            config['cell'] = {\n                'className': this.cell.getClassName(),\n                'config': cellConfig,\n            };\n        }\n        // this order is necessary, to prevent cell name from replacing layer name\n        return Object.assign({}, cellConfig, baseConfig, config);\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const cellConfig = config['cell'];\n        const cell = deserialize(cellConfig, customObjects);\n        return new cls(Object.assign(config, { cell }));\n    }\n}\n/** @nocollapse */\nRNN.className = 'RNN';\nserialization.registerClass(RNN);\n// Porting Note: This is a common parent class for RNN cells. There is no\n// equivalent of this in PyKeras. Having a common parent class forgoes the\n//  need for `has_attr(cell, ...)` checks or its TypeScript equivalent.\n/**\n * An RNNCell layer.\n *\n * @doc {heading: 'Layers', subheading: 'Classes'}\n */\nexport class RNNCell extends Layer {\n}\nexport class SimpleRNNCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        this.units = args.units;\n        assertPositiveInteger(this.units, `units`);\n        this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.stateSize = this.units;\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        // TODO(cais): Use regularizer.\n        this.kernel = this.addWeight('kernel', [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.built = true;\n    }\n    // Porting Note: PyKeras' equivalent of this method takes two tensor inputs:\n    //   `inputs` and `states`. Here, the two tensors are combined into an\n    //   `Tensor[]` Array as the first input argument.\n    //   Similarly, PyKeras' equivalent of this method returns two values:\n    //    `output` and `[output]`. Here the two are combined into one length-2\n    //    `Tensor[]`, consisting of `output` repeated.\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (inputs.length !== 2) {\n                throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);\n            }\n            let prevOutput = inputs[1];\n            inputs = inputs[0];\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(prevOutput),\n                    rate: this.recurrentDropout,\n                    training\n                });\n            }\n            let h;\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            if (dpMask != null) {\n                h = K.dot(tfc.mul(inputs, dpMask), this.kernel.read());\n            }\n            else {\n                h = K.dot(inputs, this.kernel.read());\n            }\n            if (this.bias != null) {\n                h = K.biasAdd(h, this.bias.read());\n            }\n            if (recDpMask != null) {\n                prevOutput = tfc.mul(prevOutput, recDpMask);\n            }\n            let output = tfc.add(h, K.dot(prevOutput, this.recurrentKernel.read()));\n            if (this.activation != null) {\n                output = this.activation.apply(output);\n            }\n            // TODO(cais): Properly set learning phase on output tensor?\n            return [output, output];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nSimpleRNNCell.className = 'SimpleRNNCell';\nserialization.registerClass(SimpleRNNCell);\nexport class SimpleRNN extends RNN {\n    constructor(args) {\n        args.cell = new SimpleRNNCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nSimpleRNN.className = 'SimpleRNN';\nserialization.registerClass(SimpleRNN);\nexport class GRUCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        if (args.resetAfter) {\n            throw new ValueError(`GRUCell does not support reset_after parameter set to true.`);\n        }\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :\n            args.activation);\n        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.implementation = args.implementation;\n        this.stateSize = this.units;\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const inputDim = inputShape[inputShape.length - 1];\n        this.kernel = this.addWeight('kernel', [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n        //   of the weights and bias in the call() method, at execution time.\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (inputs.length !== 2) {\n                throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            let hTMinus1 = inputs[1]; // Previous memory state.\n            inputs = inputs[0];\n            // Note: For superior performance, TensorFlow.js always uses\n            // implementation 2, regardless of the actual value of\n            // config.implementation.\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training,\n                    count: 3\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: 3\n                });\n            }\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            let z;\n            let r;\n            let hh;\n            if (0 < this.dropout && this.dropout < 1) {\n                inputs = tfc.mul(inputs, dpMask[0]);\n            }\n            let matrixX = K.dot(inputs, this.kernel.read());\n            if (this.useBias) {\n                matrixX = K.biasAdd(matrixX, this.bias.read());\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n            }\n            const recurrentKernelValue = this.recurrentKernel.read();\n            const [rk1, rk2] = tfc.split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1);\n            const matrixInner = K.dot(hTMinus1, rk1);\n            const [xZ, xR, xH] = tfc.split(matrixX, 3, matrixX.rank - 1);\n            const [recurrentZ, recurrentR] = tfc.split(matrixInner, 2, matrixInner.rank - 1);\n            z = this.recurrentActivation.apply(tfc.add(xZ, recurrentZ));\n            r = this.recurrentActivation.apply(tfc.add(xR, recurrentR));\n            const recurrentH = K.dot(tfc.mul(r, hTMinus1), rk2);\n            hh = this.activation.apply(tfc.add(xH, recurrentH));\n            const h = tfc.add(tfc.mul(z, hTMinus1), tfc.mul(tfc.add(1, tfc.neg(z)), hh));\n            // TODO(cais): Add use_learning_phase flag properly.\n            return [h, h];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            recurrentActivation: serializeActivation(this.recurrentActivation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n            implementation: this.implementation,\n            resetAfter: false\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nGRUCell.className = 'GRUCell';\nserialization.registerClass(GRUCell);\nexport class GRU extends RNN {\n    constructor(args) {\n        if (args.implementation === 0) {\n            console.warn('`implementation=0` has been deprecated, and now defaults to ' +\n                '`implementation=1`. Please update your layer call.');\n        }\n        args.cell = new GRUCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        if (config['implmentation'] === 0) {\n            config['implementation'] = 1;\n        }\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nGRU.className = 'GRU';\nserialization.registerClass(GRU);\nexport class LSTMCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :\n            args.activation);\n        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.unitForgetBias = args.unitForgetBias;\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.implementation = args.implementation;\n        this.stateSize = [this.units, this.units];\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        var _a;\n        inputShape = getExactlyOneShape(inputShape);\n        const inputDim = inputShape[inputShape.length - 1];\n        this.kernel = this.addWeight('kernel', [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        let biasInitializer;\n        if (this.useBias) {\n            if (this.unitForgetBias) {\n                const capturedBiasInit = this.biasInitializer;\n                const capturedUnits = this.units;\n                biasInitializer = new (_a = class CustomInit extends Initializer {\n                        apply(shape, dtype) {\n                            // TODO(cais): More informative variable names?\n                            const bI = capturedBiasInit.apply([capturedUnits]);\n                            const bF = (new Ones()).apply([capturedUnits]);\n                            const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);\n                            return K.concatAlongFirstAxis(K.concatAlongFirstAxis(bI, bF), bCAndH);\n                        }\n                    },\n                    /** @nocollapse */\n                    _a.className = 'CustomInit',\n                    _a)();\n            }\n            else {\n                biasInitializer = this.biasInitializer;\n            }\n            this.bias = this.addWeight('bias', [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n        //   of the weights and bias in the call() method, at execution time.\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            inputs = inputs;\n            if (inputs.length !== 3) {\n                throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            let hTMinus1 = inputs[1]; // Previous memory state.\n            const cTMinus1 = inputs[2]; // Previous carry state.\n            inputs = inputs[0];\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training,\n                    count: 4\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: 4\n                });\n            }\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            // Note: For superior performance, TensorFlow.js always uses\n            // implementation 2 regardless of the actual value of\n            // config.implementation.\n            let i;\n            let f;\n            let c;\n            let o;\n            if (0 < this.dropout && this.dropout < 1) {\n                inputs = tfc.mul(inputs, dpMask[0]);\n            }\n            let z = K.dot(inputs, this.kernel.read());\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n            }\n            z = tfc.add(z, K.dot(hTMinus1, this.recurrentKernel.read()));\n            if (this.useBias) {\n                z = K.biasAdd(z, this.bias.read());\n            }\n            const [z0, z1, z2, z3] = tfc.split(z, 4, z.rank - 1);\n            i = this.recurrentActivation.apply(z0);\n            f = this.recurrentActivation.apply(z1);\n            c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(z2)));\n            o = this.recurrentActivation.apply(z3);\n            const h = tfc.mul(o, this.activation.apply(c));\n            // TODO(cais): Add use_learning_phase flag properly.\n            return [h, h, c];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            recurrentActivation: serializeActivation(this.recurrentActivation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            unitForgetBias: this.unitForgetBias,\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n            implementation: this.implementation,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nLSTMCell.className = 'LSTMCell';\nserialization.registerClass(LSTMCell);\nexport class LSTM extends RNN {\n    constructor(args) {\n        if (args.implementation === 0) {\n            console.warn('`implementation=0` has been deprecated, and now defaults to ' +\n                '`implementation=1`. Please update your layer call.');\n        }\n        args.cell = new LSTMCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        if (config['implmentation'] === 0) {\n            config['implementation'] = 1;\n        }\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nLSTM.className = 'LSTM';\nserialization.registerClass(LSTM);\nexport class StackedRNNCells extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.cells = args.cells;\n    }\n    get stateSize() {\n        // States are a flat list in reverse order of the cell stack.\n        // This allows perserving the requirement `stack.statesize[0] ===\n        // outputDim`. E.g., states of a 2-layer LSTM would be `[h2, c2, h1, c1]`,\n        // assuming one LSTM has states `[h, c]`.\n        const stateSize = [];\n        for (const cell of this.cells.slice().reverse()) {\n            if (Array.isArray(cell.stateSize)) {\n                stateSize.push(...cell.stateSize);\n            }\n            else {\n                stateSize.push(cell.stateSize);\n            }\n        }\n        return stateSize;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            let states = inputs.slice(1);\n            // Recover per-cell states.\n            const nestedStates = [];\n            for (const cell of this.cells.slice().reverse()) {\n                if (Array.isArray(cell.stateSize)) {\n                    nestedStates.push(states.splice(0, cell.stateSize.length));\n                }\n                else {\n                    nestedStates.push(states.splice(0, 1));\n                }\n            }\n            nestedStates.reverse();\n            // Call the cells in order and store the returned states.\n            const newNestedStates = [];\n            let callInputs;\n            for (let i = 0; i < this.cells.length; ++i) {\n                const cell = this.cells[i];\n                states = nestedStates[i];\n                // TODO(cais): Take care of constants.\n                if (i === 0) {\n                    callInputs = [inputs[0]].concat(states);\n                }\n                else {\n                    callInputs = [callInputs[0]].concat(states);\n                }\n                callInputs = cell.call(callInputs, kwargs);\n                newNestedStates.push(callInputs.slice(1));\n            }\n            // Format the new states as a flat list in reverse cell order.\n            states = [];\n            for (const cellStates of newNestedStates.slice().reverse()) {\n                states.push(...cellStates);\n            }\n            return [callInputs[0]].concat(states);\n        });\n    }\n    build(inputShape) {\n        if (isArrayOfShapes(inputShape)) {\n            // TODO(cais): Take care of input constants.\n            // const constantShape = inputShape.slice(1);\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        let outputDim;\n        this.cells.forEach((cell, i) => {\n            nameScope(`RNNCell_${i}`, () => {\n                // TODO(cais): Take care of input constants.\n                cell.build(inputShape);\n                if (Array.isArray(cell.stateSize)) {\n                    outputDim = cell.stateSize[0];\n                }\n                else {\n                    outputDim = cell.stateSize;\n                }\n                inputShape = [inputShape[0], outputDim];\n            });\n        });\n        this.built = true;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const getCellConfig = (cell) => {\n            return {\n                'className': cell.getClassName(),\n                'config': cell.getConfig(),\n            };\n        };\n        const cellConfigs = this.cells.map(getCellConfig);\n        const config = { 'cells': cellConfigs };\n        return Object.assign({}, baseConfig, config);\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const cells = [];\n        for (const cellConfig of config['cells']) {\n            cells.push(deserialize(cellConfig, customObjects));\n        }\n        return new cls({ cells });\n    }\n    get trainableWeights() {\n        if (!this.trainable) {\n            return [];\n        }\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.trainableWeights);\n        }\n        return weights;\n    }\n    get nonTrainableWeights() {\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.nonTrainableWeights);\n        }\n        if (!this.trainable) {\n            const trainableWeights = [];\n            for (const cell of this.cells) {\n                trainableWeights.push(...cell.trainableWeights);\n            }\n            return trainableWeights.concat(weights);\n        }\n        return weights;\n    }\n    /**\n     * Retrieve the weights of a the model.\n     *\n     * @returns A flat `Array` of `tf.Tensor`s.\n     */\n    getWeights() {\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.weights);\n        }\n        return batchGetValue(weights);\n    }\n    /**\n     * Set the weights of the model.\n     *\n     * @param weights An `Array` of `tf.Tensor`s with shapes and types matching\n     *     the output of `getWeights()`.\n     */\n    setWeights(weights) {\n        const tuples = [];\n        for (const cell of this.cells) {\n            const numParams = cell.weights.length;\n            const inputWeights = weights.splice(numParams);\n            for (let i = 0; i < cell.weights.length; ++i) {\n                tuples.push([cell.weights[i], inputWeights[i]]);\n            }\n        }\n        batchSetValue(tuples);\n    }\n}\n/** @nocollapse */\nStackedRNNCells.className = 'StackedRNNCells';\nserialization.registerClass(StackedRNNCells);\nexport function generateDropoutMask(args) {\n    const { ones, rate, training = false, count = 1 } = args;\n    const droppedInputs = () => K.dropout(ones(), rate);\n    const createMask = () => K.inTrainPhase(droppedInputs, ones, training);\n    // just in case count is provided with null or undefined\n    if (!count || count <= 1) {\n        return tfc.keep(createMask().clone());\n    }\n    const masks = Array(count).fill(undefined).map(createMask);\n    return masks.map(m => tfc.keep(m.clone()));\n}\n//# sourceMappingURL=recurrent.js.map", "/**\n * @license\n * Copyright 2020 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nvar __rest = (this && this.__rest) || function (s, e) {\n    var t = {};\n    for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\n        t[p] = s[p];\n    if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\n        for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\n            if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\n                t[p[i]] = s[p[i]];\n        }\n    return t;\n};\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode } from '../common';\nimport { InputSpec } from '../engine/topology';\nimport { AttributeError, NotImplementedError, ValueError } from '../errors';\nimport { Initializer } from '../initializers';\nimport { convOutputLength, normalizeArray } from '../utils/conv_utils';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport { getExactlyOneShape } from '../utils/types_utils';\nimport { generateDropoutMask, LSTMCell, RNN, RNNCell } from './recurrent';\nclass ConvRNN2DCell extends RNNCell {\n}\n/**\n * Base class for convolutional-recurrent layers.\n */\nclass ConvRNN2D extends RNN {\n    constructor(args) {\n        if (args.unroll) {\n            throw new NotImplementedError('Unrolling is not possible with convolutional RNNs.');\n        }\n        if (Array.isArray(args.cell)) {\n            throw new NotImplementedError('It is not possible at the moment to stack convolutional cells.');\n        }\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 5 })];\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            if (kwargs && kwargs['constants']) {\n                throw new ValueError('ConvRNN2D cell does not support constants');\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    computeOutputShape(inputShape) {\n        let outShape = this.computeSingleOutputShape(inputShape);\n        if (!this.returnSequences) {\n            outShape = [outShape[0], ...outShape.slice(2)];\n        }\n        if (this.returnState) {\n            outShape =\n                [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])];\n        }\n        return outShape;\n    }\n    getInitialState(inputs) {\n        return tfc.tidy(() => {\n            const { stateSize } = this.cell;\n            const inputShape = inputs.shape;\n            const outputShape = this.computeSingleOutputShape(inputShape);\n            const stateShape = [outputShape[0], ...outputShape.slice(2)];\n            const initialState = tfc.zeros(stateShape);\n            if (Array.isArray(stateSize)) {\n                return Array(stateSize.length).fill(initialState);\n            }\n            return [initialState];\n        });\n    }\n    resetStates(states, training = false) {\n        tfc.tidy(() => {\n            if (!this.stateful) {\n                throw new AttributeError('Cannot call resetStates() on an RNN Layer that is not stateful.');\n            }\n            const inputShape = this.inputSpec[0].shape;\n            const outputShape = this.computeSingleOutputShape(inputShape);\n            const stateShape = [outputShape[0], ...outputShape.slice(2)];\n            const batchSize = inputShape[0];\n            if (batchSize == null) {\n                throw new ValueError('If an RNN is stateful, it needs to know its batch size. Specify ' +\n                    'the batch size of your input tensors: \\n' +\n                    '- If using a Sequential model, specify the batch size by ' +\n                    'passing a `batchInputShape` option to your first layer.\\n' +\n                    '- If using the functional API, specify the batch size by ' +\n                    'passing a `batchShape` option to your Input layer.');\n            }\n            // Initialize state if null.\n            if (this.getStates() == null) {\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));\n                }\n                else {\n                    this.states_ = [tfc.zeros(stateShape)];\n                }\n            }\n            else if (states == null) {\n                // Dispose old state tensors.\n                tfc.dispose(this.states_);\n                // For stateful RNNs, fully dispose kept old states.\n                if (this.keptStates != null) {\n                    tfc.dispose(this.keptStates);\n                    this.keptStates = [];\n                }\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));\n                }\n                else {\n                    this.states_[0] = tfc.zeros(stateShape);\n                }\n            }\n            else {\n                if (!Array.isArray(states)) {\n                    states = [states];\n                }\n                if (states.length !== this.states_.length) {\n                    throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), ` +\n                        `but it received ${states.length} state value(s). Input ` +\n                        `received: ${states}`);\n                }\n                if (training) {\n                    // Store old state tensors for complete disposal later, i.e., during\n                    // the next no-arg call to this method. We do not dispose the old\n                    // states immediately because that BPTT (among other things) require\n                    // them.\n                    this.keptStates.push(this.states_.slice());\n                }\n                else {\n                    tfc.dispose(this.states_);\n                }\n                for (let index = 0; index < this.states_.length; ++index) {\n                    const value = states[index];\n                    const expectedShape = stateShape;\n                    if (!util.arraysEqual(value.shape, expectedShape)) {\n                        throw new ValueError(`State ${index} is incompatible with layer ${this.name}: ` +\n                            `expected shape=${expectedShape}, received shape=${value.shape}`);\n                    }\n                    this.states_[index] = value;\n                }\n            }\n            this.states_ = this.states_.map(state => tfc.keep(state.clone()));\n        });\n    }\n    computeSingleOutputShape(inputShape) {\n        const { dataFormat, filters, kernelSize, padding, strides, dilationRate } = this.cell;\n        const isChannelsFirst = dataFormat === 'channelsFirst';\n        const h = inputShape[isChannelsFirst ? 3 : 2];\n        const w = inputShape[isChannelsFirst ? 4 : 3];\n        const hOut = convOutputLength(h, kernelSize[0], padding, strides[0], dilationRate[0]);\n        const wOut = convOutputLength(w, kernelSize[1], padding, strides[1], dilationRate[1]);\n        const outShape = [\n            ...inputShape.slice(0, 2),\n            ...(isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters])\n        ];\n        return outShape;\n    }\n}\n/** @nocollapse */\nConvRNN2D.className = 'ConvRNN2D';\nexport class ConvLSTM2DCell extends LSTMCell {\n    constructor(args) {\n        const { filters, kernelSize, strides, padding, dataFormat, dilationRate, } = args;\n        super(Object.assign({}, args, { units: filters }));\n        this.filters = filters;\n        assertPositiveInteger(this.filters, 'filters');\n        this.kernelSize = normalizeArray(kernelSize, 2, 'kernelSize');\n        this.kernelSize.forEach(size => assertPositiveInteger(size, 'kernelSize'));\n        this.strides = normalizeArray(strides || 1, 2, 'strides');\n        this.strides.forEach(stride => assertPositiveInteger(stride, 'strides'));\n        this.padding = padding || 'valid';\n        checkPaddingMode(this.padding);\n        this.dataFormat = dataFormat || 'channelsLast';\n        checkDataFormat(this.dataFormat);\n        this.dilationRate = normalizeArray(dilationRate || 1, 2, 'dilationRate');\n        this.dilationRate.forEach(rate => assertPositiveInteger(rate, 'dilationRate'));\n    }\n    build(inputShape) {\n        var _a;\n        inputShape = getExactlyOneShape(inputShape);\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError(`The channel dimension of the input should be defined. ` +\n                `Found ${inputShape[channelAxis]}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const numOfKernels = 4;\n        const kernelShape = this.kernelSize.concat([inputDim, this.filters * numOfKernels]);\n        this.kernel = this.addWeight('kernel', kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        const recurrentKernelShape = this.kernelSize.concat([this.filters, this.filters * numOfKernels]);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', recurrentKernelShape, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            let biasInitializer;\n            if (this.unitForgetBias) {\n                const init = this.biasInitializer;\n                const filters = this.filters;\n                biasInitializer = new (_a = class CustomInit extends Initializer {\n                        apply(shape, dtype) {\n                            const biasI = init.apply([filters]);\n                            const biasF = tfc.ones([filters]);\n                            const biasCAndO = init.apply([filters * 2]);\n                            return K.concatenate([biasI, biasF, biasCAndO]);\n                        }\n                    },\n                    /** @nocollapse */\n                    _a.className = 'CustomInit',\n                    _a)();\n            }\n            else {\n                biasInitializer = this.biasInitializer;\n            }\n            this.bias = this.addWeight('bias', [this.filters * numOfKernels], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            if (inputs.length !== 3) {\n                throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            const training = kwargs['training'] || false;\n            const x = inputs[0]; // Current input\n            const hTMinus1 = inputs[1]; // Previous memory state.\n            const cTMinus1 = inputs[2]; // Previous carry state.\n            const numOfKernels = 4;\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(x),\n                    rate: this.dropout,\n                    training,\n                    count: numOfKernels\n                });\n            }\n            const dropoutMask = this.dropoutMask;\n            const applyDropout = (x, mask, index) => {\n                if (!mask || !mask[index]) {\n                    return x;\n                }\n                return tfc.mul(mask[index], x);\n            };\n            let xI = applyDropout(x, dropoutMask, 0);\n            let xF = applyDropout(x, dropoutMask, 1);\n            let xC = applyDropout(x, dropoutMask, 2);\n            let xO = applyDropout(x, dropoutMask, 3);\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: numOfKernels\n                });\n            }\n            const recDropoutMask = this.recurrentDropoutMask;\n            let hI = applyDropout(hTMinus1, recDropoutMask, 0);\n            let hF = applyDropout(hTMinus1, recDropoutMask, 1);\n            let hC = applyDropout(hTMinus1, recDropoutMask, 2);\n            let hO = applyDropout(hTMinus1, recDropoutMask, 3);\n            const kernelChannelAxis = 3;\n            const [kernelI, kernelF, kernelC, kernelO] = tfc.split(this.kernel.read(), numOfKernels, kernelChannelAxis);\n            const [biasI, biasF, biasC, biasO] = this.useBias ?\n                tfc.split(this.bias.read(), numOfKernels) :\n                [null, null, null, null];\n            xI = this.inputConv(xI, kernelI, biasI, this.padding);\n            xF = this.inputConv(xF, kernelF, biasF, this.padding);\n            xC = this.inputConv(xC, kernelC, biasC, this.padding);\n            xO = this.inputConv(xO, kernelO, biasO, this.padding);\n            const [recKernelI, recKernelF, recKernelC, recKernelO] = tfc.split(this.recurrentKernel.read(), numOfKernels, kernelChannelAxis);\n            hI = this.recurrentConv(hI, recKernelI);\n            hF = this.recurrentConv(hF, recKernelF);\n            hC = this.recurrentConv(hC, recKernelC);\n            hO = this.recurrentConv(hO, recKernelO);\n            const i = this.recurrentActivation.apply(tfc.add(xI, hI));\n            const f = this.recurrentActivation.apply(tfc.add(xF, hF));\n            const c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(tfc.add(xC, hC))));\n            const h = tfc.mul(this.recurrentActivation.apply(tfc.add(xO, hO)), this.activation.apply(c));\n            return [h, h, c];\n        });\n    }\n    getConfig() {\n        const _a = super.getConfig(), { 'units': _ } = _a, baseConfig = __rest(_a, ['units']);\n        const config = {\n            filters: this.filters,\n            kernelSize: this.kernelSize,\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n            dilationRate: this.dilationRate,\n            strides: this.strides,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n    inputConv(x, w, b, padding) {\n        const out = tfc.conv2d(x, w, this.strides, (padding || 'valid'), this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC', this.dilationRate);\n        if (b) {\n            return K.biasAdd(out, b, this.dataFormat);\n        }\n        return out;\n    }\n    recurrentConv(x, w) {\n        const strides = 1;\n        return tfc.conv2d(x, w, strides, 'same', this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC');\n    }\n}\n/** @nocollapse */\nConvLSTM2DCell.className = 'ConvLSTM2DCell';\ntfc.serialization.registerClass(ConvLSTM2DCell);\nexport class ConvLSTM2D extends ConvRNN2D {\n    constructor(args) {\n        const cell = new ConvLSTM2DCell(args);\n        super(Object.assign({}, args, { cell }));\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nConvLSTM2D.className = 'ConvLSTM2D';\ntfc.serialization.registerClass(ConvLSTM2D);\n//# sourceMappingURL=convolutional_recurrent.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Basic Layers.\n */\nimport { any, notEqual, serialization, tidy, transpose, util } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport * as K from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { assertPositiveInteger, mapActivationToFusedKernel } from '../utils/generic_utils';\nimport { arrayProd, range } from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class Dropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.rate = Math.max(Math.min(args.rate, 1), 0);\n        // So that the scalar doesn't get tidied up between executions.\n        this.noiseShape = args.noiseShape;\n        this.seed = args.seed;\n        this.supportsMasking = true;\n    }\n    getNoiseShape(input) {\n        if (this.noiseShape == null) {\n            return this.noiseShape;\n        }\n        const inputShape = input.shape;\n        const noiseShape = [];\n        for (let i = 0; i < this.noiseShape.length; ++i) {\n            noiseShape.push(this.noiseShape[i] == null ? inputShape[i] : this.noiseShape[i]);\n        }\n        return noiseShape;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            if (0 < this.rate && this.rate < 1) {\n                const training = kwargs['training'] == null ? false : kwargs['training'];\n                const noiseShape = this.getNoiseShape(input);\n                const output = K.inTrainPhase(() => K.dropout(input, this.rate, noiseShape, this.seed), () => input, training);\n                return output;\n            }\n            return inputs;\n        });\n    }\n    getConfig() {\n        const config = {\n            rate: this.rate,\n            noiseShape: this.noiseShape,\n            seed: this.seed,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    dispose() {\n        return super.dispose();\n    }\n}\n/** @nocollapse */\nDropout.className = 'Dropout';\nserialization.registerClass(Dropout);\nexport class SpatialDropout1D extends Dropout {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [{ ndim: 3 }];\n    }\n    getNoiseShape(input) {\n        const inputShape = input.shape;\n        return [inputShape[0], 1, inputShape[2]];\n    }\n}\n/** @nocollapse */\nSpatialDropout1D.className = 'SpatialDropout1D';\nserialization.registerClass(SpatialDropout1D);\nexport class Dense extends Layer {\n    constructor(args) {\n        super(args);\n        // Default activation: Linear (none).\n        this.activation = null;\n        this.useBias = true;\n        this.kernel = null;\n        this.bias = null;\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        if (args.batchInputShape == null && args.inputShape == null &&\n            args.inputDim != null) {\n            // This logic is copied from Layer's constructor, since we can't\n            // do exactly what the Python constructor does for Dense().\n            let batchSize = null;\n            if (args.batchSize != null) {\n                batchSize = args.batchSize;\n            }\n            this.batchInputShape = [batchSize, args.inputDim];\n        }\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation);\n        if (args.useBias != null) {\n            this.useBias = args.useBias;\n        }\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.supportsMasking = true;\n        this.inputSpec = [{ minNDim: 2 }];\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const inputLastDim = inputShape[inputShape.length - 1];\n        if (this.kernel == null) {\n            this.kernel = this.addWeight('kernel', [inputLastDim, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n            if (this.useBias) {\n                this.bias = this.addWeight('bias', [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n            }\n        }\n        this.inputSpec = [{ minNDim: 2, axes: { [-1]: inputLastDim } }];\n        this.built = true;\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        outputShape[outputShape.length - 1] = this.units;\n        return outputShape;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Dense layer accepts only a single input.\n            const input = getExactlyOneTensor(inputs);\n            const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());\n            let output;\n            if (fusedActivationName != null) {\n                output = K.dot(input, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null);\n            }\n            else {\n                output = K.dot(input, this.kernel.read());\n                if (this.bias != null) {\n                    output = K.biasAdd(output, this.bias.read());\n                }\n                if (this.activation != null) {\n                    output = this.activation.apply(output);\n                }\n            }\n            return output;\n        });\n    }\n    getConfig() {\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nDense.className = 'Dense';\nserialization.registerClass(Dense);\nexport class Flatten extends Layer {\n    constructor(args) {\n        args = args || {};\n        super(args);\n        this.inputSpec = [{ minNDim: 3 }];\n        this.dataFormat = args.dataFormat;\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        for (const dim of inputShape.slice(1)) {\n            if (dim == null) {\n                throw new ValueError(`The shape of the input to \"Flatten\" is not fully defined ` +\n                    `(got ${inputShape.slice(1)}). Make sure to pass a complete ` +\n                    `\"input_shape\" or \"batch_input_shape\" argument to the first ` +\n                    `layer in your model.`);\n            }\n        }\n        return [inputShape[0], arrayProd(inputShape, 1)];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            let input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsFirst' && input.rank > 1) {\n                const permutation = [0];\n                for (let i = 2; i < input.rank; ++i) {\n                    permutation.push(i);\n                }\n                permutation.push(1);\n                input = input.transpose(permutation);\n            }\n            return K.batchFlatten(input);\n        });\n    }\n    getConfig() {\n        const config = {};\n        if (this.dataFormat != null) {\n            config['dataFormat'] = this.dataFormat;\n        }\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nFlatten.className = 'Flatten';\nserialization.registerClass(Flatten);\nexport class Activation extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.activation = getActivation(args.activation);\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            return this.activation.apply(input);\n        });\n    }\n    getConfig() {\n        const config = { activation: serializeActivation(this.activation) };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nActivation.className = 'Activation';\nserialization.registerClass(Activation);\nexport class RepeatVector extends Layer {\n    constructor(args) {\n        super(args);\n        this.n = args.n;\n        this.inputSpec = [{ ndim: 2 }];\n    }\n    computeOutputShape(inputShape) {\n        return [inputShape[0], this.n, inputShape[1]];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            return K.repeat(inputs, this.n);\n        });\n    }\n    getConfig() {\n        const config = {\n            n: this.n,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nRepeatVector.className = 'RepeatVector';\nserialization.registerClass(RepeatVector);\nexport class Reshape extends Layer {\n    constructor(args) {\n        super(args);\n        this.targetShape = args.targetShape;\n        // Make sure that all unknown dimensions are represented as `null`.\n        for (let i = 0; i < this.targetShape.length; ++i) {\n            if (this.isUnknown(this.targetShape[i])) {\n                this.targetShape[i] = null;\n            }\n        }\n    }\n    isUnknown(dim) {\n        return dim < 0 || dim == null;\n    }\n    /**\n     * Finds and replaces a missing dimension in output shape.\n     *\n     * This is a near direct port of the internal Numpy function\n     * `_fix_unknown_dimension` in `numpy/core/src/multiarray/shape.c`.\n     *\n     * @param inputShape: Original shape of array begin reshape.\n     * @param outputShape: Target shape of the array, with at most a single\n     * `null` or negative number, which indicates an underdetermined dimension\n     * that should be derived from `inputShape` and the known dimensions of\n     *   `outputShape`.\n     * @returns: The output shape with `null` replaced with its computed value.\n     * @throws: ValueError: If `inputShape` and `outputShape` do not match.\n     */\n    fixUnknownDimension(inputShape, outputShape) {\n        const errorMsg = 'Total size of new array must be unchanged.';\n        const finalShape = outputShape.slice();\n        let known = 1;\n        let unknown = null;\n        for (let i = 0; i < finalShape.length; ++i) {\n            const dim = finalShape[i];\n            if (this.isUnknown(dim)) {\n                if (unknown === null) {\n                    unknown = i;\n                }\n                else {\n                    throw new ValueError('Can only specifiy one unknown dimension.');\n                }\n            }\n            else {\n                known *= dim;\n            }\n        }\n        const originalSize = arrayProd(inputShape);\n        if (unknown !== null) {\n            if (known === 0 || originalSize % known !== 0) {\n                throw new ValueError(errorMsg);\n            }\n            finalShape[unknown] = originalSize / known;\n        }\n        else if (originalSize !== known) {\n            throw new ValueError(errorMsg);\n        }\n        return finalShape;\n    }\n    computeOutputShape(inputShape) {\n        let anyUnknownDims = false;\n        for (let i = 0; i < inputShape.length; ++i) {\n            if (this.isUnknown(inputShape[i])) {\n                anyUnknownDims = true;\n                break;\n            }\n        }\n        if (anyUnknownDims) {\n            return inputShape.slice(0, 1).concat(this.targetShape);\n        }\n        else {\n            return inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            const outputShape = inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n            return input.reshape(outputShape);\n        });\n    }\n    getConfig() {\n        const config = {\n            targetShape: this.targetShape,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nReshape.className = 'Reshape';\nserialization.registerClass(Reshape);\nexport class Permute extends Layer {\n    constructor(args) {\n        super(args);\n        if (args.dims == null) {\n            throw new Error('Required configuration field `dims` is missing during Permute ' +\n                'constructor call.');\n        }\n        if (!Array.isArray(args.dims)) {\n            throw new Error('Permute constructor requires `dims` to be an Array, but received ' +\n                `${args.dims} instead.`);\n        }\n        // Check the validity of the permutation indices.\n        const expectedSortedIndices = range(1, args.dims.length + 1);\n        if (!util.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) {\n            throw new Error('Invalid permutation `dims`: ' + JSON.stringify(args.dims) +\n                ' `dims` must contain consecutive integers starting from 1.');\n        }\n        this.dims = args.dims;\n        this.dimsIncludingBatch = [0].concat(this.dims);\n        this.inputSpec = [new InputSpec({ ndim: this.dims.length + 1 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        this.dims.forEach((dim, i) => {\n            outputShape[i + 1] = inputShape[dim];\n        });\n        return outputShape;\n    }\n    call(inputs, kwargs) {\n        return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch);\n    }\n    getConfig() {\n        const config = {\n            dims: this.dims,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nPermute.className = 'Permute';\nserialization.registerClass(Permute);\nexport class Masking extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.supportsMasking = true;\n        if (args != null) {\n            this.maskValue = args.maskValue == null ? 0 : args.maskValue;\n        }\n        else {\n            this.maskValue = 0;\n        }\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { maskValue: this.maskValue };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    computeMask(inputs, mask) {\n        const input = getExactlyOneTensor(inputs);\n        const axis = -1;\n        return any(notEqual(input, this.maskValue), axis);\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const axis = -1;\n            const keepDims = true;\n            const booleanMask = any(notEqual(input, this.maskValue), axis, keepDims);\n            const output = input.mul(booleanMask.asType(input.dtype));\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMasking.className = 'Masking';\nserialization.registerClass(Masking);\n//# sourceMappingURL=core.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Embedding Layer.\n *\n * Original source: keras/constraints.py\n */\nimport { notEqual, serialization, tidy, zerosLike } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class Embedding extends Layer {\n    constructor(args) {\n        super(args);\n        this.embeddings = null;\n        this.DEFAULT_EMBEDDINGS_INITIALIZER = 'randomUniform';\n        if (args.batchInputShape == null && args.inputShape == null) {\n            // Porting Note: This logic is copied from Layer's constructor, since we\n            // can't do exactly what the Python constructor does for Embedding().\n            // Specifically, the super constructor can not be called after the\n            // mutation of the `config` argument.\n            let batchSize = null;\n            if (args.batchSize != null) {\n                batchSize = args.batchSize;\n            }\n            if (args.inputLength == null) {\n                // Fix super-constructor to what it would have done if\n                // 'config.inputShape' were (None, )\n                this.batchInputShape = [batchSize, null];\n            }\n            else {\n                // Fix super-constructor to what it would have done if\n                // 'config.inputShape' were (config.inputLength, )\n                this.batchInputShape =\n                    [batchSize].concat(generic_utils.toList(args.inputLength));\n            }\n        }\n        this.inputDim = args.inputDim;\n        generic_utils.assertPositiveInteger(this.inputDim, 'inputDim');\n        this.outputDim = args.outputDim;\n        generic_utils.assertPositiveInteger(this.outputDim, 'outputDim');\n        this.embeddingsInitializer = getInitializer(args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER);\n        this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.embeddingsConstraint = getConstraint(args.embeddingsConstraint);\n        this.maskZero = args.maskZero;\n        this.supportsMasking = args.maskZero;\n        this.inputLength = args.inputLength;\n    }\n    build(inputShape) {\n        this.embeddings = this.addWeight('embeddings', [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint);\n        this.built = true;\n    }\n    // Override warnOnIncompatibleInputShape because an embedding layer allows\n    // the input to have varying ranks.\n    warnOnIncompatibleInputShape(inputShape) { }\n    computeMask(inputs, mask) {\n        return tidy(() => {\n            if (!this.maskZero) {\n                return null;\n            }\n            else {\n                inputs = getExactlyOneTensor(inputs);\n                return notEqual(inputs, zerosLike(inputs));\n            }\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (this.inputLength == null) {\n            return [...inputShape, this.outputDim];\n        }\n        // inputLength can be an array if input is 3D or higher.\n        const inLens = generic_utils.toList(this.inputLength);\n        if (inLens.length !== inputShape.length - 1) {\n            throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received ` +\n                `input shape has shape ${inputShape}`);\n        }\n        else {\n            let i = 0;\n            for (let k = 0; k < inLens.length; ++k) {\n                const s1 = inLens[k];\n                const s2 = inputShape[k + 1];\n                if ((s1 != null) && (s2 != null) && (s1 !== s2)) {\n                    throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received ` +\n                        `input shape has shape ${inputShape}`);\n                }\n                else if (s1 == null) {\n                    inLens[i] = s2;\n                }\n                i++;\n            }\n        }\n        return [inputShape[0], ...inLens, this.outputDim];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Embedding layer accepts only a single input.\n            let input = getExactlyOneTensor(inputs);\n            if (input.dtype !== 'int32') {\n                input = K.cast(input, 'int32');\n            }\n            const output = K.gather(this.embeddings.read(), input.as1D());\n            return output.reshape(getExactlyOneShape(this.computeOutputShape(input.shape)));\n        });\n    }\n    getConfig() {\n        const config = {\n            inputDim: this.inputDim,\n            outputDim: this.outputDim,\n            embeddingsInitializer: serializeInitializer(this.embeddingsInitializer),\n            embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            embeddingsConstraint: serializeConstraint(this.embeddingsConstraint),\n            maskZero: this.maskZero,\n            inputLength: this.inputLength\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nEmbedding.className = 'Embedding';\nserialization.registerClass(Embedding);\n//# sourceMappingURL=embeddings.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Merge Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { l2Normalize } from '../losses';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as mathUtils from '../utils/math_utils';\nimport { getExactlyOneShape } from '../utils/types_utils';\n/**\n * Generic Merge layer for element-wise merge functions.\n *\n * Used to implement `Sum`, `Average`, `Concatenate`, etc.\n */\nexport class Merge extends Layer {\n    constructor(args) {\n        super(args || {});\n        this.supportsMasking = true;\n    }\n    /**\n     * Logic for merging multiple tensors, to be overridden by subclasses.\n     * @param inputs\n     */\n    mergeFunction(inputs) {\n        throw new NotImplementedError();\n    }\n    /**\n     * Computes the shape of the result of an elementwise operation.\n     *\n     * @param shape1: Shape of the first tensor.\n     * @param shape2: Shape of the second tensor.\n     * @returns Expected output shape when an elementwise operation is carried\n     *   out on 2 tensors with shapes `shape1` and `shape2`.\n     * @throws ValueError: If `shape1` and `shape2` are not compatible for\n     *   element-wise operations.\n     */\n    computeElementwiseOpOutputShape(shape1, shape2) {\n        if (shape1 == null || shape2 == null) {\n            return null;\n        }\n        else if (shape1.length < shape2.length) {\n            return this.computeElementwiseOpOutputShape(shape2, shape1);\n        }\n        else if (shape2.length === 0) {\n            return shape1;\n        }\n        const outputShape = shape1.slice(0, shape1.length - shape2.length);\n        for (let k = 0; k < shape2.length; ++k) {\n            const i = shape1[shape1.length - shape2.length + k];\n            const j = shape2[k];\n            if (i == null || j == null || i < 0 || j < 0) {\n                outputShape.push(null);\n            }\n            else if (i === 1) {\n                outputShape.push(j);\n            }\n            else if (j === 1) {\n                outputShape.push(i);\n            }\n            else {\n                if (i !== j) {\n                    throw new ValueError('Operands could not be broadcast together with shapes ' +\n                        JSON.stringify(shape1) + ' ' + JSON.stringify(shape2));\n                }\n                outputShape.push(i);\n            }\n        }\n        return outputShape;\n    }\n    build(inputShape) {\n        // Used purely for shape validation.\n        if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) {\n            // Make sure that inputShape is an Array of shape.\n            inputShape = [getExactlyOneShape(inputShape)];\n        }\n        inputShape = inputShape;\n        if (inputShape.length < 2) {\n            throw new ValueError('A merge layer should be called on an Array of at least 2 inputs.' +\n                ` Got ${inputShape.length} input(s).`);\n        }\n        // Make sure that there is at most one unique batch size among the input\n        // shapes.\n        let batchSizes = [];\n        for (const shape of inputShape) {\n            if (shape != null && shape[0] !== null) {\n                batchSizes.push(shape[0]);\n            }\n        }\n        batchSizes = generic_utils.unique(batchSizes);\n        if (batchSizes.length > 1) {\n            throw new ValueError(`Can not merge tensors with different batch sizes. ` +\n                `Got tensors with shapes: ${JSON.stringify(inputShape)}.`);\n        }\n        let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1);\n        for (let i = 1; i < inputShape.length; ++i) {\n            const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n            outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n        }\n        // If the inputs have different ranks, we have to reshape them to make them\n        // broadcastable.\n        const allRanks = inputShape.map(shape => shape.length);\n        if (inputShape.indexOf(null) === -1 &&\n            generic_utils.unique(allRanks).length === 1) {\n            this.reshapeRequired = false;\n        }\n        else {\n            this.reshapeRequired = true;\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (this.reshapeRequired) {\n                const reshapedInputs = [];\n                const inputDims = inputs.map(input => input.rank);\n                if (inputDims.indexOf(null) === -1) {\n                    // If ranks of all inputs are available, we simply expand each of them\n                    // at axis=1 until all of them have the same rank.\n                    const maxNDim = mathUtils.max(inputDims);\n                    for (let x of inputs) {\n                        const xNDim = x.rank;\n                        for (let k = 0; k < maxNDim - xNDim; ++k) {\n                            x = K.expandDims(x, 1);\n                        }\n                        reshapedInputs.push(x);\n                    }\n                    return this.mergeFunction(reshapedInputs);\n                }\n                else {\n                    // Transpose all inputs so that batch size is the last dimension.\n                    // [batchSize, dim1, dim2, ...] -> [dim1, dim2, ..., batchSize]\n                    let transposed = false;\n                    for (const x of inputs) {\n                        const xNDim = x.rank;\n                        if (xNDim == null) {\n                            const xShape = x.shape;\n                            const batchSize = xShape[0];\n                            const newShape = xShape.slice(1).concat([batchSize]);\n                            let xTransposed = x.reshape([batchSize].concat(mathUtils.arrayProd(xShape.slice(1))));\n                            xTransposed = tfc.transpose(xTransposed, [1, 0]);\n                            xTransposed = xTransposed.reshape(newShape);\n                            reshapedInputs.push(xTransposed);\n                            transposed = true;\n                        }\n                        else if (xNDim > 1) {\n                            const dims = mathUtils.range(1, xNDim).concat([0]);\n                            reshapedInputs.push(tfc.transpose(x, dims));\n                            transposed = true;\n                        }\n                        else {\n                            // We don't transpose inputs if they are 1D vectors or scalars.\n                            reshapedInputs.push(x);\n                        }\n                    }\n                    let y = this.mergeFunction(reshapedInputs);\n                    const yNDim = y.rank;\n                    if (transposed) {\n                        // If inputs have been transposed, we have to transpose the output\n                        // too.\n                        if (yNDim == null) {\n                            const yShape = y.shape;\n                            const yNDim = yShape.length;\n                            const batchSize = yShape[yNDim - 1];\n                            const newShape = [batchSize].concat(yShape.slice(0, yShape.length - 1));\n                            y = tfc.transpose(y.reshape([-1, batchSize]), [1, 0])\n                                .reshape(newShape);\n                        }\n                        else if (yNDim > 1) {\n                            const dims = [yNDim - 1].concat(mathUtils.range(0, yNDim - 1));\n                            y = tfc.transpose(y, dims);\n                        }\n                    }\n                    return y;\n                }\n            }\n            else {\n                return this.mergeFunction(inputs);\n            }\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = inputShape;\n        let outputShape;\n        if (inputShape[0] == null) {\n            outputShape = null;\n        }\n        else {\n            outputShape = inputShape[0].slice(1);\n        }\n        for (let i = 1; i < inputShape.length; ++i) {\n            const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n            outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n        }\n        let batchSizes = [];\n        for (const shape of inputShape) {\n            if (shape != null && shape[0] !== null) {\n                batchSizes.push(shape[0]);\n            }\n        }\n        batchSizes = generic_utils.unique(batchSizes);\n        if (batchSizes.length === 1) {\n            outputShape = batchSizes.concat(outputShape);\n        }\n        else {\n            outputShape = [null].concat(outputShape);\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        return tfc.tidy(() => {\n            if (mask == null) {\n                return null;\n            }\n            if (!Array.isArray(mask)) {\n                throw new ValueError('`mask` should be an Array');\n            }\n            if (!Array.isArray(inputs)) {\n                throw new ValueError('`inputs` should be an Array');\n            }\n            if (mask.length !== inputs.length) {\n                throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same ` +\n                    `length, but have different lengths ` +\n                    `(${inputs.length} vs ${mask.length})`);\n            }\n            if (mask.every(m => m == null)) {\n                return null;\n            }\n            mask = mask.map(m => m == null ? m : tfc.expandDims(m, 0));\n            let output = mask[0];\n            for (let i = 1; i < mask.length - 1; ++i) {\n                output = tfc.logicalAnd(output, mask[i]);\n            }\n            return output;\n        });\n    }\n}\nexport class Add extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.add(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nAdd.className = 'Add';\nserialization.registerClass(Add);\n/**\n * Calculate the element-wise sum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Add` layer, by using no input argument\n *    or a single configuration argument. The resultant `Add` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const addLayer = tf.layers.add();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = addLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.add([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.add([input1, input2]).print();\n * // Gives [[11, 22], [33, 44]].\n *\n */\nexport function add(config) {\n    if (Array.isArray(config)) {\n        const layer = new Add({});\n        return layer.apply(config);\n    }\n    else {\n        return new Add(config);\n    }\n}\nexport class Multiply extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.mul(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMultiply.className = 'Multiply';\nserialization.registerClass(Multiply);\n/**\n * Calculate the element-wise product of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Multiply` layer, by using no input argument\n *    or a single configuration argument. The resultant `Multiply` layer can\n *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const multiplyLayer = tf.layers.multiply();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = multiplyLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.multiply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.multiply([input1, input2]).print();\n * // Gives [[10, 40], [90, 160]].\n *\n */\nexport function multiply(config) {\n    if (Array.isArray(config)) {\n        const layer = new Multiply({});\n        return layer.apply(config);\n    }\n    else {\n        return new Multiply(config);\n    }\n}\nexport class Average extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.add(output, inputs[i]);\n            }\n            return tfc.mul(1 / inputs.length, output);\n        });\n    }\n}\n/** @nocollapse */\nAverage.className = 'Average';\nserialization.registerClass(Average);\n/**\n * Calculate the element-wise arithmetic mean of inputs, which all have the same\n * shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Average` layer, by using no input argument\n *    or a single configuration argument. The resultant `Average` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const averageLayer = tf.layers.average();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = averageLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.average([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.average([input1, input2]).print();\n * // Gives [[5.5, 11], [16.5, 22]].\n *\n */\nexport function average(config) {\n    if (Array.isArray(config)) {\n        const layer = new Average({});\n        return layer.apply(config);\n    }\n    else {\n        return new Average(config);\n    }\n}\nexport class Maximum extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0];\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.maximum(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMaximum.className = 'Maximum';\nserialization.registerClass(Maximum);\n/**\n * Calculate the element-wise maximum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Maximum` layer, by using no input argument\n *    or a single configuration argument. The resultant `Maximum` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const maximumLayer = tf.layers.maximum();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = maximumLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.maximum([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);\n * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);\n * tf.layers.maximum([input1, input2]).print();\n * // Gives [[10, 20], [30, 40]].\n *\n */\nexport function maximum(config) {\n    if (Array.isArray(config)) {\n        const layer = new Maximum({});\n        return layer.apply(config);\n    }\n    else {\n        return new Maximum(config);\n    }\n}\nexport class Minimum extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0];\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.minimum(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMinimum.className = 'Minimum';\nserialization.registerClass(Minimum);\n/**\n * Calculate the element-wise minimum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Minimum` layer, by using no input argument\n *    or a single configuration argument. The resultant `Minimum` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const minimumLayer = tf.layers.minimum();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = minimumLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.minimum([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);\n * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);\n * tf.layers.minimum([input1, input2]).print();\n * // Gives [[1, 2], [3, 4]].\n *\n */\nexport function minimum(config) {\n    if (Array.isArray(config)) {\n        const layer = new Minimum({});\n        return layer.apply(config);\n    }\n    else {\n        return new Minimum(config);\n    }\n}\nexport class Concatenate extends Merge {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_AXIS = -1;\n        if (args == null) {\n            args = {};\n        }\n        this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n        this.supportsMasking = true;\n        this.reshapeRequired = false;\n    }\n    build(inputShape) {\n        // Used purely for shape validation.]\n        if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) ||\n            inputShape.length === 1) {\n            throw new ValueError('A `Concatenate` layer should be called on a list of at least 2 ' +\n                'inputs');\n        }\n        inputShape = inputShape;\n        let allNoneShape = true;\n        for (const shape of inputShape) {\n            if (shape != null) {\n                allNoneShape = false;\n                break;\n            }\n        }\n        if (allNoneShape) {\n            return;\n        }\n        const shapeSet = [];\n        for (let i = 0; i < inputShape.length; ++i) {\n            const shapeWithoutConcatAxis = inputShape[i].slice();\n            shapeWithoutConcatAxis.splice(this.axis, 1);\n            let exists = false;\n            for (const shape of shapeSet) {\n                if (util.arraysEqual(shape, shapeWithoutConcatAxis)) {\n                    exists = true;\n                    break;\n                }\n            }\n            if (!exists) {\n                shapeSet.push(shapeWithoutConcatAxis);\n            }\n        }\n        if (shapeSet.length > 1) {\n            throw new ValueError('A `Concatenate` layer requires inputs with matching shapes ' +\n                'except for the concat axis. Got input shapes: ' +\n                JSON.stringify(inputShape));\n        }\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            return K.concatenate(inputs, this.axis);\n        });\n    }\n    computeOutputShape(inputShape) {\n        if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) {\n            throw new ValueError('A `Concatenate` layer should be called on a list of inputs.');\n        }\n        const inputShapes = inputShape;\n        const outputShape = inputShapes[0].slice();\n        const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis;\n        // Porting Note: the line above is because TypeScript doesn't support\n        //   negative indices.\n        for (const shape of inputShapes.slice(1)) {\n            if (outputShape[axis] == null || shape[axis] == null) {\n                outputShape[axis] = null;\n                break;\n            }\n            outputShape[axis] += shape[axis];\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        if (mask == null) {\n            return null;\n        }\n        if (!Array.isArray(mask)) {\n            throw new ValueError('`mask` should be an array for Concatenate');\n        }\n        if (!Array.isArray(inputs)) {\n            throw new ValueError('`inputs` should be an array for Concatenate');\n        }\n        if (mask.length !== inputs.length) {\n            throw new ValueError(`Mismatch in the length of mask (${mask.length}) ` +\n                `and the legnth of inputs (${inputs.length})`);\n        }\n        return tfc.tidy(() => {\n            let allNullMasks = true;\n            mask.forEach(m => {\n                if (m != null) {\n                    allNullMasks = false;\n                    return;\n                }\n            });\n            if (allNullMasks) {\n                return null;\n            }\n            const outputMasks = [];\n            for (let i = 0; i < inputs.length; ++i) {\n                if (mask[i] == null) {\n                    // Input is unmasked. Append all 1's to masks.\n                    outputMasks.push(tfc.onesLike(inputs[i]).asType('bool'));\n                }\n                else if (mask[i].rank < inputs[i].rank) {\n                    // Mask is smaller than the input, expand it.\n                    outputMasks.push(tfc.expandDims(mask[i], -1));\n                }\n                else {\n                    outputMasks.push(mask[i]);\n                }\n            }\n            const concatenatedMasks = tfc.concat(outputMasks, this.axis);\n            return tfc.all(concatenatedMasks, -1, false);\n        });\n    }\n    getConfig() {\n        const config = {\n            'axis': this.axis,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nConcatenate.className = 'Concatenate';\nserialization.registerClass(Concatenate);\n/**\n * Concatenate an `Array` of inputs.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Concatenate` layer, by using no input argument\n *    or a single configuration argument. The resultant `Concatenate` layer can\n *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const concatLayer = tf.layers.concatenate();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 3]});\n * const input2 = tf.input({shape: [2, 4]});\n * const output = concatLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 7], with the first dimension as the undetermined batch\n * // dimension and the last dimension as the result of concatenating the\n * // last dimensions of the two inputs.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 3]});\n * const input2 = tf.input({shape: [2, 4]});\n * const output = tf.layers.concatenate([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension and the last dimension as the result of concatenating the\n * // last dimensions of the two inputs.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);\n * const input2 = tf.tensor2d([[10, 20], [30, 40]], [2, 2]);\n * tf.layers.concatenate([input1, input2]).print();\n * // Gives [[1, 2, 10, 20], [3, 4, 30, 40]].\n *\n */\nexport function concatenate(config) {\n    if (Array.isArray(config)) {\n        const layer = new Concatenate({});\n        return layer.apply(config);\n    }\n    else {\n        return new Concatenate(config);\n    }\n}\n/**\n * Interpretable potentially negative axis index.\n *\n * For example, given axis = -1, and dim = 3, this function will return 2.\n *\n * @param axis The axis index, may be a positive, zero or negative integer.\n * @param dim Total number of dimensions, a positive integer.\n * @returns A non-negative axis index equivalent to the input `axis`.\n */\nfunction interpretAxis(axis, dim) {\n    while (axis < 0) {\n        axis += dim;\n    }\n    return axis;\n}\nfunction batchDot(x, y, axes) {\n    if (x.shape.length > 3 || y.shape.length > 3) {\n        throw new NotImplementedError('batchDot is not implemented for tensors of 4D or higher rank yet');\n    }\n    tfc.util.assert(x.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, ` +\n        `but got ${x.shape.length}`);\n    tfc.util.assert(x.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, ` +\n        `but got ${y.shape.length}`);\n    if (typeof axes === 'number') {\n        axes = [axes, axes];\n    }\n    if (x.dtype === 'complex64' || y.dtype === 'complex64') {\n        throw new NotImplementedError('batchDot is not implemented for complex64-type Tensors yet.');\n    }\n    const xNDim = x.shape.length;\n    const yNDim = y.shape.length;\n    if (axes == null) {\n        // Behave like batchMatmul by default.\n        axes = [xNDim - 1, yNDim - 2];\n    }\n    const axesArray = axes;\n    return tfc.tidy(() => {\n        let diff;\n        if (xNDim > yNDim) {\n            diff = xNDim - yNDim;\n            const diffShape = [];\n            for (let i = 0; i < diff; ++i) {\n                diffShape.push(1);\n            }\n            y = y.reshape(y.shape.concat(diffShape));\n        }\n        else if (yNDim > xNDim) {\n            diff = yNDim - xNDim;\n            const diffShape = [];\n            for (let i = 0; i < diff; ++i) {\n                diffShape.push(1);\n            }\n            x = x.reshape(x.shape.concat(diffShape));\n        }\n        else {\n            diff = 0;\n        }\n        let out;\n        if (x.shape.length === 2 && y.shape.length === 2) {\n            if (axesArray[0] === axesArray[1]) {\n                out = x.mul(y).sum(axesArray[0]);\n            }\n            else {\n                out = x.transpose([1, 0]).mul(y).sum(axesArray[1]);\n            }\n        }\n        else {\n            const adjX = axesArray[0] !== x.shape.length - 1;\n            const adjY = axesArray[1] === y.shape.length - 1;\n            out = x.matMul(y, adjX, adjY);\n        }\n        if (diff > 0) {\n            let idx;\n            if (xNDim > yNDim) {\n                idx = xNDim + yNDim - 3;\n            }\n            else {\n                idx = xNDim - 1;\n            }\n            const squeezeAxes = [];\n            for (let i = idx; i < idx + diff; ++i) {\n                squeezeAxes.push(i);\n            }\n            out = out.squeeze(squeezeAxes);\n        }\n        if (out.shape.length === 1) {\n            out = out.expandDims(1);\n        }\n        return out;\n    });\n}\nexport class Dot extends Merge {\n    constructor(args) {\n        super(args);\n        this.axes = args.axes;\n        this.normalize = args.normalize == null ? false : args.normalize;\n        this.supportsMasking = true;\n        this.reshapeRequired = false;\n    }\n    build(inputShape) {\n        tfc.util.assert(Array.isArray(inputShape) && inputShape.length === 2 &&\n            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');\n        const shape1 = inputShape[0];\n        const shape2 = inputShape[1];\n        if (shape1.length > 3 || shape2.length > 3) {\n            throw new NotImplementedError('Dot layer does not support tensors of 4D or higher rank yet.');\n        }\n        const axes = this.interpretAxes(shape1, shape2);\n        if (shape1[axes[0]] !== shape2[axes[1]]) {\n            throw new ValueError(`Dimension incompatibility: ` +\n                `${shape1[axes[0]]} !== ${shape2[axes[1]]}`);\n        }\n    }\n    mergeFunction(inputs) {\n        if (inputs.length !== 2) {\n            throw new ValueError('A `Dot` layer must be called on exactly 2 inputs, ' +\n                `but received ${inputs.length} input(s).`);\n        }\n        let x1 = inputs[0];\n        let x2 = inputs[1];\n        let axes;\n        if (!Array.isArray(this.axes)) {\n            axes = [\n                interpretAxis(this.axes, x1.shape.length),\n                interpretAxis(this.axes, x2.shape.length)\n            ];\n        }\n        else {\n            axes = this.axes.map((axis, i) => interpretAxis(axis, inputs[i].shape.length));\n        }\n        if (this.normalize) {\n            x1 = l2Normalize(x1, axes[0]);\n            x2 = l2Normalize(x2, axes[1]);\n        }\n        return batchDot(x1, x2, axes);\n    }\n    interpretAxes(shape1, shape2) {\n        let axes;\n        if (!Array.isArray(this.axes)) {\n            // `this.axes` is a single integer.\n            axes = [\n                interpretAxis(this.axes, shape1.length),\n                interpretAxis(this.axes, shape2.length)\n            ];\n        }\n        else {\n            // `this.axes` is an Array of integers.\n            axes = this.axes;\n        }\n        return axes;\n    }\n    computeOutputShape(inputShape) {\n        tfc.util.assert(Array.isArray(inputShape) && inputShape.length === 2 &&\n            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');\n        const shape1 = inputShape[0].slice();\n        const shape2 = inputShape[1].slice();\n        if (shape1.length > 3 || shape2.length > 3) {\n            throw new NotImplementedError('Dot layer does not support tensors of 4D or higher rank yet.');\n        }\n        const axes = this.interpretAxes(shape1, shape2);\n        shape1.splice(axes[0], 1);\n        shape2.splice(axes[1], 1);\n        shape2.splice(0, 1);\n        const outputShape = shape1.concat(shape2);\n        if (outputShape.length === 1) {\n            outputShape.push(1);\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        return null;\n    }\n    getConfig() {\n        const config = {\n            'axes': this.axes,\n            'normalize': this.normalize\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nDot.className = 'Dot';\nserialization.registerClass(Dot);\n// TODO(cais): Add functional interfaces for the merge layers.\n//# sourceMappingURL=merge.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Noise Layers.\n */\nimport { greaterEqual, randomUniform, serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { Layer } from '../engine/topology';\nimport { getExactlyOneTensor } from '../utils/types_utils';\nexport class GaussianNoise extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.stddev = args.stddev;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { stddev: this.stddev };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const noised = () => K.randomNormal(input.shape, 0, this.stddev).add(input);\n            const output = K.inTrainPhase(noised, () => input, kwargs['training'] || false);\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nGaussianNoise.className = 'GaussianNoise';\nserialization.registerClass(GaussianNoise);\nexport class GaussianDropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.rate = args.rate;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { rate: this.rate };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            if (this.rate > 0 && this.rate < 1) {\n                const noised = () => {\n                    const stddev = Math.sqrt(this.rate / (1 - this.rate));\n                    return input.mul(K.randomNormal(input.shape, 1, stddev));\n                };\n                return K.inTrainPhase(noised, () => input, kwargs['training'] || false);\n            }\n            return input;\n        });\n    }\n}\n/** @nocollapse */\nGaussianDropout.className = 'GaussianDropout';\nserialization.registerClass(GaussianDropout);\n/**\n * Applies Alpha Dropout to the input.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs\n * to their original values, in order to ensure the self-normalizing property\n * even after this dropout.\n * Alpha Dropout fits well to Scaled Exponential Linear Units\n * by randomly setting activations to the negative saturation value.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the\n *     shape for randomly generated keep/drop flags.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n */\nexport class AlphaDropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.rate = args.rate;\n        this.noiseShape = args.noiseShape;\n    }\n    _getNoiseShape(inputs) {\n        return this.noiseShape || getExactlyOneTensor(inputs).shape;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { rate: this.rate };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.rate < 1 && this.rate > 0) {\n                const noiseShape = this._getNoiseShape(inputs);\n                const droppedInputs = () => {\n                    const input = getExactlyOneTensor(inputs);\n                    const alpha = 1.6732632423543772848170429916717;\n                    const scale = 1.0507009873554804934193349852946;\n                    const alphaP = -alpha * scale;\n                    let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate);\n                    keptIdx = K.cast(keptIdx, 'float32'); // get default dtype.\n                    // Get affine transformation params.\n                    const a = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5;\n                    const b = -a * alphaP * this.rate;\n                    // Apply mask.\n                    const x = input.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP));\n                    return x.mul(a).add(b);\n                };\n                return K.inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs['training'] || false);\n            }\n            return inputs;\n        });\n    }\n}\n/** @nocollapse */\nAlphaDropout.className = 'AlphaDropout';\nserialization.registerClass(AlphaDropout);\n//# sourceMappingURL=noise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Normalization layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { moments, serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Applies batch normalization on x given mean, var, beta and gamma.\n *\n * I.e. returns:\n *   `output = (x - mean) / (sqrt(var) + epsilon) * gamma + beta`\n *\n * @param x Input tensor.\n * @param mean Mean of batch.\n * @param variance Variance of batch.\n * @param beta Tensor with which to center the input.\n * @param gamma Tensor by which to scale the input.\n * @param epsilon Fuzz factor.\n * @returns The result of the batch normalization.\n */\nexport function batchNormalization(x, mean, variance, beta, gamma, epsilon = 1e-3) {\n    let out;\n    if (x.rank === 2) {\n        out = tfc.batchNorm2d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else if (x.rank === 3) {\n        // TODO(cais): Check rank; give proper error message.\n        out = tfc.batchNorm3d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else if (x.rank === 4) {\n        out = tfc.batchNorm4d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else {\n        throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} ` +\n            `yet`);\n    }\n    return out;\n}\n/**\n * Non-broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    return tidy(() => {\n        const meanAndVariance = tfc.moments(x, reductionAxes);\n        const mean = meanAndVariance.mean;\n        const variance = meanAndVariance.variance;\n        const normed = batchNormalization(x, mean, variance, beta, gamma, epsilon);\n        return [normed, mean, variance];\n    });\n}\n/**\n * Broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    return tidy(() => {\n        const meanAndVariance = tfc.moments(x, reductionAxes);\n        const mean = meanAndVariance.mean;\n        const variance = meanAndVariance.variance;\n        const targetShape = [];\n        for (const axis of math_utils.range(0, x.rank)) {\n            if (reductionAxes.indexOf(axis) !== -1) {\n                targetShape.push(1);\n            }\n            else {\n                targetShape.push(x.shape[axis]);\n            }\n        }\n        const broadcastMean = mean.reshape(targetShape);\n        const broadcastVariance = variance.reshape(targetShape);\n        const broadcastGamma = gamma == null ? null : gamma.reshape(targetShape);\n        const broadcastBeta = beta == null ? null : beta.reshape(targetShape);\n        const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon);\n        return [normed, mean, variance];\n    });\n}\n/**\n * Batch normalization for use in training (not inference).\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nexport function normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    if (util.arraysEqual(reductionAxes.slice().sort(), math_utils.range(0, x.rank - 1))) {\n        return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);\n    }\n    else {\n        return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);\n    }\n}\nexport class BatchNormalization extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.supportsMasking = true;\n        this.axis = args.axis == null ? -1 : args.axis;\n        this.momentum = args.momentum == null ? 0.99 : args.momentum;\n        this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n        this.center = args.center == null ? true : args.center;\n        this.scale = args.scale == null ? true : args.scale;\n        this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n        this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n        this.movingMeanInitializer =\n            getInitializer(args.movingMeanInitializer || 'zeros');\n        this.movingVarianceInitializer =\n            getInitializer(args.movingVarianceInitializer || 'ones');\n        this.betaConstraint = getConstraint(args.betaConstraint);\n        this.gammaConstraint = getConstraint(args.gammaConstraint);\n        this.betaRegularizer = getRegularizer(args.betaRegularizer);\n        this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const axis = this.axis >= 0 ? this.axis : (this.axis + inputShape.length);\n        const dim = inputShape[axis];\n        if (dim == null) {\n            throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but ` +\n                `the layer received an input with shape ` +\n                `${JSON.stringify(inputShape)}.`);\n        }\n        this.inputSpec =\n            [new InputSpec({ ndim: inputShape.length, axes: { [axis]: dim } })];\n        const shape = [dim];\n        if (this.scale) {\n            this.gamma = this.addWeight('gamma', shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint);\n        }\n        if (this.center) {\n            this.beta = this.addWeight('beta', shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint);\n        }\n        this.movingMean = this.addWeight('moving_mean', shape, null, this.movingMeanInitializer, null, false);\n        this.movingVariance = this.addWeight('moving_variance', shape, null, this.movingVarianceInitializer, null, false);\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            const input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            const ndim = inputShape.length;\n            const reductionAxes = math_utils.range(0, ndim);\n            const axis = this.axis >= 0 ? this.axis : (this.axis + ndim);\n            reductionAxes.splice(axis, 1);\n            const broadcastShape = generic_utils.pyListRepeat(1, ndim);\n            broadcastShape[axis] = inputShape[axis];\n            const sortedReductionAxes = reductionAxes.slice();\n            sortedReductionAxes.sort();\n            const needsBroadcasting = !util.arraysEqual(sortedReductionAxes, math_utils.range(0, ndim).slice(0, ndim - 1));\n            const normalizeInference = () => {\n                if (needsBroadcasting) {\n                    const broadcastMovingMean = this.movingMean.read().reshape(broadcastShape);\n                    const broadcastMovingVariance = this.movingVariance.read().reshape(broadcastShape);\n                    const broadcastBeta = this.center ? this.beta.read().reshape(broadcastShape) : null;\n                    const broadcastGamma = this.scale ? this.gamma.read().reshape(broadcastShape) : null;\n                    return batchNormalization(input, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon);\n                }\n                else {\n                    return batchNormalization(input, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);\n                }\n            };\n            if (!training) {\n                return normalizeInference();\n            }\n            const [normedTraining, mean, variance] = normalizeBatchInTraining(input, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon);\n            const doMovingAverage = (variable, value, momentum) => {\n                tfc.tidy(() => {\n                    const decay = 1 - momentum;\n                    const origValue = variable.read();\n                    const updateDelta = origValue.sub(value).mul(decay);\n                    variable.write(origValue.sub(updateDelta));\n                });\n            };\n            // Perform updates to moving mean and moving variance for training.\n            // Porting Note: In PyKeras, these updates to `movingMean` and\n            //   `movingAverage` are done as a deferred Graph, added to the `Layer`'s\n            //   `update`s using the `add_update()` method. Here we do it imperatively\n            //   and encapsulate the updates in a function that is invoked\n            //   immediately.\n            const updateMovingMeanAndVariance = () => {\n                doMovingAverage(this.movingMean, mean, this.momentum);\n                doMovingAverage(this.movingVariance, variance, this.momentum);\n            };\n            updateMovingMeanAndVariance();\n            return normedTraining;\n        });\n    }\n    getConfig() {\n        const config = {\n            axis: this.axis,\n            momentum: this.momentum,\n            epsilon: this.epsilon,\n            center: this.center,\n            scale: this.scale,\n            betaInitializer: serializeInitializer(this.betaInitializer),\n            gammaInitializer: serializeInitializer(this.gammaInitializer),\n            movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),\n            movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer),\n            betaRegularizer: serializeRegularizer(this.betaRegularizer),\n            gammaRegularizer: serializeRegularizer(this.gammaRegularizer),\n            betaConstraint: serializeConstraint(this.betaConstraint),\n            gammaConstraint: serializeConstraint(this.gammaConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nBatchNormalization.className = 'BatchNormalization';\nserialization.registerClass(BatchNormalization);\nexport class LayerNormalization extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.axis = args.axis == null ? -1 : args.axis;\n        if (typeof this.axis === 'number') {\n            if (!Number.isInteger(this.axis)) {\n                throw new Error(`Expected axis to be an integer, but received ${this.axis}`);\n            }\n        }\n        else if (Array.isArray(this.axis)) {\n            for (const axis of this.axis) {\n                if (!Number.isInteger(axis)) {\n                    throw new Error(`Expected axis to be an array of integers, ` +\n                        `but received ${JSON.stringify(this.axis)}`);\n                }\n            }\n        }\n        else {\n            throw new Error(`Expected axis to be an integer or an array of integers, ` +\n                `but received ${JSON.stringify(this.axis)}`);\n        }\n        this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n        this.center = args.center == null ? true : args.center;\n        this.scale = args.scale == null ? true : args.scale;\n        this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n        this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n        this.betaRegularizer = getRegularizer(args.betaRegularizer);\n        this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n        this.supportsMasking = true;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const nDims = inputShape.length;\n        // Convert axis to array and resolve negatives.\n        if (typeof this.axis === 'number') {\n            this.axis = [this.axis];\n        }\n        for (let i = 0; i < this.axis.length; ++i) {\n            if (this.axis[i] < 0) {\n                this.axis[i] += nDims;\n            }\n        }\n        // Further validate axes.\n        for (const axis of this.axis) {\n            if (axis < 0 || axis >= nDims) {\n                throw new Error(`Invalid axis: ${axis}`);\n            }\n        }\n        if (this.axis.length !== generic_utils.unique(this.axis).length) {\n            throw new Error(`Found duplicate axes in: ${this.axis}`);\n        }\n        const paramShape = this.axis.map(axis => inputShape[axis]);\n        const trainable = true;\n        if (this.scale) {\n            this.gamma = this.addWeight('gamma', paramShape, 'float32', this.gammaInitializer, this.gammaRegularizer, trainable);\n        }\n        else {\n            this.gamma = null;\n        }\n        if (this.center) {\n            this.beta = this.addWeight('beta', paramShape, 'float32', this.betaInitializer, this.betaRegularizer, trainable);\n        }\n        else {\n            this.beta = null;\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        const input = getExactlyOneTensor(inputs);\n        const inputShape = input.shape;\n        const nDims = inputShape.length;\n        return tidy(() => {\n            const keepDims = true;\n            let { mean, variance } = moments(input, this.axis, keepDims);\n            const broadcastShape = generic_utils.pyListRepeat(1, nDims);\n            for (const dim of this.axis) {\n                broadcastShape[dim] = inputShape[dim];\n            }\n            const broadcast = (v) => {\n                if (v != null && v.shape.length !== nDims &&\n                    this.axis !== [nDims - 1]) {\n                    return v.reshape(broadcastShape);\n                }\n                else {\n                    return v;\n                }\n            };\n            let scale = broadcast(this.gamma.read());\n            let offset = broadcast(this.beta.read());\n            // TODO(https://github.com/tensorflow/tfjs/issues/2120): The tiling below\n            // is a workaround for the limitation of core's batchNormalization?d don't\n            // support broadcasting in their gradients. In addition, the tiling is\n            // necessary to ensure correctness on the browser CPU backend regardless\n            // of forward or backward computation. Remove this workaround once the\n            // limitation is addressed. See .\n            const momentsTiling = [];\n            const scaleOffsetTiling = [];\n            for (let i = 0; i < nDims; ++i) {\n                if (this.axis.indexOf(i) !== -1) {\n                    momentsTiling.push(inputShape[i]);\n                    scaleOffsetTiling.push(1);\n                }\n                else {\n                    momentsTiling.push(1);\n                    scaleOffsetTiling.push(inputShape[i]);\n                }\n            }\n            mean = mean.tile(momentsTiling);\n            variance = variance.tile(momentsTiling);\n            scale = scale.tile(scaleOffsetTiling);\n            offset = offset.tile(scaleOffsetTiling);\n            return batchNormalization(input, mean, variance, offset, scale, this.epsilon);\n        });\n    }\n    getConfig() {\n        const config = {\n            axis: this.axis,\n            epsilon: this.epsilon,\n            center: this.center,\n            scale: this.scale,\n            betaInitializer: serializeInitializer(this.betaInitializer),\n            gammaInitializer: serializeInitializer(this.gammaInitializer),\n            betaRegularizer: serializeRegularizer(this.betaRegularizer),\n            gammaRegularizer: serializeRegularizer(this.gammaRegularizer)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nLayerNormalization.className = 'LayerNormalization';\nserialization.registerClass(LayerNormalization);\n//# sourceMappingURL=normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Padding Layers.\n */\n// Porting Note: In Python Keras, the padding layers are in convolutional.py,\n//   but we decided to put them in a separate file (padding.ts) for clarity.\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Pads the middle dimension of a 3D tensor.\n *\n * @param x Input `tf.Tensor` to be padded.\n * @param padding `Array` of 2 integers, how many zeros to add at the start and\n *   end of the middle dimension (i.e., dimension 1).\n * @return A padded 3D `tf.Tensor`.\n */\nexport function temporalPadding(x, padding) {\n    return tidy(() => {\n        if (x.rank !== 3) {\n            throw new ValueError(`temporalPadding expects input tensor to be 3-D, but received a ` +\n                `${x.rank}-D tensor.`);\n        }\n        if (padding == null) {\n            padding = [1, 1];\n        }\n        if (padding.length !== 2) {\n            throw new ValueError(`temporalPadding expects input padding pattern to be a length-2 ` +\n                `array, but received a length-${padding.length} array.`);\n        }\n        const pattern = [[0, 0], padding, [0, 0]];\n        return tfc.pad(x, pattern);\n    });\n}\n/**\n * Pads the 2nd and 3rd dimensions of a 4D tensor.\n *\n * @param x Input `tf.Tensor` to be padded.\n * @param padding `Array` of two `Array`s, each of which is an `Array` of two\n *   integers. The amount of padding at the beginning and end of the 2nd and 3rd\n *   dimensions, respectively.\n * @param dataFormat 'channelsLast' (default) or 'channelsFirst'.\n * @return Padded 4D `tf.Tensor`.\n */\nexport function spatial2dPadding(x, padding, dataFormat) {\n    return tidy(() => {\n        if (x.rank !== 4) {\n            throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ` +\n                `${x.rank}-D tensor.`);\n        }\n        if (padding == null) {\n            padding = [[1, 1], [1, 1]];\n        }\n        if (padding.length !== 2 || padding[0].length !== 2 ||\n            padding[1].length !== 2) {\n            throw new ValueError('spatial2dPadding expects `padding` to be an Array of two Arrays, ' +\n                'each of which is an Array of two integers.');\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (dataFormat !== 'channelsLast' && dataFormat !== 'channelsFirst') {\n            throw new ValueError(`Unknown data format: ${dataFormat}. ` +\n                `Supported data formats are 'channelsLast' and 'channelsFirst.`);\n        }\n        let pattern;\n        if (dataFormat === 'channelsFirst') {\n            pattern = [[0, 0], [0, 0], padding[0], padding[1]];\n        }\n        else {\n            pattern = [[0, 0], padding[0], padding[1], [0, 0]];\n        }\n        return tfc.pad(x, pattern);\n    });\n}\nexport class ZeroPadding2D extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.dataFormat =\n            args.dataFormat == null ? imageDataFormat() : args.dataFormat;\n        // TODO(cais): Maybe refactor the following logic surrounding `padding`\n        //   into a helper method.\n        if (args.padding == null) {\n            this.padding = [[1, 1], [1, 1]];\n        }\n        else if (typeof args.padding === 'number') {\n            this.padding =\n                [[args.padding, args.padding], [args.padding, args.padding]];\n        }\n        else {\n            args.padding = args.padding;\n            if (args.padding.length !== 2) {\n                throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but ` +\n                    `received a length-${args.padding.length} array.`);\n            }\n            let heightPadding;\n            let widthPadding;\n            if (typeof args.padding[0] === 'number') {\n                heightPadding = [args.padding[0], args.padding[0]];\n                widthPadding = [args.padding[1], args.padding[1]];\n            }\n            else {\n                args.padding = args.padding;\n                if (args.padding[0].length !== 2) {\n                    throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, ` +\n                        `but received a length-${args.padding[0].length} array.`);\n                }\n                heightPadding = args.padding[0];\n                if (args.padding[1].length !== 2) {\n                    throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, ` +\n                        `but received a length-${args.padding[1].length} array.`);\n                }\n                widthPadding = args.padding[1];\n            }\n            this.padding = [heightPadding, widthPadding];\n        }\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let rows;\n        let cols;\n        if (this.dataFormat === 'channelsFirst') {\n            if (inputShape[2] != null && inputShape[2] >= 0) {\n                rows = inputShape[2] + this.padding[0][0] + this.padding[0][1];\n            }\n            else {\n                rows = null;\n            }\n            if (inputShape[3] != null && inputShape[3] >= 0) {\n                cols = inputShape[3] + this.padding[1][0] + this.padding[1][1];\n            }\n            else {\n                cols = null;\n            }\n            return [inputShape[0], inputShape[1], rows, cols];\n        }\n        else {\n            if (inputShape[1] != null && inputShape[1] >= 0) {\n                rows = inputShape[1] + this.padding[0][0] + this.padding[0][1];\n            }\n            else {\n                rows = null;\n            }\n            if (inputShape[2] != null && inputShape[2] >= 0) {\n                cols = inputShape[2] + this.padding[1][0] + this.padding[1][1];\n            }\n            else {\n                cols = null;\n            }\n            return [inputShape[0], rows, cols, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => spatial2dPadding(getExactlyOneTensor(inputs), this.padding, this.dataFormat));\n    }\n    getConfig() {\n        const config = {\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nZeroPadding2D.className = 'ZeroPadding2D';\nserialization.registerClass(ZeroPadding2D);\n//# sourceMappingURL=padding.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Pooling Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode, checkPoolMode } from '../common';\nimport { InputSpec } from '../engine/topology';\nimport { Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { convOutputLength } from '../utils/conv_utils';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { preprocessConv2DInput, preprocessConv3DInput } from './convolutional';\n/**\n * 2D pooling.\n * @param x\n * @param poolSize\n * @param stridesdes strides. Defaults to [1, 1].\n * @param padding padding. Defaults to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param poolMode Mode of pooling. Defaults to 'max'.\n * @returns Result of the 2D pooling.\n */\nexport function pool2d(x, poolSize, strides, padding, dataFormat, poolMode) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        checkPoolMode(poolMode);\n        checkPaddingMode(padding);\n        if (strides == null) {\n            strides = [1, 1];\n        }\n        if (padding == null) {\n            padding = 'valid';\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (poolMode == null) {\n            poolMode = 'max';\n        }\n        // TODO(cais): Remove the preprocessing step once deeplearn.js supports\n        // dataFormat as an input argument.\n        x = preprocessConv2DInput(x, dataFormat); // x is NHWC after preprocessing.\n        let y;\n        const paddingString = (padding === 'same') ? 'same' : 'valid';\n        if (poolMode === 'max') {\n            // TODO(cais): Rank check?\n            y = tfc.maxPool(x, poolSize, strides, paddingString);\n        }\n        else { // 'avg'\n            // TODO(cais): Check the dtype and rank of x and give clear error message\n            //   if those are incorrect.\n            y = tfc.avgPool(\n            // TODO(cais): Rank check?\n            x, poolSize, strides, paddingString);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]); // NHWC -> NCHW.\n        }\n        return y;\n    });\n}\n/**\n * 3D pooling.\n * @param x\n * @param poolSize. Default to [1, 1, 1].\n * @param strides strides. Defaults to [1, 1, 1].\n * @param padding padding. Defaults to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param poolMode Mode of pooling. Defaults to 'max'.\n * @returns Result of the 3D pooling.\n */\nexport function pool3d(x, poolSize, strides, padding, dataFormat, poolMode) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        checkPoolMode(poolMode);\n        checkPaddingMode(padding);\n        if (strides == null) {\n            strides = [1, 1, 1];\n        }\n        if (padding == null) {\n            padding = 'valid';\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (poolMode == null) {\n            poolMode = 'max';\n        }\n        // x is NDHWC after preprocessing.\n        x = preprocessConv3DInput(x, dataFormat);\n        let y;\n        const paddingString = (padding === 'same') ? 'same' : 'valid';\n        if (poolMode === 'max') {\n            y = tfc.maxPool3d(x, poolSize, strides, paddingString);\n        }\n        else { // 'avg'\n            y = tfc.avgPool3d(x, poolSize, strides, paddingString);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 4, 1, 2, 3]); // NDHWC -> NCDHW.\n        }\n        return y;\n    });\n}\n/**\n * Abstract class for different pooling 1D layers.\n */\nexport class Pooling1D extends Layer {\n    /**\n     *\n     * @param args Parameters for the Pooling layer.\n     *\n     * config.poolSize defaults to 2.\n     */\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = 2;\n        }\n        super(args);\n        if (typeof args.poolSize === 'number') {\n            this.poolSize = [args.poolSize];\n        }\n        else if (Array.isArray(args.poolSize) &&\n            args.poolSize.length === 1 &&\n            typeof args.poolSize[0] === 'number') {\n            this.poolSize = args.poolSize;\n        }\n        else {\n            throw new ValueError(`poolSize for 1D convolutional layer must be a number or an ` +\n                `Array of a single number, but received ` +\n                `${JSON.stringify(args.poolSize)}`);\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else {\n            if (typeof args.strides === 'number') {\n                this.strides = [args.strides];\n            }\n            else if (Array.isArray(args.strides) &&\n                args.strides.length === 1 &&\n                typeof args.strides[0] === 'number') {\n                this.strides = args.strides;\n            }\n            else {\n                throw new ValueError(`strides for 1D convolutional layer must be a number or an ` +\n                    `Array of a single number, but received ` +\n                    `${JSON.stringify(args.strides)}`);\n            }\n        }\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const length = convOutputLength(inputShape[1], this.poolSize[0], this.padding, this.strides[0]);\n        return [inputShape[0], length, inputShape[2]];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Add dummy last dimension.\n            inputs = K.expandDims(getExactlyOneTensor(inputs), 2);\n            const output = this.poolingFunction(getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, 'channelsLast');\n            // Remove dummy last dimension.\n            return tfc.squeeze(output, [2]);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling1D extends Pooling1D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling1D.className = 'MaxPooling1D';\nserialization.registerClass(MaxPooling1D);\nexport class AveragePooling1D extends Pooling1D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling1D.className = 'AveragePooling1D';\nserialization.registerClass(AveragePooling1D);\n/**\n * Abstract class for different pooling 2D layers.\n */\nexport class Pooling2D extends Layer {\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = [2, 2];\n        }\n        super(args);\n        this.poolSize = Array.isArray(args.poolSize) ?\n            args.poolSize :\n            [args.poolSize, args.poolSize];\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else if (Array.isArray(args.strides)) {\n            if (args.strides.length !== 2) {\n                throw new ValueError(`If the strides property of a 2D pooling layer is an Array, ` +\n                    `it is expected to have a length of 2, but received length ` +\n                    `${args.strides.length}.`);\n            }\n            this.strides = args.strides;\n        }\n        else {\n            // `config.strides` is a number.\n            this.strides = [args.strides, args.strides];\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let rows = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        let cols = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        rows =\n            convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]);\n        cols =\n            convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], inputShape[1], rows, cols];\n        }\n        else {\n            return [inputShape[0], rows, cols, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n            dataFormat: this.dataFormat\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling2D extends Pooling2D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling2D.className = 'MaxPooling2D';\nserialization.registerClass(MaxPooling2D);\nexport class AveragePooling2D extends Pooling2D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling2D.className = 'AveragePooling2D';\nserialization.registerClass(AveragePooling2D);\n/**\n * Abstract class for different pooling 3D layers.\n */\nexport class Pooling3D extends Layer {\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = [2, 2, 2];\n        }\n        super(args);\n        this.poolSize = Array.isArray(args.poolSize) ?\n            args.poolSize :\n            [args.poolSize, args.poolSize, args.poolSize];\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else if (Array.isArray(args.strides)) {\n            if (args.strides.length !== 3) {\n                throw new ValueError(`If the strides property of a 3D pooling layer is an Array, ` +\n                    `it is expected to have a length of 3, but received length ` +\n                    `${args.strides.length}.`);\n            }\n            this.strides = args.strides;\n        }\n        else {\n            // `config.strides` is a number.\n            this.strides = [args.strides, args.strides, args.strides];\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 5 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let depths = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        let rows = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        let cols = this.dataFormat === 'channelsFirst' ? inputShape[4] : inputShape[3];\n        depths = convOutputLength(depths, this.poolSize[0], this.padding, this.strides[0]);\n        rows =\n            convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]);\n        cols =\n            convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], inputShape[1], depths, rows, cols];\n        }\n        else {\n            return [inputShape[0], depths, rows, cols, inputShape[4]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n            dataFormat: this.dataFormat\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling3D extends Pooling3D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool3d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling3D.className = 'MaxPooling3D';\nserialization.registerClass(MaxPooling3D);\nexport class AveragePooling3D extends Pooling3D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool3d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling3D.className = 'AveragePooling3D';\nserialization.registerClass(AveragePooling3D);\n/**\n * Abstract class for different global pooling 1D layers.\n */\nexport class GlobalPooling1D extends Layer {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n    }\n    computeOutputShape(inputShape) {\n        return [inputShape[0], inputShape[2]];\n    }\n    call(inputs, kwargs) {\n        throw new NotImplementedError();\n    }\n}\nexport class GlobalAveragePooling1D extends GlobalPooling1D {\n    constructor(args) {\n        super(args || {});\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            return tfc.mean(input, 1);\n        });\n    }\n}\n/** @nocollapse */\nGlobalAveragePooling1D.className = 'GlobalAveragePooling1D';\nserialization.registerClass(GlobalAveragePooling1D);\nexport class GlobalMaxPooling1D extends GlobalPooling1D {\n    constructor(args) {\n        super(args || {});\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            return tfc.max(input, 1);\n        });\n    }\n}\n/** @nocollapse */\nGlobalMaxPooling1D.className = 'GlobalMaxPooling1D';\nserialization.registerClass(GlobalMaxPooling1D);\n/**\n * Abstract class for different global pooling 2D layers.\n */\nexport class GlobalPooling2D extends Layer {\n    constructor(args) {\n        super(args);\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = inputShape;\n        if (this.dataFormat === 'channelsLast') {\n            return [inputShape[0], inputShape[3]];\n        }\n        else {\n            return [inputShape[0], inputShape[1]];\n        }\n    }\n    call(inputs, kwargs) {\n        throw new NotImplementedError();\n    }\n    getConfig() {\n        const config = { dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class GlobalAveragePooling2D extends GlobalPooling2D {\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                return tfc.mean(input, [1, 2]);\n            }\n            else {\n                return tfc.mean(input, [2, 3]);\n            }\n        });\n    }\n}\n/** @nocollapse */\nGlobalAveragePooling2D.className = 'GlobalAveragePooling2D';\nserialization.registerClass(GlobalAveragePooling2D);\nexport class GlobalMaxPooling2D extends GlobalPooling2D {\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                return tfc.max(input, [1, 2]);\n            }\n            else {\n                return tfc.max(input, [2, 3]);\n            }\n        });\n    }\n}\n/** @nocollapse */\nGlobalMaxPooling2D.className = 'GlobalMaxPooling2D';\nserialization.registerClass(GlobalMaxPooling2D);\n//# sourceMappingURL=pooling.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Layers that augment the functionality of a base layer.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { InputSpec, Layer, SymbolicTensor } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { VALID_BIDIRECTIONAL_MERGE_MODES } from '../keras_format/common';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { rnn, standardizeArgs } from './recurrent';\nimport { deserialize } from './serialization';\n/**\n * Abstract wrapper base class.\n *\n * Wrappers take another layer and augment it in various ways.\n * Do not use this class as a layer, it is only an abstract base class.\n * Two usable wrappers are the `TimeDistributed` and `Bidirectional` wrappers.\n */\nexport class Wrapper extends Layer {\n    constructor(args) {\n        // Porting Note: In PyKeras, `self.layer` is set prior to the calling\n        //   `super()`. But we can't do that here due to TypeScript's restriction.\n        //   See: https://github.com/Microsoft/TypeScript/issues/8277\n        //   As a result, we have to add checks in `get trainable()` and\n        //   `set trainable()` below in order to prevent using `this.layer` when\n        //   its value is `undefined`. The super constructor does use the getter\n        //   and the setter of `this.layer`.\n        super(args);\n        this.layer = args.layer;\n    }\n    build(inputShape) {\n        this.built = true;\n    }\n    // TODO(cais): Implement activityRegularizer getter.\n    get trainable() {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        if (this.layer != null) {\n            return this.layer.trainable;\n        }\n        else {\n            return false;\n        }\n    }\n    set trainable(value) {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        if (this.layer != null) {\n            this.layer.trainable = value;\n        }\n    }\n    get trainableWeights() {\n        return this.layer.trainableWeights;\n    }\n    // TODO(cais): Implement setter for trainableWeights.\n    get nonTrainableWeights() {\n        return this.layer.nonTrainableWeights;\n    }\n    // TODO(cais): Implement setter for nonTrainableWeights.\n    get updates() {\n        // tslint:disable-next-line:no-any\n        return this.layer._updates;\n    }\n    // TODO(cais): Implement getUpdatesFor().\n    get losses() {\n        return this.layer.losses;\n    }\n    // TODO(cais): Implement getLossesFor().\n    getWeights() {\n        return this.layer.getWeights();\n    }\n    setWeights(weights) {\n        this.layer.setWeights(weights);\n    }\n    getConfig() {\n        const config = {\n            'layer': {\n                'className': this.layer.getClassName(),\n                'config': this.layer.getConfig(),\n            }\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.layer != null) {\n            this.layer.setFastWeightInitDuringBuild(value);\n        }\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const layerConfig = config['layer'];\n        const layer = deserialize(layerConfig, customObjects);\n        delete config['layer'];\n        const newConfig = { layer };\n        Object.assign(newConfig, config);\n        return new cls(newConfig);\n    }\n}\nexport class TimeDistributed extends Wrapper {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < 3) {\n            throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received ` +\n                `input shape ${JSON.stringify(inputShape)}`);\n        }\n        this.inputSpec = [{ shape: inputShape }];\n        const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        if (!this.layer.built) {\n            this.layer.build(childInputShape);\n            this.layer.built = true;\n        }\n        super.build(inputShape);\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        const childOutputShape = this.layer.computeOutputShape(childInputShape);\n        const timesteps = inputShape[1];\n        return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1));\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            // TODO(cais): Add 'training' and 'useLearningPhase' to kwargs.\n            inputs = getExactlyOneTensor(inputs);\n            // Porting Note: In tfjs-layers, `inputs` are always concrete tensor\n            // values. Hence the inputs can't have an undetermined first (batch)\n            // dimension, which is why we always use the K.rnn approach here.\n            const step = (inputs, states) => {\n                // TODO(cais): Add useLearningPhase.\n                // NOTE(cais): `layer.call` may return a length-1 array of Tensor in\n                //   some cases (e.g., `layer` is a `Sequential` instance), which is\n                //   why `getExactlyOneTensor` is used below.\n                const output = getExactlyOneTensor(this.layer.call(inputs, kwargs));\n                return [output, []];\n            };\n            const rnnOutputs = rnn(step, inputs, [], false /* goBackwards */, null /* mask */, null /* constants */, false /* unroll */, true /* needPerStepOutputs */);\n            const y = rnnOutputs[1];\n            // TODO(cais): Add activity regularization.\n            // TODO(cais): Add useLearningPhase.\n            return y;\n        });\n    }\n}\n/** @nocollapse */\nTimeDistributed.className = 'TimeDistributed';\nserialization.registerClass(TimeDistributed);\nexport function checkBidirectionalMergeMode(value) {\n    generic_utils.checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, 'BidirectionalMergeMode', value);\n}\nconst DEFAULT_BIDIRECTIONAL_MERGE_MODE = 'concat';\nexport class Bidirectional extends Wrapper {\n    constructor(args) {\n        super(args);\n        // Note: When creating `this.forwardLayer`, the original Layer object\n        //   (`config.layer`) ought to be cloned. This is why we call\n        //   `getConfig()` followed by `deserialize()`. Without this cloning,\n        //   the layer names saved during serialization will incorrectly contain\n        //   the 'forward_' prefix. In Python Keras, this is done using\n        //   `copy.copy` (shallow copy), which does not have a simple equivalent\n        //   in JavaScript. JavaScript's `Object.assign()` does not copy\n        //   methods.\n        const layerConfig = args.layer.getConfig();\n        const forwDict = {};\n        forwDict['className'] = args.layer.getClassName();\n        forwDict['config'] = layerConfig;\n        this.forwardLayer = deserialize(forwDict);\n        layerConfig['goBackwards'] =\n            layerConfig['goBackwards'] === true ? false : true;\n        const backDict = {};\n        backDict['className'] = args.layer.getClassName();\n        backDict['config'] = layerConfig;\n        this.backwardLayer = deserialize(backDict);\n        this.forwardLayer.name = 'forward_' + this.forwardLayer.name;\n        this.backwardLayer.name = 'backward_' + this.backwardLayer.name;\n        this.mergeMode = args.mergeMode === undefined ?\n            DEFAULT_BIDIRECTIONAL_MERGE_MODE :\n            args.mergeMode;\n        checkBidirectionalMergeMode(this.mergeMode);\n        if (args.weights) {\n            throw new NotImplementedError('weights support is not implemented for Bidirectional layer yet.');\n        }\n        this._stateful = args.layer.stateful;\n        this.returnSequences = args.layer.returnSequences;\n        this.returnState = args.layer.returnState;\n        this.supportsMasking = true;\n        this._trainable = true;\n        this.inputSpec = args.layer.inputSpec;\n        this.numConstants = null;\n    }\n    get trainable() {\n        return this._trainable;\n    }\n    set trainable(value) {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        this._trainable = value;\n        if (this.forwardLayer != null) {\n            this.forwardLayer.trainable = value;\n        }\n        if (this.backwardLayer != null) {\n            this.backwardLayer.trainable = value;\n        }\n    }\n    getWeights() {\n        return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights());\n    }\n    setWeights(weights) {\n        const numWeights = weights.length;\n        const numeightsOver2 = Math.floor(numWeights / 2);\n        this.forwardLayer.setWeights(weights.slice(0, numeightsOver2));\n        this.backwardLayer.setWeights(weights.slice(numeightsOver2));\n    }\n    computeOutputShape(inputShape) {\n        let layerShapes = this.forwardLayer.computeOutputShape(inputShape);\n        if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) {\n            layerShapes = [layerShapes];\n        }\n        layerShapes = layerShapes;\n        let outputShape;\n        let outputShapes;\n        let stateShape;\n        if (this.returnState) {\n            stateShape = layerShapes.slice(1);\n            outputShape = layerShapes[0];\n        }\n        else {\n            outputShape = layerShapes[0];\n        }\n        outputShape = outputShape;\n        if (this.mergeMode === 'concat') {\n            outputShape[outputShape.length - 1] *= 2;\n            outputShapes = [outputShape];\n        }\n        else if (this.mergeMode == null) {\n            outputShapes = [outputShape, outputShape.slice()];\n        }\n        else {\n            outputShapes = [outputShape];\n        }\n        if (this.returnState) {\n            if (this.mergeMode == null) {\n                return outputShapes.concat(stateShape).concat(stateShape.slice());\n            }\n            return [outputShape].concat(stateShape).concat(stateShape.slice());\n        }\n        return generic_utils.singletonOrArray(outputShapes);\n    }\n    apply(inputs, kwargs) {\n        let initialState = kwargs == null ? null : kwargs['initialState'];\n        let constants = kwargs == null ? null : kwargs['constants'];\n        if (kwargs == null) {\n            kwargs = {};\n        }\n        const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n        inputs = standardized.inputs;\n        initialState = standardized.initialState;\n        constants = standardized.constants;\n        if (Array.isArray(inputs)) {\n            initialState = inputs.slice(1);\n            inputs = inputs[0];\n        }\n        if ((initialState == null || initialState.length === 0) &&\n            constants == null) {\n            return super.apply(inputs, kwargs);\n        }\n        const additionalInputs = [];\n        const additionalSpecs = [];\n        if (initialState != null) {\n            const numStates = initialState.length;\n            if (numStates % 2 > 0) {\n                throw new ValueError('When passing `initialState` to a Bidrectional RNN, ' +\n                    'the state should be an Array containing the states of ' +\n                    'the underlying RNNs.');\n            }\n            kwargs['initialState'] = initialState;\n            additionalInputs.push(...initialState);\n            const stateSpecs = initialState\n                .map(state => new InputSpec({ shape: state.shape }));\n            this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2);\n            this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2);\n            additionalSpecs.push(...stateSpecs);\n        }\n        if (constants != null) {\n            throw new NotImplementedError('Support for constants in Bidirectional layers is not ' +\n                'implemented yet.');\n        }\n        const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor;\n        for (const tensor of additionalInputs) {\n            if (tensor instanceof SymbolicTensor !== isSymbolicTensor) {\n                throw new ValueError('The initial state of a Bidirectional layer cannot be ' +\n                    'specified as a mix of symbolic and non-symbolic tensors');\n            }\n        }\n        if (isSymbolicTensor) {\n            // Compute the full input and specs, including the states.\n            const fullInput = [inputs].concat(additionalInputs);\n            const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n            // Perform the call temporarily and replace inputSpec.\n            // Note: with initial states symbolic calls and non-symbolic calls to\n            // this method differ in how the initial states are passed. For\n            // symbolic calls, the initial states are passed in the first arg, as\n            // an Array of SymbolicTensors; for non-symbolic calls, they are\n            // passed in the second arg as a part of the kwargs. Hence the need to\n            // temporarily modify inputSpec here.\n            // TODO(cais): Make refactoring so that this hacky code below is no\n            // longer needed.\n            const originalInputSpec = this.inputSpec;\n            this.inputSpec = fullInputSpec;\n            const output = super.apply(fullInput, kwargs);\n            this.inputSpec = originalInputSpec;\n            return output;\n        }\n        else {\n            return super.apply(inputs, kwargs);\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const initialState = kwargs['initialState'];\n            let y;\n            let yRev;\n            if (initialState == null) {\n                y = this.forwardLayer.call(inputs, kwargs);\n                yRev = this.backwardLayer.call(inputs, kwargs);\n            }\n            else {\n                const forwardState = initialState.slice(0, initialState.length / 2);\n                const backwardState = initialState.slice(initialState.length / 2);\n                y = this.forwardLayer.call(inputs, Object.assign(kwargs, { initialState: forwardState }));\n                yRev = this.backwardLayer.call(inputs, Object.assign(kwargs, { initialState: backwardState }));\n            }\n            let states;\n            if (this.returnState) {\n                if (Array.isArray(y)) {\n                    states = y.slice(1).concat(yRev.slice(1));\n                }\n                else {\n                }\n                y = y[0];\n                yRev = yRev[0];\n            }\n            if (this.returnSequences) {\n                yRev = tfc.reverse(yRev, 1);\n            }\n            let output;\n            if (this.mergeMode === 'concat') {\n                output = K.concatenate([y, yRev]);\n            }\n            else if (this.mergeMode === 'sum') {\n                output = tfc.add(y, yRev);\n            }\n            else if (this.mergeMode === 'ave') {\n                output = tfc.mul(.5, tfc.add(y, yRev));\n            }\n            else if (this.mergeMode === 'mul') {\n                output = tfc.mul(y, yRev);\n            }\n            else if (this.mergeMode == null) {\n                output = [y, yRev];\n            }\n            // TODO(cais): Properly set learning phase.\n            if (this.returnState) {\n                if (this.mergeMode == null) {\n                    return output.concat(states);\n                }\n                return [output].concat(states);\n            }\n            return output;\n        });\n    }\n    resetStates(states) {\n        this.forwardLayer.resetStates();\n        this.backwardLayer.resetStates();\n    }\n    build(inputShape) {\n        nameScope(this.forwardLayer.name, () => {\n            this.forwardLayer.build(inputShape);\n        });\n        nameScope(this.backwardLayer.name, () => {\n            this.backwardLayer.build(inputShape);\n        });\n        this.built = true;\n    }\n    computeMask(inputs, mask) {\n        if (Array.isArray(mask)) {\n            mask = mask[0];\n        }\n        let outputMask;\n        if (this.returnSequences) {\n            if (this.mergeMode == null) {\n                outputMask = [mask, mask];\n            }\n            else {\n                outputMask = mask;\n            }\n        }\n        else {\n            if (this.mergeMode == null) {\n                outputMask = [null, null];\n            }\n            else {\n                outputMask = null;\n            }\n        }\n        if (this.returnState) {\n            const states = this.forwardLayer.states;\n            const stateMask = states.map(state => null);\n            if (Array.isArray(outputMask)) {\n                return outputMask.concat(stateMask).concat(stateMask);\n            }\n            else {\n                return [outputMask].concat(stateMask).concat(stateMask);\n            }\n        }\n        else {\n            return outputMask;\n        }\n    }\n    get trainableWeights() {\n        return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights);\n    }\n    get nonTrainableWeights() {\n        return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights);\n    }\n    // TODO(cais): Implement constraints().\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.forwardLayer != null) {\n            this.forwardLayer.setFastWeightInitDuringBuild(value);\n        }\n        if (this.backwardLayer != null) {\n            this.backwardLayer.setFastWeightInitDuringBuild(value);\n        }\n    }\n    getConfig() {\n        const config = {\n            'mergeMode': this.mergeMode,\n        };\n        // TODO(cais): Add logic for `numConstants` once the property is added.\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        const rnnLayer = deserialize(config['layer']);\n        delete config['layer'];\n        // TODO(cais): Add logic for `numConstants` once the property is added.\n        if (config['numConstants'] != null) {\n            throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants ` +\n                `present is not supported yet.`);\n        }\n        // tslint:disable-next-line:no-any\n        const newConfig = config;\n        newConfig['layer'] = rnnLayer;\n        return new cls(newConfig);\n    }\n}\n/** @nocollapse */\nBidirectional.className = 'Bidirectional';\nserialization.registerClass(Bidirectional);\n//# sourceMappingURL=wrappers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { InputLayer } from './engine/input_layer';\nimport { Layer } from './engine/topology';\nimport { input } from './exports';\nimport { ELU, LeakyReLU, PReLU, ReLU, Softmax, ThresholdedReLU } from './layers/advanced_activations';\nimport { Conv1D, Conv2D, Conv2DTranspose, Conv3D, Cropping2D, SeparableConv2D, UpSampling2D } from './layers/convolutional';\nimport { DepthwiseConv2D } from './layers/convolutional_depthwise';\nimport { ConvLSTM2D, ConvLSTM2DCell } from './layers/convolutional_recurrent';\nimport { Activation, Dense, Dropout, Flatten, Masking, Permute, RepeatVector, Reshape, SpatialDropout1D } from './layers/core';\nimport { Embedding } from './layers/embeddings';\nimport { Add, Average, Concatenate, Dot, Maximum, Minimum, Multiply } from './layers/merge';\nimport { AlphaDropout, GaussianDropout, GaussianNoise } from './layers/noise';\nimport { BatchNormalization, LayerNormalization } from './layers/normalization';\nimport { ZeroPadding2D } from './layers/padding';\nimport { AveragePooling1D, AveragePooling2D, AveragePooling3D, GlobalAveragePooling1D, GlobalAveragePooling2D, GlobalMaxPooling1D, GlobalMaxPooling2D, MaxPooling1D, MaxPooling2D, MaxPooling3D } from './layers/pooling';\nimport { GRU, GRUCell, LSTM, LSTMCell, RNN, RNNCell, SimpleRNN, SimpleRNNCell, StackedRNNCells } from './layers/recurrent';\nimport { Bidirectional, TimeDistributed } from './layers/wrappers';\n// TODO(cais): Add doc string to all the public static functions in this\n//   class; include exectuable JavaScript code snippets where applicable\n//   (b/74074458).\n// Input Layer.\n/**\n * An input layer is an entry point into a `tf.LayersModel`.\n *\n * `InputLayer` is generated automatically for `tf.Sequential`` models by\n * specifying the `inputshape` or `batchInputShape` for the first layer.  It\n * should not be specified explicitly. However, it can be useful sometimes,\n * e.g., when constructing a sequential model from a subset of another\n * sequential model's layers. Like the code snippet below shows.\n *\n * ```js\n * // Define a model which simply adds two inputs.\n * const model1 = tf.sequential();\n * model1.add(tf.layers.dense({inputShape: [4], units: 3, activation: 'relu'}));\n * model1.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));\n * model1.summary();\n * model1.predict(tf.zeros([1, 4])).print();\n *\n * // Construct another model, reusing the second layer of `model1` while\n * // not using the first layer of `model1`. Note that you cannot add the second\n * // layer of `model` directly as the first layer of the new sequential model,\n * // because doing so will lead to an error related to the fact that the layer\n * // is not an input layer. Instead, you need to create an `inputLayer` and add\n * // it to the new sequential model before adding the reused layer.\n * const model2 = tf.sequential();\n * // Use an inputShape that matches the input shape of `model1`'s second\n * // layer.\n * model2.add(tf.layers.inputLayer({inputShape: [3]}));\n * model2.add(model1.layers[1]);\n * model2.summary();\n * model2.predict(tf.zeros([1, 3])).print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Inputs', namespace: 'layers'}\n */\nexport function inputLayer(args) {\n    return new InputLayer(args);\n}\n// Advanced Activation Layers.\n/**\n * Exponetial Linear Unit (ELU).\n *\n * It follows:\n * `f(x) =  alpha * (exp(x) - 1.) for x < 0`,\n * `f(x) = x for x >= 0`.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * References:\n *   - [Fast and Accurate Deep Network Learning by Exponential Linear Units\n * (ELUs)](https://arxiv.org/abs/1511.07289v1)\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function elu(args) {\n    return new ELU(args);\n}\n/**\n * Rectified Linear Unit activation function.\n *\n * Input shape:\n *   Arbitrary. Use the config field `inputShape` (Array of integers, does\n *   not include the sample axis) when using this layer as the first layer\n *   in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function reLU(args) {\n    return new ReLU(args);\n}\n/**\n * Leaky version of a rectified linear unit.\n *\n * It allows a small gradient when the unit is not active:\n * `f(x) = alpha * x for x < 0.`\n * `f(x) = x for x >= 0.`\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function leakyReLU(args) {\n    return new LeakyReLU(args);\n}\n/**\n * Parameterized version of a leaky rectified linear unit.\n *\n * It follows\n * `f(x) = alpha * x for x < 0.`\n * `f(x) = x for x >= 0.`\n * wherein `alpha` is a trainable weight.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function prelu(args) {\n    return new PReLU(args);\n}\n/**\n * Softmax activation layer.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function softmax(args) {\n    return new Softmax(args);\n}\n/**\n * Thresholded Rectified Linear Unit.\n *\n * It follows:\n * `f(x) = x for x > theta`,\n * `f(x) = 0 otherwise`.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * References:\n *   - [Zero-Bias Autoencoders and the Benefits of Co-Adapting\n * Features](http://arxiv.org/abs/1402.3337)\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function thresholdedReLU(args) {\n    return new ThresholdedReLU(args);\n}\n// Convolutional Layers.\n/**\n * 1D convolution layer (e.g., temporal convolution).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input over a single spatial (or temporal) dimension\n * to produce a tensor of outputs.\n *\n * If `use_bias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model, provide an\n * `inputShape` argument `Array` or `null`.\n *\n * For example, `inputShape` would be:\n * - `[10, 128]` for sequences of 10 vectors of 128-dimensional vectors\n * - `[null, 128]` for variable-length sequences of 128-dimensional vectors.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional',  namespace: 'layers'}\n */\nexport function conv1d(args) {\n    return new Conv1D(args);\n}\n/**\n * 2D convolution layer (e.g. spatial convolution over images).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input to produce a tensor of outputs.\n *\n * If `useBias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model,\n * provide the keyword argument `inputShape`\n * (Array of integers, does not include the sample axis),\n * e.g. `inputShape=[128, 128, 3]` for 128x128 RGB pictures\n * in `dataFormat='channelsLast'`.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv2d(args) {\n    return new Conv2D(args);\n}\n/**\n * Transposed convolutional layer (sometimes called Deconvolution).\n *\n * The need for transposed convolutions generally arises\n * from the desire to use a transformation going in the opposite direction of\n * a normal convolution, i.e., from something that has the shape of the output\n * of some convolution to something that has the shape of its input while\n * maintaining a connectivity pattern that is compatible with said\n * convolution.\n *\n * When using this layer as the first layer in a model, provide the\n * configuration `inputShape` (`Array` of integers, does not include the\n * sample axis), e.g., `inputShape: [128, 128, 3]` for 128x128 RGB pictures in\n * `dataFormat: 'channelsLast'`.\n *\n * Input shape:\n *   4D tensor with shape:\n *   `[batch, channels, rows, cols]` if `dataFormat` is `'channelsFirst'`.\n *   or 4D tensor with shape\n *   `[batch, rows, cols, channels]` if `dataFormat` is `'channelsLast`.\n *\n * Output shape:\n *   4D tensor with shape:\n *   `[batch, filters, newRows, newCols]` if `dataFormat` is\n * `'channelsFirst'`. or 4D tensor with shape:\n *   `[batch, newRows, newCols, filters]` if `dataFormat` is `'channelsLast'`.\n *\n * References:\n *   - [A guide to convolution arithmetic for deep\n * learning](https://arxiv.org/abs/1603.07285v1)\n *   - [Deconvolutional\n * Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv2dTranspose(args) {\n    return new Conv2DTranspose(args);\n}\n/**\n * 3D convolution layer (e.g. spatial convolution over volumes).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input to produce a tensor of outputs.\n *\n * If `useBias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model,\n * provide the keyword argument `inputShape`\n * (Array of integers, does not include the sample axis),\n * e.g. `inputShape=[128, 128, 128, 1]` for 128x128x128 grayscale volumes\n * in `dataFormat='channelsLast'`.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv3d(args) {\n    return new Conv3D(args);\n}\n/**\n * Depthwise separable 2D convolution.\n *\n * Separable convolution consists of first performing\n * a depthwise spatial convolution\n * (which acts on each input channel separately)\n * followed by a pointwise convolution which mixes together the resulting\n * output channels. The `depthMultiplier` argument controls how many\n * output channels are generated per input channel in the depthwise step.\n *\n * Intuitively, separable convolutions can be understood as\n * a way to factorize a convolution kernel into two smaller kernels,\n * or as an extreme version of an Inception block.\n *\n * Input shape:\n *   4D tensor with shape:\n *     `[batch, channels, rows, cols]` if data_format='channelsFirst'\n *   or 4D tensor with shape:\n *     `[batch, rows, cols, channels]` if data_format='channelsLast'.\n *\n * Output shape:\n *   4D tensor with shape:\n *     `[batch, filters, newRows, newCols]` if data_format='channelsFirst'\n *   or 4D tensor with shape:\n *     `[batch, newRows, newCols, filters]` if data_format='channelsLast'.\n *     `rows` and `cols` values might have changed due to padding.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function separableConv2d(args) {\n    return new SeparableConv2D(args);\n}\n/**\n * Cropping layer for 2D input (e.g., image).\n *\n * This layer can crop an input\n * at the top, bottom, left and right side of an image tensor.\n *\n * Input shape:\n *   4D tensor with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, rows, cols, channels]`\n *   - If `data_format` is `\"channels_first\"`:\n *     `[batch, channels, rows, cols]`.\n *\n * Output shape:\n *   4D with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, croppedRows, croppedCols, channels]`\n *    - If `dataFormat` is `\"channelsFirst\"`:\n *     `[batch, channels, croppedRows, croppedCols]`.\n *\n * Examples\n * ```js\n *\n * const model = tf.sequential();\n * model.add(tf.layers.cropping2D({cropping:[[2, 2], [2, 2]],\n *                                inputShape: [128, 128, 3]}));\n * //now output shape is [batch, 124, 124, 3]\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function cropping2D(args) {\n    return new Cropping2D(args);\n}\n/**\n * Upsampling layer for 2D inputs.\n *\n * Repeats the rows and columns of the data\n * by size[0] and size[1] respectively.\n *\n *\n * Input shape:\n *    4D tensor with shape:\n *     - If `dataFormat` is `\"channelsLast\"`:\n *         `[batch, rows, cols, channels]`\n *     - If `dataFormat` is `\"channelsFirst\"`:\n *        `[batch, channels, rows, cols]`\n *\n * Output shape:\n *     4D tensor with shape:\n *     - If `dataFormat` is `\"channelsLast\"`:\n *        `[batch, upsampledRows, upsampledCols, channels]`\n *     - If `dataFormat` is `\"channelsFirst\"`:\n *         `[batch, channels, upsampledRows, upsampledCols]`\n *\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function upSampling2d(args) {\n    return new UpSampling2D(args);\n}\n// Convolutional(depthwise) Layers.\n/**\n * Depthwise separable 2D convolution.\n *\n * Depthwise Separable convolutions consists in performing just the first step\n * in a depthwise spatial convolution (which acts on each input channel\n * separately). The `depthMultplier` argument controls how many output channels\n * are generated per input channel in the depthwise step.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function depthwiseConv2d(args) {\n    return new DepthwiseConv2D(args);\n}\n// Basic Layers.\n/**\n * Applies an activation function to an output.\n *\n * This layer applies element-wise activation function.  Other layers, notably\n * `dense` can also apply activation functions.  Use this isolated activation\n * function to extract the values before and after the\n * activation. For instance:\n *\n * ```js\n * const input = tf.input({shape: [5]});\n * const denseLayer = tf.layers.dense({units: 1});\n * const activationLayer = tf.layers.activation({activation: 'relu6'});\n *\n * // Obtain the output symbolic tensors by applying the layers in order.\n * const denseOutput = denseLayer.apply(input);\n * const activationOutput = activationLayer.apply(denseOutput);\n *\n * // Create the model based on the inputs.\n * const model = tf.model({\n *     inputs: input,\n *     outputs: [denseOutput, activationOutput]\n * });\n *\n * // Collect both outputs and print separately.\n * const [denseOut, activationOut] = model.predict(tf.randomNormal([6, 5]));\n * denseOut.print();\n * activationOut.print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function activation(args) {\n    return new Activation(args);\n}\n/**\n * Creates a dense (fully connected) layer.\n *\n * This layer implements the operation:\n *   `output = activation(dot(input, kernel) + bias)`\n *\n * `activation` is the element-wise activation function\n *   passed as the `activation` argument.\n *\n * `kernel` is a weights matrix created by the layer.\n *\n * `bias` is a bias vector created by the layer (only applicable if `useBias`\n * is `true`).\n *\n * **Input shape:**\n *\n *   nD `tf.Tensor` with shape: `(batchSize, ..., inputDim)`.\n *\n *   The most common situation would be\n *   a 2D input with shape `(batchSize, inputDim)`.\n *\n * **Output shape:**\n *\n *   nD tensor with shape: `(batchSize, ..., units)`.\n *\n *   For instance, for a 2D input with shape `(batchSize, inputDim)`,\n *   the output would have shape `(batchSize, units)`.\n *\n * Note: if the input to the layer has a rank greater than 2, then it is\n * flattened prior to the initial dot product with the kernel.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function dense(args) {\n    return new Dense(args);\n}\n/**\n * Applies\n * [dropout](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf) to\n * the input.\n *\n * Dropout consists in randomly setting a fraction `rate` of input units to 0 at\n * each update during training time, which helps prevent overfitting.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function dropout(args) {\n    return new Dropout(args);\n}\n/**\n * Spatial 1D version of Dropout.\n *\n * This Layer type performs the same function as the Dropout layer, but it drops\n * entire 1D feature maps instead of individual elements. For example, if an\n * input example consists of 3 timesteps and the feature map for each timestep\n * has a size of 4, a `spatialDropout1d` layer may zero out the feature maps\n * of the 1st timesteps and 2nd timesteps completely while sparing all feature\n * elements of the 3rd timestep.\n *\n * If adjacent frames (timesteps) are strongly correlated (as is normally the\n * case in early convolution layers), regular dropout will not regularize the\n * activation and will otherwise just result in merely an effective learning\n * rate decrease. In this case, `spatialDropout1d` will help promote\n * independence among feature maps and should be used instead.\n *\n * **Arguments:**\n *   rate: A floating-point number >=0 and <=1. Fraction of the input elements\n *     to drop.\n *\n * **Input shape:**\n *   3D tensor with shape `(samples, timesteps, channels)`.\n *\n * **Output shape:**\n *   Same as the input shape.\n *\n * References:\n *   - [Efficient Object Localization Using Convolutional\n *      Networks](https://arxiv.org/abs/1411.4280)\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function spatialDropout1d(args) {\n    return new SpatialDropout1D(args);\n}\n/**\n * Flattens the input. Does not affect the batch size.\n *\n * A `Flatten` layer flattens each batch in its inputs to 1D (making the output\n * 2D).\n *\n * For example:\n *\n * ```js\n * const input = tf.input({shape: [4, 3]});\n * const flattenLayer = tf.layers.flatten();\n * // Inspect the inferred output shape of the flatten layer, which\n * // equals `[null, 12]`. The 2nd dimension is 4 * 3, i.e., the result of the\n * // flattening. (The 1st dimension is the undermined batch size.)\n * console.log(JSON.stringify(flattenLayer.apply(input).shape));\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function flatten(args) {\n    return new Flatten(args);\n}\n/**\n * Repeats the input n times in a new dimension.\n *\n * ```js\n *  const model = tf.sequential();\n *  model.add(tf.layers.repeatVector({n: 4, inputShape: [2]}));\n *  const x = tf.tensor2d([[10, 20]]);\n *  // Use the model to do inference on a data point the model hasn't see\n *  model.predict(x).print();\n *  // output shape is now [batch, 2, 4]\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function repeatVector(args) {\n    return new RepeatVector(args);\n}\n/**\n * Reshapes an input to a certain shape.\n *\n * ```js\n * const input = tf.input({shape: [4, 3]});\n * const reshapeLayer = tf.layers.reshape({targetShape: [2, 6]});\n * // Inspect the inferred output shape of the Reshape layer, which\n * // equals `[null, 2, 6]`. (The 1st dimension is the undermined batch size.)\n * console.log(JSON.stringify(reshapeLayer.apply(input).shape));\n * ```\n *\n * Input shape:\n *   Arbitrary, although all dimensions in the input shape must be fixed.\n *   Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n *\n * Output shape:\n *   [batchSize, targetShape[0], targetShape[1], ...,\n *    targetShape[targetShape.length - 1]].\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function reshape(args) {\n    return new Reshape(args);\n}\n/**\n * Permutes the dimensions of the input according to a given pattern.\n *\n * Useful for, e.g., connecting RNNs and convnets together.\n *\n * Example:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.permute({\n *   dims: [2, 1],\n *   inputShape: [10, 64]\n * }));\n * console.log(model.outputShape);\n * // Now model's output shape is [null, 64, 10], where null is the\n * // unpermuted sample (batch) dimension.\n * ```\n *\n * Input shape:\n *   Arbitrary. Use the configuration field `inputShape` when using this\n *   layer as the first layer in a model.\n *\n * Output shape:\n *   Same rank as the input shape, but with the dimensions re-ordered (i.e.,\n *   permuted) according to the `dims` configuration of this layer.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function permute(args) {\n    return new Permute(args);\n}\n/**\n * Maps positive integers (indices) into dense vectors of fixed size.\n * eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]\n *\n * **Input shape:** 2D tensor with shape: `[batchSize, sequenceLength]`.\n *\n * **Output shape:** 3D tensor with shape: `[batchSize, sequenceLength,\n * outputDim]`.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function embedding(args) {\n    return new Embedding(args);\n}\n// Merge Layers.\n/**\n * Layer that performs element-wise addition on an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape, and returns a\n * single tensor (also of the same shape). The inputs are specified as an\n * `Array` when the `apply` method of the `Add` layer instance is called. For\n * example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const addLayer = tf.layers.add();\n * const sum = addLayer.apply([input1, input2]);\n * console.log(JSON.stringify(sum.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function add(args) {\n    return new Add(args);\n}\n/**\n * Layer that performs element-wise averaging on an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape, and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const averageLayer = tf.layers.average();\n * const average = averageLayer.apply([input1, input2]);\n * console.log(JSON.stringify(average.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function average(args) {\n    return new Average(args);\n}\n/**\n * Layer that concatenates an `Array` of inputs.\n *\n * It takes a list of tensors, all of the same shape except for the\n * concatenation axis, and returns a single tensor, the concatenation\n * of all inputs. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 3]});\n * const concatLayer = tf.layers.concatenate();\n * const output = concatLayer.apply([input1, input2]);\n * console.log(JSON.stringify(output.shape));\n * // You get [null, 2, 5], with the first dimension as the undetermined batch\n * // dimension. The last dimension (5) is the result of concatenating the\n * // last dimensions of the inputs (2 and 3).\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function concatenate(args) {\n    return new Concatenate(args);\n}\n/**\n * Layer that computes the element-wise maximum an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const maxLayer = tf.layers.maximum();\n * const max = maxLayer.apply([input1, input2]);\n * console.log(JSON.stringify(max.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function maximum(args) {\n    return new Maximum(args);\n}\n/**\n * Layer that computes the element-wise minimum of an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const minLayer = tf.layers.minimum();\n * const min = minLayer.apply([input1, input2]);\n * console.log(JSON.stringify(min.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function minimum(args) {\n    return new Minimum(args);\n}\n/**\n * Layer that multiplies (element-wise) an `Array` of inputs.\n *\n * It takes as input an Array of tensors, all of the same\n * shape, and returns a single tensor (also of the same shape).\n * For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const input3 = tf.input({shape: [2, 2]});\n * const multiplyLayer = tf.layers.multiply();\n * const product = multiplyLayer.apply([input1, input2, input3]);\n * console.log(product.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function multiply(args) {\n    return new Multiply(args);\n}\n/**\n * Layer that computes a dot product between samples in two tensors.\n *\n * E.g., if applied to a list of two tensors `a` and `b` both of shape\n * `[batchSize, n]`, the output will be a tensor of shape `[batchSize, 1]`,\n * where each entry at index `[i, 0]` will be the dot product between\n * `a[i, :]` and `b[i, :]`.\n *\n * Example:\n *\n * ```js\n * const dotLayer = tf.layers.dot({axes: -1});\n * const x1 = tf.tensor2d([[10, 20], [30, 40]]);\n * const x2 = tf.tensor2d([[-1, -2], [-3, -4]]);\n *\n * // Invoke the layer's apply() method in eager (imperative) mode.\n * const y = dotLayer.apply([x1, x2]);\n * y.print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function dot(args) {\n    return new Dot(args);\n}\n// Normalization Layers.\n/**\n * Batch normalization layer (Ioffe and Szegedy, 2014).\n *\n * Normalize the activations of the previous layer at each batch,\n * i.e. applies a transformation that maintains the mean activation\n * close to 0 and the activation standard deviation close to 1.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape` (Array of integers, does\n *   not include the sample axis) when calling the constructor of this class,\n *   if this layer is used as a first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Batch Normalization: Accelerating Deep Network Training by Reducing\n * Internal Covariate Shift](https://arxiv.org/abs/1502.03167)\n *\n * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}\n */\nexport function batchNormalization(args) {\n    return new BatchNormalization(args);\n}\n/**\n * Layer-normalization layer (Ba et al., 2016).\n *\n * Normalizes the activations of the previous layer for each given example in a\n * batch independently, instead of across a batch like in `batchNormalization`.\n * In other words, this layer applies a transformation that maintanis the mean\n * activation within each example close to0 and activation variance close to 1.\n *\n * Input shape:\n *   Arbitrary. Use the argument `inputShape` when using this layer as the first\n *   layer in a model.\n *\n * Output shape:\n *   Same as input.\n *\n * References:\n *   - [Layer Normalization](https://arxiv.org/abs/1607.06450)\n *\n * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}\n */\nexport function layerNormalization(args) {\n    return new LayerNormalization(args);\n}\n// Padding Layers.\n/**\n * Zero-padding layer for 2D input (e.g., image).\n *\n * This layer can add rows and columns of zeros\n * at the top, bottom, left and right side of an image tensor.\n *\n * Input shape:\n *   4D tensor with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, rows, cols, channels]`\n *   - If `data_format` is `\"channels_first\"`:\n *     `[batch, channels, rows, cols]`.\n *\n * Output shape:\n *   4D with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, paddedRows, paddedCols, channels]`\n *    - If `dataFormat` is `\"channelsFirst\"`:\n *     `[batch, channels, paddedRows, paddedCols]`.\n *\n * @doc {heading: 'Layers', subheading: 'Padding', namespace: 'layers'}\n */\nexport function zeroPadding2d(args) {\n    return new ZeroPadding2D(args);\n}\n// Pooling Layers.\n/**\n * Average pooling operation for spatial data.\n *\n * Input shape: `[batchSize, inLength, channels]`\n *\n * Output shape: `[batchSize, pooledLength, channels]`\n *\n * `tf.avgPool1d` is an alias.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling1d(args) {\n    return new AveragePooling1D(args);\n}\nexport function avgPool1d(args) {\n    return averagePooling1d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling1d(args) {\n    return averagePooling1d(args);\n}\n/**\n * Average pooling operation for spatial data.\n *\n * Input shape:\n *  - If `dataFormat === CHANNEL_LAST`:\n *      4D tensor with shape:\n *      `[batchSize, rows, cols, channels]`\n *  - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *      `[batchSize, channels, rows, cols]`\n *\n * Output shape\n *  - If `dataFormat === CHANNEL_LAST`:\n *      4D tensor with shape:\n *      `[batchSize, pooleRows, pooledCols, channels]`\n *  - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *      `[batchSize, channels, pooleRows, pooledCols]`\n *\n * `tf.avgPool2d` is an alias.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling2d(args) {\n    return new AveragePooling2D(args);\n}\nexport function avgPool2d(args) {\n    return averagePooling2d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling2d(args) {\n    return averagePooling2d(args);\n}\n/**\n * Average pooling operation for 3D data.\n *\n * Input shape\n *   - If `dataFormat === channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, depths, rows, cols, channels]`\n *   - If `dataFormat === channelsFirst`:\n *      4D tensor with shape:\n *       `[batchSize, channels, depths, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`\n *   - If `dataFormat=channelsFirst`:\n *       5D tensor with shape:\n *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling3d(args) {\n    return new AveragePooling3D(args);\n}\nexport function avgPool3d(args) {\n    return averagePooling3d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling3d(args) {\n    return averagePooling3d(args);\n}\n/**\n * Global average pooling operation for temporal data.\n *\n * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.\n *\n * Output Shape:2D tensor with shape: `[batchSize, features]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalAveragePooling1d(args) {\n    return new GlobalAveragePooling1D(args);\n}\n/**\n * Global average pooling operation for spatial data.\n *\n * Input shape:\n *   - If `dataFormat` is `CHANNEL_LAST`:\n *       4D tensor with shape: `[batchSize, rows, cols, channels]`.\n *   - If `dataFormat` is `CHANNEL_FIRST`:\n *       4D tensor with shape: `[batchSize, channels, rows, cols]`.\n *\n * Output shape:\n *   2D tensor with shape: `[batchSize, channels]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalAveragePooling2d(args) {\n    return new GlobalAveragePooling2D(args);\n}\n/**\n * Global max pooling operation for temporal data.\n *\n * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.\n *\n * Output Shape:2D tensor with shape: `[batchSize, features]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalMaxPooling1d(args) {\n    return new GlobalMaxPooling1D(args);\n}\n/**\n * Global max pooling operation for spatial data.\n *\n * Input shape:\n *   - If `dataFormat` is `CHANNEL_LAST`:\n *       4D tensor with shape: `[batchSize, rows, cols, channels]`.\n *   - If `dataFormat` is `CHANNEL_FIRST`:\n *       4D tensor with shape: `[batchSize, channels, rows, cols]`.\n *\n * Output shape:\n *   2D tensor with shape: `[batchSize, channels]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalMaxPooling2d(args) {\n    return new GlobalMaxPooling2D(args);\n}\n/**\n * Max pooling operation for temporal data.\n *\n * Input shape:  `[batchSize, inLength, channels]`\n *\n * Output shape: `[batchSize, pooledLength, channels]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling1d(args) {\n    return new MaxPooling1D(args);\n}\n/**\n * Max pooling operation for spatial data.\n *\n * Input shape\n *   - If `dataFormat === CHANNEL_LAST`:\n *       4D tensor with shape:\n *       `[batchSize, rows, cols, channels]`\n *   - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *       `[batchSize, channels, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=CHANNEL_LAST`:\n *       4D tensor with shape:\n *       `[batchSize, pooleRows, pooledCols, channels]`\n *   - If `dataFormat=CHANNEL_FIRST`:\n *       4D tensor with shape:\n *       `[batchSize, channels, pooleRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling2d(args) {\n    return new MaxPooling2D(args);\n}\n/**\n * Max pooling operation for 3D data.\n *\n * Input shape\n *   - If `dataFormat === channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, depths, rows, cols, channels]`\n *   - If `dataFormat === channelsFirst`:\n *      5D tensor with shape:\n *       `[batchSize, channels, depths, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`\n *   - If `dataFormat=channelsFirst`:\n *       5D tensor with shape:\n *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling3d(args) {\n    return new MaxPooling3D(args);\n}\n// Recurrent Layers.\n/**\n * Gated Recurrent Unit - Cho et al. 2014.\n *\n * This is an `RNN` layer consisting of one `GRUCell`. However, unlike\n * the underlying `GRUCell`, the `apply` method of `SimpleRNN` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const rnn = tf.layers.gru({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `GRUCell`'s number of units.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function gru(args) {\n    return new GRU(args);\n}\n/**\n * Cell class for `GRU`.\n *\n * `GRUCell` is distinct from the `RNN` subclass `GRU` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `GRU` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.gruCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `GRUCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.gruCell({units: 4}),\n *   tf.layers.gruCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `gruCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `GRUCell`, use the\n * `tf.layers.gru`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function gruCell(args) {\n    return new GRUCell(args);\n}\n/**\n * Long-Short Term Memory layer - Hochreiter 1997.\n *\n * This is an `RNN` layer consisting of one `LSTMCell`. However, unlike\n * the underlying `LSTMCell`, the `apply` method of `LSTM` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const lstm = tf.layers.lstm({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = lstm.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `LSTMCell`'s number of units.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function lstm(args) {\n    return new LSTM(args);\n}\n/**\n * Cell class for `LSTM`.\n *\n * `LSTMCell` is distinct from the `RNN` subclass `LSTM` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `LSTM` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.lstmCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `LSTMCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.lstmCell({units: 4}),\n *   tf.layers.lstmCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `lstmCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `LSTMCell`, use the\n * `tf.layers.lstm`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function lstmCell(args) {\n    return new LSTMCell(args);\n}\n/**\n * Fully-connected RNN where the output is to be fed back to input.\n *\n * This is an `RNN` layer consisting of one `SimpleRNNCell`. However, unlike\n * the underlying `SimpleRNNCell`, the `apply` method of `SimpleRNN` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const rnn = tf.layers.simpleRNN({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `SimpleRNNCell`'s number of units.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function simpleRNN(args) {\n    return new SimpleRNN(args);\n}\n/**\n * Cell class for `SimpleRNN`.\n *\n * `SimpleRNNCell` is distinct from the `RNN` subclass `SimpleRNN` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `SimpleRNN` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.simpleRNNCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `SimpleRNNCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.simpleRNNCell({units: 4}),\n *   tf.layers.simpleRNNCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `SimpleRNNCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `SimpleRNNCell`, use the\n * `tf.layers.simpleRNN`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function simpleRNNCell(args) {\n    return new SimpleRNNCell(args);\n}\n/**\n * Convolutional LSTM layer - Xingjian Shi 2015.\n *\n * This is an `ConvRNN2D` layer consisting of one `ConvLSTM2DCell`. However,\n * unlike the underlying `ConvLSTM2DCell`, the `apply` method of `ConvLSTM2D`\n * operates on a sequence of inputs. The shape of the input (not including the\n * first, batch dimension) needs to be 4-D, with the first dimension being time\n * steps. For example:\n *\n * ```js\n * const filters = 3;\n * const kernelSize = 3;\n *\n * const batchSize = 4;\n * const sequenceLength = 2;\n * const size = 5;\n * const channels = 3;\n *\n * const inputShape = [batchSize, sequenceLength, size, size, channels];\n * const input = tf.ones(inputShape);\n *\n * const layer = tf.layers.convLstm2d({filters, kernelSize});\n *\n * const output = layer.apply(input);\n * ```\n */\n/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */\nexport function convLstm2d(args) {\n    return new ConvLSTM2D(args);\n}\n/**\n * Cell class for `ConvLSTM2D`.\n *\n * `ConvLSTM2DCell` is distinct from the `ConvRNN2D` subclass `ConvLSTM2D` in\n * that its `call` method takes the input data of only a single time step and\n * returns the cell's output at the time step, while `ConvLSTM2D` takes the\n * input data over a number of time steps. For example:\n *\n * ```js\n * const filters = 3;\n * const kernelSize = 3;\n *\n * const sequenceLength = 1;\n * const size = 5;\n * const channels = 3;\n *\n * const inputShape = [sequenceLength, size, size, channels];\n * const input = tf.ones(inputShape);\n *\n * const cell = tf.layers.convLstm2dCell({filters, kernelSize});\n *\n * cell.build(input.shape);\n *\n * const outputSize = size - kernelSize + 1;\n * const outShape = [sequenceLength, outputSize, outputSize, filters];\n *\n * const initialH = tf.zeros(outShape);\n * const initialC = tf.zeros(outShape);\n *\n * const [o, h, c] = cell.call([input, initialH, initialC], {});\n * ```\n */\n/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */\nexport function convLstm2dCell(args) {\n    return new ConvLSTM2DCell(args);\n}\n/**\n * Base class for recurrent layers.\n *\n * Input shape:\n *   3D tensor with shape `[batchSize, timeSteps, inputDim]`.\n *\n * Output shape:\n *   - if `returnState`, an Array of tensors (i.e., `tf.Tensor`s). The first\n *     tensor is the output. The remaining tensors are the states at the\n *     last time step, each with shape `[batchSize, units]`.\n *   - if `returnSequences`, the output will have shape\n *     `[batchSize, timeSteps, units]`.\n *   - else, the output will have shape `[batchSize, units]`.\n *\n * Masking:\n *   This layer supports masking for input data with a variable number\n *   of timesteps. To introduce masks to your data,\n *   use an embedding layer with the `mask_zero` parameter\n *   set to `True`.\n *\n * Notes on using statefulness in RNNs:\n *   You can set RNN layers to be 'stateful', which means that the states\n *   computed for the samples in one batch will be reused as initial states\n *   for the samples in the next batch. This assumes a one-to-one mapping\n *   between samples in different successive batches.\n *\n *   To enable statefulness:\n *     - specify `stateful: true` in the layer constructor.\n *     - specify a fixed batch size for your model, by passing\n *       if sequential model:\n *         `batchInputShape=[...]` to the first layer in your model.\n *       else for functional model with 1 or more Input layers:\n *         `batchShape=[...]` to all the first layers in your model.\n *       This is the expected shape of your inputs *including the batch size*.\n *       It should be a tuple of integers, e.g. `(32, 10, 100)`.\n *     - specify `shuffle=False` when calling fit().\n *\n *   To reset the states of your model, call `.resetStates()` on either\n *   a specific layer, or on your entire model.\n *\n * Note on specifying the initial state of RNNs\n *   You can specify the initial state of RNN layers symbolically by\n *   calling them with the option `initialState`. The value of\n *   `initialState` should be a tensor or list of tensors representing\n *   the initial state of the RNN layer.\n *\n *   You can specify the initial state of RNN layers numerically by\n *   calling `resetStates` with the keyword argument `states`. The value of\n *   `states` should be a numpy array or list of numpy arrays representing\n *   the initial state of the RNN layer.\n *\n * Note on passing external constants to RNNs\n *   You can pass \"external\" constants to the cell using the `constants`\n *   keyword argument of `RNN.call` method. This requires that the `cell.call`\n *   method accepts the same keyword argument `constants`. Such constants\n *   can be used to conditon the cell transformation on additional static inputs\n *   (not changing over time), a.k.a an attention mechanism.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function rnn(args) {\n    return new RNN(args);\n}\n/**\n * Wrapper allowing a stack of RNN cells to behave as a single cell.\n *\n * Used to implement efficient stacked RNNs.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function stackedRNNCells(args) {\n    return new StackedRNNCells(args);\n}\n// Wrapper Layers.\n/** @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'} */\nexport function bidirectional(args) {\n    return new Bidirectional(args);\n}\n/**\n * This wrapper applies a layer to every temporal slice of an input.\n *\n * The input should be at least 3D,  and the dimension of the index `1` will be\n * considered to be the temporal dimension.\n *\n * Consider a batch of 32 samples, where each sample is a sequence of 10 vectors\n * of 16 dimensions. The batch input shape of the layer is then `[32,  10,\n * 16]`, and the `inputShape`, not including the sample dimension, is\n * `[10, 16]`.\n *\n * You can then use `TimeDistributed` to apply a `Dense` layer to each of the 10\n * timesteps, independently:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.timeDistributed({\n *   layer: tf.layers.dense({units: 8}),\n *   inputShape: [10, 16],\n * }));\n *\n * // Now model.outputShape = [null, 10, 8].\n * // The output will then have shape `[32, 10, 8]`.\n *\n * // In subsequent layers, there is no need for `inputShape`:\n * model.add(tf.layers.timeDistributed({layer: tf.layers.dense({units: 32})}));\n * console.log(JSON.stringify(model.outputs[0].shape));\n * // Now model.outputShape = [null, 10, 32].\n * ```\n *\n * The output will then have shape `[32, 10, 32]`.\n *\n * `TimeDistributed` can be used with arbitrary layers, not just `Dense`, for\n * instance a `Conv2D` layer.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.timeDistributed({\n *   layer: tf.layers.conv2d({filters: 64, kernelSize: [3, 3]}),\n *   inputShape: [10, 299, 299, 3],\n * }));\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'}\n */\nexport function timeDistributed(args) {\n    return new TimeDistributed(args);\n}\n// Aliases for pooling.\nexport const globalMaxPool1d = globalMaxPooling1d;\nexport const globalMaxPool2d = globalMaxPooling2d;\nexport const maxPool1d = maxPooling1d;\nexport const maxPool2d = maxPooling2d;\nexport { Layer, RNN, RNNCell, input /* alias for tf.input */ };\n/**\n * Apply additive zero-centered Gaussian noise.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * This is useful to mitigate overfitting\n * (you could see it as a form of random data augmentation).\n * Gaussian Noise (GS) is a natural choice as corruption process\n * for real valued inputs.\n *\n * # Arguments\n *     stddev: float, standard deviation of the noise distribution.\n *\n * # Input shape\n *         Arbitrary. Use the keyword argument `input_shape`\n *         (tuple of integers, does not include the samples axis)\n *         when using this layer as the first layer in a model.\n *\n * # Output shape\n *         Same shape as input.\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function gaussianNoise(args) {\n    return new GaussianNoise(args);\n}\n/**\n * Apply multiplicative 1-centered Gaussian noise.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](\n *      http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function gaussianDropout(args) {\n    return new GaussianDropout(args);\n}\n/**\n * Applies Alpha Dropout to the input.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs\n * to their original values, in order to ensure the self-normalizing property\n * even after this dropout.\n * Alpha Dropout fits well to Scaled Exponential Linear Units\n * by randomly setting activations to the negative saturation value.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the\n *     shape for randomly generated keep/drop flags.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function alphaDropout(args) {\n    return new AlphaDropout(args);\n}\n/**\n * Masks a sequence by using a mask value to skip timesteps.\n *\n * If all features for a given sample timestep are equal to `mask_value`,\n * then the sample timestep will be masked (skipped) in all downstream layers\n * (as long as they support masking).\n *\n * If any downstream layer does not support masking yet receives such\n * an input mask, an exception will be raised.\n *\n * Arguments:\n *   - `maskValue`: Either None or mask value to skip.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * @doc {heading: 'Layers', subheading: 'Mask', namespace: 'layers'}\n */\nexport function masking(args) {\n    return new Masking(args);\n}\n//# sourceMappingURL=exports_layers.js.map", "import * as losses from './losses';\nimport * as metrics from './metrics';\n/**\n * Binary accuracy metric function.\n *\n * `yTrue` and `yPred` can have 0-1 values. Example:\n * ```js\n * const x = tf.tensor2d([[1, 1, 1, 1], [0, 0, 0, 0]], [2, 4]);\n * const y = tf.tensor2d([[1, 0, 1, 0], [0, 0, 0, 1]], [2, 4]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * `yTrue` and `yPred` can also have floating-number values between 0 and 1, in\n * which case the values will be thresholded at 0.5 to yield 0-1 values (i.e.,\n * a value >= 0.5 and <= 1.0 is interpreted as 1.\n * )\n * Example:\n * ```js\n * const x = tf.tensor1d([1, 1, 1, 1, 0, 0, 0, 0]);\n * const y = tf.tensor1d([0.2, 0.4, 0.6, 0.8, 0.2, 0.3, 0.4, 0.7]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryAccuracy(yTrue, yPred) {\n    return metrics.binaryAccuracy(yTrue, yPred);\n}\n/**\n * Binary crossentropy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0], [1], [1], [1]]);\n * const y = tf.tensor2d([[0], [0], [0.5], [1]]);\n * const crossentropy = tf.metrics.binaryCrossentropy(x, y);\n * crossentropy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction, probabilities for the `1` case.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryCrossentropy(yTrue, yPred) {\n    return metrics.binaryCrossentropy(yTrue, yPred);\n}\n/**\n * Sparse categorical accuracy metric function.\n *\n * Example:\n * ```js\n *\n * const yTrue = tf.tensor1d([1, 1, 2, 2, 0]);\n * const yPred = tf.tensor2d(\n *      [[0, 1, 0], [1, 0, 0], [0, 0.4, 0.6], [0, 0.6, 0.4], [0.7, 0.3, 0]]);\n * const crossentropy = tf.metrics.sparseCategoricalAccuracy(yTrue, yPred);\n * crossentropy.print();\n * ```\n *\n * @param yTrue True labels: indices.\n * @param yPred Predicted probabilities or logits.\n * @returns Accuracy tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function sparseCategoricalAccuracy(yTrue, yPred) {\n    return metrics.sparseCategoricalAccuracy(yTrue, yPred);\n}\n/**\n * Categorical accuracy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0, 0, 0, 1], [0, 0, 0, 1]]);\n * const y = tf.tensor2d([[0.1, 0.8, 0.05, 0.05], [0.1, 0.05, 0.05, 0.8]]);\n * const accuracy = tf.metrics.categoricalAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth: one-hot encoding of categories.\n * @param yPred Binary Tensor of prediction: probabilities or logits for the\n *   same categories as in `yTrue`.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalAccuracy(yTrue, yPred) {\n    return metrics.categoricalAccuracy(yTrue, yPred);\n}\n/**\n * Categorical crossentropy between an output tensor and a target tensor.\n *\n * @param target A tensor of the same shape as `output`.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalCrossentropy(yTrue, yPred) {\n    return metrics.categoricalCrossentropy(yTrue, yPred);\n}\n/**\n * Computes the precision of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const precision = tf.metrics.precision(x, y);\n * precision.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to be contain only 0-1 values.\n * @param yPred The predicted values. Expected to be contain only 0-1 values.\n * @return Precision Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function precision(yTrue, yPred) {\n    return metrics.precision(yTrue, yPred);\n}\n/**\n * Computes the recall of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const recall = tf.metrics.recall(x, y);\n * recall.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to be contain only 0-1 values.\n * @param yPred The predicted values. Expected to be contain only 0-1 values.\n * @return Recall Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function recall(yTrue, yPred) {\n    return metrics.recall(yTrue, yPred);\n}\n/**\n * Loss or metric function: Cosine proximity.\n *\n * Mathematically, cosine proximity is defined as:\n *   `-sum(l2Normalize(yTrue) * l2Normalize(yPred))`,\n * wherein `l2Normalize()` normalizes the L2 norm of the input to 1 and `*`\n * represents element-wise multiplication.\n *\n * ```js\n * const yTrue = tf.tensor2d([[1, 0], [1, 0]]);\n * const yPred = tf.tensor2d([[1 / Math.sqrt(2), 1 / Math.sqrt(2)], [0, 1]]);\n * const proximity = tf.metrics.cosineProximity(yTrue, yPred);\n * proximity.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Cosine proximity Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function cosineProximity(yTrue, yPred) {\n    return losses.cosineProximity(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean absolute error.\n *\n * Mathematically, mean absolute error is defined as:\n *   `mean(abs(yPred - yTrue))`,\n * wherein the `mean` is applied over feature dimensions.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [0, 0], [2, 3]]);\n * const yPred = tf.tensor2d([[0, 1], [0, 1], [-2, -3]]);\n * const mse = tf.metrics.meanAbsoluteError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsoluteError(yTrue, yPred) {\n    return losses.meanAbsoluteError(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean absolute percentage error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [10, 20]]);\n * const yPred = tf.tensor2d([[0, 1], [11, 24]]);\n * const mse = tf.metrics.meanAbsolutePercentageError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MAPE`, `tf.metrics.mape`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute percentage error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsolutePercentageError(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\nexport function MAPE(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\nexport function mape(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean squared error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [3, 4]]);\n * const yPred = tf.tensor2d([[0, 1], [-3, -4]]);\n * const mse = tf.metrics.meanSquaredError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MSE`, `tf.metrics.mse`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean squared error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanSquaredError(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\nexport function MSE(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\nexport function mse(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\n//# sourceMappingURL=exports_metrics.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport { modelFromJSON } from './models';\n//# sourceMappingURL=exports_models.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as regularizers from './regularizers';\n// tslint:disable-next-line:max-line-length\nimport { L1L2 } from './regularizers';\n/**\n * Regularizer for L1 and L2 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l1 * abs(x)) + sum(l2 * x^2)\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l1l2(config) {\n    return new L1L2(config);\n}\n/**\n * Regularizer for L1 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l1 * abs(x))\n * @param args l1 config.\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l1(config) {\n    return regularizers.l1(config);\n}\n/**\n * Regularizer for L2 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l2 * x^2)\n * @param args l2 config.\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l2(config) {\n    return regularizers.l2(config);\n}\n//# sourceMappingURL=exports_regularizers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/callbacks.py */\nimport { BaseCallback } from './base_callbacks';\nimport { LayersModel } from './engine/training';\nimport { NotImplementedError } from './errors';\nimport { resolveScalarsInLogs } from './logs';\nexport class Callback extends BaseCallback {\n    constructor() {\n        super(...arguments);\n        /** Instance of `keras.models.Model`. Reference of the model being trained. */\n        this.model = null;\n    }\n    setModel(model) {\n        if (!(model instanceof LayersModel)) {\n            throw new Error('model must be a LayersModel, not some other Container');\n        }\n        this.model = model;\n    }\n}\nfunction less(currVal, prevVal) {\n    return currVal < prevVal;\n}\nfunction greater(currVal, prevVal) {\n    return currVal > prevVal;\n}\n/**\n * A Callback that stops training when a monitored quantity has stopped\n * improving.\n */\nexport class EarlyStopping extends Callback {\n    constructor(args) {\n        super();\n        if (args == null) {\n            args = {};\n        }\n        if (args.restoreBestWeights) {\n            throw new NotImplementedError('restoreBestWeights = True is not implemented in EarlyStopping yet.');\n        }\n        this.monitor = args.monitor || 'val_loss';\n        this.minDelta = Math.abs(args.minDelta || 0);\n        this.patience = args.patience || 0;\n        this.verbose = args.verbose || 0;\n        this.mode = args.mode || 'auto';\n        this.baseline = args.baseline;\n        if (['auto', 'min', 'max'].indexOf(this.mode) === -1) {\n            console.warn(`EarlyStopping mode '${this.mode}' is invalid. ` +\n                `Falling back to mode 'auto'.`);\n            this.mode = 'auto';\n        }\n        if (this.mode === 'min') {\n            this.monitorFunc = less;\n        }\n        else if (this.mode === 'max') {\n            this.monitorFunc = greater;\n        }\n        else {\n            // For mode === 'auto'.\n            if (this.monitor.indexOf('acc') !== -1) {\n                this.monitorFunc = greater;\n            }\n            else {\n                this.monitorFunc = less;\n            }\n        }\n        if (this.monitorFunc === less) {\n            this.minDelta *= -1;\n        }\n    }\n    async onTrainBegin(logs) {\n        this.wait = 0;\n        this.stoppedEpoch = 0;\n        if (this.baseline != null) {\n            this.best = this.baseline;\n        }\n        else {\n            this.best = this.monitorFunc === less ? Infinity : -Infinity;\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        await resolveScalarsInLogs(logs);\n        const current = this.getMonitorValue(logs);\n        if (current == null) {\n            return;\n        }\n        if (this.monitorFunc(current - this.minDelta, this.best)) {\n            this.best = current;\n            this.wait = 0;\n            // TODO(cais): Logic for restoreBestWeights.\n        }\n        else {\n            this.wait++;\n            if (this.wait >= this.patience) {\n                this.stoppedEpoch = epoch;\n                this.model.stopTraining = true;\n            }\n            // TODO(cais): Logic for restoreBestWeights.\n        }\n    }\n    async onTrainEnd(logs) {\n        if (this.stoppedEpoch > 0 && this.verbose) {\n            console.log(`Epoch ${this.stoppedEpoch}: early stopping.`);\n        }\n    }\n    getMonitorValue(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        const monitorValue = logs[this.monitor];\n        if (monitorValue == null) {\n            console.warn(`Metric for EarlyStopping ${this.monitor} is not available. ` +\n                `Available metrics are: ${Object.keys(logs)}`);\n        }\n        return monitorValue;\n    }\n}\n/**\n * Factory function for a Callback that stops training when a monitored\n * quantity has stopped improving.\n *\n * Early stopping is a type of regularization, and protects model against\n * overfitting.\n *\n * The following example based on fake data illustrates how this callback\n * can be used during `tf.LayersModel.fit()`:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense({\n *   units: 3,\n *   activation: 'softmax',\n *   kernelInitializer: 'ones',\n *   inputShape: [2]\n * }));\n * const xs = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const ys = tf.tensor2d([[1, 0, 0], [0, 1, 0]], [2, 3]);\n * const xsVal = tf.tensor2d([4, 3, 2, 1], [2, 2]);\n * const ysVal = tf.tensor2d([[0, 0, 1], [0, 1, 0]], [2, 3]);\n * model.compile(\n *     {loss: 'categoricalCrossentropy', optimizer: 'sgd', metrics: ['acc']});\n *\n * // Without the EarlyStopping callback, the val_acc value would be:\n * //   0.5, 0.5, 0.5, 0.5, ...\n * // With val_acc being monitored, training should stop after the 2nd epoch.\n * const history = await model.fit(xs, ys, {\n *   epochs: 10,\n *   validationData: [xsVal, ysVal],\n *   callbacks: tf.callbacks.earlyStopping({monitor: 'val_acc'})\n * });\n *\n * // Expect to see a length-2 array.\n * console.log(history.history.val_acc);\n * ```\n *\n * @doc {\n *   heading: 'Callbacks',\n *   namespace: 'callbacks'\n * }\n */\nexport function earlyStopping(args) {\n    return new EarlyStopping(args);\n}\nexport const callbacks = { earlyStopping };\n//# sourceMappingURL=callbacks.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// This file lists all exports of TensorFlow.js Layers\nimport * as constraints from './exports_constraints';\nimport * as initializers from './exports_initializers';\nimport * as layers from './exports_layers';\nimport * as metrics from './exports_metrics';\nimport * as models from './exports_models';\nimport * as regularizers from './exports_regularizers';\nexport { CallbackList, CustomCallback, History } from './base_callbacks';\nexport { Callback, callbacks, EarlyStopping } from './callbacks';\nexport { InputSpec, SymbolicTensor } from './engine/topology';\nexport { LayersModel } from './engine/training';\nexport { input, loadLayersModel, model, registerCallbackConstructor, sequential } from './exports';\nexport { RNN } from './layers/recurrent';\nexport { Sequential } from './models';\nexport { LayerVariable } from './variables';\nexport { version as version_layers } from './version';\nexport { constraints, initializers, layers, metrics, models, regularizers };\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/** DataType enum. */\nexport var DataType;\n(function (DataType) {\n    DataType[DataType[\"DT_INVALID\"] = 0] = \"DT_INVALID\";\n    DataType[DataType[\"DT_FLOAT\"] = 1] = \"DT_FLOAT\";\n    DataType[DataType[\"DT_DOUBLE\"] = 2] = \"DT_DOUBLE\";\n    DataType[DataType[\"DT_INT32\"] = 3] = \"DT_INT32\";\n    DataType[DataType[\"DT_UINT8\"] = 4] = \"DT_UINT8\";\n    DataType[DataType[\"DT_INT16\"] = 5] = \"DT_INT16\";\n    DataType[DataType[\"DT_INT8\"] = 6] = \"DT_INT8\";\n    DataType[DataType[\"DT_STRING\"] = 7] = \"DT_STRING\";\n    DataType[DataType[\"DT_COMPLEX64\"] = 8] = \"DT_COMPLEX64\";\n    DataType[DataType[\"DT_INT64\"] = 9] = \"DT_INT64\";\n    DataType[DataType[\"DT_BOOL\"] = 10] = \"DT_BOOL\";\n    DataType[DataType[\"DT_QINT8\"] = 11] = \"DT_QINT8\";\n    DataType[DataType[\"DT_QUINT8\"] = 12] = \"DT_QUINT8\";\n    DataType[DataType[\"DT_QINT32\"] = 13] = \"DT_QINT32\";\n    DataType[DataType[\"DT_BFLOAT16\"] = 14] = \"DT_BFLOAT16\";\n    DataType[DataType[\"DT_FLOAT_REF\"] = 101] = \"DT_FLOAT_REF\";\n    DataType[DataType[\"DT_DOUBLE_REF\"] = 102] = \"DT_DOUBLE_REF\";\n    DataType[DataType[\"DT_INT32_REF\"] = 103] = \"DT_INT32_REF\";\n    DataType[DataType[\"DT_UINT8_REF\"] = 104] = \"DT_UINT8_REF\";\n    DataType[DataType[\"DT_INT16_REF\"] = 105] = \"DT_INT16_REF\";\n    DataType[DataType[\"DT_INT8_REF\"] = 106] = \"DT_INT8_REF\";\n    DataType[DataType[\"DT_STRING_REF\"] = 107] = \"DT_STRING_REF\";\n    DataType[DataType[\"DT_COMPLEX64_REF\"] = 108] = \"DT_COMPLEX64_REF\";\n    DataType[DataType[\"DT_INT64_REF\"] = 109] = \"DT_INT64_REF\";\n    DataType[DataType[\"DT_BOOL_REF\"] = 110] = \"DT_BOOL_REF\";\n    DataType[DataType[\"DT_QINT8_REF\"] = 111] = \"DT_QINT8_REF\";\n    DataType[DataType[\"DT_QUINT8_REF\"] = 112] = \"DT_QUINT8_REF\";\n    DataType[DataType[\"DT_QINT32_REF\"] = 113] = \"DT_QINT32_REF\";\n    DataType[DataType[\"DT_BFLOAT16_REF\"] = 114] = \"DT_BFLOAT16_REF\";\n})(DataType || (DataType = {}));\nexport var SaverDef;\n(function (SaverDef) {\n    /** CheckpointFormatVersion enum. */\n    let CheckpointFormatVersion;\n    (function (CheckpointFormatVersion) {\n        CheckpointFormatVersion[CheckpointFormatVersion[\"LEGACY\"] = 0] = \"LEGACY\";\n        CheckpointFormatVersion[CheckpointFormatVersion[\"V1\"] = 1] = \"V1\";\n        CheckpointFormatVersion[CheckpointFormatVersion[\"V2\"] = 2] = \"V2\";\n    })(CheckpointFormatVersion = SaverDef.CheckpointFormatVersion || (SaverDef.CheckpointFormatVersion = {}));\n})(SaverDef || (SaverDef = {}));\n//# sourceMappingURL=compiled_api.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst CUSTOM_OPS = {};\n/**\n * Register an Op for graph model executor. This allow you to register\n * TensorFlow custom op or override existing op.\n *\n * Here is an example of registering a new MatMul Op.\n * ```js\n * const customMatmul = (node) =>\n *    tf.matMul(\n *        node.inputs[0], node.inputs[1],\n *        node.attrs['transpose_a'], node.attrs['transpose_b']);\n *\n * tf.registerOp('MatMul', customMatmul);\n * ```\n * The inputs and attrs of the node object is based on the TensorFlow op\n * registry.\n *\n * @param name The Tensorflow Op name.\n * @param opFunc An op function which is called with the current graph node\n * during execution and needs to return a tensor or a list of tensors. The node\n * has the following attributes:\n *    - attr: A map from attribute name to its value\n *    - inputs: A list of input tensors\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function registerOp(name, opFunc) {\n    const opMapper = {\n        tfOpName: name,\n        category: 'custom',\n        inputs: [],\n        attrs: [],\n        customExecutor: opFunc\n    };\n    CUSTOM_OPS[name] = opMapper;\n}\n/**\n * Retrieve the OpMapper object for the registered op.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function getRegisteredOp(name) {\n    return CUSTOM_OPS[name];\n}\n/**\n * Deregister the Op for graph model executor.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function deregisterOp(name) {\n    delete CUSTOM_OPS[name];\n}\n//# sourceMappingURL=register.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { clone, util } from '@tensorflow/tfjs-core';\nexport function getParamValue(paramName, node, tensorMap, context) {\n    const inputParam = node.inputParams[paramName];\n    if (inputParam && inputParam.inputIndexStart !== undefined) {\n        const start = inputParam.inputIndexStart;\n        const end = inputParam.inputIndexEnd === 0 ?\n            undefined :\n            (inputParam.inputIndexEnd === undefined ? start + 1 :\n                inputParam.inputIndexEnd);\n        if (inputParam.type === 'tensor') {\n            return getTensor(node.inputNames[inputParam.inputIndexStart], tensorMap, context);\n        }\n        if (inputParam.type === 'tensors') {\n            const inputs = node.inputNames.slice(start, end);\n            return inputs.map(name => getTensor(name, tensorMap, context));\n        }\n        const tensor = getTensor(node.inputNames.slice(start)[0], tensorMap, context);\n        const data = tensor.dataSync();\n        return inputParam.type === 'number' ?\n            data[0] :\n            util.toNestedArray(tensor.shape, data);\n    }\n    const attrParam = node.attrParams[paramName];\n    return attrParam && attrParam.value;\n}\n/**\n * Retrieve the tensor based on input name by extracting the node name and\n * output index information.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n */\nexport function getTensor(name, tensorsMap, context) {\n    const [nodeName, index] = parseNodeName(name);\n    const contextId = context.currentContextIds.find(contextId => {\n        return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId)];\n    });\n    return contextId !== undefined ?\n        tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] :\n        undefined;\n}\n/**\n * Retrieve the tensors based on input name for current context.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n */\nexport function getTensorsForCurrentContenxt(name, tensorsMap, context) {\n    return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];\n}\n/**\n * Returns the node name and index from the Node input name.\n * @param inputName The input name of the node, in format of\n * node_name:output_index, i.e. MatMul:0, if the output_index is not set, it is\n * default to 0.\n */\nexport function getNodeNameAndIndex(inputName, context) {\n    const [nodeName, index] = parseNodeName(inputName);\n    return [\n        getNodeNameWithContextId(nodeName, context && context.currentContextId),\n        index\n    ];\n}\nfunction getNodeNameWithContextId(name, contextId) {\n    return !!contextId ? `${name}-${contextId}` : name;\n}\nexport function parseNodeName(name) {\n    const parts = name.split(':');\n    if (parts.length === 1) {\n        return [name, 0];\n    }\n    const nodeName = parts[0];\n    return [nodeName, Number(parts[parts.length - 1])];\n}\nexport function split(arr, size) {\n    const res = [];\n    for (let i = 0; i < arr.length; i += size) {\n        res.push(arr.slice(i, i + size));\n    }\n    return res;\n}\nexport function getPadding(node, tensorMap, context) {\n    let pad = getParamValue('pad', node, tensorMap, context);\n    if (pad === 'explicit') {\n        // This is 1d array, we need to convert it to 2d array\n        pad = getParamValue('explicitPaddings', node, tensorMap, context);\n        const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];\n        for (let i = 0; i < 4; i++) {\n            explicitPadding[i][0] = pad[i * 2];\n            explicitPadding[i][1] = pad[i * 2 + 1];\n        }\n        return explicitPadding;\n    }\n    return pad;\n}\n/**\n *  Reuse the tensor if it is marked as keep, otherwise clone the tensor to\n *  avoid disposal. This is important for TensorArray and TensorList ops, since\n *  internally they use a tensor as the id for TensorArray and TensorList, and\n * to simplify lookup, they also use Tensor.id as the key to the internal map.\n * These id tensors have been marked as kept in the backend, we need avoid clone\n * them in order to create new Tensor.id.\n * @param tensor\n */\nexport function cloneTensor(tensor) {\n    return tensor.kept ? tensor : clone(tensor);\n}\n//# sourceMappingURL=utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Add',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AddV2',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AddN',\n        'category': 'arithmetic',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'BiasAdd',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sub',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'RealDiv',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Div',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'DivNoNan',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'FloorDiv',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Mul',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Maximum',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Minimum',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Pow',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'SquaredDifference',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Mod',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'FloorMod',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    }\n];\n//# sourceMappingURL=arithmetic.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Abs',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Acos',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Asin',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atan',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atan2',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'y', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Ceil',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ClipByValue',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'clip_value_min', 'name': 'clipValueMin', 'type': 'number' },\n            { 'tfName': 'clip_value_max', 'name': 'clipValueMax', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Complex',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'real', 'type': 'tensor' },\n            { 'start': 1, 'name': 'imag', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ComplexAbs',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Cos',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Cosh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Elu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Exp',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Floor',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Log',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Imag',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'outputType',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Neg',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Real',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'outputType',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Prelu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'alpha', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Relu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Relu6',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'clipValueMin',\n                'name': 'clipValueMin',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'clipValueMax',\n                'name': 'clipValueMax',\n                'type': 'number',\n                'defaultValue': 6\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Selu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sigmoid',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sin',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sinh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sqrt',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Rsqrt',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Square',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Tan',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Tanh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sign',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Round',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Expm1',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Log1p',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Reciprocal',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Softplus',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Asinh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Acosh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atanh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Erf',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Prod',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axes', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'keep_dims',\n                'name': 'keepDims',\n                'type': 'bool',\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LeakyRelu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'alpha',\n                'name': 'alpha',\n                'type': 'number',\n                'defaultValue': 0.2\n            },\n            {\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=basic_math.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'LoopCond',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'name': 'pred', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Switch',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'data', 'type': 'tensor' },\n            { 'start': 1, 'name': 'pred', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Merge',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Enter',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'frame_name', 'name': 'frameName', 'type': 'string' },\n            { 'tfName': 'is_constant', 'name': 'isConstant', 'type': 'bool' }\n        ]\n    },\n    {\n        'tfOpName': 'Exit',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'NextIteration',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'size', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },\n            { 'tfName': 'dynamic_size', 'name': 'dynamicSize', 'type': 'bool' },\n            { 'tfName': 'clear_after_read', 'name': 'clearAfterRead', 'type': 'bool' },\n            {\n                'tfName': 'identical_element_shapes',\n                'name': 'identicalElementShapes',\n                'type': 'bool'\n            },\n            { 'tfName': 'tensor_array_name', 'name': 'name', 'type': 'string' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayWriteV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayReadV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{\n                'tfName': 'dtype',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    },\n    {\n        'tfOpName': 'TensorArrayGatherV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayScatterV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorArrayConcatV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }, {\n                'tfName': 'element_shape_except0',\n                'name': 'elementShapeExcept0',\n                'type': 'shape',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArraySplitV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 2, 'name': 'lengths', 'type': 'number[]' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorArraySizeV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'flowIn', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayCloseV3',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'StatelessIf',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'cond', 'type': 'tensor' },\n            { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }\n        ],\n        'attrs': [\n            { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },\n            { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'If',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'cond', 'type': 'tensor' },\n            { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }\n        ],\n        'attrs': [\n            { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },\n            { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'StatelessWhile',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },\n            { 'tfName': 'body', 'name': 'body', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'While',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },\n            { 'tfName': 'body', 'name': 'body', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListScatter',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' }\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListScatterV2',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 3, 'name': 'numElements', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListGather',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListGetItem',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListSetItem',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListReserve',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 1, 'name': 'numElements', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListFromTensor',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' }\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListStack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' },\n            { 'tfName': 'num_elements', 'name': 'numElements', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListSplit',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 2, 'name': 'lengths', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListConcat',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListPopBack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListPushBack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n];\n//# sourceMappingURL=control.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'AvgPool',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPool',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPoolWithArgmax',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' }, {\n                'tfName': 'include_batch_in_index',\n                'name': 'includeBatchInIndex',\n                'type': 'bool'\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AvgPool3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPool3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Conv1D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'stride', 'name': 'stride', 'type': 'number' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NWC'\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'dilation',\n                'name': 'dilation',\n                'type': 'number',\n                'defaultValue': 1\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Conv2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            { 'tfName': 'useCudnnOnGpu', 'name': 'useCudnnOnGpu', 'type': 'bool' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': '_FusedConv2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'use_cudnn_on_gpu',\n                'name': 'useCudnnOnGpu',\n                'type': 'bool',\n                'defaultValue': true\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'dilations',\n                'name': 'dilations',\n                'type': 'number[]',\n                'defaultValue': [1, 1, 1, 1]\n            },\n            {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.0001\n            },\n        ]\n    },\n    {\n        'tfOpName': 'Conv2DBackpropInput',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 2, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 0, 'name': 'outputShape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n        ]\n    },\n    {\n        'tfOpName': 'DepthwiseConv2d',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'input', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'DepthwiseConv2dNative',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'input', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'FusedDepthwiseConv2dNative',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'dilations',\n                'name': 'dilations',\n                'type': 'number[]',\n                'defaultValue': [1, 1, 1, 1]\n            },\n            {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Conv3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ],\n    },\n    {\n        'tfOpName': 'Dilation2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'rates', 'name': 'dilations', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }\n        ]\n    }\n];\n//# sourceMappingURL=convolution.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Fill',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n            { 'start': 1, 'name': 'value', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'LinSpace',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'start', 'type': 'number' },\n            { 'start': 1, 'name': 'stop', 'type': 'number' },\n            { 'start': 2, 'name': 'num', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'OneHot',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'depth', 'type': 'number' },\n            { 'start': 2, 'name': 'onValue', 'type': 'number', 'defaultValue': 1 },\n            { 'start': 3, 'name': 'offValue', 'type': 'number', 'defaultValue': 0 },\n        ],\n        'attrs': [\n            {\n                'tfName': 'axis',\n                'name': 'axis',\n                'type': 'number',\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Ones',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'OnesLike',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'RandomUniform',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'minval',\n                'name': 'minval',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'maxval',\n                'name': 'maxval',\n                'type': 'number',\n                'defaultValue': 1\n            },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'seed2',\n                'name': 'seed2',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Range',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'start', 'type': 'number' },\n            { 'start': 1, 'name': 'stop', 'type': 'number' },\n            { 'start': 2, 'name': 'step', 'type': 'number', 'defaultValue': 0 },\n        ],\n        'attrs': [{ 'tfName': 'Tidx', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TruncatedNormal',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'means',\n                'name': 'mean',\n                'type': 'number',\n                'defaultValue': 0.0\n            },\n            {\n                'tfName': 'stddev',\n                'name': 'stdDev',\n                'type': 'number',\n                'defaultValue': 1.0\n            },\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number' }, {\n                'tfName': 'seed2',\n                'name': 'seed2',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Zeros',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'ZerosLike',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'Multinomial',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'logits', 'type': 'tensor' },\n            { 'start': 1, 'name': 'numSamples', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number' },\n            { 'tfName': 'seed2', 'name': 'seed2', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'output_dtype', 'name': 'output_dtype', 'type': 'dtype' }\n        ]\n    }\n];\n//# sourceMappingURL=creation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'NonMaxSuppressionV2',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV3',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV4',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'T_threshold',\n                'name': 'threshold',\n                'type': 'dtype',\n                'notSupported': true\n            },\n            {\n                'tfName': 'pad_to_max_output_size',\n                'name': 'padToMaxOutputSize',\n                'type': 'bool'\n            }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV5',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' },\n            { 'start': 5, 'name': 'softNmsSigma', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Where',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ListDiff',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'y', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=dynamic.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'TopKV2',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'k', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'sorted', 'name': 'sorted', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Unique',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n    },\n    {\n        'tfOpName': 'UniqueV2',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' },\n        ],\n    },\n];\n//# sourceMappingURL=evaluation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'PlaceholderWithDefault',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'default', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'Placeholder',\n        'category': 'graph',\n        'attrs': [\n            { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    { 'tfOpName': 'Const', 'category': 'graph' }, {\n        'tfOpName': 'Identity',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'IdentityN',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Snapshot',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Rank',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Size',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Shape',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'ShapeN',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Print',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'data', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'message', 'name': 'message', 'type': 'string' }, {\n                'tfName': 'first_n',\n                'name': 'firstN',\n                'type': 'number',\n                'notSupported': true\n            },\n            {\n                'tfName': 'summarize',\n                'name': 'summarize',\n                'type': 'number',\n                'defaultValue': 3\n            }\n        ]\n    },\n    { 'tfOpName': 'NoOp', 'category': 'graph', 'inputs': [] }, {\n        'tfOpName': 'StopGradient',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'FakeQuantWithMinMaxVars',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'min', 'name': 'min', 'type': 'number' },\n            { 'tfName': 'max', 'name': 'max', 'type': 'number' }\n        ]\n    }\n];\n//# sourceMappingURL=graph.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'ResizeBilinear',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'images', 'type': 'tensor' },\n            { 'start': 1, 'name': 'size', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ResizeNearestNeighbor',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'images', 'type': 'tensor' },\n            { 'start': 1, 'name': 'size', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'CropAndResize',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'image', 'type': 'tensor' },\n            { 'start': 1, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 2, 'name': 'boxInd', 'type': 'tensor' },\n            { 'start': 3, 'name': 'cropSize', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'method', 'name': 'method', 'type': 'string' }, {\n                'tfName': 'extrapolation_value',\n                'name': 'extrapolationValue',\n                'type': 'number'\n            }\n        ]\n    }\n];\n//# sourceMappingURL=image.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Equal',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'NotEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Greater',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'GreaterEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Less',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LessEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalAnd',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalNot',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalOr',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Select',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n            { 'start': 1, 'name': 'a', 'type': 'tensor' },\n            { 'start': 2, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'SelectV2',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n            { 'start': 1, 'name': 'a', 'type': 'tensor' },\n            { 'start': 2, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=logical.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': '_FusedMatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' }, {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.0001\n            },\n            {\n                'tfName': 'transpose_a',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'transpose_b',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'transpose_a',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'transpose_b',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'BatchMatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'adj_x',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'adj_y',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'BatchMatMulV2',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'adj_x',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'adj_y',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Transpose',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'perm', 'type': 'number[]' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=matrices.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'FusedBatchNorm',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'FusedBatchNormV2',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'FusedBatchNormV3',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'LRN',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'depth_radius',\n                'name': 'radius',\n                'type': 'number',\n                'defaultValue': 5\n            },\n            { 'tfName': 'bias', 'name': 'bias', 'type': 'number', 'defaultValue': 1.0 },\n            {\n                'tfName': 'alpha',\n                'name': 'alpha',\n                'type': 'number',\n                'defaultValue': 1.0\n            },\n            {\n                'tfName': 'beta',\n                'name': 'beta',\n                'type': 'number',\n                'defaultValue': 0.5\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Softmax',\n        'category': 'normalization',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'LogSoftmax',\n        'category': 'normalization',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'SparseToDense',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },\n            { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'defaultValue': true,\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Max',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Mean',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Min',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Sum',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'All',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Any',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'ArgMax',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'ArgMin',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Prod',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Cumsum',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'exclusive', 'name': 'exclusive', 'type': 'bool' },\n            { 'tfName': 'reverse', 'name': 'reverse', 'type': 'bool' }\n        ]\n    }\n];\n//# sourceMappingURL=reduction.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'ConcatV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'end': -1, 'name': 'tensors', 'type': 'tensors' },\n            { 'start': -1, 'name': 'axis', 'type': 'number' }\n        ],\n        'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]\n    },\n    {\n        'tfOpName': 'Concat',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 1, 'end': 0, 'name': 'tensors', 'type': 'tensors' },\n            { 'start': 0, 'name': 'axis', 'type': 'number' }\n        ],\n        'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]\n    },\n    {\n        'tfOpName': 'GatherV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'Gather',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Reverse',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'dims', 'type': 'bool', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ReverseV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Slice',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'begin', 'type': 'number[]' },\n            { 'start': 2, 'name': 'size', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'StridedSlice',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'begin', 'type': 'number[]' },\n            { 'start': 2, 'name': 'end', 'type': 'number[]' },\n            { 'start': 3, 'name': 'strides', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'begin_mask',\n                'name': 'beginMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'end_mask',\n                'name': 'endMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'new_axis_mask',\n                'name': 'newAxisMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'ellipsis_mask',\n                'name': 'ellipsisMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'shrink_axis_mask',\n                'name': 'shrinkAxisMask',\n                'type': 'number',\n                'defaultValue': 0\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Pack',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'Unpack',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'num',\n                'name': 'num',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Tile',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'reps', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Split',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'axis', 'type': 'number', 'defaultValue': 0 },\n            { 'start': 1, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'num_split',\n                'name': 'numOrSizeSplits',\n                'type': 'number',\n                'defaultValue': 1\n            }]\n    },\n    {\n        'tfOpName': 'SplitV',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'numOrSizeSplits', 'type': 'number[]' },\n            { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'ScatterNd',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'values', 'type': 'tensor' },\n            { 'start': 2, 'name': 'shape', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'GatherNd',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'SparseToDense',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },\n            { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'defaultValue': false,\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=slice_join.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'FFT',\n        'category': 'spectral',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'IFFT',\n        'category': 'spectral',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'RFFT',\n        'category': 'spectral',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' }, {\n                'start': 1,\n                'name': 'fft_length',\n                'type': 'number',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'IRFFT',\n        'category': 'spectral',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' }, {\n                'start': 1,\n                'name': 'fft_length',\n                'type': 'number',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=spectral.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Cast',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'SrcT',\n                'name': 'sdtype',\n                'type': 'dtype',\n                'notSupported': true\n            },\n            { 'tfName': 'DstT', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'ExpandDims',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Pad',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' },\n        ],\n        'attrs': [{\n                'tfName': 'constant_value',\n                'name': 'constantValue',\n                'type': 'number',\n                'defaultValue': 0\n            }]\n    },\n    {\n        'tfOpName': 'PadV2',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' }, {\n                'start': 2,\n                'name': 'constantValue',\n                'type': 'number',\n                'defaultValue': 0\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Reshape',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'shape', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Squeeze',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'axis',\n                'tfDeprecatedName': 'squeeze_dims',\n                'name': 'axis',\n                'type': 'number[]'\n            }]\n    },\n    {\n        'tfOpName': 'SpaceToBatchND',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'paddings', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'BatchToSpaceND',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'crops', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'DepthToSpace',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'block_size', 'name': 'blockSize', 'type': 'number' },\n            { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string' }\n        ]\n    },\n    {\n        'tfOpName': 'BroadcastTo',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': []\n    }\n];\n//# sourceMappingURL=transformation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport * as tensorflow from '../data/compiled_api';\nimport { getRegisteredOp } from './custom_op/register';\nimport { getNodeNameAndIndex } from './executors/utils';\nimport * as arithmetic from './op_list/arithmetic';\nimport * as basicMath from './op_list/basic_math';\nimport * as control from './op_list/control';\nimport * as convolution from './op_list/convolution';\nimport * as creation from './op_list/creation';\nimport * as dynamic from './op_list/dynamic';\nimport * as evaluation from './op_list/evaluation';\nimport * as graph from './op_list/graph';\nimport * as image from './op_list/image';\nimport * as logical from './op_list/logical';\nimport * as matrices from './op_list/matrices';\nimport * as normalization from './op_list/normalization';\nimport * as reduction from './op_list/reduction';\nimport * as sliceJoin from './op_list/slice_join';\nimport * as spectral from './op_list/spectral';\nimport * as transformation from './op_list/transformation';\nexport class OperationMapper {\n    // Singleton instance for the mapper\n    static get Instance() {\n        return this._instance || (this._instance = new this());\n    }\n    // Loads the op mapping from the JSON file.\n    constructor() {\n        const ops = [\n            arithmetic, basicMath, control, convolution, creation, dynamic,\n            evaluation, logical, image, graph, matrices, normalization, reduction,\n            sliceJoin, spectral, transformation\n        ];\n        const mappersJson = [].concat(...ops.map(op => op.json));\n        this.opMappers = mappersJson.reduce((map, mapper) => {\n            map[mapper.tfOpName] = mapper;\n            return map;\n        }, {});\n    }\n    // Converts the model inference graph from Tensorflow GraphDef to local\n    // representation for TensorFlow.js API\n    transformGraph(graph, signature = {}) {\n        const tfNodes = graph.node;\n        const placeholders = [];\n        const weights = [];\n        const initNodes = [];\n        const nodes = tfNodes.reduce((map, node) => {\n            map[node.name] = this.mapNode(node);\n            if (node.op.startsWith('Placeholder')) {\n                placeholders.push(map[node.name]);\n            }\n            else if (node.op === 'Const') {\n                weights.push(map[node.name]);\n            }\n            else if (node.input == null || node.input.length === 0) {\n                initNodes.push(map[node.name]);\n            }\n            return map;\n        }, {});\n        let inputs = [];\n        const outputs = [];\n        let inputNodeNameToKey = {};\n        let outputNodeNameToKey = {};\n        if (signature != null) {\n            inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);\n            outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);\n        }\n        const allNodes = Object.keys(nodes);\n        allNodes.forEach(key => {\n            const node = nodes[key];\n            node.inputNames.forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                node.inputs.push(nodes[nodeName]);\n                nodes[nodeName].children.push(node);\n            });\n        });\n        // if signature has not outputs set, add any node that does not have\n        // outputs.\n        if (Object.keys(outputNodeNameToKey).length === 0) {\n            allNodes.forEach(key => {\n                const node = nodes[key];\n                if (node.children.length === 0) {\n                    outputs.push(node);\n                }\n            });\n        }\n        else {\n            Object.keys(outputNodeNameToKey).forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                const node = nodes[nodeName];\n                if (node != null) {\n                    node.signatureKey = outputNodeNameToKey[name];\n                    outputs.push(node);\n                }\n            });\n        }\n        if (Object.keys(inputNodeNameToKey).length > 0) {\n            Object.keys(inputNodeNameToKey).forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                const node = nodes[nodeName];\n                if (node) {\n                    node.signatureKey = inputNodeNameToKey[name];\n                    inputs.push(node);\n                }\n            });\n        }\n        else {\n            inputs = placeholders;\n        }\n        let functions = {};\n        if (graph.library != null && graph.library.function != null) {\n            functions = graph.library.function.reduce((functions, func) => {\n                functions[func.signature.name] = this.mapFunction(func);\n                return functions;\n            }, {});\n        }\n        const result = { nodes, inputs, outputs, weights, placeholders, signature, functions };\n        if (initNodes.length > 0) {\n            result.initNodes = initNodes;\n        }\n        return result;\n    }\n    mapSignatureEntries(entries) {\n        return Object.keys(entries || {})\n            .reduce((prev, curr) => {\n            prev[entries[curr].name] = curr;\n            return prev;\n        }, {});\n    }\n    mapNode(node) {\n        // Unsupported ops will cause an error at run-time (not parse time), since\n        // they may not be used by the actual execution subgraph.\n        const mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {};\n        if (node.attr == null) {\n            node.attr = {};\n        }\n        const newNode = {\n            name: node.name,\n            op: node.op,\n            category: mapper.category,\n            inputNames: (node.input ||\n                []).map(input => input.startsWith('^') ? input.substr(1) : input),\n            inputs: [],\n            children: [],\n            inputParams: {},\n            attrParams: {},\n            rawAttrs: node.attr\n        };\n        if (mapper.inputs != null) {\n            newNode.inputParams =\n                mapper.inputs.reduce((map, param) => {\n                    map[param.name] = {\n                        type: param.type,\n                        inputIndexStart: param.start,\n                        inputIndexEnd: param.end\n                    };\n                    return map;\n                }, {});\n        }\n        if (mapper.attrs != null) {\n            newNode.attrParams =\n                mapper.attrs.reduce((map, param) => {\n                    const type = param.type;\n                    let value = undefined;\n                    switch (param.type) {\n                        case 'string':\n                            value = getStringParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'string[]':\n                            value = getStringArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'number':\n                            value = getNumberParam(node.attr, param.tfName, (param.defaultValue || 0));\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'number[]':\n                            value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'bool':\n                            value = getBoolParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'bool[]':\n                            value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'shape':\n                            value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'shape[]':\n                            value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'dtype':\n                            value = getDtypeParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'dtype[]':\n                            value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'func':\n                            value = getFuncParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'tensor':\n                        case 'tensors':\n                            break;\n                        default:\n                            throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`);\n                    }\n                    map[param.name] = { value, type };\n                    return map;\n                }, {});\n        }\n        return newNode;\n    }\n    // map the TFunctionDef to TFJS graph object\n    mapFunction(functionDef) {\n        const tfNodes = functionDef.nodeDef;\n        const placeholders = [];\n        const weights = [];\n        let nodes = {};\n        if (tfNodes != null) {\n            nodes = tfNodes.reduce((map, node) => {\n                map[node.name] = this.mapNode(node);\n                if (node.op === 'Const') {\n                    weights.push(map[node.name]);\n                }\n                return map;\n            }, {});\n        }\n        const inputs = [];\n        const outputs = [];\n        functionDef.signature.inputArg.forEach(arg => {\n            const [nodeName,] = getNodeNameAndIndex(arg.name);\n            const node = {\n                name: nodeName,\n                op: 'Placeholder',\n                inputs: [],\n                inputNames: [],\n                category: 'graph',\n                inputParams: {},\n                attrParams: { dtype: { value: parseDtypeParam(arg.type), type: 'dtype' } },\n                children: []\n            };\n            node.signatureKey = arg.name;\n            inputs.push(node);\n            nodes[nodeName] = node;\n        });\n        const allNodes = Object.keys(nodes);\n        allNodes.forEach(key => {\n            const node = nodes[key];\n            node.inputNames.forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                node.inputs.push(nodes[nodeName]);\n                nodes[nodeName].children.push(node);\n            });\n        });\n        const returnNodeMap = functionDef.ret;\n        functionDef.signature.outputArg.forEach(output => {\n            const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);\n            const node = nodes[nodeName];\n            if (node != null) {\n                node.defaultOutput = index;\n                outputs.push(node);\n            }\n        });\n        const signature = this.mapArgsToSignature(functionDef);\n        return { nodes, inputs, outputs, weights, placeholders, signature };\n    }\n    mapArgsToSignature(functionDef) {\n        return {\n            methodName: functionDef.signature.name,\n            inputs: functionDef.signature.inputArg.reduce((map, arg) => {\n                map[arg.name] = this.mapArgToTensorInfo(arg);\n                return map;\n            }, {}),\n            outputs: functionDef.signature.outputArg.reduce((map, arg) => {\n                map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);\n                return map;\n            }, {}),\n        };\n    }\n    mapArgToTensorInfo(arg, nameMap) {\n        let name = arg.name;\n        if (nameMap != null) {\n            name = nameMap[name];\n        }\n        return { name, dtype: arg.type };\n    }\n}\nexport function decodeBase64(text) {\n    const global = env().global;\n    if (typeof global.atob !== 'undefined') {\n        return global.atob(text);\n    }\n    else if (typeof Buffer !== 'undefined') {\n        return new Buffer(text, 'base64').toString();\n    }\n    else {\n        throw new Error('Unable to decode base64 in this environment. ' +\n            'Missing built-in atob() or Buffer()');\n    }\n}\nexport function parseStringParam(s, keepCase) {\n    const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);\n    return keepCase ? value : value.toLowerCase();\n}\nexport function getStringParam(attrs, name, def, keepCase = false) {\n    const param = attrs[name];\n    if (param != null) {\n        return parseStringParam(param.s, keepCase);\n    }\n    return def;\n}\nexport function getBoolParam(attrs, name, def) {\n    const param = attrs[name];\n    return param ? param.b : def;\n}\nexport function getNumberParam(attrs, name, def) {\n    const param = attrs[name] || {};\n    const value = param['i'] != null ? param['i'] : (param['f'] != null ? param['f'] : def);\n    return (typeof value === 'number') ? value : parseInt(value, 10);\n}\nexport function parseDtypeParam(value) {\n    if (typeof (value) === 'string') {\n        // tslint:disable-next-line:no-any\n        value = tensorflow.DataType[value];\n    }\n    switch (value) {\n        case tensorflow.DataType.DT_FLOAT:\n            return 'float32';\n        case tensorflow.DataType.DT_INT32:\n        case tensorflow.DataType.DT_INT64:\n        case tensorflow.DataType.DT_INT8:\n        case tensorflow.DataType.DT_UINT8:\n            return 'int32';\n        case tensorflow.DataType.DT_BOOL:\n            return 'bool';\n        case tensorflow.DataType.DT_DOUBLE:\n            return 'float32';\n        case tensorflow.DataType.DT_STRING:\n            return 'string';\n        default:\n            // Unknown dtype error will happen at runtime (instead of parse time),\n            // since these nodes might not be used by the actual subgraph execution.\n            return null;\n    }\n}\nexport function getFuncParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.func) {\n        return param.func.name;\n    }\n    return def;\n}\nexport function getDtypeParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.type) {\n        return parseDtypeParam(param.type);\n    }\n    return def;\n}\nexport function getDtypeArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.type) {\n        return param.list.type.map(v => parseDtypeParam(v));\n    }\n    return def;\n}\nexport function parseTensorShapeParam(shape) {\n    if (shape.unknownRank) {\n        return undefined;\n    }\n    if (shape.dim != null) {\n        return shape.dim.map(dim => (typeof dim.size === 'number') ? dim.size : parseInt(dim.size, 10));\n    }\n    return [];\n}\nexport function getTensorShapeParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.shape) {\n        return parseTensorShapeParam(param.shape);\n    }\n    return def;\n}\nexport function getNumericArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param) {\n        return ((param.list.f && param.list.f.length ? param.list.f :\n            param.list.i) ||\n            [])\n            .map(v => (typeof v === 'number') ? v : parseInt(v, 10));\n    }\n    return def;\n}\nexport function getStringArrayParam(attrs, name, def, keepCase = false) {\n    const param = attrs[name];\n    if (param && param.list && param.list.s) {\n        return param.list.s.map((v) => {\n            return parseStringParam(v, keepCase);\n        });\n    }\n    return def;\n}\nexport function getTensorShapeArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.shape) {\n        return param.list.shape.map((v) => {\n            return parseTensorShapeParam(v);\n        });\n    }\n    return def;\n}\nexport function getBoolArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.b) {\n        return param.list.b;\n    }\n    return def;\n}\n//# sourceMappingURL=operation_mapper.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getTensor } from '../executors/utils';\nimport { getBoolArrayParam, getBoolParam, getDtypeArrayParam, getDtypeParam, getNumberParam, getNumericArrayParam, getStringArrayParam, getStringParam, getTensorShapeArrayParam, getTensorShapeParam } from '../operation_mapper';\n/**\n * Helper class for lookup inputs and params for nodes in the model graph.\n */\nexport class NodeValueImpl {\n    constructor(node, tensorMap, context) {\n        this.node = node;\n        this.tensorMap = tensorMap;\n        this.context = context;\n        this.inputs = [];\n        this.attrs = {};\n        this.inputs = node.inputNames.map(name => this.getInput(name));\n        if (node.rawAttrs != null) {\n            this.attrs = Object.keys(node.rawAttrs)\n                .reduce((attrs, key) => {\n                attrs[key] = this.getAttr(key);\n                return attrs;\n            }, {});\n        }\n    }\n    /**\n     * Return the value of the attribute or input param.\n     * @param name String: name of attribute or input param.\n     */\n    getInput(name) {\n        return getTensor(name, this.tensorMap, this.context);\n    }\n    /**\n     * Return the value of the attribute or input param.\n     * @param name String: name of attribute or input param.\n     */\n    getAttr(name, defaultValue) {\n        const value = this.node.rawAttrs[name];\n        if (value.tensor != null) {\n            return getTensor(name, this.tensorMap, this.context);\n        }\n        if (value.i != null || value.f != null) {\n            return getNumberParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.s != null) {\n            return getStringParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.b != null) {\n            return getBoolParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.shape != null) {\n            return getTensorShapeParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.type != null) {\n            return getDtypeParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.list != null) {\n            if (value.list.i != null || value.list.f != null) {\n                return getNumericArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.s != null) {\n                return getStringArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.shape != null) {\n                return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.b != null) {\n                return getBoolArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.type != null) {\n                return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n        }\n        return defaultValue;\n    }\n}\n//# sourceMappingURL=node_value_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This file exports ops used by the converters executors. By default it\n * re-exports all ops. In a custom build this is aliased to a file that will\n * only exports ops for a given model.json.\n */\nexport * from './ops';\n//# sourceMappingURL=ops_for_converter.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'BiasAdd':\n        case 'AddV2':\n        case 'Add': {\n            return [tfOps.add(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'AddN': {\n            return [tfOps.addN(getParamValue('tensors', node, tensorMap, context))];\n        }\n        case 'FloorMod':\n        case 'Mod':\n            return [tfOps.mod(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        case 'Mul':\n            return [tfOps.mul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        case 'RealDiv':\n        case 'Div': {\n            return [tfOps.div(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'DivNoNan': {\n            return [tfOps.divNoNan(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'FloorDiv': {\n            return [tfOps.floorDiv(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Sub': {\n            return [tfOps.sub(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Minimum': {\n            return [tfOps.minimum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Maximum': {\n            return [tfOps.maximum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Pow': {\n            return [tfOps.pow(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'SquaredDifference': {\n            return [tfOps.squaredDifference(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'arithmetic';\n//# sourceMappingURL=arithmetic_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Abs':\n        case 'ComplexAbs':\n            return [tfOps.abs(getParamValue('x', node, tensorMap, context))];\n        case 'Acos':\n            return [tfOps.acos(getParamValue('x', node, tensorMap, context))];\n        case 'Acosh':\n            return [tfOps.acosh(getParamValue('x', node, tensorMap, context))];\n        case 'Asin':\n            return [tfOps.asin(getParamValue('x', node, tensorMap, context))];\n        case 'Asinh':\n            return [tfOps.asinh(getParamValue('x', node, tensorMap, context))];\n        case 'Atan':\n            return [tfOps.atan(getParamValue('x', node, tensorMap, context))];\n        case 'Atan2':\n            return [tfOps.atan2(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context))];\n        case 'Atanh':\n            return [tfOps.atanh(getParamValue('x', node, tensorMap, context))];\n        case 'Ceil':\n            return [tfOps.ceil(getParamValue('x', node, tensorMap, context))];\n        case 'Complex':\n            return [tfOps.complex(getParamValue('real', node, tensorMap, context), getParamValue('imag', node, tensorMap, context))];\n        case 'Cos':\n            return [tfOps.cos(getParamValue('x', node, tensorMap, context))];\n        case 'Cosh':\n            return [tfOps.cosh(getParamValue('x', node, tensorMap, context))];\n        case 'Elu':\n            return [tfOps.elu(getParamValue('x', node, tensorMap, context))];\n        case 'Erf':\n            return [tfOps.erf(getParamValue('x', node, tensorMap, context))];\n        case 'Exp':\n            return [tfOps.exp(getParamValue('x', node, tensorMap, context))];\n        case 'Expm1': {\n            return [tfOps.expm1(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Floor':\n            return [tfOps.floor(getParamValue('x', node, tensorMap, context))];\n        case 'Log':\n            return [tfOps.log(getParamValue('x', node, tensorMap, context))];\n        case 'Log1p': {\n            return [tfOps.log1p(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Imag':\n            return [tfOps.imag(getParamValue('x', node, tensorMap, context))];\n        case 'Neg':\n            return [tfOps.neg(getParamValue('x', node, tensorMap, context))];\n        case 'Reciprocal': {\n            return [tfOps.reciprocal(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Real':\n            return [tfOps.real(getParamValue('x', node, tensorMap, context))];\n        case 'Relu':\n            return [tfOps.relu(getParamValue('x', node, tensorMap, context))];\n        case 'Round': {\n            return [tfOps.round(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Selu':\n            return [tfOps.selu(getParamValue('x', node, tensorMap, context))];\n        case 'Sigmoid':\n            return [tfOps.sigmoid(getParamValue('x', node, tensorMap, context))];\n        case 'Sin':\n            return [tfOps.sin(getParamValue('x', node, tensorMap, context))];\n        case 'Sign': {\n            return [tfOps.sign(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Sinh': {\n            return [tfOps.sinh(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Softplus': {\n            return [tfOps.softplus(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Sqrt': {\n            return [tfOps.sqrt(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Square': {\n            return [tfOps.square(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Tanh': {\n            return [tfOps.tanh(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Tan':\n            return [tfOps.tan(getParamValue('x', node, tensorMap, context))];\n        case 'Relu6':\n        case 'ClipByValue':\n            return [tfOps.clipByValue(getParamValue('x', node, tensorMap, context), getParamValue('clipValueMin', node, tensorMap, context), getParamValue('clipValueMax', node, tensorMap, context))];\n        case 'Rsqrt':\n            return [tfOps.rsqrt(getTensor(node.inputNames[0], tensorMap, context))];\n        case 'Prod':\n            return [tfOps.prod(getParamValue('x', node, tensorMap, context), getParamValue('axes', node, tensorMap, context))];\n        case 'LeakyRelu':\n            return [tfOps.leakyRelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n        case 'Prelu':\n            return [tfOps.prelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'basic_math';\n//# sourceMappingURL=basic_math_executor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This differs from util.assertShapesMatch in that it allows values of\n * negative one, an undefined size of a dimensinon, in a shape to match\n * anything.\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = '') {\n    util.assert(shapesEqualAllowUndefinedSize(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nexport function shapesEqualAllowUndefinedSize(n1, n2) {\n    if (n1.length !== n2.length) {\n        return false;\n    }\n    for (let i = 0; i < n1.length; i++) {\n        if (n1[i] !== -1 && n2[i] !== -1 && n1[i] !== n2[i]) {\n            return false;\n        }\n    }\n    return true;\n}\n//# sourceMappingURL=tensor_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize } from './tensor_utils';\n/**\n * The TensorArray object keeps an array of Tensors.  It\n * allows reading from the array and writing to the array.\n */\nexport class TensorArray {\n    constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) {\n        this.name = name;\n        this.dtype = dtype;\n        this.maxSize = maxSize;\n        this.elementShape = elementShape;\n        this.identicalElementShapes = identicalElementShapes;\n        this.dynamicSize = dynamicSize;\n        this.clearAfterRead = clearAfterRead;\n        this.tensors = [];\n        this.closed_ = false;\n        this.idTensor = scalar(0);\n        keep(this.idTensor);\n    }\n    get id() {\n        return this.idTensor.id;\n    }\n    get closed() {\n        return this.closed_;\n    }\n    /**\n     * Dispose the tensors and idTensor and mark the TensoryArray as closed.\n     */\n    clearAndClose(keepIds) {\n        this.tensors.forEach(tensor => {\n            if (keepIds == null || !keepIds.has(tensor.tensor.id)) {\n                tensor.tensor.dispose();\n            }\n        });\n        this.tensors = [];\n        this.closed_ = true;\n        this.idTensor.dispose();\n    }\n    size() {\n        return this.tensors.length;\n    }\n    /**\n     * Read the value at location index in the TensorArray.\n     * @param index Number the index to read from.\n     */\n    read(index) {\n        if (this.closed_) {\n            throw new Error(`TensorArray ${this.name} has already been closed.`);\n        }\n        if (index < 0 || index >= this.size()) {\n            throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);\n        }\n        const tensorWithState = this.tensors[index];\n        if (tensorWithState.cleared) {\n            throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read ` +\n                `(perhaps try setting clear_after_read = false?).`);\n        }\n        if (this.clearAfterRead) {\n            tensorWithState.cleared = true;\n        }\n        tensorWithState.read = true;\n        return tensorWithState.tensor;\n    }\n    /**\n     * Helper method to read multiple tensors from the specified indices.\n     */\n    readMany(indices) {\n        return indices.map(index => this.read(index));\n    }\n    /**\n     * Write value into the index of the TensorArray.\n     * @param index number the index to write to.\n     * @param tensor\n     */\n    write(index, tensor) {\n        if (this.closed_) {\n            throw new Error(`TensorArray ${this.name} has already been closed.`);\n        }\n        if (index < 0 || !this.dynamicSize && index >= this.maxSize) {\n            throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);\n        }\n        const t = this.tensors[index] || {};\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},\n          because the value dtype is ${tensor.dtype}, but TensorArray dtype is ${this.dtype}.`);\n        }\n        // Set the shape for the first time write to unknow shape tensor array\n        if (this.size() === 0 &&\n            (this.elementShape == null || this.elementShape.length === 0)) {\n            this.elementShape = tensor.shape;\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`);\n        if (t.read) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);\n        }\n        if (t.written) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);\n        }\n        t.tensor = tensor;\n        keep(tensor);\n        t.written = true;\n        this.tensors[index] = t;\n    }\n    /**\n     * Helper method to write multiple tensors to the specified indices.\n     */\n    writeMany(indices, tensors) {\n        if (indices.length !== tensors.length) {\n            throw new Error(`TensorArray ${this.name}: could not write multiple tensors,` +\n                `because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);\n        }\n        indices.forEach((i, index) => this.write(i, tensors[index]));\n    }\n    /**\n     * Return selected values in the TensorArray as a packed Tensor. All of\n     * selected values must have been written and their shapes must all match.\n     * @param [indices] number[] Optional. Taking values in [0, max_value). If the\n     *    TensorArray is not dynamic, max_value=size(). If not specified returns\n     *    all tensors in the original order.\n     * @param [dtype]\n     */\n    gather(indices, dtype) {\n        if (!!dtype && dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);\n        }\n        if (!indices) {\n            indices = [];\n            for (let i = 0; i < this.size(); i++) {\n                indices.push(i);\n            }\n        }\n        else {\n            indices = indices.slice(0, this.size());\n        }\n        if (indices.length === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        // Read all the PersistentTensors into a vector to keep track of\n        // their memory.\n        const tensors = this.readMany(indices);\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, 'TensorArray shape mismatch: ');\n        return stack(tensors, 0);\n    }\n    /**\n     * Return the values in the TensorArray as a concatenated Tensor.\n     */\n    concat(dtype) {\n        if (!!dtype && dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);\n        }\n        if (this.size() === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        const indices = [];\n        for (let i = 0; i < this.size(); i++) {\n            indices.push(i);\n        }\n        // Collect all the tensors from the tensors array.\n        const tensors = this.readMany(indices);\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);\n        return concat(tensors, 0);\n    }\n    /**\n     * Scatter the values of a Tensor in specific indices of a TensorArray.\n     * @param indices nummber[] values in [0, max_value). If the\n     *    TensorArray is not dynamic, max_value=size().\n     * @param tensor Tensor input tensor.\n     */\n    scatter(indices, tensor) {\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n        }\n        if (indices.length !== tensor.shape[0]) {\n            throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n        }\n        const maxIndex = Math.max(...indices);\n        if (!this.dynamicSize && maxIndex >= this.maxSize) {\n            throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);\n        }\n        this.writeMany(indices, unstack(tensor, 0));\n    }\n    /**\n     * Split the values of a Tensor into the TensorArray.\n     * @param length number[] with the lengths to use when splitting value along\n     *    its first dimension.\n     * @param tensor Tensor, the tensor to split.\n     */\n    split(length, tensor) {\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n        }\n        let totalLength = 0;\n        const cumulativeLengths = length.map(len => {\n            totalLength += len;\n            return totalLength;\n        });\n        if (totalLength !== tensor.shape[0]) {\n            throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n        }\n        if (!this.dynamicSize && length.length !== this.maxSize) {\n            throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), ` +\n                'and the TensorArray is not marked as dynamically resizeable');\n        }\n        const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n        const tensors = [];\n        tidy(() => {\n            tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n            for (let i = 0; i < length.length; ++i) {\n                const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n                const indices = [0, previousLength, 0];\n                const sizes = [1, length[i], elementPerRow];\n                tensors[i] = reshape(slice(tensor, indices, sizes), this.elementShape);\n            }\n            return tensors;\n        });\n        const indices = [];\n        for (let i = 0; i < length.length; i++) {\n            indices[i] = i;\n        }\n        this.writeMany(indices, tensors);\n    }\n}\n//# sourceMappingURL=tensor_array.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize } from './tensor_utils';\n/**\n * TensorList stores a container of `tf.Tensor` objects, which are accessible\n * via tensors field.\n *\n * In order to get a copy of the underlying list, use the copy method:\n * ```\n *    TensorList b = a.copy();\n *    b.tensors().pushBack(t);  // This does not modify a.tensors().\n * ```\n *\n * Note that this is not a deep copy: the memory locations of the underlying\n * tensors will still point to the same locations of the corresponding tensors\n * in the original.\n */\nexport class TensorList {\n    /**\n     *\n     * @param tensors list of tensors\n     * @param elementShape shape of each tensor\n     * @param elementDtype data type of each tensor\n     * @param maxNumElements The maximum allowed size of `tensors`. Defaults to -1\n     *   meaning that the size of `tensors` is unbounded.\n     */\n    constructor(tensors, elementShape, elementDtype, maxNumElements = -1) {\n        this.tensors = tensors;\n        this.elementShape = elementShape;\n        this.elementDtype = elementDtype;\n        if (tensors != null) {\n            tensors.forEach(tensor => {\n                if (elementDtype !== tensor.dtype) {\n                    throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor.dtype}`);\n                }\n                assertShapesMatchAllowUndefinedSize(elementShape, tensor.shape, 'TensorList shape mismatch: ');\n                keep(tensor);\n            });\n        }\n        this.idTensor = scalar(0);\n        this.maxNumElements = maxNumElements;\n        keep(this.idTensor);\n    }\n    get id() {\n        return this.idTensor.id;\n    }\n    /**\n     * Get a new TensorList containing a copy of the underlying tensor container.\n     */\n    copy() {\n        return new TensorList([...this.tensors], this.elementShape, this.elementDtype);\n    }\n    /**\n     * Dispose the tensors and idTensor and clear the tensor list.\n     */\n    clearAndClose(keepIds) {\n        this.tensors.forEach(tensor => {\n            if (keepIds == null || !keepIds.has(tensor.id)) {\n                tensor.dispose();\n            }\n        });\n        this.tensors.length = 0;\n        this.idTensor.dispose();\n    }\n    /**\n     * The size of the tensors in the tensor list.\n     */\n    size() {\n        return this.tensors.length;\n    }\n    /**\n     * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1)\n     * tf.Tensor.\n     * @param elementShape shape of each tensor\n     * @param elementDtype data type of each tensor\n     * @param numElements the number of elements to stack\n     */\n    stack(elementShape, elementDtype, numElements = -1) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (numElements !== -1 && this.tensors.length !== numElements) {\n            throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);\n        }\n        assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, 'TensorList shape mismatch: ');\n        return tidy(() => {\n            const reshapedTensors = this.tensors.map(tensor => reshape(tensor, elementShape));\n            return stack(reshapedTensors, 0);\n        });\n    }\n    /**\n     * Pop a tensor from the end of the list.\n     * @param elementShape shape of the tensor\n     * @param elementDtype data type of the tensor\n     */\n    popBack(elementShape, elementDtype) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (this.size() === 0) {\n            throw new Error('Trying to pop from an empty list.');\n        }\n        const tensor = this.tensors.pop();\n        assertShapesMatchAllowUndefinedSize(tensor.shape, elementShape, 'TensorList shape mismatch: ');\n        return reshape(tensor, elementShape);\n    }\n    /**\n     * Push a tensor to the end of the list.\n     * @param tensor Tensor to be pushed.\n     */\n    pushBack(tensor) {\n        if (tensor.dtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(tensor.shape, this.elementShape, 'TensorList shape mismatch: ');\n        if (this.maxNumElements === this.size()) {\n            throw new Error(`Trying to push element into a full list.`);\n        }\n        keep(tensor);\n        this.tensors.push(tensor);\n    }\n    /**\n     * Update the size of the list.\n     * @param size the new size of the list.\n     */\n    resize(size) {\n        if (size < 0) {\n            throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);\n        }\n        if (this.maxNumElements !== -1 && size > this.maxNumElements) {\n            throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);\n        }\n        this.tensors.length = size;\n    }\n    /**\n     * Retrieve the element at the provided index\n     * @param elementShape shape of the tensor\n     * @param elementDtype dtype of the tensor\n     * @param elementIndex index of the tensor\n     */\n    getItem(elementIndex, elementShape, elementDtype) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (elementIndex < 0 || elementIndex > this.tensors.length) {\n            throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);\n        }\n        if (this.tensors[elementIndex] == null) {\n            throw new Error(`element at index ${elementIndex} is null.`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, 'TensorList shape mismatch: ');\n        return this.tensors[elementIndex];\n    }\n    /**\n     * Set the tensor at the index\n     * @param elementIndex index of the tensor\n     * @param tensor the tensor to be inserted into the list\n     */\n    setItem(elementIndex, tensor) {\n        if (tensor.dtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n        }\n        if (elementIndex < 0 ||\n            this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {\n            throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, 'TensorList shape mismatch: ');\n        keep(tensor);\n        this.tensors[elementIndex] = tensor;\n    }\n    /**\n     * Return selected values in the TensorList as a stacked Tensor. All of\n     * selected values must have been written and their shapes must all match.\n     * @param indices indices of tensors to gather\n     * @param elementDtype output tensor dtype\n     * @param elementShape output tensor element shape\n     */\n    gather(indices, elementDtype, elementShape) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n        // When indices is greater than the size of the list, indices beyond the\n        // size of the list are ignored.\n        indices = indices.slice(0, this.size());\n        if (indices.length === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        return tidy(() => {\n            const tensors = indices.map(i => reshape(this.tensors[i], elementShape));\n            return stack(tensors, 0);\n        });\n    }\n    /**\n     * Return the values in the TensorList as a concatenated Tensor.\n     * @param elementDtype output tensor dtype\n     * @param elementShape output tensor element shape\n     */\n    concat(elementDtype, elementShape) {\n        if (!!elementDtype && elementDtype !== this.elementDtype) {\n            throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n        if (this.size() === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        return tidy(() => {\n            const tensors = this.tensors.map(t => reshape(t, elementShape));\n            return concat(tensors, 0);\n        });\n    }\n}\n/**\n * Creates a TensorList which, when stacked, has the value of tensor.\n * @param tensor from tensor\n * @param elementShape output tensor element shape\n */\nexport function fromTensor(tensor, elementShape, elementDtype) {\n    const dtype = tensor.dtype;\n    if (tensor.shape.length < 1) {\n        throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor.shape}`);\n    }\n    if (tensor.dtype !== elementDtype) {\n        throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${elementDtype}`);\n    }\n    const outputShape = tensor.shape.slice(1);\n    assertShapesMatchAllowUndefinedSize(outputShape, elementShape, 'TensorList shape mismatch: ');\n    const tensorList = unstack(tensor);\n    return new TensorList(tensorList, elementShape, dtype);\n}\n/**\n * Return a TensorList of the given size with empty elements.\n * @param elementShape the shape of the future elements of the list\n * @param elementDtype the desired type of elements in the list\n * @param numElements the number of elements to reserve\n */\nexport function reserve(elementShape, elementDtype, numElements) {\n    return new TensorList([], elementShape, elementDtype, numElements);\n}\n/**\n * Put tensors at specific indices of a stacked tensor into a TensorList.\n * @param indices list of indices on how to scatter the tensor.\n * @param tensor input tensor.\n * @param elementShape the shape of the future elements of the list\n * @param numElements the number of elements to scatter\n */\nexport function scatter(tensor, indices, elementShape, numElements) {\n    if (indices.length !== tensor.shape[0]) {\n        throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n    }\n    const maxIndex = Math.max(...indices);\n    if (numElements != null && numElements !== -1 && maxIndex >= numElements) {\n        throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);\n    }\n    const list = new TensorList([], elementShape, tensor.dtype, numElements);\n    const tensors = unstack(tensor, 0);\n    indices.forEach((value, index) => {\n        list.setItem(value, tensors[index]);\n    });\n    return list;\n}\n/**\n * Split the values of a Tensor into a TensorList.\n * @param length the lengths to use when splitting value along\n *    its first dimension.\n * @param tensor the tensor to split.\n * @param elementShape the shape of the future elements of the list\n */\nexport function split(tensor, length, elementShape) {\n    let totalLength = 0;\n    const cumulativeLengths = length.map(len => {\n        totalLength += len;\n        return totalLength;\n    });\n    if (totalLength !== tensor.shape[0]) {\n        throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n    }\n    const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n    const tensors = tidy(() => {\n        const tensors = [];\n        tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n        for (let i = 0; i < length.length; ++i) {\n            const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n            const indices = [0, previousLength, 0];\n            const sizes = [1, length[i], elementPerRow];\n            tensors[i] = reshape(slice(tensor, indices, sizes), elementShape);\n        }\n        tensor.dispose();\n        return tensors;\n    });\n    const list = new TensorList([], elementShape, tensor.dtype, length.length);\n    for (let i = 0; i < tensors.length; i++) {\n        list.setItem(i, tensors[i]);\n    }\n    return list;\n}\n//# sourceMappingURL=tensor_list.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { scalar } from '@tensorflow/tfjs-core';\nimport { TensorArray } from '../../executor/tensor_array';\nimport { fromTensor, reserve, scatter, split } from '../../executor/tensor_list';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = async (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'If':\n        case 'StatelessIf': {\n            const thenFunc = getParamValue('thenBranch', node, tensorMap, context);\n            const elseFunc = getParamValue('elseBranch', node, tensorMap, context);\n            const cond = getParamValue('cond', node, tensorMap, context);\n            const args = getParamValue('args', node, tensorMap, context);\n            const condValue = await cond.data();\n            if (condValue[0]) {\n                return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n            }\n            else {\n                return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n            }\n        }\n        case 'While':\n        case 'StatelessWhile': {\n            const bodyFunc = getParamValue('body', node, tensorMap, context);\n            const condFunc = getParamValue('cond', node, tensorMap, context);\n            const args = getParamValue('args', node, tensorMap, context);\n            // Calculate the condition of the loop\n            const condResult = (await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap));\n            const argIds = args.map(tensor => tensor.id);\n            let condValue = await condResult[0].data();\n            // Dispose the intermediate tensors for condition function\n            condResult.forEach(tensor => {\n                if (!tensor.kept && argIds.indexOf(tensor.id) === -1) {\n                    tensor.dispose();\n                }\n            });\n            let result = args;\n            while (condValue[0]) {\n                // Record the previous result for intermediate tensor tracking\n                const origResult = result;\n                // Execution the body of the loop\n                result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);\n                const resultIds = result.map(tensor => tensor.id);\n                // Dispose the intermediate tensor for body function that is not global\n                // kept, not input/output of the body function\n                origResult.forEach(tensor => {\n                    if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n                        resultIds.indexOf(tensor.id) === -1) {\n                        tensor.dispose();\n                    }\n                });\n                // Recalcuate the condition of the loop using the latest results.\n                const condResult = (await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap));\n                condValue = await condResult[0].data();\n                // Dispose the intermediate tensors for condition function\n                condResult.forEach(tensor => {\n                    if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n                        resultIds.indexOf(tensor.id) === -1) {\n                        tensor.dispose();\n                    }\n                });\n            }\n            return result;\n        }\n        case 'LoopCond': {\n            const pred = getParamValue('pred', node, tensorMap, context);\n            return [cloneTensor(pred)];\n        }\n        case 'Switch': {\n            const pred = getParamValue('pred', node, tensorMap, context);\n            let data = getParamValue('data', node, tensorMap, context);\n            if (!data.kept) {\n                data = cloneTensor(data);\n            }\n            // Outputs nodes :0 => false, :1 => true\n            return (await pred.data())[0] ? [undefined, data] : [data, undefined];\n        }\n        case 'Merge': {\n            const inputName = node.inputNames.find(name => getTensor(name, tensorMap, context) !== undefined);\n            if (inputName) {\n                const data = getTensor(inputName, tensorMap, context);\n                return [cloneTensor(data)];\n            }\n            return undefined;\n        }\n        case 'Enter': {\n            const frameId = getParamValue('frameName', node, tensorMap, context);\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.enterFrame(frameId);\n            return [cloneTensor(data)];\n        }\n        case 'Exit': {\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.exitFrame();\n            return [cloneTensor(data)];\n        }\n        case 'NextIteration': {\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.nextIteration();\n            return [cloneTensor(data)];\n        }\n        case 'TensorArrayV3': {\n            const size = getParamValue('size', node, tensorMap, context);\n            const dtype = getParamValue('dtype', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const dynamicSize = getParamValue('dynamicSize', node, tensorMap, context);\n            const clearAfterRead = getParamValue('clearAfterRead', node, tensorMap, context);\n            const identicalElementShapes = getParamValue('identicalElementShapes', node, tensorMap, context);\n            const name = getParamValue('name', node, tensorMap, context);\n            const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);\n            context.addTensorArray(tensorArray);\n            return [tensorArray.idTensor, scalar(1.0)];\n        }\n        case 'TensorArrayWriteV3': {\n            const id = getParamValue('tensorArrayId', node, tensorMap, context);\n            const index = getParamValue('index', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const writeTensorArray = context.getTensorArray(id.id);\n            writeTensorArray.write(index, writeTensor);\n            return [writeTensorArray.idTensor];\n        }\n        case 'TensorArrayReadV3': {\n            const readId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const readIndex = getParamValue('index', node, tensorMap, context);\n            const readTensorArray = context.getTensorArray(readId.id);\n            return [readTensorArray.read(readIndex)];\n        }\n        case 'TensorArrayGatherV3': {\n            const gatherId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const gatherIndices = getParamValue('indices', node, tensorMap, context);\n            const gatherDtype = getParamValue('dtype', node, tensorMap, context);\n            const gatherTensorArray = context.getTensorArray(gatherId.id);\n            return [gatherTensorArray.gather(gatherIndices, gatherDtype)];\n        }\n        case 'TensorArrayScatterV3': {\n            const scatterId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const scatterIndices = getParamValue('indices', node, tensorMap, context);\n            const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n            const scatterTensorArray = context.getTensorArray(scatterId.id);\n            scatterTensorArray.scatter(scatterIndices, scatterTensor);\n            return [scatterTensorArray.idTensor];\n        }\n        case 'TensorArrayConcatV3': {\n            const concatId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const concatTensorArray = context.getTensorArray(concatId.id);\n            const concatDtype = getParamValue('dtype', node, tensorMap, context);\n            return [concatTensorArray.concat(concatDtype)];\n        }\n        case 'TensorArraySplitV3': {\n            const splitId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const splitTensor = getParamValue('tensor', node, tensorMap, context);\n            const lengths = getParamValue('lengths', node, tensorMap, context);\n            const splitTensorArray = context.getTensorArray(splitId.id);\n            splitTensorArray.split(lengths, splitTensor);\n            return [splitTensorArray.idTensor];\n        }\n        case 'TensorArraySizeV3': {\n            const sizeId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const sizeTensorArray = context.getTensorArray(sizeId.id);\n            return [scalar(sizeTensorArray.size(), 'int32')];\n        }\n        case 'TensorArrayCloseV3': {\n            const closeId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const closeTensorArray = context.getTensorArray(closeId.id);\n            closeTensorArray.clearAndClose();\n            return [closeTensorArray.idTensor];\n        }\n        case 'TensorListSetItem': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const index = getParamValue('index', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            tensorList.setItem(index, writeTensor);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListGetItem': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const readIndex = getParamValue('index', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDType = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.getItem(readIndex, elementShape, elementDType)];\n        }\n        case 'TensorListScatterV2':\n        case 'TensorListScatter': {\n            const scatterIndices = getParamValue('indices', node, tensorMap, context);\n            const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListReserve': {\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = reserve(elementShape, elementDtype, numElements);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListGather': {\n            const gatherId = getParamValue('tensorListId', node, tensorMap, context);\n            const gatherIndices = getParamValue('indices', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(gatherId.id);\n            return [tensorList.gather(gatherIndices, elementDtype, elementShape)];\n        }\n        case 'TensorListStack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.stack(elementShape, elementDtype, numElements)];\n        }\n        case 'TensorListFromTensor': {\n            const tensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = fromTensor(tensor, elementShape, elementDtype);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListConcat': {\n            const concatId = getParamValue('tensorListId', node, tensorMap, context);\n            const tensorList = context.getTensorList(concatId.id);\n            const concatDtype = getParamValue('dtype', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            return [tensorList.concat(concatDtype, elementShape)];\n        }\n        case 'TensorListPushBack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            tensorList.pushBack(writeTensor);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListPopBack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDType = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.popBack(elementShape, elementDType)];\n        }\n        case 'TensorListSplit': {\n            const splitTensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const lengths = getParamValue('lengths', node, tensorMap, context);\n            const tensorList = split(splitTensor, lengths, elementShape);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'control';\n//# sourceMappingURL=control_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getPadding, getParamValue } from './utils';\nfunction fusedConvAndDepthWiseParams(node, tensorMap, context) {\n    const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n    const isBiasAdd = extraOp === 'biasadd';\n    const isPrelu = activationFunc === 'prelu';\n    const isBatchNorm = extraOp === 'fusedbatchnorm';\n    const numArgs = getParamValue('numArgs', node, tensorMap, context);\n    if (isBiasAdd) {\n        if (isPrelu && numArgs !== 2) {\n            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu ' +\n                'must have two extra arguments: bias and alpha.');\n        }\n        if (!isPrelu && numArgs !== 1) {\n            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd must have ' +\n                'one extra argument: bias.');\n        }\n    }\n    if (isBatchNorm) {\n        throw new Error('FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.');\n    }\n    const stride = getParamValue('strides', node, tensorMap, context);\n    const pad = getPadding(node, tensorMap, context);\n    const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n        .toUpperCase();\n    const dilations = getParamValue('dilations', node, tensorMap, context);\n    const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n    return {\n        stride,\n        pad,\n        dataFormat,\n        dilations,\n        biasArg,\n        preluArg,\n        activationFunc\n    };\n}\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Conv1D': {\n            const stride = getParamValue('stride', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilation = getParamValue('dilation', node, tensorMap, context);\n            return [tfOps.conv1d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), stride, pad, dataFormat, dilation)];\n        }\n        case 'Conv2D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            return [tfOps.conv2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n        }\n        case '_FusedConv2D': {\n            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n            return [tfOps.fused.conv2d({\n                    x: getParamValue('x', node, tensorMap, context),\n                    filter: getParamValue('filter', node, tensorMap, context),\n                    strides: [stride[1], stride[2]],\n                    pad: pad,\n                    dataFormat: dataFormat,\n                    dilations: [dilations[1], dilations[2]],\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        }\n        case 'FusedDepthwiseConv2dNative': {\n            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n            return [tfOps.fused.depthwiseConv2d({\n                    x: getParamValue('x', node, tensorMap, context),\n                    filter: getParamValue('filter', node, tensorMap, context),\n                    strides: [stride[1], stride[2]],\n                    pad: pad,\n                    dataFormat: dataFormat,\n                    dilations: [dilations[1], dilations[2]],\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        }\n        case 'Conv2DBackpropInput':\n        case 'Conv2dTranspose': {\n            const shape = getParamValue('outputShape', node, tensorMap, context);\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            return [tfOps.conv2dTranspose(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), shape, [stride[1], stride[2]], pad)];\n        }\n        case 'DepthwiseConv2dNative':\n        case 'DepthwiseConv2d': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            return [tfOps.depthwiseConv2d(getParamValue('input', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n        }\n        case 'Conv3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            return [tfOps.conv3d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2], stride[3]], pad, dataFormat, [dilations[1], dilations[2], dilations[3]])];\n        }\n        case 'AvgPool': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.avgPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n        }\n        case 'MaxPool': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.maxPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n        }\n        case 'MaxPoolWithArgmax': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            const includeBatchInIndex = getParamValue('includeBatchInIndex', node, tensorMap, context);\n            const { result, indexes } = tfOps.maxPoolWithArgmax(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad, includeBatchInIndex);\n            return [result, indexes];\n        }\n        case 'AvgPool3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.avgPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n        }\n        case 'MaxPool3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.maxPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n        }\n        case 'Dilation2D': {\n            const strides = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            // strides: [1, stride_height, stride_width, 1].\n            const strideHeight = strides[1];\n            const strideWidth = strides[2];\n            // dilations: [1, dilation_height, dilation_width, 1].\n            const dilationHeight = dilations[1];\n            const dilationWidth = dilations[2];\n            return [tfOps.dilation2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [strideHeight, strideWidth], pad, [dilationHeight, dilationWidth], 'NHWC' /* dataFormat */)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'convolution';\n//# sourceMappingURL=convolution_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Fill': {\n            const shape = getParamValue('shape', node, tensorMap, context);\n            const dtype = getParamValue('dtype', node, tensorMap, context);\n            const value = getParamValue('value', node, tensorMap, context);\n            return [tfOps.fill(shape, value, dtype)];\n        }\n        case 'LinSpace': {\n            const start = getParamValue('start', node, tensorMap, context);\n            const stop = getParamValue('stop', node, tensorMap, context);\n            const num = getParamValue('num', node, tensorMap, context);\n            return [tfOps.linspace(start, stop, num)];\n        }\n        case 'Multinomial': {\n            const logits = getParamValue('logits', node, tensorMap, context);\n            const numSamples = getParamValue('numSamples', node, tensorMap, context);\n            const seed = getParamValue('seed', node, tensorMap, context);\n            return [tfOps.multinomial(logits, numSamples, seed)];\n        }\n        case 'OneHot': {\n            const indices = getParamValue('indices', node, tensorMap, context);\n            const depth = getParamValue('depth', node, tensorMap, context);\n            const onValue = getParamValue('onValue', node, tensorMap, context);\n            const offValue = getParamValue('offValue', node, tensorMap, context);\n            return [tfOps.oneHot(indices, depth, onValue, offValue)];\n        }\n        case 'Ones': {\n            return [tfOps.ones(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'OnesLike': {\n            return [tfOps.onesLike(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'RandomUniform': {\n            return [tfOps.randomUniform(\n                // tslint:disable-next-line:no-any\n                getParamValue('shape', node, tensorMap, context), getParamValue('minval', node, tensorMap, context), getParamValue('maxval', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'Range': {\n            const start = getParamValue('start', node, tensorMap, context);\n            const stop = getParamValue('stop', node, tensorMap, context);\n            const step = getParamValue('step', node, tensorMap, context);\n            return [tfOps.range(start, stop, step, getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'TruncatedNormal': {\n            const shape = getParamValue('shape', node, tensorMap, context);\n            const mean = getParamValue('mean', node, tensorMap, context);\n            const stdDev = getParamValue('stdDev', node, tensorMap, context);\n            const seed = getParamValue('seed', node, tensorMap, context);\n            return [tfOps.truncatedNormal(shape, mean, stdDev, getParamValue('dtype', node, tensorMap, context), seed)];\n        }\n        case 'Zeros': {\n            return [tfOps.zeros(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'ZerosLike': {\n            return [tfOps.zerosLike(getParamValue('x', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'creation';\n//# sourceMappingURL=creation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nfunction nmsParams(node, tensorMap, context) {\n    const boxes = getParamValue('boxes', node, tensorMap, context);\n    const scores = getParamValue('scores', node, tensorMap, context);\n    const maxOutputSize = getParamValue('maxOutputSize', node, tensorMap, context);\n    const iouThreshold = getParamValue('iouThreshold', node, tensorMap, context);\n    const scoreThreshold = getParamValue('scoreThreshold', node, tensorMap, context);\n    const softNmsSigma = getParamValue('softNmsSigma', node, tensorMap, context);\n    return {\n        boxes,\n        scores,\n        maxOutputSize,\n        iouThreshold,\n        scoreThreshold,\n        softNmsSigma\n    };\n}\nexport const executeOp = async (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'NonMaxSuppressionV5': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node, tensorMap, context);\n            const result = await tfOps.image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n            return [result.selectedIndices, result.selectedScores];\n        }\n        case 'NonMaxSuppressionV4': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n            const padToMaxOutputSize = getParamValue('padToMaxOutputSize', node, tensorMap, context);\n            const result = await tfOps.image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n            return [result.selectedIndices, result.validOutputs];\n        }\n        case 'NonMaxSuppressionV3':\n        case 'NonMaxSuppressionV2': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n            return [await tfOps.image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];\n        }\n        case 'Where': {\n            const condition = tfOps.cast(getParamValue('condition', node, tensorMap, context), 'bool');\n            const result = [await tfOps.whereAsync(condition)];\n            condition.dispose();\n            return result;\n        }\n        case 'ListDiff': {\n            return tfOps.setdiff1dAsync(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context));\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'dynamic';\n//# sourceMappingURL=dynamic_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'TopKV2': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const k = getParamValue('k', node, tensorMap, context);\n            const sorted = getParamValue('sorted', node, tensorMap, context);\n            const result = tfOps.topk(x, k, sorted);\n            return [result.values, result.indices];\n        }\n        case 'Unique': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const result = tfOps.unique(x);\n            return [result.values, result.indices];\n        }\n        case 'UniqueV2': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const result = tfOps.unique(x, axis);\n            return [result.values, result.indices];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'evaluation';\n//# sourceMappingURL=evaluation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Const': {\n            return tensorMap[node.name];\n        }\n        case 'PlaceholderWithDefault':\n            const def = getParamValue('default', node, tensorMap, context);\n            return [getTensor(node.name, tensorMap, context) || def];\n        case 'Placeholder':\n            return [getTensor(node.name, tensorMap, context)];\n        case 'Identity':\n        case 'StopGradient':\n        case 'FakeQuantWithMinMaxVars': { // This op is currently ignored.\n            const data = getParamValue('x', node, tensorMap, context);\n            return [cloneTensor(data)];\n        }\n        case 'IdentityN':\n            return getParamValue('x', node, tensorMap, context)\n                .map((t) => cloneTensor(t));\n        case 'Snapshot':\n            const snapshot = getParamValue('x', node, tensorMap, context);\n            return [cloneTensor(snapshot)];\n        case 'Shape':\n            return [tfOps.tensor1d(getParamValue('x', node, tensorMap, context).shape, 'int32')];\n        case 'ShapeN':\n            return getParamValue('x', node, tensorMap, context)\n                .map((t) => tfOps.tensor1d(t.shape));\n        case 'Size':\n            return [tfOps.scalar(getParamValue('x', node, tensorMap, context).size, 'int32')];\n        case 'Rank':\n            return [tfOps.scalar(getParamValue('x', node, tensorMap, context).rank, 'int32')];\n        case 'NoOp':\n            return [tfOps.scalar(1)];\n        case 'Print':\n            const input = getParamValue('x', node, tensorMap, context);\n            const data = getParamValue('data', node, tensorMap, context);\n            const message = getParamValue('message', node, tensorMap, context);\n            const summarize = getParamValue('summarize', node, tensorMap, context);\n            console.warn('The graph has a tf.print() operation,' +\n                'usually used for debugging, which slows down performance.');\n            console.log(message);\n            for (let i = 0; i < data.length; i++) {\n                console.log(Array.prototype.slice.call(data[i].dataSync())\n                    .slice(0, summarize));\n            }\n            return [input];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'graph';\n//# sourceMappingURL=graph_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'ResizeBilinear': {\n            const images = getParamValue('images', node, tensorMap, context);\n            const size = getParamValue('size', node, tensorMap, context);\n            const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n            return [tfOps.image.resizeBilinear(images, [size[0], size[1]], alignCorners)];\n        }\n        case 'ResizeNearestNeighbor': {\n            const images = getParamValue('images', node, tensorMap, context);\n            const size = getParamValue('size', node, tensorMap, context);\n            const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n            return [tfOps.image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners)];\n        }\n        case 'CropAndResize': {\n            const image = getParamValue('image', node, tensorMap, context);\n            const boxes = getParamValue('boxes', node, tensorMap, context);\n            const boxInd = getParamValue('boxInd', node, tensorMap, context);\n            const cropSize = getParamValue('cropSize', node, tensorMap, context);\n            const method = getParamValue('method', node, tensorMap, context);\n            const extrapolationValue = getParamValue('extrapolationValue', node, tensorMap, context);\n            return [tfOps.image.cropAndResize(image, boxes, boxInd, cropSize, method, extrapolationValue)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'image';\n//# sourceMappingURL=image_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Equal': {\n            return [tfOps.equal(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'NotEqual': {\n            return [tfOps.notEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Greater': {\n            return [tfOps.greater(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'GreaterEqual': {\n            return [tfOps.greaterEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Less': {\n            return [tfOps.less(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LessEqual': {\n            return [tfOps.lessEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LogicalAnd': {\n            return [tfOps.logicalAnd(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LogicalNot': {\n            return [tfOps.logicalNot(getParamValue('a', node, tensorMap, context))];\n        }\n        case 'LogicalOr': {\n            return [tfOps.logicalOr(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Select':\n        case 'SelectV2': {\n            return [tfOps.where(getParamValue('condition', node, tensorMap, context), getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'logical';\n//# sourceMappingURL=logical_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'BatchMatMul':\n        case 'BatchMatMulV2':\n        case 'MatMul':\n            return [tfOps.matMul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context), getParamValue('transposeA', node, tensorMap, context), getParamValue('transposeB', node, tensorMap, context))];\n        case 'Transpose':\n            return [tfOps.transpose(getParamValue('x', node, tensorMap, context), getParamValue('perm', node, tensorMap, context))];\n        case '_FusedMatMul':\n            const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n            const isBiasAdd = extraOp === 'biasadd';\n            const isPrelu = activationFunc === 'prelu';\n            const numArgs = getParamValue('numArgs', node, tensorMap, context);\n            if (isBiasAdd) {\n                if (isPrelu && numArgs !== 2) {\n                    throw new Error('Fused MatMul with BiasAdd and Prelu must have two ' +\n                        'extra arguments: bias and alpha.');\n                }\n                if (!isPrelu && numArgs !== 1) {\n                    throw new Error('Fused MatMul with BiasAdd must have one extra argument: bias.');\n                }\n            }\n            const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n            return [tfOps.fused.matMul({\n                    a: getParamValue('a', node, tensorMap, context),\n                    b: getParamValue('b', node, tensorMap, context),\n                    transposeA: getParamValue('transposeA', node, tensorMap, context),\n                    transposeB: getParamValue('transposeB', node, tensorMap, context),\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'matrices';\n//# sourceMappingURL=matrices_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'FusedBatchNorm':\n        case 'FusedBatchNormV2': {\n            return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n        }\n        case 'FusedBatchNormV3': {\n            return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n        }\n        case 'LRN': {\n            return [tfOps.localResponseNormalization(getParamValue('x', node, tensorMap, context), getParamValue('radius', node, tensorMap, context), getParamValue('bias', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context), getParamValue('beta', node, tensorMap, context))];\n        }\n        case 'Softmax': {\n            return [tfOps.softmax(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'LogSoftmax': {\n            return [tfOps.logSoftmax(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'SparseToDense': {\n            return [tfOps.sparseToDense(getParamValue('sparseIndices', node, tensorMap, context), getParamValue('outputShape', node, tensorMap, context), getParamValue('sparseValues', node, tensorMap, context), getParamValue('defaultValue', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'normalization';\n//# sourceMappingURL=normalization_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Max': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.max(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Mean': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.mean(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Min': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.min(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Sum': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.sum(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'All': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.all(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Any': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.any(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'ArgMax': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.argMax(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'ArgMin': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.argMin(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Prod': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.prod(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Cumsum': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const exclusive = getParamValue('exclusive', node, tensorMap, context);\n            const reverse = getParamValue('reverse', node, tensorMap, context);\n            return [tfOps.cumsum(getParamValue('x', node, tensorMap, context), axis, exclusive, reverse)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'reduction';\n//# sourceMappingURL=reduction_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tidy, util } from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'ConcatV2':\n        case 'Concat': {\n            const n = getParamValue('n', node, tensorMap, context);\n            const axis = getParamValue('axis', node, tensorMap, context);\n            let inputs = getParamValue('tensors', node, tensorMap, context);\n            inputs = inputs.slice(0, n);\n            return [tfOps.concat(inputs, axis)];\n        }\n        case 'GatherV2':\n        case 'Gather': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const input = getParamValue('x', node, tensorMap, context);\n            const indices = getParamValue('indices', node, tensorMap, context);\n            return [tfOps.gather(input, tfOps.cast(indices, 'int32'), axis)];\n        }\n        case 'ReverseV2':\n        case 'Reverse': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const input = getParamValue('x', node, tensorMap, context);\n            return [tfOps.reverse(input, axis)];\n        }\n        case 'Slice': {\n            // tslint:disable-next-line:no-any\n            const begin = getParamValue('begin', node, tensorMap, context);\n            // tslint:disable-next-line:no-any\n            const size = getParamValue('size', node, tensorMap, context);\n            return [tfOps.slice(getParamValue('x', node, tensorMap, context), begin, size)];\n        }\n        case 'StridedSlice': {\n            const begin = getParamValue('begin', node, tensorMap, context);\n            const end = getParamValue('end', node, tensorMap, context);\n            const strides = getParamValue('strides', node, tensorMap, context);\n            const beginMask = getParamValue('beginMask', node, tensorMap, context);\n            const endMask = getParamValue('endMask', node, tensorMap, context);\n            const ellipsisMask = getParamValue('ellipsisMask', node, tensorMap, context);\n            const newAxisMask = getParamValue('newAxisMask', node, tensorMap, context);\n            const shrinkAxisMask = getParamValue('shrinkAxisMask', node, tensorMap, context);\n            const tensor = getParamValue('x', node, tensorMap, context);\n            return [tfOps.stridedSlice(tensor, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)];\n        }\n        case 'Pack': {\n            return tidy(() => {\n                const axis = getParamValue('axis', node, tensorMap, context);\n                const tensors = getParamValue('tensors', node, tensorMap, context);\n                // Reshape the tensors to the first tensor's shape if they don't\n                // match.\n                const shape = tensors[0].shape;\n                const squeezedShape = tfOps.squeeze(tensors[0]).shape;\n                const mapped = tensors.map(tensor => {\n                    const sameShape = util.arraysEqual(tensor.shape, shape);\n                    if (!sameShape &&\n                        !util.arraysEqual(tfOps.squeeze(tensor).shape, squeezedShape)) {\n                        throw new Error('the input tensors shape does not match');\n                    }\n                    return sameShape ? tensor : tfOps.reshape(tensor, shape);\n                });\n                return [tfOps.stack(mapped, axis)];\n            });\n        }\n        case 'Unpack': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const tensor = getParamValue('tensor', node, tensorMap, context);\n            return tfOps.unstack(tensor, axis);\n        }\n        case 'Tile': {\n            const reps = getParamValue('reps', node, tensorMap, context);\n            return [tfOps.tile(getParamValue('x', node, tensorMap, context), reps)];\n        }\n        case 'Split':\n        case 'SplitV': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const numOrSizeSplits = getParamValue('numOrSizeSplits', node, tensorMap, context);\n            const tensor = getParamValue('x', node, tensorMap, context);\n            return tfOps.split(tensor, numOrSizeSplits, axis);\n        }\n        case 'ScatterNd': {\n            const indices = getParamValue('indices', node, tensorMap, context);\n            const values = getParamValue('values', node, tensorMap, context);\n            const shape = getParamValue('shape', node, tensorMap, context);\n            return [tfOps.scatterND(indices, values, shape)];\n        }\n        case 'GatherNd': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const indices = getParamValue('indices', node, tensorMap, context);\n            return [tfOps.gatherND(x, indices)];\n        }\n        case 'SparseToDense': {\n            const indices = getParamValue('sparseIndices', node, tensorMap, context);\n            const shape = getParamValue('outputShape', node, tensorMap, context);\n            const sparseValues = getParamValue('sparseValues', node, tensorMap, context);\n            const defaultValue = getParamValue('defaultValue', node, tensorMap, context);\n            return [tfOps.sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ?\n                    defaultValue :\n                    tfOps.cast(defaultValue, sparseValues.dtype))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'slice_join';\n//# sourceMappingURL=slice_join_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'FFT': {\n            return [tfOps.fft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'IFFT': {\n            return [tfOps.ifft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'RFFT': {\n            return [tfOps.rfft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'IRFFT': {\n            return [tfOps.irfft(getParamValue('x', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'spectral';\n//# sourceMappingURL=spectral_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Cast': {\n            return [tfOps.cast(getParamValue('x', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'ExpandDims': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.expandDims(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Squeeze': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.squeeze(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Reshape': {\n            return [tfOps.reshape(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n        }\n        case 'PadV2':\n        case 'Pad': {\n            return [tfOps.pad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('constantValue', node, tensorMap, context))];\n        }\n        case 'SpaceToBatchND': {\n            const blockShape = getParamValue('blockShape', node, tensorMap, context);\n            const paddings = getParamValue('paddings', node, tensorMap, context);\n            return [tfOps.spaceToBatchND(getParamValue('x', node, tensorMap, context), blockShape, paddings)];\n        }\n        case 'BatchToSpaceND': {\n            const blockShape = getParamValue('blockShape', node, tensorMap, context);\n            const crops = getParamValue('crops', node, tensorMap, context);\n            return [tfOps.batchToSpaceND(getParamValue('x', node, tensorMap, context), blockShape, crops)];\n        }\n        case 'DepthToSpace': {\n            const blockSize = getParamValue('blockSize', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context).toUpperCase();\n            return [tfOps.depthToSpace(getParamValue('x', node, tensorMap, context), blockSize, dataFormat)];\n        }\n        case 'BroadcastTo': {\n            return [tfOps.broadcastTo(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'transformation';\n//# sourceMappingURL=transformation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { NodeValueImpl } from './custom_op/node_value_impl';\nimport { getRegisteredOp } from './custom_op/register';\nimport * as arithmetic from './executors/arithmetic_executor';\nimport * as basicMath from './executors/basic_math_executor';\nimport * as control from './executors/control_executor';\nimport * as convolution from './executors/convolution_executor';\nimport * as creation from './executors/creation_executor';\nimport * as dynamic from './executors/dynamic_executor';\nimport * as evaluation from './executors/evaluation_executor';\nimport * as graph from './executors/graph_executor';\nimport * as image from './executors/image_executor';\nimport * as logical from './executors/logical_executor';\nimport * as matrices from './executors/matrices_executor';\nimport * as normalization from './executors/normalization_executor';\nimport * as reduction from './executors/reduction_executor';\nimport * as sliceJoin from './executors/slice_join_executor';\nimport * as spectral from './executors/spectral_executor';\nimport * as transformation from './executors/transformation_executor';\n/**\n * Executes the op defined by the node object.\n * @param node\n * @param tensorMap contains tensors for executed nodes and weights\n */\nexport function executeOp(node, tensorMap, context) {\n    const value = ((node, tensorMap, context) => {\n        switch (node.category) {\n            case 'arithmetic':\n                return tfc.tidy(() => arithmetic.executeOp(node, tensorMap, context));\n            case 'basic_math':\n                return tfc.tidy(() => basicMath.executeOp(node, tensorMap, context));\n            case 'control':\n                return control.executeOp(node, tensorMap, context);\n            case 'convolution':\n                return tfc.tidy(() => convolution.executeOp(node, tensorMap, context));\n            case 'creation':\n                return tfc.tidy(() => creation.executeOp(node, tensorMap, context));\n            case 'dynamic':\n                return dynamic.executeOp(node, tensorMap, context);\n            case 'evaluation':\n                return tfc.tidy(() => evaluation.executeOp(node, tensorMap, context));\n            case 'image':\n                return tfc.tidy(() => image.executeOp(node, tensorMap, context));\n            case 'graph':\n                return tfc.tidy(() => graph.executeOp(node, tensorMap, context));\n            case 'logical':\n                return tfc.tidy(() => logical.executeOp(node, tensorMap, context));\n            case 'matrices':\n                return tfc.tidy(() => matrices.executeOp(node, tensorMap, context));\n            case 'normalization':\n                return tfc.tidy(() => normalization.executeOp(node, tensorMap, context));\n            case 'reduction':\n                return tfc.tidy(() => reduction.executeOp(node, tensorMap, context));\n            case 'slice_join':\n                return tfc.tidy(() => sliceJoin.executeOp(node, tensorMap, context));\n            case 'spectral':\n                return tfc.tidy(() => spectral.executeOp(node, tensorMap, context));\n            case 'transformation':\n                return tfc.tidy(() => transformation.executeOp(node, tensorMap, context));\n            case 'custom':\n                const opMapper = getRegisteredOp(node.op);\n                if (opMapper && opMapper.customExecutor) {\n                    return opMapper.customExecutor(new NodeValueImpl(node, tensorMap, context));\n                }\n                else {\n                    throw TypeError(`Custom op ${node.op} is not registered.`);\n                }\n            default:\n                throw TypeError(`Unknown op '${node.op}'. File an issue at ` +\n                    `https://github.com/tensorflow/tfjs/issues so we can add it` +\n                    `, or register a custom execution with tf.registerOp()`);\n        }\n    })(node, tensorMap, context);\n    if (value instanceof Promise) {\n        return value.then((data) => [].concat(data));\n    }\n    return [].concat(value);\n}\n//# sourceMappingURL=operation_executor.js.map", "/**\n * ExecutionContext captures the runtime environment of the node. It keeps\n * track of the current frame and iteration for the control flow ops.\n *\n * For example, typical Dynamic RNN model may contain loops, for which\n * TensorFlow will generate graphs with Enter/Exit nodes to control the\n * current execution frame, and NextIteration Nodes for iteration id increment.\n * For model with branch logic, TensorFLow will generate Switch/Merge ops.\n */\nexport class ExecutionContext {\n    constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}) {\n        this.weightMap = weightMap;\n        this.tensorArrayMap = tensorArrayMap;\n        this.tensorListMap = tensorListMap;\n        this.functionMap = functionMap;\n        this.rootContext = { id: 0, frameName: '', iterationId: 0 };\n        this.contexts = [this.rootContext];\n        this.lastId = 0;\n        this.generateCurrentContextIds();\n    }\n    newFrame(id, frameName) {\n        return { id, frameName, iterationId: 0 };\n    }\n    /**\n     * Set the current context\n     * @param contexts: ExecutionContextInfo[] the current path of execution\n     * frames\n     */\n    set currentContext(contexts) {\n        if (this.contexts !== contexts) {\n            this.contexts = contexts;\n            this.generateCurrentContextIds();\n        }\n    }\n    get currentContext() {\n        return this.contexts;\n    }\n    /**\n     * Returns the current context in string format.\n     */\n    get currentContextId() {\n        return this._currentContextIds[0];\n    }\n    /**\n     * Returns the current context and all parent contexts in string format.\n     * This allow access to the nodes in the current and parent frames.\n     */\n    get currentContextIds() {\n        return this._currentContextIds;\n    }\n    generateCurrentContextIds() {\n        const names = [];\n        for (let i = 0; i < this.contexts.length - 1; i++) {\n            const contexts = this.contexts.slice(0, this.contexts.length - i);\n            names.push(this.contextIdforContexts(contexts));\n        }\n        names.push('');\n        this._currentContextIds = names;\n    }\n    contextIdforContexts(contexts) {\n        return contexts ?\n            contexts\n                .map(context => (context.id === 0 && context.iterationId === 0) ?\n                '' :\n                `${context.frameName}-${context.iterationId}`)\n                .join('/') :\n            '';\n    }\n    /**\n     * Enter a new frame, a new context is pushed on the current context list.\n     * @param frameId new frame id\n     */\n    enterFrame(frameId) {\n        if (this.contexts) {\n            this.lastId++;\n            this.contexts = this.contexts.slice();\n            this.contexts.push(this.newFrame(this.lastId, frameId));\n            this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));\n        }\n    }\n    /**\n     * Exit the current frame, the last context is removed from the current\n     * context list.\n     */\n    exitFrame() {\n        if (this.contexts && this.contexts.length > 1) {\n            this.contexts = this.contexts.slice();\n            this.contexts.splice(-1);\n            this.currentContextIds.shift();\n        }\n        else {\n            throw new Error('Cannot exit frame, the context is empty');\n        }\n    }\n    /**\n     * Enter the next iteration of a loop, the iteration id of last context is\n     * increased.\n     */\n    nextIteration() {\n        if (this.contexts && this.contexts.length > 0) {\n            this.contexts = this.contexts.slice();\n            this.lastId++;\n            const context = Object.assign({}, this.contexts[this.contexts.length - 1]);\n            context.iterationId += 1;\n            context.id = this.lastId;\n            this.contexts.splice(-1, 1, context);\n            this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));\n        }\n        else {\n            throw new Error('Cannot increase frame iteration, the context is empty');\n        }\n    }\n    getWeight(name) {\n        return this.weightMap[name];\n    }\n    addTensorArray(tensorArray) {\n        this.tensorArrayMap[tensorArray.id] = tensorArray;\n    }\n    getTensorArray(id) {\n        return this.tensorArrayMap[id];\n    }\n    addTensorList(tensorList) {\n        this.tensorListMap[tensorList.id] = tensorList;\n    }\n    getTensorList(id) {\n        return this.tensorListMap[id];\n    }\n    dispose(keepIds) {\n        for (const key in this.tensorArrayMap) {\n            this.tensorArrayMap[key].clearAndClose(keepIds);\n        }\n        for (const key in this.tensorListMap) {\n            this.tensorListMap[key].clearAndClose(keepIds);\n        }\n    }\n}\n//# sourceMappingURL=execution_context.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { parseNodeName } from '../operations/executors/utils';\n/**\n * Given graph inputs and desired outputs, find the minimal set of nodes\n * to execute in order to compute the outputs. In addition return other useful\n * info such:\n * - Missing inputs needed to compute the output.\n * - Whether the subgraph contains dynamic ops (control flow, dynamic shape).\n * - Alternative inputs in order to avoid async (dynamic op) execution.\n */\nexport function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {\n    const usedNodes = new Set();\n    const missingInputs = [];\n    let dynamicNode = null;\n    let syncInputs = null;\n    // Start with the outputs, going backwards and find all the nodes that are\n    // needed to compute those outputs.\n    const seen = new Set();\n    const inputNodeNames = Object.keys(inputs).map(name => parseNodeName(name)[0]);\n    let initNodeNames = [];\n    if (initNodes != null) {\n        initNodeNames = initNodes.map(node => parseNodeName(node.name)[0]);\n    }\n    const frontier = [...outputs];\n    while (frontier.length > 0) {\n        const node = frontier.pop();\n        if (isControlFlow(node) || isDynamicShape(node)) {\n            if (dynamicNode == null) {\n                dynamicNode = node;\n                syncInputs = dynamicNode.children.map(child => child.name)\n                    .filter(name => usedNodes.has(name));\n            }\n        }\n        usedNodes.add(node.name);\n        // Weights are dead end since we already have their values.\n        if (weightMap[node.name] != null) {\n            continue;\n        }\n        // This node is a dead end since it's one of the user-provided inputs.\n        if (inputNodeNames.indexOf(node.name) !== -1) {\n            continue;\n        }\n        // This node is a dead end since it doesn't have any inputs.\n        if (initNodeNames.indexOf(node.name) !== -1) {\n            continue;\n        }\n        if (node.inputs.length === 0) {\n            missingInputs.push(node.name);\n            continue;\n        }\n        node.inputs.forEach(input => {\n            // Don't add to the frontier if it is already there.\n            if (seen.has(input.name)) {\n                return;\n            }\n            seen.add(input.name);\n            frontier.push(input);\n        });\n    }\n    return { inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs };\n}\n/**\n * Given the execution info, return a list of nodes in topological order that\n * need to be executed to compute the output.\n */\nexport function getNodesInTopologicalOrder(graph, weightMap, executionInfo) {\n    const { usedNodes, inputs } = executionInfo;\n    const frontier = [];\n    const inputNodes = Object.keys(inputs)\n        .map(name => parseNodeName(name)[0])\n        .map(name => graph.nodes[name]);\n    const initNodes = graph.initNodes;\n    inputNodes.forEach(input => {\n        if (usedNodes.has(input.name)) {\n            frontier.push(input);\n        }\n    });\n    graph.weights.forEach(weight => {\n        if (usedNodes.has(weight.name)) {\n            frontier.push(weight);\n        }\n    });\n    if (initNodes != null) {\n        initNodes.forEach(node => {\n            if (usedNodes.has(node.name)) {\n                frontier.push(node);\n            }\n        });\n    }\n    const seen = new Set();\n    const orderedNodes = [];\n    while (frontier.length > 0) {\n        const node = frontier.pop();\n        seen.add(node.name);\n        if (!weightMap[node.name]) {\n            orderedNodes.push(node);\n        }\n        node.children.forEach(child => {\n            if (!seen.has(child.name) && usedNodes.has(child.name) &&\n                child.inputs.every(input => seen.has(input.name))) {\n                frontier.push(child);\n            }\n        });\n    }\n    return orderedNodes;\n}\nconst CONTROL_FLOW_OPS = [\n    'Switch', 'Merge', 'Enter', 'Exit', 'NextIteration', 'StatelessIf',\n    'StatelessWhile', 'if', 'While'\n];\nconst DYNAMIC_SHAPE_OPS = [\n    'NonMaxSuppressionV2', 'NonMaxSuppressionV3', 'NonMaxSuppressionV5', 'Where'\n];\nexport function isControlFlow(node) {\n    return CONTROL_FLOW_OPS.indexOf(node.op) >= 0;\n}\nexport function isDynamicShape(node) {\n    return DYNAMIC_SHAPE_OPS.indexOf(node.op) >= 0;\n}\n//# sourceMappingURL=model_analysis.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tidy, util } from '@tensorflow/tfjs-core';\nimport { getNodeNameAndIndex, getParamValue, getTensor, getTensorsForCurrentContenxt, parseNodeName } from '../operations/executors/utils';\nimport { executeOp } from '../operations/operation_executor';\nimport { ExecutionContext } from './execution_context';\nimport { getExecutionSubgraph, getNodesInTopologicalOrder, isControlFlow } from './model_analysis';\nexport class GraphExecutor {\n    /**\n     *\n     * @param graph Graph the model or function graph to be executed.\n     * @param parent When building function exector you need to set the parent\n     * executor. Since the weights and function executor maps are set at parant\n     * level, that function executor can access the function maps and weight maps\n     * through the parent.\n     */\n    constructor(graph, parent) {\n        this.graph = graph;\n        this.parent = parent;\n        this.compiledMap = new Map();\n        this._weightMap = {};\n        this.SEPERATOR = ',';\n        this._functions = {};\n        this._functionExecutorMap = {};\n        this._outputs = graph.outputs;\n        this._inputs = graph.inputs;\n        this._initNodes = graph.initNodes;\n        this._signature = graph.signature;\n        this._functions = graph.functions;\n        // create sub-graph executors\n        if (graph.functions != null) {\n            Object.keys(graph.functions).forEach(name => {\n                this._functionExecutorMap[name] =\n                    new GraphExecutor(graph.functions[name], this);\n            });\n        }\n    }\n    get weightIds() {\n        return this.parent ? this.parent.weightIds : this._weightIds;\n    }\n    get functionExecutorMap() {\n        return this.parent ? this.parent.functionExecutorMap :\n            this._functionExecutorMap;\n    }\n    get weightMap() {\n        return this.parent ? this.parent.weightMap : this._weightMap;\n    }\n    set weightMap(weightMap) {\n        const weightIds = Object.keys(weightMap).map(key => weightMap[key].map(tensor => tensor.id));\n        this._weightIds = [].concat(...weightIds);\n        this._weightMap = weightMap;\n    }\n    get inputs() {\n        return this._inputs.map(node => {\n            return {\n                name: node.name,\n                shape: node.attrParams['shape'] ?\n                    node.attrParams['shape'].value :\n                    undefined,\n                dtype: node.attrParams['dtype'] ?\n                    node.attrParams['dtype'].value :\n                    undefined\n            };\n        });\n    }\n    get outputs() {\n        return this._outputs.map(node => {\n            return {\n                name: node.name,\n                shape: node.attrParams['shape'] ?\n                    node.attrParams['shape'].value :\n                    undefined,\n                dtype: node.attrParams['dtype'] ?\n                    node.attrParams['dtype'].value :\n                    undefined\n            };\n        });\n    }\n    get inputNodes() {\n        return this._inputs.map(node => node.signatureKey || node.name);\n    }\n    get outputNodes() {\n        return this._outputs.map((node) => {\n            const name = node.signatureKey || node.name;\n            return node.defaultOutput ? (`${name}:${node.defaultOutput}`) : name;\n        });\n    }\n    get functions() {\n        return Object.keys(this._functions).reduce((map, key) => {\n            map[key] = this._functions[key].signature;\n            return map;\n        }, {});\n    }\n    getCompilationKey(inputs, outputs) {\n        const sortedInputs = inputs.map(node => node.name).sort();\n        const sortedOutputs = outputs.map(node => node.name).sort();\n        return sortedInputs.join(this.SEPERATOR) + '--' +\n            sortedOutputs.join(this.SEPERATOR);\n    }\n    /**\n     * Compiles the inference graph and returns the minimal set of nodes that are\n     * required for execution, in the correct execution order.\n     */\n    compile(inputs, outputs) {\n        const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);\n        const { missingInputs, dynamicNode, syncInputs } = executionInfo;\n        if (dynamicNode != null) {\n            throw new Error(`This execution contains the node '${dynamicNode.name}', which has ` +\n                `the dynamic op '${dynamicNode.op}'. Please use ` +\n                `model.executeAsync() instead. Alternatively, to avoid the ` +\n                `dynamic ops, specify the inputs [${syncInputs}]`);\n        }\n        if (missingInputs.length > 0) {\n            const outNames = outputs.map(n => n.name);\n            const inNames = Object.keys(inputs);\n            throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs ` +\n                `[${inNames}]. Missing the following inputs: [${missingInputs}]`);\n        }\n        return getNodesInTopologicalOrder(this.graph, this.weightMap, executionInfo);\n    }\n    /**\n     * Executes the inference for given input tensors.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     */\n    execute(inputs, outputs) {\n        inputs = this.mapInputs(inputs);\n        const names = Object.keys(inputs).sort();\n        this.checkInputs(inputs);\n        this.checkInputShapeAndType(inputs);\n        outputs = this.mapOutputs(outputs);\n        this.checkOutputs(outputs);\n        const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n        const outputNodeNames = outputs.map(name => parseNodeName(name)[0]);\n        let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n        // If no outputs are specified, then use the default outputs of the model.\n        if (outputNodes.length === 0) {\n            outputNodes = this._outputs;\n        }\n        const compilationKey = this.getCompilationKey(inputNodes, outputNodes);\n        // Do nothing if the compiled graph cache contains the input.\n        let orderedNodes = this.compiledMap.get(compilationKey);\n        if (orderedNodes == null) {\n            orderedNodes = this.compile(inputs, outputNodes);\n            this.compiledMap.set(compilationKey, orderedNodes);\n        }\n        const tensorArrayMap = {};\n        const tensorListMap = {};\n        return tidy(() => {\n            const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n            const tensorsMap = Object.assign({}, this.weightMap);\n            Object.keys(inputs).forEach(name => {\n                const [nodeName, index] = parseNodeName(name);\n                const tensors = [];\n                tensors[index] = inputs[name];\n                tensorsMap[nodeName] = tensors;\n            });\n            const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n            const intermediateTensorConsumerCount = {};\n            for (let i = 0; i < orderedNodes.length; i++) {\n                const node = orderedNodes[i];\n                if (!tensorsMap[node.name]) {\n                    const tensors = executeOp(node, tensorsMap, context);\n                    if (tensors instanceof Promise) {\n                        throw new Error(`The execution of the op '${node.op}' returned a promise. ` +\n                            `Please use model.executeAsync() instead.`);\n                    }\n                    tensorsMap[node.name] = tensors;\n                    this.checkTensorForDisposal(node.name, node, tensorsMap, context, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount);\n                }\n            }\n            // dispose the context for the root executor\n            if (this.parent == null) {\n                context.dispose(tensorsToKeep);\n            }\n            return outputs.map(name => getTensor(name, tensorsMap, context));\n        });\n    }\n    getFrozenTensorIds(tensorMap) {\n        const ids = [].concat.apply([], Object.keys(tensorMap)\n            .map(key => tensorMap[key])\n            .map(tensors => tensors.map(tensor => tensor.id)));\n        return new Set(ids);\n    }\n    checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount) {\n        // Skip output nodes and any control flow nodes, since its dependency is\n        // tricky to track correctly.\n        if (node.category === 'control' || outputNames.indexOf(nodeName) !== -1) {\n            return;\n        }\n        tensorMap[nodeName].forEach(tensor => {\n            if (tensor != null) {\n                intermediateTensorConsumerCount[tensor.id] =\n                    (intermediateTensorConsumerCount[tensor.id] || 0) +\n                        node.children.length;\n            }\n        });\n        node.inputs.forEach(input => {\n            // Skip any control flow nodes, since its dependency is tricky to track\n            // correctly.\n            if (input.category !== 'control') {\n                const tensors = getTensorsForCurrentContenxt(input.name, tensorMap, context);\n                if (tensors != null) {\n                    tensors.forEach(tensor => {\n                        if (tensor && !tensorsToKeep.has(tensor.id)) {\n                            const count = intermediateTensorConsumerCount[tensor.id];\n                            if (count === 1) {\n                                tensor.dispose();\n                                delete intermediateTensorConsumerCount[tensor.id];\n                            }\n                            else if (count != null) {\n                                // only intermediate nodes has count set, inputs and weights are\n                                // not.\n                                intermediateTensorConsumerCount[tensor.id]--;\n                            }\n                        }\n                    });\n                }\n            }\n        });\n    }\n    /**\n     * Executes the inference for given input tensors in Async fashion.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     */\n    async executeAsync(inputs, outputs) {\n        return this._executeAsync(inputs, outputs);\n    }\n    /**\n     * Executes the inference for given input tensors in Async fashion.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     * @param isFunctionExecution Flag for executing a function.\n     * @param tensorArrayMap Optional, global TensorArray map by id. Used for\n     * function execution.\n     * @param tensorArrayMap Optinal global TensorList map by id. Used for\n     * function execution.\n     */\n    async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {\n        if (!isFunctionExecution) {\n            inputs = this.mapInputs(inputs);\n            this.checkInputs(inputs);\n            this.checkInputShapeAndType(inputs);\n            outputs = this.mapOutputs(outputs);\n            this.checkOutputs(outputs);\n        }\n        const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n        // Graph with control flow op requires runtime evaluation of the execution\n        // order, while without control flow the execution order is pre-determined\n        // in the compile method.\n        const tensorMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);\n        const results = outputs.map(name => getTensor(name, tensorMap, context));\n        // dispose all the intermediate tensors\n        const outputIds = results.map(t => t.id);\n        const inputIds = Object.keys(inputs).map(name => inputs[name].id);\n        const keepIds = new Set([...outputIds, ...inputIds, ...this.weightIds]);\n        Object.keys(tensorMap).forEach(key => {\n            const tensorArray = tensorMap[key];\n            tensorArray.forEach(tensor => {\n                if (tensor && !tensor.isDisposed && !keepIds.has(tensor.id)) {\n                    tensor.dispose();\n                }\n            });\n        });\n        // dispose the context for the root executor\n        if (this.parent == null) {\n            context.dispose(keepIds);\n        }\n        return results;\n    }\n    async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {\n        const mappedInputs = inputs.reduce((map, tensor, index) => {\n            map[this.inputs[index].name] = tensor;\n            return map;\n        }, {});\n        return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);\n    }\n    /**\n     * When there are control flow nodes in the graph, the graph execution use\n     * ExecutionContext to keep track of the frames and loop iterators.\n     * @param inputs placeholder tensors for the graph.\n     * @param context the execution context object for current execution.\n     * @param isFunctionExecution Flag for executing a function.\n     */\n    async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {\n        const names = Object.keys(inputs);\n        const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n        const outputNodeNames = outputNames.map(name => parseNodeName(name)[0]);\n        const outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n        const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes, this.weightMap);\n        const stack = [...inputNodes, ...this.graph.weights].map(node => {\n            return { node, contexts: context.currentContext };\n        });\n        const tensorsMap = Object.assign({}, this.weightMap);\n        Object.keys(inputs).forEach(name => {\n            const [nodeName, index] = parseNodeName(name);\n            const tensors = [];\n            tensors[index] = inputs[name];\n            tensorsMap[nodeName] = tensors;\n        });\n        const intermediateTensorConsumerCount = {};\n        const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n        const added = {};\n        while (stack.length > 0) {\n            const promises = this.processStack(inputNodes, stack, context, tensorsMap, added, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount, usedNodes);\n            await Promise.all(promises);\n        }\n        if (dynamicNode == null && !isFunctionExecution) {\n            console.warn(`This model execution did not contain any nodes with control flow ` +\n                `or dynamic output shapes. You can use model.execute() instead.`);\n        }\n        const missingOutputs = outputNodes\n            .filter(node => !isControlFlow(node) &&\n            !getTensor(node.name, tensorsMap, context))\n            .map(node => node.name);\n        if (missingOutputs.length > 0) {\n            let alternativeMsg = '';\n            if (dynamicNode != null) {\n                alternativeMsg =\n                    `Alternatively, to avoid the dynamic ops, use model.execute() ` +\n                        `and specify the inputs [${syncInputs}]`;\n            }\n            throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided ` +\n                `inputs [${names}]. Consider providing the following inputs: ` +\n                `[${missingInputs}]. ${alternativeMsg}`);\n        }\n        return tensorsMap;\n    }\n    processStack(inputNodes, stack, context, tensorMap, added, tensorsToKeep, outputNames, intermediateTensorConsumerCount, usedNodes) {\n        const promises = [];\n        while (stack.length > 0) {\n            const item = stack.pop();\n            context.currentContext = item.contexts;\n            let nodeName = '';\n            // The tensor of the Enter op with isConstant set should be set\n            // in the parent scope, so it will be available as constant for the\n            // whole loop.\n            if (item.node.op === 'Enter' &&\n                getParamValue('isConstant', item.node, tensorMap, context)) {\n                [nodeName] = getNodeNameAndIndex(item.node.name, context);\n            }\n            // only process nodes that are not provided as input nodes.\n            if (inputNodes.indexOf(item.node) === -1) {\n                const tensors = executeOp(item.node, tensorMap, context);\n                if (!nodeName) {\n                    [nodeName] = getNodeNameAndIndex(item.node.name, context);\n                }\n                const currentContext = context.currentContext;\n                if (tensors instanceof Promise) {\n                    promises.push(tensors.then(t => {\n                        tensorMap[nodeName] = t;\n                        context.currentContext = currentContext;\n                        this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n                        this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n                        return t;\n                    }));\n                }\n                else {\n                    tensorMap[nodeName] = tensors;\n                    this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n                    this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n                }\n            }\n            else {\n                this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n            }\n        }\n        return promises;\n    }\n    processChildNodes(node, stack, context, tensorMap, added, usedNodes) {\n        node.children.forEach((childNode) => {\n            const [nodeName,] = getNodeNameAndIndex(childNode.name, context);\n            if (added[nodeName] || !usedNodes.has(childNode.name)) {\n                return;\n            }\n            // Merge op can be pushed if any of its inputs has value.\n            if (childNode.op === 'Merge') {\n                if (childNode.inputNames.some(name => {\n                    return !!getTensor(name, tensorMap, context);\n                })) {\n                    added[nodeName] = true;\n                    stack.push({ contexts: context.currentContext, node: childNode });\n                }\n            }\n            else // Otherwise all inputs must to have value.\n             if (childNode.inputNames.every(name => {\n                return !!getTensor(name, tensorMap, context);\n            })) {\n                added[nodeName] = true;\n                stack.push({ contexts: context.currentContext, node: childNode });\n            }\n        });\n    }\n    /**\n     * Releases the memory used by the weight tensors.\n     */\n    dispose() {\n        Object.keys(this.weightMap)\n            .forEach(key => this.weightMap[key].forEach(tensor => tensor.dispose()));\n    }\n    checkInputShapeAndType(inputs) {\n        Object.keys(inputs).forEach(name => {\n            const input = inputs[name];\n            const [nodeName,] = parseNodeName(name);\n            const node = this.graph.nodes[nodeName];\n            if (node.attrParams['shape'] && node.attrParams['shape'].value) {\n                const shape = node.attrParams['shape'].value;\n                const match = shape.length === input.shape.length &&\n                    input.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim);\n                util.assert(match, () => `The shape of dict['${node.name}'] provided in ` +\n                    `model.execute(dict) must be [${shape}], but was ` +\n                    `[${input.shape}]`);\n            }\n            if (node.attrParams['dtype'] && node.attrParams['dtype'].value) {\n                util.assert(input.dtype === node.attrParams['dtype'].value, () => `The dtype of dict['${node.name}'] provided in ` +\n                    `model.execute(dict) must be ` +\n                    `${node.attrParams['dtype'].value}, but was ${input.dtype}`);\n            }\n        });\n    }\n    mapInputs(inputs) {\n        const result = {};\n        for (const inputName in inputs) {\n            if (this._signature != null && this._signature.inputs != null &&\n                this._signature.inputs[inputName] != null) {\n                const tensor = this._signature.inputs[inputName];\n                result[tensor.name] = inputs[inputName];\n            }\n            else {\n                result[inputName] = inputs[inputName];\n            }\n        }\n        return result;\n    }\n    checkInputs(inputs) {\n        const notInGraph = Object.keys(inputs).filter(name => {\n            const [nodeName] = parseNodeName(name);\n            return this.graph.nodes[nodeName] == null;\n        });\n        if (notInGraph.length > 0) {\n            throw new Error(`The dict provided in model.execute(dict) has ` +\n                `keys: [${notInGraph}] that are not part of graph`);\n        }\n    }\n    mapOutputs(outputs) {\n        return outputs.map(name => {\n            if (this._signature != null && this._signature.outputs != null &&\n                this._signature.outputs[name] != null) {\n                const tensor = this._signature.outputs[name];\n                return tensor.name;\n            }\n            return name;\n        }, {});\n    }\n    checkOutputs(outputs) {\n        outputs.forEach(name => {\n            const [normalizedName] = parseNodeName(name);\n            if (!this.graph.nodes[normalizedName]) {\n                throw new Error(`The output '${name}' is not found in the graph`);\n            }\n        });\n    }\n}\n//# sourceMappingURL=graph_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { io, Tensor } from '@tensorflow/tfjs-core';\nimport { OperationMapper } from '../operations/operation_mapper';\nimport { GraphExecutor } from './graph_executor';\nexport const TFHUB_SEARCH_PARAM = '?tfjs-format=file';\nexport const DEFAULT_MODEL_NAME = 'model.json';\n/**\n * A `tf.GraphModel` is a directed, acyclic graph built from a\n * SavedModel GraphDef and allows inference execution.\n *\n * A `tf.GraphModel` can only be created by loading from a model converted from\n * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using\n * the command line converter tool and loaded via `tf.loadGraphModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class GraphModel {\n    /**\n     * @param modelUrl url for the model, or an `io.IOHandler`.\n     * @param weightManifestUrl url for the weight file generated by\n     * scripts/convert.py script.\n     * @param requestOption options for Request, which allows to send credentials\n     * and custom headers.\n     * @param onProgress Optional, progress callback function, fired periodically\n     * before the load is completed.\n     */\n    constructor(modelUrl, loadOptions = {}) {\n        this.modelUrl = modelUrl;\n        this.loadOptions = loadOptions;\n        this.version = 'n/a';\n        if (loadOptions == null) {\n            this.loadOptions = {};\n        }\n    }\n    // Returns the version information for the tensorflow model GraphDef.\n    get modelVersion() {\n        return this.version;\n    }\n    get inputNodes() {\n        return this.executor.inputNodes;\n    }\n    get outputNodes() {\n        return this.executor.outputNodes;\n    }\n    get inputs() {\n        return this.executor.inputs;\n    }\n    get outputs() {\n        return this.executor.outputs;\n    }\n    get weights() {\n        return this.executor.weightMap;\n    }\n    findIOHandler() {\n        const path = this.modelUrl;\n        if (path.load != null) {\n            // Path is an IO Handler.\n            this.handler = path;\n        }\n        else if (this.loadOptions.requestInit != null) {\n            this.handler = io.browserHTTPRequest(path, this.loadOptions);\n        }\n        else {\n            const handlers = io.getLoadHandlers(path, this.loadOptions);\n            if (handlers.length === 0) {\n                // For backward compatibility: if no load handler can be found,\n                // assume it is a relative http path.\n                handlers.push(io.browserHTTPRequest(path, this.loadOptions));\n            }\n            else if (handlers.length > 1) {\n                throw new Error(`Found more than one (${handlers.length}) load handlers for ` +\n                    `URL '${[path]}'`);\n            }\n            this.handler = handlers[0];\n        }\n    }\n    /**\n     * Loads the model and weight files, construct the in memory weight map and\n     * compile the inference graph.\n     */\n    async load() {\n        this.findIOHandler();\n        if (this.handler.load == null) {\n            throw new Error('Cannot proceed with model loading because the IOHandler provided ' +\n                'does not have the `load` method implemented.');\n        }\n        const artifacts = await this.handler.load();\n        return this.loadSync(artifacts);\n    }\n    /**\n     * Synchronously construct the in memory weight map and\n     * compile the inference graph. Also initialize hashtable if any.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    loadSync(artifacts) {\n        this.artifacts = artifacts;\n        const graph = this.artifacts.modelTopology;\n        let signature = {};\n        if (this.artifacts.userDefinedMetadata != null) {\n            signature = // tslint:disable-next-line:no-any\n                this.artifacts.userDefinedMetadata.signature;\n        }\n        this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;\n        const weightMap = io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);\n        this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, signature));\n        this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);\n        if (artifacts.modelInitializer != null) {\n            const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer);\n            this.initializer = new GraphExecutor(initializer);\n            this.initializer.weightMap = this.executor.weightMap;\n            this.initializer.execute({}, []);\n        }\n        return true;\n    }\n    /**\n     * Save the configuration and/or weights of the GraphModel.\n     *\n     * An `IOHandler` is an object that has a `save` method of the proper\n     * signature defined. The `save` method manages the storing or\n     * transmission of serialized data (\"artifacts\") that represent the\n     * model's topology and weights onto or via a specific medium, such as\n     * file downloads, local storage, IndexedDB in the web browser and HTTP\n     * requests to a server. TensorFlow.js provides `IOHandler`\n     * implementations for a number of frequently used saving mediums, such as\n     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n     * for more details.\n     *\n     * This method also allows you to refer to certain types of `IOHandler`s\n     * as URL-like string shortcuts, such as 'localstorage://' and\n     * 'indexeddb://'.\n     *\n     * Example 1: Save `model`'s topology and weights to browser [local\n     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n     * then load it back.\n     *\n     * ```js\n     * const modelUrl =\n     *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n     * const model = await tf.loadGraphModel(modelUrl);\n     * const zeros = tf.zeros([1, 224, 224, 3]);\n     * model.predict(zeros).print();\n     *\n     * const saveResults = await model.save('localstorage://my-model-1');\n     *\n     * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * model.predict(zeros).print();\n     * ```\n     *\n     * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n     * scheme-based string shortcut for `IOHandler`.\n     * @param config Options for saving the model.\n     * @returns A `Promise` of `SaveResult`, which summarizes the result of\n     * the saving, such as byte sizes of the saved artifacts for the model's\n     *   topology and weight values.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async save(handlerOrURL, config) {\n        if (typeof handlerOrURL === 'string') {\n            const handlers = io.getSaveHandlers(handlerOrURL);\n            if (handlers.length === 0) {\n                throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n            }\n            else if (handlers.length > 1) {\n                throw new Error(`Found more than one (${handlers.length}) save handlers for ` +\n                    `URL '${handlerOrURL}'`);\n            }\n            handlerOrURL = handlers[0];\n        }\n        if (handlerOrURL.save == null) {\n            throw new Error('GraphModel.save() cannot proceed because the IOHandler ' +\n                'provided does not have the `save` attribute defined.');\n        }\n        return handlerOrURL.save(this.artifacts);\n    }\n    /**\n     * Execute the inference for the input tensors.\n     *\n     * @param input The input tensors, when there is single input for the model,\n     * inputs param should be a `tf.Tensor`. For models with mutliple inputs,\n     * inputs params should be in either `tf.Tensor`[] if the input order is\n     * fixed, or otherwise NamedTensorMap format.\n     *\n     * For model with multiple inputs, we recommend you use NamedTensorMap as the\n     * input type, if you use `tf.Tensor`[], the order of the array needs to\n     * follow the\n     * order of inputNodes array. @see {@link GraphModel.inputNodes}\n     *\n     * You can also feed any intermediate nodes using the NamedTensorMap as the\n     * input type. For example, given the graph\n     *    InputNode => Intermediate => OutputNode,\n     * you can execute the subgraph Intermediate => OutputNode by calling\n     *    model.execute('IntermediateNode' : tf.tensor(...));\n     *\n     * This is useful for models that uses tf.dynamic_rnn, where the intermediate\n     * state needs to be fed manually.\n     *\n     * For batch inference execution, the tensors for each input need to be\n     * concatenated together. For example with mobilenet, the required input shape\n     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].\n     * If we are provide a batched data of 100 images, the input tensor should be\n     * in the shape of [100, 244, 244, 3].\n     *\n     * @param config Prediction configuration for specifying the batch size and\n     * output node names. Currently the batch size option is ignored for graph\n     * model.\n     *\n     * @returns Inference result tensors. The output would be single `tf.Tensor`\n     * if model has single output node, otherwise Tensor[] or NamedTensorMap[]\n     * will be returned for model with multiple outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(inputs, config) {\n        return this.execute(inputs, this.outputNodes);\n    }\n    normalizeInputs(inputs) {\n        if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {\n            // The input is already a NamedTensorMap.\n            return inputs;\n        }\n        inputs = Array.isArray(inputs) ? inputs : [inputs];\n        if (inputs.length !== this.inputNodes.length) {\n            throw new Error('Input tensor count mismatch,' +\n                `the graph model has ${this.inputNodes.length} placeholders, ` +\n                `while there are ${inputs.length} input tensors.`);\n        }\n        return this.inputNodes.reduce((map, inputName, i) => {\n            map[inputName] = inputs[i];\n            return map;\n        }, {});\n    }\n    normalizeOutputs(outputs) {\n        outputs = outputs || this.outputNodes;\n        return !Array.isArray(outputs) ? [outputs] : outputs;\n    }\n    /**\n     * Executes inference for the model for given input tensors.\n     * @param inputs tensor, tensor array or tensor map of the inputs for the\n     * model, keyed by the input node names.\n     * @param outputs output node name from the Tensorflow model, if no\n     * outputs are specified, the default outputs of the model would be used.\n     * You can inspect intermediate nodes of the model by adding them to the\n     * outputs array.\n     *\n     * @returns A single tensor if provided with a single output or no outputs\n     * are provided and there is only one default output, otherwise return a\n     * tensor array. The order of the tensor array is the same as the outputs\n     * if provided, otherwise the order of outputNodes attribute of the model.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    execute(inputs, outputs) {\n        inputs = this.normalizeInputs(inputs);\n        outputs = this.normalizeOutputs(outputs);\n        const result = this.executor.execute(inputs, outputs);\n        return result.length > 1 ? result : result[0];\n    }\n    /**\n     * Executes inference for the model for given input tensors in async\n     * fashion, use this method when your model contains control flow ops.\n     * @param inputs tensor, tensor array or tensor map of the inputs for the\n     * model, keyed by the input node names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     *\n     * @returns A Promise of single tensor if provided with a single output or\n     * no outputs are provided and there is only one default output, otherwise\n     * return a tensor map.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async executeAsync(inputs, outputs) {\n        inputs = this.normalizeInputs(inputs);\n        outputs = this.normalizeOutputs(outputs);\n        const result = await this.executor.executeAsync(inputs, outputs);\n        return result.length > 1 ? result : result[0];\n    }\n    convertTensorMapToTensorsMap(map) {\n        return Object.keys(map).reduce((newMap, key) => {\n            newMap[key] = [map[key]];\n            return newMap;\n        }, {});\n    }\n    /**\n     * Releases the memory used by the weight tensors.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    dispose() {\n        this.executor.dispose();\n        if (this.initializer) {\n            this.initializer.dispose();\n        }\n    }\n}\n/**\n * Load a graph model given a URL to the model definition.\n *\n * Example of loading MobileNetV2 from a URL and making a prediction with a\n * zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n * const model = await tf.loadGraphModel(modelUrl);\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n *\n * Example of loading MobileNetV2 from a TF Hub URL and making a prediction with\n * a zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';\n * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n * @param modelUrl The url or an `io.IOHandler` that loads the model.\n * @param options Options for the HTTP request, which allows to send credentials\n *    and custom headers.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport async function loadGraphModel(modelUrl, options = {}) {\n    if (modelUrl == null) {\n        throw new Error('modelUrl in loadGraphModel() cannot be null. Please provide a url ' +\n            'or an IOHandler that loads the model');\n    }\n    if (options == null) {\n        options = {};\n    }\n    if (options.fromTFHub) {\n        if (modelUrl.load == null) {\n            if (!modelUrl.endsWith('/')) {\n                modelUrl = modelUrl + '/';\n            }\n            modelUrl = `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;\n        }\n    }\n    const model = new GraphModel(modelUrl, options);\n    await model.load();\n    return model;\n}\n//# sourceMappingURL=graph_model.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { GraphModel, loadGraphModel } from './executor/graph_model';\nexport { deregisterOp, registerOp } from './operations/custom_op/register';\nexport { version as version_converter } from './version';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\n/**\n * Apply a mapping function to a nested structure in a recursive manner.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapResult`.  The `DeepMapResult` either provides a\n *   replacement value for that node (i.e., replacing the subtree), or indicates\n *   that the node should be processed recursively.\n */\nexport function deepMap(input, mapFn) {\n    return deepMapInternal(input, mapFn);\n}\n/**\n * @param seen: A Map of known object mappings (i.e., memoized results of\n *   `mapFn()`)\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepMapInternal(input, mapFn, seen = new Map(), containedIn = new Set()) {\n    if (input == null) {\n        return null;\n    }\n    if (containedIn.has(input)) {\n        throw new Error('Circular references are not supported.');\n    }\n    if (seen.has(input)) {\n        return seen.get(input);\n    }\n    const result = mapFn(input);\n    if (result.recurse && result.value !== null) {\n        throw new Error('A deep map function may not return both a value and recurse=true.');\n    }\n    if (!result.recurse) {\n        seen.set(input, result.value);\n        return result.value;\n    }\n    else if (isIterable(input)) {\n        // tslint:disable-next-line:no-any\n        const mappedIterable = Array.isArray(input) ? [] : {};\n        containedIn.add(input);\n        for (const k in input) {\n            const child = input[k];\n            const childResult = deepMapInternal(child, mapFn, seen, containedIn);\n            mappedIterable[k] = childResult;\n        }\n        containedIn.delete(input);\n        return mappedIterable;\n    }\n    else {\n        throw new Error(`Can't recurse into non-iterable type: ${input}`);\n    }\n}\n// TODO(soergel, kangyizhang) Reconsider naming of deepZip() to avoid confusion\n// with zip()\n/**\n * Zip nested structures together in a recursive manner.\n *\n * This has the effect of transposing or pivoting data, e.g. converting it from\n * a row-major representation to a column-major representation.\n *\n * For example, `deepZip([{a: 1, b: 2}, {a: 3, b: 4}])` returns\n * `{a: [1, 3], b: [2, 4]}`.\n *\n * The inputs should all have the same nested structure (i.e., of arrays and\n * dicts).  The result is a single object with the same nested structure, where\n * the leaves are arrays collecting the values of the inputs at that location\n * (or, optionally, the result of a custom function applied to those arrays).\n *\n * @param inputs: An array of the objects to zip together.\n * @param zipFn: (optional) A function that expects an array of elements at a\n *   single node of the object tree, and returns a `DeepMapResult`.  The\n *   `DeepMapResult` either provides a result value for that node (i.e.,\n *   representing the subtree), or indicates that the node should be processed\n *   recursively.  The default zipFn recurses as far as possible and places\n *   arrays at the leaves.\n */\nexport function deepZip(inputs, zipFn = zipToList) {\n    return deepZipInternal(inputs, zipFn);\n}\n/**\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepZipInternal(inputs, zipFn, containedIn = new Set()) {\n    // The recursion follows the structure of input 0; it's assumed that all the\n    // other inputs have the same structure.\n    const input = inputs[0];\n    if (containedIn.has(input)) {\n        throw new Error('Circular references are not supported.');\n    }\n    const result = zipFn(inputs);\n    if (result.recurse && result.value !== null) {\n        throw new Error('A deep zip function may not return both a value and recurse=true.');\n    }\n    if (!result.recurse) {\n        return result.value;\n    }\n    else if (isIterable(input)) {\n        // tslint:disable-next-line:no-any\n        const mappedIterable = Array.isArray(input) ? [] : {};\n        containedIn.add(input);\n        for (const k in input) {\n            const children = inputs.map(x => x[k]);\n            const childResult = deepZipInternal(children, zipFn, containedIn);\n            mappedIterable[k] = childResult;\n        }\n        containedIn.delete(input);\n        return mappedIterable;\n    }\n    else {\n        throw new Error(`Can't recurse into non-iterable type: ${input}`);\n    }\n}\n// tslint:disable-next-line:no-any\nexport function zipToList(x) {\n    if (x === null) {\n        return null;\n    }\n    // TODO(soergel): validate array type?\n    if (isIterable(x[0])) {\n        return { value: null, recurse: true };\n    }\n    else {\n        return { value: x, recurse: false };\n    }\n}\n/**\n * Apply an async mapping function to a nested structure in a recursive manner.\n *\n * This first creates a nested structure of Promises, and then awaits all of\n * those, resulting in a single Promise for a resolved nested structure.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapAsyncResult`.  The `DeepMapAsyncResult` either provides\n *   a `Promise` for a replacement value for that node (i.e., replacing the\n *   subtree), or indicates that the node should be processed recursively.  Note\n *   that the decision whether or not to recurse must be made immediately; only\n *   the mapped value may be promised.\n */\nexport async function deepMapAndAwaitAll(input, mapFn) {\n    const seen = new Map();\n    // First do a normal deepMap, collecting Promises in 'seen' as a side effect.\n    deepMapInternal(input, mapFn, seen);\n    // Replace the Promises in 'seen' in place.\n    // Note TypeScript provides no async map iteration, and regular map iteration\n    // is broken too, so sadly we have to do Array.from() to make it work.\n    // (There's no advantage to Promise.all(), and that would be tricky anyway.)\n    for (const key of Array.from(seen.keys())) {\n        const value = seen.get(key);\n        if (value instanceof Promise) {\n            const mappedValue = await value;\n            seen.set(key, mappedValue);\n        }\n    }\n    // Normal deepMap again, this time filling in the resolved values.\n    // It's unfortunate that we have to do two passes.\n    // TODO(soergel): test performance and think harder about a fast solution.\n    const result = deepMapInternal(input, mapFn, seen);\n    return result;\n}\n/**\n * Determine whether the argument is iterable.\n *\n * @returns true if the argument is an array or any non-Tensor object.\n */\n// tslint:disable-next-line:no-any\nexport function isIterable(obj) {\n    return obj != null && (!ArrayBuffer.isView(obj)) &&\n        (Array.isArray(obj) ||\n            (typeof obj === 'object' && !(obj instanceof tf.Tensor)));\n}\n/**\n * Determine whether the argument can be converted to Tensor.\n *\n * Tensors, primitives, arrays, and TypedArrays all qualify; anything else does\n * not.\n *\n * @returns true if the argument can be converted to Tensor.\n */\n// tslint:disable-next-line:no-any\nexport function canTensorify(obj) {\n    return obj == null || isPrimitive(obj) || Array.isArray(obj) ||\n        (typeof obj === 'object' && (obj instanceof tf.Tensor)) ||\n        tf.util.isTypedArray(obj);\n}\n/**\n * Returns true if the given `value` is a primitive type. Otherwise returns\n * false. This is equivalant to node util.isPrimitive\n */\nfunction isPrimitive(value) {\n    return (value === null ||\n        (typeof value !== 'object' && typeof value !== 'function'));\n}\n//# sourceMappingURL=deep_map.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { deepMap, isIterable } from './deep_map';\nexport function deepClone(container) {\n    return deepMap(container, cloneIfTensor);\n}\n// tslint:disable-next-line: no-any\nfunction cloneIfTensor(item) {\n    if (item instanceof tf.Tensor) {\n        return ({ value: item.clone(), recurse: false });\n    }\n    else if (isIterable(item)) {\n        return { value: null, recurse: true };\n    }\n    else {\n        return { value: item, recurse: false };\n    }\n}\n//# sourceMappingURL=deep_clone.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * A ring buffer, providing O(1) FIFO, LIFO, and related operations.\n */\nexport class RingBuffer {\n    /**\n     * Constructs a `RingBuffer`.\n     * @param capacity The number of items that the buffer can accomodate.\n     */\n    constructor(capacity) {\n        this.capacity = capacity;\n        // Note we store the indices in the range 0 <= index < 2*capacity.\n        // This allows us to distinguish the full from the empty case.\n        // See https://www.snellman.net/blog/archive/2016-12-13-ring-buffers/\n        this.begin = 0; // inclusive\n        this.end = 0; // exclusive\n        if (capacity == null) {\n            throw new RangeError('Can\\'t create a ring buffer of unknown capacity.');\n        }\n        if (capacity < 1) {\n            throw new RangeError('Can\\'t create ring buffer of capacity < 1.');\n        }\n        this.data = new Array(capacity);\n        this.doubledCapacity = 2 * capacity;\n    }\n    /**\n     * Map any index into the range 0 <= index < 2*capacity.\n     */\n    wrap(index) {\n        // don't trust % on negative numbers\n        while (index < 0) {\n            index += this.doubledCapacity;\n        }\n        return index % this.doubledCapacity;\n    }\n    get(index) {\n        if (index < 0) {\n            throw new RangeError('Can\\'t get item at a negative index.');\n        }\n        return this.data[index % this.capacity];\n    }\n    set(index, value) {\n        if (index < 0) {\n            throw new RangeError('Can\\'t set item at a negative index.');\n        }\n        this.data[index % this.capacity] = value;\n    }\n    /**\n     * Returns the current number of items in the buffer.\n     */\n    length() {\n        let length = this.end - this.begin;\n        if (length < 0) {\n            length = this.doubledCapacity + length;\n        }\n        return length;\n    }\n    /**\n     * Reports whether the buffer is full.\n     * @returns true if the number of items in the buffer equals its capacity, and\n     *   false otherwise.\n     */\n    isFull() {\n        return this.length() === this.capacity;\n    }\n    /**\n     * Reports whether the buffer is empty.\n     * @returns true if the number of items in the buffer equals zero, and\n     *   false otherwise.\n     */\n    isEmpty() {\n        return this.length() === 0;\n    }\n    /**\n     * Adds an item to the end of the buffer.\n     */\n    push(value) {\n        if (this.isFull()) {\n            throw new RangeError('Ring buffer is full.');\n        }\n        this.set(this.end, value);\n        this.end = this.wrap(this.end + 1);\n    }\n    /**\n     * Adds many items to the end of the buffer, in order.\n     */\n    pushAll(values) {\n        for (const value of values) {\n            this.push(value);\n        }\n    }\n    /**\n     * Removes and returns the last item in the buffer.\n     */\n    pop() {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        this.end = this.wrap(this.end - 1);\n        const result = this.get(this.end);\n        this.set(this.end, undefined);\n        return result;\n    }\n    /**\n     * Adds an item to the beginning of the buffer.\n     */\n    unshift(value) {\n        if (this.isFull()) {\n            throw new RangeError('Ring buffer is full.');\n        }\n        this.begin = this.wrap(this.begin - 1);\n        this.set(this.begin, value);\n    }\n    /**\n     * Removes and returns the first item in the buffer.\n     */\n    shift() {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        const result = this.get(this.begin);\n        this.set(this.begin, undefined);\n        this.begin = this.wrap(this.begin + 1);\n        return result;\n    }\n    /**\n     * Removes and returns a specific item in the buffer, and moves the last item\n     * to the vacated slot.  This is useful for implementing a shuffling stream.\n     * Note that this operation necessarily scrambles the original order.\n     *\n     * @param relativeIndex: the index of the item to remove, relative to the\n     *   first item in the buffer (e.g., hiding the ring nature of the underlying\n     *   storage).\n     */\n    shuffleExcise(relativeIndex) {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        const index = this.wrap(this.begin + relativeIndex);\n        const result = this.get(index);\n        this.set(index, this.pop());\n        return result;\n    }\n}\n//# sourceMappingURL=ring_buffer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { RingBuffer } from './ring_buffer';\nexport class GrowingRingBuffer extends RingBuffer {\n    /**\n     * Constructs a `GrowingRingBuffer`.\n     */\n    constructor() {\n        super(GrowingRingBuffer.INITIAL_CAPACITY);\n    }\n    isFull() {\n        return false;\n    }\n    push(value) {\n        if (super.isFull()) {\n            this.expand();\n        }\n        super.push(value);\n    }\n    unshift(value) {\n        if (super.isFull()) {\n            this.expand();\n        }\n        super.unshift(value);\n    }\n    /**\n     * Doubles the capacity of the buffer.\n     */\n    expand() {\n        const newCapacity = this.capacity * 2;\n        const newData = new Array(newCapacity);\n        const len = this.length();\n        // Rotate the buffer to start at index 0 again, since we can't just\n        // allocate more space at the end.\n        for (let i = 0; i < len; i++) {\n            newData[i] = this.get(this.wrap(this.begin + i));\n        }\n        this.data = newData;\n        this.capacity = newCapacity;\n        this.doubledCapacity = 2 * this.capacity;\n        this.begin = 0;\n        this.end = len;\n    }\n}\nGrowingRingBuffer.INITIAL_CAPACITY = 32;\n//# sourceMappingURL=growing_ring_buffer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport * as seedrandom from 'seedrandom';\nimport { deepClone } from '../util/deep_clone';\nimport { deepMapAndAwaitAll, deepZip, zipToList } from '../util/deep_map';\nimport { GrowingRingBuffer } from '../util/growing_ring_buffer';\nimport { RingBuffer } from '../util/ring_buffer';\n// Here we implement a simple asynchronous iterator.\n// This lets us avoid using either third-party stream libraries or\n// recent TypeScript language support requiring polyfills.\n/**\n * Create a `LazyIterator` from an array of items.\n */\nexport function iteratorFromItems(items) {\n    return new ArrayIterator(items);\n}\n/**\n * Create a `LazyIterator` of incrementing integers.\n */\nexport function iteratorFromIncrementing(start) {\n    let i = start;\n    return iteratorFromFunction(() => ({ value: i++, done: false }));\n}\n/**\n * Create a `LazyIterator` from a function.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const iter = tf.data.iteratorFromFunction(func);\n * await iter.forEachAsync(e => console.log(e));\n * ```\n *\n * @param func A function that produces data on each call.\n */\nexport function iteratorFromFunction(func) {\n    return new FunctionCallIterator(func);\n}\n/**\n * Create a `LazyIterator` by concatenating underlying streams, which are\n * themselves provided as a stream.\n *\n * This can also be thought of as a \"stream flatten\" operation.\n *\n * @param baseIterators A stream of streams to be concatenated.\n * @param baseErrorHandler An optional function that can intercept `Error`s\n *   raised during a `next()` call on the base stream.  This function can decide\n *   whether the error should be propagated, whether the error should be\n *   ignored, or whether the base stream should be terminated.\n */\nexport function iteratorFromConcatenated(baseIterators, baseErrorHandler) {\n    return new ChainedIterator(baseIterators, baseErrorHandler);\n}\n/**\n * Create a `LazyIterator` by concatenating streams produced by calling a\n * stream-generating function a given number of times.\n *\n * Since a `LazyIterator` is read-once, it cannot be repeated, but this\n * function can be used to achieve a similar effect:\n *\n *   LazyIterator.ofConcatenatedFunction(() => new MyIterator(), 6);\n *\n * @param iteratorFunc: A function that produces a new stream on each call.\n * @param count: The number of times to call the function.\n * @param baseErrorHandler An optional function that can intercept `Error`s\n *   raised during a `next()` call on the base stream.  This function can decide\n *   whether the error should be propagated, whether the error should be\n *   ignored, or whether the base stream should be terminated.\n */\nexport function iteratorFromConcatenatedFunction(iteratorFunc, count, baseErrorHandler) {\n    return iteratorFromConcatenated(iteratorFromFunction(iteratorFunc).take(count), baseErrorHandler);\n}\n/**\n * Create a `LazyIterator` by zipping together an array, dict, or nested\n * structure of `LazyIterator`s (and perhaps additional constants).\n *\n * The underlying streams must provide elements in a consistent order such\n * that they correspond.\n *\n * Typically, the underlying streams should have the same number of\n * elements. If they do not, the behavior is determined by the\n * `mismatchMode` argument.\n *\n * The nested structure of the `iterators` argument determines the\n * structure of elements in the resulting iterator.\n *\n * @param iterators: An array or object containing LazyIterators at the\n * leaves.\n * @param mismatchMode: Determines what to do when one underlying iterator\n * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)\n * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`\n * causes the zipped iterator to terminate with the furst underlying\n * streams, so elements remaining on the longer streams are ignored.\n * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling\n * in nulls for the exhausted streams, until all streams are exhausted.\n */\nexport function iteratorFromZipped(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n    return new ZipIterator(iterators, mismatchMode);\n}\n/**\n * An asynchronous iterator, providing lazy access to a potentially\n * unbounded stream of elements.\n *\n * Iterator can be obtained from a dataset:\n * `const iter = await dataset.iterator();`\n */\nexport class LazyIterator {\n    /**\n     * Collect all remaining elements of a bounded stream into an array.\n     * Obviously this will succeed only for small streams that fit in memory.\n     * Useful for testing.\n     *\n     * @returns A Promise for an array of stream elements, which will resolve\n     *   when the stream is exhausted.\n     */\n    async toArray() {\n        const result = [];\n        let x = await this.next();\n        while (!x.done) {\n            result.push(x.value);\n            x = await this.next();\n        }\n        return result;\n    }\n    /**\n     * Collect all elements of this dataset into an array with prefetching 100\n     * elements. This is useful for testing, because the prefetch changes the\n     * order in which the Promises are resolved along the processing pipeline.\n     * This may help expose bugs where results are dependent on the order of\n     * Promise resolution rather than on the logical order of the stream (i.e.,\n     * due to hidden mutable state).\n     *\n     * @returns A Promise for an array of stream elements, which will resolve\n     *   when the stream is exhausted.\n     */\n    async toArrayForTest() {\n        const stream = this.prefetch(100);\n        const result = [];\n        let x = await stream.next();\n        while (!x.done) {\n            result.push(x.value);\n            x = await stream.next();\n        }\n        return result;\n    }\n    /**\n     * Draw items from the stream until it is exhausted.\n     *\n     * This can be useful when the stream has side effects but no output.  In\n     * that case, calling this function guarantees that the stream will be\n     * fully processed.\n     */\n    async resolveFully() {\n        let x = await this.next();\n        while (!x.done) {\n            x = await this.next();\n        }\n    }\n    /**\n     * Draw items from the stream until it is exhausted, or a predicate fails.\n     *\n     * This can be useful when the stream has side effects but no output.  In\n     * that case, calling this function guarantees that the stream will be\n     * fully processed.\n     */\n    async resolveWhile(predicate) {\n        let x = await this.next();\n        let shouldContinue = predicate(x.value);\n        while ((!x.done) && shouldContinue) {\n            x = await this.next();\n            shouldContinue = predicate(x.value);\n        }\n    }\n    /**\n     * Handles errors thrown on this stream using a provided handler function.\n     *\n     * @param handler A function that handles any `Error` thrown during a `next()`\n     *   call and returns true if the stream should continue (dropping the failed\n     *   call) or false if the stream should quietly terminate.  If the handler\n     *   itself throws (or rethrows) an `Error`, that will be propagated.\n     *\n     * @returns A `LazyIterator` of elements passed through from upstream,\n     *   possibly filtering or terminating on upstream `next()` calls that\n     *   throw an `Error`.\n     */\n    handleErrors(handler) {\n        return new ErrorHandlingLazyIterator(this, handler);\n    }\n    // TODO(soergel): Implement reduce() etc.\n    /**\n     * Filters this stream according to `predicate`.\n     *\n     * @param predicate A function mapping a stream element to a boolean or a\n     * `Promise` for one.\n     *\n     * @returns A `LazyIterator` of elements for which the predicate was true.\n     */\n    filter(predicate) {\n        return new FilterIterator(this, predicate);\n    }\n    /**\n     * Maps this stream through a 1-to-1 transform.\n     *\n     * @param transform A function mapping a stream element to a transformed\n     *   element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    map(transform) {\n        return new MapIterator(this, transform);\n    }\n    /**\n     * Maps this stream through an async 1-to-1 transform.\n     *\n     * @param transform A function mapping a stream element to a `Promise` for a\n     *   transformed stream element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    mapAsync(transform) {\n        return new AsyncMapIterator(this, transform);\n    }\n    /**\n     * Maps this stream through a 1-to-1 transform, forcing serial execution.\n     *\n     * @param transform A function mapping a stream element to a transformed\n     *   element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    serialMapAsync(transform) {\n        return new AsyncMapIterator(this, transform).serial();\n    }\n    /**\n     * Maps this stream through a 1-to-many transform.\n     *\n     * @param transform A function mapping a stream element to an array of\n     *   transformed elements.\n     *\n     * @returns A `DataStream` of transformed elements.\n     */\n    flatmap(transform) {\n        return new FlatmapIterator(this, transform);\n    }\n    /**\n     * Apply a function to every element of the stream.\n     *\n     * @param f A function to apply to each stream element.\n     */\n    async forEachAsync(f) {\n        return this.map(f).resolveFully();\n    }\n    /**\n     * Apply a function to every element of the stream, forcing serial execution.\n     *\n     * @param f A function to apply to each stream element.  Should return 'true'\n     *   to indicate that the stream should continue, or 'false' to cause it to\n     *   terminate.\n     */\n    async serialForEach(f) {\n        return this.serialMapAsync(f).resolveWhile(x => (x === true));\n    }\n    /**\n     * Groups elements into batches, represented as arrays of elements.\n     *\n     * We can think of the elements of this iterator as 'rows' (even if they are\n     * nested structures).  By the same token, consecutive values for a given\n     * key within the elements form a 'column'.  This matches the usual sense of\n     * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n     *\n     * Thus, \"Row-major\" means that the resulting batch is simply a collection of\n     * rows: `[row1, row2, row3, ...]`.  This is contrast to the column-major\n     * form, which is needed for vectorized computation.\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @returns A `LazyIterator` of batches of elements, represented as arrays\n     *   of the original element type.\n     */\n    rowMajorBatch(batchSize, smallLastBatch = true) {\n        return new RowMajorBatchIterator(this, batchSize, smallLastBatch);\n    }\n    /**\n     * Groups elements into batches, represented in column-major form.\n     *\n     * We can think of the elements of this iterator as 'rows' (even if they are\n     * nested structures).  By the same token, consecutive values for a given\n     * key within the elements form a 'column'.  This matches the usual sense of\n     * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n     *\n     * Thus, \"column-major\" means that the resulting batch is a (potentially\n     * nested) structure representing the columns.  Each column entry, then,\n     * contains a collection of the values found in that column for a range of\n     * input elements.  This representation allows for vectorized computation, in\n     * contrast to the row-major form.\n     *\n     * The inputs should all have the same nested structure (i.e., of arrays and\n     * dicts).  The result is a single object with the same nested structure,\n     * where the leaves are arrays collecting the values of the inputs at that\n     * location (or, optionally, the result of a custom function applied to those\n     * arrays).\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @param zipFn: (optional) A function that expects an array of elements at a\n     *   single node of the object tree, and returns a `DeepMapResult`.  The\n     *   `DeepMapResult` either provides a result value for that node (i.e.,\n     *   representing the subtree), or indicates that the node should be processed\n     *   recursively.  The default zipFn recurses as far as possible and places\n     *   arrays at the leaves.\n     * @returns A `LazyIterator` of batches of elements, represented as an object\n     *   with collections at the leaves.\n     */\n    columnMajorBatch(batchSize, smallLastBatch = true, \n    // tslint:disable-next-line:no-any\n    zipFn = zipToList) {\n        // First collect the desired number of input elements as a row-major batch.\n        const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch);\n        // Now 'rotate' or 'pivot' the data, collecting all values from each column\n        // in the batch (i.e., for each key within the elements) into an array.\n        return rowBatches.map(x => deepZip(x, zipFn));\n    }\n    /**\n     * Concatenate this `LazyIterator` with another.\n     *\n     * @param iterator A `LazyIterator` to be concatenated onto this one.\n     * @param baseErrorHandler An optional function that can intercept `Error`s\n     *   raised during a `next()` call on the base stream.  This function can\n     *   decide whether the error should be propagated, whether the error should\n     *   be ignored, or whether the base stream should be terminated.\n     * @returns A `LazyIterator`.\n     */\n    concatenate(iterator, baseErrorHandler) {\n        return new ChainedIterator(iteratorFromItems([this, iterator]), baseErrorHandler);\n    }\n    /**\n     * Limits this stream to return at most `count` items.\n     *\n     * @param count The maximum number of items to provide from the stream. If\n     * a negative or undefined value is given, the entire stream is returned\n     *   unaltered.\n     */\n    take(count) {\n        if (count < 0 || count == null) {\n            return this;\n        }\n        return new TakeIterator(this, count);\n    }\n    /**\n     * Skips the first `count` items in this stream.\n     *\n     * @param count The number of items to skip.  If a negative or undefined\n     * value is given, the entire stream is returned unaltered.\n     */\n    skip(count) {\n        if (count < 0 || count == null) {\n            return this;\n        }\n        return new SkipIterator(this, count);\n    }\n    /**\n     * Prefetch the first `bufferSize` items in this stream.\n     *\n     * Note this prefetches Promises, but makes no guarantees about when those\n     * Promises resolve.\n     *\n     * @param bufferSize: An integer specifying the number of elements to be\n     *   prefetched.\n     */\n    prefetch(bufferSize) {\n        return new PrefetchIterator(this, bufferSize);\n    }\n    // TODO(soergel): deep sharded shuffle, where supported\n    /**\n     * Randomly shuffles the elements of this stream.\n     *\n     * @param bufferSize: An integer specifying the number of elements from\n     * this stream from which the new stream will sample.\n     * @param seed: (Optional.) An integer specifying the random seed that\n     * will be used to create the distribution.\n     */\n    shuffle(windowSize, seed) {\n        return new ShuffleIterator(this, windowSize, seed);\n    }\n    /**\n     * Force an iterator to execute serially: each next() call will await the\n     * prior one, so that they cannot execute concurrently.\n     */\n    serial() {\n        return new SerialIterator(this);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on LazyIterator.  Unfortunately they can't be placed in separate files,\n// due to resulting trouble with circular imports.\n// ============================================================================\n// Iterators that just extend LazyIterator directly\n// ============================================================================\nclass ArrayIterator extends LazyIterator {\n    constructor(items) {\n        super();\n        this.items = items;\n        this.trav = 0;\n    }\n    summary() {\n        return `Array of ${this.items.length} items`;\n    }\n    async next() {\n        if (this.trav >= this.items.length) {\n            return { value: null, done: true };\n        }\n        const item = this.items[this.trav];\n        this.trav++;\n        return { value: deepClone(item), done: false };\n    }\n}\nclass FunctionCallIterator extends LazyIterator {\n    constructor(nextFn) {\n        super();\n        this.nextFn = nextFn;\n    }\n    summary() {\n        return `Function call`;\n    }\n    async next() {\n        try {\n            return this.nextFn();\n        }\n        catch (e) {\n            // Modify the error message but leave the stack trace intact\n            e.message =\n                `Error thrown while iterating through a dataset: ${e.message}`;\n            throw e;\n        }\n    }\n}\nclass SerialIterator extends LazyIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Serial`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        return this.upstream.next();\n    }\n}\nclass SkipIterator extends LazyIterator {\n    constructor(upstream, maxCount) {\n        super();\n        this.upstream = upstream;\n        this.maxCount = maxCount;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.count = 0;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Skip`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        // TODO(soergel): consider tradeoffs of reading in parallel, eg.\n        // collecting next() promises in an Array and then waiting for\n        // Promise.all() of those. Benefit: pseudo-parallel execution.  Drawback:\n        // maybe delayed GC.\n        while (this.count++ < this.maxCount) {\n            const skipped = await this.upstream.next();\n            // short-circuit if upstream is already empty\n            if (skipped.done) {\n                return skipped;\n            }\n            tf.dispose(skipped.value);\n        }\n        return this.upstream.next();\n    }\n}\nclass TakeIterator extends LazyIterator {\n    constructor(upstream, maxCount) {\n        super();\n        this.upstream = upstream;\n        this.maxCount = maxCount;\n        this.count = 0;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Take`;\n    }\n    async next() {\n        if (this.count++ >= this.maxCount) {\n            return { value: null, done: true };\n        }\n        return this.upstream.next();\n    }\n}\n// Note this batch just groups items into row-wise element arrays.\n// Rotating these to a column-wise representation happens only at the dataset\n// level.\nclass RowMajorBatchIterator extends LazyIterator {\n    constructor(upstream, batchSize, enableSmallLastBatch = true) {\n        super();\n        this.upstream = upstream;\n        this.batchSize = batchSize;\n        this.enableSmallLastBatch = enableSmallLastBatch;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> RowMajorBatch`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        const batch = [];\n        while (batch.length < this.batchSize) {\n            const item = await this.upstream.next();\n            if (item.done) {\n                if (this.enableSmallLastBatch && batch.length > 0) {\n                    return { value: batch, done: false };\n                }\n                return { value: null, done: true };\n            }\n            batch.push(item.value);\n        }\n        return { value: batch, done: false };\n    }\n}\nclass FilterIterator extends LazyIterator {\n    constructor(upstream, predicate) {\n        super();\n        this.upstream = upstream;\n        this.predicate = predicate;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Filter`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        while (true) {\n            const item = await this.upstream.next();\n            if (item.done || this.predicate(item.value)) {\n                return item;\n            }\n            tf.dispose(item.value);\n        }\n    }\n}\nclass MapIterator extends LazyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Map`;\n    }\n    async next() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return { value: null, done: true };\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // That's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying\n        // any intermediate Tensors.  Here we are concerned only about the\n        // inputs.\n        const mapped = this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mapped);\n        // TODO(soergel) faster intersection\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return { value: mapped, done: false };\n    }\n}\nclass ErrorHandlingLazyIterator extends LazyIterator {\n    constructor(upstream, handler) {\n        super();\n        this.upstream = upstream;\n        this.handler = handler;\n        this.count = 0;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> handleErrors`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        while (true) {\n            try {\n                return await this.upstream.next();\n            }\n            catch (e) {\n                if (!this.handler(e)) {\n                    return { value: null, done: true };\n                }\n                // If the handler returns true, loop and fetch the next upstream item.\n                // If the upstream iterator throws an endless stream of errors, and if\n                // the handler says to ignore them, then we loop forever here.  That is\n                // the correct behavior-- it's up to the handler to decide when to stop.\n            }\n        }\n    }\n}\nclass AsyncMapIterator extends LazyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> AsyncMap`;\n    }\n    async next() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return { value: null, done: true };\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // That's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying\n        // any intermediate Tensors.  Here we are concerned only about the\n        // inputs.\n        const mapped = await this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mapped);\n        // TODO(soergel) faster intersection\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return { value: mapped, done: false };\n    }\n}\n// Iterators that maintain a queue of pending items\n// ============================================================================\n/**\n * A base class for transforming streams that operate by maintaining an\n * output queue of elements that are ready to return via next().  This is\n * commonly required when the transformation is 1-to-many:  A call to next()\n * may trigger a call to the underlying stream, which will produce many\n * mapped elements of this stream-- of which we need to return only one, so\n * we have to queue the rest.\n */\nexport class OneToManyIterator extends LazyIterator {\n    constructor() {\n        super();\n        this.outputQueue = new GrowingRingBuffer();\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        // Fetch so that the queue contains at least one item if possible.\n        // If the upstream source is exhausted, AND there are no items left in\n        // the output queue, then this stream is also exhausted.\n        while (this.outputQueue.length() === 0) {\n            // TODO(soergel): consider parallel reads.\n            if (!await this.pump()) {\n                return { value: null, done: true };\n            }\n        }\n        return { value: this.outputQueue.shift(), done: false };\n    }\n}\nclass FlatmapIterator extends OneToManyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Flatmap`;\n    }\n    async pump() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return false;\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // that's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying any\n        // intermediate Tensors.  Here we are concerned only about the inputs.\n        const mappedArray = this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mappedArray);\n        this.outputQueue.pushAll(mappedArray);\n        // TODO(soergel) faster intersection, and deduplicate outputTensors\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return true;\n    }\n}\n/**\n * Provides a `LazyIterator` that concatenates a stream of underlying\n * streams.\n *\n * Doing this in a concurrency-safe way requires some trickery.  In\n * particular, we want this stream to return the elements from the\n * underlying streams in the correct order according to when next() was\n * called, even if the resulting Promises resolve in a different order.\n */\nexport class ChainedIterator extends LazyIterator {\n    constructor(iterators, baseErrorHandler) {\n        super();\n        this.baseErrorHandler = baseErrorHandler;\n        // Strict Promise execution order:\n        // a next() call may not even begin until the previous one completes.\n        this.lastRead = null;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.iterator = null;\n        this.moreIterators = iterators;\n    }\n    summary() {\n        const upstreamSummaries = 'TODO: fill in upstream of chained summaries';\n        return `${upstreamSummaries} -> Chained`;\n    }\n    async next() {\n        this.lastRead = this.readFromChain(this.lastRead);\n        return this.lastRead;\n    }\n    async readFromChain(lastRead) {\n        // Must await on the previous read since the previous read may have advanced\n        // the stream of streams, from which we need to read.\n        // This is unfortunate since we can't parallelize reads. Which means\n        // prefetching of chained streams is a no-op.\n        // One solution is to prefetch immediately upstream of this.\n        await lastRead;\n        if (this.iterator == null) {\n            const iteratorResult = await this.moreIterators.next();\n            if (iteratorResult.done) {\n                // No more streams to stream from.\n                return { value: null, done: true };\n            }\n            this.iterator = iteratorResult.value;\n            if (this.baseErrorHandler != null) {\n                this.iterator = this.iterator.handleErrors(this.baseErrorHandler);\n            }\n        }\n        const itemResult = await this.iterator.next();\n        if (itemResult.done) {\n            this.iterator = null;\n            return this.readFromChain(lastRead);\n        }\n        return itemResult;\n    }\n}\nexport var ZipMismatchMode;\n(function (ZipMismatchMode) {\n    ZipMismatchMode[ZipMismatchMode[\"FAIL\"] = 0] = \"FAIL\";\n    ZipMismatchMode[ZipMismatchMode[\"SHORTEST\"] = 1] = \"SHORTEST\";\n    ZipMismatchMode[ZipMismatchMode[\"LONGEST\"] = 2] = \"LONGEST\"; // use nulls for exhausted streams; use up the longest stream.\n})(ZipMismatchMode || (ZipMismatchMode = {}));\n/**\n * Provides a `LazyIterator` that zips together an array, dict, or nested\n * structure of `LazyIterator`s (and perhaps additional constants).\n *\n * The underlying streams must provide elements in a consistent order such\n * that they correspond.\n *\n * Typically, the underlying streams should have the same number of\n * elements. If they do not, the behavior is determined by the\n * `mismatchMode` argument.\n *\n * The nested structure of the `iterators` argument determines the\n * structure of elements in the resulting iterator.\n *\n * Doing this in a concurrency-safe way requires some trickery.  In\n * particular, we want this stream to return the elements from the\n * underlying streams in the correct order according to when next() was\n * called, even if the resulting Promises resolve in a different order.\n *\n * @param iterators: An array or object containing LazyIterators at the\n * leaves.\n * @param mismatchMode: Determines what to do when one underlying iterator\n * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)\n * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`\n * causes the zipped iterator to terminate with the furst underlying\n * streams, so elements remaining on the longer streams are ignored.\n * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling\n * in nulls for the exhausted streams, until all streams are exhausted.\n */\nclass ZipIterator extends LazyIterator {\n    constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n        super();\n        this.iterators = iterators;\n        this.mismatchMode = mismatchMode;\n        this.count = 0;\n        this.currentPromise = null;\n    }\n    summary() {\n        const upstreamSummaries = 'TODO: fill in upstream of zip summaries';\n        return `{${upstreamSummaries}} -> Zip`;\n    }\n    async nextState(afterState) {\n        // This chaining ensures that the underlying next() are not even called\n        // before the previous ones have resolved.\n        await afterState;\n        // Collect underlying iterator \"done\" signals as a side effect in\n        // getNext()\n        let numIterators = 0;\n        let iteratorsDone = 0;\n        function getNext(container) {\n            if (container instanceof LazyIterator) {\n                const result = container.next();\n                return {\n                    value: result.then(x => {\n                        numIterators++;\n                        if (x.done) {\n                            iteratorsDone++;\n                        }\n                        return x.value;\n                    }),\n                    recurse: false\n                };\n            }\n            else {\n                return { value: null, recurse: true };\n            }\n        }\n        const mapped = await deepMapAndAwaitAll(this.iterators, getNext);\n        if (numIterators === iteratorsDone) {\n            // The streams have all ended.\n            return { value: null, done: true };\n        }\n        if (iteratorsDone > 0) {\n            switch (this.mismatchMode) {\n                case ZipMismatchMode.FAIL:\n                    throw new Error('Zipped streams should have the same length. ' +\n                        `Mismatched at element ${this.count}.`);\n                case ZipMismatchMode.SHORTEST:\n                    return { value: null, done: true };\n                case ZipMismatchMode.LONGEST:\n                default:\n                // Continue.  The exhausted streams already produced value: null.\n            }\n        }\n        this.count++;\n        return { value: mapped, done: false };\n    }\n    async next() {\n        this.currentPromise = this.nextState(this.currentPromise);\n        return this.currentPromise;\n    }\n}\n// Iterators that maintain a ring buffer of pending promises\n// ============================================================================\n/**\n * A stream that prefetches a given number of items from an upstream source,\n * returning them in FIFO order.\n *\n * Note this prefetches Promises, but makes no guarantees about when those\n * Promises resolve.\n */\nexport class PrefetchIterator extends LazyIterator {\n    constructor(upstream, bufferSize) {\n        super();\n        this.upstream = upstream;\n        this.bufferSize = bufferSize;\n        this.buffer = new RingBuffer(bufferSize);\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Prefetch`;\n    }\n    /**\n     * Refill the prefetch buffer.  Returns only after the buffer is full, or\n     * the upstream source is exhausted.\n     */\n    refill() {\n        while (!this.buffer.isFull()) {\n            const v = this.upstream.next();\n            this.buffer.push(v);\n        }\n    }\n    next() {\n        this.refill();\n        // This shift will never throw an error because the buffer is always\n        // full after a refill. If the stream is exhausted, the buffer will be\n        // full of Promises that will resolve to the end-of-stream signal.\n        return this.buffer.shift();\n    }\n}\n/**\n * A stream that performs a sliding-window random shuffle on an upstream\n * source. This is like a `PrefetchIterator` except that the items are\n * returned in randomized order.  Mixing naturally improves as the buffer\n * size increases.\n */\nexport class ShuffleIterator extends PrefetchIterator {\n    constructor(upstream, windowSize, seed) {\n        super(upstream, windowSize);\n        this.upstream = upstream;\n        this.windowSize = windowSize;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.upstreamExhausted = false;\n        this.random = seedrandom.alea(seed || tf.util.now().toString());\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    randomInt(max) {\n        return Math.floor(this.random() * max);\n    }\n    chooseIndex() {\n        return this.randomInt(this.buffer.length());\n    }\n    async serialNext() {\n        // TODO(soergel): consider performance\n        if (!this.upstreamExhausted) {\n            this.refill();\n        }\n        while (!this.buffer.isEmpty()) {\n            const chosenIndex = this.chooseIndex();\n            const result = await this.buffer.shuffleExcise(chosenIndex);\n            if (result.done) {\n                this.upstreamExhausted = true;\n            }\n            else {\n                this.refill();\n                return result;\n            }\n        }\n        return { value: null, done: true };\n    }\n}\n//# sourceMappingURL=lazy_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport * as seedrandom from 'seedrandom';\nimport { iteratorFromConcatenated, iteratorFromFunction, iteratorFromItems, iteratorFromZipped, ZipMismatchMode } from './iterators/lazy_iterator';\nimport { canTensorify, deepMapAndAwaitAll, isIterable } from './util/deep_map';\n// TODO(soergel): consider vectorized operations within the pipeline.\n/**\n * Represents a potentially large list of independent data elements (typically\n * 'samples' or 'examples').\n *\n * A 'data example' may be a primitive, an array, a map from string keys to\n * values, or any nested structure of these.\n *\n * A `Dataset` represents an ordered collection of elements, together with a\n * chain of transformations to be performed on those elements. Each\n * transformation is a method of `Dataset` that returns another `Dataset`, so\n * these may be chained, e.g.\n * `const processedDataset = rawDataset.filter(...).map(...).batch(...)`.\n *\n * Data loading and transformation is done in a lazy, streaming fashion.  The\n * dataset may be iterated over multiple times; each iteration starts the data\n * loading anew and recapitulates the transformations.\n *\n * A `Dataset` is typically processed as a stream of unbatched examples --i.e.,\n * its transformations are applied one example at a time. Batching produces a\n * new `Dataset` where each element is a batch. Batching should usually come\n * last in a pipeline, because data transformations are easier to express on a\n * per-example basis than on a per-batch basis.\n *\n * The following code examples are calling `await dataset.forEachAsync(...)` to\n * iterate once over the entire dataset in order to print out the data.\n *\n * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}\n */\nexport class Dataset {\n    constructor() {\n        this.size = null;\n    }\n    // TODO(soergel): Make Datasets report whether repeated iterator() calls\n    // produce the same result (e.g., reading from a file) or different results\n    // (e.g., from the webcam).  Currently we don't make this distinction but it\n    // could be important for the user to know.\n    // abstract isDeterministic(): boolean;\n    /**\n     * Groups elements into batches.\n     *\n     * It is assumed that each of the incoming dataset elements has the same\n     * structure-- i.e. the same set of keys at each location in an object\n     * hierarchy.  For each key, the resulting `Dataset` provides a batched\n     * element collecting all of the incoming values for that key.\n     *\n     *  * Incoming primitives are grouped into a 1-D Tensor.\n     *  * Incoming Tensors are grouped into a new Tensor where the 0'th axis is\n     *    the batch dimension.\n     *  * Incoming arrays are converted to Tensor and then batched.\n     *  * A nested array is interpreted as an n-D Tensor, so the batched result\n     *    has n+1 dimensions.\n     *  * An array that cannot be converted to Tensor produces an error.\n     *\n     * If an array should not be batched as a unit, it should first be converted\n     * to an object with integer keys.\n     *\n     * Here are a few examples:\n     *\n     * Batch a dataset of numbers:\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8]).batch(4);\n     * await a.forEachAsync(e => e.print());\n     * ```\n     *\n     * Batch a dataset of arrays:\n     * ```js\n     * const b = tf.data.array([[1], [2], [3], [4], [5], [6], [7], [8]]).batch(4);\n     * await b.forEachAsync(e => e.print());\n     * ```\n     *\n     * Batch a dataset of objects:\n     * ```js\n     * const c = tf.data.array([{a: 1, b: 11}, {a: 2, b: 12}, {a: 3, b: 13},\n     *   {a: 4, b: 14}, {a: 5, b: 15}, {a: 6, b: 16}, {a: 7, b: 17},\n     *   {a: 8, b: 18}]).batch(4);\n     * await c.forEachAsync(e => {\n     *   console.log('{');\n     *   for(var key in e) {\n     *     console.log(key+':');\n     *     e[key].print();\n     *   }\n     *   console.log('}');\n     * })\n     * ```\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @returns A `Dataset`, from which a stream of batches can be obtained.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    batch(batchSize, smallLastBatch = true) {\n        const base = this;\n        tf.util.assert(batchSize > 0, () => `batchSize needs to be positive, but it is\n      ${batchSize}`);\n        let size;\n        if (this.size === Infinity || this.size == null) {\n            // If the size of this dataset is infinity or null, the new size keeps the\n            // same.\n            size = this.size;\n        }\n        else if (smallLastBatch) {\n            // If the size of this dataset is known and include small last batch, the\n            // new size is full batch count plus last batch.\n            size = Math.ceil(this.size / batchSize);\n        }\n        else {\n            // If the size of this dataset is known and not include small last batch,\n            // the new size is full batch count.\n            size = Math.floor(this.size / batchSize);\n        }\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator())\n                .columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat);\n        }, size);\n    }\n    /**\n     * Concatenates this `Dataset` with another.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]);\n     * const b = tf.data.array([4, 5, 6]);\n     * const c = a.concatenate(b);\n     * await c.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param dataset A `Dataset` to be concatenated onto this one.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    concatenate(dataset) {\n        const base = this;\n        let size;\n        if (this.size === Infinity || dataset.size === Infinity) {\n            // If the size of any of these two dataset is infinity, new size is\n            // infinity.\n            size = Infinity;\n        }\n        else if (this.size != null && dataset.size != null) {\n            // If the size of both datasets are known and not infinity, new size is\n            // sum the size of these two datasets.\n            size = this.size + dataset.size;\n        }\n        else {\n            // If neither of these two datasets has infinite size and any of these two\n            // datasets' size is null, the new size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).concatenate(await dataset.iterator()), size);\n    }\n    /**\n     * Filters this dataset according to `predicate`.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n     *   .filter(x => x%2 === 0);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param predicate A function mapping a dataset element to a boolean or a\n     * `Promise` for one.\n     *\n     * @returns A `Dataset` of elements for which the predicate was true.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    filter(predicate) {\n        const base = this;\n        let size;\n        if (this.size === Infinity) {\n            // If the size of this dataset is infinity, new size is infinity\n            size = Infinity;\n        }\n        else {\n            // If this dataset has limited elements, new size is null because it might\n            // exhausted randomly.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).filter(x => tf.tidy(() => predicate(x)));\n        }, size);\n    }\n    /**\n     * Apply a function to every element of the dataset.\n     *\n     * After the function is applied to a dataset element, any Tensors contained\n     * within that element are disposed.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param f A function to apply to each dataset element.\n     * @returns A `Promise` that resolves after all elements have been processed.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async forEachAsync(f) {\n        return (await this.iterator()).forEachAsync(f);\n    }\n    /**\n     * Maps this dataset through a 1-to-1 transform.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]).map(x => x*x);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param transform A function mapping a dataset element to a transformed\n     *   dataset element.\n     *\n     * @returns A `Dataset` of transformed elements.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    map(transform) {\n        const base = this;\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).map(x => tf.tidy(() => transform(x)));\n        }, this.size);\n    }\n    /**\n     * Maps this dataset through an async 1-to-1 transform.\n     *\n     * ```js\n     * const a =\n     *  tf.data.array([1, 2, 3]).mapAsync(x => new Promise(function(resolve){\n     *    setTimeout(() => {\n     *      resolve(x * x);\n     *    }, Math.random()*1000 + 500);\n     *  }));\n     * console.log(await a.toArray());\n     * ```\n     *\n     * @param transform A function mapping a dataset element to a `Promise` for a\n     *   transformed dataset element.  This transform is responsible for disposing\n     *   any intermediate `Tensor`s, i.e. by wrapping its computation in\n     *   `tf.tidy()`; that cannot be automated here (as it is in the synchronous\n     *   `map()` case).\n     *\n     * @returns A `Dataset` of transformed elements.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    mapAsync(transform) {\n        const base = this;\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).mapAsync(transform);\n        }, this.size);\n    }\n    /**\n     *  Creates a `Dataset` that prefetches elements from this dataset.\n     *\n     * @param bufferSize: An integer specifying the number of elements to be\n     *   prefetched.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    prefetch(bufferSize) {\n        if (bufferSize == null) {\n            throw new RangeError('`Dataset.prefetch()` requires bufferSize to be specified.');\n        }\n        const base = this;\n        return datasetFromIteratorFn(async () => (await base.iterator()).prefetch(bufferSize), this.size);\n    }\n    /**\n     * Repeats this dataset `count` times.\n     *\n     * NOTE: If this dataset is a function of global state (e.g. a random number\n     * generator), then different repetitions may produce different elements.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]).repeat(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: (Optional) An integer, representing the number of times\n     *   the dataset should be repeated. The default behavior (if `count` is\n     *   `undefined` or negative) is for the dataset be repeated indefinitely.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    repeat(count) {\n        const base = this;\n        let size;\n        if (this.size != null && count > 0) {\n            // If this dataset has size and count is positive, new size is current\n            // size multiply count. This also covers the case that current size is\n            // infinity.\n            size = this.size * count;\n        }\n        else if (count === 0) {\n            // If count is 0, new size is 0.\n            size = 0;\n        }\n        else if (this.size != null && (count === undefined || count < 0)) {\n            // If this dataset has size and count is undefined or negative, the\n            // dataset will be repeated indefinitely and new size is infinity.\n            size = Infinity;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => {\n            const iteratorIterator = iteratorFromFunction(async () => ({ value: await base.iterator(), done: false }));\n            return iteratorFromConcatenated(iteratorIterator.take(count));\n        }, size);\n    }\n    /**\n     * Creates a `Dataset` that skips `count` initial elements from this dataset.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).skip(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: The number of elements of this dataset that should be skipped\n     *   to form the new dataset.  If `count` is greater than the size of this\n     *   dataset, the new dataset will contain no elements.  If `count`\n     *   is `undefined` or negative, skips the entire dataset.\n     *\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    skip(count) {\n        const base = this;\n        let size;\n        if (this.size != null && count >= 0 && this.size >= count) {\n            // If the size of this dataset is greater than count, the new dataset's\n            // size is current size minus skipped size.This also covers the case that\n            // current size is infinity.\n            size = this.size - count;\n        }\n        else if (this.size != null &&\n            (this.size < count || count === undefined || count < 0)) {\n            // If the size of this dataset is smaller than count, or count is\n            // undefined or negative, skips the entire dataset and the new size is 0.\n            size = 0;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).skip(count), size);\n    }\n    /**\n     * Pseudorandomly shuffles the elements of this dataset. This is done in a\n     * streaming manner, by sampling from a given number of prefetched elements.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).shuffle(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param bufferSize: An integer specifying the number of elements from this\n     *   dataset from which the new dataset will sample.\n     * @param seed: (Optional) An integer specifying the random seed that will\n     *   be used to create the distribution.\n     * @param reshuffleEachIteration: (Optional) A boolean, which if true\n     *   indicates that the dataset should be pseudorandomly reshuffled each time\n     *   it is iterated over. If false, elements will be returned in the same\n     *   shuffled order on each iteration. (Defaults to `true`.)\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    shuffle(bufferSize, seed, reshuffleEachIteration = true) {\n        if (bufferSize == null || bufferSize < 0) {\n            if (this.size == null) {\n                throw new RangeError('`Dataset.shuffle()` requires bufferSize to be specified.');\n            }\n            else {\n                throw new RangeError('`Dataset.shuffle()` requires bufferSize to be specified.  ' +\n                    'If your data fits in main memory (for regular JS objects), ' +\n                    'and/or GPU memory (for `tf.Tensor`s), consider setting ' +\n                    `bufferSize to the dataset size (${this.size} elements)`);\n            }\n        }\n        const base = this;\n        const random = seedrandom.alea(seed || tf.util.now().toString());\n        return datasetFromIteratorFn(async () => {\n            let seed2 = random.int32();\n            if (reshuffleEachIteration) {\n                seed2 += random.int32();\n            }\n            return (await base.iterator()).shuffle(bufferSize, seed2.toString());\n        }, this.size);\n    }\n    /**\n     * Creates a `Dataset` with at most `count` initial elements from this\n     * dataset.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).take(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: The number of elements of this dataset that should be taken\n     *   to form the new dataset.  If `count` is `undefined` or negative, or if\n     *   `count` is greater than the size of this dataset, the new dataset will\n     *   contain all elements of this dataset.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    take(count) {\n        const base = this;\n        let size;\n        if (this.size != null && this.size > count) {\n            // If the size of this dataset is greater than count, the new dataset's\n            // size is count.\n            size = count;\n        }\n        else if (this.size != null && this.size <= count) {\n            // If the size of this dataset is equal or smaller than count, the new\n            // dataset's size is the size of this dataset.\n            size = this.size;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).take(count), size);\n    }\n    /**\n     * Collect all elements of this dataset into an array.\n     *\n     * Obviously this will succeed only for small datasets that fit in memory.\n     * Useful for testing and generally should be avoided if possible.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]);\n     * console.log(await a.toArray());\n     * ```\n     *\n     * @returns A Promise for an array of elements, which will resolve\n     *   when a new stream has been obtained and fully consumed.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async toArray() {\n        if (this.size === Infinity) {\n            throw new Error('Can not convert infinite data stream to array.');\n        }\n        return (await this.iterator()).toArray();\n    }\n    /**\n     * Collect all elements of this dataset into an array with prefetching 100\n     * elements. This is useful for testing, because the prefetch changes the\n     * order in which the Promises are resolved along the processing pipeline.\n     * This may help expose bugs where results are dependent on the order of\n     * Promise resolution rather than on the logical order of the stream (i.e.,\n     * due to hidden mutable state).\n     *\n     * @returns A Promise for an array of elements, which will resolve\n     *   when a new stream has been obtained and fully consumed.\n     */\n    async toArrayForTest() {\n        if (this.size === Infinity) {\n            throw new Error('Can not convert infinite data stream to array.');\n        }\n        return (await this.iterator()).toArrayForTest();\n    }\n}\n// TODO(soergel): deep sharded shuffle, where supported\nDataset.MAX_BUFFER_SIZE = 10000;\n/**\n * Create a `Dataset` defined by a provided iterator() function.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const iter = tf.data.iteratorFromFunction(func);\n * const ds = tf.data.datasetFromIteratorFn(iter);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n */\nexport function datasetFromIteratorFn(iteratorFn, size = null) {\n    return new class extends Dataset {\n        constructor() {\n            super(...arguments);\n            this.size = size;\n        }\n        /*\n         * Provide a new stream of elements.  Note this will also start new streams\n         * from any underlying `Dataset`s.\n         */\n        async iterator() {\n            return iteratorFn();\n        }\n    }();\n}\n/**\n * Create a `Dataset` from an array of elements.\n *\n * Create a Dataset from an array of objects:\n * ```js\n * const a = tf.data.array([{'item': 1}, {'item': 2}, {'item': 3}]);\n * await a.forEachAsync(e => console.log(e));\n * ```\n *\n * Create a Dataset from an array of numbers:\n * ```js\n * const a = tf.data.array([4, 5, 6]);\n * await a.forEachAsync(e => console.log(e));\n * ```\n * @param items An array of elements that will be parsed as items in a dataset.\n *\n * @doc {heading: 'Data', subheading: 'Creation', namespace: 'data'}\n */\nexport function array(items) {\n    return datasetFromIteratorFn(async () => iteratorFromItems(items), items.length);\n}\n/**\n * Create a `Dataset` by zipping together an array, dict, or nested\n * structure of `Dataset`s (and perhaps additional constants).\n * The underlying datasets must provide elements in a consistent order such that\n * they correspond.\n *\n * The number of elements in the resulting dataset is the same as the size of\n * the smallest dataset in datasets.\n *\n * The nested structure of the `datasets` argument determines the\n * structure of elements in the resulting iterator.\n *\n * Note this means that, given an array of two datasets that produce dict\n * elements, the result is a dataset that produces elements that are arrays\n * of two dicts:\n *\n * Zip an array of datasets:\n * ```js\n * console.log('Zip two datasets of objects:');\n * const ds1 = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);\n * const ds2 = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);\n * const ds3 = tf.data.zip([ds1, ds2]);\n * await ds3.forEachAsync(e => console.log(JSON.stringify(e)));\n *\n * // If the goal is to merge the dicts in order to produce elements like\n * // {a: ..., b: ...}, this requires a second step such as:\n * console.log('Merge the objects:');\n * const ds4 = ds3.map(x => {return {a: x[0].a, b: x[1].b}});\n * await ds4.forEachAsync(e => console.log(e));\n * ```\n *\n * Zip a dict of datasets:\n * ```js\n * const a = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);\n * const b = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);\n * const c = tf.data.zip({c: a, d: b});\n * await c.forEachAsync(e => console.log(JSON.stringify(e)));\n * ```\n *\n * @doc {heading: 'Data', subheading: 'Operations', namespace: 'data'}\n */\nexport function zip(datasets) {\n    // manually type-check the argument for JS users\n    if (!isIterable(datasets)) {\n        throw new Error('The argument to zip() must be an object or array.');\n    }\n    let size;\n    if (Array.isArray(datasets)) {\n        for (let i = 0; i < datasets.length; i++) {\n            size = size == null ? datasets[i].size :\n                Math.min(size, datasets[i].size);\n        }\n    }\n    else if (datasets instanceof Object) {\n        for (const ds in datasets) {\n            size = size == null ? datasets[ds].size :\n                Math.min(size, datasets[ds].size);\n        }\n    }\n    return datasetFromIteratorFn(async () => {\n        const streams = await deepMapAndAwaitAll(datasets, d => {\n            if (d instanceof Dataset) {\n                return { value: d.iterator(), recurse: false };\n            }\n            else if (isIterable(d)) {\n                return { value: null, recurse: true };\n            }\n            else {\n                throw new Error('Leaves of the structure passed to zip() must be Datasets, ' +\n                    'not primitives.');\n            }\n        });\n        return iteratorFromZipped(streams, ZipMismatchMode.SHORTEST);\n    }, size);\n}\n/**\n * A zip function for use with deepZip, passed via the columnMajorBatch call.\n *\n * Accepts an array of identically-structured nested elements and either batches\n * them (if they are primitives, numeric arrays, or Tensors) or requests\n * recursion (if not).\n */\n// tslint:disable-next-line:no-any\nfunction deepBatchConcat(rows) {\n    if (rows === null) {\n        return null;\n    }\n    // use the first item to decide whether to recurse or batch here.\n    const exampleRow = rows[0];\n    if (canTensorify(exampleRow)) {\n        // rows is an array of primitives, Tensors, or arrays.  Batch them.\n        const value = batchConcat(rows);\n        return { value, recurse: false };\n    }\n    // the example row is an object, so recurse into it.\n    return { value: null, recurse: true };\n}\n/**\n * Assembles a list of same-shaped numbers, number arrays, or Tensors\n * into a single new Tensor where axis 0 is the batch dimension.\n */\nfunction batchConcat(arrays) {\n    if (arrays.length === 0) {\n        // We can't return an empty Tensor because we don't know the element shape.\n        throw new Error('Can\\'t make a batch of zero elements.');\n    }\n    if (arrays[0] instanceof tf.Tensor) {\n        // Input is an array of Tensors\n        return tf.stack(arrays);\n    }\n    else {\n        // Input is a possibly-nested array of numbers.\n        return tf.tensor(arrays);\n    }\n}\n//# sourceMappingURL=dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { Dataset } from '../dataset';\n/**\n * Represents a potentially large collection of text lines.\n *\n * The results are not batched.\n */\nexport class TextLineDataset extends Dataset {\n    /**\n     * Create a `TextLineDataset`.\n     *\n     * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n     */\n    constructor(input) {\n        super();\n        this.input = input;\n    }\n    async iterator() {\n        const inputIterator = await this.input.iterator();\n        const utf8Iterator = inputIterator.decodeUTF8();\n        const lineIterator = utf8Iterator.split('\\n').map(line => {\n            // Windows/DOS format text file has extra line breaker at the end of line.\n            if (line.endsWith('\\r')) {\n                line = line.slice(0, -1);\n            }\n            return line;\n        });\n        return lineIterator;\n    }\n}\n//# sourceMappingURL=text_line_dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { Dataset } from '../dataset';\nimport { TextLineDataset } from './text_line_dataset';\nconst CODE_QUOTE = '\"';\nconst STATE_OUT = Symbol('out');\nconst STATE_FIELD = Symbol('field');\nconst STATE_QUOTE = Symbol('quote');\nconst STATE_QUOTE_AFTER_QUOTE = Symbol('quoteafterquote');\nconst STATE_WITHIN_QUOTE_IN_QUOTE = Symbol('quoteinquote');\n/**\n * Represents a potentially large collection of delimited text records.\n *\n * The produced `TensorContainer`s each contain one key-value pair for\n * every column of the table.  When a field is empty in the incoming data, the\n * resulting value is `undefined`, or throw error if it is required.  Values\n * that can be parsed as numbers are emitted as type `number`, other values\n * are parsed as `string`.\n *\n * The results are not batched.\n *\n * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}\n */\nexport class CSVDataset extends Dataset {\n    /**\n     * Create a `CSVDataset`.\n     *\n     * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n     * @param csvConfig (Optional) A CSVConfig object that contains configurations\n     *     of reading and decoding from CSV file(s).\n     *\n     *     hasHeader: (Optional) A boolean value that indicates whether the first\n     *     row of provided CSV file is a header line with column names, and should\n     *     not be included in the data. Defaults to `true`.\n     *\n     *     columnNames: (Optional) A list of strings that corresponds to\n     *     the CSV column names, in order. If provided, it ignores the column\n     *     names inferred from the header row. If not provided, infers the column\n     *     names from the first row of the records. If hasHeader is false and\n     *     columnNames is not provided, this method throws an error.\n     *\n     *     columnConfigs: (Optional) A dictionary whose key is column names, value\n     *     is an object stating if this column is required, column's data type,\n     *     default value, and if this column is label. If provided, keys must\n     *     correspond to names provided in columnNames or inferred from the file\n     *     header lines. If isLabel is true any column, returns an array of two\n     *     items: the first item is a dict of features key/value pairs, the second\n     *     item is a dict of labels key/value pairs. If no feature is marked as\n     *     label, returns a dict of features only.\n     *\n     *     configuredColumnsOnly (Optional) If true, only columns provided in\n     *     columnConfigs will be parsed and provided during iteration.\n     *\n     *     delimiter (Optional) The string used to parse each line of the input\n     *     file. Defaults to `,`.\n     */\n    constructor(input, csvConfig) {\n        super();\n        this.input = input;\n        this.hasHeader = true;\n        this.fullColumnNames = null;\n        this.columnNamesValidated = false;\n        this.columnConfigs = null;\n        this.configuredColumnsOnly = false;\n        this.delimiter = ',';\n        this.delimWhitespace = false;\n        this.base = new TextLineDataset(input);\n        if (!csvConfig) {\n            csvConfig = {};\n        }\n        this.hasHeader = csvConfig.hasHeader === false ? false : true;\n        this.fullColumnNames = csvConfig.columnNames;\n        this.columnConfigs = csvConfig.columnConfigs;\n        this.configuredColumnsOnly = csvConfig.configuredColumnsOnly;\n        if (csvConfig.delimWhitespace) {\n            util.assert(csvConfig.delimiter == null, () => 'Delimiter should not be provided when delimWhitespace is true.');\n            this.delimWhitespace = true;\n            this.delimiter = ' ';\n        }\n        else {\n            this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : ',';\n        }\n    }\n    /**\n     * Returns column names of the csv dataset. If `configuredColumnsOnly` is\n     * true, return column names in `columnConfigs`. If `configuredColumnsOnly` is\n     * false and `columnNames` is provided, `columnNames`. If\n     * `configuredColumnsOnly` is false and `columnNames` is not provided, return\n     * all column names parsed from the csv file. For example usage please go to\n     * `tf.data.csv`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async columnNames() {\n        if (!this.columnNamesValidated) {\n            await this.setColumnNames();\n        }\n        return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) :\n            this.fullColumnNames;\n    }\n    /* 1) If `columnNames` is provided as string[], use this string[] as output\n     * keys in corresponding order. The length must match the number of inferred\n     * columns if `hasHeader` is true .\n     * 2) If `columnNames` is not provided, parse header line as `columnNames` if\n     * hasHeader is true. If `hasHeader` is false, throw an error.\n     * 3) If `columnConfigs` is provided, all the keys in `columnConfigs` must\n     * exist in parsed `columnNames`.\n     */\n    async setColumnNames() {\n        const columnNamesFromFile = await this.maybeReadHeaderLine();\n        if (!this.fullColumnNames && !columnNamesFromFile) {\n            // Throw an error if columnNames is not provided and no header line.\n            throw new Error('Column names must be provided if there is no header line.');\n        }\n        else if (this.fullColumnNames && columnNamesFromFile) {\n            // Check provided columnNames match header line.\n            util.assert(columnNamesFromFile.length === this.fullColumnNames.length, () => 'The length of provided columnNames (' +\n                this.fullColumnNames.length.toString() +\n                ') does not match the length of the header line read from ' +\n                'file (' + columnNamesFromFile.length.toString() + ').');\n        }\n        if (!this.fullColumnNames) {\n            this.fullColumnNames = columnNamesFromFile;\n        }\n        // Check if there are duplicate column names.\n        const counts = this.fullColumnNames.reduce((countAcc, name) => {\n            countAcc[name] = (countAcc[name] + 1) || 1;\n            return countAcc;\n        }, {});\n        const duplicateNames = Object.keys(counts).filter((name) => (counts[name] > 1));\n        util.assert(duplicateNames.length === 0, () => 'Duplicate column names found: ' + duplicateNames.toString());\n        // Check if keys in columnConfigs match columnNames.\n        if (this.columnConfigs) {\n            for (const key of Object.keys(this.columnConfigs)) {\n                const index = this.fullColumnNames.indexOf(key);\n                if (index === -1) {\n                    throw new Error('The key \"' + key +\n                        '\" provided in columnConfigs does not match any of the column ' +\n                        'names (' + this.fullColumnNames.toString() + ').');\n                }\n            }\n        }\n        this.columnNamesValidated = true;\n    }\n    async maybeReadHeaderLine() {\n        if (this.hasHeader) {\n            const iter = await this.base.iterator();\n            const firstElement = await iter.next();\n            if (firstElement.done) {\n                throw new Error('No data was found for CSV parsing.');\n            }\n            const firstLine = firstElement.value;\n            const headers = this.parseRow(firstLine, false);\n            return headers;\n        }\n        else {\n            return null;\n        }\n    }\n    async iterator() {\n        if (!this.columnNamesValidated) {\n            await this.setColumnNames();\n        }\n        let lines = await this.base.iterator();\n        if (this.hasHeader) {\n            // We previously read the first line to get the columnNames.\n            // Now that we're providing data, skip it.\n            lines = lines.skip(1);\n        }\n        return lines.map(x => this.makeDataElement(x));\n    }\n    makeDataElement(line) {\n        const values = this.parseRow(line);\n        const features = {};\n        const labels = {};\n        for (let i = 0; i < this.fullColumnNames.length; i++) {\n            const key = this.fullColumnNames[i];\n            const config = this.columnConfigs ? this.columnConfigs[key] : null;\n            if (this.configuredColumnsOnly && !config) {\n                // This column is not selected.\n                continue;\n            }\n            else {\n                const value = values[i];\n                let parsedValue = null;\n                if (value === '') {\n                    // If default value is provided, use it. If default value is not\n                    // provided, set as undefined.\n                    if (config && config.default !== undefined) {\n                        parsedValue = config.default;\n                    }\n                    else if (config && (config.required || config.isLabel)) {\n                        throw new Error(`Required column ${key} is empty in this line: ${line}`);\n                    }\n                    else {\n                        parsedValue = undefined;\n                    }\n                }\n                else {\n                    // A value is present, so parse it based on type\n                    const valueAsNum = Number(value);\n                    if (isNaN(valueAsNum)) {\n                        // The value is a string and this column is declared as boolean\n                        // in config, parse it as boolean.\n                        if (config && config.dtype === 'bool') {\n                            parsedValue = this.getBoolean(value);\n                        }\n                        else {\n                            // Set value as string\n                            parsedValue = value;\n                        }\n                    }\n                    else if (!config || !config.dtype) {\n                        // If this value is a number and no type config is provided, return\n                        // it as number.\n                        parsedValue = valueAsNum;\n                    }\n                    else {\n                        // If this value is a number and data type is provided, parse it\n                        // according to provided data type.\n                        switch (config.dtype) {\n                            case 'float32':\n                                parsedValue = valueAsNum;\n                                break;\n                            case 'int32':\n                                parsedValue = Math.floor(valueAsNum);\n                                break;\n                            case 'bool':\n                                parsedValue = this.getBoolean(value);\n                                break;\n                            default:\n                                parsedValue = valueAsNum;\n                        }\n                    }\n                }\n                // Check if this column is label.\n                (config && config.isLabel) ? labels[key] = parsedValue :\n                    features[key] = parsedValue;\n            }\n        }\n        // If label exists, return an object of features and labels as {xs:features,\n        // ys:labels}, otherwise return features only.\n        if (Object.keys(labels).length === 0) {\n            return features;\n        }\n        else {\n            return { xs: features, ys: labels };\n        }\n    }\n    getBoolean(value) {\n        if (value === '1' || value.toLowerCase() === 'true') {\n            return 1;\n        }\n        else {\n            return 0;\n        }\n    }\n    // adapted from https://beta.observablehq.com/@mbostock/streaming-csv\n    parseRow(line, validateElementCount = true) {\n        const result = [];\n        let readOffset = 0;\n        const readLength = line.length;\n        let currentState = STATE_OUT;\n        // Goes through the line to parse quote.\n        for (let i = 0; i < readLength; i++) {\n            switch (currentState) {\n                // Before enter a new field\n                case STATE_OUT:\n                    switch (line.charAt(i)) {\n                        // Enter a quoted field\n                        case CODE_QUOTE:\n                            readOffset = i + 1;\n                            currentState = STATE_QUOTE;\n                            break;\n                        // Read an empty field\n                        case this.delimiter:\n                            readOffset = i + 1;\n                            // If delimiter is white space and configured to collapse\n                            // multiple white spaces, ignore this white space.\n                            if (this.delimiter === ' ' && this.delimWhitespace) {\n                                break;\n                            }\n                            result.push('');\n                            currentState = STATE_OUT;\n                            break;\n                        // Enter an unquoted field\n                        default:\n                            currentState = STATE_FIELD;\n                            readOffset = i;\n                            break;\n                    }\n                    break;\n                // In an unquoted field\n                case STATE_FIELD:\n                    switch (line.charAt(i)) {\n                        // Exit an unquoted field, add it to result\n                        case this.delimiter:\n                            result.push(line.substring(readOffset, i));\n                            currentState = STATE_OUT;\n                            readOffset = i + 1;\n                            break;\n                        default:\n                    }\n                    break;\n                // In a quoted field\n                case STATE_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Read a quote after a quote\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE_AFTER_QUOTE;\n                            break;\n                        default:\n                    }\n                    break;\n                // This state means it's right after a second quote in a field\n                case STATE_QUOTE_AFTER_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Finished a quoted field\n                        case this.delimiter:\n                            result.push(line.substring(readOffset, i - 1));\n                            currentState = STATE_OUT;\n                            readOffset = i + 1;\n                            break;\n                        // Finished a quoted part in a quoted field\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE;\n                            break;\n                        // In a quoted part in a quoted field\n                        default:\n                            currentState = STATE_WITHIN_QUOTE_IN_QUOTE;\n                            break;\n                    }\n                    break;\n                case STATE_WITHIN_QUOTE_IN_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Exit a quoted part in a quoted field\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE;\n                            break;\n                        default:\n                    }\n                    break;\n                default:\n            }\n        }\n        // Adds last item based on if it is quoted.\n        if (currentState === STATE_QUOTE_AFTER_QUOTE) {\n            result.push(line.substring(readOffset, readLength - 1));\n        }\n        else {\n            result.push(line.substring(readOffset));\n        }\n        // Check if each row has the same number of elements as column names.\n        if (validateElementCount && result.length !== this.fullColumnNames.length) {\n            throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);\n        }\n        return result;\n    }\n}\n// TODO(soergel): add more basic datasets for parity with tf.data\n// tf.data.FixedLengthRecordDataset()\n// tf.data.TFRecordDataset()\n//# sourceMappingURL=csv_dataset.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env, tensor, util } from '@tensorflow/tfjs-core';\nimport { LazyIterator } from './lazy_iterator';\n/**\n * Provide a stream of tensors from microphone audio stream. The tensors are\n * representing audio data as frequency-domain spectrogram generated with\n * browser's native FFT. Tensors representing time-domain waveform is available\n * based on configuration. Only works in browser environment.\n */\nexport class MicrophoneIterator extends LazyIterator {\n    constructor(microphoneConfig) {\n        super();\n        this.microphoneConfig = microphoneConfig;\n        this.isClosed = false;\n        this.fftSize = microphoneConfig.fftSize || 1024;\n        const fftSizeLog2 = Math.log2(this.fftSize);\n        if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 ||\n            !Number.isInteger(fftSizeLog2)) {\n            throw new Error(`Invalid fftSize: it must be a power of 2 between ` +\n                `2 to 4 and 2 to 14, but got ${this.fftSize}`);\n        }\n        this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43;\n        this.sampleRateHz = microphoneConfig.sampleRateHz;\n        this.columnTruncateLength =\n            microphoneConfig.columnTruncateLength || this.fftSize;\n        this.audioTrackConstraints = microphoneConfig.audioTrackConstraints;\n        this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0;\n        this.includeSpectrogram =\n            microphoneConfig.includeSpectrogram === false ? false : true;\n        this.includeWaveform =\n            microphoneConfig.includeWaveform === true ? true : false;\n        if (!this.includeSpectrogram && !this.includeWaveform) {\n            throw new Error('Both includeSpectrogram and includeWaveform are false. ' +\n                'At least one type of data should be returned.');\n        }\n    }\n    summary() {\n        return `microphone`;\n    }\n    // Construct a MicrophoneIterator and start the audio stream.\n    static async create(microphoneConfig = {}) {\n        if (env().get('IS_NODE')) {\n            throw new Error('microphone API is only supported in browser environment.');\n        }\n        const microphoneIterator = new MicrophoneIterator(microphoneConfig);\n        // Call async function start() to initialize the audio stream.\n        await microphoneIterator.start();\n        return microphoneIterator;\n    }\n    // Start the audio stream and FFT.\n    async start() {\n        try {\n            this.stream = await navigator.mediaDevices.getUserMedia({\n                audio: this.audioTrackConstraints == null ? true :\n                    this.audioTrackConstraints,\n                video: false\n            });\n        }\n        catch (e) {\n            throw new Error(`Error thrown while initializing video stream: ${e.message}`);\n        }\n        if (!this.stream) {\n            throw new Error('Could not obtain audio from microphone.');\n        }\n        const ctxConstructor = \n        // tslint:disable-next-line:no-any\n        window.AudioContext || window.webkitAudioContext;\n        this.audioContext = new ctxConstructor();\n        if (!this.sampleRateHz) {\n            // If sample rate is not provided, use the available sample rate on\n            // device.\n            this.sampleRateHz = this.audioContext.sampleRate;\n        }\n        else if (this.audioContext.sampleRate !== this.sampleRateHz) {\n            throw new Error(`Mismatch in sampling rate: ` +\n                `Expected: ${this.sampleRateHz}; ` +\n                `Actual: ${this.audioContext.sampleRate}`);\n        }\n        const streamSource = this.audioContext.createMediaStreamSource(this.stream);\n        this.analyser = this.audioContext.createAnalyser();\n        this.analyser.fftSize = this.fftSize * 2;\n        this.analyser.smoothingTimeConstant = this.smoothingTimeConstant;\n        streamSource.connect(this.analyser);\n        this.freqData = new Float32Array(this.fftSize);\n        this.timeData = new Float32Array(this.fftSize);\n        return;\n    }\n    async next() {\n        if (this.isClosed) {\n            return { value: null, done: true };\n        }\n        let spectrogramTensor;\n        let waveformTensor;\n        const audioDataQueue = await this.getAudioData();\n        if (this.includeSpectrogram) {\n            const freqData = this.flattenQueue(audioDataQueue.freqDataQueue);\n            spectrogramTensor = this.getTensorFromAudioDataArray(freqData, [this.numFrames, this.columnTruncateLength, 1]);\n        }\n        if (this.includeWaveform) {\n            const timeData = this.flattenQueue(audioDataQueue.timeDataQueue);\n            waveformTensor = this.getTensorFromAudioDataArray(timeData, [this.numFrames * this.fftSize, 1]);\n        }\n        return {\n            value: { 'spectrogram': spectrogramTensor, 'waveform': waveformTensor },\n            done: false\n        };\n    }\n    // Capture one result from the audio stream, and extract the value from\n    // iterator.next() result.\n    async capture() {\n        return (await this.next()).value;\n    }\n    async getAudioData() {\n        const freqDataQueue = [];\n        const timeDataQueue = [];\n        let currentFrames = 0;\n        return new Promise(resolve => {\n            const intervalID = setInterval(() => {\n                if (this.includeSpectrogram) {\n                    this.analyser.getFloatFrequencyData(this.freqData);\n                    // If the audio stream is initializing, return empty queue.\n                    if (this.freqData[0] === -Infinity) {\n                        resolve({ freqDataQueue, timeDataQueue });\n                    }\n                    freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength));\n                }\n                if (this.includeWaveform) {\n                    this.analyser.getFloatTimeDomainData(this.timeData);\n                    timeDataQueue.push(this.timeData.slice());\n                }\n                // Clean interval and return when all frames have been collected\n                if (++currentFrames === this.numFrames) {\n                    clearInterval(intervalID);\n                    resolve({ freqDataQueue, timeDataQueue });\n                }\n            }, this.fftSize / this.sampleRateHz * 1e3);\n        });\n    }\n    // Stop the audio stream and pause the iterator.\n    stop() {\n        if (!this.isClosed) {\n            this.isClosed = true;\n            this.analyser.disconnect();\n            this.audioContext.close();\n            if (this.stream != null && this.stream.getTracks().length > 0) {\n                this.stream.getTracks()[0].stop();\n            }\n        }\n    }\n    // Override toArray() function to prevent collecting.\n    toArray() {\n        throw new Error('Can not convert infinite audio stream to array.');\n    }\n    // Return audio sampling rate in Hz\n    getSampleRate() {\n        return this.sampleRateHz;\n    }\n    flattenQueue(queue) {\n        const frameSize = queue[0].length;\n        const freqData = new Float32Array(queue.length * frameSize);\n        queue.forEach((data, i) => freqData.set(data, i * frameSize));\n        return freqData;\n    }\n    getTensorFromAudioDataArray(freqData, shape) {\n        const vals = new Float32Array(util.sizeFromShape(shape));\n        // If the data is less than the output shape, the rest is padded with zeros.\n        vals.set(freqData, vals.length - freqData.length);\n        return tensor(vals, shape);\n    }\n}\n//# sourceMappingURL=microphone_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { browser, env, image, tensor1d, tensor2d, tidy, util } from '@tensorflow/tfjs-core';\nimport { LazyIterator } from './lazy_iterator';\n/**\n * Provide a stream of image tensors from webcam video stream. Only works in\n * browser environment.\n */\nexport class WebcamIterator extends LazyIterator {\n    constructor(webcamVideoElement, webcamConfig) {\n        super();\n        this.webcamVideoElement = webcamVideoElement;\n        this.webcamConfig = webcamConfig;\n        this.isClosed = true;\n        this.resize = false;\n        if (this.needToResize()) {\n            this.resize = true;\n            this.cropSize =\n                [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth];\n            this.cropBoxInd = tensor1d([0], 'int32');\n            if (this.webcamConfig.centerCrop) {\n                // Calculate the box based on resizing shape.\n                const widthCroppingRatio = this.webcamConfig.resizeWidth * 1.0 / this.webcamVideoElement.width;\n                const heightCroppingRatio = this.webcamConfig.resizeHeight * 1.0 /\n                    this.webcamVideoElement.height;\n                const widthCropStart = (1 - widthCroppingRatio) / 2;\n                const heightCropStart = (1 - heightCroppingRatio) / 2;\n                const widthCropEnd = widthCropStart + widthCroppingRatio;\n                const heightCropEnd = heightCroppingRatio + heightCropStart;\n                this.cropBox = tensor2d([heightCropStart, widthCropStart, heightCropEnd, widthCropEnd], [1, 4]);\n            }\n            else {\n                this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]);\n            }\n        }\n    }\n    summary() {\n        return `webcam`;\n    }\n    // Construct a WebcamIterator and start it's video stream.\n    static async create(webcamVideoElement, webcamConfig = {}) {\n        if (env().get('IS_NODE')) {\n            throw new Error('tf.data.webcam is only supported in browser environment.');\n        }\n        if (!webcamVideoElement) {\n            // If webcam video element is not provided, create a hidden video element\n            // with provided width and height.\n            webcamVideoElement = document.createElement('video');\n            if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) {\n                throw new Error('Please provide webcam video element, or resizeWidth and ' +\n                    'resizeHeight to create a hidden video element.');\n            }\n            webcamVideoElement.width = webcamConfig.resizeWidth;\n            webcamVideoElement.height = webcamConfig.resizeHeight;\n        }\n        const webcamIterator = new WebcamIterator(webcamVideoElement, webcamConfig);\n        // Call async function to initialize the video stream.\n        await webcamIterator.start();\n        return webcamIterator;\n    }\n    // Async function to start video stream.\n    async start() {\n        if (this.webcamConfig.facingMode) {\n            util.assert((this.webcamConfig.facingMode === 'user') ||\n                (this.webcamConfig.facingMode === 'environment'), () => `Invalid webcam facing mode: ${this.webcamConfig.facingMode}. ` +\n                `Please provide 'user' or 'environment'`);\n        }\n        try {\n            this.stream = await navigator.mediaDevices.getUserMedia({\n                video: {\n                    deviceId: this.webcamConfig.deviceId,\n                    facingMode: this.webcamConfig.facingMode ?\n                        this.webcamConfig.facingMode :\n                        'user',\n                    width: this.webcamVideoElement.width,\n                    height: this.webcamVideoElement.height\n                }\n            });\n        }\n        catch (e) {\n            // Modify the error message but leave the stack trace intact\n            e.message = `Error thrown while initializing video stream: ${e.message}`;\n            throw e;\n        }\n        if (!this.stream) {\n            throw new Error('Could not obtain video from webcam.');\n        }\n        // Older browsers may not have srcObject\n        try {\n            this.webcamVideoElement.srcObject = this.stream;\n        }\n        catch (error) {\n            console.log(error);\n            this.webcamVideoElement.src = window.URL.createObjectURL(this.stream);\n        }\n        // Start the webcam video stream\n        this.webcamVideoElement.play();\n        this.isClosed = false;\n        return new Promise(resolve => {\n            // Add event listener to make sure the webcam has been fully initialized.\n            this.webcamVideoElement.onloadedmetadata = () => {\n                resolve();\n            };\n        });\n    }\n    async next() {\n        if (this.isClosed) {\n            return { value: null, done: true };\n        }\n        let img;\n        try {\n            img = browser.fromPixels(this.webcamVideoElement);\n        }\n        catch (e) {\n            throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`);\n        }\n        if (this.resize) {\n            try {\n                return { value: this.cropAndResizeFrame(img), done: false };\n            }\n            catch (e) {\n                throw new Error(`Error thrown cropping the video: ${e.message}`);\n            }\n            finally {\n                img.dispose();\n            }\n        }\n        else {\n            return { value: img, done: false };\n        }\n    }\n    needToResize() {\n        // If resizeWidth and resizeHeight are provided, and different from the\n        // width and height of original HTMLVideoElement, then resizing and cropping\n        // is required.\n        if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight &&\n            (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth ||\n                this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) {\n            return true;\n        }\n        return false;\n    }\n    // Cropping and resizing each frame based on config\n    cropAndResizeFrame(img) {\n        return tidy(() => {\n            const expandedImage = img.toFloat().expandDims(0);\n            let resizedImage;\n            resizedImage = image.cropAndResize(expandedImage, this.cropBox, this.cropBoxInd, this.cropSize, 'bilinear');\n            // Extract image from batch cropping.\n            const shape = resizedImage.shape;\n            return resizedImage.reshape(shape.slice(1));\n        });\n    }\n    // Capture one frame from the video stream, and extract the value from\n    // iterator.next() result.\n    async capture() {\n        return (await this.next()).value;\n    }\n    // Stop the video stream and pause webcam iterator.\n    stop() {\n        const tracks = this.stream.getTracks();\n        tracks.forEach(track => track.stop());\n        try {\n            this.webcamVideoElement.srcObject = null;\n        }\n        catch (error) {\n            console.log(error);\n            this.webcamVideoElement.src = null;\n        }\n        this.isClosed = true;\n    }\n    // Override toArray() function to prevent collecting.\n    toArray() {\n        throw new Error('Can not convert infinite video stream to array.');\n    }\n}\n//# sourceMappingURL=webcam_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * Represents a data source readable as a stream of binary data chunks.\n *\n * Because `Dataset`s can be read repeatedly (via `Dataset.iterator()`), this\n * provides a means to repeatedly create streams from the underlying data\n * sources.\n */\nexport class DataSource {\n}\n// TODO(soergel): consider convenience factory functions here\n// in combination with chainable source->dataset above, e.g.:\n// tf.data.url(...).asCsvDataset().shuffle().batch()\n//# sourceMappingURL=datasource.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nexport class StringIterator extends LazyIterator {\n    /**\n     * Splits a string stream on a given separator.\n     *\n     * It is assumed that the incoming chunk boundaries have no semantic meaning,\n     * so conceptually the incoming stream is treated simply as the concatenation\n     * of its elements.\n     *\n     * The outgoing stream provides chunks corresponding to the results of the\n     * standard string split() operation (even if such a chunk spanned incoming\n     * chunks).  The separators are not included.\n     *\n     * A typical usage is to split a text file (represented as a stream with\n     * arbitrary chunk boundaries) into lines.\n     *\n     * @param upstream A readable stream of strings that can be treated as\n     *   concatenated.\n     * @param separator A character to split on.\n     */\n    split(separator) {\n        return new SplitIterator(this, separator);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on StringIterator.  Unfortunately they can't be placed in separate files, due\n// to resulting trouble with circular imports.\n// ============================================================================\n// We wanted multiple inheritance, e.g.\n//   class SplitIterator extends QueueIterator<string>, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\nclass SplitIterator extends StringIterator {\n    constructor(upstream, separator) {\n        super();\n        this.upstream = upstream;\n        this.impl = new SplitIteratorImpl(upstream, separator);\n    }\n    summary() {\n        return this.impl.summary();\n    }\n    async next() {\n        return this.impl.next();\n    }\n}\nclass SplitIteratorImpl extends OneToManyIterator {\n    constructor(upstream, separator) {\n        super();\n        this.upstream = upstream;\n        this.separator = separator;\n        // A partial string at the end of an upstream chunk\n        this.carryover = '';\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Split('${this.separator}')`;\n    }\n    async pump() {\n        const chunkResult = await this.upstream.next();\n        if (chunkResult.done) {\n            if (this.carryover === '') {\n                return false;\n            }\n            // Pretend that the pump succeeded in order to emit the small last batch.\n            // The next pump() call will actually fail.\n            this.outputQueue.push(this.carryover);\n            this.carryover = '';\n            return true;\n        }\n        const lines = chunkResult.value.split(this.separator);\n        // Note the behavior: \" ab \".split(' ') === ['', 'ab', '']\n        // Thus the carryover may be '' if the separator falls on a chunk\n        // boundary; this produces the correct result.\n        lines[0] = this.carryover + lines[0];\n        for (const line of lines.slice(0, -1)) {\n            this.outputQueue.push(line);\n        }\n        this.carryover = lines[lines.length - 1];\n        return true;\n    }\n}\n//# sourceMappingURL=string_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nimport { StringIterator } from './string_iterator';\nexport class ByteChunkIterator extends LazyIterator {\n    /**\n     * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n     *\n     * The byte arrays producetd from the ByteChunkIterator on which this is\n     * called will be interpreted as concatenated.  No assumptions are made about\n     * the boundaries of the incoming chunks, so a multi-byte UTF8 encoding of a\n     * character may span the boundary between chunks.  This naturally happens,\n     * for instance, when reading fixed-size byte arrays from a file.\n     */\n    decodeUTF8() {\n        return new Utf8Iterator(this);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on ByteChunkIterator.  Unfortunately they can't be placed in separate files,\n// due to resulting trouble with circular imports.\n// ============================================================================\n// We wanted multiple inheritance, e.g.\n//   class Utf8Iterator extends QueueIterator<string>, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\nclass Utf8Iterator extends StringIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        this.impl = new Utf8IteratorImpl(upstream);\n    }\n    summary() {\n        return this.impl.summary();\n    }\n    async next() {\n        return this.impl.next();\n    }\n}\n/**\n * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n *\n * This is tricky because the incoming byte array boundaries may disrupt a\n * multi-byte UTF8 character. Thus any incomplete character data at the end of\n * a chunk must be carried over and prepended to the next chunk before\n * decoding. Luckily with native decoder, TextDecoder in browser and\n * string_decoder in node, byte array boundaries are handled automatically.\n *\n * In the context of an input pipeline for machine learning, UTF8 decoding is\n * needed to parse text files containing training examples or prediction\n * requests (e.g., formatted as CSV or JSON). We cannot use the built-in\n * decoding provided by FileReader.readAsText() because here we are in a\n * streaming context, which FileReader does not support.\n *\n * @param upstream A `LazyIterator` of `Uint8Arrays` containing UTF8-encoded\n *   text, which should be interpreted as concatenated.  No assumptions are\n *   made about the boundaries of the incoming chunks, so a multi-byte UTF8\n *   encoding of a character may span the boundary between chunks.  This\n *   naturally happens, for instance, when reading fixed-size byte arrays from a\n *   file.\n */\nclass Utf8IteratorImpl extends OneToManyIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        if (env().get('IS_BROWSER')) {\n            this.decoder = new TextDecoder('utf-8');\n        }\n        else {\n            // tslint:disable-next-line:no-require-imports\n            const { StringDecoder } = require('string_decoder');\n            this.decoder = new StringDecoder('utf8');\n        }\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Utf8`;\n    }\n    async pump() {\n        const chunkResult = await this.upstream.next();\n        let chunk;\n        if (chunkResult.done) {\n            return false;\n        }\n        else {\n            chunk = chunkResult.value;\n        }\n        let text;\n        if (env().get('IS_BROWSER')) {\n            text = this.decoder.decode(chunk, { stream: true });\n        }\n        else {\n            text = this.decoder.write(Buffer.from(chunk.buffer));\n        }\n        this.outputQueue.push(text);\n        return true;\n    }\n}\n//# sourceMappingURL=byte_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n// inspired by https://github.com/maxogden/filereader-stream\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { ByteChunkIterator } from './byte_chunk_iterator';\n/**\n * Provide a stream of chunks from a File, Blob, or Uint8Array.\n * @param file The source File, Blob or Uint8Array.\n * @param options Optional settings controlling file reading.\n * @returns a lazy Iterator of Uint8Arrays containing sequential chunks of the\n *   input File, Blob or Uint8Array.\n */\nexport class FileChunkIterator extends ByteChunkIterator {\n    constructor(file, options = {}) {\n        super();\n        this.file = file;\n        this.options = options;\n        util.assert((file instanceof Uint8Array) ||\n            (env().get('IS_BROWSER') ?\n                (file instanceof File || file instanceof Blob) :\n                false), () => 'FileChunkIterator only supports File, Blob and Uint8Array ' +\n            'right now.');\n        this.offset = options.offset || 0;\n        // default 1MB chunk has tolerable perf on large files\n        this.chunkSize = options.chunkSize || 1024 * 1024;\n    }\n    summary() {\n        return `FileChunks ${this.file}`;\n    }\n    async next() {\n        if (this.offset >= ((this.file instanceof Uint8Array) ?\n            this.file.byteLength :\n            this.file.size)) {\n            return { value: null, done: true };\n        }\n        const chunk = new Promise((resolve, reject) => {\n            const end = this.offset + this.chunkSize;\n            if (this.file instanceof Uint8Array) {\n                // Note if end > this.uint8Array.byteLength, we just get a small last\n                // chunk.\n                resolve(new Uint8Array(this.file.slice(this.offset, end)));\n            }\n            else {\n                // This branch assumes that this.file type is File or Blob, which\n                // means it is in the browser environment.\n                // TODO(soergel): is this a performance issue?\n                const fileReader = new FileReader();\n                fileReader.onload = (event) => {\n                    let data = fileReader.result;\n                    // Not sure we can trust the return type of\n                    // FileReader.readAsArrayBuffer See e.g.\n                    // https://github.com/node-file-api/FileReader/issues/2\n                    if (data instanceof ArrayBuffer) {\n                        data = new Uint8Array(data);\n                    }\n                    if (!(data instanceof Uint8Array)) {\n                        return reject(new TypeError('FileReader returned unknown type.'));\n                    }\n                    resolve(data);\n                };\n                fileReader.onabort = (event) => {\n                    return reject(new Error('Aborted'));\n                };\n                fileReader.onerror = (event) => {\n                    return reject(new Error(event.type));\n                };\n                // TODO(soergel): better handle onabort, onerror\n                // Note if end > this.file.size, we just get a small last chunk.\n                const slice = this.file.slice(this.offset, end);\n                // We can't use readAsText here (even if we know the file is text)\n                // because the slice boundary may fall within a multi-byte character.\n                fileReader.readAsArrayBuffer(slice);\n            }\n            this.offset = end;\n        });\n        return { value: (await chunk), done: false };\n    }\n}\n//# sourceMappingURL=file_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { FileChunkIterator } from './file_chunk_iterator';\n/**\n * Provide a stream of chunks from a URL.\n *\n * Note this class first downloads the entire file into memory before providing\n * the first element from the stream.  This is because the Fetch API does not\n * yet reliably provide a reader stream for the response body.\n */\nexport async function urlChunkIterator(url, options = {}) {\n    let urlString;\n    let requestInit;\n    if ((typeof url) === 'string') {\n        urlString = url;\n    }\n    else {\n        urlString = url.url;\n        requestInit = getRequestInitFromRequest(url);\n    }\n    const response = await util.fetch(urlString, requestInit);\n    if (response.ok) {\n        const uint8Array = new Uint8Array(await response.arrayBuffer());\n        return new FileChunkIterator(uint8Array, options);\n    }\n    else {\n        throw new Error(response.statusText);\n    }\n}\n// Generate RequestInit from Request to match tf.util.fetch signature.\nconst getRequestInitFromRequest = (request) => {\n    const init = {\n        method: request.method,\n        headers: request.headers,\n        body: request.body,\n        mode: request.mode,\n        credentials: request.credentials,\n        cache: request.cache,\n        redirect: request.redirect,\n        referrer: request.referrer,\n        integrity: request.integrity,\n    };\n    return init;\n};\n//# sourceMappingURL=url_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n// Skip tslint any type check cause this method is aiming to check type of\n// input.\n// tslint:disable-next-line:no-any\nexport function isLocalPath(source) {\n    return (typeof source === 'string') && source.substr(0, 7) === 'file://';\n}\n//# sourceMappingURL=source_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { DataSource } from '../datasource';\nimport { FileChunkIterator } from '../iterators/file_chunk_iterator';\nimport { isLocalPath } from '../util/source_util';\n/**\n * Represents a file, blob, or Uint8Array readable as a stream of binary data\n * chunks.\n */\nexport class FileDataSource extends DataSource {\n    /**\n     * Create a `FileDataSource`.\n     *\n     * @param input Local file path, or `File`/`Blob`/`Uint8Array` object to\n     *     read. Local file only works in node environment.\n     * @param options Options passed to the underlying `FileChunkIterator`s,\n     *   such as {chunksize: 1024}.\n     */\n    constructor(input, options = {}) {\n        super();\n        this.input = input;\n        this.options = options;\n    }\n    async iterator() {\n        if (isLocalPath(this.input) && env().get('IS_NODE')) {\n            // tslint:disable-next-line:no-require-imports\n            const fs = require('fs');\n            this.input = fs.readFileSync(this.input.substr(7));\n        }\n        // TODO(kangyizhang): Add LocalFileChunkIterator to split local streaming\n        // with file in browser.\n        return new FileChunkIterator(this.input, this.options);\n    }\n}\n//# sourceMappingURL=file_data_source.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { DataSource } from '../datasource';\nimport { urlChunkIterator } from '../iterators/url_chunk_iterator';\nimport { isLocalPath } from '../util/source_util';\nimport { FileDataSource } from './file_data_source';\n/*\n * Represents a URL readable as a stream of binary data chunks.\n */\nexport class URLDataSource extends DataSource {\n    /**\n     * Create a `URLDataSource`.\n     *\n     * @param url A source URL string, or a `Request` object.\n     * @param options Options passed to the underlying `FileChunkIterator`s,\n     *   such as {chunksize: 1024}.\n     */\n    constructor(url, fileOptions = {}) {\n        super();\n        this.url = url;\n        this.fileOptions = fileOptions;\n    }\n    // TODO(soergel): provide appropriate caching options.  Currently this\n    // will download the URL anew for each call to iterator().  Since we have\n    // to treat the downloaded file as a blob/buffer anyway, we may as well retain\n    // it-- but that raises GC issues.  Also we may want a persistent disk cache.\n    async iterator() {\n        if (isLocalPath(this.url)) {\n            return (new FileDataSource(this.url, this.fileOptions))\n                .iterator();\n        }\n        else {\n            return urlChunkIterator(this.url, this.fileOptions);\n        }\n    }\n}\n//# sourceMappingURL=url_data_source.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { datasetFromIteratorFn } from './dataset';\nimport { CSVDataset } from './datasets/csv_dataset';\nimport { iteratorFromFunction } from './iterators/lazy_iterator';\nimport { MicrophoneIterator } from './iterators/microphone_iterator';\nimport { WebcamIterator } from './iterators/webcam_iterator';\nimport { URLDataSource } from './sources/url_data_source';\n/**\n * Create a `CSVDataset` by reading and decoding CSV file(s) from provided URL\n * or local path if it's in Node environment.\n *\n * Note: If isLabel in columnConfigs is `true` for at least one column, the\n * element in returned `CSVDataset` will be an object of\n * `{xs:features, ys:labels}`: xs is a dict of features key/value pairs, ys\n * is a dict of labels key/value pairs. If no column is marked as label,\n * returns a dict of features only.\n *\n * ```js\n * const csvUrl =\n * 'https://storage.googleapis.com/tfjs-examples/multivariate-linear-regression/data/boston-housing-train.csv';\n *\n * async function run() {\n *   // We want to predict the column \"medv\", which represents a median value of\n *   // a home (in $1000s), so we mark it as a label.\n *   const csvDataset = tf.data.csv(\n *     csvUrl, {\n *       columnConfigs: {\n *         medv: {\n *           isLabel: true\n *         }\n *       }\n *     });\n *\n *   // Number of features is the number of column names minus one for the label\n *   // column.\n *   const numOfFeatures = (await csvDataset.columnNames()).length - 1;\n *\n *   // Prepare the Dataset for training.\n *   const flattenedDataset =\n *     csvDataset\n *     .map(({xs, ys}) =>\n *       {\n *         // Convert xs(features) and ys(labels) from object form (keyed by\n *         // column name) to array form.\n *         return {xs:Object.values(xs), ys:Object.values(ys)};\n *       })\n *     .batch(10);\n *\n *   // Define the model.\n *   const model = tf.sequential();\n *   model.add(tf.layers.dense({\n *     inputShape: [numOfFeatures],\n *     units: 1\n *   }));\n *   model.compile({\n *     optimizer: tf.train.sgd(0.000001),\n *     loss: 'meanSquaredError'\n *   });\n *\n *   // Fit the model using the prepared Dataset\n *   return model.fitDataset(flattenedDataset, {\n *     epochs: 10,\n *     callbacks: {\n *       onEpochEnd: async (epoch, logs) => {\n *         console.log(epoch + ':' + logs.loss);\n *       }\n *     }\n *   });\n * }\n *\n * await run();\n * ```\n *\n * @param source URL or local path to get CSV file. If it's a local path, it\n * must have prefix `file://` and it only works in node environment.\n * @param csvConfig (Optional) A CSVConfig object that contains configurations\n *     of reading and decoding from CSV file(s).\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   configParamIndices: [1]\n *  }\n */\nexport function csv(source, csvConfig = {}) {\n    return new CSVDataset(new URLDataSource(source), csvConfig);\n}\n/**\n * Create a `Dataset` that produces each element by calling a provided function.\n *\n * Note that repeated iterations over this `Dataset` may produce different\n * results, because the function will be called anew for each element of each\n * iteration.\n *\n * Also, beware that the sequence of calls to this function may be out of order\n * in time with respect to the logical order of the Dataset. This is due to the\n * asynchronous lazy nature of stream processing, and depends on downstream\n * transformations (e.g. .shuffle()). If the provided function is pure, this is\n * no problem, but if it is a closure over a mutable state (e.g., a traversal\n * pointer), then the order of the produced elements may be scrambled.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const ds = tf.data.func(func);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * @param f A function that produces one data element on each call.\n */\nexport function func(f) {\n    const iter = iteratorFromFunction(f);\n    return datasetFromIteratorFn(async () => iter);\n}\n/**\n * Create a `Dataset` that produces each element from provided JavaScript\n * generator, which is a function*\n * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions),\n * or a function that returns an\n * iterator\n * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions).\n *\n * The returned iterator should have `.next()` function that returns element in\n * format of `{value: TensorContainer, done:boolean}`.\n *\n * Example of creating a dataset from an iterator factory:\n * ```js\n * function makeIterator() {\n *   const numElements = 10;\n *   let index = 0;\n *\n *   const iterator = {\n *     next: () => {\n *       let result;\n *       if (index < numElements) {\n *         result = {value: index, done: false};\n *         index++;\n *         return result;\n *       }\n *       return {value: index, done: true};\n *     }\n *   };\n *   return iterator;\n * }\n * const ds = tf.data.generator(makeIterator);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * Example of creating a dataset from a generator:\n * ```js\n * function* dataGenerator() {\n *   const numElements = 10;\n *   let index = 0;\n *   while (index < numElements) {\n *     const x = index;\n *     index++;\n *     yield x;\n *   }\n * }\n *\n * const ds = tf.data.generator(dataGenerator);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * @param generator A Javascript generator function that returns a JavaScript\n *     iterator.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   configParamIndices: [1]\n *  }\n */\nexport function generator(generator) {\n    return datasetFromIteratorFn(async () => {\n        const gen = await generator();\n        return iteratorFromFunction(() => gen.next());\n    });\n}\n/**\n * Create an iterator that generate `Tensor`s from webcam video stream. This API\n * only works in Browser environment when the device has webcam.\n *\n * Note: this code snippet only works when the device has a webcam. It will\n * request permission to open the webcam when running.\n * ```js\n * const videoElement = document.createElement('video');\n * videoElement.width = 100;\n * videoElement.height = 100;\n * const cam = await tf.data.webcam(videoElement);\n * const img = await cam.capture();\n * img.print();\n * cam.stop();\n * ```\n *\n * @param webcamVideoElement A `HTMLVideoElement` used to play video from\n *     webcam. If this element is not provided, a hidden `HTMLVideoElement` will\n *     be created. In that case, `resizeWidth` and `resizeHeight` must be\n *     provided to set the generated tensor shape.\n * @param webcamConfig A `WebcamConfig` object that contains configurations of\n *     reading and manipulating data from webcam video stream.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   ignoreCI: true\n *  }\n */\nexport async function webcam(webcamVideoElement, webcamConfig) {\n    return WebcamIterator.create(webcamVideoElement, webcamConfig);\n}\n/**\n * Create an iterator that generate frequency-domain spectrogram `Tensor`s from\n * microphone audio stream with browser's native FFT. This API only works in\n * browser environment when the device has microphone.\n *\n * Note: this code snippet only works when the device has a microphone. It will\n * request permission to open the microphone when running.\n * ```js\n * const mic = await tf.data.microphone({\n *   fftSize: 1024,\n *   columnTruncateLength: 232,\n *   numFramesPerSpectrogram: 43,\n *   sampleRateHz:44100,\n *   includeSpectrogram: true,\n *   includeWaveform: true\n * });\n * const audioData = await mic.capture();\n * const spectrogramTensor = audioData.spectrogram;\n * spectrogramTensor.print();\n * const waveformTensor = audioData.waveform;\n * waveformTensor.print();\n * mic.stop();\n * ```\n *\n * @param microphoneConfig A `MicrophoneConfig` object that contains\n *     configurations of reading audio data from microphone.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   ignoreCI: true\n *  }\n */\nexport async function microphone(microphoneConfig) {\n    return MicrophoneIterator.create(microphoneConfig);\n}\n//# sourceMappingURL=readers.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { array, Dataset, zip } from './dataset';\nexport { CSVDataset } from './datasets/csv_dataset';\nexport { TextLineDataset } from './datasets/text_line_dataset';\nexport { csv, func, generator, microphone, webcam } from './readers';\nexport { FileDataSource } from './sources/file_data_source';\nexport { URLDataSource } from './sources/url_data_source';\nexport { version as version_data } from './version';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function assertNotComplex(tensor, opName) {\n    if (!Array.isArray(tensor)) {\n        tensor = [tensor];\n    }\n    tensor.forEach(t => {\n        if (t != null) {\n            util.assert(t.dtype !== 'complex64', () => `${opName} does not support complex64 tensors in the CPU backend.`);\n        }\n    });\n}\n//# sourceMappingURL=cpu_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { backend_util, DataStorage, engine, env, kernel_impls, KernelBackend, max, slice_util, TensorBuffer, upcastType, util } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV3Impl = kernel_impls.nonMaxSuppressionV3Impl;\nconst split = kernel_impls.split;\nconst tile = kernel_impls.tile;\nconst topkImpl = kernel_impls.topkImpl;\nconst whereImpl = kernel_impls.whereImpl;\nimport * as seedrandom from 'seedrandom';\nimport { assertNotComplex } from './cpu_util';\nfunction mapActivation(backend, x, activation, preluActivationWeights) {\n    if (activation === 'linear') {\n        return backend.linear(x);\n    }\n    else if (activation === 'relu') {\n        return backend.relu(x);\n    }\n    else if (activation === 'elu') {\n        return tf.elu(x);\n    }\n    else if (activation === 'relu6') {\n        return backend.relu6(x);\n    }\n    else if (activation === 'prelu') {\n        return backend.prelu(x, preluActivationWeights);\n    }\n    throw new Error(`Activation ${activation} has not been implemented for the CPU backend.`);\n}\nexport class MathBackendCPU extends KernelBackend {\n    constructor() {\n        super();\n        this.blockSize = 48;\n        this.firstUse = true;\n        this.data = new DataStorage(this, engine());\n    }\n    write(values, shape, dtype) {\n        if (this.firstUse) {\n            this.firstUse = false;\n            if (env().get('IS_NODE')) {\n                backend_util.warn('\\n============================\\n' +\n                    'Hi there \uD83D\uDC4B. Looks like you are running TensorFlow.js in ' +\n                    'Node.js. To speed things up dramatically, install our node ' +\n                    'backend, which binds to TensorFlow C++, by running ' +\n                    'npm i @tensorflow/tfjs-node, ' +\n                    'or npm i @tensorflow/tfjs-node-gpu if you have CUDA. ' +\n                    'Then call require(\\'@tensorflow/tfjs-node\\'); (-gpu ' +\n                    'suffix for CUDA) at the start of your program. ' +\n                    'Visit https://github.com/tensorflow/tfjs-node for more details.' +\n                    '\\n============================');\n            }\n        }\n        const dataId = {};\n        this.data.set(dataId, { values, dtype, refCount: 1 });\n        return dataId;\n    }\n    /**\n     * Create a data bucket in cpu backend.\n     * @param shape Shape of the `TensorInfo`.\n     * @param dtype DType of the `TensorInfo`.\n     * @param values The value of the `TensorInfo` stored as a flattened array.\n     */\n    makeTensorInfo(shape, dtype, values) {\n        const outId = this.write(values, shape, dtype);\n        return { dataId: outId, shape, dtype };\n    }\n    /** Increase refCount of a `TensorData`. */\n    incRef(dataId) {\n        const tensorData = this.data.get(dataId);\n        tensorData.refCount++;\n    }\n    /** Decrease refCount of a `TensorData`. */\n    decRef(dataId) {\n        if (this.data.has(dataId)) {\n            const tensorData = this.data.get(dataId);\n            tensorData.refCount--;\n        }\n    }\n    move(dataId, values, shape, dtype) {\n        this.data.set(dataId, { values, dtype, refCount: 1 });\n    }\n    numDataIds() {\n        return this.data.numDataIds();\n    }\n    async read(dataId) {\n        return this.readSync(dataId);\n    }\n    readSync(dataId) {\n        const { dtype, complexTensorInfos } = this.data.get(dataId);\n        if (dtype === 'complex64') {\n            const realValues = this.readSync(complexTensorInfos.real.dataId);\n            const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n            return backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        return this.data.get(dataId).values;\n    }\n    bufferSync(t) {\n        const data = this.readSync(t.dataId);\n        let decodedData = data;\n        if (t.dtype === 'string') {\n            try {\n                // Decode the bytes into string.\n                decodedData = data.map(d => util.decodeString(d));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode encoded string bytes into utf-8');\n            }\n        }\n        return tf.buffer(t.shape, t.dtype, decodedData);\n    }\n    makeOutput(values, shape, dtype) {\n        const dataId = this.write(values, shape, dtype);\n        return engine().makeTensorFromDataId(dataId, shape, dtype, this);\n    }\n    disposeData(dataId) {\n        if (this.data.has(dataId)) {\n            const { complexTensorInfos } = this.data.get(dataId);\n            if (complexTensorInfos != null) {\n                this.disposeData(complexTensorInfos.real.dataId);\n                this.disposeData(complexTensorInfos.imag.dataId);\n            }\n            this.data.delete(dataId);\n        }\n    }\n    disposeIntermediateTensorInfo(tensorInfo) {\n        const dataId = tensorInfo.dataId;\n        if (this.data.has(dataId)) {\n            const tensorData = this.data.get(dataId);\n            tensorData.refCount--;\n            if (tensorData.refCount < 1) {\n                this.disposeData(dataId);\n            }\n        }\n    }\n    async time(f) {\n        const start = util.now();\n        f();\n        const kernelMs = util.now() - start;\n        return { kernelMs };\n    }\n    memory() {\n        return {\n            // Unreliable due to automatic gc. The numbers above are cumulative.\n            unreliable: true,\n            reasons: ['The reported memory is an upper bound. Due to automatic garbage ' +\n                    'collection, the true allocated memory may be less.']\n        };\n    }\n    stridedSlice(x, begin, end, strides) {\n        assertNotComplex(x, 'stridedSlice');\n        const outShape = slice_util.computeOutShape(begin, end, strides);\n        if (outShape.some(axis => axis === 0)) {\n            return tf.tensor([], outShape);\n        }\n        const buffer = tf.buffer(outShape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < buffer.size; i++) {\n            const loc = buffer.indexToLoc(i);\n            const newLoc = new Array(loc.length);\n            for (let j = 0; j < newLoc.length; j++) {\n                newLoc[j] = loc[j] * strides[j] + begin[j];\n            }\n            buffer.set(xBuf.get(...newLoc), ...loc);\n        }\n        return buffer.toTensor();\n    }\n    diag(x) {\n        const xVals = this.readSync(x.dataId);\n        const buffer = tf.buffer([x.size, x.size], x.dtype);\n        const vals = buffer.values;\n        for (let i = 0; i < xVals.length; i++) {\n            vals[i * x.size + i] = xVals[i];\n        }\n        return buffer.toTensor();\n    }\n    unstack(x, axis) {\n        const num = x.shape[axis];\n        const outShape = new Array(x.rank - 1);\n        let outIndex = 0;\n        for (let i = 0; i < x.rank; i++) {\n            if (i !== axis) {\n                outShape[outIndex++] = x.shape[i];\n            }\n        }\n        const begin = new Array(x.rank).fill(0);\n        const size = x.shape.slice();\n        size[axis] = 1;\n        const res = new Array(num);\n        for (let i = 0; i < res.length; i++) {\n            begin[axis] = i;\n            res[i] = tf.slice(x, begin, size).reshape(outShape);\n        }\n        return res;\n    }\n    reverse(x, axis) {\n        assertNotComplex(x, 'reverse');\n        const buffer = tf.buffer(x.shape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < buffer.size; i++) {\n            const outLoc = buffer.indexToLoc(i);\n            const inLoc = outLoc.slice();\n            axis.forEach(ax => inLoc[ax] = x.shape[ax] - 1 - inLoc[ax]);\n            buffer.set(xBuf.get(...inLoc), ...outLoc);\n        }\n        return buffer.toTensor();\n    }\n    neg(x) {\n        assertNotComplex(x, 'neg');\n        // TODO(lina128): Use mul directly once neg is modularized.\n        return tf.mul(tf.scalar(-1), x);\n    }\n    addN(tensors) {\n        assertNotComplex(tensors, 'addN');\n        const vals = tensors.map(t => this.readSync(t.dataId));\n        const result = tf.buffer(tensors[0].shape, tensors[0].dtype);\n        const resultVals = result.values;\n        for (let i = 0; i < tensors.length; i++) {\n            const currVals = vals[i];\n            for (let j = 0; j < resultVals.length; j++) {\n                resultVals[j] += currVals[j];\n            }\n        }\n        return result.toTensor();\n    }\n    softmax(logits, dim) {\n        const axes = util.parseAxisParam([dim], logits.shape);\n        // TODO(annxingyuan): Call maxImpl rather than op as part of softmax kernel\n        // modularization.\n        const maxLogit = max(logits, axes);\n        const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n        // TODO(lina128): Use sub directly once softmax is modularized.\n        const a = tf.sub(logits, maxLogit.reshape(expandedShape));\n        const b = tf.exp(a);\n        const sumExp = this.sum(b, axes).reshape(expandedShape);\n        // TODO(annxingyuan): Call divImpl rather than op as part of softmax\n        // kernel modularization.\n        return tf.div(b, sumExp);\n    }\n    pow(a, b) {\n        assertNotComplex([a, b], 'pow');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aValue, bValue) => Math.pow(aValue, bValue));\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        assertNotComplex([a, b], 'matMul');\n        const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n        const leftDim = transposeA ? a.shape[2] : a.shape[1];\n        const rightDim = transposeB ? b.shape[1] : b.shape[2];\n        const batchDim = a.shape[0];\n        const aValues = this.readSync(a.dataId);\n        const bValues = this.readSync(b.dataId);\n        const [aBatch, aOuterStep, aInnerStep] = transposeA ?\n            [a.strides[0], 1, a.strides[1]] :\n            [a.strides[0], a.strides[1], 1];\n        const [bInnerStep, bOuterStep, bBatch] = transposeB ?\n            [1, b.strides[1], b.strides[0]] :\n            [b.strides[1], 1, b.strides[0]];\n        const size = leftDim * rightDim;\n        const result = tf.buffer([batchDim, leftDim, rightDim], a.dtype);\n        const resVals = result.values;\n        const blockSize = this.blockSize;\n        for (let b = 0; b < batchDim; b++) {\n            for (let i0 = 0; i0 < leftDim; i0 += blockSize) {\n                for (let j0 = 0; j0 < rightDim; j0 += blockSize) {\n                    for (let k0 = 0; k0 < sharedDim; k0 += blockSize) {\n                        // for when blockSize doesn't evenly divide the input\n                        const iBlock = Math.min(i0 + blockSize, leftDim);\n                        const jBlock = Math.min(j0 + blockSize, rightDim);\n                        const kBlock = Math.min(k0 + blockSize, sharedDim);\n                        for (let i = i0; i < iBlock; i++) {\n                            for (let j = j0; j < jBlock; j++) {\n                                let sum = 0.0;\n                                for (let k = k0; k < kBlock; k++) {\n                                    sum += aValues[b * aBatch + i * aOuterStep + k * aInnerStep] *\n                                        bValues[k * bInnerStep + j * bOuterStep + b * bBatch];\n                                }\n                                resVals[b * size + (i * rightDim + j)] += sum;\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return result.toTensor();\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        let result = this.batchMatMul(a, b, transposeA, transposeB);\n        if (bias) {\n            // TODO(lina128): Use add directly once fusedBatchMatMul is modularized.\n            result = tf.add(result, bias);\n        }\n        if (activation) {\n            result =\n                mapActivation(this, result, activation, preluActivationWeights);\n        }\n        return result;\n    }\n    floorDiv(a, b) {\n        assertNotComplex([a, b], 'floorDiv');\n        const op = (a, b) => Math.floor(a / b);\n        const outputDtype = 'int32';\n        return this.broadcastedBinaryOp(a, b, outputDtype, op);\n    }\n    sum(x, axes) {\n        assertNotComplex(x, 'sum');\n        backend_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(outShape, resultDtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let sum = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                sum += aVals[offset + j];\n            }\n            vals[i] = sum;\n        }\n        return result;\n    }\n    prod(x, axes) {\n        assertNotComplex(x, 'sum');\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(outShape, resultDtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let prod = 1;\n            for (let j = 0; j < reduceSize; ++j) {\n                prod *= aVals[offset + j];\n            }\n            vals[i] = prod;\n        }\n        return result;\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        assertNotComplex(x, 'unsortedSegmentSum');\n        const res = [];\n        // Reshape the segment id's so that they can be broadcast with\n        // x. The new shape should be [segmentIds.shape, 1, ..., 1]\n        const numIters = x.rank - segmentIds.rank;\n        for (let i = 0; i < numIters; ++i) {\n            segmentIds = segmentIds.expandDims(i + 1);\n        }\n        for (let i = 0; i < numSegments; ++i) {\n            const segmentId = tf.scalar(i, 'int32');\n            const mask = tf.equal(segmentId, segmentIds).asType('float32');\n            const sum = mask.mul(x).sum(0);\n            res.push(sum);\n        }\n        return tf.stack(res);\n    }\n    argMin(x, axis) {\n        assertNotComplex(x, 'argMin');\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('argMin', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, 'int32');\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let min = aVals[offset];\n            let minIndex = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value < min) {\n                    min = value;\n                    minIndex = j;\n                }\n            }\n            vals[i] = minIndex;\n        }\n        return result;\n    }\n    argMax(x, axis) {\n        assertNotComplex(x, 'argMax');\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('argMax', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, 'int32');\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let max = aVals[offset];\n            let maxIndex = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value > max) {\n                    max = value;\n                    maxIndex = j;\n                }\n            }\n            vals[i] = maxIndex;\n        }\n        return result;\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        assertNotComplex(x, 'cumsum');\n        if (axis !== x.rank - 1) {\n            throw new Error(`backend.cumsum in CPU expects an inner-most axis=${x.rank - 1} ` +\n                `but got axis=${axis}`);\n        }\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(x.shape, resultDtype);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        const finalDim = x.shape[x.rank - 1];\n        const indexAdjuster = reverse ?\n            (i, j) => i + finalDim - j - 1 :\n            (i, j) => i + j;\n        for (let i = 0; i < aVals.length; i += finalDim) {\n            for (let j = 0; j < finalDim; j++) {\n                const idx = indexAdjuster(i, j);\n                if (j === 0) {\n                    vals[idx] = exclusive ? 0 : aVals[idx];\n                }\n                else {\n                    const prevIdx = indexAdjuster(i, j - 1);\n                    vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :\n                        aVals[idx] + vals[prevIdx];\n                }\n            }\n        }\n        return result;\n    }\n    equal(a, b) {\n        assertNotComplex([a, b], 'equal');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal === bVal) ? 1 : 0;\n        });\n    }\n    notEqual(a, b) {\n        assertNotComplex([a, b], 'notEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal !== bVal) ? 1 : 0;\n        });\n    }\n    less(a, b) {\n        assertNotComplex([a, b], 'less');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal < bVal) ? 1 : 0;\n        });\n    }\n    lessEqual(a, b) {\n        assertNotComplex([a, b], 'lessEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal <= bVal) ? 1 : 0;\n        });\n    }\n    greater(a, b) {\n        assertNotComplex([a, b], 'greater');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal > bVal) ? 1 : 0;\n        });\n    }\n    greaterEqual(a, b) {\n        assertNotComplex([a, b], 'greaterEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal >= bVal) ? 1 : 0;\n        });\n    }\n    logicalAnd(a, b) {\n        assertNotComplex([a, b], 'logicalAnd');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return aVal && bVal;\n        });\n    }\n    logicalOr(a, b) {\n        assertNotComplex([a, b], 'logicalOr');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return aVal || bVal;\n        });\n    }\n    select(condition, a, b) {\n        assertNotComplex([condition, a, b], 'select');\n        const values = this.readSync(condition.dataId);\n        const aValues = this.readSync(a.dataId);\n        const bValues = this.readSync(b.dataId);\n        const result = tf.zeros(a.shape, upcastType(a.dtype, b.dtype));\n        const newValues = this.readSync(result.dataId);\n        let index = 0;\n        const offset = condition.rank === 0 || condition.rank > 1 || a.rank === 1 ?\n            1 :\n            util.sizeFromShape(a.shape.slice(1));\n        for (let i = 0; i < values.length; i++) {\n            for (let j = 0; j < offset; j++) {\n                if (values[i] === 1) {\n                    newValues[index++] = aValues[i];\n                }\n                else {\n                    newValues[index++] = bValues[i];\n                }\n            }\n        }\n        return result;\n    }\n    where(condition) {\n        assertNotComplex([condition], 'where');\n        const condVals = this.readSync(condition.dataId);\n        return whereImpl(condition.shape, condVals);\n    }\n    topk(x, k, sorted) {\n        assertNotComplex(x, 'topk');\n        const xVals = this.readSync(x.dataId);\n        return topkImpl(xVals, x.shape, x.dtype, k, sorted);\n    }\n    min(x, axes) {\n        assertNotComplex(x, 'min');\n        backend_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let min = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value < min) {\n                    min = value;\n                }\n            }\n            vals[i] = min;\n        }\n        return result;\n    }\n    minimum(a, b) {\n        assertNotComplex([a, b], 'minimum');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => Math.min(aVal, bVal));\n    }\n    mod(a, b) {\n        assertNotComplex([a, b], 'mod');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n            const rem = aVal % bVal;\n            if ((aVal < 0 && bVal < 0) || (aVal >= 0 && bVal >= 0)) {\n                return rem;\n            }\n            else {\n                return (rem + bVal) % bVal;\n            }\n        });\n    }\n    maximum(a, b) {\n        assertNotComplex([a, b], 'maximum');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => Math.max(aVal, bVal));\n    }\n    all(x, axes) {\n        assertNotComplex(x, 'all');\n        backend_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let all = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                all = all && value;\n            }\n            vals[i] = all;\n        }\n        return result;\n    }\n    any(x, axes) {\n        assertNotComplex(x, 'any');\n        backend_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let anyVal = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                anyVal = anyVal || value;\n            }\n            vals[i] = anyVal;\n        }\n        return result;\n    }\n    squaredDifference(a, b) {\n        assertNotComplex([a, b], 'squaredDifference');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n            const diff = aVal - bVal;\n            return diff * diff;\n        });\n    }\n    linear(x) {\n        return x;\n    }\n    relu(x) {\n        assertNotComplex(x, 'relu');\n        const res = tf.zeros(x.shape, x.dtype);\n        const resVals = this.readSync(res.dataId);\n        const inVals = this.readSync(x.dataId);\n        for (let i = 0; i < inVals.length; ++i) {\n            resVals[i] = Math.max(0, inVals[i]);\n        }\n        return res;\n    }\n    relu6(x) {\n        assertNotComplex(x, 'relu');\n        const res = tf.zeros(x.shape, x.dtype);\n        const resVals = this.readSync(res.dataId);\n        const inVals = this.readSync(x.dataId);\n        for (let i = 0; i < inVals.length; ++i) {\n            resVals[i] = Math.min(Math.max(0, inVals[i]), 6);\n        }\n        return res;\n    }\n    prelu(x, a) {\n        assertNotComplex([x, a], 'prelu');\n        return this.broadcastedBinaryOp(x, a, x.dtype, (xValue, aValue) => xValue < 0 ? aValue * xValue : xValue);\n    }\n    eluDer(dy, y) {\n        assertNotComplex([dy, y], 'eluDer');\n        const resultValues = new Float32Array(y.size);\n        const values = this.readSync(y.dataId);\n        const dyValues = this.readSync(dy.dataId);\n        for (let i = 0; i < values.length; ++i) {\n            const v = values[i];\n            if (v >= 1) {\n                resultValues[i] = dyValues[i];\n            }\n            else {\n                resultValues[i] = dyValues[i] * (v + 1);\n            }\n        }\n        return this.makeOutput(resultValues, y.shape, 'float32');\n    }\n    atan2(a, b) {\n        assertNotComplex([a, b], 'atan2');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aValue, bValue) => Math.atan2(aValue, bValue));\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        let result = this.conv2d(input, filter, convInfo);\n        if (bias) {\n            // TODO(lina128): Use add directly once fusedConv2d is modularized.\n            result = tf.add(result, bias);\n        }\n        if (activation) {\n            result =\n                mapActivation(this, result, activation, preluActivationWeights);\n        }\n        return result;\n    }\n    conv2d(x, filter, convInfo) {\n        assertNotComplex([x, filter], 'conv2d');\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const padLeft = convInfo.padInfo.left;\n        const padTop = convInfo.padInfo.top;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const y = tf.buffer(convInfo.outShape, x.dtype);\n        const xBatchStride = x.strides[0];\n        const xRowStride = isChannelsLast ? x.strides[1] : x.strides[2];\n        const xColStride = isChannelsLast ? x.strides[2] : 1;\n        const xChannelStride = isChannelsLast ? 1 : x.strides[1];\n        const yBatchStride = y.strides[0];\n        const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];\n        const yColStride = isChannelsLast ? y.strides[2] : 1;\n        const yChannelStride = isChannelsLast ? 1 : y.strides[1];\n        const xVals = this.readSync(x.dataId);\n        const wVals = this.readSync(filter.dataId);\n        const yVals = y.values;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n            const xOffset1 = b * xBatchStride;\n            const yOffset1 = b * yBatchStride;\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const yOffset2 = yOffset1 + yR * yRowStride;\n                const xRCorner = yR * convInfo.strideHeight - padTop;\n                for (let wR = 0; wR < filterHeight; wR++) {\n                    const xR = xRCorner + wR * dilationHeight;\n                    if (xR < 0 || xR >= convInfo.inHeight) {\n                        continue;\n                    }\n                    const wOffset1 = wR * filter.strides[0];\n                    const xOffset2 = xOffset1 + xR * xRowStride;\n                    for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                        const yOffset3 = yOffset2 + yC * yColStride;\n                        const xCCorner = yC * convInfo.strideWidth - padLeft;\n                        for (let wC = 0; wC < filterWidth; wC++) {\n                            const xC = xCCorner + wC * dilationWidth;\n                            if (xC < 0 || xC >= convInfo.inWidth) {\n                                continue;\n                            }\n                            const wOffset2 = wOffset1 + wC * filter.strides[1];\n                            const xOffset3 = xOffset2 + xC * xColStride;\n                            let wOffset3 = wOffset2;\n                            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                const xVal = xVals[xOffset3 + d1 * xChannelStride];\n                                for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                                    yVals[yOffset3 + d2 * yChannelStride] +=\n                                        xVal * wVals[wOffset3 + d2];\n                                }\n                                wOffset3 += convInfo.outChannels;\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return y.toTensor();\n    }\n    conv3d(x, filter, convInfo) {\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const padFront = convInfo.padInfo.front;\n        const padLeft = convInfo.padInfo.left;\n        const padTop = convInfo.padInfo.top;\n        const y = tf.buffer(convInfo.outShape, x.dtype);\n        const xVals = this.readSync(x.dataId);\n        const wVals = this.readSync(filter.dataId);\n        const yVals = y.values;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n            const xOffset1 = b * x.strides[0];\n            const yOffset1 = b * y.strides[0];\n            for (let yF = 0; yF < convInfo.outDepth; ++yF) {\n                const yOffset2 = yOffset1 + yF * y.strides[1];\n                const xFCorner = yF * convInfo.strideDepth - padFront;\n                for (let wF = 0; wF < filterDepth; wF++) {\n                    const xF = xFCorner + wF * dilationDepth;\n                    if (xF < 0 || xF >= convInfo.inDepth) {\n                        continue;\n                    }\n                    const wOffset1 = wF * filter.strides[0];\n                    const xOffset2 = xOffset1 + xF * x.strides[1];\n                    for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                        const yOffset3 = yOffset2 + yR * y.strides[2];\n                        const xRCorner = yR * convInfo.strideHeight - padTop;\n                        for (let wR = 0; wR < filterHeight; wR++) {\n                            const xR = xRCorner + wR * dilationHeight;\n                            if (xR < 0 || xR >= convInfo.inHeight) {\n                                continue;\n                            }\n                            const wOffset2 = wOffset1 + wR * filter.strides[1];\n                            const xOffset3 = xOffset2 + xR * x.strides[2];\n                            for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                                const yOffset4 = yOffset3 + yC * convInfo.outChannels;\n                                const xCCorner = yC * convInfo.strideWidth - padLeft;\n                                for (let wC = 0; wC < filterWidth; wC++) {\n                                    const xC = xCCorner + wC * dilationWidth;\n                                    if (xC < 0 || xC >= convInfo.inWidth) {\n                                        continue;\n                                    }\n                                    const wOffset3 = wOffset2 + wC * filter.strides[2];\n                                    const xOffset4 = xOffset3 + xC * convInfo.inChannels;\n                                    let wOffset4 = wOffset3;\n                                    for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                        const xVal = xVals[xOffset4 + d1];\n                                        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                                            yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];\n                                        }\n                                        wOffset4 += convInfo.outChannels;\n                                    }\n                                }\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return y.toTensor();\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        assertNotComplex([dy, filter], 'conv2dDerInput');\n        const dx = tf.buffer(convInfo.inShape, 'float32');\n        const dxValues = dx.values;\n        const dyValues = this.readSync(dy.dataId);\n        const fltValues = this.readSync(filter.dataId);\n        const [fltS0, fltS1, fltS2] = filter.strides;\n        const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth, dataFormat } = convInfo;\n        const topPad = filterHeight - 1 - convInfo.padInfo.top;\n        const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const xBatchStride = dx.strides[0];\n        const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];\n        const xColStride = isChannelsLast ? dx.strides[2] : 1;\n        const xChannelStride = isChannelsLast ? 1 : dx.strides[1];\n        const yBatchStride = dy.strides[0];\n        const yRowStride = isChannelsLast ? dy.strides[1] : dy.strides[2];\n        const yColStride = isChannelsLast ? dy.strides[2] : 1;\n        const yChannelStride = isChannelsLast ? 1 : dy.strides[1];\n        for (let b = 0; b < batchSize; ++b) {\n            for (let d1 = 0; d1 < inChannels; ++d1) {\n                for (let xR = 0; xR < inHeight; ++xR) {\n                    const xRCorner = xR - topPad;\n                    const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                    const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                    for (let xC = 0; xC < inWidth; ++xC) {\n                        const xCCorner = xC - leftPad;\n                        const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                        const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                        let dotProd = 0;\n                        for (let yR = xRMin; yR < yRMax; ++yR) {\n                            const wR = yR * strideHeight - xRCorner;\n                            for (let yC = xCMin; yC < yCMax; ++yC) {\n                                const wC = yC * strideWidth - xCCorner;\n                                const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;\n                                const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n                                for (let d2 = 0; d2 < outChannels; ++d2) {\n                                    const pixel = dyValues[dyOffset + yChannelStride * d2];\n                                    const weight = fltValues[fltOffset + d2];\n                                    dotProd += pixel * weight;\n                                }\n                            }\n                        }\n                        const dxOffset = xBatchStride * b + xRowStride * xR +\n                            xColStride * xC + xChannelStride * d1;\n                        dxValues[dxOffset] = dotProd;\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        const dx = tf.buffer(convInfo.inShape, 'float32');\n        const dxValues = dx.values;\n        const [dxS0, dxS1, dxS2, dxS3] = dx.strides;\n        const dyValues = this.readSync(dy.dataId);\n        const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n        const fltValues = this.readSync(filter.dataId);\n        const [fltS0, fltS1, fltS2, fltS3] = filter.strides;\n        const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo;\n        const frontPad = filterDepth - 1 - convInfo.padInfo.front;\n        const topPad = filterHeight - 1 - convInfo.padInfo.top;\n        const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let d1 = 0; d1 < inChannels; ++d1) {\n                // Frames of depth\n                for (let xF = 0; xF < inDepth; ++xF) {\n                    const xFCorner = xF - frontPad;\n                    const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));\n                    const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);\n                    // Rows as per standard 2d matrix notation\n                    for (let xR = 0; xR < inHeight; ++xR) {\n                        const xRCorner = xR - topPad;\n                        const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                        const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                        // Columns as per standard 2d matrix notation\n                        for (let xC = 0; xC < inWidth; ++xC) {\n                            const xCCorner = xC - leftPad;\n                            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                            const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                            let dotProd = 0;\n                            for (let yF = xFMin; yF < yFMax; ++yF) {\n                                const wF = yF * strideDepth - xFCorner;\n                                for (let yR = xRMin; yR < yRMax; ++yR) {\n                                    const wR = yR * strideHeight - xRCorner;\n                                    for (let yC = xCMin; yC < yCMax; ++yC) {\n                                        const wC = yC * strideWidth - xCCorner;\n                                        const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;\n                                        const fltOffset = fltS0 * (filterDepth - 1 - wF) +\n                                            fltS1 * (filterHeight - 1 - wR) +\n                                            fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;\n                                        for (let d2 = 0; d2 < outChannels; ++d2) {\n                                            const pixel = dyValues[dyOffset + d2];\n                                            const weight = fltValues[fltOffset + d2];\n                                            dotProd += pixel * weight;\n                                        }\n                                    }\n                                }\n                            }\n                            dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =\n                                dotProd;\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    conv2dDerFilter(x, dy, convInfo) {\n        assertNotComplex([x, dy], 'conv2dDerFilter');\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const dW = tf.buffer(convInfo.filterShape, 'float32');\n        const leftPad = convInfo.padInfo.left;\n        const topPad = convInfo.padInfo.top;\n        const xBuf = this.bufferSync(x);\n        const dyBuf = this.bufferSync(dy);\n        for (let wR = 0; wR < filterHeight; ++wR) {\n            const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n            const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n            for (let wC = 0; wC < filterWidth; ++wC) {\n                const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                    for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                        // Need to convolve.\n                        let dotProd = 0;\n                        for (let b = 0; b < convInfo.batchSize; ++b) {\n                            for (let yR = yRMin; yR < yRMax; ++yR) {\n                                const xR = wR + yR * strideHeight - topPad;\n                                for (let yC = yCMin; yC < yCMax; ++yC) {\n                                    const xC = wC + yC * strideWidth - leftPad;\n                                    if (isChannelsLast) {\n                                        dotProd +=\n                                            xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n                                    }\n                                    else {\n                                        dotProd +=\n                                            xBuf.get(b, d1, xR, xC) * dyBuf.get(b, d2, yR, yC);\n                                    }\n                                }\n                            }\n                        }\n                        dW.set(dotProd, wR, wC, d1, d2);\n                    }\n                }\n            }\n        }\n        return dW.toTensor();\n    }\n    conv3dDerFilter(x, dy, convInfo) {\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dw = tf.buffer(convInfo.filterShape, 'float32');\n        const dwValues = dw.values;\n        const [dwS0, dwS1, dwS2, dwS3] = dw.strides;\n        const dyValues = this.readSync(dy.dataId);\n        const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n        const xValues = this.readSync(x.dataId);\n        const [xS0, xS1, xS2, xS3] = x.strides;\n        const frontPad = convInfo.padInfo.front;\n        const leftPad = convInfo.padInfo.left;\n        const topPad = convInfo.padInfo.top;\n        for (let wF = 0; wF < filterDepth; ++wF) {\n            const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));\n            const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);\n            const wOffset1 = wF * dwS0;\n            for (let wR = 0; wR < filterHeight; ++wR) {\n                const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n                const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n                const wOffset2 = wR * dwS1 + wOffset1;\n                for (let wC = 0; wC < filterWidth; ++wC) {\n                    const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                    const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                    const wOffset3 = wC * dwS2 + wOffset2;\n                    for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                        const wOffset4 = d1 * dwS3 + wOffset3;\n                        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                            let dotProd = 0;\n                            for (let b = 0; b < convInfo.batchSize; ++b) {\n                                const xOffset1 = b * xS0;\n                                const yOffset1 = b * dyS0;\n                                for (let yF = yFMin; yF < yFMax; ++yF) {\n                                    const xF = wF + yF * strideDepth - frontPad;\n                                    const xOffset2 = xF * xS1 + xOffset1;\n                                    const yOffset2 = yF * dyS1 + yOffset1;\n                                    for (let yR = yRMin; yR < yRMax; ++yR) {\n                                        const xR = wR + yR * strideHeight - topPad;\n                                        const xOffset3 = xR * xS2 + xOffset2;\n                                        const yOffset3 = yR * dyS2 + yOffset2;\n                                        for (let yC = yCMin; yC < yCMax; ++yC) {\n                                            const xC = wC + yC * strideWidth - leftPad;\n                                            const xOffset4 = xC * xS3 + xOffset3;\n                                            const yOffset4 = yC * dyS3 + yOffset3;\n                                            dotProd +=\n                                                xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];\n                                        }\n                                    }\n                                }\n                            }\n                            dwValues[wOffset4 + d2] = dotProd;\n                        }\n                    }\n                }\n            }\n        }\n        return dw.toTensor();\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        let result = this.depthwiseConv2D(input, filter, convInfo);\n        if (bias) {\n            // TODO(lina128): Use add directly once fusedDepthwiseConv2D is\n            // modularized.\n            result = tf.add(result, bias);\n        }\n        if (activation) {\n            result =\n                mapActivation(this, result, activation, preluActivationWeights);\n        }\n        return result;\n    }\n    depthwiseConv2D(x, filter, convInfo) {\n        assertNotComplex([x, filter], 'depthwiseConv2D');\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const padLeft = convInfo.padInfo.left;\n        const padTop = convInfo.padInfo.top;\n        const chMul = convInfo.outChannels / convInfo.inChannels;\n        const y = tf.buffer(convInfo.outShape, x.dtype);\n        const xVals = this.readSync(x.dataId);\n        const wVals = this.readSync(filter.dataId);\n        const yVals = y.values;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n            const xOffset1 = b * x.strides[0];\n            const yOffset1 = b * y.strides[0];\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const yOffset2 = yOffset1 + yR * y.strides[1];\n                const xRCorner = yR * convInfo.strideHeight - padLeft;\n                for (let wR = 0; wR < filterHeight; ++wR) {\n                    const xR = xRCorner + wR * dilationHeight;\n                    if (xR < 0 || xR >= convInfo.inHeight) {\n                        continue;\n                    }\n                    const wOffset1 = wR * filter.strides[0];\n                    const xOffset2 = xOffset1 + xR * x.strides[1];\n                    for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                        const yOffset3 = yOffset2 + yC * y.strides[2];\n                        const xCCorner = yC * convInfo.strideWidth - padTop;\n                        for (let wC = 0; wC < filterWidth; ++wC) {\n                            const xC = xCCorner + wC * dilationWidth;\n                            if (xC < 0 || xC >= convInfo.inWidth) {\n                                continue;\n                            }\n                            const wOffset2 = wOffset1 + wC * filter.strides[1];\n                            const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n                            let yOffset4 = yOffset3;\n                            let wOffset3 = wOffset2;\n                            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                const xVal = xVals[xOffset3 + d1];\n                                for (let q = 0; q < chMul; ++q) {\n                                    yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];\n                                }\n                                yOffset4 += chMul;\n                                wOffset3 += chMul;\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return y.toTensor();\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        assertNotComplex([dy, filter], 'depthwiseConv2DDerInput');\n        const dx = tf.buffer(convInfo.inShape, 'float32');\n        const dxValues = dx.values;\n        const [dxS0, dxS1, dxS2] = dx.strides;\n        const dyValues = this.readSync(dy.dataId);\n        const [dyS0, dyS1, dyS2] = dy.strides;\n        const fltValues = this.readSync(filter.dataId);\n        const [fltS0, fltS1, fltS2] = filter.strides;\n        const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n        const topPad = filterHeight - 1 - convInfo.padInfo.top;\n        const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n        const chMul = outChannels / inChannels;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let d1 = 0; d1 < inChannels; ++d1) {\n                for (let xR = 0; xR < inHeight; ++xR) {\n                    const xRCorner = xR - topPad;\n                    const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                    const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                    for (let xC = 0; xC < inWidth; ++xC) {\n                        const xCCorner = xC - leftPad;\n                        const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                        const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                        let dotProd = 0;\n                        for (let yR = xRMin; yR < yRMax; ++yR) {\n                            const wR = yR * strideHeight - xRCorner;\n                            for (let yC = xCMin; yC < yCMax; ++yC) {\n                                const wC = yC * strideWidth - xCCorner;\n                                const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n                                const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n                                for (let dm = 0; dm < chMul; ++dm) {\n                                    const d2 = d1 * chMul + dm;\n                                    const pixel = dyValues[dyOffset + d2];\n                                    const weight = fltValues[fltOffset + dm];\n                                    dotProd += pixel * weight;\n                                }\n                            }\n                        }\n                        dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    depthwiseConv2DDerFilter(x, dy, convInfo) {\n        assertNotComplex([x, dy], 'depthwiseConv2DDerFilter');\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dW = tf.buffer(convInfo.filterShape, 'float32');\n        const leftPad = convInfo.padInfo.left;\n        const topPad = convInfo.padInfo.top;\n        const chMul = convInfo.outChannels / convInfo.inChannels;\n        const xBuf = this.bufferSync(x);\n        const dyBuf = this.bufferSync(dy);\n        for (let wR = 0; wR < filterHeight; ++wR) {\n            const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n            const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n            for (let wC = 0; wC < filterWidth; ++wC) {\n                const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                    const d1 = Math.trunc(d2 / chMul);\n                    const dm = d2 % chMul;\n                    let dotProd = 0;\n                    for (let b = 0; b < convInfo.batchSize; ++b) {\n                        for (let yR = yRMin; yR < yRMax; ++yR) {\n                            const xR = wR + yR * strideHeight - topPad;\n                            for (let yC = yCMin; yC < yCMax; ++yC) {\n                                const xC = wC + yC * strideWidth - leftPad;\n                                dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n                            }\n                        }\n                    }\n                    dW.set(dotProd, wR, wC, d1, dm);\n                }\n            }\n        }\n        return dW.toTensor();\n    }\n    tile(x, reps) {\n        assertNotComplex(x, 'tile');\n        return tile(this.bufferSync(x), reps);\n    }\n    gather(x, indices, axis) {\n        assertNotComplex([x, indices], 'gather');\n        const newShape = x.shape.slice();\n        const indicesValues = this.readSync(indices.dataId);\n        newShape[axis] = indicesValues.length;\n        const result = tf.buffer(newShape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < result.size; ++i) {\n            const newLoc = result.indexToLoc(i);\n            const originalLoc = newLoc.slice();\n            originalLoc[axis] = indicesValues[newLoc[axis]];\n            const originalIndex = xBuf.locToIndex(originalLoc);\n            result.values[i] = xBuf.values[originalIndex];\n        }\n        return result.toTensor();\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        assertNotComplex([x], 'batchToSpaceND');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n        const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n        const reshapedPermuted = backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n        const sliceBeginCoords = backend_util.getSliceBeginCoords(crops, blockShape.length);\n        const sliceSize = backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n        return tf.transpose(x.reshape(reshaped), permuted)\n            .reshape(reshapedPermuted)\n            .slice(sliceBeginCoords, sliceSize);\n    }\n    pool3d(x, convInfo, poolType) {\n        assertNotComplex(x, 'pool3d');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n            Number.POSITIVE_INFINITY);\n        const xValues = this.readSync(x.dataId);\n        const output = tf.buffer(convInfo.outShape, x.dtype);\n        const outputVals = output.values;\n        const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] *\n            convInfo.outShape[3] * convInfo.outShape[4];\n        const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];\n        const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];\n        const outputColStrides = convInfo.outShape[4];\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            const outputBatchOffset = batch * outputBatchStrides;\n            const inputBatchOffset = batch * x.strides[0];\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n                    const xDepthCorner = yDepth * strideDepth - padFront;\n                    let xDepthMin = xDepthCorner;\n                    while (xDepthMin < 0) {\n                        xDepthMin += dilationDepth;\n                    }\n                    const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n                    const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides;\n                    for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n                        const xRowCorner = yRow * strideHeight - padTop;\n                        let xRowMin = xRowCorner;\n                        while (xRowMin < 0) {\n                            xRowMin += dilationHeight;\n                        }\n                        const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n                        const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;\n                        for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n                            const xColCorner = yCol * strideWidth - padLeft;\n                            let xColMin = xColCorner;\n                            while (xColMin < 0) {\n                                xColMin += dilationWidth;\n                            }\n                            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n                            // Shader code begins\n                            const outputColOffset = outputRowOffset + yCol * outputColStrides;\n                            let minMaxValue = initialValue;\n                            let avgValue = 0;\n                            let count = 0;\n                            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n                                const xDepthOffset = inputBatchOffset + xDepth * x.strides[1];\n                                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                                    const xRowOffset = xDepthOffset + xRow * x.strides[2];\n                                    for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                                        const xColOffset = xRowOffset + xCol * x.strides[3];\n                                        const pixel = xValues[xColOffset + channel];\n                                        if ((poolType === 'max' && pixel > minMaxValue)) {\n                                            minMaxValue = pixel;\n                                        }\n                                        else if (poolType === 'avg') {\n                                            avgValue += pixel;\n                                            count++;\n                                        }\n                                        if (isNaN(minMaxValue)) {\n                                            break;\n                                        }\n                                    }\n                                    if (isNaN(minMaxValue)) {\n                                        break;\n                                    }\n                                }\n                                if (isNaN(minMaxValue)) {\n                                    break;\n                                }\n                            }\n                            const outputOffset = outputColOffset + channel;\n                            outputVals[outputOffset] =\n                                poolType === 'avg' ? avgValue / count : minMaxValue;\n                        }\n                    }\n                }\n            }\n        }\n        return output.toTensor();\n    }\n    avgPool3d(x, convInfo) {\n        assertNotComplex(x, 'avgPool3d');\n        return this.pool3d(x, convInfo, 'avg').toFloat();\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        assertNotComplex([dy, x], 'avgPool3dBackprop');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const dx = tf.buffer(x.shape, 'float32');\n        const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n        const dyBuf = this.bufferSync(dy);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n                    for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n                        for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n                            // Shader code begins.\n                            const dyDepthCorner = dxDepth - padFront;\n                            const dyRowCorner = dxRow - padTop;\n                            const dyColCorner = dxCol - padLeft;\n                            let dotProd = 0;\n                            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n                                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n                                    Math.floor(dyDepth) !== dyDepth) {\n                                    continue;\n                                }\n                                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                                    const dyRow = (dyRowCorner + wRow) / strideHeight;\n                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n                                        Math.floor(dyRow) !== dyRow) {\n                                        continue;\n                                    }\n                                    for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                                        const dyCol = (dyColCorner + wCol) / strideWidth;\n                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n                                            Math.floor(dyCol) !== dyCol) {\n                                            continue;\n                                        }\n                                        const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        dotProd += pixel;\n                                    }\n                                }\n                            }\n                            dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    maxPool3d(x, convInfo) {\n        assertNotComplex(x, 'maxPool3d');\n        return this.pool3d(x, convInfo, 'max').toFloat();\n    }\n    maxPool3dPositions(x, convInfo) {\n        const maxPositions = tf.buffer(convInfo.outShape, 'int32');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const xBuf = this.bufferSync(x);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n                    const xDepthCorner = yDepth * strideDepth - padFront;\n                    let xDepthMin = xDepthCorner;\n                    while (xDepthMin < 0) {\n                        xDepthMin += dilationDepth;\n                    }\n                    const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n                    for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n                        const xRowCorner = yRow * strideHeight - padTop;\n                        let xRowMin = xRowCorner;\n                        while (xRowMin < 0) {\n                            xRowMin += dilationHeight;\n                        }\n                        const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n                        for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n                            const xColCorner = yCol * strideWidth - padLeft;\n                            let xColMin = xColCorner;\n                            while (xColMin < 0) {\n                                xColMin += dilationWidth;\n                            }\n                            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n                            // Shader code begins\n                            let maxValue = Number.NEGATIVE_INFINITY;\n                            let maxPosition = -1;\n                            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n                                const wDepth = xDepth - xDepthCorner;\n                                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                                    const wRow = xRow - xRowCorner;\n                                    for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                                        const wCol = xCol - xColCorner;\n                                        const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);\n                                        if (pixel >= maxValue) {\n                                            maxValue = pixel;\n                                            maxPosition = wDepth * effectiveFilterHeight *\n                                                effectiveFilterWidth +\n                                                wRow * effectiveFilterHeight + wCol;\n                                        }\n                                    }\n                                }\n                            }\n                            maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return maxPositions.toTensor();\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        assertNotComplex([x, y], 'maxPool3dBackprop');\n        const maxPositions = this.maxPool3dPositions(x, convInfo);\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const dx = tf.buffer(x.shape, 'float32');\n        const maxPosBuf = this.bufferSync(maxPositions);\n        const dyBuf = this.bufferSync(dy);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n                    for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n                        for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n                            // Shader code begins\n                            const dyDepthCorner = dxDepth - padFront;\n                            const dyRowCorner = dxRow - padTop;\n                            const dyColCorner = dxCol - padLeft;\n                            let dotProd = 0;\n                            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n                                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n                                    Math.floor(dyDepth) !== dyDepth) {\n                                    continue;\n                                }\n                                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                                    const dyRow = (dyRowCorner + wRow) / strideHeight;\n                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n                                        Math.floor(dyRow) !== dyRow) {\n                                        continue;\n                                    }\n                                    for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                                        const dyCol = (dyColCorner + wCol) / strideWidth;\n                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n                                            Math.floor(dyCol) !== dyCol) {\n                                            continue;\n                                        }\n                                        const maxPos = effectiveFilterDepth *\n                                            effectiveFilterHeight * effectiveFilterWidth -\n                                            1 -\n                                            maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth +\n                                            wRow * effectiveFilterWidth + wCol;\n                                        const mask = maxPos === curPos ? 1 : 0;\n                                        if (mask === 0) {\n                                            continue;\n                                        }\n                                        const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        dotProd += pixel * mask;\n                                    }\n                                }\n                            }\n                            dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        assertNotComplex(x, 'resizeBilinear');\n        const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n        const xValues = this.readSync(x.dataId);\n        const result = new Float32Array(util.sizeFromShape([batch, newHeight, newWidth, numChannels]));\n        const effectiveInputSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutputSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        let outputIdx = 0;\n        const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n        const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n        for (let b = 0; b < batch; b++) {\n            for (let r = 0; r < newHeight; r++) {\n                const sourceFracRow = effectiveRowSizeRatio * r;\n                const sourceRowFloor = Math.floor(sourceFracRow);\n                const rowFrac = sourceFracRow - sourceRowFloor;\n                const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));\n                const topRowOffset = b * x.strides[0] + sourceRowFloor * x.strides[1];\n                const botRowOffset = b * x.strides[0] + sourceRowCeil * x.strides[1];\n                for (let c = 0; c < newWidth; c++) {\n                    const sourceFracCol = effectiveColSizeRatio * c;\n                    const sourceColFloor = Math.floor(sourceFracCol);\n                    const colFrac = sourceFracCol - sourceColFloor;\n                    const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));\n                    const topLeftOffest = topRowOffset + sourceColFloor * x.strides[2];\n                    const botLeftOffset = botRowOffset + sourceColFloor * x.strides[2];\n                    const topRightOffset = topRowOffset + sourceColCeil * x.strides[2];\n                    const botRightOffest = botRowOffset + sourceColCeil * x.strides[2];\n                    for (let d = 0; d < numChannels; d++) {\n                        // Begin shader.\n                        // Compute the fractional index of the source.\n                        const topLeft = xValues[topLeftOffest + d];\n                        const bottomLeft = xValues[botLeftOffset + d];\n                        const topRight = xValues[topRightOffset + d];\n                        const bottomRight = xValues[botRightOffest + d];\n                        const top = topLeft + (topRight - topLeft) * colFrac;\n                        const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;\n                        const newValue = top + (bottom - top) * rowFrac;\n                        result[outputIdx++] = newValue;\n                    }\n                }\n            }\n        }\n        return tf.tensor(result, [batch, newHeight, newWidth, numChannels]);\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        assertNotComplex([dy, x], 'resizeBilinearBackprop');\n        const [batch, xHeight, xWidth, depth] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        const output = new Float32Array(batch * xHeight * xWidth * depth);\n        // In the backwards pass, we want to find the pixels that were generated\n        // for each pixel in the input image the forward pass and add the\n        // corresponding coefficient from dy to the gradient (with some\n        // interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/3039375c86a5bbc9610c7725dcaa95d635f87ba2/tensorflow/core/kernels/resize_bilinear_op.cc#L275\n        const dyValues = this.readSync(dy.dataId);\n        let offset = 0;\n        for (let b = 0; b < batch; b++) {\n            const bOffset = b * x.strides[0];\n            for (let r = 0; r < yHeight; r++) {\n                const dxR = r * heightScale;\n                const topDxRIndex = Math.floor(dxR);\n                const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);\n                const topDxROffset = bOffset + topDxRIndex * x.strides[1];\n                const bottomDxROffset = bOffset + bottomDxRIndex * x.strides[1];\n                const dxRLerp = dxR - topDxRIndex;\n                const inverseDxRLerp = 1.0 - dxRLerp;\n                for (let c = 0; c < yWidth; c++) {\n                    const dxC = c * widthScale;\n                    const leftDxCIndex = Math.floor(dxC);\n                    const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);\n                    const dxCLerp = dxC - leftDxCIndex;\n                    const inverseDxCLerp = 1.0 - dxCLerp;\n                    const topLeftRCOffset = topDxROffset + leftDxCIndex * x.strides[2];\n                    const topRightRCOffset = topDxROffset + rightDxCIndex * x.strides[2];\n                    const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * x.strides[2];\n                    const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * x.strides[2];\n                    const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;\n                    const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;\n                    const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;\n                    const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;\n                    for (let d = 0; d < depth; d++) {\n                        const dyVal = dyValues[offset++];\n                        output[topLeftRCOffset + d] +=\n                            dyVal * inverseDxRLerpTimesInverseDxCLerp;\n                        output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;\n                        output[bottomLeftRCOffset + d] +=\n                            dyVal * dxRLerpTimesInverseDxCLerp;\n                        output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(output, [batch, xWidth, xHeight, depth], x.dtype);\n    }\n    resizeNearestNeighbor(x, newHeight, newWidth, alignCorners) {\n        assertNotComplex(x, 'resizeNearestNeighbor');\n        const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n        const xValues = this.readSync(x.dataId);\n        const output = new Float32Array(batch * newHeight * newWidth * numChannels);\n        const effectiveInputSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutputSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n        const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n        let outputOffset = 0;\n        for (let b = 0; b < batch; b++) {\n            const batchOffset = b * x.strides[0];\n            for (let r = 0; r < newHeight; r++) {\n                const sourceFracRow = effectiveRowSizeRatio * r;\n                const sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) :\n                    Math.floor(sourceFracRow));\n                const rowOffset = batchOffset + sourceNearestRow * x.strides[1];\n                for (let c = 0; c < newWidth; c++) {\n                    const sourceFracCol = effectiveColSizeRatio * c;\n                    const sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) :\n                        Math.floor(sourceFracCol));\n                    const colOffset = rowOffset + sourceNearestCol * x.strides[2];\n                    for (let d = 0; d < numChannels; d++) {\n                        // Begin shader.\n                        // Compute the fractional index of the source.\n                        const newVal = xValues[colOffset + d];\n                        output[outputOffset++] = newVal;\n                    }\n                }\n            }\n        }\n        return tf.tensor(output, [batch, newHeight, newWidth, numChannels], x.dtype);\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        assertNotComplex([dy, x], 'resizeNearestNeighborBackprop');\n        const [batch, xHeight, xWidth, depth] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        const output = new Float32Array(batch * xHeight * xWidth * depth);\n        const dyValues = this.readSync(dy.dataId);\n        // In the backwards pass, we want to find the pixels that were generated\n        // for each pixel in the input image the forward pass\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        // Loop over the output space.\n        for (let b = 0; b < batch; b++) {\n            const batchOffset = b * x.strides[0];\n            for (let r = 0; r < xHeight; r++) {\n                const rowOffset = batchOffset + r * x.strides[1];\n                // Compute bounds for where in dy we will look\n                const startRLerp = Math.floor(r * invHeightScale);\n                const startDyR = Math.floor(startRLerp - (winHeight / 2));\n                for (let c = 0; c < xWidth; c++) {\n                    const colOffset = rowOffset + c * x.strides[2];\n                    // Compute bounds for where in dy we will look\n                    const startCLerp = Math.floor(c * invWidthScale);\n                    const startDyC = Math.floor(startCLerp - (winWidth / 2));\n                    for (let d = 0; d < depth; d++) {\n                        let accum = 0;\n                        // loop over dy\n                        for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {\n                            const dyR = dyRIndex + startDyR;\n                            // Guard against the window exceeding the bounds of dy\n                            if (dyR < 0 || dyR >= yHeight) {\n                                continue;\n                            }\n                            const dyROffset = batchOffset + dyR * dy.strides[1];\n                            const sourceFracRow = dyR * heightScale;\n                            const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) :\n                                Math.floor(sourceFracRow));\n                            if (r !== sourceNearestRow) {\n                                continue;\n                            }\n                            for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {\n                                const dyC = dyCIndex + startDyC;\n                                // Guard against the window exceeding the bounds of dy\n                                if (dyC < 0 || dyC >= yWidth) {\n                                    continue;\n                                }\n                                const dyCOffset = dyROffset + dyC * dy.strides[2];\n                                const sourceFracCol = dyC * widthScale;\n                                const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) :\n                                    Math.floor(sourceFracCol));\n                                if (c === sourceNearestCol) {\n                                    accum += dyValues[dyCOffset + d];\n                                }\n                            }\n                        }\n                        output[colOffset + d] = accum;\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(output, x.shape, x.dtype);\n    }\n    localResponseNormalization4D(x, depthRadius, bias, alpha, beta) {\n        assertNotComplex(x, 'localResponseNormalization4D');\n        const channels = x.shape[3];\n        const maxD = channels - 1;\n        const xValues = this.readSync(x.dataId);\n        const size = x.size;\n        const result = new Float32Array(size);\n        function sumAcrossChannels(offset) {\n            const currentChannel = offset % channels;\n            let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n            const endSumOffset = offset - currentChannel +\n                Math.min(currentChannel + depthRadius, maxD);\n            let sum = 0.0;\n            for (; beginSumOffset <= endSumOffset; beginSumOffset++) {\n                const z = xValues[beginSumOffset];\n                sum += z * z;\n            }\n            return sum;\n        }\n        for (let offset = 0; offset < size; offset++) {\n            const sum = sumAcrossChannels(offset);\n            const val = xValues[offset] * Math.pow(bias + alpha * sum, -beta);\n            result[offset] = val;\n        }\n        return tf.tensor4d(result, x.shape);\n    }\n    LRNGrad(dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {\n        assertNotComplex(dy, 'LRNGrad');\n        const channels = dy.shape[3];\n        const dyValues = this.readSync(dy.dataId);\n        const inputImageValues = this.readSync(inputImage.dataId);\n        const outputImageValues = this.readSync(outputImage.dataId);\n        const result = new Float32Array(dy.size);\n        const size = dy.size;\n        for (let offset = 0; offset < size; offset++) {\n            const currentChannel = offset % channels;\n            const depthBegin = (offset - currentChannel) + Math.max(0, currentChannel - depthRadius);\n            const depthEnd = (offset - currentChannel) +\n                Math.min(channels, currentChannel + depthRadius + 1);\n            let norm = 0;\n            for (let k = depthBegin; k < depthEnd; k++) {\n                norm += Math.pow(inputImageValues[k], 2);\n            }\n            norm = alpha * norm + bias;\n            for (let k = depthBegin; k < depthEnd; k++) {\n                let dyi = -2 * alpha * beta * inputImageValues[k] *\n                    outputImageValues[offset] / norm;\n                if (offset === k) {\n                    dyi += Math.pow(norm, -beta);\n                }\n                dyi *= dyValues[offset];\n                result[k] += dyi;\n            }\n        }\n        return tf.tensor4d(result, dy.shape);\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        assertNotComplex(logits, 'multinomial');\n        const probabilities = normalized ? logits : tf.softmax(logits);\n        const batchSize = probabilities.shape[0];\n        const numEvents = probabilities.shape[1];\n        const res = tf.zeros([batchSize, numSamples], 'int32');\n        const resVals = this.readSync(res.dataId);\n        const probVals = this.readSync(probabilities.dataId);\n        for (let b = 0; b < batchSize; ++b) {\n            const offset = b * numEvents;\n            // The cdf won't include the last event. It will be implicit if no other\n            // event happened.\n            const cdf = new Float32Array(numEvents - 1);\n            cdf[0] = probVals[offset];\n            for (let event = 1; event < cdf.length; ++event) {\n                cdf[event] = cdf[event - 1] + probVals[offset + event];\n            }\n            const random = seedrandom.alea(seed.toString());\n            const outOffset = b * numSamples;\n            for (let sampleId = 0; sampleId < numSamples; ++sampleId) {\n                const r = random();\n                // Assume last event happened by default.\n                resVals[outOffset + sampleId] = cdf.length;\n                for (let event = 0; event < cdf.length; event++) {\n                    if (r < cdf[event]) {\n                        resVals[outOffset + sampleId] = event;\n                        break;\n                    }\n                }\n            }\n        }\n        return res;\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        assertNotComplex(indices, 'oneHot');\n        const res = new Float32Array(indices.size * depth);\n        res.fill(offValue);\n        const indicesVal = this.readSync(indices.dataId);\n        for (let event = 0; event < indices.size; ++event) {\n            if (indicesVal[event] >= 0 && indicesVal[event] < depth) {\n                res[event * depth + indicesVal[event]] = onValue;\n            }\n        }\n        return tf.tensor2d(res, [indices.size, depth], 'int32');\n    }\n    nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n        assertNotComplex(boxes, 'nonMaxSuppression');\n        const boxesVals = this.readSync(boxes.dataId);\n        const scoresVals = this.readSync(scores.dataId);\n        return nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        util.assert(dataFormat === 'NHWC', () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`);\n        util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n        const batchSize = x.shape[0];\n        const inputHeight = x.shape[1];\n        const inputWidth = x.shape[2];\n        const inputDepth = x.shape[3];\n        const outputHeight = inputHeight * blockSize;\n        const outputWidth = inputWidth * blockSize;\n        const outputDepth = inputDepth / (blockSize * blockSize);\n        const xValues = this.readSync(x.dataId);\n        const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);\n        let outputIdx = 0;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let h = 0; h < outputHeight; ++h) {\n                const inH = Math.floor(h / blockSize);\n                const offsetH = (h % blockSize);\n                for (let w = 0; w < outputWidth; ++w) {\n                    const inW = Math.floor(w / blockSize);\n                    const offsetW = (w % blockSize);\n                    const offsetD = (offsetH * blockSize + offsetW) * outputDepth;\n                    for (let d = 0; d < outputDepth; ++d) {\n                        const inD = d + offsetD;\n                        const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));\n                        result[outputIdx++] = xValues[inputIdx];\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(result, [batchSize, outputHeight, outputWidth, outputDepth]);\n    }\n    broadcastedBinaryOp(a, b, dtype, op) {\n        const newShape = backend_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const result = tf.buffer(newShape, dtype);\n        const aVals = this.readSync(a.dataId);\n        const bVals = this.readSync(b.dataId);\n        const aBroadcastDims = backend_util.getBroadcastDims(a.shape, newShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(b.shape, newShape);\n        const resVals = result.values;\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < resVals.length; ++i) {\n                resVals[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n            }\n        }\n        else {\n            const aBuf = this.bufferSync(a);\n            const bBuf = this.bufferSync(b);\n            for (let i = 0; i < resVals.length; ++i) {\n                const loc = result.indexToLoc(i);\n                const aLoc = loc.slice(-a.rank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = aBuf.locToIndex(aLoc);\n                const bLoc = loc.slice(-b.rank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = bBuf.locToIndex(bLoc);\n                resVals[i] = op(aVals[aIndex], bVals[bIndex]);\n            }\n        }\n        return result.toTensor();\n    }\n    split(x, sizeSplits, axis) {\n        return split(x, sizeSplits, axis);\n    }\n    dispose() { }\n    floatPrecision() {\n        return 32;\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return super.epsilon();\n    }\n    cropAndResize(images, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        const [batch, imageHeight, imageWidth, numChannels] = images.shape;\n        const numBoxes = boxes.shape[0];\n        const [cropHeight, cropWidth] = cropSize;\n        const output = tf.buffer([numBoxes, cropHeight, cropWidth, numChannels], 'float32');\n        const boxVals = this.readSync(boxes.dataId);\n        const boxIndVals = this.readSync(boxIndex.dataId);\n        const imageVals = this.readSync(images.dataId);\n        const inStride = images.strides; // to calculate flat indexes into image\n        const outStride = output.strides; // to calculate flat indexes into output\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc\n        for (let b = 0; b < numBoxes; b++) {\n            const startInd = b * 4;\n            const y1 = boxVals[startInd];\n            const x1 = boxVals[startInd + 1];\n            const y2 = boxVals[startInd + 2];\n            const x2 = boxVals[startInd + 3];\n            const bInd = boxIndVals[b];\n            if (bInd >= batch) {\n                continue;\n            }\n            const heightScale = (cropHeight > 1) ?\n                (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) :\n                0;\n            const widthScale = (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;\n            for (let y = 0; y < cropHeight; y++) {\n                const yInd = (cropHeight > 1) ?\n                    y1 * (imageHeight - 1) + y * (heightScale) :\n                    0.5 * (y1 + y2) * (imageHeight - 1);\n                if (yInd < 0 || yInd > imageHeight - 1) {\n                    for (let x = 0; x < cropWidth; x++) {\n                        for (let c = 0; c < numChannels; c++) {\n                            const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[ind] = extrapolationValue;\n                        }\n                    }\n                    continue;\n                }\n                if (method === 'bilinear') {\n                    const topInd = Math.floor(yInd);\n                    const bottomInd = Math.ceil(yInd);\n                    const yLerp = yInd - topInd;\n                    for (let x = 0; x < cropWidth; x++) {\n                        const xInd = (cropWidth > 1) ?\n                            x1 * (imageWidth - 1) + x * widthScale :\n                            0.5 * (x1 + x2) * (imageWidth - 1);\n                        if (xInd < 0 || xInd > imageWidth - 1) {\n                            for (let c = 0; c < numChannels; c++) {\n                                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                                output.values[ind] = extrapolationValue;\n                            }\n                            continue;\n                        }\n                        const leftInd = Math.floor(xInd);\n                        const rightInd = Math.ceil(xInd);\n                        const xLerp = xInd - leftInd;\n                        for (let c = 0; c < numChannels; c++) {\n                            let ind = c + leftInd * inStride[2] + topInd * inStride[1] +\n                                bInd * inStride[0];\n                            const topLeft = imageVals[ind];\n                            ind = c + rightInd * inStride[2] + topInd * inStride[1] +\n                                bInd * inStride[0];\n                            const topRight = imageVals[ind];\n                            ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +\n                                bInd * inStride[0];\n                            const bottomLeft = imageVals[ind];\n                            ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +\n                                bInd * inStride[0];\n                            const bottomRight = imageVals[ind];\n                            const top = topLeft + (topRight - topLeft) * xLerp;\n                            const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;\n                            ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[ind] = top + ((bottom - top) * yLerp);\n                        }\n                    }\n                }\n                else { // method == \"nearest\"\n                    for (let x = 0; x < cropWidth; ++x) {\n                        const xInd = (cropWidth > 1) ?\n                            x1 * (imageWidth - 1) + x * widthScale :\n                            0.5 * (x1 + x2) * (imageWidth - 1);\n                        if (xInd < 0 || xInd > imageWidth - 1) {\n                            for (let c = 0; c < numChannels; c++) {\n                                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                                output.values[ind] = extrapolationValue;\n                            }\n                            continue;\n                        }\n                        const closestX = Math.round(xInd);\n                        const closestY = Math.round(yInd);\n                        for (let c = 0; c < numChannels; c++) {\n                            const inInd = c + closestX * inStride[2] +\n                                closestY * inStride[1] + bInd * inStride[0];\n                            const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[outInd] = imageVals[inInd];\n                        }\n                    }\n                }\n            }\n        }\n        return output.toTensor();\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n        const sumDupeIndices = false;\n        return this.scatter(sparseIndices, sparseValues, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n    }\n    gatherND(x, indices) {\n        const indicesShape = indices.shape;\n        const sliceRank = indicesShape[indicesShape.length - 1];\n        const [resultShape, numSlices, sliceSize, strides] = backend_util.prepareAndValidate(x, indices);\n        if (numSlices === 0) {\n            return tf.tensor([], resultShape, x.dtype);\n        }\n        const buffer = new TensorBuffer([numSlices, sliceSize], x.dtype);\n        const indicesData = this.readSync(indices.dataId);\n        const xData = this.readSync(x.dataId);\n        for (let i = 0; i < numSlices; i++) {\n            const index = [];\n            let flattenIndex = 0;\n            for (let j = 0; j < sliceRank; j++) {\n                const dim = indicesData[i * sliceRank + j];\n                flattenIndex += dim * strides[j];\n                index.push(dim);\n            }\n            if (flattenIndex < 0 || flattenIndex >= x.size / sliceSize) {\n                throw new Error(`Invalid indices: ${index} does not index into ${x.shape}`);\n            }\n            for (let k = 0; k < sliceSize; k++) {\n                buffer.values[i * sliceSize + k] = xData[flattenIndex * sliceSize + k];\n            }\n        }\n        return buffer.toTensor().reshape(resultShape);\n    }\n    scatterND(indices, updates, shape) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(updates, indices, shape);\n        const defaultValue = tf.scalar(0);\n        const sumDupeIndices = true;\n        return this.scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n    }\n    fill(shape, value, dtype) {\n        dtype = dtype || util.inferDtype(value);\n        const values = util.getArrayFromDType(dtype, util.sizeFromShape(shape));\n        values.fill(value);\n        return engine().makeTensor(values, shape, dtype, this);\n    }\n    onesLike(x) {\n        if (x.dtype === 'string') {\n            throw new Error('onesLike is not supported for string tensors');\n        }\n        else {\n            return this.fill(x.shape, 1, x.dtype);\n        }\n    }\n    zerosLike(x) {\n        const values = util.getArrayFromDType(x.dtype, util.sizeFromShape(x.shape));\n        return this.makeOutput(values, x.shape, x.dtype);\n    }\n    linspace(start, stop, num) {\n        return backend_util.linspaceImpl(start, stop, num);\n    }\n    scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {\n        const flattenShape = [outputSize / sliceSize, sliceSize];\n        const indicesData = this.readSync(indices.dataId);\n        const updatesData = this.readSync(updates.dataId);\n        if (outputSize === 0) {\n            return tf.tensor([], shape, updates.dtype);\n        }\n        const buffer = new TensorBuffer(flattenShape, updates.dtype);\n        buffer.values.fill(this.readSync(defaultValue.dataId)[0]);\n        for (let i = 0; i < numUpdates; i++) {\n            const index = [];\n            let flattenIndex = 0;\n            for (let j = 0; j < sliceRank; j++) {\n                const dim = indicesData[i * sliceRank + j];\n                index.push(dim);\n                flattenIndex += dim * strides[j];\n            }\n            if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {\n                throw new Error(`Invalid indices: ${index} does not index into ${shape}`);\n            }\n            for (let k = 0; k < sliceSize; k++) {\n                if (sumDupeIndices) {\n                    buffer.values[flattenIndex * sliceSize + k] +=\n                        updatesData[i * sliceSize + k];\n                }\n                else {\n                    buffer.values[flattenIndex * sliceSize + k] = updates.rank === 0 ?\n                        updatesData[0] :\n                        updatesData[i * sliceSize + k];\n                }\n            }\n        }\n        return buffer.toTensor().reshape(shape);\n    }\n}\n//# sourceMappingURL=backend_cpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs, util } from '@tensorflow/tfjs-core';\nexport function simpleAbsImpl(vals) {\n    const resultValues = new Float32Array(vals.length);\n    for (let i = 0; i < vals.length; ++i) {\n        resultValues[i] = Math.abs(vals[i]);\n    }\n    return resultValues;\n}\nexport const absKernelFunc = (args) => {\n    const { x } = args.inputs;\n    const cpuBackend = args.backend;\n    let resultValues = new Float32Array(util.sizeFromShape(x.shape));\n    if (x.dtype !== 'complex64') {\n        const values = cpuBackend.data.get(x.dataId).values;\n        resultValues = simpleAbsImpl(values);\n    }\n    else {\n        const complexVals = cpuBackend.data.get(x.dataId);\n        const real = complexVals.complexTensorInfos.real;\n        const imag = complexVals.complexTensorInfos.imag;\n        const realVals = cpuBackend.data.get(real.dataId).values;\n        const imagVals = cpuBackend.data.get(imag.dataId).values;\n        for (let i = 0; i < realVals.length; i++) {\n            const real = realVals[i];\n            const imag = imagVals[i];\n            resultValues[i] = Math.hypot(real, imag);\n        }\n    }\n    return cpuBackend.makeOutput(resultValues, x.shape, 'float32');\n};\nexport const absConfig = {\n    kernelName: Abs,\n    backendName: 'cpu',\n    kernelFunc: absKernelFunc,\n};\n//# sourceMappingURL=Abs.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for binary ops. Supports broadcast.\n */\nexport function createSimpleBinaryKernelImpl(op) {\n    return (aShape, bShape, aVals, bVals, dtype) => {\n        const newShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const resultRank = newShape.length;\n        const resultStrides = util.computeStrides(newShape);\n        const resultSize = util.sizeFromShape(newShape);\n        const result = util.getTypedArrayFromDType(dtype, resultSize);\n        const aRank = aShape.length;\n        const bRank = bShape.length;\n        const aStrides = util.computeStrides(aShape);\n        const bStrides = util.computeStrides(bShape);\n        const aBroadcastDims = backend_util.getBroadcastDims(aShape, newShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(bShape, newShape);\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < result.length; ++i) {\n                result[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n            }\n        }\n        else {\n            for (let i = 0; i < result.length; ++i) {\n                const loc = util.indexToLoc(i, resultRank, resultStrides);\n                const aLoc = loc.slice(-aRank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n                const bLoc = loc.slice(-bRank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n                result[i] = op(aVals[aIndex], bVals[bIndex]);\n            }\n        }\n        return [result, newShape];\n    };\n}\n//# sourceMappingURL=binary_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Complex } from '@tensorflow/tfjs-core';\nexport function complex(args) {\n    const { inputs, backend } = args;\n    const { real, imag } = inputs;\n    const realVals = backend.data.get(real.dataId).values;\n    const imagVals = backend.data.get(imag.dataId).values;\n    const complexInfo = backend.makeTensorInfo(real.shape, 'complex64');\n    const complex = backend.data.get(complexInfo.dataId);\n    // The complex tensor owns the underlying real and imag tensorInfos, only the\n    // complex tensor tracks refCount, when complexData is disposed the\n    // underlying tensorData will be disposed.\n    complex.complexTensorInfos = {\n        real: backend.makeTensorInfo(real.shape, 'float32', realVals),\n        imag: backend.makeTensorInfo(imag.shape, 'float32', imagVals)\n    };\n    return complexInfo;\n}\nexport const complexConfig = {\n    kernelName: Complex,\n    backendName: 'cpu',\n    kernelFunc: complex\n};\n//# sourceMappingURL=Complex.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '@tensorflow/tfjs-core';\nexport function identity(args) {\n    const { inputs, backend } = args;\n    const { x } = inputs;\n    backend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nexport const identityConfig = {\n    kernelName: Identity,\n    backendName: 'cpu',\n    kernelFunc: identity\n};\n//# sourceMappingURL=Identity.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Real } from '@tensorflow/tfjs-core';\nexport function real(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const real = backend.data.get(input.dataId).complexTensorInfos.real;\n    const realVal = backend.data.get(real.dataId).values;\n    // When complex tensor is disposed, its underlying parts will be disposed too.\n    // Make new tensor out of the real value of the complex. This makes sure the\n    // value is still accessible even if complex tensor is disposed.\n    return backend.makeTensorInfo(real.shape, real.dtype, realVal);\n}\nexport const realConfig = {\n    kernelName: Real,\n    backendName: 'cpu',\n    kernelFunc: real\n};\n//# sourceMappingURL=Real.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { Cast, util } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { complex } from './Complex';\nimport { identity } from './Identity';\nimport { real } from './Real';\nexport function cast(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { dtype } = attrs;\n    // Casting to complex64.\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return identity({ inputs: { x }, backend });\n        }\n        // TODO(lina128): Import kernel function once zeros is modularized.\n        const zerosTensor = tf.zeros(x.shape);\n        const floatX = cast({ inputs: { x }, backend, attrs: { dtype: 'float32' } });\n        const result = complex({ inputs: { real: floatX, imag: zerosTensor }, backend });\n        zerosTensor.dispose();\n        backend.disposeIntermediateTensorInfo(floatX);\n        return result;\n    }\n    // Casting from complex64\n    if (x.dtype === 'complex64') {\n        const realPart = real({ inputs: { input: x }, backend });\n        const result = cast({ inputs: { x: realPart }, backend, attrs: { dtype } });\n        backend.disposeIntermediateTensorInfo(realPart);\n        return result;\n    }\n    if (!util.hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        const result = identity({ inputs: { x }, backend });\n        return { dataId: result.dataId, shape: result.shape, dtype };\n    }\n    if (dtype === 'int32') {\n        const values = backend.data.get(x.dataId).values;\n        const resultValues = Int32Array.from(values);\n        return backend.makeTensorInfo(x.shape, 'int32', resultValues);\n    }\n    if (dtype === 'bool') {\n        // This is essentially the result of notEqual(x, 0). We avoid using\n        // kernel notEqual to avoid circular dependency, i.e. binary_utils ->\n        // cast -> notEqual -> binary_utils.\n        const xVals = backend.data.get(x.dataId).values;\n        const zero = util.toTypedArray([0], x.dtype);\n        const [resultData, resultShape] = createSimpleBinaryKernelImpl((a, b) => (a !== b) ? 1 : 0)(x.shape, [], xVals, zero, 'bool');\n        return backend.makeTensorInfo(resultShape, 'bool', resultData);\n    }\n    throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nexport const castConfig = {\n    kernelName: Cast,\n    backendName: 'cpu',\n    kernelFunc: cast\n};\n//# sourceMappingURL=Cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { cast } from '../kernels/Cast';\nimport { complex } from '../kernels/Complex';\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param name Kernel name.\n * @param binaryKernelImpl A `SimpleBinaryKernelImpl` for the kernel.\n * @param binaryKernelComplexImpl Optional. If exists, represents a\n *     `ComplexBinaryKernelImpl` for the kernel, will be used when input dtype\n *     is `complex64`.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc(name, simpleImpl, complexImpl, dtype) {\n    if (complexImpl == null) {\n        return ({ inputs, backend }) => {\n            const { a, b } = inputs;\n            const cpuBackend = backend;\n            assertNotComplex([a, b], name);\n            const aVals = cpuBackend.data.get(a.dataId).values;\n            const bVals = cpuBackend.data.get(b.dataId).values;\n            const $dtype = dtype || a.dtype;\n            const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n            return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n        };\n    }\n    return ({ inputs, backend }) => {\n        const { a, b } = inputs;\n        const cpuBackend = backend;\n        if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n            const $aComplex = cast({ inputs: { x: a }, backend: cpuBackend, attrs: { dtype: 'complex64' } });\n            const $aComplexVals = cpuBackend.data.get($aComplex.dataId);\n            const aReal = $aComplexVals.complexTensorInfos.real;\n            const aImag = $aComplexVals.complexTensorInfos.imag;\n            const aRealVals = cpuBackend.data.get(aReal.dataId).values;\n            const aImagVals = cpuBackend.data.get(aImag.dataId).values;\n            const $bComplex = cast({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: 'complex64' } });\n            const $bComplexVals = cpuBackend.data.get($bComplex.dataId);\n            const bReal = $bComplexVals.complexTensorInfos.real;\n            const bImag = $bComplexVals.complexTensorInfos.imag;\n            const bRealVals = cpuBackend.data.get(bReal.dataId).values;\n            const bImagVals = cpuBackend.data.get(bImag.dataId).values;\n            const [resultRealData, resultImagData, resultShape] = complexImpl(a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);\n            const resultReal = cpuBackend.makeTensorInfo(resultShape, 'float32', resultRealData);\n            const resultImag = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImagData);\n            const result = complex({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend });\n            cpuBackend.disposeIntermediateTensorInfo($aComplex);\n            cpuBackend.disposeIntermediateTensorInfo($bComplex);\n            cpuBackend.disposeIntermediateTensorInfo(resultReal);\n            cpuBackend.disposeIntermediateTensorInfo(resultImag);\n            return result;\n        }\n        else {\n            const aVals = cpuBackend.data.get(a.dataId).values;\n            const bVals = cpuBackend.data.get(b.dataId).values;\n            const $dtype = dtype || a.dtype;\n            const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n            return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n        }\n    };\n}\n/**\n * Template that creates the complex type implementation for binary ops.\n * Supports broadcast.\n */\nexport function createComplexBinaryKernelImpl(op) {\n    return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) => {\n        const resultShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const resultSize = util.sizeFromShape(resultShape);\n        const resultRank = resultShape.length;\n        const resultStrides = util.computeStrides(resultShape);\n        const resultRealVals = util.getTypedArrayFromDType('float32', resultSize);\n        const resultImagVals = util.getTypedArrayFromDType('float32', resultSize);\n        const aBroadcastDims = backend_util.getBroadcastDims(aShape, resultShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(bShape, resultShape);\n        const aVals = backend_util.mergeRealAndImagArrays(aRealVals, aImagVals);\n        const bVals = backend_util.mergeRealAndImagArrays(bRealVals, bImagVals);\n        const aRank = aShape.length;\n        const aStrides = util.computeStrides(aShape);\n        const bRank = bShape.length;\n        const bStrides = util.computeStrides(bShape);\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < resultRealVals.length; i++) {\n                const aIdx = i % aVals.length;\n                const bIdx = i % bVals.length;\n                const result = op(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);\n                resultRealVals[i] = result.real;\n                resultImagVals[i] = result.imag;\n            }\n        }\n        else {\n            for (let i = 0; i < resultRealVals.length; i++) {\n                const loc = util.indexToLoc(i, resultRank, resultStrides);\n                const aLoc = loc.slice(-aRank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n                const bLoc = loc.slice(-bRank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n                const opResult = op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);\n                resultRealVals[i] = opResult.real;\n                resultImagVals[i] = opResult.imag;\n            }\n        }\n        return [resultRealVals, resultImagVals, resultShape];\n    };\n}\n//# sourceMappingURL=kernel_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const addImpl = createSimpleBinaryKernelImpl(((a, b) => a + b));\nexport const addComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal + bReal, imag: aImag + bImag };\n}));\nexport const add = binaryKernelFunc(Add, addImpl, addComplexImpl);\nexport const addConfig = {\n    kernelName: Add,\n    backendName: 'cpu',\n    kernelFunc: add\n};\n//# sourceMappingURL=Add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for unary op.\n */\nexport function createSimpleUnaryImpl(op) {\n    return (values, dtype, attrs) => {\n        const newValues = util.getTypedArrayFromDType(dtype, values.length);\n        for (let i = 0; i < values.length; ++i) {\n            newValues[i] = op(values[i], attrs);\n        }\n        return newValues;\n    };\n}\n//# sourceMappingURL=unary_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param name Kernel name.\n * @param op A `SimpleUnaryOperation` for the kernel.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the input. This is mainly used in certain\n *     kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFunc(name, op, dtype) {\n    return ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        assertNotComplex(x, name);\n        if (x.dtype === 'string' || dtype === 'string') {\n            throw new Error('unaryKernelFunc does not support string input/output');\n        }\n        const cpuBackend = backend;\n        const values = cpuBackend.data.get(x.dataId).values;\n        const xSize = util.sizeFromShape(x.shape);\n        const $dtype = dtype || x.dtype;\n        const newValues = util.getArrayFromDType($dtype, xSize);\n        for (let i = 0; i < xSize; ++i) {\n            newValues[i] = op(values[i], attrs);\n        }\n        return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n    };\n}\n/**\n * Template that creates a `KernelFunc` for unary ops from the given\n * `SimpleUnaryImpl`..\n * @param name Kernel name.\n * @param unaryImpl A `SimpleUnaryImpl` that implements the op.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the input. This is mainly used in certain\n *     kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFuncFromImpl(name, unaryImpl, dtype) {\n    return ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        assertNotComplex(x, name);\n        if (x.dtype === 'string' || dtype === 'string') {\n            throw new Error('unaryKernelFunc does not support string input/output');\n        }\n        const cpuBackend = backend;\n        const values = cpuBackend.data.get(x.dataId).values;\n        const $dtype = dtype || x.dtype;\n        const newValues = unaryImpl(values, $dtype, attrs);\n        return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n    };\n}\n//# sourceMappingURL=unary_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));\nexport const ceilKernelFunc = unaryKernelFuncFromImpl(Ceil, ceilImpl);\nexport const ceilConfig = {\n    kernelName: Ceil,\n    backendName: 'cpu',\n    kernelFunc: ceilKernelFunc,\n};\n//# sourceMappingURL=Ceil.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));\nexport const expKernelFunc = unaryKernelFuncFromImpl(Exp, expImpl);\nexport const expConfig = {\n    kernelName: Exp,\n    backendName: 'cpu',\n    kernelFunc: expKernelFunc,\n};\n//# sourceMappingURL=Exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));\nexport const expm1KernelFunc = unaryKernelFuncFromImpl(Expm1, expm1Impl);\nexport const expm1Config = {\n    kernelName: Expm1,\n    backendName: 'cpu',\n    kernelFunc: expm1KernelFunc,\n};\n//# sourceMappingURL=Expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));\nexport const floorKernelFunc = unaryKernelFuncFromImpl(Floor, floorImpl);\nexport const floorConfig = {\n    kernelName: Floor,\n    backendName: 'cpu',\n    kernelFunc: floorKernelFunc,\n};\n//# sourceMappingURL=Floor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));\nexport const logKernelFunc = unaryKernelFuncFromImpl(Log, logImpl);\nexport const logConfig = {\n    kernelName: Log,\n    backendName: 'cpu',\n    kernelFunc: logKernelFunc,\n};\n//# sourceMappingURL=Log.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function maxImpl(aVals, reduceSize, outShape, dtype) {\n    const vals = util.getTypedArrayFromDType(dtype, util.sizeFromShape(outShape));\n    for (let i = 0; i < vals.length; ++i) {\n        const offset = i * reduceSize;\n        let max = aVals[offset];\n        for (let j = 0; j < reduceSize; ++j) {\n            const value = aVals[offset + j];\n            if (value > max) {\n                max = value;\n            }\n        }\n        vals[i] = max;\n    }\n    return vals;\n}\n//# sourceMappingURL=Max_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const multiplyImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue * bValue));\nexport const multiplyComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return {\n        real: aReal * bReal - aImag * bImag,\n        imag: aReal * bImag + aImag * bReal\n    };\n}));\nexport const multiply = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);\nexport const multiplyConfig = {\n    kernelName: Multiply,\n    backendName: 'cpu',\n    kernelFunc: multiply\n};\n//# sourceMappingURL=Multiply.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));\nexport const rsqrtKernelFunc = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);\nexport const rsqrtConfig = {\n    kernelName: Rsqrt,\n    backendName: 'cpu',\n    kernelFunc: rsqrtKernelFunc,\n};\n//# sourceMappingURL=Rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Slice, slice_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function sliceImpl(vals, begin, size, shape, dtype) {\n    const isContinous = slice_util.isSliceContinous(shape, begin, size);\n    const length = util.sizeFromShape(size);\n    const xStrides = util.computeStrides(shape);\n    if (isContinous) {\n        const flatOffset = slice_util.computeFlatOffset(begin, xStrides);\n        return vals.subarray(flatOffset, flatOffset + length);\n    }\n    const outVals = util.getTypedArrayFromDType(dtype, length);\n    for (let i = 0; i < length; ++i) {\n        const rank = size.length;\n        const strides = util.computeStrides(size);\n        const loc = util.indexToLoc(i, rank, strides);\n        const xLoc = loc.map((idx, j) => idx + begin[j]);\n        const xIndex = util.locToIndex(xLoc, shape.length, xStrides);\n        outVals[i] = vals[xIndex];\n    }\n    return outVals;\n}\nexport function slice(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { begin, size } = attrs;\n    assertNotComplex(x, 'slice');\n    const [$begin, $size] = slice_util.parseSliceParams(x, begin, size);\n    slice_util.assertParamsValid(x, $begin, $size);\n    const vals = backend.data.get(x.dataId).values;\n    const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);\n    return backend.makeTensorInfo($size, x.dtype, outVals);\n}\nexport const sliceConfig = {\n    kernelName: Slice,\n    backendName: 'cpu',\n    kernelFunc: slice\n};\n//# sourceMappingURL=Slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const subImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue - bValue));\nexport const subComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal - bReal, imag: aImag - bImag };\n}));\nexport const sub = binaryKernelFunc(Sub, subImpl, subComplexImpl);\nexport const subConfig = {\n    kernelName: Sub,\n    backendName: 'cpu',\n    kernelFunc: sub\n};\n//# sourceMappingURL=Sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function transposeImpl(xVals, xShape, dtype, perm, newShape) {\n    const xRank = xShape.length;\n    const xSize = util.sizeFromShape(xShape);\n    const xStrides = util.computeStrides(xShape);\n    const newStrides = util.computeStrides(newShape);\n    const result = util.getTypedArrayFromDType(dtype, util.sizeFromShape(newShape));\n    for (let i = 0; i < xSize; ++i) {\n        const loc = util.indexToLoc(i, xRank, xStrides);\n        // Permute location.\n        const newLoc = new Array(loc.length);\n        for (let i = 0; i < newLoc.length; i++) {\n            newLoc[i] = loc[perm[i]];\n        }\n        const newIndex = util.locToIndex(newLoc, xRank, newStrides);\n        result[newIndex] = xVals[i];\n    }\n    return result;\n}\n//# sourceMappingURL=Transpose_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer, util } from '@tensorflow/tfjs-core';\nexport function uniqueImpl(values, axis, shape, dtype) {\n    // Normalize and validate axis.\n    const $axis = util.parseAxisParam(axis, shape)[0];\n    // Calculate the new shape that is suitable for extracting data along the\n    // given axis.\n    //\n    // The rank is 3.\n    // The size of the 1st dimension is the size of all the axes < the given axis.\n    // The size of the 2nd dimension is the same as the size of the given axis.\n    // The size of the 3rd dimension is the size of all the axes > the given axis.\n    //\n    // For example, for a 4D tensor with shape=[2, 3, 5, 4] and axis=2, the\n    // newShape would be: [2*3, 5, 4].\n    //\n    // Note that this is not the final output shape. This will be the shape for an\n    // intermediate TensorBuffer (see inputBuffer below) to allow us to extract\n    // values along the given axis. To demonstrate how it works, consider the\n    // following example:\n    //\n    // Input: a 3D tensor, with shape [1, 2, 3]\n    // [\n    //   [\n    //      [1,2,3],\n    //      [4,5,6]\n    //   ]\n    // ]\n    // Axis: 2 (the last axis).\n    // Along axis 2, we expect to extract 3 tensors: [1,4], [2,5], [3,6].\n    //\n    // For this example, newShape would be: [2, 3, 1], where 2 is calculated from\n    // 1*2. The re-shaped data would look like:\n    //\n    // [\n    //   [\n    //     [1], [2], [3]\n    //   ],\n    //   [\n    //     [4], [5], [6]\n    //   ]\n    // ]\n    //\n    // Then, we can construct a 3-level nested loop by the following dimension\n    // order to extract the values along the axis (dimension1):\n    // i: dimension1       // 0,1,2 (newShape[1])\n    //   m: dimension0     // 0,1   (newShape[0])\n    //     n: dimension2   // 0     (newShape[2])\n    //\n    //                       m, i, n\n    //                      ---------\n    // Iteration 0: data at [0, 0, 0] => \"1\"\n    // Iteration 1: data at [1, 0, 0] => \"4\"\n    // We got [1,4].\n    // Iteration 2: data at [0, 1, 0] => \"2\"\n    // Iteration 3: data at [1, 1, 0] => \"5\"\n    // We got [2,5].\n    // Iteration 4: data at [0, 2, 0] => \"3\"\n    // Iteration 5: data at [1, 2, 0] => \"6\"\n    // We got [3,6].\n    const newShape = [1, shape[0], 1];\n    for (let i = 0; i < $axis; i++) {\n        newShape[0] *= shape[i];\n    }\n    newShape[1] = shape[$axis];\n    for (let i = $axis + 1; i < shape.length; i++) {\n        newShape[2] *= shape[i];\n    }\n    // A map from unique elements (their string representations) to their values\n    // in \"indices\" (below).\n    const uniqueElements = {};\n    // The indices of each unique element in the original tensor along the given\n    // axis. It is 1D and has the same size as the given axis.\n    const indices = new Int32Array(shape[$axis]);\n    // Create a buffer so we can easily extract value at a given location.\n    const inputBuffer = new TensorBuffer(newShape, dtype, values);\n    // The indices along the given axis that have unique elements. This is a\n    // de-duped version of \"indices\" above.\n    const uniqueIndices = [];\n    const is1DTensor = newShape[0] === 1 && newShape[2] === 1;\n    for (let i = 0; i < shape[$axis]; i++) {\n        // Extract values along the axis.\n        let element;\n        if (is1DTensor) {\n            // Fast path for 1D tensor input.\n            element = values[i].toString();\n        }\n        else {\n            const axisValues = [];\n            for (let m = 0; m < newShape[0]; m++) {\n                for (let n = 0; n < newShape[2]; n++) {\n                    axisValues.push(inputBuffer.get(m, i, n));\n                }\n            }\n            element = axisValues.join(',');\n        }\n        // Dedup and update various indices.\n        if (uniqueElements[element] !== undefined) {\n            indices[i] = uniqueElements[element];\n        }\n        else {\n            const uniqueIndex = Object.keys(uniqueElements).length;\n            uniqueElements[element] = uniqueIndex;\n            indices[i] = uniqueIndex;\n            uniqueIndices.push(i);\n        }\n    }\n    // Now we know where each of the unique elements are located along the axis\n    // (uniqueIndices). Extract them from input buffer and store them in the\n    // output buffer.\n    const outputTmpShape = newShape.slice();\n    outputTmpShape[1] = Object.keys(uniqueElements).length;\n    const outputBuffer = new TensorBuffer(outputTmpShape, dtype);\n    uniqueIndices.forEach((uniqueElementIndex, i) => {\n        for (let m = 0; m < newShape[0]; m++) {\n            for (let n = 0; n < newShape[2]; n++) {\n                outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);\n            }\n        }\n    });\n    // The output shape can be calculated from the input shape with the size of\n    // the given axis replaced by the number of unique elements along that axis.\n    const outputShape = shape.slice();\n    outputShape[$axis] = outputTmpShape[1];\n    return {\n        outputValues: outputBuffer.values,\n        outputShape,\n        indices,\n    };\n}\n//# sourceMappingURL=Unique_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Shared kernel impls for use in other backends.\nexport { simpleAbsImpl } from './kernels/Abs';\nexport { addImpl } from './kernels/Add';\nexport { ceilImpl } from './kernels/Ceil';\nexport { expImpl } from './kernels/Exp';\nexport { expm1Impl } from './kernels/Expm1';\nexport { floorImpl } from './kernels/Floor';\nexport { logImpl } from './kernels/Log';\nexport { maxImpl } from './kernels/Max_impl';\nexport { multiplyImpl } from './kernels/Multiply';\nexport { rsqrtImpl } from './kernels/Rsqrt';\nexport { sliceImpl } from './kernels/Slice';\nexport { subImpl } from './kernels/Sub';\nexport { transposeImpl } from './kernels/Transpose_impl';\nexport { uniqueImpl } from './kernels/Unique_impl';\n//# sourceMappingURL=shared.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/*\n * base.ts contains all the exports from tfjs-backend-cpu\n * without auto-kernel registration\n */\nimport { registerBackend } from '@tensorflow/tfjs-core';\nimport { MathBackendCPU } from './backend_cpu';\nimport * as shared from './shared';\nexport { MathBackendCPU } from './backend_cpu';\nexport { version as version_cpu } from './version';\nexport { shared };\n// Side effects for default initialization of MathBackendCPU\nregisterBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acos } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const acosKernelFunc = unaryKernelFunc(Acos, (xi) => Math.acos(xi));\nexport const acosConfig = {\n    kernelName: Acos,\n    backendName: 'cpu',\n    kernelFunc: acosKernelFunc,\n};\n//# sourceMappingURL=Acos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acosh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const acoshKernelFunc = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi));\nexport const acoshConfig = {\n    kernelName: Acosh,\n    backendName: 'cpu',\n    kernelFunc: acoshKernelFunc,\n};\n//# sourceMappingURL=Acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asin } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const asinKernelFunc = unaryKernelFunc(Asin, (xi) => Math.asin(xi));\nexport const asinConfig = {\n    kernelName: Asin,\n    backendName: 'cpu',\n    kernelFunc: asinKernelFunc,\n};\n//# sourceMappingURL=Asin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asinh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const asinhKernelFunc = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi));\nexport const asinhConfig = {\n    kernelName: Asinh,\n    backendName: 'cpu',\n    kernelFunc: asinhKernelFunc,\n};\n//# sourceMappingURL=Asinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const atanKernelFunc = unaryKernelFunc(Atan, (xi) => Math.atan(xi));\nexport const atanConfig = {\n    kernelName: Atan,\n    backendName: 'cpu',\n    kernelFunc: atanKernelFunc,\n};\n//# sourceMappingURL=Atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atanh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const atanhKernelFunc = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi));\nexport const atanhConfig = {\n    kernelName: Atanh,\n    backendName: 'cpu',\n    kernelFunc: atanhKernelFunc,\n};\n//# sourceMappingURL=Atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from '@tensorflow/tfjs-core';\nexport function pool(xValues, xShape, dtype, strides, convInfo, poolType) {\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n        Number.POSITIVE_INFINITY);\n    const output = buffer(convInfo.outShape, dtype);\n    const outputVals = output.values;\n    const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];\n    const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];\n    const outputColStrides = convInfo.outShape[3];\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        const outputBatchOffset = b * outputBatchStrides;\n        const inputBatchOffset = b * strides[0];\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const xRCorner = yR * strideHeight - padTop;\n                const xRMin = Math.max(0, xRCorner);\n                const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n                const outputRowOffset = outputBatchOffset + yR * outputRowStrides;\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const xCCorner = yC * strideWidth - padLeft;\n                    const xCMin = Math.max(0, xCCorner);\n                    const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n                    let minMaxValue = initialValue;\n                    let avgValue = 0;\n                    let count = 0;\n                    for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n                        const xROffset = inputBatchOffset + xR * strides[1];\n                        for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                            const xCOffset = xROffset + xC * strides[2];\n                            const pixel = xValues[xCOffset + d];\n                            if ((poolType === 'max' && pixel > minMaxValue)) {\n                                minMaxValue = pixel;\n                            }\n                            else if (poolType === 'avg') {\n                                avgValue += pixel;\n                                count++;\n                            }\n                        }\n                        if (isNaN(minMaxValue)) {\n                            break;\n                        }\n                    }\n                    const outputOffset = outputRowOffset + yC * outputColStrides + d;\n                    outputVals[outputOffset] =\n                        poolType === 'avg' ? avgValue / count : minMaxValue;\n                }\n            }\n        }\n    }\n    return output;\n}\nexport function maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) {\n    const maxPositions = buffer(convInfo.outShape, 'int32');\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const xBuf = buffer(xShape, dtype, xValues);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const xRCorner = yR * strideHeight - padTop;\n                let xRMin = xRCorner;\n                while (xRMin < 0) {\n                    xRMin += dilationHeight;\n                }\n                // const xRMin = Math.max(0, xRCorner);\n                const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const xCCorner = yC * strideWidth - padLeft;\n                    let xCMin = xCCorner;\n                    while (xCMin < 0) {\n                        xCMin += dilationWidth;\n                    }\n                    const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n                    let maxValue = Number.NEGATIVE_INFINITY;\n                    let maxPosition = -1;\n                    for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n                        const wR = xR - xRCorner;\n                        for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                            const wC = xC - xCCorner;\n                            const pixel = xBuf.get(b, xR, xC, d);\n                            if (pixel > maxValue) {\n                                maxValue = pixel;\n                                if (flattenPositions) {\n                                    maxPosition = includeBatchInIndex ?\n                                        ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) *\n                                            convInfo.inChannels +\n                                            d :\n                                        (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;\n                                }\n                                else {\n                                    maxPosition = wR * effectiveFilterWidth + wC;\n                                }\n                            }\n                        }\n                    }\n                    maxPositions.set(maxPosition, b, yR, yC, d);\n                }\n            }\n        }\n    }\n    return maxPositions;\n}\n//# sourceMappingURL=pool_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool, backend_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { pool } from '../utils/pool_utils';\nimport { identity } from './Identity';\nexport function avgPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'avgPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    let res;\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        res = identity({ inputs: { x }, backend });\n    }\n    else {\n        const xValues = backend.data.get(x.dataId).values;\n        const strides = util.computeStrides(x.shape);\n        const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'avg');\n        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);\n    }\n    return res;\n}\nexport const avgPoolConfig = {\n    kernelName: AvgPool,\n    backendName: 'cpu',\n    kernelFunc: avgPool\n};\n//# sourceMappingURL=AvgPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPoolBackprop, backend_util, buffer } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function avgPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input } = inputs;\n    const x = input;\n    assertNotComplex([dy, input], 'avgPoolBackprop');\n    const { filterSize, strides, pad } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = buffer(x.shape, 'float32');\n    const avgMultiplier = 1 / (filterHeight * filterWidth);\n    const dyData = backend.data.get(dy.dataId).values;\n    const dyBuf = buffer(dy.shape, 'float32', dyData);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n                for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n                    // Shader code begins.\n                    const dyRCorner = dxR - padTop;\n                    const dyCCorner = dxC - padLeft;\n                    let dotProd = 0;\n                    for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n                        const dyR = (dyRCorner + wR) / strideHeight;\n                        if (dyR < 0 || dyR >= convInfo.outHeight ||\n                            Math.floor(dyR) !== dyR) {\n                            continue;\n                        }\n                        for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                            const dyC = (dyCCorner + wC) / strideWidth;\n                            if (dyC < 0 || dyC >= convInfo.outWidth ||\n                                Math.floor(dyC) !== dyC) {\n                                continue;\n                            }\n                            const pixel = dyBuf.get(b, dyR, dyC, d);\n                            dotProd += pixel;\n                        }\n                    }\n                    dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const avgPoolBackpropConfig = {\n    kernelName: AvgPoolBackprop,\n    backendName: 'cpu',\n    kernelFunc: avgPoolBackprop\n};\n//# sourceMappingURL=AvgPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedBatchNorm, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function batchNormKernelFunc(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, scale, offset, mean, variance } = inputs;\n    util.assert(mean.shape.length === variance.shape.length, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert(offset == null || mean.shape.length === offset.shape.length, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert(scale == null || mean.shape.length === scale.shape.length, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    assertNotComplex([x, mean, variance, scale, offset], 'batchNorm');\n    let { varianceEpsilon } = attrs;\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const xVals = backend.data.get(x.dataId).values;\n    const mVals = backend.data.get(mean.dataId).values;\n    const varVals = backend.data.get(variance.dataId).values;\n    const sVals = scale ? backend.data.get(scale.dataId).values :\n        new Float32Array([1]);\n    const offVals = offset ?\n        backend.data.get(offset.dataId).values :\n        new Float32Array([0]);\n    const outVals = new Float32Array(xVals.length);\n    const offValsLength = offVals.length;\n    const sValsLength = sVals.length;\n    const varValsLength = varVals.length;\n    const mValsLength = mVals.length;\n    let offi = 0;\n    let mi = 0;\n    let si = 0;\n    let vi = 0;\n    for (let i = 0; i < xVals.length; ++i) {\n        outVals[i] = offVals[offi++] +\n            (xVals[i] - mVals[mi++]) * sVals[si++] /\n                Math.sqrt(varVals[vi++] + varianceEpsilon);\n        if (offi >= offValsLength) {\n            offi = 0;\n        }\n        if (mi >= mValsLength) {\n            mi = 0;\n        }\n        if (si >= sValsLength) {\n            si = 0;\n        }\n        if (vi >= varValsLength) {\n            vi = 0;\n        }\n    }\n    return backend.makeTensorInfo(x.shape, x.dtype, outVals);\n}\nexport const batchNormConfig = {\n    kernelName: FusedBatchNorm,\n    backendName: 'cpu',\n    kernelFunc: batchNormKernelFunc,\n};\n//# sourceMappingURL=BatchNorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ClipByValue } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const clipKernelFunc = unaryKernelFunc(ClipByValue, (xi, attrs) => {\n    const clipAttrs = attrs;\n    if (xi > clipAttrs.clipValueMax) {\n        return clipAttrs.clipValueMax;\n    }\n    return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;\n});\nexport const clipConfig = {\n    kernelName: ClipByValue,\n    backendName: 'cpu',\n    kernelFunc: clipKernelFunc,\n};\n//# sourceMappingURL=Clip.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Imag } from '@tensorflow/tfjs-core';\nexport function imag(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const imag = backend.data.get(input.dataId).complexTensorInfos.imag;\n    const imagVal = backend.data.get(imag.dataId).values;\n    // When complex tensor is disposed, its underlying parts will be disposed too.\n    // Make new tensor out of the imag value of the complex. This makes sure the\n    // value is still accessible even if complex tensor is disposed.\n    return backend.makeTensorInfo(imag.shape, imag.dtype, imagVal);\n}\nexport const imagConfig = {\n    kernelName: Imag,\n    backendName: 'cpu',\n    kernelFunc: imag\n};\n//# sourceMappingURL=Imag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape, util } from '@tensorflow/tfjs-core';\nexport function reshape(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { shape } = attrs;\n    const xSize = util.sizeFromShape(x.shape);\n    const $shape = util.inferFromImplicitShape(shape, xSize);\n    const $xSize = util.sizeFromShape($shape);\n    util.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n        `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n        `shape must have the same number of elements.`);\n    backend.incRef(x.dataId);\n    const xData = backend.data.get(x.dataId);\n    if (xData.complexTensorInfos != null) {\n        const real = xData.complexTensorInfos.real;\n        const imag = xData.complexTensorInfos.imag;\n        real.shape = $shape;\n        imag.shape = $shape;\n    }\n    return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nexport const reshapeConfig = {\n    kernelName: Reshape,\n    backendName: 'cpu',\n    kernelFunc: reshape\n};\n//# sourceMappingURL=Reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Concat, util } from '@tensorflow/tfjs-core';\nimport { complex } from './Complex';\nimport { imag } from './Imag';\nimport { real } from './Real';\nimport { reshape } from './Reshape';\nexport function concat(args) {\n    const { inputs, backend, attrs } = args;\n    const { axis } = attrs;\n    const $axis = util.parseAxisParam(axis, inputs[0].shape)[0];\n    let outShape = backend_util.computeOutShape(inputs.map(t => t.shape), $axis);\n    if (util.sizeFromShape(outShape) === 0) {\n        return backend.makeTensorInfo(outShape, inputs[0].dtype, []);\n    }\n    // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n    const $inputs = inputs.filter(t => util.sizeFromShape(t.shape) > 0);\n    if ($inputs.length === 1) {\n        return $inputs[0];\n    }\n    const shapes = $inputs.map(t => t.shape);\n    backend_util.assertParamsConsistent(shapes, $axis);\n    if ($inputs[0].dtype === 'complex64') {\n        const reals = $inputs.map((t) => real({ inputs: { input: t }, backend }));\n        const imags = $inputs.map((t) => imag({ inputs: { input: t }, backend }));\n        const realConcated = concat({ inputs: reals, backend, attrs: { axis } });\n        const imagConcated = concat({ inputs: imags, backend, attrs: { axis } });\n        const result = complex({ inputs: { real: realConcated, imag: imagConcated }, backend });\n        reals.forEach(r => backend.disposeIntermediateTensorInfo(r));\n        imags.forEach(i => backend.disposeIntermediateTensorInfo(i));\n        backend.disposeIntermediateTensorInfo(realConcated);\n        backend.disposeIntermediateTensorInfo(imagConcated);\n        return result;\n    }\n    // Any concat of n-dimensional tensors across any axis can be reduced to\n    // a concatenation of two-dimensional tensors across the axis 1 by first\n    // partitioning the axes of the original tensors into those less than the\n    // axis to be concatenated and the rest. Then reshape the tensors\n    // into a two-dimensional tensor by collapsing these two sets of axes and\n    // concatenate the resulting matrices across the axis 1, finally reshaping\n    // the result to have the proper shape.\n    const inputs2D = $inputs.map(t => {\n        const innerSize = util.sizeFromShape(t.shape.slice($axis));\n        const shape = [-1, innerSize];\n        return reshape({ inputs: { x: t }, backend, attrs: { shape } });\n    });\n    // Concats 2d tensors along axis=1.\n    outShape =\n        backend_util.computeOutShape(inputs2D.map(t => t.shape), 1 /* axis */);\n    const outVals = util.getTypedArrayFromDType($inputs[0].dtype, util.sizeFromShape(outShape));\n    if (inputs2D[0].shape[0] === 1) {\n        // Use built-in TypedArray.set() method for speed.\n        let offset = 0;\n        inputs2D.forEach(t => {\n            const val = backend.data.get(t.dataId).values;\n            const size = util.sizeFromShape(t.shape);\n            outVals.set(val, offset);\n            offset += size;\n        });\n    }\n    else {\n        let colOffset = 0;\n        inputs2D.forEach(t => {\n            const tVals = backend.data.get(t.dataId).values;\n            let tIdx = 0;\n            for (let row = 0; row < t.shape[0]; ++row) {\n                const resIdx = row * outShape[1] + colOffset;\n                for (let col = 0; col < t.shape[1]; ++col) {\n                    outVals[resIdx + col] = tVals[tIdx++];\n                }\n            }\n            colOffset += t.shape[1];\n        });\n    }\n    const finalOutShape = backend_util.computeOutShape($inputs.map(t => t.shape), $axis);\n    const outInfo = backend.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);\n    inputs2D.forEach(t => backend.disposeIntermediateTensorInfo(t));\n    return outInfo;\n}\nexport const concatConfig = {\n    kernelName: Concat,\n    backendName: 'cpu',\n    kernelFunc: concat\n};\n//# sourceMappingURL=Concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const cosKernelFunc = unaryKernelFunc(Cos, (xi) => Math.cos(xi));\nexport const cosConfig = {\n    kernelName: Cos,\n    backendName: 'cpu',\n    kernelFunc: cosKernelFunc,\n};\n//# sourceMappingURL=Cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cosh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const coshKernelFunc = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi));\nexport const coshConfig = {\n    kernelName: Cosh,\n    backendName: 'cpu',\n    kernelFunc: coshKernelFunc,\n};\n//# sourceMappingURL=Cosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2D, util } from '@tensorflow/tfjs-core';\nexport const dilation2dConfig = {\n    kernelName: Dilation2D,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const xVals = cpuBackend.data.get(x.dataId).values;\n        const xRank = x.shape.length;\n        const filterVals = cpuBackend.data.get(filter.dataId).values;\n        const filterRank = filter.shape.length;\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        const outSize = util.sizeFromShape(outShape);\n        const outRank = outShape.length;\n        const outputVals = util.getArrayFromDType(x.dtype, outSize);\n        // Upsampling the input by fill in `dilation size - 1` values between each\n        // input value.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const xIndex = util.locToIndex([b, hIn, wIn, d], xRank, util.computeStrides(x.shape));\n                                        const filterIndex = util.locToIndex([h, w, d], filterRank, util.computeStrides(filter.shape));\n                                        const val = xVals[xIndex] + filterVals[filterIndex];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        const outputIndex = util.locToIndex([b, hOut, wOut, d], outRank, util.computeStrides(outShape));\n                        outputVals[outputIndex] = curVal;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(outputVals, x.dtype), outShape, x.dtype);\n        return { dataId, shape: outShape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2DBackpropFilter, util } from '@tensorflow/tfjs-core';\nexport const dilation2dBackpropFilterConfig = {\n    kernelName: Dilation2DBackpropFilter,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter, dy } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const $x = util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n        const $filter = util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        util.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy ` +\n            `must have the same rank as output ${outShape.length}, but got ` +\n            `${dy.rank}`);\n        const $dy = util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n        // The computed filter gradients has the same dimensions as the filter:\n        // [filterHeight, filterWidth, depth]\n        const gradients = util.makeZerosNestedTypedArray(filter.shape, filter.dtype);\n        // In the case of multiple argmax branches, we only back-propagate along the\n        // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n        // similarly to the max-pooling backward routines.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        let hMax = 0;\n                        let wMax = 0;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                            hMax = h;\n                                            wMax = w;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);\n        return { dataId, shape: filter.shape, dtype: filter.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2DBackpropFilter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2DBackpropInput, util } from '@tensorflow/tfjs-core';\nexport const dilation2dBackpropInputConfig = {\n    kernelName: Dilation2DBackpropInput,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter, dy } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const $x = util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n        const $filter = util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        util.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy ` +\n            `must have the same rank as output ${outShape.length}, but got ` +\n            `${dy.rank}`);\n        const $dy = util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n        // The computed gradients has the same dimensions as the input:\n        // [batch, inputHeight, inputCols, inChannel]\n        const gradients = util.makeZerosNestedTypedArray(x.shape, x.dtype);\n        // In the case of multiple argmax branches, we only back-propagate along the\n        // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n        // similarly to the max-pooling backward routines.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        let hInMax = (hBeg < 0) ? 0 : hBeg;\n                        let wInMax = (wBeg < 0) ? 0 : wBeg;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                            hInMax = hIn;\n                                            wInMax = wIn;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(gradients, x.dtype), x.shape, x.dtype);\n        return { dataId, shape: x.shape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2DBackpropInput.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const divImpl = createSimpleBinaryKernelImpl((a, b) => a / b);\nexport const div = binaryKernelFunc(Div, divImpl);\nexport const divConfig = {\n    kernelName: Div,\n    backendName: 'cpu',\n    kernelFunc: div\n};\n//# sourceMappingURL=Div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Elu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const eluKernelFunc = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : (Math.exp(xi) - 1));\nexport const eluConfig = {\n    kernelName: Elu,\n    backendName: 'cpu',\n    kernelFunc: eluKernelFunc,\n};\n//# sourceMappingURL=Elu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Erf } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nconst p = backend_util.ERF_P;\nconst a1 = backend_util.ERF_A1;\nconst a2 = backend_util.ERF_A2;\nconst a3 = backend_util.ERF_A3;\nconst a4 = backend_util.ERF_A4;\nconst a5 = backend_util.ERF_A5;\nexport const erfKernelFunc = unaryKernelFunc(Erf, (xi) => {\n    const sign = Math.sign(xi);\n    const v = Math.abs(xi);\n    const t = 1.0 / (1.0 + p * v);\n    return sign *\n        (1.0 -\n            (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t *\n                Math.exp(-v * v));\n});\nexport const erfConfig = {\n    kernelName: Erf,\n    backendName: 'cpu',\n    kernelFunc: erfKernelFunc,\n};\n//# sourceMappingURL=Erf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { add } from '../kernels/Add';\nimport { complex } from '../kernels/Complex';\nimport { concat } from '../kernels/Concat';\nimport { divConfig } from '../kernels/Div';\nimport { identity } from '../kernels/Identity';\nimport { imag } from '../kernels/Imag';\nimport { multiply } from '../kernels/Multiply';\nimport { real } from '../kernels/Real';\nimport { slice } from '../kernels/Slice';\nimport { sub } from '../kernels/Sub';\n/**\n * Calculate FFT of inner most elements of batch tensor.\n */\nexport function fftBatch(input, inverse, cpuBackend) {\n    const inputShape = input.shape;\n    const batch = inputShape[0];\n    const innerDim = inputShape[1];\n    const inputVals = cpuBackend.data.get(input.dataId);\n    const real2D = inputVals.complexTensorInfos.real;\n    const imag2D = inputVals.complexTensorInfos.imag;\n    // Collects real and imaginary values separately.\n    const resultShape = [batch, innerDim];\n    const resultSize = util.sizeFromShape(resultShape);\n    const resultReal = util.getTypedArrayFromDType('float32', resultSize);\n    const resultImag = util.getTypedArrayFromDType('float32', resultSize);\n    for (let b = 0; b < batch; b++) {\n        // TODO: Support slice ops for complex type.\n        const r = slice({\n            inputs: { x: real2D },\n            backend: cpuBackend,\n            attrs: { begin: [b, 0], size: [1, innerDim] }\n        });\n        const i = slice({\n            inputs: { x: imag2D },\n            backend: cpuBackend,\n            attrs: { begin: [b, 0], size: [1, innerDim] }\n        });\n        const input = complex({ inputs: { real: r, imag: i }, backend: cpuBackend });\n        // Run FFT by batch element.\n        const { real, imag } = fftImpl(input, inverse, cpuBackend);\n        const res = backend_util.mergeRealAndImagArrays(real, imag);\n        for (let d = 0; d < innerDim; d++) {\n            const c = backend_util.getComplexWithIndex(res, d);\n            resultReal[b * innerDim + d] = c.real;\n            resultImag[b * innerDim + d] = c.imag;\n        }\n        cpuBackend.disposeIntermediateTensorInfo(r);\n        cpuBackend.disposeIntermediateTensorInfo(i);\n        cpuBackend.disposeIntermediateTensorInfo(input);\n    }\n    const $realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultReal);\n    const $imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImag);\n    const result = complex({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend });\n    cpuBackend.disposeIntermediateTensorInfo($realInfo);\n    cpuBackend.disposeIntermediateTensorInfo($imagInfo);\n    return result;\n}\nexport function fftImpl(input, inverse, cpuBackend) {\n    const inputSize = util.sizeFromShape(input.shape);\n    const inputVals = cpuBackend.data.get(input.dataId);\n    const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values;\n    const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;\n    if (isExponentOf2(inputSize)) {\n        const result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);\n        const resultShape = [input.shape[0], input.shape[1]];\n        if (inverse) {\n            const realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.real);\n            const imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.imag);\n            const sizeInfo = cpuBackend.makeTensorInfo([], 'float32', util.createScalarValue(inputSize, 'float32'));\n            const sizeInfoCopy = identity({ inputs: { x: sizeInfo }, backend: cpuBackend });\n            const divRealInfo = divConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend });\n            const divImagInfo = divConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend });\n            const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values;\n            const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values;\n            cpuBackend.disposeIntermediateTensorInfo(realInfo);\n            cpuBackend.disposeIntermediateTensorInfo(imagInfo);\n            cpuBackend.disposeIntermediateTensorInfo(sizeInfo);\n            cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);\n            cpuBackend.disposeIntermediateTensorInfo(divRealInfo);\n            cpuBackend.disposeIntermediateTensorInfo(divImagInfo);\n            return { real: divRealVals, imag: divImagVals };\n        }\n        return result;\n    }\n    else {\n        const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n        const rawOutput = fourierTransformByMatmul(data, inputSize, inverse);\n        return backend_util.splitRealAndImagArrays(rawOutput);\n    }\n}\nfunction isExponentOf2(size) {\n    return (size & size - 1) === 0;\n}\n// FFT using Cooley-Tukey algorithm on radix 2 dimensional input.\nfunction fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {\n    if (size === 1) {\n        return { real: realVals, imag: imagVals };\n    }\n    const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n    const half = size / 2;\n    const evenComplex = backend_util.complexWithEvenIndex(data);\n    const evenRealVals = evenComplex.real;\n    const evenImagVals = evenComplex.imag;\n    const evenShape = [evenRealVals.length];\n    const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenRealVals);\n    const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenImagVals);\n    const evenTensorInfo = complex({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend });\n    const oddComplex = backend_util.complexWithOddIndex(data);\n    const oddRealVals = oddComplex.real;\n    const oddImagVals = oddComplex.imag;\n    const oddShape = [oddRealVals.length];\n    const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddRealVals);\n    const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddImagVals);\n    const oddTensorInfo = complex({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend });\n    // Recursive call for half part of original input.\n    const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);\n    const $evenRealVals = $evenComplex.real;\n    const $evenImagVals = $evenComplex.imag;\n    const $evenShape = [$evenRealVals.length];\n    const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenRealVals);\n    const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenImagVals);\n    const $evenTensorInfo = complex({\n        inputs: { real: $evenRealInfo, imag: $evenImagInfo },\n        backend: cpuBackend\n    });\n    const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);\n    const $oddRealVals = $oddComplex.real;\n    const $oddImagVals = $oddComplex.imag;\n    const $oddShape = [$oddRealVals.length];\n    const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddRealVals);\n    const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddImagVals);\n    const $oddTensorInfo = complex({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend });\n    const e = backend_util.exponents(size, inverse);\n    const eShape = [e.real.length];\n    const eRealInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.real);\n    const eImagInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.imag);\n    const complexInfo = complex({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend });\n    const exponentInfo = multiply({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend });\n    const addPart = add({\n        inputs: { a: $evenTensorInfo, b: exponentInfo },\n        backend: cpuBackend\n    });\n    const subPart = sub({\n        inputs: { a: $evenTensorInfo, b: exponentInfo },\n        backend: cpuBackend\n    });\n    const addPartReal = real({ inputs: { input: addPart }, backend: cpuBackend });\n    const subPartReal = real({ inputs: { input: subPart }, backend: cpuBackend });\n    const addPartImag = imag({ inputs: { input: addPart }, backend: cpuBackend });\n    const subPartImag = imag({ inputs: { input: subPart }, backend: cpuBackend });\n    const $real = concat({\n        inputs: [addPartReal, subPartReal],\n        backend: cpuBackend,\n        attrs: { axis: 0 }\n    });\n    const $imag = concat({\n        inputs: [addPartImag, subPartImag],\n        backend: cpuBackend,\n        attrs: { axis: 0 }\n    });\n    const $realVals = cpuBackend.data.get($real.dataId).values;\n    const $imagVals = cpuBackend.data.get($imag.dataId).values;\n    cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo(eRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(eImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(complexInfo);\n    cpuBackend.disposeIntermediateTensorInfo(exponentInfo);\n    cpuBackend.disposeIntermediateTensorInfo(addPart);\n    cpuBackend.disposeIntermediateTensorInfo(subPart);\n    cpuBackend.disposeIntermediateTensorInfo(addPartReal);\n    cpuBackend.disposeIntermediateTensorInfo(addPartImag);\n    cpuBackend.disposeIntermediateTensorInfo(subPartReal);\n    cpuBackend.disposeIntermediateTensorInfo(subPartImag);\n    cpuBackend.disposeIntermediateTensorInfo($real);\n    cpuBackend.disposeIntermediateTensorInfo($imag);\n    return { real: $realVals, imag: $imagVals };\n}\n// Calculate fourier transform by multplying sinusoid matrix.\nfunction fourierTransformByMatmul(data, size, inverse) {\n    const ret = new Float32Array(size * 2);\n    // TODO: Use matmul instead once it supports complex64 type.\n    for (let r = 0; r < size; r++) {\n        let real = 0.0;\n        let imag = 0.0;\n        for (let c = 0; c < size; c++) {\n            const e = backend_util.exponent(r * c, size, inverse);\n            const term = backend_util.getComplexWithIndex(data, c);\n            real += term.real * e.real - term.imag * e.imag;\n            imag += term.real * e.imag + term.imag * e.real;\n        }\n        if (inverse) {\n            real /= size;\n            imag /= size;\n        }\n        backend_util.assignToTypedArray(ret, real, imag, r);\n    }\n    return ret;\n}\n//# sourceMappingURL=fft_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FFT, util } from '@tensorflow/tfjs-core';\nimport { fftBatch } from '../utils/fft_utils';\nimport { reshape } from './Reshape';\nexport function fft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputSize = util.sizeFromShape(input.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({\n        inputs: { x: input },\n        backend,\n        attrs: { shape: [batch, innerDimensionSize] }\n    });\n    const result = fftBatch(input2D, false, backend);\n    const resultReshaped = reshape({ inputs: { x: result }, backend, attrs: { shape: input.shape } });\n    backend.disposeIntermediateTensorInfo(input2D);\n    backend.disposeIntermediateTensorInfo(result);\n    return resultReshaped;\n}\nexport const fftConfig = {\n    kernelName: FFT,\n    backendName: 'cpu',\n    kernelFunc: fft\n};\n//# sourceMappingURL=FFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FlipLeftRight, util } from '@tensorflow/tfjs-core';\nexport const flipLeftRightConfig = {\n    kernelName: FlipLeftRight,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const cpuBackend = backend;\n        const output = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(image.shape));\n        const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n        const imageVals = cpuBackend.data.get(image.dataId).values;\n        for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n            for (let row = 0; row < imageHeight; row++) {\n                const rowOffset = row * (imageWidth * numChannels);\n                for (let col = 0; col < imageWidth; col++) {\n                    const colOffset = col * numChannels;\n                    for (let channel = 0; channel < numChannels; channel++) {\n                        const coords = [batch, row, col, channel];\n                        const x = coords[2];\n                        const coordX = Math.round(imageWidth - x);\n                        const outIdx = batchOffset + rowOffset + colOffset + channel;\n                        let outputValue = imageVals[outIdx];\n                        // If the coordinate position falls within the image boundaries...\n                        if (coordX >= 0 && coordX < imageWidth) {\n                            // set the output to the image value at the coordinate position.\n                            const rotatedColOffset = coordX * numChannels;\n                            const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel;\n                            outputValue = imageVals[imageIdx];\n                        }\n                        output[outIdx] = outputValue;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(output, image.shape, image.dtype);\n        return { dataId, shape: image.shape, dtype: image.dtype };\n    }\n};\n//# sourceMappingURL=FlipLeftRight.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IFFT, util } from '@tensorflow/tfjs-core';\nimport { fftBatch } from '../utils/fft_utils';\nimport { reshape } from './Reshape';\nexport function ifft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputSize = util.sizeFromShape(input.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({\n        inputs: { x: input },\n        backend,\n        attrs: { shape: [batch, innerDimensionSize] }\n    });\n    const result = fftBatch(input2D, true, backend);\n    const resultReshaped = reshape({ inputs: { x: result }, backend, attrs: { shape: input.shape } });\n    backend.disposeIntermediateTensorInfo(input2D);\n    backend.disposeIntermediateTensorInfo(result);\n    return resultReshaped;\n}\nexport const ifftConfig = {\n    kernelName: IFFT,\n    backendName: 'cpu',\n    kernelFunc: ifft\n};\n//# sourceMappingURL=IFFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsFinite } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isFiniteKernelFunc = unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, 'bool');\nexport const isFiniteConfig = {\n    kernelName: IsFinite,\n    backendName: 'cpu',\n    kernelFunc: isFiniteKernelFunc,\n};\n//# sourceMappingURL=IsFinite.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsInf } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isInfKernelFunc = unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, 'bool');\nexport const isInfConfig = {\n    kernelName: IsInf,\n    backendName: 'cpu',\n    kernelFunc: isInfKernelFunc,\n};\n//# sourceMappingURL=IsInf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsNan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isNaNKernelFunc = unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, 'bool');\nexport const isNaNConfig = {\n    kernelName: IsNan,\n    backendName: 'cpu',\n    kernelFunc: isNaNKernelFunc,\n};\n//# sourceMappingURL=IsNaN.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log1p } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const log1pKernelFunc = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi));\nexport const log1pConfig = {\n    kernelName: Log1p,\n    backendName: 'cpu',\n    kernelFunc: log1pKernelFunc,\n};\n//# sourceMappingURL=Log1p.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LogicalNot } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const logicalNotKernelFunc = unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, 'bool');\nexport const logicalNotConfig = {\n    kernelName: LogicalNot,\n    backendName: 'cpu',\n    kernelFunc: logicalNotKernelFunc,\n};\n//# sourceMappingURL=LogicalNot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '@tensorflow/tfjs-core';\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxImpl } from './Max_impl';\nimport { transposeImpl } from './Transpose_impl';\nexport const maxConfig = {\n    kernelName: Max,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { reductionIndices, keepDims } = attrs;\n        const cpuBackend = backend;\n        let xShape = x.shape;\n        const xRank = xShape.length;\n        const origAxes = util.parseAxisParam(reductionIndices, xShape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        let xVals = cpuBackend.data.get(x.dataId).values;\n        if (permutedAxes != null) {\n            const newShape = new Array(xRank);\n            for (let i = 0; i < newShape.length; i++) {\n                newShape[i] = xShape[permutedAxes[i]];\n            }\n            xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n            xShape = newShape;\n        }\n        assertNotComplex(x, 'max');\n        backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n        const [maxOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(xShape, axes);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);\n        const dataId = cpuBackend.write(result, maxOutShape, x.dtype);\n        let outShape = maxOutShape;\n        if (keepDims) {\n            // reshape\n            const newShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n            outShape = newShape;\n        }\n        return { dataId, shape: outShape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPool, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { pool } from '../utils/pool_utils';\nimport { identity } from './Identity';\nexport function maxPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'maxPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    let res;\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        res = identity({ inputs: { x }, backend });\n    }\n    else {\n        const xValues = backend.data.get(x.dataId).values;\n        const strides = util.computeStrides(x.shape);\n        const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'max');\n        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);\n    }\n    return res;\n}\nexport const maxPoolConfig = {\n    kernelName: MaxPool,\n    backendName: 'cpu',\n    kernelFunc: maxPool\n};\n//# sourceMappingURL=MaxPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, buffer, MaxPoolBackprop } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxPoolPositions } from '../utils/pool_utils';\nexport function maxPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input, output } = inputs;\n    const x = input;\n    assertNotComplex([input, output], 'maxPoolBackprop');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n    const xValues = backend.data.get(x.dataId).values;\n    const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = buffer(x.shape, 'float32');\n    const dyData = backend.data.get(dy.dataId).values;\n    const dyBuf = buffer(dy.shape, 'float32', dyData);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n                for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n                    // Shader code begins.\n                    const dyRCorner = dxR - padTop;\n                    const dyCCorner = dxC - padLeft;\n                    let dotProd = 0;\n                    for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n                        const dyR = (dyRCorner + wR) / strideHeight;\n                        if (dyR < 0 || dyR >= convInfo.outHeight ||\n                            Math.floor(dyR) !== dyR) {\n                            continue;\n                        }\n                        for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                            const dyC = (dyCCorner + wC) / strideWidth;\n                            if (dyC < 0 || dyC >= convInfo.outWidth ||\n                                Math.floor(dyC) !== dyC) {\n                                continue;\n                            }\n                            const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 -\n                                maxPosBuf.get(b, dyR, dyC, d);\n                            const curPos = wR * effectiveFilterWidth + wC;\n                            const mask = maxPos === curPos ? 1 : 0;\n                            if (mask === 0) {\n                                continue;\n                            }\n                            const pixel = dyBuf.get(b, dyR, dyC, d);\n                            dotProd += pixel * mask;\n                        }\n                    }\n                    dx.set(dotProd, b, dxR, dxC, d);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const maxPoolBackpropConfig = {\n    kernelName: MaxPoolBackprop,\n    backendName: 'cpu',\n    kernelFunc: maxPoolBackprop\n};\n//# sourceMappingURL=MaxPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { maxPoolPositions, pool } from '../utils/pool_utils';\nexport function maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) {\n    const strides = util.computeStrides(xShape);\n    const maxPools = pool(xValues, xShape, dtype, strides, convInfo, 'max');\n    const maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex);\n    return [maxPools.values, maxPositions.values];\n}\n//# sourceMappingURL=MaxPoolWithArgmax_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPoolWithArgmax } from '@tensorflow/tfjs-core';\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxPoolWithArgmaxImpl } from './MaxPoolWithArgmax_impl';\nexport const maxPoolWithArgmaxConfig = {\n    kernelName: MaxPoolWithArgmax,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { filterSize, strides, pad, includeBatchInIndex } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(x, 'MaxPoolWithArgmax');\n        const values = cpuBackend.data.get(x.dataId).values;\n        const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad);\n        const [pooled, indexes] = maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo);\n        const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype);\n        const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype);\n        return [\n            { dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype },\n            { dataId: indexesDataId, shape: convInfo.outShape, dtype: 'int32' }\n        ];\n    }\n};\n//# sourceMappingURL=MaxPoolWithArgmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NonMaxSuppressionV4 } from '@tensorflow/tfjs-core';\nimport { kernel_impls } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\nimport { assertNotComplex } from '../cpu_util';\nexport const nonMaxSuppressionV4Config = {\n    kernelName: NonMaxSuppressionV4,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(boxes, 'NonMaxSuppressionPadded');\n        const boxesVals = cpuBackend.data.get(boxes.dataId).values;\n        const scoresVals = cpuBackend.data.get(scores.dataId).values;\n        const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n        return [selectedIndices, validOutputs];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV4.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NonMaxSuppressionV5 } from '@tensorflow/tfjs-core';\nimport { kernel_impls } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nimport { assertNotComplex } from '../cpu_util';\nexport const nonMaxSuppressionV5Config = {\n    kernelName: NonMaxSuppressionV5,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(boxes, 'NonMaxSuppressionWithScore');\n        const boxesVals = cpuBackend.data.get(boxes.dataId).values;\n        const scoresVals = cpuBackend.data.get(scores.dataId).values;\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        const softNmsSigmaVal = softNmsSigma;\n        const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n        return [selectedIndices, selectedScores];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV5.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NotEqual } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const notEqualImpl = createSimpleBinaryKernelImpl(((a, b) => (a !== b) ? 1 : 0));\nexport const notEqual = binaryKernelFunc(NotEqual, notEqualImpl, null /* complexOp */, 'bool');\nexport const notEqualConfig = {\n    kernelName: NotEqual,\n    backendName: 'cpu',\n    kernelFunc: notEqual\n};\n//# sourceMappingURL=NotEqual.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { PadV2, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function padV2(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { paddings, constantValue } = attrs;\n    assertNotComplex(x, 'pad');\n    const outShape = paddings.map((p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);\n    const start = paddings.map(p => p[0]);\n    const xVals = backend.data.get(x.dataId).values;\n    const xSize = util.sizeFromShape(x.shape);\n    const xRank = x.shape.length;\n    const xStrides = util.computeStrides(x.shape);\n    const resultSize = util.sizeFromShape(outShape);\n    const resultRank = outShape.length;\n    const resultStrides = util.computeStrides(outShape);\n    const resVals = util.getTypedArrayFromDType(x.dtype, resultSize);\n    if (constantValue !== 0) {\n        resVals.fill(constantValue);\n    }\n    for (let i = 0; i < xSize; i++) {\n        const coords = util.indexToLoc(i, xRank, xStrides);\n        const outCoords = coords.map((c, i) => c + start[i]);\n        const outIndex = util.locToIndex(outCoords, resultRank, resultStrides);\n        resVals[outIndex] = xVals[i];\n    }\n    const outId = backend.write(resVals, outShape, x.dtype);\n    return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nexport const padV2Config = {\n    kernelName: PadV2,\n    backendName: 'cpu',\n    kernelFunc: padV2\n};\n//# sourceMappingURL=PadV2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reciprocal } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const reciprocalKernelFunc = unaryKernelFunc(Reciprocal, (xi) => 1 / xi);\nexport const reciprocalConfig = {\n    kernelName: Reciprocal,\n    backendName: 'cpu',\n    kernelFunc: reciprocalKernelFunc,\n};\n//# sourceMappingURL=Reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, RotateWithOffset, util } from '@tensorflow/tfjs-core';\nexport const rotateWithOffsetConfig = {\n    kernelName: RotateWithOffset,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const { radians, fillValue, center } = attrs;\n        const cpuBackend = backend;\n        const output = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(image.shape));\n        const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n        const [centerX, centerY] = backend_util.getImageCenter(center, imageHeight, imageWidth);\n        const fullOpacityValue = 255;\n        const sinFactor = Math.sin(radians);\n        const cosFactor = Math.cos(radians);\n        const imageVals = cpuBackend.data.get(image.dataId).values;\n        for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n            for (let row = 0; row < imageHeight; row++) {\n                const rowOffset = row * (imageWidth * numChannels);\n                for (let col = 0; col < imageWidth; col++) {\n                    const colOffset = col * numChannels;\n                    for (let channel = 0; channel < numChannels; channel++) {\n                        const coords = [batch, row, col, channel];\n                        const x = coords[2];\n                        const y = coords[1];\n                        // coordX/coordY are the result of rotating and translating x/y.\n                        let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;\n                        let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;\n                        coordX = Math.round(coordX + centerX);\n                        coordY = Math.round(coordY + centerY);\n                        let outputValue = fillValue;\n                        if (typeof fillValue !== 'number') {\n                            if (channel === 3) {\n                                outputValue = fullOpacityValue;\n                            }\n                            else {\n                                outputValue = fillValue[channel];\n                            }\n                        }\n                        // If the coordinate position falls within the image boundaries...\n                        if (coordX >= 0 && coordX < imageWidth && coordY >= 0 &&\n                            coordY < imageHeight) {\n                            // set the output to the image value at the coordinate position.\n                            const rotatedRowOffset = coordY * (imageWidth * numChannels);\n                            const rotatedColOffset = coordX * numChannels;\n                            const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;\n                            outputValue = imageVals[imageIdx];\n                        }\n                        const outIdx = batchOffset + rowOffset + colOffset + channel;\n                        output[outIdx] = outputValue;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(output, image.shape, image.dtype);\n        return { dataId, shape: image.shape, dtype: image.dtype };\n    }\n};\n//# sourceMappingURL=RotateWithOffset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Round } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const roundKernelFunc = unaryKernelFunc(Round, (xi) => {\n    // The algorithm is based on banker's rounding.\n    const base = Math.floor(xi);\n    if (xi - base < 0.5) {\n        return Math.floor(xi);\n    }\n    else if (xi - base > 0.5) {\n        return Math.ceil(xi);\n    }\n    else {\n        if (base % 2.0 === 0.0) {\n            return base;\n        }\n        else {\n            return base + 1.0;\n        }\n    }\n});\nexport const roundConfig = {\n    kernelName: Round,\n    backendName: 'cpu',\n    kernelFunc: roundKernelFunc,\n};\n//# sourceMappingURL=Round.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Selu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nconst scaleAlpha = backend_util.SELU_SCALEALPHA;\nconst scale = backend_util.SELU_SCALE;\nexport const seluKernelFunc = unaryKernelFunc(Selu, (xi) => {\n    if (xi >= 0) {\n        return scale * xi;\n    }\n    else {\n        return scaleAlpha * (Math.exp(xi) - 1);\n    }\n});\nexport const seluConfig = {\n    kernelName: Selu,\n    backendName: 'cpu',\n    kernelFunc: seluKernelFunc,\n};\n//# sourceMappingURL=Selu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sigmoid } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sigmoidKernelFunc = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));\nexport const sigmoidConfig = {\n    kernelName: Sigmoid,\n    backendName: 'cpu',\n    kernelFunc: sigmoidKernelFunc,\n};\n//# sourceMappingURL=Sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sign } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const signKernelFunc = unaryKernelFunc(Sign, (xi) => {\n    if (xi < 0) {\n        return -1;\n    }\n    else if (xi > 0) {\n        return 1;\n    }\n    else {\n        return 0;\n    }\n});\nexport const signConfig = {\n    kernelName: Sign,\n    backendName: 'cpu',\n    kernelFunc: signKernelFunc,\n};\n//# sourceMappingURL=Sign.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sinKernelFunc = unaryKernelFunc(Sin, (xi) => Math.sin(xi));\nexport const sinConfig = {\n    kernelName: Sin,\n    backendName: 'cpu',\n    kernelFunc: sinKernelFunc,\n};\n//# sourceMappingURL=Sin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sinh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sinhKernelFunc = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi));\nexport const sinhConfig = {\n    kernelName: Sinh,\n    backendName: 'cpu',\n    kernelFunc: sinhKernelFunc,\n};\n//# sourceMappingURL=Sinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softplus } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\n// mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n// epsilon is the difference between 1.0 and the next representable float.\n// For a single precision 32 bit float this should be 2^-23, see:\n// https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\nconst epsilon = 1.1920928955078125e-7;\nconst threshold = Math.log(epsilon) + 2.0;\nexport const softplusKernelFunc = unaryKernelFunc(Softplus, (xi) => {\n    // Value above which exp(x) may overflow, but softplus(x) == x\n    // is within machine epsilon.\n    const tooLarge = xi > -threshold;\n    // Value below which exp(x) may underflow, but softplus(x) == exp(x)\n    // is within machine epsilon.\n    const tooSmall = xi < threshold;\n    const expX = Math.exp(xi);\n    let result;\n    if (tooSmall) {\n        result = expX;\n    }\n    else if (tooLarge) {\n        result = xi;\n    }\n    else {\n        result = Math.log(1.0 + expX);\n    }\n    return result;\n});\nexport const softplusConfig = {\n    kernelName: Softplus,\n    backendName: 'cpu',\n    kernelFunc: softplusKernelFunc,\n};\n//# sourceMappingURL=Softplus.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { transposeImpl } from './Transpose_impl';\nexport function transpose(args) {\n    const { inputs, attrs, backend } = args;\n    const { x } = inputs;\n    const { perm } = attrs;\n    assertNotComplex(x, 'transpose');\n    const xRank = x.shape.length;\n    const newShape = new Array(xRank);\n    for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = x.shape[perm[i]];\n    }\n    const values = backend.data.get(x.dataId).values;\n    const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);\n    const dataId = backend.write(result, newShape, x.dtype);\n    return { dataId, shape: newShape, dtype: x.dtype };\n}\nexport const transposeConfig = {\n    kernelName: Transpose,\n    backendName: 'cpu',\n    kernelFunc: transpose\n};\n//# sourceMappingURL=Transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, SpaceToBatchND, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { padV2Config } from './PadV2';\nimport { reshape } from './Reshape';\nimport { transpose } from './Transpose';\nexport function spaceToBatchND(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { blockShape, paddings } = attrs;\n    assertNotComplex([x], 'spaceToBatchND');\n    const prod = util.sizeFromShape(blockShape);\n    const completePaddings = [[0, 0]];\n    completePaddings.push(...paddings);\n    for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n        completePaddings.push([0, 0]);\n    }\n    const paddedX = padV2Config.kernelFunc({\n        inputs: { x },\n        backend,\n        attrs: { paddings: completePaddings, constantValue: 0 }\n    });\n    const reshapedPaddedShape = backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n    const permutedReshapedPaddedPermutation = backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n    const flattenShape = backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n    const reshapeInputs = { x: paddedX };\n    const reshapeAttrs = { shape: reshapedPaddedShape };\n    const paddedXReshaped = reshape({ inputs: reshapeInputs, backend, attrs: reshapeAttrs });\n    const transposeInputs = { x: paddedXReshaped };\n    const transposeAttrs = { perm: permutedReshapedPaddedPermutation };\n    const paddedXT = transpose({ inputs: transposeInputs, backend, attrs: transposeAttrs });\n    const resultReshapeInputs = { x: paddedXT };\n    const resultReshapeAttrs = { shape: flattenShape };\n    const result = reshape({ inputs: resultReshapeInputs, backend, attrs: resultReshapeAttrs });\n    backend.disposeIntermediateTensorInfo(paddedX);\n    backend.disposeIntermediateTensorInfo(paddedXReshaped);\n    backend.disposeIntermediateTensorInfo(paddedXT);\n    return result;\n}\nexport const spaceToBatchNDConfig = {\n    kernelName: SpaceToBatchND,\n    backendName: 'cpu',\n    kernelFunc: spaceToBatchND\n};\n//# sourceMappingURL=SpaceToBatchND.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sqrt } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sqrtKernelFunc = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));\nexport const sqrtConfig = {\n    kernelName: Sqrt,\n    backendName: 'cpu',\n    kernelFunc: sqrtKernelFunc,\n};\n//# sourceMappingURL=Sqrt.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport const squareConfig = {\n    kernelName: Square,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend }) => {\n        const { x } = inputs;\n        const cpuBackend = backend;\n        assertNotComplex(x, 'square');\n        const values = cpuBackend.data.get(x.dataId).values;\n        const newValues = new Float32Array(values.length);\n        for (let i = 0; i < values.length; ++i) {\n            const value = values[i];\n            newValues[i] = value * value;\n        }\n        const dataId = cpuBackend.write(newValues, x.shape, x.dtype);\n        return { dataId, shape: x.shape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const squaredDifferenceImpl = createSimpleBinaryKernelImpl(((a, b) => {\n    const diff = a - b;\n    return diff * diff;\n}));\nexport const squaredDifference = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);\nexport const squaredDifferenceConfig = {\n    kernelName: SquaredDifference,\n    backendName: 'cpu',\n    kernelFunc: squaredDifference\n};\n//# sourceMappingURL=SquaredDifference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Step } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const stepKernelFunc = unaryKernelFunc(Step, (xi, attrs) => {\n    const stepAttrs = attrs;\n    if (isNaN(xi)) {\n        return NaN;\n    }\n    else {\n        return xi > 0 ? 1 : stepAttrs.alpha;\n    }\n});\nexport const stepConfig = {\n    kernelName: Step,\n    backendName: 'cpu',\n    kernelFunc: stepKernelFunc,\n};\n//# sourceMappingURL=Step.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const tanKernelFunc = unaryKernelFunc(Tan, (xi) => Math.tan(xi));\nexport const tanConfig = {\n    kernelName: Tan,\n    backendName: 'cpu',\n    kernelFunc: tanKernelFunc,\n};\n//# sourceMappingURL=Tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tanh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const tanhKernelFunc = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi));\nexport const tanhConfig = {\n    kernelName: Tanh,\n    backendName: 'cpu',\n    kernelFunc: tanhKernelFunc,\n};\n//# sourceMappingURL=Tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unique } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { uniqueImpl } from './Unique_impl';\nexport function unique(args) {\n    const { inputs, attrs, backend } = args;\n    const { axis } = attrs;\n    const { x } = inputs;\n    assertNotComplex(x, 'unique');\n    const values = backend.data.get(x.dataId).values;\n    const { outputValues, outputShape, indices } = uniqueImpl(values, axis, x.shape, x.dtype);\n    return [\n        backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n        backend.makeTensorInfo([indices.length], 'int32', indices),\n    ];\n}\nexport const uniqueConfig = {\n    kernelName: Unique,\n    backendName: 'cpu',\n    kernelFunc: unique,\n};\n//# sourceMappingURL=Unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// We explicitly import the modular kernels so they get registered in the\n// global registry when we compile the library. A modular build would replace\n// the contents of this file and import only the kernels that are needed.\nimport { registerKernel } from '@tensorflow/tfjs-core';\nimport { absConfig } from './kernels/Abs';\nimport { acosConfig } from './kernels/Acos';\nimport { acoshConfig } from './kernels/Acosh';\nimport { addConfig } from './kernels/Add';\nimport { asinConfig } from './kernels/Asin';\nimport { asinhConfig } from './kernels/Asinh';\nimport { atanConfig } from './kernels/Atan';\nimport { atanhConfig } from './kernels/Atanh';\nimport { avgPoolConfig } from './kernels/AvgPool';\nimport { avgPoolBackpropConfig } from './kernels/AvgPoolBackprop';\nimport { batchNormConfig } from './kernels/BatchNorm';\nimport { castConfig } from './kernels/Cast';\nimport { ceilConfig } from './kernels/Ceil';\nimport { clipConfig } from './kernels/Clip';\nimport { complexConfig } from './kernels/Complex';\nimport { concatConfig } from './kernels/Concat';\nimport { cosConfig } from './kernels/Cos';\nimport { coshConfig } from './kernels/Cosh';\nimport { dilation2dConfig } from './kernels/Dilation2D';\nimport { dilation2dBackpropFilterConfig } from './kernels/Dilation2DBackpropFilter';\nimport { dilation2dBackpropInputConfig } from './kernels/Dilation2DBackpropInput';\nimport { divConfig } from './kernels/Div';\nimport { eluConfig } from './kernels/Elu';\nimport { erfConfig } from './kernels/Erf';\nimport { expConfig } from './kernels/Exp';\nimport { expm1Config } from './kernels/Expm1';\nimport { fftConfig } from './kernels/FFT';\nimport { flipLeftRightConfig } from './kernels/FlipLeftRight';\nimport { floorConfig } from './kernels/Floor';\nimport { identityConfig } from './kernels/Identity';\nimport { ifftConfig } from './kernels/IFFT';\nimport { imagConfig } from './kernels/Imag';\nimport { isFiniteConfig } from './kernels/IsFinite';\nimport { isInfConfig } from './kernels/IsInf';\nimport { isNaNConfig } from './kernels/IsNaN';\nimport { logConfig } from './kernels/Log';\nimport { log1pConfig } from './kernels/Log1p';\nimport { logicalNotConfig } from './kernels/LogicalNot';\nimport { maxConfig } from './kernels/Max';\nimport { maxPoolConfig } from './kernels/MaxPool';\nimport { maxPoolBackpropConfig } from './kernels/MaxPoolBackprop';\nimport { maxPoolWithArgmaxConfig } from './kernels/MaxPoolWithArgmax';\nimport { multiplyConfig } from './kernels/Multiply';\nimport { nonMaxSuppressionV4Config } from './kernels/NonMaxSuppressionV4';\nimport { nonMaxSuppressionV5Config } from './kernels/NonMaxSuppressionV5';\nimport { notEqualConfig } from './kernels/NotEqual';\nimport { padV2Config } from './kernels/PadV2';\nimport { realConfig } from './kernels/Real';\nimport { reciprocalConfig } from './kernels/Reciprocal';\nimport { reshapeConfig } from './kernels/Reshape';\nimport { rotateWithOffsetConfig } from './kernels/RotateWithOffset';\nimport { roundConfig } from './kernels/Round';\nimport { rsqrtConfig } from './kernels/Rsqrt';\nimport { seluConfig } from './kernels/Selu';\nimport { sigmoidConfig } from './kernels/Sigmoid';\nimport { signConfig } from './kernels/Sign';\nimport { sinConfig } from './kernels/Sin';\nimport { sinhConfig } from './kernels/Sinh';\nimport { sliceConfig } from './kernels/Slice';\nimport { softplusConfig } from './kernels/Softplus';\nimport { spaceToBatchNDConfig } from './kernels/SpaceToBatchND';\nimport { sqrtConfig } from './kernels/Sqrt';\nimport { squareConfig } from './kernels/Square';\nimport { squaredDifferenceConfig } from './kernels/SquaredDifference';\nimport { stepConfig } from './kernels/Step';\nimport { subConfig } from './kernels/Sub';\nimport { tanConfig } from './kernels/Tan';\nimport { tanhConfig } from './kernels/Tanh';\nimport { transposeConfig } from './kernels/Transpose';\nimport { uniqueConfig } from './kernels/Unique';\n// List all kernel configs here\nconst kernelConfigs = [\n    absConfig,\n    acosConfig,\n    acoshConfig,\n    addConfig,\n    asinConfig,\n    asinhConfig,\n    atanConfig,\n    atanhConfig,\n    avgPoolConfig,\n    avgPoolBackpropConfig,\n    batchNormConfig,\n    castConfig,\n    ceilConfig,\n    clipConfig,\n    complexConfig,\n    concatConfig,\n    cosConfig,\n    coshConfig,\n    dilation2dConfig,\n    dilation2dBackpropInputConfig,\n    dilation2dBackpropFilterConfig,\n    divConfig,\n    eluConfig,\n    erfConfig,\n    expConfig,\n    expm1Config,\n    fftConfig,\n    flipLeftRightConfig,\n    floorConfig,\n    identityConfig,\n    ifftConfig,\n    imagConfig,\n    isFiniteConfig,\n    isInfConfig,\n    isNaNConfig,\n    logConfig,\n    log1pConfig,\n    logicalNotConfig,\n    maxPoolConfig,\n    maxPoolBackpropConfig,\n    maxPoolWithArgmaxConfig,\n    maxConfig,\n    multiplyConfig,\n    nonMaxSuppressionV4Config,\n    nonMaxSuppressionV5Config,\n    notEqualConfig,\n    padV2Config,\n    realConfig,\n    reciprocalConfig,\n    reshapeConfig,\n    rotateWithOffsetConfig,\n    roundConfig,\n    rsqrtConfig,\n    seluConfig,\n    sigmoidConfig,\n    signConfig,\n    sinConfig,\n    sinhConfig,\n    sliceConfig,\n    softplusConfig,\n    spaceToBatchNDConfig,\n    sqrtConfig,\n    squareConfig,\n    squaredDifferenceConfig,\n    stepConfig,\n    subConfig,\n    tanConfig,\n    tanhConfig,\n    transposeConfig,\n    uniqueConfig,\n];\nfor (const kernelConfig of kernelConfigs) {\n    registerKernel(kernelConfig);\n}\n//# sourceMappingURL=register_all_kernels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// All exports from this package should be in base.\nexport * from './base';\nimport './register_all_kernels';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst contexts = {};\nconst WEBGL_ATTRIBUTES = {\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: true\n};\nexport function setWebGLContext(webGLVersion, gl) {\n    contexts[webGLVersion] = gl;\n}\nexport function getWebGLContext(webGLVersion) {\n    if (!(webGLVersion in contexts)) {\n        const newCtx = getWebGLRenderingContext(webGLVersion);\n        if (newCtx !== null) {\n            contexts[webGLVersion] = newCtx;\n        }\n        else {\n            console.log('Could not get context for WebGL version', webGLVersion);\n            return null;\n        }\n    }\n    const gl = contexts[webGLVersion];\n    if (gl.isContextLost()) {\n        delete contexts[webGLVersion];\n        return getWebGLContext(webGLVersion);\n    }\n    gl.disable(gl.DEPTH_TEST);\n    gl.disable(gl.STENCIL_TEST);\n    gl.disable(gl.BLEND);\n    gl.disable(gl.DITHER);\n    gl.disable(gl.POLYGON_OFFSET_FILL);\n    gl.disable(gl.SAMPLE_COVERAGE);\n    gl.enable(gl.SCISSOR_TEST);\n    gl.enable(gl.CULL_FACE);\n    gl.cullFace(gl.BACK);\n    return contexts[webGLVersion];\n}\nfunction createCanvas(webGLVersion) {\n    if (typeof OffscreenCanvas !== 'undefined' && webGLVersion === 2) {\n        return new OffscreenCanvas(300, 150);\n    }\n    else if (typeof document !== 'undefined') {\n        return document.createElement('canvas');\n    }\n    else {\n        throw new Error('Cannot create a canvas in this context');\n    }\n}\nfunction getWebGLRenderingContext(webGLVersion) {\n    if (webGLVersion !== 1 && webGLVersion !== 2) {\n        throw new Error('Cannot get WebGL rendering context, WebGL is disabled.');\n    }\n    const canvas = createCanvas(webGLVersion);\n    canvas.addEventListener('webglcontextlost', (ev) => {\n        ev.preventDefault();\n        delete contexts[webGLVersion];\n    }, false);\n    if (webGLVersion === 1) {\n        return (canvas.getContext('webgl', WEBGL_ATTRIBUTES) ||\n            canvas.getContext('experimental-webgl', WEBGL_ATTRIBUTES));\n    }\n    return canvas.getContext('webgl2', WEBGL_ATTRIBUTES);\n}\n//# sourceMappingURL=canvas_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nexport var PackingScheme;\n(function (PackingScheme) {\n    /**\n     * All values in a single texel are densely packed without any constraints.\n     *\n     * This is how the shader encodes a tensor with shape = [2, 3, 4]\n     * (indices are [batch, row, col]).\n     *\n     * 000|001   010|011   020|021\n     * -------   -------   -------\n     * 002|003   012|013   022|023\n     *\n     * 100|101   110|111   120|121\n     * -------   -------   -------\n     * 102|103   112|113   122|123\n     *\n     */\n    PackingScheme[PackingScheme[\"DENSE\"] = 0] = \"DENSE\";\n    /**\n     * Single texels contain only values from the same batch, and from adjacent\n     * rows and columns.\n     *\n     * This is how the shader encodes a tensor with shape = [2, 3, 5]\n     * (indices are [batch, row, col]).\n     *\n     * 000|001   002|003   004|xxx   020|021   022|023   024|xxx\n     * -------   -------   -------   -------   -------   -------\n     * 010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n     *\n     * 100|101   102|103   104|xxx   120|121   122|123   124|xxx\n     * -------   -------   -------   -------   -------   -------\n     * 110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n     *\n     */\n    PackingScheme[PackingScheme[\"SHARED_BATCH\"] = 1] = \"SHARED_BATCH\";\n})(PackingScheme || (PackingScheme = {}));\nexport var TextureUsage;\n(function (TextureUsage) {\n    TextureUsage[TextureUsage[\"RENDER\"] = 0] = \"RENDER\";\n    TextureUsage[TextureUsage[\"UPLOAD\"] = 1] = \"UPLOAD\";\n    TextureUsage[TextureUsage[\"PIXELS\"] = 2] = \"PIXELS\";\n    TextureUsage[TextureUsage[\"DOWNLOAD\"] = 3] = \"DOWNLOAD\";\n})(TextureUsage || (TextureUsage = {}));\nexport var PhysicalTextureType;\n(function (PhysicalTextureType) {\n    PhysicalTextureType[PhysicalTextureType[\"UNPACKED_FLOAT16\"] = 0] = \"UNPACKED_FLOAT16\";\n    PhysicalTextureType[PhysicalTextureType[\"UNPACKED_FLOAT32\"] = 1] = \"UNPACKED_FLOAT32\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_4X1_UNSIGNED_BYTE\"] = 2] = \"PACKED_4X1_UNSIGNED_BYTE\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_2X2_FLOAT32\"] = 3] = \"PACKED_2X2_FLOAT32\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_2X2_FLOAT16\"] = 4] = \"PACKED_2X2_FLOAT16\";\n})(PhysicalTextureType || (PhysicalTextureType = {}));\nexport function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {\n    return [columns, rows];\n}\nexport function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {\n    return matrixSize * channelsPerTexture;\n}\nexport function getColorMatrixTextureShapeWidthHeight(rows, columns) {\n    return [columns * 4, rows];\n}\n/**\n * Get shape for densely packed RGBA texture.\n */\nexport function getDenseTexShape(shape) {\n    const size = util.sizeFromShape(shape);\n    const texelsNeeded = Math.ceil(size / 4);\n    return util.sizeToSquarishShape(texelsNeeded);\n}\nexport function getMatrixSizeFromUnpackedArraySize(unpackedSize, channelsPerTexture) {\n    if (unpackedSize % channelsPerTexture !== 0) {\n        throw new Error(`unpackedSize (${unpackedSize}) must be a multiple of ` +\n            `${channelsPerTexture}`);\n    }\n    return unpackedSize / channelsPerTexture;\n}\nexport function decodeMatrixFromUnpackedColorRGBAArray(unpackedArray, matrix, channels) {\n    const requiredSize = unpackedArray.length * channels / 4;\n    if (matrix.length < requiredSize) {\n        throw new Error(`matrix length (${matrix.length}) must be >= ${requiredSize}`);\n    }\n    let dst = 0;\n    for (let src = 0; src < unpackedArray.length; src += 4) {\n        for (let c = 0; c < channels; c++) {\n            matrix[dst++] = unpackedArray[src + c];\n        }\n    }\n}\nexport function getPackedMatrixTextureShapeWidthHeight(rows, columns) {\n    return [\n        Math.max(1, Math.ceil(columns / 2)), Math.max(1, Math.ceil(rows / 2))\n    ];\n}\nexport function getPackedRGBAArraySizeFromMatrixShape(rows, columns) {\n    const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return w * h * 4;\n}\nexport function getTextureConfig(\n// tslint:disable-next-line:no-any\ngl, textureHalfFloatExtension) {\n    // tslint:disable-next-line:no-any\n    const glany = gl;\n    let internalFormatFloat;\n    let internalFormatHalfFloat;\n    let internalFormatPackedHalfFloat;\n    let internalFormatPackedFloat;\n    let textureFormatFloat;\n    let downloadTextureFormat;\n    let downloadUnpackNumChannels;\n    let defaultNumChannels;\n    let textureTypeHalfFloat;\n    let textureTypeFloat;\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        internalFormatFloat = glany.R32F;\n        internalFormatHalfFloat = glany.R16F;\n        internalFormatPackedHalfFloat = glany.RGBA16F;\n        internalFormatPackedFloat = glany.RGBA32F;\n        textureFormatFloat = glany.RED;\n        downloadUnpackNumChannels = 4;\n        defaultNumChannels = 1;\n        textureTypeHalfFloat = glany.HALF_FLOAT;\n        textureTypeFloat = glany.FLOAT;\n    }\n    else {\n        internalFormatFloat = gl.RGBA;\n        internalFormatHalfFloat = gl.RGBA;\n        internalFormatPackedHalfFloat = gl.RGBA;\n        internalFormatPackedFloat = glany.RGBA;\n        textureFormatFloat = gl.RGBA;\n        downloadUnpackNumChannels = 4;\n        defaultNumChannels = 4;\n        textureTypeHalfFloat = textureHalfFloatExtension != null ?\n            textureHalfFloatExtension.HALF_FLOAT_OES :\n            null;\n        textureTypeFloat = gl.FLOAT;\n    }\n    downloadTextureFormat = gl.RGBA;\n    return {\n        internalFormatFloat,\n        internalFormatHalfFloat,\n        internalFormatPackedHalfFloat,\n        internalFormatPackedFloat,\n        textureFormatFloat,\n        downloadTextureFormat,\n        downloadUnpackNumChannels,\n        defaultNumChannels,\n        textureTypeHalfFloat,\n        textureTypeFloat\n    };\n}\n//# sourceMappingURL=tex_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { getWebGLContext } from './canvas_util';\nimport { getTextureConfig } from './tex_util';\nexport function callAndCheck(gl, func) {\n    const returnValue = func();\n    if (env().getBool('DEBUG')) {\n        checkWebGLError(gl);\n    }\n    return returnValue;\n}\nfunction checkWebGLError(gl) {\n    const error = gl.getError();\n    if (error !== gl.NO_ERROR) {\n        throw new Error('WebGL Error: ' + getWebGLErrorMessage(gl, error));\n    }\n}\n// https://en.wikipedia.org/wiki/Half-precision_floating-point_format\nconst MIN_FLOAT16 = 5.96e-8;\nconst MAX_FLOAT16 = 65504;\nexport function canBeRepresented(num) {\n    if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED') || num === 0 ||\n        (MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16)) {\n        return true;\n    }\n    return false;\n}\nexport function getWebGLErrorMessage(gl, status) {\n    switch (status) {\n        case gl.NO_ERROR:\n            return 'NO_ERROR';\n        case gl.INVALID_ENUM:\n            return 'INVALID_ENUM';\n        case gl.INVALID_VALUE:\n            return 'INVALID_VALUE';\n        case gl.INVALID_OPERATION:\n            return 'INVALID_OPERATION';\n        case gl.INVALID_FRAMEBUFFER_OPERATION:\n            return 'INVALID_FRAMEBUFFER_OPERATION';\n        case gl.OUT_OF_MEMORY:\n            return 'OUT_OF_MEMORY';\n        case gl.CONTEXT_LOST_WEBGL:\n            return 'CONTEXT_LOST_WEBGL';\n        default:\n            return `Unknown error code ${status}`;\n    }\n}\nexport function getExtensionOrThrow(gl, extensionName) {\n    return throwIfNull(gl, () => gl.getExtension(extensionName), 'Extension \"' + extensionName + '\" not supported on this browser.');\n}\nexport function createVertexShader(gl, vertexShaderSource) {\n    const vertexShader = throwIfNull(gl, () => gl.createShader(gl.VERTEX_SHADER), 'Unable to create vertex WebGLShader.');\n    callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));\n    callAndCheck(gl, () => gl.compileShader(vertexShader));\n    if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {\n        console.log(gl.getShaderInfoLog(vertexShader));\n        throw new Error('Failed to compile vertex shader.');\n    }\n    return vertexShader;\n}\nexport function createFragmentShader(gl, fragmentShaderSource) {\n    const fragmentShader = throwIfNull(gl, () => gl.createShader(gl.FRAGMENT_SHADER), 'Unable to create fragment WebGLShader.');\n    callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));\n    callAndCheck(gl, () => gl.compileShader(fragmentShader));\n    if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {\n        logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));\n        throw new Error('Failed to compile fragment shader.');\n    }\n    return fragmentShader;\n}\nconst lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;\nfunction logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {\n    const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);\n    if (lineNumberRegexResult == null) {\n        console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);\n        console.log(shaderSource);\n        return;\n    }\n    const lineNumber = +lineNumberRegexResult[1];\n    const shaderLines = shaderSource.split('\\n');\n    const pad = shaderLines.length.toString().length + 2;\n    const linesWithLineNumbers = shaderLines.map((line, lineNumber) => util.rightPad((lineNumber + 1).toString(), pad) + line);\n    let maxLineLength = 0;\n    for (let i = 0; i < linesWithLineNumbers.length; i++) {\n        maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);\n    }\n    const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);\n    const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);\n    const afterErrorLines = linesWithLineNumbers.slice(lineNumber);\n    console.log(beforeErrorLines.join('\\n'));\n    console.log(shaderInfoLog.split('\\n')[0]);\n    console.log(`%c ${util.rightPad(errorLine[0], maxLineLength)}`, 'border:1px solid red; background-color:#e3d2d2; color:#a61717');\n    console.log(afterErrorLines.join('\\n'));\n}\nexport function createProgram(gl) {\n    return throwIfNull(gl, () => gl.createProgram(), 'Unable to create WebGLProgram.');\n}\nexport function linkProgram(gl, program) {\n    callAndCheck(gl, () => gl.linkProgram(program));\n    if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {\n        console.log(gl.getProgramInfoLog(program));\n        throw new Error('Failed to link vertex and fragment shaders.');\n    }\n}\nexport function validateProgram(gl, program) {\n    callAndCheck(gl, () => gl.validateProgram(program));\n    if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {\n        console.log(gl.getProgramInfoLog(program));\n        throw new Error('Shader program validation failed.');\n    }\n}\nexport function createStaticVertexBuffer(gl, data) {\n    const buffer = throwIfNull(gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));\n    return buffer;\n}\nexport function createStaticIndexBuffer(gl, data) {\n    const buffer = throwIfNull(gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n    callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));\n    return buffer;\n}\nexport function getNumChannels() {\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        return 1;\n    }\n    return 4;\n}\nexport function createTexture(gl) {\n    return throwIfNull(gl, () => gl.createTexture(), 'Unable to create WebGLTexture.');\n}\nexport function validateTextureSize(width, height) {\n    const maxTextureSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n    if ((width <= 0) || (height <= 0)) {\n        const requested = `[${width}x${height}]`;\n        throw new Error('Requested texture size ' + requested + ' is invalid.');\n    }\n    if ((width > maxTextureSize) || (height > maxTextureSize)) {\n        const requested = `[${width}x${height}]`;\n        const max = `[${maxTextureSize}x${maxTextureSize}]`;\n        throw new Error('Requested texture size ' + requested +\n            ' greater than WebGL maximum on this browser / GPU ' + max + '.');\n    }\n}\nexport function createFramebuffer(gl) {\n    return throwIfNull(gl, () => gl.createFramebuffer(), 'Unable to create WebGLFramebuffer.');\n}\nexport function bindVertexBufferToProgramAttribute(gl, program, attribute, buffer, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {\n    const loc = gl.getAttribLocation(program, attribute);\n    if (loc === -1) {\n        // The GPU compiler decided to strip out this attribute because it's unused,\n        // thus no need to bind.\n        return false;\n    }\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes));\n    callAndCheck(gl, () => gl.enableVertexAttribArray(loc));\n    return true;\n}\nexport function bindTextureUnit(gl, texture, textureUnit) {\n    validateTextureUnit(gl, textureUnit);\n    callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n}\nexport function unbindTextureUnit(gl, textureUnit) {\n    validateTextureUnit(gl, textureUnit);\n    callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function getProgramUniformLocationOrThrow(gl, program, uniformName) {\n    return throwIfNull(gl, () => gl.getUniformLocation(program, uniformName), 'uniform \"' + uniformName + '\" not present in program.');\n}\nexport function getProgramUniformLocation(gl, program, uniformName) {\n    return gl.getUniformLocation(program, uniformName);\n}\nexport function bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) {\n    callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));\n    callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));\n}\nexport function bindCanvasToFramebuffer(gl) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n    callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));\n    callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));\n}\nexport function bindColorTextureToFramebuffer(gl, texture, framebuffer) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));\n}\nexport function unbindColorTextureFromFramebuffer(gl, framebuffer) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));\n}\nexport function validateFramebuffer(gl) {\n    const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);\n    if (status !== gl.FRAMEBUFFER_COMPLETE) {\n        throw new Error('Error binding framebuffer: ' + getFramebufferErrorMessage(gl, status));\n    }\n}\nexport function getFramebufferErrorMessage(gl, status) {\n    switch (status) {\n        case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:\n            return 'FRAMEBUFFER_INCOMPLETE_ATTACHMENT';\n        case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:\n            return 'FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT';\n        case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:\n            return 'FRAMEBUFFER_INCOMPLETE_DIMENSIONS';\n        case gl.FRAMEBUFFER_UNSUPPORTED:\n            return 'FRAMEBUFFER_UNSUPPORTED';\n        default:\n            return `unknown error ${status}`;\n    }\n}\nfunction throwIfNull(gl, returnTOrNull, failureMessage) {\n    const tOrNull = callAndCheck(gl, () => returnTOrNull());\n    if (tOrNull == null) {\n        throw new Error(failureMessage);\n    }\n    return tOrNull;\n}\nfunction validateTextureUnit(gl, textureUnit) {\n    const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;\n    const glTextureUnit = textureUnit + gl.TEXTURE0;\n    if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {\n        const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;\n        throw new Error(`textureUnit must be in ${textureUnitRange}.`);\n    }\n}\nexport function getBatchDim(shape, dimsToSkip = 2) {\n    return util.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));\n}\nexport function getRowsCols(shape) {\n    if (shape.length === 0) {\n        throw Error('Cannot get rows and columns of an empty shape array.');\n    }\n    return [\n        shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]\n    ];\n}\nexport function getShapeAs3D(shape) {\n    let shapeAs3D = [1, 1, 1];\n    const isScalar = shape.length === 0 || (shape.length === 1 && shape[0] === 1);\n    if (!isScalar) {\n        shapeAs3D =\n            [getBatchDim(shape), ...getRowsCols(shape)];\n    }\n    return shapeAs3D;\n}\nexport function getTextureShapeFromLogicalShape(logShape, isPacked = false) {\n    let maxTexSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n    if (isPacked) {\n        maxTexSize = maxTexSize * 2;\n        // This logic ensures we accurately count the number of packed texels needed\n        // to accommodate the tensor. We can only pack values in the same texel if\n        // they are from adjacent pairs of rows/cols within the same batch. So if a\n        // tensor has 3 rows, we pretend it has 4 rows in order to account for the\n        // fact that the texels containing the third row are half empty.\n        logShape = logShape.map((d, i) => i >= logShape.length - 2 ?\n            util.nearestLargerEven(logShape[i]) :\n            logShape[i]);\n        // Packed texture height is at least 2 (the channel height of a single\n        // texel).\n        if (logShape.length === 1) {\n            logShape = [2, logShape[0]];\n        }\n    }\n    // If logical shape is 2, we don't squeeze, since we want to match physical.\n    if (logShape.length !== 2) {\n        const squeezeResult = util.squeezeShape(logShape);\n        logShape = squeezeResult.newShape;\n    }\n    let size = util.sizeFromShape(logShape);\n    if (logShape.length <= 1 && size <= maxTexSize) {\n        return [1, size];\n    }\n    else if (logShape.length === 2 && logShape[0] <= maxTexSize &&\n        logShape[1] <= maxTexSize) {\n        return logShape;\n    }\n    else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize &&\n        logShape[2] <= maxTexSize) {\n        return [logShape[0] * logShape[1], logShape[2]];\n    }\n    else if (logShape.length === 3 && logShape[0] <= maxTexSize &&\n        logShape[1] * logShape[2] <= maxTexSize) {\n        return [logShape[0], logShape[1] * logShape[2]];\n    }\n    else if (logShape.length === 4 &&\n        logShape[0] * logShape[1] * logShape[2] <= maxTexSize &&\n        logShape[3] <= maxTexSize) {\n        return [logShape[0] * logShape[1] * logShape[2], logShape[3]];\n    }\n    else if (logShape.length === 4 && logShape[0] <= maxTexSize &&\n        logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {\n        return [logShape[0], logShape[1] * logShape[2] * logShape[3]];\n    }\n    else {\n        if (isPacked) {\n            // For packed textures size equals the number of channels required to\n            // accommodate the texture data. However in order to squarify such that\n            // inner dimensions stay even, we rewrite size to equal the number of\n            // texels. Then in the return statement we rehydrate the squarified\n            // dimensions to channel units.\n            const batchDim = getBatchDim(logShape);\n            let rows = 2, cols = 2;\n            if (logShape.length) {\n                [rows, cols] = getRowsCols(logShape);\n            }\n            size = batchDim * (rows / 2) * (cols / 2);\n            return util.sizeToSquarishShape(size).map(d => d * 2);\n        }\n        return util.sizeToSquarishShape(size);\n    }\n}\nfunction isEven(n) {\n    return n % 2 === 0;\n}\n/**\n * This determines whether reshaping a packed texture requires rearranging\n * the data within the texture, assuming 2x2 packing.\n */\nexport function isReshapeFree(shape1, shape2) {\n    shape1 = shape1.slice(-2);\n    shape2 = shape2.slice(-2);\n    if (util.arraysEqual(shape1, shape2)) {\n        return true;\n    }\n    if (!shape1.length || !shape2.length) { // One of the shapes is a scalar.\n        return true;\n    }\n    if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 ||\n        shape2[1] === 0) {\n        return true;\n    }\n    if (shape1.length !== shape2.length) { // One of the shapes is a vector.\n        const shape1Cols = shape1.slice(-1)[0];\n        const shape2Cols = shape2.slice(-1)[0];\n        if (shape1Cols === shape2Cols) {\n            return true;\n        }\n        if (isEven(shape1Cols) && isEven(shape2Cols) &&\n            (shape1[0] === 1 || shape2[0] === 1)) {\n            return true;\n        }\n    }\n    return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);\n}\n// We cache webgl params because the environment gets reset between\n// unit tests and we don't want to constantly query the WebGLContext for\n// MAX_TEXTURE_SIZE.\nlet MAX_TEXTURE_SIZE;\nlet MAX_TEXTURES_IN_SHADER;\nexport function getWebGLMaxTextureSize(webGLVersion) {\n    if (MAX_TEXTURE_SIZE == null) {\n        const gl = getWebGLContext(webGLVersion);\n        MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n    }\n    return MAX_TEXTURE_SIZE;\n}\nexport function resetMaxTextureSize() {\n    MAX_TEXTURE_SIZE = null;\n}\nexport function resetMaxTexturesInShader() {\n    MAX_TEXTURES_IN_SHADER = null;\n}\nexport function getMaxTexturesInShader(webGLVersion) {\n    if (MAX_TEXTURES_IN_SHADER == null) {\n        const gl = getWebGLContext(webGLVersion);\n        MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);\n    }\n    // We cap at 16 to avoid spurious runtime \"memory exhausted\" error.\n    return Math.min(16, MAX_TEXTURES_IN_SHADER);\n}\nexport function getWebGLDisjointQueryTimerVersion(webGLVersion) {\n    if (webGLVersion === 0) {\n        return 0;\n    }\n    let queryTimerVersion;\n    const gl = getWebGLContext(webGLVersion);\n    if (hasExtension(gl, 'EXT_disjoint_timer_query_webgl2') &&\n        webGLVersion === 2) {\n        queryTimerVersion = 2;\n    }\n    else if (hasExtension(gl, 'EXT_disjoint_timer_query')) {\n        queryTimerVersion = 1;\n    }\n    else {\n        queryTimerVersion = 0;\n    }\n    return queryTimerVersion;\n}\nexport function hasExtension(gl, extensionName) {\n    const ext = gl.getExtension(extensionName);\n    return ext != null;\n}\nexport function isWebGLVersionEnabled(webGLVersion) {\n    try {\n        const gl = getWebGLContext(webGLVersion);\n        if (gl != null) {\n            return true;\n        }\n    }\n    catch (e) {\n        console.log('Error when getting WebGL context: ', e);\n        return false;\n    }\n    return false;\n}\nexport function isCapableOfRenderingToFloatTexture(webGLVersion) {\n    if (webGLVersion === 0) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    if (webGLVersion === 1) {\n        if (!hasExtension(gl, 'OES_texture_float')) {\n            return false;\n        }\n    }\n    else {\n        if (!hasExtension(gl, 'EXT_color_buffer_float')) {\n            return false;\n        }\n    }\n    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n    return isFrameBufferComplete;\n}\n/**\n * Check if we can download values from a float/half-float texture.\n *\n * Note that for performance reasons we use binding a texture to a framebuffer\n * as a proxy for ability to download float values later using readPixels. The\n * texture params of this texture will not match those in readPixels exactly\n * but if we are unable to bind some kind of float texture to the frameBuffer\n * then we definitely will not be able to read float values from it.\n */\nexport function isDownloadFloatTextureEnabled(webGLVersion) {\n    if (webGLVersion === 0) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    if (webGLVersion === 1) {\n        if (!hasExtension(gl, 'OES_texture_float')) {\n            return false;\n        }\n        if (!hasExtension(gl, 'WEBGL_color_buffer_float')) {\n            return false;\n        }\n    }\n    else {\n        if (hasExtension(gl, 'EXT_color_buffer_float')) {\n            return createFloatTextureAndBindToFramebuffer(gl);\n        }\n        const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n        if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {\n            const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n            return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);\n        }\n        return false;\n    }\n    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n    return isFrameBufferComplete;\n}\nfunction createFloatTextureAndBindToFramebuffer(gl) {\n    const texConfig = getTextureConfig(gl);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    const width = 1;\n    const height = 1;\n    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);\n    const frameBuffer = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    gl.deleteTexture(texture);\n    gl.deleteFramebuffer(frameBuffer);\n    return isFrameBufferComplete;\n}\nfunction createHalfFloatTextureAndBindToFramebuffer(\n// tslint:disable-next-line:no-any\ngl, textureHalfFloatExtension) {\n    const texConfig = getTextureConfig(gl, textureHalfFloatExtension);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    const width = 1;\n    const height = 1;\n    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);\n    const frameBuffer = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    gl.deleteTexture(texture);\n    gl.deleteFramebuffer(frameBuffer);\n    return isFrameBufferComplete;\n}\nexport function isWebGLFenceEnabled(webGLVersion) {\n    if (webGLVersion !== 2) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    // tslint:disable-next-line:no-any\n    const isEnabled = gl.fenceSync != null;\n    return isEnabled;\n}\nexport function assertNotComplex(tensor, opName) {\n    if (!Array.isArray(tensor)) {\n        tensor = [tensor];\n    }\n    tensor.forEach(t => {\n        if (t != null) {\n            util.assert(t.dtype !== 'complex64', () => `${opName} does not support complex64 tensors ` +\n                'in the WebGL backend.');\n        }\n    });\n}\n//# sourceMappingURL=webgl_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { device_util, env } from '@tensorflow/tfjs-core';\nimport { getMaxTexturesInShader, getWebGLDisjointQueryTimerVersion, getWebGLMaxTextureSize, isCapableOfRenderingToFloatTexture, isDownloadFloatTextureEnabled, isWebGLFenceEnabled, isWebGLVersionEnabled } from './webgl_util';\nconst ENV = env();\n/**\n * This file contains WebGL-specific flag registrations.\n */\n/**\n * True if WebGL is supported.\n */\nENV.registerFlag('HAS_WEBGL', () => ENV.getNumber('WEBGL_VERSION') > 0);\n/** 0: No WebGL, 1: WebGL 1.0, 2: WebGL 2.0. */\nENV.registerFlag('WEBGL_VERSION', () => {\n    if (isWebGLVersionEnabled(2)) {\n        return 2;\n    }\n    else if (isWebGLVersionEnabled(1)) {\n        return 1;\n    }\n    return 0;\n});\n/** Whether to check for numerical representation problems. */\nENV.registerFlag('WEBGL_CHECK_NUMERICAL_PROBLEMS', () => false);\nENV.registerFlag('WEBGL_BUFFER_SUPPORTED', () => ENV.get('WEBGL_VERSION') === 2);\n/** Whether the WebGL backend will sometimes forward ops to the CPU. */\nENV.registerFlag('WEBGL_CPU_FORWARD', () => true);\n/** Whether the WebGL backend will always use f16 textures for rendering. */\nENV.registerFlag('WEBGL_FORCE_F16_TEXTURES', () => false);\n/** Whether to turn all packing related flags on. */\nENV.registerFlag('WEBGL_PACK', () => ENV.getBool('HAS_WEBGL'));\n/** Whether we will pack the batchnormalization op. */\nENV.registerFlag('WEBGL_PACK_NORMALIZATION', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack the clip op. */\nENV.registerFlag('WEBGL_PACK_CLIP', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack the depthwise conv op. */\n// TODO: https://github.com/tensorflow/tfjs/issues/1679\nENV.registerFlag('WEBGL_PACK_DEPTHWISECONV', () => false);\n/** Whether we will pack binary ops. */\nENV.registerFlag('WEBGL_PACK_BINARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack unary ops. */\nENV.registerFlag('WEBGL_PACK_UNARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack array ops. */\nENV.registerFlag('WEBGL_PACK_ARRAY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack image ops. */\nENV.registerFlag('WEBGL_PACK_IMAGE_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack reduce ops. */\nENV.registerFlag('WEBGL_PACK_REDUCE', () => ENV.getBool('WEBGL_PACK'));\n/** Whether packed WebGL kernels lazily unpack their outputs. */\nENV.registerFlag('WEBGL_LAZILY_UNPACK', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will use the im2col algorithm to speed up convolutions. */\nENV.registerFlag('WEBGL_CONV_IM2COL', () => ENV.getBool('WEBGL_PACK'));\n/** The maximum texture dimension. */\nENV.registerFlag('WEBGL_MAX_TEXTURE_SIZE', () => getWebGLMaxTextureSize(ENV.getNumber('WEBGL_VERSION')));\n/** The maximum texture dimension. */\nENV.registerFlag('WEBGL_MAX_TEXTURES_IN_SHADER', () => getMaxTexturesInShader(ENV.getNumber('WEBGL_VERSION')));\n/**\n * The disjoint_query_timer extension version.\n * 0: disabled, 1: EXT_disjoint_timer_query, 2:\n * EXT_disjoint_timer_query_webgl2.\n * In Firefox with WebGL 2.0,\n * EXT_disjoint_timer_query_webgl2 is not available, so we must use the\n * WebGL 1.0 extension.\n */\nENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', () => {\n    const webGLVersion = ENV.getNumber('WEBGL_VERSION');\n    if (webGLVersion === 0) {\n        return 0;\n    }\n    return getWebGLDisjointQueryTimerVersion(webGLVersion);\n});\n/**\n * Whether the timer object from the disjoint_query_timer extension gives\n * timing information that is reliable.\n */\nENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', () => ENV.getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&\n    !device_util.isMobile());\n/**\n * Whether the device is physically capable of rendering to float32 textures.\n */\nENV.registerFlag('WEBGL_RENDER_FLOAT32_CAPABLE', () => isCapableOfRenderingToFloatTexture(ENV.getNumber('WEBGL_VERSION')));\n/**\n * Whether rendering to float32 textures is enabled. If disabled, renders to\n * float16 textures.\n */\nENV.registerFlag('WEBGL_RENDER_FLOAT32_ENABLED', () => {\n    return ENV.getBool('WEBGL_FORCE_F16_TEXTURES') ?\n        false :\n        ENV.getBool('WEBGL_RENDER_FLOAT32_CAPABLE');\n});\n/**\n * Whether downloading float textures is enabled (16 or 32 bit). If disabled,\n * uses IEEE 754 encoding of the float32 values to 4 uint8 when downloading.\n */\nENV.registerFlag('WEBGL_DOWNLOAD_FLOAT_ENABLED', () => isDownloadFloatTextureEnabled(ENV.getNumber('WEBGL_VERSION')));\n/** Whether the fence API is available. */\nENV.registerFlag('WEBGL_FENCE_API_ENABLED', () => isWebGLFenceEnabled(ENV.getNumber('WEBGL_VERSION')));\n/**\n * Tensors with size <= than this will be uploaded as uniforms, not textures.\n */\nENV.registerFlag('WEBGL_SIZE_UPLOAD_UNIFORM', () => {\n    // Use uniform uploads only when 32bit floats are supported. In\n    // 16bit\n    // environments there are problems with comparing a 16bit texture value\n    // with a 32bit uniform value.\n    const useUniforms = ENV.getBool('WEBGL_RENDER_FLOAT32_ENABLED');\n    return useUniforms ? 4 : 0;\n});\n/**\n * If the total number of bytes allocated on the GPU is greater than this\n * number, we will aggressively delete textures upon disposal with\n * gl.deleteMatrixTexture, rather than making them available for reuse.\n *\n * Default value -1 indicates that we will never aggressively delete textures.\n */\nENV.registerFlag('WEBGL_DELETE_TEXTURE_THRESHOLD', () => {\n    return -1;\n}, threshold => {\n    if (threshold < 0 && threshold !== -1) {\n        throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never ` +\n            `delete) or at least 0, but got ${threshold}.`);\n    }\n});\n//# sourceMappingURL=flags_webgl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Import shared functionality from tfjs-backend-cpu without triggering\n// side effects.\n// tslint:disable-next-line: no-imports-from-dist\nimport * as shared from '@tensorflow/tfjs-backend-cpu/dist/shared';\nconst { simpleAbsImpl: simpleAbsImplCPU, addImpl: addImplCPU, ceilImpl: ceilImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, multiplyImpl: multiplyImplCPU, rsqrtImpl: rsqrtImplCPU, sliceImpl: sliceImplCPU, subImpl: subImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU, } = shared;\nexport { simpleAbsImplCPU, addImplCPU, ceilImplCPU, expImplCPU, expm1ImplCPU, logImplCPU, multiplyImplCPU, sliceImplCPU, subImplCPU, floorImplCPU, maxImplCPU, rsqrtImplCPU, transposeImplCPU, uniqueImplCPU, };\n//# sourceMappingURL=shared.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AddNProgram {\n    constructor(outputShape, shapes) {\n        this.outputShape = [];\n        this.outputShape = outputShape;\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const snippets = [];\n        // Get target elements from every input tensor.\n        this.variableNames.forEach(variable => {\n            snippets.push(`float v${variable} = get${variable}AtOutCoords();`);\n        });\n        // Calculate the sum of all elements.\n        const operation = this.variableNames\n            .map(variable => {\n            return `v${variable}`;\n        })\n            .join(' + ');\n        this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        float result = ${operation};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=addn_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AddNPackedProgram {\n    constructor(outputShape, shapes) {\n        this.outputShape = [];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const snippets = [];\n        // Get target elements from every input tensor.\n        this.variableNames.forEach(variable => {\n            snippets.push(`vec4 v${variable} = get${variable}AtOutCoords();`);\n        });\n        // Calculate the sum of all elements.\n        const operation = this.variableNames\n            .map(variable => {\n            return `v${variable}`;\n        })\n            .join(' + ');\n        this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        vec4 result = ${operation};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=addn_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ArgMinMaxProgram {\n    constructor(reduceInfo, op, firstPass) {\n        this.variableNames = ['A'];\n        const { windowSize, batchSize, outSize } = reduceInfo;\n        if (!firstPass) {\n            this.variableNames.push('bestIndicesA');\n        }\n        this.outputShape = [batchSize, outSize];\n        const compOp = (op === 'max') ? '>' : '<';\n        const indexSnippet = firstPass ?\n            'inOffset + i;' :\n            'round(getBestIndicesA(batch, inOffset + i));';\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          int inIdx = ${indexSnippet};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${compOp} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=argminmax_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport function getVecChannels(name, rank) {\n    return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank).map(d => `${name}.${d}`);\n}\nexport function getChannels(name, rank) {\n    if (rank === 1) {\n        return [name];\n    }\n    return getVecChannels(name, rank);\n}\nexport function getSourceCoords(rank, dims) {\n    if (rank === 1) {\n        return 'rc';\n    }\n    let coords = '';\n    for (let i = 0; i < rank; i++) {\n        coords += dims[i];\n        if (i < rank - 1) {\n            coords += ',';\n        }\n    }\n    return coords;\n}\n//# sourceMappingURL=packing_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nexport function getGlslDifferences() {\n    let version;\n    let attribute;\n    let varyingVs;\n    let varyingFs;\n    let texture2D;\n    let output;\n    let defineOutput;\n    let defineSpecialNaN;\n    let defineSpecialInf;\n    let defineRound;\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        version = '#version 300 es';\n        attribute = 'in';\n        varyingVs = 'out';\n        varyingFs = 'in';\n        texture2D = 'texture';\n        output = 'outputColor';\n        defineOutput = 'out vec4 outputColor;';\n        // Use custom isnan definition to work across differences between\n        // implementations on various platforms. While this should happen in ANGLE\n        // we still see differences between android and windows (on chrome) when\n        // using isnan directly.\n        defineSpecialNaN = `\n      bool isnan_custom(float val) {\n        return (val > 0.0 || val < 0.0) ? false : val != 0.0;\n      }\n\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan_custom(val.x),\n          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n      }\n\n      #define isnan(value) isnan_custom(value)\n    `;\n        // In webgl 2 we do not need to specify a custom isinf so there is no\n        // need for a special INFINITY constant.\n        defineSpecialInf = ``;\n        defineRound = `\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n    }\n    else {\n        version = '';\n        attribute = 'attribute';\n        varyingVs = 'varying';\n        varyingFs = 'varying';\n        texture2D = 'texture2D';\n        output = 'gl_FragColor';\n        defineOutput = '';\n        // WebGL1 has no built in isnan so we define one here.\n        defineSpecialNaN = `\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    `;\n        defineSpecialInf = `\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    `;\n        defineRound = `\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n    }\n    return {\n        version,\n        attribute,\n        varyingVs,\n        varyingFs,\n        texture2D,\n        output,\n        defineOutput,\n        defineSpecialNaN,\n        defineSpecialInf,\n        defineRound\n    };\n}\n//# sourceMappingURL=glsl_version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Produces GLSL code that derives logical coordinates from a flat\n * index. The code performs integer division with each stride and decrements\n * the index until the index equals the final dimension coordinate.\n */\nexport function getLogicalCoordinatesFromFlatIndex(coords, shape, index = 'index') {\n    const strides = util.computeStrides(shape);\n    return strides\n        .map((stride, i) => {\n        const line1 = `int ${coords[i]} = ${index} / ${stride}`;\n        const line2 = i === strides.length - 1 ?\n            `int ${coords[i + 1]} = ${index} - ${coords[i]} * ${stride}` :\n            `index -= ${coords[i]} * ${stride}`;\n        return `${line1}; ${line2};`;\n    })\n        .join('');\n}\nfunction buildVec(x) {\n    if (x.length === 1) {\n        return `${x[0]}`;\n    }\n    return `vec${x.length}(${x.join(',')})`;\n}\n/**\n * Produces GLSL code that computes the dot product of the input x and y\n * vectors. Handles splitting inputs into increments of vec4s when necessary.\n */\nexport function dotify(x, y) {\n    if (x.length !== y.length) {\n        throw new Error(`Vectors to be dotted must be of the same length -` +\n            `got ${x.length} and ${y.length}`);\n    }\n    const slices = [];\n    const nearestVec4 = Math.floor(x.length / 4);\n    const nearestVec4Remainder = x.length % 4;\n    for (let i = 0; i < nearestVec4; i++) {\n        const xSlice = x.slice(i * 4, i * 4 + 4);\n        const ySlice = y.slice(i * 4, i * 4 + 4);\n        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n    }\n    if (nearestVec4Remainder !== 0) {\n        let xSlice = x.slice(nearestVec4 * 4);\n        let ySlice = y.slice(nearestVec4 * 4);\n        if (xSlice.length === 1) {\n            xSlice = xSlice.map(d => `float(${d})`);\n            ySlice = ySlice.map(d => `float(${d})`);\n        }\n        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n    }\n    return slices.map((d, i) => `dot(${d})`).join('+');\n}\n/**\n * Produces GLSL that computes the flat index from 3D coordinates.\n */\nexport function getFlatIndexFrom3D(shape) {\n    const strides = util.computeStrides(shape).map(d => d.toString());\n    return `\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;\n  }\n`;\n}\nexport const ENCODE_FLOAT_SNIPPET = `\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`;\n//# sourceMappingURL=shader_compiler_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nconst { getBroadcastDims } = backend_util;\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nexport function makeShader(inputsInfo, outputShape, userCode, usesPackedTextures) {\n    const prefixSnippets = [];\n    inputsInfo.forEach(x => {\n        const size = util.sizeFromShape(x.shapeInfo.logicalShape);\n        // Snippet when we decided to upload the values as uniform.\n        if (x.shapeInfo.isUniform) {\n            prefixSnippets.push(`uniform float ${x.name}${size > 1 ? `[${size}]` : ''};`);\n        }\n        else {\n            prefixSnippets.push(`uniform sampler2D ${x.name};`);\n            prefixSnippets.push(`uniform int offset${x.name};`);\n        }\n    });\n    const inputPrefixSnippet = prefixSnippets.join('\\n');\n    const inputSamplingSnippet = inputsInfo\n        .map(x => getInputSamplingSnippet(x, outputShape, usesPackedTextures))\n        .join('\\n');\n    const outTexShape = outputShape.texShape;\n    const glsl = getGlslDifferences();\n    const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);\n    let outputSamplingSnippet;\n    let floatTextureSetOutputSnippet;\n    let shaderPrefix = getShaderPrefix(glsl);\n    if (outputShape.isPacked) {\n        outputSamplingSnippet =\n            getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n        floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);\n    }\n    else {\n        outputSamplingSnippet =\n            getOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n        floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);\n    }\n    if (usesPackedTextures) {\n        shaderPrefix += SHADER_PACKED_PREFIX;\n    }\n    const source = [\n        shaderPrefix, floatTextureSampleSnippet, floatTextureSetOutputSnippet,\n        inputPrefixSnippet, outputSamplingSnippet, inputSamplingSnippet, userCode\n    ].join('\\n');\n    return source;\n}\nfunction getSamplerFromInInfo(inInfo) {\n    const shape = inInfo.shapeInfo.logicalShape;\n    switch (shape.length) {\n        case 0:\n            return getSamplerScalar(inInfo);\n        case 1:\n            return getSampler1D(inInfo);\n        case 2:\n            return getSampler2D(inInfo);\n        case 3:\n            return getSampler3D(inInfo);\n        case 4:\n            return getSampler4D(inInfo);\n        case 5:\n            return getSampler5D(inInfo);\n        case 6:\n            return getSampler6D(inInfo);\n        default:\n            throw new Error(`${shape.length}-D input sampling` +\n                ` is not yet supported`);\n    }\n}\nfunction getPackedSamplerFromInInfo(inInfo) {\n    const shape = inInfo.shapeInfo.logicalShape;\n    switch (shape.length) {\n        case 0:\n            return getPackedSamplerScalar(inInfo);\n        case 1:\n            return getPackedSampler1D(inInfo);\n        case 2:\n            return getPackedSampler2D(inInfo);\n        case 3:\n            return getPackedSampler3D(inInfo);\n        default:\n            return getPackedSamplerND(inInfo);\n    }\n}\nfunction getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false) {\n    let res = '';\n    if (usesPackedTextures) {\n        res += getPackedSamplerFromInInfo(inInfo);\n    }\n    else {\n        res += getSamplerFromInInfo(inInfo);\n    }\n    const inShape = inInfo.shapeInfo.logicalShape;\n    const outShape = outShapeInfo.logicalShape;\n    if (inShape.length <= outShape.length) {\n        if (usesPackedTextures) {\n            res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);\n        }\n        else {\n            res += getSamplerAtOutputCoords(inInfo, outShapeInfo);\n        }\n    }\n    return res;\n}\nfunction getPackedOutputSamplingSnippet(outShape, outTexShape) {\n    switch (outShape.length) {\n        case 0:\n            return getOutputScalarCoords();\n        case 1:\n            return getOutputPacked1DCoords(outShape, outTexShape);\n        case 2:\n            return getOutputPacked2DCoords(outShape, outTexShape);\n        case 3:\n            return getOutputPacked3DCoords(outShape, outTexShape);\n        default:\n            return getOutputPackedNDCoords(outShape, outTexShape);\n    }\n}\nfunction getOutputSamplingSnippet(outShape, outTexShape) {\n    switch (outShape.length) {\n        case 0:\n            return getOutputScalarCoords();\n        case 1:\n            return getOutput1DCoords(outShape, outTexShape);\n        case 2:\n            return getOutput2DCoords(outShape, outTexShape);\n        case 3:\n            return getOutput3DCoords(outShape, outTexShape);\n        case 4:\n            return getOutput4DCoords(outShape, outTexShape);\n        case 5:\n            return getOutput5DCoords(outShape, outTexShape);\n        case 6:\n            return getOutput6DCoords(outShape, outTexShape);\n        default:\n            throw new Error(`${outShape.length}-D output sampling is not yet supported`);\n    }\n}\nfunction getFloatTextureSampleSnippet(glsl) {\n    return `\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${glsl.texture2D}(textureSampler, uv).r;\n    }\n  `;\n}\nfunction getFloatTextureSetRSnippet(glsl) {\n    return `\n    void setOutput(float val) {\n      ${glsl.output} = vec4(val, 0, 0, 0);\n    }\n  `;\n}\nfunction getFloatTextureSetRGBASnippet(glsl) {\n    return `\n    void setOutput(vec4 val) {\n      ${glsl.output} = val;\n    }\n  `;\n}\nfunction getShaderPrefix(glsl) {\n    const SHADER_PREFIX = `${glsl.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${glsl.varyingFs} vec2 resultUV;\n    ${glsl.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${glsl.defineSpecialNaN}\n    ${glsl.defineSpecialInf}\n    ${glsl.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${SAMPLE_1D_SNIPPET}\n    ${SAMPLE_2D_SNIPPET}\n    ${SAMPLE_3D_SNIPPET}\n  `;\n    return SHADER_PREFIX;\n}\nconst SAMPLE_1D_SNIPPET = `\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SAMPLE_2D_SNIPPET = `\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SAMPLE_3D_SNIPPET = `\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SHADER_PACKED_PREFIX = `\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;\nfunction getOutputScalarCoords() {\n    return `\n    int getOutputCoords() {\n      return 0;\n    }\n  `;\n}\nfunction getOutputPacked1DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (packedTexShape[0] === 1) {\n        return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);\n      }\n    `;\n    }\n    if (packedTexShape[1] === 1) {\n        return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);\n      }\n    `;\n    }\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);\n    }\n  `;\n}\nfunction getOutput1DCoords(shape, texShape) {\n    if (texShape[0] === 1) {\n        return `\n      int getOutputCoords() {\n        return int(resultUV.x * ${texShape[1]}.0);\n      }\n    `;\n    }\n    if (texShape[1] === 1) {\n        return `\n      int getOutputCoords() {\n        return int(resultUV.y * ${texShape[0]}.0);\n      }\n    `;\n    }\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      return resTexRC.x * ${texShape[1]} + resTexRC.y;\n    }\n  `;\n}\nfunction getOutputPacked3DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texelsInLogicalRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;\n}\nfunction getOutput3DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\nfunction getOutputPackedNDCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);\n    let texelsInBatchN = texelsInBatch;\n    let batches = ``;\n    let coords = 'b, r, c';\n    for (let b = 2; b < shape.length - 1; b++) {\n        texelsInBatchN *= shape[shape.length - b - 1];\n        batches = `\n      int b${b} = index / ${texelsInBatchN};\n      index -= b${b} * ${texelsInBatchN};\n    ` + batches;\n        coords = `b${b}, ` + coords;\n    }\n    return `\n    ivec${shape.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      ${batches}\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec${shape.length}(${coords});\n    }\n  `;\n}\nfunction getOutput4DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2'], shape);\n    return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec4(r, c, d, d2);\n    }\n  `;\n}\nfunction getOutput5DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3'], shape);\n    return `\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},\n                             ${texShape[1]}));\n\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `;\n}\nfunction getOutput6DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3', 'd4'], shape);\n    return `\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `;\n}\nfunction getOutputPacked2DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (util.arraysEqual(shape, texShape)) {\n        return `\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      }\n    `;\n    }\n    // texels needed to accommodate a logical row\n    const texelsInLogicalRow = Math.ceil(shape[1] / 2);\n    /**\n     * getOutputCoords\n     *\n     * resTexRC: The rows and columns of the texels. If you move over one\n     * texel to the right in the packed texture, you are moving over one column\n     * (not two).\n     *\n     * index: The texel index\n     */\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getOutput2DCoords(shape, texShape) {\n    if (util.arraysEqual(shape, texShape)) {\n        return `\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));\n      }\n    `;\n    }\n    if (shape[1] === 1) {\n        return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;\n    }\n    if (shape[0] === 1) {\n        return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;\n    }\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      int r = index / ${shape[1]};\n      int c = index - r * ${shape[1]};\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getFlatOffsetUniformName(texName) {\n    return `offset${texName}`;\n}\nfunction getPackedSamplerScalar(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}() {\n      return ${glsl.texture2D}(${texName}, halfCR);\n    }\n  `;\n}\nfunction getSamplerScalar(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    if (inputInfo.shapeInfo.isUniform) {\n        return `float ${funcName}() {return ${texName};}`;\n    }\n    const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;\n    if (texNumR === 1 && texNumC === 1) {\n        return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n    }\n    const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}() {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler1D(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${packedTexShape[0]}, ${packedTexShape[1]}, index);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler1D(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int index) {\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texShape = inputInfo.shapeInfo.texShape;\n    const tNumR = texShape[0];\n    const tNumC = texShape[1];\n    if (tNumC === 1 && tNumR === 1) {\n        return `\n      float ${funcName}(int index) {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    if (tNumC === 1) {\n        return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (tNumR === 1) {\n        return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n    float ${funcName}(int index) {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler2D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const glsl = getGlslDifferences();\n    if (texShape != null && util.arraysEqual(shape, texShape)) {\n        return `\n      vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n\n        return ${glsl.texture2D}(${texName}, uv);\n      }\n    `;\n    }\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const valuesPerRow = Math.ceil(shape[1] / 2);\n    return `\n    vec4 ${funcName}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler2D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    if (texShape != null && util.arraysEqual(shape, texShape)) {\n        const texNumR = texShape[0];\n        const texNumC = texShape[1];\n        return `\n    float ${funcName}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['row', 'col'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const offset = getFlatOffsetUniformName(texName);\n    if (texNumC === 1) {\n        // index is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    if (texNumR === 1) {\n        // index is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    return `\n  float ${funcName}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${shape[1]} + col + ${offset};\n    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n    return sampleTexture(${texName}, uv);\n  }\n`;\n}\nfunction getPackedSampler3D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (shape[0] === 1) {\n        const squeezedShape = shape.slice(1);\n        const keptDims = [1, 2];\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['b', 'row', 'col'];\n        return `\n        ${getPackedSamplerFromInInfo(newInputInfo)}\n        vec4 ${funcName}(int b, int row, int col) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n    }\n    const texNumR = packedTexShape[0];\n    const texNumC = packedTexShape[1];\n    const valuesPerRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler3D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride0 = shape[1] * shape[2];\n    const stride1 = shape[2];\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['row', 'col', 'depth'];\n        return `\n        ${getSamplerFromInInfo(newInputInfo)}\n        float ${funcName}(int row, int col, int depth) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${stride0}, ${stride1}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n        float ${funcName}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${texNumC}.0, ${texNumR}.0);\n          return sampleTexture(${texName}, uv);\n        }\n      `;\n    }\n    if (texNumC === stride1 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n      float ${funcName}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};\n        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n        return sampleTexture(${texName}, uv);\n      }\n  `;\n}\nfunction getPackedSamplerND(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const rank = shape.length;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texNumR = packedTexShape[0];\n    const texNumC = packedTexShape[1];\n    const valuesPerRow = Math.ceil(shape[rank - 1] / 2);\n    let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);\n    let params = `int b, int row, int col`;\n    let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;\n    for (let b = 2; b < rank - 1; b++) {\n        params = `int b${b}, ` + params;\n        texelsInBatch *= shape[rank - b - 1];\n        index = `b${b} * ${texelsInBatch} + ` + index;\n    }\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(${params}) {\n      int index = ${index};\n      int texR = index / ${texNumC};\n      int texC = index - texR * ${texNumC};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler4D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride2 = shape[3];\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${stride1}, ${stride2}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride2 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} +\n          depth * ${stride2} + depth2;\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler5D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride3 = shape[4];\n    const stride2 = shape[3] * stride3;\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2', 'depth3'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          depth3;\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${stride1}, ${stride2}, ${stride3}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride3 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3]},\n               ${shape[2] * shape[3]}, ${shape[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler6D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    const stride4 = shape[5];\n    const stride3 = shape[4] * stride4;\n    const stride2 = shape[3] * stride3;\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${stride4}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride4 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},\n               ${shape[2] * shape[3] * shape[4]},\n               ${shape[3] * shape[4]},\n               ${shape[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getUniformSampler(inputInfo) {\n    const texName = inputInfo.name;\n    const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n    if (inSize < 2) {\n        return `return ${texName};`;\n    }\n    return `\n    for (int i = 0; i < ${inSize}; i++) {\n      if (i == index) {\n        return ${texName}[i];\n      }\n    }\n  `;\n}\nfunction getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n    const texName = inputInfo.name;\n    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n    const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n    const inRank = inputInfo.shapeInfo.logicalShape.length;\n    const outRank = outShapeInfo.logicalShape.length;\n    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n    const type = getCoordsDataType(outRank);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet;\n    const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n    if (inRank === 0) {\n        coordsSnippet = '';\n    }\n    else if (outRank < 2 && broadcastDims.length >= 1) {\n        coordsSnippet = 'coords = 0;';\n    }\n    else {\n        coordsSnippet =\n            broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n                .join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n        unpackedCoordsSnippet = 'coords';\n    }\n    else {\n        unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n            .map((s, i) => `coords.${fields[i + rankDiff]}`)\n            .join(', ');\n    }\n    let output = `return outputValue;`;\n    const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n    const isInputScalar = inSize === 1;\n    const outSize = util.sizeFromShape(outShapeInfo.logicalShape);\n    const isOutputScalar = outSize === 1;\n    if (inRank === 1 && !isInputScalar && !isOutputScalar) {\n        output = `\n      return vec4(outputValue.xy, outputValue.xy);\n    `;\n    }\n    else if (isInputScalar && !isOutputScalar) {\n        if (outRank === 1) {\n            output = `\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `;\n        }\n        else {\n            output = `\n        return vec4(outputValue.x);\n      `;\n        }\n    }\n    else if (broadcastDims.length) {\n        const rows = inRank - 2;\n        const cols = inRank - 1;\n        if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {\n            output = `return vec4(outputValue.x);`;\n        }\n        else if (broadcastDims.indexOf(rows) > -1) {\n            output = `return vec4(outputValue.x, outputValue.y, ` +\n                `outputValue.x, outputValue.y);`;\n        }\n        else if (broadcastDims.indexOf(cols) > -1) {\n            output = `return vec4(outputValue.xx, outputValue.zz);`;\n        }\n    }\n    return `\n    vec4 ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});\n      ${output}\n    }\n  `;\n}\nfunction getSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n    const texName = inputInfo.name;\n    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n    const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n    const outTexShape = outShapeInfo.texShape;\n    const inTexShape = inputInfo.shapeInfo.texShape;\n    const inRank = inputInfo.shapeInfo.logicalShape.length;\n    const outRank = outShapeInfo.logicalShape.length;\n    if (!inputInfo.shapeInfo.isUniform && inRank === outRank &&\n        inputInfo.shapeInfo.flatOffset == null &&\n        util.arraysEqual(inTexShape, outTexShape)) {\n        return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, resultUV);\n      }\n    `;\n    }\n    const type = getCoordsDataType(outRank);\n    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet;\n    const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n    if (inRank === 0) {\n        coordsSnippet = '';\n    }\n    else if (outRank < 2 && broadcastDims.length >= 1) {\n        coordsSnippet = 'coords = 0;';\n    }\n    else {\n        coordsSnippet =\n            broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n                .join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n        unpackedCoordsSnippet = 'coords';\n    }\n    else {\n        unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n            .map((s, i) => `coords.${fields[i + rankDiff]}`)\n            .join(', ');\n    }\n    return `\n    float ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      return get${texFuncSnippet}(${unpackedCoordsSnippet});\n    }\n  `;\n}\nexport function getCoordsDataType(rank) {\n    if (rank <= 1) {\n        return 'int';\n    }\n    else if (rank === 2) {\n        return 'ivec2';\n    }\n    else if (rank === 3) {\n        return 'ivec3';\n    }\n    else if (rank === 4) {\n        return 'ivec4';\n    }\n    else if (rank === 5) {\n        return 'ivec5';\n    }\n    else if (rank === 6) {\n        return 'ivec6';\n    }\n    else {\n        throw Error(`GPU for rank ${rank} is not yet supported`);\n    }\n}\n/** Returns a new input info (a copy) that has a squeezed logical shape. */\nfunction squeezeInputInfo(inInfo, squeezedShape) {\n    // Deep copy.\n    const newInputInfo = JSON.parse(JSON.stringify(inInfo));\n    newInputInfo.shapeInfo.logicalShape = squeezedShape;\n    return newInputInfo;\n}\nfunction getSqueezedParams(params, keptDims) {\n    return keptDims.map(d => params[d]).join(', ');\n}\n//# sourceMappingURL=shader_compiler.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ArgMinMaxPackedProgram {\n    constructor(shape, windowSize, op, firstPass) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        util.assert(shape.length > 2, () => `Packed arg${op.charAt(0).toUpperCase() +\n            op.slice(1)} supports only inputs with rank above 2.`);\n        const inSize = shape[shape.length - 1];\n        const outSize = Math.ceil(inSize / windowSize);\n        this.outputShape = shape.slice(0, -1);\n        if (outSize > 1) {\n            this.outputShape.push(outSize);\n        }\n        if (!firstPass) {\n            this.variableNames.push('bestIndicesA');\n        }\n        const outShape = this.outputShape;\n        const rank = outShape.length;\n        const dtype = getCoordsDataType(rank);\n        const coords = getChannels('coords', rank);\n        let sourceLocSetup;\n        let sourceRank;\n        if (outSize === 1) {\n            sourceRank = rank + 1;\n            const sourceLocDType = getCoordsDataType(sourceRank);\n            sourceLocSetup = `\n        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 1]};\n        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 2]};\n        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 1]};\n        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 2]};`;\n        }\n        else {\n            sourceRank = rank;\n            sourceLocSetup = `\n        ${dtype} sourceLocR = coords;\n        ++${coords[rank - 1]};\n        ${dtype} sourceLocG = coords;\n        ++${coords[rank - 2]};\n        ${dtype} sourceLocA = coords;\n        --${coords[rank - 1]};\n        ${dtype} sourceLocB = coords;\n        --${coords[rank - 2]};`;\n        }\n        const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, sourceRank);\n        const inChannel = '.' + channels[sourceRank - 1]; // e.g. \".b\" for rank 3.\n        const intChannels = channels.map(x => 'int ' + x);\n        const srcRCoords = getChannels('sourceLocR', sourceRank - 1).concat('inIdx.r');\n        const srcGCoords = getChannels('sourceLocG', sourceRank - 1).concat('inIdx.g');\n        const srcBCoords = getChannels('sourceLocB', sourceRank - 1).concat('inIdx.b');\n        const srcACoords = getChannels('sourceLocA', sourceRank - 1).concat('inIdx.a');\n        const compOp = (op === 'max') ? 'greaterThan' : 'lessThan';\n        const fetchCandidateIdx = firstPass ? '' : `\n          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),\n                             getBestIndicesAChannel(${srcGCoords.join()}),\n                             getBestIndicesAChannel(${srcBCoords.join()}),\n                             getBestIndicesAChannel(${srcACoords.join()})));`;\n        const fetchValue = `vec4(\n            getAChannel(${srcRCoords.join()}),\n            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,\n            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;\n        const getBestIndicesAChannelSnippet = firstPass ? '' : `\n      float getBestIndicesAChannel(${intChannels.join()}) {\n        return getChannel(getBestIndicesA(${channels.join()}),\n                                          vec2(${channels.slice(-2).join()}));\n      }`;\n        this.userCode = `\n      float getAChannel(${intChannels.join()}) {\n        return getChannel(getA(${channels.join()}),\n                               vec2(${channels.slice(-2).join()}));\n      }\n      ${getBestIndicesAChannelSnippet}\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        bool hasNextCol = ${coords[rank - 1]} < ${outShape[rank - 1] - 1};\n        bool hasNextRow = ${coords[rank - 2]} < ${outShape[rank - 2] - 1};\n        ${sourceLocSetup}\n        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},\n          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${fetchValue};\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          inIdx = srcIdx;\n          ${fetchCandidateIdx}\n          vec4 candidate = ${fetchValue};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=argminmax_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AvgPool2DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const avgMultiplier = 1 / (filterHeight * filterWidth);\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth};\n            wC+= ${dilationWidth}) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class AvgPool3DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy'];\n        this.outputShape = convInfo.inShape;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=avg_pool_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\n// (Ar + Ai)(Br + Bi) =\n// ArBr + ArBi + AiBr + AiBi = ArBr - AB + ArBi + AiBr\n// Yr = ArBr - AB\n// Yi = ArBi + AiBr\nexport const COMPLEX_MULTIPLY = {\n    REAL: 'return areal * breal - aimag * bimag;',\n    IMAG: 'return areal * bimag + aimag * breal;'\n};\nexport class BinaryOpComplexProgram {\n    constructor(op, aShape, bShape) {\n        this.variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        this.userCode = `\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${op}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_complex_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nconst CHECK_NAN_SNIPPET = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nexport const ADD = 'return a + b;';\nexport const SUB = 'return a - b;';\nexport const MUL = 'return a * b;';\n// We use native integer division to deal with floating point imprecision. Since\n// we implement floor division and glsl implements truncated division, we\n// correct for this by subtracting 1 from result when the result is negative and\n// there is a remainder.\nexport const INT_DIV = `\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`;\nexport const POW = `\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nif (b == 0.0) {\n  return 1.0;\n}\nreturn (round(mod(b, 2.0)) != 1) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`;\nexport const SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport const EQUAL = `return float(a == b);`;\nexport const NOT_EQUAL = `return float(a != b);`;\nexport const LESS = `return float(a < b);`;\nexport const LESS_EQUAL = `return float(a <= b);`;\nexport const GREATER = `return float(a > b);`;\nexport const GREATER_EQUAL = `return float(a >= b);`;\nexport const LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;\nexport const LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;\nexport const MAX = CHECK_NAN_SNIPPET + `\n  return max(a, b);\n`;\nexport const MIN = CHECK_NAN_SNIPPET + `\n  return min(a, b);\n`;\nexport const MOD = `if (b == 0.0) return NAN;\n  return mod(a, b);`;\nexport const ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;\nexport const PRELU = `return (a < 0.) ? b * a : a;`;\nexport class BinaryOpProgram {\n    constructor(op, aShape, bShape) {\n        this.variableNames = ['A', 'B'];\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        this.userCode = `\n      float binaryOperation(float a, float b) {\n        ${op}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nconst CHECK_NAN_SNIPPET = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\nexport const INT_DIV = `\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n`;\nexport const POW = `\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const PRELU = `\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;\nexport const ELU_DER = `\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`;\nexport const EQUAL = `\n  return vec4(equal(a, b));\n`;\nexport const NOT_EQUAL = `\n  return vec4(notEqual(a, b));\n`;\nexport const LESS = `\n  return vec4(lessThan(a, b));\n`;\nexport const LESS_EQUAL = `\n  return vec4(lessThanEqual(a, b));\n`;\nexport const GREATER = `\n  return vec4(greaterThan(a, b));\n`;\nexport const GREATER_EQUAL = `\n  return vec4(greaterThanEqual(a, b));\n`;\nexport const LOGICAL_AND = `\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`;\nexport const LOGICAL_OR = `\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`;\nexport const MAX = `\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const MIN = `\n  vec4 result = vec4(min(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const MOD = `\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport class BinaryOpPackedProgram {\n    constructor(op, aShape, bShape, checkOutOfBounds = false) {\n        this.variableNames = ['A', 'B'];\n        this.supportsBroadcasting = true;\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const rank = this.outputShape.length;\n        let checkOutOfBoundsString = '';\n        if (checkOutOfBounds) {\n            if (rank === 0 || util.sizeFromShape(this.outputShape) === 1) {\n                checkOutOfBoundsString = `\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;\n            }\n            else {\n                const dtype = getCoordsDataType(rank);\n                checkOutOfBoundsString = `\n          ${dtype} coords = getOutputCoords();\n        `;\n                if (rank === 1) {\n                    checkOutOfBoundsString += `\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;\n                }\n                else {\n                    const channels = getChannels('coords', rank);\n                    checkOutOfBoundsString += `\n            bool nextRowOutOfBounds =\n              (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};\n            bool nextColOutOfBounds =\n              (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `;\n                }\n            }\n        }\n        this.userCode = `\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${op}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${checkOutOfBoundsString}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ClipProgram {\n    constructor(aShape) {\n        this.variableNames = ['A'];\n        this.outputShape = aShape;\n        this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    `;\n    }\n    getCustomSetupFunc(min, max) {\n        return (gpgpu, webGLProgram) => {\n            if (this.minLoc == null) {\n                this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n                this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n            }\n            gpgpu.gl.uniform1f(this.minLoc, min);\n            gpgpu.gl.uniform1f(this.maxLoc, max);\n        };\n    }\n}\n//# sourceMappingURL=clip_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ClipPackedProgram {\n    constructor(aShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = aShape;\n        this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    `;\n    }\n    getCustomSetupFunc(min, max) {\n        return (gpgpu, webGLProgram) => {\n            if (this.minLoc == null) {\n                this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n                this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n            }\n            gpgpu.gl.uniform1f(this.minLoc, min);\n            gpgpu.gl.uniform1f(this.maxLoc, max);\n        };\n    }\n}\n//# sourceMappingURL=clip_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ComplexAbsProgram {\n    constructor(shape) {\n        this.variableNames = ['real', 'imag'];\n        this.outputShape = shape;\n        this.userCode = `\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `;\n    }\n}\n//# sourceMappingURL=complex_abs_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class ConcatProgram {\n    // Concats 2d tensors along axis=1. See comments in MathBackendWebGL.concat().\n    constructor(shapes) {\n        this.outputShape = [];\n        this.outputShape = backend_util.computeOutShape(shapes, 1 /* axis */);\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const offsets = new Array(shapes.length - 1);\n        offsets[0] = shapes[0][1];\n        for (let i = 1; i < offsets.length; i++) {\n            offsets[i] = offsets[i - 1] + shapes[i][1];\n        }\n        const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];\n        for (let i = 1; i < offsets.length; i++) {\n            const shift = offsets[i - 1];\n            snippets.push(`else if (yC < ${offsets[i]}) ` +\n                `setOutput(getT${i}(yR, yC-${shift}));`);\n        }\n        const lastIndex = offsets.length;\n        const lastShift = offsets[offsets.length - 1];\n        snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${snippets.join('\\n        ')}\n      }\n    `;\n    }\n}\n//# sourceMappingURL=concat_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ConcatPackedProgram {\n    constructor(shapes, axis) {\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = [];\n        this.outputShape = backend_util.computeOutShape(shapes, axis);\n        const shape = this.outputShape;\n        const rank = shape.length;\n        const dtype = getCoordsDataType(rank);\n        const coords = getChannels('coords', rank);\n        const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank);\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const offsets = new Array(shapes.length - 1);\n        offsets[0] = shapes[0][axis];\n        for (let i = 1; i < offsets.length; i++) {\n            offsets[i] = offsets[i - 1] + shapes[i][axis];\n        }\n        const channel = channels[axis];\n        const lastChannels = channels.slice(-2);\n        const allChannels = channels.join();\n        let getValueSnippet = `if (${channel} < ${offsets[0]}) {\n        return getChannel(\n            getT0(${allChannels}), vec2(${lastChannels.join()}));\n        }`;\n        for (let i = 1; i < offsets.length; i++) {\n            const shift = offsets[i - 1];\n            // Note: the >= comparison below may seem unnecessary given the check\n            // above but is needed to workaround branch execution issues on some\n            // devices. It makes all the conditions exclusive without relying on\n            // execution order.\n            getValueSnippet += `\n        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i - 1]}) {\n          return getChannel(\n            getT${i}(${shiftedChannels(channels, channel, shift)}),\n            vec2(${shiftedChannels(lastChannels, channel, shift)}));\n        }`;\n        }\n        const lastIndex = offsets.length;\n        const shift = offsets[offsets.length - 1];\n        getValueSnippet += `\n        return getChannel(\n          getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),\n          vec2(${shiftedChannels(lastChannels, channel, shift)}));`;\n        this.userCode = `\n      float getValue(${channels.map(x => 'int ' + x)}) {\n        ${getValueSnippet}\n      }\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${coords}), 0., 0., 0.);\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} + 1;\n        if (${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.g = getValue(${coords});\n        }\n\n        ${coords[rank - 2]} = ${coords[rank - 2]} + 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]}) {\n          result.a = getValue(${coords});\n        }\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} - 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]} &&\n            ${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.b = getValue(${coords});\n        }\n        setOutput(result);\n      }\n    `;\n    }\n}\n/**\n * Return an expression for coordinates into a vector where a given channel\n * will be offset by [shift].\n *\n * @param channels the channels to consider\n * @param channel the channel we want shifted\n * @param shift  the amount to subtract from the channel.\n *\n * @returns a string of the form 'x, y-[shift], z' where any one channel can\n * have the shift applied.\n */\nfunction shiftedChannels(channels, channel, shift) {\n    const channelIdx = channels.indexOf(channel);\n    const res = channels.map((c, idx) => {\n        if (idx === channelIdx) {\n            return `${c} - ${shift}`;\n        }\n        else {\n            return c;\n        }\n    });\n    return res.join();\n}\n//# sourceMappingURL=concat_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Conv2DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              if (${isChannelsLast}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv2DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        const rowDim = isChannelsLast ? 1 : 2;\n        const colDim = isChannelsLast ? 2 : 3;\n        const channelDim = isChannelsLast ? 3 : 1;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${channelDim}];\n\n        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n\n              if (${isChannelsLast}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv3DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.userCode = `\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {\n            int xF = wF + yF * ${strideDepth} - ${padFront};\n\n            if (xF < 0 || xF >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n              int xR = wR + yR * ${strideHeight} - ${padTop};\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n                int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv3DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padFront = filterDepth - 1 - convInfo.padInfo.front;\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;\n\n          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${filterDepth} - 1 - wF;\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${filterHeight} - 1 - wR;\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${filterWidth} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthwiseConv2DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${channelMul} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class DepthwiseConv2DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${channelMul}; dm++) {\n              int d2 = d1 * ${channelMul} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_backprop_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Conv2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivationWeights = false) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n        const inputDepthVec4Remainder = convInfo.inChannels % 4;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const rowDim = isChannelsLast ? 1 : 2;\n        const colDim = isChannelsLast ? 2 : 3;\n        const channelDim = isChannelsLast ? 3 : 1;\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivationWeights) {\n                activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivationWeights) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${channelDim}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${convInfo.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${inputDepthVec4Remainder === 1}) {\n\n              if (${isChannelsLast}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              }\n\n            } else if (${inputDepthVec4Remainder === 2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${inputDepthVec4Remainder === 3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\nexport class Conv3DProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n        const inputDepthVec4Remainder = convInfo.inChannels % 4;\n        this.userCode = `\n      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          int xF = xFCorner + wF * ${dilationDepth};\n\n          if (xF < 0 || xF >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            int xR = xRCorner + wR * ${dilationHeight};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${inputDepthVec4Remainder === 1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);\n              } else if (${inputDepthVec4Remainder === 2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${inputDepthVec4Remainder === 3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthwiseConv2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const xNumRows = convInfo.inHeight;\n        const xNumCols = convInfo.inWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${xNumRows}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${xNumCols}) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport class DepthwiseConvPacked2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['x', 'W'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = convInfo.outShape;\n        const xNumRows = convInfo.inHeight;\n        const xNumCols = convInfo.inWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const texelsAcross = filterWidth;\n        let mainLoop = `int xR; int xC; int xCOffset;`;\n        for (let r = 0; r < filterHeight; r++) {\n            for (let c = 0; c < filterWidth; c++) {\n                mainLoop += `\n          vec4 xTexelR${r}C${c * 2} = vec4(0.);\n          vec4 wR${r}C${c} = vec4(0.);\n          vec4 xR${r}C${c} = vec4(0.);`;\n            }\n        }\n        /**\n         * This vectorized implementation works by gathering the values needed for\n         * each output channel's dot product into vec4's and then multiplying them\n         * all together (this happens in the final double for-loop below). Most of\n         * the main loop consists of constructing these vec4's with the minimum\n         * number of texture2D calls, which means making use of all four returned\n         * values from a texture2D call at once.\n         */\n        for (let r = 0; r < filterHeight; r++) {\n            for (let texelC = 0; texelC < texelsAcross; texelC++) {\n                const c = texelC * 2;\n                mainLoop += `\n          xR = xRCorner + ${r * dilationHeight};\n          xC = xCCorner + ${c * dilationWidth};\n        `;\n                if (strideWidth === 1) {\n                    if (c < filterWidth) {\n                        // If padding is odd, the outer texels have to be composed.\n                        if (padLeft % 2 === 1) {\n                            // TODO: Ensure vec4 previous does not result in redundant sample,\n                            // and avoid setting xTexelRC's that exceed the boundary in the\n                            // first place rather than resetting them to vec4(0)).\n                            // To compute xCOffset:\n                            // - If padding is odd, we must add 1 to ensure we ask for an\n                            // even-numbered row.\n                            // - We subtract 2 to access the previous texel.\n                            mainLoop += `\n                xCOffset = xC + 1;\n                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= ${xNumCols}) {\n                    xTexelR${r}C${c}.zw = vec2(0.);\n                  }\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xCOffset = xC + 1 - 2;\n                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  vec4 previous = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= ${xNumCols}) {\n                    previous.zw = vec2(0.);\n                  }\n\n                  xR${r}C${c} = vec4(previous.zw, xTexelR${r}C${c}.xy);\n                } else {\n                  xR${r}C${c} = vec4(0, 0, xTexelR${r}C${c}.xy);\n                }\n              `;\n                        }\n                        else {\n                            // Padding is even, so xRC corresponds to a single texel.\n                            mainLoop += `\n                if(xR >= 0 && xR < ${xNumRows} && xC >= 0 && xC < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xR${r}C${c} = xTexelR${r}C${c};\n              `;\n                        }\n                        if (c + 1 < filterWidth) {\n                            // If dilation is even, the second entry should match the first\n                            // (either both are composed or both are single samples). But if\n                            // dilation is odd, then the second entry should be the opposite\n                            // of the first (if the first is composed, the second is a single\n                            // sample, and vice versa.)\n                            const nextTexelOffset = padLeft % 2 === 0 ?\n                                util.nearestLargerEven(dilationWidth) :\n                                dilationWidth;\n                            if ((dilationWidth % 2 === 0 && padLeft % 2 === 1) ||\n                                (dilationWidth % 2 !== 0 && padLeft % 2 !== 1)) {\n                                mainLoop += `\n                  xCOffset = xC + ${padLeft % 2} + ${nextTexelOffset};\n\n                  if(xR >= 0 && xR < ${xNumRows} &&\n                    xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                  }\n                `;\n                                // If dilation > 1 then the xRC's will not be able to share any\n                                // values, so each xRC will require two unique calls to getX.\n                                if (dilationWidth > 1) {\n                                    mainLoop += `\n                    xCOffset -= 2;\n                    if(xR >= 0 && xR < ${xNumRows} &&\n                      xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                      xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n                    } else {\n                      xTexelR${r}C${c} = vec4(0.);\n                    }\n                  `;\n                                }\n                                mainLoop += `\n                  xR${r}C${c + 1} = vec4(\n                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.xy);\n                `;\n                            }\n                            else {\n                                mainLoop += `\n                  xCOffset = xC + ${nextTexelOffset};\n\n                  if(xR >= 0 && xR < ${xNumRows} &&\n                    xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                  }\n\n                  xR${r}C${c + 1} = xTexelR${r}C${c + 2};\n                `;\n                            }\n                        }\n                    }\n                }\n                else { // stride > 1\n                    if (c < filterWidth) {\n                        mainLoop += `\n              if(xR >= 0 && xR < ${xNumRows}) {\n            `;\n                        // Depending on whether padLeft is even or odd, we want either the\n                        // xy or zw channels from X texels for xR${r}C${c}. If padLeft is\n                        // even, xR${r}C${c + 1} is simply the zw channels of texels we've\n                        // already sampled. But if padLeft is odd, xR${r}C{$c + 1}.zw will\n                        // need to come from the xy channels of a new texel, hence the `vec4\n                        // final` initialized below.\n                        if (padLeft % 2 === 1) {\n                            mainLoop += `\n                xCOffset = xC + 1 - ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < ${xNumCols}) {\n                  xTexelR${r}C${c + 2} = getX(batch, xR, xC + 1, d1);\n                } else {\n                  xTexelR${r}C${c + 2} = vec4(0.);\n                }\n\n                xR${r}C${c} = vec4(\n                  xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n              `;\n                            if (c + 1 < filterWidth) {\n                                mainLoop += `\n                  vec4 final = vec4(0.);\n                  xCOffset = xC + 1 + ${strideWidth};\n                  if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xR${r}C${c + 1} = vec4(xTexelR${r}C${c + 2}.xy, final.xy);\n                `;\n                            }\n                        }\n                        else {\n                            mainLoop += `\n                if(xC >= 0 && xC < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xCOffset = xC + ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${r}C${c + 2} = vec4(0.);\n                }\n\n                xR${r}C${c} = vec4(\n                  xTexelR${r}C${c}.xy, xTexelR${r}C${c + 2}.xy);\n              `;\n                            if (c + 1 < filterWidth) {\n                                mainLoop += `\n                  xR${r}C${c + 1} = vec4(\n                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n                `;\n                            }\n                        }\n                        mainLoop += `}`;\n                    }\n                }\n                if (c < filterWidth) {\n                    mainLoop += `\n            vec4 wTexelR${r}C${c} = getW(${r}, ${c}, d1, q);\n            wR${r}C${c} = vec4(wTexelR${r}C${c}.xz, wTexelR${r}C${c}.xz);\n          `;\n                    if (c + 1 < filterWidth) {\n                        mainLoop += `\n              vec4 wTexelR${r}C${c + 1} = getW(${r}, ${c + 1}, d1, q);\n              wR${r}C${c + 1} =\n                vec4(wTexelR${r}C${c + 1}.xz, wTexelR${r}C${c + 1}.xz);`;\n                    }\n                }\n            }\n        }\n        for (let r = 0; r < filterHeight; r++) {\n            for (let c = 0; c < filterWidth; c++) {\n                mainLoop += `dotProd += xR${r}C${c} * wR${r}C${c};`;\n            }\n        }\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 dotProd = vec4(0.);\n\n        ${mainLoop}\n\n        vec4 result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_packed_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class CropAndResizeProgram {\n    constructor(imageShape, boxShape, cropSize, method, extrapolationValue) {\n        this.variableNames = ['Image', 'Boxes', 'BoxInd'];\n        this.outputShape = [];\n        const [batch, imageHeight, imageWidth, depth] = imageShape;\n        const [numBoxes,] = boxShape;\n        const [cropHeight, cropWidth] = cropSize;\n        this.outputShape = [numBoxes, cropHeight, cropWidth, depth];\n        const methodId = method === 'bilinear' ? 1 : 0;\n        const [inputHeightFloat, inputWidthFloat] = [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];\n        const [heightRatio, heightScale, inY] = cropHeight > 1 ?\n            [\n                `${(imageHeight - 1) / (cropHeight - 1)}`,\n                '(y2-y1) * height_ratio',\n                `y1*${inputHeightFloat} + float(y)*(height_scale)`,\n            ] :\n            [\n                '0.0',\n                '0.0',\n                `0.5 * (y1+y2) * ${inputHeightFloat}`,\n            ];\n        const [widthRatio, widthScale, inX] = cropWidth > 1 ?\n            [\n                `${(imageWidth - 1) / (cropWidth - 1)}`,\n                '(x2-x1) * width_ratio',\n                `x1*${inputWidthFloat} + float(x)*(width_scale)`,\n            ] :\n            [\n                '0.0',\n                '0.0',\n                `0.5 * (x1+x2) * ${inputWidthFloat}`,\n            ];\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc\n        this.userCode = `\n      const float height_ratio = float(${heightRatio});\n      const float width_ratio = float(${widthRatio});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${batch}) {\n          return;\n        }\n\n        float height_scale = ${heightScale};\n        float width_scale = ${widthScale};\n\n        float in_y = ${inY};\n        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n        float in_x = ${inX};\n        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${methodId} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=crop_and_resize_gpu.js.map", "import { getCoordsDataType } from './shader_compiler';\nexport class CumSumProgram {\n    constructor(shape, exclusive, reverse) {\n        this.variableNames = ['x'];\n        this.outputShape = shape;\n        const rank = shape.length;\n        const val = exclusive ? '0.0' : `getX(${getCoords(rank, 'coords')})`;\n        const length = shape[shape.length - 1];\n        let condition = '';\n        let idxString = '';\n        // When exclusive is set, the cumsum op becomes roll op that copies the\n        // value from the previous index based on the direction specified by the\n        // reverse flag.\n        if (exclusive) {\n            condition = reverse ? `end != ${length - 1}` : 'end != 0';\n            idxString = reverse ? 'end + 1' : 'end - 1';\n        }\n        else {\n            condition = reverse ? `end + pow2 < ${length}` : 'end >= pow2';\n            idxString = (reverse ? 'end + pow2' : 'end - pow2');\n        }\n        this.userCode = `\n      uniform float index;\n      void main() {\n        ${getCoordsDataType(rank)} coords = getOutputCoords();\n        int end = ${getFinalCoord(rank, 'coords')};\n        float val = ${val};\n        int pow2 = int(pow(2.0, index));\n        if (${condition}) {\n          int idx = ${idxString};\n          ${getFinalCoord(rank, 'coords')} = idx;\n          val += getX(${getCoords(rank, 'coords')});\n        }\n        setOutput(val);\n      }\n    `;\n    }\n    getCustomSetupFunc(index) {\n        return (gpgpu, webGLProgram) => {\n            if (this.index == null) {\n                this.index = gpgpu.getUniformLocation(webGLProgram, 'index');\n            }\n            gpgpu.gl.uniform1f(this.index, index);\n        };\n    }\n}\nfunction getCoords(rank, name) {\n    if (rank === 1) {\n        return `${name}`;\n    }\n    else if (rank === 2) {\n        return `${name}.x, ${name}.y`;\n    }\n    else if (rank === 3) {\n        return `${name}.x, ${name}.y, ${name}.z`;\n    }\n    else if (rank === 4) {\n        return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n    }\n    else {\n        throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n    }\n}\nfunction getFinalCoord(rank, name) {\n    if (rank === 1) {\n        return `${name}`;\n    }\n    else if (rank === 2) {\n        return `${name}.y`;\n    }\n    else if (rank === 3) {\n        return `${name}.z`;\n    }\n    else if (rank === 4) {\n        return `${name}.w`;\n    }\n    else {\n        throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n    }\n}\n//# sourceMappingURL=cumsum_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nimport { getDenseTexShape, PackingScheme } from './tex_util';\nexport class DecodeMatrixProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        this.outPackingScheme = PackingScheme.DENSE;\n        const texShape = getDenseTexShape(outputShape);\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=decode_matrix_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nimport { getDenseTexShape, PackingScheme } from './tex_util';\nexport class DecodeMatrixPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outPackingScheme = PackingScheme.DENSE;\n        const texShape = getDenseTexShape(outputShape);\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=decode_matrix_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthToSpaceProgram {\n    constructor(outputShape, blockSize, dataFormat) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        this.outputShape = outputShape;\n        this.blockSize = blockSize;\n        this.dataFormat = dataFormat;\n        this.userCode = `\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${blockSize};\n      int offset_h = imod(h, ${blockSize});\n      int in_w = w / ${blockSize};\n      int offset_w = imod(w, ${blockSize});\n      int offset_d = (offset_h * ${blockSize} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `;\n    }\n    getHeightCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[1]`;\n        }\n        else {\n            return `coords[2]`;\n        }\n    }\n    getWidthCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[2]`;\n        }\n        else {\n            return `coords[3]`;\n        }\n    }\n    getDepthCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[3]`;\n        }\n        else {\n            return `coords[1]`;\n        }\n    }\n    getOutputDepthSize() {\n        if (this.dataFormat === 'NHWC') {\n            return this.outputShape[3];\n        }\n        else {\n            return this.outputShape[1];\n        }\n    }\n    getInputSamplingString() {\n        if (this.dataFormat === 'NHWC') {\n            return `getX(b, in_h, in_w, in_d)`;\n        }\n        else {\n            return `getX(b, in_d, in_h, in_w)`;\n        }\n    }\n}\n//# sourceMappingURL=depth_to_space_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DiagProgram {\n    constructor(size) {\n        this.variableNames = ['X'];\n        this.outputShape = [size, size];\n        this.userCode = `\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=diag_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport { ENCODE_FLOAT_SNIPPET } from './shader_compiler_util';\nimport { TextureUsage } from './tex_util';\nexport class EncodeFloatProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.outTexUsage = TextureUsage.DOWNLOAD;\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_float_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport { ENCODE_FLOAT_SNIPPET } from './shader_compiler_util';\nimport { TextureUsage } from './tex_util';\nexport class EncodeFloatPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = false;\n        this.outTexUsage = TextureUsage.DOWNLOAD;\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_float_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nexport class EncodeMatrixProgram {\n    constructor(outputShape, texShape, inputIsUnsignedByte = false) {\n        this.variableNames = ['A'];\n        const glsl = getGlslDifferences();\n        const [height, width] = texShape;\n        this.outputShape = outputShape;\n        let output = `result`;\n        if (inputIsUnsignedByte) {\n            output = `floor(result * 255. + 0.5)`;\n        }\n        this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / ${width};\n        int c = imod(flatIndex, ${width});\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n        vec4 values = ${glsl.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${glsl.output} = vec4(${output}, 0., 0., 0.);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_matrix_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\n/*\nThis is how the shader encodes a tensor with shape = [2, 3, 5]\n(indices are [batch, row, col]).\n\n000|001   002|003   004|xxx   020|021   022|023   024|xxx\n-------   -------   -------   -------   -------   -------\n010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\n100|101   102|103   104|xxx   120|121   122|123   124|xxx\n-------   -------   -------   -------   -------   -------\n110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\nSingle texels contain only values from the same batch, and from adjacent rows\nand columns.\n */\nexport class EncodeMatrixPackedProgram {\n    constructor(outputShape, texShape, inputIsUnsignedByte = false) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        const glsl = getGlslDifferences();\n        const [height, width] = texShape;\n        this.outputShape = outputShape;\n        let mainLoop = '';\n        let output = 'result';\n        if (inputIsUnsignedByte) {\n            output = 'floor(result * 255. + 0.5)';\n        }\n        for (let row = 0; row <= 1; row++) {\n            for (let col = 0; col <= 1; col++) {\n                const channel = row * 2 + col;\n                mainLoop += `\n          localCoords = coords;\n          if(localCoords[2] + ${col} < ${outputShape[2]}) {\n            localCoords[2] += ${col};\n            if(localCoords[1] + ${row} < ${outputShape[1]}) {\n              localCoords[1] += ${row};\n\n              flatIndex = getFlatIndex(localCoords);\n              offset = imod(flatIndex, 4);\n\n              flatIndex = idiv(flatIndex, 4, 1.);\n\n              r = flatIndex / ${width};\n              c = imod(flatIndex, ${width});\n              uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n              values = ${glsl.texture2D}(A, uv);\n\n              if(offset == 0) {\n                result[${channel}] = values[0];\n              } else if(offset == 1) {\n                result[${channel}] = values[1];\n              } else if(offset == 2) {\n                result[${channel}] = values[2];\n              } else {\n                result[${channel}] = values[3];\n              }\n            }\n          }\n        `;\n            }\n        }\n        this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        vec4 result = vec4(0.);\n        int flatIndex, r, c, offset;\n        ivec3 localCoords;\n        vec2 uv;\n        vec4 values;\n\n        ${mainLoop}\n\n        ${glsl.output} = ${output};\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_matrix_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const COMPLEX_FFT = {\n    REAL: 'return real * expR - imag * expI;',\n    IMAG: 'return real * expI + imag * expR;'\n};\nexport class FFTProgram {\n    constructor(op, inputShape, inverse) {\n        this.variableNames = ['real', 'imag'];\n        const innerDim = inputShape[1];\n        this.outputShape = inputShape;\n        const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;\n        const resultDenominator = inverse ? `${innerDim}.0` : '1.0';\n        this.userCode = `\n      const float exponentMultiplier = ${exponentMultiplierSnippet};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${op}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${innerDim});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${innerDim}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=fft_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FillProgram {\n    constructor(shape, value) {\n        this.outputShape = [];\n        this.variableNames = ['x'];\n        this.outputShape = shape;\n        this.userCode = `\n      uniform float value;\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    `;\n    }\n    getCustomSetupFunc(value) {\n        return (gpgpu, webGLProgram) => {\n            if (this.valueLoc == null) {\n                this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'value');\n            }\n            gpgpu.gl.uniform1f(this.valueLoc, value);\n        };\n    }\n}\n//# sourceMappingURL=fill_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class GatherProgram {\n    constructor(aShape, indicesLength, axis) {\n        this.variableNames = ['A', 'indices'];\n        const outputShape = aShape.slice();\n        outputShape[axis] = indicesLength;\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const sourceCoords = getSourceCoords(aShape, axis);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n    }\n}\nfunction getSourceCoords(aShape, axis) {\n    const rank = aShape.length;\n    if (rank > 4) {\n        throw Error(`Gather for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n        return `int(getIndices(resRC))`;\n    }\n    const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n    const sourceCoords = [];\n    for (let i = 0; i < aShape.length; i++) {\n        if (i === axis) {\n            sourceCoords.push(`int(getIndices(${currentCoords[i]}))`);\n        }\n        else {\n            sourceCoords.push(`${currentCoords[i]}`);\n        }\n    }\n    return sourceCoords.join();\n}\n//# sourceMappingURL=gather_gpu.js.map", "import { getCoordsDataType } from './shader_compiler';\nexport class GatherNDProgram {\n    constructor(sliceDim, strides, shape) {\n        this.sliceDim = sliceDim;\n        this.strides = strides;\n        this.variableNames = ['x', 'indices'];\n        this.outputShape = shape;\n        const stridesType = getCoordsDataType(strides.length);\n        const dtype = getCoordsDataType(shape.length);\n        const strideString = this.sliceDim > 1 ? 'strides[j]' : 'strides';\n        this.userCode = `\n        ${stridesType} strides = ${stridesType}(${this.strides});\n         void main() {\n          ${dtype} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${strideString};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `;\n    }\n}\n//# sourceMappingURL=gather_nd_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\nexport function createVertexShader(gl) {\n    const glsl = getGlslDifferences();\n    const vertexShaderSource = `${glsl.version}\n    precision highp float;\n    ${glsl.attribute} vec3 clipSpacePos;\n    ${glsl.attribute} vec2 uv;\n    ${glsl.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;\n    return webgl_util.createVertexShader(gl, vertexShaderSource);\n}\nexport function createVertexBuffer(gl) {\n    // [x y z u v] * [upper-left, lower-left, upper-right, lower-right]\n    const vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);\n    return webgl_util.createStaticVertexBuffer(gl, vertexArray);\n}\nexport function createIndexBuffer(gl) {\n    // OpenGL (and WebGL) have \"CCW == front\" winding\n    const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);\n    return webgl_util.createStaticIndexBuffer(gl, triangleVertexIndices);\n}\nfunction createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {\n    webgl_util.validateTextureSize(width, height);\n    const texture = webgl_util.createTexture(gl);\n    const tex2d = gl.TEXTURE_2D;\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(tex2d, texture));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));\n    webgl_util.callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null));\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n    return texture;\n}\nexport function getInternalFormatForFloat32MatrixTexture(textureConfig) {\n    return textureConfig.internalFormatFloat;\n}\nexport function createFloat32MatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT);\n}\nexport function getInternalFormatForFloat16MatrixTexture(textureConfig) {\n    return textureConfig.internalFormatHalfFloat;\n}\nexport function createFloat16MatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);\n}\nexport function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) {\n    return textureConfig.downloadTextureFormat;\n}\nexport function createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE);\n}\nexport function getInternalFormatForPackedMatrixTexture(textureConfig) {\n    return textureConfig.internalFormatPackedFloat;\n}\nexport function createPackedMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT);\n}\nexport function getInternalFormatForFloat16PackedMatrixTexture(textureConfig) {\n    return textureConfig.internalFormatPackedHalfFloat;\n}\nexport function createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat);\n}\nexport function bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {\n    const posOffset = 0; // x is the first buffer element\n    const uvOffset = 3 * 4; // uv comes after [x y z]\n    const stride = (3 * 4) + (2 * 4); // xyz + uv, each entry is 4-byte float.\n    webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));\n    const success = webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'clipSpacePos', vertexBuffer, 3, stride, posOffset);\n    return success &&\n        webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'uv', vertexBuffer, 2, stride, uvOffset);\n}\nexport function uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) {\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n    let dataForUpload, texelDataType, internalFormat;\n    if (data instanceof Uint8Array) {\n        dataForUpload = new Uint8Array(width * height * 4);\n        texelDataType = gl.UNSIGNED_BYTE;\n        internalFormat = gl.RGBA;\n    }\n    else {\n        dataForUpload = new Float32Array(width * height * 4);\n        texelDataType = gl.FLOAT;\n        internalFormat = textureConfig.internalFormatPackedFloat;\n    }\n    dataForUpload.set(data);\n    webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload));\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function uploadPixelDataToTexture(gl, texture, pixels) {\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n    if (pixels.data instanceof Uint8Array) {\n        webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));\n    }\n    else {\n        webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n    }\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function createBufferFromOutputTexture(gl2, rows, columns, textureConfig) {\n    // Create and bind the buffer.\n    const buffer = gl2.createBuffer();\n    webgl_util.callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer));\n    // Initialize the buffer to the size of the texture in bytes.\n    const bytesPerFloat = 4;\n    const valuesPerTexel = 4;\n    const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;\n    webgl_util.callAndCheck(gl2, () => gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));\n    // Enqueue a command on the GPU command queue to copy of texture into the\n    // buffer.\n    webgl_util.callAndCheck(gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));\n    webgl_util.callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));\n    return buffer;\n}\nexport function downloadFloat32MatrixFromBuffer(gl, buffer, size) {\n    const gl2 = gl;\n    const downloadTarget = new Float32Array(size);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n    return downloadTarget;\n}\nexport function downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {\n    const [w, h] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    const numChannels = 4;\n    const downloadTarget = new Uint8Array(tex_util.getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));\n    webgl_util.callAndCheck(gl, () => gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget));\n    // By wrapping the buffer in a Float32Array, we use native browser IEEE 754\n    // decoding of the 4 bytes that back each 32 bit float.\n    return new Float32Array(downloadTarget.buffer);\n}\nexport function downloadPackedMatrixFromBuffer(gl, buffer, batch, rows, cols, physicalRows, physicalCols, textureConfig) {\n    const gl2 = gl;\n    const downloadTarget = new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n    return downloadTarget;\n}\nexport function downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {\n    const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);\n    webgl_util.callAndCheck(gl, () => gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));\n    return packedRGBA;\n}\n//# sourceMappingURL=gpgpu_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { getWebGLContext, setWebGLContext } from './canvas_util';\nimport * as gpgpu_util from './gpgpu_util';\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\nexport class GPGPUContext {\n    constructor(gl) {\n        this.outputTexture = null;\n        this.program = null;\n        this.disposed = false;\n        this.vertexAttrsAreBound = false;\n        this.itemsToPoll = [];\n        const glVersion = env().getNumber('WEBGL_VERSION');\n        if (gl != null) {\n            this.gl = gl;\n            setWebGLContext(glVersion, gl);\n        }\n        else {\n            this.gl = getWebGLContext(glVersion);\n        }\n        // WebGL 2.0 enables texture floats without an extension.\n        let COLOR_BUFFER_FLOAT = 'WEBGL_color_buffer_float';\n        const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n        if (env().getNumber('WEBGL_VERSION') === 1) {\n            const TEXTURE_FLOAT = 'OES_texture_float';\n            const TEXTURE_HALF_FLOAT = 'OES_texture_half_float';\n            this.textureFloatExtension =\n                webgl_util.getExtensionOrThrow(this.gl, TEXTURE_FLOAT);\n            if (webgl_util.hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {\n                this.textureHalfFloatExtension =\n                    webgl_util.getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);\n            }\n            else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n                throw new Error('GL context does not support half float textures, yet the ' +\n                    'environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n            }\n            this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);\n            if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n                this.colorBufferHalfFloatExtension =\n                    webgl_util.getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);\n            }\n            else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n                throw new Error('GL context does not support color renderable half floats, yet ' +\n                    'the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n            }\n        }\n        else {\n            COLOR_BUFFER_FLOAT = 'EXT_color_buffer_float';\n            if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {\n                this.colorBufferFloatExtension =\n                    this.gl.getExtension(COLOR_BUFFER_FLOAT);\n            }\n            else if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n                this.colorBufferHalfFloatExtension =\n                    this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n            }\n            else {\n                throw new Error('GL context does not support color renderable floats');\n            }\n        }\n        this.vertexBuffer = gpgpu_util.createVertexBuffer(this.gl);\n        this.indexBuffer = gpgpu_util.createIndexBuffer(this.gl);\n        this.framebuffer = webgl_util.createFramebuffer(this.gl);\n        this.textureConfig =\n            tex_util.getTextureConfig(this.gl, this.textureHalfFloatExtension);\n    }\n    get debug() {\n        return env().getBool('DEBUG');\n    }\n    dispose() {\n        if (this.disposed) {\n            return;\n        }\n        if (this.program != null) {\n            console.warn('Disposing a GPGPUContext that still has a bound WebGLProgram.' +\n                ' This is probably a resource leak, delete the program with ' +\n                'GPGPUContext.deleteProgram before disposing.');\n        }\n        if (this.outputTexture != null) {\n            console.warn('Disposing a GPGPUContext that still has a bound output matrix ' +\n                'texture.  This is probably a resource leak, delete the output ' +\n                'matrix texture with GPGPUContext.deleteMatrixTexture before ' +\n                'disposing.');\n        }\n        const gl = this.gl;\n        webgl_util.callAndCheck(gl, () => gl.finish());\n        webgl_util.callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));\n        webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));\n        this.disposed = true;\n    }\n    createFloat32MatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createFloat16MatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createUnsignedBytesMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    uploadPixelDataToTexture(texture, pixels) {\n        this.throwIfDisposed();\n        gpgpu_util.uploadPixelDataToTexture(this.gl, texture, pixels);\n    }\n    uploadDenseMatrixToTexture(texture, width, height, data) {\n        this.throwIfDisposed();\n        gpgpu_util.uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig);\n    }\n    createFloat16PackedMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createPackedMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    deleteMatrixTexture(texture) {\n        this.throwIfDisposed();\n        if (this.outputTexture === texture) {\n            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n            this.outputTexture = null;\n        }\n        webgl_util.callAndCheck(this.gl, () => this.gl.deleteTexture(texture));\n    }\n    downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) {\n        return this.downloadMatrixDriver(texture, () => gpgpu_util.downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig));\n    }\n    downloadPackedMatrixFromBuffer(buffer, batch, rows, columns, physicalRows, physicalCols) {\n        return gpgpu_util.downloadPackedMatrixFromBuffer(this.gl, buffer, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);\n    }\n    downloadFloat32MatrixFromBuffer(buffer, size) {\n        return gpgpu_util.downloadFloat32MatrixFromBuffer(this.gl, buffer, size);\n    }\n    createBufferFromTexture(texture, rows, columns) {\n        this.bindTextureToFrameBuffer(texture);\n        const result = gpgpu_util.createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig);\n        this.unbindTextureToFrameBuffer();\n        return result;\n    }\n    createAndWaitForFence() {\n        const fenceContext = this.createFence(this.gl);\n        return this.pollFence(fenceContext);\n    }\n    createFence(gl) {\n        let query;\n        let isFencePassed;\n        if (env().getBool('WEBGL_FENCE_API_ENABLED')) {\n            const gl2 = gl;\n            const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);\n            gl.flush();\n            isFencePassed = () => {\n                const status = gl2.clientWaitSync(sync, 0, 0);\n                return status === gl2.ALREADY_SIGNALED ||\n                    status === gl2.CONDITION_SATISFIED;\n            };\n            query = sync;\n        }\n        else if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n            query = this.beginQuery();\n            this.endQuery();\n            isFencePassed = () => this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n        }\n        else {\n            // If we have no way to fence, return true immediately. This will fire in\n            // WebGL 1.0 when there is no disjoint query timer. In this case, because\n            // the fence passes immediately, we'll immediately ask for a download of\n            // the texture, which will cause the UI thread to hang.\n            isFencePassed = () => true;\n        }\n        return { query, isFencePassed };\n    }\n    downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {\n        return this.downloadMatrixDriver(texture, () => gpgpu_util.downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));\n    }\n    createProgram(fragmentShaderSource) {\n        this.throwIfDisposed();\n        const gl = this.gl;\n        const fragmentShader = webgl_util.createFragmentShader(gl, fragmentShaderSource);\n        const vertexShader = gpgpu_util.createVertexShader(gl);\n        const program = webgl_util.createProgram(gl);\n        webgl_util.callAndCheck(gl, () => gl.attachShader(program, vertexShader));\n        webgl_util.callAndCheck(gl, () => gl.attachShader(program, fragmentShader));\n        webgl_util.linkProgram(gl, program);\n        if (this.debug) {\n            webgl_util.validateProgram(gl, program);\n        }\n        if (!this.vertexAttrsAreBound) {\n            this.setProgram(program);\n            this.vertexAttrsAreBound = gpgpu_util.bindVertexProgramAttributeStreams(gl, this.program, this.vertexBuffer);\n        }\n        return program;\n    }\n    deleteProgram(program) {\n        this.throwIfDisposed();\n        if (program === this.program) {\n            this.program = null;\n        }\n        if (program != null) {\n            webgl_util.callAndCheck(this.gl, () => this.gl.deleteProgram(program));\n        }\n    }\n    setProgram(program) {\n        this.throwIfDisposed();\n        this.program = program;\n        if ((this.program != null) && this.debug) {\n            webgl_util.validateProgram(this.gl, this.program);\n        }\n        webgl_util.callAndCheck(this.gl, () => this.gl.useProgram(program));\n    }\n    getUniformLocation(program, uniformName, shouldThrow = true) {\n        this.throwIfDisposed();\n        if (shouldThrow) {\n            return webgl_util.getProgramUniformLocationOrThrow(this.gl, program, uniformName);\n        }\n        else {\n            return webgl_util.getProgramUniformLocation(this.gl, program, uniformName);\n        }\n    }\n    getAttributeLocation(program, attribute) {\n        this.throwIfDisposed();\n        return webgl_util.callAndCheck(this.gl, () => this.gl.getAttribLocation(program, attribute));\n    }\n    getUniformLocationNoThrow(program, uniformName) {\n        this.throwIfDisposed();\n        return this.gl.getUniformLocation(program, uniformName);\n    }\n    setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) {\n        this.throwIfDisposed();\n        this.throwIfNoProgram();\n        webgl_util.bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit);\n    }\n    setOutputMatrixTexture(outputMatrixTexture, rows, columns) {\n        this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);\n    }\n    setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) {\n        this.throwIfDisposed();\n        const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n        this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);\n    }\n    setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n        this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);\n    }\n    setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n        throw new Error('setOutputPackedMatrixWriteRegion not implemented.');\n    }\n    debugValidate() {\n        if (this.program != null) {\n            webgl_util.validateProgram(this.gl, this.program);\n        }\n        webgl_util.validateFramebuffer(this.gl);\n    }\n    executeProgram() {\n        this.throwIfDisposed();\n        this.throwIfNoProgram();\n        const gl = this.gl;\n        if (this.debug) {\n            this.debugValidate();\n        }\n        webgl_util.callAndCheck(gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));\n    }\n    blockUntilAllProgramsCompleted() {\n        this.throwIfDisposed();\n        webgl_util.callAndCheck(this.gl, () => this.gl.finish());\n    }\n    getQueryTimerExtension() {\n        if (this.disjointQueryTimerExtension == null) {\n            this.disjointQueryTimerExtension =\n                webgl_util.getExtensionOrThrow(this.gl, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2 ?\n                    'EXT_disjoint_timer_query_webgl2' :\n                    'EXT_disjoint_timer_query');\n        }\n        return this.disjointQueryTimerExtension;\n    }\n    getQueryTimerExtensionWebGL2() {\n        return this.getQueryTimerExtension();\n    }\n    getQueryTimerExtensionWebGL1() {\n        return this.getQueryTimerExtension();\n    }\n    beginQuery() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            const query = gl2.createQuery();\n            gl2.beginQuery(ext.TIME_ELAPSED_EXT, query);\n            return query;\n        }\n        const ext = this.getQueryTimerExtensionWebGL1();\n        const query = ext.createQueryEXT();\n        ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);\n        return query;\n    }\n    endQuery() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            gl2.endQuery(ext.TIME_ELAPSED_EXT);\n            return;\n        }\n        const ext = this.getQueryTimerExtensionWebGL1();\n        ext.endQueryEXT(ext.TIME_ELAPSED_EXT);\n    }\n    async waitForQueryAndGetTime(query) {\n        await util.repeatedTry(() => this.disposed || // while testing contexts are created / disposed\n            // in rapid succession, so without this check we\n            // may poll for the query timer indefinitely\n            this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')));\n        return this.getQueryTime(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n    }\n    getQueryTime(query, queryTimerVersion) {\n        if (queryTimerVersion === 0) {\n            return null;\n        }\n        if (queryTimerVersion === 2) {\n            const gl2 = this.gl;\n            const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);\n            // Return milliseconds.\n            return timeElapsedNanos / 1000000;\n        }\n        else {\n            const ext = this.getQueryTimerExtensionWebGL1();\n            const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);\n            // Return milliseconds.\n            return timeElapsedNanos / 1000000;\n        }\n    }\n    isQueryAvailable(query, queryTimerVersion) {\n        if (queryTimerVersion === 0) {\n            return true;\n        }\n        if (queryTimerVersion === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);\n            if (this.disjoint == null) {\n                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n            }\n            return available && !this.disjoint;\n        }\n        else {\n            const ext = this.getQueryTimerExtensionWebGL1();\n            const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);\n            if (this.disjoint == null) {\n                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n            }\n            return available && !this.disjoint;\n        }\n    }\n    pollFence(fenceContext) {\n        return new Promise(resolve => {\n            this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());\n        });\n    }\n    pollItems() {\n        // Find the last query that has finished.\n        const index = linearSearchLastTrue(this.itemsToPoll.map(x => x.isDoneFn));\n        for (let i = 0; i <= index; ++i) {\n            const { resolveFn } = this.itemsToPoll[i];\n            resolveFn();\n        }\n        this.itemsToPoll = this.itemsToPoll.slice(index + 1);\n    }\n    addItemToPoll(isDoneFn, resolveFn) {\n        this.itemsToPoll.push({ isDoneFn, resolveFn });\n        if (this.itemsToPoll.length > 1) {\n            // We already have a running loop that polls.\n            return;\n        }\n        // Start a new loop that polls.\n        util.repeatedTry(() => {\n            this.pollItems();\n            // End the loop if no more items to poll.\n            return this.itemsToPoll.length === 0;\n        });\n    }\n    bindTextureToFrameBuffer(texture) {\n        this.throwIfDisposed();\n        webgl_util.bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);\n        if (this.debug) {\n            webgl_util.validateFramebuffer(this.gl);\n        }\n    }\n    unbindTextureToFrameBuffer() {\n        if (this.outputTexture != null) {\n            webgl_util.bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);\n            if (this.debug) {\n                webgl_util.validateFramebuffer(this.gl);\n            }\n        }\n        else {\n            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n        }\n    }\n    downloadMatrixDriver(texture, downloadAndDecode) {\n        this.bindTextureToFrameBuffer(texture);\n        const result = downloadAndDecode();\n        this.unbindTextureToFrameBuffer();\n        return result;\n    }\n    setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) {\n        this.throwIfDisposed();\n        const gl = this.gl;\n        webgl_util.bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);\n        if (this.debug) {\n            webgl_util.validateFramebuffer(gl);\n        }\n        this.outputTexture = outputMatrixTextureMaybePacked;\n        webgl_util.callAndCheck(gl, () => gl.viewport(0, 0, width, height));\n        webgl_util.callAndCheck(gl, () => gl.scissor(0, 0, width, height));\n    }\n    setOutputMatrixWriteRegionDriver(x, y, width, height) {\n        this.throwIfDisposed();\n        webgl_util.callAndCheck(this.gl, () => this.gl.scissor(x, y, width, height));\n    }\n    throwIfDisposed() {\n        if (this.disposed) {\n            throw new Error('Attempted to use disposed GPGPUContext.');\n        }\n    }\n    throwIfNoProgram() {\n        if (this.program == null) {\n            throw new Error('No GPU program is currently set.');\n        }\n    }\n}\n/**\n * Finds the index of the last true element using linear search.\n * Note: We can't do binary search because Chrome expects us to explicitly\n * test all fences before download:\n * https://github.com/tensorflow/tfjs/issues/1145\n */\nexport function linearSearchLastTrue(arr) {\n    let i = 0;\n    for (; i < arr.length; ++i) {\n        const isDone = arr[i]();\n        if (!isDone) {\n            break;\n        }\n    }\n    return i - 1;\n}\n//# sourceMappingURL=gpgpu_context.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport * as shader_compiler from './shader_compiler';\nexport function compileProgram(gpgpu, program, inputs, output) {\n    const userCode = program.userCode;\n    const inputInfos = inputs.map((input, i) => {\n        const shapeInfo = {\n            logicalShape: input.shape,\n            texShape: input.isUniform ? null : input.texData.texShape,\n            isUniform: input.isUniform,\n            isPacked: input.isUniform ? false : input.texData.isPacked,\n            flatOffset: null\n        };\n        if (input.texData != null && input.texData.slice != null &&\n            input.texData.slice.flatOffset > 0) {\n            shapeInfo.flatOffset = input.texData.slice.flatOffset;\n        }\n        return { name: program.variableNames[i], shapeInfo };\n    });\n    const inShapeInfos = inputInfos.map(x => x.shapeInfo);\n    const outShapeInfo = {\n        logicalShape: output.shape,\n        texShape: output.texData.texShape,\n        isUniform: false,\n        isPacked: output.texData.isPacked,\n        flatOffset: null\n    };\n    const source = shader_compiler.makeShader(inputInfos, outShapeInfo, userCode, program.packedInputs);\n    const webGLProgram = gpgpu.createProgram(source);\n    // Add special uniforms (NAN, INFINITY)\n    let infLoc = null;\n    const nanLoc = gpgpu.getUniformLocation(webGLProgram, 'NAN', false);\n    if (env().getNumber('WEBGL_VERSION') === 1) {\n        infLoc = gpgpu.getUniformLocation(webGLProgram, 'INFINITY', false);\n    }\n    // Add user-defined uniforms\n    const uniformLocations = {};\n    for (let i = 0; i < program.variableNames.length; i++) {\n        const varName = program.variableNames[i];\n        const shouldThrow = false;\n        uniformLocations[varName] =\n            gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);\n        uniformLocations[`offset${varName}`] =\n            gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);\n    }\n    return {\n        program,\n        source,\n        webGLProgram,\n        uniformLocations,\n        inShapeInfos,\n        outShapeInfo,\n        infLoc,\n        nanLoc,\n    };\n}\nfunction validateBinaryAndProgram(shapeInfos, inputs) {\n    if (shapeInfos.length !== inputs.length) {\n        throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but ` +\n            `was executed with ${inputs.length} inputs`);\n    }\n    shapeInfos.forEach((s, i) => {\n        const shapeA = s.logicalShape;\n        const input = inputs[i];\n        const shapeB = input.shape;\n        if (!util.arraysEqual(shapeA, shapeB)) {\n            throw Error(`Binary was compiled with different shapes than ` +\n                `the current args. Shapes ${shapeA} and ${shapeB} must match`);\n        }\n        // The input is uploaded as uniform.\n        if (s.isUniform && input.isUniform) {\n            return;\n        }\n        const texShapeA = s.texShape;\n        const texShapeB = input.isUniform ? null : input.texData.texShape;\n        if (!util.arraysEqual(texShapeA, texShapeB)) {\n            throw Error(`Binary was compiled with different texture shapes than the` +\n                ` current args. Shape ${texShapeA} and ${texShapeB} must match`);\n        }\n    });\n}\nexport function runProgram(gpgpu, binary, inputs, output, customSetup) {\n    validateBinaryAndProgram(binary.inShapeInfos, inputs);\n    validateBinaryAndProgram([binary.outShapeInfo], [output]);\n    const outTex = output.texData.texture;\n    const outTexShape = output.texData.texShape;\n    if (output.texData.isPacked) {\n        gpgpu.setOutputPackedMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n    }\n    else {\n        gpgpu.setOutputMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n    }\n    gpgpu.setProgram(binary.webGLProgram);\n    // Set special uniforms (NAN, INFINITY)\n    if (env().getNumber('WEBGL_VERSION') === 1) {\n        if (binary.infLoc !== null) {\n            gpgpu.gl.uniform1f(binary.infLoc, Infinity);\n        }\n    }\n    if (binary.nanLoc !== null) {\n        gpgpu.gl.uniform1f(binary.nanLoc, NaN);\n    }\n    // Set user-defined inputs\n    inputs.forEach((input, i) => {\n        const varName = binary.program.variableNames[i];\n        const varLoc = binary.uniformLocations[varName];\n        const varOffsetLoc = binary.uniformLocations[`offset${varName}`];\n        if (varLoc == null) {\n            // The compiler inferred that this variable is not used in this shader.\n            return;\n        }\n        if (input.isUniform) {\n            // Upload the values of the tensor as uniform.\n            if (util.sizeFromShape(input.shape) < 2) {\n                gpgpu.gl.uniform1f(varLoc, input.uniformValues[0]);\n            }\n            else {\n                let vals = input.uniformValues;\n                if (!(vals instanceof Float32Array)) {\n                    vals = new Float32Array(vals);\n                }\n                gpgpu.gl.uniform1fv(varLoc, vals);\n            }\n            return;\n        }\n        // If the input was sliced, upload the flat offset index.\n        if (input.texData.slice != null && varOffsetLoc != null) {\n            gpgpu.gl.uniform1i(varOffsetLoc, input.texData.slice.flatOffset);\n        }\n        gpgpu.setInputMatrixTexture(input.texData.texture, varLoc, i);\n    });\n    if (customSetup != null) {\n        customSetup(gpgpu, binary.webGLProgram);\n    }\n    gpgpu.executeProgram();\n}\nexport function makeShaderKey(program, inputs, output) {\n    let keyInputs = '';\n    inputs.concat(output).forEach(x => {\n        const hasOffset = x.texData != null && x.texData.slice != null &&\n            x.texData.slice.flatOffset > 0;\n        const texShape = x.isUniform ? 'uniform' : x.texData.texShape;\n        keyInputs += `${x.shape}_${texShape}_${hasOffset}`;\n    });\n    const keyUserCode = program.userCode;\n    let key = program.constructor.name;\n    // Fast string concat. See https://jsperf.com/string-concatenation/14.\n    key += '_' + keyInputs + '_' + keyUserCode;\n    return key;\n}\n//# sourceMappingURL=gpgpu_math.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nexport class Im2ColPackedProgram {\n    constructor(outputShape, inputShape, convInfo) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        const { filterWidth, inChannels, strideWidth, strideHeight, padInfo, outWidth, dilationWidth, dilationHeight, dataFormat } = convInfo;\n        const { left, top } = padInfo;\n        const itemsPerBlockRow = inChannels * filterWidth;\n        const glsl = getGlslDifferences();\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const rowDim = isChannelsLast ? 0 : 1;\n        const colDim = isChannelsLast ? 1 : 2;\n        let unrolled = ``;\n        for (let row = 0; row <= 1; row++) {\n            for (let col = 0; col <= 1; col++) {\n                unrolled += `\n          blockIndex = rc.y + ${col};\n          pos = rc.x + ${row};\n\n          if(blockIndex < ${outputShape[1]} && pos < ${outputShape[0]}) {\n            offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${top};\n            d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});\n\n            if(d0 < ${inputShape[rowDim]} && d0 >= 0) {\n\n              offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${strideWidth}. - ${left}.);\n              d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${itemsPerBlockRow}.) / ${inChannels}.));\n\n              if(d1 < ${inputShape[colDim]} && d1 >= 0) {\n\n                ch = int(mod(float(pos), ${inChannels}.));\n\n                if (${isChannelsLast}) {\n                  innerDims = vec2(d1, ch);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;\n            }\n        }\n        this.userCode = `\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${unrolled}\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=im2col_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNProgram {\n    constructor(xShape, radius, bias, alpha, beta) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        const rad = radius;\n        const maxD = xShape[3] - 1;\n        this.outputShape = xShape;\n        // optimize pow(bias + alpha * sum, -beta)\n        // src: https://github.com/tensorflow/tensorflow/..\n        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n        // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n        let powOperator;\n        const basis = `float(${bias}) + float(${alpha}) * sum`;\n        if (beta === 0.5) {\n            powOperator = `inversesqrt(${basis})`;\n        }\n        else if (beta === 1.0) {\n            powOperator = `1.0/(${basis})`;\n        }\n        else {\n            powOperator = `exp(log(${basis}) * float(-${beta}));`;\n        }\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${rad}; j <= ${rad}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${maxD}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${powOperator};\n        setOutput(val);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNGradProgram {\n    constructor(inputShape, depthRadius, bias, alpha, beta) {\n        this.variableNames = ['inputImage', 'outputImage', 'dy'];\n        this.outputShape = [];\n        this.outputShape = inputShape;\n        this.depth = inputShape[3];\n        this.depthRadius = depthRadius;\n        this.bias = bias;\n        this.alpha = alpha;\n        this.beta = beta;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${depthRadius})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${depthRadius} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${alpha}) * norm + float(${bias});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${alpha})\n                * float(${beta})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${beta});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_grad_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNPackedProgram {\n    constructor(xShape, radius, bias, alpha, beta) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const rad = radius;\n        const maxD = xShape[3] - 1;\n        this.outputShape = xShape;\n        // optimize pow(bias + alpha * sum, -beta)\n        // src: https://github.com/tensorflow/tensorflow/..\n        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n        // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n        let powOperator;\n        const basis = `float(${bias}) + float(${alpha}) * sum`;\n        if (beta === 0.5) {\n            powOperator = `inversesqrt(${basis})`;\n        }\n        else if (beta === 1.0) {\n            powOperator = `1.0/(${basis})`;\n        }\n        else {\n            powOperator = `exp(log(${basis}) * float(-${beta}));`;\n        }\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${rad};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${rad}; j <= ${rad}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${powOperator};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MaxPool2DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'maxPos'];\n        this.outputShape = convInfo.inShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n          wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${effectiveFilterWidth} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class MaxPool3DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'maxPos'];\n        this.outputShape = convInfo.inShape;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n           wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${lastIndex} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                  wR * ${effectiveFilterWidth} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=max_pool_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MatMulPackedProgram {\n    constructor(aShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['matrixA', 'matrixB'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        const sharedDim = transposeA ? aShape[1] : aShape[2];\n        const sharedDimensionPacked = Math.ceil(sharedDim / 2);\n        const aSample = transposeA ? 'i * 2, rc.y' : 'rc.y, i * 2';\n        const bSample = transposeB ? 'rc.z, i * 2' : 'i * 2, rc.z';\n        const aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];\n        const bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const float sharedDimension = ${sharedDimensionPacked}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${sharedDimensionPacked}; i++) {\n          vec4 a = getMatrixA(rc.x, ${aSample});\n          vec4 b = getMatrixB(rc.x, ${bSample});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${aSwizzle[0]} * ${bSwizzle[0]});\n          result += (${aSwizzle[1]} * ${bSwizzle[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${addBiasSnippet}\n\n        ${applyActivationSnippet}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=mulmat_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MultinomialProgram {\n    constructor(batchSize, numOutcomes, numSamples) {\n        this.variableNames = ['probs'];\n        this.outputShape = [batchSize, numSamples];\n        this.userCode = `\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < ${numOutcomes - 1}; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(${numOutcomes - 1}));\n      }\n    `;\n    }\n    getCustomSetupFunc(seed) {\n        return (gpgpu, webGLProgram) => {\n            if (this.seedLoc == null) {\n                this.seedLoc = gpgpu.getUniformLocation(webGLProgram, 'seed');\n            }\n            gpgpu.gl.uniform1f(this.seedLoc, seed);\n        };\n    }\n}\n//# sourceMappingURL=multinomial_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class OneHotProgram {\n    constructor(numIndices, depth, onValue, offValue) {\n        this.variableNames = ['indices'];\n        this.outputShape = [numIndices, depth];\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${offValue}), float(${onValue}),\n                      float(index == coords.y)));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=onehot_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class PackProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        // Only input / output 3D tensors.\n        this.outputShape = outputShape;\n        const rank = outputShape.length;\n        if (rank === 0) {\n            this.userCode = `\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;\n        }\n        else {\n            const channels = getChannels('rc', rank);\n            const dtype = getCoordsDataType(rank);\n            const outOfBoundsCondition = getOutOfBoundsCondition(rank, outputShape, channels);\n            const setup = getSetup(rank, outputShape[outputShape.length - 1], outputShape[outputShape.length - 2], channels);\n            const output = getOutput(outputShape, channels);\n            this.userCode = `\n        void main() {\n          ${dtype} rc = getOutputCoords();\n\n          if(${outOfBoundsCondition}) {\n            setOutput(vec4(0));\n          } else {\n            ${setup}\n\n            setOutput(vec4(${output}));\n          }\n        }\n      `;\n        }\n    }\n}\nfunction getSourceCoordsArr(rank, dims) {\n    const coords = [];\n    for (let row = 0; row <= 1; row++) {\n        for (let col = 0; col <= 1; col++) {\n            let coord = `${row === 0 ? 'r' : 'rp1'}, ${col === 0 ? 'c' : 'cp1'}`;\n            for (let d = 2; d < rank; d++) {\n                coord = `${dims[dims.length - 1 - d]},` + coord;\n            }\n            coords.push(coord);\n        }\n    }\n    return coords;\n}\nfunction getOutOfBoundsCondition(rank, shape, dims) {\n    if (rank === 1) {\n        return `rc > ${shape[0]}`;\n    }\n    let cond = '';\n    for (let i = rank - 2; i < rank; i++) {\n        cond += `${dims[i]} >= ${shape[i]}`;\n        if (i < rank - 1) {\n            cond += '||';\n        }\n    }\n    return cond;\n}\nfunction getSetup(rank, cols, rows, dims) {\n    if (rank === 1) {\n        return '';\n    }\n    const innerDims = dims.slice(-2);\n    return `\n    int r = ${innerDims[0]};\n    int c = ${innerDims[1]};\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= ${cols};\n    bool rEdge = rp1 >= ${rows};\n  `;\n}\nfunction getOutput(shape, dims) {\n    const rank = shape.length;\n    const sourceCoords = getSourceCoordsArr(rank, dims);\n    if (rank === 1) {\n        return `getA(rc),\n            rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),\n            0, 0`;\n    }\n    return `getA(${sourceCoords[0]}),\n          cEdge ? 0. : getA(${sourceCoords[1]}),\n          rEdge ? 0. : getA(${sourceCoords[2]}),\n          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;\n}\n//# sourceMappingURL=pack_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class PadProgram {\n    constructor(xShape, paddings, constantValue) {\n        this.variableNames = ['x'];\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const type = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const unpackedCoords = ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);\n        if (rank === 1) {\n            this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float(${constantValue}));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `;\n            return;\n        }\n        this.userCode = `\n      ${type} start = ${type}(${start});\n      ${type} end = ${type}(${end});\n\n      void main() {\n        ${type} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float(${constantValue}));\n        } else {\n          ${type} coords = outC - start;\n          setOutput(getX(${unpackedCoords}));\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pad_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class PadPackedProgram {\n    constructor(xShape, paddings, constantValue) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const dtype = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const coords = getChannels('rc', rank);\n        const source = getChannels('source', rank);\n        const cLimit = `${coords[rank - 1]} < ${this.outputShape[rank - 1]}`;\n        const innerDims = rank === 1 ? 'source' : `vec2(${source.slice(-2).join()})`;\n        const componentSetup = [\n            `${dtype} rc = outputLoc;`, `${coords[rank - 1]} += 1;\n       if(${cLimit}) {\n      `,\n            rank === 1 ? '' : `}\n       rc = outputLoc;\n       ${coords[rank - 2]} += 1;\n       if(${coords[rank - 2]} < ${this.outputShape[rank - 2]}) {`,\n            rank === 1 ? '' : `  ${coords[rank - 1]} += 1;\n         if(${cLimit}) {`\n        ];\n        const paddingArea = rank === 1 ?\n            'rc < start || rc >= end' :\n            'any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))';\n        let mainLoop = '';\n        for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {\n            mainLoop += `\n        ${componentSetup[i]}\n        if (${paddingArea}) {\n          result[${i}] = float(${constantValue});\n        } else {\n          ${dtype} source = rc - start;\n          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n        }\n        mainLoop += (rank === 1 ? `} ` : `}}`);\n        this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pad_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Pool2DProgram {\n    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n        this.variableNames = ['x'];\n        if (poolType === 'avg' && computePositions) {\n            throw new Error('Cannot compute positions for average pool.');\n        }\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.outputShape = convInfo.outShape;\n        const isAvgPool = poolType === 'avg';\n        const batchFlattenPositionStr = `((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n        const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n        let initializationValue = '0.0';\n        if (!isAvgPool) {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n        }\n        if (computePositions) {\n            const compareOp = '>=';\n            this.userCode = `\n        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n        const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${compareOp} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${flattenPositions ? (includeBatchInIndex ? batchFlattenPositionStr :\n                flattenPositionStr) :\n                `wR * ${effectiveFilterWidth} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n            return;\n        }\n        const compareOp = 'max';\n        let returnValue = `${poolType}(${poolType}(${poolType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (poolType === 'avg') {\n            returnValue = `avgValue / count`;\n        }\n        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n        const filterWidthVec4Remainder = filterWidth % 4;\n        const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)\n            );\n\n            ${updateSnippet}\n          }\n\n          int xC = xCCorner + ${filterWidthNearestVec4};\n          if (${filterWidthVec4Remainder === 1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              initializationValue\n            );\n\n            ${updateSnippet}\n          }\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\nexport class Pool3DProgram {\n    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n        this.variableNames = ['x'];\n        if (poolType === 'avg' && computePositions) {\n            throw new Error('Cannot compute positions for average pool.');\n        }\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.outputShape = convInfo.outShape;\n        const isAvgPool = poolType === 'avg';\n        let initializationValue = '0.0';\n        if (!isAvgPool) {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n        }\n        if (computePositions) {\n            const compareOp = '>=';\n            this.userCode = `\n        const ivec3 strides =\n            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${effectiveFilterDepth};\n              wD += ${dilationDepth}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${effectiveFilterHeight};\n                wR += ${dilationHeight}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${effectiveFilterWidth};\n                  wC += ${dilationWidth}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${compareOp} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${flattenPositions ?\n                (includeBatchInIndex ?\n                    `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` :\n                    `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch`) :\n                `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                      wR * ${effectiveFilterWidth} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n            return;\n        }\n        const compareOp = 'max';\n        let returnValue = `${poolType}(${poolType}(${poolType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (poolType === 'avg') {\n            returnValue = `avgValue / count`;\n        }\n        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n        const filterWidthVec4Remainder = filterWidth % 4;\n        const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        this.userCode = `\n      const ivec3 strides =\n        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)\n              );\n\n              ${updateSnippet}\n            }\n\n            int xC = xCCorner + ${filterWidthNearestVec4};\n            if (${filterWidthVec4Remainder === 1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                initializationValue\n              );\n\n              ${updateSnippet}\n            }\n          }\n          setOutput(${returnValue});\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pool_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ReduceProgram {\n    constructor(reduceInfo, reduceType) {\n        this.variableNames = ['x'];\n        const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n        this.outputShape = [batchSize, outSize];\n        let initializationValue = '0.0';\n        let compareOp = ``;\n        if (reduceType === 'prod') {\n            initializationValue = '1.0';\n        }\n        else if (reduceType === 'min') {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '1.0 / 1e-20';\n            compareOp = `min`;\n        }\n        else if (reduceType === 'max') {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n            compareOp = `max`;\n        }\n        let returnValue = `${reduceType}(${reduceType}(${reduceType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (reduceType === 'sum') {\n            returnValue = `sumValue`;\n        }\n        else if (reduceType === 'prod') {\n            returnValue = `prodValue`;\n        }\n        else if (reduceType === 'all') {\n            returnValue = `allValue`;\n        }\n        else if (reduceType === 'any') {\n            returnValue = `anyValue`;\n        }\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        let updateSnippet = `\n      if (${reduceType === 'sum'}) {\n        sumValue += dot(values, ones);\n      } else if (${reduceType === 'prod'}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        let vecType = `vec4`;\n        if (reduceType === 'all') {\n            initializationValue = '1.0';\n            updateSnippet = `\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `;\n            vecType = `bvec4`;\n        }\n        else if (reduceType === 'any') {\n            initializationValue = '0.0';\n            updateSnippet = `\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `;\n            vecType = `bvec4`;\n        }\n        let checkOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n        }\n        this.userCode = `\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        vec4 minMaxValue = vec4(${initializationValue});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\n//# sourceMappingURL=reduce_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as shader_util from './shader_compiler_util';\nexport class ReshapePackedProgram {\n    constructor(outputShape, inputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        let mainLoop = ``;\n        for (let i = 0; i < 4; i++) {\n            let thisRC = `thisRC = rc;`;\n            if (i % 2 === 1) {\n                thisRC += `thisRC.z += 1;`;\n            }\n            if (i > 1) {\n                thisRC += `thisRC.y += 1;`;\n            }\n            mainLoop += `\n        ${thisRC}\n        ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ''}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${i}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${i > 0 ? '}' : ''}\n      `;\n        }\n        this.userCode = `\n      ${getReshapedInputCoords(inputShape)}\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${outputShape[1]};\n        int cols = ${outputShape[2]};\n\n        ${mainLoop}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\nfunction getReshapedInputCoords(shape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n    return `\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\n//# sourceMappingURL=reshape_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearBackpropProgram {\n    constructor(dy, x, alignCorners) {\n        this.variableNames = ['dy'];\n        this.outputShape = [];\n        this.outputShape = x.shape;\n        const [, xHeight, xWidth,] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        // In the backwards pass, we want to find the pixels that were generated for\n        // each pixel in the input image the forward pass and add the corresponding\n        // coefficient from dy to the gradient (with some interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearPackedProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        this.userCode = `\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,\n                                     ${oldWidth}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${depth - 1};\n        bool hasNextRow = coords.z < ${newWidth - 1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeNearestNeigborBackpropProgram {\n    constructor(dy, x, alignCorners) {\n        this.variableNames = ['dy'];\n        this.outputShape = [];\n        this.outputShape = x.shape;\n        const [, xHeight, xWidth,] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        // In the backwards pass, we want to find the pixels that were generated for\n        // each pixel in the input image the forward pass and add the corresponding\n        // coefficient from dy to the gradient (with some interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${effectiveXSize[0]}) *\n                (float(dyR) / float(${effectiveYSize[0]}));\n\n            float sourceFracCol =\n                float(${effectiveXSize[1]}) *\n                  (float(dyC) / float(${effectiveYSize[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${xHeight}) - 1),\n                ${alignCorners} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${xWidth}) - 1),\n                ${alignCorners} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_nearest_neighbor_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeNearestNeighborProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        // When align corners is false, we rounds the value with floor.\n        const roundBase = alignCorners ? '0.5' : '0.0';\n        this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_nearest_neighbor_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class ReverseProgram {\n    constructor(xShape, axis) {\n        this.variableNames = ['x'];\n        const rank = xShape.length;\n        if (rank > 4) {\n            throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n        }\n        this.outputShape = xShape;\n        if (rank === 1) {\n            this.userCode = `\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${xShape[0]} - coord - 1));\n        }\n      `;\n            return;\n        }\n        const getInCoord = (i) => {\n            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n                return `${xShape[i]} - coords[${i}] - 1`;\n            }\n            return `coords[${i}]`;\n        };\n        const inCoords = xShape.map((_, i) => getInCoord(i)).join(',');\n        const type = getCoordsDataType(rank);\n        this.userCode = `\n      void main() {\n        ${type} coords = getOutputCoords();\n        setOutput(getX(${inCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=reverse_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ReversePackedProgram {\n    constructor(xShape, axis) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const rank = xShape.length;\n        if (rank > 4) {\n            throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n        }\n        this.outputShape = xShape;\n        const channels = getChannels('rc', rank);\n        const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;\n        const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;\n        const type = getCoordsDataType(rank);\n        if (rank === 1) {\n            this.userCode = `\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${xShape[0]} - rc - 1),\n            ${xShape[0]} - rc - 1);\n          if(${nextColumn}){\n              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),\n                ${xShape[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `;\n        }\n        else {\n            this.userCode = `\n        void main() {\n          ${type} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${getR(channels.slice())};\n          if(${nextColumn}){\n            result.g = ${getG(channels.slice())};\n          }\n          if(${nextRow}) {\n            result.b = ${getB(channels.slice())};\n            if(${nextColumn}) {\n              result.a = ${getA(channels.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `;\n        }\n        function getR(channels) {\n            return getChannel(channels);\n        }\n        function getG(channels) {\n            channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getB(channels) {\n            channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getA(channels) {\n            channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n            channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getChannel(channels) {\n            const inCoordsArray = xShape.map((_, i) => getInCoord(i, channels));\n            const inCoords = inCoordsArray.join(',');\n            const innerDims = inCoordsArray.slice(-2).join(',');\n            return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;\n        }\n        function getInCoord(i, channels1) {\n            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n                return `${xShape[i]} - ${channels1[i]} - 1`;\n            }\n            else {\n                return `${channels1[i]}`;\n            }\n        }\n    }\n}\n//# sourceMappingURL=reverse_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class ScatterProgram {\n    constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true) {\n        this.variableNames = ['updates', 'indices', 'defaultValue'];\n        this.outputShape = shape;\n        const stridesType = getCoordsDataType(strides.length);\n        const dtype = getCoordsDataType(shape.length);\n        let indicesString = '';\n        if (indicesRank === 1) {\n            indicesString = 'i';\n        }\n        else if (indicesRank === 2) {\n            indicesString = 'i, j';\n        }\n        const indicesSnippet = `getIndices(${indicesString})`;\n        let updatesString = '';\n        if (updatesRank === 1) {\n            updatesString = 'i';\n        }\n        else if (updatesRank === 2) {\n            updatesString = 'i, coords[1]';\n        }\n        const updatesSnippet = `getUpdates(${updatesString})`;\n        const strideString = sliceDim > 1 ? 'strides[j]' : 'strides';\n        this.userCode = `\n        ${stridesType} strides = ${stridesType}(${strides});\n\n        void main() {\n          ${dtype} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${updateSize}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${sliceDim}; j++) {\n              int index = round(${indicesSnippet});\n              flattenedIndex += index * ${strideString};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${updatesSnippet};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `;\n    }\n}\n//# sourceMappingURL=scatter_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class SegmentOpProgram {\n    constructor(segOpInfo, segOpType) {\n        this.variableNames = ['x', 'segmentIds'];\n        const windowSize = segOpInfo.windowSize;\n        const batchSize = segOpInfo.batchSize;\n        const inSize = segOpInfo.inSize;\n        const numSegments = segOpInfo.numSegments;\n        const outSize = numSegments * Math.ceil(inSize / windowSize);\n        this.outputShape = [batchSize, outSize];\n        const initializationValue = '0.0';\n        const returnValue = `sumValue`;\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        const updateSnippet = `\n        sumValue += dot(values, segFilter);\n    `;\n        let checkValueOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkValueOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n        }\n        let checkSegmentIdOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkSegmentIdOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return -1.0;\n        }\n      `;\n        }\n        this.userCode = `\n      const float initializationValue = ${initializationValue};\n\n      float getValue(int batch, int inIdx) {\n        ${checkValueOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${checkSegmentIdOutOfBounds}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${numSegments})) * float(${windowSize}));\n        int currentSeg = int(mod(float(outIdx), float(${numSegments})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\n//# sourceMappingURL=segment_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class SelectProgram {\n    constructor(cRank, shape, rank) {\n        this.variableNames = ['c', 'a', 'b'];\n        this.outputShape = shape;\n        let cCoords;\n        let abCoords;\n        if (rank > 4) {\n            throw Error(`Where for rank ${rank} is not yet supported`);\n        }\n        if (rank === 1) {\n            abCoords = `resRC`;\n            cCoords = `resRC`;\n        }\n        else {\n            const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n            const cCoordVars = [];\n            const abCoordVars = [];\n            for (let i = 0; i < shape.length; i++) {\n                abCoordVars.push(`${currentCoords[i]}`);\n                if (i < cRank) {\n                    cCoordVars.push(`${currentCoords[i]}`);\n                }\n            }\n            cCoords = cCoordVars.join();\n            abCoords = abCoordVars.join();\n        }\n        const dtype = getCoordsDataType(rank);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        float cVal = getC(${cCoords});\n        if (cVal >= 1.0) {\n          setOutput(getA(${abCoords}));\n        } else {\n          setOutput(getB(${abCoords}));\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=select_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class SliceProgram {\n    constructor(destSize) {\n        this.variableNames = ['source'];\n        this.outputShape = destSize;\n        this.rank = destSize.length;\n        const dtype = getCoordsDataType(this.rank);\n        const uniformPart = `uniform int start[${this.rank}];`;\n        const sourceCoords = getCoords(this.rank);\n        let body;\n        const coordSum = destSize.map((_, i) => {\n            return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`;\n        });\n        body = `\n        ${dtype} sourceLoc;\n        ${dtype} coords = getOutputCoords();\n        ${coordSum.join('\\n')}\n      `;\n        this.userCode = `\n      ${uniformPart}\n      void main() {\n        ${body}\n        setOutput(getSource(${sourceCoords}));\n      }\n    `;\n    }\n    getCustomSetupFunc(start) {\n        if (start.length !== this.rank) {\n            throw Error(`The rank (${this.rank}) of the program must match the ` +\n                `length of start (${start.length})`);\n        }\n        return (gpgpu, webGLProgram) => {\n            if (this.startLoc == null) {\n                this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n                if (this.startLoc == null) {\n                    // This means the compiler has optimized and realized it doesn't need\n                    // the uniform.\n                    return;\n                }\n            }\n            gpgpu.gl.uniform1iv(this.startLoc, start);\n        };\n    }\n}\nconst coords = ['x', 'y', 'z', 'w', 'u', 'v'];\nfunction getCoords(rank) {\n    if (rank === 1) {\n        return 'sourceLoc';\n    }\n    else if (rank <= 6) {\n        return coords.slice(0, rank).map(x => 'sourceLoc.' + x).join(',');\n    }\n    else {\n        throw Error(`Slicing for rank ${rank} is not yet supported`);\n    }\n}\n//# sourceMappingURL=slice_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class SlicePackedProgram {\n    constructor(destSize) {\n        this.variableNames = ['source'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = destSize;\n        this.rank = destSize.length;\n        const dtype = getCoordsDataType(this.rank);\n        const coords = getChannels('coords', this.rank);\n        const sourceLoc = getChannels('sourceLoc', this.rank);\n        const innerDims = this.rank === 1 ? 'sourceLoc' : `vec2(${sourceLoc.slice(-2).join()})`;\n        const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;\n        const upperRow = `\n      result.x = ${getChannel};\n      if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n        ++${sourceLoc[this.rank - 1]};\n        result.y = ${getChannel};\n        --${sourceLoc[this.rank - 1]};\n      }\n    `;\n        const lowerRow = this.rank === 1 ? '' : `\n      --${coords[this.rank - 1]};\n      if (++${coords[this.rank - 2]} < ${destSize[this.rank - 2]}) {\n        ++${sourceLoc[this.rank - 2]};\n        result.z = ${getChannel};\n        if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n          ++${sourceLoc[this.rank - 1]};\n          result.w = ${getChannel};\n        }\n      }\n    `;\n        const sourceLocSetup = this.rank <= 4 ?\n            `sourceLoc = coords +\n            ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` :\n            destSize.map((_, i) => `${sourceLoc[i]} = ${coords[i]} + start[${i}];`)\n                .join('\\n');\n        this.userCode = `\n      uniform int start[${this.rank}];\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        ${dtype} sourceLoc;\n        ${sourceLocSetup}\n        vec4 result = vec4(0.);\n        ${upperRow}\n        ${lowerRow}\n        setOutput(result);\n      }\n    `;\n    }\n    getCustomSetupFunc(start) {\n        if (start.length !== this.rank) {\n            throw Error(`The rank (${this.rank}) of the program must match the ` +\n                `length of start (${start.length})`);\n        }\n        return (gpgpu, webGLProgram) => {\n            if (this.startLoc == null) {\n                this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n                if (this.startLoc == null) {\n                    // This means the compiler has optimized and realized it doesn't need\n                    // the uniform.\n                    return;\n                }\n            }\n            gpgpu.gl.uniform1iv(this.startLoc, start);\n        };\n    }\n}\n//# sourceMappingURL=slice_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class StridedSliceProgram {\n    constructor(begin, strides, size) {\n        this.variableNames = ['x'];\n        this.outputShape = size;\n        const rank = size.length;\n        const inputDtype = getCoordsDataType(size.length);\n        const dtype = getCoordsDataType(size.length);\n        let newCoords = '';\n        if (rank === 1) {\n            newCoords = 'coords * strides + begin';\n        }\n        else {\n            let outputAxis = 0;\n            newCoords =\n                size.map((_, i) => {\n                    outputAxis++;\n                    return size.length === 1 ?\n                        `coords * strides[${i}] + begin[${i}]` :\n                        `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;\n                })\n                    .join(',');\n        }\n        this.userCode = `\n      ${inputDtype} begin = ${inputDtype}(${begin});\n      ${inputDtype} strides = ${inputDtype}(${strides});\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        setOutput(getX(${newCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=strided_slice_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { getInternalFormatForFloat16MatrixTexture, getInternalFormatForFloat16PackedMatrixTexture, getInternalFormatForFloat32MatrixTexture, getInternalFormatForPackedMatrixTexture, getInternalFormatForUnsignedBytesMatrixTexture } from './gpgpu_util';\nimport { getPackedMatrixTextureShapeWidthHeight, getUnpackedMatrixTextureShapeWidthHeight, PhysicalTextureType, TextureUsage } from './tex_util';\nexport class TextureManager {\n    constructor(gpgpu) {\n        this.gpgpu = gpgpu;\n        this.numUsedTextures = 0;\n        this.numFreeTextures = 0;\n        this._numBytesAllocated = 0;\n        this._numBytesFree = 0; // How many bytes that have been allocated\n        // are available for reuse.\n        this.freeTextures = {};\n        this.logEnabled = false;\n        this.usedTextures = {};\n    }\n    acquireTexture(shapeRC, usage, isPacked) {\n        const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);\n        const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);\n        if (!(shapeKey in this.freeTextures)) {\n            this.freeTextures[shapeKey] = [];\n        }\n        if (!(shapeKey in this.usedTextures)) {\n            this.usedTextures[shapeKey] = [];\n        }\n        const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n        if (this.freeTextures[shapeKey].length > 0) {\n            this.numFreeTextures--;\n            this.numUsedTextures++;\n            this._numBytesFree -= texBytes;\n            this.log();\n            const newTexture = this.freeTextures[shapeKey].shift();\n            this.usedTextures[shapeKey].push(newTexture);\n            return newTexture;\n        }\n        let newTexture;\n        if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {\n            newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {\n            newTexture =\n                this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {\n            newTexture =\n                this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {\n            newTexture =\n                this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {\n            newTexture =\n                this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        this.usedTextures[shapeKey].push(newTexture);\n        this.numUsedTextures++;\n        this._numBytesAllocated += texBytes;\n        this.log();\n        return newTexture;\n    }\n    releaseTexture(texture, shape, logicalTexType, isPacked) {\n        if (this.freeTextures == null) {\n            // Already disposed.\n            return;\n        }\n        const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);\n        const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);\n        if (!(shapeKey in this.freeTextures)) {\n            this.freeTextures[shapeKey] = [];\n        }\n        const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n        const deleteTexThreshold = env().get('WEBGL_DELETE_TEXTURE_THRESHOLD');\n        if (deleteTexThreshold !== -1 &&\n            this._numBytesAllocated > deleteTexThreshold) {\n            this.gpgpu.deleteMatrixTexture(texture);\n            this._numBytesAllocated -= texBytes;\n        }\n        else {\n            this.freeTextures[shapeKey].push(texture);\n            this.numFreeTextures++;\n            this._numBytesFree += texBytes;\n        }\n        this.numUsedTextures--;\n        const texList = this.usedTextures[shapeKey];\n        const texIndex = texList.indexOf(texture);\n        if (texIndex < 0) {\n            throw new Error('Cannot release a texture that was never provided by this ' +\n                'texture manager');\n        }\n        texList.splice(texIndex, 1);\n        this.log();\n    }\n    log() {\n        if (!this.logEnabled) {\n            return;\n        }\n        const total = this.numFreeTextures + this.numUsedTextures;\n        console.log('Free/Used', `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`);\n        const freeRatio = this._numBytesFree / this._numBytesAllocated;\n        console.log(`Bytes allocated: ${this._numBytesAllocated}`);\n        console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`);\n    }\n    get numBytesAllocated() {\n        return this._numBytesAllocated;\n    }\n    get numBytesFree() {\n        return this._numBytesFree;\n    }\n    getNumUsedTextures() {\n        return this.numUsedTextures;\n    }\n    getNumFreeTextures() {\n        return this.numFreeTextures;\n    }\n    dispose() {\n        if (this.freeTextures == null) {\n            // Already disposed.\n            return;\n        }\n        for (const texShape in this.freeTextures) {\n            this.freeTextures[texShape].forEach(tex => {\n                this.gpgpu.deleteMatrixTexture(tex);\n            });\n        }\n        for (const texShape in this.usedTextures) {\n            this.usedTextures[texShape].forEach(tex => {\n                this.gpgpu.deleteMatrixTexture(tex);\n            });\n        }\n        this.freeTextures = null;\n        this.usedTextures = null;\n        this.numUsedTextures = 0;\n        this.numFreeTextures = 0;\n        this._numBytesAllocated = 0;\n        this._numBytesFree = 0;\n    }\n}\nfunction numBytesForInternalFormat(gl, internalFormat) {\n    // tslint:disable-next-line:no-any\n    const glany = gl;\n    if (internalFormat === glany.R32F) {\n        return 4;\n    }\n    else if (internalFormat === glany.R16F) {\n        return 2;\n    }\n    else if (internalFormat === glany.RGBA32F) {\n        return 16;\n    }\n    else if (internalFormat === gl.RGBA) {\n        return 16;\n    }\n    else if (internalFormat === glany.RGBA16F) {\n        return 8;\n    }\n    throw new Error(`Unknown internal format ${internalFormat}`);\n}\nexport function computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) {\n    // It is not possible to infer packed status from the texture type because\n    // depending on the textureConfig, different  texture types may resolve to the\n    // same internal format (e.g. in WebGL1, the internal format for\n    // UNPACKED_FLOAT16 textures is gl.RGBA). Therefore we pass in `isPacked`\n    // explicitly.\n    const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig);\n    let numElements;\n    if (isPacked) {\n        const [packedWidth, packedHeight] = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n        numElements = packedWidth * packedHeight;\n    }\n    else {\n        const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n        numElements = width * height;\n    }\n    const bytesPerElement = numBytesForInternalFormat(gl, internalFormat);\n    return numElements * bytesPerElement;\n}\nfunction internalFormatForPhysicalTexType(physicalTexType, textureConfig) {\n    switch (physicalTexType) {\n        case PhysicalTextureType.PACKED_2X2_FLOAT32:\n            return getInternalFormatForPackedMatrixTexture(textureConfig);\n        case PhysicalTextureType.PACKED_2X2_FLOAT16:\n            return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);\n        case PhysicalTextureType.UNPACKED_FLOAT32:\n            return getInternalFormatForFloat32MatrixTexture(textureConfig);\n        case PhysicalTextureType.UNPACKED_FLOAT16:\n            return getInternalFormatForFloat16MatrixTexture(textureConfig);\n        case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:\n            return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);\n        default:\n            throw new Error(`Unknown physical texture type ${physicalTexType}`);\n    }\n}\nfunction getPhysicalTextureForRendering(isPacked) {\n    if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED')) {\n        if (isPacked) {\n            return PhysicalTextureType.PACKED_2X2_FLOAT32;\n        }\n        return PhysicalTextureType.UNPACKED_FLOAT32;\n    }\n    if (isPacked) {\n        return PhysicalTextureType.PACKED_2X2_FLOAT16;\n    }\n    return PhysicalTextureType.UNPACKED_FLOAT16;\n}\nfunction getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {\n    if (logicalTexType === TextureUsage.UPLOAD) {\n        return PhysicalTextureType.PACKED_2X2_FLOAT32;\n    }\n    else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {\n        return getPhysicalTextureForRendering(isPacked);\n    }\n    else if (logicalTexType === TextureUsage.DOWNLOAD ||\n        logicalTexType === TextureUsage.PIXELS) {\n        return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;\n    }\n    throw new Error(`Unknown logical texture type ${logicalTexType}`);\n}\nfunction getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {\n    return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;\n}\n//# sourceMappingURL=texture_manager.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class TileProgram {\n    constructor(aShape, reps) {\n        this.variableNames = ['A'];\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[i] * reps[i];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const sourceCoords = getSourceCoords(aShape);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n    }\n}\nfunction getSourceCoords(aShape) {\n    const rank = aShape.length;\n    if (rank > 5) {\n        throw Error(`Tile for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n        return `imod(resRC, ${aShape[0]})`;\n    }\n    const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u'];\n    const sourceCoords = [];\n    for (let i = 0; i < aShape.length; i++) {\n        sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);\n    }\n    return sourceCoords.join();\n}\n//# sourceMappingURL=tile_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class UnaryOpProgram {\n    constructor(aShape, opSnippet) {\n        this.variableNames = ['A'];\n        this.outputShape = aShape;\n        this.userCode = `\n      float unaryOperation(float x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n    }\n}\nconst CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;\nexport const LINEAR = `return x;`;\nexport const ABS = `return abs(x);`;\nexport const RELU = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\nexport const RELU6 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`;\nexport const ELU = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\nexport const SELU = `\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${backend_util.SELU_SCALEALPHA};\n  float scale = ${backend_util.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`;\nexport function STEP(alpha = 0.0) {\n    return CHECK_NAN_SNIPPET + `\n    return x > 0.0 ? 1.0 : float(${alpha});\n  `;\n}\nexport const NEG = `return -x;`;\nexport const CEIL = `return ceil(x);`;\nexport const FLOOR = `return floor(x);`;\nexport const SIGN = `\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n`;\nexport const IS_NAN = `return float(isnan(x));`;\nexport const IS_INF = `return float(isinf(x));`;\nexport const IS_FINITE = `return float(!isnan(x) && !isinf(x));`;\nexport const ROUND = `\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`;\nexport const EXP = `return exp(x);`;\nexport const EXPM1 = `return exp(x) - 1.0;`;\nexport const LOG = `if (x < 0.0) return NAN;\n  return log(x);`;\nexport const LOG1P = `return log(1.0 + x);`;\nexport const SQRT = `return sqrt(x);`;\nexport const RSQRT = `return inversesqrt(x);`;\nexport const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n/**\n * mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n *\n * epsilon is the difference between 1.0 and the next representable\n * float. For a single precision 32 bit float this should be 2^-23, see:\n * https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\n *\n * too_large = (x > -threshold) is value above which exp(x) may overflow\n * but softplus(x) == x is within machine epsilon\n *\n * too_small = (x < threshold) is value below which exp(x) may underflow,\n * but softplus(x) == exp(x) is within machine epsilon.\n */\nexport const SOFTPLUS = `\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`;\nexport const ASIN = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`;\nexport const ACOS = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`;\nexport const ATAN = CHECK_NAN_SNIPPET + `\n  return atan(x);\n`;\nexport const SINH = `\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`;\nexport const COSH = `\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`;\nexport const TANH = `\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`;\nexport const ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`;\nexport const ACOSH = CHECK_NAN_SNIPPET + `\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));`;\nexport const ATANH = CHECK_NAN_SNIPPET + `\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;\nexport const ERF = `\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = ${backend_util.ERF_P};\n  float a1 = ${backend_util.ERF_A1};\n  float a2 = ${backend_util.ERF_A2};\n  float a3 = ${backend_util.ERF_A3};\n  float a4 = ${backend_util.ERF_A4};\n  float a5 = ${backend_util.ERF_A5};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`;\nexport const RECIPROCAL = `return 1.0 / x;`;\nexport const LOGICAL_NOT = `return float(!(x >= 1.0));`;\nexport const TO_INT = `return float(int(x));`;\nexport const CLONE = 'return x;';\n//# sourceMappingURL=unaryop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const LINEAR = `return x;`;\nexport const LOG = `\n  vec4 result = log(x);\n  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\n  result.r = isNaN.r == 1.0 ? NAN : result.r;\n  result.g = isNaN.g == 1.0 ? NAN : result.g;\n  result.b = isNaN.b == 1.0 ? NAN : result.b;\n  result.a = isNaN.a == 1.0 ? NAN : result.a;\n\n  return result;\n`;\nexport const RELU = `\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nexport const RELU6 = `\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nexport const ELU = `\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`;\nexport class UnaryOpPackedProgram {\n    constructor(aShape, opSnippet) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = aShape;\n        this.userCode = `\n      vec4 unaryOperation(vec4 x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=unaryop_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels, getSourceCoords } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class UnpackProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = false;\n        this.outputShape = outputShape;\n        const rank = outputShape.length;\n        const channels = getChannels('rc', rank);\n        const dtype = getCoordsDataType(rank);\n        const sourceCoords = getSourceCoords(rank, channels);\n        const innerDims = channels.slice(-2);\n        const coords = rank <= 1 ? 'rc' : `vec2(${innerDims.join(',')})`;\n        this.userCode = `\n      void main() {\n        ${dtype} rc = getOutputCoords();\n        vec4 packedInput = getA(${sourceCoords});\n\n        setOutput(getChannel(packedInput, ${coords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=unpack_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Import webgl flags.\nimport './flags_webgl';\nimport * as tf from '@tensorflow/tfjs-core';\nimport { complex, div, engine, env, imag, max, range, real, reshape, scalar, softmax, tensor, tidy, transpose } from '@tensorflow/tfjs-core';\nimport { backend_util, buffer, kernel_impls, slice_util, util } from '@tensorflow/tfjs-core';\nimport { DataStorage, KernelBackend, upcastType } from '@tensorflow/tfjs-core';\nimport { addImplCPU, ceilImplCPU, expImplCPU, expm1ImplCPU, floorImplCPU, logImplCPU, multiplyImplCPU, rsqrtImplCPU, simpleAbsImplCPU, sliceImplCPU, subImplCPU } from './kernel_utils/shared';\nconst { segment_util } = backend_util;\nconst split = kernel_impls.split;\nconst tile = kernel_impls.tile;\nconst topkImpl = kernel_impls.topkImpl;\nconst whereImpl = kernel_impls.whereImpl;\nimport { AddNProgram } from './addn_gpu';\nimport { AddNPackedProgram } from './addn_packed_gpu';\nimport { ArgMinMaxProgram } from './argminmax_gpu';\nimport { ArgMinMaxPackedProgram } from './argminmax_packed_gpu';\nimport { AvgPool3DBackpropProgram } from './avg_pool_backprop_gpu';\nimport * as binaryop_complex_gpu from './binaryop_complex_gpu';\nimport { BinaryOpComplexProgram } from './binaryop_complex_gpu';\nimport * as binaryop_gpu from './binaryop_gpu';\nimport { BinaryOpProgram } from './binaryop_gpu';\nimport * as binaryop_packed_gpu from './binaryop_packed_gpu';\nimport { BinaryOpPackedProgram } from './binaryop_packed_gpu';\nimport { getWebGLContext } from './canvas_util';\nimport { ClipProgram } from './clip_gpu';\nimport { ClipPackedProgram } from './clip_packed_gpu';\nimport { ComplexAbsProgram } from './complex_abs_gpu';\nimport { ConcatProgram } from './concat_gpu';\nimport { ConcatPackedProgram } from './concat_packed_gpu';\nimport { Conv2DDerFilterProgram, Conv2DDerInputProgram, Conv3DDerFilterProgram, Conv3DDerInputProgram } from './conv_backprop_gpu';\nimport { DepthwiseConv2DDerFilterProgram, DepthwiseConv2DDerInputProgram } from './conv_backprop_gpu_depthwise';\nimport { Conv2DProgram, Conv3DProgram } from './conv_gpu';\nimport { DepthwiseConv2DProgram } from './conv_gpu_depthwise';\nimport { DepthwiseConvPacked2DProgram } from './conv_packed_gpu_depthwise';\nimport { CropAndResizeProgram } from './crop_and_resize_gpu';\nimport { CumSumProgram } from './cumsum_gpu';\nimport { DecodeMatrixProgram } from './decode_matrix_gpu';\nimport { DecodeMatrixPackedProgram } from './decode_matrix_packed_gpu';\nimport { DepthToSpaceProgram } from './depth_to_space_gpu';\nimport { DiagProgram } from './diag_gpu';\nimport { EncodeFloatProgram } from './encode_float_gpu';\nimport { EncodeFloatPackedProgram } from './encode_float_packed_gpu';\nimport { EncodeMatrixProgram } from './encode_matrix_gpu';\nimport { EncodeMatrixPackedProgram } from './encode_matrix_packed_gpu';\nimport * as fft_gpu from './fft_gpu';\nimport { FFTProgram } from './fft_gpu';\nimport { FillProgram } from './fill_gpu';\nimport { GatherProgram } from './gather_gpu';\nimport { GatherNDProgram } from './gather_nd_gpu';\nimport { GPGPUContext } from './gpgpu_context';\nimport * as gpgpu_math from './gpgpu_math';\nimport { Im2ColPackedProgram } from './im2col_packed_gpu';\nimport { LRNProgram } from './lrn_gpu';\nimport { LRNGradProgram } from './lrn_grad_gpu';\nimport { LRNPackedProgram } from './lrn_packed_gpu';\nimport { MaxPool3DBackpropProgram } from './max_pool_backprop_gpu';\nimport { MatMulPackedProgram } from './mulmat_packed_gpu';\nimport { MultinomialProgram } from './multinomial_gpu';\nimport { OneHotProgram } from './onehot_gpu';\nimport { PackProgram } from './pack_gpu';\nimport { PadProgram } from './pad_gpu';\nimport { PadPackedProgram } from './pad_packed_gpu';\nimport { Pool3DProgram } from './pool_gpu';\nimport { ReduceProgram } from './reduce_gpu';\nimport { ReshapePackedProgram } from './reshape_packed_gpu';\nimport { ResizeBilinearBackpropProgram } from './resize_bilinear_backprop_gpu';\nimport { ResizeBilinearProgram } from './resize_bilinear_gpu';\nimport { ResizeBilinearPackedProgram } from './resize_bilinear_packed_gpu';\nimport { ResizeNearestNeigborBackpropProgram } from './resize_nearest_neighbor_backprop_gpu';\nimport { ResizeNearestNeighborProgram } from './resize_nearest_neighbor_gpu';\nimport { ReverseProgram } from './reverse_gpu';\nimport { ReversePackedProgram } from './reverse_packed_gpu';\nimport { ScatterProgram } from './scatter_gpu';\nimport { SegmentOpProgram } from './segment_gpu';\nimport { SelectProgram } from './select_gpu';\nimport { SliceProgram } from './slice_gpu';\nimport { SlicePackedProgram } from './slice_packed_gpu';\nimport { StridedSliceProgram } from './strided_slice_gpu';\nimport * as tex_util from './tex_util';\nimport { TextureUsage } from './tex_util';\nimport { TextureManager } from './texture_manager';\nimport { TileProgram } from './tile_gpu';\nimport * as unary_op from './unaryop_gpu';\nimport { UnaryOpProgram } from './unaryop_gpu';\nimport * as unary_packed_op from './unaryop_packed_gpu';\nimport { UnaryOpPackedProgram } from './unaryop_packed_gpu';\nimport { UnpackProgram } from './unpack_gpu';\nimport * as webgl_util from './webgl_util';\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\nconst binaryCaches = {};\nexport function getBinaryCache(webGLVersion) {\n    if (webGLVersion in binaryCaches) {\n        return binaryCaches[webGLVersion];\n    }\n    binaryCaches[webGLVersion] = {};\n    return binaryCaches[webGLVersion];\n}\nfunction mapActivationToShaderProgram(activation, packed = false) {\n    if (activation === 'linear') {\n        if (packed) {\n            return unary_packed_op.LINEAR;\n        }\n        return unary_op.LINEAR;\n    }\n    else if (activation === 'relu') {\n        if (packed) {\n            return unary_packed_op.RELU;\n        }\n        return unary_op.RELU;\n    }\n    else if (activation === 'elu') {\n        if (packed) {\n            return unary_packed_op.ELU;\n        }\n        return unary_op.ELU;\n    }\n    else if (activation === 'relu6') {\n        if (packed) {\n            return unary_packed_op.RELU6;\n        }\n        return unary_op.RELU6;\n    }\n    else if (activation === 'prelu') {\n        if (packed) {\n            return binaryop_packed_gpu.PRELU;\n        }\n        return binaryop_gpu.PRELU;\n    }\n    throw new Error(`Activation ${activation} has not been implemented for the WebGL backend.`);\n}\n// Empirically determined constant used to determine size threshold for handing\n// off execution to the CPU.\nconst CPU_HANDOFF_SIZE_THRESHOLD = 128;\n// Empirically determined constant used to decide the number of MB on GPU\n// before we warn about high memory use. The MB are this constant * screen area\n// * dpi / 1024 / 1024.\nconst BEFORE_PAGING_CONSTANT = 600;\nfunction numMBBeforeWarning() {\n    if (env().global.screen == null) {\n        return 1024; // 1 GB.\n    }\n    return (env().global.screen.height * env().global.screen.width *\n        window.devicePixelRatio) *\n        BEFORE_PAGING_CONSTANT / 1024 / 1024;\n}\n// Empirically determined minimal shared dimension in matmul before we forward\n// to a.mul(b).sum() in order to take advantage of GPU parallelism. See\n// https://github.com/tensorflow/tfjs-core/pull/1379 for benchmarks.\nexport const MATMUL_SHARED_DIM_THRESHOLD = 1000;\nexport class MathBackendWebGL extends KernelBackend {\n    constructor(gpgpu) {\n        super();\n        // Maps data ids that have a pending read operation, to list of subscribers.\n        this.pendingRead = new WeakMap();\n        // List of data ids that are scheduled for disposal, but are waiting on a\n        // pending read operation.\n        this.pendingDisposal = new WeakSet();\n        // Used to count the number of 'shallow' sliced tensors that point to the\n        // same data id.\n        this.dataRefCount = new WeakMap();\n        this.numBytesInGPU = 0;\n        // Accumulated time spent (including blocking) in uploading data to webgl.\n        this.uploadWaitMs = 0;\n        // Accumulated time spent (including blocking in downloading data from webgl.\n        this.downloadWaitMs = 0;\n        this.warnedAboutMemory = false;\n        this.warnedAboutCPUBackend = false;\n        this.pendingDeletes = 0;\n        this.disposed = false;\n        if (!env().getBool('HAS_WEBGL')) {\n            throw new Error('WebGL is not supported on this device');\n        }\n        if (gpgpu == null) {\n            const gl = getWebGLContext(env().getNumber('WEBGL_VERSION'));\n            this.binaryCache = getBinaryCache(env().getNumber('WEBGL_VERSION'));\n            this.gpgpu = new GPGPUContext(gl);\n            this.canvas = gl.canvas;\n            this.gpgpuCreatedLocally = true;\n        }\n        else {\n            this.gpgpu = gpgpu;\n            this.binaryCache = {};\n            this.gpgpuCreatedLocally = false;\n            this.canvas = gpgpu.gl.canvas;\n        }\n        this.textureManager = new TextureManager(this.gpgpu);\n        this.numMBBeforeWarning = numMBBeforeWarning();\n        this.texData = new DataStorage(this, engine());\n    }\n    numDataIds() {\n        return this.texData.numDataIds() +\n            (this.cpuBackend ? this.cpuBackend.numDataIds() : 0) -\n            this.pendingDeletes;\n    }\n    write(values, shape, dtype) {\n        if (env().getBool('WEBGL_CHECK_NUMERICAL_PROBLEMS') ||\n            env().getBool('DEBUG')) {\n            this.checkNumericalProblems(values);\n        }\n        if (dtype === 'complex64' && values != null) {\n            throw new Error(`Cannot write to a complex64 dtype. ` +\n                `Please use tf.complex(real, imag).`);\n        }\n        const dataId = {};\n        this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });\n        return dataId;\n    }\n    /** Increase refCount of a `TextureData`. */\n    incRef(dataId) {\n        const texData = this.texData.get(dataId);\n        texData.refCount++;\n    }\n    /** Decrease refCount of a `TextureData`. */\n    decRef(dataId) {\n        if (this.texData.has(dataId)) {\n            const texData = this.texData.get(dataId);\n            texData.refCount--;\n        }\n    }\n    move(dataId, values, shape, dtype) {\n        if (env().getBool('DEBUG')) {\n            this.checkNumericalProblems(values);\n        }\n        if (dtype === 'complex64') {\n            throw new Error(`Cannot write to a complex64 dtype. ` +\n                `Please use tf.complex(real, imag).`);\n        }\n        this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });\n    }\n    disposeIntermediateTensorInfo(tensorInfo) {\n        const dataId = tensorInfo.dataId;\n        if (this.texData.has(dataId)) {\n            const textureData = this.texData.get(dataId);\n            textureData.refCount--;\n            if (textureData.refCount < 1) {\n                this.disposeData(dataId);\n            }\n        }\n    }\n    readSync(dataId) {\n        const texData = this.texData.get(dataId);\n        const { values, dtype, complexTensors, slice, shape, isPacked } = texData;\n        // The presence of `slice` indicates this tensor is a shallow slice of a\n        // different tensor, and is using that original tensor's texture. Run\n        // `clone` in order to copy that texture and read from it.\n        if (slice != null) {\n            let program;\n            if (isPacked) {\n                program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n            }\n            else {\n                program = new UnaryOpProgram(shape, unary_op.CLONE);\n            }\n            const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n            const data = this.readSync(res.dataId);\n            this.disposeIntermediateTensorInfo(res);\n            return data;\n        }\n        if (values != null) {\n            return this.convertAndCacheOnCPU(dataId);\n        }\n        if (dtype === 'string') {\n            return values;\n        }\n        const shouldTimeProgram = this.activeTimers != null;\n        let start;\n        if (shouldTimeProgram) {\n            start = util.now();\n        }\n        let result;\n        if (dtype === 'complex64') {\n            const realValues = complexTensors.real.dataSync();\n            const imagValues = complexTensors.imag.dataSync();\n            result = backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        else {\n            result = this.getValuesFromTexture(dataId);\n        }\n        if (shouldTimeProgram) {\n            this.downloadWaitMs += util.now() - start;\n        }\n        return this.convertAndCacheOnCPU(dataId, result);\n    }\n    async read(dataId) {\n        if (this.pendingRead.has(dataId)) {\n            const subscribers = this.pendingRead.get(dataId);\n            return new Promise(resolve => subscribers.push(resolve));\n        }\n        const texData = this.texData.get(dataId);\n        const { values, shape, slice, dtype, complexTensors, isPacked } = texData;\n        // The presence of `slice` indicates this tensor is a shallow slice of a\n        // different tensor, and is using that original tensor's texture. Run\n        // `clone` in order to copy that texture and read from it.\n        if (slice != null) {\n            let program;\n            if (isPacked) {\n                program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n            }\n            else {\n                program = new UnaryOpProgram(shape, unary_op.CLONE);\n            }\n            const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n            const data = this.read(res.dataId);\n            this.disposeIntermediateTensorInfo(res);\n            return data;\n        }\n        if (values != null) {\n            return this.convertAndCacheOnCPU(dataId);\n        }\n        if (!env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED') &&\n            env().getNumber('WEBGL_VERSION') === 2) {\n            throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and ` +\n                `WEBGL_VERSION=2 not yet supported.`);\n        }\n        let buffer = null;\n        let tmpDownloadTarget;\n        if (dtype !== 'complex64' && env().get('WEBGL_BUFFER_SUPPORTED')) {\n            // Possibly copy the texture into a buffer before inserting a fence.\n            tmpDownloadTarget = this.decode(dataId);\n            const tmpData = this.texData.get(tmpDownloadTarget.dataId);\n            buffer = this.gpgpu.createBufferFromTexture(tmpData.texture, ...tex_util.getDenseTexShape(shape));\n        }\n        this.pendingRead.set(dataId, []);\n        if (dtype !== 'complex64') {\n            // Create a fence and wait for it to resolve.\n            await this.gpgpu.createAndWaitForFence();\n        }\n        // Download the values from the GPU.\n        let vals;\n        if (dtype === 'complex64') {\n            const ps = await Promise.all([complexTensors.real.data(), complexTensors.imag.data()]);\n            const realValues = ps[0];\n            const imagValues = ps[1];\n            vals = backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        else if (buffer == null) {\n            vals = this.getValuesFromTexture(dataId);\n        }\n        else {\n            const size = util.sizeFromShape(shape);\n            vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer, size);\n        }\n        if (tmpDownloadTarget != null) {\n            this.disposeIntermediateTensorInfo(tmpDownloadTarget);\n        }\n        const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);\n        const subscribers = this.pendingRead.get(dataId);\n        this.pendingRead.delete(dataId);\n        // Notify all pending reads.\n        subscribers.forEach(resolve => resolve(dTypeVals));\n        if (this.pendingDisposal.has(dataId)) {\n            this.pendingDisposal.delete(dataId);\n            this.disposeData(dataId);\n            this.pendingDeletes--;\n        }\n        return dTypeVals;\n    }\n    checkNumericalProblems(values) {\n        if (values == null) {\n            return;\n        }\n        for (let i = 0; i < values.length; i++) {\n            const num = values[i];\n            if (!webgl_util.canBeRepresented(num)) {\n                if (env().getBool('WEBGL_RENDER_FLOAT32_CAPABLE')) {\n                    throw Error(`The value ${num} cannot be represented with your ` +\n                        `current settings. Consider enabling float32 rendering: ` +\n                        `'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);\n                }\n                throw Error(`The value ${num} cannot be represented on this device.`);\n            }\n        }\n    }\n    getValuesFromTexture(dataId) {\n        const { shape, dtype, isPacked } = this.texData.get(dataId);\n        const size = util.sizeFromShape(shape);\n        if (env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {\n            const tmpTarget = this.decode(dataId);\n            const tmpData = this.texData.get(tmpTarget.dataId);\n            const vals = this.gpgpu\n                .downloadMatrixFromPackedTexture(tmpData.texture, ...tex_util.getDenseTexShape(shape))\n                .subarray(0, size);\n            this.disposeIntermediateTensorInfo(tmpTarget);\n            return vals;\n        }\n        const shouldUsePackedProgram = env().getBool('WEBGL_PACK') && isPacked === true;\n        const outputShape = shouldUsePackedProgram ? webgl_util.getShapeAs3D(shape) : shape;\n        const program = shouldUsePackedProgram ?\n            new EncodeFloatPackedProgram(outputShape) :\n            new EncodeFloatProgram(outputShape);\n        const output = this.runWebGLProgram(program, [{ shape: outputShape, dtype, dataId }], 'float32');\n        const tmpData = this.texData.get(output.dataId);\n        const vals = this.gpgpu\n            .downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture, tmpData.texShape[0], tmpData.texShape[1])\n            .subarray(0, size);\n        this.disposeIntermediateTensorInfo(output);\n        return vals;\n    }\n    async time(f) {\n        const oldActiveTimers = this.activeTimers;\n        const newActiveTimers = [];\n        let outerMostTime = false;\n        if (this.programTimersStack == null) {\n            this.programTimersStack = newActiveTimers;\n            outerMostTime = true;\n        }\n        else {\n            this.activeTimers.push(newActiveTimers);\n        }\n        this.activeTimers = newActiveTimers;\n        f();\n        // needing to split these up because util.flatten only accepts certain types\n        const flattenedActiveTimerQueries = util.flatten(this.activeTimers.map((d) => d.query))\n            .filter(d => d != null);\n        const flattenedActiveTimerNames = util.flatten(this.activeTimers.map((d) => d.name))\n            .filter(d => d != null);\n        this.activeTimers = oldActiveTimers;\n        if (outerMostTime) {\n            this.programTimersStack = null;\n        }\n        const res = {\n            uploadWaitMs: this.uploadWaitMs,\n            downloadWaitMs: this.downloadWaitMs,\n            kernelMs: null,\n            wallMs: null // will be filled by the engine\n        };\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n            res['kernelMs'] = util.sum(kernelMs);\n            res['getExtraProfileInfo'] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d }))\n                .map(d => `${d.name}: ${d.ms}`)\n                .join(', ');\n        }\n        else {\n            res['kernelMs'] = {\n                error: 'WebGL query timers are not supported in this environment.'\n            };\n        }\n        this.uploadWaitMs = 0;\n        this.downloadWaitMs = 0;\n        return res;\n    }\n    memory() {\n        return {\n            unreliable: false,\n            numBytesInGPU: this.numBytesInGPU,\n            numBytesInGPUAllocated: this.textureManager.numBytesAllocated,\n            numBytesInGPUFree: this.textureManager.numBytesFree\n        };\n    }\n    startTimer() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            return this.gpgpu.beginQuery();\n        }\n        return { startMs: util.now(), endMs: null };\n    }\n    endTimer(query) {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            this.gpgpu.endQuery();\n            return query;\n        }\n        query.endMs = util.now();\n        return query;\n    }\n    async getQueryTime(query) {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            return this.gpgpu.waitForQueryAndGetTime(query);\n        }\n        const timerQuery = query;\n        return timerQuery.endMs - timerQuery.startMs;\n    }\n    disposeData(dataId) {\n        if (this.pendingDisposal.has(dataId)) {\n            return;\n        }\n        if (this.pendingRead.has(dataId)) {\n            this.pendingDisposal.add(dataId);\n            this.pendingDeletes++;\n            return;\n        }\n        // No-op if already disposed.\n        if (!this.texData.has(dataId)) {\n            return;\n        }\n        this.releaseGPUData(dataId);\n        const { complexTensors } = this.texData.get(dataId);\n        if (complexTensors != null) {\n            complexTensors.real.dispose();\n            complexTensors.imag.dispose();\n        }\n        this.texData.delete(dataId);\n    }\n    releaseGPUData(dataId) {\n        const { texture, dtype, texShape, usage, isPacked, slice } = this.texData.get(dataId);\n        const key = slice && slice.origDataId || dataId;\n        const refCount = this.dataRefCount.get(key);\n        if (refCount > 1) {\n            this.dataRefCount.set(key, refCount - 1);\n        }\n        else {\n            this.dataRefCount.delete(key);\n            if (texture != null) {\n                this.numBytesInGPU -= this.computeBytes(texShape, dtype);\n                this.textureManager.releaseTexture(texture, texShape, usage, isPacked);\n            }\n        }\n        const texData = this.texData.get(dataId);\n        texData.texture = null;\n        texData.texShape = null;\n        texData.isPacked = false;\n        texData.slice = null;\n    }\n    getTexture(dataId) {\n        this.uploadToGPU(dataId);\n        return this.texData.get(dataId).texture;\n    }\n    /**\n     * Returns internal information for the specific data bucket. Used in unit\n     * tests.\n     */\n    getDataInfo(dataId) {\n        return this.texData.get(dataId);\n    }\n    getCPUBackend() {\n        if (!env().getBool('WEBGL_CPU_FORWARD')) {\n            return null;\n        }\n        if (this.cpuBackend == null) {\n            this.cpuBackend = engine().findBackend('cpu');\n        }\n        return this.cpuBackend;\n    }\n    /*\n    Tests whether all the inputs to an op are small and on the CPU. This heuristic\n    determines when it would be faster to execute a kernel on the CPU. WebGL\n    kernels opt into running this check and forwarding when appropriate.\n    TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more\n    sustainable strategy for optimizing backend execution of ops.\n     */\n    shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) {\n        const cpuBackend = this.getCPUBackend();\n        if (!this.warnedAboutCPUBackend && cpuBackend == null) {\n            console.warn('Your application contains ops that are small enough to be ' +\n                'executed on the CPU backend, however the CPU backend cannot ' +\n                'be found. Consider importing the CPU backend ' +\n                '(@tensorflow/tfjs-backend-cpu) for better performance.');\n            this.warnedAboutCPUBackend = true;\n        }\n        return cpuBackend != null &&\n            inputs.every(input => this.texData.get(input.dataId).texture == null &&\n                util.sizeFromShape(input.shape) < sizeThreshold);\n    }\n    getGPGPUContext() {\n        return this.gpgpu;\n    }\n    complex(real, imag) {\n        const result = this.makeOutput(real.shape, 'complex64');\n        const resultData = this.texData.get(result.dataId);\n        // The backend owns the reference to the underlying real and imaginary\n        // clones. These will explicitly get disposed when the complex tensor is\n        // disposed.\n        resultData.complexTensors = {\n            real: engine().keep(real.clone()),\n            imag: engine().keep(imag.clone())\n        };\n        return result;\n    }\n    real(input) {\n        const resultData = this.texData.get(input.dataId);\n        return resultData.complexTensors.real.clone();\n    }\n    imag(input) {\n        const resultData = this.texData.get(input.dataId);\n        return resultData.complexTensors.imag.clone();\n    }\n    slice(x, begin, size) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = sliceImplCPU(this.texData.get(x.dataId).values, begin, size, x.shape, x.dtype);\n            return this.makeOutput(size, x.dtype, outValues);\n        }\n        // Short-circuit computation if the slice is zero-sized.\n        if (util.sizeFromShape(size) === 0) {\n            return tensor([], size, x.dtype);\n        }\n        const { isPacked } = this.texData.get(x.dataId);\n        const isContinous = slice_util.isSliceContinous(x.shape, begin, size);\n        if (isPacked || !isContinous) {\n            const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n                new SlicePackedProgram(size) :\n                new SliceProgram(size);\n            const customSetup = program.getCustomSetupFunc(begin);\n            return this.compileAndRun(program, [x], null, customSetup);\n        }\n        this.uploadToGPU(x.dataId);\n        return this.shallowSlice(x, begin, size);\n    }\n    shallowSlice(x, begin, size) {\n        const xTexData = this.texData.get(x.dataId);\n        const t = this.makeOutput(size, x.dtype);\n        const newTexData = this.texData.get(t.dataId);\n        // Copy texture data from the original tensor.\n        Object.assign(newTexData, xTexData);\n        newTexData.shape = size;\n        newTexData.dtype = x.dtype;\n        let flatOffset = slice_util.computeFlatOffset(begin, x.strides);\n        if (xTexData.slice) {\n            // We are slicing an already sliced tensor, so we have to accumulate\n            // the offset.\n            flatOffset += xTexData.slice.flatOffset;\n        }\n        newTexData.slice = {\n            flatOffset,\n            // Point to the original dataId, which is used to do ref counting.\n            origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId\n        };\n        // Increase the ref count for that data bucket.\n        const refCount = this.dataRefCount.get(newTexData.slice.origDataId) || 1;\n        this.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);\n        return t;\n    }\n    stridedSlice(x, begin, end, strides) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.stridedSlice(x, begin, end, strides));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const outShape = slice_util.computeOutShape(begin, end, strides);\n        if (outShape.some(axis => axis === 0)) {\n            return tensor([], outShape);\n        }\n        const program = new StridedSliceProgram(begin, strides, outShape);\n        return this.compileAndRun(program, [x]);\n    }\n    reverse(x, axis) {\n        const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n            new ReversePackedProgram(x.shape, axis) :\n            new ReverseProgram(x.shape, axis);\n        return this.compileAndRun(program, [x]);\n    }\n    concat(tensors, axis) {\n        if (tensors[0].dtype === 'complex64') {\n            const reals = tensors.map((t) => real(t));\n            const imags = tensors.map((t) => imag(t));\n            return complex(this.concat(reals, axis), this.concat(imags, axis));\n        }\n        if (tensors.length === 1) {\n            return tensors[0];\n        }\n        if (tensors.length > env().getNumber('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n            const midIndex = Math.floor(tensors.length / 2);\n            const leftSide = this.concat(tensors.slice(0, midIndex), axis);\n            const rightSide = this.concat(tensors.slice(midIndex), axis);\n            return this.concat([leftSide, rightSide], axis);\n        }\n        if (env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') && tensors[0].rank > 1) {\n            const program = new ConcatPackedProgram(tensors.map(t => t.shape), axis);\n            return this.compileAndRun(program, tensors);\n        }\n        // Any concat of n-dimensional tensors across any axis can be reduced to\n        // a concatenation of two-dimensional tensors across the axis 1 by first\n        // partitioning the axes of the original tensors into those less than the\n        // axis to be concatenated and the rest. Then reshape the tensors\n        // into a two-dimensional tensor by collapsing these two sets of axes and\n        // concatenate the resulting matrices across the axis 1, finally reshaping\n        // the result to have the proper shape.\n        const outShape = backend_util.computeOutShape(tensors.map(t => t.shape), axis);\n        const tensors2D = tensors.map(t => t.as2D(-1, util.sizeFromShape(t.shape.slice(axis))));\n        const program = new ConcatProgram(tensors2D.map(t => t.shape));\n        const res = this.compileAndRun(program, tensors2D);\n        return res.reshape(outShape);\n    }\n    neg(x) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.neg(x));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.NEG, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.NEG);\n        return this.compileAndRun(program, [x]);\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n        const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n        const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n        const [batch, ,] = a.shape;\n        // Since the matrices are vectors, it is faster to call mul().sum()\n        // because sum() is O(sqrt(N)) due to divide-and-conquer.\n        if ((outerShapeA === 1 || outerShapeB === 1) &&\n            sharedDim > MATMUL_SHARED_DIM_THRESHOLD) {\n            if (transposeA) {\n                a = transpose(a, [0, 2, 1]);\n            }\n            if (transposeB) {\n                b = transpose(b, [0, 2, 1]);\n            }\n            const a3D = outerShapeB === 1 ? a : a.as3D(batch, sharedDim, 1);\n            const axis = outerShapeB === 1 ? 2 : 1;\n            const b3D = outerShapeB === 1 ? b.as3D(batch, 1, sharedDim) : b;\n            return this.multiply(a3D, b3D).sum(axis, true /* keepDims */);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        const program = new MatMulPackedProgram(a.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n        const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n        const [batch, ,] = a.shape;\n        const dtype = upcastType(a.dtype, b.dtype);\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;\n        const program = new MatMulPackedProgram(a.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [a, b];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (preluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        return this.compileAndRun(program, inputs, dtype);\n    }\n    multiply(a, b) {\n        if (a.dtype === 'complex64') {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const realProgram = new BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.REAL, a.shape, b.shape);\n            const imagProgram = new BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);\n            const inputs = [\n                this.makeComplexComponentTensorInfo(a, aData.complexTensors.real),\n                this.makeComplexComponentTensorInfo(a, aData.complexTensors.imag),\n                this.makeComplexComponentTensorInfo(b, bData.complexTensors.real),\n                this.makeComplexComponentTensorInfo(b, bData.complexTensors.imag)\n            ];\n            const real = this.compileAndRun(realProgram, inputs);\n            const imag = this.compileAndRun(imagProgram, inputs);\n            const complex = this.complex(real, imag);\n            real.dispose();\n            imag.dispose();\n            return complex;\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        if (this.shouldExecuteOnCPU([a, b])) {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const [outValues, outShape] = multiplyImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n            return this.makeOutput(outShape, dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_gpu.MUL, a.dtype);\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.MUL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], a.dtype);\n    }\n    localResponseNormalization4D(x, radius, bias, alpha, beta) {\n        const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n            new LRNPackedProgram(x.shape, radius, bias, alpha, beta) :\n            new LRNProgram(x.shape, radius, bias, alpha, beta);\n        return this.compileAndRun(program, [x]);\n    }\n    LRNGrad(dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {\n        const program = new LRNGradProgram(inputImage.shape, depthRadius, bias, alpha, beta);\n        return this.compileAndRun(program, [inputImage, outputImage, dy]);\n    }\n    tile(x, reps) {\n        if (x.dtype === 'string') {\n            const data = this.readSync(x.dataId);\n            const decodedData = data.map(d => util.decodeString(d));\n            const buf = buffer(x.shape, x.dtype, decodedData);\n            return tile(buf, reps);\n        }\n        const program = new TileProgram(x.shape, reps);\n        return this.compileAndRun(program, [x]);\n    }\n    pad(x, paddings, constantValue) {\n        const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n            new PadPackedProgram(x.shape, paddings, constantValue) :\n            new PadProgram(x.shape, paddings, constantValue);\n        return this.compileAndRun(program, [x]);\n    }\n    gather(x, indices, axis) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x, indices], () => this.cpuBackend.gather(x, indices, axis));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = new GatherProgram(x.shape, indices.size, axis);\n        return this.compileAndRun(program, [x, indices]);\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        util.assert(x.rank <= 4, () => 'batchToSpaceND for rank > 4 with a WebGL backend not ' +\n            'implemented yet');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n        const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n        const reshapedPermuted = backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n        const sliceBeginCoords = backend_util.getSliceBeginCoords(crops, blockShape.length);\n        const sliceSize = backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n        return transpose(x.reshape(reshaped), permuted)\n            .reshape(reshapedPermuted)\n            .slice(sliceBeginCoords, sliceSize);\n    }\n    spaceToBatchND(x, blockShape, paddings) {\n        util.assert(x.rank <= 4, () => 'spaceToBatchND for rank > 4 with a WebGL backend not ' +\n            'implemented yet');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const completePaddings = [[0, 0]];\n        completePaddings.push(...paddings);\n        for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n            completePaddings.push([0, 0]);\n        }\n        const paddedX = x.pad(completePaddings);\n        const reshapedPaddedShape = backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n        const permutedReshapedPaddedPermutation = backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n        const flattenShape = backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n        const paddedXT = transpose(paddedX.reshape(reshapedPaddedShape), permutedReshapedPaddedPermutation);\n        return reshape(paddedXT, flattenShape);\n    }\n    reduce(x, reduceType, dtype) {\n        const batchSize = x.shape[0];\n        const inSize = x.shape[1];\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const outSize = Math.ceil(inSize / windowSize);\n        const reduceInfo = { windowSize, inSize, batchSize, outSize };\n        const program = new ReduceProgram(reduceInfo, reduceType);\n        const output = this.compileAndRun(program, [x], dtype);\n        // No need to run another GPGPU program.\n        if (output.shape[1] === 1) {\n            return output;\n        }\n        return this.reduce(output, reduceType, dtype);\n    }\n    argReduce(x, reduceType, bestIndicesA = null) {\n        let batchSize = x.shape[0];\n        let inSize = x.shape[1];\n        if (bestIndicesA != null) {\n            batchSize = bestIndicesA.shape[0];\n            inSize = bestIndicesA.shape[1];\n        }\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const reduceInfo = {\n            windowSize,\n            inSize,\n            batchSize,\n            outSize: Math.ceil(inSize / windowSize)\n        };\n        const program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);\n        const inputs = [x];\n        if (bestIndicesA != null) {\n            inputs.push(bestIndicesA);\n        }\n        const output = this.compileAndRun(program, inputs, 'int32');\n        // No need to run another GPGPU program.\n        if (output.shape[1] === 1) {\n            return output;\n        }\n        return this.argReduce(x, reduceType, output);\n    }\n    argReducePacked(x, reduceType, bestIndicesA = null) {\n        const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;\n        const inSize = inShape[inShape.length - 1];\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null);\n        const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];\n        const output = this.compileAndRun(program, inputs, 'int32');\n        if (output.rank === x.rank) {\n            return this.argReducePacked(x, reduceType, output);\n        }\n        return output;\n    }\n    sum(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        return this.reduce(a2D, 'sum', outputDType).reshape(outShape);\n    }\n    prod(x, axes) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.prod(x, axes));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        return this.reduce(a2D, 'prod', outputDType).reshape(outShape);\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        let axis = 0;\n        const permutation = backend_util.getAxesPermutation([axis], x.rank);\n        let permutedX = x;\n        if (permutation != null) {\n            permutedX = transpose(x, permutation);\n            axis = backend_util.getInnerMostAxes(1, x.rank)[0];\n        }\n        const outShape = segment_util.computeOutShape(permutedX.shape, axis, numSegments);\n        const inSize = util.sizeFromShape([permutedX.shape[axis]]);\n        const a2D = permutedX.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        let result = this.segOpCompute(a2D, 'unsortedSegmentSum', segmentIds, outputDType, numSegments)\n            .reshape(outShape);\n        if (permutation != null) {\n            result =\n                transpose(result, backend_util.getUndoAxesPermutation(permutation));\n        }\n        return result;\n    }\n    segOpCompute(x, segOpType, segmentIds, dtype, numSegments) {\n        const batchSize = x.shape[0];\n        const inSize = x.shape[1];\n        const windowSize = segment_util.segOpComputeOptimalWindowSize(inSize, numSegments);\n        const segOpInfo = { windowSize, inSize, batchSize, numSegments };\n        const program = new SegmentOpProgram(segOpInfo, segOpType);\n        const output = this.compileAndRun(program, [x, segmentIds], dtype);\n        // No need to run another GPGPU program.\n        if (output.shape[1] === numSegments) {\n            return output;\n        }\n        segmentIds = range(0, numSegments).tile([inSize / windowSize]);\n        return this.segOpCompute(output, segOpType, segmentIds, dtype, numSegments);\n    }\n    argMinMaxReduce(x, axis, reduceType) {\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('arg' + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.rank);\n        if (!env().getBool('WEBGL_PACK_REDUCE') || x.rank <= 2) {\n            const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n            const inSize = util.sizeFromShape(reduceShape);\n            const a2D = x.as2D(-1, inSize);\n            return this.argReduce(a2D, reduceType).reshape(outShape);\n        }\n        return this.argReducePacked(x, reduceType);\n    }\n    argMin(x, axis) {\n        return this.argMinMaxReduce(x, axis, 'min');\n    }\n    argMax(x, axis) {\n        return this.argMinMaxReduce(x, axis, 'max');\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        if (axis !== x.rank - 1) {\n            throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.rank - 1} ` +\n                `but got axis=${axis}`);\n        }\n        const size = x.shape[axis];\n        let result = x;\n        // Use cumsum parallel algorithm, ref:\n        // https://developer.nvidia.com/gpugems/gpugems3/part-vi-gpu-computing/chapter-39-parallel-prefix-sum-scan-cuda\n        for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {\n            const program = new CumSumProgram(x.shape, false, reverse);\n            const customSetup = program.getCustomSetupFunc(i);\n            const prevResult = result;\n            result = this.compileAndRun(program, [result], result.dtype, customSetup);\n            prevResult.dispose();\n        }\n        // For exclusive cumsum, shift the end result in the direction of sum and\n        // add 0 to the front index.\n        if (exclusive) {\n            const program = new CumSumProgram(x.shape, exclusive, reverse);\n            const prevResult = result;\n            result = this.compileAndRun(program, [result]);\n            prevResult.dispose();\n        }\n        return result;\n    }\n    equal(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    notEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.NOT_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.NOT_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    less(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.less(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LESS, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LESS, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    lessEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LESS_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LESS_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    greater(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.greater(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.GREATER, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.GREATER, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    greaterEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.GREATER_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.GREATER_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    logicalNot(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.LOGICAL_NOT);\n        return this.compileAndRun(program, [x]);\n    }\n    logicalAnd(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LOGICAL_AND, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LOGICAL_AND, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    logicalOr(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LOGICAL_OR, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LOGICAL_OR, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    select(condition, a, b) {\n        const program = new SelectProgram(condition.rank, a.shape, a.rank);\n        return this.compileAndRun(program, [condition, a, b], upcastType(a.dtype, b.dtype));\n    }\n    where(condition) {\n        backend_util.warn('tf.where() in webgl locks the UI thread. ' +\n            'Call tf.whereAsync() instead');\n        const condVals = condition.dataSync();\n        return whereImpl(condition.shape, condVals);\n    }\n    topk(x, k, sorted) {\n        const xVals = x.dataSync();\n        return topkImpl(xVals, x.shape, x.dtype, k, sorted);\n    }\n    min(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'min', a2D.dtype).reshape(outShape);\n    }\n    minimum(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.minimum(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MIN, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MIN, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    mod(a, b) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MOD, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MOD, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    maximum(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.maximum(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MAX, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MAX, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    all(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'all', a2D.dtype).reshape(outShape);\n    }\n    any(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'any', a2D.dtype).reshape(outShape);\n    }\n    floorDiv(a, b) {\n        const op = binaryop_gpu.INT_DIV;\n        const outputDtype = 'int32';\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.INT_DIV, outputDtype);\n        }\n        const program = new BinaryOpProgram(op, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], outputDtype);\n    }\n    add(a, b) {\n        if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n            return this.complexSeparableBinaryOp(a, b, binaryop_gpu.ADD);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        if (this.shouldExecuteOnCPU([a, b])) {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const [outValues, outShape] = addImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n            return this.makeOutput(outShape, dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_gpu.ADD, dtype);\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.ADD, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    packedUnaryOp(x, op, dtype) {\n        const program = new UnaryOpPackedProgram(x.shape, op);\n        return this.compileAndRun(program, [x], dtype);\n    }\n    packedBinaryOp(a, b, op, dtype, checkOutOfBounds = false) {\n        const program = new BinaryOpPackedProgram(op, a.shape, b.shape, checkOutOfBounds);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    /**\n     * Computes a complex binary operation that can be decomposed into a simple\n     * binary operation on both the real and imagary parts.\n     */\n    complexSeparableBinaryOp(a, b, op) {\n        const aData = this.texData.get(a.dataId);\n        const bData = this.texData.get(b.dataId);\n        const [real, imag] = [\n            [aData.complexTensors.real, bData.complexTensors.real],\n            [aData.complexTensors.imag, bData.complexTensors.imag]\n        ].map(complexParts => {\n            const [aPart, bPart] = complexParts;\n            const aHandle = this.makeComplexComponentTensorInfo(a, aPart);\n            const bHandle = this.makeComplexComponentTensorInfo(b, bPart);\n            const program = new BinaryOpProgram(op, a.shape, b.shape);\n            return this.compileAndRun(program, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));\n        });\n        const complex = this.complex(real, imag);\n        real.dispose();\n        imag.dispose();\n        return complex;\n    }\n    // Returns a TensorInfo with the complex shape and the dataId of the\n    // underlying part. We need to do this because a reshaped complex tensor is\n    // not reflected in its parts.\n    makeComplexComponentTensorInfo(complexTensor, complexPart) {\n        return {\n            dataId: complexPart.dataId,\n            dtype: complexPart.dtype,\n            shape: complexTensor.shape\n        };\n    }\n    addN(tensors) {\n        if (tensors.length === 1) {\n            return tensors[0];\n        }\n        // Limit the number of uploaded textures for optimization.\n        if (tensors.length > env().get('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n            const midIndex = Math.floor(tensors.length / 2);\n            const leftSide = this.addN(tensors.slice(0, midIndex));\n            const rightSide = this.addN(tensors.slice(midIndex));\n            return this.addN([leftSide, rightSide]);\n        }\n        const dtype = tensors.map(t => t.dtype).reduce((d1, d2) => upcastType(d1, d2));\n        const shapes = tensors.map(t => t.shape);\n        // We can make sure shapes are identical in op level.\n        const usePackedOp = env().getBool('WEBGL_PACK');\n        const program = usePackedOp ?\n            new AddNPackedProgram(tensors[0].shape, shapes) :\n            new AddNProgram(tensors[0].shape, shapes);\n        return this.compileAndRun(program, tensors, dtype);\n    }\n    subtract(a, b) {\n        if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n            return this.complexSeparableBinaryOp(a, b, binaryop_gpu.SUB);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        if (this.shouldExecuteOnCPU([a, b])) {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const [outValues, outShape] = subImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n            return this.makeOutput(outShape, dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_gpu.SUB, a.dtype);\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.SUB, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    pow(a, b) {\n        const usePackedOp = env().getBool('WEBGL_PACK_BINARY_OPERATIONS');\n        const program = usePackedOp ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.POW, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.POW, a.shape, b.shape);\n        const dtype = upcastType(a.dtype, b.dtype);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    ceil(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = ceilImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.CEIL, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.CEIL);\n        return this.compileAndRun(program, [x]);\n    }\n    floor(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = floorImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.FLOOR, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.FLOOR);\n        return this.compileAndRun(program, [x]);\n    }\n    sign(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SIGN);\n        return this.compileAndRun(program, [x]);\n    }\n    isNaN(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_NAN);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    isInf(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_INF);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    isFinite(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_FINITE);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    round(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ROUND);\n        return this.compileAndRun(program, [x]);\n    }\n    exp(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = expImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.EXP, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.EXP);\n        return this.compileAndRun(program, [x]);\n    }\n    expm1(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = expm1ImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.EXPM1, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.EXPM1);\n        return this.compileAndRun(program, [x]);\n    }\n    softmax(logits, dim) {\n        const axes = util.parseAxisParam([dim], logits.shape);\n        // TODO(annxingyuan): Call maxImpl rather than op as part of softmax kernel\n        // modularization.\n        const maxLogit = max(logits, axes);\n        const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n        const a = this.subtract(logits, maxLogit.reshape(expandedShape));\n        const b = this.exp(a);\n        const sumExp = this.sum(b, axes).reshape(expandedShape);\n        // TODO(annxingyuan): Call divImpl rather than op as part of softmax kernel\n        // modularization.\n        return div(b, sumExp);\n    }\n    log(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = logImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_packed_op.LOG, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.LOG);\n        return this.compileAndRun(program, [x]);\n    }\n    log1p(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.LOG1P);\n        return this.compileAndRun(program, [x]);\n    }\n    sqrt(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SQRT);\n        return this.compileAndRun(program, [x]);\n    }\n    rsqrt(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = rsqrtImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.RSQRT);\n        return this.compileAndRun(program, [x]);\n    }\n    reciprocal(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.RECIPROCAL);\n        return this.compileAndRun(program, [x]);\n    }\n    relu(x) {\n        let program;\n        if (env().getBool('WEBGL_PACK')) {\n            program = new UnaryOpPackedProgram(x.shape, unary_packed_op.RELU);\n        }\n        else {\n            program = new UnaryOpProgram(x.shape, unary_op.RELU);\n        }\n        return this.compileAndRun(program, [x]);\n    }\n    relu6(x) {\n        let program;\n        if (env().getBool('WEBGL_PACK')) {\n            program = new UnaryOpPackedProgram(x.shape, unary_packed_op.RELU6);\n        }\n        else {\n            program = new UnaryOpProgram(x.shape, unary_op.RELU6);\n        }\n        return this.compileAndRun(program, [x]);\n    }\n    prelu(x, alpha) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.PRELU, x.shape, alpha.shape) :\n            new BinaryOpProgram(binaryop_gpu.PRELU, x.shape, alpha.shape);\n        return this.compileAndRun(program, [x, alpha]);\n    }\n    elu(x) {\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_packed_op.ELU, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.ELU);\n        return this.compileAndRun(program, [x]);\n    }\n    eluDer(dy, y) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.ELU_DER, dy.shape, y.shape) :\n            new BinaryOpProgram(binaryop_gpu.ELU_DER, dy.shape, y.shape);\n        return this.compileAndRun(program, [dy, y]);\n    }\n    selu(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SELU);\n        return this.compileAndRun(program, [x]);\n    }\n    int(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.TO_INT);\n        return this.compileAndRun(program, [x], 'int32');\n    }\n    clip(x, min, max) {\n        let program;\n        if (env().getBool('WEBGL_PACK_CLIP')) {\n            program = new ClipPackedProgram(x.shape);\n        }\n        else {\n            program = new ClipProgram(x.shape);\n        }\n        const customSetup = program.getCustomSetupFunc(min, max);\n        return this.compileAndRun(program, [x], null, customSetup);\n    }\n    abs(x) {\n        // TODO: handle cases when x is complex.\n        if (this.shouldExecuteOnCPU([x]) && x.dtype !== 'complex64') {\n            const outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.ABS, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.ABS);\n        return this.compileAndRun(program, [x]);\n    }\n    complexAbs(x) {\n        const xData = this.texData.get(x.dataId);\n        const program = new ComplexAbsProgram(x.shape);\n        const inputs = [\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.real),\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.imag),\n        ];\n        return this.compileAndRun(program, inputs);\n    }\n    sigmoid(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SIGMOID);\n        return this.compileAndRun(program, [x]);\n    }\n    softplus(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SOFTPLUS);\n        return this.compileAndRun(program, [x]);\n    }\n    asin(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ASIN);\n        return this.compileAndRun(program, [x]);\n    }\n    acos(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ACOS);\n        return this.compileAndRun(program, [x]);\n    }\n    atan(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ATAN);\n        return this.compileAndRun(program, [x]);\n    }\n    sinh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SINH);\n        return this.compileAndRun(program, [x]);\n    }\n    cosh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.COSH);\n        return this.compileAndRun(program, [x]);\n    }\n    tanh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.TANH);\n        return this.compileAndRun(program, [x]);\n    }\n    asinh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ASINH);\n        return this.compileAndRun(program, [x]);\n    }\n    acosh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ACOSH);\n        return this.compileAndRun(program, [x]);\n    }\n    atanh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ATANH);\n        return this.compileAndRun(program, [x]);\n    }\n    erf(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ERF);\n        return this.compileAndRun(program, [x]);\n    }\n    step(x, alpha) {\n        const program = new UnaryOpProgram(x.shape, unary_op.STEP(alpha));\n        return this.compileAndRun(program, [x]);\n    }\n    conv2dByMatMul(x, filter, convInfo, bias, activation, preluActivationWeights) {\n        // Reshapes conv2D input to 2D tensors, uses matMul and then reshape the\n        // result from 2D to 4D.\n        const xShape = x.shape;\n        const xTexData = this.texData.get(x.dataId);\n        const sharedMatMulDim = convInfo.inChannels;\n        const outerShapeX = xShape[0] * xShape[1] * xShape[2];\n        const outerShapeFilter = convInfo.outChannels;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const transposeA = false;\n        const transposeB = false;\n        // TODO: Once reduction ops are packed, batchMatMul will always be packed\n        // and we can remove this condition.\n        const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) &&\n            sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;\n        const reshapeWillBeExpensive = xShape[2] % 2 !== 0 && !!xTexData.isPacked;\n        if (batchMatMulWillBeUnpacked || !env().getBool('WEBGL_LAZILY_UNPACK') ||\n            !env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ||\n            !reshapeWillBeExpensive) {\n            const targetShape = isChannelsLast ? xShape[0] * xShape[1] * xShape[2] :\n                xShape[0] * xShape[2] * xShape[3];\n            const xReshaped = reshape(x, [1, targetShape, convInfo.inChannels]);\n            const filterReshaped = reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);\n            const result = this.fusedBatchMatMul({\n                a: xReshaped,\n                b: filterReshaped,\n                transposeA,\n                transposeB,\n                bias,\n                activation,\n                preluActivationWeights\n            });\n            return reshape(result, convInfo.outShape);\n        }\n        // Following optimization is specific to packed |x| with odd row count\n        // (For example, in channelLast mode, 'row count' refers to x.shape[2]):\n        // we avoid expensive packed 2x2 reshape by padding row count to next,\n        // even number. When x.shape[2] is odd, the result of packed batchMatMul is\n        // the same (has the same texture layout and and values in the texture) as\n        // it is for even x.shape[2] + 1. We make the odd-rows tensor to look like\n        // even-rows tensor before the operation and, after the batchMatMul,\n        // fix the even-rows result to have odd number of rows.\n        const targetShape = isChannelsLast ?\n            xShape[0] * xShape[1] * (xShape[2] + 1) :\n            xShape[0] * xShape[2] * (xShape[3] + 1);\n        const xReshaped = {\n            dataId: x.dataId,\n            shape: [1, targetShape, convInfo.inChannels],\n            dtype: x.dtype\n        };\n        // xTexData.shape gets referenced from GPGPUBinary.inShapeInfos.\n        // Decrementing row count, after batchMatMul->...->compileProgram leads to\n        // invalid row count within the reference in GPGPUBinary.inShapeInfos.\n        // Alternative fix would be to provide a copy to GPGPUBinary.inShapeInfos\n        // in compileProgram method, but that would affect compilation of all\n        // programs - instead, provide a copy here, with even row count, before\n        // calling batchMatMul->...->compileProgram and after that, the original\n        // xTexData.shape is restored.\n        const originalXTexDataShape = xTexData.shape;\n        xTexData.shape = xTexData.shape.slice();\n        xTexData.shape[xTexData.shape.length - 2]++;\n        util.assert(webgl_util.isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);\n        const filterReshaped = reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);\n        const pointwiseConv = this.fusedBatchMatMul({\n            a: xReshaped,\n            b: filterReshaped,\n            transposeA,\n            transposeB,\n            bias,\n            activation,\n            preluActivationWeights\n        });\n        const pointwiseConvTexData = this.texData.get(pointwiseConv.dataId);\n        util.assert(pointwiseConvTexData.isPacked, () => 'batchMatMul result is expected to be packed');\n        // Restore the input shape to original.\n        xTexData.shape = originalXTexDataShape;\n        // Set the output shape - there is no need for expensive reshape as data\n        // layout is already correct.\n        pointwiseConvTexData.shape = convInfo.outShape;\n        return engine().makeTensorFromDataId(pointwiseConv.dataId, convInfo.outShape, pointwiseConv.dtype);\n    }\n    conv2dWithIm2Row(x, filter, convInfo, bias, activation, preluActivationWeights) {\n        // Rearranges conv2d input so each block to be convolved over forms the\n        // column of a new matrix with shape [filterWidth * filterHeight *\n        // inChannels, outHeight * outWidth]. The filter is also rearranged so each\n        // output channel forms a row of a new matrix with shape [outChannels,\n        // filterWidth * filterHeight * inChannels]. The convolution is then\n        // computed by multiplying these matrices and reshaping the result.\n        const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo;\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const sharedDim = filterWidth * filterHeight * inChannels;\n        const numCols = outHeight * outWidth;\n        const x2ColShape = [sharedDim, numCols];\n        const transposeA = true;\n        const transposeB = false;\n        const xSqueezed = x.squeeze([0]);\n        const w2Row = filter.reshape([1, sharedDim, -1]);\n        const im2ColProgram = new Im2ColPackedProgram(x2ColShape, xSqueezed.shape, convInfo);\n        const im2Col = this.compileAndRun(im2ColProgram, [xSqueezed]).reshape([\n            1, x2ColShape[0], x2ColShape[1]\n        ]);\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;\n        const matmulProgram = new MatMulPackedProgram(im2Col.shape, [1, numCols, convInfo.outChannels], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [im2Col, w2Row];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (hasPreluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        const product = this.compileAndRun(matmulProgram, inputs);\n        if (isChannelsLast) {\n            return product.reshape([1, outHeight, outWidth, convInfo.outChannels]);\n        }\n        else {\n            return product.reshape([1, convInfo.outChannels, outHeight, outWidth]);\n        }\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n            (convInfo.padInfo.type === 'SAME' ||\n                convInfo.padInfo.type === 'VALID')) {\n            return this.conv2dByMatMul(input, filter, convInfo, bias, activation, preluActivationWeights);\n        }\n        if (env().getBool('WEBGL_CONV_IM2COL') && input.shape[0] === 1) {\n            return this.conv2dWithIm2Row(input, filter, convInfo, bias, activation, preluActivationWeights);\n        }\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, false) : null;\n        const program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [input, filter];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (preluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        return this.compileAndRun(program, inputs);\n    }\n    conv2d(x, filter, convInfo) {\n        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n            (convInfo.padInfo.type === 'SAME' ||\n                convInfo.padInfo.type === 'VALID')) {\n            return this.conv2dByMatMul(x, filter, convInfo);\n        }\n        if (env().getBool('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {\n            return this.conv2dWithIm2Row(x, filter, convInfo);\n        }\n        const program = new Conv2DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        const program = new Conv2DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    conv2dDerFilter(x, dy, convInfo) {\n        const program = new Conv2DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        const shouldPackDepthwiseConv = env().getBool('WEBGL_PACK_DEPTHWISECONV') &&\n            convInfo.strideWidth <= 2 &&\n            convInfo.outChannels / convInfo.inChannels === 1;\n        const fusedActivation = activation ?\n            mapActivationToShaderProgram(activation, shouldPackDepthwiseConv) :\n            null;\n        const inputs = [input, filter];\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        if (hasBias) {\n            inputs.push(bias);\n        }\n        if (hasPreluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        let program;\n        if (shouldPackDepthwiseConv) {\n            program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n            return this.compileAndRun(program, inputs);\n        }\n        program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n        return this.compileAndRun(program, inputs);\n    }\n    depthwiseConv2D(x, filter, convInfo) {\n        let program;\n        if (env().getBool('WEBGL_PACK_DEPTHWISECONV') &&\n            convInfo.strideWidth <= 2 &&\n            convInfo.outChannels / convInfo.inChannels === 1) {\n            program = new DepthwiseConvPacked2DProgram(convInfo);\n            return this.compileAndRun(program, [x, filter]);\n        }\n        program = new DepthwiseConv2DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        const program = new DepthwiseConv2DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    depthwiseConv2DDerFilter(x, dy, convInfo) {\n        const program = new DepthwiseConv2DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    conv3d(x, filter, convInfo) {\n        const program = new Conv3DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        const program = new Conv3DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    conv3dDerFilter(x, dy, convInfo) {\n        const program = new Conv3DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    cast(x, dtype) {\n        return backend_util.castTensor(x, dtype, this);\n    }\n    unstack(x, axis) {\n        const num = x.shape[axis];\n        const outShape = new Array(x.rank - 1);\n        let outIndex = 0;\n        for (let i = 0; i < x.rank; i++) {\n            if (i !== axis) {\n                outShape[outIndex++] = x.shape[i];\n            }\n        }\n        const begin = new Array(x.rank).fill(0);\n        const size = x.shape.slice();\n        size[axis] = 1;\n        const res = new Array(num);\n        for (let i = 0; i < res.length; i++) {\n            begin[axis] = i;\n            res[i] = this.slice(x, begin, size).reshape(outShape);\n        }\n        return res;\n    }\n    avgPool3d(x, convInfo) {\n        const program = new Pool3DProgram(convInfo, 'avg', false);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        const avgPool3dBackpropProgram = new AvgPool3DBackpropProgram(convInfo);\n        return this.compileAndRun(avgPool3dBackpropProgram, [dy], x.dtype);\n    }\n    maxPool3d(x, convInfo) {\n        const program = new Pool3DProgram(convInfo, 'max', false);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        const getPositions = true;\n        const maxPool3dPositionsProgram = new Pool3DProgram(convInfo, 'max', getPositions);\n        const maxPool3dPositions = this.compileAndRun(maxPool3dPositionsProgram, [x]);\n        const maxPool3dBackPropProgram = new MaxPool3DBackpropProgram(convInfo);\n        const result = this.compileAndRun(maxPool3dBackPropProgram, [dy, maxPool3dPositions], x.dtype);\n        maxPool3dPositions.dispose();\n        return result;\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        const program = env().getBool('WEBGL_PACK_IMAGE_OPERATIONS') ?\n            new ResizeBilinearPackedProgram(x.shape, newHeight, newWidth, alignCorners) :\n            new ResizeBilinearProgram(x.shape, newHeight, newWidth, alignCorners);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        const program = new ResizeBilinearBackpropProgram(dy, x, alignCorners);\n        return this.compileAndRun(program, [dy]);\n    }\n    resizeNearestNeighbor(x, newHeight, newWidth, alignCorners) {\n        const program = new ResizeNearestNeighborProgram(x.shape, newHeight, newWidth, alignCorners);\n        return this.compileAndRun(program, [x]);\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        const program = new ResizeNearestNeigborBackpropProgram(dy, x, alignCorners);\n        return this.compileAndRun(program, [dy]);\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        const probs = normalized ? logits : softmax(logits);\n        const batchSize = probs.shape[0];\n        const numOutcomes = probs.shape[1];\n        const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);\n        const customSetup = program.getCustomSetupFunc(seed);\n        return this.compileAndRun(program, [probs], 'int32', customSetup);\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        const program = new OneHotProgram(indices.size, depth, onValue, offValue);\n        return this.compileAndRun(program, [indices]);\n    }\n    diag(x) {\n        const program = new DiagProgram(x.size);\n        return this.compileAndRun(program, [x]);\n    }\n    cropAndResize(image, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        const program = new CropAndResizeProgram(image.shape, boxes.shape, cropSize, method, extrapolationValue);\n        return this.compileAndRun(program, [image, boxes, boxIndex], 'float32');\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n        const batchSize = x.shape[0];\n        const inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];\n        const inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];\n        const inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];\n        const outputHeight = inputHeight * blockSize;\n        const outputWidth = inputWidth * blockSize;\n        const outputDepth = inputDepth / (blockSize * blockSize);\n        const outputShape = (dataFormat === 'NHWC') ?\n            [batchSize, outputHeight, outputWidth, outputDepth] :\n            [batchSize, outputDepth, outputHeight, outputWidth];\n        const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);\n        return this.compileAndRun(program, [x]);\n    }\n    split(x, sizeSplits, axis) {\n        return split(x, sizeSplits, axis);\n    }\n    scatterND(indices, updates, shape) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(updates, indices, shape);\n        const flattenShape = [outputSize / sliceSize, sliceSize];\n        const flattenIndices = indices.reshape([numUpdates, sliceRank]);\n        const flattenX = updates.reshape([numUpdates, sliceSize]);\n        if (outputSize === 0) {\n            return backend_util.reshapeTensor(tensor([]), shape);\n        }\n        const defaultValue = scalar(0);\n        const program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.rank, flattenX.rank, strides, flattenShape);\n        const res = this.compileAndRun(program, [flattenX, flattenIndices, defaultValue]);\n        return res.reshape(shape);\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        const { sliceRank, numUpdates, strides, outputSize } = backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n        const sumDupeIndices = false;\n        const program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.rank, sparseValues.rank, strides, [outputSize, 1], sumDupeIndices);\n        const res = this.compileAndRun(program, [sparseValues, sparseIndices, defaultValue]);\n        return res.reshape(outputShape);\n    }\n    fft(x) {\n        const inverse = false;\n        return this.fftImpl(x, inverse);\n    }\n    ifft(x) {\n        const inverse = true;\n        return this.fftImpl(x, inverse);\n    }\n    fftImpl(x, inverse) {\n        const xData = this.texData.get(x.dataId);\n        const realProgram = new FFTProgram(fft_gpu.COMPLEX_FFT.REAL, x.shape, inverse);\n        const imagProgram = new FFTProgram(fft_gpu.COMPLEX_FFT.IMAG, x.shape, inverse);\n        const inputs = [\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.real),\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.imag),\n        ];\n        const real = this.compileAndRun(realProgram, inputs);\n        const imag = this.compileAndRun(imagProgram, inputs);\n        const complex = this.complex(real, imag).as2D(x.shape[0], x.shape[1]);\n        real.dispose();\n        imag.dispose();\n        return complex;\n    }\n    gatherND(x, indices) {\n        const indicesShape = indices.shape;\n        const sliceRank = indicesShape[indicesShape.length - 1];\n        const [resultShape, numSlices, sliceSize, strides] = backend_util.prepareAndValidate(x, indices);\n        const flattenIndices = indices.reshape([numSlices, sliceRank]);\n        const flattenX = x.reshape([x.size / sliceSize, sliceSize]);\n        const program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);\n        const res = this.compileAndRun(program, [flattenX, flattenIndices]);\n        return res.reshape(resultShape);\n    }\n    fill(shape, value, dtype) {\n        dtype = dtype || util.inferDtype(value);\n        if (dtype === 'string') {\n            // String type should be handled in CPU memory.\n            const values = util.getArrayFromDType(dtype, util.sizeFromShape(shape));\n            values.fill(value);\n            return engine().makeTensor(values, shape, dtype, this);\n        }\n        else {\n            const program = new FillProgram(shape, value);\n            const customSetup = program.getCustomSetupFunc(value);\n            return this.compileAndRun(program, [], dtype, customSetup);\n        }\n    }\n    onesLike(x) {\n        if (x.dtype === 'string') {\n            throw new Error('onesLike is not supported under string dtype');\n        }\n        else {\n            // TODO(cais, smilkov): Add WebGL shader for onesLike:\n            //   https://github.com/tensorflow/tfjs/issues/1293\n            return this.fill(x.shape, 1, x.dtype);\n        }\n    }\n    zerosLike(x) {\n        return this.fill(x.shape, x.dtype === 'string' ? '' : 0, x.dtype);\n    }\n    linspace(start, stop, num) {\n        // TODO: Use CPU implementation due to the precision problem in Safari.\n        return backend_util.linspaceImpl(start, stop, num);\n    }\n    makeTensorInfo(shape, dtype, values) {\n        const dataId = this.write(values, shape, dtype);\n        this.texData.get(dataId).usage = null;\n        return { dataId, shape, dtype };\n    }\n    makeOutput(shape, dtype, values) {\n        const { dataId } = this.makeTensorInfo(shape, dtype, values);\n        return engine().makeTensorFromDataId(dataId, shape, dtype, this);\n    }\n    unpackTensor(input) {\n        const program = new UnpackProgram(input.shape);\n        return this.runWebGLProgram(program, [input], input.dtype);\n    }\n    packTensor(input) {\n        const program = new PackProgram(input.shape);\n        const preventEagerUnpackingOutput = true;\n        return this.runWebGLProgram(program, [input], input.dtype, null /* customSetup */, preventEagerUnpackingOutput);\n    }\n    packedReshape(input, afterShape) {\n        const input3DShape = [\n            webgl_util.getBatchDim(input.shape),\n            ...webgl_util.getRowsCols(input.shape)\n        ];\n        const input3D = {\n            dtype: input.dtype,\n            shape: input3DShape,\n            dataId: input.dataId\n        };\n        const afterShapeAs3D = [\n            webgl_util.getBatchDim(afterShape), ...webgl_util.getRowsCols(afterShape)\n        ];\n        const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n        const preventEagerUnpackingOfOutput = true;\n        const output = this.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n        return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n    }\n    decode(dataId) {\n        const texData = this.texData.get(dataId);\n        const { isPacked, shape, dtype } = texData;\n        const shapeAs3D = webgl_util.getShapeAs3D(shape);\n        let program;\n        if (isPacked) {\n            program = new DecodeMatrixPackedProgram(shapeAs3D);\n        }\n        else {\n            program = new DecodeMatrixProgram(shapeAs3D);\n        }\n        const preventEagerUnpackingOfOutput = true;\n        const out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype, dataId }], dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n        return { dtype, shape, dataId: out.dataId };\n    }\n    runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {\n        const output = this.makeTensorInfo(program.outputShape, outputDtype);\n        const outData = this.texData.get(output.dataId);\n        if (program.packedOutput) {\n            outData.isPacked = true;\n        }\n        if (program.outPackingScheme === tex_util.PackingScheme.DENSE) {\n            const texelShape = tex_util.getDenseTexShape(program.outputShape);\n            // For a densely packed output, we explicitly set texShape\n            // so it doesn't get assigned later according to our typical packing\n            // scheme wherein a single texel can only contain values from adjacent\n            // rows/cols.\n            outData.texShape = texelShape.map(d => d * 2);\n        }\n        if (program.outTexUsage != null) {\n            outData.usage = program.outTexUsage;\n        }\n        if (util.sizeFromShape(output.shape) === 0) {\n            // Short-circuit the computation since the result is empty (has 0 in its\n            // shape).\n            outData.values =\n                util.getTypedArrayFromDType(output.dtype, 0);\n            return output;\n        }\n        const dataToDispose = [];\n        const inputsData = inputs.map(input => {\n            if (input.dtype === 'complex64') {\n                throw new Error(`GPGPUProgram does not support complex64 input. For complex64 ` +\n                    `dtypes, please separate the program into real and imaginary ` +\n                    `parts.`);\n            }\n            let texData = this.texData.get(input.dataId);\n            if (texData.texture == null) {\n                if (!program.packedInputs &&\n                    util.sizeFromShape(input.shape) <=\n                        env().getNumber('WEBGL_SIZE_UPLOAD_UNIFORM')) {\n                    // Upload small tensors that live on the CPU as uniforms, not as\n                    // textures. Do this only when the environment supports 32bit floats\n                    // due to problems when comparing 16bit floats with 32bit floats.\n                    // TODO(https://github.com/tensorflow/tfjs/issues/821): Make it\n                    // possible for packed shaders to sample from uniforms.\n                    return {\n                        shape: input.shape,\n                        texData: null,\n                        isUniform: true,\n                        uniformValues: texData.values\n                    };\n                }\n                // This ensures that if a packed program's inputs have not yet been\n                // uploaded to the GPU, they get uploaded as packed right off the bat.\n                if (program.packedInputs) {\n                    texData.isPacked = true;\n                    texData.shape = input.shape;\n                }\n            }\n            else if (!!texData.isPacked !== !!program.packedInputs) {\n                input = texData.isPacked ? this.unpackTensor(input) :\n                    this.packTensor(input);\n                dataToDispose.push(input);\n                texData = this.texData.get(input.dataId);\n            }\n            else if (texData.isPacked &&\n                !webgl_util.isReshapeFree(texData.shape, input.shape)) {\n                // This is a special case where a texture exists for a tensor\n                // but the shapes are incompatible (due to packing constraints) because\n                // the tensor did not have a chance to go through the packed reshape\n                // shader. This only happens when we reshape the *same* tensor to form\n                // *distinct* inputs to an op, e.g. dotting a vector with itself. This\n                // case will disappear once packed uploading is the default.\n                const savedInput = input;\n                const targetShape = input.shape;\n                input.shape = texData.shape;\n                input = this.packedReshape(input, targetShape);\n                dataToDispose.push(input);\n                texData = this.texData.get(input.dataId);\n                savedInput.shape = targetShape;\n            }\n            this.uploadToGPU(input.dataId);\n            return { shape: input.shape, texData, isUniform: false };\n        });\n        this.uploadToGPU(output.dataId);\n        const outputData = { shape: output.shape, texData: outData, isUniform: false };\n        const key = gpgpu_math.makeShaderKey(program, inputsData, outputData);\n        const binary = this.getAndSaveBinary(key, () => {\n            return gpgpu_math.compileProgram(this.gpgpu, program, inputsData, outputData);\n        });\n        const shouldTimeProgram = this.activeTimers != null;\n        let query;\n        if (shouldTimeProgram) {\n            query = this.startTimer();\n        }\n        gpgpu_math.runProgram(this.gpgpu, binary, inputsData, outputData, customSetup);\n        dataToDispose.forEach(info => this.disposeIntermediateTensorInfo(info));\n        if (shouldTimeProgram) {\n            query = this.endTimer(query);\n            this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });\n        }\n        if (!env().getBool('WEBGL_LAZILY_UNPACK') && outData.isPacked &&\n            preventEagerUnpackingOfOutput === false) {\n            const unpacked = this.unpackTensor(output);\n            this.disposeIntermediateTensorInfo(output);\n            return unpacked;\n        }\n        return output;\n    }\n    compileAndRun(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {\n        outputDtype = outputDtype || inputs[0].dtype;\n        const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput);\n        return engine().makeTensorFromDataId(outInfo.dataId, outInfo.shape, outInfo.dtype);\n    }\n    getAndSaveBinary(key, getBinary) {\n        if (!(key in this.binaryCache)) {\n            this.binaryCache[key] = getBinary();\n        }\n        return this.binaryCache[key];\n    }\n    getTextureManager() {\n        return this.textureManager;\n    }\n    dispose() {\n        if (this.disposed) {\n            return;\n        }\n        // Avoid disposing the compiled webgl programs during unit testing because\n        // it slows down test execution.\n        if (!env().getBool('IS_TEST')) {\n            const allKeys = Object.keys(this.binaryCache);\n            allKeys.forEach(key => {\n                this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);\n                delete this.binaryCache[key];\n            });\n        }\n        this.textureManager.dispose();\n        if (this.canvas != null &&\n            (typeof (HTMLCanvasElement) !== 'undefined' &&\n                this.canvas instanceof HTMLCanvasElement)) {\n            this.canvas.remove();\n        }\n        else {\n            this.canvas = null;\n        }\n        if (this.gpgpuCreatedLocally) {\n            this.gpgpu.program = null;\n            this.gpgpu.dispose();\n        }\n        this.disposed = true;\n    }\n    floatPrecision() {\n        if (this.floatPrecisionValue == null) {\n            this.floatPrecisionValue = tidy(() => {\n                if (!env().get('WEBGL_RENDER_FLOAT32_ENABLED')) {\n                    // Momentarily switching DEBUG flag to false so we don't throw an\n                    // error trying to upload a small value.\n                    const debugFlag = env().getBool('DEBUG');\n                    env().set('DEBUG', false);\n                    const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];\n                    env().set('DEBUG', debugFlag);\n                    if (underflowCheckValue > 0) {\n                        return 32;\n                    }\n                }\n                return 16;\n            });\n        }\n        return this.floatPrecisionValue;\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n    }\n    uploadToGPU(dataId) {\n        const texData = this.texData.get(dataId);\n        const { shape, dtype, values, texture, usage, isPacked } = texData;\n        if (texture != null) {\n            // Array is already on GPU. No-op.\n            return;\n        }\n        const shouldTimeProgram = this.activeTimers != null;\n        let start;\n        if (shouldTimeProgram) {\n            start = util.now();\n        }\n        let texShape = texData.texShape;\n        if (texShape == null) {\n            texShape = webgl_util.getTextureShapeFromLogicalShape(shape, isPacked);\n            texData.texShape = texShape;\n        }\n        if (values != null) {\n            const shapeAs3D = webgl_util.getShapeAs3D(shape);\n            let program;\n            let width = texShape[1], height = texShape[0];\n            const isByteArray = values instanceof Uint8Array;\n            if (isPacked) {\n                [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]);\n                program = new EncodeMatrixPackedProgram(shapeAs3D, [height, width], isByteArray);\n            }\n            else {\n                program =\n                    new EncodeMatrixProgram(shapeAs3D, [height, width], isByteArray);\n            }\n            const tempDenseInputHandle = this.makeTensorInfo([height, width], dtype);\n            if (isByteArray) {\n                this.texData.get(tempDenseInputHandle.dataId).usage =\n                    TextureUsage.PIXELS;\n            }\n            else {\n                this.texData.get(tempDenseInputHandle.dataId).usage =\n                    TextureUsage.UPLOAD;\n            }\n            this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);\n            // We want the output to remain packed regardless of the value of\n            // WEBGL_PACK.\n            const preventEagerUnpacking = true;\n            const encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, null, preventEagerUnpacking);\n            // Have the original texture assume the identity of the encoded output.\n            const outputTexData = this.texData.get(encodedOutputTarget.dataId);\n            texData.texture = outputTexData.texture;\n            texData.texShape = outputTexData.texShape;\n            texData.isPacked = outputTexData.isPacked;\n            texData.usage = outputTexData.usage;\n            this.disposeIntermediateTensorInfo(tempDenseInputHandle);\n            this.texData.delete(encodedOutputTarget.dataId);\n            // Once uploaded, don't store the values on cpu.\n            texData.values = null;\n            if (shouldTimeProgram) {\n                this.uploadWaitMs += util.now() - start;\n            }\n        }\n        else {\n            const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);\n            texData.texture = newTexture;\n        }\n    }\n    convertAndCacheOnCPU(dataId, float32Values) {\n        const texData = this.texData.get(dataId);\n        const { dtype } = texData;\n        this.releaseGPUData(dataId);\n        if (float32Values != null) {\n            texData.values = float32ToTypedArray(float32Values, dtype);\n        }\n        return texData.values;\n    }\n    acquireTexture(texShape, texType, dtype, isPacked) {\n        this.numBytesInGPU += this.computeBytes(texShape, dtype);\n        if (!this.warnedAboutMemory &&\n            this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {\n            const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);\n            this.warnedAboutMemory = true;\n            console.warn(`High memory usage in GPU: ${mb} MB, ` +\n                `most likely due to a memory leak`);\n        }\n        return this.textureManager.acquireTexture(texShape, texType, isPacked);\n    }\n    computeBytes(shape, dtype) {\n        return shape[0] * shape[1] * util.bytesPerElement(dtype);\n    }\n    tryRunOnCpuOrThrow(inputs, fn) {\n        if (this.shouldExecuteOnCPU(inputs)) {\n            try {\n                return fn();\n            }\n            catch (e) {\n                if (env().getBool('IS_TEST')) {\n                    throw new Error('CPU forwarding failed');\n                }\n            }\n        }\n        return null;\n    }\n}\nfunction float32ToTypedArray(a, dtype) {\n    if (dtype === 'float32' || dtype === 'complex64') {\n        return a;\n    }\n    else if (dtype === 'int32' || dtype === 'bool') {\n        const result = (dtype === 'int32') ? new Int32Array(a.length) :\n            new Uint8Array(a.length);\n        for (let i = 0; i < result.length; ++i) {\n            result[i] = Math.round(a[i]);\n        }\n        return result;\n    }\n    else {\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n}\n//# sourceMappingURL=backend_webgl.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport * as gpgpu_util from './gpgpu_util';\nimport * as webgl_util from './webgl_util';\nexport { MathBackendWebGL } from './backend_webgl';\nexport { setWebGLContext } from './canvas_util';\nexport { GPGPUContext } from './gpgpu_context';\n// WebGL specific utils.\nexport { gpgpu_util, webgl_util };\n/**\n * Enforce use of half precision textures if available on the platform.\n *\n * @doc {heading: 'Environment', namespace: 'webgl'}\n */\nexport function forceHalfFloat() {\n    env().set('WEBGL_FORCE_F16_TEXTURES', true);\n}\n//# sourceMappingURL=webgl.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// base.ts is the webgl backend without auto kernel registration.\nimport { device_util, registerBackend } from '@tensorflow/tfjs-core';\nimport { MathBackendWebGL } from './backend_webgl';\nexport { version as version_webgl } from './version';\nif (device_util.isBrowser()) {\n    registerBackend('webgl', () => new MathBackendWebGL(), 2 /* priority */);\n}\n// Export webgl utilities\nexport * from './webgl';\n// Export forceHalfFlost under webgl namespace for the union bundle.\nimport { forceHalfFloat } from './webgl';\nexport const webgl = { forceHalfFloat };\n//# sourceMappingURL=base.js.map", "import { env } from '@tensorflow/tfjs-core';\nimport { BinaryOpProgram } from '../binaryop_gpu';\nimport { BinaryOpPackedProgram } from '../binaryop_packed_gpu';\nimport { UnaryOpProgram } from '../unaryop_gpu';\nexport const CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;\nexport const CHECK_NAN_SNIPPET_BINARY = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nexport const CHECK_NAN_SNIPPET_BINARY_PACKED = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param opSnippets Op snippet to create `UnaryOpProgram`.\n */\nexport function unaryKernelFunc(opSnippet) {\n    return ({ inputs, backend }) => {\n        const { x } = inputs;\n        const webglBackend = backend;\n        const program = new UnaryOpProgram(x.shape, opSnippet);\n        return webglBackend.runWebGLProgram(program, [x], x.dtype);\n    };\n}\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param opSnippet Op snippet to create `BinaryOpProgram`.\n * @param packedOpSnippet Op snippet to create `BinaryOpPackedProgram`.\n * @param checkOutOfBoundsForPackedProgram Whether to set checkOutOfBounds=true\n *     when creating BinaryOpPackedProgram.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc(opSnippet, packedOpSnippet, checkOutOfBoundsForPackedProgram, dtype) {\n    // TODO(jingjin): handle complex64.\n    return ({ inputs, backend }) => {\n        const { a, b } = inputs;\n        const webglBackend = backend;\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(packedOpSnippet, a.shape, b.shape, !!checkOutOfBoundsForPackedProgram) :\n            new BinaryOpProgram(opSnippet, a.shape, b.shape);\n        const $dtype = dtype || a.dtype;\n        const output = webglBackend.runWebGLProgram(program, [a, b], $dtype);\n        return output;\n    };\n}\n//# sourceMappingURL=kernel_funcs_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan2 } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc, CHECK_NAN_SNIPPET_BINARY, CHECK_NAN_SNIPPET_BINARY_PACKED } from '../kernel_utils/kernel_funcs_utils';\nconst ATAN2 = CHECK_NAN_SNIPPET_BINARY + `\n  return atan(a, b);\n`;\nconst ATAN2_PACKED = `\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET_BINARY_PACKED + `\n  return result;\n`;\nexport const atan2KernelFunc = binaryKernelFunc(ATAN2, ATAN2_PACKED);\nexport const atan2Config = {\n    kernelName: Atan2,\n    backendName: 'webgl',\n    kernelFunc: atan2KernelFunc,\n};\n//# sourceMappingURL=Atan2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '@tensorflow/tfjs-core';\nexport function identity(args) {\n    const { inputs, backend } = args;\n    const { x } = inputs;\n    backend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nexport const identityConfig = {\n    kernelName: Identity,\n    backendName: 'webgl',\n    kernelFunc: identity\n};\n//# sourceMappingURL=Identity.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool, backend_util, util } from '@tensorflow/tfjs-core';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nimport { identity } from './Identity';\nexport function avgPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'avgPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        return identity({ inputs: { x }, backend });\n    }\n    const avgPoolProgram = new Pool2DProgram(convInfo, 'avg', false);\n    return backend.runWebGLProgram(avgPoolProgram, [x], 'float32');\n}\nexport const avgPoolConfig = {\n    kernelName: AvgPool,\n    backendName: 'webgl',\n    kernelFunc: avgPool\n};\n//# sourceMappingURL=AvgPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPoolBackprop, backend_util } from '@tensorflow/tfjs-core';\nimport { AvgPool2DBackpropProgram } from '../avg_pool_backprop_gpu';\nimport { assertNotComplex } from '../webgl_util';\nexport function avgPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input } = inputs;\n    const x = input;\n    assertNotComplex([dy, input], 'avgPoolBackprop');\n    const { filterSize, strides, pad } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);\n    const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);\n    return backend.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);\n}\nexport const avgPoolBackpropConfig = {\n    kernelName: AvgPoolBackprop,\n    backendName: 'webgl',\n    kernelFunc: avgPoolBackprop\n};\n//# sourceMappingURL=AvgPoolBackprop.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class BatchNormProgram {\n    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n        this.outputShape = [];\n        this.variableNames = ['x', 'mean', 'variance'];\n        backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n        backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n        let offsetSnippet = '0.0';\n        if (offsetShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n            this.variableNames.push('offset');\n            offsetSnippet = 'getOffsetAtOutCoords()';\n        }\n        let scaleSnippet = '1.0';\n        if (scaleShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n            this.variableNames.push('scale');\n            scaleSnippet = 'getScaleAtOutCoords()';\n        }\n        this.outputShape = xShape;\n        this.userCode = `\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${offsetSnippet};\n        float scale = ${scaleSnippet};\n        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=batchnorm_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class BatchNormPackedProgram {\n    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.variableNames = ['x', 'mean', 'variance'];\n        backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n        backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n        let offsetSnippet = 'vec4(0.0)';\n        if (offsetShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n            this.variableNames.push('offset');\n            offsetSnippet = 'getOffsetAtOutCoords()';\n        }\n        let scaleSnippet = 'vec4(1.0)';\n        if (scaleShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n            this.variableNames.push('scale');\n            scaleSnippet = 'getScaleAtOutCoords()';\n        }\n        this.outputShape = xShape;\n        this.userCode = `\n      void main() {\n        vec4 offset = ${offsetSnippet};\n        vec4 scale = ${scaleSnippet};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=batchnorm_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, FusedBatchNorm, util } from '@tensorflow/tfjs-core';\nimport { BatchNormProgram } from '../batchnorm_gpu';\nimport { BatchNormPackedProgram } from '../batchnorm_packed_gpu';\nexport const batchNormKernelFunc = ({ inputs, backend, attrs }) => {\n    const { x, mean, variance, offset, scale } = inputs;\n    util.assert(mean.shape.length === variance.shape.length, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert(offset == null || mean.shape.length === offset.shape.length, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert(scale == null || mean.shape.length === scale.shape.length, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    let { varianceEpsilon } = attrs;\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const finalInputs = [x, mean, variance];\n    let offsetShape = null;\n    if (offset != null) {\n        offsetShape = offset.shape;\n        finalInputs.push(offset);\n    }\n    let scaleShape = null;\n    if (scale != null) {\n        scaleShape = scale.shape;\n        finalInputs.push(scale);\n    }\n    const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n        new BatchNormPackedProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) :\n        new BatchNormProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);\n    const output = backend.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);\n    return output;\n};\nexport const batchNormConfig = {\n    kernelName: FusedBatchNorm,\n    backendName: 'webgl',\n    kernelFunc: batchNormKernelFunc,\n};\n//# sourceMappingURL=BatchNorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '@tensorflow/tfjs-core';\nimport { CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst COS = CHECK_NAN_SNIPPET_UNARY + `\n  return cos(x);\n`;\nexport const cosKernelFunc = unaryKernelFunc(COS);\nexport const cosConfig = {\n    kernelName: Cos,\n    backendName: 'webgl',\n    kernelFunc: cosKernelFunc,\n};\n//# sourceMappingURL=Cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\n// Without the equality check div produces 0.9999 for a = b, which when\n// floored can cause errors.\nconst DIV = `\nif (a == b) {\n  return 1.0;\n};\nreturn a / b;`;\n// We do the same as in ./binaryop_gpu, with vec4 and ivec4.\n// On Linux, the vectorized implementation produces NaNs when a and b are 0.\nconst DIV_PACKED = `\n  // vec4 one = vec4(equal(a, b));\n  // return one + (vec4(1.0) - one) * a / b;\n  vec4 result = a / b;\n  if(a.x == b.x) {\n    result.x = 1.;\n  }\n  if(a.y == b.y) {\n    result.y = 1.;\n  }\n  if(a.z == b.z) {\n    result.z = 1.;\n  }\n  if(a.w == b.w) {\n    result.w = 1.;\n  }\n\n  return result;\n`;\nexport const divKernelFunc = binaryKernelFunc(DIV, DIV_PACKED, true /* checkOutOfBoundsForPackedProgram */);\nexport const divConfig = {\n    kernelName: Div,\n    backendName: 'webgl',\n    kernelFunc: divKernelFunc,\n};\n//# sourceMappingURL=Div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FlipLeftRightProgram {\n    constructor(imageShape) {\n        this.variableNames = ['Image'];\n        this.outputShape = [];\n        const imageWidth = imageShape[2];\n        this.outputShape = imageShape;\n        this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${imageWidth} - x;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${imageWidth}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n    }\n}\n//# sourceMappingURL=flip_left_right_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FlipLeftRight } from '@tensorflow/tfjs-core';\nimport { FlipLeftRightProgram } from '../flip_left_right_gpu';\nexport const flipLeftRightConfig = {\n    kernelName: FlipLeftRight,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend }) => {\n        const { image } = inputs;\n        const webglBackend = backend;\n        const program = new FlipLeftRightProgram(image.shape);\n        const output = webglBackend.runWebGLProgram(program, [image], image.dtype);\n        return output;\n    }\n};\n//# sourceMappingURL=FlipLeftRight.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from '../../glsl_version';\nexport class FromPixelsProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        const glsl = getGlslDifferences();\n        const [height, width,] = outputShape;\n        this.outputShape = outputShape;\n        this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);\n\n        vec4 values = ${glsl.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=from_pixels_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from '../../glsl_version';\nexport class FromPixelsPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        const glsl = getGlslDifferences();\n        const [height, width,] = outputShape;\n        this.outputShape = outputShape;\n        this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(${width}.0, ${height}.0);\n            vec4 values = ${glsl.texture2D}(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=from_pixels_packed_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { FromPixels } from '@tensorflow/tfjs-core';\nimport { TextureUsage } from '../tex_util';\nimport { FromPixelsProgram } from './FromPixels_utils/from_pixels_gpu';\nimport { FromPixelsPackedProgram } from './FromPixels_utils/from_pixels_packed_gpu';\nexport const fromPixelsConfig = {\n    kernelName: FromPixels,\n    backendName: 'webgl',\n    kernelFunc: fromPixels,\n};\nlet fromPixels2DContext;\nfunction fromPixels(args) {\n    const { inputs, backend, attrs } = args;\n    let { pixels } = inputs;\n    const { numChannels } = attrs;\n    const isVideo = typeof (HTMLVideoElement) !== 'undefined' &&\n        pixels instanceof HTMLVideoElement;\n    const isImage = typeof (HTMLImageElement) !== 'undefined' &&\n        pixels instanceof HTMLImageElement;\n    const [width, height] = isVideo ?\n        [\n            pixels.videoWidth,\n            pixels.videoHeight\n        ] :\n        [pixels.width, pixels.height];\n    const texShape = [height, width];\n    const outShape = [height, width, numChannels];\n    if (isImage || isVideo) {\n        if (fromPixels2DContext == null) {\n            fromPixels2DContext = document.createElement('canvas').getContext('2d');\n        }\n        fromPixels2DContext.canvas.width = width;\n        fromPixels2DContext.canvas.height = height;\n        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n        pixels = fromPixels2DContext.canvas;\n    }\n    const tempPixelHandle = backend.makeTensorInfo(texShape, 'int32');\n    // This is a byte texture with pixels.\n    backend.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;\n    backend.gpgpu.uploadPixelDataToTexture(backend.getTexture(tempPixelHandle.dataId), pixels);\n    const program = env().getBool('WEBGL_PACK') ?\n        new FromPixelsPackedProgram(outShape) :\n        new FromPixelsProgram(outShape);\n    const res = backend.runWebGLProgram(program, [tempPixelHandle], 'int32');\n    backend.disposeData(tempPixelHandle.dataId);\n    return res;\n}\n//# sourceMappingURL=FromPixels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { ReduceProgram } from '../reduce_gpu';\n// Returns an array of configuration objects that describe each stage of the\n// reduction.\nfunction getReductionStages(inShape) {\n    const stages = [];\n    while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {\n        const outSize = stages.length ? stages[stages.length - 1].outSize : inShape[1];\n        const windowSize = backend_util.computeOptimalWindowSize(outSize);\n        stages.push({\n            inSize: outSize,\n            windowSize,\n            outSize: Math.ceil(outSize / windowSize)\n        });\n    }\n    return stages;\n}\nexport function reduce(x, dtype, reductionType, backend) {\n    const reductionStages = getReductionStages(x.shape);\n    let result = x;\n    for (let i = 0; i < reductionStages.length; i++) {\n        const { inSize, windowSize, outSize } = reductionStages[i];\n        const program = new ReduceProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, reductionType);\n        const previousResult = result;\n        result = backend.runWebGLProgram(program, [result], dtype);\n        if (previousResult.dataId !== x.dataId) {\n            backend.disposeData(previousResult.dataId);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=reduce.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ReshapePackedProgram } from '../reshape_packed_gpu';\nimport { getBatchDim, getRowsCols } from '../webgl_util';\nexport function packedReshape(input, afterShape, backend) {\n    const input3DShape = [getBatchDim(input.shape),\n        ...getRowsCols(input.shape)];\n    const input3D = {\n        dtype: input.dtype,\n        shape: input3DShape,\n        dataId: input.dataId\n    };\n    const afterShapeAs3D = [getBatchDim(afterShape),\n        ...getRowsCols(afterShape)];\n    const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n    const preventEagerUnpackingOfOutput = true;\n    const output = backend.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n    return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n}\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape, util } from '@tensorflow/tfjs-core';\nimport { packedReshape } from '../kernel_utils/reshape';\nimport { isReshapeFree } from '../webgl_util';\nexport function reshape(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { shape } = attrs;\n    const webglBackend = backend;\n    const xSize = util.sizeFromShape(x.shape);\n    const $shape = util.inferFromImplicitShape(shape, xSize);\n    const $xSize = util.sizeFromShape($shape);\n    util.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n        `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n        `shape must have the same number of elements.`);\n    const xTexData = webglBackend.texData.get(x.dataId);\n    if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) &&\n        !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {\n        return packedReshape(x, $shape, webglBackend);\n    }\n    webglBackend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nexport const reshapeConfig = {\n    kernelName: Reshape,\n    backendName: 'webgl',\n    kernelFunc: reshape\n};\n//# sourceMappingURL=Reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { reduce } from '../kernel_utils/reduce';\nimport { reshape } from '../kernels/Reshape';\nexport function maxImpl(x, reduceShape, outShape, backend) {\n    const inSize = util.sizeFromShape(reduceShape);\n    const xSize = util.sizeFromShape(x.shape);\n    const batchSize = xSize / inSize;\n    const reshapedInput = reshape({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend });\n    const reduced = reduce(reshapedInput, x.dtype, 'max', backend);\n    const reshapedOutput = reshape({ inputs: { x: reduced }, attrs: { shape: outShape }, backend });\n    backend.disposeIntermediateTensorInfo(reshapedInput);\n    backend.disposeIntermediateTensorInfo(reduced);\n    return reshapedOutput;\n}\n//# sourceMappingURL=Max_impl.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class TransposeProgram {\n    constructor(aShape, newDim) {\n        this.variableNames = ['A'];\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[newDim[i]];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const switched = getSwitchedCoords(newDim);\n        this.userCode = `\n    void main() {\n      ${dtype} resRC = getOutputCoords();\n      setOutput(getA(${switched}));\n    }\n    `;\n    }\n}\nfunction getSwitchedCoords(newDim) {\n    const rank = newDim.length;\n    if (rank > 6) {\n        throw Error(`Transpose for rank ${rank} is not yet supported`);\n    }\n    const originalOrder = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u', 'resRC.v'];\n    const switchedCoords = new Array(rank);\n    for (let i = 0; i < newDim.length; i++) {\n        switchedCoords[newDim[i]] = originalOrder[i];\n    }\n    return switchedCoords.join();\n}\n//# sourceMappingURL=transpose_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getVecChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class TransposePackedProgram {\n    constructor(aShape, newDim) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[newDim[i]];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        if (this.rank > 6) {\n            throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);\n        }\n        const dtype = getCoordsDataType(this.rank);\n        const outputOrder = getVecChannels('rc', this.rank);\n        const switchedOrder = new Array(this.rank);\n        for (let i = 0; i < newDim.length; i++) {\n            switchedOrder[newDim[i]] = outputOrder[i];\n        }\n        const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;\n        const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;\n        const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;\n        this.userCode = `\n    void main() {\n      ${dtype} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${getc};\n      if(${nextColumn}) {\n        result[1] = ${getc};\n      }\n      --${outputOrder[this.rank - 1]};\n      if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {\n        result[2] = ${getc};\n        if(${nextColumn}) {\n          result[3] = ${getc};\n        }\n      }\n      setOutput(result);\n    }\n    `;\n    }\n}\n//# sourceMappingURL=transpose_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { transposeImplCPU } from '../kernel_utils/shared';\nimport { TransposeProgram } from '../transpose_gpu';\nimport { TransposePackedProgram } from '../transpose_packed_gpu';\nexport function transposeImpl(x, perm, backend) {\n    const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n        new TransposePackedProgram(x.shape, perm) :\n        new TransposeProgram(x.shape, perm);\n    return backend.runWebGLProgram(program, [x], x.dtype);\n}\nexport { transposeImplCPU };\n//# sourceMappingURL=Transpose_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '@tensorflow/tfjs-core';\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { maxImplCPU } from '../kernel_utils/shared';\nimport { maxImpl } from './Max_impl';\nimport { transposeImpl, transposeImplCPU } from './Transpose_impl';\nexport const maxConfig = {\n    kernelName: Max,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { reductionIndices, keepDims } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        const maxInputIsTransposed = permutedAxes != null;\n        const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);\n        let maxInput = x;\n        if (maxInputIsTransposed) {\n            if (shouldExecuteOnCPU) {\n                const xTexData = webglBackend.texData.get(maxInput.dataId);\n                const values = xTexData.values;\n                const newShape = new Array(xRank);\n                for (let i = 0; i < newShape.length; i++) {\n                    newShape[i] = x.shape[permutedAxes[i]];\n                }\n                const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n                maxInput = webglBackend.makeTensorInfo(newShape, x.dtype);\n                const maxInputData = webglBackend.texData.get(maxInput.dataId);\n                maxInputData.values = maxInputValues;\n            }\n            else {\n                maxInput = transposeImpl(x, permutedAxes, webglBackend);\n            }\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n        }\n        backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n        const [maxOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(maxInput.shape, axes);\n        let outShape = maxOutShape;\n        if (keepDims) {\n            // rather than reshape at the end, set the target shape here.\n            outShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n        }\n        let out;\n        if (shouldExecuteOnCPU) {\n            const xTexData = webglBackend.texData.get(maxInput.dataId);\n            const values = xTexData.values;\n            const outValues = maxImplCPU(values, util.sizeFromShape(reduceShape), outShape, x.dtype);\n            out = webglBackend.makeTensorInfo(outShape, x.dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n        }\n        else {\n            out = maxImpl(maxInput, reduceShape, outShape, webglBackend);\n        }\n        if (maxInputIsTransposed) {\n            webglBackend.disposeIntermediateTensorInfo(maxInput);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPool, util } from '@tensorflow/tfjs-core';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nimport { identity } from './Identity';\nexport function maxPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'maxPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        return identity({ inputs: { x }, backend });\n    }\n    const maxPoolProgram = new Pool2DProgram(convInfo, 'max', false);\n    return backend.runWebGLProgram(maxPoolProgram, [x], x.dtype);\n}\nexport const maxPoolConfig = {\n    kernelName: MaxPool,\n    backendName: 'webgl',\n    kernelFunc: maxPool\n};\n//# sourceMappingURL=MaxPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPoolBackprop } from '@tensorflow/tfjs-core';\nimport { MaxPool2DBackpropProgram } from '../max_pool_backprop_gpu';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nexport function maxPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input, output } = inputs;\n    const x = input;\n    assertNotComplex([input, output], 'maxPoolBackprop');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n    const getPositions = true;\n    const maxPoolPositionsProgram = new Pool2DProgram(convInfo, 'max', getPositions);\n    const maxPoolPositions = backend.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);\n    const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);\n    const result = backend.runWebGLProgram(maxPoolBackPropProgram, [dy, maxPoolPositions], x.dtype);\n    backend.disposeIntermediateTensorInfo(maxPoolPositions);\n    return result;\n}\nexport const maxPoolBackpropConfig = {\n    kernelName: MaxPoolBackprop,\n    backendName: 'webgl',\n    kernelFunc: maxPoolBackprop\n};\n//# sourceMappingURL=MaxPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Pool2DProgram } from '../pool_gpu';\nexport function maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, backend) {\n    let program = new Pool2DProgram(convInfo, 'max', false);\n    const poolOutput = backend.runWebGLProgram(program, [x], 'float32');\n    program = new Pool2DProgram(convInfo, 'max', true, true, includeBatchInIndex);\n    const indexOutput = backend.runWebGLProgram(program, [x], 'float32');\n    return [poolOutput, indexOutput];\n}\n//# sourceMappingURL=MaxPoolWithArgmax_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPoolWithArgmax } from '@tensorflow/tfjs-core';\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { maxPoolWithArgmaxImpl } from './MaxPoolWithArgmax_impl';\nexport const maxPoolWithArgmaxConfig = {\n    kernelName: MaxPoolWithArgmax,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { filterSize, strides, pad, includeBatchInIndex } = attrs;\n        const webglBackend = backend;\n        util.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);\n        const dilations = [1, 1];\n        util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n            `Got strides ${strides} and dilations '${dilations}'`);\n        const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad);\n        const [result, indexes] = maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, webglBackend);\n        return [result, indexes];\n    }\n};\n//# sourceMappingURL=MaxPoolWithArgmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls, NonMaxSuppressionV3 } from '@tensorflow/tfjs-core';\nexport const nonMaxSuppressionV3Config = {\n    kernelName: NonMaxSuppressionV3,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        return kernel_impls.nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal);\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV3.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls, NonMaxSuppressionV4 } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\nexport const nonMaxSuppressionV4Config = {\n    kernelName: NonMaxSuppressionV4,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n        return [selectedIndices, validOutputs];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV4.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls } from '@tensorflow/tfjs-core';\nimport { NonMaxSuppressionV5 } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nexport const nonMaxSuppressionV5Config = {\n    kernelName: NonMaxSuppressionV5,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        const softNmsSigmaVal = softNmsSigma;\n        const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n        return [selectedIndices, selectedScores];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV5.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class RotateProgram {\n    constructor(imageShape, radians, fillValue, center) {\n        this.variableNames = ['Image'];\n        this.outputShape = [];\n        const imageHeight = imageShape[1];\n        const imageWidth = imageShape[2];\n        const sinFactor = Math.sin(radians).toFixed(3);\n        const cosFactor = Math.cos(radians).toFixed(3);\n        this.outputShape = imageShape;\n        const [centerX, centerY] = backend_util.getImageCenter(center, imageHeight, imageWidth);\n        const centerXString = centerX.toFixed(3);\n        const centerYString = centerY.toFixed(3);\n        let fillSnippet = '';\n        if (typeof fillValue === 'number') {\n            fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;\n        }\n        else {\n            fillSnippet = `\n        vec3 fill = vec3(${fillValue.join(',')});\n        float outputValue = fill[coords[3]];`;\n        }\n        this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - ${centerXString}) * ${cosFactor} - (float(y) - ${centerYString}) * ${sinFactor};\n          float coordYFloat = (float(x) - ${centerXString}) * ${sinFactor} + (float(y) - ${centerYString}) * ${cosFactor};\n          int coordX = int(round(coordXFloat + ${centerXString}));\n          int coordY = int(round(coordYFloat + ${centerYString}));\n          ${fillSnippet}\n          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n    }\n}\n//# sourceMappingURL=rotate_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { RotateWithOffset } from '@tensorflow/tfjs-core';\nimport { RotateProgram } from '../rotate_gpu';\nexport const rotateWithOffsetConfig = {\n    kernelName: RotateWithOffset,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const { radians, fillValue, center } = attrs;\n        const webglBackend = backend;\n        const program = new RotateProgram(image.shape, radians, fillValue, center);\n        const output = webglBackend.runWebGLProgram(program, [image], image.dtype);\n        return output;\n    }\n};\n//# sourceMappingURL=RotateWithOffset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '@tensorflow/tfjs-core';\nimport { CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SIN = CHECK_NAN_SNIPPET_UNARY + `\n  return sin(x);\n`;\nexport const sinKernelFunc = unaryKernelFunc(SIN);\nexport const sinConfig = {\n    kernelName: Sin,\n    backendName: 'webgl',\n    kernelFunc: sinKernelFunc,\n};\n//# sourceMappingURL=Sin.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SQUARE = `return x * x;`;\nexport const squareKernelFunc = unaryKernelFunc(SQUARE);\nexport const squareConfig = {\n    kernelName: Square,\n    backendName: 'webgl',\n    kernelFunc: squareKernelFunc,\n};\n//# sourceMappingURL=Square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport const squaredDifferenceKernelFunc = binaryKernelFunc(SQUARED_DIFFERENCE, SQUARED_DIFFERENCE);\nexport const squaredDifferenceConfig = {\n    kernelName: SquaredDifference,\n    backendName: 'webgl',\n    kernelFunc: squaredDifferenceKernelFunc,\n};\n//# sourceMappingURL=SquaredDifference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst TAN = `return tan(x);`;\nexport const tanKernelFunc = unaryKernelFunc(TAN);\nexport const tanConfig = {\n    kernelName: Tan,\n    backendName: 'webgl',\n    kernelFunc: tanKernelFunc,\n};\n//# sourceMappingURL=Tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '@tensorflow/tfjs-core';\nimport { transposeImpl } from './Transpose_impl';\nimport { transposeImplCPU as cpuTranspose } from './Transpose_impl';\nexport const transposeConfig = {\n    kernelName: Transpose,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { perm } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const newShape = new Array(xRank);\n        for (let i = 0; i < newShape.length; i++) {\n            newShape[i] = x.shape[perm[i]];\n        }\n        let out;\n        if (webglBackend.shouldExecuteOnCPU([x])) {\n            const xTexData = webglBackend.texData.get(x.dataId);\n            const values = xTexData.values;\n            const outValues = cpuTranspose(values, x.shape, x.dtype, perm, newShape);\n            out = webglBackend.makeTensorInfo(newShape, x.dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n        }\n        else {\n            out = transposeImpl(x, perm, webglBackend);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unique } from '@tensorflow/tfjs-core';\nimport { uniqueImplCPU } from '../kernel_utils/shared';\nimport { assertNotComplex } from '../webgl_util';\nexport function unique(args) {\n    const { inputs, attrs, backend } = args;\n    const { axis } = attrs;\n    const { x } = inputs;\n    assertNotComplex(x, 'unique');\n    // For now, always forward calculation to the CPU backend.\n    console.warn('WARNING: ', 'UI might be locked temporarily as data is being downloaded');\n    const values = backend.readSync(x.dataId);\n    const { outputValues, outputShape, indices } = uniqueImplCPU(values, axis, x.shape, x.dtype);\n    return [\n        backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n        backend.makeTensorInfo([indices.length], 'int32', indices),\n    ];\n}\nexport const uniqueConfig = {\n    kernelName: Unique,\n    backendName: 'webgl',\n    kernelFunc: unique,\n};\n//# sourceMappingURL=Unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { registerKernel } from '@tensorflow/tfjs-core';\nimport { atan2Config } from './kernels/Atan2';\nimport { avgPoolConfig } from './kernels/AvgPool';\nimport { avgPoolBackpropConfig } from './kernels/AvgPoolBackprop';\nimport { batchNormConfig } from './kernels/BatchNorm';\nimport { cosConfig } from './kernels/Cos';\nimport { divConfig } from './kernels/Div';\nimport { flipLeftRightConfig } from './kernels/FlipLeftRight';\nimport { fromPixelsConfig } from './kernels/FromPixels';\nimport { identityConfig } from './kernels/Identity';\nimport { maxConfig } from './kernels/Max';\nimport { maxPoolConfig } from './kernels/MaxPool';\nimport { maxPoolBackpropConfig } from './kernels/MaxPoolBackprop';\nimport { maxPoolWithArgmaxConfig } from './kernels/MaxPoolWithArgmax';\nimport { nonMaxSuppressionV3Config } from './kernels/NonMaxSuppressionV3';\nimport { nonMaxSuppressionV4Config } from './kernels/NonMaxSuppressionV4';\nimport { nonMaxSuppressionV5Config } from './kernels/NonMaxSuppressionV5';\nimport { reshapeConfig } from './kernels/Reshape';\nimport { rotateWithOffsetConfig } from './kernels/RotateWithOffset';\nimport { sinConfig } from './kernels/Sin';\nimport { squareConfig } from './kernels/Square';\nimport { squaredDifferenceConfig } from './kernels/SquaredDifference';\nimport { tanConfig } from './kernels/Tan';\nimport { transposeConfig } from './kernels/Transpose';\nimport { uniqueConfig } from './kernels/Unique';\n// List all kernel configs here\nconst kernelConfigs = [\n    atan2Config,\n    avgPoolConfig,\n    avgPoolBackpropConfig,\n    batchNormConfig,\n    cosConfig,\n    divConfig,\n    flipLeftRightConfig,\n    fromPixelsConfig,\n    identityConfig,\n    maxConfig,\n    maxPoolConfig,\n    maxPoolBackpropConfig,\n    maxPoolWithArgmaxConfig,\n    nonMaxSuppressionV3Config,\n    nonMaxSuppressionV4Config,\n    nonMaxSuppressionV5Config,\n    reshapeConfig,\n    rotateWithOffsetConfig,\n    sinConfig,\n    squareConfig,\n    squaredDifferenceConfig,\n    tanConfig,\n    transposeConfig,\n    uniqueConfig,\n];\nfor (const kernelConfig of kernelConfigs) {\n    registerKernel(kernelConfig);\n}\n//# sourceMappingURL=register_all_kernels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// All exports from this package should be in base.\nexport * from './base';\nimport './register_all_kernels';\n//# sourceMappingURL=index.js.map", "/** @license See the LICENSE file. */\n\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport {version};\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport * from '@tensorflow/tfjs-core';\nexport * from '@tensorflow/tfjs-layers';\nexport * from '@tensorflow/tfjs-converter';\n\n// Export data api as tf.data\nimport * as data from '@tensorflow/tfjs-data';\nexport {data};\n\n// Import and register backends.\nimport '@tensorflow/tfjs-backend-cpu';\nimport '@tensorflow/tfjs-backend-webgl';\n\n// Import versions of all sub-packages.\nimport {version_core} from '@tensorflow/tfjs-core';\nimport {version_cpu} from '@tensorflow/tfjs-backend-cpu';\nimport {version_webgl} from '@tensorflow/tfjs-backend-webgl';\nimport {version_data} from '@tensorflow/tfjs-data';\nimport {version_layers} from '@tensorflow/tfjs-layers';\nimport {version_converter} from '@tensorflow/tfjs-converter';\nimport {version as version_union} from './version';\n\nexport const version = {\n  'tfjs-core': version_core,\n  'tfjs-backend-cpu': version_cpu,\n  'tfjs-backend-webgl': version_webgl,\n  'tfjs-data': version_data,\n  'tfjs-layers': version_layers,\n  'tfjs-converter': version_converter,\n  'tfjs': version_union\n};\n", "export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r)\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g)\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b)\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0)\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = input.toBatchTensor(112, true)\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = input.toBatchTensor(112, true)\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([\n          tf.fill([68], fillX),\n          tf.fill([68], fillY)\n        ], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = input.toBatchTensor(112, true)\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n"],
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+  "sourcesContent": ["import Stream from 'stream';\nimport http from 'http';\nimport Url from 'url';\nimport https from 'https';\nimport zlib from 'zlib';\n\n// Based on https://github.com/tmpvar/jsdom/blob/aa85b2abf07766ff7bf5c1f6daafb3726f2f2db5/lib/jsdom/living/blob.js\n\n// fix for \"Readable\" isn't a named export issue\nconst Readable = Stream.Readable;\n\nconst BUFFER = Symbol('buffer');\nconst TYPE = Symbol('type');\n\nclass Blob {\n\tconstructor() {\n\t\tthis[TYPE] = '';\n\n\t\tconst blobParts = arguments[0];\n\t\tconst options = arguments[1];\n\n\t\tconst buffers = [];\n\t\tlet size = 0;\n\n\t\tif (blobParts) {\n\t\t\tconst a = blobParts;\n\t\t\tconst length = Number(a.length);\n\t\t\tfor (let i = 0; i < length; i++) {\n\t\t\t\tconst element = a[i];\n\t\t\t\tlet buffer;\n\t\t\t\tif (element instanceof Buffer) {\n\t\t\t\t\tbuffer = element;\n\t\t\t\t} else if (ArrayBuffer.isView(element)) {\n\t\t\t\t\tbuffer = Buffer.from(element.buffer, element.byteOffset, element.byteLength);\n\t\t\t\t} else if (element instanceof ArrayBuffer) {\n\t\t\t\t\tbuffer = Buffer.from(element);\n\t\t\t\t} else if (element instanceof Blob) {\n\t\t\t\t\tbuffer = element[BUFFER];\n\t\t\t\t} else {\n\t\t\t\t\tbuffer = Buffer.from(typeof element === 'string' ? element : String(element));\n\t\t\t\t}\n\t\t\t\tsize += buffer.length;\n\t\t\t\tbuffers.push(buffer);\n\t\t\t}\n\t\t}\n\n\t\tthis[BUFFER] = Buffer.concat(buffers);\n\n\t\tlet type = options && options.type !== undefined && String(options.type).toLowerCase();\n\t\tif (type && !/[^\\u0020-\\u007E]/.test(type)) {\n\t\t\tthis[TYPE] = type;\n\t\t}\n\t}\n\tget size() {\n\t\treturn this[BUFFER].length;\n\t}\n\tget type() {\n\t\treturn this[TYPE];\n\t}\n\ttext() {\n\t\treturn Promise.resolve(this[BUFFER].toString());\n\t}\n\tarrayBuffer() {\n\t\tconst buf = this[BUFFER];\n\t\tconst ab = buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n\t\treturn Promise.resolve(ab);\n\t}\n\tstream() {\n\t\tconst readable = new Readable();\n\t\treadable._read = function () {};\n\t\treadable.push(this[BUFFER]);\n\t\treadable.push(null);\n\t\treturn readable;\n\t}\n\ttoString() {\n\t\treturn '[object Blob]';\n\t}\n\tslice() {\n\t\tconst size = this.size;\n\n\t\tconst start = arguments[0];\n\t\tconst end = arguments[1];\n\t\tlet relativeStart, relativeEnd;\n\t\tif (start === undefined) {\n\t\t\trelativeStart = 0;\n\t\t} else if (start < 0) {\n\t\t\trelativeStart = Math.max(size + start, 0);\n\t\t} else {\n\t\t\trelativeStart = Math.min(start, size);\n\t\t}\n\t\tif (end === undefined) {\n\t\t\trelativeEnd = size;\n\t\t} else if (end < 0) {\n\t\t\trelativeEnd = Math.max(size + end, 0);\n\t\t} else {\n\t\t\trelativeEnd = Math.min(end, size);\n\t\t}\n\t\tconst span = Math.max(relativeEnd - relativeStart, 0);\n\n\t\tconst buffer = this[BUFFER];\n\t\tconst slicedBuffer = buffer.slice(relativeStart, relativeStart + span);\n\t\tconst blob = new Blob([], { type: arguments[2] });\n\t\tblob[BUFFER] = slicedBuffer;\n\t\treturn blob;\n\t}\n}\n\nObject.defineProperties(Blob.prototype, {\n\tsize: { enumerable: true },\n\ttype: { enumerable: true },\n\tslice: { enumerable: true }\n});\n\nObject.defineProperty(Blob.prototype, Symbol.toStringTag, {\n\tvalue: 'Blob',\n\twritable: false,\n\tenumerable: false,\n\tconfigurable: true\n});\n\n/**\n * fetch-error.js\n *\n * FetchError interface for operational errors\n */\n\n/**\n * Create FetchError instance\n *\n * @param   String      message      Error message for human\n * @param   String      type         Error type for machine\n * @param   String      systemError  For Node.js system error\n * @return  FetchError\n */\nfunction FetchError(message, type, systemError) {\n  Error.call(this, message);\n\n  this.message = message;\n  this.type = type;\n\n  // when err.type is `system`, err.code contains system error code\n  if (systemError) {\n    this.code = this.errno = systemError.code;\n  }\n\n  // hide custom error implementation details from end-users\n  Error.captureStackTrace(this, this.constructor);\n}\n\nFetchError.prototype = Object.create(Error.prototype);\nFetchError.prototype.constructor = FetchError;\nFetchError.prototype.name = 'FetchError';\n\nlet convert;\ntry {\n\tconvert = require('encoding').convert;\n} catch (e) {}\n\nconst INTERNALS = Symbol('Body internals');\n\n// fix an issue where \"PassThrough\" isn't a named export for node <10\nconst PassThrough = Stream.PassThrough;\n\n/**\n * Body mixin\n *\n * Ref: https://fetch.spec.whatwg.org/#body\n *\n * @param   Stream  body  Readable stream\n * @param   Object  opts  Response options\n * @return  Void\n */\nfunction Body(body) {\n\tvar _this = this;\n\n\tvar _ref = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {},\n\t    _ref$size = _ref.size;\n\n\tlet size = _ref$size === undefined ? 0 : _ref$size;\n\tvar _ref$timeout = _ref.timeout;\n\tlet timeout = _ref$timeout === undefined ? 0 : _ref$timeout;\n\n\tif (body == null) {\n\t\t// body is undefined or null\n\t\tbody = null;\n\t} else if (isURLSearchParams(body)) {\n\t\t// body is a URLSearchParams\n\t\tbody = Buffer.from(body.toString());\n\t} else if (isBlob(body)) ; else if (Buffer.isBuffer(body)) ; else if (Object.prototype.toString.call(body) === '[object ArrayBuffer]') {\n\t\t// body is ArrayBuffer\n\t\tbody = Buffer.from(body);\n\t} else if (ArrayBuffer.isView(body)) {\n\t\t// body is ArrayBufferView\n\t\tbody = Buffer.from(body.buffer, body.byteOffset, body.byteLength);\n\t} else if (body instanceof Stream) ; else {\n\t\t// none of the above\n\t\t// coerce to string then buffer\n\t\tbody = Buffer.from(String(body));\n\t}\n\tthis[INTERNALS] = {\n\t\tbody,\n\t\tdisturbed: false,\n\t\terror: null\n\t};\n\tthis.size = size;\n\tthis.timeout = timeout;\n\n\tif (body instanceof Stream) {\n\t\tbody.on('error', function (err) {\n\t\t\tconst error = err.name === 'AbortError' ? err : new FetchError(`Invalid response body while trying to fetch ${_this.url}: ${err.message}`, 'system', err);\n\t\t\t_this[INTERNALS].error = error;\n\t\t});\n\t}\n}\n\nBody.prototype = {\n\tget body() {\n\t\treturn this[INTERNALS].body;\n\t},\n\n\tget bodyUsed() {\n\t\treturn this[INTERNALS].disturbed;\n\t},\n\n\t/**\n  * Decode response as ArrayBuffer\n  *\n  * @return  Promise\n  */\n\tarrayBuffer() {\n\t\treturn consumeBody.call(this).then(function (buf) {\n\t\t\treturn buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n\t\t});\n\t},\n\n\t/**\n  * Return raw response as Blob\n  *\n  * @return Promise\n  */\n\tblob() {\n\t\tlet ct = this.headers && this.headers.get('content-type') || '';\n\t\treturn consumeBody.call(this).then(function (buf) {\n\t\t\treturn Object.assign(\n\t\t\t// Prevent copying\n\t\t\tnew Blob([], {\n\t\t\t\ttype: ct.toLowerCase()\n\t\t\t}), {\n\t\t\t\t[BUFFER]: buf\n\t\t\t});\n\t\t});\n\t},\n\n\t/**\n  * Decode response as json\n  *\n  * @return  Promise\n  */\n\tjson() {\n\t\tvar _this2 = this;\n\n\t\treturn consumeBody.call(this).then(function (buffer) {\n\t\t\ttry {\n\t\t\t\treturn JSON.parse(buffer.toString());\n\t\t\t} catch (err) {\n\t\t\t\treturn Body.Promise.reject(new FetchError(`invalid json response body at ${_this2.url} reason: ${err.message}`, 'invalid-json'));\n\t\t\t}\n\t\t});\n\t},\n\n\t/**\n  * Decode response as text\n  *\n  * @return  Promise\n  */\n\ttext() {\n\t\treturn consumeBody.call(this).then(function (buffer) {\n\t\t\treturn buffer.toString();\n\t\t});\n\t},\n\n\t/**\n  * Decode response as buffer (non-spec api)\n  *\n  * @return  Promise\n  */\n\tbuffer() {\n\t\treturn consumeBody.call(this);\n\t},\n\n\t/**\n  * Decode response as text, while automatically detecting the encoding and\n  * trying to decode to UTF-8 (non-spec api)\n  *\n  * @return  Promise\n  */\n\ttextConverted() {\n\t\tvar _this3 = this;\n\n\t\treturn consumeBody.call(this).then(function (buffer) {\n\t\t\treturn convertBody(buffer, _this3.headers);\n\t\t});\n\t}\n};\n\n// In browsers, all properties are enumerable.\nObject.defineProperties(Body.prototype, {\n\tbody: { enumerable: true },\n\tbodyUsed: { enumerable: true },\n\tarrayBuffer: { enumerable: true },\n\tblob: { enumerable: true },\n\tjson: { enumerable: true },\n\ttext: { enumerable: true }\n});\n\nBody.mixIn = function (proto) {\n\tfor (const name of Object.getOwnPropertyNames(Body.prototype)) {\n\t\t// istanbul ignore else: future proof\n\t\tif (!(name in proto)) {\n\t\t\tconst desc = Object.getOwnPropertyDescriptor(Body.prototype, name);\n\t\t\tObject.defineProperty(proto, name, desc);\n\t\t}\n\t}\n};\n\n/**\n * Consume and convert an entire Body to a Buffer.\n *\n * Ref: https://fetch.spec.whatwg.org/#concept-body-consume-body\n *\n * @return  Promise\n */\nfunction consumeBody() {\n\tvar _this4 = this;\n\n\tif (this[INTERNALS].disturbed) {\n\t\treturn Body.Promise.reject(new TypeError(`body used already for: ${this.url}`));\n\t}\n\n\tthis[INTERNALS].disturbed = true;\n\n\tif (this[INTERNALS].error) {\n\t\treturn Body.Promise.reject(this[INTERNALS].error);\n\t}\n\n\tlet body = this.body;\n\n\t// body is null\n\tif (body === null) {\n\t\treturn Body.Promise.resolve(Buffer.alloc(0));\n\t}\n\n\t// body is blob\n\tif (isBlob(body)) {\n\t\tbody = body.stream();\n\t}\n\n\t// body is buffer\n\tif (Buffer.isBuffer(body)) {\n\t\treturn Body.Promise.resolve(body);\n\t}\n\n\t// istanbul ignore if: should never happen\n\tif (!(body instanceof Stream)) {\n\t\treturn Body.Promise.resolve(Buffer.alloc(0));\n\t}\n\n\t// body is stream\n\t// get ready to actually consume the body\n\tlet accum = [];\n\tlet accumBytes = 0;\n\tlet abort = false;\n\n\treturn new Body.Promise(function (resolve, reject) {\n\t\tlet resTimeout;\n\n\t\t// allow timeout on slow response body\n\t\tif (_this4.timeout) {\n\t\t\tresTimeout = setTimeout(function () {\n\t\t\t\tabort = true;\n\t\t\t\treject(new FetchError(`Response timeout while trying to fetch ${_this4.url} (over ${_this4.timeout}ms)`, 'body-timeout'));\n\t\t\t}, _this4.timeout);\n\t\t}\n\n\t\t// handle stream errors\n\t\tbody.on('error', function (err) {\n\t\t\tif (err.name === 'AbortError') {\n\t\t\t\t// if the request was aborted, reject with this Error\n\t\t\t\tabort = true;\n\t\t\t\treject(err);\n\t\t\t} else {\n\t\t\t\t// other errors, such as incorrect content-encoding\n\t\t\t\treject(new FetchError(`Invalid response body while trying to fetch ${_this4.url}: ${err.message}`, 'system', err));\n\t\t\t}\n\t\t});\n\n\t\tbody.on('data', function (chunk) {\n\t\t\tif (abort || chunk === null) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tif (_this4.size && accumBytes + chunk.length > _this4.size) {\n\t\t\t\tabort = true;\n\t\t\t\treject(new FetchError(`content size at ${_this4.url} over limit: ${_this4.size}`, 'max-size'));\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\taccumBytes += chunk.length;\n\t\t\taccum.push(chunk);\n\t\t});\n\n\t\tbody.on('end', function () {\n\t\t\tif (abort) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tclearTimeout(resTimeout);\n\n\t\t\ttry {\n\t\t\t\tresolve(Buffer.concat(accum, accumBytes));\n\t\t\t} catch (err) {\n\t\t\t\t// handle streams that have accumulated too much data (issue #414)\n\t\t\t\treject(new FetchError(`Could not create Buffer from response body for ${_this4.url}: ${err.message}`, 'system', err));\n\t\t\t}\n\t\t});\n\t});\n}\n\n/**\n * Detect buffer encoding and convert to target encoding\n * ref: http://www.w3.org/TR/2011/WD-html5-20110113/parsing.html#determining-the-character-encoding\n *\n * @param   Buffer  buffer    Incoming buffer\n * @param   String  encoding  Target encoding\n * @return  String\n */\nfunction convertBody(buffer, headers) {\n\tif (typeof convert !== 'function') {\n\t\tthrow new Error('The package `encoding` must be installed to use the textConverted() function');\n\t}\n\n\tconst ct = headers.get('content-type');\n\tlet charset = 'utf-8';\n\tlet res, str;\n\n\t// header\n\tif (ct) {\n\t\tres = /charset=([^;]*)/i.exec(ct);\n\t}\n\n\t// no charset in content type, peek at response body for at most 1024 bytes\n\tstr = buffer.slice(0, 1024).toString();\n\n\t// html5\n\tif (!res && str) {\n\t\tres = /<meta.+?charset=(['\"])(.+?)\\1/i.exec(str);\n\t}\n\n\t// html4\n\tif (!res && str) {\n\t\tres = /<meta[\\s]+?http-equiv=(['\"])content-type\\1[\\s]+?content=(['\"])(.+?)\\2/i.exec(str);\n\t\tif (!res) {\n\t\t\tres = /<meta[\\s]+?content=(['\"])(.+?)\\1[\\s]+?http-equiv=(['\"])content-type\\3/i.exec(str);\n\t\t\tif (res) {\n\t\t\t\tres.pop(); // drop last quote\n\t\t\t}\n\t\t}\n\n\t\tif (res) {\n\t\t\tres = /charset=(.*)/i.exec(res.pop());\n\t\t}\n\t}\n\n\t// xml\n\tif (!res && str) {\n\t\tres = /<\\?xml.+?encoding=(['\"])(.+?)\\1/i.exec(str);\n\t}\n\n\t// found charset\n\tif (res) {\n\t\tcharset = res.pop();\n\n\t\t// prevent decode issues when sites use incorrect encoding\n\t\t// ref: https://hsivonen.fi/encoding-menu/\n\t\tif (charset === 'gb2312' || charset === 'gbk') {\n\t\t\tcharset = 'gb18030';\n\t\t}\n\t}\n\n\t// turn raw buffers into a single utf-8 buffer\n\treturn convert(buffer, 'UTF-8', charset).toString();\n}\n\n/**\n * Detect a URLSearchParams object\n * ref: https://github.com/bitinn/node-fetch/issues/296#issuecomment-307598143\n *\n * @param   Object  obj     Object to detect by type or brand\n * @return  String\n */\nfunction isURLSearchParams(obj) {\n\t// Duck-typing as a necessary condition.\n\tif (typeof obj !== 'object' || typeof obj.append !== 'function' || typeof obj.delete !== 'function' || typeof obj.get !== 'function' || typeof obj.getAll !== 'function' || typeof obj.has !== 'function' || typeof obj.set !== 'function') {\n\t\treturn false;\n\t}\n\n\t// Brand-checking and more duck-typing as optional condition.\n\treturn obj.constructor.name === 'URLSearchParams' || Object.prototype.toString.call(obj) === '[object URLSearchParams]' || typeof obj.sort === 'function';\n}\n\n/**\n * Check if `obj` is a W3C `Blob` object (which `File` inherits from)\n * @param  {*} obj\n * @return {boolean}\n */\nfunction isBlob(obj) {\n\treturn typeof obj === 'object' && typeof obj.arrayBuffer === 'function' && typeof obj.type === 'string' && typeof obj.stream === 'function' && typeof obj.constructor === 'function' && typeof obj.constructor.name === 'string' && /^(Blob|File)$/.test(obj.constructor.name) && /^(Blob|File)$/.test(obj[Symbol.toStringTag]);\n}\n\n/**\n * Clone body given Res/Req instance\n *\n * @param   Mixed  instance  Response or Request instance\n * @return  Mixed\n */\nfunction clone(instance) {\n\tlet p1, p2;\n\tlet body = instance.body;\n\n\t// don't allow cloning a used body\n\tif (instance.bodyUsed) {\n\t\tthrow new Error('cannot clone body after it is used');\n\t}\n\n\t// check that body is a stream and not form-data object\n\t// note: we can't clone the form-data object without having it as a dependency\n\tif (body instanceof Stream && typeof body.getBoundary !== 'function') {\n\t\t// tee instance body\n\t\tp1 = new PassThrough();\n\t\tp2 = new PassThrough();\n\t\tbody.pipe(p1);\n\t\tbody.pipe(p2);\n\t\t// set instance body to teed body and return the other teed body\n\t\tinstance[INTERNALS].body = p1;\n\t\tbody = p2;\n\t}\n\n\treturn body;\n}\n\n/**\n * Performs the operation \"extract a `Content-Type` value from |object|\" as\n * specified in the specification:\n * https://fetch.spec.whatwg.org/#concept-bodyinit-extract\n *\n * This function assumes that instance.body is present.\n *\n * @param   Mixed  instance  Any options.body input\n */\nfunction extractContentType(body) {\n\tif (body === null) {\n\t\t// body is null\n\t\treturn null;\n\t} else if (typeof body === 'string') {\n\t\t// body is string\n\t\treturn 'text/plain;charset=UTF-8';\n\t} else if (isURLSearchParams(body)) {\n\t\t// body is a URLSearchParams\n\t\treturn 'application/x-www-form-urlencoded;charset=UTF-8';\n\t} else if (isBlob(body)) {\n\t\t// body is blob\n\t\treturn body.type || null;\n\t} else if (Buffer.isBuffer(body)) {\n\t\t// body is buffer\n\t\treturn null;\n\t} else if (Object.prototype.toString.call(body) === '[object ArrayBuffer]') {\n\t\t// body is ArrayBuffer\n\t\treturn null;\n\t} else if (ArrayBuffer.isView(body)) {\n\t\t// body is ArrayBufferView\n\t\treturn null;\n\t} else if (typeof body.getBoundary === 'function') {\n\t\t// detect form data input from form-data module\n\t\treturn `multipart/form-data;boundary=${body.getBoundary()}`;\n\t} else if (body instanceof Stream) {\n\t\t// body is stream\n\t\t// can't really do much about this\n\t\treturn null;\n\t} else {\n\t\t// Body constructor defaults other things to string\n\t\treturn 'text/plain;charset=UTF-8';\n\t}\n}\n\n/**\n * The Fetch Standard treats this as if \"total bytes\" is a property on the body.\n * For us, we have to explicitly get it with a function.\n *\n * ref: https://fetch.spec.whatwg.org/#concept-body-total-bytes\n *\n * @param   Body    instance   Instance of Body\n * @return  Number?            Number of bytes, or null if not possible\n */\nfunction getTotalBytes(instance) {\n\tconst body = instance.body;\n\n\n\tif (body === null) {\n\t\t// body is null\n\t\treturn 0;\n\t} else if (isBlob(body)) {\n\t\treturn body.size;\n\t} else if (Buffer.isBuffer(body)) {\n\t\t// body is buffer\n\t\treturn body.length;\n\t} else if (body && typeof body.getLengthSync === 'function') {\n\t\t// detect form data input from form-data module\n\t\tif (body._lengthRetrievers && body._lengthRetrievers.length == 0 || // 1.x\n\t\tbody.hasKnownLength && body.hasKnownLength()) {\n\t\t\t// 2.x\n\t\t\treturn body.getLengthSync();\n\t\t}\n\t\treturn null;\n\t} else {\n\t\t// body is stream\n\t\treturn null;\n\t}\n}\n\n/**\n * Write a Body to a Node.js WritableStream (e.g. http.Request) object.\n *\n * @param   Body    instance   Instance of Body\n * @return  Void\n */\nfunction writeToStream(dest, instance) {\n\tconst body = instance.body;\n\n\n\tif (body === null) {\n\t\t// body is null\n\t\tdest.end();\n\t} else if (isBlob(body)) {\n\t\tbody.stream().pipe(dest);\n\t} else if (Buffer.isBuffer(body)) {\n\t\t// body is buffer\n\t\tdest.write(body);\n\t\tdest.end();\n\t} else {\n\t\t// body is stream\n\t\tbody.pipe(dest);\n\t}\n}\n\n// expose Promise\nBody.Promise = global.Promise;\n\n/**\n * headers.js\n *\n * Headers class offers convenient helpers\n */\n\nconst invalidTokenRegex = /[^\\^_`a-zA-Z\\-0-9!#$%&'*+.|~]/;\nconst invalidHeaderCharRegex = /[^\\t\\x20-\\x7e\\x80-\\xff]/;\n\nfunction validateName(name) {\n\tname = `${name}`;\n\tif (invalidTokenRegex.test(name) || name === '') {\n\t\tthrow new TypeError(`${name} is not a legal HTTP header name`);\n\t}\n}\n\nfunction validateValue(value) {\n\tvalue = `${value}`;\n\tif (invalidHeaderCharRegex.test(value)) {\n\t\tthrow new TypeError(`${value} is not a legal HTTP header value`);\n\t}\n}\n\n/**\n * Find the key in the map object given a header name.\n *\n * Returns undefined if not found.\n *\n * @param   String  name  Header name\n * @return  String|Undefined\n */\nfunction find(map, name) {\n\tname = name.toLowerCase();\n\tfor (const key in map) {\n\t\tif (key.toLowerCase() === name) {\n\t\t\treturn key;\n\t\t}\n\t}\n\treturn undefined;\n}\n\nconst MAP = Symbol('map');\nclass Headers {\n\t/**\n  * Headers class\n  *\n  * @param   Object  headers  Response headers\n  * @return  Void\n  */\n\tconstructor() {\n\t\tlet init = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : undefined;\n\n\t\tthis[MAP] = Object.create(null);\n\n\t\tif (init instanceof Headers) {\n\t\t\tconst rawHeaders = init.raw();\n\t\t\tconst headerNames = Object.keys(rawHeaders);\n\n\t\t\tfor (const headerName of headerNames) {\n\t\t\t\tfor (const value of rawHeaders[headerName]) {\n\t\t\t\t\tthis.append(headerName, value);\n\t\t\t\t}\n\t\t\t}\n\n\t\t\treturn;\n\t\t}\n\n\t\t// We don't worry about converting prop to ByteString here as append()\n\t\t// will handle it.\n\t\tif (init == null) ; else if (typeof init === 'object') {\n\t\t\tconst method = init[Symbol.iterator];\n\t\t\tif (method != null) {\n\t\t\t\tif (typeof method !== 'function') {\n\t\t\t\t\tthrow new TypeError('Header pairs must be iterable');\n\t\t\t\t}\n\n\t\t\t\t// sequence<sequence<ByteString>>\n\t\t\t\t// Note: per spec we have to first exhaust the lists then process them\n\t\t\t\tconst pairs = [];\n\t\t\t\tfor (const pair of init) {\n\t\t\t\t\tif (typeof pair !== 'object' || typeof pair[Symbol.iterator] !== 'function') {\n\t\t\t\t\t\tthrow new TypeError('Each header pair must be iterable');\n\t\t\t\t\t}\n\t\t\t\t\tpairs.push(Array.from(pair));\n\t\t\t\t}\n\n\t\t\t\tfor (const pair of pairs) {\n\t\t\t\t\tif (pair.length !== 2) {\n\t\t\t\t\t\tthrow new TypeError('Each header pair must be a name/value tuple');\n\t\t\t\t\t}\n\t\t\t\t\tthis.append(pair[0], pair[1]);\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\t// record<ByteString, ByteString>\n\t\t\t\tfor (const key of Object.keys(init)) {\n\t\t\t\t\tconst value = init[key];\n\t\t\t\t\tthis.append(key, value);\n\t\t\t\t}\n\t\t\t}\n\t\t} else {\n\t\t\tthrow new TypeError('Provided initializer must be an object');\n\t\t}\n\t}\n\n\t/**\n  * Return combined header value given name\n  *\n  * @param   String  name  Header name\n  * @return  Mixed\n  */\n\tget(name) {\n\t\tname = `${name}`;\n\t\tvalidateName(name);\n\t\tconst key = find(this[MAP], name);\n\t\tif (key === undefined) {\n\t\t\treturn null;\n\t\t}\n\n\t\treturn this[MAP][key].join(', ');\n\t}\n\n\t/**\n  * Iterate over all headers\n  *\n  * @param   Function  callback  Executed for each item with parameters (value, name, thisArg)\n  * @param   Boolean   thisArg   `this` context for callback function\n  * @return  Void\n  */\n\tforEach(callback) {\n\t\tlet thisArg = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : undefined;\n\n\t\tlet pairs = getHeaders(this);\n\t\tlet i = 0;\n\t\twhile (i < pairs.length) {\n\t\t\tvar _pairs$i = pairs[i];\n\t\t\tconst name = _pairs$i[0],\n\t\t\t      value = _pairs$i[1];\n\n\t\t\tcallback.call(thisArg, value, name, this);\n\t\t\tpairs = getHeaders(this);\n\t\t\ti++;\n\t\t}\n\t}\n\n\t/**\n  * Overwrite header values given name\n  *\n  * @param   String  name   Header name\n  * @param   String  value  Header value\n  * @return  Void\n  */\n\tset(name, value) {\n\t\tname = `${name}`;\n\t\tvalue = `${value}`;\n\t\tvalidateName(name);\n\t\tvalidateValue(value);\n\t\tconst key = find(this[MAP], name);\n\t\tthis[MAP][key !== undefined ? key : name] = [value];\n\t}\n\n\t/**\n  * Append a value onto existing header\n  *\n  * @param   String  name   Header name\n  * @param   String  value  Header value\n  * @return  Void\n  */\n\tappend(name, value) {\n\t\tname = `${name}`;\n\t\tvalue = `${value}`;\n\t\tvalidateName(name);\n\t\tvalidateValue(value);\n\t\tconst key = find(this[MAP], name);\n\t\tif (key !== undefined) {\n\t\t\tthis[MAP][key].push(value);\n\t\t} else {\n\t\t\tthis[MAP][name] = [value];\n\t\t}\n\t}\n\n\t/**\n  * Check for header name existence\n  *\n  * @param   String   name  Header name\n  * @return  Boolean\n  */\n\thas(name) {\n\t\tname = `${name}`;\n\t\tvalidateName(name);\n\t\treturn find(this[MAP], name) !== undefined;\n\t}\n\n\t/**\n  * Delete all header values given name\n  *\n  * @param   String  name  Header name\n  * @return  Void\n  */\n\tdelete(name) {\n\t\tname = `${name}`;\n\t\tvalidateName(name);\n\t\tconst key = find(this[MAP], name);\n\t\tif (key !== undefined) {\n\t\t\tdelete this[MAP][key];\n\t\t}\n\t}\n\n\t/**\n  * Return raw headers (non-spec api)\n  *\n  * @return  Object\n  */\n\traw() {\n\t\treturn this[MAP];\n\t}\n\n\t/**\n  * Get an iterator on keys.\n  *\n  * @return  Iterator\n  */\n\tkeys() {\n\t\treturn createHeadersIterator(this, 'key');\n\t}\n\n\t/**\n  * Get an iterator on values.\n  *\n  * @return  Iterator\n  */\n\tvalues() {\n\t\treturn createHeadersIterator(this, 'value');\n\t}\n\n\t/**\n  * Get an iterator on entries.\n  *\n  * This is the default iterator of the Headers object.\n  *\n  * @return  Iterator\n  */\n\t[Symbol.iterator]() {\n\t\treturn createHeadersIterator(this, 'key+value');\n\t}\n}\nHeaders.prototype.entries = Headers.prototype[Symbol.iterator];\n\nObject.defineProperty(Headers.prototype, Symbol.toStringTag, {\n\tvalue: 'Headers',\n\twritable: false,\n\tenumerable: false,\n\tconfigurable: true\n});\n\nObject.defineProperties(Headers.prototype, {\n\tget: { enumerable: true },\n\tforEach: { enumerable: true },\n\tset: { enumerable: true },\n\tappend: { enumerable: true },\n\thas: { enumerable: true },\n\tdelete: { enumerable: true },\n\tkeys: { enumerable: true },\n\tvalues: { enumerable: true },\n\tentries: { enumerable: true }\n});\n\nfunction getHeaders(headers) {\n\tlet kind = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : 'key+value';\n\n\tconst keys = Object.keys(headers[MAP]).sort();\n\treturn keys.map(kind === 'key' ? function (k) {\n\t\treturn k.toLowerCase();\n\t} : kind === 'value' ? function (k) {\n\t\treturn headers[MAP][k].join(', ');\n\t} : function (k) {\n\t\treturn [k.toLowerCase(), headers[MAP][k].join(', ')];\n\t});\n}\n\nconst INTERNAL = Symbol('internal');\n\nfunction createHeadersIterator(target, kind) {\n\tconst iterator = Object.create(HeadersIteratorPrototype);\n\titerator[INTERNAL] = {\n\t\ttarget,\n\t\tkind,\n\t\tindex: 0\n\t};\n\treturn iterator;\n}\n\nconst HeadersIteratorPrototype = Object.setPrototypeOf({\n\tnext() {\n\t\t// istanbul ignore if\n\t\tif (!this || Object.getPrototypeOf(this) !== HeadersIteratorPrototype) {\n\t\t\tthrow new TypeError('Value of `this` is not a HeadersIterator');\n\t\t}\n\n\t\tvar _INTERNAL = this[INTERNAL];\n\t\tconst target = _INTERNAL.target,\n\t\t      kind = _INTERNAL.kind,\n\t\t      index = _INTERNAL.index;\n\n\t\tconst values = getHeaders(target, kind);\n\t\tconst len = values.length;\n\t\tif (index >= len) {\n\t\t\treturn {\n\t\t\t\tvalue: undefined,\n\t\t\t\tdone: true\n\t\t\t};\n\t\t}\n\n\t\tthis[INTERNAL].index = index + 1;\n\n\t\treturn {\n\t\t\tvalue: values[index],\n\t\t\tdone: false\n\t\t};\n\t}\n}, Object.getPrototypeOf(Object.getPrototypeOf([][Symbol.iterator]())));\n\nObject.defineProperty(HeadersIteratorPrototype, Symbol.toStringTag, {\n\tvalue: 'HeadersIterator',\n\twritable: false,\n\tenumerable: false,\n\tconfigurable: true\n});\n\n/**\n * Export the Headers object in a form that Node.js can consume.\n *\n * @param   Headers  headers\n * @return  Object\n */\nfunction exportNodeCompatibleHeaders(headers) {\n\tconst obj = Object.assign({ __proto__: null }, headers[MAP]);\n\n\t// http.request() only supports string as Host header. This hack makes\n\t// specifying custom Host header possible.\n\tconst hostHeaderKey = find(headers[MAP], 'Host');\n\tif (hostHeaderKey !== undefined) {\n\t\tobj[hostHeaderKey] = obj[hostHeaderKey][0];\n\t}\n\n\treturn obj;\n}\n\n/**\n * Create a Headers object from an object of headers, ignoring those that do\n * not conform to HTTP grammar productions.\n *\n * @param   Object  obj  Object of headers\n * @return  Headers\n */\nfunction createHeadersLenient(obj) {\n\tconst headers = new Headers();\n\tfor (const name of Object.keys(obj)) {\n\t\tif (invalidTokenRegex.test(name)) {\n\t\t\tcontinue;\n\t\t}\n\t\tif (Array.isArray(obj[name])) {\n\t\t\tfor (const val of obj[name]) {\n\t\t\t\tif (invalidHeaderCharRegex.test(val)) {\n\t\t\t\t\tcontinue;\n\t\t\t\t}\n\t\t\t\tif (headers[MAP][name] === undefined) {\n\t\t\t\t\theaders[MAP][name] = [val];\n\t\t\t\t} else {\n\t\t\t\t\theaders[MAP][name].push(val);\n\t\t\t\t}\n\t\t\t}\n\t\t} else if (!invalidHeaderCharRegex.test(obj[name])) {\n\t\t\theaders[MAP][name] = [obj[name]];\n\t\t}\n\t}\n\treturn headers;\n}\n\nconst INTERNALS$1 = Symbol('Response internals');\n\n// fix an issue where \"STATUS_CODES\" aren't a named export for node <10\nconst STATUS_CODES = http.STATUS_CODES;\n\n/**\n * Response class\n *\n * @param   Stream  body  Readable stream\n * @param   Object  opts  Response options\n * @return  Void\n */\nclass Response {\n\tconstructor() {\n\t\tlet body = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null;\n\t\tlet opts = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n\t\tBody.call(this, body, opts);\n\n\t\tconst status = opts.status || 200;\n\t\tconst headers = new Headers(opts.headers);\n\n\t\tif (body != null && !headers.has('Content-Type')) {\n\t\t\tconst contentType = extractContentType(body);\n\t\t\tif (contentType) {\n\t\t\t\theaders.append('Content-Type', contentType);\n\t\t\t}\n\t\t}\n\n\t\tthis[INTERNALS$1] = {\n\t\t\turl: opts.url,\n\t\t\tstatus,\n\t\t\tstatusText: opts.statusText || STATUS_CODES[status],\n\t\t\theaders,\n\t\t\tcounter: opts.counter\n\t\t};\n\t}\n\n\tget url() {\n\t\treturn this[INTERNALS$1].url || '';\n\t}\n\n\tget status() {\n\t\treturn this[INTERNALS$1].status;\n\t}\n\n\t/**\n  * Convenience property representing if the request ended normally\n  */\n\tget ok() {\n\t\treturn this[INTERNALS$1].status >= 200 && this[INTERNALS$1].status < 300;\n\t}\n\n\tget redirected() {\n\t\treturn this[INTERNALS$1].counter > 0;\n\t}\n\n\tget statusText() {\n\t\treturn this[INTERNALS$1].statusText;\n\t}\n\n\tget headers() {\n\t\treturn this[INTERNALS$1].headers;\n\t}\n\n\t/**\n  * Clone this response\n  *\n  * @return  Response\n  */\n\tclone() {\n\t\treturn new Response(clone(this), {\n\t\t\turl: this.url,\n\t\t\tstatus: this.status,\n\t\t\tstatusText: this.statusText,\n\t\t\theaders: this.headers,\n\t\t\tok: this.ok,\n\t\t\tredirected: this.redirected\n\t\t});\n\t}\n}\n\nBody.mixIn(Response.prototype);\n\nObject.defineProperties(Response.prototype, {\n\turl: { enumerable: true },\n\tstatus: { enumerable: true },\n\tok: { enumerable: true },\n\tredirected: { enumerable: true },\n\tstatusText: { enumerable: true },\n\theaders: { enumerable: true },\n\tclone: { enumerable: true }\n});\n\nObject.defineProperty(Response.prototype, Symbol.toStringTag, {\n\tvalue: 'Response',\n\twritable: false,\n\tenumerable: false,\n\tconfigurable: true\n});\n\nconst INTERNALS$2 = Symbol('Request internals');\n\n// fix an issue where \"format\", \"parse\" aren't a named export for node <10\nconst parse_url = Url.parse;\nconst format_url = Url.format;\n\nconst streamDestructionSupported = 'destroy' in Stream.Readable.prototype;\n\n/**\n * Check if a value is an instance of Request.\n *\n * @param   Mixed   input\n * @return  Boolean\n */\nfunction isRequest(input) {\n\treturn typeof input === 'object' && typeof input[INTERNALS$2] === 'object';\n}\n\nfunction isAbortSignal(signal) {\n\tconst proto = signal && typeof signal === 'object' && Object.getPrototypeOf(signal);\n\treturn !!(proto && proto.constructor.name === 'AbortSignal');\n}\n\n/**\n * Request class\n *\n * @param   Mixed   input  Url or Request instance\n * @param   Object  init   Custom options\n * @return  Void\n */\nclass Request {\n\tconstructor(input) {\n\t\tlet init = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n\t\tlet parsedURL;\n\n\t\t// normalize input\n\t\tif (!isRequest(input)) {\n\t\t\tif (input && input.href) {\n\t\t\t\t// in order to support Node.js' Url objects; though WHATWG's URL objects\n\t\t\t\t// will fall into this branch also (since their `toString()` will return\n\t\t\t\t// `href` property anyway)\n\t\t\t\tparsedURL = parse_url(input.href);\n\t\t\t} else {\n\t\t\t\t// coerce input to a string before attempting to parse\n\t\t\t\tparsedURL = parse_url(`${input}`);\n\t\t\t}\n\t\t\tinput = {};\n\t\t} else {\n\t\t\tparsedURL = parse_url(input.url);\n\t\t}\n\n\t\tlet method = init.method || input.method || 'GET';\n\t\tmethod = method.toUpperCase();\n\n\t\tif ((init.body != null || isRequest(input) && input.body !== null) && (method === 'GET' || method === 'HEAD')) {\n\t\t\tthrow new TypeError('Request with GET/HEAD method cannot have body');\n\t\t}\n\n\t\tlet inputBody = init.body != null ? init.body : isRequest(input) && input.body !== null ? clone(input) : null;\n\n\t\tBody.call(this, inputBody, {\n\t\t\ttimeout: init.timeout || input.timeout || 0,\n\t\t\tsize: init.size || input.size || 0\n\t\t});\n\n\t\tconst headers = new Headers(init.headers || input.headers || {});\n\n\t\tif (inputBody != null && !headers.has('Content-Type')) {\n\t\t\tconst contentType = extractContentType(inputBody);\n\t\t\tif (contentType) {\n\t\t\t\theaders.append('Content-Type', contentType);\n\t\t\t}\n\t\t}\n\n\t\tlet signal = isRequest(input) ? input.signal : null;\n\t\tif ('signal' in init) signal = init.signal;\n\n\t\tif (signal != null && !isAbortSignal(signal)) {\n\t\t\tthrow new TypeError('Expected signal to be an instanceof AbortSignal');\n\t\t}\n\n\t\tthis[INTERNALS$2] = {\n\t\t\tmethod,\n\t\t\tredirect: init.redirect || input.redirect || 'follow',\n\t\t\theaders,\n\t\t\tparsedURL,\n\t\t\tsignal\n\t\t};\n\n\t\t// node-fetch-only options\n\t\tthis.follow = init.follow !== undefined ? init.follow : input.follow !== undefined ? input.follow : 20;\n\t\tthis.compress = init.compress !== undefined ? init.compress : input.compress !== undefined ? input.compress : true;\n\t\tthis.counter = init.counter || input.counter || 0;\n\t\tthis.agent = init.agent || input.agent;\n\t}\n\n\tget method() {\n\t\treturn this[INTERNALS$2].method;\n\t}\n\n\tget url() {\n\t\treturn format_url(this[INTERNALS$2].parsedURL);\n\t}\n\n\tget headers() {\n\t\treturn this[INTERNALS$2].headers;\n\t}\n\n\tget redirect() {\n\t\treturn this[INTERNALS$2].redirect;\n\t}\n\n\tget signal() {\n\t\treturn this[INTERNALS$2].signal;\n\t}\n\n\t/**\n  * Clone this request\n  *\n  * @return  Request\n  */\n\tclone() {\n\t\treturn new Request(this);\n\t}\n}\n\nBody.mixIn(Request.prototype);\n\nObject.defineProperty(Request.prototype, Symbol.toStringTag, {\n\tvalue: 'Request',\n\twritable: false,\n\tenumerable: false,\n\tconfigurable: true\n});\n\nObject.defineProperties(Request.prototype, {\n\tmethod: { enumerable: true },\n\turl: { enumerable: true },\n\theaders: { enumerable: true },\n\tredirect: { enumerable: true },\n\tclone: { enumerable: true },\n\tsignal: { enumerable: true }\n});\n\n/**\n * Convert a Request to Node.js http request options.\n *\n * @param   Request  A Request instance\n * @return  Object   The options object to be passed to http.request\n */\nfunction getNodeRequestOptions(request) {\n\tconst parsedURL = request[INTERNALS$2].parsedURL;\n\tconst headers = new Headers(request[INTERNALS$2].headers);\n\n\t// fetch step 1.3\n\tif (!headers.has('Accept')) {\n\t\theaders.set('Accept', '*/*');\n\t}\n\n\t// Basic fetch\n\tif (!parsedURL.protocol || !parsedURL.hostname) {\n\t\tthrow new TypeError('Only absolute URLs are supported');\n\t}\n\n\tif (!/^https?:$/.test(parsedURL.protocol)) {\n\t\tthrow new TypeError('Only HTTP(S) protocols are supported');\n\t}\n\n\tif (request.signal && request.body instanceof Stream.Readable && !streamDestructionSupported) {\n\t\tthrow new Error('Cancellation of streamed requests with AbortSignal is not supported in node < 8');\n\t}\n\n\t// HTTP-network-or-cache fetch steps 2.4-2.7\n\tlet contentLengthValue = null;\n\tif (request.body == null && /^(POST|PUT)$/i.test(request.method)) {\n\t\tcontentLengthValue = '0';\n\t}\n\tif (request.body != null) {\n\t\tconst totalBytes = getTotalBytes(request);\n\t\tif (typeof totalBytes === 'number') {\n\t\t\tcontentLengthValue = String(totalBytes);\n\t\t}\n\t}\n\tif (contentLengthValue) {\n\t\theaders.set('Content-Length', contentLengthValue);\n\t}\n\n\t// HTTP-network-or-cache fetch step 2.11\n\tif (!headers.has('User-Agent')) {\n\t\theaders.set('User-Agent', 'node-fetch/1.0 (+https://github.com/bitinn/node-fetch)');\n\t}\n\n\t// HTTP-network-or-cache fetch step 2.15\n\tif (request.compress && !headers.has('Accept-Encoding')) {\n\t\theaders.set('Accept-Encoding', 'gzip,deflate');\n\t}\n\n\tlet agent = request.agent;\n\tif (typeof agent === 'function') {\n\t\tagent = agent(parsedURL);\n\t}\n\n\tif (!headers.has('Connection') && !agent) {\n\t\theaders.set('Connection', 'close');\n\t}\n\n\t// HTTP-network fetch step 4.2\n\t// chunked encoding is handled by Node.js\n\n\treturn Object.assign({}, parsedURL, {\n\t\tmethod: request.method,\n\t\theaders: exportNodeCompatibleHeaders(headers),\n\t\tagent\n\t});\n}\n\n/**\n * abort-error.js\n *\n * AbortError interface for cancelled requests\n */\n\n/**\n * Create AbortError instance\n *\n * @param   String      message      Error message for human\n * @return  AbortError\n */\nfunction AbortError(message) {\n  Error.call(this, message);\n\n  this.type = 'aborted';\n  this.message = message;\n\n  // hide custom error implementation details from end-users\n  Error.captureStackTrace(this, this.constructor);\n}\n\nAbortError.prototype = Object.create(Error.prototype);\nAbortError.prototype.constructor = AbortError;\nAbortError.prototype.name = 'AbortError';\n\n// fix an issue where \"PassThrough\", \"resolve\" aren't a named export for node <10\nconst PassThrough$1 = Stream.PassThrough;\nconst resolve_url = Url.resolve;\n\n/**\n * Fetch function\n *\n * @param   Mixed    url   Absolute url or Request instance\n * @param   Object   opts  Fetch options\n * @return  Promise\n */\nfunction fetch(url, opts) {\n\n\t// allow custom promise\n\tif (!fetch.Promise) {\n\t\tthrow new Error('native promise missing, set fetch.Promise to your favorite alternative');\n\t}\n\n\tBody.Promise = fetch.Promise;\n\n\t// wrap http.request into fetch\n\treturn new fetch.Promise(function (resolve, reject) {\n\t\t// build request object\n\t\tconst request = new Request(url, opts);\n\t\tconst options = getNodeRequestOptions(request);\n\n\t\tconst send = (options.protocol === 'https:' ? https : http).request;\n\t\tconst signal = request.signal;\n\n\t\tlet response = null;\n\n\t\tconst abort = function abort() {\n\t\t\tlet error = new AbortError('The user aborted a request.');\n\t\t\treject(error);\n\t\t\tif (request.body && request.body instanceof Stream.Readable) {\n\t\t\t\trequest.body.destroy(error);\n\t\t\t}\n\t\t\tif (!response || !response.body) return;\n\t\t\tresponse.body.emit('error', error);\n\t\t};\n\n\t\tif (signal && signal.aborted) {\n\t\t\tabort();\n\t\t\treturn;\n\t\t}\n\n\t\tconst abortAndFinalize = function abortAndFinalize() {\n\t\t\tabort();\n\t\t\tfinalize();\n\t\t};\n\n\t\t// send request\n\t\tconst req = send(options);\n\t\tlet reqTimeout;\n\n\t\tif (signal) {\n\t\t\tsignal.addEventListener('abort', abortAndFinalize);\n\t\t}\n\n\t\tfunction finalize() {\n\t\t\treq.abort();\n\t\t\tif (signal) signal.removeEventListener('abort', abortAndFinalize);\n\t\t\tclearTimeout(reqTimeout);\n\t\t}\n\n\t\tif (request.timeout) {\n\t\t\treq.once('socket', function (socket) {\n\t\t\t\treqTimeout = setTimeout(function () {\n\t\t\t\t\treject(new FetchError(`network timeout at: ${request.url}`, 'request-timeout'));\n\t\t\t\t\tfinalize();\n\t\t\t\t}, request.timeout);\n\t\t\t});\n\t\t}\n\n\t\treq.on('error', function (err) {\n\t\t\treject(new FetchError(`request to ${request.url} failed, reason: ${err.message}`, 'system', err));\n\t\t\tfinalize();\n\t\t});\n\n\t\treq.on('response', function (res) {\n\t\t\tclearTimeout(reqTimeout);\n\n\t\t\tconst headers = createHeadersLenient(res.headers);\n\n\t\t\t// HTTP fetch step 5\n\t\t\tif (fetch.isRedirect(res.statusCode)) {\n\t\t\t\t// HTTP fetch step 5.2\n\t\t\t\tconst location = headers.get('Location');\n\n\t\t\t\t// HTTP fetch step 5.3\n\t\t\t\tconst locationURL = location === null ? null : resolve_url(request.url, location);\n\n\t\t\t\t// HTTP fetch step 5.5\n\t\t\t\tswitch (request.redirect) {\n\t\t\t\t\tcase 'error':\n\t\t\t\t\t\treject(new FetchError(`uri requested responds with a redirect, redirect mode is set to error: ${request.url}`, 'no-redirect'));\n\t\t\t\t\t\tfinalize();\n\t\t\t\t\t\treturn;\n\t\t\t\t\tcase 'manual':\n\t\t\t\t\t\t// node-fetch-specific step: make manual redirect a bit easier to use by setting the Location header value to the resolved URL.\n\t\t\t\t\t\tif (locationURL !== null) {\n\t\t\t\t\t\t\t// handle corrupted header\n\t\t\t\t\t\t\ttry {\n\t\t\t\t\t\t\t\theaders.set('Location', locationURL);\n\t\t\t\t\t\t\t} catch (err) {\n\t\t\t\t\t\t\t\t// istanbul ignore next: nodejs server prevent invalid response headers, we can't test this through normal request\n\t\t\t\t\t\t\t\treject(err);\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tcase 'follow':\n\t\t\t\t\t\t// HTTP-redirect fetch step 2\n\t\t\t\t\t\tif (locationURL === null) {\n\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// HTTP-redirect fetch step 5\n\t\t\t\t\t\tif (request.counter >= request.follow) {\n\t\t\t\t\t\t\treject(new FetchError(`maximum redirect reached at: ${request.url}`, 'max-redirect'));\n\t\t\t\t\t\t\tfinalize();\n\t\t\t\t\t\t\treturn;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// HTTP-redirect fetch step 6 (counter increment)\n\t\t\t\t\t\t// Create a new Request object.\n\t\t\t\t\t\tconst requestOpts = {\n\t\t\t\t\t\t\theaders: new Headers(request.headers),\n\t\t\t\t\t\t\tfollow: request.follow,\n\t\t\t\t\t\t\tcounter: request.counter + 1,\n\t\t\t\t\t\t\tagent: request.agent,\n\t\t\t\t\t\t\tcompress: request.compress,\n\t\t\t\t\t\t\tmethod: request.method,\n\t\t\t\t\t\t\tbody: request.body,\n\t\t\t\t\t\t\tsignal: request.signal,\n\t\t\t\t\t\t\ttimeout: request.timeout,\n\t\t\t\t\t\t\tsize: request.size\n\t\t\t\t\t\t};\n\n\t\t\t\t\t\t// HTTP-redirect fetch step 9\n\t\t\t\t\t\tif (res.statusCode !== 303 && request.body && getTotalBytes(request) === null) {\n\t\t\t\t\t\t\treject(new FetchError('Cannot follow redirect with body being a readable stream', 'unsupported-redirect'));\n\t\t\t\t\t\t\tfinalize();\n\t\t\t\t\t\t\treturn;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// HTTP-redirect fetch step 11\n\t\t\t\t\t\tif (res.statusCode === 303 || (res.statusCode === 301 || res.statusCode === 302) && request.method === 'POST') {\n\t\t\t\t\t\t\trequestOpts.method = 'GET';\n\t\t\t\t\t\t\trequestOpts.body = undefined;\n\t\t\t\t\t\t\trequestOpts.headers.delete('content-length');\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// HTTP-redirect fetch step 15\n\t\t\t\t\t\tresolve(fetch(new Request(locationURL, requestOpts)));\n\t\t\t\t\t\tfinalize();\n\t\t\t\t\t\treturn;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// prepare response\n\t\t\tres.once('end', function () {\n\t\t\t\tif (signal) signal.removeEventListener('abort', abortAndFinalize);\n\t\t\t});\n\t\t\tlet body = res.pipe(new PassThrough$1());\n\n\t\t\tconst response_options = {\n\t\t\t\turl: request.url,\n\t\t\t\tstatus: res.statusCode,\n\t\t\t\tstatusText: res.statusMessage,\n\t\t\t\theaders: headers,\n\t\t\t\tsize: request.size,\n\t\t\t\ttimeout: request.timeout,\n\t\t\t\tcounter: request.counter\n\t\t\t};\n\n\t\t\t// HTTP-network fetch step 12.1.1.3\n\t\t\tconst codings = headers.get('Content-Encoding');\n\n\t\t\t// HTTP-network fetch step 12.1.1.4: handle content codings\n\n\t\t\t// in following scenarios we ignore compression support\n\t\t\t// 1. compression support is disabled\n\t\t\t// 2. HEAD request\n\t\t\t// 3. no Content-Encoding header\n\t\t\t// 4. no content response (204)\n\t\t\t// 5. content not modified response (304)\n\t\t\tif (!request.compress || request.method === 'HEAD' || codings === null || res.statusCode === 204 || res.statusCode === 304) {\n\t\t\t\tresponse = new Response(body, response_options);\n\t\t\t\tresolve(response);\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// For Node v6+\n\t\t\t// Be less strict when decoding compressed responses, since sometimes\n\t\t\t// servers send slightly invalid responses that are still accepted\n\t\t\t// by common browsers.\n\t\t\t// Always using Z_SYNC_FLUSH is what cURL does.\n\t\t\tconst zlibOptions = {\n\t\t\t\tflush: zlib.Z_SYNC_FLUSH,\n\t\t\t\tfinishFlush: zlib.Z_SYNC_FLUSH\n\t\t\t};\n\n\t\t\t// for gzip\n\t\t\tif (codings == 'gzip' || codings == 'x-gzip') {\n\t\t\t\tbody = body.pipe(zlib.createGunzip(zlibOptions));\n\t\t\t\tresponse = new Response(body, response_options);\n\t\t\t\tresolve(response);\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// for deflate\n\t\t\tif (codings == 'deflate' || codings == 'x-deflate') {\n\t\t\t\t// handle the infamous raw deflate response from old servers\n\t\t\t\t// a hack for old IIS and Apache servers\n\t\t\t\tconst raw = res.pipe(new PassThrough$1());\n\t\t\t\traw.once('data', function (chunk) {\n\t\t\t\t\t// see http://stackoverflow.com/questions/37519828\n\t\t\t\t\tif ((chunk[0] & 0x0F) === 0x08) {\n\t\t\t\t\t\tbody = body.pipe(zlib.createInflate());\n\t\t\t\t\t} else {\n\t\t\t\t\t\tbody = body.pipe(zlib.createInflateRaw());\n\t\t\t\t\t}\n\t\t\t\t\tresponse = new Response(body, response_options);\n\t\t\t\t\tresolve(response);\n\t\t\t\t});\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// for br\n\t\t\tif (codings == 'br' && typeof zlib.createBrotliDecompress === 'function') {\n\t\t\t\tbody = body.pipe(zlib.createBrotliDecompress());\n\t\t\t\tresponse = new Response(body, response_options);\n\t\t\t\tresolve(response);\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// otherwise, use response as-is\n\t\t\tresponse = new Response(body, response_options);\n\t\t\tresolve(response);\n\t\t});\n\n\t\twriteToStream(req, request);\n\t});\n}\n/**\n * Redirect code matching\n *\n * @param   Number   code  Status code\n * @return  Boolean\n */\nfetch.isRedirect = function (code) {\n\treturn code === 301 || code === 302 || code === 303 || code === 307 || code === 308;\n};\n\n// expose Promise\nfetch.Promise = global.Promise;\n\nexport default fetch;\nexport { Headers, Request, Response, FetchError };\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage {\n    constructor(backend, dataMover) {\n        this.backend = backend;\n        this.dataMover = dataMover;\n        this.data = new WeakMap();\n        this.dataIdsCount = 0;\n    }\n    get(dataId) {\n        if (!this.data.has(dataId)) {\n            this.dataMover.moveData(this.backend, dataId);\n        }\n        return this.data.get(dataId);\n    }\n    set(dataId, value) {\n        this.dataIdsCount++;\n        this.data.set(dataId, value);\n    }\n    has(dataId) {\n        return this.data.has(dataId);\n    }\n    delete(dataId) {\n        this.dataIdsCount--;\n        return this.data.delete(dataId);\n    }\n    numDataIds() {\n        return this.dataIdsCount;\n    }\n}\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend {\n    time(f) {\n        return notYetImplemented('time');\n    }\n    read(dataId) {\n        return notYetImplemented('read');\n    }\n    readSync(dataId) {\n        return notYetImplemented('readSync');\n    }\n    numDataIds() {\n        return notYetImplemented('numDataIds');\n    }\n    disposeData(dataId) {\n        return notYetImplemented('disposeData');\n    }\n    write(values, shape, dtype) {\n        return notYetImplemented('write');\n    }\n    move(dataId, values, shape, dtype) {\n        return notYetImplemented('move');\n    }\n    memory() {\n        return notYetImplemented('memory');\n    }\n    /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n    floatPrecision() {\n        return notYetImplemented('floatPrecision');\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        return notYetImplemented('batchMatMul');\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedBatchMatMul');\n    }\n    slice(x, begin, size) {\n        return notYetImplemented('slice');\n    }\n    stridedSlice(x, begin, end, strides) {\n        return notYetImplemented('stridedSlice');\n    }\n    unstack(x, axis) {\n        return notYetImplemented('unstack');\n    }\n    reverse(a, axis) {\n        return notYetImplemented('reverse');\n    }\n    concat(tensors, axis) {\n        return notYetImplemented('concat');\n    }\n    neg(a) {\n        return notYetImplemented('neg');\n    }\n    add(a, b) {\n        return notYetImplemented('add');\n    }\n    addN(tensors) {\n        return notYetImplemented('addN');\n    }\n    subtract(a, b) {\n        return notYetImplemented('subtract');\n    }\n    multiply(a, b) {\n        return notYetImplemented('multiply');\n    }\n    realDivide(a, b) {\n        return notYetImplemented('realDivide');\n    }\n    floorDiv(a, b) {\n        return notYetImplemented('floorDiv');\n    }\n    sum(x, axes) {\n        return notYetImplemented('sum');\n    }\n    prod(x, axes) {\n        return notYetImplemented('prod');\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        return notYetImplemented('unsortedSegmentSum');\n    }\n    argMin(x, axis) {\n        return notYetImplemented('argMin');\n    }\n    argMax(x, axis) {\n        return notYetImplemented('argMax');\n    }\n    equal(a, b) {\n        return notYetImplemented('equal');\n    }\n    notEqual(a, b) {\n        return notYetImplemented('notEqual');\n    }\n    less(a, b) {\n        return notYetImplemented('less');\n    }\n    lessEqual(a, b) {\n        return notYetImplemented('lessEqual');\n    }\n    greater(a, b) {\n        return notYetImplemented('greater');\n    }\n    greaterEqual(a, b) {\n        return notYetImplemented('greaterEqual');\n    }\n    logicalNot(a) {\n        return notYetImplemented('logicalNot');\n    }\n    logicalAnd(a, b) {\n        return notYetImplemented('logicalAnd');\n    }\n    logicalOr(a, b) {\n        return notYetImplemented('logicalOr');\n    }\n    where(condition) {\n        return notYetImplemented('where');\n    }\n    select(condition, a, b) {\n        return notYetImplemented('select');\n    }\n    topk(x, k, sorted) {\n        return notYetImplemented('topk');\n    }\n    min(x, axes) {\n        return notYetImplemented('min');\n    }\n    minimum(a, b) {\n        return notYetImplemented('minimum');\n    }\n    mod(a, b) {\n        return notYetImplemented('mod');\n    }\n    max(x, axes) {\n        return notYetImplemented('max');\n    }\n    maximum(a, b) {\n        return notYetImplemented('maximum');\n    }\n    all(x, axes) {\n        return notYetImplemented('all');\n    }\n    any(x, axes) {\n        return notYetImplemented('any');\n    }\n    squaredDifference(a, b) {\n        return notYetImplemented('squaredDifference');\n    }\n    ceil(x) {\n        return notYetImplemented('ceil');\n    }\n    floor(x) {\n        return notYetImplemented('floor');\n    }\n    round(x) {\n        return notYetImplemented('round');\n    }\n    sign(x) {\n        return notYetImplemented('sign');\n    }\n    isNaN(x) {\n        return notYetImplemented('isNaN');\n    }\n    isInf(x) {\n        return notYetImplemented('isInf');\n    }\n    isFinite(x) {\n        return notYetImplemented('isFinite');\n    }\n    pow(a, b) {\n        return notYetImplemented('pow');\n    }\n    exp(x) {\n        return notYetImplemented('exp');\n    }\n    expm1(x) {\n        return notYetImplemented('expm1');\n    }\n    softmax(x, dim) {\n        return notYetImplemented('softmax');\n    }\n    log(x) {\n        return notYetImplemented('log');\n    }\n    log1p(x) {\n        return notYetImplemented('log1p');\n    }\n    sqrt(x) {\n        return notYetImplemented('sqrt');\n    }\n    rsqrt(x) {\n        return notYetImplemented('rsqrt');\n    }\n    square(x) {\n        return notYetImplemented('square');\n    }\n    reciprocal(x) {\n        return notYetImplemented('reciprocal');\n    }\n    relu(x) {\n        return notYetImplemented('relu');\n    }\n    relu6(x) {\n        return notYetImplemented('relu6');\n    }\n    prelu(x, a) {\n        return notYetImplemented('prelu');\n    }\n    elu(x) {\n        return notYetImplemented('elu');\n    }\n    eluDer(dy, y) {\n        return notYetImplemented('eluDer');\n    }\n    selu(x) {\n        return notYetImplemented('selu');\n    }\n    int(x) {\n        return notYetImplemented('int');\n    }\n    clip(x, min, max) {\n        return notYetImplemented('clip');\n    }\n    abs(x) {\n        return notYetImplemented('abs');\n    }\n    complexAbs(x) {\n        return notYetImplemented('complexAbs');\n    }\n    sigmoid(x) {\n        return notYetImplemented('sigmoid');\n    }\n    softplus(x) {\n        return notYetImplemented('softplus');\n    }\n    sin(x) {\n        return notYetImplemented('sin');\n    }\n    cos(x) {\n        return notYetImplemented('cos');\n    }\n    tan(x) {\n        return notYetImplemented('tan');\n    }\n    asin(x) {\n        return notYetImplemented('asin');\n    }\n    acos(x) {\n        return notYetImplemented('acos');\n    }\n    atan(x) {\n        return notYetImplemented('atan');\n    }\n    atan2(a, b) {\n        return notYetImplemented('atan2');\n    }\n    sinh(x) {\n        return notYetImplemented('sinh');\n    }\n    cosh(x) {\n        return notYetImplemented('cosh');\n    }\n    tanh(x) {\n        return notYetImplemented('tanh');\n    }\n    asinh(x) {\n        return notYetImplemented('asinh');\n    }\n    acosh(x) {\n        return notYetImplemented('acosh');\n    }\n    atanh(x) {\n        return notYetImplemented('atanh');\n    }\n    erf(x) {\n        return notYetImplemented('erf');\n    }\n    step(x, alpha) {\n        return notYetImplemented('step');\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedConv2d');\n    }\n    conv2d(x, filter, convInfo) {\n        return notYetImplemented('conv2d');\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        return notYetImplemented('conv2dDerInput');\n    }\n    conv2dDerFilter(x, dY, convInfo) {\n        return notYetImplemented('conv2dDerFilter');\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedDepthwiseConv2D');\n    }\n    depthwiseConv2D(input, filter, convInfo) {\n        return notYetImplemented('depthwiseConv2D');\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        return notYetImplemented('depthwiseConv2DDerInput');\n    }\n    depthwiseConv2DDerFilter(x, dY, convInfo) {\n        return notYetImplemented('depthwiseConv2DDerFilter');\n    }\n    conv3d(x, filter, convInfo) {\n        return notYetImplemented('conv3d');\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        return notYetImplemented('conv3dDerInput');\n    }\n    conv3dDerFilter(x, dY, convInfo) {\n        return notYetImplemented('conv3dDerFilter');\n    }\n    maxPool(x, convInfo) {\n        return notYetImplemented('maxPool');\n    }\n    maxPoolBackprop(dy, x, y, convInfo) {\n        return notYetImplemented('maxPoolBackprop');\n    }\n    avgPool(x, convInfo) {\n        return notYetImplemented('avgPool');\n    }\n    avgPoolBackprop(dy, x, convInfo) {\n        return notYetImplemented('avgPoolBackprop');\n    }\n    avgPool3d(x, convInfo) {\n        return notYetImplemented('avgPool3d');\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        return notYetImplemented('avgPool3dBackprop');\n    }\n    maxPool3d(x, convInfo) {\n        return notYetImplemented('maxPool3d');\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        return notYetImplemented('maxPool3dBackprop');\n    }\n    reshape(x, shape) {\n        return notYetImplemented('reshape');\n    }\n    cast(x, dtype) {\n        return notYetImplemented('cast');\n    }\n    tile(x, reps) {\n        return notYetImplemented('tile');\n    }\n    pad(x, paddings, constantValue) {\n        return notYetImplemented('pad');\n    }\n    transpose(x, perm) {\n        return notYetImplemented('transpose');\n    }\n    gather(x, indices, axis) {\n        return notYetImplemented('gather');\n    }\n    gatherND(x, indices) {\n        return notYetImplemented('gatherND');\n    }\n    scatterND(indices, updates, shape) {\n        return notYetImplemented('scatterND');\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        return notYetImplemented('batchToSpaceND');\n    }\n    spaceToBatchND(x, blockShape, paddings) {\n        return notYetImplemented('spaceToBatchND');\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        return notYetImplemented('resizeBilinear');\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        return notYetImplemented('resizeBilinearBackprop');\n    }\n    resizeNearestNeighbor(x, newHEight, newWidth, alignCorners) {\n        return notYetImplemented('resizeNearestNeighbor');\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        return notYetImplemented('resizeNearestNeighborBackprop');\n    }\n    batchNorm(x, mean, variance, offset, scale, varianceEpsilon) {\n        return notYetImplemented('batchNorm');\n    }\n    localResponseNormalization4D(x, radius, bias, alpha, beta) {\n        return notYetImplemented('localResponseNormalization4D');\n    }\n    LRNGrad(dy, inputImage, outputImage, radius, bias, alpha, beta) {\n        return notYetImplemented('LRNGrad');\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        return notYetImplemented('multinomial');\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        return notYetImplemented('oneHot');\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        return notYetImplemented('cumsum');\n    }\n    nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n        return notYetImplemented('nonMaxSuppression');\n    }\n    fft(x) {\n        return notYetImplemented('fft');\n    }\n    ifft(x) {\n        return notYetImplemented('ifft');\n    }\n    complex(real, imag) {\n        return notYetImplemented('complex');\n    }\n    real(input) {\n        return notYetImplemented('real');\n    }\n    imag(input) {\n        return notYetImplemented('imag');\n    }\n    cropAndResize(image, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        return notYetImplemented('cropAndResize');\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        return notYetImplemented('depthToSpace');\n    }\n    // Aligns with the \"SplitV\" kernel in TensorFlow.\n    split(value, sizeSplits, axis) {\n        return notYetImplemented('split');\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        return notYetImplemented('sparseToDense');\n    }\n    diag(x) {\n        return notYetImplemented('diag');\n    }\n    fill(shape, value, dtype) {\n        return notYetImplemented('fill');\n    }\n    onesLike(x) {\n        return notYetImplemented('onesLike');\n    }\n    zerosLike(x) {\n        return notYetImplemented('zerosLike');\n    }\n    linspace(start, stop, num) {\n        return notYetImplemented('linspace');\n    }\n    dispose() {\n        return notYetImplemented('dispose');\n    }\n}\nfunction notYetImplemented(kernelName) {\n    throw new Error(`'${kernelName}' not yet implemented or not found in the registry. ` +\n        `This kernel may not be supported by the tfjs backend you have chosen`);\n}\n//# sourceMappingURL=backend.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.\nconst TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';\n/**\n * The environment contains evaluated flags as well as the registered platform.\n * This is always used as a global singleton and can be retrieved with\n * `tf.env()`.\n *\n * @doc {heading: 'Environment'}\n */\nexport class Environment {\n    // tslint:disable-next-line: no-any\n    constructor(global) {\n        this.global = global;\n        this.flags = {};\n        this.flagRegistry = {};\n        this.urlFlags = {};\n        this.populateURLFlags();\n    }\n    setPlatform(platformName, platform) {\n        if (this.platform != null) {\n            console.warn(`Platform ${this.platformName} has already been set. ` +\n                `Overwriting the platform with ${platform}.`);\n        }\n        this.platformName = platformName;\n        this.platform = platform;\n    }\n    registerFlag(flagName, evaluationFn, setHook) {\n        this.flagRegistry[flagName] = { evaluationFn, setHook };\n        // Override the flag value from the URL. This has to happen here because the\n        // environment is initialized before flags get registered.\n        if (this.urlFlags[flagName] != null) {\n            const flagValue = this.urlFlags[flagName];\n            console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);\n            this.set(flagName, flagValue);\n        }\n    }\n    async getAsync(flagName) {\n        if (flagName in this.flags) {\n            return this.flags[flagName];\n        }\n        this.flags[flagName] = await this.evaluateFlag(flagName);\n        return this.flags[flagName];\n    }\n    get(flagName) {\n        if (flagName in this.flags) {\n            return this.flags[flagName];\n        }\n        const flagValue = this.evaluateFlag(flagName);\n        if (flagValue instanceof Promise) {\n            throw new Error(`Flag ${flagName} cannot be synchronously evaluated. ` +\n                `Please use getAsync() instead.`);\n        }\n        this.flags[flagName] = flagValue;\n        return this.flags[flagName];\n    }\n    getNumber(flagName) {\n        return this.get(flagName);\n    }\n    getBool(flagName) {\n        return this.get(flagName);\n    }\n    getFlags() {\n        return this.flags;\n    }\n    // For backwards compatibility.\n    get features() {\n        return this.flags;\n    }\n    set(flagName, value) {\n        if (this.flagRegistry[flagName] == null) {\n            throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);\n        }\n        this.flags[flagName] = value;\n        if (this.flagRegistry[flagName].setHook != null) {\n            this.flagRegistry[flagName].setHook(value);\n        }\n    }\n    evaluateFlag(flagName) {\n        if (this.flagRegistry[flagName] == null) {\n            throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);\n        }\n        return this.flagRegistry[flagName].evaluationFn();\n    }\n    setFlags(flags) {\n        this.flags = Object.assign({}, flags);\n    }\n    reset() {\n        this.flags = {};\n        this.urlFlags = {};\n        this.populateURLFlags();\n    }\n    populateURLFlags() {\n        if (typeof this.global === 'undefined' ||\n            typeof this.global.location === 'undefined' ||\n            typeof this.global.location.search === 'undefined') {\n            return;\n        }\n        const urlParams = getQueryParams(this.global.location.search);\n        if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n            const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');\n            keyValues.forEach(keyValue => {\n                const [key, value] = keyValue.split(':');\n                this.urlFlags[key] = parseValue(key, value);\n            });\n        }\n    }\n}\nexport function getQueryParams(queryString) {\n    const params = {};\n    queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n        decodeParam(params, t[0], t[1]);\n        return t.join('=');\n    });\n    return params;\n}\nfunction decodeParam(params, name, value) {\n    params[decodeURIComponent(name)] = decodeURIComponent(value || '');\n}\nfunction parseValue(flagName, value) {\n    value = value.toLowerCase();\n    if (value === 'true' || value === 'false') {\n        return value === 'true';\n    }\n    else if (`${+value}` === value) {\n        return +value;\n    }\n    throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);\n}\n/**\n * Returns the current environment (a global singleton).\n *\n * The environment object contains the evaluated feature values as well as the\n * active platform.\n *\n * @doc {heading: 'Environment'}\n */\nexport function env() {\n    return ENV;\n}\nexport let ENV = null;\nexport function setEnvironmentGlobal(environment) {\n    ENV = environment;\n}\n//# sourceMappingURL=environment.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Note that the identifier globalNameSpace is scoped to this module, but will\n// always resolve to the same global object regardless of how the module is\n// resolved.\n// tslint:disable-next-line:no-any\nlet globalNameSpace;\n// tslint:disable-next-line:no-any\nexport function getGlobalNamespace() {\n    if (globalNameSpace == null) {\n        // tslint:disable-next-line:no-any\n        let ns;\n        if (typeof (window) !== 'undefined') {\n            ns = window;\n        }\n        else if (typeof (global) !== 'undefined') {\n            ns = global;\n        }\n        else if (typeof (process) !== 'undefined') {\n            ns = process;\n        }\n        else if (typeof (self) !== 'undefined') {\n            ns = self;\n        }\n        else {\n            throw new Error('Could not find a global object');\n        }\n        globalNameSpace = ns;\n    }\n    return globalNameSpace;\n}\n// tslint:disable-next-line:no-any\nfunction getGlobalMap() {\n    const ns = getGlobalNamespace();\n    if (ns._tfGlobals == null) {\n        ns._tfGlobals = new Map();\n    }\n    return ns._tfGlobals;\n}\n/**\n * Returns a globally accessible 'singleton' object.\n *\n * @param key the name of the object\n * @param init a function to initialize to initialize this object\n *             the first time it is fetched.\n */\nexport function getGlobal(key, init) {\n    const globalMap = getGlobalMap();\n    if (globalMap.has(key)) {\n        return globalMap.get(key);\n    }\n    else {\n        const singleton = init();\n        globalMap.set(key, singleton);\n        return globalMap.get(key);\n    }\n}\n//# sourceMappingURL=global_util.js.map", "export const Abs = 'Abs';\nexport const Acos = 'Acos';\nexport const Acosh = 'Acosh';\nexport const Add = 'Add';\nexport const AddN = 'AddN';\nexport const All = 'All';\nexport const Any = 'Any';\nexport const ArgMax = 'ArgMax';\nexport const ArgMin = 'ArgMin';\nexport const Asin = 'Asin';\nexport const Asinh = 'Asinh';\nexport const Atan = 'Atan';\nexport const Atanh = 'Atanh';\nexport const Atan2 = 'Atan2';\nexport const AvgPool = 'AvgPool';\nexport const AvgPoolBackprop = 'AvgPoolBackprop';\nexport const AvgPool3D = 'AvgPool3D';\nexport const AvgPool3DBackprop = 'AvgPool3DBackprop';\nexport const BatchMatMul = 'BatchMatMul';\nexport const BatchToSpaceND = 'BatchToSpaceND';\nexport const BroadcastTo = 'BroadcastTo';\nexport const Cast = 'Cast';\nexport const Ceil = 'Ceil';\nexport const ClipByValue = 'ClipByValue';\nexport const Complex = 'Complex';\nexport const Concat = 'Concat';\nexport const Conv2D = 'Conv2D';\nexport const Conv2DBackpropFilter = 'Conv2DBackpropFilter';\nexport const Conv2DBackpropInput = 'Conv2DBackpropInput';\nexport const Conv3D = 'Conv3D';\nexport const Conv3DBackpropFilterV2 = 'Conv3DBackpropFilterV2';\nexport const Conv3DBackpropInputV2 = 'Conv3DBackpropInputV2';\nexport const Cos = 'Cos';\nexport const Cosh = 'Cosh';\nexport const Cumsum = 'Cumsum';\nexport const CropAndResize = 'CropAndResize';\nexport const DepthToSpace = 'DepthToSpace';\nexport const DepthwiseConv2dNative = 'DepthwiseConv2dNative';\nexport const DepthwiseConv2dNativeBackpropFilter = 'DepthwiseConv2dNativeBackpropFilter';\nexport const DepthwiseConv2dNativeBackpropInput = 'DepthwiseConv2dNativeBackpropInput';\nexport const Diag = 'Diag';\nexport const Dilation2D = 'Dilation2D';\nexport const Dilation2DBackpropInput = 'Dilation2DBackpropInput';\nexport const Dilation2DBackpropFilter = 'Dilation2DBackpropFilter';\nexport const Div = 'Div';\nexport const Elu = 'Elu';\nexport const EluGrad = 'EluGrad';\nexport const Erf = 'Erf';\nexport const Equal = 'Equal';\nexport const Exp = 'Exp';\nexport const Expm1 = 'Expm1';\nexport const FFT = 'FFT';\nexport const Fill = 'Fill';\nexport const FlipLeftRight = 'FlipLeftRight';\nexport const Floor = 'Floor';\nexport const FloorDiv = 'FloorDiv';\nexport const FusedBatchNorm = 'FusedBatchNorm';\nexport const GatherV2 = 'GatherV2';\nexport const GatherNd = 'GatherNd';\nexport const Greater = 'Greater';\nexport const GreaterEqual = 'GreaterEqual';\nexport const Identity = 'Identity';\nexport const IFFT = 'IFFT';\nexport const Imag = 'Imag';\nexport const IsFinite = 'IsFinite';\nexport const IsInf = 'IsInf';\nexport const IsNan = 'IsNan';\nexport const Less = 'Less';\nexport const LessEqual = 'LessEqual';\nexport const LinSpace = 'LinSpace';\nexport const Log = 'Log';\nexport const Log1p = 'Log1p';\nexport const LogicalAnd = 'LogicalAnd';\nexport const LogicalNot = 'LogicalNot';\nexport const LogicalOr = 'LogicalOr';\nexport const LogSoftmax = 'LogSoftmax';\nexport const LRN = 'LRN';\nexport const LRNBackprop = 'LRNBackprop';\nexport const Max = 'Max';\nexport const Maximum = 'Maximum';\nexport const MaxPool = 'MaxPool';\nexport const MaxPoolBackprop = 'MaxPoolBackprop';\nexport const MaxPool3D = 'MaxPool3D';\nexport const MaxPool3DBackprop = 'MaxPool3DBackprop';\nexport const MaxPoolWithArgmax = 'MaxPoolWithArgmax';\nexport const Mean = 'Mean';\nexport const Min = 'Min';\nexport const Minimum = 'Minimum';\nexport const Mod = 'Mod';\nexport const Multiply = 'Multiply';\nexport const Negate = 'Negate';\nexport const NotEqual = 'NotEqual';\nexport const NonMaxSuppressionV3 = 'NonMaxSuppressionV3';\nexport const NonMaxSuppressionV4 = 'NonMaxSuppressionV4';\nexport const NonMaxSuppressionV5 = 'NonMaxSuppressionV5';\nexport const OnesLike = 'OnesLike';\nexport const OneHot = 'OneHot';\nexport const PadV2 = 'PadV2';\nexport const Pool = 'Pool';\nexport const Pow = 'Pow';\nexport const Prelu = 'Prelu';\nexport const Prod = 'Prod';\nexport const Range = 'Range';\nexport const Real = 'Real';\nexport const Reciprocal = 'Reciprocal';\nexport const Relu = 'Relu';\nexport const Reshape = 'Reshape';\nexport const ResizeNearestNeighbor = 'ResizeNearestNeighbor';\nexport const ResizeNearestNeighborGrad = 'ResizeNearestNeighborGrad';\nexport const ResizeBilinear = 'ResizeBilinear';\nexport const ResizeBilinearGrad = 'ResizeBilinearGrad';\nexport const Relu6 = 'Relu6';\nexport const Reverse = 'Reverse';\nexport const Round = 'Round';\nexport const Rsqrt = 'Rsqrt';\nexport const ScatterNd = 'ScatterNd';\nexport const SelectV2 = 'SelectV2';\nexport const Selu = 'Selu';\nexport const Slice = 'Slice';\nexport const Sin = 'Sin';\nexport const Sinh = 'Sinh';\nexport const Sign = 'Sign';\nexport const Sigmoid = 'Sigmoid';\nexport const Softplus = 'Softplus';\nexport const Sqrt = 'Sqrt';\nexport const Sum = 'Sum';\nexport const SpaceToBatchND = 'SpaceToBatchND';\nexport const SplitV = 'SplitV';\nexport const Softmax = 'Softmax';\nexport const SquaredDifference = 'SquaredDifference';\nexport const Square = 'Square';\nexport const Sub = 'Sub';\nexport const SparseToDense = 'SparseToDense';\nexport const StridedSlice = 'StridedSlice';\nexport const Tan = 'Tan';\nexport const Tanh = 'Tanh';\nexport const Tile = 'Tile';\nexport const TopK = 'TopK';\nexport const Transpose = 'Transpose';\nexport const Unique = 'Unique';\nexport const Unpack = 'Unpack';\nexport const UnsortedSegmentSum = 'UnsortedSegmentSum';\nexport const ZerosLike = 'ZerosLike';\n/**\n * TensorFlow.js-only kernels\n */\nexport const Step = 'Step';\nexport const FromPixels = 'FromPixels';\nexport const RotateWithOffset = 'RotateWithOffset';\nexport const _FusedMatMul = '_FusedMatMul';\nexport const FusedConv2D = 'FusedConv2D';\nexport const FusedDepthwiseConv2D = 'FusedDepthwiseConv2D';\n//# sourceMappingURL=kernel_names.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nimport { getGlobal } from './global_util';\nconst kernelRegistry = getGlobal('kernelRegistry', () => new Map());\nconst gradRegistry = getGlobal('gradRegistry', () => new Map());\n/**\n * Returns the kernel function (code) associated with the provided names.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n */\nexport function getKernel(kernelName, backendName) {\n    const key = makeKey(kernelName, backendName);\n    return kernelRegistry.get(key);\n}\n/**\n * Returns the registered gradient info associated with the provided kernel.\n * @param kernelName The official TF kernel name.\n */\nexport function getGradient(kernelName) {\n    return gradRegistry.get(kernelName);\n}\nexport function getKernelsForBackend(backendName) {\n    const it = kernelRegistry.entries();\n    const result = [];\n    while (true) {\n        const { done, value } = it.next();\n        if (done) {\n            break;\n        }\n        const [key, config] = value;\n        const [backend,] = key.split('_');\n        if (backend === backendName) {\n            result.push(config);\n        }\n    }\n    return result;\n}\n/**\n * Registers the function (forward pass) for the kernel in a global registry.\n *\n * @param config A config object with the following properties:\n * - `kernelName` The official name of the kernel.\n * - `backendName` The official name of the backend.\n * - `kernelFunc` The function to run during the forward pass of the kernel.\n * - `setupFunc` Optional. Gets called once, after the backend initializes.\n * - `disposeFunc` Optional. Gets called once, right before the backend is\n * disposed.\n */\nexport function registerKernel(config) {\n    const { kernelName, backendName } = config;\n    const key = makeKey(kernelName, backendName);\n    if (kernelRegistry.has(key)) {\n        console.warn(`The kernel '${kernelName}' for backend ` +\n            `'${backendName}' is already registered`);\n    }\n    kernelRegistry.set(key, config);\n}\n/**\n * Registers a gradient function for a given kernel in the global registry,\n * to be used during the back-propagation of that kernel.\n *\n * @param config An object with the following properties:\n * - `kernelName` The name of the kernel that the gradient function is for.\n * - `gradFunc` The function to run during back-propagation.\n */\nexport function registerGradient(config) {\n    const { kernelName } = config;\n    if (gradRegistry.has(kernelName)) {\n        // TODO (yassogba) after 3.0 assess whether we need to keep this gated\n        // to debug mode.\n        if (env().getBool('DEBUG')) {\n            console.warn(`Overriding the gradient for '${kernelName}'`);\n        }\n    }\n    gradRegistry.set(kernelName, config);\n}\n/**\n * Removes the kernel function from the registry.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n *\n */\nexport function unregisterKernel(kernelName, backendName) {\n    const key = makeKey(kernelName, backendName);\n    if (!kernelRegistry.has(key)) {\n        throw new Error(`The kernel '${kernelName}' for backend ` +\n            `'${backendName}' is not registered`);\n    }\n    kernelRegistry.delete(key);\n}\n/** Removes the registered gradient from the global registry. */\nexport function unregisterGradient(kernelName) {\n    if (!gradRegistry.has(kernelName)) {\n        throw new Error(`The gradient '${kernelName}' for backend is not registered`);\n    }\n    gradRegistry.delete(kernelName);\n}\n/**\n * Finds kernels that have already been registered to a backend and re-registers\n * them for a new backend. Useful for registering custom backends.\n * @param registeredBackendName Already registered backend.\n * @param newBackendName New backend.\n */\nexport function copyRegisteredKernels(registeredBackendName, newBackendName) {\n    const kernels = getKernelsForBackend(registeredBackendName);\n    kernels.forEach(kernelConfig => {\n        const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName });\n        registerKernel(newKernelConfig);\n    });\n}\nfunction makeKey(kernelName, backendName) {\n    return `${backendName}_${kernelName}`;\n}\n//# sourceMappingURL=kernel_registry.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\n/**\n * Shuffles the array in-place using Fisher-Yates algorithm.\n *\n * ```js\n * const a = [1, 2, 3, 4, 5];\n * tf.util.shuffle(a);\n * console.log(a);\n * ```\n *\n * @param array The array to shuffle in-place.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\n// tslint:disable-next-line:no-any\nexport function shuffle(array) {\n    let counter = array.length;\n    let temp = 0;\n    let index = 0;\n    // While there are elements in the array\n    while (counter > 0) {\n        // Pick a random index\n        index = (Math.random() * counter) | 0;\n        // Decrease counter by 1\n        counter--;\n        // And swap the last element with it\n        temp = array[counter];\n        array[counter] = array[index];\n        array[index] = temp;\n    }\n}\n/** Clamps a value to a specified range. */\nexport function clamp(min, x, max) {\n    return Math.max(min, Math.min(x, max));\n}\nexport function nearestLargerEven(val) {\n    return val % 2 === 0 ? val : val + 1;\n}\nexport function sum(arr) {\n    let sum = 0;\n    for (let i = 0; i < arr.length; i++) {\n        sum += arr[i];\n    }\n    return sum;\n}\n/**\n * Returns a sample from a uniform [a, b) distribution.\n *\n * @param a The minimum support (inclusive).\n * @param b The maximum support (exclusive).\n * @return A pseudorandom number on the half-open interval [a,b).\n */\nexport function randUniform(a, b) {\n    const r = Math.random();\n    return (b * r) + (1 - r) * a;\n}\n/** Returns the squared Euclidean distance between two vectors. */\nexport function distSquared(a, b) {\n    let result = 0;\n    for (let i = 0; i < a.length; i++) {\n        const diff = Number(a[i]) - Number(b[i]);\n        result += diff * diff;\n    }\n    return result;\n}\n/**\n * Asserts that the expression is true. Otherwise throws an error with the\n * provided message.\n *\n * ```js\n * const x = 2;\n * tf.util.assert(x === 2, 'x is not 2');\n * ```\n *\n * @param expr The expression to assert (as a boolean).\n * @param msg A function that returns the message to report when throwing an\n *     error. We use a function for performance reasons.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function assert(expr, msg) {\n    if (!expr) {\n        throw new Error(typeof msg === 'string' ? msg : msg());\n    }\n}\nexport function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = '') {\n    assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nexport function assertNonNull(a) {\n    assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);\n}\n// NOTE: We explicitly type out what T extends instead of any so that\n// util.flatten on a nested array of number doesn't try to infer T as a\n// number[][], causing us to explicitly type util.flatten<number>().\n/**\n *  Flattens an arbitrarily nested array.\n *\n * ```js\n * const a = [[1, 2], [3, 4], [5, [6, [7]]]];\n * const flat = tf.util.flatten(a);\n * console.log(flat);\n * ```\n *\n *  @param arr The nested array to flatten.\n *  @param result The destination array which holds the elements.\n *  @param skipTypedArray If true, avoids flattening the typed arrays. Defaults\n *      to false.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function flatten(arr, result = [], skipTypedArray = false) {\n    if (result == null) {\n        result = [];\n    }\n    if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {\n        for (let i = 0; i < arr.length; ++i) {\n            flatten(arr[i], result, skipTypedArray);\n        }\n    }\n    else {\n        result.push(arr);\n    }\n    return result;\n}\n/**\n * Returns the size (number of elements) of the tensor given its shape.\n *\n * ```js\n * const shape = [3, 4, 2];\n * const size = tf.util.sizeFromShape(shape);\n * console.log(size);\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function sizeFromShape(shape) {\n    if (shape.length === 0) {\n        // Scalar.\n        return 1;\n    }\n    let size = shape[0];\n    for (let i = 1; i < shape.length; i++) {\n        size *= shape[i];\n    }\n    return size;\n}\nexport function isScalarShape(shape) {\n    return shape.length === 0;\n}\nexport function arraysEqual(n1, n2) {\n    if (n1 === n2) {\n        return true;\n    }\n    if (n1 == null || n2 == null) {\n        return false;\n    }\n    if (n1.length !== n2.length) {\n        return false;\n    }\n    for (let i = 0; i < n1.length; i++) {\n        if (n1[i] !== n2[i]) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function isInt(a) {\n    return a % 1 === 0;\n}\nexport function tanh(x) {\n    // tslint:disable-next-line:no-any\n    if (Math.tanh != null) {\n        // tslint:disable-next-line:no-any\n        return Math.tanh(x);\n    }\n    if (x === Infinity) {\n        return 1;\n    }\n    else if (x === -Infinity) {\n        return -1;\n    }\n    else {\n        const e2x = Math.exp(2 * x);\n        return (e2x - 1) / (e2x + 1);\n    }\n}\nexport function sizeToSquarishShape(size) {\n    const width = Math.ceil(Math.sqrt(size));\n    return [width, Math.ceil(size / width)];\n}\n/**\n * Creates a new array with randomized indicies to a given quantity.\n *\n * ```js\n * const randomTen = tf.util.createShuffledIndices(10);\n * console.log(randomTen);\n * ```\n *\n * @param number Quantity of how many shuffled indicies to create.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function createShuffledIndices(n) {\n    const shuffledIndices = new Uint32Array(n);\n    for (let i = 0; i < n; ++i) {\n        shuffledIndices[i] = i;\n    }\n    shuffle(shuffledIndices);\n    return shuffledIndices;\n}\nexport function rightPad(a, size) {\n    if (size <= a.length) {\n        return a;\n    }\n    return a + ' '.repeat(size - a.length);\n}\nexport function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {\n    return new Promise((resolve, reject) => {\n        let tryCount = 0;\n        const tryFn = () => {\n            if (checkFn()) {\n                resolve();\n                return;\n            }\n            tryCount++;\n            const nextBackoff = delayFn(tryCount);\n            if (maxCounter != null && tryCount >= maxCounter) {\n                reject();\n                return;\n            }\n            setTimeout(tryFn, nextBackoff);\n        };\n        tryFn();\n    });\n}\n/**\n * Given the full size of the array and a shape that may contain -1 as the\n * implicit dimension, returns the inferred shape where -1 is replaced.\n * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].\n *\n * @param shape The shape, which may contain -1 in some dimension.\n * @param size The full size (number of elements) of the array.\n * @return The inferred shape where -1 is replaced with the inferred size.\n */\nexport function inferFromImplicitShape(shape, size) {\n    let shapeProd = 1;\n    let implicitIdx = -1;\n    for (let i = 0; i < shape.length; ++i) {\n        if (shape[i] >= 0) {\n            shapeProd *= shape[i];\n        }\n        else if (shape[i] === -1) {\n            if (implicitIdx !== -1) {\n                throw Error(`Shapes can only have 1 implicit size. ` +\n                    `Found -1 at dim ${implicitIdx} and dim ${i}`);\n            }\n            implicitIdx = i;\n        }\n        else if (shape[i] < 0) {\n            throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n        }\n    }\n    if (implicitIdx === -1) {\n        if (size > 0 && size !== shapeProd) {\n            throw Error(`Size(${size}) must match the product of shape ${shape}`);\n        }\n        return shape;\n    }\n    if (shapeProd === 0) {\n        throw Error(`Cannot infer the missing size in [${shape}] when ` +\n            `there are 0 elements`);\n    }\n    if (size % shapeProd !== 0) {\n        throw Error(`The implicit shape can't be a fractional number. ` +\n            `Got ${size} / ${shapeProd}`);\n    }\n    const newShape = shape.slice();\n    newShape[implicitIdx] = size / shapeProd;\n    return newShape;\n}\nexport function parseAxisParam(axis, shape) {\n    const rank = shape.length;\n    // Normalize input\n    axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n    // Check for valid range\n    assert(axis.every(ax => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but ` +\n        `got axis ${axis}`);\n    // Check for only integers\n    assert(axis.every(ax => isInt(ax)), () => `All values in axis param must be integers but ` +\n        `got axis ${axis}`);\n    // Handle negative axis.\n    return axis.map(a => a < 0 ? rank + a : a);\n}\n/** Reduces the shape by removing all dimensions of shape 1. */\nexport function squeezeShape(shape, axis) {\n    const newShape = [];\n    const keptDims = [];\n    const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;\n    const axes = (axis == null || isEmptyArray) ?\n        null :\n        parseAxisParam(axis, shape).sort();\n    let j = 0;\n    for (let i = 0; i < shape.length; ++i) {\n        if (axes != null) {\n            if (axes[j] === i && shape[i] !== 1) {\n                throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n            }\n            if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {\n                newShape.push(shape[i]);\n                keptDims.push(i);\n            }\n            if (axes[j] <= i) {\n                j++;\n            }\n        }\n        if (shape[i] !== 1) {\n            newShape.push(shape[i]);\n            keptDims.push(i);\n        }\n    }\n    return { newShape, keptDims };\n}\nexport function getTypedArrayFromDType(dtype, size) {\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    return values;\n}\nexport function getArrayFromDType(dtype, size) {\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else if (dtype === 'string') {\n        values = new Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    return values;\n}\nexport function checkConversionForErrors(vals, dtype) {\n    for (let i = 0; i < vals.length; i++) {\n        const num = vals[i];\n        if (isNaN(num) || !isFinite(num)) {\n            throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n        }\n    }\n}\n/** Returns true if the dtype is valid. */\nexport function isValidDtype(dtype) {\n    return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' ||\n        dtype === 'int32' || dtype === 'string';\n}\n/**\n * Returns true if the new type can't encode the old type without loss of\n * precision.\n */\nexport function hasEncodingLoss(oldType, newType) {\n    if (newType === 'complex64') {\n        return false;\n    }\n    if (newType === 'float32' && oldType !== 'complex64') {\n        return false;\n    }\n    if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {\n        return false;\n    }\n    if (newType === 'bool' && oldType === 'bool') {\n        return false;\n    }\n    return true;\n}\nexport function isTypedArray(a) {\n    return a instanceof Float32Array || a instanceof Int32Array ||\n        a instanceof Uint8Array;\n}\nexport function bytesPerElement(dtype) {\n    if (dtype === 'float32' || dtype === 'int32') {\n        return 4;\n    }\n    else if (dtype === 'complex64') {\n        return 8;\n    }\n    else if (dtype === 'bool') {\n        return 1;\n    }\n    else {\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n}\n/**\n * Returns the approximate number of bytes allocated in the string array - 2\n * bytes per character. Computing the exact bytes for a native string in JS is\n * not possible since it depends on the encoding of the html page that serves\n * the website.\n */\nexport function bytesFromStringArray(arr) {\n    if (arr == null) {\n        return 0;\n    }\n    let bytes = 0;\n    arr.forEach(x => bytes += x.length);\n    return bytes;\n}\n/** Returns true if the value is a string. */\nexport function isString(value) {\n    return typeof value === 'string' || value instanceof String;\n}\nexport function isBoolean(value) {\n    return typeof value === 'boolean';\n}\nexport function isNumber(value) {\n    return typeof value === 'number';\n}\nexport function inferDtype(values) {\n    if (Array.isArray(values)) {\n        return inferDtype(values[0]);\n    }\n    if (values instanceof Float32Array) {\n        return 'float32';\n    }\n    else if (values instanceof Int32Array || values instanceof Uint8Array) {\n        return 'int32';\n    }\n    else if (isNumber(values)) {\n        return 'float32';\n    }\n    else if (isString(values)) {\n        return 'string';\n    }\n    else if (isBoolean(values)) {\n        return 'bool';\n    }\n    return 'float32';\n}\nexport function isFunction(f) {\n    return !!(f && f.constructor && f.call && f.apply);\n}\nexport function nearestDivisor(size, start) {\n    for (let i = start; i < size; ++i) {\n        if (size % i === 0) {\n            return i;\n        }\n    }\n    return size;\n}\nexport function computeStrides(shape) {\n    const rank = shape.length;\n    if (rank < 2) {\n        return [];\n    }\n    // Last dimension has implicit stride of 1, thus having D-1 (instead of D)\n    // strides.\n    const strides = new Array(rank - 1);\n    strides[rank - 2] = shape[rank - 1];\n    for (let i = rank - 3; i >= 0; --i) {\n        strides[i] = strides[i + 1] * shape[i + 1];\n    }\n    return strides;\n}\n/**\n * Create typed array for scalar value. Used for storing in `DataStorage`.\n */\nexport function createScalarValue(value, dtype) {\n    if (dtype === 'string') {\n        return encodeString(value);\n    }\n    return toTypedArray([value], dtype);\n}\nexport function toTypedArray(a, dtype) {\n    if (dtype === 'string') {\n        throw new Error('Cannot convert a string[] to a TypedArray');\n    }\n    if (Array.isArray(a)) {\n        a = flatten(a);\n    }\n    if (env().getBool('DEBUG')) {\n        checkConversionForErrors(a, dtype);\n    }\n    if (noConversionNeeded(a, dtype)) {\n        return a;\n    }\n    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n        return new Float32Array(a);\n    }\n    else if (dtype === 'int32') {\n        return new Int32Array(a);\n    }\n    else if (dtype === 'bool') {\n        const bool = new Uint8Array(a.length);\n        for (let i = 0; i < bool.length; ++i) {\n            if (Math.round(a[i]) !== 0) {\n                bool[i] = 1;\n            }\n        }\n        return bool;\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\nfunction createNestedArray(offset, shape, a) {\n    const ret = new Array();\n    if (shape.length === 1) {\n        const d = shape[0];\n        for (let i = 0; i < d; i++) {\n            ret[i] = a[offset + i];\n        }\n    }\n    else {\n        const d = shape[0];\n        const rest = shape.slice(1);\n        const len = rest.reduce((acc, c) => acc * c);\n        for (let i = 0; i < d; i++) {\n            ret[i] = createNestedArray(offset + i * len, rest, a);\n        }\n    }\n    return ret;\n}\n// Provide a nested array of TypedArray in given shape.\nexport function toNestedArray(shape, a) {\n    if (shape.length === 0) {\n        // Scalar type should return a single number.\n        return a[0];\n    }\n    const size = shape.reduce((acc, c) => acc * c);\n    if (size === 0) {\n        // A tensor with shape zero should be turned into empty list.\n        return [];\n    }\n    if (size !== a.length) {\n        throw new Error(`[${shape}] does not match the input size ${a.length}.`);\n    }\n    return createNestedArray(0, shape, a);\n}\nfunction noConversionNeeded(a, dtype) {\n    return (a instanceof Float32Array && dtype === 'float32') ||\n        (a instanceof Int32Array && dtype === 'int32') ||\n        (a instanceof Uint8Array && dtype === 'bool');\n}\nexport function makeOnesTypedArray(size, dtype) {\n    const array = makeZerosTypedArray(size, dtype);\n    for (let i = 0; i < array.length; i++) {\n        array[i] = 1;\n    }\n    return array;\n}\nexport function makeZerosTypedArray(size, dtype) {\n    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n        return new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        return new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        return new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\n/**\n * Make nested `TypedArray` filled with zeros.\n * @param shape The shape information for the nested array.\n * @param dtype dtype of the array element.\n */\nexport function makeZerosNestedTypedArray(shape, dtype) {\n    const size = shape.reduce((prev, curr) => prev * curr, 1);\n    if (dtype == null || dtype === 'float32') {\n        return toNestedArray(shape, new Float32Array(size));\n    }\n    else if (dtype === 'int32') {\n        return toNestedArray(shape, new Int32Array(size));\n    }\n    else if (dtype === 'bool') {\n        return toNestedArray(shape, new Uint8Array(size));\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\n/**\n * Returns the current high-resolution time in milliseconds relative to an\n * arbitrary time in the past. It works across different platforms (node.js,\n * browsers).\n *\n * ```js\n * console.log(tf.util.now());\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function now() {\n    return env().platform.now();\n}\nexport function assertNonNegativeIntegerDimensions(shape) {\n    shape.forEach(dimSize => {\n        assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got ` +\n            `shape [${shape}].`);\n    });\n}\n/**\n * Returns a platform-specific implementation of\n * [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n *\n * If `fetch` is defined on the global object (`window`, `process`, etc.),\n * `tf.util.fetch` returns that function.\n *\n * If not, `tf.util.fetch` returns a platform-specific solution.\n *\n * ```js\n * const resource = await tf.util.fetch('https://unpkg.com/@tensorflow/tfjs');\n * // handle response\n * ```\n *\n * @doc {heading: 'Util'}\n */\nexport function fetch(path, requestInits) {\n    return env().platform.fetch(path, requestInits);\n}\n/**\n * Encodes the provided string into bytes using the provided encoding scheme.\n *\n * @param s The string to encode.\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function encodeString(s, encoding = 'utf-8') {\n    encoding = encoding || 'utf-8';\n    return env().platform.encode(s, encoding);\n}\n/**\n * Decodes the provided bytes into a string using the provided encoding scheme.\n * @param bytes The bytes to decode.\n *\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function decodeString(bytes, encoding = 'utf-8') {\n    encoding = encoding || 'utf-8';\n    return env().platform.decode(bytes, encoding);\n}\n/**\n * Computes flat index for a given location (multidimentionsal index) in a\n * Tensor/multidimensional array.\n *\n * @param locs Location in the tensor.\n * @param rank Rank of the tensor.\n * @param strides Tensor strides.\n */\nexport function locToIndex(locs, rank, strides) {\n    if (rank === 0) {\n        return 0;\n    }\n    else if (rank === 1) {\n        return locs[0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n        index += strides[i] * locs[i];\n    }\n    return index;\n}\n/**\n * Computes the location (multidimensional index) in a tensor/multidimentional\n * array for a given flat index.\n *\n * @param index Index in flat array.\n * @param rank Rank of tensor.\n * @param strides Strides of tensor.\n */\nexport function indexToLoc(index, rank, strides) {\n    if (rank === 0) {\n        return [];\n    }\n    else if (rank === 1) {\n        return [index];\n    }\n    const locs = new Array(rank);\n    for (let i = 0; i < locs.length - 1; ++i) {\n        locs[i] = Math.floor(index / strides[i]);\n        index -= locs[i] * strides[i];\n    }\n    locs[locs.length - 1] = index;\n    return locs;\n}\n//# sourceMappingURL=util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from './util';\nexport class Profiler {\n    constructor(backendTimer, logger) {\n        this.backendTimer = backendTimer;\n        this.logger = logger;\n        if (logger == null) {\n            this.logger = new Logger();\n        }\n    }\n    profileKernel(kernelName, inputs, f) {\n        let outputs;\n        const holdResultWrapperFn = () => {\n            outputs = f();\n        };\n        const timer = this.backendTimer.time(holdResultWrapperFn);\n        for (let i = 0; i < outputs.length; i++) {\n            const output = outputs[i];\n            // Dangling promise here because we don't want to propagate up\n            // asynchronicity.\n            output.data().then(tensorVals => {\n                checkComputationForErrors(tensorVals, output.dtype, kernelName);\n            });\n        }\n        const kernelProfile = {\n            kernelName,\n            outputs,\n            inputs,\n            timeMs: timer.then(timing => timing.kernelMs),\n            extraInfo: timer.then(timing => timing.getExtraProfileInfo != null ?\n                timing.getExtraProfileInfo() :\n                '')\n        };\n        return kernelProfile;\n    }\n    logKernelProfile(kernelProfile) {\n        const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile;\n        outputs.forEach(result => {\n            Promise.all([result.data(), timeMs, extraInfo]).then(valueContainer => {\n                this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);\n            });\n        });\n    }\n}\nexport function checkComputationForErrors(vals, dtype, kernelName) {\n    if (dtype !== 'float32') {\n        // Only floating point computations will generate NaN values\n        return false;\n    }\n    for (let i = 0; i < vals.length; i++) {\n        const num = vals[i];\n        if (isNaN(num) || !isFinite(num)) {\n            // Throwing custom exception so behavior is testable.\n            console.warn(`Found ${num} in the result of '${kernelName}'`);\n            return true;\n        }\n    }\n    return false;\n}\nexport class Logger {\n    logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {\n        const time = typeof timeMs === 'number' ? util.rightPad(`${timeMs}ms`, 9) :\n            timeMs['error'];\n        const paddedName = util.rightPad(name, 25);\n        const rank = result.rank;\n        const size = result.size;\n        const shape = util.rightPad(result.shape.toString(), 14);\n        let inputShapesDescription = '';\n        for (const name in inputs) {\n            const input = inputs[name];\n            if (input != null) {\n                // The input might be a non-tensor (e.g HTMLImageElement), in which case\n                // we claim the output shape as input shape.\n                const inputShape = input.shape || result.shape;\n                const inputRank = inputShape.length;\n                inputShapesDescription +=\n                    `${name}: ${inputRank}D ${inputRank > 0 ? inputShape : ''} `;\n            }\n        }\n        console.log(`%c${paddedName}\\t%c${time}\\t%c${rank}D ${shape}\\t%c${size}\\t%c${inputShapesDescription}\\t%c${extraInfo}`, 'font-weight:bold', 'color:red', 'color:blue', 'color: orange', 'color: green', 'color: steelblue');\n    }\n}\n//# sourceMappingURL=profiler.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from './util';\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nexport function getFilteredNodesXToY(tape, xs, y) {\n    // Forward pass to compute all the nodes and Tensors that are transitively a\n    // function of x.\n    const tensorsFromX = {};\n    const nodesFromX = {};\n    for (let i = 0; i < xs.length; i++) {\n        tensorsFromX[xs[i].id] = true;\n    }\n    for (let i = 0; i < tape.length; i++) {\n        const node = tape[i];\n        const nodeInputs = node.inputs;\n        for (const inputName in nodeInputs) {\n            const input = nodeInputs[inputName];\n            let anyInputFromX = false;\n            for (let j = 0; j < xs.length; j++) {\n                if (tensorsFromX[input.id]) {\n                    node.outputs.forEach(output => tensorsFromX[output.id] = true);\n                    anyInputFromX = true;\n                    nodesFromX[node.id] = true;\n                    break;\n                }\n            }\n            if (anyInputFromX) {\n                break;\n            }\n        }\n    }\n    // Backward pass to find all of the nodes and Tensors that lead to y.\n    const tensorsLeadToY = {};\n    tensorsLeadToY[y.id] = true;\n    const nodesToY = {};\n    for (let i = tape.length - 1; i >= 0; i--) {\n        const node = tape[i];\n        const nodeInputs = node.inputs;\n        // If any of the outputs lead to y, mark all of the inputs as leading to y.\n        for (let j = 0; j < node.outputs.length; j++) {\n            if (tensorsLeadToY[node.outputs[j].id]) {\n                for (const inputName in nodeInputs) {\n                    tensorsLeadToY[nodeInputs[inputName].id] = true;\n                    nodesToY[node.id] = true;\n                }\n                break;\n            }\n        }\n    }\n    // Return the paths that come from x and lead to y.\n    const filteredTape = [];\n    for (let i = 0; i < tape.length; i++) {\n        const node = tape[i];\n        if (nodesFromX[node.id] && nodesToY[node.id]) {\n            // Prune the inputs from the node that aren't a function of x.\n            const prunedInputs = {};\n            for (const inputName in node.inputs) {\n                const nodeInput = node.inputs[inputName];\n                if (tensorsFromX[nodeInput.id]) {\n                    prunedInputs[inputName] = nodeInput;\n                }\n            }\n            // Copy the node and overwrite inputsAndArgs to the pruned version.\n            const prunedNode = Object.assign({}, node);\n            prunedNode.inputs = prunedInputs;\n            prunedNode.outputs = node.outputs;\n            filteredTape.push(prunedNode);\n        }\n    }\n    return filteredTape;\n}\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nexport function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy, add) {\n    // Walk the tape backward and keep a map of Tensor to its gradient.\n    for (let i = filteredTape.length - 1; i >= 0; i--) {\n        const node = filteredTape[i];\n        const dys = [];\n        node.outputs.forEach(o => {\n            const gradTensor = tensorAccumulatedGradientMap[o.id];\n            if (gradTensor != null) {\n                dys.push(gradTensor);\n            }\n            else {\n                // This particular output is not in the back-propagation subgraph, so it\n                // does not affect the final output, thus we put null for its dy.\n                dys.push(null);\n            }\n        });\n        if (node.gradient == null) {\n            throw new Error(`Cannot compute gradient: gradient function not found ` +\n                `for ${node.kernelName}.`);\n        }\n        // Backprop dy through this node and accumulate gradients over the inputs.\n        const inputGradients = node.gradient(dys);\n        for (const inputName in node.inputs) {\n            if (!(inputName in inputGradients)) {\n                throw new Error(`Cannot backprop through input ${inputName}. ` +\n                    `Available gradients found: ${Object.keys(inputGradients)}.`);\n            }\n            // Call the gradient function.\n            const dx = tidy(() => inputGradients[inputName]());\n            if (dx.dtype !== 'float32') {\n                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n                    `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n            }\n            const x = node.inputs[inputName];\n            if (!util.arraysEqual(dx.shape, x.shape)) {\n                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n                    `'${inputName}' has shape '${dx.shape}', which does not match ` +\n                    `the shape of the input '${x.shape}'`);\n            }\n            if (tensorAccumulatedGradientMap[x.id] == null) {\n                tensorAccumulatedGradientMap[x.id] = dx;\n            }\n            else {\n                const curGradient = tensorAccumulatedGradientMap[x.id];\n                tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);\n                curGradient.dispose();\n            }\n        }\n    }\n}\n//# sourceMappingURL=tape.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { computeStrides, isString, rightPad, sizeFromShape } from './util';\n// Maximum number of values before we decide to show ellipsis.\nconst FORMAT_LIMIT_NUM_VALS = 20;\n// Number of first and last values to show when displaying a, b,...,y, z.\nconst FORMAT_NUM_FIRST_LAST_VALS = 3;\n// Number of significant digits to show.\nconst FORMAT_NUM_SIG_DIGITS = 7;\nexport function tensorToString(vals, shape, dtype, verbose) {\n    const strides = computeStrides(shape);\n    const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n    const rank = shape.length;\n    const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n    const lines = ['Tensor'];\n    if (verbose) {\n        lines.push(`  dtype: ${dtype}`);\n        lines.push(`  rank: ${rank}`);\n        lines.push(`  shape: [${shape}]`);\n        lines.push(`  values:`);\n    }\n    lines.push(valsLines.map(l => '    ' + l).join('\\n'));\n    return lines.join('\\n');\n}\nfunction computeMaxSizePerColumn(vals, shape, dtype, strides) {\n    const n = sizeFromShape(shape);\n    const numCols = strides[strides.length - 1];\n    const padPerCol = new Array(numCols).fill(0);\n    const rank = shape.length;\n    const valuesOrTuples = dtype === 'complex64' ? createComplexTuples(vals) : vals;\n    if (rank > 1) {\n        for (let row = 0; row < n / numCols; row++) {\n            const offset = row * numCols;\n            for (let j = 0; j < numCols; j++) {\n                padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);\n            }\n        }\n    }\n    return padPerCol;\n}\nfunction valToString(val, pad, dtype) {\n    let valStr;\n    if (Array.isArray(val)) {\n        valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +\n            `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n    }\n    else if (isString(val)) {\n        valStr = `'${val}'`;\n    }\n    else if (dtype === 'bool') {\n        valStr = boolNumToString(val);\n    }\n    else {\n        valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n    }\n    return rightPad(valStr, pad);\n}\nfunction boolNumToString(v) {\n    return v === 0 ? 'false' : 'true';\n}\nfunction subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {\n    const storagePerElement = dtype === 'complex64' ? 2 : 1;\n    const size = shape[0];\n    const rank = shape.length;\n    if (rank === 0) {\n        if (dtype === 'complex64') {\n            const complexTuple = createComplexTuples(vals);\n            return [valToString(complexTuple[0], 0, dtype)];\n        }\n        if (dtype === 'bool') {\n            return [boolNumToString(vals[0])];\n        }\n        return [vals[0].toString()];\n    }\n    if (rank === 1) {\n        if (size > FORMAT_LIMIT_NUM_VALS) {\n            const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n            let firstVals = Array.from(vals.slice(0, firstValsSize));\n            let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));\n            if (dtype === 'complex64') {\n                firstVals = createComplexTuples(firstVals);\n                lastVals = createComplexTuples(lastVals);\n            }\n            return [\n                '[' +\n                    firstVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n                        .join(', ') +\n                    ', ..., ' +\n                    lastVals\n                        .map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype))\n                        .join(', ') +\n                    ']'\n            ];\n        }\n        const displayVals = dtype === 'complex64' ? createComplexTuples(vals) :\n            Array.from(vals);\n        return [\n            '[' +\n                displayVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n                    .join(', ') +\n                ']'\n        ];\n    }\n    // The array is rank 2 or more.\n    const subshape = shape.slice(1);\n    const substrides = strides.slice(1);\n    const stride = strides[0] * storagePerElement;\n    const lines = [];\n    if (size > FORMAT_LIMIT_NUM_VALS) {\n        for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false /* isLast */));\n        }\n        lines.push('...');\n        for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n        }\n    }\n    else {\n        for (let i = 0; i < size; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n        }\n    }\n    const sep = rank === 2 ? ',' : '';\n    lines[0] = '[' + lines[0] + sep;\n    for (let i = 1; i < lines.length - 1; i++) {\n        lines[i] = ' ' + lines[i] + sep;\n    }\n    let newLineSep = ',\\n';\n    for (let i = 2; i < rank; i++) {\n        newLineSep += '\\n';\n    }\n    lines[lines.length - 1] =\n        ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);\n    return lines;\n}\nfunction createComplexTuples(vals) {\n    const complexTuples = [];\n    for (let i = 0; i < vals.length; i += 2) {\n        complexTuples.push([vals[i], vals[i + 1]]);\n    }\n    return complexTuples;\n}\n//# sourceMappingURL=tensor_format.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensorToString } from './tensor_format';\nimport * as util from './util';\nimport { computeStrides, toNestedArray } from './util';\n/**\n * A mutable object, similar to `tf.Tensor`, that allows users to set values\n * at locations before converting to an immutable `tf.Tensor`.\n *\n * See `tf.buffer` for creating a tensor buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class TensorBuffer {\n    constructor(shape, dtype, values) {\n        this.dtype = dtype;\n        this.shape = shape.slice();\n        this.size = util.sizeFromShape(shape);\n        if (values != null) {\n            const n = values.length;\n            util.assert(n === this.size, () => `Length of values '${n}' does not match the size ` +\n                `inferred by the shape '${this.size}'.`);\n        }\n        if (dtype === 'complex64') {\n            throw new Error(`complex64 dtype TensorBuffers are not supported. Please create ` +\n                `a TensorBuffer for the real and imaginary parts separately and ` +\n                `call tf.complex(real, imag).`);\n        }\n        this.values = values || util.getArrayFromDType(dtype, this.size);\n        this.strides = computeStrides(shape);\n    }\n    /**\n     * Sets a value in the buffer at a given location.\n     *\n     * @param value The value to set.\n     * @param locs  The location indices.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    set(value, ...locs) {\n        if (locs.length === 0) {\n            locs = [0];\n        }\n        util.assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must ` +\n            `match the rank (${this.rank})`);\n        const index = this.locToIndex(locs);\n        this.values[index] = value;\n    }\n    /**\n     * Returns the value in the buffer at the provided location.\n     *\n     * @param locs The location indices.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    get(...locs) {\n        if (locs.length === 0) {\n            locs = [0];\n        }\n        let i = 0;\n        for (const loc of locs) {\n            if (loc < 0 || loc >= this.shape[i]) {\n                const msg = `Requested out of range element at ${locs}. ` +\n                    `  Buffer shape=${this.shape}`;\n                throw new Error(msg);\n            }\n            i++;\n        }\n        let index = locs[locs.length - 1];\n        for (let i = 0; i < locs.length - 1; ++i) {\n            index += this.strides[i] * locs[i];\n        }\n        return this.values[index];\n    }\n    locToIndex(locs) {\n        if (this.rank === 0) {\n            return 0;\n        }\n        else if (this.rank === 1) {\n            return locs[0];\n        }\n        let index = locs[locs.length - 1];\n        for (let i = 0; i < locs.length - 1; ++i) {\n            index += this.strides[i] * locs[i];\n        }\n        return index;\n    }\n    indexToLoc(index) {\n        if (this.rank === 0) {\n            return [];\n        }\n        else if (this.rank === 1) {\n            return [index];\n        }\n        const locs = new Array(this.shape.length);\n        for (let i = 0; i < locs.length - 1; ++i) {\n            locs[i] = Math.floor(index / this.strides[i]);\n            index -= locs[i] * this.strides[i];\n        }\n        locs[locs.length - 1] = index;\n        return locs;\n    }\n    get rank() {\n        return this.shape.length;\n    }\n    /**\n     * Creates an immutable `tf.Tensor` object from the buffer.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    toTensor() {\n        return trackerFn().makeTensor(this.values, this.shape, this.dtype);\n    }\n}\n// For tracking tensor creation and disposal.\nlet trackerFn = null;\n// Used by chaining methods to call into ops.\nlet opHandler = null;\n// Used to warn about deprecated methods.\nlet deprecationWarningFn = null;\n// This here so that we can use this method on dev branches and keep the\n// functionality at master.\n// tslint:disable-next-line:no-unused-expression\n[deprecationWarningFn];\n/**\n * An external consumer can register itself as the tensor tracker. This way\n * the Tensor class can notify the tracker for every tensor created and\n * disposed.\n */\nexport function setTensorTracker(fn) {\n    trackerFn = fn;\n}\n/**\n * An external consumer can register itself as the op handler. This way the\n * Tensor class can have chaining methods that call into ops via the op\n * handler.\n */\nexport function setOpHandler(handler) {\n    opHandler = handler;\n}\n/**\n * Sets the deprecation warning function to be used by this file. This way the\n * Tensor class can be a leaf but still use the environment.\n */\nexport function setDeprecationWarningFn(fn) {\n    deprecationWarningFn = fn;\n}\n/**\n * A `tf.Tensor` object represents an immutable, multidimensional array of\n * numbers that has a shape and a data type.\n *\n * See `tf.tensor` for details on how to create a `tf.Tensor`.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Tensor {\n    constructor(shape, dtype, dataId, id) {\n        /** Whether this tensor has been globally kept. */\n        this.kept = false;\n        this.isDisposedInternal = false;\n        this.shape = shape.slice();\n        this.dtype = dtype || 'float32';\n        this.size = util.sizeFromShape(shape);\n        this.strides = computeStrides(shape);\n        this.dataId = dataId;\n        this.id = id;\n        this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher');\n    }\n    get rank() {\n        return this.shape.length;\n    }\n    /**\n     * Returns a promise of `tf.TensorBuffer` that holds the underlying data.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async buffer() {\n        const vals = await this.data();\n        return opHandler.buffer(this.shape, this.dtype, vals);\n    }\n    /**\n     * Returns a `tf.TensorBuffer` that holds the underlying data.\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    bufferSync() {\n        return opHandler.buffer(this.shape, this.dtype, this.dataSync());\n    }\n    /**\n     * Returns the tensor data as a nested array. The transfer of data is done\n     * asynchronously.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async array() {\n        const vals = await this.data();\n        return toNestedArray(this.shape, vals);\n    }\n    /**\n     * Returns the tensor data as a nested array. The transfer of data is done\n     * synchronously.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    arraySync() {\n        return toNestedArray(this.shape, this.dataSync());\n    }\n    /**\n     * Asynchronously downloads the values from the `tf.Tensor`. Returns a\n     * promise of `TypedArray` that resolves when the computation has finished.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async data() {\n        this.throwIfDisposed();\n        const data = trackerFn().read(this.dataId);\n        if (this.dtype === 'string') {\n            const bytes = await data;\n            try {\n                return bytes.map(b => util.decodeString(b));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode the string bytes into utf-8. ' +\n                    'To get the original bytes, call tensor.bytes().');\n            }\n        }\n        return data;\n    }\n    /**\n     * Synchronously downloads the values from the `tf.Tensor`. This blocks the\n     * UI thread until the values are ready, which can cause performance issues.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    dataSync() {\n        this.throwIfDisposed();\n        const data = trackerFn().readSync(this.dataId);\n        if (this.dtype === 'string') {\n            try {\n                return data.map(b => util.decodeString(b));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode the string bytes into utf-8. ' +\n                    'To get the original bytes, call tensor.bytes().');\n            }\n        }\n        return data;\n    }\n    /** Returns the underlying bytes of the tensor's data. */\n    async bytes() {\n        this.throwIfDisposed();\n        const data = await trackerFn().read(this.dataId);\n        if (this.dtype === 'string') {\n            return data;\n        }\n        else {\n            return new Uint8Array(data.buffer);\n        }\n    }\n    /**\n     * Disposes `tf.Tensor` from memory.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    dispose() {\n        if (this.isDisposed) {\n            return;\n        }\n        trackerFn().disposeTensor(this);\n        this.isDisposedInternal = true;\n    }\n    get isDisposed() {\n        return this.isDisposedInternal;\n    }\n    throwIfDisposed() {\n        if (this.isDisposed) {\n            throw new Error(`Tensor is disposed.`);\n        }\n    }\n    /**\n     * Prints the `tf.Tensor`. See `tf.print` for details.\n     *\n     * @param verbose Whether to print verbose information about the tensor,\n     *    including dtype and size.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    print(verbose = false) {\n        return opHandler.print(this, verbose);\n    }\n    /**\n     * Returns a copy of the tensor. See `tf.clone` for details.\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    clone() {\n        this.throwIfDisposed();\n        return opHandler.clone(this);\n    }\n    /**\n     * Returns a human-readable description of the tensor. Useful for logging.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    toString(verbose = false) {\n        const vals = this.dataSync();\n        return tensorToString(vals, this.shape, this.dtype, verbose);\n    }\n    cast(dtype) {\n        this.throwIfDisposed();\n        return opHandler.cast(this, dtype);\n    }\n    variable(trainable = true, name, dtype) {\n        this.throwIfDisposed();\n        return trackerFn().makeVariable(this, trainable, name, dtype);\n    }\n}\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n    value: (instance) => {\n        // Implementation note: we should use properties of the object that will be\n        // defined before the constructor body has finished executing (methods).\n        // This is because when this code is transpiled by babel, babel will call\n        // classCallCheck before the constructor body is run.\n        // See https://github.com/tensorflow/tfjs/issues/3384 for backstory.\n        return !!instance && instance.data != null && instance.dataSync != null &&\n            instance.throwIfDisposed != null;\n    }\n});\n/**\n * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Variable extends Tensor {\n    constructor(initialValue, trainable, name, tensorId) {\n        super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);\n        this.trainable = trainable;\n        this.name = name;\n    }\n    /**\n     * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n     * the same shape and dtype as the old `tf.Tensor`.\n     *\n     * @param newValue New tensor to be assigned to this variable.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    assign(newValue) {\n        if (newValue.dtype !== this.dtype) {\n            throw new Error(`dtype of the new value (${newValue.dtype}) and ` +\n                `previous value (${this.dtype}) must match`);\n        }\n        if (!util.arraysEqual(newValue.shape, this.shape)) {\n            throw new Error(`shape of the new value (${newValue.shape}) and ` +\n                `previous value (${this.shape}) must match`);\n        }\n        trackerFn().disposeTensor(this);\n        this.dataId = newValue.dataId;\n        trackerFn().incRef(this, null /* backend */);\n    }\n    dispose() {\n        trackerFn().disposeVariable(this);\n        this.isDisposedInternal = true;\n    }\n}\nObject.defineProperty(Variable, Symbol.hasInstance, {\n    value: (instance) => {\n        return instance instanceof Tensor && instance.assign != null &&\n            instance.assign instanceof Function;\n    }\n});\n//# sourceMappingURL=tensor.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Rank;\n(function (Rank) {\n    Rank[\"R0\"] = \"R0\";\n    Rank[\"R1\"] = \"R1\";\n    Rank[\"R2\"] = \"R2\";\n    Rank[\"R3\"] = \"R3\";\n    Rank[\"R4\"] = \"R4\";\n    Rank[\"R5\"] = \"R5\";\n    Rank[\"R6\"] = \"R6\";\n})(Rank || (Rank = {}));\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nvar UpcastInt32AndMap;\n(function (UpcastInt32AndMap) {\n    UpcastInt32AndMap[\"float32\"] = \"float32\";\n    UpcastInt32AndMap[\"int32\"] = \"int32\";\n    UpcastInt32AndMap[\"bool\"] = \"int32\";\n    UpcastInt32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));\nvar UpcastBoolAndMap;\n(function (UpcastBoolAndMap) {\n    UpcastBoolAndMap[\"float32\"] = \"float32\";\n    UpcastBoolAndMap[\"int32\"] = \"int32\";\n    UpcastBoolAndMap[\"bool\"] = \"bool\";\n    UpcastBoolAndMap[\"complex64\"] = \"complex64\";\n})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));\nvar UpcastFloat32AndMap;\n(function (UpcastFloat32AndMap) {\n    UpcastFloat32AndMap[\"float32\"] = \"float32\";\n    UpcastFloat32AndMap[\"int32\"] = \"float32\";\n    UpcastFloat32AndMap[\"bool\"] = \"float32\";\n    UpcastFloat32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));\nvar UpcastComplex64AndMap;\n(function (UpcastComplex64AndMap) {\n    UpcastComplex64AndMap[\"float32\"] = \"complex64\";\n    UpcastComplex64AndMap[\"int32\"] = \"complex64\";\n    UpcastComplex64AndMap[\"bool\"] = \"complex64\";\n    UpcastComplex64AndMap[\"complex64\"] = \"complex64\";\n})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));\nconst upcastTypeMap = {\n    'float32': UpcastFloat32AndMap,\n    'int32': UpcastInt32AndMap,\n    'bool': UpcastBoolAndMap,\n    'complex64': UpcastComplex64AndMap\n};\nexport function upcastType(typeA, typeB) {\n    if (typeA === 'string' || typeB === 'string') {\n        if (typeA === 'string' && typeB === 'string') {\n            return 'string';\n        }\n        throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n    }\n    return upcastTypeMap[typeA][typeB];\n}\n/** Returns the output type after summation. */\nexport function sumOutType(type) {\n    return upcastType(type, 'int32');\n}\n//# sourceMappingURL=types.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from './tensor';\nimport { upcastType } from './types';\nimport { assert } from './util';\nexport function makeTypesMatch(a, b) {\n    if (a.dtype === b.dtype) {\n        return [a, b];\n    }\n    const dtype = upcastType(a.dtype, b.dtype);\n    return [a.cast(dtype), b.cast(dtype)];\n}\nexport function assertTypesMatch(a, b) {\n    assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and` +\n        ` second(${b.dtype}) input must match`);\n}\nexport function isTensorInList(tensor, tensorList) {\n    return tensorList.some(x => x.id === tensor.id);\n}\n/**\n * Extracts any `Tensor`s found within the provided object.\n *\n * @param container an object that may be a `Tensor` or may directly contain\n *   `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it\n *   is safe to pass any object here, except that `Promise`s are not\n *   supported.\n * @returns An array of `Tensors` found within the passed object. If the\n *   argument is simply a `Tensor', a list containing that `Tensor` is\n *   returned. If the object is not a `Tensor` or does not\n *   contain `Tensors`, an empty list is returned.\n */\nexport function getTensorsInContainer(result) {\n    const list = [];\n    const seen = new Set();\n    walkTensorContainer(result, list, seen);\n    return list;\n}\nfunction walkTensorContainer(container, list, seen) {\n    if (container == null) {\n        return;\n    }\n    if (container instanceof Tensor) {\n        list.push(container);\n        return;\n    }\n    if (!isIterable(container)) {\n        return;\n    }\n    // Iteration over keys works also for arrays.\n    const iterable = container;\n    for (const k in iterable) {\n        const val = iterable[k];\n        if (!seen.has(val)) {\n            seen.add(val);\n            walkTensorContainer(val, list, seen);\n        }\n    }\n}\n// tslint:disable-next-line:no-any\nfunction isIterable(obj) {\n    return Array.isArray(obj) || typeof obj === 'object';\n}\n//# sourceMappingURL=tensor_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { KernelBackend } from './backends/backend';\nimport { Environment, setEnvironmentGlobal } from './environment';\nimport { getGlobalNamespace } from './global_util';\nimport { Add, Cast } from './kernel_names';\nimport { getGradient, getKernel, getKernelsForBackend } from './kernel_registry';\nimport { Profiler } from './profiler';\nimport { backpropagateGradients, getFilteredNodesXToY } from './tape';\nimport { setTensorTracker, Tensor, Variable } from './tensor';\nimport { getTensorsInContainer } from './tensor_util';\nimport * as util from './util';\nimport { bytesFromStringArray, makeOnesTypedArray, now, sizeFromShape } from './util';\nclass EngineState {\n    constructor() {\n        // Public since optimizers will use it.\n        this.registeredVariables = {};\n        this.nextTapeNodeId = 0;\n        this.numBytes = 0;\n        this.numTensors = 0;\n        this.numStringTensors = 0;\n        this.numDataBuffers = 0;\n        // Number of nested tf.grad() statements when computing higher-order\n        // gradients. E.g. `1` for first-order gradients and `2` for second-order\n        // gradients. Used to track if the tape should be removed after a backprop.\n        this.gradientDepth = 0;\n        // Number of nested kernel calls. When kernel depth is greater than 1, we turn\n        // off the tape.\n        this.kernelDepth = 0;\n        this.scopeStack = [];\n        /**\n         * Keeps track of the number of data moves during a kernel execution. We\n         * maintain a stack since kernels can call other kernels, recursively.\n         */\n        this.numDataMovesStack = [];\n        this.nextScopeId = 0;\n        this.tensorInfo = new WeakMap();\n        this.profiling = false;\n        this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null };\n    }\n    dispose() {\n        for (const variableName in this.registeredVariables) {\n            this.registeredVariables[variableName].dispose();\n        }\n    }\n}\nexport class Engine {\n    constructor(ENV) {\n        this.ENV = ENV;\n        this.registry = {};\n        this.registryFactory = {};\n        this.pendingBackendInitId = 0;\n        this.state = new EngineState();\n    }\n    async ready() {\n        if (this.pendingBackendInit != null) {\n            return this.pendingBackendInit.then(() => { });\n        }\n        if (this.backendInstance != null) {\n            return;\n        }\n        const sortedBackends = this.getSortedBackends();\n        for (let i = 0; i < sortedBackends.length; i++) {\n            const backendName = sortedBackends[i];\n            const success = await this.initializeBackend(backendName).success;\n            if (success) {\n                await this.setBackend(backendName);\n                return;\n            }\n        }\n        throw new Error(`Could not initialize any backends, all backend initializations ` +\n            `failed.`);\n    }\n    get backend() {\n        if (this.pendingBackendInit != null) {\n            throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make ` +\n                `sure to await tf.ready() or await tf.setBackend() before calling ` +\n                `other methods`);\n        }\n        if (this.backendInstance == null) {\n            const { name, asyncInit } = this.initializeBackendsAndReturnBest();\n            if (asyncInit) {\n                throw new Error(`The highest priority backend '${name}' has not yet been ` +\n                    `initialized. Make sure to await tf.ready() or ` +\n                    `await tf.setBackend() before calling other methods`);\n            }\n            this.setBackend(name);\n        }\n        return this.backendInstance;\n    }\n    backendNames() {\n        return Object.keys(this.registryFactory);\n    }\n    findBackend(backendName) {\n        if (!(backendName in this.registry)) {\n            // If the backend hasn't been initialized but we have a registry entry for\n            // it, initialize it and return it.\n            if (backendName in this.registryFactory) {\n                const { asyncInit } = this.initializeBackend(backendName);\n                if (asyncInit) {\n                    // Backend is not ready yet.\n                    return null;\n                }\n            }\n            else {\n                return null;\n            }\n        }\n        return this.registry[backendName];\n    }\n    findBackendFactory(backendName) {\n        if (!(backendName in this.registryFactory)) {\n            return null;\n        }\n        return this.registryFactory[backendName].factory;\n    }\n    registerBackend(backendName, factory, priority = 1) {\n        if (backendName in this.registryFactory) {\n            console.warn(`${backendName} backend was already registered. ` +\n                `Reusing existing backend factory.`);\n            return false;\n        }\n        this.registryFactory[backendName] = { factory, priority };\n        return true;\n    }\n    async setBackend(backendName) {\n        if (this.registryFactory[backendName] == null) {\n            throw new Error(`Backend name '${backendName}' not found in registry`);\n        }\n        this.backendName = backendName;\n        if (this.registry[backendName] == null) {\n            this.backendInstance = null;\n            const { success, asyncInit } = this.initializeBackend(backendName);\n            const result = asyncInit ? await success : success;\n            if (!result) {\n                return false;\n            }\n        }\n        this.backendInstance = this.registry[backendName];\n        this.setupRegisteredKernels();\n        // Reset the profiler.\n        this.profiler = new Profiler(this.backendInstance);\n        return true;\n    }\n    setupRegisteredKernels() {\n        const kernels = getKernelsForBackend(this.backendName);\n        kernels.forEach(kernel => {\n            if (kernel.setupFunc != null) {\n                kernel.setupFunc(this.backendInstance);\n            }\n        });\n    }\n    disposeRegisteredKernels(backendName) {\n        const kernels = getKernelsForBackend(backendName);\n        kernels.forEach(kernel => {\n            if (kernel.disposeFunc != null) {\n                kernel.disposeFunc(this.registry[backendName]);\n            }\n        });\n    }\n    /**\n     * Initializes a backend by looking up the backend name in the factory\n     * registry and calling the factory method. Returns a boolean representing\n     * whether the initialization of the backend suceeded. Throws an error if\n     * there is no backend in the factory registry.\n     */\n    initializeBackend(backendName) {\n        const registryFactoryEntry = this.registryFactory[backendName];\n        if (registryFactoryEntry == null) {\n            throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);\n        }\n        try {\n            const backend = registryFactoryEntry.factory();\n            /* Test if the factory returns a promise.\n            Done in a more liberal way than\n            previous 'Promise.resolve(backend)===backend'\n            as we needed to account for custom Promise\n            implementations (e.g. Angular) */\n            if (backend && !(backend instanceof KernelBackend)\n                && typeof backend.then === 'function') {\n                const promiseId = ++this.pendingBackendInitId;\n                const success = backend\n                    .then(backendInstance => {\n                    // Outdated promise. Another backend was set in the meantime.\n                    if (promiseId < this.pendingBackendInitId) {\n                        return false;\n                    }\n                    this.registry[backendName] = backendInstance;\n                    this.pendingBackendInit = null;\n                    return true;\n                })\n                    .catch(err => {\n                    // Outdated promise. Another backend was set in the meantime.\n                    if (promiseId < this.pendingBackendInitId) {\n                        return false;\n                    }\n                    this.pendingBackendInit = null;\n                    console.warn(`Initialization of backend ${backendName} failed`);\n                    console.warn(err.stack || err.message);\n                    return false;\n                });\n                this.pendingBackendInit = success;\n                return { success, asyncInit: true };\n            }\n            else {\n                this.registry[backendName] = backend;\n                return { success: true, asyncInit: false };\n            }\n        }\n        catch (err) {\n            console.warn(`Initialization of backend ${backendName} failed`);\n            console.warn(err.stack || err.message);\n            return { success: false, asyncInit: false };\n        }\n    }\n    removeBackend(backendName) {\n        if (!(backendName in this.registryFactory)) {\n            throw new Error(`${backendName} backend not found in registry`);\n        }\n        if (this.backendName === backendName && this.pendingBackendInit != null) {\n            // There is a pending promise of the backend we want to remove. Make it\n            // obsolete.\n            this.pendingBackendInitId++;\n        }\n        if (backendName in this.registry) {\n            this.disposeRegisteredKernels(backendName);\n            this.registry[backendName].dispose();\n            delete this.registry[backendName];\n        }\n        delete this.registryFactory[backendName];\n        // Unset the backend if it is active.\n        if (this.backendName === backendName) {\n            this.pendingBackendInit = null;\n            this.backendName = null;\n            this.backendInstance = null;\n        }\n    }\n    getSortedBackends() {\n        if (Object.keys(this.registryFactory).length === 0) {\n            throw new Error('No backend found in registry.');\n        }\n        return Object.keys(this.registryFactory).sort((a, b) => {\n            // Highest priority comes first.\n            return this.registryFactory[b].priority -\n                this.registryFactory[a].priority;\n        });\n    }\n    initializeBackendsAndReturnBest() {\n        const sortedBackends = this.getSortedBackends();\n        for (let i = 0; i < sortedBackends.length; i++) {\n            const backendName = sortedBackends[i];\n            const { success, asyncInit } = this.initializeBackend(backendName);\n            if (asyncInit || success) {\n                return { name: backendName, asyncInit };\n            }\n        }\n        throw new Error(`Could not initialize any backends, all backend initializations ` +\n            `failed.`);\n    }\n    moveData(backend, dataId) {\n        const info = this.state.tensorInfo.get(dataId);\n        const srcBackend = info.backend;\n        const values = this.readSync(dataId);\n        // Delete the tensor from the old backend and move it to the new\n        // backend.\n        srcBackend.disposeData(dataId);\n        info.backend = backend;\n        backend.move(dataId, values, info.shape, info.dtype);\n        if (this.shouldCheckForMemLeaks()) {\n            // Track the number of moves during a kernel execution to correctly\n            // detect memory leaks.\n            this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;\n        }\n    }\n    tidy(nameOrFn, fn) {\n        let name = null;\n        if (fn == null) {\n            // Called with only 1 argument.\n            if (typeof nameOrFn !== 'function') {\n                throw new Error('Please provide a function to tidy()');\n            }\n            fn = nameOrFn;\n        }\n        else {\n            // Called with 2 arguments.\n            if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {\n                throw new Error('When calling with two arguments, the first argument ' +\n                    'to tidy() must be a string');\n            }\n            if (typeof fn !== 'function') {\n                throw new Error('When calling with two arguments, the 2nd argument ' +\n                    'to tidy() must be a function');\n            }\n            name = nameOrFn;\n            // TODO(nsthorat,smilkov): Do operation logging and performance\n            // profiling.\n        }\n        let result;\n        return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {\n            result = fn();\n            if (result instanceof Promise) {\n                console.error('Cannot return a Promise inside of tidy.');\n            }\n            return result;\n        });\n    }\n    scopedRun(start, end, f) {\n        start();\n        try {\n            const res = f();\n            end();\n            return res;\n        }\n        catch (ex) {\n            end();\n            throw ex;\n        }\n    }\n    nextTensorId() {\n        return Engine.nextTensorId++;\n    }\n    nextVariableId() {\n        return Engine.nextVariableId++;\n    }\n    /**\n     * This method is called instead of the public-facing tensor.clone() when\n     * saving a tensor for backwards pass. It makes sure to add the clone\n     * operation to the tape regardless of being called inside a kernel\n     * execution.\n     *\n     * This method will go away once all kernels are modularized since we won't\n     * need to turn off the tape inside runKernel().\n     */\n    clone(x) {\n        const y = this.makeTensorFromDataId(x.dataId, x.shape, x.dtype);\n        const inputs = { x };\n        const grad = (dy) => ({\n            x: () => {\n                const dtype = 'float32';\n                const gradInputs = { x: dy };\n                const attrs = { dtype };\n                return ENGINE.runKernelFunc(backend => backend.cast(dy, dtype), gradInputs, null /* grad */, Cast, attrs);\n            }\n        });\n        const saved = [];\n        this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {});\n        return y;\n    }\n    /**\n     * Execute a kernel with the given name and return the output tensor.\n     *\n     * @param kernelName The name of the kernel to execute.\n     * @param inputs A map of input names to tensors.\n     * @param attrs A map of attribute names to their values. An attribute is a\n     *     primitive (non-tensor) input to the kernel.\n     * @param inputsToSave A list of tensors, inputs to save for the backprop\n     *     computation.\n     * @param outputsToSave A list of booleans, specifying which output to save\n     *     for the backprop computation. These are booleans since the output\n     * tensors are not visible to the user.\n     */\n    runKernel(kernelName, inputs, attrs, inputsToSave, outputsToSave) {\n        const forwardFunc = null;\n        const backwardsFunc = null;\n        // Call runKernel as a stop-gap until we modularize all kernels.\n        // Once we modularize all kernels, we will remove the existing\n        // `runKernelFunc`.\n        return this.runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave);\n    }\n    shouldCheckForMemLeaks() {\n        return this.ENV.getBool('IS_TEST');\n    }\n    checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {\n        const numDataIdsAfter = this.backend.numDataIds();\n        // Count the number of data ids associated with the result of the kernel.\n        let numOutputDataIds = 0;\n        outInfos.forEach(info => {\n            // Complex numbers allocate 3 data ids, one for 'real', one for\n            // 'imaginary', and one for the container that holds the former two.\n            numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1);\n        });\n        // Account for the number of moves during kernel execution. A \"data move\"\n        // can happen in the middle of a kernel execution, placing a new (key,value)\n        // pair in the data storage. Since data moves have net zero effect (we\n        // always remove the data from the old backend), we have to cancel them out\n        // when detecting memory leaks.\n        const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];\n        const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;\n        if (dataIdsLeaked > 0) {\n            throw new Error(`Backend '${this.backendName}' has an internal memory leak ` +\n                `(${dataIdsLeaked} data ids) after running '${kernelName}'`);\n        }\n    }\n    /**\n     * @deprecated Use `runKernel` for newly added kernels. Keep using this method\n     *     only for kernels that are not yet fully modularized.\n     */\n    runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave) {\n        let outputs;\n        let saved = [];\n        const isTapeOn = this.isTapeOn();\n        if (kernelName == null) {\n            kernelName =\n                this.state.activeScope != null ? this.state.activeScope.name : '';\n        }\n        const startingBytecount = this.state.numBytes;\n        const startingNumTensors = this.state.numTensors;\n        if (this.shouldCheckForMemLeaks()) {\n            this.state.numDataMovesStack.push(0);\n        }\n        let kernelFunc;\n        const kernel = getKernel(kernelName, this.backendName);\n        let out;\n        if (kernel != null) {\n            kernelFunc = () => {\n                const numDataIdsBefore = this.backend.numDataIds();\n                out = kernel.kernelFunc({ inputs, attrs, backend: this.backend });\n                const outInfos = Array.isArray(out) ? out : [out];\n                if (this.shouldCheckForMemLeaks()) {\n                    this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);\n                }\n                const outTensors = outInfos.map(({ dataId, shape, dtype }) => this.makeTensorFromDataId(dataId, shape, dtype));\n                // Save the inputs and outputs.\n                // Do not save unless we are recording to the tape. Otherwise it would\n                // cause a mem leak since we would never run backprop, which disposes\n                // the kept tensors.\n                if (isTapeOn) {\n                    let tensorsToSave = this.getTensorsForGradient(kernelName, inputs, outTensors);\n                    if (tensorsToSave == null) {\n                        // Fallback for ops that call runKernelFunc and pass in\n                        // inputsToSave and outputsToSave. Currently this is the set of ops\n                        // with kernel support in the WASM backend. Once those ops and\n                        // respective gradients are modularised we can remove this path.\n                        if (outputsToSave == null) {\n                            outputsToSave = [];\n                        }\n                        const outsToSave = outTensors.filter((_, i) => outputsToSave[i]);\n                        tensorsToSave = (inputsToSave || []).slice().concat(outsToSave);\n                    }\n                    saved = this.saveTensorsForBackwardMode(tensorsToSave);\n                }\n                return outTensors;\n            };\n        }\n        else {\n            const saveFunc = (tensors) => {\n                // Do not save unless we are recording to the tape. Otherwise it would\n                // cause a mem leak since we would never run backprop, which disposes\n                // the kept tensors.\n                if (!isTapeOn) {\n                    return;\n                }\n                saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n            };\n            kernelFunc = () => {\n                const numDataIdsBefore = this.backend.numDataIds();\n                out = this.tidy(() => forwardFunc(this.backend, saveFunc));\n                const outs = (Array.isArray(out) ? out : [out]);\n                if (this.shouldCheckForMemLeaks()) {\n                    this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outs);\n                }\n                return outs;\n            };\n        }\n        // Stop recording to a tape when running a kernel.\n        let kernelProfile;\n        this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {\n            if (!this.ENV.getBool('DEBUG') && !this.state.profiling) {\n                outputs = kernelFunc();\n            }\n            else {\n                kernelProfile = this.profiler.profileKernel(kernelName, inputs, () => kernelFunc());\n                if (this.ENV.getBool('DEBUG')) {\n                    this.profiler.logKernelProfile(kernelProfile);\n                }\n                outputs = kernelProfile.outputs;\n            }\n        });\n        if (isTapeOn) {\n            this.addTapeNode(kernelName, inputs, outputs, backwardsFunc, saved, attrs);\n        }\n        if (this.state.profiling) {\n            this.state.activeProfile.kernels.push({\n                name: kernelName,\n                bytesAdded: this.state.numBytes - startingBytecount,\n                totalBytesSnapshot: this.state.numBytes,\n                tensorsAdded: this.state.numTensors - startingNumTensors,\n                totalTensorsSnapshot: this.state.numTensors,\n                inputShapes: Object.keys(inputs).map(key => inputs[key] != null ? inputs[key].shape : null),\n                outputShapes: outputs.map(item => item.shape),\n                kernelTimeMs: kernelProfile.timeMs,\n                extraInfo: kernelProfile.extraInfo\n            });\n        }\n        return (Array.isArray(out) ? outputs : outputs[0]);\n    }\n    /**\n     * Saves tensors used in forward mode for use in backward mode.\n     *\n     * @param tensors the list of tensors to save.\n     */\n    saveTensorsForBackwardMode(tensors) {\n        const saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n        return saved;\n    }\n    /**\n     * Returns a list of tensors to save for a given gradient calculation.\n     *\n     * Returns undefined if their is no registered gradient for this kernel in the\n     * gradient registry.\n     *\n     * @param kernelName name of kernel to look up gradient for.\n     * @param inputs a map of input tensors.\n     * @param outputs an array of output tensors from forward mode of kernel.\n     */\n    getTensorsForGradient(kernelName, inputs, outputs) {\n        const gradConfig = getGradient(kernelName);\n        if (gradConfig != null) {\n            const inputsToSave = gradConfig.inputsToSave || [];\n            const outputsToSave = gradConfig.outputsToSave || [];\n            // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs\n            // specified in inputsToSave will be saved.\n            let inputTensorsToSave;\n            if (gradConfig.saveAllInputs) {\n                util.assert(Array.isArray(inputs), () => 'saveAllInputs is true, expected inputs to be an array.');\n                inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);\n            }\n            else {\n                inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);\n            }\n            const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);\n            return inputTensorsToSave.concat(outputTensorsToSave);\n        }\n        // TODO(yassogba) throw exception here once all runkernelFunc calls with\n        // inputsToSave/outputsToSave are removed\n        return null;\n    }\n    /**\n     * Internal method used by public APIs for tensor creation. Makes a new\n     * tensor with the provided shape, dtype and values. It always\n     * creates a new data id and writes the values to the underlying backend.\n     */\n    makeTensor(values, shape, dtype, backend) {\n        if (values == null) {\n            throw new Error('Values passed to engine.makeTensor() are null');\n        }\n        dtype = dtype || 'float32';\n        backend = backend || this.backend;\n        let backendVals = values;\n        if (dtype === 'string' && util.isString(values[0])) {\n            backendVals = values.map(d => util.encodeString(d));\n        }\n        const dataId = backend.write(backendVals, shape, dtype);\n        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n        this.incRef(t, backend);\n        // Count bytes for string tensors.\n        if (dtype === 'string') {\n            const info = this.state.tensorInfo.get(dataId);\n            const newBytes = bytesFromStringArray(backendVals);\n            this.state.numBytes += newBytes - info.bytes;\n            info.bytes = newBytes;\n        }\n        return t;\n    }\n    /**\n     * Internal method used by backends. Makes a new tensor\n     * that is a wrapper around an existing data id. It doesn't create\n     * a new data id, only increments the ref count used in memory tracking.\n     */\n    makeTensorFromDataId(dataId, shape, dtype, backend) {\n        dtype = dtype || 'float32';\n        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n        this.incRef(t, backend);\n        return t;\n    }\n    makeVariable(initialValue, trainable = true, name, dtype) {\n        name = name || this.nextVariableId().toString();\n        if (dtype != null && dtype !== initialValue.dtype) {\n            initialValue = initialValue.cast(dtype);\n        }\n        const v = new Variable(initialValue, trainable, name, this.nextTensorId());\n        if (this.state.registeredVariables[v.name] != null) {\n            throw new Error(`Variable with name ${v.name} was already registered`);\n        }\n        this.state.registeredVariables[v.name] = v;\n        this.incRef(v, this.backend);\n        return v;\n    }\n    incRef(a, backend) {\n        const refCount = this.state.tensorInfo.has(a.dataId) ?\n            this.state.tensorInfo.get(a.dataId).refCount :\n            0;\n        this.state.numTensors++;\n        if (a.dtype === 'string') {\n            this.state.numStringTensors++;\n        }\n        if (refCount === 0) {\n            this.state.numDataBuffers++;\n            // Bytes for complex numbers are counted by their components. Bytes for\n            // string tensors are counted when writing values.\n            let bytes = 0;\n            if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n                bytes = a.size * util.bytesPerElement(a.dtype);\n            }\n            this.state.tensorInfo.set(a.dataId, {\n                backend: backend || this.backend,\n                dtype: a.dtype,\n                shape: a.shape,\n                bytes,\n                refCount: 0\n            });\n            this.state.numBytes += bytes;\n        }\n        this.state.tensorInfo.get(a.dataId).refCount++;\n        if (!(a instanceof Variable)) {\n            this.track(a);\n        }\n    }\n    disposeTensor(a) {\n        if (!this.state.tensorInfo.has(a.dataId)) {\n            return;\n        }\n        this.state.numTensors--;\n        if (a.dtype === 'string') {\n            this.state.numStringTensors--;\n        }\n        const info = this.state.tensorInfo.get(a.dataId);\n        const refCount = info.refCount;\n        if (refCount <= 1) {\n            // Don't count bytes for complex numbers as they are counted by their\n            // components.\n            if (a.dtype !== 'complex64') {\n                this.state.numBytes -= info.bytes;\n            }\n            this.state.numDataBuffers--;\n            info.backend.disposeData(a.dataId);\n            this.state.tensorInfo.delete(a.dataId);\n        }\n        else {\n            this.state.tensorInfo.get(a.dataId).refCount--;\n        }\n        // TODO(nsthorat): Construct an error and save the stack trace for\n        // debugging when in debug mode. Creating a stack trace is too expensive\n        // to do unconditionally.\n    }\n    disposeVariables() {\n        for (const varName in this.state.registeredVariables) {\n            const v = this.state.registeredVariables[varName];\n            this.disposeVariable(v);\n        }\n    }\n    disposeVariable(v) {\n        this.disposeTensor(v);\n        if (this.state.registeredVariables[v.name] != null) {\n            delete this.state.registeredVariables[v.name];\n        }\n    }\n    memory() {\n        const info = this.backend.memory();\n        info.numTensors = this.state.numTensors;\n        info.numDataBuffers = this.state.numDataBuffers;\n        info.numBytes = this.state.numBytes;\n        if (this.state.numStringTensors > 0) {\n            info.unreliable = true;\n            if (info.reasons == null) {\n                info.reasons = [];\n            }\n            info.reasons.push('Memory usage by string tensors is approximate ' +\n                '(2 bytes per character)');\n        }\n        return info;\n    }\n    async profile(query) {\n        this.state.profiling = true;\n        const startBytes = this.state.numBytes;\n        const startNumTensors = this.state.numTensors;\n        this.state.activeProfile.kernels = [];\n        this.state.activeProfile.result = await query();\n        this.state.profiling = false;\n        this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map(d => d.totalBytesSnapshot));\n        this.state.activeProfile.newBytes = this.state.numBytes - startBytes;\n        this.state.activeProfile.newTensors =\n            this.state.numTensors - startNumTensors;\n        for (const kernel of this.state.activeProfile.kernels) {\n            kernel.kernelTimeMs = await kernel.kernelTimeMs;\n            kernel.extraInfo = await kernel.extraInfo;\n        }\n        return this.state.activeProfile;\n    }\n    isTapeOn() {\n        return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;\n    }\n    addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {\n        const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved };\n        const gradConfig = getGradient(kernelName);\n        if (gradConfig != null) {\n            gradientsFunc = gradConfig.gradFunc;\n        }\n        if (gradientsFunc != null) {\n            tapeNode.gradient = (dys) => {\n                // TODO(smilkov): To optimize back-prop, pass dys that are not used in\n                // the backprop graph to the user as null instead of zeros\n                dys = dys.map((dy, i) => {\n                    if (dy == null) {\n                        const output = outputs[i];\n                        const vals = util.makeZerosTypedArray(output.size, output.dtype);\n                        return this.makeTensor(vals, output.shape, output.dtype);\n                    }\n                    return dy;\n                });\n                // Grad functions of ops with single outputs expect a dy, while ops\n                // with multiple outputs expect dys (array of dy).\n                return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);\n            };\n        }\n        this.state.activeTape.push(tapeNode);\n    }\n    keep(result) {\n        result.kept = true;\n        return result;\n    }\n    startTape() {\n        if (this.state.gradientDepth === 0) {\n            this.state.activeTape = [];\n        }\n        this.state.gradientDepth++;\n    }\n    endTape() {\n        this.state.gradientDepth--;\n    }\n    /**\n     * Start a scope. Use this with endScope() to achieve the same functionality\n     * as scope() without the need for a function closure.\n     */\n    startScope(name) {\n        const scopeInfo = {\n            track: [],\n            name: 'unnamed scope',\n            id: this.state.nextScopeId++\n        };\n        if (name) {\n            scopeInfo.name = name;\n        }\n        this.state.scopeStack.push(scopeInfo);\n        this.state.activeScope = scopeInfo;\n    }\n    /**\n     * End a scope. Use this with startScope() to achieve the same functionality\n     * as scope() without the need for a function closure.\n     */\n    endScope(result) {\n        const tensorsToTrackInParent = getTensorsInContainer(result);\n        const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map(t => t.id));\n        // Dispose the arrays tracked in this scope.\n        for (let i = 0; i < this.state.activeScope.track.length; i++) {\n            const tensor = this.state.activeScope.track[i];\n            if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) {\n                tensor.dispose();\n            }\n        }\n        const oldScope = this.state.scopeStack.pop();\n        this.state.activeScope = this.state.scopeStack.length === 0 ?\n            null :\n            this.state.scopeStack[this.state.scopeStack.length - 1];\n        // Track the current result in the parent scope.\n        tensorsToTrackInParent.forEach(tensor => {\n            // Only track the tensor if was allocated in the inner scope and is not\n            // globally kept.\n            if (!tensor.kept && tensor.scopeId === oldScope.id) {\n                this.track(tensor);\n            }\n        });\n    }\n    /**\n     * Returns gradients of `f` with respect to each of the `xs`. The gradients\n     * returned are of the same length as `xs`, but some might be null if `f`\n     * was not a function of that `x`. It also takes optional dy to multiply the\n     * gradient, which defaults to `1`.\n     */\n    gradients(f, xs, dy, allowNoGradients = false) {\n        util.assert(xs.length > 0, () => 'gradients() received an empty list of xs.');\n        if (dy != null && dy.dtype !== 'float32') {\n            throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n        }\n        const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy('forward', f));\n        util.assert(y instanceof Tensor, () => 'The result y returned by f() must be a tensor.');\n        // Filter out the nodes that don't connect x => y.\n        const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);\n        if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n            throw new Error('Cannot compute gradient of y=f(x) with respect to x. Make sure ' +\n                'that the f you passed encloses all operations that lead from x ' +\n                'to y.');\n        }\n        return this.tidy('backward', () => {\n            const accumulatedGradientMap = {};\n            accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;\n            // Backprop gradients through the filtered nodes.\n            backpropagateGradients(accumulatedGradientMap, filteredTape, \n            // Pass the tidy function to avoid circular dep with `tape.ts`.\n            f => this.tidy(f), \n            // Pass an add function to avoide a circular dep with `tape.ts`.\n            add);\n            const grads = xs.map(x => accumulatedGradientMap[x.id]);\n            if (this.state.gradientDepth === 0) {\n                // This means that we are not computing higher-order gradients\n                // and can clean up the tape.\n                this.state.activeTape.forEach(node => {\n                    for (const tensor of node.saved) {\n                        tensor.dispose();\n                    }\n                });\n                this.state.activeTape = null;\n            }\n            return { value: y, grads };\n        });\n    }\n    customGrad(f) {\n        util.assert(util.isFunction(f), () => 'The f passed in customGrad(f) must be a function.');\n        return (...inputs) => {\n            util.assert(inputs.every(t => t instanceof Tensor), () => 'The args passed in customGrad(f)(x1, x2,...) must all be ' +\n                'tensors');\n            let res;\n            const inputMap = {};\n            inputs.forEach((input, i) => {\n                inputMap[i] = input;\n            });\n            return this.runKernelFunc((_, save) => {\n                res = f(...[...inputs, save]);\n                util.assert(res.value instanceof Tensor, () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.value` is a tensor');\n                util.assert(util.isFunction(res.gradFunc), () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function.');\n                return res.value;\n            }, inputMap, (dy, saved) => {\n                const gradRes = res.gradFunc(dy, saved);\n                const grads = Array.isArray(gradRes) ? gradRes : [gradRes];\n                util.assert(grads.length === inputs.length, () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'the same number of tensors as inputs passed to f(...).');\n                util.assert(grads.every(t => t instanceof Tensor), () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'a list of only tensors.');\n                const gradMap = {};\n                grads.forEach((grad, i) => {\n                    gradMap[i] = () => grad;\n                });\n                return gradMap;\n            });\n        };\n    }\n    readSync(dataId) {\n        // Route the read to the correct backend.\n        const info = this.state.tensorInfo.get(dataId);\n        return info.backend.readSync(dataId);\n    }\n    read(dataId) {\n        // Route the read to the correct backend.\n        const info = this.state.tensorInfo.get(dataId);\n        return info.backend.read(dataId);\n    }\n    async time(query) {\n        const start = now();\n        const timingInfo = await this.backend.time(query);\n        timingInfo.wallMs = now() - start;\n        return timingInfo;\n    }\n    /**\n     * Tracks a Tensor in the current scope to be automatically cleaned up\n     * when the current scope ends, and returns the value.\n     *\n     * @param result The Tensor to track in the current scope.\n     */\n    track(result) {\n        if (this.state.activeScope != null) {\n            result.scopeId = this.state.activeScope.id;\n            this.state.activeScope.track.push(result);\n        }\n        return result;\n    }\n    get registeredVariables() {\n        return this.state.registeredVariables;\n    }\n    /**\n     * Resets the engine state. Removes all backends but does not remove\n     * registered backend factories.\n     */\n    reset() {\n        // Make any pending promise obsolete.\n        this.pendingBackendInitId++;\n        this.state.dispose();\n        this.ENV.reset();\n        this.state = new EngineState();\n        for (const backendName in this.registry) {\n            this.disposeRegisteredKernels(backendName);\n            this.registry[backendName].dispose();\n            delete this.registry[backendName];\n        }\n        this.backendName = null;\n        this.backendInstance = null;\n        this.pendingBackendInit = null;\n    }\n}\nEngine.nextTensorId = 0;\nEngine.nextVariableId = 0;\nfunction ones(shape) {\n    const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');\n    return ENGINE.makeTensor(values, shape, 'float32');\n}\nexport function getOrMakeEngine() {\n    const ns = getGlobalNamespace();\n    if (ns._tfengine == null) {\n        const environment = new Environment(ns);\n        ns._tfengine = new Engine(environment);\n    }\n    setEnvironmentGlobal(ns._tfengine.ENV);\n    // Tell the current tensor interface that the global engine is responsible\n    // for tracking.\n    setTensorTracker(() => ns._tfengine);\n    return ns._tfengine;\n}\nexport const ENGINE = getOrMakeEngine();\n/**\n * A implementation of the add op for use within engine and tape.\n *\n * This allows us to avoid a circular dependency between add.ts and engine.\n * It is exported to be available in tape tests.\n */\nexport function add(a, b) {\n    // We duplicate Add here to avoid a circular dependency with add.ts.\n    const inputs = { a, b };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.add(a, b);\n        save([a, b]);\n        return res;\n    }, inputs, null /* gradient */, Add);\n}\n//# sourceMappingURL=engine.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line:no-any\nfunction _isNavigatorDefined() {\n    return typeof navigator !== 'undefined' && navigator != null;\n}\nexport function isMobile() {\n    if (_isNavigatorDefined()) {\n        // tslint:disable-next-line:no-any\n        const a = navigator.userAgent || navigator.vendor || window.opera;\n        // tslint:disable-next-line:max-line-length\n        return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n            .test(a) ||\n            // tslint:disable-next-line:max-line-length\n            /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i\n                .test(a.substr(0, 4));\n    }\n    return false;\n}\nexport function isBrowser() {\n    return (typeof window !== 'undefined' && window.document != null) ||\n        //@ts-ignore\n        (typeof WorkerGlobalScope !== 'undefined');\n}\n//# sourceMappingURL=device_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './engine';\nimport * as device_util from './device_util';\nimport { env } from './environment';\nconst ENV = env();\n/**\n * This file contains environment-related flag registrations.\n */\n/** Whether to enable debug mode. */\nENV.registerFlag('DEBUG', () => false, debugValue => {\n    if (debugValue) {\n        console.warn('Debugging mode is ON. The output of every math call will ' +\n            'be downloaded to CPU and checked for NaNs. ' +\n            'This significantly impacts performance.');\n    }\n});\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_BROWSER', () => device_util.isBrowser());\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_NODE', () => (typeof process !== 'undefined') &&\n    (typeof process.versions !== 'undefined') &&\n    (typeof process.versions.node !== 'undefined'));\n/** Whether this browser is Chrome. */\nENV.registerFlag('IS_CHROME', () => typeof navigator !== 'undefined' && navigator != null &&\n    navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&\n    /Google Inc/.test(navigator.vendor));\n/**\n * True when the environment is \"production\" where we disable safety checks\n * to gain performance.\n */\nENV.registerFlag('PROD', () => false);\n/**\n * Whether to do sanity checks when inferring a shape from user-provided\n * values, used when creating a new tensor.\n */\nENV.registerFlag('TENSORLIKE_CHECK_SHAPE_CONSISTENCY', () => ENV.getBool('DEBUG'));\n/** Whether deprecation warnings are enabled. */\nENV.registerFlag('DEPRECATION_WARNINGS_ENABLED', () => true);\n/** True if running unit tests. */\nENV.registerFlag('IS_TEST', () => false);\n//# sourceMappingURL=flags.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { env } from './environment';\nimport { Tensor } from './tensor';\nimport { assert, flatten, inferDtype, isTypedArray, toTypedArray } from './util';\nexport function inferShape(val, dtype) {\n    let firstElem = val;\n    if (isTypedArray(val)) {\n        return dtype === 'string' ? [] : [val.length];\n    }\n    if (!Array.isArray(val)) {\n        return []; // Scalar.\n    }\n    const shape = [];\n    while (Array.isArray(firstElem) ||\n        isTypedArray(firstElem) && dtype !== 'string') {\n        shape.push(firstElem.length);\n        firstElem = firstElem[0];\n    }\n    if (Array.isArray(val) &&\n        env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {\n        deepAssertShapeConsistency(val, shape, []);\n    }\n    return shape;\n}\nfunction deepAssertShapeConsistency(val, shape, indices) {\n    indices = indices || [];\n    if (!(Array.isArray(val)) && !isTypedArray(val)) {\n        assert(shape.length === 0, () => `Element arr[${indices.join('][')}] is a primitive, ` +\n            `but should be an array/TypedArray of ${shape[0]} elements`);\n        return;\n    }\n    assert(shape.length > 0, () => `Element arr[${indices.join('][')}] should be a primitive, ` +\n        `but is an array of ${val.length} elements`);\n    assert(val.length === shape[0], () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +\n        `elements, but has ${val.length} elements`);\n    const subShape = shape.slice(1);\n    for (let i = 0; i < val.length; ++i) {\n        deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n    }\n}\nfunction assertDtype(expectedDtype, actualDType, argName, functionName) {\n    if (expectedDtype == null) {\n        return;\n    }\n    if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||\n        expectedDtype === 'numeric' && actualDType === 'string') {\n        throw new Error(`Argument '${argName}' passed to '${functionName}' must ` +\n            `be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n    }\n}\nexport function convertToTensor(x, argName, functionName, parseAsDtype = 'numeric') {\n    if (x instanceof Tensor) {\n        assertDtype(parseAsDtype, x.dtype, argName, functionName);\n        return x;\n    }\n    let inferredDtype = inferDtype(x);\n    // If the user expects a bool/int/float, use that info to update the\n    // inferredDtype when it is not a string.\n    if (inferredDtype !== 'string' &&\n        ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {\n        inferredDtype = parseAsDtype;\n    }\n    assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n    if ((x == null) ||\n        (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&\n            typeof x !== 'boolean' && typeof x !== 'string')) {\n        const type = x == null ? 'null' : x.constructor.name;\n        throw new Error(`Argument '${argName}' passed to '${functionName}' must be a ` +\n            `Tensor or TensorLike, but got '${type}'`);\n    }\n    const inferredShape = inferShape(x, inferredDtype);\n    if (!isTypedArray(x) && !Array.isArray(x)) {\n        x = [x];\n    }\n    const skipTypedArray = true;\n    const values = inferredDtype !== 'string' ?\n        toTypedArray(x, inferredDtype) :\n        flatten(x, [], skipTypedArray);\n    return ENGINE.makeTensor(values, inferredShape, inferredDtype);\n}\nexport function convertToTensorArray(arg, argName, functionName, parseAsDtype = 'numeric') {\n    if (!Array.isArray(arg)) {\n        throw new Error(`Argument ${argName} passed to ${functionName} must be a ` +\n            '`Tensor[]` or `TensorLike[]`');\n    }\n    const tensors = arg;\n    return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName), parseAsDtype);\n}\n//# sourceMappingURL=tensor_util_env.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nexport const OP_SCOPE_SUFFIX = '__op';\n/**\n * Used for wrapping functions that perform math operations on\n * Tensors. The function will be wrapped in a named scope that cleans all\n * memory usage after the function is done.\n */\nexport function op(f) {\n    const keys = Object.keys(f);\n    if (keys.length !== 1) {\n        throw new Error(`Please provide an object with a single key ` +\n            `(operation name) mapping to a function. Got an object with ` +\n            `${keys.length} keys.`);\n    }\n    let opName = keys[0];\n    const fn = f[opName];\n    // Strip the underscore from the end of the function name.\n    if (opName.endsWith('_')) {\n        opName = opName.substring(0, opName.length - 1);\n    }\n    // add an __op suffix to distinguish ops from kernels in tf.profile\n    opName = opName + OP_SCOPE_SUFFIX;\n    // tslint:disable-next-line:no-any\n    const f2 = (...args) => {\n        ENGINE.startScope(opName);\n        try {\n            const result = fn(...args);\n            if (result instanceof Promise) {\n                console.error('Cannot return a Promise inside of tidy.');\n            }\n            ENGINE.endScope(result);\n            return result;\n        }\n        catch (ex) {\n            ENGINE.endScope(null);\n            throw ex;\n        }\n    };\n    Object.defineProperty(f2, 'name', { value: opName, configurable: true });\n    // tslint:disable-next-line:no-any\n    return f2;\n}\n//# sourceMappingURL=operation.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Complex } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction complex_(real, imag) {\n    const $real = convertToTensor(real, 'real', 'complex');\n    const $imag = convertToTensor(imag, 'imag', 'complex');\n    util.assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n        `must match in call to tf.complex().`);\n    const forward = (backend) => {\n        return backend.complex($real, $imag);\n    };\n    const inputs = { real: $real, imag: $imag };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Complex);\n}\nexport const complex = op({ complex_ });\n//# sourceMappingURL=complex.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { assert, assertNonNegativeIntegerDimensions, flatten, inferDtype, isTypedArray, sizeFromShape, toTypedArray } from '../util';\n/** This is shared code across all tensor creation methods. */\nexport function makeTensor(values, shape, inferredShape, dtype) {\n    if (dtype == null) {\n        dtype = inferDtype(values);\n    }\n    if (dtype === 'complex64') {\n        throw new Error(`Cannot construct a complex64 tensor directly. ` +\n            `Please use tf.complex(real, imag).`);\n    }\n    if (!isTypedArray(values) && !Array.isArray(values) &&\n        typeof values !== 'number' && typeof values !== 'boolean' &&\n        typeof values !== 'string') {\n        throw new Error('values passed to tensor(values) must be a number/boolean/string or ' +\n            'an array of numbers/booleans/strings, or a TypedArray');\n    }\n    if (shape != null) {\n        assertNonNegativeIntegerDimensions(shape);\n        const providedSize = sizeFromShape(shape);\n        const inferredSize = sizeFromShape(inferredShape);\n        assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ` +\n            `${providedSize} values but has ${inferredSize}`);\n        for (let i = 0; i < inferredShape.length; ++i) {\n            const inferred = inferredShape[i];\n            const flatDimsDontMatch = i === inferredShape.length - 1 ?\n                inferred !== sizeFromShape(shape.slice(i)) :\n                true;\n            assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape ` +\n                `(${inferredShape}) does not match the provided ` +\n                `shape (${shape}). `);\n        }\n    }\n    if (!isTypedArray(values) && !Array.isArray(values)) {\n        values = [values];\n    }\n    shape = shape || inferredShape;\n    values = dtype !== 'string' ?\n        toTypedArray(values, dtype) :\n        flatten(values, [], true);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=tensor_ops_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`. If the values are strings,\n *     they will be encoded as utf-8 and kept as `Uint8Array[]`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor(values, shape, dtype) {\n    const inferredShape = inferShape(values, dtype);\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/* Type definitions for exporting and importing of models. */\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP = {\n    'float32': 4,\n    'float16': 2,\n    'int32': 4,\n    'uint16': 2,\n    'uint8': 1,\n    'bool': 1,\n    'complex64': 8\n};\n//# sourceMappingURL=types.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../ops/complex';\nimport { tensor } from '../ops/tensor';\nimport { sizeFromShape } from '../util';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/** Number of bytes reserved for the length of the string. (32bit integer). */\nconst NUM_BYTES_STRING_LENGTH = 4;\n/**\n * Encode a map from names to weight values as an ArrayBuffer, along with an\n * `Array` of `WeightsManifestEntry` as specification of the encoded weights.\n *\n * This function does not perform sharding.\n *\n * This function is the reverse of `decodeWeights`.\n *\n * @param tensors A map (\"dict\") from names to tensors.\n * @param group Group to which the weights belong (optional).\n * @returns A `Promise` of\n *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s\n *     concatenated.\n *   - An `Array` of `WeightManifestEntry`s, carrying information including\n *     tensor names, `dtype`s and shapes.\n * @throws Error: on unsupported tensor `dtype`.\n */\nexport async function encodeWeights(tensors, group) {\n    // TODO(adarob, cais): Support quantization.\n    const specs = [];\n    const dataPromises = [];\n    const names = Array.isArray(tensors) ?\n        tensors.map(tensor => tensor.name) :\n        Object.keys(tensors);\n    for (let i = 0; i < names.length; ++i) {\n        const name = names[i];\n        const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];\n        if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool' &&\n            t.dtype !== 'string' && t.dtype !== 'complex64') {\n            throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n        }\n        const spec = { name, shape: t.shape, dtype: t.dtype };\n        if (t.dtype === 'string') {\n            const utf8bytes = new Promise(async (resolve) => {\n                const vals = await t.bytes();\n                const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) +\n                    NUM_BYTES_STRING_LENGTH * vals.length;\n                const bytes = new Uint8Array(totalNumBytes);\n                let offset = 0;\n                for (let i = 0; i < vals.length; i++) {\n                    const val = vals[i];\n                    const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);\n                    bytes.set(bytesOfLength, offset);\n                    offset += NUM_BYTES_STRING_LENGTH;\n                    bytes.set(val, offset);\n                    offset += val.length;\n                }\n                resolve(bytes);\n            });\n            dataPromises.push(utf8bytes);\n        }\n        else {\n            dataPromises.push(t.data());\n        }\n        if (group != null) {\n            spec.group = group;\n        }\n        specs.push(spec);\n    }\n    const tensorValues = await Promise.all(dataPromises);\n    return { data: concatenateTypedArrays(tensorValues), specs };\n}\n/**\n * Decode flat ArrayBuffer as weights.\n *\n * This function does not handle sharding.\n *\n * This function is the reverse of `encodeWeights`.\n *\n * @param buffer A flat ArrayBuffer carrying the binary values of the tensors\n *   concatenated in the order specified in `specs`.\n * @param specs Specifications of the names, dtypes and shapes of the tensors\n *   whose value are encoded by `buffer`.\n * @return A map from tensor name to tensor value, with the names corresponding\n *   to names in `specs`.\n * @throws Error, if any of the tensors has unsupported dtype.\n */\nexport function decodeWeights(buffer, specs) {\n    // TODO(adarob, cais): Support quantization.\n    const out = {};\n    let float16Decode;\n    let offset = 0;\n    for (const spec of specs) {\n        const name = spec.name;\n        const dtype = spec.dtype;\n        const shape = spec.shape;\n        const size = sizeFromShape(shape);\n        let values;\n        if ('quantization' in spec) {\n            const quantization = spec.quantization;\n            if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n                if (!('min' in quantization && 'scale' in quantization)) {\n                    throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} ` +\n                        `doesn't have corresponding metadata min and scale.`);\n                }\n            }\n            else if (quantization.dtype === 'float16') {\n                if (dtype !== 'float32') {\n                    throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} ` +\n                        `which only supports weights of type float32 not ${dtype}.`);\n                }\n            }\n            else {\n                throw new Error(`Weight ${spec.name} has unknown ` +\n                    `quantization dtype ${quantization.dtype}. ` +\n                    `Supported quantization dtypes are: ` +\n                    `'uint8', 'uint16', and 'float16'.`);\n            }\n            const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n            const byteBuffer = buffer.slice(offset, offset + size * quantizationSizeFactor);\n            const quantizedArray = (quantization.dtype === 'uint8') ?\n                new Uint8Array(byteBuffer) :\n                new Uint16Array(byteBuffer);\n            if (dtype === 'float32') {\n                if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n                    values = new Float32Array(quantizedArray.length);\n                    for (let i = 0; i < quantizedArray.length; i++) {\n                        const v = quantizedArray[i];\n                        values[i] = v * quantization.scale + quantization.min;\n                    }\n                }\n                else if (quantization.dtype === 'float16') {\n                    if (float16Decode === undefined) {\n                        float16Decode = getFloat16Decoder();\n                    }\n                    values = float16Decode(quantizedArray);\n                }\n                else {\n                    throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n                        `for weight type float32.`);\n                }\n            }\n            else if (dtype === 'int32') {\n                if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {\n                    throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n                        `for weight type int32.`);\n                }\n                values = new Int32Array(quantizedArray.length);\n                for (let i = 0; i < quantizedArray.length; i++) {\n                    const v = quantizedArray[i];\n                    values[i] = Math.round(v * quantization.scale + quantization.min);\n                }\n            }\n            else {\n                throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n            }\n            offset += size * quantizationSizeFactor;\n        }\n        else if (dtype === 'string') {\n            const size = sizeFromShape(spec.shape);\n            values = [];\n            for (let i = 0; i < size; i++) {\n                const byteLength = new Uint32Array(buffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];\n                offset += NUM_BYTES_STRING_LENGTH;\n                const bytes = new Uint8Array(buffer.slice(offset, offset + byteLength));\n                values.push(bytes);\n                offset += byteLength;\n            }\n        }\n        else {\n            const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n            const byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);\n            if (dtype === 'float32') {\n                values = new Float32Array(byteBuffer);\n            }\n            else if (dtype === 'int32') {\n                values = new Int32Array(byteBuffer);\n            }\n            else if (dtype === 'bool') {\n                values = new Uint8Array(byteBuffer);\n            }\n            else if (dtype === 'complex64') {\n                values = new Float32Array(byteBuffer);\n                const real = new Float32Array(values.length / 2);\n                const image = new Float32Array(values.length / 2);\n                for (let i = 0; i < real.length; i++) {\n                    real[i] = values[i * 2];\n                    image[i] = values[i * 2 + 1];\n                }\n                const realTensor = tensor(real, shape, 'float32');\n                const imageTensor = tensor(image, shape, 'float32');\n                out[name] = complex(realTensor, imageTensor);\n            }\n            else {\n                throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n            }\n            offset += size * dtypeFactor;\n        }\n        if (dtype !== 'complex64') {\n            out[name] = tensor(values, shape, dtype);\n        }\n    }\n    return out;\n}\n/**\n * Concatenate TypedArrays into an ArrayBuffer.\n */\nexport function concatenateTypedArrays(xs) {\n    // TODO(adarob, cais): Support quantization.\n    if (xs === null) {\n        throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n    }\n    let totalByteLength = 0;\n    // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'\n    // can have a different byte length from that of the `TypedArray` itself,\n    // for example, when the `TypedArray` is created from an offset in an\n    // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match\n    // the `TypedArray` in byte length. If an element of `xs` does not show\n    // this property, a new `TypedArray` that satisfy this property will be\n    // constructed and pushed into `normalizedXs`.\n    const normalizedXs = [];\n    xs.forEach((x) => {\n        totalByteLength += x.byteLength;\n        // tslint:disable:no-any\n        normalizedXs.push(x.byteLength === x.buffer.byteLength ? x :\n            new x.constructor(x));\n        if (!(x instanceof Float32Array || x instanceof Int32Array ||\n            x instanceof Uint8Array)) {\n            throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n        }\n        // tslint:enable:no-any\n    });\n    const y = new Uint8Array(totalByteLength);\n    let offset = 0;\n    normalizedXs.forEach((x) => {\n        y.set(new Uint8Array(x.buffer), offset);\n        offset += x.byteLength;\n    });\n    return y.buffer;\n}\n// Use Buffer on Node.js instead of Blob/atob/btoa\nconst useNodeBuffer = typeof Buffer !== 'undefined' &&\n    (typeof Blob === 'undefined' || typeof atob === 'undefined' ||\n        typeof btoa === 'undefined');\n/**\n * Calculate the byte length of a JavaScript string.\n *\n * Note that a JavaScript string can contain wide characters, therefore the\n * length of the string is not necessarily equal to the byte length.\n *\n * @param str Input string.\n * @returns Byte length.\n */\nexport function stringByteLength(str) {\n    if (useNodeBuffer) {\n        return Buffer.byteLength(str);\n    }\n    return new Blob([str]).size;\n}\n/**\n * Encode an ArrayBuffer as a base64 encoded string.\n *\n * @param buffer `ArrayBuffer` to be converted.\n * @returns A string that base64-encodes `buffer`.\n */\nexport function arrayBufferToBase64String(buffer) {\n    if (useNodeBuffer) {\n        return Buffer.from(buffer).toString('base64');\n    }\n    const buf = new Uint8Array(buffer);\n    let s = '';\n    for (let i = 0, l = buf.length; i < l; i++) {\n        s += String.fromCharCode(buf[i]);\n    }\n    return btoa(s);\n}\n/**\n * Decode a base64 string as an ArrayBuffer.\n *\n * @param str Base64 string.\n * @returns Decoded `ArrayBuffer`.\n */\nexport function base64StringToArrayBuffer(str) {\n    if (useNodeBuffer) {\n        const buf = Buffer.from(str, 'base64');\n        return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n    }\n    const s = atob(str);\n    const buffer = new Uint8Array(s.length);\n    for (let i = 0; i < s.length; ++i) {\n        buffer.set([s.charCodeAt(i)], i);\n    }\n    return buffer.buffer;\n}\n/**\n * Concatenate a number of ArrayBuffers into one.\n *\n * @param buffers A number of array buffers to concatenate.\n * @returns Result of concatenating `buffers` in order.\n */\nexport function concatenateArrayBuffers(buffers) {\n    if (buffers.length === 1) {\n        return buffers[0];\n    }\n    let totalByteLength = 0;\n    buffers.forEach((buffer) => {\n        totalByteLength += buffer.byteLength;\n    });\n    const temp = new Uint8Array(totalByteLength);\n    let offset = 0;\n    buffers.forEach((buffer) => {\n        temp.set(new Uint8Array(buffer), offset);\n        offset += buffer.byteLength;\n    });\n    return temp.buffer;\n}\n/**\n * Get the basename of a path.\n *\n * Behaves in a way analogous to Linux's basename command.\n *\n * @param path\n */\nexport function basename(path) {\n    const SEPARATOR = '/';\n    path = path.trim();\n    while (path.endsWith(SEPARATOR)) {\n        path = path.slice(0, path.length - 1);\n    }\n    const items = path.split(SEPARATOR);\n    return items[items.length - 1];\n}\n/**\n * Populate ModelArtifactsInfo fields for a model with JSON topology.\n * @param modelArtifacts\n * @returns A ModelArtifactsInfo object.\n */\nexport function getModelArtifactsInfoForJSON(modelArtifacts) {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n        throw new Error('Expected JSON model topology, received ArrayBuffer.');\n    }\n    return {\n        dateSaved: new Date(),\n        modelTopologyType: 'JSON',\n        modelTopologyBytes: modelArtifacts.modelTopology == null ?\n            0 :\n            stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n        weightSpecsBytes: modelArtifacts.weightSpecs == null ?\n            0 :\n            stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n        weightDataBytes: modelArtifacts.weightData == null ?\n            0 :\n            modelArtifacts.weightData.byteLength,\n    };\n}\n/**\n * Computes mantisa table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 2048 mantissa lookup values.\n */\nfunction computeFloat16MantisaTable() {\n    const convertMantissa = (i) => {\n        let m = i << 13;\n        let e = 0;\n        while ((m & 0x00800000) === 0) {\n            e -= 0x00800000;\n            m <<= 1;\n        }\n        m &= ~0x00800000;\n        e += 0x38800000;\n        return m | e;\n    };\n    const mantisaTable = new Uint32Array(2048);\n    mantisaTable[0] = 0;\n    for (let i = 1; i < 1024; i++) {\n        mantisaTable[i] = convertMantissa(i);\n    }\n    for (let i = 1024; i < 2048; i++) {\n        mantisaTable[i] = 0x38000000 + ((i - 1024) << 13);\n    }\n    return mantisaTable;\n}\n/**\n * Computes exponent table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 64 exponent lookup values.\n */\nfunction computeFloat16ExponentTable() {\n    const exponentTable = new Uint32Array(64);\n    exponentTable[0] = 0;\n    exponentTable[31] = 0x47800000;\n    exponentTable[32] = 0x80000000;\n    exponentTable[63] = 0xc7800000;\n    for (let i = 1; i < 31; i++) {\n        exponentTable[i] = i << 23;\n    }\n    for (let i = 33; i < 63; i++) {\n        exponentTable[i] = 0x80000000 + ((i - 32) << 23);\n    }\n    return exponentTable;\n}\n/**\n * Computes offset table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 6d offset values.\n */\nfunction computeFloat16OffsetTable() {\n    const offsetTable = new Uint32Array(64);\n    for (let i = 0; i < 64; i++) {\n        offsetTable[i] = 1024;\n    }\n    offsetTable[0] = offsetTable[32] = 0;\n    return offsetTable;\n}\n/**\n * Retrieve a Float16 decoder which will decode a ByteArray of Float16 values\n * to a Float32Array.\n *\n * @returns Function (buffer: Uint16Array) => Float32Array which decodes\n *          the Uint16Array of Float16 bytes to a Float32Array.\n */\nexport function getFloat16Decoder() {\n    // Algorithm is based off of\n    // http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n    // Cache lookup tables\n    const mantisaTable = computeFloat16MantisaTable();\n    const exponentTable = computeFloat16ExponentTable();\n    const offsetTable = computeFloat16OffsetTable();\n    return (quantizedArray) => {\n        const buffer = new ArrayBuffer(4 * quantizedArray.length);\n        const bufferUint32View = new Uint32Array(buffer);\n        for (let index = 0; index < quantizedArray.length; index++) {\n            const float16Bits = quantizedArray[index];\n            const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 0x3ff)] +\n                exponentTable[float16Bits >> 10];\n            bufferUint32View[index] = float32Bits;\n        }\n        return new Float32Array(buffer);\n    };\n}\n//# sourceMappingURL=io_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class IORouterRegistry {\n    constructor() {\n        this.saveRouters = [];\n        this.loadRouters = [];\n    }\n    static getInstance() {\n        if (IORouterRegistry.instance == null) {\n            IORouterRegistry.instance = new IORouterRegistry();\n        }\n        return IORouterRegistry.instance;\n    }\n    /**\n     * Register a save-handler router.\n     *\n     * @param saveRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `save` method defined or `null`.\n     */\n    static registerSaveRouter(saveRouter) {\n        IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n    }\n    /**\n     * Register a load-handler router.\n     *\n     * @param loadRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `load` method defined or `null`.\n     */\n    static registerLoadRouter(loadRouter) {\n        IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n    }\n    /**\n     * Look up IOHandler for saving, given a URL-like string.\n     *\n     * @param url\n     * @returns If only one match is found, an instance of IOHandler with the\n     * `save` method defined. If no match is found, `null`.\n     * @throws Error, if more than one match is found.\n     */\n    static getSaveHandlers(url) {\n        return IORouterRegistry.getHandlers(url, 'save');\n    }\n    /**\n     * Look up IOHandler for loading, given a URL-like string.\n     *\n     * @param url\n     * @param loadOptions Optional, custom load options.\n     * @returns All valid handlers for `url`, given the currently registered\n     *   handler routers.\n     */\n    static getLoadHandlers(url, loadOptions) {\n        return IORouterRegistry.getHandlers(url, 'load', loadOptions);\n    }\n    static getHandlers(url, handlerType, loadOptions) {\n        const validHandlers = [];\n        const routers = handlerType === 'load' ?\n            IORouterRegistry.getInstance().loadRouters :\n            IORouterRegistry.getInstance().saveRouters;\n        routers.forEach(router => {\n            const handler = router(url, loadOptions);\n            if (handler !== null) {\n                validHandlers.push(handler);\n            }\n        });\n        return validHandlers;\n    }\n}\nexport const registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);\nexport const registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);\nexport const getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);\nexport const getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);\n//# sourceMappingURL=router_registry.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DATABASE_NAME = 'tensorflowjs';\nconst DATABASE_VERSION = 1;\n// Model data and ModelArtifactsInfo (metadata) are stored in two separate\n// stores for efficient access of the list of stored models and their metadata.\n// 1. The object store for model data: topology, weights and weight manifests.\nconst MODEL_STORE_NAME = 'models_store';\n// 2. The object store for ModelArtifactsInfo, including meta-information such\n//    as the type of topology (JSON vs binary), byte size of the topology, byte\n//    size of the weights, etc.\nconst INFO_STORE_NAME = 'model_info_store';\n/**\n * Delete the entire database for tensorflow.js, including the models store.\n */\nexport async function deleteDatabase() {\n    const idbFactory = getIndexedDBFactory();\n    return new Promise((resolve, reject) => {\n        const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);\n        deleteRequest.onsuccess = () => resolve();\n        deleteRequest.onerror = error => reject(error);\n    });\n}\nfunction getIndexedDBFactory() {\n    if (!env().getBool('IS_BROWSER')) {\n        // TODO(cais): Add more info about what IOHandler subtypes are available.\n        //   Maybe point to a doc page on the web and/or automatically determine\n        //   the available IOHandlers and print them in the error message.\n        throw new Error('Failed to obtain IndexedDB factory because the current environment' +\n            'is not a web browser.');\n    }\n    // tslint:disable-next-line:no-any\n    const theWindow = typeof window === 'undefined' ? self : window;\n    const factory = theWindow.indexedDB || theWindow.mozIndexedDB ||\n        theWindow.webkitIndexedDB || theWindow.msIndexedDB ||\n        theWindow.shimIndexedDB;\n    if (factory == null) {\n        throw new Error('The current browser does not appear to support IndexedDB.');\n    }\n    return factory;\n}\nfunction setUpDatabase(openRequest) {\n    const db = openRequest.result;\n    db.createObjectStore(MODEL_STORE_NAME, { keyPath: 'modelPath' });\n    db.createObjectStore(INFO_STORE_NAME, { keyPath: 'modelPath' });\n}\n/**\n * IOHandler subclass: Browser IndexedDB.\n *\n * See the doc string of `browserIndexedDB` for more details.\n */\nexport class BrowserIndexedDB {\n    constructor(modelPath) {\n        this.indexedDB = getIndexedDBFactory();\n        if (modelPath == null || !modelPath) {\n            throw new Error('For IndexedDB, modelPath must not be null, undefined or empty.');\n        }\n        this.modelPath = modelPath;\n    }\n    async save(modelArtifacts) {\n        // TODO(cais): Support saving GraphDef models.\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        return this.databaseAction(this.modelPath, modelArtifacts);\n    }\n    async load() {\n        return this.databaseAction(this.modelPath);\n    }\n    /**\n     * Perform database action to put model artifacts into or read model artifacts\n     * from IndexedDB object store.\n     *\n     * Whether the action is put or get depends on whether `modelArtifacts` is\n     * specified. If it is specified, the action will be put; otherwise the action\n     * will be get.\n     *\n     * @param modelPath A unique string path for the model.\n     * @param modelArtifacts If specified, it will be the model artifacts to be\n     *   stored in IndexedDB.\n     * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`\n     *   of `ModelArtifacts`, if the action is get.\n     */\n    databaseAction(modelPath, modelArtifacts) {\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                if (modelArtifacts == null) {\n                    // Read model out from object store.\n                    const modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');\n                    const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                    const getRequest = modelStore.get(this.modelPath);\n                    getRequest.onsuccess = () => {\n                        if (getRequest.result == null) {\n                            db.close();\n                            return reject(new Error(`Cannot find model with path '${this.modelPath}' ` +\n                                `in IndexedDB.`));\n                        }\n                        else {\n                            resolve(getRequest.result.modelArtifacts);\n                        }\n                    };\n                    getRequest.onerror = error => {\n                        db.close();\n                        return reject(getRequest.error);\n                    };\n                    modelTx.oncomplete = () => db.close();\n                }\n                else {\n                    // Put model into object store.\n                    const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n                    // First, put ModelArtifactsInfo into info store.\n                    const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n                    let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                    const putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo });\n                    let modelTx;\n                    putInfoRequest.onsuccess = () => {\n                        // Second, put model data into model store.\n                        modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n                        const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                        const putModelRequest = modelStore.put({\n                            modelPath: this.modelPath,\n                            modelArtifacts,\n                            modelArtifactsInfo\n                        });\n                        putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo });\n                        putModelRequest.onerror = error => {\n                            // If the put-model request fails, roll back the info entry as\n                            // well.\n                            infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                            const deleteInfoRequest = infoStore.delete(this.modelPath);\n                            deleteInfoRequest.onsuccess = () => {\n                                db.close();\n                                return reject(putModelRequest.error);\n                            };\n                            deleteInfoRequest.onerror = error => {\n                                db.close();\n                                return reject(putModelRequest.error);\n                            };\n                        };\n                    };\n                    putInfoRequest.onerror = error => {\n                        db.close();\n                        return reject(putInfoRequest.error);\n                    };\n                    infoTx.oncomplete = () => {\n                        if (modelTx == null) {\n                            db.close();\n                        }\n                        else {\n                            modelTx.oncomplete = () => db.close();\n                        }\n                    };\n                }\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n}\nBrowserIndexedDB.URL_SCHEME = 'indexeddb://';\nexport const indexedDBRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n            return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\n/**\n * Creates a browser IndexedDB IOHandler for saving and loading models.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save('indexeddb://MyModel'));\n * console.log(saveResult);\n * ```\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),\n *   which can be used with, e.g., `tf.Model.save`.\n */\nexport function browserIndexedDB(modelPath) {\n    return new BrowserIndexedDB(modelPath);\n}\nfunction maybeStripScheme(key) {\n    return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?\n        key.slice(BrowserIndexedDB.URL_SCHEME.length) :\n        key;\n}\nexport class BrowserIndexedDBManager {\n    constructor() {\n        this.indexedDB = getIndexedDBFactory();\n    }\n    async listModels() {\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                const tx = db.transaction(INFO_STORE_NAME, 'readonly');\n                const store = tx.objectStore(INFO_STORE_NAME);\n                // tslint:disable:max-line-length\n                // Need to cast `store` as `any` here because TypeScript's DOM\n                // library does not have the `getAll()` method even though the\n                // method is supported in the latest version of most mainstream\n                // browsers:\n                // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll\n                // tslint:enable:max-line-length\n                // tslint:disable-next-line:no-any\n                const getAllInfoRequest = store.getAll();\n                getAllInfoRequest.onsuccess = () => {\n                    const out = {};\n                    for (const item of getAllInfoRequest.result) {\n                        out[item.modelPath] = item.modelArtifactsInfo;\n                    }\n                    resolve(out);\n                };\n                getAllInfoRequest.onerror = error => {\n                    db.close();\n                    return reject(getAllInfoRequest.error);\n                };\n                tx.oncomplete = () => db.close();\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n    async removeModel(path) {\n        path = maybeStripScheme(path);\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n                const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                const getInfoRequest = infoStore.get(path);\n                let modelTx;\n                getInfoRequest.onsuccess = () => {\n                    if (getInfoRequest.result == null) {\n                        db.close();\n                        return reject(new Error(`Cannot find model with path '${path}' ` +\n                            `in IndexedDB.`));\n                    }\n                    else {\n                        // First, delete the entry in the info store.\n                        const deleteInfoRequest = infoStore.delete(path);\n                        const deleteModelData = () => {\n                            // Second, delete the entry in the model store.\n                            modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n                            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                            const deleteModelRequest = modelStore.delete(path);\n                            deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo);\n                            deleteModelRequest.onerror = error => reject(getInfoRequest.error);\n                        };\n                        // Proceed with deleting model data regardless of whether deletion\n                        // of info data succeeds or not.\n                        deleteInfoRequest.onsuccess = deleteModelData;\n                        deleteInfoRequest.onerror = error => {\n                            deleteModelData();\n                            db.close();\n                            return reject(getInfoRequest.error);\n                        };\n                    }\n                };\n                getInfoRequest.onerror = error => {\n                    db.close();\n                    return reject(getInfoRequest.error);\n                };\n                infoTx.oncomplete = () => {\n                    if (modelTx == null) {\n                        db.close();\n                    }\n                    else {\n                        modelTx.oncomplete = () => db.close();\n                    }\n                };\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n}\n//# sourceMappingURL=indexed_db.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { arrayBufferToBase64String, base64StringToArrayBuffer, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst PATH_SEPARATOR = '/';\nconst PATH_PREFIX = 'tensorflowjs_models';\nconst INFO_SUFFIX = 'info';\nconst MODEL_TOPOLOGY_SUFFIX = 'model_topology';\nconst WEIGHT_SPECS_SUFFIX = 'weight_specs';\nconst WEIGHT_DATA_SUFFIX = 'weight_data';\nconst MODEL_METADATA_SUFFIX = 'model_metadata';\n/**\n * Purge all tensorflow.js-saved model artifacts from local storage.\n *\n * @returns Paths of the models purged.\n */\nexport function purgeLocalStorageArtifacts() {\n    if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n        typeof window.localStorage === 'undefined') {\n        throw new Error('purgeLocalStorageModels() cannot proceed because local storage is ' +\n            'unavailable in the current environment.');\n    }\n    const LS = window.localStorage;\n    const purgedModelPaths = [];\n    for (let i = 0; i < LS.length; ++i) {\n        const key = LS.key(i);\n        const prefix = PATH_PREFIX + PATH_SEPARATOR;\n        if (key.startsWith(prefix) && key.length > prefix.length) {\n            LS.removeItem(key);\n            const modelName = getModelPathFromKey(key);\n            if (purgedModelPaths.indexOf(modelName) === -1) {\n                purgedModelPaths.push(modelName);\n            }\n        }\n    }\n    return purgedModelPaths;\n}\nfunction getModelKeys(path) {\n    return {\n        info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n        topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n        weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n        weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),\n        modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)\n    };\n}\n/**\n * Get model path from a local-storage key.\n *\n * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'\n *\n * @param key\n */\nfunction getModelPathFromKey(key) {\n    const items = key.split(PATH_SEPARATOR);\n    if (items.length < 3) {\n        throw new Error(`Invalid key format: ${key}`);\n    }\n    return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\nfunction maybeStripScheme(key) {\n    return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?\n        key.slice(BrowserLocalStorage.URL_SCHEME.length) :\n        key;\n}\n/**\n * IOHandler subclass: Browser Local Storage.\n *\n * See the doc string to `browserLocalStorage` for more details.\n */\nexport class BrowserLocalStorage {\n    constructor(modelPath) {\n        if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n            typeof window.localStorage === 'undefined') {\n            // TODO(cais): Add more info about what IOHandler subtypes are\n            // available.\n            //   Maybe point to a doc page on the web and/or automatically determine\n            //   the available IOHandlers and print them in the error message.\n            throw new Error('The current environment does not support local storage.');\n        }\n        this.LS = window.localStorage;\n        if (modelPath == null || !modelPath) {\n            throw new Error('For local storage, modelPath must not be null, undefined or empty.');\n        }\n        this.modelPath = modelPath;\n        this.keys = getModelKeys(this.modelPath);\n    }\n    /**\n     * Save model artifacts to browser local storage.\n     *\n     * See the documentation to `browserLocalStorage` for details on the saved\n     * artifacts.\n     *\n     * @param modelArtifacts The model artifacts to be stored.\n     * @returns An instance of SaveResult.\n     */\n    async save(modelArtifacts) {\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        else {\n            const topology = JSON.stringify(modelArtifacts.modelTopology);\n            const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n            const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n            try {\n                this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n                this.LS.setItem(this.keys.topology, topology);\n                this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n                this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData));\n                this.LS.setItem(this.keys.modelMetadata, JSON.stringify({\n                    format: modelArtifacts.format,\n                    generatedBy: modelArtifacts.generatedBy,\n                    convertedBy: modelArtifacts.convertedBy,\n                    userDefinedMetadata: modelArtifacts.userDefinedMetadata\n                }));\n                return { modelArtifactsInfo };\n            }\n            catch (err) {\n                // If saving failed, clean up all items saved so far.\n                this.LS.removeItem(this.keys.info);\n                this.LS.removeItem(this.keys.topology);\n                this.LS.removeItem(this.keys.weightSpecs);\n                this.LS.removeItem(this.keys.weightData);\n                this.LS.removeItem(this.keys.modelMetadata);\n                throw new Error(`Failed to save model '${this.modelPath}' to local storage: ` +\n                    `size quota being exceeded is a possible cause of this failure: ` +\n                    `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` +\n                    `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` +\n                    `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n            }\n        }\n    }\n    /**\n     * Load a model from local storage.\n     *\n     * See the documentation to `browserLocalStorage` for details on the saved\n     * artifacts.\n     *\n     * @returns The loaded model (if loading succeeds).\n     */\n    async load() {\n        const info = JSON.parse(this.LS.getItem(this.keys.info));\n        if (info == null) {\n            throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);\n        }\n        if (info.modelTopologyType !== 'JSON') {\n            throw new Error('BrowserLocalStorage does not support loading non-JSON model ' +\n                'topology yet.');\n        }\n        const out = {};\n        // Load topology.\n        const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n        if (topology == null) {\n            throw new Error(`In local storage, the topology of model '${this.modelPath}' ` +\n                `is missing.`);\n        }\n        out.modelTopology = topology;\n        // Load weight specs.\n        const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n        if (weightSpecs == null) {\n            throw new Error(`In local storage, the weight specs of model '${this.modelPath}' ` +\n                `are missing.`);\n        }\n        out.weightSpecs = weightSpecs;\n        // Load meta-data fields.\n        const metadataString = this.LS.getItem(this.keys.modelMetadata);\n        if (metadataString != null) {\n            const metadata = JSON.parse(metadataString);\n            out.format = metadata['format'];\n            out.generatedBy = metadata['generatedBy'];\n            out.convertedBy = metadata['convertedBy'];\n            out.userDefinedMetadata = metadata['userDefinedMetadata'];\n        }\n        // Load weight data.\n        const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n        if (weightDataBase64 == null) {\n            throw new Error(`In local storage, the binary weight values of model ` +\n                `'${this.modelPath}' are missing.`);\n        }\n        out.weightData = base64StringToArrayBuffer(weightDataBase64);\n        return out;\n    }\n}\nBrowserLocalStorage.URL_SCHEME = 'localstorage://';\nexport const localStorageRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n            return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\n/**\n * Factory function for local storage IOHandler.\n *\n * This `IOHandler` supports both `save` and `load`.\n *\n * For each model's saved artifacts, four items are saved to local storage.\n *   - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the\n *     model, such as date saved, type of the topology, size in bytes, etc.\n *   - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-\n *     style models, this is a stringized JSON.\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the\n *     model, can be used to decode the saved binary weight values (see\n *     item below).\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary\n *     weight values, stored as a base64-encoded string.\n *\n * Saving may throw an `Error` if the total size of the artifacts exceed the\n * browser-specific quota.\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `IOHandler`, which can be used with, e.g.,\n *   `tf.Model.save`.\n */\nexport function browserLocalStorage(modelPath) {\n    return new BrowserLocalStorage(modelPath);\n}\nexport class BrowserLocalStorageManager {\n    constructor() {\n        assert(env().getBool('IS_BROWSER'), () => 'Current environment is not a web browser');\n        assert(typeof window === 'undefined' ||\n            typeof window.localStorage !== 'undefined', () => 'Current browser does not appear to support localStorage');\n        this.LS = window.localStorage;\n    }\n    async listModels() {\n        const out = {};\n        const prefix = PATH_PREFIX + PATH_SEPARATOR;\n        const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n        for (let i = 0; i < this.LS.length; ++i) {\n            const key = this.LS.key(i);\n            if (key.startsWith(prefix) && key.endsWith(suffix)) {\n                const modelPath = getModelPathFromKey(key);\n                out[modelPath] = JSON.parse(this.LS.getItem(key));\n            }\n        }\n        return out;\n    }\n    async removeModel(path) {\n        path = maybeStripScheme(path);\n        const keys = getModelKeys(path);\n        if (this.LS.getItem(keys.info) == null) {\n            throw new Error(`Cannot find model at path '${path}'`);\n        }\n        const info = JSON.parse(this.LS.getItem(keys.info));\n        this.LS.removeItem(keys.info);\n        this.LS.removeItem(keys.topology);\n        this.LS.removeItem(keys.weightSpecs);\n        this.LS.removeItem(keys.weightData);\n        return info;\n    }\n}\n//# sourceMappingURL=local_storage.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Classes and functions for model management across multiple storage mediums.\n *\n * Supported client actions:\n * - Listing models on all registered storage mediums.\n * - Remove model by URL from any registered storage mediums, by using URL\n *   string.\n * - Moving or copying model from one path to another in the same medium or from\n *   one medium to another, by using URL strings.\n */\nimport { assert } from '../util';\nimport { IORouterRegistry } from './router_registry';\nconst URL_SCHEME_SUFFIX = '://';\nexport class ModelStoreManagerRegistry {\n    constructor() {\n        this.managers = {};\n    }\n    static getInstance() {\n        if (ModelStoreManagerRegistry.instance == null) {\n            ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n        }\n        return ModelStoreManagerRegistry.instance;\n    }\n    /**\n     * Register a save-handler router.\n     *\n     * @param saveRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `save` method defined or `null`.\n     */\n    static registerManager(scheme, manager) {\n        assert(scheme != null, () => 'scheme must not be undefined or null.');\n        if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n            scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n        }\n        assert(scheme.length > 0, () => 'scheme must not be an empty string.');\n        const registry = ModelStoreManagerRegistry.getInstance();\n        assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);\n        registry.managers[scheme] = manager;\n    }\n    static getManager(scheme) {\n        const manager = this.getInstance().managers[scheme];\n        if (manager == null) {\n            throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n        }\n        return manager;\n    }\n    static getSchemes() {\n        return Object.keys(this.getInstance().managers);\n    }\n}\n/**\n * Helper method for parsing a URL string into a scheme and a path.\n *\n * @param url E.g., 'localstorage://my-model'\n * @returns A dictionary with two fields: scheme and path.\n *   Scheme: e.g., 'localstorage' in the example above.\n *   Path: e.g., 'my-model' in the example above.\n */\nfunction parseURL(url) {\n    if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n        throw new Error(`The url string provided does not contain a scheme. ` +\n            `Supported schemes are: ` +\n            `${ModelStoreManagerRegistry.getSchemes().join(',')}`);\n    }\n    return {\n        scheme: url.split(URL_SCHEME_SUFFIX)[0],\n        path: url.split(URL_SCHEME_SUFFIX)[1],\n    };\n}\nasync function cloneModelInternal(sourceURL, destURL, deleteSource = false) {\n    assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);\n    const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n    assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);\n    assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n        `load handlers for source URL ${sourceURL}.`);\n    const loadHandler = loadHandlers[0];\n    const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n    assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination ` +\n        `URL ${destURL}.`);\n    assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n        `save handlers for destination URL ${destURL}.`);\n    const saveHandler = saveHandlers[0];\n    const sourceScheme = parseURL(sourceURL).scheme;\n    const sourcePath = parseURL(sourceURL).path;\n    const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n    const modelArtifacts = await loadHandler.load();\n    // If moving within the same storage medium, remove the old model as soon as\n    // the loading is done. Without doing this, it is possible that the combined\n    // size of the two models will cause the cloning to fail.\n    if (deleteSource && sameMedium) {\n        await ModelStoreManagerRegistry.getManager(sourceScheme)\n            .removeModel(sourcePath);\n    }\n    const saveResult = await saveHandler.save(modelArtifacts);\n    // If moving between mediums, the deletion is done after the save succeeds.\n    // This guards against the case in which saving to the destination medium\n    // fails.\n    if (deleteSource && !sameMedium) {\n        await ModelStoreManagerRegistry.getManager(sourceScheme)\n            .removeModel(sourcePath);\n    }\n    return saveResult.modelArtifactsInfo;\n}\n/**\n * List all models stored in registered storage mediums.\n *\n * For a web browser environment, the registered mediums are Local Storage and\n * IndexedDB.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @returns A `Promise` of a dictionary mapping URLs of existing models to\n * their model artifacts info. URLs include medium-specific schemes, e.g.,\n *   'indexeddb://my/model/1'. Model artifacts info include type of the\n * model's topology, byte sizes of the topology, weights, etc.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function listModels() {\n    const schemes = ModelStoreManagerRegistry.getSchemes();\n    const out = {};\n    for (const scheme of schemes) {\n        const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();\n        for (const path in schemeOut) {\n            const url = scheme + URL_SCHEME_SUFFIX + path;\n            out[url] = schemeOut[path];\n        }\n    }\n    return out;\n}\n/**\n * Remove a model specified by URL from a reigstered storage medium.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @param url A URL to a stored model, with a scheme prefix, e.g.,\n *   'localstorage://my-model-1', 'indexeddb://my/model/2'.\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n *   is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function removeModel(url) {\n    const schemeAndPath = parseURL(url);\n    const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n    return manager.removeModel(schemeAndPath.path);\n}\n/**\n * Copy a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Copying within a storage medium, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Copying between two storage mediums, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Copy the model, from Local Storage to IndexedDB.\n * await tf.io.copyModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove both models.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of copying.\n * @param destURL Destination URL of copying.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function copyModel(sourceURL, destURL) {\n    const deleteSource = false;\n    return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n/**\n * Move a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Moving within a storage medium, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Moving between two storage mediums, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Move the model, from Local Storage to IndexedDB.\n * await tf.io.moveModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove the moved model.\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of moving.\n * @param destURL Destination URL of moving.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function moveModel(sourceURL, destURL) {\n    const deleteSource = true;\n    return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\nexport { moveModel, copyModel, removeModel, listModels };\n//# sourceMappingURL=model_management.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { BrowserIndexedDB, BrowserIndexedDBManager } from '../io/indexed_db';\nimport { BrowserLocalStorage, BrowserLocalStorageManager } from '../io/local_storage';\nimport { ModelStoreManagerRegistry } from '../io/model_management';\nexport class PlatformBrowser {\n    fetch(path, init) {\n        return fetch(path, init);\n    }\n    now() {\n        return performance.now();\n    }\n    encode(text, encoding) {\n        if (encoding !== 'utf-8' && encoding !== 'utf8') {\n            throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);\n        }\n        if (this.textEncoder == null) {\n            this.textEncoder = new TextEncoder();\n        }\n        return this.textEncoder.encode(text);\n    }\n    decode(bytes, encoding) {\n        return new TextDecoder(encoding).decode(bytes);\n    }\n}\nif (env().get('IS_BROWSER')) {\n    env().setPlatform('browser', new PlatformBrowser());\n    // Register LocalStorage IOHandler\n    try {\n        ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n    }\n    catch (err) {\n    }\n    // Register IndexedDB IOHandler\n    try {\n        ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n    }\n    catch (err) {\n    }\n}\n//# sourceMappingURL=platform_browser.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\n// We are wrapping this within an object so it can be stubbed by Jasmine.\nexport const getNodeFetch = {\n    // tslint:disable-next-line:no-require-imports\n    importFetch: () => require('node-fetch')\n};\nlet systemFetch;\n// These getters and setters are for testing so we don't export a mutable\n// variable.\nexport function resetSystemFetch() {\n    systemFetch = null;\n}\nexport function setSystemFetch(fetchFn) {\n    systemFetch = fetchFn;\n}\nexport function getSystemFetch() {\n    return systemFetch;\n}\nexport class PlatformNode {\n    constructor() {\n        // tslint:disable-next-line:no-require-imports\n        this.util = require('util');\n        // According to the spec, the built-in encoder can do only UTF-8 encoding.\n        // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder\n        this.textEncoder = new this.util.TextEncoder();\n    }\n    fetch(path, requestInits) {\n        if (env().global.fetch != null) {\n            return env().global.fetch(path, requestInits);\n        }\n        if (systemFetch == null) {\n            systemFetch = getNodeFetch.importFetch();\n        }\n        return systemFetch(path, requestInits);\n    }\n    now() {\n        const time = process.hrtime();\n        return time[0] * 1000 + time[1] / 1000000;\n    }\n    encode(text, encoding) {\n        if (encoding !== 'utf-8' && encoding !== 'utf8') {\n            throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);\n        }\n        return this.textEncoder.encode(text);\n    }\n    decode(bytes, encoding) {\n        if (bytes.length === 0) {\n            return '';\n        }\n        return new this.util.TextDecoder(encoding).decode(bytes);\n    }\n}\nif (env().get('IS_NODE')) {\n    env().setPlatform('node', new PlatformNode());\n}\n//# sourceMappingURL=platform_node.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport * as util from '../util';\n/**\n * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.\n *\n * The values are stored in CPU as `TypedArray`. Fill the buffer using\n * `buffer.set()`, or by modifying directly `buffer.values`.\n *\n * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with\n * those values.\n *\n * ```js\n * // Create a buffer and set values at particular indices.\n * const buffer = tf.buffer([2, 2]);\n * buffer.set(3, 0, 0);\n * buffer.set(5, 1, 0);\n *\n * // Convert the buffer back to a tensor.\n * buffer.toTensor().print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The dtype of the buffer. Defaults to 'float32'.\n * @param values The values of the buffer as `TypedArray`. Defaults to\n * zeros.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function buffer(shape, dtype = 'float32', values) {\n    dtype = dtype || 'float32';\n    util.assertNonNegativeIntegerDimensions(shape);\n    return new TensorBuffer(shape, dtype, values);\n}\n//# sourceMappingURL=buffer.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cast } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Casts a `tf.Tensor` to a new dtype.\n *\n * ```js\n * const x = tf.tensor1d([1.5, 2.5, 3]);\n * tf.cast(x, 'int32').print();\n * ```\n * @param x The input tensor to be casted.\n * @param dtype The dtype to cast the input tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction cast_(x, dtype) {\n    const $x = convertToTensor(x, 'x', 'cast');\n    // Sanity checks.\n    if (!util.isValidDtype(dtype)) {\n        throw new Error(`Failed to cast to unknown dtype ${dtype}`);\n    }\n    if (dtype === 'string' && $x.dtype !== 'string' ||\n        dtype !== 'string' && $x.dtype === 'string') {\n        throw new Error('Only strings can be casted to strings');\n    }\n    const inputs = { x: $x };\n    const attrs = { dtype };\n    return ENGINE.runKernelFunc(backend => backend.cast($x, dtype), inputs, null /* grad */, Cast, attrs);\n}\nexport const cast = op({ cast_ });\n//# sourceMappingURL=cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Identity } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction clone_(x) {\n    const $x = convertToTensor(x, 'x', 'clone', null);\n    const forward = () => ENGINE.makeTensorFromDataId($x.dataId, $x.shape, $x.dtype);\n    const inputs = { x: $x };\n    // Note this op is called tf.identity in python. Hence the kernel name used\n    // here.\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Identity);\n}\nexport const clone = op({ clone_ });\n//# sourceMappingURL=clone.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function print(x, verbose = false) {\n    console.log(x.toString(verbose));\n}\n//# sourceMappingURL=print.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Required side effectful code for tfjs-core\n// Set up Engine and ENV\nimport { getOrMakeEngine } from './engine';\ngetOrMakeEngine();\n// Register backend-agnostic flags.\nimport './flags';\n// Register platforms\nimport './platforms/platform_browser';\nimport './platforms/platform_node';\n// Set up OpHandler\nimport { buffer } from './ops/buffer';\nimport { cast } from './ops/cast';\nimport { clone } from './ops/clone';\nimport { print } from './ops/print';\nimport { setOpHandler } from './tensor';\nconst opHandler = {\n    buffer,\n    cast,\n    clone,\n    print\n};\nsetOpHandler(opHandler);\n//# sourceMappingURL=base_side_effects.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandlers related to files, such as browser-triggered file downloads,\n * user-selected files in browser.\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { basename, concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DEFAULT_FILE_NAME_PREFIX = 'model';\nconst DEFAULT_JSON_EXTENSION_NAME = '.json';\nconst DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';\nfunction defer(f) {\n    return new Promise(resolve => setTimeout(resolve)).then(f);\n}\nexport class BrowserDownloads {\n    constructor(fileNamePrefix) {\n        if (!env().getBool('IS_BROWSER')) {\n            // TODO(cais): Provide info on what IOHandlers are available under the\n            //   current environment.\n            throw new Error('browserDownloads() cannot proceed because the current environment ' +\n                'is not a browser.');\n        }\n        if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n            fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n        }\n        if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n            fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n        }\n        this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n        this.weightDataFileName =\n            fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n    }\n    async save(modelArtifacts) {\n        if (typeof (document) === 'undefined') {\n            throw new Error('Browser downloads are not supported in ' +\n                'this environment since `document` is not present');\n        }\n        const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: 'application/octet-stream' }));\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserDownloads.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        else {\n            const weightsManifest = [{\n                    paths: ['./' + this.weightDataFileName],\n                    weights: modelArtifacts.weightSpecs\n                }];\n            const modelTopologyAndWeightManifest = {\n                modelTopology: modelArtifacts.modelTopology,\n                format: modelArtifacts.format,\n                generatedBy: modelArtifacts.generatedBy,\n                convertedBy: modelArtifacts.convertedBy,\n                weightsManifest\n            };\n            const modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: 'application/json' }));\n            // If anchor elements are not provided, create them without attaching them\n            // to parents, so that the downloaded file names can be controlled.\n            const jsonAnchor = this.jsonAnchor == null ? document.createElement('a') :\n                this.jsonAnchor;\n            jsonAnchor.download = this.modelTopologyFileName;\n            jsonAnchor.href = modelTopologyAndWeightManifestURL;\n            // Trigger downloads by evoking a click event on the download anchors.\n            // When multiple downloads are started synchronously, Firefox will only\n            // save the last one.\n            await defer(() => jsonAnchor.dispatchEvent(new MouseEvent('click')));\n            if (modelArtifacts.weightData != null) {\n                const weightDataAnchor = this.weightDataAnchor == null ?\n                    document.createElement('a') :\n                    this.weightDataAnchor;\n                weightDataAnchor.download = this.weightDataFileName;\n                weightDataAnchor.href = weightsURL;\n                await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent('click')));\n            }\n            return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) };\n        }\n    }\n}\nBrowserDownloads.URL_SCHEME = 'downloads://';\nclass BrowserFiles {\n    constructor(files) {\n        if (files == null || files.length < 1) {\n            throw new Error(`When calling browserFiles, at least 1 file is required, ` +\n                `but received ${files}`);\n        }\n        this.files = files;\n    }\n    async load() {\n        const jsonFile = this.files[0];\n        const weightFiles = this.files.slice(1);\n        return new Promise((resolve, reject) => {\n            const jsonReader = new FileReader();\n            jsonReader.onload = (event) => {\n                // tslint:disable-next-line:no-any\n                const modelJSON = JSON.parse(event.target.result);\n                const modelTopology = modelJSON.modelTopology;\n                if (modelTopology == null) {\n                    reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));\n                    return;\n                }\n                if (weightFiles.length === 0) {\n                    resolve({ modelTopology });\n                }\n                const weightsManifest = modelJSON.weightsManifest;\n                if (weightsManifest == null) {\n                    reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));\n                    return;\n                }\n                let pathToFile;\n                try {\n                    pathToFile =\n                        this.checkManifestAndWeightFiles(weightsManifest, weightFiles);\n                }\n                catch (err) {\n                    reject(err);\n                    return;\n                }\n                const weightSpecs = [];\n                const paths = [];\n                const perFileBuffers = [];\n                weightsManifest.forEach(weightsGroup => {\n                    weightsGroup.paths.forEach(path => {\n                        paths.push(path);\n                        perFileBuffers.push(null);\n                    });\n                    weightSpecs.push(...weightsGroup.weights);\n                });\n                weightsManifest.forEach(weightsGroup => {\n                    weightsGroup.paths.forEach(path => {\n                        const weightFileReader = new FileReader();\n                        weightFileReader.onload = (event) => {\n                            // tslint:disable-next-line:no-any\n                            const weightData = event.target.result;\n                            const index = paths.indexOf(path);\n                            perFileBuffers[index] = weightData;\n                            if (perFileBuffers.indexOf(null) === -1) {\n                                resolve({\n                                    modelTopology,\n                                    weightSpecs,\n                                    weightData: concatenateArrayBuffers(perFileBuffers),\n                                    format: modelJSON.format,\n                                    generatedBy: modelJSON.generatedBy,\n                                    convertedBy: modelJSON.convertedBy,\n                                    userDefinedMetadata: modelJSON.userDefinedMetadata\n                                });\n                            }\n                        };\n                        weightFileReader.onerror = error => reject(`Failed to weights data from file of path '${path}'.`);\n                        weightFileReader.readAsArrayBuffer(pathToFile[path]);\n                    });\n                });\n            };\n            jsonReader.onerror = error => reject(`Failed to read model topology and weights manifest JSON ` +\n                `from file '${jsonFile.name}'. BrowserFiles supports loading ` +\n                `Keras-style tf.Model artifacts only.`);\n            jsonReader.readAsText(jsonFile);\n        });\n    }\n    /**\n     * Check the compatibility between weights manifest and weight files.\n     */\n    checkManifestAndWeightFiles(manifest, files) {\n        const basenames = [];\n        const fileNames = files.map(file => basename(file.name));\n        const pathToFile = {};\n        for (const group of manifest) {\n            group.paths.forEach(path => {\n                const pathBasename = basename(path);\n                if (basenames.indexOf(pathBasename) !== -1) {\n                    throw new Error(`Duplicate file basename found in weights manifest: ` +\n                        `'${pathBasename}'`);\n                }\n                basenames.push(pathBasename);\n                if (fileNames.indexOf(pathBasename) === -1) {\n                    throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);\n                }\n                else {\n                    pathToFile[path] = files[fileNames.indexOf(pathBasename)];\n                }\n            });\n        }\n        if (basenames.length !== files.length) {\n            throw new Error(`Mismatch in the number of files in weights manifest ` +\n                `(${basenames.length}) and the number of weight files provided ` +\n                `(${files.length}).`);\n        }\n        return pathToFile;\n    }\n}\nexport const browserDownloadsRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n            return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\n/**\n * Creates an IOHandler that triggers file downloads from the browser.\n *\n * The returned `IOHandler` instance can be used as model exporting methods such\n * as `tf.Model.save` and supports only saving.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * const saveResult = await model.save('downloads://mymodel');\n * // This will trigger downloading of two files:\n * //   'mymodel.json' and 'mymodel.weights.bin'.\n * console.log(saveResult);\n * ```\n *\n * @param fileNamePrefix Prefix name of the files to be downloaded. For use with\n *   `tf.Model`, `fileNamePrefix` should follow either of the following two\n *   formats:\n *   1. `null` or `undefined`, in which case the default file\n *      names will be used:\n *      - 'model.json' for the JSON file containing the model topology and\n *        weights manifest.\n *      - 'model.weights.bin' for the binary file containing the binary weight\n *        values.\n *   2. A single string or an Array of a single string, as the file name prefix.\n *      For example, if `'foo'` is provided, the downloaded JSON\n *      file and binary weights file will be named 'foo.json' and\n *      'foo.weights.bin', respectively.\n * @param config Additional configuration for triggering downloads.\n * @returns An instance of `BrowserDownloads` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserDownloads(fileNamePrefix = 'model') {\n    return new BrowserDownloads(fileNamePrefix);\n}\n/**\n * Creates an IOHandler that loads model artifacts from user-selected files.\n *\n * This method can be used for loading from files such as user-selected files\n * in the browser.\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * // Note: This code snippet won't run properly without the actual file input\n * //   elements in the HTML DOM.\n *\n * // Suppose there are two HTML file input (`<input type=\"file\" ...>`)\n * // elements.\n * const uploadJSONInput = document.getElementById('upload-json');\n * const uploadWeightsInput = document.getElementById('upload-weights');\n * const model = await tf.loadLayersModel(tf.io.browserFiles(\n *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));\n * ```\n *\n * @param files `File`s to load from. Currently, this function supports only\n *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for\n *   which an `Array` of `File`s is expected (in that order):\n *   - A JSON file containing the model topology and weight manifest.\n *   - Optionally, One or more binary files containing the binary weights.\n *     These files must have names that match the paths in the `weightsManifest`\n *     contained by the aforementioned JSON file, or errors will be thrown\n *     during loading. These weights files have the same format as the ones\n *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`\n *     Python PIP package. If no weights files are provided, only the model\n *     topology will be loaded from the JSON file above.\n * @returns An instance of `Files` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserFiles(files) {\n    return new BrowserFiles(files);\n}\n//# sourceMappingURL=browser_files.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../util';\n/**\n * Monitor Promise.all progress, fire onProgress callback function.\n *\n * @param promises Promise list going to be monitored\n * @param onProgress Callback function. Fired when a promise resolved.\n * @param startFraction Optional fraction start. Default to 0.\n * @param endFraction Optional fraction end. Default to 1.\n */\nexport function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {\n    checkPromises(promises);\n    startFraction = startFraction == null ? 0 : startFraction;\n    endFraction = endFraction == null ? 1 : endFraction;\n    checkFraction(startFraction, endFraction);\n    let resolvedPromise = 0;\n    const registerMonitor = (promise) => {\n        promise.then(value => {\n            const fraction = startFraction +\n                ++resolvedPromise / promises.length * (endFraction - startFraction);\n            // pass fraction as parameter to callback function.\n            onProgress(fraction);\n            return value;\n        });\n        return promise;\n    };\n    function checkPromises(promises) {\n        assert(promises != null && Array.isArray(promises) && promises.length > 0, () => 'promises must be a none empty array');\n    }\n    function checkFraction(startFraction, endFraction) {\n        assert(startFraction >= 0 && startFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n            `got startFraction ${startFraction}`);\n        assert(endFraction >= 0 && endFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n            `got endFraction ${endFraction}`);\n        assert(endFraction >= startFraction, () => `startFraction must be no more than endFraction, but ` +\n            `got startFraction ${startFraction} and endFraction ` +\n            `${endFraction}`);\n    }\n    return Promise.all(promises.map(registerMonitor));\n}\n//# sourceMappingURL=progress.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\nimport * as util from '../util';\nimport { decodeWeights } from './io_utils';\nimport { monitorPromisesProgress } from './progress';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/**\n * Reads binary weights data from a number of URLs.\n *\n * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.\n * @param requestOptions RequestInit (options) for the HTTP requests.\n * @param fetchFunc Optional overriding value for the `window.fetch` function.\n * @param onProgress Optional, progress callback function, fired periodically\n *   before the load is completed.\n * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same\n *   length as `fetchURLs`.\n */\nexport async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {\n    if (loadOptions == null) {\n        loadOptions = {};\n    }\n    const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch :\n        loadOptions.fetchFunc;\n    // Create the requests for all of the weights in parallel.\n    const requests = fetchURLs.map(fetchURL => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true }));\n    const fetchStartFraction = 0;\n    const fetchEndFraction = 0.5;\n    const responses = loadOptions.onProgress == null ?\n        await Promise.all(requests) :\n        await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);\n    const bufferPromises = responses.map(response => response.arrayBuffer());\n    const bufferStartFraction = 0.5;\n    const bufferEndFraction = 1;\n    const buffers = loadOptions.onProgress == null ?\n        await Promise.all(bufferPromises) :\n        await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);\n    return buffers;\n}\n/**\n * Reads a weights manifest JSON configuration, fetches the weights and\n * returns them as `Tensor`s.\n *\n * @param manifest The weights manifest JSON.\n * @param filePathPrefix The path prefix for filenames given in the manifest.\n *     Defaults to the empty string.\n * @param weightNames The names of the weights to be fetched.\n */\nexport async function loadWeights(manifest, filePathPrefix = '', weightNames, requestInit) {\n    // TODO(nsthorat): Groups are currently fetched atomically. If you need a\n    // single weight from a group, the whole group will be fetched. At a future\n    // date, we should support fetching only the individual shards within a\n    // group that are needed to reconstruct the requested weight.\n    // TODO(cais): Use `decodeWeights` for implementation.\n    const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit });\n    const loadWeights = weightsLoaderFactory(fetchWeights);\n    return loadWeights(manifest, filePathPrefix, weightNames);\n}\n/**\n * Creates a function, which reads a weights manifest JSON configuration,\n * fetches the weight files using the specified function and returns them as\n * `Tensor`s.\n *\n * ```js\n * // example for creating a nodejs weight loader, which reads the weight files\n * // from disk using fs.readFileSync\n *\n * import * as fs from 'fs'\n *\n * const fetchWeightsFromDisk = (filePaths: string[]) =>\n *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)\n *\n * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n *\n * const manifest = JSON.parse(\n *   fs.readFileSync('./my_model-weights_manifest').toString()\n * )\n * const weightMap = await loadWeights(manifest, './')\n * ```\n * @param fetchWeightsFunction The function used for fetching the weight files.\n * @returns Weight loading function.\n */\nexport function weightsLoaderFactory(fetchWeightsFunction) {\n    return async (manifest, filePathPrefix = '', weightNames) => {\n        // Collect all the groups, weights, and their relative offsets to be\n        // fetched.\n        const groupIndicesToFetchMap = manifest.map(() => false);\n        const groupWeightsToFetch = {};\n        const weightsFound = weightNames != null ? weightNames.map(() => false) : [];\n        const allManifestWeightNames = [];\n        manifest.forEach((manifestGroupConfig, groupIndex) => {\n            let groupOffset = 0;\n            manifestGroupConfig.weights.forEach(weightsEntry => {\n                const rawDtype = ('quantization' in weightsEntry) ?\n                    weightsEntry.quantization.dtype :\n                    weightsEntry.dtype;\n                const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *\n                    util.sizeFromShape(weightsEntry.shape);\n                const enqueueWeightsForFetchingFn = () => {\n                    groupIndicesToFetchMap[groupIndex] = true;\n                    if (groupWeightsToFetch[groupIndex] == null) {\n                        groupWeightsToFetch[groupIndex] = [];\n                    }\n                    groupWeightsToFetch[groupIndex].push({\n                        manifestEntry: weightsEntry,\n                        groupOffset,\n                        sizeBytes: weightsBytes\n                    });\n                };\n                if (weightNames != null) {\n                    weightNames.forEach((weightName, weightIndex) => {\n                        if (weightName === weightsEntry.name) {\n                            enqueueWeightsForFetchingFn();\n                            weightsFound[weightIndex] = true;\n                        }\n                    });\n                }\n                else {\n                    enqueueWeightsForFetchingFn();\n                }\n                allManifestWeightNames.push(weightsEntry.name);\n                groupOffset += weightsBytes;\n            });\n        });\n        if (!weightsFound.every(found => found)) {\n            const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n            throw new Error(`Could not find weights in manifest with names: ` +\n                `${weightsNotFound.join(', ')}. \\n` +\n                `Manifest JSON has weights with names: ` +\n                `${allManifestWeightNames.join(', ')}.`);\n        }\n        // Convert the one-hot boolean groupId => shouldFetch map to a list of group\n        // IDs.\n        const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n            if (shouldFetch) {\n                accumulator.push(i);\n            }\n            return accumulator;\n        }, []);\n        const fetchUrls = [];\n        groupIndicesToFetch.forEach(i => {\n            manifest[i].paths.forEach(filepath => {\n                const fetchUrl = filePathPrefix +\n                    (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;\n                fetchUrls.push(fetchUrl);\n            });\n        });\n        const buffers = await fetchWeightsFunction(fetchUrls);\n        const weightsTensorMap = {};\n        let bufferIndexOffset = 0;\n        groupIndicesToFetch.forEach(i => {\n            const numBuffers = manifest[i].paths.length;\n            let groupBytes = 0;\n            for (let i = 0; i < numBuffers; i++) {\n                groupBytes += buffers[bufferIndexOffset + i].byteLength;\n            }\n            // Create a buffer for the whole group.\n            const groupBuffer = new ArrayBuffer(groupBytes);\n            const groupByteBuffer = new Uint8Array(groupBuffer);\n            let groupBufferOffset = 0;\n            for (let i = 0; i < numBuffers; i++) {\n                const buffer = new Uint8Array(buffers[bufferIndexOffset + i]);\n                groupByteBuffer.set(buffer, groupBufferOffset);\n                groupBufferOffset += buffer.byteLength;\n            }\n            const weightsEntries = groupWeightsToFetch[i];\n            weightsEntries.forEach(weightsEntry => {\n                const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);\n                const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n                for (const name in nameToTensorMap) {\n                    weightsTensorMap[name] = nameToTensorMap[name];\n                }\n            });\n            bufferIndexOffset += numBuffers;\n        });\n        return weightsTensorMap;\n    };\n}\n//# sourceMappingURL=weights_loader.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandler implementations based on HTTP requests in the web browser.\n *\n * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n */\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nimport { loadWeightsAsArrayBuffer } from './weights_loader';\nconst OCTET_STREAM_MIME_TYPE = 'application/octet-stream';\nconst JSON_TYPE = 'application/json';\nexport class HTTPRequest {\n    constructor(path, loadOptions) {\n        this.DEFAULT_METHOD = 'POST';\n        if (loadOptions == null) {\n            loadOptions = {};\n        }\n        this.weightPathPrefix = loadOptions.weightPathPrefix;\n        this.onProgress = loadOptions.onProgress;\n        this.weightUrlConverter = loadOptions.weightUrlConverter;\n        if (loadOptions.fetchFunc != null) {\n            assert(typeof loadOptions.fetchFunc === 'function', () => 'Must pass a function that matches the signature of ' +\n                '`fetch` (see ' +\n                'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');\n            this.fetch = loadOptions.fetchFunc;\n        }\n        else {\n            this.fetch = env().platform.fetch;\n        }\n        assert(path != null && path.length > 0, () => 'URL path for http must not be null, undefined or ' +\n            'empty.');\n        if (Array.isArray(path)) {\n            assert(path.length === 2, () => 'URL paths for http must have a length of 2, ' +\n                `(actual length is ${path.length}).`);\n        }\n        this.path = path;\n        if (loadOptions.requestInit != null &&\n            loadOptions.requestInit.body != null) {\n            throw new Error('requestInit is expected to have no pre-existing body, but has one.');\n        }\n        this.requestInit = loadOptions.requestInit || {};\n    }\n    async save(modelArtifacts) {\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserHTTPRequest.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        const init = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);\n        init.body = new FormData();\n        const weightsManifest = [{\n                paths: ['./model.weights.bin'],\n                weights: modelArtifacts.weightSpecs,\n            }];\n        const modelTopologyAndWeightManifest = {\n            modelTopology: modelArtifacts.modelTopology,\n            format: modelArtifacts.format,\n            generatedBy: modelArtifacts.generatedBy,\n            convertedBy: modelArtifacts.convertedBy,\n            userDefinedMetadata: modelArtifacts.userDefinedMetadata,\n            weightsManifest\n        };\n        init.body.append('model.json', new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), 'model.json');\n        if (modelArtifacts.weightData != null) {\n            init.body.append('model.weights.bin', new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), 'model.weights.bin');\n        }\n        const response = await this.fetch(this.path, init);\n        if (response.ok) {\n            return {\n                modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n                responses: [response],\n            };\n        }\n        else {\n            throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ` +\n                `${response.status}.`);\n        }\n    }\n    /**\n     * Load model artifacts via HTTP request(s).\n     *\n     * See the documentation to `tf.io.http` for details on the saved\n     * artifacts.\n     *\n     * @returns The loaded model artifacts (if loading succeeds).\n     */\n    async load() {\n        const modelConfigRequest = await this.fetch(this.path, this.requestInit);\n        if (!modelConfigRequest.ok) {\n            throw new Error(`Request to ${this.path} failed with status code ` +\n                `${modelConfigRequest.status}. Please verify this URL points to ` +\n                `the model JSON of the model to load.`);\n        }\n        let modelConfig;\n        try {\n            modelConfig = await modelConfigRequest.json();\n        }\n        catch (e) {\n            let message = `Failed to parse model JSON of response from ${this.path}.`;\n            // TODO(nsthorat): Remove this after some time when we're comfortable that\n            // .pb files are mostly gone.\n            if (this.path.endsWith('.pb')) {\n                message += ' Your path contains a .pb file extension. ' +\n                    'Support for .pb models have been removed in TensorFlow.js 1.0 ' +\n                    'in favor of .json models. You can re-convert your Python ' +\n                    'TensorFlow model using the TensorFlow.js 1.0 conversion scripts ' +\n                    'or you can convert your.pb models with the \\'pb2json\\'' +\n                    'NPM script in the tensorflow/tfjs-converter repository.';\n            }\n            else {\n                message += ' Please make sure the server is serving valid ' +\n                    'JSON for this request.';\n            }\n            throw new Error(message);\n        }\n        const modelTopology = modelConfig.modelTopology;\n        const weightsManifest = modelConfig.weightsManifest;\n        const generatedBy = modelConfig.generatedBy;\n        const convertedBy = modelConfig.convertedBy;\n        const format = modelConfig.format;\n        const userDefinedMetadata = modelConfig.userDefinedMetadata;\n        // We do not allow both modelTopology and weightsManifest to be missing.\n        if (modelTopology == null && weightsManifest == null) {\n            throw new Error(`The JSON from HTTP path ${this.path} contains neither model ` +\n                `topology or manifest for weights.`);\n        }\n        let weightSpecs;\n        let weightData;\n        if (weightsManifest != null) {\n            const results = await this.loadWeights(weightsManifest);\n            [weightSpecs, weightData] = results;\n        }\n        const artifacts = {\n            modelTopology,\n            weightSpecs,\n            weightData,\n            userDefinedMetadata,\n            generatedBy,\n            convertedBy,\n            format\n        };\n        const initializer = modelConfig.modelInitializer;\n        if (initializer) {\n            artifacts.modelInitializer = initializer;\n        }\n        return artifacts;\n    }\n    async loadWeights(weightsManifest) {\n        const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n        const [prefix, suffix] = parseUrl(weightPath);\n        const pathPrefix = this.weightPathPrefix || prefix;\n        const weightSpecs = [];\n        for (const entry of weightsManifest) {\n            weightSpecs.push(...entry.weights);\n        }\n        const fetchURLs = [];\n        const urlPromises = [];\n        for (const weightsGroup of weightsManifest) {\n            for (const path of weightsGroup.paths) {\n                if (this.weightUrlConverter != null) {\n                    urlPromises.push(this.weightUrlConverter(path));\n                }\n                else {\n                    fetchURLs.push(pathPrefix + path + suffix);\n                }\n            }\n        }\n        if (this.weightUrlConverter) {\n            fetchURLs.push(...await Promise.all(urlPromises));\n        }\n        const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {\n            requestInit: this.requestInit,\n            fetchFunc: this.fetch,\n            onProgress: this.onProgress\n        });\n        return [weightSpecs, concatenateArrayBuffers(buffers)];\n    }\n}\nHTTPRequest.URL_SCHEME_REGEX = /^https?:\\/\\//;\n/**\n * Extract the prefix and suffix of the url, where the prefix is the path before\n * the last file, and suffix is the search params after the last file.\n * ```\n * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'\n * [prefix, suffix] = parseUrl(url)\n * // prefix = 'http://tfhub.dev/model/1/'\n * // suffix = '?tfjs-format=file'\n * ```\n * @param url the model url to be parsed.\n */\nexport function parseUrl(url) {\n    const lastSlash = url.lastIndexOf('/');\n    const lastSearchParam = url.lastIndexOf('?');\n    const prefix = url.substring(0, lastSlash);\n    const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';\n    return [prefix + '/', suffix];\n}\nexport function isHTTPScheme(url) {\n    return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;\n}\nexport const httpRouter = (url, loadOptions) => {\n    if (typeof fetch === 'undefined' &&\n        (loadOptions == null || loadOptions.fetchFunc == null)) {\n        // `http` uses `fetch` or `node-fetch`, if one wants to use it in\n        // an environment that is not the browser or node they have to setup a\n        // global fetch polyfill.\n        return null;\n    }\n    else {\n        let isHTTP = true;\n        if (Array.isArray(url)) {\n            isHTTP = url.every(urlItem => isHTTPScheme(urlItem));\n        }\n        else {\n            isHTTP = isHTTPScheme(url);\n        }\n        if (isHTTP) {\n            return http(url, loadOptions);\n        }\n    }\n    return null;\n};\nIORouterRegistry.registerSaveRouter(httpRouter);\nIORouterRegistry.registerLoadRouter(httpRouter);\n/**\n * Creates an IOHandler subtype that sends model artifacts to HTTP server.\n *\n * An HTTP request of the `multipart/form-data` mime type will be sent to the\n * `path` URL. The form data includes artifacts that represent the topology\n * and/or weights of the model. In the case of Keras-style `tf.Model`, two\n * blobs (files) exist in form-data:\n *   - A JSON file consisting of `modelTopology` and `weightsManifest`.\n *   - A binary weights file consisting of the concatenated weight values.\n * These files are in the same format as the one generated by\n * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).\n *\n * The following code snippet exemplifies the client-side code that uses this\n * function:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save(tf.io.http(\n *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));\n * console.log(saveResult);\n * ```\n *\n * If the default `POST` method is to be used, without any custom parameters\n * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:\n *\n * ```js\n * const saveResult = await model.save('http://model-server:5000/upload');\n * ```\n *\n * The following GitHub Gist\n * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864\n * implements a server based on [flask](https://github.com/pallets/flask) that\n * can receive the request. Upon receiving the model artifacts via the requst,\n * this particular server reconsistutes instances of [Keras\n * Models](https://keras.io/models/model/) in memory.\n *\n *\n * @param path A URL path to the model.\n *   Can be an absolute HTTP path (e.g.,\n *   'http://localhost:8000/model-upload)') or a relative path (e.g.,\n *   './model-upload').\n * @param requestInit Request configurations to be used when sending\n *    HTTP request to server using `fetch`. It can contain fields such as\n *    `method`, `credentials`, `headers`, `mode`, etc. See\n *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request\n *    for more information. `requestInit` must not have a body, because the\n * body will be set by TensorFlow.js. File blobs representing the model\n * topology (filename: 'model.json') and the weights of the model (filename:\n * 'model.weights.bin') will be appended to the body. If `requestInit` has a\n * `body`, an Error will be thrown.\n * @param loadOptions Optional configuration for the loading. It includes the\n *   following fields:\n *   - weightPathPrefix Optional, this specifies the path prefix for weight\n *     files, by default this is calculated from the path param.\n *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,\n *     the `fetch` from node-fetch can be used here.\n *   - onProgress Optional, progress callback function, fired periodically\n *     before the load is completed.\n * @returns An instance of `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function http(path, loadOptions) {\n    return new HTTPRequest(path, loadOptions);\n}\n/**\n * Deprecated. Use `tf.io.http`.\n * @param path\n * @param loadOptions\n */\nexport function browserHTTPRequest(path, loadOptions) {\n    return http(path, loadOptions);\n}\n//# sourceMappingURL=http.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nclass PassthroughLoader {\n    constructor(modelArtifacts) {\n        this.modelArtifacts = modelArtifacts;\n    }\n    async load() {\n        return this.modelArtifacts;\n    }\n}\nclass PassthroughSaver {\n    constructor(saveHandler) {\n        this.saveHandler = saveHandler;\n    }\n    async save(modelArtifacts) {\n        return this.saveHandler(modelArtifacts);\n    }\n}\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadLayersModel(tf.io.fromMemory(\n *     modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelArtifacts a object containing model topology (i.e., parsed from\n *   the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n *   names, shapes, types, and quantization of the weight data.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n *   concatenated in the order described by the weightSpecs.\n * @param trainingConfig Model training configuration. Optional.\n *\n * @returns A passthrough `IOHandler` that simply loads the provided data.\n */\nexport function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {\n    if (arguments.length === 1) {\n        const isModelArtifacts = modelArtifacts.modelTopology != null ||\n            modelArtifacts.weightSpecs != null;\n        if (isModelArtifacts) {\n            return new PassthroughLoader(modelArtifacts);\n        }\n        else {\n            // Legacy support: with only modelTopology.\n            // TODO(cais): Remove this deprecated API.\n            console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n                'The argument should be of type ModelArtifacts. ' +\n                'The multi-argument signature of tf.io.fromMemory() has been ' +\n                'deprecated and will be removed in a future release.');\n            return new PassthroughLoader({ modelTopology: modelArtifacts });\n        }\n    }\n    else {\n        // Legacy support.\n        // TODO(cais): Remove this deprecated API.\n        console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n            'The argument should be of type ModelArtifacts. ' +\n            'The multi-argument signature of tf.io.fromMemory() has been ' +\n            'deprecated and will be removed in a future release.');\n        return new PassthroughLoader({\n            modelTopology: modelArtifacts,\n            weightSpecs,\n            weightData,\n            trainingConfig\n        });\n    }\n}\n/**\n * Creates an IOHandler that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n *   // ... do something with the artifacts ...\n *   return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n *     `SaveResult`.\n */\nexport function withSaveHandler(saveHandler) {\n    return new PassthroughSaver(saveHandler);\n}\n//# sourceMappingURL=passthrough.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Importing local_storage and indexed_db is necessary for the routers to be\n// registered.\nimport './indexed_db';\nimport './local_storage';\nimport { browserFiles } from './browser_files';\nimport { browserHTTPRequest, http, isHTTPScheme } from './http';\nimport { concatenateArrayBuffers, decodeWeights, encodeWeights, getModelArtifactsInfoForJSON } from './io_utils';\nimport { fromMemory, withSaveHandler } from './passthrough';\nimport { getLoadHandlers, getSaveHandlers, registerLoadRouter, registerSaveRouter } from './router_registry';\nimport { loadWeights, weightsLoaderFactory } from './weights_loader';\nexport { copyModel, listModels, moveModel, removeModel } from './model_management';\nexport { browserFiles, browserHTTPRequest, concatenateArrayBuffers, decodeWeights, encodeWeights, fromMemory, getLoadHandlers, getModelArtifactsInfoForJSON, getSaveHandlers, http, isHTTPScheme, loadWeights, registerLoadRouter, registerSaveRouter, weightsLoaderFactory, withSaveHandler };\n//# sourceMappingURL=io.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reshape } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Reshapes a `tf.Tensor` to a given shape.\n *\n * Given an input tensor, returns a new tensor with the same values as the\n * input tensor with shape `shape`.\n *\n * If one component of shape is the special value -1, the size of that\n * dimension is computed so that the total size remains constant. In\n * particular, a shape of [-1] flattens into 1-D. At most one component of\n * shape can be -1.\n *\n * If shape is 1-D or higher, then the operation returns a tensor with shape\n * shape filled with the values of tensor. In this case, the number of\n * elements implied by shape must be the same as the number of elements in\n * tensor.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.reshape([2, 2]).print();\n * ```\n *\n * @param x The input tensor to be reshaped.\n * @param shape An array of integers defining the output tensor shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction reshape_(x, shape) {\n    const $x = convertToTensor(x, 'x', 'reshape', null);\n    const inputs = { x: $x };\n    const attrs = { shape };\n    const forward = (backend, save) => {\n        shape = util.inferFromImplicitShape(shape, $x.size);\n        util.assert($x.size === util.sizeFromShape(shape), () => 'new shape and old shape must have the same number of elements.');\n        save([$x]);\n        return backend.reshape($x, shape);\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Reshape, attrs);\n}\nexport const reshape = op({ reshape_ });\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchMatMul } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the dot product of two matrices, A * B. These must be matrices.\n *\n * ```js\n * const a = tf.tensor2d([1, 2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.matMul(b).print();  // or tf.matMul(a, b)\n * ```\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction matMul_(a, b, transposeA = false, transposeB = false) {\n    let $a = convertToTensor(a, 'a', 'matMul');\n    let $b = convertToTensor(b, 'b', 'matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    util.assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in matMul: inputs must have the same rank of at least 2, ` +\n        `got ranks ${$a.rank} and ${$b.rank}.`);\n    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n    const outerDimsA = $a.shape.slice(0, -2);\n    const outerDimsB = $b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n    util.assert(util.arraysEqual(outerDimsA, outerDimsB), () => `Error in matMul: outer dimensions (${outerDimsA}) and (` +\n        `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} must match.`);\n    util.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (` +\n        `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} and transposeA=${transposeA}` +\n        ` and transposeB=${transposeB} must match.`);\n    const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n    const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n        reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n    const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n        reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n    const forward = (backend, save) => {\n        save([a3D, b3D]);\n        return backend.batchMatMul(a3D, b3D, transposeA, transposeB);\n    };\n    const inputs = { a: a3D, b: b3D };\n    const attrs = { transposeA, transposeB };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, BatchMatMul, attrs);\n    return reshape(res, outShape);\n}\nexport const matMul = op({ matMul_ });\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OneHot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank\n * `R+1` with the last axis of size `depth`.\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor` of indices with dtype `int32`.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n *     not match the location.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction oneHot_(indices, depth, onValue = 1, offValue = 0) {\n    if (depth < 2) {\n        throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n    }\n    const $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');\n    const outShape = [...$indices.shape, depth];\n    const forward = (backend, save) => {\n        save([$indices]);\n        return reshape(backend.oneHot(reshape($indices, [$indices.size]), depth, onValue, offValue), outShape);\n    };\n    const inputs = { indices: $indices };\n    const attrs = { depth, onValue, offValue };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, OneHot, attrs);\n}\nexport const oneHot = op({ oneHot_ });\n//# sourceMappingURL=one_hot.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Transpose } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print();  // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction transpose_(x, perm) {\n    const $x = convertToTensor(x, 'x', 'transpose');\n    if (perm == null) {\n        perm = $x.shape.map((s, i) => i).reverse();\n    }\n    util.assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n        `must match length of perm ${perm}.`);\n    perm.forEach(axis => {\n        util.assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n            ` but got ${perm}`);\n    });\n    if ($x.rank <= 1) {\n        return $x.clone();\n    }\n    const inputs = { x: $x };\n    const attrs = { perm };\n    return ENGINE.runKernelFunc(backend => backend.transpose($x, perm), inputs, null /* gradient */, Transpose, attrs);\n}\nexport const transpose = op({ transpose_ });\n//# sourceMappingURL=transpose.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { matMul } from './mat_mul';\nimport { oneHot } from './one_hot';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Computes the confusion matrix from true labels and predicted labels.\n *\n * ```js\n * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n * const numClasses = 3;\n * const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n * out.print();\n * // Expected output matrix:\n * // [[2, 0, 0],\n * //  [0, 1, 1],\n * //  [0, 0, 1]]\n * ```\n *\n * @param labels The target labels, assumed to be 0-based integers\n *   for the classes. The shape is `[numExamples]`, where\n *   `numExamples` is the number of examples included.\n * @param predictions The predicted classes, assumed to be\n *   0-based integers for the classes. Must have the same shape as `labels`.\n * @param numClasses Number of all classes, as an integer.\n *   Its value must be larger than the largest element in `labels` and\n *   `predictions`.\n * @returns The confusion matrix as a int32-type 2D tensor. The value at\n *   row `r` and column `c` is the number of times examples of actual class\n *   `r` were predicted as class `c`.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nexport function confusionMatrix_(labels, predictions, numClasses) {\n    const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');\n    const $predictions = convertToTensor(predictions, 'predictions', 'confusionMatrix');\n    util.assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, ` +\n        `but got ${numClasses}`);\n    util.assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n    util.assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, ` +\n        `but got ${$predictions.rank}`);\n    util.assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ` +\n        `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +\n        `Labels and predictions should have the same number of elements.`);\n    util.assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ` +\n        `${numClasses}`);\n    // TODO(cais): In the future, if oneHot supports tensors inputs for\n    //   `numClasses`, `confusionMatrix` can make `numClasses` optional.\n    const oneHotLabels = oneHot(cast($labels, 'int32'), numClasses);\n    const oneHotPredictions = oneHot(cast($predictions, 'int32'), numClasses);\n    const oneHotLabelsT = transpose(oneHotLabels);\n    return cast(matMul(oneHotLabelsT, oneHotPredictions), 'int32');\n}\nexport const confusionMatrix = op({ confusionMatrix_ });\n//# sourceMappingURL=confusion_matrix.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Exports under the tf.math.* namespace.\n */\nimport { confusionMatrix } from './ops/confusion_matrix';\nexport { confusionMatrix };\n//# sourceMappingURL=math.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor3d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided,  it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor3d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 3) {\n        throw new Error('tensor3d() requires shape to have three numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n        throw new Error('tensor3d() requires values to be number[][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor3d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor3d.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FromPixels } from '../kernel_names';\nimport { getKernel } from '../kernel_registry';\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { tensor3d } from './tensor3d';\nlet fromPixels2DContext;\n/**\n * Creates a `tf.Tensor` from an image.\n *\n * ```js\n * const image = new ImageData(1, 1);\n * image.data[0] = 100;\n * image.data[1] = 150;\n * image.data[2] = 200;\n * image.data[3] = 255;\n *\n * tf.browser.fromPixels(image).print();\n * ```\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel. You can also pass in an image\n * object with following attributes:\n * `{data: Uint8Array; width: number; height: number}`\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n *\n * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}\n */\nfunction fromPixels_(pixels, numChannels = 3) {\n    // Sanity checks.\n    if (numChannels > 4) {\n        throw new Error('Cannot construct Tensor with more than 4 channels from pixels.');\n    }\n    if (pixels == null) {\n        throw new Error('pixels passed to tf.browser.fromPixels() can not be null');\n    }\n    let isPixelData = false;\n    let isImageData = false;\n    let isVideo = false;\n    let isImage = false;\n    let isCanvasLike = false;\n    if (pixels.data instanceof Uint8Array) {\n        isPixelData = true;\n    }\n    else if (typeof (ImageData) !== 'undefined' && pixels instanceof ImageData) {\n        isImageData = true;\n    }\n    else if (typeof (HTMLVideoElement) !== 'undefined' &&\n        pixels instanceof HTMLVideoElement) {\n        isVideo = true;\n    }\n    else if (typeof (HTMLImageElement) !== 'undefined' &&\n        pixels instanceof HTMLImageElement) {\n        isImage = true;\n        // tslint:disable-next-line: no-any\n    }\n    else if (pixels.getContext != null) {\n        isCanvasLike = true;\n    }\n    else {\n        throw new Error('pixels passed to tf.browser.fromPixels() must be either an ' +\n            `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData ` +\n            `in browser, or OffscreenCanvas, ImageData in webworker` +\n            ` or {data: Uint32Array, width: number, height: number}, ` +\n            `but was ${pixels.constructor.name}`);\n    }\n    if (isVideo) {\n        const HAVE_CURRENT_DATA_READY_STATE = 2;\n        if (isVideo &&\n            pixels.readyState <\n                HAVE_CURRENT_DATA_READY_STATE) {\n            throw new Error('The video element has not loaded data yet. Please wait for ' +\n                '`loadeddata` event on the <video> element.');\n        }\n    }\n    // If the current backend has 'FromPixels' registered, it has a more\n    // efficient way of handling pixel uploads, so we call that.\n    const kernel = getKernel(FromPixels, ENGINE.backendName);\n    if (kernel != null) {\n        const inputs = { pixels };\n        const attrs = { numChannels };\n        return ENGINE.runKernel(FromPixels, inputs, attrs);\n    }\n    const [width, height] = isVideo ?\n        [\n            pixels.videoWidth,\n            pixels.videoHeight\n        ] :\n        [pixels.width, pixels.height];\n    let vals;\n    if (isCanvasLike) {\n        vals =\n            // tslint:disable-next-line:no-any\n            pixels.getContext('2d').getImageData(0, 0, width, height).data;\n    }\n    else if (isImageData || isPixelData) {\n        vals = pixels.data;\n    }\n    else if (isImage || isVideo) {\n        if (fromPixels2DContext == null) {\n            fromPixels2DContext = document.createElement('canvas').getContext('2d');\n        }\n        fromPixels2DContext.canvas.width = width;\n        fromPixels2DContext.canvas.height = height;\n        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n        vals = fromPixels2DContext.getImageData(0, 0, width, height).data;\n    }\n    let values;\n    if (numChannels === 4) {\n        values = new Int32Array(vals);\n    }\n    else {\n        const numPixels = width * height;\n        values = new Int32Array(numPixels * numChannels);\n        for (let i = 0; i < numPixels; i++) {\n            for (let channel = 0; channel < numChannels; ++channel) {\n                values[i * numChannels + channel] = vals[i * 4 + channel];\n            }\n        }\n    }\n    const outShape = [height, width, numChannels];\n    return tensor3d(values, outShape, 'int32');\n}\n/**\n * Draws a `tf.Tensor` of pixel values to a byte array or optionally a\n * canvas.\n *\n * When the dtype of the input is 'float32', we assume values in the range\n * [0-1]. Otherwise, when input is 'int32', we assume values in the range\n * [0-255].\n *\n * Returns a promise that resolves when the canvas has been drawn to.\n *\n * @param img A rank-2 or rank-3 tensor. If rank-2, draws grayscale. If\n *     rank-3, must have depth of 1, 3 or 4. When depth of 1, draws\n * grayscale. When depth of 3, we draw with the first three components of\n * the depth dimension corresponding to r, g, b and alpha = 1. When depth of\n * 4, all four components of the depth dimension correspond to r, g, b, a.\n * @param canvas The canvas to draw to.\n *\n * @doc {heading: 'Browser', namespace: 'browser'}\n */\nexport async function toPixels(img, canvas) {\n    let $img = convertToTensor(img, 'img', 'toPixels');\n    if (!(img instanceof Tensor)) {\n        // Assume int32 if user passed a native array.\n        const originalImgTensor = $img;\n        $img = cast(originalImgTensor, 'int32');\n        originalImgTensor.dispose();\n    }\n    if ($img.rank !== 2 && $img.rank !== 3) {\n        throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);\n    }\n    const [height, width] = $img.shape.slice(0, 2);\n    const depth = $img.rank === 2 ? 1 : $img.shape[2];\n    if (depth > 4 || depth === 2) {\n        throw new Error(`toPixels only supports depth of size ` +\n            `1, 3 or 4 but got ${depth}`);\n    }\n    if ($img.dtype !== 'float32' && $img.dtype !== 'int32') {\n        throw new Error(`Unsupported type for toPixels: ${$img.dtype}.` +\n            ` Please use float32 or int32 tensors.`);\n    }\n    const data = await $img.data();\n    const multiplier = $img.dtype === 'float32' ? 255 : 1;\n    const bytes = new Uint8ClampedArray(width * height * 4);\n    for (let i = 0; i < height * width; ++i) {\n        const rgba = [0, 0, 0, 255];\n        for (let d = 0; d < depth; d++) {\n            const value = data[i * depth + d];\n            if ($img.dtype === 'float32') {\n                if (value < 0 || value > 1) {\n                    throw new Error(`Tensor values for a float32 Tensor must be in the ` +\n                        `range [0 - 1] but encountered ${value}.`);\n                }\n            }\n            else if ($img.dtype === 'int32') {\n                if (value < 0 || value > 255) {\n                    throw new Error(`Tensor values for a int32 Tensor must be in the ` +\n                        `range [0 - 255] but encountered ${value}.`);\n                }\n            }\n            if (depth === 1) {\n                rgba[0] = value * multiplier;\n                rgba[1] = value * multiplier;\n                rgba[2] = value * multiplier;\n            }\n            else {\n                rgba[d] = value * multiplier;\n            }\n        }\n        const j = i * 4;\n        bytes[j + 0] = Math.round(rgba[0]);\n        bytes[j + 1] = Math.round(rgba[1]);\n        bytes[j + 2] = Math.round(rgba[2]);\n        bytes[j + 3] = Math.round(rgba[3]);\n    }\n    if (canvas != null) {\n        canvas.width = width;\n        canvas.height = height;\n        const ctx = canvas.getContext('2d');\n        const imageData = new ImageData(bytes, width, height);\n        ctx.putImageData(imageData, 0, 0);\n    }\n    if ($img !== img) {\n        $img.dispose();\n    }\n    return bytes;\n}\nexport const fromPixels = op({ fromPixels_ });\n//# sourceMappingURL=browser.js.map", "import { computeStrides } from '../util';\n/**\n * Validate gather nd inputs.\n *\n * @param tensor The tensor contains the source values.\n * @param indices The tensor contains the indices to slice the source.\n *\n * @returns [resultShape, numUpdates, sliceSize, strides]\n */\nexport function prepareAndValidate(tensor, indices) {\n    if (tensor.rank < 1) {\n        throw new Error('tf.gatherND() expects the input to be rank 1 or higher,' +\n            ` but the rank was ${tensor.rank}.`);\n    }\n    if (indices.rank < 1) {\n        throw new Error('tf.gatherND() expects the indices to be rank 1 or higher,' +\n            ` but the rank was ${indices.rank}.`);\n    }\n    if (indices.dtype !== 'int32') {\n        throw new Error('tf.gatherND() expects the indices to be int32 type,' +\n            ` but the dtype was ${indices.dtype}.`);\n    }\n    if (indices.shape[indices.rank - 1] > tensor.rank) {\n        throw new Error('index innermost dimension length must be <= tensor rank; saw: ' +\n            `${indices.shape[indices.rank - 1]} vs. ${tensor.rank}`);\n    }\n    if (tensor.size === 0) {\n        throw new Error('Requested more than 0 entries, but input is empty.' +\n            ` Input shape: ${tensor.shape}.`);\n    }\n    const indicesShape = indices.shape;\n    const sliceRank = indicesShape[indicesShape.length - 1];\n    // The result shape is\n    //   indices.shape[:-1] + params.shape[indices.shape[-1]:]\n    let nResult = 1;\n    for (let i = 0; i < indicesShape.length - 1; ++i) {\n        nResult *= indicesShape[i];\n    }\n    const inputShape = tensor.shape;\n    const resultShape = indicesShape.slice();\n    resultShape.pop();\n    let sliceSize = 1;\n    for (let i = sliceRank; i < tensor.rank; ++i) {\n        sliceSize *= inputShape[i];\n        resultShape.push(inputShape[i]);\n    }\n    const strides = [...computeStrides(tensor.shape).map(stride => stride / sliceSize),\n        1].slice(0, sliceRank);\n    return [resultShape, nResult, sliceSize, strides];\n}\n//# sourceMappingURL=gather_nd_util.js.map", "import { computeStrides, sizeFromShape } from '../util';\n/**\n * Check whether updates.shape = indices.shape[:batchDim] +\n * shape[sliceDim:]\n *\n * @param x The input tensor.\n */\nexport function validateUpdateShape(shape, indices, updates) {\n    const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n    const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;\n    const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +\n        `shape[sliceDim:], got updates.shape: ${updates.shape}` +\n        `, indices.shape: ${indices.shape}, shape: ${shape}` +\n        `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n    if (updates.rank < batchDim) {\n        throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n    }\n    if (shape.length < sliceDim + (updates.rank - batchDim)) {\n        throw new Error(shapeError +\n            ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n    }\n    if (updates.rank !== batchDim + shape.length - sliceDim) {\n        throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n    }\n    for (let d = 0; d < batchDim; ++d) {\n        if (updates.shape[d] !== indices.shape[d]) {\n            throw new Error(shapeError +\n                ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);\n        }\n    }\n    for (let d = 0; d < updates.rank - batchDim; ++d) {\n        if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n            throw new Error(shapeError +\n                ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);\n        }\n    }\n}\n/**\n * Validate scatter nd inputs.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n */\nexport function validateInput(updates, indices, shape) {\n    if (indices.rank < 1) {\n        throw new Error('tf.scatterND() expects the indices to be rank 1 or higher,' +\n            ` but the rank was ${indices.rank}.`);\n    }\n    if (updates.rank < 1) {\n        throw new Error('tf.scatterND() expects the updates to be rank 1 or higher,' +\n            ` but the rank was ${updates.rank}.`);\n    }\n    if (indices.dtype !== 'int32') {\n        throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);\n    }\n    if (shape.length < 1) {\n        throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);\n    }\n    if (shape.length === 0) {\n        if (indices.size === 0) {\n            throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);\n        }\n        if (updates.size === 0) {\n            throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);\n        }\n    }\n    validateUpdateShape(shape, indices, updates);\n}\n/**\n * Calculate the shape information for the output.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n *\n * @returns ScatterShapeInfo\n */\nexport function calculateShapes(updates, indices, shape) {\n    // Calculate the number of dimensions in indices\n    const indicesRank = indices.shape.length;\n    const sliceRank = (indicesRank > 1) ? indices.shape[indicesRank - 1] : 1;\n    // Calculate the number of elements that make up each slice of our updated\n    // tensor. This allows us to work with flattened tensors and copy over whole\n    // slices at a time.\n    const totalNd = shape.length;\n    let sliceSize = 1;\n    for (let i = sliceRank; i < totalNd; ++i) {\n        sliceSize *= shape[i];\n    }\n    const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;\n    const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;\n    const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];\n    const outputSize = sizeFromShape(shape);\n    return { sliceRank, numUpdates, sliceSize, strides, outputSize };\n}\n//# sourceMappingURL=scatter_nd_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nexport function assertParamsValid(input, begin, size) {\n    const inputRank = input.shape.length;\n    util.assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must ` +\n        `match the rank of the array (${inputRank}).`);\n    util.assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must ` +\n        `match the rank of the array (${inputRank}).`);\n    for (let i = 0; i < inputRank; ++i) {\n        util.assert(begin[i] + size[i] <= input.shape[i], () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] ` +\n            `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${input.shape[i]})`);\n    }\n}\n/** Converts a binary mask to an array of axes. Used in stridedSlice(). */\nexport function maskToAxes(mask) {\n    const axes = [];\n    let axis = 0;\n    while (mask > 0) {\n        if (mask & 1) {\n            axes.push(axis);\n        }\n        mask /= 2;\n        axis++;\n    }\n    return axes;\n}\n/** Computes the output shape given the strided slice params. */\nexport function computeOutShape(begin, end, strides) {\n    const size = [];\n    for (let axis = 0; axis < begin.length; axis++) {\n        size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);\n    }\n    return size;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stride value. Otherwise, insert.\nexport function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {\n    const newStrides = [...strides];\n    for (let i = newStrides.length; i < inputShape.length; i++) {\n        newStrides.push(1);\n    }\n    for (let i = 0; i < numElidedAxes; i++) {\n        if (i === 0) {\n            newStrides[ellipsisInsertionIndex] = 1;\n        }\n        else {\n            newStrides.splice(ellipsisInsertionIndex, 0 /* num elements to delete */, 1 /* element to add */);\n            newStrides.pop();\n        }\n    }\n    return newStrides;\n}\nfunction unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {\n    if (normalizedAxis <= ellipsisInsertionIndex) {\n        return normalizedAxis;\n    }\n    return normalizedAxis - (numElidedAxes - 1);\n}\nfunction getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {\n    const elidedAxes = [];\n    for (let i = 0; i < numElidedAxes; i++) {\n        elidedAxes.push(ellipsisInsertionIndex + i);\n    }\n    return elidedAxes;\n}\n// Normalize the start, end and strides.\nexport function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {\n    const inputRank = inputShape.length;\n    let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);\n    if (ellipsisAxes.length && numInterpolatedAxes > 0) {\n        const fullIndex = ellipsisAxes[0];\n        // The ellipsis applies to the masked index as well as any dimensions\n        // that are interpolated.\n        const numElidedAxes = numInterpolatedAxes + 1;\n        normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);\n        normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);\n        normalizedStrides =\n            stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);\n    }\n    else {\n        for (let axis = 0; axis < inputRank; axis++) {\n            normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);\n            normalizedEnd[axis] =\n                stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);\n            normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);\n        }\n    }\n    return {\n        begin: normalizedBegin,\n        end: normalizedEnd,\n        strides: normalizedStrides\n    };\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current start value. Otherwise, insert.\nexport function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {\n    const newIndices = [...inputShape];\n    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n    for (let axis = 0; axis < newIndices.length; axis++) {\n        if (elidedAxes.indexOf(axis) > -1) {\n            newIndices[axis] = 0;\n        }\n        else {\n            const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n            let originalValue = originalBegin[originalAxis];\n            if (beginMask & 1 << originalAxis) {\n                originalValue = 0;\n            }\n            newIndices[axis] = originalValue;\n        }\n    }\n    return newIndices;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stop value. Otherwise, insert.\nexport function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {\n    const newIndices = [...inputShape];\n    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n    for (let axis = 0; axis < newIndices.length; axis++) {\n        if (elidedAxes.indexOf(axis) > -1) {\n            newIndices[axis] = Number.MAX_SAFE_INTEGER;\n        }\n        else {\n            const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n            let originalValue = originalEnd[originalAxis];\n            if (endMask & 1 << originalAxis) {\n                originalValue = Number.MAX_SAFE_INTEGER;\n            }\n            newIndices[axis] = originalValue;\n        }\n    }\n    for (let i = 0; i < newIndices.length; i++) {\n        // Handle negative indices\n        const axisSize = inputShape[i];\n        if (newIndices[i] < 0) {\n            newIndices[i] += axisSize;\n        }\n        newIndices[i] = util.clamp(0, newIndices[i], inputShape[i]);\n    }\n    return newIndices;\n}\nexport function stridesForAxis(strides, axis, ellipsisMask) {\n    let stride = strides[axis];\n    if (ellipsisMask & (1 << axis) || stride == null) {\n        stride = 1;\n    }\n    return stride;\n}\nexport function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {\n    // Begin with the specified index\n    let start = startIndices[axis];\n    const stride = strides[axis] || 1;\n    // Check the axis bit from right of masked axes, or the begin index is not set\n    // for the axis.\n    if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {\n        if (stride > 0) {\n            // Forward iteration - use the first element. These values will get\n            // clamped below (Note: We could have set them to 0 and axis_size-1, but\n            // use lowest() and max() to maintain symmetry with StopForAxis())\n            start = Number.MIN_SAFE_INTEGER;\n        }\n        else {\n            // Backward iteration - use the last element.\n            start = Number.MAX_SAFE_INTEGER;\n        }\n    }\n    // Handle negative indices\n    const axisSize = inputShape[axis];\n    if (start < 0) {\n        start += axisSize;\n    }\n    // Clamping\n    start = util.clamp(0, start, axisSize - 1);\n    return start;\n}\nexport function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {\n    // Begin with the specified index\n    let stop = stopIndices[axis];\n    const stride = strides[axis] || 1;\n    // Check the axis bit from right of masked axes, or if the stop index is not\n    // set for this axis.\n    if (endMask & (1 << axis) || ellipsisMask & (1 << axis) || stop == null) {\n        if (stride > 0) {\n            // Forward iteration - use the last element. These values will get\n            // clamped below\n            stop = Number.MAX_SAFE_INTEGER;\n        }\n        else {\n            // Backward iteration - use the first element.\n            stop = Number.MIN_SAFE_INTEGER;\n        }\n    }\n    // Handle negative indices\n    const axisSize = inputShape[axis];\n    if (stop < 0) {\n        stop += axisSize;\n    }\n    // Clamping\n    // Because the end index points one past the last element, we need slightly\n    // different clamping ranges depending on the direction.\n    if (stride > 0) {\n        // Forward iteration\n        stop = util.clamp(0, stop, axisSize);\n    }\n    else {\n        // Backward iteration\n        stop = util.clamp(-1, stop, axisSize - 1);\n    }\n    return stop;\n}\n/**\n * Returns true if the slice occupies a continous set of elements in the\n * 'flat' space.\n */\nexport function isSliceContinous(shape, begin, size) {\n    // Index of the first axis that has size > 1.\n    let firstNonOneAxis = size.length;\n    for (let i = 0; i < size.length; i++) {\n        if (size[i] > 1) {\n            firstNonOneAxis = i;\n            break;\n        }\n    }\n    for (let i = firstNonOneAxis + 1; i < size.length; i++) {\n        if (begin[i] > 0 || size[i] !== shape[i]) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function computeFlatOffset(begin, strides) {\n    let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;\n    for (let i = 0; i < begin.length - 1; i++) {\n        flatOffset += begin[i] * strides[i];\n    }\n    return flatOffset;\n}\nexport function parseSliceParams(x, begin, size) {\n    // The following logic allows for more ergonomic calls.\n    let begin_;\n    const xRank = x.shape.length;\n    if (typeof begin === 'number') {\n        begin_ = [begin, ...new Array(xRank - 1).fill(0)];\n    }\n    else if (begin.length < xRank) {\n        begin_ = begin.concat(new Array(xRank - begin.length).fill(0));\n    }\n    else {\n        begin_ = begin.slice();\n    }\n    begin_.forEach(d => {\n        util.assert(d !== -1, () => 'slice() does not support negative begin indexing.');\n    });\n    let size_;\n    if (size == null) {\n        size_ = new Array(xRank).fill(-1);\n    }\n    else if (typeof size === 'number') {\n        size_ = [size, ...new Array(xRank - 1).fill(-1)];\n    }\n    else if (size.length < xRank) {\n        size_ = size.concat(new Array(xRank - size.length).fill(-1));\n    }\n    else {\n        size_ = size;\n    }\n    size_ = size_.map((d, i) => {\n        if (d >= 0) {\n            return d;\n        }\n        else {\n            util.assert(d === -1, () => `Negative size values should be exactly -1 but got ` +\n                `${d} for the slice() size at index ${i}.`);\n            return x.shape[i] - begin_[i];\n        }\n    });\n    return [begin_, size_];\n}\n//# sourceMappingURL=slice_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from './util';\n/**\n * Serializable defines the serialization contract.\n *\n * TFJS requires serializable classes to return their className when asked\n * to avoid issues with minification.\n */\nexport class Serializable {\n    /**\n     * Return the class name for this class to use in serialization contexts.\n     *\n     * Generally speaking this will be the same thing that constructor.name\n     * would have returned.  However, the class name needs to be robust\n     * against minification for serialization/deserialization to work properly.\n     *\n     * There's also places such as initializers.VarianceScaling, where\n     * implementation details between different languages led to different\n     * class hierarchies and a non-leaf node is used for serialization purposes.\n     */\n    getClassName() {\n        return this.constructor\n            .className;\n    }\n    /**\n     * Creates an instance of T from a ConfigDict.\n     *\n     * This works for most descendants of serializable.  A few need to\n     * provide special handling.\n     * @param cls A Constructor for the class to instantiate.\n     * @param config The Configuration for the object.\n     */\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/**\n * Maps string keys to class constructors.\n *\n * Used during (de)serialization from the cross-language JSON format, which\n * requires the class name in the serialization format matches the class\n * names as used in Python, should it exist.\n */\nexport class SerializationMap {\n    constructor() {\n        this.classNameMap = {};\n    }\n    /**\n     * Returns the singleton instance of the map.\n     */\n    static getMap() {\n        if (SerializationMap.instance == null) {\n            SerializationMap.instance = new SerializationMap();\n        }\n        return SerializationMap.instance;\n    }\n    /**\n     * Registers the class as serializable.\n     */\n    static register(cls) {\n        SerializationMap.getMap().classNameMap[cls.className] =\n            [cls, cls.fromConfig];\n    }\n}\n/**\n * Register a class with the serialization map of TensorFlow.js.\n *\n * This is often used for registering custom Layers, so they can be\n * serialized and deserialized.\n *\n * Example:\n *\n * ```js\n * class MyCustomLayer extends tf.layers.Layer {\n *   static className = 'MyCustomLayer';\n *\n *   constructor(config) {\n *     super(config);\n *   }\n * }\n * tf.serialization.registerClass(MyCustomLayer);\n * ```\n *\n * @param cls The class to be registered. It must have a public static member\n *   called `className` defined and the value must be a non-empty string.\n *\n * @doc {heading: 'Models', subheading: 'Serialization', ignoreCI: true}\n */\nexport function registerClass(cls) {\n    assert(cls.className != null, () => `Class being registered does not have the static className ` +\n        `property defined.`);\n    assert(typeof cls.className === 'string', () => `className is required to be a string, but got type ` +\n        typeof cls.className);\n    assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, ` +\n        `which is disallowed.`);\n    SerializationMap.register(cls);\n}\n//# sourceMappingURL=serialization.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { inferShape } from './tensor_util_env';\nimport { arraysEqual, flatten, isString, isTypedArray } from './util';\nconst TEST_EPSILON_FLOAT32 = 1e-3;\nexport const TEST_EPSILON_FLOAT16 = 1e-1;\nexport function expectArraysClose(actual, expected, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon));\n}\nexport function testEpsilon() {\n    return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :\n        TEST_EPSILON_FLOAT16;\n}\nfunction expectArraysPredicate(actual, expected, predicate) {\n    let checkClassType = true;\n    if (isTypedArray(actual) || isTypedArray(expected)) {\n        checkClassType = false;\n    }\n    if (isTypedArray(actual) && isTypedArray(expected)) {\n        checkClassType = true;\n    }\n    if (checkClassType) {\n        const aType = actual.constructor.name;\n        const bType = expected.constructor.name;\n        if (aType !== bType) {\n            throw new Error(`Arrays are of different type. Actual: ${aType}. ` +\n                `Expected: ${bType}`);\n        }\n    }\n    if (Array.isArray(actual) && Array.isArray(expected)) {\n        const actualShape = inferShape(actual);\n        const expectedShape = inferShape(expected);\n        if (!arraysEqual(actualShape, expectedShape)) {\n            throw new Error(`Arrays have different shapes. ` +\n                `Actual: [${actualShape}]. Expected: [${expectedShape}]`);\n        }\n    }\n    const actualFlat = isTypedArray(actual) ? actual : flatten(actual);\n    const expectedFlat = isTypedArray(expected) ?\n        expected :\n        flatten(expected);\n    if (actualFlat.length !== expectedFlat.length) {\n        throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs ` +\n            `expected: ${expectedFlat.length}.\\n` +\n            `Actual:   ${actualFlat}.\\n` +\n            `Expected: ${expectedFlat}.`);\n    }\n    for (let i = 0; i < expectedFlat.length; ++i) {\n        const a = actualFlat[i];\n        const e = expectedFlat[i];\n        if (!predicate(a, e)) {\n            throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\\n` +\n                `Actual:   ${actualFlat}.\\n` +\n                `Expected: ${expectedFlat}.`);\n        }\n    }\n}\nexport function expectPromiseToFail(fn, done) {\n    fn().then(() => done.fail(), () => done());\n}\nexport function expectArraysEqual(actual, expected) {\n    const exp = typeof expected === 'string' || typeof expected === 'number' ||\n        typeof expected === 'boolean' ?\n        [expected] :\n        expected;\n    if (isString(actual) || isString(actual[0]) ||\n        isString(expected) || isString(expected[0])) {\n        // tslint:disable-next-line: triple-equals\n        return expectArraysPredicate(actual, exp, (a, b) => a == b);\n    }\n    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));\n}\nexport function expectNumbersClose(a, e, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    if (!areClose(a, e, epsilon)) {\n        throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n    }\n}\nfunction areClose(a, e, epsilon) {\n    if (!isFinite(a) && !isFinite(e)) {\n        return true;\n    }\n    if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {\n        return false;\n    }\n    return true;\n}\nexport function expectValuesInRange(actual, low, high) {\n    for (let i = 0; i < actual.length; i++) {\n        if (actual[i] < low || actual[i] > high) {\n            throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);\n        }\n    }\n}\nexport function expectArrayBuffersEqual(actual, expected) {\n    // Safari & Jasmine don't like comparing ArrayBuffers directly. Wrapping in\n    // a Float32Array solves this issue.\n    expect(new Float32Array(actual)).toEqual(new Float32Array(expected));\n}\n//# sourceMappingURL=test_util.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { env } from './environment';\nimport { setDeprecationWarningFn } from './tensor';\nimport { getTensorsInContainer } from './tensor_util';\n/**\n * Enables production mode which disables correctness checks in favor of\n * performance.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableProdMode() {\n    env().set('PROD', true);\n}\n/**\n * Enables debug mode which will log information about all executed kernels:\n * the elapsed time of the kernel execution, as well as the rank, shape, and\n * size of the output tensor.\n *\n * Debug mode will significantly slow down your application as it will\n * download the result of every operation to the CPU. This should not be used in\n * production. Debug mode does not affect the timing information of the kernel\n * execution as we do not measure download time in the kernel execution time.\n *\n * See also: `tf.profile`, `tf.memory`.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableDebugMode() {\n    env().set('DEBUG', true);\n}\n/** Globally disables deprecation warnings */\nexport function disableDeprecationWarnings() {\n    env().set('DEPRECATION_WARNINGS_ENABLED', false);\n    console.warn(`TensorFlow.js deprecation warnings have been disabled.`);\n}\n/** Warn users about deprecated functionality. */\nexport function deprecationWarn(msg) {\n    if (env().getBool('DEPRECATION_WARNINGS_ENABLED')) {\n        console.warn(msg + ' You can disable deprecation warnings with ' +\n            'tf.disableDeprecationWarnings().');\n    }\n}\nsetDeprecationWarningFn(deprecationWarn);\n/**\n * Dispose all variables kept in backend engine.\n *\n * @doc {heading: 'Environment'}\n */\nexport function disposeVariables() {\n    ENGINE.disposeVariables();\n}\n/**\n * It returns the global engine that keeps track of all tensors and backends.\n *\n * @doc {heading: 'Environment'}\n */\nexport function engine() {\n    return ENGINE;\n}\n/**\n * Returns memory info at the current time in the program. The result is an\n * object with the following properties:\n *\n * - `numBytes`: Number of bytes allocated (undisposed) at this time.\n * - `numTensors`: Number of unique tensors allocated.\n * - `numDataBuffers`: Number of unique data buffers allocated\n *   (undisposed) at this time, which is \u2264 the number of tensors\n *   (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same\n *   data buffer with `a`).\n * - `unreliable`: True if the memory usage is unreliable. See `reasons` when\n *    `unreliable` is true.\n * - `reasons`: `string[]`, reasons why the memory is unreliable, present if\n *    `unreliable` is true.\n *\n * WebGL Properties:\n * - `numBytesInGPU`: Number of bytes allocated (undisposed) in the GPU only at\n *     this time.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function memory() {\n    return ENGINE.memory();\n}\n/**\n * Executes the provided function `f()` and returns a promise that resolves\n * with information about the function's memory use:\n * - `newBytes`: the number of new bytes allocated\n * - `newTensors`: the number of new tensors created\n * - `peakBytes`: the peak number of bytes allocated\n * - `kernels`: an array of objects for each kernel involved that reports\n * their input and output shapes, number of bytes used, and number of new\n * tensors created.\n *\n * ```js\n * const profile = await tf.profile(() => {\n *   const x = tf.tensor1d([1, 2, 3]);\n *   let x2 = x.square();\n *   x2.dispose();\n *   x2 = x.square();\n *   x2.dispose();\n *   return x;\n * });\n *\n * console.log(`newBytes: ${profile.newBytes}`);\n * console.log(`newTensors: ${profile.newTensors}`);\n * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>\n * k.totalBytesSnapshot)}`);\n * ```\n *\n *\n * @doc {heading: 'Performance', subheading: 'Profile'}\n */\nexport function profile(f) {\n    return ENGINE.profile(f);\n}\n/**\n * Executes the provided function `fn` and after it is executed, cleans up all\n * intermediate tensors allocated by `fn` except those returned by `fn`.\n * `fn` must not return a Promise (async functions not allowed). The returned\n * result can be a complex object.\n *\n * Using this method helps avoid memory leaks. In general, wrap calls to\n * operations in `tf.tidy` for automatic memory cleanup.\n *\n * NOTE: Variables do *not* get cleaned up when inside a tidy(). If you want to\n * dispose variables, please use `tf.disposeVariables` or call dispose()\n * directly on variables.\n *\n * ```js\n * // y = 2 ^ 2 + 1\n * const y = tf.tidy(() => {\n *   // a, b, and one will be cleaned up when the tidy ends.\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *   const b = a.square();\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * y.print();\n * ```\n *\n * @param nameOrFn The name of the closure, or the function to execute.\n *     If a name is provided, the 2nd argument should be the function.\n *     If debug mode is on, the timing and the memory usage of the function\n *     will be tracked and displayed on the console using the provided name.\n * @param fn The function to execute.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function tidy(nameOrFn, fn) {\n    return ENGINE.tidy(nameOrFn, fn);\n}\n/**\n * Disposes any `tf.Tensor`s found within the provided object.\n *\n * @param container an object that may be a `tf.Tensor` or may directly\n *     contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If\n *     the object is not a `tf.Tensor` or does not contain `Tensors`, nothing\n *     happens. In general it is safe to pass any object here, except that\n *     `Promise`s are not supported.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function dispose(container) {\n    const tensors = getTensorsInContainer(container);\n    tensors.forEach(tensor => tensor.dispose());\n}\n/**\n * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed\n * automatically.\n *\n * ```js\n * let b;\n * const y = tf.tidy(() => {\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *\n *   // b will not be cleaned up by the tidy. a and one will be cleaned up\n *   // when the tidy ends.\n *   b = tf.keep(a.square());\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * console.log('y:');\n * y.print();\n * console.log('b:');\n * b.print();\n * ```\n *\n * @param result The tensor to keep from being disposed.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function keep(result) {\n    return ENGINE.keep(result);\n}\n/**\n * Executes `f()` and returns a promise that resolves with timing\n * information.\n *\n * The result is an object with the following properties:\n *\n * - `wallMs`: Wall execution time.\n * - `kernelMs`: Kernel execution time, ignoring data transfer. If using the\n * WebGL backend and the query timer extension is not available, this will\n * return an error object.\n * - On `WebGL` The following additional properties exist:\n *   - `uploadWaitMs`: CPU blocking time on texture uploads.\n *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).\n *\n * ```js\n * const x = tf.randomNormal([20, 20]);\n * const time = await tf.time(() => x.matMul(x));\n *\n * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);\n * ```\n *\n * @param f The function to execute and time.\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nexport function time(f) {\n    return ENGINE.time(f);\n}\n/**\n * Sets the backend (cpu, webgl, wasm, etc) responsible for creating tensors and\n * executing operations on those tensors. Returns a promise that resolves\n * to a boolean if the backend initialization was successful.\n *\n * Note this disposes the current backend, if any, as well as any tensors\n * associated with it. A new backend is initialized, even if it is of the\n * same type as the previous one.\n *\n * @param backendName The name of the backend. Currently supports\n *     `'webgl'|'cpu'` in the browser, `'tensorflow'` under node.js\n *     (requires tfjs-node), and `'wasm'` (requires tfjs-backend-wasm).\n *\n * @doc {heading: 'Backends'}\n */\nexport function setBackend(backendName) {\n    return ENGINE.setBackend(backendName);\n}\n/**\n * Returns a promise that resolves when the currently selected backend (or the\n * highest priority one) has initialized. Await this promise when you are using\n * a backend that has async initialization.\n *\n * @doc {heading: 'Backends'}\n */\nexport function ready() {\n    return ENGINE.ready();\n}\n/**\n * Returns the current backend name (cpu, webgl, etc). The backend is\n * responsible for creating tensors and executing operations on those tensors.\n *\n * @doc {heading: 'Backends'}\n */\nexport function getBackend() {\n    return ENGINE.backendName;\n}\n/**\n * Removes a backend and the registered factory.\n *\n * @doc {heading: 'Backends'}\n */\nexport function removeBackend(name) {\n    ENGINE.removeBackend(name);\n}\n/**\n * Finds the backend registered under the provided name. Returns null if the\n * name is not in the registry, or the registration hasn't finished yet.\n */\nexport function findBackend(name) {\n    return ENGINE.findBackend(name);\n}\n/**\n * Finds the backend factory registered under the provided name. Returns a\n * function that produces a new backend when called. Returns null if the name\n * is not in the registry.\n */\nexport function findBackendFactory(name) {\n    return ENGINE.findBackendFactory(name);\n}\n/**\n * Registers a global backend. The registration should happen when importing\n * a module file (e.g. when importing `backend_webgl.ts`), and is used for\n * modular builds (e.g. custom tfjs bundle with only webgl support).\n *\n * @param factory The backend factory function. When called, it should\n * return a backend instance, or a promise of an instance.\n * @param priority The priority of the backend (higher = more important).\n *     In case multiple backends are registered, the priority is used to find\n *     the best backend. Defaults to 1.\n * @return False if there is already a registered backend under this name, true\n *     if not.\n *\n * @doc {heading: 'Backends'}\n */\nexport function registerBackend(name, factory, priority = 1) {\n    return ENGINE.registerBackend(name, factory, priority);\n}\n/**\n * Gets the current backend. If no backends have been initialized, this will\n * attempt to initialize the best backend. Will throw an error if the highest\n * priority backend has async initialization, in which case, you should call\n * 'await tf.ready()' before running other code.\n *\n * @doc {heading: 'Backends'}\n */\nexport function backend() {\n    return ENGINE.backend;\n}\n/**\n * Sets the global platform.\n *\n * @param platformName The name of this platform.\n * @param platform A platform implementation.\n */\nexport function setPlatform(platformName, platform) {\n    env().setPlatform(platformName, platform);\n}\n//# sourceMappingURL=globals.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Add } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n *\n * ```js\n * // Broadcast add a with b.\n * const a = tf.scalar(5);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n * @param a The first `tf.Tensor` to add.\n * @param b The second `tf.Tensor` to add. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction add_(a, b) {\n    let $a = convertToTensor(a, 'a', 'add');\n    let $b = convertToTensor(b, 'b', 'add');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.add($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Add);\n}\nexport const add = op({ add_ });\n//# sourceMappingURL=add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FloorDiv } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n * The result is rounded with floor function.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.floorDiv(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.floorDiv(b).print();  // or tf.floorDiv(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction floorDiv_(a, b) {\n    let $a = convertToTensor(a, 'a', 'floorDiv');\n    let $b = convertToTensor(b, 'b', 'floorDiv');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.floorDiv($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, FloorDiv);\n}\nexport const floorDiv = op({ floorDiv_ });\n//# sourceMappingURL=floorDiv.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Div } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { floorDiv } from './floorDiv';\nimport { op } from './operation';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction div_(a, b) {\n    let $a = convertToTensor(a, 'a', 'div');\n    let $b = convertToTensor(b, 'b', 'div');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'int32' && $b.dtype === 'int32') {\n        return floorDiv($a, $b);\n    }\n    const forward = (backend, save) => {\n        const res = backend.realDivide($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = {};\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Div, attrs);\n}\nexport const div = op({ div_ });\n//# sourceMappingURL=div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Multiply } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.\n *\n * We also expose `tf.mulStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([2, 3, 4, 5]);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n *\n * ```js\n * // Broadcast mul a with b.\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.scalar(5);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n * @param a The first tensor to multiply.\n * @param b The second tensor to multiply. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mul_(a, b) {\n    let $a = convertToTensor(a, 'a', 'mul');\n    let $b = convertToTensor(b, 'b', 'mul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.multiply($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Multiply);\n}\nexport const mul = op({ mul_ });\n//# sourceMappingURL=mul.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Abs } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes absolute value element-wise: `abs(x)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.abs().print();  // or tf.abs(x)\n * ```\n * @param x The input `tf.Tensor`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction abs_(x) {\n    const $x = convertToTensor(x, 'x', 'abs');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'complex64') {\n            return backend.complexAbs($x);\n        }\n        return backend.abs($x);\n    }, inputs, null /* grad */, Abs);\n}\nexport const abs = op({ abs_ });\n//# sourceMappingURL=abs.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.acos().print();  // or tf.acos(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acos_(x) {\n    const $x = convertToTensor(x, 'x', 'acos');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.acos($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Acos);\n}\nexport const acos = op({ acos_ });\n//# sourceMappingURL=acos.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:\n * `acosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([10, 1, 3, 5.7]);\n *\n * x.acosh().print();  // or tf.acosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acosh_(x) {\n    const $x = convertToTensor(x, 'x', 'acosh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.acosh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Acosh);\n}\nexport const acosh = op({ acosh_ });\n//# sourceMappingURL=acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AddN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n *\n * tf.addN([a, b, c]).print();\n * ```\n * @param tensors A list of tensors with the same shape and dtype.\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction addN_(tensors) {\n    util.assert(Array.isArray(tensors), () => 'The argument passed to tf.addN() must be a list of tensors');\n    util.assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ` +\n        `${tensors.length}`);\n    const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));\n    const firstTensor = $tensors[0];\n    $tensors.forEach(t => {\n        if (t.dtype !== firstTensor.dtype) {\n            throw new Error('All tensors passed to tf.addN() must have the same dtype');\n        }\n    });\n    $tensors.forEach(t => {\n        if (!util.arraysEqual(t.shape, firstTensor.shape)) {\n            throw new Error('All tensors passed to tf.addN() must have the same shape');\n        }\n    });\n    const forward = (backend, save) => {\n        const res = backend.addN($tensors);\n        save($tensors);\n        return res;\n    };\n    const inputs = $tensors;\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, AddN);\n}\nexport const addN = op({ addN_ });\n//# sourceMappingURL=add_n.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n * Returns true if the axis specifies the inner most dimensions of the\n * array.\n */\nexport function axesAreInnerMostDims(axes, rank) {\n    for (let i = 0; i < axes.length; ++i) {\n        if (axes[axes.length - i - 1] !== rank - 1 - i) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function combineLocations(outputLoc, reduceLoc, axes) {\n    const rank = outputLoc.length + reduceLoc.length;\n    const loc = [];\n    let outIdx = 0;\n    let reduceIdx = 0;\n    for (let dim = 0; dim < rank; dim++) {\n        if (axes.indexOf(dim) === -1) {\n            loc.push(outputLoc[outIdx++]);\n        }\n        else {\n            loc.push(reduceLoc[reduceIdx++]);\n        }\n    }\n    return loc;\n}\nexport function computeOutAndReduceShapes(aShape, axes) {\n    const outShape = [];\n    const rank = aShape.length;\n    for (let dim = 0; dim < rank; dim++) {\n        if (axes.indexOf(dim) === -1) {\n            outShape.push(aShape[dim]);\n        }\n    }\n    const reduceShape = axes.map(dim => aShape[dim]);\n    return [outShape, reduceShape];\n}\nexport function expandShapeToKeepDim(shape, axes) {\n    const reduceSubShape = axes.map(x => 1);\n    return combineLocations(shape, reduceSubShape, axes);\n}\nexport function assertAxesAreInnerMostDims(msg, axes, rank) {\n    util.assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. ` +\n        `Got axes ${axes} and rank-${rank} input.`);\n}\n/**\n * Returns the axes permutation to be used with `tf.transpose`, if such\n * permutation is necessary. Otherwise it returns null. This method is used by\n * operations that operate only on inner-most axes.\n */\nexport function getAxesPermutation(axes, rank) {\n    if (axesAreInnerMostDims(axes, rank)) {\n        return null;\n    }\n    const result = [];\n    for (let i = 0; i < rank; ++i) {\n        if (axes.indexOf(i) === -1) {\n            result.push(i);\n        }\n    }\n    axes.forEach(axis => result.push(axis));\n    return result;\n}\n/** Returns the axes permutation that undoes the original permutation. */\nexport function getUndoAxesPermutation(axes) {\n    return axes.map((axis, i) => [i, axis])\n        .sort((a, b) => a[1] - b[1])\n        .map(x => x[0]);\n}\nexport function getInnerMostAxes(numAxes, rank) {\n    const res = [];\n    for (let i = rank - numAxes; i < rank; ++i) {\n        res.push(i);\n    }\n    return res;\n}\n//# sourceMappingURL=axis_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { All } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the logical and of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.all().print();  // or tf.all(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.all(axis).print();  // or tf.all(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction all_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'all', 'bool');\n    const forward = (backend) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = getInnerMostAxes(axes.length, $x.rank);\n        }\n        const res = backend.all($x, axes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(res.shape, origAxes);\n            return reshape(res, newShape);\n        }\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, All, attrs);\n}\nexport const all = op({ all_ });\n//# sourceMappingURL=all.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Any } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the logical or of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.any().print();  // or tf.any(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.any(axis).print();  // or tf.any(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction any_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'any', 'bool');\n    const forward = (backend) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = getInnerMostAxes(axes.length, $x.rank);\n        }\n        const res = backend.any($x, axes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(res.shape, origAxes);\n            return reshape(res, newShape);\n        }\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Any, attrs);\n}\n// tslint:disable-next-line:variable-name\nexport const any = op({ any_ });\n//# sourceMappingURL=any.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Returns the indices of the maximum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMax().print();  // or tf.argMax(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMax(axis).print();  // or tf.argMax(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMax_(x, axis = 0) {\n    let $x = convertToTensor(x, 'x', 'argMax');\n    const forward = (backend, save) => {\n        save([$x]);\n        let axes = util.parseAxisParam(axis, $x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        return backend.argMax($x, axes[0]);\n    };\n    const inputs = { x: $x };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ArgMax, attrs);\n}\nexport const argMax = op({ argMax_ });\n//# sourceMappingURL=arg_max.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Returns the indices of the minimum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMin().print();  // or tf.argMin(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMin(axis).print();  // or tf.argMin(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMin_(x, axis = 0) {\n    let $x = convertToTensor(x, 'x', 'argMin');\n    const forward = (backend, save) => {\n        save([$x]);\n        if (axis == null) {\n            axis = 0;\n        }\n        let axes = util.parseAxisParam(axis, $x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        return backend.argMin($x, axes[0]);\n    };\n    const inputs = { x: $x };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ArgMin, attrs);\n}\nexport const argMin = op({ argMin_ });\n//# sourceMappingURL=arg_min.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asin().print();  // or tf.asin(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asin_(x) {\n    const $x = convertToTensor(x, 'x', 'asin');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.asin($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Asin);\n}\nexport const asin = op({ asin_ });\n//# sourceMappingURL=asin.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:\n * `asinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asinh().print();  // or tf.asinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asinh_(x) {\n    const $x = convertToTensor(x, 'x', 'asinh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.asinh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Asinh);\n}\nexport const asinh = op({ asinh_ });\n//# sourceMappingURL=asinh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.atan().print();  // or tf.atan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan_(x) {\n    const $x = convertToTensor(x, 'x', 'atan');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.atan($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Atan);\n}\nexport const atan = op({ atan_ });\n//# sourceMappingURL=atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan2 } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);\n * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);\n *\n * tf.atan2(a, b).print()\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan2_(a, b) {\n    let $a = convertToTensor(a, 'a', 'atan2');\n    let $b = convertToTensor(b, 'b', 'atan2');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.atan2($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Atan2);\n}\nexport const atan2 = op({ atan2_ });\n//# sourceMappingURL=atan2.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:\n * `atanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.atanh().print();  // or tf.atanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atanh_(x) {\n    const $x = convertToTensor(x, 'x', 'atanh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.atanh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Atanh);\n}\nexport const atanh = op({ atanh_ });\n//# sourceMappingURL=atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n *\n * @param inputShape Input tensor shape is of the following dimensions:\n *     `[batch, height, width, inChannels]`.\n * @param filterShape The filter shape is of the following dimensions:\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat The data format of the input and output data.\n *     Defaults to 'NHWC'.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`.\n *     Defaults to `[1, 1]`. If `dilations` is a single number, then\n *     `dilationHeight == dilationWidth`.\n */\nexport function computeDilation2DInfo(inputShape, filterShape, strides, pad, dataFormat = 'NHWC', dilations) {\n    // `computerConv2DInfo` require filterShape to be in the dimension of:\n    // `[filterHeight, filterWidth, depth, outDepth]`, dilation2d doesn't have\n    // outDepth, it should have the same depth as the input.\n    // Input shape: [batch, height, width, inChannels]\n    const inputChannels = inputShape[3];\n    const $filterShape = [...filterShape, inputChannels];\n    const $dataFormat = convertConv2DDataFormat(dataFormat);\n    return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad, null /* roundingMode */, null /* depthWise */, $dataFormat);\n}\nexport function computePool2DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'channelsLast') {\n    const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n    let filterShape;\n    if (dataFormat === 'channelsLast') {\n        filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n    }\n    else if (dataFormat === 'channelsFirst') {\n        filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    return computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, false, dataFormat);\n}\n/**\n * Computes the information for a forward pass of a pooling3D operation.\n */\nexport function computePool3DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'NDHWC') {\n    const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);\n    let filterShape;\n    let $dataFormat;\n    if (dataFormat === 'NDHWC') {\n        $dataFormat = 'channelsLast';\n        filterShape =\n            [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];\n    }\n    else if (dataFormat === 'NCDHW') {\n        $dataFormat = 'channelsFirst';\n        filterShape =\n            [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    return computeConv3DInfo(inShape, filterShape, strides, dilations, pad, false, $dataFormat, roundingMode);\n}\n/**\n * Computes the information for a forward pass of a convolution/pooling\n * operation.\n */\nexport function computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, depthwise = false, dataFormat = 'channelsLast') {\n    let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n    if (dataFormat === 'channelsLast') {\n        [batchSize, inHeight, inWidth, inChannels] = inShape;\n    }\n    else if (dataFormat === 'channelsFirst') {\n        [batchSize, inChannels, inHeight, inWidth] = inShape;\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    const [filterHeight, filterWidth, , filterChannels] = filterShape;\n    const [strideHeight, strideWidth] = parseTupleParam(strides);\n    const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n    const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);\n    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n    let outShape;\n    if (dataFormat === 'channelsFirst') {\n        outShape = [batchSize, outChannels, outHeight, outWidth];\n    }\n    else if (dataFormat === 'channelsLast') {\n        outShape = [batchSize, outHeight, outWidth, outChannels];\n    }\n    return {\n        batchSize,\n        dataFormat,\n        inHeight,\n        inWidth,\n        inChannels,\n        outHeight,\n        outWidth,\n        outChannels,\n        padInfo,\n        strideHeight,\n        strideWidth,\n        filterHeight,\n        filterWidth,\n        effectiveFilterHeight,\n        effectiveFilterWidth,\n        dilationHeight,\n        dilationWidth,\n        inShape,\n        outShape,\n        filterShape\n    };\n}\n/**\n * Computes the information for a forward pass of a 3D convolution/pooling\n * operation.\n */\nexport function computeConv3DInfo(inShape, filterShape, strides, dilations, pad, depthwise = false, dataFormat = 'channelsLast', roundingMode) {\n    let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];\n    if (dataFormat === 'channelsLast') {\n        [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n    }\n    else if (dataFormat === 'channelsFirst') {\n        [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;\n    const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n    const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);\n    const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);\n    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n    const { padInfo, outDepth, outHeight, outWidth } = get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);\n    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n    let outShape;\n    if (dataFormat === 'channelsFirst') {\n        outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n    }\n    else if (dataFormat === 'channelsLast') {\n        outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n    }\n    return {\n        batchSize,\n        dataFormat,\n        inDepth,\n        inHeight,\n        inWidth,\n        inChannels,\n        outDepth,\n        outHeight,\n        outWidth,\n        outChannels,\n        padInfo,\n        strideDepth,\n        strideHeight,\n        strideWidth,\n        filterDepth,\n        filterHeight,\n        filterWidth,\n        effectiveFilterDepth,\n        effectiveFilterHeight,\n        effectiveFilterWidth,\n        dilationDepth,\n        dilationHeight,\n        dilationWidth,\n        inShape,\n        outShape,\n        filterShape\n    };\n}\nfunction computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {\n    if (zeroPad == null) {\n        zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n    }\n    const inputRows = inShape[0];\n    const inputCols = inShape[1];\n    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    return [outputRows, outputCols];\n}\nfunction computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {\n    if (zeroPad == null) {\n        zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n    }\n    const inputDepth = inShape[0];\n    const inputRows = inShape[1];\n    const inputCols = inShape[2];\n    const outputDepths = conditionalRound((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputDepths), () => `The output # of depths (${outputDepths}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    return [outputDepths, outputRows, outputCols, outChannels];\n}\nexport function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {\n    const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n    return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\nfunction parseTupleParam(param) {\n    if (typeof param === 'number') {\n        return [param, param, param];\n    }\n    if (param.length === 2) {\n        return [param[0], param[1], 1];\n    }\n    return param;\n}\nfunction parse3TupleParam(param) {\n    return typeof param === 'number' ? [param, param, param] : param;\n}\n/* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d\n * Atrous convolution is equivalent to standard convolution with upsampled\n * filters with effective_filter_height =\n * filter_height + (filter_height - 1) * (dilation - 1)\n * and effective_filter_width =\n * filter_width + (filter_width - 1) * (dilation - 1),\n * produced by inserting dilation - 1 zeros along consecutive elements across\n * the filters' spatial dimensions.\n * When there is a dilation, this converts a filter dimension to the\n * effective filter dimension, so it can be used in a standard convolution.\n */\nfunction getEffectiveFilterSize(filterSize, dilation) {\n    if (dilation <= 1) {\n        return filterSize;\n    }\n    return filterSize + (filterSize - 1) * (dilation - 1);\n}\nfunction getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {\n    let padInfo;\n    let outHeight;\n    let outWidth;\n    if (typeof pad === 'number') {\n        const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n        padInfo = { top: pad, bottom: pad, left: pad, right: pad, type: padType };\n        const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad, roundingMode);\n        outHeight = outShape[0];\n        outWidth = outShape[1];\n    }\n    else if (pad === 'same') {\n        outHeight = Math.ceil(inHeight / strideHeight);\n        outWidth = Math.ceil(inWidth / strideWidth);\n        const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);\n        const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);\n        const top = Math.floor(padAlongHeight / 2);\n        const bottom = padAlongHeight - top;\n        const left = Math.floor(padAlongWidth / 2);\n        const right = padAlongWidth - left;\n        padInfo = { top, bottom, left, right, type: 'SAME' };\n    }\n    else if (pad === 'valid') {\n        padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: 'VALID' };\n        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n    }\n    else if (typeof pad === 'object') {\n        const top = dataFormat === 'channelsLast' ? pad[1][0] : pad[2][0];\n        const bottom = dataFormat === 'channelsLast' ? pad[1][1] : pad[2][1];\n        const left = dataFormat === 'channelsLast' ? pad[2][0] : pad[3][0];\n        const right = dataFormat === 'channelsLast' ? pad[2][1] : pad[3][1];\n        const padType = (top === 0 && bottom === 0 && left === 0 && right === 0) ?\n            'VALID' :\n            'EXPLICIT';\n        padInfo = { top, bottom, left, right, type: padType };\n        outHeight = conditionalRound((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);\n        outWidth = conditionalRound((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);\n    }\n    else {\n        throw Error(`Unknown padding parameter: ${pad}`);\n    }\n    return { padInfo, outHeight, outWidth };\n}\nfunction get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {\n    let padInfo;\n    let outDepth;\n    let outHeight;\n    let outWidth;\n    if (typeof pad === 'number') {\n        const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n        padInfo = {\n            top: pad,\n            bottom: pad,\n            left: pad,\n            right: pad,\n            front: pad,\n            back: pad,\n            type: padType\n        };\n        const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad, roundingMode);\n        outDepth = outShape[0];\n        outHeight = outShape[1];\n        outWidth = outShape[2];\n    }\n    else if (pad === 'same') {\n        outDepth = Math.ceil(inDepth / strideDepth);\n        outHeight = Math.ceil(inHeight / strideHeight);\n        outWidth = Math.ceil(inWidth / strideWidth);\n        const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n        const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;\n        const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n        const front = Math.floor(padAlongDepth / 2);\n        const back = padAlongDepth - front;\n        const top = Math.floor(padAlongHeight / 2);\n        const bottom = padAlongHeight - top;\n        const left = Math.floor(padAlongWidth / 2);\n        const right = padAlongWidth - left;\n        padInfo = { top, bottom, left, right, front, back, type: 'SAME' };\n    }\n    else if (pad === 'valid') {\n        padInfo = {\n            top: 0,\n            bottom: 0,\n            left: 0,\n            right: 0,\n            front: 0,\n            back: 0,\n            type: 'VALID'\n        };\n        outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);\n        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n    }\n    else {\n        throw Error(`Unknown padding parameter: ${pad}`);\n    }\n    return { padInfo, outDepth, outHeight, outWidth };\n}\n/**\n * Rounds a value depending on the rounding mode\n * @param value\n * @param roundingMode\n */\nfunction conditionalRound(value, roundingMode) {\n    if (!roundingMode) {\n        return value;\n    }\n    switch (roundingMode) {\n        case 'round':\n            // used for Caffe Conv\n            return Math.round(value);\n        case 'ceil':\n            // used for Caffe Pool\n            return Math.ceil(value);\n        case 'floor':\n            return Math.floor(value);\n        default:\n            throw new Error(`Unknown roundingMode ${roundingMode}`);\n    }\n}\nexport function tupleValuesAreOne(param) {\n    const [dimA, dimB, dimC] = parseTupleParam(param);\n    return dimA === 1 && dimB === 1 && dimC === 1;\n}\nexport function eitherStridesOrDilationsAreOne(strides, dilations) {\n    return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n/**\n * Convert Conv2D dataFormat from 'NHWC'|'NCHW' to\n *    'channelsLast'|'channelsFirst'\n * @param dataFormat in 'NHWC'|'NCHW' mode\n * @return dataFormat in 'channelsLast'|'channelsFirst' mode\n * @throws unknown dataFormat\n */\nexport function convertConv2DDataFormat(dataFormat) {\n    if (dataFormat === 'NHWC') {\n        return 'channelsLast';\n    }\n    else if (dataFormat === 'NCHW') {\n        return 'channelsFirst';\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n}\n//# sourceMappingURL=conv_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction avgPool_(x, filterSize, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'avgPool', 'float32');\n    const dilations = 1;\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in avgPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        save([x4D]);\n        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n            util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n            return x4D.clone();\n        }\n        return backend.avgPool(x4D, convInfo);\n    };\n    const inputs = { x: x4D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool, attrs);\n    res = cast(res, $x.dtype);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const avgPool = op({ avgPool_ });\n//# sourceMappingURL=avg_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { deprecationWarn } from '../globals';\nimport { AvgPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D average pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.avgPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates:\n *     `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction avgPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat = 'NDHWC', dilations) {\n    if (dilations == null) {\n        dilations = [1, 1, 1];\n    }\n    else {\n        deprecationWarn('dilations is deprecated, this field will be gone in ' +\n            'v3.0.0.');\n    }\n    const $x = convertToTensor(x, 'x', 'avgPool3d', 'float32');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in avgPool3d: Only NDHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool3d: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in avgPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1, 1];\n        }\n        const convInfo = conv_util.computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);\n        save([x5D]);\n        return backend.avgPool3d(x5D, convInfo);\n    };\n    const inputs = { x: x5D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat, dilations };\n    let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool3D, attrs);\n    res = cast(res, x5D.dtype);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const avgPool3d = op({ avgPool3d_ });\n//# sourceMappingURL=avg_pool_3d.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nexport function assertParamsConsistent(shapes, axis) {\n    const rank = shapes[0].length;\n    shapes.forEach((shape, i) => {\n        util.assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same ` +\n            `as the rank of the rest (${rank})`);\n    });\n    util.assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);\n    const firstShape = shapes[0];\n    shapes.forEach((shape, i) => {\n        for (let r = 0; r < rank; r++) {\n            util.assert((r === axis) || (shape[r] === firstShape[r]), () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) ` +\n                `does not match the shape of the rest (${firstShape}) ` +\n                `along the non-concatenated axis ${i}.`);\n        }\n    });\n}\nexport function computeOutShape(shapes, axis) {\n    const outputShape = shapes[0].slice();\n    for (let i = 1; i < shapes.length; i++) {\n        outputShape[axis] += shapes[i][axis];\n    }\n    return outputShape;\n}\n//# sourceMappingURL=concat_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Concat } from '../kernel_names';\nimport { convertToTensorArray } from '../tensor_util_env';\nimport { assert, parseAxisParam, sizeFromShape } from '../util';\nimport { assertParamsConsistent, computeOutShape } from './concat_util';\nimport { op } from './operation';\nimport { tensor } from './tensor';\n/**\n * Concatenates a list of `tf.Tensor`s along a given axis.\n *\n * The tensors ranks and types must match, and their sizes must match in all\n * dimensions except `axis`.\n *\n * Also available are stricter rank-specific methods that assert that\n * `tensors` are of the given rank:\n *   - `tf.concat1d`\n *   - `tf.concat2d`\n *   - `tf.concat3d`\n *   - `tf.concat4d`\n *\n * Except `tf.concat1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * a.concat(b).print();  // or a.concat(b)\n * ```\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.concat([a, b, c]).print();\n * ```\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [10, 20]]);\n * const b = tf.tensor2d([[3, 4], [30, 40]]);\n * const axis = 1;\n * tf.concat([a, b], axis).print();\n * ```\n * @param tensors A list of tensors to concatenate.\n * @param axis The axis to concate along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction concat_(tensors, axis = 0) {\n    assert(tensors.length >= 1, () => 'Pass at least one tensor to concat');\n    let $tensors = convertToTensorArray(tensors, 'tensors', 'concat');\n    if ($tensors[0].dtype === 'complex64') {\n        $tensors.forEach(tensor => {\n            if (tensor.dtype !== 'complex64') {\n                throw new Error(`Cannot concatenate complex64 tensors with a tensor\n          with dtype ${tensor.dtype}. `);\n            }\n        });\n    }\n    const forward = (backend, save) => {\n        const $axis = parseAxisParam(axis, $tensors[0].shape)[0];\n        const outShape = computeOutShape($tensors.map(t => t.shape), $axis);\n        if (sizeFromShape(outShape) === 0) {\n            return tensor([], outShape);\n        }\n        // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n        $tensors = $tensors.filter(t => t.size > 0);\n        if ($tensors.length === 1) {\n            return $tensors[0];\n        }\n        const shapes = $tensors.map(t => t.shape);\n        assertParamsConsistent(shapes, $axis);\n        const res = backend.concat($tensors, $axis);\n        save($tensors);\n        return res;\n    };\n    const inputs = $tensors;\n    const attr = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Concat, attr);\n}\nexport const concat = op({ concat_ });\n//# sourceMappingURL=concat.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sigmoid } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sigmoid element-wise, `1 / (1 + exp(-x))`\n *\n * ```js\n * const x = tf.tensor1d([0, -1, 2, -3]);\n *\n * x.sigmoid().print();  // or tf.sigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sigmoid_(x) {\n    const $x = convertToTensor(x, 'x', 'sigmoid');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sigmoid($x);\n        save([res]);\n        return res;\n    }, inputs, null /* grad */, Sigmoid);\n}\nexport const sigmoid = op({ sigmoid_ });\n//# sourceMappingURL=sigmoid.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Slice } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport * as slice_util from './slice_util';\n/**\n * Extracts a slice from a `tf.Tensor` starting at coordinates `begin`\n * and is of size `size`.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `x` is of the given rank:\n *   - `tf.slice1d`\n *   - `tf.slice2d`\n *   - `tf.slice3d`\n *   - `tf.slice4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.slice([1], [2]).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * x.slice([1, 0], [1, 2]).print();\n * ```\n * @param x The input `tf.Tensor` to slice from.\n * @param begin The coordinates to start the slice from. The length can be\n *     less than the rank of x - the rest of the axes will have implicit 0 as\n *     start. Can also be a single number, in which case it specifies the\n *     first axis.\n * @param size The size of the slice. The length can be less than the rank of\n *     x - the rest of the axes will have implicit -1. A value of -1 requests\n *     the rest of the dimensions in the axis. Can also be a single number,\n *     in which case it specifies the size of the first axis.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction slice_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice');\n    if ($x.rank === 0) {\n        throw new Error('Slicing scalar is not possible');\n    }\n    const forward = (backend, save) => {\n        const [begin_, size_] = slice_util.parseSliceParams($x, begin, size);\n        slice_util.assertParamsValid($x, begin_, size_);\n        save([$x]);\n        return backend.slice($x, begin_, size_);\n    };\n    const inputs = { x: $x };\n    const attrs = { begin, size };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Slice, attrs);\n}\nexport const slice = op({ slice_ });\n//# sourceMappingURL=slice.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, 70]);\n *\n * x.tanh().print();  // or tf.tanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tanh_(x) {\n    const $x = convertToTensor(x, 'x', 'tanh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const y = backend.tanh($x);\n        save([y]);\n        return y;\n    }, inputs, null /* grad */, Tanh);\n}\nexport const tanh = op({ tanh_ });\n//# sourceMappingURL=tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { add } from './add';\nimport { concat } from './concat';\nimport { matMul } from './mat_mul';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { slice } from './slice';\nimport { tanh } from './tanh';\n/**\n * Computes the next state and output of a BasicLSTMCell.\n *\n * Returns `[newC, newH]`.\n *\n * Derived from tf.contrib.rnn.BasicLSTMCell.\n *\n * @param forgetBias Forget bias for the cell.\n * @param lstmKernel The weights for the cell.\n * @param lstmBias The bias for the cell.\n * @param data The input to the cell.\n * @param c Previous cell state.\n * @param h Previous cell output.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {\n    const $forgetBias = convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');\n    const $lstmKernel = convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');\n    const $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');\n    const $data = convertToTensor(data, 'data', 'basicLSTMCell');\n    const $c = convertToTensor(c, 'c', 'basicLSTMCell');\n    const $h = convertToTensor(h, 'h', 'basicLSTMCell');\n    const combined = concat([$data, $h], 1);\n    const weighted = matMul(combined, $lstmKernel);\n    const res = add(weighted, $lstmBias);\n    // i = input_gate, j = new_input, f = forget_gate, o = output_gate\n    const batchSize = res.shape[0];\n    const sliceCols = res.shape[1] / 4;\n    const sliceSize = [batchSize, sliceCols];\n    const i = slice(res, [0, 0], sliceSize);\n    const j = slice(res, [0, sliceCols], sliceSize);\n    const f = slice(res, [0, sliceCols * 2], sliceSize);\n    const o = slice(res, [0, sliceCols * 3], sliceSize);\n    const newC = add(mul(sigmoid(i), tanh(j)), mul($c, sigmoid(add($forgetBias, f))));\n    const newH = mul(tanh(newC), sigmoid(o));\n    return [newC, newH];\n}\nexport const basicLSTMCell = op({ basicLSTMCell_ });\n//# sourceMappingURL=basic_lstm_cell.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchToSpaceND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation reshapes the \"batch\" dimension 0 into `M + 1` dimensions of\n * shape `blockShape + [batch]`, interleaves these blocks back into the grid\n * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with\n * the same rank as the input. The spatial dimensions of this intermediate\n * result are then optionally cropped according to `crops` to produce the\n * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise\n * description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n * const blockShape = [2, 2];\n * const crops = [[0, 0], [0, 0]];\n *\n * x.batchToSpaceND(blockShape, crops).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param crops A 2-D array.  Must have shape `[M, 2]`, all values must be >= 0.\n * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input\n * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required\n * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,\n * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,\n * x.shape[N-1]]`\n *\n * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /\n * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`\n * according to `crops` to produce the output of shape: `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],\n * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -\n * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction batchToSpaceND_(x, blockShape, crops) {\n    const $x = convertToTensor(x, 'x', 'batchToSpaceND');\n    const prod = blockShape.reduce((a, b) => a * b);\n    util.assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);\n    util.assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);\n    util.assert($x.shape[0] % prod === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of ` +\n        `the elements of blockShape ${blockShape.join(' * ')} === ${prod}`);\n    const forward = backend => {\n        return backend.batchToSpaceND($x, blockShape, crops);\n    };\n    const inputs = { x: $x };\n    const attrs = { blockShape, crops };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, BatchToSpaceND, attrs);\n}\nexport const batchToSpaceND = op({ batchToSpaceND_ });\n//# sourceMappingURL=batch_to_space_nd.js.map", "import { reshape } from './reshape';\nexport function xAs4D(x) {\n    let x4D;\n    if (x.rank === 0 || x.rank === 1) {\n        x4D = reshape(x, [1, 1, 1, x.size]);\n    }\n    else if (x.rank === 2) {\n        x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n    }\n    else if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    else {\n        x4D = x;\n    }\n    return x4D;\n}\n//# sourceMappingURL=batchnorm_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FusedBatchNorm } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { xAs4D } from './batchnorm_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Batch normalization.\n *\n * As described in\n * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).\n *\n * Mean, variance, scale, and offset can be of two shapes:\n *   - The same shape as the input.\n *   - In the common case, the depth dimension is the last dimension of x, so\n *     the values would be an `tf.Tensor1D` of shape [depth].\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that parameters passed are of given rank\n *   - `tf.batchNorm2d`\n *   - `tf.batchNorm3d`\n *   - `tf.batchNorm4d`\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction batchNorm_(x, mean, variance, offset, scale, varianceEpsilon) {\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($mean.rank === $variance.rank, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert($offset == null || $mean.rank === $offset.rank, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert($scale == null || $mean.rank === $scale.rank, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    const x4D = xAs4D($x);\n    const forward = (backend, save) => {\n        save([x4D, $mean, $variance, $scale]);\n        return backend.batchNorm(x4D, as1DOr4D($mean), as1DOr4D($variance), as1DOr4D($offset), as1DOr4D($scale), varianceEpsilon);\n    };\n    const inputs = {\n        x: x4D,\n        scale: $scale,\n        offset: $offset,\n        mean: $mean,\n        variance: $variance\n    };\n    const attrs = { varianceEpsilon };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, FusedBatchNorm, attrs);\n    return reshape(res, $x.shape);\n}\nfunction as1DOr4D(x) {\n    if (x == null) {\n        return null;\n    }\n    if (x.rank === 0) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [x.size]);\n    }\n    else if (x.rank === 1) {\n        return x;\n    }\n    else if (x.rank === 2) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n    }\n    else if (x.rank === 3) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    return x;\n}\nexport const batchNorm = op({ batchNorm_ });\n//# sourceMappingURL=batchnorm.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 2D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm2d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm2d = op({ batchNorm2d_ });\n//# sourceMappingURL=batchnorm2d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 3D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm3d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm3d = op({ batchNorm3d_ });\n//# sourceMappingURL=batchnorm3d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 4D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm4d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm4d = op({ batchNorm4d_ });\n//# sourceMappingURL=batchnorm4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BroadcastTo } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { clone } from './clone';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Broadcast an array to a compatible shape NumPy-style.\n *\n * The tensor's shape is compared to the broadcast shape from end to beginning.\n * Ones are prepended to the tensor's shape until is has the same length as\n * the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is\n * already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then\n * the input tensor is tiled N times along that axis (using tf.tile).\n *\n * @param input The tensor that is to be broadcasted.\n * @param shape The input is to be broadcast to this shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction broadcastTo_(x, shape) {\n    let input = convertToTensor(x, 'broadcastTo', 'x');\n    const xShape = input.shape;\n    if (shape.some(d => !(d > 0) || d % 1 !== 0)) {\n        throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);\n    }\n    if (shape.length < input.rank) {\n        throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input.rank}.`);\n    }\n    if (shape.length > input.rank) {\n        const newShape = input.shape.slice();\n        while (newShape.length < shape.length) {\n            newShape.unshift(1);\n        }\n        input = reshape(input, newShape);\n    }\n    const inputShape = input.shape;\n    const reps = Array.from(shape);\n    for (let i = shape.length - 1; i >= 0; i--) {\n        if (inputShape[i] === shape[i]) {\n            reps[i] = 1;\n        }\n        else if (input.shape[i] !== 1) {\n            throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);\n        }\n    }\n    const axes = reps.map((n, i) => n > 1 ? i : -1).filter(i => i >= 0);\n    if (axes.length === 0) {\n        return clone(input);\n    }\n    const forward = (backend) => backend.tile(input, reps);\n    const inputs = { x: input };\n    const attrs = { shape, inputShape };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, BroadcastTo, attrs);\n}\nexport const broadcastTo = op({ broadcastTo_ });\n//# sourceMappingURL=broadcast_to.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Ceil } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.ceil().print();  // or tf.ceil(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction ceil_(x) {\n    const $x = convertToTensor(x, 'x', 'ceil');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.ceil($x), inputs, null /* grad */, Ceil);\n}\nexport const ceil = op({ ceil_ });\n//# sourceMappingURL=ceil.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ClipByValue } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)\n * ```\n * @param x The input tensor.\n * @param clipValueMin Lower-bound of range to be clipped to.\n * @param clipValueMax Upper-bound of range to be clipped to.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction clipByValue_(x, clipValueMin, clipValueMax) {\n    const $x = convertToTensor(x, 'x', 'clipByValue');\n    util.assert((clipValueMin <= clipValueMax), () => `Error in clip: min (${clipValueMin}) must be ` +\n        `less than or equal to max (${clipValueMax}).`);\n    const inputs = { x: $x };\n    const attrs = { clipValueMin, clipValueMax };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.clip($x, clipValueMin, clipValueMax);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, ClipByValue, attrs);\n}\nexport const clipByValue = op({ clipByValue_ });\n//# sourceMappingURL=clip_by_value.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(3) = |r1, g1, b1|\n * B: shape(2) = |r2, g2|\n * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @return The concatenated array.\n */\nfunction concat1d_(tensors) {\n    return concat(tensors, 0 /* axis */);\n}\nexport const concat1d = op({ concat1d_ });\n//# sourceMappingURL=concat_1d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *\n * B: shape(2, 3) = | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * C = tf.concat2d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *                  | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * if axis = 1:\n * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |\n *                   | r2, g2, b2, r4, g4, b4 |\n *\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concatenate along.\n * @return The concatenated array.\n */\nfunction concat2d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat2d = op({ concat2d_ });\n//# sourceMappingURL=concat_2d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor3D`s along an axis.\n * See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *\n * B: shape(2, 1, 3) = | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * C = tf.concat3d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *                     | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * if axis = 1:\n * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |\n *                     | r2, g2, b2, r4, g4, b4 |\n *\n * if axis = 2:\n * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |\n *                      | r2, g2, b2, r4, g4, b4 |\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat3d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat3d = op({ concat3d_ });\n//# sourceMappingURL=concat_3d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor4D`s along an axis.\n * See `concat` for details.\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat4d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat4d = op({ concat4d_ });\n//# sourceMappingURL=concat_4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 2D convolution over the input x.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv2d');\n    const $filter = convertToTensor(filter, 'filter', 'conv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n    util.assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match ` +\n        `input depth for filter ${$filter.shape[2]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const forward = (backend, save) => {\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        const res = backend.conv2d(x4D, $filter, convInfo);\n        save([x4D, $filter]);\n        return res;\n    };\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv2D, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const conv2d = op({ conv2d_ });\n//# sourceMappingURL=conv2d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 1D convolution over the input x.\n *\n * @param x The input tensor, of rank 3 or rank 2, of shape\n *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.\n * @param filter The filter, rank 3, of shape\n *     `[filterWidth, inDepth, outDepth]`.\n * @param stride The number of entries by which the filter is moved right at\n *     each step.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from \"NWC\", \"NCW\". Defaults to \"NWC\",\n *     the data is stored in the order of [batch, in_width, in_channels]. Only\n *     \"NWC\" is currently supported.\n * @param dilation The dilation rate in which we sample input values in\n *     atrous convolution. Defaults to `1`. If it is greater than 1, then\n *     stride must be `1`.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv1d_(x, filter, stride, pad, dataFormat = 'NWC', dilation = 1, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv1d');\n    const $filter = convertToTensor(filter, 'filter', 'conv1d');\n    let x3D = $x;\n    let reshapedTo3D = false;\n    if ($x.rank === 2) {\n        reshapedTo3D = true;\n        x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);\n    }\n    util.assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n    util.assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv1d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    util.assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +\n        `input depth for filter ${$filter.shape[1]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(stride, dilation), () => 'Error in conv1D: Either stride or dilation must be 1. ' +\n        `Got stride ${stride} and dilation '${dilation}'`);\n    util.assert(dataFormat === 'NWC', () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);\n    const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);\n    const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);\n    const strides = [1, stride];\n    const dilations = [1, dilation];\n    const conv2dDataFormat = 'NHWC';\n    const res = conv2d(input4D, filter4D, strides, pad, conv2dDataFormat, dilations, dimRoundingMode);\n    if (reshapedTo3D) {\n        return reshape(res, [res.shape[2], res.shape[3]]);\n    }\n    return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);\n}\nexport const conv1d = op({ conv1d_ });\n//# sourceMappingURL=conv1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropInput } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 2D convolution.\n *\n * @param xShape The shape of the input: [batch, height, width, inDepth].\n * If length of 3, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 4 or rank 3 of shape\n *   `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction conv2DBackpropInput_(xShape, dy, filter, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n    util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n        `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n    let xShape4D = xShape;\n    let dy4D = dy;\n    let reshapedTo4D = false;\n    if (dy.rank === 3) {\n        reshapedTo4D = true;\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n        xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n    }\n    util.assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ` +\n        `${xShape4D.length}.`);\n    util.assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got ` +\n        `rank ${dy4D.rank}`);\n    util.assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got ` +\n        `rank ${filter.rank}`);\n    const inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1];\n    const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n    util.assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must ` +\n        `match input depth for filter ${filter.shape[2]}.`);\n    util.assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must ` +\n        `match output depth for filter ${filter.shape[3]}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2dDerInput: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const dilations = 1;\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(xShape4D, filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        const res = backend.conv2dDerInput(dy4D, filter, convInfo);\n        save([dy4D, filter]);\n        return res;\n    };\n    const inputs = { dy: dy4D, filter };\n    const attrs = { strides, pad, dataFormat, dimRoundingMode, inputShape: xShape4D };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv2DBackpropInput, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const conv2DBackpropInput = op({ conv2DBackpropInput_ });\n//# sourceMappingURL=conv2d_backprop_input.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport { conv2DBackpropInput } from './conv2d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 2D convolution of an image, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 4 or rank 3, of shape\n *   `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 4 or rank 3:\n *     `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.\n * @param strides The strides of the original convolution:\n *     `[strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n * @param dimRoundingMode The rounding mode used when computing output\n *    dimensions if pad is a number. If none is provided, it will not round\n *    and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2dTranspose_(x, filter, outputShape, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv2dTranspose');\n    const $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');\n    return conv2DBackpropInput(outputShape, $x, $filter, strides, pad, 'NHWC', dimRoundingMode);\n}\nexport const conv2dTranspose = op({ conv2dTranspose_ });\n//# sourceMappingURL=conv2d_transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { eitherStridesOrDilationsAreOne } from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 3D convolution over the input x.\n *\n * @param x The input tensor, of rank 5 or rank 4, of shape\n *     `[batch, depth, height, width, channels]`. If rank 4,\n * batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.\n *      inChannels must match between input and filter.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationDepth, dilationHeight,\n *     dilationWidth]` in which we sample input values across the height\n *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.\n *     If `dilations` is a single number, then\n *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater\n *     than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3d_(x, filter, strides, pad, dataFormat = 'NDHWC', dilations = [1, 1, 1]) {\n    const $x = convertToTensor(x, 'x', 'conv3d');\n    const $filter = convertToTensor(filter, 'filter', 'conv3d');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n    util.assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +\n        `input depth for filter ${$filter.shape[3]}.`);\n    util.assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv3D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computeConv3DInfo(x5D.shape, $filter.shape, strides, dilations, pad);\n        const res = backend.conv3d(x5D, $filter, convInfo);\n        save([x5D, $filter]);\n        return res;\n    };\n    const inputs = { x: x5D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv3D, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const conv3d = op({ conv3d_ });\n//# sourceMappingURL=conv3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3DBackpropInputV2 } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 3D convolution.\n *\n * @param xShape The shape of the input: [batch, depth, height, width,\n * in_channels]. If length of 4, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 5 or rank 4 of shape\n *   `[batch, outDepth, outHeight, outWidth, in_channels]`.\n * If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n */\nfunction conv3DBackpropInput_(xShape, dy, filter, strides, pad) {\n    util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n        `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n    let xShape5D = xShape;\n    let dy5D = dy;\n    let reshapedTo5D = false;\n    if (dy.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n        xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n    }\n    const inDepth = xShape5D[4];\n    const outDepth = dy5D.shape[4];\n    util.assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ` +\n        `${xShape5D.length}.`);\n    util.assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got ` +\n        `rank ${dy5D.rank}`);\n    util.assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got ` +\n        `rank ${filter.rank}`);\n    util.assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must ` +\n        `match input depth for filter ${filter.shape[3]}.`);\n    util.assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must ` +\n        `match output depth for filter ${filter.shape[4]}.`);\n    const forward = backend => {\n        const dilations = 1;\n        const convInfo = conv_util.computeConv3DInfo(xShape5D, filter.shape, strides, dilations, pad);\n        return backend.conv3dDerInput(dy5D, filter, convInfo);\n    };\n    const inputs = { dy: dy5D };\n    const attrs = { pad };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropInputV2, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const conv3DBackpropInput = op({ conv3DBackpropInput_ });\n//# sourceMappingURL=conv3d_backprop_input.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport { conv3DBackpropInput } from './conv3d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 3D convolution of a volume, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 5 or rank 4, of shape\n *   `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[depth, filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 5 or rank 4:\n *     `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is\n *    assumed.\n * @param strides The strides of the original convolution:\n *     `[strideDepth, strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3dTranspose_(x, filter, outputShape, strides, pad) {\n    const $x = convertToTensor(x, 'x', 'conv3dTranspose');\n    const $filter = convertToTensor(filter, 'filter', 'conv3dTranspose');\n    return conv3DBackpropInput(outputShape, $x, $filter, strides, pad);\n}\nexport const conv3dTranspose = op({ conv3dTranspose_ });\n//# sourceMappingURL=conv3d_transpose.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.cos().print();  // or tf.cos(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cos_(x) {\n    const $x = convertToTensor(x, 'x', 'cos');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.cos($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Cos);\n}\nexport const cos = op({ cos_ });\n//# sourceMappingURL=cos.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.cosh().print();  // or tf.cosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cosh_(x) {\n    const $x = convertToTensor(x, 'x', 'cosh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.cosh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Cosh);\n}\nexport const cosh = op({ cosh_ });\n//# sourceMappingURL=cosh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cumsum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { getAxesPermutation, getInnerMostAxes, getUndoAxesPermutation } from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Computes the cumulative sum of a `tf.Tensor` along `axis`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4]);\n * x.cumsum().print();\n * ```\n * ```js\n * const x = tf.tensor([[1, 2], [3, 4]]);\n * x.cumsum().print();\n * ```\n *\n * @param x The input tensor to be summed.\n * @param axis The axis along which to sum. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative sum. Optional.\n *     Defaults to false. If set to true then the sum of each tensor entry\n *     does not include its own value, but only the values previous to it\n *     along the specified axis.\n * @param reverse Whether to sum in the opposite direction. Optional.\n *     Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Scan'}\n */\nfunction cumsum_(x, axis = 0, exclusive = false, reverse = false) {\n    const $x = convertToTensor(x, 'x', 'cumsum');\n    const forward = (backend, save) => {\n        const permutation = getAxesPermutation([axis], $x.rank);\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n        }\n        const permutedAxis = getInnerMostAxes(1, $x.rank)[0];\n        let value = backend.cumsum(permutedX, permutedAxis, exclusive, reverse);\n        save([$x]);\n        if (permutation != null) {\n            const reversePermutation = getUndoAxesPermutation(permutation);\n            value = transpose(value, reversePermutation);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, exclusive, reverse };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Cumsum, attrs);\n}\nexport const cumsum = op({ cumsum_ });\n//# sourceMappingURL=cumsum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthToSpace } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Rearranges data from depth into blocks of spatial data. More specifically,\n * this op outputs a copy of the input tensor where values from the `depth`\n * dimension are moved in spatial blocks to the `height` and `width` dimensions.\n * The attr `blockSize` indicates the input block size and how the data is\n * moved.\n *\n *  - Chunks of data of size `blockSize * blockSize` from depth are rearranged\n * into non-overlapping blocks of size `blockSize x blockSize`\n *\n *  - The width the output tensor is `inputWidth * blockSize`, whereas the\n * height is `inputHeight * blockSize`\n *\n *  - The Y, X coordinates within each block of the output image are determined\n * by the high order component of the input channel index\n *\n *  - The depth of the input tensor must be divisible by `blockSize *\n * blockSize`\n *\n * The `dataFormat` attr specifies the layout of the input and output tensors\n * with the following options: \"NHWC\": [ `batch, height, width, channels` ]\n * \"NCHW\": [ `batch, channels, height, width` ]\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);\n * const blockSize = 2;\n * const dataFormat = \"NHWC\";\n *\n * tf.depthToSpace(x, blockSize, dataFormat).print();\n * ```\n *\n * @param x The input tensor of rank 4\n * @param blockSIze  An `int` that is `>= 2`. The size of the spatial block\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to \"NHWC\"\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction depthToSpace_(x, blockSize, dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'depthToSpace');\n    const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];\n    const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];\n    const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];\n    util.assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputHeight} and ${blockSize}  for depthToSpace with input shape\n    ${$x.shape}`);\n    util.assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputWidth} and ${blockSize} for depthToSpace with input shape\n        ${$x.shape}`);\n    util.assert((inputDepth % (blockSize * blockSize) === 0), () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);\n    const forward = backend => backend.depthToSpace($x, blockSize, dataFormat);\n    const inputs = { x: $x };\n    const attrs = { blockSize, dataFormat };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, DepthToSpace, attrs);\n}\nexport const depthToSpace = op({ depthToSpace_ });\n//# sourceMappingURL=depth_to_space.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNative } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Depthwise 2D convolution.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction depthwiseConv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n    const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels ` +\n        `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n        `filter ${$filter.shape[2]}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1];\n        }\n        util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +\n            `1. Got strides ${strides} and dilations '${dilations}'`);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n        const res = backend.depthwiseConv2D(x4D, $filter, convInfo);\n        save([x4D, $filter]);\n        return res;\n    };\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, DepthwiseConv2dNative, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const depthwiseConv2d = op({ depthwiseConv2d_ });\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Diag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Returns a diagonal tensor with a given diagonal values.\n *\n * Given a diagonal, this operation returns a tensor with the diagonal and\n * everything else padded with zeros.\n *\n * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor\n * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * tf.diag(x).print()\n * ```\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4, 5, 6, 6, 8], [4, 2])\n *\n * tf.diag(x).print()\n * ```\n * @param x The input tensor.\n */\nfunction diag_(x) {\n    const $x = convertToTensor(x, 'x', 'diag');\n    const forward = backend => {\n        const flat = reshape($x, [$x.size]);\n        const result = backend.diag(flat);\n        const outShape = [...x.shape, ...x.shape];\n        return reshape(result, outShape);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Diag);\n}\nexport const diag = op({ diag_ });\n//# sourceMappingURL=diag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Dilation2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the grayscale dilation over the input `x`.\n *\n * @param x The input tensor, rank 3 or rank 4 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter tensor, rank 3, of shape\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat Specify the data format of the input and output data.\n *      Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the\n *      default format \"NHWC\", the data is stored in the order of: [batch,\n *      height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations`\n *     is a single number, then `dilationHeight == dilationWidth`. If it is\n *     greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction dilation2d_(x, filter, strides, pad, dilations = [1, 1], dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'dilation2d');\n    const $filter = convertToTensor(filter, 'filter', 'dilation2d');\n    util.assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ` +\n        `${$x.rank}.`);\n    util.assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(dataFormat === 'NHWC', () => `Error in dilation2d: Only NHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n        reshapedTo4D = true;\n    }\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dilations };\n    const res = ENGINE.runKernel(Dilation2D, inputs, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const dilation2d = op({ dilation2d_ });\n//# sourceMappingURL=dilation2d.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the dimensions in the input shape that are broadcasted to\n * produce the provided output shape.\n *\n * The returned dimensions are 0-indexed and sorted. An example:\n * inShape = [4, 1, 3]\n * outShape = [5, 4, 3, 3]\n * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.\n */\nexport function getBroadcastDims(inShape, outShape) {\n    const inRank = inShape.length;\n    const dims = [];\n    for (let i = 0; i < inRank; i++) {\n        const dim = inRank - 1 - i;\n        const a = inShape[dim] || 1;\n        const b = outShape[outShape.length - 1 - i] || 1;\n        if (b > 1 && a === 1) {\n            dims.unshift(dim);\n        }\n    }\n    return dims;\n}\n/**\n * Returns the axes in the output space that should be reduced to produce\n * the input space.\n */\nexport function getReductionAxes(inShape, outShape) {\n    const result = [];\n    for (let i = 0; i < outShape.length; i++) {\n        const inDim = inShape[inShape.length - i - 1];\n        const outAxis = outShape.length - i - 1;\n        const outDim = outShape[outAxis];\n        if (inDim == null || (inDim === 1 && outDim > 1)) {\n            result.unshift(outAxis);\n        }\n    }\n    return result;\n}\nexport function assertAndGetBroadcastShape(shapeA, shapeB) {\n    const result = [];\n    const l = Math.max(shapeA.length, shapeB.length);\n    for (let i = 0; i < l; i++) {\n        let a = shapeA[shapeA.length - i - 1];\n        if (a == null) {\n            a = 1;\n        }\n        let b = shapeB[shapeB.length - i - 1];\n        if (b == null) {\n            b = 1;\n        }\n        if (a === 1) {\n            result.unshift(b);\n        }\n        else if (b === 1) {\n            result.unshift(a);\n        }\n        else if (a !== b) {\n            const errMsg = `Operands could not be broadcast together with shapes ` +\n                `${shapeA} and ${shapeB}.`;\n            throw Error(errMsg);\n        }\n        else {\n            result.unshift(a);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=broadcast_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Equal } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a == b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.equal(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction equal_(a, b) {\n    let $a = convertToTensor(a, 'a', 'equal');\n    let $b = convertToTensor(b, 'b', 'equal');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.equal($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null, Equal);\n}\nexport const equal = op({ equal_ });\n//# sourceMappingURL=equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SelectV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, assertShapesMatch } from '../util';\nimport { broadcastTo } from './broadcast_to';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the elements, either `a` or `b` depending on the `condition`.\n *\n * If the condition is true, select from `a`, otherwise select from `b`.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const a = tf.tensor1d([1 , 2, 3]);\n * const b = tf.tensor1d([-1, -2, -3]);\n *\n * a.where(cond, b).print();\n * ```\n *\n * @param condition The input condition. Must be of dtype bool.\n * @param a If `condition` is rank 1, `a` may have a higher rank but\n *     its first dimension must match the size of `condition`.\n * @param b A tensor with the same dtype as `a` and with shape that is\n *     compatible with `a`.\n * @return A tensor with same dtype as `a` and `b`, and shape that is\n *     broadcastable from `a` and `b`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction where_(condition, a, b) {\n    const $a = convertToTensor(a, 'a', 'where');\n    const $b = convertToTensor(b, 'b', 'where');\n    const $condition = convertToTensor(condition, 'condition', 'where', 'bool');\n    // TODO: move this logic to forward function when the broadcastTo op is\n    // implemented in WASM.\n    // Find the broadcastable shape for $a and $b.\n    const broadcastShape = assertAndGetBroadcastShape($a.shape, $b.shape);\n    const $broadcastedA = broadcastTo($a, broadcastShape);\n    const $broadcastedB = broadcastTo($b, broadcastShape);\n    if ($condition.rank === 1) {\n        // If condition rank is 1, then the first dimension must match the size of\n        // condition.\n        assert($condition.shape[0] === $a.shape[0], () => 'The first dimension of `a` must match the size of `condition`.');\n    }\n    if ($condition.rank !== 1) {\n        // A must have the same shape as condition.\n        assertShapesMatch($condition.shape, $broadcastedB.shape, 'Error in where: ');\n    }\n    const forward = (backend, save) => {\n        const res = backend.select($condition, $broadcastedA, $broadcastedB);\n        save([$condition]);\n        return res;\n    };\n    const inputs = {\n        condition: $condition,\n        t: $broadcastedA,\n        e: $broadcastedB\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, SelectV2);\n}\nexport const where = op({ where_ });\n//# sourceMappingURL=where.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ZerosLike } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.zerosLike(x).print();\n * ```\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction zerosLike_(x) {\n    const $x = convertToTensor(x, 'x', 'zerosLike');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.zerosLike($x), inputs, null /* grad */, ZerosLike);\n}\nexport const zerosLike = op({ zerosLike_ });\n//# sourceMappingURL=zeros_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { div } from './div';\nimport { equal } from './equal';\nimport { op } from './operation';\nimport { where } from './where';\nimport { zerosLike } from './zeros_like';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0\n * if denominator is 0.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n * const c = tf.tensor1d([0, 0, 0, 0]);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n * const c = tf.scalar(0);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction divNoNan_(a, b) {\n    // TODO: Make this into its own kernel.\n    let $a = convertToTensor(a, 'a', 'div');\n    let $b = convertToTensor(b, 'b', 'div');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const divResult = div($a, $b);\n    const zeros = zerosLike(divResult);\n    const bEqualsZero = equal($b, zeros);\n    return where(bEqualsZero, zeros, divResult);\n}\nexport const divNoNan = op({ divNoNan_ });\n//# sourceMappingURL=div_no_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor2d([[1, 2], [3, 4]]);\n * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n *\n * a.dot(b).print();  // or tf.dot(a, b)\n * b.dot(a).print();\n * b.dot(c).print();\n * ```\n * @param t1 The first tensor in the dot operation.\n * @param t2 The second tensor in the dot operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction dot_(t1, t2) {\n    const $t1 = convertToTensor(t1, 't1', 'dot');\n    const $t2 = convertToTensor(t2, 't2', 'dot');\n    util.assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ` +\n        `${$t1.rank} and ${$t2.rank}.`);\n    const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);\n    const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);\n    util.assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ` +\n        `${t1Inner} and ${t2Inner}.`);\n    if ($t1.rank === 1 && $t2.rank === 1) {\n        const t12D = reshape($t1, [1, -1]);\n        const t22D = reshape($t2, [-1, 1]);\n        const t1t2 = matMul(t12D, t22D);\n        return reshape(t1t2, []);\n    }\n    else if ($t1.rank === 1 && $t2.rank === 2) {\n        const t12D = reshape($t1, [1, -1]);\n        const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n        const t1t2 = matMul(t12D, t22D);\n        return reshape(t1t2, [t1t2.size]);\n    }\n    else if ($t1.rank === 2 && $t2.rank === 1) {\n        const t22D = reshape($t2, [-1, 1]);\n        const t1t2 = matMul($t1, t22D);\n        return reshape(t1t2, [t1t2.size]);\n    }\n    else {\n        const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n        const t1t2 = matMul($t1, t22D);\n        return t1t2;\n    }\n}\nexport const dot = op({ dot_ });\n//# sourceMappingURL=dot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Elu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential linear element-wise: `x > 0 ? e ^ x - 1 : 0`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 1, -3, 2]);\n *\n * x.elu().print();  // or tf.elu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction elu_(x) {\n    const $x = convertToTensor(x, 'x', 'elu');\n    const forward = (backend, save) => {\n        const y = backend.elu($x);\n        save([y]);\n        return y;\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Elu);\n}\nexport const elu = op({ elu_ });\n//# sourceMappingURL=elu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Erf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes gause error function of the input `tf.Tensor` element-wise:\n * `erf(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.erf().print(); // or tf.erf(x);\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction erf_(x) {\n    let $x = convertToTensor(x, 'x', 'erf');\n    util.assert($x.dtype === 'int32' || $x.dtype === 'float32', () => 'Input dtype must be `int32` or `float32`.');\n    if ($x.dtype === 'int32') {\n        $x = cast($x, 'float32');\n    }\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.erf($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Erf);\n}\nexport const erf = op({ erf_ });\n//# sourceMappingURL=erf.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Exp } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.exp().print();  // or tf.exp(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction exp_(x) {\n    const $x = convertToTensor(x, 'x', 'exp');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.exp($x);\n        save([res]);\n        return res;\n    }, inputs, null /* grad */, Exp);\n}\nexport const exp = op({ exp_ });\n//# sourceMappingURL=exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n * into the tensor's shape.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const axis = 1;\n * x.expandDims(axis).print();\n * ```\n *\n * @param x The input tensor whose dimensions to be expanded.\n * @param axis The dimension index at which to insert shape of `1`. Defaults\n *     to 0 (the first dimension).\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction expandDims_(x, axis = 0) {\n    const parseAs = null;\n    const $x = convertToTensor(x, 'x', 'expandDims', parseAs);\n    util.assert(axis <= $x.rank, () => 'Axis must be <= rank of the tensor');\n    const newShape = $x.shape.slice();\n    if (axis < 0) {\n        // Negative value is counted from the tail of rank.\n        util.assert(-($x.rank + 1) <= axis, () => `Axis must be in the interval [${-($x.rank + 1)}, ${$x.rank}]`);\n        axis = $x.rank + axis + 1;\n    }\n    newShape.splice(axis, 0, 1);\n    return reshape($x, newShape);\n}\nexport const expandDims = op({ expandDims_ });\n//# sourceMappingURL=expand_dims.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Expm1 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential of the input `tf.Tensor` minus one element-wise.\n * `e ^ x - 1`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.expm1().print();  // or tf.expm1(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction expm1_(x) {\n    const $x = convertToTensor(x, 'x', 'expm1');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.expm1($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Expm1);\n}\nexport const expm1 = op({ expm1_ });\n//# sourceMappingURL=expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tile } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Construct a tensor by repeating it the number of times given by reps.\n *\n * This operation creates a new tensor by replicating `input` `reps`\n * times. The output tensor's i'th dimension has `input.shape[i] *\n * reps[i]` elements, and the values of `input` are replicated\n * `reps[i]` times along the i'th dimension. For example, tiling\n * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n *\n * a.tile([2]).print();    // or a.tile([2])\n * ```\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.tile([1, 2]).print();  // or a.tile([1, 2])\n * ```\n * @param x The tensor to tile.\n * @param reps Determines the number of replications per dimension.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction tile_(x, reps) {\n    const parseAs = null;\n    const $x = convertToTensor(x, 'x', 'tile', parseAs);\n    util.assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n        `must match length of reps ${reps}.`);\n    const forward = (backend, save) => {\n        const res = backend.tile($x, reps);\n        save([$x]);\n        return res;\n    };\n    const inputsToSave = [$x];\n    const inputs = { x: $x };\n    const attrs = { reps };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Tile, attrs, inputsToSave);\n}\nexport const tile = op({ tile_ });\n//# sourceMappingURL=tile.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { expandDims } from './expand_dims';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { tile } from './tile';\n/**\n * Create an identity matrix.\n *\n * @param numRows Number of rows.\n * @param numColumns Number of columns. Defaults to `numRows`.\n * @param batchShape If provided, will add the batch shape to the beginning\n *   of the shape of the returned `tf.Tensor` by repeating the identity\n *   matrix.\n * @param dtype Data type.\n * @returns Identity matrix of the specified size and data type, possibly\n *   with batch repetition if `batchShape` is specified.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction eye_(numRows, numColumns, batchShape, dtype = 'float32') {\n    if (numColumns == null) {\n        numColumns = numRows;\n    }\n    const buff = buffer([numRows, numColumns], dtype);\n    const n = numRows <= numColumns ? numRows : numColumns;\n    for (let i = 0; i < n; ++i) {\n        buff.set(1, i, i);\n    }\n    const out = reshape(buff.toTensor(), [numRows, numColumns]);\n    if (batchShape == null) {\n        return out;\n    }\n    else {\n        if (batchShape.length === 1) {\n            return tile(expandDims(out, 0), [batchShape[0], 1, 1]);\n        }\n        else if (batchShape.length === 2) {\n            // tslint:disable-next-line:no-unnecessary-type-assertion\n            return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);\n        }\n        else if (batchShape.length === 3) {\n            // tslint:disable-next-line:no-unnecessary-type-assertion\n            return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [\n                batchShape[0], batchShape[1], batchShape[2], 1, 1\n            ]);\n        }\n        else {\n            throw new Error(`eye() currently supports only 1D and 2D ` +\n                // tslint:disable-next-line:no-any\n                `batchShapes, but received ${batchShape.length}D.`);\n        }\n    }\n}\nexport const eye = op({ eye_ });\n//# sourceMappingURL=eye.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Fill } from '../kernel_names';\n/**\n * Creates a `tf.Tensor` filled with a scalar value.\n *\n * ```js\n * tf.fill([2, 2], 4).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param value The scalar value to fill the tensor with.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction fill(shape, value, dtype) {\n    const attrs = { shape, value, dtype };\n    return ENGINE.runKernelFunc(backend => backend.fill(shape, value, dtype), {}, null, Fill, attrs);\n}\nexport { fill };\n//# sourceMappingURL=fill.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Floor } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.floor().print();  // or tf.floor(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction floor_(x) {\n    const $x = convertToTensor(x, 'x', 'floor');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.floor($x), inputs, null /* grad */, Floor);\n}\nexport const floor = op({ floor_ });\n//# sourceMappingURL=floor.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inputs of size above this threshold will be parallelized by calling multiple\n * shader programs.\n */\nimport { nearestDivisor } from '../util';\nexport const PARALLELIZE_THRESHOLD = 30;\nexport function computeOptimalWindowSize(inSize) {\n    if (inSize <= PARALLELIZE_THRESHOLD) {\n        return inSize;\n    }\n    return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n}\n//# sourceMappingURL=reduce_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nearestDivisor } from '../util';\nimport { PARALLELIZE_THRESHOLD } from './reduce_util';\nexport function segOpComputeOptimalWindowSize(inSize, numSegments) {\n    let done = false;\n    let res;\n    if (inSize <= PARALLELIZE_THRESHOLD) {\n        res = inSize;\n        done = true;\n    }\n    else {\n        res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n    }\n    while (!done) {\n        if (res > numSegments || res === inSize) {\n            done = true;\n        }\n        else {\n            res = nearestDivisor(inSize, res + 1);\n        }\n    }\n    return res;\n}\nexport function computeOutShape(aShape, axis, numSegments) {\n    const outShape = [];\n    const rank = aShape.length;\n    for (let dim = 0; dim < rank; dim++) {\n        if (dim !== axis) {\n            outShape.push(aShape[dim]);\n        }\n        else {\n            outShape.push(numSegments);\n        }\n    }\n    return outShape;\n}\nexport function collectGatherOpShapeInfo(x, indices, axis) {\n    const dimSize = x.shape[axis];\n    const outputShape = [];\n    let batchSize = 1;\n    let sliceSize = 1;\n    for (let i = 0; i < axis; i++) {\n        outputShape.push(x.shape[i]);\n        batchSize *= x.shape[i];\n    }\n    for (let i = 0; i < indices.rank; i++) {\n        outputShape.push(indices.shape[i]);\n    }\n    for (let i = axis + 1; i < x.rank; i++) {\n        outputShape.push(x.shape[i]);\n        sliceSize *= x.shape[i];\n    }\n    return { batchSize, sliceSize, dimSize, outputShape };\n}\n//# sourceMappingURL=segment_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { collectGatherOpShapeInfo } from './segment_util';\n/**\n * Gather slices from tensor `x`'s axis `axis` according to `indices`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const indices = tf.tensor1d([1, 3, 3], 'int32');\n *\n * x.gather(indices).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const indices = tf.tensor1d([1, 1, 0], 'int32');\n *\n * x.gather(indices).print();\n * ```\n * @param x The input tensor whose slices to be gathered.\n * @param indices The indices of the values to extract.\n * @param axis The axis over which to select values. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction gather_(x, indices, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'gather');\n    const $indices = convertToTensor(indices, 'indices', 'gather', 'int32');\n    const inputs = { x: $x, indices: $indices };\n    const attrs = { axis };\n    const forward = (backend, save) => {\n        const parsedAxis = parseAxisParam(axis, $x.shape)[0];\n        const shapeInfo = collectGatherOpShapeInfo($x, $indices, parsedAxis);\n        const res = backend.gather($x, reshape($indices, [$indices.size]), parsedAxis);\n        save([$x, $indices]);\n        return reshape(res, shapeInfo.outputShape);\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, GatherV2, attrs);\n}\nexport const gather = op({ gather_ });\n//# sourceMappingURL=gather.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Greater } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a > b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greater(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greater_(a, b) {\n    let $a = convertToTensor(a, 'a', 'greater');\n    let $b = convertToTensor(b, 'b', 'greater');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.greater($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Greater);\n}\nexport const greater = op({ greater_ });\n//# sourceMappingURL=greater.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GreaterEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a >= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greaterEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greaterEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'greaterEqual');\n    let $b = convertToTensor(b, 'b', 'greaterEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.greaterEqual($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, GreaterEqual);\n}\nexport const greaterEqual = op({ greaterEqual_ });\n//# sourceMappingURL=greater_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Imag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the imaginary part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the imaginary part of each element in input considered as a complex number.\n * If input is real, a tensor of all zeros is returned.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.imag(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction imag_(input) {\n    const $input = convertToTensor(input, 'input', 'imag');\n    const forward = (backend) => {\n        return backend.imag($input);\n    };\n    const inputs = { input: $input };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Imag);\n}\nexport const imag = op({ imag_ });\n//# sourceMappingURL=imag.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsFinite } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are finite.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isFinite().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isFinite_(x) {\n    const $x = convertToTensor(x, 'x', 'isFinite');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.isFinite($x), inputs, null /* grad */, IsFinite);\n}\nexport const isFinite = op({ isFinite_ });\n//# sourceMappingURL=is_finite.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsInf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are Infinity or -Infinity.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isInf().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isInf_(x) {\n    const $x = convertToTensor(x, 'x', 'isInf');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.isInf($x), inputs, null /* grad */, IsInf);\n}\nexport const isInf = op({ isInf_ });\n//# sourceMappingURL=is_inf.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsNan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * RReturns which elements of x are NaN.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isNaN().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isNaN_(x) {\n    const $x = convertToTensor(x, 'x', 'isNaN');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.isNaN($x), inputs, null /* grad */, IsNan);\n}\nexport const isNaN = op({ isNaN_ });\n//# sourceMappingURL=is_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Maximum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.maximumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * ```js\n * // Broadcast maximum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction maximum_(a, b) {\n    let $a = convertToTensor(a, 'a', 'maximum');\n    let $b = convertToTensor(b, 'b', 'maximum');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'bool') {\n        $a = cast($a, 'int32');\n        $b = cast($b, 'int32');\n    }\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.maximum($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Maximum);\n}\nexport const maximum = op({ maximum_ });\n//# sourceMappingURL=maximum.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { isTypedArray } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.scalar` as it makes the code more readable.\n *\n * ```js\n * tf.scalar(3.14).print();\n * ```\n *\n * @param value The value of the scalar.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function scalar(value, dtype) {\n    if (((isTypedArray(value) && dtype !== 'string') || Array.isArray(value)) &&\n        dtype !== 'complex64') {\n        throw new Error('Error creating a new Scalar: value must be a primitive ' +\n            '(number|boolean|string)');\n    }\n    if (dtype === 'string' && isTypedArray(value) &&\n        !(value instanceof Uint8Array)) {\n        throw new Error('When making a scalar from encoded string, ' +\n            'the value must be `Uint8Array`.');\n    }\n    const shape = [];\n    const inferredShape = [];\n    return makeTensor(value, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=scalar.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { maximum } from './maximum';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { scalar } from './scalar';\n/**\n * Computes leaky rectified linear element-wise.\n *\n * See\n * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](\n *     http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.leakyRelu(0.1).print();  // or tf.leakyRelu(x, 0.1)\n * ```\n * @param x The input tensor.\n * @param alpha The scaling factor for negative values, defaults to 0.2.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction leakyRelu_(x, alpha = 0.2) {\n    const $x = convertToTensor(x, 'x', 'leakyRelu');\n    return maximum(mul(scalar(alpha), $x), $x);\n}\nexport const leakyRelu = op({ leakyRelu_ });\n//# sourceMappingURL=leaky_relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Less } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a < b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.less(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction less_(a, b) {\n    let $a = convertToTensor(a, 'a', 'less');\n    let $b = convertToTensor(b, 'b', 'less');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.less($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Less);\n}\nexport const less = op({ less_ });\n//# sourceMappingURL=less.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LessEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a <= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.lessEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction lessEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'lessEqual');\n    let $b = convertToTensor(b, 'b', 'lessEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.lessEqual($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LessEqual);\n}\nexport const lessEqual = op({ lessEqual_ });\n//# sourceMappingURL=less_equal.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LinSpace } from '../kernel_names';\n/**\n * Return an evenly spaced sequence of numbers over the given interval.\n *\n * ```js\n * tf.linspace(0, 9, 10).print();\n * ```\n * @param start The start value of the sequence.\n * @param stop The end value of the sequence.\n * @param num The number of values to generate.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function linspace(start, stop, num) {\n    if (num <= 0) {\n        throw new Error('The number of values should be positive.');\n    }\n    const attrs = { start, stop, num };\n    return ENGINE.runKernelFunc(backend => backend.linspace(start, stop, num), {} /* inputs */, null /* grad */, LinSpace, attrs);\n}\n//# sourceMappingURL=linspace.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LRN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Normalizes the activation of a local neighborhood across or within\n * channels.\n *\n * @param x The input tensor. The 4-D input tensor is treated as a 3-D array\n *     of 1D vectors (along the last dimension), and each vector is\n *     normalized independently.\n * @param depthRadius The number of adjacent channels in the 1D normalization\n *     window.\n * @param bias A constant bias term for the basis.\n * @param alpha A scale factor, usually positive.\n * @param beta An exponent.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n    const $x = convertToTensor(x, 'x', 'localResponseNormalization');\n    util.assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${$x.rank}.`);\n    util.assert(util.isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an ` +\n        `integer but got depthRadius ${depthRadius}.`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    const forward = (backend, save) => {\n        const y = backend.localResponseNormalization4D(x4D, depthRadius, bias, alpha, beta);\n        save([x4D, y]);\n        return y;\n    };\n    const inputs = { x: x4D };\n    const attrs = { depthRadius, bias, alpha, beta };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, LRN, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    else {\n        return res;\n    }\n}\nexport const localResponseNormalization = op({ localResponseNormalization_ });\n//# sourceMappingURL=local_response_normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E]);\n *\n * x.log().print();  // or tf.log(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log_(x) {\n    const $x = convertToTensor(x, 'x', 'log');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.log($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Log);\n}\nexport const log = op({ log_ });\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log1p } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` plus one\n * element-wise: `ln(1 + x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E - 1]);\n *\n * x.log1p().print();  // or tf.log1p(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log1p_(x) {\n    const $x = convertToTensor(x, 'x', 'log1p');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.log1p($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Log1p);\n}\nexport const log1p = op({ log1p_ });\n//# sourceMappingURL=log1p.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { Tensor, Variable } from './tensor';\nimport { convertToTensor, convertToTensorArray } from './tensor_util_env';\nimport * as util from './util';\n/**\n * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the\n * gradient of `f(x)` with respect to `x`.\n *\n * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to\n * `x` is computed instead. `f(x)` must take a single tensor `x` and return a\n * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.grad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * g(x).print();\n * ```\n *\n * ```js\n * // f(x) = x ^ 3\n * const f = x => x.pow(tf.scalar(3, 'int32'));\n * // f'(x) = 3x ^ 2\n * const g = tf.grad(f);\n * // f''(x) = 6x\n * const gg = tf.grad(g);\n *\n * const x = tf.tensor1d([2, 3]);\n * gg(x).print();\n * ```\n *\n * @param f The function f(x), to compute gradient for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grad(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in grad(f) must be a function');\n    return (x, dy) => {\n        // x can be of any dtype, thus null as the last argument.\n        const $x = convertToTensor(x, 'x', 'tf.grad', null);\n        const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grad') : null;\n        return ENGINE.tidy(() => {\n            const { value, grads } = ENGINE.gradients(() => f($x), [$x], $dy);\n            if ($dy != null) {\n                util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grad(f)(x, dy) must match the shape ' +\n                    'returned by f(x)');\n            }\n            checkGrads(grads);\n            return grads[0];\n        });\n    };\n}\n/**\n * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,\n * which gives an array of gradients of `f()` with respect to each input\n * [`x1`,`x2`,...].\n *\n * If `dy` is passed when calling `g()`, the gradient of\n * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.\n * The provided `f` must take one or more tensors and return a single tensor\n * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df / da = b, df / db = a\n * const g = tf.grads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const [da, db] = g([a, b]);\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @param f The function `f(x1, x2,...)` to compute gradients for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grads(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in grads(f) must be a function');\n    return (args, dy) => {\n        util.assert(Array.isArray(args), () => 'The args passed in grads(f)(args) must be an array ' +\n            'of `Tensor`s or `TensorLike`s');\n        // args can be of any dtype, thus null as the last argument.\n        const $args = convertToTensorArray(args, 'args', 'tf.grads', null);\n        const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grads') : null;\n        return ENGINE.tidy(() => {\n            const { value, grads } = ENGINE.gradients(() => f(...$args), $args, $dy);\n            if ($dy != null) {\n                util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grads(f)([x1,...], dy) must ' +\n                    'match the shape returned by f([x1,...])');\n            }\n            checkGrads(grads);\n            return grads;\n        });\n    };\n}\n/**\n * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.valueAndGrad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * const {value, grad} = g(x);\n *\n * console.log('value');\n * value.print();\n * console.log('grad');\n * grad.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrad(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in valueAndGrad(f) must be a function');\n    return (x, dy) => {\n        util.assert(x instanceof Tensor, () => 'The x passed in valueAndGrad(f)(x) must be a tensor');\n        util.assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrad(f)(x, dy) must be a tensor');\n        const { grads, value } = ENGINE.gradients(() => f(x), [x], dy);\n        checkGrads(grads);\n        return { grad: grads[0], value };\n    };\n}\n/**\n * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df/da = b, df/db = a\n * const g = tf.valueAndGrads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const {value, grads} = g([a, b]);\n *\n * const [da, db] = grads;\n *\n * console.log('value');\n * value.print();\n *\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrads(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in valueAndGrads(f) must be a function');\n    return (args, dy) => {\n        util.assert(Array.isArray(args) && args.every(arg => arg instanceof Tensor), () => 'The args passed in valueAndGrads(f)(args) must be array of ' +\n            'tensors');\n        util.assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrads(f)(args, dy) must be a tensor');\n        const res = ENGINE.gradients(() => f(...args), args, dy);\n        if (dy != null) {\n            util.assertShapesMatch(res.value.shape, dy.shape, 'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +\n                'match the shape returned by f([x1,...])');\n        }\n        checkGrads(res.grads);\n        return res;\n    };\n}\n/**\n * Computes and returns the gradient of f(x) with respect to the list of\n * trainable variables provided by `varList`. If no list is provided, it\n * defaults to all trainable variables.\n *\n * ```js\n * const a = tf.variable(tf.tensor1d([3, 4]));\n * const b = tf.variable(tf.tensor1d([5, 6]));\n * const x = tf.tensor1d([1, 2]);\n *\n * // f(a, b) = a * x ^ 2 + b * x\n * const f = () => a.mul(x.square()).add(b.mul(x)).sum();\n * // df/da = x ^ 2, df/db = x\n * const {value, grads} = tf.variableGrads(f);\n *\n * Object.keys(grads).forEach(varName => grads[varName].print());\n * ```\n *\n * @param f The function to execute. f() should return a scalar.\n * @param varList The list of variables to compute the gradients with respect\n *     to. Defaults to all trainable variables.\n * @returns An object with the following keys and values:\n *   - `value`: The value of the function `f`.\n *   - `grads`: A map from the names of the variables to the gradients.\n *     If the `varList` argument is provided explicitly and contains a subset of\n *     non-trainable variables, this map in the return value will contain keys\n *     that map the names of the non-trainable variables to `null`.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction variableGrads(f, varList) {\n    util.assert(util.isFunction(f), () => 'The f passed in variableGrads(f) must be a function');\n    util.assert(varList == null ||\n        Array.isArray(varList) && varList.every(v => v instanceof Variable), () => 'The varList passed in variableGrads(f, varList) must be an array ' +\n        'of variables');\n    const specifiedVarList = varList != null;\n    if (!specifiedVarList) {\n        // Get all of the trainable variables.\n        varList = [];\n        for (const varName in ENGINE.registeredVariables) {\n            varList.push(ENGINE.registeredVariables[varName]);\n        }\n    }\n    const specifiedNonTrainable = specifiedVarList ? varList.filter(variable => !variable.trainable) : null;\n    // Prune non-trainable variables.\n    const originalVarCount = varList.length;\n    varList = varList.filter(variable => variable.trainable);\n    util.assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to ` +\n        `be trainable, but none of the ${originalVarCount} variables is ` +\n        `trainable.`);\n    const allowNoGradients = true;\n    const { value, grads } = ENGINE.gradients(f, varList, null, allowNoGradients);\n    util.assert(grads.some(g => g != null), () => 'Cannot find a connection between any variable and the result of ' +\n        'the loss function y=f(x). Please make sure the operations that ' +\n        'use variables are inside the function f passed to minimize().');\n    util.assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it ` +\n        `returned a rank-${value.rank} tensor`);\n    const namedGrads = {};\n    varList.forEach((v, i) => {\n        if (grads[i] != null) {\n            namedGrads[v.name] = grads[i];\n        }\n    });\n    if (specifiedNonTrainable != null) {\n        // If varList is explicitly provided and contains non-trainable values,\n        // add them to the returned gradients with `null` values.\n        specifiedNonTrainable.forEach(v => namedGrads[v.name] = null);\n    }\n    return { value, grads: namedGrads };\n}\n/**\n * Overrides the gradient computation of a function `f`.\n *\n * Takes a function\n * `f(...inputs, save) => {value: Tensor, gradFunc: (dy, saved) => Tensor[]}`\n * and returns another function `g(...inputs)` which takes the same inputs as\n * `f`. When called, `g` returns `f().value`. In backward mode, custom gradients\n * with respect to each input of `f` are computed using `f().gradFunc`.\n *\n * The `save` function passsed to `f` should be used for saving tensors needed\n * in the gradient. And the `saved` passed to the `gradFunc` is a\n * `NamedTensorMap`, which contains those saved tensor.\n *\n * ```js\n * const customOp = tf.customGrad((x, save) => {\n *   // Save x to make sure it's available later for the gradient.\n *   save([x]);\n *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);\n *   return {\n *     value: x.square(),\n *     // Note `saved.x` which points to the `x` we saved earlier.\n *     gradFunc: (dy, saved) => [dy.mul(saved[0].abs())]\n *   };\n * });\n *\n * const x = tf.tensor1d([-1, -2, 3]);\n * const dx = tf.grad(x => customOp(x));\n *\n * console.log(`f(x):`);\n * customOp(x).print();\n * console.log(`f'(x):`);\n * dx(x).print();\n * ```\n *\n * @param f The function to evaluate in forward mode, which should return\n *     `{value: Tensor, gradFunc: (dy, saved) => Tensor[]}`, where `gradFunc`\n *     returns the custom gradients of `f` with respect to its inputs.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction customGrad(f) {\n    return ENGINE.customGrad(f);\n}\nfunction checkGrads(grads) {\n    const numNullGradients = grads.filter(g => g == null).length;\n    if (numNullGradients > 0) {\n        throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.`);\n    }\n}\nexport { customGrad, variableGrads, valueAndGrad, valueAndGrads, grad, grads, };\n//# sourceMappingURL=gradients.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Negate } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes `-1 * x` element-wise.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);\n *\n * x.neg().print();  // or tf.neg(x)\n * ```\n *\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction neg_(x) {\n    const $x = convertToTensor(x, 'x', 'neg');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.neg($x), inputs, null /* grad */, Negate);\n}\nexport const neg = op({ neg_ });\n//# sourceMappingURL=neg.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softplus } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.softplus().print();  // or tf.softplus(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction softplus_(x) {\n    const $x = convertToTensor(x, 'x', 'softplus');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.softplus($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Softplus);\n}\nexport const softplus = op({ softplus_ });\n//# sourceMappingURL=softplus.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../gradients';\nimport { convertToTensor } from '../tensor_util_env';\nimport { mul } from './mul';\nimport { neg } from './neg';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { softplus } from './softplus';\n/**\n * Computes log sigmoid of the input `tf.Tensor` element-wise:\n * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.logSigmoid().print();  // or tf.logSigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction logSigmoid_(x) {\n    const $x = convertToTensor(x, 'x', 'logSigmoid');\n    // Use a custom gradient to maintain previous implementation.\n    // There is no LogSigmoid kernel in TF so we can't use engine.runKernel\n    // directly\n    const customOp = customGrad((x) => {\n        // TODO(yassogba) we can remove the chained softplus call here only\n        // after backends have modualrized softplus at which point we can call\n        // engine runKernel(..., Sotfplus, ...) directly.\n        const value = neg(softplus(neg(x)));\n        const gradFunc = (dy) => {\n            const derX = mul(dy, sigmoid(neg(x)));\n            return derX;\n        };\n        return { value, gradFunc };\n    });\n    return customOp($x);\n}\nexport const logSigmoid = op({ logSigmoid_ });\n//# sourceMappingURL=log_sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Max } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the maximum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.max().print();  // or tf.max(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.max(axis).print();  // or tf.max(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction max_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'max');\n    const forward = (backend, save) => {\n        const origAxes = util.parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let maxInput = $x;\n        if (permutedAxes != null) {\n            maxInput = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, maxInput.rank);\n        }\n        const y = backend.max(maxInput, axes);\n        if (permutedAxes != null) {\n            maxInput.dispose();\n        }\n        let res = y;\n        if (keepDims) {\n            const expandedShape = axis_util.expandShapeToKeepDim(res.shape, util.parseAxisParam(axis, $x.shape));\n            res = reshape(res, expandedShape);\n            y.dispose();\n        }\n        save([$x, res]);\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { reductionIndices: axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Max, attrs);\n}\nexport const max = op({ max_ });\n//# sourceMappingURL=max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sub } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n *\n * ```js\n * // Broadcast subtract a with b.\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.scalar(5);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n * @param a The first `tf.Tensor` to subtract from.\n * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction sub_(a, b) {\n    let $a = convertToTensor(a, 'a', 'sub');\n    let $b = convertToTensor(b, 'b', 'sub');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.subtract($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Sub);\n}\nexport const sub = op({ sub_ });\n//# sourceMappingURL=sub.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the sum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If axes has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.sum().print();  // or tf.sum(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.sum(axis).print();  // or tf.sum(x, axis)\n * ```\n *\n * @param x The input tensor to compute the sum over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction sum_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'sum');\n    if ($x.dtype === 'bool') {\n        $x = cast($x, 'int32');\n    }\n    const forward = (backend, save) => {\n        save([$x]);\n        const axes = parseAxisParam(axis, $x.shape);\n        const permutation = getAxesPermutation(axes, $x.rank);\n        let reductionAxes = axes;\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n        }\n        let value = backend.sum(permutedX, reductionAxes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(value.shape, axes);\n            value = reshape(value, newShape);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Sum, attrs);\n}\nexport const sum = op({ sum_ });\n//# sourceMappingURL=sum.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogSoftmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { op } from './operation';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log softmax.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param axis The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction logSoftmax_(logits, axis = -1) {\n    const $logits = convertToTensor(logits, 'logits', 'logSoftmax');\n    if (axis === -1) {\n        axis = $logits.rank - 1;\n    }\n    if (axis !== $logits.rank - 1) {\n        throw Error('Log Softmax along a non-last dimension is not yet supported. ' +\n            `Logits was rank ${$logits.rank} and axis was ${axis}`);\n    }\n    const forward = (backend, save) => {\n        const keepDims = true;\n        const xMax = max(logits, axis, true);\n        const shifted = sub(logits, xMax);\n        const value = sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis, keepDims)));\n        save([value]);\n        return value;\n    };\n    const inputs = { logits: $logits };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LogSoftmax, attrs);\n}\nexport const logSoftmax = op({ logSoftmax_ });\n//# sourceMappingURL=log_softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { add } from './add';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log(sum(exp(elements across the reduction dimensions)).\n *\n * Reduces the input along the dimensions given in `axis`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.logSumExp().print();  // or tf.logSumExp(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.logSumExp(axis).print();  // or tf.logSumExp(a, axis)\n * ```\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. If null (the default),\n *     reduces all dimensions.\n * @param keepDims If true, retains reduced dimensions with length\n *     of 1. Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction logSumExp_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'logSumExp');\n    const axes = parseAxisParam(axis, $x.shape);\n    const xMax = max($x, axes, true /* keepDims */);\n    const a = sub($x, xMax);\n    const b = exp(a);\n    const c = sum(b, axes);\n    const d = log(c);\n    const res = add(reshape(xMax, d.shape), d);\n    if (keepDims) {\n        const newShape = expandShapeToKeepDim(res.shape, axes);\n        return reshape(res, newShape);\n    }\n    return res;\n}\nexport const logSumExp = op({ logSumExp_ });\n//# sourceMappingURL=log_sum_exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalAnd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a AND b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalAnd(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalAnd_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(backend => backend.logicalAnd($a, $b), inputs, null /* grad */, LogicalAnd);\n}\nexport const logicalAnd = op({ logicalAnd_ });\n//# sourceMappingURL=logical_and.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalNot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the truth value of `NOT x` element-wise.\n *\n * ```js\n * const a = tf.tensor1d([false, true], 'bool');\n *\n * a.logicalNot().print();\n * ```\n *\n * @param x The input tensor. Must be of dtype 'bool'.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalNot_(x) {\n    const $x = convertToTensor(x, 'x', 'logicalNot', 'bool');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.logicalNot($x), inputs, null /* grad */, LogicalNot);\n}\nexport const logicalNot = op({ logicalNot_ });\n//# sourceMappingURL=logical_not.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalOr } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a OR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalOr(b).print();\n * ```\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalOr_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalOr', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalOr', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(backend => backend.logicalOr($a, $b), inputs, null /* grad */, LogicalOr);\n}\nexport const logicalOr = op({ logicalOr_ });\n//# sourceMappingURL=logical_or.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { logicalAnd } from './logical_and';\nimport { logicalNot } from './logical_not';\nimport { logicalOr } from './logical_or';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalXor(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalXor_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalXor', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalXor', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    // x ^ y = (x | y) & ~(x & y)\n    return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));\n}\nexport const logicalXor = op({ logicalXor_ });\n//# sourceMappingURL=logical_xor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction maxPool_(x, filterSize, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'maxPool');\n    const dilations = 1;\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        let y;\n        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n            util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n            y = x4D.clone();\n        }\n        else {\n            y = backend.maxPool(x4D, convInfo);\n        }\n        save([x4D, y]);\n        return y;\n    };\n    const inputs = { x: x4D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const maxPool = op({ maxPool_ });\n//# sourceMappingURL=max_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { deprecationWarn } from '../globals';\nimport { MaxPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D max pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.maxPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPool3d_(x, filterSize = [1, 1, 1], strides, pad, dimRoundingMode, dataFormat = 'NDHWC', dilations) {\n    if (dilations == null) {\n        dilations = [1, 1, 1];\n    }\n    else {\n        deprecationWarn('dilations is deprecated, this field will be gone in ' +\n            'v3.0.0.');\n    }\n    const $x = convertToTensor(x, 'x', 'maxPool3d');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in maxPool3d: Only NDHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool3d: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1, 1];\n        }\n        const convInfo = conv_util.computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);\n        const y = backend.maxPool3d(x5D, convInfo);\n        save([x5D, y]);\n        return y;\n    };\n    const inputs = { x: x5D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat, dilations };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool3D, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const maxPool3d = op({ maxPool3d_ });\n//# sourceMappingURL=max_pool_3d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPoolWithArgmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the 2D max pooling of an image with Argmax index.\n * The indices in argmax are flattened, so that a maximum value at position `[b,\n * y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if\n * include_batch_in_index is False; `((b * height + y) * width + x) * channels\n * +c` if include_batch_in_index is True.\n *\n * The indices returned are always in `[0, height) x [0, width)` before\n * flattening.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param includeBatchIndex Defaults to False. Whether to include batch\n *    dimension in flattened index of argmax.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPoolWithArgmax_(x, filterSize, strides, pad, includeBatchInIndex = false) {\n    const $x = convertToTensor(x, 'x', 'maxPoolWithArgmax');\n    const inputs = { x: $x };\n    const attrs = { filterSize, strides, pad, includeBatchInIndex };\n    const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);\n    return { result: result[0], indexes: result[1] };\n}\nexport const maxPoolWithArgmax = op({ maxPoolWithArgmax_ });\n//# sourceMappingURL=max_pool_with_argmax.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeZerosTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\n/**\n * Creates a `tf.Tensor` with all elements set to 0.\n *\n * ```js\n * tf.zeros([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Can\n *     be 'float32', 'int32' or 'bool'. Defaults to 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function zeros(shape, dtype = 'float32') {\n    if (dtype === 'complex64') {\n        const real = zeros(shape, 'float32');\n        const imag = zeros(shape, 'float32');\n        return complex(real, imag);\n    }\n    const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=zeros.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeOnesTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\nimport { zeros } from './zeros';\n/**\n * Creates a `tf.Tensor` with all elements set to 1.\n *\n * ```js\n * tf.ones([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n *     'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function ones(shape, dtype = 'float32') {\n    if (dtype === 'complex64') {\n        const real = ones(shape, 'float32');\n        const imag = zeros(shape, 'float32');\n        return complex(real, imag);\n    }\n    const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=ones.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../gradients';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam, sizeFromShape } from '../util';\nimport { computeOutAndReduceShapes } from './axis_util';\nimport { cast } from './cast';\nimport { div } from './div';\nimport { mul } from './mul';\nimport { ones } from './ones';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { scalar } from './scalar';\nimport { sum } from './sum';\n/**\n * Computes the mean of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is\n * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with\n * a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.mean().print();  // or tf.mean(a)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.mean(axis).print();  // or tf.mean(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction mean_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'mean');\n    const axes = parseAxisParam(axis, $x.shape);\n    const shapes = computeOutAndReduceShapes($x.shape, axes);\n    const reduceShape = shapes[1];\n    const reduceSize = sizeFromShape(reduceShape);\n    // Use a custom gradient to bypass 2 gradient backprops since mean is used\n    // extremely often.\n    const customOp = customGrad((x) => {\n        const reduceSizeScalar = scalar(reduceSize);\n        // Cast if needed.\n        const xReduce = reduceSizeScalar.dtype === x.dtype ?\n            x :\n            cast(x, reduceSizeScalar.dtype);\n        const res = div(xReduce, reduceSizeScalar);\n        const value = sum(res, axis, keepDims);\n        const gradFunc = (dy) => {\n            const expandedDyShape = x.shape.slice();\n            axes.forEach(axis => {\n                expandedDyShape[axis] = 1;\n            });\n            const expandedDy = reshape(dy, expandedDyShape);\n            const derX = div(mul(expandedDy, ones(x.shape, 'float32')), reduceSize);\n            return derX;\n        };\n        return { value, gradFunc };\n    });\n    return customOp($x);\n}\nexport const mean = op({ mean_ });\n//# sourceMappingURL=mean.js.map", "import { ENGINE } from '../engine';\nimport { Min } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the minimum value from the input.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axes`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axes` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.min().print();  // or tf.min(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.min(axis).print();  // or tf.min(x, axis)\n * ```\n *\n * @param x The input Tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction min_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'min');\n    const forward = (backend, save) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let minInput = $x;\n        if (permutedAxes != null) {\n            minInput = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        const y = backend.min(minInput, axes);\n        if (permutedAxes != null) {\n            minInput.dispose();\n        }\n        let res = y;\n        if (keepDims) {\n            const expandedShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n            res = reshape(y, expandedShape);\n            y.dispose();\n        }\n        save([$x, res]);\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Min, attrs);\n}\nexport const min = op({ min_ });\n//# sourceMappingURL=min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Minimum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `minimumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * ```js\n * // Broadcast minimum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction minimum_(a, b) {\n    let $a = convertToTensor(a, 'a', 'minimum');\n    let $b = convertToTensor(b, 'b', 'minimum');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'bool') {\n        $a = cast($a, 'int32');\n        $b = cast($b, 'int32');\n    }\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.minimum($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Minimum);\n}\nexport const minimum = op({ minimum_ });\n//# sourceMappingURL=minimum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Mod } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the mod of a and b element-wise.\n * `floor(x / y) * y + mod(x, y) = x`\n * Supports broadcasting.\n *\n * We also expose `tf.modStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * ```js\n * // Broadcast a mod b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mod_(a, b) {\n    let $a = convertToTensor(a, 'a', 'mod');\n    let $b = convertToTensor(b, 'b', 'mod');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.mod($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Mod);\n}\nexport const mod = op({ mod_ });\n//# sourceMappingURL=mod.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes square of `x` element-wise: `x ^ 2`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);\n *\n * x.square().print();  // or tf.square(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction square_(x) {\n    const $x = convertToTensor(x, 'x', 'square');\n    const attrs = {};\n    const inputsToSave = [$x];\n    const outputsToSave = [];\n    return ENGINE.runKernelFunc((backend, save) => {\n        save([$x]);\n        return backend.square($x);\n    }, { x: $x }, null /* grad */, 'Square', attrs, inputsToSave, outputsToSave);\n}\nexport const square = op({ square_ });\n//# sourceMappingURL=square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { cast } from './cast';\nimport { mean } from './mean';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { square } from './square';\nimport { sub } from './sub';\n/**\n * Calculates the mean and variance of `x`. The mean and variance are\n * calculated by aggregating the contents of `x` across `axes`. If `x` is\n * 1-D and `axes = [0]` this is just the mean and variance of a vector.\n *\n * @param x The input tensor.\n * @param axis The dimension(s) along with to compute mean and\n *     variance. By default it reduces all dimensions.\n * @param keepDims If true, the moments have the same dimensionality as the\n *     input.\n * @return An object with two keys: `mean` and `variance`.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction moments_(x, axis = null, keepDims = false) {\n    x = convertToTensor(x, 'x', 'moments');\n    const axes = parseAxisParam(axis, x.shape);\n    const xMean = mean(x, axes, keepDims);\n    let keepDimsShape = xMean.shape;\n    if (!keepDims) {\n        keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);\n    }\n    const devSquared = square(sub(cast(x, 'float32'), reshape(xMean, keepDimsShape)));\n    const variance = mean(devSquared, axes, keepDims);\n    return { mean: xMean, variance };\n}\nexport const moments = op({ moments_ });\n//# sourceMappingURL=moments.js.map", "import { convertToTensor, convertToTensorArray } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the next states and outputs of a stack of LSTMCells.\n *\n * Each cell output is used as input to the next cell.\n *\n * Returns `[cellState, cellOutput]`.\n *\n * Derived from tf.contrib.rn.MultiRNNCell.\n *\n * @param lstmCells Array of LSTMCell functions.\n * @param data The input to the cell.\n * @param c Array of previous cell states.\n * @param h Array of previous cell outputs.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction multiRNNCell_(lstmCells, data, c, h) {\n    const $data = convertToTensor(data, 'data', 'multiRNNCell');\n    const $c = convertToTensorArray(c, 'c', 'multiRNNCell');\n    const $h = convertToTensorArray(h, 'h', 'multiRNNCell');\n    let input = $data;\n    const newStates = [];\n    for (let i = 0; i < lstmCells.length; i++) {\n        const output = lstmCells[i](input, $c[i], $h[i]);\n        newStates.push(output[0]);\n        newStates.push(output[1]);\n        input = output[1];\n    }\n    const newC = [];\n    const newH = [];\n    for (let i = 0; i < newStates.length; i += 2) {\n        newC.push(newStates[i]);\n        newH.push(newStates[i + 1]);\n    }\n    return [newC, newH];\n}\nexport const multiRNNCell = op({ multiRNNCell_ });\n//# sourceMappingURL=multi_rnn_cell.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Creates a `tf.Tensor` with values drawn from a multinomial distribution.\n *\n * ```js\n * const probs = tf.tensor([.75, .25]);\n * tf.multinomial(probs, 3).print();\n * ```\n *\n * @param logits 1D array with unnormalized log-probabilities, or\n *     2D array of shape `[batchSize, numOutcomes]`. See the `normalized`\n *     parameter.\n * @param numSamples Number of samples to draw for each row slice.\n * @param seed The seed number.\n * @param normalized Whether the provided `logits` are normalized true\n *     probabilities (sum to 1). Defaults to false.\n * @return 1D array of shape `[numSamples]`, or 2D array of shape\n *     `[batchSize, numSamples]`, depending on the rank of the input.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction multinomial_(logits, numSamples, seed, normalized = false) {\n    const $logits = convertToTensor(logits, 'logits', 'multinomial');\n    const numOutcomes = $logits.size;\n    const origRank = $logits.rank;\n    if (numOutcomes < 2) {\n        throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ` +\n            `${numOutcomes}.`);\n    }\n    if (origRank > 2) {\n        throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n    }\n    seed = seed || Math.random();\n    const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;\n    const res = ENGINE.runKernelFunc(backend => backend.multinomial(logits2D, normalized, numSamples, seed), { logits2D });\n    // tslint:disable-next-line:no-unnecessary-type-assertion\n    return origRank === 1 ? reshape(res, [res.size]) : res;\n}\nexport const multinomial = op({ multinomial_ });\n//# sourceMappingURL=multinomial.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { NotEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a != b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([0, 2, 3]);\n *\n * a.notEqual(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction notEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'notEqual');\n    let $b = convertToTensor(b, 'b', 'notEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend) => backend.notEqual($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, NotEqual);\n}\nexport const notEqual = op({ notEqual_ });\n//# sourceMappingURL=not_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Real } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the real part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the real part of each element in input considered as a complex number.\n *\n * If the input is real, it simply makes a clone.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.real(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction real_(input) {\n    const $input = convertToTensor(input, 'input', 'real');\n    const forward = (backend) => {\n        return backend.real($input);\n    };\n    const inputs = { input: $input };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Real);\n}\nexport const real = op({ real_ });\n//# sourceMappingURL=real.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OnesLike } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { complex } from './complex';\nimport { imag } from './imag';\nimport { op } from './operation';\nimport { real } from './real';\nimport { zerosLike } from './zeros_like';\n/**\n * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.onesLike(x).print();\n * ```\n * @param x A tensor.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction onesLike_(x) {\n    const $x = convertToTensor(x, 'x', 'onesLike');\n    const forward = (backend, save) => {\n        if ($x.dtype === 'complex64') {\n            const r = onesLike(real($x));\n            const i = zerosLike(imag($x));\n            return complex(r, i);\n        }\n        return backend.onesLike($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, OnesLike);\n}\nexport const onesLike = op({ onesLike_ });\n//# sourceMappingURL=ones_like.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the outer product of two vectors, `v1` and `v2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([3, 4, 5]);\n *\n * tf.outerProduct(a, b).print();\n * ```\n * @param v1 The first vector in the outer product operation.\n * @param v2 The second vector in the outer product operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction outerProduct_(v1, v2) {\n    const $v1 = convertToTensor(v1, 'v1', 'outerProduct');\n    const $v2 = convertToTensor(v2, 'v2', 'outerProduct');\n    util.assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ` +\n        `${$v1.rank} and ${$v2.rank}.`);\n    const v12D = reshape($v1, [-1, 1]);\n    const v22D = reshape($v2, [1, -1]);\n    return matMul(v12D, v22D);\n}\nexport const outerProduct = op({ outerProduct_ });\n//# sourceMappingURL=outer_product.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { PadV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Pads a `tf.Tensor` with a given value and paddings.\n *\n * This operation currently only implements the `CONSTANT` mode.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `paddings` is of given length.\n *   - `tf.pad1d`\n *   - `tf.pad2d`\n *   - `tf.pad3d`\n *   - `tf.pad4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.pad([[1, 2]]).print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * @param constantValue The pad value to use. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction pad_(x, paddings, constantValue = 0) {\n    const $x = convertToTensor(x, 'x', 'pad');\n    if ($x.rank === 0) {\n        throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');\n    }\n    const forward = (backend, save) => {\n        save([$x]);\n        return backend.pad($x, paddings, constantValue);\n    };\n    const attrs = { paddings, constantValue };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, PadV2, attrs);\n}\nexport const pad = op({ pad_ });\n//# sourceMappingURL=pad.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.\n */\nfunction pad1d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 2, () => 'Invalid number of paddings. Must be length of 2.');\n    return pad(x, [paddings], constantValue);\n}\nexport const pad1d = op({ pad1d_ });\n//# sourceMappingURL=pad1d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.\n */\nfunction pad2d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 2 && paddings[0].length === 2 &&\n        paddings[1].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad2d = op({ pad2d_ });\n//# sourceMappingURL=pad2d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.\n */\nfunction pad3d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 3 && paddings[0].length === 2 &&\n        paddings[1].length === 2 && paddings[2].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad3d = op({ pad3d_ });\n//# sourceMappingURL=pad3d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.\n */\nfunction pad4d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 4 && paddings[0].length === 2 &&\n        paddings[1].length === 2 && paddings[2].length === 2 &&\n        paddings[3].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad4d = op({ pad4d_ });\n//# sourceMappingURL=pad4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SpaceToBatchND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation divides \"spatial\" dimensions `[1, ..., M]` of the input into\n * a grid of blocks of shape `blockShape`, and interleaves these blocks with\n * the \"batch\" dimension (0) such that in the output, the spatial\n * dimensions `[1, ..., M]` correspond to the position within the grid,\n * and the batch dimension combines both the position within a spatial block\n * and the original batch position. Prior to division into blocks,\n * the spatial dimensions of the input are optionally zero padded\n * according to `paddings`. See below for a precise description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n * const blockShape = [2, 2];\n * const paddings = [[0, 0], [0, 0]];\n *\n * x.spaceToBatchND(blockShape, paddings).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=\n *     0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad\n * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It\n * is required that\n * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input\n * according to `paddings` to produce `padded` of shape paddedShape.\n *\n * 2. Reshape `padded` to `reshapedPadded` of shape:\n * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`\n *\n * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`\n * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the\n * batch dimension, producing an output tensor of shape:\n * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction spaceToBatchND_(x, blockShape, paddings) {\n    const $x = convertToTensor(x, 'x', 'spaceToBatchND');\n    util.assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);\n    util.assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);\n    util.assert($x.shape.reduce((a, b, i) => {\n        if (i > 0 && i <= blockShape.length) {\n            return a &&\n                ((b + paddings[i - 1][0] + paddings[i - 1][1]) %\n                    blockShape[i - 1] ===\n                    0);\n        }\n        return a;\n    }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);\n    const forward = backend => backend.spaceToBatchND($x, blockShape, paddings);\n    const inputs = { x: $x };\n    const attrs = { blockShape, paddings };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, SpaceToBatchND, attrs);\n}\nexport const spaceToBatchND = op({ spaceToBatchND_ });\n//# sourceMappingURL=space_to_batch_nd.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { avgPool } from './avg_pool';\nimport { batchToSpaceND } from './batch_to_space_nd';\nimport * as conv_util from './conv_util';\nimport { maxPool } from './max_pool';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { spaceToBatchND } from './space_to_batch_nd';\n/**\n * Performs an N-D pooling operation\n *\n * @param input The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param windowShape The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param poolingType The type of pooling, either 'max' or 'avg'.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction pool_(input, windowShape, poolingType, pad, dilations, strides) {\n    if (dilations == null) {\n        dilations = [1, 1];\n    }\n    if (strides == null) {\n        strides = 1;\n    }\n    if (pad === 0) {\n        pad = 'valid';\n    }\n    const $x = convertToTensor(input, 'x', 'maxPool');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in pool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = conv_util.computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad);\n    const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];\n    // The following implementation does batchToSpace(pool(spaceToBatch(x)))\n    // whenever dilation > 1 since the TF kernels do not support dilation > 1.\n    // tslint:disable-next-line:max-line-length\n    // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037\n    let basePadding;\n    if (pad === 'same') {\n        basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);\n    }\n    else {\n        basePadding = [[0, 0], [0, 0]];\n    }\n    const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n    const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n    const convertedPad = isDilationOne ? pad : 'valid';\n    const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n    const forwardOp = poolingType === 'avg' ?\n        () => avgPool(convertedX, windowShape, strides, convertedPad) :\n        () => maxPool(convertedX, windowShape, strides, convertedPad);\n    const y = forwardOp();\n    const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\n// Helper function to compute crops and paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184\nfunction requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {\n    const padStart = basePadding.map(b => b[0]);\n    const origPadEnd = basePadding.map(b => b[1]);\n    const fullInputShape = inputShape.concat(padStart, origPadEnd);\n    const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n    const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n    const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n    const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n    return [paddings, crops];\n}\n// Helper function to compute base paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524\nfunction withSpaceToBatchBasePaddings(filterShape, dilation) {\n    // Spatial dimensions of the filters and the upsampled filters in which we\n    // introduce (rate - 1) zeros between consecutive filter values.\n    const dilatedFilterShape = filterShape.map((s, i) => {\n        return s + (s - 1) * (dilation[i] - 1);\n    });\n    const padExtraShape = dilatedFilterShape.map(s => s - 1);\n    // When padding is odd, we pad more at end, following the same\n    // convention as conv2d.\n    const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));\n    const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n    return padExtraShape.map((_, i) => {\n        return [padExtraStart[i], padExtraEnd[i]];\n    });\n}\nexport const pool = op({ pool_ });\n//# sourceMappingURL=pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Pow } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the power of one `tf.Tensor` to another. Supports broadcasting.\n *\n * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for\n * corresponding elements in x and y. The result's dtype will be the upcasted\n * type of the `base` and `exp` dtypes.\n *\n * ```js\n * const a = tf.tensor([[2, 3], [4, 5]])\n * const b = tf.tensor([[1, 2], [3, 0]]).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n *\n * ```js\n * const a = tf.tensor([[1, 2], [3, 4]])\n * const b = tf.tensor(2).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n * We also expose `powStrict` which has the same signature as this op and\n * asserts that `base` and `exp` are the same shape (does not broadcast).\n *\n * @param base The base `tf.Tensor` to pow element-wise.\n * @param exp The exponent `tf.Tensor` to pow element-wise.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction pow_(base, exp) {\n    let $base = convertToTensor(base, 'base', 'pow');\n    let $exp = convertToTensor(exp, 'exp', 'pow');\n    [$base, $exp] = makeTypesMatch($base, $exp);\n    const inputs = { a: $base, b: $exp };\n    const forward = (backend, save) => {\n        const y = backend.pow($base, $exp);\n        save([$base, $exp, y]);\n        return y;\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Pow);\n}\nexport const pow = op({ pow_ });\n//# sourceMappingURL=pow.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prelu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes leaky rectified linear element-wise with parametric alphas.\n *\n * `x < 0 ? alpha * x : f(x) = x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n * const alpha = tf.scalar(0.1);\n *\n * x.prelu(alpha).print();  // or tf.prelu(x, alpha)\n * ```\n * @param x The input tensor.\n * @param alpha Scaling factor for negative values.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction prelu_(x, alpha) {\n    const $x = convertToTensor(x, 'x', 'prelu');\n    const $alpha = convertToTensor(alpha, 'alpha', 'prelu');\n    const forward = (backend, save) => {\n        const res = backend.prelu($x, $alpha);\n        save([$x, $alpha]);\n        return res;\n    };\n    const inputs = { x: $x, alpha: $alpha };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Prelu);\n}\nexport const prelu = op({ prelu_ });\n//# sourceMappingURL=prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prod } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the product of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.prod().print();  // or tf.prod(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.prod(axis).print();  // or tf.prod(x, axis)\n * ```\n *\n * @param x The input tensor to compute the product over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction prod_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'prod');\n    const forward = (backend) => {\n        if ($x.dtype === 'bool') {\n            $x = cast($x, 'int32');\n        }\n        const axes = parseAxisParam(axis, $x.shape);\n        const permutation = getAxesPermutation(axes, $x.rank);\n        let reductionAxes = axes;\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n        }\n        let value = backend.prod(permutedX, reductionAxes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(value.shape, axes);\n            value = reshape(value, newShape);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Prod, attrs);\n}\nexport const prod = op({ prod_ });\n//# sourceMappingURL=prod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { sizeFromShape } from '../util';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with values sampled from a random number generator\n * function defined by the user.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param randFunction A random number generator function which is called\n * for each element in the output tensor.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\nfunction rand_(shape, randFunction, dtype) {\n    const size = sizeFromShape(shape);\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    for (let i = 0; i < size; i++) {\n        values[i] = randFunction();\n    }\n    return ENGINE.makeTensor(values, shape, dtype);\n}\nexport const rand = op({ rand_ });\n//# sourceMappingURL=rand.js.map", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as seedrandom from 'seedrandom';\nimport { expectNumbersClose, testEpsilon } from '../test_util';\n// https://en.wikipedia.org/wiki/Marsaglia_polar_method\nexport class MPRandGauss {\n    constructor(mean, stdDeviation, dtype, truncated, seed) {\n        this.mean = mean;\n        this.stdDev = stdDeviation;\n        this.dtype = dtype;\n        this.nextVal = NaN;\n        this.truncated = truncated;\n        if (this.truncated) {\n            this.upper = this.mean + this.stdDev * 2;\n            this.lower = this.mean - this.stdDev * 2;\n        }\n        const seedValue = seed ? seed : Math.random();\n        this.random = seedrandom.alea(seedValue.toString());\n    }\n    /** Returns next sample from a Gaussian distribution. */\n    nextValue() {\n        if (!isNaN(this.nextVal)) {\n            const value = this.nextVal;\n            this.nextVal = NaN;\n            return value;\n        }\n        let resultX, resultY;\n        let isValid = false;\n        while (!isValid) {\n            let v1, v2, s;\n            do {\n                v1 = 2 * this.random() - 1;\n                v2 = 2 * this.random() - 1;\n                s = v1 * v1 + v2 * v2;\n            } while (s >= 1 || s === 0);\n            const mul = Math.sqrt(-2.0 * Math.log(s) / s);\n            resultX = this.mean + this.stdDev * v1 * mul;\n            resultY = this.mean + this.stdDev * v2 * mul;\n            if (!this.truncated || this.isValidTruncated(resultX)) {\n                isValid = true;\n            }\n        }\n        if (!this.truncated || this.isValidTruncated(resultY)) {\n            this.nextVal = this.convertValue(resultY);\n        }\n        return this.convertValue(resultX);\n    }\n    /** Handles proper rounding for non-floating-point numbers. */\n    convertValue(value) {\n        if (this.dtype == null || this.dtype === 'float32') {\n            return value;\n        }\n        return Math.round(value);\n    }\n    /** Returns true if less than 2-standard-deviations from the mean. */\n    isValidTruncated(value) {\n        return value <= this.upper && value >= this.lower;\n    }\n}\n// Marsaglia, George, and Wai Wan Tsang. 2000. \"A Simple Method for Generating\n// Gamma Variables.\"\nexport class RandGamma {\n    constructor(alpha, beta, dtype, seed) {\n        this.alpha = alpha;\n        this.beta = 1 / beta; // convert rate to scale parameter\n        this.dtype = dtype;\n        const seedValue = seed ? seed : Math.random();\n        this.randu = seedrandom.alea(seedValue.toString());\n        this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());\n        if (alpha < 1) {\n            this.d = alpha + (2 / 3);\n        }\n        else {\n            this.d = alpha - (1 / 3);\n        }\n        this.c = 1 / Math.sqrt(9 * this.d);\n    }\n    /** Returns next sample from a gamma distribution. */\n    nextValue() {\n        let x2, v0, v1, x, u, v;\n        while (true) {\n            do {\n                x = this.randn.nextValue();\n                v = 1 + (this.c * x);\n            } while (v <= 0);\n            v *= v * v;\n            x2 = x * x;\n            v0 = 1 - (0.331 * x2 * x2);\n            v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v)));\n            u = this.randu();\n            if (u < v0 || Math.log(u) < v1) {\n                break;\n            }\n        }\n        v = (1 / this.beta) * this.d * v;\n        if (this.alpha < 1) {\n            v *= Math.pow(this.randu(), 1 / this.alpha);\n        }\n        return this.convertValue(v);\n    }\n    /** Handles proper rounding for non-floating-point numbers. */\n    convertValue(value) {\n        if (this.dtype === 'float32') {\n            return value;\n        }\n        return Math.round(value);\n    }\n}\nexport class UniformRandom {\n    constructor(min = 0, max = 1, dtype, seed) {\n        /** Handles proper rounding for non floating point numbers. */\n        this.canReturnFloat = () => (this.dtype == null || this.dtype === 'float32');\n        this.min = min;\n        this.range = max - min;\n        this.dtype = dtype;\n        if (seed == null) {\n            seed = Math.random();\n        }\n        if (typeof seed === 'number') {\n            seed = seed.toString();\n        }\n        if (!this.canReturnFloat() && this.range <= 1) {\n            throw new Error(`The difference between ${min} - ${max} <= 1 and dtype is not float`);\n        }\n        this.random = seedrandom.alea(seed);\n    }\n    convertValue(value) {\n        if (this.canReturnFloat()) {\n            return value;\n        }\n        return Math.round(value);\n    }\n    nextValue() {\n        return this.convertValue(this.min + this.range * this.random());\n    }\n}\nexport function jarqueBeraNormalityTest(values) {\n    // https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test\n    const n = values.length;\n    const s = skewness(values);\n    const k = kurtosis(values);\n    const jb = n / 6 * (Math.pow(s, 2) + 0.25 * Math.pow(k - 3, 2));\n    // JB test requires 2-degress of freedom from Chi-Square @ 0.95:\n    // http://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm\n    const CHI_SQUARE_2DEG = 5.991;\n    if (jb > CHI_SQUARE_2DEG) {\n        throw new Error(`Invalid p-value for JB: ${jb}`);\n    }\n}\nexport function expectArrayInMeanStdRange(actual, expectedMean, expectedStdDev, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    const actualMean = mean(actual);\n    expectNumbersClose(actualMean, expectedMean, epsilon);\n    expectNumbersClose(standardDeviation(actual, actualMean), expectedStdDev, epsilon);\n}\nfunction mean(values) {\n    let sum = 0;\n    for (let i = 0; i < values.length; i++) {\n        sum += values[i];\n    }\n    return sum / values.length;\n}\nfunction standardDeviation(values, mean) {\n    let squareDiffSum = 0;\n    for (let i = 0; i < values.length; i++) {\n        const diff = values[i] - mean;\n        squareDiffSum += diff * diff;\n    }\n    return Math.sqrt(squareDiffSum / values.length);\n}\nfunction kurtosis(values) {\n    // https://en.wikipedia.org/wiki/Kurtosis\n    const valuesMean = mean(values);\n    const n = values.length;\n    let sum2 = 0;\n    let sum4 = 0;\n    for (let i = 0; i < n; i++) {\n        const v = values[i] - valuesMean;\n        sum2 += Math.pow(v, 2);\n        sum4 += Math.pow(v, 4);\n    }\n    return (1 / n) * sum4 / Math.pow((1 / n) * sum2, 2);\n}\nfunction skewness(values) {\n    // https://en.wikipedia.org/wiki/Skewness\n    const valuesMean = mean(values);\n    const n = values.length;\n    let sum2 = 0;\n    let sum3 = 0;\n    for (let i = 0; i < n; i++) {\n        const v = values[i] - valuesMean;\n        sum2 += Math.pow(v, 2);\n        sum3 += Math.pow(v, 3);\n    }\n    return (1 / n) * sum3 / Math.pow((1 / (n - 1)) * sum2, 3 / 2);\n}\n//# sourceMappingURL=rand_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { RandGamma } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a gamma distribution.\n *\n * ```js\n * tf.randomGamma([2, 2], 1).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param alpha The shape parameter of the gamma distribution.\n * @param beta The inverse scale parameter of the gamma distribution. Defaults\n *     to 1.\n * @param dtype The data type of the output. Defaults to float32.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomGamma_(shape, alpha, beta = 1, dtype = 'float32', seed) {\n    if (beta == null) {\n        beta = 1;\n    }\n    if (dtype == null) {\n        dtype = 'float32';\n    }\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new Error(`Unsupported data type ${dtype}`);\n    }\n    const rgamma = new RandGamma(alpha, beta, dtype, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = rgamma.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomGamma = op({ randomGamma_ });\n//# sourceMappingURL=random_gamma.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a normal distribution.\n *\n * ```js\n * tf.randomNormal([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n    if (dtype != null && dtype === 'bool') {\n        throw new Error(`Unsupported data type ${dtype}`);\n    }\n    const randGauss = new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = randGauss.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomNormal = op({ randomNormal_ });\n//# sourceMappingURL=random_normal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { UniformRandom } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a uniform distribution.\n *\n * The generated values follow a uniform distribution in the range [minval,\n * maxval). The lower bound minval is included in the range, while the upper\n * bound maxval is excluded.\n *\n * ```js\n * tf.randomUniform([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param minval The lower bound on the range of random values to generate.\n *   Defaults to 0.\n * @param maxval The upper bound on the range of random values to generate.\n *   Defaults to 1.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomUniform_(shape, minval = 0, maxval = 1, dtype = 'float32', seed) {\n    const res = buffer(shape, dtype);\n    const random = new UniformRandom(minval, maxval, null, seed);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = random.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomUniform = op({ randomUniform_ });\n//# sourceMappingURL=random_uniform.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor1d` as it makes the code more readable.\n *\n * ```js\n * tf.tensor1d([1, 2, 3]).print();\n * ```\n *\n * @param values The values of the tensor. Can be array of numbers,\n *     or a `TypedArray`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor1d(values, dtype) {\n    assertNonNull(values);\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 1) {\n        throw new Error('tensor1d() requires values to be a flat/TypedArray');\n    }\n    const shape = null;\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor1d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Range } from '../kernel_names';\nimport { makeZerosTypedArray } from '../util';\nimport { tensor1d } from './tensor1d';\nimport { zeros } from './zeros';\n/**\n * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.\n *\n * The tensor is a is half-open interval meaning it includes start, but\n * excludes stop. Decrementing ranges and negative step values are also\n * supported.sv\n *\n *\n * ```js\n * tf.range(0, 9, 2).print();\n * ```\n *\n * @param start An integer start value\n * @param stop An integer stop value\n * @param step An integer increment (will default to 1 or -1)\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function range(start, stop, step = 1, dtype = 'float32') {\n    if (step === 0) {\n        throw new Error('Cannot have a step of zero');\n    }\n    const forward = () => {\n        const sameStartStop = start === stop;\n        const increasingRangeNegativeStep = start < stop && step < 0;\n        const decreasingRangePositiveStep = stop < start && step > 1;\n        if (sameStartStop || increasingRangeNegativeStep ||\n            decreasingRangePositiveStep) {\n            return zeros([0], dtype);\n        }\n        const numElements = Math.abs(Math.ceil((stop - start) / step));\n        const values = makeZerosTypedArray(numElements, dtype);\n        if (stop < start && step === 1) {\n            // Auto adjust the step's sign if it hasn't been set\n            // (or was set to 1)\n            step = -1;\n        }\n        values[0] = start;\n        for (let i = 1; i < values.length; i++) {\n            values[i] = values[i - 1] + step;\n        }\n        return tensor1d(values, dtype);\n    };\n    const attrs = { start, stop, step, dtype };\n    return ENGINE.runKernelFunc(forward, {} /* inputs */, null /* grad */, Range, attrs);\n}\n//# sourceMappingURL=range.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reciprocal } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of x element-wise: `1 / x`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, 2]);\n *\n * x.reciprocal().print();  // or tf.reciprocal(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction reciprocal_(x) {\n    const $x = convertToTensor(x, 'x', 'reciprocal');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.reciprocal($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Reciprocal);\n}\nexport const reciprocal = op({ reciprocal_ });\n//# sourceMappingURL=reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { cast } from './cast';\n/**\n * Computes rectified linear element-wise: `max(x, 0)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.relu().print();  // or tf.relu(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu_(x) {\n    const $x = convertToTensor(x, 'x', 'relu');\n    const forward = (backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'bool') {\n            return cast($x, 'int32');\n        }\n        return backend.relu($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Relu);\n}\nexport const relu = op({ relu_ });\n//# sourceMappingURL=relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu6 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes rectified linear 6 element-wise: `min(max(x, 0), 6)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 8]);\n *\n * x.relu6().print();  // or tf.relu6(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu6_(x) {\n    const $x = convertToTensor(x, 'x', 'relu6');\n    const forward = (backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'bool') {\n            return cast($x, 'int32');\n        }\n        return backend.relu6($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Relu6);\n}\nexport const relu6 = op({ relu6_ });\n//# sourceMappingURL=relu6.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reverse } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { clone } from './clone';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Reverses a `tf.Tensor` along a specified axis.\n *\n * Also available are stricter rank-specific methods that assert that `x` is\n * of the given rank:\n *   - `tf.reverse1d`\n *   - `tf.reverse2d`\n *   - `tf.reverse3d`\n *   - `tf.reverse4d`\n *\n * Except `tf.reverse1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.reverse().print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.reverse(axis).print();\n * ```\n * @param x The input tensor to be reversed.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction reverse_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    const forward = (backend) => {\n        const axes = parseAxisParam(axis, $x.shape);\n        if ($x.rank === 0) {\n            return clone($x);\n        }\n        const res = backend.reverse($x, axes);\n        return reshape(res, $x.shape);\n    };\n    const inputs = { x: $x };\n    const attrs = { dims: axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Reverse, attrs);\n}\nexport const reverse = op({ reverse_ });\n//# sourceMappingURL=reverse.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor1D`.\n *\n * @param x The input tensor.\n */\nfunction reverse1d_(x) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);\n    return reverse($x, 0);\n}\nexport const reverse1d = op({ reverse1d_ });\n//# sourceMappingURL=reverse_1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor2D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse2d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse2d = op({ reverse2d_ });\n//# sourceMappingURL=reverse_2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor3D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse3d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse3d = op({ reverse3d_ });\n//# sourceMappingURL=reverse_3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor4D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse4d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse4d = op({ reverse4d_ });\n//# sourceMappingURL=reverse_4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Round } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes round of input `tf.Tensor` element-wise: `round(x)`.\n * It implements banker's rounding.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.round().print();  // or tf.round(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction round_(x) {\n    const $x = convertToTensor(x, 'x', 'round');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.round($x), inputs, null /* grad */, Round);\n}\nexport const round = op({ round_ });\n//# sourceMappingURL=round.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Rsqrt } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of square root of the input `tf.Tensor` element-wise:\n * `y = 1 / sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.rsqrt().print();  // or tf.rsqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction rsqrt_(x) {\n    const $x = convertToTensor(x, 'x', 'rsqrt');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.rsqrt($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Rsqrt);\n}\nexport const rsqrt = op({ rsqrt_ });\n//# sourceMappingURL=rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Selu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes scaled exponential linear element-wise.\n *\n * `x < 0 ? scale * alpha * (exp(x) - 1) : x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.selu().print();  // or tf.selu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction selu_(x) {\n    const $x = convertToTensor(x, 'x', 'selu');\n    const forward = (backend, save) => {\n        const res = backend.selu($x);\n        save([$x]);\n        return res;\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Selu);\n}\nexport const selu = op({ selu_ });\n//# sourceMappingURL=selu.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport { depthwiseConv2d } from './depthwise_conv2d';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * 2-D convolution with separable filters.\n *\n * Performs a depthwise convolution that acts separately on channels followed\n * by a pointwise convolution that mixes channels. Note that this is\n * separability between dimensions [1, 2] and 3, not spatial separability\n * between dimensions 1 and 2.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is\n *     the filter used in the first step.\n * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape\n *     `[1, 1, inChannels * channelMultiplier, outChannels]`. This is\n *     the filter used in the second step.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation = [1, 1], dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'separableConv2d');\n    const $depthwiseFilter = convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');\n    const $pointwiseFilter = convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    if (dataFormat === 'NCHW') {\n        throw new Error('separableConv2d currently does not support dataFormat NCHW; only ' +\n            'NHWC is supported');\n    }\n    util.assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but ` +\n        `got rank ${$depthwiseFilter.rank}.`);\n    util.assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but ` +\n        `got rank ${$depthwiseFilter.rank}.`);\n    util.assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter ` +\n        ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n    util.assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise ` +\n        `filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n    const inChannels = $depthwiseFilter.shape[2];\n    const channelMultiplier = $depthwiseFilter.shape[3];\n    util.assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter ` +\n        `must be ${inChannels * channelMultiplier}, ` +\n        `but got ${$pointwiseFilter.shape[2]}.`);\n    const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);\n    const pointwiseStride = 1;\n    const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const separableConv2d = op({ separableConv2d_ });\n//# sourceMappingURL=separable_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\n/**\n * Computes the difference between two lists of numbers.\n *\n * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`\n * that represents all values that are in `x` but not in `y`. The returned\n * Tensor `out` is sorted in the same order that the numbers appear in `x`\n * (duplicates are preserved). This operation also returns a Tensor indices that\n * represents the position of each out element in `x`. In other words:\n *\n * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`\n *\n * ```js\n * const x = [1, 2, 3, 4, 5, 6];\n * const y = [1, 3, 5];\n *\n * const [out, indices] = await tf.setdiff1dAsync(x, y);\n * out.print(); // [2, 4, 6]\n * indices.print(); // [1, 3, 5]\n * ```\n *\n * @param x 1-D Tensor. Values to keep.\n * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the\n *     output.\n * @returns Promise of Tensor tuple [out, indices].\n *  out: Tensor with the same type as x.\n *  indices: A Tensor of type int32.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nasync function setdiff1dAsync_(x, y) {\n    const $x = convertToTensor(x, 'x', 'setdiff1d');\n    const $y = convertToTensor(y, 'y', 'setdiff1d');\n    util.assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);\n    util.assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);\n    util.assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);\n    const xVals = await $x.data();\n    const yVals = await $y.data();\n    const ySet = new Set(yVals);\n    let outputSize = 0;\n    for (let i = 0; i < xVals.length; i++) {\n        if (!ySet.has(xVals[i])) {\n            outputSize++;\n        }\n    }\n    const buffer = new TensorBuffer([outputSize], $x.dtype);\n    const indices = new TensorBuffer([outputSize], 'int32');\n    for (let i = 0, p = 0; i < xVals.length; i++) {\n        if (!ySet.has(xVals[i])) {\n            buffer.values[p] = xVals[i];\n            indices.values[p] = i;\n            p++;\n        }\n    }\n    return [buffer.toTensor(), indices.toTensor()];\n}\nexport const setdiff1dAsync = setdiff1dAsync_;\n//# sourceMappingURL=setdiff1d_async.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sign } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns an element-wise indication of the sign of a number.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);\n *\n * x.sign().print();  // or tf.sign(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sign_(x) {\n    const $x = convertToTensor(x, 'x', 'sign');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.sign($x), inputs, null /* grad */, Sign);\n}\nexport const sign = op({ sign_ });\n//# sourceMappingURL=sign.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sin of the input Tensor element-wise: `sin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.sin().print();  // or tf.sin(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sin_(x) {\n    const $x = convertToTensor(x, 'x', 'sin');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sin($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sin);\n}\nexport const sin = op({ sin_ });\n//# sourceMappingURL=sin.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.sinh().print();  // or tf.sinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sinh_(x) {\n    const $x = convertToTensor(x, 'x', 'sinh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sinh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sinh);\n}\nexport const sinh = op({ sinh_ });\n//# sourceMappingURL=sinh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 1D slice from 1D array starting at coordinates `begin` and is\n * of length `size`. See `slice` for details.\n */\nfunction slice1d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice1d');\n    util.assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, [begin], [size]);\n}\nexport const slice1d = op({ slice1d_ });\n//# sourceMappingURL=slice1d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 2D slice from a 2D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice2d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice2d');\n    util.assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice2d = op({ slice2d_ });\n//# sourceMappingURL=slice2d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 3D slice from a 3D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice3d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice3d');\n    util.assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice3d = op({ slice3d_ });\n//# sourceMappingURL=slice3d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 4D slice from a 4D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice4d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice4d');\n    util.assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice4d = op({ slice4d_ });\n//# sourceMappingURL=slice4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the softmax normalized vector given the logits.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction softmax_(logits, dim = -1) {\n    const $logits = convertToTensor(logits, 'logits', 'softmax', 'float32');\n    if (dim === -1) {\n        dim = $logits.rank - 1;\n    }\n    if (dim !== $logits.rank - 1) {\n        throw Error('Softmax along a non-last dimension is not yet supported. ' +\n            `Logits was rank ${$logits.rank} and dim was ${dim}`);\n    }\n    const inputs = { logits: $logits };\n    const attrs = { dim };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const y = backend.softmax($logits, dim);\n        save([y]);\n        return y;\n    }, inputs, null /* grad */, Softmax, attrs);\n}\nexport const softmax = op({ softmax_ });\n//# sourceMappingURL=softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\n/**\n * Fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the inner-most\n * dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.fft().print();  // tf.spectral.fft(x).print();\n * ```\n * @param input The complex input to compute an fft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction fft_(input) {\n    assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.fft() must be complex64 ` +\n        `but got ${input.dtype}.`);\n    const inputs = { input };\n    return ENGINE.runKernelFunc(backend => {\n        // Collapse all outer dimensions to a single batch dimension.\n        const innerDimensionSize = input.shape[input.shape.length - 1];\n        const batch = input.size / innerDimensionSize;\n        const input2D = input.as2D(batch, innerDimensionSize);\n        const result = backend.fft(input2D);\n        return result.reshape(input.shape);\n    }, inputs, null /* gradient */, FFT);\n}\nexport const fft = op({ fft_ });\n//# sourceMappingURL=fft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { IFFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Inverse fast Fourier transform.\n *\n * Computes the inverse 1-dimensional discrete Fourier transform over the\n * inner-most dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.ifft().print();  // tf.spectral.ifft(x).print();\n * ```\n * @param input The complex input to compute an ifft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction ifft_(input) {\n    assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.ifft() must be complex64 ` +\n        `but got ${input.dtype}.`);\n    const inputs = { input };\n    return ENGINE.runKernelFunc(backend => {\n        // Collapse all outer dimensions to a single batch dimension.\n        const innerDimensionSize = input.shape[input.shape.length - 1];\n        const batch = input.size / innerDimensionSize;\n        const input2D = reshape(input, [batch, innerDimensionSize]);\n        const result = backend.ifft(input2D);\n        return reshape(result, input.shape);\n    }, inputs, null /* gradient */, IFFT);\n}\nexport const ifft = op({ ifft_ });\n//# sourceMappingURL=ifft.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { reverse } from '../reverse';\nimport { scalar } from '../scalar';\nimport { slice } from '../slice';\nimport { ifft } from './ifft';\n/**\n * Inversed real value input fast Fourier transform.\n *\n * Computes the 1-dimensional inversed discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([0, 0, 0]);\n * const x = tf.complex(real, imag);\n *\n * x.irfft().print();\n * ```\n * @param input The real value input to compute an irfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction irfft_(input) {\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n    let ret;\n    if (innerDimensionSize <= 2) {\n        const complexInput = reshape(input, [batch, innerDimensionSize]);\n        ret = ifft(complexInput);\n    }\n    else {\n        // The length of unique components of the DFT of a real-valued signal\n        // is 2 * (input_len - 1)\n        const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n        const realInput = reshape(real(input), [batch, innerDimensionSize]);\n        const imagInput = reshape(imag(input), [batch, innerDimensionSize]);\n        const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);\n        const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));\n        const r = concat([realInput, realConjugate], 1);\n        const i = concat([imagInput, imagConjugate], 1);\n        const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);\n        ret = ifft(complexInput);\n    }\n    ret = real(ret);\n    // reshape the result if the input is 3D tensor.\n    if (input.rank === 3 && input.shape[0] !== 0) {\n        const temp = ret;\n        const batch = input.shape[0];\n        ret = reshape(ret, [batch, ret.shape[0] / batch, ret.shape[1]]);\n        temp.dispose();\n    }\n    return ret;\n}\nexport const irfft = op({ irfft_ });\n//# sourceMappingURL=irfft.js.map", "import { assert } from '../util';\n/**\n * Prepare the split size array. When the input is a number, the axis is evenly\n * divided among the split size. When the input contains the negative value, the\n * rest of the axis is allocated toward that.\n */\nexport function prepareSplitSize(x, numOrSizeSplits, axis = 0) {\n    let splitSizes = [];\n    if (typeof (numOrSizeSplits) === 'number') {\n        assert(x.shape[axis] % numOrSizeSplits === 0, () => 'Number of splits must evenly divide the axis.');\n        splitSizes =\n            new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);\n    }\n    else {\n        const numOfNegs = numOrSizeSplits.reduce((count, value) => {\n            if (value === -1) {\n                count += 1;\n            }\n            return count;\n        }, 0);\n        assert(numOfNegs <= 1, () => 'There should be only one negative value in split array.');\n        const negIndex = numOrSizeSplits.indexOf(-1);\n        // Allow the number of split array to be -1, which indicates the rest\n        // of dimension is allocated to that split.\n        if (negIndex !== -1) {\n            const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);\n            numOrSizeSplits[negIndex] = x.shape[axis] - total;\n        }\n        assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => 'The sum of sizes must match the size of the axis dimension.');\n        splitSizes = numOrSizeSplits;\n    }\n    return splitSizes;\n}\n//# sourceMappingURL=split_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SplitV } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { op } from './operation';\nimport { prepareSplitSize } from './split_util';\n/**\n * Splits a `tf.Tensor` into sub tensors.\n *\n * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`\n * into `numOrSizeSplits` smaller tensors.\n * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.\n *\n * If `numOrSizeSplits` is a number array, splits `x` into\n * `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the\n * same size as `x` except along dimension `axis` where the size is\n * `numOrSizeSplits[i]`.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);\n * const [a, b] = tf.split(x, 2, 1);\n * a.print();\n * b.print();\n *\n * const [c, d, e] = tf.split(x, [1, 2, 1], 1);\n * c.print();\n * d.print();\n * e.print();\n * ```\n *\n * @param x The input tensor to split.\n * @param numOrSizeSplits Either an integer indicating the number of\n * splits along the axis or an array of integers containing the sizes of\n * each output tensor along the axis. If a number then it must evenly divide\n * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.\n * Can contain one -1 indicating that dimension is to be inferred.\n * @param axis The dimension along which to split. Defaults to 0 (the first\n * dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction split_(x, numOrSizeSplits, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'split');\n    const forward = (backend, _) => {\n        const $axis = parseAxisParam(axis, $x.shape)[0];\n        const splitSizes = prepareSplitSize($x, numOrSizeSplits, $axis);\n        return backend.split($x, splitSizes, $axis);\n    };\n    const inputs = { x: $x };\n    const attr = { numOrSizeSplits, axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, SplitV, attr);\n}\nexport const split = op({ split_ });\n//# sourceMappingURL=split.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../../util';\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { split } from '../split';\nimport { zeros } from '../zeros';\nimport { zerosLike } from '../zeros_like';\nimport { fft } from './fft';\n/**\n * Real value input fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * real.rfft().print();\n * ```\n * @param input The real value input to compute an rfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction rfft_(input, fftLength) {\n    assert(input.dtype === 'float32', () => `The dtype for rfft() must be real value but got ${input.dtype}`);\n    let innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n    let adjustedInput;\n    if (fftLength != null && fftLength < innerDimensionSize) {\n        // Need to crop\n        const begin = input.shape.map(v => 0);\n        const size = input.shape.map(v => v);\n        size[input.shape.length - 1] = fftLength;\n        adjustedInput = slice(input, begin, size);\n        innerDimensionSize = fftLength;\n    }\n    else if (fftLength != null && fftLength > innerDimensionSize) {\n        // Need to pad with zeros\n        const zerosShape = input.shape.map(v => v);\n        zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;\n        adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);\n        innerDimensionSize = fftLength;\n    }\n    else {\n        adjustedInput = input;\n    }\n    // Complement the input with zero imaginary numbers.\n    const zerosInput = zerosLike(adjustedInput);\n    const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);\n    const ret = fft(complexInput);\n    // Exclude complex conjugations. These conjugations are put symmetrically.\n    const half = Math.floor(innerDimensionSize / 2) + 1;\n    const realValues = real(ret);\n    const imagValues = imag(ret);\n    const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);\n    const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);\n    const outputShape = adjustedInput.shape.slice();\n    outputShape[adjustedInput.shape.length - 1] = half;\n    return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);\n}\nexport const rfft = op({ rfft_ });\n//# sourceMappingURL=rfft.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sqrt } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.sqrt().print();  // or tf.sqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sqrt_(x) {\n    const $x = convertToTensor(x, 'x', 'sqrt');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sqrt($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sqrt);\n}\nexport const sqrt = op({ sqrt_ });\n//# sourceMappingURL=sqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SquaredDifference } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns (a - b) * (a - b) element-wise.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * ```js\n * // Broadcast squared difference  a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction squaredDifference_(a, b) {\n    let $a = convertToTensor(a, 'a', 'squaredDifference');\n    let $b = convertToTensor(b, 'b', 'squaredDifference');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.squaredDifference($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = {};\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, SquaredDifference, attrs);\n}\nexport const squaredDifference = op({ squaredDifference_ });\n//# sourceMappingURL=squared_difference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { squeezeShape } from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Removes dimensions of size 1 from the shape of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);\n * x.squeeze().print();\n * ```\n *\n * @param x The input tensor to be squeezed.\n * @param axis An optional list of numbers. If specified, only\n *     squeezes the dimensions listed. The dimension index starts at 0. It\n * is an error to squeeze a dimension that is not 1.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction squeeze_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'squeeze');\n    return reshape($x, squeezeShape($x.shape, axis).newShape);\n}\nexport const squeeze = op({ squeeze_ });\n//# sourceMappingURL=squeeze.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensorArray } from '../tensor_util_env';\nimport * as util from '../util';\nimport { concat } from './concat';\nimport { expandDims } from './expand_dims';\nimport { op } from './operation';\n/**\n * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.stack([a, b, c]).print();\n * ```\n *\n * @param tensors A list of tensor objects with the same shape and dtype.\n * @param axis The axis to stack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction stack_(tensors, axis = 0) {\n    const $tensors = convertToTensorArray(tensors, 'tensors', 'stack');\n    util.assert($tensors.length >= 1, () => 'Pass at least one tensor to tf.stack');\n    if ($tensors.length === 1) {\n        return expandDims($tensors[0], axis);\n    }\n    const rank = $tensors[0].rank;\n    const shape = $tensors[0].shape;\n    const dtype = $tensors[0].dtype;\n    util.assert(axis <= rank, () => 'Axis must be <= rank of the tensor');\n    $tensors.forEach(t => {\n        util.assertShapesMatch(shape, t.shape, 'All tensors passed to stack must have matching shapes');\n        util.assert(dtype === t.dtype, () => 'All tensors passed to stack must have matching dtypes');\n    });\n    const expandedTensors = $tensors.map(t => expandDims(t, axis));\n    // Stack exists in the TensorFlow C++ API\n    // (https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/stack) but not\n    // in\n    // https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/core/ops/ops.pbtxt.\n    // Therefore we are treating it like a high-level op rather than\n    // creating a dedicated stack kernel.\n    return concat(expandedTensors, axis);\n}\nexport const stack = op({ stack_ });\n//# sourceMappingURL=stack.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Step } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha * x`\n *\n * ```js\n * const x = tf.tensor1d([0, 2, -1, -3]);\n *\n * x.step(.5).print();  // or tf.step(x, .5)\n * ```\n * @param x The input tensor.\n * @param alpha The gradient when input is negative.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction step_(x, alpha = 0.0) {\n    const $x = convertToTensor(x, 'x', 'step');\n    const inputs = { x: $x };\n    const attrs = { alpha };\n    return ENGINE.runKernelFunc(backend => backend.step($x, alpha), inputs, null /* grad */, Step, attrs);\n}\nexport const step = op({ step_ });\n//# sourceMappingURL=step.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { StridedSlice } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { slice } from './slice';\nimport { computeOutShape, getNormalizedAxes, maskToAxes } from './slice_util';\n/**\n * Extracts a strided slice of a tensor.\n *\n * Roughly speaking, this op extracts a slice of size (end-begin)/stride from\n * the given input tensor (x). Starting at the location specified by begin the\n * slice continues by adding stride to the index until all dimensions are not\n * less than end. Note that a stride can be negative, which causes a reverse\n * slice.\n *\n * ```js\n * const t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],\n *    [3, 2, 3]);\n * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print()  // [[[3, 3, 3]]]\n * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print()  // [[[3, 3, 3],\n *                                                     // [4, 4, 4]]]\n * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],\n *                                                     // [3, 3, 3]]]\n * ```\n *\n * @param x The tensor to stride slice.\n * @param begin The coordinates to start the slice from.\n * @param end: The coordinates to end the slice at.\n * @param strides: The size of the slice.\n * @param beginMask: If the ith bit of beginMask is set, begin[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param endMask: If the ith bit of endMask is set, end[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param shrinkAxisMask: a bitmask where bit i implies that\n * the ith specification should shrink the dimensionality. begin and end must\n * imply a slice of size 1 in the dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {\n    let $x = convertToTensor(x, 'x', 'stridedSlice');\n    const forward = (backend) => {\n        if (strides == null) {\n            strides = new Array(begin.length);\n        }\n        const ellipsisAxes = maskToAxes(ellipsisMask);\n        if (ellipsisAxes.length > 1) {\n            throw new Error('Multiple ellipses in slice is not allowed.');\n        }\n        if (ellipsisMask !== 0 && newAxisMask !== 0) {\n            throw new Error('Using both ellipsisMask and newAxisMask is not yet supported.');\n        }\n        if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {\n            throw new Error('Using both ellipsisMask and shrinkAxisMask is not yet supported.');\n        }\n        const numInterpolatedAxes = $x.rank - begin.length;\n        // Expand the dims of x based on the newAxisMask.\n        const expandAxes = maskToAxes(newAxisMask);\n        const newShape = $x.shape.slice();\n        expandAxes.forEach(axis => {\n            begin[axis] = 0;\n            end[axis] = 1;\n            newShape.splice(axis, 0, 1);\n        });\n        $x = reshape($x, newShape);\n        const { begin: normalizedBegin, end: normalizedEnd, strides: normalizedStrides } = getNormalizedAxes($x.shape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask);\n        begin = normalizedBegin;\n        end = normalizedEnd;\n        strides = normalizedStrides;\n        const shrinkAxes = maskToAxes(shrinkAxisMask);\n        // Adjust the ends based on the shrink mask.\n        shrinkAxes.forEach(axis => {\n            end[axis] = begin[axis] + 1;\n            strides[axis] = 1;\n        });\n        // Figure out the output shape.\n        const size = computeOutShape(begin, end, strides);\n        // Remove the axes based on shrinkMask.\n        const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);\n        const nonStrided = strides.every(v => v === 1);\n        if (nonStrided) {\n            return reshape(slice($x, begin, size), outShape);\n        }\n        const res = backend.stridedSlice($x, begin, end, strides);\n        return reshape(res, outShape);\n    };\n    const inputs = { x: $x };\n    const attrs = {\n        begin,\n        end,\n        strides,\n        beginMask,\n        endMask,\n        ellipsisMask,\n        newAxisMask,\n        shrinkAxisMask\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, StridedSlice, attrs);\n}\nexport const stridedSlice = op({ stridedSlice_ });\n//# sourceMappingURL=strided_slice.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.tan().print();  // or tf.tan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tan_(x) {\n    const $x = convertToTensor(x, 'x', 'tan');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.tan($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Tan);\n}\nexport const tan = op({ tan_ });\n//# sourceMappingURL=tan.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor2d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor2d([[1, 2], [3, 4]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor2d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 2) {\n        throw new Error('tensor2d() requires shape to have two numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n        throw new Error('tensor2d() requires values to be number[][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor2d() requires shape to be provided when `values` ' +\n            'are a flat/TypedArray');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor2d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor4d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor4d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 4) {\n        throw new Error('tensor4d() requires shape to have four numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n        throw new Error('tensor4d() requires values to be number[][][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor4d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor5d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor5d([[[[[1], [2]], [[3], [4]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor5d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 5) {\n        throw new Error('tensor5d() requires shape to have five numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n        throw new Error('tensor5d() requires values to be ' +\n            'number[][][][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor5d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor5d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor6d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor6d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 6) {\n        throw new Error('tensor6d() requires shape to have six numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n        throw new Error('tensor6d() requires values to be number[][][][][][] or ' +\n            'flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor6d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    shape = shape ||\n        inferredShape;\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor6d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { TopK } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Finds the values and indices of the `k` largest entries along the last\n * dimension.\n *\n * If the input is a vector (rank=1), finds the k largest entries in the vector\n * and outputs their values and indices as vectors. Thus values[j] is the j-th\n * largest entry in input, and its index is indices[j].\n * For higher rank inputs, computes the top k entries along the last dimension.\n *\n * If two elements are equal, the lower-index element appears first.\n *\n * ```js\n * const a = tf.tensor2d([[1, 5], [4, 3]]);\n * const {values, indices} = tf.topk(a);\n * values.print();\n * indices.print();\n * ```\n * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.\n * @param k Number of top elements to look for along the last dimension.\n * @param sorted If true, the resulting `k` elements will be sorted by the\n *     values in descending order.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction topk_(x, k = 1, sorted = true) {\n    const $x = convertToTensor(x, 'x', 'topk');\n    if ($x.rank === 0) {\n        throw new Error('topk() expects the input to be of rank 1 or higher');\n    }\n    const lastDim = $x.shape[$x.shape.length - 1];\n    if (k > lastDim) {\n        throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) ` +\n            `but got ${k}`);\n    }\n    const inputs = { x: $x };\n    const attrs = { k, sorted };\n    const [values, indices] = ENGINE.runKernelFunc(b => b.topk($x, k, sorted), inputs, null /* grad */, TopK, attrs);\n    return { values, indices };\n}\nexport const topk = op({ topk_ });\n//# sourceMappingURL=topk.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a truncated normal\n * distribution.\n *\n * ```js\n * tf.truncatedNormal([2, 2]).print();\n * ```\n *\n * The generated values follow a normal distribution with specified mean and\n * standard deviation, except that values whose magnitude is more than 2\n * standard deviations from the mean are dropped and re-picked.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output tensor.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction truncatedNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n    if (dtype != null && dtype === 'bool') {\n        throw new Error(`Unsupported data type $ { dtype }`);\n    }\n    const randGauss = new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = randGauss.nextValue();\n    }\n    return res.toTensor();\n}\nexport const truncatedNormal = op({ truncatedNormal_ });\n//# sourceMappingURL=truncated_normal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unique } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert } from '../util';\nimport { op } from './operation';\n/**\n * Finds unique elements along an axis of a tensor.\n *\n * It returns a tensor `values` containing all of the unique elements along the\n * `axis` of the given tensor `x` in the same order that they occur along the\n * `axis` in `x`; `x` does not need to be sorted. It also returns a tensor\n * `indices` the same size as the number of the elements in `x` along the `axis`\n * dimension. It contains the index in the unique output `values`.\n *\n * ```js\n * // A 1-D tensor\n * const a = tf.tensor1d([1, 1, 2, 4, 4, 4, 7, 8, 8]);\n * const {values, indices} = tf.unique(a);\n * values.print();   // [1, 2, 4, 7, 8,]\n * indices.print();  // [0, 0, 1, 2, 2, 2, 3, 4, 4]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=0\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 0)\n * values.print();   // [[1, 0, 0],\n *                   //  [2, 0, 0]]\n * indices.print();  // [0, 0, 1]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=1\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 1)\n * values.print();   // [[1, 0],\n *                   //  [1, 0],\n *                   //  [2, 0]]\n * indices.print();  // [0, 1, 1]\n * ```\n * @param x A tensor (int32, string, bool).\n * @param axis The axis of the tensor to find the unique elements.\n * @returns [uniqueElements, indices] (see above for details)\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction unique_(x, axis = 0) {\n    // x can be of any dtype, thus null as the last argument.\n    const $x = convertToTensor(x, 'x', 'unique', null);\n    assert($x.rank > 0, () => 'The input tensor must be at least 1D');\n    const inputs = { x: $x };\n    const attrs = { axis };\n    const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs);\n    return { values, indices };\n}\nexport const unique = op({ unique_ });\n//# sourceMappingURL=unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { UnsortedSegmentSum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, isInt } from '../util';\nimport { op } from './operation';\n/**\n * Computes the sum along segments of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');\n * const numSegments = 3;\n *\n * x.unsortedSegmentSum(segmentIds, numSegments).print()\n * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)\n * ```\n * @param x The `tf.Tensor` that will be summed along its segments.\n * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s\n * dimension along the `axis`.  Maps each element of `x` to a segment.\n * @param numSegments The number of distinct `segmentIds`.\n *\n * @doc {heading: 'Operations', subheading: 'Segment'}\n */\nfunction unsortedSegmentSum_(x, segmentIds, numSegments) {\n    const $x = convertToTensor(x, 'x', 'unsortedSegmentSum');\n    const $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');\n    assert(isInt(numSegments), () => 'numSegments must be of dtype int');\n    const inputs = { x: $x, segmentIds: $segmentIds };\n    const attrs = { numSegments };\n    const forward = (backend, save) => {\n        const res = backend.unsortedSegmentSum($x, $segmentIds, numSegments);\n        save([$segmentIds]);\n        return res;\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, UnsortedSegmentSum, attrs);\n}\nexport const unsortedSegmentSum = op({ unsortedSegmentSum_ });\n//# sourceMappingURL=unsorted_segment_sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unpack } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * tf.unstack(a).forEach(tensor => tensor.print());\n * ```\n *\n * @param x A tensor object.\n * @param axis The axis to unstack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction unstack_(x, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'unstack');\n    util.assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);\n    if (axis < 0) {\n        axis += $x.shape.length;\n    }\n    const inputs = { value: $x };\n    const attrs = { axis };\n    const forward = (backend) => backend.unstack($x, axis);\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Unpack, attrs);\n}\nexport const unstack = op({ unstack_ });\n//# sourceMappingURL=unstack.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\n/**\n * Creates a new variable with the provided initial value.\n * ```js\n * const x = tf.variable(tf.tensor([1, 2, 3]));\n * x.assign(tf.tensor([4, 5, 6]));\n *\n * x.print();\n * ```\n *\n * @param initialValue Initial value for the tensor.\n * @param trainable If true, optimizers are allowed to update it.\n * @param name Name of the variable. Defaults to a unique id.\n * @param dtype If set, initialValue will be converted to the given type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function variable(initialValue, trainable = true, name, dtype) {\n    return ENGINE.makeVariable(initialValue, trainable, name, dtype);\n}\n//# sourceMappingURL=variable.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the Where kernel shared between cpu and webgl */\nimport { buffer } from '../ops/buffer';\nexport function whereImpl(condShape, condVals) {\n    const indices = [];\n    for (let i = 0; i < condVals.length; i++) {\n        if (condVals[i]) {\n            indices.push(i);\n        }\n    }\n    const inBuffer = buffer(condShape, 'int32');\n    const out = buffer([indices.length, condShape.length], 'int32');\n    for (let i = 0; i < indices.length; i++) {\n        const loc = inBuffer.indexToLoc(indices[i]);\n        const offset = i * condShape.length;\n        out.values.set(loc, offset);\n    }\n    return out.toTensor();\n}\n//# sourceMappingURL=where_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { whereImpl } from '../backends/where_impl';\nimport { convertToTensor } from '../tensor_util_env';\n/**\n * Returns the coordinates of true elements of condition.\n *\n * The coordinates are returned in a 2-D tensor where the first dimension (rows)\n * represents the number of true elements, and the second dimension (columns)\n * represents the coordinates of the true elements. Keep in mind, the shape of\n * the output tensor can vary depending on how many true values there are in\n * input. Indices are output in row-major order. The resulting tensor has the\n * shape `[numTrueElems, condition.rank]`.\n *\n * This is analogous to calling the python `tf.where(cond)` without an x or y.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const result = await tf.whereAsync(cond);\n * result.print();\n * ```\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nasync function whereAsync_(condition) {\n    const $condition = convertToTensor(condition, 'condition', 'whereAsync', 'bool');\n    const vals = await $condition.data();\n    const res = whereImpl($condition.shape, vals);\n    if (condition !== $condition) {\n        $condition.dispose();\n    }\n    return res;\n}\nexport const whereAsync = whereAsync_;\n//# sourceMappingURL=where_async.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { gather } from './gather';\nimport { reshape } from './reshape';\nimport { squeeze } from './squeeze';\nimport { whereAsync } from './where_async';\n/**\n * Apply boolean mask to tensor.\n *\n * ```js\n * const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);\n * const mask = tf.tensor1d([1, 0, 1], 'bool');\n * const result = await tf.booleanMaskAsync(tensor, mask);\n * result.print();\n * ```\n *\n * @param tensor N-D tensor.\n * @param mask K-D boolean tensor, K <= N and K must be known statically.\n * @param axis A 0-D int Tensor representing the axis in tensor to mask from.\n *     By default, axis is 0 which will mask from the first dimension.\n *     Otherwise K + axis <= N.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nasync function booleanMaskAsync_(tensor, mask, axis) {\n    const $tensor = convertToTensor(tensor, 'tensor', 'boolMask');\n    const $mask = convertToTensor(mask, 'mask', 'boolMask', 'bool');\n    const axisFrom = axis == null ? 0 : axis;\n    const maskDim = $mask.rank;\n    const tensorShape = $tensor.shape;\n    util.assert(maskDim > 0, () => 'mask cannot be scalar');\n    util.assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);\n    let leadingSize = 1;\n    for (let i = axisFrom; i < axisFrom + maskDim; i++) {\n        leadingSize *= tensorShape[i];\n    }\n    const targetTensorShape = tensorShape.slice(0, axisFrom)\n        .concat([leadingSize], tensorShape.slice(axisFrom + maskDim));\n    const reshapedTensor = reshape($tensor, targetTensorShape);\n    const reshapedMask = reshape($mask, [-1]);\n    const positivePositions = await whereAsync(reshapedMask);\n    const indices = squeeze(positivePositions, [1]);\n    const res = gather(reshapedTensor, indices, axisFrom);\n    // Ensure no memory leak.\n    if (tensor !== $tensor) {\n        $tensor.dispose();\n    }\n    if (mask !== $mask) {\n        $mask.dispose();\n    }\n    indices.dispose();\n    reshapedTensor.dispose();\n    reshapedMask.dispose();\n    positivePositions.dispose();\n    return res;\n}\nexport const booleanMaskAsync = booleanMaskAsync_;\n//# sourceMappingURL=boolean_mask.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../globals';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertShapesMatch } from '../util';\nimport { equal } from './equal';\nimport { greater } from './greater';\nimport { greaterEqual } from './greater_equal';\nimport { less } from './less';\nimport { lessEqual } from './less_equal';\nimport { notEqual } from './not_equal';\nimport { op } from './operation';\n/**\n * @deprecated\n * Strict version of `tf.notEqual` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction notEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'notEqualStrict');\n    const $b = convertToTensor(b, 'b', 'notEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in notEqualStrict: ');\n    return notEqual($a, $b);\n}\n/**\n * @deprecated\n * Strict version of `tf.less` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction lessStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'lessStrict');\n    const $b = convertToTensor(b, 'b', 'lessStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in lessStrict: ');\n    return less($a, $b);\n}\nfunction equalStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'equalStrict');\n    const $b = convertToTensor(b, 'b', 'equalStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in equalStrict: ');\n    return equal($a, $b);\n}\nfunction lessEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'lessEqualStrict');\n    const $b = convertToTensor(b, 'b', 'lessEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in lessEqualStrict: ');\n    return lessEqual($a, $b);\n}\nfunction greaterStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'greaterStrict');\n    const $b = convertToTensor(b, 'b', 'greaterStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in greaterStrict: ');\n    return greater($a, $b);\n}\nfunction greaterEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'greaterEqualStrict');\n    const $b = convertToTensor(b, 'b', 'greaterEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in greaterEqualStrict: ');\n    return greaterEqual($a, $b);\n}\nexport const equalStrict = op({ equalStrict_ });\nexport const greaterEqualStrict = op({ greaterEqualStrict_ });\nexport const greaterStrict = op({ greaterStrict_ });\nexport const lessEqualStrict = op({ lessEqualStrict_ });\nexport const lessStrict = op({ lessStrict_ });\nexport const notEqualStrict = op({ notEqualStrict_ });\n//# sourceMappingURL=compare.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../globals';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { maximum } from './maximum';\nimport { minimum } from './minimum';\nimport { mod } from './mod';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { squaredDifference } from './squared_difference';\nimport { sub } from './sub';\n/**\n * @deprecated\n * Adds two `tf.Tensor`s element-wise, A + B.\n *\n * Inputs must be the same shape. For broadcasting support, use add() instead.\n *\n * @param a The first Tensor to add element-wise.\n * @param b The second Tensor to add element-wise.\n */\nfunction addStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'addStrict');\n    const $b = convertToTensor(b, 'b', 'addStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in addStrict: ');\n    return add($a, $b);\n}\n/**\n * @deprecated\n * Subtracts two `tf.Tensor`s element-wise, A - B. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.sub` instead.\n *\n * @param a The first Tensor to subtract element-wise.\n * @param b The second Tensor to subtract element-wise.\n */\nfunction subStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'subStrict');\n    const $b = convertToTensor(b, 'b', 'subStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in subStrict: ');\n    return sub($a, $b);\n}\n/**\n * @deprecated\n * Computes the power of one `tf.Tensor` to another. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.pow` instead.\n *\n * @param base The base tensor to pow element-wise.\n * @param exp The exponent tensor to pow element-wise.\n */\nfunction powStrict_(base, exp) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    util.assertShapesMatch(base.shape, exp.shape, 'Error in powStrict: ');\n    return pow(base, exp);\n}\n/**\n * @deprecated\n * Multiplies two `tf.Tensor`s element-wise, A * B.\n *\n * Inputs must be the same shape. For broadcasting support, use `tf.mul`.\n *\n * @param a The first tensor to multiply.\n * @param b The first tensor to multiply. Must have the same\n *    dtype as `a`.\n */\nfunction mulStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'mul');\n    const $b = convertToTensor(b, 'b', 'mul');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in multiplyStrict: ');\n    return mul($a, $b);\n}\n/**\n * @deprecated\n * Divides two `tf.Tensor`s element-wise, A / B. Inputs must\n * be the same shape.\n *\n * @param a The first tensor as the numerator for element-wise division.\n * @param b The second tensor as the denominator for element-wise division.\n */\nfunction divStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'div');\n    const $b = convertToTensor(b, 'b', 'div');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in divideStrict: ');\n    return div($a, $b);\n}\n/**\n * @deprecated\n * Returns the mod of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use mod().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction modStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'modStrict');\n    const $b = convertToTensor(b, 'b', 'modStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in modStrict: ');\n    return mod($a, $b);\n}\n/**\n * @deprecated\n * Returns the min of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use minimum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction minimumStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'minimumStrict');\n    const $b = convertToTensor(b, 'b', 'minimumStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in minimumStrict: ');\n    return minimum($a, $b);\n}\n/**\n * @deprecated\n * Returns the max of a and b (`a > b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use maximum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction maximumStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'maximumStrict');\n    const $b = convertToTensor(b, 'b', 'maximumStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in maximumStrict: ');\n    return maximum($a, $b);\n}\n/**\n * @deprecated\n * Returns (a - b) * (a - b) element-wise.\n *\n * Inputs must be the same shape. For broadcasting support, use\n * `tf.squaredDifference` instead.\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\nfunction squaredDifferenceStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'squaredDifferenceStrict');\n    const $b = convertToTensor(b, 'b', 'squaredDifferenceStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in squaredDifferenceStrict: ');\n    return squaredDifference($a, $b);\n}\nexport const addStrict = op({ addStrict_ });\nexport const divStrict = op({ divStrict_ });\nexport const maximumStrict = op({ maximumStrict_ });\nexport const minimumStrict = op({ minimumStrict_ });\nexport const modStrict = op({ modStrict_ });\nexport const mulStrict = op({ mulStrict_ });\nexport const powStrict = op({ powStrict_ });\nexport const squaredDifferenceStrict = op({ squaredDifferenceStrict_ });\nexport const subStrict = op({ subStrict_ });\n//# sourceMappingURL=binary_ops.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { abs } from './abs';\nimport * as axis_util from './axis_util';\nimport { max } from './max';\nimport { min } from './min';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { reshape } from './reshape';\nimport { scalar } from './scalar';\nimport { sqrt } from './sqrt';\nimport { square } from './square';\nimport { sum } from './sum';\n/**\n * Computes the norm of scalar, vectors, and matrices.\n * This function can compute several different vector norms (the 1-norm, the\n * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)\n * and matrix norms (Frobenius, 1-norm, and inf-norm).\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.norm().print();  // or tf.norm(x)\n * ```\n *\n * @param x The input array.\n * @param ord Optional. Order of the norm. Supported norm types are\n * following:\n *\n *  | ord        | norm for matrices         | norm for vectors\n *  |------------|---------------------------|---------------------\n *  |'euclidean' |Frobenius norm             |2-norm\n *  |'fro'       |Frobenius norm\t           |\n *  |Infinity    |max(sum(abs(x), axis=1))   |max(abs(x))\n *  |-Infinity   |min(sum(abs(x), axis=1))   |min(abs(x))\n *  |1           |max(sum(abs(x), axis=0))   |sum(abs(x))\n *  |2           |                           |sum(abs(x)^2)^1/2*\n *\n * @param axis Optional. If axis is null (the default), the input is\n * considered a vector and a single vector norm is computed over the entire\n * set of values in the Tensor, i.e. norm(x, ord) is equivalent\n * to norm(x.reshape([-1]), ord). If axis is a integer, the input\n * is considered a batch of vectors, and axis determines the axis in x\n * over which to compute vector norms. If axis is a 2-tuple of integer it is\n * considered a batch of matrices and axis determines the axes in NDArray\n * over which to compute a matrix norm.\n * @param keepDims Optional. If true, the norm have the same dimensionality\n * as the input.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction norm_(x, ord = 'euclidean', axis = null, keepDims = false) {\n    x = convertToTensor(x, 'x', 'norm');\n    const norm = normImpl(x, ord, axis);\n    let keepDimsShape = norm.shape;\n    if (keepDims) {\n        const axes = parseAxisParam(axis, x.shape);\n        keepDimsShape = axis_util.expandShapeToKeepDim(norm.shape, axes);\n    }\n    return reshape(norm, keepDimsShape);\n}\nfunction normImpl(x, p, axis = null) {\n    if (x.rank === 0) {\n        return abs(x);\n    }\n    // consider vector when no axis is specified\n    if (x.rank !== 1 && axis === null) {\n        return normImpl(reshape(x, [-1]), p, axis);\n    }\n    // vector\n    if (x.rank === 1 || typeof axis === 'number' ||\n        Array.isArray(axis) && axis.length === 1) {\n        if (p === 1) {\n            return sum(abs(x), axis);\n        }\n        if (p === Infinity) {\n            return max(abs(x), axis);\n        }\n        if (p === -Infinity) {\n            return min(abs(x), axis);\n        }\n        if (p === 'euclidean' || p === 2) {\n            // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2\n            return sqrt(sum(pow(abs(x), scalar(2, 'int32')), axis));\n        }\n        throw new Error(`Error in norm: invalid ord value: ${p}`);\n    }\n    // matrix (assumption axis[0] < axis[1])\n    if (Array.isArray(axis) && axis.length === 2) {\n        if (p === 1) {\n            return max(sum(abs(x), axis[0]), axis[1] - 1);\n        }\n        if (p === Infinity) {\n            return max(sum(abs(x), axis[1]), axis[0]);\n        }\n        if (p === -Infinity) {\n            return min(sum(abs(x), axis[1]), axis[0]);\n        }\n        if (p === 'fro' || p === 'euclidean') {\n            // norm(x) = sqrt(sum(pow(x, 2)))\n            return sqrt(sum(square(x), axis));\n        }\n        throw new Error(`Error in norm: invalid ord value: ${p}`);\n    }\n    throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\nexport const norm = op({ norm_ });\n//# sourceMappingURL=norm.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assertTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { scalar } from './scalar';\nimport { sub } from './sub';\n/**\n * Compute the moving average of a variable.\n *\n * Without zeroDebias, the moving average operation is defined by:\n *   `v += delta`\n * where\n *   `delta = (1 - decay) * (x - v)`\n *\n * With zeroDebias (default), the `delta` term is scaled to debias the\n * effect of the (assumed) zero-initialization of `v`.\n *   `delta /= (1 - decay ^ step)`\n *\n * For more details on the zero-debiasing algorithm, see:\n *   https://arxiv.org/abs/1412.6980\n *\n * Note that this function is completely stateless and does not keep track of\n * step count. The step count needs to be maintained by the caller and passed\n * in as `step`.\n *\n * @param v The current moving average value.\n * @param x New input value, must have the same shape and dtype as `v`.\n * @param decay The decay factor. Typical values are 0.95 and 0.99.\n * @param step Step count.\n * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).\n * @returns The new moving average value.\n *\n * @doc {heading: 'Operations', subheading: 'Moving Average'}\n */\nfunction movingAverage_(v, x, decay, step, zeroDebias = true) {\n    const $v = convertToTensor(v, 'v', 'movingAverage');\n    const $x = convertToTensor(x, 'x', 'movingAverage');\n    const $decay = convertToTensor(decay, 'decay', 'movingAverage');\n    assertTypesMatch($v, $x);\n    util.assert(util.arraysEqual($v.shape, $x.shape), () => 'Shape mismatch in v and x');\n    const one = scalar(1);\n    const oneMinusDecay = sub(one, $decay);\n    let update = mul(sub($x, $v), oneMinusDecay);\n    if (zeroDebias) {\n        util.assert(step != null, () => 'When using zeroDebias: true, step is required.');\n        const $step = convertToTensor(step, 'step', 'movingAverage');\n        update = div(update, sub(one, pow($decay, $step)));\n    }\n    return add($v, update);\n}\nexport const movingAverage = op({ movingAverage_ });\n//# sourceMappingURL=moving_average.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ScatterNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport * as scatter_nd_util from './scatter_nd_util';\n/**\n * Creates a new tensor by applying sparse updates to individual\n * values or slices within a zero tensor of the given shape tensor according to\n * indices. This operator is the inverse of the `tf.gatherND` operator which\n * extracts values or slices from a given tensor.\n *\n * ```js\n * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32');\n * const updates = tf.tensor1d([9, 10, 11, 12]);\n * const shape = [8];\n * tf.scatterND(indices, updates, shape).print() //[0, 11, 0, 10, 9, 0, 0, 12]\n * ```\n *\n * @param indices The tensor contains the indices into the output tensor.\n * @param updates The tensor contains the value for the indices.\n * @param shape: The shape of the output tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction scatterND_(indices, updates, shape) {\n    const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');\n    const $updates = convertToTensor(updates, 'updates', 'scatterND');\n    scatter_nd_util.validateInput($updates, $indices, shape);\n    const forward = (backend) => {\n        return backend.scatterND($indices, $updates, shape);\n    };\n    const inputs = { indices: $indices, updates: $updates };\n    const attrs = { shape };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ScatterNd, attrs);\n}\nexport const scatterND = op({ scatterND_ });\n//# sourceMappingURL=scatter_nd.js.map", "/**\n * Validate sparseToDense inputs.\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape number[]. Shape of the dense output tensor.\n * @param validateIndices boolean. indice validation is not supported, error\n * will be thrown if it is set.\n */\nexport function validateInput(sparseIndices, sparseValues, outputShape, defaultValues) {\n    if (sparseIndices.dtype !== 'int32') {\n        throw new Error('tf.sparseToDense() expects the indices to be int32 type,' +\n            ` but the dtype was ${sparseIndices.dtype}.`);\n    }\n    if (sparseIndices.rank > 2) {\n        throw new Error('sparseIndices should be a scalar, vector, or matrix,' +\n            ` but got shape ${sparseIndices.shape}.`);\n    }\n    const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n    const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n    if (outputShape.length !== numDims) {\n        throw new Error('outputShape has incorrect number of elements:,' +\n            ` ${outputShape.length}, should be: ${numDims}.`);\n    }\n    const numValues = sparseValues.size;\n    if (!(sparseValues.rank === 0 ||\n        sparseValues.rank === 1 && numValues === numElems)) {\n        throw new Error('sparseValues has incorrect shape ' +\n            `${sparseValues.shape}, should be [] or [${numElems}]`);\n    }\n    if (sparseValues.dtype !== defaultValues.dtype) {\n        throw new Error('sparseValues.dtype must match defaultValues.dtype');\n    }\n}\n//# sourceMappingURL=sparse_to_dense_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SparseToDense } from '../kernel_names';\nimport * as sparse_to_dense from '../ops/sparse_to_dense_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Converts a sparse representation into a dense tensor.\n *\n * Builds an array dense with shape outputShape such that:\n *\n * // If sparseIndices is scalar\n * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)\n *\n * // If sparseIndices is a vector, then for each i\n * dense[sparseIndices[i]] = sparseValues[i]\n *\n * // If sparseIndices is an n by d matrix, then for each i in [0, n)\n * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]\n * All other values in dense are set to defaultValue. If sparseValues is a\n * scalar, all sparse indices are set to this single value.\n *\n * If indices are repeated the final value is summed over all values for those\n * indices.\n *\n * ```js\n * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');\n * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');\n * const shape = [8];\n * tf.sparseToDense(indices, values, shape).print();\n * ```\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape Shape of the dense output tensor. the type is inferred.\n * @param defaultValue Scalar. Value to set for indices not specified in\n * sparseIndices. Defaults to zero.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {\n    const $sparseIndices = convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');\n    const $sparseValues = convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');\n    const $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);\n    sparse_to_dense.validateInput($sparseIndices, $sparseValues, outputShape, $defaultValue);\n    const inputs = {\n        sparseIndices: $sparseIndices,\n        sparseValues: $sparseValues,\n        defaultValue: $defaultValue\n    };\n    const attrs = { outputShape };\n    return ENGINE.runKernelFunc(backend => backend.sparseToDense($sparseIndices, $sparseValues, outputShape, $defaultValue), inputs, null /* grad */, SparseToDense, attrs);\n}\nexport const sparseToDense = op({ sparseToDense_ });\n//# sourceMappingURL=sparse_to_dense.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Gather slices from input tensor into a Tensor with shape specified by\n * `indices`.\n *\n * `indices` is an K-dimensional integer tensor, best thought of as a\n * (K-1)-dimensional tensor of indices into input, where each element defines a\n * slice of input:\n * output[\\\\(i_0, ..., i_{K-2}\\\\)] = input[indices[\\\\(i_0, ..., i_{K-2}\\\\)]]\n *\n * Whereas in `tf.gather`, `indices` defines slices into the first dimension of\n * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions\n * of input, where N = indices.shape[-1].\n *\n * The last dimension of indices can be at most the rank of input:\n * indices.shape[-1] <= input.rank\n *\n * The last dimension of `indices` corresponds to elements\n * (if indices.shape[-1] == input.rank) or slices\n * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of\n * input.\n * The output tensor has shape\n * indices.shape[:-1] + input.shape[indices.shape[-1]:]\n *\n * Note that on CPU, if an out of bound index is found, an error is returned. On\n * GPU, if an out of bound index is found, a 0 is stored in the corresponding\n * output value.\n *\n * ```js\n * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');\n * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);\n * tf.gatherND(input, indices).print() // [10, 11]\n * ```\n *\n * @param x The tensor from which to gather values.\n * @param indices Index tensor, must be of type int32.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction gatherND_(x, indices) {\n    const $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');\n    const $x = convertToTensor(x, 'x', 'gatherND');\n    const forward = (backend) => {\n        return backend.gatherND($x, $indices);\n    };\n    const inputs = { params: $x, indices: $indices };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, GatherNd);\n}\nexport const gatherND = op({ gatherND_ });\n//# sourceMappingURL=gather_nd.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n * Normalize noise shape based on provided tensor and noise shape.\n *\n * @param x Tensor.\n * @param noiseShape The shape for the randomly generated keep/drop flags, as\n *   an array of numbers. Optional.\n * @returns Normalized noise shape.\n */\nexport function getNoiseShape(x, noiseShape) {\n    if (noiseShape == null) {\n        return x.shape.slice();\n    }\n    if (util.arraysEqual(x.shape, noiseShape)) {\n        return noiseShape;\n    }\n    if (x.shape.length === noiseShape.length) {\n        const newDimension = [];\n        for (let i = 0; i < x.shape.length; i++) {\n            if (noiseShape[i] == null && x.shape[i] != null) {\n                newDimension.push(x.shape[i]);\n            }\n            else {\n                newDimension.push(noiseShape[i]);\n            }\n        }\n        return newDimension;\n    }\n    return noiseShape;\n}\n//# sourceMappingURL=dropout_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { getNoiseShape } from './dropout_util';\nimport { floor } from './floor';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { randomUniform } from './random_uniform';\n/**\n * Computes dropout.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 2, 1]);\n * const rate = 0.75;\n * const output = tf.dropout(x, rate);\n * output.print();\n * ```\n *\n * @param x A floating point Tensor or TensorLike.\n * @param rate A float in the range [0, 1). The probability that each element\n *   of x is discarded.\n * @param noiseShape An array of numbers of type int32, representing the\n * shape for randomly generated keep/drop flags. If the noiseShape has null\n * value, it will be automatically replaced with the x's relative dimension\n * size. Optional.\n * @param seed Used to create random seeds. Optional.\n * @returns A Tensor of the same shape of x.\n *\n * @doc {heading: 'Operations', subheading: 'Dropout'}\n */\nfunction dropout_(x, rate, noiseShape, seed) {\n    const $x = convertToTensor(x, 'x', 'dropout');\n    util.assert($x.dtype === 'float32', () => `x has to be a floating point tensor since it's going to be ` +\n        `scaled, but got a ${$x.dtype} tensor instead.`);\n    util.assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);\n    if (rate === 0) {\n        return x instanceof Tensor ? $x.clone() : $x;\n    }\n    const $noiseShape = getNoiseShape($x, noiseShape);\n    const keepProb = 1 - rate;\n    const multiplier = div(floor(add(randomUniform($noiseShape, 0, 1, 'float32', seed), keepProb)), keepProb);\n    return mul($x, multiplier);\n}\nexport const dropout = op({ dropout_ });\n//# sourceMappingURL=dropout.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensor1d } from './tensor1d';\nexport function enclosingPowerOfTwo(value) {\n    // Return 2**N for integer N such that 2**N >= value.\n    return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2.0))));\n}\nexport function cosineWindow(windowLength, a, b) {\n    const even = 1 - windowLength % 2;\n    const newValues = new Float32Array(windowLength);\n    for (let i = 0; i < windowLength; ++i) {\n        const cosArg = (2.0 * Math.PI * i) / (windowLength + even - 1);\n        newValues[i] = a - b * Math.cos(cosArg);\n    }\n    return tensor1d(newValues, 'float32');\n}\n//# sourceMappingURL=signal_ops_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, assertShapesMatch, getTypedArrayFromDType } from '../util';\nimport { tensor } from './tensor';\n/**\n * Returns whether the targets are in the top K predictions.\n *\n * ```js\n * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);\n * const targets = tf.tensor1d([2, 0]);\n * const precision = await tf.inTopKAsync(predictions, targets);\n * precision.print();\n * ```\n * @param predictions 2-D or higher `tf.Tensor` with last dimension being\n *     at least `k`.\n * @param targets 1-D or higher `tf.Tensor`.\n * @param k Optional Number of top elements to look at for computing precision,\n *     default to 1.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nasync function inTopKAsync_(predictions, targets, k = 1) {\n    const $predictions = convertToTensor(predictions, 'predictions', 'inTopK');\n    const $targets = convertToTensor(targets, 'targets', 'inTopK');\n    assert($predictions.rank > 1, () => 'inTopK() expects the predictions to be of rank 2 or higher, ' +\n        `but got ${$predictions.rank}`);\n    assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than ` +\n        `targets rank, but got predictions rank ` +\n        `${$predictions.rank} and targets rank ${$targets.rank}`);\n    assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, ` +\n        'except the last dimension.');\n    const lastDim = $predictions.shape[$predictions.shape.length - 1];\n    assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last ` +\n        `dimension (${lastDim}), but got ${k}`);\n    const predictionsVals = await $predictions.data();\n    const targetsVals = await $targets.data();\n    // Reshape predictionsVals into a 2d tensor [batch, lastDim]\n    // and look up topK along lastDim.\n    const [batch, size] = [predictionsVals.length / lastDim, lastDim];\n    const precision = getTypedArrayFromDType('bool', batch);\n    for (let b = 0; b < batch; b++) {\n        const offset = b * size;\n        const vals = predictionsVals.subarray(offset, offset + size);\n        const valAndInd = [];\n        for (let i = 0; i < vals.length; i++) {\n            valAndInd.push({ value: vals[i], index: i });\n        }\n        valAndInd.sort((a, b) => b.value - a.value);\n        precision[b] = 0;\n        for (let i = 0; i < k; i++) {\n            if (valAndInd[i].index === targetsVals[b]) {\n                precision[b] = 1;\n                break;\n            }\n        }\n    }\n    if (predictions !== $predictions) {\n        $predictions.dispose();\n    }\n    if (targets !== $targets) {\n        $targets.dispose();\n    }\n    // Output precision has the same shape as targets.\n    return tensor(precision, $targets.shape, 'bool');\n}\nexport const inTopKAsync = inTopKAsync_;\n//# sourceMappingURL=in_top_k.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropFilter } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the filter of a 2D convolution.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.\n * @param dy The dy image, of rank 4 or rank 3, of shape\n *     [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.\n * @param filterShape The shape of the filter, length 4,\n *     [filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction conv2DBackpropFilter_(x, dy, filterShape, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n    let x4D = x;\n    if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    let dy4D = dy;\n    if (dy4D.rank === 3) {\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ` +\n        `${x4D.shape}.`);\n    util.assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ` +\n        `${dy4D.shape}.`);\n    util.assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ` +\n        `${filterShape}.`);\n    const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n    const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n    util.assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must ` +\n        `match input depth in filter (${filterShape[2]}.`);\n    util.assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must ` +\n        `match output depth for filter (${filterShape[3]}).`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2dDerFilter: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const dilations = 1;\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, filterShape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        return backend.conv2dDerFilter(x4D, dy4D, convInfo);\n    };\n    const inputs = { x: x4D, dy: dy4D };\n    const attrs = { strides, pad, dataFormat, dimRoundingMode };\n    return ENGINE.runKernelFunc(forward, inputs, null, Conv2DBackpropFilter, attrs);\n}\nexport const conv2DBackpropFilter = op({ conv2DBackpropFilter_ });\n//# sourceMappingURL=conv2d_backprop_filter.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as broadcast_util from './broadcast_util';\nimport { elu } from './elu';\nimport { mul } from './mul';\nimport { prelu } from './prelu';\nimport { relu } from './relu';\nimport { relu6 } from './relu6';\nimport { reshape } from './reshape';\nimport { step } from './step';\nimport { sum } from './sum';\n// Returns gradient for fused activation.\nexport function getFusedDyActivation(dy, y, activation) {\n    if (activation == null || activation === 'linear') {\n        return dy;\n    }\n    if (activation === 'relu') {\n        return mul(dy, step(y));\n    }\n    throw new Error(`Cannot compute gradient for fused activation ${activation}.`);\n}\n// Returns gradient for fused bias.\nexport function getFusedBiasGradient(bias, dyActivation) {\n    let res = dyActivation;\n    const reduceAxes = broadcast_util.getReductionAxes(bias.shape, dyActivation.shape);\n    if (reduceAxes.length > 0) {\n        res = sum(res, reduceAxes);\n    }\n    return reshape(res, bias.shape);\n}\nexport function applyActivation(x, activation, preluActivationWeights) {\n    if (activation === 'linear') {\n        return x;\n    }\n    else if (activation === 'relu') {\n        return relu(x);\n    }\n    else if (activation === 'elu') {\n        return elu(x);\n    }\n    else if (activation === 'relu6') {\n        return relu6(x);\n    }\n    else if (activation === 'prelu') {\n        return prelu(x, preluActivationWeights);\n    }\n    throw new Error(`Unknown fused activation ${activation}.`);\n}\n// Whether we should call fused ops.\nexport const shouldFuse = (gradientDepth, activation) => {\n    const gradientMode = gradientDepth > 0;\n    return !gradientMode || activation === 'linear';\n};\n//# sourceMappingURL=fused_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { conv2d as unfusedConv2d } from '../conv2d';\nimport { conv2DBackpropFilter } from '../conv2d_backprop_filter';\nimport { conv2DBackpropInput } from '../conv2d_backprop_input';\nimport * as conv_util from '../conv_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes a 2D convolution over the input x, optionally fused with adding a\n * bias and applying an activation.\n *\n * ```js\n * const inputDepth = 2;\n * const inShape = [2, 2, 2, inputDepth];\n * const outputDepth = 2;\n * const fSize = 1;\n * const pad = 0;\n * const strides = 1;\n *\n * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n * 16], inShape);\n * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,\n * outputDepth]);\n *\n * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',\n * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();\n * ```\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid` output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`) to be\n *     applied\n *      after biasAdd.\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights }) {\n    activation = activation || 'linear';\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    const $x = convertToTensor(x, 'x', 'conv2d');\n    const $filter = convertToTensor(filter, 'filter', 'conv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ` +\n        `${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in fused conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match ` +\n        `input depth for filter ${$filter.shape[2]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    util.assert(dataFormat === 'NHWC', () => `Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);\n    const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n        [$bias] = makeTypesMatch($bias, $x);\n        broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused conv2d');\n    }\n    const grad = (dy, saved) => {\n        const [$filter, x4D, y, $bias] = saved;\n        const dyActivation = getFusedDyActivation(dy, y, activation);\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused conv2D: ' +\n            `dilation rates greater than 1 ` +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n        const xDer = conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad);\n        const filterDer = conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad);\n        const der = [xDer, filterDer];\n        if ($bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            der.push(biasDer);\n        }\n        return der;\n    };\n    const forward = (backend) => {\n        const res = backend.fusedConv2d({\n            input: x4D,\n            filter: $filter,\n            convInfo,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return res;\n    };\n    const inputs = {\n        x: x4D,\n        filter: $filter,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((x4D, filter, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedConv2D, attrs);\n            save([filter, x4D, res]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOp(x4D, $filter);\n    }\n    else {\n        const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedConv2D, attrs);\n            save([filter, x4D, res, bias]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOpWithBias(x4D, $filter, $bias);\n    }\n}\nexport const conv2d = op({ fusedConv2d_ });\n//# sourceMappingURL=conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropFilter } from '../kernel_names';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, convInfo) {\n    let x4D = x;\n    if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    let dy4D = dy;\n    if (dy4D.rank === 3) {\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    const forward = backend => backend.depthwiseConv2DDerFilter(x4D, dy4D, convInfo);\n    const inputs = { x: x4D, dy: dy4D };\n    return ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropFilter);\n}\nexport const depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ });\n//# sourceMappingURL=depthwise_conv2d_native_backprop_filter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropInput } from '../kernel_names';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, convInfo) {\n    let dy4D = dy;\n    let reshapedTo4D = false;\n    if (dy.rank === 3) {\n        reshapedTo4D = true;\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    const forward = backend => backend.depthwiseConv2DDerInput(dy4D, filter, convInfo);\n    const inputs = { dy: dy4D };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropInput);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ });\n//# sourceMappingURL=depthwise_conv2d_native_backprop_input.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedDepthwiseConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport * as conv_util from '../conv_util';\nimport { depthwiseConv2d as unfusedDepthwiseConv2d } from '../depthwise_conv2d';\nimport { depthwiseConv2dNativeBackpropFilter } from '../depthwise_conv2d_native_backprop_filter';\nimport { depthwiseConv2dNativeBackpropInput } from '../depthwise_conv2d_native_backprop_input';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes depthwise 2D convolution, optionally fused with adding a\n * bias and applying an activation.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`).\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedDepthwiseConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights }) {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedDepthwiseConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n    const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, ` +\n        `but got rank ${$filter.rank}.`);\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels ` +\n        `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n        `filter ${$filter.shape[2]}.`);\n    if (dilations == null) {\n        dilations = [1, 1];\n    }\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in fused depthwiseConv2d: Either strides or dilations must ' +\n        `be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in fused depthwiseConv2d: pad must be an integer when ` +\n            `using dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n        [$bias] = makeTypesMatch($bias, $x);\n        broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');\n    }\n    const grad = (dy, saved) => {\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused depthwiseConv2d: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${dilations}'`);\n        const [$filter, x4D, y, bias] = saved;\n        const dyActivation = getFusedDyActivation(dy, y, activation);\n        const xDer = depthwiseConv2dNativeBackpropInput(x4D.shape, dyActivation, $filter, convInfo);\n        const filterDer = depthwiseConv2dNativeBackpropFilter(x4D, dyActivation, $filter.shape, convInfo);\n        if (bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            return [xDer, filterDer, biasDer];\n        }\n        return [xDer, filterDer];\n    };\n    const forward = (backend) => {\n        const res = backend.fusedDepthwiseConv2D({\n            input: x4D,\n            filter: $filter,\n            convInfo,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return res;\n    };\n    const inputs = {\n        x: x4D,\n        filter: $filter,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((x4D, filter, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedDepthwiseConv2D, attrs);\n            save([filter, x4D, res]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOp(x4D, $filter);\n    }\n    else {\n        const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedDepthwiseConv2D, attrs);\n            save([filter, x4D, res, bias]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOpWithBias(x4D, $filter, $bias);\n    }\n}\nexport const depthwiseConv2d = op({ fusedDepthwiseConv2d_ });\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { _FusedMatMul } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { matMul as unfusedMatMul } from '../mat_mul';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes the dot product of two matrices with optional activation and bias.\n *\n * ```js\n * const a = tf.tensor2d([-1, -2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const bias = tf.tensor2d([1, 2], [1, 2]);\n *\n * tf.fused.matMul({a, b, bias, activation: 'relu'}).print();\n * ```\n *\n * @param obj An object with the following properties:\n * - `a` First matrix in dot product operation.\n * - `b` Second matrix in dot product operation.\n * - `transposeA` If true, `a` is transposed before multiplication.\n * - `transposeB` If true, `b` is transposed before multiplication.\n * - `bias` Matrix to be added to the result.\n * - `activation` Name of activation kernel (defaults to `linear`).\n * - `preluActivationWeights` Tensor of prelu weights.\n */\nfunction fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation = 'linear', preluActivationWeights }) {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedMatMul(a, b, transposeA, transposeB);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    let $a = convertToTensor(a, 'a', 'fused matMul');\n    let $b = convertToTensor(b, 'b', 'fused matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n    const outerDimsA = $a.shape.slice(0, -2);\n    const outerDimsB = $b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n    util.assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in fused matMul: inputs must have the same rank of at least ` +\n        `2, got ranks ${$a.rank} and ${$b.rank}.`);\n    util.assert(util.arraysEqual(outerDimsA, outerDimsB), () => `Error in fused matMul: outer dimensions (${outerDimsA}) and (` +\n        `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} must match.`);\n    util.assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (` +\n        `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} and transposeA=${transposeA}` +\n        ` and transposeB=${transposeB} must match.`);\n    const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n    const a3D = transposeA ?\n        reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n        reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n    const b3D = transposeB ?\n        reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n        reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused matMul');\n        [$bias] = makeTypesMatch($bias, $a);\n        broadcast_util.assertAndGetBroadcastShape(outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused matMul');\n    }\n    const grad = (dy, saved) => {\n        const [a3D, b3D, y, $bias] = saved;\n        // we reshape dy because the result of the forward is not\n        // necessarily going to be a 3d tensor due to a reshape done at the end of\n        // the customOp.\n        const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation);\n        let aDer;\n        let bDer;\n        if (!transposeA && !transposeB) {\n            aDer = unfusedMatMul(dyActivation, b3D, false, true);\n            bDer = unfusedMatMul(a3D, dyActivation, true, false);\n        }\n        else if (!transposeA && transposeB) {\n            aDer = unfusedMatMul(dyActivation, b3D, false, false);\n            bDer = unfusedMatMul(dyActivation, a3D, true, false);\n        }\n        else if (transposeA && !transposeB) {\n            aDer = unfusedMatMul(b3D, dyActivation, false, true);\n            bDer = unfusedMatMul(a3D, dyActivation, false, false);\n        }\n        else {\n            aDer = unfusedMatMul(b3D, dyActivation, true, true);\n            bDer = unfusedMatMul(dyActivation, a3D, true, true);\n        }\n        if (bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            return [aDer, bDer, biasDer];\n        }\n        else {\n            return [aDer, bDer];\n        }\n    };\n    const forward = (backend) => {\n        const y = backend.fusedBatchMatMul({\n            a: a3D,\n            b: b3D,\n            transposeA,\n            transposeB,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return y;\n    };\n    const inputs = {\n        a: a3D,\n        b: b3D,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { transposeA, transposeB, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((a3D, b3D, save) => {\n            const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, _FusedMatMul, attrs);\n            save([a3D, b3D, res]);\n            return { value: reshape(res, outShape), gradFunc: grad };\n        });\n        return customOp(a3D, b3D);\n    }\n    else {\n        const customOpWithBias = customGrad((a3D, b3D, $bias, save) => {\n            const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, _FusedMatMul, attrs);\n            save([a3D, b3D, res, $bias]);\n            return { value: reshape(res, outShape), gradFunc: grad };\n        });\n        return customOpWithBias(a3D, b3D, $bias);\n    }\n}\nexport const matMul = op({ fusedMatMul_ });\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2d } from './fused/conv2d';\nimport { depthwiseConv2d } from './fused/depthwise_conv2d';\nimport { matMul } from './fused/mat_mul';\nexport { conv2d, depthwiseConv2d, matMul };\n//# sourceMappingURL=fused_ops.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a hamming window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hammingWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hammingWindow_(windowLength) {\n    return cosineWindow(windowLength, 0.54, 0.46);\n}\nexport const hammingWindow = op({ hammingWindow_ });\n//# sourceMappingURL=hamming_window.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a Hann window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hannWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hannWindow_(windowLength) {\n    return cosineWindow(windowLength, 0.5, 0.5);\n}\nexport const hannWindow = op({ hannWindow_ });\n//# sourceMappingURL=hann_window.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../concat';\nimport { fill } from '../fill';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { tensor2d } from '../tensor2d';\n/**\n * Expands input into frames of frameLength.\n * Slides a window size with frameStep.\n *\n * ```js\n * tf.signal.frame([1, 2, 3], 2, 1).print();\n * ```\n * @param signal The input tensor to be expanded\n * @param frameLength Length of each frame\n * @param frameStep The frame hop size in samples.\n * @param padEnd Whether to pad the end of signal with padValue.\n * @param padValue An number to use where the input signal does\n *     not exist when padEnd is True.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction frame_(signal, frameLength, frameStep, padEnd = false, padValue = 0) {\n    let start = 0;\n    const output = [];\n    while (start + frameLength <= signal.size) {\n        output.push(slice(signal, start, frameLength));\n        start += frameStep;\n    }\n    if (padEnd) {\n        while (start < signal.size) {\n            const padLen = (start + frameLength) - signal.size;\n            const pad = concat([\n                slice(signal, start, frameLength - padLen), fill([padLen], padValue)\n            ]);\n            output.push(pad);\n            start += frameStep;\n        }\n    }\n    if (output.length === 0) {\n        return tensor2d([], [0, frameLength]);\n    }\n    return reshape(concat(output), [output.length, frameLength]);\n}\nexport const frame = op({ frame_ });\n//# sourceMappingURL=frame.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../concat';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { enclosingPowerOfTwo } from '../signal_ops_util';\nimport { slice } from '../slice';\nimport { rfft } from '../spectral/rfft';\nimport { frame } from './frame';\nimport { hannWindow } from './hann_window';\n/**\n * Computes the Short-time Fourier Transform of signals\n * See: https://en.wikipedia.org/wiki/Short-time_Fourier_transform\n *\n * ```js\n * const input = tf.tensor1d([1, 1, 1, 1, 1])\n * tf.signal.stft(input, 3, 1).print();\n * ```\n * @param signal 1-dimensional real value tensor.\n * @param frameLength The window length of samples.\n * @param frameStep The number of samples to step.\n * @param fftLength The size of the FFT to apply.\n * @param windowFn A callable that takes a window length and returns 1-d tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction stft_(signal, frameLength, frameStep, fftLength, windowFn = hannWindow) {\n    if (fftLength == null) {\n        fftLength = enclosingPowerOfTwo(frameLength);\n    }\n    const framedSignal = frame(signal, frameLength, frameStep);\n    const windowedSignal = mul(framedSignal, windowFn(frameLength));\n    const output = [];\n    for (let i = 0; i < framedSignal.shape[0]; i++) {\n        output.push(rfft(slice(windowedSignal, [i, 0], [1, frameLength]), fftLength));\n    }\n    return concat(output);\n}\nexport const stft = op({ stft_ });\n//# sourceMappingURL=stft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { CropAndResize } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Extracts crops from the input image tensor and resizes them using bilinear\n * sampling or nearest neighbor sampling (possibly with aspect ratio change)\n * to a common output size specified by crop_size.\n *\n * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,\n *     where imageHeight and imageWidth must be positive, specifying the\n *     batch of images from which to take crops\n * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized\n *     coordinates of the box in the boxInd[i]'th image in the batch\n * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range\n *     `[0, batch)` that specifies the image that the `i`-th box refers to.\n * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`\n *     specifying the size to which all crops are resized to.\n * @param method Optional string from `'bilinear' | 'nearest'`,\n *     defaults to bilinear, which specifies the sampling method for resizing\n * @param extrapolationValue A threshold for deciding when to remove boxes based\n *     on score. Defaults to 0.\n * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction cropAndResize_(image, boxes, boxInd, cropSize, method, extrapolationValue) {\n    const $image = convertToTensor(image, 'image', 'cropAndResize');\n    const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');\n    const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');\n    method = method || 'bilinear';\n    extrapolationValue = extrapolationValue || 0;\n    const numBoxes = $boxes.shape[0];\n    util.assert($image.rank === 4, () => 'Error in cropAndResize: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    util.assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +\n        `but had shape ${$boxes.shape}.`);\n    util.assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +\n        `but had shape ${$boxes.shape}.`);\n    util.assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got ` +\n        `length ${cropSize.length}.`);\n    util.assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);\n    util.assert(method === 'bilinear' || method === 'nearest', () => `method must be bilinear or nearest, but was ${method}`);\n    const forward = (backend) => backend.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);\n    const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };\n    const attrs = { method, extrapolationValue, cropSize };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, CropAndResize, attrs);\n    return res;\n}\nexport const cropAndResize = op({ cropAndResize_ });\n//# sourceMappingURL=crop_and_resize.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FlipLeftRight } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Flips the image left to right. Currently available in the CPU, WebGL, and\n * WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction flipLeftRight_(image) {\n    const $image = convertToTensor(image, 'image', 'flipLeftRight', 'float32');\n    util.assert($image.rank === 4, () => 'Error in flipLeftRight: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    const inputs = { image: $image };\n    const res = ENGINE.runKernel(FlipLeftRight, inputs, {});\n    return res;\n}\nexport const flipLeftRight = op({ flipLeftRight_ });\n//# sourceMappingURL=flip_left_right.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { RotateWithOffset } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Rotates the input image tensor counter-clockwise with an optional offset\n * center of rotation. Currently available in the CPU, WebGL, and WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n * @param radians The amount of rotation.\n * @param fillValue The value to fill in the empty space leftover\n *     after rotation. Can be either a single grayscale value (0-255), or an\n *     array of three numbers `[red, green, blue]` specifying the red, green,\n *     and blue channels. Defaults to `0` (black).\n * @param center The center of rotation. Can be either a single value (0-1), or\n *     an array of two numbers `[centerX, centerY]`. Defaults to `0.5` (rotates\n *     the image around its center).\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction rotateWithOffset_(image, radians, fillValue = 0, center = 0.5) {\n    const $image = convertToTensor(image, 'image', 'rotateWithOffset', 'float32');\n    util.assert($image.rank === 4, () => 'Error in rotateWithOffset: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    const inputs = { image: $image };\n    const attrs = { radians, fillValue, center };\n    const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);\n    return res;\n}\nexport const rotateWithOffset = op({ rotateWithOffset_ });\n//# sourceMappingURL=rotate_with_offset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nfunction nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n    if (iouThreshold == null) {\n        iouThreshold = 0.5;\n    }\n    if (scoreThreshold == null) {\n        scoreThreshold = Number.NEGATIVE_INFINITY;\n    }\n    if (softNmsSigma == null) {\n        softNmsSigma = 0.0;\n    }\n    const numBoxes = boxes.shape[0];\n    maxOutputSize = Math.min(maxOutputSize, numBoxes);\n    util.assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n    util.assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n    util.assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n    util.assert(scores.rank === 1, () => 'scores must be a 1D tensor');\n    util.assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, ` +\n        `but was ${scores.shape[0]}`);\n    util.assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);\n    return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n}\nexport { nonMaxSuppSanityCheck };\n//# sourceMappingURL=nonmax_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV3 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\nfunction nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n    maxOutputSize = inputs.maxOutputSize;\n    iouThreshold = inputs.iouThreshold;\n    scoreThreshold = inputs.scoreThreshold;\n    const attrs = { maxOutputSize, iouThreshold, scoreThreshold };\n    return ENGINE.runKernelFunc(b => b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold), { boxes: $boxes, scores: $scores }, null /* grad */, NonMaxSuppressionV3, attrs);\n}\nexport const nonMaxSuppression = op({ nonMaxSuppression_ });\n//# sourceMappingURL=non_max_suppression.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inserts a value into a sorted array. This method allows duplicate, meaning it\n * allows inserting duplicate value, in which case, the element will be inserted\n * at the lowest index of the value.\n * @param arr The array to modify.\n * @param element The element to insert.\n * @param comparator Optional. If no comparator is specified, elements are\n * compared using array_util.defaultComparator, which is suitable for Strings\n * and Numbers in ascending arrays. If the array contains multiple instances of\n * the target value, the left-most instance will be returned. To provide a\n * comparator, it should take 2 arguments to compare and return a negative,\n * zero, or a positive number.\n */\nexport function binaryInsert(arr, element, comparator) {\n    const index = binarySearch(arr, element, comparator);\n    const insertionPoint = index < 0 ? -(index + 1) : index;\n    arr.splice(insertionPoint, 0, element);\n}\n/**\n * Searches the array for the target using binary search, returns the index\n * of the found element, or position to insert if element not found. If no\n * comparator is specified, elements are compared using array_\n * util.defaultComparator, which is suitable for Strings and Numbers in\n * ascending arrays. If the array contains multiple instances of the target\n * value, the left-most instance will be returned.\n * @param arr The array to be searched in.\n * @param target The target to be searched for.\n * @param comparator Should take 2 arguments to compare and return a negative,\n *    zero, or a positive number.\n * @return Lowest index of the target value if found, otherwise the insertion\n *    point where the target should be inserted, in the form of\n *    (-insertionPoint - 1).\n */\nexport function binarySearch(arr, target, comparator) {\n    return binarySearch_(arr, target, comparator || defaultComparator);\n}\n/**\n * Compares its two arguments for order.\n * @param a The first element to be compared.\n * @param b The second element to be compared.\n * @return A negative number, zero, or a positive number as the first\n *     argument is less than, equal to, or greater than the second.\n */\nfunction defaultComparator(a, b) {\n    return a > b ? 1 : a < b ? -1 : 0;\n}\nfunction binarySearch_(arr, target, comparator) {\n    let left = 0;\n    let right = arr.length;\n    let middle = 0;\n    let found = false;\n    while (left < right) {\n        middle = left + ((right - left) >>> 1);\n        const compareResult = comparator(target, arr[middle]);\n        if (compareResult > 0) {\n            left = middle + 1;\n        }\n        else {\n            right = middle;\n            // If compareResult is 0, the value is found. We record it is found,\n            // and then keep looking because there may be duplicate.\n            found = !compareResult;\n        }\n    }\n    return found ? left : -left - 1;\n}\n//# sourceMappingURL=array_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Implementation of the NonMaxSuppression kernel shared between webgl and cpu.\n */\nimport { scalar } from '../ops/scalar';\nimport { tensor1d } from '../ops/tensor1d';\nimport { binaryInsert } from './array_util';\nexport function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */)\n        .selectedIndices;\n}\nexport function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */, false /* returnScoresTensor */, padToMaxOutputSize /* padToMaxOutputSize */, true\n    /* returnValidOutputs */ );\n}\nexport function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true /* returnScoresTensor */);\n}\nfunction nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {\n    // The list is sorted in ascending order, so that we can always pop the\n    // candidate with the largest score in O(1) time.\n    const candidates = [];\n    for (let i = 0; i < scores.length; i++) {\n        if (scores[i] > scoreThreshold) {\n            candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 });\n        }\n    }\n    candidates.sort(ascendingComparator);\n    // If softNmsSigma is 0, the outcome of this algorithm is exactly same as\n    // before.\n    const scale = softNmsSigma > 0 ? (-0.5 / softNmsSigma) : 0.0;\n    const selectedIndices = [];\n    const selectedScores = [];\n    while (selectedIndices.length < maxOutputSize && candidates.length > 0) {\n        const candidate = candidates.pop();\n        const { score: originalScore, boxIndex, suppressBeginIndex } = candidate;\n        if (originalScore < scoreThreshold) {\n            break;\n        }\n        // Overlapping boxes are likely to have similar scores, therefore we\n        // iterate through the previously selected boxes backwards in order to\n        // see if candidate's score should be suppressed. We use\n        // suppressBeginIndex to track and ensure a candidate can be suppressed\n        // by a selected box no more than once. Also, if the overlap exceeds\n        // iouThreshold, we simply ignore the candidate.\n        let ignoreCandidate = false;\n        for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {\n            const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);\n            if (iou >= iouThreshold) {\n                ignoreCandidate = true;\n                break;\n            }\n            candidate.score =\n                candidate.score * suppressWeight(iouThreshold, scale, iou);\n            if (candidate.score <= scoreThreshold) {\n                break;\n            }\n        }\n        // At this point, if `candidate.score` has not dropped below\n        // `scoreThreshold`, then we know that we went through all of the\n        // previous selections and can safely update `suppressBeginIndex` to the\n        // end of the selected array. Then we can re-insert the candidate with\n        // the updated score and suppressBeginIndex back in the candidate list.\n        // If on the other hand, `candidate.score` has dropped below the score\n        // threshold, we will not add it back to the candidates list.\n        candidate.suppressBeginIndex = selectedIndices.length;\n        if (!ignoreCandidate) {\n            // Candidate has passed all the tests, and is not suppressed, so\n            // select the candidate.\n            if (candidate.score === originalScore) {\n                selectedIndices.push(boxIndex);\n                selectedScores.push(candidate.score);\n            }\n            else if (candidate.score > scoreThreshold) {\n                // Candidate's score is suppressed but is still high enough to be\n                // considered, so add back to the candidates list.\n                binaryInsert(candidates, candidate, ascendingComparator);\n            }\n        }\n    }\n    // NonMaxSuppressionV4 feature: padding output to maxOutputSize.\n    const validOutputs = selectedIndices.length;\n    const elemsToPad = maxOutputSize - validOutputs;\n    if (padToMaxOutputSize && elemsToPad > 0) {\n        selectedIndices.push(...new Array(elemsToPad).fill(0));\n        selectedScores.push(...new Array(elemsToPad).fill(0.0));\n    }\n    const result = { selectedIndices: tensor1d(selectedIndices, 'int32') };\n    if (returnScoresTensor) {\n        result['selectedScores'] = tensor1d(selectedScores, 'float32');\n    }\n    if (returnValidOutputs) {\n        result['validOutputs'] = scalar(validOutputs, 'int32');\n    }\n    return result;\n}\nfunction intersectionOverUnion(boxes, i, j) {\n    const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n    const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n    const yminI = Math.min(iCoord[0], iCoord[2]);\n    const xminI = Math.min(iCoord[1], iCoord[3]);\n    const ymaxI = Math.max(iCoord[0], iCoord[2]);\n    const xmaxI = Math.max(iCoord[1], iCoord[3]);\n    const yminJ = Math.min(jCoord[0], jCoord[2]);\n    const xminJ = Math.min(jCoord[1], jCoord[3]);\n    const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n    const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n    const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n    const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n    if (areaI <= 0 || areaJ <= 0) {\n        return 0.0;\n    }\n    const intersectionYmin = Math.max(yminI, yminJ);\n    const intersectionXmin = Math.max(xminI, xminJ);\n    const intersectionYmax = Math.min(ymaxI, ymaxJ);\n    const intersectionXmax = Math.min(xmaxI, xmaxJ);\n    const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *\n        Math.max(intersectionXmax - intersectionXmin, 0.0);\n    return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n// A Gaussian penalty function, this method always returns values in [0, 1].\n// The weight is a function of similarity, the more overlap two boxes are, the\n// smaller the weight is, meaning highly overlapping boxe will be significantly\n// penalized. On the other hand, a non-overlapping box will not be penalized.\nfunction suppressWeight(iouThreshold, scale, iou) {\n    const weight = Math.exp(scale * iou * iou);\n    return iou <= iouThreshold ? weight : 0.0;\n}\nfunction ascendingComparator(c1, c2) {\n    // For objects with same scores, we make the object with the larger index go\n    // first. In an array that pops from the end, this means that the object with\n    // the smaller index will be popped first. This ensures the same output as\n    // the TensorFlow python version.\n    return (c1.score - c2.score) ||\n        ((c1.score === c2.score) && (c2.boxIndex - c1.boxIndex));\n}\n//# sourceMappingURL=non_max_suppression_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV3Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This is the async version of `nonMaxSuppression`\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n    maxOutputSize = inputs.maxOutputSize;\n    iouThreshold = inputs.iouThreshold;\n    scoreThreshold = inputs.scoreThreshold;\n    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n    const boxesVals = boxesAndScores[0];\n    const scoresVals = boxesAndScores[1];\n    // We call a cpu based impl directly with the typedarray data  here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\n//# sourceMappingURL=non_max_suppression_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV5 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    maxOutputSize = params.maxOutputSize;\n    iouThreshold = params.iouThreshold;\n    scoreThreshold = params.scoreThreshold;\n    softNmsSigma = params.softNmsSigma;\n    const inputs = { boxes: $boxes, scores: $scores };\n    const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n    const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);\n    return { selectedIndices: result[0], selectedScores: result[1] };\n}\nexport const nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ });\n//# sourceMappingURL=non_max_suppression_with_score.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV5Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    maxOutputSize = params.maxOutputSize;\n    iouThreshold = params.iouThreshold;\n    scoreThreshold = params.scoreThreshold;\n    softNmsSigma = params.softNmsSigma;\n    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n    const boxesVals = boxesAndScores[0];\n    const scoresVals = boxesAndScores[1];\n    // We call a cpu based impl directly with the typedarray data  here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;\n//# sourceMappingURL=non_max_suppression_with_score_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV4 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n    const $maxOutputSize = params.maxOutputSize;\n    const $iouThreshold = params.iouThreshold;\n    const $scoreThreshold = params.scoreThreshold;\n    const inputs = { boxes: $boxes, scores: $scores };\n    const attrs = {\n        maxOutputSize: $maxOutputSize,\n        iouThreshold: $iouThreshold,\n        scoreThreshold: $scoreThreshold,\n        padToMaxOutputSize\n    };\n    const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);\n    return { selectedIndices: result[0], validOutputs: result[1] };\n}\nexport const nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ });\n//# sourceMappingURL=non_max_suppression_padded.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV4Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n    const $maxOutputSize = params.maxOutputSize;\n    const $iouThreshold = params.iouThreshold;\n    const $scoreThreshold = params.scoreThreshold;\n    const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);\n    // We call a cpu based impl directly with the typedarray data here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;\n//# sourceMappingURL=non_max_suppression_padded_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeBilinear } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Bilinear resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeBilinear_(images, size, alignCorners = false) {\n    const $images = convertToTensor(images, 'images', 'resizeBilinear');\n    util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got ` +\n        `rank ${$images.rank}.`);\n    util.assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ` +\n        `${size}.`);\n    let batchImages = $images;\n    let reshapedTo4D = false;\n    if ($images.rank === 3) {\n        reshapedTo4D = true;\n        batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n    }\n    const [newHeight, newWidth] = size;\n    const forward = (backend, save) => {\n        save([batchImages]);\n        return backend.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);\n    };\n    const inputs = { images: batchImages };\n    const attrs = { alignCorners, size };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, ResizeBilinear, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const resizeBilinear = op({ resizeBilinear_ });\n//# sourceMappingURL=resize_bilinear.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeNearestNeighbor } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * NearestNeighbor resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeNearestNeighbor_(images, size, alignCorners = false) {\n    const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');\n    util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` +\n        `rank ${$images.rank}.`);\n    util.assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ` +\n        `${size}.`);\n    util.assert($images.dtype === 'float32' || $images.dtype === 'int32', () => '`images` must have `int32` or `float32` as dtype');\n    let batchImages = $images;\n    let reshapedTo4D = false;\n    if ($images.rank === 3) {\n        reshapedTo4D = true;\n        batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n    }\n    const [newHeight, newWidth] = size;\n    const inputs = { images: batchImages };\n    const attrs = { alignCorners, size };\n    const forward = (backend, save) => {\n        save([batchImages]);\n        return backend.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);\n    };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, ResizeNearestNeighbor, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const resizeNearestNeighbor = op({ resizeNearestNeighbor_ });\n//# sourceMappingURL=resize_nearest_neighbor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assert } from '../../util';\nimport { greaterEqual } from '../greater_equal';\nimport { lessEqual } from '../less_equal';\nimport { logicalAnd } from '../logical_and';\nimport { op } from '../operation';\nimport { range } from '../range';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\nimport { zeros } from '../zeros';\n/**\n * Copy a tensor setting everything outside a central band in each innermost\n * matrix to zero.\n *\n * The band part is computed as follows: Assume input has `k` dimensions\n * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where\n * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.\n * The indicator function\n * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower))`\n * `&& (num_upper < 0 || (n-m) <= num_upper)`\n *\n * ```js\n * const x = tf.tensor2d([[ 0,  1,  2, 3],\n *                        [-1,  0,  1, 2],\n *                        [-2, -1,  0, 1],\n *                        [-3, -2, -1, 0]]);\n * let y = tf.linalg.bandPart(x, 1, -1);\n * y.print(); // [[ 0,  1,  2, 3],\n *            //  [-1,  0,  1, 2],\n *            //  [ 0, -1,  0, 1],\n *            //  [ 0, 0 , -1, 0]]\n * let z = tf.linalg.bandPart(x, 2, 1);\n * z.print(); // [[ 0,  1,  0, 0],\n *            //  [-1,  0,  1, 0],\n *            //  [-2, -1,  0, 1],\n *            //  [ 0, -2, -1, 0]]\n * ```\n *\n * @param x Rank `k` tensor\n * @param numLower Number of subdiagonals to keep.\n *   If negative, keep entire lower triangle.\n * @param numUpper Number of subdiagonals to keep.\n *   If negative, keep entire upper triangle.\n * @returns Rank `k` tensor of the same shape as input.\n *   The extracted banded tensor.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction bandPart_(a, numLower, numUpper) {\n    assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);\n    assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);\n    const $a = convertToTensor(a, 'a', 'bandPart');\n    assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);\n    const shape = $a.shape;\n    const [M, N] = $a.shape.slice(-2);\n    if (!(numLower <= M)) {\n        throw new Error(`bandPart(): numLower (${numLower})` +\n            ` must not be greater than the number of rows (${M}).`);\n    }\n    if (!(numUpper <= N)) {\n        throw new Error(`bandPart(): numUpper (${numUpper})` +\n            ` must not be greater than the number of columns (${N}).`);\n    }\n    if (numLower < 0) {\n        numLower = M;\n    }\n    if (numUpper < 0) {\n        numUpper = N;\n    }\n    const i = reshape(range(0, M, 1, 'int32'), [-1, 1]);\n    const j = range(0, N, 1, 'int32');\n    const ij = sub(i, j);\n    const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, 'int32')), greaterEqual(ij, scalar(-numUpper, 'int32')));\n    const zero = zeros([M, N], $a.dtype);\n    return reshape(stack(unstack(reshape($a, [-1, M, N]))\n        .map(mat => where(inBand, mat, zero))), shape);\n}\nexport const bandPart = op({ bandPart_ });\n//# sourceMappingURL=band_part.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { assert } from '../../util';\nimport { div } from '../div';\nimport { mul } from '../mul';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { split } from '../split';\nimport { squeeze } from '../squeeze';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\n/**\n * Gram-Schmidt orthogonalization.\n *\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * let y = tf.linalg.gramSchmidt(x);\n * y.print();\n * console.log('Othogonalized:');\n * y.dot(y.transpose()).print();  // should be nearly the identity matrix.\n * console.log('First row direction maintained:');\n * const data = await y.array();\n * console.log(data[0][1] / data[0][0]);  // should be nearly 2.\n * ```\n *\n * @param xs The vectors to be orthogonalized, in one of the two following\n *   formats:\n *   - An Array of `tf.Tensor1D`.\n *   - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows\n *     of `xs`.\n *   In each case, all the vectors must have the same length and the length\n *   must be greater than or equal to the number of vectors.\n * @returns The orthogonalized and normalized vectors or matrix.\n *   Orthogonalization means that the vectors or the rows of the matrix\n *   are orthogonal (zero inner products). Normalization means that each\n *   vector or each row of the matrix has an L2 norm that equals `1`.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction gramSchmidt_(xs) {\n    let inputIsTensor2D;\n    if (Array.isArray(xs)) {\n        inputIsTensor2D = false;\n        assert(xs != null && xs.length > 0, () => 'Gram-Schmidt process: input must not be null, undefined, or ' +\n            'empty');\n        const dim = xs[0].shape[0];\n        for (let i = 1; i < xs.length; ++i) {\n            assert(xs[i].shape[0] === dim, () => 'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +\n                `(${xs[i].shape[0]} vs. ${dim})`);\n        }\n    }\n    else {\n        inputIsTensor2D = true;\n        xs = split(xs, xs.shape[0], 0).map(x => squeeze(x, [0]));\n    }\n    assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds ` +\n        `number of dimensions (${xs[0].shape[0]}).`);\n    const ys = [];\n    const xs1d = xs;\n    for (let i = 0; i < xs.length; ++i) {\n        ys.push(ENGINE.tidy(() => {\n            let x = xs1d[i];\n            if (i > 0) {\n                for (let j = 0; j < i; ++j) {\n                    const proj = mul(sum(mul(ys[j], x)), ys[j]);\n                    x = sub(x, proj);\n                }\n            }\n            return div(x, norm(x, 'euclidean'));\n        }));\n    }\n    if (inputIsTensor2D) {\n        return stack(ys, 0);\n    }\n    else {\n        return ys;\n    }\n}\nexport const gramSchmidt = op({ gramSchmidt_ });\n//# sourceMappingURL=gram_schmidt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { dispose } from '../../globals';\nimport { assert } from '../../util';\nimport { clone } from '../clone';\nimport { concat } from '../concat';\nimport { div } from '../div';\nimport { eye } from '../eye';\nimport { greater } from '../greater';\nimport { matMul } from '../mat_mul';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { tensor2d } from '../tensor2d';\nimport { transpose } from '../transpose';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\n/**\n * Compute QR decomposition of m-by-n matrix using Householder transformation.\n *\n * Implementation based on\n *   [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]\n * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [3, 4]]);\n * let [q, r] = tf.linalg.qr(a);\n * console.log('Q');\n * q.print();\n * console.log('R');\n * r.print();\n * console.log('Orthogonalized');\n * q.dot(q.transpose()).print()  // should be nearly the identity matrix.\n * console.log('Reconstructed');\n * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];\n * ```\n *\n * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose\n *   it has the shape `[..., M, N]`.\n * @param fullMatrices An optional boolean parameter. Defaults to `false`.\n *   If `true`, compute full-sized `Q`. If `false` (the default),\n *   compute only the leading N columns of `Q` and `R`.\n * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,\n *   i.e., its columns all have unit norm and are mutually orthogonal.\n *   If `M >= N`,\n *     If `fullMatrices` is `false` (default),\n *       - `Q` has a shape of `[..., M, N]`,\n *       - `R` has a shape of `[..., N, N]`.\n *     If `fullMatrices` is `true` (default),\n *       - `Q` has a shape of `[..., M, M]`,\n *       - `R` has a shape of `[..., M, N]`.\n *   If `M < N`,\n *     - `Q` has a shape of `[..., M, M]`,\n *     - `R` has a shape of `[..., M, N]`.\n * @throws If the rank of `x` is less than 2.\n *\n * @doc {heading:'Operations',\n *       subheading:'Linear Algebra',\n *       namespace:'linalg'}\n */\nfunction qr_(x, fullMatrices = false) {\n    assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);\n    if (x.rank === 2) {\n        return qr2d(x, fullMatrices);\n    }\n    else {\n        // Rank > 2.\n        // TODO(cais): Below we split the input into individual 2D tensors,\n        //   perform QR decomposition on them and then stack the results back\n        //   together. We should explore whether this can be parallelized.\n        const outerDimsProd = x.shape.slice(0, x.shape.length - 2)\n            .reduce((value, prev) => value * prev);\n        const x2ds = unstack(reshape(x, [\n            outerDimsProd, x.shape[x.shape.length - 2],\n            x.shape[x.shape.length - 1]\n        ]), 0);\n        const q2ds = [];\n        const r2ds = [];\n        x2ds.forEach(x2d => {\n            const [q2d, r2d] = qr2d(x2d, fullMatrices);\n            q2ds.push(q2d);\n            r2ds.push(r2d);\n        });\n        const q = reshape(stack(q2ds, 0), x.shape);\n        const r = reshape(stack(r2ds, 0), x.shape);\n        return [q, r];\n    }\n}\nfunction qr2d(x, fullMatrices = false) {\n    return ENGINE.tidy(() => {\n        assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);\n        const m = x.shape[0];\n        const n = x.shape[1];\n        let q = eye(m); // Orthogonal transform so far.\n        let r = clone(x); // Transformed matrix so far.\n        const one2D = tensor2d([[1]], [1, 1]);\n        let w = clone(one2D);\n        const iters = m >= n ? n : m;\n        for (let j = 0; j < iters; ++j) {\n            // This tidy within the for-loop ensures we clean up temporary\n            // tensors as soon as they are no longer needed.\n            const rTemp = r;\n            const wTemp = w;\n            const qTemp = q;\n            [w, r, q] = ENGINE.tidy(() => {\n                // Find H = I - tau * w * w', to put zeros below R(j, j).\n                const rjEnd1 = slice(r, [j, j], [m - j, 1]);\n                const normX = norm(rjEnd1);\n                const rjj = slice(r, [j, j], [1, 1]);\n                // The sign() function returns 0 on 0, which causes division by zero.\n                const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));\n                const u1 = sub(rjj, mul(s, normX));\n                const wPre = div(rjEnd1, u1);\n                if (wPre.shape[0] === 1) {\n                    w = clone(one2D);\n                }\n                else {\n                    w = concat([\n                        one2D,\n                        slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])\n                    ], 0);\n                }\n                const tau = neg(div(matMul(s, u1), normX));\n                // -- R := HR, Q := QH.\n                const rjEndAll = slice(r, [j, 0], [m - j, n]);\n                const tauTimesW = mul(tau, w);\n                const wT = transpose(w);\n                if (j === 0) {\n                    r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n                }\n                else {\n                    const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n                    r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);\n                }\n                const tawTimesWT = transpose(tauTimesW);\n                const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);\n                if (j === 0) {\n                    q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n                }\n                else {\n                    const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n                    q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);\n                }\n                return [w, r, q];\n            });\n            dispose([rTemp, wTemp, qTemp]);\n        }\n        if (!fullMatrices && m > n) {\n            q = slice(q, [0, 0], [m, n]);\n            r = slice(r, [0, 0], [n, n]);\n        }\n        return [q, r];\n    });\n}\nexport const qr = op({ qr_ });\n//# sourceMappingURL=qr.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Reduction;\n(function (Reduction) {\n    Reduction[Reduction[\"NONE\"] = 0] = \"NONE\";\n    Reduction[Reduction[\"MEAN\"] = 1] = \"MEAN\";\n    Reduction[Reduction[\"SUM\"] = 2] = \"SUM\";\n    Reduction[Reduction[\"SUM_BY_NONZERO_WEIGHTS\"] = 3] = \"SUM_BY_NONZERO_WEIGHTS\";\n})(Reduction || (Reduction = {}));\n//# sourceMappingURL=loss_ops_utils.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { Reduction } from '../loss_ops_utils';\nimport { mean } from '../mean';\nimport { mul } from '../mul';\nimport { notEqual } from '../not_equal';\nimport { ones } from '../ones';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sum } from '../sum';\n/**\n * Computes the weighted loss between two tensors.\n *\n * @param losses Tensor of shape `[batch_size, d1, ... dN]`.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `losses`, and must be broadcastable to `losses` (i.e., all\n *    dimensions must be either `1`, or the same as the corresponding\n *    `losses` dimension).\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction computeWeightedLoss_(losses, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');\n    }\n    const weightedLoss = ($weights == null) ? $losses : mul($losses, $weights);\n    if (reduction === Reduction.NONE) {\n        return weightedLoss;\n    }\n    if (reduction === Reduction.SUM) {\n        return sum(weightedLoss);\n    }\n    if (reduction === Reduction.MEAN) {\n        if ($weights == null) {\n            return mean(weightedLoss);\n        }\n        else {\n            const broadcastFactor = $losses.size / $weights.size;\n            const result = div(sum(weightedLoss), sum($weights));\n            return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) :\n                result;\n        }\n    }\n    if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n        if ($weights == null) {\n            return div(sum(weightedLoss), scalar($losses.size));\n        }\n        else {\n            const broadcastedWeights = mul($weights, ones($losses.shape));\n            const numNonZeros = cast(sum(notEqual(broadcastedWeights, scalar(0))), 'float32');\n            return div(sum(weightedLoss), numNonZeros);\n        }\n    }\n    throw Error(`Unknown reduction: ${reduction}`);\n}\nexport const computeWeightedLoss = op({ computeWeightedLoss_ });\n//# sourceMappingURL=compute_weighted_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the absolute difference loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'absoluteDifference');\n    const $predictions = convertToTensor(predictions, 'predictions', 'absoluteDifference');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'absoluteDifference');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in absoluteDifference: ');\n    const losses = abs(sub($labels, $predictions));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const absoluteDifference = op({ absoluteDifference_ });\n//# sourceMappingURL=absolute_difference.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the cosine distance loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param axis The dimension along which the cosine distance is computed.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'cosineDistance');\n    const $predictions = convertToTensor(predictions, 'predictions', 'cosineDistance');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'cosineDistance');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in cosineDistance: ');\n    const one = scalar(1);\n    const losses = sub(one, sum(mul($labels, $predictions), axis, true));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const cosineDistance = op({ cosineDistance_ });\n//# sourceMappingURL=cosine_distance.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the Hinge loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $labels = convertToTensor(labels, 'labels', 'hingeLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'hingeLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');\n    const one = scalar(1);\n    // Convert binary labels to (-1, 1)\n    $labels = sub(mul(scalar(2), $labels), one);\n    const losses = relu(sub(one, mul($labels, $predictions)));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const hingeLoss = op({ hingeLoss_ });\n//# sourceMappingURL=hinge_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { Reduction } from '../loss_ops_utils';\nimport { minimum } from '../minimum';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { square } from '../square';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the huber loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param delta Point where huber loss changes from quadratic to linear.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`.\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction huberLoss_(labels, predictions, weights, delta = 1.0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'huberLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'huberLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');\n    const deltaScalar = scalar(delta);\n    const error = abs(sub($predictions, $labels));\n    const quadratic = minimum(error, deltaScalar);\n    const linear = sub(error, quadratic);\n    const losses = add(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const huberLoss = op({ huberLoss_ });\n//# sourceMappingURL=huber_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { log } from '../log';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the log loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param epsilon A small increment to avoid taking log of zero\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction logLoss_(labels, predictions, weights, epsilon = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'logLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'logLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'logLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');\n    const one = scalar(1);\n    const epsilonScalar = scalar(epsilon);\n    const l1 = neg(mul($labels, log(add($predictions, epsilonScalar))));\n    const l2 = mul(sub(one, $labels), log(add(sub(one, $predictions), epsilonScalar)));\n    const losses = sub(l1, l2);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const logLoss = op({ logLoss_ });\n//# sourceMappingURL=log_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { squaredDifference } from '../squared_difference';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the mean squared error between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'meanSquaredError');\n    const $predictions = convertToTensor(predictions, 'predictions', 'meanSquaredError');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'meanSquaredError');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in meanSquaredError: ');\n    const losses = squaredDifference($labels, $predictions);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const meanSquaredError = op({ meanSquaredError_ });\n//# sourceMappingURL=mean_squared_error.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { exp } from '../exp';\nimport { log1p } from '../log1p';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\nfunction sigmoidCrossEntropyWithLogits_(labels, logits) {\n    const $labels = convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');\n    const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');\n    assertShapesMatch($labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');\n    /**\n     * Implementation Details:\n     *\n     * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n     *     z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n     *   = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n     *   = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n     *   = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n     *   = (1 - z) * x + log(1 + exp(-x))\n     *   = x - x * z + log(1 + exp(-x))\n     *\n     *   For x < 0, to avoid overflow in exp(-x), we reformulate the above\n     *     x - x * z + log(1 + exp(-x))\n     *   = log(exp(x)) - x * z + log(1 + exp(-x))\n     *   = - x * z + log(1 + exp(x))\n     *\n     * Hence, to ensure stability and avoid overflow, the implementation uses\n     * this equivalent formulation:\n     *     max(x, 0) - x * z + log(1 + exp(-abs(x)))\n     */\n    const maxOutput = relu($logits);\n    const outputXTarget = mul($logits, $labels);\n    const sigmoidOutput = log1p(exp(neg(abs($logits))));\n    return add(sub(maxOutput, outputXTarget), sigmoidOutput);\n}\n/**\n * Computes the sigmoid cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)\n *                         + 0.5 * labelSmoothing\n *\n * @param multiClassLabels The ground truth output tensor of shape\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $multiClassLabels = convertToTensor(multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');\n    const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');\n    }\n    assertShapesMatch($multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');\n    if (labelSmoothing > 0) {\n        const labelSmoothingScalar = scalar(labelSmoothing);\n        const one = scalar(1);\n        const half = scalar(0.5);\n        $multiClassLabels =\n            add(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));\n    }\n    const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ });\n//# sourceMappingURL=sigmoid_cross_entropy.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../../gradients';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { expandShapeToKeepDim } from '../axis_util';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { exp } from '../exp';\nimport { logSumExp } from '../log_sum_exp';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes softmax cross entropy between logits and labels.\n *\n * Measures the probability error in discrete classification tasks in which\n * the classes are mutually exclusive (each entry is in exactly one class).\n * For example, each CIFAR-10 image is labeled with one and only one label: an\n * image can be a dog or a truck, but not both.\n *\n * `NOTE`: While the classes are mutually exclusive, their probabilities need\n * not be. All that is required is that each row of labels is a valid\n * probability distribution. If they are not, the computation of the gradient\n * will be incorrect.\n *\n * `WARNING`: This op expects unscaled logits, since it performs a softmax on\n * logits internally for efficiency. Do not call this op with the output of\n * softmax, as it will produce incorrect results.\n *\n * logits and labels must have the same shape, e.g. [batch_size, num_classes]\n * and the same dtype.\n * @param labels The labels array.\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\nfunction softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {\n    if (dim === -1) {\n        dim = logits.rank - 1;\n    }\n    if (dim !== logits.rank - 1) {\n        throw Error(`Softmax cross entropy along a non-last dimension is not yet ` +\n            `supported. Labels / logits was rank ${logits.rank} ` +\n            `and dim was ${dim}`);\n    }\n    // Use a custom gradient for numerical stability.\n    const customOp = customGrad((labels, logits, save) => {\n        // Reference:\n        //   1. http://cs231n.github.io/linear-classify/#softmax\n        //   2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/\n        const keepDims = true;\n        const lse = logSumExp(logits, [dim], keepDims);\n        const logResult = sub(cast(logits, 'float32'), lse);\n        save([labels, logResult]);\n        const costVector = neg(mul(logResult, labels));\n        const value = sum(costVector, [dim]);\n        const gradFunc = (dy, saved) => {\n            const [labels, logResult] = saved;\n            const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n            return [\n                mul(reshape(dy, dyShape), sub(cast(labels, 'float32'), exp(logResult))),\n                mul(reshape(dy, dyShape), sub(exp(logResult), cast(labels, 'float32'))),\n            ];\n        };\n        return { value, gradFunc };\n    });\n    return customOp(labels, logits);\n}\n/**\n * Computes the softmax cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newOnehotLabels = onehotLabels * (1 - labelSmoothing)\n *                         + labelSmoothing / numClasses\n *\n * @param onehotLabels One hot encoded labels\n *    [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or 1, and must be\n *    broadcastable to `loss`  of shape [batch_size]\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $onehotLabels = convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');\n    const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');\n    }\n    assertShapesMatch($onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');\n    if (labelSmoothing > 0) {\n        const labelSmoothingScalar = scalar(labelSmoothing);\n        const one = scalar(1);\n        const numClasses = scalar($onehotLabels.shape[1]);\n        $onehotLabels =\n            add(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));\n    }\n    const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const softmaxCrossEntropy = op({ softmaxCrossEntropy_ });\n//# sourceMappingURL=softmax_cross_entropy.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Modularized ops.\nexport { abs } from './abs';\nexport { acos } from './acos';\nexport { acosh } from './acosh';\nexport { add } from './add';\nexport { addN } from './add_n';\nexport { all } from './all';\nexport { any } from './any';\nexport { argMax } from './arg_max';\nexport { argMin } from './arg_min';\nexport { asin } from './asin';\nexport { asinh } from './asinh';\nexport { atan } from './atan';\nexport { atan2 } from './atan2';\nexport { atanh } from './atanh';\nexport { avgPool } from './avg_pool';\nexport { avgPool3d } from './avg_pool_3d';\nexport { basicLSTMCell } from './basic_lstm_cell';\nexport { batchToSpaceND } from './batch_to_space_nd';\nexport { batchNorm } from './batchnorm';\nexport { batchNorm2d } from './batchnorm2d';\nexport { batchNorm3d } from './batchnorm3d';\nexport { batchNorm4d } from './batchnorm4d';\nexport { broadcastTo } from './broadcast_to';\nexport { buffer } from './buffer';\nexport { cast } from './cast';\nexport { ceil } from './ceil';\nexport { clipByValue } from './clip_by_value';\nexport { clone } from './clone';\nexport { complex } from './complex';\nexport { concat } from './concat';\nexport { concat1d } from './concat_1d';\nexport { concat2d } from './concat_2d';\nexport { concat3d } from './concat_3d';\nexport { concat4d } from './concat_4d';\nexport { conv1d } from './conv1d';\nexport { conv2d } from './conv2d';\nexport { conv2dTranspose } from './conv2d_transpose';\nexport { conv3d } from './conv3d';\nexport { conv3dTranspose } from './conv3d_transpose';\nexport { cos } from './cos';\nexport { cosh } from './cosh';\nexport { cumsum } from './cumsum';\nexport { depthToSpace } from './depth_to_space';\nexport { depthwiseConv2d } from './depthwise_conv2d';\nexport { diag } from './diag';\nexport { dilation2d } from './dilation2d';\nexport { div } from './div';\nexport { divNoNan } from './div_no_nan';\nexport { dot } from './dot';\nexport { elu } from './elu';\nexport { equal } from './equal';\nexport { erf } from './erf';\nexport { exp } from './exp';\nexport { expandDims } from './expand_dims';\nexport { expm1 } from './expm1';\nexport { eye } from './eye';\nexport { fill } from './fill';\nexport { floor } from './floor';\nexport { floorDiv } from './floorDiv';\nexport { gather } from './gather';\nexport { greater } from './greater';\nexport { greaterEqual } from './greater_equal';\nexport { imag } from './imag';\nexport { isFinite } from './is_finite';\nexport { isInf } from './is_inf';\nexport { isNaN } from './is_nan';\nexport { leakyRelu } from './leaky_relu';\nexport { less } from './less';\nexport { lessEqual } from './less_equal';\nexport { linspace } from './linspace';\nexport { localResponseNormalization } from './local_response_normalization';\nexport { log } from './log';\nexport { log1p } from './log1p';\nexport { logSigmoid } from './log_sigmoid';\nexport { logSoftmax } from './log_softmax';\nexport { logSumExp } from './log_sum_exp';\nexport { logicalAnd } from './logical_and';\nexport { logicalNot } from './logical_not';\nexport { logicalOr } from './logical_or';\nexport { logicalXor } from './logical_xor';\nexport { matMul } from './mat_mul';\nexport { max } from './max';\nexport { maxPool } from './max_pool';\nexport { maxPool3d } from './max_pool_3d';\nexport { maxPoolWithArgmax } from './max_pool_with_argmax';\nexport { maximum } from './maximum';\nexport { mean } from './mean';\nexport { min } from './min';\nexport { minimum } from './minimum';\nexport { mod } from './mod';\nexport { moments } from './moments';\nexport { mul } from './mul';\nexport { multiRNNCell } from './multi_rnn_cell';\nexport { multinomial } from './multinomial';\nexport { neg } from './neg';\nexport { notEqual } from './not_equal';\nexport { oneHot } from './one_hot';\nexport { ones } from './ones';\nexport { onesLike } from './ones_like';\nexport { outerProduct } from './outer_product';\nexport { pad } from './pad';\nexport { pad1d } from './pad1d';\nexport { pad2d } from './pad2d';\nexport { pad3d } from './pad3d';\nexport { pad4d } from './pad4d';\nexport { pool } from './pool';\nexport { pow } from './pow';\nexport { prelu } from './prelu';\nexport { print } from './print';\nexport { prod } from './prod';\nexport { rand } from './rand';\nexport { randomGamma } from './random_gamma';\nexport { randomNormal } from './random_normal';\nexport { randomUniform } from './random_uniform';\nexport { range } from './range';\nexport { real } from './real';\nexport { reciprocal } from './reciprocal';\nexport { relu } from './relu';\nexport { relu6 } from './relu6';\nexport { reshape } from './reshape';\nexport { reverse } from './reverse';\nexport { reverse1d } from './reverse_1d';\nexport { reverse2d } from './reverse_2d';\nexport { reverse3d } from './reverse_3d';\nexport { reverse4d } from './reverse_4d';\nexport { round } from './round';\nexport { rsqrt } from './rsqrt';\nexport { scalar } from './scalar';\nexport { selu } from './selu';\nexport { separableConv2d } from './separable_conv2d';\nexport { setdiff1dAsync } from './setdiff1d_async';\nexport { sigmoid } from './sigmoid';\nexport { sign } from './sign';\nexport { sin } from './sin';\nexport { sinh } from './sinh';\nexport { slice } from './slice';\nexport { slice1d } from './slice1d';\nexport { slice2d } from './slice2d';\nexport { slice3d } from './slice3d';\nexport { slice4d } from './slice4d';\nexport { softmax } from './softmax';\nexport { softplus } from './softplus';\nexport { spaceToBatchND } from './space_to_batch_nd';\nexport { fft } from './spectral/fft';\nexport { ifft } from './spectral/ifft';\nexport { irfft } from './spectral/irfft';\nexport { rfft } from './spectral/rfft';\nexport { split } from './split';\nexport { sqrt } from './sqrt';\nexport { square } from './square';\nexport { squaredDifference } from './squared_difference';\nexport { squeeze } from './squeeze';\nexport { stack } from './stack';\nexport { step } from './step';\nexport { stridedSlice } from './strided_slice';\nexport { sub } from './sub';\nexport { sum } from './sum';\nexport { tan } from './tan';\nexport { tanh } from './tanh';\nexport { tensor } from './tensor';\nexport { tensor1d } from './tensor1d';\nexport { tensor2d } from './tensor2d';\nexport { tensor3d } from './tensor3d';\nexport { tensor4d } from './tensor4d';\nexport { tensor5d } from './tensor5d';\nexport { tensor6d } from './tensor6d';\nexport { tile } from './tile';\nexport { topk } from './topk';\nexport { truncatedNormal } from './truncated_normal';\nexport { unique } from './unique';\nexport { unsortedSegmentSum } from './unsorted_segment_sum';\nexport { unstack } from './unstack';\nexport { variable } from './variable';\nexport { where } from './where';\nexport { whereAsync } from './where_async';\nexport { zeros } from './zeros';\nexport { zerosLike } from './zeros_like';\nexport * from './boolean_mask';\nexport * from './compare';\nexport * from './binary_ops';\nexport * from './transpose';\nexport * from './norm';\nexport * from './moving_average';\nexport * from './scatter_nd';\nexport * from './sparse_to_dense';\nexport * from './gather_nd';\nexport * from './dropout';\nexport * from './signal_ops_util';\nexport * from './in_top_k';\nexport { op, OP_SCOPE_SUFFIX } from './operation';\nimport { rfft } from './spectral/rfft';\nimport { fft } from './spectral/fft';\nimport { ifft } from './spectral/ifft';\nimport { irfft } from './spectral/irfft';\nconst spectral = {\n    fft,\n    ifft,\n    rfft,\n    irfft\n};\nimport * as fused from './fused_ops';\nimport { hammingWindow } from './signal/hamming_window';\nimport { hannWindow } from './signal/hann_window';\nimport { frame } from './signal/frame';\nimport { stft } from './signal/stft';\nconst signal = {\n    hammingWindow,\n    hannWindow,\n    frame,\n    stft,\n};\n// Image Ops namespace\nimport { cropAndResize } from './image/crop_and_resize';\nimport { flipLeftRight } from './image/flip_left_right';\nimport { rotateWithOffset } from './image/rotate_with_offset';\nimport { nonMaxSuppression } from './image/non_max_suppression';\nimport { nonMaxSuppressionAsync } from './image/non_max_suppression_async';\nimport { nonMaxSuppressionWithScore } from './image/non_max_suppression_with_score';\nimport { nonMaxSuppressionWithScoreAsync } from './image/non_max_suppression_with_score_async';\nimport { nonMaxSuppressionPadded } from './image/non_max_suppression_padded';\nimport { nonMaxSuppressionPaddedAsync } from './image/non_max_suppression_padded_async';\nimport { resizeBilinear } from './image/resize_bilinear';\nimport { resizeNearestNeighbor } from './image/resize_nearest_neighbor';\nconst image = {\n    flipLeftRight,\n    resizeNearestNeighbor,\n    resizeBilinear,\n    rotateWithOffset,\n    cropAndResize,\n    nonMaxSuppression,\n    nonMaxSuppressionAsync,\n    nonMaxSuppressionWithScore,\n    nonMaxSuppressionWithScoreAsync,\n    nonMaxSuppressionPadded,\n    nonMaxSuppressionPaddedAsync\n};\n// linalg namespace\nimport { bandPart } from './linalg/band_part';\nimport { gramSchmidt } from './linalg/gram_schmidt';\nimport { qr } from './linalg/qr';\nconst linalg = {\n    bandPart,\n    gramSchmidt,\n    qr\n};\n// losses namespace;\nimport { absoluteDifference } from './losses/absolute_difference';\nimport { computeWeightedLoss } from './losses/compute_weighted_loss';\nimport { cosineDistance } from './losses/cosine_distance';\nimport { hingeLoss } from './losses/hinge_loss';\nimport { huberLoss } from './losses/huber_loss';\nimport { logLoss } from './losses/log_loss';\nimport { meanSquaredError } from './losses/mean_squared_error';\nimport { sigmoidCrossEntropy } from './losses/sigmoid_cross_entropy';\nimport { softmaxCrossEntropy } from './losses/softmax_cross_entropy';\nconst losses = {\n    absoluteDifference,\n    computeWeightedLoss,\n    cosineDistance,\n    hingeLoss,\n    huberLoss,\n    logLoss,\n    meanSquaredError,\n    sigmoidCrossEntropy,\n    softmaxCrossEntropy\n};\n// Second level exports.\nexport { image, linalg, losses, spectral, fused, signal };\n//# sourceMappingURL=ops.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dispose } from '../globals';\nimport { variableGrads } from '../gradients';\nimport { scalar } from '../ops/ops';\nimport { Serializable } from '../serialization';\n/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */\nexport class Optimizer extends Serializable {\n    /**\n     * Executes `f()` and minimizes the scalar output of `f()` by computing\n     * gradients of y with respect to the list of trainable variables provided by\n     * `varList`. If no list is provided, it defaults to all trainable variables.\n     *\n     * @param f The function to execute and whose output to minimize.\n     * @param returnCost Whether to return the scalar cost value produced by\n     * executing `f()`.\n     * @param varList An optional list of variables to update. If specified, only\n     * the trainable variables in varList will be updated by minimize. Defaults to\n     * all trainable variables.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers'}\n     */\n    minimize(f, returnCost = false, varList) {\n        const { value, grads } = this.computeGradients(f, varList);\n        if (varList != null) {\n            const gradArray = varList.map(v => ({ name: v.name, tensor: grads[v.name] }));\n            this.applyGradients(gradArray);\n        }\n        else {\n            this.applyGradients(grads);\n        }\n        // Dispose gradients.\n        dispose(grads);\n        if (returnCost) {\n            return value;\n        }\n        else {\n            value.dispose();\n            return null;\n        }\n    }\n    /**\n     * The number of iterations that this optimizer instance has been invoked for.\n     */\n    get iterations() {\n        if (this.iterations_ == null) {\n            this.iterations_ = 0;\n        }\n        return this.iterations_;\n    }\n    incrementIterations() {\n        this.iterations_ = this.iterations + 1;\n    }\n    /**\n     * Executes f() and computes the gradient of the scalar output of f() with\n     * respect to the list of trainable variables provided by `varList`. If no\n     * list is provided, it defaults to all trainable variables.\n     *\n     * @param f The function to execute and whose output to use for computing\n     * gradients with respect to variables.\n     * @param varList An optional list of variables to compute gradients with\n     * respect to. If specified, only the trainable variables in varList will have\n     * gradients computed with respect to. Defaults to all trainable variables.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers'}\n     */\n    computeGradients(f, varList) {\n        return variableGrads(f, varList);\n    }\n    /**\n     * Dispose the variables (if any) owned by this optimizer instance.\n     */\n    dispose() {\n        if (this.iterations_ != null) {\n            dispose(this.iterations_);\n        }\n    }\n    async saveIterations() {\n        if (this.iterations_ == null) {\n            this.iterations_ = 0;\n        }\n        return {\n            name: 'iter',\n            // TODO(cais): Use 'int64' type when available.\n            tensor: scalar(this.iterations_, 'int32')\n        };\n    }\n    async getWeights() {\n        throw new Error('getWeights() is not implemented for this optimizer yet.');\n    }\n    async setWeights(weightValues) {\n        throw new Error(`setWeights() is not implemented for this optimizer class ` +\n            `${this.getClassName()}`);\n    }\n    /**\n     * Extract the first element of the weight values and set it\n     * as the iterations counter variable of this instance of optimizer.\n     *\n     * @param weightValues\n     * @returns Weight values with the first element consumed and excluded.\n     */\n    async extractIterations(weightValues) {\n        this.iterations_ = (await weightValues[0].tensor.data())[0];\n        return weightValues.slice(1);\n    }\n}\nObject.defineProperty(Optimizer, Symbol.hasInstance, {\n    value: (instance) => {\n        return instance.minimize != null && instance.computeGradients != null &&\n            instance.applyGradients != null;\n    }\n});\n//# sourceMappingURL=optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/ops';\nimport { square } from '../ops/square';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class AdadeltaOptimizer extends Optimizer {\n    constructor(learningRate, rho, epsilon = null) {\n        super();\n        this.learningRate = learningRate;\n        this.rho = rho;\n        this.epsilon = epsilon;\n        this.accumulatedGrads = [];\n        this.accumulatedUpdates = [];\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            const trainable = false;\n            if (this.accumulatedGrads[i] == null) {\n                this.accumulatedGrads[i] = {\n                    originalName: `${name}/accum_grad`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedUpdates[i] == null) {\n                this.accumulatedUpdates[i] = {\n                    originalName: `${name}/accum_var`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedGrad = this.accumulatedGrads[i].variable;\n            const accumulatedUpdate = this.accumulatedUpdates[i].variable;\n            tidy(() => {\n                const newAccumulatedGrad = add(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));\n                const updates = mul(div(sqrt(add(accumulatedUpdate, this.epsilon)), sqrt(add(accumulatedGrad, this.epsilon))), gradient);\n                const newAccumulatedUpdate = add(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));\n                accumulatedGrad.assign(newAccumulatedGrad);\n                accumulatedUpdate.assign(newAccumulatedUpdate);\n                const newValue = add(mul(updates, -this.learningRate), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedUpdates != null) {\n            dispose(this.accumulatedGrads.map(v => v.variable));\n            dispose(this.accumulatedUpdates.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const variableCount = weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedGrads =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedUpdates =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'rho': this.rho,\n            'epsilon': this.epsilon\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['rho'], config['epsilon']);\n    }\n}\n/** @nocollapse */\nAdadeltaOptimizer.className = 'Adadelta'; // Name matters for Python compatibility.\nregisterClass(AdadeltaOptimizer);\n//# sourceMappingURL=adadelta_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { fill } from '../ops/fill';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class AdagradOptimizer extends Optimizer {\n    constructor(learningRate, initialAccumulatorValue = 0.1) {\n        super();\n        this.learningRate = learningRate;\n        this.initialAccumulatorValue = initialAccumulatorValue;\n        this.accumulatedGrads = [];\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            if (this.accumulatedGrads[i] == null) {\n                const trainable = false;\n                this.accumulatedGrads[i] = {\n                    originalName: `${name}/accumulator`,\n                    variable: tidy(() => fill(value.shape, this.initialAccumulatorValue)\n                        .variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedGrad = this.accumulatedGrads[i].variable;\n            tidy(() => {\n                const newAccumulatedGrad = add(accumulatedGrad, square(gradient));\n                accumulatedGrad.assign(newAccumulatedGrad);\n                const newValue = add(mul(div(gradient, sqrt(add(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedGrads != null) {\n            dispose(this.accumulatedGrads.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        return [await this.saveIterations()].concat(this.accumulatedGrads.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const trainable = false;\n        this.accumulatedGrads = weightValues.map(v => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'initialAccumulatorValue': this.initialAccumulatorValue,\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['initialAccumulatorValue']);\n    }\n}\n/** @nocollapse */\nAdagradOptimizer.className = 'Adagrad'; // Note: Name matters for Python compatibility.\nregisterClass(AdagradOptimizer);\n//# sourceMappingURL=adagrad_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { pow } from '../ops/pow';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\nexport class AdamOptimizer extends Optimizer {\n    constructor(learningRate, beta1, beta2, epsilon = null) {\n        super();\n        this.learningRate = learningRate;\n        this.beta1 = beta1;\n        this.beta2 = beta2;\n        this.epsilon = epsilon;\n        this.accumulatedFirstMoment = [];\n        this.accumulatedSecondMoment = [];\n        tidy(() => {\n            // accB* will be updated by batch.\n            this.accBeta1 = scalar(beta1).variable();\n            this.accBeta2 = scalar(beta2).variable();\n        });\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const varNames = Array.isArray(variableGradients) ?\n            variableGradients.map(v => v.name) :\n            Object.keys(variableGradients);\n        tidy(() => {\n            const oneMinusAccBeta1 = sub(1, this.accBeta1);\n            const oneMinusAccBeta2 = sub(1, this.accBeta2);\n            varNames.forEach((name, i) => {\n                const value = ENGINE.registeredVariables[name];\n                const trainable = false;\n                if (this.accumulatedFirstMoment[i] == null) {\n                    this.accumulatedFirstMoment[i] = {\n                        originalName: `${name}/m`,\n                        variable: tidy(() => zerosLike(value).variable(trainable))\n                    };\n                }\n                if (this.accumulatedSecondMoment[i] == null) {\n                    this.accumulatedSecondMoment[i] = {\n                        originalName: `${name}/v`,\n                        variable: tidy(() => zerosLike(value).variable(trainable))\n                    };\n                }\n                const gradient = Array.isArray(variableGradients) ?\n                    variableGradients[i].tensor :\n                    variableGradients[name];\n                if (gradient == null) {\n                    return;\n                }\n                const firstMoment = this.accumulatedFirstMoment[i].variable;\n                const secondMoment = this.accumulatedSecondMoment[i].variable;\n                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n                const newSecondMoment = add(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));\n                const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);\n                const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);\n                firstMoment.assign(newFirstMoment);\n                secondMoment.assign(newSecondMoment);\n                const newValue = add(mul(div(biasCorrectedFirstMoment, add(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);\n                value.assign(newValue);\n            });\n            this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n            this.accBeta2.assign(mul(this.accBeta2, this.beta2));\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.accBeta1.dispose();\n        this.accBeta2.dispose();\n        if (this.accumulatedFirstMoment != null) {\n            dispose(this.accumulatedFirstMoment.map(v => v.variable));\n        }\n        if (this.accumulatedSecondMoment != null) {\n            dispose(this.accumulatedSecondMoment.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        tidy(() => {\n            this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));\n            this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));\n        });\n        const variableCount = weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedFirstMoment =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedSecondMoment =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'beta1': this.beta1,\n            'beta2': this.beta2,\n            'epsilon': this.epsilon,\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon']);\n    }\n}\n/** @nocollapse */\nAdamOptimizer.className = 'Adam'; // Note: Name matters for Python compatibility.\nregisterClass(AdamOptimizer);\n//# sourceMappingURL=adam_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { abs } from '../ops/abs';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { maximum } from '../ops/maximum';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\nexport class AdamaxOptimizer extends Optimizer {\n    constructor(learningRate, beta1, beta2, epsilon = null, decay = 0.0) {\n        super();\n        this.learningRate = learningRate;\n        this.beta1 = beta1;\n        this.beta2 = beta2;\n        this.epsilon = epsilon;\n        this.decay = decay;\n        this.accumulatedFirstMoment = [];\n        this.accumulatedWeightedInfNorm = [];\n        tidy(() => {\n            this.iteration = scalar(0).variable();\n            this.accBeta1 = scalar(beta1).variable();\n        });\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        tidy(() => {\n            const oneMinusAccBeta1 = sub(1, this.accBeta1);\n            const lr = div(-this.learningRate, add(mul(this.iteration, this.decay), 1));\n            variableNames.forEach((name, i) => {\n                const value = ENGINE.registeredVariables[name];\n                const trainable = false;\n                if (this.accumulatedFirstMoment[i] == null) {\n                    this.accumulatedFirstMoment[i] = {\n                        originalName: `${name}/m`,\n                        variable: zerosLike(value).variable(trainable)\n                    };\n                }\n                if (this.accumulatedWeightedInfNorm[i] == null) {\n                    this.accumulatedWeightedInfNorm[i] = {\n                        originalName: `${name}/v`,\n                        variable: zerosLike(value).variable(trainable)\n                    };\n                }\n                const gradient = Array.isArray(variableGradients) ?\n                    variableGradients[i].tensor :\n                    variableGradients[name];\n                if (gradient == null) {\n                    return;\n                }\n                const firstMoment = this.accumulatedFirstMoment[i].variable;\n                const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;\n                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n                const ut0 = mul(weightedInfNorm, this.beta2);\n                const ut1 = abs(gradient);\n                const newWeightedInfNorm = maximum(ut0, ut1);\n                firstMoment.assign(newFirstMoment);\n                weightedInfNorm.assign(newWeightedInfNorm);\n                const newValue = add(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add(newWeightedInfNorm, this.epsilon))), value);\n                value.assign(newValue);\n            });\n            this.iteration.assign(add(this.iteration, 1));\n            this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.accBeta1.dispose();\n        this.iteration.dispose();\n        if (this.accumulatedFirstMoment != null) {\n            dispose(this.accumulatedFirstMoment.map(v => v.variable));\n        }\n        if (this.accumulatedWeightedInfNorm != null) {\n            dispose(this.accumulatedWeightedInfNorm.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        throw new Error('getWeights() is not implemented for Adamax yet.');\n    }\n    async setWeights(weightValues) {\n        throw new Error('setWeights() is not implemented for Adamax yet.');\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'beta1': this.beta1,\n            'beta2': this.beta2,\n            'epsilon': this.epsilon,\n            'decay': this.decay\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon'], config['decay']);\n    }\n}\n/** @nocollapse */\nAdamaxOptimizer.className = 'Adamax'; // Note: Name matters for Python compatbility.\nregisterClass(AdamaxOptimizer);\n//# sourceMappingURL=adamax_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { keep, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class SGDOptimizer extends Optimizer {\n    constructor(learningRate) {\n        super();\n        this.learningRate = learningRate;\n        this.setLearningRate(learningRate);\n    }\n    applyGradients(variableGradients) {\n        const varNames = Array.isArray(variableGradients) ?\n            variableGradients.map(v => v.name) :\n            Object.keys(variableGradients);\n        varNames.forEach((name, i) => {\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const value = ENGINE.registeredVariables[name];\n            tidy(() => {\n                const newValue = add(mul(this.c, gradient), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    /**\n     * Sets the learning rate of the optimizer.\n     */\n    setLearningRate(learningRate) {\n        this.learningRate = learningRate;\n        if (this.c != null) {\n            this.c.dispose();\n        }\n        this.c = keep(scalar(-learningRate));\n    }\n    dispose() {\n        this.c.dispose();\n    }\n    async getWeights() {\n        return [await this.saveIterations()];\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        if (weightValues.length !== 0) {\n            throw new Error('SGD optimizer does not have settable weights.');\n        }\n    }\n    getConfig() {\n        return { 'learningRate': this.learningRate };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate']);\n    }\n}\n/** @nocollapse */\nSGDOptimizer.className = 'SGD'; // Note: Name matters for Python compatibility.\nregisterClass(SGDOptimizer);\n//# sourceMappingURL=sgd_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { SGDOptimizer } from './sgd_optimizer';\n/** @doclink Optimizer */\nexport class MomentumOptimizer extends SGDOptimizer {\n    constructor(learningRate, momentum, useNesterov = false) {\n        super(learningRate);\n        this.learningRate = learningRate;\n        this.momentum = momentum;\n        this.useNesterov = useNesterov;\n        this.accumulations = [];\n        this.m = scalar(this.momentum);\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            if (this.accumulations[i] == null) {\n                const trainable = false;\n                this.accumulations[i] = {\n                    originalName: `${name}/momentum`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const accumulation = this.accumulations[i].variable;\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            tidy(() => {\n                let newValue;\n                const newAccumulation = add(mul(this.m, accumulation), gradient);\n                if (this.useNesterov) {\n                    newValue = add(mul(this.c, add(gradient, mul(newAccumulation, this.m))), value);\n                }\n                else {\n                    newValue = add(mul(this.c, newAccumulation), value);\n                }\n                accumulation.assign(newAccumulation);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.m.dispose();\n        if (this.accumulations != null) {\n            dispose(this.accumulations.map(v => v.variable));\n        }\n    }\n    /**\n     * Sets the momentum of the optimizer.\n     *\n     * @param momentum\n     */\n    setMomentum(momentum) {\n        this.momentum = momentum;\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        return [await this.saveIterations()].concat(this.accumulations.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const trainable = false;\n        this.accumulations = weightValues.map(v => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'momentum': this.momentum,\n            'useNesterov': this.useNesterov\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['momentum'], config['useNesterov']);\n    }\n}\n/** @nocollapse */\nMomentumOptimizer.className = 'Momentum'; // Name matters for Python compatibility.\nregisterClass(MomentumOptimizer);\n//# sourceMappingURL=momentum_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class RMSPropOptimizer extends Optimizer {\n    constructor(learningRate, decay = 0.9, momentum = 0.0, epsilon = null, centered = false) {\n        super();\n        this.learningRate = learningRate;\n        this.decay = decay;\n        this.momentum = momentum;\n        this.epsilon = epsilon;\n        this.accumulatedMeanSquares = [];\n        this.accumulatedMoments = [];\n        this.accumulatedMeanGrads = [];\n        this.centered = centered;\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n        if (learningRate == null) {\n            throw new Error(`learningRate for RMSPropOptimizer must be defined.`);\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            const trainable = false;\n            if (this.accumulatedMeanSquares[i] == null) {\n                this.accumulatedMeanSquares[i] = {\n                    originalName: `${name}/rms`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedMoments[i] == null) {\n                this.accumulatedMoments[i] = {\n                    originalName: `${name}/momentum`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedMeanGrads[i] == null && this.centered) {\n                this.accumulatedMeanGrads[i] = {\n                    originalName: `${name}/mg`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;\n            const accumulatedMoments = this.accumulatedMoments[i].variable;\n            tidy(() => {\n                const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n                if (this.centered) {\n                    const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;\n                    // Centered gradient\n                    const newAccumulatedMeanGrad = add(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));\n                    const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add(square(newAccumulatedMeanGrad), this.epsilon))));\n                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), gradContribution);\n                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n                    accumulatedMeanGrad.assign(newAccumulatedMeanGrad);\n                    accumulatedMoments.assign(newAccumulatedMoments);\n                    const newValue = sub(value, newAccumulatedMoments);\n                    value.assign(newValue);\n                }\n                else {\n                    // Plain gradient\n                    const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add(newAccumulatedMeanSquare, this.epsilon))));\n                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n                    accumulatedMoments.assign(newAccumulatedMoments);\n                    const newValue = sub(value, newAccumulatedMoments);\n                    value.assign(newValue);\n                }\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedMeanSquares != null) {\n            dispose(this.accumulatedMeanSquares.map(v => v.variable));\n        }\n        if (this.accumulatedMeanGrads != null && this.centered) {\n            dispose(this.accumulatedMeanGrads.map(v => v.variable));\n        }\n        if (this.accumulatedMoments != null) {\n            dispose(this.accumulatedMoments.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];\n        if (this.centered) {\n            variables.push(...this.accumulatedMeanGrads);\n        }\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedMeanSquares =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedMoments =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        if (this.centered) {\n            this.accumulatedMeanGrads =\n                weightValues.slice(variableCount * 2, variableCount * 3)\n                    .map(v => ({\n                    originalName: v.name,\n                    variable: v.tensor.variable(trainable)\n                }));\n        }\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'decay': this.decay,\n            'momentum': this.momentum,\n            'epsilon': this.epsilon,\n            'centered': this.centered\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['decay'], config['momentum'], config['epsilon'], config['centered']);\n    }\n}\n/** @nocollapse */\nRMSPropOptimizer.className = 'RMSProp'; // Note: Name matters for Python compatibility.\nregisterClass(RMSPropOptimizer);\n//# sourceMappingURL=rmsprop_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AdadeltaOptimizer } from './adadelta_optimizer';\nimport { AdagradOptimizer } from './adagrad_optimizer';\nimport { AdamOptimizer } from './adam_optimizer';\nimport { AdamaxOptimizer } from './adamax_optimizer';\nimport { MomentumOptimizer } from './momentum_optimizer';\nimport { RMSPropOptimizer } from './rmsprop_optimizer';\nimport { SGDOptimizer } from './sgd_optimizer';\nexport class OptimizerConstructors {\n    /**\n     * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.\n     *\n     * ```js\n     * // Fit a quadratic function by learning the coefficients a, b, c.\n     * const xs = tf.tensor1d([0, 1, 2, 3]);\n     * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);\n     *\n     * const a = tf.scalar(Math.random()).variable();\n     * const b = tf.scalar(Math.random()).variable();\n     * const c = tf.scalar(Math.random()).variable();\n     *\n     * // y = a * x^2 + b * x + c.\n     * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);\n     * const loss = (pred, label) => pred.sub(label).square().mean();\n     *\n     * const learningRate = 0.01;\n     * const optimizer = tf.train.sgd(learningRate);\n     *\n     * // Train the model.\n     * for (let i = 0; i < 10; i++) {\n     *   optimizer.minimize(() => loss(f(xs), ys));\n     * }\n     *\n     * // Make predictions.\n     * console.log(\n     *     `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);\n     * const preds = f(xs).dataSync();\n     * preds.forEach((pred, i) => {\n     *   console.log(`x: ${i}, pred: ${pred}`);\n     * });\n     * ```\n     *\n     * @param learningRate The learning rate to use for the SGD algorithm.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static sgd(learningRate) {\n        return new SGDOptimizer(learningRate);\n    }\n    /**\n     * Constructs a `tf.MomentumOptimizer` that uses momentum gradient\n     * descent.\n     *\n     * See\n     * [http://proceedings.mlr.press/v28/sutskever13.pdf](\n     * http://proceedings.mlr.press/v28/sutskever13.pdf)\n     *\n     * @param learningRate The learning rate to use for the Momentum gradient\n     * descent algorithm.\n     * @param momentum The momentum to use for the momentum gradient descent\n     * algorithm.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static momentum(learningRate, momentum, useNesterov = false) {\n        return new MomentumOptimizer(learningRate, momentum, useNesterov);\n    }\n    /**\n     * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient\n     * descent. This implementation uses plain momentum and is not centered\n     * version of RMSProp.\n     *\n     * See\n     * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](\n     * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\n     *\n     * @param learningRate The learning rate to use for the RMSProp gradient\n     * descent algorithm.\n     * @param decay The discounting factor for the history/coming gradient.\n     * @param momentum The momentum to use for the RMSProp gradient descent\n     * algorithm.\n     * @param epsilon Small value to avoid zero denominator.\n     * @param centered If true, gradients are normalized by the estimated\n     * variance of the gradient.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static rmsprop(learningRate, decay = .9, momentum = 0.0, epsilon = null, centered = false) {\n        return new RMSPropOptimizer(learningRate, decay, momentum, epsilon, centered);\n    }\n    /**\n     * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.\n     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n     *\n     * @param learningRate The learning rate to use for the Adam gradient\n     * descent algorithm.\n     * @param beta1 The exponential decay rate for the 1st moment estimates.\n     * @param beta2 The exponential decay rate for the 2nd moment estimates.\n     * @param epsilon A small constant for numerical stability.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adam(learningRate = 0.001, beta1 = 0.9, beta2 = 0.999, epsilon = null) {\n        return new AdamOptimizer(learningRate, beta1, beta2, epsilon);\n    }\n    /**\n     * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.\n     * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)\n     *\n     * @param learningRate The learning rate to use for the Adadelta gradient\n     * descent algorithm.\n     * @param rho The learning rate decay over each update.\n     * @param epsilon A constant epsilon used to better condition the grad\n     * update.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adadelta(learningRate = .001, rho = .95, epsilon = null) {\n        return new AdadeltaOptimizer(learningRate, rho, epsilon);\n    }\n    /**\n     * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.\n     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n     *\n     * @param learningRate The learning rate to use for the Adamax gradient\n     * descent algorithm.\n     * @param beta1 The exponential decay rate for the 1st moment estimates.\n     * @param beta2 The exponential decay rate for the 2nd moment estimates.\n     * @param epsilon A small constant for numerical stability.\n     * @param decay The learning rate decay over each update.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adamax(learningRate = 0.002, beta1 = 0.9, beta2 = 0.999, epsilon = null, decay = 0.0) {\n        return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);\n    }\n    /**\n     * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.\n     * See\n     * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](\n     * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\n     * or\n     * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](\n     * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)\n     *\n     * @param learningRate The learning rate to use for the Adagrad gradient\n     * descent algorithm.\n     * @param initialAccumulatorValue Starting value for the accumulators, must be\n     * positive.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adagrad(learningRate, initialAccumulatorValue = 0.1) {\n        return new AdagradOptimizer(learningRate, initialAccumulatorValue);\n    }\n}\n//# sourceMappingURL=optimizer_constructors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// So typings can propagate.\nimport { AdadeltaOptimizer } from './optimizers/adadelta_optimizer';\nimport { AdagradOptimizer } from './optimizers/adagrad_optimizer';\nimport { AdamOptimizer } from './optimizers/adam_optimizer';\nimport { AdamaxOptimizer } from './optimizers/adamax_optimizer';\nimport { MomentumOptimizer } from './optimizers/momentum_optimizer';\nimport { OptimizerConstructors } from './optimizers/optimizer_constructors';\nimport { RMSPropOptimizer } from './optimizers/rmsprop_optimizer';\nimport { SGDOptimizer } from './optimizers/sgd_optimizer';\n// tslint:disable-next-line:no-unused-expression\n[MomentumOptimizer, SGDOptimizer, AdadeltaOptimizer, AdagradOptimizer,\n    RMSPropOptimizer, AdamaxOptimizer, AdamOptimizer];\nexport const train = {\n    sgd: OptimizerConstructors.sgd,\n    momentum: OptimizerConstructors.momentum,\n    adadelta: OptimizerConstructors.adadelta,\n    adagrad: OptimizerConstructors.adagrad,\n    rmsprop: OptimizerConstructors.rmsprop,\n    adamax: OptimizerConstructors.adamax,\n    adam: OptimizerConstructors.adam\n};\n//# sourceMappingURL=train.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst delayCallback = (() => {\n    if (typeof requestAnimationFrame !== 'undefined') {\n        return requestAnimationFrame;\n    }\n    else if (typeof setImmediate !== 'undefined') {\n        return setImmediate;\n    }\n    return (f) => f(); // no delays\n})();\n/**\n * Returns a promise that resolve when a requestAnimationFrame has completed.\n *\n * On Node.js this uses setImmediate instead of requestAnimationFrame.\n *\n * This is simply a sugar method so that users can do the following:\n * `await tf.nextFrame();`\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nfunction nextFrame() {\n    return new Promise(resolve => delayCallback(() => resolve()));\n}\nexport { nextFrame };\n//# sourceMappingURL=browser_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Returns the image center in pixels.\nexport function getImageCenter(center, imageHeight, imageWidth) {\n    const centerX = imageWidth * (typeof center === 'number' ? center : center[0]);\n    const centerY = imageHeight * (typeof center === 'number' ? center : center[1]);\n    return [centerX, centerY];\n}\n//# sourceMappingURL=rotate_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Gets the new shape of the input Tensor after it's been reshaped\n * to:\n * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),\n * inputShape[1], ..., inputShape[N-1]]\n *\n * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshaped(inputShape, blockShape, prod, batchToSpace = true) {\n    let reshaped = [];\n    if (batchToSpace) {\n        reshaped = reshaped.concat(blockShape.slice(0));\n        reshaped.push(inputShape[0] / prod);\n        reshaped = reshaped.concat(inputShape.slice(1));\n    }\n    else {\n        reshaped = reshaped.concat(inputShape[0]);\n        const spatialLength = blockShape.length;\n        for (let i = 0; i < spatialLength; ++i) {\n            reshaped =\n                reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);\n        }\n        reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n    }\n    return reshaped;\n}\n/**\n * Gets the permutation that will transpose the dimensions of the\n * reshaped tensor to shape:\n *\n * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,\n * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {\n    const permuted = [];\n    if (batchToSpace) {\n        permuted.push(blockShapeRank);\n        for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {\n            if (i <= 2 * blockShapeRank) {\n                permuted.push(i);\n                permuted.push(i - (blockShapeRank + 1));\n            }\n            else {\n                permuted.push(i);\n            }\n        }\n    }\n    else {\n        const permutedBeforeBatch = [];\n        const permutedAfterBatch = [];\n        for (let i = 1; i < reshapedRank; ++i) {\n            if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {\n                permutedAfterBatch.push(i);\n            }\n            else {\n                permutedBeforeBatch.push(i);\n            }\n        }\n        permuted.push(...permutedBeforeBatch);\n        permuted.push(0);\n        permuted.push(...permutedAfterBatch);\n    }\n    return permuted;\n}\n/**\n * Gets the shape of the reshaped and permuted input Tensor before any cropping\n * is applied.  The new shape will be:\n *\n * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,\n * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshapedPermuted(inputShape, blockShape, prod, batchToSpace = true) {\n    const reshapedPermuted = [];\n    if (batchToSpace) {\n        reshapedPermuted.push(inputShape[0] / prod);\n    }\n    else {\n        reshapedPermuted.push(inputShape[0] * prod);\n    }\n    for (let i = 1; i < inputShape.length; ++i) {\n        if (i <= blockShape.length) {\n            if (batchToSpace) {\n                reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);\n            }\n            else {\n                reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);\n            }\n        }\n        else {\n            reshapedPermuted.push(inputShape[i]);\n        }\n    }\n    return reshapedPermuted;\n}\n/**\n * Converts the crops argument into the beginning coordinates of a slice\n * operation.\n */\nexport function getSliceBeginCoords(crops, blockShape) {\n    const sliceBeginCoords = [0];\n    for (let i = 0; i < blockShape; ++i) {\n        sliceBeginCoords.push(crops[i][0]);\n    }\n    return sliceBeginCoords;\n}\n/**\n * Converts the crops argument into the size of a slice operation.  When\n * combined with getSliceBeginCoords this function allows the reshaped and\n * permuted Tensor to be cropped to its final output shape of:\n *\n * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,\n * inputShape[M] * blockShape[M-1] -crops[M-1,0] -\n * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getSliceSize(uncroppedShape, crops, blockShape) {\n    const sliceSize = uncroppedShape.slice(0, 1);\n    for (let i = 0; i < blockShape; ++i) {\n        sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);\n    }\n    return sliceSize;\n}\n//# sourceMappingURL=array_ops_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const SELU_SCALEALPHA = 1.7580993408473768599402175208123;\nexport const SELU_SCALE = 1.0507009873554804934193349852946;\n//# sourceMappingURL=selu_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const ERF_P = 0.3275911;\nexport const ERF_A1 = 0.254829592;\nexport const ERF_A2 = -0.284496736;\nexport const ERF_A3 = 1.421413741;\nexport const ERF_A4 = -1.453152027;\nexport const ERF_A5 = 1.061405429;\n//# sourceMappingURL=erf_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nexport function warn(...msg) {\n    if (!env().getBool('IS_TEST')) {\n        console.warn(...msg);\n    }\n}\nexport function log(...msg) {\n    if (!env().getBool('IS_TEST')) {\n        console.log(...msg);\n    }\n}\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Merges real and imaginary Float32Arrays into a single complex Float32Array.\n *\n * The memory layout is interleaved as follows:\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n * complex: [r0, i0, r1, i1, r2, i2]\n *\n * This is the inverse of splitRealAndImagArrays.\n *\n * @param real The real values of the complex tensor values.\n * @param imag The imag values of the complex tensor values.\n * @returns A complex tensor as a Float32Array with merged values.\n */\nexport function mergeRealAndImagArrays(real, imag) {\n    if (real.length !== imag.length) {\n        throw new Error(`Cannot merge real and imag arrays of different lengths. real:` +\n            `${real.length}, imag: ${imag.length}.`);\n    }\n    const result = new Float32Array(real.length * 2);\n    for (let i = 0; i < result.length; i += 2) {\n        result[i] = real[i / 2];\n        result[i + 1] = imag[i / 2];\n    }\n    return result;\n}\n/**\n * Splits a complex Float32Array into real and imag parts.\n *\n * The memory layout is interleaved as follows:\n * complex: [r0, i0, r1, i1, r2, i2]\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n *\n * This is the inverse of mergeRealAndImagArrays.\n *\n * @param complex The complex tensor values.\n * @returns An object with real and imag Float32Array components of the complex\n *     tensor.\n */\nexport function splitRealAndImagArrays(complex) {\n    const real = new Float32Array(complex.length / 2);\n    const imag = new Float32Array(complex.length / 2);\n    for (let i = 0; i < complex.length; i += 2) {\n        real[i / 2] = complex[i];\n        imag[i / 2] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Extracts even indexed complex values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithEvenIndex(complex) {\n    const len = Math.ceil(complex.length / 4);\n    const real = new Float32Array(len);\n    const imag = new Float32Array(len);\n    for (let i = 0; i < complex.length; i += 4) {\n        real[Math.floor(i / 4)] = complex[i];\n        imag[Math.floor(i / 4)] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Extracts odd indexed comple values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithOddIndex(complex) {\n    const len = Math.floor(complex.length / 4);\n    const real = new Float32Array(len);\n    const imag = new Float32Array(len);\n    for (let i = 2; i < complex.length; i += 4) {\n        real[Math.floor(i / 4)] = complex[i];\n        imag[Math.floor(i / 4)] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Get the map representing a complex value in the given array.\n * @param complex The complex tensor values.\n * @param index An index of the target complex value.\n */\nexport function getComplexWithIndex(complex, index) {\n    const real = complex[index * 2];\n    const imag = complex[index * 2 + 1];\n    return { real, imag };\n}\n/**\n * Insert a given complex value into the TypedArray.\n * @param data The array in which the complex value is inserted.\n * @param c The complex value to be inserted.\n * @param index An index of the target complex value.\n */\nexport function assignToTypedArray(data, real, imag, index) {\n    data[index * 2] = real;\n    data[index * 2 + 1] = imag;\n}\n/**\n * Make the list of exponent terms used by FFT.\n */\nexport function exponents(n, inverse) {\n    const real = new Float32Array(n / 2);\n    const imag = new Float32Array(n / 2);\n    for (let i = 0; i < Math.ceil(n / 2); i++) {\n        const x = (inverse ? 2 : -2) * Math.PI * (i / n);\n        real[i] = Math.cos(x);\n        imag[i] = Math.sin(x);\n    }\n    return { real, imag };\n}\n/**\n * Make the exponent term used by FFT.\n */\nexport function exponent(k, n, inverse) {\n    const x = (inverse ? 2 : -2) * Math.PI * (k / n);\n    const real = Math.cos(x);\n    const imag = Math.sin(x);\n    return { real, imag };\n}\n//# sourceMappingURL=complex_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { cast } from '../ops/cast';\nimport { scalar } from '../ops/scalar';\nimport { tensor1d } from '../ops/tensor1d';\nimport { zeros } from '../ops/zeros';\nimport { hasEncodingLoss, makeZerosTypedArray } from '../util';\n// Utilities needed by backend consumers of tf-core.\nexport * from '../ops/axis_util';\nexport * from '../ops/broadcast_util';\nexport * from '../ops/concat_util';\nexport * from '../ops/conv_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/reduce_util';\nimport * as slice_util from '../ops/slice_util';\nexport { slice_util };\nexport { upcastType } from '../types';\nexport * from '../ops/rotate_util';\nexport * from '../ops/array_ops_util';\nexport * from '../ops/gather_nd_util';\nexport * from '../ops/scatter_nd_util';\nexport * from '../ops/selu_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/erf_util';\nexport * from '../log';\nexport * from '../backends/complex_util';\nexport * from '../ops/split_util';\nimport * as segment_util from '../ops/segment_util';\nexport { segment_util };\nexport function castTensor(x, dtype, backend) {\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return x.clone();\n        }\n        const zerosTensor = zeros(x.shape);\n        const floatX = cast(x, 'float32');\n        const result = backend.complex(floatX, zerosTensor);\n        zerosTensor.dispose();\n        floatX.dispose();\n        return result;\n    }\n    if (!hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        return ENGINE.makeTensorFromDataId(x.dataId, x.shape, dtype);\n    }\n    if (x.dtype === 'complex64') {\n        const real = backend.real(x);\n        const result = cast(real, dtype);\n        real.dispose();\n        return result;\n    }\n    if (dtype === 'int32') {\n        return backend.int(x);\n    }\n    else if (dtype === 'bool') {\n        const zero = scalar(0, x.dtype);\n        const result = backend.notEqual(x, zero);\n        zero.dispose();\n        return result;\n    }\n    else {\n        throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n    }\n}\nexport function reshapeTensor(x, shape) {\n    return ENGINE.makeTensorFromDataId(x.dataId, shape, x.dtype);\n}\nexport function linspaceImpl(start, stop, num) {\n    const step = (stop - start) / (num - 1);\n    const values = makeZerosTypedArray(num, 'float32');\n    values[0] = start;\n    for (let i = 1; i < values.length; i++) {\n        values[i] = values[i - 1] + step;\n    }\n    return tensor1d(values, 'float32');\n}\n//# sourceMappingURL=backend_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { slice } from '../ops/slice';\n// TODO(annxingyuan): Use this helper in WASM Split kernel once intermediate\n// kernels have been modularized in WebGL and CPU\n// https://github.com/tensorflow/tfjs/issues/2822.\n/** Shared implementation of the split kernel across WebGL and CPU. */\nexport function split(x, sizeSplits, axis) {\n    const begin = new Array(x.rank).fill(0);\n    const size = x.shape.slice();\n    return sizeSplits.map(s => {\n        const sliceSize = [...size];\n        sliceSize[axis] = s;\n        const sliceT = slice(x, begin, sliceSize);\n        begin[axis] += s;\n        return sliceT;\n    });\n}\n//# sourceMappingURL=split_shared.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * An implementation of the tile kernel shared between webgl and cpu for string\n * tensors only.\n */\nimport { buffer } from '../ops/buffer';\nexport function tile(xBuf, reps) {\n    const newShape = new Array(xBuf.rank);\n    for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = xBuf.shape[i] * reps[i];\n    }\n    const result = buffer(newShape, xBuf.dtype);\n    for (let i = 0; i < result.values.length; ++i) {\n        const newLoc = result.indexToLoc(i);\n        const originalLoc = new Array(xBuf.rank);\n        for (let j = 0; j < originalLoc.length; j++) {\n            originalLoc[j] = newLoc[j] % xBuf.shape[j];\n        }\n        const originalIndex = xBuf.locToIndex(originalLoc);\n        result.values[i] = xBuf.values[originalIndex];\n    }\n    return result.toTensor();\n}\n//# sourceMappingURL=tile_impl.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the TopK kernel shared between webgl and cpu. */\nimport { tensor } from '../ops/tensor';\nimport { getTypedArrayFromDType } from '../util';\nexport function topkImpl(x, xShape, xDtype, k, sorted) {\n    // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.\n    const lastDim = xShape[xShape.length - 1];\n    const [batch, size] = [x.length / lastDim, lastDim];\n    const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);\n    const allTopKIndices = getTypedArrayFromDType('int32', batch * k);\n    for (let b = 0; b < batch; b++) {\n        const offset = b * size;\n        const vals = x.subarray(offset, offset + size);\n        const valAndInd = [];\n        for (let i = 0; i < vals.length; i++) {\n            valAndInd.push({ value: vals[i], index: i });\n        }\n        valAndInd.sort((a, b) => b.value - a.value);\n        const outOffset = b * k;\n        const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n        const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n        for (let i = 0; i < k; i++) {\n            topKVals[i] = valAndInd[i].value;\n            topKIndices[i] = valAndInd[i].index;\n        }\n    }\n    // Reshape back to the original input shape, except that the last\n    // dimension is k.\n    const outputShape = xShape.slice();\n    outputShape[outputShape.length - 1] = k;\n    return [\n        tensor(allTopKVals, outputShape, xDtype),\n        tensor(allTopKIndices, outputShape, 'int32')\n    ];\n}\n//# sourceMappingURL=topk_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { nonMaxSuppressionV3Impl, nonMaxSuppressionV4Impl, nonMaxSuppressionV5Impl } from './non_max_suppression_impl';\nexport { split } from './split_shared';\nexport { tile } from './tile_impl';\nexport { topkImpl } from './topk_impl';\nexport { whereImpl } from './where_impl';\n//# sourceMappingURL=kernel_impls.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// base.ts is tfjs-core without auto registration of things like flags,\n// gradients, chained ops or the opHandler. See base_side_effects.ts for parts\n// tfjs core that are required side effects.\n/**\n * @fileoverview\n * @suppress {partialAlias} Optimization disabled due to passing the module\n * object into a function below:\n *\n *   import * as ops from './ops/ops';\n *   setOpHandler(ops);\n */\n// Serialization.\nimport * as io from './io/io';\nimport * as math from './math';\nimport * as browser from './ops/browser';\nimport * as gather_util from './ops/gather_nd_util';\nimport * as scatter_util from './ops/scatter_nd_util';\nimport * as slice_util from './ops/slice_util';\nimport * as serialization from './serialization';\nimport * as tensor_util from './tensor_util';\nimport * as test_util from './test_util';\nimport * as util from './util';\nimport { version } from './version';\n// Optimizers.\nexport { AdadeltaOptimizer } from './optimizers/adadelta_optimizer';\nexport { AdagradOptimizer } from './optimizers/adagrad_optimizer';\nexport { AdamOptimizer } from './optimizers/adam_optimizer';\nexport { AdamaxOptimizer } from './optimizers/adamax_optimizer';\nexport { MomentumOptimizer } from './optimizers/momentum_optimizer';\nexport { Optimizer } from './optimizers/optimizer';\nexport { RMSPropOptimizer } from './optimizers/rmsprop_optimizer';\nexport { SGDOptimizer } from './optimizers/sgd_optimizer';\nexport { Tensor, TensorBuffer, Variable } from './tensor';\nexport { Rank, sumOutType, upcastType } from './types';\nexport * from './ops/ops';\nexport { Reduction } from './ops/loss_ops_utils';\nexport * from './train';\nexport * from './globals';\nexport * from './kernel_registry';\nexport { customGrad, grad, grads, valueAndGrad, valueAndGrads, variableGrads } from './gradients';\nexport { Environment, env, ENV } from './environment';\nexport { version as version_core };\n// Top-level method exports.\nexport { nextFrame } from './browser_util';\n// Second level exports.\nimport * as backend_util from './backends/backend_util';\nimport * as device_util from './device_util';\nexport { browser, io, math, serialization, test_util, util, backend_util, tensor_util, slice_util, gather_util, scatter_util, device_util };\nimport * as kernel_impls from './backends/kernel_impls';\nexport { kernel_impls };\n// Backend specific.\nexport { KernelBackend, DataStorage } from './backends/backend';\n// Export all kernel names / info.\nexport * from './kernel_names';\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const absGradConfig = {\n    kernelName: Abs,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, step(cast(x, 'float32'), -1)) };\n    }\n};\n//# sourceMappingURL=Abs_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acos } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { neg } from '../ops/neg';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const acosGradConfig = {\n    kernelName: Acos,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = square(cast(x, 'float32'));\n                const b = sqrt(sub(scalar(1), a));\n                return neg(div(dy, b));\n            }\n        };\n    }\n};\n//# sourceMappingURL=Acos_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acosh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const acoshGradConfig = {\n    kernelName: Acosh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = sqrt(sub(square(cast(x, 'float32')), 1));\n                return div(dy, a);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Acosh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const addGradConfig = {\n    kernelName: Add,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Add_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AddN } from '../kernel_names';\nexport const addNGradConfig = {\n    kernelName: AddN,\n    saveAllInputs: true,\n    gradFunc: (dy, saved) => {\n        const ders = {};\n        saved.forEach((_, i) => {\n            ders[i] = () => dy.clone();\n        });\n        return ders;\n    }\n};\n//# sourceMappingURL=AddN_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ArgMax } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const argMaxGradConfig = {\n    kernelName: ArgMax,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => zerosLike(x) };\n    }\n};\n//# sourceMappingURL=ArgMax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ArgMin } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const argMinGradConfig = {\n    kernelName: ArgMin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => zerosLike(x) };\n    }\n};\n//# sourceMappingURL=ArgMin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asin } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const asinGradConfig = {\n    kernelName: Asin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, 'float32'))))) };\n    }\n};\n//# sourceMappingURL=Asin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asinh } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nexport const asinhGradConfig = {\n    kernelName: Asinh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = sqrt(add(scalar(1), square(cast(x, 'float32'))));\n                return div(dy, a);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Asinh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan2 } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const atan2GradConfig = {\n    kernelName: Atan2,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const d = add(square(a), square(b));\n            let res = mul(dy, div(b, d));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            const d = add(square(a), square(b));\n            let res = neg(mul(dy, div(a, d)));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Atan2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nexport const atanGradConfig = {\n    kernelName: Atan,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, add(square(cast(x, 'float32')), 1)) };\n    }\n};\n//# sourceMappingURL=Atan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atanh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { scalar } from '../ops/scalar';\nexport const atanhGradConfig = {\n    kernelName: Atanh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, sub(scalar(1), square(cast(x, 'float32')))) };\n    }\n};\n//# sourceMappingURL=Atanh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool3DBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of a 3d avg pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0. The dilation\n *     rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction avgPool3dBackprop_(dy, input, filterSize, strides, dilations = [1, 1, 1], pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'avgPool3dBackprop');\n    const $input = convertToTensor(input, 'input', 'avgPool3dBackprop');\n    let dy5D = $dy;\n    let input5D = $input;\n    let reshapedTo5D = false;\n    if ($input.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n        input5D = reshape($input, [\n            1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n        ]);\n    }\n    util.assert(dy5D.rank === 5, () => `Error in avgPool3dBackprop: dy must be rank 5 but got rank ` +\n        `${dy5D.rank}.`);\n    util.assert(input5D.rank === 5, () => `Error in avgPool3dBackprop: input must be rank 5 but got rank ` +\n        `${input5D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool3dBackprop: Either strides or dilations ' +\n        `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n        return backend.avgPool3dBackprop(dy5D, input5D, convInfo);\n    };\n    const inputs = { dy: dy5D, input: input5D };\n    const attrs = { filterSize, strides, dilations, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool3DBackprop, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const avgPool3dBackprop = op({ avgPool3dBackprop_ });\n//# sourceMappingURL=avg_pool_3d_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool3D } from '../kernel_names';\nimport { avgPool3dBackprop } from '../ops/avg_pool_3d_backprop';\nexport const avgPool3DGradConfig = {\n    kernelName: AvgPool3D,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { filterSize, strides, dilations, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1, 1] : dilations;\n        return {\n            x: () => avgPool3dBackprop(dy, x, filterSize, strides, $dilations, pad, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=AvgPool3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPoolBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of an 2D avg pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The input image, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction avgPoolBackprop_(dy, input, filterSize, strides, pad) {\n    const $dy = convertToTensor(dy, 'dy', 'avgPoolBackprop');\n    const $input = convertToTensor(input, 'input', 'avgPoolBackprop');\n    util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n    let input4D = $input;\n    let dy4D = $dy;\n    let reshapedTo4D = false;\n    if ($input.rank === 3) {\n        reshapedTo4D = true;\n        input4D =\n            reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);\n        dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);\n    }\n    util.assert(dy4D.rank === 4, () => `Error in avgPoolBackprop: dy must be rank 4 but got rank ` +\n        `${dy4D.rank}.`);\n    util.assert(input4D.rank === 4, () => `Error in avgPoolBackprop: input must be rank 4 but got rank ` +\n        `${input4D.rank}.`);\n    const forward = backend => {\n        const convInfo = conv_util.computePool2DInfo(input4D.shape, filterSize, strides, 1 /* dilations */, pad);\n        return backend.avgPoolBackprop(dy4D, input4D, convInfo);\n    };\n    const inputs = { dy: dy4D, input: input4D };\n    const attrs = { filterSize, strides, pad };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, AvgPoolBackprop, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const avgPoolBackprop = op({ avgPoolBackprop_ });\n//# sourceMappingURL=avg_pool_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool } from '../kernel_names';\nimport { avgPoolBackprop } from '../ops/avg_pool_backprop';\nexport const avgPoolGradConfig = {\n    kernelName: AvgPool,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { filterSize, strides, pad } = attrs;\n        return {\n            x: () => avgPoolBackprop(dy, x, filterSize, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=AvgPool_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchMatMul } from '../kernel_names';\nimport { matMul } from '../ops/mat_mul';\nexport const batchMatMulGradConfig = {\n    kernelName: BatchMatMul,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved, attrs) => {\n        const [a, b] = saved;\n        const { transposeA, transposeB } = attrs;\n        if (!transposeA && !transposeB) {\n            return {\n                a: () => matMul(dy, b, false, true),\n                b: () => matMul(a, dy, true, false)\n            };\n        }\n        else if (!transposeA && transposeB) {\n            return {\n                a: () => matMul(dy, b, false, false),\n                b: () => matMul(dy, a, true, false)\n            };\n        }\n        else if (transposeA && !transposeB) {\n            return {\n                a: () => matMul(b, dy, false, true),\n                b: () => matMul(a, dy, false, false)\n            };\n        }\n        else {\n            return {\n                a: () => matMul(b, dy, true, true),\n                b: () => matMul(dy, a, true, true)\n            };\n        }\n    }\n};\n//# sourceMappingURL=BatchMatMul_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchToSpaceND } from '../kernel_names';\nimport { spaceToBatchND } from '../ops/space_to_batch_nd';\nexport const batchToSpaceNDGradConfig = {\n    kernelName: BatchToSpaceND,\n    gradFunc: (dy, saved, attrs) => {\n        const { blockShape, crops } = attrs;\n        return { x: () => spaceToBatchND(dy, blockShape, crops) };\n    }\n};\n//# sourceMappingURL=BatchToSpaceND_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BroadcastTo } from '../kernel_names';\nimport { sum } from '../ops/sum';\nexport const broadcastToGradConfig = {\n    kernelName: BroadcastTo,\n    gradFunc: (dy, saved, attrs) => {\n        const broadCastToAttrs = attrs;\n        const inputShape = broadCastToAttrs.inputShape;\n        const outputShape = broadCastToAttrs.shape;\n        const reps = Array.from(outputShape);\n        for (let i = inputShape.length - 1; i >= 0; i--) {\n            if (inputShape[i] === outputShape[i]) {\n                reps[i] = 1;\n            }\n            else if (inputShape[i] !== 1) {\n                throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);\n            }\n        }\n        const axes = [];\n        for (let i = 0; i < reps.length; i++) {\n            if (reps[i] > 1) {\n                axes.push(i);\n            }\n        }\n        return { x: () => sum(dy, axes, true /* keepDims */) };\n    }\n};\n//# sourceMappingURL=BroadcastTo_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cast } from '../kernel_names';\nexport const castGradConfig = {\n    kernelName: Cast,\n    gradFunc: (dy) => {\n        return { x: () => dy.clone() };\n    }\n};\n//# sourceMappingURL=Cast_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const ceilGradConfig = {\n    kernelName: Ceil,\n    gradFunc: (dy) => {\n        // TODO(manrajgrover): Return null for gradients when backprop supports it.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Ceil_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ClipByValue } from '../kernel_names';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { lessEqual } from '../ops/less_equal';\nimport { logicalAnd } from '../ops/logical_and';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const clipByValueGradConfig = {\n    kernelName: ClipByValue,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { clipValueMin, clipValueMax } = attrs;\n        return {\n            x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy)),\n        };\n    }\n};\n//# sourceMappingURL=ClipByValue_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Concat } from '../kernel_names';\nimport { split } from '../ops/split';\nimport { parseAxisParam } from '../util';\nexport const concatGradConfig = {\n    kernelName: Concat,\n    saveAllInputs: true,\n    gradFunc: (dy, saved, attrs) => {\n        const shapes = saved.map(t => t.shape);\n        const { axis } = attrs;\n        const $axis = parseAxisParam(axis, saved[0].shape)[0];\n        const sizeSplits = shapes.map(s => s[$axis]);\n        const derTensors = split(dy, sizeSplits, $axis);\n        return derTensors.map(t => () => t);\n    }\n};\n//# sourceMappingURL=Concat_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv2D } from '../kernel_names';\nimport { conv2DBackpropFilter } from '../ops/conv2d_backprop_filter';\nimport { conv2DBackpropInput } from '../ops/conv2d_backprop_input';\nimport * as conv_util from '../ops/conv_util';\nimport * as util from '../util';\nexport const conv2DGradConfig = {\n    kernelName: Conv2D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x4D, $filter] = saved;\n        const { dilations, strides, pad, dataFormat } = attrs;\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of conv2D: dilation rates greater than 1 ' +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n        return {\n            x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad, dataFormat),\n            filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad, dataFormat)\n        };\n    }\n};\n//# sourceMappingURL=Conv2D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv2DBackpropInput } from '../kernel_names';\nimport { conv2d } from '../ops/conv2d';\nimport { conv2DBackpropFilter } from '../ops/conv2d_backprop_filter';\nexport const conv2DBackpropInputGradConfig = {\n    kernelName: Conv2DBackpropInput,\n    inputsToSave: ['dy', 'filter'],\n    gradFunc: (ddx, saved, attrs) => {\n        const [dy, filter] = saved;\n        const { strides, pad, dataFormat, dimRoundingMode } = attrs;\n        return {\n            dy: () => conv2d(ddx, filter, strides, pad, dataFormat, 1 /* dilations */, dimRoundingMode),\n            filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad, dataFormat, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=Conv2DBackpropInput_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3DBackpropFilterV2 } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the filter of a 3D convolution.\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     [batch, depth, height, width, inChannels]. If rank 4, batch of 1 is\n *     assumed.\n * @param dy The dy image, of rank 5 or rank 4, of shape\n *     [batch, depth, height, width, outDepth]. If rank 4, batch of 1 is\n *     assumed.\n * @param filterShape The shape of the filter, length 5,\n *     [filterDepth, filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideDepth, strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction conv3DBackpropFilter_(x, dy, filterShape, strides, pad) {\n    let x5D = x;\n    if (x.rank === 4) {\n        x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);\n    }\n    let dy5D = dy;\n    if (dy5D.rank === 4) {\n        dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ` +\n        `${x5D.shape}.`);\n    util.assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ` +\n        `${dy5D.shape}.`);\n    util.assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ` +\n        `${filterShape}.`);\n    util.assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must ` +\n        `match input depth in filter (${filterShape[3]}.`);\n    util.assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must ` +\n        `match output depth for filter (${filterShape[4]}).`);\n    const forward = backend => {\n        const dilations = 1;\n        const convInfo = conv_util.computeConv3DInfo(x5D.shape, filterShape, strides, dilations, pad);\n        return backend.conv3dDerFilter(x5D, dy5D, convInfo);\n    };\n    const inputs = { x: x5D, y: dy5D };\n    const attrs = { strides, pad };\n    return ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropFilterV2, attrs);\n}\nexport const conv3DBackpropFilter = op({ conv3DBackpropFilter_ });\n//# sourceMappingURL=conv3d_backprop_filter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv3D } from '../kernel_names';\nimport { conv3DBackpropFilter } from '../ops/conv3d_backprop_filter';\nimport { conv3DBackpropInput } from '../ops/conv3d_backprop_input';\nimport { tupleValuesAreOne } from '../ops/conv_util';\nimport * as util from '../util';\nexport const conv3DGradConfig = {\n    kernelName: Conv3D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const { dilations, strides, pad } = attrs;\n        util.assert(tupleValuesAreOne(dilations), () => 'Error in gradient of conv3D: dilation rates greater than 1 are ' +\n            `not yet supported in gradients. Got dilations '${dilations}'`);\n        const [x5D, $filter] = saved;\n        return {\n            x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad),\n            filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=Conv3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { sin } from '../ops/sin';\nexport const cosGradConfig = {\n    kernelName: Cos,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(neg(sin(cast(x, 'float32'))), dy) };\n    }\n};\n//# sourceMappingURL=Cos_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cosh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { sinh } from '../ops/sinh';\nexport const coshGradConfig = {\n    kernelName: Cosh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(sinh(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Cosh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cumsum } from '../kernel_names';\nimport { getAxesPermutation } from '../ops/axis_util';\nimport { cumsum } from '../ops/cumsum';\nimport { transpose } from '../ops/transpose';\nexport const cumsumGradConfig = {\n    kernelName: Cumsum,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { axis, exclusive, reverse } = attrs;\n        return {\n            x: () => {\n                const permutation = getAxesPermutation([axis], x.rank);\n                let out = cumsum(dy, axis, exclusive, !reverse);\n                if (permutation != null) {\n                    out = transpose(out, permutation);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Cumsum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { DepthwiseConv2dNative } from '../kernel_names';\nimport * as conv_util from '../ops/conv_util';\nimport { depthwiseConv2dNativeBackpropFilter } from '../ops/depthwise_conv2d_native_backprop_filter';\nimport { depthwiseConv2dNativeBackpropInput } from '../ops/depthwise_conv2d_native_backprop_input';\nimport * as util from '../util';\nexport const depthwiseConv2dNativeGradConfig = {\n    kernelName: DepthwiseConv2dNative,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const { dilations, strides, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1] : dilations;\n        util.assert(conv_util.tupleValuesAreOne($dilations), () => 'Error in gradient of depthwiseConv2dNative: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${$dilations}'`);\n        const [x, filter] = saved;\n        util.assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be ` +\n            `rank 4, but got rank ${x.rank}.`);\n        util.assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be ` +\n            `rank 4, but got rank ${filter.rank}.`);\n        util.assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input ` +\n            `channels (${x.shape[3]}) must match the inChannels dimension ` +\n            `in filter ${filter.shape[2]}.`);\n        util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, $dilations), () => 'Error in gradient of depthwiseConv2d: Either strides or ' +\n            `dilations must be  1. Got strides ${strides} and dilations ` +\n            `'${$dilations}'.`);\n        if (dimRoundingMode != null) {\n            util.assert(util.isInt(pad), () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n                `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n        }\n        const convInfo = conv_util.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad, dimRoundingMode, true /* depthwise */);\n        return {\n            x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, convInfo),\n            filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, convInfo),\n        };\n    }\n};\n//# sourceMappingURL=DepthwiseConv2dNative_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput } from '../kernel_names';\nexport const dilation2dGradConfig = {\n    kernelName: Dilation2D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, filter] = saved;\n        const inputInputs = { x, filter, dy };\n        const filterInputs = { x, filter, dy };\n        return {\n            x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),\n            filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)\n        };\n    }\n};\n//# sourceMappingURL=Dilation2D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const divGradConfig = {\n    kernelName: Div,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = div(dy, cast(b, 'float32'));\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            let res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = reshape(sum(res, reduceAxes), b.shape);\n            }\n            const tmp = square(b);\n            return neg(div(res, cast(tmp, 'float32')));\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Div_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Elu, EluGrad } from '../kernel_names';\nexport const eluGradConfig = {\n    kernelName: Elu,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        const backPropKernelFunc = (backend) => {\n            return backend.eluDer(dy, y);\n        };\n        const inputs = { dy, y };\n        return {\n            x: () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* grad */, EluGrad)\n        };\n    }\n};\n//# sourceMappingURL=Elu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Erf } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { square } from '../ops/square';\nexport const erfGradConfig = {\n    kernelName: Erf,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));\n        return { x: () => mul(dy, a) };\n    }\n};\n//# sourceMappingURL=Erf_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '../kernel_names';\nimport { mul } from '../ops/mul';\nexport const expGradConfig = {\n    kernelName: Exp,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(dy, y) };\n    }\n};\n//# sourceMappingURL=Exp_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nexport const expm1GradConfig = {\n    kernelName: Expm1,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, exp(x)) };\n    }\n};\n//# sourceMappingURL=Expm1_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const floorGradConfig = {\n    kernelName: Floor,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Floor_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FloorDiv } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const floorDivGradConfig = {\n    kernelName: FloorDiv,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = div(dy, cast(b, 'float32'));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            let res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = reshape(sum(res, reduceAxes), b.shape);\n            }\n            const tmp = square(b);\n            return neg(div(res, cast(tmp, 'float32')));\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=FloorDiv_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedBatchNorm } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { getReductionAxes } from '../ops/broadcast_util';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { rsqrt } from '../ops/rsqrt';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nimport { tile } from '../ops/tile';\nexport const fusedBatchNormGradConfig = {\n    kernelName: FusedBatchNorm,\n    inputsToSave: ['x', 'mean', 'variance', 'scale'],\n    gradFunc: (dy, saved, attrs) => {\n        const { varianceEpsilon } = attrs;\n        const [x, mean, variance, scale] = saved;\n        const scaleValue = scale == null ? scalar(1) : scale;\n        const reductionAxes = getReductionAxes(mean.shape, x.shape);\n        const tileShape = [];\n        if (mean.rank === 1) {\n            for (let i = 0; i < x.shape.length - 1; ++i) {\n                tileShape.push(x.shape[i]);\n            }\n            tileShape.push(1);\n        }\n        const xMinusMean = sub(x, mean);\n        const dyTimesScaleValue = mul(dy, scaleValue);\n        const oneOverSqrtVariance = rsqrt(add(variance, scalar(varianceEpsilon)));\n        const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));\n        const derX = () => {\n            if (mean.rank === 1) {\n                return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean.shape[0]]), tileShape)), scaleValue), x.shape);\n            }\n            else {\n                return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);\n            }\n        };\n        const derMean = () => {\n            let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);\n            if (mean.rank === 1) {\n                meanDer = sum(meanDer, reductionAxes);\n            }\n            return reshape(meanDer, mean.shape);\n        };\n        const derVariance = () => {\n            let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);\n            if (mean.rank === 1) {\n                varianceDer = sum(varianceDer, reductionAxes);\n            }\n            return reshape(varianceDer, mean.shape);\n        };\n        const derScale = () => {\n            const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);\n            let scaleDer = mul(dy, xMinusMean2TimesRsqrt);\n            if (mean.rank === 1) {\n                scaleDer = sum(scaleDer, reductionAxes);\n            }\n            return reshape(scaleDer, mean.shape);\n        };\n        const derOffset = () => {\n            let offsetDer = dy;\n            if (mean.rank === 1) {\n                offsetDer = sum(offsetDer, reductionAxes);\n            }\n            return reshape(offsetDer, mean.shape);\n        };\n        return {\n            x: derX,\n            mean: derMean,\n            variance: derVariance,\n            scale: derScale,\n            offset: derOffset\n        };\n    }\n};\n//# sourceMappingURL=FusedBatchNorm_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GatherV2 } from '../kernel_names';\nimport { getUndoAxesPermutation } from '../ops/axis_util';\nimport { reshape } from '../ops/reshape';\nimport { transpose } from '../ops/transpose';\nimport { unsortedSegmentSum } from '../ops/unsorted_segment_sum';\nimport { parseAxisParam } from '../util';\nexport const gatherGradConfig = {\n    kernelName: GatherV2,\n    inputsToSave: ['x', 'indices'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, indices] = saved;\n        const { axis } = attrs;\n        const parsedAxis = parseAxisParam(axis, x.shape)[0];\n        const derX = () => {\n            const paramsShape = x.shape;\n            const indicesSize = indices.size;\n            const outerShape = paramsShape.slice(0, parsedAxis);\n            const outerDims = outerShape.length;\n            const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);\n            const innerDims = innerShape.length;\n            const outerAxesIndices = arrayRange(0, outerDims);\n            const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);\n            const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);\n            const values = reshape(dy, valuesShape);\n            const reshapedIndices = reshape(indices, [indicesSize]);\n            const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);\n            const valuesTranspose = transpose(values, transposeDims);\n            let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);\n            const invertTransposeDims = getUndoAxesPermutation(transposeDims);\n            paramsGrad = transpose(paramsGrad, invertTransposeDims);\n            return paramsGrad;\n        };\n        return { x: derX, indices: () => indices };\n    }\n};\nfunction arrayRange(start, stop) {\n    const result = [];\n    for (let i = start; i < stop; ++i) {\n        result.push(i);\n    }\n    return result;\n}\nfunction arrayConcat(arrays) {\n    const result = [];\n    for (let i = 0; i < arrays.length; ++i) {\n        for (let j = 0; j < arrays[i].length; ++j) {\n            result.push(arrays[i][j]);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=GatherV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GreaterEqual } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const greaterEqualGradConfig = {\n    kernelName: GreaterEqual,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        return { a: () => zerosLike(a), b: () => zerosLike(b) };\n    }\n};\n//# sourceMappingURL=GreaterEqual_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '../kernel_names';\nimport { cast } from '../ops/cast';\nexport const identityGradConfig = {\n    kernelName: Identity,\n    gradFunc: (dy) => {\n        return { x: () => cast(dy, 'float32') };\n    }\n};\n//# sourceMappingURL=Identity_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsFinite } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isFiniteGradConfig = {\n    kernelName: IsFinite,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsFinite_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsInf } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isInfGradConfig = {\n    kernelName: IsInf,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsInf_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsNan } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isNanGradConfig = {\n    kernelName: IsNan,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsNan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log1p } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nexport const log1pGradConfig = {\n    kernelName: Log1p,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, add(x, 1)) };\n    }\n};\n//# sourceMappingURL=Log1p_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nexport const logGradConfig = {\n    kernelName: Log,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, cast(x, 'float32')) };\n    }\n};\n//# sourceMappingURL=Log_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LogSoftmax } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nexport const logSoftmaxGradConfig = {\n    kernelName: LogSoftmax,\n    inputsToSave: [],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [value] = saved;\n        const { axis } = attrs;\n        return {\n            logits: () => {\n                const keepDims = true;\n                const softmax = exp(value);\n                return sub(dy, mul(sum(dy, axis, keepDims), softmax));\n            }\n        };\n    }\n};\n//# sourceMappingURL=LogSoftmax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LRNBackprop } from '../kernel_names';\nimport { op } from './operation';\nfunction localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n    const forward = backend => backend.LRNGrad(dy, x, y, depthRadius, bias, alpha, beta);\n    const inputs = { x, y, dy };\n    const attrs = { depthRadius, bias, alpha, beta };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LRNBackprop, attrs);\n}\nexport const localResponseNormalizationBackprop = op({ localResponseNormalizationBackprop_ });\n//# sourceMappingURL=local_response_normalization_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LRN } from '../kernel_names';\nimport { localResponseNormalizationBackprop } from '../ops/local_response_normalization_backprop';\nexport const lrnGradConfig = {\n    kernelName: LRN,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { depthRadius, bias, alpha, beta } = attrs;\n        return {\n            x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)\n        };\n    }\n};\n//# sourceMappingURL=LRN_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as axis_util from '../ops/axis_util';\nimport { cast } from '../ops/cast';\nimport { equal } from '../ops/equal';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { transpose } from '../ops/transpose';\n/**\n * Gradient helper function for the min and max operations.\n */\nexport function gradForMinAndMax(dy, y, xOrig, origAxes, permutedAxes) {\n    if (y.rank < xOrig.rank) {\n        y = reshape(y, axis_util.expandShapeToKeepDim(y.shape, origAxes));\n    }\n    if (dy.rank < xOrig.rank) {\n        dy = reshape(dy, axis_util.expandShapeToKeepDim(dy.shape, origAxes));\n    }\n    return {\n        x: () => {\n            const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));\n            return permutedAxes == null ? dx : transpose(dx, permutedAxes);\n        }\n    };\n}\n//# sourceMappingURL=min_max_grad_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nimport * as util from '../util';\nimport { gradForMinAndMax } from './min_max_grad_util';\nexport const maxGradConfig = {\n    kernelName: Max,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const maxAttrs = attrs;\n        const { reductionIndices } = maxAttrs;\n        const [x, y] = saved;\n        const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(origAxes, x.rank);\n        const maxGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);\n        return {\n            x: () => {\n                let out = maxGrad['x']();\n                if (permutedAxes != null) {\n                    out = transpose(out);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Max_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Maximum } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { less } from '../ops/less';\nimport { mul } from '../ops/mul';\nexport const maximumGradConfig = {\n    kernelName: Maximum,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const derA = () => mul(dy, cast(greaterEqual(a, b), 'float32'));\n        const derB = () => mul(dy, cast(less(a, b), 'float32'));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Maximum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool3DBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of a 3d max pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank 4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param output The original output image, of rank 5 of shape\n *     [batchSize, outDepth, outHeight, outWidth, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPool3dBackprop_(dy, input, output, filterSize, strides, dilations = [1, 1, 1], pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'maxPool3dBackprop');\n    const $input = convertToTensor(input, 'input', 'maxPool3dBackprop');\n    const $output = convertToTensor(output, 'output', 'maxPool3dBackprop');\n    let dy5D = $dy;\n    let input5D = $input;\n    let output5D = $output;\n    let reshapedTo5D = false;\n    if ($input.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n        input5D = reshape($input, [\n            1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n        ]);\n        output5D = reshape($output, [\n            1, $output.shape[0], $output.shape[1], $output.shape[2], $output.shape[3]\n        ]);\n    }\n    util.assert(dy5D.rank === 5, () => `Error in maxPool3dBackprop: dy must be rank 5 but got rank ` +\n        `${dy5D.rank}.`);\n    util.assert(input5D.rank === 5, () => `Error in maxPool3dBackprop: input must be rank 5 but got rank ` +\n        `${input5D.rank}.`);\n    util.assert(output5D.rank === 5, () => `Error in maxPool3dBackprop: output must be rank 5 but got rank ` +\n        `${output5D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool3dBackprop: Either strides or dilations ' +\n        `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n        return backend.maxPool3dBackprop(dy5D, input5D, output5D, convInfo);\n    };\n    const inputs = { dy: dy5D, input: input5D, output: output5D };\n    const attrs = { filterSize, strides, dilations, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool3DBackprop, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const maxPool3dBackprop = op({ maxPool3dBackprop_ });\n//# sourceMappingURL=max_pool_3d_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPool3D } from '../kernel_names';\nimport { maxPool3dBackprop } from '../ops/max_pool_3d_backprop';\nexport const maxPool3DGradConfig = {\n    kernelName: MaxPool3D,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { filterSize, strides, dilations, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1, 1] : dilations;\n        return {\n            x: () => maxPool3dBackprop(dy, x, y, filterSize, strides, $dilations, pad, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=MaxPool3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPoolBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\n/**\n * Computes the backprop of a 2D max pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The original input image, of rank 4, of shape\n *     [batchSize, height, width, channels].\n * @param output The original output image, of rank 4, of shape\n *     [batchSize, outHeight, outWidth, channels].\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPoolBackprop_(dy, input, output, filterSize, strides, pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'maxPoolBackprop');\n    const $input = convertToTensor(input, 'input', 'maxPoolBackprop');\n    const $output = convertToTensor(output, 'output', 'maxPoolBackprop');\n    util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy ` +\n        `(${$dy.rank})`);\n    util.assert($dy.rank === 4, () => `Error in maxPoolBackprop: dy must be rank 4 but got rank ` +\n        `${$dy.rank}.`);\n    util.assert($input.rank === 4, () => `Error in maxPoolBackprop: input must be rank 4 but got rank ` +\n        `${$input.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPoolBackprop: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool2DInfo($input.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        return backend.maxPoolBackprop($dy, $input, $output, convInfo);\n    };\n    const inputs = { dy: $dy, input: $input, output: $output };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    return ENGINE.runKernelFunc(forward, inputs, null, MaxPoolBackprop, attrs);\n}\nexport const maxPoolBackprop = op({ maxPoolBackprop_ });\n//# sourceMappingURL=max_pool_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPool } from '../kernel_names';\nimport { maxPoolBackprop } from '../ops/max_pool_backprop';\nexport const maxPoolGradConfig = {\n    kernelName: MaxPool,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { filterSize, strides, pad } = attrs;\n        return {\n            x: () => maxPoolBackprop(dy, x, y, filterSize, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=MaxPool_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Min } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nimport * as util from '../util';\nimport { gradForMinAndMax } from './min_max_grad_util';\nexport const minGradConfig = {\n    kernelName: Min,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const minAttrs = attrs;\n        const { axis } = minAttrs;\n        const [x, y] = saved;\n        const origAxes = util.parseAxisParam(axis, x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(origAxes, x.rank);\n        const minGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);\n        return {\n            x: () => {\n                let out = minGrad['x']();\n                if (permutedAxes != null) {\n                    out = transpose(out);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Min_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Minimum } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { greater } from '../ops/greater';\nimport { lessEqual } from '../ops/less_equal';\nimport { mul } from '../ops/mul';\nexport const minimumGradConfig = {\n    kernelName: Minimum,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const derA = () => mul(dy, cast(lessEqual(a, b), 'float32'));\n        const derB = () => mul(dy, cast(greater(a, b), 'float32'));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Minimum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Mod } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { div } from '../ops/div';\nimport { floor } from '../ops/floor';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const modGradConfig = {\n    kernelName: Mod,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(dy, reduceAxes), a.shape);\n            }\n            return dy;\n        };\n        const derB = () => {\n            const res = mul(dy, neg(floor(div(a, b))));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), b.shape);\n            }\n            return res;\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Mod_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const multiplyGradConfig = {\n    kernelName: Multiply,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = mul(dy, cast(b, 'float32'));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            const res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), b.shape);\n            }\n            return res;\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Multiply_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Negate } from '../kernel_names';\nimport { neg } from '../ops/neg';\nexport const negateGradConfig = {\n    kernelName: Negate,\n    gradFunc: (dy) => {\n        return { x: () => neg(dy) };\n    }\n};\n//# sourceMappingURL=Negate_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { OneHot } from '../kernel_names';\nimport { zeros } from '../ops/zeros';\nexport const oneHotGradConfig = {\n    kernelName: OneHot,\n    inputsToSave: ['indices'],\n    gradFunc: (dy, saved) => {\n        const indices = saved[0];\n        return { indices: () => zeros(indices.shape, 'float32') };\n    }\n};\n//# sourceMappingURL=OneHot_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { OnesLike } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const onesLikeGradConfig = {\n    kernelName: OnesLike,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=OnesLike_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { PadV2 } from '../kernel_names';\nimport { slice } from '../ops/slice';\nexport const padV2GradConfig = {\n    kernelName: PadV2,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        // Pad introduces values around the original tensor, so the gradient\n        // slices the original shape out of the gradient.\n        const x = saved[0];\n        const { paddings } = attrs;\n        const begin = paddings.map(p => p[0]);\n        return { x: () => slice(dy, begin, x.shape) };\n    }\n};\n//# sourceMappingURL=PadV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Pow } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { greater } from '../ops/greater';\nimport { log } from '../ops/log';\nimport { mul } from '../ops/mul';\nimport { pow } from '../ops/pow';\nimport { reshape } from '../ops/reshape';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const powGradConfig = {\n    kernelName: Pow,\n    inputsToSave: ['a', 'b'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [a, b, y] = saved;\n        const base = a;\n        const exp = b;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(base.shape, exp.shape);\n        const derBase = () => {\n            const expFloat = cast(exp, 'float32');\n            let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));\n            const reduceAxes = broadcast_util.getReductionAxes(base.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, base.shape);\n        };\n        const derExp = () => {\n            const condition = greater(base, 0);\n            const logBase = where(condition, log(base), zerosLike(base));\n            let res = mul(dy, mul(y, logBase));\n            const reduceAxes = broadcast_util.getReductionAxes(exp.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, exp.shape);\n        };\n        return { a: derBase, b: derExp };\n    }\n};\n//# sourceMappingURL=Pow_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Prelu } from '../kernel_names';\nimport { getReductionAxes } from '../ops/broadcast_util';\nimport { greater } from '../ops/greater';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const preluGradConfig = {\n    kernelName: Prelu,\n    inputsToSave: ['x', 'alpha'],\n    gradFunc: (dy, saved) => {\n        const [x, alpha] = saved;\n        const mask = greater(x, 0);\n        return {\n            x: () => where(mask, dy, mul(dy, alpha)),\n            alpha: () => {\n                let res = where(mask, zerosLike(dy), mul(dy, x));\n                const reduceAxes = getReductionAxes(alpha.shape, dy.shape);\n                if (reduceAxes.length > 0) {\n                    res = sum(res, reduceAxes);\n                }\n                return reshape(res, alpha.shape);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Prelu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reciprocal } from '../kernel_names';\nimport { div } from '../ops/div';\nimport { neg } from '../ops/neg';\nimport { square } from '../ops/square';\nexport const reciprocalGradConfig = {\n    kernelName: Reciprocal,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, neg(square(x))) };\n    }\n};\n//# sourceMappingURL=Reciprocal_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu6 } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { lessEqual } from '../ops/less_equal';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const relu6GradConfig = {\n    kernelName: Relu6,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        const mask = mul(lessEqual(x, 6), step(x));\n        return { x: () => mul(dy, cast(mask, 'float32')) };\n    }\n};\n//# sourceMappingURL=Relu6_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const reluGradConfig = {\n    kernelName: Relu,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, cast(step(x), 'float32')) };\n    }\n};\n//# sourceMappingURL=Relu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape } from '../kernel_names';\nimport { reshape } from '../ops/reshape';\nexport const reshapeGradConfig = {\n    kernelName: Reshape,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => reshape(dy, x.shape) };\n    }\n};\n//# sourceMappingURL=Reshape_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ResizeBilinear, ResizeBilinearGrad } from '../kernel_names';\nexport const resizeBilinearGradConfig = {\n    kernelName: ResizeBilinear,\n    inputsToSave: ['images'],\n    gradFunc: (dy, saved, attrs) => {\n        const [images] = saved;\n        const backPropKernelFunc = (backend) => {\n            const { alignCorners } = attrs;\n            return backend.resizeBilinearBackprop(dy, images, alignCorners);\n        };\n        const inputs = { images };\n        const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* gradient */, ResizeBilinearGrad, attrs);\n        return { images: imagesDer };\n    }\n};\n//# sourceMappingURL=ResizeBilinear_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ResizeNearestNeighbor, ResizeNearestNeighborGrad } from '../kernel_names';\nexport const resizeNearestNeighborGradConfig = {\n    kernelName: ResizeNearestNeighbor,\n    inputsToSave: ['images'],\n    gradFunc: (dy, saved, attrs) => {\n        const [images] = saved;\n        const backPropKernelFunc = (backend) => {\n            const { alignCorners } = attrs;\n            return backend.resizeNearestNeighborBackprop(dy, images, alignCorners);\n        };\n        const inputs = { images };\n        const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* gradient */, ResizeNearestNeighborGrad, attrs);\n        return { images: imagesDer };\n    }\n};\n//# sourceMappingURL=ResizeNearestNeighbor_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reverse } from '../kernel_names';\nimport { reverse } from '../ops/reverse';\nimport { parseAxisParam } from '../util';\nexport const reverseGradConfig = {\n    kernelName: Reverse,\n    gradFunc: (dy, saved, attrs) => {\n        const { dims } = attrs;\n        const axes = parseAxisParam(dims, dy.shape);\n        return { x: () => reverse(dy, axes) };\n    }\n};\n//# sourceMappingURL=Reverse_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Round } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const roundGradConfig = {\n    kernelName: Round,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Round_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '../kernel_names';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { pow } from '../ops/pow';\nexport const rsqrtGradConfig = {\n    kernelName: Rsqrt,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => neg(div(dy, mul(pow(x, 1.5), 2))) };\n    }\n};\n//# sourceMappingURL=Rsqrt_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SelectV2 } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { logicalNot } from '../ops/logical_not';\nimport { mul } from '../ops/mul';\nimport { zerosLike } from '../ops/zeros_like';\nexport const selectV2PoolGradConfig = {\n    kernelName: SelectV2,\n    inputsToSave: ['condition'],\n    gradFunc: (dy, saved) => {\n        const [condition] = saved;\n        return {\n            // TODO(julianoks): Return null for condition gradient\n            // when backprop supports it.\n            condition: () => cast(zerosLike(condition), 'float32'),\n            t: () => mul(dy, cast(condition, dy.dtype)),\n            e: () => mul(dy, cast(logicalNot(condition), dy.dtype))\n        };\n    }\n};\n//# sourceMappingURL=SelectV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Selu } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { exp } from '../ops/exp';\nimport { greater } from '../ops/greater';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { SELU_SCALE, SELU_SCALEALPHA } from '../ops/selu_util';\nimport { where } from '../ops/where';\nexport const seluGradConfig = {\n    kernelName: Selu,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const mask = greater(x, scalar(0));\n                const scaleAlpha = scalar(SELU_SCALEALPHA);\n                const scale = scalar(SELU_SCALE);\n                const greaterThanZeroDer = mul(dy, scale);\n                const lessEqualZeroDer = mul(mul(dy, scaleAlpha), exp(cast(x, 'float32')));\n                return where(mask, greaterThanZeroDer, lessEqualZeroDer);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Selu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sigmoid } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nexport const sigmoidGradConfig = {\n    kernelName: Sigmoid,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(dy, mul(y, sub(scalar(1), y))) };\n    }\n};\n//# sourceMappingURL=Sigmoid_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sign } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const signGradConfig = {\n    kernelName: Sign,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Sign_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { cos } from '../ops/cos';\nimport { mul } from '../ops/mul';\nexport const sinGradConfig = {\n    kernelName: Sin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(cos(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Sin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sinh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { cosh } from '../ops/cosh';\nimport { mul } from '../ops/mul';\nexport const sinhGradConfig = {\n    kernelName: Sinh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(cosh(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Sinh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Slice } from '../kernel_names';\nimport { pad } from '../ops/pad';\nimport { parseSliceParams } from '../ops/slice_util';\nexport const sliceGradConfig = {\n    kernelName: Slice,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { begin, size } = attrs;\n        const inputShape = x.shape;\n        const [begin_, size_] = parseSliceParams(x, begin, size);\n        // Create an Nx2 padding where the first column represents how many\n        // zeros are prepended (at start) for each dimension, and the second\n        // column indicates how many zeros are appended (at end).\n        // The number of zeros to append is the shape of the input\n        // elementwise-subtracted by both the begin vector and sizes vector.\n        const paddings = [];\n        for (let i = 0; i < dy.rank; i++) {\n            paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);\n        }\n        return { x: () => pad(dy, paddings) };\n    }\n};\n//# sourceMappingURL=Slice_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softmax } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nexport const softmaxGradConfig = {\n    kernelName: Softmax,\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [y] = saved;\n        const { dim } = attrs;\n        const keepDims = true;\n        const dyTimesY = mul(dy, y);\n        return {\n            logits: () => sub(dyTimesY, mul(sum(dyTimesY, [dim], keepDims), y))\n        };\n    }\n};\n//# sourceMappingURL=Softmax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softplus } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { sigmoid } from '../ops/sigmoid';\nexport const softplusGradConfig = {\n    kernelName: Softplus,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, sigmoid(x)) };\n    }\n};\n//# sourceMappingURL=Softplus_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SpaceToBatchND } from '../kernel_names';\nimport { batchToSpaceND } from '../ops/batch_to_space_nd';\nexport const spaceToBatchNDGradConfig = {\n    kernelName: SpaceToBatchND,\n    gradFunc: (dy, saved, attrs) => {\n        const { blockShape, paddings } = attrs;\n        return { x: () => batchToSpaceND(dy, blockShape, paddings) };\n    }\n};\n//# sourceMappingURL=SpaceToBatchND_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SplitV } from '../kernel_names';\nimport { concat } from '../ops/concat';\nexport const splitVGradConfig = {\n    kernelName: SplitV,\n    gradFunc: (dy, saved, attrs) => {\n        const { axis } = attrs;\n        return { x: () => concat(dy, axis) };\n    }\n};\n//# sourceMappingURL=SplitV_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sqrt } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nexport const sqrtGradConfig = {\n    kernelName: Sqrt,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, mul(sqrt(cast(x, 'float32')), 2)) };\n    }\n};\n//# sourceMappingURL=Sqrt_grad.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nexport const squareGradConfig = {\n    kernelName: Square,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, mul(cast(x, 'float32'), 2)) };\n    }\n};\n//# sourceMappingURL=Square_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nexport const squaredDifferenceGradConfig = {\n    kernelName: SquaredDifference,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const two = scalar(2);\n        const derA = () => mul(dy, mul(two, sub(a, b)));\n        const derB = () => mul(dy, mul(two, sub(b, a)));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=SquaredDifference_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Step } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const stepGradConfig = {\n    kernelName: Step,\n    gradFunc: (dy) => {\n        // TODO(manrajgrover): Return null for gradients when backprop supports\n        // it.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Step_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const subGradConfig = {\n    kernelName: Sub,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(neg(res), b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Sub_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sum } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { ones } from '../ops/ones';\nimport { reshape } from '../ops/reshape';\nimport { parseAxisParam } from '../util';\nexport const sumGradConfig = {\n    kernelName: Sum,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const expandedDyShape = x.shape.slice();\n        const { axis } = attrs;\n        const axes = parseAxisParam(axis, x.shape);\n        axes.forEach(axis => {\n            expandedDyShape[axis] = 1;\n        });\n        const expandedDy = reshape(dy, expandedDyShape);\n        const derX = mul(expandedDy, ones(x.shape, 'float32'));\n        return { x: () => derX };\n    }\n};\n//# sourceMappingURL=Sum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '../kernel_names';\nimport { cos } from '../ops/cos';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nexport const tanGradConfig = {\n    kernelName: Tan,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, square(cos(x))) };\n    }\n};\n//# sourceMappingURL=Tan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tanh } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const tanhGradConfig = {\n    kernelName: Tanh,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(sub(scalar(1), square(y)), dy) };\n    }\n};\n//# sourceMappingURL=Tanh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tile } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { slice } from '../ops/slice';\nimport { zerosLike } from '../ops/zeros_like';\nexport const tileGradConfig = {\n    kernelName: Tile,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { reps } = attrs;\n        const derX = () => {\n            let xGrad = zerosLike(x);\n            // TODO(cais): Maybe reduce memory footprint by avoiding repeated\n            // slicing.\n            if (x.rank === 1) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    xGrad = add(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));\n                }\n            }\n            else if (x.rank === 2) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        xGrad = add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [\n                            x.shape[0], x.shape[1]\n                        ]));\n                    }\n                }\n            }\n            else if (x.rank === 3) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        for (let k = 0; k < reps[2]; ++k) {\n                            xGrad =\n                                add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));\n                        }\n                    }\n                }\n            }\n            else if (x.rank === 4) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        for (let k = 0; k < reps[2]; ++k) {\n                            for (let l = 0; l < reps[3]; ++l) {\n                                xGrad =\n                                    add(xGrad, slice(dy, [\n                                        i * x.shape[0], j * x.shape[1], k * x.shape[2],\n                                        l * x.shape[3]\n                                    ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));\n                            }\n                        }\n                    }\n                }\n            }\n            else {\n                throw new Error(`Gradient for tile operation is not implemented for rank-` +\n                    `${x.rank} tensors yet.`);\n            }\n            return xGrad;\n        };\n        return { x: derX };\n    },\n};\n//# sourceMappingURL=Tile_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nexport const transposeGradConfig = {\n    kernelName: Transpose,\n    gradFunc: (dy, saved, attrs) => {\n        const transposeAttrs = attrs;\n        const { perm } = transposeAttrs;\n        const undoPerm = axis_util.getUndoAxesPermutation(perm);\n        return { x: () => transpose(dy, undoPerm) };\n    }\n};\n//# sourceMappingURL=Transpose_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unpack } from '../kernel_names';\nimport { stack } from '../ops/stack';\nexport const unpackGradConfig = {\n    kernelName: Unpack,\n    gradFunc: (dy, saved, attrs) => {\n        const unpackAttrs = attrs;\n        const { axis } = unpackAttrs;\n        return { value: () => stack(dy, axis) };\n    }\n};\n//# sourceMappingURL=Unpack_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { UnsortedSegmentSum } from '../kernel_names';\nimport { expandDims } from '../ops/expand_dims';\nimport { gather } from '../ops/gather';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { logicalAnd } from '../ops/logical_and';\nimport { maximum } from '../ops/maximum';\nimport { ones } from '../ops/ones';\nimport { scalar } from '../ops/scalar';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const unsortedSegmentSumGradConfig = {\n    kernelName: UnsortedSegmentSum,\n    inputsToSave: ['segmentIds'],\n    gradFunc: (dy, saved) => {\n        const [segmentIds] = saved;\n        const derX = () => {\n            return gatherDropNegatives(dy, segmentIds);\n        };\n        return { x: derX };\n    }\n};\nfunction gatherDropNegatives(x, indices) {\n    // Helper function for unsorted segment ops. Gathers params for\n    // positive segment ids and gathers 0 for inputs with negative segment id.\n    // Mirrors _GatherDropNegatives from tensorflow/python/ops/math_grad.py\n    const zeroClippedIndices = maximum(indices, zerosLike(indices));\n    const gathered = gather(x, zeroClippedIndices);\n    let isPositive = greaterEqual(indices, scalar(0, 'int32'));\n    const numIters = gathered.rank - isPositive.rank;\n    for (let i = 0; i < numIters; ++i) {\n        isPositive = expandDims(isPositive, i + 1);\n    }\n    isPositive = logicalAnd(isPositive, ones(gathered.shape, 'bool'));\n    const zeroSlice = zerosLike(gathered);\n    return where(isPositive, gathered, zeroSlice);\n}\n//# sourceMappingURL=UnsortedSegmentSum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ZerosLike } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const zerosLikeGradConfig = {\n    kernelName: ZerosLike,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=ZerosLike_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { absGradConfig } from './gradients/Abs_grad';\nimport { acosGradConfig } from './gradients/Acos_grad';\nimport { acoshGradConfig } from './gradients/Acosh_grad';\nimport { addGradConfig } from './gradients/Add_grad';\nimport { addNGradConfig } from './gradients/AddN_grad';\nimport { argMaxGradConfig } from './gradients/ArgMax_grad';\nimport { argMinGradConfig } from './gradients/ArgMin_grad';\nimport { asinGradConfig } from './gradients/Asin_grad';\nimport { asinhGradConfig } from './gradients/Asinh_grad';\nimport { atan2GradConfig } from './gradients/Atan2_grad';\nimport { atanGradConfig } from './gradients/Atan_grad';\nimport { atanhGradConfig } from './gradients/Atanh_grad';\nimport { avgPool3DGradConfig } from './gradients/AvgPool3D_grad';\nimport { avgPoolGradConfig } from './gradients/AvgPool_grad';\nimport { batchMatMulGradConfig } from './gradients/BatchMatMul_grad';\nimport { batchToSpaceNDGradConfig } from './gradients/BatchToSpaceND_grad';\nimport { broadcastToGradConfig } from './gradients/BroadcastTo_grad';\nimport { castGradConfig } from './gradients/Cast_grad';\nimport { ceilGradConfig } from './gradients/Ceil_grad';\nimport { clipByValueGradConfig } from './gradients/ClipByValue_grad';\nimport { concatGradConfig } from './gradients/Concat_grad';\nimport { conv2DGradConfig } from './gradients/Conv2D_grad';\nimport { conv2DBackpropInputGradConfig } from './gradients/Conv2DBackpropInput_grad';\nimport { conv3DGradConfig } from './gradients/Conv3D_grad';\nimport { cosGradConfig } from './gradients/Cos_grad';\nimport { coshGradConfig } from './gradients/Cosh_grad';\nimport { cumsumGradConfig } from './gradients/Cumsum_grad';\nimport { depthwiseConv2dNativeGradConfig } from './gradients/DepthwiseConv2dNative_grad';\nimport { dilation2dGradConfig } from './gradients/Dilation2D_grad';\nimport { divGradConfig } from './gradients/Div_grad';\nimport { eluGradConfig } from './gradients/Elu_grad';\nimport { erfGradConfig } from './gradients/Erf_grad';\nimport { expGradConfig } from './gradients/Exp_grad';\nimport { expm1GradConfig } from './gradients/Expm1_grad';\nimport { floorGradConfig } from './gradients/Floor_grad';\nimport { floorDivGradConfig } from './gradients/FloorDiv_grad';\nimport { fusedBatchNormGradConfig } from './gradients/FusedBatchNorm_grad';\nimport { gatherGradConfig } from './gradients/GatherV2_grad';\nimport { greaterEqualGradConfig } from './gradients/GreaterEqual_grad';\nimport { identityGradConfig } from './gradients/Identity_grad';\nimport { isFiniteGradConfig } from './gradients/IsFinite_grad';\nimport { isInfGradConfig } from './gradients/IsInf_grad';\nimport { isNanGradConfig } from './gradients/IsNan_grad';\nimport { log1pGradConfig } from './gradients/Log1p_grad';\nimport { logGradConfig } from './gradients/Log_grad';\nimport { logSoftmaxGradConfig } from './gradients/LogSoftmax_grad';\nimport { lrnGradConfig } from './gradients/LRN_grad';\nimport { maxGradConfig } from './gradients/Max_grad';\nimport { maximumGradConfig } from './gradients/Maximum_grad';\nimport { maxPool3DGradConfig } from './gradients/MaxPool3D_grad';\nimport { maxPoolGradConfig } from './gradients/MaxPool_grad';\nimport { minGradConfig } from './gradients/Min_grad';\nimport { minimumGradConfig } from './gradients/Minimum_grad';\nimport { modGradConfig } from './gradients/Mod_grad';\nimport { multiplyGradConfig } from './gradients/Multiply_grad';\nimport { negateGradConfig } from './gradients/Negate_grad';\nimport { oneHotGradConfig } from './gradients/OneHot_grad';\nimport { onesLikeGradConfig } from './gradients/OnesLike_grad';\nimport { padV2GradConfig } from './gradients/PadV2_grad';\nimport { powGradConfig } from './gradients/Pow_grad';\nimport { preluGradConfig } from './gradients/Prelu_grad';\nimport { reciprocalGradConfig } from './gradients/Reciprocal_grad';\nimport { relu6GradConfig } from './gradients/Relu6_grad';\nimport { reluGradConfig } from './gradients/Relu_grad';\nimport { reshapeGradConfig } from './gradients/Reshape_grad';\nimport { resizeBilinearGradConfig } from './gradients/ResizeBilinear_grad';\nimport { resizeNearestNeighborGradConfig } from './gradients/ResizeNearestNeighbor_grad';\nimport { reverseGradConfig } from './gradients/Reverse_grad';\nimport { roundGradConfig } from './gradients/Round_grad';\nimport { rsqrtGradConfig } from './gradients/Rsqrt_grad';\nimport { selectV2PoolGradConfig } from './gradients/SelectV2_grad';\nimport { seluGradConfig } from './gradients/Selu_grad';\nimport { sigmoidGradConfig } from './gradients/Sigmoid_grad';\nimport { signGradConfig } from './gradients/Sign_grad';\nimport { sinGradConfig } from './gradients/Sin_grad';\nimport { sinhGradConfig } from './gradients/Sinh_grad';\nimport { sliceGradConfig } from './gradients/Slice_grad';\nimport { softmaxGradConfig } from './gradients/Softmax_grad';\nimport { softplusGradConfig } from './gradients/Softplus_grad';\nimport { spaceToBatchNDGradConfig } from './gradients/SpaceToBatchND_grad';\nimport { splitVGradConfig } from './gradients/SplitV_grad';\nimport { sqrtGradConfig } from './gradients/Sqrt_grad';\nimport { squareGradConfig } from './gradients/Square_grad';\nimport { squaredDifferenceGradConfig } from './gradients/SquaredDifference_grad';\nimport { stepGradConfig } from './gradients/Step_grad';\nimport { subGradConfig } from './gradients/Sub_grad';\nimport { sumGradConfig } from './gradients/Sum_grad';\nimport { tanGradConfig } from './gradients/Tan_grad';\nimport { tanhGradConfig } from './gradients/Tanh_grad';\nimport { tileGradConfig } from './gradients/Tile_grad';\nimport { transposeGradConfig } from './gradients/Transpose_grad';\nimport { unpackGradConfig } from './gradients/Unpack_grad';\nimport { unsortedSegmentSumGradConfig } from './gradients/UnsortedSegmentSum_grad';\nimport { zerosLikeGradConfig } from './gradients/ZerosLike_grad';\nimport { registerGradient } from './kernel_registry';\n// Export all kernel configs here so that the package can auto register them\nconst gradConfigs = [\n    absGradConfig,\n    acosGradConfig,\n    acoshGradConfig,\n    addGradConfig,\n    addNGradConfig,\n    argMaxGradConfig,\n    argMinGradConfig,\n    asinGradConfig,\n    asinhGradConfig,\n    atan2GradConfig,\n    atanGradConfig,\n    atanhGradConfig,\n    avgPool3DGradConfig,\n    avgPoolGradConfig,\n    batchMatMulGradConfig,\n    batchToSpaceNDGradConfig,\n    broadcastToGradConfig,\n    castGradConfig,\n    ceilGradConfig,\n    clipByValueGradConfig,\n    concatGradConfig,\n    conv2DBackpropInputGradConfig,\n    conv2DGradConfig,\n    conv3DGradConfig,\n    cosGradConfig,\n    coshGradConfig,\n    cumsumGradConfig,\n    depthwiseConv2dNativeGradConfig,\n    dilation2dGradConfig,\n    divGradConfig,\n    eluGradConfig,\n    erfGradConfig,\n    expGradConfig,\n    expm1GradConfig,\n    floorDivGradConfig,\n    floorGradConfig,\n    fusedBatchNormGradConfig,\n    gatherGradConfig,\n    greaterEqualGradConfig,\n    identityGradConfig,\n    isFiniteGradConfig,\n    isInfGradConfig,\n    isNanGradConfig,\n    log1pGradConfig,\n    logGradConfig,\n    logSoftmaxGradConfig,\n    lrnGradConfig,\n    maxGradConfig,\n    maxGradConfig,\n    maximumGradConfig,\n    maxPool3DGradConfig,\n    maxPoolGradConfig,\n    minGradConfig,\n    minimumGradConfig,\n    modGradConfig,\n    multiplyGradConfig,\n    negateGradConfig,\n    oneHotGradConfig,\n    onesLikeGradConfig,\n    padV2GradConfig,\n    padV2GradConfig,\n    powGradConfig,\n    preluGradConfig,\n    reciprocalGradConfig,\n    relu6GradConfig,\n    reluGradConfig,\n    reshapeGradConfig,\n    resizeBilinearGradConfig,\n    resizeNearestNeighborGradConfig,\n    reverseGradConfig,\n    roundGradConfig,\n    rsqrtGradConfig,\n    selectV2PoolGradConfig,\n    seluGradConfig,\n    sigmoidGradConfig,\n    signGradConfig,\n    sinGradConfig,\n    sinhGradConfig,\n    sliceGradConfig,\n    softmaxGradConfig,\n    softplusGradConfig,\n    spaceToBatchNDGradConfig,\n    spaceToBatchNDGradConfig,\n    splitVGradConfig,\n    splitVGradConfig,\n    sqrtGradConfig,\n    squaredDifferenceGradConfig,\n    squareGradConfig,\n    stepGradConfig,\n    subGradConfig,\n    sumGradConfig,\n    tanGradConfig,\n    tanhGradConfig,\n    tileGradConfig,\n    transposeGradConfig,\n    unpackGradConfig,\n    unsortedSegmentSumGradConfig,\n    zerosLikeGradConfig\n];\nfor (const gradientConfig of gradConfigs) {\n    registerGradient(gradientConfig);\n}\n//# sourceMappingURL=register_all_gradients.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { abs } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.abs = function () {\n    this.throwIfDisposed();\n    return abs(this);\n};\n//# sourceMappingURL=abs.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { acos } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.acos = function () {\n    this.throwIfDisposed();\n    return acos(this);\n};\n//# sourceMappingURL=acos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { acosh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.acosh = function () {\n    this.throwIfDisposed();\n    return acosh(this);\n};\n//# sourceMappingURL=acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { addStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.addStrict = function (x) {\n    this.throwIfDisposed();\n    return addStrict(this, x);\n};\n//# sourceMappingURL=add_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { add } from '../../ops/add';\nimport { Tensor } from '../../tensor';\nTensor.prototype.add = function (b) {\n    this.throwIfDisposed();\n    return add(this, b);\n};\n//# sourceMappingURL=add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { all } from '../../ops/all';\nimport { Tensor } from '../../tensor';\nTensor.prototype.all = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return all(this, axis, keepDims);\n};\n//# sourceMappingURL=all.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { any } from '../../ops/any';\nimport { Tensor } from '../../tensor';\nTensor.prototype.any = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return any(this, axis, keepDims);\n};\n//# sourceMappingURL=any.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { argMax } from '../../ops/arg_max';\nimport { Tensor } from '../../tensor';\nTensor.prototype.argMax = function (axis) {\n    this.throwIfDisposed();\n    return argMax(this, axis);\n};\n//# sourceMappingURL=arg_max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { argMin } from '../../ops/arg_min';\nimport { Tensor } from '../../tensor';\nTensor.prototype.argMin = function (axis) {\n    this.throwIfDisposed();\n    return argMin(this, axis);\n};\n//# sourceMappingURL=arg_min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\nimport { assert } from '../../util';\n/** Converts a size-1 `tf.Tensor` to a `tf.Scalar`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asScalar = function () {\n    this.throwIfDisposed();\n    assert(this.size === 1, () => 'The array must have only 1 element.');\n    return reshape(this, []);\n};\n//# sourceMappingURL=as_scalar.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * Casts a `tf.Tensor` to a specified dtype.\n *\n * @param dtype Data-type to cast the tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asType = function (dtype) {\n    this.throwIfDisposed();\n    return cast(this, dtype);\n};\n//# sourceMappingURL=as_type.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/** Converts a `tf.Tensor` to a `tf.Tensor1D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as1D = function () {\n    this.throwIfDisposed();\n    return reshape(this, [this.size]);\n};\n//# sourceMappingURL=as1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor2D`.\n *\n * @param rows Number of rows in `tf.Tensor2D`.\n * @param columns Number of columns in `tf.Tensor2D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as2D = function (rows, columns) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns]);\n};\n//# sourceMappingURL=as2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor3D`.\n *\n * @param rows Number of rows in `tf.Tensor3D`.\n * @param columns Number of columns in `tf.Tensor3D`.\n * @param depth Depth of `tf.Tensor3D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as3D = function (rows, columns, depth) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth]);\n};\n//# sourceMappingURL=as3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor4D`.\n *\n * @param rows Number of rows in `tf.Tensor4D`.\n * @param columns Number of columns in `tf.Tensor4D`.\n * @param depth Depth of `tf.Tensor4D`.\n * @param depth2 4th dimension of `tf.Tensor4D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as4D = function (rows, columns, depth, depth2) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth, depth2]);\n};\n//# sourceMappingURL=as4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor5D`.\n *\n * @param rows Number of rows in `tf.Tensor5D`.\n * @param columns Number of columns in `tf.Tensor5D`.\n * @param depth Depth of `tf.Tensor5D`.\n * @param depth2 4th dimension of `tf.Tensor5D`.\n * @param depth3 5th dimension of 'tf.Tensor5D'\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as5D = function (rows, columns, depth, depth2, depth3) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth, depth2, depth3]);\n};\n//# sourceMappingURL=as5d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { asin } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.asin = function () {\n    this.throwIfDisposed();\n    return asin(this);\n};\n//# sourceMappingURL=asin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { asinh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.asinh = function () {\n    this.throwIfDisposed();\n    return asinh(this);\n};\n//# sourceMappingURL=asinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { atan } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atan = function () {\n    this.throwIfDisposed();\n    return atan(this);\n};\n//# sourceMappingURL=atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { atan2 } from '../../ops/atan2';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atan2 = function (b) {\n    this.throwIfDisposed();\n    return atan2(this, b);\n};\n//# sourceMappingURL=atan2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { atanh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atanh = function () {\n    this.throwIfDisposed();\n    return atanh(this);\n};\n//# sourceMappingURL=atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { avgPool } from '../../ops/avg_pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.avgPool = function (filterSize, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return avgPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=avg_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { batchToSpaceND } from '../../ops/batch_to_space_nd';\nimport { Tensor } from '../../tensor';\nTensor.prototype.batchToSpaceND = function (blockShape, crops) {\n    this.throwIfDisposed();\n    return batchToSpaceND(this, blockShape, crops);\n};\n//# sourceMappingURL=batch_to_space_nd.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { batchNorm } from '../../ops/batchnorm';\nimport { Tensor } from '../../tensor';\nTensor.prototype.batchNorm = function (mean, variance, offset, scale, varianceEpsilon) {\n    this.throwIfDisposed();\n    return batchNorm(this, mean, variance, offset, scale, varianceEpsilon);\n};\n//# sourceMappingURL=batchnorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { broadcastTo } from '../../ops/broadcast_to';\nimport { Tensor } from '../../tensor';\nTensor.prototype.broadcastTo = function (shape) {\n    this.throwIfDisposed();\n    return broadcastTo(this, shape);\n};\n//# sourceMappingURL=broadcast_to.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cast = function (dtype) {\n    this.throwIfDisposed();\n    return cast(this, dtype);\n};\n//# sourceMappingURL=cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { ceil } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.ceil = function () {\n    this.throwIfDisposed();\n    return ceil(this);\n};\n//# sourceMappingURL=ceil.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { clipByValue } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.clipByValue = function (min, max) {\n    this.throwIfDisposed();\n    return clipByValue(this, min, max);\n};\n//# sourceMappingURL=clip_by_value.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../../ops/concat';\nimport { Tensor } from '../../tensor';\nTensor.prototype.concat = function (x, axis) {\n    this.throwIfDisposed();\n    if (x instanceof Tensor) {\n        x = [x];\n    }\n    return concat([this, ...x], axis);\n};\n//# sourceMappingURL=concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv1d } from '../../ops/conv1d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv1d = function (filter, stride, pad, dataFormat, dilation, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv1d(this, filter, stride, pad, dataFormat, dilation, dimRoundingMode);\n};\n//# sourceMappingURL=conv1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2dTranspose } from '../../ops/conv2d_transpose';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv2dTranspose = function (filter, outputShape, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv2dTranspose(this, filter, outputShape, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=conv2d_transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2d } from '../../ops/conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cos } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cos = function () {\n    this.throwIfDisposed();\n    return cos(this);\n};\n//# sourceMappingURL=cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cosh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cosh = function () {\n    this.throwIfDisposed();\n    return cosh(this);\n};\n//# sourceMappingURL=cosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { cumsum } from '../../ops/cumsum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cumsum = function (axis, exclusive, reverse) {\n    this.throwIfDisposed();\n    return cumsum(this, axis, exclusive, reverse);\n};\n//# sourceMappingURL=cumsum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { depthToSpace } from '../../ops/depth_to_space';\nimport { Tensor } from '../../tensor';\nTensor.prototype.depthToSpace = function (blockSize, dataFormat) {\n    this.throwIfDisposed();\n    return depthToSpace(this, blockSize, dataFormat);\n};\n//# sourceMappingURL=depth_to_space.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../../globals';\nimport { depthwiseConv2d } from '../../ops/depthwise_conv2d';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated Use `depthwiseConv2d` instead.\n */\nTensor.prototype.depthwiseConv2D = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    deprecationWarn('depthwiseConv2D is deprecated, use depthwiseConv2d instead');\n    this.throwIfDisposed();\n    return depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=depthwise_conv2D_deprecated.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { depthwiseConv2d } from '../../ops/depthwise_conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.depthwiseConv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    this.throwIfDisposed();\n    return depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dilation2d } from '../../ops/dilation2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.dilation2d = function (filter, strides, pad, dilations, dataFormat) {\n    this.throwIfDisposed();\n    return dilation2d(this, filter, strides, pad, dilations, dataFormat);\n};\n//# sourceMappingURL=dilation2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { divNoNan } from '../../ops/div_no_nan';\nimport { Tensor } from '../../tensor';\nTensor.prototype.divNoNan = function (b) {\n    this.throwIfDisposed();\n    return divNoNan(this, b);\n};\n//# sourceMappingURL=div_no_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { divStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.divStrict = function (x) {\n    this.throwIfDisposed();\n    return divStrict(this, x);\n};\n//# sourceMappingURL=div_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { div } from '../../ops/div';\nimport { Tensor } from '../../tensor';\nTensor.prototype.div = function (b) {\n    this.throwIfDisposed();\n    return div(this, b);\n};\n//# sourceMappingURL=div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dot } from '../../ops/dot';\nimport { Tensor } from '../../tensor';\nTensor.prototype.dot = function (b) {\n    this.throwIfDisposed();\n    return dot(this, b);\n};\n//# sourceMappingURL=dot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { elu } from '../../ops/elu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.elu = function () {\n    this.throwIfDisposed();\n    return elu(this);\n};\n//# sourceMappingURL=elu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { equalStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.equalStrict = function (x) {\n    this.throwIfDisposed();\n    return equalStrict(this, x);\n};\n//# sourceMappingURL=equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { equal } from '../../ops/equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.equal = function (b) {\n    this.throwIfDisposed();\n    return equal(this, b);\n};\n//# sourceMappingURL=equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { erf } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.erf = function () {\n    this.throwIfDisposed();\n    return erf(this);\n};\n//# sourceMappingURL=erf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { exp } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.exp = function () {\n    this.throwIfDisposed();\n    return exp(this);\n};\n//# sourceMappingURL=exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { expandDims } from '../../ops/expand_dims';\nimport { Tensor } from '../../tensor';\nTensor.prototype.expandDims = function (axis) {\n    this.throwIfDisposed();\n    return expandDims(this, axis);\n};\n//# sourceMappingURL=expand_dims.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { expm1 } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.expm1 = function () {\n    this.throwIfDisposed();\n    return expm1(this);\n};\n//# sourceMappingURL=expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { fft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.fft = function () {\n    this.throwIfDisposed();\n    return fft(this);\n};\n//# sourceMappingURL=fft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/** Flatten a Tensor to a 1D array.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.flatten = function () {\n    this.throwIfDisposed();\n    return reshape(this, [this.size]);\n};\n//# sourceMappingURL=flatten.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { floor } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.floor = function () {\n    this.throwIfDisposed();\n    return floor(this);\n};\n//# sourceMappingURL=floor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { floorDiv } from '../../ops/floorDiv';\nimport { Tensor } from '../../tensor';\nTensor.prototype.floorDiv = function (b) {\n    this.throwIfDisposed();\n    return floorDiv(this, b);\n};\n//# sourceMappingURL=floorDiv.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { gather } from '../../ops/gather';\nimport { Tensor } from '../../tensor';\nTensor.prototype.gather = function (indices, axis) {\n    this.throwIfDisposed();\n    return gather(this, indices, axis);\n};\n//# sourceMappingURL=gather.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { greaterEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return greaterEqualStrict(this, x);\n};\n//# sourceMappingURL=greater_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { greaterEqual } from '../../ops/greater_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.greaterEqual = function (b) {\n    this.throwIfDisposed();\n    return greaterEqual(this, b);\n};\n//# sourceMappingURL=greater_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { greaterStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterStrict = function (x) {\n    this.throwIfDisposed();\n    return greaterStrict(this, x);\n};\n//# sourceMappingURL=greater_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { greater } from '../../ops/greater';\nimport { Tensor } from '../../tensor';\nTensor.prototype.greater = function (b) {\n    this.throwIfDisposed();\n    return greater(this, b);\n};\n//# sourceMappingURL=greater.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { ifft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.ifft = function () {\n    this.throwIfDisposed();\n    return ifft(this);\n};\n//# sourceMappingURL=ifft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { irfft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.irfft = function () {\n    this.throwIfDisposed();\n    return irfft(this);\n};\n//# sourceMappingURL=irfft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isFinite } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isFinite = function () {\n    this.throwIfDisposed();\n    return isFinite(this);\n};\n//# sourceMappingURL=is_finite.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isInf } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isInf = function () {\n    this.throwIfDisposed();\n    return isInf(this);\n};\n//# sourceMappingURL=is_inf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isNaN } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isNaN = function () {\n    this.throwIfDisposed();\n    return isNaN(this);\n};\n//# sourceMappingURL=is_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { leakyRelu } from '../../ops/leaky_relu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.leakyRelu = function (alpha) {\n    this.throwIfDisposed();\n    return leakyRelu(this, alpha);\n};\n//# sourceMappingURL=leaky_relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { lessEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.lessEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return lessEqualStrict(this, x);\n};\n//# sourceMappingURL=less_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { lessEqual } from '../../ops/less_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.lessEqual = function (b) {\n    this.throwIfDisposed();\n    return lessEqual(this, b);\n};\n//# sourceMappingURL=less_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { lessStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.lessStrict = function (x) {\n    this.throwIfDisposed();\n    return lessStrict(this, x);\n};\n//# sourceMappingURL=less_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { less } from '../../ops/less';\nimport { Tensor } from '../../tensor';\nTensor.prototype.less = function (b) {\n    this.throwIfDisposed();\n    return less(this, b);\n};\n//# sourceMappingURL=less.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { localResponseNormalization } from '../../ops/local_response_normalization';\nimport { Tensor } from '../../tensor';\nTensor.prototype.localResponseNormalization = function (depthRadius, bias, alpha, beta) {\n    this.throwIfDisposed();\n    return localResponseNormalization(this, depthRadius, bias, alpha, beta);\n};\n//# sourceMappingURL=local_response_normalization.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { logSigmoid } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSigmoid = function () {\n    this.throwIfDisposed();\n    return logSigmoid(this);\n};\n//# sourceMappingURL=log_sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { logSoftmax } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSoftmax = function (axis) {\n    this.throwIfDisposed();\n    return logSoftmax(this, axis);\n};\n//# sourceMappingURL=log_softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logSumExp } from '../../ops/log_sum_exp';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSumExp = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return logSumExp(this, axis, keepDims);\n};\n//# sourceMappingURL=log_sum_exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { log } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.log = function () {\n    this.throwIfDisposed();\n    return log(this);\n};\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { log1p } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.log1p = function () {\n    this.throwIfDisposed();\n    return log1p(this);\n};\n//# sourceMappingURL=log1p.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalAnd } from '../../ops/logical_and';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalAnd = function (b) {\n    this.throwIfDisposed();\n    return logicalAnd(this, b);\n};\n//# sourceMappingURL=logical_and.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalNot } from '../../ops/logical_not';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalNot = function () {\n    this.throwIfDisposed();\n    return logicalNot(this);\n};\n//# sourceMappingURL=logical_not.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalOr } from '../../ops/logical_or';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalOr = function (b) {\n    this.throwIfDisposed();\n    return logicalOr(this, b);\n};\n//# sourceMappingURL=logical_or.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalXor } from '../../ops/logical_xor';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalXor = function (b) {\n    this.throwIfDisposed();\n    return logicalXor(this, b);\n};\n//# sourceMappingURL=logical_xor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { matMul } from '../../ops/mat_mul';\nimport { Tensor } from '../../tensor';\nTensor.prototype.matMul = function (b, transposeA, transposeB) {\n    this.throwIfDisposed();\n    return matMul(this, b, transposeA, transposeB);\n};\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { maxPool } from '../../ops/max_pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.maxPool = function (filterSize, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return maxPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=max_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { max } from '../../ops/max';\nimport { Tensor } from '../../tensor';\nTensor.prototype.max = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return max(this, axis, keepDims);\n};\n//# sourceMappingURL=max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { maximumStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.maximumStrict = function (x) {\n    this.throwIfDisposed();\n    return maximumStrict(this, x);\n};\n//# sourceMappingURL=maximum_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { maximum } from '../../ops/maximum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.maximum = function (b) {\n    this.throwIfDisposed();\n    return maximum(this, b);\n};\n//# sourceMappingURL=maximum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mean } from '../../ops/mean';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mean = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return mean(this, axis, keepDims);\n};\n//# sourceMappingURL=mean.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { min } from '../../ops/min';\nimport { Tensor } from '../../tensor';\nTensor.prototype.min = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return min(this, axis, keepDims);\n};\n//# sourceMappingURL=min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { minimumStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.minimumStrict = function (x) {\n    this.throwIfDisposed();\n    return minimumStrict(this, x);\n};\n//# sourceMappingURL=minimum_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { minimum } from '../../ops/minimum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.minimum = function (b) {\n    this.throwIfDisposed();\n    return minimum(this, b);\n};\n//# sourceMappingURL=minimum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { modStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.modStrict = function (x) {\n    this.throwIfDisposed();\n    return modStrict(this, x);\n};\n//# sourceMappingURL=mod_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mod } from '../../ops/mod';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mod = function (b) {\n    this.throwIfDisposed();\n    return mod(this, b);\n};\n//# sourceMappingURL=mod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { mulStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.mulStrict = function (x) {\n    this.throwIfDisposed();\n    return mulStrict(this, x);\n};\n//# sourceMappingURL=mul_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mul } from '../../ops/mul';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mul = function (b) {\n    this.throwIfDisposed();\n    return mul(this, b);\n};\n//# sourceMappingURL=mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { neg } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.neg = function () {\n    this.throwIfDisposed();\n    return neg(this);\n};\n//# sourceMappingURL=neg.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { norm } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.norm = function (ord, axis, keepDims) {\n    this.throwIfDisposed();\n    return norm(this, ord, axis, keepDims);\n};\n//# sourceMappingURL=norm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { notEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.notEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return notEqualStrict(this, x);\n};\n//# sourceMappingURL=not_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { notEqual } from '../../ops/not_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.notEqual = function (b) {\n    this.throwIfDisposed();\n    return notEqual(this, b);\n};\n//# sourceMappingURL=not_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { oneHot } from '../../ops/one_hot';\nimport { Tensor } from '../../tensor';\nTensor.prototype.oneHot = function (depth, onValue = 1, offValue = 0) {\n    this.throwIfDisposed();\n    return oneHot(this, depth, onValue, offValue);\n};\n//# sourceMappingURL=one_hot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { onesLike } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.onesLike = function () {\n    this.throwIfDisposed();\n    return onesLike(this);\n};\n//# sourceMappingURL=ones_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pad } from '../../ops/pad';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pad = function (paddings, constantValue) {\n    this.throwIfDisposed();\n    return pad(this, paddings, constantValue);\n};\n//# sourceMappingURL=pad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pool } from '../../ops/pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pool = function (windowShape, poolingType, padding, dilationRate, strides) {\n    this.throwIfDisposed();\n    return pool(this, windowShape, poolingType, padding, dilationRate, strides);\n};\n//# sourceMappingURL=pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { powStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.powStrict = function (exp) {\n    this.throwIfDisposed();\n    return powStrict(this, exp);\n};\n//# sourceMappingURL=pow_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pow } from '../../ops/pow';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pow = function (exp) {\n    this.throwIfDisposed();\n    return pow(this, exp);\n};\n//# sourceMappingURL=pow.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { prelu } from '../../ops/prelu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.prelu = function (alpha) {\n    this.throwIfDisposed();\n    return prelu(this, alpha);\n};\n//# sourceMappingURL=prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { prod } from '../../ops/prod';\nimport { Tensor } from '../../tensor';\nTensor.prototype.prod = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return prod(this, axis, keepDims);\n};\n//# sourceMappingURL=prod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { reciprocal } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reciprocal = function () {\n    this.throwIfDisposed();\n    return reciprocal(this);\n};\n//# sourceMappingURL=reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { relu } from '../../ops/relu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.relu = function () {\n    this.throwIfDisposed();\n    return relu(this);\n};\n//# sourceMappingURL=relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { relu6 } from '../../ops/relu6';\nimport { Tensor } from '../../tensor';\nTensor.prototype.relu6 = function () {\n    this.throwIfDisposed();\n    return relu6(this);\n};\n//# sourceMappingURL=relu6.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Reshapes the tensor into the shape of the provided tensor.\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.reshapeAs = function (x) {\n    this.throwIfDisposed();\n    return reshape(this, x.shape);\n};\n//# sourceMappingURL=reshape_as.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reshape = function (shape) {\n    this.throwIfDisposed();\n    return reshape(this, shape);\n};\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { resizeBilinear } from '../../ops/image/resize_bilinear';\nimport { Tensor } from '../../tensor';\nTensor.prototype.resizeBilinear = function (newShape2D, alignCorners) {\n    this.throwIfDisposed();\n    return resizeBilinear(this, newShape2D, alignCorners);\n};\n//# sourceMappingURL=resize_bilinear.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { resizeNearestNeighbor } from '../../ops/image/resize_nearest_neighbor';\nimport { Tensor } from '../../tensor';\nTensor.prototype.resizeNearestNeighbor = function (newShape2D, alignCorners) {\n    this.throwIfDisposed();\n    return resizeNearestNeighbor(this, newShape2D, alignCorners);\n};\n//# sourceMappingURL=resize_nearest_neighbor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reverse } from '../../ops/reverse';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reverse = function (axis) {\n    this.throwIfDisposed();\n    return reverse(this, axis);\n};\n//# sourceMappingURL=reverse.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { rfft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.rfft = function () {\n    this.throwIfDisposed();\n    return rfft(this);\n};\n//# sourceMappingURL=rfft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { round } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.round = function () {\n    this.throwIfDisposed();\n    return round(this);\n};\n//# sourceMappingURL=round.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { rsqrt } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.rsqrt = function () {\n    this.throwIfDisposed();\n    return rsqrt(this);\n};\n//# sourceMappingURL=rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { selu } from '../../ops/selu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.selu = function () {\n    this.throwIfDisposed();\n    return selu(this);\n};\n//# sourceMappingURL=selu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { separableConv2d } from '../../ops/separable_conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.separableConv2d = function (depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat) {\n    this.throwIfDisposed();\n    return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat);\n};\n//# sourceMappingURL=separable_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sigmoid } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sigmoid = function () {\n    this.throwIfDisposed();\n    return sigmoid(this);\n};\n//# sourceMappingURL=sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sign } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sign = function () {\n    this.throwIfDisposed();\n    return sign(this);\n};\n//# sourceMappingURL=sign.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sin } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sin = function () {\n    this.throwIfDisposed();\n    return sin(this);\n};\n//# sourceMappingURL=sin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sinh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sinh = function () {\n    this.throwIfDisposed();\n    return sinh(this);\n};\n//# sourceMappingURL=sinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { slice } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.slice = function (begin, size) {\n    this.throwIfDisposed();\n    return slice(this, begin, size);\n};\n//# sourceMappingURL=slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { softmax } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.softmax = function (dim) {\n    this.throwIfDisposed();\n    return softmax(this, dim);\n};\n//# sourceMappingURL=softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { softplus } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.softplus = function () {\n    this.throwIfDisposed();\n    return softplus(this);\n};\n//# sourceMappingURL=softplus.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { spaceToBatchND } from '../../ops/space_to_batch_nd';\nimport { Tensor } from '../../tensor';\nTensor.prototype.spaceToBatchND = function (blockShape, paddings) {\n    this.throwIfDisposed();\n    return spaceToBatchND(this, blockShape, paddings);\n};\n//# sourceMappingURL=space_to_batch_nd.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { split } from '../../ops/split';\nimport { Tensor } from '../../tensor';\nTensor.prototype.split = function (numOrSizeSplits, axis) {\n    this.throwIfDisposed();\n    return split(this, numOrSizeSplits, axis);\n};\n//# sourceMappingURL=split.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sqrt } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sqrt = function () {\n    this.throwIfDisposed();\n    return sqrt(this);\n};\n//# sourceMappingURL=sqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { square } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.square = function () {\n    this.throwIfDisposed();\n    return square(this);\n};\n//# sourceMappingURL=square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { squaredDifference } from '../../ops/squared_difference';\nimport { Tensor } from '../../tensor';\nTensor.prototype.squaredDifference = function (b) {\n    this.throwIfDisposed();\n    return squaredDifference(this, b);\n};\n//# sourceMappingURL=squared_difference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { squaredDifferenceStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.squaredDifferenceStrict = function (x) {\n    this.throwIfDisposed();\n    return squaredDifferenceStrict(this, x);\n};\n//# sourceMappingURL=squared_difference_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { squeeze } from '../../ops/squeeze';\nimport { Tensor } from '../../tensor';\nTensor.prototype.squeeze = function (axis) {\n    this.throwIfDisposed();\n    return squeeze(this, axis);\n};\n//# sourceMappingURL=squeeze.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { stack } from '../../ops/stack';\nimport { Tensor } from '../../tensor';\nTensor.prototype.stack = function (x, axis) {\n    this.throwIfDisposed();\n    const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];\n    return stack(tensorsToBeStacked, axis);\n};\n//# sourceMappingURL=stack.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { step } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.step = function (alpha) {\n    this.throwIfDisposed();\n    return step(this, alpha);\n};\n//# sourceMappingURL=step.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { stridedSlice } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.stridedSlice = function (begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n    this.throwIfDisposed();\n    return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n};\n//# sourceMappingURL=strided_slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { subStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.subStrict = function (x) {\n    this.throwIfDisposed();\n    return subStrict(this, x);\n};\n//# sourceMappingURL=sub_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { sub } from '../../ops/sub';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sub = function (b) {\n    this.throwIfDisposed();\n    return sub(this, b);\n};\n//# sourceMappingURL=sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { sum } from '../../ops/sum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sum = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return sum(this, axis, keepDims);\n};\n//# sourceMappingURL=sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { tan } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tan = function () {\n    this.throwIfDisposed();\n    return tan(this);\n};\n//# sourceMappingURL=tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { tanh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tanh = function () {\n    this.throwIfDisposed();\n    return tanh(this);\n};\n//# sourceMappingURL=tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tile } from '../../ops/tile';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tile = function (reps) {\n    this.throwIfDisposed();\n    return tile(this, reps);\n};\n//# sourceMappingURL=tile.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `bool`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toBool = function () {\n    this.throwIfDisposed();\n    return cast(this, 'bool');\n};\n//# sourceMappingURL=to_bool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `float32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toFloat = function () {\n    this.throwIfDisposed();\n    return cast(this, 'float32');\n};\n//# sourceMappingURL=to_float.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `int32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toInt = function () {\n    this.throwIfDisposed();\n    return cast(this, 'int32');\n};\n//# sourceMappingURL=to_int.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { topk } from '../../ops/topk';\nimport { Tensor } from '../../tensor';\nTensor.prototype.topk = function (k, sorted) {\n    this.throwIfDisposed();\n    return topk(this, k, sorted);\n};\n//# sourceMappingURL=topk.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { transpose } from '../../ops/transpose';\nimport { Tensor } from '../../tensor';\nTensor.prototype.transpose = function (perm) {\n    this.throwIfDisposed();\n    return transpose(this, perm);\n};\n//# sourceMappingURL=transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unique } from '../../ops/unique';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unique = function (axis) {\n    this.throwIfDisposed();\n    return unique(this, axis);\n};\n//# sourceMappingURL=unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unsortedSegmentSum } from '../../ops/unsorted_segment_sum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unsortedSegmentSum = function (segmentIds, numSegments) {\n    this.throwIfDisposed();\n    return unsortedSegmentSum(this, segmentIds, numSegments);\n};\n//# sourceMappingURL=unsorted_segment_sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unstack } from '../../ops/unstack';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unstack = function (axis) {\n    this.throwIfDisposed();\n    return unstack(this, axis);\n};\n//# sourceMappingURL=unstack.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { where } from '../../ops/where';\nimport { Tensor } from '../../tensor';\nTensor.prototype.where = function (condition, x) {\n    this.throwIfDisposed();\n    return where(condition, this, x);\n};\n//# sourceMappingURL=where.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { zerosLike } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.zerosLike = function () {\n    this.throwIfDisposed();\n    return zerosLike(this);\n};\n//# sourceMappingURL=zeros_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './abs';\nimport './acos';\nimport './acosh';\nimport './add_strict';\nimport './add';\nimport './all';\nimport './any';\nimport './arg_max';\nimport './arg_min';\nimport './as_scalar';\nimport './as_type';\nimport './as1d';\nimport './as2d';\nimport './as3d';\nimport './as4d';\nimport './as5d';\nimport './asin';\nimport './asinh';\nimport './atan';\nimport './atan2';\nimport './atanh';\nimport './avg_pool';\nimport './batch_to_space_nd';\nimport './batchnorm';\nimport './broadcast_to';\nimport './cast';\nimport './ceil';\nimport './clip_by_value';\nimport './concat';\nimport './conv1d';\nimport './conv2d_transpose';\nimport './conv2d';\nimport './cos';\nimport './cosh';\nimport './cumsum';\nimport './depth_to_space';\nimport './depthwise_conv2D_deprecated';\nimport './depthwise_conv2d';\nimport './dilation2d';\nimport './div_no_nan';\nimport './div_strict';\nimport './div';\nimport './dot';\nimport './elu';\nimport './equal_strict';\nimport './equal';\nimport './erf';\nimport './exp';\nimport './expand_dims';\nimport './expm1';\nimport './fft';\nimport './flatten';\nimport './floor';\nimport './floorDiv';\nimport './gather';\nimport './greater_equal_strict';\nimport './greater_equal';\nimport './greater_strict';\nimport './greater';\nimport './ifft';\nimport './irfft';\nimport './is_finite';\nimport './is_inf';\nimport './is_nan';\nimport './leaky_relu';\nimport './less_equal_strict';\nimport './less_equal';\nimport './less_strict';\nimport './less';\nimport './local_response_normalization';\nimport './log_sigmoid';\nimport './log_softmax';\nimport './log_sum_exp';\nimport './log';\nimport './log1p';\nimport './logical_and';\nimport './logical_not';\nimport './logical_or';\nimport './logical_xor';\nimport './mat_mul';\nimport './max_pool';\nimport './max';\nimport './maximum_strict';\nimport './maximum';\nimport './mean';\nimport './min';\nimport './minimum_strict';\nimport './minimum';\nimport './mod_strict';\nimport './mod';\nimport './mul_strict';\nimport './mul';\nimport './neg';\nimport './norm';\nimport './not_equal_strict';\nimport './not_equal';\nimport './one_hot';\nimport './ones_like';\nimport './pad';\nimport './pool';\nimport './pow_strict';\nimport './pow';\nimport './prelu';\nimport './prod';\nimport './reciprocal';\nimport './relu';\nimport './relu6';\nimport './reshape_as';\nimport './reshape';\nimport './resize_bilinear';\nimport './resize_nearest_neighbor';\nimport './reverse';\nimport './rfft';\nimport './round';\nimport './rsqrt';\nimport './selu';\nimport './separable_conv2d';\nimport './sigmoid';\nimport './sign';\nimport './sin';\nimport './sinh';\nimport './slice';\nimport './softmax';\nimport './softplus';\nimport './space_to_batch_nd';\nimport './split';\nimport './sqrt';\nimport './square';\nimport './squared_difference';\nimport './squared_difference_strict';\nimport './squeeze';\nimport './stack';\nimport './step';\nimport './strided_slice';\nimport './sub_strict';\nimport './sub';\nimport './sum';\nimport './tan';\nimport './tanh';\nimport './tile';\nimport './to_bool';\nimport './to_float';\nimport './to_int';\nimport './topk';\nimport './transpose';\nimport './unique';\nimport './unsorted_segment_sum';\nimport './unstack';\nimport './where';\nimport './zeros_like';\n//# sourceMappingURL=register_all_chained_ops.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Required side effectful code.\nimport './base_side_effects';\n// All exports from this package should be in base.\nexport * from './base';\n// Register all the gradients.\nimport './register_all_gradients';\n// Import all op chainers and add type info to Tensor.\nimport './public/chained_ops/register_all_chained_ops';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { backend } from '@tensorflow/tfjs-core';\nlet _epsilon;\n/**\n * Returns the value of the fuzz factor used in numeric expressions.\n */\nexport function epsilon() {\n    if (_epsilon == null) {\n        _epsilon = backend().epsilon();\n    }\n    return _epsilon;\n}\n/**\n * Sets the value of the fuzz factor used in numeric expressions.\n * @param e New value of epsilon.\n */\nexport function setEpsilon(e) {\n    _epsilon = e;\n}\n/**\n * Returns the default image data format convention.\n */\nexport function imageDataFormat() {\n    return 'channelsLast';\n}\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Explicit error types.\n *\n * See the following link for more information about why the code includes\n * calls to setPrototypeOf:\n *\n * https://github.com/Microsoft/TypeScript-wiki/blob/master/Breaking-Changes.md#extending-built-ins-like-error-array-and-map-may-no-longer-work\n */\n// tslint:enable\n/**\n * Equivalent of Python's AttributeError.\n */\nexport class AttributeError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, AttributeError.prototype);\n    }\n}\n/**\n * Equivalent of Python's RuntimeError.\n */\nexport class RuntimeError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, RuntimeError.prototype);\n    }\n}\n/**\n * Equivalent of Python's ValueError.\n */\nexport class ValueError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, ValueError.prototype);\n    }\n}\n/**\n * Equivalent of Python's NotImplementedError.\n */\nexport class NotImplementedError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, NotImplementedError.prototype);\n    }\n}\n/**\n * Equivalent of Python's AssertionError.\n */\nexport class AssertionError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, AssertionError.prototype);\n    }\n}\n/**\n * Equivalent of Python's IndexError.\n */\nexport class IndexError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, IndexError.prototype);\n    }\n}\n//# sourceMappingURL=errors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: utils/generic_utils.py */\nimport { util } from '@tensorflow/tfjs-core';\nimport { AssertionError, ValueError } from '../errors';\n// tslint:enable\n/**\n * If `value` is an Array, equivalent to Python's `value * numValues`.\n * If `value` is not an Array, equivalent to Python's `[value] * numValues`\n */\n// tslint:disable-next-line:no-any\nexport function pyListRepeat(value, numValues) {\n    if (Array.isArray(value)) {\n        // tslint:disable-next-line:no-any\n        let newArray = [];\n        for (let i = 0; i < numValues; i++) {\n            newArray = newArray.concat(value);\n        }\n        return newArray;\n    }\n    else {\n        const newArray = new Array(numValues);\n        newArray.fill(value);\n        return newArray;\n    }\n}\nexport function assert(val, message) {\n    if (!val) {\n        throw new AssertionError(message);\n    }\n}\n/**\n * Count the number of elements of the `array` that are equal to `reference`.\n */\nexport function count(array, refernce) {\n    let counter = 0;\n    for (const item of array) {\n        if (item === refernce) {\n            counter++;\n        }\n    }\n    return counter;\n}\n/**\n * If an array is of length 1, just return the first element. Otherwise, return\n * the full array.\n * @param tensors\n */\nexport function singletonOrArray(xs) {\n    if (xs.length === 1) {\n        return xs[0];\n    }\n    return xs;\n}\n/**\n * Normalizes a list/tensor into a list.\n *\n * If a tensor is passed, we return\n * a list of size 1 containing the tensor.\n *\n * @param x target object to be normalized.\n */\n// tslint:disable-next-line:no-any\nexport function toList(x) {\n    if (Array.isArray(x)) {\n        return x;\n    }\n    return [x];\n}\n/**\n * Generate a UID for a list\n */\n// tslint:disable-next-line:no-any\nexport function objectListUid(objs) {\n    const objectList = toList(objs);\n    let retVal = '';\n    for (const obj of objectList) {\n        if (obj.id == null) {\n            throw new ValueError(`Object ${obj} passed to objectListUid without an id`);\n        }\n        if (retVal !== '') {\n            retVal = retVal + ', ';\n        }\n        retVal = `${retVal}${Math.abs(obj.id)}`;\n    }\n    return retVal;\n}\n/**\n * Converts string to snake-case.\n * @param name\n */\nexport function toSnakeCase(name) {\n    const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, '$1_$2');\n    const insecure = intermediate.replace(/([a-z])([A-Z])/g, '$1_$2').toLowerCase();\n    /*\n     If the class is private the name starts with \"_\" which is not secure\n     for creating scopes. We prefix the name with \"private\" in this case.\n     */\n    if (insecure[0] !== '_') {\n        return insecure;\n    }\n    return 'private' + insecure;\n}\nexport function toCamelCase(identifier) {\n    // quick return for empty string or single character strings\n    if (identifier.length <= 1) {\n        return identifier;\n    }\n    // Check for the underscore indicating snake_case\n    if (identifier.indexOf('_') === -1) {\n        return identifier;\n    }\n    return identifier.replace(/[_]+(\\w|$)/g, (m, p1) => p1.toUpperCase());\n}\n// tslint:disable-next-line:no-any\nlet _GLOBAL_CUSTOM_OBJECTS = {};\nexport function serializeKerasObject(instance) {\n    if (instance === null || instance === undefined) {\n        return null;\n    }\n    const dict = {};\n    dict['className'] = instance.getClassName();\n    dict['config'] = instance.getConfig();\n    return dict;\n}\n/**\n * Replace ndarray-style scalar objects in serialization objects with numbers.\n *\n * Background: In some versions of tf.keras, certain scalar values in the HDF5\n * model save file can be serialized as: `{'type': 'ndarray', 'value': num}`,\n * where in `num` is a plain number. This method converts such serialization\n * to a `number`.\n *\n * @param config The keras-format serialization object to be processed\n *   (in place).\n */\nfunction convertNDArrayScalarsInConfig(config) {\n    if (config == null || typeof config !== 'object') {\n        return;\n    }\n    else if (Array.isArray(config)) {\n        config.forEach(configItem => convertNDArrayScalarsInConfig(configItem));\n    }\n    else {\n        const fields = Object.keys(config);\n        for (const field of fields) {\n            const value = config[field];\n            if (value != null && typeof value === 'object') {\n                if (!Array.isArray(value) && value['type'] === 'ndarray' &&\n                    typeof value['value'] === 'number') {\n                    config[field] = value['value'];\n                }\n                else {\n                    convertNDArrayScalarsInConfig(value);\n                }\n            }\n        }\n    }\n}\n/**\n * Deserialize a saved Keras Object\n * @param identifier either a string ID or a saved Keras dictionary\n * @param moduleObjects a list of Python class names to object constructors\n * @param customObjects a list of Python class names to object constructors\n * @param printableModuleName debug text for the object being reconstituted\n * @param fastWeightInit Optional flag to use fast weight initialization\n *   during deserialization. This is applicable to cases in which\n *   the initialization will be immediately overwritten by loaded weight\n *   values. Default: `false`.\n * @returns a TensorFlow.js Layers object\n */\n// tslint:disable:no-any\nexport function deserializeKerasObject(identifier, moduleObjects = {}, customObjects = {}, printableModuleName = 'object', fastWeightInit = false) {\n    // tslint:enable\n    if (typeof identifier === 'string') {\n        const functionName = identifier;\n        let fn;\n        if (functionName in customObjects) {\n            fn = customObjects[functionName];\n        }\n        else if (functionName in _GLOBAL_CUSTOM_OBJECTS) {\n            fn = _GLOBAL_CUSTOM_OBJECTS[functionName];\n        }\n        else {\n            fn = moduleObjects[functionName];\n            if (fn == null) {\n                throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. ` +\n                    `This may be due to one of the following reasons:\\n` +\n                    `1. The ${printableModuleName} is defined in Python, in which ` +\n                    `case it needs to be ported to TensorFlow.js or your JavaScript ` +\n                    `code.\\n` +\n                    `2. The custom ${printableModuleName} is defined in JavaScript, ` +\n                    `but is not registered properly with ` +\n                    `tf.serialization.registerClass().`);\n                // TODO(cais): Add link to tutorial page on custom layers.\n            }\n        }\n        return fn;\n    }\n    else {\n        // In this case we are dealing with a Keras config dictionary.\n        const config = identifier;\n        if (config['className'] == null || config['config'] == null) {\n            throw new ValueError(`${printableModuleName}: Improper config format: ` +\n                `${JSON.stringify(config)}.\\n` +\n                `'className' and 'config' must set.`);\n        }\n        const className = config['className'];\n        let cls, fromConfig;\n        if (className in customObjects) {\n            [cls, fromConfig] = customObjects[className];\n        }\n        else if (className in _GLOBAL_CUSTOM_OBJECTS) {\n            [cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS['className'];\n        }\n        else if (className in moduleObjects) {\n            [cls, fromConfig] = moduleObjects[className];\n        }\n        if (cls == null) {\n            throw new ValueError(`Unknown ${printableModuleName}: ${className}. ` +\n                `This may be due to one of the following reasons:\\n` +\n                `1. The ${printableModuleName} is defined in Python, in which ` +\n                `case it needs to be ported to TensorFlow.js or your JavaScript ` +\n                `code.\\n` +\n                `2. The custom ${printableModuleName} is defined in JavaScript, ` +\n                `but is not registered properly with ` +\n                `tf.serialization.registerClass().`);\n            // TODO(cais): Add link to tutorial page on custom layers.\n        }\n        if (fromConfig != null) {\n            // Porting notes: Instead of checking to see whether fromConfig accepts\n            // customObjects, we create a customObjects dictionary and tack it on to\n            // config['config'] as config['config'].customObjects. Objects can use it,\n            // if they want.\n            // tslint:disable-next-line:no-any\n            const customObjectsCombined = {};\n            for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) {\n                customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key];\n            }\n            for (const key of Object.keys(customObjects)) {\n                customObjectsCombined[key] = customObjects[key];\n            }\n            // Add the customObjects to config\n            const nestedConfig = config['config'];\n            nestedConfig['customObjects'] = customObjectsCombined;\n            const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n            for (const key of Object.keys(customObjects)) {\n                _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n            }\n            convertNDArrayScalarsInConfig(config['config']);\n            const returnObj = fromConfig(cls, config['config'], customObjects, fastWeightInit);\n            _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n            return returnObj;\n        }\n        else {\n            // Then `cls` may be a function returning a class.\n            // In this case by convention `config` holds\n            // the kwargs of the function.\n            const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n            for (const key of Object.keys(customObjects)) {\n                _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n            }\n            // In python this is **config['config'], for tfjs-layers we require\n            // classes that use this fall-through construction method to take\n            // a config interface that mimics the expansion of named parameters.\n            const returnObj = new cls(config['config']);\n            _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n            return returnObj;\n        }\n    }\n}\n/**\n * Compares two numbers for sorting.\n * @param a\n * @param b\n */\nexport function numberCompare(a, b) {\n    return (a < b) ? -1 : ((a > b) ? 1 : 0);\n}\n/**\n * Comparison of two numbers for reverse sorting.\n * @param a\n * @param b\n */\nexport function reverseNumberCompare(a, b) {\n    return -1 * numberCompare(a, b);\n}\n/**\n * Convert a string into the corresponding DType.\n * @param dtype\n * @returns An instance of DType.\n */\nexport function stringToDType(dtype) {\n    switch (dtype) {\n        case 'float32':\n            return 'float32';\n        default:\n            throw new ValueError(`Invalid dtype: ${dtype}`);\n    }\n}\n/**\n * Test the element-by-element equality of two Arrays of strings.\n * @param xs First array of strings.\n * @param ys Second array of strings.\n * @returns Wether the two arrays are all equal, element by element.\n */\nexport function stringsEqual(xs, ys) {\n    if (xs == null || ys == null) {\n        return xs === ys;\n    }\n    if (xs.length !== ys.length) {\n        return false;\n    }\n    for (let i = 0; i < xs.length; ++i) {\n        if (xs[i] !== ys[i]) {\n            return false;\n        }\n    }\n    return true;\n}\n/**\n * Get the unique elements of an array.\n * @param xs Array.\n * @returns An Array consisting of the unique elements in `xs`.\n */\nexport function unique(xs) {\n    if (xs == null) {\n        return xs;\n    }\n    const out = [];\n    // TODO(cais): Maybe improve performance by sorting.\n    for (const x of xs) {\n        if (out.indexOf(x) === -1) {\n            out.push(x);\n        }\n    }\n    return out;\n}\n/**\n * Determine if an Object is empty (i.e., does not have own properties).\n * @param obj Object\n * @returns Whether the Object is empty.\n * @throws ValueError: If object is `null` or `undefined`.\n */\nexport function isObjectEmpty(obj) {\n    if (obj == null) {\n        throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);\n    }\n    for (const key in obj) {\n        if (obj.hasOwnProperty(key)) {\n            return false;\n        }\n    }\n    return true;\n}\n/**\n * Helper function used to build type union/enum run-time checkers.\n * @param values The list of allowed values.\n * @param label A string name for the type\n * @param value The value to test.\n * @throws ValueError: If the value is not in values nor `undefined`/`null`.\n */\nexport function checkStringTypeUnionValue(values, label, value) {\n    if (value == null) {\n        return;\n    }\n    if (values.indexOf(value) < 0) {\n        throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`);\n    }\n}\n/**\n * Helper function for verifying the types of inputs.\n *\n * Ensures that the elements of `x` are all of type `expectedType`.\n * Also verifies that the length of `x` is within bounds.\n *\n * @param x Object to test.\n * @param expectedType The string expected type of all of the elements in the\n * Array.\n * @param minLength Return false if x.length is less than this.\n * @param maxLength Return false if x.length is greater than this.\n * @returns true if and only if `x` is an `Array<expectedType>` with\n * length >= `minLength` and <= `maxLength`.\n */\n// tslint:disable:no-any\nexport function checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) {\n    assert(minLength >= 0);\n    assert(maxLength >= minLength);\n    return (Array.isArray(x) && x.length >= minLength && x.length <= maxLength &&\n        x.every(e => typeof e === expectedType));\n}\n// tslint:enable:no-any\n/**\n * Assert that a value or an array of value are positive integer.\n *\n * @param value The value being asserted on. May be a single number or an array\n *   of numbers.\n * @param name Name of the value, used to make the error message.\n */\nexport function assertPositiveInteger(value, name) {\n    if (Array.isArray(value)) {\n        util.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`);\n        value.forEach((v, i) => assertPositiveInteger(v, `element ${i + 1} of ${name}`));\n    }\n    else {\n        util.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ` +\n            `${formatAsFriendlyString(value)}.`);\n    }\n}\n/**\n * Format a value into a display-friendly, human-readable fashion.\n *\n * - `null` is formatted as `'null'`\n * - Strings are formated with flanking pair of quotes.\n * - Arrays are formatted with flanking pair of square brackets.\n *\n * @param value The value to display.\n * @return Formatted string.\n */\n// tslint:disable-next-line:no-any\nexport function formatAsFriendlyString(value) {\n    if (value === null) {\n        return 'null';\n    }\n    else if (Array.isArray(value)) {\n        return '[' + value.map(v => formatAsFriendlyString(v)).join(',') + ']';\n    }\n    else if (typeof value === 'string') {\n        return `\"${value}\"`;\n    }\n    else {\n        return `${value}`;\n    }\n}\n/**\n * Returns a function `f2` (decorator) which wraps the original function\n * `f`. `f2` guarantees that `f` can be called at most once\n * every `waitMs` ms. If `f2` is called more often, it will return\n * the last returned result of `f`.\n *\n * @param f The original function `f` to wrap.\n * @param waitMs The time between two consecutive calls to `f` in ms.\n */\nexport function debounce(f, waitMs) {\n    let lastTime = util.now();\n    let lastResult;\n    const f2 = (...args) => {\n        const now = util.now();\n        if (now - lastTime < waitMs) {\n            return lastResult;\n        }\n        lastTime = now;\n        lastResult = f(...args);\n        return lastResult;\n    };\n    return f2;\n}\n/**\n * Returns the fusable activation given a layers identifier.\n *\n * @param activationName The layers identifier string.\n * @return The name of the fusable activation.\n */\nexport function mapActivationToFusedKernel(activationName) {\n    if (activationName === 'relu') {\n        return 'relu';\n    }\n    if (activationName === 'linear') {\n        return 'linear';\n    }\n    if (activationName === 'elu') {\n        return 'elu';\n    }\n    return null;\n}\n/**\n * Returns the cartesian product of sets of values.\n * This works the same as itertools.product in Python.\n *\n * Example:\n *\n * filters = [128, 256, 512]\n * paddings = ['same', 'valid']\n *\n * product = [ [128, 'same'], [128, 'valid'], [256, 'same'], [256, 'valid'],\n * [512, 'same'], [512, 'valid']]\n *\n * @param arrayOfValues List/array of values.\n * @return The cartesian product.\n */\nexport function getCartesianProductOfValues(...arrayOfValues) {\n    assert(arrayOfValues.length > 0, 'arrayOfValues is empty');\n    for (const values of arrayOfValues) {\n        assert(Array.isArray(values), 'one of the values is not an array');\n        assert(values.length > 0, 'one of the values is empty');\n    }\n    return arrayOfValues.reduce((products, values) => {\n        if (products.length === 0) {\n            return values.map(value => [value]);\n        }\n        return values\n            .map(value => {\n            return products.map((prevValue) => [...prevValue, value]);\n        })\n            .reduce((flattenedProduct, unflattenedProduct) => {\n            return flattenedProduct.concat(unflattenedProduct);\n        }, []);\n    }, []);\n}\n//# sourceMappingURL=generic_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/contraints.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport { deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\n/**\n * Helper function used by many of the Constraints to find the L2Norms.\n */\nfunction calcL2Norms(w, axis) {\n    return tidy(() => tfc.sqrt(tfc.sum(tfc.mul(w, w), axis, true)));\n}\n/**\n * Base class for functions that impose constraints on weight values\n *\n * @doc {\n *   heading: 'Constraints',\n *   subheading: 'Classes',\n *   namespace: 'constraints'\n * }\n */\nexport class Constraint extends serialization.Serializable {\n    getConfig() {\n        return {};\n    }\n}\nexport class MaxNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultMaxValue = 2;\n        this.defaultAxis = 0;\n        this.maxValue =\n            args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => {\n            const norms = calcL2Norms(w, this.axis);\n            const desired = tfc.clipByValue(norms, 0, this.maxValue);\n            return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));\n        });\n    }\n    getConfig() {\n        return { maxValue: this.maxValue, axis: this.axis };\n    }\n}\n/** @nocollapse */\nMaxNorm.className = 'MaxNorm';\nserialization.registerClass(MaxNorm);\nexport class UnitNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultAxis = 0;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => tfc.div(w, tfc.add(epsilon(), calcL2Norms(w, this.axis))));\n    }\n    getConfig() {\n        return { axis: this.axis };\n    }\n}\n/** @nocollapse */\nUnitNorm.className = 'UnitNorm';\nserialization.registerClass(UnitNorm);\nexport class NonNeg extends Constraint {\n    apply(w) {\n        return tfc.relu(w);\n    }\n}\n/** @nocollapse */\nNonNeg.className = 'NonNeg';\nserialization.registerClass(NonNeg);\nexport class MinMaxNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultMinValue = 0.0;\n        this.defaultMaxValue = 1.0;\n        this.defaultRate = 1.0;\n        this.defaultAxis = 0;\n        this.minValue =\n            args.minValue != null ? args.minValue : this.defaultMinValue;\n        this.maxValue =\n            args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n        this.rate = args.rate != null ? args.rate : this.defaultRate;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => {\n            const norms = calcL2Norms(w, this.axis);\n            const desired = tfc.add(tfc.mul(this.rate, tfc.clipByValue(norms, this.minValue, this.maxValue)), tfc.mul(1.0 - this.rate, norms));\n            return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));\n        });\n    }\n    getConfig() {\n        return {\n            minValue: this.minValue,\n            maxValue: this.maxValue,\n            rate: this.rate,\n            axis: this.axis\n        };\n    }\n}\n/** @nocollapse */\nMinMaxNorm.className = 'MinMaxNorm';\nserialization.registerClass(MinMaxNorm);\n// Maps the JavaScript-like identifier keys to the corresponding registry\n// symbols.\nexport const CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'maxNorm': 'MaxNorm',\n    'minMaxNorm': 'MinMaxNorm',\n    'nonNeg': 'NonNeg',\n    'unitNorm': 'UnitNorm'\n};\nexport function serializeConstraint(constraint) {\n    return serializeKerasObject(constraint);\n}\nexport function deserializeConstraint(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'constraint');\n}\nexport function getConstraint(identifier) {\n    if (identifier == null) {\n        return null;\n    }\n    if (typeof identifier === 'string') {\n        const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        const config = { className, config: {} };\n        return deserializeConstraint(config);\n    }\n    else if (identifier instanceof Constraint) {\n        return identifier;\n    }\n    else {\n        return deserializeConstraint(identifier);\n    }\n}\n//# sourceMappingURL=constraints.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// tslint:disable-next-line:max-line-length\nimport { MaxNorm, MinMaxNorm, NonNeg, UnitNorm } from './constraints';\n/**\n * MaxNorm weight constraint.\n *\n * Constrains the weights incident to each hidden unit\n * to have a norm less than or equal to a desired value.\n *\n * References\n *       - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting\n * Srivastava, Hinton, et al.\n * 2014](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)\n *\n * @doc {heading: 'Constraints',namespace: 'constraints'}\n */\nexport function maxNorm(args) {\n    return new MaxNorm(args);\n}\n/**\n * Constrains the weights incident to each hidden unit to have unit norm.\n *\n * @doc {heading: 'Constraints', namespace: 'constraints'}\n */\nexport function unitNorm(args) {\n    return new UnitNorm(args);\n}\n/**\n * Constains the weight to be non-negative.\n *\n * @doc {heading: 'Constraints', namespace: 'constraints'}\n */\nexport function nonNeg() {\n    return new NonNeg();\n}\n/** @doc {heading: 'Constraints', namespace: 'constraints'} */\nexport function minMaxNorm(config) {\n    return new MinMaxNorm(config);\n}\n//# sourceMappingURL=exports_constraints.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport const VALID_DATA_FORMAT_VALUES = ['channelsFirst', 'channelsLast'];\nexport const VALID_PADDING_MODE_VALUES = ['valid', 'same', 'causal'];\nexport const VALID_POOL_MODE_VALUES = ['max', 'avg'];\nexport const VALID_BIDIRECTIONAL_MERGE_MODES = ['sum', 'mul', 'concat', 'ave'];\nexport const VALID_SAMPLE_WEIGHT_MODES = ['temporal'];\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Common functions for TensorFlow.js Layers.\n */\nimport { VALID_DATA_FORMAT_VALUES, VALID_PADDING_MODE_VALUES, VALID_POOL_MODE_VALUES } from './keras_format/common';\nimport { checkStringTypeUnionValue } from './utils/generic_utils';\n// A map from the requested scoped name of a Tensor to the number of Tensors\n// wanting that name so far.  This allows enforcing name uniqueness by appending\n// an incrementing index, e.g. scope/name, scope/name_1, scope/name_2, etc.\nconst nameMap = new Map();\nexport function checkDataFormat(value) {\n    checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, 'DataFormat', value);\n}\nexport function checkPaddingMode(value) {\n    checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, 'PaddingMode', value);\n}\nexport function checkPoolMode(value) {\n    checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, 'PoolMode', value);\n}\nconst _nameScopeStack = [];\nconst _nameScopeDivider = '/';\n/**\n * Enter namescope, which can be nested.\n */\nexport function nameScope(name, fn) {\n    _nameScopeStack.push(name);\n    try {\n        const val = fn();\n        _nameScopeStack.pop();\n        return val;\n    }\n    catch (e) {\n        _nameScopeStack.pop();\n        throw e;\n    }\n}\n/**\n * Get the current namescope as a flat, concatenated string.\n */\nfunction currentNameScopePrefix() {\n    if (_nameScopeStack.length === 0) {\n        return '';\n    }\n    else {\n        return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider;\n    }\n}\n/**\n * Get the name a Tensor (or Variable) would have if not uniqueified.\n * @param tensorName\n * @return Scoped name string.\n */\nexport function getScopedTensorName(tensorName) {\n    if (!isValidTensorName(tensorName)) {\n        throw new Error('Not a valid tensor name: \\'' + tensorName + '\\'');\n    }\n    return currentNameScopePrefix() + tensorName;\n}\n/**\n * Get unique names for Tensors and Variables.\n * @param scopedName The fully-qualified name of the Tensor, i.e. as produced by\n *  `getScopedTensorName()`.\n * @return A unique version of the given fully scoped name.\n *   If this is the first time that the scoped name is seen in this session,\n *   then the given `scopedName` is returned unaltered.  If the same name is\n *   seen again (producing a collision), an incrementing suffix is added to the\n *   end of the name, so it takes the form 'scope/name_1', 'scope/name_2', etc.\n */\nexport function getUniqueTensorName(scopedName) {\n    if (!isValidTensorName(scopedName)) {\n        throw new Error('Not a valid tensor name: \\'' + scopedName + '\\'');\n    }\n    if (!nameMap.has(scopedName)) {\n        nameMap.set(scopedName, 0);\n    }\n    const index = nameMap.get(scopedName);\n    nameMap.set(scopedName, nameMap.get(scopedName) + 1);\n    if (index > 0) {\n        const result = `${scopedName}_${index}`;\n        // Mark the composed name as used in case someone wants\n        // to call getUniqueTensorName(\"name_1\").\n        nameMap.set(result, 1);\n        return result;\n    }\n    else {\n        return scopedName;\n    }\n}\nconst tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\\._\\/]*$/);\n/**\n * Determine whether a string is a valid tensor name.\n * @param name\n * @returns A Boolean indicating whether `name` is a valid tensor name.\n */\nexport function isValidTensorName(name) {\n    return !!name.match(tensorNameRegex);\n}\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Math utility functions.\n *\n * This file contains some frequently used math function that operates on\n * number[] or Float32Array and return a number. Many of these functions are\n * not-so-thick wrappers around TF.js Core functions. But they offer the\n * convenience of\n * 1) not having to convert the inputs into Tensors,\n * 2) not having to convert the returned Tensors to numbers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { scalar, tensor1d } from '@tensorflow/tfjs-core';\nimport { ValueError } from '../errors';\n/**\n * Determine if a number is an integer.\n */\nexport function isInteger(x) {\n    return x === parseInt(x.toString(), 10);\n}\n/**\n * Calculate the product of an array of numbers.\n * @param array The array to calculate the product over.\n * @param begin Beginning index, inclusive.\n * @param end Ending index, exclusive.\n * @return The product.\n */\nexport function arrayProd(array, begin, end) {\n    if (begin == null) {\n        begin = 0;\n    }\n    if (end == null) {\n        end = array.length;\n    }\n    let prod = 1;\n    for (let i = begin; i < end; ++i) {\n        prod *= array[i];\n    }\n    return prod;\n}\n/**\n * A helper function transforms the two input types to an instance of Tensor1D,\n * so the return value can be fed directly into various TF.js Core functions.\n * @param array\n */\nfunction toArray1D(array) {\n    array = Array.isArray(array) ? new Float32Array(array) : array;\n    return tensor1d(array);\n}\n/**\n * Compute minimum value.\n * @param array\n * @return minimum value.\n */\nexport function min(array) {\n    return tfc.min(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute maximum value.\n * @param array\n * @return maximum value\n */\nexport function max(array) {\n    return tfc.max(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute sum of array.\n * @param array\n * @return The sum.\n */\nexport function sum(array) {\n    return tfc.sum(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute mean of array.\n * @param array\n * @return The mean.\n */\nexport function mean(array) {\n    return sum(array) / array.length;\n}\n/**\n * Compute variance of array.\n * @param array\n * @return The variance.\n */\nexport function variance(array) {\n    const demeaned = tfc.sub(toArray1D(array), scalar(mean(array)));\n    const sumSquare = tfc.sum(tfc.mul(demeaned, demeaned)).dataSync()[0];\n    return sumSquare / array.length;\n}\n/**\n * Compute median of array.\n * @param array\n * @return The median value.\n */\nexport function median(array) {\n    const arraySorted = array.slice().sort((a, b) => a - b);\n    const lowIdx = Math.floor((arraySorted.length - 1) / 2);\n    const highIdx = Math.ceil((arraySorted.length - 1) / 2);\n    if (lowIdx === highIdx) {\n        return arraySorted[lowIdx];\n    }\n    return (arraySorted[lowIdx] + arraySorted[highIdx]) / 2;\n}\n/**\n * Generate an array of integers in [begin, end).\n * @param begin Beginning integer, inclusive.\n * @param end Ending integer, exclusive.\n * @returns Range array.\n * @throws ValueError, iff `end` < `begin`.\n */\nexport function range(begin, end) {\n    if (end < begin) {\n        throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);\n    }\n    const out = [];\n    for (let i = begin; i < end; ++i) {\n        out.push(i);\n    }\n    return out;\n}\n//# sourceMappingURL=math_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * deeplearn.js backend.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { onesLike as coreOnesLike, scalar, tensor1d, tidy, where, zerosLike as coreZerosLike } from '@tensorflow/tfjs-core';\nimport { checkDataFormat } from '../common';\nimport { NotImplementedError, ValueError } from '../errors';\nimport * as math_utils from '../utils/math_utils';\nimport { imageDataFormat } from './common';\n// tslint:enable\n/* Setting and getting backend from deeplearn.js. */\n// Default deeplearn.js backend is WebGL (GPU).\nlet backend = 'webgl';\nexport function setBackend(requestedBackend) {\n    tfc.setBackend(requestedBackend);\n    backend = requestedBackend;\n}\nexport function getBackend() {\n    return backend;\n}\n/**\n * Indicates whether the backend is operating symbolically.\n *\n * This function will be used to determine how to interpret user code. If\n * it returns true, calls to the backend construct a symbolic graph; if\n * it returns false, calls to the backend execute immediately.\n */\nexport function isBackendSymbolic() {\n    return false;\n}\n/**\n * Get the number of elements in a Tensor.\n * @param x The Tensor.\n * @return Number of elements in `x`.\n */\nexport function countParams(x) {\n    const shape = x.shape;\n    if (shape.length > 0) {\n        return shape.reduce((a, b) => a * b);\n    }\n    else {\n        // Scalar.\n        return 1;\n    }\n}\n/**\n * Casts a tensor to a different dtype and returns it.\n * @param x Input tensor.\n * @param dtype String: 'float32'|'int32'|'bool'.\n * @returns Tensor of the specified `dtype`.\n */\nexport function cast(x, dtype) {\n    return x.asType(dtype);\n}\n/**\n * Adds a 1-sized dimension at index \"axis\".\n * @param x Input tensor.\n * @param axis Position where to add the new axis.\n * @returns Result of the dimension expansion.\n */\nexport function expandDims(x, axis = -1) {\n    const outShape = x.shape.slice();\n    if (axis < 0) {\n        axis = outShape.length + axis + 1;\n    }\n    outShape.splice(axis, 0, 1);\n    return x.reshape(outShape);\n}\n/**\n * Repeats a 2D tensor.\n *\n * If `x` has shape `[samples, dim]` and `n` is 2, for example, the output\n * will have shape `[samples, 2, dim]`.\n *\n * @param x Input tensor.\n * @param n Integer, number of times to repeat.\n * @returns The result of the repeat operation.\n * @throws ValueError: If input tensor is not 2D.\n */\nexport function repeat(x, n) {\n    return tidy(() => {\n        if (x.shape.length !== 2) {\n            throw new ValueError(`repeat() expects a rank-2 tensor, but received a ` +\n                `rank-${x.shape.length} tensor.`);\n        }\n        const y = expandDims(x, 1);\n        return tile(y, [1, n, 1]);\n    });\n}\n/**\n * Flatten a Tensor into 1D.\n * @param x Input tensor.\n * @return The result of the flattening `x`.\n */\nexport function flatten(x) {\n    const newShape = [math_utils.arrayProd(x.shape)];\n    return x.reshape(newShape);\n}\n/**\n * Turn a nD tensor into a 2D tensor with same 0th dimension.\n * In other words, it flattens each data samples of a batch.\n *\n * @param x The tensor to flatten. The rank of this tensor is required to be 2\n *   or higher.\n * @return The result of the flattening.\n */\nexport function batchFlatten(x) {\n    if (x.rank <= 1) {\n        throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);\n    }\n    const newShape = [x.shape[0], math_utils.arrayProd(x.shape, 1)];\n    return x.reshape(newShape);\n}\n/**\n * Do slicing along the first axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size size of the slice along the first axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongFirstAxis(array, start, size) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                return tfc.slice2d(array, [start, 0], [size, array.shape[1]]);\n            case 3:\n                return tfc.slice3d(array, [start, 0, 0], [size, array.shape[1], array.shape[2]]);\n            case 4:\n                return tfc.slice4d(array, [start, 0, 0, 0], [size, array.shape[1], array.shape[2], array.shape[3]]);\n            case 5:\n                return tfc.slice(array, [start, 0, 0, 0, 0], [\n                    size, array.shape[1], array.shape[2], array.shape[3], array.shape[4]\n                ]);\n            case 6:\n                return tfc.slice(array, [start, 0, 0, 0, 0, 0], [\n                    size, array.shape[1], array.shape[2], array.shape[3], array.shape[4],\n                    array.shape[5]\n                ]);\n            default:\n                throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Do slicing along the last axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size size of the slice along the last axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongLastAxis(array, start, size) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                return tfc.slice2d(array, [0, start], [array.shape[0], size]);\n            case 3:\n                return tfc.slice3d(array, [0, 0, start], [array.shape[0], array.shape[1], size]);\n            case 4:\n                return tfc.slice4d(array, [0, 0, 0, start], [array.shape[0], array.shape[1], array.shape[2], size]);\n            default:\n                throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Do slicing along the sepcified axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size of the slice along the chosen axis.\n * @param choose an axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongAxis(array, start, size, axis) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            case 3:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return tfc.slice3d(array, [0, start, 0], [array.shape[0], size, array.shape[2]]);\n                    case 3:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            case 4:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return tfc.slice4d(array, [0, start, 0, 0], [array.shape[0], size, array.shape[2], array.shape[3]]);\n                    case 3:\n                        return tfc.slice4d(array, [0, 0, start, 0], [array.shape[0], array.shape[1], size, array.shape[3]]);\n                    case 4:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            default:\n                throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Concatenates a list of tensors alongside the specified axis.\n * @param tensors `Array` of tensors to concatenate.\n * @param axis Concatenation axis.\n * @returns The result of the concatenation.\n */\nexport function concatenate(tensors, axis = -1) {\n    let rank;\n    if (axis < 0) {\n        rank = tensors[0].rank;\n        if (rank !== 0) {\n            axis = rank;\n        }\n        else {\n            axis = 0;\n        }\n    }\n    if (axis === tensors[0].rank) {\n        // Porting Note: This is necessary because tfc.concat() requires axis to be\n        //   in the interval [-rank, rank).\n        axis = -1;\n    }\n    // Porting Note: Sparse concat is not supported yet.\n    return tfc.concat(tensors, axis);\n}\n/**\n * Concatenate two arrays along the first dimension.\n * @param a The 1st `tf.Tensor` to concatenate.\n * @param b The 2nd `tf.Tensor` to concatenate.\n * @returns Result of the concatenation.\n * @throws ValueError: If `a` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function concatAlongFirstAxis(a, b) {\n    switch (a.rank) {\n        case 1:\n            return tfc.concat1d([a, b]);\n        case 2:\n            return tfc.concat2d([a, b], 0);\n        case 3:\n            return tfc.concat3d([a, b], 0);\n        case 4:\n            return tfc.concat4d([a, b], 0);\n        default:\n            throw new ValueError(`concatAlongFirstAxis() received an unsupported ` +\n                `tensor rank: ${a.rank}`);\n    }\n}\n/**\n * Creates a tensor by tiling `x` by `n`.\n * @param x A tensor.\n * @param n An Array of integers or a single integer. If an Array, the length\n *   must be the same as the number of dimensions in `x`. If a single integer,\n *   it will be treated as an Array of length 1.\n */\nexport function tile(x, n) {\n    if (!Array.isArray(n)) {\n        n = [n];\n    }\n    if (x.rank !== n.length) {\n        throw new ValueError(`The length of input n (${n.length}) does not match ` +\n            `the number of dimensions in input x (${x.rank})`);\n    }\n    return tfc.tile(x, n);\n}\n/* Creation of random tensors. */\n/**\n * Get a tensor with normal distribution of values.\n *\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @return The normal tensor.\n */\nexport function randomNormal(shape, mean = 0.0, stddev = 1.0, dtype, seed) {\n    return tfc.randomNormal(shape, mean, stddev, dtype, seed);\n}\n/* Linear Algebra */\n/**\n * Multiply two tensors and returns the result as a tensor.\n *\n * For 2D tensors, this is equivalent to matrix multiplication (matMul).\n * For tensors of higher ranks, it follows the Theano behavior,\n * (e.g. `(2, 3) * (4, 3, 5) -> (2, 4, 5)`).  From the Theano documentation:\n *\n * For N dimensions it is a sum product over the last axis of x and the\n * second-to-last of y:\n *\n * @param a A tensor of at least rank 2.\n * @param b A tensor of at least rank 2.\n * @param activation (optional) A string identifying the activation\n *   function.\n * @return Result of the dot operation.\n */\nexport function dot(a, b, activation, bias) {\n    if ((a.rank < 2) || (b.rank < 2)) {\n        throw new NotImplementedError(`dot requires both inputs to be rank >= 2` +\n            ` but got x shape = ${a.shape} and y shape = ${b.shape}`);\n    }\n    if (b.rank >= 3) {\n        const xLastDim = a.shape.slice(-1)[0];\n        const ySecondLastDim = b.shape.slice(-2)[0];\n        if (xLastDim !== ySecondLastDim) {\n            throw new NotImplementedError(`If rank y >= 3, then the second last dim` +\n                ` of y must equal the last dim of x but got x shape = ${a.shape} and ` +\n                ` y shape = ${b.shape}`);\n        }\n    }\n    // Handle basic 2D x 2D case.\n    if ((a.rank === 2) && (b.rank === 2)) {\n        const transposeA = false;\n        const transposeB = false;\n        // tfc.fused.matMul only fuses certain activation functions. Unsupported\n        // activation functions are treated as 'linear' activations, which is\n        // equivalent to a no-op.\n        return tfc.fused.matMul({\n            a,\n            b: b,\n            transposeA,\n            transposeB,\n            bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n            activation\n        });\n    }\n    else {\n        // Reshape x into the analogous 2D Tensor.\n        const aFirstDims = a.shape.slice(); // Holds all but the last dim of x.\n        const aLastDim = aFirstDims.pop();\n        a = a.reshape([-1, aLastDim]);\n        // Reshape y into the analogous 2D Tensor, and keep track of the\n        // required dimensions to reproduce the output shape.\n        const bShape = b.shape.slice();\n        const bLastDim = bShape.pop();\n        const ySecondLastDim = bShape.pop();\n        const yOtherDims = [...bShape, bLastDim];\n        // permutation should be like [r-2, 0, 1, 2, ... r-4, r-3, r-1]\n        // where r is the rank of y.\n        const perm = Array.from({ length: b.rank }, (_, i) => {\n            if (i === 0) {\n                return b.rank - 2;\n            }\n            else if (i <= b.rank - 2) {\n                return i - 1;\n            }\n            return i;\n        });\n        b = b.transpose(perm).reshape([ySecondLastDim, -1]);\n        // Multiply x and y as 2D Tensors, and then reshape back to original.\n        const outputShape = [...aFirstDims, ...yOtherDims];\n        const transposeA = false;\n        const transposeB = false;\n        return tfc.fused\n            .matMul({\n            a,\n            b,\n            transposeA,\n            transposeB,\n            bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n            activation\n        })\n            .reshape(outputShape);\n    }\n}\n/**\n * Compute the sign Tensor of an input Tensor.\n *\n * Elements of the input `tf.Tensor` that are === 0 are mapped to 0.\n * Elements of the input `tf.Tensor` that are > 0 are mapped to 1.\n * Elements of the input `tf.Tensor` that are < 0 are mapped to -1.\n *\n * @param x Input `tf.Tensor`.\n * @return The sign `tf.Tensor`.\n */\nexport function sign(x) {\n    // TODO(cais): Move to the core.\n    return tidy(() => {\n        const zerosLikeX = coreZerosLike(x);\n        const onesLikeX = coreOnesLike(x);\n        return where(tfc.equal(x, zerosLikeX), zerosLikeX, where(tfc.greater(x, coreZerosLike(x)), onesLikeX, tfc.mul(-1, onesLikeX)));\n    });\n}\n/**\n * Computes the one-hot representation of an integer tensor.\n * @param indices nD integer tensor of shape\n *   `(batch_size, dim1, dim2, ... dim(n-1))`\n * @param numClasses Integer, number of classes to consider.\n * @returns (n + 1)D one hot representation of the input\n *   with shape `(batch_size, dim1, dim2, ... dim(n-1), num_classes)`\n */\nexport function oneHot(indices, numClasses) {\n    return tidy(() => {\n        if (indices.rank !== 1) {\n            throw new Error('Only 1D one-hot tensors are supported in the ' +\n                'deeplearn backend, at present.');\n        }\n        indices = indices.toInt();\n        return tfc.oneHot(indices, numClasses).toFloat();\n    });\n}\n/* Elementary math functions. */\n/**\n * Retrieves the elements of indices `indices` in the tensor `reference`.\n * @param reference A tensor.\n * @param indices An integer tensor of indices or an `Array` of integers.\n * @param axis Axis along which to perform the gather operation.\n * @returns The result of the gathering as a tensor.\n */\nexport function gather(reference, indices, axis) {\n    return tidy(() => {\n        if (Array.isArray(indices)) {\n            indices = tensor1d(indices, 'int32');\n        }\n        else {\n            indices = indices.toInt();\n        }\n        return tfc.gather(reference, indices, axis);\n    });\n}\n/**\n * Element-wise square.\n * @param x Input tensor.\n * @return element-wise x^2\n */\nexport function square(x) {\n    return tfc.mul(x, x);\n}\n/**\n * Element-wise exponentiation.\n *\n * Porting Note: In PyKeras, `a` (the exponent) is a Python integer, which\n *   takes advatnage of the backend's (e.g., TensorFlow's) automatic\n * conversion to tensor. Here we allow `a` to be either a number or a tensor.\n *\n * @param x The base tensor.\n * @param a The exponent, tensor or number. If a number, it is rounded to the\n *   nearest integer and converted to a tensor.\n * @returns A tensor of the same shape as `x`.\n */\nexport function pow(x, a) {\n    return tidy(() => {\n        if (typeof (a) === 'number') {\n            a = scalar(Math.round(a), 'int32');\n        }\n        if (a.dtype !== 'int32') {\n            throw new NotImplementedError(`Non-int32 dtype (${a.dtype}) is not supported by pow() yet`);\n        }\n        return tfc.pow(x, a);\n    });\n}\n/**\n * Reshapes bias tensor according to rank of x.\n */\nfunction reshapeBias(xRank, bias, dataFormat) {\n    const biasShape = bias.shape;\n    if (bias.rank !== 1 && bias.rank !== xRank) {\n        throw new ValueError(`Unexpected bias dimensions: ${bias.rank}` +\n            `; expected it to be 1 or ${xRank}`);\n    }\n    if (xRank === 5) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1, 1, 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, 1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank === 4) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1, 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[2], biasShape[0], biasShape[1]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank === 3) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[1], biasShape[0]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank < 3) {\n        return bias;\n    }\n    throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);\n}\n/* Neural-network operations. */\n/**\n * Add a bias to a tensor.\n *\n * @param x The tensor to add the bias to.\n * @param bias The bias to add to `x`. Must be 1D or the same rank as `x`.\n * @return Result of the bias adding.\n * @throws ValueError: If the rank of `bias` is incorrect.\n */\nexport function biasAdd(x, bias, dataFormat) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        return x.add(reshapeBias(x.rank, bias, dataFormat));\n    });\n}\n/**\n * Exponential linear unit (ELU).\n * @param x A tensor or variable to compute the activation function for.\n * @param alpha: A scalar, a scaling factor for the negative section.\n * @return Output of the ELU operation.\n */\nexport function elu(x, alpha = 1) {\n    // TODO(cais): Add support for alpha values other than 1.\n    if (alpha !== 1) {\n        throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented ` +\n            `yet.`);\n    }\n    return tfc.elu(x);\n}\n/**\n * Softsign of a tensor.\n *\n * Defined as x / (abs(x) + 1), element-wise.\n *\n * @param x: Input.\n * @returns Output.\n */\nexport function softsign(x) {\n    return tidy(() => tfc.div(x, tfc.abs(x).add(1)));\n}\n/**\n * Sets entries in `x` to zero at random, while scaling the entire tensor.\n *\n * @param x input tensor.\n * @param level fraction of the entries in the tensor that will be set to 0.\n * @param noiseShape shape of randomly generated keep/drop flags, must be\n *   broadcastable to the shape of `x`. Optional.\n * @param seed random seed to ensure determinism. Optional.\n * @returns Result of the dropout operation.\n */\nexport function dropout(x, level, noiseShape, seed) {\n    return tidy(() => tfc.dropout(x, level, noiseShape, seed));\n}\n/**\n * Element-wise, segment-wise linear approximation of sigmoid.\n *\n * Returns `0.` if `x < -2.5`, `1.` if `x > 2.5`.\n * In `-2.5 <= x <= 2.5`, returns `0.2 * x + 0.5`.\n *\n * @param x Input tensor.\n * @returns Output tensor.\n */\nexport function hardSigmoid(x) {\n    return tidy(() => {\n        const y = tfc.add(.5, tfc.mul(.2, x));\n        return tfc.clipByValue(y, 0, 1);\n    });\n}\n/**\n * Invoke `x` in the training phase, and `alt` otherwise.\n *\n * Porting Note: We do not create placeholder tensors for the `training`\n * boolean flag here, because there is no such thing in the TF.js imperative\n * backend.\n *\n * @param x The function to invoke iff `training` is `true`.\n * @param alt The function to invoke iff `training` is `false`.\n * @param training Boolean flag for whether training phase is active.\n * @returns The return value of `x()` if `training` is `true`, or the return\n *   value of `alt()` if `training` is `false`.\n */\nexport function inTrainPhase(x, alt, training = false) {\n    return training ? x() : alt();\n}\n//# sourceMappingURL=tfjs_backend.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport const VALID_FAN_MODE_VALUES = ['fanIn', 'fanOut', 'fanAvg'];\nexport const VALID_DISTRIBUTION_VALUES = ['normal', 'uniform', 'truncatedNormal'];\n// We can't easily extract a string[] from the string union type, but we can\n// recapitulate the list, enforcing at compile time that the values are valid\n// and that we have the right number of them.\n/**\n * A string array of valid Initializer class names.\n *\n * This is guaranteed to match the `InitializerClassName` union type.\n */\nexport const initializerClassNames = [\n    'Zeros', 'Ones', 'Constant', 'RandomNormal', 'RandomUniform',\n    'TruncatedNormal', 'VarianceScaling', 'Orthogonal', 'Identity'\n];\n//# sourceMappingURL=initializer_config.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { eye, linalg, mul, ones, randomUniform, scalar, serialization, tidy, truncatedNormal, zeros } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { checkDataFormat } from './common';\nimport { NotImplementedError, ValueError } from './errors';\nimport { VALID_DISTRIBUTION_VALUES, VALID_FAN_MODE_VALUES } from './keras_format/initializer_config';\nimport { checkStringTypeUnionValue, deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\nimport { arrayProd } from './utils/math_utils';\nexport function checkFanMode(value) {\n    checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, 'FanMode', value);\n}\nexport function checkDistribution(value) {\n    checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, 'Distribution', value);\n}\n/**\n * Initializer base class.\n *\n * @doc {\n *   heading: 'Initializers', subheading: 'Classes', namespace: 'initializers'}\n */\nexport class Initializer extends serialization.Serializable {\n    fromConfigUsesCustomObjects() {\n        return false;\n    }\n    getConfig() {\n        return {};\n    }\n}\nexport class Zeros extends Initializer {\n    apply(shape, dtype) {\n        return zeros(shape, dtype);\n    }\n}\n/** @nocollapse */\nZeros.className = 'Zeros';\nserialization.registerClass(Zeros);\nexport class Ones extends Initializer {\n    apply(shape, dtype) {\n        return ones(shape, dtype);\n    }\n}\n/** @nocollapse */\nOnes.className = 'Ones';\nserialization.registerClass(Ones);\nexport class Constant extends Initializer {\n    constructor(args) {\n        super();\n        if (typeof args !== 'object') {\n            throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);\n        }\n        if (args.value === undefined) {\n            throw new ValueError(`config must have value set but got ${args}`);\n        }\n        this.value = args.value;\n    }\n    apply(shape, dtype) {\n        return tidy(() => mul(scalar(this.value), ones(shape, dtype)));\n    }\n    getConfig() {\n        return {\n            value: this.value,\n        };\n    }\n}\n/** @nocollapse */\nConstant.className = 'Constant';\nserialization.registerClass(Constant);\nexport class RandomUniform extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MINVAL = -0.05;\n        this.DEFAULT_MAXVAL = 0.05;\n        this.minval = args.minval || this.DEFAULT_MINVAL;\n        this.maxval = args.maxval || this.DEFAULT_MAXVAL;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        return randomUniform(shape, this.minval, this.maxval, dtype);\n    }\n    getConfig() {\n        return { minval: this.minval, maxval: this.maxval, seed: this.seed };\n    }\n}\n/** @nocollapse */\nRandomUniform.className = 'RandomUniform';\nserialization.registerClass(RandomUniform);\nexport class RandomNormal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MEAN = 0.;\n        this.DEFAULT_STDDEV = 0.05;\n        this.mean = args.mean || this.DEFAULT_MEAN;\n        this.stddev = args.stddev || this.DEFAULT_STDDEV;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        dtype = dtype || 'float32';\n        if (dtype !== 'float32' && dtype !== 'int32') {\n            throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n        }\n        return K.randomNormal(shape, this.mean, this.stddev, dtype, this.seed);\n    }\n    getConfig() {\n        return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n    }\n}\n/** @nocollapse */\nRandomNormal.className = 'RandomNormal';\nserialization.registerClass(RandomNormal);\nexport class TruncatedNormal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MEAN = 0.;\n        this.DEFAULT_STDDEV = 0.05;\n        this.mean = args.mean || this.DEFAULT_MEAN;\n        this.stddev = args.stddev || this.DEFAULT_STDDEV;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        dtype = dtype || 'float32';\n        if (dtype !== 'float32' && dtype !== 'int32') {\n            throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);\n        }\n        return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed);\n    }\n    getConfig() {\n        return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n    }\n}\n/** @nocollapse */\nTruncatedNormal.className = 'TruncatedNormal';\nserialization.registerClass(TruncatedNormal);\nexport class Identity extends Initializer {\n    constructor(args) {\n        super();\n        this.gain = args.gain != null ? args.gain : 1.0;\n    }\n    apply(shape, dtype) {\n        return tidy(() => {\n            if (shape.length !== 2 || shape[0] !== shape[1]) {\n                throw new ValueError('Identity matrix initializer can only be used for' +\n                    ' 2D square matrices.');\n            }\n            else {\n                return mul(this.gain, eye(shape[0]));\n            }\n        });\n    }\n    getConfig() {\n        return { gain: this.gain };\n    }\n}\n/** @nocollapse */\nIdentity.className = 'Identity';\nserialization.registerClass(Identity);\n/**\n * Computes the number of input and output units for a weight shape.\n * @param shape Shape of weight.\n * @param dataFormat data format to use for convolution kernels.\n *   Note that all kernels in Keras are standardized on the\n *   CHANNEL_LAST ordering (even when inputs are set to CHANNEL_FIRST).\n * @return An length-2 array: fanIn, fanOut.\n */\nfunction computeFans(shape, dataFormat = 'channelsLast') {\n    let fanIn;\n    let fanOut;\n    checkDataFormat(dataFormat);\n    if (shape.length === 2) {\n        fanIn = shape[0];\n        fanOut = shape[1];\n    }\n    else if ([3, 4, 5].indexOf(shape.length) !== -1) {\n        if (dataFormat === 'channelsFirst') {\n            const receptiveFieldSize = arrayProd(shape, 2);\n            fanIn = shape[1] * receptiveFieldSize;\n            fanOut = shape[0] * receptiveFieldSize;\n        }\n        else if (dataFormat === 'channelsLast') {\n            const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2);\n            fanIn = shape[shape.length - 2] * receptiveFieldSize;\n            fanOut = shape[shape.length - 1] * receptiveFieldSize;\n        }\n    }\n    else {\n        const shapeProd = arrayProd(shape);\n        fanIn = Math.sqrt(shapeProd);\n        fanOut = Math.sqrt(shapeProd);\n    }\n    return [fanIn, fanOut];\n}\nexport class VarianceScaling extends Initializer {\n    /**\n     * Constructor of VarianceScaling.\n     * @throws ValueError for invalid value in scale.\n     */\n    constructor(args) {\n        super();\n        if (args.scale < 0.0) {\n            throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);\n        }\n        this.scale = args.scale == null ? 1.0 : args.scale;\n        this.mode = args.mode == null ? 'fanIn' : args.mode;\n        checkFanMode(this.mode);\n        this.distribution =\n            args.distribution == null ? 'normal' : args.distribution;\n        checkDistribution(this.distribution);\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        const fans = computeFans(shape);\n        const fanIn = fans[0];\n        const fanOut = fans[1];\n        let scale = this.scale;\n        if (this.mode === 'fanIn') {\n            scale /= Math.max(1, fanIn);\n        }\n        else if (this.mode === 'fanOut') {\n            scale /= Math.max(1, fanOut);\n        }\n        else {\n            scale /= Math.max(1, (fanIn + fanOut) / 2);\n        }\n        if (this.distribution === 'normal') {\n            const stddev = Math.sqrt(scale);\n            dtype = dtype || 'float32';\n            if (dtype !== 'float32' && dtype !== 'int32') {\n                throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);\n            }\n            return truncatedNormal(shape, 0, stddev, dtype, this.seed);\n        }\n        else {\n            const limit = Math.sqrt(3 * scale);\n            return randomUniform(shape, -limit, limit, dtype);\n        }\n    }\n    getConfig() {\n        return {\n            scale: this.scale,\n            mode: this.mode,\n            distribution: this.distribution,\n            seed: this.seed\n        };\n    }\n}\n/** @nocollapse */\nVarianceScaling.className = 'VarianceScaling';\nserialization.registerClass(VarianceScaling);\nexport class GlorotUniform extends VarianceScaling {\n    /**\n     * Constructor of GlorotUniform\n     * @param scale\n     * @param mode\n     * @param distribution\n     * @param seed\n     */\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanAvg',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, GlorotUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nGlorotUniform.className = 'GlorotUniform';\nserialization.registerClass(GlorotUniform);\nexport class GlorotNormal extends VarianceScaling {\n    /**\n     * Constructor of GlorotNormal.\n     * @param scale\n     * @param mode\n     * @param distribution\n     * @param seed\n     */\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanAvg',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, GlorotNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nGlorotNormal.className = 'GlorotNormal';\nserialization.registerClass(GlorotNormal);\nexport class HeNormal extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 2.0,\n            mode: 'fanIn',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, HeNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nHeNormal.className = 'HeNormal';\nserialization.registerClass(HeNormal);\nexport class HeUniform extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 2.0,\n            mode: 'fanIn',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, HeUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nHeUniform.className = 'HeUniform';\nserialization.registerClass(HeUniform);\nexport class LeCunNormal extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanIn',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, LeCunNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nLeCunNormal.className = 'LeCunNormal';\nserialization.registerClass(LeCunNormal);\nexport class LeCunUniform extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanIn',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, LeCunUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nLeCunUniform.className = 'LeCunNormal';\nserialization.registerClass(LeCunUniform);\nexport class Orthogonal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_GAIN = 1;\n        this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain;\n        this.seed = args.seed;\n        if (this.seed != null) {\n            throw new NotImplementedError('Random seed is not implemented for Orthogonal Initializer yet.');\n        }\n    }\n    apply(shape, dtype) {\n        return tidy(() => {\n            if (shape.length < 2) {\n                throw new NotImplementedError('Shape must be at least 2D.');\n            }\n            if (shape[0] * shape[1] > 2000) {\n                console.warn(`Orthogonal initializer is being called on a matrix with more ` +\n                    `than 2000 (${shape[0] * shape[1]}) elements: ` +\n                    `Slowness may result.`);\n            }\n            // TODO(cais): Add seed support.\n            const normalizedShape = shape[0] > shape[1] ? [shape[1], shape[0]] : shape;\n            const a = K.randomNormal(normalizedShape, 0, 1, 'float32');\n            let q = linalg.gramSchmidt(a);\n            if (shape[0] > shape[1]) {\n                q = q.transpose();\n            }\n            return mul(this.gain, q);\n        });\n    }\n    getConfig() {\n        return {\n            gain: this.gain,\n            seed: this.seed,\n        };\n    }\n}\n/** @nocollapse */\nOrthogonal.className = 'Orthogonal';\nserialization.registerClass(Orthogonal);\n// Maps the JavaScript-like identifier keys to the corresponding registry\n// symbols.\nexport const INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'constant': 'Constant',\n    'glorotNormal': 'GlorotNormal',\n    'glorotUniform': 'GlorotUniform',\n    'heNormal': 'HeNormal',\n    'heUniform': 'HeUniform',\n    'identity': 'Identity',\n    'leCunNormal': 'LeCunNormal',\n    'leCunUniform': 'LeCunUniform',\n    'ones': 'Ones',\n    'orthogonal': 'Orthogonal',\n    'randomNormal': 'RandomNormal',\n    'randomUniform': 'RandomUniform',\n    'truncatedNormal': 'TruncatedNormal',\n    'varianceScaling': 'VarianceScaling',\n    'zeros': 'Zeros'\n};\nfunction deserializeInitializer(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'initializer');\n}\nexport function serializeInitializer(initializer) {\n    return serializeKerasObject(initializer);\n}\nexport function getInitializer(identifier) {\n    if (typeof identifier === 'string') {\n        const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        /* We have four 'helper' classes for common initializers that\n        all get serialized as 'VarianceScaling' and shouldn't go through\n        the deserializeInitializer pathway. */\n        if (className === 'GlorotNormal') {\n            return new GlorotNormal();\n        }\n        else if (className === 'GlorotUniform') {\n            return new GlorotUniform();\n        }\n        else if (className === 'HeNormal') {\n            return new HeNormal();\n        }\n        else if (className === 'HeUniform') {\n            return new HeUniform();\n        }\n        else if (className === 'LeCunNormal') {\n            return new LeCunNormal();\n        }\n        else if (className === 'LeCunUniform') {\n            return new LeCunUniform();\n        }\n        else {\n            const config = {};\n            config['className'] = className;\n            config['config'] = {};\n            return deserializeInitializer(config);\n        }\n    }\n    else if (identifier instanceof Initializer) {\n        return identifier;\n    }\n    else {\n        return deserializeInitializer(identifier);\n    }\n}\n//# sourceMappingURL=initializers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// tslint:disable-next-line:max-line-length\nimport { Constant, GlorotNormal, GlorotUniform, HeNormal, HeUniform, Identity, LeCunNormal, LeCunUniform, Ones, Orthogonal, RandomNormal, RandomUniform, TruncatedNormal, VarianceScaling, Zeros } from './initializers';\n/**\n * Initializer that generates tensors initialized to 0.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function zeros() {\n    return new Zeros();\n}\n/**\n * Initializer that generates tensors initialized to 1.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function ones() {\n    return new Ones();\n}\n/**\n * Initializer that generates values initialized to some constant.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function constant(args) {\n    return new Constant(args);\n}\n/**\n * Initializer that generates random values initialized to a uniform\n * distribution.\n *\n * Values will be distributed uniformly between the configured minval and\n * maxval.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function randomUniform(args) {\n    return new RandomUniform(args);\n}\n/**\n * Initializer that generates random values initialized to a normal\n * distribution.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function randomNormal(args) {\n    return new RandomNormal(args);\n}\n/**\n * Initializer that generates random values initialized to a truncated normal.\n * distribution.\n *\n * These values are similar to values from a `RandomNormal` except that values\n * more than two standard deviations from the mean are discarded and re-drawn.\n * This is the recommended initializer for neural network weights and filters.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function truncatedNormal(args) {\n    return new TruncatedNormal(args);\n}\n/**\n * Initializer that generates the identity matrix.\n * Only use for square 2D matrices.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function identity(args) {\n    return new Identity(args);\n}\n/**\n * Initializer capable of adapting its scale to the shape of weights.\n * With distribution=NORMAL, samples are drawn from a truncated normal\n * distribution centered on zero, with `stddev = sqrt(scale / n)` where n is:\n *   - number of input units in the weight tensor, if mode = FAN_IN.\n *   - number of output units, if mode = FAN_OUT.\n *   - average of the numbers of input and output units, if mode = FAN_AVG.\n * With distribution=UNIFORM,\n * samples are drawn from a uniform distribution\n * within [-limit, limit], with `limit = sqrt(3 * scale / n)`.\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function varianceScaling(config) {\n    return new VarianceScaling(config);\n}\n/**\n * Glorot uniform initializer, also called Xavier uniform initializer.\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotUniform(args) {\n    return new GlorotUniform(args);\n}\n/**\n * Glorot normal initializer, also called Xavier normal initializer.\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor.\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotNormal(args) {\n    return new GlorotNormal(args);\n}\n/**\n * He normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function heNormal(args) {\n    return new HeNormal(args);\n}\n/**\n * He uniform initializer.\n *\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / fan_in)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function heUniform(args) {\n    return new HeUniform(args);\n}\n/**\n * LeCun normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(1 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * References:\n *   [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n *   [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunNormal(args) {\n    return new LeCunNormal(args);\n}\n/**\n * LeCun uniform initializer.\n *\n * It draws samples from a uniform distribution in the interval\n * `[-limit, limit]` with `limit = sqrt(3 / fanIn)`,\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunUniform(args) {\n    return new LeCunUniform(args);\n}\n/**\n * Initializer that generates a random orthogonal matrix.\n *\n * Reference:\n * [Saxe et al., http://arxiv.org/abs/1312.6120](http://arxiv.org/abs/1312.6120)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function orthogonal(args) {\n    return new Orthogonal(args);\n}\n//# sourceMappingURL=exports_initializers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Utilities related to persistent state in the backend.\n */\n/**\n * An ID to track `tf.SymbolicTensor`s and derived classes.\n * Required in different places in engine/topology.ts to identify unique\n * tensors.\n */\nlet _nextUniqueTensorId = 0;\nexport function getNextUniqueTensorId() {\n    return _nextUniqueTensorId++;\n}\nconst _uidPrefixes = {};\n/**\n * Provides a unique UID given a string prefix.\n *\n * @param prefix\n */\nexport function getUid(prefix = '') {\n    if (!(prefix in _uidPrefixes)) {\n        _uidPrefixes[prefix] = 0;\n    }\n    _uidPrefixes[prefix] += 1;\n    return prefix + _uidPrefixes[prefix].toString();\n}\n//# sourceMappingURL=state.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { ValueError } from '../errors';\n// tslint:enable\n/**\n * Determine whether the input is an Array of Shapes.\n */\nexport function isArrayOfShapes(x) {\n    return Array.isArray(x) && Array.isArray(x[0]);\n}\n/**\n * Special case of normalizing shapes to lists.\n *\n * @param x A shape or list of shapes to normalize into a list of Shapes.\n * @return A list of Shapes.\n */\nexport function normalizeShapeList(x) {\n    if (x.length === 0) {\n        return [];\n    }\n    if (!Array.isArray(x[0])) {\n        return [x];\n    }\n    return x;\n}\n/**\n * Helper function to obtain exactly one Tensor.\n * @param xs: A single `tf.Tensor` or an `Array` of `tf.Tensor`s.\n * @return A single `tf.Tensor`. If `xs` is an `Array`, return the first one.\n * @throws ValueError: If `xs` is an `Array` and its length is not 1.\n */\nexport function getExactlyOneTensor(xs) {\n    let x;\n    if (Array.isArray(xs)) {\n        if (xs.length !== 1) {\n            throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);\n        }\n        x = xs[0];\n    }\n    else {\n        x = xs;\n    }\n    return x;\n}\n/**\n * Helper function to obtain exactly on instance of Shape.\n *\n * @param shapes Input single `Shape` or Array of `Shape`s.\n * @returns If input is a single `Shape`, return it unchanged. If the input is\n *   an `Array` containing exactly one instance of `Shape`, return the instance.\n *   Otherwise, throw a `ValueError`.\n * @throws ValueError: If input is an `Array` of `Shape`s, and its length is not\n *   1.\n */\nexport function getExactlyOneShape(shapes) {\n    if (Array.isArray(shapes) && Array.isArray(shapes[0])) {\n        if (shapes.length === 1) {\n            shapes = shapes;\n            return shapes[0];\n        }\n        else {\n            throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`);\n        }\n    }\n    else {\n        return shapes;\n    }\n}\n//# sourceMappingURL=types_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Count the elements in an Array of LayerVariables.\n *\n * @param weights: The LayerVariables of which the constituent numbers are to\n *   be counted.\n * @returns A count of the elements in all the LayerVariables\n */\nexport function countParamsInWeights(weights) {\n    let count = 0;\n    for (const weight of weights) {\n        if (weight.shape.length === 0) {\n            count += 1;\n        }\n        else {\n            count += weight.shape.reduce((a, b) => a * b);\n        }\n    }\n    return count;\n}\n//# sourceMappingURL=variable_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { variableGrads } from '@tensorflow/tfjs-core';\nimport { getNextUniqueTensorId } from './backend/state';\nimport { getScopedTensorName, getUniqueTensorName } from './common';\nimport { NotImplementedError } from './errors';\nconst DEFAULT_VARIABLE_NAME_PREFIX = 'Variable';\n/**\n * A `tf.layers.LayerVariable` is similar to a `tf.Tensor` in that it has a\n * dtype and shape, but its value is mutable.  The value is itself represented\n * as a`tf.Tensor`, and can be read with the `read()` method and updated with\n * the `write()` method.\n */\nexport class LayerVariable {\n    /**\n     * Construct Variable from a `tf.Tensor`.\n     *\n     * If not explicitly named, the Variable will be given a name with the\n     * prefix 'Variable'. Variable names are unique. In the case of name\n     * collision, suffixies '_<num>' will be added to the name.\n     *\n     * @param val Initial value of the Variable.\n     * @param name Name of the variable. If `null` or `undefined` is provided, it\n     *   will default a name with the prefix 'Variable'.\n     * @param constraint Optional, projection function to be applied to the\n     * variable after optimize updates\n     * @throws ValueError if `name` is `null` or `undefined`.\n     */\n    constructor(val, dtype = 'float32', name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true, constraint = null) {\n        this.dtype = dtype == null ? 'float32' : dtype;\n        this.shape = val.shape;\n        this.id = getNextUniqueTensorId();\n        name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name;\n        this.originalName = getScopedTensorName(name);\n        this.name = getUniqueTensorName(this.originalName);\n        this.trainable_ = trainable;\n        this.constraint = constraint;\n        this.val = tfc.variable(val, this.trainable_, this.name, this.dtype);\n    }\n    /**\n     * Get a snapshot of the Variable's value.\n     *\n     * The returned value is a snapshot of the Variable's value at the time of\n     * the invocation. Future mutations in the value of the tensor will only\n     * be reflected by future calls to this method.\n     */\n    read() {\n        this.assertNotDisposed();\n        return this.val;\n    }\n    /**\n     * Update the value of the Variable.\n     *\n     * @param newVal: The new value to update to. Must be consistent with the\n     *   dtype and shape of the Variable.\n     * @return This Variable.\n     */\n    write(newVal) {\n        // TODO(cais): Once  TF.js Core supports Tensor.dtype, check dtype match.\n        this.assertNotDisposed();\n        checkShapesMatch(this.val, newVal);\n        // Skip updating if this is the exact same tensor.\n        if (this.val.id !== newVal.id) {\n            this.val.assign(newVal);\n            if (this.constraint != null) {\n                this.val.assign(this.constraint.apply(this.val));\n            }\n        }\n        return this;\n    }\n    /**\n     * Dispose this LayersVariable instance from memory.\n     */\n    dispose() {\n        this.assertNotDisposed();\n        this.val.dispose();\n    }\n    assertNotDisposed() {\n        if (this.val.isDisposed) {\n            throw new Error(`LayersVariable ${this.name} is already disposed.`);\n        }\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this.trainable_ = trainable;\n        this.val.trainable = trainable;\n    }\n}\nfunction checkShapesMatch(x, y) {\n    if (x.shape.toString() !== y.shape.toString()) {\n        throw new Error('Shape mismatch: ' + JSON.stringify(x.shape) + ' vs. ' +\n            JSON.stringify(y.shape));\n    }\n}\n/**\n * Create a Variable.\n * @param x The initial value of the `Variable`.\n * @param dtype optional, the type of the variable.\n * @param name optional, the name of the variable, default provided by\n * Variable.\n * @param constraint optional, a constraint to be applied after every update.\n * @return The newly instantiated `Variable`.\n */\nexport function variable(x, dtype, name, constraint) {\n    return new LayerVariable(x, dtype, name, true, constraint);\n}\n/**\n * Instantiates an all-zeros Variable and returns it.\n *\n * @param shape Shape of the tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return An all-zero Variable.\n */\nexport function zerosVariable(shape, dtype, name) {\n    // TODO(cais): Implement logic for dtype.\n    return new LayerVariable(tfc.zeros(shape), dtype, name);\n}\n/**\n * Instantiates an all-zeros tensor of the same shape as another tensor.\n *\n * @param x The other tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return A newly instantiated Variable.\n */\nexport function zerosLike(x, dtype, name) {\n    return new LayerVariable(tfc.zerosLike(x), dtype, name);\n}\n/**\n * Instantiates an all-ones tensor and returns it.\n *\n * @param shape Shape of the tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return An all-ones Variable.\n */\nexport function onesVariable(shape, dtype, name) {\n    // TODO(cais): Implement logic for dtype.\n    const allocated = tfc.ones(shape);\n    return new LayerVariable(allocated, dtype, name);\n}\n/**\n * Instantiates an all-ones tensor of the same shape as another tensor.\n *\n * @param x The other tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return A newly instantiated Variable.\n */\nexport function onesLike(x, dtype, name) {\n    const allocated = tfc.onesLike(x);\n    return new LayerVariable(allocated, dtype, name);\n}\n/**\n * Instantiate an identity matrix and returns it, as a Variable\n *\n * @param size Number of rows/columns.\n * @param dtype Data type of returned Variable.\n * @param name Name of returned Variable.\n * @return A Variable, an identity matrix.\n */\nexport function eyeVariable(size, dtype, name) {\n    return new LayerVariable(tfc.eye(size), dtype, name);\n}\n/**\n * Get a Variable with uniform distribution of values.\n * @param shape Shape of the tensor.\n * @param minval Lower bound of the uniform distribution.\n * @param maxval Upper bound of the uniform distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The uniform-random Variable.\n */\nexport function randomUniformVariable(shape, minval, maxval, dtype, seed, name = 'randomUniform') {\n    return new LayerVariable(tfc.randomUniform(shape, minval, maxval, dtype), dtype, name);\n}\n/**\n * Get a Variable with truncated-normal distribution of values.\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The truncated-normal-random Variable.\n */\nexport function truncatedNormalVariable(shape, mean = 0.0, stddev = 1.0, dtype, seed, name = 'truncatedNormal') {\n    // TODO(cais): Implement logic for dtype and seed once they are supported\n    // by deeplearn.js.\n    dtype = dtype || 'float32';\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n    }\n    return new LayerVariable(tfc.truncatedNormal(shape, mean, stddev, dtype, seed), dtype, name);\n}\n/**\n * Get a Variable with normal distribution of values.\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The truncated-normal-random Variable.\n */\nexport function randomNormalVariable(shape, mean = 0.0, stddev = 1.0, dtype, seed, name = 'randomNormal') {\n    dtype = dtype || 'float32';\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new NotImplementedError(`randomNormalVariable does not support dType ${dtype}.`);\n    }\n    return new LayerVariable(tfc.randomNormal(shape, mean, stddev, dtype, seed), dtype, name);\n}\n/**\n * Update the value of a Variable.\n * @param x The Variable to be updated.\n * @param xNew The new value to update to.\n * @return The Variable updated.\n */\nexport function update(x, xNew) {\n    return x.write(xNew);\n}\n/**\n * Update the value of a Variable by adding an increment.\n * @param x The Variable to be updated.\n * @param increment The incrment to add to `x`.\n * @return The Variable updated.\n */\nexport function updateAdd(x, increment) {\n    return x.write(tfc.add(x.read(), increment));\n}\n/**\n * Update the value of a Variable by subtracting a decrement.\n * @param x The Variable to be updated.\n * @param decrement The decrement to subtract from `x`.\n * @return The Variable updated.\n */\nexport function updateSub(x, decrement) {\n    return x.write(tfc.sub(x.read(), decrement));\n}\n/**\n * Get the values of an array of Variables.\n *\n * @param tensors An `Array` of `Variable`s to get the values of.\n * @return The values of the inputs, as an `Array` of`tf.Tensor`s.\n */\nexport function batchGetValue(xs) {\n    return xs.map(x => x.read());\n}\n/**\n * Update the value of multiple Variables at once.\n *\n * @param variablesAndValues An `Array`, each element is of type\n *   [Variable, Tensor]. The first item is the\n *   `Variable` of which the value is to be updated. The second item\n *   carries the new value.\n */\nexport function batchSetValue(variablesAndValues) {\n    variablesAndValues.forEach(variableAndValue => {\n        const variable = variableAndValue[0];\n        variable.write(variableAndValue[1]);\n    });\n}\n/**\n * Returns the gradients of `variables` w.r.t. the return value of `lossFn`.\n * @param lossFn A function which returns a Scalar to be used as the function\n *   value (i.e., numerator) for differentiation.\n * @param variables List of variables to be used as the independent variables\n *   (i.e., denominator) for differentiation.\n * @returns An Array of gradients tensors.\n */\nexport function gradients(lossFn, variables) {\n    // TODO(cais): The return type signature can be simplified if deeplearn makes\n    //   the corresponding type public.\n    const variableList = variables.map(variable => variable.read());\n    const valudAndGrads = variableGrads(lossFn, variableList);\n    return variables.map(variable => valudAndGrads.grads[variable.name]);\n}\n//# sourceMappingURL=variables.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/engine/topology.py */\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getNextUniqueTensorId, getUid } from '../backend/state';\nimport { getScopedTensorName, getUniqueTensorName, nameScope } from '../common';\nimport { AttributeError, NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { getInitializer } from '../initializers';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as types_utils from '../utils/types_utils';\nimport * as variable_utils from '../utils/variable_utils';\nimport { batchGetValue, batchSetValue, LayerVariable } from '../variables';\n/**\n * Specifies the ndim, dtype and shape of every input to a layer.\n *\n * Every layer should expose (if appropriate) an `inputSpec` attribute:\n * a list of instances of InputSpec (one per input tensor).\n *\n * A null entry in a shape is compatible with any dimension,\n * a null shape is compatible with any shape.\n */\nexport class InputSpec {\n    constructor(args) {\n        this.dtype = args.dtype;\n        this.shape = args.shape;\n        /*\n          TODO(michaelterry): Could throw error if ndim and shape are both defined\n            (then backport).\n        */\n        if (args.shape != null) {\n            this.ndim = args.shape.length;\n        }\n        else {\n            this.ndim = args.ndim;\n        }\n        this.maxNDim = args.maxNDim;\n        this.minNDim = args.minNDim;\n        this.axes = args.axes || {};\n    }\n}\n/**\n * `tf.SymbolicTensor` is a placeholder for a Tensor without any concrete value.\n *\n * They are most often encountered when building a graph of `Layer`s for a\n * a `tf.LayersModel` and the input data's shape, but not values are known.\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\nexport class SymbolicTensor {\n    /**\n     *\n     * @param dtype\n     * @param shape\n     * @param sourceLayer The Layer that produced this symbolic tensor.\n     * @param inputs The inputs passed to sourceLayer's __call__() method.\n     * @param nodeIndex\n     * @param tensorIndex\n     * @param callArgs The keyword arguments passed to the __call__() method.\n     * @param name\n     * @param outputTensorIndex The index of this tensor in the list of outputs\n     *   returned by apply().\n     */\n    constructor(dtype, shape, sourceLayer, inputs, callArgs, name, outputTensorIndex) {\n        this.dtype = dtype;\n        this.shape = shape;\n        this.sourceLayer = sourceLayer;\n        this.inputs = inputs;\n        this.callArgs = callArgs;\n        this.outputTensorIndex = outputTensorIndex;\n        this.id = getNextUniqueTensorId();\n        if (name != null) {\n            this.originalName = getScopedTensorName(name);\n            this.name = getUniqueTensorName(this.originalName);\n        }\n        this.rank = shape.length;\n    }\n}\nlet _nextNodeID = 0;\n/**\n * A `Node` describes the connectivity between two layers.\n *\n * Each time a layer is connected to some new input,\n * a node is added to `layer.inboundNodes`.\n *\n * Each time the output of a layer is used by another layer,\n * a node is added to `layer.outboundNodes`.\n *\n * `nodeIndices` and `tensorIndices` are basically fine-grained coordinates\n * describing the origin of the `inputTensors`, verifying the following:\n *\n * `inputTensors[i] ==\n * inboundLayers[i].inboundNodes[nodeIndices[i]].outputTensors[\n *   tensorIndices[i]]`\n *\n * A node from layer A to layer B is added to:\n *     A.outboundNodes\n *     B.inboundNodes\n */\nexport class Node {\n    constructor(args, \n    // TODO(michaelterry): Define actual type for this.\n    callArgs) {\n        this.callArgs = callArgs;\n        this.id = _nextNodeID++;\n        /*\n          Layer instance (NOT a list).\n          this is the layer that takes a list of input tensors\n          and turns them into a list of output tensors.\n          the current node will be added to\n          the inboundNodes of outboundLayer.\n        */\n        this.outboundLayer = args.outboundLayer;\n        /*\n            The following 3 properties describe where\n            the input tensors come from: which layers,\n            and for each layer, which node and which\n            tensor output of each node.\n        */\n        // List of layer instances.\n        this.inboundLayers = args.inboundLayers;\n        // List of integers, 1:1 mapping with inboundLayers.\n        this.nodeIndices = args.nodeIndices;\n        // List of integers, 1:1 mapping with inboundLayers.\n        this.tensorIndices = args.tensorIndices;\n        /*\n            Following 2 properties:\n            tensor inputs and outputs of outboundLayer.\n        */\n        // List of tensors. 1:1 mapping with inboundLayers.\n        this.inputTensors = args.inputTensors;\n        // List of tensors, created by outboundLayer.call().\n        this.outputTensors = args.outputTensors;\n        /*\n            Following 2 properties: input and output masks.\n            List of tensors, 1:1 mapping with inputTensor.\n        */\n        this.inputMasks = args.inputMasks;\n        // List of tensors, created by outboundLayer.computeMask().\n        this.outputMasks = args.outputMasks;\n        // Following 2 properties: input and output shapes.\n        // List of shape tuples, shapes of inputTensors.\n        this.inputShapes = args.inputShapes;\n        // List of shape tuples, shapes of outputTensors.\n        this.outputShapes = args.outputShapes;\n        // Add nodes to all layers involved.\n        for (const layer of args.inboundLayers) {\n            if (layer != null) {\n                layer.outboundNodes.push(this);\n            }\n        }\n        args.outboundLayer.inboundNodes.push(this);\n    }\n    getConfig() {\n        const inboundNames = [];\n        for (const layer of this.inboundLayers) {\n            if (layer != null) {\n                inboundNames.push(layer.name);\n            }\n            else {\n                inboundNames.push(null);\n            }\n        }\n        return {\n            outboundLayer: this.outboundLayer ? this.outboundLayer.name : null,\n            inboundLayers: inboundNames,\n            nodeIndices: this.nodeIndices,\n            tensorIndices: this.tensorIndices\n        };\n    }\n}\nlet _nextLayerID = 0;\n/**\n * A layer is a grouping of operations and weights that can be composed to\n * create a `tf.LayersModel`.\n *\n * Layers are constructed by using the functions under the\n * [tf.layers](#Layers-Basic) namespace.\n *\n * @doc {heading: 'Layers', subheading: 'Classes', namespace: 'layers'}\n */\nexport class Layer extends serialization.Serializable {\n    constructor(args = {}) {\n        super();\n        this._callHook = null;\n        this._addedWeightNames = [];\n        // Porting Notes: PyKeras does not have this property in this base Layer\n        //   class. Instead lets Layer subclass set it dynamically and checks the\n        //   value with `hasattr`. In tfjs-layers, we let this be a member of this\n        //   base class.\n        this._stateful = false;\n        this.id = _nextLayerID++;\n        this.activityRegularizer = null;\n        this.inputSpec = null;\n        this.supportsMasking = false;\n        // These properties will be set upon call of this.build()\n        this._trainableWeights = [];\n        this._nonTrainableWeights = [];\n        this._losses = [];\n        this._updates = [];\n        this._built = false;\n        /*\n          These lists will be filled via successive calls\n          to this.addInboundNode().\n         */\n        this.inboundNodes = [];\n        this.outboundNodes = [];\n        let name = args.name;\n        if (!name) {\n            const prefix = this.getClassName();\n            name = generic_utils.toSnakeCase(prefix) + '_' + getUid(prefix);\n        }\n        this.name = name;\n        this.trainable_ = args.trainable == null ? true : args.trainable;\n        if (args.inputShape != null || args.batchInputShape != null) {\n            /*\n              In this case we will later create an input layer\n              to insert before the current layer\n             */\n            let batchInputShape;\n            if (args.batchInputShape != null) {\n                batchInputShape = args.batchInputShape;\n            }\n            else if (args.inputShape != null) {\n                let batchSize = null;\n                if (args.batchSize != null) {\n                    batchSize = args.batchSize;\n                }\n                batchInputShape = [batchSize].concat(args.inputShape);\n            }\n            this.batchInputShape = batchInputShape;\n            // Set dtype.\n            let dtype = args.dtype;\n            if (dtype == null) {\n                dtype = args.inputDType;\n            }\n            if (dtype == null) {\n                dtype = 'float32';\n            }\n            this.dtype = dtype;\n        }\n        if (args.weights != null) {\n            this.initialWeights = args.weights;\n        }\n        else {\n            this.initialWeights = null;\n        }\n        // The value of `_refCount` is initialized to null. When the layer is used\n        // in a symbolic way for the first time, it will be set to 1.\n        this._refCount = null;\n        this.fastWeightInitDuringBuild = false;\n    }\n    /**\n     * Converts a layer and its index to a unique (immutable type) name.\n     * This function is used internally with `this.containerNodes`.\n     * @param layer The layer.\n     * @param nodeIndex The layer's position (e.g. via enumerate) in a list of\n     *   nodes.\n     *\n     * @returns The unique name.\n     */\n    static nodeKey(layer, nodeIndex) {\n        return layer.name + '_ib-' + nodeIndex.toString();\n    }\n    /**\n     * Returns this.inboundNode at index nodeIndex.\n     *\n     * Porting note: This is a replacement for _get_node_attribute_at_index()\n     * @param nodeIndex\n     * @param attrName The name of the attribute related to request for this node.\n     */\n    getNodeAtIndex(nodeIndex, attrName) {\n        if (this.inboundNodes.length === 0) {\n            throw new RuntimeError('The layer has never been called ' +\n                `and thus has no defined ${attrName}.`);\n        }\n        if (this.inboundNodes.length <= nodeIndex) {\n            throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, ` +\n                `but the layer has only ${this.inboundNodes.length} inbound nodes.`);\n        }\n        return this.inboundNodes[nodeIndex];\n    }\n    /**\n     * Retrieves the input tensor(s) of a layer at a given node.\n     *\n     * @param nodeIndex Integer, index of the node from which to retrieve the\n     *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n     *   was called.\n     *\n     * @return A tensor (or list of tensors if the layer has multiple inputs).\n     */\n    getInputAt(nodeIndex) {\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(nodeIndex, 'input').inputTensors);\n    }\n    /**\n     * Retrieves the output tensor(s) of a layer at a given node.\n     *\n     * @param nodeIndex Integer, index of the node from which to retrieve the\n     *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n     *   was called.\n     *\n     * @return A tensor (or list of tensors if the layer has multiple outputs).\n     */\n    getOutputAt(nodeIndex) {\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(nodeIndex, 'output').outputTensors);\n    }\n    // Properties\n    /**\n     * Retrieves the input tensor(s) of a layer.\n     *\n     * Only applicable if the layer has exactly one inbound node,\n     * i.e. if it is connected to one incoming layer.\n     *\n     * @return Input tensor or list of input tensors.\n     *\n     * @exception AttributeError if the layer is connected to more than one\n     *   incoming layers.\n     */\n    get input() {\n        if (this.inboundNodes.length > 1) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has multiple inbound nodes, ' +\n                'hence the notion of \"layer input\" ' +\n                'is ill-defined. ' +\n                'Use `getInputAt(nodeIndex)` instead.');\n        }\n        else if (this.inboundNodes.length === 0) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' is not connected, no input to return.');\n        }\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(0, 'input').inputTensors);\n    }\n    /**\n     * Retrieves the output tensor(s) of a layer.\n     *\n     * Only applicable if the layer has exactly one inbound node,\n     * i.e. if it is connected to one incoming layer.\n     *\n     * @return Output tensor or list of output tensors.\n     *\n     * @exception AttributeError if the layer is connected to more than one\n     *   incoming layers.\n     */\n    get output() {\n        if (this.inboundNodes.length === 0) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has no inbound nodes.');\n        }\n        if (this.inboundNodes.length > 1) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has multiple inbound nodes, ' +\n                'hence the notion of \"layer output\" ' +\n                'is ill-defined. ' +\n                'Use `getOutputAt(nodeIndex)` instead.');\n        }\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(0, 'output').outputTensors);\n    }\n    get losses() {\n        return this._losses;\n    }\n    /**\n     * Retrieves the Layer's current loss values.\n     *\n     * Used for regularizers during training.\n     */\n    calculateLosses() {\n        // Porting Node: This is an augmentation to Layer.loss in PyKeras.\n        //   In PyKeras, Layer.loss returns symbolic tensors. Here a concrete\n        //   Tensor (specifically Scalar) values are returned. This is due to the\n        //   imperative backend.\n        return this.losses.map(lossFn => lossFn());\n    }\n    get updates() {\n        return this._updates;\n    }\n    get built() {\n        return this._built;\n    }\n    set built(built) {\n        this._built = built;\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this._trainableWeights.forEach(w => w.trainable = trainable);\n        this.trainable_ = trainable;\n    }\n    get trainableWeights() {\n        if (this.trainable_) {\n            return this._trainableWeights.filter(w => w.trainable);\n        }\n        else {\n            return [];\n        }\n    }\n    set trainableWeights(weights) {\n        this._trainableWeights = weights;\n    }\n    get nonTrainableWeights() {\n        if (this.trainable) {\n            return this._trainableWeights.filter(w => !w.trainable)\n                .concat(this._nonTrainableWeights);\n        }\n        else {\n            return this._trainableWeights.concat(this._nonTrainableWeights);\n        }\n    }\n    set nonTrainableWeights(weights) {\n        this._nonTrainableWeights = weights;\n    }\n    /**\n     * The concatenation of the lists trainableWeights and nonTrainableWeights\n     * (in this order).\n     */\n    get weights() {\n        return this.trainableWeights.concat(this.nonTrainableWeights);\n    }\n    get stateful() {\n        return this._stateful;\n    }\n    /**\n     * Reset the states of the layer.\n     *\n     * This method of the base Layer class is essentially a no-op.\n     * Subclasses that are stateful (e.g., stateful RNNs) should override this\n     * method.\n     */\n    resetStates() {\n        if (!this.stateful) {\n            throw new Error('Cannot call the resetStates() method of a non-stateful Layer ' +\n                'object.');\n        }\n    }\n    /**\n     * Checks compatibility between the layer and provided inputs.\n     *\n     * This checks that the tensor(s) `input`\n     * verify the input assumptions of the layer\n     * (if any). If not, exceptions are raised.\n     *\n     * @param inputs Input tensor or list of input tensors.\n     *\n     * @exception ValueError in case of mismatch between\n     *   the provided inputs and the expectations of the layer.\n     */\n    assertInputCompatibility(inputs) {\n        inputs = generic_utils.toList(inputs);\n        if (this.inputSpec == null || this.inputSpec.length === 0) {\n            return;\n        }\n        const inputSpec = generic_utils.toList(this.inputSpec);\n        if (inputs.length !== inputSpec.length) {\n            throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, ` +\n                `but it received ${inputs.length} input tensors. ` +\n                `Input received: ${inputs}`);\n        }\n        for (let inputIndex = 0; inputIndex < inputs.length; inputIndex++) {\n            const x = inputs[inputIndex];\n            const spec = inputSpec[inputIndex];\n            if (spec == null) {\n                continue;\n            }\n            // Check ndim.\n            const ndim = x.rank;\n            if (spec.ndim != null) {\n                if (ndim !== spec.ndim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: ` +\n                        `expected ndim=${spec.ndim}, found ndim=${ndim}`);\n                }\n            }\n            if (spec.maxNDim != null) {\n                if (ndim > spec.maxNDim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}` +\n                        `: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);\n                }\n            }\n            if (spec.minNDim != null) {\n                if (ndim < spec.minNDim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}` +\n                        `: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);\n                }\n            }\n            // Check dtype.\n            if (spec.dtype != null) {\n                if (x.dtype !== spec.dtype) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} ` +\n                        `: expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);\n                }\n            }\n            // Check specific shape axes.\n            if (spec.axes) {\n                const xShape = x.shape;\n                for (const key in spec.axes) {\n                    const axis = Number(key);\n                    const value = spec.axes[key];\n                    // Perform Python-style slicing in case axis < 0;\n                    // TODO(cais): Use https://github.com/alvivi/typescript-underscore to\n                    // ensure type safety through Underscore calls.\n                    const xShapeAtAxis = axis >= 0 ? xShape[axis] : xShape[xShape.length + axis];\n                    if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) {\n                        throw new ValueError(`Input ${inputIndex} is incompatible with layer ` +\n                            `${this.name}: expected axis ${axis} of input shape to ` +\n                            `have value ${value} but got shape ${xShape}.`);\n                    }\n                }\n            }\n            // Check shape.\n            if (spec.shape != null) {\n                for (let i = 0; i < spec.shape.length; ++i) {\n                    const specDim = spec.shape[i];\n                    const dim = x.shape[i];\n                    if (specDim != null && dim != null) {\n                        if (specDim !== dim) {\n                            throw new ValueError(`Input ${inputIndex} is incompatible with layer ` +\n                                `${this.name}: expected shape=${spec.shape}, ` +\n                                `found shape=${x.shape}.`);\n                        }\n                    }\n                }\n            }\n        }\n    }\n    /**\n     * This is where the layer's logic lives.\n     *\n     * @param inputs Input tensor, or list/tuple of input tensors.\n     * @param kwargs Additional keyword arguments.\n     *\n     * @return A tensor or list/tuple of tensors.\n     */\n    call(inputs, kwargs) {\n        return inputs;\n    }\n    invokeCallHook(inputs, kwargs) {\n        if (this._callHook != null) {\n            this._callHook(inputs, kwargs);\n        }\n    }\n    /**\n     * Set call hook.\n     * This is currently used for testing only.\n     * @param callHook\n     */\n    setCallHook(callHook) {\n        this._callHook = callHook;\n    }\n    /**\n     * Clear call hook.\n     * This is currently used for testing only.\n     */\n    clearCallHook() {\n        this._callHook = null;\n    }\n    /**\n     * Builds or executes a `Layer's logic.\n     *\n     * When called with `tf.Tensor`(s), execute the `Layer`s computation and\n     * return Tensor(s). For example:\n     *\n     * ```js\n     * const denseLayer = tf.layers.dense({\n     *   units: 1,\n     *   kernelInitializer: 'zeros',\n     *   useBias: false\n     * });\n     *\n     * // Invoke the layer's apply() method with a `tf.Tensor` (with concrete\n     * // numeric values).\n     * const input = tf.ones([2, 2]);\n     * const output = denseLayer.apply(input);\n     *\n     * // The output's value is expected to be [[0], [0]], due to the fact that\n     * // the dense layer has a kernel initialized to all-zeros and does not have\n     * // a bias.\n     * output.print();\n     * ```\n     *\n     * When called with `tf.SymbolicTensor`(s), this will prepare the layer for\n     * future execution.  This entails internal book-keeping on shapes of\n     * expected Tensors, wiring layers together, and initializing weights.\n     *\n     * Calling `apply` with `tf.SymbolicTensor`s are typically used during the\n     * building of non-`tf.Sequential` models. For example:\n     *\n     * ```js\n     * const flattenLayer = tf.layers.flatten();\n     * const denseLayer = tf.layers.dense({units: 1});\n     *\n     * // Use tf.layers.input() to obtain a SymbolicTensor as input to apply().\n     * const input = tf.input({shape: [2, 2]});\n     * const output1 = flattenLayer.apply(input);\n     *\n     * // output1.shape is [null, 4]. The first dimension is the undetermined\n     * // batch size. The second dimension comes from flattening the [2, 2]\n     * // shape.\n     * console.log(JSON.stringify(output1.shape));\n     *\n     * // The output SymbolicTensor of the flatten layer can be used to call\n     * // the apply() of the dense layer:\n     * const output2 = denseLayer.apply(output1);\n     *\n     * // output2.shape is [null, 1]. The first dimension is the undetermined\n     * // batch size. The second dimension matches the number of units of the\n     * // dense layer.\n     * console.log(JSON.stringify(output2.shape));\n     *\n     * // The input and output and be used to construct a model that consists\n     * // of the flatten and dense layers.\n     * const model = tf.model({inputs: input, outputs: output2});\n     * ```\n     *\n     * @param inputs a `tf.Tensor` or `tf.SymbolicTensor` or an Array of them.\n     * @param kwargs Additional keyword arguments to be passed to `call()`.\n     *\n     * @return Output of the layer's `call` method.\n     *\n     * @exception ValueError error in case the layer is missing shape information\n     *   for its `build` call.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    // Porting Note: This is a replacement for __call__() in Python.\n    apply(inputs, kwargs) {\n        kwargs = kwargs || {};\n        this.assertNotDisposed();\n        // Ensure inputs are all the same type.\n        const inputsList = generic_utils.toList(inputs);\n        let allAreSymbolic = true;\n        for (const input of inputsList) {\n            if (!(input instanceof SymbolicTensor)) {\n                allAreSymbolic = false;\n                break;\n            }\n        }\n        let noneAreSymbolic = true;\n        for (const input of inputsList) {\n            if (input instanceof SymbolicTensor) {\n                noneAreSymbolic = false;\n                break;\n            }\n        }\n        if (allAreSymbolic === noneAreSymbolic) {\n            throw new ValueError('Arguments to apply() must be all ' +\n                'SymbolicTensors or all Tensors');\n        }\n        // TODO(michaelterry): nameScope() may not be necessary.\n        return nameScope(this.name, () => {\n            // Handle laying building (weight creating, input spec locking).\n            if (!this.built) {\n                /*\n                  Throw exceptions in case the input is not compatible\n                  with the inputSpec specified in the layer constructor.\n                 */\n                this.assertInputCompatibility(inputs);\n                // Collect input shapes to build layer.\n                const inputShapes = [];\n                for (const xElem of generic_utils.toList(inputs)) {\n                    inputShapes.push(xElem.shape);\n                }\n                this.build(generic_utils.singletonOrArray(inputShapes));\n                this.built = true;\n                // Load weights that were specified at layer instantiation.\n                if (this.initialWeights) {\n                    this.setWeights(this.initialWeights);\n                }\n                if (this._refCount === null && noneAreSymbolic) {\n                    // The first use of this layer is a non-symbolic call, set ref count\n                    // to 1 so the Layer can be properly disposed if its dispose() method\n                    // is called.\n                    this._refCount = 1;\n                }\n            }\n            /*\n              Throw exceptions in case the input is not compatible\n              with the inputSpec set at build time.\n            */\n            this.assertInputCompatibility(inputs);\n            // Handle mask propagation.\n            // TODO(michaelterry): Mask propagation not currently implemented.\n            // Actually call the layer, collecting output(s), mask(s), and shape(s).\n            if (noneAreSymbolic) {\n                let output = this.call(inputs, kwargs);\n                // TODO(michaelterry): Compute the outputMask\n                // If the layer returns tensors from its inputs, unmodified,\n                // we copy them to avoid loss of tensor metadata.\n                const outputList = generic_utils.toList(output);\n                const outputListCopy = [];\n                // TODO(michaelterry): This copying may not be necessary given our eager\n                // backend.\n                for (let x of outputList) {\n                    if (inputsList.indexOf(x) !== -1) {\n                        x = x.clone();\n                    }\n                    outputListCopy.push(x);\n                }\n                output = generic_utils.singletonOrArray(outputListCopy);\n                if (this.activityRegularizer != null) {\n                    throw new NotImplementedError('Layer invocation in the presence of activity ' +\n                        'regularizer(s) is not supported yet.');\n                }\n                // TODO(michaelterry): Call addInboundNode()?\n                return output;\n            }\n            else {\n                const inputShape = collectInputShape(inputs);\n                const outputShape = this.computeOutputShape(inputShape);\n                let output;\n                const outputDType = guessOutputDType(inputs);\n                this.warnOnIncompatibleInputShape(Array.isArray(inputs) ? inputShape[0] :\n                    inputShape);\n                if (outputShape != null && outputShape.length > 0 &&\n                    Array.isArray(outputShape[0])) {\n                    // We have multiple output shapes. Create multiple output tensors.\n                    output = outputShape\n                        .map((shape, index) => new SymbolicTensor(outputDType, shape, this, generic_utils.toList(inputs), kwargs, this.name, index));\n                }\n                else {\n                    output = new SymbolicTensor(outputDType, outputShape, this, generic_utils.toList(inputs), kwargs, this.name);\n                }\n                /*\n                  Add an inbound node to the layer, so that it keeps track\n                  of the call and of all new variables created during the call.\n                  This also updates the layer history of the output tensor(s).\n                  If the input tensor(s) had no previous history,\n                  this does nothing.\n                */\n                this.addInboundNode(inputs, output, null, null, inputShape, outputShape, kwargs);\n                this._refCount++;\n                if (this.activityRegularizer != null) {\n                    throw new NotImplementedError('Layer invocation in the presence of activity ' +\n                        'regularizer(s) is not supported yet.');\n                }\n                return output;\n            }\n        });\n    }\n    /**\n     * Check compatibility between input shape and this layer's batchInputShape.\n     *\n     * Print warning if any incompatibility is found.\n     *\n     * @param inputShape Input shape to be checked.\n     */\n    warnOnIncompatibleInputShape(inputShape) {\n        if (this.batchInputShape == null) {\n            return;\n        }\n        else if (inputShape.length !== this.batchInputShape.length) {\n            console.warn(`The rank of the input tensor provided (shape: ` +\n                `${JSON.stringify(inputShape)}) does not match that of the ` +\n                `batchInputShape (${JSON.stringify(this.batchInputShape)}) ` +\n                `of the layer ${this.name}`);\n        }\n        else {\n            let dimMismatch = false;\n            this.batchInputShape.forEach((dimension, i) => {\n                if (dimension != null && inputShape[i] != null &&\n                    inputShape[i] !== dimension) {\n                    dimMismatch = true;\n                }\n            });\n            if (dimMismatch) {\n                console.warn(`The shape of the input tensor ` +\n                    `(${JSON.stringify(inputShape)}) does not ` +\n                    `match the expectation of layer ${this.name}: ` +\n                    `${JSON.stringify(this.batchInputShape)}`);\n            }\n        }\n    }\n    /**\n     * Retrieves the output shape(s) of a layer.\n     *\n     * Only applicable if the layer has only one inbound node, or if all inbound\n     * nodes have the same output shape.\n     *\n     * @returns Output shape or shapes.\n     * @throws AttributeError: if the layer is connected to more than one incoming\n     *   nodes.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    get outputShape() {\n        if (this.inboundNodes == null || this.inboundNodes.length === 0) {\n            throw new AttributeError(`The layer ${this.name} has never been called and thus has no ` +\n                `defined output shape.`);\n        }\n        const allOutputShapes = [];\n        for (const node of this.inboundNodes) {\n            const shapeString = JSON.stringify(node.outputShapes);\n            if (allOutputShapes.indexOf(shapeString) === -1) {\n                allOutputShapes.push(shapeString);\n            }\n        }\n        if (allOutputShapes.length === 1) {\n            const outputShapes = this.inboundNodes[0].outputShapes;\n            if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) &&\n                outputShapes.length === 1) {\n                return outputShapes[0];\n            }\n            else {\n                return outputShapes;\n            }\n        }\n        else {\n            throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different ` +\n                `output shapes. Hence the notion of \"output shape\" is ill-defined ` +\n                `for the layer.`);\n            // TODO(cais): Implement getOutputShapeAt().\n        }\n    }\n    /**\n     * Counts the total number of numbers (e.g., float32, int32) in the\n     * weights.\n     *\n     * @returns An integer count.\n     * @throws RuntimeError: If the layer is not built yet (in which case its\n     *   weights are not defined yet.)\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    countParams() {\n        if (!this.built) {\n            throw new RuntimeError(`You tried to call countParams() on ${this.name}, ` +\n                `but the layer is not built yet. Build it first by calling ` +\n                `build(batchInputShape).`);\n        }\n        return variable_utils.countParamsInWeights(this.weights);\n    }\n    /**\n     * Creates the layer weights.\n     *\n     * Must be implemented on all layers that have weights.\n     *\n     * Called when apply() is called to construct the weights.\n     *\n     * @param inputShape A `Shape` or array of `Shape` (unused).\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    build(inputShape) {\n        this.built = true;\n    }\n    /**\n     * Returns the current values of the weights of the layer.\n     *\n     * @param trainableOnly Whether to get the values of only trainable weights.\n     * @returns Weight values as an `Array` of `tf.Tensor`s.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    getWeights(trainableOnly = false) {\n        return batchGetValue(trainableOnly ? this.trainableWeights : this.weights);\n    }\n    /**\n     * Sets the weights of the layer, from Tensors.\n     *\n     * @param weights a list of Tensors. The number of arrays and their shape\n     *   must match number of the dimensions of the weights of the layer (i.e.\n     *   it should match the output of `getWeights`).\n     *\n     * @exception ValueError If the provided weights list does not match the\n     *   layer's specifications.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    setWeights(weights) {\n        tidy(() => {\n            const params = this.weights;\n            if (params.length !== weights.length) {\n                // TODO(cais): Restore the following and use `providedWeights`, instead\n                // of `weights` in the error message, once the deeplearn.js bug is\n                // fixed: https://github.com/PAIR-code/deeplearnjs/issues/498 const\n                // providedWeights = JSON.stringify(weights).substr(0, 50);\n                throw new ValueError(`You called setWeights(weights) on layer \"${this.name}\" ` +\n                    `with a weight list of length ${weights.length}, ` +\n                    `but the layer was expecting ${params.length} weights. ` +\n                    `Provided weights: ${weights}...`);\n            }\n            if (params.length === 0) {\n                return;\n            }\n            const weightValueTuples = [];\n            const paramValues = batchGetValue(params);\n            for (let i = 0; i < paramValues.length; ++i) {\n                const pv = paramValues[i];\n                const p = params[i];\n                const w = weights[i];\n                if (!util.arraysEqual(pv.shape, w.shape)) {\n                    throw new ValueError(`Layer weight shape ${pv.shape} ` +\n                        `not compatible with provided weight shape ${w.shape}`);\n                }\n                weightValueTuples.push([p, w]);\n            }\n            batchSetValue(weightValueTuples);\n        });\n    }\n    /**\n     * Adds a weight variable to the layer.\n     *\n     * @param name Name of the new weight variable.\n     * @param shape The shape of the weight.\n     * @param dtype The dtype of the weight.\n     * @param initializer An initializer instance.\n     * @param regularizer A regularizer instance.\n     * @param trainable Whether the weight should be trained via backprop or not\n     *   (assuming that the layer itself is also trainable).\n     * @param constraint An optional trainable.\n     * @return The created weight variable.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint) {\n        // Reject duplicate weight names.\n        if (this._addedWeightNames.indexOf(name) !== -1) {\n            throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);\n        }\n        this._addedWeightNames.push(name);\n        if (dtype == null) {\n            dtype = 'float32';\n        }\n        if (this.fastWeightInitDuringBuild) {\n            initializer = getInitializer('zeros');\n        }\n        const initValue = initializer.apply(shape, dtype);\n        const weight = new LayerVariable(initValue, dtype, name, trainable, constraint);\n        initValue.dispose();\n        // Request backend not to dispose the weights of the model on scope() exit.\n        if (regularizer != null) {\n            this.addLoss(() => regularizer.apply(weight.read()));\n        }\n        if (trainable == null) {\n            trainable = true;\n        }\n        if (trainable) {\n            this._trainableWeights.push(weight);\n        }\n        else {\n            this._nonTrainableWeights.push(weight);\n        }\n        return weight;\n    }\n    /**\n     * Set the fast-weight-initialization flag.\n     *\n     * In cases where the initialized weight values will be immediately\n     * overwritten by loaded weight values during model loading, setting\n     * the flag to `true` saves unnecessary calls to potentially expensive\n     * initializers and speeds up the loading process.\n     *\n     * @param value Target value of the flag.\n     */\n    setFastWeightInitDuringBuild(value) {\n        this.fastWeightInitDuringBuild = value;\n    }\n    /**\n     * Add losses to the layer.\n     *\n     * The loss may potentionally be conditional on some inputs tensors,\n     * for instance activity losses are conditional on the layer's inputs.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    addLoss(losses) {\n        if (losses == null || Array.isArray(losses) && losses.length === 0) {\n            return;\n        }\n        // Update this.losses\n        losses = generic_utils.toList(losses);\n        if (this._losses !== undefined && this._losses !== null) {\n            this.losses.push(...losses);\n        }\n    }\n    /**\n     * Computes the output shape of the layer.\n     *\n     * Assumes that the layer will be built to match that input shape provided.\n     *\n     * @param inputShape A shape (tuple of integers) or a list of shape tuples\n     *   (one per output tensor of the layer). Shape tuples can include null for\n     *   free dimensions, instead of an integer.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    /**\n     * Computes an output mask tensor.\n     *\n     * @param inputs Tensor or list of tensors.\n     * @param mask Tensor or list of tensors.\n     *\n     * @return null or a tensor (or list of tensors, one per output tensor of the\n     * layer).\n     */\n    computeMask(inputs, mask) {\n        if (!this.supportsMasking) {\n            if (mask != null) {\n                if (Array.isArray(mask)) {\n                    mask.forEach(maskElement => {\n                        if (maskElement != null) {\n                            throw new TypeError(`Layer ${this.name} does not support masking, ` +\n                                'but was passed an inputMask.');\n                        }\n                    });\n                }\n                else {\n                    throw new TypeError(`Layer ${this.name} does not support masking, ` +\n                        'but was passed an inputMask.');\n                }\n            }\n            // masking not explicitly supported: return null as mask\n            return null;\n        }\n        // if masking is explictly supported, by default\n        // carry over the input mask\n        return mask;\n    }\n    /**\n     * Internal method to create an inbound node for the layer.\n     *\n     * @param inputTensors List of input tensors.\n     * @param outputTensors List of output tensors.\n     * @param inputMasks List of input masks (a mask can be a tensor, or null).\n     * @param outputMasks List of output masks (a mask can be a tensor, or null).\n     * @param inputShapes List of input shape tuples.\n     * @param outputShapes List of output shape tuples.\n     * @param kwargs Dictionary of keyword arguments that were passed to the\n     *   `call` method of the layer at the call that created the node.\n     */\n    addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) {\n        const inputTensorList = generic_utils.toList(inputTensors);\n        outputTensors = generic_utils.toList(outputTensors);\n        inputMasks = generic_utils.toList(inputMasks);\n        outputMasks = generic_utils.toList(outputMasks);\n        inputShapes = types_utils.normalizeShapeList(inputShapes);\n        outputShapes = types_utils.normalizeShapeList(outputShapes);\n        // Collect input tensor(s) coordinates.\n        const inboundLayers = [];\n        const nodeIndices = [];\n        const tensorIndices = [];\n        for (const x of inputTensorList) {\n            /*\n             * TODO(michaelterry): Keras adds this value to tensors; it's not\n             * clear whether we'll use this or not.\n             */\n            inboundLayers.push(x.sourceLayer);\n            nodeIndices.push(x.nodeIndex);\n            tensorIndices.push(x.tensorIndex);\n        }\n        // Create node, add it to inbound nodes.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers,\n            nodeIndices,\n            tensorIndices,\n            inputTensors: inputTensorList,\n            outputTensors,\n            inputMasks,\n            outputMasks,\n            inputShapes,\n            outputShapes\n        }, kwargs);\n        // Update tensor history\n        for (let i = 0; i < outputTensors.length; i++) {\n            // TODO(michaelterry: _uses_learning_phase not tracked.\n            outputTensors[i].sourceLayer = this;\n            outputTensors[i].nodeIndex = this.inboundNodes.length - 1;\n            outputTensors[i].tensorIndex = i;\n        }\n    }\n    /**\n     * Returns the config of the layer.\n     *\n     * A layer config is a TS dictionary (serializable)\n     * containing the configuration of a layer.\n     * The same layer can be reinstantiated later\n     * (without its trained weights) from this configuration.\n     *\n     * The config of a layer does not include connectivity\n     * information, nor the layer class name.  These are handled\n     * by 'Container' (one layer of abstraction above).\n     *\n     * Porting Note: The TS dictionary follows TS naming standrds for\n     * keys, and uses tfjs-layers type-safe Enums.  Serialization methods\n     * should use a helper function to convert to the pythonic storage\n     * standard. (see serialization_utils.convertTsToPythonic)\n     *\n     * @returns TS dictionary of configuration.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    getConfig() {\n        const config = { name: this.name, trainable: this.trainable };\n        if (this.batchInputShape != null) {\n            config['batchInputShape'] = this.batchInputShape;\n        }\n        if (this.dtype != null) {\n            config['dtype'] = this.dtype;\n        }\n        return config;\n    }\n    /**\n     * Dispose the weight variables that this Layer instance holds.\n     *\n     * @returns {number} Number of disposed variables.\n     */\n    disposeWeights() {\n        this.weights.forEach(weight => weight.dispose());\n        return this.weights.length;\n    }\n    assertNotDisposed() {\n        if (this._refCount === 0) {\n            throw new Error(`Layer '${this.name}' is already disposed.`);\n        }\n    }\n    /**\n     * Attempt to dispose layer's weights.\n     *\n     * This method decrease the reference count of the Layer object by 1.\n     *\n     * A Layer is reference-counted. Its reference count is incremented by 1\n     * the first item its `apply()` method is called and when it becomes a part\n     * of a new `Node` (through calling the `apply()`) method on a\n     * `tf.SymbolicTensor`).\n     *\n     * If the reference count of a Layer becomes 0, all the weights will be\n     * disposed and the underlying memory (e.g., the textures allocated in WebGL)\n     * will be freed.\n     *\n     * Note: If the reference count is greater than 0 after the decrement, the\n     * weights of the Layer will *not* be disposed.\n     *\n     * After a Layer is disposed, it cannot be used in calls such as `apply()`,\n     * `getWeights()` or `setWeights()` anymore.\n     *\n     * @returns A DisposeResult Object with the following fields:\n     *   - refCountAfterDispose: The reference count of the Container after this\n     *     `dispose()` call.\n     *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n     *     during this `dispose()` call.\n     * @throws {Error} If the layer is not built yet, or if the layer has already\n     *   been disposed.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    dispose() {\n        if (!this.built) {\n            throw new Error(`Cannot dispose Layer ${this.name} because it has not been ` +\n                `built yet.`);\n        }\n        if (this._refCount === null) {\n            throw new Error(`Cannot dispose Layer ${this.name} because it has not been used ` +\n                `yet.`);\n        }\n        this.assertNotDisposed();\n        let numDisposedVariables = 0;\n        if (--this._refCount === 0) {\n            numDisposedVariables = this.disposeWeights();\n        }\n        return { refCountAfterDispose: this._refCount, numDisposedVariables };\n    }\n}\n/**\n * Collects the input shape(s) of a list of `tf.Tensor`s or\n * `tf.SymbolicTensor`s.\n *\n * TODO(michaelterry): Update PyKeras docs (backport).\n *\n * @param inputTensors List of input tensors (or single input tensor).\n *\n * @return List of shape tuples (or single tuple), one tuple per input.\n */\nfunction collectInputShape(inputTensors) {\n    inputTensors =\n        generic_utils.toList(inputTensors);\n    const shapes = [];\n    for (const x of inputTensors) {\n        shapes.push(x.shape);\n    }\n    return generic_utils.singletonOrArray(shapes);\n}\n/**\n * Guesses output dtype based on inputs.\n *\n * At present, just returns 'float32' for any input.\n *\n * @param inputTensors List of input tensors (or single input tensor).\n *\n * @return The guessed DType. At present, always returns 'float32'.\n */\nfunction guessOutputDType(inputTensors) {\n    return 'float32';\n}\n/**\n * Returns the list of input tensors necessary to compute `tensor`.\n *\n * Output will always be a list of tensors (potentially with 1 element).\n *\n * @param tensor The tensor to start from.\n * @param layer Origin layer of the tensor.\n * @param nodeIndex Origin node index of the tensor.\n *\n * @return Array of input tensors.\n */\nexport function getSourceInputs(tensor, layer, nodeIndex) {\n    if (layer == null || (nodeIndex != null && nodeIndex > 0)) {\n        layer = tensor.sourceLayer;\n        nodeIndex = tensor.nodeIndex;\n    }\n    if (layer.inboundNodes.length === 0) {\n        return [tensor];\n    }\n    else {\n        const node = layer.inboundNodes[nodeIndex];\n        if (node.inboundLayers.length === 0) {\n            return node.inputTensors;\n        }\n        else {\n            const sourceTensors = [];\n            for (let i = 0; i < node.inboundLayers.length; i++) {\n                const x = node.inputTensors[i];\n                const layer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const previousSources = getSourceInputs(x, layer, nodeIndex);\n                // Avoid input redundancy.\n                for (const x of previousSources) {\n                    if (sourceTensors.indexOf(x) === -1) {\n                        sourceTensors.push(x);\n                    }\n                }\n            }\n            return sourceTensors;\n        }\n    }\n}\n//# sourceMappingURL=topology.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { serialization } from '@tensorflow/tfjs-core';\nimport { getUid } from '../backend/state';\nimport { ValueError } from '../errors';\nimport { Layer, Node, SymbolicTensor } from './topology';\nexport class InputLayer extends Layer {\n    constructor(args) {\n        super({\n            dtype: args.dtype,\n            name: args.name != null ? args.name : getUid('input').toString()\n        });\n        // Normalize config.batchSize and config.sparse\n        if (args.batchSize == null) {\n            args.batchSize = null;\n        }\n        if (args.sparse == null) {\n            args.sparse = false;\n        }\n        this.trainable = false;\n        this.built = true;\n        this.sparse = args.sparse;\n        if (args.inputShape != null && args.batchInputShape != null) {\n            throw new ValueError('Only provide the inputShape OR ' +\n                'batchInputShape argument to inputLayer, not both at the same time.');\n        }\n        let batchInputShape = args.batchInputShape;\n        if (batchInputShape == null) {\n            if (args.inputShape == null) {\n                throw new ValueError('An InputLayer should be passed either a ' +\n                    '`batchInputShape` or an `inputShape`.');\n            }\n            else {\n                batchInputShape = [args.batchSize].concat(args.inputShape);\n            }\n        }\n        else {\n            // TODO(michaelterry): Backport to PyKeras\n            if (args.batchSize != null) {\n                throw new ValueError('Cannot specify batchSize if batchInputShape is ' +\n                    'specified when creating an InputLayer.');\n            }\n        }\n        const dtype = args.dtype || 'float32';\n        this.batchInputShape = batchInputShape;\n        this.dtype = dtype;\n        // TODO(michaelterry): Backport this to PyKeras?\n        this.inputSpec = [{ shape: batchInputShape }];\n        const inputTensor = new SymbolicTensor(this.dtype, this.batchInputShape, this, [], {}, this.name);\n        inputTensor.nodeIndex = 0;\n        inputTensor.tensorIndex = 0;\n        // Create an input node to add to this.outboundNode.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers: [],\n            nodeIndices: [],\n            tensorIndices: [],\n            inputTensors: [inputTensor],\n            outputTensors: [inputTensor],\n            inputMasks: [null],\n            outputMasks: [null],\n            inputShapes: [batchInputShape],\n            outputShapes: [batchInputShape]\n        });\n    }\n    apply(inputs, kwargs) {\n        throw new ValueError('Cannot pass any input to an ' +\n            `InputLayer's apply() method. InputLayer name: ${this.name}`);\n    }\n    dispose() {\n        // dispose() for InputLayer is overridden as no-op.\n        return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 };\n    }\n    getConfig() {\n        return {\n            batchInputShape: this.batchInputShape,\n            dtype: this.dtype,\n            sparse: this.sparse,\n            name: this.name\n        };\n    }\n}\n/** @nocollapse */\nInputLayer.className = 'InputLayer';\nserialization.registerClass(InputLayer);\nexport function Input(config) {\n    if (config.batchShape == null && config.shape == null) {\n        throw new Error('Please provide to Input either a `shape`' +\n            ' or a `batchShape` argument. Note that ' +\n            '`shape` does not include the batch ' +\n            'dimension.');\n    }\n    if (config.batchShape != null && config.shape != null) {\n        // TODO(michaelterry): Backport to PyKeras.\n        throw new ValueError('Please provide either a `shape` or `batchShape` ' +\n            'argument to Input, but not both.');\n    }\n    let batchShape = config.batchShape;\n    if (config.shape != null && batchShape == null) {\n        batchShape = [null].concat(config.shape);\n    }\n    let dtype = config.dtype;\n    if (dtype == null) {\n        dtype = 'float32';\n    }\n    const inputLayer = new InputLayer({\n        batchInputShape: batchShape,\n        name: config.name,\n        dtype,\n        sparse: config.sparse\n    });\n    const outputs = inputLayer.inboundNodes[0].outputTensors;\n    return outputs[0];\n}\n//# sourceMappingURL=input_layer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { dispose } from '@tensorflow/tfjs-core';\n/**\n * Turn any Scalar values in a Logs object into actual number values.\n *\n * @param logs The `Logs` object to be resolved in place.\n */\nexport async function resolveScalarsInLogs(logs) {\n    if (logs == null) {\n        return;\n    }\n    const promises = [];\n    const keys = [];\n    const scalarsToDispose = [];\n    for (const key in logs) {\n        const value = logs[key];\n        if (typeof value !== 'number') {\n            const valueScalar = value;\n            promises.push(valueScalar.data());\n            keys.push(key);\n            scalarsToDispose.push(valueScalar);\n        }\n    }\n    if (promises.length > 0) {\n        const values = await Promise.all(promises);\n        for (let i = 0; i < values.length; ++i) {\n            logs[keys[i]] = values[i][0];\n        }\n        // Dispose the original scalar tensors.\n        dispose(scalarsToDispose);\n    }\n}\n/**\n * Dispose all Tensors in an UnresolvedLogs object.\n *\n * @param logs An `UnresolvedLogs` object potentially containing `tf.Tensor`s in\n *   places where the values can be `tf.Tensor` or `number`.\n */\nexport function disposeTensorsInLogs(logs) {\n    if (logs == null) {\n        return;\n    }\n    for (const key in logs) {\n        const value = logs[key];\n        if (typeof value !== 'number') {\n            value.dispose();\n        }\n    }\n}\n//# sourceMappingURL=logs.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/callbacks.py */\nimport { add, div, keep, mul, nextFrame, tidy, util } from '@tensorflow/tfjs-core';\nimport { ValueError } from './errors';\nimport { resolveScalarsInLogs } from './logs';\nimport * as generic_utils from './utils/generic_utils';\n/** Verbosity logging level when fitting a model. */\nexport var ModelLoggingVerbosity;\n(function (ModelLoggingVerbosity) {\n    ModelLoggingVerbosity[ModelLoggingVerbosity[\"SILENT\"] = 0] = \"SILENT\";\n    ModelLoggingVerbosity[ModelLoggingVerbosity[\"VERBOSE\"] = 1] = \"VERBOSE\";\n})(ModelLoggingVerbosity || (ModelLoggingVerbosity = {}));\n/** How often to yield to the main thread when training (in ms). */\nexport const DEFAULT_YIELD_EVERY_MS = 125;\n/**\n * Abstract base class used to build new callbacks.\n *\n * The `logs` dictionary that callback methods take as argument will contain\n * keys for quantities relevant to the current batch or epoch.\n *\n * Currently, the `.fit()` method of the `Sequential` model class\n * will include the following quantities in the `logs` that\n * it passes to its callbacks:\n *\n * onEpochEnd: Logs include `acc` and `loss`, and optionally include `valLoss`\n *   (if validation is enabled in `fit`), and `valAcc` (if validation and\n *   accuracy monitoring are enabled).\n * onBatchBegin: Logs include `size`, the number of samples in the current\n *   batch.\n * onBatchEnd: Logs include `loss`, and optionally `acc` (if accuracy monitoring\n *   is enabled).\n */\nexport class BaseCallback {\n    constructor() {\n        // TODO(michaelterry): This type is a best guess.\n        this.validationData = null;\n    }\n    setParams(params) {\n        this.params = params;\n    }\n    async onEpochBegin(epoch, logs) { }\n    async onEpochEnd(epoch, logs) { }\n    async onBatchBegin(batch, logs) { }\n    async onBatchEnd(batch, logs) { }\n    async onTrainBegin(logs) { }\n    async onTrainEnd(logs) { }\n    // LayersModel needs to call Callback.setModel(), but cannot actually depend\n    // on Callback because that creates a cyclic dependency.  Providing this no-op\n    // method on BaseCallback breaks the cycle: this way LayersModel can depend on\n    // BaseCallback but not on Callback.  The argument is typed as `Container`\n    // (the superclass of LayersModel) to avoid recapitulating the cycle. Callback\n    // overrides this method and enforces that the argument is really a\n    // LayersModel.\n    setModel(model) {\n        // Do nothing. Use Callback instead of BaseCallback to track the model.\n    }\n}\n/**\n * Container abstracting a list of callbacks.\n */\nexport class CallbackList {\n    // TODO(cais): When the need arises, uncomment the following lines and\n    // implement the queue for time values.\n    // private deltaTBatch: number;\n    // private deltaTsBatchBegin: Array<number>;\n    // private deltaTsBatchEnd: Array<number>;\n    /**\n     * Constructor of CallbackList.\n     * @param callbacks Array of `Callback` instances.\n     * @param queueLength Queue length for keeping running statistics over\n     *   callback execution time.\n     */\n    constructor(callbacks, queueLength = 10) {\n        // TODO(cais): Make use of queueLength when implementing the queue for time\n        // values.\n        if (callbacks == null) {\n            callbacks = [];\n        }\n        this.callbacks = callbacks;\n        this.queueLength = queueLength;\n    }\n    append(callback) {\n        this.callbacks.push(callback);\n    }\n    setParams(params) {\n        for (const callback of this.callbacks) {\n            callback.setParams(params);\n        }\n    }\n    setModel(model) {\n        for (const callback of this.callbacks) {\n            callback.setModel(model);\n        }\n    }\n    /**\n     * Called at the start of an epoch.\n     * @param epoch Index of epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onEpochBegin(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onEpochBegin(epoch, logs);\n        }\n    }\n    /**\n     * Called at the end of an epoch.\n     * @param epoch Index of epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onEpochEnd(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onEpochEnd(epoch, logs);\n        }\n    }\n    /**\n     * Called  right before processing a batch.\n     * @param batch Index of batch within the current epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onBatchBegin(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onBatchBegin(batch, logs);\n        }\n    }\n    /**\n     * Called at the end of a batch.\n     * @param batch Index of batch within the current epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onBatchEnd(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onBatchEnd(batch, logs);\n        }\n    }\n    /**\n     * Called at the beginning of training.\n     * @param logs Dictionary of logs.\n     */\n    async onTrainBegin(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onTrainBegin(logs);\n        }\n    }\n    /**\n     * Called at the end of training.\n     * @param logs Dictionary of logs.\n     */\n    async onTrainEnd(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onTrainEnd(logs);\n        }\n    }\n}\n/**\n * Callback that accumulates epoch averages of metrics.\n *\n * This callback is automatically applied to every LayersModel.\n */\nexport class BaseLogger extends BaseCallback {\n    constructor() {\n        super();\n    }\n    async onEpochBegin(epoch) {\n        this.seen = 0;\n        this.totals = {};\n    }\n    async onBatchEnd(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        const batchSize = logs['size'] == null ? 0 : logs['size'];\n        this.seen += batchSize;\n        for (const key in logs) {\n            const value = logs[key];\n            if (typeof value === 'number') {\n                if (!this.totals.hasOwnProperty(key)) {\n                    this.totals[key] = 0;\n                }\n                this.totals[key] = this.totals[key] + value * batchSize;\n            }\n            else {\n                let oldTotalsToDispose;\n                if (key in this.totals) {\n                    oldTotalsToDispose = this.totals[key];\n                }\n                else {\n                    this.totals[key] = 0;\n                }\n                const total = tidy(() => add((this.totals[key]), mul(value, batchSize)));\n                this.totals[key] = total;\n                if (oldTotalsToDispose != null) {\n                    oldTotalsToDispose.dispose();\n                }\n            }\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        if (logs != null) {\n            for (const key of this.params['metrics']) {\n                if (this.totals[key] == null) {\n                    continue;\n                }\n                if (typeof this.totals[key] === 'number') {\n                    logs[key] = this.totals[key] / this.seen;\n                }\n                else {\n                    tidy(() => {\n                        const log = mul(div(1, this.seen), this.totals[key]);\n                        logs[key] = log;\n                        this.totals[key].dispose();\n                        keep(logs[key]);\n                    });\n                }\n            }\n        }\n    }\n}\n/**\n * Callback that records events into a `History` object. This callback is\n * automatically applied to every TF.js Layers model. The `History` object\n * gets returned by the `fit` method of models.\n */\nexport class History extends BaseCallback {\n    async onTrainBegin(logs) {\n        this.epoch = [];\n        this.history = {};\n    }\n    async onEpochEnd(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        this.epoch.push(epoch);\n        for (const key in logs) {\n            if (this.history[key] == null) {\n                this.history[key] = [];\n            }\n            this.history[key].push(logs[key]);\n        }\n    }\n    /**\n     * Await the values of all losses and metrics.\n     */\n    async syncData() {\n        const promises = [];\n        const keys = [];\n        const indices = [];\n        for (const key in this.history) {\n            const valueArray = this.history[key];\n            for (let i = 0; i < valueArray.length; ++i) {\n                if (typeof valueArray[i] !== 'number') {\n                    const valueScalar = valueArray[i];\n                    promises.push(valueScalar.data());\n                    keys.push(key);\n                    indices.push(i);\n                }\n            }\n        }\n        const values = await Promise.all(promises);\n        for (let n = 0; n < values.length; ++n) {\n            const tensorToDispose = this.history[keys[n]][indices[n]];\n            tensorToDispose.dispose();\n            this.history[keys[n]][indices[n]] = values[n][0];\n        }\n    }\n}\n/**\n * Custom callback for training.\n */\nexport class CustomCallback extends BaseCallback {\n    constructor(args, yieldEvery) {\n        super();\n        this.currentEpoch = 0;\n        this.yieldEvery = yieldEvery || 'auto';\n        if (this.yieldEvery === 'auto') {\n            this.yieldEvery = DEFAULT_YIELD_EVERY_MS;\n        }\n        if (this.yieldEvery === 'never' && args.onYield != null) {\n            throw new Error('yieldEvery is `never` but you provided an `onYield` callback. ' +\n                'Either change `yieldEvery` or remove the callback');\n        }\n        if (util.isNumber(this.yieldEvery)) {\n            // Decorate `maybeWait` so it will be called at most once every\n            // `yieldEvery` ms.\n            this.maybeWait = generic_utils.debounce(this.maybeWait.bind(this), this.yieldEvery);\n        }\n        this.trainBegin = args.onTrainBegin;\n        this.trainEnd = args.onTrainEnd;\n        this.epochBegin = args.onEpochBegin;\n        this.epochEnd = args.onEpochEnd;\n        this.batchBegin = args.onBatchBegin;\n        this.batchEnd = args.onBatchEnd;\n        this.yield = args.onYield;\n    }\n    async maybeWait(epoch, batch, logs) {\n        const ps = [];\n        if (this.yield != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.yield(epoch, batch, logs));\n        }\n        ps.push(nextFrame());\n        await Promise.all(ps);\n    }\n    async onEpochBegin(epoch, logs) {\n        this.currentEpoch = epoch;\n        if (this.epochBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.epochBegin(epoch, logs);\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        const ps = [];\n        if (this.epochEnd != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.epochEnd(epoch, logs));\n        }\n        if (this.yieldEvery === 'epoch') {\n            ps.push(nextFrame());\n        }\n        await Promise.all(ps);\n    }\n    async onBatchBegin(batch, logs) {\n        if (this.batchBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.batchBegin(batch, logs);\n        }\n    }\n    async onBatchEnd(batch, logs) {\n        const ps = [];\n        if (this.batchEnd != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.batchEnd(batch, logs));\n        }\n        if (this.yieldEvery === 'batch') {\n            ps.push(nextFrame());\n        }\n        else if (util.isNumber(this.yieldEvery)) {\n            ps.push(this.maybeWait(this.currentEpoch, batch, logs));\n        }\n        await Promise.all(ps);\n    }\n    async onTrainBegin(logs) {\n        if (this.trainBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.trainBegin(logs);\n        }\n    }\n    async onTrainEnd(logs) {\n        if (this.trainEnd != null) {\n            await resolveScalarsInLogs(logs);\n            await this.trainEnd(logs);\n        }\n    }\n}\n/**\n * Standardize callbacks or configurations of them to an Array of callbacks.\n */\nexport function standardizeCallbacks(callbacks, yieldEvery) {\n    if (callbacks == null) {\n        callbacks = {};\n    }\n    if (callbacks instanceof BaseCallback) {\n        return [callbacks];\n    }\n    if (Array.isArray(callbacks) && callbacks[0] instanceof BaseCallback) {\n        return callbacks;\n    }\n    // Convert custom callback configs to custom callback objects.\n    const callbackConfigs = generic_utils.toList(callbacks);\n    return callbackConfigs.map(callbackConfig => new CustomCallback(callbackConfig, yieldEvery));\n}\n/**\n * A global registry for callback constructors to be used during\n * LayersModel.fit().\n */\nexport class CallbackConstructorRegistry {\n    /**\n     * Blocks public access to constructor.\n     */\n    constructor() { }\n    /**\n     * Register a tf.LayersModel.fit() callback constructor.\n     *\n     * The registered callback constructor will be used to instantiate\n     * callbacks for every tf.LayersModel.fit() call afterwards.\n     *\n     * @param verbosityLevel Level of verbosity at which the `callbackConstructor`\n     *   is to be reigstered.\n     * @param callbackConstructor A no-arg constructor for `tf.Callback`.\n     * @throws Error, if the same callbackConstructor has been registered before,\n     *   either at the same or a different `verbosityLevel`.\n     */\n    static registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n        util.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, ` +\n            `but got ${verbosityLevel}`);\n        CallbackConstructorRegistry.checkForDuplicate(callbackConstructor);\n        if (CallbackConstructorRegistry.constructors[verbosityLevel] == null) {\n            CallbackConstructorRegistry.constructors[verbosityLevel] = [];\n        }\n        CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor);\n    }\n    static checkForDuplicate(callbackConstructor) {\n        for (const levelName in CallbackConstructorRegistry.constructors) {\n            const constructors = CallbackConstructorRegistry.constructors[+levelName];\n            constructors.forEach(ctor => {\n                if (ctor === callbackConstructor) {\n                    throw new ValueError('Duplicate callback constructor.');\n                }\n            });\n        }\n    }\n    /**\n     * Clear all registered callback constructors.\n     */\n    static clear() {\n        CallbackConstructorRegistry.constructors = {};\n    }\n    /**\n     * Create callbacks using the registered callback constructors.\n     *\n     * Given `verbosityLevel`, all constructors registered at that level or above\n     * will be called and the instantiated callbacks will be used.\n     *\n     * @param verbosityLevel: Level of verbosity.\n     */\n    static createCallbacks(verbosityLevel) {\n        const constructors = [];\n        for (const levelName in CallbackConstructorRegistry.constructors) {\n            const level = +levelName;\n            if (verbosityLevel >= level) {\n                constructors.push(...CallbackConstructorRegistry.constructors[level]);\n            }\n        }\n        return constructors.map(ctor => new ctor());\n    }\n}\nCallbackConstructorRegistry.constructors = {};\nexport function configureCallbacks(callbacks, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) {\n    const history = new History();\n    const actualCallbacks = [\n        new BaseLogger(), ...CallbackConstructorRegistry.createCallbacks(verbose)\n    ];\n    if (callbacks != null) {\n        actualCallbacks.push(...callbacks);\n    }\n    actualCallbacks.push(history);\n    const callbackList = new CallbackList(actualCallbacks);\n    // TODO(cais): Figure out when this LayersModel instance can have a\n    // dynamically\n    //   set property called 'callback_model' as in PyKeras.\n    callbackList.setParams({\n        epochs,\n        initialEpoch,\n        samples: numTrainSamples,\n        steps: stepsPerEpoch,\n        batchSize,\n        verbose,\n        doValidation,\n        metrics: callbackMetrics,\n    });\n    return { callbackList, history };\n}\n//# sourceMappingURL=base_callbacks.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source layers/__init__.py */\nimport { serialization } from '@tensorflow/tfjs-core';\nimport { deserializeKerasObject } from '../utils/generic_utils';\n/**\n * Instantiate a layer from a config dictionary.\n * @param config dict of the form {class_name: str, config: dict}\n * @param customObjects dict mapping class names (or function names)\n *   of custom (non-Keras) objects to class/functions\n * @param fastWeightInit Optional flag to use fast weight initialization\n *   during deserialization. This is applicable to cases in which\n *   the initialization will be immediately overwritten by loaded weight\n *   values. Default: `false`.\n * @returns Layer instance (may be LayersModel, Sequential, Layer...)\n */\nexport function deserialize(config, customObjects = {}, fastWeightInit = false) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'layer', fastWeightInit);\n}\n//# sourceMappingURL=serialization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source: losses.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { tidy, util } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport * as K from './backend/tfjs_backend';\nimport { ValueError } from './errors';\n/**\n * Normalizes a tensor wrt the L2 norm alongside the specified axis.\n * @param x\n * @param axis Axis along which to perform normalization.\n */\nexport function l2Normalize(x, axis) {\n    return tidy(() => {\n        if (x.dtype !== 'float32') {\n            x = x.asType('float32');\n        }\n        const squareSum = tfc.sum(K.square(x), axis, true);\n        const epsilonTensor = tfc.fill(squareSum.shape, epsilon());\n        const norm = tfc.sqrt(tfc.maximum(squareSum, epsilonTensor));\n        return tfc.div(x, norm);\n    });\n}\nexport function meanSquaredError(yTrue, yPred) {\n    return tidy(() => tfc.mean(K.square(tfc.sub(yPred, yTrue)), -1));\n}\nexport function meanAbsoluteError(yTrue, yPred) {\n    return tidy(() => tfc.mean(tfc.abs(tfc.sub(yPred, yTrue)), -1));\n}\nexport function meanAbsolutePercentageError(yTrue, yPred) {\n    return tidy(() => {\n        const diff = tfc.sub(yTrue, yPred);\n        const clippedTrue = tfc.clipByValue(tfc.abs(yTrue), epsilon(), Number.MAX_VALUE);\n        const absResult = tfc.abs(tfc.div(diff, clippedTrue));\n        return tfc.mul(100, tfc.mean(absResult, -1));\n    });\n}\nexport function meanSquaredLogarithmicError(yTrue, yPred) {\n    return tidy(() => {\n        const clippedPred = tfc.clipByValue(yPred, epsilon(), Number.MAX_VALUE);\n        const firstLog = tfc.log(tfc.add(1, clippedPred));\n        const clippedTrue = tfc.clipByValue(yTrue, epsilon(), Number.MAX_VALUE);\n        const secondLog = tfc.log(tfc.add(1, clippedTrue));\n        return tfc.mean(K.square(tfc.sub(firstLog, secondLog)), -1);\n    });\n}\nexport function squaredHinge(yTrue, yPred) {\n    return tidy(() => {\n        const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n        return tfc.mean(K.square(maxResult), -1);\n    });\n}\nexport function hinge(yTrue, yPred) {\n    return tidy(() => {\n        const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n        return tfc.mean(maxResult, -1);\n    });\n}\nexport function categoricalHinge(yTrue, yPred) {\n    return tidy(() => {\n        const pos = tfc.sum(tfc.mul(yTrue, yPred), -1);\n        const neg = tfc.max(tfc.mul(tfc.sub(1, yTrue), yPred), -1);\n        return tfc.maximum(0, tfc.add(1, tfc.sub(neg, pos)));\n    });\n}\n/**\n * Logarithm of the hyperbolic cosine of the prediction error.\n *\n * `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and\n * to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly\n * like the mean squared error, but will not be so strongly affected by the\n * occasional wildly incorrect prediction.\n */\nexport function logcosh(yTrue, yPred) {\n    return tidy(() => {\n        const log2 = Math.log(2);\n        const predictionDiff = tfc.sub(yPred, yTrue);\n        const logcoshResult = tfc.sub(tfc.add(predictionDiff, tfc.softplus(tfc.mul(-2, predictionDiff))), log2);\n        return tfc.mean(logcoshResult, -1);\n    });\n}\nexport function categoricalCrossentropy(target, output, fromLogits = false) {\n    return tidy(() => {\n        if (fromLogits) {\n            output = tfc.softmax(output);\n        }\n        else {\n            // scale preds so that the class probabilities of each sample sum to 1.\n            const outputSum = tfc.sum(output, output.shape.length - 1, true);\n            output = tfc.div(output, outputSum);\n        }\n        output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n        return tfc.neg(tfc.sum(tfc.mul(target.toFloat(), tfc.log(output)), output.shape.length - 1));\n    });\n}\n/**\n * Categorical crossentropy with integer targets.\n *\n * @param target An integer tensor.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n */\nexport function sparseCategoricalCrossentropy(target, output, fromLogits = false) {\n    return tidy(() => {\n        const flatTarget = tfc.floor(K.flatten(target)).toInt();\n        output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n        const outputShape = output.shape;\n        const oneHotTarget = tfc.oneHot(flatTarget, outputShape[outputShape.length - 1])\n            .reshape(outputShape);\n        return categoricalCrossentropy(oneHotTarget, output, fromLogits);\n    });\n}\n/**\n * From TensorFlow's implementation in nn_impl.py:\n *\n * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n *      z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n *    = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n *    = (1 - z) * x + log(1 + exp(-x))\n *    = x - x * z + log(1 + exp(-x))\n * For x < 0, to avoid overflow in exp(-x), we reformulate the above\n *      x - x * z + log(1 + exp(-x))\n *    = log(exp(x)) - x * z + log(1 + exp(-x))\n *    = - x * z + log(1 + exp(x))\n * Hence, to ensure stability and avoid overflow, the implementation uses this\n * equivalent formulation\n *    max(x, 0) - x * z + log(1 + exp(-abs(x)))\n *\n * @param labels The labels.\n * @param logits The logits.\n */\nexport function sigmoidCrossEntropyWithLogits(labels, logits) {\n    if (!util.arraysEqual(labels.shape, logits.shape)) {\n        throw new ValueError(`logits and labels must have the same shape, but got shapes ` +\n            `${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);\n    }\n    return tidy(() => {\n        // The logistic loss formula from above is\n        //   x - x * z + log(1 + exp(-x))\n        // For x < 0, a more numerically stable formula is\n        //   -x * z + log(1 + exp(x))\n        // Note that these two expressions can be combined into the following:\n        //   max(x, 0) - x * z + log(1 + exp(-abs(x)))\n        const reluLogits = logits.relu();\n        const negAbsLogits = logits.abs().neg();\n        return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p());\n    });\n}\nexport function binaryCrossentropy(yTrue, yPred) {\n    return tidy(() => {\n        let y;\n        y = tfc.clipByValue(yPred, epsilon(), 1 - epsilon());\n        y = tfc.log(tfc.div(y, tfc.sub(1, y)));\n        return tfc.mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);\n    });\n}\nexport function kullbackLeiblerDivergence(yTrue, yPred) {\n    return tidy(() => {\n        const clippedTrue = tfc.clipByValue(yTrue, epsilon(), 1);\n        const clippedPred = tfc.clipByValue(yPred, epsilon(), 1);\n        return tfc.sum(tfc.mul(yTrue, tfc.log(tfc.div(clippedTrue, clippedPred))), -1);\n    });\n}\nexport function poisson(yTrue, yPred) {\n    return tidy(() => {\n        const logPred = tfc.log(tfc.add(epsilon(), yPred));\n        return tfc.mean(tfc.sub(yPred, tfc.mul(yTrue, logPred)), -1);\n    });\n}\nexport function cosineProximity(yTrue, yPred) {\n    return tidy(() => {\n        const trueNormalized = l2Normalize(yTrue, -1);\n        const predNormalized = l2Normalize(yPred, -1);\n        const trueXPred = tfc.mul(trueNormalized, predNormalized);\n        return tfc.neg(tfc.sum(trueXPred, -1));\n    });\n}\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const msle = meanSquaredLogarithmicError;\nexport const MSLE = meanSquaredLogarithmicError;\nexport const kld = kullbackLeiblerDivergence;\nexport const KLD = kullbackLeiblerDivergence;\nexport const cosine = cosineProximity;\n// TODO(michaelterry): Add deserialize() function.\nexport const lossesMap = {\n    meanSquaredError,\n    meanAbsoluteError,\n    meanAbsolutePercentageError,\n    meanSquaredLogarithmicError,\n    squaredHinge,\n    hinge,\n    categoricalHinge,\n    logcosh,\n    categoricalCrossentropy,\n    sparseCategoricalCrossentropy,\n    binaryCrossentropy,\n    kullbackLeiblerDivergence,\n    poisson,\n    cosineProximity\n};\n// Porting note: This diverges from the PyKeras implementation and may need to\n// change based on (de)serialization requirements.\nexport function get(identifierOrFn) {\n    if (typeof identifierOrFn === 'string') {\n        if (identifierOrFn in lossesMap) {\n            return lossesMap[identifierOrFn];\n        }\n        let errMsg = `Unknown loss ${identifierOrFn}`;\n        if (identifierOrFn.toLowerCase().includes('softmaxcrossentropy')) {\n            errMsg = `Unknown loss ${identifierOrFn}. ` +\n                'Use \"categoricalCrossentropy\" as the string name for ' +\n                'tf.losses.softmaxCrossEntropy';\n        }\n        throw new ValueError(errMsg);\n    }\n    else {\n        return identifierOrFn;\n    }\n}\n//# sourceMappingURL=losses.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Built-in metrics.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { tidy } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { NotImplementedError, ValueError } from './errors';\nimport { categoricalCrossentropy as categoricalCrossentropyLoss, cosineProximity, meanAbsoluteError, meanAbsolutePercentageError, meanSquaredError, sparseCategoricalCrossentropy as sparseCategoricalCrossentropyLoss } from './losses';\nimport { binaryCrossentropy as lossBinaryCrossentropy } from './losses';\nimport { lossesMap } from './losses';\nimport * as util from './utils/generic_utils';\nexport function binaryAccuracy(yTrue, yPred) {\n    return tidy(() => {\n        const threshold = tfc.mul(.5, tfc.onesLike(yPred));\n        const yPredThresholded = K.cast(tfc.greater(yPred, threshold), yTrue.dtype);\n        return tfc.mean(tfc.equal(yTrue, yPredThresholded), -1);\n    });\n}\nexport function categoricalAccuracy(yTrue, yPred) {\n    return tidy(() => K.cast(tfc.equal(tfc.argMax(yTrue, -1), tfc.argMax(yPred, -1)), 'float32'));\n}\nfunction truePositives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(1), yPred.equal(1)).sum().cast('float32');\n    });\n}\nfunction falseNegatives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(1), yPred.equal(0)).sum().cast('float32');\n    });\n}\nfunction falsePositives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(0), yPred.equal(1)).sum().cast('float32');\n    });\n}\nexport function precision(yTrue, yPred) {\n    return tidy(() => {\n        const tp = truePositives(yTrue, yPred);\n        const fp = falsePositives(yTrue, yPred);\n        const denominator = tp.add(fp);\n        return tfc.where(tfc.greater(denominator, 0), tp.div(denominator), 0)\n            .cast('float32');\n    });\n}\nexport function recall(yTrue, yPred) {\n    return tidy(() => {\n        const tp = truePositives(yTrue, yPred);\n        const fn = falseNegatives(yTrue, yPred);\n        const denominator = tp.add(fn);\n        return tfc.where(tfc.greater(denominator, 0), tp.div(denominator), 0)\n            .cast('float32');\n    });\n}\nexport function binaryCrossentropy(yTrue, yPred) {\n    return lossBinaryCrossentropy(yTrue, yPred);\n}\nexport function sparseCategoricalAccuracy(yTrue, yPred) {\n    if (yTrue.rank === yPred.rank) {\n        yTrue = yTrue.squeeze([yTrue.rank - 1]);\n    }\n    yPred = yPred.argMax(-1);\n    if (yPred.dtype !== yTrue.dtype) {\n        yPred = yPred.asType(yTrue.dtype);\n    }\n    return tfc.equal(yTrue, yPred).asType('float32');\n}\nexport function topKCategoricalAccuracy(yTrue, yPred) {\n    throw new NotImplementedError();\n}\nexport function sparseTopKCategoricalAccuracy(yTrue, yPred) {\n    throw new NotImplementedError();\n}\n// Aliases.\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const categoricalCrossentropy = categoricalCrossentropyLoss;\nexport const cosine = cosineProximity;\nexport const sparseCategoricalCrossentropy = sparseCategoricalCrossentropyLoss;\n// TODO(cais, nielsene): Add serialize().\nexport const metricsMap = {\n    binaryAccuracy,\n    categoricalAccuracy,\n    precision,\n    categoricalCrossentropy,\n    sparseCategoricalCrossentropy,\n    mse,\n    MSE,\n    mae,\n    MAE,\n    mape,\n    MAPE,\n    cosine\n};\nexport function get(identifier) {\n    if (typeof identifier === 'string' && identifier in metricsMap) {\n        return metricsMap[identifier];\n    }\n    else if (typeof identifier !== 'string' && identifier != null) {\n        return identifier;\n    }\n    else {\n        throw new ValueError(`Unknown metric ${identifier}`);\n    }\n}\n/**\n * Get the shortcut function name.\n *\n * If the fn name is a string,\n *   directly return the string name.\n * If the function is included in metricsMap or lossesMap,\n *   return key of the map.\n *   - If the function relative to multiple keys,\n *     return the first found key as the function name.\n *   - If the function exists in both lossesMap and metricsMap,\n *     search lossesMap first.\n * If the function is not included in metricsMap or lossesMap,\n *   return the function name.\n *\n * @param fn loss function, metric function, or short cut name.\n * @returns Loss or Metric name in string.\n */\nexport function getLossOrMetricName(fn) {\n    util.assert(fn !== null, `Unknown LossOrMetricFn ${fn}`);\n    if (typeof fn === 'string') {\n        return fn;\n    }\n    else {\n        let fnName;\n        for (const key of Object.keys(lossesMap)) {\n            if (lossesMap[key] === fn) {\n                fnName = key;\n                break;\n            }\n        }\n        if (fnName !== undefined) {\n            return fnName;\n        }\n        for (const key of Object.keys(metricsMap)) {\n            if (metricsMap[key] === fn) {\n                fnName = key;\n                break;\n            }\n        }\n        if (fnName !== undefined) {\n            return fnName;\n        }\n        return fn.name;\n    }\n}\n//# sourceMappingURL=metrics.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Optimizers.\n */\nimport { train } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport { ValueError } from './errors';\n// Add (de)serialize()\n// Porting note: This diverges from the PyKeras implementation and may need to\n// change based on (de)serialization requirements.\nexport function getOptimizer(identifier) {\n    const optimizerMap = {\n        'Adagrad': () => train.adagrad(0.01),\n        'Adadelta': () => train.adadelta(1, 0.95, epsilon()),\n        'Adam': () => train.adam(0.001, 0.9, 0.999, epsilon()),\n        'Adamax': () => train.adamax(0.002, 0.9, 0.999, epsilon(), 0),\n        'RMSProp': () => train.rmsprop(0.001, 0.9, 0, epsilon()),\n        'SGD': () => train.sgd(0.01)\n    };\n    optimizerMap['adagrad'] = optimizerMap['Adagrad'];\n    optimizerMap['adadelta'] = optimizerMap['Adadelta'];\n    optimizerMap['adam'] = optimizerMap['Adam'];\n    optimizerMap['adamax'] = optimizerMap['Adamax'];\n    optimizerMap['rmsprop'] = optimizerMap['RMSProp'];\n    optimizerMap['sgd'] = optimizerMap['SGD'];\n    if (identifier in optimizerMap) {\n        return optimizerMap[identifier]();\n    }\n    throw new ValueError(`Unknown Optimizer ${identifier}`);\n}\n//# sourceMappingURL=optimizers.js.map", "/**\n * @license\n * Copyright 2019 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/** Utility functions related to user-defined metadata. */\n// Maximum recommended serialized size for user-defined metadata.\n// Beyond this limit, a warning message will be printed during model loading and\n// saving.\nexport const MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024;\n/**\n * Check validity of user-defined metadata.\n *\n * @param userDefinedMetadata\n * @param modelName Name of the model that the user-defined metadata belongs to.\n *   Used during construction of error messages.\n * @param checkSize Whether to check the size of the metadata is under\n *   recommended limit. Default: `false`. If `true`, will try stringify the\n *   JSON object and print a console warning if the serialzied size is above the\n *   limit.\n * @throws Error if `userDefinedMetadata` is not a plain JSON object.\n */\nexport function checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) {\n    if (userDefinedMetadata == null ||\n        typeof userDefinedMetadata !== 'object' ||\n        Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype ||\n        !plainObjectCheck(userDefinedMetadata)) {\n        throw new Error('User-defined metadata is expected to be a JSON object, but is not.');\n    }\n    if (checkSize) {\n        const out = JSON.stringify(userDefinedMetadata);\n        if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) {\n            console.warn(`User-defined metadata of model \"${modelName}\" is too large in ` +\n                `size (length=${out.length} when serialized). It is not ` +\n                `recommended to store such large objects in user-defined metadata. ` +\n                `Please make sure its serialized length is <= ` +\n                `${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`);\n        }\n    }\n}\n/**\n * Check if an input is plain JSON object or any valid subfield of it.\n *\n * @param x The input to be checked.\n * @param assertObject Whether to assert `x` is a JSON object, i.e., reject\n *   cases of arrays and primitives.\n * @return Returns `true` if and only if `x` is a plain JSON object,\n *   a JSON-valid primitive including string, number, boolean and null,\n *   or an array of the said types.\n */\n// tslint:disable-next-line:no-any\nexport function plainObjectCheck(x) {\n    if (x === null) {\n        // Note: typeof `null` is 'object', and `null` is valid in JSON.\n        return true;\n    }\n    else if (typeof x === 'object') {\n        if (Object.getPrototypeOf(x) === Object.prototype) {\n            // `x` is a JavaScript object and its prototype is Object.\n            const keys = Object.keys(x);\n            for (const key of keys) {\n                if (typeof key !== 'string') {\n                    // JSON keys must be strings.\n                    return false;\n                }\n                if (!plainObjectCheck(x[key])) { // Recursive call.\n                    return false;\n                }\n            }\n            return true;\n        }\n        else {\n            // `x` is a JavaScript object but its prototype is not Object.\n            if (Array.isArray(x)) {\n                // `x` is a JavaScript array.\n                for (const item of x) {\n                    if (!plainObjectCheck(item)) { // Recursive call.\n                        return false;\n                    }\n                }\n                return true;\n            }\n            else {\n                // `x` is a JavaScript object and its prototype is not Object,\n                // and it's not an Array. I.e., it's a complex object such as\n                // `Error` and `Date`.\n                return false;\n            }\n        }\n    }\n    else {\n        // `x` is not a JavaScript object or `null`.\n        const xType = typeof x;\n        return xType === 'string' || xType === 'number' || xType === 'boolean';\n    }\n}\n//# sourceMappingURL=user_defined_metadata.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { countParamsInWeights } from './variable_utils';\n/**\n * Print the summary of a LayersModel object.\n *\n * @param model tf.LayersModel instance.\n * @param lineLength Total length of printed lines. Set this to adapt to the\n *   display to different terminal or console sizes.\n * @param positions Relative or absolute positions of log elements in each\n *   line. Each number corresponds to right-most (i.e., ending) position of a\n *   column.\n *   If not provided, defaults to `[0.45, 0.85, 1]` for sequential-like\n *   models and `[0.33, 0.55, 0.67, 1]` for non-sequential like models.\n * @param printFn Print function to use.\n *   It will be called on each line of the summary. You can provide a custom\n *   function in order to capture the string summary. Defaults to `console.log`.\n */\nexport function printSummary(model, lineLength, positions, \n// tslint:disable-next-line:no-any\nprintFn = console.log) {\n    const sequentialLike = isModelSequentialLike(model);\n    // Header names for different log elements.\n    const toDisplay = ['Layer (type)', 'Output shape', 'Param #'];\n    if (sequentialLike) {\n        lineLength = lineLength || 65;\n        positions = positions || [0.45, 0.85, 1];\n    }\n    else {\n        lineLength = lineLength || 98;\n        positions = positions || [0.33, 0.55, 0.67, 1];\n        // Header names for different log elements.\n    }\n    if (positions[positions.length - 1] <= 1) {\n        // `positions` is relative. Convert it to absolute positioning.\n        positions = positions.map(p => Math.floor(lineLength * p));\n    }\n    let relevantNodes;\n    if (!sequentialLike) {\n        toDisplay.push('Receives inputs');\n        relevantNodes = [];\n        for (const depth in model.nodesByDepth) {\n            relevantNodes.push(...model.nodesByDepth[depth]);\n        }\n    }\n    printFn('_'.repeat(lineLength));\n    printRow(toDisplay, positions, printFn);\n    printFn('='.repeat(lineLength));\n    const layers = model.layers;\n    for (let i = 0; i < layers.length; ++i) {\n        if (sequentialLike) {\n            printLayerSummary(layers[i], positions, printFn);\n        }\n        else {\n            printLayerSummaryWithConnections(layers[i], positions, relevantNodes, printFn);\n        }\n        printFn((i === layers.length - 1 ? '=' : '_').repeat(lineLength));\n    }\n    // tslint:disable-next-line:no-any\n    model.checkTrainableWeightsConsistency();\n    const trainableCount = countTrainableParams(model);\n    const nonTrainableCount = countParamsInWeights(model.nonTrainableWeights);\n    printFn(`Total params: ${trainableCount + nonTrainableCount}`);\n    printFn(`Trainable params: ${trainableCount}`);\n    printFn(`Non-trainable params: ${nonTrainableCount}`);\n    printFn('_'.repeat(lineLength));\n}\nfunction countTrainableParams(model) {\n    let trainableCount;\n    // tslint:disable:no-any\n    if (model.collectedTrainableWeights != null) {\n        trainableCount =\n            countParamsInWeights(model.collectedTrainableWeights);\n    }\n    else {\n        trainableCount = countParamsInWeights(model.trainableWeights);\n    }\n    // tslint:enable:no-any\n    return trainableCount;\n}\nfunction isModelSequentialLike(model) {\n    let sequentialLike = true;\n    const nodesByDepth = [];\n    const nodes = [];\n    for (const depth in model.nodesByDepth) {\n        nodesByDepth.push(model.nodesByDepth[depth]);\n    }\n    for (const depthNodes of nodesByDepth) {\n        if (depthNodes.length > 1 ||\n            depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) {\n            sequentialLike = false;\n            break;\n        }\n        nodes.push(...depthNodes);\n    }\n    if (sequentialLike) {\n        // Search for shared layers.\n        for (const layer of model.layers) {\n            let flag = false;\n            for (const node of layer.inboundNodes) {\n                if (nodes.indexOf(node) !== -1) {\n                    if (flag) {\n                        sequentialLike = false;\n                        break;\n                    }\n                    else {\n                        flag = true;\n                    }\n                }\n            }\n            if (!sequentialLike) {\n                break;\n            }\n        }\n    }\n    return sequentialLike;\n}\nfunction printRow(fields, positions, \n// tslint:disable-next-line:no-any\nprintFn = console.log) {\n    let line = '';\n    for (let i = 0; i < fields.length; ++i) {\n        if (i > 0) {\n            line = line.slice(0, line.length - 1) + ' ';\n        }\n        line += fields[i];\n        line = line.slice(0, positions[i]);\n        line += ' '.repeat(positions[i] - line.length);\n    }\n    printFn(line);\n}\n/**\n * Prints a summary for a single Layer, without connectivity information.\n *\n * @param layer: Layer instance to print.\n */\nfunction printLayerSummary(layer, positions, \n// tslint:disable-next-line:no-any\nprintFn) {\n    let outputShape;\n    try {\n        outputShape = JSON.stringify(layer.outputShape);\n    }\n    catch (err) {\n        outputShape = 'multiple';\n    }\n    const name = layer.name;\n    const className = layer.getClassName();\n    const fields = [`${name} (${className})`, outputShape, layer.countParams().toString()];\n    printRow(fields, positions, printFn);\n}\n/**\n * Prints a summary for a single Layer, with connectivity information.\n */\nfunction printLayerSummaryWithConnections(layer, positions, relevantNodes, \n// tslint:disable-next-line:no-any\nprintFn) {\n    let outputShape;\n    try {\n        outputShape = JSON.stringify(layer.outputShape);\n    }\n    catch (err) {\n        outputShape = 'multiple';\n    }\n    const connections = [];\n    for (const node of layer.inboundNodes) {\n        if (relevantNodes != null && relevantNodes.length > 0 &&\n            relevantNodes.indexOf(node) === -1) {\n            continue;\n        }\n        for (let i = 0; i < node.inboundLayers.length; ++i) {\n            const inboundLayer = node.inboundLayers[i].name;\n            const inboundLayerIndex = node.nodeIndices[i];\n            const inboundTensorIndex = node.tensorIndices[i];\n            connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);\n        }\n    }\n    const name = layer.name;\n    const className = layer.getClassName();\n    const firstConnection = connections.length === 0 ? '' : connections[0];\n    const fields = [\n        `${name} (${className})`, outputShape, layer.countParams().toString(),\n        firstConnection\n    ];\n    printRow(fields, positions, printFn);\n    for (let i = 1; i < connections.length; ++i) {\n        printRow(['', '', '', connections[i]], positions, printFn);\n    }\n}\n//# sourceMappingURL=layer_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as generic_utils from '../utils/generic_utils';\n// tslint:enable\n/**\n * Test whether a value in an array is the name of a LayersModel or Layer.\n * @param key The key name that the value is found under. Note that the key\n *   may not be at the level immediately above the value, if the value is in a\n *   nested array.\n * @param index Index of the value in the Array that it is found in.\n * @param value The value object.\n * @returns A boolean indicating whether value is a name.\n */\nfunction isArrayItemInputOrOutputName(key, index, value) {\n    return (key === 'inboundNodes' || key === 'outputLayers' ||\n        key === 'inputLayers') &&\n        index === 0 && typeof value === 'string';\n}\n/**\n * Convert a Pythonic config object to TypeScript config object.\n * @param pythonicConfig The config object to convert.\n * @param key Optional key name of the object being converted.\n * @returns Result of the conversion.\n */\nexport function convertPythonicToTs(pythonicConfig, key) {\n    if (pythonicConfig === null) {\n        return null;\n    }\n    else if (typeof pythonicConfig === 'string') {\n        return generic_utils.toCamelCase(pythonicConfig);\n    }\n    else if ((typeof pythonicConfig === 'number') ||\n        (typeof pythonicConfig === 'boolean')) {\n        return pythonicConfig;\n    }\n    else if (pythonicConfig instanceof Array) {\n        const tsArray = [];\n        const arrayLength = pythonicConfig.length;\n        for (let i = 0; i < arrayLength; ++i) {\n            const item = pythonicConfig[i];\n            if (isArrayItemInputOrOutputName(key, i, item)) {\n                tsArray.push(item);\n            }\n            else {\n                tsArray.push(convertPythonicToTs(item, key));\n            }\n        }\n        return tsArray;\n    }\n    else {\n        const tsDict = {};\n        for (const pythonicKey of Object.keys(pythonicConfig)) {\n            const pythonicValue = pythonicConfig[pythonicKey];\n            if (pythonicKey === 'name' && typeof pythonicValue === 'string') {\n                // Special case the 'name' key with a string value. Name values, such as\n                // the names of LayersModel and Layer instances, should not undergo the\n                // camel-case conversion.\n                tsDict[pythonicKey] = pythonicValue;\n            }\n            else {\n                const tsKey = generic_utils.toCamelCase(pythonicKey);\n                tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey);\n            }\n        }\n        return tsDict;\n    }\n}\n/**\n * Convert a TypeScript config object to Python config object.\n * @param tsConfig The config object to convert.\n * @param key Optional key name of the object being converted.\n * @returns Result of the conversion.\n */\nexport function convertTsToPythonic(tsConfig, key) {\n    if (tsConfig === null || tsConfig === undefined) {\n        return null;\n    }\n    else if (typeof tsConfig === 'string') {\n        return generic_utils.toSnakeCase(tsConfig);\n    }\n    else if ((typeof tsConfig === 'number') || (typeof tsConfig === 'boolean')) {\n        return tsConfig;\n    }\n    else if (tsConfig instanceof Array) {\n        const pyArray = [];\n        const arrayLength = tsConfig.length;\n        for (let i = 0; i < arrayLength; ++i) {\n            const item = tsConfig[i];\n            if (isArrayItemInputOrOutputName(key, i, item)) {\n                pyArray.push(item);\n            }\n            else {\n                pyArray.push(convertTsToPythonic(item, key));\n            }\n        }\n        return pyArray;\n    }\n    else {\n        const pyDict = {};\n        for (const tsKey of Object.keys(tsConfig)) {\n            const tsValue = tsConfig[tsKey];\n            const pyKey = generic_utils.toSnakeCase(tsKey);\n            if ((tsKey === 'name' || tsKey === 'className') &&\n                typeof tsValue === 'string') {\n                // Special case the 'name' key with a string value. Name values, such as\n                // the names of LayersModel and Layer instances, should not undergo the\n                // snake-case conversion.\n                pyDict[pyKey] = tsValue;\n            }\n            else {\n                pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey);\n            }\n        }\n        return pyDict;\n    }\n}\n//# sourceMappingURL=serialization_utils.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Executor: Evaluates SymbolicTensor based on feeds.\n */\nimport { cast, dispose, memory, util } from '@tensorflow/tfjs-core';\nimport { ValueError } from '../errors';\nimport { toList } from '../utils/generic_utils';\nimport { InputLayer } from './input_layer';\nimport { SymbolicTensor } from './topology';\n/**\n * Helper function to check the dtype and shape compatibility of a feed value.\n */\nfunction assertFeedCompatibility(key, val) {\n    // Check dtype compatibility.\n    if (key.dtype == null || key.dtype === val.dtype) {\n        //  a.  If types match, return val tensor as is.\n        return val;\n    }\n    try {\n        //  b. Attempt to convert to expected type.\n        return cast(val, key.dtype);\n    }\n    catch (err) {\n        //  c. If conversion fails, return helpful error.\n        throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype ` +\n            `of the key '${key.name}' (${key.dtype}).`);\n    }\n}\n/**\n * FeedDict: A mapping from unique SymbolicTensors to feed values for them.\n * A feed value is a concrete value represented as an `Tensor`.\n */\nexport class FeedDict {\n    /**\n     * Constructor, optionally does copy-construction.\n     * @param feeds An Array of `Feed`s, or another `FeedDict`, in which case\n     *   copy-construction will be performed.\n     */\n    constructor(feeds) {\n        this.id2Value = {};\n        this.id2Mask = {};\n        this.name2Id = {};\n        if (feeds instanceof FeedDict) {\n            for (const id in feeds.id2Value) {\n                this.id2Value[id] = feeds.id2Value[id];\n                if (id in feeds.id2Mask) {\n                    this.id2Mask[id] = feeds.id2Mask[id];\n                }\n            }\n        }\n        else {\n            if (feeds == null) {\n                return;\n            }\n            for (const feed of feeds) {\n                this.add(feed.key, feed.value);\n            }\n        }\n    }\n    /**\n     * Add a key-value pair to the FeedDict.\n     *\n     * @param key The key of the feed.\n     * @param value The value of the tensor feed.\n     * @param mask The value of the mask feed (optional).\n     * @returns This `FeedDict`.\n     * @throws ValueError: If the key `SymbolicTensor` already exists in the\n     *   `FeedDict`.\n     */\n    add(key, value, mask) {\n        if (this.id2Value[key.id] == null) {\n            this.id2Value[key.id] = assertFeedCompatibility(key, value);\n            this.name2Id[key.name] = key.id;\n            if (mask != null) {\n                this.id2Mask[key.id] = mask;\n            }\n        }\n        else {\n            throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);\n        }\n        return this;\n    }\n    /**\n     * Add a Feed to the FeedDict.\n     * @param feed The new `Feed` to add.\n     * @returns This `FeedDict`.\n     */\n    addFeed(feed) {\n        this.add(feed.key, feed.value);\n    }\n    /**\n     * Probe whether a key already exists in the FeedDict.\n     * @param key\n     */\n    hasKey(key) {\n        return this.id2Value[key.id] != null;\n    }\n    /**\n     * Get all the SymbolicTensor available in this FeedDict.\n     */\n    names() {\n        return Object.keys(this.name2Id);\n    }\n    /**\n     * Get the feed value for given key.\n     * @param key The SymbolicTensor, or its name (as a string), of which the\n     *     value is sought.\n     * @returns If `key` exists, the corresponding feed value.\n     * @throws ValueError: If `key` does not exist in this `FeedDict`.\n     */\n    getValue(key) {\n        if (key instanceof SymbolicTensor) {\n            if (this.id2Value[key.id] == null) {\n                throw new ValueError(`Nonexistent key: ${key.name}`);\n            }\n            else {\n                return this.id2Value[key.id];\n            }\n        }\n        else {\n            const id = this.name2Id[key];\n            if (id == null) {\n                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n            }\n            return this.id2Value[id];\n        }\n    }\n    /**\n     * Get the feed mask for given key.\n     * @param key The SymbolicTensor, or its name (as a string), of which the\n     *     value is sought.\n     * @returns If `key` exists, the corresponding feed mask.\n     * @throws ValueError: If `key` does not exist in this `FeedDict`.\n     */\n    getMask(key) {\n        if (key instanceof SymbolicTensor) {\n            if (this.id2Value[key.id] == null) {\n                throw new ValueError(`Nonexistent key: ${key.name}`);\n            }\n            else {\n                return this.id2Mask[key.id];\n            }\n        }\n        else {\n            const id = this.name2Id[key];\n            if (id == null) {\n                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n            }\n            return this.id2Mask[id];\n        }\n    }\n    /** Dispose all mask Tensors held by this object. */\n    disposeMasks() {\n        if (this.id2Mask != null) {\n            dispose(this.id2Mask);\n        }\n    }\n}\n// Cache for topologically sorted SymbolicTensors for given execution\n// targets (i.e., fetches).\nconst cachedSorted = {};\n// Cache for recipient count maps for given execution targets (i.e., fetches).\nconst cachedRecipientCounts = {};\n/**\n * Execute a SymbolicTensor by using concrete feed values.\n *\n * A `SymbolicTensor` object is a node in a computation graph of TF.js\n * Layers. The object is backed by a source layer and input\n * `SymbolicTensor`s to the source layer. This method evaluates\n * the `call()` method of the source layer, using concrete values of the\n * inputs obtained from either\n * * `feedDict`, if the input key exists in `feedDict`, or else,\n * * a recursive call to `execute()` itself.\n *\n * @param x: The `SymbolicTensor` to execute.\n * @param feedDict: The feed values, as base condition of the recursion.\n *   execution.\n * @param kwargs: Optional keyword arguments.\n * @param probe: A probe object (of interface `ExecutionProbe`) used for\n *   testing memory footprint of `execute` calls.\n * @returns Result of the execution.\n * @throws ValueError: If any `SymbolicTensor`s from `InputLayer`s\n *   encountered during the execution lacks a feed value in `feedDict`.\n */\nexport function execute(fetches, feedDict, kwargs, probe) {\n    const training = kwargs == null ? false : kwargs['training'];\n    const arrayFetches = Array.isArray(fetches);\n    const fetchArray = arrayFetches ? fetches : [fetches];\n    const outputNames = fetchArray.map(t => t.name);\n    const finalOutputs = [];\n    const feedNames = feedDict.names();\n    for (const outputName of outputNames) {\n        if (feedNames.indexOf(outputName) !== -1) {\n            finalOutputs.push(feedDict.getValue(outputName));\n        }\n        else {\n            finalOutputs.push(null);\n        }\n    }\n    if (probe != null) {\n        // For optional probing of memory footprint during execution.\n        probe.maxNumTensors = -Infinity;\n        probe.minNumTensors = Infinity;\n    }\n    // Check cache.\n    const fetchAndFeedKey = outputNames.join(',') + '|' + feedDict.names().join(',');\n    let sorted;\n    let recipientCounts;\n    if (cachedSorted[fetchAndFeedKey] == null) {\n        // Cache doesn't contain the desired combination of fetches. Compute\n        // topological sort for the combination for the first time.\n        const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);\n        sorted = out.sorted;\n        recipientCounts = out.recipientCounts;\n        // Store results in cache for future use.\n        cachedSorted[fetchAndFeedKey] = sorted;\n        cachedRecipientCounts[fetchAndFeedKey] = recipientCounts;\n    }\n    sorted = cachedSorted[fetchAndFeedKey];\n    recipientCounts = {};\n    if (!training) {\n        Object.assign(recipientCounts, cachedRecipientCounts[fetchAndFeedKey]);\n    }\n    const internalFeedDict = new FeedDict(feedDict);\n    // Start iterative execution on the topologically-sorted SymbolicTensors.\n    for (let i = 0; i < sorted.length; ++i) {\n        if (probe != null) {\n            // For optional probing of memory usage during execution.\n            const numTensors = memory().numTensors;\n            if (numTensors > probe.maxNumTensors) {\n                probe.maxNumTensors = numTensors;\n            }\n            if (numTensors < probe.minNumTensors) {\n                probe.minNumTensors = numTensors;\n            }\n        }\n        const symbolic = sorted[i];\n        const srcLayer = symbolic.sourceLayer;\n        if (srcLayer instanceof InputLayer) {\n            continue;\n        }\n        const inputValues = [];\n        const inputMasks = [];\n        const tensorsToDispose = [];\n        let maskExists = false;\n        for (const input of symbolic.inputs) {\n            const value = internalFeedDict.getValue(input);\n            const mask = internalFeedDict.getMask(input);\n            inputValues.push(value);\n            inputMasks.push(mask);\n            if (mask != null) {\n                maskExists = true;\n            }\n            if (!training) {\n                recipientCounts[input.name]--;\n                if (recipientCounts[input.name] === 0 && !feedDict.hasKey(input) &&\n                    outputNames.indexOf(input.name) === -1 && !value.isDisposed &&\n                    input.sourceLayer.stateful !== true) {\n                    tensorsToDispose.push(value);\n                }\n            }\n        }\n        if (maskExists) {\n            kwargs = kwargs || {};\n            kwargs['mask'] = inputMasks[0];\n        }\n        const outputTensors = toList(srcLayer.apply(inputValues, kwargs));\n        let outputMask = null;\n        if (srcLayer.supportsMasking) {\n            outputMask = srcLayer.computeMask(inputValues, inputMasks);\n        }\n        const layerOutputs = getNodeOutputs(symbolic);\n        const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];\n        for (let i = 0; i < outputSymbolicTensors.length; ++i) {\n            if (!internalFeedDict.hasKey(outputSymbolicTensors[i])) {\n                internalFeedDict.add(outputSymbolicTensors[i], outputTensors[i], Array.isArray(outputMask) ? outputMask[0] : outputMask);\n            }\n            const index = outputNames.indexOf(outputSymbolicTensors[i].name);\n            if (index !== -1) {\n                finalOutputs[index] = outputTensors[i];\n            }\n        }\n        if (!training) {\n            // Clean up Tensors that are no longer needed.\n            dispose(tensorsToDispose);\n        }\n    }\n    // NOTE(cais): Unlike intermediate tensors, we don't discard mask\n    // tensors as we go, because these tensors are sometimes passed over a\n    // series of mutliple layers, i.e., not obeying the immediate input\n    // relations in the graph. If this becomes a memory-usage concern,\n    // we can improve this in the future.\n    internalFeedDict.disposeMasks();\n    return arrayFetches ? finalOutputs : finalOutputs[0];\n}\n/**\n * Sort the `SymbolicTensor`s topologically, for an array of fetches.\n *\n * This function calls getTopologicalSortAndRecipientCountsForOneFetch and\n * merges their results.\n *\n * @param fetch The array of fetches requested. Must be a non-empty array.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientCounts: Recipient counts for all SymbolicTensors in `sorted`.\n */\nfunction getTopologicalSortAndRecipientCounts(fetches, feedDict) {\n    util.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`);\n    let finalSorted = [];\n    let finalRecipientMap = {};\n    if (fetches.length === 1) {\n        // Special-casing 1 fetch for efficiency.\n        const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);\n        finalSorted = out.sorted;\n        finalRecipientMap = out.recipientMap;\n    }\n    else {\n        const visited = new Set();\n        for (const fetch of fetches) {\n            const { sorted, recipientMap } = getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict);\n            // Merge sorted SymbolicTensor Arrays.\n            for (const symbolicTensor of sorted) {\n                if (!visited.has(symbolicTensor.name)) {\n                    finalSorted.push(symbolicTensor);\n                    visited.add(symbolicTensor.name);\n                }\n            }\n            // Merge recipient maps.\n            for (const name in recipientMap) {\n                if (finalRecipientMap[name] == null) {\n                    finalRecipientMap[name] = new Set();\n                }\n                recipientMap[name].forEach(recipient => finalRecipientMap[name].add(recipient));\n            }\n        }\n    }\n    return {\n        sorted: finalSorted,\n        recipientCounts: recipientMap2Counts(finalRecipientMap)\n    };\n}\nfunction recipientMap2Counts(recipientMap) {\n    const recipientCounts = {};\n    for (const name in recipientMap) {\n        recipientCounts[name] = recipientMap[name].size;\n    }\n    return recipientCounts;\n}\n/**\n * Sort the `SymbolicTensor`s topologically, for a single fetch.\n *\n * This helper function processes the upstream SymbolicTensors of a single\n * fetch.\n *\n * @param fetch The single fetch requested.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientMap: Recipient names for all SymbolicTensors in `sorted`.\n */\nexport function getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict) {\n    const visited = new Set();\n    const sorted = [];\n    const recipientMap = {};\n    // Put keys of the feedDict into visited first, so they don't have to be\n    // walked. This is needed in case where there are feeds for intermediate\n    // SymbolicTensors of the graph.\n    for (const key of feedDict.names()) {\n        visited.add(key);\n    }\n    const stack = [];\n    const marks = [];\n    // Initial population of stack and marks.\n    stack.push(fetch);\n    while (stack.length > 0) {\n        const top = stack[stack.length - 1];\n        if (visited.has(top.name)) {\n            stack.pop();\n            continue;\n        }\n        const topIsMarked = marks[marks.length - 1] === stack.length - 1;\n        if (top.inputs.length === 0 || topIsMarked) {\n            // Input SymbolicTensor or all children have been visited.\n            stack.pop();\n            sorted.push(top);\n            visited.add(top.name);\n            if (topIsMarked) {\n                marks.pop();\n            }\n        }\n        else {\n            // A non-input SymbolicTensor whose upstream SymbolicTensors haven't\n            // been visited yet. Push them onto the stack.\n            marks.push(stack.length - 1);\n            for (const input of top.inputs) {\n                // Increment the recipient count. Note that this needs to happen\n                // regardless of whether the SymbolicTensor has been visited before.\n                if (recipientMap[input.name] == null) {\n                    recipientMap[input.name] = new Set();\n                }\n                recipientMap[input.name].add(top.name);\n                if (visited.has(input.name)) {\n                    continue; // Avoid repeated visits to the same SymbolicTensor.\n                }\n                stack.push(input);\n            }\n        }\n    }\n    return { sorted, recipientMap };\n}\n/**\n * Get the symbolic output tensors of the node to which a given fetch belongs.\n * @param fetch The fetched symbolic tensor.\n * @returns The Array of symbolic tensors output by the node to which `fetch`\n *   belongs.\n */\nfunction getNodeOutputs(fetch) {\n    let layerOutputs;\n    if (fetch.sourceLayer.inboundNodes.length === 1) {\n        layerOutputs = fetch.sourceLayer.output;\n    }\n    else {\n        let nodeIndex = null;\n        for (let i = 0; i < fetch.sourceLayer.inboundNodes.length; ++i) {\n            for (const outputTensor of fetch.sourceLayer.inboundNodes[i]\n                .outputTensors) {\n                if (outputTensor.id === fetch.id) {\n                    nodeIndex = i;\n                    break;\n                }\n            }\n        }\n        layerOutputs = fetch.sourceLayer.getOutputAt(nodeIndex);\n    }\n    return layerOutputs;\n}\n//# sourceMappingURL=executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/engine/topology.py */\nimport { tidy } from '@tensorflow/tfjs-core';\nimport { getUid } from '../backend/state';\nimport { NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { deserialize as deserializeLayer } from '../layers/serialization';\nimport * as generic_utils from '../utils/generic_utils';\nimport { convertTsToPythonic } from '../utils/serialization_utils';\nimport * as types_utils from '../utils/types_utils';\nimport { batchSetValue } from '../variables';\nimport { version as layersVersion } from '../version';\nimport { execute, FeedDict } from './executor';\nimport { InputLayer } from './input_layer';\nimport { Layer, Node } from './topology';\n/**\n * A Container is a directed acyclic graph of layers.\n *\n * It is the topological form of a \"model\". A LayersModel\n * is simply a Container with added training routines.\n *\n */\nexport class Container extends Layer {\n    constructor(args) {\n        // No args passed to super's constructor.\n        super({});\n        this.containerNodes = new Set();\n        this.name = args.name;\n        if (this.name == null) {\n            const prefix = this.getClassName().toLowerCase();\n            this.name = getUid(prefix);\n        }\n        this.supportsMasking = false;\n        this.trainable_ = true;\n        // TODO(michaelterry): Initialize perInputLosses/Updates here.\n        // Container-specific properties.\n        if (Array.isArray(args.inputs)) {\n            this.inputs = args.inputs.slice();\n        }\n        else {\n            this.inputs = [args.inputs];\n        }\n        if (Array.isArray(args.outputs)) {\n            this.outputs = args.outputs.slice();\n        }\n        else {\n            this.outputs = [args.outputs];\n        }\n        // Check for redundancy in inputs.\n        if (generic_utils.unique(this.inputs).length !== this.inputs.length) {\n            throw new ValueError('The list of inputs passed to the model is ' +\n                'redundant. All inputs should only appear once. Found: ' +\n                `${this.inputs.map(x => x.name)}`);\n        }\n        // Check for redundancy in outputs.\n        if (generic_utils.unique(this.outputs).length !== this.outputs.length) {\n            console.warn('The list of outputs passed to the model is redundant. ' +\n                'All outputs should only appear once. Found: ' +\n                `${this.outputs.map(x => x.name)}`);\n        }\n        /*\n          List of initial layers (1 to 1 mapping with this.inputs, hence the same\n          layer might appear twice)\n        */\n        this.inputLayers = [];\n        this.inputLayersNodeIndices = [];\n        this.inputLayersTensorIndices = [];\n        /*\n          List of layers (1 to 1 mapping with this.outputs, hence the same layer\n          might appear twice)\n        */\n        this.outputLayers = [];\n        this.outputLayersNodeIndices = [];\n        this.outputLayersTensorIndices = [];\n        /*\n          All layers in order of horizontal graph traversal. Entries are unique.\n          Includes input and output layers.\n        */\n        this.layers = [];\n        /*\n          References to container layers that were constructed internally. We need\n          these to properly dispose of tensors from nested containers.\n        */\n        this.internalContainerRefs = [];\n        // TODO(michaelterry): Determine if caching still needed with eager\n        // backend.\n        /*\n          This is for performance optimization when calling the Container on new\n          inputs. Every time the Container is called on a set on input tensors,\n          we compute the output tensors, output masks and output shapes in one pass,\n          then cache them here. When one of these outputs is queried later,\n          we retrieve it from there instead of recomputing it.\n        */\n        // this.outputTensorCache = {};\n        // this.outputShapeCache = {};\n        // Build this.outputLayers:\n        for (const x of this.outputs) {\n            const layer = x.sourceLayer;\n            const nodeIndex = x.nodeIndex;\n            const tensorIndex = x.tensorIndex;\n            this.outputLayers.push(layer);\n            this.outputLayersNodeIndices.push(nodeIndex);\n            this.outputLayersTensorIndices.push(tensorIndex);\n        }\n        // TODO(michaelterry): Add output mask cache code.\n        // Build this.inputLayers:\n        for (const x of this.inputs) {\n            const layer = x.sourceLayer;\n            const nodeIndex = x.nodeIndex;\n            const tensorIndex = x.tensorIndex;\n            /*\n              It's supposed to be an input layer, so only one node\n              and one tensor output.\n            */\n            generic_utils.assert(nodeIndex === 0, 'input layer has >1 nodes');\n            generic_utils.assert(tensorIndex === 0, 'input layer has >1 tensors');\n            this.inputLayers.push(layer);\n            this.inputLayersNodeIndices.push(nodeIndex);\n            this.inputLayersTensorIndices.push(tensorIndex);\n        }\n        // Build this.inputNames and this.outputNames.\n        this.inputNames = [];\n        this.outputNames = [];\n        this.feedInputShapes = [];\n        this.feedInputNames = [];\n        this.feedOutputNames = [];\n        for (let i = 0; i < this.inputLayers.length; i++) {\n            const layer = this.inputLayers[i];\n            // Check that layer is an InputLayer.\n            if (!(layer instanceof InputLayer)) {\n                throw new TypeError('Input layers to a LayersModel must be InputLayer objects. ' +\n                    `Received inputs: ${args.inputs}. ` +\n                    `Input ${i} (0-based) originates ` +\n                    `from layer type ${layer.getClassName()}.`);\n            }\n            this.inputNames.push(layer.name);\n            this.feedInputShapes.push(layer.batchInputShape);\n            this.feedInputNames.push(layer.name);\n        }\n        for (const layer of this.outputLayers) {\n            this.outputNames.push(layer.name);\n        }\n        this.internalInputShapes = this.inputs.map(x => x.shape);\n        this.internalOutputShapes = this.outputs.map(x => x.shape);\n        /*\n          Container_nodes: set of nodes included in the graph (not all nodes\n          included in the layers are relevant to the current graph).\n        */\n        // ids of all nodes relevant to the Container:\n        const nodesDepths = {};\n        // To recover nodes from their ID.\n        const nodeIDToNode = {};\n        const layersDepths = {};\n        // To layers from their ID.\n        const layerIDToLayer = {};\n        const layerIndices = {};\n        const nodesInDecreasingDepth = [];\n        /**\n         * Builds a map of the graph of layers.\n         *\n         * This recursively updates the map `layerIndices`,\n         * the list `nodesInDecreasingDepth` and the set `containerNodes`.\n         *\n         * @param tensor Some tensor in a graph.\n         * @param finishedNodes Set of nodes whose subgraphs have been traversed\n         *         completely. Useful to prevent duplicated work.\n         * @param nodesInProgress Set of nodes that are currently active on the\n         *         recursion stack. Useful to detect cycles.\n         * @param layer Layer from which `tensor` comes from. If not provided,\n         *   will be obtained from tensor.sourceLayer.\n         * @param nodeIndex Node index from which `tensor` comes from.\n         * @param tensorIndex TensorIndex from which `tensor` comes from.\n         *\n         * @exception RuntimeError if a cycle is detected.\n         */\n        const buildMapOfGraph = (tensor, finishedNodes, nodesInProgress, layer, nodeIndex, tensorIndex) => {\n            if (layer == null || nodeIndex == null || tensorIndex == null) {\n                layer = tensor.sourceLayer;\n                nodeIndex = tensor.nodeIndex;\n                tensorIndex = tensor.tensorIndex;\n            }\n            const node = layer.inboundNodes[nodeIndex];\n            // Prevent cycles.\n            if (nodesInProgress.indexOf(node) !== -1) {\n                throw new RuntimeError(`The tensor ${tensor.name} at layer \"${layer.name}\" ` +\n                    'is part of a cycle.');\n            }\n            // Don't repeat work for shared subgraphs\n            if (finishedNodes.indexOf(node) !== -1) {\n                return;\n            }\n            // Update containerNodes.\n            this.containerNodes.add(Container.nodeKey(layer, nodeIndex));\n            // Store the traversal order for layer sorting.\n            if (!(layer.id in layerIndices)) {\n                layerIndices[layer.id] = Object.keys(layerIndices).length;\n            }\n            if (nodesInProgress.indexOf(node) === -1) {\n                nodesInProgress.push(node);\n            }\n            // Propagate to all previous tensors connected to this node.\n            const numInboundLayers = node.inboundLayers.length;\n            for (let i = 0; i < numInboundLayers; i++) {\n                const x = node.inputTensors[i];\n                const layer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const tensorIndex = node.tensorIndices[i];\n                buildMapOfGraph(x, finishedNodes, nodesInProgress, layer, nodeIndex, tensorIndex);\n            }\n            finishedNodes.push(node);\n            while (nodesInProgress.indexOf(node) >= 0) {\n                nodesInProgress.splice(nodesInProgress.indexOf(node), 1);\n            }\n            nodesInDecreasingDepth.push(node);\n        };\n        const finishedNodes = [];\n        const nodesInProgress = [];\n        for (const x of this.outputs) {\n            buildMapOfGraph(x, finishedNodes, nodesInProgress);\n        }\n        const reversedNodesInDecreasingDepth = nodesInDecreasingDepth.slice().reverse();\n        for (const node of reversedNodesInDecreasingDepth) {\n            nodeIDToNode[node.id] = node;\n            // If the depth is not set, the node has no outbound nodes (depth 0).\n            if (!(node.id in nodesDepths)) {\n                nodesDepths[node.id] = 0;\n            }\n            let depth = nodesDepths[node.id];\n            // Update the depth of the corresponding layer\n            const previousDepth = (layersDepths[node.outboundLayer.id] == null ?\n                0 :\n                layersDepths[node.outboundLayer.id]);\n            /*\n              If we've seen this layer before at a higher depth, we should use that\n              depth instead of the node depth.  This is necessary for shared layers\n              that have inputs at different depth levels in the graph.\n            */\n            depth = Math.max(depth, previousDepth);\n            layersDepths[node.outboundLayer.id] = depth;\n            layerIDToLayer[node.outboundLayer.id] = node.outboundLayer;\n            nodesDepths[node.id] = depth;\n            // Update the depth of inbound nodes.\n            for (let i = 0; i < node.inboundLayers.length; i++) {\n                const inboundLayer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const inboundNode = inboundLayer.inboundNodes[nodeIndex];\n                const previousDepth = (nodesDepths[inboundNode.id] == null ? 0 :\n                    nodesDepths[inboundNode.id]);\n                nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth);\n                nodeIDToNode[inboundNode.id] = inboundNode;\n            }\n        }\n        // Build a dict {depth: list of nodes with this depth}\n        const nodesByDepth = {};\n        for (const nodeID in nodesDepths) {\n            const depth = nodesDepths[nodeID];\n            if (!(depth in nodesByDepth)) {\n                nodesByDepth[depth] = [];\n            }\n            nodesByDepth[depth].push(nodeIDToNode[nodeID]);\n        }\n        // Build a dict {depth: list of layers with this depth}\n        const layersByDepth = {};\n        for (const layerID in layersDepths) {\n            const depth = layersDepths[layerID];\n            if (!(depth in layersByDepth)) {\n                layersByDepth[depth] = [];\n            }\n            layersByDepth[depth].push(layerIDToLayer[layerID]);\n        }\n        // Get sorted list of layer depths.\n        let depthKeys = Object.keys(layersByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Set this.layers and this.layersByDepth.\n        this.layers = [];\n        for (const depth of depthKeys) {\n            const layersForDepth = layersByDepth[depth];\n            // Container.layers needs to have a deterministic order:\n            // here we order them by traversal order.\n            layersForDepth.sort((a, b) => {\n                const aIndex = layerIndices[a.id];\n                const bIndex = layerIndices[b.id];\n                if (aIndex < bIndex) {\n                    return -1;\n                }\n                if (aIndex > bIndex) {\n                    return 1;\n                }\n                return 0;\n            });\n            for (const layer of layersForDepth) {\n                if (layer instanceof Container) {\n                    this.internalContainerRefs.push(layer);\n                }\n                this.layers.push(layer);\n            }\n        }\n        this.layersByDepth = layersByDepth;\n        // Get sorted list of node depths;\n        depthKeys = Object.keys(nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Check that all tensors required are computable.\n        // computable_tensors: all tensors in the graph\n        // that can be computed from the inputs provided.\n        const computableTensors = this.inputs.slice();\n        // To provide a better error msg.\n        const layersWithCompleteInput = [];\n        for (const depth of depthKeys) {\n            for (const node of nodesByDepth[depth]) {\n                const layer = node.outboundLayer;\n                if (layer != null) {\n                    for (const x of node.inputTensors) {\n                        if (computableTensors.indexOf(x) === -1) {\n                            throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x}` +\n                                ` at layer \"${layer.name}\". ` +\n                                'The following previous layers were accessed without ' +\n                                `issue: ${layersWithCompleteInput}`);\n                        }\n                    }\n                    for (const x of node.outputTensors) {\n                        computableTensors.push(x);\n                    }\n                    layersWithCompleteInput.push(layer.name);\n                }\n            }\n        }\n        // Set this.containerNodes and this.nodesByDepth.\n        this.nodesByDepth = nodesByDepth;\n        // Ensure name unicity, which will be crucial for serialization\n        // (since serialized nodes refer to layers by their name).\n        const allNames = this.layers.map(x => x.name);\n        for (const name of allNames) {\n            const numOccurrences = allNames.filter(x => x === name).length;\n            if (numOccurrences !== 1) {\n                throw new RuntimeError(`The name \"${name}\" is used ${numOccurrences} times ` +\n                    'in the model. All layer names should be unique. Layer names: ' +\n                    JSON.stringify(allNames));\n            }\n        }\n        // Layer parameters.\n        // The new container starts with a single inbound node\n        // for its inputs, and no outbound nodes.\n        // Will be appended to by future calls to apply().\n        this.outboundNodes = [];\n        // Will be appended to below, and by future calls to apply().\n        this.inboundNodes = [];\n        // Create the node linking internal inputs to internal outputs.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers: [],\n            nodeIndices: [],\n            tensorIndices: [],\n            inputTensors: this.inputs,\n            outputTensors: this.outputs,\n            inputMasks: this.inputs.map(x => null),\n            outputMasks: this.outputs.map(x => null),\n            inputShapes: this.inputs.map(x => x.shape),\n            outputShapes: this.outputs.map(x => x.shape)\n        });\n        this.built = true;\n        this._refCount = 1; // The ref count of a container always start at 1.\n    }\n    assertNotDisposed() {\n        if (this._refCount === 0) {\n            throw new Error(`Container '${this.name}' is already disposed.`);\n        }\n    }\n    /**\n     * Attempt to dispose a LayersModel's weights.\n     *\n     * This method decrease the reference count of the LayersModel object by 1.\n     *\n     * A LayersModel is reference-counted. Its reference count is incremented by 1\n     * when it is first constructed and when it is used as a Layer of another\n     * LayersModel.\n     *\n     * If the reference count of a LayersModel becomes 0, the `dispose` method of\n     * all its constituent `Layer`s will be called.\n     *\n     * Note: If the reference count is greater than 0 after the decrement, the\n     * `dispose` method of its constituent `Layer`s will *not* be called.\n     *\n     * After a LayersModel is disposed, it cannot be used in calls such as\n     * 'predict`, `evaluate` or `fit` anymore.\n     *\n     * @returns A DisposeResult Object with the following fields:\n     *   - refCountAfterDispose: The reference count of the LayersModel after this\n     *     `dispose()` call.\n     *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n     *     during this `dispose()` call.\n     * @throws {Error} If the layer is not built yet, or if the LayersModel has\n     *   already been disposed.\n     */\n    dispose() {\n        this.assertNotDisposed();\n        const result = { refCountAfterDispose: null, numDisposedVariables: 0 };\n        if (--this._refCount === 0) {\n            for (const layer of this.layers) {\n                result.numDisposedVariables += layer.dispose().numDisposedVariables;\n            }\n            // Call dispose on each internally created container layer again to ensure\n            // their refCounts hit zero and their tensors are subsequently deleted.\n            for (const container of this.internalContainerRefs) {\n                result.numDisposedVariables += container.dispose().numDisposedVariables;\n            }\n        }\n        result.refCountAfterDispose = this._refCount;\n        return result;\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this.layers.forEach(layer => {\n            // tslint:disable-next-line:no-any\n            layer._trainableWeights\n                .forEach(w => w.trainable = trainable);\n        });\n        this.trainable_ = trainable;\n    }\n    get trainableWeights() {\n        // Porting Note: This check below is to prevent errors where the\n        //   _trainableWeights inherited from the parent class (Layer) gets\n        //   inadvertently used.\n        if (this._trainableWeights.length > 0) {\n            throw new ValueError('Container instance unexpectedly contains _trainableWeights.' +\n                'The trainable weights of a Container are a union of the ' +\n                'trainable weights of its consituent Layers. Its own ' +\n                '_trainableWeights must remain an empty Array.');\n        }\n        if (!this.trainable) {\n            return [];\n        }\n        let weights = [];\n        for (const layer of this.layers) {\n            weights = weights.concat(layer.trainableWeights);\n        }\n        return weights;\n    }\n    get nonTrainableWeights() {\n        const weights = [];\n        for (const layer of this.layers) {\n            weights.push(...layer.nonTrainableWeights);\n        }\n        if (!this.trainable) {\n            const trainableWeights = [];\n            for (const layer of this.layers) {\n                trainableWeights.push(...layer.trainableWeights);\n            }\n            return trainableWeights.concat(weights);\n        }\n        return weights;\n    }\n    get weights() {\n        return this.trainableWeights.concat(this.nonTrainableWeights);\n    }\n    /**\n     * Loads all layer weights from a JSON object.\n     *\n     * Porting Note: HDF5 weight files cannot be directly loaded in JavaScript /\n     *   TypeScript. The utility script at `scripts/pykeras.py` offers means\n     *   to convert them into JSON strings compatible with this method.\n     * Porting Note: TensorFlow.js Layers supports only loading by name currently.\n     *\n     * @param weights A JSON mapping weight names to weight values as nested\n     *   arrays of numbers, or a `NamedTensorMap`, i.e., a JSON mapping weight\n     *   names to `tf.Tensor` objects.\n     * @param strict Require that the provided weights exactly match those\n     *   required by the container.  Default: `true`.  Passing `false` means that\n     *   extra weights and missing weights will be silently ignored.\n     */\n    loadWeights(weights, strict = true) {\n        const nameToWeight = {};\n        let totalWeightsCount = 0;\n        for (const layer of this.layers) {\n            for (const weight of layer.weights) {\n                if (nameToWeight[weight.originalName] != null) {\n                    throw new ValueError(`Duplicate weight name: ${weight.originalName}`);\n                }\n                nameToWeight[weight.originalName] = weight;\n                totalWeightsCount++;\n            }\n        }\n        const weightValueTuples = [];\n        for (const name in weights) {\n            // TF 2.2.0 added cell name to the weight name in the format of\n            // layer_name/cell_name/weight_name, we need to remove\n            // the inner cell name.\n            let validatedName = name;\n            if (nameToWeight[name] == null) {\n                const tokens = name.split('/');\n                const shortenNameArray = tokens.slice(0, -2).concat([tokens[tokens.length - 1]]);\n                validatedName = shortenNameArray.join('/');\n            }\n            if (nameToWeight[validatedName] != null) {\n                weightValueTuples.push([nameToWeight[validatedName], weights[name]]);\n            }\n            else if (strict) {\n                throw new ValueError(`Provided weight data has no target variable: ${name}`);\n            }\n            delete nameToWeight[validatedName];\n        }\n        if (strict) {\n            // Check that all weights are set.\n            const unsetNames = [];\n            for (const name in nameToWeight) {\n                unsetNames.push(name);\n            }\n            if (unsetNames.length > 0) {\n                throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ` +\n                    `${unsetNames}`);\n            }\n        }\n        batchSetValue(weightValueTuples);\n    }\n    /**\n     * Util shared between different serialization methods.\n     * @returns LayersModel config with Keras version information added.\n     */\n    updatedConfig() {\n        const theConfig = this.getConfig();\n        const modelConfig = {};\n        modelConfig['className'] = this.getClassName();\n        modelConfig['config'] = theConfig;\n        modelConfig['kerasVersion'] = `tfjs-layers ${layersVersion}`;\n        // TODO(nielsene): Replace something like K.backend() once\n        // possible.\n        modelConfig['backend'] = 'TensorFlow.js';\n        return modelConfig;\n    }\n    /**\n     * Returns a JSON string containing the network configuration.\n     *\n     * To load a network from a JSON save file, use\n     * models.modelFromJSON(jsonString);\n     * @param extraJsonArgs Unused in tfjs-layers, maintained for PyKeras\n     * @param returnString Whether the return value should be stringified\n     *    (default: `true`).\n     * @returns a JSON string if `returnString` (default), or a JSON object if\n     *   `!returnString`.\n     */\n    // tslint:disable-next-line:no-any\n    toJSON(unused, returnString = true) {\n        const modelConfig = convertTsToPythonic(this.updatedConfig());\n        return returnString ? JSON.stringify(modelConfig) : modelConfig;\n    }\n    /**\n     * Call the model on new inputs.\n     *\n     * In this case `call` just reapplies all ops in the graph to the new inputs\n     * (e.g. build a new computational graph from the provided inputs).\n     *\n     * @param inputs A tensor or list of tensors.\n     * @param mask A mask or list of masks. A mask can be either a tensor or null\n     *   (no mask).\n     *\n     * @return A tensor if there is a single output, or a list of tensors if there\n     *   are more than one outputs.\n     */\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = generic_utils.toList(inputs);\n            const feedDict = new FeedDict();\n            for (let i = 0; i < this.inputs.length; ++i) {\n                feedDict.add(this.inputs[i], inputs[i]);\n            }\n            return execute(this.outputs, feedDict, kwargs);\n        });\n    }\n    /**\n     * Computes an output mask tensor.\n     *\n     * @param inputs Tensor or list of tensors.\n     * @param mask Tensor or list of tensors.\n     *\n     * @return null or a tensor (or list of tensors, one per output tensor of the\n     * layer).\n     */\n    computeMask(inputs, mask) {\n        return tidy(() => {\n            inputs = generic_utils.toList(inputs);\n            let masks;\n            if (mask == null) {\n                masks = generic_utils.pyListRepeat(null, inputs.length);\n            }\n            else {\n                masks = generic_utils.toList(mask);\n            }\n            // TODO(michaelterry): Add support for mask caching.\n            return this.runInternalGraph(inputs, masks)[1];\n        });\n    }\n    /**\n     * Computes the output shape of the layer.\n     *\n     * Assumes that the layer will be built to match that input shape provided.\n     *\n     * @param inputShape A shape (tuple of integers) or a list of shape tuples\n     *   (one per output tensor of the layer). Shape tuples can include null for\n     *   free dimensions, instead of an integer.\n     */\n    computeOutputShape(inputShape) {\n        const inputShapes = types_utils.normalizeShapeList(inputShape);\n        if (inputShapes.length !== this.inputLayers.length) {\n            throw new ValueError(`Invalid inputShape argument ${inputShape}: ` +\n                `model has ${this.inputLayers.length} tensor inputs.`);\n        }\n        // TODO(michaelterry): Add caching\n        const layersToOutputShapes = {};\n        for (let i = 0; i < inputShapes.length; i++) {\n            const layer = this.inputLayers[i];\n            const inputShape = inputShapes[i];\n            // It's an input layer: computeOutputShape is identity,\n            // and there is only one node and one tensor output.\n            const shapeKey = layer.name + '_0_0';\n            layersToOutputShapes[shapeKey] = inputShape;\n        }\n        const depthKeys = Object.keys(this.nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Iterate over nodes, by depth level.\n        if (depthKeys.length > 1) {\n            for (const depth of depthKeys) {\n                const nodes = this.nodesByDepth[depth];\n                for (const node of nodes) {\n                    // This is always a single layer, never a list.\n                    const layer = node.outboundLayer;\n                    if (this.inputLayers.map(x => x.id).indexOf(layer.id) !== -1) {\n                        // We've already covered the input layers a few lines above.\n                        continue;\n                    }\n                    // Potentially redundant list, same size of node.inputTensors.\n                    const inputShapes = [];\n                    for (let j = 0; j < node.inboundLayers.length; j++) {\n                        const inboundLayer = node.inboundLayers[j];\n                        const nodeIndex = node.nodeIndices[j];\n                        const tensorIndex = node.tensorIndices[j];\n                        const shapeKey = `${inboundLayer.name}_${nodeIndex}_${tensorIndex}`;\n                        const inputShape = layersToOutputShapes[shapeKey];\n                        inputShapes.push(inputShape);\n                    }\n                    const outputShape = layer.computeOutputShape(generic_utils.singletonOrArray(inputShapes));\n                    const outputShapes = types_utils.normalizeShapeList(outputShape);\n                    const nodeIndex = layer.inboundNodes.indexOf(node);\n                    for (let j = 0; j < outputShapes.length; j++) {\n                        const shapeKey = `${layer.name}_${nodeIndex}_${j}`;\n                        layersToOutputShapes[shapeKey] = outputShapes[j];\n                    }\n                }\n            }\n        }\n        // Read final output shapes from layersToOutputShapes.\n        const outputShapes = [];\n        const outputShapeKeys = [];\n        for (let i = 0; i < this.outputLayers.length; i++) {\n            const layer = this.outputLayers[i];\n            const nodeIndex = this.outputLayersNodeIndices[i];\n            const tensorIndex = this.outputLayersTensorIndices[i];\n            const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;\n            outputShapeKeys.push(shapeKey);\n        }\n        for (let i = 0; i < outputShapeKeys.length; i++) {\n            const key = outputShapeKeys[i];\n            generic_utils.assert(key in layersToOutputShapes);\n            outputShapes.push(layersToOutputShapes[key]);\n        }\n        // TODO(michaelterry): Update cache\n        return generic_utils.singletonOrArray(outputShapes);\n    }\n    /**\n     * Computes output tensors for new inputs.\n     *\n     * Note:\n     *   - Expects `inputs` to be a list (potentially with 1 element).\n     *\n     * @param inputs List of tensors\n     * @param masks List of masks (tensors or null).\n     * @return Three lists: outputTensors, outputMasks, outputShapes\n     */\n    runInternalGraph(inputs, masks) {\n        if (masks == null) {\n            masks = generic_utils.pyListRepeat(null, inputs.length);\n        }\n        // Dictionary mapping reference tensors to tuples\n        // (computed tensor, compute mask)\n        // we assume a 1:1 mapping from tensor to mask\n        // TODO: raise exception when a `.computeMask()` call\n        // does not return a list the same size as `call`\n        const tensorMap = {};\n        for (let i = 0; i < this.inputs.length; ++i) {\n            const x = this.inputs[i];\n            const y = inputs[i];\n            const mask = masks[i];\n            tensorMap[x.id] = [y, mask];\n        }\n        const depthKeys = Object.keys(this.nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        for (const depth of depthKeys) {\n            const nodes = this.nodesByDepth[depth];\n            for (const node of nodes) {\n                // This is always a single layer, never a list.\n                const layer = node.outboundLayer;\n                const referenceInputTensors = node.inputTensors;\n                const referenceOutputTensors = node.outputTensors;\n                // If all previous input tensors are available in tensorMap,\n                // then call node.inboundLayer on them.\n                // List of tuples [input, mask]:\n                const computedData = new Array();\n                for (const x of referenceInputTensors) {\n                    if (x.id in tensorMap) {\n                        computedData.push(tensorMap[x.id]);\n                    }\n                }\n                if (computedData.length === referenceInputTensors.length) {\n                    // TODO(michaelterry): Add K.name_scope here, if we need it.\n                    let kwargs = {};\n                    let computedTensors;\n                    let computedMasks;\n                    let outputTensors;\n                    let outputMasks;\n                    // call layer\n                    if (node.callArgs != null) {\n                        kwargs = node.callArgs;\n                    }\n                    if (computedData.length === 1) {\n                        const [computedTensor, computedMask] = computedData[0];\n                        if (kwargs['mask'] == null) {\n                            kwargs['mask'] = computedMask;\n                        }\n                        outputTensors =\n                            generic_utils.toList(layer.call(computedTensor, kwargs));\n                        outputMasks = generic_utils.toList(layer.computeMask(computedTensor, computedMask));\n                        computedTensors = [computedTensor];\n                        computedMasks = [computedMask];\n                    }\n                    else {\n                        computedTensors = computedData.map(x => x[0]);\n                        computedMasks = computedData.map(x => x[1]);\n                        if (kwargs['mask'] == null) {\n                            kwargs['mask'] = computedMasks;\n                        }\n                        outputTensors =\n                            generic_utils.toList(layer.call(computedTensors, kwargs));\n                        outputMasks = generic_utils.toList(layer.computeMask(computedTensors, computedMasks));\n                    }\n                    if (layer.activityRegularizer) {\n                        throw new NotImplementedError('LayersModel invocation with concrete Tensor value(s) in the ' +\n                            'presence of activity regularizer(s) is not supported yet.');\n                    }\n                    // TODO(michaelterry): Add model updates and losses\n                    // Update tensor map.\n                    for (let i = 0; i < referenceOutputTensors.length; ++i) {\n                        const x = referenceOutputTensors[i];\n                        const y = outputTensors[i];\n                        const mask = outputMasks[i];\n                        tensorMap[x.id] = [y, mask];\n                    }\n                }\n            }\n        }\n        const outputTensors = [];\n        const outputMasks = [];\n        const outputShapes = [];\n        for (const x of this.outputs) {\n            generic_utils.assert(x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`);\n            const [tensor, mask] = tensorMap[x.id];\n            outputShapes.push(tensor.shape);\n            outputTensors.push(tensor);\n            outputMasks.push(mask);\n        }\n        // TODO(michaelterry): Add support for caches.\n        return [outputTensors, outputMasks, outputShapes];\n    }\n    /**\n     * Builds a map of internal node keys to node ordering.\n     * Used in serializaion a node orderings may change as unused nodes are\n     * dropped. Porting Note:  This helper method was pulled out of getConfig to\n     * improve readability.\n     * @param layers An array of Layers in the model.\n     * @returns Map of Node Keys to index order within the layer.\n     */\n    buildNodeConversionMap(layers) {\n        const nodeConversionMap = {};\n        let keptNodes;\n        for (const layer of this.layers) {\n            keptNodes = layer instanceof Container ? 1 : 0;\n            for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n                const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n                if (this.containerNodes.has(nodeKey)) {\n                    // i.e. we mark it to be saved\n                    nodeConversionMap[nodeKey] = keptNodes;\n                    keptNodes += 1;\n                }\n            }\n        }\n        return nodeConversionMap;\n    }\n    /**\n     * Retrieves a layer based on either its name (unique) or index.\n     *\n     * Indices are based on order of horizontal graph traversal (bottom-up).\n     *\n     * If both `name` and `index` are specified, `index` takes precedence.\n     *\n     * @param name Name of layer.\n     * @param index Index of layer.\n     * @returns A Layer instance.\n     * @throws ValueError: In case of invalid layer name or index.\n     *\n     * @doc {\n     *    heading: 'Layers',\n     *    subheading: 'Classes',\n     *    namespace: 'layers',\n     *    subclasses: ['LayersModel']\n     * }\n     */\n    getLayer(name, index) {\n        if (index != null) {\n            if (this.layers.length <= index) {\n                throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only ` +\n                    `has ${this.layers.length} layer(s).`);\n            }\n            else {\n                return this.layers[index];\n            }\n        }\n        else {\n            if (name == null) {\n                throw new ValueError('Provide either a layer name or layer index');\n            }\n        }\n        for (const layer of this.layers) {\n            if (layer.name === name) {\n                return layer;\n            }\n        }\n        throw new ValueError(`No such layer: ${name}`);\n    }\n    /**\n     * Retrieves the Container's current loss values.\n     *\n     * Used for regularizers during training.\n     */\n    calculateLosses() {\n        // Porting Node: This is an augmentation to Container.loss in PyKeras.\n        //   In PyKeras, Container.loss returns symbolic tensors. Here a concrete\n        //   Tensor (specifically Scalar) values are returned. This is due to the\n        //   imperative backend.\n        return tidy(() => {\n            const losses = [];\n            for (const layer of this.layers) {\n                for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length; ++nodeIndex) {\n                    const nodeKey = Container.nodeKey(layer, nodeIndex);\n                    if (this.containerNodes.has(nodeKey)) {\n                        losses.push(...layer.calculateLosses());\n                    }\n                }\n            }\n            // TODO(cais): Add any unconditional model-level losses?\n            return losses;\n        });\n    }\n    getConfig() {\n        const config = { name: this.name };\n        // Build a map from layer unique name (self._node_key)\n        // to the index of the nodes that are saved in the config.\n        // Only nodes in container_nodes are saved.\n        const nodeConversionMap = this.buildNodeConversionMap(this.layers);\n        // Serialize and save the layers in layerConfigs\n        const layerConfigs = [];\n        for (const layer of this.layers) {\n            const layerClassName = layer.getClassName();\n            const layerConfig = layer.getConfig();\n            const filteredInboundNodes = [];\n            for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n                const node = layer.inboundNodes[originalNodeIndex];\n                const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n                let kwargs = {};\n                if (this.containerNodes.has(nodeKey)) {\n                    // The node is relevant to the model:\n                    // add to filteredInboundNodes.\n                    if (node.callArgs) {\n                        try {\n                            JSON.stringify(node.callArgs);\n                            kwargs = node.callArgs;\n                        }\n                        catch (err) {\n                            console.warn(`Layer ${layer.name} was passed ` +\n                                `non-serializable keyword arguments: ` +\n                                `${node.callArgs}. They will not be included ` +\n                                `in the serialized model (and thus will be ` +\n                                `missing at deserialization time).`);\n                            kwargs = {};\n                        }\n                    }\n                    if (node.inboundLayers.length > 0) {\n                        const nodeData = [];\n                        for (let i = 0; i < node.inboundLayers.length; i++) {\n                            const inboundLayer = node.inboundLayers[i];\n                            const nodeIndex = node.nodeIndices[i];\n                            const tensorIndex = node.tensorIndices[i];\n                            const nodeKey = Container.nodeKey(inboundLayer, nodeIndex);\n                            let newNodeIndex = nodeConversionMap[nodeKey];\n                            if (newNodeIndex == null) {\n                                newNodeIndex = 0;\n                            }\n                            nodeData.push([inboundLayer.name, newNodeIndex, tensorIndex, kwargs]);\n                        }\n                        filteredInboundNodes.push(nodeData);\n                    }\n                }\n            }\n            const dict = {};\n            dict['name'] = layer.name;\n            dict['className'] = layerClassName;\n            dict['config'] = layerConfig;\n            dict['inboundNodes'] = filteredInboundNodes;\n            layerConfigs.push(dict);\n        }\n        config['layers'] = layerConfigs;\n        // Gather info about inputs and outputs\n        const modelInputs = [];\n        for (let i = 0; i < this.inputLayers.length; i++) {\n            const layer = this.inputLayers[i];\n            const nodeIndex = this.inputLayersNodeIndices[i];\n            const nodeKey = Container.nodeKey(layer, nodeIndex);\n            if (!this.containerNodes.has(nodeKey)) {\n                continue;\n            }\n            let newNodeIndex = nodeConversionMap[nodeKey];\n            if (newNodeIndex === null || newNodeIndex === undefined) {\n                newNodeIndex = 0;\n            }\n            const tensorIndex = this.inputLayersTensorIndices[i];\n            modelInputs.push([layer.name, newNodeIndex, tensorIndex]);\n        }\n        config['inputLayers'] = modelInputs;\n        const modelOutputs = [];\n        for (let i = 0; i < this.outputLayers.length; i++) {\n            const layer = this.outputLayers[i];\n            const nodeIndex = this.outputLayersNodeIndices[i];\n            const nodeKey = Container.nodeKey(layer, nodeIndex);\n            if (!this.containerNodes.has(nodeKey)) {\n                continue;\n            }\n            let newNodeIndex = nodeConversionMap[nodeKey];\n            if (newNodeIndex === null || newNodeIndex === undefined) {\n                newNodeIndex = 0;\n            }\n            const tensorIndex = this.outputLayersTensorIndices[i];\n            modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);\n        }\n        config['outputLayers'] = modelOutputs;\n        return config;\n    }\n    /**\n     * Instantiates a LayersModel from its config (output of `get_config()`).\n     * @param cls the class to create\n     * @param config LayersModel config dictionary.\n     * @param customObjects An optional dictionary of custom objects.\n     * @param fastWeightInit Optional flag to use fast weight initialization\n     *   during deserialization. This is applicable to cases in which\n     *   the initialization will be immediately overwritten by loaded weight\n     *   values. Default: `false`.\n     * @returns A LayersModel instance.\n     * @throws ValueError: In case of improperly formatted config dict.\n     */\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n        // Layer instances created during\n        // the graph reconstruction process\n        const createdLayers = {};\n        // Dictionary mapping layer instances to\n        // node data that specifies a layer call.\n        // It acts as a queue that maintains any unprocessed\n        // layer call until it becomes possible to process it\n        // (i.e. until the input tensors to the call all exist).\n        const unprocessedNodes = {};\n        function addUnprocessedNode(layer, nodeData) {\n            if (!(layer.name in unprocessedNodes)) {\n                unprocessedNodes[layer.name] = [nodeData];\n            }\n            else {\n                unprocessedNodes[layer.name].push(nodeData);\n            }\n        }\n        function processNode(layer, nodeData) {\n            const inputTensors = [];\n            let kwargs;\n            for (const inputData of nodeData) {\n                const inboundLayerName = inputData[0];\n                const inboundNodeIndex = inputData[1];\n                const inboundTensorIndex = inputData[2];\n                kwargs = inputData[3] == null ?\n                    {} :\n                    inputData[3];\n                if (!(inboundLayerName in createdLayers)) {\n                    addUnprocessedNode(layer, nodeData);\n                    return;\n                }\n                const inboundLayer = createdLayers[inboundLayerName];\n                if (inboundLayer.inboundNodes.length <= inboundNodeIndex) {\n                    addUnprocessedNode(layer, nodeData);\n                    return;\n                }\n                const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex];\n                inputTensors.push(inboundNode.outputTensors[inboundTensorIndex]);\n            }\n            // Call layer on its inputs, thus creating the node\n            // and building the layer if needed.\n            // Note: This has Eager vs Graph Implications.\n            if (inputTensors.length > 0) {\n                layer.apply(generic_utils.singletonOrArray(inputTensors), kwargs); // was ** kwargs\n            }\n        }\n        /**\n         * Deserialize a layer, then call it on appropriate inputs.\n         * @param layerData: layer config dict.\n         * @throws ValueError: In case of improperly formatted `layer_data`\n         * dict.\n         */\n        function processLayer(layerData) {\n            const layerName = layerData['name'];\n            // Instantiate layer.\n            const layer = deserializeLayer(layerData, config['customObjects'] != null ?\n                config['customObjects'] :\n                {});\n            layer.setFastWeightInitDuringBuild(fastWeightInit);\n            createdLayers[layerName] = layer;\n            // Gather layer inputs.\n            const inboundNodesData = layerData['inboundNodes'];\n            inboundNodesData.forEach(nodeData => {\n                if (!(nodeData instanceof Array)) {\n                    throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);\n                }\n                // We don't process nodes (i.e. make layer calls)\n                // on the fly because the inbound node may not yet exist,\n                // in case of layer shared at different topological depths\n                // (e.g.a model such as A(B(A(B(x)))))\n                addUnprocessedNode(layer, nodeData);\n            });\n        }\n        // First, we create all layers and enqueue nodes to be processed.\n        const name = config['name'];\n        const layersFromConfig = config['layers'];\n        for (const layerData of layersFromConfig) {\n            processLayer(layerData);\n        }\n        // Then we process nodes in order of layer depth.\n        // Nodes that cannot yet be processed(if the inbound node\n        // does not yet exist) are re - enqueued, and the process\n        // is repeated until all nodes are processed.\n        while (!generic_utils.isObjectEmpty(unprocessedNodes)) {\n            for (const layerData of layersFromConfig) {\n                const layer = createdLayers[layerData['name']];\n                if (layer.name in unprocessedNodes) {\n                    const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name];\n                    delete unprocessedNodes[layer.name];\n                    for (const nodeData of currentUnprocessedNodesForLayer) {\n                        processNode(layer, nodeData);\n                    }\n                }\n            }\n        }\n        const inputTensors = [];\n        const outputTensors = [];\n        const inputLayersFromConfig = config['inputLayers'];\n        for (const layerData of inputLayersFromConfig) {\n            const layerName = layerData[0];\n            const nodeIndex = layerData[1];\n            const tensorIndex = layerData[2];\n            generic_utils.assert(layerName in createdLayers);\n            const layer = createdLayers[layerName];\n            const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n            inputTensors.push(layerOutputTensors[tensorIndex]);\n        }\n        const outputLayersFromConfig = config['outputLayers'];\n        for (const layerData of outputLayersFromConfig) {\n            const layerName = layerData[0];\n            const nodeIndex = layerData[1];\n            const tensorIndex = layerData[2];\n            generic_utils.assert(layerName in createdLayers);\n            const layer = createdLayers[layerName];\n            const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n            outputTensors.push(layerOutputTensors[tensorIndex]);\n        }\n        return new cls({ inputs: inputTensors, outputs: outputTensors, name });\n    }\n    /**\n     * Determine whether the container is stateful.\n     *\n     * Porting Note: this is the equivalent of the stateful @property of\n     *   the Container class in PyKeras.\n     */\n    get stateful() {\n        // Porting Note: This check is to prevent inadvertent setting of the\n        //   _stateful property of the Container instance.\n        if (this._stateful) {\n            throw new ValueError('Container instance unexpectedly has _stateful = true. The ' +\n                'statefulness of a Container is determined by the Layers it ' +\n                'contains. Its _stateful property must remain the default false.');\n        }\n        for (const layer of this.layers) {\n            if (layer.stateful) {\n                return true;\n            }\n        }\n        return false;\n    }\n    /**\n     * Reset the state of all stateful constituent layers (if any).\n     *\n     * Examples of stateful layers include RNN layers whose `stateful` property\n     * is set as `true`.\n     */\n    resetStates() {\n        tidy(() => {\n            this.layers.forEach(layer => {\n                // tslint:disable:no-any\n                if (layer.stateful) {\n                    layer.resetStates();\n                }\n                // tslint:enable:no-any\n            });\n        });\n    }\n}\n//# sourceMappingURL=container.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { dispose, mul, tensor1d, tidy } from '@tensorflow/tfjs-core';\nfunction standardizeSampleOrClassWeights(xWeight, outputNames, weightType) {\n    const numOutputs = outputNames.length;\n    if (xWeight == null || (Array.isArray(xWeight) && xWeight.length === 0)) {\n        return outputNames.map(name => null);\n    }\n    if (numOutputs === 1) {\n        if (Array.isArray(xWeight) && xWeight.length === 1) {\n            return xWeight;\n        }\n        else if (typeof xWeight === 'object' && outputNames[0] in xWeight) {\n            return [xWeight[outputNames[0]]];\n        }\n        else {\n            return [xWeight];\n        }\n    }\n    if (Array.isArray(xWeight)) {\n        if (xWeight.length !== numOutputs) {\n            throw new Error(`Provided ${weightType} is an array of ${xWeight.length} ` +\n                `element(s), but the model has ${numOutputs} outputs. ` +\n                `Make sure a set of weights is provided for each model output.`);\n        }\n        return xWeight;\n    }\n    else if (typeof xWeight === 'object' && Object.keys(xWeight).length > 0 &&\n        typeof xWeight[Object.keys(xWeight)[0]] ===\n            'object') {\n        const output = [];\n        outputNames.forEach(outputName => {\n            if (outputName in xWeight) {\n                output.push(xWeight[outputName]);\n            }\n            else {\n                output.push(null);\n            }\n        });\n        return output;\n    }\n    else {\n        throw new Error(`The model has multiple (${numOutputs}) outputs, ` +\n            `so ${weightType} must be either an array with ` +\n            `${numOutputs} elements or an object with ${outputNames} keys. ` +\n            `Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);\n    }\n}\n/**\n * Standardize class weighting objects.\n *\n * This function takes a single class-weighting object, an array of them,\n * or a map from output name to class-weighting object. It compares it to the\n * output name(s) of the model, base on which it outputs an array of\n * class-weighting objects of which the length matches the number of outputs.\n *\n * @param classWeight Input class-weighting object(s).\n * @param outputNames All output name(s) of the model.\n * @return An array of class-weighting objects. The length of the array matches\n *   the model's number of outputs.\n */\nexport function standardizeClassWeights(classWeight, outputNames) {\n    return standardizeSampleOrClassWeights(classWeight, outputNames, 'classWeight');\n}\nexport function standardizeSampleWeights(classWeight, outputNames) {\n    return standardizeSampleOrClassWeights(classWeight, outputNames, 'sampleWeight');\n}\n/**\n * Standardize by-sample and/or by-class weights for training.\n *\n * Note that this function operates on one model output at a time. For a model\n * with multiple outputs, you must call this function multiple times.\n *\n * @param y The target tensor that the by-sample and/or by-class weight is for.\n *     The values of y are assumed to encode the classes, either directly\n *     as an integer index, or as one-hot encoding.\n * @param sampleWeight By-sample weights.\n * @param classWeight By-class weights: an object mapping class indices\n *     (integers) to a weight (float) to apply to the model's loss for the\n *     samples from this class during training. This can be useful to tell the\n *     model to \"pay more attention\" to samples from an under-represented class.\n * @param sampleWeightMode The mode for the sample weights.\n * @return A Promise of weight tensor, of which the size of the first dimension\n *     matches that of `y`.\n */\nexport async function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) {\n    if (sampleWeight != null || sampleWeightMode != null) {\n        // TODO(cais): Once 'temporal' mode is implemented, document it in the doc\n        // string.\n        throw new Error('Support sampleWeight is not implemented yet');\n    }\n    if (classWeight != null) {\n        // Apply class weights per sample.\n        const yClasses = tidy(() => {\n            if (y.shape.length === 1) {\n                // Assume class indices.\n                return y.clone();\n            }\n            else if (y.shape.length === 2) {\n                if (y.shape[1] > 1) {\n                    // Assume one-hot encoding of classes.\n                    const axis = 1;\n                    return y.argMax(axis);\n                }\n                else if (y.shape[1] === 1) {\n                    // Class index.\n                    return y.reshape([y.shape[0]]);\n                }\n                else {\n                    throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) ` +\n                        `during handling of class weights. The size is expected to be ` +\n                        `>= 1.`);\n                }\n            }\n            else {\n                throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during ` +\n                    `handling of class weights. The rank is expected to be 1 or 2.`);\n            }\n        });\n        const yClassIndices = Array.from(await yClasses.data());\n        dispose(yClasses);\n        const classSampleWeight = [];\n        yClassIndices.forEach(classIndex => {\n            if (classWeight[classIndex] == null) {\n                throw new Error(`classWeight must contain all classes in the training data. ` +\n                    `The class ${classIndex} exists in the data but not in ` +\n                    `classWeight`);\n            }\n            else {\n                classSampleWeight.push(classWeight[classIndex]);\n            }\n        });\n        return tensor1d(classSampleWeight, 'float32');\n    }\n    else {\n        return null;\n    }\n}\n/**\n * Apply per-sample weights on the loss values from a number of samples.\n *\n * @param losses Loss tensor of shape `[batchSize]`.\n * @param sampleWeights Per-sample weight tensor of shape `[batchSize]`.\n * @returns Tensor of the same shape as`losses`.\n */\nexport function computeWeightedLoss(losses, sampleWeights) {\n    return mul(losses, sampleWeights);\n}\n//# sourceMappingURL=training_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Interfaces and methods for training models using TensorFlow.js datasets.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { scalar } from '@tensorflow/tfjs-core';\nimport { configureCallbacks, standardizeCallbacks } from '../base_callbacks';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { disposeTensorsInLogs } from '../logs';\nimport { singletonOrArray, toList } from '../utils/generic_utils';\nimport { standardizeClassWeights, standardizeWeights } from './training_utils';\n// Default batch size used during tensor-based validation.\nconst DEFAULT_VALIDATION_BATCH_SIZE = 32;\n/**\n * Standardize the output of a dataset iterator for use by\n * LayersModel.fitDataset().\n *\n * @param model: A `tf.LayersModel` object.\n * @param iteratorOut The output of a dataset iterator. It is required to be\n *   an object of the form `{xs: TensorOrArrayOrMap, ys:\n * TensorOrArrayOrMap}`, where `TensorOrArrayOrMap` is a single `tf.Tensor`,\n * a `tf.Tensor[]`, or a flat map from string names to `tf.Tensor`s.\n * @returns A flat array of `tf.Tensor` objects: the input `tf.Tensor`s\n *   followed by the target `tf.Tensor`s.  When `tf.Tensor`s are provided\n *   as a map, the order in the resulting array is taken from the `inputNames`\n *   and `outputNames` of the model.\n */\nfunction standardizeDataIteratorOutput(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, iteratorOut) {\n    let xs;\n    let ys;\n    const iteratorOutObj = iteratorOut;\n    xs = iteratorOutObj['xs'];\n    ys = iteratorOutObj['ys'];\n    tfc.util.assert(xs != null && ys != null, () => 'A Dataset iterator for fitDataset() is expected to generate ' +\n        'objects of the form `{xs: xVal, ys: yVal}`, where the two ' +\n        'values may be `tf.Tensor`, an array of Tensors, or a map of ' +\n        'string to Tensor.  The provided Dataset instead generates ' +\n        `${iteratorOut}`);\n    const flattenedXs = flattenTensorOrArrayOrMap('input', model.inputNames, xs);\n    const flattenedYs = flattenTensorOrArrayOrMap('output', model.outputNames, ys);\n    const batchSize = flattenedXs[0].shape[0];\n    tfc.util.assert(flattenedXs.length === model.inputs.length, () => `LayersModel has ${model.inputs.length} inputs, but the dataset ` +\n        `provides ${flattenedXs.length} inputs.  (Expected input keys: ` +\n        `${JSON.stringify(model.inputNames)})`);\n    tfc.util.assert(flattenedYs.length === model.outputs.length, () => `LayersModel has ${model.outputs.length} outputs, but the dataset ` +\n        `provides ${flattenedYs.length} outputs.  (Expected output keys: ` +\n        `${JSON.stringify(model.outputNames)})`);\n    for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {\n        tfc.util.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ` +\n            `${model.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; ` +\n            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);\n    }\n    for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {\n        tfc.util.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ` +\n            `${model.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; ` +\n            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);\n    }\n    return { xs: flattenedXs, ys: flattenedYs };\n}\nfunction flattenTensorOrArrayOrMap(inputOrOutput, names, values) {\n    if (values instanceof tfc.Tensor) {\n        return [values];\n    }\n    else if (Array.isArray(values)) {\n        tfc.util.assert(values.length === names.length, () => `Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`);\n        return values;\n    }\n    else {\n        const result = [];\n        // Check that all the required keys are available.\n        for (const name of names) {\n            if (values[name] == null) {\n                throw new ValueError(`The feature data generated by the dataset lacks the required ` +\n                    `${inputOrOutput} key '${name}'.`);\n            }\n            result.push(values[name]);\n        }\n        return result;\n    }\n}\nfunction standardizeTensorValidationData(data) {\n    if (data.length === 3) {\n        throw new NotImplementedError('Validation with sample weights is not implemented yet.');\n    }\n    return { xs: data[0], ys: data[1] };\n}\nexport async function fitDataset(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, dataset, args) {\n    const hasBatchesPerEpoch = args.batchesPerEpoch != null;\n    tfc.util.assert(model.optimizer != null, () => 'You must compile a model before training/testing. Use ' +\n        'LayersModel.compile(modelCompileConfig).');\n    tfc.util.assert(args != null, () => `For fitDataset(), the 2nd argument (config) is required, ` +\n        `but it is not provided in this call.`);\n    tfc.util.assert(args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs), () => `For fitDataset(), config.epochs is expected to be a positive ` +\n        `integer, but got ${args.epochs}`);\n    tfc.util.assert(!hasBatchesPerEpoch ||\n        (args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch)), () => `For fitDataset(), config.batchesPerEpoch is expected to be a ` +\n        `positive integer if specified, but got ${args.batchesPerEpoch}`);\n    tfc.util.assert(\n    // tslint:disable-next-line:no-any\n    args['validationSplit'] == null, () => '`validationSplit` is not supported by `fitDataset()`. ' +\n        'Use validationData instead.');\n    if (model.isTraining) {\n        throw new Error('Cannot start training because another fit() call is ongoing.');\n    }\n    model.isTraining = true;\n    try {\n        const doValidation = args.validationData != null;\n        let valXs;\n        let valYs;\n        if (doValidation) {\n            if (isDatasetObject(args.validationData)) {\n                tfc.util.assert(args.validationBatches == null ||\n                    (args.validationBatches > 0 &&\n                        Number.isInteger(args.validationBatches)), () => `For fitDataset() with dataset-based validation, ` +\n                    `config.validationBatches is expected not to be provided, ` +\n                    `or to be a positive integer, ` +\n                    `but got ${args.validationBatches}`);\n            }\n            else {\n                const validationData = standardizeTensorValidationData(args.validationData);\n                valXs = validationData.xs;\n                valYs = validationData.ys;\n            }\n        }\n        const trainFunction = model.makeTrainFunction();\n        const outLabels = model.getDedupedMetricsNames();\n        let callbackMetrics;\n        if (doValidation) {\n            callbackMetrics =\n                outLabels.slice().concat(outLabels.map(n => 'val_' + n));\n        }\n        else {\n            callbackMetrics = outLabels.slice();\n        }\n        const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);\n        const verbose = args.verbose == null ? 1 : args.verbose;\n        const { callbackList, history } = configureCallbacks(callbacks, verbose, args.epochs, null, null, getStepsPerEpoch(dataset, args), null, // Batch size determined by the dataset itself.\n        doValidation, callbackMetrics);\n        callbackList.setModel(model);\n        model.history = history;\n        await callbackList.onTrainBegin();\n        model.stopTraining_ = false;\n        let epoch = args.initialEpoch == null ? 0 : args.initialEpoch;\n        let dataIterator = await dataset.iterator();\n        while (epoch < args.epochs) {\n            const epochLogs = {};\n            await callbackList.onEpochBegin(epoch);\n            let stepsDone = 0;\n            let batchIndex = 0;\n            if (!hasBatchesPerEpoch) {\n                dataIterator = await dataset.iterator();\n            }\n            while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) {\n                const iteratorOut = await dataIterator.next();\n                // If `batchesPerEpoch` is specified, the dataset should not be\n                // exhausted until all epoches are done.\n                if (hasBatchesPerEpoch && iteratorOut.done) {\n                    console.warn('You provided `batchesPerEpoch` as ' +\n                        `${args.batchesPerEpoch}, ` +\n                        'but your dataset iterator ran out of data after ' +\n                        `${stepsDone} batches; ` +\n                        'interrupting training. Make sure that your ' +\n                        'dataset can generate at least `batchesPerEpoch * epochs` ' +\n                        'batches (in this case, ' +\n                        `${args.batchesPerEpoch * args.epochs} batches). ` +\n                        'You may need to use the repeat() function when building ' +\n                        'your dataset.');\n                    break;\n                }\n                if (iteratorOut.value != null) {\n                    const { xs, ys } = standardizeDataIteratorOutput(model, iteratorOut.value);\n                    const batchLogs = {};\n                    batchLogs['batch'] = batchIndex;\n                    batchLogs['size'] = xs[0].shape[0];\n                    await callbackList.onBatchBegin(batchIndex, batchLogs);\n                    const sampleWeights = [];\n                    if (args.classWeight != null) {\n                        const standardClassWeights = standardizeClassWeights(args.classWeight, model.outputNames);\n                        for (let i = 0; i < standardClassWeights.length; ++i) {\n                            sampleWeights.push(await standardizeWeights(ys[i], null, standardClassWeights[i]));\n                        }\n                    }\n                    // Train on batch.\n                    const ins = xs.concat(ys).concat(sampleWeights);\n                    const outs = trainFunction(ins);\n                    tfc.dispose(ins);\n                    for (let i = 0; i < outLabels.length; ++i) {\n                        const label = outLabels[i];\n                        const out = outs[i];\n                        batchLogs[label] = out;\n                        tfc.keep(out);\n                    }\n                    await callbackList.onBatchEnd(batchIndex, batchLogs);\n                    disposeTensorsInLogs(batchLogs);\n                    batchIndex++;\n                    stepsDone++;\n                }\n                if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch :\n                    iteratorOut.done) {\n                    // Epoch finished. Perform validation.\n                    if (doValidation) {\n                        let valOuts;\n                        if (isDatasetObject(args.validationData)) {\n                            valOuts = toList(await model.evaluateDataset(args.validationData, { batches: args.validationBatches }));\n                        }\n                        else {\n                            valOuts = toList(model.evaluate(valXs, valYs, {\n                                batchSize: args.validationBatchSize == null ?\n                                    DEFAULT_VALIDATION_BATCH_SIZE :\n                                    args.validationBatchSize,\n                                verbose: 0\n                            }));\n                        }\n                        for (let i = 0; i < model.metricsNames.length; ++i) {\n                            epochLogs[`val_${model.metricsNames[i]}`] = valOuts[i];\n                        }\n                    }\n                    // Call `break` to exit one epoch lopp after validation is done. If\n                    // config.batchesPerEpoch is specified, an epoch while loop will\n                    // stop when `stepsDone >= config.batchesPerEpoch`. When\n                    // config.batchesPerEpoch is not provided, the following `break` is\n                    // required to exit the while lopp after dataset is exhausted.\n                    break;\n                }\n                if (model.stopTraining_) {\n                    break;\n                }\n            }\n            await callbackList.onEpochEnd(epoch, epochLogs);\n            epoch++;\n            if (model.stopTraining_) {\n                break;\n            }\n        }\n        await callbackList.onTrainEnd();\n        await model.history.syncData();\n        return model.history;\n    }\n    finally {\n        model.isTraining = false;\n    }\n}\n/** Helper function that determines number of steps (batches) per epoch. */\nfunction getStepsPerEpoch(dataset, args) {\n    // Attempt to determine # of batches in an epoch.\n    let stepsPerEpoch = null;\n    if (args.batchesPerEpoch != null) {\n        stepsPerEpoch = args.batchesPerEpoch;\n    }\n    else if (Number.isFinite(dataset.size)) {\n        stepsPerEpoch = dataset.size;\n    }\n    return stepsPerEpoch;\n}\n// Check if provided object is a Dataset object by checking its .iterator\n// element.\nfunction isDatasetObject(dataset) {\n    return (typeof dataset.iterator === 'function');\n}\n// Check if provided object is a LazyIterator object by checking it's .next\n// element.\nfunction isLazyIteratorObject(iterator) {\n    return (typeof iterator.next === 'function');\n}\nexport async function evaluateDataset(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, dataset, args) {\n    args = args || {};\n    const hasBatches = args.batches != null;\n    const f = model.testFunction;\n    let outs = [];\n    if (args.verbose > 0) {\n        throw new NotImplementedError('Verbose mode is not implemented yet.');\n    }\n    tfc.util.assert(!hasBatches || (args.batches > 0 && Number.isInteger(args.batches)), () => 'Test loop expects `batches` to be a positive integer, but ' +\n        `received ${JSON.stringify(args.batches)}`);\n    const dataIterator = isLazyIteratorObject(dataset) ?\n        dataset :\n        await dataset.iterator();\n    // Keeps track of number of examples used in this evaluation.\n    let numExamples = 0;\n    let batch = 0;\n    while (hasBatches ? batch < args.batches : true) {\n        const iteratorOut = await dataIterator.next();\n        outs = tfc.tidy(() => {\n            if (iteratorOut.value) {\n                // TODO(cais): Once real dataset is available, use\n                //   `map(x => standardizeDataIteratorOutput(model, x).map(f)`.\n                const { xs, ys } = standardizeDataIteratorOutput(model, iteratorOut.value);\n                const xsAndYs = xs.concat(ys);\n                const batchOuts = tfc.tidy(() => f(xsAndYs));\n                tfc.dispose(xsAndYs);\n                if (batch === 0) {\n                    for (let i = 0; i < batchOuts.length; ++i) {\n                        outs.push(scalar(0));\n                    }\n                }\n                const batchSize = xsAndYs[0].shape[0];\n                for (let i = 0; i < batchOuts.length; ++i) {\n                    const batchOut = batchOuts[i];\n                    const oldScalar = outs[i];\n                    outs[i] =\n                        tfc.tidy(() => tfc.add(outs[i], tfc.mul(batchSize, batchOut)));\n                    if (batch > 0) {\n                        tfc.dispose(oldScalar);\n                    }\n                }\n                tfc.dispose(batchOuts);\n                numExamples += batchSize;\n                ++batch;\n            }\n            return outs;\n        });\n        if (iteratorOut.done) {\n            if (hasBatches) {\n                console.warn('Your dataset iterator ran out of data during evaluateDataset(). ' +\n                    'Interrupting evalution. Make sure that your ' +\n                    'dataset can generate at least `batches` ' +\n                    `batches (in this case, ${args.batches} batches). ` +\n                    'You may need to use the repeat() function when building ' +\n                    'your dataset.');\n            }\n            break;\n        }\n    }\n    for (let i = 0; i < outs.length; ++i) {\n        const oldScalar = outs[i];\n        outs[i] = tfc.div(outs[i], numExamples);\n        tfc.dispose(oldScalar);\n    }\n    return singletonOrArray(outs);\n}\n//# sourceMappingURL=training_dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Interfaces and methods for training models using tf.Tensor objects.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { Tensor, tensor1d, util } from '@tensorflow/tfjs-core';\nimport { expandDims, gather, sliceAlongFirstAxis } from '../backend/tfjs_backend';\nimport { configureCallbacks, standardizeCallbacks } from '../base_callbacks';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { disposeTensorsInLogs } from '../logs';\nimport { range } from '../utils/math_utils';\nexport function checkBatchSize(batchSize) {\n    tfc.util.assert(batchSize > 0 && Number.isInteger(batchSize), () => `batchSize is required to be a positive integer, but got ${batchSize}`);\n}\n/**\n * Slice a Tensor or an Array of Tensors, by start and stop indices.\n *\n * Porting Note: The `_slice_arrays` function in PyKeras is covered by this\n *   function and `sliceArraysByIndices()` together.\n *\n * @param arrays: the input.\n * @param start: the starting index (inclusive).\n * @param stop: the stopping index (exclusive).\n * @returns The result of the slicing. If `arrays` is an `Array` of\n *   `tf.Tensor`s, the slicing will be applied to all elements of the `Array`\n *   in the same way.\n */\nexport function sliceArrays(arrays, start, stop) {\n    if (arrays == null) {\n        return [null];\n    }\n    else if (Array.isArray(arrays)) {\n        return arrays.map(array => sliceAlongFirstAxis(array, start, stop - start));\n    }\n    else { // Tensor.\n        return sliceAlongFirstAxis(arrays, start, stop - start);\n    }\n}\n/**\n * Slice a Tensor or an Array of Tensors, by random-order indices.\n *\n * Porting Note: The `_slice_arrays` function in PyKeras is covered by this\n *   function and `sliceArrays()` together.\n *\n * @param arrays The input `tf.Tensor` or `Array` of `tf.Tensor`s to slice.\n *   If an `Array` of `tf.Tensor`s, all `tf.Tensor`s will be sliced in the\n *   same fashion.\n * @param indices The indices to use for slicing along the first (batch)\n *   dimension.\n * @returns Result(s) of the slicing.\n */\nexport function sliceArraysByIndices(arrays, indices) {\n    return tfc.tidy(() => {\n        if (arrays == null) {\n            return null;\n        }\n        else if (Array.isArray(arrays)) {\n            return arrays.map(array => sliceArraysByIndices(array, indices));\n        }\n        else {\n            // TODO(cais): indices should be a pre-constructed Tensor1D to avoid\n            //   tensor1d() calls.\n            return gather(arrays, indices.dtype === 'int32' ? indices : indices.toInt());\n        }\n    });\n}\n/**\n * Returns a list of batch indices (tuples of indices).\n * @param size: Integer, total size of the data to slice into batches.\n * @param batchSize: Integer, batch size.\n * @returns An Array of [batchStart, batchEnd] tuples. batchStart is\n *   inclusive; batchEnd is exclusive. I.e., each batch consists of indices x\n *   that satisfy batchStart <= x < batchEnd.\n */\nexport function makeBatches(size, batchSize) {\n    const output = [];\n    let batchStart = 0;\n    let batchEnd = null;\n    while (batchStart < size) {\n        batchEnd = batchStart + batchSize;\n        if (batchEnd >= size) {\n            batchEnd = size;\n        }\n        output.push([batchStart, batchEnd]);\n        batchStart = batchEnd;\n    }\n    return output;\n}\n/**\n * Abstract fit function for `f(ins)`.\n * @param f A Function returning a list of tensors. For training, this\n *   function is expected to perform the updates to the variables.\n * @param ins List of tensors to be fed to `f`.\n * @param outLabels List of strings, display names of the outputs of `f`.\n * @param batchSize Integer batch size or `== null` if unknown. Default : 32.\n * @param epochs Number of times to iterate over the data. Default : 1.\n * @param verbose Verbosity mode: 0, 1, or 2. Default: 1.\n * @param callbacks List of callbacks to be called during training.\n * @param valF Function to call for validation.\n * @param valIns List of tensors to be fed to `valF`.\n * @param shuffle Whether to shuffle the data at the beginning of every\n * epoch. Default : true.\n * @param callbackMetrics List of strings, the display names of the metrics\n *   passed to the callbacks. They should be the concatenation of the\n *   display names of the outputs of `f` and the list of display names\n *   of the outputs of `valF`.\n * @param initialEpoch Epoch at which to start training (useful for\n *   resuming a previous training run). Default : 0.\n * @param stepsPerEpoch Total number of steps (batches on samples) before\n *   declaring one epoch finished and starting the next epoch. Ignored with\n *   the default value of `undefined` or `null`.\n * @param validationSteps Number of steps to run validation for (only if\n *   doing validation from data tensors). Not applicable for tfjs-layers.\n * @returns A `History` object.\n */\nasync function fitLoop(\n// Type `model` as `any` here to avoid circular dependency w/ training.ts.\n// tslint:disable-next-line:no-any\nmodel, f, ins, outLabels, batchSize, epochs, verbose, callbacks, valF, valIns, shuffle, callbackMetrics, initialEpoch, stepsPerEpoch, validationSteps) {\n    if (batchSize == null) {\n        batchSize = 32;\n    }\n    if (epochs == null) {\n        epochs = 1;\n    }\n    if (shuffle == null) {\n        shuffle = true;\n    }\n    if (initialEpoch == null) {\n        initialEpoch = 0;\n    }\n    // TODO(cais): Change const to let below when implementing validation.\n    let doValidation = false;\n    if (valF != null && valIns != null) {\n        doValidation = true;\n        // TODO(cais): verbose message.\n    }\n    if (validationSteps != null) {\n        doValidation = true;\n        if (stepsPerEpoch == null) {\n            throw new ValueError('Can only use `validationSteps` when doing step-wise training, ' +\n                'i.e., `stepsPerEpoch` must be set.');\n        }\n    }\n    const numTrainSamples = model.checkNumSamples(ins, batchSize, stepsPerEpoch, 'steps_per_epoch');\n    let indexArray;\n    if (numTrainSamples != null) {\n        indexArray = range(0, numTrainSamples);\n    }\n    if (verbose == null) {\n        verbose = 1;\n    }\n    const { callbackList, history } = configureCallbacks(callbacks, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics);\n    callbackList.setModel(model);\n    model.history = history;\n    await callbackList.onTrainBegin();\n    model.stopTraining_ = false;\n    // TODO(cais): Take care of callbacks.validation_data as in PyKeras.\n    // TODO(cais): Pre-convert feeds for performance as in PyKeras.\n    for (let epoch = initialEpoch; epoch < epochs; ++epoch) {\n        await callbackList.onEpochBegin(epoch);\n        const epochLogs = {};\n        if (stepsPerEpoch != null) {\n            throw new NotImplementedError('stepsPerEpoch mode is not implemented yet.');\n        }\n        else {\n            if (shuffle === 'batch') {\n                throw new NotImplementedError('batch shuffling is not implemneted yet');\n            }\n            else if (shuffle) {\n                util.shuffle(indexArray);\n            }\n            // Convert the potentially shuffled indices to Tensor1D, to avoid the\n            // cost of repeated creation of Array1Ds later on.\n            const epochIndexArray1D = tensor1d(indexArray);\n            const batches = makeBatches(numTrainSamples, batchSize);\n            for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                const batchLogs = {};\n                await callbackList.onBatchBegin(batchIndex, batchLogs);\n                tfc.tidy(() => {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    const batchIds = sliceAlongFirstAxis(epochIndexArray1D, batchStart, batchEnd - batchStart);\n                    batchLogs['batch'] = batchIndex;\n                    batchLogs['size'] = batchEnd - batchStart;\n                    // TODO(cais): In ins, train flag can be a number, instead of an\n                    //   Tensor? Do we need to handle this in tfjs-layers?\n                    const insBatch = sliceArraysByIndices(ins, batchIds);\n                    const outs = f(insBatch);\n                    for (let i = 0; i < outLabels.length; ++i) {\n                        const label = outLabels[i];\n                        const out = outs[i];\n                        batchLogs[label] = out;\n                        tfc.keep(out);\n                        // TODO(cais): Use scope() to avoid ownership.\n                    }\n                    if (batchIndex === batches.length - 1) { // Last batch.\n                        if (doValidation) {\n                            const valOuts = model.testLoop(valF, valIns, batchSize);\n                            // Porting Notes: In tfjs-layers, valOuts is always an Array.\n                            for (let i = 0; i < outLabels.length; ++i) {\n                                const label = outLabels[i];\n                                const out = valOuts[i];\n                                tfc.keep(out);\n                                // TODO(cais): Use scope() to avoid ownership.\n                                epochLogs['val_' + label] = out;\n                            }\n                        }\n                    }\n                });\n                await callbackList.onBatchEnd(batchIndex, batchLogs);\n                disposeTensorsInLogs(batchLogs);\n                if (model.stopTraining_) {\n                    break;\n                }\n                // TODO(cais): return outs as list of Tensor.\n            }\n            epochIndexArray1D.dispose();\n        }\n        // TODO(cais): Run validation at the end of the epoch.\n        await callbackList.onEpochEnd(epoch, epochLogs);\n        if (model.stopTraining_) {\n            break;\n        }\n    }\n    await callbackList.onTrainEnd();\n    await model.history.syncData();\n    return model.history;\n}\nexport async function fitTensors(\n// Type `model` as `any` here to avoid circular dependency w/ training.ts.\n// tslint:disable-next-line:no-any\nmodel, x, y, args = {}) {\n    if (model.isTraining) {\n        throw new Error('Cannot start training because another fit() call is ongoing.');\n    }\n    model.isTraining = true;\n    let inputs;\n    let targets;\n    let inputValX;\n    let inputValY;\n    let valX;\n    let valY;\n    let sampleWeights;\n    try {\n        const batchSize = args.batchSize == null ? 32 : args.batchSize;\n        checkBatchSize(batchSize);\n        // Validate user data.\n        // TODO(cais): Support sampleWeight.\n        const checkBatchAxis = false;\n        const standardizedOuts = await model.standardizeUserData(x, y, args.sampleWeight, args.classWeight, checkBatchAxis, batchSize);\n        inputs = standardizedOuts[0];\n        targets = standardizedOuts[1];\n        sampleWeights = standardizedOuts[2];\n        // Prepare validation data.\n        let doValidation = false;\n        let valIns;\n        if (args.validationData != null && args.validationData.length > 0) {\n            doValidation = true;\n            if (args.validationData.length === 2) {\n                // config.validationData consists of valX and valY.\n                inputValX = args.validationData[0];\n                inputValY = args.validationData[1];\n            }\n            else if (args.validationData.length === 3) {\n                throw new NotImplementedError('validationData including sample weights is not supported yet.');\n            }\n            else {\n                throw new ValueError(`When passing validation data, it must contain 2 (valX, valY) ` +\n                    `or 3 (valX, valY, valSampleWeight) items; ` +\n                    `${args.validationData} is invalid.`);\n            }\n            const checkBatchAxis = true;\n            const valStandardized = await model.standardizeUserData(inputValX, inputValY, null, /** Unused sample weights. */ null, /** Unused class weights. */ checkBatchAxis, batchSize);\n            valX = valStandardized[0];\n            valY = valStandardized[1];\n            valIns = valX.concat(valY);\n            // TODO(cais): Add useLearningPhase data properly.\n        }\n        else if (args.validationSplit != null && args.validationSplit > 0 &&\n            args.validationSplit < 1) {\n            doValidation = true;\n            // Porting Note: In tfjs-layers, inputs[0] is always a Tensor.\n            const splitAt = Math.floor(inputs[0].shape[0] * (1 - args.validationSplit));\n            const originalBatchSize = inputs[0].shape[0];\n            valX = sliceArrays(inputs, splitAt, originalBatchSize);\n            inputs = sliceArrays(inputs, 0, splitAt);\n            valY = sliceArrays(targets, splitAt, originalBatchSize);\n            targets = sliceArrays(targets, 0, splitAt);\n            // TODO(cais): Once sampleWeights becomes available, slice it to get\n            //   valSampleWeights.\n            valIns = valX.concat(valY);\n            // TODO(cais): Add useLearningPhase data properly.\n        }\n        else if (args.validationSteps != null) {\n            doValidation = true;\n            // TODO(cais): Add useLearningPhase.\n        }\n        const ins = inputs.concat(targets).concat(sampleWeights);\n        model.checkTrainableWeightsConsistency();\n        // TODO(cais): Handle use_learning_phase and learning_phase?\n        // Porting Note: Here we see a key deviation of tfjs-layers from\n        // Keras.\n        //  Due to the imperative nature of tfjs-layers' backend (tfjs-core),\n        //  we do not construct symbolic computation graphs to embody the\n        //  training process. Instead, we define a function that performs the\n        //  training action. In PyKeras, the data (inputs and targets) are fed\n        //  through graph placeholders. In tfjs-layers, the data are fed as\n        //  function arguments. Since the function are defined below in the\n        //  scope, we don't have equivalents of PyKeras's\n        //  `_make_train_funciton`.\n        const trainFunction = model.makeTrainFunction();\n        const outLabels = model.getDedupedMetricsNames();\n        let valFunction;\n        let callbackMetrics;\n        if (doValidation) {\n            model.makeTestFunction();\n            valFunction = model.testFunction;\n            callbackMetrics =\n                outLabels.slice().concat(outLabels.map(n => 'val_' + n));\n        }\n        else {\n            valFunction = null;\n            valIns = [];\n            callbackMetrics = outLabels.slice();\n        }\n        const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);\n        const out = await fitLoop(model, trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null);\n        return out;\n    }\n    finally {\n        model.isTraining = false;\n        // Memory clean up.\n        disposeNewTensors(inputs, x);\n        disposeNewTensors(targets, y);\n        disposeNewTensors(valX, inputValX);\n        disposeNewTensors(valY, inputValY);\n        if (sampleWeights != null) {\n            tfc.dispose(sampleWeights);\n        }\n    }\n    // TODO(cais): Add value to outLabels.\n}\n/**\n * Ensure tensors all have a rank of at least 2.\n *\n * If a tensor has a rank of 1, it is dimension-expanded to rank 2.\n * If any tensor has a rank of 0 (i.e., is a scalar), an error will be thrown.\n */\nexport function ensureTensorsRank2OrHigher(tensors) {\n    const outs = [];\n    if (tensors instanceof Tensor) {\n        tensors = [tensors];\n    }\n    // Make Tensors at least 2D.\n    for (let i = 0; i < tensors.length; ++i) {\n        const tensor = tensors[i];\n        if (tensor.rank === 1) {\n            outs.push(expandDims(tensor, 1));\n        }\n        else if (tensor.rank === 0) {\n            throw new Error('Expected tensor to be at least 1D, but received a 0D tensor ' +\n                '(scalar).');\n        }\n        else {\n            outs.push(tensor);\n        }\n    }\n    return outs;\n}\n/**\n * Compare a set of tensors with a reference (old) set, discard the ones\n * in the new set that are not present in the reference set.\n *\n * This method is used for memory clenaup during calls such as\n * LayersModel.fit().\n *\n * @param tensors New set which may contain Tensors not present in\n *   `refTensors`.\n * @param refTensors Reference Tensor set.\n */\n// TODO(cais, kangyizhang): Deduplicate with tfjs-data.\nexport function disposeNewTensors(tensors, refTensors) {\n    if (tensors == null) {\n        return;\n    }\n    const oldTensorIds = [];\n    if (refTensors instanceof Tensor) {\n        oldTensorIds.push(refTensors.id);\n    }\n    else if (Array.isArray(refTensors)) {\n        refTensors.forEach(t => oldTensorIds.push(t.id));\n    }\n    else if (refTensors != null) {\n        // `oldTensors` is a map from string name to Tensor.\n        for (const name in refTensors) {\n            const oldTensor = refTensors[name];\n            oldTensorIds.push(oldTensor.id);\n        }\n    }\n    const tensorsToDispose = [];\n    if (tensors instanceof Tensor) {\n        if (oldTensorIds.indexOf(tensors.id) === -1) {\n            tensorsToDispose.push(tensors);\n        }\n    }\n    else if (Array.isArray(tensors)) {\n        tensors.forEach(t => {\n            if (oldTensorIds.indexOf(t.id) === -1) {\n                tensorsToDispose.push(t);\n            }\n        });\n    }\n    else if (tensors != null) {\n        // `oldTensors` is a map from string name to Tensor.\n        for (const name in tensors) {\n            const tensor = tensors[name];\n            if (oldTensorIds.indexOf(tensor.id) === -1) {\n                tensorsToDispose.push(tensor);\n            }\n        }\n    }\n    tensorsToDispose.forEach(t => {\n        if (!t.isDisposed) {\n            t.dispose();\n        }\n    });\n}\n//# sourceMappingURL=training_tensors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source: engine/training.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { io, Optimizer, scalar, serialization, Tensor, tensor1d, util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { deserialize } from '../layers/serialization';\nimport * as losses from '../losses';\nimport * as Metrics from '../metrics';\nimport * as optimizers from '../optimizers';\nimport { checkUserDefinedMetadata } from '../user_defined_metadata';\nimport { count, pyListRepeat, singletonOrArray, toCamelCase, toSnakeCase, unique } from '../utils/generic_utils';\nimport { printSummary } from '../utils/layer_utils';\nimport { range } from '../utils/math_utils';\nimport { convertPythonicToTs } from '../utils/serialization_utils';\nimport { version } from '../version';\nimport { Container } from './container';\nimport { execute, FeedDict } from './executor';\nimport { evaluateDataset, fitDataset } from './training_dataset';\nimport { checkBatchSize, disposeNewTensors, ensureTensorsRank2OrHigher, fitTensors, makeBatches, sliceArrays, sliceArraysByIndices } from './training_tensors';\nimport { computeWeightedLoss, standardizeClassWeights, standardizeWeights } from './training_utils';\n/**\n * Helper function for polymorphic input data: 1. singleton Tensor.\n */\nexport function isDataTensor(x) {\n    return x instanceof Tensor;\n}\n/**\n * Helper function for polymorphic input data: 2. Array of Tensor.\n */\nexport function isDataArray(x) {\n    return Array.isArray(x);\n}\n/**\n * Helper function for polymorphic input data: 3. \"dict\" of Tensor.\n */\nexport function isDataDict(x) {\n    return !isDataTensor(x) && !isDataArray(x);\n}\n/**\n * Normalizes inputs and targets provided by users.\n * @param data User-provided input data (polymorphic).\n * @param names An Array of expected Tensor names.\n * @param shapes Optional Array of expected Tensor shapes.\n * @param checkBatchAxis Whether to check that the batch axis of the arrays\n *   match  the expected value found in `shapes`.\n * @param exceptionPrefix String prefix used for exception formatting.\n * @returns List of standardized input Tensors (one Tensor per model input).\n * @throws ValueError: in case of improperly formatted user data.\n */\nexport function standardizeInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = '') {\n    if (names == null || names.length === 0) {\n        // Check for the case where the model expected no data, but some data got\n        // sent.\n        if (data != null) {\n            let gotUnexpectedData = false;\n            if (isDataArray(data) && data.length > 0) {\n                gotUnexpectedData = true;\n            }\n            else if (isDataDict(data)) {\n                for (const key in data) {\n                    if (data.hasOwnProperty(key)) {\n                        gotUnexpectedData = true;\n                        break;\n                    }\n                }\n            }\n            else {\n                // `data` is a singleton Tensor in this case.\n                gotUnexpectedData = true;\n            }\n            if (gotUnexpectedData) {\n                throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, ` +\n                    `but got ${data}`);\n            }\n        }\n        return [];\n    }\n    if (data == null) {\n        return names.map(name => null);\n    }\n    let arrays;\n    if (isDataDict(data)) {\n        data = data;\n        arrays = [];\n        for (const name of names) {\n            if (data[name] == null) {\n                throw new ValueError(`No data provided for \"${name}\". Need data for each key in: ` +\n                    `${names}`);\n            }\n            arrays.push(data[name]);\n        }\n    }\n    else if (isDataArray(data)) {\n        data = data;\n        if (data.length !== names.length) {\n            throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of ` +\n                `Tensors that you are passing to your model is not the size the ` +\n                `model expected. Expected to see ${names.length} Tensor(s), but ` +\n                `instead got the following list of Tensor(s): ${data}`);\n        }\n        arrays = data;\n    }\n    else {\n        data = data;\n        if (names.length > 1) {\n            throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), ` +\n                `but only received one Tensor. Found: Tensor with shape ${data.shape}`);\n        }\n        arrays = [data];\n    }\n    arrays = ensureTensorsRank2OrHigher(arrays);\n    // Check shape compatibility.\n    if (shapes != null) {\n        for (let i = 0; i < names.length; ++i) {\n            if (shapes[i] == null) {\n                continue;\n            }\n            const array = arrays[i];\n            if (array.shape.length !== shapes[i].length) {\n                throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                    `to have ${shapes[i].length} dimension(s). but got array with ` +\n                    `shape ${array.shape}`);\n            }\n            for (let j = 0; j < shapes[i].length; ++j) {\n                if (j === 0 && !checkBatchAxis) {\n                    // Skip the first (batch) axis.\n                    continue;\n                }\n                const dim = array.shape[j];\n                const refDim = shapes[i][j];\n                if (refDim != null && refDim >= 0 && dim !== refDim) {\n                    throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                        `to have shape [${shapes[i]}], but got array with shape ` +\n                        `[${array.shape}].`);\n                }\n            }\n        }\n    }\n    return arrays;\n}\n/**\n * User input validation for Tensors.\n * @param inputs `Array` of `tf.Tensor`s for inputs.\n * @param targets `Array` of `tf.Tensor`s for targets.\n * @param weights Optional `Array` of `tf.Tensor`s for sample weights.\n * @throws ValueError: in case of incorrectly formatted data.\n */\nexport function checkArrayLengths(inputs, targets, weights) {\n    const setX = unique(inputs.map(input => input.shape[0]));\n    setX.sort();\n    const setY = unique(targets.map(target => target.shape[0]));\n    setY.sort();\n    // TODO(cais): Check `weights` as well.\n    if (setX.length > 1) {\n        throw new ValueError(`All input Tensors (x) should have the same number of samples. ` +\n            `Got array shapes: ` +\n            `${JSON.stringify(inputs.map(input => input.shape))}`);\n    }\n    if (setY.length > 1) {\n        throw new ValueError(`All target Tensors (y) should have the same number of samples. ` +\n            `Got array shapes: ` +\n            `${JSON.stringify(targets.map(target => target.shape))}`);\n    }\n    if (setX.length > 0 && setY.length > 0 && !util.arraysEqual(setX, setY)) {\n        throw new ValueError(`Input Tensors should have the same number of samples as target ` +\n            `Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target ` +\n            `sample(s).`);\n    }\n}\n/**\n * Validation on the compatibility of targes and loss functions.\n *\n * This helps prevent users from using loss functions incorrectly.\n *\n * @param targets `Array` of `tf.Tensor`s of targets.\n * @param lossFns `Array` of loss functions.\n * @param outputShapes `Array` of shapes of model outputs.\n */\nfunction checkLossAndTargetCompatibility(targets, lossFns, outputShapes) {\n    // TODO(cais): Dedicated test coverage?\n    const keyLosses = [\n        losses.meanSquaredError, losses.binaryCrossentropy,\n        losses.categoricalCrossentropy\n    ];\n    for (let i = 0; i < targets.length; ++i) {\n        const y = targets[i];\n        const loss = lossFns[i];\n        const shape = outputShapes[i];\n        if (loss == null) {\n            continue;\n        }\n        if (loss === losses.categoricalCrossentropy) {\n            if (y.shape[y.shape.length - 1] === 1) {\n                throw new ValueError(`You are passing a target array of shape ${y.shape} while using ` +\n                    `a loss 'categorical_crossentropy'. 'categorical_crossentropy'` +\n                    `expects targets to be binary matrices (1s and 0s) of shape ` +\n                    `[samples, classes].`);\n                // TODO(cais): Example code in error message.\n            }\n        }\n        if (keyLosses.indexOf(loss) !== -1) {\n            const slicedYShape = y.shape.slice(1);\n            const slicedShape = shape.slice(1);\n            for (let j = 0; j < slicedYShape.length; ++j) {\n                const targetDim = slicedYShape[j];\n                const outDim = slicedShape[j];\n                if (outDim != null && targetDim !== outDim) {\n                    throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an ` +\n                        `output of shape ${shape}, while using a loss function that ` +\n                        `expects targets to have the same shape as the output.`);\n                }\n            }\n        }\n    }\n}\n/**\n * Check inputs provided by the user.\n *\n * Porting Note: This corresponds to _standardize_input_data() in Python\n *   Keras. Because of the strong typing in TF.js, we do not need to convert\n *   the data. Specifically:\n *   1) in PyKeras, `data` can be `DataFrame` instances from pandas, for\n *      example. We don't need to worry about that here because there is no\n *      widely popular javascript/typesdcript equivalent of pandas (so far).\n *      If one becomes available in the future, we can add support.\n *   2) in PyKeras, inputs can be Python dict. But here we are stipulating\n * that the data is either a single `tf.Tensor` or an Array of `tf.Tensor`s. We\n * may add support for `Object` data inputs in the future when the need\n * arises.\n *\n * Instead, we perform basic checks for number of parameters and shapes.\n *\n * @param data: The input data.\n * @param names: Name for the inputs, from the model.\n * @param shapes: Expected shapes for the input data, from the model.\n * @param checkBatchAxis: Whether the size along the batch axis (i.e., the\n *   first dimension) will be checked for matching.\n * @param exceptionPrefix: Execption prefix message, used in generating error\n *   messages.\n * @throws ValueError: on incorrect number of inputs or mismatches in shapes.\n */\nfunction checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = '') {\n    let arrays;\n    if (Array.isArray(data)) {\n        if (data.length !== names.length) {\n            throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of ` +\n                `Tensors that you are passing to your model is not the size the ` +\n                `the model expected. Expected to see ${names.length} Tensor(s),` +\n                ` but instead got ${data.length} Tensors(s).`);\n        }\n        arrays = data;\n    }\n    else {\n        if (names.length > 1) {\n            throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, ` +\n                `but only received one Tensor. Found: array with shape ` +\n                `${JSON.stringify(data.shape)}.`);\n        }\n        arrays = [data];\n    }\n    if (shapes != null) {\n        for (let i = 0; i < names.length; ++i) {\n            if (shapes[i] == null) {\n                continue;\n            }\n            const array = arrays[i];\n            if (array.shape.length !== shapes[i].length) {\n                throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                    `to have ${shapes[i].length} dimension(s), but got array with ` +\n                    `shape ${JSON.stringify(array.shape)}`);\n            }\n            for (let j = 0; j < shapes[i].length; ++j) {\n                if (j === 0 && !checkBatchAxis) {\n                    continue;\n                }\n                const dim = array.shape[j];\n                const refDim = shapes[i][j];\n                if (refDim != null) {\n                    if (refDim !== dim) {\n                        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ` +\n                            `${names[i]} to have shape ${JSON.stringify(shapes[i])} but ` +\n                            `got array with shape ${JSON.stringify(array.shape)}.`);\n                    }\n                }\n            }\n        }\n    }\n}\n/**\n * Maps metric functions to model outputs.\n * @param metrics An shortcut strings name, metric function, `Array` or dict\n *   (`Object`) of metric functions.\n * @param outputNames An `Array` of the names of model outputs.\n * @returns An `Array` (one entry per model output) of `Array` of metric\n *   functions. For instance, if the model has 2 outputs, and for the first\n *   output we want to compute `binaryAccuracy` and `binaryCrossentropy`,\n *   and just `binaryAccuracy` for the second output, the `Array` would look\n *   like:\n *     `[[binaryAccuracy, binaryCrossentropy],  [binaryAccuracy]]`\n * @throws TypeError: incompatible metrics format.\n */\nexport function collectMetrics(metrics, outputNames) {\n    if (metrics == null || Array.isArray(metrics) && metrics.length === 0) {\n        return outputNames.map(name => []);\n    }\n    let wrappedMetrics;\n    if (typeof metrics === 'string' || typeof metrics === 'function') {\n        wrappedMetrics = [metrics];\n    }\n    else if (Array.isArray(metrics) || typeof metrics === 'object') {\n        wrappedMetrics = metrics;\n    }\n    else {\n        throw new TypeError('Type of metrics argument not understood. Expected an string,' +\n            `function, Array, or Object, found: ${metrics}`);\n    }\n    if (Array.isArray(wrappedMetrics)) {\n        // We then apply all metrics to all outputs.\n        return outputNames.map(name => wrappedMetrics);\n    }\n    else {\n        // In this case, metrics is a dict.\n        const nestedMetrics = [];\n        for (const name of outputNames) {\n            let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : [];\n            if (!Array.isArray(outputMetrics)) {\n                outputMetrics = [outputMetrics];\n            }\n            nestedMetrics.push(outputMetrics);\n        }\n        return nestedMetrics;\n    }\n}\nconst LAYERS_MODEL_FORMAT_NAME = 'layers-model';\n/**\n * A `tf.LayersModel` is a directed, acyclic graph of `tf.Layer`s plus methods\n * for training, evaluation, prediction and saving.\n *\n * `tf.LayersModel` is the basic unit of training, inference and evaluation in\n * TensorFlow.js. To create a `tf.LayersModel`, use `tf.LayersModel`.\n *\n * See also:\n *   `tf.Sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class LayersModel extends Container {\n    constructor(args) {\n        super(args);\n        this.isTraining = false;\n    }\n    /**\n     * Print a text summary of the model's layers.\n     *\n     * The summary includes\n     * - Name and type of all layers that comprise the model.\n     * - Output shape(s) of the layers\n     * - Number of weight parameters of each layer\n     * - If the model has non-sequential-like topology, the inputs each layer\n     *   receives\n     * - The total number of trainable and non-trainable parameters of the model.\n     *\n     * ```js\n     * const input1 = tf.input({shape: [10]});\n     * const input2 = tf.input({shape: [20]});\n     * const dense1 = tf.layers.dense({units: 4}).apply(input1);\n     * const dense2 = tf.layers.dense({units: 8}).apply(input2);\n     * const concat = tf.layers.concatenate().apply([dense1, dense2]);\n     * const output =\n     *     tf.layers.dense({units: 3, activation: 'softmax'}).apply(concat);\n     *\n     * const model = tf.model({inputs: [input1, input2], outputs: output});\n     * model.summary();\n     * ```\n     *\n     * @param lineLength Custom line length, in number of characters.\n     * @param positions Custom widths of each of the columns, as either\n     *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n     *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n     *   right-most (i.e., ending) position of a column.\n     * @param printFn Custom print function. Can be used to replace the default\n     *   `console.log`. For example, you can use `x => {}` to mute the printed\n     *   messages in the console.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    summary(lineLength, positions, printFn = console.log) {\n        if (!this.built) {\n            throw new ValueError(`This model has never been called, thus its weights have not been ` +\n                `created yet. So no summary can be displayed. Build the model ` +\n                `first (e.g., by calling it on some test data).`);\n        }\n        printSummary(this, lineLength, positions, printFn);\n    }\n    /**\n     * Configures and prepares the model for training and evaluation.  Compiling\n     * outfits the model with an optimizer, loss, and/or metrics.  Calling `fit`\n     * or `evaluate` on an un-compiled model will throw an error.\n     *\n     * @param args a `ModelCompileArgs` specifying the loss, optimizer, and\n     * metrics to be used for fitting and evaluating this model.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    compile(args) {\n        if (args.loss == null) {\n            args.loss = [];\n        }\n        this.loss = args.loss;\n        if (typeof args.optimizer === 'string') {\n            this.optimizer_ = optimizers.getOptimizer(args.optimizer);\n            this.isOptimizerOwned = true;\n        }\n        else {\n            if (!(args.optimizer instanceof Optimizer)) {\n                throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`);\n            }\n            this.optimizer_ = args.optimizer;\n            this.isOptimizerOwned = false;\n        }\n        // TODO(cais): Add lossWeights.\n        // TODO(cais): Add sampleWeightMode.\n        // Prepare loss functions.\n        let lossFunctions = [];\n        if (!Array.isArray(args.loss) && typeof args.loss !== 'string' &&\n            typeof args.loss !== 'function') {\n            args.loss = args.loss;\n            for (const name in args.loss) {\n                if (this.outputNames.indexOf(name) === -1) {\n                    throw new ValueError(`Unknown entry in loss dictionary: \"${name}\". ` +\n                        `Only expected the following keys: ${this.outputNames}`);\n                }\n            }\n            for (const name of this.outputNames) {\n                if (args.loss[name] == null) {\n                    console.warn(`Output \"${name}\" is missing from loss dictionary. We assume ` +\n                        `this was done on purpose, and we will not be expecting data ` +\n                        `to be passed to ${name} during training`);\n                }\n                lossFunctions.push(losses.get(args.loss[name]));\n            }\n        }\n        else if (Array.isArray(args.loss)) {\n            if (args.loss.length !== this.outputs.length) {\n                throw new ValueError(`When passing an Array as loss, it should have one entry per ` +\n                    `model output. The model has ${this.outputs.length} output(s), ` +\n                    `but you passed loss=${args.loss}.`);\n            }\n            const theLosses = args.loss;\n            lossFunctions = theLosses.map(l => losses.get(l));\n        }\n        else {\n            const lossFunction = losses.get(args.loss);\n            this.outputs.forEach(_ => {\n                lossFunctions.push(lossFunction);\n            });\n        }\n        this.lossFunctions = lossFunctions;\n        this.feedOutputNames = [];\n        this.feedOutputShapes = [];\n        this.feedLossFns = [];\n        for (let i = 0; i < this.outputs.length; ++i) {\n            // TODO(cais): Logic for skipping target(s).\n            const shape = this.internalOutputShapes[i];\n            const name = this.outputNames[i];\n            this.feedOutputNames.push(name);\n            this.feedOutputShapes.push(shape);\n            this.feedLossFns.push(this.lossFunctions[i]);\n        }\n        // TODO(cais): Add logic for output masks.\n        // TODO(cais): Add logic for sample weights.\n        const skipTargetIndices = [];\n        // Prepare metrics.\n        this.metrics = args.metrics;\n        // TODO(cais): Add weightedMetrics.\n        this.metricsNames = ['loss'];\n        this.metricsTensors = [];\n        // Compute total loss.\n        // Porting Note: In PyKeras, metrics_tensors are symbolic tensor objects.\n        //   Here, metricsTensors are TypeScript functions. This difference is due\n        //   to the difference in symbolic/imperative property of the backends.\n        nameScope('loss', () => {\n            for (let i = 0; i < this.outputs.length; ++i) {\n                if (skipTargetIndices.indexOf(i) !== -1) {\n                    continue;\n                }\n                // TODO(cais): Add weightedLoss, sampleWeight and mask.\n                //   The following line should be weightedLoss\n                const weightedLoss = this.lossFunctions[i];\n                if (this.outputs.length > 1) {\n                    this.metricsTensors.push([weightedLoss, i]);\n                    this.metricsNames.push(this.outputNames[i] + '_loss');\n                }\n            }\n            // Porting Note: Due to the imperative nature of the backend, we calculate\n            //   the regularizer penalties in the totalLossFunction, instead of here.\n        });\n        const nestedMetrics = collectMetrics(args.metrics, this.outputNames);\n        // TODO(cais): Add nestedWeightedMetrics.\n        /**\n         * Helper function used in loop below.\n         */\n        const appendMetric = (outputIndex, metricName, metricTensor) => {\n            if (this.outputNames.length > 1) {\n                metricName = this.outputNames[outputIndex] + '_' + metricName;\n            }\n            this.metricsNames.push(metricName);\n            this.metricsTensors.push([metricTensor, outputIndex]);\n        };\n        nameScope('metric', () => {\n            for (let i = 0; i < this.outputs.length; ++i) {\n                if (skipTargetIndices.indexOf(i) !== -1) {\n                    continue;\n                }\n                const outputMetrics = nestedMetrics[i];\n                // TODO(cais): Add weights and outputWeightedMetrics.\n                // TODO(cais): Add optional arg `weights` to the following function.\n                const handleMetrics = (metrics) => {\n                    const metricNamePrefix = '';\n                    let metricName;\n                    let accFn;\n                    let weightedMetricFn;\n                    //  TODO(cais): Use 'weights_' for weighted metrics.\n                    for (const metric of metrics) {\n                        if (typeof metric === 'string' &&\n                            ['accuracy', 'acc', 'crossentropy', 'ce'].indexOf(metric) !==\n                                -1) {\n                            const outputShape = this.internalOutputShapes[i];\n                            if (outputShape[outputShape.length - 1] === 1 ||\n                                this.lossFunctions[i] === losses.binaryCrossentropy) {\n                                // case: binary accuracy/crossentropy.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.binaryAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.binaryCrossentropy;\n                                }\n                            }\n                            else if (this.lossFunctions[i] ===\n                                losses.sparseCategoricalCrossentropy) {\n                                // case: categorical accuracy / crossentropy with sparse\n                                // targets.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.sparseCategoricalAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.sparseCategoricalCrossentropy;\n                                }\n                            }\n                            else {\n                                // case: categorical accuracy / crossentropy.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.categoricalAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.categoricalCrossentropy;\n                                }\n                            }\n                            let suffix;\n                            if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                suffix = 'acc';\n                            }\n                            else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                suffix = 'ce';\n                            }\n                            // TODO(cais): Add weighting actually.\n                            weightedMetricFn = accFn;\n                            metricName = metricNamePrefix + suffix;\n                        }\n                        else {\n                            const metricFn = Metrics.get(metric);\n                            // TODO(cais): Add weighting actually.\n                            weightedMetricFn = metricFn;\n                            metricName =\n                                metricNamePrefix + Metrics.getLossOrMetricName(metric);\n                        }\n                        // TODO(cais): Add weighting and masking to metricResult.\n                        let metricResult;\n                        nameScope(metricName, () => {\n                            metricResult = weightedMetricFn;\n                        });\n                        appendMetric(i, metricName, metricResult);\n                    }\n                };\n                handleMetrics(outputMetrics);\n                // TODO(cais): Call handleMetrics with weights.\n            }\n        });\n        // Porting Notes: Given the imperative backend of tfjs-core,\n        //   there is no need for constructing the symbolic graph and placeholders.\n        this.collectedTrainableWeights = this.trainableWeights;\n    }\n    /**\n     * Check trainable weights count consistency.\n     *\n     * This will raise a warning if `this.trainableWeights` and\n     * `this.collectedTrainableWeights` are inconsistent (i.e., have different\n     * numbers of parameters).\n     * Inconsistency will typically arise when one modifies `model.trainable`\n     * without calling `model.compile()` again.\n     */\n    checkTrainableWeightsConsistency() {\n        if (this.collectedTrainableWeights == null) {\n            return;\n        }\n        if (this.trainableWeights.length !==\n            this.collectedTrainableWeights.length) {\n            console.warn('Discrepancy between trainableweights and collected trainable ' +\n                'weights. Did you set `model.trainable` without calling ' +\n                '`model.compile()` afterwards?');\n        }\n    }\n    /**\n     * Returns the loss value & metrics values for the model in test mode.\n     *\n     * Loss and metrics are specified during `compile()`, which needs to happen\n     * before calls to `evaluate()`.\n     *\n     * Computation is done in batches.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const result = model.evaluate(\n     *     tf.ones([8, 10]), tf.ones([8, 1]), {batchSize: 4});\n     * result.print();\n     * ```\n     *\n     * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple inputs.\n     * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple outputs.\n     * @param args A `ModelEvaluateArgs`, containing optional fields.\n     *\n     * @return `Scalar` test loss (if the model has a single output and no\n     *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n     *   and/or metrics). The attribute `model.metricsNames`\n     *   will give you the display labels for the scalar outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    evaluate(x, y, args = {}) {\n        const batchSize = args.batchSize == null ? 32 : args.batchSize;\n        checkBatchSize(batchSize);\n        // TODO(cais): Standardize `config.sampleWeights` as well.\n        // Validate user data.\n        const checkBatchAxis = true;\n        const standardizedOuts = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n        try {\n            // TODO(cais): If uses `useLearningPhase`, set the corresponding element\n            // of the input to 0.\n            const ins = standardizedOuts[0].concat(standardizedOuts[1]);\n            this.makeTestFunction();\n            const f = this.testFunction;\n            const testOuts = this.testLoop(f, ins, batchSize, args.verbose, args.steps);\n            return singletonOrArray(testOuts);\n        }\n        finally {\n            disposeNewTensors(standardizedOuts[0], x);\n            disposeNewTensors(standardizedOuts[1], y);\n        }\n    }\n    // TODO(cais): Add code snippet below once real dataset objects are\n    //   available.\n    /**\n     * Evaluate model using a dataset object.\n     *\n     * Note: Unlike `evaluate()`, this method is asynchronous (`async`);\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for evaluation. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs. Of the two items in the array, the\n     *   first is the input feature(s) and the second is the output target(s).\n     * @param args A configuration object for the dataset-based evaluation.\n     * @returns Loss and metric values as an Array of `Scalar` objects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async evaluateDataset(dataset, args) {\n        this.makeTestFunction();\n        return evaluateDataset(this, dataset, args);\n    }\n    /**\n     * Get number of samples provided for training, evaluation or prediction.\n     *\n     * @param ins Input `tf.Tensor`.\n     * @param batchSize Integer batch size, optional.\n     * @param steps Total number of steps (batches of samples) before\n     * declaring loop finished. Optional.\n     * @param stepsName The public API's parameter name for `steps`.\n     * @returns Number of samples provided.\n     */\n    checkNumSamples(ins, batchSize, steps, stepsName = 'steps') {\n        let numSamples;\n        if (steps != null) {\n            numSamples = null;\n            if (batchSize != null) {\n                throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.` +\n                    `Got batchSize = ${batchSize}`);\n            }\n        }\n        else if (ins != null) {\n            if (Array.isArray(ins)) {\n                numSamples = ins[0].shape[0];\n            }\n            else {\n                numSamples = ins.shape[0];\n            }\n        }\n        else {\n            throw new ValueError(`Either the input data should have a defined shape, or ` +\n                `${stepsName} shoud be specified.`);\n        }\n        return numSamples;\n    }\n    /**\n     * Execute internal tensors of the model with input data feed.\n     * @param inputs Input data feed. Must match the inputs of the model.\n     * @param outputs Names of the output tensors to be fetched. Must match\n     *   names of the SymbolicTensors that belong to the graph.\n     * @returns Fetched values for `outputs`.\n     */\n    execute(inputs, outputs) {\n        if (Array.isArray(outputs) && outputs.length === 0) {\n            throw new ValueError('`outputs` is an empty Array, which is not allowed.');\n        }\n        const outputsIsArray = Array.isArray(outputs);\n        const outputNames = (outputsIsArray ? outputs : [outputs]);\n        const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames);\n        // Format the input into a FeedDict.\n        const feedDict = new FeedDict();\n        if (inputs instanceof Tensor) {\n            inputs = [inputs];\n        }\n        if (Array.isArray(inputs)) {\n            if (inputs.length !== this.inputs.length) {\n                throw new ValueError(`The number of inputs provided (${inputs.length}) ` +\n                    `does not match the number of inputs of this model ` +\n                    `(${this.inputs.length}).`);\n            }\n            for (let i = 0; i < this.inputs.length; ++i) {\n                feedDict.add(this.inputs[i], inputs[i]);\n            }\n        }\n        else {\n            for (const input of this.inputs) {\n                const tensorValue = inputs[input.name];\n                if (tensorValue == null) {\n                    throw new ValueError(`No value is provided for the model's input ${input.name}`);\n                }\n                feedDict.add(input, tensorValue);\n            }\n        }\n        // Run execution.\n        const executeOutputs = execute(outputSymbolicTensors, feedDict);\n        return outputsIsArray ? executeOutputs : executeOutputs[0];\n    }\n    /**\n     * Retrieve the model's internal symbolic tensors from symbolic-tensor names.\n     */\n    retrieveSymbolicTensors(symbolicTensorNames) {\n        const outputSymbolicTensors = pyListRepeat(null, symbolicTensorNames.length);\n        let outputsRemaining = symbolicTensorNames.length;\n        for (const layer of this.layers) {\n            const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output];\n            const layerOutputNames = layerOutputs.map(output => output.name);\n            for (let i = 0; i < symbolicTensorNames.length; ++i) {\n                const index = layerOutputNames.indexOf(symbolicTensorNames[i]);\n                if (index !== -1) {\n                    outputSymbolicTensors[i] = layerOutputs[index];\n                    outputsRemaining--;\n                }\n                if (outputsRemaining === 0) {\n                    break;\n                }\n            }\n            if (outputsRemaining === 0) {\n                break;\n            }\n        }\n        if (outputsRemaining > 0) {\n            const remainingNames = [];\n            outputSymbolicTensors.forEach((tensor, i) => {\n                if (tensor == null) {\n                    remainingNames.push(symbolicTensorNames[i]);\n                }\n            });\n            throw new ValueError(`Cannot find SymbolicTensors for output name(s): ` +\n                `${JSON.stringify(remainingNames)}`);\n        }\n        return outputSymbolicTensors;\n    }\n    /**\n     * Helper method to loop over some data in batches.\n     *\n     * Porting Note: Not using the functional approach in the Python equivalent\n     *   due to the imperative backend.\n     * Porting Note: Does not support step mode currently.\n     *\n     * @param ins: input data\n     * @param batchSize: integer batch size.\n     * @param verbose: verbosity model\n     * @returns: Predictions as `tf.Tensor` (if a single output) or an `Array` of\n     *   `tf.Tensor` (if multipe outputs).\n     */\n    predictLoop(ins, batchSize = 32, verbose = false) {\n        return tfc.tidy(() => {\n            const numSamples = this.checkNumSamples(ins);\n            if (verbose) {\n                throw new NotImplementedError('Verbose predictLoop() is not implemented yet.');\n            }\n            // Sample-based predictions.\n            // Porting Note: Tensor currently does not support sliced assignments as\n            //   in numpy, e.g., x[1:3] = y. Therefore we use concatenation while\n            //   iterating over the batches.\n            const batches = makeBatches(numSamples, batchSize);\n            const outsBatches = this.outputs.map(output => []);\n            // TODO(cais): Can the scope() be pushed down inside the for loop?\n            for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                const batchOuts = tfc.tidy(() => {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    // TODO(cais): Take care of the case of the last element is a flag for\n                    //   training/test.\n                    const insBatch = sliceArrays(ins, batchStart, batchEnd);\n                    // Construct the feeds for execute();\n                    const feeds = [];\n                    if (Array.isArray(insBatch)) {\n                        for (let i = 0; i < insBatch.length; ++i) {\n                            feeds.push({ key: this.inputs[i], value: insBatch[i] });\n                        }\n                    }\n                    else {\n                        feeds.push({ key: this.inputs[0], value: insBatch });\n                    }\n                    const feedDict = new FeedDict(feeds);\n                    return execute(this.outputs, feedDict);\n                });\n                batchOuts.forEach((batchOut, i) => outsBatches[i].push(batchOut));\n            }\n            return singletonOrArray(outsBatches.map(batches => tfc.concat(batches, 0)));\n        });\n    }\n    /**\n     * Generates output predictions for the input samples.\n     *\n     * Computation is done in batches.\n     *\n     * Note: the \"step\" mode of predict() is currently not supported.\n     *   This is because the TensorFlow.js core backend is imperative only.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predict(tf.ones([8, 10]), {batchSize: 4}).print();\n     * ```\n     *\n     * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n     *   the model has multiple inputs.\n     * @param args A `ModelPredictArgs` object containing optional fields.\n     *\n     * @return Prediction results as a `tf.Tensor`(s).\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and the model's expectations, or in case a stateful model receives a\n     *   number of samples that is not a multiple of the batch size.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(x, args = {}) {\n        const xsRank2OrHigher = ensureTensorsRank2OrHigher(x);\n        checkInputData(xsRank2OrHigher, this.inputNames, this.feedInputShapes, false);\n        try {\n            // TODO(cais): Take care of stateful models.\n            //   if (this.stateful) ...\n            // TODO(cais): Take care of the learning_phase boolean flag.\n            //   if (this.useLearningPhase) ...\n            const batchSize = args.batchSize == null ? 32 : args.batchSize;\n            checkBatchSize(batchSize);\n            return this.predictLoop(xsRank2OrHigher, batchSize);\n        }\n        finally {\n            disposeNewTensors(xsRank2OrHigher, x);\n        }\n    }\n    /**\n     * Returns predictions for a single batch of samples.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predictOnBatch(tf.ones([8, 10])).print();\n     * ```\n     * @param x: Input samples, as a Tensor (for models with exactly one\n     *   input) or an array of Tensors (for models with more than one input).\n     * @return Tensor(s) of predictions\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predictOnBatch(x) {\n        checkInputData(x, this.inputNames, this.feedInputShapes, true);\n        // TODO(cais): Take care of the learning_phase boolean flag.\n        //   if (this.useLearningPhase) ...\n        const batchSize = (Array.isArray(x) ? x[0] : x).shape[0];\n        return this.predictLoop(x, batchSize);\n    }\n    standardizeUserDataXY(x, y, checkBatchAxis = true, batchSize) {\n        // TODO(cais): Add sampleWeight, classWeight\n        if (this.optimizer_ == null) {\n            throw new RuntimeError('You must compile a model before training/testing. Use ' +\n                'LayersModel.compile(modelCompileArgs).');\n        }\n        const outputShapes = [];\n        for (let i = 0; i < this.feedOutputShapes.length; ++i) {\n            const outputShape = this.feedOutputShapes[i];\n            const lossFn = this.feedLossFns[i];\n            if (lossFn === losses.sparseCategoricalCrossentropy) {\n                outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1]));\n            }\n            else {\n                // Porting Note: Because of strong typing `lossFn` must be a function.\n                outputShapes.push(outputShape);\n            }\n        }\n        x = standardizeInputData(x, this.feedInputNames, this.feedInputShapes, false, 'input');\n        y = standardizeInputData(y, this.feedOutputNames, outputShapes, false, 'target');\n        // TODO(cais): Standardize sampleWeights & classWeights.\n        checkArrayLengths(x, y, null);\n        // TODO(cais): Check sampleWeights as well.\n        checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes);\n        if (this.stateful && batchSize != null && batchSize > 0) {\n            if (x[0].shape[0] % batchSize !== 0) {\n                throw new ValueError(`In a stateful network, you should only pass inputs with a ` +\n                    `number of samples that is divisible by the batch size ` +\n                    `${batchSize}. Found: ${x[0].shape[0]} sample(s).`);\n            }\n        }\n        return [x, y];\n    }\n    async standardizeUserData(x, y, sampleWeight, classWeight, checkBatchAxis = true, batchSize) {\n        const [standardXs, standardYs] = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n        // TODO(cais): Handle sampleWeights.\n        if (sampleWeight != null) {\n            throw new Error('sample weight is not supported yet.');\n        }\n        let standardSampleWeights = null;\n        if (classWeight != null) {\n            const classWeights = standardizeClassWeights(classWeight, this.outputNames);\n            standardSampleWeights = [];\n            for (let i = 0; i < classWeights.length; ++i) {\n                standardSampleWeights.push(await standardizeWeights(standardYs[i], null, classWeights[i]));\n            }\n        }\n        // TODO(cais): Deal with the case of model.stateful == true.\n        return [standardXs, standardYs, standardSampleWeights];\n    }\n    /**\n     * Loop over some test data in batches.\n     * @param f A Function returning a list of tensors.\n     * @param ins Array of tensors to be fed to `f`.\n     * @param batchSize Integer batch size or `null` / `undefined`.\n     * @param verbose verbosity mode.\n     * @param steps Total number of steps (batches of samples) before\n     * declaring test finished. Ignored with the default value of `null` /\n     * `undefined`.\n     * @returns Array of Scalars.\n     */\n    testLoop(f, ins, batchSize, verbose = 0, steps) {\n        return tfc.tidy(() => {\n            const numSamples = this.checkNumSamples(ins, batchSize, steps, 'steps');\n            const outs = [];\n            if (verbose > 0) {\n                throw new NotImplementedError('Verbose mode is not implemented yet.');\n            }\n            // TODO(cais): Use `indicesForConversionToDense' to prevent slow down.\n            if (steps != null) {\n                throw new NotImplementedError('steps mode in testLoop() is not implemented yet');\n            }\n            else {\n                const batches = makeBatches(numSamples, batchSize);\n                const indexArray = tensor1d(range(0, numSamples));\n                for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    const batchIds = K.sliceAlongFirstAxis(indexArray, batchStart, batchEnd - batchStart);\n                    // TODO(cais): In ins, train flag can be a number, instead of an\n                    //   Tensor? Do we need to handle this in tfjs-layers?\n                    const insBatch = sliceArraysByIndices(ins, batchIds);\n                    const batchOuts = f(insBatch);\n                    if (batchIndex === 0) {\n                        for (let i = 0; i < batchOuts.length; ++i) {\n                            outs.push(scalar(0));\n                        }\n                    }\n                    for (let i = 0; i < batchOuts.length; ++i) {\n                        const batchOut = batchOuts[i];\n                        outs[i] =\n                            tfc.add(outs[i], tfc.mul(batchEnd - batchStart, batchOut));\n                    }\n                }\n                for (let i = 0; i < outs.length; ++i) {\n                    outs[i] = tfc.div(outs[i], numSamples);\n                }\n            }\n            return outs;\n        });\n    }\n    getDedupedMetricsNames() {\n        const outLabels = this.metricsNames;\n        // Rename duplicated metrics names (can happen with an output layer\n        // shared among multiple dataflows).\n        const dedupedOutLabels = [];\n        for (let i = 0; i < outLabels.length; ++i) {\n            const label = outLabels[i];\n            let newLabel = label;\n            if (count(outLabels, label) > 1) {\n                const dupIndex = count(outLabels.slice(0, i), label);\n                newLabel += `_${dupIndex}`;\n            }\n            dedupedOutLabels.push(newLabel);\n        }\n        return dedupedOutLabels;\n    }\n    /**\n     * Creates a function that performs the following actions:\n     *\n     * 1. computes the losses\n     * 2. sums them to get the total loss\n     * 3. call the optimizer computes the gradients of the LayersModel's\n     *    trainable weights w.r.t. the total loss and update the variables\n     * 4. calculates the metrics\n     * 5. returns the values of the losses and metrics.\n     */\n    makeTrainFunction() {\n        return (data) => {\n            const lossValues = [];\n            const inputs = data.slice(0, this.inputs.length);\n            const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n            const sampleWeights = data.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2);\n            const metricsValues = [];\n            // Create a function that computes the total loss based on the\n            // inputs. This function is used for obtaining gradients through\n            // backprop.\n            const totalLossFunction = () => {\n                const feeds = [];\n                for (let i = 0; i < this.inputs.length; ++i) {\n                    feeds.push({ key: this.inputs[i], value: inputs[i] });\n                }\n                const feedDict = new FeedDict(feeds);\n                const outputs = execute(this.outputs, feedDict, { 'training': true });\n                // TODO(cais): Take care of the case of multiple outputs from a\n                //   single layer?\n                let totalLoss;\n                for (let i = 0; i < this.lossFunctions.length; ++i) {\n                    const lossFunction = this.lossFunctions[i];\n                    let loss = lossFunction(targets[i], outputs[i]);\n                    if (sampleWeights[i] != null) {\n                        loss = computeWeightedLoss(loss, sampleWeights[i]);\n                    }\n                    // TODO(cais): push Scalar instead.\n                    const meanLoss = tfc.mean(loss);\n                    // TODO(cais): Use a scope() instead, to avoid ownership.\n                    lossValues.push(meanLoss);\n                    if (i === 0) {\n                        totalLoss = loss;\n                    }\n                    else {\n                        totalLoss = tfc.add(totalLoss, loss);\n                    }\n                }\n                // Compute the metrics.\n                // TODO(cais): These should probably be calculated outside\n                //   totalLossFunction to benefit speed?\n                for (let i = 0; i < this.metricsTensors.length; ++i) {\n                    let weightedMetric;\n                    if (this.outputs.length > 1 && i < this.outputs.length) {\n                        weightedMetric = lossValues[i];\n                    }\n                    else {\n                        const metric = this.metricsTensors[i][0];\n                        const outputIndex = this.metricsTensors[i][1];\n                        weightedMetric =\n                            tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));\n                    }\n                    tfc.keep(weightedMetric);\n                    // TODO(cais): Use a scope() instead, to avoid ownership.\n                    metricsValues.push(weightedMetric);\n                }\n                totalLoss = tfc.mean(totalLoss);\n                // Add regularizer penalties.\n                this.calculateLosses().forEach(regularizerLoss => {\n                    totalLoss = tfc.add(totalLoss, regularizerLoss);\n                });\n                return totalLoss;\n            };\n            const variables = this.collectedTrainableWeights.map(param => param.read());\n            const returnCost = true;\n            const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables);\n            return [totalLossValue].concat(metricsValues);\n        };\n    }\n    /**\n     * Create a function which, when invoked with an array of `tf.Tensor`s as a\n     * batch of inputs, returns the prespecified loss and metrics of the model\n     * under the batch of input data.\n     */\n    makeTestFunction() {\n        this.testFunction = (data) => {\n            return tfc.tidy(() => {\n                const valOutputs = [];\n                let totalLoss;\n                const inputs = data.slice(0, this.inputs.length);\n                const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n                const feeds = [];\n                for (let i = 0; i < this.inputs.length; ++i) {\n                    feeds.push({ key: this.inputs[i], value: inputs[i] });\n                }\n                const feedDict = new FeedDict(feeds);\n                const outputs = execute(this.outputs, feedDict);\n                // Compute total loss.\n                for (let i = 0; i < this.lossFunctions.length; ++i) {\n                    const lossFunction = this.lossFunctions[i];\n                    // TODO(cais): Add sample weighting and replace the simple\n                    // averaging.\n                    const loss = tfc.mean(lossFunction(targets[i], outputs[i]));\n                    if (i === 0) {\n                        totalLoss = loss;\n                    }\n                    else {\n                        totalLoss = tfc.add(totalLoss, loss);\n                    }\n                    valOutputs.push(totalLoss);\n                }\n                // Compute the metrics.\n                for (let i = 0; i < this.metricsTensors.length; ++i) {\n                    const metric = this.metricsTensors[i][0];\n                    const outputIndex = this.metricsTensors[i][1];\n                    // TODO(cais): Replace K.mean() with a proper weighting function.\n                    const meanMetric = tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));\n                    valOutputs.push(meanMetric);\n                }\n                return valOutputs;\n            });\n        };\n    }\n    /**\n     * Trains the model for a fixed number of epochs (iterations on a\n     * dataset).\n     *\n     * ```js\n     * const model = tf.sequential({\n     *     layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * for (let i = 1; i < 5 ; ++i) {\n     *   const h = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *       batchSize: 4,\n     *       epochs: 3\n     *   });\n     *   console.log(\"Loss after Epoch \" + i + \" : \" + h.history.loss[0]);\n     * }\n     * ```\n     *\n     * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n     * model has multiple inputs. If all inputs in the model are named, you\n     * can also pass a dictionary mapping input names to `tf.Tensor`s.\n     * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n     * the model has multiple outputs. If all outputs in the model are named,\n     * you can also pass a dictionary mapping output names to `tf.Tensor`s.\n     * @param args A `ModelFitArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @exception ValueError In case of mismatch between the provided input\n     * data and what the model expects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fit(x, y, args = {}) {\n        return fitTensors(this, x, y, args);\n    }\n    // TODO(cais): Add code snippet below when it's possible to instantiate\n    //   actual dataset objects.\n    /**\n     * Trains the model using a dataset object.\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for training. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs.\n     *   Of the two items in the array, the first is the input feature(s) and\n     *   the second is the output target(s).\n     * @param args A `ModelFitDatasetArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fitDataset(dataset, args) {\n        return fitDataset(this, dataset, args);\n    }\n    /**\n     * Runs a single gradient update on a single batch of data.\n     *\n     * This method differs from `fit()` and `fitDataset()` in the following\n     * regards:\n     *   - It operates on exactly one batch of data.\n     *   - It returns only the loss and matric values, instead of\n     *     returning the batch-by-batch loss and metric values.\n     *   - It doesn't support fine-grained options such as verbosity and\n     *     callbacks.\n     *\n     * @param x Input data. It could be one of the following:\n     *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n     *     multiple inputs).\n     *   - An Object mapping input names to corresponding `tf.Tensor` (if the\n     *     model has named inputs).\n     * @param y Target darta. It could be either a `tf.Tensor` a multiple\n     *   `tf.Tensor`s. It should be consistent with `x`.\n     * @returns Training loss or losses (in case the model has\n     *   multiple outputs), along with metrics (if any), as numbers.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async trainOnBatch(x, y) {\n        // TODO(cais): Support sampleWeight and classWeight.\n        // TODO(cais): Support Dataset objects.\n        const standardizeOut = await this.standardizeUserData(x, y);\n        const inputs = standardizeOut[0];\n        const targets = standardizeOut[1];\n        const trainFunction = this.makeTrainFunction();\n        const losses = trainFunction(inputs.concat(targets));\n        const lossValues = [];\n        for (const loss of losses) {\n            const v = await loss.data();\n            lossValues.push(v[0]);\n        }\n        tfc.dispose(losses);\n        return singletonOrArray(lossValues);\n    }\n    /**\n     * Extract weight values of the model.\n     *\n     * @param config: An instance of `io.SaveConfig`, which specifies\n     * model-saving options such as whether only trainable weights are to be\n     * saved.\n     * @returns A `NamedTensorMap` mapping original weight names (i.e.,\n     *   non-uniqueified weight names) to their values.\n     */\n    getNamedWeights(config) {\n        const namedWeights = [];\n        const trainableOnly = config != null && config.trainableOnly;\n        const weights = trainableOnly ? this.trainableWeights : this.weights;\n        const weightValues = this.getWeights(trainableOnly);\n        for (let i = 0; i < weights.length; ++i) {\n            if (trainableOnly && !weights[i].trainable) {\n                // Optionally skip non-trainable weights.\n                continue;\n            }\n            namedWeights.push({ name: weights[i].originalName, tensor: weightValues[i] });\n        }\n        return namedWeights;\n    }\n    /**\n     * Setter used for force stopping of LayersModel.fit() (i.e., training).\n     *\n     * Example:\n     *\n     * ```js\n     * const input = tf.input({shape: [10]});\n     * const output = tf.layers.dense({units: 1}).apply(input);\n     * const model = tf.model({inputs: [input], outputs: [output]});\n     * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n     * const xs = tf.ones([8, 10]);\n     * const ys = tf.zeros([8, 1]);\n     *\n     * const history = await model.fit(xs, ys, {\n     *   epochs: 10,\n     *   callbacks: {\n     *     onEpochEnd: async (epoch, logs) => {\n     *       if (epoch === 2) {\n     *         model.stopTraining = true;\n     *       }\n     *     }\n     *   }\n     * });\n     *\n     * // There should be only 3 values in the loss array, instead of 10\n     * values,\n     * // due to the stopping after 3 epochs.\n     * console.log(history.history.loss);\n     * ```\n     */\n    set stopTraining(stop) {\n        this.stopTraining_ = stop;\n    }\n    get stopTraining() {\n        return this.stopTraining_;\n    }\n    get optimizer() {\n        return this.optimizer_;\n    }\n    set optimizer(optimizer) {\n        if (this.optimizer_ !== optimizer) {\n            this.optimizer_ = optimizer;\n            this.isOptimizerOwned = false;\n        }\n    }\n    dispose() {\n        const result = super.dispose();\n        if (result.refCountAfterDispose === 0 && this.optimizer != null &&\n            this.isOptimizerOwned) {\n            const numTensorsBeforeOptmizerDisposal = tfc.memory().numTensors;\n            this.optimizer_.dispose();\n            result.numDisposedVariables +=\n                numTensorsBeforeOptmizerDisposal - tfc.memory().numTensors;\n        }\n        return result;\n    }\n    getLossIdentifiers() {\n        let lossNames;\n        if (typeof this.loss === 'string') {\n            lossNames = toSnakeCase(this.loss);\n        }\n        else if (Array.isArray(this.loss)) {\n            for (const loss of this.loss) {\n                if (typeof loss !== 'string') {\n                    throw new Error('Serialization of non-string loss is not supported.');\n                }\n            }\n            lossNames = this.loss.map(name => toSnakeCase(name));\n        }\n        else {\n            const outputNames = Object.keys(this.loss);\n            lossNames = {};\n            const losses = this.loss;\n            for (const outputName of outputNames) {\n                if (typeof losses[outputName] === 'string') {\n                    lossNames[outputName] =\n                        toSnakeCase(losses[outputName]);\n                }\n                else {\n                    throw new Error('Serialization of non-string loss is not supported.');\n                }\n            }\n        }\n        return lossNames;\n    }\n    getMetricIdentifiers() {\n        if (typeof this.metrics === 'string' ||\n            typeof this.metrics === 'function') {\n            return [toSnakeCase(Metrics.getLossOrMetricName(this.metrics))];\n        }\n        else if (Array.isArray(this.metrics)) {\n            return this.metrics.map(metric => toSnakeCase(Metrics.getLossOrMetricName(metric)));\n        }\n        else {\n            const metricsIdentifiers = {};\n            for (const key in this.metrics) {\n                metricsIdentifiers[key] =\n                    toSnakeCase(Metrics.getLossOrMetricName(this.metrics[key]));\n            }\n            return metricsIdentifiers;\n        }\n    }\n    getTrainingConfig() {\n        return {\n            loss: this.getLossIdentifiers(),\n            metrics: this.getMetricIdentifiers(),\n            optimizer_config: {\n                class_name: this.optimizer.getClassName(),\n                config: this.optimizer.getConfig()\n            }\n        };\n        // TODO(cais): Add weight_metrics when they are supported.\n        // TODO(cais): Add sample_weight_mode when it's supported.\n        // TODO(cais): Add loss_weights when it's supported.\n    }\n    loadTrainingConfig(trainingConfig) {\n        if (trainingConfig.weighted_metrics != null) {\n            throw new Error('Loading weight_metrics is not supported yet.');\n        }\n        if (trainingConfig.loss_weights != null) {\n            throw new Error('Loading loss_weights is not supported yet.');\n        }\n        if (trainingConfig.sample_weight_mode != null) {\n            throw new Error('Loading sample_weight_mode is not supported yet.');\n        }\n        const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config);\n        const optimizer = deserialize(tsConfig);\n        let loss;\n        if (typeof trainingConfig.loss === 'string') {\n            loss = toCamelCase(trainingConfig.loss);\n        }\n        else if (Array.isArray(trainingConfig.loss)) {\n            loss = trainingConfig.loss.map(lossEntry => toCamelCase(lossEntry));\n        }\n        else if (trainingConfig.loss != null) {\n            loss = {};\n            for (const key in trainingConfig.loss) {\n                loss[key] = toCamelCase(trainingConfig.loss[key]);\n            }\n        }\n        let metrics;\n        if (Array.isArray(trainingConfig.metrics)) {\n            metrics = trainingConfig.metrics.map(metric => toCamelCase(metric));\n        }\n        else if (trainingConfig.metrics != null) {\n            metrics = {};\n            for (const key in trainingConfig.metrics) {\n                metrics[key] = toCamelCase(trainingConfig.metrics[key]);\n            }\n        }\n        this.compile({ loss, metrics, optimizer });\n    }\n    /**\n     * Save the configuration and/or weights of the LayersModel.\n     *\n     * An `IOHandler` is an object that has a `save` method of the proper\n     * signature defined. The `save` method manages the storing or\n     * transmission of serialized data (\"artifacts\") that represent the\n     * model's topology and weights onto or via a specific medium, such as\n     * file downloads, local storage, IndexedDB in the web browser and HTTP\n     * requests to a server. TensorFlow.js provides `IOHandler`\n     * implementations for a number of frequently used saving mediums, such as\n     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n     * for more details.\n     *\n     * This method also allows you to refer to certain types of `IOHandler`s\n     * as URL-like string shortcuts, such as 'localstorage://' and\n     * 'indexeddb://'.\n     *\n     * Example 1: Save `model`'s topology and weights to browser [local\n     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n     * then load it back.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * console.log('Prediction from original model:');\n     * model.predict(tf.ones([1, 3])).print();\n     *\n     * const saveResults = await model.save('localstorage://my-model-1');\n     *\n     * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * loadedModel.predict(tf.ones([1, 3])).print();\n     * ```\n     *\n     * Example 2. Saving `model`'s topology and weights to browser\n     * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n     * then load it back.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * console.log('Prediction from original model:');\n     * model.predict(tf.ones([1, 3])).print();\n     *\n     * const saveResults = await model.save('indexeddb://my-model-1');\n     *\n     * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * loadedModel.predict(tf.ones([1, 3])).print();\n     * ```\n     *\n     * Example 3. Saving `model`'s topology and weights as two files\n     * (`my-model-1.json` and `my-model-1.weights.bin`) downloaded from\n     * browser.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * const saveResults = await model.save('downloads://my-model-1');\n     * ```\n     *\n     * Example 4. Send  `model`'s topology and weights to an HTTP server.\n     * See the documentation of `tf.io.http` for more details\n     * including specifying request parameters and implementation of the\n     * server.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * const saveResults = await model.save('http://my-server/model/upload');\n     * ```\n     *\n     * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n     * scheme-based string shortcut for `IOHandler`.\n     * @param config Options for saving the model.\n     * @returns A `Promise` of `SaveResult`, which summarizes the result of\n     * the saving, such as byte sizes of the saved artifacts for the model's\n     *   topology and weight values.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async save(handlerOrURL, config) {\n        if (typeof handlerOrURL === 'string') {\n            const handlers = io.getSaveHandlers(handlerOrURL);\n            if (handlers.length === 0) {\n                throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n            }\n            else if (handlers.length > 1) {\n                throw new ValueError(`Found more than one (${handlers.length}) save handlers for ` +\n                    `URL '${handlerOrURL}'`);\n            }\n            handlerOrURL = handlers[0];\n        }\n        if (handlerOrURL.save == null) {\n            throw new ValueError('LayersModel.save() cannot proceed because the IOHandler ' +\n                'provided does not have the `save` attribute defined.');\n        }\n        const weightDataAndSpecs = await io.encodeWeights(this.getNamedWeights(config));\n        const returnString = false;\n        const unusedArg = null;\n        const modelConfig = this.toJSON(unusedArg, returnString);\n        const modelArtifacts = {\n            modelTopology: modelConfig,\n            format: LAYERS_MODEL_FORMAT_NAME,\n            generatedBy: `TensorFlow.js tfjs-layers v${version}`,\n            convertedBy: null,\n        };\n        const includeOptimizer = config == null ? false : config.includeOptimizer;\n        if (includeOptimizer && this.optimizer != null) {\n            modelArtifacts.trainingConfig = this.getTrainingConfig();\n            const weightType = 'optimizer';\n            const { data: optimizerWeightData, specs: optimizerWeightSpecs } = await io.encodeWeights(await this.optimizer.getWeights(), weightType);\n            weightDataAndSpecs.specs.push(...optimizerWeightSpecs);\n            weightDataAndSpecs.data = io.concatenateArrayBuffers([weightDataAndSpecs.data, optimizerWeightData]);\n        }\n        if (this.userDefinedMetadata != null) {\n            // Check serialized size of user-defined metadata.\n            const checkSize = true;\n            checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize);\n            modelArtifacts.userDefinedMetadata = this.userDefinedMetadata;\n        }\n        modelArtifacts.weightData = weightDataAndSpecs.data;\n        modelArtifacts.weightSpecs = weightDataAndSpecs.specs;\n        return handlerOrURL.save(modelArtifacts);\n    }\n    /**\n     * Set user-defined metadata.\n     *\n     * The set metadata will be serialized together with the topology\n     * and weights of the model during `save()` calls.\n     *\n     * @param setUserDefinedMetadata\n     */\n    setUserDefinedMetadata(userDefinedMetadata) {\n        checkUserDefinedMetadata(userDefinedMetadata, this.name);\n        this.userDefinedMetadata = userDefinedMetadata;\n    }\n    /**\n     * Get user-defined metadata.\n     *\n     * The metadata is supplied via one of the two routes:\n     *   1. By calling `setUserDefinedMetadata()`.\n     *   2. Loaded during model loading (if the model is constructed\n     *      via `tf.loadLayersModel()`.)\n     *\n     * If no user-defined metadata is available from either of the\n     * two routes, this function will return `undefined`.\n     */\n    getUserDefinedMetadata() {\n        return this.userDefinedMetadata;\n    }\n}\n// The class name is 'Model' rather than 'LayersModel' for backwards\n// compatibility since this class name shows up in the serialization format.\n/** @nocollapse */\nLayersModel.className = 'Model';\nserialization.registerClass(LayersModel);\n/**\n * A `tf.Functional` is an alias to `tf.LayersModel`.\n *\n * See also:\n *   `tf.LayersModel`, `tf.Sequential`, `tf.loadLayersModel`.\n */\n/** @doc {heading: 'Models', subheading: 'Classes'} */\nexport class Functional extends LayersModel {\n}\nFunctional.className = 'Functional';\nserialization.registerClass(Functional);\n//# sourceMappingURL=training.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source keras/models.py */\nimport { dispose, io, serialization, util } from '@tensorflow/tfjs-core';\nimport { getUid } from './backend/state';\nimport { Input } from './engine/input_layer';\nimport { getSourceInputs, Node } from './engine/topology';\nimport { LayersModel } from './engine/training';\nimport { NotImplementedError, RuntimeError, ValueError } from './errors';\nimport { deserialize } from './layers/serialization';\nimport * as generic_utils from './utils/generic_utils';\nimport { convertPythonicToTs } from './utils/serialization_utils';\nimport { getExactlyOneShape } from './utils/types_utils';\n/**\n * Parses a JSON model configuration file and returns a model instance.\n *\n * ```js\n * // This example shows how to serialize a model using `toJSON()` and\n * // deserialize it as another model using `tf.models.modelFromJSON()`.\n * // Note: this example serializes and deserializes only the topology\n * // of the model; the weights of the loaded model will be different\n * // from those of the the original model, due to random weight\n * // initialization.\n * // To load the topology and weights of a model, use `tf.loadLayersModel()`.\n * const model1 = tf.sequential();\n * model1.add(tf.layers.repeatVector({inputShape: [2], n: 4}));\n * // Serialize `model1` as a JSON object.\n * const model1JSON = model1.toJSON(null, false);\n * model1.summary();\n *\n * const model2 = await tf.models.modelFromJSON(model1JSON);\n * model2.summary();\n * ```\n *\n *  @param modelAndWeightsConfig JSON object or string encoding a model and\n *       weights configuration. It can also be only the topology JSON of the\n *       model, in which case the weights will not be loaded.\n *  @param custom_objects Optional dictionary mapping names\n *       (strings) to custom classes or functions to be\n *       considered during deserialization.\n * @returns A TensorFlow.js Layers `tf.LayersModel` instance (uncompiled).\n */\nexport async function modelFromJSON(modelAndWeightsConfig, customObjects) {\n    if (!('modelTopology' in modelAndWeightsConfig)) {\n        modelAndWeightsConfig = { modelTopology: modelAndWeightsConfig };\n    }\n    modelAndWeightsConfig = modelAndWeightsConfig;\n    let modelTopology = modelAndWeightsConfig.modelTopology;\n    if (modelTopology['model_config'] != null) {\n        // If the model-topology JSON contains a 'model_config' field, then it is\n        // a full model JSON (e.g., from `keras.Model.save()`), which contains\n        // not only the model's architecture in its 'model_config' field, but\n        // additional information such as the model's optimizer. We use only the\n        // 'model_config' field currently.\n        modelTopology = modelTopology['model_config'];\n    }\n    const tsConfig = convertPythonicToTs(modelTopology);\n    const model = deserialize(tsConfig, customObjects);\n    if (modelAndWeightsConfig.weightsManifest != null) {\n        // Load the weight values keyed by the original tensor names in the model\n        // file that was loaded.  These should match the keys of the weight\n        // manifest.\n        const weightValues = await io.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model.weights.map(weight => weight.originalName));\n        // Map the weights to the unique tensor names generated during model loading\n        const uniqueWeightValues = {};\n        for (const weight of model.weights) {\n            uniqueWeightValues[weight.originalName] =\n                weightValues[weight.originalName];\n        }\n        model.loadWeights(uniqueWeightValues);\n        // Dispose temporary weight values.\n        dispose(weightValues);\n    }\n    return model;\n}\n/**\n * Load a model, including its topology and optionally weights.  See the\n * Tutorial named \"How to import a Keras Model\" for usage examples.\n *\n * Example 1: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 2. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Load a model from user-selected files from HTML\n * [file input\n * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).\n *\n * ```js\n * // Note: this code snippet will not work without the HTML elements in the\n * //   page\n * const jsonUpload = document.getElementById('json-upload');\n * const weightsUpload = document.getElementById('weights-upload');\n *\n * const model = await tf.loadLayersModel(\n *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));\n * ```\n *\n * Example 4. Load a model from an HTTP server.\n *\n * ```js\n * const model = await\n *     tf.loadLayersModel('https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');\n * model.summary();\n * ```\n *\n * @param pathOrIOHandler Can be either of the two formats\n *   1. A string path to the `ModelAndWeightsConfig` JSON describing\n *      the model in the canonical TensorFlow.js format. This path will be\n *      interpreted as a relative HTTP path, to which `fetch` will be used to\n *      request the model topology and weight manifest JSON.\n *      The content of the JSON file is assumed to be a JSON object with the\n *      following fields and values:\n *      - 'modelTopology': A JSON object that can be either of:\n *        1. a model architecture JSON consistent with the format of the return\n *            value of `keras.Model.to_json()`\n *        2. a full model JSON in the format of `keras.models.save_model()`.\n *      - 'weightsManifest': A TensorFlow.js weights manifest.\n *      See the Python converter function `save_model()` for more details.\n *      It is also assumed that model weights can be accessed from relative\n *      paths described by the `paths` fields in weights manifest.\n *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`\n *      method.\n * @param options Optional configuration arguments for the model loading,\n *   including:\n *   - `strict`: Require that the provided weights exactly match those required\n *     by the layers.  Default true.  Passing false means that both extra\n *     weights and missing weights will be silently ignored.\n *   - `onProgress`: A progress callback of the form:\n *     `(fraction: number) => void`. This callback can be used to monitor the\n *     model-loading process.\n * @returns A `Promise` of `tf.LayersModel`, with the topology and weights\n *     loaded.\n */\nexport async function loadLayersModelInternal(pathOrIOHandler, options) {\n    if (options == null) {\n        options = {};\n    }\n    if (typeof pathOrIOHandler === 'string') {\n        const handlers = io.getLoadHandlers(pathOrIOHandler, options);\n        if (handlers.length === 0) {\n            // For backward compatibility: if no load handler can be found,\n            // assume it is a relative http path.\n            // TODO(cais): Reformat the args into a single `LoadOptions` once the core\n            // is refactored.\n            handlers.push(io.browserHTTPRequest(pathOrIOHandler, options));\n        }\n        else if (handlers.length > 1) {\n            throw new ValueError(`Found more than one (${handlers.length}) load handlers for ` +\n                `URL '${pathOrIOHandler}'`);\n        }\n        pathOrIOHandler = handlers[0];\n    }\n    return loadLayersModelFromIOHandler(pathOrIOHandler, undefined, options);\n}\n/**\n * Load a model and optionally its weights, using an IOHandler object.\n *\n * @param handler The instance of `IOHandler` to be used during the model\n *   loading.\n * @param customObjects Any optional custom objects to be used during model\n *   loading.\n * @param strict Whether the weight loading will be done in strict mode.\n *   Default: `true`.\n */\nexport async function loadLayersModelFromIOHandler(handler, customObjects, options) {\n    if (options == null) {\n        options = {};\n    }\n    if (handler.load == null) {\n        throw new ValueError('Cannot proceed with model loading because the IOHandler provided ' +\n            'does not have the `load` method implemented.');\n    }\n    const artifacts = await handler.load();\n    let modelTopology = artifacts.modelTopology;\n    if (modelTopology['model_config'] != null) {\n        modelTopology = modelTopology['model_config'];\n    }\n    const strict = options.strict == null ? true : options.strict;\n    // If weights are provided and the weight-loading mode is strict, use\n    // fast weight initialization. This skips costly initializers such as\n    // 'orthogonal' and saves unnecessary computation in cases where\n    // the initialized weight values will immediately be overwritten by\n    // loaded weight values.\n    const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict;\n    const model = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit);\n    const trainingConfig = artifacts.trainingConfig;\n    if (trainingConfig != null) {\n        model.loadTrainingConfig(trainingConfig);\n    }\n    if (artifacts.userDefinedMetadata != null) {\n        model.setUserDefinedMetadata(artifacts.userDefinedMetadata);\n    }\n    // If weightData is present, load the weights into the model.\n    if (artifacts.weightData != null) {\n        // Loading weights requires weightSpecs.\n        if (artifacts.weightSpecs == null) {\n            throw new ValueError('LayersModel artifacts contains weight data, but not weight specs. ' +\n                'Therefore loading of weights cannot proceed.');\n        }\n        const { modelWeights, optimizerWeights } = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs);\n        model.loadWeights(modelWeights, strict);\n        if (model.optimizer != null && optimizerWeights.length > 0) {\n            await model.optimizer.setWeights(optimizerWeights);\n        }\n        // Dispose temporary weight values.\n        dispose(modelWeights);\n        dispose(optimizerWeights.map(w => w.tensor));\n    }\n    return model;\n}\nfunction decodeModelAndOptimizerWeights(buffer, specs) {\n    const name2Tensor = io.decodeWeights(buffer, specs);\n    const modelWeights = {};\n    const optimizerWeights = [];\n    specs.forEach(spec => {\n        if (spec.group === 'optimizer') {\n            optimizerWeights.push({ name: spec.name, tensor: name2Tensor[spec.name] });\n        }\n        else {\n            modelWeights[spec.name] = name2Tensor[spec.name];\n        }\n    });\n    return { modelWeights, optimizerWeights };\n}\n/**\n * A model with a stack of layers, feeding linearly from one to the next.\n *\n * `tf.sequential` is a factory function that creates an instance of\n * `tf.Sequential`.\n *\n * ```js\n *  // Define a model for linear regression.\n *  const model = tf.sequential();\n *  model.add(tf.layers.dense({units: 1, inputShape: [1]}));\n *\n *  // Prepare the model for training: Specify the loss and the optimizer.\n *  model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n *\n *  // Generate some synthetic data for training.\n *  const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);\n *  const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);\n *\n *  // Train the model using the data then do inference on a data point the\n *  // model hasn't seen:\n *  await model.fit(xs, ys);\n *  model.predict(tf.tensor2d([5], [1, 1])).print();\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class Sequential extends LayersModel {\n    constructor(args) {\n        super({ inputs: [], outputs: [] });\n        args = args || {};\n        this.trainable = true;\n        this.built = false;\n        // Set model name.\n        this.name = (args.name != null) ? args.name : getUid('sequential_');\n        // Add to the model any layers passed to the constructor.\n        if (args.layers != null) {\n            for (const layer of args.layers) {\n                this.add(layer);\n            }\n        }\n    }\n    // Helper function to Sequential.add  Throws if the new output shape will be\n    // invalid.\n    checkShape(layer) {\n        const shape = layer.inboundNodes[0].outputTensors[0].shape;\n        if (shape.some(x => x < 0)) {\n            throw new ValueError('Negative dimension size caused by adding layer ' +\n                `${layer.name} with input shape [` +\n                `${layer.inboundNodes[0].inputTensors[0].shape}]`);\n        }\n    }\n    /**\n     * Adds a layer instance on top of the layer stack.\n     *\n     * ```js\n     *  const model = tf.sequential();\n     *  model.add(tf.layers.dense({units: 8, inputShape: [1]}));\n     *  model.add(tf.layers.dense({units: 4, activation: 'relu6'}));\n     *  model.add(tf.layers.dense({units: 1, activation: 'relu6'}));\n     *  // Note that the untrained model is random at this point.\n     *  model.predict(tf.randomNormal([10, 1])).print();\n     * ```\n     * @param layer Layer instance.\n     *\n     * @exception ValueError In case the `layer` argument does not know its\n     * input shape.\n     * @exception ValueError In case the `layer` argument has multiple output\n     *   tensors, or is already connected somewhere else (forbidden in\n     *   `Sequential` models).\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    add(layer) {\n        const isLayerModelInstance = layer instanceof Sequential || layer instanceof LayersModel;\n        let modelLayer;\n        if (isLayerModelInstance) {\n            modelLayer = layer;\n            if (modelLayer.outputs.length !== 1) {\n                throw new ValueError('All layers in a Sequential model ' +\n                    'should have a single output tensor. ' +\n                    'For multi-output layers, ' +\n                    'use the functional API.');\n            }\n            if (modelLayer.inputs.length !== 1) {\n                throw new ValueError('All layers in a Sequential model ' +\n                    'should have a single input tensor. ' +\n                    'For multi-input layers, ' +\n                    'use the functional API.');\n            }\n        }\n        if (this.outputs.length === 0) {\n            // first layer in model: check that it is an input layer\n            if (layer.inboundNodes.length === 0) {\n                // create an input layer\n                if (layer.batchInputShape == null) {\n                    throw new ValueError('The first layer in a Sequential model must ' +\n                        'get an `inputShape` or `batchInputShape` argument.');\n                }\n                // Instantiate the input layer.\n                const x = Input({\n                    batchShape: layer.batchInputShape,\n                    dtype: layer.dtype,\n                    name: layer.name + '_input'\n                });\n                // This will build the current layer and create the node connecting\n                // the current layer to the input layer we just created.\n                layer.apply(x);\n            }\n            if (isLayerModelInstance) {\n                this.outputs = modelLayer.outputs;\n                this.inputs = modelLayer.inputs;\n            }\n            else {\n                if (layer.inboundNodes.length !== 1) {\n                    throw new ValueError('A layer added to a Sequential model must not already be ' +\n                        `connected somewhere else. LayersModel received layer ${layer.name} ` +\n                        `which has ${layer.inboundNodes.length} pre-existing inbound ` +\n                        'connections.');\n                }\n                if (layer.inboundNodes[0].outputTensors.length !== 1) {\n                    throw new ValueError('All layers in a Sequential model ' +\n                        'should have a single output tensor. ' +\n                        'For multi-output layers, ' +\n                        'use the functional API.');\n                }\n                this.checkShape(layer);\n                this.outputs = [layer.inboundNodes[0].outputTensors[0]];\n                this.inputs = getSourceInputs(this.outputs[0]);\n            }\n            this.inboundNodes = [];\n            // We create an input node, which we will keep updated\n            // as we add more layers.\n            // (This call has side effects.)\n            // tslint:disable-next-line:no-unused-expression\n            new Node({\n                outboundLayer: this,\n                inboundLayers: [],\n                nodeIndices: [],\n                tensorIndices: [],\n                inputTensors: this.inputs,\n                outputTensors: this.outputs,\n                // no model-level masking for now\n                inputMasks: generic_utils.pyListRepeat(null, this.inputs.length),\n                outputMasks: [null],\n                inputShapes: this.inputs.map(x => x.shape),\n                outputShapes: this.outputs[0].shape\n            });\n        }\n        else {\n            const outputTensor = layer.apply(this.outputs[0]);\n            if (Array.isArray(outputTensor)) {\n                throw new TypeError('All layers in a Sequential model ' +\n                    'should have a single output tensor. ' +\n                    'For multi-output layers, ' +\n                    'use the functional API.');\n            }\n            this.checkShape(layer);\n            this.outputs = [outputTensor];\n            // update self.inbound_nodes\n            this.inboundNodes[0].outputTensors = this.outputs;\n            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n        }\n        this.layers.push(layer);\n        this.built = false;\n    }\n    /**\n     * Removes the last layer in the model.\n     *\n     * @exception TypeError if there are no layers in the model.\n     */\n    pop() {\n        if (this.layers.length === 0) {\n            throw new TypeError('There are no layers in the model.');\n        }\n        this.layers.pop();\n        if (this.layers.length === 0) {\n            this.outputs = [];\n            this.inboundNodes = [];\n            this.outboundNodes = [];\n        }\n        else {\n            const lastLayerIndex = this.layers.length - 1;\n            this.layers[lastLayerIndex].outboundNodes = [];\n            this.outputs = [this.layers[lastLayerIndex].output];\n            // update self.inbound_nodes\n            this.inboundNodes[0].outputTensors = this.outputs;\n            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n        }\n    }\n    call(inputs, kwargs) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.call(inputs, kwargs);\n    }\n    build(inputShape) {\n        // Call `getExactlyOneShape` without using its return value,\n        // to verify that exactly one input shape is provided.\n        getExactlyOneShape(inputShape);\n        if (this.inputs.length === 0 || this.outputs.length === 0) {\n            throw new TypeError('Sequential model cannot be built: model is empty.' +\n                ' Add some layers first.');\n        }\n        // actually create the model\n        this.model = new LayersModel({\n            inputs: this.inputs,\n            outputs: this.outputs[0],\n            name: this.name + '_model'\n        });\n        this.model.trainable = this.trainable;\n        // mirror model attributes\n        this.supportsMasking = this.model.supportsMasking;\n        // TODO(michaelterry): Add caches\n        this.inputLayers = this.model.inputLayers;\n        this.inputLayersNodeIndices = this.model.inputLayersNodeIndices;\n        this.inputLayersTensorIndices = this.model.inputLayersTensorIndices;\n        this.outputLayers = this.model.outputLayers;\n        this.outputLayersNodeIndices = this.model.outputLayersNodeIndices;\n        this.outputLayersTensorIndices = this.model.outputLayersTensorIndices;\n        this.nodesByDepth = this.model.nodesByDepth;\n        this.containerNodes = this.model.containerNodes;\n        this.outputNames = this.model.outputNames;\n        this.inputNames = this.model.inputNames;\n        // TODO(michaelterry): Add feedInputNames, feedInputs, if needed.\n        // TODO(michaelterry): Add callbackModel if needed.\n        this.built = true;\n    }\n    countParams() {\n        if (!this.built) {\n            this.build();\n        }\n        return super.countParams();\n    }\n    /**\n     * Print a text summary of the Sequential model's layers.\n     *\n     * The summary includes\n     * - Name and type of all layers that comprise the model.\n     * - Output shape(s) of the layers\n     * - Number of weight parameters of each layer\n     * - The total number of trainable and non-trainable parameters of the\n     * model.\n     *\n     * ```js\n     * const model = tf.sequential();\n     * model.add(\n     *     tf.layers.dense({units: 100, inputShape: [10], activation: 'relu'}));\n     * model.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));\n     *\n     * model.summary();\n     * ```\n     *\n     * @param lineLength Custom line length, in number of characters.\n     * @param positions Custom widths of each of the columns, as either\n     *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n     *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n     *   right-most (i.e., ending) position of a column.\n     * @param printFn Custom print function. Can be used to replace the default\n     *   `console.log`. For example, you can use `x => {}` to mute the printed\n     *   messages in the console.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    summary(lineLength, positions, printFn = console.log) {\n        if (!this.built) {\n            this.build();\n        }\n        super.summary(lineLength, positions, printFn);\n    }\n    /**\n     * Sets the weights of the model.\n     *\n     * @param weights Should be a list of Tensors with shapes and types matching\n     *   the output of `model.getWeights()`.\n     */\n    setWeights(weights) {\n        if (this.model == null) {\n            this.build();\n        }\n        this.model.setWeights(weights);\n    }\n    /**\n     * Returns the loss value & metrics values for the model in test mode.\n     *\n     * Loss and metrics are specified during `compile()`, which needs to happen\n     * before calls to `evaluate()`.\n     *\n     * Computation is done in batches.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const result = model.evaluate(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *   batchSize: 4,\n     * });\n     * result.print();\n     * ```\n     *\n     * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple inputs.\n     * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple outputs.\n     * @param args A `ModelEvaluateConfig`, containing optional fields.\n     *\n     * @return `Scalar` test loss (if the model has a single output and no\n     *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n     *   and/or metrics). The attribute `model.metricsNames`\n     *   will give you the display labels for the scalar outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    evaluate(x, y, args = {}) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before being used.');\n        }\n        return this.model.evaluate(x, y, args);\n    }\n    // TODO(cais): Add code snippet below once real dataset objects are\n    //   available.\n    /**\n     * Evaluate model using a dataset object.\n     *\n     * Note: Unlike `evaluate()`, this method is asynchronous (`async`);\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for evaluation. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs. Of the two items in the array, the\n     *   first is the input feature(s) and the second is the output target(s).\n     * @param args A configuration object for the dataset-based evaluation.\n     * @returns Loss and metric values as an Array of `Scalar` objects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async evaluateDataset(dataset, args) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before being used.');\n        }\n        return this.model.evaluateDataset(dataset, args);\n    }\n    /**\n     * Generates output predictions for the input samples.\n     *\n     * Computation is done in batches.\n     *\n     * Note: the \"step\" mode of predict() is currently not supported.\n     *   This is because the TensorFow.js core backend is imperative only.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predict(tf.ones([2, 10])).print();\n     * ```\n     *\n     * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n     *   the model has multiple inputs.\n     * @param conifg A `ModelPredictConfig` object containing optional fields.\n     *\n     * @return `tf.Tensor`(s) of predictions.\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and the model's expectations, or in case a stateful model receives a\n     *   number of samples that is not a multiple of the batch size.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(x, args = {}) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.predict(x, args);\n    }\n    /**\n     * Returns predictions for a single batch of samples.\n     *\n     * @param x: Input samples, as a Tensor, or list of Tensors (if the model\n     *   has multiple inputs).\n     * @return Tensor(s) of predictions\n     */\n    predictOnBatch(x) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.predictOnBatch(x);\n    }\n    /**\n     * See `LayersModel.compile`.\n     *\n     * @param args\n     */\n    compile(args) {\n        this.build();\n        this.model.compile(args);\n        this.optimizer_ = this.model.optimizer;\n        // tslint:disable-next-line:no-any\n        this.isOptimizerOwned = this.model.isOptimizerOwned;\n        this.loss = this.model.loss;\n        this.metrics = this.model.metrics;\n        // TODO(cais): Add this.lossWeights, this.sampleWeightMode,\n        //   this.weightedMetrics, this.targets.\n        this.metricsTensors = this.model.metricsTensors;\n        this.metricsNames = this.model.metricsNames;\n        // TODO(cais): Add sampleWeights.\n    }\n    get optimizer() {\n        return this.model == null ? undefined : this.model.optimizer;\n    }\n    set optimizer(optimizer) {\n        this.model.optimizer = optimizer;\n    }\n    /**\n     * Trains the model for a fixed number of epochs (iterations on a dataset).\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const history = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *   batchSize: 4,\n     *   epochs: 3\n     * });\n     * console.log(history.history.loss[0]);\n     * ```\n     *\n     * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n     * model has multiple inputs. If all inputs in the model are named, you can\n     * also pass a dictionary mapping input names to `tf.Tensor`s.\n     * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n     * the model has multiple outputs. If all outputs in the model are named, you\n     *  can also pass a dictionary mapping output names to `tf.Tensor`s.\n     * @param args  A `ModelFitConfig`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and what the model expects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fit(x, y, args = {}) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before ' +\n                'being used.');\n        }\n        return this.model.fit(x, y, args);\n    }\n    /**\n     * Trains the model using a dataset object.\n     *\n     * ```js\n     * const xArray = [\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     * ];\n     * const yArray = [1, 1, 1, 1];\n     * // Create a dataset from the JavaScript array.\n     * const xDataset = tf.data.array(xArray);\n     * const yDataset = tf.data.array(yArray);\n     * // Zip combines the `x` and `y` Datasets into a single Dataset, the\n     * // iterator of which will return an object containing of two tensors,\n     * // corresponding to `x` and `y`.  The call to `batch(4)` will bundle\n     * // four such samples into a single object, with the same keys now pointing\n     * // to tensors that hold 4 examples, organized along the batch dimension.\n     * // The call to `shuffle(4)` causes each iteration through the dataset to\n     * // happen in a different order.  The size of the shuffle window is 4.\n     * const xyDataset = tf.data.zip({xs: xDataset, ys: yDataset})\n     *     .batch(4)\n     *     .shuffle(4);\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [9]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const history = await model.fitDataset(xyDataset, {\n     *   epochs: 4,\n     *   callbacks: {onEpochEnd: (epoch, logs) => console.log(logs.loss)}\n     * });\n     * ```\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected to\n     *   generate a dataset iterator object, the `next()` method of which is\n     *   expected to produce data batches for evaluation. The return value of the\n     *   `next()` call ought to contain a boolean `done` field and a `value`\n     *   field.\n     *\n     *   The `value` field is expected to be an object of with fields\n     *   `xs` and `ys`, which point to the feature tensor and the target tensor,\n     *   respectively. This case is for models with exactly one input and one\n     *   output (e.g.. a sequential model). For example:\n     *   ```js\n     *   {value: {xs: xsTensor, ys: ysTensor}, done: false}\n     *   ```\n     *\n     *   If the model has multiple inputs, the `xs` field of `value` should\n     *   be an object mapping input names to their respective feature tensors.\n     *   For example:\n     *   ```js\n     *   {\n     *     value: {\n     *       xs: {\n     *         input_1: xsTensor1,\n     *         input_2: xsTensor2\n     *       },\n     *       ys: ysTensor\n     *     },\n     *     done: false\n     *   }\n     *   ```\n     *   If the model has multiple outputs, the `ys` field of `value` should\n     *   be an object mapping output names to their respective target tensors.\n     *   For example:\n     *   ```js\n     *   {\n     *     value: {\n     *       xs: xsTensor,\n     *       ys: {\n     *         output_1: ysTensor1,\n     *         output_2: ysTensor2\n     *       },\n     *     },\n     *     done: false\n     *   }\n     *   ```\n     * @param args A `ModelFitDatasetArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async fitDataset(dataset, args) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before ' +\n                'being used.');\n        }\n        return this.model.fitDataset(dataset, args);\n    }\n    /**\n     * Runs a single gradient update on a single batch of data.\n     *\n     * This method differs from `fit()` and `fitDataset()` in the following\n     * regards:\n     *   - It operates on exactly one batch of data.\n     *   - It returns only the loss and matric values, instead of\n     *     returning the batch-by-batch loss and metric values.\n     *   - It doesn't support fine-grained options such as verbosity and\n     *     callbacks.\n     *\n     * @param x Input data. It could be one of the following:\n     *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n     *     multiple inputs).\n     *   - An Object mapping input names to corresponding `tf.Tensor` (if the\n     *     model has named inputs).\n     * @param y Target darta. It could be either a `tf.Tensor` a multiple\n     *   `tf.Tensor`s. It should be consistent with `x`.\n     * @returns Training loss or losses (in case the model has\n     *   multiple outputs), along with metrics (if any), as numbers.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async trainOnBatch(x, y) {\n        return this.model.trainOnBatch(x, y);\n    }\n    /* See parent class for JsDoc */\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n        let configArray;\n        let extraModelConfig = {};\n        if (config instanceof Array) {\n            if (!(config[0].className != null) ||\n                config[0]['className'] === 'Merge') {\n                throw new ValueError('Legacy serialization format not supported yet.');\n            }\n            configArray = config;\n        }\n        else {\n            util.assert(config['layers'] != null, () => `When the config data for a Sequential model is not an Array, ` +\n                `it must be an Object that contains the 'layers' field.`);\n            configArray = config['layers'];\n            delete config['layers'];\n            extraModelConfig = config;\n        }\n        const model = new cls(extraModelConfig);\n        if (!(model instanceof Sequential)) {\n            throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model}`);\n        }\n        for (const conf of configArray) {\n            const customObjects = undefined;\n            const layer = deserialize(conf, customObjects, fastWeightInit);\n            if (fastWeightInit) {\n                layer.setFastWeightInitDuringBuild(true);\n            }\n            model.add(layer);\n        }\n        return model;\n    }\n    /**\n     * Setter used for force stopping of LayersModel.fit() (i.e., training).\n     *\n     * Example:\n     *\n     * ```js\n     * const model = tf.sequential();\n     * model.add(tf.layers.dense({units: 1, inputShape: [10]}));\n     * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n     * const xs = tf.ones([8, 10]);\n     * const ys = tf.zeros([8, 1]);\n     *\n     * const history = await model.fit(xs, ys, {\n     *   epochs: 10,\n     *   callbacks: {\n     *     onEpochEnd: async (epoch, logs) => {\n     *       if (epoch === 2) {\n     *         model.stopTraining = true;\n     *       }\n     *     }\n     *   }\n     * });\n     *\n     * // There should be only 3 values in the loss array, instead of 10 values,\n     * // due to the stopping after 3 epochs.\n     * console.log(history.history.loss);\n     * ```\n     */\n    set stopTraining(stop) {\n        // TODO(cais): When refactoring to remove the composition pattern happens,\n        // remove this method overriding.\n        if (this.model == null) {\n            throw new ValueError('Cannot set the stopTraining property of a sequential model before ' +\n                'it is compiled.');\n        }\n        this.model.stopTraining = stop;\n    }\n    get stopTraining() {\n        if (this.model == null) {\n            throw new ValueError('Cannot get the stopTraining property of a sequential model before ' +\n                'it is compiled.');\n        }\n        return this.model.stopTraining;\n    }\n    // TODO(cais): Override get trainableWeights() here\n    // tslint:disable-next-line:no-any\n    getConfig() {\n        // NOTE(cais): We override the return type of getConfig() to `any` here,\n        //   because the `Sequential` class is a special case among `Container`\n        //   subtypes in that its getConfig() method returns an Array (not a\n        //   dict).\n        const layers = [];\n        for (const layer of this.layers) {\n            const dict = {};\n            dict['className'] = layer.getClassName();\n            dict['config'] = layer.getConfig();\n            layers.push(dict);\n        }\n        return { name: this.name, layers };\n    }\n}\n/** @nocollapse */\nSequential.className = 'Sequential';\nserialization.registerClass(Sequential);\n//# sourceMappingURL=models.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { CallbackConstructorRegistry } from './base_callbacks';\nimport { Input, } from './engine/input_layer';\nimport { LayersModel } from './engine/training';\nimport { loadLayersModelInternal, Sequential } from './models';\n// TODO(cais): Add doc string to all the public static functions in this\n//   class; include exectuable JavaScript code snippets where applicable\n//   (b/74074458).\n// LayersModel and related factory methods.\n/**\n * A model is a data structure that consists of `Layers` and defines inputs\n * and outputs.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.model` is more generic, supporting an arbitrary graph (without\n * cycles) of layers. `tf.sequential` is less generic and supports only a linear\n * stack of layers.\n *\n * When creating a `tf.LayersModel`, specify its input(s) and output(s). Layers\n * are used to wire input(s) to output(s).\n *\n * For example, the following code snippet defines a model consisting of\n * two `dense` layers, with 10 and 4 units, respectively.\n *\n * ```js\n * // Define input, which has a size of 5 (not including batch dimension).\n * const input = tf.input({shape: [5]});\n *\n * // First dense layer uses relu activation.\n * const denseLayer1 = tf.layers.dense({units: 10, activation: 'relu'});\n * // Second dense layer uses softmax activation.\n * const denseLayer2 = tf.layers.dense({units: 4, activation: 'softmax'});\n *\n * // Obtain the output symbolic tensor by applying the layers on the input.\n * const output = denseLayer2.apply(denseLayer1.apply(input));\n *\n * // Create the model based on the inputs.\n * const model = tf.model({inputs: input, outputs: output});\n *\n * // The model can be used for training, evaluation and prediction.\n * // For example, the following line runs prediction with the model on\n * // some fake data.\n * model.predict(tf.ones([2, 5])).print();\n * ```\n * See also:\n *   `tf.sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function model(args) {\n    return new LayersModel(args);\n}\n/**\n * Creates a `tf.Sequential` model.  A sequential model is any model where the\n * outputs of one layer are the inputs to the next layer, i.e. the model\n * topology is a simple 'stack' of layers, with no branching or skipping.\n *\n * This means that the first layer passed to a `tf.Sequential` model should have\n * a defined input shape. What that means is that it should have received an\n * `inputShape` or `batchInputShape` argument, or for some type of layers\n * (recurrent, Dense...) an `inputDim` argument.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.sequential` is less generic, supporting only a linear stack of layers.\n * `tf.model` is more generic and supports an arbitrary graph (without\n * cycles) of layers.\n *\n * Examples:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have an input shape defined.\n * model.add(tf.layers.dense({units: 32, inputShape: [50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output, which equals\n * // `[null, 4]`. The 1st dimension is the undetermined batch dimension; the\n * // 2nd is the output size of the model's last layer.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * It is also possible to specify a batch size (with potentially undetermined\n * batch dimension, denoted by \"null\") for the first layer using the\n * `batchInputShape` key. The following example is equivalent to the above:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have a defined input shape\n * model.add(tf.layers.dense({units: 32, batchInputShape: [null, 50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * You can also use an `Array` of already-constructed `Layer`s to create\n * a `tf.Sequential` model:\n *\n * ```js\n * const model = tf.sequential({\n *   layers: [tf.layers.dense({units: 32, inputShape: [50]}),\n *            tf.layers.dense({units: 4})]\n * });\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function sequential(config) {\n    return new Sequential(config);\n}\n/**\n * Load a model composed of Layer objects, including its topology and optionally\n * weights. See the Tutorial named \"How to import a Keras Model\" for usage\n * examples.\n *\n * This method is applicable to:\n *\n * 1. Models created with the `tf.layers.*`, `tf.sequential`, and\n * `tf.model` APIs of TensorFlow.js and later saved with the\n * `tf.LayersModel.save` method.\n * 2. Models converted from Keras or TensorFlow tf.keras using the\n * [tensorflowjs_converter](https://github.com/tensorflow/tfjs/tree/master/tfjs-converter).\n *\n * This mode is *not* applicable to TensorFlow `SavedModel`s or their converted\n * forms. For those models, use `tf.loadGraphModel`.\n *\n * Example 1. Load a model from an HTTP server.\n *\n * ```js\n * const model = await tf.loadLayersModel(\n *     'https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');\n * model.summary();\n * ```\n *\n * Example 2: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 4. Load a model from user-selected files from HTML\n * [file input\n * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).\n *\n * ```js\n * // Note: this code snippet will not work without the HTML elements in the\n * //   page\n * const jsonUpload = document.getElementById('json-upload');\n * const weightsUpload = document.getElementById('weights-upload');\n *\n * const model = await tf.loadLayersModel(\n *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));\n * ```\n *\n * @param pathOrIOHandler Can be either of the two formats\n *   1. A string path to the `ModelAndWeightsConfig` JSON describing\n *      the model in the canonical TensorFlow.js format. For file://\n *      (tfjs-node-only), http:// and https:// schemas, the path can be\n *      either absolute or relative.\n *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`\n *      method.\n * @param options Optional configuration arguments for the model loading,\n *   including:\n *   - `strict`: Require that the provided weights exactly match those required\n *     by the layers.  Default true.  Passing false means that both extra\n *     weights and missing weights will be silently ignored.\n *   - `onProgress`: A function of the signature `(fraction: number) => void',\n *     that can be used as the progress callback for the model loading.\n * @returns A `Promise` of `tf.LayersModel`, with the topology and weights\n *     loaded.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport function loadLayersModel(pathOrIOHandler, options) {\n    if (options == null) {\n        options = {};\n    }\n    return loadLayersModelInternal(pathOrIOHandler, options);\n}\n/**\n * Used to instantiate an input to a model as a `tf.SymbolicTensor`.\n *\n * Users should call the `input` factory function for\n * consistency with other generator functions.\n *\n * Example:\n *\n * ```js\n * // Defines a simple logistic regression model with 32 dimensional input\n * // and 3 dimensional output.\n * const x = tf.input({shape: [32]});\n * const y = tf.layers.dense({units: 3, activation: 'softmax'}).apply(x);\n * const model = tf.model({inputs: x, outputs: y});\n * model.predict(tf.ones([2, 32])).print();\n * ```\n *\n * Note: `input` is only necessary when using `model`. When using\n * `sequential`, specify `inputShape` for the first layer or use `inputLayer`\n * as the first layer.\n *\n * @doc {heading: 'Models', subheading: 'Inputs'}\n */\nexport function input(config) {\n    return Input(config);\n}\nexport function registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n    CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);\n}\n//# sourceMappingURL=exports.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// Layer activation functions\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { deserializeKerasObject } from './utils/generic_utils';\n/**\n * Base class for Activations.\n *\n * Special note: due to cross-language compatibility reasons, the\n * static readonly className field in this family of classes must be set to\n * the initialLowerCamelCase name of the activation.\n */\nexport class Activation extends serialization.Serializable {\n    getConfig() {\n        return {};\n    }\n}\n/**\n * Exponential linear unit (ELU).\n * Reference: https://arxiv.org/abs/1511.07289\n */\nexport class Elu extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x: Input.\n     * @param alpha: Scaling factor the negative section.\n     * @return Output of the ELU activation.\n     */\n    apply(x, alpha = 1) {\n        return K.elu(x, alpha);\n    }\n}\n/** @nocollapse */\nElu.className = 'elu';\nserialization.registerClass(Elu);\n/**\n * Scaled Exponential Linear Unit. (Klambauer et al., 2017).\n * Reference: Self-Normalizing Neural Networks, https://arxiv.org/abs/1706.02515\n * Notes:\n *   - To be used together with the initialization \"lecunNormal\".\n *   - To be used together with the dropout variant \"AlphaDropout\".\n */\nexport class Selu extends Activation {\n    apply(x) {\n        return tfc.selu(x);\n    }\n}\n/** @nocollapse */\nSelu.className = 'selu';\nserialization.registerClass(Selu);\n/**\n *  Rectified linear unit\n */\nexport class Relu extends Activation {\n    apply(x) {\n        return tfc.relu(x);\n    }\n}\n/** @nocollapse */\nRelu.className = 'relu';\nserialization.registerClass(Relu);\n/**\n * Rectified linear unit activation maxing out at 6.0.\n */\nexport class Relu6 extends Activation {\n    apply(x) {\n        return tidy(() => tfc.minimum(6.0, tfc.relu(x)));\n    }\n}\n/** @nocollapse */\nRelu6.className = 'relu6';\nserialization.registerClass(Relu6);\n//* Linear activation (no-op) */\nexport class Linear extends Activation {\n    apply(x) {\n        return x;\n    }\n}\n/** @nocollapse */\nLinear.className = 'linear';\nserialization.registerClass(Linear);\n/**\n * Sigmoid activation function.\n */\nexport class Sigmoid extends Activation {\n    apply(x) {\n        return tfc.sigmoid(x);\n    }\n}\n/** @nocollapse */\nSigmoid.className = 'sigmoid';\nserialization.registerClass(Sigmoid);\n/**\n * Segment-wise linear approximation of sigmoid.\n */\nexport class HardSigmoid extends Activation {\n    apply(x) {\n        return K.hardSigmoid(x);\n    }\n}\n/** @nocollapse */\nHardSigmoid.className = 'hardSigmoid';\nserialization.registerClass(HardSigmoid);\n/**\n * Softplus activation function.\n */\nexport class Softplus extends Activation {\n    apply(x) {\n        return tfc.softplus(x);\n    }\n}\n/** @nocollapse */\nSoftplus.className = 'softplus';\nserialization.registerClass(Softplus);\n/**\n * Softsign activation function.\n */\nexport class Softsign extends Activation {\n    apply(x) {\n        return K.softsign(x);\n    }\n}\n/** @nocollapse */\nSoftsign.className = 'softsign';\nserialization.registerClass(Softsign);\n/**\n * Hyperbolic tangent function.\n */\nexport class Tanh extends Activation {\n    apply(x) {\n        return tfc.tanh(x);\n    }\n}\n/** @nocollapse */\nTanh.className = 'tanh';\nserialization.registerClass(Tanh);\n/**\n * Softmax activation function\n */\nexport class Softmax extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x Tensor.\n     * @param axis Integer, axis along which the softmax normalization is applied.\n     * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n     * an error.\n     *\n     * @returns a Tensor of the same shape as x\n     *\n     * @throws ValueError: In case `dim(x) < 2`.\n     */\n    apply(x, axis = (-1)) {\n        return tfc.softmax(x, axis);\n    }\n}\n/** @nocollapse */\nSoftmax.className = 'softmax';\nserialization.registerClass(Softmax);\n/**\n * Log softmax activation function\n */\nexport class LogSoftmax extends Activation {\n    /**\n     * Calculate the activation function of log softmax:\n     * log( exp(x_i) / sum(exp(x)) )\n     *\n     * @param x Tensor.\n     * @param axis Integer, axis along which the softmax normalization is applied.\n     * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n     * an error.\n     *\n     * @returns a Tensor of the same shape as x\n     *\n     * @throws ValueError: In case `dim(x) < 2`.\n     */\n    apply(x, axis = (-1)) {\n        return tfc.logSoftmax(x, axis);\n    }\n}\n/** @nocollapse */\nLogSoftmax.className = 'logSoftmax';\nserialization.registerClass(LogSoftmax);\n/**\n * Swish activation function\n */\nexport class Swish extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x Tensor.\n     * @param alpha Scaling factor for the sigmoid function.\n     * @returns a Tensor of the same shape as x\n     */\n    apply(x, alpha = 1) {\n        return tidy(() => tfc.sigmoid(x.mul(alpha)).mul(x));\n    }\n}\n/** @nocollapse */\nSwish.className = 'swish';\nserialization.registerClass(Swish);\nexport function serializeActivation(activation) {\n    return activation.getClassName();\n}\nexport function deserializeActivation(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'activation');\n}\nexport function getActivation(identifier) {\n    if (identifier == null) {\n        const config = {};\n        config['className'] = 'linear';\n        config['config'] = {};\n        return deserializeActivation(config);\n    }\n    if (typeof identifier === 'string') {\n        const config = {};\n        config['className'] = identifier;\n        config['config'] = {};\n        return deserializeActivation(config);\n    }\n    else if (identifier instanceof Activation) {\n        return identifier;\n    }\n    else {\n        return deserializeActivation(identifier);\n    }\n}\n//# sourceMappingURL=activations.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* original source: keras/regularizers.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { abs, add, serialization, sum, tidy, zeros } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\nfunction assertObjectArgs(args) {\n    if (args != null && typeof args !== 'object') {\n        throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an ` +\n            `object, but received: ${args}`);\n    }\n}\n/**\n * Regularizer base class.\n */\nexport class Regularizer extends serialization.Serializable {\n}\nexport class L1L2 extends Regularizer {\n    constructor(args) {\n        super();\n        assertObjectArgs(args);\n        this.l1 = args == null || args.l1 == null ? 0.01 : args.l1;\n        this.l2 = args == null || args.l2 == null ? 0.01 : args.l2;\n        this.hasL1 = this.l1 !== 0;\n        this.hasL2 = this.l2 !== 0;\n    }\n    /**\n     * Porting note: Renamed from __call__.\n     * @param x Variable of which to calculate the regularization score.\n     */\n    apply(x) {\n        return tidy(() => {\n            let regularization = zeros([1]);\n            if (this.hasL1) {\n                regularization = add(regularization, sum(tfc.mul(this.l1, abs(x))));\n            }\n            if (this.hasL2) {\n                regularization =\n                    add(regularization, sum(tfc.mul(this.l2, K.square(x))));\n            }\n            return regularization.asScalar();\n        });\n    }\n    getConfig() {\n        return { 'l1': this.l1, 'l2': this.l2 };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls({ l1: config['l1'], l2: config['l2'] });\n    }\n}\n/** @nocollapse */\nL1L2.className = 'L1L2';\nserialization.registerClass(L1L2);\nexport function l1(args) {\n    assertObjectArgs(args);\n    return new L1L2({ l1: args != null ? args.l1 : null, l2: 0 });\n}\nexport function l2(args) {\n    assertObjectArgs(args);\n    return new L1L2({ l2: args != null ? args.l2 : null, l1: 0 });\n}\n// Maps the JavaScript-like identifier keys to the corresponding keras symbols.\nexport const REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'l1l2': 'L1L2'\n};\nexport function serializeRegularizer(constraint) {\n    return serializeKerasObject(constraint);\n}\nexport function deserializeRegularizer(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'regularizer');\n}\nexport function getRegularizer(identifier) {\n    if (identifier == null) {\n        return null;\n    }\n    if (typeof identifier === 'string') {\n        const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        const config = { className, config: {} };\n        return deserializeRegularizer(config);\n    }\n    else if (identifier instanceof Regularizer) {\n        return identifier;\n    }\n    else {\n        return deserializeRegularizer(identifier);\n    }\n}\n//# sourceMappingURL=regularizers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n *  Advanced activation layers.\n */\nimport { clipByValue, elu, leakyRelu, prelu, relu, serialization } from '@tensorflow/tfjs-core';\nimport { Softmax as softmaxActivation } from '../activations';\nimport { cast } from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class ReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.supportsMasking = true;\n        if (args != null) {\n            this.maxValue = args.maxValue;\n        }\n    }\n    call(inputs, kwargs) {\n        inputs = getExactlyOneTensor(inputs);\n        let output = relu(inputs);\n        if (this.maxValue != null) {\n            output = clipByValue(output, 0, this.maxValue);\n        }\n        return output;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { maxValue: this.maxValue };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nReLU.className = 'ReLU';\nserialization.registerClass(ReLU);\nexport class LeakyReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA = 0.3;\n        if (args == null) {\n            args = {};\n        }\n        this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return leakyRelu(x, this.alpha);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { alpha: this.alpha };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nLeakyReLU.className = 'LeakyReLU';\nserialization.registerClass(LeakyReLU);\nexport class PReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA_INITIALIZER = 'zeros';\n        if (args == null) {\n            args = {};\n        }\n        this.supportsMasking = true;\n        this.alphaInitializer =\n            getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER);\n        this.alphaRegularizer = getRegularizer(args.alphaRegularizer);\n        this.alphaConstraint = getConstraint(args.alphaConstraint);\n        if (args.sharedAxes == null) {\n            this.sharedAxes = null;\n        }\n        else if (Array.isArray(args.sharedAxes)) {\n            this.sharedAxes = args.sharedAxes;\n        }\n        else if (typeof args.sharedAxes === 'number') {\n            this.sharedAxes = [args.sharedAxes];\n        }\n        else {\n            throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, ` +\n                `but got ${args.sharedAxes}`);\n        }\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const paramShape = inputShape.slice(1);\n        if (this.sharedAxes != null) {\n            for (const i of this.sharedAxes) {\n                paramShape[i - 1] = 1;\n            }\n        }\n        this.alpha = this.addWeight('alpha', paramShape, 'float32', this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);\n        // Set input spec.\n        const axes = {};\n        if (this.sharedAxes != null) {\n            for (let i = 1; i < inputShape.length; ++i) {\n                axes[i] = inputShape[i];\n            }\n        }\n        this.inputSpec = [new InputSpec({\n                ndim: inputShape.length,\n                axes,\n            })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        inputs = getExactlyOneTensor(inputs);\n        return prelu(inputs, this.alpha.read());\n    }\n    getConfig() {\n        const config = {\n            alphaInitializer: serializeInitializer(this.alphaInitializer),\n            alphaRegularizer: serializeRegularizer(this.alphaRegularizer),\n            alphaConstraint: serializeConstraint(this.alphaConstraint),\n            sharedAxes: this.sharedAxes\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nPReLU.className = 'PReLU';\nserialization.registerClass(PReLU);\nexport class ELU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) {\n            throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ` +\n                `ELU layer yet.`);\n        }\n        this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return elu(x);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { alpha: this.alpha };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nELU.className = 'ELU';\nserialization.registerClass(ELU);\nexport class ThresholdedReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_THETA = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return x.mul(cast(x.greater(this.theta), 'float32'));\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { theta: this.theta };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nThresholdedReLU.className = 'ThresholdedReLU';\nserialization.registerClass(ThresholdedReLU);\nexport class Softmax extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_AXIS = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        this.softmax = new softmaxActivation().apply;\n        this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return this.softmax(x, this.axis);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { axis: this.axis };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nSoftmax.className = 'Softmax';\nserialization.registerClass(Softmax);\n//# sourceMappingURL=advanced_activations.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { ValueError } from '../errors';\nimport { pyListRepeat } from './generic_utils';\nimport { isInteger, max } from './math_utils';\n/**\n * Transforms a single number of array of numbers into an array of numbers.\n * @param value\n * @param n: The size of the tuple to be returned.\n * @param name: Name of the parameter, used for generating error messages.\n * @returns An array of numbers.\n */\nexport function normalizeArray(value, n, name) {\n    if (typeof value === 'number') {\n        return pyListRepeat(value, n);\n    }\n    else {\n        if (value.length !== n) {\n            throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers.` +\n                ` Received: ${value.length} elements.`);\n        }\n        for (let i = 0; i < n; ++i) {\n            const singleValue = value[i];\n            if (!isInteger(singleValue)) {\n                throw new ValueError(`The ${name} argument must be an integer or tuple of ${n}` +\n                    ` integers. Received: ${JSON.stringify(value)} including a` +\n                    ` non-integer number ${singleValue}`);\n            }\n        }\n        return value;\n    }\n}\n/**\n * Determines output length of a convolution given input length.\n * @param inputLength\n * @param filterSize\n * @param padding\n * @param stride\n * @param dilation: dilation rate.\n */\nexport function convOutputLength(inputLength, filterSize, padding, stride, dilation = 1) {\n    if (inputLength == null) {\n        return inputLength;\n    }\n    const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1);\n    let outputLength;\n    if (padding === 'same') {\n        outputLength = inputLength;\n    }\n    else { // VALID\n        outputLength = inputLength - dilatedFilterSize + 1;\n    }\n    return Math.floor((outputLength + stride - 1) / stride);\n}\nexport function deconvLength(dimSize, strideSize, kernelSize, padding) {\n    if (dimSize == null) {\n        return null;\n    }\n    if (padding === 'valid') {\n        dimSize = dimSize * strideSize + max([kernelSize - strideSize, 0]);\n    }\n    else if (padding === 'same') {\n        dimSize = dimSize * strideSize;\n    }\n    else {\n        throw new ValueError(`Unsupport padding mode: ${padding}.`);\n    }\n    return dimSize;\n}\n//# sourceMappingURL=conv_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Convolutional Layers\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { convOutputLength, deconvLength, normalizeArray } from '../utils/conv_utils';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Transpose and cast the input before the conv2d.\n * @param x Input image tensor.\n * @param dataFormat\n */\nexport function preprocessConv2DInput(x, dataFormat) {\n    // TODO(cais): Cast type to float32 if not.\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        if (dataFormat === 'channelsFirst') {\n            return tfc.transpose(x, [0, 2, 3, 1]); // NCHW -> NHWC.\n        }\n        else {\n            return x;\n        }\n    });\n}\n/**\n * Transpose and cast the input before the conv3d.\n * @param x Input image tensor.\n * @param dataFormat\n */\nexport function preprocessConv3DInput(x, dataFormat) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        if (dataFormat === 'channelsFirst') {\n            return tfc.transpose(x, [0, 2, 3, 4, 1]); // NCDHW -> NDHWC.\n        }\n        else {\n            return x;\n        }\n    });\n}\n/**\n * 1D-convolution with bias added.\n *\n * Porting Note: This function does not exist in the Python Keras backend.\n *   It is exactly the same as `conv2d`, except the added `bias`.\n *\n * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.\n * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.\n * @param bias Bias, rank-3, of shape `[outDepth]`.\n * @param strides\n * @param padding Padding mode.\n * @param dataFormat Data format.\n * @param dilationRate\n * @returns The result of the 1D convolution.\n * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.\n */\nexport function conv1dWithBias(x, kernel, bias, strides = 1, padding = 'valid', dataFormat, dilationRate = 1) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        // Check the ranks of x, kernel and bias.\n        if (x.shape.length !== 3) {\n            throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ` +\n                `${x.shape.length} instead.`);\n        }\n        if (kernel.shape.length !== 3) {\n            throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ` +\n                `${kernel.shape.length} instead`);\n        }\n        if (bias != null && bias.shape.length !== 1) {\n            throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ` +\n                `${kernel.shape.length} instead`);\n        }\n        // TODO(cais): Support CAUSAL padding mode.\n        if (dataFormat === 'channelsFirst') {\n            x = tfc.transpose(x, [0, 2, 1]); // NCW -> NWC.\n        }\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv1dWithBias is not ' +\n                'implemented yet.');\n        }\n        let y = tfc.conv1d(x, kernel, strides, padding === 'same' ? 'same' : 'valid', 'NWC', dilationRate);\n        if (bias != null) {\n            y = K.biasAdd(y, bias);\n        }\n        return y;\n    });\n}\n/**\n * 1D-convolution.\n *\n * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.\n * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.s\n * @param strides\n * @param padding Padding mode.\n * @param dataFormat Data format.\n * @param dilationRate\n * @returns The result of the 1D convolution.\n * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.\n */\nexport function conv1d(x, kernel, strides = 1, padding = 'valid', dataFormat, dilationRate = 1) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv1dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 2D Convolution\n * @param x\n * @param kernel kernel of the convolution.\n * @param strides strides array.\n * @param padding padding mode. Default to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param dilationRate dilation rate array.\n * @returns Result of the 2D pooling.\n */\nexport function conv2d(x, kernel, strides = [1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv2dWithBiasActivation(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 2D Convolution with an added bias and optional activation.\n * Note: This function does not exist in the Python Keras Backend. This function\n * is exactly the same as `conv2d`, except the added `bias`.\n */\nexport function conv2dWithBiasActivation(x, kernel, bias, strides = [1, 1], padding = 'valid', dataFormat, dilationRate, activation = null) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        if (x.rank !== 3 && x.rank !== 4) {\n            throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, ` +\n                `but received ${x.rank}.`);\n        }\n        if (kernel.rank !== 3 && kernel.rank !== 4) {\n            throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, ` +\n                `but received ${x.rank}.`);\n        }\n        let y = preprocessConv2DInput(x, dataFormat);\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv1dWithBias is not ' +\n                'implemented yet.');\n        }\n        y = tfc.fused.conv2d({\n            x: y,\n            filter: kernel,\n            strides: strides,\n            pad: padding === 'same' ? 'same' : 'valid',\n            dilations: dilationRate,\n            dataFormat: 'NHWC',\n            bias,\n            activation\n        });\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]);\n        }\n        return y;\n    });\n}\n/**\n * 3D Convolution.\n * @param x\n * @param kernel kernel of the convolution.\n * @param strides strides array.\n * @param padding padding mode. Default to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param dilationRate dilation rate array.\n * @returns Result of the 3D convolution.\n */\nexport function conv3d(x, kernel, strides = [1, 1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv3dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 3D Convolution with an added bias.\n * Note: This function does not exist in the Python Keras Backend. This function\n * is exactly the same as `conv3d`, except the added `bias`.\n */\nexport function conv3dWithBias(x, kernel, bias, strides = [1, 1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        if (x.rank !== 4 && x.rank !== 5) {\n            throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ` +\n                `${x.rank}.`);\n        }\n        if (kernel.rank !== 4 && kernel.rank !== 5) {\n            throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ` +\n                `${x.rank}.`);\n        }\n        let y = preprocessConv3DInput(x, dataFormat);\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv3dWithBias is not ' +\n                'implemented yet.');\n        }\n        y = tfc.conv3d(y, kernel, strides, padding === 'same' ? 'same' : 'valid', 'NDHWC', dilationRate);\n        if (bias != null) {\n            y = K.biasAdd(y, bias);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 4, 1, 2, 3]);\n        }\n        return y;\n    });\n}\n/**\n * Abstract convolution layer.\n */\nexport class BaseConv extends Layer {\n    constructor(rank, args) {\n        super(args);\n        this.bias = null;\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        BaseConv.verifyArgs(args);\n        this.rank = rank;\n        generic_utils.assertPositiveInteger(this.rank, 'rank');\n        if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) {\n            throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is ` +\n                `not implemented yet.`);\n        }\n        this.kernelSize = normalizeArray(args.kernelSize, rank, 'kernelSize');\n        this.strides = normalizeArray(args.strides == null ? 1 : args.strides, rank, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        checkPaddingMode(this.padding);\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        this.activation = getActivation(args.activation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.dilationRate = normalizeArray(args.dilationRate == null ? 1 : args.dilationRate, rank, 'dilationRate');\n        if (this.rank === 1 &&\n            (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) {\n            throw new ValueError(`dilationRate must be a number or an array of a single number ` +\n                `for 1D convolution, but received ` +\n                `${JSON.stringify(this.dilationRate)}`);\n        }\n        else if (this.rank === 2) {\n            if (typeof this.dilationRate === 'number') {\n                this.dilationRate = [this.dilationRate, this.dilationRate];\n            }\n            else if (this.dilationRate.length !== 2) {\n                throw new ValueError(`dilationRate must be a number or array of two numbers for 2D ` +\n                    `convolution, but received ${JSON.stringify(this.dilationRate)}`);\n            }\n        }\n        else if (this.rank === 3) {\n            if (typeof this.dilationRate === 'number') {\n                this.dilationRate =\n                    [this.dilationRate, this.dilationRate, this.dilationRate];\n            }\n            else if (this.dilationRate.length !== 3) {\n                throw new ValueError(`dilationRate must be a number or array of three numbers for 3D ` +\n                    `convolution, but received ${JSON.stringify(this.dilationRate)}`);\n            }\n        }\n    }\n    static verifyArgs(args) {\n        // Check config.kernelSize type and shape.\n        generic_utils.assert('kernelSize' in args, `required key 'kernelSize' not in config`);\n        if (typeof args.kernelSize !== 'number' &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 3)) {\n            throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with ` +\n                `length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n    getConfig() {\n        const config = {\n            kernelSize: this.kernelSize,\n            strides: this.strides,\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n            dilationRate: this.dilationRate,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            biasConstraint: serializeConstraint(this.biasConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/**\n * Abstract nD convolution layer.  Ancestor of convolution layers which reduce\n * across channels, i.e., Conv1D and Conv2D, but not DepthwiseConv2D.\n */\nexport class Conv extends BaseConv {\n    constructor(rank, args) {\n        super(rank, args);\n        this.kernel = null;\n        Conv.verifyArgs(args);\n        this.filters = args.filters;\n        generic_utils.assertPositiveInteger(this.filters, 'filters');\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError(`The channel dimension of the input should be defined. ` +\n                `Found ${inputShape[channelAxis]}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const kernelShape = this.kernelSize.concat([inputDim, this.filters]);\n        this.kernel = this.addWeight('kernel', kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        this.inputSpec = [{ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } }];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let outputs;\n            const biasValue = this.bias == null ? null : this.bias.read();\n            const fusedActivationName = generic_utils.mapActivationToFusedKernel(this.activation.getClassName());\n            if (fusedActivationName != null && this.rank === 2) {\n                outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate, fusedActivationName);\n            }\n            else {\n                if (this.rank === 1) {\n                    outputs = conv1dWithBias(inputs, this.kernel.read(), biasValue, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);\n                }\n                else if (this.rank === 2) {\n                    // TODO(cais): Move up to constructor.\n                    outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n                }\n                else if (this.rank === 3) {\n                    outputs = conv3dWithBias(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n                }\n                else {\n                    throw new NotImplementedError('convolutions greater than 3D are not implemented yet.');\n                }\n                if (this.activation != null) {\n                    outputs = this.activation.apply(outputs);\n                }\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const newSpace = [];\n        const space = (this.dataFormat === 'channelsLast') ?\n            inputShape.slice(1, inputShape.length - 1) :\n            inputShape.slice(2);\n        for (let i = 0; i < space.length; ++i) {\n            const newDim = convOutputLength(space[i], this.kernelSize[i], this.padding, this.strides[i], typeof this.dilationRate === 'number' ? this.dilationRate :\n                this.dilationRate[i]);\n            newSpace.push(newDim);\n        }\n        let outputShape = [inputShape[0]];\n        if (this.dataFormat === 'channelsLast') {\n            outputShape = outputShape.concat(newSpace);\n            outputShape.push(this.filters);\n        }\n        else {\n            outputShape.push(this.filters);\n            outputShape = outputShape.concat(newSpace);\n        }\n        return outputShape;\n    }\n    getConfig() {\n        const config = {\n            filters: this.filters,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    static verifyArgs(args) {\n        // Check config.filters type, shape, and value.\n        if (!('filters' in args) || typeof args.filters !== 'number' ||\n            args.filters < 1) {\n            throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 ` +\n                `but got ${JSON.stringify(args.filters)}`);\n        }\n    }\n}\nexport class Conv2D extends Conv {\n    constructor(args) {\n        super(2, args);\n        Conv2D.verifyArgs(args);\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if ((typeof args.kernelSize !== 'number') &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 2)) {\n            throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with ` +\n                `length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n}\n/** @nocollapse */\nConv2D.className = 'Conv2D';\nserialization.registerClass(Conv2D);\nexport class Conv3D extends Conv {\n    constructor(args) {\n        super(3, args);\n        Conv3D.verifyArgs(args);\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if (typeof args.kernelSize !== 'number') {\n            if (!(Array.isArray(args.kernelSize) &&\n                (args.kernelSize.length === 1 || args.kernelSize.length === 3))) {\n                throw new ValueError(`Conv3D expects config.kernelSize to be number or` +\n                    ` [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`);\n            }\n        }\n    }\n}\n/** @nocollapse */\nConv3D.className = 'Conv3D';\nserialization.registerClass(Conv3D);\nexport class Conv2DTranspose extends Conv2D {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n        if (this.padding !== 'same' && this.padding !== 'valid') {\n            throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' ` +\n                `and 'valid', but received padding mode ${this.padding}`);\n        }\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length !== 4) {\n            throw new ValueError('Input should have rank 4; Received input shape: ' +\n                JSON.stringify(inputShape));\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError('The channel dimension of the inputs should be defined. ' +\n                'Found `None`.');\n        }\n        const inputDim = inputShape[channelAxis];\n        const kernelShape = this.kernelSize.concat([this.filters, inputDim]);\n        this.kernel = this.addWeight('kernel', kernelShape, 'float32', this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], 'float32', this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        // Set input spec.\n        this.inputSpec =\n            [new InputSpec({ ndim: 4, axes: { [channelAxis]: inputDim } })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            let input = getExactlyOneTensor(inputs);\n            if (input.shape.length !== 4) {\n                throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but ` +\n                    `received a tensor of rank-${input.shape.length}`);\n            }\n            const inputShape = input.shape;\n            const batchSize = inputShape[0];\n            let hAxis;\n            let wAxis;\n            if (this.dataFormat === 'channelsFirst') {\n                hAxis = 2;\n                wAxis = 3;\n            }\n            else {\n                hAxis = 1;\n                wAxis = 2;\n            }\n            const height = inputShape[hAxis];\n            const width = inputShape[wAxis];\n            const kernelH = this.kernelSize[0];\n            const kernelW = this.kernelSize[1];\n            const strideH = this.strides[0];\n            const strideW = this.strides[1];\n            // Infer the dynamic output shape.\n            const outHeight = deconvLength(height, strideH, kernelH, this.padding);\n            const outWidth = deconvLength(width, strideW, kernelW, this.padding);\n            // Porting Note: We don't branch based on `this.dataFormat` here,\n            // because\n            //   the tjfs-core function `conv2dTranspose` called below always\n            //   assumes channelsLast.\n            const outputShape = [batchSize, outHeight, outWidth, this.filters];\n            if (this.dataFormat !== 'channelsLast') {\n                input = tfc.transpose(input, [0, 2, 3, 1]);\n            }\n            let outputs = tfc.conv2dTranspose(input, this.kernel.read(), outputShape, this.strides, this.padding);\n            if (this.dataFormat !== 'channelsLast') {\n                outputs = tfc.transpose(outputs, [0, 3, 1, 2]);\n            }\n            if (this.bias != null) {\n                outputs =\n                    K.biasAdd(outputs, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                outputs = this.activation.apply(outputs);\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        let channelAxis;\n        let heightAxis;\n        let widthAxis;\n        if (this.dataFormat === 'channelsFirst') {\n            channelAxis = 1;\n            heightAxis = 2;\n            widthAxis = 3;\n        }\n        else {\n            channelAxis = 3;\n            heightAxis = 1;\n            widthAxis = 2;\n        }\n        const kernelH = this.kernelSize[0];\n        const kernelW = this.kernelSize[1];\n        const strideH = this.strides[0];\n        const strideW = this.strides[1];\n        outputShape[channelAxis] = this.filters;\n        outputShape[heightAxis] =\n            deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);\n        outputShape[widthAxis] =\n            deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);\n        return outputShape;\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['dilationRate'];\n        return config;\n    }\n}\n/** @nocollapse */\nConv2DTranspose.className = 'Conv2DTranspose';\nserialization.registerClass(Conv2DTranspose);\nexport class SeparableConv extends Conv {\n    constructor(rank, config) {\n        super(rank, config);\n        this.DEFAULT_DEPTHWISE_INITIALIZER = 'glorotUniform';\n        this.DEFAULT_POINTWISE_INITIALIZER = 'glorotUniform';\n        this.depthwiseKernel = null;\n        this.pointwiseKernel = null;\n        if (config.filters == null) {\n            throw new ValueError('The `filters` configuration field is required by SeparableConv, ' +\n                'but is unspecified.');\n        }\n        if (config.kernelInitializer != null || config.kernelRegularizer != null ||\n            config.kernelConstraint != null) {\n            throw new ValueError('Fields kernelInitializer, kernelRegularizer and kernelConstraint ' +\n                'are invalid for SeparableConv2D. Use depthwiseInitializer, ' +\n                'depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, ' +\n                'pointwiseRegularizer and pointwiseConstraint instead.');\n        }\n        if (config.padding != null && config.padding !== 'same' &&\n            config.padding !== 'valid') {\n            throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: ` +\n                `'same' and 'valid', but received ${JSON.stringify(config.padding)}`);\n        }\n        this.depthMultiplier =\n            config.depthMultiplier == null ? 1 : config.depthMultiplier;\n        this.depthwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER);\n        this.depthwiseRegularizer = getRegularizer(config.depthwiseRegularizer);\n        this.depthwiseConstraint = getConstraint(config.depthwiseConstraint);\n        this.pointwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER);\n        this.pointwiseRegularizer = getRegularizer(config.pointwiseRegularizer);\n        this.pointwiseConstraint = getConstraint(config.pointwiseConstraint);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < this.rank + 2) {\n            throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ` +\n                `${this.rank + 2}, but received input shape: ` +\n                `${JSON.stringify(inputShape)}`);\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n            throw new ValueError(`The channel dimension of the inputs should be defined, ` +\n                `but found ${JSON.stringify(inputShape[channelAxis])}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]);\n        const pointwiseKernelShape = [];\n        for (let i = 0; i < this.rank; ++i) {\n            pointwiseKernelShape.push(1);\n        }\n        pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);\n        const trainable = true;\n        this.depthwiseKernel = this.addWeight('depthwise_kernel', depthwiseKernelShape, 'float32', this.depthwiseInitializer, this.depthwiseRegularizer, trainable, this.depthwiseConstraint);\n        this.pointwiseKernel = this.addWeight('pointwise_kernel', pointwiseKernelShape, 'float32', this.pointwiseInitializer, this.pointwiseRegularizer, trainable, this.pointwiseConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], 'float32', this.biasInitializer, this.biasRegularizer, trainable, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.inputSpec =\n            [new InputSpec({ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let output;\n            if (this.rank === 1) {\n                throw new NotImplementedError('1D separable convolution is not implemented yet.');\n            }\n            else if (this.rank === 2) {\n                if (this.dataFormat === 'channelsFirst') {\n                    inputs = tfc.transpose(inputs, [0, 2, 3, 1]); // NCHW -> NHWC.\n                }\n                output = tfc.separableConv2d(inputs, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, 'NHWC');\n            }\n            if (this.useBias) {\n                output = K.biasAdd(output, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                output = this.activation.apply(output);\n            }\n            if (this.dataFormat === 'channelsFirst') {\n                output = tfc.transpose(output, [0, 3, 1, 2]); // NHWC -> NCHW.\n            }\n            return output;\n        });\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        delete config['kernelInitializer'];\n        delete config['kernelRegularizer'];\n        delete config['kernelConstraint'];\n        config['depthwiseInitializer'] =\n            serializeInitializer(this.depthwiseInitializer);\n        config['pointwiseInitializer'] =\n            serializeInitializer(this.pointwiseInitializer);\n        config['depthwiseRegularizer'] =\n            serializeRegularizer(this.depthwiseRegularizer);\n        config['pointwiseRegularizer'] =\n            serializeRegularizer(this.pointwiseRegularizer);\n        config['depthwiseConstraint'] =\n            serializeConstraint(this.depthwiseConstraint);\n        config['pointwiseConstraint'] =\n            serializeConstraint(this.pointwiseConstraint);\n        return config;\n    }\n}\n/** @nocollapse */\nSeparableConv.className = 'SeparableConv';\nexport class SeparableConv2D extends SeparableConv {\n    constructor(args) {\n        super(2, args);\n    }\n}\n/** @nocollapse */\nSeparableConv2D.className = 'SeparableConv2D';\nserialization.registerClass(SeparableConv2D);\nexport class Conv1D extends Conv {\n    constructor(args) {\n        super(1, args);\n        Conv1D.verifyArgs(args);\n        this.inputSpec = [{ ndim: 3 }];\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        delete config['dataFormat'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if (typeof args.kernelSize !== 'number' &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 1)) {\n            throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with ` +\n                `length 1, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n}\n/** @nocollapse */\nConv1D.className = 'Conv1D';\nserialization.registerClass(Conv1D);\nexport class Cropping2D extends Layer {\n    constructor(args) {\n        super(args);\n        if (typeof args.cropping === 'number') {\n            this.cropping =\n                [[args.cropping, args.cropping], [args.cropping, args.cropping]];\n        }\n        else if (typeof args.cropping[0] === 'number') {\n            this.cropping = [\n                [args.cropping[0], args.cropping[0]],\n                [args.cropping[1], args.cropping[1]]\n            ];\n        }\n        else {\n            this.cropping = args.cropping;\n        }\n        this.dataFormat =\n            args.dataFormat === undefined ? 'channelsLast' : args.dataFormat;\n        this.inputSpec = [{ ndim: 4 }];\n    }\n    computeOutputShape(inputShape) {\n        if (this.dataFormat === 'channelsFirst') {\n            return [\n                inputShape[0], inputShape[1],\n                inputShape[2] - this.cropping[0][0] - this.cropping[0][1],\n                inputShape[3] - this.cropping[1][0] - this.cropping[1][1]\n            ];\n        }\n        else {\n            return [\n                inputShape[0],\n                inputShape[1] - this.cropping[0][0] - this.cropping[0][1],\n                inputShape[2] - this.cropping[1][0] - this.cropping[1][1], inputShape[3]\n            ];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                const hSliced = K.sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);\n                return K.sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);\n            }\n            else {\n                const hSliced = K.sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);\n                return K.sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);\n            }\n        });\n    }\n    getConfig() {\n        const config = { cropping: this.cropping, dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nCropping2D.className = 'Cropping2D';\nserialization.registerClass(Cropping2D);\nexport class UpSampling2D extends Layer {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_SIZE = [2, 2];\n        this.inputSpec = [{ ndim: 4 }];\n        this.size = args.size == null ? this.DEFAULT_SIZE : args.size;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n    }\n    computeOutputShape(inputShape) {\n        if (this.dataFormat === 'channelsFirst') {\n            const height = inputShape[2] == null ? null : this.size[0] * inputShape[2];\n            const width = inputShape[3] == null ? null : this.size[1] * inputShape[3];\n            return [inputShape[0], inputShape[1], height, width];\n        }\n        else {\n            const height = inputShape[1] == null ? null : this.size[0] * inputShape[1];\n            const width = inputShape[2] == null ? null : this.size[1] * inputShape[2];\n            return [inputShape[0], height, width, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            let input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            if (this.dataFormat === 'channelsFirst') {\n                input = tfc.transpose(input, [0, 2, 3, 1]);\n                const height = this.size[0] * inputShape[2];\n                const width = this.size[1] * inputShape[3];\n                const resized = input.resizeNearestNeighbor([height, width]);\n                return tfc.transpose(resized, [0, 3, 1, 2]);\n            }\n            else {\n                const height = this.size[0] * inputShape[1];\n                const width = this.size[1] * inputShape[2];\n                return input.resizeNearestNeighbor([height, width]);\n            }\n        });\n    }\n    getConfig() {\n        const config = { size: this.size, dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nUpSampling2D.className = 'UpSampling2D';\nserialization.registerClass(UpSampling2D);\n//# sourceMappingURL=convolutional.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Depthwise Convolutional Layers\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { convOutputLength } from '../utils/conv_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { BaseConv, preprocessConv2DInput } from './convolutional';\n/**\n * 2D convolution with separable filters.\n * @param x Input tensor.\n * @param depthwiseKernel Convolution kernel for depthwise convolution.\n * @param strides Strides (Array of two integers).\n * @param padding Padding model.\n * @param dataFormat Data format.\n * @param dilationRate Array of two integers, dilation rates for the separable\n *   convolution.\n * @returns Output tensor.\n * @throws ValueError If depthwiseKernel is not a 4D array.\n */\nexport function depthwiseConv2d(x, depthwiseKernel, strides = [1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        let y = preprocessConv2DInput(x, dataFormat);\n        if (x.rank !== 4) {\n            throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ` +\n                `${x.rank}-D`);\n        }\n        if (depthwiseKernel.rank !== 4) {\n            throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ` +\n                `${depthwiseKernel.rank}-D`);\n        }\n        y = tfc.depthwiseConv2d(y, depthwiseKernel, strides, padding === 'same' ? 'same' : 'valid', 'NHWC', dilationRate);\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]);\n        }\n        return y;\n    });\n}\nexport class DepthwiseConv2D extends BaseConv {\n    constructor(args) {\n        super(2, args);\n        this.depthwiseKernel = null;\n        this.depthMultiplier =\n            args.depthMultiplier == null ? 1 : args.depthMultiplier;\n        this.depthwiseInitializer = getInitializer(args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.depthwiseConstraint = getConstraint(args.depthwiseConstraint);\n        this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < 4) {\n            throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. ` +\n                `Received input shape: ${JSON.stringify(inputShape)}.`);\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : 3;\n        if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n            throw new ValueError('The channel dimension of the inputs to DepthwiseConv2D should ' +\n                `be defined, but is not (${inputShape[channelAxis]}).`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const depthwiseKernelShape = [\n            this.kernelSize[0], this.kernelSize[1], inputDim, this.depthMultiplier\n        ];\n        this.depthwiseKernel = this.addWeight('depthwise_kernel', depthwiseKernelShape, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [inputDim * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let outputs = depthwiseConv2d(inputs, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);\n            // TODO(cais): Add support for dilation.\n            if (this.useBias) {\n                outputs = K.biasAdd(outputs, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                outputs = this.activation.apply(outputs);\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const rows = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        const cols = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        const outFilters = this.dataFormat === 'channelsFirst' ?\n            inputShape[1] * this.depthMultiplier :\n            inputShape[3] * this.depthMultiplier;\n        const outRows = convOutputLength(rows, this.kernelSize[0], this.padding, this.strides[0]);\n        const outCols = convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], outFilters, outRows, outCols];\n        }\n        else {\n            // In this case, assume 'channelsLast'.\n            return [inputShape[0], outRows, outCols, outFilters];\n        }\n    }\n    getConfig() {\n        const config = super.getConfig();\n        config['depthMultiplier'] = this.depthMultiplier;\n        config['depthwiseInitializer'] =\n            serializeInitializer(this.depthwiseInitializer);\n        config['depthwiseRegularizer'] =\n            serializeRegularizer(this.depthwiseRegularizer);\n        config['depthwiseConstraint'] =\n            serializeConstraint(this.depthwiseRegularizer);\n        return config;\n    }\n}\n/** @nocollapse */\nDepthwiseConv2D.className = 'DepthwiseConv2D';\nserialization.registerClass(DepthwiseConv2D);\n//# sourceMappingURL=convolutional_depthwise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Recurrent Neural Network Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, SymbolicTensor } from '../engine/topology';\nimport { Layer } from '../engine/topology';\nimport { AttributeError, NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, Initializer, Ones, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor, isArrayOfShapes } from '../utils/types_utils';\nimport { batchGetValue, batchSetValue } from '../variables';\nimport { deserialize } from './serialization';\n/**\n * Standardize `apply()` args to a single list of tensor inputs.\n *\n * When running a model loaded from file, the input tensors `initialState` and\n * `constants` are passed to `RNN.apply()` as part of `inputs` instead of the\n * dedicated kwargs fields. `inputs` consists of\n * `[inputs, initialState0, initialState1, ..., constant0, constant1]` in this\n * case.\n * This method makes sure that arguments are\n * separated and that `initialState` and `constants` are `Array`s of tensors\n * (or None).\n *\n * @param inputs Tensor or `Array` of  tensors.\n * @param initialState Tensor or `Array` of tensors or `null`/`undefined`.\n * @param constants Tensor or `Array` of tensors or `null`/`undefined`.\n * @returns An object consisting of\n *   inputs: A tensor.\n *   initialState: `Array` of tensors or `null`.\n *   constants: `Array` of tensors or `null`.\n * @throws ValueError, if `inputs` is an `Array` but either `initialState` or\n *   `constants` is provided.\n */\nexport function standardizeArgs(inputs, initialState, constants, numConstants) {\n    if (Array.isArray(inputs)) {\n        if (initialState != null || constants != null) {\n            throw new ValueError('When inputs is an array, neither initialState or constants ' +\n                'should be provided');\n        }\n        if (numConstants != null) {\n            constants = inputs.slice(inputs.length - numConstants, inputs.length);\n            inputs = inputs.slice(0, inputs.length - numConstants);\n        }\n        if (inputs.length > 1) {\n            initialState = inputs.slice(1, inputs.length);\n        }\n        inputs = inputs[0];\n    }\n    function toListOrNull(x) {\n        if (x == null || Array.isArray(x)) {\n            return x;\n        }\n        else {\n            return [x];\n        }\n    }\n    initialState = toListOrNull(initialState);\n    constants = toListOrNull(constants);\n    return { inputs, initialState, constants };\n}\n/**\n * Iterates over the time dimension of a tensor.\n *\n * @param stepFunction RNN step function.\n *   Parameters:\n *     inputs: tensor with shape `[samples, ...]` (no time dimension),\n *       representing input for the batch of samples at a certain time step.\n *     states: an Array of tensors.\n *   Returns:\n *     outputs: tensor with shape `[samples, outputDim]` (no time dimension).\n *     newStates: list of tensors, same length and shapes as `states`. The first\n *       state in the list must be the output tensor at the previous timestep.\n * @param inputs Tensor of temporal data of shape `[samples, time, ...]` (at\n *   least 3D).\n * @param initialStates Tensor with shape `[samples, outputDim]` (no time\n *   dimension), containing the initial values of the states used in the step\n *   function.\n * @param goBackwards If `true`, do the iteration over the time dimension in\n *   reverse order and return the reversed sequence.\n * @param mask Binary tensor with shape `[sample, time, 1]`, with a zero for\n *   every element that is masked.\n * @param constants An Array of constant values passed at each step.\n * @param unroll Whether to unroll the RNN or to use a symbolic loop. *Not*\n *   applicable to this imperative deeplearn.js backend. Its value is ignored.\n * @param needPerStepOutputs Whether the per-step outputs are to be\n *   concatenated into a single tensor and returned (as the second return\n *   value). Default: `false`. This arg is included so that the relatively\n *   expensive concatenation of the stepwise outputs can be omitted unless\n *   the stepwise outputs need to be kept (e.g., for an LSTM layer of which\n *   `returnSequence` is `true`.)\n * @returns An Array: `[lastOutput, outputs, newStates]`.\n *   lastOutput: the lastest output of the RNN, of shape `[samples, ...]`.\n *   outputs: tensor with shape `[samples, time, ...]` where each entry\n *     `output[s, t]` is the output of the step function at time `t` for sample\n *     `s`. This return value is provided if and only if the\n *     `needPerStepOutputs` is set as `true`. If it is set as `false`, this\n *     return value will be `undefined`.\n *   newStates: Array of tensors, latest states returned by the step function,\n *      of shape `(samples, ...)`.\n * @throws ValueError If input dimension is less than 3.\n *\n * TODO(nielsene): This needs to be tidy-ed.\n */\nexport function rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, constants, unroll = false, needPerStepOutputs = false) {\n    return tfc.tidy(() => {\n        const ndim = inputs.shape.length;\n        if (ndim < 3) {\n            throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);\n        }\n        // Transpose to time-major, i.e., from [batch, time, ...] to [time, batch,\n        // ...].\n        const axes = [1, 0].concat(math_utils.range(2, ndim));\n        inputs = tfc.transpose(inputs, axes);\n        if (constants != null) {\n            throw new NotImplementedError('The rnn() functoin of the deeplearn.js backend does not support ' +\n                'constants yet.');\n        }\n        // Porting Note: the unroll option is ignored by the imperative backend.\n        if (unroll) {\n            console.warn('Backend rnn(): the unroll = true option is not applicable to the ' +\n                'imperative deeplearn.js backend.');\n        }\n        if (mask != null) {\n            mask = mask.asType('bool').asType('float32');\n            if (mask.rank === ndim - 1) {\n                mask = tfc.expandDims(mask, -1);\n            }\n            mask = tfc.transpose(mask, axes);\n        }\n        if (goBackwards) {\n            inputs = tfc.reverse(inputs, 0);\n            if (mask != null) {\n                mask = tfc.reverse(mask, 0);\n            }\n        }\n        // Porting Note: PyKeras with TensorFlow backend uses a symbolic loop\n        //   (tf.while_loop). But for the imperative deeplearn.js backend, we just\n        //   use the usual TypeScript control flow to iterate over the time steps in\n        //   the inputs.\n        // Porting Note: PyKeras patches a \"_use_learning_phase\" attribute to\n        // outputs.\n        //   This is not idiomatic in TypeScript. The info regarding whether we are\n        //   in a learning (i.e., training) phase for RNN is passed in a different\n        //   way.\n        const perStepOutputs = [];\n        let lastOutput;\n        let states = initialStates;\n        const timeSteps = inputs.shape[0];\n        const perStepInputs = tfc.unstack(inputs);\n        let perStepMasks;\n        if (mask != null) {\n            perStepMasks = tfc.unstack(mask);\n        }\n        for (let t = 0; t < timeSteps; ++t) {\n            const currentInput = perStepInputs[t];\n            const stepOutputs = tfc.tidy(() => stepFunction(currentInput, states));\n            if (mask == null) {\n                lastOutput = stepOutputs[0];\n                states = stepOutputs[1];\n            }\n            else {\n                const maskedOutputs = tfc.tidy(() => {\n                    const stepMask = perStepMasks[t];\n                    const negStepMask = tfc.onesLike(stepMask).sub(stepMask);\n                    // TODO(cais): Would tfc.where() be better for performance?\n                    const output = stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask));\n                    const newStates = states.map((state, i) => {\n                        return stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask));\n                    });\n                    return { output, newStates };\n                });\n                lastOutput = maskedOutputs.output;\n                states = maskedOutputs.newStates;\n            }\n            if (needPerStepOutputs) {\n                perStepOutputs.push(lastOutput);\n            }\n        }\n        let outputs;\n        if (needPerStepOutputs) {\n            const axis = 1;\n            outputs = tfc.stack(perStepOutputs, axis);\n        }\n        return [lastOutput, outputs, states];\n    });\n}\nexport class RNN extends Layer {\n    constructor(args) {\n        super(args);\n        let cell;\n        if (args.cell == null) {\n            throw new ValueError('cell property is missing for the constructor of RNN.');\n        }\n        else if (Array.isArray(args.cell)) {\n            cell = new StackedRNNCells({ cells: args.cell });\n        }\n        else {\n            cell = args.cell;\n        }\n        if (cell.stateSize == null) {\n            throw new ValueError('The RNN cell should have an attribute `stateSize` (tuple of ' +\n                'integers, one integer per RNN state).');\n        }\n        this.cell = cell;\n        this.returnSequences =\n            args.returnSequences == null ? false : args.returnSequences;\n        this.returnState = args.returnState == null ? false : args.returnState;\n        this.goBackwards = args.goBackwards == null ? false : args.goBackwards;\n        this._stateful = args.stateful == null ? false : args.stateful;\n        this.unroll = args.unroll == null ? false : args.unroll;\n        this.supportsMasking = true;\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n        this.stateSpec = null;\n        this.states_ = null;\n        // TODO(cais): Add constantsSpec and numConstants.\n        this.numConstants = null;\n        // TODO(cais): Look into the use of initial_state in the kwargs of the\n        //   constructor.\n        this.keptStates = [];\n    }\n    // Porting Note: This is the equivalent of `RNN.states` property getter in\n    //   PyKeras.\n    getStates() {\n        if (this.states_ == null) {\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            return math_utils.range(0, numStates).map(x => null);\n        }\n        else {\n            return this.states_;\n        }\n    }\n    // Porting Note: This is the equivalent of the `RNN.states` property setter in\n    //   PyKeras.\n    setStates(states) {\n        this.states_ = states;\n    }\n    computeOutputShape(inputShape) {\n        if (isArrayOfShapes(inputShape)) {\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        // TODO(cais): Remove the casting once stacked RNN cells become supported.\n        let stateSize = this.cell.stateSize;\n        if (!Array.isArray(stateSize)) {\n            stateSize = [stateSize];\n        }\n        const outputDim = stateSize[0];\n        let outputShape;\n        if (this.returnSequences) {\n            outputShape = [inputShape[0], inputShape[1], outputDim];\n        }\n        else {\n            outputShape = [inputShape[0], outputDim];\n        }\n        if (this.returnState) {\n            const stateShape = [];\n            for (const dim of stateSize) {\n                stateShape.push([inputShape[0], dim]);\n            }\n            return [outputShape].concat(stateShape);\n        }\n        else {\n            return outputShape;\n        }\n    }\n    computeMask(inputs, mask) {\n        return tfc.tidy(() => {\n            if (Array.isArray(mask)) {\n                mask = mask[0];\n            }\n            const outputMask = this.returnSequences ? mask : null;\n            if (this.returnState) {\n                const stateMask = this.states.map(s => null);\n                return [outputMask].concat(stateMask);\n            }\n            else {\n                return outputMask;\n            }\n        });\n    }\n    /**\n     * Get the current state tensors of the RNN.\n     *\n     * If the state hasn't been set, return an array of `null`s of the correct\n     * length.\n     */\n    get states() {\n        if (this.states_ == null) {\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            const output = [];\n            for (let i = 0; i < numStates; ++i) {\n                output.push(null);\n            }\n            return output;\n        }\n        else {\n            return this.states_;\n        }\n    }\n    set states(s) {\n        this.states_ = s;\n    }\n    build(inputShape) {\n        // Note inputShape will be an Array of Shapes of initial states and\n        // constants if these are passed in apply().\n        const constantShape = null;\n        if (this.numConstants != null) {\n            throw new NotImplementedError('Constants support is not implemented in RNN yet.');\n        }\n        if (isArrayOfShapes(inputShape)) {\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        const batchSize = this.stateful ? inputShape[0] : null;\n        const inputDim = inputShape.slice(2);\n        this.inputSpec[0] = new InputSpec({ shape: [batchSize, null, ...inputDim] });\n        // Allow cell (if RNNCell Layer) to build before we set or validate\n        // stateSpec.\n        const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        if (constantShape != null) {\n            throw new NotImplementedError('Constants support is not implemented in RNN yet.');\n        }\n        else {\n            this.cell.build(stepInputShape);\n        }\n        // Set or validate stateSpec.\n        let stateSize;\n        if (Array.isArray(this.cell.stateSize)) {\n            stateSize = this.cell.stateSize;\n        }\n        else {\n            stateSize = [this.cell.stateSize];\n        }\n        if (this.stateSpec != null) {\n            if (!util.arraysEqual(this.stateSpec.map(spec => spec.shape[spec.shape.length - 1]), stateSize)) {\n                throw new ValueError(`An initialState was passed that is not compatible with ` +\n                    `cell.stateSize. Received stateSpec=${this.stateSpec}; ` +\n                    `However cell.stateSize is ${this.cell.stateSize}`);\n            }\n        }\n        else {\n            this.stateSpec =\n                stateSize.map(dim => new InputSpec({ shape: [null, dim] }));\n        }\n        if (this.stateful) {\n            this.resetStates();\n        }\n    }\n    /**\n     * Reset the state tensors of the RNN.\n     *\n     * If the `states` argument is `undefined` or `null`, will set the\n     * state tensor(s) of the RNN to all-zero tensors of the appropriate\n     * shape(s).\n     *\n     * If `states` is provided, will set the state tensors of the RNN to its\n     * value.\n     *\n     * @param states Optional externally-provided initial states.\n     * @param training Whether this call is done during training. For stateful\n     *   RNNs, this affects whether the old states are kept or discarded. In\n     *   particular, if `training` is `true`, the old states will be kept so\n     *   that subsequent backpropgataion through time (BPTT) may work properly.\n     *   Else, the old states will be discarded.\n     */\n    resetStates(states, training = false) {\n        tidy(() => {\n            if (!this.stateful) {\n                throw new AttributeError('Cannot call resetStates() on an RNN Layer that is not stateful.');\n            }\n            const batchSize = this.inputSpec[0].shape[0];\n            if (batchSize == null) {\n                throw new ValueError('If an RNN is stateful, it needs to know its batch size. Specify ' +\n                    'the batch size of your input tensors: \\n' +\n                    '- If using a Sequential model, specify the batch size by ' +\n                    'passing a `batchInputShape` option to your first layer.\\n' +\n                    '- If using the functional API, specify the batch size by ' +\n                    'passing a `batchShape` option to your Input layer.');\n            }\n            // Initialize state if null.\n            if (this.states_ == null) {\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ =\n                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n                }\n                else {\n                    this.states_ = [tfc.zeros([batchSize, this.cell.stateSize])];\n                }\n            }\n            else if (states == null) {\n                // Dispose old state tensors.\n                tfc.dispose(this.states_);\n                // For stateful RNNs, fully dispose kept old states.\n                if (this.keptStates != null) {\n                    tfc.dispose(this.keptStates);\n                    this.keptStates = [];\n                }\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ =\n                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n                }\n                else {\n                    this.states_[0] = tfc.zeros([batchSize, this.cell.stateSize]);\n                }\n            }\n            else {\n                if (!Array.isArray(states)) {\n                    states = [states];\n                }\n                if (states.length !== this.states_.length) {\n                    throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), ` +\n                        `but it received ${states.length} state value(s). Input ` +\n                        `received: ${states}`);\n                }\n                if (training === true) {\n                    // Store old state tensors for complete disposal later, i.e., during\n                    // the next no-arg call to this method. We do not dispose the old\n                    // states immediately because that BPTT (among other things) require\n                    // them.\n                    this.keptStates.push(this.states_.slice());\n                }\n                else {\n                    tfc.dispose(this.states_);\n                }\n                for (let index = 0; index < this.states_.length; ++index) {\n                    const value = states[index];\n                    const dim = Array.isArray(this.cell.stateSize) ?\n                        this.cell.stateSize[index] :\n                        this.cell.stateSize;\n                    const expectedShape = [batchSize, dim];\n                    if (!util.arraysEqual(value.shape, expectedShape)) {\n                        throw new ValueError(`State ${index} is incompatible with layer ${this.name}: ` +\n                            `expected shape=${expectedShape}, received shape=${value.shape}`);\n                    }\n                    this.states_[index] = value;\n                }\n            }\n            this.states_ = this.states_.map(state => tfc.keep(state.clone()));\n        });\n    }\n    apply(inputs, kwargs) {\n        // TODO(cais): Figure out whether initialState is in kwargs or inputs.\n        let initialState = kwargs == null ? null : kwargs['initialState'];\n        let constants = kwargs == null ? null : kwargs['constants'];\n        if (kwargs == null) {\n            kwargs = {};\n        }\n        const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n        inputs = standardized.inputs;\n        initialState = standardized.initialState;\n        constants = standardized.constants;\n        // If any of `initial_state` or `constants` are specified and are\n        // `tf.SymbolicTensor`s, then add them to the inputs and temporarily modify\n        // the input_spec to include them.\n        let additionalInputs = [];\n        let additionalSpecs = [];\n        if (initialState != null) {\n            kwargs['initialState'] = initialState;\n            additionalInputs = additionalInputs.concat(initialState);\n            this.stateSpec = [];\n            for (const state of initialState) {\n                this.stateSpec.push(new InputSpec({ shape: state.shape }));\n            }\n            // TODO(cais): Use the following instead.\n            // this.stateSpec = initialState.map(state => new InputSpec({shape:\n            // state.shape}));\n            additionalSpecs = additionalSpecs.concat(this.stateSpec);\n        }\n        if (constants != null) {\n            kwargs['constants'] = constants;\n            additionalInputs = additionalInputs.concat(constants);\n            // TODO(cais): Add this.constantsSpec.\n            this.numConstants = constants.length;\n        }\n        const isTensor = additionalInputs[0] instanceof SymbolicTensor;\n        if (isTensor) {\n            // Compute full input spec, including state and constants.\n            const fullInput = [inputs].concat(additionalInputs);\n            const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n            // Perform the call with temporarily replaced inputSpec.\n            const originalInputSpec = this.inputSpec;\n            this.inputSpec = fullInputSpec;\n            const output = super.apply(fullInput, kwargs);\n            this.inputSpec = originalInputSpec;\n            return output;\n        }\n        else {\n            return super.apply(inputs, kwargs);\n        }\n    }\n    // tslint:disable-next-line:no-any\n    call(inputs, kwargs) {\n        // Input shape: `[samples, time (padded with zeros), input_dim]`.\n        // Note that the .build() method of subclasses **must** define\n        // this.inputSpec and this.stateSpec owith complete input shapes.\n        return tidy(() => {\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            let initialState = kwargs == null ? null : kwargs['initialState'];\n            inputs = getExactlyOneTensor(inputs);\n            if (initialState == null) {\n                if (this.stateful) {\n                    initialState = this.states_;\n                }\n                else {\n                    initialState = this.getInitialState(inputs);\n                }\n            }\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            if (initialState.length !== numStates) {\n                throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ` +\n                    `${initialState.length} initial state(s).`);\n            }\n            if (this.unroll) {\n                console.warn('Ignoring unroll = true for RNN layer, due to imperative backend.');\n            }\n            const cellCallKwargs = { training };\n            // TODO(cais): Add support for constants.\n            const step = (inputs, states) => {\n                // `inputs` and `states` are concatenated to form a single `Array` of\n                // `tf.Tensor`s as the input to `cell.call()`.\n                const outputs = this.cell.call([inputs].concat(states), cellCallKwargs);\n                // Marshall the return value into output and new states.\n                return [outputs[0], outputs.slice(1)];\n            };\n            // TODO(cais): Add support for constants.\n            const rnnOutputs = rnn(step, inputs, initialState, this.goBackwards, mask, null, this.unroll, this.returnSequences);\n            const lastOutput = rnnOutputs[0];\n            const outputs = rnnOutputs[1];\n            const states = rnnOutputs[2];\n            if (this.stateful) {\n                this.resetStates(states, training);\n            }\n            const output = this.returnSequences ? outputs : lastOutput;\n            // TODO(cais): Porperty set learning phase flag.\n            if (this.returnState) {\n                return [output].concat(states);\n            }\n            else {\n                return output;\n            }\n        });\n    }\n    getInitialState(inputs) {\n        return tidy(() => {\n            // Build an all-zero tensor of shape [samples, outputDim].\n            // [Samples, timeSteps, inputDim].\n            let initialState = tfc.zeros(inputs.shape);\n            // [Samples].\n            initialState = tfc.sum(initialState, [1, 2]);\n            initialState = K.expandDims(initialState); // [Samples, 1].\n            if (Array.isArray(this.cell.stateSize)) {\n                return this.cell.stateSize.map(dim => dim > 1 ? K.tile(initialState, [1, dim]) : initialState);\n            }\n            else {\n                return this.cell.stateSize > 1 ?\n                    [K.tile(initialState, [1, this.cell.stateSize])] :\n                    [initialState];\n            }\n        });\n    }\n    get trainableWeights() {\n        if (!this.trainable) {\n            return [];\n        }\n        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n        return this.cell.trainableWeights;\n    }\n    get nonTrainableWeights() {\n        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n        if (!this.trainable) {\n            return this.cell.weights;\n        }\n        return this.cell.nonTrainableWeights;\n    }\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.cell != null) {\n            this.cell.setFastWeightInitDuringBuild(value);\n        }\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            returnSequences: this.returnSequences,\n            returnState: this.returnState,\n            goBackwards: this.goBackwards,\n            stateful: this.stateful,\n            unroll: this.unroll,\n        };\n        if (this.numConstants != null) {\n            config['numConstants'] = this.numConstants;\n        }\n        const cellConfig = this.cell.getConfig();\n        if (this.getClassName() === RNN.className) {\n            config['cell'] = {\n                'className': this.cell.getClassName(),\n                'config': cellConfig,\n            };\n        }\n        // this order is necessary, to prevent cell name from replacing layer name\n        return Object.assign({}, cellConfig, baseConfig, config);\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const cellConfig = config['cell'];\n        const cell = deserialize(cellConfig, customObjects);\n        return new cls(Object.assign(config, { cell }));\n    }\n}\n/** @nocollapse */\nRNN.className = 'RNN';\nserialization.registerClass(RNN);\n// Porting Note: This is a common parent class for RNN cells. There is no\n// equivalent of this in PyKeras. Having a common parent class forgoes the\n//  need for `has_attr(cell, ...)` checks or its TypeScript equivalent.\n/**\n * An RNNCell layer.\n *\n * @doc {heading: 'Layers', subheading: 'Classes'}\n */\nexport class RNNCell extends Layer {\n}\nexport class SimpleRNNCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        this.units = args.units;\n        assertPositiveInteger(this.units, `units`);\n        this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.stateSize = this.units;\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        // TODO(cais): Use regularizer.\n        this.kernel = this.addWeight('kernel', [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.built = true;\n    }\n    // Porting Note: PyKeras' equivalent of this method takes two tensor inputs:\n    //   `inputs` and `states`. Here, the two tensors are combined into an\n    //   `Tensor[]` Array as the first input argument.\n    //   Similarly, PyKeras' equivalent of this method returns two values:\n    //    `output` and `[output]`. Here the two are combined into one length-2\n    //    `Tensor[]`, consisting of `output` repeated.\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (inputs.length !== 2) {\n                throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);\n            }\n            let prevOutput = inputs[1];\n            inputs = inputs[0];\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(prevOutput),\n                    rate: this.recurrentDropout,\n                    training\n                });\n            }\n            let h;\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            if (dpMask != null) {\n                h = K.dot(tfc.mul(inputs, dpMask), this.kernel.read());\n            }\n            else {\n                h = K.dot(inputs, this.kernel.read());\n            }\n            if (this.bias != null) {\n                h = K.biasAdd(h, this.bias.read());\n            }\n            if (recDpMask != null) {\n                prevOutput = tfc.mul(prevOutput, recDpMask);\n            }\n            let output = tfc.add(h, K.dot(prevOutput, this.recurrentKernel.read()));\n            if (this.activation != null) {\n                output = this.activation.apply(output);\n            }\n            // TODO(cais): Properly set learning phase on output tensor?\n            return [output, output];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nSimpleRNNCell.className = 'SimpleRNNCell';\nserialization.registerClass(SimpleRNNCell);\nexport class SimpleRNN extends RNN {\n    constructor(args) {\n        args.cell = new SimpleRNNCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nSimpleRNN.className = 'SimpleRNN';\nserialization.registerClass(SimpleRNN);\nexport class GRUCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        if (args.resetAfter) {\n            throw new ValueError(`GRUCell does not support reset_after parameter set to true.`);\n        }\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :\n            args.activation);\n        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.implementation = args.implementation;\n        this.stateSize = this.units;\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const inputDim = inputShape[inputShape.length - 1];\n        this.kernel = this.addWeight('kernel', [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n        //   of the weights and bias in the call() method, at execution time.\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (inputs.length !== 2) {\n                throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            let hTMinus1 = inputs[1]; // Previous memory state.\n            inputs = inputs[0];\n            // Note: For superior performance, TensorFlow.js always uses\n            // implementation 2, regardless of the actual value of\n            // config.implementation.\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training,\n                    count: 3\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: 3\n                });\n            }\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            let z;\n            let r;\n            let hh;\n            if (0 < this.dropout && this.dropout < 1) {\n                inputs = tfc.mul(inputs, dpMask[0]);\n            }\n            let matrixX = K.dot(inputs, this.kernel.read());\n            if (this.useBias) {\n                matrixX = K.biasAdd(matrixX, this.bias.read());\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n            }\n            const recurrentKernelValue = this.recurrentKernel.read();\n            const [rk1, rk2] = tfc.split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1);\n            const matrixInner = K.dot(hTMinus1, rk1);\n            const [xZ, xR, xH] = tfc.split(matrixX, 3, matrixX.rank - 1);\n            const [recurrentZ, recurrentR] = tfc.split(matrixInner, 2, matrixInner.rank - 1);\n            z = this.recurrentActivation.apply(tfc.add(xZ, recurrentZ));\n            r = this.recurrentActivation.apply(tfc.add(xR, recurrentR));\n            const recurrentH = K.dot(tfc.mul(r, hTMinus1), rk2);\n            hh = this.activation.apply(tfc.add(xH, recurrentH));\n            const h = tfc.add(tfc.mul(z, hTMinus1), tfc.mul(tfc.add(1, tfc.neg(z)), hh));\n            // TODO(cais): Add use_learning_phase flag properly.\n            return [h, h];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            recurrentActivation: serializeActivation(this.recurrentActivation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n            implementation: this.implementation,\n            resetAfter: false\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nGRUCell.className = 'GRUCell';\nserialization.registerClass(GRUCell);\nexport class GRU extends RNN {\n    constructor(args) {\n        if (args.implementation === 0) {\n            console.warn('`implementation=0` has been deprecated, and now defaults to ' +\n                '`implementation=1`. Please update your layer call.');\n        }\n        args.cell = new GRUCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        if (config['implmentation'] === 0) {\n            config['implementation'] = 1;\n        }\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nGRU.className = 'GRU';\nserialization.registerClass(GRU);\nexport class LSTMCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :\n            args.activation);\n        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.unitForgetBias = args.unitForgetBias;\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.implementation = args.implementation;\n        this.stateSize = [this.units, this.units];\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        var _a;\n        inputShape = getExactlyOneShape(inputShape);\n        const inputDim = inputShape[inputShape.length - 1];\n        this.kernel = this.addWeight('kernel', [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        let biasInitializer;\n        if (this.useBias) {\n            if (this.unitForgetBias) {\n                const capturedBiasInit = this.biasInitializer;\n                const capturedUnits = this.units;\n                biasInitializer = new (_a = class CustomInit extends Initializer {\n                        apply(shape, dtype) {\n                            // TODO(cais): More informative variable names?\n                            const bI = capturedBiasInit.apply([capturedUnits]);\n                            const bF = (new Ones()).apply([capturedUnits]);\n                            const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);\n                            return K.concatAlongFirstAxis(K.concatAlongFirstAxis(bI, bF), bCAndH);\n                        }\n                    },\n                    /** @nocollapse */\n                    _a.className = 'CustomInit',\n                    _a)();\n            }\n            else {\n                biasInitializer = this.biasInitializer;\n            }\n            this.bias = this.addWeight('bias', [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n        //   of the weights and bias in the call() method, at execution time.\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            inputs = inputs;\n            if (inputs.length !== 3) {\n                throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            let hTMinus1 = inputs[1]; // Previous memory state.\n            const cTMinus1 = inputs[2]; // Previous carry state.\n            inputs = inputs[0];\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training,\n                    count: 4\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: 4\n                });\n            }\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            // Note: For superior performance, TensorFlow.js always uses\n            // implementation 2 regardless of the actual value of\n            // config.implementation.\n            let i;\n            let f;\n            let c;\n            let o;\n            if (0 < this.dropout && this.dropout < 1) {\n                inputs = tfc.mul(inputs, dpMask[0]);\n            }\n            let z = K.dot(inputs, this.kernel.read());\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n            }\n            z = tfc.add(z, K.dot(hTMinus1, this.recurrentKernel.read()));\n            if (this.useBias) {\n                z = K.biasAdd(z, this.bias.read());\n            }\n            const [z0, z1, z2, z3] = tfc.split(z, 4, z.rank - 1);\n            i = this.recurrentActivation.apply(z0);\n            f = this.recurrentActivation.apply(z1);\n            c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(z2)));\n            o = this.recurrentActivation.apply(z3);\n            const h = tfc.mul(o, this.activation.apply(c));\n            // TODO(cais): Add use_learning_phase flag properly.\n            return [h, h, c];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            recurrentActivation: serializeActivation(this.recurrentActivation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            unitForgetBias: this.unitForgetBias,\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n            implementation: this.implementation,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nLSTMCell.className = 'LSTMCell';\nserialization.registerClass(LSTMCell);\nexport class LSTM extends RNN {\n    constructor(args) {\n        if (args.implementation === 0) {\n            console.warn('`implementation=0` has been deprecated, and now defaults to ' +\n                '`implementation=1`. Please update your layer call.');\n        }\n        args.cell = new LSTMCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        if (config['implmentation'] === 0) {\n            config['implementation'] = 1;\n        }\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nLSTM.className = 'LSTM';\nserialization.registerClass(LSTM);\nexport class StackedRNNCells extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.cells = args.cells;\n    }\n    get stateSize() {\n        // States are a flat list in reverse order of the cell stack.\n        // This allows perserving the requirement `stack.statesize[0] ===\n        // outputDim`. E.g., states of a 2-layer LSTM would be `[h2, c2, h1, c1]`,\n        // assuming one LSTM has states `[h, c]`.\n        const stateSize = [];\n        for (const cell of this.cells.slice().reverse()) {\n            if (Array.isArray(cell.stateSize)) {\n                stateSize.push(...cell.stateSize);\n            }\n            else {\n                stateSize.push(cell.stateSize);\n            }\n        }\n        return stateSize;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            let states = inputs.slice(1);\n            // Recover per-cell states.\n            const nestedStates = [];\n            for (const cell of this.cells.slice().reverse()) {\n                if (Array.isArray(cell.stateSize)) {\n                    nestedStates.push(states.splice(0, cell.stateSize.length));\n                }\n                else {\n                    nestedStates.push(states.splice(0, 1));\n                }\n            }\n            nestedStates.reverse();\n            // Call the cells in order and store the returned states.\n            const newNestedStates = [];\n            let callInputs;\n            for (let i = 0; i < this.cells.length; ++i) {\n                const cell = this.cells[i];\n                states = nestedStates[i];\n                // TODO(cais): Take care of constants.\n                if (i === 0) {\n                    callInputs = [inputs[0]].concat(states);\n                }\n                else {\n                    callInputs = [callInputs[0]].concat(states);\n                }\n                callInputs = cell.call(callInputs, kwargs);\n                newNestedStates.push(callInputs.slice(1));\n            }\n            // Format the new states as a flat list in reverse cell order.\n            states = [];\n            for (const cellStates of newNestedStates.slice().reverse()) {\n                states.push(...cellStates);\n            }\n            return [callInputs[0]].concat(states);\n        });\n    }\n    build(inputShape) {\n        if (isArrayOfShapes(inputShape)) {\n            // TODO(cais): Take care of input constants.\n            // const constantShape = inputShape.slice(1);\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        let outputDim;\n        this.cells.forEach((cell, i) => {\n            nameScope(`RNNCell_${i}`, () => {\n                // TODO(cais): Take care of input constants.\n                cell.build(inputShape);\n                if (Array.isArray(cell.stateSize)) {\n                    outputDim = cell.stateSize[0];\n                }\n                else {\n                    outputDim = cell.stateSize;\n                }\n                inputShape = [inputShape[0], outputDim];\n            });\n        });\n        this.built = true;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const getCellConfig = (cell) => {\n            return {\n                'className': cell.getClassName(),\n                'config': cell.getConfig(),\n            };\n        };\n        const cellConfigs = this.cells.map(getCellConfig);\n        const config = { 'cells': cellConfigs };\n        return Object.assign({}, baseConfig, config);\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const cells = [];\n        for (const cellConfig of config['cells']) {\n            cells.push(deserialize(cellConfig, customObjects));\n        }\n        return new cls({ cells });\n    }\n    get trainableWeights() {\n        if (!this.trainable) {\n            return [];\n        }\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.trainableWeights);\n        }\n        return weights;\n    }\n    get nonTrainableWeights() {\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.nonTrainableWeights);\n        }\n        if (!this.trainable) {\n            const trainableWeights = [];\n            for (const cell of this.cells) {\n                trainableWeights.push(...cell.trainableWeights);\n            }\n            return trainableWeights.concat(weights);\n        }\n        return weights;\n    }\n    /**\n     * Retrieve the weights of a the model.\n     *\n     * @returns A flat `Array` of `tf.Tensor`s.\n     */\n    getWeights() {\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.weights);\n        }\n        return batchGetValue(weights);\n    }\n    /**\n     * Set the weights of the model.\n     *\n     * @param weights An `Array` of `tf.Tensor`s with shapes and types matching\n     *     the output of `getWeights()`.\n     */\n    setWeights(weights) {\n        const tuples = [];\n        for (const cell of this.cells) {\n            const numParams = cell.weights.length;\n            const inputWeights = weights.splice(numParams);\n            for (let i = 0; i < cell.weights.length; ++i) {\n                tuples.push([cell.weights[i], inputWeights[i]]);\n            }\n        }\n        batchSetValue(tuples);\n    }\n}\n/** @nocollapse */\nStackedRNNCells.className = 'StackedRNNCells';\nserialization.registerClass(StackedRNNCells);\nexport function generateDropoutMask(args) {\n    const { ones, rate, training = false, count = 1 } = args;\n    const droppedInputs = () => K.dropout(ones(), rate);\n    const createMask = () => K.inTrainPhase(droppedInputs, ones, training);\n    // just in case count is provided with null or undefined\n    if (!count || count <= 1) {\n        return tfc.keep(createMask().clone());\n    }\n    const masks = Array(count).fill(undefined).map(createMask);\n    return masks.map(m => tfc.keep(m.clone()));\n}\n//# sourceMappingURL=recurrent.js.map", "/**\n * @license\n * Copyright 2020 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nvar __rest = (this && this.__rest) || function (s, e) {\n    var t = {};\n    for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\n        t[p] = s[p];\n    if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\n        for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\n            if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\n                t[p[i]] = s[p[i]];\n        }\n    return t;\n};\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode } from '../common';\nimport { InputSpec } from '../engine/topology';\nimport { AttributeError, NotImplementedError, ValueError } from '../errors';\nimport { Initializer } from '../initializers';\nimport { convOutputLength, normalizeArray } from '../utils/conv_utils';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport { getExactlyOneShape } from '../utils/types_utils';\nimport { generateDropoutMask, LSTMCell, RNN, RNNCell } from './recurrent';\nclass ConvRNN2DCell extends RNNCell {\n}\n/**\n * Base class for convolutional-recurrent layers.\n */\nclass ConvRNN2D extends RNN {\n    constructor(args) {\n        if (args.unroll) {\n            throw new NotImplementedError('Unrolling is not possible with convolutional RNNs.');\n        }\n        if (Array.isArray(args.cell)) {\n            throw new NotImplementedError('It is not possible at the moment to stack convolutional cells.');\n        }\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 5 })];\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            if (kwargs && kwargs['constants']) {\n                throw new ValueError('ConvRNN2D cell does not support constants');\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    computeOutputShape(inputShape) {\n        let outShape = this.computeSingleOutputShape(inputShape);\n        if (!this.returnSequences) {\n            outShape = [outShape[0], ...outShape.slice(2)];\n        }\n        if (this.returnState) {\n            outShape =\n                [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])];\n        }\n        return outShape;\n    }\n    getInitialState(inputs) {\n        return tfc.tidy(() => {\n            const { stateSize } = this.cell;\n            const inputShape = inputs.shape;\n            const outputShape = this.computeSingleOutputShape(inputShape);\n            const stateShape = [outputShape[0], ...outputShape.slice(2)];\n            const initialState = tfc.zeros(stateShape);\n            if (Array.isArray(stateSize)) {\n                return Array(stateSize.length).fill(initialState);\n            }\n            return [initialState];\n        });\n    }\n    resetStates(states, training = false) {\n        tfc.tidy(() => {\n            if (!this.stateful) {\n                throw new AttributeError('Cannot call resetStates() on an RNN Layer that is not stateful.');\n            }\n            const inputShape = this.inputSpec[0].shape;\n            const outputShape = this.computeSingleOutputShape(inputShape);\n            const stateShape = [outputShape[0], ...outputShape.slice(2)];\n            const batchSize = inputShape[0];\n            if (batchSize == null) {\n                throw new ValueError('If an RNN is stateful, it needs to know its batch size. Specify ' +\n                    'the batch size of your input tensors: \\n' +\n                    '- If using a Sequential model, specify the batch size by ' +\n                    'passing a `batchInputShape` option to your first layer.\\n' +\n                    '- If using the functional API, specify the batch size by ' +\n                    'passing a `batchShape` option to your Input layer.');\n            }\n            // Initialize state if null.\n            if (this.getStates() == null) {\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));\n                }\n                else {\n                    this.states_ = [tfc.zeros(stateShape)];\n                }\n            }\n            else if (states == null) {\n                // Dispose old state tensors.\n                tfc.dispose(this.states_);\n                // For stateful RNNs, fully dispose kept old states.\n                if (this.keptStates != null) {\n                    tfc.dispose(this.keptStates);\n                    this.keptStates = [];\n                }\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));\n                }\n                else {\n                    this.states_[0] = tfc.zeros(stateShape);\n                }\n            }\n            else {\n                if (!Array.isArray(states)) {\n                    states = [states];\n                }\n                if (states.length !== this.states_.length) {\n                    throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), ` +\n                        `but it received ${states.length} state value(s). Input ` +\n                        `received: ${states}`);\n                }\n                if (training) {\n                    // Store old state tensors for complete disposal later, i.e., during\n                    // the next no-arg call to this method. We do not dispose the old\n                    // states immediately because that BPTT (among other things) require\n                    // them.\n                    this.keptStates.push(this.states_.slice());\n                }\n                else {\n                    tfc.dispose(this.states_);\n                }\n                for (let index = 0; index < this.states_.length; ++index) {\n                    const value = states[index];\n                    const expectedShape = stateShape;\n                    if (!util.arraysEqual(value.shape, expectedShape)) {\n                        throw new ValueError(`State ${index} is incompatible with layer ${this.name}: ` +\n                            `expected shape=${expectedShape}, received shape=${value.shape}`);\n                    }\n                    this.states_[index] = value;\n                }\n            }\n            this.states_ = this.states_.map(state => tfc.keep(state.clone()));\n        });\n    }\n    computeSingleOutputShape(inputShape) {\n        const { dataFormat, filters, kernelSize, padding, strides, dilationRate } = this.cell;\n        const isChannelsFirst = dataFormat === 'channelsFirst';\n        const h = inputShape[isChannelsFirst ? 3 : 2];\n        const w = inputShape[isChannelsFirst ? 4 : 3];\n        const hOut = convOutputLength(h, kernelSize[0], padding, strides[0], dilationRate[0]);\n        const wOut = convOutputLength(w, kernelSize[1], padding, strides[1], dilationRate[1]);\n        const outShape = [\n            ...inputShape.slice(0, 2),\n            ...(isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters])\n        ];\n        return outShape;\n    }\n}\n/** @nocollapse */\nConvRNN2D.className = 'ConvRNN2D';\nexport class ConvLSTM2DCell extends LSTMCell {\n    constructor(args) {\n        const { filters, kernelSize, strides, padding, dataFormat, dilationRate, } = args;\n        super(Object.assign({}, args, { units: filters }));\n        this.filters = filters;\n        assertPositiveInteger(this.filters, 'filters');\n        this.kernelSize = normalizeArray(kernelSize, 2, 'kernelSize');\n        this.kernelSize.forEach(size => assertPositiveInteger(size, 'kernelSize'));\n        this.strides = normalizeArray(strides || 1, 2, 'strides');\n        this.strides.forEach(stride => assertPositiveInteger(stride, 'strides'));\n        this.padding = padding || 'valid';\n        checkPaddingMode(this.padding);\n        this.dataFormat = dataFormat || 'channelsLast';\n        checkDataFormat(this.dataFormat);\n        this.dilationRate = normalizeArray(dilationRate || 1, 2, 'dilationRate');\n        this.dilationRate.forEach(rate => assertPositiveInteger(rate, 'dilationRate'));\n    }\n    build(inputShape) {\n        var _a;\n        inputShape = getExactlyOneShape(inputShape);\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError(`The channel dimension of the input should be defined. ` +\n                `Found ${inputShape[channelAxis]}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const numOfKernels = 4;\n        const kernelShape = this.kernelSize.concat([inputDim, this.filters * numOfKernels]);\n        this.kernel = this.addWeight('kernel', kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        const recurrentKernelShape = this.kernelSize.concat([this.filters, this.filters * numOfKernels]);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', recurrentKernelShape, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            let biasInitializer;\n            if (this.unitForgetBias) {\n                const init = this.biasInitializer;\n                const filters = this.filters;\n                biasInitializer = new (_a = class CustomInit extends Initializer {\n                        apply(shape, dtype) {\n                            const biasI = init.apply([filters]);\n                            const biasF = tfc.ones([filters]);\n                            const biasCAndO = init.apply([filters * 2]);\n                            return K.concatenate([biasI, biasF, biasCAndO]);\n                        }\n                    },\n                    /** @nocollapse */\n                    _a.className = 'CustomInit',\n                    _a)();\n            }\n            else {\n                biasInitializer = this.biasInitializer;\n            }\n            this.bias = this.addWeight('bias', [this.filters * numOfKernels], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            if (inputs.length !== 3) {\n                throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            const training = kwargs['training'] || false;\n            const x = inputs[0]; // Current input\n            const hTMinus1 = inputs[1]; // Previous memory state.\n            const cTMinus1 = inputs[2]; // Previous carry state.\n            const numOfKernels = 4;\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(x),\n                    rate: this.dropout,\n                    training,\n                    count: numOfKernels\n                });\n            }\n            const dropoutMask = this.dropoutMask;\n            const applyDropout = (x, mask, index) => {\n                if (!mask || !mask[index]) {\n                    return x;\n                }\n                return tfc.mul(mask[index], x);\n            };\n            let xI = applyDropout(x, dropoutMask, 0);\n            let xF = applyDropout(x, dropoutMask, 1);\n            let xC = applyDropout(x, dropoutMask, 2);\n            let xO = applyDropout(x, dropoutMask, 3);\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: numOfKernels\n                });\n            }\n            const recDropoutMask = this.recurrentDropoutMask;\n            let hI = applyDropout(hTMinus1, recDropoutMask, 0);\n            let hF = applyDropout(hTMinus1, recDropoutMask, 1);\n            let hC = applyDropout(hTMinus1, recDropoutMask, 2);\n            let hO = applyDropout(hTMinus1, recDropoutMask, 3);\n            const kernelChannelAxis = 3;\n            const [kernelI, kernelF, kernelC, kernelO] = tfc.split(this.kernel.read(), numOfKernels, kernelChannelAxis);\n            const [biasI, biasF, biasC, biasO] = this.useBias ?\n                tfc.split(this.bias.read(), numOfKernels) :\n                [null, null, null, null];\n            xI = this.inputConv(xI, kernelI, biasI, this.padding);\n            xF = this.inputConv(xF, kernelF, biasF, this.padding);\n            xC = this.inputConv(xC, kernelC, biasC, this.padding);\n            xO = this.inputConv(xO, kernelO, biasO, this.padding);\n            const [recKernelI, recKernelF, recKernelC, recKernelO] = tfc.split(this.recurrentKernel.read(), numOfKernels, kernelChannelAxis);\n            hI = this.recurrentConv(hI, recKernelI);\n            hF = this.recurrentConv(hF, recKernelF);\n            hC = this.recurrentConv(hC, recKernelC);\n            hO = this.recurrentConv(hO, recKernelO);\n            const i = this.recurrentActivation.apply(tfc.add(xI, hI));\n            const f = this.recurrentActivation.apply(tfc.add(xF, hF));\n            const c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(tfc.add(xC, hC))));\n            const h = tfc.mul(this.recurrentActivation.apply(tfc.add(xO, hO)), this.activation.apply(c));\n            return [h, h, c];\n        });\n    }\n    getConfig() {\n        const _a = super.getConfig(), { 'units': _ } = _a, baseConfig = __rest(_a, ['units']);\n        const config = {\n            filters: this.filters,\n            kernelSize: this.kernelSize,\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n            dilationRate: this.dilationRate,\n            strides: this.strides,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n    inputConv(x, w, b, padding) {\n        const out = tfc.conv2d(x, w, this.strides, (padding || 'valid'), this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC', this.dilationRate);\n        if (b) {\n            return K.biasAdd(out, b, this.dataFormat);\n        }\n        return out;\n    }\n    recurrentConv(x, w) {\n        const strides = 1;\n        return tfc.conv2d(x, w, strides, 'same', this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC');\n    }\n}\n/** @nocollapse */\nConvLSTM2DCell.className = 'ConvLSTM2DCell';\ntfc.serialization.registerClass(ConvLSTM2DCell);\nexport class ConvLSTM2D extends ConvRNN2D {\n    constructor(args) {\n        const cell = new ConvLSTM2DCell(args);\n        super(Object.assign({}, args, { cell }));\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nConvLSTM2D.className = 'ConvLSTM2D';\ntfc.serialization.registerClass(ConvLSTM2D);\n//# sourceMappingURL=convolutional_recurrent.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Basic Layers.\n */\nimport { any, notEqual, serialization, tidy, transpose, util } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport * as K from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { assertPositiveInteger, mapActivationToFusedKernel } from '../utils/generic_utils';\nimport { arrayProd, range } from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class Dropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.rate = Math.max(Math.min(args.rate, 1), 0);\n        // So that the scalar doesn't get tidied up between executions.\n        this.noiseShape = args.noiseShape;\n        this.seed = args.seed;\n        this.supportsMasking = true;\n    }\n    getNoiseShape(input) {\n        if (this.noiseShape == null) {\n            return this.noiseShape;\n        }\n        const inputShape = input.shape;\n        const noiseShape = [];\n        for (let i = 0; i < this.noiseShape.length; ++i) {\n            noiseShape.push(this.noiseShape[i] == null ? inputShape[i] : this.noiseShape[i]);\n        }\n        return noiseShape;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            if (0 < this.rate && this.rate < 1) {\n                const training = kwargs['training'] == null ? false : kwargs['training'];\n                const noiseShape = this.getNoiseShape(input);\n                const output = K.inTrainPhase(() => K.dropout(input, this.rate, noiseShape, this.seed), () => input, training);\n                return output;\n            }\n            return inputs;\n        });\n    }\n    getConfig() {\n        const config = {\n            rate: this.rate,\n            noiseShape: this.noiseShape,\n            seed: this.seed,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    dispose() {\n        return super.dispose();\n    }\n}\n/** @nocollapse */\nDropout.className = 'Dropout';\nserialization.registerClass(Dropout);\nexport class SpatialDropout1D extends Dropout {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [{ ndim: 3 }];\n    }\n    getNoiseShape(input) {\n        const inputShape = input.shape;\n        return [inputShape[0], 1, inputShape[2]];\n    }\n}\n/** @nocollapse */\nSpatialDropout1D.className = 'SpatialDropout1D';\nserialization.registerClass(SpatialDropout1D);\nexport class Dense extends Layer {\n    constructor(args) {\n        super(args);\n        // Default activation: Linear (none).\n        this.activation = null;\n        this.useBias = true;\n        this.kernel = null;\n        this.bias = null;\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        if (args.batchInputShape == null && args.inputShape == null &&\n            args.inputDim != null) {\n            // This logic is copied from Layer's constructor, since we can't\n            // do exactly what the Python constructor does for Dense().\n            let batchSize = null;\n            if (args.batchSize != null) {\n                batchSize = args.batchSize;\n            }\n            this.batchInputShape = [batchSize, args.inputDim];\n        }\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation);\n        if (args.useBias != null) {\n            this.useBias = args.useBias;\n        }\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.supportsMasking = true;\n        this.inputSpec = [{ minNDim: 2 }];\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const inputLastDim = inputShape[inputShape.length - 1];\n        if (this.kernel == null) {\n            this.kernel = this.addWeight('kernel', [inputLastDim, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n            if (this.useBias) {\n                this.bias = this.addWeight('bias', [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n            }\n        }\n        this.inputSpec = [{ minNDim: 2, axes: { [-1]: inputLastDim } }];\n        this.built = true;\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        outputShape[outputShape.length - 1] = this.units;\n        return outputShape;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Dense layer accepts only a single input.\n            const input = getExactlyOneTensor(inputs);\n            const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());\n            let output;\n            if (fusedActivationName != null) {\n                output = K.dot(input, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null);\n            }\n            else {\n                output = K.dot(input, this.kernel.read());\n                if (this.bias != null) {\n                    output = K.biasAdd(output, this.bias.read());\n                }\n                if (this.activation != null) {\n                    output = this.activation.apply(output);\n                }\n            }\n            return output;\n        });\n    }\n    getConfig() {\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nDense.className = 'Dense';\nserialization.registerClass(Dense);\nexport class Flatten extends Layer {\n    constructor(args) {\n        args = args || {};\n        super(args);\n        this.inputSpec = [{ minNDim: 3 }];\n        this.dataFormat = args.dataFormat;\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        for (const dim of inputShape.slice(1)) {\n            if (dim == null) {\n                throw new ValueError(`The shape of the input to \"Flatten\" is not fully defined ` +\n                    `(got ${inputShape.slice(1)}). Make sure to pass a complete ` +\n                    `\"input_shape\" or \"batch_input_shape\" argument to the first ` +\n                    `layer in your model.`);\n            }\n        }\n        return [inputShape[0], arrayProd(inputShape, 1)];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            let input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsFirst' && input.rank > 1) {\n                const permutation = [0];\n                for (let i = 2; i < input.rank; ++i) {\n                    permutation.push(i);\n                }\n                permutation.push(1);\n                input = input.transpose(permutation);\n            }\n            return K.batchFlatten(input);\n        });\n    }\n    getConfig() {\n        const config = {};\n        if (this.dataFormat != null) {\n            config['dataFormat'] = this.dataFormat;\n        }\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nFlatten.className = 'Flatten';\nserialization.registerClass(Flatten);\nexport class Activation extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.activation = getActivation(args.activation);\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            return this.activation.apply(input);\n        });\n    }\n    getConfig() {\n        const config = { activation: serializeActivation(this.activation) };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nActivation.className = 'Activation';\nserialization.registerClass(Activation);\nexport class RepeatVector extends Layer {\n    constructor(args) {\n        super(args);\n        this.n = args.n;\n        this.inputSpec = [{ ndim: 2 }];\n    }\n    computeOutputShape(inputShape) {\n        return [inputShape[0], this.n, inputShape[1]];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            return K.repeat(inputs, this.n);\n        });\n    }\n    getConfig() {\n        const config = {\n            n: this.n,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nRepeatVector.className = 'RepeatVector';\nserialization.registerClass(RepeatVector);\nexport class Reshape extends Layer {\n    constructor(args) {\n        super(args);\n        this.targetShape = args.targetShape;\n        // Make sure that all unknown dimensions are represented as `null`.\n        for (let i = 0; i < this.targetShape.length; ++i) {\n            if (this.isUnknown(this.targetShape[i])) {\n                this.targetShape[i] = null;\n            }\n        }\n    }\n    isUnknown(dim) {\n        return dim < 0 || dim == null;\n    }\n    /**\n     * Finds and replaces a missing dimension in output shape.\n     *\n     * This is a near direct port of the internal Numpy function\n     * `_fix_unknown_dimension` in `numpy/core/src/multiarray/shape.c`.\n     *\n     * @param inputShape: Original shape of array begin reshape.\n     * @param outputShape: Target shape of the array, with at most a single\n     * `null` or negative number, which indicates an underdetermined dimension\n     * that should be derived from `inputShape` and the known dimensions of\n     *   `outputShape`.\n     * @returns: The output shape with `null` replaced with its computed value.\n     * @throws: ValueError: If `inputShape` and `outputShape` do not match.\n     */\n    fixUnknownDimension(inputShape, outputShape) {\n        const errorMsg = 'Total size of new array must be unchanged.';\n        const finalShape = outputShape.slice();\n        let known = 1;\n        let unknown = null;\n        for (let i = 0; i < finalShape.length; ++i) {\n            const dim = finalShape[i];\n            if (this.isUnknown(dim)) {\n                if (unknown === null) {\n                    unknown = i;\n                }\n                else {\n                    throw new ValueError('Can only specifiy one unknown dimension.');\n                }\n            }\n            else {\n                known *= dim;\n            }\n        }\n        const originalSize = arrayProd(inputShape);\n        if (unknown !== null) {\n            if (known === 0 || originalSize % known !== 0) {\n                throw new ValueError(errorMsg);\n            }\n            finalShape[unknown] = originalSize / known;\n        }\n        else if (originalSize !== known) {\n            throw new ValueError(errorMsg);\n        }\n        return finalShape;\n    }\n    computeOutputShape(inputShape) {\n        let anyUnknownDims = false;\n        for (let i = 0; i < inputShape.length; ++i) {\n            if (this.isUnknown(inputShape[i])) {\n                anyUnknownDims = true;\n                break;\n            }\n        }\n        if (anyUnknownDims) {\n            return inputShape.slice(0, 1).concat(this.targetShape);\n        }\n        else {\n            return inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            const outputShape = inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n            return input.reshape(outputShape);\n        });\n    }\n    getConfig() {\n        const config = {\n            targetShape: this.targetShape,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nReshape.className = 'Reshape';\nserialization.registerClass(Reshape);\nexport class Permute extends Layer {\n    constructor(args) {\n        super(args);\n        if (args.dims == null) {\n            throw new Error('Required configuration field `dims` is missing during Permute ' +\n                'constructor call.');\n        }\n        if (!Array.isArray(args.dims)) {\n            throw new Error('Permute constructor requires `dims` to be an Array, but received ' +\n                `${args.dims} instead.`);\n        }\n        // Check the validity of the permutation indices.\n        const expectedSortedIndices = range(1, args.dims.length + 1);\n        if (!util.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) {\n            throw new Error('Invalid permutation `dims`: ' + JSON.stringify(args.dims) +\n                ' `dims` must contain consecutive integers starting from 1.');\n        }\n        this.dims = args.dims;\n        this.dimsIncludingBatch = [0].concat(this.dims);\n        this.inputSpec = [new InputSpec({ ndim: this.dims.length + 1 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        this.dims.forEach((dim, i) => {\n            outputShape[i + 1] = inputShape[dim];\n        });\n        return outputShape;\n    }\n    call(inputs, kwargs) {\n        return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch);\n    }\n    getConfig() {\n        const config = {\n            dims: this.dims,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nPermute.className = 'Permute';\nserialization.registerClass(Permute);\nexport class Masking extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.supportsMasking = true;\n        if (args != null) {\n            this.maskValue = args.maskValue == null ? 0 : args.maskValue;\n        }\n        else {\n            this.maskValue = 0;\n        }\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { maskValue: this.maskValue };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    computeMask(inputs, mask) {\n        const input = getExactlyOneTensor(inputs);\n        const axis = -1;\n        return any(notEqual(input, this.maskValue), axis);\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const axis = -1;\n            const keepDims = true;\n            const booleanMask = any(notEqual(input, this.maskValue), axis, keepDims);\n            const output = input.mul(booleanMask.asType(input.dtype));\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMasking.className = 'Masking';\nserialization.registerClass(Masking);\n//# sourceMappingURL=core.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Embedding Layer.\n *\n * Original source: keras/constraints.py\n */\nimport { notEqual, serialization, tidy, zerosLike } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class Embedding extends Layer {\n    constructor(args) {\n        super(args);\n        this.embeddings = null;\n        this.DEFAULT_EMBEDDINGS_INITIALIZER = 'randomUniform';\n        if (args.batchInputShape == null && args.inputShape == null) {\n            // Porting Note: This logic is copied from Layer's constructor, since we\n            // can't do exactly what the Python constructor does for Embedding().\n            // Specifically, the super constructor can not be called after the\n            // mutation of the `config` argument.\n            let batchSize = null;\n            if (args.batchSize != null) {\n                batchSize = args.batchSize;\n            }\n            if (args.inputLength == null) {\n                // Fix super-constructor to what it would have done if\n                // 'config.inputShape' were (None, )\n                this.batchInputShape = [batchSize, null];\n            }\n            else {\n                // Fix super-constructor to what it would have done if\n                // 'config.inputShape' were (config.inputLength, )\n                this.batchInputShape =\n                    [batchSize].concat(generic_utils.toList(args.inputLength));\n            }\n        }\n        this.inputDim = args.inputDim;\n        generic_utils.assertPositiveInteger(this.inputDim, 'inputDim');\n        this.outputDim = args.outputDim;\n        generic_utils.assertPositiveInteger(this.outputDim, 'outputDim');\n        this.embeddingsInitializer = getInitializer(args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER);\n        this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.embeddingsConstraint = getConstraint(args.embeddingsConstraint);\n        this.maskZero = args.maskZero;\n        this.supportsMasking = args.maskZero;\n        this.inputLength = args.inputLength;\n    }\n    build(inputShape) {\n        this.embeddings = this.addWeight('embeddings', [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint);\n        this.built = true;\n    }\n    // Override warnOnIncompatibleInputShape because an embedding layer allows\n    // the input to have varying ranks.\n    warnOnIncompatibleInputShape(inputShape) { }\n    computeMask(inputs, mask) {\n        return tidy(() => {\n            if (!this.maskZero) {\n                return null;\n            }\n            else {\n                inputs = getExactlyOneTensor(inputs);\n                return notEqual(inputs, zerosLike(inputs));\n            }\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (this.inputLength == null) {\n            return [...inputShape, this.outputDim];\n        }\n        // inputLength can be an array if input is 3D or higher.\n        const inLens = generic_utils.toList(this.inputLength);\n        if (inLens.length !== inputShape.length - 1) {\n            throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received ` +\n                `input shape has shape ${inputShape}`);\n        }\n        else {\n            let i = 0;\n            for (let k = 0; k < inLens.length; ++k) {\n                const s1 = inLens[k];\n                const s2 = inputShape[k + 1];\n                if ((s1 != null) && (s2 != null) && (s1 !== s2)) {\n                    throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received ` +\n                        `input shape has shape ${inputShape}`);\n                }\n                else if (s1 == null) {\n                    inLens[i] = s2;\n                }\n                i++;\n            }\n        }\n        return [inputShape[0], ...inLens, this.outputDim];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Embedding layer accepts only a single input.\n            let input = getExactlyOneTensor(inputs);\n            if (input.dtype !== 'int32') {\n                input = K.cast(input, 'int32');\n            }\n            const output = K.gather(this.embeddings.read(), input.as1D());\n            return output.reshape(getExactlyOneShape(this.computeOutputShape(input.shape)));\n        });\n    }\n    getConfig() {\n        const config = {\n            inputDim: this.inputDim,\n            outputDim: this.outputDim,\n            embeddingsInitializer: serializeInitializer(this.embeddingsInitializer),\n            embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            embeddingsConstraint: serializeConstraint(this.embeddingsConstraint),\n            maskZero: this.maskZero,\n            inputLength: this.inputLength\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nEmbedding.className = 'Embedding';\nserialization.registerClass(Embedding);\n//# sourceMappingURL=embeddings.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Merge Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { l2Normalize } from '../losses';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as mathUtils from '../utils/math_utils';\nimport { getExactlyOneShape } from '../utils/types_utils';\n/**\n * Generic Merge layer for element-wise merge functions.\n *\n * Used to implement `Sum`, `Average`, `Concatenate`, etc.\n */\nexport class Merge extends Layer {\n    constructor(args) {\n        super(args || {});\n        this.supportsMasking = true;\n    }\n    /**\n     * Logic for merging multiple tensors, to be overridden by subclasses.\n     * @param inputs\n     */\n    mergeFunction(inputs) {\n        throw new NotImplementedError();\n    }\n    /**\n     * Computes the shape of the result of an elementwise operation.\n     *\n     * @param shape1: Shape of the first tensor.\n     * @param shape2: Shape of the second tensor.\n     * @returns Expected output shape when an elementwise operation is carried\n     *   out on 2 tensors with shapes `shape1` and `shape2`.\n     * @throws ValueError: If `shape1` and `shape2` are not compatible for\n     *   element-wise operations.\n     */\n    computeElementwiseOpOutputShape(shape1, shape2) {\n        if (shape1 == null || shape2 == null) {\n            return null;\n        }\n        else if (shape1.length < shape2.length) {\n            return this.computeElementwiseOpOutputShape(shape2, shape1);\n        }\n        else if (shape2.length === 0) {\n            return shape1;\n        }\n        const outputShape = shape1.slice(0, shape1.length - shape2.length);\n        for (let k = 0; k < shape2.length; ++k) {\n            const i = shape1[shape1.length - shape2.length + k];\n            const j = shape2[k];\n            if (i == null || j == null || i < 0 || j < 0) {\n                outputShape.push(null);\n            }\n            else if (i === 1) {\n                outputShape.push(j);\n            }\n            else if (j === 1) {\n                outputShape.push(i);\n            }\n            else {\n                if (i !== j) {\n                    throw new ValueError('Operands could not be broadcast together with shapes ' +\n                        JSON.stringify(shape1) + ' ' + JSON.stringify(shape2));\n                }\n                outputShape.push(i);\n            }\n        }\n        return outputShape;\n    }\n    build(inputShape) {\n        // Used purely for shape validation.\n        if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) {\n            // Make sure that inputShape is an Array of shape.\n            inputShape = [getExactlyOneShape(inputShape)];\n        }\n        inputShape = inputShape;\n        if (inputShape.length < 2) {\n            throw new ValueError('A merge layer should be called on an Array of at least 2 inputs.' +\n                ` Got ${inputShape.length} input(s).`);\n        }\n        // Make sure that there is at most one unique batch size among the input\n        // shapes.\n        let batchSizes = [];\n        for (const shape of inputShape) {\n            if (shape != null && shape[0] !== null) {\n                batchSizes.push(shape[0]);\n            }\n        }\n        batchSizes = generic_utils.unique(batchSizes);\n        if (batchSizes.length > 1) {\n            throw new ValueError(`Can not merge tensors with different batch sizes. ` +\n                `Got tensors with shapes: ${JSON.stringify(inputShape)}.`);\n        }\n        let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1);\n        for (let i = 1; i < inputShape.length; ++i) {\n            const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n            outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n        }\n        // If the inputs have different ranks, we have to reshape them to make them\n        // broadcastable.\n        const allRanks = inputShape.map(shape => shape.length);\n        if (inputShape.indexOf(null) === -1 &&\n            generic_utils.unique(allRanks).length === 1) {\n            this.reshapeRequired = false;\n        }\n        else {\n            this.reshapeRequired = true;\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (this.reshapeRequired) {\n                const reshapedInputs = [];\n                const inputDims = inputs.map(input => input.rank);\n                if (inputDims.indexOf(null) === -1) {\n                    // If ranks of all inputs are available, we simply expand each of them\n                    // at axis=1 until all of them have the same rank.\n                    const maxNDim = mathUtils.max(inputDims);\n                    for (let x of inputs) {\n                        const xNDim = x.rank;\n                        for (let k = 0; k < maxNDim - xNDim; ++k) {\n                            x = K.expandDims(x, 1);\n                        }\n                        reshapedInputs.push(x);\n                    }\n                    return this.mergeFunction(reshapedInputs);\n                }\n                else {\n                    // Transpose all inputs so that batch size is the last dimension.\n                    // [batchSize, dim1, dim2, ...] -> [dim1, dim2, ..., batchSize]\n                    let transposed = false;\n                    for (const x of inputs) {\n                        const xNDim = x.rank;\n                        if (xNDim == null) {\n                            const xShape = x.shape;\n                            const batchSize = xShape[0];\n                            const newShape = xShape.slice(1).concat([batchSize]);\n                            let xTransposed = x.reshape([batchSize].concat(mathUtils.arrayProd(xShape.slice(1))));\n                            xTransposed = tfc.transpose(xTransposed, [1, 0]);\n                            xTransposed = xTransposed.reshape(newShape);\n                            reshapedInputs.push(xTransposed);\n                            transposed = true;\n                        }\n                        else if (xNDim > 1) {\n                            const dims = mathUtils.range(1, xNDim).concat([0]);\n                            reshapedInputs.push(tfc.transpose(x, dims));\n                            transposed = true;\n                        }\n                        else {\n                            // We don't transpose inputs if they are 1D vectors or scalars.\n                            reshapedInputs.push(x);\n                        }\n                    }\n                    let y = this.mergeFunction(reshapedInputs);\n                    const yNDim = y.rank;\n                    if (transposed) {\n                        // If inputs have been transposed, we have to transpose the output\n                        // too.\n                        if (yNDim == null) {\n                            const yShape = y.shape;\n                            const yNDim = yShape.length;\n                            const batchSize = yShape[yNDim - 1];\n                            const newShape = [batchSize].concat(yShape.slice(0, yShape.length - 1));\n                            y = tfc.transpose(y.reshape([-1, batchSize]), [1, 0])\n                                .reshape(newShape);\n                        }\n                        else if (yNDim > 1) {\n                            const dims = [yNDim - 1].concat(mathUtils.range(0, yNDim - 1));\n                            y = tfc.transpose(y, dims);\n                        }\n                    }\n                    return y;\n                }\n            }\n            else {\n                return this.mergeFunction(inputs);\n            }\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = inputShape;\n        let outputShape;\n        if (inputShape[0] == null) {\n            outputShape = null;\n        }\n        else {\n            outputShape = inputShape[0].slice(1);\n        }\n        for (let i = 1; i < inputShape.length; ++i) {\n            const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n            outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n        }\n        let batchSizes = [];\n        for (const shape of inputShape) {\n            if (shape != null && shape[0] !== null) {\n                batchSizes.push(shape[0]);\n            }\n        }\n        batchSizes = generic_utils.unique(batchSizes);\n        if (batchSizes.length === 1) {\n            outputShape = batchSizes.concat(outputShape);\n        }\n        else {\n            outputShape = [null].concat(outputShape);\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        return tfc.tidy(() => {\n            if (mask == null) {\n                return null;\n            }\n            if (!Array.isArray(mask)) {\n                throw new ValueError('`mask` should be an Array');\n            }\n            if (!Array.isArray(inputs)) {\n                throw new ValueError('`inputs` should be an Array');\n            }\n            if (mask.length !== inputs.length) {\n                throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same ` +\n                    `length, but have different lengths ` +\n                    `(${inputs.length} vs ${mask.length})`);\n            }\n            if (mask.every(m => m == null)) {\n                return null;\n            }\n            mask = mask.map(m => m == null ? m : tfc.expandDims(m, 0));\n            let output = mask[0];\n            for (let i = 1; i < mask.length - 1; ++i) {\n                output = tfc.logicalAnd(output, mask[i]);\n            }\n            return output;\n        });\n    }\n}\nexport class Add extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.add(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nAdd.className = 'Add';\nserialization.registerClass(Add);\n/**\n * Calculate the element-wise sum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Add` layer, by using no input argument\n *    or a single configuration argument. The resultant `Add` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const addLayer = tf.layers.add();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = addLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.add([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.add([input1, input2]).print();\n * // Gives [[11, 22], [33, 44]].\n *\n */\nexport function add(config) {\n    if (Array.isArray(config)) {\n        const layer = new Add({});\n        return layer.apply(config);\n    }\n    else {\n        return new Add(config);\n    }\n}\nexport class Multiply extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.mul(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMultiply.className = 'Multiply';\nserialization.registerClass(Multiply);\n/**\n * Calculate the element-wise product of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Multiply` layer, by using no input argument\n *    or a single configuration argument. The resultant `Multiply` layer can\n *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const multiplyLayer = tf.layers.multiply();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = multiplyLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.multiply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.multiply([input1, input2]).print();\n * // Gives [[10, 40], [90, 160]].\n *\n */\nexport function multiply(config) {\n    if (Array.isArray(config)) {\n        const layer = new Multiply({});\n        return layer.apply(config);\n    }\n    else {\n        return new Multiply(config);\n    }\n}\nexport class Average extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.add(output, inputs[i]);\n            }\n            return tfc.mul(1 / inputs.length, output);\n        });\n    }\n}\n/** @nocollapse */\nAverage.className = 'Average';\nserialization.registerClass(Average);\n/**\n * Calculate the element-wise arithmetic mean of inputs, which all have the same\n * shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Average` layer, by using no input argument\n *    or a single configuration argument. The resultant `Average` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const averageLayer = tf.layers.average();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = averageLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.average([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.average([input1, input2]).print();\n * // Gives [[5.5, 11], [16.5, 22]].\n *\n */\nexport function average(config) {\n    if (Array.isArray(config)) {\n        const layer = new Average({});\n        return layer.apply(config);\n    }\n    else {\n        return new Average(config);\n    }\n}\nexport class Maximum extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0];\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.maximum(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMaximum.className = 'Maximum';\nserialization.registerClass(Maximum);\n/**\n * Calculate the element-wise maximum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Maximum` layer, by using no input argument\n *    or a single configuration argument. The resultant `Maximum` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const maximumLayer = tf.layers.maximum();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = maximumLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.maximum([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);\n * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);\n * tf.layers.maximum([input1, input2]).print();\n * // Gives [[10, 20], [30, 40]].\n *\n */\nexport function maximum(config) {\n    if (Array.isArray(config)) {\n        const layer = new Maximum({});\n        return layer.apply(config);\n    }\n    else {\n        return new Maximum(config);\n    }\n}\nexport class Minimum extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0];\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.minimum(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMinimum.className = 'Minimum';\nserialization.registerClass(Minimum);\n/**\n * Calculate the element-wise minimum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Minimum` layer, by using no input argument\n *    or a single configuration argument. The resultant `Minimum` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const minimumLayer = tf.layers.minimum();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = minimumLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.minimum([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);\n * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);\n * tf.layers.minimum([input1, input2]).print();\n * // Gives [[1, 2], [3, 4]].\n *\n */\nexport function minimum(config) {\n    if (Array.isArray(config)) {\n        const layer = new Minimum({});\n        return layer.apply(config);\n    }\n    else {\n        return new Minimum(config);\n    }\n}\nexport class Concatenate extends Merge {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_AXIS = -1;\n        if (args == null) {\n            args = {};\n        }\n        this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n        this.supportsMasking = true;\n        this.reshapeRequired = false;\n    }\n    build(inputShape) {\n        // Used purely for shape validation.]\n        if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) ||\n            inputShape.length === 1) {\n            throw new ValueError('A `Concatenate` layer should be called on a list of at least 2 ' +\n                'inputs');\n        }\n        inputShape = inputShape;\n        let allNoneShape = true;\n        for (const shape of inputShape) {\n            if (shape != null) {\n                allNoneShape = false;\n                break;\n            }\n        }\n        if (allNoneShape) {\n            return;\n        }\n        const shapeSet = [];\n        for (let i = 0; i < inputShape.length; ++i) {\n            const shapeWithoutConcatAxis = inputShape[i].slice();\n            shapeWithoutConcatAxis.splice(this.axis, 1);\n            let exists = false;\n            for (const shape of shapeSet) {\n                if (util.arraysEqual(shape, shapeWithoutConcatAxis)) {\n                    exists = true;\n                    break;\n                }\n            }\n            if (!exists) {\n                shapeSet.push(shapeWithoutConcatAxis);\n            }\n        }\n        if (shapeSet.length > 1) {\n            throw new ValueError('A `Concatenate` layer requires inputs with matching shapes ' +\n                'except for the concat axis. Got input shapes: ' +\n                JSON.stringify(inputShape));\n        }\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            return K.concatenate(inputs, this.axis);\n        });\n    }\n    computeOutputShape(inputShape) {\n        if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) {\n            throw new ValueError('A `Concatenate` layer should be called on a list of inputs.');\n        }\n        const inputShapes = inputShape;\n        const outputShape = inputShapes[0].slice();\n        const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis;\n        // Porting Note: the line above is because TypeScript doesn't support\n        //   negative indices.\n        for (const shape of inputShapes.slice(1)) {\n            if (outputShape[axis] == null || shape[axis] == null) {\n                outputShape[axis] = null;\n                break;\n            }\n            outputShape[axis] += shape[axis];\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        if (mask == null) {\n            return null;\n        }\n        if (!Array.isArray(mask)) {\n            throw new ValueError('`mask` should be an array for Concatenate');\n        }\n        if (!Array.isArray(inputs)) {\n            throw new ValueError('`inputs` should be an array for Concatenate');\n        }\n        if (mask.length !== inputs.length) {\n            throw new ValueError(`Mismatch in the length of mask (${mask.length}) ` +\n                `and the legnth of inputs (${inputs.length})`);\n        }\n        return tfc.tidy(() => {\n            let allNullMasks = true;\n            mask.forEach(m => {\n                if (m != null) {\n                    allNullMasks = false;\n                    return;\n                }\n            });\n            if (allNullMasks) {\n                return null;\n            }\n            const outputMasks = [];\n            for (let i = 0; i < inputs.length; ++i) {\n                if (mask[i] == null) {\n                    // Input is unmasked. Append all 1's to masks.\n                    outputMasks.push(tfc.onesLike(inputs[i]).asType('bool'));\n                }\n                else if (mask[i].rank < inputs[i].rank) {\n                    // Mask is smaller than the input, expand it.\n                    outputMasks.push(tfc.expandDims(mask[i], -1));\n                }\n                else {\n                    outputMasks.push(mask[i]);\n                }\n            }\n            const concatenatedMasks = tfc.concat(outputMasks, this.axis);\n            return tfc.all(concatenatedMasks, -1, false);\n        });\n    }\n    getConfig() {\n        const config = {\n            'axis': this.axis,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nConcatenate.className = 'Concatenate';\nserialization.registerClass(Concatenate);\n/**\n * Concatenate an `Array` of inputs.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Concatenate` layer, by using no input argument\n *    or a single configuration argument. The resultant `Concatenate` layer can\n *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const concatLayer = tf.layers.concatenate();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 3]});\n * const input2 = tf.input({shape: [2, 4]});\n * const output = concatLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 7], with the first dimension as the undetermined batch\n * // dimension and the last dimension as the result of concatenating the\n * // last dimensions of the two inputs.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 3]});\n * const input2 = tf.input({shape: [2, 4]});\n * const output = tf.layers.concatenate([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension and the last dimension as the result of concatenating the\n * // last dimensions of the two inputs.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);\n * const input2 = tf.tensor2d([[10, 20], [30, 40]], [2, 2]);\n * tf.layers.concatenate([input1, input2]).print();\n * // Gives [[1, 2, 10, 20], [3, 4, 30, 40]].\n *\n */\nexport function concatenate(config) {\n    if (Array.isArray(config)) {\n        const layer = new Concatenate({});\n        return layer.apply(config);\n    }\n    else {\n        return new Concatenate(config);\n    }\n}\n/**\n * Interpretable potentially negative axis index.\n *\n * For example, given axis = -1, and dim = 3, this function will return 2.\n *\n * @param axis The axis index, may be a positive, zero or negative integer.\n * @param dim Total number of dimensions, a positive integer.\n * @returns A non-negative axis index equivalent to the input `axis`.\n */\nfunction interpretAxis(axis, dim) {\n    while (axis < 0) {\n        axis += dim;\n    }\n    return axis;\n}\nfunction batchDot(x, y, axes) {\n    if (x.shape.length > 3 || y.shape.length > 3) {\n        throw new NotImplementedError('batchDot is not implemented for tensors of 4D or higher rank yet');\n    }\n    tfc.util.assert(x.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, ` +\n        `but got ${x.shape.length}`);\n    tfc.util.assert(x.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, ` +\n        `but got ${y.shape.length}`);\n    if (typeof axes === 'number') {\n        axes = [axes, axes];\n    }\n    if (x.dtype === 'complex64' || y.dtype === 'complex64') {\n        throw new NotImplementedError('batchDot is not implemented for complex64-type Tensors yet.');\n    }\n    const xNDim = x.shape.length;\n    const yNDim = y.shape.length;\n    if (axes == null) {\n        // Behave like batchMatmul by default.\n        axes = [xNDim - 1, yNDim - 2];\n    }\n    const axesArray = axes;\n    return tfc.tidy(() => {\n        let diff;\n        if (xNDim > yNDim) {\n            diff = xNDim - yNDim;\n            const diffShape = [];\n            for (let i = 0; i < diff; ++i) {\n                diffShape.push(1);\n            }\n            y = y.reshape(y.shape.concat(diffShape));\n        }\n        else if (yNDim > xNDim) {\n            diff = yNDim - xNDim;\n            const diffShape = [];\n            for (let i = 0; i < diff; ++i) {\n                diffShape.push(1);\n            }\n            x = x.reshape(x.shape.concat(diffShape));\n        }\n        else {\n            diff = 0;\n        }\n        let out;\n        if (x.shape.length === 2 && y.shape.length === 2) {\n            if (axesArray[0] === axesArray[1]) {\n                out = x.mul(y).sum(axesArray[0]);\n            }\n            else {\n                out = x.transpose([1, 0]).mul(y).sum(axesArray[1]);\n            }\n        }\n        else {\n            const adjX = axesArray[0] !== x.shape.length - 1;\n            const adjY = axesArray[1] === y.shape.length - 1;\n            out = x.matMul(y, adjX, adjY);\n        }\n        if (diff > 0) {\n            let idx;\n            if (xNDim > yNDim) {\n                idx = xNDim + yNDim - 3;\n            }\n            else {\n                idx = xNDim - 1;\n            }\n            const squeezeAxes = [];\n            for (let i = idx; i < idx + diff; ++i) {\n                squeezeAxes.push(i);\n            }\n            out = out.squeeze(squeezeAxes);\n        }\n        if (out.shape.length === 1) {\n            out = out.expandDims(1);\n        }\n        return out;\n    });\n}\nexport class Dot extends Merge {\n    constructor(args) {\n        super(args);\n        this.axes = args.axes;\n        this.normalize = args.normalize == null ? false : args.normalize;\n        this.supportsMasking = true;\n        this.reshapeRequired = false;\n    }\n    build(inputShape) {\n        tfc.util.assert(Array.isArray(inputShape) && inputShape.length === 2 &&\n            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');\n        const shape1 = inputShape[0];\n        const shape2 = inputShape[1];\n        if (shape1.length > 3 || shape2.length > 3) {\n            throw new NotImplementedError('Dot layer does not support tensors of 4D or higher rank yet.');\n        }\n        const axes = this.interpretAxes(shape1, shape2);\n        if (shape1[axes[0]] !== shape2[axes[1]]) {\n            throw new ValueError(`Dimension incompatibility: ` +\n                `${shape1[axes[0]]} !== ${shape2[axes[1]]}`);\n        }\n    }\n    mergeFunction(inputs) {\n        if (inputs.length !== 2) {\n            throw new ValueError('A `Dot` layer must be called on exactly 2 inputs, ' +\n                `but received ${inputs.length} input(s).`);\n        }\n        let x1 = inputs[0];\n        let x2 = inputs[1];\n        let axes;\n        if (!Array.isArray(this.axes)) {\n            axes = [\n                interpretAxis(this.axes, x1.shape.length),\n                interpretAxis(this.axes, x2.shape.length)\n            ];\n        }\n        else {\n            axes = this.axes.map((axis, i) => interpretAxis(axis, inputs[i].shape.length));\n        }\n        if (this.normalize) {\n            x1 = l2Normalize(x1, axes[0]);\n            x2 = l2Normalize(x2, axes[1]);\n        }\n        return batchDot(x1, x2, axes);\n    }\n    interpretAxes(shape1, shape2) {\n        let axes;\n        if (!Array.isArray(this.axes)) {\n            // `this.axes` is a single integer.\n            axes = [\n                interpretAxis(this.axes, shape1.length),\n                interpretAxis(this.axes, shape2.length)\n            ];\n        }\n        else {\n            // `this.axes` is an Array of integers.\n            axes = this.axes;\n        }\n        return axes;\n    }\n    computeOutputShape(inputShape) {\n        tfc.util.assert(Array.isArray(inputShape) && inputShape.length === 2 &&\n            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');\n        const shape1 = inputShape[0].slice();\n        const shape2 = inputShape[1].slice();\n        if (shape1.length > 3 || shape2.length > 3) {\n            throw new NotImplementedError('Dot layer does not support tensors of 4D or higher rank yet.');\n        }\n        const axes = this.interpretAxes(shape1, shape2);\n        shape1.splice(axes[0], 1);\n        shape2.splice(axes[1], 1);\n        shape2.splice(0, 1);\n        const outputShape = shape1.concat(shape2);\n        if (outputShape.length === 1) {\n            outputShape.push(1);\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        return null;\n    }\n    getConfig() {\n        const config = {\n            'axes': this.axes,\n            'normalize': this.normalize\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nDot.className = 'Dot';\nserialization.registerClass(Dot);\n// TODO(cais): Add functional interfaces for the merge layers.\n//# sourceMappingURL=merge.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Noise Layers.\n */\nimport { greaterEqual, randomUniform, serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { Layer } from '../engine/topology';\nimport { getExactlyOneTensor } from '../utils/types_utils';\nexport class GaussianNoise extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.stddev = args.stddev;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { stddev: this.stddev };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const noised = () => K.randomNormal(input.shape, 0, this.stddev).add(input);\n            const output = K.inTrainPhase(noised, () => input, kwargs['training'] || false);\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nGaussianNoise.className = 'GaussianNoise';\nserialization.registerClass(GaussianNoise);\nexport class GaussianDropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.rate = args.rate;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { rate: this.rate };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            if (this.rate > 0 && this.rate < 1) {\n                const noised = () => {\n                    const stddev = Math.sqrt(this.rate / (1 - this.rate));\n                    return input.mul(K.randomNormal(input.shape, 1, stddev));\n                };\n                return K.inTrainPhase(noised, () => input, kwargs['training'] || false);\n            }\n            return input;\n        });\n    }\n}\n/** @nocollapse */\nGaussianDropout.className = 'GaussianDropout';\nserialization.registerClass(GaussianDropout);\n/**\n * Applies Alpha Dropout to the input.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs\n * to their original values, in order to ensure the self-normalizing property\n * even after this dropout.\n * Alpha Dropout fits well to Scaled Exponential Linear Units\n * by randomly setting activations to the negative saturation value.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the\n *     shape for randomly generated keep/drop flags.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n */\nexport class AlphaDropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.rate = args.rate;\n        this.noiseShape = args.noiseShape;\n    }\n    _getNoiseShape(inputs) {\n        return this.noiseShape || getExactlyOneTensor(inputs).shape;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { rate: this.rate };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.rate < 1 && this.rate > 0) {\n                const noiseShape = this._getNoiseShape(inputs);\n                const droppedInputs = () => {\n                    const input = getExactlyOneTensor(inputs);\n                    const alpha = 1.6732632423543772848170429916717;\n                    const scale = 1.0507009873554804934193349852946;\n                    const alphaP = -alpha * scale;\n                    let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate);\n                    keptIdx = K.cast(keptIdx, 'float32'); // get default dtype.\n                    // Get affine transformation params.\n                    const a = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5;\n                    const b = -a * alphaP * this.rate;\n                    // Apply mask.\n                    const x = input.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP));\n                    return x.mul(a).add(b);\n                };\n                return K.inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs['training'] || false);\n            }\n            return inputs;\n        });\n    }\n}\n/** @nocollapse */\nAlphaDropout.className = 'AlphaDropout';\nserialization.registerClass(AlphaDropout);\n//# sourceMappingURL=noise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Normalization layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { moments, serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Applies batch normalization on x given mean, var, beta and gamma.\n *\n * I.e. returns:\n *   `output = (x - mean) / (sqrt(var) + epsilon) * gamma + beta`\n *\n * @param x Input tensor.\n * @param mean Mean of batch.\n * @param variance Variance of batch.\n * @param beta Tensor with which to center the input.\n * @param gamma Tensor by which to scale the input.\n * @param epsilon Fuzz factor.\n * @returns The result of the batch normalization.\n */\nexport function batchNormalization(x, mean, variance, beta, gamma, epsilon = 1e-3) {\n    let out;\n    if (x.rank === 2) {\n        out = tfc.batchNorm2d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else if (x.rank === 3) {\n        // TODO(cais): Check rank; give proper error message.\n        out = tfc.batchNorm3d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else if (x.rank === 4) {\n        out = tfc.batchNorm4d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else {\n        throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} ` +\n            `yet`);\n    }\n    return out;\n}\n/**\n * Non-broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    return tidy(() => {\n        const meanAndVariance = tfc.moments(x, reductionAxes);\n        const mean = meanAndVariance.mean;\n        const variance = meanAndVariance.variance;\n        const normed = batchNormalization(x, mean, variance, beta, gamma, epsilon);\n        return [normed, mean, variance];\n    });\n}\n/**\n * Broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    return tidy(() => {\n        const meanAndVariance = tfc.moments(x, reductionAxes);\n        const mean = meanAndVariance.mean;\n        const variance = meanAndVariance.variance;\n        const targetShape = [];\n        for (const axis of math_utils.range(0, x.rank)) {\n            if (reductionAxes.indexOf(axis) !== -1) {\n                targetShape.push(1);\n            }\n            else {\n                targetShape.push(x.shape[axis]);\n            }\n        }\n        const broadcastMean = mean.reshape(targetShape);\n        const broadcastVariance = variance.reshape(targetShape);\n        const broadcastGamma = gamma == null ? null : gamma.reshape(targetShape);\n        const broadcastBeta = beta == null ? null : beta.reshape(targetShape);\n        const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon);\n        return [normed, mean, variance];\n    });\n}\n/**\n * Batch normalization for use in training (not inference).\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nexport function normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    if (util.arraysEqual(reductionAxes.slice().sort(), math_utils.range(0, x.rank - 1))) {\n        return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);\n    }\n    else {\n        return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);\n    }\n}\nexport class BatchNormalization extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.supportsMasking = true;\n        this.axis = args.axis == null ? -1 : args.axis;\n        this.momentum = args.momentum == null ? 0.99 : args.momentum;\n        this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n        this.center = args.center == null ? true : args.center;\n        this.scale = args.scale == null ? true : args.scale;\n        this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n        this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n        this.movingMeanInitializer =\n            getInitializer(args.movingMeanInitializer || 'zeros');\n        this.movingVarianceInitializer =\n            getInitializer(args.movingVarianceInitializer || 'ones');\n        this.betaConstraint = getConstraint(args.betaConstraint);\n        this.gammaConstraint = getConstraint(args.gammaConstraint);\n        this.betaRegularizer = getRegularizer(args.betaRegularizer);\n        this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const axis = this.axis >= 0 ? this.axis : (this.axis + inputShape.length);\n        const dim = inputShape[axis];\n        if (dim == null) {\n            throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but ` +\n                `the layer received an input with shape ` +\n                `${JSON.stringify(inputShape)}.`);\n        }\n        this.inputSpec =\n            [new InputSpec({ ndim: inputShape.length, axes: { [axis]: dim } })];\n        const shape = [dim];\n        if (this.scale) {\n            this.gamma = this.addWeight('gamma', shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint);\n        }\n        if (this.center) {\n            this.beta = this.addWeight('beta', shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint);\n        }\n        this.movingMean = this.addWeight('moving_mean', shape, null, this.movingMeanInitializer, null, false);\n        this.movingVariance = this.addWeight('moving_variance', shape, null, this.movingVarianceInitializer, null, false);\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            const input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            const ndim = inputShape.length;\n            const reductionAxes = math_utils.range(0, ndim);\n            const axis = this.axis >= 0 ? this.axis : (this.axis + ndim);\n            reductionAxes.splice(axis, 1);\n            const broadcastShape = generic_utils.pyListRepeat(1, ndim);\n            broadcastShape[axis] = inputShape[axis];\n            const sortedReductionAxes = reductionAxes.slice();\n            sortedReductionAxes.sort();\n            const needsBroadcasting = !util.arraysEqual(sortedReductionAxes, math_utils.range(0, ndim).slice(0, ndim - 1));\n            const normalizeInference = () => {\n                if (needsBroadcasting) {\n                    const broadcastMovingMean = this.movingMean.read().reshape(broadcastShape);\n                    const broadcastMovingVariance = this.movingVariance.read().reshape(broadcastShape);\n                    const broadcastBeta = this.center ? this.beta.read().reshape(broadcastShape) : null;\n                    const broadcastGamma = this.scale ? this.gamma.read().reshape(broadcastShape) : null;\n                    return batchNormalization(input, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon);\n                }\n                else {\n                    return batchNormalization(input, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);\n                }\n            };\n            if (!training) {\n                return normalizeInference();\n            }\n            const [normedTraining, mean, variance] = normalizeBatchInTraining(input, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon);\n            const doMovingAverage = (variable, value, momentum) => {\n                tfc.tidy(() => {\n                    const decay = 1 - momentum;\n                    const origValue = variable.read();\n                    const updateDelta = origValue.sub(value).mul(decay);\n                    variable.write(origValue.sub(updateDelta));\n                });\n            };\n            // Perform updates to moving mean and moving variance for training.\n            // Porting Note: In PyKeras, these updates to `movingMean` and\n            //   `movingAverage` are done as a deferred Graph, added to the `Layer`'s\n            //   `update`s using the `add_update()` method. Here we do it imperatively\n            //   and encapsulate the updates in a function that is invoked\n            //   immediately.\n            const updateMovingMeanAndVariance = () => {\n                doMovingAverage(this.movingMean, mean, this.momentum);\n                doMovingAverage(this.movingVariance, variance, this.momentum);\n            };\n            updateMovingMeanAndVariance();\n            return normedTraining;\n        });\n    }\n    getConfig() {\n        const config = {\n            axis: this.axis,\n            momentum: this.momentum,\n            epsilon: this.epsilon,\n            center: this.center,\n            scale: this.scale,\n            betaInitializer: serializeInitializer(this.betaInitializer),\n            gammaInitializer: serializeInitializer(this.gammaInitializer),\n            movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),\n            movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer),\n            betaRegularizer: serializeRegularizer(this.betaRegularizer),\n            gammaRegularizer: serializeRegularizer(this.gammaRegularizer),\n            betaConstraint: serializeConstraint(this.betaConstraint),\n            gammaConstraint: serializeConstraint(this.gammaConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nBatchNormalization.className = 'BatchNormalization';\nserialization.registerClass(BatchNormalization);\nexport class LayerNormalization extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.axis = args.axis == null ? -1 : args.axis;\n        if (typeof this.axis === 'number') {\n            if (!Number.isInteger(this.axis)) {\n                throw new Error(`Expected axis to be an integer, but received ${this.axis}`);\n            }\n        }\n        else if (Array.isArray(this.axis)) {\n            for (const axis of this.axis) {\n                if (!Number.isInteger(axis)) {\n                    throw new Error(`Expected axis to be an array of integers, ` +\n                        `but received ${JSON.stringify(this.axis)}`);\n                }\n            }\n        }\n        else {\n            throw new Error(`Expected axis to be an integer or an array of integers, ` +\n                `but received ${JSON.stringify(this.axis)}`);\n        }\n        this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n        this.center = args.center == null ? true : args.center;\n        this.scale = args.scale == null ? true : args.scale;\n        this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n        this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n        this.betaRegularizer = getRegularizer(args.betaRegularizer);\n        this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n        this.supportsMasking = true;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const nDims = inputShape.length;\n        // Convert axis to array and resolve negatives.\n        if (typeof this.axis === 'number') {\n            this.axis = [this.axis];\n        }\n        for (let i = 0; i < this.axis.length; ++i) {\n            if (this.axis[i] < 0) {\n                this.axis[i] += nDims;\n            }\n        }\n        // Further validate axes.\n        for (const axis of this.axis) {\n            if (axis < 0 || axis >= nDims) {\n                throw new Error(`Invalid axis: ${axis}`);\n            }\n        }\n        if (this.axis.length !== generic_utils.unique(this.axis).length) {\n            throw new Error(`Found duplicate axes in: ${this.axis}`);\n        }\n        const paramShape = this.axis.map(axis => inputShape[axis]);\n        const trainable = true;\n        if (this.scale) {\n            this.gamma = this.addWeight('gamma', paramShape, 'float32', this.gammaInitializer, this.gammaRegularizer, trainable);\n        }\n        else {\n            this.gamma = null;\n        }\n        if (this.center) {\n            this.beta = this.addWeight('beta', paramShape, 'float32', this.betaInitializer, this.betaRegularizer, trainable);\n        }\n        else {\n            this.beta = null;\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        const input = getExactlyOneTensor(inputs);\n        const inputShape = input.shape;\n        const nDims = inputShape.length;\n        return tidy(() => {\n            const keepDims = true;\n            let { mean, variance } = moments(input, this.axis, keepDims);\n            const broadcastShape = generic_utils.pyListRepeat(1, nDims);\n            for (const dim of this.axis) {\n                broadcastShape[dim] = inputShape[dim];\n            }\n            const broadcast = (v) => {\n                if (v != null && v.shape.length !== nDims &&\n                    this.axis !== [nDims - 1]) {\n                    return v.reshape(broadcastShape);\n                }\n                else {\n                    return v;\n                }\n            };\n            let scale = broadcast(this.gamma.read());\n            let offset = broadcast(this.beta.read());\n            // TODO(https://github.com/tensorflow/tfjs/issues/2120): The tiling below\n            // is a workaround for the limitation of core's batchNormalization?d don't\n            // support broadcasting in their gradients. In addition, the tiling is\n            // necessary to ensure correctness on the browser CPU backend regardless\n            // of forward or backward computation. Remove this workaround once the\n            // limitation is addressed. See .\n            const momentsTiling = [];\n            const scaleOffsetTiling = [];\n            for (let i = 0; i < nDims; ++i) {\n                if (this.axis.indexOf(i) !== -1) {\n                    momentsTiling.push(inputShape[i]);\n                    scaleOffsetTiling.push(1);\n                }\n                else {\n                    momentsTiling.push(1);\n                    scaleOffsetTiling.push(inputShape[i]);\n                }\n            }\n            mean = mean.tile(momentsTiling);\n            variance = variance.tile(momentsTiling);\n            scale = scale.tile(scaleOffsetTiling);\n            offset = offset.tile(scaleOffsetTiling);\n            return batchNormalization(input, mean, variance, offset, scale, this.epsilon);\n        });\n    }\n    getConfig() {\n        const config = {\n            axis: this.axis,\n            epsilon: this.epsilon,\n            center: this.center,\n            scale: this.scale,\n            betaInitializer: serializeInitializer(this.betaInitializer),\n            gammaInitializer: serializeInitializer(this.gammaInitializer),\n            betaRegularizer: serializeRegularizer(this.betaRegularizer),\n            gammaRegularizer: serializeRegularizer(this.gammaRegularizer)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nLayerNormalization.className = 'LayerNormalization';\nserialization.registerClass(LayerNormalization);\n//# sourceMappingURL=normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Padding Layers.\n */\n// Porting Note: In Python Keras, the padding layers are in convolutional.py,\n//   but we decided to put them in a separate file (padding.ts) for clarity.\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Pads the middle dimension of a 3D tensor.\n *\n * @param x Input `tf.Tensor` to be padded.\n * @param padding `Array` of 2 integers, how many zeros to add at the start and\n *   end of the middle dimension (i.e., dimension 1).\n * @return A padded 3D `tf.Tensor`.\n */\nexport function temporalPadding(x, padding) {\n    return tidy(() => {\n        if (x.rank !== 3) {\n            throw new ValueError(`temporalPadding expects input tensor to be 3-D, but received a ` +\n                `${x.rank}-D tensor.`);\n        }\n        if (padding == null) {\n            padding = [1, 1];\n        }\n        if (padding.length !== 2) {\n            throw new ValueError(`temporalPadding expects input padding pattern to be a length-2 ` +\n                `array, but received a length-${padding.length} array.`);\n        }\n        const pattern = [[0, 0], padding, [0, 0]];\n        return tfc.pad(x, pattern);\n    });\n}\n/**\n * Pads the 2nd and 3rd dimensions of a 4D tensor.\n *\n * @param x Input `tf.Tensor` to be padded.\n * @param padding `Array` of two `Array`s, each of which is an `Array` of two\n *   integers. The amount of padding at the beginning and end of the 2nd and 3rd\n *   dimensions, respectively.\n * @param dataFormat 'channelsLast' (default) or 'channelsFirst'.\n * @return Padded 4D `tf.Tensor`.\n */\nexport function spatial2dPadding(x, padding, dataFormat) {\n    return tidy(() => {\n        if (x.rank !== 4) {\n            throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ` +\n                `${x.rank}-D tensor.`);\n        }\n        if (padding == null) {\n            padding = [[1, 1], [1, 1]];\n        }\n        if (padding.length !== 2 || padding[0].length !== 2 ||\n            padding[1].length !== 2) {\n            throw new ValueError('spatial2dPadding expects `padding` to be an Array of two Arrays, ' +\n                'each of which is an Array of two integers.');\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (dataFormat !== 'channelsLast' && dataFormat !== 'channelsFirst') {\n            throw new ValueError(`Unknown data format: ${dataFormat}. ` +\n                `Supported data formats are 'channelsLast' and 'channelsFirst.`);\n        }\n        let pattern;\n        if (dataFormat === 'channelsFirst') {\n            pattern = [[0, 0], [0, 0], padding[0], padding[1]];\n        }\n        else {\n            pattern = [[0, 0], padding[0], padding[1], [0, 0]];\n        }\n        return tfc.pad(x, pattern);\n    });\n}\nexport class ZeroPadding2D extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.dataFormat =\n            args.dataFormat == null ? imageDataFormat() : args.dataFormat;\n        // TODO(cais): Maybe refactor the following logic surrounding `padding`\n        //   into a helper method.\n        if (args.padding == null) {\n            this.padding = [[1, 1], [1, 1]];\n        }\n        else if (typeof args.padding === 'number') {\n            this.padding =\n                [[args.padding, args.padding], [args.padding, args.padding]];\n        }\n        else {\n            args.padding = args.padding;\n            if (args.padding.length !== 2) {\n                throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but ` +\n                    `received a length-${args.padding.length} array.`);\n            }\n            let heightPadding;\n            let widthPadding;\n            if (typeof args.padding[0] === 'number') {\n                heightPadding = [args.padding[0], args.padding[0]];\n                widthPadding = [args.padding[1], args.padding[1]];\n            }\n            else {\n                args.padding = args.padding;\n                if (args.padding[0].length !== 2) {\n                    throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, ` +\n                        `but received a length-${args.padding[0].length} array.`);\n                }\n                heightPadding = args.padding[0];\n                if (args.padding[1].length !== 2) {\n                    throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, ` +\n                        `but received a length-${args.padding[1].length} array.`);\n                }\n                widthPadding = args.padding[1];\n            }\n            this.padding = [heightPadding, widthPadding];\n        }\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let rows;\n        let cols;\n        if (this.dataFormat === 'channelsFirst') {\n            if (inputShape[2] != null && inputShape[2] >= 0) {\n                rows = inputShape[2] + this.padding[0][0] + this.padding[0][1];\n            }\n            else {\n                rows = null;\n            }\n            if (inputShape[3] != null && inputShape[3] >= 0) {\n                cols = inputShape[3] + this.padding[1][0] + this.padding[1][1];\n            }\n            else {\n                cols = null;\n            }\n            return [inputShape[0], inputShape[1], rows, cols];\n        }\n        else {\n            if (inputShape[1] != null && inputShape[1] >= 0) {\n                rows = inputShape[1] + this.padding[0][0] + this.padding[0][1];\n            }\n            else {\n                rows = null;\n            }\n            if (inputShape[2] != null && inputShape[2] >= 0) {\n                cols = inputShape[2] + this.padding[1][0] + this.padding[1][1];\n            }\n            else {\n                cols = null;\n            }\n            return [inputShape[0], rows, cols, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => spatial2dPadding(getExactlyOneTensor(inputs), this.padding, this.dataFormat));\n    }\n    getConfig() {\n        const config = {\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nZeroPadding2D.className = 'ZeroPadding2D';\nserialization.registerClass(ZeroPadding2D);\n//# sourceMappingURL=padding.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Pooling Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode, checkPoolMode } from '../common';\nimport { InputSpec } from '../engine/topology';\nimport { Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { convOutputLength } from '../utils/conv_utils';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { preprocessConv2DInput, preprocessConv3DInput } from './convolutional';\n/**\n * 2D pooling.\n * @param x\n * @param poolSize\n * @param stridesdes strides. Defaults to [1, 1].\n * @param padding padding. Defaults to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param poolMode Mode of pooling. Defaults to 'max'.\n * @returns Result of the 2D pooling.\n */\nexport function pool2d(x, poolSize, strides, padding, dataFormat, poolMode) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        checkPoolMode(poolMode);\n        checkPaddingMode(padding);\n        if (strides == null) {\n            strides = [1, 1];\n        }\n        if (padding == null) {\n            padding = 'valid';\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (poolMode == null) {\n            poolMode = 'max';\n        }\n        // TODO(cais): Remove the preprocessing step once deeplearn.js supports\n        // dataFormat as an input argument.\n        x = preprocessConv2DInput(x, dataFormat); // x is NHWC after preprocessing.\n        let y;\n        const paddingString = (padding === 'same') ? 'same' : 'valid';\n        if (poolMode === 'max') {\n            // TODO(cais): Rank check?\n            y = tfc.maxPool(x, poolSize, strides, paddingString);\n        }\n        else { // 'avg'\n            // TODO(cais): Check the dtype and rank of x and give clear error message\n            //   if those are incorrect.\n            y = tfc.avgPool(\n            // TODO(cais): Rank check?\n            x, poolSize, strides, paddingString);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]); // NHWC -> NCHW.\n        }\n        return y;\n    });\n}\n/**\n * 3D pooling.\n * @param x\n * @param poolSize. Default to [1, 1, 1].\n * @param strides strides. Defaults to [1, 1, 1].\n * @param padding padding. Defaults to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param poolMode Mode of pooling. Defaults to 'max'.\n * @returns Result of the 3D pooling.\n */\nexport function pool3d(x, poolSize, strides, padding, dataFormat, poolMode) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        checkPoolMode(poolMode);\n        checkPaddingMode(padding);\n        if (strides == null) {\n            strides = [1, 1, 1];\n        }\n        if (padding == null) {\n            padding = 'valid';\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (poolMode == null) {\n            poolMode = 'max';\n        }\n        // x is NDHWC after preprocessing.\n        x = preprocessConv3DInput(x, dataFormat);\n        let y;\n        const paddingString = (padding === 'same') ? 'same' : 'valid';\n        if (poolMode === 'max') {\n            y = tfc.maxPool3d(x, poolSize, strides, paddingString);\n        }\n        else { // 'avg'\n            y = tfc.avgPool3d(x, poolSize, strides, paddingString);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 4, 1, 2, 3]); // NDHWC -> NCDHW.\n        }\n        return y;\n    });\n}\n/**\n * Abstract class for different pooling 1D layers.\n */\nexport class Pooling1D extends Layer {\n    /**\n     *\n     * @param args Parameters for the Pooling layer.\n     *\n     * config.poolSize defaults to 2.\n     */\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = 2;\n        }\n        super(args);\n        if (typeof args.poolSize === 'number') {\n            this.poolSize = [args.poolSize];\n        }\n        else if (Array.isArray(args.poolSize) &&\n            args.poolSize.length === 1 &&\n            typeof args.poolSize[0] === 'number') {\n            this.poolSize = args.poolSize;\n        }\n        else {\n            throw new ValueError(`poolSize for 1D convolutional layer must be a number or an ` +\n                `Array of a single number, but received ` +\n                `${JSON.stringify(args.poolSize)}`);\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else {\n            if (typeof args.strides === 'number') {\n                this.strides = [args.strides];\n            }\n            else if (Array.isArray(args.strides) &&\n                args.strides.length === 1 &&\n                typeof args.strides[0] === 'number') {\n                this.strides = args.strides;\n            }\n            else {\n                throw new ValueError(`strides for 1D convolutional layer must be a number or an ` +\n                    `Array of a single number, but received ` +\n                    `${JSON.stringify(args.strides)}`);\n            }\n        }\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const length = convOutputLength(inputShape[1], this.poolSize[0], this.padding, this.strides[0]);\n        return [inputShape[0], length, inputShape[2]];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Add dummy last dimension.\n            inputs = K.expandDims(getExactlyOneTensor(inputs), 2);\n            const output = this.poolingFunction(getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, 'channelsLast');\n            // Remove dummy last dimension.\n            return tfc.squeeze(output, [2]);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling1D extends Pooling1D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling1D.className = 'MaxPooling1D';\nserialization.registerClass(MaxPooling1D);\nexport class AveragePooling1D extends Pooling1D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling1D.className = 'AveragePooling1D';\nserialization.registerClass(AveragePooling1D);\n/**\n * Abstract class for different pooling 2D layers.\n */\nexport class Pooling2D extends Layer {\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = [2, 2];\n        }\n        super(args);\n        this.poolSize = Array.isArray(args.poolSize) ?\n            args.poolSize :\n            [args.poolSize, args.poolSize];\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else if (Array.isArray(args.strides)) {\n            if (args.strides.length !== 2) {\n                throw new ValueError(`If the strides property of a 2D pooling layer is an Array, ` +\n                    `it is expected to have a length of 2, but received length ` +\n                    `${args.strides.length}.`);\n            }\n            this.strides = args.strides;\n        }\n        else {\n            // `config.strides` is a number.\n            this.strides = [args.strides, args.strides];\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let rows = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        let cols = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        rows =\n            convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]);\n        cols =\n            convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], inputShape[1], rows, cols];\n        }\n        else {\n            return [inputShape[0], rows, cols, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n            dataFormat: this.dataFormat\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling2D extends Pooling2D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling2D.className = 'MaxPooling2D';\nserialization.registerClass(MaxPooling2D);\nexport class AveragePooling2D extends Pooling2D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling2D.className = 'AveragePooling2D';\nserialization.registerClass(AveragePooling2D);\n/**\n * Abstract class for different pooling 3D layers.\n */\nexport class Pooling3D extends Layer {\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = [2, 2, 2];\n        }\n        super(args);\n        this.poolSize = Array.isArray(args.poolSize) ?\n            args.poolSize :\n            [args.poolSize, args.poolSize, args.poolSize];\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else if (Array.isArray(args.strides)) {\n            if (args.strides.length !== 3) {\n                throw new ValueError(`If the strides property of a 3D pooling layer is an Array, ` +\n                    `it is expected to have a length of 3, but received length ` +\n                    `${args.strides.length}.`);\n            }\n            this.strides = args.strides;\n        }\n        else {\n            // `config.strides` is a number.\n            this.strides = [args.strides, args.strides, args.strides];\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 5 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let depths = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        let rows = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        let cols = this.dataFormat === 'channelsFirst' ? inputShape[4] : inputShape[3];\n        depths = convOutputLength(depths, this.poolSize[0], this.padding, this.strides[0]);\n        rows =\n            convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]);\n        cols =\n            convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], inputShape[1], depths, rows, cols];\n        }\n        else {\n            return [inputShape[0], depths, rows, cols, inputShape[4]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n            dataFormat: this.dataFormat\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling3D extends Pooling3D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool3d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling3D.className = 'MaxPooling3D';\nserialization.registerClass(MaxPooling3D);\nexport class AveragePooling3D extends Pooling3D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool3d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling3D.className = 'AveragePooling3D';\nserialization.registerClass(AveragePooling3D);\n/**\n * Abstract class for different global pooling 1D layers.\n */\nexport class GlobalPooling1D extends Layer {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n    }\n    computeOutputShape(inputShape) {\n        return [inputShape[0], inputShape[2]];\n    }\n    call(inputs, kwargs) {\n        throw new NotImplementedError();\n    }\n}\nexport class GlobalAveragePooling1D extends GlobalPooling1D {\n    constructor(args) {\n        super(args || {});\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            return tfc.mean(input, 1);\n        });\n    }\n}\n/** @nocollapse */\nGlobalAveragePooling1D.className = 'GlobalAveragePooling1D';\nserialization.registerClass(GlobalAveragePooling1D);\nexport class GlobalMaxPooling1D extends GlobalPooling1D {\n    constructor(args) {\n        super(args || {});\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            return tfc.max(input, 1);\n        });\n    }\n}\n/** @nocollapse */\nGlobalMaxPooling1D.className = 'GlobalMaxPooling1D';\nserialization.registerClass(GlobalMaxPooling1D);\n/**\n * Abstract class for different global pooling 2D layers.\n */\nexport class GlobalPooling2D extends Layer {\n    constructor(args) {\n        super(args);\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = inputShape;\n        if (this.dataFormat === 'channelsLast') {\n            return [inputShape[0], inputShape[3]];\n        }\n        else {\n            return [inputShape[0], inputShape[1]];\n        }\n    }\n    call(inputs, kwargs) {\n        throw new NotImplementedError();\n    }\n    getConfig() {\n        const config = { dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class GlobalAveragePooling2D extends GlobalPooling2D {\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                return tfc.mean(input, [1, 2]);\n            }\n            else {\n                return tfc.mean(input, [2, 3]);\n            }\n        });\n    }\n}\n/** @nocollapse */\nGlobalAveragePooling2D.className = 'GlobalAveragePooling2D';\nserialization.registerClass(GlobalAveragePooling2D);\nexport class GlobalMaxPooling2D extends GlobalPooling2D {\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                return tfc.max(input, [1, 2]);\n            }\n            else {\n                return tfc.max(input, [2, 3]);\n            }\n        });\n    }\n}\n/** @nocollapse */\nGlobalMaxPooling2D.className = 'GlobalMaxPooling2D';\nserialization.registerClass(GlobalMaxPooling2D);\n//# sourceMappingURL=pooling.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Layers that augment the functionality of a base layer.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { InputSpec, Layer, SymbolicTensor } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { VALID_BIDIRECTIONAL_MERGE_MODES } from '../keras_format/common';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { rnn, standardizeArgs } from './recurrent';\nimport { deserialize } from './serialization';\n/**\n * Abstract wrapper base class.\n *\n * Wrappers take another layer and augment it in various ways.\n * Do not use this class as a layer, it is only an abstract base class.\n * Two usable wrappers are the `TimeDistributed` and `Bidirectional` wrappers.\n */\nexport class Wrapper extends Layer {\n    constructor(args) {\n        // Porting Note: In PyKeras, `self.layer` is set prior to the calling\n        //   `super()`. But we can't do that here due to TypeScript's restriction.\n        //   See: https://github.com/Microsoft/TypeScript/issues/8277\n        //   As a result, we have to add checks in `get trainable()` and\n        //   `set trainable()` below in order to prevent using `this.layer` when\n        //   its value is `undefined`. The super constructor does use the getter\n        //   and the setter of `this.layer`.\n        super(args);\n        this.layer = args.layer;\n    }\n    build(inputShape) {\n        this.built = true;\n    }\n    // TODO(cais): Implement activityRegularizer getter.\n    get trainable() {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        if (this.layer != null) {\n            return this.layer.trainable;\n        }\n        else {\n            return false;\n        }\n    }\n    set trainable(value) {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        if (this.layer != null) {\n            this.layer.trainable = value;\n        }\n    }\n    get trainableWeights() {\n        return this.layer.trainableWeights;\n    }\n    // TODO(cais): Implement setter for trainableWeights.\n    get nonTrainableWeights() {\n        return this.layer.nonTrainableWeights;\n    }\n    // TODO(cais): Implement setter for nonTrainableWeights.\n    get updates() {\n        // tslint:disable-next-line:no-any\n        return this.layer._updates;\n    }\n    // TODO(cais): Implement getUpdatesFor().\n    get losses() {\n        return this.layer.losses;\n    }\n    // TODO(cais): Implement getLossesFor().\n    getWeights() {\n        return this.layer.getWeights();\n    }\n    setWeights(weights) {\n        this.layer.setWeights(weights);\n    }\n    getConfig() {\n        const config = {\n            'layer': {\n                'className': this.layer.getClassName(),\n                'config': this.layer.getConfig(),\n            }\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.layer != null) {\n            this.layer.setFastWeightInitDuringBuild(value);\n        }\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const layerConfig = config['layer'];\n        const layer = deserialize(layerConfig, customObjects);\n        delete config['layer'];\n        const newConfig = { layer };\n        Object.assign(newConfig, config);\n        return new cls(newConfig);\n    }\n}\nexport class TimeDistributed extends Wrapper {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < 3) {\n            throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received ` +\n                `input shape ${JSON.stringify(inputShape)}`);\n        }\n        this.inputSpec = [{ shape: inputShape }];\n        const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        if (!this.layer.built) {\n            this.layer.build(childInputShape);\n            this.layer.built = true;\n        }\n        super.build(inputShape);\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        const childOutputShape = this.layer.computeOutputShape(childInputShape);\n        const timesteps = inputShape[1];\n        return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1));\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            // TODO(cais): Add 'training' and 'useLearningPhase' to kwargs.\n            inputs = getExactlyOneTensor(inputs);\n            // Porting Note: In tfjs-layers, `inputs` are always concrete tensor\n            // values. Hence the inputs can't have an undetermined first (batch)\n            // dimension, which is why we always use the K.rnn approach here.\n            const step = (inputs, states) => {\n                // TODO(cais): Add useLearningPhase.\n                // NOTE(cais): `layer.call` may return a length-1 array of Tensor in\n                //   some cases (e.g., `layer` is a `Sequential` instance), which is\n                //   why `getExactlyOneTensor` is used below.\n                const output = getExactlyOneTensor(this.layer.call(inputs, kwargs));\n                return [output, []];\n            };\n            const rnnOutputs = rnn(step, inputs, [], false /* goBackwards */, null /* mask */, null /* constants */, false /* unroll */, true /* needPerStepOutputs */);\n            const y = rnnOutputs[1];\n            // TODO(cais): Add activity regularization.\n            // TODO(cais): Add useLearningPhase.\n            return y;\n        });\n    }\n}\n/** @nocollapse */\nTimeDistributed.className = 'TimeDistributed';\nserialization.registerClass(TimeDistributed);\nexport function checkBidirectionalMergeMode(value) {\n    generic_utils.checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, 'BidirectionalMergeMode', value);\n}\nconst DEFAULT_BIDIRECTIONAL_MERGE_MODE = 'concat';\nexport class Bidirectional extends Wrapper {\n    constructor(args) {\n        super(args);\n        // Note: When creating `this.forwardLayer`, the original Layer object\n        //   (`config.layer`) ought to be cloned. This is why we call\n        //   `getConfig()` followed by `deserialize()`. Without this cloning,\n        //   the layer names saved during serialization will incorrectly contain\n        //   the 'forward_' prefix. In Python Keras, this is done using\n        //   `copy.copy` (shallow copy), which does not have a simple equivalent\n        //   in JavaScript. JavaScript's `Object.assign()` does not copy\n        //   methods.\n        const layerConfig = args.layer.getConfig();\n        const forwDict = {};\n        forwDict['className'] = args.layer.getClassName();\n        forwDict['config'] = layerConfig;\n        this.forwardLayer = deserialize(forwDict);\n        layerConfig['goBackwards'] =\n            layerConfig['goBackwards'] === true ? false : true;\n        const backDict = {};\n        backDict['className'] = args.layer.getClassName();\n        backDict['config'] = layerConfig;\n        this.backwardLayer = deserialize(backDict);\n        this.forwardLayer.name = 'forward_' + this.forwardLayer.name;\n        this.backwardLayer.name = 'backward_' + this.backwardLayer.name;\n        this.mergeMode = args.mergeMode === undefined ?\n            DEFAULT_BIDIRECTIONAL_MERGE_MODE :\n            args.mergeMode;\n        checkBidirectionalMergeMode(this.mergeMode);\n        if (args.weights) {\n            throw new NotImplementedError('weights support is not implemented for Bidirectional layer yet.');\n        }\n        this._stateful = args.layer.stateful;\n        this.returnSequences = args.layer.returnSequences;\n        this.returnState = args.layer.returnState;\n        this.supportsMasking = true;\n        this._trainable = true;\n        this.inputSpec = args.layer.inputSpec;\n        this.numConstants = null;\n    }\n    get trainable() {\n        return this._trainable;\n    }\n    set trainable(value) {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        this._trainable = value;\n        if (this.forwardLayer != null) {\n            this.forwardLayer.trainable = value;\n        }\n        if (this.backwardLayer != null) {\n            this.backwardLayer.trainable = value;\n        }\n    }\n    getWeights() {\n        return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights());\n    }\n    setWeights(weights) {\n        const numWeights = weights.length;\n        const numeightsOver2 = Math.floor(numWeights / 2);\n        this.forwardLayer.setWeights(weights.slice(0, numeightsOver2));\n        this.backwardLayer.setWeights(weights.slice(numeightsOver2));\n    }\n    computeOutputShape(inputShape) {\n        let layerShapes = this.forwardLayer.computeOutputShape(inputShape);\n        if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) {\n            layerShapes = [layerShapes];\n        }\n        layerShapes = layerShapes;\n        let outputShape;\n        let outputShapes;\n        let stateShape;\n        if (this.returnState) {\n            stateShape = layerShapes.slice(1);\n            outputShape = layerShapes[0];\n        }\n        else {\n            outputShape = layerShapes[0];\n        }\n        outputShape = outputShape;\n        if (this.mergeMode === 'concat') {\n            outputShape[outputShape.length - 1] *= 2;\n            outputShapes = [outputShape];\n        }\n        else if (this.mergeMode == null) {\n            outputShapes = [outputShape, outputShape.slice()];\n        }\n        else {\n            outputShapes = [outputShape];\n        }\n        if (this.returnState) {\n            if (this.mergeMode == null) {\n                return outputShapes.concat(stateShape).concat(stateShape.slice());\n            }\n            return [outputShape].concat(stateShape).concat(stateShape.slice());\n        }\n        return generic_utils.singletonOrArray(outputShapes);\n    }\n    apply(inputs, kwargs) {\n        let initialState = kwargs == null ? null : kwargs['initialState'];\n        let constants = kwargs == null ? null : kwargs['constants'];\n        if (kwargs == null) {\n            kwargs = {};\n        }\n        const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n        inputs = standardized.inputs;\n        initialState = standardized.initialState;\n        constants = standardized.constants;\n        if (Array.isArray(inputs)) {\n            initialState = inputs.slice(1);\n            inputs = inputs[0];\n        }\n        if ((initialState == null || initialState.length === 0) &&\n            constants == null) {\n            return super.apply(inputs, kwargs);\n        }\n        const additionalInputs = [];\n        const additionalSpecs = [];\n        if (initialState != null) {\n            const numStates = initialState.length;\n            if (numStates % 2 > 0) {\n                throw new ValueError('When passing `initialState` to a Bidrectional RNN, ' +\n                    'the state should be an Array containing the states of ' +\n                    'the underlying RNNs.');\n            }\n            kwargs['initialState'] = initialState;\n            additionalInputs.push(...initialState);\n            const stateSpecs = initialState\n                .map(state => new InputSpec({ shape: state.shape }));\n            this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2);\n            this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2);\n            additionalSpecs.push(...stateSpecs);\n        }\n        if (constants != null) {\n            throw new NotImplementedError('Support for constants in Bidirectional layers is not ' +\n                'implemented yet.');\n        }\n        const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor;\n        for (const tensor of additionalInputs) {\n            if (tensor instanceof SymbolicTensor !== isSymbolicTensor) {\n                throw new ValueError('The initial state of a Bidirectional layer cannot be ' +\n                    'specified as a mix of symbolic and non-symbolic tensors');\n            }\n        }\n        if (isSymbolicTensor) {\n            // Compute the full input and specs, including the states.\n            const fullInput = [inputs].concat(additionalInputs);\n            const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n            // Perform the call temporarily and replace inputSpec.\n            // Note: with initial states symbolic calls and non-symbolic calls to\n            // this method differ in how the initial states are passed. For\n            // symbolic calls, the initial states are passed in the first arg, as\n            // an Array of SymbolicTensors; for non-symbolic calls, they are\n            // passed in the second arg as a part of the kwargs. Hence the need to\n            // temporarily modify inputSpec here.\n            // TODO(cais): Make refactoring so that this hacky code below is no\n            // longer needed.\n            const originalInputSpec = this.inputSpec;\n            this.inputSpec = fullInputSpec;\n            const output = super.apply(fullInput, kwargs);\n            this.inputSpec = originalInputSpec;\n            return output;\n        }\n        else {\n            return super.apply(inputs, kwargs);\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const initialState = kwargs['initialState'];\n            let y;\n            let yRev;\n            if (initialState == null) {\n                y = this.forwardLayer.call(inputs, kwargs);\n                yRev = this.backwardLayer.call(inputs, kwargs);\n            }\n            else {\n                const forwardState = initialState.slice(0, initialState.length / 2);\n                const backwardState = initialState.slice(initialState.length / 2);\n                y = this.forwardLayer.call(inputs, Object.assign(kwargs, { initialState: forwardState }));\n                yRev = this.backwardLayer.call(inputs, Object.assign(kwargs, { initialState: backwardState }));\n            }\n            let states;\n            if (this.returnState) {\n                if (Array.isArray(y)) {\n                    states = y.slice(1).concat(yRev.slice(1));\n                }\n                else {\n                }\n                y = y[0];\n                yRev = yRev[0];\n            }\n            if (this.returnSequences) {\n                yRev = tfc.reverse(yRev, 1);\n            }\n            let output;\n            if (this.mergeMode === 'concat') {\n                output = K.concatenate([y, yRev]);\n            }\n            else if (this.mergeMode === 'sum') {\n                output = tfc.add(y, yRev);\n            }\n            else if (this.mergeMode === 'ave') {\n                output = tfc.mul(.5, tfc.add(y, yRev));\n            }\n            else if (this.mergeMode === 'mul') {\n                output = tfc.mul(y, yRev);\n            }\n            else if (this.mergeMode == null) {\n                output = [y, yRev];\n            }\n            // TODO(cais): Properly set learning phase.\n            if (this.returnState) {\n                if (this.mergeMode == null) {\n                    return output.concat(states);\n                }\n                return [output].concat(states);\n            }\n            return output;\n        });\n    }\n    resetStates(states) {\n        this.forwardLayer.resetStates();\n        this.backwardLayer.resetStates();\n    }\n    build(inputShape) {\n        nameScope(this.forwardLayer.name, () => {\n            this.forwardLayer.build(inputShape);\n        });\n        nameScope(this.backwardLayer.name, () => {\n            this.backwardLayer.build(inputShape);\n        });\n        this.built = true;\n    }\n    computeMask(inputs, mask) {\n        if (Array.isArray(mask)) {\n            mask = mask[0];\n        }\n        let outputMask;\n        if (this.returnSequences) {\n            if (this.mergeMode == null) {\n                outputMask = [mask, mask];\n            }\n            else {\n                outputMask = mask;\n            }\n        }\n        else {\n            if (this.mergeMode == null) {\n                outputMask = [null, null];\n            }\n            else {\n                outputMask = null;\n            }\n        }\n        if (this.returnState) {\n            const states = this.forwardLayer.states;\n            const stateMask = states.map(state => null);\n            if (Array.isArray(outputMask)) {\n                return outputMask.concat(stateMask).concat(stateMask);\n            }\n            else {\n                return [outputMask].concat(stateMask).concat(stateMask);\n            }\n        }\n        else {\n            return outputMask;\n        }\n    }\n    get trainableWeights() {\n        return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights);\n    }\n    get nonTrainableWeights() {\n        return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights);\n    }\n    // TODO(cais): Implement constraints().\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.forwardLayer != null) {\n            this.forwardLayer.setFastWeightInitDuringBuild(value);\n        }\n        if (this.backwardLayer != null) {\n            this.backwardLayer.setFastWeightInitDuringBuild(value);\n        }\n    }\n    getConfig() {\n        const config = {\n            'mergeMode': this.mergeMode,\n        };\n        // TODO(cais): Add logic for `numConstants` once the property is added.\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        const rnnLayer = deserialize(config['layer']);\n        delete config['layer'];\n        // TODO(cais): Add logic for `numConstants` once the property is added.\n        if (config['numConstants'] != null) {\n            throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants ` +\n                `present is not supported yet.`);\n        }\n        // tslint:disable-next-line:no-any\n        const newConfig = config;\n        newConfig['layer'] = rnnLayer;\n        return new cls(newConfig);\n    }\n}\n/** @nocollapse */\nBidirectional.className = 'Bidirectional';\nserialization.registerClass(Bidirectional);\n//# sourceMappingURL=wrappers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { InputLayer } from './engine/input_layer';\nimport { Layer } from './engine/topology';\nimport { input } from './exports';\nimport { ELU, LeakyReLU, PReLU, ReLU, Softmax, ThresholdedReLU } from './layers/advanced_activations';\nimport { Conv1D, Conv2D, Conv2DTranspose, Conv3D, Cropping2D, SeparableConv2D, UpSampling2D } from './layers/convolutional';\nimport { DepthwiseConv2D } from './layers/convolutional_depthwise';\nimport { ConvLSTM2D, ConvLSTM2DCell } from './layers/convolutional_recurrent';\nimport { Activation, Dense, Dropout, Flatten, Masking, Permute, RepeatVector, Reshape, SpatialDropout1D } from './layers/core';\nimport { Embedding } from './layers/embeddings';\nimport { Add, Average, Concatenate, Dot, Maximum, Minimum, Multiply } from './layers/merge';\nimport { AlphaDropout, GaussianDropout, GaussianNoise } from './layers/noise';\nimport { BatchNormalization, LayerNormalization } from './layers/normalization';\nimport { ZeroPadding2D } from './layers/padding';\nimport { AveragePooling1D, AveragePooling2D, AveragePooling3D, GlobalAveragePooling1D, GlobalAveragePooling2D, GlobalMaxPooling1D, GlobalMaxPooling2D, MaxPooling1D, MaxPooling2D, MaxPooling3D } from './layers/pooling';\nimport { GRU, GRUCell, LSTM, LSTMCell, RNN, RNNCell, SimpleRNN, SimpleRNNCell, StackedRNNCells } from './layers/recurrent';\nimport { Bidirectional, TimeDistributed } from './layers/wrappers';\n// TODO(cais): Add doc string to all the public static functions in this\n//   class; include exectuable JavaScript code snippets where applicable\n//   (b/74074458).\n// Input Layer.\n/**\n * An input layer is an entry point into a `tf.LayersModel`.\n *\n * `InputLayer` is generated automatically for `tf.Sequential`` models by\n * specifying the `inputshape` or `batchInputShape` for the first layer.  It\n * should not be specified explicitly. However, it can be useful sometimes,\n * e.g., when constructing a sequential model from a subset of another\n * sequential model's layers. Like the code snippet below shows.\n *\n * ```js\n * // Define a model which simply adds two inputs.\n * const model1 = tf.sequential();\n * model1.add(tf.layers.dense({inputShape: [4], units: 3, activation: 'relu'}));\n * model1.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));\n * model1.summary();\n * model1.predict(tf.zeros([1, 4])).print();\n *\n * // Construct another model, reusing the second layer of `model1` while\n * // not using the first layer of `model1`. Note that you cannot add the second\n * // layer of `model` directly as the first layer of the new sequential model,\n * // because doing so will lead to an error related to the fact that the layer\n * // is not an input layer. Instead, you need to create an `inputLayer` and add\n * // it to the new sequential model before adding the reused layer.\n * const model2 = tf.sequential();\n * // Use an inputShape that matches the input shape of `model1`'s second\n * // layer.\n * model2.add(tf.layers.inputLayer({inputShape: [3]}));\n * model2.add(model1.layers[1]);\n * model2.summary();\n * model2.predict(tf.zeros([1, 3])).print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Inputs', namespace: 'layers'}\n */\nexport function inputLayer(args) {\n    return new InputLayer(args);\n}\n// Advanced Activation Layers.\n/**\n * Exponetial Linear Unit (ELU).\n *\n * It follows:\n * `f(x) =  alpha * (exp(x) - 1.) for x < 0`,\n * `f(x) = x for x >= 0`.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * References:\n *   - [Fast and Accurate Deep Network Learning by Exponential Linear Units\n * (ELUs)](https://arxiv.org/abs/1511.07289v1)\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function elu(args) {\n    return new ELU(args);\n}\n/**\n * Rectified Linear Unit activation function.\n *\n * Input shape:\n *   Arbitrary. Use the config field `inputShape` (Array of integers, does\n *   not include the sample axis) when using this layer as the first layer\n *   in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function reLU(args) {\n    return new ReLU(args);\n}\n/**\n * Leaky version of a rectified linear unit.\n *\n * It allows a small gradient when the unit is not active:\n * `f(x) = alpha * x for x < 0.`\n * `f(x) = x for x >= 0.`\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function leakyReLU(args) {\n    return new LeakyReLU(args);\n}\n/**\n * Parameterized version of a leaky rectified linear unit.\n *\n * It follows\n * `f(x) = alpha * x for x < 0.`\n * `f(x) = x for x >= 0.`\n * wherein `alpha` is a trainable weight.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function prelu(args) {\n    return new PReLU(args);\n}\n/**\n * Softmax activation layer.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function softmax(args) {\n    return new Softmax(args);\n}\n/**\n * Thresholded Rectified Linear Unit.\n *\n * It follows:\n * `f(x) = x for x > theta`,\n * `f(x) = 0 otherwise`.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * References:\n *   - [Zero-Bias Autoencoders and the Benefits of Co-Adapting\n * Features](http://arxiv.org/abs/1402.3337)\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function thresholdedReLU(args) {\n    return new ThresholdedReLU(args);\n}\n// Convolutional Layers.\n/**\n * 1D convolution layer (e.g., temporal convolution).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input over a single spatial (or temporal) dimension\n * to produce a tensor of outputs.\n *\n * If `use_bias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model, provide an\n * `inputShape` argument `Array` or `null`.\n *\n * For example, `inputShape` would be:\n * - `[10, 128]` for sequences of 10 vectors of 128-dimensional vectors\n * - `[null, 128]` for variable-length sequences of 128-dimensional vectors.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional',  namespace: 'layers'}\n */\nexport function conv1d(args) {\n    return new Conv1D(args);\n}\n/**\n * 2D convolution layer (e.g. spatial convolution over images).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input to produce a tensor of outputs.\n *\n * If `useBias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model,\n * provide the keyword argument `inputShape`\n * (Array of integers, does not include the sample axis),\n * e.g. `inputShape=[128, 128, 3]` for 128x128 RGB pictures\n * in `dataFormat='channelsLast'`.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv2d(args) {\n    return new Conv2D(args);\n}\n/**\n * Transposed convolutional layer (sometimes called Deconvolution).\n *\n * The need for transposed convolutions generally arises\n * from the desire to use a transformation going in the opposite direction of\n * a normal convolution, i.e., from something that has the shape of the output\n * of some convolution to something that has the shape of its input while\n * maintaining a connectivity pattern that is compatible with said\n * convolution.\n *\n * When using this layer as the first layer in a model, provide the\n * configuration `inputShape` (`Array` of integers, does not include the\n * sample axis), e.g., `inputShape: [128, 128, 3]` for 128x128 RGB pictures in\n * `dataFormat: 'channelsLast'`.\n *\n * Input shape:\n *   4D tensor with shape:\n *   `[batch, channels, rows, cols]` if `dataFormat` is `'channelsFirst'`.\n *   or 4D tensor with shape\n *   `[batch, rows, cols, channels]` if `dataFormat` is `'channelsLast`.\n *\n * Output shape:\n *   4D tensor with shape:\n *   `[batch, filters, newRows, newCols]` if `dataFormat` is\n * `'channelsFirst'`. or 4D tensor with shape:\n *   `[batch, newRows, newCols, filters]` if `dataFormat` is `'channelsLast'`.\n *\n * References:\n *   - [A guide to convolution arithmetic for deep\n * learning](https://arxiv.org/abs/1603.07285v1)\n *   - [Deconvolutional\n * Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv2dTranspose(args) {\n    return new Conv2DTranspose(args);\n}\n/**\n * 3D convolution layer (e.g. spatial convolution over volumes).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input to produce a tensor of outputs.\n *\n * If `useBias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model,\n * provide the keyword argument `inputShape`\n * (Array of integers, does not include the sample axis),\n * e.g. `inputShape=[128, 128, 128, 1]` for 128x128x128 grayscale volumes\n * in `dataFormat='channelsLast'`.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv3d(args) {\n    return new Conv3D(args);\n}\n/**\n * Depthwise separable 2D convolution.\n *\n * Separable convolution consists of first performing\n * a depthwise spatial convolution\n * (which acts on each input channel separately)\n * followed by a pointwise convolution which mixes together the resulting\n * output channels. The `depthMultiplier` argument controls how many\n * output channels are generated per input channel in the depthwise step.\n *\n * Intuitively, separable convolutions can be understood as\n * a way to factorize a convolution kernel into two smaller kernels,\n * or as an extreme version of an Inception block.\n *\n * Input shape:\n *   4D tensor with shape:\n *     `[batch, channels, rows, cols]` if data_format='channelsFirst'\n *   or 4D tensor with shape:\n *     `[batch, rows, cols, channels]` if data_format='channelsLast'.\n *\n * Output shape:\n *   4D tensor with shape:\n *     `[batch, filters, newRows, newCols]` if data_format='channelsFirst'\n *   or 4D tensor with shape:\n *     `[batch, newRows, newCols, filters]` if data_format='channelsLast'.\n *     `rows` and `cols` values might have changed due to padding.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function separableConv2d(args) {\n    return new SeparableConv2D(args);\n}\n/**\n * Cropping layer for 2D input (e.g., image).\n *\n * This layer can crop an input\n * at the top, bottom, left and right side of an image tensor.\n *\n * Input shape:\n *   4D tensor with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, rows, cols, channels]`\n *   - If `data_format` is `\"channels_first\"`:\n *     `[batch, channels, rows, cols]`.\n *\n * Output shape:\n *   4D with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, croppedRows, croppedCols, channels]`\n *    - If `dataFormat` is `\"channelsFirst\"`:\n *     `[batch, channels, croppedRows, croppedCols]`.\n *\n * Examples\n * ```js\n *\n * const model = tf.sequential();\n * model.add(tf.layers.cropping2D({cropping:[[2, 2], [2, 2]],\n *                                inputShape: [128, 128, 3]}));\n * //now output shape is [batch, 124, 124, 3]\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function cropping2D(args) {\n    return new Cropping2D(args);\n}\n/**\n * Upsampling layer for 2D inputs.\n *\n * Repeats the rows and columns of the data\n * by size[0] and size[1] respectively.\n *\n *\n * Input shape:\n *    4D tensor with shape:\n *     - If `dataFormat` is `\"channelsLast\"`:\n *         `[batch, rows, cols, channels]`\n *     - If `dataFormat` is `\"channelsFirst\"`:\n *        `[batch, channels, rows, cols]`\n *\n * Output shape:\n *     4D tensor with shape:\n *     - If `dataFormat` is `\"channelsLast\"`:\n *        `[batch, upsampledRows, upsampledCols, channels]`\n *     - If `dataFormat` is `\"channelsFirst\"`:\n *         `[batch, channels, upsampledRows, upsampledCols]`\n *\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function upSampling2d(args) {\n    return new UpSampling2D(args);\n}\n// Convolutional(depthwise) Layers.\n/**\n * Depthwise separable 2D convolution.\n *\n * Depthwise Separable convolutions consists in performing just the first step\n * in a depthwise spatial convolution (which acts on each input channel\n * separately). The `depthMultplier` argument controls how many output channels\n * are generated per input channel in the depthwise step.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function depthwiseConv2d(args) {\n    return new DepthwiseConv2D(args);\n}\n// Basic Layers.\n/**\n * Applies an activation function to an output.\n *\n * This layer applies element-wise activation function.  Other layers, notably\n * `dense` can also apply activation functions.  Use this isolated activation\n * function to extract the values before and after the\n * activation. For instance:\n *\n * ```js\n * const input = tf.input({shape: [5]});\n * const denseLayer = tf.layers.dense({units: 1});\n * const activationLayer = tf.layers.activation({activation: 'relu6'});\n *\n * // Obtain the output symbolic tensors by applying the layers in order.\n * const denseOutput = denseLayer.apply(input);\n * const activationOutput = activationLayer.apply(denseOutput);\n *\n * // Create the model based on the inputs.\n * const model = tf.model({\n *     inputs: input,\n *     outputs: [denseOutput, activationOutput]\n * });\n *\n * // Collect both outputs and print separately.\n * const [denseOut, activationOut] = model.predict(tf.randomNormal([6, 5]));\n * denseOut.print();\n * activationOut.print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function activation(args) {\n    return new Activation(args);\n}\n/**\n * Creates a dense (fully connected) layer.\n *\n * This layer implements the operation:\n *   `output = activation(dot(input, kernel) + bias)`\n *\n * `activation` is the element-wise activation function\n *   passed as the `activation` argument.\n *\n * `kernel` is a weights matrix created by the layer.\n *\n * `bias` is a bias vector created by the layer (only applicable if `useBias`\n * is `true`).\n *\n * **Input shape:**\n *\n *   nD `tf.Tensor` with shape: `(batchSize, ..., inputDim)`.\n *\n *   The most common situation would be\n *   a 2D input with shape `(batchSize, inputDim)`.\n *\n * **Output shape:**\n *\n *   nD tensor with shape: `(batchSize, ..., units)`.\n *\n *   For instance, for a 2D input with shape `(batchSize, inputDim)`,\n *   the output would have shape `(batchSize, units)`.\n *\n * Note: if the input to the layer has a rank greater than 2, then it is\n * flattened prior to the initial dot product with the kernel.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function dense(args) {\n    return new Dense(args);\n}\n/**\n * Applies\n * [dropout](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf) to\n * the input.\n *\n * Dropout consists in randomly setting a fraction `rate` of input units to 0 at\n * each update during training time, which helps prevent overfitting.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function dropout(args) {\n    return new Dropout(args);\n}\n/**\n * Spatial 1D version of Dropout.\n *\n * This Layer type performs the same function as the Dropout layer, but it drops\n * entire 1D feature maps instead of individual elements. For example, if an\n * input example consists of 3 timesteps and the feature map for each timestep\n * has a size of 4, a `spatialDropout1d` layer may zero out the feature maps\n * of the 1st timesteps and 2nd timesteps completely while sparing all feature\n * elements of the 3rd timestep.\n *\n * If adjacent frames (timesteps) are strongly correlated (as is normally the\n * case in early convolution layers), regular dropout will not regularize the\n * activation and will otherwise just result in merely an effective learning\n * rate decrease. In this case, `spatialDropout1d` will help promote\n * independence among feature maps and should be used instead.\n *\n * **Arguments:**\n *   rate: A floating-point number >=0 and <=1. Fraction of the input elements\n *     to drop.\n *\n * **Input shape:**\n *   3D tensor with shape `(samples, timesteps, channels)`.\n *\n * **Output shape:**\n *   Same as the input shape.\n *\n * References:\n *   - [Efficient Object Localization Using Convolutional\n *      Networks](https://arxiv.org/abs/1411.4280)\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function spatialDropout1d(args) {\n    return new SpatialDropout1D(args);\n}\n/**\n * Flattens the input. Does not affect the batch size.\n *\n * A `Flatten` layer flattens each batch in its inputs to 1D (making the output\n * 2D).\n *\n * For example:\n *\n * ```js\n * const input = tf.input({shape: [4, 3]});\n * const flattenLayer = tf.layers.flatten();\n * // Inspect the inferred output shape of the flatten layer, which\n * // equals `[null, 12]`. The 2nd dimension is 4 * 3, i.e., the result of the\n * // flattening. (The 1st dimension is the undermined batch size.)\n * console.log(JSON.stringify(flattenLayer.apply(input).shape));\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function flatten(args) {\n    return new Flatten(args);\n}\n/**\n * Repeats the input n times in a new dimension.\n *\n * ```js\n *  const model = tf.sequential();\n *  model.add(tf.layers.repeatVector({n: 4, inputShape: [2]}));\n *  const x = tf.tensor2d([[10, 20]]);\n *  // Use the model to do inference on a data point the model hasn't see\n *  model.predict(x).print();\n *  // output shape is now [batch, 2, 4]\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function repeatVector(args) {\n    return new RepeatVector(args);\n}\n/**\n * Reshapes an input to a certain shape.\n *\n * ```js\n * const input = tf.input({shape: [4, 3]});\n * const reshapeLayer = tf.layers.reshape({targetShape: [2, 6]});\n * // Inspect the inferred output shape of the Reshape layer, which\n * // equals `[null, 2, 6]`. (The 1st dimension is the undermined batch size.)\n * console.log(JSON.stringify(reshapeLayer.apply(input).shape));\n * ```\n *\n * Input shape:\n *   Arbitrary, although all dimensions in the input shape must be fixed.\n *   Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n *\n * Output shape:\n *   [batchSize, targetShape[0], targetShape[1], ...,\n *    targetShape[targetShape.length - 1]].\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function reshape(args) {\n    return new Reshape(args);\n}\n/**\n * Permutes the dimensions of the input according to a given pattern.\n *\n * Useful for, e.g., connecting RNNs and convnets together.\n *\n * Example:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.permute({\n *   dims: [2, 1],\n *   inputShape: [10, 64]\n * }));\n * console.log(model.outputShape);\n * // Now model's output shape is [null, 64, 10], where null is the\n * // unpermuted sample (batch) dimension.\n * ```\n *\n * Input shape:\n *   Arbitrary. Use the configuration field `inputShape` when using this\n *   layer as the first layer in a model.\n *\n * Output shape:\n *   Same rank as the input shape, but with the dimensions re-ordered (i.e.,\n *   permuted) according to the `dims` configuration of this layer.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function permute(args) {\n    return new Permute(args);\n}\n/**\n * Maps positive integers (indices) into dense vectors of fixed size.\n * eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]\n *\n * **Input shape:** 2D tensor with shape: `[batchSize, sequenceLength]`.\n *\n * **Output shape:** 3D tensor with shape: `[batchSize, sequenceLength,\n * outputDim]`.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function embedding(args) {\n    return new Embedding(args);\n}\n// Merge Layers.\n/**\n * Layer that performs element-wise addition on an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape, and returns a\n * single tensor (also of the same shape). The inputs are specified as an\n * `Array` when the `apply` method of the `Add` layer instance is called. For\n * example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const addLayer = tf.layers.add();\n * const sum = addLayer.apply([input1, input2]);\n * console.log(JSON.stringify(sum.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function add(args) {\n    return new Add(args);\n}\n/**\n * Layer that performs element-wise averaging on an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape, and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const averageLayer = tf.layers.average();\n * const average = averageLayer.apply([input1, input2]);\n * console.log(JSON.stringify(average.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function average(args) {\n    return new Average(args);\n}\n/**\n * Layer that concatenates an `Array` of inputs.\n *\n * It takes a list of tensors, all of the same shape except for the\n * concatenation axis, and returns a single tensor, the concatenation\n * of all inputs. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 3]});\n * const concatLayer = tf.layers.concatenate();\n * const output = concatLayer.apply([input1, input2]);\n * console.log(JSON.stringify(output.shape));\n * // You get [null, 2, 5], with the first dimension as the undetermined batch\n * // dimension. The last dimension (5) is the result of concatenating the\n * // last dimensions of the inputs (2 and 3).\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function concatenate(args) {\n    return new Concatenate(args);\n}\n/**\n * Layer that computes the element-wise maximum an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const maxLayer = tf.layers.maximum();\n * const max = maxLayer.apply([input1, input2]);\n * console.log(JSON.stringify(max.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function maximum(args) {\n    return new Maximum(args);\n}\n/**\n * Layer that computes the element-wise minimum of an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const minLayer = tf.layers.minimum();\n * const min = minLayer.apply([input1, input2]);\n * console.log(JSON.stringify(min.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function minimum(args) {\n    return new Minimum(args);\n}\n/**\n * Layer that multiplies (element-wise) an `Array` of inputs.\n *\n * It takes as input an Array of tensors, all of the same\n * shape, and returns a single tensor (also of the same shape).\n * For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const input3 = tf.input({shape: [2, 2]});\n * const multiplyLayer = tf.layers.multiply();\n * const product = multiplyLayer.apply([input1, input2, input3]);\n * console.log(product.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function multiply(args) {\n    return new Multiply(args);\n}\n/**\n * Layer that computes a dot product between samples in two tensors.\n *\n * E.g., if applied to a list of two tensors `a` and `b` both of shape\n * `[batchSize, n]`, the output will be a tensor of shape `[batchSize, 1]`,\n * where each entry at index `[i, 0]` will be the dot product between\n * `a[i, :]` and `b[i, :]`.\n *\n * Example:\n *\n * ```js\n * const dotLayer = tf.layers.dot({axes: -1});\n * const x1 = tf.tensor2d([[10, 20], [30, 40]]);\n * const x2 = tf.tensor2d([[-1, -2], [-3, -4]]);\n *\n * // Invoke the layer's apply() method in eager (imperative) mode.\n * const y = dotLayer.apply([x1, x2]);\n * y.print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function dot(args) {\n    return new Dot(args);\n}\n// Normalization Layers.\n/**\n * Batch normalization layer (Ioffe and Szegedy, 2014).\n *\n * Normalize the activations of the previous layer at each batch,\n * i.e. applies a transformation that maintains the mean activation\n * close to 0 and the activation standard deviation close to 1.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape` (Array of integers, does\n *   not include the sample axis) when calling the constructor of this class,\n *   if this layer is used as a first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Batch Normalization: Accelerating Deep Network Training by Reducing\n * Internal Covariate Shift](https://arxiv.org/abs/1502.03167)\n *\n * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}\n */\nexport function batchNormalization(args) {\n    return new BatchNormalization(args);\n}\n/**\n * Layer-normalization layer (Ba et al., 2016).\n *\n * Normalizes the activations of the previous layer for each given example in a\n * batch independently, instead of across a batch like in `batchNormalization`.\n * In other words, this layer applies a transformation that maintanis the mean\n * activation within each example close to0 and activation variance close to 1.\n *\n * Input shape:\n *   Arbitrary. Use the argument `inputShape` when using this layer as the first\n *   layer in a model.\n *\n * Output shape:\n *   Same as input.\n *\n * References:\n *   - [Layer Normalization](https://arxiv.org/abs/1607.06450)\n *\n * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}\n */\nexport function layerNormalization(args) {\n    return new LayerNormalization(args);\n}\n// Padding Layers.\n/**\n * Zero-padding layer for 2D input (e.g., image).\n *\n * This layer can add rows and columns of zeros\n * at the top, bottom, left and right side of an image tensor.\n *\n * Input shape:\n *   4D tensor with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, rows, cols, channels]`\n *   - If `data_format` is `\"channels_first\"`:\n *     `[batch, channels, rows, cols]`.\n *\n * Output shape:\n *   4D with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, paddedRows, paddedCols, channels]`\n *    - If `dataFormat` is `\"channelsFirst\"`:\n *     `[batch, channels, paddedRows, paddedCols]`.\n *\n * @doc {heading: 'Layers', subheading: 'Padding', namespace: 'layers'}\n */\nexport function zeroPadding2d(args) {\n    return new ZeroPadding2D(args);\n}\n// Pooling Layers.\n/**\n * Average pooling operation for spatial data.\n *\n * Input shape: `[batchSize, inLength, channels]`\n *\n * Output shape: `[batchSize, pooledLength, channels]`\n *\n * `tf.avgPool1d` is an alias.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling1d(args) {\n    return new AveragePooling1D(args);\n}\nexport function avgPool1d(args) {\n    return averagePooling1d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling1d(args) {\n    return averagePooling1d(args);\n}\n/**\n * Average pooling operation for spatial data.\n *\n * Input shape:\n *  - If `dataFormat === CHANNEL_LAST`:\n *      4D tensor with shape:\n *      `[batchSize, rows, cols, channels]`\n *  - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *      `[batchSize, channels, rows, cols]`\n *\n * Output shape\n *  - If `dataFormat === CHANNEL_LAST`:\n *      4D tensor with shape:\n *      `[batchSize, pooleRows, pooledCols, channels]`\n *  - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *      `[batchSize, channels, pooleRows, pooledCols]`\n *\n * `tf.avgPool2d` is an alias.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling2d(args) {\n    return new AveragePooling2D(args);\n}\nexport function avgPool2d(args) {\n    return averagePooling2d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling2d(args) {\n    return averagePooling2d(args);\n}\n/**\n * Average pooling operation for 3D data.\n *\n * Input shape\n *   - If `dataFormat === channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, depths, rows, cols, channels]`\n *   - If `dataFormat === channelsFirst`:\n *      4D tensor with shape:\n *       `[batchSize, channels, depths, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`\n *   - If `dataFormat=channelsFirst`:\n *       5D tensor with shape:\n *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling3d(args) {\n    return new AveragePooling3D(args);\n}\nexport function avgPool3d(args) {\n    return averagePooling3d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling3d(args) {\n    return averagePooling3d(args);\n}\n/**\n * Global average pooling operation for temporal data.\n *\n * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.\n *\n * Output Shape:2D tensor with shape: `[batchSize, features]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalAveragePooling1d(args) {\n    return new GlobalAveragePooling1D(args);\n}\n/**\n * Global average pooling operation for spatial data.\n *\n * Input shape:\n *   - If `dataFormat` is `CHANNEL_LAST`:\n *       4D tensor with shape: `[batchSize, rows, cols, channels]`.\n *   - If `dataFormat` is `CHANNEL_FIRST`:\n *       4D tensor with shape: `[batchSize, channels, rows, cols]`.\n *\n * Output shape:\n *   2D tensor with shape: `[batchSize, channels]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalAveragePooling2d(args) {\n    return new GlobalAveragePooling2D(args);\n}\n/**\n * Global max pooling operation for temporal data.\n *\n * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.\n *\n * Output Shape:2D tensor with shape: `[batchSize, features]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalMaxPooling1d(args) {\n    return new GlobalMaxPooling1D(args);\n}\n/**\n * Global max pooling operation for spatial data.\n *\n * Input shape:\n *   - If `dataFormat` is `CHANNEL_LAST`:\n *       4D tensor with shape: `[batchSize, rows, cols, channels]`.\n *   - If `dataFormat` is `CHANNEL_FIRST`:\n *       4D tensor with shape: `[batchSize, channels, rows, cols]`.\n *\n * Output shape:\n *   2D tensor with shape: `[batchSize, channels]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalMaxPooling2d(args) {\n    return new GlobalMaxPooling2D(args);\n}\n/**\n * Max pooling operation for temporal data.\n *\n * Input shape:  `[batchSize, inLength, channels]`\n *\n * Output shape: `[batchSize, pooledLength, channels]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling1d(args) {\n    return new MaxPooling1D(args);\n}\n/**\n * Max pooling operation for spatial data.\n *\n * Input shape\n *   - If `dataFormat === CHANNEL_LAST`:\n *       4D tensor with shape:\n *       `[batchSize, rows, cols, channels]`\n *   - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *       `[batchSize, channels, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=CHANNEL_LAST`:\n *       4D tensor with shape:\n *       `[batchSize, pooleRows, pooledCols, channels]`\n *   - If `dataFormat=CHANNEL_FIRST`:\n *       4D tensor with shape:\n *       `[batchSize, channels, pooleRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling2d(args) {\n    return new MaxPooling2D(args);\n}\n/**\n * Max pooling operation for 3D data.\n *\n * Input shape\n *   - If `dataFormat === channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, depths, rows, cols, channels]`\n *   - If `dataFormat === channelsFirst`:\n *      5D tensor with shape:\n *       `[batchSize, channels, depths, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`\n *   - If `dataFormat=channelsFirst`:\n *       5D tensor with shape:\n *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling3d(args) {\n    return new MaxPooling3D(args);\n}\n// Recurrent Layers.\n/**\n * Gated Recurrent Unit - Cho et al. 2014.\n *\n * This is an `RNN` layer consisting of one `GRUCell`. However, unlike\n * the underlying `GRUCell`, the `apply` method of `SimpleRNN` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const rnn = tf.layers.gru({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `GRUCell`'s number of units.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function gru(args) {\n    return new GRU(args);\n}\n/**\n * Cell class for `GRU`.\n *\n * `GRUCell` is distinct from the `RNN` subclass `GRU` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `GRU` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.gruCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `GRUCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.gruCell({units: 4}),\n *   tf.layers.gruCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `gruCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `GRUCell`, use the\n * `tf.layers.gru`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function gruCell(args) {\n    return new GRUCell(args);\n}\n/**\n * Long-Short Term Memory layer - Hochreiter 1997.\n *\n * This is an `RNN` layer consisting of one `LSTMCell`. However, unlike\n * the underlying `LSTMCell`, the `apply` method of `LSTM` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const lstm = tf.layers.lstm({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = lstm.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `LSTMCell`'s number of units.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function lstm(args) {\n    return new LSTM(args);\n}\n/**\n * Cell class for `LSTM`.\n *\n * `LSTMCell` is distinct from the `RNN` subclass `LSTM` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `LSTM` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.lstmCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `LSTMCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.lstmCell({units: 4}),\n *   tf.layers.lstmCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `lstmCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `LSTMCell`, use the\n * `tf.layers.lstm`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function lstmCell(args) {\n    return new LSTMCell(args);\n}\n/**\n * Fully-connected RNN where the output is to be fed back to input.\n *\n * This is an `RNN` layer consisting of one `SimpleRNNCell`. However, unlike\n * the underlying `SimpleRNNCell`, the `apply` method of `SimpleRNN` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const rnn = tf.layers.simpleRNN({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `SimpleRNNCell`'s number of units.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function simpleRNN(args) {\n    return new SimpleRNN(args);\n}\n/**\n * Cell class for `SimpleRNN`.\n *\n * `SimpleRNNCell` is distinct from the `RNN` subclass `SimpleRNN` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `SimpleRNN` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.simpleRNNCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `SimpleRNNCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.simpleRNNCell({units: 4}),\n *   tf.layers.simpleRNNCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `SimpleRNNCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `SimpleRNNCell`, use the\n * `tf.layers.simpleRNN`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function simpleRNNCell(args) {\n    return new SimpleRNNCell(args);\n}\n/**\n * Convolutional LSTM layer - Xingjian Shi 2015.\n *\n * This is an `ConvRNN2D` layer consisting of one `ConvLSTM2DCell`. However,\n * unlike the underlying `ConvLSTM2DCell`, the `apply` method of `ConvLSTM2D`\n * operates on a sequence of inputs. The shape of the input (not including the\n * first, batch dimension) needs to be 4-D, with the first dimension being time\n * steps. For example:\n *\n * ```js\n * const filters = 3;\n * const kernelSize = 3;\n *\n * const batchSize = 4;\n * const sequenceLength = 2;\n * const size = 5;\n * const channels = 3;\n *\n * const inputShape = [batchSize, sequenceLength, size, size, channels];\n * const input = tf.ones(inputShape);\n *\n * const layer = tf.layers.convLstm2d({filters, kernelSize});\n *\n * const output = layer.apply(input);\n * ```\n */\n/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */\nexport function convLstm2d(args) {\n    return new ConvLSTM2D(args);\n}\n/**\n * Cell class for `ConvLSTM2D`.\n *\n * `ConvLSTM2DCell` is distinct from the `ConvRNN2D` subclass `ConvLSTM2D` in\n * that its `call` method takes the input data of only a single time step and\n * returns the cell's output at the time step, while `ConvLSTM2D` takes the\n * input data over a number of time steps. For example:\n *\n * ```js\n * const filters = 3;\n * const kernelSize = 3;\n *\n * const sequenceLength = 1;\n * const size = 5;\n * const channels = 3;\n *\n * const inputShape = [sequenceLength, size, size, channels];\n * const input = tf.ones(inputShape);\n *\n * const cell = tf.layers.convLstm2dCell({filters, kernelSize});\n *\n * cell.build(input.shape);\n *\n * const outputSize = size - kernelSize + 1;\n * const outShape = [sequenceLength, outputSize, outputSize, filters];\n *\n * const initialH = tf.zeros(outShape);\n * const initialC = tf.zeros(outShape);\n *\n * const [o, h, c] = cell.call([input, initialH, initialC], {});\n * ```\n */\n/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */\nexport function convLstm2dCell(args) {\n    return new ConvLSTM2DCell(args);\n}\n/**\n * Base class for recurrent layers.\n *\n * Input shape:\n *   3D tensor with shape `[batchSize, timeSteps, inputDim]`.\n *\n * Output shape:\n *   - if `returnState`, an Array of tensors (i.e., `tf.Tensor`s). The first\n *     tensor is the output. The remaining tensors are the states at the\n *     last time step, each with shape `[batchSize, units]`.\n *   - if `returnSequences`, the output will have shape\n *     `[batchSize, timeSteps, units]`.\n *   - else, the output will have shape `[batchSize, units]`.\n *\n * Masking:\n *   This layer supports masking for input data with a variable number\n *   of timesteps. To introduce masks to your data,\n *   use an embedding layer with the `mask_zero` parameter\n *   set to `True`.\n *\n * Notes on using statefulness in RNNs:\n *   You can set RNN layers to be 'stateful', which means that the states\n *   computed for the samples in one batch will be reused as initial states\n *   for the samples in the next batch. This assumes a one-to-one mapping\n *   between samples in different successive batches.\n *\n *   To enable statefulness:\n *     - specify `stateful: true` in the layer constructor.\n *     - specify a fixed batch size for your model, by passing\n *       if sequential model:\n *         `batchInputShape=[...]` to the first layer in your model.\n *       else for functional model with 1 or more Input layers:\n *         `batchShape=[...]` to all the first layers in your model.\n *       This is the expected shape of your inputs *including the batch size*.\n *       It should be a tuple of integers, e.g. `(32, 10, 100)`.\n *     - specify `shuffle=False` when calling fit().\n *\n *   To reset the states of your model, call `.resetStates()` on either\n *   a specific layer, or on your entire model.\n *\n * Note on specifying the initial state of RNNs\n *   You can specify the initial state of RNN layers symbolically by\n *   calling them with the option `initialState`. The value of\n *   `initialState` should be a tensor or list of tensors representing\n *   the initial state of the RNN layer.\n *\n *   You can specify the initial state of RNN layers numerically by\n *   calling `resetStates` with the keyword argument `states`. The value of\n *   `states` should be a numpy array or list of numpy arrays representing\n *   the initial state of the RNN layer.\n *\n * Note on passing external constants to RNNs\n *   You can pass \"external\" constants to the cell using the `constants`\n *   keyword argument of `RNN.call` method. This requires that the `cell.call`\n *   method accepts the same keyword argument `constants`. Such constants\n *   can be used to conditon the cell transformation on additional static inputs\n *   (not changing over time), a.k.a an attention mechanism.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function rnn(args) {\n    return new RNN(args);\n}\n/**\n * Wrapper allowing a stack of RNN cells to behave as a single cell.\n *\n * Used to implement efficient stacked RNNs.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function stackedRNNCells(args) {\n    return new StackedRNNCells(args);\n}\n// Wrapper Layers.\n/** @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'} */\nexport function bidirectional(args) {\n    return new Bidirectional(args);\n}\n/**\n * This wrapper applies a layer to every temporal slice of an input.\n *\n * The input should be at least 3D,  and the dimension of the index `1` will be\n * considered to be the temporal dimension.\n *\n * Consider a batch of 32 samples, where each sample is a sequence of 10 vectors\n * of 16 dimensions. The batch input shape of the layer is then `[32,  10,\n * 16]`, and the `inputShape`, not including the sample dimension, is\n * `[10, 16]`.\n *\n * You can then use `TimeDistributed` to apply a `Dense` layer to each of the 10\n * timesteps, independently:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.timeDistributed({\n *   layer: tf.layers.dense({units: 8}),\n *   inputShape: [10, 16],\n * }));\n *\n * // Now model.outputShape = [null, 10, 8].\n * // The output will then have shape `[32, 10, 8]`.\n *\n * // In subsequent layers, there is no need for `inputShape`:\n * model.add(tf.layers.timeDistributed({layer: tf.layers.dense({units: 32})}));\n * console.log(JSON.stringify(model.outputs[0].shape));\n * // Now model.outputShape = [null, 10, 32].\n * ```\n *\n * The output will then have shape `[32, 10, 32]`.\n *\n * `TimeDistributed` can be used with arbitrary layers, not just `Dense`, for\n * instance a `Conv2D` layer.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.timeDistributed({\n *   layer: tf.layers.conv2d({filters: 64, kernelSize: [3, 3]}),\n *   inputShape: [10, 299, 299, 3],\n * }));\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'}\n */\nexport function timeDistributed(args) {\n    return new TimeDistributed(args);\n}\n// Aliases for pooling.\nexport const globalMaxPool1d = globalMaxPooling1d;\nexport const globalMaxPool2d = globalMaxPooling2d;\nexport const maxPool1d = maxPooling1d;\nexport const maxPool2d = maxPooling2d;\nexport { Layer, RNN, RNNCell, input /* alias for tf.input */ };\n/**\n * Apply additive zero-centered Gaussian noise.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * This is useful to mitigate overfitting\n * (you could see it as a form of random data augmentation).\n * Gaussian Noise (GS) is a natural choice as corruption process\n * for real valued inputs.\n *\n * # Arguments\n *     stddev: float, standard deviation of the noise distribution.\n *\n * # Input shape\n *         Arbitrary. Use the keyword argument `input_shape`\n *         (tuple of integers, does not include the samples axis)\n *         when using this layer as the first layer in a model.\n *\n * # Output shape\n *         Same shape as input.\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function gaussianNoise(args) {\n    return new GaussianNoise(args);\n}\n/**\n * Apply multiplicative 1-centered Gaussian noise.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](\n *      http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function gaussianDropout(args) {\n    return new GaussianDropout(args);\n}\n/**\n * Applies Alpha Dropout to the input.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs\n * to their original values, in order to ensure the self-normalizing property\n * even after this dropout.\n * Alpha Dropout fits well to Scaled Exponential Linear Units\n * by randomly setting activations to the negative saturation value.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the\n *     shape for randomly generated keep/drop flags.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function alphaDropout(args) {\n    return new AlphaDropout(args);\n}\n/**\n * Masks a sequence by using a mask value to skip timesteps.\n *\n * If all features for a given sample timestep are equal to `mask_value`,\n * then the sample timestep will be masked (skipped) in all downstream layers\n * (as long as they support masking).\n *\n * If any downstream layer does not support masking yet receives such\n * an input mask, an exception will be raised.\n *\n * Arguments:\n *   - `maskValue`: Either None or mask value to skip.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * @doc {heading: 'Layers', subheading: 'Mask', namespace: 'layers'}\n */\nexport function masking(args) {\n    return new Masking(args);\n}\n//# sourceMappingURL=exports_layers.js.map", "import * as losses from './losses';\nimport * as metrics from './metrics';\n/**\n * Binary accuracy metric function.\n *\n * `yTrue` and `yPred` can have 0-1 values. Example:\n * ```js\n * const x = tf.tensor2d([[1, 1, 1, 1], [0, 0, 0, 0]], [2, 4]);\n * const y = tf.tensor2d([[1, 0, 1, 0], [0, 0, 0, 1]], [2, 4]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * `yTrue` and `yPred` can also have floating-number values between 0 and 1, in\n * which case the values will be thresholded at 0.5 to yield 0-1 values (i.e.,\n * a value >= 0.5 and <= 1.0 is interpreted as 1.\n * )\n * Example:\n * ```js\n * const x = tf.tensor1d([1, 1, 1, 1, 0, 0, 0, 0]);\n * const y = tf.tensor1d([0.2, 0.4, 0.6, 0.8, 0.2, 0.3, 0.4, 0.7]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryAccuracy(yTrue, yPred) {\n    return metrics.binaryAccuracy(yTrue, yPred);\n}\n/**\n * Binary crossentropy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0], [1], [1], [1]]);\n * const y = tf.tensor2d([[0], [0], [0.5], [1]]);\n * const crossentropy = tf.metrics.binaryCrossentropy(x, y);\n * crossentropy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction, probabilities for the `1` case.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryCrossentropy(yTrue, yPred) {\n    return metrics.binaryCrossentropy(yTrue, yPred);\n}\n/**\n * Sparse categorical accuracy metric function.\n *\n * Example:\n * ```js\n *\n * const yTrue = tf.tensor1d([1, 1, 2, 2, 0]);\n * const yPred = tf.tensor2d(\n *      [[0, 1, 0], [1, 0, 0], [0, 0.4, 0.6], [0, 0.6, 0.4], [0.7, 0.3, 0]]);\n * const crossentropy = tf.metrics.sparseCategoricalAccuracy(yTrue, yPred);\n * crossentropy.print();\n * ```\n *\n * @param yTrue True labels: indices.\n * @param yPred Predicted probabilities or logits.\n * @returns Accuracy tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function sparseCategoricalAccuracy(yTrue, yPred) {\n    return metrics.sparseCategoricalAccuracy(yTrue, yPred);\n}\n/**\n * Categorical accuracy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0, 0, 0, 1], [0, 0, 0, 1]]);\n * const y = tf.tensor2d([[0.1, 0.8, 0.05, 0.05], [0.1, 0.05, 0.05, 0.8]]);\n * const accuracy = tf.metrics.categoricalAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth: one-hot encoding of categories.\n * @param yPred Binary Tensor of prediction: probabilities or logits for the\n *   same categories as in `yTrue`.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalAccuracy(yTrue, yPred) {\n    return metrics.categoricalAccuracy(yTrue, yPred);\n}\n/**\n * Categorical crossentropy between an output tensor and a target tensor.\n *\n * @param target A tensor of the same shape as `output`.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalCrossentropy(yTrue, yPred) {\n    return metrics.categoricalCrossentropy(yTrue, yPred);\n}\n/**\n * Computes the precision of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const precision = tf.metrics.precision(x, y);\n * precision.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to be contain only 0-1 values.\n * @param yPred The predicted values. Expected to be contain only 0-1 values.\n * @return Precision Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function precision(yTrue, yPred) {\n    return metrics.precision(yTrue, yPred);\n}\n/**\n * Computes the recall of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const recall = tf.metrics.recall(x, y);\n * recall.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to be contain only 0-1 values.\n * @param yPred The predicted values. Expected to be contain only 0-1 values.\n * @return Recall Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function recall(yTrue, yPred) {\n    return metrics.recall(yTrue, yPred);\n}\n/**\n * Loss or metric function: Cosine proximity.\n *\n * Mathematically, cosine proximity is defined as:\n *   `-sum(l2Normalize(yTrue) * l2Normalize(yPred))`,\n * wherein `l2Normalize()` normalizes the L2 norm of the input to 1 and `*`\n * represents element-wise multiplication.\n *\n * ```js\n * const yTrue = tf.tensor2d([[1, 0], [1, 0]]);\n * const yPred = tf.tensor2d([[1 / Math.sqrt(2), 1 / Math.sqrt(2)], [0, 1]]);\n * const proximity = tf.metrics.cosineProximity(yTrue, yPred);\n * proximity.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Cosine proximity Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function cosineProximity(yTrue, yPred) {\n    return losses.cosineProximity(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean absolute error.\n *\n * Mathematically, mean absolute error is defined as:\n *   `mean(abs(yPred - yTrue))`,\n * wherein the `mean` is applied over feature dimensions.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [0, 0], [2, 3]]);\n * const yPred = tf.tensor2d([[0, 1], [0, 1], [-2, -3]]);\n * const mse = tf.metrics.meanAbsoluteError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsoluteError(yTrue, yPred) {\n    return losses.meanAbsoluteError(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean absolute percentage error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [10, 20]]);\n * const yPred = tf.tensor2d([[0, 1], [11, 24]]);\n * const mse = tf.metrics.meanAbsolutePercentageError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MAPE`, `tf.metrics.mape`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute percentage error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsolutePercentageError(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\nexport function MAPE(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\nexport function mape(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean squared error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [3, 4]]);\n * const yPred = tf.tensor2d([[0, 1], [-3, -4]]);\n * const mse = tf.metrics.meanSquaredError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MSE`, `tf.metrics.mse`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean squared error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanSquaredError(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\nexport function MSE(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\nexport function mse(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\n//# sourceMappingURL=exports_metrics.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport { modelFromJSON } from './models';\n//# sourceMappingURL=exports_models.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as regularizers from './regularizers';\n// tslint:disable-next-line:max-line-length\nimport { L1L2 } from './regularizers';\n/**\n * Regularizer for L1 and L2 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l1 * abs(x)) + sum(l2 * x^2)\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l1l2(config) {\n    return new L1L2(config);\n}\n/**\n * Regularizer for L1 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l1 * abs(x))\n * @param args l1 config.\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l1(config) {\n    return regularizers.l1(config);\n}\n/**\n * Regularizer for L2 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l2 * x^2)\n * @param args l2 config.\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l2(config) {\n    return regularizers.l2(config);\n}\n//# sourceMappingURL=exports_regularizers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/callbacks.py */\nimport { BaseCallback } from './base_callbacks';\nimport { LayersModel } from './engine/training';\nimport { NotImplementedError } from './errors';\nimport { resolveScalarsInLogs } from './logs';\nexport class Callback extends BaseCallback {\n    constructor() {\n        super(...arguments);\n        /** Instance of `keras.models.Model`. Reference of the model being trained. */\n        this.model = null;\n    }\n    setModel(model) {\n        if (!(model instanceof LayersModel)) {\n            throw new Error('model must be a LayersModel, not some other Container');\n        }\n        this.model = model;\n    }\n}\nfunction less(currVal, prevVal) {\n    return currVal < prevVal;\n}\nfunction greater(currVal, prevVal) {\n    return currVal > prevVal;\n}\n/**\n * A Callback that stops training when a monitored quantity has stopped\n * improving.\n */\nexport class EarlyStopping extends Callback {\n    constructor(args) {\n        super();\n        if (args == null) {\n            args = {};\n        }\n        if (args.restoreBestWeights) {\n            throw new NotImplementedError('restoreBestWeights = True is not implemented in EarlyStopping yet.');\n        }\n        this.monitor = args.monitor || 'val_loss';\n        this.minDelta = Math.abs(args.minDelta || 0);\n        this.patience = args.patience || 0;\n        this.verbose = args.verbose || 0;\n        this.mode = args.mode || 'auto';\n        this.baseline = args.baseline;\n        if (['auto', 'min', 'max'].indexOf(this.mode) === -1) {\n            console.warn(`EarlyStopping mode '${this.mode}' is invalid. ` +\n                `Falling back to mode 'auto'.`);\n            this.mode = 'auto';\n        }\n        if (this.mode === 'min') {\n            this.monitorFunc = less;\n        }\n        else if (this.mode === 'max') {\n            this.monitorFunc = greater;\n        }\n        else {\n            // For mode === 'auto'.\n            if (this.monitor.indexOf('acc') !== -1) {\n                this.monitorFunc = greater;\n            }\n            else {\n                this.monitorFunc = less;\n            }\n        }\n        if (this.monitorFunc === less) {\n            this.minDelta *= -1;\n        }\n    }\n    async onTrainBegin(logs) {\n        this.wait = 0;\n        this.stoppedEpoch = 0;\n        if (this.baseline != null) {\n            this.best = this.baseline;\n        }\n        else {\n            this.best = this.monitorFunc === less ? Infinity : -Infinity;\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        await resolveScalarsInLogs(logs);\n        const current = this.getMonitorValue(logs);\n        if (current == null) {\n            return;\n        }\n        if (this.monitorFunc(current - this.minDelta, this.best)) {\n            this.best = current;\n            this.wait = 0;\n            // TODO(cais): Logic for restoreBestWeights.\n        }\n        else {\n            this.wait++;\n            if (this.wait >= this.patience) {\n                this.stoppedEpoch = epoch;\n                this.model.stopTraining = true;\n            }\n            // TODO(cais): Logic for restoreBestWeights.\n        }\n    }\n    async onTrainEnd(logs) {\n        if (this.stoppedEpoch > 0 && this.verbose) {\n            console.log(`Epoch ${this.stoppedEpoch}: early stopping.`);\n        }\n    }\n    getMonitorValue(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        const monitorValue = logs[this.monitor];\n        if (monitorValue == null) {\n            console.warn(`Metric for EarlyStopping ${this.monitor} is not available. ` +\n                `Available metrics are: ${Object.keys(logs)}`);\n        }\n        return monitorValue;\n    }\n}\n/**\n * Factory function for a Callback that stops training when a monitored\n * quantity has stopped improving.\n *\n * Early stopping is a type of regularization, and protects model against\n * overfitting.\n *\n * The following example based on fake data illustrates how this callback\n * can be used during `tf.LayersModel.fit()`:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense({\n *   units: 3,\n *   activation: 'softmax',\n *   kernelInitializer: 'ones',\n *   inputShape: [2]\n * }));\n * const xs = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const ys = tf.tensor2d([[1, 0, 0], [0, 1, 0]], [2, 3]);\n * const xsVal = tf.tensor2d([4, 3, 2, 1], [2, 2]);\n * const ysVal = tf.tensor2d([[0, 0, 1], [0, 1, 0]], [2, 3]);\n * model.compile(\n *     {loss: 'categoricalCrossentropy', optimizer: 'sgd', metrics: ['acc']});\n *\n * // Without the EarlyStopping callback, the val_acc value would be:\n * //   0.5, 0.5, 0.5, 0.5, ...\n * // With val_acc being monitored, training should stop after the 2nd epoch.\n * const history = await model.fit(xs, ys, {\n *   epochs: 10,\n *   validationData: [xsVal, ysVal],\n *   callbacks: tf.callbacks.earlyStopping({monitor: 'val_acc'})\n * });\n *\n * // Expect to see a length-2 array.\n * console.log(history.history.val_acc);\n * ```\n *\n * @doc {\n *   heading: 'Callbacks',\n *   namespace: 'callbacks'\n * }\n */\nexport function earlyStopping(args) {\n    return new EarlyStopping(args);\n}\nexport const callbacks = { earlyStopping };\n//# sourceMappingURL=callbacks.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// This file lists all exports of TensorFlow.js Layers\nimport * as constraints from './exports_constraints';\nimport * as initializers from './exports_initializers';\nimport * as layers from './exports_layers';\nimport * as metrics from './exports_metrics';\nimport * as models from './exports_models';\nimport * as regularizers from './exports_regularizers';\nexport { CallbackList, CustomCallback, History } from './base_callbacks';\nexport { Callback, callbacks, EarlyStopping } from './callbacks';\nexport { InputSpec, SymbolicTensor } from './engine/topology';\nexport { LayersModel } from './engine/training';\nexport { input, loadLayersModel, model, registerCallbackConstructor, sequential } from './exports';\nexport { RNN } from './layers/recurrent';\nexport { Sequential } from './models';\nexport { LayerVariable } from './variables';\nexport { version as version_layers } from './version';\nexport { constraints, initializers, layers, metrics, models, regularizers };\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/** DataType enum. */\nexport var DataType;\n(function (DataType) {\n    DataType[DataType[\"DT_INVALID\"] = 0] = \"DT_INVALID\";\n    DataType[DataType[\"DT_FLOAT\"] = 1] = \"DT_FLOAT\";\n    DataType[DataType[\"DT_DOUBLE\"] = 2] = \"DT_DOUBLE\";\n    DataType[DataType[\"DT_INT32\"] = 3] = \"DT_INT32\";\n    DataType[DataType[\"DT_UINT8\"] = 4] = \"DT_UINT8\";\n    DataType[DataType[\"DT_INT16\"] = 5] = \"DT_INT16\";\n    DataType[DataType[\"DT_INT8\"] = 6] = \"DT_INT8\";\n    DataType[DataType[\"DT_STRING\"] = 7] = \"DT_STRING\";\n    DataType[DataType[\"DT_COMPLEX64\"] = 8] = \"DT_COMPLEX64\";\n    DataType[DataType[\"DT_INT64\"] = 9] = \"DT_INT64\";\n    DataType[DataType[\"DT_BOOL\"] = 10] = \"DT_BOOL\";\n    DataType[DataType[\"DT_QINT8\"] = 11] = \"DT_QINT8\";\n    DataType[DataType[\"DT_QUINT8\"] = 12] = \"DT_QUINT8\";\n    DataType[DataType[\"DT_QINT32\"] = 13] = \"DT_QINT32\";\n    DataType[DataType[\"DT_BFLOAT16\"] = 14] = \"DT_BFLOAT16\";\n    DataType[DataType[\"DT_FLOAT_REF\"] = 101] = \"DT_FLOAT_REF\";\n    DataType[DataType[\"DT_DOUBLE_REF\"] = 102] = \"DT_DOUBLE_REF\";\n    DataType[DataType[\"DT_INT32_REF\"] = 103] = \"DT_INT32_REF\";\n    DataType[DataType[\"DT_UINT8_REF\"] = 104] = \"DT_UINT8_REF\";\n    DataType[DataType[\"DT_INT16_REF\"] = 105] = \"DT_INT16_REF\";\n    DataType[DataType[\"DT_INT8_REF\"] = 106] = \"DT_INT8_REF\";\n    DataType[DataType[\"DT_STRING_REF\"] = 107] = \"DT_STRING_REF\";\n    DataType[DataType[\"DT_COMPLEX64_REF\"] = 108] = \"DT_COMPLEX64_REF\";\n    DataType[DataType[\"DT_INT64_REF\"] = 109] = \"DT_INT64_REF\";\n    DataType[DataType[\"DT_BOOL_REF\"] = 110] = \"DT_BOOL_REF\";\n    DataType[DataType[\"DT_QINT8_REF\"] = 111] = \"DT_QINT8_REF\";\n    DataType[DataType[\"DT_QUINT8_REF\"] = 112] = \"DT_QUINT8_REF\";\n    DataType[DataType[\"DT_QINT32_REF\"] = 113] = \"DT_QINT32_REF\";\n    DataType[DataType[\"DT_BFLOAT16_REF\"] = 114] = \"DT_BFLOAT16_REF\";\n})(DataType || (DataType = {}));\nexport var SaverDef;\n(function (SaverDef) {\n    /** CheckpointFormatVersion enum. */\n    let CheckpointFormatVersion;\n    (function (CheckpointFormatVersion) {\n        CheckpointFormatVersion[CheckpointFormatVersion[\"LEGACY\"] = 0] = \"LEGACY\";\n        CheckpointFormatVersion[CheckpointFormatVersion[\"V1\"] = 1] = \"V1\";\n        CheckpointFormatVersion[CheckpointFormatVersion[\"V2\"] = 2] = \"V2\";\n    })(CheckpointFormatVersion = SaverDef.CheckpointFormatVersion || (SaverDef.CheckpointFormatVersion = {}));\n})(SaverDef || (SaverDef = {}));\n//# sourceMappingURL=compiled_api.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst CUSTOM_OPS = {};\n/**\n * Register an Op for graph model executor. This allow you to register\n * TensorFlow custom op or override existing op.\n *\n * Here is an example of registering a new MatMul Op.\n * ```js\n * const customMatmul = (node) =>\n *    tf.matMul(\n *        node.inputs[0], node.inputs[1],\n *        node.attrs['transpose_a'], node.attrs['transpose_b']);\n *\n * tf.registerOp('MatMul', customMatmul);\n * ```\n * The inputs and attrs of the node object is based on the TensorFlow op\n * registry.\n *\n * @param name The Tensorflow Op name.\n * @param opFunc An op function which is called with the current graph node\n * during execution and needs to return a tensor or a list of tensors. The node\n * has the following attributes:\n *    - attr: A map from attribute name to its value\n *    - inputs: A list of input tensors\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function registerOp(name, opFunc) {\n    const opMapper = {\n        tfOpName: name,\n        category: 'custom',\n        inputs: [],\n        attrs: [],\n        customExecutor: opFunc\n    };\n    CUSTOM_OPS[name] = opMapper;\n}\n/**\n * Retrieve the OpMapper object for the registered op.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function getRegisteredOp(name) {\n    return CUSTOM_OPS[name];\n}\n/**\n * Deregister the Op for graph model executor.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function deregisterOp(name) {\n    delete CUSTOM_OPS[name];\n}\n//# sourceMappingURL=register.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { clone, util } from '@tensorflow/tfjs-core';\nexport function getParamValue(paramName, node, tensorMap, context) {\n    const inputParam = node.inputParams[paramName];\n    if (inputParam && inputParam.inputIndexStart !== undefined) {\n        const start = inputParam.inputIndexStart;\n        const end = inputParam.inputIndexEnd === 0 ?\n            undefined :\n            (inputParam.inputIndexEnd === undefined ? start + 1 :\n                inputParam.inputIndexEnd);\n        if (inputParam.type === 'tensor') {\n            return getTensor(node.inputNames[inputParam.inputIndexStart], tensorMap, context);\n        }\n        if (inputParam.type === 'tensors') {\n            const inputs = node.inputNames.slice(start, end);\n            return inputs.map(name => getTensor(name, tensorMap, context));\n        }\n        const tensor = getTensor(node.inputNames.slice(start)[0], tensorMap, context);\n        const data = tensor.dataSync();\n        return inputParam.type === 'number' ?\n            data[0] :\n            util.toNestedArray(tensor.shape, data);\n    }\n    const attrParam = node.attrParams[paramName];\n    return attrParam && attrParam.value;\n}\n/**\n * Retrieve the tensor based on input name by extracting the node name and\n * output index information.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n */\nexport function getTensor(name, tensorsMap, context) {\n    const [nodeName, index] = parseNodeName(name);\n    const contextId = context.currentContextIds.find(contextId => {\n        return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId)];\n    });\n    return contextId !== undefined ?\n        tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] :\n        undefined;\n}\n/**\n * Retrieve the tensors based on input name for current context.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n */\nexport function getTensorsForCurrentContenxt(name, tensorsMap, context) {\n    return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];\n}\n/**\n * Returns the node name and index from the Node input name.\n * @param inputName The input name of the node, in format of\n * node_name:output_index, i.e. MatMul:0, if the output_index is not set, it is\n * default to 0.\n */\nexport function getNodeNameAndIndex(inputName, context) {\n    const [nodeName, index] = parseNodeName(inputName);\n    return [\n        getNodeNameWithContextId(nodeName, context && context.currentContextId),\n        index\n    ];\n}\nfunction getNodeNameWithContextId(name, contextId) {\n    return !!contextId ? `${name}-${contextId}` : name;\n}\nexport function parseNodeName(name) {\n    const parts = name.split(':');\n    if (parts.length === 1) {\n        return [name, 0];\n    }\n    const nodeName = parts[0];\n    return [nodeName, Number(parts[parts.length - 1])];\n}\nexport function split(arr, size) {\n    const res = [];\n    for (let i = 0; i < arr.length; i += size) {\n        res.push(arr.slice(i, i + size));\n    }\n    return res;\n}\nexport function getPadding(node, tensorMap, context) {\n    let pad = getParamValue('pad', node, tensorMap, context);\n    if (pad === 'explicit') {\n        // This is 1d array, we need to convert it to 2d array\n        pad = getParamValue('explicitPaddings', node, tensorMap, context);\n        const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];\n        for (let i = 0; i < 4; i++) {\n            explicitPadding[i][0] = pad[i * 2];\n            explicitPadding[i][1] = pad[i * 2 + 1];\n        }\n        return explicitPadding;\n    }\n    return pad;\n}\n/**\n *  Reuse the tensor if it is marked as keep, otherwise clone the tensor to\n *  avoid disposal. This is important for TensorArray and TensorList ops, since\n *  internally they use a tensor as the id for TensorArray and TensorList, and\n * to simplify lookup, they also use Tensor.id as the key to the internal map.\n * These id tensors have been marked as kept in the backend, we need avoid clone\n * them in order to create new Tensor.id.\n * @param tensor\n */\nexport function cloneTensor(tensor) {\n    return tensor.kept ? tensor : clone(tensor);\n}\n//# sourceMappingURL=utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Add',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AddV2',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AddN',\n        'category': 'arithmetic',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'BiasAdd',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sub',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'RealDiv',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Div',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'DivNoNan',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'FloorDiv',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Mul',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Maximum',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Minimum',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Pow',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'SquaredDifference',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Mod',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'FloorMod',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    }\n];\n//# sourceMappingURL=arithmetic.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Abs',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Acos',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Asin',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atan',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atan2',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'y', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Ceil',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ClipByValue',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'clip_value_min', 'name': 'clipValueMin', 'type': 'number' },\n            { 'tfName': 'clip_value_max', 'name': 'clipValueMax', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Complex',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'real', 'type': 'tensor' },\n            { 'start': 1, 'name': 'imag', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ComplexAbs',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Cos',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Cosh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Elu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Exp',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Floor',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Log',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Imag',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'outputType',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Neg',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Real',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'outputType',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Prelu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'alpha', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Relu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Relu6',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'clipValueMin',\n                'name': 'clipValueMin',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'clipValueMax',\n                'name': 'clipValueMax',\n                'type': 'number',\n                'defaultValue': 6\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Selu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sigmoid',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sin',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sinh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sqrt',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Rsqrt',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Square',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Tan',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Tanh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sign',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Round',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Expm1',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Log1p',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Reciprocal',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Softplus',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Asinh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Acosh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atanh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Erf',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Prod',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axes', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'keep_dims',\n                'name': 'keepDims',\n                'type': 'bool',\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LeakyRelu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'alpha',\n                'name': 'alpha',\n                'type': 'number',\n                'defaultValue': 0.2\n            },\n            {\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=basic_math.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'LoopCond',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'name': 'pred', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Switch',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'data', 'type': 'tensor' },\n            { 'start': 1, 'name': 'pred', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Merge',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Enter',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'frame_name', 'name': 'frameName', 'type': 'string' },\n            { 'tfName': 'is_constant', 'name': 'isConstant', 'type': 'bool' }\n        ]\n    },\n    {\n        'tfOpName': 'Exit',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'NextIteration',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'size', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },\n            { 'tfName': 'dynamic_size', 'name': 'dynamicSize', 'type': 'bool' },\n            { 'tfName': 'clear_after_read', 'name': 'clearAfterRead', 'type': 'bool' },\n            {\n                'tfName': 'identical_element_shapes',\n                'name': 'identicalElementShapes',\n                'type': 'bool'\n            },\n            { 'tfName': 'tensor_array_name', 'name': 'name', 'type': 'string' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayWriteV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayReadV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{\n                'tfName': 'dtype',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    },\n    {\n        'tfOpName': 'TensorArrayGatherV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayScatterV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorArrayConcatV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }, {\n                'tfName': 'element_shape_except0',\n                'name': 'elementShapeExcept0',\n                'type': 'shape',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArraySplitV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 2, 'name': 'lengths', 'type': 'number[]' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorArraySizeV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'flowIn', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayCloseV3',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'StatelessIf',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'cond', 'type': 'tensor' },\n            { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }\n        ],\n        'attrs': [\n            { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },\n            { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'If',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'cond', 'type': 'tensor' },\n            { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }\n        ],\n        'attrs': [\n            { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },\n            { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'StatelessWhile',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },\n            { 'tfName': 'body', 'name': 'body', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'While',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },\n            { 'tfName': 'body', 'name': 'body', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListScatter',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' }\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListScatterV2',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 3, 'name': 'numElements', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListGather',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListGetItem',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListSetItem',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListReserve',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 1, 'name': 'numElements', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListFromTensor',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' }\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListStack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' },\n            { 'tfName': 'num_elements', 'name': 'numElements', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListSplit',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 2, 'name': 'lengths', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListConcat',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListPopBack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListPushBack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n];\n//# sourceMappingURL=control.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'AvgPool',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPool',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPoolWithArgmax',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' }, {\n                'tfName': 'include_batch_in_index',\n                'name': 'includeBatchInIndex',\n                'type': 'bool'\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AvgPool3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPool3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Conv1D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'stride', 'name': 'stride', 'type': 'number' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NWC'\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'dilation',\n                'name': 'dilation',\n                'type': 'number',\n                'defaultValue': 1\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Conv2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            { 'tfName': 'useCudnnOnGpu', 'name': 'useCudnnOnGpu', 'type': 'bool' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': '_FusedConv2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'use_cudnn_on_gpu',\n                'name': 'useCudnnOnGpu',\n                'type': 'bool',\n                'defaultValue': true\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'dilations',\n                'name': 'dilations',\n                'type': 'number[]',\n                'defaultValue': [1, 1, 1, 1]\n            },\n            {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.0001\n            },\n        ]\n    },\n    {\n        'tfOpName': 'Conv2DBackpropInput',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 2, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 0, 'name': 'outputShape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n        ]\n    },\n    {\n        'tfOpName': 'DepthwiseConv2d',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'input', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'DepthwiseConv2dNative',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'input', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'FusedDepthwiseConv2dNative',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'dilations',\n                'name': 'dilations',\n                'type': 'number[]',\n                'defaultValue': [1, 1, 1, 1]\n            },\n            {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Conv3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ],\n    },\n    {\n        'tfOpName': 'Dilation2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'rates', 'name': 'dilations', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }\n        ]\n    }\n];\n//# sourceMappingURL=convolution.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Fill',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n            { 'start': 1, 'name': 'value', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'LinSpace',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'start', 'type': 'number' },\n            { 'start': 1, 'name': 'stop', 'type': 'number' },\n            { 'start': 2, 'name': 'num', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'OneHot',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'depth', 'type': 'number' },\n            { 'start': 2, 'name': 'onValue', 'type': 'number', 'defaultValue': 1 },\n            { 'start': 3, 'name': 'offValue', 'type': 'number', 'defaultValue': 0 },\n        ],\n        'attrs': [\n            {\n                'tfName': 'axis',\n                'name': 'axis',\n                'type': 'number',\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Ones',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'OnesLike',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'RandomUniform',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'minval',\n                'name': 'minval',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'maxval',\n                'name': 'maxval',\n                'type': 'number',\n                'defaultValue': 1\n            },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'seed2',\n                'name': 'seed2',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Range',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'start', 'type': 'number' },\n            { 'start': 1, 'name': 'stop', 'type': 'number' },\n            { 'start': 2, 'name': 'step', 'type': 'number', 'defaultValue': 0 },\n        ],\n        'attrs': [{ 'tfName': 'Tidx', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TruncatedNormal',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'means',\n                'name': 'mean',\n                'type': 'number',\n                'defaultValue': 0.0\n            },\n            {\n                'tfName': 'stddev',\n                'name': 'stdDev',\n                'type': 'number',\n                'defaultValue': 1.0\n            },\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number' }, {\n                'tfName': 'seed2',\n                'name': 'seed2',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Zeros',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'ZerosLike',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'Multinomial',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'logits', 'type': 'tensor' },\n            { 'start': 1, 'name': 'numSamples', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number' },\n            { 'tfName': 'seed2', 'name': 'seed2', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'output_dtype', 'name': 'output_dtype', 'type': 'dtype' }\n        ]\n    }\n];\n//# sourceMappingURL=creation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'NonMaxSuppressionV2',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV3',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV4',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'T_threshold',\n                'name': 'threshold',\n                'type': 'dtype',\n                'notSupported': true\n            },\n            {\n                'tfName': 'pad_to_max_output_size',\n                'name': 'padToMaxOutputSize',\n                'type': 'bool'\n            }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV5',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' },\n            { 'start': 5, 'name': 'softNmsSigma', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Where',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ListDiff',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'y', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=dynamic.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'TopKV2',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'k', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'sorted', 'name': 'sorted', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Unique',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n    },\n    {\n        'tfOpName': 'UniqueV2',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' },\n        ],\n    },\n];\n//# sourceMappingURL=evaluation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'PlaceholderWithDefault',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'default', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'Placeholder',\n        'category': 'graph',\n        'attrs': [\n            { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    { 'tfOpName': 'Const', 'category': 'graph' }, {\n        'tfOpName': 'Identity',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'IdentityN',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Snapshot',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Rank',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Size',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Shape',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'ShapeN',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Print',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'data', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'message', 'name': 'message', 'type': 'string' }, {\n                'tfName': 'first_n',\n                'name': 'firstN',\n                'type': 'number',\n                'notSupported': true\n            },\n            {\n                'tfName': 'summarize',\n                'name': 'summarize',\n                'type': 'number',\n                'defaultValue': 3\n            }\n        ]\n    },\n    { 'tfOpName': 'NoOp', 'category': 'graph', 'inputs': [] }, {\n        'tfOpName': 'StopGradient',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'FakeQuantWithMinMaxVars',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'min', 'name': 'min', 'type': 'number' },\n            { 'tfName': 'max', 'name': 'max', 'type': 'number' }\n        ]\n    }\n];\n//# sourceMappingURL=graph.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'ResizeBilinear',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'images', 'type': 'tensor' },\n            { 'start': 1, 'name': 'size', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ResizeNearestNeighbor',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'images', 'type': 'tensor' },\n            { 'start': 1, 'name': 'size', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'CropAndResize',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'image', 'type': 'tensor' },\n            { 'start': 1, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 2, 'name': 'boxInd', 'type': 'tensor' },\n            { 'start': 3, 'name': 'cropSize', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'method', 'name': 'method', 'type': 'string' }, {\n                'tfName': 'extrapolation_value',\n                'name': 'extrapolationValue',\n                'type': 'number'\n            }\n        ]\n    }\n];\n//# sourceMappingURL=image.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Equal',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'NotEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Greater',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'GreaterEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Less',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LessEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalAnd',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalNot',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalOr',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Select',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n            { 'start': 1, 'name': 'a', 'type': 'tensor' },\n            { 'start': 2, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'SelectV2',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n            { 'start': 1, 'name': 'a', 'type': 'tensor' },\n            { 'start': 2, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=logical.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': '_FusedMatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' }, {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.0001\n            },\n            {\n                'tfName': 'transpose_a',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'transpose_b',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'transpose_a',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'transpose_b',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'BatchMatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'adj_x',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'adj_y',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'BatchMatMulV2',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'adj_x',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'adj_y',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Transpose',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'perm', 'type': 'number[]' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=matrices.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'FusedBatchNorm',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'FusedBatchNormV2',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'FusedBatchNormV3',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'LRN',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'depth_radius',\n                'name': 'radius',\n                'type': 'number',\n                'defaultValue': 5\n            },\n            { 'tfName': 'bias', 'name': 'bias', 'type': 'number', 'defaultValue': 1.0 },\n            {\n                'tfName': 'alpha',\n                'name': 'alpha',\n                'type': 'number',\n                'defaultValue': 1.0\n            },\n            {\n                'tfName': 'beta',\n                'name': 'beta',\n                'type': 'number',\n                'defaultValue': 0.5\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Softmax',\n        'category': 'normalization',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'LogSoftmax',\n        'category': 'normalization',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'SparseToDense',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },\n            { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'defaultValue': true,\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Max',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Mean',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Min',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Sum',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'All',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Any',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'ArgMax',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'ArgMin',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Prod',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Cumsum',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'exclusive', 'name': 'exclusive', 'type': 'bool' },\n            { 'tfName': 'reverse', 'name': 'reverse', 'type': 'bool' }\n        ]\n    }\n];\n//# sourceMappingURL=reduction.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'ConcatV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'end': -1, 'name': 'tensors', 'type': 'tensors' },\n            { 'start': -1, 'name': 'axis', 'type': 'number' }\n        ],\n        'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]\n    },\n    {\n        'tfOpName': 'Concat',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 1, 'end': 0, 'name': 'tensors', 'type': 'tensors' },\n            { 'start': 0, 'name': 'axis', 'type': 'number' }\n        ],\n        'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]\n    },\n    {\n        'tfOpName': 'GatherV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'Gather',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Reverse',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'dims', 'type': 'bool', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ReverseV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Slice',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'begin', 'type': 'number[]' },\n            { 'start': 2, 'name': 'size', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'StridedSlice',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'begin', 'type': 'number[]' },\n            { 'start': 2, 'name': 'end', 'type': 'number[]' },\n            { 'start': 3, 'name': 'strides', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'begin_mask',\n                'name': 'beginMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'end_mask',\n                'name': 'endMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'new_axis_mask',\n                'name': 'newAxisMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'ellipsis_mask',\n                'name': 'ellipsisMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'shrink_axis_mask',\n                'name': 'shrinkAxisMask',\n                'type': 'number',\n                'defaultValue': 0\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Pack',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'Unpack',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'num',\n                'name': 'num',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Tile',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'reps', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Split',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'axis', 'type': 'number', 'defaultValue': 0 },\n            { 'start': 1, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'num_split',\n                'name': 'numOrSizeSplits',\n                'type': 'number',\n                'defaultValue': 1\n            }]\n    },\n    {\n        'tfOpName': 'SplitV',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'numOrSizeSplits', 'type': 'number[]' },\n            { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'ScatterNd',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'values', 'type': 'tensor' },\n            { 'start': 2, 'name': 'shape', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'GatherNd',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'SparseToDense',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },\n            { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'defaultValue': false,\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=slice_join.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'FFT',\n        'category': 'spectral',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'IFFT',\n        'category': 'spectral',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'RFFT',\n        'category': 'spectral',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' }, {\n                'start': 1,\n                'name': 'fft_length',\n                'type': 'number',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'IRFFT',\n        'category': 'spectral',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' }, {\n                'start': 1,\n                'name': 'fft_length',\n                'type': 'number',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=spectral.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Cast',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'SrcT',\n                'name': 'sdtype',\n                'type': 'dtype',\n                'notSupported': true\n            },\n            { 'tfName': 'DstT', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'ExpandDims',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Pad',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' },\n        ],\n        'attrs': [{\n                'tfName': 'constant_value',\n                'name': 'constantValue',\n                'type': 'number',\n                'defaultValue': 0\n            }]\n    },\n    {\n        'tfOpName': 'PadV2',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' }, {\n                'start': 2,\n                'name': 'constantValue',\n                'type': 'number',\n                'defaultValue': 0\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Reshape',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'shape', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Squeeze',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'axis',\n                'tfDeprecatedName': 'squeeze_dims',\n                'name': 'axis',\n                'type': 'number[]'\n            }]\n    },\n    {\n        'tfOpName': 'SpaceToBatchND',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'paddings', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'BatchToSpaceND',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'crops', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'DepthToSpace',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'block_size', 'name': 'blockSize', 'type': 'number' },\n            { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string' }\n        ]\n    },\n    {\n        'tfOpName': 'BroadcastTo',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': []\n    }\n];\n//# sourceMappingURL=transformation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport * as tensorflow from '../data/compiled_api';\nimport { getRegisteredOp } from './custom_op/register';\nimport { getNodeNameAndIndex } from './executors/utils';\nimport * as arithmetic from './op_list/arithmetic';\nimport * as basicMath from './op_list/basic_math';\nimport * as control from './op_list/control';\nimport * as convolution from './op_list/convolution';\nimport * as creation from './op_list/creation';\nimport * as dynamic from './op_list/dynamic';\nimport * as evaluation from './op_list/evaluation';\nimport * as graph from './op_list/graph';\nimport * as image from './op_list/image';\nimport * as logical from './op_list/logical';\nimport * as matrices from './op_list/matrices';\nimport * as normalization from './op_list/normalization';\nimport * as reduction from './op_list/reduction';\nimport * as sliceJoin from './op_list/slice_join';\nimport * as spectral from './op_list/spectral';\nimport * as transformation from './op_list/transformation';\nexport class OperationMapper {\n    // Singleton instance for the mapper\n    static get Instance() {\n        return this._instance || (this._instance = new this());\n    }\n    // Loads the op mapping from the JSON file.\n    constructor() {\n        const ops = [\n            arithmetic, basicMath, control, convolution, creation, dynamic,\n            evaluation, logical, image, graph, matrices, normalization, reduction,\n            sliceJoin, spectral, transformation\n        ];\n        const mappersJson = [].concat(...ops.map(op => op.json));\n        this.opMappers = mappersJson.reduce((map, mapper) => {\n            map[mapper.tfOpName] = mapper;\n            return map;\n        }, {});\n    }\n    // Converts the model inference graph from Tensorflow GraphDef to local\n    // representation for TensorFlow.js API\n    transformGraph(graph, signature = {}) {\n        const tfNodes = graph.node;\n        const placeholders = [];\n        const weights = [];\n        const initNodes = [];\n        const nodes = tfNodes.reduce((map, node) => {\n            map[node.name] = this.mapNode(node);\n            if (node.op.startsWith('Placeholder')) {\n                placeholders.push(map[node.name]);\n            }\n            else if (node.op === 'Const') {\n                weights.push(map[node.name]);\n            }\n            else if (node.input == null || node.input.length === 0) {\n                initNodes.push(map[node.name]);\n            }\n            return map;\n        }, {});\n        let inputs = [];\n        const outputs = [];\n        let inputNodeNameToKey = {};\n        let outputNodeNameToKey = {};\n        if (signature != null) {\n            inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);\n            outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);\n        }\n        const allNodes = Object.keys(nodes);\n        allNodes.forEach(key => {\n            const node = nodes[key];\n            node.inputNames.forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                node.inputs.push(nodes[nodeName]);\n                nodes[nodeName].children.push(node);\n            });\n        });\n        // if signature has not outputs set, add any node that does not have\n        // outputs.\n        if (Object.keys(outputNodeNameToKey).length === 0) {\n            allNodes.forEach(key => {\n                const node = nodes[key];\n                if (node.children.length === 0) {\n                    outputs.push(node);\n                }\n            });\n        }\n        else {\n            Object.keys(outputNodeNameToKey).forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                const node = nodes[nodeName];\n                if (node != null) {\n                    node.signatureKey = outputNodeNameToKey[name];\n                    outputs.push(node);\n                }\n            });\n        }\n        if (Object.keys(inputNodeNameToKey).length > 0) {\n            Object.keys(inputNodeNameToKey).forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                const node = nodes[nodeName];\n                if (node) {\n                    node.signatureKey = inputNodeNameToKey[name];\n                    inputs.push(node);\n                }\n            });\n        }\n        else {\n            inputs = placeholders;\n        }\n        let functions = {};\n        if (graph.library != null && graph.library.function != null) {\n            functions = graph.library.function.reduce((functions, func) => {\n                functions[func.signature.name] = this.mapFunction(func);\n                return functions;\n            }, {});\n        }\n        const result = { nodes, inputs, outputs, weights, placeholders, signature, functions };\n        if (initNodes.length > 0) {\n            result.initNodes = initNodes;\n        }\n        return result;\n    }\n    mapSignatureEntries(entries) {\n        return Object.keys(entries || {})\n            .reduce((prev, curr) => {\n            prev[entries[curr].name] = curr;\n            return prev;\n        }, {});\n    }\n    mapNode(node) {\n        // Unsupported ops will cause an error at run-time (not parse time), since\n        // they may not be used by the actual execution subgraph.\n        const mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {};\n        if (node.attr == null) {\n            node.attr = {};\n        }\n        const newNode = {\n            name: node.name,\n            op: node.op,\n            category: mapper.category,\n            inputNames: (node.input ||\n                []).map(input => input.startsWith('^') ? input.substr(1) : input),\n            inputs: [],\n            children: [],\n            inputParams: {},\n            attrParams: {},\n            rawAttrs: node.attr\n        };\n        if (mapper.inputs != null) {\n            newNode.inputParams =\n                mapper.inputs.reduce((map, param) => {\n                    map[param.name] = {\n                        type: param.type,\n                        inputIndexStart: param.start,\n                        inputIndexEnd: param.end\n                    };\n                    return map;\n                }, {});\n        }\n        if (mapper.attrs != null) {\n            newNode.attrParams =\n                mapper.attrs.reduce((map, param) => {\n                    const type = param.type;\n                    let value = undefined;\n                    switch (param.type) {\n                        case 'string':\n                            value = getStringParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'string[]':\n                            value = getStringArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'number':\n                            value = getNumberParam(node.attr, param.tfName, (param.defaultValue || 0));\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'number[]':\n                            value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'bool':\n                            value = getBoolParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'bool[]':\n                            value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'shape':\n                            value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'shape[]':\n                            value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'dtype':\n                            value = getDtypeParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'dtype[]':\n                            value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'func':\n                            value = getFuncParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'tensor':\n                        case 'tensors':\n                            break;\n                        default:\n                            throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`);\n                    }\n                    map[param.name] = { value, type };\n                    return map;\n                }, {});\n        }\n        return newNode;\n    }\n    // map the TFunctionDef to TFJS graph object\n    mapFunction(functionDef) {\n        const tfNodes = functionDef.nodeDef;\n        const placeholders = [];\n        const weights = [];\n        let nodes = {};\n        if (tfNodes != null) {\n            nodes = tfNodes.reduce((map, node) => {\n                map[node.name] = this.mapNode(node);\n                if (node.op === 'Const') {\n                    weights.push(map[node.name]);\n                }\n                return map;\n            }, {});\n        }\n        const inputs = [];\n        const outputs = [];\n        functionDef.signature.inputArg.forEach(arg => {\n            const [nodeName,] = getNodeNameAndIndex(arg.name);\n            const node = {\n                name: nodeName,\n                op: 'Placeholder',\n                inputs: [],\n                inputNames: [],\n                category: 'graph',\n                inputParams: {},\n                attrParams: { dtype: { value: parseDtypeParam(arg.type), type: 'dtype' } },\n                children: []\n            };\n            node.signatureKey = arg.name;\n            inputs.push(node);\n            nodes[nodeName] = node;\n        });\n        const allNodes = Object.keys(nodes);\n        allNodes.forEach(key => {\n            const node = nodes[key];\n            node.inputNames.forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                node.inputs.push(nodes[nodeName]);\n                nodes[nodeName].children.push(node);\n            });\n        });\n        const returnNodeMap = functionDef.ret;\n        functionDef.signature.outputArg.forEach(output => {\n            const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);\n            const node = nodes[nodeName];\n            if (node != null) {\n                node.defaultOutput = index;\n                outputs.push(node);\n            }\n        });\n        const signature = this.mapArgsToSignature(functionDef);\n        return { nodes, inputs, outputs, weights, placeholders, signature };\n    }\n    mapArgsToSignature(functionDef) {\n        return {\n            methodName: functionDef.signature.name,\n            inputs: functionDef.signature.inputArg.reduce((map, arg) => {\n                map[arg.name] = this.mapArgToTensorInfo(arg);\n                return map;\n            }, {}),\n            outputs: functionDef.signature.outputArg.reduce((map, arg) => {\n                map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);\n                return map;\n            }, {}),\n        };\n    }\n    mapArgToTensorInfo(arg, nameMap) {\n        let name = arg.name;\n        if (nameMap != null) {\n            name = nameMap[name];\n        }\n        return { name, dtype: arg.type };\n    }\n}\nexport function decodeBase64(text) {\n    const global = env().global;\n    if (typeof global.atob !== 'undefined') {\n        return global.atob(text);\n    }\n    else if (typeof Buffer !== 'undefined') {\n        return new Buffer(text, 'base64').toString();\n    }\n    else {\n        throw new Error('Unable to decode base64 in this environment. ' +\n            'Missing built-in atob() or Buffer()');\n    }\n}\nexport function parseStringParam(s, keepCase) {\n    const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);\n    return keepCase ? value : value.toLowerCase();\n}\nexport function getStringParam(attrs, name, def, keepCase = false) {\n    const param = attrs[name];\n    if (param != null) {\n        return parseStringParam(param.s, keepCase);\n    }\n    return def;\n}\nexport function getBoolParam(attrs, name, def) {\n    const param = attrs[name];\n    return param ? param.b : def;\n}\nexport function getNumberParam(attrs, name, def) {\n    const param = attrs[name] || {};\n    const value = param['i'] != null ? param['i'] : (param['f'] != null ? param['f'] : def);\n    return (typeof value === 'number') ? value : parseInt(value, 10);\n}\nexport function parseDtypeParam(value) {\n    if (typeof (value) === 'string') {\n        // tslint:disable-next-line:no-any\n        value = tensorflow.DataType[value];\n    }\n    switch (value) {\n        case tensorflow.DataType.DT_FLOAT:\n            return 'float32';\n        case tensorflow.DataType.DT_INT32:\n        case tensorflow.DataType.DT_INT64:\n        case tensorflow.DataType.DT_INT8:\n        case tensorflow.DataType.DT_UINT8:\n            return 'int32';\n        case tensorflow.DataType.DT_BOOL:\n            return 'bool';\n        case tensorflow.DataType.DT_DOUBLE:\n            return 'float32';\n        case tensorflow.DataType.DT_STRING:\n            return 'string';\n        default:\n            // Unknown dtype error will happen at runtime (instead of parse time),\n            // since these nodes might not be used by the actual subgraph execution.\n            return null;\n    }\n}\nexport function getFuncParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.func) {\n        return param.func.name;\n    }\n    return def;\n}\nexport function getDtypeParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.type) {\n        return parseDtypeParam(param.type);\n    }\n    return def;\n}\nexport function getDtypeArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.type) {\n        return param.list.type.map(v => parseDtypeParam(v));\n    }\n    return def;\n}\nexport function parseTensorShapeParam(shape) {\n    if (shape.unknownRank) {\n        return undefined;\n    }\n    if (shape.dim != null) {\n        return shape.dim.map(dim => (typeof dim.size === 'number') ? dim.size : parseInt(dim.size, 10));\n    }\n    return [];\n}\nexport function getTensorShapeParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.shape) {\n        return parseTensorShapeParam(param.shape);\n    }\n    return def;\n}\nexport function getNumericArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param) {\n        return ((param.list.f && param.list.f.length ? param.list.f :\n            param.list.i) ||\n            [])\n            .map(v => (typeof v === 'number') ? v : parseInt(v, 10));\n    }\n    return def;\n}\nexport function getStringArrayParam(attrs, name, def, keepCase = false) {\n    const param = attrs[name];\n    if (param && param.list && param.list.s) {\n        return param.list.s.map((v) => {\n            return parseStringParam(v, keepCase);\n        });\n    }\n    return def;\n}\nexport function getTensorShapeArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.shape) {\n        return param.list.shape.map((v) => {\n            return parseTensorShapeParam(v);\n        });\n    }\n    return def;\n}\nexport function getBoolArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.b) {\n        return param.list.b;\n    }\n    return def;\n}\n//# sourceMappingURL=operation_mapper.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getTensor } from '../executors/utils';\nimport { getBoolArrayParam, getBoolParam, getDtypeArrayParam, getDtypeParam, getNumberParam, getNumericArrayParam, getStringArrayParam, getStringParam, getTensorShapeArrayParam, getTensorShapeParam } from '../operation_mapper';\n/**\n * Helper class for lookup inputs and params for nodes in the model graph.\n */\nexport class NodeValueImpl {\n    constructor(node, tensorMap, context) {\n        this.node = node;\n        this.tensorMap = tensorMap;\n        this.context = context;\n        this.inputs = [];\n        this.attrs = {};\n        this.inputs = node.inputNames.map(name => this.getInput(name));\n        if (node.rawAttrs != null) {\n            this.attrs = Object.keys(node.rawAttrs)\n                .reduce((attrs, key) => {\n                attrs[key] = this.getAttr(key);\n                return attrs;\n            }, {});\n        }\n    }\n    /**\n     * Return the value of the attribute or input param.\n     * @param name String: name of attribute or input param.\n     */\n    getInput(name) {\n        return getTensor(name, this.tensorMap, this.context);\n    }\n    /**\n     * Return the value of the attribute or input param.\n     * @param name String: name of attribute or input param.\n     */\n    getAttr(name, defaultValue) {\n        const value = this.node.rawAttrs[name];\n        if (value.tensor != null) {\n            return getTensor(name, this.tensorMap, this.context);\n        }\n        if (value.i != null || value.f != null) {\n            return getNumberParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.s != null) {\n            return getStringParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.b != null) {\n            return getBoolParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.shape != null) {\n            return getTensorShapeParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.type != null) {\n            return getDtypeParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.list != null) {\n            if (value.list.i != null || value.list.f != null) {\n                return getNumericArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.s != null) {\n                return getStringArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.shape != null) {\n                return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.b != null) {\n                return getBoolArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.type != null) {\n                return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n        }\n        return defaultValue;\n    }\n}\n//# sourceMappingURL=node_value_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This file exports ops used by the converters executors. By default it\n * re-exports all ops. In a custom build this is aliased to a file that will\n * only exports ops for a given model.json.\n */\nexport * from './ops';\n//# sourceMappingURL=ops_for_converter.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'BiasAdd':\n        case 'AddV2':\n        case 'Add': {\n            return [tfOps.add(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'AddN': {\n            return [tfOps.addN(getParamValue('tensors', node, tensorMap, context))];\n        }\n        case 'FloorMod':\n        case 'Mod':\n            return [tfOps.mod(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        case 'Mul':\n            return [tfOps.mul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        case 'RealDiv':\n        case 'Div': {\n            return [tfOps.div(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'DivNoNan': {\n            return [tfOps.divNoNan(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'FloorDiv': {\n            return [tfOps.floorDiv(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Sub': {\n            return [tfOps.sub(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Minimum': {\n            return [tfOps.minimum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Maximum': {\n            return [tfOps.maximum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Pow': {\n            return [tfOps.pow(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'SquaredDifference': {\n            return [tfOps.squaredDifference(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'arithmetic';\n//# sourceMappingURL=arithmetic_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Abs':\n        case 'ComplexAbs':\n            return [tfOps.abs(getParamValue('x', node, tensorMap, context))];\n        case 'Acos':\n            return [tfOps.acos(getParamValue('x', node, tensorMap, context))];\n        case 'Acosh':\n            return [tfOps.acosh(getParamValue('x', node, tensorMap, context))];\n        case 'Asin':\n            return [tfOps.asin(getParamValue('x', node, tensorMap, context))];\n        case 'Asinh':\n            return [tfOps.asinh(getParamValue('x', node, tensorMap, context))];\n        case 'Atan':\n            return [tfOps.atan(getParamValue('x', node, tensorMap, context))];\n        case 'Atan2':\n            return [tfOps.atan2(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context))];\n        case 'Atanh':\n            return [tfOps.atanh(getParamValue('x', node, tensorMap, context))];\n        case 'Ceil':\n            return [tfOps.ceil(getParamValue('x', node, tensorMap, context))];\n        case 'Complex':\n            return [tfOps.complex(getParamValue('real', node, tensorMap, context), getParamValue('imag', node, tensorMap, context))];\n        case 'Cos':\n            return [tfOps.cos(getParamValue('x', node, tensorMap, context))];\n        case 'Cosh':\n            return [tfOps.cosh(getParamValue('x', node, tensorMap, context))];\n        case 'Elu':\n            return [tfOps.elu(getParamValue('x', node, tensorMap, context))];\n        case 'Erf':\n            return [tfOps.erf(getParamValue('x', node, tensorMap, context))];\n        case 'Exp':\n            return [tfOps.exp(getParamValue('x', node, tensorMap, context))];\n        case 'Expm1': {\n            return [tfOps.expm1(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Floor':\n            return [tfOps.floor(getParamValue('x', node, tensorMap, context))];\n        case 'Log':\n            return [tfOps.log(getParamValue('x', node, tensorMap, context))];\n        case 'Log1p': {\n            return [tfOps.log1p(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Imag':\n            return [tfOps.imag(getParamValue('x', node, tensorMap, context))];\n        case 'Neg':\n            return [tfOps.neg(getParamValue('x', node, tensorMap, context))];\n        case 'Reciprocal': {\n            return [tfOps.reciprocal(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Real':\n            return [tfOps.real(getParamValue('x', node, tensorMap, context))];\n        case 'Relu':\n            return [tfOps.relu(getParamValue('x', node, tensorMap, context))];\n        case 'Round': {\n            return [tfOps.round(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Selu':\n            return [tfOps.selu(getParamValue('x', node, tensorMap, context))];\n        case 'Sigmoid':\n            return [tfOps.sigmoid(getParamValue('x', node, tensorMap, context))];\n        case 'Sin':\n            return [tfOps.sin(getParamValue('x', node, tensorMap, context))];\n        case 'Sign': {\n            return [tfOps.sign(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Sinh': {\n            return [tfOps.sinh(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Softplus': {\n            return [tfOps.softplus(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Sqrt': {\n            return [tfOps.sqrt(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Square': {\n            return [tfOps.square(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Tanh': {\n            return [tfOps.tanh(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Tan':\n            return [tfOps.tan(getParamValue('x', node, tensorMap, context))];\n        case 'Relu6':\n        case 'ClipByValue':\n            return [tfOps.clipByValue(getParamValue('x', node, tensorMap, context), getParamValue('clipValueMin', node, tensorMap, context), getParamValue('clipValueMax', node, tensorMap, context))];\n        case 'Rsqrt':\n            return [tfOps.rsqrt(getTensor(node.inputNames[0], tensorMap, context))];\n        case 'Prod':\n            return [tfOps.prod(getParamValue('x', node, tensorMap, context), getParamValue('axes', node, tensorMap, context))];\n        case 'LeakyRelu':\n            return [tfOps.leakyRelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n        case 'Prelu':\n            return [tfOps.prelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'basic_math';\n//# sourceMappingURL=basic_math_executor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This differs from util.assertShapesMatch in that it allows values of\n * negative one, an undefined size of a dimensinon, in a shape to match\n * anything.\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = '') {\n    util.assert(shapesEqualAllowUndefinedSize(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nexport function shapesEqualAllowUndefinedSize(n1, n2) {\n    if (n1.length !== n2.length) {\n        return false;\n    }\n    for (let i = 0; i < n1.length; i++) {\n        if (n1[i] !== -1 && n2[i] !== -1 && n1[i] !== n2[i]) {\n            return false;\n        }\n    }\n    return true;\n}\n//# sourceMappingURL=tensor_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize } from './tensor_utils';\n/**\n * The TensorArray object keeps an array of Tensors.  It\n * allows reading from the array and writing to the array.\n */\nexport class TensorArray {\n    constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) {\n        this.name = name;\n        this.dtype = dtype;\n        this.maxSize = maxSize;\n        this.elementShape = elementShape;\n        this.identicalElementShapes = identicalElementShapes;\n        this.dynamicSize = dynamicSize;\n        this.clearAfterRead = clearAfterRead;\n        this.tensors = [];\n        this.closed_ = false;\n        this.idTensor = scalar(0);\n        keep(this.idTensor);\n    }\n    get id() {\n        return this.idTensor.id;\n    }\n    get closed() {\n        return this.closed_;\n    }\n    /**\n     * Dispose the tensors and idTensor and mark the TensoryArray as closed.\n     */\n    clearAndClose(keepIds) {\n        this.tensors.forEach(tensor => {\n            if (keepIds == null || !keepIds.has(tensor.tensor.id)) {\n                tensor.tensor.dispose();\n            }\n        });\n        this.tensors = [];\n        this.closed_ = true;\n        this.idTensor.dispose();\n    }\n    size() {\n        return this.tensors.length;\n    }\n    /**\n     * Read the value at location index in the TensorArray.\n     * @param index Number the index to read from.\n     */\n    read(index) {\n        if (this.closed_) {\n            throw new Error(`TensorArray ${this.name} has already been closed.`);\n        }\n        if (index < 0 || index >= this.size()) {\n            throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);\n        }\n        const tensorWithState = this.tensors[index];\n        if (tensorWithState.cleared) {\n            throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read ` +\n                `(perhaps try setting clear_after_read = false?).`);\n        }\n        if (this.clearAfterRead) {\n            tensorWithState.cleared = true;\n        }\n        tensorWithState.read = true;\n        return tensorWithState.tensor;\n    }\n    /**\n     * Helper method to read multiple tensors from the specified indices.\n     */\n    readMany(indices) {\n        return indices.map(index => this.read(index));\n    }\n    /**\n     * Write value into the index of the TensorArray.\n     * @param index number the index to write to.\n     * @param tensor\n     */\n    write(index, tensor) {\n        if (this.closed_) {\n            throw new Error(`TensorArray ${this.name} has already been closed.`);\n        }\n        if (index < 0 || !this.dynamicSize && index >= this.maxSize) {\n            throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);\n        }\n        const t = this.tensors[index] || {};\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},\n          because the value dtype is ${tensor.dtype}, but TensorArray dtype is ${this.dtype}.`);\n        }\n        // Set the shape for the first time write to unknow shape tensor array\n        if (this.size() === 0 &&\n            (this.elementShape == null || this.elementShape.length === 0)) {\n            this.elementShape = tensor.shape;\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`);\n        if (t.read) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);\n        }\n        if (t.written) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);\n        }\n        t.tensor = tensor;\n        keep(tensor);\n        t.written = true;\n        this.tensors[index] = t;\n    }\n    /**\n     * Helper method to write multiple tensors to the specified indices.\n     */\n    writeMany(indices, tensors) {\n        if (indices.length !== tensors.length) {\n            throw new Error(`TensorArray ${this.name}: could not write multiple tensors,` +\n                `because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);\n        }\n        indices.forEach((i, index) => this.write(i, tensors[index]));\n    }\n    /**\n     * Return selected values in the TensorArray as a packed Tensor. All of\n     * selected values must have been written and their shapes must all match.\n     * @param [indices] number[] Optional. Taking values in [0, max_value). If the\n     *    TensorArray is not dynamic, max_value=size(). If not specified returns\n     *    all tensors in the original order.\n     * @param [dtype]\n     */\n    gather(indices, dtype) {\n        if (!!dtype && dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);\n        }\n        if (!indices) {\n            indices = [];\n            for (let i = 0; i < this.size(); i++) {\n                indices.push(i);\n            }\n        }\n        else {\n            indices = indices.slice(0, this.size());\n        }\n        if (indices.length === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        // Read all the PersistentTensors into a vector to keep track of\n        // their memory.\n        const tensors = this.readMany(indices);\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, 'TensorArray shape mismatch: ');\n        return stack(tensors, 0);\n    }\n    /**\n     * Return the values in the TensorArray as a concatenated Tensor.\n     */\n    concat(dtype) {\n        if (!!dtype && dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);\n        }\n        if (this.size() === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        const indices = [];\n        for (let i = 0; i < this.size(); i++) {\n            indices.push(i);\n        }\n        // Collect all the tensors from the tensors array.\n        const tensors = this.readMany(indices);\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);\n        return concat(tensors, 0);\n    }\n    /**\n     * Scatter the values of a Tensor in specific indices of a TensorArray.\n     * @param indices nummber[] values in [0, max_value). If the\n     *    TensorArray is not dynamic, max_value=size().\n     * @param tensor Tensor input tensor.\n     */\n    scatter(indices, tensor) {\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n        }\n        if (indices.length !== tensor.shape[0]) {\n            throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n        }\n        const maxIndex = Math.max(...indices);\n        if (!this.dynamicSize && maxIndex >= this.maxSize) {\n            throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);\n        }\n        this.writeMany(indices, unstack(tensor, 0));\n    }\n    /**\n     * Split the values of a Tensor into the TensorArray.\n     * @param length number[] with the lengths to use when splitting value along\n     *    its first dimension.\n     * @param tensor Tensor, the tensor to split.\n     */\n    split(length, tensor) {\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n        }\n        let totalLength = 0;\n        const cumulativeLengths = length.map(len => {\n            totalLength += len;\n            return totalLength;\n        });\n        if (totalLength !== tensor.shape[0]) {\n            throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n        }\n        if (!this.dynamicSize && length.length !== this.maxSize) {\n            throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), ` +\n                'and the TensorArray is not marked as dynamically resizeable');\n        }\n        const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n        const tensors = [];\n        tidy(() => {\n            tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n            for (let i = 0; i < length.length; ++i) {\n                const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n                const indices = [0, previousLength, 0];\n                const sizes = [1, length[i], elementPerRow];\n                tensors[i] = reshape(slice(tensor, indices, sizes), this.elementShape);\n            }\n            return tensors;\n        });\n        const indices = [];\n        for (let i = 0; i < length.length; i++) {\n            indices[i] = i;\n        }\n        this.writeMany(indices, tensors);\n    }\n}\n//# sourceMappingURL=tensor_array.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize } from './tensor_utils';\n/**\n * TensorList stores a container of `tf.Tensor` objects, which are accessible\n * via tensors field.\n *\n * In order to get a copy of the underlying list, use the copy method:\n * ```\n *    TensorList b = a.copy();\n *    b.tensors().pushBack(t);  // This does not modify a.tensors().\n * ```\n *\n * Note that this is not a deep copy: the memory locations of the underlying\n * tensors will still point to the same locations of the corresponding tensors\n * in the original.\n */\nexport class TensorList {\n    /**\n     *\n     * @param tensors list of tensors\n     * @param elementShape shape of each tensor\n     * @param elementDtype data type of each tensor\n     * @param maxNumElements The maximum allowed size of `tensors`. Defaults to -1\n     *   meaning that the size of `tensors` is unbounded.\n     */\n    constructor(tensors, elementShape, elementDtype, maxNumElements = -1) {\n        this.tensors = tensors;\n        this.elementShape = elementShape;\n        this.elementDtype = elementDtype;\n        if (tensors != null) {\n            tensors.forEach(tensor => {\n                if (elementDtype !== tensor.dtype) {\n                    throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor.dtype}`);\n                }\n                assertShapesMatchAllowUndefinedSize(elementShape, tensor.shape, 'TensorList shape mismatch: ');\n                keep(tensor);\n            });\n        }\n        this.idTensor = scalar(0);\n        this.maxNumElements = maxNumElements;\n        keep(this.idTensor);\n    }\n    get id() {\n        return this.idTensor.id;\n    }\n    /**\n     * Get a new TensorList containing a copy of the underlying tensor container.\n     */\n    copy() {\n        return new TensorList([...this.tensors], this.elementShape, this.elementDtype);\n    }\n    /**\n     * Dispose the tensors and idTensor and clear the tensor list.\n     */\n    clearAndClose(keepIds) {\n        this.tensors.forEach(tensor => {\n            if (keepIds == null || !keepIds.has(tensor.id)) {\n                tensor.dispose();\n            }\n        });\n        this.tensors.length = 0;\n        this.idTensor.dispose();\n    }\n    /**\n     * The size of the tensors in the tensor list.\n     */\n    size() {\n        return this.tensors.length;\n    }\n    /**\n     * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1)\n     * tf.Tensor.\n     * @param elementShape shape of each tensor\n     * @param elementDtype data type of each tensor\n     * @param numElements the number of elements to stack\n     */\n    stack(elementShape, elementDtype, numElements = -1) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (numElements !== -1 && this.tensors.length !== numElements) {\n            throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);\n        }\n        assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, 'TensorList shape mismatch: ');\n        return tidy(() => {\n            const reshapedTensors = this.tensors.map(tensor => reshape(tensor, elementShape));\n            return stack(reshapedTensors, 0);\n        });\n    }\n    /**\n     * Pop a tensor from the end of the list.\n     * @param elementShape shape of the tensor\n     * @param elementDtype data type of the tensor\n     */\n    popBack(elementShape, elementDtype) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (this.size() === 0) {\n            throw new Error('Trying to pop from an empty list.');\n        }\n        const tensor = this.tensors.pop();\n        assertShapesMatchAllowUndefinedSize(tensor.shape, elementShape, 'TensorList shape mismatch: ');\n        return reshape(tensor, elementShape);\n    }\n    /**\n     * Push a tensor to the end of the list.\n     * @param tensor Tensor to be pushed.\n     */\n    pushBack(tensor) {\n        if (tensor.dtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(tensor.shape, this.elementShape, 'TensorList shape mismatch: ');\n        if (this.maxNumElements === this.size()) {\n            throw new Error(`Trying to push element into a full list.`);\n        }\n        keep(tensor);\n        this.tensors.push(tensor);\n    }\n    /**\n     * Update the size of the list.\n     * @param size the new size of the list.\n     */\n    resize(size) {\n        if (size < 0) {\n            throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);\n        }\n        if (this.maxNumElements !== -1 && size > this.maxNumElements) {\n            throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);\n        }\n        this.tensors.length = size;\n    }\n    /**\n     * Retrieve the element at the provided index\n     * @param elementShape shape of the tensor\n     * @param elementDtype dtype of the tensor\n     * @param elementIndex index of the tensor\n     */\n    getItem(elementIndex, elementShape, elementDtype) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (elementIndex < 0 || elementIndex > this.tensors.length) {\n            throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);\n        }\n        if (this.tensors[elementIndex] == null) {\n            throw new Error(`element at index ${elementIndex} is null.`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, 'TensorList shape mismatch: ');\n        return this.tensors[elementIndex];\n    }\n    /**\n     * Set the tensor at the index\n     * @param elementIndex index of the tensor\n     * @param tensor the tensor to be inserted into the list\n     */\n    setItem(elementIndex, tensor) {\n        if (tensor.dtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n        }\n        if (elementIndex < 0 ||\n            this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {\n            throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, 'TensorList shape mismatch: ');\n        keep(tensor);\n        this.tensors[elementIndex] = tensor;\n    }\n    /**\n     * Return selected values in the TensorList as a stacked Tensor. All of\n     * selected values must have been written and their shapes must all match.\n     * @param indices indices of tensors to gather\n     * @param elementDtype output tensor dtype\n     * @param elementShape output tensor element shape\n     */\n    gather(indices, elementDtype, elementShape) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n        // When indices is greater than the size of the list, indices beyond the\n        // size of the list are ignored.\n        indices = indices.slice(0, this.size());\n        if (indices.length === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        return tidy(() => {\n            const tensors = indices.map(i => reshape(this.tensors[i], elementShape));\n            return stack(tensors, 0);\n        });\n    }\n    /**\n     * Return the values in the TensorList as a concatenated Tensor.\n     * @param elementDtype output tensor dtype\n     * @param elementShape output tensor element shape\n     */\n    concat(elementDtype, elementShape) {\n        if (!!elementDtype && elementDtype !== this.elementDtype) {\n            throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n        if (this.size() === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        return tidy(() => {\n            const tensors = this.tensors.map(t => reshape(t, elementShape));\n            return concat(tensors, 0);\n        });\n    }\n}\n/**\n * Creates a TensorList which, when stacked, has the value of tensor.\n * @param tensor from tensor\n * @param elementShape output tensor element shape\n */\nexport function fromTensor(tensor, elementShape, elementDtype) {\n    const dtype = tensor.dtype;\n    if (tensor.shape.length < 1) {\n        throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor.shape}`);\n    }\n    if (tensor.dtype !== elementDtype) {\n        throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${elementDtype}`);\n    }\n    const outputShape = tensor.shape.slice(1);\n    assertShapesMatchAllowUndefinedSize(outputShape, elementShape, 'TensorList shape mismatch: ');\n    const tensorList = unstack(tensor);\n    return new TensorList(tensorList, elementShape, dtype);\n}\n/**\n * Return a TensorList of the given size with empty elements.\n * @param elementShape the shape of the future elements of the list\n * @param elementDtype the desired type of elements in the list\n * @param numElements the number of elements to reserve\n */\nexport function reserve(elementShape, elementDtype, numElements) {\n    return new TensorList([], elementShape, elementDtype, numElements);\n}\n/**\n * Put tensors at specific indices of a stacked tensor into a TensorList.\n * @param indices list of indices on how to scatter the tensor.\n * @param tensor input tensor.\n * @param elementShape the shape of the future elements of the list\n * @param numElements the number of elements to scatter\n */\nexport function scatter(tensor, indices, elementShape, numElements) {\n    if (indices.length !== tensor.shape[0]) {\n        throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n    }\n    const maxIndex = Math.max(...indices);\n    if (numElements != null && numElements !== -1 && maxIndex >= numElements) {\n        throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);\n    }\n    const list = new TensorList([], elementShape, tensor.dtype, numElements);\n    const tensors = unstack(tensor, 0);\n    indices.forEach((value, index) => {\n        list.setItem(value, tensors[index]);\n    });\n    return list;\n}\n/**\n * Split the values of a Tensor into a TensorList.\n * @param length the lengths to use when splitting value along\n *    its first dimension.\n * @param tensor the tensor to split.\n * @param elementShape the shape of the future elements of the list\n */\nexport function split(tensor, length, elementShape) {\n    let totalLength = 0;\n    const cumulativeLengths = length.map(len => {\n        totalLength += len;\n        return totalLength;\n    });\n    if (totalLength !== tensor.shape[0]) {\n        throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n    }\n    const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n    const tensors = tidy(() => {\n        const tensors = [];\n        tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n        for (let i = 0; i < length.length; ++i) {\n            const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n            const indices = [0, previousLength, 0];\n            const sizes = [1, length[i], elementPerRow];\n            tensors[i] = reshape(slice(tensor, indices, sizes), elementShape);\n        }\n        tensor.dispose();\n        return tensors;\n    });\n    const list = new TensorList([], elementShape, tensor.dtype, length.length);\n    for (let i = 0; i < tensors.length; i++) {\n        list.setItem(i, tensors[i]);\n    }\n    return list;\n}\n//# sourceMappingURL=tensor_list.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { scalar } from '@tensorflow/tfjs-core';\nimport { TensorArray } from '../../executor/tensor_array';\nimport { fromTensor, reserve, scatter, split } from '../../executor/tensor_list';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = async (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'If':\n        case 'StatelessIf': {\n            const thenFunc = getParamValue('thenBranch', node, tensorMap, context);\n            const elseFunc = getParamValue('elseBranch', node, tensorMap, context);\n            const cond = getParamValue('cond', node, tensorMap, context);\n            const args = getParamValue('args', node, tensorMap, context);\n            const condValue = await cond.data();\n            if (condValue[0]) {\n                return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n            }\n            else {\n                return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n            }\n        }\n        case 'While':\n        case 'StatelessWhile': {\n            const bodyFunc = getParamValue('body', node, tensorMap, context);\n            const condFunc = getParamValue('cond', node, tensorMap, context);\n            const args = getParamValue('args', node, tensorMap, context);\n            // Calculate the condition of the loop\n            const condResult = (await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap));\n            const argIds = args.map(tensor => tensor.id);\n            let condValue = await condResult[0].data();\n            // Dispose the intermediate tensors for condition function\n            condResult.forEach(tensor => {\n                if (!tensor.kept && argIds.indexOf(tensor.id) === -1) {\n                    tensor.dispose();\n                }\n            });\n            let result = args;\n            while (condValue[0]) {\n                // Record the previous result for intermediate tensor tracking\n                const origResult = result;\n                // Execution the body of the loop\n                result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);\n                const resultIds = result.map(tensor => tensor.id);\n                // Dispose the intermediate tensor for body function that is not global\n                // kept, not input/output of the body function\n                origResult.forEach(tensor => {\n                    if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n                        resultIds.indexOf(tensor.id) === -1) {\n                        tensor.dispose();\n                    }\n                });\n                // Recalcuate the condition of the loop using the latest results.\n                const condResult = (await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap));\n                condValue = await condResult[0].data();\n                // Dispose the intermediate tensors for condition function\n                condResult.forEach(tensor => {\n                    if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n                        resultIds.indexOf(tensor.id) === -1) {\n                        tensor.dispose();\n                    }\n                });\n            }\n            return result;\n        }\n        case 'LoopCond': {\n            const pred = getParamValue('pred', node, tensorMap, context);\n            return [cloneTensor(pred)];\n        }\n        case 'Switch': {\n            const pred = getParamValue('pred', node, tensorMap, context);\n            let data = getParamValue('data', node, tensorMap, context);\n            if (!data.kept) {\n                data = cloneTensor(data);\n            }\n            // Outputs nodes :0 => false, :1 => true\n            return (await pred.data())[0] ? [undefined, data] : [data, undefined];\n        }\n        case 'Merge': {\n            const inputName = node.inputNames.find(name => getTensor(name, tensorMap, context) !== undefined);\n            if (inputName) {\n                const data = getTensor(inputName, tensorMap, context);\n                return [cloneTensor(data)];\n            }\n            return undefined;\n        }\n        case 'Enter': {\n            const frameId = getParamValue('frameName', node, tensorMap, context);\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.enterFrame(frameId);\n            return [cloneTensor(data)];\n        }\n        case 'Exit': {\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.exitFrame();\n            return [cloneTensor(data)];\n        }\n        case 'NextIteration': {\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.nextIteration();\n            return [cloneTensor(data)];\n        }\n        case 'TensorArrayV3': {\n            const size = getParamValue('size', node, tensorMap, context);\n            const dtype = getParamValue('dtype', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const dynamicSize = getParamValue('dynamicSize', node, tensorMap, context);\n            const clearAfterRead = getParamValue('clearAfterRead', node, tensorMap, context);\n            const identicalElementShapes = getParamValue('identicalElementShapes', node, tensorMap, context);\n            const name = getParamValue('name', node, tensorMap, context);\n            const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);\n            context.addTensorArray(tensorArray);\n            return [tensorArray.idTensor, scalar(1.0)];\n        }\n        case 'TensorArrayWriteV3': {\n            const id = getParamValue('tensorArrayId', node, tensorMap, context);\n            const index = getParamValue('index', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const writeTensorArray = context.getTensorArray(id.id);\n            writeTensorArray.write(index, writeTensor);\n            return [writeTensorArray.idTensor];\n        }\n        case 'TensorArrayReadV3': {\n            const readId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const readIndex = getParamValue('index', node, tensorMap, context);\n            const readTensorArray = context.getTensorArray(readId.id);\n            return [readTensorArray.read(readIndex)];\n        }\n        case 'TensorArrayGatherV3': {\n            const gatherId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const gatherIndices = getParamValue('indices', node, tensorMap, context);\n            const gatherDtype = getParamValue('dtype', node, tensorMap, context);\n            const gatherTensorArray = context.getTensorArray(gatherId.id);\n            return [gatherTensorArray.gather(gatherIndices, gatherDtype)];\n        }\n        case 'TensorArrayScatterV3': {\n            const scatterId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const scatterIndices = getParamValue('indices', node, tensorMap, context);\n            const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n            const scatterTensorArray = context.getTensorArray(scatterId.id);\n            scatterTensorArray.scatter(scatterIndices, scatterTensor);\n            return [scatterTensorArray.idTensor];\n        }\n        case 'TensorArrayConcatV3': {\n            const concatId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const concatTensorArray = context.getTensorArray(concatId.id);\n            const concatDtype = getParamValue('dtype', node, tensorMap, context);\n            return [concatTensorArray.concat(concatDtype)];\n        }\n        case 'TensorArraySplitV3': {\n            const splitId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const splitTensor = getParamValue('tensor', node, tensorMap, context);\n            const lengths = getParamValue('lengths', node, tensorMap, context);\n            const splitTensorArray = context.getTensorArray(splitId.id);\n            splitTensorArray.split(lengths, splitTensor);\n            return [splitTensorArray.idTensor];\n        }\n        case 'TensorArraySizeV3': {\n            const sizeId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const sizeTensorArray = context.getTensorArray(sizeId.id);\n            return [scalar(sizeTensorArray.size(), 'int32')];\n        }\n        case 'TensorArrayCloseV3': {\n            const closeId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const closeTensorArray = context.getTensorArray(closeId.id);\n            closeTensorArray.clearAndClose();\n            return [closeTensorArray.idTensor];\n        }\n        case 'TensorListSetItem': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const index = getParamValue('index', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            tensorList.setItem(index, writeTensor);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListGetItem': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const readIndex = getParamValue('index', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDType = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.getItem(readIndex, elementShape, elementDType)];\n        }\n        case 'TensorListScatterV2':\n        case 'TensorListScatter': {\n            const scatterIndices = getParamValue('indices', node, tensorMap, context);\n            const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListReserve': {\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = reserve(elementShape, elementDtype, numElements);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListGather': {\n            const gatherId = getParamValue('tensorListId', node, tensorMap, context);\n            const gatherIndices = getParamValue('indices', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(gatherId.id);\n            return [tensorList.gather(gatherIndices, elementDtype, elementShape)];\n        }\n        case 'TensorListStack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.stack(elementShape, elementDtype, numElements)];\n        }\n        case 'TensorListFromTensor': {\n            const tensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = fromTensor(tensor, elementShape, elementDtype);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListConcat': {\n            const concatId = getParamValue('tensorListId', node, tensorMap, context);\n            const tensorList = context.getTensorList(concatId.id);\n            const concatDtype = getParamValue('dtype', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            return [tensorList.concat(concatDtype, elementShape)];\n        }\n        case 'TensorListPushBack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            tensorList.pushBack(writeTensor);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListPopBack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDType = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.popBack(elementShape, elementDType)];\n        }\n        case 'TensorListSplit': {\n            const splitTensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const lengths = getParamValue('lengths', node, tensorMap, context);\n            const tensorList = split(splitTensor, lengths, elementShape);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'control';\n//# sourceMappingURL=control_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getPadding, getParamValue } from './utils';\nfunction fusedConvAndDepthWiseParams(node, tensorMap, context) {\n    const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n    const isBiasAdd = extraOp === 'biasadd';\n    const isPrelu = activationFunc === 'prelu';\n    const isBatchNorm = extraOp === 'fusedbatchnorm';\n    const numArgs = getParamValue('numArgs', node, tensorMap, context);\n    if (isBiasAdd) {\n        if (isPrelu && numArgs !== 2) {\n            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu ' +\n                'must have two extra arguments: bias and alpha.');\n        }\n        if (!isPrelu && numArgs !== 1) {\n            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd must have ' +\n                'one extra argument: bias.');\n        }\n    }\n    if (isBatchNorm) {\n        throw new Error('FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.');\n    }\n    const stride = getParamValue('strides', node, tensorMap, context);\n    const pad = getPadding(node, tensorMap, context);\n    const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n        .toUpperCase();\n    const dilations = getParamValue('dilations', node, tensorMap, context);\n    const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n    return {\n        stride,\n        pad,\n        dataFormat,\n        dilations,\n        biasArg,\n        preluArg,\n        activationFunc\n    };\n}\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Conv1D': {\n            const stride = getParamValue('stride', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilation = getParamValue('dilation', node, tensorMap, context);\n            return [tfOps.conv1d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), stride, pad, dataFormat, dilation)];\n        }\n        case 'Conv2D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            return [tfOps.conv2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n        }\n        case '_FusedConv2D': {\n            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n            return [tfOps.fused.conv2d({\n                    x: getParamValue('x', node, tensorMap, context),\n                    filter: getParamValue('filter', node, tensorMap, context),\n                    strides: [stride[1], stride[2]],\n                    pad: pad,\n                    dataFormat: dataFormat,\n                    dilations: [dilations[1], dilations[2]],\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        }\n        case 'FusedDepthwiseConv2dNative': {\n            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n            return [tfOps.fused.depthwiseConv2d({\n                    x: getParamValue('x', node, tensorMap, context),\n                    filter: getParamValue('filter', node, tensorMap, context),\n                    strides: [stride[1], stride[2]],\n                    pad: pad,\n                    dataFormat: dataFormat,\n                    dilations: [dilations[1], dilations[2]],\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        }\n        case 'Conv2DBackpropInput':\n        case 'Conv2dTranspose': {\n            const shape = getParamValue('outputShape', node, tensorMap, context);\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            return [tfOps.conv2dTranspose(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), shape, [stride[1], stride[2]], pad)];\n        }\n        case 'DepthwiseConv2dNative':\n        case 'DepthwiseConv2d': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            return [tfOps.depthwiseConv2d(getParamValue('input', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n        }\n        case 'Conv3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            return [tfOps.conv3d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2], stride[3]], pad, dataFormat, [dilations[1], dilations[2], dilations[3]])];\n        }\n        case 'AvgPool': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.avgPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n        }\n        case 'MaxPool': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.maxPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n        }\n        case 'MaxPoolWithArgmax': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            const includeBatchInIndex = getParamValue('includeBatchInIndex', node, tensorMap, context);\n            const { result, indexes } = tfOps.maxPoolWithArgmax(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad, includeBatchInIndex);\n            return [result, indexes];\n        }\n        case 'AvgPool3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.avgPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n        }\n        case 'MaxPool3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.maxPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n        }\n        case 'Dilation2D': {\n            const strides = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            // strides: [1, stride_height, stride_width, 1].\n            const strideHeight = strides[1];\n            const strideWidth = strides[2];\n            // dilations: [1, dilation_height, dilation_width, 1].\n            const dilationHeight = dilations[1];\n            const dilationWidth = dilations[2];\n            return [tfOps.dilation2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [strideHeight, strideWidth], pad, [dilationHeight, dilationWidth], 'NHWC' /* dataFormat */)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'convolution';\n//# sourceMappingURL=convolution_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Fill': {\n            const shape = getParamValue('shape', node, tensorMap, context);\n            const dtype = getParamValue('dtype', node, tensorMap, context);\n            const value = getParamValue('value', node, tensorMap, context);\n            return [tfOps.fill(shape, value, dtype)];\n        }\n        case 'LinSpace': {\n            const start = getParamValue('start', node, tensorMap, context);\n            const stop = getParamValue('stop', node, tensorMap, context);\n            const num = getParamValue('num', node, tensorMap, context);\n            return [tfOps.linspace(start, stop, num)];\n        }\n        case 'Multinomial': {\n            const logits = getParamValue('logits', node, tensorMap, context);\n            const numSamples = getParamValue('numSamples', node, tensorMap, context);\n            const seed = getParamValue('seed', node, tensorMap, context);\n            return [tfOps.multinomial(logits, numSamples, seed)];\n        }\n        case 'OneHot': {\n            const indices = getParamValue('indices', node, tensorMap, context);\n            const depth = getParamValue('depth', node, tensorMap, context);\n            const onValue = getParamValue('onValue', node, tensorMap, context);\n            const offValue = getParamValue('offValue', node, tensorMap, context);\n            return [tfOps.oneHot(indices, depth, onValue, offValue)];\n        }\n        case 'Ones': {\n            return [tfOps.ones(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'OnesLike': {\n            return [tfOps.onesLike(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'RandomUniform': {\n            return [tfOps.randomUniform(\n                // tslint:disable-next-line:no-any\n                getParamValue('shape', node, tensorMap, context), getParamValue('minval', node, tensorMap, context), getParamValue('maxval', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'Range': {\n            const start = getParamValue('start', node, tensorMap, context);\n            const stop = getParamValue('stop', node, tensorMap, context);\n            const step = getParamValue('step', node, tensorMap, context);\n            return [tfOps.range(start, stop, step, getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'TruncatedNormal': {\n            const shape = getParamValue('shape', node, tensorMap, context);\n            const mean = getParamValue('mean', node, tensorMap, context);\n            const stdDev = getParamValue('stdDev', node, tensorMap, context);\n            const seed = getParamValue('seed', node, tensorMap, context);\n            return [tfOps.truncatedNormal(shape, mean, stdDev, getParamValue('dtype', node, tensorMap, context), seed)];\n        }\n        case 'Zeros': {\n            return [tfOps.zeros(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'ZerosLike': {\n            return [tfOps.zerosLike(getParamValue('x', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'creation';\n//# sourceMappingURL=creation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nfunction nmsParams(node, tensorMap, context) {\n    const boxes = getParamValue('boxes', node, tensorMap, context);\n    const scores = getParamValue('scores', node, tensorMap, context);\n    const maxOutputSize = getParamValue('maxOutputSize', node, tensorMap, context);\n    const iouThreshold = getParamValue('iouThreshold', node, tensorMap, context);\n    const scoreThreshold = getParamValue('scoreThreshold', node, tensorMap, context);\n    const softNmsSigma = getParamValue('softNmsSigma', node, tensorMap, context);\n    return {\n        boxes,\n        scores,\n        maxOutputSize,\n        iouThreshold,\n        scoreThreshold,\n        softNmsSigma\n    };\n}\nexport const executeOp = async (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'NonMaxSuppressionV5': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node, tensorMap, context);\n            const result = await tfOps.image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n            return [result.selectedIndices, result.selectedScores];\n        }\n        case 'NonMaxSuppressionV4': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n            const padToMaxOutputSize = getParamValue('padToMaxOutputSize', node, tensorMap, context);\n            const result = await tfOps.image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n            return [result.selectedIndices, result.validOutputs];\n        }\n        case 'NonMaxSuppressionV3':\n        case 'NonMaxSuppressionV2': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n            return [await tfOps.image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];\n        }\n        case 'Where': {\n            const condition = tfOps.cast(getParamValue('condition', node, tensorMap, context), 'bool');\n            const result = [await tfOps.whereAsync(condition)];\n            condition.dispose();\n            return result;\n        }\n        case 'ListDiff': {\n            return tfOps.setdiff1dAsync(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context));\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'dynamic';\n//# sourceMappingURL=dynamic_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'TopKV2': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const k = getParamValue('k', node, tensorMap, context);\n            const sorted = getParamValue('sorted', node, tensorMap, context);\n            const result = tfOps.topk(x, k, sorted);\n            return [result.values, result.indices];\n        }\n        case 'Unique': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const result = tfOps.unique(x);\n            return [result.values, result.indices];\n        }\n        case 'UniqueV2': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const result = tfOps.unique(x, axis);\n            return [result.values, result.indices];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'evaluation';\n//# sourceMappingURL=evaluation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Const': {\n            return tensorMap[node.name];\n        }\n        case 'PlaceholderWithDefault':\n            const def = getParamValue('default', node, tensorMap, context);\n            return [getTensor(node.name, tensorMap, context) || def];\n        case 'Placeholder':\n            return [getTensor(node.name, tensorMap, context)];\n        case 'Identity':\n        case 'StopGradient':\n        case 'FakeQuantWithMinMaxVars': { // This op is currently ignored.\n            const data = getParamValue('x', node, tensorMap, context);\n            return [cloneTensor(data)];\n        }\n        case 'IdentityN':\n            return getParamValue('x', node, tensorMap, context)\n                .map((t) => cloneTensor(t));\n        case 'Snapshot':\n            const snapshot = getParamValue('x', node, tensorMap, context);\n            return [cloneTensor(snapshot)];\n        case 'Shape':\n            return [tfOps.tensor1d(getParamValue('x', node, tensorMap, context).shape, 'int32')];\n        case 'ShapeN':\n            return getParamValue('x', node, tensorMap, context)\n                .map((t) => tfOps.tensor1d(t.shape));\n        case 'Size':\n            return [tfOps.scalar(getParamValue('x', node, tensorMap, context).size, 'int32')];\n        case 'Rank':\n            return [tfOps.scalar(getParamValue('x', node, tensorMap, context).rank, 'int32')];\n        case 'NoOp':\n            return [tfOps.scalar(1)];\n        case 'Print':\n            const input = getParamValue('x', node, tensorMap, context);\n            const data = getParamValue('data', node, tensorMap, context);\n            const message = getParamValue('message', node, tensorMap, context);\n            const summarize = getParamValue('summarize', node, tensorMap, context);\n            console.warn('The graph has a tf.print() operation,' +\n                'usually used for debugging, which slows down performance.');\n            console.log(message);\n            for (let i = 0; i < data.length; i++) {\n                console.log(Array.prototype.slice.call(data[i].dataSync())\n                    .slice(0, summarize));\n            }\n            return [input];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'graph';\n//# sourceMappingURL=graph_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'ResizeBilinear': {\n            const images = getParamValue('images', node, tensorMap, context);\n            const size = getParamValue('size', node, tensorMap, context);\n            const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n            return [tfOps.image.resizeBilinear(images, [size[0], size[1]], alignCorners)];\n        }\n        case 'ResizeNearestNeighbor': {\n            const images = getParamValue('images', node, tensorMap, context);\n            const size = getParamValue('size', node, tensorMap, context);\n            const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n            return [tfOps.image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners)];\n        }\n        case 'CropAndResize': {\n            const image = getParamValue('image', node, tensorMap, context);\n            const boxes = getParamValue('boxes', node, tensorMap, context);\n            const boxInd = getParamValue('boxInd', node, tensorMap, context);\n            const cropSize = getParamValue('cropSize', node, tensorMap, context);\n            const method = getParamValue('method', node, tensorMap, context);\n            const extrapolationValue = getParamValue('extrapolationValue', node, tensorMap, context);\n            return [tfOps.image.cropAndResize(image, boxes, boxInd, cropSize, method, extrapolationValue)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'image';\n//# sourceMappingURL=image_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Equal': {\n            return [tfOps.equal(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'NotEqual': {\n            return [tfOps.notEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Greater': {\n            return [tfOps.greater(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'GreaterEqual': {\n            return [tfOps.greaterEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Less': {\n            return [tfOps.less(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LessEqual': {\n            return [tfOps.lessEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LogicalAnd': {\n            return [tfOps.logicalAnd(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LogicalNot': {\n            return [tfOps.logicalNot(getParamValue('a', node, tensorMap, context))];\n        }\n        case 'LogicalOr': {\n            return [tfOps.logicalOr(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Select':\n        case 'SelectV2': {\n            return [tfOps.where(getParamValue('condition', node, tensorMap, context), getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'logical';\n//# sourceMappingURL=logical_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'BatchMatMul':\n        case 'BatchMatMulV2':\n        case 'MatMul':\n            return [tfOps.matMul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context), getParamValue('transposeA', node, tensorMap, context), getParamValue('transposeB', node, tensorMap, context))];\n        case 'Transpose':\n            return [tfOps.transpose(getParamValue('x', node, tensorMap, context), getParamValue('perm', node, tensorMap, context))];\n        case '_FusedMatMul':\n            const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n            const isBiasAdd = extraOp === 'biasadd';\n            const isPrelu = activationFunc === 'prelu';\n            const numArgs = getParamValue('numArgs', node, tensorMap, context);\n            if (isBiasAdd) {\n                if (isPrelu && numArgs !== 2) {\n                    throw new Error('Fused MatMul with BiasAdd and Prelu must have two ' +\n                        'extra arguments: bias and alpha.');\n                }\n                if (!isPrelu && numArgs !== 1) {\n                    throw new Error('Fused MatMul with BiasAdd must have one extra argument: bias.');\n                }\n            }\n            const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n            return [tfOps.fused.matMul({\n                    a: getParamValue('a', node, tensorMap, context),\n                    b: getParamValue('b', node, tensorMap, context),\n                    transposeA: getParamValue('transposeA', node, tensorMap, context),\n                    transposeB: getParamValue('transposeB', node, tensorMap, context),\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'matrices';\n//# sourceMappingURL=matrices_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'FusedBatchNorm':\n        case 'FusedBatchNormV2': {\n            return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n        }\n        case 'FusedBatchNormV3': {\n            return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n        }\n        case 'LRN': {\n            return [tfOps.localResponseNormalization(getParamValue('x', node, tensorMap, context), getParamValue('radius', node, tensorMap, context), getParamValue('bias', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context), getParamValue('beta', node, tensorMap, context))];\n        }\n        case 'Softmax': {\n            return [tfOps.softmax(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'LogSoftmax': {\n            return [tfOps.logSoftmax(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'SparseToDense': {\n            return [tfOps.sparseToDense(getParamValue('sparseIndices', node, tensorMap, context), getParamValue('outputShape', node, tensorMap, context), getParamValue('sparseValues', node, tensorMap, context), getParamValue('defaultValue', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'normalization';\n//# sourceMappingURL=normalization_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Max': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.max(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Mean': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.mean(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Min': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.min(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Sum': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.sum(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'All': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.all(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Any': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.any(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'ArgMax': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.argMax(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'ArgMin': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.argMin(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Prod': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.prod(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Cumsum': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const exclusive = getParamValue('exclusive', node, tensorMap, context);\n            const reverse = getParamValue('reverse', node, tensorMap, context);\n            return [tfOps.cumsum(getParamValue('x', node, tensorMap, context), axis, exclusive, reverse)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'reduction';\n//# sourceMappingURL=reduction_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tidy, util } from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'ConcatV2':\n        case 'Concat': {\n            const n = getParamValue('n', node, tensorMap, context);\n            const axis = getParamValue('axis', node, tensorMap, context);\n            let inputs = getParamValue('tensors', node, tensorMap, context);\n            inputs = inputs.slice(0, n);\n            return [tfOps.concat(inputs, axis)];\n        }\n        case 'GatherV2':\n        case 'Gather': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const input = getParamValue('x', node, tensorMap, context);\n            const indices = getParamValue('indices', node, tensorMap, context);\n            return [tfOps.gather(input, tfOps.cast(indices, 'int32'), axis)];\n        }\n        case 'ReverseV2':\n        case 'Reverse': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const input = getParamValue('x', node, tensorMap, context);\n            return [tfOps.reverse(input, axis)];\n        }\n        case 'Slice': {\n            // tslint:disable-next-line:no-any\n            const begin = getParamValue('begin', node, tensorMap, context);\n            // tslint:disable-next-line:no-any\n            const size = getParamValue('size', node, tensorMap, context);\n            return [tfOps.slice(getParamValue('x', node, tensorMap, context), begin, size)];\n        }\n        case 'StridedSlice': {\n            const begin = getParamValue('begin', node, tensorMap, context);\n            const end = getParamValue('end', node, tensorMap, context);\n            const strides = getParamValue('strides', node, tensorMap, context);\n            const beginMask = getParamValue('beginMask', node, tensorMap, context);\n            const endMask = getParamValue('endMask', node, tensorMap, context);\n            const ellipsisMask = getParamValue('ellipsisMask', node, tensorMap, context);\n            const newAxisMask = getParamValue('newAxisMask', node, tensorMap, context);\n            const shrinkAxisMask = getParamValue('shrinkAxisMask', node, tensorMap, context);\n            const tensor = getParamValue('x', node, tensorMap, context);\n            return [tfOps.stridedSlice(tensor, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)];\n        }\n        case 'Pack': {\n            return tidy(() => {\n                const axis = getParamValue('axis', node, tensorMap, context);\n                const tensors = getParamValue('tensors', node, tensorMap, context);\n                // Reshape the tensors to the first tensor's shape if they don't\n                // match.\n                const shape = tensors[0].shape;\n                const squeezedShape = tfOps.squeeze(tensors[0]).shape;\n                const mapped = tensors.map(tensor => {\n                    const sameShape = util.arraysEqual(tensor.shape, shape);\n                    if (!sameShape &&\n                        !util.arraysEqual(tfOps.squeeze(tensor).shape, squeezedShape)) {\n                        throw new Error('the input tensors shape does not match');\n                    }\n                    return sameShape ? tensor : tfOps.reshape(tensor, shape);\n                });\n                return [tfOps.stack(mapped, axis)];\n            });\n        }\n        case 'Unpack': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const tensor = getParamValue('tensor', node, tensorMap, context);\n            return tfOps.unstack(tensor, axis);\n        }\n        case 'Tile': {\n            const reps = getParamValue('reps', node, tensorMap, context);\n            return [tfOps.tile(getParamValue('x', node, tensorMap, context), reps)];\n        }\n        case 'Split':\n        case 'SplitV': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const numOrSizeSplits = getParamValue('numOrSizeSplits', node, tensorMap, context);\n            const tensor = getParamValue('x', node, tensorMap, context);\n            return tfOps.split(tensor, numOrSizeSplits, axis);\n        }\n        case 'ScatterNd': {\n            const indices = getParamValue('indices', node, tensorMap, context);\n            const values = getParamValue('values', node, tensorMap, context);\n            const shape = getParamValue('shape', node, tensorMap, context);\n            return [tfOps.scatterND(indices, values, shape)];\n        }\n        case 'GatherNd': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const indices = getParamValue('indices', node, tensorMap, context);\n            return [tfOps.gatherND(x, indices)];\n        }\n        case 'SparseToDense': {\n            const indices = getParamValue('sparseIndices', node, tensorMap, context);\n            const shape = getParamValue('outputShape', node, tensorMap, context);\n            const sparseValues = getParamValue('sparseValues', node, tensorMap, context);\n            const defaultValue = getParamValue('defaultValue', node, tensorMap, context);\n            return [tfOps.sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ?\n                    defaultValue :\n                    tfOps.cast(defaultValue, sparseValues.dtype))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'slice_join';\n//# sourceMappingURL=slice_join_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'FFT': {\n            return [tfOps.fft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'IFFT': {\n            return [tfOps.ifft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'RFFT': {\n            return [tfOps.rfft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'IRFFT': {\n            return [tfOps.irfft(getParamValue('x', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'spectral';\n//# sourceMappingURL=spectral_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Cast': {\n            return [tfOps.cast(getParamValue('x', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'ExpandDims': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.expandDims(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Squeeze': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.squeeze(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Reshape': {\n            return [tfOps.reshape(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n        }\n        case 'PadV2':\n        case 'Pad': {\n            return [tfOps.pad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('constantValue', node, tensorMap, context))];\n        }\n        case 'SpaceToBatchND': {\n            const blockShape = getParamValue('blockShape', node, tensorMap, context);\n            const paddings = getParamValue('paddings', node, tensorMap, context);\n            return [tfOps.spaceToBatchND(getParamValue('x', node, tensorMap, context), blockShape, paddings)];\n        }\n        case 'BatchToSpaceND': {\n            const blockShape = getParamValue('blockShape', node, tensorMap, context);\n            const crops = getParamValue('crops', node, tensorMap, context);\n            return [tfOps.batchToSpaceND(getParamValue('x', node, tensorMap, context), blockShape, crops)];\n        }\n        case 'DepthToSpace': {\n            const blockSize = getParamValue('blockSize', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context).toUpperCase();\n            return [tfOps.depthToSpace(getParamValue('x', node, tensorMap, context), blockSize, dataFormat)];\n        }\n        case 'BroadcastTo': {\n            return [tfOps.broadcastTo(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'transformation';\n//# sourceMappingURL=transformation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { NodeValueImpl } from './custom_op/node_value_impl';\nimport { getRegisteredOp } from './custom_op/register';\nimport * as arithmetic from './executors/arithmetic_executor';\nimport * as basicMath from './executors/basic_math_executor';\nimport * as control from './executors/control_executor';\nimport * as convolution from './executors/convolution_executor';\nimport * as creation from './executors/creation_executor';\nimport * as dynamic from './executors/dynamic_executor';\nimport * as evaluation from './executors/evaluation_executor';\nimport * as graph from './executors/graph_executor';\nimport * as image from './executors/image_executor';\nimport * as logical from './executors/logical_executor';\nimport * as matrices from './executors/matrices_executor';\nimport * as normalization from './executors/normalization_executor';\nimport * as reduction from './executors/reduction_executor';\nimport * as sliceJoin from './executors/slice_join_executor';\nimport * as spectral from './executors/spectral_executor';\nimport * as transformation from './executors/transformation_executor';\n/**\n * Executes the op defined by the node object.\n * @param node\n * @param tensorMap contains tensors for executed nodes and weights\n */\nexport function executeOp(node, tensorMap, context) {\n    const value = ((node, tensorMap, context) => {\n        switch (node.category) {\n            case 'arithmetic':\n                return tfc.tidy(() => arithmetic.executeOp(node, tensorMap, context));\n            case 'basic_math':\n                return tfc.tidy(() => basicMath.executeOp(node, tensorMap, context));\n            case 'control':\n                return control.executeOp(node, tensorMap, context);\n            case 'convolution':\n                return tfc.tidy(() => convolution.executeOp(node, tensorMap, context));\n            case 'creation':\n                return tfc.tidy(() => creation.executeOp(node, tensorMap, context));\n            case 'dynamic':\n                return dynamic.executeOp(node, tensorMap, context);\n            case 'evaluation':\n                return tfc.tidy(() => evaluation.executeOp(node, tensorMap, context));\n            case 'image':\n                return tfc.tidy(() => image.executeOp(node, tensorMap, context));\n            case 'graph':\n                return tfc.tidy(() => graph.executeOp(node, tensorMap, context));\n            case 'logical':\n                return tfc.tidy(() => logical.executeOp(node, tensorMap, context));\n            case 'matrices':\n                return tfc.tidy(() => matrices.executeOp(node, tensorMap, context));\n            case 'normalization':\n                return tfc.tidy(() => normalization.executeOp(node, tensorMap, context));\n            case 'reduction':\n                return tfc.tidy(() => reduction.executeOp(node, tensorMap, context));\n            case 'slice_join':\n                return tfc.tidy(() => sliceJoin.executeOp(node, tensorMap, context));\n            case 'spectral':\n                return tfc.tidy(() => spectral.executeOp(node, tensorMap, context));\n            case 'transformation':\n                return tfc.tidy(() => transformation.executeOp(node, tensorMap, context));\n            case 'custom':\n                const opMapper = getRegisteredOp(node.op);\n                if (opMapper && opMapper.customExecutor) {\n                    return opMapper.customExecutor(new NodeValueImpl(node, tensorMap, context));\n                }\n                else {\n                    throw TypeError(`Custom op ${node.op} is not registered.`);\n                }\n            default:\n                throw TypeError(`Unknown op '${node.op}'. File an issue at ` +\n                    `https://github.com/tensorflow/tfjs/issues so we can add it` +\n                    `, or register a custom execution with tf.registerOp()`);\n        }\n    })(node, tensorMap, context);\n    if (value instanceof Promise) {\n        return value.then((data) => [].concat(data));\n    }\n    return [].concat(value);\n}\n//# sourceMappingURL=operation_executor.js.map", "/**\n * ExecutionContext captures the runtime environment of the node. It keeps\n * track of the current frame and iteration for the control flow ops.\n *\n * For example, typical Dynamic RNN model may contain loops, for which\n * TensorFlow will generate graphs with Enter/Exit nodes to control the\n * current execution frame, and NextIteration Nodes for iteration id increment.\n * For model with branch logic, TensorFLow will generate Switch/Merge ops.\n */\nexport class ExecutionContext {\n    constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}) {\n        this.weightMap = weightMap;\n        this.tensorArrayMap = tensorArrayMap;\n        this.tensorListMap = tensorListMap;\n        this.functionMap = functionMap;\n        this.rootContext = { id: 0, frameName: '', iterationId: 0 };\n        this.contexts = [this.rootContext];\n        this.lastId = 0;\n        this.generateCurrentContextIds();\n    }\n    newFrame(id, frameName) {\n        return { id, frameName, iterationId: 0 };\n    }\n    /**\n     * Set the current context\n     * @param contexts: ExecutionContextInfo[] the current path of execution\n     * frames\n     */\n    set currentContext(contexts) {\n        if (this.contexts !== contexts) {\n            this.contexts = contexts;\n            this.generateCurrentContextIds();\n        }\n    }\n    get currentContext() {\n        return this.contexts;\n    }\n    /**\n     * Returns the current context in string format.\n     */\n    get currentContextId() {\n        return this._currentContextIds[0];\n    }\n    /**\n     * Returns the current context and all parent contexts in string format.\n     * This allow access to the nodes in the current and parent frames.\n     */\n    get currentContextIds() {\n        return this._currentContextIds;\n    }\n    generateCurrentContextIds() {\n        const names = [];\n        for (let i = 0; i < this.contexts.length - 1; i++) {\n            const contexts = this.contexts.slice(0, this.contexts.length - i);\n            names.push(this.contextIdforContexts(contexts));\n        }\n        names.push('');\n        this._currentContextIds = names;\n    }\n    contextIdforContexts(contexts) {\n        return contexts ?\n            contexts\n                .map(context => (context.id === 0 && context.iterationId === 0) ?\n                '' :\n                `${context.frameName}-${context.iterationId}`)\n                .join('/') :\n            '';\n    }\n    /**\n     * Enter a new frame, a new context is pushed on the current context list.\n     * @param frameId new frame id\n     */\n    enterFrame(frameId) {\n        if (this.contexts) {\n            this.lastId++;\n            this.contexts = this.contexts.slice();\n            this.contexts.push(this.newFrame(this.lastId, frameId));\n            this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));\n        }\n    }\n    /**\n     * Exit the current frame, the last context is removed from the current\n     * context list.\n     */\n    exitFrame() {\n        if (this.contexts && this.contexts.length > 1) {\n            this.contexts = this.contexts.slice();\n            this.contexts.splice(-1);\n            this.currentContextIds.shift();\n        }\n        else {\n            throw new Error('Cannot exit frame, the context is empty');\n        }\n    }\n    /**\n     * Enter the next iteration of a loop, the iteration id of last context is\n     * increased.\n     */\n    nextIteration() {\n        if (this.contexts && this.contexts.length > 0) {\n            this.contexts = this.contexts.slice();\n            this.lastId++;\n            const context = Object.assign({}, this.contexts[this.contexts.length - 1]);\n            context.iterationId += 1;\n            context.id = this.lastId;\n            this.contexts.splice(-1, 1, context);\n            this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));\n        }\n        else {\n            throw new Error('Cannot increase frame iteration, the context is empty');\n        }\n    }\n    getWeight(name) {\n        return this.weightMap[name];\n    }\n    addTensorArray(tensorArray) {\n        this.tensorArrayMap[tensorArray.id] = tensorArray;\n    }\n    getTensorArray(id) {\n        return this.tensorArrayMap[id];\n    }\n    addTensorList(tensorList) {\n        this.tensorListMap[tensorList.id] = tensorList;\n    }\n    getTensorList(id) {\n        return this.tensorListMap[id];\n    }\n    dispose(keepIds) {\n        for (const key in this.tensorArrayMap) {\n            this.tensorArrayMap[key].clearAndClose(keepIds);\n        }\n        for (const key in this.tensorListMap) {\n            this.tensorListMap[key].clearAndClose(keepIds);\n        }\n    }\n}\n//# sourceMappingURL=execution_context.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { parseNodeName } from '../operations/executors/utils';\n/**\n * Given graph inputs and desired outputs, find the minimal set of nodes\n * to execute in order to compute the outputs. In addition return other useful\n * info such:\n * - Missing inputs needed to compute the output.\n * - Whether the subgraph contains dynamic ops (control flow, dynamic shape).\n * - Alternative inputs in order to avoid async (dynamic op) execution.\n */\nexport function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {\n    const usedNodes = new Set();\n    const missingInputs = [];\n    let dynamicNode = null;\n    let syncInputs = null;\n    // Start with the outputs, going backwards and find all the nodes that are\n    // needed to compute those outputs.\n    const seen = new Set();\n    const inputNodeNames = Object.keys(inputs).map(name => parseNodeName(name)[0]);\n    let initNodeNames = [];\n    if (initNodes != null) {\n        initNodeNames = initNodes.map(node => parseNodeName(node.name)[0]);\n    }\n    const frontier = [...outputs];\n    while (frontier.length > 0) {\n        const node = frontier.pop();\n        if (isControlFlow(node) || isDynamicShape(node)) {\n            if (dynamicNode == null) {\n                dynamicNode = node;\n                syncInputs = dynamicNode.children.map(child => child.name)\n                    .filter(name => usedNodes.has(name));\n            }\n        }\n        usedNodes.add(node.name);\n        // Weights are dead end since we already have their values.\n        if (weightMap[node.name] != null) {\n            continue;\n        }\n        // This node is a dead end since it's one of the user-provided inputs.\n        if (inputNodeNames.indexOf(node.name) !== -1) {\n            continue;\n        }\n        // This node is a dead end since it doesn't have any inputs.\n        if (initNodeNames.indexOf(node.name) !== -1) {\n            continue;\n        }\n        if (node.inputs.length === 0) {\n            missingInputs.push(node.name);\n            continue;\n        }\n        node.inputs.forEach(input => {\n            // Don't add to the frontier if it is already there.\n            if (seen.has(input.name)) {\n                return;\n            }\n            seen.add(input.name);\n            frontier.push(input);\n        });\n    }\n    return { inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs };\n}\n/**\n * Given the execution info, return a list of nodes in topological order that\n * need to be executed to compute the output.\n */\nexport function getNodesInTopologicalOrder(graph, weightMap, executionInfo) {\n    const { usedNodes, inputs } = executionInfo;\n    const frontier = [];\n    const inputNodes = Object.keys(inputs)\n        .map(name => parseNodeName(name)[0])\n        .map(name => graph.nodes[name]);\n    const initNodes = graph.initNodes;\n    inputNodes.forEach(input => {\n        if (usedNodes.has(input.name)) {\n            frontier.push(input);\n        }\n    });\n    graph.weights.forEach(weight => {\n        if (usedNodes.has(weight.name)) {\n            frontier.push(weight);\n        }\n    });\n    if (initNodes != null) {\n        initNodes.forEach(node => {\n            if (usedNodes.has(node.name)) {\n                frontier.push(node);\n            }\n        });\n    }\n    const seen = new Set();\n    const orderedNodes = [];\n    while (frontier.length > 0) {\n        const node = frontier.pop();\n        seen.add(node.name);\n        if (!weightMap[node.name]) {\n            orderedNodes.push(node);\n        }\n        node.children.forEach(child => {\n            if (!seen.has(child.name) && usedNodes.has(child.name) &&\n                child.inputs.every(input => seen.has(input.name))) {\n                frontier.push(child);\n            }\n        });\n    }\n    return orderedNodes;\n}\nconst CONTROL_FLOW_OPS = [\n    'Switch', 'Merge', 'Enter', 'Exit', 'NextIteration', 'StatelessIf',\n    'StatelessWhile', 'if', 'While'\n];\nconst DYNAMIC_SHAPE_OPS = [\n    'NonMaxSuppressionV2', 'NonMaxSuppressionV3', 'NonMaxSuppressionV5', 'Where'\n];\nexport function isControlFlow(node) {\n    return CONTROL_FLOW_OPS.indexOf(node.op) >= 0;\n}\nexport function isDynamicShape(node) {\n    return DYNAMIC_SHAPE_OPS.indexOf(node.op) >= 0;\n}\n//# sourceMappingURL=model_analysis.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tidy, util } from '@tensorflow/tfjs-core';\nimport { getNodeNameAndIndex, getParamValue, getTensor, getTensorsForCurrentContenxt, parseNodeName } from '../operations/executors/utils';\nimport { executeOp } from '../operations/operation_executor';\nimport { ExecutionContext } from './execution_context';\nimport { getExecutionSubgraph, getNodesInTopologicalOrder, isControlFlow } from './model_analysis';\nexport class GraphExecutor {\n    /**\n     *\n     * @param graph Graph the model or function graph to be executed.\n     * @param parent When building function exector you need to set the parent\n     * executor. Since the weights and function executor maps are set at parant\n     * level, that function executor can access the function maps and weight maps\n     * through the parent.\n     */\n    constructor(graph, parent) {\n        this.graph = graph;\n        this.parent = parent;\n        this.compiledMap = new Map();\n        this._weightMap = {};\n        this.SEPERATOR = ',';\n        this._functions = {};\n        this._functionExecutorMap = {};\n        this._outputs = graph.outputs;\n        this._inputs = graph.inputs;\n        this._initNodes = graph.initNodes;\n        this._signature = graph.signature;\n        this._functions = graph.functions;\n        // create sub-graph executors\n        if (graph.functions != null) {\n            Object.keys(graph.functions).forEach(name => {\n                this._functionExecutorMap[name] =\n                    new GraphExecutor(graph.functions[name], this);\n            });\n        }\n    }\n    get weightIds() {\n        return this.parent ? this.parent.weightIds : this._weightIds;\n    }\n    get functionExecutorMap() {\n        return this.parent ? this.parent.functionExecutorMap :\n            this._functionExecutorMap;\n    }\n    get weightMap() {\n        return this.parent ? this.parent.weightMap : this._weightMap;\n    }\n    set weightMap(weightMap) {\n        const weightIds = Object.keys(weightMap).map(key => weightMap[key].map(tensor => tensor.id));\n        this._weightIds = [].concat(...weightIds);\n        this._weightMap = weightMap;\n    }\n    get inputs() {\n        return this._inputs.map(node => {\n            return {\n                name: node.name,\n                shape: node.attrParams['shape'] ?\n                    node.attrParams['shape'].value :\n                    undefined,\n                dtype: node.attrParams['dtype'] ?\n                    node.attrParams['dtype'].value :\n                    undefined\n            };\n        });\n    }\n    get outputs() {\n        return this._outputs.map(node => {\n            return {\n                name: node.name,\n                shape: node.attrParams['shape'] ?\n                    node.attrParams['shape'].value :\n                    undefined,\n                dtype: node.attrParams['dtype'] ?\n                    node.attrParams['dtype'].value :\n                    undefined\n            };\n        });\n    }\n    get inputNodes() {\n        return this._inputs.map(node => node.signatureKey || node.name);\n    }\n    get outputNodes() {\n        return this._outputs.map((node) => {\n            const name = node.signatureKey || node.name;\n            return node.defaultOutput ? (`${name}:${node.defaultOutput}`) : name;\n        });\n    }\n    get functions() {\n        return Object.keys(this._functions).reduce((map, key) => {\n            map[key] = this._functions[key].signature;\n            return map;\n        }, {});\n    }\n    getCompilationKey(inputs, outputs) {\n        const sortedInputs = inputs.map(node => node.name).sort();\n        const sortedOutputs = outputs.map(node => node.name).sort();\n        return sortedInputs.join(this.SEPERATOR) + '--' +\n            sortedOutputs.join(this.SEPERATOR);\n    }\n    /**\n     * Compiles the inference graph and returns the minimal set of nodes that are\n     * required for execution, in the correct execution order.\n     */\n    compile(inputs, outputs) {\n        const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);\n        const { missingInputs, dynamicNode, syncInputs } = executionInfo;\n        if (dynamicNode != null) {\n            throw new Error(`This execution contains the node '${dynamicNode.name}', which has ` +\n                `the dynamic op '${dynamicNode.op}'. Please use ` +\n                `model.executeAsync() instead. Alternatively, to avoid the ` +\n                `dynamic ops, specify the inputs [${syncInputs}]`);\n        }\n        if (missingInputs.length > 0) {\n            const outNames = outputs.map(n => n.name);\n            const inNames = Object.keys(inputs);\n            throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs ` +\n                `[${inNames}]. Missing the following inputs: [${missingInputs}]`);\n        }\n        return getNodesInTopologicalOrder(this.graph, this.weightMap, executionInfo);\n    }\n    /**\n     * Executes the inference for given input tensors.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     */\n    execute(inputs, outputs) {\n        inputs = this.mapInputs(inputs);\n        const names = Object.keys(inputs).sort();\n        this.checkInputs(inputs);\n        this.checkInputShapeAndType(inputs);\n        outputs = this.mapOutputs(outputs);\n        this.checkOutputs(outputs);\n        const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n        const outputNodeNames = outputs.map(name => parseNodeName(name)[0]);\n        let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n        // If no outputs are specified, then use the default outputs of the model.\n        if (outputNodes.length === 0) {\n            outputNodes = this._outputs;\n        }\n        const compilationKey = this.getCompilationKey(inputNodes, outputNodes);\n        // Do nothing if the compiled graph cache contains the input.\n        let orderedNodes = this.compiledMap.get(compilationKey);\n        if (orderedNodes == null) {\n            orderedNodes = this.compile(inputs, outputNodes);\n            this.compiledMap.set(compilationKey, orderedNodes);\n        }\n        const tensorArrayMap = {};\n        const tensorListMap = {};\n        return tidy(() => {\n            const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n            const tensorsMap = Object.assign({}, this.weightMap);\n            Object.keys(inputs).forEach(name => {\n                const [nodeName, index] = parseNodeName(name);\n                const tensors = [];\n                tensors[index] = inputs[name];\n                tensorsMap[nodeName] = tensors;\n            });\n            const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n            const intermediateTensorConsumerCount = {};\n            for (let i = 0; i < orderedNodes.length; i++) {\n                const node = orderedNodes[i];\n                if (!tensorsMap[node.name]) {\n                    const tensors = executeOp(node, tensorsMap, context);\n                    if (tensors instanceof Promise) {\n                        throw new Error(`The execution of the op '${node.op}' returned a promise. ` +\n                            `Please use model.executeAsync() instead.`);\n                    }\n                    tensorsMap[node.name] = tensors;\n                    this.checkTensorForDisposal(node.name, node, tensorsMap, context, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount);\n                }\n            }\n            // dispose the context for the root executor\n            if (this.parent == null) {\n                context.dispose(tensorsToKeep);\n            }\n            return outputs.map(name => getTensor(name, tensorsMap, context));\n        });\n    }\n    getFrozenTensorIds(tensorMap) {\n        const ids = [].concat.apply([], Object.keys(tensorMap)\n            .map(key => tensorMap[key])\n            .map(tensors => tensors.map(tensor => tensor.id)));\n        return new Set(ids);\n    }\n    checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount) {\n        // Skip output nodes and any control flow nodes, since its dependency is\n        // tricky to track correctly.\n        if (node.category === 'control' || outputNames.indexOf(nodeName) !== -1) {\n            return;\n        }\n        tensorMap[nodeName].forEach(tensor => {\n            if (tensor != null) {\n                intermediateTensorConsumerCount[tensor.id] =\n                    (intermediateTensorConsumerCount[tensor.id] || 0) +\n                        node.children.length;\n            }\n        });\n        node.inputs.forEach(input => {\n            // Skip any control flow nodes, since its dependency is tricky to track\n            // correctly.\n            if (input.category !== 'control') {\n                const tensors = getTensorsForCurrentContenxt(input.name, tensorMap, context);\n                if (tensors != null) {\n                    tensors.forEach(tensor => {\n                        if (tensor && !tensorsToKeep.has(tensor.id)) {\n                            const count = intermediateTensorConsumerCount[tensor.id];\n                            if (count === 1) {\n                                tensor.dispose();\n                                delete intermediateTensorConsumerCount[tensor.id];\n                            }\n                            else if (count != null) {\n                                // only intermediate nodes has count set, inputs and weights are\n                                // not.\n                                intermediateTensorConsumerCount[tensor.id]--;\n                            }\n                        }\n                    });\n                }\n            }\n        });\n    }\n    /**\n     * Executes the inference for given input tensors in Async fashion.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     */\n    async executeAsync(inputs, outputs) {\n        return this._executeAsync(inputs, outputs);\n    }\n    /**\n     * Executes the inference for given input tensors in Async fashion.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     * @param isFunctionExecution Flag for executing a function.\n     * @param tensorArrayMap Optional, global TensorArray map by id. Used for\n     * function execution.\n     * @param tensorArrayMap Optinal global TensorList map by id. Used for\n     * function execution.\n     */\n    async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {\n        if (!isFunctionExecution) {\n            inputs = this.mapInputs(inputs);\n            this.checkInputs(inputs);\n            this.checkInputShapeAndType(inputs);\n            outputs = this.mapOutputs(outputs);\n            this.checkOutputs(outputs);\n        }\n        const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n        // Graph with control flow op requires runtime evaluation of the execution\n        // order, while without control flow the execution order is pre-determined\n        // in the compile method.\n        const tensorMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);\n        const results = outputs.map(name => getTensor(name, tensorMap, context));\n        // dispose all the intermediate tensors\n        const outputIds = results.map(t => t.id);\n        const inputIds = Object.keys(inputs).map(name => inputs[name].id);\n        const keepIds = new Set([...outputIds, ...inputIds, ...this.weightIds]);\n        Object.keys(tensorMap).forEach(key => {\n            const tensorArray = tensorMap[key];\n            tensorArray.forEach(tensor => {\n                if (tensor && !tensor.isDisposed && !keepIds.has(tensor.id)) {\n                    tensor.dispose();\n                }\n            });\n        });\n        // dispose the context for the root executor\n        if (this.parent == null) {\n            context.dispose(keepIds);\n        }\n        return results;\n    }\n    async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {\n        const mappedInputs = inputs.reduce((map, tensor, index) => {\n            map[this.inputs[index].name] = tensor;\n            return map;\n        }, {});\n        return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);\n    }\n    /**\n     * When there are control flow nodes in the graph, the graph execution use\n     * ExecutionContext to keep track of the frames and loop iterators.\n     * @param inputs placeholder tensors for the graph.\n     * @param context the execution context object for current execution.\n     * @param isFunctionExecution Flag for executing a function.\n     */\n    async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {\n        const names = Object.keys(inputs);\n        const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n        const outputNodeNames = outputNames.map(name => parseNodeName(name)[0]);\n        const outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n        const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes, this.weightMap);\n        const stack = [...inputNodes, ...this.graph.weights].map(node => {\n            return { node, contexts: context.currentContext };\n        });\n        const tensorsMap = Object.assign({}, this.weightMap);\n        Object.keys(inputs).forEach(name => {\n            const [nodeName, index] = parseNodeName(name);\n            const tensors = [];\n            tensors[index] = inputs[name];\n            tensorsMap[nodeName] = tensors;\n        });\n        const intermediateTensorConsumerCount = {};\n        const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n        const added = {};\n        while (stack.length > 0) {\n            const promises = this.processStack(inputNodes, stack, context, tensorsMap, added, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount, usedNodes);\n            await Promise.all(promises);\n        }\n        if (dynamicNode == null && !isFunctionExecution) {\n            console.warn(`This model execution did not contain any nodes with control flow ` +\n                `or dynamic output shapes. You can use model.execute() instead.`);\n        }\n        const missingOutputs = outputNodes\n            .filter(node => !isControlFlow(node) &&\n            !getTensor(node.name, tensorsMap, context))\n            .map(node => node.name);\n        if (missingOutputs.length > 0) {\n            let alternativeMsg = '';\n            if (dynamicNode != null) {\n                alternativeMsg =\n                    `Alternatively, to avoid the dynamic ops, use model.execute() ` +\n                        `and specify the inputs [${syncInputs}]`;\n            }\n            throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided ` +\n                `inputs [${names}]. Consider providing the following inputs: ` +\n                `[${missingInputs}]. ${alternativeMsg}`);\n        }\n        return tensorsMap;\n    }\n    processStack(inputNodes, stack, context, tensorMap, added, tensorsToKeep, outputNames, intermediateTensorConsumerCount, usedNodes) {\n        const promises = [];\n        while (stack.length > 0) {\n            const item = stack.pop();\n            context.currentContext = item.contexts;\n            let nodeName = '';\n            // The tensor of the Enter op with isConstant set should be set\n            // in the parent scope, so it will be available as constant for the\n            // whole loop.\n            if (item.node.op === 'Enter' &&\n                getParamValue('isConstant', item.node, tensorMap, context)) {\n                [nodeName] = getNodeNameAndIndex(item.node.name, context);\n            }\n            // only process nodes that are not provided as input nodes.\n            if (inputNodes.indexOf(item.node) === -1) {\n                const tensors = executeOp(item.node, tensorMap, context);\n                if (!nodeName) {\n                    [nodeName] = getNodeNameAndIndex(item.node.name, context);\n                }\n                const currentContext = context.currentContext;\n                if (tensors instanceof Promise) {\n                    promises.push(tensors.then(t => {\n                        tensorMap[nodeName] = t;\n                        context.currentContext = currentContext;\n                        this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n                        this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n                        return t;\n                    }));\n                }\n                else {\n                    tensorMap[nodeName] = tensors;\n                    this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n                    this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n                }\n            }\n            else {\n                this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n            }\n        }\n        return promises;\n    }\n    processChildNodes(node, stack, context, tensorMap, added, usedNodes) {\n        node.children.forEach((childNode) => {\n            const [nodeName,] = getNodeNameAndIndex(childNode.name, context);\n            if (added[nodeName] || !usedNodes.has(childNode.name)) {\n                return;\n            }\n            // Merge op can be pushed if any of its inputs has value.\n            if (childNode.op === 'Merge') {\n                if (childNode.inputNames.some(name => {\n                    return !!getTensor(name, tensorMap, context);\n                })) {\n                    added[nodeName] = true;\n                    stack.push({ contexts: context.currentContext, node: childNode });\n                }\n            }\n            else // Otherwise all inputs must to have value.\n             if (childNode.inputNames.every(name => {\n                return !!getTensor(name, tensorMap, context);\n            })) {\n                added[nodeName] = true;\n                stack.push({ contexts: context.currentContext, node: childNode });\n            }\n        });\n    }\n    /**\n     * Releases the memory used by the weight tensors.\n     */\n    dispose() {\n        Object.keys(this.weightMap)\n            .forEach(key => this.weightMap[key].forEach(tensor => tensor.dispose()));\n    }\n    checkInputShapeAndType(inputs) {\n        Object.keys(inputs).forEach(name => {\n            const input = inputs[name];\n            const [nodeName,] = parseNodeName(name);\n            const node = this.graph.nodes[nodeName];\n            if (node.attrParams['shape'] && node.attrParams['shape'].value) {\n                const shape = node.attrParams['shape'].value;\n                const match = shape.length === input.shape.length &&\n                    input.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim);\n                util.assert(match, () => `The shape of dict['${node.name}'] provided in ` +\n                    `model.execute(dict) must be [${shape}], but was ` +\n                    `[${input.shape}]`);\n            }\n            if (node.attrParams['dtype'] && node.attrParams['dtype'].value) {\n                util.assert(input.dtype === node.attrParams['dtype'].value, () => `The dtype of dict['${node.name}'] provided in ` +\n                    `model.execute(dict) must be ` +\n                    `${node.attrParams['dtype'].value}, but was ${input.dtype}`);\n            }\n        });\n    }\n    mapInputs(inputs) {\n        const result = {};\n        for (const inputName in inputs) {\n            if (this._signature != null && this._signature.inputs != null &&\n                this._signature.inputs[inputName] != null) {\n                const tensor = this._signature.inputs[inputName];\n                result[tensor.name] = inputs[inputName];\n            }\n            else {\n                result[inputName] = inputs[inputName];\n            }\n        }\n        return result;\n    }\n    checkInputs(inputs) {\n        const notInGraph = Object.keys(inputs).filter(name => {\n            const [nodeName] = parseNodeName(name);\n            return this.graph.nodes[nodeName] == null;\n        });\n        if (notInGraph.length > 0) {\n            throw new Error(`The dict provided in model.execute(dict) has ` +\n                `keys: [${notInGraph}] that are not part of graph`);\n        }\n    }\n    mapOutputs(outputs) {\n        return outputs.map(name => {\n            if (this._signature != null && this._signature.outputs != null &&\n                this._signature.outputs[name] != null) {\n                const tensor = this._signature.outputs[name];\n                return tensor.name;\n            }\n            return name;\n        }, {});\n    }\n    checkOutputs(outputs) {\n        outputs.forEach(name => {\n            const [normalizedName] = parseNodeName(name);\n            if (!this.graph.nodes[normalizedName]) {\n                throw new Error(`The output '${name}' is not found in the graph`);\n            }\n        });\n    }\n}\n//# sourceMappingURL=graph_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { io, Tensor } from '@tensorflow/tfjs-core';\nimport { OperationMapper } from '../operations/operation_mapper';\nimport { GraphExecutor } from './graph_executor';\nexport const TFHUB_SEARCH_PARAM = '?tfjs-format=file';\nexport const DEFAULT_MODEL_NAME = 'model.json';\n/**\n * A `tf.GraphModel` is a directed, acyclic graph built from a\n * SavedModel GraphDef and allows inference execution.\n *\n * A `tf.GraphModel` can only be created by loading from a model converted from\n * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using\n * the command line converter tool and loaded via `tf.loadGraphModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class GraphModel {\n    /**\n     * @param modelUrl url for the model, or an `io.IOHandler`.\n     * @param weightManifestUrl url for the weight file generated by\n     * scripts/convert.py script.\n     * @param requestOption options for Request, which allows to send credentials\n     * and custom headers.\n     * @param onProgress Optional, progress callback function, fired periodically\n     * before the load is completed.\n     */\n    constructor(modelUrl, loadOptions = {}) {\n        this.modelUrl = modelUrl;\n        this.loadOptions = loadOptions;\n        this.version = 'n/a';\n        if (loadOptions == null) {\n            this.loadOptions = {};\n        }\n    }\n    // Returns the version information for the tensorflow model GraphDef.\n    get modelVersion() {\n        return this.version;\n    }\n    get inputNodes() {\n        return this.executor.inputNodes;\n    }\n    get outputNodes() {\n        return this.executor.outputNodes;\n    }\n    get inputs() {\n        return this.executor.inputs;\n    }\n    get outputs() {\n        return this.executor.outputs;\n    }\n    get weights() {\n        return this.executor.weightMap;\n    }\n    findIOHandler() {\n        const path = this.modelUrl;\n        if (path.load != null) {\n            // Path is an IO Handler.\n            this.handler = path;\n        }\n        else if (this.loadOptions.requestInit != null) {\n            this.handler = io.browserHTTPRequest(path, this.loadOptions);\n        }\n        else {\n            const handlers = io.getLoadHandlers(path, this.loadOptions);\n            if (handlers.length === 0) {\n                // For backward compatibility: if no load handler can be found,\n                // assume it is a relative http path.\n                handlers.push(io.browserHTTPRequest(path, this.loadOptions));\n            }\n            else if (handlers.length > 1) {\n                throw new Error(`Found more than one (${handlers.length}) load handlers for ` +\n                    `URL '${[path]}'`);\n            }\n            this.handler = handlers[0];\n        }\n    }\n    /**\n     * Loads the model and weight files, construct the in memory weight map and\n     * compile the inference graph.\n     */\n    async load() {\n        this.findIOHandler();\n        if (this.handler.load == null) {\n            throw new Error('Cannot proceed with model loading because the IOHandler provided ' +\n                'does not have the `load` method implemented.');\n        }\n        const artifacts = await this.handler.load();\n        return this.loadSync(artifacts);\n    }\n    /**\n     * Synchronously construct the in memory weight map and\n     * compile the inference graph. Also initialize hashtable if any.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    loadSync(artifacts) {\n        this.artifacts = artifacts;\n        const graph = this.artifacts.modelTopology;\n        let signature = {};\n        if (this.artifacts.userDefinedMetadata != null) {\n            signature = // tslint:disable-next-line:no-any\n                this.artifacts.userDefinedMetadata.signature;\n        }\n        this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;\n        const weightMap = io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);\n        this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, signature));\n        this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);\n        if (artifacts.modelInitializer != null) {\n            const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer);\n            this.initializer = new GraphExecutor(initializer);\n            this.initializer.weightMap = this.executor.weightMap;\n            this.initializer.execute({}, []);\n        }\n        return true;\n    }\n    /**\n     * Save the configuration and/or weights of the GraphModel.\n     *\n     * An `IOHandler` is an object that has a `save` method of the proper\n     * signature defined. The `save` method manages the storing or\n     * transmission of serialized data (\"artifacts\") that represent the\n     * model's topology and weights onto or via a specific medium, such as\n     * file downloads, local storage, IndexedDB in the web browser and HTTP\n     * requests to a server. TensorFlow.js provides `IOHandler`\n     * implementations for a number of frequently used saving mediums, such as\n     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n     * for more details.\n     *\n     * This method also allows you to refer to certain types of `IOHandler`s\n     * as URL-like string shortcuts, such as 'localstorage://' and\n     * 'indexeddb://'.\n     *\n     * Example 1: Save `model`'s topology and weights to browser [local\n     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n     * then load it back.\n     *\n     * ```js\n     * const modelUrl =\n     *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n     * const model = await tf.loadGraphModel(modelUrl);\n     * const zeros = tf.zeros([1, 224, 224, 3]);\n     * model.predict(zeros).print();\n     *\n     * const saveResults = await model.save('localstorage://my-model-1');\n     *\n     * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * model.predict(zeros).print();\n     * ```\n     *\n     * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n     * scheme-based string shortcut for `IOHandler`.\n     * @param config Options for saving the model.\n     * @returns A `Promise` of `SaveResult`, which summarizes the result of\n     * the saving, such as byte sizes of the saved artifacts for the model's\n     *   topology and weight values.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async save(handlerOrURL, config) {\n        if (typeof handlerOrURL === 'string') {\n            const handlers = io.getSaveHandlers(handlerOrURL);\n            if (handlers.length === 0) {\n                throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n            }\n            else if (handlers.length > 1) {\n                throw new Error(`Found more than one (${handlers.length}) save handlers for ` +\n                    `URL '${handlerOrURL}'`);\n            }\n            handlerOrURL = handlers[0];\n        }\n        if (handlerOrURL.save == null) {\n            throw new Error('GraphModel.save() cannot proceed because the IOHandler ' +\n                'provided does not have the `save` attribute defined.');\n        }\n        return handlerOrURL.save(this.artifacts);\n    }\n    /**\n     * Execute the inference for the input tensors.\n     *\n     * @param input The input tensors, when there is single input for the model,\n     * inputs param should be a `tf.Tensor`. For models with mutliple inputs,\n     * inputs params should be in either `tf.Tensor`[] if the input order is\n     * fixed, or otherwise NamedTensorMap format.\n     *\n     * For model with multiple inputs, we recommend you use NamedTensorMap as the\n     * input type, if you use `tf.Tensor`[], the order of the array needs to\n     * follow the\n     * order of inputNodes array. @see {@link GraphModel.inputNodes}\n     *\n     * You can also feed any intermediate nodes using the NamedTensorMap as the\n     * input type. For example, given the graph\n     *    InputNode => Intermediate => OutputNode,\n     * you can execute the subgraph Intermediate => OutputNode by calling\n     *    model.execute('IntermediateNode' : tf.tensor(...));\n     *\n     * This is useful for models that uses tf.dynamic_rnn, where the intermediate\n     * state needs to be fed manually.\n     *\n     * For batch inference execution, the tensors for each input need to be\n     * concatenated together. For example with mobilenet, the required input shape\n     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].\n     * If we are provide a batched data of 100 images, the input tensor should be\n     * in the shape of [100, 244, 244, 3].\n     *\n     * @param config Prediction configuration for specifying the batch size and\n     * output node names. Currently the batch size option is ignored for graph\n     * model.\n     *\n     * @returns Inference result tensors. The output would be single `tf.Tensor`\n     * if model has single output node, otherwise Tensor[] or NamedTensorMap[]\n     * will be returned for model with multiple outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(inputs, config) {\n        return this.execute(inputs, this.outputNodes);\n    }\n    normalizeInputs(inputs) {\n        if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {\n            // The input is already a NamedTensorMap.\n            return inputs;\n        }\n        inputs = Array.isArray(inputs) ? inputs : [inputs];\n        if (inputs.length !== this.inputNodes.length) {\n            throw new Error('Input tensor count mismatch,' +\n                `the graph model has ${this.inputNodes.length} placeholders, ` +\n                `while there are ${inputs.length} input tensors.`);\n        }\n        return this.inputNodes.reduce((map, inputName, i) => {\n            map[inputName] = inputs[i];\n            return map;\n        }, {});\n    }\n    normalizeOutputs(outputs) {\n        outputs = outputs || this.outputNodes;\n        return !Array.isArray(outputs) ? [outputs] : outputs;\n    }\n    /**\n     * Executes inference for the model for given input tensors.\n     * @param inputs tensor, tensor array or tensor map of the inputs for the\n     * model, keyed by the input node names.\n     * @param outputs output node name from the Tensorflow model, if no\n     * outputs are specified, the default outputs of the model would be used.\n     * You can inspect intermediate nodes of the model by adding them to the\n     * outputs array.\n     *\n     * @returns A single tensor if provided with a single output or no outputs\n     * are provided and there is only one default output, otherwise return a\n     * tensor array. The order of the tensor array is the same as the outputs\n     * if provided, otherwise the order of outputNodes attribute of the model.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    execute(inputs, outputs) {\n        inputs = this.normalizeInputs(inputs);\n        outputs = this.normalizeOutputs(outputs);\n        const result = this.executor.execute(inputs, outputs);\n        return result.length > 1 ? result : result[0];\n    }\n    /**\n     * Executes inference for the model for given input tensors in async\n     * fashion, use this method when your model contains control flow ops.\n     * @param inputs tensor, tensor array or tensor map of the inputs for the\n     * model, keyed by the input node names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     *\n     * @returns A Promise of single tensor if provided with a single output or\n     * no outputs are provided and there is only one default output, otherwise\n     * return a tensor map.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async executeAsync(inputs, outputs) {\n        inputs = this.normalizeInputs(inputs);\n        outputs = this.normalizeOutputs(outputs);\n        const result = await this.executor.executeAsync(inputs, outputs);\n        return result.length > 1 ? result : result[0];\n    }\n    convertTensorMapToTensorsMap(map) {\n        return Object.keys(map).reduce((newMap, key) => {\n            newMap[key] = [map[key]];\n            return newMap;\n        }, {});\n    }\n    /**\n     * Releases the memory used by the weight tensors.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    dispose() {\n        this.executor.dispose();\n        if (this.initializer) {\n            this.initializer.dispose();\n        }\n    }\n}\n/**\n * Load a graph model given a URL to the model definition.\n *\n * Example of loading MobileNetV2 from a URL and making a prediction with a\n * zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n * const model = await tf.loadGraphModel(modelUrl);\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n *\n * Example of loading MobileNetV2 from a TF Hub URL and making a prediction with\n * a zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';\n * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n * @param modelUrl The url or an `io.IOHandler` that loads the model.\n * @param options Options for the HTTP request, which allows to send credentials\n *    and custom headers.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport async function loadGraphModel(modelUrl, options = {}) {\n    if (modelUrl == null) {\n        throw new Error('modelUrl in loadGraphModel() cannot be null. Please provide a url ' +\n            'or an IOHandler that loads the model');\n    }\n    if (options == null) {\n        options = {};\n    }\n    if (options.fromTFHub) {\n        if (modelUrl.load == null) {\n            if (!modelUrl.endsWith('/')) {\n                modelUrl = modelUrl + '/';\n            }\n            modelUrl = `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;\n        }\n    }\n    const model = new GraphModel(modelUrl, options);\n    await model.load();\n    return model;\n}\n//# sourceMappingURL=graph_model.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { GraphModel, loadGraphModel } from './executor/graph_model';\nexport { deregisterOp, registerOp } from './operations/custom_op/register';\nexport { version as version_converter } from './version';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\n/**\n * Apply a mapping function to a nested structure in a recursive manner.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapResult`.  The `DeepMapResult` either provides a\n *   replacement value for that node (i.e., replacing the subtree), or indicates\n *   that the node should be processed recursively.\n */\nexport function deepMap(input, mapFn) {\n    return deepMapInternal(input, mapFn);\n}\n/**\n * @param seen: A Map of known object mappings (i.e., memoized results of\n *   `mapFn()`)\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepMapInternal(input, mapFn, seen = new Map(), containedIn = new Set()) {\n    if (input == null) {\n        return null;\n    }\n    if (containedIn.has(input)) {\n        throw new Error('Circular references are not supported.');\n    }\n    if (seen.has(input)) {\n        return seen.get(input);\n    }\n    const result = mapFn(input);\n    if (result.recurse && result.value !== null) {\n        throw new Error('A deep map function may not return both a value and recurse=true.');\n    }\n    if (!result.recurse) {\n        seen.set(input, result.value);\n        return result.value;\n    }\n    else if (isIterable(input)) {\n        // tslint:disable-next-line:no-any\n        const mappedIterable = Array.isArray(input) ? [] : {};\n        containedIn.add(input);\n        for (const k in input) {\n            const child = input[k];\n            const childResult = deepMapInternal(child, mapFn, seen, containedIn);\n            mappedIterable[k] = childResult;\n        }\n        containedIn.delete(input);\n        return mappedIterable;\n    }\n    else {\n        throw new Error(`Can't recurse into non-iterable type: ${input}`);\n    }\n}\n// TODO(soergel, kangyizhang) Reconsider naming of deepZip() to avoid confusion\n// with zip()\n/**\n * Zip nested structures together in a recursive manner.\n *\n * This has the effect of transposing or pivoting data, e.g. converting it from\n * a row-major representation to a column-major representation.\n *\n * For example, `deepZip([{a: 1, b: 2}, {a: 3, b: 4}])` returns\n * `{a: [1, 3], b: [2, 4]}`.\n *\n * The inputs should all have the same nested structure (i.e., of arrays and\n * dicts).  The result is a single object with the same nested structure, where\n * the leaves are arrays collecting the values of the inputs at that location\n * (or, optionally, the result of a custom function applied to those arrays).\n *\n * @param inputs: An array of the objects to zip together.\n * @param zipFn: (optional) A function that expects an array of elements at a\n *   single node of the object tree, and returns a `DeepMapResult`.  The\n *   `DeepMapResult` either provides a result value for that node (i.e.,\n *   representing the subtree), or indicates that the node should be processed\n *   recursively.  The default zipFn recurses as far as possible and places\n *   arrays at the leaves.\n */\nexport function deepZip(inputs, zipFn = zipToList) {\n    return deepZipInternal(inputs, zipFn);\n}\n/**\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepZipInternal(inputs, zipFn, containedIn = new Set()) {\n    // The recursion follows the structure of input 0; it's assumed that all the\n    // other inputs have the same structure.\n    const input = inputs[0];\n    if (containedIn.has(input)) {\n        throw new Error('Circular references are not supported.');\n    }\n    const result = zipFn(inputs);\n    if (result.recurse && result.value !== null) {\n        throw new Error('A deep zip function may not return both a value and recurse=true.');\n    }\n    if (!result.recurse) {\n        return result.value;\n    }\n    else if (isIterable(input)) {\n        // tslint:disable-next-line:no-any\n        const mappedIterable = Array.isArray(input) ? [] : {};\n        containedIn.add(input);\n        for (const k in input) {\n            const children = inputs.map(x => x[k]);\n            const childResult = deepZipInternal(children, zipFn, containedIn);\n            mappedIterable[k] = childResult;\n        }\n        containedIn.delete(input);\n        return mappedIterable;\n    }\n    else {\n        throw new Error(`Can't recurse into non-iterable type: ${input}`);\n    }\n}\n// tslint:disable-next-line:no-any\nexport function zipToList(x) {\n    if (x === null) {\n        return null;\n    }\n    // TODO(soergel): validate array type?\n    if (isIterable(x[0])) {\n        return { value: null, recurse: true };\n    }\n    else {\n        return { value: x, recurse: false };\n    }\n}\n/**\n * Apply an async mapping function to a nested structure in a recursive manner.\n *\n * This first creates a nested structure of Promises, and then awaits all of\n * those, resulting in a single Promise for a resolved nested structure.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapAsyncResult`.  The `DeepMapAsyncResult` either provides\n *   a `Promise` for a replacement value for that node (i.e., replacing the\n *   subtree), or indicates that the node should be processed recursively.  Note\n *   that the decision whether or not to recurse must be made immediately; only\n *   the mapped value may be promised.\n */\nexport async function deepMapAndAwaitAll(input, mapFn) {\n    const seen = new Map();\n    // First do a normal deepMap, collecting Promises in 'seen' as a side effect.\n    deepMapInternal(input, mapFn, seen);\n    // Replace the Promises in 'seen' in place.\n    // Note TypeScript provides no async map iteration, and regular map iteration\n    // is broken too, so sadly we have to do Array.from() to make it work.\n    // (There's no advantage to Promise.all(), and that would be tricky anyway.)\n    for (const key of Array.from(seen.keys())) {\n        const value = seen.get(key);\n        if (value instanceof Promise) {\n            const mappedValue = await value;\n            seen.set(key, mappedValue);\n        }\n    }\n    // Normal deepMap again, this time filling in the resolved values.\n    // It's unfortunate that we have to do two passes.\n    // TODO(soergel): test performance and think harder about a fast solution.\n    const result = deepMapInternal(input, mapFn, seen);\n    return result;\n}\n/**\n * Determine whether the argument is iterable.\n *\n * @returns true if the argument is an array or any non-Tensor object.\n */\n// tslint:disable-next-line:no-any\nexport function isIterable(obj) {\n    return obj != null && (!ArrayBuffer.isView(obj)) &&\n        (Array.isArray(obj) ||\n            (typeof obj === 'object' && !(obj instanceof tf.Tensor)));\n}\n/**\n * Determine whether the argument can be converted to Tensor.\n *\n * Tensors, primitives, arrays, and TypedArrays all qualify; anything else does\n * not.\n *\n * @returns true if the argument can be converted to Tensor.\n */\n// tslint:disable-next-line:no-any\nexport function canTensorify(obj) {\n    return obj == null || isPrimitive(obj) || Array.isArray(obj) ||\n        (typeof obj === 'object' && (obj instanceof tf.Tensor)) ||\n        tf.util.isTypedArray(obj);\n}\n/**\n * Returns true if the given `value` is a primitive type. Otherwise returns\n * false. This is equivalant to node util.isPrimitive\n */\nfunction isPrimitive(value) {\n    return (value === null ||\n        (typeof value !== 'object' && typeof value !== 'function'));\n}\n//# sourceMappingURL=deep_map.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { deepMap, isIterable } from './deep_map';\nexport function deepClone(container) {\n    return deepMap(container, cloneIfTensor);\n}\n// tslint:disable-next-line: no-any\nfunction cloneIfTensor(item) {\n    if (item instanceof tf.Tensor) {\n        return ({ value: item.clone(), recurse: false });\n    }\n    else if (isIterable(item)) {\n        return { value: null, recurse: true };\n    }\n    else {\n        return { value: item, recurse: false };\n    }\n}\n//# sourceMappingURL=deep_clone.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * A ring buffer, providing O(1) FIFO, LIFO, and related operations.\n */\nexport class RingBuffer {\n    /**\n     * Constructs a `RingBuffer`.\n     * @param capacity The number of items that the buffer can accomodate.\n     */\n    constructor(capacity) {\n        this.capacity = capacity;\n        // Note we store the indices in the range 0 <= index < 2*capacity.\n        // This allows us to distinguish the full from the empty case.\n        // See https://www.snellman.net/blog/archive/2016-12-13-ring-buffers/\n        this.begin = 0; // inclusive\n        this.end = 0; // exclusive\n        if (capacity == null) {\n            throw new RangeError('Can\\'t create a ring buffer of unknown capacity.');\n        }\n        if (capacity < 1) {\n            throw new RangeError('Can\\'t create ring buffer of capacity < 1.');\n        }\n        this.data = new Array(capacity);\n        this.doubledCapacity = 2 * capacity;\n    }\n    /**\n     * Map any index into the range 0 <= index < 2*capacity.\n     */\n    wrap(index) {\n        // don't trust % on negative numbers\n        while (index < 0) {\n            index += this.doubledCapacity;\n        }\n        return index % this.doubledCapacity;\n    }\n    get(index) {\n        if (index < 0) {\n            throw new RangeError('Can\\'t get item at a negative index.');\n        }\n        return this.data[index % this.capacity];\n    }\n    set(index, value) {\n        if (index < 0) {\n            throw new RangeError('Can\\'t set item at a negative index.');\n        }\n        this.data[index % this.capacity] = value;\n    }\n    /**\n     * Returns the current number of items in the buffer.\n     */\n    length() {\n        let length = this.end - this.begin;\n        if (length < 0) {\n            length = this.doubledCapacity + length;\n        }\n        return length;\n    }\n    /**\n     * Reports whether the buffer is full.\n     * @returns true if the number of items in the buffer equals its capacity, and\n     *   false otherwise.\n     */\n    isFull() {\n        return this.length() === this.capacity;\n    }\n    /**\n     * Reports whether the buffer is empty.\n     * @returns true if the number of items in the buffer equals zero, and\n     *   false otherwise.\n     */\n    isEmpty() {\n        return this.length() === 0;\n    }\n    /**\n     * Adds an item to the end of the buffer.\n     */\n    push(value) {\n        if (this.isFull()) {\n            throw new RangeError('Ring buffer is full.');\n        }\n        this.set(this.end, value);\n        this.end = this.wrap(this.end + 1);\n    }\n    /**\n     * Adds many items to the end of the buffer, in order.\n     */\n    pushAll(values) {\n        for (const value of values) {\n            this.push(value);\n        }\n    }\n    /**\n     * Removes and returns the last item in the buffer.\n     */\n    pop() {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        this.end = this.wrap(this.end - 1);\n        const result = this.get(this.end);\n        this.set(this.end, undefined);\n        return result;\n    }\n    /**\n     * Adds an item to the beginning of the buffer.\n     */\n    unshift(value) {\n        if (this.isFull()) {\n            throw new RangeError('Ring buffer is full.');\n        }\n        this.begin = this.wrap(this.begin - 1);\n        this.set(this.begin, value);\n    }\n    /**\n     * Removes and returns the first item in the buffer.\n     */\n    shift() {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        const result = this.get(this.begin);\n        this.set(this.begin, undefined);\n        this.begin = this.wrap(this.begin + 1);\n        return result;\n    }\n    /**\n     * Removes and returns a specific item in the buffer, and moves the last item\n     * to the vacated slot.  This is useful for implementing a shuffling stream.\n     * Note that this operation necessarily scrambles the original order.\n     *\n     * @param relativeIndex: the index of the item to remove, relative to the\n     *   first item in the buffer (e.g., hiding the ring nature of the underlying\n     *   storage).\n     */\n    shuffleExcise(relativeIndex) {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        const index = this.wrap(this.begin + relativeIndex);\n        const result = this.get(index);\n        this.set(index, this.pop());\n        return result;\n    }\n}\n//# sourceMappingURL=ring_buffer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { RingBuffer } from './ring_buffer';\nexport class GrowingRingBuffer extends RingBuffer {\n    /**\n     * Constructs a `GrowingRingBuffer`.\n     */\n    constructor() {\n        super(GrowingRingBuffer.INITIAL_CAPACITY);\n    }\n    isFull() {\n        return false;\n    }\n    push(value) {\n        if (super.isFull()) {\n            this.expand();\n        }\n        super.push(value);\n    }\n    unshift(value) {\n        if (super.isFull()) {\n            this.expand();\n        }\n        super.unshift(value);\n    }\n    /**\n     * Doubles the capacity of the buffer.\n     */\n    expand() {\n        const newCapacity = this.capacity * 2;\n        const newData = new Array(newCapacity);\n        const len = this.length();\n        // Rotate the buffer to start at index 0 again, since we can't just\n        // allocate more space at the end.\n        for (let i = 0; i < len; i++) {\n            newData[i] = this.get(this.wrap(this.begin + i));\n        }\n        this.data = newData;\n        this.capacity = newCapacity;\n        this.doubledCapacity = 2 * this.capacity;\n        this.begin = 0;\n        this.end = len;\n    }\n}\nGrowingRingBuffer.INITIAL_CAPACITY = 32;\n//# sourceMappingURL=growing_ring_buffer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport * as seedrandom from 'seedrandom';\nimport { deepClone } from '../util/deep_clone';\nimport { deepMapAndAwaitAll, deepZip, zipToList } from '../util/deep_map';\nimport { GrowingRingBuffer } from '../util/growing_ring_buffer';\nimport { RingBuffer } from '../util/ring_buffer';\n// Here we implement a simple asynchronous iterator.\n// This lets us avoid using either third-party stream libraries or\n// recent TypeScript language support requiring polyfills.\n/**\n * Create a `LazyIterator` from an array of items.\n */\nexport function iteratorFromItems(items) {\n    return new ArrayIterator(items);\n}\n/**\n * Create a `LazyIterator` of incrementing integers.\n */\nexport function iteratorFromIncrementing(start) {\n    let i = start;\n    return iteratorFromFunction(() => ({ value: i++, done: false }));\n}\n/**\n * Create a `LazyIterator` from a function.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const iter = tf.data.iteratorFromFunction(func);\n * await iter.forEachAsync(e => console.log(e));\n * ```\n *\n * @param func A function that produces data on each call.\n */\nexport function iteratorFromFunction(func) {\n    return new FunctionCallIterator(func);\n}\n/**\n * Create a `LazyIterator` by concatenating underlying streams, which are\n * themselves provided as a stream.\n *\n * This can also be thought of as a \"stream flatten\" operation.\n *\n * @param baseIterators A stream of streams to be concatenated.\n * @param baseErrorHandler An optional function that can intercept `Error`s\n *   raised during a `next()` call on the base stream.  This function can decide\n *   whether the error should be propagated, whether the error should be\n *   ignored, or whether the base stream should be terminated.\n */\nexport function iteratorFromConcatenated(baseIterators, baseErrorHandler) {\n    return new ChainedIterator(baseIterators, baseErrorHandler);\n}\n/**\n * Create a `LazyIterator` by concatenating streams produced by calling a\n * stream-generating function a given number of times.\n *\n * Since a `LazyIterator` is read-once, it cannot be repeated, but this\n * function can be used to achieve a similar effect:\n *\n *   LazyIterator.ofConcatenatedFunction(() => new MyIterator(), 6);\n *\n * @param iteratorFunc: A function that produces a new stream on each call.\n * @param count: The number of times to call the function.\n * @param baseErrorHandler An optional function that can intercept `Error`s\n *   raised during a `next()` call on the base stream.  This function can decide\n *   whether the error should be propagated, whether the error should be\n *   ignored, or whether the base stream should be terminated.\n */\nexport function iteratorFromConcatenatedFunction(iteratorFunc, count, baseErrorHandler) {\n    return iteratorFromConcatenated(iteratorFromFunction(iteratorFunc).take(count), baseErrorHandler);\n}\n/**\n * Create a `LazyIterator` by zipping together an array, dict, or nested\n * structure of `LazyIterator`s (and perhaps additional constants).\n *\n * The underlying streams must provide elements in a consistent order such\n * that they correspond.\n *\n * Typically, the underlying streams should have the same number of\n * elements. If they do not, the behavior is determined by the\n * `mismatchMode` argument.\n *\n * The nested structure of the `iterators` argument determines the\n * structure of elements in the resulting iterator.\n *\n * @param iterators: An array or object containing LazyIterators at the\n * leaves.\n * @param mismatchMode: Determines what to do when one underlying iterator\n * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)\n * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`\n * causes the zipped iterator to terminate with the furst underlying\n * streams, so elements remaining on the longer streams are ignored.\n * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling\n * in nulls for the exhausted streams, until all streams are exhausted.\n */\nexport function iteratorFromZipped(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n    return new ZipIterator(iterators, mismatchMode);\n}\n/**\n * An asynchronous iterator, providing lazy access to a potentially\n * unbounded stream of elements.\n *\n * Iterator can be obtained from a dataset:\n * `const iter = await dataset.iterator();`\n */\nexport class LazyIterator {\n    /**\n     * Collect all remaining elements of a bounded stream into an array.\n     * Obviously this will succeed only for small streams that fit in memory.\n     * Useful for testing.\n     *\n     * @returns A Promise for an array of stream elements, which will resolve\n     *   when the stream is exhausted.\n     */\n    async toArray() {\n        const result = [];\n        let x = await this.next();\n        while (!x.done) {\n            result.push(x.value);\n            x = await this.next();\n        }\n        return result;\n    }\n    /**\n     * Collect all elements of this dataset into an array with prefetching 100\n     * elements. This is useful for testing, because the prefetch changes the\n     * order in which the Promises are resolved along the processing pipeline.\n     * This may help expose bugs where results are dependent on the order of\n     * Promise resolution rather than on the logical order of the stream (i.e.,\n     * due to hidden mutable state).\n     *\n     * @returns A Promise for an array of stream elements, which will resolve\n     *   when the stream is exhausted.\n     */\n    async toArrayForTest() {\n        const stream = this.prefetch(100);\n        const result = [];\n        let x = await stream.next();\n        while (!x.done) {\n            result.push(x.value);\n            x = await stream.next();\n        }\n        return result;\n    }\n    /**\n     * Draw items from the stream until it is exhausted.\n     *\n     * This can be useful when the stream has side effects but no output.  In\n     * that case, calling this function guarantees that the stream will be\n     * fully processed.\n     */\n    async resolveFully() {\n        let x = await this.next();\n        while (!x.done) {\n            x = await this.next();\n        }\n    }\n    /**\n     * Draw items from the stream until it is exhausted, or a predicate fails.\n     *\n     * This can be useful when the stream has side effects but no output.  In\n     * that case, calling this function guarantees that the stream will be\n     * fully processed.\n     */\n    async resolveWhile(predicate) {\n        let x = await this.next();\n        let shouldContinue = predicate(x.value);\n        while ((!x.done) && shouldContinue) {\n            x = await this.next();\n            shouldContinue = predicate(x.value);\n        }\n    }\n    /**\n     * Handles errors thrown on this stream using a provided handler function.\n     *\n     * @param handler A function that handles any `Error` thrown during a `next()`\n     *   call and returns true if the stream should continue (dropping the failed\n     *   call) or false if the stream should quietly terminate.  If the handler\n     *   itself throws (or rethrows) an `Error`, that will be propagated.\n     *\n     * @returns A `LazyIterator` of elements passed through from upstream,\n     *   possibly filtering or terminating on upstream `next()` calls that\n     *   throw an `Error`.\n     */\n    handleErrors(handler) {\n        return new ErrorHandlingLazyIterator(this, handler);\n    }\n    // TODO(soergel): Implement reduce() etc.\n    /**\n     * Filters this stream according to `predicate`.\n     *\n     * @param predicate A function mapping a stream element to a boolean or a\n     * `Promise` for one.\n     *\n     * @returns A `LazyIterator` of elements for which the predicate was true.\n     */\n    filter(predicate) {\n        return new FilterIterator(this, predicate);\n    }\n    /**\n     * Maps this stream through a 1-to-1 transform.\n     *\n     * @param transform A function mapping a stream element to a transformed\n     *   element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    map(transform) {\n        return new MapIterator(this, transform);\n    }\n    /**\n     * Maps this stream through an async 1-to-1 transform.\n     *\n     * @param transform A function mapping a stream element to a `Promise` for a\n     *   transformed stream element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    mapAsync(transform) {\n        return new AsyncMapIterator(this, transform);\n    }\n    /**\n     * Maps this stream through a 1-to-1 transform, forcing serial execution.\n     *\n     * @param transform A function mapping a stream element to a transformed\n     *   element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    serialMapAsync(transform) {\n        return new AsyncMapIterator(this, transform).serial();\n    }\n    /**\n     * Maps this stream through a 1-to-many transform.\n     *\n     * @param transform A function mapping a stream element to an array of\n     *   transformed elements.\n     *\n     * @returns A `DataStream` of transformed elements.\n     */\n    flatmap(transform) {\n        return new FlatmapIterator(this, transform);\n    }\n    /**\n     * Apply a function to every element of the stream.\n     *\n     * @param f A function to apply to each stream element.\n     */\n    async forEachAsync(f) {\n        return this.map(f).resolveFully();\n    }\n    /**\n     * Apply a function to every element of the stream, forcing serial execution.\n     *\n     * @param f A function to apply to each stream element.  Should return 'true'\n     *   to indicate that the stream should continue, or 'false' to cause it to\n     *   terminate.\n     */\n    async serialForEach(f) {\n        return this.serialMapAsync(f).resolveWhile(x => (x === true));\n    }\n    /**\n     * Groups elements into batches, represented as arrays of elements.\n     *\n     * We can think of the elements of this iterator as 'rows' (even if they are\n     * nested structures).  By the same token, consecutive values for a given\n     * key within the elements form a 'column'.  This matches the usual sense of\n     * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n     *\n     * Thus, \"Row-major\" means that the resulting batch is simply a collection of\n     * rows: `[row1, row2, row3, ...]`.  This is contrast to the column-major\n     * form, which is needed for vectorized computation.\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @returns A `LazyIterator` of batches of elements, represented as arrays\n     *   of the original element type.\n     */\n    rowMajorBatch(batchSize, smallLastBatch = true) {\n        return new RowMajorBatchIterator(this, batchSize, smallLastBatch);\n    }\n    /**\n     * Groups elements into batches, represented in column-major form.\n     *\n     * We can think of the elements of this iterator as 'rows' (even if they are\n     * nested structures).  By the same token, consecutive values for a given\n     * key within the elements form a 'column'.  This matches the usual sense of\n     * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n     *\n     * Thus, \"column-major\" means that the resulting batch is a (potentially\n     * nested) structure representing the columns.  Each column entry, then,\n     * contains a collection of the values found in that column for a range of\n     * input elements.  This representation allows for vectorized computation, in\n     * contrast to the row-major form.\n     *\n     * The inputs should all have the same nested structure (i.e., of arrays and\n     * dicts).  The result is a single object with the same nested structure,\n     * where the leaves are arrays collecting the values of the inputs at that\n     * location (or, optionally, the result of a custom function applied to those\n     * arrays).\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @param zipFn: (optional) A function that expects an array of elements at a\n     *   single node of the object tree, and returns a `DeepMapResult`.  The\n     *   `DeepMapResult` either provides a result value for that node (i.e.,\n     *   representing the subtree), or indicates that the node should be processed\n     *   recursively.  The default zipFn recurses as far as possible and places\n     *   arrays at the leaves.\n     * @returns A `LazyIterator` of batches of elements, represented as an object\n     *   with collections at the leaves.\n     */\n    columnMajorBatch(batchSize, smallLastBatch = true, \n    // tslint:disable-next-line:no-any\n    zipFn = zipToList) {\n        // First collect the desired number of input elements as a row-major batch.\n        const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch);\n        // Now 'rotate' or 'pivot' the data, collecting all values from each column\n        // in the batch (i.e., for each key within the elements) into an array.\n        return rowBatches.map(x => deepZip(x, zipFn));\n    }\n    /**\n     * Concatenate this `LazyIterator` with another.\n     *\n     * @param iterator A `LazyIterator` to be concatenated onto this one.\n     * @param baseErrorHandler An optional function that can intercept `Error`s\n     *   raised during a `next()` call on the base stream.  This function can\n     *   decide whether the error should be propagated, whether the error should\n     *   be ignored, or whether the base stream should be terminated.\n     * @returns A `LazyIterator`.\n     */\n    concatenate(iterator, baseErrorHandler) {\n        return new ChainedIterator(iteratorFromItems([this, iterator]), baseErrorHandler);\n    }\n    /**\n     * Limits this stream to return at most `count` items.\n     *\n     * @param count The maximum number of items to provide from the stream. If\n     * a negative or undefined value is given, the entire stream is returned\n     *   unaltered.\n     */\n    take(count) {\n        if (count < 0 || count == null) {\n            return this;\n        }\n        return new TakeIterator(this, count);\n    }\n    /**\n     * Skips the first `count` items in this stream.\n     *\n     * @param count The number of items to skip.  If a negative or undefined\n     * value is given, the entire stream is returned unaltered.\n     */\n    skip(count) {\n        if (count < 0 || count == null) {\n            return this;\n        }\n        return new SkipIterator(this, count);\n    }\n    /**\n     * Prefetch the first `bufferSize` items in this stream.\n     *\n     * Note this prefetches Promises, but makes no guarantees about when those\n     * Promises resolve.\n     *\n     * @param bufferSize: An integer specifying the number of elements to be\n     *   prefetched.\n     */\n    prefetch(bufferSize) {\n        return new PrefetchIterator(this, bufferSize);\n    }\n    // TODO(soergel): deep sharded shuffle, where supported\n    /**\n     * Randomly shuffles the elements of this stream.\n     *\n     * @param bufferSize: An integer specifying the number of elements from\n     * this stream from which the new stream will sample.\n     * @param seed: (Optional.) An integer specifying the random seed that\n     * will be used to create the distribution.\n     */\n    shuffle(windowSize, seed) {\n        return new ShuffleIterator(this, windowSize, seed);\n    }\n    /**\n     * Force an iterator to execute serially: each next() call will await the\n     * prior one, so that they cannot execute concurrently.\n     */\n    serial() {\n        return new SerialIterator(this);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on LazyIterator.  Unfortunately they can't be placed in separate files,\n// due to resulting trouble with circular imports.\n// ============================================================================\n// Iterators that just extend LazyIterator directly\n// ============================================================================\nclass ArrayIterator extends LazyIterator {\n    constructor(items) {\n        super();\n        this.items = items;\n        this.trav = 0;\n    }\n    summary() {\n        return `Array of ${this.items.length} items`;\n    }\n    async next() {\n        if (this.trav >= this.items.length) {\n            return { value: null, done: true };\n        }\n        const item = this.items[this.trav];\n        this.trav++;\n        return { value: deepClone(item), done: false };\n    }\n}\nclass FunctionCallIterator extends LazyIterator {\n    constructor(nextFn) {\n        super();\n        this.nextFn = nextFn;\n    }\n    summary() {\n        return `Function call`;\n    }\n    async next() {\n        try {\n            return this.nextFn();\n        }\n        catch (e) {\n            // Modify the error message but leave the stack trace intact\n            e.message =\n                `Error thrown while iterating through a dataset: ${e.message}`;\n            throw e;\n        }\n    }\n}\nclass SerialIterator extends LazyIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Serial`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        return this.upstream.next();\n    }\n}\nclass SkipIterator extends LazyIterator {\n    constructor(upstream, maxCount) {\n        super();\n        this.upstream = upstream;\n        this.maxCount = maxCount;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.count = 0;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Skip`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        // TODO(soergel): consider tradeoffs of reading in parallel, eg.\n        // collecting next() promises in an Array and then waiting for\n        // Promise.all() of those. Benefit: pseudo-parallel execution.  Drawback:\n        // maybe delayed GC.\n        while (this.count++ < this.maxCount) {\n            const skipped = await this.upstream.next();\n            // short-circuit if upstream is already empty\n            if (skipped.done) {\n                return skipped;\n            }\n            tf.dispose(skipped.value);\n        }\n        return this.upstream.next();\n    }\n}\nclass TakeIterator extends LazyIterator {\n    constructor(upstream, maxCount) {\n        super();\n        this.upstream = upstream;\n        this.maxCount = maxCount;\n        this.count = 0;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Take`;\n    }\n    async next() {\n        if (this.count++ >= this.maxCount) {\n            return { value: null, done: true };\n        }\n        return this.upstream.next();\n    }\n}\n// Note this batch just groups items into row-wise element arrays.\n// Rotating these to a column-wise representation happens only at the dataset\n// level.\nclass RowMajorBatchIterator extends LazyIterator {\n    constructor(upstream, batchSize, enableSmallLastBatch = true) {\n        super();\n        this.upstream = upstream;\n        this.batchSize = batchSize;\n        this.enableSmallLastBatch = enableSmallLastBatch;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> RowMajorBatch`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        const batch = [];\n        while (batch.length < this.batchSize) {\n            const item = await this.upstream.next();\n            if (item.done) {\n                if (this.enableSmallLastBatch && batch.length > 0) {\n                    return { value: batch, done: false };\n                }\n                return { value: null, done: true };\n            }\n            batch.push(item.value);\n        }\n        return { value: batch, done: false };\n    }\n}\nclass FilterIterator extends LazyIterator {\n    constructor(upstream, predicate) {\n        super();\n        this.upstream = upstream;\n        this.predicate = predicate;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Filter`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        while (true) {\n            const item = await this.upstream.next();\n            if (item.done || this.predicate(item.value)) {\n                return item;\n            }\n            tf.dispose(item.value);\n        }\n    }\n}\nclass MapIterator extends LazyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Map`;\n    }\n    async next() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return { value: null, done: true };\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // That's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying\n        // any intermediate Tensors.  Here we are concerned only about the\n        // inputs.\n        const mapped = this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mapped);\n        // TODO(soergel) faster intersection\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return { value: mapped, done: false };\n    }\n}\nclass ErrorHandlingLazyIterator extends LazyIterator {\n    constructor(upstream, handler) {\n        super();\n        this.upstream = upstream;\n        this.handler = handler;\n        this.count = 0;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> handleErrors`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        while (true) {\n            try {\n                return await this.upstream.next();\n            }\n            catch (e) {\n                if (!this.handler(e)) {\n                    return { value: null, done: true };\n                }\n                // If the handler returns true, loop and fetch the next upstream item.\n                // If the upstream iterator throws an endless stream of errors, and if\n                // the handler says to ignore them, then we loop forever here.  That is\n                // the correct behavior-- it's up to the handler to decide when to stop.\n            }\n        }\n    }\n}\nclass AsyncMapIterator extends LazyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> AsyncMap`;\n    }\n    async next() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return { value: null, done: true };\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // That's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying\n        // any intermediate Tensors.  Here we are concerned only about the\n        // inputs.\n        const mapped = await this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mapped);\n        // TODO(soergel) faster intersection\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return { value: mapped, done: false };\n    }\n}\n// Iterators that maintain a queue of pending items\n// ============================================================================\n/**\n * A base class for transforming streams that operate by maintaining an\n * output queue of elements that are ready to return via next().  This is\n * commonly required when the transformation is 1-to-many:  A call to next()\n * may trigger a call to the underlying stream, which will produce many\n * mapped elements of this stream-- of which we need to return only one, so\n * we have to queue the rest.\n */\nexport class OneToManyIterator extends LazyIterator {\n    constructor() {\n        super();\n        this.outputQueue = new GrowingRingBuffer();\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        // Fetch so that the queue contains at least one item if possible.\n        // If the upstream source is exhausted, AND there are no items left in\n        // the output queue, then this stream is also exhausted.\n        while (this.outputQueue.length() === 0) {\n            // TODO(soergel): consider parallel reads.\n            if (!await this.pump()) {\n                return { value: null, done: true };\n            }\n        }\n        return { value: this.outputQueue.shift(), done: false };\n    }\n}\nclass FlatmapIterator extends OneToManyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Flatmap`;\n    }\n    async pump() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return false;\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // that's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying any\n        // intermediate Tensors.  Here we are concerned only about the inputs.\n        const mappedArray = this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mappedArray);\n        this.outputQueue.pushAll(mappedArray);\n        // TODO(soergel) faster intersection, and deduplicate outputTensors\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return true;\n    }\n}\n/**\n * Provides a `LazyIterator` that concatenates a stream of underlying\n * streams.\n *\n * Doing this in a concurrency-safe way requires some trickery.  In\n * particular, we want this stream to return the elements from the\n * underlying streams in the correct order according to when next() was\n * called, even if the resulting Promises resolve in a different order.\n */\nexport class ChainedIterator extends LazyIterator {\n    constructor(iterators, baseErrorHandler) {\n        super();\n        this.baseErrorHandler = baseErrorHandler;\n        // Strict Promise execution order:\n        // a next() call may not even begin until the previous one completes.\n        this.lastRead = null;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.iterator = null;\n        this.moreIterators = iterators;\n    }\n    summary() {\n        const upstreamSummaries = 'TODO: fill in upstream of chained summaries';\n        return `${upstreamSummaries} -> Chained`;\n    }\n    async next() {\n        this.lastRead = this.readFromChain(this.lastRead);\n        return this.lastRead;\n    }\n    async readFromChain(lastRead) {\n        // Must await on the previous read since the previous read may have advanced\n        // the stream of streams, from which we need to read.\n        // This is unfortunate since we can't parallelize reads. Which means\n        // prefetching of chained streams is a no-op.\n        // One solution is to prefetch immediately upstream of this.\n        await lastRead;\n        if (this.iterator == null) {\n            const iteratorResult = await this.moreIterators.next();\n            if (iteratorResult.done) {\n                // No more streams to stream from.\n                return { value: null, done: true };\n            }\n            this.iterator = iteratorResult.value;\n            if (this.baseErrorHandler != null) {\n                this.iterator = this.iterator.handleErrors(this.baseErrorHandler);\n            }\n        }\n        const itemResult = await this.iterator.next();\n        if (itemResult.done) {\n            this.iterator = null;\n            return this.readFromChain(lastRead);\n        }\n        return itemResult;\n    }\n}\nexport var ZipMismatchMode;\n(function (ZipMismatchMode) {\n    ZipMismatchMode[ZipMismatchMode[\"FAIL\"] = 0] = \"FAIL\";\n    ZipMismatchMode[ZipMismatchMode[\"SHORTEST\"] = 1] = \"SHORTEST\";\n    ZipMismatchMode[ZipMismatchMode[\"LONGEST\"] = 2] = \"LONGEST\"; // use nulls for exhausted streams; use up the longest stream.\n})(ZipMismatchMode || (ZipMismatchMode = {}));\n/**\n * Provides a `LazyIterator` that zips together an array, dict, or nested\n * structure of `LazyIterator`s (and perhaps additional constants).\n *\n * The underlying streams must provide elements in a consistent order such\n * that they correspond.\n *\n * Typically, the underlying streams should have the same number of\n * elements. If they do not, the behavior is determined by the\n * `mismatchMode` argument.\n *\n * The nested structure of the `iterators` argument determines the\n * structure of elements in the resulting iterator.\n *\n * Doing this in a concurrency-safe way requires some trickery.  In\n * particular, we want this stream to return the elements from the\n * underlying streams in the correct order according to when next() was\n * called, even if the resulting Promises resolve in a different order.\n *\n * @param iterators: An array or object containing LazyIterators at the\n * leaves.\n * @param mismatchMode: Determines what to do when one underlying iterator\n * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)\n * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`\n * causes the zipped iterator to terminate with the furst underlying\n * streams, so elements remaining on the longer streams are ignored.\n * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling\n * in nulls for the exhausted streams, until all streams are exhausted.\n */\nclass ZipIterator extends LazyIterator {\n    constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n        super();\n        this.iterators = iterators;\n        this.mismatchMode = mismatchMode;\n        this.count = 0;\n        this.currentPromise = null;\n    }\n    summary() {\n        const upstreamSummaries = 'TODO: fill in upstream of zip summaries';\n        return `{${upstreamSummaries}} -> Zip`;\n    }\n    async nextState(afterState) {\n        // This chaining ensures that the underlying next() are not even called\n        // before the previous ones have resolved.\n        await afterState;\n        // Collect underlying iterator \"done\" signals as a side effect in\n        // getNext()\n        let numIterators = 0;\n        let iteratorsDone = 0;\n        function getNext(container) {\n            if (container instanceof LazyIterator) {\n                const result = container.next();\n                return {\n                    value: result.then(x => {\n                        numIterators++;\n                        if (x.done) {\n                            iteratorsDone++;\n                        }\n                        return x.value;\n                    }),\n                    recurse: false\n                };\n            }\n            else {\n                return { value: null, recurse: true };\n            }\n        }\n        const mapped = await deepMapAndAwaitAll(this.iterators, getNext);\n        if (numIterators === iteratorsDone) {\n            // The streams have all ended.\n            return { value: null, done: true };\n        }\n        if (iteratorsDone > 0) {\n            switch (this.mismatchMode) {\n                case ZipMismatchMode.FAIL:\n                    throw new Error('Zipped streams should have the same length. ' +\n                        `Mismatched at element ${this.count}.`);\n                case ZipMismatchMode.SHORTEST:\n                    return { value: null, done: true };\n                case ZipMismatchMode.LONGEST:\n                default:\n                // Continue.  The exhausted streams already produced value: null.\n            }\n        }\n        this.count++;\n        return { value: mapped, done: false };\n    }\n    async next() {\n        this.currentPromise = this.nextState(this.currentPromise);\n        return this.currentPromise;\n    }\n}\n// Iterators that maintain a ring buffer of pending promises\n// ============================================================================\n/**\n * A stream that prefetches a given number of items from an upstream source,\n * returning them in FIFO order.\n *\n * Note this prefetches Promises, but makes no guarantees about when those\n * Promises resolve.\n */\nexport class PrefetchIterator extends LazyIterator {\n    constructor(upstream, bufferSize) {\n        super();\n        this.upstream = upstream;\n        this.bufferSize = bufferSize;\n        this.buffer = new RingBuffer(bufferSize);\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Prefetch`;\n    }\n    /**\n     * Refill the prefetch buffer.  Returns only after the buffer is full, or\n     * the upstream source is exhausted.\n     */\n    refill() {\n        while (!this.buffer.isFull()) {\n            const v = this.upstream.next();\n            this.buffer.push(v);\n        }\n    }\n    next() {\n        this.refill();\n        // This shift will never throw an error because the buffer is always\n        // full after a refill. If the stream is exhausted, the buffer will be\n        // full of Promises that will resolve to the end-of-stream signal.\n        return this.buffer.shift();\n    }\n}\n/**\n * A stream that performs a sliding-window random shuffle on an upstream\n * source. This is like a `PrefetchIterator` except that the items are\n * returned in randomized order.  Mixing naturally improves as the buffer\n * size increases.\n */\nexport class ShuffleIterator extends PrefetchIterator {\n    constructor(upstream, windowSize, seed) {\n        super(upstream, windowSize);\n        this.upstream = upstream;\n        this.windowSize = windowSize;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.upstreamExhausted = false;\n        this.random = seedrandom.alea(seed || tf.util.now().toString());\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    randomInt(max) {\n        return Math.floor(this.random() * max);\n    }\n    chooseIndex() {\n        return this.randomInt(this.buffer.length());\n    }\n    async serialNext() {\n        // TODO(soergel): consider performance\n        if (!this.upstreamExhausted) {\n            this.refill();\n        }\n        while (!this.buffer.isEmpty()) {\n            const chosenIndex = this.chooseIndex();\n            const result = await this.buffer.shuffleExcise(chosenIndex);\n            if (result.done) {\n                this.upstreamExhausted = true;\n            }\n            else {\n                this.refill();\n                return result;\n            }\n        }\n        return { value: null, done: true };\n    }\n}\n//# sourceMappingURL=lazy_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport * as seedrandom from 'seedrandom';\nimport { iteratorFromConcatenated, iteratorFromFunction, iteratorFromItems, iteratorFromZipped, ZipMismatchMode } from './iterators/lazy_iterator';\nimport { canTensorify, deepMapAndAwaitAll, isIterable } from './util/deep_map';\n// TODO(soergel): consider vectorized operations within the pipeline.\n/**\n * Represents a potentially large list of independent data elements (typically\n * 'samples' or 'examples').\n *\n * A 'data example' may be a primitive, an array, a map from string keys to\n * values, or any nested structure of these.\n *\n * A `Dataset` represents an ordered collection of elements, together with a\n * chain of transformations to be performed on those elements. Each\n * transformation is a method of `Dataset` that returns another `Dataset`, so\n * these may be chained, e.g.\n * `const processedDataset = rawDataset.filter(...).map(...).batch(...)`.\n *\n * Data loading and transformation is done in a lazy, streaming fashion.  The\n * dataset may be iterated over multiple times; each iteration starts the data\n * loading anew and recapitulates the transformations.\n *\n * A `Dataset` is typically processed as a stream of unbatched examples --i.e.,\n * its transformations are applied one example at a time. Batching produces a\n * new `Dataset` where each element is a batch. Batching should usually come\n * last in a pipeline, because data transformations are easier to express on a\n * per-example basis than on a per-batch basis.\n *\n * The following code examples are calling `await dataset.forEachAsync(...)` to\n * iterate once over the entire dataset in order to print out the data.\n *\n * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}\n */\nexport class Dataset {\n    constructor() {\n        this.size = null;\n    }\n    // TODO(soergel): Make Datasets report whether repeated iterator() calls\n    // produce the same result (e.g., reading from a file) or different results\n    // (e.g., from the webcam).  Currently we don't make this distinction but it\n    // could be important for the user to know.\n    // abstract isDeterministic(): boolean;\n    /**\n     * Groups elements into batches.\n     *\n     * It is assumed that each of the incoming dataset elements has the same\n     * structure-- i.e. the same set of keys at each location in an object\n     * hierarchy.  For each key, the resulting `Dataset` provides a batched\n     * element collecting all of the incoming values for that key.\n     *\n     *  * Incoming primitives are grouped into a 1-D Tensor.\n     *  * Incoming Tensors are grouped into a new Tensor where the 0'th axis is\n     *    the batch dimension.\n     *  * Incoming arrays are converted to Tensor and then batched.\n     *  * A nested array is interpreted as an n-D Tensor, so the batched result\n     *    has n+1 dimensions.\n     *  * An array that cannot be converted to Tensor produces an error.\n     *\n     * If an array should not be batched as a unit, it should first be converted\n     * to an object with integer keys.\n     *\n     * Here are a few examples:\n     *\n     * Batch a dataset of numbers:\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8]).batch(4);\n     * await a.forEachAsync(e => e.print());\n     * ```\n     *\n     * Batch a dataset of arrays:\n     * ```js\n     * const b = tf.data.array([[1], [2], [3], [4], [5], [6], [7], [8]]).batch(4);\n     * await b.forEachAsync(e => e.print());\n     * ```\n     *\n     * Batch a dataset of objects:\n     * ```js\n     * const c = tf.data.array([{a: 1, b: 11}, {a: 2, b: 12}, {a: 3, b: 13},\n     *   {a: 4, b: 14}, {a: 5, b: 15}, {a: 6, b: 16}, {a: 7, b: 17},\n     *   {a: 8, b: 18}]).batch(4);\n     * await c.forEachAsync(e => {\n     *   console.log('{');\n     *   for(var key in e) {\n     *     console.log(key+':');\n     *     e[key].print();\n     *   }\n     *   console.log('}');\n     * })\n     * ```\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @returns A `Dataset`, from which a stream of batches can be obtained.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    batch(batchSize, smallLastBatch = true) {\n        const base = this;\n        tf.util.assert(batchSize > 0, () => `batchSize needs to be positive, but it is\n      ${batchSize}`);\n        let size;\n        if (this.size === Infinity || this.size == null) {\n            // If the size of this dataset is infinity or null, the new size keeps the\n            // same.\n            size = this.size;\n        }\n        else if (smallLastBatch) {\n            // If the size of this dataset is known and include small last batch, the\n            // new size is full batch count plus last batch.\n            size = Math.ceil(this.size / batchSize);\n        }\n        else {\n            // If the size of this dataset is known and not include small last batch,\n            // the new size is full batch count.\n            size = Math.floor(this.size / batchSize);\n        }\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator())\n                .columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat);\n        }, size);\n    }\n    /**\n     * Concatenates this `Dataset` with another.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]);\n     * const b = tf.data.array([4, 5, 6]);\n     * const c = a.concatenate(b);\n     * await c.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param dataset A `Dataset` to be concatenated onto this one.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    concatenate(dataset) {\n        const base = this;\n        let size;\n        if (this.size === Infinity || dataset.size === Infinity) {\n            // If the size of any of these two dataset is infinity, new size is\n            // infinity.\n            size = Infinity;\n        }\n        else if (this.size != null && dataset.size != null) {\n            // If the size of both datasets are known and not infinity, new size is\n            // sum the size of these two datasets.\n            size = this.size + dataset.size;\n        }\n        else {\n            // If neither of these two datasets has infinite size and any of these two\n            // datasets' size is null, the new size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).concatenate(await dataset.iterator()), size);\n    }\n    /**\n     * Filters this dataset according to `predicate`.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n     *   .filter(x => x%2 === 0);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param predicate A function mapping a dataset element to a boolean or a\n     * `Promise` for one.\n     *\n     * @returns A `Dataset` of elements for which the predicate was true.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    filter(predicate) {\n        const base = this;\n        let size;\n        if (this.size === Infinity) {\n            // If the size of this dataset is infinity, new size is infinity\n            size = Infinity;\n        }\n        else {\n            // If this dataset has limited elements, new size is null because it might\n            // exhausted randomly.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).filter(x => tf.tidy(() => predicate(x)));\n        }, size);\n    }\n    /**\n     * Apply a function to every element of the dataset.\n     *\n     * After the function is applied to a dataset element, any Tensors contained\n     * within that element are disposed.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param f A function to apply to each dataset element.\n     * @returns A `Promise` that resolves after all elements have been processed.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async forEachAsync(f) {\n        return (await this.iterator()).forEachAsync(f);\n    }\n    /**\n     * Maps this dataset through a 1-to-1 transform.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]).map(x => x*x);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param transform A function mapping a dataset element to a transformed\n     *   dataset element.\n     *\n     * @returns A `Dataset` of transformed elements.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    map(transform) {\n        const base = this;\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).map(x => tf.tidy(() => transform(x)));\n        }, this.size);\n    }\n    /**\n     * Maps this dataset through an async 1-to-1 transform.\n     *\n     * ```js\n     * const a =\n     *  tf.data.array([1, 2, 3]).mapAsync(x => new Promise(function(resolve){\n     *    setTimeout(() => {\n     *      resolve(x * x);\n     *    }, Math.random()*1000 + 500);\n     *  }));\n     * console.log(await a.toArray());\n     * ```\n     *\n     * @param transform A function mapping a dataset element to a `Promise` for a\n     *   transformed dataset element.  This transform is responsible for disposing\n     *   any intermediate `Tensor`s, i.e. by wrapping its computation in\n     *   `tf.tidy()`; that cannot be automated here (as it is in the synchronous\n     *   `map()` case).\n     *\n     * @returns A `Dataset` of transformed elements.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    mapAsync(transform) {\n        const base = this;\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).mapAsync(transform);\n        }, this.size);\n    }\n    /**\n     *  Creates a `Dataset` that prefetches elements from this dataset.\n     *\n     * @param bufferSize: An integer specifying the number of elements to be\n     *   prefetched.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    prefetch(bufferSize) {\n        if (bufferSize == null) {\n            throw new RangeError('`Dataset.prefetch()` requires bufferSize to be specified.');\n        }\n        const base = this;\n        return datasetFromIteratorFn(async () => (await base.iterator()).prefetch(bufferSize), this.size);\n    }\n    /**\n     * Repeats this dataset `count` times.\n     *\n     * NOTE: If this dataset is a function of global state (e.g. a random number\n     * generator), then different repetitions may produce different elements.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]).repeat(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: (Optional) An integer, representing the number of times\n     *   the dataset should be repeated. The default behavior (if `count` is\n     *   `undefined` or negative) is for the dataset be repeated indefinitely.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    repeat(count) {\n        const base = this;\n        let size;\n        if (this.size != null && count > 0) {\n            // If this dataset has size and count is positive, new size is current\n            // size multiply count. This also covers the case that current size is\n            // infinity.\n            size = this.size * count;\n        }\n        else if (count === 0) {\n            // If count is 0, new size is 0.\n            size = 0;\n        }\n        else if (this.size != null && (count === undefined || count < 0)) {\n            // If this dataset has size and count is undefined or negative, the\n            // dataset will be repeated indefinitely and new size is infinity.\n            size = Infinity;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => {\n            const iteratorIterator = iteratorFromFunction(async () => ({ value: await base.iterator(), done: false }));\n            return iteratorFromConcatenated(iteratorIterator.take(count));\n        }, size);\n    }\n    /**\n     * Creates a `Dataset` that skips `count` initial elements from this dataset.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).skip(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: The number of elements of this dataset that should be skipped\n     *   to form the new dataset.  If `count` is greater than the size of this\n     *   dataset, the new dataset will contain no elements.  If `count`\n     *   is `undefined` or negative, skips the entire dataset.\n     *\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    skip(count) {\n        const base = this;\n        let size;\n        if (this.size != null && count >= 0 && this.size >= count) {\n            // If the size of this dataset is greater than count, the new dataset's\n            // size is current size minus skipped size.This also covers the case that\n            // current size is infinity.\n            size = this.size - count;\n        }\n        else if (this.size != null &&\n            (this.size < count || count === undefined || count < 0)) {\n            // If the size of this dataset is smaller than count, or count is\n            // undefined or negative, skips the entire dataset and the new size is 0.\n            size = 0;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).skip(count), size);\n    }\n    /**\n     * Pseudorandomly shuffles the elements of this dataset. This is done in a\n     * streaming manner, by sampling from a given number of prefetched elements.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).shuffle(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param bufferSize: An integer specifying the number of elements from this\n     *   dataset from which the new dataset will sample.\n     * @param seed: (Optional) An integer specifying the random seed that will\n     *   be used to create the distribution.\n     * @param reshuffleEachIteration: (Optional) A boolean, which if true\n     *   indicates that the dataset should be pseudorandomly reshuffled each time\n     *   it is iterated over. If false, elements will be returned in the same\n     *   shuffled order on each iteration. (Defaults to `true`.)\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    shuffle(bufferSize, seed, reshuffleEachIteration = true) {\n        if (bufferSize == null || bufferSize < 0) {\n            if (this.size == null) {\n                throw new RangeError('`Dataset.shuffle()` requires bufferSize to be specified.');\n            }\n            else {\n                throw new RangeError('`Dataset.shuffle()` requires bufferSize to be specified.  ' +\n                    'If your data fits in main memory (for regular JS objects), ' +\n                    'and/or GPU memory (for `tf.Tensor`s), consider setting ' +\n                    `bufferSize to the dataset size (${this.size} elements)`);\n            }\n        }\n        const base = this;\n        const random = seedrandom.alea(seed || tf.util.now().toString());\n        return datasetFromIteratorFn(async () => {\n            let seed2 = random.int32();\n            if (reshuffleEachIteration) {\n                seed2 += random.int32();\n            }\n            return (await base.iterator()).shuffle(bufferSize, seed2.toString());\n        }, this.size);\n    }\n    /**\n     * Creates a `Dataset` with at most `count` initial elements from this\n     * dataset.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).take(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: The number of elements of this dataset that should be taken\n     *   to form the new dataset.  If `count` is `undefined` or negative, or if\n     *   `count` is greater than the size of this dataset, the new dataset will\n     *   contain all elements of this dataset.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    take(count) {\n        const base = this;\n        let size;\n        if (this.size != null && this.size > count) {\n            // If the size of this dataset is greater than count, the new dataset's\n            // size is count.\n            size = count;\n        }\n        else if (this.size != null && this.size <= count) {\n            // If the size of this dataset is equal or smaller than count, the new\n            // dataset's size is the size of this dataset.\n            size = this.size;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).take(count), size);\n    }\n    /**\n     * Collect all elements of this dataset into an array.\n     *\n     * Obviously this will succeed only for small datasets that fit in memory.\n     * Useful for testing and generally should be avoided if possible.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]);\n     * console.log(await a.toArray());\n     * ```\n     *\n     * @returns A Promise for an array of elements, which will resolve\n     *   when a new stream has been obtained and fully consumed.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async toArray() {\n        if (this.size === Infinity) {\n            throw new Error('Can not convert infinite data stream to array.');\n        }\n        return (await this.iterator()).toArray();\n    }\n    /**\n     * Collect all elements of this dataset into an array with prefetching 100\n     * elements. This is useful for testing, because the prefetch changes the\n     * order in which the Promises are resolved along the processing pipeline.\n     * This may help expose bugs where results are dependent on the order of\n     * Promise resolution rather than on the logical order of the stream (i.e.,\n     * due to hidden mutable state).\n     *\n     * @returns A Promise for an array of elements, which will resolve\n     *   when a new stream has been obtained and fully consumed.\n     */\n    async toArrayForTest() {\n        if (this.size === Infinity) {\n            throw new Error('Can not convert infinite data stream to array.');\n        }\n        return (await this.iterator()).toArrayForTest();\n    }\n}\n// TODO(soergel): deep sharded shuffle, where supported\nDataset.MAX_BUFFER_SIZE = 10000;\n/**\n * Create a `Dataset` defined by a provided iterator() function.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const iter = tf.data.iteratorFromFunction(func);\n * const ds = tf.data.datasetFromIteratorFn(iter);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n */\nexport function datasetFromIteratorFn(iteratorFn, size = null) {\n    return new class extends Dataset {\n        constructor() {\n            super(...arguments);\n            this.size = size;\n        }\n        /*\n         * Provide a new stream of elements.  Note this will also start new streams\n         * from any underlying `Dataset`s.\n         */\n        async iterator() {\n            return iteratorFn();\n        }\n    }();\n}\n/**\n * Create a `Dataset` from an array of elements.\n *\n * Create a Dataset from an array of objects:\n * ```js\n * const a = tf.data.array([{'item': 1}, {'item': 2}, {'item': 3}]);\n * await a.forEachAsync(e => console.log(e));\n * ```\n *\n * Create a Dataset from an array of numbers:\n * ```js\n * const a = tf.data.array([4, 5, 6]);\n * await a.forEachAsync(e => console.log(e));\n * ```\n * @param items An array of elements that will be parsed as items in a dataset.\n *\n * @doc {heading: 'Data', subheading: 'Creation', namespace: 'data'}\n */\nexport function array(items) {\n    return datasetFromIteratorFn(async () => iteratorFromItems(items), items.length);\n}\n/**\n * Create a `Dataset` by zipping together an array, dict, or nested\n * structure of `Dataset`s (and perhaps additional constants).\n * The underlying datasets must provide elements in a consistent order such that\n * they correspond.\n *\n * The number of elements in the resulting dataset is the same as the size of\n * the smallest dataset in datasets.\n *\n * The nested structure of the `datasets` argument determines the\n * structure of elements in the resulting iterator.\n *\n * Note this means that, given an array of two datasets that produce dict\n * elements, the result is a dataset that produces elements that are arrays\n * of two dicts:\n *\n * Zip an array of datasets:\n * ```js\n * console.log('Zip two datasets of objects:');\n * const ds1 = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);\n * const ds2 = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);\n * const ds3 = tf.data.zip([ds1, ds2]);\n * await ds3.forEachAsync(e => console.log(JSON.stringify(e)));\n *\n * // If the goal is to merge the dicts in order to produce elements like\n * // {a: ..., b: ...}, this requires a second step such as:\n * console.log('Merge the objects:');\n * const ds4 = ds3.map(x => {return {a: x[0].a, b: x[1].b}});\n * await ds4.forEachAsync(e => console.log(e));\n * ```\n *\n * Zip a dict of datasets:\n * ```js\n * const a = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);\n * const b = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);\n * const c = tf.data.zip({c: a, d: b});\n * await c.forEachAsync(e => console.log(JSON.stringify(e)));\n * ```\n *\n * @doc {heading: 'Data', subheading: 'Operations', namespace: 'data'}\n */\nexport function zip(datasets) {\n    // manually type-check the argument for JS users\n    if (!isIterable(datasets)) {\n        throw new Error('The argument to zip() must be an object or array.');\n    }\n    let size;\n    if (Array.isArray(datasets)) {\n        for (let i = 0; i < datasets.length; i++) {\n            size = size == null ? datasets[i].size :\n                Math.min(size, datasets[i].size);\n        }\n    }\n    else if (datasets instanceof Object) {\n        for (const ds in datasets) {\n            size = size == null ? datasets[ds].size :\n                Math.min(size, datasets[ds].size);\n        }\n    }\n    return datasetFromIteratorFn(async () => {\n        const streams = await deepMapAndAwaitAll(datasets, d => {\n            if (d instanceof Dataset) {\n                return { value: d.iterator(), recurse: false };\n            }\n            else if (isIterable(d)) {\n                return { value: null, recurse: true };\n            }\n            else {\n                throw new Error('Leaves of the structure passed to zip() must be Datasets, ' +\n                    'not primitives.');\n            }\n        });\n        return iteratorFromZipped(streams, ZipMismatchMode.SHORTEST);\n    }, size);\n}\n/**\n * A zip function for use with deepZip, passed via the columnMajorBatch call.\n *\n * Accepts an array of identically-structured nested elements and either batches\n * them (if they are primitives, numeric arrays, or Tensors) or requests\n * recursion (if not).\n */\n// tslint:disable-next-line:no-any\nfunction deepBatchConcat(rows) {\n    if (rows === null) {\n        return null;\n    }\n    // use the first item to decide whether to recurse or batch here.\n    const exampleRow = rows[0];\n    if (canTensorify(exampleRow)) {\n        // rows is an array of primitives, Tensors, or arrays.  Batch them.\n        const value = batchConcat(rows);\n        return { value, recurse: false };\n    }\n    // the example row is an object, so recurse into it.\n    return { value: null, recurse: true };\n}\n/**\n * Assembles a list of same-shaped numbers, number arrays, or Tensors\n * into a single new Tensor where axis 0 is the batch dimension.\n */\nfunction batchConcat(arrays) {\n    if (arrays.length === 0) {\n        // We can't return an empty Tensor because we don't know the element shape.\n        throw new Error('Can\\'t make a batch of zero elements.');\n    }\n    if (arrays[0] instanceof tf.Tensor) {\n        // Input is an array of Tensors\n        return tf.stack(arrays);\n    }\n    else {\n        // Input is a possibly-nested array of numbers.\n        return tf.tensor(arrays);\n    }\n}\n//# sourceMappingURL=dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { Dataset } from '../dataset';\n/**\n * Represents a potentially large collection of text lines.\n *\n * The results are not batched.\n */\nexport class TextLineDataset extends Dataset {\n    /**\n     * Create a `TextLineDataset`.\n     *\n     * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n     */\n    constructor(input) {\n        super();\n        this.input = input;\n    }\n    async iterator() {\n        const inputIterator = await this.input.iterator();\n        const utf8Iterator = inputIterator.decodeUTF8();\n        const lineIterator = utf8Iterator.split('\\n').map(line => {\n            // Windows/DOS format text file has extra line breaker at the end of line.\n            if (line.endsWith('\\r')) {\n                line = line.slice(0, -1);\n            }\n            return line;\n        });\n        return lineIterator;\n    }\n}\n//# sourceMappingURL=text_line_dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { Dataset } from '../dataset';\nimport { TextLineDataset } from './text_line_dataset';\nconst CODE_QUOTE = '\"';\nconst STATE_OUT = Symbol('out');\nconst STATE_FIELD = Symbol('field');\nconst STATE_QUOTE = Symbol('quote');\nconst STATE_QUOTE_AFTER_QUOTE = Symbol('quoteafterquote');\nconst STATE_WITHIN_QUOTE_IN_QUOTE = Symbol('quoteinquote');\n/**\n * Represents a potentially large collection of delimited text records.\n *\n * The produced `TensorContainer`s each contain one key-value pair for\n * every column of the table.  When a field is empty in the incoming data, the\n * resulting value is `undefined`, or throw error if it is required.  Values\n * that can be parsed as numbers are emitted as type `number`, other values\n * are parsed as `string`.\n *\n * The results are not batched.\n *\n * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}\n */\nexport class CSVDataset extends Dataset {\n    /**\n     * Create a `CSVDataset`.\n     *\n     * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n     * @param csvConfig (Optional) A CSVConfig object that contains configurations\n     *     of reading and decoding from CSV file(s).\n     *\n     *     hasHeader: (Optional) A boolean value that indicates whether the first\n     *     row of provided CSV file is a header line with column names, and should\n     *     not be included in the data. Defaults to `true`.\n     *\n     *     columnNames: (Optional) A list of strings that corresponds to\n     *     the CSV column names, in order. If provided, it ignores the column\n     *     names inferred from the header row. If not provided, infers the column\n     *     names from the first row of the records. If hasHeader is false and\n     *     columnNames is not provided, this method throws an error.\n     *\n     *     columnConfigs: (Optional) A dictionary whose key is column names, value\n     *     is an object stating if this column is required, column's data type,\n     *     default value, and if this column is label. If provided, keys must\n     *     correspond to names provided in columnNames or inferred from the file\n     *     header lines. If isLabel is true any column, returns an array of two\n     *     items: the first item is a dict of features key/value pairs, the second\n     *     item is a dict of labels key/value pairs. If no feature is marked as\n     *     label, returns a dict of features only.\n     *\n     *     configuredColumnsOnly (Optional) If true, only columns provided in\n     *     columnConfigs will be parsed and provided during iteration.\n     *\n     *     delimiter (Optional) The string used to parse each line of the input\n     *     file. Defaults to `,`.\n     */\n    constructor(input, csvConfig) {\n        super();\n        this.input = input;\n        this.hasHeader = true;\n        this.fullColumnNames = null;\n        this.columnNamesValidated = false;\n        this.columnConfigs = null;\n        this.configuredColumnsOnly = false;\n        this.delimiter = ',';\n        this.delimWhitespace = false;\n        this.base = new TextLineDataset(input);\n        if (!csvConfig) {\n            csvConfig = {};\n        }\n        this.hasHeader = csvConfig.hasHeader === false ? false : true;\n        this.fullColumnNames = csvConfig.columnNames;\n        this.columnConfigs = csvConfig.columnConfigs;\n        this.configuredColumnsOnly = csvConfig.configuredColumnsOnly;\n        if (csvConfig.delimWhitespace) {\n            util.assert(csvConfig.delimiter == null, () => 'Delimiter should not be provided when delimWhitespace is true.');\n            this.delimWhitespace = true;\n            this.delimiter = ' ';\n        }\n        else {\n            this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : ',';\n        }\n    }\n    /**\n     * Returns column names of the csv dataset. If `configuredColumnsOnly` is\n     * true, return column names in `columnConfigs`. If `configuredColumnsOnly` is\n     * false and `columnNames` is provided, `columnNames`. If\n     * `configuredColumnsOnly` is false and `columnNames` is not provided, return\n     * all column names parsed from the csv file. For example usage please go to\n     * `tf.data.csv`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async columnNames() {\n        if (!this.columnNamesValidated) {\n            await this.setColumnNames();\n        }\n        return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) :\n            this.fullColumnNames;\n    }\n    /* 1) If `columnNames` is provided as string[], use this string[] as output\n     * keys in corresponding order. The length must match the number of inferred\n     * columns if `hasHeader` is true .\n     * 2) If `columnNames` is not provided, parse header line as `columnNames` if\n     * hasHeader is true. If `hasHeader` is false, throw an error.\n     * 3) If `columnConfigs` is provided, all the keys in `columnConfigs` must\n     * exist in parsed `columnNames`.\n     */\n    async setColumnNames() {\n        const columnNamesFromFile = await this.maybeReadHeaderLine();\n        if (!this.fullColumnNames && !columnNamesFromFile) {\n            // Throw an error if columnNames is not provided and no header line.\n            throw new Error('Column names must be provided if there is no header line.');\n        }\n        else if (this.fullColumnNames && columnNamesFromFile) {\n            // Check provided columnNames match header line.\n            util.assert(columnNamesFromFile.length === this.fullColumnNames.length, () => 'The length of provided columnNames (' +\n                this.fullColumnNames.length.toString() +\n                ') does not match the length of the header line read from ' +\n                'file (' + columnNamesFromFile.length.toString() + ').');\n        }\n        if (!this.fullColumnNames) {\n            this.fullColumnNames = columnNamesFromFile;\n        }\n        // Check if there are duplicate column names.\n        const counts = this.fullColumnNames.reduce((countAcc, name) => {\n            countAcc[name] = (countAcc[name] + 1) || 1;\n            return countAcc;\n        }, {});\n        const duplicateNames = Object.keys(counts).filter((name) => (counts[name] > 1));\n        util.assert(duplicateNames.length === 0, () => 'Duplicate column names found: ' + duplicateNames.toString());\n        // Check if keys in columnConfigs match columnNames.\n        if (this.columnConfigs) {\n            for (const key of Object.keys(this.columnConfigs)) {\n                const index = this.fullColumnNames.indexOf(key);\n                if (index === -1) {\n                    throw new Error('The key \"' + key +\n                        '\" provided in columnConfigs does not match any of the column ' +\n                        'names (' + this.fullColumnNames.toString() + ').');\n                }\n            }\n        }\n        this.columnNamesValidated = true;\n    }\n    async maybeReadHeaderLine() {\n        if (this.hasHeader) {\n            const iter = await this.base.iterator();\n            const firstElement = await iter.next();\n            if (firstElement.done) {\n                throw new Error('No data was found for CSV parsing.');\n            }\n            const firstLine = firstElement.value;\n            const headers = this.parseRow(firstLine, false);\n            return headers;\n        }\n        else {\n            return null;\n        }\n    }\n    async iterator() {\n        if (!this.columnNamesValidated) {\n            await this.setColumnNames();\n        }\n        let lines = await this.base.iterator();\n        if (this.hasHeader) {\n            // We previously read the first line to get the columnNames.\n            // Now that we're providing data, skip it.\n            lines = lines.skip(1);\n        }\n        return lines.map(x => this.makeDataElement(x));\n    }\n    makeDataElement(line) {\n        const values = this.parseRow(line);\n        const features = {};\n        const labels = {};\n        for (let i = 0; i < this.fullColumnNames.length; i++) {\n            const key = this.fullColumnNames[i];\n            const config = this.columnConfigs ? this.columnConfigs[key] : null;\n            if (this.configuredColumnsOnly && !config) {\n                // This column is not selected.\n                continue;\n            }\n            else {\n                const value = values[i];\n                let parsedValue = null;\n                if (value === '') {\n                    // If default value is provided, use it. If default value is not\n                    // provided, set as undefined.\n                    if (config && config.default !== undefined) {\n                        parsedValue = config.default;\n                    }\n                    else if (config && (config.required || config.isLabel)) {\n                        throw new Error(`Required column ${key} is empty in this line: ${line}`);\n                    }\n                    else {\n                        parsedValue = undefined;\n                    }\n                }\n                else {\n                    // A value is present, so parse it based on type\n                    const valueAsNum = Number(value);\n                    if (isNaN(valueAsNum)) {\n                        // The value is a string and this column is declared as boolean\n                        // in config, parse it as boolean.\n                        if (config && config.dtype === 'bool') {\n                            parsedValue = this.getBoolean(value);\n                        }\n                        else {\n                            // Set value as string\n                            parsedValue = value;\n                        }\n                    }\n                    else if (!config || !config.dtype) {\n                        // If this value is a number and no type config is provided, return\n                        // it as number.\n                        parsedValue = valueAsNum;\n                    }\n                    else {\n                        // If this value is a number and data type is provided, parse it\n                        // according to provided data type.\n                        switch (config.dtype) {\n                            case 'float32':\n                                parsedValue = valueAsNum;\n                                break;\n                            case 'int32':\n                                parsedValue = Math.floor(valueAsNum);\n                                break;\n                            case 'bool':\n                                parsedValue = this.getBoolean(value);\n                                break;\n                            default:\n                                parsedValue = valueAsNum;\n                        }\n                    }\n                }\n                // Check if this column is label.\n                (config && config.isLabel) ? labels[key] = parsedValue :\n                    features[key] = parsedValue;\n            }\n        }\n        // If label exists, return an object of features and labels as {xs:features,\n        // ys:labels}, otherwise return features only.\n        if (Object.keys(labels).length === 0) {\n            return features;\n        }\n        else {\n            return { xs: features, ys: labels };\n        }\n    }\n    getBoolean(value) {\n        if (value === '1' || value.toLowerCase() === 'true') {\n            return 1;\n        }\n        else {\n            return 0;\n        }\n    }\n    // adapted from https://beta.observablehq.com/@mbostock/streaming-csv\n    parseRow(line, validateElementCount = true) {\n        const result = [];\n        let readOffset = 0;\n        const readLength = line.length;\n        let currentState = STATE_OUT;\n        // Goes through the line to parse quote.\n        for (let i = 0; i < readLength; i++) {\n            switch (currentState) {\n                // Before enter a new field\n                case STATE_OUT:\n                    switch (line.charAt(i)) {\n                        // Enter a quoted field\n                        case CODE_QUOTE:\n                            readOffset = i + 1;\n                            currentState = STATE_QUOTE;\n                            break;\n                        // Read an empty field\n                        case this.delimiter:\n                            readOffset = i + 1;\n                            // If delimiter is white space and configured to collapse\n                            // multiple white spaces, ignore this white space.\n                            if (this.delimiter === ' ' && this.delimWhitespace) {\n                                break;\n                            }\n                            result.push('');\n                            currentState = STATE_OUT;\n                            break;\n                        // Enter an unquoted field\n                        default:\n                            currentState = STATE_FIELD;\n                            readOffset = i;\n                            break;\n                    }\n                    break;\n                // In an unquoted field\n                case STATE_FIELD:\n                    switch (line.charAt(i)) {\n                        // Exit an unquoted field, add it to result\n                        case this.delimiter:\n                            result.push(line.substring(readOffset, i));\n                            currentState = STATE_OUT;\n                            readOffset = i + 1;\n                            break;\n                        default:\n                    }\n                    break;\n                // In a quoted field\n                case STATE_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Read a quote after a quote\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE_AFTER_QUOTE;\n                            break;\n                        default:\n                    }\n                    break;\n                // This state means it's right after a second quote in a field\n                case STATE_QUOTE_AFTER_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Finished a quoted field\n                        case this.delimiter:\n                            result.push(line.substring(readOffset, i - 1));\n                            currentState = STATE_OUT;\n                            readOffset = i + 1;\n                            break;\n                        // Finished a quoted part in a quoted field\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE;\n                            break;\n                        // In a quoted part in a quoted field\n                        default:\n                            currentState = STATE_WITHIN_QUOTE_IN_QUOTE;\n                            break;\n                    }\n                    break;\n                case STATE_WITHIN_QUOTE_IN_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Exit a quoted part in a quoted field\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE;\n                            break;\n                        default:\n                    }\n                    break;\n                default:\n            }\n        }\n        // Adds last item based on if it is quoted.\n        if (currentState === STATE_QUOTE_AFTER_QUOTE) {\n            result.push(line.substring(readOffset, readLength - 1));\n        }\n        else {\n            result.push(line.substring(readOffset));\n        }\n        // Check if each row has the same number of elements as column names.\n        if (validateElementCount && result.length !== this.fullColumnNames.length) {\n            throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);\n        }\n        return result;\n    }\n}\n// TODO(soergel): add more basic datasets for parity with tf.data\n// tf.data.FixedLengthRecordDataset()\n// tf.data.TFRecordDataset()\n//# sourceMappingURL=csv_dataset.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env, tensor, util } from '@tensorflow/tfjs-core';\nimport { LazyIterator } from './lazy_iterator';\n/**\n * Provide a stream of tensors from microphone audio stream. The tensors are\n * representing audio data as frequency-domain spectrogram generated with\n * browser's native FFT. Tensors representing time-domain waveform is available\n * based on configuration. Only works in browser environment.\n */\nexport class MicrophoneIterator extends LazyIterator {\n    constructor(microphoneConfig) {\n        super();\n        this.microphoneConfig = microphoneConfig;\n        this.isClosed = false;\n        this.fftSize = microphoneConfig.fftSize || 1024;\n        const fftSizeLog2 = Math.log2(this.fftSize);\n        if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 ||\n            !Number.isInteger(fftSizeLog2)) {\n            throw new Error(`Invalid fftSize: it must be a power of 2 between ` +\n                `2 to 4 and 2 to 14, but got ${this.fftSize}`);\n        }\n        this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43;\n        this.sampleRateHz = microphoneConfig.sampleRateHz;\n        this.columnTruncateLength =\n            microphoneConfig.columnTruncateLength || this.fftSize;\n        this.audioTrackConstraints = microphoneConfig.audioTrackConstraints;\n        this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0;\n        this.includeSpectrogram =\n            microphoneConfig.includeSpectrogram === false ? false : true;\n        this.includeWaveform =\n            microphoneConfig.includeWaveform === true ? true : false;\n        if (!this.includeSpectrogram && !this.includeWaveform) {\n            throw new Error('Both includeSpectrogram and includeWaveform are false. ' +\n                'At least one type of data should be returned.');\n        }\n    }\n    summary() {\n        return `microphone`;\n    }\n    // Construct a MicrophoneIterator and start the audio stream.\n    static async create(microphoneConfig = {}) {\n        if (env().get('IS_NODE')) {\n            throw new Error('microphone API is only supported in browser environment.');\n        }\n        const microphoneIterator = new MicrophoneIterator(microphoneConfig);\n        // Call async function start() to initialize the audio stream.\n        await microphoneIterator.start();\n        return microphoneIterator;\n    }\n    // Start the audio stream and FFT.\n    async start() {\n        try {\n            this.stream = await navigator.mediaDevices.getUserMedia({\n                audio: this.audioTrackConstraints == null ? true :\n                    this.audioTrackConstraints,\n                video: false\n            });\n        }\n        catch (e) {\n            throw new Error(`Error thrown while initializing video stream: ${e.message}`);\n        }\n        if (!this.stream) {\n            throw new Error('Could not obtain audio from microphone.');\n        }\n        const ctxConstructor = \n        // tslint:disable-next-line:no-any\n        window.AudioContext || window.webkitAudioContext;\n        this.audioContext = new ctxConstructor();\n        if (!this.sampleRateHz) {\n            // If sample rate is not provided, use the available sample rate on\n            // device.\n            this.sampleRateHz = this.audioContext.sampleRate;\n        }\n        else if (this.audioContext.sampleRate !== this.sampleRateHz) {\n            throw new Error(`Mismatch in sampling rate: ` +\n                `Expected: ${this.sampleRateHz}; ` +\n                `Actual: ${this.audioContext.sampleRate}`);\n        }\n        const streamSource = this.audioContext.createMediaStreamSource(this.stream);\n        this.analyser = this.audioContext.createAnalyser();\n        this.analyser.fftSize = this.fftSize * 2;\n        this.analyser.smoothingTimeConstant = this.smoothingTimeConstant;\n        streamSource.connect(this.analyser);\n        this.freqData = new Float32Array(this.fftSize);\n        this.timeData = new Float32Array(this.fftSize);\n        return;\n    }\n    async next() {\n        if (this.isClosed) {\n            return { value: null, done: true };\n        }\n        let spectrogramTensor;\n        let waveformTensor;\n        const audioDataQueue = await this.getAudioData();\n        if (this.includeSpectrogram) {\n            const freqData = this.flattenQueue(audioDataQueue.freqDataQueue);\n            spectrogramTensor = this.getTensorFromAudioDataArray(freqData, [this.numFrames, this.columnTruncateLength, 1]);\n        }\n        if (this.includeWaveform) {\n            const timeData = this.flattenQueue(audioDataQueue.timeDataQueue);\n            waveformTensor = this.getTensorFromAudioDataArray(timeData, [this.numFrames * this.fftSize, 1]);\n        }\n        return {\n            value: { 'spectrogram': spectrogramTensor, 'waveform': waveformTensor },\n            done: false\n        };\n    }\n    // Capture one result from the audio stream, and extract the value from\n    // iterator.next() result.\n    async capture() {\n        return (await this.next()).value;\n    }\n    async getAudioData() {\n        const freqDataQueue = [];\n        const timeDataQueue = [];\n        let currentFrames = 0;\n        return new Promise(resolve => {\n            const intervalID = setInterval(() => {\n                if (this.includeSpectrogram) {\n                    this.analyser.getFloatFrequencyData(this.freqData);\n                    // If the audio stream is initializing, return empty queue.\n                    if (this.freqData[0] === -Infinity) {\n                        resolve({ freqDataQueue, timeDataQueue });\n                    }\n                    freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength));\n                }\n                if (this.includeWaveform) {\n                    this.analyser.getFloatTimeDomainData(this.timeData);\n                    timeDataQueue.push(this.timeData.slice());\n                }\n                // Clean interval and return when all frames have been collected\n                if (++currentFrames === this.numFrames) {\n                    clearInterval(intervalID);\n                    resolve({ freqDataQueue, timeDataQueue });\n                }\n            }, this.fftSize / this.sampleRateHz * 1e3);\n        });\n    }\n    // Stop the audio stream and pause the iterator.\n    stop() {\n        if (!this.isClosed) {\n            this.isClosed = true;\n            this.analyser.disconnect();\n            this.audioContext.close();\n            if (this.stream != null && this.stream.getTracks().length > 0) {\n                this.stream.getTracks()[0].stop();\n            }\n        }\n    }\n    // Override toArray() function to prevent collecting.\n    toArray() {\n        throw new Error('Can not convert infinite audio stream to array.');\n    }\n    // Return audio sampling rate in Hz\n    getSampleRate() {\n        return this.sampleRateHz;\n    }\n    flattenQueue(queue) {\n        const frameSize = queue[0].length;\n        const freqData = new Float32Array(queue.length * frameSize);\n        queue.forEach((data, i) => freqData.set(data, i * frameSize));\n        return freqData;\n    }\n    getTensorFromAudioDataArray(freqData, shape) {\n        const vals = new Float32Array(util.sizeFromShape(shape));\n        // If the data is less than the output shape, the rest is padded with zeros.\n        vals.set(freqData, vals.length - freqData.length);\n        return tensor(vals, shape);\n    }\n}\n//# sourceMappingURL=microphone_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { browser, env, image, tensor1d, tensor2d, tidy, util } from '@tensorflow/tfjs-core';\nimport { LazyIterator } from './lazy_iterator';\n/**\n * Provide a stream of image tensors from webcam video stream. Only works in\n * browser environment.\n */\nexport class WebcamIterator extends LazyIterator {\n    constructor(webcamVideoElement, webcamConfig) {\n        super();\n        this.webcamVideoElement = webcamVideoElement;\n        this.webcamConfig = webcamConfig;\n        this.isClosed = true;\n        this.resize = false;\n        if (this.needToResize()) {\n            this.resize = true;\n            this.cropSize =\n                [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth];\n            this.cropBoxInd = tensor1d([0], 'int32');\n            if (this.webcamConfig.centerCrop) {\n                // Calculate the box based on resizing shape.\n                const widthCroppingRatio = this.webcamConfig.resizeWidth * 1.0 / this.webcamVideoElement.width;\n                const heightCroppingRatio = this.webcamConfig.resizeHeight * 1.0 /\n                    this.webcamVideoElement.height;\n                const widthCropStart = (1 - widthCroppingRatio) / 2;\n                const heightCropStart = (1 - heightCroppingRatio) / 2;\n                const widthCropEnd = widthCropStart + widthCroppingRatio;\n                const heightCropEnd = heightCroppingRatio + heightCropStart;\n                this.cropBox = tensor2d([heightCropStart, widthCropStart, heightCropEnd, widthCropEnd], [1, 4]);\n            }\n            else {\n                this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]);\n            }\n        }\n    }\n    summary() {\n        return `webcam`;\n    }\n    // Construct a WebcamIterator and start it's video stream.\n    static async create(webcamVideoElement, webcamConfig = {}) {\n        if (env().get('IS_NODE')) {\n            throw new Error('tf.data.webcam is only supported in browser environment.');\n        }\n        if (!webcamVideoElement) {\n            // If webcam video element is not provided, create a hidden video element\n            // with provided width and height.\n            webcamVideoElement = document.createElement('video');\n            if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) {\n                throw new Error('Please provide webcam video element, or resizeWidth and ' +\n                    'resizeHeight to create a hidden video element.');\n            }\n            webcamVideoElement.width = webcamConfig.resizeWidth;\n            webcamVideoElement.height = webcamConfig.resizeHeight;\n        }\n        const webcamIterator = new WebcamIterator(webcamVideoElement, webcamConfig);\n        // Call async function to initialize the video stream.\n        await webcamIterator.start();\n        return webcamIterator;\n    }\n    // Async function to start video stream.\n    async start() {\n        if (this.webcamConfig.facingMode) {\n            util.assert((this.webcamConfig.facingMode === 'user') ||\n                (this.webcamConfig.facingMode === 'environment'), () => `Invalid webcam facing mode: ${this.webcamConfig.facingMode}. ` +\n                `Please provide 'user' or 'environment'`);\n        }\n        try {\n            this.stream = await navigator.mediaDevices.getUserMedia({\n                video: {\n                    deviceId: this.webcamConfig.deviceId,\n                    facingMode: this.webcamConfig.facingMode ?\n                        this.webcamConfig.facingMode :\n                        'user',\n                    width: this.webcamVideoElement.width,\n                    height: this.webcamVideoElement.height\n                }\n            });\n        }\n        catch (e) {\n            // Modify the error message but leave the stack trace intact\n            e.message = `Error thrown while initializing video stream: ${e.message}`;\n            throw e;\n        }\n        if (!this.stream) {\n            throw new Error('Could not obtain video from webcam.');\n        }\n        // Older browsers may not have srcObject\n        try {\n            this.webcamVideoElement.srcObject = this.stream;\n        }\n        catch (error) {\n            console.log(error);\n            this.webcamVideoElement.src = window.URL.createObjectURL(this.stream);\n        }\n        // Start the webcam video stream\n        this.webcamVideoElement.play();\n        this.isClosed = false;\n        return new Promise(resolve => {\n            // Add event listener to make sure the webcam has been fully initialized.\n            this.webcamVideoElement.onloadedmetadata = () => {\n                resolve();\n            };\n        });\n    }\n    async next() {\n        if (this.isClosed) {\n            return { value: null, done: true };\n        }\n        let img;\n        try {\n            img = browser.fromPixels(this.webcamVideoElement);\n        }\n        catch (e) {\n            throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`);\n        }\n        if (this.resize) {\n            try {\n                return { value: this.cropAndResizeFrame(img), done: false };\n            }\n            catch (e) {\n                throw new Error(`Error thrown cropping the video: ${e.message}`);\n            }\n            finally {\n                img.dispose();\n            }\n        }\n        else {\n            return { value: img, done: false };\n        }\n    }\n    needToResize() {\n        // If resizeWidth and resizeHeight are provided, and different from the\n        // width and height of original HTMLVideoElement, then resizing and cropping\n        // is required.\n        if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight &&\n            (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth ||\n                this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) {\n            return true;\n        }\n        return false;\n    }\n    // Cropping and resizing each frame based on config\n    cropAndResizeFrame(img) {\n        return tidy(() => {\n            const expandedImage = img.toFloat().expandDims(0);\n            let resizedImage;\n            resizedImage = image.cropAndResize(expandedImage, this.cropBox, this.cropBoxInd, this.cropSize, 'bilinear');\n            // Extract image from batch cropping.\n            const shape = resizedImage.shape;\n            return resizedImage.reshape(shape.slice(1));\n        });\n    }\n    // Capture one frame from the video stream, and extract the value from\n    // iterator.next() result.\n    async capture() {\n        return (await this.next()).value;\n    }\n    // Stop the video stream and pause webcam iterator.\n    stop() {\n        const tracks = this.stream.getTracks();\n        tracks.forEach(track => track.stop());\n        try {\n            this.webcamVideoElement.srcObject = null;\n        }\n        catch (error) {\n            console.log(error);\n            this.webcamVideoElement.src = null;\n        }\n        this.isClosed = true;\n    }\n    // Override toArray() function to prevent collecting.\n    toArray() {\n        throw new Error('Can not convert infinite video stream to array.');\n    }\n}\n//# sourceMappingURL=webcam_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * Represents a data source readable as a stream of binary data chunks.\n *\n * Because `Dataset`s can be read repeatedly (via `Dataset.iterator()`), this\n * provides a means to repeatedly create streams from the underlying data\n * sources.\n */\nexport class DataSource {\n}\n// TODO(soergel): consider convenience factory functions here\n// in combination with chainable source->dataset above, e.g.:\n// tf.data.url(...).asCsvDataset().shuffle().batch()\n//# sourceMappingURL=datasource.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nexport class StringIterator extends LazyIterator {\n    /**\n     * Splits a string stream on a given separator.\n     *\n     * It is assumed that the incoming chunk boundaries have no semantic meaning,\n     * so conceptually the incoming stream is treated simply as the concatenation\n     * of its elements.\n     *\n     * The outgoing stream provides chunks corresponding to the results of the\n     * standard string split() operation (even if such a chunk spanned incoming\n     * chunks).  The separators are not included.\n     *\n     * A typical usage is to split a text file (represented as a stream with\n     * arbitrary chunk boundaries) into lines.\n     *\n     * @param upstream A readable stream of strings that can be treated as\n     *   concatenated.\n     * @param separator A character to split on.\n     */\n    split(separator) {\n        return new SplitIterator(this, separator);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on StringIterator.  Unfortunately they can't be placed in separate files, due\n// to resulting trouble with circular imports.\n// ============================================================================\n// We wanted multiple inheritance, e.g.\n//   class SplitIterator extends QueueIterator<string>, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\nclass SplitIterator extends StringIterator {\n    constructor(upstream, separator) {\n        super();\n        this.upstream = upstream;\n        this.impl = new SplitIteratorImpl(upstream, separator);\n    }\n    summary() {\n        return this.impl.summary();\n    }\n    async next() {\n        return this.impl.next();\n    }\n}\nclass SplitIteratorImpl extends OneToManyIterator {\n    constructor(upstream, separator) {\n        super();\n        this.upstream = upstream;\n        this.separator = separator;\n        // A partial string at the end of an upstream chunk\n        this.carryover = '';\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Split('${this.separator}')`;\n    }\n    async pump() {\n        const chunkResult = await this.upstream.next();\n        if (chunkResult.done) {\n            if (this.carryover === '') {\n                return false;\n            }\n            // Pretend that the pump succeeded in order to emit the small last batch.\n            // The next pump() call will actually fail.\n            this.outputQueue.push(this.carryover);\n            this.carryover = '';\n            return true;\n        }\n        const lines = chunkResult.value.split(this.separator);\n        // Note the behavior: \" ab \".split(' ') === ['', 'ab', '']\n        // Thus the carryover may be '' if the separator falls on a chunk\n        // boundary; this produces the correct result.\n        lines[0] = this.carryover + lines[0];\n        for (const line of lines.slice(0, -1)) {\n            this.outputQueue.push(line);\n        }\n        this.carryover = lines[lines.length - 1];\n        return true;\n    }\n}\n//# sourceMappingURL=string_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nimport { StringIterator } from './string_iterator';\nexport class ByteChunkIterator extends LazyIterator {\n    /**\n     * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n     *\n     * The byte arrays producetd from the ByteChunkIterator on which this is\n     * called will be interpreted as concatenated.  No assumptions are made about\n     * the boundaries of the incoming chunks, so a multi-byte UTF8 encoding of a\n     * character may span the boundary between chunks.  This naturally happens,\n     * for instance, when reading fixed-size byte arrays from a file.\n     */\n    decodeUTF8() {\n        return new Utf8Iterator(this);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on ByteChunkIterator.  Unfortunately they can't be placed in separate files,\n// due to resulting trouble with circular imports.\n// ============================================================================\n// We wanted multiple inheritance, e.g.\n//   class Utf8Iterator extends QueueIterator<string>, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\nclass Utf8Iterator extends StringIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        this.impl = new Utf8IteratorImpl(upstream);\n    }\n    summary() {\n        return this.impl.summary();\n    }\n    async next() {\n        return this.impl.next();\n    }\n}\n/**\n * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n *\n * This is tricky because the incoming byte array boundaries may disrupt a\n * multi-byte UTF8 character. Thus any incomplete character data at the end of\n * a chunk must be carried over and prepended to the next chunk before\n * decoding. Luckily with native decoder, TextDecoder in browser and\n * string_decoder in node, byte array boundaries are handled automatically.\n *\n * In the context of an input pipeline for machine learning, UTF8 decoding is\n * needed to parse text files containing training examples or prediction\n * requests (e.g., formatted as CSV or JSON). We cannot use the built-in\n * decoding provided by FileReader.readAsText() because here we are in a\n * streaming context, which FileReader does not support.\n *\n * @param upstream A `LazyIterator` of `Uint8Arrays` containing UTF8-encoded\n *   text, which should be interpreted as concatenated.  No assumptions are\n *   made about the boundaries of the incoming chunks, so a multi-byte UTF8\n *   encoding of a character may span the boundary between chunks.  This\n *   naturally happens, for instance, when reading fixed-size byte arrays from a\n *   file.\n */\nclass Utf8IteratorImpl extends OneToManyIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        if (env().get('IS_BROWSER')) {\n            this.decoder = new TextDecoder('utf-8');\n        }\n        else {\n            // tslint:disable-next-line:no-require-imports\n            const { StringDecoder } = require('string_decoder');\n            this.decoder = new StringDecoder('utf8');\n        }\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Utf8`;\n    }\n    async pump() {\n        const chunkResult = await this.upstream.next();\n        let chunk;\n        if (chunkResult.done) {\n            return false;\n        }\n        else {\n            chunk = chunkResult.value;\n        }\n        let text;\n        if (env().get('IS_BROWSER')) {\n            text = this.decoder.decode(chunk, { stream: true });\n        }\n        else {\n            text = this.decoder.write(Buffer.from(chunk.buffer));\n        }\n        this.outputQueue.push(text);\n        return true;\n    }\n}\n//# sourceMappingURL=byte_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n// inspired by https://github.com/maxogden/filereader-stream\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { ByteChunkIterator } from './byte_chunk_iterator';\n/**\n * Provide a stream of chunks from a File, Blob, or Uint8Array.\n * @param file The source File, Blob or Uint8Array.\n * @param options Optional settings controlling file reading.\n * @returns a lazy Iterator of Uint8Arrays containing sequential chunks of the\n *   input File, Blob or Uint8Array.\n */\nexport class FileChunkIterator extends ByteChunkIterator {\n    constructor(file, options = {}) {\n        super();\n        this.file = file;\n        this.options = options;\n        util.assert((file instanceof Uint8Array) ||\n            (env().get('IS_BROWSER') ?\n                (file instanceof File || file instanceof Blob) :\n                false), () => 'FileChunkIterator only supports File, Blob and Uint8Array ' +\n            'right now.');\n        this.offset = options.offset || 0;\n        // default 1MB chunk has tolerable perf on large files\n        this.chunkSize = options.chunkSize || 1024 * 1024;\n    }\n    summary() {\n        return `FileChunks ${this.file}`;\n    }\n    async next() {\n        if (this.offset >= ((this.file instanceof Uint8Array) ?\n            this.file.byteLength :\n            this.file.size)) {\n            return { value: null, done: true };\n        }\n        const chunk = new Promise((resolve, reject) => {\n            const end = this.offset + this.chunkSize;\n            if (this.file instanceof Uint8Array) {\n                // Note if end > this.uint8Array.byteLength, we just get a small last\n                // chunk.\n                resolve(new Uint8Array(this.file.slice(this.offset, end)));\n            }\n            else {\n                // This branch assumes that this.file type is File or Blob, which\n                // means it is in the browser environment.\n                // TODO(soergel): is this a performance issue?\n                const fileReader = new FileReader();\n                fileReader.onload = (event) => {\n                    let data = fileReader.result;\n                    // Not sure we can trust the return type of\n                    // FileReader.readAsArrayBuffer See e.g.\n                    // https://github.com/node-file-api/FileReader/issues/2\n                    if (data instanceof ArrayBuffer) {\n                        data = new Uint8Array(data);\n                    }\n                    if (!(data instanceof Uint8Array)) {\n                        return reject(new TypeError('FileReader returned unknown type.'));\n                    }\n                    resolve(data);\n                };\n                fileReader.onabort = (event) => {\n                    return reject(new Error('Aborted'));\n                };\n                fileReader.onerror = (event) => {\n                    return reject(new Error(event.type));\n                };\n                // TODO(soergel): better handle onabort, onerror\n                // Note if end > this.file.size, we just get a small last chunk.\n                const slice = this.file.slice(this.offset, end);\n                // We can't use readAsText here (even if we know the file is text)\n                // because the slice boundary may fall within a multi-byte character.\n                fileReader.readAsArrayBuffer(slice);\n            }\n            this.offset = end;\n        });\n        return { value: (await chunk), done: false };\n    }\n}\n//# sourceMappingURL=file_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { FileChunkIterator } from './file_chunk_iterator';\n/**\n * Provide a stream of chunks from a URL.\n *\n * Note this class first downloads the entire file into memory before providing\n * the first element from the stream.  This is because the Fetch API does not\n * yet reliably provide a reader stream for the response body.\n */\nexport async function urlChunkIterator(url, options = {}) {\n    let urlString;\n    let requestInit;\n    if ((typeof url) === 'string') {\n        urlString = url;\n    }\n    else {\n        urlString = url.url;\n        requestInit = getRequestInitFromRequest(url);\n    }\n    const response = await util.fetch(urlString, requestInit);\n    if (response.ok) {\n        const uint8Array = new Uint8Array(await response.arrayBuffer());\n        return new FileChunkIterator(uint8Array, options);\n    }\n    else {\n        throw new Error(response.statusText);\n    }\n}\n// Generate RequestInit from Request to match tf.util.fetch signature.\nconst getRequestInitFromRequest = (request) => {\n    const init = {\n        method: request.method,\n        headers: request.headers,\n        body: request.body,\n        mode: request.mode,\n        credentials: request.credentials,\n        cache: request.cache,\n        redirect: request.redirect,\n        referrer: request.referrer,\n        integrity: request.integrity,\n    };\n    return init;\n};\n//# sourceMappingURL=url_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n// Skip tslint any type check cause this method is aiming to check type of\n// input.\n// tslint:disable-next-line:no-any\nexport function isLocalPath(source) {\n    return (typeof source === 'string') && source.substr(0, 7) === 'file://';\n}\n//# sourceMappingURL=source_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { DataSource } from '../datasource';\nimport { FileChunkIterator } from '../iterators/file_chunk_iterator';\nimport { isLocalPath } from '../util/source_util';\n/**\n * Represents a file, blob, or Uint8Array readable as a stream of binary data\n * chunks.\n */\nexport class FileDataSource extends DataSource {\n    /**\n     * Create a `FileDataSource`.\n     *\n     * @param input Local file path, or `File`/`Blob`/`Uint8Array` object to\n     *     read. Local file only works in node environment.\n     * @param options Options passed to the underlying `FileChunkIterator`s,\n     *   such as {chunksize: 1024}.\n     */\n    constructor(input, options = {}) {\n        super();\n        this.input = input;\n        this.options = options;\n    }\n    async iterator() {\n        if (isLocalPath(this.input) && env().get('IS_NODE')) {\n            // tslint:disable-next-line:no-require-imports\n            const fs = require('fs');\n            this.input = fs.readFileSync(this.input.substr(7));\n        }\n        // TODO(kangyizhang): Add LocalFileChunkIterator to split local streaming\n        // with file in browser.\n        return new FileChunkIterator(this.input, this.options);\n    }\n}\n//# sourceMappingURL=file_data_source.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { DataSource } from '../datasource';\nimport { urlChunkIterator } from '../iterators/url_chunk_iterator';\nimport { isLocalPath } from '../util/source_util';\nimport { FileDataSource } from './file_data_source';\n/*\n * Represents a URL readable as a stream of binary data chunks.\n */\nexport class URLDataSource extends DataSource {\n    /**\n     * Create a `URLDataSource`.\n     *\n     * @param url A source URL string, or a `Request` object.\n     * @param options Options passed to the underlying `FileChunkIterator`s,\n     *   such as {chunksize: 1024}.\n     */\n    constructor(url, fileOptions = {}) {\n        super();\n        this.url = url;\n        this.fileOptions = fileOptions;\n    }\n    // TODO(soergel): provide appropriate caching options.  Currently this\n    // will download the URL anew for each call to iterator().  Since we have\n    // to treat the downloaded file as a blob/buffer anyway, we may as well retain\n    // it-- but that raises GC issues.  Also we may want a persistent disk cache.\n    async iterator() {\n        if (isLocalPath(this.url)) {\n            return (new FileDataSource(this.url, this.fileOptions))\n                .iterator();\n        }\n        else {\n            return urlChunkIterator(this.url, this.fileOptions);\n        }\n    }\n}\n//# sourceMappingURL=url_data_source.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { datasetFromIteratorFn } from './dataset';\nimport { CSVDataset } from './datasets/csv_dataset';\nimport { iteratorFromFunction } from './iterators/lazy_iterator';\nimport { MicrophoneIterator } from './iterators/microphone_iterator';\nimport { WebcamIterator } from './iterators/webcam_iterator';\nimport { URLDataSource } from './sources/url_data_source';\n/**\n * Create a `CSVDataset` by reading and decoding CSV file(s) from provided URL\n * or local path if it's in Node environment.\n *\n * Note: If isLabel in columnConfigs is `true` for at least one column, the\n * element in returned `CSVDataset` will be an object of\n * `{xs:features, ys:labels}`: xs is a dict of features key/value pairs, ys\n * is a dict of labels key/value pairs. If no column is marked as label,\n * returns a dict of features only.\n *\n * ```js\n * const csvUrl =\n * 'https://storage.googleapis.com/tfjs-examples/multivariate-linear-regression/data/boston-housing-train.csv';\n *\n * async function run() {\n *   // We want to predict the column \"medv\", which represents a median value of\n *   // a home (in $1000s), so we mark it as a label.\n *   const csvDataset = tf.data.csv(\n *     csvUrl, {\n *       columnConfigs: {\n *         medv: {\n *           isLabel: true\n *         }\n *       }\n *     });\n *\n *   // Number of features is the number of column names minus one for the label\n *   // column.\n *   const numOfFeatures = (await csvDataset.columnNames()).length - 1;\n *\n *   // Prepare the Dataset for training.\n *   const flattenedDataset =\n *     csvDataset\n *     .map(({xs, ys}) =>\n *       {\n *         // Convert xs(features) and ys(labels) from object form (keyed by\n *         // column name) to array form.\n *         return {xs:Object.values(xs), ys:Object.values(ys)};\n *       })\n *     .batch(10);\n *\n *   // Define the model.\n *   const model = tf.sequential();\n *   model.add(tf.layers.dense({\n *     inputShape: [numOfFeatures],\n *     units: 1\n *   }));\n *   model.compile({\n *     optimizer: tf.train.sgd(0.000001),\n *     loss: 'meanSquaredError'\n *   });\n *\n *   // Fit the model using the prepared Dataset\n *   return model.fitDataset(flattenedDataset, {\n *     epochs: 10,\n *     callbacks: {\n *       onEpochEnd: async (epoch, logs) => {\n *         console.log(epoch + ':' + logs.loss);\n *       }\n *     }\n *   });\n * }\n *\n * await run();\n * ```\n *\n * @param source URL or local path to get CSV file. If it's a local path, it\n * must have prefix `file://` and it only works in node environment.\n * @param csvConfig (Optional) A CSVConfig object that contains configurations\n *     of reading and decoding from CSV file(s).\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   configParamIndices: [1]\n *  }\n */\nexport function csv(source, csvConfig = {}) {\n    return new CSVDataset(new URLDataSource(source), csvConfig);\n}\n/**\n * Create a `Dataset` that produces each element by calling a provided function.\n *\n * Note that repeated iterations over this `Dataset` may produce different\n * results, because the function will be called anew for each element of each\n * iteration.\n *\n * Also, beware that the sequence of calls to this function may be out of order\n * in time with respect to the logical order of the Dataset. This is due to the\n * asynchronous lazy nature of stream processing, and depends on downstream\n * transformations (e.g. .shuffle()). If the provided function is pure, this is\n * no problem, but if it is a closure over a mutable state (e.g., a traversal\n * pointer), then the order of the produced elements may be scrambled.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const ds = tf.data.func(func);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * @param f A function that produces one data element on each call.\n */\nexport function func(f) {\n    const iter = iteratorFromFunction(f);\n    return datasetFromIteratorFn(async () => iter);\n}\n/**\n * Create a `Dataset` that produces each element from provided JavaScript\n * generator, which is a function*\n * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions),\n * or a function that returns an\n * iterator\n * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions).\n *\n * The returned iterator should have `.next()` function that returns element in\n * format of `{value: TensorContainer, done:boolean}`.\n *\n * Example of creating a dataset from an iterator factory:\n * ```js\n * function makeIterator() {\n *   const numElements = 10;\n *   let index = 0;\n *\n *   const iterator = {\n *     next: () => {\n *       let result;\n *       if (index < numElements) {\n *         result = {value: index, done: false};\n *         index++;\n *         return result;\n *       }\n *       return {value: index, done: true};\n *     }\n *   };\n *   return iterator;\n * }\n * const ds = tf.data.generator(makeIterator);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * Example of creating a dataset from a generator:\n * ```js\n * function* dataGenerator() {\n *   const numElements = 10;\n *   let index = 0;\n *   while (index < numElements) {\n *     const x = index;\n *     index++;\n *     yield x;\n *   }\n * }\n *\n * const ds = tf.data.generator(dataGenerator);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * @param generator A Javascript generator function that returns a JavaScript\n *     iterator.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   configParamIndices: [1]\n *  }\n */\nexport function generator(generator) {\n    return datasetFromIteratorFn(async () => {\n        const gen = await generator();\n        return iteratorFromFunction(() => gen.next());\n    });\n}\n/**\n * Create an iterator that generate `Tensor`s from webcam video stream. This API\n * only works in Browser environment when the device has webcam.\n *\n * Note: this code snippet only works when the device has a webcam. It will\n * request permission to open the webcam when running.\n * ```js\n * const videoElement = document.createElement('video');\n * videoElement.width = 100;\n * videoElement.height = 100;\n * const cam = await tf.data.webcam(videoElement);\n * const img = await cam.capture();\n * img.print();\n * cam.stop();\n * ```\n *\n * @param webcamVideoElement A `HTMLVideoElement` used to play video from\n *     webcam. If this element is not provided, a hidden `HTMLVideoElement` will\n *     be created. In that case, `resizeWidth` and `resizeHeight` must be\n *     provided to set the generated tensor shape.\n * @param webcamConfig A `WebcamConfig` object that contains configurations of\n *     reading and manipulating data from webcam video stream.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   ignoreCI: true\n *  }\n */\nexport async function webcam(webcamVideoElement, webcamConfig) {\n    return WebcamIterator.create(webcamVideoElement, webcamConfig);\n}\n/**\n * Create an iterator that generate frequency-domain spectrogram `Tensor`s from\n * microphone audio stream with browser's native FFT. This API only works in\n * browser environment when the device has microphone.\n *\n * Note: this code snippet only works when the device has a microphone. It will\n * request permission to open the microphone when running.\n * ```js\n * const mic = await tf.data.microphone({\n *   fftSize: 1024,\n *   columnTruncateLength: 232,\n *   numFramesPerSpectrogram: 43,\n *   sampleRateHz:44100,\n *   includeSpectrogram: true,\n *   includeWaveform: true\n * });\n * const audioData = await mic.capture();\n * const spectrogramTensor = audioData.spectrogram;\n * spectrogramTensor.print();\n * const waveformTensor = audioData.waveform;\n * waveformTensor.print();\n * mic.stop();\n * ```\n *\n * @param microphoneConfig A `MicrophoneConfig` object that contains\n *     configurations of reading audio data from microphone.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   ignoreCI: true\n *  }\n */\nexport async function microphone(microphoneConfig) {\n    return MicrophoneIterator.create(microphoneConfig);\n}\n//# sourceMappingURL=readers.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { array, Dataset, zip } from './dataset';\nexport { CSVDataset } from './datasets/csv_dataset';\nexport { TextLineDataset } from './datasets/text_line_dataset';\nexport { csv, func, generator, microphone, webcam } from './readers';\nexport { FileDataSource } from './sources/file_data_source';\nexport { URLDataSource } from './sources/url_data_source';\nexport { version as version_data } from './version';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function assertNotComplex(tensor, opName) {\n    if (!Array.isArray(tensor)) {\n        tensor = [tensor];\n    }\n    tensor.forEach(t => {\n        if (t != null) {\n            util.assert(t.dtype !== 'complex64', () => `${opName} does not support complex64 tensors in the CPU backend.`);\n        }\n    });\n}\n//# sourceMappingURL=cpu_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { backend_util, DataStorage, engine, env, kernel_impls, KernelBackend, max, slice_util, TensorBuffer, upcastType, util } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV3Impl = kernel_impls.nonMaxSuppressionV3Impl;\nconst split = kernel_impls.split;\nconst tile = kernel_impls.tile;\nconst topkImpl = kernel_impls.topkImpl;\nconst whereImpl = kernel_impls.whereImpl;\nimport * as seedrandom from 'seedrandom';\nimport { assertNotComplex } from './cpu_util';\nfunction mapActivation(backend, x, activation, preluActivationWeights) {\n    if (activation === 'linear') {\n        return backend.linear(x);\n    }\n    else if (activation === 'relu') {\n        return backend.relu(x);\n    }\n    else if (activation === 'elu') {\n        return tf.elu(x);\n    }\n    else if (activation === 'relu6') {\n        return backend.relu6(x);\n    }\n    else if (activation === 'prelu') {\n        return backend.prelu(x, preluActivationWeights);\n    }\n    throw new Error(`Activation ${activation} has not been implemented for the CPU backend.`);\n}\nexport class MathBackendCPU extends KernelBackend {\n    constructor() {\n        super();\n        this.blockSize = 48;\n        this.firstUse = true;\n        this.data = new DataStorage(this, engine());\n    }\n    write(values, shape, dtype) {\n        if (this.firstUse) {\n            this.firstUse = false;\n            if (env().get('IS_NODE')) {\n                backend_util.warn('\\n============================\\n' +\n                    'Hi there \uD83D\uDC4B. Looks like you are running TensorFlow.js in ' +\n                    'Node.js. To speed things up dramatically, install our node ' +\n                    'backend, which binds to TensorFlow C++, by running ' +\n                    'npm i @tensorflow/tfjs-node, ' +\n                    'or npm i @tensorflow/tfjs-node-gpu if you have CUDA. ' +\n                    'Then call require(\\'@tensorflow/tfjs-node\\'); (-gpu ' +\n                    'suffix for CUDA) at the start of your program. ' +\n                    'Visit https://github.com/tensorflow/tfjs-node for more details.' +\n                    '\\n============================');\n            }\n        }\n        const dataId = {};\n        this.data.set(dataId, { values, dtype, refCount: 1 });\n        return dataId;\n    }\n    /**\n     * Create a data bucket in cpu backend.\n     * @param shape Shape of the `TensorInfo`.\n     * @param dtype DType of the `TensorInfo`.\n     * @param values The value of the `TensorInfo` stored as a flattened array.\n     */\n    makeTensorInfo(shape, dtype, values) {\n        const outId = this.write(values, shape, dtype);\n        return { dataId: outId, shape, dtype };\n    }\n    /** Increase refCount of a `TensorData`. */\n    incRef(dataId) {\n        const tensorData = this.data.get(dataId);\n        tensorData.refCount++;\n    }\n    /** Decrease refCount of a `TensorData`. */\n    decRef(dataId) {\n        if (this.data.has(dataId)) {\n            const tensorData = this.data.get(dataId);\n            tensorData.refCount--;\n        }\n    }\n    move(dataId, values, shape, dtype) {\n        this.data.set(dataId, { values, dtype, refCount: 1 });\n    }\n    numDataIds() {\n        return this.data.numDataIds();\n    }\n    async read(dataId) {\n        return this.readSync(dataId);\n    }\n    readSync(dataId) {\n        const { dtype, complexTensorInfos } = this.data.get(dataId);\n        if (dtype === 'complex64') {\n            const realValues = this.readSync(complexTensorInfos.real.dataId);\n            const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n            return backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        return this.data.get(dataId).values;\n    }\n    bufferSync(t) {\n        const data = this.readSync(t.dataId);\n        let decodedData = data;\n        if (t.dtype === 'string') {\n            try {\n                // Decode the bytes into string.\n                decodedData = data.map(d => util.decodeString(d));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode encoded string bytes into utf-8');\n            }\n        }\n        return tf.buffer(t.shape, t.dtype, decodedData);\n    }\n    makeOutput(values, shape, dtype) {\n        const dataId = this.write(values, shape, dtype);\n        return engine().makeTensorFromDataId(dataId, shape, dtype, this);\n    }\n    disposeData(dataId) {\n        if (this.data.has(dataId)) {\n            const { complexTensorInfos } = this.data.get(dataId);\n            if (complexTensorInfos != null) {\n                this.disposeData(complexTensorInfos.real.dataId);\n                this.disposeData(complexTensorInfos.imag.dataId);\n            }\n            this.data.delete(dataId);\n        }\n    }\n    disposeIntermediateTensorInfo(tensorInfo) {\n        const dataId = tensorInfo.dataId;\n        if (this.data.has(dataId)) {\n            const tensorData = this.data.get(dataId);\n            tensorData.refCount--;\n            if (tensorData.refCount < 1) {\n                this.disposeData(dataId);\n            }\n        }\n    }\n    async time(f) {\n        const start = util.now();\n        f();\n        const kernelMs = util.now() - start;\n        return { kernelMs };\n    }\n    memory() {\n        return {\n            // Unreliable due to automatic gc. The numbers above are cumulative.\n            unreliable: true,\n            reasons: ['The reported memory is an upper bound. Due to automatic garbage ' +\n                    'collection, the true allocated memory may be less.']\n        };\n    }\n    stridedSlice(x, begin, end, strides) {\n        assertNotComplex(x, 'stridedSlice');\n        const outShape = slice_util.computeOutShape(begin, end, strides);\n        if (outShape.some(axis => axis === 0)) {\n            return tf.tensor([], outShape);\n        }\n        const buffer = tf.buffer(outShape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < buffer.size; i++) {\n            const loc = buffer.indexToLoc(i);\n            const newLoc = new Array(loc.length);\n            for (let j = 0; j < newLoc.length; j++) {\n                newLoc[j] = loc[j] * strides[j] + begin[j];\n            }\n            buffer.set(xBuf.get(...newLoc), ...loc);\n        }\n        return buffer.toTensor();\n    }\n    diag(x) {\n        const xVals = this.readSync(x.dataId);\n        const buffer = tf.buffer([x.size, x.size], x.dtype);\n        const vals = buffer.values;\n        for (let i = 0; i < xVals.length; i++) {\n            vals[i * x.size + i] = xVals[i];\n        }\n        return buffer.toTensor();\n    }\n    unstack(x, axis) {\n        const num = x.shape[axis];\n        const outShape = new Array(x.rank - 1);\n        let outIndex = 0;\n        for (let i = 0; i < x.rank; i++) {\n            if (i !== axis) {\n                outShape[outIndex++] = x.shape[i];\n            }\n        }\n        const begin = new Array(x.rank).fill(0);\n        const size = x.shape.slice();\n        size[axis] = 1;\n        const res = new Array(num);\n        for (let i = 0; i < res.length; i++) {\n            begin[axis] = i;\n            res[i] = tf.slice(x, begin, size).reshape(outShape);\n        }\n        return res;\n    }\n    reverse(x, axis) {\n        assertNotComplex(x, 'reverse');\n        const buffer = tf.buffer(x.shape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < buffer.size; i++) {\n            const outLoc = buffer.indexToLoc(i);\n            const inLoc = outLoc.slice();\n            axis.forEach(ax => inLoc[ax] = x.shape[ax] - 1 - inLoc[ax]);\n            buffer.set(xBuf.get(...inLoc), ...outLoc);\n        }\n        return buffer.toTensor();\n    }\n    neg(x) {\n        assertNotComplex(x, 'neg');\n        // TODO(lina128): Use mul directly once neg is modularized.\n        return tf.mul(tf.scalar(-1), x);\n    }\n    addN(tensors) {\n        assertNotComplex(tensors, 'addN');\n        const vals = tensors.map(t => this.readSync(t.dataId));\n        const result = tf.buffer(tensors[0].shape, tensors[0].dtype);\n        const resultVals = result.values;\n        for (let i = 0; i < tensors.length; i++) {\n            const currVals = vals[i];\n            for (let j = 0; j < resultVals.length; j++) {\n                resultVals[j] += currVals[j];\n            }\n        }\n        return result.toTensor();\n    }\n    softmax(logits, dim) {\n        const axes = util.parseAxisParam([dim], logits.shape);\n        // TODO(annxingyuan): Call maxImpl rather than op as part of softmax kernel\n        // modularization.\n        const maxLogit = max(logits, axes);\n        const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n        // TODO(lina128): Use sub directly once softmax is modularized.\n        const a = tf.sub(logits, maxLogit.reshape(expandedShape));\n        const b = tf.exp(a);\n        const sumExp = this.sum(b, axes).reshape(expandedShape);\n        // TODO(annxingyuan): Call divImpl rather than op as part of softmax\n        // kernel modularization.\n        return tf.div(b, sumExp);\n    }\n    pow(a, b) {\n        assertNotComplex([a, b], 'pow');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aValue, bValue) => Math.pow(aValue, bValue));\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        assertNotComplex([a, b], 'matMul');\n        const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n        const leftDim = transposeA ? a.shape[2] : a.shape[1];\n        const rightDim = transposeB ? b.shape[1] : b.shape[2];\n        const batchDim = a.shape[0];\n        const aValues = this.readSync(a.dataId);\n        const bValues = this.readSync(b.dataId);\n        const [aBatch, aOuterStep, aInnerStep] = transposeA ?\n            [a.strides[0], 1, a.strides[1]] :\n            [a.strides[0], a.strides[1], 1];\n        const [bInnerStep, bOuterStep, bBatch] = transposeB ?\n            [1, b.strides[1], b.strides[0]] :\n            [b.strides[1], 1, b.strides[0]];\n        const size = leftDim * rightDim;\n        const result = tf.buffer([batchDim, leftDim, rightDim], a.dtype);\n        const resVals = result.values;\n        const blockSize = this.blockSize;\n        for (let b = 0; b < batchDim; b++) {\n            for (let i0 = 0; i0 < leftDim; i0 += blockSize) {\n                for (let j0 = 0; j0 < rightDim; j0 += blockSize) {\n                    for (let k0 = 0; k0 < sharedDim; k0 += blockSize) {\n                        // for when blockSize doesn't evenly divide the input\n                        const iBlock = Math.min(i0 + blockSize, leftDim);\n                        const jBlock = Math.min(j0 + blockSize, rightDim);\n                        const kBlock = Math.min(k0 + blockSize, sharedDim);\n                        for (let i = i0; i < iBlock; i++) {\n                            for (let j = j0; j < jBlock; j++) {\n                                let sum = 0.0;\n                                for (let k = k0; k < kBlock; k++) {\n                                    sum += aValues[b * aBatch + i * aOuterStep + k * aInnerStep] *\n                                        bValues[k * bInnerStep + j * bOuterStep + b * bBatch];\n                                }\n                                resVals[b * size + (i * rightDim + j)] += sum;\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return result.toTensor();\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        let result = this.batchMatMul(a, b, transposeA, transposeB);\n        if (bias) {\n            // TODO(lina128): Use add directly once fusedBatchMatMul is modularized.\n            result = tf.add(result, bias);\n        }\n        if (activation) {\n            result =\n                mapActivation(this, result, activation, preluActivationWeights);\n        }\n        return result;\n    }\n    floorDiv(a, b) {\n        assertNotComplex([a, b], 'floorDiv');\n        const op = (a, b) => Math.floor(a / b);\n        const outputDtype = 'int32';\n        return this.broadcastedBinaryOp(a, b, outputDtype, op);\n    }\n    sum(x, axes) {\n        assertNotComplex(x, 'sum');\n        backend_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(outShape, resultDtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let sum = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                sum += aVals[offset + j];\n            }\n            vals[i] = sum;\n        }\n        return result;\n    }\n    prod(x, axes) {\n        assertNotComplex(x, 'sum');\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(outShape, resultDtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let prod = 1;\n            for (let j = 0; j < reduceSize; ++j) {\n                prod *= aVals[offset + j];\n            }\n            vals[i] = prod;\n        }\n        return result;\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        assertNotComplex(x, 'unsortedSegmentSum');\n        const res = [];\n        // Reshape the segment id's so that they can be broadcast with\n        // x. The new shape should be [segmentIds.shape, 1, ..., 1]\n        const numIters = x.rank - segmentIds.rank;\n        for (let i = 0; i < numIters; ++i) {\n            segmentIds = segmentIds.expandDims(i + 1);\n        }\n        for (let i = 0; i < numSegments; ++i) {\n            const segmentId = tf.scalar(i, 'int32');\n            const mask = tf.equal(segmentId, segmentIds).asType('float32');\n            const sum = mask.mul(x).sum(0);\n            res.push(sum);\n        }\n        return tf.stack(res);\n    }\n    argMin(x, axis) {\n        assertNotComplex(x, 'argMin');\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('argMin', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, 'int32');\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let min = aVals[offset];\n            let minIndex = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value < min) {\n                    min = value;\n                    minIndex = j;\n                }\n            }\n            vals[i] = minIndex;\n        }\n        return result;\n    }\n    argMax(x, axis) {\n        assertNotComplex(x, 'argMax');\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('argMax', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, 'int32');\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let max = aVals[offset];\n            let maxIndex = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value > max) {\n                    max = value;\n                    maxIndex = j;\n                }\n            }\n            vals[i] = maxIndex;\n        }\n        return result;\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        assertNotComplex(x, 'cumsum');\n        if (axis !== x.rank - 1) {\n            throw new Error(`backend.cumsum in CPU expects an inner-most axis=${x.rank - 1} ` +\n                `but got axis=${axis}`);\n        }\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(x.shape, resultDtype);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        const finalDim = x.shape[x.rank - 1];\n        const indexAdjuster = reverse ?\n            (i, j) => i + finalDim - j - 1 :\n            (i, j) => i + j;\n        for (let i = 0; i < aVals.length; i += finalDim) {\n            for (let j = 0; j < finalDim; j++) {\n                const idx = indexAdjuster(i, j);\n                if (j === 0) {\n                    vals[idx] = exclusive ? 0 : aVals[idx];\n                }\n                else {\n                    const prevIdx = indexAdjuster(i, j - 1);\n                    vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :\n                        aVals[idx] + vals[prevIdx];\n                }\n            }\n        }\n        return result;\n    }\n    equal(a, b) {\n        assertNotComplex([a, b], 'equal');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal === bVal) ? 1 : 0;\n        });\n    }\n    notEqual(a, b) {\n        assertNotComplex([a, b], 'notEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal !== bVal) ? 1 : 0;\n        });\n    }\n    less(a, b) {\n        assertNotComplex([a, b], 'less');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal < bVal) ? 1 : 0;\n        });\n    }\n    lessEqual(a, b) {\n        assertNotComplex([a, b], 'lessEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal <= bVal) ? 1 : 0;\n        });\n    }\n    greater(a, b) {\n        assertNotComplex([a, b], 'greater');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal > bVal) ? 1 : 0;\n        });\n    }\n    greaterEqual(a, b) {\n        assertNotComplex([a, b], 'greaterEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal >= bVal) ? 1 : 0;\n        });\n    }\n    logicalAnd(a, b) {\n        assertNotComplex([a, b], 'logicalAnd');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return aVal && bVal;\n        });\n    }\n    logicalOr(a, b) {\n        assertNotComplex([a, b], 'logicalOr');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return aVal || bVal;\n        });\n    }\n    select(condition, a, b) {\n        assertNotComplex([condition, a, b], 'select');\n        const values = this.readSync(condition.dataId);\n        const aValues = this.readSync(a.dataId);\n        const bValues = this.readSync(b.dataId);\n        const result = tf.zeros(a.shape, upcastType(a.dtype, b.dtype));\n        const newValues = this.readSync(result.dataId);\n        let index = 0;\n        const offset = condition.rank === 0 || condition.rank > 1 || a.rank === 1 ?\n            1 :\n            util.sizeFromShape(a.shape.slice(1));\n        for (let i = 0; i < values.length; i++) {\n            for (let j = 0; j < offset; j++) {\n                if (values[i] === 1) {\n                    newValues[index++] = aValues[i];\n                }\n                else {\n                    newValues[index++] = bValues[i];\n                }\n            }\n        }\n        return result;\n    }\n    where(condition) {\n        assertNotComplex([condition], 'where');\n        const condVals = this.readSync(condition.dataId);\n        return whereImpl(condition.shape, condVals);\n    }\n    topk(x, k, sorted) {\n        assertNotComplex(x, 'topk');\n        const xVals = this.readSync(x.dataId);\n        return topkImpl(xVals, x.shape, x.dtype, k, sorted);\n    }\n    min(x, axes) {\n        assertNotComplex(x, 'min');\n        backend_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let min = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value < min) {\n                    min = value;\n                }\n            }\n            vals[i] = min;\n        }\n        return result;\n    }\n    minimum(a, b) {\n        assertNotComplex([a, b], 'minimum');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => Math.min(aVal, bVal));\n    }\n    mod(a, b) {\n        assertNotComplex([a, b], 'mod');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n            const rem = aVal % bVal;\n            if ((aVal < 0 && bVal < 0) || (aVal >= 0 && bVal >= 0)) {\n                return rem;\n            }\n            else {\n                return (rem + bVal) % bVal;\n            }\n        });\n    }\n    maximum(a, b) {\n        assertNotComplex([a, b], 'maximum');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => Math.max(aVal, bVal));\n    }\n    all(x, axes) {\n        assertNotComplex(x, 'all');\n        backend_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let all = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                all = all && value;\n            }\n            vals[i] = all;\n        }\n        return result;\n    }\n    any(x, axes) {\n        assertNotComplex(x, 'any');\n        backend_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let anyVal = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                anyVal = anyVal || value;\n            }\n            vals[i] = anyVal;\n        }\n        return result;\n    }\n    squaredDifference(a, b) {\n        assertNotComplex([a, b], 'squaredDifference');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n            const diff = aVal - bVal;\n            return diff * diff;\n        });\n    }\n    linear(x) {\n        return x;\n    }\n    relu(x) {\n        assertNotComplex(x, 'relu');\n        const res = tf.zeros(x.shape, x.dtype);\n        const resVals = this.readSync(res.dataId);\n        const inVals = this.readSync(x.dataId);\n        for (let i = 0; i < inVals.length; ++i) {\n            resVals[i] = Math.max(0, inVals[i]);\n        }\n        return res;\n    }\n    relu6(x) {\n        assertNotComplex(x, 'relu');\n        const res = tf.zeros(x.shape, x.dtype);\n        const resVals = this.readSync(res.dataId);\n        const inVals = this.readSync(x.dataId);\n        for (let i = 0; i < inVals.length; ++i) {\n            resVals[i] = Math.min(Math.max(0, inVals[i]), 6);\n        }\n        return res;\n    }\n    prelu(x, a) {\n        assertNotComplex([x, a], 'prelu');\n        return this.broadcastedBinaryOp(x, a, x.dtype, (xValue, aValue) => xValue < 0 ? aValue * xValue : xValue);\n    }\n    eluDer(dy, y) {\n        assertNotComplex([dy, y], 'eluDer');\n        const resultValues = new Float32Array(y.size);\n        const values = this.readSync(y.dataId);\n        const dyValues = this.readSync(dy.dataId);\n        for (let i = 0; i < values.length; ++i) {\n            const v = values[i];\n            if (v >= 1) {\n                resultValues[i] = dyValues[i];\n            }\n            else {\n                resultValues[i] = dyValues[i] * (v + 1);\n            }\n        }\n        return this.makeOutput(resultValues, y.shape, 'float32');\n    }\n    atan2(a, b) {\n        assertNotComplex([a, b], 'atan2');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aValue, bValue) => Math.atan2(aValue, bValue));\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        let result = this.conv2d(input, filter, convInfo);\n        if (bias) {\n            // TODO(lina128): Use add directly once fusedConv2d is modularized.\n            result = tf.add(result, bias);\n        }\n        if (activation) {\n            result =\n                mapActivation(this, result, activation, preluActivationWeights);\n        }\n        return result;\n    }\n    conv2d(x, filter, convInfo) {\n        assertNotComplex([x, filter], 'conv2d');\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const padLeft = convInfo.padInfo.left;\n        const padTop = convInfo.padInfo.top;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const y = tf.buffer(convInfo.outShape, x.dtype);\n        const xBatchStride = x.strides[0];\n        const xRowStride = isChannelsLast ? x.strides[1] : x.strides[2];\n        const xColStride = isChannelsLast ? x.strides[2] : 1;\n        const xChannelStride = isChannelsLast ? 1 : x.strides[1];\n        const yBatchStride = y.strides[0];\n        const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];\n        const yColStride = isChannelsLast ? y.strides[2] : 1;\n        const yChannelStride = isChannelsLast ? 1 : y.strides[1];\n        const xVals = this.readSync(x.dataId);\n        const wVals = this.readSync(filter.dataId);\n        const yVals = y.values;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n            const xOffset1 = b * xBatchStride;\n            const yOffset1 = b * yBatchStride;\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const yOffset2 = yOffset1 + yR * yRowStride;\n                const xRCorner = yR * convInfo.strideHeight - padTop;\n                for (let wR = 0; wR < filterHeight; wR++) {\n                    const xR = xRCorner + wR * dilationHeight;\n                    if (xR < 0 || xR >= convInfo.inHeight) {\n                        continue;\n                    }\n                    const wOffset1 = wR * filter.strides[0];\n                    const xOffset2 = xOffset1 + xR * xRowStride;\n                    for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                        const yOffset3 = yOffset2 + yC * yColStride;\n                        const xCCorner = yC * convInfo.strideWidth - padLeft;\n                        for (let wC = 0; wC < filterWidth; wC++) {\n                            const xC = xCCorner + wC * dilationWidth;\n                            if (xC < 0 || xC >= convInfo.inWidth) {\n                                continue;\n                            }\n                            const wOffset2 = wOffset1 + wC * filter.strides[1];\n                            const xOffset3 = xOffset2 + xC * xColStride;\n                            let wOffset3 = wOffset2;\n                            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                const xVal = xVals[xOffset3 + d1 * xChannelStride];\n                                for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                                    yVals[yOffset3 + d2 * yChannelStride] +=\n                                        xVal * wVals[wOffset3 + d2];\n                                }\n                                wOffset3 += convInfo.outChannels;\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return y.toTensor();\n    }\n    conv3d(x, filter, convInfo) {\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const padFront = convInfo.padInfo.front;\n        const padLeft = convInfo.padInfo.left;\n        const padTop = convInfo.padInfo.top;\n        const y = tf.buffer(convInfo.outShape, x.dtype);\n        const xVals = this.readSync(x.dataId);\n        const wVals = this.readSync(filter.dataId);\n        const yVals = y.values;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n            const xOffset1 = b * x.strides[0];\n            const yOffset1 = b * y.strides[0];\n            for (let yF = 0; yF < convInfo.outDepth; ++yF) {\n                const yOffset2 = yOffset1 + yF * y.strides[1];\n                const xFCorner = yF * convInfo.strideDepth - padFront;\n                for (let wF = 0; wF < filterDepth; wF++) {\n                    const xF = xFCorner + wF * dilationDepth;\n                    if (xF < 0 || xF >= convInfo.inDepth) {\n                        continue;\n                    }\n                    const wOffset1 = wF * filter.strides[0];\n                    const xOffset2 = xOffset1 + xF * x.strides[1];\n                    for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                        const yOffset3 = yOffset2 + yR * y.strides[2];\n                        const xRCorner = yR * convInfo.strideHeight - padTop;\n                        for (let wR = 0; wR < filterHeight; wR++) {\n                            const xR = xRCorner + wR * dilationHeight;\n                            if (xR < 0 || xR >= convInfo.inHeight) {\n                                continue;\n                            }\n                            const wOffset2 = wOffset1 + wR * filter.strides[1];\n                            const xOffset3 = xOffset2 + xR * x.strides[2];\n                            for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                                const yOffset4 = yOffset3 + yC * convInfo.outChannels;\n                                const xCCorner = yC * convInfo.strideWidth - padLeft;\n                                for (let wC = 0; wC < filterWidth; wC++) {\n                                    const xC = xCCorner + wC * dilationWidth;\n                                    if (xC < 0 || xC >= convInfo.inWidth) {\n                                        continue;\n                                    }\n                                    const wOffset3 = wOffset2 + wC * filter.strides[2];\n                                    const xOffset4 = xOffset3 + xC * convInfo.inChannels;\n                                    let wOffset4 = wOffset3;\n                                    for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                        const xVal = xVals[xOffset4 + d1];\n                                        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                                            yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];\n                                        }\n                                        wOffset4 += convInfo.outChannels;\n                                    }\n                                }\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return y.toTensor();\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        assertNotComplex([dy, filter], 'conv2dDerInput');\n        const dx = tf.buffer(convInfo.inShape, 'float32');\n        const dxValues = dx.values;\n        const dyValues = this.readSync(dy.dataId);\n        const fltValues = this.readSync(filter.dataId);\n        const [fltS0, fltS1, fltS2] = filter.strides;\n        const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth, dataFormat } = convInfo;\n        const topPad = filterHeight - 1 - convInfo.padInfo.top;\n        const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const xBatchStride = dx.strides[0];\n        const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];\n        const xColStride = isChannelsLast ? dx.strides[2] : 1;\n        const xChannelStride = isChannelsLast ? 1 : dx.strides[1];\n        const yBatchStride = dy.strides[0];\n        const yRowStride = isChannelsLast ? dy.strides[1] : dy.strides[2];\n        const yColStride = isChannelsLast ? dy.strides[2] : 1;\n        const yChannelStride = isChannelsLast ? 1 : dy.strides[1];\n        for (let b = 0; b < batchSize; ++b) {\n            for (let d1 = 0; d1 < inChannels; ++d1) {\n                for (let xR = 0; xR < inHeight; ++xR) {\n                    const xRCorner = xR - topPad;\n                    const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                    const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                    for (let xC = 0; xC < inWidth; ++xC) {\n                        const xCCorner = xC - leftPad;\n                        const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                        const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                        let dotProd = 0;\n                        for (let yR = xRMin; yR < yRMax; ++yR) {\n                            const wR = yR * strideHeight - xRCorner;\n                            for (let yC = xCMin; yC < yCMax; ++yC) {\n                                const wC = yC * strideWidth - xCCorner;\n                                const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;\n                                const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n                                for (let d2 = 0; d2 < outChannels; ++d2) {\n                                    const pixel = dyValues[dyOffset + yChannelStride * d2];\n                                    const weight = fltValues[fltOffset + d2];\n                                    dotProd += pixel * weight;\n                                }\n                            }\n                        }\n                        const dxOffset = xBatchStride * b + xRowStride * xR +\n                            xColStride * xC + xChannelStride * d1;\n                        dxValues[dxOffset] = dotProd;\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        const dx = tf.buffer(convInfo.inShape, 'float32');\n        const dxValues = dx.values;\n        const [dxS0, dxS1, dxS2, dxS3] = dx.strides;\n        const dyValues = this.readSync(dy.dataId);\n        const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n        const fltValues = this.readSync(filter.dataId);\n        const [fltS0, fltS1, fltS2, fltS3] = filter.strides;\n        const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo;\n        const frontPad = filterDepth - 1 - convInfo.padInfo.front;\n        const topPad = filterHeight - 1 - convInfo.padInfo.top;\n        const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let d1 = 0; d1 < inChannels; ++d1) {\n                // Frames of depth\n                for (let xF = 0; xF < inDepth; ++xF) {\n                    const xFCorner = xF - frontPad;\n                    const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));\n                    const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);\n                    // Rows as per standard 2d matrix notation\n                    for (let xR = 0; xR < inHeight; ++xR) {\n                        const xRCorner = xR - topPad;\n                        const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                        const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                        // Columns as per standard 2d matrix notation\n                        for (let xC = 0; xC < inWidth; ++xC) {\n                            const xCCorner = xC - leftPad;\n                            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                            const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                            let dotProd = 0;\n                            for (let yF = xFMin; yF < yFMax; ++yF) {\n                                const wF = yF * strideDepth - xFCorner;\n                                for (let yR = xRMin; yR < yRMax; ++yR) {\n                                    const wR = yR * strideHeight - xRCorner;\n                                    for (let yC = xCMin; yC < yCMax; ++yC) {\n                                        const wC = yC * strideWidth - xCCorner;\n                                        const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;\n                                        const fltOffset = fltS0 * (filterDepth - 1 - wF) +\n                                            fltS1 * (filterHeight - 1 - wR) +\n                                            fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;\n                                        for (let d2 = 0; d2 < outChannels; ++d2) {\n                                            const pixel = dyValues[dyOffset + d2];\n                                            const weight = fltValues[fltOffset + d2];\n                                            dotProd += pixel * weight;\n                                        }\n                                    }\n                                }\n                            }\n                            dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =\n                                dotProd;\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    conv2dDerFilter(x, dy, convInfo) {\n        assertNotComplex([x, dy], 'conv2dDerFilter');\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const dW = tf.buffer(convInfo.filterShape, 'float32');\n        const leftPad = convInfo.padInfo.left;\n        const topPad = convInfo.padInfo.top;\n        const xBuf = this.bufferSync(x);\n        const dyBuf = this.bufferSync(dy);\n        for (let wR = 0; wR < filterHeight; ++wR) {\n            const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n            const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n            for (let wC = 0; wC < filterWidth; ++wC) {\n                const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                    for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                        // Need to convolve.\n                        let dotProd = 0;\n                        for (let b = 0; b < convInfo.batchSize; ++b) {\n                            for (let yR = yRMin; yR < yRMax; ++yR) {\n                                const xR = wR + yR * strideHeight - topPad;\n                                for (let yC = yCMin; yC < yCMax; ++yC) {\n                                    const xC = wC + yC * strideWidth - leftPad;\n                                    if (isChannelsLast) {\n                                        dotProd +=\n                                            xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n                                    }\n                                    else {\n                                        dotProd +=\n                                            xBuf.get(b, d1, xR, xC) * dyBuf.get(b, d2, yR, yC);\n                                    }\n                                }\n                            }\n                        }\n                        dW.set(dotProd, wR, wC, d1, d2);\n                    }\n                }\n            }\n        }\n        return dW.toTensor();\n    }\n    conv3dDerFilter(x, dy, convInfo) {\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dw = tf.buffer(convInfo.filterShape, 'float32');\n        const dwValues = dw.values;\n        const [dwS0, dwS1, dwS2, dwS3] = dw.strides;\n        const dyValues = this.readSync(dy.dataId);\n        const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n        const xValues = this.readSync(x.dataId);\n        const [xS0, xS1, xS2, xS3] = x.strides;\n        const frontPad = convInfo.padInfo.front;\n        const leftPad = convInfo.padInfo.left;\n        const topPad = convInfo.padInfo.top;\n        for (let wF = 0; wF < filterDepth; ++wF) {\n            const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));\n            const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);\n            const wOffset1 = wF * dwS0;\n            for (let wR = 0; wR < filterHeight; ++wR) {\n                const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n                const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n                const wOffset2 = wR * dwS1 + wOffset1;\n                for (let wC = 0; wC < filterWidth; ++wC) {\n                    const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                    const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                    const wOffset3 = wC * dwS2 + wOffset2;\n                    for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                        const wOffset4 = d1 * dwS3 + wOffset3;\n                        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                            let dotProd = 0;\n                            for (let b = 0; b < convInfo.batchSize; ++b) {\n                                const xOffset1 = b * xS0;\n                                const yOffset1 = b * dyS0;\n                                for (let yF = yFMin; yF < yFMax; ++yF) {\n                                    const xF = wF + yF * strideDepth - frontPad;\n                                    const xOffset2 = xF * xS1 + xOffset1;\n                                    const yOffset2 = yF * dyS1 + yOffset1;\n                                    for (let yR = yRMin; yR < yRMax; ++yR) {\n                                        const xR = wR + yR * strideHeight - topPad;\n                                        const xOffset3 = xR * xS2 + xOffset2;\n                                        const yOffset3 = yR * dyS2 + yOffset2;\n                                        for (let yC = yCMin; yC < yCMax; ++yC) {\n                                            const xC = wC + yC * strideWidth - leftPad;\n                                            const xOffset4 = xC * xS3 + xOffset3;\n                                            const yOffset4 = yC * dyS3 + yOffset3;\n                                            dotProd +=\n                                                xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];\n                                        }\n                                    }\n                                }\n                            }\n                            dwValues[wOffset4 + d2] = dotProd;\n                        }\n                    }\n                }\n            }\n        }\n        return dw.toTensor();\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        let result = this.depthwiseConv2D(input, filter, convInfo);\n        if (bias) {\n            // TODO(lina128): Use add directly once fusedDepthwiseConv2D is\n            // modularized.\n            result = tf.add(result, bias);\n        }\n        if (activation) {\n            result =\n                mapActivation(this, result, activation, preluActivationWeights);\n        }\n        return result;\n    }\n    depthwiseConv2D(x, filter, convInfo) {\n        assertNotComplex([x, filter], 'depthwiseConv2D');\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const padLeft = convInfo.padInfo.left;\n        const padTop = convInfo.padInfo.top;\n        const chMul = convInfo.outChannels / convInfo.inChannels;\n        const y = tf.buffer(convInfo.outShape, x.dtype);\n        const xVals = this.readSync(x.dataId);\n        const wVals = this.readSync(filter.dataId);\n        const yVals = y.values;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n            const xOffset1 = b * x.strides[0];\n            const yOffset1 = b * y.strides[0];\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const yOffset2 = yOffset1 + yR * y.strides[1];\n                const xRCorner = yR * convInfo.strideHeight - padLeft;\n                for (let wR = 0; wR < filterHeight; ++wR) {\n                    const xR = xRCorner + wR * dilationHeight;\n                    if (xR < 0 || xR >= convInfo.inHeight) {\n                        continue;\n                    }\n                    const wOffset1 = wR * filter.strides[0];\n                    const xOffset2 = xOffset1 + xR * x.strides[1];\n                    for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                        const yOffset3 = yOffset2 + yC * y.strides[2];\n                        const xCCorner = yC * convInfo.strideWidth - padTop;\n                        for (let wC = 0; wC < filterWidth; ++wC) {\n                            const xC = xCCorner + wC * dilationWidth;\n                            if (xC < 0 || xC >= convInfo.inWidth) {\n                                continue;\n                            }\n                            const wOffset2 = wOffset1 + wC * filter.strides[1];\n                            const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n                            let yOffset4 = yOffset3;\n                            let wOffset3 = wOffset2;\n                            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                const xVal = xVals[xOffset3 + d1];\n                                for (let q = 0; q < chMul; ++q) {\n                                    yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];\n                                }\n                                yOffset4 += chMul;\n                                wOffset3 += chMul;\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return y.toTensor();\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        assertNotComplex([dy, filter], 'depthwiseConv2DDerInput');\n        const dx = tf.buffer(convInfo.inShape, 'float32');\n        const dxValues = dx.values;\n        const [dxS0, dxS1, dxS2] = dx.strides;\n        const dyValues = this.readSync(dy.dataId);\n        const [dyS0, dyS1, dyS2] = dy.strides;\n        const fltValues = this.readSync(filter.dataId);\n        const [fltS0, fltS1, fltS2] = filter.strides;\n        const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n        const topPad = filterHeight - 1 - convInfo.padInfo.top;\n        const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n        const chMul = outChannels / inChannels;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let d1 = 0; d1 < inChannels; ++d1) {\n                for (let xR = 0; xR < inHeight; ++xR) {\n                    const xRCorner = xR - topPad;\n                    const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                    const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                    for (let xC = 0; xC < inWidth; ++xC) {\n                        const xCCorner = xC - leftPad;\n                        const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                        const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                        let dotProd = 0;\n                        for (let yR = xRMin; yR < yRMax; ++yR) {\n                            const wR = yR * strideHeight - xRCorner;\n                            for (let yC = xCMin; yC < yCMax; ++yC) {\n                                const wC = yC * strideWidth - xCCorner;\n                                const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n                                const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n                                for (let dm = 0; dm < chMul; ++dm) {\n                                    const d2 = d1 * chMul + dm;\n                                    const pixel = dyValues[dyOffset + d2];\n                                    const weight = fltValues[fltOffset + dm];\n                                    dotProd += pixel * weight;\n                                }\n                            }\n                        }\n                        dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    depthwiseConv2DDerFilter(x, dy, convInfo) {\n        assertNotComplex([x, dy], 'depthwiseConv2DDerFilter');\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dW = tf.buffer(convInfo.filterShape, 'float32');\n        const leftPad = convInfo.padInfo.left;\n        const topPad = convInfo.padInfo.top;\n        const chMul = convInfo.outChannels / convInfo.inChannels;\n        const xBuf = this.bufferSync(x);\n        const dyBuf = this.bufferSync(dy);\n        for (let wR = 0; wR < filterHeight; ++wR) {\n            const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n            const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n            for (let wC = 0; wC < filterWidth; ++wC) {\n                const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                    const d1 = Math.trunc(d2 / chMul);\n                    const dm = d2 % chMul;\n                    let dotProd = 0;\n                    for (let b = 0; b < convInfo.batchSize; ++b) {\n                        for (let yR = yRMin; yR < yRMax; ++yR) {\n                            const xR = wR + yR * strideHeight - topPad;\n                            for (let yC = yCMin; yC < yCMax; ++yC) {\n                                const xC = wC + yC * strideWidth - leftPad;\n                                dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n                            }\n                        }\n                    }\n                    dW.set(dotProd, wR, wC, d1, dm);\n                }\n            }\n        }\n        return dW.toTensor();\n    }\n    tile(x, reps) {\n        assertNotComplex(x, 'tile');\n        return tile(this.bufferSync(x), reps);\n    }\n    gather(x, indices, axis) {\n        assertNotComplex([x, indices], 'gather');\n        const newShape = x.shape.slice();\n        const indicesValues = this.readSync(indices.dataId);\n        newShape[axis] = indicesValues.length;\n        const result = tf.buffer(newShape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < result.size; ++i) {\n            const newLoc = result.indexToLoc(i);\n            const originalLoc = newLoc.slice();\n            originalLoc[axis] = indicesValues[newLoc[axis]];\n            const originalIndex = xBuf.locToIndex(originalLoc);\n            result.values[i] = xBuf.values[originalIndex];\n        }\n        return result.toTensor();\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        assertNotComplex([x], 'batchToSpaceND');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n        const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n        const reshapedPermuted = backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n        const sliceBeginCoords = backend_util.getSliceBeginCoords(crops, blockShape.length);\n        const sliceSize = backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n        return tf.transpose(x.reshape(reshaped), permuted)\n            .reshape(reshapedPermuted)\n            .slice(sliceBeginCoords, sliceSize);\n    }\n    pool3d(x, convInfo, poolType) {\n        assertNotComplex(x, 'pool3d');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n            Number.POSITIVE_INFINITY);\n        const xValues = this.readSync(x.dataId);\n        const output = tf.buffer(convInfo.outShape, x.dtype);\n        const outputVals = output.values;\n        const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] *\n            convInfo.outShape[3] * convInfo.outShape[4];\n        const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];\n        const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];\n        const outputColStrides = convInfo.outShape[4];\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            const outputBatchOffset = batch * outputBatchStrides;\n            const inputBatchOffset = batch * x.strides[0];\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n                    const xDepthCorner = yDepth * strideDepth - padFront;\n                    let xDepthMin = xDepthCorner;\n                    while (xDepthMin < 0) {\n                        xDepthMin += dilationDepth;\n                    }\n                    const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n                    const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides;\n                    for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n                        const xRowCorner = yRow * strideHeight - padTop;\n                        let xRowMin = xRowCorner;\n                        while (xRowMin < 0) {\n                            xRowMin += dilationHeight;\n                        }\n                        const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n                        const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;\n                        for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n                            const xColCorner = yCol * strideWidth - padLeft;\n                            let xColMin = xColCorner;\n                            while (xColMin < 0) {\n                                xColMin += dilationWidth;\n                            }\n                            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n                            // Shader code begins\n                            const outputColOffset = outputRowOffset + yCol * outputColStrides;\n                            let minMaxValue = initialValue;\n                            let avgValue = 0;\n                            let count = 0;\n                            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n                                const xDepthOffset = inputBatchOffset + xDepth * x.strides[1];\n                                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                                    const xRowOffset = xDepthOffset + xRow * x.strides[2];\n                                    for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                                        const xColOffset = xRowOffset + xCol * x.strides[3];\n                                        const pixel = xValues[xColOffset + channel];\n                                        if ((poolType === 'max' && pixel > minMaxValue)) {\n                                            minMaxValue = pixel;\n                                        }\n                                        else if (poolType === 'avg') {\n                                            avgValue += pixel;\n                                            count++;\n                                        }\n                                        if (isNaN(minMaxValue)) {\n                                            break;\n                                        }\n                                    }\n                                    if (isNaN(minMaxValue)) {\n                                        break;\n                                    }\n                                }\n                                if (isNaN(minMaxValue)) {\n                                    break;\n                                }\n                            }\n                            const outputOffset = outputColOffset + channel;\n                            outputVals[outputOffset] =\n                                poolType === 'avg' ? avgValue / count : minMaxValue;\n                        }\n                    }\n                }\n            }\n        }\n        return output.toTensor();\n    }\n    avgPool3d(x, convInfo) {\n        assertNotComplex(x, 'avgPool3d');\n        return this.pool3d(x, convInfo, 'avg').toFloat();\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        assertNotComplex([dy, x], 'avgPool3dBackprop');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const dx = tf.buffer(x.shape, 'float32');\n        const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n        const dyBuf = this.bufferSync(dy);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n                    for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n                        for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n                            // Shader code begins.\n                            const dyDepthCorner = dxDepth - padFront;\n                            const dyRowCorner = dxRow - padTop;\n                            const dyColCorner = dxCol - padLeft;\n                            let dotProd = 0;\n                            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n                                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n                                    Math.floor(dyDepth) !== dyDepth) {\n                                    continue;\n                                }\n                                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                                    const dyRow = (dyRowCorner + wRow) / strideHeight;\n                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n                                        Math.floor(dyRow) !== dyRow) {\n                                        continue;\n                                    }\n                                    for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                                        const dyCol = (dyColCorner + wCol) / strideWidth;\n                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n                                            Math.floor(dyCol) !== dyCol) {\n                                            continue;\n                                        }\n                                        const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        dotProd += pixel;\n                                    }\n                                }\n                            }\n                            dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    maxPool3d(x, convInfo) {\n        assertNotComplex(x, 'maxPool3d');\n        return this.pool3d(x, convInfo, 'max').toFloat();\n    }\n    maxPool3dPositions(x, convInfo) {\n        const maxPositions = tf.buffer(convInfo.outShape, 'int32');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const xBuf = this.bufferSync(x);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n                    const xDepthCorner = yDepth * strideDepth - padFront;\n                    let xDepthMin = xDepthCorner;\n                    while (xDepthMin < 0) {\n                        xDepthMin += dilationDepth;\n                    }\n                    const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n                    for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n                        const xRowCorner = yRow * strideHeight - padTop;\n                        let xRowMin = xRowCorner;\n                        while (xRowMin < 0) {\n                            xRowMin += dilationHeight;\n                        }\n                        const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n                        for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n                            const xColCorner = yCol * strideWidth - padLeft;\n                            let xColMin = xColCorner;\n                            while (xColMin < 0) {\n                                xColMin += dilationWidth;\n                            }\n                            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n                            // Shader code begins\n                            let maxValue = Number.NEGATIVE_INFINITY;\n                            let maxPosition = -1;\n                            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n                                const wDepth = xDepth - xDepthCorner;\n                                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                                    const wRow = xRow - xRowCorner;\n                                    for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                                        const wCol = xCol - xColCorner;\n                                        const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);\n                                        if (pixel >= maxValue) {\n                                            maxValue = pixel;\n                                            maxPosition = wDepth * effectiveFilterHeight *\n                                                effectiveFilterWidth +\n                                                wRow * effectiveFilterHeight + wCol;\n                                        }\n                                    }\n                                }\n                            }\n                            maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return maxPositions.toTensor();\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        assertNotComplex([x, y], 'maxPool3dBackprop');\n        const maxPositions = this.maxPool3dPositions(x, convInfo);\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const dx = tf.buffer(x.shape, 'float32');\n        const maxPosBuf = this.bufferSync(maxPositions);\n        const dyBuf = this.bufferSync(dy);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n                    for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n                        for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n                            // Shader code begins\n                            const dyDepthCorner = dxDepth - padFront;\n                            const dyRowCorner = dxRow - padTop;\n                            const dyColCorner = dxCol - padLeft;\n                            let dotProd = 0;\n                            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n                                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n                                    Math.floor(dyDepth) !== dyDepth) {\n                                    continue;\n                                }\n                                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                                    const dyRow = (dyRowCorner + wRow) / strideHeight;\n                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n                                        Math.floor(dyRow) !== dyRow) {\n                                        continue;\n                                    }\n                                    for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                                        const dyCol = (dyColCorner + wCol) / strideWidth;\n                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n                                            Math.floor(dyCol) !== dyCol) {\n                                            continue;\n                                        }\n                                        const maxPos = effectiveFilterDepth *\n                                            effectiveFilterHeight * effectiveFilterWidth -\n                                            1 -\n                                            maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth +\n                                            wRow * effectiveFilterWidth + wCol;\n                                        const mask = maxPos === curPos ? 1 : 0;\n                                        if (mask === 0) {\n                                            continue;\n                                        }\n                                        const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        dotProd += pixel * mask;\n                                    }\n                                }\n                            }\n                            dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        assertNotComplex(x, 'resizeBilinear');\n        const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n        const xValues = this.readSync(x.dataId);\n        const result = new Float32Array(util.sizeFromShape([batch, newHeight, newWidth, numChannels]));\n        const effectiveInputSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutputSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        let outputIdx = 0;\n        const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n        const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n        for (let b = 0; b < batch; b++) {\n            for (let r = 0; r < newHeight; r++) {\n                const sourceFracRow = effectiveRowSizeRatio * r;\n                const sourceRowFloor = Math.floor(sourceFracRow);\n                const rowFrac = sourceFracRow - sourceRowFloor;\n                const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));\n                const topRowOffset = b * x.strides[0] + sourceRowFloor * x.strides[1];\n                const botRowOffset = b * x.strides[0] + sourceRowCeil * x.strides[1];\n                for (let c = 0; c < newWidth; c++) {\n                    const sourceFracCol = effectiveColSizeRatio * c;\n                    const sourceColFloor = Math.floor(sourceFracCol);\n                    const colFrac = sourceFracCol - sourceColFloor;\n                    const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));\n                    const topLeftOffest = topRowOffset + sourceColFloor * x.strides[2];\n                    const botLeftOffset = botRowOffset + sourceColFloor * x.strides[2];\n                    const topRightOffset = topRowOffset + sourceColCeil * x.strides[2];\n                    const botRightOffest = botRowOffset + sourceColCeil * x.strides[2];\n                    for (let d = 0; d < numChannels; d++) {\n                        // Begin shader.\n                        // Compute the fractional index of the source.\n                        const topLeft = xValues[topLeftOffest + d];\n                        const bottomLeft = xValues[botLeftOffset + d];\n                        const topRight = xValues[topRightOffset + d];\n                        const bottomRight = xValues[botRightOffest + d];\n                        const top = topLeft + (topRight - topLeft) * colFrac;\n                        const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;\n                        const newValue = top + (bottom - top) * rowFrac;\n                        result[outputIdx++] = newValue;\n                    }\n                }\n            }\n        }\n        return tf.tensor(result, [batch, newHeight, newWidth, numChannels]);\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        assertNotComplex([dy, x], 'resizeBilinearBackprop');\n        const [batch, xHeight, xWidth, depth] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        const output = new Float32Array(batch * xHeight * xWidth * depth);\n        // In the backwards pass, we want to find the pixels that were generated\n        // for each pixel in the input image the forward pass and add the\n        // corresponding coefficient from dy to the gradient (with some\n        // interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/3039375c86a5bbc9610c7725dcaa95d635f87ba2/tensorflow/core/kernels/resize_bilinear_op.cc#L275\n        const dyValues = this.readSync(dy.dataId);\n        let offset = 0;\n        for (let b = 0; b < batch; b++) {\n            const bOffset = b * x.strides[0];\n            for (let r = 0; r < yHeight; r++) {\n                const dxR = r * heightScale;\n                const topDxRIndex = Math.floor(dxR);\n                const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);\n                const topDxROffset = bOffset + topDxRIndex * x.strides[1];\n                const bottomDxROffset = bOffset + bottomDxRIndex * x.strides[1];\n                const dxRLerp = dxR - topDxRIndex;\n                const inverseDxRLerp = 1.0 - dxRLerp;\n                for (let c = 0; c < yWidth; c++) {\n                    const dxC = c * widthScale;\n                    const leftDxCIndex = Math.floor(dxC);\n                    const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);\n                    const dxCLerp = dxC - leftDxCIndex;\n                    const inverseDxCLerp = 1.0 - dxCLerp;\n                    const topLeftRCOffset = topDxROffset + leftDxCIndex * x.strides[2];\n                    const topRightRCOffset = topDxROffset + rightDxCIndex * x.strides[2];\n                    const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * x.strides[2];\n                    const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * x.strides[2];\n                    const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;\n                    const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;\n                    const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;\n                    const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;\n                    for (let d = 0; d < depth; d++) {\n                        const dyVal = dyValues[offset++];\n                        output[topLeftRCOffset + d] +=\n                            dyVal * inverseDxRLerpTimesInverseDxCLerp;\n                        output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;\n                        output[bottomLeftRCOffset + d] +=\n                            dyVal * dxRLerpTimesInverseDxCLerp;\n                        output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(output, [batch, xWidth, xHeight, depth], x.dtype);\n    }\n    resizeNearestNeighbor(x, newHeight, newWidth, alignCorners) {\n        assertNotComplex(x, 'resizeNearestNeighbor');\n        const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n        const xValues = this.readSync(x.dataId);\n        const output = new Float32Array(batch * newHeight * newWidth * numChannels);\n        const effectiveInputSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutputSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n        const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n        let outputOffset = 0;\n        for (let b = 0; b < batch; b++) {\n            const batchOffset = b * x.strides[0];\n            for (let r = 0; r < newHeight; r++) {\n                const sourceFracRow = effectiveRowSizeRatio * r;\n                const sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) :\n                    Math.floor(sourceFracRow));\n                const rowOffset = batchOffset + sourceNearestRow * x.strides[1];\n                for (let c = 0; c < newWidth; c++) {\n                    const sourceFracCol = effectiveColSizeRatio * c;\n                    const sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) :\n                        Math.floor(sourceFracCol));\n                    const colOffset = rowOffset + sourceNearestCol * x.strides[2];\n                    for (let d = 0; d < numChannels; d++) {\n                        // Begin shader.\n                        // Compute the fractional index of the source.\n                        const newVal = xValues[colOffset + d];\n                        output[outputOffset++] = newVal;\n                    }\n                }\n            }\n        }\n        return tf.tensor(output, [batch, newHeight, newWidth, numChannels], x.dtype);\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        assertNotComplex([dy, x], 'resizeNearestNeighborBackprop');\n        const [batch, xHeight, xWidth, depth] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        const output = new Float32Array(batch * xHeight * xWidth * depth);\n        const dyValues = this.readSync(dy.dataId);\n        // In the backwards pass, we want to find the pixels that were generated\n        // for each pixel in the input image the forward pass\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        // Loop over the output space.\n        for (let b = 0; b < batch; b++) {\n            const batchOffset = b * x.strides[0];\n            for (let r = 0; r < xHeight; r++) {\n                const rowOffset = batchOffset + r * x.strides[1];\n                // Compute bounds for where in dy we will look\n                const startRLerp = Math.floor(r * invHeightScale);\n                const startDyR = Math.floor(startRLerp - (winHeight / 2));\n                for (let c = 0; c < xWidth; c++) {\n                    const colOffset = rowOffset + c * x.strides[2];\n                    // Compute bounds for where in dy we will look\n                    const startCLerp = Math.floor(c * invWidthScale);\n                    const startDyC = Math.floor(startCLerp - (winWidth / 2));\n                    for (let d = 0; d < depth; d++) {\n                        let accum = 0;\n                        // loop over dy\n                        for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {\n                            const dyR = dyRIndex + startDyR;\n                            // Guard against the window exceeding the bounds of dy\n                            if (dyR < 0 || dyR >= yHeight) {\n                                continue;\n                            }\n                            const dyROffset = batchOffset + dyR * dy.strides[1];\n                            const sourceFracRow = dyR * heightScale;\n                            const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) :\n                                Math.floor(sourceFracRow));\n                            if (r !== sourceNearestRow) {\n                                continue;\n                            }\n                            for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {\n                                const dyC = dyCIndex + startDyC;\n                                // Guard against the window exceeding the bounds of dy\n                                if (dyC < 0 || dyC >= yWidth) {\n                                    continue;\n                                }\n                                const dyCOffset = dyROffset + dyC * dy.strides[2];\n                                const sourceFracCol = dyC * widthScale;\n                                const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) :\n                                    Math.floor(sourceFracCol));\n                                if (c === sourceNearestCol) {\n                                    accum += dyValues[dyCOffset + d];\n                                }\n                            }\n                        }\n                        output[colOffset + d] = accum;\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(output, x.shape, x.dtype);\n    }\n    localResponseNormalization4D(x, depthRadius, bias, alpha, beta) {\n        assertNotComplex(x, 'localResponseNormalization4D');\n        const channels = x.shape[3];\n        const maxD = channels - 1;\n        const xValues = this.readSync(x.dataId);\n        const size = x.size;\n        const result = new Float32Array(size);\n        function sumAcrossChannels(offset) {\n            const currentChannel = offset % channels;\n            let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n            const endSumOffset = offset - currentChannel +\n                Math.min(currentChannel + depthRadius, maxD);\n            let sum = 0.0;\n            for (; beginSumOffset <= endSumOffset; beginSumOffset++) {\n                const z = xValues[beginSumOffset];\n                sum += z * z;\n            }\n            return sum;\n        }\n        for (let offset = 0; offset < size; offset++) {\n            const sum = sumAcrossChannels(offset);\n            const val = xValues[offset] * Math.pow(bias + alpha * sum, -beta);\n            result[offset] = val;\n        }\n        return tf.tensor4d(result, x.shape);\n    }\n    LRNGrad(dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {\n        assertNotComplex(dy, 'LRNGrad');\n        const channels = dy.shape[3];\n        const dyValues = this.readSync(dy.dataId);\n        const inputImageValues = this.readSync(inputImage.dataId);\n        const outputImageValues = this.readSync(outputImage.dataId);\n        const result = new Float32Array(dy.size);\n        const size = dy.size;\n        for (let offset = 0; offset < size; offset++) {\n            const currentChannel = offset % channels;\n            const depthBegin = (offset - currentChannel) + Math.max(0, currentChannel - depthRadius);\n            const depthEnd = (offset - currentChannel) +\n                Math.min(channels, currentChannel + depthRadius + 1);\n            let norm = 0;\n            for (let k = depthBegin; k < depthEnd; k++) {\n                norm += Math.pow(inputImageValues[k], 2);\n            }\n            norm = alpha * norm + bias;\n            for (let k = depthBegin; k < depthEnd; k++) {\n                let dyi = -2 * alpha * beta * inputImageValues[k] *\n                    outputImageValues[offset] / norm;\n                if (offset === k) {\n                    dyi += Math.pow(norm, -beta);\n                }\n                dyi *= dyValues[offset];\n                result[k] += dyi;\n            }\n        }\n        return tf.tensor4d(result, dy.shape);\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        assertNotComplex(logits, 'multinomial');\n        const probabilities = normalized ? logits : tf.softmax(logits);\n        const batchSize = probabilities.shape[0];\n        const numEvents = probabilities.shape[1];\n        const res = tf.zeros([batchSize, numSamples], 'int32');\n        const resVals = this.readSync(res.dataId);\n        const probVals = this.readSync(probabilities.dataId);\n        for (let b = 0; b < batchSize; ++b) {\n            const offset = b * numEvents;\n            // The cdf won't include the last event. It will be implicit if no other\n            // event happened.\n            const cdf = new Float32Array(numEvents - 1);\n            cdf[0] = probVals[offset];\n            for (let event = 1; event < cdf.length; ++event) {\n                cdf[event] = cdf[event - 1] + probVals[offset + event];\n            }\n            const random = seedrandom.alea(seed.toString());\n            const outOffset = b * numSamples;\n            for (let sampleId = 0; sampleId < numSamples; ++sampleId) {\n                const r = random();\n                // Assume last event happened by default.\n                resVals[outOffset + sampleId] = cdf.length;\n                for (let event = 0; event < cdf.length; event++) {\n                    if (r < cdf[event]) {\n                        resVals[outOffset + sampleId] = event;\n                        break;\n                    }\n                }\n            }\n        }\n        return res;\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        assertNotComplex(indices, 'oneHot');\n        const res = new Float32Array(indices.size * depth);\n        res.fill(offValue);\n        const indicesVal = this.readSync(indices.dataId);\n        for (let event = 0; event < indices.size; ++event) {\n            if (indicesVal[event] >= 0 && indicesVal[event] < depth) {\n                res[event * depth + indicesVal[event]] = onValue;\n            }\n        }\n        return tf.tensor2d(res, [indices.size, depth], 'int32');\n    }\n    nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n        assertNotComplex(boxes, 'nonMaxSuppression');\n        const boxesVals = this.readSync(boxes.dataId);\n        const scoresVals = this.readSync(scores.dataId);\n        return nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        util.assert(dataFormat === 'NHWC', () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`);\n        util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n        const batchSize = x.shape[0];\n        const inputHeight = x.shape[1];\n        const inputWidth = x.shape[2];\n        const inputDepth = x.shape[3];\n        const outputHeight = inputHeight * blockSize;\n        const outputWidth = inputWidth * blockSize;\n        const outputDepth = inputDepth / (blockSize * blockSize);\n        const xValues = this.readSync(x.dataId);\n        const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);\n        let outputIdx = 0;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let h = 0; h < outputHeight; ++h) {\n                const inH = Math.floor(h / blockSize);\n                const offsetH = (h % blockSize);\n                for (let w = 0; w < outputWidth; ++w) {\n                    const inW = Math.floor(w / blockSize);\n                    const offsetW = (w % blockSize);\n                    const offsetD = (offsetH * blockSize + offsetW) * outputDepth;\n                    for (let d = 0; d < outputDepth; ++d) {\n                        const inD = d + offsetD;\n                        const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));\n                        result[outputIdx++] = xValues[inputIdx];\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(result, [batchSize, outputHeight, outputWidth, outputDepth]);\n    }\n    broadcastedBinaryOp(a, b, dtype, op) {\n        const newShape = backend_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const result = tf.buffer(newShape, dtype);\n        const aVals = this.readSync(a.dataId);\n        const bVals = this.readSync(b.dataId);\n        const aBroadcastDims = backend_util.getBroadcastDims(a.shape, newShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(b.shape, newShape);\n        const resVals = result.values;\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < resVals.length; ++i) {\n                resVals[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n            }\n        }\n        else {\n            const aBuf = this.bufferSync(a);\n            const bBuf = this.bufferSync(b);\n            for (let i = 0; i < resVals.length; ++i) {\n                const loc = result.indexToLoc(i);\n                const aLoc = loc.slice(-a.rank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = aBuf.locToIndex(aLoc);\n                const bLoc = loc.slice(-b.rank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = bBuf.locToIndex(bLoc);\n                resVals[i] = op(aVals[aIndex], bVals[bIndex]);\n            }\n        }\n        return result.toTensor();\n    }\n    split(x, sizeSplits, axis) {\n        return split(x, sizeSplits, axis);\n    }\n    dispose() { }\n    floatPrecision() {\n        return 32;\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return super.epsilon();\n    }\n    cropAndResize(images, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        const [batch, imageHeight, imageWidth, numChannels] = images.shape;\n        const numBoxes = boxes.shape[0];\n        const [cropHeight, cropWidth] = cropSize;\n        const output = tf.buffer([numBoxes, cropHeight, cropWidth, numChannels], 'float32');\n        const boxVals = this.readSync(boxes.dataId);\n        const boxIndVals = this.readSync(boxIndex.dataId);\n        const imageVals = this.readSync(images.dataId);\n        const inStride = images.strides; // to calculate flat indexes into image\n        const outStride = output.strides; // to calculate flat indexes into output\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc\n        for (let b = 0; b < numBoxes; b++) {\n            const startInd = b * 4;\n            const y1 = boxVals[startInd];\n            const x1 = boxVals[startInd + 1];\n            const y2 = boxVals[startInd + 2];\n            const x2 = boxVals[startInd + 3];\n            const bInd = boxIndVals[b];\n            if (bInd >= batch) {\n                continue;\n            }\n            const heightScale = (cropHeight > 1) ?\n                (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) :\n                0;\n            const widthScale = (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;\n            for (let y = 0; y < cropHeight; y++) {\n                const yInd = (cropHeight > 1) ?\n                    y1 * (imageHeight - 1) + y * (heightScale) :\n                    0.5 * (y1 + y2) * (imageHeight - 1);\n                if (yInd < 0 || yInd > imageHeight - 1) {\n                    for (let x = 0; x < cropWidth; x++) {\n                        for (let c = 0; c < numChannels; c++) {\n                            const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[ind] = extrapolationValue;\n                        }\n                    }\n                    continue;\n                }\n                if (method === 'bilinear') {\n                    const topInd = Math.floor(yInd);\n                    const bottomInd = Math.ceil(yInd);\n                    const yLerp = yInd - topInd;\n                    for (let x = 0; x < cropWidth; x++) {\n                        const xInd = (cropWidth > 1) ?\n                            x1 * (imageWidth - 1) + x * widthScale :\n                            0.5 * (x1 + x2) * (imageWidth - 1);\n                        if (xInd < 0 || xInd > imageWidth - 1) {\n                            for (let c = 0; c < numChannels; c++) {\n                                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                                output.values[ind] = extrapolationValue;\n                            }\n                            continue;\n                        }\n                        const leftInd = Math.floor(xInd);\n                        const rightInd = Math.ceil(xInd);\n                        const xLerp = xInd - leftInd;\n                        for (let c = 0; c < numChannels; c++) {\n                            let ind = c + leftInd * inStride[2] + topInd * inStride[1] +\n                                bInd * inStride[0];\n                            const topLeft = imageVals[ind];\n                            ind = c + rightInd * inStride[2] + topInd * inStride[1] +\n                                bInd * inStride[0];\n                            const topRight = imageVals[ind];\n                            ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +\n                                bInd * inStride[0];\n                            const bottomLeft = imageVals[ind];\n                            ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +\n                                bInd * inStride[0];\n                            const bottomRight = imageVals[ind];\n                            const top = topLeft + (topRight - topLeft) * xLerp;\n                            const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;\n                            ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[ind] = top + ((bottom - top) * yLerp);\n                        }\n                    }\n                }\n                else { // method == \"nearest\"\n                    for (let x = 0; x < cropWidth; ++x) {\n                        const xInd = (cropWidth > 1) ?\n                            x1 * (imageWidth - 1) + x * widthScale :\n                            0.5 * (x1 + x2) * (imageWidth - 1);\n                        if (xInd < 0 || xInd > imageWidth - 1) {\n                            for (let c = 0; c < numChannels; c++) {\n                                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                                output.values[ind] = extrapolationValue;\n                            }\n                            continue;\n                        }\n                        const closestX = Math.round(xInd);\n                        const closestY = Math.round(yInd);\n                        for (let c = 0; c < numChannels; c++) {\n                            const inInd = c + closestX * inStride[2] +\n                                closestY * inStride[1] + bInd * inStride[0];\n                            const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[outInd] = imageVals[inInd];\n                        }\n                    }\n                }\n            }\n        }\n        return output.toTensor();\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n        const sumDupeIndices = false;\n        return this.scatter(sparseIndices, sparseValues, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n    }\n    gatherND(x, indices) {\n        const indicesShape = indices.shape;\n        const sliceRank = indicesShape[indicesShape.length - 1];\n        const [resultShape, numSlices, sliceSize, strides] = backend_util.prepareAndValidate(x, indices);\n        if (numSlices === 0) {\n            return tf.tensor([], resultShape, x.dtype);\n        }\n        const buffer = new TensorBuffer([numSlices, sliceSize], x.dtype);\n        const indicesData = this.readSync(indices.dataId);\n        const xData = this.readSync(x.dataId);\n        for (let i = 0; i < numSlices; i++) {\n            const index = [];\n            let flattenIndex = 0;\n            for (let j = 0; j < sliceRank; j++) {\n                const dim = indicesData[i * sliceRank + j];\n                flattenIndex += dim * strides[j];\n                index.push(dim);\n            }\n            if (flattenIndex < 0 || flattenIndex >= x.size / sliceSize) {\n                throw new Error(`Invalid indices: ${index} does not index into ${x.shape}`);\n            }\n            for (let k = 0; k < sliceSize; k++) {\n                buffer.values[i * sliceSize + k] = xData[flattenIndex * sliceSize + k];\n            }\n        }\n        return buffer.toTensor().reshape(resultShape);\n    }\n    scatterND(indices, updates, shape) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(updates, indices, shape);\n        const defaultValue = tf.scalar(0);\n        const sumDupeIndices = true;\n        return this.scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n    }\n    fill(shape, value, dtype) {\n        dtype = dtype || util.inferDtype(value);\n        const values = util.getArrayFromDType(dtype, util.sizeFromShape(shape));\n        values.fill(value);\n        return engine().makeTensor(values, shape, dtype, this);\n    }\n    onesLike(x) {\n        if (x.dtype === 'string') {\n            throw new Error('onesLike is not supported for string tensors');\n        }\n        else {\n            return this.fill(x.shape, 1, x.dtype);\n        }\n    }\n    zerosLike(x) {\n        const values = util.getArrayFromDType(x.dtype, util.sizeFromShape(x.shape));\n        return this.makeOutput(values, x.shape, x.dtype);\n    }\n    linspace(start, stop, num) {\n        return backend_util.linspaceImpl(start, stop, num);\n    }\n    scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {\n        const flattenShape = [outputSize / sliceSize, sliceSize];\n        const indicesData = this.readSync(indices.dataId);\n        const updatesData = this.readSync(updates.dataId);\n        if (outputSize === 0) {\n            return tf.tensor([], shape, updates.dtype);\n        }\n        const buffer = new TensorBuffer(flattenShape, updates.dtype);\n        buffer.values.fill(this.readSync(defaultValue.dataId)[0]);\n        for (let i = 0; i < numUpdates; i++) {\n            const index = [];\n            let flattenIndex = 0;\n            for (let j = 0; j < sliceRank; j++) {\n                const dim = indicesData[i * sliceRank + j];\n                index.push(dim);\n                flattenIndex += dim * strides[j];\n            }\n            if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {\n                throw new Error(`Invalid indices: ${index} does not index into ${shape}`);\n            }\n            for (let k = 0; k < sliceSize; k++) {\n                if (sumDupeIndices) {\n                    buffer.values[flattenIndex * sliceSize + k] +=\n                        updatesData[i * sliceSize + k];\n                }\n                else {\n                    buffer.values[flattenIndex * sliceSize + k] = updates.rank === 0 ?\n                        updatesData[0] :\n                        updatesData[i * sliceSize + k];\n                }\n            }\n        }\n        return buffer.toTensor().reshape(shape);\n    }\n}\n//# sourceMappingURL=backend_cpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs, util } from '@tensorflow/tfjs-core';\nexport function simpleAbsImpl(vals) {\n    const resultValues = new Float32Array(vals.length);\n    for (let i = 0; i < vals.length; ++i) {\n        resultValues[i] = Math.abs(vals[i]);\n    }\n    return resultValues;\n}\nexport const absKernelFunc = (args) => {\n    const { x } = args.inputs;\n    const cpuBackend = args.backend;\n    let resultValues = new Float32Array(util.sizeFromShape(x.shape));\n    if (x.dtype !== 'complex64') {\n        const values = cpuBackend.data.get(x.dataId).values;\n        resultValues = simpleAbsImpl(values);\n    }\n    else {\n        const complexVals = cpuBackend.data.get(x.dataId);\n        const real = complexVals.complexTensorInfos.real;\n        const imag = complexVals.complexTensorInfos.imag;\n        const realVals = cpuBackend.data.get(real.dataId).values;\n        const imagVals = cpuBackend.data.get(imag.dataId).values;\n        for (let i = 0; i < realVals.length; i++) {\n            const real = realVals[i];\n            const imag = imagVals[i];\n            resultValues[i] = Math.hypot(real, imag);\n        }\n    }\n    return cpuBackend.makeOutput(resultValues, x.shape, 'float32');\n};\nexport const absConfig = {\n    kernelName: Abs,\n    backendName: 'cpu',\n    kernelFunc: absKernelFunc,\n};\n//# sourceMappingURL=Abs.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for binary ops. Supports broadcast.\n */\nexport function createSimpleBinaryKernelImpl(op) {\n    return (aShape, bShape, aVals, bVals, dtype) => {\n        const newShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const resultRank = newShape.length;\n        const resultStrides = util.computeStrides(newShape);\n        const resultSize = util.sizeFromShape(newShape);\n        const result = util.getTypedArrayFromDType(dtype, resultSize);\n        const aRank = aShape.length;\n        const bRank = bShape.length;\n        const aStrides = util.computeStrides(aShape);\n        const bStrides = util.computeStrides(bShape);\n        const aBroadcastDims = backend_util.getBroadcastDims(aShape, newShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(bShape, newShape);\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < result.length; ++i) {\n                result[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n            }\n        }\n        else {\n            for (let i = 0; i < result.length; ++i) {\n                const loc = util.indexToLoc(i, resultRank, resultStrides);\n                const aLoc = loc.slice(-aRank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n                const bLoc = loc.slice(-bRank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n                result[i] = op(aVals[aIndex], bVals[bIndex]);\n            }\n        }\n        return [result, newShape];\n    };\n}\n//# sourceMappingURL=binary_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Complex } from '@tensorflow/tfjs-core';\nexport function complex(args) {\n    const { inputs, backend } = args;\n    const { real, imag } = inputs;\n    const realVals = backend.data.get(real.dataId).values;\n    const imagVals = backend.data.get(imag.dataId).values;\n    const complexInfo = backend.makeTensorInfo(real.shape, 'complex64');\n    const complex = backend.data.get(complexInfo.dataId);\n    // The complex tensor owns the underlying real and imag tensorInfos, only the\n    // complex tensor tracks refCount, when complexData is disposed the\n    // underlying tensorData will be disposed.\n    complex.complexTensorInfos = {\n        real: backend.makeTensorInfo(real.shape, 'float32', realVals),\n        imag: backend.makeTensorInfo(imag.shape, 'float32', imagVals)\n    };\n    return complexInfo;\n}\nexport const complexConfig = {\n    kernelName: Complex,\n    backendName: 'cpu',\n    kernelFunc: complex\n};\n//# sourceMappingURL=Complex.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '@tensorflow/tfjs-core';\nexport function identity(args) {\n    const { inputs, backend } = args;\n    const { x } = inputs;\n    backend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nexport const identityConfig = {\n    kernelName: Identity,\n    backendName: 'cpu',\n    kernelFunc: identity\n};\n//# sourceMappingURL=Identity.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Real } from '@tensorflow/tfjs-core';\nexport function real(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const real = backend.data.get(input.dataId).complexTensorInfos.real;\n    const realVal = backend.data.get(real.dataId).values;\n    // When complex tensor is disposed, its underlying parts will be disposed too.\n    // Make new tensor out of the real value of the complex. This makes sure the\n    // value is still accessible even if complex tensor is disposed.\n    return backend.makeTensorInfo(real.shape, real.dtype, realVal);\n}\nexport const realConfig = {\n    kernelName: Real,\n    backendName: 'cpu',\n    kernelFunc: real\n};\n//# sourceMappingURL=Real.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { Cast, util } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { complex } from './Complex';\nimport { identity } from './Identity';\nimport { real } from './Real';\nexport function cast(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { dtype } = attrs;\n    // Casting to complex64.\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return identity({ inputs: { x }, backend });\n        }\n        // TODO(lina128): Import kernel function once zeros is modularized.\n        const zerosTensor = tf.zeros(x.shape);\n        const floatX = cast({ inputs: { x }, backend, attrs: { dtype: 'float32' } });\n        const result = complex({ inputs: { real: floatX, imag: zerosTensor }, backend });\n        zerosTensor.dispose();\n        backend.disposeIntermediateTensorInfo(floatX);\n        return result;\n    }\n    // Casting from complex64\n    if (x.dtype === 'complex64') {\n        const realPart = real({ inputs: { input: x }, backend });\n        const result = cast({ inputs: { x: realPart }, backend, attrs: { dtype } });\n        backend.disposeIntermediateTensorInfo(realPart);\n        return result;\n    }\n    if (!util.hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        const result = identity({ inputs: { x }, backend });\n        return { dataId: result.dataId, shape: result.shape, dtype };\n    }\n    if (dtype === 'int32') {\n        const values = backend.data.get(x.dataId).values;\n        const resultValues = Int32Array.from(values);\n        return backend.makeTensorInfo(x.shape, 'int32', resultValues);\n    }\n    if (dtype === 'bool') {\n        // This is essentially the result of notEqual(x, 0). We avoid using\n        // kernel notEqual to avoid circular dependency, i.e. binary_utils ->\n        // cast -> notEqual -> binary_utils.\n        const xVals = backend.data.get(x.dataId).values;\n        const zero = util.toTypedArray([0], x.dtype);\n        const [resultData, resultShape] = createSimpleBinaryKernelImpl((a, b) => (a !== b) ? 1 : 0)(x.shape, [], xVals, zero, 'bool');\n        return backend.makeTensorInfo(resultShape, 'bool', resultData);\n    }\n    throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nexport const castConfig = {\n    kernelName: Cast,\n    backendName: 'cpu',\n    kernelFunc: cast\n};\n//# sourceMappingURL=Cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { cast } from '../kernels/Cast';\nimport { complex } from '../kernels/Complex';\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param name Kernel name.\n * @param binaryKernelImpl A `SimpleBinaryKernelImpl` for the kernel.\n * @param binaryKernelComplexImpl Optional. If exists, represents a\n *     `ComplexBinaryKernelImpl` for the kernel, will be used when input dtype\n *     is `complex64`.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc(name, simpleImpl, complexImpl, dtype) {\n    if (complexImpl == null) {\n        return ({ inputs, backend }) => {\n            const { a, b } = inputs;\n            const cpuBackend = backend;\n            assertNotComplex([a, b], name);\n            const aVals = cpuBackend.data.get(a.dataId).values;\n            const bVals = cpuBackend.data.get(b.dataId).values;\n            const $dtype = dtype || a.dtype;\n            const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n            return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n        };\n    }\n    return ({ inputs, backend }) => {\n        const { a, b } = inputs;\n        const cpuBackend = backend;\n        if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n            const $aComplex = cast({ inputs: { x: a }, backend: cpuBackend, attrs: { dtype: 'complex64' } });\n            const $aComplexVals = cpuBackend.data.get($aComplex.dataId);\n            const aReal = $aComplexVals.complexTensorInfos.real;\n            const aImag = $aComplexVals.complexTensorInfos.imag;\n            const aRealVals = cpuBackend.data.get(aReal.dataId).values;\n            const aImagVals = cpuBackend.data.get(aImag.dataId).values;\n            const $bComplex = cast({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: 'complex64' } });\n            const $bComplexVals = cpuBackend.data.get($bComplex.dataId);\n            const bReal = $bComplexVals.complexTensorInfos.real;\n            const bImag = $bComplexVals.complexTensorInfos.imag;\n            const bRealVals = cpuBackend.data.get(bReal.dataId).values;\n            const bImagVals = cpuBackend.data.get(bImag.dataId).values;\n            const [resultRealData, resultImagData, resultShape] = complexImpl(a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);\n            const resultReal = cpuBackend.makeTensorInfo(resultShape, 'float32', resultRealData);\n            const resultImag = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImagData);\n            const result = complex({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend });\n            cpuBackend.disposeIntermediateTensorInfo($aComplex);\n            cpuBackend.disposeIntermediateTensorInfo($bComplex);\n            cpuBackend.disposeIntermediateTensorInfo(resultReal);\n            cpuBackend.disposeIntermediateTensorInfo(resultImag);\n            return result;\n        }\n        else {\n            const aVals = cpuBackend.data.get(a.dataId).values;\n            const bVals = cpuBackend.data.get(b.dataId).values;\n            const $dtype = dtype || a.dtype;\n            const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n            return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n        }\n    };\n}\n/**\n * Template that creates the complex type implementation for binary ops.\n * Supports broadcast.\n */\nexport function createComplexBinaryKernelImpl(op) {\n    return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) => {\n        const resultShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const resultSize = util.sizeFromShape(resultShape);\n        const resultRank = resultShape.length;\n        const resultStrides = util.computeStrides(resultShape);\n        const resultRealVals = util.getTypedArrayFromDType('float32', resultSize);\n        const resultImagVals = util.getTypedArrayFromDType('float32', resultSize);\n        const aBroadcastDims = backend_util.getBroadcastDims(aShape, resultShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(bShape, resultShape);\n        const aVals = backend_util.mergeRealAndImagArrays(aRealVals, aImagVals);\n        const bVals = backend_util.mergeRealAndImagArrays(bRealVals, bImagVals);\n        const aRank = aShape.length;\n        const aStrides = util.computeStrides(aShape);\n        const bRank = bShape.length;\n        const bStrides = util.computeStrides(bShape);\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < resultRealVals.length; i++) {\n                const aIdx = i % aVals.length;\n                const bIdx = i % bVals.length;\n                const result = op(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);\n                resultRealVals[i] = result.real;\n                resultImagVals[i] = result.imag;\n            }\n        }\n        else {\n            for (let i = 0; i < resultRealVals.length; i++) {\n                const loc = util.indexToLoc(i, resultRank, resultStrides);\n                const aLoc = loc.slice(-aRank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n                const bLoc = loc.slice(-bRank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n                const opResult = op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);\n                resultRealVals[i] = opResult.real;\n                resultImagVals[i] = opResult.imag;\n            }\n        }\n        return [resultRealVals, resultImagVals, resultShape];\n    };\n}\n//# sourceMappingURL=kernel_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const addImpl = createSimpleBinaryKernelImpl(((a, b) => a + b));\nexport const addComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal + bReal, imag: aImag + bImag };\n}));\nexport const add = binaryKernelFunc(Add, addImpl, addComplexImpl);\nexport const addConfig = {\n    kernelName: Add,\n    backendName: 'cpu',\n    kernelFunc: add\n};\n//# sourceMappingURL=Add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for unary op.\n */\nexport function createSimpleUnaryImpl(op) {\n    return (values, dtype, attrs) => {\n        const newValues = util.getTypedArrayFromDType(dtype, values.length);\n        for (let i = 0; i < values.length; ++i) {\n            newValues[i] = op(values[i], attrs);\n        }\n        return newValues;\n    };\n}\n//# sourceMappingURL=unary_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param name Kernel name.\n * @param op A `SimpleUnaryOperation` for the kernel.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the input. This is mainly used in certain\n *     kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFunc(name, op, dtype) {\n    return ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        assertNotComplex(x, name);\n        if (x.dtype === 'string' || dtype === 'string') {\n            throw new Error('unaryKernelFunc does not support string input/output');\n        }\n        const cpuBackend = backend;\n        const values = cpuBackend.data.get(x.dataId).values;\n        const xSize = util.sizeFromShape(x.shape);\n        const $dtype = dtype || x.dtype;\n        const newValues = util.getArrayFromDType($dtype, xSize);\n        for (let i = 0; i < xSize; ++i) {\n            newValues[i] = op(values[i], attrs);\n        }\n        return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n    };\n}\n/**\n * Template that creates a `KernelFunc` for unary ops from the given\n * `SimpleUnaryImpl`..\n * @param name Kernel name.\n * @param unaryImpl A `SimpleUnaryImpl` that implements the op.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the input. This is mainly used in certain\n *     kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFuncFromImpl(name, unaryImpl, dtype) {\n    return ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        assertNotComplex(x, name);\n        if (x.dtype === 'string' || dtype === 'string') {\n            throw new Error('unaryKernelFunc does not support string input/output');\n        }\n        const cpuBackend = backend;\n        const values = cpuBackend.data.get(x.dataId).values;\n        const $dtype = dtype || x.dtype;\n        const newValues = unaryImpl(values, $dtype, attrs);\n        return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n    };\n}\n//# sourceMappingURL=unary_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));\nexport const ceilKernelFunc = unaryKernelFuncFromImpl(Ceil, ceilImpl);\nexport const ceilConfig = {\n    kernelName: Ceil,\n    backendName: 'cpu',\n    kernelFunc: ceilKernelFunc,\n};\n//# sourceMappingURL=Ceil.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));\nexport const expKernelFunc = unaryKernelFuncFromImpl(Exp, expImpl);\nexport const expConfig = {\n    kernelName: Exp,\n    backendName: 'cpu',\n    kernelFunc: expKernelFunc,\n};\n//# sourceMappingURL=Exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));\nexport const expm1KernelFunc = unaryKernelFuncFromImpl(Expm1, expm1Impl);\nexport const expm1Config = {\n    kernelName: Expm1,\n    backendName: 'cpu',\n    kernelFunc: expm1KernelFunc,\n};\n//# sourceMappingURL=Expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));\nexport const floorKernelFunc = unaryKernelFuncFromImpl(Floor, floorImpl);\nexport const floorConfig = {\n    kernelName: Floor,\n    backendName: 'cpu',\n    kernelFunc: floorKernelFunc,\n};\n//# sourceMappingURL=Floor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));\nexport const logKernelFunc = unaryKernelFuncFromImpl(Log, logImpl);\nexport const logConfig = {\n    kernelName: Log,\n    backendName: 'cpu',\n    kernelFunc: logKernelFunc,\n};\n//# sourceMappingURL=Log.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function maxImpl(aVals, reduceSize, outShape, dtype) {\n    const vals = util.getTypedArrayFromDType(dtype, util.sizeFromShape(outShape));\n    for (let i = 0; i < vals.length; ++i) {\n        const offset = i * reduceSize;\n        let max = aVals[offset];\n        for (let j = 0; j < reduceSize; ++j) {\n            const value = aVals[offset + j];\n            if (value > max) {\n                max = value;\n            }\n        }\n        vals[i] = max;\n    }\n    return vals;\n}\n//# sourceMappingURL=Max_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const multiplyImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue * bValue));\nexport const multiplyComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return {\n        real: aReal * bReal - aImag * bImag,\n        imag: aReal * bImag + aImag * bReal\n    };\n}));\nexport const multiply = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);\nexport const multiplyConfig = {\n    kernelName: Multiply,\n    backendName: 'cpu',\n    kernelFunc: multiply\n};\n//# sourceMappingURL=Multiply.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));\nexport const rsqrtKernelFunc = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);\nexport const rsqrtConfig = {\n    kernelName: Rsqrt,\n    backendName: 'cpu',\n    kernelFunc: rsqrtKernelFunc,\n};\n//# sourceMappingURL=Rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Slice, slice_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function sliceImpl(vals, begin, size, shape, dtype) {\n    const isContinous = slice_util.isSliceContinous(shape, begin, size);\n    const length = util.sizeFromShape(size);\n    const xStrides = util.computeStrides(shape);\n    if (isContinous) {\n        const flatOffset = slice_util.computeFlatOffset(begin, xStrides);\n        return vals.subarray(flatOffset, flatOffset + length);\n    }\n    const outVals = util.getTypedArrayFromDType(dtype, length);\n    for (let i = 0; i < length; ++i) {\n        const rank = size.length;\n        const strides = util.computeStrides(size);\n        const loc = util.indexToLoc(i, rank, strides);\n        const xLoc = loc.map((idx, j) => idx + begin[j]);\n        const xIndex = util.locToIndex(xLoc, shape.length, xStrides);\n        outVals[i] = vals[xIndex];\n    }\n    return outVals;\n}\nexport function slice(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { begin, size } = attrs;\n    assertNotComplex(x, 'slice');\n    const [$begin, $size] = slice_util.parseSliceParams(x, begin, size);\n    slice_util.assertParamsValid(x, $begin, $size);\n    const vals = backend.data.get(x.dataId).values;\n    const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);\n    return backend.makeTensorInfo($size, x.dtype, outVals);\n}\nexport const sliceConfig = {\n    kernelName: Slice,\n    backendName: 'cpu',\n    kernelFunc: slice\n};\n//# sourceMappingURL=Slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const subImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue - bValue));\nexport const subComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal - bReal, imag: aImag - bImag };\n}));\nexport const sub = binaryKernelFunc(Sub, subImpl, subComplexImpl);\nexport const subConfig = {\n    kernelName: Sub,\n    backendName: 'cpu',\n    kernelFunc: sub\n};\n//# sourceMappingURL=Sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function transposeImpl(xVals, xShape, dtype, perm, newShape) {\n    const xRank = xShape.length;\n    const xSize = util.sizeFromShape(xShape);\n    const xStrides = util.computeStrides(xShape);\n    const newStrides = util.computeStrides(newShape);\n    const result = util.getTypedArrayFromDType(dtype, util.sizeFromShape(newShape));\n    for (let i = 0; i < xSize; ++i) {\n        const loc = util.indexToLoc(i, xRank, xStrides);\n        // Permute location.\n        const newLoc = new Array(loc.length);\n        for (let i = 0; i < newLoc.length; i++) {\n            newLoc[i] = loc[perm[i]];\n        }\n        const newIndex = util.locToIndex(newLoc, xRank, newStrides);\n        result[newIndex] = xVals[i];\n    }\n    return result;\n}\n//# sourceMappingURL=Transpose_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer, util } from '@tensorflow/tfjs-core';\nexport function uniqueImpl(values, axis, shape, dtype) {\n    // Normalize and validate axis.\n    const $axis = util.parseAxisParam(axis, shape)[0];\n    // Calculate the new shape that is suitable for extracting data along the\n    // given axis.\n    //\n    // The rank is 3.\n    // The size of the 1st dimension is the size of all the axes < the given axis.\n    // The size of the 2nd dimension is the same as the size of the given axis.\n    // The size of the 3rd dimension is the size of all the axes > the given axis.\n    //\n    // For example, for a 4D tensor with shape=[2, 3, 5, 4] and axis=2, the\n    // newShape would be: [2*3, 5, 4].\n    //\n    // Note that this is not the final output shape. This will be the shape for an\n    // intermediate TensorBuffer (see inputBuffer below) to allow us to extract\n    // values along the given axis. To demonstrate how it works, consider the\n    // following example:\n    //\n    // Input: a 3D tensor, with shape [1, 2, 3]\n    // [\n    //   [\n    //      [1,2,3],\n    //      [4,5,6]\n    //   ]\n    // ]\n    // Axis: 2 (the last axis).\n    // Along axis 2, we expect to extract 3 tensors: [1,4], [2,5], [3,6].\n    //\n    // For this example, newShape would be: [2, 3, 1], where 2 is calculated from\n    // 1*2. The re-shaped data would look like:\n    //\n    // [\n    //   [\n    //     [1], [2], [3]\n    //   ],\n    //   [\n    //     [4], [5], [6]\n    //   ]\n    // ]\n    //\n    // Then, we can construct a 3-level nested loop by the following dimension\n    // order to extract the values along the axis (dimension1):\n    // i: dimension1       // 0,1,2 (newShape[1])\n    //   m: dimension0     // 0,1   (newShape[0])\n    //     n: dimension2   // 0     (newShape[2])\n    //\n    //                       m, i, n\n    //                      ---------\n    // Iteration 0: data at [0, 0, 0] => \"1\"\n    // Iteration 1: data at [1, 0, 0] => \"4\"\n    // We got [1,4].\n    // Iteration 2: data at [0, 1, 0] => \"2\"\n    // Iteration 3: data at [1, 1, 0] => \"5\"\n    // We got [2,5].\n    // Iteration 4: data at [0, 2, 0] => \"3\"\n    // Iteration 5: data at [1, 2, 0] => \"6\"\n    // We got [3,6].\n    const newShape = [1, shape[0], 1];\n    for (let i = 0; i < $axis; i++) {\n        newShape[0] *= shape[i];\n    }\n    newShape[1] = shape[$axis];\n    for (let i = $axis + 1; i < shape.length; i++) {\n        newShape[2] *= shape[i];\n    }\n    // A map from unique elements (their string representations) to their values\n    // in \"indices\" (below).\n    const uniqueElements = {};\n    // The indices of each unique element in the original tensor along the given\n    // axis. It is 1D and has the same size as the given axis.\n    const indices = new Int32Array(shape[$axis]);\n    // Create a buffer so we can easily extract value at a given location.\n    const inputBuffer = new TensorBuffer(newShape, dtype, values);\n    // The indices along the given axis that have unique elements. This is a\n    // de-duped version of \"indices\" above.\n    const uniqueIndices = [];\n    const is1DTensor = newShape[0] === 1 && newShape[2] === 1;\n    for (let i = 0; i < shape[$axis]; i++) {\n        // Extract values along the axis.\n        let element;\n        if (is1DTensor) {\n            // Fast path for 1D tensor input.\n            element = values[i].toString();\n        }\n        else {\n            const axisValues = [];\n            for (let m = 0; m < newShape[0]; m++) {\n                for (let n = 0; n < newShape[2]; n++) {\n                    axisValues.push(inputBuffer.get(m, i, n));\n                }\n            }\n            element = axisValues.join(',');\n        }\n        // Dedup and update various indices.\n        if (uniqueElements[element] !== undefined) {\n            indices[i] = uniqueElements[element];\n        }\n        else {\n            const uniqueIndex = Object.keys(uniqueElements).length;\n            uniqueElements[element] = uniqueIndex;\n            indices[i] = uniqueIndex;\n            uniqueIndices.push(i);\n        }\n    }\n    // Now we know where each of the unique elements are located along the axis\n    // (uniqueIndices). Extract them from input buffer and store them in the\n    // output buffer.\n    const outputTmpShape = newShape.slice();\n    outputTmpShape[1] = Object.keys(uniqueElements).length;\n    const outputBuffer = new TensorBuffer(outputTmpShape, dtype);\n    uniqueIndices.forEach((uniqueElementIndex, i) => {\n        for (let m = 0; m < newShape[0]; m++) {\n            for (let n = 0; n < newShape[2]; n++) {\n                outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);\n            }\n        }\n    });\n    // The output shape can be calculated from the input shape with the size of\n    // the given axis replaced by the number of unique elements along that axis.\n    const outputShape = shape.slice();\n    outputShape[$axis] = outputTmpShape[1];\n    return {\n        outputValues: outputBuffer.values,\n        outputShape,\n        indices,\n    };\n}\n//# sourceMappingURL=Unique_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Shared kernel impls for use in other backends.\nexport { simpleAbsImpl } from './kernels/Abs';\nexport { addImpl } from './kernels/Add';\nexport { ceilImpl } from './kernels/Ceil';\nexport { expImpl } from './kernels/Exp';\nexport { expm1Impl } from './kernels/Expm1';\nexport { floorImpl } from './kernels/Floor';\nexport { logImpl } from './kernels/Log';\nexport { maxImpl } from './kernels/Max_impl';\nexport { multiplyImpl } from './kernels/Multiply';\nexport { rsqrtImpl } from './kernels/Rsqrt';\nexport { sliceImpl } from './kernels/Slice';\nexport { subImpl } from './kernels/Sub';\nexport { transposeImpl } from './kernels/Transpose_impl';\nexport { uniqueImpl } from './kernels/Unique_impl';\n//# sourceMappingURL=shared.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/*\n * base.ts contains all the exports from tfjs-backend-cpu\n * without auto-kernel registration\n */\nimport { registerBackend } from '@tensorflow/tfjs-core';\nimport { MathBackendCPU } from './backend_cpu';\nimport * as shared from './shared';\nexport { MathBackendCPU } from './backend_cpu';\nexport { version as version_cpu } from './version';\nexport { shared };\n// Side effects for default initialization of MathBackendCPU\nregisterBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acos } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const acosKernelFunc = unaryKernelFunc(Acos, (xi) => Math.acos(xi));\nexport const acosConfig = {\n    kernelName: Acos,\n    backendName: 'cpu',\n    kernelFunc: acosKernelFunc,\n};\n//# sourceMappingURL=Acos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acosh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const acoshKernelFunc = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi));\nexport const acoshConfig = {\n    kernelName: Acosh,\n    backendName: 'cpu',\n    kernelFunc: acoshKernelFunc,\n};\n//# sourceMappingURL=Acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asin } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const asinKernelFunc = unaryKernelFunc(Asin, (xi) => Math.asin(xi));\nexport const asinConfig = {\n    kernelName: Asin,\n    backendName: 'cpu',\n    kernelFunc: asinKernelFunc,\n};\n//# sourceMappingURL=Asin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asinh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const asinhKernelFunc = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi));\nexport const asinhConfig = {\n    kernelName: Asinh,\n    backendName: 'cpu',\n    kernelFunc: asinhKernelFunc,\n};\n//# sourceMappingURL=Asinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const atanKernelFunc = unaryKernelFunc(Atan, (xi) => Math.atan(xi));\nexport const atanConfig = {\n    kernelName: Atan,\n    backendName: 'cpu',\n    kernelFunc: atanKernelFunc,\n};\n//# sourceMappingURL=Atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atanh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const atanhKernelFunc = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi));\nexport const atanhConfig = {\n    kernelName: Atanh,\n    backendName: 'cpu',\n    kernelFunc: atanhKernelFunc,\n};\n//# sourceMappingURL=Atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from '@tensorflow/tfjs-core';\nexport function pool(xValues, xShape, dtype, strides, convInfo, poolType) {\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n        Number.POSITIVE_INFINITY);\n    const output = buffer(convInfo.outShape, dtype);\n    const outputVals = output.values;\n    const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];\n    const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];\n    const outputColStrides = convInfo.outShape[3];\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        const outputBatchOffset = b * outputBatchStrides;\n        const inputBatchOffset = b * strides[0];\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const xRCorner = yR * strideHeight - padTop;\n                const xRMin = Math.max(0, xRCorner);\n                const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n                const outputRowOffset = outputBatchOffset + yR * outputRowStrides;\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const xCCorner = yC * strideWidth - padLeft;\n                    const xCMin = Math.max(0, xCCorner);\n                    const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n                    let minMaxValue = initialValue;\n                    let avgValue = 0;\n                    let count = 0;\n                    for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n                        const xROffset = inputBatchOffset + xR * strides[1];\n                        for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                            const xCOffset = xROffset + xC * strides[2];\n                            const pixel = xValues[xCOffset + d];\n                            if ((poolType === 'max' && pixel > minMaxValue)) {\n                                minMaxValue = pixel;\n                            }\n                            else if (poolType === 'avg') {\n                                avgValue += pixel;\n                                count++;\n                            }\n                        }\n                        if (isNaN(minMaxValue)) {\n                            break;\n                        }\n                    }\n                    const outputOffset = outputRowOffset + yC * outputColStrides + d;\n                    outputVals[outputOffset] =\n                        poolType === 'avg' ? avgValue / count : minMaxValue;\n                }\n            }\n        }\n    }\n    return output;\n}\nexport function maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) {\n    const maxPositions = buffer(convInfo.outShape, 'int32');\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const xBuf = buffer(xShape, dtype, xValues);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const xRCorner = yR * strideHeight - padTop;\n                let xRMin = xRCorner;\n                while (xRMin < 0) {\n                    xRMin += dilationHeight;\n                }\n                // const xRMin = Math.max(0, xRCorner);\n                const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const xCCorner = yC * strideWidth - padLeft;\n                    let xCMin = xCCorner;\n                    while (xCMin < 0) {\n                        xCMin += dilationWidth;\n                    }\n                    const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n                    let maxValue = Number.NEGATIVE_INFINITY;\n                    let maxPosition = -1;\n                    for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n                        const wR = xR - xRCorner;\n                        for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                            const wC = xC - xCCorner;\n                            const pixel = xBuf.get(b, xR, xC, d);\n                            if (pixel > maxValue) {\n                                maxValue = pixel;\n                                if (flattenPositions) {\n                                    maxPosition = includeBatchInIndex ?\n                                        ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) *\n                                            convInfo.inChannels +\n                                            d :\n                                        (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;\n                                }\n                                else {\n                                    maxPosition = wR * effectiveFilterWidth + wC;\n                                }\n                            }\n                        }\n                    }\n                    maxPositions.set(maxPosition, b, yR, yC, d);\n                }\n            }\n        }\n    }\n    return maxPositions;\n}\n//# sourceMappingURL=pool_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool, backend_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { pool } from '../utils/pool_utils';\nimport { identity } from './Identity';\nexport function avgPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'avgPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    let res;\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        res = identity({ inputs: { x }, backend });\n    }\n    else {\n        const xValues = backend.data.get(x.dataId).values;\n        const strides = util.computeStrides(x.shape);\n        const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'avg');\n        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);\n    }\n    return res;\n}\nexport const avgPoolConfig = {\n    kernelName: AvgPool,\n    backendName: 'cpu',\n    kernelFunc: avgPool\n};\n//# sourceMappingURL=AvgPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPoolBackprop, backend_util, buffer } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function avgPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input } = inputs;\n    const x = input;\n    assertNotComplex([dy, input], 'avgPoolBackprop');\n    const { filterSize, strides, pad } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = buffer(x.shape, 'float32');\n    const avgMultiplier = 1 / (filterHeight * filterWidth);\n    const dyData = backend.data.get(dy.dataId).values;\n    const dyBuf = buffer(dy.shape, 'float32', dyData);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n                for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n                    // Shader code begins.\n                    const dyRCorner = dxR - padTop;\n                    const dyCCorner = dxC - padLeft;\n                    let dotProd = 0;\n                    for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n                        const dyR = (dyRCorner + wR) / strideHeight;\n                        if (dyR < 0 || dyR >= convInfo.outHeight ||\n                            Math.floor(dyR) !== dyR) {\n                            continue;\n                        }\n                        for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                            const dyC = (dyCCorner + wC) / strideWidth;\n                            if (dyC < 0 || dyC >= convInfo.outWidth ||\n                                Math.floor(dyC) !== dyC) {\n                                continue;\n                            }\n                            const pixel = dyBuf.get(b, dyR, dyC, d);\n                            dotProd += pixel;\n                        }\n                    }\n                    dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const avgPoolBackpropConfig = {\n    kernelName: AvgPoolBackprop,\n    backendName: 'cpu',\n    kernelFunc: avgPoolBackprop\n};\n//# sourceMappingURL=AvgPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedBatchNorm, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function batchNormKernelFunc(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, scale, offset, mean, variance } = inputs;\n    util.assert(mean.shape.length === variance.shape.length, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert(offset == null || mean.shape.length === offset.shape.length, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert(scale == null || mean.shape.length === scale.shape.length, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    assertNotComplex([x, mean, variance, scale, offset], 'batchNorm');\n    let { varianceEpsilon } = attrs;\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const xVals = backend.data.get(x.dataId).values;\n    const mVals = backend.data.get(mean.dataId).values;\n    const varVals = backend.data.get(variance.dataId).values;\n    const sVals = scale ? backend.data.get(scale.dataId).values :\n        new Float32Array([1]);\n    const offVals = offset ?\n        backend.data.get(offset.dataId).values :\n        new Float32Array([0]);\n    const outVals = new Float32Array(xVals.length);\n    const offValsLength = offVals.length;\n    const sValsLength = sVals.length;\n    const varValsLength = varVals.length;\n    const mValsLength = mVals.length;\n    let offi = 0;\n    let mi = 0;\n    let si = 0;\n    let vi = 0;\n    for (let i = 0; i < xVals.length; ++i) {\n        outVals[i] = offVals[offi++] +\n            (xVals[i] - mVals[mi++]) * sVals[si++] /\n                Math.sqrt(varVals[vi++] + varianceEpsilon);\n        if (offi >= offValsLength) {\n            offi = 0;\n        }\n        if (mi >= mValsLength) {\n            mi = 0;\n        }\n        if (si >= sValsLength) {\n            si = 0;\n        }\n        if (vi >= varValsLength) {\n            vi = 0;\n        }\n    }\n    return backend.makeTensorInfo(x.shape, x.dtype, outVals);\n}\nexport const batchNormConfig = {\n    kernelName: FusedBatchNorm,\n    backendName: 'cpu',\n    kernelFunc: batchNormKernelFunc,\n};\n//# sourceMappingURL=BatchNorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ClipByValue } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const clipKernelFunc = unaryKernelFunc(ClipByValue, (xi, attrs) => {\n    const clipAttrs = attrs;\n    if (xi > clipAttrs.clipValueMax) {\n        return clipAttrs.clipValueMax;\n    }\n    return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;\n});\nexport const clipConfig = {\n    kernelName: ClipByValue,\n    backendName: 'cpu',\n    kernelFunc: clipKernelFunc,\n};\n//# sourceMappingURL=Clip.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Imag } from '@tensorflow/tfjs-core';\nexport function imag(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const imag = backend.data.get(input.dataId).complexTensorInfos.imag;\n    const imagVal = backend.data.get(imag.dataId).values;\n    // When complex tensor is disposed, its underlying parts will be disposed too.\n    // Make new tensor out of the imag value of the complex. This makes sure the\n    // value is still accessible even if complex tensor is disposed.\n    return backend.makeTensorInfo(imag.shape, imag.dtype, imagVal);\n}\nexport const imagConfig = {\n    kernelName: Imag,\n    backendName: 'cpu',\n    kernelFunc: imag\n};\n//# sourceMappingURL=Imag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape, util } from '@tensorflow/tfjs-core';\nexport function reshape(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { shape } = attrs;\n    const xSize = util.sizeFromShape(x.shape);\n    const $shape = util.inferFromImplicitShape(shape, xSize);\n    const $xSize = util.sizeFromShape($shape);\n    util.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n        `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n        `shape must have the same number of elements.`);\n    backend.incRef(x.dataId);\n    const xData = backend.data.get(x.dataId);\n    if (xData.complexTensorInfos != null) {\n        const real = xData.complexTensorInfos.real;\n        const imag = xData.complexTensorInfos.imag;\n        real.shape = $shape;\n        imag.shape = $shape;\n    }\n    return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nexport const reshapeConfig = {\n    kernelName: Reshape,\n    backendName: 'cpu',\n    kernelFunc: reshape\n};\n//# sourceMappingURL=Reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Concat, util } from '@tensorflow/tfjs-core';\nimport { complex } from './Complex';\nimport { imag } from './Imag';\nimport { real } from './Real';\nimport { reshape } from './Reshape';\nexport function concat(args) {\n    const { inputs, backend, attrs } = args;\n    const { axis } = attrs;\n    const $axis = util.parseAxisParam(axis, inputs[0].shape)[0];\n    let outShape = backend_util.computeOutShape(inputs.map(t => t.shape), $axis);\n    if (util.sizeFromShape(outShape) === 0) {\n        return backend.makeTensorInfo(outShape, inputs[0].dtype, []);\n    }\n    // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n    const $inputs = inputs.filter(t => util.sizeFromShape(t.shape) > 0);\n    if ($inputs.length === 1) {\n        return $inputs[0];\n    }\n    const shapes = $inputs.map(t => t.shape);\n    backend_util.assertParamsConsistent(shapes, $axis);\n    if ($inputs[0].dtype === 'complex64') {\n        const reals = $inputs.map((t) => real({ inputs: { input: t }, backend }));\n        const imags = $inputs.map((t) => imag({ inputs: { input: t }, backend }));\n        const realConcated = concat({ inputs: reals, backend, attrs: { axis } });\n        const imagConcated = concat({ inputs: imags, backend, attrs: { axis } });\n        const result = complex({ inputs: { real: realConcated, imag: imagConcated }, backend });\n        reals.forEach(r => backend.disposeIntermediateTensorInfo(r));\n        imags.forEach(i => backend.disposeIntermediateTensorInfo(i));\n        backend.disposeIntermediateTensorInfo(realConcated);\n        backend.disposeIntermediateTensorInfo(imagConcated);\n        return result;\n    }\n    // Any concat of n-dimensional tensors across any axis can be reduced to\n    // a concatenation of two-dimensional tensors across the axis 1 by first\n    // partitioning the axes of the original tensors into those less than the\n    // axis to be concatenated and the rest. Then reshape the tensors\n    // into a two-dimensional tensor by collapsing these two sets of axes and\n    // concatenate the resulting matrices across the axis 1, finally reshaping\n    // the result to have the proper shape.\n    const inputs2D = $inputs.map(t => {\n        const innerSize = util.sizeFromShape(t.shape.slice($axis));\n        const shape = [-1, innerSize];\n        return reshape({ inputs: { x: t }, backend, attrs: { shape } });\n    });\n    // Concats 2d tensors along axis=1.\n    outShape =\n        backend_util.computeOutShape(inputs2D.map(t => t.shape), 1 /* axis */);\n    const outVals = util.getTypedArrayFromDType($inputs[0].dtype, util.sizeFromShape(outShape));\n    if (inputs2D[0].shape[0] === 1) {\n        // Use built-in TypedArray.set() method for speed.\n        let offset = 0;\n        inputs2D.forEach(t => {\n            const val = backend.data.get(t.dataId).values;\n            const size = util.sizeFromShape(t.shape);\n            outVals.set(val, offset);\n            offset += size;\n        });\n    }\n    else {\n        let colOffset = 0;\n        inputs2D.forEach(t => {\n            const tVals = backend.data.get(t.dataId).values;\n            let tIdx = 0;\n            for (let row = 0; row < t.shape[0]; ++row) {\n                const resIdx = row * outShape[1] + colOffset;\n                for (let col = 0; col < t.shape[1]; ++col) {\n                    outVals[resIdx + col] = tVals[tIdx++];\n                }\n            }\n            colOffset += t.shape[1];\n        });\n    }\n    const finalOutShape = backend_util.computeOutShape($inputs.map(t => t.shape), $axis);\n    const outInfo = backend.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);\n    inputs2D.forEach(t => backend.disposeIntermediateTensorInfo(t));\n    return outInfo;\n}\nexport const concatConfig = {\n    kernelName: Concat,\n    backendName: 'cpu',\n    kernelFunc: concat\n};\n//# sourceMappingURL=Concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const cosKernelFunc = unaryKernelFunc(Cos, (xi) => Math.cos(xi));\nexport const cosConfig = {\n    kernelName: Cos,\n    backendName: 'cpu',\n    kernelFunc: cosKernelFunc,\n};\n//# sourceMappingURL=Cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cosh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const coshKernelFunc = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi));\nexport const coshConfig = {\n    kernelName: Cosh,\n    backendName: 'cpu',\n    kernelFunc: coshKernelFunc,\n};\n//# sourceMappingURL=Cosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2D, util } from '@tensorflow/tfjs-core';\nexport const dilation2dConfig = {\n    kernelName: Dilation2D,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const xVals = cpuBackend.data.get(x.dataId).values;\n        const xRank = x.shape.length;\n        const filterVals = cpuBackend.data.get(filter.dataId).values;\n        const filterRank = filter.shape.length;\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        const outSize = util.sizeFromShape(outShape);\n        const outRank = outShape.length;\n        const outputVals = util.getArrayFromDType(x.dtype, outSize);\n        // Upsampling the input by fill in `dilation size - 1` values between each\n        // input value.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const xIndex = util.locToIndex([b, hIn, wIn, d], xRank, util.computeStrides(x.shape));\n                                        const filterIndex = util.locToIndex([h, w, d], filterRank, util.computeStrides(filter.shape));\n                                        const val = xVals[xIndex] + filterVals[filterIndex];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        const outputIndex = util.locToIndex([b, hOut, wOut, d], outRank, util.computeStrides(outShape));\n                        outputVals[outputIndex] = curVal;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(outputVals, x.dtype), outShape, x.dtype);\n        return { dataId, shape: outShape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2DBackpropFilter, util } from '@tensorflow/tfjs-core';\nexport const dilation2dBackpropFilterConfig = {\n    kernelName: Dilation2DBackpropFilter,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter, dy } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const $x = util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n        const $filter = util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        util.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy ` +\n            `must have the same rank as output ${outShape.length}, but got ` +\n            `${dy.rank}`);\n        const $dy = util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n        // The computed filter gradients has the same dimensions as the filter:\n        // [filterHeight, filterWidth, depth]\n        const gradients = util.makeZerosNestedTypedArray(filter.shape, filter.dtype);\n        // In the case of multiple argmax branches, we only back-propagate along the\n        // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n        // similarly to the max-pooling backward routines.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        let hMax = 0;\n                        let wMax = 0;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                            hMax = h;\n                                            wMax = w;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);\n        return { dataId, shape: filter.shape, dtype: filter.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2DBackpropFilter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2DBackpropInput, util } from '@tensorflow/tfjs-core';\nexport const dilation2dBackpropInputConfig = {\n    kernelName: Dilation2DBackpropInput,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter, dy } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const $x = util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n        const $filter = util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        util.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy ` +\n            `must have the same rank as output ${outShape.length}, but got ` +\n            `${dy.rank}`);\n        const $dy = util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n        // The computed gradients has the same dimensions as the input:\n        // [batch, inputHeight, inputCols, inChannel]\n        const gradients = util.makeZerosNestedTypedArray(x.shape, x.dtype);\n        // In the case of multiple argmax branches, we only back-propagate along the\n        // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n        // similarly to the max-pooling backward routines.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        let hInMax = (hBeg < 0) ? 0 : hBeg;\n                        let wInMax = (wBeg < 0) ? 0 : wBeg;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                            hInMax = hIn;\n                                            wInMax = wIn;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(gradients, x.dtype), x.shape, x.dtype);\n        return { dataId, shape: x.shape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2DBackpropInput.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const divImpl = createSimpleBinaryKernelImpl((a, b) => a / b);\nexport const div = binaryKernelFunc(Div, divImpl);\nexport const divConfig = {\n    kernelName: Div,\n    backendName: 'cpu',\n    kernelFunc: div\n};\n//# sourceMappingURL=Div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Elu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const eluKernelFunc = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : (Math.exp(xi) - 1));\nexport const eluConfig = {\n    kernelName: Elu,\n    backendName: 'cpu',\n    kernelFunc: eluKernelFunc,\n};\n//# sourceMappingURL=Elu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Erf } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nconst p = backend_util.ERF_P;\nconst a1 = backend_util.ERF_A1;\nconst a2 = backend_util.ERF_A2;\nconst a3 = backend_util.ERF_A3;\nconst a4 = backend_util.ERF_A4;\nconst a5 = backend_util.ERF_A5;\nexport const erfKernelFunc = unaryKernelFunc(Erf, (xi) => {\n    const sign = Math.sign(xi);\n    const v = Math.abs(xi);\n    const t = 1.0 / (1.0 + p * v);\n    return sign *\n        (1.0 -\n            (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t *\n                Math.exp(-v * v));\n});\nexport const erfConfig = {\n    kernelName: Erf,\n    backendName: 'cpu',\n    kernelFunc: erfKernelFunc,\n};\n//# sourceMappingURL=Erf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { add } from '../kernels/Add';\nimport { complex } from '../kernels/Complex';\nimport { concat } from '../kernels/Concat';\nimport { divConfig } from '../kernels/Div';\nimport { identity } from '../kernels/Identity';\nimport { imag } from '../kernels/Imag';\nimport { multiply } from '../kernels/Multiply';\nimport { real } from '../kernels/Real';\nimport { slice } from '../kernels/Slice';\nimport { sub } from '../kernels/Sub';\n/**\n * Calculate FFT of inner most elements of batch tensor.\n */\nexport function fftBatch(input, inverse, cpuBackend) {\n    const inputShape = input.shape;\n    const batch = inputShape[0];\n    const innerDim = inputShape[1];\n    const inputVals = cpuBackend.data.get(input.dataId);\n    const real2D = inputVals.complexTensorInfos.real;\n    const imag2D = inputVals.complexTensorInfos.imag;\n    // Collects real and imaginary values separately.\n    const resultShape = [batch, innerDim];\n    const resultSize = util.sizeFromShape(resultShape);\n    const resultReal = util.getTypedArrayFromDType('float32', resultSize);\n    const resultImag = util.getTypedArrayFromDType('float32', resultSize);\n    for (let b = 0; b < batch; b++) {\n        // TODO: Support slice ops for complex type.\n        const r = slice({\n            inputs: { x: real2D },\n            backend: cpuBackend,\n            attrs: { begin: [b, 0], size: [1, innerDim] }\n        });\n        const i = slice({\n            inputs: { x: imag2D },\n            backend: cpuBackend,\n            attrs: { begin: [b, 0], size: [1, innerDim] }\n        });\n        const input = complex({ inputs: { real: r, imag: i }, backend: cpuBackend });\n        // Run FFT by batch element.\n        const { real, imag } = fftImpl(input, inverse, cpuBackend);\n        const res = backend_util.mergeRealAndImagArrays(real, imag);\n        for (let d = 0; d < innerDim; d++) {\n            const c = backend_util.getComplexWithIndex(res, d);\n            resultReal[b * innerDim + d] = c.real;\n            resultImag[b * innerDim + d] = c.imag;\n        }\n        cpuBackend.disposeIntermediateTensorInfo(r);\n        cpuBackend.disposeIntermediateTensorInfo(i);\n        cpuBackend.disposeIntermediateTensorInfo(input);\n    }\n    const $realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultReal);\n    const $imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImag);\n    const result = complex({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend });\n    cpuBackend.disposeIntermediateTensorInfo($realInfo);\n    cpuBackend.disposeIntermediateTensorInfo($imagInfo);\n    return result;\n}\nexport function fftImpl(input, inverse, cpuBackend) {\n    const inputSize = util.sizeFromShape(input.shape);\n    const inputVals = cpuBackend.data.get(input.dataId);\n    const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values;\n    const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;\n    if (isExponentOf2(inputSize)) {\n        const result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);\n        const resultShape = [input.shape[0], input.shape[1]];\n        if (inverse) {\n            const realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.real);\n            const imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.imag);\n            const sizeInfo = cpuBackend.makeTensorInfo([], 'float32', util.createScalarValue(inputSize, 'float32'));\n            const sizeInfoCopy = identity({ inputs: { x: sizeInfo }, backend: cpuBackend });\n            const divRealInfo = divConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend });\n            const divImagInfo = divConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend });\n            const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values;\n            const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values;\n            cpuBackend.disposeIntermediateTensorInfo(realInfo);\n            cpuBackend.disposeIntermediateTensorInfo(imagInfo);\n            cpuBackend.disposeIntermediateTensorInfo(sizeInfo);\n            cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);\n            cpuBackend.disposeIntermediateTensorInfo(divRealInfo);\n            cpuBackend.disposeIntermediateTensorInfo(divImagInfo);\n            return { real: divRealVals, imag: divImagVals };\n        }\n        return result;\n    }\n    else {\n        const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n        const rawOutput = fourierTransformByMatmul(data, inputSize, inverse);\n        return backend_util.splitRealAndImagArrays(rawOutput);\n    }\n}\nfunction isExponentOf2(size) {\n    return (size & size - 1) === 0;\n}\n// FFT using Cooley-Tukey algorithm on radix 2 dimensional input.\nfunction fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {\n    if (size === 1) {\n        return { real: realVals, imag: imagVals };\n    }\n    const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n    const half = size / 2;\n    const evenComplex = backend_util.complexWithEvenIndex(data);\n    const evenRealVals = evenComplex.real;\n    const evenImagVals = evenComplex.imag;\n    const evenShape = [evenRealVals.length];\n    const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenRealVals);\n    const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenImagVals);\n    const evenTensorInfo = complex({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend });\n    const oddComplex = backend_util.complexWithOddIndex(data);\n    const oddRealVals = oddComplex.real;\n    const oddImagVals = oddComplex.imag;\n    const oddShape = [oddRealVals.length];\n    const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddRealVals);\n    const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddImagVals);\n    const oddTensorInfo = complex({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend });\n    // Recursive call for half part of original input.\n    const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);\n    const $evenRealVals = $evenComplex.real;\n    const $evenImagVals = $evenComplex.imag;\n    const $evenShape = [$evenRealVals.length];\n    const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenRealVals);\n    const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenImagVals);\n    const $evenTensorInfo = complex({\n        inputs: { real: $evenRealInfo, imag: $evenImagInfo },\n        backend: cpuBackend\n    });\n    const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);\n    const $oddRealVals = $oddComplex.real;\n    const $oddImagVals = $oddComplex.imag;\n    const $oddShape = [$oddRealVals.length];\n    const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddRealVals);\n    const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddImagVals);\n    const $oddTensorInfo = complex({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend });\n    const e = backend_util.exponents(size, inverse);\n    const eShape = [e.real.length];\n    const eRealInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.real);\n    const eImagInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.imag);\n    const complexInfo = complex({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend });\n    const exponentInfo = multiply({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend });\n    const addPart = add({\n        inputs: { a: $evenTensorInfo, b: exponentInfo },\n        backend: cpuBackend\n    });\n    const subPart = sub({\n        inputs: { a: $evenTensorInfo, b: exponentInfo },\n        backend: cpuBackend\n    });\n    const addPartReal = real({ inputs: { input: addPart }, backend: cpuBackend });\n    const subPartReal = real({ inputs: { input: subPart }, backend: cpuBackend });\n    const addPartImag = imag({ inputs: { input: addPart }, backend: cpuBackend });\n    const subPartImag = imag({ inputs: { input: subPart }, backend: cpuBackend });\n    const $real = concat({\n        inputs: [addPartReal, subPartReal],\n        backend: cpuBackend,\n        attrs: { axis: 0 }\n    });\n    const $imag = concat({\n        inputs: [addPartImag, subPartImag],\n        backend: cpuBackend,\n        attrs: { axis: 0 }\n    });\n    const $realVals = cpuBackend.data.get($real.dataId).values;\n    const $imagVals = cpuBackend.data.get($imag.dataId).values;\n    cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo(eRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(eImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(complexInfo);\n    cpuBackend.disposeIntermediateTensorInfo(exponentInfo);\n    cpuBackend.disposeIntermediateTensorInfo(addPart);\n    cpuBackend.disposeIntermediateTensorInfo(subPart);\n    cpuBackend.disposeIntermediateTensorInfo(addPartReal);\n    cpuBackend.disposeIntermediateTensorInfo(addPartImag);\n    cpuBackend.disposeIntermediateTensorInfo(subPartReal);\n    cpuBackend.disposeIntermediateTensorInfo(subPartImag);\n    cpuBackend.disposeIntermediateTensorInfo($real);\n    cpuBackend.disposeIntermediateTensorInfo($imag);\n    return { real: $realVals, imag: $imagVals };\n}\n// Calculate fourier transform by multplying sinusoid matrix.\nfunction fourierTransformByMatmul(data, size, inverse) {\n    const ret = new Float32Array(size * 2);\n    // TODO: Use matmul instead once it supports complex64 type.\n    for (let r = 0; r < size; r++) {\n        let real = 0.0;\n        let imag = 0.0;\n        for (let c = 0; c < size; c++) {\n            const e = backend_util.exponent(r * c, size, inverse);\n            const term = backend_util.getComplexWithIndex(data, c);\n            real += term.real * e.real - term.imag * e.imag;\n            imag += term.real * e.imag + term.imag * e.real;\n        }\n        if (inverse) {\n            real /= size;\n            imag /= size;\n        }\n        backend_util.assignToTypedArray(ret, real, imag, r);\n    }\n    return ret;\n}\n//# sourceMappingURL=fft_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FFT, util } from '@tensorflow/tfjs-core';\nimport { fftBatch } from '../utils/fft_utils';\nimport { reshape } from './Reshape';\nexport function fft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputSize = util.sizeFromShape(input.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({\n        inputs: { x: input },\n        backend,\n        attrs: { shape: [batch, innerDimensionSize] }\n    });\n    const result = fftBatch(input2D, false, backend);\n    const resultReshaped = reshape({ inputs: { x: result }, backend, attrs: { shape: input.shape } });\n    backend.disposeIntermediateTensorInfo(input2D);\n    backend.disposeIntermediateTensorInfo(result);\n    return resultReshaped;\n}\nexport const fftConfig = {\n    kernelName: FFT,\n    backendName: 'cpu',\n    kernelFunc: fft\n};\n//# sourceMappingURL=FFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FlipLeftRight, util } from '@tensorflow/tfjs-core';\nexport const flipLeftRightConfig = {\n    kernelName: FlipLeftRight,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const cpuBackend = backend;\n        const output = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(image.shape));\n        const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n        const imageVals = cpuBackend.data.get(image.dataId).values;\n        for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n            for (let row = 0; row < imageHeight; row++) {\n                const rowOffset = row * (imageWidth * numChannels);\n                for (let col = 0; col < imageWidth; col++) {\n                    const colOffset = col * numChannels;\n                    for (let channel = 0; channel < numChannels; channel++) {\n                        const coords = [batch, row, col, channel];\n                        const x = coords[2];\n                        const coordX = Math.round(imageWidth - x);\n                        const outIdx = batchOffset + rowOffset + colOffset + channel;\n                        let outputValue = imageVals[outIdx];\n                        // If the coordinate position falls within the image boundaries...\n                        if (coordX >= 0 && coordX < imageWidth) {\n                            // set the output to the image value at the coordinate position.\n                            const rotatedColOffset = coordX * numChannels;\n                            const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel;\n                            outputValue = imageVals[imageIdx];\n                        }\n                        output[outIdx] = outputValue;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(output, image.shape, image.dtype);\n        return { dataId, shape: image.shape, dtype: image.dtype };\n    }\n};\n//# sourceMappingURL=FlipLeftRight.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IFFT, util } from '@tensorflow/tfjs-core';\nimport { fftBatch } from '../utils/fft_utils';\nimport { reshape } from './Reshape';\nexport function ifft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputSize = util.sizeFromShape(input.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({\n        inputs: { x: input },\n        backend,\n        attrs: { shape: [batch, innerDimensionSize] }\n    });\n    const result = fftBatch(input2D, true, backend);\n    const resultReshaped = reshape({ inputs: { x: result }, backend, attrs: { shape: input.shape } });\n    backend.disposeIntermediateTensorInfo(input2D);\n    backend.disposeIntermediateTensorInfo(result);\n    return resultReshaped;\n}\nexport const ifftConfig = {\n    kernelName: IFFT,\n    backendName: 'cpu',\n    kernelFunc: ifft\n};\n//# sourceMappingURL=IFFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsFinite } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isFiniteKernelFunc = unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, 'bool');\nexport const isFiniteConfig = {\n    kernelName: IsFinite,\n    backendName: 'cpu',\n    kernelFunc: isFiniteKernelFunc,\n};\n//# sourceMappingURL=IsFinite.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsInf } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isInfKernelFunc = unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, 'bool');\nexport const isInfConfig = {\n    kernelName: IsInf,\n    backendName: 'cpu',\n    kernelFunc: isInfKernelFunc,\n};\n//# sourceMappingURL=IsInf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsNan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isNaNKernelFunc = unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, 'bool');\nexport const isNaNConfig = {\n    kernelName: IsNan,\n    backendName: 'cpu',\n    kernelFunc: isNaNKernelFunc,\n};\n//# sourceMappingURL=IsNaN.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log1p } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const log1pKernelFunc = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi));\nexport const log1pConfig = {\n    kernelName: Log1p,\n    backendName: 'cpu',\n    kernelFunc: log1pKernelFunc,\n};\n//# sourceMappingURL=Log1p.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LogicalNot } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const logicalNotKernelFunc = unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, 'bool');\nexport const logicalNotConfig = {\n    kernelName: LogicalNot,\n    backendName: 'cpu',\n    kernelFunc: logicalNotKernelFunc,\n};\n//# sourceMappingURL=LogicalNot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '@tensorflow/tfjs-core';\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxImpl } from './Max_impl';\nimport { transposeImpl } from './Transpose_impl';\nexport const maxConfig = {\n    kernelName: Max,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { reductionIndices, keepDims } = attrs;\n        const cpuBackend = backend;\n        let xShape = x.shape;\n        const xRank = xShape.length;\n        const origAxes = util.parseAxisParam(reductionIndices, xShape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        let xVals = cpuBackend.data.get(x.dataId).values;\n        if (permutedAxes != null) {\n            const newShape = new Array(xRank);\n            for (let i = 0; i < newShape.length; i++) {\n                newShape[i] = xShape[permutedAxes[i]];\n            }\n            xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n            xShape = newShape;\n        }\n        assertNotComplex(x, 'max');\n        backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n        const [maxOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(xShape, axes);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);\n        const dataId = cpuBackend.write(result, maxOutShape, x.dtype);\n        let outShape = maxOutShape;\n        if (keepDims) {\n            // reshape\n            const newShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n            outShape = newShape;\n        }\n        return { dataId, shape: outShape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPool, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { pool } from '../utils/pool_utils';\nimport { identity } from './Identity';\nexport function maxPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'maxPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    let res;\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        res = identity({ inputs: { x }, backend });\n    }\n    else {\n        const xValues = backend.data.get(x.dataId).values;\n        const strides = util.computeStrides(x.shape);\n        const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'max');\n        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);\n    }\n    return res;\n}\nexport const maxPoolConfig = {\n    kernelName: MaxPool,\n    backendName: 'cpu',\n    kernelFunc: maxPool\n};\n//# sourceMappingURL=MaxPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, buffer, MaxPoolBackprop } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxPoolPositions } from '../utils/pool_utils';\nexport function maxPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input, output } = inputs;\n    const x = input;\n    assertNotComplex([input, output], 'maxPoolBackprop');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n    const xValues = backend.data.get(x.dataId).values;\n    const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = buffer(x.shape, 'float32');\n    const dyData = backend.data.get(dy.dataId).values;\n    const dyBuf = buffer(dy.shape, 'float32', dyData);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n                for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n                    // Shader code begins.\n                    const dyRCorner = dxR - padTop;\n                    const dyCCorner = dxC - padLeft;\n                    let dotProd = 0;\n                    for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n                        const dyR = (dyRCorner + wR) / strideHeight;\n                        if (dyR < 0 || dyR >= convInfo.outHeight ||\n                            Math.floor(dyR) !== dyR) {\n                            continue;\n                        }\n                        for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                            const dyC = (dyCCorner + wC) / strideWidth;\n                            if (dyC < 0 || dyC >= convInfo.outWidth ||\n                                Math.floor(dyC) !== dyC) {\n                                continue;\n                            }\n                            const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 -\n                                maxPosBuf.get(b, dyR, dyC, d);\n                            const curPos = wR * effectiveFilterWidth + wC;\n                            const mask = maxPos === curPos ? 1 : 0;\n                            if (mask === 0) {\n                                continue;\n                            }\n                            const pixel = dyBuf.get(b, dyR, dyC, d);\n                            dotProd += pixel * mask;\n                        }\n                    }\n                    dx.set(dotProd, b, dxR, dxC, d);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const maxPoolBackpropConfig = {\n    kernelName: MaxPoolBackprop,\n    backendName: 'cpu',\n    kernelFunc: maxPoolBackprop\n};\n//# sourceMappingURL=MaxPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { maxPoolPositions, pool } from '../utils/pool_utils';\nexport function maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) {\n    const strides = util.computeStrides(xShape);\n    const maxPools = pool(xValues, xShape, dtype, strides, convInfo, 'max');\n    const maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex);\n    return [maxPools.values, maxPositions.values];\n}\n//# sourceMappingURL=MaxPoolWithArgmax_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPoolWithArgmax } from '@tensorflow/tfjs-core';\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxPoolWithArgmaxImpl } from './MaxPoolWithArgmax_impl';\nexport const maxPoolWithArgmaxConfig = {\n    kernelName: MaxPoolWithArgmax,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { filterSize, strides, pad, includeBatchInIndex } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(x, 'MaxPoolWithArgmax');\n        const values = cpuBackend.data.get(x.dataId).values;\n        const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad);\n        const [pooled, indexes] = maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo);\n        const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype);\n        const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype);\n        return [\n            { dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype },\n            { dataId: indexesDataId, shape: convInfo.outShape, dtype: 'int32' }\n        ];\n    }\n};\n//# sourceMappingURL=MaxPoolWithArgmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NonMaxSuppressionV4 } from '@tensorflow/tfjs-core';\nimport { kernel_impls } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\nimport { assertNotComplex } from '../cpu_util';\nexport const nonMaxSuppressionV4Config = {\n    kernelName: NonMaxSuppressionV4,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(boxes, 'NonMaxSuppressionPadded');\n        const boxesVals = cpuBackend.data.get(boxes.dataId).values;\n        const scoresVals = cpuBackend.data.get(scores.dataId).values;\n        const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n        return [selectedIndices, validOutputs];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV4.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NonMaxSuppressionV5 } from '@tensorflow/tfjs-core';\nimport { kernel_impls } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nimport { assertNotComplex } from '../cpu_util';\nexport const nonMaxSuppressionV5Config = {\n    kernelName: NonMaxSuppressionV5,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(boxes, 'NonMaxSuppressionWithScore');\n        const boxesVals = cpuBackend.data.get(boxes.dataId).values;\n        const scoresVals = cpuBackend.data.get(scores.dataId).values;\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        const softNmsSigmaVal = softNmsSigma;\n        const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n        return [selectedIndices, selectedScores];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV5.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NotEqual } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const notEqualImpl = createSimpleBinaryKernelImpl(((a, b) => (a !== b) ? 1 : 0));\nexport const notEqual = binaryKernelFunc(NotEqual, notEqualImpl, null /* complexOp */, 'bool');\nexport const notEqualConfig = {\n    kernelName: NotEqual,\n    backendName: 'cpu',\n    kernelFunc: notEqual\n};\n//# sourceMappingURL=NotEqual.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { PadV2, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function padV2(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { paddings, constantValue } = attrs;\n    assertNotComplex(x, 'pad');\n    const outShape = paddings.map((p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);\n    const start = paddings.map(p => p[0]);\n    const xVals = backend.data.get(x.dataId).values;\n    const xSize = util.sizeFromShape(x.shape);\n    const xRank = x.shape.length;\n    const xStrides = util.computeStrides(x.shape);\n    const resultSize = util.sizeFromShape(outShape);\n    const resultRank = outShape.length;\n    const resultStrides = util.computeStrides(outShape);\n    const resVals = util.getTypedArrayFromDType(x.dtype, resultSize);\n    if (constantValue !== 0) {\n        resVals.fill(constantValue);\n    }\n    for (let i = 0; i < xSize; i++) {\n        const coords = util.indexToLoc(i, xRank, xStrides);\n        const outCoords = coords.map((c, i) => c + start[i]);\n        const outIndex = util.locToIndex(outCoords, resultRank, resultStrides);\n        resVals[outIndex] = xVals[i];\n    }\n    const outId = backend.write(resVals, outShape, x.dtype);\n    return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nexport const padV2Config = {\n    kernelName: PadV2,\n    backendName: 'cpu',\n    kernelFunc: padV2\n};\n//# sourceMappingURL=PadV2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reciprocal } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const reciprocalKernelFunc = unaryKernelFunc(Reciprocal, (xi) => 1 / xi);\nexport const reciprocalConfig = {\n    kernelName: Reciprocal,\n    backendName: 'cpu',\n    kernelFunc: reciprocalKernelFunc,\n};\n//# sourceMappingURL=Reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, RotateWithOffset, util } from '@tensorflow/tfjs-core';\nexport const rotateWithOffsetConfig = {\n    kernelName: RotateWithOffset,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const { radians, fillValue, center } = attrs;\n        const cpuBackend = backend;\n        const output = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(image.shape));\n        const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n        const [centerX, centerY] = backend_util.getImageCenter(center, imageHeight, imageWidth);\n        const fullOpacityValue = 255;\n        const sinFactor = Math.sin(radians);\n        const cosFactor = Math.cos(radians);\n        const imageVals = cpuBackend.data.get(image.dataId).values;\n        for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n            for (let row = 0; row < imageHeight; row++) {\n                const rowOffset = row * (imageWidth * numChannels);\n                for (let col = 0; col < imageWidth; col++) {\n                    const colOffset = col * numChannels;\n                    for (let channel = 0; channel < numChannels; channel++) {\n                        const coords = [batch, row, col, channel];\n                        const x = coords[2];\n                        const y = coords[1];\n                        // coordX/coordY are the result of rotating and translating x/y.\n                        let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;\n                        let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;\n                        coordX = Math.round(coordX + centerX);\n                        coordY = Math.round(coordY + centerY);\n                        let outputValue = fillValue;\n                        if (typeof fillValue !== 'number') {\n                            if (channel === 3) {\n                                outputValue = fullOpacityValue;\n                            }\n                            else {\n                                outputValue = fillValue[channel];\n                            }\n                        }\n                        // If the coordinate position falls within the image boundaries...\n                        if (coordX >= 0 && coordX < imageWidth && coordY >= 0 &&\n                            coordY < imageHeight) {\n                            // set the output to the image value at the coordinate position.\n                            const rotatedRowOffset = coordY * (imageWidth * numChannels);\n                            const rotatedColOffset = coordX * numChannels;\n                            const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;\n                            outputValue = imageVals[imageIdx];\n                        }\n                        const outIdx = batchOffset + rowOffset + colOffset + channel;\n                        output[outIdx] = outputValue;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(output, image.shape, image.dtype);\n        return { dataId, shape: image.shape, dtype: image.dtype };\n    }\n};\n//# sourceMappingURL=RotateWithOffset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Round } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const roundKernelFunc = unaryKernelFunc(Round, (xi) => {\n    // The algorithm is based on banker's rounding.\n    const base = Math.floor(xi);\n    if (xi - base < 0.5) {\n        return Math.floor(xi);\n    }\n    else if (xi - base > 0.5) {\n        return Math.ceil(xi);\n    }\n    else {\n        if (base % 2.0 === 0.0) {\n            return base;\n        }\n        else {\n            return base + 1.0;\n        }\n    }\n});\nexport const roundConfig = {\n    kernelName: Round,\n    backendName: 'cpu',\n    kernelFunc: roundKernelFunc,\n};\n//# sourceMappingURL=Round.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Selu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nconst scaleAlpha = backend_util.SELU_SCALEALPHA;\nconst scale = backend_util.SELU_SCALE;\nexport const seluKernelFunc = unaryKernelFunc(Selu, (xi) => {\n    if (xi >= 0) {\n        return scale * xi;\n    }\n    else {\n        return scaleAlpha * (Math.exp(xi) - 1);\n    }\n});\nexport const seluConfig = {\n    kernelName: Selu,\n    backendName: 'cpu',\n    kernelFunc: seluKernelFunc,\n};\n//# sourceMappingURL=Selu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sigmoid } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sigmoidKernelFunc = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));\nexport const sigmoidConfig = {\n    kernelName: Sigmoid,\n    backendName: 'cpu',\n    kernelFunc: sigmoidKernelFunc,\n};\n//# sourceMappingURL=Sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sign } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const signKernelFunc = unaryKernelFunc(Sign, (xi) => {\n    if (xi < 0) {\n        return -1;\n    }\n    else if (xi > 0) {\n        return 1;\n    }\n    else {\n        return 0;\n    }\n});\nexport const signConfig = {\n    kernelName: Sign,\n    backendName: 'cpu',\n    kernelFunc: signKernelFunc,\n};\n//# sourceMappingURL=Sign.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sinKernelFunc = unaryKernelFunc(Sin, (xi) => Math.sin(xi));\nexport const sinConfig = {\n    kernelName: Sin,\n    backendName: 'cpu',\n    kernelFunc: sinKernelFunc,\n};\n//# sourceMappingURL=Sin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sinh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sinhKernelFunc = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi));\nexport const sinhConfig = {\n    kernelName: Sinh,\n    backendName: 'cpu',\n    kernelFunc: sinhKernelFunc,\n};\n//# sourceMappingURL=Sinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softplus } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\n// mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n// epsilon is the difference between 1.0 and the next representable float.\n// For a single precision 32 bit float this should be 2^-23, see:\n// https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\nconst epsilon = 1.1920928955078125e-7;\nconst threshold = Math.log(epsilon) + 2.0;\nexport const softplusKernelFunc = unaryKernelFunc(Softplus, (xi) => {\n    // Value above which exp(x) may overflow, but softplus(x) == x\n    // is within machine epsilon.\n    const tooLarge = xi > -threshold;\n    // Value below which exp(x) may underflow, but softplus(x) == exp(x)\n    // is within machine epsilon.\n    const tooSmall = xi < threshold;\n    const expX = Math.exp(xi);\n    let result;\n    if (tooSmall) {\n        result = expX;\n    }\n    else if (tooLarge) {\n        result = xi;\n    }\n    else {\n        result = Math.log(1.0 + expX);\n    }\n    return result;\n});\nexport const softplusConfig = {\n    kernelName: Softplus,\n    backendName: 'cpu',\n    kernelFunc: softplusKernelFunc,\n};\n//# sourceMappingURL=Softplus.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { transposeImpl } from './Transpose_impl';\nexport function transpose(args) {\n    const { inputs, attrs, backend } = args;\n    const { x } = inputs;\n    const { perm } = attrs;\n    assertNotComplex(x, 'transpose');\n    const xRank = x.shape.length;\n    const newShape = new Array(xRank);\n    for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = x.shape[perm[i]];\n    }\n    const values = backend.data.get(x.dataId).values;\n    const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);\n    const dataId = backend.write(result, newShape, x.dtype);\n    return { dataId, shape: newShape, dtype: x.dtype };\n}\nexport const transposeConfig = {\n    kernelName: Transpose,\n    backendName: 'cpu',\n    kernelFunc: transpose\n};\n//# sourceMappingURL=Transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, SpaceToBatchND, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { padV2Config } from './PadV2';\nimport { reshape } from './Reshape';\nimport { transpose } from './Transpose';\nexport function spaceToBatchND(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { blockShape, paddings } = attrs;\n    assertNotComplex([x], 'spaceToBatchND');\n    const prod = util.sizeFromShape(blockShape);\n    const completePaddings = [[0, 0]];\n    completePaddings.push(...paddings);\n    for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n        completePaddings.push([0, 0]);\n    }\n    const paddedX = padV2Config.kernelFunc({\n        inputs: { x },\n        backend,\n        attrs: { paddings: completePaddings, constantValue: 0 }\n    });\n    const reshapedPaddedShape = backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n    const permutedReshapedPaddedPermutation = backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n    const flattenShape = backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n    const reshapeInputs = { x: paddedX };\n    const reshapeAttrs = { shape: reshapedPaddedShape };\n    const paddedXReshaped = reshape({ inputs: reshapeInputs, backend, attrs: reshapeAttrs });\n    const transposeInputs = { x: paddedXReshaped };\n    const transposeAttrs = { perm: permutedReshapedPaddedPermutation };\n    const paddedXT = transpose({ inputs: transposeInputs, backend, attrs: transposeAttrs });\n    const resultReshapeInputs = { x: paddedXT };\n    const resultReshapeAttrs = { shape: flattenShape };\n    const result = reshape({ inputs: resultReshapeInputs, backend, attrs: resultReshapeAttrs });\n    backend.disposeIntermediateTensorInfo(paddedX);\n    backend.disposeIntermediateTensorInfo(paddedXReshaped);\n    backend.disposeIntermediateTensorInfo(paddedXT);\n    return result;\n}\nexport const spaceToBatchNDConfig = {\n    kernelName: SpaceToBatchND,\n    backendName: 'cpu',\n    kernelFunc: spaceToBatchND\n};\n//# sourceMappingURL=SpaceToBatchND.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sqrt } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sqrtKernelFunc = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));\nexport const sqrtConfig = {\n    kernelName: Sqrt,\n    backendName: 'cpu',\n    kernelFunc: sqrtKernelFunc,\n};\n//# sourceMappingURL=Sqrt.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport const squareConfig = {\n    kernelName: Square,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend }) => {\n        const { x } = inputs;\n        const cpuBackend = backend;\n        assertNotComplex(x, 'square');\n        const values = cpuBackend.data.get(x.dataId).values;\n        const newValues = new Float32Array(values.length);\n        for (let i = 0; i < values.length; ++i) {\n            const value = values[i];\n            newValues[i] = value * value;\n        }\n        const dataId = cpuBackend.write(newValues, x.shape, x.dtype);\n        return { dataId, shape: x.shape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const squaredDifferenceImpl = createSimpleBinaryKernelImpl(((a, b) => {\n    const diff = a - b;\n    return diff * diff;\n}));\nexport const squaredDifference = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);\nexport const squaredDifferenceConfig = {\n    kernelName: SquaredDifference,\n    backendName: 'cpu',\n    kernelFunc: squaredDifference\n};\n//# sourceMappingURL=SquaredDifference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Step } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const stepKernelFunc = unaryKernelFunc(Step, (xi, attrs) => {\n    const stepAttrs = attrs;\n    if (isNaN(xi)) {\n        return NaN;\n    }\n    else {\n        return xi > 0 ? 1 : stepAttrs.alpha;\n    }\n});\nexport const stepConfig = {\n    kernelName: Step,\n    backendName: 'cpu',\n    kernelFunc: stepKernelFunc,\n};\n//# sourceMappingURL=Step.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const tanKernelFunc = unaryKernelFunc(Tan, (xi) => Math.tan(xi));\nexport const tanConfig = {\n    kernelName: Tan,\n    backendName: 'cpu',\n    kernelFunc: tanKernelFunc,\n};\n//# sourceMappingURL=Tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tanh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const tanhKernelFunc = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi));\nexport const tanhConfig = {\n    kernelName: Tanh,\n    backendName: 'cpu',\n    kernelFunc: tanhKernelFunc,\n};\n//# sourceMappingURL=Tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unique } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { uniqueImpl } from './Unique_impl';\nexport function unique(args) {\n    const { inputs, attrs, backend } = args;\n    const { axis } = attrs;\n    const { x } = inputs;\n    assertNotComplex(x, 'unique');\n    const values = backend.data.get(x.dataId).values;\n    const { outputValues, outputShape, indices } = uniqueImpl(values, axis, x.shape, x.dtype);\n    return [\n        backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n        backend.makeTensorInfo([indices.length], 'int32', indices),\n    ];\n}\nexport const uniqueConfig = {\n    kernelName: Unique,\n    backendName: 'cpu',\n    kernelFunc: unique,\n};\n//# sourceMappingURL=Unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// We explicitly import the modular kernels so they get registered in the\n// global registry when we compile the library. A modular build would replace\n// the contents of this file and import only the kernels that are needed.\nimport { registerKernel } from '@tensorflow/tfjs-core';\nimport { absConfig } from './kernels/Abs';\nimport { acosConfig } from './kernels/Acos';\nimport { acoshConfig } from './kernels/Acosh';\nimport { addConfig } from './kernels/Add';\nimport { asinConfig } from './kernels/Asin';\nimport { asinhConfig } from './kernels/Asinh';\nimport { atanConfig } from './kernels/Atan';\nimport { atanhConfig } from './kernels/Atanh';\nimport { avgPoolConfig } from './kernels/AvgPool';\nimport { avgPoolBackpropConfig } from './kernels/AvgPoolBackprop';\nimport { batchNormConfig } from './kernels/BatchNorm';\nimport { castConfig } from './kernels/Cast';\nimport { ceilConfig } from './kernels/Ceil';\nimport { clipConfig } from './kernels/Clip';\nimport { complexConfig } from './kernels/Complex';\nimport { concatConfig } from './kernels/Concat';\nimport { cosConfig } from './kernels/Cos';\nimport { coshConfig } from './kernels/Cosh';\nimport { dilation2dConfig } from './kernels/Dilation2D';\nimport { dilation2dBackpropFilterConfig } from './kernels/Dilation2DBackpropFilter';\nimport { dilation2dBackpropInputConfig } from './kernels/Dilation2DBackpropInput';\nimport { divConfig } from './kernels/Div';\nimport { eluConfig } from './kernels/Elu';\nimport { erfConfig } from './kernels/Erf';\nimport { expConfig } from './kernels/Exp';\nimport { expm1Config } from './kernels/Expm1';\nimport { fftConfig } from './kernels/FFT';\nimport { flipLeftRightConfig } from './kernels/FlipLeftRight';\nimport { floorConfig } from './kernels/Floor';\nimport { identityConfig } from './kernels/Identity';\nimport { ifftConfig } from './kernels/IFFT';\nimport { imagConfig } from './kernels/Imag';\nimport { isFiniteConfig } from './kernels/IsFinite';\nimport { isInfConfig } from './kernels/IsInf';\nimport { isNaNConfig } from './kernels/IsNaN';\nimport { logConfig } from './kernels/Log';\nimport { log1pConfig } from './kernels/Log1p';\nimport { logicalNotConfig } from './kernels/LogicalNot';\nimport { maxConfig } from './kernels/Max';\nimport { maxPoolConfig } from './kernels/MaxPool';\nimport { maxPoolBackpropConfig } from './kernels/MaxPoolBackprop';\nimport { maxPoolWithArgmaxConfig } from './kernels/MaxPoolWithArgmax';\nimport { multiplyConfig } from './kernels/Multiply';\nimport { nonMaxSuppressionV4Config } from './kernels/NonMaxSuppressionV4';\nimport { nonMaxSuppressionV5Config } from './kernels/NonMaxSuppressionV5';\nimport { notEqualConfig } from './kernels/NotEqual';\nimport { padV2Config } from './kernels/PadV2';\nimport { realConfig } from './kernels/Real';\nimport { reciprocalConfig } from './kernels/Reciprocal';\nimport { reshapeConfig } from './kernels/Reshape';\nimport { rotateWithOffsetConfig } from './kernels/RotateWithOffset';\nimport { roundConfig } from './kernels/Round';\nimport { rsqrtConfig } from './kernels/Rsqrt';\nimport { seluConfig } from './kernels/Selu';\nimport { sigmoidConfig } from './kernels/Sigmoid';\nimport { signConfig } from './kernels/Sign';\nimport { sinConfig } from './kernels/Sin';\nimport { sinhConfig } from './kernels/Sinh';\nimport { sliceConfig } from './kernels/Slice';\nimport { softplusConfig } from './kernels/Softplus';\nimport { spaceToBatchNDConfig } from './kernels/SpaceToBatchND';\nimport { sqrtConfig } from './kernels/Sqrt';\nimport { squareConfig } from './kernels/Square';\nimport { squaredDifferenceConfig } from './kernels/SquaredDifference';\nimport { stepConfig } from './kernels/Step';\nimport { subConfig } from './kernels/Sub';\nimport { tanConfig } from './kernels/Tan';\nimport { tanhConfig } from './kernels/Tanh';\nimport { transposeConfig } from './kernels/Transpose';\nimport { uniqueConfig } from './kernels/Unique';\n// List all kernel configs here\nconst kernelConfigs = [\n    absConfig,\n    acosConfig,\n    acoshConfig,\n    addConfig,\n    asinConfig,\n    asinhConfig,\n    atanConfig,\n    atanhConfig,\n    avgPoolConfig,\n    avgPoolBackpropConfig,\n    batchNormConfig,\n    castConfig,\n    ceilConfig,\n    clipConfig,\n    complexConfig,\n    concatConfig,\n    cosConfig,\n    coshConfig,\n    dilation2dConfig,\n    dilation2dBackpropInputConfig,\n    dilation2dBackpropFilterConfig,\n    divConfig,\n    eluConfig,\n    erfConfig,\n    expConfig,\n    expm1Config,\n    fftConfig,\n    flipLeftRightConfig,\n    floorConfig,\n    identityConfig,\n    ifftConfig,\n    imagConfig,\n    isFiniteConfig,\n    isInfConfig,\n    isNaNConfig,\n    logConfig,\n    log1pConfig,\n    logicalNotConfig,\n    maxPoolConfig,\n    maxPoolBackpropConfig,\n    maxPoolWithArgmaxConfig,\n    maxConfig,\n    multiplyConfig,\n    nonMaxSuppressionV4Config,\n    nonMaxSuppressionV5Config,\n    notEqualConfig,\n    padV2Config,\n    realConfig,\n    reciprocalConfig,\n    reshapeConfig,\n    rotateWithOffsetConfig,\n    roundConfig,\n    rsqrtConfig,\n    seluConfig,\n    sigmoidConfig,\n    signConfig,\n    sinConfig,\n    sinhConfig,\n    sliceConfig,\n    softplusConfig,\n    spaceToBatchNDConfig,\n    sqrtConfig,\n    squareConfig,\n    squaredDifferenceConfig,\n    stepConfig,\n    subConfig,\n    tanConfig,\n    tanhConfig,\n    transposeConfig,\n    uniqueConfig,\n];\nfor (const kernelConfig of kernelConfigs) {\n    registerKernel(kernelConfig);\n}\n//# sourceMappingURL=register_all_kernels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// All exports from this package should be in base.\nexport * from './base';\nimport './register_all_kernels';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst contexts = {};\nconst WEBGL_ATTRIBUTES = {\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: true\n};\nexport function setWebGLContext(webGLVersion, gl) {\n    contexts[webGLVersion] = gl;\n}\nexport function getWebGLContext(webGLVersion) {\n    if (!(webGLVersion in contexts)) {\n        const newCtx = getWebGLRenderingContext(webGLVersion);\n        if (newCtx !== null) {\n            contexts[webGLVersion] = newCtx;\n        }\n        else {\n            console.log('Could not get context for WebGL version', webGLVersion);\n            return null;\n        }\n    }\n    const gl = contexts[webGLVersion];\n    if (gl.isContextLost()) {\n        delete contexts[webGLVersion];\n        return getWebGLContext(webGLVersion);\n    }\n    gl.disable(gl.DEPTH_TEST);\n    gl.disable(gl.STENCIL_TEST);\n    gl.disable(gl.BLEND);\n    gl.disable(gl.DITHER);\n    gl.disable(gl.POLYGON_OFFSET_FILL);\n    gl.disable(gl.SAMPLE_COVERAGE);\n    gl.enable(gl.SCISSOR_TEST);\n    gl.enable(gl.CULL_FACE);\n    gl.cullFace(gl.BACK);\n    return contexts[webGLVersion];\n}\nfunction createCanvas(webGLVersion) {\n    if (typeof OffscreenCanvas !== 'undefined' && webGLVersion === 2) {\n        return new OffscreenCanvas(300, 150);\n    }\n    else if (typeof document !== 'undefined') {\n        return document.createElement('canvas');\n    }\n    else {\n        throw new Error('Cannot create a canvas in this context');\n    }\n}\nfunction getWebGLRenderingContext(webGLVersion) {\n    if (webGLVersion !== 1 && webGLVersion !== 2) {\n        throw new Error('Cannot get WebGL rendering context, WebGL is disabled.');\n    }\n    const canvas = createCanvas(webGLVersion);\n    canvas.addEventListener('webglcontextlost', (ev) => {\n        ev.preventDefault();\n        delete contexts[webGLVersion];\n    }, false);\n    if (webGLVersion === 1) {\n        return (canvas.getContext('webgl', WEBGL_ATTRIBUTES) ||\n            canvas.getContext('experimental-webgl', WEBGL_ATTRIBUTES));\n    }\n    return canvas.getContext('webgl2', WEBGL_ATTRIBUTES);\n}\n//# sourceMappingURL=canvas_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nexport var PackingScheme;\n(function (PackingScheme) {\n    /**\n     * All values in a single texel are densely packed without any constraints.\n     *\n     * This is how the shader encodes a tensor with shape = [2, 3, 4]\n     * (indices are [batch, row, col]).\n     *\n     * 000|001   010|011   020|021\n     * -------   -------   -------\n     * 002|003   012|013   022|023\n     *\n     * 100|101   110|111   120|121\n     * -------   -------   -------\n     * 102|103   112|113   122|123\n     *\n     */\n    PackingScheme[PackingScheme[\"DENSE\"] = 0] = \"DENSE\";\n    /**\n     * Single texels contain only values from the same batch, and from adjacent\n     * rows and columns.\n     *\n     * This is how the shader encodes a tensor with shape = [2, 3, 5]\n     * (indices are [batch, row, col]).\n     *\n     * 000|001   002|003   004|xxx   020|021   022|023   024|xxx\n     * -------   -------   -------   -------   -------   -------\n     * 010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n     *\n     * 100|101   102|103   104|xxx   120|121   122|123   124|xxx\n     * -------   -------   -------   -------   -------   -------\n     * 110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n     *\n     */\n    PackingScheme[PackingScheme[\"SHARED_BATCH\"] = 1] = \"SHARED_BATCH\";\n})(PackingScheme || (PackingScheme = {}));\nexport var TextureUsage;\n(function (TextureUsage) {\n    TextureUsage[TextureUsage[\"RENDER\"] = 0] = \"RENDER\";\n    TextureUsage[TextureUsage[\"UPLOAD\"] = 1] = \"UPLOAD\";\n    TextureUsage[TextureUsage[\"PIXELS\"] = 2] = \"PIXELS\";\n    TextureUsage[TextureUsage[\"DOWNLOAD\"] = 3] = \"DOWNLOAD\";\n})(TextureUsage || (TextureUsage = {}));\nexport var PhysicalTextureType;\n(function (PhysicalTextureType) {\n    PhysicalTextureType[PhysicalTextureType[\"UNPACKED_FLOAT16\"] = 0] = \"UNPACKED_FLOAT16\";\n    PhysicalTextureType[PhysicalTextureType[\"UNPACKED_FLOAT32\"] = 1] = \"UNPACKED_FLOAT32\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_4X1_UNSIGNED_BYTE\"] = 2] = \"PACKED_4X1_UNSIGNED_BYTE\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_2X2_FLOAT32\"] = 3] = \"PACKED_2X2_FLOAT32\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_2X2_FLOAT16\"] = 4] = \"PACKED_2X2_FLOAT16\";\n})(PhysicalTextureType || (PhysicalTextureType = {}));\nexport function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {\n    return [columns, rows];\n}\nexport function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {\n    return matrixSize * channelsPerTexture;\n}\nexport function getColorMatrixTextureShapeWidthHeight(rows, columns) {\n    return [columns * 4, rows];\n}\n/**\n * Get shape for densely packed RGBA texture.\n */\nexport function getDenseTexShape(shape) {\n    const size = util.sizeFromShape(shape);\n    const texelsNeeded = Math.ceil(size / 4);\n    return util.sizeToSquarishShape(texelsNeeded);\n}\nexport function getMatrixSizeFromUnpackedArraySize(unpackedSize, channelsPerTexture) {\n    if (unpackedSize % channelsPerTexture !== 0) {\n        throw new Error(`unpackedSize (${unpackedSize}) must be a multiple of ` +\n            `${channelsPerTexture}`);\n    }\n    return unpackedSize / channelsPerTexture;\n}\nexport function decodeMatrixFromUnpackedColorRGBAArray(unpackedArray, matrix, channels) {\n    const requiredSize = unpackedArray.length * channels / 4;\n    if (matrix.length < requiredSize) {\n        throw new Error(`matrix length (${matrix.length}) must be >= ${requiredSize}`);\n    }\n    let dst = 0;\n    for (let src = 0; src < unpackedArray.length; src += 4) {\n        for (let c = 0; c < channels; c++) {\n            matrix[dst++] = unpackedArray[src + c];\n        }\n    }\n}\nexport function getPackedMatrixTextureShapeWidthHeight(rows, columns) {\n    return [\n        Math.max(1, Math.ceil(columns / 2)), Math.max(1, Math.ceil(rows / 2))\n    ];\n}\nexport function getPackedRGBAArraySizeFromMatrixShape(rows, columns) {\n    const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return w * h * 4;\n}\nexport function getTextureConfig(\n// tslint:disable-next-line:no-any\ngl, textureHalfFloatExtension) {\n    // tslint:disable-next-line:no-any\n    const glany = gl;\n    let internalFormatFloat;\n    let internalFormatHalfFloat;\n    let internalFormatPackedHalfFloat;\n    let internalFormatPackedFloat;\n    let textureFormatFloat;\n    let downloadTextureFormat;\n    let downloadUnpackNumChannels;\n    let defaultNumChannels;\n    let textureTypeHalfFloat;\n    let textureTypeFloat;\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        internalFormatFloat = glany.R32F;\n        internalFormatHalfFloat = glany.R16F;\n        internalFormatPackedHalfFloat = glany.RGBA16F;\n        internalFormatPackedFloat = glany.RGBA32F;\n        textureFormatFloat = glany.RED;\n        downloadUnpackNumChannels = 4;\n        defaultNumChannels = 1;\n        textureTypeHalfFloat = glany.HALF_FLOAT;\n        textureTypeFloat = glany.FLOAT;\n    }\n    else {\n        internalFormatFloat = gl.RGBA;\n        internalFormatHalfFloat = gl.RGBA;\n        internalFormatPackedHalfFloat = gl.RGBA;\n        internalFormatPackedFloat = glany.RGBA;\n        textureFormatFloat = gl.RGBA;\n        downloadUnpackNumChannels = 4;\n        defaultNumChannels = 4;\n        textureTypeHalfFloat = textureHalfFloatExtension != null ?\n            textureHalfFloatExtension.HALF_FLOAT_OES :\n            null;\n        textureTypeFloat = gl.FLOAT;\n    }\n    downloadTextureFormat = gl.RGBA;\n    return {\n        internalFormatFloat,\n        internalFormatHalfFloat,\n        internalFormatPackedHalfFloat,\n        internalFormatPackedFloat,\n        textureFormatFloat,\n        downloadTextureFormat,\n        downloadUnpackNumChannels,\n        defaultNumChannels,\n        textureTypeHalfFloat,\n        textureTypeFloat\n    };\n}\n//# sourceMappingURL=tex_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { getWebGLContext } from './canvas_util';\nimport { getTextureConfig } from './tex_util';\nexport function callAndCheck(gl, func) {\n    const returnValue = func();\n    if (env().getBool('DEBUG')) {\n        checkWebGLError(gl);\n    }\n    return returnValue;\n}\nfunction checkWebGLError(gl) {\n    const error = gl.getError();\n    if (error !== gl.NO_ERROR) {\n        throw new Error('WebGL Error: ' + getWebGLErrorMessage(gl, error));\n    }\n}\n// https://en.wikipedia.org/wiki/Half-precision_floating-point_format\nconst MIN_FLOAT16 = 5.96e-8;\nconst MAX_FLOAT16 = 65504;\nexport function canBeRepresented(num) {\n    if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED') || num === 0 ||\n        (MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16)) {\n        return true;\n    }\n    return false;\n}\nexport function getWebGLErrorMessage(gl, status) {\n    switch (status) {\n        case gl.NO_ERROR:\n            return 'NO_ERROR';\n        case gl.INVALID_ENUM:\n            return 'INVALID_ENUM';\n        case gl.INVALID_VALUE:\n            return 'INVALID_VALUE';\n        case gl.INVALID_OPERATION:\n            return 'INVALID_OPERATION';\n        case gl.INVALID_FRAMEBUFFER_OPERATION:\n            return 'INVALID_FRAMEBUFFER_OPERATION';\n        case gl.OUT_OF_MEMORY:\n            return 'OUT_OF_MEMORY';\n        case gl.CONTEXT_LOST_WEBGL:\n            return 'CONTEXT_LOST_WEBGL';\n        default:\n            return `Unknown error code ${status}`;\n    }\n}\nexport function getExtensionOrThrow(gl, extensionName) {\n    return throwIfNull(gl, () => gl.getExtension(extensionName), 'Extension \"' + extensionName + '\" not supported on this browser.');\n}\nexport function createVertexShader(gl, vertexShaderSource) {\n    const vertexShader = throwIfNull(gl, () => gl.createShader(gl.VERTEX_SHADER), 'Unable to create vertex WebGLShader.');\n    callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));\n    callAndCheck(gl, () => gl.compileShader(vertexShader));\n    if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {\n        console.log(gl.getShaderInfoLog(vertexShader));\n        throw new Error('Failed to compile vertex shader.');\n    }\n    return vertexShader;\n}\nexport function createFragmentShader(gl, fragmentShaderSource) {\n    const fragmentShader = throwIfNull(gl, () => gl.createShader(gl.FRAGMENT_SHADER), 'Unable to create fragment WebGLShader.');\n    callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));\n    callAndCheck(gl, () => gl.compileShader(fragmentShader));\n    if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {\n        logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));\n        throw new Error('Failed to compile fragment shader.');\n    }\n    return fragmentShader;\n}\nconst lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;\nfunction logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {\n    const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);\n    if (lineNumberRegexResult == null) {\n        console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);\n        console.log(shaderSource);\n        return;\n    }\n    const lineNumber = +lineNumberRegexResult[1];\n    const shaderLines = shaderSource.split('\\n');\n    const pad = shaderLines.length.toString().length + 2;\n    const linesWithLineNumbers = shaderLines.map((line, lineNumber) => util.rightPad((lineNumber + 1).toString(), pad) + line);\n    let maxLineLength = 0;\n    for (let i = 0; i < linesWithLineNumbers.length; i++) {\n        maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);\n    }\n    const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);\n    const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);\n    const afterErrorLines = linesWithLineNumbers.slice(lineNumber);\n    console.log(beforeErrorLines.join('\\n'));\n    console.log(shaderInfoLog.split('\\n')[0]);\n    console.log(`%c ${util.rightPad(errorLine[0], maxLineLength)}`, 'border:1px solid red; background-color:#e3d2d2; color:#a61717');\n    console.log(afterErrorLines.join('\\n'));\n}\nexport function createProgram(gl) {\n    return throwIfNull(gl, () => gl.createProgram(), 'Unable to create WebGLProgram.');\n}\nexport function linkProgram(gl, program) {\n    callAndCheck(gl, () => gl.linkProgram(program));\n    if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {\n        console.log(gl.getProgramInfoLog(program));\n        throw new Error('Failed to link vertex and fragment shaders.');\n    }\n}\nexport function validateProgram(gl, program) {\n    callAndCheck(gl, () => gl.validateProgram(program));\n    if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {\n        console.log(gl.getProgramInfoLog(program));\n        throw new Error('Shader program validation failed.');\n    }\n}\nexport function createStaticVertexBuffer(gl, data) {\n    const buffer = throwIfNull(gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));\n    return buffer;\n}\nexport function createStaticIndexBuffer(gl, data) {\n    const buffer = throwIfNull(gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n    callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));\n    return buffer;\n}\nexport function getNumChannels() {\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        return 1;\n    }\n    return 4;\n}\nexport function createTexture(gl) {\n    return throwIfNull(gl, () => gl.createTexture(), 'Unable to create WebGLTexture.');\n}\nexport function validateTextureSize(width, height) {\n    const maxTextureSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n    if ((width <= 0) || (height <= 0)) {\n        const requested = `[${width}x${height}]`;\n        throw new Error('Requested texture size ' + requested + ' is invalid.');\n    }\n    if ((width > maxTextureSize) || (height > maxTextureSize)) {\n        const requested = `[${width}x${height}]`;\n        const max = `[${maxTextureSize}x${maxTextureSize}]`;\n        throw new Error('Requested texture size ' + requested +\n            ' greater than WebGL maximum on this browser / GPU ' + max + '.');\n    }\n}\nexport function createFramebuffer(gl) {\n    return throwIfNull(gl, () => gl.createFramebuffer(), 'Unable to create WebGLFramebuffer.');\n}\nexport function bindVertexBufferToProgramAttribute(gl, program, attribute, buffer, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {\n    const loc = gl.getAttribLocation(program, attribute);\n    if (loc === -1) {\n        // The GPU compiler decided to strip out this attribute because it's unused,\n        // thus no need to bind.\n        return false;\n    }\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes));\n    callAndCheck(gl, () => gl.enableVertexAttribArray(loc));\n    return true;\n}\nexport function bindTextureUnit(gl, texture, textureUnit) {\n    validateTextureUnit(gl, textureUnit);\n    callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n}\nexport function unbindTextureUnit(gl, textureUnit) {\n    validateTextureUnit(gl, textureUnit);\n    callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function getProgramUniformLocationOrThrow(gl, program, uniformName) {\n    return throwIfNull(gl, () => gl.getUniformLocation(program, uniformName), 'uniform \"' + uniformName + '\" not present in program.');\n}\nexport function getProgramUniformLocation(gl, program, uniformName) {\n    return gl.getUniformLocation(program, uniformName);\n}\nexport function bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) {\n    callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));\n    callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));\n}\nexport function bindCanvasToFramebuffer(gl) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n    callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));\n    callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));\n}\nexport function bindColorTextureToFramebuffer(gl, texture, framebuffer) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));\n}\nexport function unbindColorTextureFromFramebuffer(gl, framebuffer) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));\n}\nexport function validateFramebuffer(gl) {\n    const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);\n    if (status !== gl.FRAMEBUFFER_COMPLETE) {\n        throw new Error('Error binding framebuffer: ' + getFramebufferErrorMessage(gl, status));\n    }\n}\nexport function getFramebufferErrorMessage(gl, status) {\n    switch (status) {\n        case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:\n            return 'FRAMEBUFFER_INCOMPLETE_ATTACHMENT';\n        case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:\n            return 'FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT';\n        case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:\n            return 'FRAMEBUFFER_INCOMPLETE_DIMENSIONS';\n        case gl.FRAMEBUFFER_UNSUPPORTED:\n            return 'FRAMEBUFFER_UNSUPPORTED';\n        default:\n            return `unknown error ${status}`;\n    }\n}\nfunction throwIfNull(gl, returnTOrNull, failureMessage) {\n    const tOrNull = callAndCheck(gl, () => returnTOrNull());\n    if (tOrNull == null) {\n        throw new Error(failureMessage);\n    }\n    return tOrNull;\n}\nfunction validateTextureUnit(gl, textureUnit) {\n    const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;\n    const glTextureUnit = textureUnit + gl.TEXTURE0;\n    if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {\n        const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;\n        throw new Error(`textureUnit must be in ${textureUnitRange}.`);\n    }\n}\nexport function getBatchDim(shape, dimsToSkip = 2) {\n    return util.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));\n}\nexport function getRowsCols(shape) {\n    if (shape.length === 0) {\n        throw Error('Cannot get rows and columns of an empty shape array.');\n    }\n    return [\n        shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]\n    ];\n}\nexport function getShapeAs3D(shape) {\n    let shapeAs3D = [1, 1, 1];\n    const isScalar = shape.length === 0 || (shape.length === 1 && shape[0] === 1);\n    if (!isScalar) {\n        shapeAs3D =\n            [getBatchDim(shape), ...getRowsCols(shape)];\n    }\n    return shapeAs3D;\n}\nexport function getTextureShapeFromLogicalShape(logShape, isPacked = false) {\n    let maxTexSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n    if (isPacked) {\n        maxTexSize = maxTexSize * 2;\n        // This logic ensures we accurately count the number of packed texels needed\n        // to accommodate the tensor. We can only pack values in the same texel if\n        // they are from adjacent pairs of rows/cols within the same batch. So if a\n        // tensor has 3 rows, we pretend it has 4 rows in order to account for the\n        // fact that the texels containing the third row are half empty.\n        logShape = logShape.map((d, i) => i >= logShape.length - 2 ?\n            util.nearestLargerEven(logShape[i]) :\n            logShape[i]);\n        // Packed texture height is at least 2 (the channel height of a single\n        // texel).\n        if (logShape.length === 1) {\n            logShape = [2, logShape[0]];\n        }\n    }\n    // If logical shape is 2, we don't squeeze, since we want to match physical.\n    if (logShape.length !== 2) {\n        const squeezeResult = util.squeezeShape(logShape);\n        logShape = squeezeResult.newShape;\n    }\n    let size = util.sizeFromShape(logShape);\n    if (logShape.length <= 1 && size <= maxTexSize) {\n        return [1, size];\n    }\n    else if (logShape.length === 2 && logShape[0] <= maxTexSize &&\n        logShape[1] <= maxTexSize) {\n        return logShape;\n    }\n    else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize &&\n        logShape[2] <= maxTexSize) {\n        return [logShape[0] * logShape[1], logShape[2]];\n    }\n    else if (logShape.length === 3 && logShape[0] <= maxTexSize &&\n        logShape[1] * logShape[2] <= maxTexSize) {\n        return [logShape[0], logShape[1] * logShape[2]];\n    }\n    else if (logShape.length === 4 &&\n        logShape[0] * logShape[1] * logShape[2] <= maxTexSize &&\n        logShape[3] <= maxTexSize) {\n        return [logShape[0] * logShape[1] * logShape[2], logShape[3]];\n    }\n    else if (logShape.length === 4 && logShape[0] <= maxTexSize &&\n        logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {\n        return [logShape[0], logShape[1] * logShape[2] * logShape[3]];\n    }\n    else {\n        if (isPacked) {\n            // For packed textures size equals the number of channels required to\n            // accommodate the texture data. However in order to squarify such that\n            // inner dimensions stay even, we rewrite size to equal the number of\n            // texels. Then in the return statement we rehydrate the squarified\n            // dimensions to channel units.\n            const batchDim = getBatchDim(logShape);\n            let rows = 2, cols = 2;\n            if (logShape.length) {\n                [rows, cols] = getRowsCols(logShape);\n            }\n            size = batchDim * (rows / 2) * (cols / 2);\n            return util.sizeToSquarishShape(size).map(d => d * 2);\n        }\n        return util.sizeToSquarishShape(size);\n    }\n}\nfunction isEven(n) {\n    return n % 2 === 0;\n}\n/**\n * This determines whether reshaping a packed texture requires rearranging\n * the data within the texture, assuming 2x2 packing.\n */\nexport function isReshapeFree(shape1, shape2) {\n    shape1 = shape1.slice(-2);\n    shape2 = shape2.slice(-2);\n    if (util.arraysEqual(shape1, shape2)) {\n        return true;\n    }\n    if (!shape1.length || !shape2.length) { // One of the shapes is a scalar.\n        return true;\n    }\n    if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 ||\n        shape2[1] === 0) {\n        return true;\n    }\n    if (shape1.length !== shape2.length) { // One of the shapes is a vector.\n        const shape1Cols = shape1.slice(-1)[0];\n        const shape2Cols = shape2.slice(-1)[0];\n        if (shape1Cols === shape2Cols) {\n            return true;\n        }\n        if (isEven(shape1Cols) && isEven(shape2Cols) &&\n            (shape1[0] === 1 || shape2[0] === 1)) {\n            return true;\n        }\n    }\n    return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);\n}\n// We cache webgl params because the environment gets reset between\n// unit tests and we don't want to constantly query the WebGLContext for\n// MAX_TEXTURE_SIZE.\nlet MAX_TEXTURE_SIZE;\nlet MAX_TEXTURES_IN_SHADER;\nexport function getWebGLMaxTextureSize(webGLVersion) {\n    if (MAX_TEXTURE_SIZE == null) {\n        const gl = getWebGLContext(webGLVersion);\n        MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n    }\n    return MAX_TEXTURE_SIZE;\n}\nexport function resetMaxTextureSize() {\n    MAX_TEXTURE_SIZE = null;\n}\nexport function resetMaxTexturesInShader() {\n    MAX_TEXTURES_IN_SHADER = null;\n}\nexport function getMaxTexturesInShader(webGLVersion) {\n    if (MAX_TEXTURES_IN_SHADER == null) {\n        const gl = getWebGLContext(webGLVersion);\n        MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);\n    }\n    // We cap at 16 to avoid spurious runtime \"memory exhausted\" error.\n    return Math.min(16, MAX_TEXTURES_IN_SHADER);\n}\nexport function getWebGLDisjointQueryTimerVersion(webGLVersion) {\n    if (webGLVersion === 0) {\n        return 0;\n    }\n    let queryTimerVersion;\n    const gl = getWebGLContext(webGLVersion);\n    if (hasExtension(gl, 'EXT_disjoint_timer_query_webgl2') &&\n        webGLVersion === 2) {\n        queryTimerVersion = 2;\n    }\n    else if (hasExtension(gl, 'EXT_disjoint_timer_query')) {\n        queryTimerVersion = 1;\n    }\n    else {\n        queryTimerVersion = 0;\n    }\n    return queryTimerVersion;\n}\nexport function hasExtension(gl, extensionName) {\n    const ext = gl.getExtension(extensionName);\n    return ext != null;\n}\nexport function isWebGLVersionEnabled(webGLVersion) {\n    try {\n        const gl = getWebGLContext(webGLVersion);\n        if (gl != null) {\n            return true;\n        }\n    }\n    catch (e) {\n        console.log('Error when getting WebGL context: ', e);\n        return false;\n    }\n    return false;\n}\nexport function isCapableOfRenderingToFloatTexture(webGLVersion) {\n    if (webGLVersion === 0) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    if (webGLVersion === 1) {\n        if (!hasExtension(gl, 'OES_texture_float')) {\n            return false;\n        }\n    }\n    else {\n        if (!hasExtension(gl, 'EXT_color_buffer_float')) {\n            return false;\n        }\n    }\n    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n    return isFrameBufferComplete;\n}\n/**\n * Check if we can download values from a float/half-float texture.\n *\n * Note that for performance reasons we use binding a texture to a framebuffer\n * as a proxy for ability to download float values later using readPixels. The\n * texture params of this texture will not match those in readPixels exactly\n * but if we are unable to bind some kind of float texture to the frameBuffer\n * then we definitely will not be able to read float values from it.\n */\nexport function isDownloadFloatTextureEnabled(webGLVersion) {\n    if (webGLVersion === 0) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    if (webGLVersion === 1) {\n        if (!hasExtension(gl, 'OES_texture_float')) {\n            return false;\n        }\n        if (!hasExtension(gl, 'WEBGL_color_buffer_float')) {\n            return false;\n        }\n    }\n    else {\n        if (hasExtension(gl, 'EXT_color_buffer_float')) {\n            return createFloatTextureAndBindToFramebuffer(gl);\n        }\n        const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n        if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {\n            const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n            return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);\n        }\n        return false;\n    }\n    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n    return isFrameBufferComplete;\n}\nfunction createFloatTextureAndBindToFramebuffer(gl) {\n    const texConfig = getTextureConfig(gl);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    const width = 1;\n    const height = 1;\n    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);\n    const frameBuffer = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    gl.deleteTexture(texture);\n    gl.deleteFramebuffer(frameBuffer);\n    return isFrameBufferComplete;\n}\nfunction createHalfFloatTextureAndBindToFramebuffer(\n// tslint:disable-next-line:no-any\ngl, textureHalfFloatExtension) {\n    const texConfig = getTextureConfig(gl, textureHalfFloatExtension);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    const width = 1;\n    const height = 1;\n    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);\n    const frameBuffer = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    gl.deleteTexture(texture);\n    gl.deleteFramebuffer(frameBuffer);\n    return isFrameBufferComplete;\n}\nexport function isWebGLFenceEnabled(webGLVersion) {\n    if (webGLVersion !== 2) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    // tslint:disable-next-line:no-any\n    const isEnabled = gl.fenceSync != null;\n    return isEnabled;\n}\nexport function assertNotComplex(tensor, opName) {\n    if (!Array.isArray(tensor)) {\n        tensor = [tensor];\n    }\n    tensor.forEach(t => {\n        if (t != null) {\n            util.assert(t.dtype !== 'complex64', () => `${opName} does not support complex64 tensors ` +\n                'in the WebGL backend.');\n        }\n    });\n}\n//# sourceMappingURL=webgl_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { device_util, env } from '@tensorflow/tfjs-core';\nimport { getMaxTexturesInShader, getWebGLDisjointQueryTimerVersion, getWebGLMaxTextureSize, isCapableOfRenderingToFloatTexture, isDownloadFloatTextureEnabled, isWebGLFenceEnabled, isWebGLVersionEnabled } from './webgl_util';\nconst ENV = env();\n/**\n * This file contains WebGL-specific flag registrations.\n */\n/**\n * True if WebGL is supported.\n */\nENV.registerFlag('HAS_WEBGL', () => ENV.getNumber('WEBGL_VERSION') > 0);\n/** 0: No WebGL, 1: WebGL 1.0, 2: WebGL 2.0. */\nENV.registerFlag('WEBGL_VERSION', () => {\n    if (isWebGLVersionEnabled(2)) {\n        return 2;\n    }\n    else if (isWebGLVersionEnabled(1)) {\n        return 1;\n    }\n    return 0;\n});\n/** Whether to check for numerical representation problems. */\nENV.registerFlag('WEBGL_CHECK_NUMERICAL_PROBLEMS', () => false);\nENV.registerFlag('WEBGL_BUFFER_SUPPORTED', () => ENV.get('WEBGL_VERSION') === 2);\n/** Whether the WebGL backend will sometimes forward ops to the CPU. */\nENV.registerFlag('WEBGL_CPU_FORWARD', () => true);\n/** Whether the WebGL backend will always use f16 textures for rendering. */\nENV.registerFlag('WEBGL_FORCE_F16_TEXTURES', () => false);\n/** Whether to turn all packing related flags on. */\nENV.registerFlag('WEBGL_PACK', () => ENV.getBool('HAS_WEBGL'));\n/** Whether we will pack the batchnormalization op. */\nENV.registerFlag('WEBGL_PACK_NORMALIZATION', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack the clip op. */\nENV.registerFlag('WEBGL_PACK_CLIP', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack the depthwise conv op. */\n// TODO: https://github.com/tensorflow/tfjs/issues/1679\nENV.registerFlag('WEBGL_PACK_DEPTHWISECONV', () => false);\n/** Whether we will pack binary ops. */\nENV.registerFlag('WEBGL_PACK_BINARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack unary ops. */\nENV.registerFlag('WEBGL_PACK_UNARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack array ops. */\nENV.registerFlag('WEBGL_PACK_ARRAY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack image ops. */\nENV.registerFlag('WEBGL_PACK_IMAGE_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack reduce ops. */\nENV.registerFlag('WEBGL_PACK_REDUCE', () => ENV.getBool('WEBGL_PACK'));\n/** Whether packed WebGL kernels lazily unpack their outputs. */\nENV.registerFlag('WEBGL_LAZILY_UNPACK', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will use the im2col algorithm to speed up convolutions. */\nENV.registerFlag('WEBGL_CONV_IM2COL', () => ENV.getBool('WEBGL_PACK'));\n/** The maximum texture dimension. */\nENV.registerFlag('WEBGL_MAX_TEXTURE_SIZE', () => getWebGLMaxTextureSize(ENV.getNumber('WEBGL_VERSION')));\n/** The maximum texture dimension. */\nENV.registerFlag('WEBGL_MAX_TEXTURES_IN_SHADER', () => getMaxTexturesInShader(ENV.getNumber('WEBGL_VERSION')));\n/**\n * The disjoint_query_timer extension version.\n * 0: disabled, 1: EXT_disjoint_timer_query, 2:\n * EXT_disjoint_timer_query_webgl2.\n * In Firefox with WebGL 2.0,\n * EXT_disjoint_timer_query_webgl2 is not available, so we must use the\n * WebGL 1.0 extension.\n */\nENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', () => {\n    const webGLVersion = ENV.getNumber('WEBGL_VERSION');\n    if (webGLVersion === 0) {\n        return 0;\n    }\n    return getWebGLDisjointQueryTimerVersion(webGLVersion);\n});\n/**\n * Whether the timer object from the disjoint_query_timer extension gives\n * timing information that is reliable.\n */\nENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', () => ENV.getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&\n    !device_util.isMobile());\n/**\n * Whether the device is physically capable of rendering to float32 textures.\n */\nENV.registerFlag('WEBGL_RENDER_FLOAT32_CAPABLE', () => isCapableOfRenderingToFloatTexture(ENV.getNumber('WEBGL_VERSION')));\n/**\n * Whether rendering to float32 textures is enabled. If disabled, renders to\n * float16 textures.\n */\nENV.registerFlag('WEBGL_RENDER_FLOAT32_ENABLED', () => {\n    return ENV.getBool('WEBGL_FORCE_F16_TEXTURES') ?\n        false :\n        ENV.getBool('WEBGL_RENDER_FLOAT32_CAPABLE');\n});\n/**\n * Whether downloading float textures is enabled (16 or 32 bit). If disabled,\n * uses IEEE 754 encoding of the float32 values to 4 uint8 when downloading.\n */\nENV.registerFlag('WEBGL_DOWNLOAD_FLOAT_ENABLED', () => isDownloadFloatTextureEnabled(ENV.getNumber('WEBGL_VERSION')));\n/** Whether the fence API is available. */\nENV.registerFlag('WEBGL_FENCE_API_ENABLED', () => isWebGLFenceEnabled(ENV.getNumber('WEBGL_VERSION')));\n/**\n * Tensors with size <= than this will be uploaded as uniforms, not textures.\n */\nENV.registerFlag('WEBGL_SIZE_UPLOAD_UNIFORM', () => {\n    // Use uniform uploads only when 32bit floats are supported. In\n    // 16bit\n    // environments there are problems with comparing a 16bit texture value\n    // with a 32bit uniform value.\n    const useUniforms = ENV.getBool('WEBGL_RENDER_FLOAT32_ENABLED');\n    return useUniforms ? 4 : 0;\n});\n/**\n * If the total number of bytes allocated on the GPU is greater than this\n * number, we will aggressively delete textures upon disposal with\n * gl.deleteMatrixTexture, rather than making them available for reuse.\n *\n * Default value -1 indicates that we will never aggressively delete textures.\n */\nENV.registerFlag('WEBGL_DELETE_TEXTURE_THRESHOLD', () => {\n    return -1;\n}, threshold => {\n    if (threshold < 0 && threshold !== -1) {\n        throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never ` +\n            `delete) or at least 0, but got ${threshold}.`);\n    }\n});\n//# sourceMappingURL=flags_webgl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Import shared functionality from tfjs-backend-cpu without triggering\n// side effects.\n// tslint:disable-next-line: no-imports-from-dist\nimport * as shared from '@tensorflow/tfjs-backend-cpu/dist/shared';\nconst { simpleAbsImpl: simpleAbsImplCPU, addImpl: addImplCPU, ceilImpl: ceilImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, multiplyImpl: multiplyImplCPU, rsqrtImpl: rsqrtImplCPU, sliceImpl: sliceImplCPU, subImpl: subImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU, } = shared;\nexport { simpleAbsImplCPU, addImplCPU, ceilImplCPU, expImplCPU, expm1ImplCPU, logImplCPU, multiplyImplCPU, sliceImplCPU, subImplCPU, floorImplCPU, maxImplCPU, rsqrtImplCPU, transposeImplCPU, uniqueImplCPU, };\n//# sourceMappingURL=shared.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AddNProgram {\n    constructor(outputShape, shapes) {\n        this.outputShape = [];\n        this.outputShape = outputShape;\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const snippets = [];\n        // Get target elements from every input tensor.\n        this.variableNames.forEach(variable => {\n            snippets.push(`float v${variable} = get${variable}AtOutCoords();`);\n        });\n        // Calculate the sum of all elements.\n        const operation = this.variableNames\n            .map(variable => {\n            return `v${variable}`;\n        })\n            .join(' + ');\n        this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        float result = ${operation};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=addn_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AddNPackedProgram {\n    constructor(outputShape, shapes) {\n        this.outputShape = [];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const snippets = [];\n        // Get target elements from every input tensor.\n        this.variableNames.forEach(variable => {\n            snippets.push(`vec4 v${variable} = get${variable}AtOutCoords();`);\n        });\n        // Calculate the sum of all elements.\n        const operation = this.variableNames\n            .map(variable => {\n            return `v${variable}`;\n        })\n            .join(' + ');\n        this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        vec4 result = ${operation};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=addn_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ArgMinMaxProgram {\n    constructor(reduceInfo, op, firstPass) {\n        this.variableNames = ['A'];\n        const { windowSize, batchSize, outSize } = reduceInfo;\n        if (!firstPass) {\n            this.variableNames.push('bestIndicesA');\n        }\n        this.outputShape = [batchSize, outSize];\n        const compOp = (op === 'max') ? '>' : '<';\n        const indexSnippet = firstPass ?\n            'inOffset + i;' :\n            'round(getBestIndicesA(batch, inOffset + i));';\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          int inIdx = ${indexSnippet};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${compOp} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=argminmax_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport function getVecChannels(name, rank) {\n    return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank).map(d => `${name}.${d}`);\n}\nexport function getChannels(name, rank) {\n    if (rank === 1) {\n        return [name];\n    }\n    return getVecChannels(name, rank);\n}\nexport function getSourceCoords(rank, dims) {\n    if (rank === 1) {\n        return 'rc';\n    }\n    let coords = '';\n    for (let i = 0; i < rank; i++) {\n        coords += dims[i];\n        if (i < rank - 1) {\n            coords += ',';\n        }\n    }\n    return coords;\n}\n//# sourceMappingURL=packing_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nexport function getGlslDifferences() {\n    let version;\n    let attribute;\n    let varyingVs;\n    let varyingFs;\n    let texture2D;\n    let output;\n    let defineOutput;\n    let defineSpecialNaN;\n    let defineSpecialInf;\n    let defineRound;\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        version = '#version 300 es';\n        attribute = 'in';\n        varyingVs = 'out';\n        varyingFs = 'in';\n        texture2D = 'texture';\n        output = 'outputColor';\n        defineOutput = 'out vec4 outputColor;';\n        // Use custom isnan definition to work across differences between\n        // implementations on various platforms. While this should happen in ANGLE\n        // we still see differences between android and windows (on chrome) when\n        // using isnan directly.\n        defineSpecialNaN = `\n      bool isnan_custom(float val) {\n        return (val > 0.0 || val < 0.0) ? false : val != 0.0;\n      }\n\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan_custom(val.x),\n          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n      }\n\n      #define isnan(value) isnan_custom(value)\n    `;\n        // In webgl 2 we do not need to specify a custom isinf so there is no\n        // need for a special INFINITY constant.\n        defineSpecialInf = ``;\n        defineRound = `\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n    }\n    else {\n        version = '';\n        attribute = 'attribute';\n        varyingVs = 'varying';\n        varyingFs = 'varying';\n        texture2D = 'texture2D';\n        output = 'gl_FragColor';\n        defineOutput = '';\n        // WebGL1 has no built in isnan so we define one here.\n        defineSpecialNaN = `\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    `;\n        defineSpecialInf = `\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    `;\n        defineRound = `\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n    }\n    return {\n        version,\n        attribute,\n        varyingVs,\n        varyingFs,\n        texture2D,\n        output,\n        defineOutput,\n        defineSpecialNaN,\n        defineSpecialInf,\n        defineRound\n    };\n}\n//# sourceMappingURL=glsl_version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Produces GLSL code that derives logical coordinates from a flat\n * index. The code performs integer division with each stride and decrements\n * the index until the index equals the final dimension coordinate.\n */\nexport function getLogicalCoordinatesFromFlatIndex(coords, shape, index = 'index') {\n    const strides = util.computeStrides(shape);\n    return strides\n        .map((stride, i) => {\n        const line1 = `int ${coords[i]} = ${index} / ${stride}`;\n        const line2 = i === strides.length - 1 ?\n            `int ${coords[i + 1]} = ${index} - ${coords[i]} * ${stride}` :\n            `index -= ${coords[i]} * ${stride}`;\n        return `${line1}; ${line2};`;\n    })\n        .join('');\n}\nfunction buildVec(x) {\n    if (x.length === 1) {\n        return `${x[0]}`;\n    }\n    return `vec${x.length}(${x.join(',')})`;\n}\n/**\n * Produces GLSL code that computes the dot product of the input x and y\n * vectors. Handles splitting inputs into increments of vec4s when necessary.\n */\nexport function dotify(x, y) {\n    if (x.length !== y.length) {\n        throw new Error(`Vectors to be dotted must be of the same length -` +\n            `got ${x.length} and ${y.length}`);\n    }\n    const slices = [];\n    const nearestVec4 = Math.floor(x.length / 4);\n    const nearestVec4Remainder = x.length % 4;\n    for (let i = 0; i < nearestVec4; i++) {\n        const xSlice = x.slice(i * 4, i * 4 + 4);\n        const ySlice = y.slice(i * 4, i * 4 + 4);\n        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n    }\n    if (nearestVec4Remainder !== 0) {\n        let xSlice = x.slice(nearestVec4 * 4);\n        let ySlice = y.slice(nearestVec4 * 4);\n        if (xSlice.length === 1) {\n            xSlice = xSlice.map(d => `float(${d})`);\n            ySlice = ySlice.map(d => `float(${d})`);\n        }\n        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n    }\n    return slices.map((d, i) => `dot(${d})`).join('+');\n}\n/**\n * Produces GLSL that computes the flat index from 3D coordinates.\n */\nexport function getFlatIndexFrom3D(shape) {\n    const strides = util.computeStrides(shape).map(d => d.toString());\n    return `\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;\n  }\n`;\n}\nexport const ENCODE_FLOAT_SNIPPET = `\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`;\n//# sourceMappingURL=shader_compiler_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nconst { getBroadcastDims } = backend_util;\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nexport function makeShader(inputsInfo, outputShape, userCode, usesPackedTextures) {\n    const prefixSnippets = [];\n    inputsInfo.forEach(x => {\n        const size = util.sizeFromShape(x.shapeInfo.logicalShape);\n        // Snippet when we decided to upload the values as uniform.\n        if (x.shapeInfo.isUniform) {\n            prefixSnippets.push(`uniform float ${x.name}${size > 1 ? `[${size}]` : ''};`);\n        }\n        else {\n            prefixSnippets.push(`uniform sampler2D ${x.name};`);\n            prefixSnippets.push(`uniform int offset${x.name};`);\n        }\n    });\n    const inputPrefixSnippet = prefixSnippets.join('\\n');\n    const inputSamplingSnippet = inputsInfo\n        .map(x => getInputSamplingSnippet(x, outputShape, usesPackedTextures))\n        .join('\\n');\n    const outTexShape = outputShape.texShape;\n    const glsl = getGlslDifferences();\n    const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);\n    let outputSamplingSnippet;\n    let floatTextureSetOutputSnippet;\n    let shaderPrefix = getShaderPrefix(glsl);\n    if (outputShape.isPacked) {\n        outputSamplingSnippet =\n            getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n        floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);\n    }\n    else {\n        outputSamplingSnippet =\n            getOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n        floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);\n    }\n    if (usesPackedTextures) {\n        shaderPrefix += SHADER_PACKED_PREFIX;\n    }\n    const source = [\n        shaderPrefix, floatTextureSampleSnippet, floatTextureSetOutputSnippet,\n        inputPrefixSnippet, outputSamplingSnippet, inputSamplingSnippet, userCode\n    ].join('\\n');\n    return source;\n}\nfunction getSamplerFromInInfo(inInfo) {\n    const shape = inInfo.shapeInfo.logicalShape;\n    switch (shape.length) {\n        case 0:\n            return getSamplerScalar(inInfo);\n        case 1:\n            return getSampler1D(inInfo);\n        case 2:\n            return getSampler2D(inInfo);\n        case 3:\n            return getSampler3D(inInfo);\n        case 4:\n            return getSampler4D(inInfo);\n        case 5:\n            return getSampler5D(inInfo);\n        case 6:\n            return getSampler6D(inInfo);\n        default:\n            throw new Error(`${shape.length}-D input sampling` +\n                ` is not yet supported`);\n    }\n}\nfunction getPackedSamplerFromInInfo(inInfo) {\n    const shape = inInfo.shapeInfo.logicalShape;\n    switch (shape.length) {\n        case 0:\n            return getPackedSamplerScalar(inInfo);\n        case 1:\n            return getPackedSampler1D(inInfo);\n        case 2:\n            return getPackedSampler2D(inInfo);\n        case 3:\n            return getPackedSampler3D(inInfo);\n        default:\n            return getPackedSamplerND(inInfo);\n    }\n}\nfunction getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false) {\n    let res = '';\n    if (usesPackedTextures) {\n        res += getPackedSamplerFromInInfo(inInfo);\n    }\n    else {\n        res += getSamplerFromInInfo(inInfo);\n    }\n    const inShape = inInfo.shapeInfo.logicalShape;\n    const outShape = outShapeInfo.logicalShape;\n    if (inShape.length <= outShape.length) {\n        if (usesPackedTextures) {\n            res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);\n        }\n        else {\n            res += getSamplerAtOutputCoords(inInfo, outShapeInfo);\n        }\n    }\n    return res;\n}\nfunction getPackedOutputSamplingSnippet(outShape, outTexShape) {\n    switch (outShape.length) {\n        case 0:\n            return getOutputScalarCoords();\n        case 1:\n            return getOutputPacked1DCoords(outShape, outTexShape);\n        case 2:\n            return getOutputPacked2DCoords(outShape, outTexShape);\n        case 3:\n            return getOutputPacked3DCoords(outShape, outTexShape);\n        default:\n            return getOutputPackedNDCoords(outShape, outTexShape);\n    }\n}\nfunction getOutputSamplingSnippet(outShape, outTexShape) {\n    switch (outShape.length) {\n        case 0:\n            return getOutputScalarCoords();\n        case 1:\n            return getOutput1DCoords(outShape, outTexShape);\n        case 2:\n            return getOutput2DCoords(outShape, outTexShape);\n        case 3:\n            return getOutput3DCoords(outShape, outTexShape);\n        case 4:\n            return getOutput4DCoords(outShape, outTexShape);\n        case 5:\n            return getOutput5DCoords(outShape, outTexShape);\n        case 6:\n            return getOutput6DCoords(outShape, outTexShape);\n        default:\n            throw new Error(`${outShape.length}-D output sampling is not yet supported`);\n    }\n}\nfunction getFloatTextureSampleSnippet(glsl) {\n    return `\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${glsl.texture2D}(textureSampler, uv).r;\n    }\n  `;\n}\nfunction getFloatTextureSetRSnippet(glsl) {\n    return `\n    void setOutput(float val) {\n      ${glsl.output} = vec4(val, 0, 0, 0);\n    }\n  `;\n}\nfunction getFloatTextureSetRGBASnippet(glsl) {\n    return `\n    void setOutput(vec4 val) {\n      ${glsl.output} = val;\n    }\n  `;\n}\nfunction getShaderPrefix(glsl) {\n    const SHADER_PREFIX = `${glsl.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${glsl.varyingFs} vec2 resultUV;\n    ${glsl.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${glsl.defineSpecialNaN}\n    ${glsl.defineSpecialInf}\n    ${glsl.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${SAMPLE_1D_SNIPPET}\n    ${SAMPLE_2D_SNIPPET}\n    ${SAMPLE_3D_SNIPPET}\n  `;\n    return SHADER_PREFIX;\n}\nconst SAMPLE_1D_SNIPPET = `\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SAMPLE_2D_SNIPPET = `\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SAMPLE_3D_SNIPPET = `\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SHADER_PACKED_PREFIX = `\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;\nfunction getOutputScalarCoords() {\n    return `\n    int getOutputCoords() {\n      return 0;\n    }\n  `;\n}\nfunction getOutputPacked1DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (packedTexShape[0] === 1) {\n        return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);\n      }\n    `;\n    }\n    if (packedTexShape[1] === 1) {\n        return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);\n      }\n    `;\n    }\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);\n    }\n  `;\n}\nfunction getOutput1DCoords(shape, texShape) {\n    if (texShape[0] === 1) {\n        return `\n      int getOutputCoords() {\n        return int(resultUV.x * ${texShape[1]}.0);\n      }\n    `;\n    }\n    if (texShape[1] === 1) {\n        return `\n      int getOutputCoords() {\n        return int(resultUV.y * ${texShape[0]}.0);\n      }\n    `;\n    }\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      return resTexRC.x * ${texShape[1]} + resTexRC.y;\n    }\n  `;\n}\nfunction getOutputPacked3DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texelsInLogicalRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;\n}\nfunction getOutput3DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\nfunction getOutputPackedNDCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);\n    let texelsInBatchN = texelsInBatch;\n    let batches = ``;\n    let coords = 'b, r, c';\n    for (let b = 2; b < shape.length - 1; b++) {\n        texelsInBatchN *= shape[shape.length - b - 1];\n        batches = `\n      int b${b} = index / ${texelsInBatchN};\n      index -= b${b} * ${texelsInBatchN};\n    ` + batches;\n        coords = `b${b}, ` + coords;\n    }\n    return `\n    ivec${shape.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      ${batches}\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec${shape.length}(${coords});\n    }\n  `;\n}\nfunction getOutput4DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2'], shape);\n    return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec4(r, c, d, d2);\n    }\n  `;\n}\nfunction getOutput5DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3'], shape);\n    return `\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},\n                             ${texShape[1]}));\n\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `;\n}\nfunction getOutput6DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3', 'd4'], shape);\n    return `\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `;\n}\nfunction getOutputPacked2DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (util.arraysEqual(shape, texShape)) {\n        return `\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      }\n    `;\n    }\n    // texels needed to accommodate a logical row\n    const texelsInLogicalRow = Math.ceil(shape[1] / 2);\n    /**\n     * getOutputCoords\n     *\n     * resTexRC: The rows and columns of the texels. If you move over one\n     * texel to the right in the packed texture, you are moving over one column\n     * (not two).\n     *\n     * index: The texel index\n     */\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getOutput2DCoords(shape, texShape) {\n    if (util.arraysEqual(shape, texShape)) {\n        return `\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));\n      }\n    `;\n    }\n    if (shape[1] === 1) {\n        return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;\n    }\n    if (shape[0] === 1) {\n        return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;\n    }\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      int r = index / ${shape[1]};\n      int c = index - r * ${shape[1]};\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getFlatOffsetUniformName(texName) {\n    return `offset${texName}`;\n}\nfunction getPackedSamplerScalar(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}() {\n      return ${glsl.texture2D}(${texName}, halfCR);\n    }\n  `;\n}\nfunction getSamplerScalar(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    if (inputInfo.shapeInfo.isUniform) {\n        return `float ${funcName}() {return ${texName};}`;\n    }\n    const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;\n    if (texNumR === 1 && texNumC === 1) {\n        return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n    }\n    const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}() {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler1D(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${packedTexShape[0]}, ${packedTexShape[1]}, index);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler1D(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int index) {\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texShape = inputInfo.shapeInfo.texShape;\n    const tNumR = texShape[0];\n    const tNumC = texShape[1];\n    if (tNumC === 1 && tNumR === 1) {\n        return `\n      float ${funcName}(int index) {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    if (tNumC === 1) {\n        return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (tNumR === 1) {\n        return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n    float ${funcName}(int index) {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler2D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const glsl = getGlslDifferences();\n    if (texShape != null && util.arraysEqual(shape, texShape)) {\n        return `\n      vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n\n        return ${glsl.texture2D}(${texName}, uv);\n      }\n    `;\n    }\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const valuesPerRow = Math.ceil(shape[1] / 2);\n    return `\n    vec4 ${funcName}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler2D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    if (texShape != null && util.arraysEqual(shape, texShape)) {\n        const texNumR = texShape[0];\n        const texNumC = texShape[1];\n        return `\n    float ${funcName}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['row', 'col'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const offset = getFlatOffsetUniformName(texName);\n    if (texNumC === 1) {\n        // index is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    if (texNumR === 1) {\n        // index is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    return `\n  float ${funcName}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${shape[1]} + col + ${offset};\n    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n    return sampleTexture(${texName}, uv);\n  }\n`;\n}\nfunction getPackedSampler3D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (shape[0] === 1) {\n        const squeezedShape = shape.slice(1);\n        const keptDims = [1, 2];\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['b', 'row', 'col'];\n        return `\n        ${getPackedSamplerFromInInfo(newInputInfo)}\n        vec4 ${funcName}(int b, int row, int col) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n    }\n    const texNumR = packedTexShape[0];\n    const texNumC = packedTexShape[1];\n    const valuesPerRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler3D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride0 = shape[1] * shape[2];\n    const stride1 = shape[2];\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['row', 'col', 'depth'];\n        return `\n        ${getSamplerFromInInfo(newInputInfo)}\n        float ${funcName}(int row, int col, int depth) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${stride0}, ${stride1}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n        float ${funcName}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${texNumC}.0, ${texNumR}.0);\n          return sampleTexture(${texName}, uv);\n        }\n      `;\n    }\n    if (texNumC === stride1 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n      float ${funcName}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};\n        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n        return sampleTexture(${texName}, uv);\n      }\n  `;\n}\nfunction getPackedSamplerND(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const rank = shape.length;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texNumR = packedTexShape[0];\n    const texNumC = packedTexShape[1];\n    const valuesPerRow = Math.ceil(shape[rank - 1] / 2);\n    let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);\n    let params = `int b, int row, int col`;\n    let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;\n    for (let b = 2; b < rank - 1; b++) {\n        params = `int b${b}, ` + params;\n        texelsInBatch *= shape[rank - b - 1];\n        index = `b${b} * ${texelsInBatch} + ` + index;\n    }\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(${params}) {\n      int index = ${index};\n      int texR = index / ${texNumC};\n      int texC = index - texR * ${texNumC};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler4D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride2 = shape[3];\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${stride1}, ${stride2}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride2 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} +\n          depth * ${stride2} + depth2;\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler5D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride3 = shape[4];\n    const stride2 = shape[3] * stride3;\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2', 'depth3'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          depth3;\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${stride1}, ${stride2}, ${stride3}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride3 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3]},\n               ${shape[2] * shape[3]}, ${shape[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler6D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    const stride4 = shape[5];\n    const stride3 = shape[4] * stride4;\n    const stride2 = shape[3] * stride3;\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${stride4}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride4 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},\n               ${shape[2] * shape[3] * shape[4]},\n               ${shape[3] * shape[4]},\n               ${shape[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getUniformSampler(inputInfo) {\n    const texName = inputInfo.name;\n    const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n    if (inSize < 2) {\n        return `return ${texName};`;\n    }\n    return `\n    for (int i = 0; i < ${inSize}; i++) {\n      if (i == index) {\n        return ${texName}[i];\n      }\n    }\n  `;\n}\nfunction getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n    const texName = inputInfo.name;\n    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n    const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n    const inRank = inputInfo.shapeInfo.logicalShape.length;\n    const outRank = outShapeInfo.logicalShape.length;\n    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n    const type = getCoordsDataType(outRank);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet;\n    const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n    if (inRank === 0) {\n        coordsSnippet = '';\n    }\n    else if (outRank < 2 && broadcastDims.length >= 1) {\n        coordsSnippet = 'coords = 0;';\n    }\n    else {\n        coordsSnippet =\n            broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n                .join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n        unpackedCoordsSnippet = 'coords';\n    }\n    else {\n        unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n            .map((s, i) => `coords.${fields[i + rankDiff]}`)\n            .join(', ');\n    }\n    let output = `return outputValue;`;\n    const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n    const isInputScalar = inSize === 1;\n    const outSize = util.sizeFromShape(outShapeInfo.logicalShape);\n    const isOutputScalar = outSize === 1;\n    if (inRank === 1 && !isInputScalar && !isOutputScalar) {\n        output = `\n      return vec4(outputValue.xy, outputValue.xy);\n    `;\n    }\n    else if (isInputScalar && !isOutputScalar) {\n        if (outRank === 1) {\n            output = `\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `;\n        }\n        else {\n            output = `\n        return vec4(outputValue.x);\n      `;\n        }\n    }\n    else if (broadcastDims.length) {\n        const rows = inRank - 2;\n        const cols = inRank - 1;\n        if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {\n            output = `return vec4(outputValue.x);`;\n        }\n        else if (broadcastDims.indexOf(rows) > -1) {\n            output = `return vec4(outputValue.x, outputValue.y, ` +\n                `outputValue.x, outputValue.y);`;\n        }\n        else if (broadcastDims.indexOf(cols) > -1) {\n            output = `return vec4(outputValue.xx, outputValue.zz);`;\n        }\n    }\n    return `\n    vec4 ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});\n      ${output}\n    }\n  `;\n}\nfunction getSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n    const texName = inputInfo.name;\n    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n    const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n    const outTexShape = outShapeInfo.texShape;\n    const inTexShape = inputInfo.shapeInfo.texShape;\n    const inRank = inputInfo.shapeInfo.logicalShape.length;\n    const outRank = outShapeInfo.logicalShape.length;\n    if (!inputInfo.shapeInfo.isUniform && inRank === outRank &&\n        inputInfo.shapeInfo.flatOffset == null &&\n        util.arraysEqual(inTexShape, outTexShape)) {\n        return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, resultUV);\n      }\n    `;\n    }\n    const type = getCoordsDataType(outRank);\n    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet;\n    const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n    if (inRank === 0) {\n        coordsSnippet = '';\n    }\n    else if (outRank < 2 && broadcastDims.length >= 1) {\n        coordsSnippet = 'coords = 0;';\n    }\n    else {\n        coordsSnippet =\n            broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n                .join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n        unpackedCoordsSnippet = 'coords';\n    }\n    else {\n        unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n            .map((s, i) => `coords.${fields[i + rankDiff]}`)\n            .join(', ');\n    }\n    return `\n    float ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      return get${texFuncSnippet}(${unpackedCoordsSnippet});\n    }\n  `;\n}\nexport function getCoordsDataType(rank) {\n    if (rank <= 1) {\n        return 'int';\n    }\n    else if (rank === 2) {\n        return 'ivec2';\n    }\n    else if (rank === 3) {\n        return 'ivec3';\n    }\n    else if (rank === 4) {\n        return 'ivec4';\n    }\n    else if (rank === 5) {\n        return 'ivec5';\n    }\n    else if (rank === 6) {\n        return 'ivec6';\n    }\n    else {\n        throw Error(`GPU for rank ${rank} is not yet supported`);\n    }\n}\n/** Returns a new input info (a copy) that has a squeezed logical shape. */\nfunction squeezeInputInfo(inInfo, squeezedShape) {\n    // Deep copy.\n    const newInputInfo = JSON.parse(JSON.stringify(inInfo));\n    newInputInfo.shapeInfo.logicalShape = squeezedShape;\n    return newInputInfo;\n}\nfunction getSqueezedParams(params, keptDims) {\n    return keptDims.map(d => params[d]).join(', ');\n}\n//# sourceMappingURL=shader_compiler.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ArgMinMaxPackedProgram {\n    constructor(shape, windowSize, op, firstPass) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        util.assert(shape.length > 2, () => `Packed arg${op.charAt(0).toUpperCase() +\n            op.slice(1)} supports only inputs with rank above 2.`);\n        const inSize = shape[shape.length - 1];\n        const outSize = Math.ceil(inSize / windowSize);\n        this.outputShape = shape.slice(0, -1);\n        if (outSize > 1) {\n            this.outputShape.push(outSize);\n        }\n        if (!firstPass) {\n            this.variableNames.push('bestIndicesA');\n        }\n        const outShape = this.outputShape;\n        const rank = outShape.length;\n        const dtype = getCoordsDataType(rank);\n        const coords = getChannels('coords', rank);\n        let sourceLocSetup;\n        let sourceRank;\n        if (outSize === 1) {\n            sourceRank = rank + 1;\n            const sourceLocDType = getCoordsDataType(sourceRank);\n            sourceLocSetup = `\n        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 1]};\n        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 2]};\n        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 1]};\n        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 2]};`;\n        }\n        else {\n            sourceRank = rank;\n            sourceLocSetup = `\n        ${dtype} sourceLocR = coords;\n        ++${coords[rank - 1]};\n        ${dtype} sourceLocG = coords;\n        ++${coords[rank - 2]};\n        ${dtype} sourceLocA = coords;\n        --${coords[rank - 1]};\n        ${dtype} sourceLocB = coords;\n        --${coords[rank - 2]};`;\n        }\n        const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, sourceRank);\n        const inChannel = '.' + channels[sourceRank - 1]; // e.g. \".b\" for rank 3.\n        const intChannels = channels.map(x => 'int ' + x);\n        const srcRCoords = getChannels('sourceLocR', sourceRank - 1).concat('inIdx.r');\n        const srcGCoords = getChannels('sourceLocG', sourceRank - 1).concat('inIdx.g');\n        const srcBCoords = getChannels('sourceLocB', sourceRank - 1).concat('inIdx.b');\n        const srcACoords = getChannels('sourceLocA', sourceRank - 1).concat('inIdx.a');\n        const compOp = (op === 'max') ? 'greaterThan' : 'lessThan';\n        const fetchCandidateIdx = firstPass ? '' : `\n          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),\n                             getBestIndicesAChannel(${srcGCoords.join()}),\n                             getBestIndicesAChannel(${srcBCoords.join()}),\n                             getBestIndicesAChannel(${srcACoords.join()})));`;\n        const fetchValue = `vec4(\n            getAChannel(${srcRCoords.join()}),\n            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,\n            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;\n        const getBestIndicesAChannelSnippet = firstPass ? '' : `\n      float getBestIndicesAChannel(${intChannels.join()}) {\n        return getChannel(getBestIndicesA(${channels.join()}),\n                                          vec2(${channels.slice(-2).join()}));\n      }`;\n        this.userCode = `\n      float getAChannel(${intChannels.join()}) {\n        return getChannel(getA(${channels.join()}),\n                               vec2(${channels.slice(-2).join()}));\n      }\n      ${getBestIndicesAChannelSnippet}\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        bool hasNextCol = ${coords[rank - 1]} < ${outShape[rank - 1] - 1};\n        bool hasNextRow = ${coords[rank - 2]} < ${outShape[rank - 2] - 1};\n        ${sourceLocSetup}\n        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},\n          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${fetchValue};\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          inIdx = srcIdx;\n          ${fetchCandidateIdx}\n          vec4 candidate = ${fetchValue};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=argminmax_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AvgPool2DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const avgMultiplier = 1 / (filterHeight * filterWidth);\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth};\n            wC+= ${dilationWidth}) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class AvgPool3DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy'];\n        this.outputShape = convInfo.inShape;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=avg_pool_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\n// (Ar + Ai)(Br + Bi) =\n// ArBr + ArBi + AiBr + AiBi = ArBr - AB + ArBi + AiBr\n// Yr = ArBr - AB\n// Yi = ArBi + AiBr\nexport const COMPLEX_MULTIPLY = {\n    REAL: 'return areal * breal - aimag * bimag;',\n    IMAG: 'return areal * bimag + aimag * breal;'\n};\nexport class BinaryOpComplexProgram {\n    constructor(op, aShape, bShape) {\n        this.variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        this.userCode = `\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${op}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_complex_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nconst CHECK_NAN_SNIPPET = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nexport const ADD = 'return a + b;';\nexport const SUB = 'return a - b;';\nexport const MUL = 'return a * b;';\n// We use native integer division to deal with floating point imprecision. Since\n// we implement floor division and glsl implements truncated division, we\n// correct for this by subtracting 1 from result when the result is negative and\n// there is a remainder.\nexport const INT_DIV = `\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`;\nexport const POW = `\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nif (b == 0.0) {\n  return 1.0;\n}\nreturn (round(mod(b, 2.0)) != 1) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`;\nexport const SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport const EQUAL = `return float(a == b);`;\nexport const NOT_EQUAL = `return float(a != b);`;\nexport const LESS = `return float(a < b);`;\nexport const LESS_EQUAL = `return float(a <= b);`;\nexport const GREATER = `return float(a > b);`;\nexport const GREATER_EQUAL = `return float(a >= b);`;\nexport const LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;\nexport const LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;\nexport const MAX = CHECK_NAN_SNIPPET + `\n  return max(a, b);\n`;\nexport const MIN = CHECK_NAN_SNIPPET + `\n  return min(a, b);\n`;\nexport const MOD = `if (b == 0.0) return NAN;\n  return mod(a, b);`;\nexport const ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;\nexport const PRELU = `return (a < 0.) ? b * a : a;`;\nexport class BinaryOpProgram {\n    constructor(op, aShape, bShape) {\n        this.variableNames = ['A', 'B'];\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        this.userCode = `\n      float binaryOperation(float a, float b) {\n        ${op}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nconst CHECK_NAN_SNIPPET = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\nexport const INT_DIV = `\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n`;\nexport const POW = `\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const PRELU = `\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;\nexport const ELU_DER = `\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`;\nexport const EQUAL = `\n  return vec4(equal(a, b));\n`;\nexport const NOT_EQUAL = `\n  return vec4(notEqual(a, b));\n`;\nexport const LESS = `\n  return vec4(lessThan(a, b));\n`;\nexport const LESS_EQUAL = `\n  return vec4(lessThanEqual(a, b));\n`;\nexport const GREATER = `\n  return vec4(greaterThan(a, b));\n`;\nexport const GREATER_EQUAL = `\n  return vec4(greaterThanEqual(a, b));\n`;\nexport const LOGICAL_AND = `\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`;\nexport const LOGICAL_OR = `\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`;\nexport const MAX = `\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const MIN = `\n  vec4 result = vec4(min(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const MOD = `\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport class BinaryOpPackedProgram {\n    constructor(op, aShape, bShape, checkOutOfBounds = false) {\n        this.variableNames = ['A', 'B'];\n        this.supportsBroadcasting = true;\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const rank = this.outputShape.length;\n        let checkOutOfBoundsString = '';\n        if (checkOutOfBounds) {\n            if (rank === 0 || util.sizeFromShape(this.outputShape) === 1) {\n                checkOutOfBoundsString = `\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;\n            }\n            else {\n                const dtype = getCoordsDataType(rank);\n                checkOutOfBoundsString = `\n          ${dtype} coords = getOutputCoords();\n        `;\n                if (rank === 1) {\n                    checkOutOfBoundsString += `\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;\n                }\n                else {\n                    const channels = getChannels('coords', rank);\n                    checkOutOfBoundsString += `\n            bool nextRowOutOfBounds =\n              (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};\n            bool nextColOutOfBounds =\n              (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `;\n                }\n            }\n        }\n        this.userCode = `\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${op}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${checkOutOfBoundsString}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ClipProgram {\n    constructor(aShape) {\n        this.variableNames = ['A'];\n        this.outputShape = aShape;\n        this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    `;\n    }\n    getCustomSetupFunc(min, max) {\n        return (gpgpu, webGLProgram) => {\n            if (this.minLoc == null) {\n                this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n                this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n            }\n            gpgpu.gl.uniform1f(this.minLoc, min);\n            gpgpu.gl.uniform1f(this.maxLoc, max);\n        };\n    }\n}\n//# sourceMappingURL=clip_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ClipPackedProgram {\n    constructor(aShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = aShape;\n        this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    `;\n    }\n    getCustomSetupFunc(min, max) {\n        return (gpgpu, webGLProgram) => {\n            if (this.minLoc == null) {\n                this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n                this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n            }\n            gpgpu.gl.uniform1f(this.minLoc, min);\n            gpgpu.gl.uniform1f(this.maxLoc, max);\n        };\n    }\n}\n//# sourceMappingURL=clip_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ComplexAbsProgram {\n    constructor(shape) {\n        this.variableNames = ['real', 'imag'];\n        this.outputShape = shape;\n        this.userCode = `\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `;\n    }\n}\n//# sourceMappingURL=complex_abs_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class ConcatProgram {\n    // Concats 2d tensors along axis=1. See comments in MathBackendWebGL.concat().\n    constructor(shapes) {\n        this.outputShape = [];\n        this.outputShape = backend_util.computeOutShape(shapes, 1 /* axis */);\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const offsets = new Array(shapes.length - 1);\n        offsets[0] = shapes[0][1];\n        for (let i = 1; i < offsets.length; i++) {\n            offsets[i] = offsets[i - 1] + shapes[i][1];\n        }\n        const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];\n        for (let i = 1; i < offsets.length; i++) {\n            const shift = offsets[i - 1];\n            snippets.push(`else if (yC < ${offsets[i]}) ` +\n                `setOutput(getT${i}(yR, yC-${shift}));`);\n        }\n        const lastIndex = offsets.length;\n        const lastShift = offsets[offsets.length - 1];\n        snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${snippets.join('\\n        ')}\n      }\n    `;\n    }\n}\n//# sourceMappingURL=concat_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ConcatPackedProgram {\n    constructor(shapes, axis) {\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = [];\n        this.outputShape = backend_util.computeOutShape(shapes, axis);\n        const shape = this.outputShape;\n        const rank = shape.length;\n        const dtype = getCoordsDataType(rank);\n        const coords = getChannels('coords', rank);\n        const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank);\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const offsets = new Array(shapes.length - 1);\n        offsets[0] = shapes[0][axis];\n        for (let i = 1; i < offsets.length; i++) {\n            offsets[i] = offsets[i - 1] + shapes[i][axis];\n        }\n        const channel = channels[axis];\n        const lastChannels = channels.slice(-2);\n        const allChannels = channels.join();\n        let getValueSnippet = `if (${channel} < ${offsets[0]}) {\n        return getChannel(\n            getT0(${allChannels}), vec2(${lastChannels.join()}));\n        }`;\n        for (let i = 1; i < offsets.length; i++) {\n            const shift = offsets[i - 1];\n            // Note: the >= comparison below may seem unnecessary given the check\n            // above but is needed to workaround branch execution issues on some\n            // devices. It makes all the conditions exclusive without relying on\n            // execution order.\n            getValueSnippet += `\n        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i - 1]}) {\n          return getChannel(\n            getT${i}(${shiftedChannels(channels, channel, shift)}),\n            vec2(${shiftedChannels(lastChannels, channel, shift)}));\n        }`;\n        }\n        const lastIndex = offsets.length;\n        const shift = offsets[offsets.length - 1];\n        getValueSnippet += `\n        return getChannel(\n          getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),\n          vec2(${shiftedChannels(lastChannels, channel, shift)}));`;\n        this.userCode = `\n      float getValue(${channels.map(x => 'int ' + x)}) {\n        ${getValueSnippet}\n      }\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${coords}), 0., 0., 0.);\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} + 1;\n        if (${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.g = getValue(${coords});\n        }\n\n        ${coords[rank - 2]} = ${coords[rank - 2]} + 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]}) {\n          result.a = getValue(${coords});\n        }\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} - 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]} &&\n            ${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.b = getValue(${coords});\n        }\n        setOutput(result);\n      }\n    `;\n    }\n}\n/**\n * Return an expression for coordinates into a vector where a given channel\n * will be offset by [shift].\n *\n * @param channels the channels to consider\n * @param channel the channel we want shifted\n * @param shift  the amount to subtract from the channel.\n *\n * @returns a string of the form 'x, y-[shift], z' where any one channel can\n * have the shift applied.\n */\nfunction shiftedChannels(channels, channel, shift) {\n    const channelIdx = channels.indexOf(channel);\n    const res = channels.map((c, idx) => {\n        if (idx === channelIdx) {\n            return `${c} - ${shift}`;\n        }\n        else {\n            return c;\n        }\n    });\n    return res.join();\n}\n//# sourceMappingURL=concat_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Conv2DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              if (${isChannelsLast}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv2DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        const rowDim = isChannelsLast ? 1 : 2;\n        const colDim = isChannelsLast ? 2 : 3;\n        const channelDim = isChannelsLast ? 3 : 1;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${channelDim}];\n\n        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n\n              if (${isChannelsLast}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv3DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.userCode = `\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {\n            int xF = wF + yF * ${strideDepth} - ${padFront};\n\n            if (xF < 0 || xF >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n              int xR = wR + yR * ${strideHeight} - ${padTop};\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n                int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv3DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padFront = filterDepth - 1 - convInfo.padInfo.front;\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;\n\n          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${filterDepth} - 1 - wF;\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${filterHeight} - 1 - wR;\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${filterWidth} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthwiseConv2DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${channelMul} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class DepthwiseConv2DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${channelMul}; dm++) {\n              int d2 = d1 * ${channelMul} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_backprop_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Conv2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivationWeights = false) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n        const inputDepthVec4Remainder = convInfo.inChannels % 4;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const rowDim = isChannelsLast ? 1 : 2;\n        const colDim = isChannelsLast ? 2 : 3;\n        const channelDim = isChannelsLast ? 3 : 1;\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivationWeights) {\n                activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivationWeights) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${channelDim}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${convInfo.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${inputDepthVec4Remainder === 1}) {\n\n              if (${isChannelsLast}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              }\n\n            } else if (${inputDepthVec4Remainder === 2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${inputDepthVec4Remainder === 3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\nexport class Conv3DProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n        const inputDepthVec4Remainder = convInfo.inChannels % 4;\n        this.userCode = `\n      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          int xF = xFCorner + wF * ${dilationDepth};\n\n          if (xF < 0 || xF >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            int xR = xRCorner + wR * ${dilationHeight};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${inputDepthVec4Remainder === 1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);\n              } else if (${inputDepthVec4Remainder === 2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${inputDepthVec4Remainder === 3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthwiseConv2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const xNumRows = convInfo.inHeight;\n        const xNumCols = convInfo.inWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${xNumRows}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${xNumCols}) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport class DepthwiseConvPacked2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['x', 'W'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = convInfo.outShape;\n        const xNumRows = convInfo.inHeight;\n        const xNumCols = convInfo.inWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const texelsAcross = filterWidth;\n        let mainLoop = `int xR; int xC; int xCOffset;`;\n        for (let r = 0; r < filterHeight; r++) {\n            for (let c = 0; c < filterWidth; c++) {\n                mainLoop += `\n          vec4 xTexelR${r}C${c * 2} = vec4(0.);\n          vec4 wR${r}C${c} = vec4(0.);\n          vec4 xR${r}C${c} = vec4(0.);`;\n            }\n        }\n        /**\n         * This vectorized implementation works by gathering the values needed for\n         * each output channel's dot product into vec4's and then multiplying them\n         * all together (this happens in the final double for-loop below). Most of\n         * the main loop consists of constructing these vec4's with the minimum\n         * number of texture2D calls, which means making use of all four returned\n         * values from a texture2D call at once.\n         */\n        for (let r = 0; r < filterHeight; r++) {\n            for (let texelC = 0; texelC < texelsAcross; texelC++) {\n                const c = texelC * 2;\n                mainLoop += `\n          xR = xRCorner + ${r * dilationHeight};\n          xC = xCCorner + ${c * dilationWidth};\n        `;\n                if (strideWidth === 1) {\n                    if (c < filterWidth) {\n                        // If padding is odd, the outer texels have to be composed.\n                        if (padLeft % 2 === 1) {\n                            // TODO: Ensure vec4 previous does not result in redundant sample,\n                            // and avoid setting xTexelRC's that exceed the boundary in the\n                            // first place rather than resetting them to vec4(0)).\n                            // To compute xCOffset:\n                            // - If padding is odd, we must add 1 to ensure we ask for an\n                            // even-numbered row.\n                            // - We subtract 2 to access the previous texel.\n                            mainLoop += `\n                xCOffset = xC + 1;\n                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= ${xNumCols}) {\n                    xTexelR${r}C${c}.zw = vec2(0.);\n                  }\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xCOffset = xC + 1 - 2;\n                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  vec4 previous = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= ${xNumCols}) {\n                    previous.zw = vec2(0.);\n                  }\n\n                  xR${r}C${c} = vec4(previous.zw, xTexelR${r}C${c}.xy);\n                } else {\n                  xR${r}C${c} = vec4(0, 0, xTexelR${r}C${c}.xy);\n                }\n              `;\n                        }\n                        else {\n                            // Padding is even, so xRC corresponds to a single texel.\n                            mainLoop += `\n                if(xR >= 0 && xR < ${xNumRows} && xC >= 0 && xC < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xR${r}C${c} = xTexelR${r}C${c};\n              `;\n                        }\n                        if (c + 1 < filterWidth) {\n                            // If dilation is even, the second entry should match the first\n                            // (either both are composed or both are single samples). But if\n                            // dilation is odd, then the second entry should be the opposite\n                            // of the first (if the first is composed, the second is a single\n                            // sample, and vice versa.)\n                            const nextTexelOffset = padLeft % 2 === 0 ?\n                                util.nearestLargerEven(dilationWidth) :\n                                dilationWidth;\n                            if ((dilationWidth % 2 === 0 && padLeft % 2 === 1) ||\n                                (dilationWidth % 2 !== 0 && padLeft % 2 !== 1)) {\n                                mainLoop += `\n                  xCOffset = xC + ${padLeft % 2} + ${nextTexelOffset};\n\n                  if(xR >= 0 && xR < ${xNumRows} &&\n                    xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                  }\n                `;\n                                // If dilation > 1 then the xRC's will not be able to share any\n                                // values, so each xRC will require two unique calls to getX.\n                                if (dilationWidth > 1) {\n                                    mainLoop += `\n                    xCOffset -= 2;\n                    if(xR >= 0 && xR < ${xNumRows} &&\n                      xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                      xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n                    } else {\n                      xTexelR${r}C${c} = vec4(0.);\n                    }\n                  `;\n                                }\n                                mainLoop += `\n                  xR${r}C${c + 1} = vec4(\n                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.xy);\n                `;\n                            }\n                            else {\n                                mainLoop += `\n                  xCOffset = xC + ${nextTexelOffset};\n\n                  if(xR >= 0 && xR < ${xNumRows} &&\n                    xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                  }\n\n                  xR${r}C${c + 1} = xTexelR${r}C${c + 2};\n                `;\n                            }\n                        }\n                    }\n                }\n                else { // stride > 1\n                    if (c < filterWidth) {\n                        mainLoop += `\n              if(xR >= 0 && xR < ${xNumRows}) {\n            `;\n                        // Depending on whether padLeft is even or odd, we want either the\n                        // xy or zw channels from X texels for xR${r}C${c}. If padLeft is\n                        // even, xR${r}C${c + 1} is simply the zw channels of texels we've\n                        // already sampled. But if padLeft is odd, xR${r}C{$c + 1}.zw will\n                        // need to come from the xy channels of a new texel, hence the `vec4\n                        // final` initialized below.\n                        if (padLeft % 2 === 1) {\n                            mainLoop += `\n                xCOffset = xC + 1 - ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < ${xNumCols}) {\n                  xTexelR${r}C${c + 2} = getX(batch, xR, xC + 1, d1);\n                } else {\n                  xTexelR${r}C${c + 2} = vec4(0.);\n                }\n\n                xR${r}C${c} = vec4(\n                  xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n              `;\n                            if (c + 1 < filterWidth) {\n                                mainLoop += `\n                  vec4 final = vec4(0.);\n                  xCOffset = xC + 1 + ${strideWidth};\n                  if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xR${r}C${c + 1} = vec4(xTexelR${r}C${c + 2}.xy, final.xy);\n                `;\n                            }\n                        }\n                        else {\n                            mainLoop += `\n                if(xC >= 0 && xC < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xCOffset = xC + ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${r}C${c + 2} = vec4(0.);\n                }\n\n                xR${r}C${c} = vec4(\n                  xTexelR${r}C${c}.xy, xTexelR${r}C${c + 2}.xy);\n              `;\n                            if (c + 1 < filterWidth) {\n                                mainLoop += `\n                  xR${r}C${c + 1} = vec4(\n                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n                `;\n                            }\n                        }\n                        mainLoop += `}`;\n                    }\n                }\n                if (c < filterWidth) {\n                    mainLoop += `\n            vec4 wTexelR${r}C${c} = getW(${r}, ${c}, d1, q);\n            wR${r}C${c} = vec4(wTexelR${r}C${c}.xz, wTexelR${r}C${c}.xz);\n          `;\n                    if (c + 1 < filterWidth) {\n                        mainLoop += `\n              vec4 wTexelR${r}C${c + 1} = getW(${r}, ${c + 1}, d1, q);\n              wR${r}C${c + 1} =\n                vec4(wTexelR${r}C${c + 1}.xz, wTexelR${r}C${c + 1}.xz);`;\n                    }\n                }\n            }\n        }\n        for (let r = 0; r < filterHeight; r++) {\n            for (let c = 0; c < filterWidth; c++) {\n                mainLoop += `dotProd += xR${r}C${c} * wR${r}C${c};`;\n            }\n        }\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 dotProd = vec4(0.);\n\n        ${mainLoop}\n\n        vec4 result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_packed_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class CropAndResizeProgram {\n    constructor(imageShape, boxShape, cropSize, method, extrapolationValue) {\n        this.variableNames = ['Image', 'Boxes', 'BoxInd'];\n        this.outputShape = [];\n        const [batch, imageHeight, imageWidth, depth] = imageShape;\n        const [numBoxes,] = boxShape;\n        const [cropHeight, cropWidth] = cropSize;\n        this.outputShape = [numBoxes, cropHeight, cropWidth, depth];\n        const methodId = method === 'bilinear' ? 1 : 0;\n        const [inputHeightFloat, inputWidthFloat] = [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];\n        const [heightRatio, heightScale, inY] = cropHeight > 1 ?\n            [\n                `${(imageHeight - 1) / (cropHeight - 1)}`,\n                '(y2-y1) * height_ratio',\n                `y1*${inputHeightFloat} + float(y)*(height_scale)`,\n            ] :\n            [\n                '0.0',\n                '0.0',\n                `0.5 * (y1+y2) * ${inputHeightFloat}`,\n            ];\n        const [widthRatio, widthScale, inX] = cropWidth > 1 ?\n            [\n                `${(imageWidth - 1) / (cropWidth - 1)}`,\n                '(x2-x1) * width_ratio',\n                `x1*${inputWidthFloat} + float(x)*(width_scale)`,\n            ] :\n            [\n                '0.0',\n                '0.0',\n                `0.5 * (x1+x2) * ${inputWidthFloat}`,\n            ];\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc\n        this.userCode = `\n      const float height_ratio = float(${heightRatio});\n      const float width_ratio = float(${widthRatio});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${batch}) {\n          return;\n        }\n\n        float height_scale = ${heightScale};\n        float width_scale = ${widthScale};\n\n        float in_y = ${inY};\n        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n        float in_x = ${inX};\n        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${methodId} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=crop_and_resize_gpu.js.map", "import { getCoordsDataType } from './shader_compiler';\nexport class CumSumProgram {\n    constructor(shape, exclusive, reverse) {\n        this.variableNames = ['x'];\n        this.outputShape = shape;\n        const rank = shape.length;\n        const val = exclusive ? '0.0' : `getX(${getCoords(rank, 'coords')})`;\n        const length = shape[shape.length - 1];\n        let condition = '';\n        let idxString = '';\n        // When exclusive is set, the cumsum op becomes roll op that copies the\n        // value from the previous index based on the direction specified by the\n        // reverse flag.\n        if (exclusive) {\n            condition = reverse ? `end != ${length - 1}` : 'end != 0';\n            idxString = reverse ? 'end + 1' : 'end - 1';\n        }\n        else {\n            condition = reverse ? `end + pow2 < ${length}` : 'end >= pow2';\n            idxString = (reverse ? 'end + pow2' : 'end - pow2');\n        }\n        this.userCode = `\n      uniform float index;\n      void main() {\n        ${getCoordsDataType(rank)} coords = getOutputCoords();\n        int end = ${getFinalCoord(rank, 'coords')};\n        float val = ${val};\n        int pow2 = int(pow(2.0, index));\n        if (${condition}) {\n          int idx = ${idxString};\n          ${getFinalCoord(rank, 'coords')} = idx;\n          val += getX(${getCoords(rank, 'coords')});\n        }\n        setOutput(val);\n      }\n    `;\n    }\n    getCustomSetupFunc(index) {\n        return (gpgpu, webGLProgram) => {\n            if (this.index == null) {\n                this.index = gpgpu.getUniformLocation(webGLProgram, 'index');\n            }\n            gpgpu.gl.uniform1f(this.index, index);\n        };\n    }\n}\nfunction getCoords(rank, name) {\n    if (rank === 1) {\n        return `${name}`;\n    }\n    else if (rank === 2) {\n        return `${name}.x, ${name}.y`;\n    }\n    else if (rank === 3) {\n        return `${name}.x, ${name}.y, ${name}.z`;\n    }\n    else if (rank === 4) {\n        return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n    }\n    else {\n        throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n    }\n}\nfunction getFinalCoord(rank, name) {\n    if (rank === 1) {\n        return `${name}`;\n    }\n    else if (rank === 2) {\n        return `${name}.y`;\n    }\n    else if (rank === 3) {\n        return `${name}.z`;\n    }\n    else if (rank === 4) {\n        return `${name}.w`;\n    }\n    else {\n        throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n    }\n}\n//# sourceMappingURL=cumsum_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nimport { getDenseTexShape, PackingScheme } from './tex_util';\nexport class DecodeMatrixProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        this.outPackingScheme = PackingScheme.DENSE;\n        const texShape = getDenseTexShape(outputShape);\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=decode_matrix_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nimport { getDenseTexShape, PackingScheme } from './tex_util';\nexport class DecodeMatrixPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outPackingScheme = PackingScheme.DENSE;\n        const texShape = getDenseTexShape(outputShape);\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=decode_matrix_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthToSpaceProgram {\n    constructor(outputShape, blockSize, dataFormat) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        this.outputShape = outputShape;\n        this.blockSize = blockSize;\n        this.dataFormat = dataFormat;\n        this.userCode = `\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${blockSize};\n      int offset_h = imod(h, ${blockSize});\n      int in_w = w / ${blockSize};\n      int offset_w = imod(w, ${blockSize});\n      int offset_d = (offset_h * ${blockSize} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `;\n    }\n    getHeightCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[1]`;\n        }\n        else {\n            return `coords[2]`;\n        }\n    }\n    getWidthCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[2]`;\n        }\n        else {\n            return `coords[3]`;\n        }\n    }\n    getDepthCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[3]`;\n        }\n        else {\n            return `coords[1]`;\n        }\n    }\n    getOutputDepthSize() {\n        if (this.dataFormat === 'NHWC') {\n            return this.outputShape[3];\n        }\n        else {\n            return this.outputShape[1];\n        }\n    }\n    getInputSamplingString() {\n        if (this.dataFormat === 'NHWC') {\n            return `getX(b, in_h, in_w, in_d)`;\n        }\n        else {\n            return `getX(b, in_d, in_h, in_w)`;\n        }\n    }\n}\n//# sourceMappingURL=depth_to_space_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DiagProgram {\n    constructor(size) {\n        this.variableNames = ['X'];\n        this.outputShape = [size, size];\n        this.userCode = `\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=diag_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport { ENCODE_FLOAT_SNIPPET } from './shader_compiler_util';\nimport { TextureUsage } from './tex_util';\nexport class EncodeFloatProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.outTexUsage = TextureUsage.DOWNLOAD;\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_float_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport { ENCODE_FLOAT_SNIPPET } from './shader_compiler_util';\nimport { TextureUsage } from './tex_util';\nexport class EncodeFloatPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = false;\n        this.outTexUsage = TextureUsage.DOWNLOAD;\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_float_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nexport class EncodeMatrixProgram {\n    constructor(outputShape, texShape, inputIsUnsignedByte = false) {\n        this.variableNames = ['A'];\n        const glsl = getGlslDifferences();\n        const [height, width] = texShape;\n        this.outputShape = outputShape;\n        let output = `result`;\n        if (inputIsUnsignedByte) {\n            output = `floor(result * 255. + 0.5)`;\n        }\n        this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / ${width};\n        int c = imod(flatIndex, ${width});\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n        vec4 values = ${glsl.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${glsl.output} = vec4(${output}, 0., 0., 0.);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_matrix_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\n/*\nThis is how the shader encodes a tensor with shape = [2, 3, 5]\n(indices are [batch, row, col]).\n\n000|001   002|003   004|xxx   020|021   022|023   024|xxx\n-------   -------   -------   -------   -------   -------\n010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\n100|101   102|103   104|xxx   120|121   122|123   124|xxx\n-------   -------   -------   -------   -------   -------\n110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\nSingle texels contain only values from the same batch, and from adjacent rows\nand columns.\n */\nexport class EncodeMatrixPackedProgram {\n    constructor(outputShape, texShape, inputIsUnsignedByte = false) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        const glsl = getGlslDifferences();\n        const [height, width] = texShape;\n        this.outputShape = outputShape;\n        let mainLoop = '';\n        let output = 'result';\n        if (inputIsUnsignedByte) {\n            output = 'floor(result * 255. + 0.5)';\n        }\n        for (let row = 0; row <= 1; row++) {\n            for (let col = 0; col <= 1; col++) {\n                const channel = row * 2 + col;\n                mainLoop += `\n          localCoords = coords;\n          if(localCoords[2] + ${col} < ${outputShape[2]}) {\n            localCoords[2] += ${col};\n            if(localCoords[1] + ${row} < ${outputShape[1]}) {\n              localCoords[1] += ${row};\n\n              flatIndex = getFlatIndex(localCoords);\n              offset = imod(flatIndex, 4);\n\n              flatIndex = idiv(flatIndex, 4, 1.);\n\n              r = flatIndex / ${width};\n              c = imod(flatIndex, ${width});\n              uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n              values = ${glsl.texture2D}(A, uv);\n\n              if(offset == 0) {\n                result[${channel}] = values[0];\n              } else if(offset == 1) {\n                result[${channel}] = values[1];\n              } else if(offset == 2) {\n                result[${channel}] = values[2];\n              } else {\n                result[${channel}] = values[3];\n              }\n            }\n          }\n        `;\n            }\n        }\n        this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        vec4 result = vec4(0.);\n        int flatIndex, r, c, offset;\n        ivec3 localCoords;\n        vec2 uv;\n        vec4 values;\n\n        ${mainLoop}\n\n        ${glsl.output} = ${output};\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_matrix_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const COMPLEX_FFT = {\n    REAL: 'return real * expR - imag * expI;',\n    IMAG: 'return real * expI + imag * expR;'\n};\nexport class FFTProgram {\n    constructor(op, inputShape, inverse) {\n        this.variableNames = ['real', 'imag'];\n        const innerDim = inputShape[1];\n        this.outputShape = inputShape;\n        const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;\n        const resultDenominator = inverse ? `${innerDim}.0` : '1.0';\n        this.userCode = `\n      const float exponentMultiplier = ${exponentMultiplierSnippet};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${op}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${innerDim});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${innerDim}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=fft_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FillProgram {\n    constructor(shape, value) {\n        this.outputShape = [];\n        this.variableNames = ['x'];\n        this.outputShape = shape;\n        this.userCode = `\n      uniform float value;\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    `;\n    }\n    getCustomSetupFunc(value) {\n        return (gpgpu, webGLProgram) => {\n            if (this.valueLoc == null) {\n                this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'value');\n            }\n            gpgpu.gl.uniform1f(this.valueLoc, value);\n        };\n    }\n}\n//# sourceMappingURL=fill_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class GatherProgram {\n    constructor(aShape, indicesLength, axis) {\n        this.variableNames = ['A', 'indices'];\n        const outputShape = aShape.slice();\n        outputShape[axis] = indicesLength;\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const sourceCoords = getSourceCoords(aShape, axis);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n    }\n}\nfunction getSourceCoords(aShape, axis) {\n    const rank = aShape.length;\n    if (rank > 4) {\n        throw Error(`Gather for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n        return `int(getIndices(resRC))`;\n    }\n    const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n    const sourceCoords = [];\n    for (let i = 0; i < aShape.length; i++) {\n        if (i === axis) {\n            sourceCoords.push(`int(getIndices(${currentCoords[i]}))`);\n        }\n        else {\n            sourceCoords.push(`${currentCoords[i]}`);\n        }\n    }\n    return sourceCoords.join();\n}\n//# sourceMappingURL=gather_gpu.js.map", "import { getCoordsDataType } from './shader_compiler';\nexport class GatherNDProgram {\n    constructor(sliceDim, strides, shape) {\n        this.sliceDim = sliceDim;\n        this.strides = strides;\n        this.variableNames = ['x', 'indices'];\n        this.outputShape = shape;\n        const stridesType = getCoordsDataType(strides.length);\n        const dtype = getCoordsDataType(shape.length);\n        const strideString = this.sliceDim > 1 ? 'strides[j]' : 'strides';\n        this.userCode = `\n        ${stridesType} strides = ${stridesType}(${this.strides});\n         void main() {\n          ${dtype} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${strideString};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `;\n    }\n}\n//# sourceMappingURL=gather_nd_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\nexport function createVertexShader(gl) {\n    const glsl = getGlslDifferences();\n    const vertexShaderSource = `${glsl.version}\n    precision highp float;\n    ${glsl.attribute} vec3 clipSpacePos;\n    ${glsl.attribute} vec2 uv;\n    ${glsl.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;\n    return webgl_util.createVertexShader(gl, vertexShaderSource);\n}\nexport function createVertexBuffer(gl) {\n    // [x y z u v] * [upper-left, lower-left, upper-right, lower-right]\n    const vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);\n    return webgl_util.createStaticVertexBuffer(gl, vertexArray);\n}\nexport function createIndexBuffer(gl) {\n    // OpenGL (and WebGL) have \"CCW == front\" winding\n    const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);\n    return webgl_util.createStaticIndexBuffer(gl, triangleVertexIndices);\n}\nfunction createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {\n    webgl_util.validateTextureSize(width, height);\n    const texture = webgl_util.createTexture(gl);\n    const tex2d = gl.TEXTURE_2D;\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(tex2d, texture));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));\n    webgl_util.callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null));\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n    return texture;\n}\nexport function getInternalFormatForFloat32MatrixTexture(textureConfig) {\n    return textureConfig.internalFormatFloat;\n}\nexport function createFloat32MatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT);\n}\nexport function getInternalFormatForFloat16MatrixTexture(textureConfig) {\n    return textureConfig.internalFormatHalfFloat;\n}\nexport function createFloat16MatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);\n}\nexport function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) {\n    return textureConfig.downloadTextureFormat;\n}\nexport function createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE);\n}\nexport function getInternalFormatForPackedMatrixTexture(textureConfig) {\n    return textureConfig.internalFormatPackedFloat;\n}\nexport function createPackedMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT);\n}\nexport function getInternalFormatForFloat16PackedMatrixTexture(textureConfig) {\n    return textureConfig.internalFormatPackedHalfFloat;\n}\nexport function createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat);\n}\nexport function bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {\n    const posOffset = 0; // x is the first buffer element\n    const uvOffset = 3 * 4; // uv comes after [x y z]\n    const stride = (3 * 4) + (2 * 4); // xyz + uv, each entry is 4-byte float.\n    webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));\n    const success = webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'clipSpacePos', vertexBuffer, 3, stride, posOffset);\n    return success &&\n        webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'uv', vertexBuffer, 2, stride, uvOffset);\n}\nexport function uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) {\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n    let dataForUpload, texelDataType, internalFormat;\n    if (data instanceof Uint8Array) {\n        dataForUpload = new Uint8Array(width * height * 4);\n        texelDataType = gl.UNSIGNED_BYTE;\n        internalFormat = gl.RGBA;\n    }\n    else {\n        dataForUpload = new Float32Array(width * height * 4);\n        texelDataType = gl.FLOAT;\n        internalFormat = textureConfig.internalFormatPackedFloat;\n    }\n    dataForUpload.set(data);\n    webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload));\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function uploadPixelDataToTexture(gl, texture, pixels) {\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n    if (pixels.data instanceof Uint8Array) {\n        webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));\n    }\n    else {\n        webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n    }\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function createBufferFromOutputTexture(gl2, rows, columns, textureConfig) {\n    // Create and bind the buffer.\n    const buffer = gl2.createBuffer();\n    webgl_util.callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer));\n    // Initialize the buffer to the size of the texture in bytes.\n    const bytesPerFloat = 4;\n    const valuesPerTexel = 4;\n    const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;\n    webgl_util.callAndCheck(gl2, () => gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));\n    // Enqueue a command on the GPU command queue to copy of texture into the\n    // buffer.\n    webgl_util.callAndCheck(gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));\n    webgl_util.callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));\n    return buffer;\n}\nexport function downloadFloat32MatrixFromBuffer(gl, buffer, size) {\n    const gl2 = gl;\n    const downloadTarget = new Float32Array(size);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n    return downloadTarget;\n}\nexport function downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {\n    const [w, h] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    const numChannels = 4;\n    const downloadTarget = new Uint8Array(tex_util.getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));\n    webgl_util.callAndCheck(gl, () => gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget));\n    // By wrapping the buffer in a Float32Array, we use native browser IEEE 754\n    // decoding of the 4 bytes that back each 32 bit float.\n    return new Float32Array(downloadTarget.buffer);\n}\nexport function downloadPackedMatrixFromBuffer(gl, buffer, batch, rows, cols, physicalRows, physicalCols, textureConfig) {\n    const gl2 = gl;\n    const downloadTarget = new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n    return downloadTarget;\n}\nexport function downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {\n    const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);\n    webgl_util.callAndCheck(gl, () => gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));\n    return packedRGBA;\n}\n//# sourceMappingURL=gpgpu_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { getWebGLContext, setWebGLContext } from './canvas_util';\nimport * as gpgpu_util from './gpgpu_util';\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\nexport class GPGPUContext {\n    constructor(gl) {\n        this.outputTexture = null;\n        this.program = null;\n        this.disposed = false;\n        this.vertexAttrsAreBound = false;\n        this.itemsToPoll = [];\n        const glVersion = env().getNumber('WEBGL_VERSION');\n        if (gl != null) {\n            this.gl = gl;\n            setWebGLContext(glVersion, gl);\n        }\n        else {\n            this.gl = getWebGLContext(glVersion);\n        }\n        // WebGL 2.0 enables texture floats without an extension.\n        let COLOR_BUFFER_FLOAT = 'WEBGL_color_buffer_float';\n        const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n        if (env().getNumber('WEBGL_VERSION') === 1) {\n            const TEXTURE_FLOAT = 'OES_texture_float';\n            const TEXTURE_HALF_FLOAT = 'OES_texture_half_float';\n            this.textureFloatExtension =\n                webgl_util.getExtensionOrThrow(this.gl, TEXTURE_FLOAT);\n            if (webgl_util.hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {\n                this.textureHalfFloatExtension =\n                    webgl_util.getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);\n            }\n            else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n                throw new Error('GL context does not support half float textures, yet the ' +\n                    'environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n            }\n            this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);\n            if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n                this.colorBufferHalfFloatExtension =\n                    webgl_util.getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);\n            }\n            else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n                throw new Error('GL context does not support color renderable half floats, yet ' +\n                    'the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n            }\n        }\n        else {\n            COLOR_BUFFER_FLOAT = 'EXT_color_buffer_float';\n            if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {\n                this.colorBufferFloatExtension =\n                    this.gl.getExtension(COLOR_BUFFER_FLOAT);\n            }\n            else if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n                this.colorBufferHalfFloatExtension =\n                    this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n            }\n            else {\n                throw new Error('GL context does not support color renderable floats');\n            }\n        }\n        this.vertexBuffer = gpgpu_util.createVertexBuffer(this.gl);\n        this.indexBuffer = gpgpu_util.createIndexBuffer(this.gl);\n        this.framebuffer = webgl_util.createFramebuffer(this.gl);\n        this.textureConfig =\n            tex_util.getTextureConfig(this.gl, this.textureHalfFloatExtension);\n    }\n    get debug() {\n        return env().getBool('DEBUG');\n    }\n    dispose() {\n        if (this.disposed) {\n            return;\n        }\n        if (this.program != null) {\n            console.warn('Disposing a GPGPUContext that still has a bound WebGLProgram.' +\n                ' This is probably a resource leak, delete the program with ' +\n                'GPGPUContext.deleteProgram before disposing.');\n        }\n        if (this.outputTexture != null) {\n            console.warn('Disposing a GPGPUContext that still has a bound output matrix ' +\n                'texture.  This is probably a resource leak, delete the output ' +\n                'matrix texture with GPGPUContext.deleteMatrixTexture before ' +\n                'disposing.');\n        }\n        const gl = this.gl;\n        webgl_util.callAndCheck(gl, () => gl.finish());\n        webgl_util.callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));\n        webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));\n        this.disposed = true;\n    }\n    createFloat32MatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createFloat16MatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createUnsignedBytesMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    uploadPixelDataToTexture(texture, pixels) {\n        this.throwIfDisposed();\n        gpgpu_util.uploadPixelDataToTexture(this.gl, texture, pixels);\n    }\n    uploadDenseMatrixToTexture(texture, width, height, data) {\n        this.throwIfDisposed();\n        gpgpu_util.uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig);\n    }\n    createFloat16PackedMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createPackedMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    deleteMatrixTexture(texture) {\n        this.throwIfDisposed();\n        if (this.outputTexture === texture) {\n            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n            this.outputTexture = null;\n        }\n        webgl_util.callAndCheck(this.gl, () => this.gl.deleteTexture(texture));\n    }\n    downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) {\n        return this.downloadMatrixDriver(texture, () => gpgpu_util.downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig));\n    }\n    downloadPackedMatrixFromBuffer(buffer, batch, rows, columns, physicalRows, physicalCols) {\n        return gpgpu_util.downloadPackedMatrixFromBuffer(this.gl, buffer, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);\n    }\n    downloadFloat32MatrixFromBuffer(buffer, size) {\n        return gpgpu_util.downloadFloat32MatrixFromBuffer(this.gl, buffer, size);\n    }\n    createBufferFromTexture(texture, rows, columns) {\n        this.bindTextureToFrameBuffer(texture);\n        const result = gpgpu_util.createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig);\n        this.unbindTextureToFrameBuffer();\n        return result;\n    }\n    createAndWaitForFence() {\n        const fenceContext = this.createFence(this.gl);\n        return this.pollFence(fenceContext);\n    }\n    createFence(gl) {\n        let query;\n        let isFencePassed;\n        if (env().getBool('WEBGL_FENCE_API_ENABLED')) {\n            const gl2 = gl;\n            const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);\n            gl.flush();\n            isFencePassed = () => {\n                const status = gl2.clientWaitSync(sync, 0, 0);\n                return status === gl2.ALREADY_SIGNALED ||\n                    status === gl2.CONDITION_SATISFIED;\n            };\n            query = sync;\n        }\n        else if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n            query = this.beginQuery();\n            this.endQuery();\n            isFencePassed = () => this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n        }\n        else {\n            // If we have no way to fence, return true immediately. This will fire in\n            // WebGL 1.0 when there is no disjoint query timer. In this case, because\n            // the fence passes immediately, we'll immediately ask for a download of\n            // the texture, which will cause the UI thread to hang.\n            isFencePassed = () => true;\n        }\n        return { query, isFencePassed };\n    }\n    downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {\n        return this.downloadMatrixDriver(texture, () => gpgpu_util.downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));\n    }\n    createProgram(fragmentShaderSource) {\n        this.throwIfDisposed();\n        const gl = this.gl;\n        const fragmentShader = webgl_util.createFragmentShader(gl, fragmentShaderSource);\n        const vertexShader = gpgpu_util.createVertexShader(gl);\n        const program = webgl_util.createProgram(gl);\n        webgl_util.callAndCheck(gl, () => gl.attachShader(program, vertexShader));\n        webgl_util.callAndCheck(gl, () => gl.attachShader(program, fragmentShader));\n        webgl_util.linkProgram(gl, program);\n        if (this.debug) {\n            webgl_util.validateProgram(gl, program);\n        }\n        if (!this.vertexAttrsAreBound) {\n            this.setProgram(program);\n            this.vertexAttrsAreBound = gpgpu_util.bindVertexProgramAttributeStreams(gl, this.program, this.vertexBuffer);\n        }\n        return program;\n    }\n    deleteProgram(program) {\n        this.throwIfDisposed();\n        if (program === this.program) {\n            this.program = null;\n        }\n        if (program != null) {\n            webgl_util.callAndCheck(this.gl, () => this.gl.deleteProgram(program));\n        }\n    }\n    setProgram(program) {\n        this.throwIfDisposed();\n        this.program = program;\n        if ((this.program != null) && this.debug) {\n            webgl_util.validateProgram(this.gl, this.program);\n        }\n        webgl_util.callAndCheck(this.gl, () => this.gl.useProgram(program));\n    }\n    getUniformLocation(program, uniformName, shouldThrow = true) {\n        this.throwIfDisposed();\n        if (shouldThrow) {\n            return webgl_util.getProgramUniformLocationOrThrow(this.gl, program, uniformName);\n        }\n        else {\n            return webgl_util.getProgramUniformLocation(this.gl, program, uniformName);\n        }\n    }\n    getAttributeLocation(program, attribute) {\n        this.throwIfDisposed();\n        return webgl_util.callAndCheck(this.gl, () => this.gl.getAttribLocation(program, attribute));\n    }\n    getUniformLocationNoThrow(program, uniformName) {\n        this.throwIfDisposed();\n        return this.gl.getUniformLocation(program, uniformName);\n    }\n    setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) {\n        this.throwIfDisposed();\n        this.throwIfNoProgram();\n        webgl_util.bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit);\n    }\n    setOutputMatrixTexture(outputMatrixTexture, rows, columns) {\n        this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);\n    }\n    setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) {\n        this.throwIfDisposed();\n        const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n        this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);\n    }\n    setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n        this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);\n    }\n    setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n        throw new Error('setOutputPackedMatrixWriteRegion not implemented.');\n    }\n    debugValidate() {\n        if (this.program != null) {\n            webgl_util.validateProgram(this.gl, this.program);\n        }\n        webgl_util.validateFramebuffer(this.gl);\n    }\n    executeProgram() {\n        this.throwIfDisposed();\n        this.throwIfNoProgram();\n        const gl = this.gl;\n        if (this.debug) {\n            this.debugValidate();\n        }\n        webgl_util.callAndCheck(gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));\n    }\n    blockUntilAllProgramsCompleted() {\n        this.throwIfDisposed();\n        webgl_util.callAndCheck(this.gl, () => this.gl.finish());\n    }\n    getQueryTimerExtension() {\n        if (this.disjointQueryTimerExtension == null) {\n            this.disjointQueryTimerExtension =\n                webgl_util.getExtensionOrThrow(this.gl, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2 ?\n                    'EXT_disjoint_timer_query_webgl2' :\n                    'EXT_disjoint_timer_query');\n        }\n        return this.disjointQueryTimerExtension;\n    }\n    getQueryTimerExtensionWebGL2() {\n        return this.getQueryTimerExtension();\n    }\n    getQueryTimerExtensionWebGL1() {\n        return this.getQueryTimerExtension();\n    }\n    beginQuery() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            const query = gl2.createQuery();\n            gl2.beginQuery(ext.TIME_ELAPSED_EXT, query);\n            return query;\n        }\n        const ext = this.getQueryTimerExtensionWebGL1();\n        const query = ext.createQueryEXT();\n        ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);\n        return query;\n    }\n    endQuery() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            gl2.endQuery(ext.TIME_ELAPSED_EXT);\n            return;\n        }\n        const ext = this.getQueryTimerExtensionWebGL1();\n        ext.endQueryEXT(ext.TIME_ELAPSED_EXT);\n    }\n    async waitForQueryAndGetTime(query) {\n        await util.repeatedTry(() => this.disposed || // while testing contexts are created / disposed\n            // in rapid succession, so without this check we\n            // may poll for the query timer indefinitely\n            this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')));\n        return this.getQueryTime(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n    }\n    getQueryTime(query, queryTimerVersion) {\n        if (queryTimerVersion === 0) {\n            return null;\n        }\n        if (queryTimerVersion === 2) {\n            const gl2 = this.gl;\n            const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);\n            // Return milliseconds.\n            return timeElapsedNanos / 1000000;\n        }\n        else {\n            const ext = this.getQueryTimerExtensionWebGL1();\n            const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);\n            // Return milliseconds.\n            return timeElapsedNanos / 1000000;\n        }\n    }\n    isQueryAvailable(query, queryTimerVersion) {\n        if (queryTimerVersion === 0) {\n            return true;\n        }\n        if (queryTimerVersion === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);\n            if (this.disjoint == null) {\n                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n            }\n            return available && !this.disjoint;\n        }\n        else {\n            const ext = this.getQueryTimerExtensionWebGL1();\n            const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);\n            if (this.disjoint == null) {\n                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n            }\n            return available && !this.disjoint;\n        }\n    }\n    pollFence(fenceContext) {\n        return new Promise(resolve => {\n            this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());\n        });\n    }\n    pollItems() {\n        // Find the last query that has finished.\n        const index = linearSearchLastTrue(this.itemsToPoll.map(x => x.isDoneFn));\n        for (let i = 0; i <= index; ++i) {\n            const { resolveFn } = this.itemsToPoll[i];\n            resolveFn();\n        }\n        this.itemsToPoll = this.itemsToPoll.slice(index + 1);\n    }\n    addItemToPoll(isDoneFn, resolveFn) {\n        this.itemsToPoll.push({ isDoneFn, resolveFn });\n        if (this.itemsToPoll.length > 1) {\n            // We already have a running loop that polls.\n            return;\n        }\n        // Start a new loop that polls.\n        util.repeatedTry(() => {\n            this.pollItems();\n            // End the loop if no more items to poll.\n            return this.itemsToPoll.length === 0;\n        });\n    }\n    bindTextureToFrameBuffer(texture) {\n        this.throwIfDisposed();\n        webgl_util.bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);\n        if (this.debug) {\n            webgl_util.validateFramebuffer(this.gl);\n        }\n    }\n    unbindTextureToFrameBuffer() {\n        if (this.outputTexture != null) {\n            webgl_util.bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);\n            if (this.debug) {\n                webgl_util.validateFramebuffer(this.gl);\n            }\n        }\n        else {\n            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n        }\n    }\n    downloadMatrixDriver(texture, downloadAndDecode) {\n        this.bindTextureToFrameBuffer(texture);\n        const result = downloadAndDecode();\n        this.unbindTextureToFrameBuffer();\n        return result;\n    }\n    setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) {\n        this.throwIfDisposed();\n        const gl = this.gl;\n        webgl_util.bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);\n        if (this.debug) {\n            webgl_util.validateFramebuffer(gl);\n        }\n        this.outputTexture = outputMatrixTextureMaybePacked;\n        webgl_util.callAndCheck(gl, () => gl.viewport(0, 0, width, height));\n        webgl_util.callAndCheck(gl, () => gl.scissor(0, 0, width, height));\n    }\n    setOutputMatrixWriteRegionDriver(x, y, width, height) {\n        this.throwIfDisposed();\n        webgl_util.callAndCheck(this.gl, () => this.gl.scissor(x, y, width, height));\n    }\n    throwIfDisposed() {\n        if (this.disposed) {\n            throw new Error('Attempted to use disposed GPGPUContext.');\n        }\n    }\n    throwIfNoProgram() {\n        if (this.program == null) {\n            throw new Error('No GPU program is currently set.');\n        }\n    }\n}\n/**\n * Finds the index of the last true element using linear search.\n * Note: We can't do binary search because Chrome expects us to explicitly\n * test all fences before download:\n * https://github.com/tensorflow/tfjs/issues/1145\n */\nexport function linearSearchLastTrue(arr) {\n    let i = 0;\n    for (; i < arr.length; ++i) {\n        const isDone = arr[i]();\n        if (!isDone) {\n            break;\n        }\n    }\n    return i - 1;\n}\n//# sourceMappingURL=gpgpu_context.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport * as shader_compiler from './shader_compiler';\nexport function compileProgram(gpgpu, program, inputs, output) {\n    const userCode = program.userCode;\n    const inputInfos = inputs.map((input, i) => {\n        const shapeInfo = {\n            logicalShape: input.shape,\n            texShape: input.isUniform ? null : input.texData.texShape,\n            isUniform: input.isUniform,\n            isPacked: input.isUniform ? false : input.texData.isPacked,\n            flatOffset: null\n        };\n        if (input.texData != null && input.texData.slice != null &&\n            input.texData.slice.flatOffset > 0) {\n            shapeInfo.flatOffset = input.texData.slice.flatOffset;\n        }\n        return { name: program.variableNames[i], shapeInfo };\n    });\n    const inShapeInfos = inputInfos.map(x => x.shapeInfo);\n    const outShapeInfo = {\n        logicalShape: output.shape,\n        texShape: output.texData.texShape,\n        isUniform: false,\n        isPacked: output.texData.isPacked,\n        flatOffset: null\n    };\n    const source = shader_compiler.makeShader(inputInfos, outShapeInfo, userCode, program.packedInputs);\n    const webGLProgram = gpgpu.createProgram(source);\n    // Add special uniforms (NAN, INFINITY)\n    let infLoc = null;\n    const nanLoc = gpgpu.getUniformLocation(webGLProgram, 'NAN', false);\n    if (env().getNumber('WEBGL_VERSION') === 1) {\n        infLoc = gpgpu.getUniformLocation(webGLProgram, 'INFINITY', false);\n    }\n    // Add user-defined uniforms\n    const uniformLocations = {};\n    for (let i = 0; i < program.variableNames.length; i++) {\n        const varName = program.variableNames[i];\n        const shouldThrow = false;\n        uniformLocations[varName] =\n            gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);\n        uniformLocations[`offset${varName}`] =\n            gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);\n    }\n    return {\n        program,\n        source,\n        webGLProgram,\n        uniformLocations,\n        inShapeInfos,\n        outShapeInfo,\n        infLoc,\n        nanLoc,\n    };\n}\nfunction validateBinaryAndProgram(shapeInfos, inputs) {\n    if (shapeInfos.length !== inputs.length) {\n        throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but ` +\n            `was executed with ${inputs.length} inputs`);\n    }\n    shapeInfos.forEach((s, i) => {\n        const shapeA = s.logicalShape;\n        const input = inputs[i];\n        const shapeB = input.shape;\n        if (!util.arraysEqual(shapeA, shapeB)) {\n            throw Error(`Binary was compiled with different shapes than ` +\n                `the current args. Shapes ${shapeA} and ${shapeB} must match`);\n        }\n        // The input is uploaded as uniform.\n        if (s.isUniform && input.isUniform) {\n            return;\n        }\n        const texShapeA = s.texShape;\n        const texShapeB = input.isUniform ? null : input.texData.texShape;\n        if (!util.arraysEqual(texShapeA, texShapeB)) {\n            throw Error(`Binary was compiled with different texture shapes than the` +\n                ` current args. Shape ${texShapeA} and ${texShapeB} must match`);\n        }\n    });\n}\nexport function runProgram(gpgpu, binary, inputs, output, customSetup) {\n    validateBinaryAndProgram(binary.inShapeInfos, inputs);\n    validateBinaryAndProgram([binary.outShapeInfo], [output]);\n    const outTex = output.texData.texture;\n    const outTexShape = output.texData.texShape;\n    if (output.texData.isPacked) {\n        gpgpu.setOutputPackedMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n    }\n    else {\n        gpgpu.setOutputMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n    }\n    gpgpu.setProgram(binary.webGLProgram);\n    // Set special uniforms (NAN, INFINITY)\n    if (env().getNumber('WEBGL_VERSION') === 1) {\n        if (binary.infLoc !== null) {\n            gpgpu.gl.uniform1f(binary.infLoc, Infinity);\n        }\n    }\n    if (binary.nanLoc !== null) {\n        gpgpu.gl.uniform1f(binary.nanLoc, NaN);\n    }\n    // Set user-defined inputs\n    inputs.forEach((input, i) => {\n        const varName = binary.program.variableNames[i];\n        const varLoc = binary.uniformLocations[varName];\n        const varOffsetLoc = binary.uniformLocations[`offset${varName}`];\n        if (varLoc == null) {\n            // The compiler inferred that this variable is not used in this shader.\n            return;\n        }\n        if (input.isUniform) {\n            // Upload the values of the tensor as uniform.\n            if (util.sizeFromShape(input.shape) < 2) {\n                gpgpu.gl.uniform1f(varLoc, input.uniformValues[0]);\n            }\n            else {\n                let vals = input.uniformValues;\n                if (!(vals instanceof Float32Array)) {\n                    vals = new Float32Array(vals);\n                }\n                gpgpu.gl.uniform1fv(varLoc, vals);\n            }\n            return;\n        }\n        // If the input was sliced, upload the flat offset index.\n        if (input.texData.slice != null && varOffsetLoc != null) {\n            gpgpu.gl.uniform1i(varOffsetLoc, input.texData.slice.flatOffset);\n        }\n        gpgpu.setInputMatrixTexture(input.texData.texture, varLoc, i);\n    });\n    if (customSetup != null) {\n        customSetup(gpgpu, binary.webGLProgram);\n    }\n    gpgpu.executeProgram();\n}\nexport function makeShaderKey(program, inputs, output) {\n    let keyInputs = '';\n    inputs.concat(output).forEach(x => {\n        const hasOffset = x.texData != null && x.texData.slice != null &&\n            x.texData.slice.flatOffset > 0;\n        const texShape = x.isUniform ? 'uniform' : x.texData.texShape;\n        keyInputs += `${x.shape}_${texShape}_${hasOffset}`;\n    });\n    const keyUserCode = program.userCode;\n    let key = program.constructor.name;\n    // Fast string concat. See https://jsperf.com/string-concatenation/14.\n    key += '_' + keyInputs + '_' + keyUserCode;\n    return key;\n}\n//# sourceMappingURL=gpgpu_math.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nexport class Im2ColPackedProgram {\n    constructor(outputShape, inputShape, convInfo) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        const { filterWidth, inChannels, strideWidth, strideHeight, padInfo, outWidth, dilationWidth, dilationHeight, dataFormat } = convInfo;\n        const { left, top } = padInfo;\n        const itemsPerBlockRow = inChannels * filterWidth;\n        const glsl = getGlslDifferences();\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const rowDim = isChannelsLast ? 0 : 1;\n        const colDim = isChannelsLast ? 1 : 2;\n        let unrolled = ``;\n        for (let row = 0; row <= 1; row++) {\n            for (let col = 0; col <= 1; col++) {\n                unrolled += `\n          blockIndex = rc.y + ${col};\n          pos = rc.x + ${row};\n\n          if(blockIndex < ${outputShape[1]} && pos < ${outputShape[0]}) {\n            offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${top};\n            d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});\n\n            if(d0 < ${inputShape[rowDim]} && d0 >= 0) {\n\n              offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${strideWidth}. - ${left}.);\n              d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${itemsPerBlockRow}.) / ${inChannels}.));\n\n              if(d1 < ${inputShape[colDim]} && d1 >= 0) {\n\n                ch = int(mod(float(pos), ${inChannels}.));\n\n                if (${isChannelsLast}) {\n                  innerDims = vec2(d1, ch);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;\n            }\n        }\n        this.userCode = `\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${unrolled}\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=im2col_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNProgram {\n    constructor(xShape, radius, bias, alpha, beta) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        const rad = radius;\n        const maxD = xShape[3] - 1;\n        this.outputShape = xShape;\n        // optimize pow(bias + alpha * sum, -beta)\n        // src: https://github.com/tensorflow/tensorflow/..\n        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n        // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n        let powOperator;\n        const basis = `float(${bias}) + float(${alpha}) * sum`;\n        if (beta === 0.5) {\n            powOperator = `inversesqrt(${basis})`;\n        }\n        else if (beta === 1.0) {\n            powOperator = `1.0/(${basis})`;\n        }\n        else {\n            powOperator = `exp(log(${basis}) * float(-${beta}));`;\n        }\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${rad}; j <= ${rad}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${maxD}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${powOperator};\n        setOutput(val);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNGradProgram {\n    constructor(inputShape, depthRadius, bias, alpha, beta) {\n        this.variableNames = ['inputImage', 'outputImage', 'dy'];\n        this.outputShape = [];\n        this.outputShape = inputShape;\n        this.depth = inputShape[3];\n        this.depthRadius = depthRadius;\n        this.bias = bias;\n        this.alpha = alpha;\n        this.beta = beta;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${depthRadius})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${depthRadius} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${alpha}) * norm + float(${bias});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${alpha})\n                * float(${beta})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${beta});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_grad_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNPackedProgram {\n    constructor(xShape, radius, bias, alpha, beta) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const rad = radius;\n        const maxD = xShape[3] - 1;\n        this.outputShape = xShape;\n        // optimize pow(bias + alpha * sum, -beta)\n        // src: https://github.com/tensorflow/tensorflow/..\n        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n        // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n        let powOperator;\n        const basis = `float(${bias}) + float(${alpha}) * sum`;\n        if (beta === 0.5) {\n            powOperator = `inversesqrt(${basis})`;\n        }\n        else if (beta === 1.0) {\n            powOperator = `1.0/(${basis})`;\n        }\n        else {\n            powOperator = `exp(log(${basis}) * float(-${beta}));`;\n        }\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${rad};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${rad}; j <= ${rad}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${powOperator};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MaxPool2DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'maxPos'];\n        this.outputShape = convInfo.inShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n          wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${effectiveFilterWidth} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class MaxPool3DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'maxPos'];\n        this.outputShape = convInfo.inShape;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n           wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${lastIndex} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                  wR * ${effectiveFilterWidth} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=max_pool_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MatMulPackedProgram {\n    constructor(aShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['matrixA', 'matrixB'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        const sharedDim = transposeA ? aShape[1] : aShape[2];\n        const sharedDimensionPacked = Math.ceil(sharedDim / 2);\n        const aSample = transposeA ? 'i * 2, rc.y' : 'rc.y, i * 2';\n        const bSample = transposeB ? 'rc.z, i * 2' : 'i * 2, rc.z';\n        const aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];\n        const bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const float sharedDimension = ${sharedDimensionPacked}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${sharedDimensionPacked}; i++) {\n          vec4 a = getMatrixA(rc.x, ${aSample});\n          vec4 b = getMatrixB(rc.x, ${bSample});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${aSwizzle[0]} * ${bSwizzle[0]});\n          result += (${aSwizzle[1]} * ${bSwizzle[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${addBiasSnippet}\n\n        ${applyActivationSnippet}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=mulmat_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MultinomialProgram {\n    constructor(batchSize, numOutcomes, numSamples) {\n        this.variableNames = ['probs'];\n        this.outputShape = [batchSize, numSamples];\n        this.userCode = `\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < ${numOutcomes - 1}; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(${numOutcomes - 1}));\n      }\n    `;\n    }\n    getCustomSetupFunc(seed) {\n        return (gpgpu, webGLProgram) => {\n            if (this.seedLoc == null) {\n                this.seedLoc = gpgpu.getUniformLocation(webGLProgram, 'seed');\n            }\n            gpgpu.gl.uniform1f(this.seedLoc, seed);\n        };\n    }\n}\n//# sourceMappingURL=multinomial_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class OneHotProgram {\n    constructor(numIndices, depth, onValue, offValue) {\n        this.variableNames = ['indices'];\n        this.outputShape = [numIndices, depth];\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${offValue}), float(${onValue}),\n                      float(index == coords.y)));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=onehot_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class PackProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        // Only input / output 3D tensors.\n        this.outputShape = outputShape;\n        const rank = outputShape.length;\n        if (rank === 0) {\n            this.userCode = `\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;\n        }\n        else {\n            const channels = getChannels('rc', rank);\n            const dtype = getCoordsDataType(rank);\n            const outOfBoundsCondition = getOutOfBoundsCondition(rank, outputShape, channels);\n            const setup = getSetup(rank, outputShape[outputShape.length - 1], outputShape[outputShape.length - 2], channels);\n            const output = getOutput(outputShape, channels);\n            this.userCode = `\n        void main() {\n          ${dtype} rc = getOutputCoords();\n\n          if(${outOfBoundsCondition}) {\n            setOutput(vec4(0));\n          } else {\n            ${setup}\n\n            setOutput(vec4(${output}));\n          }\n        }\n      `;\n        }\n    }\n}\nfunction getSourceCoordsArr(rank, dims) {\n    const coords = [];\n    for (let row = 0; row <= 1; row++) {\n        for (let col = 0; col <= 1; col++) {\n            let coord = `${row === 0 ? 'r' : 'rp1'}, ${col === 0 ? 'c' : 'cp1'}`;\n            for (let d = 2; d < rank; d++) {\n                coord = `${dims[dims.length - 1 - d]},` + coord;\n            }\n            coords.push(coord);\n        }\n    }\n    return coords;\n}\nfunction getOutOfBoundsCondition(rank, shape, dims) {\n    if (rank === 1) {\n        return `rc > ${shape[0]}`;\n    }\n    let cond = '';\n    for (let i = rank - 2; i < rank; i++) {\n        cond += `${dims[i]} >= ${shape[i]}`;\n        if (i < rank - 1) {\n            cond += '||';\n        }\n    }\n    return cond;\n}\nfunction getSetup(rank, cols, rows, dims) {\n    if (rank === 1) {\n        return '';\n    }\n    const innerDims = dims.slice(-2);\n    return `\n    int r = ${innerDims[0]};\n    int c = ${innerDims[1]};\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= ${cols};\n    bool rEdge = rp1 >= ${rows};\n  `;\n}\nfunction getOutput(shape, dims) {\n    const rank = shape.length;\n    const sourceCoords = getSourceCoordsArr(rank, dims);\n    if (rank === 1) {\n        return `getA(rc),\n            rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),\n            0, 0`;\n    }\n    return `getA(${sourceCoords[0]}),\n          cEdge ? 0. : getA(${sourceCoords[1]}),\n          rEdge ? 0. : getA(${sourceCoords[2]}),\n          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;\n}\n//# sourceMappingURL=pack_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class PadProgram {\n    constructor(xShape, paddings, constantValue) {\n        this.variableNames = ['x'];\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const type = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const unpackedCoords = ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);\n        if (rank === 1) {\n            this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float(${constantValue}));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `;\n            return;\n        }\n        this.userCode = `\n      ${type} start = ${type}(${start});\n      ${type} end = ${type}(${end});\n\n      void main() {\n        ${type} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float(${constantValue}));\n        } else {\n          ${type} coords = outC - start;\n          setOutput(getX(${unpackedCoords}));\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pad_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class PadPackedProgram {\n    constructor(xShape, paddings, constantValue) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const dtype = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const coords = getChannels('rc', rank);\n        const source = getChannels('source', rank);\n        const cLimit = `${coords[rank - 1]} < ${this.outputShape[rank - 1]}`;\n        const innerDims = rank === 1 ? 'source' : `vec2(${source.slice(-2).join()})`;\n        const componentSetup = [\n            `${dtype} rc = outputLoc;`, `${coords[rank - 1]} += 1;\n       if(${cLimit}) {\n      `,\n            rank === 1 ? '' : `}\n       rc = outputLoc;\n       ${coords[rank - 2]} += 1;\n       if(${coords[rank - 2]} < ${this.outputShape[rank - 2]}) {`,\n            rank === 1 ? '' : `  ${coords[rank - 1]} += 1;\n         if(${cLimit}) {`\n        ];\n        const paddingArea = rank === 1 ?\n            'rc < start || rc >= end' :\n            'any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))';\n        let mainLoop = '';\n        for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {\n            mainLoop += `\n        ${componentSetup[i]}\n        if (${paddingArea}) {\n          result[${i}] = float(${constantValue});\n        } else {\n          ${dtype} source = rc - start;\n          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n        }\n        mainLoop += (rank === 1 ? `} ` : `}}`);\n        this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pad_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Pool2DProgram {\n    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n        this.variableNames = ['x'];\n        if (poolType === 'avg' && computePositions) {\n            throw new Error('Cannot compute positions for average pool.');\n        }\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.outputShape = convInfo.outShape;\n        const isAvgPool = poolType === 'avg';\n        const batchFlattenPositionStr = `((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n        const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n        let initializationValue = '0.0';\n        if (!isAvgPool) {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n        }\n        if (computePositions) {\n            const compareOp = '>=';\n            this.userCode = `\n        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n        const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${compareOp} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${flattenPositions ? (includeBatchInIndex ? batchFlattenPositionStr :\n                flattenPositionStr) :\n                `wR * ${effectiveFilterWidth} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n            return;\n        }\n        const compareOp = 'max';\n        let returnValue = `${poolType}(${poolType}(${poolType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (poolType === 'avg') {\n            returnValue = `avgValue / count`;\n        }\n        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n        const filterWidthVec4Remainder = filterWidth % 4;\n        const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)\n            );\n\n            ${updateSnippet}\n          }\n\n          int xC = xCCorner + ${filterWidthNearestVec4};\n          if (${filterWidthVec4Remainder === 1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              initializationValue\n            );\n\n            ${updateSnippet}\n          }\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\nexport class Pool3DProgram {\n    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n        this.variableNames = ['x'];\n        if (poolType === 'avg' && computePositions) {\n            throw new Error('Cannot compute positions for average pool.');\n        }\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.outputShape = convInfo.outShape;\n        const isAvgPool = poolType === 'avg';\n        let initializationValue = '0.0';\n        if (!isAvgPool) {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n        }\n        if (computePositions) {\n            const compareOp = '>=';\n            this.userCode = `\n        const ivec3 strides =\n            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${effectiveFilterDepth};\n              wD += ${dilationDepth}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${effectiveFilterHeight};\n                wR += ${dilationHeight}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${effectiveFilterWidth};\n                  wC += ${dilationWidth}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${compareOp} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${flattenPositions ?\n                (includeBatchInIndex ?\n                    `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` :\n                    `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch`) :\n                `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                      wR * ${effectiveFilterWidth} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n            return;\n        }\n        const compareOp = 'max';\n        let returnValue = `${poolType}(${poolType}(${poolType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (poolType === 'avg') {\n            returnValue = `avgValue / count`;\n        }\n        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n        const filterWidthVec4Remainder = filterWidth % 4;\n        const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        this.userCode = `\n      const ivec3 strides =\n        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)\n              );\n\n              ${updateSnippet}\n            }\n\n            int xC = xCCorner + ${filterWidthNearestVec4};\n            if (${filterWidthVec4Remainder === 1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                initializationValue\n              );\n\n              ${updateSnippet}\n            }\n          }\n          setOutput(${returnValue});\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pool_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ReduceProgram {\n    constructor(reduceInfo, reduceType) {\n        this.variableNames = ['x'];\n        const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n        this.outputShape = [batchSize, outSize];\n        let initializationValue = '0.0';\n        let compareOp = ``;\n        if (reduceType === 'prod') {\n            initializationValue = '1.0';\n        }\n        else if (reduceType === 'min') {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '1.0 / 1e-20';\n            compareOp = `min`;\n        }\n        else if (reduceType === 'max') {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n            compareOp = `max`;\n        }\n        let returnValue = `${reduceType}(${reduceType}(${reduceType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (reduceType === 'sum') {\n            returnValue = `sumValue`;\n        }\n        else if (reduceType === 'prod') {\n            returnValue = `prodValue`;\n        }\n        else if (reduceType === 'all') {\n            returnValue = `allValue`;\n        }\n        else if (reduceType === 'any') {\n            returnValue = `anyValue`;\n        }\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        let updateSnippet = `\n      if (${reduceType === 'sum'}) {\n        sumValue += dot(values, ones);\n      } else if (${reduceType === 'prod'}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        let vecType = `vec4`;\n        if (reduceType === 'all') {\n            initializationValue = '1.0';\n            updateSnippet = `\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `;\n            vecType = `bvec4`;\n        }\n        else if (reduceType === 'any') {\n            initializationValue = '0.0';\n            updateSnippet = `\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `;\n            vecType = `bvec4`;\n        }\n        let checkOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n        }\n        this.userCode = `\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        vec4 minMaxValue = vec4(${initializationValue});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\n//# sourceMappingURL=reduce_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as shader_util from './shader_compiler_util';\nexport class ReshapePackedProgram {\n    constructor(outputShape, inputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        let mainLoop = ``;\n        for (let i = 0; i < 4; i++) {\n            let thisRC = `thisRC = rc;`;\n            if (i % 2 === 1) {\n                thisRC += `thisRC.z += 1;`;\n            }\n            if (i > 1) {\n                thisRC += `thisRC.y += 1;`;\n            }\n            mainLoop += `\n        ${thisRC}\n        ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ''}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${i}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${i > 0 ? '}' : ''}\n      `;\n        }\n        this.userCode = `\n      ${getReshapedInputCoords(inputShape)}\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${outputShape[1]};\n        int cols = ${outputShape[2]};\n\n        ${mainLoop}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\nfunction getReshapedInputCoords(shape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n    return `\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\n//# sourceMappingURL=reshape_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearBackpropProgram {\n    constructor(dy, x, alignCorners) {\n        this.variableNames = ['dy'];\n        this.outputShape = [];\n        this.outputShape = x.shape;\n        const [, xHeight, xWidth,] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        // In the backwards pass, we want to find the pixels that were generated for\n        // each pixel in the input image the forward pass and add the corresponding\n        // coefficient from dy to the gradient (with some interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearPackedProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        this.userCode = `\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,\n                                     ${oldWidth}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${depth - 1};\n        bool hasNextRow = coords.z < ${newWidth - 1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeNearestNeigborBackpropProgram {\n    constructor(dy, x, alignCorners) {\n        this.variableNames = ['dy'];\n        this.outputShape = [];\n        this.outputShape = x.shape;\n        const [, xHeight, xWidth,] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        // In the backwards pass, we want to find the pixels that were generated for\n        // each pixel in the input image the forward pass and add the corresponding\n        // coefficient from dy to the gradient (with some interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${effectiveXSize[0]}) *\n                (float(dyR) / float(${effectiveYSize[0]}));\n\n            float sourceFracCol =\n                float(${effectiveXSize[1]}) *\n                  (float(dyC) / float(${effectiveYSize[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${xHeight}) - 1),\n                ${alignCorners} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${xWidth}) - 1),\n                ${alignCorners} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_nearest_neighbor_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeNearestNeighborProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        // When align corners is false, we rounds the value with floor.\n        const roundBase = alignCorners ? '0.5' : '0.0';\n        this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_nearest_neighbor_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class ReverseProgram {\n    constructor(xShape, axis) {\n        this.variableNames = ['x'];\n        const rank = xShape.length;\n        if (rank > 4) {\n            throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n        }\n        this.outputShape = xShape;\n        if (rank === 1) {\n            this.userCode = `\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${xShape[0]} - coord - 1));\n        }\n      `;\n            return;\n        }\n        const getInCoord = (i) => {\n            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n                return `${xShape[i]} - coords[${i}] - 1`;\n            }\n            return `coords[${i}]`;\n        };\n        const inCoords = xShape.map((_, i) => getInCoord(i)).join(',');\n        const type = getCoordsDataType(rank);\n        this.userCode = `\n      void main() {\n        ${type} coords = getOutputCoords();\n        setOutput(getX(${inCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=reverse_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ReversePackedProgram {\n    constructor(xShape, axis) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const rank = xShape.length;\n        if (rank > 4) {\n            throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n        }\n        this.outputShape = xShape;\n        const channels = getChannels('rc', rank);\n        const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;\n        const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;\n        const type = getCoordsDataType(rank);\n        if (rank === 1) {\n            this.userCode = `\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${xShape[0]} - rc - 1),\n            ${xShape[0]} - rc - 1);\n          if(${nextColumn}){\n              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),\n                ${xShape[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `;\n        }\n        else {\n            this.userCode = `\n        void main() {\n          ${type} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${getR(channels.slice())};\n          if(${nextColumn}){\n            result.g = ${getG(channels.slice())};\n          }\n          if(${nextRow}) {\n            result.b = ${getB(channels.slice())};\n            if(${nextColumn}) {\n              result.a = ${getA(channels.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `;\n        }\n        function getR(channels) {\n            return getChannel(channels);\n        }\n        function getG(channels) {\n            channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getB(channels) {\n            channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getA(channels) {\n            channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n            channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getChannel(channels) {\n            const inCoordsArray = xShape.map((_, i) => getInCoord(i, channels));\n            const inCoords = inCoordsArray.join(',');\n            const innerDims = inCoordsArray.slice(-2).join(',');\n            return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;\n        }\n        function getInCoord(i, channels1) {\n            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n                return `${xShape[i]} - ${channels1[i]} - 1`;\n            }\n            else {\n                return `${channels1[i]}`;\n            }\n        }\n    }\n}\n//# sourceMappingURL=reverse_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class ScatterProgram {\n    constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true) {\n        this.variableNames = ['updates', 'indices', 'defaultValue'];\n        this.outputShape = shape;\n        const stridesType = getCoordsDataType(strides.length);\n        const dtype = getCoordsDataType(shape.length);\n        let indicesString = '';\n        if (indicesRank === 1) {\n            indicesString = 'i';\n        }\n        else if (indicesRank === 2) {\n            indicesString = 'i, j';\n        }\n        const indicesSnippet = `getIndices(${indicesString})`;\n        let updatesString = '';\n        if (updatesRank === 1) {\n            updatesString = 'i';\n        }\n        else if (updatesRank === 2) {\n            updatesString = 'i, coords[1]';\n        }\n        const updatesSnippet = `getUpdates(${updatesString})`;\n        const strideString = sliceDim > 1 ? 'strides[j]' : 'strides';\n        this.userCode = `\n        ${stridesType} strides = ${stridesType}(${strides});\n\n        void main() {\n          ${dtype} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${updateSize}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${sliceDim}; j++) {\n              int index = round(${indicesSnippet});\n              flattenedIndex += index * ${strideString};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${updatesSnippet};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `;\n    }\n}\n//# sourceMappingURL=scatter_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class SegmentOpProgram {\n    constructor(segOpInfo, segOpType) {\n        this.variableNames = ['x', 'segmentIds'];\n        const windowSize = segOpInfo.windowSize;\n        const batchSize = segOpInfo.batchSize;\n        const inSize = segOpInfo.inSize;\n        const numSegments = segOpInfo.numSegments;\n        const outSize = numSegments * Math.ceil(inSize / windowSize);\n        this.outputShape = [batchSize, outSize];\n        const initializationValue = '0.0';\n        const returnValue = `sumValue`;\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        const updateSnippet = `\n        sumValue += dot(values, segFilter);\n    `;\n        let checkValueOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkValueOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n        }\n        let checkSegmentIdOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkSegmentIdOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return -1.0;\n        }\n      `;\n        }\n        this.userCode = `\n      const float initializationValue = ${initializationValue};\n\n      float getValue(int batch, int inIdx) {\n        ${checkValueOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${checkSegmentIdOutOfBounds}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${numSegments})) * float(${windowSize}));\n        int currentSeg = int(mod(float(outIdx), float(${numSegments})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\n//# sourceMappingURL=segment_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class SelectProgram {\n    constructor(cRank, shape, rank) {\n        this.variableNames = ['c', 'a', 'b'];\n        this.outputShape = shape;\n        let cCoords;\n        let abCoords;\n        if (rank > 4) {\n            throw Error(`Where for rank ${rank} is not yet supported`);\n        }\n        if (rank === 1) {\n            abCoords = `resRC`;\n            cCoords = `resRC`;\n        }\n        else {\n            const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n            const cCoordVars = [];\n            const abCoordVars = [];\n            for (let i = 0; i < shape.length; i++) {\n                abCoordVars.push(`${currentCoords[i]}`);\n                if (i < cRank) {\n                    cCoordVars.push(`${currentCoords[i]}`);\n                }\n            }\n            cCoords = cCoordVars.join();\n            abCoords = abCoordVars.join();\n        }\n        const dtype = getCoordsDataType(rank);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        float cVal = getC(${cCoords});\n        if (cVal >= 1.0) {\n          setOutput(getA(${abCoords}));\n        } else {\n          setOutput(getB(${abCoords}));\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=select_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class SliceProgram {\n    constructor(destSize) {\n        this.variableNames = ['source'];\n        this.outputShape = destSize;\n        this.rank = destSize.length;\n        const dtype = getCoordsDataType(this.rank);\n        const uniformPart = `uniform int start[${this.rank}];`;\n        const sourceCoords = getCoords(this.rank);\n        let body;\n        const coordSum = destSize.map((_, i) => {\n            return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`;\n        });\n        body = `\n        ${dtype} sourceLoc;\n        ${dtype} coords = getOutputCoords();\n        ${coordSum.join('\\n')}\n      `;\n        this.userCode = `\n      ${uniformPart}\n      void main() {\n        ${body}\n        setOutput(getSource(${sourceCoords}));\n      }\n    `;\n    }\n    getCustomSetupFunc(start) {\n        if (start.length !== this.rank) {\n            throw Error(`The rank (${this.rank}) of the program must match the ` +\n                `length of start (${start.length})`);\n        }\n        return (gpgpu, webGLProgram) => {\n            if (this.startLoc == null) {\n                this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n                if (this.startLoc == null) {\n                    // This means the compiler has optimized and realized it doesn't need\n                    // the uniform.\n                    return;\n                }\n            }\n            gpgpu.gl.uniform1iv(this.startLoc, start);\n        };\n    }\n}\nconst coords = ['x', 'y', 'z', 'w', 'u', 'v'];\nfunction getCoords(rank) {\n    if (rank === 1) {\n        return 'sourceLoc';\n    }\n    else if (rank <= 6) {\n        return coords.slice(0, rank).map(x => 'sourceLoc.' + x).join(',');\n    }\n    else {\n        throw Error(`Slicing for rank ${rank} is not yet supported`);\n    }\n}\n//# sourceMappingURL=slice_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class SlicePackedProgram {\n    constructor(destSize) {\n        this.variableNames = ['source'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = destSize;\n        this.rank = destSize.length;\n        const dtype = getCoordsDataType(this.rank);\n        const coords = getChannels('coords', this.rank);\n        const sourceLoc = getChannels('sourceLoc', this.rank);\n        const innerDims = this.rank === 1 ? 'sourceLoc' : `vec2(${sourceLoc.slice(-2).join()})`;\n        const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;\n        const upperRow = `\n      result.x = ${getChannel};\n      if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n        ++${sourceLoc[this.rank - 1]};\n        result.y = ${getChannel};\n        --${sourceLoc[this.rank - 1]};\n      }\n    `;\n        const lowerRow = this.rank === 1 ? '' : `\n      --${coords[this.rank - 1]};\n      if (++${coords[this.rank - 2]} < ${destSize[this.rank - 2]}) {\n        ++${sourceLoc[this.rank - 2]};\n        result.z = ${getChannel};\n        if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n          ++${sourceLoc[this.rank - 1]};\n          result.w = ${getChannel};\n        }\n      }\n    `;\n        const sourceLocSetup = this.rank <= 4 ?\n            `sourceLoc = coords +\n            ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` :\n            destSize.map((_, i) => `${sourceLoc[i]} = ${coords[i]} + start[${i}];`)\n                .join('\\n');\n        this.userCode = `\n      uniform int start[${this.rank}];\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        ${dtype} sourceLoc;\n        ${sourceLocSetup}\n        vec4 result = vec4(0.);\n        ${upperRow}\n        ${lowerRow}\n        setOutput(result);\n      }\n    `;\n    }\n    getCustomSetupFunc(start) {\n        if (start.length !== this.rank) {\n            throw Error(`The rank (${this.rank}) of the program must match the ` +\n                `length of start (${start.length})`);\n        }\n        return (gpgpu, webGLProgram) => {\n            if (this.startLoc == null) {\n                this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n                if (this.startLoc == null) {\n                    // This means the compiler has optimized and realized it doesn't need\n                    // the uniform.\n                    return;\n                }\n            }\n            gpgpu.gl.uniform1iv(this.startLoc, start);\n        };\n    }\n}\n//# sourceMappingURL=slice_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class StridedSliceProgram {\n    constructor(begin, strides, size) {\n        this.variableNames = ['x'];\n        this.outputShape = size;\n        const rank = size.length;\n        const inputDtype = getCoordsDataType(size.length);\n        const dtype = getCoordsDataType(size.length);\n        let newCoords = '';\n        if (rank === 1) {\n            newCoords = 'coords * strides + begin';\n        }\n        else {\n            let outputAxis = 0;\n            newCoords =\n                size.map((_, i) => {\n                    outputAxis++;\n                    return size.length === 1 ?\n                        `coords * strides[${i}] + begin[${i}]` :\n                        `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;\n                })\n                    .join(',');\n        }\n        this.userCode = `\n      ${inputDtype} begin = ${inputDtype}(${begin});\n      ${inputDtype} strides = ${inputDtype}(${strides});\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        setOutput(getX(${newCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=strided_slice_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { getInternalFormatForFloat16MatrixTexture, getInternalFormatForFloat16PackedMatrixTexture, getInternalFormatForFloat32MatrixTexture, getInternalFormatForPackedMatrixTexture, getInternalFormatForUnsignedBytesMatrixTexture } from './gpgpu_util';\nimport { getPackedMatrixTextureShapeWidthHeight, getUnpackedMatrixTextureShapeWidthHeight, PhysicalTextureType, TextureUsage } from './tex_util';\nexport class TextureManager {\n    constructor(gpgpu) {\n        this.gpgpu = gpgpu;\n        this.numUsedTextures = 0;\n        this.numFreeTextures = 0;\n        this._numBytesAllocated = 0;\n        this._numBytesFree = 0; // How many bytes that have been allocated\n        // are available for reuse.\n        this.freeTextures = {};\n        this.logEnabled = false;\n        this.usedTextures = {};\n    }\n    acquireTexture(shapeRC, usage, isPacked) {\n        const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);\n        const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);\n        if (!(shapeKey in this.freeTextures)) {\n            this.freeTextures[shapeKey] = [];\n        }\n        if (!(shapeKey in this.usedTextures)) {\n            this.usedTextures[shapeKey] = [];\n        }\n        const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n        if (this.freeTextures[shapeKey].length > 0) {\n            this.numFreeTextures--;\n            this.numUsedTextures++;\n            this._numBytesFree -= texBytes;\n            this.log();\n            const newTexture = this.freeTextures[shapeKey].shift();\n            this.usedTextures[shapeKey].push(newTexture);\n            return newTexture;\n        }\n        let newTexture;\n        if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {\n            newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {\n            newTexture =\n                this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {\n            newTexture =\n                this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {\n            newTexture =\n                this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {\n            newTexture =\n                this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        this.usedTextures[shapeKey].push(newTexture);\n        this.numUsedTextures++;\n        this._numBytesAllocated += texBytes;\n        this.log();\n        return newTexture;\n    }\n    releaseTexture(texture, shape, logicalTexType, isPacked) {\n        if (this.freeTextures == null) {\n            // Already disposed.\n            return;\n        }\n        const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);\n        const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);\n        if (!(shapeKey in this.freeTextures)) {\n            this.freeTextures[shapeKey] = [];\n        }\n        const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n        const deleteTexThreshold = env().get('WEBGL_DELETE_TEXTURE_THRESHOLD');\n        if (deleteTexThreshold !== -1 &&\n            this._numBytesAllocated > deleteTexThreshold) {\n            this.gpgpu.deleteMatrixTexture(texture);\n            this._numBytesAllocated -= texBytes;\n        }\n        else {\n            this.freeTextures[shapeKey].push(texture);\n            this.numFreeTextures++;\n            this._numBytesFree += texBytes;\n        }\n        this.numUsedTextures--;\n        const texList = this.usedTextures[shapeKey];\n        const texIndex = texList.indexOf(texture);\n        if (texIndex < 0) {\n            throw new Error('Cannot release a texture that was never provided by this ' +\n                'texture manager');\n        }\n        texList.splice(texIndex, 1);\n        this.log();\n    }\n    log() {\n        if (!this.logEnabled) {\n            return;\n        }\n        const total = this.numFreeTextures + this.numUsedTextures;\n        console.log('Free/Used', `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`);\n        const freeRatio = this._numBytesFree / this._numBytesAllocated;\n        console.log(`Bytes allocated: ${this._numBytesAllocated}`);\n        console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`);\n    }\n    get numBytesAllocated() {\n        return this._numBytesAllocated;\n    }\n    get numBytesFree() {\n        return this._numBytesFree;\n    }\n    getNumUsedTextures() {\n        return this.numUsedTextures;\n    }\n    getNumFreeTextures() {\n        return this.numFreeTextures;\n    }\n    dispose() {\n        if (this.freeTextures == null) {\n            // Already disposed.\n            return;\n        }\n        for (const texShape in this.freeTextures) {\n            this.freeTextures[texShape].forEach(tex => {\n                this.gpgpu.deleteMatrixTexture(tex);\n            });\n        }\n        for (const texShape in this.usedTextures) {\n            this.usedTextures[texShape].forEach(tex => {\n                this.gpgpu.deleteMatrixTexture(tex);\n            });\n        }\n        this.freeTextures = null;\n        this.usedTextures = null;\n        this.numUsedTextures = 0;\n        this.numFreeTextures = 0;\n        this._numBytesAllocated = 0;\n        this._numBytesFree = 0;\n    }\n}\nfunction numBytesForInternalFormat(gl, internalFormat) {\n    // tslint:disable-next-line:no-any\n    const glany = gl;\n    if (internalFormat === glany.R32F) {\n        return 4;\n    }\n    else if (internalFormat === glany.R16F) {\n        return 2;\n    }\n    else if (internalFormat === glany.RGBA32F) {\n        return 16;\n    }\n    else if (internalFormat === gl.RGBA) {\n        return 16;\n    }\n    else if (internalFormat === glany.RGBA16F) {\n        return 8;\n    }\n    throw new Error(`Unknown internal format ${internalFormat}`);\n}\nexport function computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) {\n    // It is not possible to infer packed status from the texture type because\n    // depending on the textureConfig, different  texture types may resolve to the\n    // same internal format (e.g. in WebGL1, the internal format for\n    // UNPACKED_FLOAT16 textures is gl.RGBA). Therefore we pass in `isPacked`\n    // explicitly.\n    const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig);\n    let numElements;\n    if (isPacked) {\n        const [packedWidth, packedHeight] = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n        numElements = packedWidth * packedHeight;\n    }\n    else {\n        const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n        numElements = width * height;\n    }\n    const bytesPerElement = numBytesForInternalFormat(gl, internalFormat);\n    return numElements * bytesPerElement;\n}\nfunction internalFormatForPhysicalTexType(physicalTexType, textureConfig) {\n    switch (physicalTexType) {\n        case PhysicalTextureType.PACKED_2X2_FLOAT32:\n            return getInternalFormatForPackedMatrixTexture(textureConfig);\n        case PhysicalTextureType.PACKED_2X2_FLOAT16:\n            return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);\n        case PhysicalTextureType.UNPACKED_FLOAT32:\n            return getInternalFormatForFloat32MatrixTexture(textureConfig);\n        case PhysicalTextureType.UNPACKED_FLOAT16:\n            return getInternalFormatForFloat16MatrixTexture(textureConfig);\n        case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:\n            return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);\n        default:\n            throw new Error(`Unknown physical texture type ${physicalTexType}`);\n    }\n}\nfunction getPhysicalTextureForRendering(isPacked) {\n    if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED')) {\n        if (isPacked) {\n            return PhysicalTextureType.PACKED_2X2_FLOAT32;\n        }\n        return PhysicalTextureType.UNPACKED_FLOAT32;\n    }\n    if (isPacked) {\n        return PhysicalTextureType.PACKED_2X2_FLOAT16;\n    }\n    return PhysicalTextureType.UNPACKED_FLOAT16;\n}\nfunction getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {\n    if (logicalTexType === TextureUsage.UPLOAD) {\n        return PhysicalTextureType.PACKED_2X2_FLOAT32;\n    }\n    else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {\n        return getPhysicalTextureForRendering(isPacked);\n    }\n    else if (logicalTexType === TextureUsage.DOWNLOAD ||\n        logicalTexType === TextureUsage.PIXELS) {\n        return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;\n    }\n    throw new Error(`Unknown logical texture type ${logicalTexType}`);\n}\nfunction getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {\n    return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;\n}\n//# sourceMappingURL=texture_manager.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class TileProgram {\n    constructor(aShape, reps) {\n        this.variableNames = ['A'];\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[i] * reps[i];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const sourceCoords = getSourceCoords(aShape);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n    }\n}\nfunction getSourceCoords(aShape) {\n    const rank = aShape.length;\n    if (rank > 5) {\n        throw Error(`Tile for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n        return `imod(resRC, ${aShape[0]})`;\n    }\n    const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u'];\n    const sourceCoords = [];\n    for (let i = 0; i < aShape.length; i++) {\n        sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);\n    }\n    return sourceCoords.join();\n}\n//# sourceMappingURL=tile_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class UnaryOpProgram {\n    constructor(aShape, opSnippet) {\n        this.variableNames = ['A'];\n        this.outputShape = aShape;\n        this.userCode = `\n      float unaryOperation(float x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n    }\n}\nconst CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;\nexport const LINEAR = `return x;`;\nexport const ABS = `return abs(x);`;\nexport const RELU = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\nexport const RELU6 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`;\nexport const ELU = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\nexport const SELU = `\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${backend_util.SELU_SCALEALPHA};\n  float scale = ${backend_util.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`;\nexport function STEP(alpha = 0.0) {\n    return CHECK_NAN_SNIPPET + `\n    return x > 0.0 ? 1.0 : float(${alpha});\n  `;\n}\nexport const NEG = `return -x;`;\nexport const CEIL = `return ceil(x);`;\nexport const FLOOR = `return floor(x);`;\nexport const SIGN = `\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n`;\nexport const IS_NAN = `return float(isnan(x));`;\nexport const IS_INF = `return float(isinf(x));`;\nexport const IS_FINITE = `return float(!isnan(x) && !isinf(x));`;\nexport const ROUND = `\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`;\nexport const EXP = `return exp(x);`;\nexport const EXPM1 = `return exp(x) - 1.0;`;\nexport const LOG = `if (x < 0.0) return NAN;\n  return log(x);`;\nexport const LOG1P = `return log(1.0 + x);`;\nexport const SQRT = `return sqrt(x);`;\nexport const RSQRT = `return inversesqrt(x);`;\nexport const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n/**\n * mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n *\n * epsilon is the difference between 1.0 and the next representable\n * float. For a single precision 32 bit float this should be 2^-23, see:\n * https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\n *\n * too_large = (x > -threshold) is value above which exp(x) may overflow\n * but softplus(x) == x is within machine epsilon\n *\n * too_small = (x < threshold) is value below which exp(x) may underflow,\n * but softplus(x) == exp(x) is within machine epsilon.\n */\nexport const SOFTPLUS = `\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`;\nexport const ASIN = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`;\nexport const ACOS = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`;\nexport const ATAN = CHECK_NAN_SNIPPET + `\n  return atan(x);\n`;\nexport const SINH = `\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`;\nexport const COSH = `\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`;\nexport const TANH = `\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`;\nexport const ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`;\nexport const ACOSH = CHECK_NAN_SNIPPET + `\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));`;\nexport const ATANH = CHECK_NAN_SNIPPET + `\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;\nexport const ERF = `\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = ${backend_util.ERF_P};\n  float a1 = ${backend_util.ERF_A1};\n  float a2 = ${backend_util.ERF_A2};\n  float a3 = ${backend_util.ERF_A3};\n  float a4 = ${backend_util.ERF_A4};\n  float a5 = ${backend_util.ERF_A5};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`;\nexport const RECIPROCAL = `return 1.0 / x;`;\nexport const LOGICAL_NOT = `return float(!(x >= 1.0));`;\nexport const TO_INT = `return float(int(x));`;\nexport const CLONE = 'return x;';\n//# sourceMappingURL=unaryop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const LINEAR = `return x;`;\nexport const LOG = `\n  vec4 result = log(x);\n  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\n  result.r = isNaN.r == 1.0 ? NAN : result.r;\n  result.g = isNaN.g == 1.0 ? NAN : result.g;\n  result.b = isNaN.b == 1.0 ? NAN : result.b;\n  result.a = isNaN.a == 1.0 ? NAN : result.a;\n\n  return result;\n`;\nexport const RELU = `\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nexport const RELU6 = `\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nexport const ELU = `\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`;\nexport class UnaryOpPackedProgram {\n    constructor(aShape, opSnippet) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = aShape;\n        this.userCode = `\n      vec4 unaryOperation(vec4 x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=unaryop_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels, getSourceCoords } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class UnpackProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = false;\n        this.outputShape = outputShape;\n        const rank = outputShape.length;\n        const channels = getChannels('rc', rank);\n        const dtype = getCoordsDataType(rank);\n        const sourceCoords = getSourceCoords(rank, channels);\n        const innerDims = channels.slice(-2);\n        const coords = rank <= 1 ? 'rc' : `vec2(${innerDims.join(',')})`;\n        this.userCode = `\n      void main() {\n        ${dtype} rc = getOutputCoords();\n        vec4 packedInput = getA(${sourceCoords});\n\n        setOutput(getChannel(packedInput, ${coords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=unpack_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Import webgl flags.\nimport './flags_webgl';\nimport * as tf from '@tensorflow/tfjs-core';\nimport { complex, div, engine, env, imag, max, range, real, reshape, scalar, softmax, tensor, tidy, transpose } from '@tensorflow/tfjs-core';\nimport { backend_util, buffer, kernel_impls, slice_util, util } from '@tensorflow/tfjs-core';\nimport { DataStorage, KernelBackend, upcastType } from '@tensorflow/tfjs-core';\nimport { addImplCPU, ceilImplCPU, expImplCPU, expm1ImplCPU, floorImplCPU, logImplCPU, multiplyImplCPU, rsqrtImplCPU, simpleAbsImplCPU, sliceImplCPU, subImplCPU } from './kernel_utils/shared';\nconst { segment_util } = backend_util;\nconst split = kernel_impls.split;\nconst tile = kernel_impls.tile;\nconst topkImpl = kernel_impls.topkImpl;\nconst whereImpl = kernel_impls.whereImpl;\nimport { AddNProgram } from './addn_gpu';\nimport { AddNPackedProgram } from './addn_packed_gpu';\nimport { ArgMinMaxProgram } from './argminmax_gpu';\nimport { ArgMinMaxPackedProgram } from './argminmax_packed_gpu';\nimport { AvgPool3DBackpropProgram } from './avg_pool_backprop_gpu';\nimport * as binaryop_complex_gpu from './binaryop_complex_gpu';\nimport { BinaryOpComplexProgram } from './binaryop_complex_gpu';\nimport * as binaryop_gpu from './binaryop_gpu';\nimport { BinaryOpProgram } from './binaryop_gpu';\nimport * as binaryop_packed_gpu from './binaryop_packed_gpu';\nimport { BinaryOpPackedProgram } from './binaryop_packed_gpu';\nimport { getWebGLContext } from './canvas_util';\nimport { ClipProgram } from './clip_gpu';\nimport { ClipPackedProgram } from './clip_packed_gpu';\nimport { ComplexAbsProgram } from './complex_abs_gpu';\nimport { ConcatProgram } from './concat_gpu';\nimport { ConcatPackedProgram } from './concat_packed_gpu';\nimport { Conv2DDerFilterProgram, Conv2DDerInputProgram, Conv3DDerFilterProgram, Conv3DDerInputProgram } from './conv_backprop_gpu';\nimport { DepthwiseConv2DDerFilterProgram, DepthwiseConv2DDerInputProgram } from './conv_backprop_gpu_depthwise';\nimport { Conv2DProgram, Conv3DProgram } from './conv_gpu';\nimport { DepthwiseConv2DProgram } from './conv_gpu_depthwise';\nimport { DepthwiseConvPacked2DProgram } from './conv_packed_gpu_depthwise';\nimport { CropAndResizeProgram } from './crop_and_resize_gpu';\nimport { CumSumProgram } from './cumsum_gpu';\nimport { DecodeMatrixProgram } from './decode_matrix_gpu';\nimport { DecodeMatrixPackedProgram } from './decode_matrix_packed_gpu';\nimport { DepthToSpaceProgram } from './depth_to_space_gpu';\nimport { DiagProgram } from './diag_gpu';\nimport { EncodeFloatProgram } from './encode_float_gpu';\nimport { EncodeFloatPackedProgram } from './encode_float_packed_gpu';\nimport { EncodeMatrixProgram } from './encode_matrix_gpu';\nimport { EncodeMatrixPackedProgram } from './encode_matrix_packed_gpu';\nimport * as fft_gpu from './fft_gpu';\nimport { FFTProgram } from './fft_gpu';\nimport { FillProgram } from './fill_gpu';\nimport { GatherProgram } from './gather_gpu';\nimport { GatherNDProgram } from './gather_nd_gpu';\nimport { GPGPUContext } from './gpgpu_context';\nimport * as gpgpu_math from './gpgpu_math';\nimport { Im2ColPackedProgram } from './im2col_packed_gpu';\nimport { LRNProgram } from './lrn_gpu';\nimport { LRNGradProgram } from './lrn_grad_gpu';\nimport { LRNPackedProgram } from './lrn_packed_gpu';\nimport { MaxPool3DBackpropProgram } from './max_pool_backprop_gpu';\nimport { MatMulPackedProgram } from './mulmat_packed_gpu';\nimport { MultinomialProgram } from './multinomial_gpu';\nimport { OneHotProgram } from './onehot_gpu';\nimport { PackProgram } from './pack_gpu';\nimport { PadProgram } from './pad_gpu';\nimport { PadPackedProgram } from './pad_packed_gpu';\nimport { Pool3DProgram } from './pool_gpu';\nimport { ReduceProgram } from './reduce_gpu';\nimport { ReshapePackedProgram } from './reshape_packed_gpu';\nimport { ResizeBilinearBackpropProgram } from './resize_bilinear_backprop_gpu';\nimport { ResizeBilinearProgram } from './resize_bilinear_gpu';\nimport { ResizeBilinearPackedProgram } from './resize_bilinear_packed_gpu';\nimport { ResizeNearestNeigborBackpropProgram } from './resize_nearest_neighbor_backprop_gpu';\nimport { ResizeNearestNeighborProgram } from './resize_nearest_neighbor_gpu';\nimport { ReverseProgram } from './reverse_gpu';\nimport { ReversePackedProgram } from './reverse_packed_gpu';\nimport { ScatterProgram } from './scatter_gpu';\nimport { SegmentOpProgram } from './segment_gpu';\nimport { SelectProgram } from './select_gpu';\nimport { SliceProgram } from './slice_gpu';\nimport { SlicePackedProgram } from './slice_packed_gpu';\nimport { StridedSliceProgram } from './strided_slice_gpu';\nimport * as tex_util from './tex_util';\nimport { TextureUsage } from './tex_util';\nimport { TextureManager } from './texture_manager';\nimport { TileProgram } from './tile_gpu';\nimport * as unary_op from './unaryop_gpu';\nimport { UnaryOpProgram } from './unaryop_gpu';\nimport * as unary_packed_op from './unaryop_packed_gpu';\nimport { UnaryOpPackedProgram } from './unaryop_packed_gpu';\nimport { UnpackProgram } from './unpack_gpu';\nimport * as webgl_util from './webgl_util';\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\nconst binaryCaches = {};\nexport function getBinaryCache(webGLVersion) {\n    if (webGLVersion in binaryCaches) {\n        return binaryCaches[webGLVersion];\n    }\n    binaryCaches[webGLVersion] = {};\n    return binaryCaches[webGLVersion];\n}\nfunction mapActivationToShaderProgram(activation, packed = false) {\n    if (activation === 'linear') {\n        if (packed) {\n            return unary_packed_op.LINEAR;\n        }\n        return unary_op.LINEAR;\n    }\n    else if (activation === 'relu') {\n        if (packed) {\n            return unary_packed_op.RELU;\n        }\n        return unary_op.RELU;\n    }\n    else if (activation === 'elu') {\n        if (packed) {\n            return unary_packed_op.ELU;\n        }\n        return unary_op.ELU;\n    }\n    else if (activation === 'relu6') {\n        if (packed) {\n            return unary_packed_op.RELU6;\n        }\n        return unary_op.RELU6;\n    }\n    else if (activation === 'prelu') {\n        if (packed) {\n            return binaryop_packed_gpu.PRELU;\n        }\n        return binaryop_gpu.PRELU;\n    }\n    throw new Error(`Activation ${activation} has not been implemented for the WebGL backend.`);\n}\n// Empirically determined constant used to determine size threshold for handing\n// off execution to the CPU.\nconst CPU_HANDOFF_SIZE_THRESHOLD = 128;\n// Empirically determined constant used to decide the number of MB on GPU\n// before we warn about high memory use. The MB are this constant * screen area\n// * dpi / 1024 / 1024.\nconst BEFORE_PAGING_CONSTANT = 600;\nfunction numMBBeforeWarning() {\n    if (env().global.screen == null) {\n        return 1024; // 1 GB.\n    }\n    return (env().global.screen.height * env().global.screen.width *\n        window.devicePixelRatio) *\n        BEFORE_PAGING_CONSTANT / 1024 / 1024;\n}\n// Empirically determined minimal shared dimension in matmul before we forward\n// to a.mul(b).sum() in order to take advantage of GPU parallelism. See\n// https://github.com/tensorflow/tfjs-core/pull/1379 for benchmarks.\nexport const MATMUL_SHARED_DIM_THRESHOLD = 1000;\nexport class MathBackendWebGL extends KernelBackend {\n    constructor(gpgpu) {\n        super();\n        // Maps data ids that have a pending read operation, to list of subscribers.\n        this.pendingRead = new WeakMap();\n        // List of data ids that are scheduled for disposal, but are waiting on a\n        // pending read operation.\n        this.pendingDisposal = new WeakSet();\n        // Used to count the number of 'shallow' sliced tensors that point to the\n        // same data id.\n        this.dataRefCount = new WeakMap();\n        this.numBytesInGPU = 0;\n        // Accumulated time spent (including blocking) in uploading data to webgl.\n        this.uploadWaitMs = 0;\n        // Accumulated time spent (including blocking in downloading data from webgl.\n        this.downloadWaitMs = 0;\n        this.warnedAboutMemory = false;\n        this.warnedAboutCPUBackend = false;\n        this.pendingDeletes = 0;\n        this.disposed = false;\n        if (!env().getBool('HAS_WEBGL')) {\n            throw new Error('WebGL is not supported on this device');\n        }\n        if (gpgpu == null) {\n            const gl = getWebGLContext(env().getNumber('WEBGL_VERSION'));\n            this.binaryCache = getBinaryCache(env().getNumber('WEBGL_VERSION'));\n            this.gpgpu = new GPGPUContext(gl);\n            this.canvas = gl.canvas;\n            this.gpgpuCreatedLocally = true;\n        }\n        else {\n            this.gpgpu = gpgpu;\n            this.binaryCache = {};\n            this.gpgpuCreatedLocally = false;\n            this.canvas = gpgpu.gl.canvas;\n        }\n        this.textureManager = new TextureManager(this.gpgpu);\n        this.numMBBeforeWarning = numMBBeforeWarning();\n        this.texData = new DataStorage(this, engine());\n    }\n    numDataIds() {\n        return this.texData.numDataIds() +\n            (this.cpuBackend ? this.cpuBackend.numDataIds() : 0) -\n            this.pendingDeletes;\n    }\n    write(values, shape, dtype) {\n        if (env().getBool('WEBGL_CHECK_NUMERICAL_PROBLEMS') ||\n            env().getBool('DEBUG')) {\n            this.checkNumericalProblems(values);\n        }\n        if (dtype === 'complex64' && values != null) {\n            throw new Error(`Cannot write to a complex64 dtype. ` +\n                `Please use tf.complex(real, imag).`);\n        }\n        const dataId = {};\n        this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });\n        return dataId;\n    }\n    /** Increase refCount of a `TextureData`. */\n    incRef(dataId) {\n        const texData = this.texData.get(dataId);\n        texData.refCount++;\n    }\n    /** Decrease refCount of a `TextureData`. */\n    decRef(dataId) {\n        if (this.texData.has(dataId)) {\n            const texData = this.texData.get(dataId);\n            texData.refCount--;\n        }\n    }\n    move(dataId, values, shape, dtype) {\n        if (env().getBool('DEBUG')) {\n            this.checkNumericalProblems(values);\n        }\n        if (dtype === 'complex64') {\n            throw new Error(`Cannot write to a complex64 dtype. ` +\n                `Please use tf.complex(real, imag).`);\n        }\n        this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });\n    }\n    disposeIntermediateTensorInfo(tensorInfo) {\n        const dataId = tensorInfo.dataId;\n        if (this.texData.has(dataId)) {\n            const textureData = this.texData.get(dataId);\n            textureData.refCount--;\n            if (textureData.refCount < 1) {\n                this.disposeData(dataId);\n            }\n        }\n    }\n    readSync(dataId) {\n        const texData = this.texData.get(dataId);\n        const { values, dtype, complexTensors, slice, shape, isPacked } = texData;\n        // The presence of `slice` indicates this tensor is a shallow slice of a\n        // different tensor, and is using that original tensor's texture. Run\n        // `clone` in order to copy that texture and read from it.\n        if (slice != null) {\n            let program;\n            if (isPacked) {\n                program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n            }\n            else {\n                program = new UnaryOpProgram(shape, unary_op.CLONE);\n            }\n            const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n            const data = this.readSync(res.dataId);\n            this.disposeIntermediateTensorInfo(res);\n            return data;\n        }\n        if (values != null) {\n            return this.convertAndCacheOnCPU(dataId);\n        }\n        if (dtype === 'string') {\n            return values;\n        }\n        const shouldTimeProgram = this.activeTimers != null;\n        let start;\n        if (shouldTimeProgram) {\n            start = util.now();\n        }\n        let result;\n        if (dtype === 'complex64') {\n            const realValues = complexTensors.real.dataSync();\n            const imagValues = complexTensors.imag.dataSync();\n            result = backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        else {\n            result = this.getValuesFromTexture(dataId);\n        }\n        if (shouldTimeProgram) {\n            this.downloadWaitMs += util.now() - start;\n        }\n        return this.convertAndCacheOnCPU(dataId, result);\n    }\n    async read(dataId) {\n        if (this.pendingRead.has(dataId)) {\n            const subscribers = this.pendingRead.get(dataId);\n            return new Promise(resolve => subscribers.push(resolve));\n        }\n        const texData = this.texData.get(dataId);\n        const { values, shape, slice, dtype, complexTensors, isPacked } = texData;\n        // The presence of `slice` indicates this tensor is a shallow slice of a\n        // different tensor, and is using that original tensor's texture. Run\n        // `clone` in order to copy that texture and read from it.\n        if (slice != null) {\n            let program;\n            if (isPacked) {\n                program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n            }\n            else {\n                program = new UnaryOpProgram(shape, unary_op.CLONE);\n            }\n            const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n            const data = this.read(res.dataId);\n            this.disposeIntermediateTensorInfo(res);\n            return data;\n        }\n        if (values != null) {\n            return this.convertAndCacheOnCPU(dataId);\n        }\n        if (!env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED') &&\n            env().getNumber('WEBGL_VERSION') === 2) {\n            throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and ` +\n                `WEBGL_VERSION=2 not yet supported.`);\n        }\n        let buffer = null;\n        let tmpDownloadTarget;\n        if (dtype !== 'complex64' && env().get('WEBGL_BUFFER_SUPPORTED')) {\n            // Possibly copy the texture into a buffer before inserting a fence.\n            tmpDownloadTarget = this.decode(dataId);\n            const tmpData = this.texData.get(tmpDownloadTarget.dataId);\n            buffer = this.gpgpu.createBufferFromTexture(tmpData.texture, ...tex_util.getDenseTexShape(shape));\n        }\n        this.pendingRead.set(dataId, []);\n        if (dtype !== 'complex64') {\n            // Create a fence and wait for it to resolve.\n            await this.gpgpu.createAndWaitForFence();\n        }\n        // Download the values from the GPU.\n        let vals;\n        if (dtype === 'complex64') {\n            const ps = await Promise.all([complexTensors.real.data(), complexTensors.imag.data()]);\n            const realValues = ps[0];\n            const imagValues = ps[1];\n            vals = backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        else if (buffer == null) {\n            vals = this.getValuesFromTexture(dataId);\n        }\n        else {\n            const size = util.sizeFromShape(shape);\n            vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer, size);\n        }\n        if (tmpDownloadTarget != null) {\n            this.disposeIntermediateTensorInfo(tmpDownloadTarget);\n        }\n        const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);\n        const subscribers = this.pendingRead.get(dataId);\n        this.pendingRead.delete(dataId);\n        // Notify all pending reads.\n        subscribers.forEach(resolve => resolve(dTypeVals));\n        if (this.pendingDisposal.has(dataId)) {\n            this.pendingDisposal.delete(dataId);\n            this.disposeData(dataId);\n            this.pendingDeletes--;\n        }\n        return dTypeVals;\n    }\n    checkNumericalProblems(values) {\n        if (values == null) {\n            return;\n        }\n        for (let i = 0; i < values.length; i++) {\n            const num = values[i];\n            if (!webgl_util.canBeRepresented(num)) {\n                if (env().getBool('WEBGL_RENDER_FLOAT32_CAPABLE')) {\n                    throw Error(`The value ${num} cannot be represented with your ` +\n                        `current settings. Consider enabling float32 rendering: ` +\n                        `'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);\n                }\n                throw Error(`The value ${num} cannot be represented on this device.`);\n            }\n        }\n    }\n    getValuesFromTexture(dataId) {\n        const { shape, dtype, isPacked } = this.texData.get(dataId);\n        const size = util.sizeFromShape(shape);\n        if (env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {\n            const tmpTarget = this.decode(dataId);\n            const tmpData = this.texData.get(tmpTarget.dataId);\n            const vals = this.gpgpu\n                .downloadMatrixFromPackedTexture(tmpData.texture, ...tex_util.getDenseTexShape(shape))\n                .subarray(0, size);\n            this.disposeIntermediateTensorInfo(tmpTarget);\n            return vals;\n        }\n        const shouldUsePackedProgram = env().getBool('WEBGL_PACK') && isPacked === true;\n        const outputShape = shouldUsePackedProgram ? webgl_util.getShapeAs3D(shape) : shape;\n        const program = shouldUsePackedProgram ?\n            new EncodeFloatPackedProgram(outputShape) :\n            new EncodeFloatProgram(outputShape);\n        const output = this.runWebGLProgram(program, [{ shape: outputShape, dtype, dataId }], 'float32');\n        const tmpData = this.texData.get(output.dataId);\n        const vals = this.gpgpu\n            .downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture, tmpData.texShape[0], tmpData.texShape[1])\n            .subarray(0, size);\n        this.disposeIntermediateTensorInfo(output);\n        return vals;\n    }\n    async time(f) {\n        const oldActiveTimers = this.activeTimers;\n        const newActiveTimers = [];\n        let outerMostTime = false;\n        if (this.programTimersStack == null) {\n            this.programTimersStack = newActiveTimers;\n            outerMostTime = true;\n        }\n        else {\n            this.activeTimers.push(newActiveTimers);\n        }\n        this.activeTimers = newActiveTimers;\n        f();\n        // needing to split these up because util.flatten only accepts certain types\n        const flattenedActiveTimerQueries = util.flatten(this.activeTimers.map((d) => d.query))\n            .filter(d => d != null);\n        const flattenedActiveTimerNames = util.flatten(this.activeTimers.map((d) => d.name))\n            .filter(d => d != null);\n        this.activeTimers = oldActiveTimers;\n        if (outerMostTime) {\n            this.programTimersStack = null;\n        }\n        const res = {\n            uploadWaitMs: this.uploadWaitMs,\n            downloadWaitMs: this.downloadWaitMs,\n            kernelMs: null,\n            wallMs: null // will be filled by the engine\n        };\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n            res['kernelMs'] = util.sum(kernelMs);\n            res['getExtraProfileInfo'] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d }))\n                .map(d => `${d.name}: ${d.ms}`)\n                .join(', ');\n        }\n        else {\n            res['kernelMs'] = {\n                error: 'WebGL query timers are not supported in this environment.'\n            };\n        }\n        this.uploadWaitMs = 0;\n        this.downloadWaitMs = 0;\n        return res;\n    }\n    memory() {\n        return {\n            unreliable: false,\n            numBytesInGPU: this.numBytesInGPU,\n            numBytesInGPUAllocated: this.textureManager.numBytesAllocated,\n            numBytesInGPUFree: this.textureManager.numBytesFree\n        };\n    }\n    startTimer() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            return this.gpgpu.beginQuery();\n        }\n        return { startMs: util.now(), endMs: null };\n    }\n    endTimer(query) {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            this.gpgpu.endQuery();\n            return query;\n        }\n        query.endMs = util.now();\n        return query;\n    }\n    async getQueryTime(query) {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            return this.gpgpu.waitForQueryAndGetTime(query);\n        }\n        const timerQuery = query;\n        return timerQuery.endMs - timerQuery.startMs;\n    }\n    disposeData(dataId) {\n        if (this.pendingDisposal.has(dataId)) {\n            return;\n        }\n        if (this.pendingRead.has(dataId)) {\n            this.pendingDisposal.add(dataId);\n            this.pendingDeletes++;\n            return;\n        }\n        // No-op if already disposed.\n        if (!this.texData.has(dataId)) {\n            return;\n        }\n        this.releaseGPUData(dataId);\n        const { complexTensors } = this.texData.get(dataId);\n        if (complexTensors != null) {\n            complexTensors.real.dispose();\n            complexTensors.imag.dispose();\n        }\n        this.texData.delete(dataId);\n    }\n    releaseGPUData(dataId) {\n        const { texture, dtype, texShape, usage, isPacked, slice } = this.texData.get(dataId);\n        const key = slice && slice.origDataId || dataId;\n        const refCount = this.dataRefCount.get(key);\n        if (refCount > 1) {\n            this.dataRefCount.set(key, refCount - 1);\n        }\n        else {\n            this.dataRefCount.delete(key);\n            if (texture != null) {\n                this.numBytesInGPU -= this.computeBytes(texShape, dtype);\n                this.textureManager.releaseTexture(texture, texShape, usage, isPacked);\n            }\n        }\n        const texData = this.texData.get(dataId);\n        texData.texture = null;\n        texData.texShape = null;\n        texData.isPacked = false;\n        texData.slice = null;\n    }\n    getTexture(dataId) {\n        this.uploadToGPU(dataId);\n        return this.texData.get(dataId).texture;\n    }\n    /**\n     * Returns internal information for the specific data bucket. Used in unit\n     * tests.\n     */\n    getDataInfo(dataId) {\n        return this.texData.get(dataId);\n    }\n    getCPUBackend() {\n        if (!env().getBool('WEBGL_CPU_FORWARD')) {\n            return null;\n        }\n        if (this.cpuBackend == null) {\n            this.cpuBackend = engine().findBackend('cpu');\n        }\n        return this.cpuBackend;\n    }\n    /*\n    Tests whether all the inputs to an op are small and on the CPU. This heuristic\n    determines when it would be faster to execute a kernel on the CPU. WebGL\n    kernels opt into running this check and forwarding when appropriate.\n    TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more\n    sustainable strategy for optimizing backend execution of ops.\n     */\n    shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) {\n        const cpuBackend = this.getCPUBackend();\n        if (!this.warnedAboutCPUBackend && cpuBackend == null) {\n            console.warn('Your application contains ops that are small enough to be ' +\n                'executed on the CPU backend, however the CPU backend cannot ' +\n                'be found. Consider importing the CPU backend ' +\n                '(@tensorflow/tfjs-backend-cpu) for better performance.');\n            this.warnedAboutCPUBackend = true;\n        }\n        return cpuBackend != null &&\n            inputs.every(input => this.texData.get(input.dataId).texture == null &&\n                util.sizeFromShape(input.shape) < sizeThreshold);\n    }\n    getGPGPUContext() {\n        return this.gpgpu;\n    }\n    complex(real, imag) {\n        const result = this.makeOutput(real.shape, 'complex64');\n        const resultData = this.texData.get(result.dataId);\n        // The backend owns the reference to the underlying real and imaginary\n        // clones. These will explicitly get disposed when the complex tensor is\n        // disposed.\n        resultData.complexTensors = {\n            real: engine().keep(real.clone()),\n            imag: engine().keep(imag.clone())\n        };\n        return result;\n    }\n    real(input) {\n        const resultData = this.texData.get(input.dataId);\n        return resultData.complexTensors.real.clone();\n    }\n    imag(input) {\n        const resultData = this.texData.get(input.dataId);\n        return resultData.complexTensors.imag.clone();\n    }\n    slice(x, begin, size) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = sliceImplCPU(this.texData.get(x.dataId).values, begin, size, x.shape, x.dtype);\n            return this.makeOutput(size, x.dtype, outValues);\n        }\n        // Short-circuit computation if the slice is zero-sized.\n        if (util.sizeFromShape(size) === 0) {\n            return tensor([], size, x.dtype);\n        }\n        const { isPacked } = this.texData.get(x.dataId);\n        const isContinous = slice_util.isSliceContinous(x.shape, begin, size);\n        if (isPacked || !isContinous) {\n            const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n                new SlicePackedProgram(size) :\n                new SliceProgram(size);\n            const customSetup = program.getCustomSetupFunc(begin);\n            return this.compileAndRun(program, [x], null, customSetup);\n        }\n        this.uploadToGPU(x.dataId);\n        return this.shallowSlice(x, begin, size);\n    }\n    shallowSlice(x, begin, size) {\n        const xTexData = this.texData.get(x.dataId);\n        const t = this.makeOutput(size, x.dtype);\n        const newTexData = this.texData.get(t.dataId);\n        // Copy texture data from the original tensor.\n        Object.assign(newTexData, xTexData);\n        newTexData.shape = size;\n        newTexData.dtype = x.dtype;\n        let flatOffset = slice_util.computeFlatOffset(begin, x.strides);\n        if (xTexData.slice) {\n            // We are slicing an already sliced tensor, so we have to accumulate\n            // the offset.\n            flatOffset += xTexData.slice.flatOffset;\n        }\n        newTexData.slice = {\n            flatOffset,\n            // Point to the original dataId, which is used to do ref counting.\n            origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId\n        };\n        // Increase the ref count for that data bucket.\n        const refCount = this.dataRefCount.get(newTexData.slice.origDataId) || 1;\n        this.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);\n        return t;\n    }\n    stridedSlice(x, begin, end, strides) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.stridedSlice(x, begin, end, strides));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const outShape = slice_util.computeOutShape(begin, end, strides);\n        if (outShape.some(axis => axis === 0)) {\n            return tensor([], outShape);\n        }\n        const program = new StridedSliceProgram(begin, strides, outShape);\n        return this.compileAndRun(program, [x]);\n    }\n    reverse(x, axis) {\n        const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n            new ReversePackedProgram(x.shape, axis) :\n            new ReverseProgram(x.shape, axis);\n        return this.compileAndRun(program, [x]);\n    }\n    concat(tensors, axis) {\n        if (tensors[0].dtype === 'complex64') {\n            const reals = tensors.map((t) => real(t));\n            const imags = tensors.map((t) => imag(t));\n            return complex(this.concat(reals, axis), this.concat(imags, axis));\n        }\n        if (tensors.length === 1) {\n            return tensors[0];\n        }\n        if (tensors.length > env().getNumber('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n            const midIndex = Math.floor(tensors.length / 2);\n            const leftSide = this.concat(tensors.slice(0, midIndex), axis);\n            const rightSide = this.concat(tensors.slice(midIndex), axis);\n            return this.concat([leftSide, rightSide], axis);\n        }\n        if (env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') && tensors[0].rank > 1) {\n            const program = new ConcatPackedProgram(tensors.map(t => t.shape), axis);\n            return this.compileAndRun(program, tensors);\n        }\n        // Any concat of n-dimensional tensors across any axis can be reduced to\n        // a concatenation of two-dimensional tensors across the axis 1 by first\n        // partitioning the axes of the original tensors into those less than the\n        // axis to be concatenated and the rest. Then reshape the tensors\n        // into a two-dimensional tensor by collapsing these two sets of axes and\n        // concatenate the resulting matrices across the axis 1, finally reshaping\n        // the result to have the proper shape.\n        const outShape = backend_util.computeOutShape(tensors.map(t => t.shape), axis);\n        const tensors2D = tensors.map(t => t.as2D(-1, util.sizeFromShape(t.shape.slice(axis))));\n        const program = new ConcatProgram(tensors2D.map(t => t.shape));\n        const res = this.compileAndRun(program, tensors2D);\n        return res.reshape(outShape);\n    }\n    neg(x) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.neg(x));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.NEG, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.NEG);\n        return this.compileAndRun(program, [x]);\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n        const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n        const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n        const [batch, ,] = a.shape;\n        // Since the matrices are vectors, it is faster to call mul().sum()\n        // because sum() is O(sqrt(N)) due to divide-and-conquer.\n        if ((outerShapeA === 1 || outerShapeB === 1) &&\n            sharedDim > MATMUL_SHARED_DIM_THRESHOLD) {\n            if (transposeA) {\n                a = transpose(a, [0, 2, 1]);\n            }\n            if (transposeB) {\n                b = transpose(b, [0, 2, 1]);\n            }\n            const a3D = outerShapeB === 1 ? a : a.as3D(batch, sharedDim, 1);\n            const axis = outerShapeB === 1 ? 2 : 1;\n            const b3D = outerShapeB === 1 ? b.as3D(batch, 1, sharedDim) : b;\n            return this.multiply(a3D, b3D).sum(axis, true /* keepDims */);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        const program = new MatMulPackedProgram(a.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n        const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n        const [batch, ,] = a.shape;\n        const dtype = upcastType(a.dtype, b.dtype);\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;\n        const program = new MatMulPackedProgram(a.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [a, b];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (preluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        return this.compileAndRun(program, inputs, dtype);\n    }\n    multiply(a, b) {\n        if (a.dtype === 'complex64') {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const realProgram = new BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.REAL, a.shape, b.shape);\n            const imagProgram = new BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);\n            const inputs = [\n                this.makeComplexComponentTensorInfo(a, aData.complexTensors.real),\n                this.makeComplexComponentTensorInfo(a, aData.complexTensors.imag),\n                this.makeComplexComponentTensorInfo(b, bData.complexTensors.real),\n                this.makeComplexComponentTensorInfo(b, bData.complexTensors.imag)\n            ];\n            const real = this.compileAndRun(realProgram, inputs);\n            const imag = this.compileAndRun(imagProgram, inputs);\n            const complex = this.complex(real, imag);\n            real.dispose();\n            imag.dispose();\n            return complex;\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        if (this.shouldExecuteOnCPU([a, b])) {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const [outValues, outShape] = multiplyImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n            return this.makeOutput(outShape, dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_gpu.MUL, a.dtype);\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.MUL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], a.dtype);\n    }\n    localResponseNormalization4D(x, radius, bias, alpha, beta) {\n        const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n            new LRNPackedProgram(x.shape, radius, bias, alpha, beta) :\n            new LRNProgram(x.shape, radius, bias, alpha, beta);\n        return this.compileAndRun(program, [x]);\n    }\n    LRNGrad(dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {\n        const program = new LRNGradProgram(inputImage.shape, depthRadius, bias, alpha, beta);\n        return this.compileAndRun(program, [inputImage, outputImage, dy]);\n    }\n    tile(x, reps) {\n        if (x.dtype === 'string') {\n            const data = this.readSync(x.dataId);\n            const decodedData = data.map(d => util.decodeString(d));\n            const buf = buffer(x.shape, x.dtype, decodedData);\n            return tile(buf, reps);\n        }\n        const program = new TileProgram(x.shape, reps);\n        return this.compileAndRun(program, [x]);\n    }\n    pad(x, paddings, constantValue) {\n        const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n            new PadPackedProgram(x.shape, paddings, constantValue) :\n            new PadProgram(x.shape, paddings, constantValue);\n        return this.compileAndRun(program, [x]);\n    }\n    gather(x, indices, axis) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x, indices], () => this.cpuBackend.gather(x, indices, axis));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = new GatherProgram(x.shape, indices.size, axis);\n        return this.compileAndRun(program, [x, indices]);\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        util.assert(x.rank <= 4, () => 'batchToSpaceND for rank > 4 with a WebGL backend not ' +\n            'implemented yet');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n        const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n        const reshapedPermuted = backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n        const sliceBeginCoords = backend_util.getSliceBeginCoords(crops, blockShape.length);\n        const sliceSize = backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n        return transpose(x.reshape(reshaped), permuted)\n            .reshape(reshapedPermuted)\n            .slice(sliceBeginCoords, sliceSize);\n    }\n    spaceToBatchND(x, blockShape, paddings) {\n        util.assert(x.rank <= 4, () => 'spaceToBatchND for rank > 4 with a WebGL backend not ' +\n            'implemented yet');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const completePaddings = [[0, 0]];\n        completePaddings.push(...paddings);\n        for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n            completePaddings.push([0, 0]);\n        }\n        const paddedX = x.pad(completePaddings);\n        const reshapedPaddedShape = backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n        const permutedReshapedPaddedPermutation = backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n        const flattenShape = backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n        const paddedXT = transpose(paddedX.reshape(reshapedPaddedShape), permutedReshapedPaddedPermutation);\n        return reshape(paddedXT, flattenShape);\n    }\n    reduce(x, reduceType, dtype) {\n        const batchSize = x.shape[0];\n        const inSize = x.shape[1];\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const outSize = Math.ceil(inSize / windowSize);\n        const reduceInfo = { windowSize, inSize, batchSize, outSize };\n        const program = new ReduceProgram(reduceInfo, reduceType);\n        const output = this.compileAndRun(program, [x], dtype);\n        // No need to run another GPGPU program.\n        if (output.shape[1] === 1) {\n            return output;\n        }\n        return this.reduce(output, reduceType, dtype);\n    }\n    argReduce(x, reduceType, bestIndicesA = null) {\n        let batchSize = x.shape[0];\n        let inSize = x.shape[1];\n        if (bestIndicesA != null) {\n            batchSize = bestIndicesA.shape[0];\n            inSize = bestIndicesA.shape[1];\n        }\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const reduceInfo = {\n            windowSize,\n            inSize,\n            batchSize,\n            outSize: Math.ceil(inSize / windowSize)\n        };\n        const program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);\n        const inputs = [x];\n        if (bestIndicesA != null) {\n            inputs.push(bestIndicesA);\n        }\n        const output = this.compileAndRun(program, inputs, 'int32');\n        // No need to run another GPGPU program.\n        if (output.shape[1] === 1) {\n            return output;\n        }\n        return this.argReduce(x, reduceType, output);\n    }\n    argReducePacked(x, reduceType, bestIndicesA = null) {\n        const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;\n        const inSize = inShape[inShape.length - 1];\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null);\n        const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];\n        const output = this.compileAndRun(program, inputs, 'int32');\n        if (output.rank === x.rank) {\n            return this.argReducePacked(x, reduceType, output);\n        }\n        return output;\n    }\n    sum(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        return this.reduce(a2D, 'sum', outputDType).reshape(outShape);\n    }\n    prod(x, axes) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.prod(x, axes));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        return this.reduce(a2D, 'prod', outputDType).reshape(outShape);\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        let axis = 0;\n        const permutation = backend_util.getAxesPermutation([axis], x.rank);\n        let permutedX = x;\n        if (permutation != null) {\n            permutedX = transpose(x, permutation);\n            axis = backend_util.getInnerMostAxes(1, x.rank)[0];\n        }\n        const outShape = segment_util.computeOutShape(permutedX.shape, axis, numSegments);\n        const inSize = util.sizeFromShape([permutedX.shape[axis]]);\n        const a2D = permutedX.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        let result = this.segOpCompute(a2D, 'unsortedSegmentSum', segmentIds, outputDType, numSegments)\n            .reshape(outShape);\n        if (permutation != null) {\n            result =\n                transpose(result, backend_util.getUndoAxesPermutation(permutation));\n        }\n        return result;\n    }\n    segOpCompute(x, segOpType, segmentIds, dtype, numSegments) {\n        const batchSize = x.shape[0];\n        const inSize = x.shape[1];\n        const windowSize = segment_util.segOpComputeOptimalWindowSize(inSize, numSegments);\n        const segOpInfo = { windowSize, inSize, batchSize, numSegments };\n        const program = new SegmentOpProgram(segOpInfo, segOpType);\n        const output = this.compileAndRun(program, [x, segmentIds], dtype);\n        // No need to run another GPGPU program.\n        if (output.shape[1] === numSegments) {\n            return output;\n        }\n        segmentIds = range(0, numSegments).tile([inSize / windowSize]);\n        return this.segOpCompute(output, segOpType, segmentIds, dtype, numSegments);\n    }\n    argMinMaxReduce(x, axis, reduceType) {\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('arg' + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.rank);\n        if (!env().getBool('WEBGL_PACK_REDUCE') || x.rank <= 2) {\n            const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n            const inSize = util.sizeFromShape(reduceShape);\n            const a2D = x.as2D(-1, inSize);\n            return this.argReduce(a2D, reduceType).reshape(outShape);\n        }\n        return this.argReducePacked(x, reduceType);\n    }\n    argMin(x, axis) {\n        return this.argMinMaxReduce(x, axis, 'min');\n    }\n    argMax(x, axis) {\n        return this.argMinMaxReduce(x, axis, 'max');\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        if (axis !== x.rank - 1) {\n            throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.rank - 1} ` +\n                `but got axis=${axis}`);\n        }\n        const size = x.shape[axis];\n        let result = x;\n        // Use cumsum parallel algorithm, ref:\n        // https://developer.nvidia.com/gpugems/gpugems3/part-vi-gpu-computing/chapter-39-parallel-prefix-sum-scan-cuda\n        for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {\n            const program = new CumSumProgram(x.shape, false, reverse);\n            const customSetup = program.getCustomSetupFunc(i);\n            const prevResult = result;\n            result = this.compileAndRun(program, [result], result.dtype, customSetup);\n            prevResult.dispose();\n        }\n        // For exclusive cumsum, shift the end result in the direction of sum and\n        // add 0 to the front index.\n        if (exclusive) {\n            const program = new CumSumProgram(x.shape, exclusive, reverse);\n            const prevResult = result;\n            result = this.compileAndRun(program, [result]);\n            prevResult.dispose();\n        }\n        return result;\n    }\n    equal(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    notEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.NOT_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.NOT_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    less(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.less(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LESS, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LESS, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    lessEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LESS_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LESS_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    greater(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.greater(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.GREATER, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.GREATER, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    greaterEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.GREATER_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.GREATER_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    logicalNot(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.LOGICAL_NOT);\n        return this.compileAndRun(program, [x]);\n    }\n    logicalAnd(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LOGICAL_AND, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LOGICAL_AND, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    logicalOr(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LOGICAL_OR, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LOGICAL_OR, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    select(condition, a, b) {\n        const program = new SelectProgram(condition.rank, a.shape, a.rank);\n        return this.compileAndRun(program, [condition, a, b], upcastType(a.dtype, b.dtype));\n    }\n    where(condition) {\n        backend_util.warn('tf.where() in webgl locks the UI thread. ' +\n            'Call tf.whereAsync() instead');\n        const condVals = condition.dataSync();\n        return whereImpl(condition.shape, condVals);\n    }\n    topk(x, k, sorted) {\n        const xVals = x.dataSync();\n        return topkImpl(xVals, x.shape, x.dtype, k, sorted);\n    }\n    min(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'min', a2D.dtype).reshape(outShape);\n    }\n    minimum(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.minimum(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MIN, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MIN, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    mod(a, b) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MOD, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MOD, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    maximum(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.maximum(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MAX, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MAX, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    all(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'all', a2D.dtype).reshape(outShape);\n    }\n    any(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'any', a2D.dtype).reshape(outShape);\n    }\n    floorDiv(a, b) {\n        const op = binaryop_gpu.INT_DIV;\n        const outputDtype = 'int32';\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.INT_DIV, outputDtype);\n        }\n        const program = new BinaryOpProgram(op, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], outputDtype);\n    }\n    add(a, b) {\n        if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n            return this.complexSeparableBinaryOp(a, b, binaryop_gpu.ADD);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        if (this.shouldExecuteOnCPU([a, b])) {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const [outValues, outShape] = addImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n            return this.makeOutput(outShape, dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_gpu.ADD, dtype);\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.ADD, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    packedUnaryOp(x, op, dtype) {\n        const program = new UnaryOpPackedProgram(x.shape, op);\n        return this.compileAndRun(program, [x], dtype);\n    }\n    packedBinaryOp(a, b, op, dtype, checkOutOfBounds = false) {\n        const program = new BinaryOpPackedProgram(op, a.shape, b.shape, checkOutOfBounds);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    /**\n     * Computes a complex binary operation that can be decomposed into a simple\n     * binary operation on both the real and imagary parts.\n     */\n    complexSeparableBinaryOp(a, b, op) {\n        const aData = this.texData.get(a.dataId);\n        const bData = this.texData.get(b.dataId);\n        const [real, imag] = [\n            [aData.complexTensors.real, bData.complexTensors.real],\n            [aData.complexTensors.imag, bData.complexTensors.imag]\n        ].map(complexParts => {\n            const [aPart, bPart] = complexParts;\n            const aHandle = this.makeComplexComponentTensorInfo(a, aPart);\n            const bHandle = this.makeComplexComponentTensorInfo(b, bPart);\n            const program = new BinaryOpProgram(op, a.shape, b.shape);\n            return this.compileAndRun(program, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));\n        });\n        const complex = this.complex(real, imag);\n        real.dispose();\n        imag.dispose();\n        return complex;\n    }\n    // Returns a TensorInfo with the complex shape and the dataId of the\n    // underlying part. We need to do this because a reshaped complex tensor is\n    // not reflected in its parts.\n    makeComplexComponentTensorInfo(complexTensor, complexPart) {\n        return {\n            dataId: complexPart.dataId,\n            dtype: complexPart.dtype,\n            shape: complexTensor.shape\n        };\n    }\n    addN(tensors) {\n        if (tensors.length === 1) {\n            return tensors[0];\n        }\n        // Limit the number of uploaded textures for optimization.\n        if (tensors.length > env().get('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n            const midIndex = Math.floor(tensors.length / 2);\n            const leftSide = this.addN(tensors.slice(0, midIndex));\n            const rightSide = this.addN(tensors.slice(midIndex));\n            return this.addN([leftSide, rightSide]);\n        }\n        const dtype = tensors.map(t => t.dtype).reduce((d1, d2) => upcastType(d1, d2));\n        const shapes = tensors.map(t => t.shape);\n        // We can make sure shapes are identical in op level.\n        const usePackedOp = env().getBool('WEBGL_PACK');\n        const program = usePackedOp ?\n            new AddNPackedProgram(tensors[0].shape, shapes) :\n            new AddNProgram(tensors[0].shape, shapes);\n        return this.compileAndRun(program, tensors, dtype);\n    }\n    subtract(a, b) {\n        if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n            return this.complexSeparableBinaryOp(a, b, binaryop_gpu.SUB);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        if (this.shouldExecuteOnCPU([a, b])) {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const [outValues, outShape] = subImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n            return this.makeOutput(outShape, dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_gpu.SUB, a.dtype);\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.SUB, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    pow(a, b) {\n        const usePackedOp = env().getBool('WEBGL_PACK_BINARY_OPERATIONS');\n        const program = usePackedOp ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.POW, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.POW, a.shape, b.shape);\n        const dtype = upcastType(a.dtype, b.dtype);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    ceil(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = ceilImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.CEIL, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.CEIL);\n        return this.compileAndRun(program, [x]);\n    }\n    floor(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = floorImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.FLOOR, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.FLOOR);\n        return this.compileAndRun(program, [x]);\n    }\n    sign(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SIGN);\n        return this.compileAndRun(program, [x]);\n    }\n    isNaN(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_NAN);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    isInf(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_INF);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    isFinite(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_FINITE);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    round(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ROUND);\n        return this.compileAndRun(program, [x]);\n    }\n    exp(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = expImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.EXP, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.EXP);\n        return this.compileAndRun(program, [x]);\n    }\n    expm1(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = expm1ImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.EXPM1, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.EXPM1);\n        return this.compileAndRun(program, [x]);\n    }\n    softmax(logits, dim) {\n        const axes = util.parseAxisParam([dim], logits.shape);\n        // TODO(annxingyuan): Call maxImpl rather than op as part of softmax kernel\n        // modularization.\n        const maxLogit = max(logits, axes);\n        const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n        const a = this.subtract(logits, maxLogit.reshape(expandedShape));\n        const b = this.exp(a);\n        const sumExp = this.sum(b, axes).reshape(expandedShape);\n        // TODO(annxingyuan): Call divImpl rather than op as part of softmax kernel\n        // modularization.\n        return div(b, sumExp);\n    }\n    log(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = logImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_packed_op.LOG, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.LOG);\n        return this.compileAndRun(program, [x]);\n    }\n    log1p(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.LOG1P);\n        return this.compileAndRun(program, [x]);\n    }\n    sqrt(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SQRT);\n        return this.compileAndRun(program, [x]);\n    }\n    rsqrt(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = rsqrtImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.RSQRT);\n        return this.compileAndRun(program, [x]);\n    }\n    reciprocal(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.RECIPROCAL);\n        return this.compileAndRun(program, [x]);\n    }\n    relu(x) {\n        let program;\n        if (env().getBool('WEBGL_PACK')) {\n            program = new UnaryOpPackedProgram(x.shape, unary_packed_op.RELU);\n        }\n        else {\n            program = new UnaryOpProgram(x.shape, unary_op.RELU);\n        }\n        return this.compileAndRun(program, [x]);\n    }\n    relu6(x) {\n        let program;\n        if (env().getBool('WEBGL_PACK')) {\n            program = new UnaryOpPackedProgram(x.shape, unary_packed_op.RELU6);\n        }\n        else {\n            program = new UnaryOpProgram(x.shape, unary_op.RELU6);\n        }\n        return this.compileAndRun(program, [x]);\n    }\n    prelu(x, alpha) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.PRELU, x.shape, alpha.shape) :\n            new BinaryOpProgram(binaryop_gpu.PRELU, x.shape, alpha.shape);\n        return this.compileAndRun(program, [x, alpha]);\n    }\n    elu(x) {\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_packed_op.ELU, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.ELU);\n        return this.compileAndRun(program, [x]);\n    }\n    eluDer(dy, y) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.ELU_DER, dy.shape, y.shape) :\n            new BinaryOpProgram(binaryop_gpu.ELU_DER, dy.shape, y.shape);\n        return this.compileAndRun(program, [dy, y]);\n    }\n    selu(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SELU);\n        return this.compileAndRun(program, [x]);\n    }\n    int(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.TO_INT);\n        return this.compileAndRun(program, [x], 'int32');\n    }\n    clip(x, min, max) {\n        let program;\n        if (env().getBool('WEBGL_PACK_CLIP')) {\n            program = new ClipPackedProgram(x.shape);\n        }\n        else {\n            program = new ClipProgram(x.shape);\n        }\n        const customSetup = program.getCustomSetupFunc(min, max);\n        return this.compileAndRun(program, [x], null, customSetup);\n    }\n    abs(x) {\n        // TODO: handle cases when x is complex.\n        if (this.shouldExecuteOnCPU([x]) && x.dtype !== 'complex64') {\n            const outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.ABS, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.ABS);\n        return this.compileAndRun(program, [x]);\n    }\n    complexAbs(x) {\n        const xData = this.texData.get(x.dataId);\n        const program = new ComplexAbsProgram(x.shape);\n        const inputs = [\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.real),\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.imag),\n        ];\n        return this.compileAndRun(program, inputs);\n    }\n    sigmoid(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SIGMOID);\n        return this.compileAndRun(program, [x]);\n    }\n    softplus(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SOFTPLUS);\n        return this.compileAndRun(program, [x]);\n    }\n    asin(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ASIN);\n        return this.compileAndRun(program, [x]);\n    }\n    acos(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ACOS);\n        return this.compileAndRun(program, [x]);\n    }\n    atan(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ATAN);\n        return this.compileAndRun(program, [x]);\n    }\n    sinh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SINH);\n        return this.compileAndRun(program, [x]);\n    }\n    cosh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.COSH);\n        return this.compileAndRun(program, [x]);\n    }\n    tanh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.TANH);\n        return this.compileAndRun(program, [x]);\n    }\n    asinh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ASINH);\n        return this.compileAndRun(program, [x]);\n    }\n    acosh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ACOSH);\n        return this.compileAndRun(program, [x]);\n    }\n    atanh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ATANH);\n        return this.compileAndRun(program, [x]);\n    }\n    erf(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ERF);\n        return this.compileAndRun(program, [x]);\n    }\n    step(x, alpha) {\n        const program = new UnaryOpProgram(x.shape, unary_op.STEP(alpha));\n        return this.compileAndRun(program, [x]);\n    }\n    conv2dByMatMul(x, filter, convInfo, bias, activation, preluActivationWeights) {\n        // Reshapes conv2D input to 2D tensors, uses matMul and then reshape the\n        // result from 2D to 4D.\n        const xShape = x.shape;\n        const xTexData = this.texData.get(x.dataId);\n        const sharedMatMulDim = convInfo.inChannels;\n        const outerShapeX = xShape[0] * xShape[1] * xShape[2];\n        const outerShapeFilter = convInfo.outChannels;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const transposeA = false;\n        const transposeB = false;\n        // TODO: Once reduction ops are packed, batchMatMul will always be packed\n        // and we can remove this condition.\n        const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) &&\n            sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;\n        const reshapeWillBeExpensive = xShape[2] % 2 !== 0 && !!xTexData.isPacked;\n        if (batchMatMulWillBeUnpacked || !env().getBool('WEBGL_LAZILY_UNPACK') ||\n            !env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ||\n            !reshapeWillBeExpensive) {\n            const targetShape = isChannelsLast ? xShape[0] * xShape[1] * xShape[2] :\n                xShape[0] * xShape[2] * xShape[3];\n            const xReshaped = reshape(x, [1, targetShape, convInfo.inChannels]);\n            const filterReshaped = reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);\n            const result = this.fusedBatchMatMul({\n                a: xReshaped,\n                b: filterReshaped,\n                transposeA,\n                transposeB,\n                bias,\n                activation,\n                preluActivationWeights\n            });\n            return reshape(result, convInfo.outShape);\n        }\n        // Following optimization is specific to packed |x| with odd row count\n        // (For example, in channelLast mode, 'row count' refers to x.shape[2]):\n        // we avoid expensive packed 2x2 reshape by padding row count to next,\n        // even number. When x.shape[2] is odd, the result of packed batchMatMul is\n        // the same (has the same texture layout and and values in the texture) as\n        // it is for even x.shape[2] + 1. We make the odd-rows tensor to look like\n        // even-rows tensor before the operation and, after the batchMatMul,\n        // fix the even-rows result to have odd number of rows.\n        const targetShape = isChannelsLast ?\n            xShape[0] * xShape[1] * (xShape[2] + 1) :\n            xShape[0] * xShape[2] * (xShape[3] + 1);\n        const xReshaped = {\n            dataId: x.dataId,\n            shape: [1, targetShape, convInfo.inChannels],\n            dtype: x.dtype\n        };\n        // xTexData.shape gets referenced from GPGPUBinary.inShapeInfos.\n        // Decrementing row count, after batchMatMul->...->compileProgram leads to\n        // invalid row count within the reference in GPGPUBinary.inShapeInfos.\n        // Alternative fix would be to provide a copy to GPGPUBinary.inShapeInfos\n        // in compileProgram method, but that would affect compilation of all\n        // programs - instead, provide a copy here, with even row count, before\n        // calling batchMatMul->...->compileProgram and after that, the original\n        // xTexData.shape is restored.\n        const originalXTexDataShape = xTexData.shape;\n        xTexData.shape = xTexData.shape.slice();\n        xTexData.shape[xTexData.shape.length - 2]++;\n        util.assert(webgl_util.isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);\n        const filterReshaped = reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);\n        const pointwiseConv = this.fusedBatchMatMul({\n            a: xReshaped,\n            b: filterReshaped,\n            transposeA,\n            transposeB,\n            bias,\n            activation,\n            preluActivationWeights\n        });\n        const pointwiseConvTexData = this.texData.get(pointwiseConv.dataId);\n        util.assert(pointwiseConvTexData.isPacked, () => 'batchMatMul result is expected to be packed');\n        // Restore the input shape to original.\n        xTexData.shape = originalXTexDataShape;\n        // Set the output shape - there is no need for expensive reshape as data\n        // layout is already correct.\n        pointwiseConvTexData.shape = convInfo.outShape;\n        return engine().makeTensorFromDataId(pointwiseConv.dataId, convInfo.outShape, pointwiseConv.dtype);\n    }\n    conv2dWithIm2Row(x, filter, convInfo, bias, activation, preluActivationWeights) {\n        // Rearranges conv2d input so each block to be convolved over forms the\n        // column of a new matrix with shape [filterWidth * filterHeight *\n        // inChannels, outHeight * outWidth]. The filter is also rearranged so each\n        // output channel forms a row of a new matrix with shape [outChannels,\n        // filterWidth * filterHeight * inChannels]. The convolution is then\n        // computed by multiplying these matrices and reshaping the result.\n        const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo;\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const sharedDim = filterWidth * filterHeight * inChannels;\n        const numCols = outHeight * outWidth;\n        const x2ColShape = [sharedDim, numCols];\n        const transposeA = true;\n        const transposeB = false;\n        const xSqueezed = x.squeeze([0]);\n        const w2Row = filter.reshape([1, sharedDim, -1]);\n        const im2ColProgram = new Im2ColPackedProgram(x2ColShape, xSqueezed.shape, convInfo);\n        const im2Col = this.compileAndRun(im2ColProgram, [xSqueezed]).reshape([\n            1, x2ColShape[0], x2ColShape[1]\n        ]);\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;\n        const matmulProgram = new MatMulPackedProgram(im2Col.shape, [1, numCols, convInfo.outChannels], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [im2Col, w2Row];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (hasPreluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        const product = this.compileAndRun(matmulProgram, inputs);\n        if (isChannelsLast) {\n            return product.reshape([1, outHeight, outWidth, convInfo.outChannels]);\n        }\n        else {\n            return product.reshape([1, convInfo.outChannels, outHeight, outWidth]);\n        }\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n            (convInfo.padInfo.type === 'SAME' ||\n                convInfo.padInfo.type === 'VALID')) {\n            return this.conv2dByMatMul(input, filter, convInfo, bias, activation, preluActivationWeights);\n        }\n        if (env().getBool('WEBGL_CONV_IM2COL') && input.shape[0] === 1) {\n            return this.conv2dWithIm2Row(input, filter, convInfo, bias, activation, preluActivationWeights);\n        }\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, false) : null;\n        const program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [input, filter];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (preluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        return this.compileAndRun(program, inputs);\n    }\n    conv2d(x, filter, convInfo) {\n        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n            (convInfo.padInfo.type === 'SAME' ||\n                convInfo.padInfo.type === 'VALID')) {\n            return this.conv2dByMatMul(x, filter, convInfo);\n        }\n        if (env().getBool('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {\n            return this.conv2dWithIm2Row(x, filter, convInfo);\n        }\n        const program = new Conv2DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        const program = new Conv2DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    conv2dDerFilter(x, dy, convInfo) {\n        const program = new Conv2DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        const shouldPackDepthwiseConv = env().getBool('WEBGL_PACK_DEPTHWISECONV') &&\n            convInfo.strideWidth <= 2 &&\n            convInfo.outChannels / convInfo.inChannels === 1;\n        const fusedActivation = activation ?\n            mapActivationToShaderProgram(activation, shouldPackDepthwiseConv) :\n            null;\n        const inputs = [input, filter];\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        if (hasBias) {\n            inputs.push(bias);\n        }\n        if (hasPreluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        let program;\n        if (shouldPackDepthwiseConv) {\n            program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n            return this.compileAndRun(program, inputs);\n        }\n        program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n        return this.compileAndRun(program, inputs);\n    }\n    depthwiseConv2D(x, filter, convInfo) {\n        let program;\n        if (env().getBool('WEBGL_PACK_DEPTHWISECONV') &&\n            convInfo.strideWidth <= 2 &&\n            convInfo.outChannels / convInfo.inChannels === 1) {\n            program = new DepthwiseConvPacked2DProgram(convInfo);\n            return this.compileAndRun(program, [x, filter]);\n        }\n        program = new DepthwiseConv2DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        const program = new DepthwiseConv2DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    depthwiseConv2DDerFilter(x, dy, convInfo) {\n        const program = new DepthwiseConv2DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    conv3d(x, filter, convInfo) {\n        const program = new Conv3DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        const program = new Conv3DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    conv3dDerFilter(x, dy, convInfo) {\n        const program = new Conv3DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    cast(x, dtype) {\n        return backend_util.castTensor(x, dtype, this);\n    }\n    unstack(x, axis) {\n        const num = x.shape[axis];\n        const outShape = new Array(x.rank - 1);\n        let outIndex = 0;\n        for (let i = 0; i < x.rank; i++) {\n            if (i !== axis) {\n                outShape[outIndex++] = x.shape[i];\n            }\n        }\n        const begin = new Array(x.rank).fill(0);\n        const size = x.shape.slice();\n        size[axis] = 1;\n        const res = new Array(num);\n        for (let i = 0; i < res.length; i++) {\n            begin[axis] = i;\n            res[i] = this.slice(x, begin, size).reshape(outShape);\n        }\n        return res;\n    }\n    avgPool3d(x, convInfo) {\n        const program = new Pool3DProgram(convInfo, 'avg', false);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        const avgPool3dBackpropProgram = new AvgPool3DBackpropProgram(convInfo);\n        return this.compileAndRun(avgPool3dBackpropProgram, [dy], x.dtype);\n    }\n    maxPool3d(x, convInfo) {\n        const program = new Pool3DProgram(convInfo, 'max', false);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        const getPositions = true;\n        const maxPool3dPositionsProgram = new Pool3DProgram(convInfo, 'max', getPositions);\n        const maxPool3dPositions = this.compileAndRun(maxPool3dPositionsProgram, [x]);\n        const maxPool3dBackPropProgram = new MaxPool3DBackpropProgram(convInfo);\n        const result = this.compileAndRun(maxPool3dBackPropProgram, [dy, maxPool3dPositions], x.dtype);\n        maxPool3dPositions.dispose();\n        return result;\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        const program = env().getBool('WEBGL_PACK_IMAGE_OPERATIONS') ?\n            new ResizeBilinearPackedProgram(x.shape, newHeight, newWidth, alignCorners) :\n            new ResizeBilinearProgram(x.shape, newHeight, newWidth, alignCorners);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        const program = new ResizeBilinearBackpropProgram(dy, x, alignCorners);\n        return this.compileAndRun(program, [dy]);\n    }\n    resizeNearestNeighbor(x, newHeight, newWidth, alignCorners) {\n        const program = new ResizeNearestNeighborProgram(x.shape, newHeight, newWidth, alignCorners);\n        return this.compileAndRun(program, [x]);\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        const program = new ResizeNearestNeigborBackpropProgram(dy, x, alignCorners);\n        return this.compileAndRun(program, [dy]);\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        const probs = normalized ? logits : softmax(logits);\n        const batchSize = probs.shape[0];\n        const numOutcomes = probs.shape[1];\n        const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);\n        const customSetup = program.getCustomSetupFunc(seed);\n        return this.compileAndRun(program, [probs], 'int32', customSetup);\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        const program = new OneHotProgram(indices.size, depth, onValue, offValue);\n        return this.compileAndRun(program, [indices]);\n    }\n    diag(x) {\n        const program = new DiagProgram(x.size);\n        return this.compileAndRun(program, [x]);\n    }\n    cropAndResize(image, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        const program = new CropAndResizeProgram(image.shape, boxes.shape, cropSize, method, extrapolationValue);\n        return this.compileAndRun(program, [image, boxes, boxIndex], 'float32');\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n        const batchSize = x.shape[0];\n        const inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];\n        const inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];\n        const inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];\n        const outputHeight = inputHeight * blockSize;\n        const outputWidth = inputWidth * blockSize;\n        const outputDepth = inputDepth / (blockSize * blockSize);\n        const outputShape = (dataFormat === 'NHWC') ?\n            [batchSize, outputHeight, outputWidth, outputDepth] :\n            [batchSize, outputDepth, outputHeight, outputWidth];\n        const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);\n        return this.compileAndRun(program, [x]);\n    }\n    split(x, sizeSplits, axis) {\n        return split(x, sizeSplits, axis);\n    }\n    scatterND(indices, updates, shape) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(updates, indices, shape);\n        const flattenShape = [outputSize / sliceSize, sliceSize];\n        const flattenIndices = indices.reshape([numUpdates, sliceRank]);\n        const flattenX = updates.reshape([numUpdates, sliceSize]);\n        if (outputSize === 0) {\n            return backend_util.reshapeTensor(tensor([]), shape);\n        }\n        const defaultValue = scalar(0);\n        const program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.rank, flattenX.rank, strides, flattenShape);\n        const res = this.compileAndRun(program, [flattenX, flattenIndices, defaultValue]);\n        return res.reshape(shape);\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        const { sliceRank, numUpdates, strides, outputSize } = backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n        const sumDupeIndices = false;\n        const program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.rank, sparseValues.rank, strides, [outputSize, 1], sumDupeIndices);\n        const res = this.compileAndRun(program, [sparseValues, sparseIndices, defaultValue]);\n        return res.reshape(outputShape);\n    }\n    fft(x) {\n        const inverse = false;\n        return this.fftImpl(x, inverse);\n    }\n    ifft(x) {\n        const inverse = true;\n        return this.fftImpl(x, inverse);\n    }\n    fftImpl(x, inverse) {\n        const xData = this.texData.get(x.dataId);\n        const realProgram = new FFTProgram(fft_gpu.COMPLEX_FFT.REAL, x.shape, inverse);\n        const imagProgram = new FFTProgram(fft_gpu.COMPLEX_FFT.IMAG, x.shape, inverse);\n        const inputs = [\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.real),\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.imag),\n        ];\n        const real = this.compileAndRun(realProgram, inputs);\n        const imag = this.compileAndRun(imagProgram, inputs);\n        const complex = this.complex(real, imag).as2D(x.shape[0], x.shape[1]);\n        real.dispose();\n        imag.dispose();\n        return complex;\n    }\n    gatherND(x, indices) {\n        const indicesShape = indices.shape;\n        const sliceRank = indicesShape[indicesShape.length - 1];\n        const [resultShape, numSlices, sliceSize, strides] = backend_util.prepareAndValidate(x, indices);\n        const flattenIndices = indices.reshape([numSlices, sliceRank]);\n        const flattenX = x.reshape([x.size / sliceSize, sliceSize]);\n        const program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);\n        const res = this.compileAndRun(program, [flattenX, flattenIndices]);\n        return res.reshape(resultShape);\n    }\n    fill(shape, value, dtype) {\n        dtype = dtype || util.inferDtype(value);\n        if (dtype === 'string') {\n            // String type should be handled in CPU memory.\n            const values = util.getArrayFromDType(dtype, util.sizeFromShape(shape));\n            values.fill(value);\n            return engine().makeTensor(values, shape, dtype, this);\n        }\n        else {\n            const program = new FillProgram(shape, value);\n            const customSetup = program.getCustomSetupFunc(value);\n            return this.compileAndRun(program, [], dtype, customSetup);\n        }\n    }\n    onesLike(x) {\n        if (x.dtype === 'string') {\n            throw new Error('onesLike is not supported under string dtype');\n        }\n        else {\n            // TODO(cais, smilkov): Add WebGL shader for onesLike:\n            //   https://github.com/tensorflow/tfjs/issues/1293\n            return this.fill(x.shape, 1, x.dtype);\n        }\n    }\n    zerosLike(x) {\n        return this.fill(x.shape, x.dtype === 'string' ? '' : 0, x.dtype);\n    }\n    linspace(start, stop, num) {\n        // TODO: Use CPU implementation due to the precision problem in Safari.\n        return backend_util.linspaceImpl(start, stop, num);\n    }\n    makeTensorInfo(shape, dtype, values) {\n        const dataId = this.write(values, shape, dtype);\n        this.texData.get(dataId).usage = null;\n        return { dataId, shape, dtype };\n    }\n    makeOutput(shape, dtype, values) {\n        const { dataId } = this.makeTensorInfo(shape, dtype, values);\n        return engine().makeTensorFromDataId(dataId, shape, dtype, this);\n    }\n    unpackTensor(input) {\n        const program = new UnpackProgram(input.shape);\n        return this.runWebGLProgram(program, [input], input.dtype);\n    }\n    packTensor(input) {\n        const program = new PackProgram(input.shape);\n        const preventEagerUnpackingOutput = true;\n        return this.runWebGLProgram(program, [input], input.dtype, null /* customSetup */, preventEagerUnpackingOutput);\n    }\n    packedReshape(input, afterShape) {\n        const input3DShape = [\n            webgl_util.getBatchDim(input.shape),\n            ...webgl_util.getRowsCols(input.shape)\n        ];\n        const input3D = {\n            dtype: input.dtype,\n            shape: input3DShape,\n            dataId: input.dataId\n        };\n        const afterShapeAs3D = [\n            webgl_util.getBatchDim(afterShape), ...webgl_util.getRowsCols(afterShape)\n        ];\n        const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n        const preventEagerUnpackingOfOutput = true;\n        const output = this.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n        return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n    }\n    decode(dataId) {\n        const texData = this.texData.get(dataId);\n        const { isPacked, shape, dtype } = texData;\n        const shapeAs3D = webgl_util.getShapeAs3D(shape);\n        let program;\n        if (isPacked) {\n            program = new DecodeMatrixPackedProgram(shapeAs3D);\n        }\n        else {\n            program = new DecodeMatrixProgram(shapeAs3D);\n        }\n        const preventEagerUnpackingOfOutput = true;\n        const out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype, dataId }], dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n        return { dtype, shape, dataId: out.dataId };\n    }\n    runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {\n        const output = this.makeTensorInfo(program.outputShape, outputDtype);\n        const outData = this.texData.get(output.dataId);\n        if (program.packedOutput) {\n            outData.isPacked = true;\n        }\n        if (program.outPackingScheme === tex_util.PackingScheme.DENSE) {\n            const texelShape = tex_util.getDenseTexShape(program.outputShape);\n            // For a densely packed output, we explicitly set texShape\n            // so it doesn't get assigned later according to our typical packing\n            // scheme wherein a single texel can only contain values from adjacent\n            // rows/cols.\n            outData.texShape = texelShape.map(d => d * 2);\n        }\n        if (program.outTexUsage != null) {\n            outData.usage = program.outTexUsage;\n        }\n        if (util.sizeFromShape(output.shape) === 0) {\n            // Short-circuit the computation since the result is empty (has 0 in its\n            // shape).\n            outData.values =\n                util.getTypedArrayFromDType(output.dtype, 0);\n            return output;\n        }\n        const dataToDispose = [];\n        const inputsData = inputs.map(input => {\n            if (input.dtype === 'complex64') {\n                throw new Error(`GPGPUProgram does not support complex64 input. For complex64 ` +\n                    `dtypes, please separate the program into real and imaginary ` +\n                    `parts.`);\n            }\n            let texData = this.texData.get(input.dataId);\n            if (texData.texture == null) {\n                if (!program.packedInputs &&\n                    util.sizeFromShape(input.shape) <=\n                        env().getNumber('WEBGL_SIZE_UPLOAD_UNIFORM')) {\n                    // Upload small tensors that live on the CPU as uniforms, not as\n                    // textures. Do this only when the environment supports 32bit floats\n                    // due to problems when comparing 16bit floats with 32bit floats.\n                    // TODO(https://github.com/tensorflow/tfjs/issues/821): Make it\n                    // possible for packed shaders to sample from uniforms.\n                    return {\n                        shape: input.shape,\n                        texData: null,\n                        isUniform: true,\n                        uniformValues: texData.values\n                    };\n                }\n                // This ensures that if a packed program's inputs have not yet been\n                // uploaded to the GPU, they get uploaded as packed right off the bat.\n                if (program.packedInputs) {\n                    texData.isPacked = true;\n                    texData.shape = input.shape;\n                }\n            }\n            else if (!!texData.isPacked !== !!program.packedInputs) {\n                input = texData.isPacked ? this.unpackTensor(input) :\n                    this.packTensor(input);\n                dataToDispose.push(input);\n                texData = this.texData.get(input.dataId);\n            }\n            else if (texData.isPacked &&\n                !webgl_util.isReshapeFree(texData.shape, input.shape)) {\n                // This is a special case where a texture exists for a tensor\n                // but the shapes are incompatible (due to packing constraints) because\n                // the tensor did not have a chance to go through the packed reshape\n                // shader. This only happens when we reshape the *same* tensor to form\n                // *distinct* inputs to an op, e.g. dotting a vector with itself. This\n                // case will disappear once packed uploading is the default.\n                const savedInput = input;\n                const targetShape = input.shape;\n                input.shape = texData.shape;\n                input = this.packedReshape(input, targetShape);\n                dataToDispose.push(input);\n                texData = this.texData.get(input.dataId);\n                savedInput.shape = targetShape;\n            }\n            this.uploadToGPU(input.dataId);\n            return { shape: input.shape, texData, isUniform: false };\n        });\n        this.uploadToGPU(output.dataId);\n        const outputData = { shape: output.shape, texData: outData, isUniform: false };\n        const key = gpgpu_math.makeShaderKey(program, inputsData, outputData);\n        const binary = this.getAndSaveBinary(key, () => {\n            return gpgpu_math.compileProgram(this.gpgpu, program, inputsData, outputData);\n        });\n        const shouldTimeProgram = this.activeTimers != null;\n        let query;\n        if (shouldTimeProgram) {\n            query = this.startTimer();\n        }\n        gpgpu_math.runProgram(this.gpgpu, binary, inputsData, outputData, customSetup);\n        dataToDispose.forEach(info => this.disposeIntermediateTensorInfo(info));\n        if (shouldTimeProgram) {\n            query = this.endTimer(query);\n            this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });\n        }\n        if (!env().getBool('WEBGL_LAZILY_UNPACK') && outData.isPacked &&\n            preventEagerUnpackingOfOutput === false) {\n            const unpacked = this.unpackTensor(output);\n            this.disposeIntermediateTensorInfo(output);\n            return unpacked;\n        }\n        return output;\n    }\n    compileAndRun(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {\n        outputDtype = outputDtype || inputs[0].dtype;\n        const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput);\n        return engine().makeTensorFromDataId(outInfo.dataId, outInfo.shape, outInfo.dtype);\n    }\n    getAndSaveBinary(key, getBinary) {\n        if (!(key in this.binaryCache)) {\n            this.binaryCache[key] = getBinary();\n        }\n        return this.binaryCache[key];\n    }\n    getTextureManager() {\n        return this.textureManager;\n    }\n    dispose() {\n        if (this.disposed) {\n            return;\n        }\n        // Avoid disposing the compiled webgl programs during unit testing because\n        // it slows down test execution.\n        if (!env().getBool('IS_TEST')) {\n            const allKeys = Object.keys(this.binaryCache);\n            allKeys.forEach(key => {\n                this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);\n                delete this.binaryCache[key];\n            });\n        }\n        this.textureManager.dispose();\n        if (this.canvas != null &&\n            (typeof (HTMLCanvasElement) !== 'undefined' &&\n                this.canvas instanceof HTMLCanvasElement)) {\n            this.canvas.remove();\n        }\n        else {\n            this.canvas = null;\n        }\n        if (this.gpgpuCreatedLocally) {\n            this.gpgpu.program = null;\n            this.gpgpu.dispose();\n        }\n        this.disposed = true;\n    }\n    floatPrecision() {\n        if (this.floatPrecisionValue == null) {\n            this.floatPrecisionValue = tidy(() => {\n                if (!env().get('WEBGL_RENDER_FLOAT32_ENABLED')) {\n                    // Momentarily switching DEBUG flag to false so we don't throw an\n                    // error trying to upload a small value.\n                    const debugFlag = env().getBool('DEBUG');\n                    env().set('DEBUG', false);\n                    const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];\n                    env().set('DEBUG', debugFlag);\n                    if (underflowCheckValue > 0) {\n                        return 32;\n                    }\n                }\n                return 16;\n            });\n        }\n        return this.floatPrecisionValue;\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n    }\n    uploadToGPU(dataId) {\n        const texData = this.texData.get(dataId);\n        const { shape, dtype, values, texture, usage, isPacked } = texData;\n        if (texture != null) {\n            // Array is already on GPU. No-op.\n            return;\n        }\n        const shouldTimeProgram = this.activeTimers != null;\n        let start;\n        if (shouldTimeProgram) {\n            start = util.now();\n        }\n        let texShape = texData.texShape;\n        if (texShape == null) {\n            texShape = webgl_util.getTextureShapeFromLogicalShape(shape, isPacked);\n            texData.texShape = texShape;\n        }\n        if (values != null) {\n            const shapeAs3D = webgl_util.getShapeAs3D(shape);\n            let program;\n            let width = texShape[1], height = texShape[0];\n            const isByteArray = values instanceof Uint8Array;\n            if (isPacked) {\n                [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]);\n                program = new EncodeMatrixPackedProgram(shapeAs3D, [height, width], isByteArray);\n            }\n            else {\n                program =\n                    new EncodeMatrixProgram(shapeAs3D, [height, width], isByteArray);\n            }\n            const tempDenseInputHandle = this.makeTensorInfo([height, width], dtype);\n            if (isByteArray) {\n                this.texData.get(tempDenseInputHandle.dataId).usage =\n                    TextureUsage.PIXELS;\n            }\n            else {\n                this.texData.get(tempDenseInputHandle.dataId).usage =\n                    TextureUsage.UPLOAD;\n            }\n            this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);\n            // We want the output to remain packed regardless of the value of\n            // WEBGL_PACK.\n            const preventEagerUnpacking = true;\n            const encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, null, preventEagerUnpacking);\n            // Have the original texture assume the identity of the encoded output.\n            const outputTexData = this.texData.get(encodedOutputTarget.dataId);\n            texData.texture = outputTexData.texture;\n            texData.texShape = outputTexData.texShape;\n            texData.isPacked = outputTexData.isPacked;\n            texData.usage = outputTexData.usage;\n            this.disposeIntermediateTensorInfo(tempDenseInputHandle);\n            this.texData.delete(encodedOutputTarget.dataId);\n            // Once uploaded, don't store the values on cpu.\n            texData.values = null;\n            if (shouldTimeProgram) {\n                this.uploadWaitMs += util.now() - start;\n            }\n        }\n        else {\n            const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);\n            texData.texture = newTexture;\n        }\n    }\n    convertAndCacheOnCPU(dataId, float32Values) {\n        const texData = this.texData.get(dataId);\n        const { dtype } = texData;\n        this.releaseGPUData(dataId);\n        if (float32Values != null) {\n            texData.values = float32ToTypedArray(float32Values, dtype);\n        }\n        return texData.values;\n    }\n    acquireTexture(texShape, texType, dtype, isPacked) {\n        this.numBytesInGPU += this.computeBytes(texShape, dtype);\n        if (!this.warnedAboutMemory &&\n            this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {\n            const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);\n            this.warnedAboutMemory = true;\n            console.warn(`High memory usage in GPU: ${mb} MB, ` +\n                `most likely due to a memory leak`);\n        }\n        return this.textureManager.acquireTexture(texShape, texType, isPacked);\n    }\n    computeBytes(shape, dtype) {\n        return shape[0] * shape[1] * util.bytesPerElement(dtype);\n    }\n    tryRunOnCpuOrThrow(inputs, fn) {\n        if (this.shouldExecuteOnCPU(inputs)) {\n            try {\n                return fn();\n            }\n            catch (e) {\n                if (env().getBool('IS_TEST')) {\n                    throw new Error('CPU forwarding failed');\n                }\n            }\n        }\n        return null;\n    }\n}\nfunction float32ToTypedArray(a, dtype) {\n    if (dtype === 'float32' || dtype === 'complex64') {\n        return a;\n    }\n    else if (dtype === 'int32' || dtype === 'bool') {\n        const result = (dtype === 'int32') ? new Int32Array(a.length) :\n            new Uint8Array(a.length);\n        for (let i = 0; i < result.length; ++i) {\n            result[i] = Math.round(a[i]);\n        }\n        return result;\n    }\n    else {\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n}\n//# sourceMappingURL=backend_webgl.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport * as gpgpu_util from './gpgpu_util';\nimport * as webgl_util from './webgl_util';\nexport { MathBackendWebGL } from './backend_webgl';\nexport { setWebGLContext } from './canvas_util';\nexport { GPGPUContext } from './gpgpu_context';\n// WebGL specific utils.\nexport { gpgpu_util, webgl_util };\n/**\n * Enforce use of half precision textures if available on the platform.\n *\n * @doc {heading: 'Environment', namespace: 'webgl'}\n */\nexport function forceHalfFloat() {\n    env().set('WEBGL_FORCE_F16_TEXTURES', true);\n}\n//# sourceMappingURL=webgl.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// base.ts is the webgl backend without auto kernel registration.\nimport { device_util, registerBackend } from '@tensorflow/tfjs-core';\nimport { MathBackendWebGL } from './backend_webgl';\nexport { version as version_webgl } from './version';\nif (device_util.isBrowser()) {\n    registerBackend('webgl', () => new MathBackendWebGL(), 2 /* priority */);\n}\n// Export webgl utilities\nexport * from './webgl';\n// Export forceHalfFlost under webgl namespace for the union bundle.\nimport { forceHalfFloat } from './webgl';\nexport const webgl = { forceHalfFloat };\n//# sourceMappingURL=base.js.map", "import { env } from '@tensorflow/tfjs-core';\nimport { BinaryOpProgram } from '../binaryop_gpu';\nimport { BinaryOpPackedProgram } from '../binaryop_packed_gpu';\nimport { UnaryOpProgram } from '../unaryop_gpu';\nexport const CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;\nexport const CHECK_NAN_SNIPPET_BINARY = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nexport const CHECK_NAN_SNIPPET_BINARY_PACKED = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param opSnippets Op snippet to create `UnaryOpProgram`.\n */\nexport function unaryKernelFunc(opSnippet) {\n    return ({ inputs, backend }) => {\n        const { x } = inputs;\n        const webglBackend = backend;\n        const program = new UnaryOpProgram(x.shape, opSnippet);\n        return webglBackend.runWebGLProgram(program, [x], x.dtype);\n    };\n}\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param opSnippet Op snippet to create `BinaryOpProgram`.\n * @param packedOpSnippet Op snippet to create `BinaryOpPackedProgram`.\n * @param checkOutOfBoundsForPackedProgram Whether to set checkOutOfBounds=true\n *     when creating BinaryOpPackedProgram.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc(opSnippet, packedOpSnippet, checkOutOfBoundsForPackedProgram, dtype) {\n    // TODO(jingjin): handle complex64.\n    return ({ inputs, backend }) => {\n        const { a, b } = inputs;\n        const webglBackend = backend;\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(packedOpSnippet, a.shape, b.shape, !!checkOutOfBoundsForPackedProgram) :\n            new BinaryOpProgram(opSnippet, a.shape, b.shape);\n        const $dtype = dtype || a.dtype;\n        const output = webglBackend.runWebGLProgram(program, [a, b], $dtype);\n        return output;\n    };\n}\n//# sourceMappingURL=kernel_funcs_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan2 } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc, CHECK_NAN_SNIPPET_BINARY, CHECK_NAN_SNIPPET_BINARY_PACKED } from '../kernel_utils/kernel_funcs_utils';\nconst ATAN2 = CHECK_NAN_SNIPPET_BINARY + `\n  return atan(a, b);\n`;\nconst ATAN2_PACKED = `\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET_BINARY_PACKED + `\n  return result;\n`;\nexport const atan2KernelFunc = binaryKernelFunc(ATAN2, ATAN2_PACKED);\nexport const atan2Config = {\n    kernelName: Atan2,\n    backendName: 'webgl',\n    kernelFunc: atan2KernelFunc,\n};\n//# sourceMappingURL=Atan2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '@tensorflow/tfjs-core';\nexport function identity(args) {\n    const { inputs, backend } = args;\n    const { x } = inputs;\n    backend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nexport const identityConfig = {\n    kernelName: Identity,\n    backendName: 'webgl',\n    kernelFunc: identity\n};\n//# sourceMappingURL=Identity.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool, backend_util, util } from '@tensorflow/tfjs-core';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nimport { identity } from './Identity';\nexport function avgPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'avgPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        return identity({ inputs: { x }, backend });\n    }\n    const avgPoolProgram = new Pool2DProgram(convInfo, 'avg', false);\n    return backend.runWebGLProgram(avgPoolProgram, [x], 'float32');\n}\nexport const avgPoolConfig = {\n    kernelName: AvgPool,\n    backendName: 'webgl',\n    kernelFunc: avgPool\n};\n//# sourceMappingURL=AvgPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPoolBackprop, backend_util } from '@tensorflow/tfjs-core';\nimport { AvgPool2DBackpropProgram } from '../avg_pool_backprop_gpu';\nimport { assertNotComplex } from '../webgl_util';\nexport function avgPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input } = inputs;\n    const x = input;\n    assertNotComplex([dy, input], 'avgPoolBackprop');\n    const { filterSize, strides, pad } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);\n    const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);\n    return backend.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);\n}\nexport const avgPoolBackpropConfig = {\n    kernelName: AvgPoolBackprop,\n    backendName: 'webgl',\n    kernelFunc: avgPoolBackprop\n};\n//# sourceMappingURL=AvgPoolBackprop.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class BatchNormProgram {\n    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n        this.outputShape = [];\n        this.variableNames = ['x', 'mean', 'variance'];\n        backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n        backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n        let offsetSnippet = '0.0';\n        if (offsetShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n            this.variableNames.push('offset');\n            offsetSnippet = 'getOffsetAtOutCoords()';\n        }\n        let scaleSnippet = '1.0';\n        if (scaleShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n            this.variableNames.push('scale');\n            scaleSnippet = 'getScaleAtOutCoords()';\n        }\n        this.outputShape = xShape;\n        this.userCode = `\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${offsetSnippet};\n        float scale = ${scaleSnippet};\n        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=batchnorm_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class BatchNormPackedProgram {\n    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.variableNames = ['x', 'mean', 'variance'];\n        backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n        backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n        let offsetSnippet = 'vec4(0.0)';\n        if (offsetShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n            this.variableNames.push('offset');\n            offsetSnippet = 'getOffsetAtOutCoords()';\n        }\n        let scaleSnippet = 'vec4(1.0)';\n        if (scaleShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n            this.variableNames.push('scale');\n            scaleSnippet = 'getScaleAtOutCoords()';\n        }\n        this.outputShape = xShape;\n        this.userCode = `\n      void main() {\n        vec4 offset = ${offsetSnippet};\n        vec4 scale = ${scaleSnippet};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=batchnorm_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, FusedBatchNorm, util } from '@tensorflow/tfjs-core';\nimport { BatchNormProgram } from '../batchnorm_gpu';\nimport { BatchNormPackedProgram } from '../batchnorm_packed_gpu';\nexport const batchNormKernelFunc = ({ inputs, backend, attrs }) => {\n    const { x, mean, variance, offset, scale } = inputs;\n    util.assert(mean.shape.length === variance.shape.length, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert(offset == null || mean.shape.length === offset.shape.length, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert(scale == null || mean.shape.length === scale.shape.length, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    let { varianceEpsilon } = attrs;\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const finalInputs = [x, mean, variance];\n    let offsetShape = null;\n    if (offset != null) {\n        offsetShape = offset.shape;\n        finalInputs.push(offset);\n    }\n    let scaleShape = null;\n    if (scale != null) {\n        scaleShape = scale.shape;\n        finalInputs.push(scale);\n    }\n    const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n        new BatchNormPackedProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) :\n        new BatchNormProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);\n    const output = backend.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);\n    return output;\n};\nexport const batchNormConfig = {\n    kernelName: FusedBatchNorm,\n    backendName: 'webgl',\n    kernelFunc: batchNormKernelFunc,\n};\n//# sourceMappingURL=BatchNorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '@tensorflow/tfjs-core';\nimport { CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst COS = CHECK_NAN_SNIPPET_UNARY + `\n  return cos(x);\n`;\nexport const cosKernelFunc = unaryKernelFunc(COS);\nexport const cosConfig = {\n    kernelName: Cos,\n    backendName: 'webgl',\n    kernelFunc: cosKernelFunc,\n};\n//# sourceMappingURL=Cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\n// Without the equality check div produces 0.9999 for a = b, which when\n// floored can cause errors.\nconst DIV = `\nif (a == b) {\n  return 1.0;\n};\nreturn a / b;`;\n// We do the same as in ./binaryop_gpu, with vec4 and ivec4.\n// On Linux, the vectorized implementation produces NaNs when a and b are 0.\nconst DIV_PACKED = `\n  // vec4 one = vec4(equal(a, b));\n  // return one + (vec4(1.0) - one) * a / b;\n  vec4 result = a / b;\n  if(a.x == b.x) {\n    result.x = 1.;\n  }\n  if(a.y == b.y) {\n    result.y = 1.;\n  }\n  if(a.z == b.z) {\n    result.z = 1.;\n  }\n  if(a.w == b.w) {\n    result.w = 1.;\n  }\n\n  return result;\n`;\nexport const divKernelFunc = binaryKernelFunc(DIV, DIV_PACKED, true /* checkOutOfBoundsForPackedProgram */);\nexport const divConfig = {\n    kernelName: Div,\n    backendName: 'webgl',\n    kernelFunc: divKernelFunc,\n};\n//# sourceMappingURL=Div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FlipLeftRightProgram {\n    constructor(imageShape) {\n        this.variableNames = ['Image'];\n        this.outputShape = [];\n        const imageWidth = imageShape[2];\n        this.outputShape = imageShape;\n        this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${imageWidth} - x;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${imageWidth}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n    }\n}\n//# sourceMappingURL=flip_left_right_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FlipLeftRight } from '@tensorflow/tfjs-core';\nimport { FlipLeftRightProgram } from '../flip_left_right_gpu';\nexport const flipLeftRightConfig = {\n    kernelName: FlipLeftRight,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend }) => {\n        const { image } = inputs;\n        const webglBackend = backend;\n        const program = new FlipLeftRightProgram(image.shape);\n        const output = webglBackend.runWebGLProgram(program, [image], image.dtype);\n        return output;\n    }\n};\n//# sourceMappingURL=FlipLeftRight.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from '../../glsl_version';\nexport class FromPixelsProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        const glsl = getGlslDifferences();\n        const [height, width,] = outputShape;\n        this.outputShape = outputShape;\n        this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);\n\n        vec4 values = ${glsl.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=from_pixels_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from '../../glsl_version';\nexport class FromPixelsPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        const glsl = getGlslDifferences();\n        const [height, width,] = outputShape;\n        this.outputShape = outputShape;\n        this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(${width}.0, ${height}.0);\n            vec4 values = ${glsl.texture2D}(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=from_pixels_packed_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { FromPixels } from '@tensorflow/tfjs-core';\nimport { TextureUsage } from '../tex_util';\nimport { FromPixelsProgram } from './FromPixels_utils/from_pixels_gpu';\nimport { FromPixelsPackedProgram } from './FromPixels_utils/from_pixels_packed_gpu';\nexport const fromPixelsConfig = {\n    kernelName: FromPixels,\n    backendName: 'webgl',\n    kernelFunc: fromPixels,\n};\nlet fromPixels2DContext;\nfunction fromPixels(args) {\n    const { inputs, backend, attrs } = args;\n    let { pixels } = inputs;\n    const { numChannels } = attrs;\n    const isVideo = typeof (HTMLVideoElement) !== 'undefined' &&\n        pixels instanceof HTMLVideoElement;\n    const isImage = typeof (HTMLImageElement) !== 'undefined' &&\n        pixels instanceof HTMLImageElement;\n    const [width, height] = isVideo ?\n        [\n            pixels.videoWidth,\n            pixels.videoHeight\n        ] :\n        [pixels.width, pixels.height];\n    const texShape = [height, width];\n    const outShape = [height, width, numChannels];\n    if (isImage || isVideo) {\n        if (fromPixels2DContext == null) {\n            fromPixels2DContext = document.createElement('canvas').getContext('2d');\n        }\n        fromPixels2DContext.canvas.width = width;\n        fromPixels2DContext.canvas.height = height;\n        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n        pixels = fromPixels2DContext.canvas;\n    }\n    const tempPixelHandle = backend.makeTensorInfo(texShape, 'int32');\n    // This is a byte texture with pixels.\n    backend.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;\n    backend.gpgpu.uploadPixelDataToTexture(backend.getTexture(tempPixelHandle.dataId), pixels);\n    const program = env().getBool('WEBGL_PACK') ?\n        new FromPixelsPackedProgram(outShape) :\n        new FromPixelsProgram(outShape);\n    const res = backend.runWebGLProgram(program, [tempPixelHandle], 'int32');\n    backend.disposeData(tempPixelHandle.dataId);\n    return res;\n}\n//# sourceMappingURL=FromPixels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { ReduceProgram } from '../reduce_gpu';\n// Returns an array of configuration objects that describe each stage of the\n// reduction.\nfunction getReductionStages(inShape) {\n    const stages = [];\n    while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {\n        const outSize = stages.length ? stages[stages.length - 1].outSize : inShape[1];\n        const windowSize = backend_util.computeOptimalWindowSize(outSize);\n        stages.push({\n            inSize: outSize,\n            windowSize,\n            outSize: Math.ceil(outSize / windowSize)\n        });\n    }\n    return stages;\n}\nexport function reduce(x, dtype, reductionType, backend) {\n    const reductionStages = getReductionStages(x.shape);\n    let result = x;\n    for (let i = 0; i < reductionStages.length; i++) {\n        const { inSize, windowSize, outSize } = reductionStages[i];\n        const program = new ReduceProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, reductionType);\n        const previousResult = result;\n        result = backend.runWebGLProgram(program, [result], dtype);\n        if (previousResult.dataId !== x.dataId) {\n            backend.disposeData(previousResult.dataId);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=reduce.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ReshapePackedProgram } from '../reshape_packed_gpu';\nimport { getBatchDim, getRowsCols } from '../webgl_util';\nexport function packedReshape(input, afterShape, backend) {\n    const input3DShape = [getBatchDim(input.shape),\n        ...getRowsCols(input.shape)];\n    const input3D = {\n        dtype: input.dtype,\n        shape: input3DShape,\n        dataId: input.dataId\n    };\n    const afterShapeAs3D = [getBatchDim(afterShape),\n        ...getRowsCols(afterShape)];\n    const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n    const preventEagerUnpackingOfOutput = true;\n    const output = backend.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n    return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n}\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape, util } from '@tensorflow/tfjs-core';\nimport { packedReshape } from '../kernel_utils/reshape';\nimport { isReshapeFree } from '../webgl_util';\nexport function reshape(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { shape } = attrs;\n    const webglBackend = backend;\n    const xSize = util.sizeFromShape(x.shape);\n    const $shape = util.inferFromImplicitShape(shape, xSize);\n    const $xSize = util.sizeFromShape($shape);\n    util.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n        `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n        `shape must have the same number of elements.`);\n    const xTexData = webglBackend.texData.get(x.dataId);\n    if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) &&\n        !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {\n        return packedReshape(x, $shape, webglBackend);\n    }\n    webglBackend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nexport const reshapeConfig = {\n    kernelName: Reshape,\n    backendName: 'webgl',\n    kernelFunc: reshape\n};\n//# sourceMappingURL=Reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { reduce } from '../kernel_utils/reduce';\nimport { reshape } from '../kernels/Reshape';\nexport function maxImpl(x, reduceShape, outShape, backend) {\n    const inSize = util.sizeFromShape(reduceShape);\n    const xSize = util.sizeFromShape(x.shape);\n    const batchSize = xSize / inSize;\n    const reshapedInput = reshape({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend });\n    const reduced = reduce(reshapedInput, x.dtype, 'max', backend);\n    const reshapedOutput = reshape({ inputs: { x: reduced }, attrs: { shape: outShape }, backend });\n    backend.disposeIntermediateTensorInfo(reshapedInput);\n    backend.disposeIntermediateTensorInfo(reduced);\n    return reshapedOutput;\n}\n//# sourceMappingURL=Max_impl.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class TransposeProgram {\n    constructor(aShape, newDim) {\n        this.variableNames = ['A'];\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[newDim[i]];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const switched = getSwitchedCoords(newDim);\n        this.userCode = `\n    void main() {\n      ${dtype} resRC = getOutputCoords();\n      setOutput(getA(${switched}));\n    }\n    `;\n    }\n}\nfunction getSwitchedCoords(newDim) {\n    const rank = newDim.length;\n    if (rank > 6) {\n        throw Error(`Transpose for rank ${rank} is not yet supported`);\n    }\n    const originalOrder = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u', 'resRC.v'];\n    const switchedCoords = new Array(rank);\n    for (let i = 0; i < newDim.length; i++) {\n        switchedCoords[newDim[i]] = originalOrder[i];\n    }\n    return switchedCoords.join();\n}\n//# sourceMappingURL=transpose_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getVecChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class TransposePackedProgram {\n    constructor(aShape, newDim) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[newDim[i]];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        if (this.rank > 6) {\n            throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);\n        }\n        const dtype = getCoordsDataType(this.rank);\n        const outputOrder = getVecChannels('rc', this.rank);\n        const switchedOrder = new Array(this.rank);\n        for (let i = 0; i < newDim.length; i++) {\n            switchedOrder[newDim[i]] = outputOrder[i];\n        }\n        const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;\n        const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;\n        const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;\n        this.userCode = `\n    void main() {\n      ${dtype} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${getc};\n      if(${nextColumn}) {\n        result[1] = ${getc};\n      }\n      --${outputOrder[this.rank - 1]};\n      if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {\n        result[2] = ${getc};\n        if(${nextColumn}) {\n          result[3] = ${getc};\n        }\n      }\n      setOutput(result);\n    }\n    `;\n    }\n}\n//# sourceMappingURL=transpose_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { transposeImplCPU } from '../kernel_utils/shared';\nimport { TransposeProgram } from '../transpose_gpu';\nimport { TransposePackedProgram } from '../transpose_packed_gpu';\nexport function transposeImpl(x, perm, backend) {\n    const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n        new TransposePackedProgram(x.shape, perm) :\n        new TransposeProgram(x.shape, perm);\n    return backend.runWebGLProgram(program, [x], x.dtype);\n}\nexport { transposeImplCPU };\n//# sourceMappingURL=Transpose_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '@tensorflow/tfjs-core';\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { maxImplCPU } from '../kernel_utils/shared';\nimport { maxImpl } from './Max_impl';\nimport { transposeImpl, transposeImplCPU } from './Transpose_impl';\nexport const maxConfig = {\n    kernelName: Max,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { reductionIndices, keepDims } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        const maxInputIsTransposed = permutedAxes != null;\n        const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);\n        let maxInput = x;\n        if (maxInputIsTransposed) {\n            if (shouldExecuteOnCPU) {\n                const xTexData = webglBackend.texData.get(maxInput.dataId);\n                const values = xTexData.values;\n                const newShape = new Array(xRank);\n                for (let i = 0; i < newShape.length; i++) {\n                    newShape[i] = x.shape[permutedAxes[i]];\n                }\n                const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n                maxInput = webglBackend.makeTensorInfo(newShape, x.dtype);\n                const maxInputData = webglBackend.texData.get(maxInput.dataId);\n                maxInputData.values = maxInputValues;\n            }\n            else {\n                maxInput = transposeImpl(x, permutedAxes, webglBackend);\n            }\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n        }\n        backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n        const [maxOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(maxInput.shape, axes);\n        let outShape = maxOutShape;\n        if (keepDims) {\n            // rather than reshape at the end, set the target shape here.\n            outShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n        }\n        let out;\n        if (shouldExecuteOnCPU) {\n            const xTexData = webglBackend.texData.get(maxInput.dataId);\n            const values = xTexData.values;\n            const outValues = maxImplCPU(values, util.sizeFromShape(reduceShape), outShape, x.dtype);\n            out = webglBackend.makeTensorInfo(outShape, x.dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n        }\n        else {\n            out = maxImpl(maxInput, reduceShape, outShape, webglBackend);\n        }\n        if (maxInputIsTransposed) {\n            webglBackend.disposeIntermediateTensorInfo(maxInput);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPool, util } from '@tensorflow/tfjs-core';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nimport { identity } from './Identity';\nexport function maxPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'maxPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        return identity({ inputs: { x }, backend });\n    }\n    const maxPoolProgram = new Pool2DProgram(convInfo, 'max', false);\n    return backend.runWebGLProgram(maxPoolProgram, [x], x.dtype);\n}\nexport const maxPoolConfig = {\n    kernelName: MaxPool,\n    backendName: 'webgl',\n    kernelFunc: maxPool\n};\n//# sourceMappingURL=MaxPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPoolBackprop } from '@tensorflow/tfjs-core';\nimport { MaxPool2DBackpropProgram } from '../max_pool_backprop_gpu';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nexport function maxPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input, output } = inputs;\n    const x = input;\n    assertNotComplex([input, output], 'maxPoolBackprop');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n    const getPositions = true;\n    const maxPoolPositionsProgram = new Pool2DProgram(convInfo, 'max', getPositions);\n    const maxPoolPositions = backend.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);\n    const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);\n    const result = backend.runWebGLProgram(maxPoolBackPropProgram, [dy, maxPoolPositions], x.dtype);\n    backend.disposeIntermediateTensorInfo(maxPoolPositions);\n    return result;\n}\nexport const maxPoolBackpropConfig = {\n    kernelName: MaxPoolBackprop,\n    backendName: 'webgl',\n    kernelFunc: maxPoolBackprop\n};\n//# sourceMappingURL=MaxPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Pool2DProgram } from '../pool_gpu';\nexport function maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, backend) {\n    let program = new Pool2DProgram(convInfo, 'max', false);\n    const poolOutput = backend.runWebGLProgram(program, [x], 'float32');\n    program = new Pool2DProgram(convInfo, 'max', true, true, includeBatchInIndex);\n    const indexOutput = backend.runWebGLProgram(program, [x], 'float32');\n    return [poolOutput, indexOutput];\n}\n//# sourceMappingURL=MaxPoolWithArgmax_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPoolWithArgmax } from '@tensorflow/tfjs-core';\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { maxPoolWithArgmaxImpl } from './MaxPoolWithArgmax_impl';\nexport const maxPoolWithArgmaxConfig = {\n    kernelName: MaxPoolWithArgmax,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { filterSize, strides, pad, includeBatchInIndex } = attrs;\n        const webglBackend = backend;\n        util.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);\n        const dilations = [1, 1];\n        util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n            `Got strides ${strides} and dilations '${dilations}'`);\n        const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad);\n        const [result, indexes] = maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, webglBackend);\n        return [result, indexes];\n    }\n};\n//# sourceMappingURL=MaxPoolWithArgmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls, NonMaxSuppressionV3 } from '@tensorflow/tfjs-core';\nexport const nonMaxSuppressionV3Config = {\n    kernelName: NonMaxSuppressionV3,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        return kernel_impls.nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal);\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV3.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls, NonMaxSuppressionV4 } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\nexport const nonMaxSuppressionV4Config = {\n    kernelName: NonMaxSuppressionV4,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n        return [selectedIndices, validOutputs];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV4.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls } from '@tensorflow/tfjs-core';\nimport { NonMaxSuppressionV5 } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nexport const nonMaxSuppressionV5Config = {\n    kernelName: NonMaxSuppressionV5,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        const softNmsSigmaVal = softNmsSigma;\n        const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n        return [selectedIndices, selectedScores];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV5.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class RotateProgram {\n    constructor(imageShape, radians, fillValue, center) {\n        this.variableNames = ['Image'];\n        this.outputShape = [];\n        const imageHeight = imageShape[1];\n        const imageWidth = imageShape[2];\n        const sinFactor = Math.sin(radians).toFixed(3);\n        const cosFactor = Math.cos(radians).toFixed(3);\n        this.outputShape = imageShape;\n        const [centerX, centerY] = backend_util.getImageCenter(center, imageHeight, imageWidth);\n        const centerXString = centerX.toFixed(3);\n        const centerYString = centerY.toFixed(3);\n        let fillSnippet = '';\n        if (typeof fillValue === 'number') {\n            fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;\n        }\n        else {\n            fillSnippet = `\n        vec3 fill = vec3(${fillValue.join(',')});\n        float outputValue = fill[coords[3]];`;\n        }\n        this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - ${centerXString}) * ${cosFactor} - (float(y) - ${centerYString}) * ${sinFactor};\n          float coordYFloat = (float(x) - ${centerXString}) * ${sinFactor} + (float(y) - ${centerYString}) * ${cosFactor};\n          int coordX = int(round(coordXFloat + ${centerXString}));\n          int coordY = int(round(coordYFloat + ${centerYString}));\n          ${fillSnippet}\n          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n    }\n}\n//# sourceMappingURL=rotate_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { RotateWithOffset } from '@tensorflow/tfjs-core';\nimport { RotateProgram } from '../rotate_gpu';\nexport const rotateWithOffsetConfig = {\n    kernelName: RotateWithOffset,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const { radians, fillValue, center } = attrs;\n        const webglBackend = backend;\n        const program = new RotateProgram(image.shape, radians, fillValue, center);\n        const output = webglBackend.runWebGLProgram(program, [image], image.dtype);\n        return output;\n    }\n};\n//# sourceMappingURL=RotateWithOffset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '@tensorflow/tfjs-core';\nimport { CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SIN = CHECK_NAN_SNIPPET_UNARY + `\n  return sin(x);\n`;\nexport const sinKernelFunc = unaryKernelFunc(SIN);\nexport const sinConfig = {\n    kernelName: Sin,\n    backendName: 'webgl',\n    kernelFunc: sinKernelFunc,\n};\n//# sourceMappingURL=Sin.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SQUARE = `return x * x;`;\nexport const squareKernelFunc = unaryKernelFunc(SQUARE);\nexport const squareConfig = {\n    kernelName: Square,\n    backendName: 'webgl',\n    kernelFunc: squareKernelFunc,\n};\n//# sourceMappingURL=Square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport const squaredDifferenceKernelFunc = binaryKernelFunc(SQUARED_DIFFERENCE, SQUARED_DIFFERENCE);\nexport const squaredDifferenceConfig = {\n    kernelName: SquaredDifference,\n    backendName: 'webgl',\n    kernelFunc: squaredDifferenceKernelFunc,\n};\n//# sourceMappingURL=SquaredDifference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst TAN = `return tan(x);`;\nexport const tanKernelFunc = unaryKernelFunc(TAN);\nexport const tanConfig = {\n    kernelName: Tan,\n    backendName: 'webgl',\n    kernelFunc: tanKernelFunc,\n};\n//# sourceMappingURL=Tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '@tensorflow/tfjs-core';\nimport { transposeImpl } from './Transpose_impl';\nimport { transposeImplCPU as cpuTranspose } from './Transpose_impl';\nexport const transposeConfig = {\n    kernelName: Transpose,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { perm } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const newShape = new Array(xRank);\n        for (let i = 0; i < newShape.length; i++) {\n            newShape[i] = x.shape[perm[i]];\n        }\n        let out;\n        if (webglBackend.shouldExecuteOnCPU([x])) {\n            const xTexData = webglBackend.texData.get(x.dataId);\n            const values = xTexData.values;\n            const outValues = cpuTranspose(values, x.shape, x.dtype, perm, newShape);\n            out = webglBackend.makeTensorInfo(newShape, x.dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n        }\n        else {\n            out = transposeImpl(x, perm, webglBackend);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unique } from '@tensorflow/tfjs-core';\nimport { uniqueImplCPU } from '../kernel_utils/shared';\nimport { assertNotComplex } from '../webgl_util';\nexport function unique(args) {\n    const { inputs, attrs, backend } = args;\n    const { axis } = attrs;\n    const { x } = inputs;\n    assertNotComplex(x, 'unique');\n    // For now, always forward calculation to the CPU backend.\n    console.warn('WARNING: ', 'UI might be locked temporarily as data is being downloaded');\n    const values = backend.readSync(x.dataId);\n    const { outputValues, outputShape, indices } = uniqueImplCPU(values, axis, x.shape, x.dtype);\n    return [\n        backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n        backend.makeTensorInfo([indices.length], 'int32', indices),\n    ];\n}\nexport const uniqueConfig = {\n    kernelName: Unique,\n    backendName: 'webgl',\n    kernelFunc: unique,\n};\n//# sourceMappingURL=Unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { registerKernel } from '@tensorflow/tfjs-core';\nimport { atan2Config } from './kernels/Atan2';\nimport { avgPoolConfig } from './kernels/AvgPool';\nimport { avgPoolBackpropConfig } from './kernels/AvgPoolBackprop';\nimport { batchNormConfig } from './kernels/BatchNorm';\nimport { cosConfig } from './kernels/Cos';\nimport { divConfig } from './kernels/Div';\nimport { flipLeftRightConfig } from './kernels/FlipLeftRight';\nimport { fromPixelsConfig } from './kernels/FromPixels';\nimport { identityConfig } from './kernels/Identity';\nimport { maxConfig } from './kernels/Max';\nimport { maxPoolConfig } from './kernels/MaxPool';\nimport { maxPoolBackpropConfig } from './kernels/MaxPoolBackprop';\nimport { maxPoolWithArgmaxConfig } from './kernels/MaxPoolWithArgmax';\nimport { nonMaxSuppressionV3Config } from './kernels/NonMaxSuppressionV3';\nimport { nonMaxSuppressionV4Config } from './kernels/NonMaxSuppressionV4';\nimport { nonMaxSuppressionV5Config } from './kernels/NonMaxSuppressionV5';\nimport { reshapeConfig } from './kernels/Reshape';\nimport { rotateWithOffsetConfig } from './kernels/RotateWithOffset';\nimport { sinConfig } from './kernels/Sin';\nimport { squareConfig } from './kernels/Square';\nimport { squaredDifferenceConfig } from './kernels/SquaredDifference';\nimport { tanConfig } from './kernels/Tan';\nimport { transposeConfig } from './kernels/Transpose';\nimport { uniqueConfig } from './kernels/Unique';\n// List all kernel configs here\nconst kernelConfigs = [\n    atan2Config,\n    avgPoolConfig,\n    avgPoolBackpropConfig,\n    batchNormConfig,\n    cosConfig,\n    divConfig,\n    flipLeftRightConfig,\n    fromPixelsConfig,\n    identityConfig,\n    maxConfig,\n    maxPoolConfig,\n    maxPoolBackpropConfig,\n    maxPoolWithArgmaxConfig,\n    nonMaxSuppressionV3Config,\n    nonMaxSuppressionV4Config,\n    nonMaxSuppressionV5Config,\n    reshapeConfig,\n    rotateWithOffsetConfig,\n    sinConfig,\n    squareConfig,\n    squaredDifferenceConfig,\n    tanConfig,\n    transposeConfig,\n    uniqueConfig,\n];\nfor (const kernelConfig of kernelConfigs) {\n    registerKernel(kernelConfig);\n}\n//# sourceMappingURL=register_all_kernels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// All exports from this package should be in base.\nexport * from './base';\nimport './register_all_kernels';\n//# sourceMappingURL=index.js.map", "/** @license See the LICENSE file. */\n\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport {version};\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport * from '@tensorflow/tfjs-core';\nexport * from '@tensorflow/tfjs-layers';\nexport * from '@tensorflow/tfjs-converter';\n\n// Export data api as tf.data\nimport * as data from '@tensorflow/tfjs-data';\nexport {data};\n\n// Import and register backends.\nimport '@tensorflow/tfjs-backend-cpu';\nimport '@tensorflow/tfjs-backend-webgl';\n\n// Import versions of all sub-packages.\nimport {version_core} from '@tensorflow/tfjs-core';\nimport {version_cpu} from '@tensorflow/tfjs-backend-cpu';\nimport {version_webgl} from '@tensorflow/tfjs-backend-webgl';\nimport {version_data} from '@tensorflow/tfjs-data';\nimport {version_layers} from '@tensorflow/tfjs-layers';\nimport {version_converter} from '@tensorflow/tfjs-converter';\nimport {version as version_union} from './version';\n\nexport const version = {\n  'tfjs-core': version_core,\n  'tfjs-backend-cpu': version_cpu,\n  'tfjs-backend-webgl': version_webgl,\n  'tfjs-data': version_data,\n  'tfjs-layers': version_layers,\n  'tfjs-converter': version_converter,\n  'tfjs': version_union\n};\n", "export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32')\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32')\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32')\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0, 'float32')\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n"],
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diff --git a/dist/face-api.esm.js b/dist/face-api.esm.js
index 0e24125..986e254 100644
--- a/dist/face-api.esm.js
+++ b/dist/face-api.esm.js
@@ -1,39 +1,39 @@
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Buffer shape=${this.shape}`;throw new Error(i)}t++}let n=e[e.length-1];for(let s=0;s<e.length-1;++s)n+=this.strides[s]*e[s];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];const t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return Si().makeTensor(this.values,this.shape,this.dtype)}}let Si=null,wa=null,Mx=null;function ND(e){Si=e}function CD(e){wa=e}function RD(e){Mx=e}class Q{constructor(e,t,n,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=we(e),this.strides=Ot(e),this.dataId=n,this.id=s,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const e=await this.data();return wa.buffer(this.shape,this.dtype,e)}bufferSync(){return wa.buffer(this.shape,this.dtype,this.dataSync())}async array(){const e=await this.data();return ys(this.shape,e)}arraySync(){return ys(this.shape,this.dataSync())}async data(){this.throwIfDisposed();const e=Si().read(this.dataId);if(this.dtype==="string"){const t=await e;try{return t.map(n=>Dl(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();const e=Si().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Dl(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();const e=await Si().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){if(this.isDisposed)return;Si().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return wa.print(this,e)}clone(){return this.throwIfDisposed(),wa.clone(this)}toString(e=!1){const t=this.dataSync();return AD(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),wa.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Si().makeVariable(this,e,t,n)}}Object.defineProperty(Q,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});class Wl extends Q{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!ot(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Si().disposeTensor(this),this.dataId=e.dataId,Si().incRef(this,null)}dispose(){Si().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(Wl,Symbol.hasInstance,{value:e=>e instanceof Q&&e.assign!=null&&e.assign instanceof Function});(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(r.Rank||(r.Rank={}));var uy;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(uy||(uy={}));var dy;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(dy||(dy={}));var py;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(py||(py={}));var my;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(my||(my={}));const OD={float32:py,int32:uy,bool:dy,complex64:my};function vn(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return OD[e][t]}function cd(e){return vn(e,"int32")}function Bt(e,t){if(e.dtype===t.dtype)return[e,t];const n=vn(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function Px(e,t){k(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function ld(e,t){return t.some(n=>n.id===e.id)}function Hi(e){const t=[],n=new Set;return zx(e,t,n),t}function zx(e,t,n){if(e==null)return;if(e instanceof Q){t.push(e);return}if(!ED(e))return;const s=e;for(const i in s){const o=s[i];n.has(o)||(n.add(o),zx(o,t,n))}}function ED(e){return Array.isArray(e)||typeof e=="object"}var DD=Object.freeze({__proto__:null,makeTypesMatch:Bt,assertTypesMatch:Px,isTensorInList:ld,getTensorsInContainer:Hi});class Gx{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class $l{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Gx}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],s=await this.initializeBackend(n).success;if(s){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){const{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;const{success:t,asyncInit:n}=this.initializeBackend(e),s=n?await t:t;if(!s)return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new LD(this.backendInstance),!0}setupRegisteredKernels(){const e=td(this.backendName);e.forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){const t=td(e);t.forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){const t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{const n=t.factory();if(n&&!(n instanceof g)&&typeof n.then=="function"){const s=++this.pendingBackendInitId,i=n.then(o=>s<this.pendingBackendInitId?!1:(this.registry[e]=o,this.pendingBackendInit=null,!0)).catch(o=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(o.stack||o.message)),!1));return this.pendingBackendInit=i,{success:i,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],{success:s,asyncInit:i}=this.initializeBackend(n);if(i||s)return{name:n,asyncInit:i}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),s=n.backend,i=this.readSync(t);s.disposeData(t),n.backend=e,e.move(t,i,n.shape,n.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{const s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return $l.nextTensorId++}nextVariableId(){return $l.nextVariableId++}clone(e){const t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),n={x:e},s=o=>({x:()=>{const a="float32",c={x:o},h={dtype:a};return V.runKernelFunc(d=>d.cast(o,a),c,null,Jc,h)}}),i=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,i,{}),t}runKernel(e,t,n,s,i){const o=null,a=null;return this.runKernelFunc(o,t,a,e,n,s,i)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const s=this.backend.numDataIds();let i=0;n.forEach(c=>{i+=c.dtype==="complex64"?3:1});const o=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=s-t-i-o;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${e}'`)}runKernelFunc(e,t,n,s,i,o,a){let c,h=[];const d=this.isTapeOn();s==null&&(s=this.state.activeScope!=null?this.state.activeScope.name:"");const m=this.state.numBytes,y=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let b;const w=Zg(s,this.backendName);let L;if(w!=null)b=()=>{const A=this.backend.numDataIds();L=w.kernelFunc({inputs:t,attrs:i,backend:this.backend});const N=Array.isArray(L)?L:[L];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,A,N);const E=N.map(({dataId:D,shape:F,dtype:_})=>this.makeTensorFromDataId(D,F,_));if(d){let D=this.getTensorsForGradient(s,t,E);if(D==null){a==null&&(a=[]);const F=E.filter((_,B)=>a[B]);D=(o||[]).slice().concat(F)}h=this.saveTensorsForBackwardMode(D)}return E};else{const A=N=>{if(!d)return;h=N.map(E=>this.keep(this.clone(E)))};b=()=>{const N=this.backend.numDataIds();L=this.tidy(()=>e(this.backend,A));const E=Array.isArray(L)?L:[L];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,N,E),E}}let T;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?c=b():(T=this.profiler.profileKernel(s,t,()=>b()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(T),c=T.outputs)}),d&&this.addTapeNode(s,t,c,n,h,i),this.state.profiling&&this.state.activeProfile.kernels.push({name:s,bytesAdded:this.state.numBytes-m,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-y,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(A=>t[A]!=null?t[A].shape:null),outputShapes:c.map(A=>A.shape),kernelTimeMs:T.timeMs,extraInfo:T.extraInfo}),Array.isArray(L)?c:c[0]}saveTensorsForBackwardMode(e){const t=e.map(n=>this.keep(this.clone(n)));return t}getTensorsForGradient(e,t,n){const s=Qg(e);if(s!=null){const i=s.inputsToSave||[],o=s.outputsToSave||[];let a;s.saveAllInputs?(k(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(t).map(h=>t[h])):a=i.map(h=>t[h]);const c=n.filter((h,d)=>o[d]);return a.concat(c)}return null}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let i=e;n==="string"&&Ir(e[0])&&(i=e.map(c=>ly(c)));const o=s.write(i,t,n),a=new Q(t,n,o,this.nextTensorId());if(this.incRef(a,s),n==="string"){const c=this.state.tensorInfo.get(o),h=kx(i);this.state.numBytes+=h-c.bytes,c.bytes=h}return a}makeTensorFromDataId(e,t,n,s){n=n||"float32";const i=new Q(t,n,e,this.nextTensorId());return this.incRef(i,s),i}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));const i=new Wl(e,t,n,this.nextTensorId());if(this.state.registeredVariables[i.name]!=null)throw new Error(`Variable with name ${i.name} was already registered`);return this.state.registeredVariables[i.name]=i,this.incRef(i,this.backend),i}incRef(e,t){const n=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,n===0){this.state.numDataBuffers++;let s=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(s=e.size*ry(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:s,refCount:0}),this.state.numBytes+=s}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof Wl||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;const t=this.state.tensorInfo.get(e.dataId),n=t.refCount;n<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):this.state.tensorInfo.get(e.dataId).refCount--}disposeVariables(){for(const e in this.state.registeredVariables){const t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){const e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;const t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(const s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,i,o){const a={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:i},c=Qg(e);c!=null&&(s=c.gradFunc),s!=null&&(a.gradient=h=>(h=h.map((d,m)=>{if(d==null){const y=n[m],b=ya(y.size,y.dtype);return this.makeTensor(b,y.shape,y.dtype)}return d}),s(h.length>1?h:h[0],i,o))),this.state.activeTape.push(a)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){const t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){const t=Hi(e),n=new Set(t.map(i=>i.id));for(let i=0;i<this.state.activeScope.track.length;i++){const o=this.state.activeScope.track[i];!o.kept&&!n.has(o.id)&&o.dispose()}const s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(i=>{!i.kept&&i.scopeId===s.id&&this.track(i)})}gradients(e,t,n,s=!1){if(k(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);const i=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));k(i instanceof Q,()=>"The result y returned by f() must be a tensor.");const o=xD(this.state.activeTape,t,i);if(!s&&o.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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The output of every math call will be downloaded to CPU and checked for NaNs. 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Got an object with ${t.length} keys.`);let n=t[0];const s=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+jx;const i=(...o)=>{V.startScope(n);try{const a=s(...o);return a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),V.endScope(a),a}catch(a){throw V.endScope(null),a}};return Object.defineProperty(i,"name",{value:n,configurable:!0}),i}function $D(e,t){const n=W(e,"real","complex"),s=W(t,"imag","complex");dt(n.shape,s.shape,`real and imag shapes, ${n.shape} and ${s.shape}, must match in call to tf.complex().`);const i=a=>a.complex(n,s),o={real:n,imag:s};return V.runKernelFunc(i,o,null,hg)}const xi=P({complex_:$D});function vr(e,t,n,s){if(s==null&&(s=ga(e)),s==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!wn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){cy(t);const i=we(t),o=we(n);k(i===o,()=>`Based on the provided shape, [${t}], the tensor should have ${i} values but has ${o}`);for(let a=0;a<n.length;++a){const c=n[a],h=a===n.length-1?c!==we(t.slice(a)):!0;k(n[a]===t[a]||!h,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!wn(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=s!=="string"?Tr(e,s):Yi(e,[],!0),V.makeTensor(e,t,s)}function en(e,t,n){const s=Ii(e,n);return vr(e,t,s,n)}const gy={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8};const hd=4;async function yy(e,t){const n=[],s=[],i=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);for(let a=0;a<i.length;++a){const c=i[a],h=Array.isArray(e)?e[a].tensor:e[c];if(h.dtype!=="float32"&&h.dtype!=="int32"&&h.dtype!=="bool"&&h.dtype!=="string"&&h.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${c}': ${h.dtype}`);const d={name:c,shape:h.shape,dtype:h.dtype};if(h.dtype==="string"){const m=new Promise(async y=>{const b=await h.bytes(),w=b.reduce((A,N)=>A+N.length,0)+hd*b.length,L=new Uint8Array(w);let T=0;for(let A=0;A<b.length;A++){const N=b[A],E=new Uint8Array(new Uint32Array([N.length]).buffer);L.set(E,T),T+=hd,L.set(N,T),T+=N.length}y(L)});s.push(m)}else s.push(h.data());t!=null&&(d.group=t),n.push(d)}const o=await Promise.all(s);return{data:UD(o),specs:n}}function ud(e,t){const n={};let s,i=0;for(const o of t){const a=o.name,c=o.dtype,h=o.shape,d=we(h);let m;if("quantization"in o){const y=o.quantization;if(y.dtype==="uint8"||y.dtype==="uint16"){if(!("min"in y&&"scale"in y))throw new Error(`Weight ${o.name} with quantization ${y.dtype} doesn't have corresponding metadata min and scale.`)}else if(y.dtype==="float16"){if(c!=="float32")throw new Error(`Weight ${o.name} is quantized with ${y.dtype} which only supports weights of type float32 not ${c}.`)}else throw new Error(`Weight ${o.name} has unknown quantization dtype ${y.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);const b=gy[y.dtype],w=e.slice(i,i+d*b),L=y.dtype==="uint8"?new Uint8Array(w):new Uint16Array(w);if(c==="float32")if(y.dtype==="uint8"||y.dtype==="uint16"){m=new Float32Array(L.length);for(let T=0;T<L.length;T++){const A=L[T];m[T]=A*y.scale+y.min}}else if(y.dtype==="float16")s===void 0&&(s=VD()),m=s(L);else throw new Error(`Unsupported quantization type ${y.dtype} for weight type float32.`);else if(c==="int32"){if(y.dtype!=="uint8"&&y.dtype!=="uint16")throw new Error(`Unsupported quantization type ${y.dtype} for weight type int32.`);m=new Int32Array(L.length);for(let T=0;T<L.length;T++){const A=L[T];m[T]=Math.round(A*y.scale+y.min)}}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=d*b}else if(c==="string"){const y=we(o.shape);m=[];for(let b=0;b<y;b++){const w=new Uint32Array(e.slice(i,i+hd))[0];i+=hd;const L=new Uint8Array(e.slice(i,i+w));m.push(L),i+=w}}else{const y=gy[c],b=e.slice(i,i+d*y);if(c==="float32")m=new Float32Array(b);else if(c==="int32")m=new Int32Array(b);else if(c==="bool")m=new Uint8Array(b);else if(c==="complex64"){m=new Float32Array(b);const w=new Float32Array(m.length/2),L=new Float32Array(m.length/2);for(let N=0;N<w.length;N++)w[N]=m[N*2],L[N]=m[N*2+1];const T=en(w,h,"float32"),A=en(L,h,"float32");n[a]=xi(T,A)}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=d*y}c!=="complex64"&&(n[a]=en(m,h,c))}return n}function UD(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0;const n=[];e.forEach(o=>{if(t+=o.byteLength,n.push(o.byteLength===o.buffer.byteLength?o:new o.constructor(o)),!(o instanceof Float32Array||o instanceof Int32Array||o instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${o.constructor.name}`)});const s=new Uint8Array(t);let i=0;return n.forEach(o=>{s.set(new Uint8Array(o.buffer),i),i+=o.byteLength}),s.buffer}const by=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function Kx(e){return by?Buffer.byteLength(e):new Blob([e]).size}function BD(e){if(by)return Buffer.from(e).toString("base64");const t=new Uint8Array(e);let n="";for(let s=0,i=t.length;s<i;s++)n+=String.fromCharCode(t[s]);return btoa(n)}function MD(e){if(by){const s=Buffer.from(e,"base64");return s.buffer.slice(s.byteOffset,s.byteOffset+s.byteLength)}const t=atob(e),n=new Uint8Array(t.length);for(let s=0;s<t.length;++s)n.set([t.charCodeAt(s)],s);return n.buffer}function dd(e){if(e.length===1)return e[0];let t=0;e.forEach(i=>{t+=i.byteLength});const n=new Uint8Array(t);let s=0;return e.forEach(i=>{n.set(new Uint8Array(i),s),s+=i.byteLength}),n.buffer}function Xx(e){const t="/";for(e=e.trim();e.endsWith(t);)e=e.slice(0,e.length-1);const n=e.split(t);return n[n.length-1]}function Bl(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received 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c=s[a],h=e[n[c>>10]+(c&1023)]+t[c>>10];o[a]=h}return new Float32Array(i)}}class Xt{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Xt.instance==null&&(Xt.instance=new Xt),Xt.instance}static registerSaveRouter(e){Xt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Xt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Xt.getHandlers(e,"save")}static getLoadHandlers(e,t){return Xt.getHandlers(e,"load",t)}static getHandlers(e,t,n){const s=[],i=t==="load"?Xt.getInstance().loadRouters:Xt.getInstance().saveRouters;return i.forEach(o=>{const a=o(e,n);a!==null&&s.push(a)}),s}}const YD=e=>Xt.registerSaveRouter(e),HD=e=>Xt.registerLoadRouter(e),wy=e=>Xt.getSaveHandlers(e),Ly=(e,t)=>Xt.getLoadHandlers(e,t);const pd="tensorflowjs",Sy=1,co="models_store",Nr="model_info_store";async function kZ(){const e=Iy();return new Promise((t,n)=>{const s=e.deleteDatabase(pd);s.onsuccess=()=>t(),s.onerror=i=>n(i)})}function Iy(){if(!C().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");const e=typeof window=="undefined"?self:window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(t==null)throw new Error("The current browser does not appear to support IndexedDB.");return t}function xy(e){const t=e.result;t.createObjectStore(co,{keyPath:"modelPath"}),t.createObjectStore(Nr,{keyPath:"modelPath"})}class lo{constructor(e){if(this.indexedDB=Iy(),e==null||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,e)}async load(){return this.databaseAction(this.modelPath)}databaseAction(e,t){return new Promise((n,s)=>{const i=this.indexedDB.open(pd,Sy);i.onupgradeneeded=()=>xy(i),i.onsuccess=()=>{const o=i.result;if(t==null){const a=o.transaction(co,"readonly"),c=a.objectStore(co),h=c.get(this.modelPath);h.onsuccess=()=>{if(h.result==null)return o.close(),s(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(h.result.modelArtifacts)},h.onerror=d=>(o.close(),s(h.error)),a.oncomplete=()=>o.close()}else{const a=Bl(t),c=o.transaction(Nr,"readwrite");let h=c.objectStore(Nr);const d=h.put({modelPath:this.modelPath,modelArtifactsInfo:a});let m;d.onsuccess=()=>{m=o.transaction(co,"readwrite");const y=m.objectStore(co),b=y.put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:a});b.onsuccess=()=>n({modelArtifactsInfo:a}),b.onerror=w=>{h=c.objectStore(Nr);const L=h.delete(this.modelPath);L.onsuccess=()=>(o.close(),s(b.error)),L.onerror=T=>(o.close(),s(b.error))}},d.onerror=y=>(o.close(),s(d.error)),c.oncomplete=()=>{m==null?o.close():m.oncomplete=()=>o.close()}}},i.onerror=o=>s(i.error)})}}lo.URL_SCHEME="indexeddb://";const Jx=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(lo.URL_SCHEME))?qD(e.slice(lo.URL_SCHEME.length)):null;Xt.registerSaveRouter(Jx),Xt.registerLoadRouter(Jx);function qD(e){return new lo(e)}function jD(e){return e.startsWith(lo.URL_SCHEME)?e.slice(lo.URL_SCHEME.length):e}class KD{constructor(){this.indexedDB=Iy()}async listModels(){return new Promise((e,t)=>{const n=this.indexedDB.open(pd,Sy);n.onupgradeneeded=()=>xy(n),n.onsuccess=()=>{const s=n.result,i=s.transaction(Nr,"readonly"),o=i.objectStore(Nr),a=o.getAll();a.onsuccess=()=>{const c={};for(const h of a.result)c[h.modelPath]=h.modelArtifactsInfo;e(c)},a.onerror=c=>(s.close(),t(a.error)),i.oncomplete=()=>s.close()},n.onerror=s=>t(n.error)})}async removeModel(e){return e=jD(e),new Promise((t,n)=>{const s=this.indexedDB.open(pd,Sy);s.onupgradeneeded=()=>xy(s),s.onsuccess=()=>{const i=s.result,o=i.transaction(Nr,"readwrite"),a=o.objectStore(Nr),c=a.get(e);let h;c.onsuccess=()=>{if(c.result==null)return i.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{const d=a.delete(e),m=()=>{h=i.transaction(co,"readwrite");const y=h.objectStore(co),b=y.delete(e);b.onsuccess=()=>t(c.result.modelArtifactsInfo),b.onerror=w=>n(c.error)};d.onsuccess=m,d.onerror=y=>(m(),i.close(),n(c.error))}},c.onerror=d=>(i.close(),n(c.error)),o.oncomplete=()=>{h==null?i.close():h.oncomplete=()=>i.close()}},s.onerror=i=>n(s.error)})}}const Ti="/",ho="tensorflowjs_models",Zx="info",XD="model_topology",JD="weight_specs",ZD="weight_data",QD="model_metadata";function FZ(){if(!C().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("purgeLocalStorageModels() cannot proceed because local storage is unavailable in the current environment.");const e=window.localStorage,t=[];for(let n=0;n<e.length;++n){const s=e.key(n),i=ho+Ti;if(s.startsWith(i)&&s.length>i.length){e.removeItem(s);const o=eT(s);t.indexOf(o)===-1&&t.push(o)}}return t}function Qx(e){return{info:[ho,e,Zx].join(Ti),topology:[ho,e,XD].join(Ti),weightSpecs:[ho,e,JD].join(Ti),weightData:[ho,e,ZD].join(Ti),modelMetadata:[ho,e,QD].join(Ti)}}function eT(e){const t=e.split(Ti);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(Ti)}function ek(e){return e.startsWith(uo.URL_SCHEME)?e.slice(uo.URL_SCHEME.length):e}class uo{constructor(e){if(!C().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=Qx(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{const t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),s=Bl(e);try{return this.LS.setItem(this.keys.info,JSON.stringify(s)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,BD(e.weightData)),this.LS.setItem(this.keys.modelMetadata,JSON.stringify({format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,userDefinedMetadata:e.userDefinedMetadata})),{modelArtifactsInfo:s}}catch(i){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${s.modelTopologyBytes}, weightSpecsBytes=${s.weightSpecsBytes}, weightDataBytes=${s.weightDataBytes}.`)}}}async load(){const e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(e.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");const t={},n=JSON.parse(this.LS.getItem(this.keys.topology));if(n==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);t.modelTopology=n;const s=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(s==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=s;const i=this.LS.getItem(this.keys.modelMetadata);if(i!=null){const a=JSON.parse(i);t.format=a.format,t.generatedBy=a.generatedBy,t.convertedBy=a.convertedBy,t.userDefinedMetadata=a.userDefinedMetadata}const o=this.LS.getItem(this.keys.weightData);if(o==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=MD(o),t}}uo.URL_SCHEME="localstorage://";const tT=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(uo.URL_SCHEME))?tk(e.slice(uo.URL_SCHEME.length)):null;Xt.registerSaveRouter(tT),Xt.registerLoadRouter(tT);function tk(e){return new uo(e)}class nk{constructor(){k(C().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),k(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){const e={},t=ho+Ti,n=Ti+Zx;for(let s=0;s<this.LS.length;++s){const i=this.LS.key(s);if(i.startsWith(t)&&i.endsWith(n)){const o=eT(i);e[o]=JSON.parse(this.LS.getItem(i))}}return e}async removeModel(e){e=ek(e);const t=Qx(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);const n=JSON.parse(this.LS.getItem(t.info));return this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),n}}const La="://";class bs{constructor(){this.managers={}}static getInstance(){return bs.instance==null&&(bs.instance=new bs),bs.instance}static registerManager(e,t){k(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(La)&&(e=e.slice(0,e.indexOf(La))),k(e.length>0,()=>"scheme must not be an empty string.");const n=bs.getInstance();k(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),n.managers[e]=t}static getManager(e){const t=this.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}}function md(e){if(e.indexOf(La)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${bs.getSchemes().join(",")}`);return{scheme:e.split(La)[0],path:e.split(La)[1]}}async function nT(e,t,n=!1){k(e!==t,()=>`Old path and new path are the same: '${e}'`);const s=Xt.getLoadHandlers(e);k(s.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),k(s.length<2,()=>`Copying failed because more than one (${s.length}) load handlers for source URL ${e}.`);const i=s[0],o=Xt.getSaveHandlers(t);k(o.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),k(o.length<2,()=>`Copying failed because more than one (${s.length}) save handlers for destination URL ${t}.`);const a=o[0],c=md(e).scheme,h=md(e).path,d=c===md(e).scheme,m=await i.load();n&&d&&await bs.getManager(c).removeModel(h);const y=await a.save(m);return n&&!d&&await bs.getManager(c).removeModel(h),y.modelArtifactsInfo}async function sk(){const e=bs.getSchemes(),t={};for(const n of e){const s=await bs.getManager(n).listModels();for(const i in s){const o=n+La+i;t[o]=s[i]}}return t}async function ik(e){const t=md(e),n=bs.getManager(t.scheme);return n.removeModel(t.path)}async function rk(e,t){const n=!1;return nT(e,t,n)}async function ok(e,t){const n=!0;return nT(e,t,n)}class ak{fetch(e,t){return fetch(e,t)}now(){return performance.now()}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${t}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(e)}decode(e,t){return new TextDecoder(t).decode(e)}}if(C().get("IS_BROWSER")){C().setPlatform("browser",new ak);try{bs.registerManager(uo.URL_SCHEME,new nk)}catch(e){}try{bs.registerManager(lo.URL_SCHEME,new KD)}catch(e){}}const ck={importFetch:()=>IC()};let Sa;function _Z(){Sa=null}function WZ(e){Sa=e}function $Z(){return Sa}class lk{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return C().global.fetch!=null?C().global.fetch(e,t):(Sa==null&&(Sa=ck.importFetch()),Sa(e,t))}now(){const e=process.hrtime();return e[0]*1e3+e[1]/1e6}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${t}`);return this.textEncoder.encode(e)}decode(e,t){return e.length===0?"":new this.util.TextDecoder(t).decode(e)}}C().get("IS_NODE")&&C().setPlatform("node",new lk);function Ze(e,t="float32",n){return t=t||"float32",cy(e),new Ar(e,t,n)}function hk(e,t){const n=W(e,"x","cast");if(!Dx(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");const s={x:n},i={dtype:t};return V.runKernelFunc(o=>o.cast(n,t),s,null,Jc,i)}const ve=P({cast_:hk});function uk(e){const t=W(e,"x","clone",null),n=()=>V.makeTensorFromDataId(t.dataId,t.shape,t.dtype),s={x:t};return V.runKernelFunc(n,s,null,al)}const Cr=P({clone_:uk});function sT(e,t=!1){console.log(e.toString(t))}Vx();const dk={buffer:Ze,cast:ve,clone:Cr,print:sT};CD(dk);const pk="model",mk=".json",fk=".weights.bin";function iT(e){return new Promise(t=>setTimeout(t)).then(e)}class Ia{constructor(e){if(!C().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(Ia.URL_SCHEME)&&(e=e.slice(Ia.URL_SCHEME.length)),(e==null||e.length===0)&&(e=pk),this.modelTopologyFileName=e+mk,this.weightDataFileName=e+fk}async save(e){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");const t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{const n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],s={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n},i=window.URL.createObjectURL(new Blob([JSON.stringify(s)],{type:"application/json"})),o=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(o.download=this.modelTopologyFileName,o.href=i,await iT(()=>o.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){const a=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;a.download=this.weightDataFileName,a.href=t,await iT(()=>a.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Bl(e)}}}}Ia.URL_SCHEME="downloads://";class gk{constructor(e){if(e==null||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.files=e}async load(){const e=this.files[0],t=this.files.slice(1);return new Promise((n,s)=>{const i=new FileReader;i.onload=o=>{const a=JSON.parse(o.target.result),c=a.modelTopology;if(c==null){s(new Error(`modelTopology field is missing from file ${e.name}`));return}t.length===0&&n({modelTopology:c});const h=a.weightsManifest;if(h==null){s(new Error(`weightManifest field is missing from file ${e.name}`));return}let d;try{d=this.checkManifestAndWeightFiles(h,t)}catch(w){s(w);return}const m=[],y=[],b=[];h.forEach(w=>{w.paths.forEach(L=>{y.push(L),b.push(null)}),m.push(...w.weights)}),h.forEach(w=>{w.paths.forEach(L=>{const T=new FileReader;T.onload=A=>{const N=A.target.result,E=y.indexOf(L);b[E]=N,b.indexOf(null)===-1&&n({modelTopology:c,weightSpecs:m,weightData:dd(b),format:a.format,generatedBy:a.generatedBy,convertedBy:a.convertedBy,userDefinedMetadata:a.userDefinedMetadata})},T.onerror=A=>s(`Failed to weights data from file of path '${L}'.`),T.readAsArrayBuffer(d[L])})})},i.onerror=o=>s(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),i.readAsText(e)})}checkManifestAndWeightFiles(e,t){const n=[],s=t.map(o=>Xx(o.name)),i={};for(const o of e)o.paths.forEach(a=>{const c=Xx(a);if(n.indexOf(c)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${c}'`);if(n.push(c),s.indexOf(c)===-1)throw new Error(`Weight file with basename '${c}' is not provided.`);i[a]=t[s.indexOf(c)]});if(n.length!==t.length)throw new Error(`Mismatch in the number of files in weights manifest (${n.length}) and the number of weight files provided (${t.length}).`);return i}}const yk=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(Ia.URL_SCHEME))?bk(e.slice(Ia.URL_SCHEME.length)):null;Xt.registerSaveRouter(yk);function bk(e="model"){return new Ia(e)}function wk(e){return new gk(e)}function rT(e,t,n,s){a(e),n=n==null?0:n,s=s==null?1:s,c(n,s);let i=0;const o=h=>(h.then(d=>{const m=n+ ++i/e.length*(s-n);return t(m),d}),h);function a(h){k(h!=null&&Array.isArray(h)&&h.length>0,()=>"promises must be a none empty array")}function c(h,d){k(h>=0&&h<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${h}`),k(d>=0&&d<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${d}`),k(d>=h,()=>`startFraction must be no more than endFraction, but got startFraction ${h} and endFraction ${d}`)}return Promise.all(e.map(o))}async function oT(e,t){t==null&&(t={});const n=t.fetchFunc==null?C().platform.fetch:t.fetchFunc,s=e.map(y=>n(y,t.requestInit,{isBinary:!0})),i=0,o=.5,a=t.onProgress==null?await Promise.all(s):await rT(s,t.onProgress,i,o),c=a.map(y=>y.arrayBuffer()),h=.5,d=1,m=t.onProgress==null?await Promise.all(c):await rT(c,t.onProgress,h,d);return m}async function aT(e,t="",n,s){const i=a=>oT(a,{requestInit:s}),o=cT(i);return o(e,t,n)}function cT(e){return async(t,n="",s)=>{const i=t.map(()=>!1),o={},a=s!=null?s.map(()=>!1):[],c=[];if(t.forEach((w,L)=>{let T=0;w.weights.forEach(A=>{const N="quantization"in A?A.quantization.dtype:A.dtype,E=gy[N]*we(A.shape),D=()=>{i[L]=!0,o[L]==null&&(o[L]=[]),o[L].push({manifestEntry:A,groupOffset:T,sizeBytes:E})};s!=null?s.forEach((F,_)=>{F===A.name&&(D(),a[_]=!0)}):D(),c.push(A.name),T+=E})}),!a.every(w=>w)){const w=s.filter((L,T)=>!a[T]);throw new Error(`Could not find weights in manifest with names: ${w.join(", ")}. 
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Please verify this URL points to the model JSON of the model to load.`);let t;try{t=await e.json()}catch(b){let w=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?w+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. 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Buffer shape=${this.shape}`;throw new Error(i)}t++}let n=e[e.length-1];for(let s=0;s<e.length-1;++s)n+=this.strides[s]*e[s];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];const t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return Si().makeTensor(this.values,this.shape,this.dtype)}}let Si=null,Sa=null,Mx=null;function ND(e){Si=e}function CD(e){Sa=e}function RD(e){Mx=e}class Q{constructor(e,t,n,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=we(e),this.strides=Ot(e),this.dataId=n,this.id=s,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const e=await this.data();return Sa.buffer(this.shape,this.dtype,e)}bufferSync(){return Sa.buffer(this.shape,this.dtype,this.dataSync())}async array(){const e=await this.data();return ys(this.shape,e)}arraySync(){return ys(this.shape,this.dataSync())}async data(){this.throwIfDisposed();const e=Si().read(this.dataId);if(this.dtype==="string"){const t=await e;try{return t.map(n=>Dl(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();const e=Si().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Dl(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();const e=await Si().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){if(this.isDisposed)return;Si().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Sa.print(this,e)}clone(){return this.throwIfDisposed(),Sa.clone(this)}toString(e=!1){const t=this.dataSync();return AD(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Sa.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Si().makeVariable(this,e,t,n)}}Object.defineProperty(Q,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});class Wl extends Q{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!ot(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Si().disposeTensor(this),this.dataId=e.dataId,Si().incRef(this,null)}dispose(){Si().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(Wl,Symbol.hasInstance,{value:e=>e instanceof Q&&e.assign!=null&&e.assign instanceof Function});(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(r.Rank||(r.Rank={}));var uy;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(uy||(uy={}));var dy;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(dy||(dy={}));var py;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(py||(py={}));var my;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(my||(my={}));const OD={float32:py,int32:uy,bool:dy,complex64:my};function vn(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return OD[e][t]}function cd(e){return vn(e,"int32")}function Bt(e,t){if(e.dtype===t.dtype)return[e,t];const n=vn(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function Px(e,t){k(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function ld(e,t){return t.some(n=>n.id===e.id)}function Hi(e){const t=[],n=new Set;return zx(e,t,n),t}function zx(e,t,n){if(e==null)return;if(e instanceof Q){t.push(e);return}if(!ED(e))return;const s=e;for(const i in s){const o=s[i];n.has(o)||(n.add(o),zx(o,t,n))}}function ED(e){return Array.isArray(e)||typeof e=="object"}var DD=Object.freeze({__proto__:null,makeTypesMatch:Bt,assertTypesMatch:Px,isTensorInList:ld,getTensorsInContainer:Hi});class Gx{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class $l{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Gx}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],s=await this.initializeBackend(n).success;if(s){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){const{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;const{success:t,asyncInit:n}=this.initializeBackend(e),s=n?await t:t;if(!s)return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new LD(this.backendInstance),!0}setupRegisteredKernels(){const e=td(this.backendName);e.forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){const t=td(e);t.forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){const t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{const n=t.factory();if(n&&!(n instanceof g)&&typeof n.then=="function"){const s=++this.pendingBackendInitId,i=n.then(o=>s<this.pendingBackendInitId?!1:(this.registry[e]=o,this.pendingBackendInit=null,!0)).catch(o=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(o.stack||o.message)),!1));return this.pendingBackendInit=i,{success:i,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],{success:s,asyncInit:i}=this.initializeBackend(n);if(i||s)return{name:n,asyncInit:i}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),s=n.backend,i=this.readSync(t);s.disposeData(t),n.backend=e,e.move(t,i,n.shape,n.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{const s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return $l.nextTensorId++}nextVariableId(){return $l.nextVariableId++}clone(e){const t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),n={x:e},s=o=>({x:()=>{const a="float32",c={x:o},h={dtype:a};return V.runKernelFunc(d=>d.cast(o,a),c,null,Jc,h)}}),i=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,i,{}),t}runKernel(e,t,n,s,i){const o=null,a=null;return this.runKernelFunc(o,t,a,e,n,s,i)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const s=this.backend.numDataIds();let i=0;n.forEach(c=>{i+=c.dtype==="complex64"?3:1});const o=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=s-t-i-o;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${e}'`)}runKernelFunc(e,t,n,s,i,o,a){let c,h=[];const d=this.isTapeOn();s==null&&(s=this.state.activeScope!=null?this.state.activeScope.name:"");const m=this.state.numBytes,y=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let b;const w=Zg(s,this.backendName);let L;if(w!=null)b=()=>{const A=this.backend.numDataIds();L=w.kernelFunc({inputs:t,attrs:i,backend:this.backend});const N=Array.isArray(L)?L:[L];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,A,N);const E=N.map(({dataId:D,shape:F,dtype:_})=>this.makeTensorFromDataId(D,F,_));if(d){let D=this.getTensorsForGradient(s,t,E);if(D==null){a==null&&(a=[]);const F=E.filter((_,B)=>a[B]);D=(o||[]).slice().concat(F)}h=this.saveTensorsForBackwardMode(D)}return E};else{const A=N=>{if(!d)return;h=N.map(E=>this.keep(this.clone(E)))};b=()=>{const N=this.backend.numDataIds();L=this.tidy(()=>e(this.backend,A));const E=Array.isArray(L)?L:[L];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,N,E),E}}let T;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?c=b():(T=this.profiler.profileKernel(s,t,()=>b()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(T),c=T.outputs)}),d&&this.addTapeNode(s,t,c,n,h,i),this.state.profiling&&this.state.activeProfile.kernels.push({name:s,bytesAdded:this.state.numBytes-m,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-y,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(A=>t[A]!=null?t[A].shape:null),outputShapes:c.map(A=>A.shape),kernelTimeMs:T.timeMs,extraInfo:T.extraInfo}),Array.isArray(L)?c:c[0]}saveTensorsForBackwardMode(e){const t=e.map(n=>this.keep(this.clone(n)));return t}getTensorsForGradient(e,t,n){const s=Qg(e);if(s!=null){const i=s.inputsToSave||[],o=s.outputsToSave||[];let a;s.saveAllInputs?(k(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(t).map(h=>t[h])):a=i.map(h=>t[h]);const c=n.filter((h,d)=>o[d]);return a.concat(c)}return null}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let i=e;n==="string"&&Ir(e[0])&&(i=e.map(c=>ly(c)));const o=s.write(i,t,n),a=new Q(t,n,o,this.nextTensorId());if(this.incRef(a,s),n==="string"){const c=this.state.tensorInfo.get(o),h=kx(i);this.state.numBytes+=h-c.bytes,c.bytes=h}return a}makeTensorFromDataId(e,t,n,s){n=n||"float32";const i=new Q(t,n,e,this.nextTensorId());return this.incRef(i,s),i}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));const i=new Wl(e,t,n,this.nextTensorId());if(this.state.registeredVariables[i.name]!=null)throw new Error(`Variable with name ${i.name} was already registered`);return this.state.registeredVariables[i.name]=i,this.incRef(i,this.backend),i}incRef(e,t){const n=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,n===0){this.state.numDataBuffers++;let s=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(s=e.size*ry(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:s,refCount:0}),this.state.numBytes+=s}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof Wl||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;const t=this.state.tensorInfo.get(e.dataId),n=t.refCount;n<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):this.state.tensorInfo.get(e.dataId).refCount--}disposeVariables(){for(const e in this.state.registeredVariables){const t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){const e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;const t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(const s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,i,o){const a={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:i},c=Qg(e);c!=null&&(s=c.gradFunc),s!=null&&(a.gradient=h=>(h=h.map((d,m)=>{if(d==null){const y=n[m],b=wa(y.size,y.dtype);return this.makeTensor(b,y.shape,y.dtype)}return d}),s(h.length>1?h:h[0],i,o))),this.state.activeTape.push(a)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){const t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){const t=Hi(e),n=new Set(t.map(i=>i.id));for(let i=0;i<this.state.activeScope.track.length;i++){const o=this.state.activeScope.track[i];!o.kept&&!n.has(o.id)&&o.dispose()}const s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(i=>{!i.kept&&i.scopeId===s.id&&this.track(i)})}gradients(e,t,n,s=!1){if(k(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);const i=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));k(i instanceof Q,()=>"The result y returned by f() must be a tensor.");const o=xD(this.state.activeTape,t,i);if(!s&&o.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")}),qi.registerFlag("IS_BROWSER",()=>fy()),qi.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined"),qi.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor)),qi.registerFlag("PROD",()=>!1),qi.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>qi.getBool("DEBUG")),qi.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0),qi.registerFlag("IS_TEST",()=>!1);function Ii(e,t){let n=e;if(wn(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];const s=[];for(;Array.isArray(n)||wn(n)&&t!=="string";)s.push(n.length),n=n[0];return Array.isArray(e)&&C().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&Hx(e,s,[]),s}function Hx(e,t,n){if(n=n||[],!Array.isArray(e)&&!wn(e)){k(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}k(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),k(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);const s=t.slice(1);for(let i=0;i<e.length;++i)Hx(e[i],s,n.concat(i))}function qx(e,t,n,s){if(e==null)return;if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${s}' must be ${e} tensor, but got ${t} tensor`)}function W(e,t,n,s="numeric"){if(e instanceof Q)return qx(s,e.dtype,t,n),e;let i=ba(e);if(i!=="string"&&["bool","int32","float32"].indexOf(s)>=0&&(i=s),qx(s,i,t,n),e==null||!wn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){const h=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${h}'`)}const o=Ii(e,i);!wn(e)&&!Array.isArray(e)&&(e=[e]);const a=!0,c=i!=="string"?Tr(e,i):Yi(e,[],a);return V.makeTensor(c,o,i)}function Ul(e,t,n,s="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);const i=e;return i.map((o,a)=>W(o,`${t}[${a}]`,n),s)}const jx="__op";function P(e){const t=Object.keys(e);if(t.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${t.length} keys.`);let n=t[0];const s=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+jx;const i=(...o)=>{V.startScope(n);try{const a=s(...o);return a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),V.endScope(a),a}catch(a){throw V.endScope(null),a}};return Object.defineProperty(i,"name",{value:n,configurable:!0}),i}function $D(e,t){const n=W(e,"real","complex"),s=W(t,"imag","complex");dt(n.shape,s.shape,`real and imag shapes, ${n.shape} and ${s.shape}, must match in call to tf.complex().`);const i=a=>a.complex(n,s),o={real:n,imag:s};return V.runKernelFunc(i,o,null,hg)}const xi=P({complex_:$D});function vr(e,t,n,s){if(s==null&&(s=ba(e)),s==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!wn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){cy(t);const i=we(t),o=we(n);k(i===o,()=>`Based on the provided shape, [${t}], the tensor should have ${i} values but has ${o}`);for(let a=0;a<n.length;++a){const c=n[a],h=a===n.length-1?c!==we(t.slice(a)):!0;k(n[a]===t[a]||!h,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!wn(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=s!=="string"?Tr(e,s):Yi(e,[],!0),V.makeTensor(e,t,s)}function en(e,t,n){const s=Ii(e,n);return vr(e,t,s,n)}const gy={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8};const hd=4;async function yy(e,t){const n=[],s=[],i=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);for(let a=0;a<i.length;++a){const c=i[a],h=Array.isArray(e)?e[a].tensor:e[c];if(h.dtype!=="float32"&&h.dtype!=="int32"&&h.dtype!=="bool"&&h.dtype!=="string"&&h.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${c}': ${h.dtype}`);const d={name:c,shape:h.shape,dtype:h.dtype};if(h.dtype==="string"){const m=new Promise(async y=>{const b=await h.bytes(),w=b.reduce((A,N)=>A+N.length,0)+hd*b.length,L=new Uint8Array(w);let T=0;for(let A=0;A<b.length;A++){const N=b[A],E=new Uint8Array(new Uint32Array([N.length]).buffer);L.set(E,T),T+=hd,L.set(N,T),T+=N.length}y(L)});s.push(m)}else s.push(h.data());t!=null&&(d.group=t),n.push(d)}const o=await Promise.all(s);return{data:UD(o),specs:n}}function ud(e,t){const n={};let s,i=0;for(const o of t){const a=o.name,c=o.dtype,h=o.shape,d=we(h);let m;if("quantization"in o){const y=o.quantization;if(y.dtype==="uint8"||y.dtype==="uint16"){if(!("min"in y&&"scale"in y))throw new Error(`Weight ${o.name} with quantization ${y.dtype} doesn't have corresponding metadata min and scale.`)}else if(y.dtype==="float16"){if(c!=="float32")throw new Error(`Weight ${o.name} is quantized with ${y.dtype} which only supports weights of type float32 not ${c}.`)}else throw new Error(`Weight ${o.name} has unknown quantization dtype ${y.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);const b=gy[y.dtype],w=e.slice(i,i+d*b),L=y.dtype==="uint8"?new Uint8Array(w):new Uint16Array(w);if(c==="float32")if(y.dtype==="uint8"||y.dtype==="uint16"){m=new Float32Array(L.length);for(let T=0;T<L.length;T++){const A=L[T];m[T]=A*y.scale+y.min}}else if(y.dtype==="float16")s===void 0&&(s=VD()),m=s(L);else throw new Error(`Unsupported quantization type ${y.dtype} for weight type float32.`);else if(c==="int32"){if(y.dtype!=="uint8"&&y.dtype!=="uint16")throw new Error(`Unsupported quantization type ${y.dtype} for weight type int32.`);m=new Int32Array(L.length);for(let T=0;T<L.length;T++){const A=L[T];m[T]=Math.round(A*y.scale+y.min)}}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=d*b}else if(c==="string"){const y=we(o.shape);m=[];for(let b=0;b<y;b++){const w=new Uint32Array(e.slice(i,i+hd))[0];i+=hd;const L=new Uint8Array(e.slice(i,i+w));m.push(L),i+=w}}else{const y=gy[c],b=e.slice(i,i+d*y);if(c==="float32")m=new Float32Array(b);else if(c==="int32")m=new Int32Array(b);else if(c==="bool")m=new Uint8Array(b);else if(c==="complex64"){m=new Float32Array(b);const w=new Float32Array(m.length/2),L=new Float32Array(m.length/2);for(let N=0;N<w.length;N++)w[N]=m[N*2],L[N]=m[N*2+1];const T=en(w,h,"float32"),A=en(L,h,"float32");n[a]=xi(T,A)}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=d*y}c!=="complex64"&&(n[a]=en(m,h,c))}return n}function UD(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0;const n=[];e.forEach(o=>{if(t+=o.byteLength,n.push(o.byteLength===o.buffer.byteLength?o:new o.constructor(o)),!(o instanceof Float32Array||o instanceof Int32Array||o instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${o.constructor.name}`)});const s=new Uint8Array(t);let i=0;return n.forEach(o=>{s.set(new Uint8Array(o.buffer),i),i+=o.byteLength}),s.buffer}const by=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function Kx(e){return by?Buffer.byteLength(e):new Blob([e]).size}function BD(e){if(by)return Buffer.from(e).toString("base64");const t=new Uint8Array(e);let n="";for(let s=0,i=t.length;s<i;s++)n+=String.fromCharCode(t[s]);return btoa(n)}function MD(e){if(by){const s=Buffer.from(e,"base64");return s.buffer.slice(s.byteOffset,s.byteOffset+s.byteLength)}const t=atob(e),n=new Uint8Array(t.length);for(let s=0;s<t.length;++s)n.set([t.charCodeAt(s)],s);return n.buffer}function dd(e){if(e.length===1)return e[0];let t=0;e.forEach(i=>{t+=i.byteLength});const n=new Uint8Array(t);let s=0;return e.forEach(i=>{n.set(new Uint8Array(i),s),s+=i.byteLength}),n.buffer}function Xx(e){const t="/";for(e=e.trim();e.endsWith(t);)e=e.slice(0,e.length-1);const n=e.split(t);return n[n.length-1]}function Bl(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:e.modelTopology==null?0:Kx(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:Kx(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function PD(){const e=n=>{let s=n<<13,i=0;for(;(s&8388608)===0;)i-=8388608,s<<=1;return s&=~8388608,i+=947912704,s|i},t=new Uint32Array(2048);t[0]=0;for(let n=1;n<1024;n++)t[n]=e(n);for(let n=1024;n<2048;n++)t[n]=939524096+(n-1024<<13);return t}function zD(){const e=new Uint32Array(64);e[0]=0,e[31]=1199570944,e[32]=2147483648,e[63]=3347054592;for(let t=1;t<31;t++)e[t]=t<<23;for(let t=33;t<63;t++)e[t]=2147483648+(t-32<<23);return e}function GD(){const e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function VD(){const e=PD(),t=zD(),n=GD();return s=>{const i=new ArrayBuffer(4*s.length),o=new Uint32Array(i);for(let a=0;a<s.length;a++){const c=s[a],h=e[n[c>>10]+(c&1023)]+t[c>>10];o[a]=h}return new Float32Array(i)}}class Xt{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Xt.instance==null&&(Xt.instance=new Xt),Xt.instance}static registerSaveRouter(e){Xt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Xt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Xt.getHandlers(e,"save")}static getLoadHandlers(e,t){return Xt.getHandlers(e,"load",t)}static getHandlers(e,t,n){const s=[],i=t==="load"?Xt.getInstance().loadRouters:Xt.getInstance().saveRouters;return i.forEach(o=>{const a=o(e,n);a!==null&&s.push(a)}),s}}const YD=e=>Xt.registerSaveRouter(e),HD=e=>Xt.registerLoadRouter(e),wy=e=>Xt.getSaveHandlers(e),Ly=(e,t)=>Xt.getLoadHandlers(e,t);const pd="tensorflowjs",Sy=1,ho="models_store",Nr="model_info_store";async function kZ(){const e=Iy();return new Promise((t,n)=>{const s=e.deleteDatabase(pd);s.onsuccess=()=>t(),s.onerror=i=>n(i)})}function Iy(){if(!C().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");const e=typeof window=="undefined"?self:window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(t==null)throw new Error("The current browser does not appear to support IndexedDB.");return t}function xy(e){const t=e.result;t.createObjectStore(ho,{keyPath:"modelPath"}),t.createObjectStore(Nr,{keyPath:"modelPath"})}class uo{constructor(e){if(this.indexedDB=Iy(),e==null||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,e)}async load(){return this.databaseAction(this.modelPath)}databaseAction(e,t){return new Promise((n,s)=>{const i=this.indexedDB.open(pd,Sy);i.onupgradeneeded=()=>xy(i),i.onsuccess=()=>{const o=i.result;if(t==null){const a=o.transaction(ho,"readonly"),c=a.objectStore(ho),h=c.get(this.modelPath);h.onsuccess=()=>{if(h.result==null)return o.close(),s(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(h.result.modelArtifacts)},h.onerror=d=>(o.close(),s(h.error)),a.oncomplete=()=>o.close()}else{const a=Bl(t),c=o.transaction(Nr,"readwrite");let h=c.objectStore(Nr);const d=h.put({modelPath:this.modelPath,modelArtifactsInfo:a});let m;d.onsuccess=()=>{m=o.transaction(ho,"readwrite");const y=m.objectStore(ho),b=y.put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:a});b.onsuccess=()=>n({modelArtifactsInfo:a}),b.onerror=w=>{h=c.objectStore(Nr);const L=h.delete(this.modelPath);L.onsuccess=()=>(o.close(),s(b.error)),L.onerror=T=>(o.close(),s(b.error))}},d.onerror=y=>(o.close(),s(d.error)),c.oncomplete=()=>{m==null?o.close():m.oncomplete=()=>o.close()}}},i.onerror=o=>s(i.error)})}}uo.URL_SCHEME="indexeddb://";const Jx=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(uo.URL_SCHEME))?qD(e.slice(uo.URL_SCHEME.length)):null;Xt.registerSaveRouter(Jx),Xt.registerLoadRouter(Jx);function qD(e){return new uo(e)}function jD(e){return e.startsWith(uo.URL_SCHEME)?e.slice(uo.URL_SCHEME.length):e}class KD{constructor(){this.indexedDB=Iy()}async listModels(){return new Promise((e,t)=>{const n=this.indexedDB.open(pd,Sy);n.onupgradeneeded=()=>xy(n),n.onsuccess=()=>{const s=n.result,i=s.transaction(Nr,"readonly"),o=i.objectStore(Nr),a=o.getAll();a.onsuccess=()=>{const c={};for(const h of a.result)c[h.modelPath]=h.modelArtifactsInfo;e(c)},a.onerror=c=>(s.close(),t(a.error)),i.oncomplete=()=>s.close()},n.onerror=s=>t(n.error)})}async removeModel(e){return e=jD(e),new Promise((t,n)=>{const s=this.indexedDB.open(pd,Sy);s.onupgradeneeded=()=>xy(s),s.onsuccess=()=>{const i=s.result,o=i.transaction(Nr,"readwrite"),a=o.objectStore(Nr),c=a.get(e);let h;c.onsuccess=()=>{if(c.result==null)return i.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{const d=a.delete(e),m=()=>{h=i.transaction(ho,"readwrite");const y=h.objectStore(ho),b=y.delete(e);b.onsuccess=()=>t(c.result.modelArtifactsInfo),b.onerror=w=>n(c.error)};d.onsuccess=m,d.onerror=y=>(m(),i.close(),n(c.error))}},c.onerror=d=>(i.close(),n(c.error)),o.oncomplete=()=>{h==null?i.close():h.oncomplete=()=>i.close()}},s.onerror=i=>n(s.error)})}}const Ti="/",po="tensorflowjs_models",Zx="info",XD="model_topology",JD="weight_specs",ZD="weight_data",QD="model_metadata";function FZ(){if(!C().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("purgeLocalStorageModels() cannot proceed because local storage is unavailable in the current environment.");const e=window.localStorage,t=[];for(let n=0;n<e.length;++n){const s=e.key(n),i=po+Ti;if(s.startsWith(i)&&s.length>i.length){e.removeItem(s);const o=eT(s);t.indexOf(o)===-1&&t.push(o)}}return t}function Qx(e){return{info:[po,e,Zx].join(Ti),topology:[po,e,XD].join(Ti),weightSpecs:[po,e,JD].join(Ti),weightData:[po,e,ZD].join(Ti),modelMetadata:[po,e,QD].join(Ti)}}function eT(e){const t=e.split(Ti);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(Ti)}function ek(e){return e.startsWith(mo.URL_SCHEME)?e.slice(mo.URL_SCHEME.length):e}class mo{constructor(e){if(!C().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=Qx(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{const t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),s=Bl(e);try{return this.LS.setItem(this.keys.info,JSON.stringify(s)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,BD(e.weightData)),this.LS.setItem(this.keys.modelMetadata,JSON.stringify({format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,userDefinedMetadata:e.userDefinedMetadata})),{modelArtifactsInfo:s}}catch(i){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${s.modelTopologyBytes}, weightSpecsBytes=${s.weightSpecsBytes}, weightDataBytes=${s.weightDataBytes}.`)}}}async load(){const e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(e.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");const t={},n=JSON.parse(this.LS.getItem(this.keys.topology));if(n==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);t.modelTopology=n;const s=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(s==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=s;const i=this.LS.getItem(this.keys.modelMetadata);if(i!=null){const a=JSON.parse(i);t.format=a.format,t.generatedBy=a.generatedBy,t.convertedBy=a.convertedBy,t.userDefinedMetadata=a.userDefinedMetadata}const o=this.LS.getItem(this.keys.weightData);if(o==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=MD(o),t}}mo.URL_SCHEME="localstorage://";const tT=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(mo.URL_SCHEME))?tk(e.slice(mo.URL_SCHEME.length)):null;Xt.registerSaveRouter(tT),Xt.registerLoadRouter(tT);function tk(e){return new mo(e)}class nk{constructor(){k(C().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),k(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){const e={},t=po+Ti,n=Ti+Zx;for(let s=0;s<this.LS.length;++s){const i=this.LS.key(s);if(i.startsWith(t)&&i.endsWith(n)){const o=eT(i);e[o]=JSON.parse(this.LS.getItem(i))}}return e}async removeModel(e){e=ek(e);const t=Qx(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);const n=JSON.parse(this.LS.getItem(t.info));return this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),n}}const Ia="://";class bs{constructor(){this.managers={}}static getInstance(){return bs.instance==null&&(bs.instance=new bs),bs.instance}static registerManager(e,t){k(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(Ia)&&(e=e.slice(0,e.indexOf(Ia))),k(e.length>0,()=>"scheme must not be an empty string.");const n=bs.getInstance();k(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),n.managers[e]=t}static getManager(e){const t=this.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}}function md(e){if(e.indexOf(Ia)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${bs.getSchemes().join(",")}`);return{scheme:e.split(Ia)[0],path:e.split(Ia)[1]}}async function nT(e,t,n=!1){k(e!==t,()=>`Old path and new path are the same: '${e}'`);const s=Xt.getLoadHandlers(e);k(s.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),k(s.length<2,()=>`Copying failed because more than one (${s.length}) load handlers for source URL ${e}.`);const i=s[0],o=Xt.getSaveHandlers(t);k(o.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),k(o.length<2,()=>`Copying failed because more than one (${s.length}) save handlers for destination URL ${t}.`);const a=o[0],c=md(e).scheme,h=md(e).path,d=c===md(e).scheme,m=await i.load();n&&d&&await bs.getManager(c).removeModel(h);const y=await a.save(m);return n&&!d&&await bs.getManager(c).removeModel(h),y.modelArtifactsInfo}async function sk(){const e=bs.getSchemes(),t={};for(const n of e){const s=await bs.getManager(n).listModels();for(const i in s){const o=n+Ia+i;t[o]=s[i]}}return t}async function ik(e){const t=md(e),n=bs.getManager(t.scheme);return n.removeModel(t.path)}async function rk(e,t){const n=!1;return nT(e,t,n)}async function ok(e,t){const n=!0;return nT(e,t,n)}class ak{fetch(e,t){return fetch(e,t)}now(){return performance.now()}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${t}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(e)}decode(e,t){return new TextDecoder(t).decode(e)}}if(C().get("IS_BROWSER")){C().setPlatform("browser",new ak);try{bs.registerManager(mo.URL_SCHEME,new nk)}catch(e){}try{bs.registerManager(uo.URL_SCHEME,new KD)}catch(e){}}const ck={importFetch:()=>IC()};let xa;function _Z(){xa=null}function WZ(e){xa=e}function $Z(){return xa}class lk{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return C().global.fetch!=null?C().global.fetch(e,t):(xa==null&&(xa=ck.importFetch()),xa(e,t))}now(){const e=process.hrtime();return e[0]*1e3+e[1]/1e6}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${t}`);return this.textEncoder.encode(e)}decode(e,t){return e.length===0?"":new this.util.TextDecoder(t).decode(e)}}C().get("IS_NODE")&&C().setPlatform("node",new lk);function Ze(e,t="float32",n){return t=t||"float32",cy(e),new Ar(e,t,n)}function hk(e,t){const n=W(e,"x","cast");if(!Dx(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");const s={x:n},i={dtype:t};return V.runKernelFunc(o=>o.cast(n,t),s,null,Jc,i)}const ve=P({cast_:hk});function uk(e){const t=W(e,"x","clone",null),n=()=>V.makeTensorFromDataId(t.dataId,t.shape,t.dtype),s={x:t};return V.runKernelFunc(n,s,null,al)}const Cr=P({clone_:uk});function sT(e,t=!1){console.log(e.toString(t))}Vx();const dk={buffer:Ze,cast:ve,clone:Cr,print:sT};CD(dk);const pk="model",mk=".json",fk=".weights.bin";function iT(e){return new Promise(t=>setTimeout(t)).then(e)}class Ta{constructor(e){if(!C().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(Ta.URL_SCHEME)&&(e=e.slice(Ta.URL_SCHEME.length)),(e==null||e.length===0)&&(e=pk),this.modelTopologyFileName=e+mk,this.weightDataFileName=e+fk}async save(e){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");const t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{const n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],s={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n},i=window.URL.createObjectURL(new Blob([JSON.stringify(s)],{type:"application/json"})),o=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(o.download=this.modelTopologyFileName,o.href=i,await iT(()=>o.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){const a=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;a.download=this.weightDataFileName,a.href=t,await iT(()=>a.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Bl(e)}}}}Ta.URL_SCHEME="downloads://";class gk{constructor(e){if(e==null||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.files=e}async load(){const e=this.files[0],t=this.files.slice(1);return new Promise((n,s)=>{const i=new FileReader;i.onload=o=>{const a=JSON.parse(o.target.result),c=a.modelTopology;if(c==null){s(new Error(`modelTopology field is missing from file ${e.name}`));return}t.length===0&&n({modelTopology:c});const h=a.weightsManifest;if(h==null){s(new Error(`weightManifest field is missing from file ${e.name}`));return}let d;try{d=this.checkManifestAndWeightFiles(h,t)}catch(w){s(w);return}const m=[],y=[],b=[];h.forEach(w=>{w.paths.forEach(L=>{y.push(L),b.push(null)}),m.push(...w.weights)}),h.forEach(w=>{w.paths.forEach(L=>{const T=new FileReader;T.onload=A=>{const N=A.target.result,E=y.indexOf(L);b[E]=N,b.indexOf(null)===-1&&n({modelTopology:c,weightSpecs:m,weightData:dd(b),format:a.format,generatedBy:a.generatedBy,convertedBy:a.convertedBy,userDefinedMetadata:a.userDefinedMetadata})},T.onerror=A=>s(`Failed to weights data from file of path '${L}'.`),T.readAsArrayBuffer(d[L])})})},i.onerror=o=>s(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),i.readAsText(e)})}checkManifestAndWeightFiles(e,t){const n=[],s=t.map(o=>Xx(o.name)),i={};for(const o of e)o.paths.forEach(a=>{const c=Xx(a);if(n.indexOf(c)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${c}'`);if(n.push(c),s.indexOf(c)===-1)throw new Error(`Weight file with basename '${c}' is not provided.`);i[a]=t[s.indexOf(c)]});if(n.length!==t.length)throw new Error(`Mismatch in the number of files in weights manifest (${n.length}) and the number of weight files provided (${t.length}).`);return i}}const yk=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(Ta.URL_SCHEME))?bk(e.slice(Ta.URL_SCHEME.length)):null;Xt.registerSaveRouter(yk);function bk(e="model"){return new Ta(e)}function wk(e){return new gk(e)}function rT(e,t,n,s){a(e),n=n==null?0:n,s=s==null?1:s,c(n,s);let i=0;const o=h=>(h.then(d=>{const m=n+ ++i/e.length*(s-n);return t(m),d}),h);function a(h){k(h!=null&&Array.isArray(h)&&h.length>0,()=>"promises must be a none empty array")}function c(h,d){k(h>=0&&h<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${h}`),k(d>=0&&d<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${d}`),k(d>=h,()=>`startFraction must be no more than endFraction, but got startFraction ${h} and endFraction ${d}`)}return Promise.all(e.map(o))}async function oT(e,t){t==null&&(t={});const n=t.fetchFunc==null?C().platform.fetch:t.fetchFunc,s=e.map(y=>n(y,t.requestInit,{isBinary:!0})),i=0,o=.5,a=t.onProgress==null?await Promise.all(s):await rT(s,t.onProgress,i,o),c=a.map(y=>y.arrayBuffer()),h=.5,d=1,m=t.onProgress==null?await Promise.all(c):await rT(c,t.onProgress,h,d);return m}async function aT(e,t="",n,s){const i=a=>oT(a,{requestInit:s}),o=cT(i);return o(e,t,n)}function cT(e){return async(t,n="",s)=>{const i=t.map(()=>!1),o={},a=s!=null?s.map(()=>!1):[],c=[];if(t.forEach((w,L)=>{let T=0;w.weights.forEach(A=>{const N="quantization"in A?A.quantization.dtype:A.dtype,E=gy[N]*we(A.shape),D=()=>{i[L]=!0,o[L]==null&&(o[L]=[]),o[L].push({manifestEntry:A,groupOffset:T,sizeBytes:E})};s!=null?s.forEach((F,_)=>{F===A.name&&(D(),a[_]=!0)}):D(),c.push(A.name),T+=E})}),!a.every(w=>w)){const w=s.filter((L,T)=>!a[T]);throw new Error(`Could not find weights in manifest with names: ${w.join(", ")}. 
+Manifest JSON has weights with names: ${c.join(", ")}.`)}const h=i.reduce((w,L,T)=>(L&&w.push(T),w),[]),d=[];h.forEach(w=>{t[w].paths.forEach(L=>{const T=n+(n.endsWith("/")?"":"/")+L;d.push(T)})});const m=await e(d),y={};let b=0;return h.forEach(w=>{const L=t[w].paths.length;let T=0;for(let F=0;F<L;F++)T+=m[b+F].byteLength;const A=new ArrayBuffer(T),N=new Uint8Array(A);let E=0;for(let F=0;F<L;F++){const _=new Uint8Array(m[b+F]);N.set(_,E),E+=_.byteLength}const D=o[w];D.forEach(F=>{const _=A.slice(F.groupOffset,F.groupOffset+F.sizeBytes),B=ud(_,[F.manifestEntry]);for(const $ in B)y[$]=B[$]}),b+=L}),y}}const Lk="application/octet-stream",Sk="application/json";class Ty{constructor(e,t){if(this.DEFAULT_METHOD="POST",t==null&&(t={}),this.weightPathPrefix=t.weightPathPrefix,this.onProgress=t.onProgress,this.weightUrlConverter=t.weightUrlConverter,t.fetchFunc!=null?(k(typeof t.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=t.fetchFunc):this.fetch=C().platform.fetch,k(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&k(e.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${e.length}).`),this.path=e,t.requestInit!=null&&t.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t.requestInit||{}}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");const t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);t.body=new FormData;const n=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],s={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,userDefinedMetadata:e.userDefinedMetadata,weightsManifest:n};t.body.append("model.json",new Blob([JSON.stringify(s)],{type:Sk}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:Lk}),"model.weights.bin");const i=await this.fetch(this.path,t);if(i.ok)return{modelArtifactsInfo:Bl(e),responses:[i]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${i.status}.`)}async load(){const e=await this.fetch(this.path,this.requestInit);if(!e.ok)throw new Error(`Request to ${this.path} failed with status code ${e.status}. Please verify this URL points to the model JSON of the model to load.`);let t;try{t=await e.json()}catch(b){let w=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?w+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. 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+          with dtype ${a.dtype}. `)});const s=(a,c)=>{const h=ft(t,n[0].shape)[0],d=Or(n.map(b=>b.shape),h);if(we(d)===0)return en([],d);if(n=n.filter(b=>b.size>0),n.length===1)return n[0];const m=n.map(b=>b.shape);Ky(m,h);const y=a.concat(n,h);return c(n),y},i=n,o={axis:t};return V.runKernelFunc(s,i,null,$u,o)}const Mt=P({concat_:CF});function RF(e){const t=W(e,"x","sigmoid"),n={x:t};return V.runKernelFunc((s,i)=>{const o=s.sigmoid(t);return i([o]),o},n,null,xl)}const vi=P({sigmoid_:RF});function OF(e,t,n){const s=W(e,"x","slice");if(s.rank===0)throw new Error("Slicing scalar is not possible");const i=(c,h)=>{const[d,m]=wd(s,t,n);return Oy(s,d,m),h([s]),c.slice(s,d,m)},o={x:s},a={begin:t,size:n};return V.runKernelFunc(i,o,null,Ku,a)}const nt=P({slice_:OF});function EF(e){const t=W(e,"x","tanh"),n={x:t};return V.runKernelFunc((s,i)=>{const o=s.tanh(t);return i([o]),o},n,null,Nl)}const Ca=P({tanh_:EF});function DF(e,t,n,s,i,o){const 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ql=P({batchToSpaceND_:FF});function _F(e){let t;return e.rank===0||e.rank===1?t=K(e,[1,1,1,e.size]):e.rank===2?t=K(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=K(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function WF(e,t,n,s,i,o){o==null&&(o=.001);const a=W(e,"x","batchNorm"),c=W(t,"mean","batchNorm"),h=W(n,"variance","batchNorm");let d;i!=null&&(d=W(i,"scale","batchNorm"));let m;s!=null&&(m=W(s,"offset","batchNorm")),k(c.rank===h.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k(m==null||c.rank===m.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k(d==null||c.rank===d.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");const y=_F(a),b=(A,N)=>(N([y,c,h,d]),A.batchNorm(y,vd(c),vd(h),vd(m),vd(d),o)),w={x:y,scale:d,offset:m,mean:c,variance:h},L={varianceEpsilon:o},T=V.runKernelFunc(b,w,null,ol,L);return K(T,a.shape)}function vd(e){return 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a=W(e,"x","batchNorm"),c=W(t,"mean","batchNorm"),h=W(n,"variance","batchNorm");let d;i!=null&&(d=W(i,"scale","batchNorm"));let m;return s!=null&&(m=W(s,"offset","batchNorm")),k(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),k(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${c.rank}.`),k(h.rank===3||h.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${h.rank}.`),d!=null&&k(d.rank===3||d.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${d.rank}.`),m!=null&&k(m.rank===3||m.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${m.rank}.`),bo(a,c,h,m,d,o)}const RT=P({batchNorm3d_:UF});function BF(e,t,n,s,i,o){const a=W(e,"x","batchNorm"),c=W(t,"mean","batchNorm"),h=W(n,"variance","batchNorm");let d;i!=null&&(d=W(i,"scale","batchNorm"));let m;return s!=null&&(m=W(s,"offset","batchNorm")),k(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),k(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${c.rank}.`),k(h.rank===4||h.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${h.rank}.`),d!=null&&k(d.rank===4||d.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${d.rank}.`),m!=null&&k(m.rank===4||m.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${m.rank}.`),bo(a,c,h,m,d,o)}const OT=P({batchNorm4d_:BF});function MF(e,t){let n=W(e,"broadcastTo","x");const s=n.shape;if(t.some(m=>!(m>0)||m%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){const m=n.shape.slice();for(;m.length<t.length;)m.unshift(1);n=K(n,m)}const i=n.shape,o=Array.from(t);for(let m=t.length-1;m>=0;m--)if(i[m]===t[m])o[m]=1;else 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B=(J,re)=>{const[ce,ue,he,de]=re,le=Xd(K(J,he.shape),he,o);let ye,pe;if(!n&&!s?(ye=at(le,ue,!1,!0),pe=at(ce,le,!0,!1)):!n&&s?(ye=at(le,ue,!1,!1),pe=at(le,ce,!0,!1)):n&&!s?(ye=at(ue,le,!1,!0),pe=at(ce,le,!1,!1)):(ye=at(ue,le,!0,!0),pe=at(le,ce,!0,!0)),i!=null){const Ie=Jd(de,le);return[ye,pe,Ie]}else return[ye,pe]},$=J=>{const re=J.fusedBatchMatMul({a:E,b:D,transposeA:n,transposeB:s,bias:F,activation:o,preluActivationWeights:_});return re},H={a:E,b:D,bias:F,preluActivationWeights:_},q={transposeA:n,transposeB:s,activation:o};if(i==null){const J=Ni((re,ce,ue)=>{const he=V.runKernelFunc($,H,null,Kg,q);return ue([re,ce,he]),{value:K(he,N),gradFunc:B}});return J(E,D)}else{const J=Ni((re,ce,ue,he)=>{const de=V.runKernelFunc($,H,null,Kg,q);return he([re,ce,de,ue]),{value:K(de,N),gradFunc:B}});return J(E,D,F)}}const ep=P({fusedMatMul_:IU});var xU=Object.freeze({__proto__:null,conv2d:vb,depthwiseConv2d:bA,matMul:ep});function TU(e){return Tb(e,.54,.46)}const AU=P({hammingWindow_:TU});function 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nB(e,t,n){k(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),k(n%1===0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);const s=W(e,"a","bandPart");k(s.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${s.rank}.`);const i=s.shape,[o,a]=s.shape.slice(-2);if(!(t<=o))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${o}).`);if(!(n<=a))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${a}).`);t<0&&(t=o),n<0&&(n=a);const c=K(sh(0,o,1,"int32"),[-1,1]),h=sh(0,a,1,"int32"),d=Ce(c,h),m=Ws(Dr(d,Ne(+t,"int32")),Ki(d,Ne(-n,"int32"))),y=ct([o,a],s.dtype);return K(is(Oi(K(s,[-1,o,a])).map(b=>_n(m,b,y))),i)}const sB=P({bandPart_:nB});function iB(e){let t;if(Array.isArray(e)){t=!1,k(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");const i=e[0].shape[0];for(let o=1;o<e.length;++o)k(e[o].shape[0]===i,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[o].shape[0]} vs. ${i})`)}else t=!0,e=ss(e,e.shape[0],0).map(i=>Fr(i,[0]));k(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);const n=[],s=e;for(let i=0;i<e.length;++i)n.push(V.tidy(()=>{let o=s[i];if(i>0)for(let a=0;a<i;++a){const c=X(Ue(X(n[a],o)),n[a]);o=Ce(o,c)}return _e(o,Kd(o,"euclidean"))}));return t?is(n,0):n}const rB=P({gramSchmidt_:iB});function oB(e,t=!1){if(k(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return TA(e,t);{const n=e.shape.slice(0,e.shape.length-2).reduce((h,d)=>h*d),s=Oi(K(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),i=[],o=[];s.forEach(h=>{const[d,m]=TA(h,t);i.push(d),o.push(m)});const a=K(is(i,0),e.shape),c=K(is(o,0),e.shape);return[a,c]}}function TA(e,t=!1){return V.tidy(()=>{k(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);const n=e.shape[0],s=e.shape[1];let i=Ed(n),o=Cr(e);const a=_r([[1]],[1,1]);let c=Cr(a);const h=n>=s?s:n;for(let d=0;d<h;++d){const m=o,y=c,b=i;[c,o,i]=V.tidy(()=>{const w=nt(o,[d,d],[n-d,1]),L=Kd(w),T=nt(o,[d,d],[1,1]),A=_n(Ss(T,0),_r([[-1]]),_r([[1]])),N=Ce(T,X(A,L)),E=_e(w,N);E.shape[0]===1?c=Cr(a):c=Mt([a,nt(E,[1,0],[E.shape[0]-1,E.shape[1]])],0);const D=Pt(_e(at(A,N),L)),F=nt(o,[d,0],[n-d,s]),_=X(D,c),B=Me(c);if(d===0)o=Ce(F,at(_,at(B,F)));else{const q=Ce(F,at(_,at(B,F)));o=Mt([nt(o,[0,0],[d,s]),q],0)}const $=Me(_),H=nt(i,[0,d],[n,i.shape[1]-d]);if(d===0)i=Ce(H,at(at(H,c),$));else{const q=Ce(H,at(at(H,c),$));i=Mt([nt(i,[0,0],[n,d]),q],1)}return[c,o,i]}),He([m,y,b])}return!t&&n>s&&(i=nt(i,[0,0],[n,s]),o=nt(o,[0,0],[s,s])),[i,o]})}const aB=P({qr_:oB});(function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(r.Reduction||(r.Reduction={}));function cB(e,t,n=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const s=W(e,"losses","computeWeightedLoss");let i=null;t!=null&&(i=W(t,"weights","computeWeightedLoss"));const o=i==null?s:X(s,i);if(n===r.Reduction.NONE)return o;if(n===r.Reduction.SUM)return Ue(o);if(n===r.Reduction.MEAN){if(i==null)return zt(o);{const a=s.size/i.size,c=_e(Ue(o),Ue(i));return a>1?_e(c,Ne(a)):c}}if(n===r.Reduction.SUM_BY_NONZERO_WEIGHTS){if(i==null)return _e(Ue(o),Ne(s.size));{const a=X(i,Qs(s.shape)),c=ve(Ue(kr(a,Ne(0))),"float32");return _e(Ue(o),c)}}throw Error(`Unknown reduction: ${n}`)}const Xi=P({computeWeightedLoss_:cB});function lB(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=W(e,"labels","absoluteDifference"),o=W(t,"predictions","absoluteDifference");let a=null;n!=null&&(a=W(n,"weights","absoluteDifference")),dt(i.shape,o.shape,"Error in absoluteDifference: ");const c=nn(Ce(i,o));return Xi(c,a,s)}const hB=P({absoluteDifference_:lB});function uB(e,t,n,s,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","cosineDistance"),a=W(t,"predictions","cosineDistance");let c=null;s!=null&&(c=W(s,"weights","cosineDistance")),dt(o.shape,a.shape,"Error in cosineDistance: ");const h=Ne(1),d=Ce(h,Ue(X(o,a),n,!0));return Xi(d,c,i)}const dB=P({cosineDistance_:uB});function pB(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let i=W(e,"labels","hingeLoss");const o=W(t,"predictions","hingeLoss");let a=null;n!=null&&(a=W(n,"weights","hingeLoss")),dt(i.shape,o.shape,"Error in hingeLoss: ");const c=Ne(1);i=Ce(X(Ne(2),i),c);const h=Ri(Ce(c,X(i,o)));return Xi(h,a,s)}const mB=P({hingeLoss_:pB});function fB(e,t,n,s=1,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","huberLoss"),a=W(t,"predictions","huberLoss");let c=null;n!=null&&(c=W(n,"weights","huberLoss")),dt(o.shape,a.shape,"Error in huberLoss: ");const h=Ne(s),d=nn(Ce(a,o)),m=Lo(d,h),y=Ce(d,m),b=be(X(Ne(.5),wt(m)),X(h,y));return Xi(b,c,i)}const gB=P({huberLoss_:fB});function yB(e,t,n,s=1e-7,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","logLoss"),a=W(t,"predictions","logLoss");let c=null;n!=null&&(c=W(n,"weights","logLoss")),dt(o.shape,a.shape,"Error in logLoss: ");const h=Ne(1),d=Ne(s),m=Pt(X(o,ts(be(a,d)))),y=X(Ce(h,o),ts(be(Ce(h,a),d))),b=Ce(m,y);return Xi(b,c,i)}const bB=P({logLoss_:yB});function wB(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=W(e,"labels","meanSquaredError"),o=W(t,"predictions","meanSquaredError");let a=null;n!=null&&(a=W(n,"weights","meanSquaredError")),dt(i.shape,o.shape,"Error in meanSquaredError: ");const c=ah(i,o);return Xi(c,a,s)}const LB=P({meanSquaredError_:wB});function SB(e,t){const n=W(e,"labels","sigmoidCrossEntropyWithLogits"),s=W(t,"logits","sigmoidCrossEntropyWithLogits");dt(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");const i=Ri(s),o=X(s,n),a=kd(Ls(Pt(nn(s))));return be(Ce(i,o),a)}function IB(e,t,n,s=0,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=W(e,"multiClassLabels","sigmoidCrossEntropy");const a=W(t,"logits","sigmoidCrossEntropy");let c=null;if(n!=null&&(c=W(n,"weights","sigmoidCrossEntropy")),dt(o.shape,a.shape,"Error in sigmoidCrossEntropy: "),s>0){const d=Ne(s),m=Ne(1),y=Ne(.5);o=be(X(o,Ce(m,d)),X(y,d))}const h=SB(o,a);return Xi(h,c,i)}const xB=P({sigmoidCrossEntropy_:IB});function TB(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);const s=Ni((i,o,a)=>{const c=!0,h=ab(o,[n],c),d=Ce(ve(o,"float32"),h);a([i,d]);const m=Pt(X(d,i)),y=Ue(m,[n]),b=(w,L)=>{const[T,A]=L,N=Rn(w.shape,[n]);return[X(K(w,N),Ce(ve(T,"float32"),Ls(A))),X(K(w,N),Ce(Ls(A),ve(T,"float32")))]};return{value:y,gradFunc:b}});return s(e,t)}function AB(e,t,n,s=0,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=W(e,"onehotLabels","softmaxCrossEntropy");const a=W(t,"logits","softmaxCrossEntropy");let c=null;if(n!=null&&(c=W(n,"weights","softmaxCrossEntropy")),dt(o.shape,a.shape,"Error in softmaxCrossEntropy: "),s>0){const d=Ne(s),m=Ne(1),y=Ne(o.shape[1]);o=be(X(o,Ce(m,d)),_e(d,y))}const h=TB(o,a);return Xi(h,c,i)}const vB=P({softmaxCrossEntropy_:AB});const NB={fft:rh,ifft:Fa,rfft:oh,irfft:Hd},CB={hammingWindow:AU,hannWindow:wA,frame:LA,stft:RU},Wr={flipLeftRight:kU,resizeNearestNeighbor:xA,resizeBilinear:IA,rotateWithOffset:_U,cropAndResize:EU,nonMaxSuppression:$U,nonMaxSuppressionAsync:YU,nonMaxSuppressionWithScore:qU,nonMaxSuppressionWithScoreAsync:KU,nonMaxSuppressionPadded:JU,nonMaxSuppressionPaddedAsync:QU},AA={bandPart:sB,gramSchmidt:rB,qr:aB},RB={absoluteDifference:hB,computeWeightedLoss:Xi,cosineDistance:dB,hingeLoss:mB,huberLoss:gB,logLoss:bB,meanSquaredError:LB,sigmoidCrossEntropy:xB,softmaxCrossEntropy:vB};class Ji extends mo{minimize(e,t=!1,n){const{value:s,grads:i}=this.computeGradients(e,n);if(n!=null){const o=n.map(a=>({name:a.name,tensor:i[a.name]}));this.applyGradients(o)}else this.applyGradients(i);return He(i),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return ob(e,t)}dispose(){this.iterations_!=null&&He(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ne(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(Ji,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});class lh extends Ji{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n],o=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:ee(()=>Qe(i).variable(o))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:ee(()=>Qe(i).variable(o))});const a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;const c=this.accumulatedGrads[s].variable,h=this.accumulatedUpdates[s].variable;ee(()=>{const d=be(X(c,this.rho),X(wt(a),1-this.rho)),m=X(_e(Ln(be(h,this.epsilon)),Ln(be(c,this.epsilon))),a),y=be(X(h,this.rho),X(wt(m),1-this.rho));c.assign(d),h.assign(y);const b=be(X(m,-this.learningRate),i);i.assign(b)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(He(this.accumulatedGrads.map(e=>e.variable)),He(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}lh.className="Adadelta",me(lh);class hh extends Ji{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n];if(this.accumulatedGrads[s]==null){const c=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:ee(()=>Xl(i.shape,this.initialAccumulatorValue).variable(c))}}const o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;const a=this.accumulatedGrads[s].variable;ee(()=>{const c=be(a,wt(o));a.assign(c);const h=be(X(_e(o,Ln(be(c,V.backend.epsilon()))),-this.learningRate),i);i.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&He(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}hh.className="Adagrad",me(hh);class uh extends Ji{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],ee(()=>{this.accBeta1=Ne(t).variable(),this.accBeta2=Ne(n).variable()}),s==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);ee(()=>{const n=Ce(1,this.accBeta1),s=Ce(1,this.accBeta2);t.forEach((i,o)=>{const a=V.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:ee(()=>Qe(a).variable(c))}),this.accumulatedSecondMoment[o]==null&&(this.accumulatedSecondMoment[o]={originalName:`${i}/v`,variable:ee(()=>Qe(a).variable(c))});const h=Array.isArray(e)?e[o].tensor:e[i];if(h==null)return;const d=this.accumulatedFirstMoment[o].variable,m=this.accumulatedSecondMoment[o].variable,y=be(X(d,this.beta1),X(h,1-this.beta1)),b=be(X(m,this.beta2),X(wt(h),1-this.beta2)),w=_e(y,n),L=_e(b,s);d.assign(y),m.assign(b);const T=be(X(_e(w,be(Ln(L),this.epsilon)),-this.learningRate),a);a.assign(T)}),this.accBeta1.assign(X(this.accBeta1,this.beta1)),this.accBeta2.assign(X(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&He(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&He(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),ee(()=>{this.accBeta1.assign(ei(this.beta1,this.iterations_+1)),this.accBeta2.assign(ei(this.beta2,this.iterations_+1))});const t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}}uh.className="Adam",me(uh);class dh extends Ji{constructor(e,t,n,s=null,i=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=i,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],ee(()=>{this.iteration=Ne(0).variable(),this.accBeta1=Ne(t).variable()}),s==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);ee(()=>{const n=Ce(1,this.accBeta1),s=_e(-this.learningRate,be(X(this.iteration,this.decay),1));t.forEach((i,o)=>{const a=V.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:Qe(a).variable(c)}),this.accumulatedWeightedInfNorm[o]==null&&(this.accumulatedWeightedInfNorm[o]={originalName:`${i}/v`,variable:Qe(a).variable(c)});const h=Array.isArray(e)?e[o].tensor:e[i];if(h==null)return;const d=this.accumulatedFirstMoment[o].variable,m=this.accumulatedWeightedInfNorm[o].variable,y=be(X(d,this.beta1),X(h,1-this.beta1)),b=X(m,this.beta2),w=nn(h),L=_s(b,w);d.assign(y),m.assign(L);const T=be(X(_e(s,n),_e(y,be(L,this.epsilon))),a);a.assign(T)}),this.iteration.assign(be(this.iteration,1)),this.accBeta1.assign(X(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&He(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&He(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}}dh.className="Adamax",me(dh);class Ua extends Ji{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;const o=V.registeredVariables[n];ee(()=>{const a=be(X(this.c,i),o);o.assign(a)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Nn(Ne(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}Ua.className="SGD",me(Ua);class ph extends Ua{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Ne(this.momentum)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n];if(this.accumulations[s]==null){const c=!1;this.accumulations[s]={originalName:`${n}/momentum`,variable:ee(()=>Qe(i).variable(c))}}const o=this.accumulations[s].variable,a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;ee(()=>{let c;const h=be(X(this.m,o),a);this.useNesterov?c=be(X(this.c,be(a,X(h,this.m))),i):c=be(X(this.c,h),i),o.assign(h),i.assign(c)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&He(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}ph.className="Momentum",me(ph);class mh extends Ji{constructor(e,t=.9,n=0,s=null,i=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=i,s==null&&(this.epsilon=V.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n],o=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:ee(()=>Qe(i).variable(o))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:ee(()=>Qe(i).variable(o))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:ee(()=>Qe(i).variable(o))});const a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;const c=this.accumulatedMeanSquares[s].variable,h=this.accumulatedMoments[s].variable;ee(()=>{const d=be(X(c,this.decay),X(wt(a),1-this.decay));if(this.centered){const m=this.accumulatedMeanGrads[s].variable,y=be(X(m,this.decay),X(a,1-this.decay)),b=_e(X(a,this.learningRate),Ln(Ce(d,be(wt(y),this.epsilon)))),w=be(X(h,this.momentum),b);c.assign(d),m.assign(y),h.assign(w);const L=Ce(i,w);i.assign(L)}else{const m=be(X(c,this.decay),X(wt(a),1-this.decay)),y=be(X(h,this.momentum),_e(X(a,this.learningRate),Ln(be(m,this.epsilon))));c.assign(m),h.assign(y);const b=Ce(i,y);i.assign(b)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&He(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&He(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&He(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}}mh.className="RMSProp",me(mh);class vo{static sgd(e){return new Ua(e)}static momentum(e,t,n=!1){return new ph(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,i=!1){return new mh(e,t,n,s,i)}static 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B=(J,re)=>{const[ce,ue,he,de]=re,le=Xd(K(J,he.shape),he,o);let ye,pe;if(!n&&!s?(ye=at(le,ue,!1,!0),pe=at(ce,le,!0,!1)):!n&&s?(ye=at(le,ue,!1,!1),pe=at(le,ce,!0,!1)):n&&!s?(ye=at(ue,le,!1,!0),pe=at(ce,le,!1,!1)):(ye=at(ue,le,!0,!0),pe=at(le,ce,!0,!0)),i!=null){const Ie=Jd(de,le);return[ye,pe,Ie]}else return[ye,pe]},$=J=>{const re=J.fusedBatchMatMul({a:E,b:D,transposeA:n,transposeB:s,bias:F,activation:o,preluActivationWeights:_});return re},H={a:E,b:D,bias:F,preluActivationWeights:_},q={transposeA:n,transposeB:s,activation:o};if(i==null){const J=Ni((re,ce,ue)=>{const he=V.runKernelFunc($,H,null,Kg,q);return ue([re,ce,he]),{value:K(he,N),gradFunc:B}});return J(E,D)}else{const J=Ni((re,ce,ue,he)=>{const de=V.runKernelFunc($,H,null,Kg,q);return he([re,ce,de,ue]),{value:K(de,N),gradFunc:B}});return J(E,D,F)}}const ep=P({fusedMatMul_:IU});var xU=Object.freeze({__proto__:null,conv2d:vb,depthwiseConv2d:bA,matMul:ep});function TU(e){return Tb(e,.54,.46)}const AU=P({hammingWindow_:TU});function 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i=s,o=!1;s.rank===3&&(o=!0,i=K(s,[1,s.shape[0],s.shape[1],s.shape[2]]));const[a,c]=t,h=(b,w)=>(w([i]),b.resizeBilinear(i,a,c,n)),d={images:i},m={alignCorners:n,size:t},y=V.runKernelFunc(h,d,null,Ug,m);return o?K(y,[y.shape[1],y.shape[2],y.shape[3]]):y}const IA=P({resizeBilinear_:eB});function tB(e,t,n=!1){const s=W(e,"images","resizeNearestNeighbor");k(s.rank===3||s.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${s.rank}.`),k(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),k(s.dtype==="float32"||s.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype");let i=s,o=!1;s.rank===3&&(o=!0,i=K(s,[1,s.shape[0],s.shape[1],s.shape[2]]));const[a,c]=t,h={images:i},d={alignCorners:n,size:t},m=(b,w)=>(w([i]),b.resizeNearestNeighbor(i,a,c,n)),y=V.runKernelFunc(m,h,null,$g,d);return o?K(y,[y.shape[1],y.shape[2],y.shape[3]]):y}const xA=P({resizeNearestNeighbor_:tB});function nB(e,t,n){k(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),k(n%1===0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);const s=W(e,"a","bandPart");k(s.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${s.rank}.`);const i=s.shape,[o,a]=s.shape.slice(-2);if(!(t<=o))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${o}).`);if(!(n<=a))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${a}).`);t<0&&(t=o),n<0&&(n=a);const c=K(sh(0,o,1,"int32"),[-1,1]),h=sh(0,a,1,"int32"),d=Ce(c,h),m=Ws(Dr(d,Ne(+t,"int32")),Ki(d,Ne(-n,"int32"))),y=ct([o,a],s.dtype);return K(is(Oi(K(s,[-1,o,a])).map(b=>_n(m,b,y))),i)}const sB=P({bandPart_:nB});function iB(e){let t;if(Array.isArray(e)){t=!1,k(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");const i=e[0].shape[0];for(let o=1;o<e.length;++o)k(e[o].shape[0]===i,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[o].shape[0]} vs. ${i})`)}else t=!0,e=ss(e,e.shape[0],0).map(i=>Fr(i,[0]));k(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);const n=[],s=e;for(let i=0;i<e.length;++i)n.push(V.tidy(()=>{let o=s[i];if(i>0)for(let a=0;a<i;++a){const c=X(Ue(X(n[a],o)),n[a]);o=Ce(o,c)}return _e(o,Kd(o,"euclidean"))}));return t?is(n,0):n}const rB=P({gramSchmidt_:iB});function oB(e,t=!1){if(k(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return TA(e,t);{const n=e.shape.slice(0,e.shape.length-2).reduce((h,d)=>h*d),s=Oi(K(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),i=[],o=[];s.forEach(h=>{const[d,m]=TA(h,t);i.push(d),o.push(m)});const a=K(is(i,0),e.shape),c=K(is(o,0),e.shape);return[a,c]}}function TA(e,t=!1){return V.tidy(()=>{k(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);const n=e.shape[0],s=e.shape[1];let i=Ed(n),o=Cr(e);const a=_r([[1]],[1,1]);let c=Cr(a);const h=n>=s?s:n;for(let d=0;d<h;++d){const m=o,y=c,b=i;[c,o,i]=V.tidy(()=>{const w=nt(o,[d,d],[n-d,1]),L=Kd(w),T=nt(o,[d,d],[1,1]),A=_n(Ss(T,0),_r([[-1]]),_r([[1]])),N=Ce(T,X(A,L)),E=_e(w,N);E.shape[0]===1?c=Cr(a):c=Mt([a,nt(E,[1,0],[E.shape[0]-1,E.shape[1]])],0);const D=Pt(_e(at(A,N),L)),F=nt(o,[d,0],[n-d,s]),_=X(D,c),B=Me(c);if(d===0)o=Ce(F,at(_,at(B,F)));else{const q=Ce(F,at(_,at(B,F)));o=Mt([nt(o,[0,0],[d,s]),q],0)}const $=Me(_),H=nt(i,[0,d],[n,i.shape[1]-d]);if(d===0)i=Ce(H,at(at(H,c),$));else{const q=Ce(H,at(at(H,c),$));i=Mt([nt(i,[0,0],[n,d]),q],1)}return[c,o,i]}),He([m,y,b])}return!t&&n>s&&(i=nt(i,[0,0],[n,s]),o=nt(o,[0,0],[s,s])),[i,o]})}const aB=P({qr_:oB});(function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(r.Reduction||(r.Reduction={}));function cB(e,t,n=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const s=W(e,"losses","computeWeightedLoss");let i=null;t!=null&&(i=W(t,"weights","computeWeightedLoss"));const o=i==null?s:X(s,i);if(n===r.Reduction.NONE)return o;if(n===r.Reduction.SUM)return Ue(o);if(n===r.Reduction.MEAN){if(i==null)return zt(o);{const a=s.size/i.size,c=_e(Ue(o),Ue(i));return a>1?_e(c,Ne(a)):c}}if(n===r.Reduction.SUM_BY_NONZERO_WEIGHTS){if(i==null)return _e(Ue(o),Ne(s.size));{const a=X(i,Qs(s.shape)),c=ve(Ue(kr(a,Ne(0))),"float32");return _e(Ue(o),c)}}throw Error(`Unknown reduction: ${n}`)}const Xi=P({computeWeightedLoss_:cB});function lB(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=W(e,"labels","absoluteDifference"),o=W(t,"predictions","absoluteDifference");let a=null;n!=null&&(a=W(n,"weights","absoluteDifference")),dt(i.shape,o.shape,"Error in absoluteDifference: ");const c=sn(Ce(i,o));return Xi(c,a,s)}const hB=P({absoluteDifference_:lB});function uB(e,t,n,s,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","cosineDistance"),a=W(t,"predictions","cosineDistance");let c=null;s!=null&&(c=W(s,"weights","cosineDistance")),dt(o.shape,a.shape,"Error in cosineDistance: ");const h=Ne(1),d=Ce(h,Ue(X(o,a),n,!0));return Xi(d,c,i)}const dB=P({cosineDistance_:uB});function pB(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let i=W(e,"labels","hingeLoss");const o=W(t,"predictions","hingeLoss");let a=null;n!=null&&(a=W(n,"weights","hingeLoss")),dt(i.shape,o.shape,"Error in hingeLoss: ");const c=Ne(1);i=Ce(X(Ne(2),i),c);const h=Ri(Ce(c,X(i,o)));return Xi(h,a,s)}const mB=P({hingeLoss_:pB});function fB(e,t,n,s=1,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","huberLoss"),a=W(t,"predictions","huberLoss");let c=null;n!=null&&(c=W(n,"weights","huberLoss")),dt(o.shape,a.shape,"Error in huberLoss: ");const h=Ne(s),d=sn(Ce(a,o)),m=Io(d,h),y=Ce(d,m),b=be(X(Ne(.5),wt(m)),X(h,y));return Xi(b,c,i)}const gB=P({huberLoss_:fB});function yB(e,t,n,s=1e-7,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","logLoss"),a=W(t,"predictions","logLoss");let c=null;n!=null&&(c=W(n,"weights","logLoss")),dt(o.shape,a.shape,"Error in logLoss: ");const h=Ne(1),d=Ne(s),m=Pt(X(o,ts(be(a,d)))),y=X(Ce(h,o),ts(be(Ce(h,a),d))),b=Ce(m,y);return Xi(b,c,i)}const bB=P({logLoss_:yB});function wB(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=W(e,"labels","meanSquaredError"),o=W(t,"predictions","meanSquaredError");let a=null;n!=null&&(a=W(n,"weights","meanSquaredError")),dt(i.shape,o.shape,"Error in meanSquaredError: ");const c=ah(i,o);return Xi(c,a,s)}const LB=P({meanSquaredError_:wB});function SB(e,t){const n=W(e,"labels","sigmoidCrossEntropyWithLogits"),s=W(t,"logits","sigmoidCrossEntropyWithLogits");dt(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");const i=Ri(s),o=X(s,n),a=kd(Ls(Pt(sn(s))));return be(Ce(i,o),a)}function IB(e,t,n,s=0,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=W(e,"multiClassLabels","sigmoidCrossEntropy");const a=W(t,"logits","sigmoidCrossEntropy");let c=null;if(n!=null&&(c=W(n,"weights","sigmoidCrossEntropy")),dt(o.shape,a.shape,"Error in sigmoidCrossEntropy: "),s>0){const d=Ne(s),m=Ne(1),y=Ne(.5);o=be(X(o,Ce(m,d)),X(y,d))}const h=SB(o,a);return Xi(h,c,i)}const xB=P({sigmoidCrossEntropy_:IB});function TB(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);const s=Ni((i,o,a)=>{const c=!0,h=ab(o,[n],c),d=Ce(ve(o,"float32"),h);a([i,d]);const m=Pt(X(d,i)),y=Ue(m,[n]),b=(w,L)=>{const[T,A]=L,N=Rn(w.shape,[n]);return[X(K(w,N),Ce(ve(T,"float32"),Ls(A))),X(K(w,N),Ce(Ls(A),ve(T,"float32")))]};return{value:y,gradFunc:b}});return s(e,t)}function AB(e,t,n,s=0,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=W(e,"onehotLabels","softmaxCrossEntropy");const a=W(t,"logits","softmaxCrossEntropy");let c=null;if(n!=null&&(c=W(n,"weights","softmaxCrossEntropy")),dt(o.shape,a.shape,"Error in softmaxCrossEntropy: "),s>0){const d=Ne(s),m=Ne(1),y=Ne(o.shape[1]);o=be(X(o,Ce(m,d)),_e(d,y))}const h=TB(o,a);return Xi(h,c,i)}const vB=P({softmaxCrossEntropy_:AB});const NB={fft:rh,ifft:Wa,rfft:oh,irfft:Hd},CB={hammingWindow:AU,hannWindow:wA,frame:LA,stft:RU},Wr={flipLeftRight:kU,resizeNearestNeighbor:xA,resizeBilinear:IA,rotateWithOffset:_U,cropAndResize:EU,nonMaxSuppression:$U,nonMaxSuppressionAsync:YU,nonMaxSuppressionWithScore:qU,nonMaxSuppressionWithScoreAsync:KU,nonMaxSuppressionPadded:JU,nonMaxSuppressionPaddedAsync:QU},AA={bandPart:sB,gramSchmidt:rB,qr:aB},RB={absoluteDifference:hB,computeWeightedLoss:Xi,cosineDistance:dB,hingeLoss:mB,huberLoss:gB,logLoss:bB,meanSquaredError:LB,sigmoidCrossEntropy:xB,softmaxCrossEntropy:vB};class Ji extends go{minimize(e,t=!1,n){const{value:s,grads:i}=this.computeGradients(e,n);if(n!=null){const o=n.map(a=>({name:a.name,tensor:i[a.name]}));this.applyGradients(o)}else this.applyGradients(i);return He(i),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return ob(e,t)}dispose(){this.iterations_!=null&&He(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ne(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(Ji,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});class lh extends Ji{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n],o=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:ee(()=>Qe(i).variable(o))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:ee(()=>Qe(i).variable(o))});const a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;const c=this.accumulatedGrads[s].variable,h=this.accumulatedUpdates[s].variable;ee(()=>{const d=be(X(c,this.rho),X(wt(a),1-this.rho)),m=X(_e(Ln(be(h,this.epsilon)),Ln(be(c,this.epsilon))),a),y=be(X(h,this.rho),X(wt(m),1-this.rho));c.assign(d),h.assign(y);const b=be(X(m,-this.learningRate),i);i.assign(b)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(He(this.accumulatedGrads.map(e=>e.variable)),He(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}lh.className="Adadelta",me(lh);class hh extends Ji{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n];if(this.accumulatedGrads[s]==null){const c=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:ee(()=>Xl(i.shape,this.initialAccumulatorValue).variable(c))}}const o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;const a=this.accumulatedGrads[s].variable;ee(()=>{const c=be(a,wt(o));a.assign(c);const h=be(X(_e(o,Ln(be(c,V.backend.epsilon()))),-this.learningRate),i);i.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&He(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}hh.className="Adagrad",me(hh);class uh extends Ji{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],ee(()=>{this.accBeta1=Ne(t).variable(),this.accBeta2=Ne(n).variable()}),s==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);ee(()=>{const n=Ce(1,this.accBeta1),s=Ce(1,this.accBeta2);t.forEach((i,o)=>{const a=V.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:ee(()=>Qe(a).variable(c))}),this.accumulatedSecondMoment[o]==null&&(this.accumulatedSecondMoment[o]={originalName:`${i}/v`,variable:ee(()=>Qe(a).variable(c))});const h=Array.isArray(e)?e[o].tensor:e[i];if(h==null)return;const d=this.accumulatedFirstMoment[o].variable,m=this.accumulatedSecondMoment[o].variable,y=be(X(d,this.beta1),X(h,1-this.beta1)),b=be(X(m,this.beta2),X(wt(h),1-this.beta2)),w=_e(y,n),L=_e(b,s);d.assign(y),m.assign(b);const T=be(X(_e(w,be(Ln(L),this.epsilon)),-this.learningRate),a);a.assign(T)}),this.accBeta1.assign(X(this.accBeta1,this.beta1)),this.accBeta2.assign(X(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&He(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&He(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),ee(()=>{this.accBeta1.assign(ei(this.beta1,this.iterations_+1)),this.accBeta2.assign(ei(this.beta2,this.iterations_+1))});const t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}}uh.className="Adam",me(uh);class dh extends Ji{constructor(e,t,n,s=null,i=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=i,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],ee(()=>{this.iteration=Ne(0).variable(),this.accBeta1=Ne(t).variable()}),s==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);ee(()=>{const n=Ce(1,this.accBeta1),s=_e(-this.learningRate,be(X(this.iteration,this.decay),1));t.forEach((i,o)=>{const a=V.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:Qe(a).variable(c)}),this.accumulatedWeightedInfNorm[o]==null&&(this.accumulatedWeightedInfNorm[o]={originalName:`${i}/v`,variable:Qe(a).variable(c)});const h=Array.isArray(e)?e[o].tensor:e[i];if(h==null)return;const d=this.accumulatedFirstMoment[o].variable,m=this.accumulatedWeightedInfNorm[o].variable,y=be(X(d,this.beta1),X(h,1-this.beta1)),b=X(m,this.beta2),w=sn(h),L=_s(b,w);d.assign(y),m.assign(L);const T=be(X(_e(s,n),_e(y,be(L,this.epsilon))),a);a.assign(T)}),this.iteration.assign(be(this.iteration,1)),this.accBeta1.assign(X(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&He(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&He(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}}dh.className="Adamax",me(dh);class Ma extends Ji{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;const o=V.registeredVariables[n];ee(()=>{const a=be(X(this.c,i),o);o.assign(a)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Nn(Ne(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}Ma.className="SGD",me(Ma);class ph extends Ma{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Ne(this.momentum)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n];if(this.accumulations[s]==null){const c=!1;this.accumulations[s]={originalName:`${n}/momentum`,variable:ee(()=>Qe(i).variable(c))}}const o=this.accumulations[s].variable,a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;ee(()=>{let c;const h=be(X(this.m,o),a);this.useNesterov?c=be(X(this.c,be(a,X(h,this.m))),i):c=be(X(this.c,h),i),o.assign(h),i.assign(c)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&He(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}ph.className="Momentum",me(ph);class mh extends 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nv={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function sv(e,t={}){return bh(e,ks.getMap().classNameMap,t,"initializer")}function Vt(e){return Gb(e)}function Ft(e){if(typeof e=="string"){const t=e in nv?nv[e]:e;if(t==="GlorotNormal")return new mp;if(t==="GlorotUniform")return new pp;if(t==="HeNormal")return new fp;if(t==="HeUniform")return new gp;if(t==="LeCunNormal")return new yp;if(t==="LeCunUniform")return new bp;{const n={};return n.className=t,n.config={},sv(n)}}else return e instanceof Us?e:sv(e)}function iz(){return new tw}function rz(){return new dp}function oz(e){return new nw(e)}function az(e){return new sw(e)}function cz(e){return new iw(e)}function lz(e){return new rw(e)}function hz(e){return new ow(e)}function uz(e){return new Kn(e)}function dz(e){return new pp(e)}function pz(e){return new mp(e)}function mz(e){return new fp(e)}function fz(e){return new gp(e)}function gz(e){return new yp(e)}function yz(e){return new bp(e)}function bz(e){return new aw(e)}var wz=Object.freeze({__proto__:null,zeros:iz,ones:rz,constant:oz,randomUniform:az,randomNormal:cz,truncatedNormal:lz,identity:hz,varianceScaling:uz,glorotUniform:dz,glorotNormal:pz,heNormal:mz,heUniform:fz,leCunNormal:gz,leCunUniform:yz,orthogonal:bz});let Lz=0;function iv(){return Lz++}const wp={};function Lp(e=""){return e in wp||(wp[e]=0),wp[e]+=1,e+wp[e].toString()}function cw(e){return Array.isArray(e)&&Array.isArray(e[0])}function Sp(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function je(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new j(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function St(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new j(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function Ip(e){let t=0;for(const n of e)n.shape.length===0?t+=1:t+=n.shape.reduce((s,i)=>s*i);return t}const rv="Variable";class ii{constructor(e,t="float32",n=rv,s=!0,i=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=iv(),n=n==null?rv:n,this.originalName=KA(n),this.name=XA(this.originalName),this.trainable_=s,this.constraint=i,this.val=KT(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),Sz(this.val,e),this.val.id!==e.id&&(this.val.assign(e),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}}function Sz(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function rQ(e,t,n,s){return new ii(e,t,n,!0,s)}function oQ(e,t,n){return new ii(ct(e),t,n)}function aQ(e,t,n){return new ii(Qe(e),t,n)}function cQ(e,t,n){const s=Qs(e);return new ii(s,t,n)}function lQ(e,t,n){const s=On(e);return new ii(s,t,n)}function hQ(e,t,n){return new ii(Ed(e),t,n)}function uQ(e,t,n,s,i,o="randomUniform"){return new ii(To(e,t,n,s),s,o)}function dQ(e,t=0,n=1,s,i,o="truncatedNormal"){if(s=s||"float32",s!=="float32"&&s!=="int32")throw new ze(`randomNormal does not support dType ${s}.`);return new ii(ch(e,t,n,s,i),s,o)}function pQ(e,t=0,n=1,s,i,o="randomNormal"){if(s=s||"float32",s!=="float32"&&s!=="int32")throw new ze(`randomNormalVariable does not support dType ${s}.`);return new ii(ub(e,t,n,s,i),s,o)}function mQ(e,t){return e.write(t)}function fQ(e,t){return e.write(be(e.read(),t))}function gQ(e,t){return e.write(Ce(e.read(),t))}function lw(e){return e.map(t=>t.read())}function hw(e){e.forEach(t=>{const n=t[0];n.write(t[1])})}function yQ(e,t){const n=t.map(i=>i.read()),s=ob(e,n);return t.map(i=>s.grads[i.name])}class mn{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}}class ri{constructor(e,t,n,s,i,o,a){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=i,this.outputTensorIndex=a,this.id=iv(),o!=null&&(this.originalName=KA(o),this.name=XA(this.originalName)),this.rank=t.length}}let Iz=0;class xp{constructor(e,t){this.callArgs=t,this.id=Iz++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(const n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){const e=[];for(const t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}}let xz=0;class lt extends mo{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=xz++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){const n=this.getClassName();t=er(n)+"_"+Lp(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let i=null;e.batchSize!=null&&(i=e.batchSize),n=[i].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new ni(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new j(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return jn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return jn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Qi(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Use \`getOutputAt(nodeIndex)\` instead.`);return jn(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=Nt(e),this.inputSpec==null||this.inputSpec.length===0)return;const t=Nt(this.inputSpec);if(e.length!==t.length)throw new j(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){const s=e[n],i=t[n];if(i==null)continue;const o=s.rank;if(i.ndim!=null&&o!==i.ndim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${i.ndim}, found ndim=${o}`);if(i.maxNDim!=null&&o>i.maxNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${i.maxNDim}, found ndim=${o}`);if(i.minNDim!=null&&o<i.minNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${i.minNDim}, found ndim=${o}.`);if(i.dtype!=null&&s.dtype!==i.dtype)throw new j(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${i.dtype}, found dtype=${s.dtype}.`);if(i.axes){const a=s.shape;for(const c in i.axes){const h=Number(c),d=i.axes[c],m=h>=0?a[h]:a[a.length+h];if(d!=null&&[d,null].indexOf(m)===-1)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected axis ${h} of input shape to have value ${d} but got shape ${a}.`)}}if(i.shape!=null)for(let a=0;a<i.shape.length;++a){const c=i.shape[a],h=s.shape[a];if(c!=null&&h!=null&&c!==h)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected shape=${i.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();const n=Nt(e);let s=!0;for(const o of n)if(!(o instanceof ri)){s=!1;break}let i=!0;for(const o of n)if(o instanceof ri){i=!1;break}if(s===i)throw new j("Arguments to apply() must be all SymbolicTensors or all Tensors");return Oo(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);const o=[];for(const a of Nt(e))o.push(a.shape);this.build(jn(o)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&i&&(this._refCount=1)}if(this.assertInputCompatibility(e),i){let o=this.call(e,t);const a=Nt(o),c=[];for(let h of a)n.indexOf(h)!==-1&&(h=h.clone()),c.push(h);if(o=jn(c),this.activityRegularizer!=null)throw new ze("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}else{const o=Tz(e),a=this.computeOutputShape(o);let c;const h=Az(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?o[0]:o),a!=null&&a.length>0&&Array.isArray(a[0])?c=a.map((d,m)=>new ri(h,d,this,Nt(e),t,this.name,m)):c=new ri(h,a,this,Nt(e),t,this.name),this.addInboundNode(e,c,null,null,o,a,t),this._refCount++,this.activityRegularizer!=null)throw new ze("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return c}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape==null)return;if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Qi(`The layer ${this.name} has never been called and thus has no defined output shape.`);const e=[];for(const t of this.inboundNodes){const n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){const t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Qi(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new ni(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Ip(this.weights)}build(e){this.built=!0}getWeights(e=!1){return lw(e?this.trainableWeights:this.weights)}setWeights(e){ee(()=>{const t=this.weights;if(t.length!==e.length)throw new j(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;const n=[],s=lw(t);for(let i=0;i<s.length;++i){const o=s[i],a=t[i],c=e[i];if(!ot(o.shape,c.shape))throw new j(`Layer weight shape ${o.shape} not compatible with provided weight shape ${c.shape}`);n.push([a,c])}hw(n)})}addWeight(e,t,n,s,i,o,a){if(this._addedWeightNames.indexOf(e)!==-1)throw new j(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=Ft("zeros"));const c=s.apply(t,n),h=new ii(c,n,e,o,a);return c.dispose(),i!=null&&this.addLoss(()=>i.apply(h.read())),o==null&&(o=!0),o?this._trainableWeights.push(h):this._nonTrainableWeights.push(h),h}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){if(e==null||Array.isArray(e)&&e.length===0)return;e=Nt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e)}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,i,o,a=null){const c=Nt(e);t=Nt(t),n=Nt(n),s=Nt(s),i=Sp(i),o=Sp(o);const h=[],d=[],m=[];for(const y of c)h.push(y.sourceLayer),d.push(y.nodeIndex),m.push(y.tensorIndex);new xp({outboundLayer:this,inboundLayers:h,nodeIndices:d,tensorIndices:m,inputTensors:c,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:i,outputShapes:o},a);for(let y=0;y<t.length;y++)t[y].sourceLayer=this,t[y].nodeIndex=this.inboundNodes.length-1,t[y].tensorIndex=y}getConfig(){const e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount===0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}}function Tz(e){e=Nt(e);const t=[];for(const n of e)t.push(n.shape);return jn(t)}function Az(e){return"float32"}function ov(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{const s=t.inboundNodes[n];if(s.inboundLayers.length===0)return s.inputTensors;{const i=[];for(let o=0;o<s.inboundLayers.length;o++){const a=s.inputTensors[o],c=s.inboundLayers[o],h=s.nodeIndices[o],d=ov(a,c,h);for(const m of d)i.indexOf(m)===-1&&i.push(m)}return i}}}class Ga extends lt{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Lp("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new j("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new j("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new j("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");const n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];const s=new ri(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new xp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new j(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}}Ga.className="InputLayer",me(Ga);function av(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. 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this}addFeed(e){this.add(e.key,e.value)}hasKey(e){return this.id2Value[e.id]!=null}names(){return Object.keys(this.name2Id)}getValue(e){if(e instanceof ri){if(this.id2Value[e.id]==null)throw new j(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}else{const t=this.name2Id[e];if(t==null)throw new j(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof ri){if(this.id2Value[e.id]==null)throw new j(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}else{const t=this.name2Id[e];if(t==null)throw new j(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&He(this.id2Mask)}}const ww={},xv={};function Nh(e,t,n,s){const i=n==null?!1:n.training,o=Array.isArray(e),a=o?e:[e],c=a.map(L=>L.name),h=[],d=t.names();for(const L of c)d.indexOf(L)!==-1?h.push(t.getValue(L)):h.push(null);s!=null&&(s.maxNumTensors=-Infinity,s.minNumTensors=Infinity);const m=c.join(",")+"|"+t.names().join(",");let y,b;if(ww[m]==null){const L=Zz(a,t);y=L.sorted,b=L.recipientCounts,ww[m]=y,xv[m]=b}y=ww[m],b={},i||Object.assign(b,xv[m]);const w=new Do(t);for(let L=0;L<y.length;++L){if(s!=null){const q=Sd().numTensors;q>s.maxNumTensors&&(s.maxNumTensors=q),q<s.minNumTensors&&(s.minNumTensors=q)}const T=y[L],A=T.sourceLayer;if(A instanceof Ga)continue;const N=[],E=[],D=[];let F=!1;for(const q of T.inputs){const J=w.getValue(q),re=w.getMask(q);N.push(J),E.push(re),re!=null&&(F=!0),i||(b[q.name]--,b[q.name]===0&&!t.hasKey(q)&&c.indexOf(q.name)===-1&&!J.isDisposed&&q.sourceLayer.stateful!==!0&&D.push(J))}F&&(n=n||{},n.mask=E[0]);const _=Nt(A.apply(N,n));let B=null;A.supportsMasking&&(B=A.computeMask(N,E));const $=e3(T),H=Array.isArray($)?$:[$];for(let q=0;q<H.length;++q){w.hasKey(H[q])||w.add(H[q],_[q],Array.isArray(B)?B[0]:B);const J=c.indexOf(H[q].name);J!==-1&&(h[J]=_[q])}i||He(D)}return w.disposeMasks(),o?h:h[0]}function Zz(e,t){k(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],s={};if(e.length===1){const i=Tv(e[0],t);n=i.sorted,s=i.recipientMap}else{const i=new Set;for(const o of e){const{sorted:a,recipientMap:c}=Tv(o,t);for(const h of a)i.has(h.name)||(n.push(h),i.add(h.name));for(const h in c)s[h]==null&&(s[h]=new Set),c[h].forEach(d=>s[h].add(d))}}return{sorted:n,recipientCounts:Qz(s)}}function Qz(e){const t={};for(const n in e)t[n]=e[n].size;return t}function Tv(e,t){const n=new Set,s=[],i={};for(const c of t.names())n.add(c);const o=[],a=[];for(o.push(e);o.length>0;){const c=o[o.length-1];if(n.has(c.name)){o.pop();continue}const h=a[a.length-1]===o.length-1;if(c.inputs.length===0||h)o.pop(),s.push(c),n.add(c.name),h&&a.pop();else{a.push(o.length-1);for(const d of c.inputs){if(i[d.name]==null&&(i[d.name]=new Set),i[d.name].add(c.name),n.has(d.name))continue;o.push(d)}}}return{sorted:s,recipientMap:i}}function e3(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(const i of e.sourceLayer.inboundNodes[s].outputTensors)if(i.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}class ki extends lt{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){const N=this.getClassName().toLowerCase();this.name=Lp(N)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],$r(this.inputs).length!==this.inputs.length)throw new j(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(N=>N.name)}`);$r(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(N=>N.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(const N of this.outputs){const E=N.sourceLayer,D=N.nodeIndex,F=N.tensorIndex;this.outputLayers.push(E),this.outputLayersNodeIndices.push(D),this.outputLayersTensorIndices.push(F)}for(const N of this.inputs){const E=N.sourceLayer,D=N.nodeIndex,F=N.tensorIndex;xs(D===0,"input layer has >1 nodes"),xs(F===0,"input layer has >1 tensors"),this.inputLayers.push(E),this.inputLayersNodeIndices.push(D),this.inputLayersTensorIndices.push(F)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let N=0;N<this.inputLayers.length;N++){const E=this.inputLayers[N];if(!(E instanceof Ga))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${N} (0-based) originates from layer type ${E.getClassName()}.`);this.inputNames.push(E.name),this.feedInputShapes.push(E.batchInputShape),this.feedInputNames.push(E.name)}for(const N of this.outputLayers)this.outputNames.push(N.name);this.internalInputShapes=this.inputs.map(N=>N.shape),this.internalOutputShapes=this.outputs.map(N=>N.shape);const t={},n={},s={},i={},o={},a=[],c=(N,E,D,F,_,B)=>{(F==null||_==null||B==null)&&(F=N.sourceLayer,_=N.nodeIndex,B=N.tensorIndex);const $=F.inboundNodes[_];if(D.indexOf($)!==-1)throw new ni(`The tensor ${N.name} at layer "${F.name}" is part of a cycle.`);if(E.indexOf($)!==-1)return;this.containerNodes.add(ki.nodeKey(F,_)),F.id in o||(o[F.id]=Object.keys(o).length),D.indexOf($)===-1&&D.push($);const H=$.inboundLayers.length;for(let q=0;q<H;q++){const J=$.inputTensors[q],re=$.inboundLayers[q],ce=$.nodeIndices[q],ue=$.tensorIndices[q];c(J,E,D,re,ce,ue)}for(E.push($);D.indexOf($)>=0;)D.splice(D.indexOf($),1);a.push($)},h=[],d=[];for(const N of this.outputs)c(N,h,d);const m=a.slice().reverse();for(const N of m){n[N.id]=N,N.id in t||(t[N.id]=0);let E=t[N.id];const D=s[N.outboundLayer.id]==null?0:s[N.outboundLayer.id];E=Math.max(E,D),s[N.outboundLayer.id]=E,i[N.outboundLayer.id]=N.outboundLayer,t[N.id]=E;for(let F=0;F<N.inboundLayers.length;F++){const _=N.inboundLayers[F],B=N.nodeIndices[F],$=_.inboundNodes[B],H=t[$.id]==null?0:t[$.id];t[$.id]=Math.max(E+1,H),n[$.id]=$}}const y={};for(const N in t){const E=t[N];E in y||(y[E]=[]),y[E].push(n[N])}const b={};for(const N in s){const E=s[N];E in b||(b[E]=[]),b[E].push(i[N])}let w=Object.keys(b).map(N=>parseInt(N,10)).sort(cp);this.layers=[];for(const N of w){const E=b[N];E.sort((D,F)=>{const _=o[D.id],B=o[F.id];return _<B?-1:_>B?1:0});for(const D of E)D instanceof ki&&this.internalContainerRefs.push(D),this.layers.push(D)}this.layersByDepth=b,w=Object.keys(y).map(N=>parseInt(N,10)).sort(cp);const L=this.inputs.slice(),T=[];for(const N of w)for(const E of y[N]){const D=E.outboundLayer;if(D!=null){for(const F of E.inputTensors)if(L.indexOf(F)===-1)throw new ni(`Graph disconnected: cannot obtain value for tensor ${F} at layer "${D.name}". The following previous layers were accessed without issue: ${T}`);for(const F of E.outputTensors)L.push(F);T.push(D.name)}}this.nodesByDepth=y;const A=this.layers.map(N=>N.name);for(const N of A){const E=A.filter(D=>D===N).length;if(E!==1)throw new ni(`The name "${N}" is used ${E} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(A))}this.outboundNodes=[],this.inboundNodes=[],new xp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(N=>null),outputMasks:this.outputs.map(N=>null),inputShapes:this.inputs.map(N=>N.shape),outputShapes:this.outputs.map(N=>N.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();const e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(const t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(const t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new j("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(const t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){const n={};let s=0;for(const o of this.layers)for(const a of o.weights){if(n[a.originalName]!=null)throw new j(`Duplicate weight name: ${a.originalName}`);n[a.originalName]=a,s++}const i=[];for(const o in e){let a=o;if(n[o]==null){const c=o.split("/"),h=c.slice(0,-2).concat([c[c.length-1]]);a=h.join("/")}if(n[a]!=null)i.push([n[a],e[o]]);else if(t)throw new j(`Provided weight data has no target variable: ${o}`);delete n[a]}if(t){const o=[];for(const a in n)o.push(a);if(o.length>0)throw new j(`${o.length} of ${s} weights are not set: ${o}`)}hw(i)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${Dp}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){const n=bw(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return ee(()=>{e=Nt(e);const n=new Do;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Nh(this.outputs,n,t)})}computeMask(e,t){return ee(()=>{e=Nt(e);let n;return t==null?n=Co(null,e.length):n=Nt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){const t=Sp(e);if(t.length!==this.inputLayers.length)throw new j(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);const n={};for(let a=0;a<t.length;a++){const c=this.inputLayers[a],h=t[a],d=c.name+"_0_0";n[d]=h}const s=Object.keys(this.nodesByDepth).map(a=>parseInt(a,10)).sort(cp);if(s.length>1)for(const a of s){const c=this.nodesByDepth[a];for(const h of c){const d=h.outboundLayer;if(this.inputLayers.map(L=>L.id).indexOf(d.id)!==-1)continue;const m=[];for(let L=0;L<h.inboundLayers.length;L++){const T=h.inboundLayers[L],A=h.nodeIndices[L],N=h.tensorIndices[L],E=`${T.name}_${A}_${N}`,D=n[E];m.push(D)}const y=d.computeOutputShape(jn(m)),b=Sp(y),w=d.inboundNodes.indexOf(h);for(let L=0;L<b.length;L++){const T=`${d.name}_${w}_${L}`;n[T]=b[L]}}}const i=[],o=[];for(let a=0;a<this.outputLayers.length;a++){const c=this.outputLayers[a],h=this.outputLayersNodeIndices[a],d=this.outputLayersTensorIndices[a],m=`${c.name}_${h}_${d}`;o.push(m)}for(let a=0;a<o.length;a++){const c=o[a];xs(c in n),i.push(n[c])}return jn(i)}runInternalGraph(e,t){t==null&&(t=Co(null,e.length));const n={};for(let c=0;c<this.inputs.length;++c){const h=this.inputs[c],d=e[c],m=t[c];n[h.id]=[d,m]}const s=Object.keys(this.nodesByDepth).map(c=>parseInt(c,10)).sort(cp);for(const c of s){const h=this.nodesByDepth[c];for(const d of h){const m=d.outboundLayer,y=d.inputTensors,b=d.outputTensors,w=new Array;for(const L of y)L.id in n&&w.push(n[L.id]);if(w.length===y.length){let L={},T,A,N,E;if(d.callArgs!=null&&(L=d.callArgs),w.length===1){const[D,F]=w[0];L.mask==null&&(L.mask=F),N=Nt(m.call(D,L)),E=Nt(m.computeMask(D,F)),T=[D],A=[F]}else T=w.map(D=>D[0]),A=w.map(D=>D[1]),L.mask==null&&(L.mask=A),N=Nt(m.call(T,L)),E=Nt(m.computeMask(T,A));if(m.activityRegularizer)throw new ze("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let D=0;D<b.length;++D){const F=b[D],_=N[D],B=E[D];n[F.id]=[_,B]}}}}const i=[],o=[],a=[];for(const c of this.outputs){xs(c.id in n,`Could not compute output ${c.name} : ${c.id}`);const[h,d]=n[c.id];a.push(h.shape),i.push(h),o.push(d)}return[i,o,a]}buildNodeConversionMap(e){const t={};let n;for(const s of this.layers){n=s instanceof ki?1:0;for(let i=0;i<s.inboundNodes.length;i++){const o=ki.nodeKey(s,i);this.containerNodes.has(o)&&(t[o]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new j(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new j("Provide either a layer name or layer index");for(const n of this.layers)if(n.name===e)return n;throw new j(`No such layer: ${e}`)}calculateLosses(){return ee(()=>{const e=[];for(const t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){const s=ki.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){const e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(const o of this.layers){const a=o.getClassName(),c=o.getConfig(),h=[];for(let m=0;m<o.inboundNodes.length;m++){const y=o.inboundNodes[m],b=ki.nodeKey(o,m);let w={};if(this.containerNodes.has(b)){if(y.callArgs)try{JSON.stringify(y.callArgs),w=y.callArgs}catch(L){console.warn(`Layer ${o.name} was passed non-serializable keyword arguments: ${y.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),w={}}if(y.inboundLayers.length>0){const L=[];for(let T=0;T<y.inboundLayers.length;T++){const A=y.inboundLayers[T],N=y.nodeIndices[T],E=y.tensorIndices[T],D=ki.nodeKey(A,N);let F=t[D];F==null&&(F=0),L.push([A.name,F,E,w])}h.push(L)}}}const d={};d.name=o.name,d.className=a,d.config=c,d.inboundNodes=h,n.push(d)}e.layers=n;const s=[];for(let o=0;o<this.inputLayers.length;o++){const a=this.inputLayers[o],c=this.inputLayersNodeIndices[o],h=ki.nodeKey(a,c);if(!this.containerNodes.has(h))continue;let d=t[h];d==null&&(d=0);const m=this.inputLayersTensorIndices[o];s.push([a.name,d,m])}e.inputLayers=s;const i=[];for(let o=0;o<this.outputLayers.length;o++){const a=this.outputLayers[o],c=this.outputLayersNodeIndices[o],h=ki.nodeKey(a,c);if(!this.containerNodes.has(h))continue;let d=t[h];d==null&&(d=0);const m=this.outputLayersTensorIndices[o];i.push([a.name,d,m])}return e.outputLayers=i,e}static fromConfig(e,t,n={},s=!1){const i={},o={};function a(T,A){T.name in o?o[T.name].push(A):o[T.name]=[A]}function c(T,A){const N=[];let E;for(const D of A){const F=D[0],_=D[1],B=D[2];if(E=D[3]==null?{}:D[3],!(F in i)){a(T,A);return}const $=i[F];if($.inboundNodes.length<=_){a(T,A);return}const H=$.inboundNodes[_];N.push(H.outputTensors[B])}N.length>0&&T.apply(jn(N),E)}function h(T){const A=T.name,N=oi(T,t.customObjects!=null?t.customObjects:{});N.setFastWeightInitDuringBuild(s),i[A]=N;const E=T.inboundNodes;E.forEach(D=>{if(!(D instanceof Array))throw new j(`Corrupted configuration, expected array for nodeData: ${D}`);a(N,D)})}const d=t.name,m=t.layers;for(const T of m)h(T);for(;!EP(o);)for(const T of m){const A=i[T.name];if(A.name in o){const N=o[A.name];delete o[A.name];for(const E of N)c(A,E)}}const y=[],b=[],w=t.inputLayers;for(const T of w){const A=T[0],N=T[1],E=T[2];xs(A in i);const D=i[A],F=D.inboundNodes[N].outputTensors;y.push(F[E])}const L=t.outputLayers;for(const T of L){const A=T[0],N=T[1],E=T[2];xs(A in i);const D=i[A],F=D.inboundNodes[N].outputTensors;b.push(F[E])}return new e({inputs:y,outputs:b,name:d})}get stateful(){if(this._stateful)throw new j("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(const e of this.layers)if(e.stateful)return!0;return!1}resetStates(){ee(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}}function Av(e,t,n){const s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(i=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){const i=[];return t.forEach(o=>{o in e?i.push(e[o]):i.push(null)}),i}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function vv(e,t){return Av(e,t,"classWeight")}function EQ(e,t){return Av(e,t,"sampleWeight")}async function Nv(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){const i=ee(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){const c=1;return e.argMax(c)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),o=Array.from(await i.data());He(i);const a=[];return o.forEach(c=>{if(n[c]==null)throw new Error(`classWeight must contain all classes in the training data. 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We assume this was done on purpose, and we will not be expecting data to be passed to ${o} during training`),t.push(pw(e.loss[o]))}else if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new j(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${e.loss}.`);const o=e.loss;t=o.map(a=>pw(a))}else{const o=pw(e.loss);this.outputs.forEach(a=>{t.push(o)})}this.lossFunctions=t,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let o=0;o<this.outputs.length;++o){const a=this.internalOutputShapes[o],c=this.outputNames[o];this.feedOutputNames.push(c),this.feedOutputShapes.push(a),this.feedLossFns.push(this.lossFunctions[o])}const n=[];this.metrics=e.metrics,this.metricsNames=["loss"],this.metricsTensors=[],Oo("loss",()=>{for(let o=0;o<this.outputs.length;++o){if(n.indexOf(o)!==-1)continue;const a=this.lossFunctions[o];this.outputs.length>1&&(this.metricsTensors.push([a,o]),this.metricsNames.push(this.outputNames[o]+"_loss"))}});const s=p3(e.metrics,this.outputNames),i=(o,a,c)=>{this.outputNames.length>1&&(a=this.outputNames[o]+"_"+a),this.metricsNames.push(a),this.metricsTensors.push([c,o])};Oo("metric",()=>{for(let o=0;o<this.outputs.length;++o){if(n.indexOf(o)!==-1)continue;const a=s[o],c=h=>{const d="";let m,y,b;for(const w of h){if(typeof w=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(w)!==-1){const T=this.internalOutputShapes[o];T[T.length-1]===1||this.lossFunctions[o]===vp?["accuracy","acc"].indexOf(w)!==-1?y=mw:["crossentropy","ce"].indexOf(w)!==-1&&(y=yv):this.lossFunctions[o]===Ap?["accuracy","acc"].indexOf(w)!==-1?y=bv:["crossentropy","ce"].indexOf(w)!==-1&&(y=wv):["accuracy","acc"].indexOf(w)!==-1?y=fw:["crossentropy","ce"].indexOf(w)!==-1&&(y=gw);let A;["accuracy","acc"].indexOf(w)!==-1?A="acc":["crossentropy","ce"].indexOf(w)!==-1&&(A="ce"),b=y,m=d+A}else{const T=Vz(w);b=T,m=d+Op(w)}let L;Oo(m,()=>{L=b}),i(o,m,L)}};c(a)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){if(this.collectedTrainableWeights==null)return;this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(e,t,n={}){const s=n.batchSize==null?32:n.batchSize;Lw(s);const i=!0,o=this.standardizeUserDataXY(e,t,i,s);try{const a=o[0].concat(o[1]);this.makeTestFunction();const c=this.testFunction,h=this.testLoop(c,a,s,n.verbose,n.steps);return jn(h)}finally{ko(o[0],e),ko(o[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),a3(this,e,t)}checkNumSamples(e,t,n,s="steps"){let i;if(n!=null){if(i=null,t!=null)throw new j(`If ${s} is set, batchSize must be null or undefined.Got batchSize = ${t}`)}else if(e!=null)Array.isArray(e)?i=e[0].shape[0]:i=e.shape[0];else throw new j(`Either the input data should have a defined shape, or ${s} shoud be specified.`);return i}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new j("`outputs` is an empty Array, which is not allowed.");const n=Array.isArray(t),s=n?t:[t],i=this.retrieveSymbolicTensors(s),o=new Do;if(e instanceof Q&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new j(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let c=0;c<this.inputs.length;++c)o.add(this.inputs[c],e[c])}else for(const c of this.inputs){const h=e[c.name];if(h==null)throw new j(`No value is provided for the model's input ${c.name}`);o.add(c,h)}const a=Nh(i,o);return n?a:a[0]}retrieveSymbolicTensors(e){const t=Co(null,e.length);let n=e.length;for(const s of this.layers){const i=Array.isArray(s.output)?s.output:[s.output],o=i.map(a=>a.name);for(let a=0;a<e.length;++a){const c=o.indexOf(e[a]);if(c!==-1&&(t[a]=i[c],n--),n===0)break}if(n===0)break}if(n>0){const s=[];throw t.forEach((i,o)=>{i==null&&s.push(e[o])}),new j(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(s)}`)}return t}predictLoop(e,t=32,n=!1){return ee(()=>{const s=this.checkNumSamples(e);if(n)throw new ze("Verbose predictLoop() is not implemented yet.");const i=Iw(s,t),o=this.outputs.map(a=>[]);for(let a=0;a<i.length;++a){const c=ee(()=>{const h=i[a][0],d=i[a][1],m=Ch(e,h,d),y=[];if(Array.isArray(m))for(let w=0;w<m.length;++w)y.push({key:this.inputs[w],value:m[w]});else y.push({key:this.inputs[0],value:m});const b=new Do(y);return Nh(this.outputs,b)});c.forEach((h,d)=>o[d].push(h))}return jn(o.map(a=>Mt(a,0)))})}predict(e,t={}){const n=Ev(e);Fv(n,this.inputNames,this.feedInputShapes,!1);try{const s=t.batchSize==null?32:t.batchSize;return Lw(s),this.predictLoop(n,s)}finally{ko(n,e)}}predictOnBatch(e){Fv(e,this.inputNames,this.feedInputShapes,!0);const t=(Array.isArray(e)?e[0]:e).shape[0];return this.predictLoop(e,t)}standardizeUserDataXY(e,t,n=!0,s){if(this.optimizer_==null)throw new ni("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");const i=[];for(let o=0;o<this.feedOutputShapes.length;++o){const a=this.feedOutputShapes[o],c=this.feedLossFns[o];c===Ap?i.push(a.slice(0,a.length-1).concat([1])):i.push(a)}if(e=kv(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=kv(t,this.feedOutputNames,i,!1,"target"),u3(e,t,null),d3(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new j(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,i=!0,o){const[a,c]=this.standardizeUserDataXY(e,t,i,o);if(n!=null)throw new Error("sample weight is not supported yet.");let h=null;if(s!=null){const d=vv(s,this.outputNames);h=[];for(let m=0;m<d.length;++m)h.push(await Nv(c[m],null,d[m]))}return[a,c,h]}testLoop(e,t,n,s=0,i){return ee(()=>{const o=this.checkNumSamples(t,n,i,"steps"),a=[];if(s>0)throw new ze("Verbose mode is not implemented yet.");if(i!=null)throw new ze("steps mode in testLoop() is not implemented yet");{const c=Iw(o,n),h=ns(si(0,o));for(let d=0;d<c.length;++d){const m=c[d][0],y=c[d][1],b=Eo(h,m,y-m),w=Sw(t,b),L=e(w);if(d===0)for(let T=0;T<L.length;++T)a.push(Ne(0));for(let T=0;T<L.length;++T){const A=L[T];a[T]=be(a[T],X(y-m,A))}}for(let d=0;d<a.length;++d)a[d]=_e(a[d],o)}return a})}getDedupedMetricsNames(){const e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){const s=e[n];let i=s;if(zA(e,s)>1){const o=zA(e.slice(0,n),s);i+=`_${o}`}t.push(i)}return t}makeTrainFunction(){return e=>{const t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),i=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),o=[],a=()=>{const m=[];for(let L=0;L<this.inputs.length;++L)m.push({key:this.inputs[L],value:n[L]});const y=new Do(m),b=Nh(this.outputs,y,{training:!0});let w;for(let L=0;L<this.lossFunctions.length;++L){const T=this.lossFunctions[L];let A=T(s[L],b[L]);i[L]!=null&&(A=t3(A,i[L]));const N=zt(A);t.push(N),L===0?w=A:w=be(w,A)}for(let L=0;L<this.metricsTensors.length;++L){let T;if(this.outputs.length>1&&L<this.outputs.length)T=t[L];else{const A=this.metricsTensors[L][0],N=this.metricsTensors[L][1];T=zt(A(s[N],b[N]))}Nn(T),o.push(T)}return w=zt(w),this.calculateLosses().forEach(L=>{w=be(w,L)}),w},c=this.collectedTrainableWeights.map(m=>m.read()),h=!0,d=this.optimizer_.minimize(a,h,c);return[d].concat(o)}}makeTestFunction(){this.testFunction=e=>ee(()=>{const t=[];let n;const s=e.slice(0,this.inputs.length),i=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),o=[];for(let h=0;h<this.inputs.length;++h)o.push({key:this.inputs[h],value:s[h]});const a=new Do(o),c=Nh(this.outputs,a);for(let h=0;h<this.lossFunctions.length;++h){const d=this.lossFunctions[h],m=zt(d(i[h],c[h]));h===0?n=m:n=be(n,m),t.push(n)}for(let h=0;h<this.metricsTensors.length;++h){const d=this.metricsTensors[h][0],m=this.metricsTensors[h][1],y=zt(d(i[m],c[m]));t.push(y)}return t})}async fit(e,t,n={}){return l3(this,e,t,n)}async fitDataset(e,t){return i3(this,e,t)}async trainOnBatch(e,t){const n=await this.standardizeUserData(e,t),s=n[0],i=n[1],o=this.makeTrainFunction(),a=o(s.concat(i)),c=[];for(const h of a){const d=await h.data();c.push(d[0])}return He(a),jn(c)}getNamedWeights(e){const t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,i=this.getWeights(n);for(let o=0;o<s.length;++o){if(n&&!s[o].trainable)continue;t.push({name:s[o].originalName,tensor:i[o]})}return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){const e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){const t=Sd().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Sd().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=er(this.loss);else if(Array.isArray(this.loss)){for(const t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>er(t))}else{const t=Object.keys(this.loss);e={};const n=this.loss;for(const s of t)if(typeof n[s]=="string")e[s]=er(n[s]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[er(Op(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>er(Op(e)));{const e={};for(const t in this.metrics)e[t]=er(Op(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");const t=vh(e.optimizer_config),n=oi(t);let s;if(typeof e.loss=="string")s=Ro(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(o=>Ro(o));else if(e.loss!=null){s={};for(const o in e.loss)s[o]=Ro(e.loss[o])}let i;if(Array.isArray(e.metrics))i=e.metrics.map(o=>Ro(o));else if(e.metrics!=null){i={};for(const o in e.metrics)i[o]=Ro(e.metrics[o])}this.compile({loss:s,metrics:i,optimizer:n})}async save(e,t){if(typeof e=="string"){const h=wy(e);if(h.length===0)throw new j(`Cannot find any save handlers for URL '${e}'`);if(h.length>1)throw new j(`Found more than one (${h.length}) save handlers for URL '${e}'`);e=h[0]}if(e.save==null)throw new j("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const n=await yy(this.getNamedWeights(t)),s=!1,i=null,o=this.toJSON(i,s),a={modelTopology:o,format:m3,generatedBy:`TensorFlow.js tfjs-layers v${Dp}`,convertedBy:null},c=t==null?!1:t.includeOptimizer;if(c&&this.optimizer!=null){a.trainingConfig=this.getTrainingConfig();const h="optimizer",{data:d,specs:m}=await yy(await this.optimizer.getWeights(),h);n.specs.push(...m),n.data=dd([n.data,d])}if(this.userDefinedMetadata!=null){const h=!0;Sv(this.userDefinedMetadata,this.name,h),a.userDefinedMetadata=this.userDefinedMetadata}return a.weightData=n.data,a.weightSpecs=n.specs,e.save(a)}setUserDefinedMetadata(e){Sv(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}}nr.className="Model",me(nr);class _v extends nr{}_v.className="Functional",me(_v);async function f3(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);const s=vh(n),i=oi(s,t);if(e.weightsManifest!=null){const o=await aT(e.weightsManifest,e.pathPrefix,i.weights.map(c=>c.originalName)),a={};for(const c of i.weights)a[c.originalName]=o[c.originalName];i.loadWeights(a),He(o)}return i}async function g3(e,t){if(t==null&&(t={}),typeof e=="string"){const n=Ly(e,t);if(n.length===0)n.push(fd(e,t));else if(n.length>1)throw new j(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return y3(e,void 0,t)}async function y3(e,t,n){if(n==null&&(n={}),e.load==null)throw new j("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const s=await e.load();let i=s.modelTopology;i.model_config!=null&&(i=i.model_config);const o=n.strict==null?!0:n.strict,a=s.weightData!=null&&s.weightSpecs!=null&&o,c=oi(vh(i),t,a),h=s.trainingConfig;if(h!=null&&c.loadTrainingConfig(h),s.userDefinedMetadata!=null&&c.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new j("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");const{modelWeights:d,optimizerWeights:m}=b3(s.weightData,s.weightSpecs);c.loadWeights(d,o),c.optimizer!=null&&m.length>0&&await c.optimizer.setWeights(m),He(d),He(m.map(y=>y.tensor))}return c}function b3(e,t){const n=ud(e,t),s={},i=[];return t.forEach(o=>{o.group==="optimizer"?i.push({name:o.name,tensor:n[o.name]}):s[o.name]=n[o.name]}),{modelWeights:s,optimizerWeights:i}}class Ha extends nr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Lp("sequential_"),e.layers!=null)for(const t of e.layers)this.add(t)}checkShape(e){const t=e.inboundNodes[0].outputTensors[0].shape;if(t.some(n=>n<0))throw new j(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){const t=e instanceof Ha||e instanceof nr;let n;if(t){if(n=e,n.outputs.length!==1)throw new j("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new j("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new j("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");const s=av({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new j(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new j("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=ov(this.outputs[0])}this.inboundNodes=[],new xp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Co(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{const s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{const e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(St(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new nr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new ni("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new ni("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new ni("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new ni("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let i,o={};if(t instanceof Array){if(!(t[0].className!=null)||t[0].className==="Merge")throw new j("Legacy serialization format not supported yet.");i=t}else k(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),i=t.layers,delete t.layers,o=t;const a=new e(o);if(!(a instanceof Ha))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(const c of i){const h=void 0,d=oi(c,h,s);s&&d.setFastWeightInitDuringBuild(!0),a.add(d)}return a}set stopTraining(e){if(this.model==null)throw new j("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new j("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){const e=[];for(const t of this.layers){const n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}}Ha.className="Sequential",me(Ha);function w3(e){return new nr(e)}function L3(e){return new Ha(e)}function S3(e,t){return t==null&&(t={}),g3(e,t)}function Wv(e){return av(e)}function I3(e,t){Bs.registerCallbackConstructor(e,t)}class rs extends mo{getConfig(){return{}}}class $v extends rs{apply(e,t=1){return XP(e,t)}}$v.className="elu",me($v);class Uv extends rs{apply(e){return Pd(e)}}Uv.className="selu",me(Uv);class Bv extends rs{apply(e){return Ri(e)}}Bv.className="relu",me(Bv);class Mv extends rs{apply(e){return ee(()=>Lo(6,Ri(e)))}}Mv.className="relu6",me(Mv);class Pv extends rs{apply(e){return e}}Pv.className="linear",me(Pv);class zv extends rs{apply(e){return vi(e)}}zv.className="sigmoid",me(zv);class Gv extends rs{apply(e){return ZP(e)}}Gv.className="hardSigmoid",me(Gv);class Vv extends rs{apply(e){return Oa(e)}}Vv.className="softplus",me(Vv);class Yv extends rs{apply(e){return JP(e)}}Yv.className="softsign",me(Yv);class Hv extends rs{apply(e){return va(e)}}Hv.className="tanh",me(Hv);class Tw extends rs{apply(e,t=-1){return Ao(e,t)}}Tw.className="softmax",me(Tw);class qv extends rs{apply(e,t=-1){return _d(e,t)}}qv.className="logSoftmax",me(qv);class jv extends rs{apply(e,t=1){return ee(()=>vi(e.mul(t)).mul(e))}}jv.className="swish",me(jv);function zr(e){return e.getClassName()}function Aw(e,t={}){return bh(e,ks.getMap().classNameMap,t,"activation")}function Gr(e){if(e==null){const t={};return t.className="linear",t.config={},Aw(t)}if(typeof e=="string"){const t={};return t.className=e,t.config={},Aw(t)}else return e instanceof rs?e:Aw(e)}function vw(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}class Kv extends mo{}class Rh extends Kv{constructor(e){super();vw(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return ee(()=>{let t=ct([1]);return this.hasL1&&(t=be(t,Ue(X(this.l1,nn(e))))),this.hasL2&&(t=be(t,Ue(X(this.l2,xh(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}Rh.className="L1L2",me(Rh);function x3(e){return vw(e),new Rh({l1:e!=null?e.l1:null,l2:0})}function T3(e){return vw(e),new Rh({l2:e!=null?e.l2:null,l1:0})}const Xv={l1l2:"L1L2"};function It(e){return Gb(e)}function Jv(e,t={}){return bh(e,ks.getMap().classNameMap,t,"regularizer")}function _t(e){if(e==null)return null;if(typeof e=="string"){const t=e in Xv?Xv[e]:e,n={className:t,config:{}};return Jv(n)}else return e instanceof Kv?e:Jv(e)}class Nw extends lt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=je(e);let n=Ri(e);return this.maxValue!=null&&(n=Yn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}Nw.className="ReLU",me(Nw);class Cw extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=je(e);return Dd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}Cw.className="LeakyReLU",me(Cw);class Rw extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Ft(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=_t(e.alphaRegularizer),this.alphaConstraint=cn(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new j(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=St(e);const t=e.slice(1);if(this.sharedAxes!=null)for(const s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new mn({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=je(e),nh(e,this.alpha.read())}getConfig(){const e={alphaInitializer:Vt(this.alphaInitializer),alphaRegularizer:It(this.alphaRegularizer),alphaConstraint:an(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}}Rw.className="PReLU",me(Rw);class Ow extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new ze(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=je(e);return wo(n)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}Ow.className="ELU",me(Ow);class Ew extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){const n=je(e);return n.mul(Sh(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){const e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}}Ew.className="ThresholdedReLU",me(Ew);class Dw extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Tw().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){const n=je(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}Dw.className="Softmax",me(Dw);function qa(e,t,n){if(typeof e=="number")return Co(e,t);if(e.length!==t)throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){const i=e[s];if(!VP(i))throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${i}`)}return e}function ai(e,t,n,s,i=1){if(e==null)return e;const o=t+(t-1)*(i-1);let a;return n==="same"?a=e:a=e-o+1,Math.floor((a+s-1)/s)}function kp(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+Br([n-t,0]);else if(s==="same")e=e*t;else throw new j(`Unsupport padding mode: ${s}.`);return e}function kw(e,t){return ee(()=>(Gt(t),t==="channelsFirst"?Me(e,[0,2,3,1]):e))}function Zv(e,t){return ee(()=>(Gt(t),t==="channelsFirst"?Me(e,[0,2,3,4,1]):e))}function Qv(e,t,n,s=1,i="valid",o,a=1){return ee(()=>{if(o==null&&(o=ti()),Gt(o),e.shape.length!==3)throw new j(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new j(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new j(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(o==="channelsFirst"&&(e=Me(e,[0,2,1])),i==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let c=Nd(e,t,s,i==="same"?"same":"valid","NWC",a);return n!=null&&(c=Di(c,n)),c})}function DQ(e,t,n=1,s="valid",i,o=1){return ee(()=>(Gt(i),Qv(e,t,null,n,s,i,o)))}function kQ(e,t,n=[1,1],s="valid",i,o){return ee(()=>(Gt(i),Fw(e,t,null,n,s,i,o)))}function Fw(e,t,n,s=[1,1],i="valid",o,a,c=null){return ee(()=>{if(o==null&&(o=ti()),Gt(o),e.rank!==3&&e.rank!==4)throw new j(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new j(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let h=kw(e,o);if(i==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return h=vb({x:h,filter:t,strides:s,pad:i==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:n,activation:c}),o==="channelsFirst"&&(h=Me(h,[0,3,1,2])),h})}function FQ(e,t,n=[1,1,1],s="valid",i,o){return ee(()=>(Gt(i),eN(e,t,null,n,s,i,o)))}function eN(e,t,n,s=[1,1,1],i="valid",o,a){return ee(()=>{if(o==null&&(o=ti()),Gt(o),e.rank!==4&&e.rank!==5)throw new j(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new j(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let c=Zv(e,o);if(i==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return c=Zy(c,t,s,i==="same"?"same":"valid","NDHWC",a),n!=null&&(c=Di(c,n)),o==="channelsFirst"&&(c=Me(c,[0,4,1,2,3])),c})}class _w extends lt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",_w.verifyArgs(t),this.rank=e,pn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=qa(t.kernelSize,e,"kernelSize"),this.strides=qa(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Ts(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Gt(this.dataFormat),this.activation=Gr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Ft(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=cn(t.biasConstraint),this.biasRegularizer=_t(t.biasRegularizer),this.activityRegularizer=_t(t.activityRegularizer),this.dilationRate=qa(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new j(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new j(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new j(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(xs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Yb(e.kernelSize,"number",1,3))throw new j(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){const e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:zr(this.activation),useBias:this.useBias,biasInitializer:Vt(this.biasInitializer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),biasConstraint:an(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class Oh extends _w{constructor(e,t){super(e,t);this.kernel=null,Oh.verifyArgs(t),this.filters=t.filters,pn(this.filters,"filters"),this.kernelInitializer=Ft(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=cn(t.kernelConstraint),this.kernelRegularizer=_t(t.kernelRegularizer)}build(e){e=St(e);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[t]}`);const n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return ee(()=>{e=je(e);let n;const s=this.bias==null?null:this.bias.read(),i=VA(this.activation.getClassName());if(i!=null&&this.rank===2)n=Fw(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,i);else{if(this.rank===1)n=Qv(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Fw(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=eN(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=St(e);const t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let i=0;i<n.length;++i){const o=ai(n[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);t.push(o)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){const e={filters:this.filters,kernelInitializer:Vt(this.kernelInitializer),kernelRegularizer:It(this.kernelRegularizer),kernelConstraint:an(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new j(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}}class Eh extends Oh{constructor(e){super(2,e);Eh.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Yb(e.kernelSize,"number",1,2))throw new j(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}Eh.className="Conv2D",me(Eh);class Fp extends Oh{constructor(e){super(3,e);Fp.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new j(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}Fp.className="Conv3D",me(Fp);class Ww extends Eh{constructor(e){super(e);if(this.inputSpec=[new mn({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new j(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=St(e),e.length!==4)throw new j("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new mn({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return ee(()=>{let n=je(e);if(n.shape.length!==4)throw new j(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);const s=n.shape,i=s[0];let o,a;this.dataFormat==="channelsFirst"?(o=2,a=3):(o=1,a=2);const c=s[o],h=s[a],d=this.kernelSize[0],m=this.kernelSize[1],y=this.strides[0],b=this.strides[1],w=kp(c,y,d,this.padding),L=kp(h,b,m,this.padding),T=[i,w,L,this.filters];this.dataFormat!=="channelsLast"&&(n=Me(n,[0,2,3,1]));let A=Cd(n,this.kernel.read(),T,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=Me(A,[0,3,1,2])),this.bias!=null&&(A=Di(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=St(e);const t=e.slice();let n,s,i;this.dataFormat==="channelsFirst"?(n=1,s=2,i=3):(n=3,s=1,i=2);const o=this.kernelSize[0],a=this.kernelSize[1],c=this.strides[0],h=this.strides[1];return t[n]=this.filters,t[s]=kp(t[s],c,o,this.padding),t[i]=kp(t[i],h,a,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}Ww.className="Conv2DTranspose",me(Ww);class tN extends Oh{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new j("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new j("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new j(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Ft(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=_t(t.depthwiseRegularizer),this.depthwiseConstraint=cn(t.depthwiseConstraint),this.pointwiseInitializer=Ft(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=_t(t.pointwiseRegularizer),this.pointwiseConstraint=cn(t.pointwiseConstraint)}build(e){if(e=St(e),e.length<this.rank+2)throw new j(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);const n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),i=[];for(let a=0;a<this.rank;++a)i.push(1);i.push(n*this.depthMultiplier,this.filters);const o=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,o,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",i,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,o,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,o,this.biasConstraint):this.bias=null,this.inputSpec=[new mn({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return ee(()=>{e=je(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Me(e,[0,2,3,1])),n=fb(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Di(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Me(n,[0,3,1,2])),n})}getConfig(){const e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Vt(this.depthwiseInitializer),e.pointwiseInitializer=Vt(this.pointwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.pointwiseRegularizer=It(this.pointwiseRegularizer),e.depthwiseConstraint=an(this.depthwiseConstraint),e.pointwiseConstraint=an(this.pointwiseConstraint),e}}tN.className="SeparableConv";class $w extends tN{constructor(e){super(2,e)}}$w.className="SeparableConv2D",me($w);class _p extends Oh{constructor(e){super(1,e);_p.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){const e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Yb(e.kernelSize,"number",1,1))throw new j(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}_p.className="Conv1D",me(_p);class Uw extends lt{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return ee(()=>{if(e=je(e),this.dataFormat==="channelsLast"){const n=hp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return hp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{const n=hp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return hp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){const e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}Uw.className="Cropping2D",me(Uw);class Bw extends lt{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){const t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{const t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return ee(()=>{let n=je(e);const s=n.shape;if(this.dataFormat==="channelsFirst"){n=Me(n,[0,2,3,1]);const i=this.size[0]*s[2],o=this.size[1]*s[3],a=n.resizeNearestNeighbor([i,o]);return Me(a,[0,3,1,2])}else{const i=this.size[0]*s[1],o=this.size[1]*s[2];return n.resizeNearestNeighbor([i,o])}})}getConfig(){const e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}Bw.className="UpSampling2D",me(Bw);function A3(e,t,n=[1,1],s="valid",i,o){return ee(()=>{i==null&&(i=ti()),Gt(i);let a=kw(e,i);if(e.rank!==4)throw new j(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new j(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return a=yo(a,t,n,s==="same"?"same":"valid","NHWC",o),i==="channelsFirst"&&(a=Me(a,[0,3,1,2])),a})}class Mw extends _w{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Ft(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=cn(e.depthwiseConstraint),this.depthwiseRegularizer=_t(e.depthwiseRegularizer)}build(e){if(e=St(e),e.length<4)throw new j(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);const n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{e=je(e);let n=A3(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Di(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=St(e);const t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,i=ai(t,this.kernelSize[0],this.padding,this.strides[0]),o=ai(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,i,o]:[e[0],i,o,s]}getConfig(){const e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Vt(this.depthwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.depthwiseConstraint=an(this.depthwiseRegularizer),e}}Mw.className="DepthwiseConv2D",me(Mw);function nN(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new j("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function i(o){return o==null||Array.isArray(o)?o:[o]}return t=i(t),n=i(n),{inputs:e,initialState:t,constants:n}}function sN(e,t,n,s=!1,i,o,a=!1,c=!1){return ee(()=>{const h=t.shape.length;if(h<3)throw new j(`Input should be at least 3D, but is ${h}D.`);const d=[1,0].concat(si(2,h));if(t=Me(t,d),o!=null)throw new ze("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),i!=null&&(i=i.asType("bool").asType("float32"),i.rank===h-1&&(i=Hn(i,-1)),i=Me(i,d)),s&&(t=Is(t,0),i!=null&&(i=Is(i,0)));const m=[];let y,b=n;const w=t.shape[0],L=Oi(t);let T;i!=null&&(T=Oi(i));for(let N=0;N<w;++N){const E=L[N],D=ee(()=>e(E,b));if(i==null)y=D[0],b=D[1];else{const F=ee(()=>{const _=T[N],B=On(_).sub(_),$=D[0].mul(_).add(b[0].mul(B)),H=b.map((q,J)=>D[1][J].mul(_).add(q.mul(B)));return{output:$,newStates:H}});y=F.output,b=F.newStates}c&&m.push(y)}let A;if(c){const N=1;A=is(m,N)}return[y,A,b]})}class Fi extends lt{constructor(e){super(e);let t;if(e.cell==null)throw new j("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Up({cells:e.cell}):t=e.cell,t.stateSize==null)throw new j("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new mn({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return si(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){cw(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);const n=t[0];let s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){const i=[];for(const o of t)i.push([e[0],o]);return[s].concat(i)}else return s}computeMask(e,t){return ee(()=>{Array.isArray(t)&&(t=t[0]);const n=this.returnSequences?t:null;if(this.returnState){const s=this.states.map(i=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){const t=null;if(this.numConstants!=null)throw new ze("Constants support is not implemented in RNN yet.");cw(e)&&(e=e[0]),e=e;const n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new mn({shape:[n,null,...s]});const i=[e[0]].concat(e.slice(2));if(t!=null)throw new ze("Constants support is not implemented in RNN yet.");this.cell.build(i);let o;if(Array.isArray(this.cell.stateSize)?o=this.cell.stateSize:o=[this.cell.stateSize],this.stateSpec!=null){if(!ot(this.stateSpec.map(a=>a.shape[a.shape.length-1]),o))throw new j(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=o.map(a=>new mn({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){ee(()=>{if(!this.stateful)throw new Qi("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape[0];if(n==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>ct([n,s])):this.states_=[ct([n,this.cell.stateSize])];else if(e==null)He(this.states_),this.keptStates!=null&&(He(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>ct([n,s])):this.states_[0]=ct([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):He(this.states_);for(let s=0;s<this.states_.length;++s){const i=e[s],o=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,a=[n,o];if(!ot(i.shape,a))throw new j(`State ${s} is incompatible with layer ${this.name}: expected shape=${a}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Nn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=nN(e,n,s,this.numConstants);e=i.inputs,n=i.initialState,s=i.constants;let o=[],a=[];if(n!=null){t.initialState=n,o=o.concat(n),this.stateSpec=[];for(const h of n)this.stateSpec.push(new mn({shape:h.shape}));a=a.concat(this.stateSpec)}s!=null&&(t.constants=s,o=o.concat(s),this.numConstants=s.length);const c=o[0]instanceof ri;if(c){const h=[e].concat(o),d=this.inputSpec.concat(a),m=this.inputSpec;this.inputSpec=d;const y=super.apply(h,t);return this.inputSpec=m,y}else return super.apply(e,t)}call(e,t){return ee(()=>{const n=t==null?null:t.mask,s=t==null?null:t.training;let i=t==null?null:t.initialState;e=je(e),i==null&&(this.stateful?i=this.states_:i=this.getInitialState(e));const o=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(i.length!==o)throw new j(`RNN Layer has ${o} state(s) but was passed ${i.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const a={training:s},c=(w,L)=>{const T=this.cell.call([w].concat(L),a);return[T[0],T.slice(1)]},h=sN(c,e,i,this.goBackwards,n,null,this.unroll,this.returnSequences),d=h[0],m=h[1],y=h[2];this.stateful&&this.resetStates(y,s);const b=this.returnSequences?m:d;return this.returnState?[b].concat(y):b})}getInitialState(e){return ee(()=>{let t=ct(e.shape);return t=Ue(t,[1,2]),t=Ih(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Qb(t,[1,n]):t):this.cell.stateSize>1?[Qb(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){const e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);const n=this.cell.getConfig();return this.getClassName()===Fi.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){const s=t.cell,i=oi(s,n);return new e(Object.assign(t,{cell:i}))}}Fi.className="RNN",me(Fi);class ja extends lt{}class Wp extends ja{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,pn(this.units,"units"),this.activation=Gr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=cn(e.kernelConstraint),this.recurrentConstraint=cn(e.recurrentConstraint),this.biasConstraint=cn(e.biasConstraint),this.dropout=za([1,Br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=za([1,Br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=St(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{if(e=e,e.length!==2)throw new j(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];const s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Vr({ones:()=>On(e),rate:this.dropout,training:s})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Vr({ones:()=>On(n),rate:this.recurrentDropout,training:s}));let i;const o=this.dropoutMask,a=this.recurrentDropoutMask;o!=null?i=Ei(X(e,o),this.kernel.read()):i=Ei(e,this.kernel.read()),this.bias!=null&&(i=Di(i,this.bias.read())),a!=null&&(n=X(n,a));let c=be(i,Ei(n,this.recurrentKernel.read()));return this.activation!=null&&(c=this.activation.apply(c)),[c,c]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:zr(this.activation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),recurrentInitializer:Vt(this.recurrentInitializer),biasInitializer:Vt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}}Wp.className="SimpleRNNCell",me(Wp);class Pw extends Fi{constructor(e){e.cell=new Wp(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return new e(t)}}Pw.className="SimpleRNN",me(Pw);class $p extends ja{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new j("GRUCell does not support reset_after parameter set to true.");this.units=e.units,pn(this.units,"units"),this.activation=Gr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Gr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=cn(e.kernelConstraint),this.recurrentConstraint=cn(e.recurrentConstraint),this.biasConstraint=cn(e.biasConstraint),this.dropout=za([1,Br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=za([1,Br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=St(e);const t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{if(e=e,e.length!==2)throw new j(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training==null?!1:t.training;let s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Vr({ones:()=>On(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Vr({ones:()=>On(s),rate:this.recurrentDropout,training:n,count:3}));const i=this.dropoutMask,o=this.recurrentDropoutMask;let a,c,h;0<this.dropout&&this.dropout<1&&(e=X(e,i[0]));let d=Ei(e,this.kernel.read());this.useBias&&(d=Di(d,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,o[0]));const m=this.recurrentKernel.read(),[y,b]=ss(m,[2*this.units,this.units],m.rank-1),w=Ei(s,y),[L,T,A]=ss(d,3,d.rank-1),[N,E]=ss(w,2,w.rank-1);a=this.recurrentActivation.apply(be(L,N)),c=this.recurrentActivation.apply(be(T,E));const D=Ei(X(c,s),b);h=this.activation.apply(be(A,D));const F=be(X(a,s),X(be(1,Pt(a)),h));return[F,F]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:zr(this.activation),recurrentActivation:zr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),recurrentInitializer:Vt(this.recurrentInitializer),biasInitializer:Vt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}}$p.className="GRUCell",me($p);class zw extends Fi{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new $p(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}zw.className="GRU",me(zw);class Dh extends ja{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,pn(this.units,"units"),this.activation=Gr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Gr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=cn(e.kernelConstraint),this.recurrentConstraint=cn(e.recurrentConstraint),this.biasConstraint=cn(e.biasConstraint),this.dropout=za([1,Br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=za([1,Br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=St(e);const n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){const i=this.biasInitializer,o=this.units;s=new(t=class extends Us{apply(c,h){const d=i.apply([o]),m=new dp().apply([o]),y=i.apply([o*2]);return QA(QA(d,m),y)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return ee(()=>{const n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new j(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1];const i=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Vr({ones:()=>On(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Vr({ones:()=>On(s),rate:this.recurrentDropout,training:n,count:4}));const o=this.dropoutMask,a=this.recurrentDropoutMask;let c,h,d,m;0<this.dropout&&this.dropout<1&&(e=X(e,o[0]));let y=Ei(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,a[0])),y=be(y,Ei(s,this.recurrentKernel.read())),this.useBias&&(y=Di(y,this.bias.read()));const[b,w,L,T]=ss(y,4,y.rank-1);c=this.recurrentActivation.apply(b),h=this.recurrentActivation.apply(w),d=be(X(h,i),X(c,this.activation.apply(L))),m=this.recurrentActivation.apply(T);const A=X(m,this.activation.apply(d));return[A,A,d]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:zr(this.activation),recurrentActivation:zr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),recurrentInitializer:Vt(this.recurrentInitializer),biasInitializer:Vt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}}Dh.className="LSTMCell",me(Dh);class Gw extends Fi{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Dh(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}Gw.className="LSTM",me(Gw);class Up extends ja{constructor(e){super(e);this.cells=e.cells}get stateSize(){const e=[];for(const t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return ee(()=>{e=e;let n=e.slice(1);const s=[];for(const a of this.cells.slice().reverse())Array.isArray(a.stateSize)?s.push(n.splice(0,a.stateSize.length)):s.push(n.splice(0,1));s.reverse();const i=[];let o;for(let a=0;a<this.cells.length;++a){const c=this.cells[a];n=s[a],a===0?o=[e[0]].concat(n):o=[o[0]].concat(n),o=c.call(o,t),i.push(o.slice(1))}n=[];for(const a of i.slice().reverse())n.push(...a);return[o[0]].concat(n)})}build(e){cw(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Oo(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){const e=super.getConfig(),t=i=>({className:i.getClassName(),config:i.getConfig()}),n=this.cells.map(t),s={cells:n};return Object.assign({},e,s)}static fromConfig(e,t,n={}){const s=[];for(const i of t.cells)s.push(oi(i,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];const e=[];for(const t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){const e=[];for(const t of this.cells)e.push(...t.weights);return lw(e)}setWeights(e){const t=[];for(const n of this.cells){const s=n.weights.length,i=e.splice(s);for(let o=0;o<n.weights.length;++o)t.push([n.weights[o],i[o]])}hw(t)}}Up.className="StackedRNNCells",me(Up);function Vr(e){const{ones:t,rate:n,training:s=!1,count:i=1}=e,o=()=>tv(t(),n),a=()=>Th(o,t,s);if(!i||i<=1)return Nn(a().clone());const c=Array(i).fill(void 0).map(a);return c.map(h=>Nn(h.clone()))}var v3=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,s=Object.getOwnPropertySymbols(e);i<s.length;i++)t.indexOf(s[i])<0&&Object.prototype.propertyIsEnumerable.call(e,s[i])&&(n[s[i]]=e[s[i]]);return n};class _Q extends ja{}class iN extends Fi{constructor(e){if(e.unroll)throw new ze("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new ze("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new mn({ndim:5})]}call(e,t){return ee(()=>{if(this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new j("ConvRNN2D cell does not support constants");const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return ee(()=>{const{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),i=[s[0],...s.slice(2)],o=ct(i);return Array.isArray(t)?Array(t.length).fill(o):[o]})}resetStates(e,t=!1){ee(()=>{if(!this.stateful)throw new Qi("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),i=[s[0],...s.slice(2)],o=n[0];if(o==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ct(i)):this.states_=[ct(i)];else if(e==null)He(this.states_),this.keptStates!=null&&(He(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ct(i)):this.states_[0]=ct(i);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):He(this.states_);for(let a=0;a<this.states_.length;++a){const c=e[a],h=i;if(!ot(c.shape,h))throw new j(`State ${a} is incompatible with layer ${this.name}: expected shape=${h}, received shape=${c.shape}`);this.states_[a]=c}}this.states_=this.states_.map(a=>Nn(a.clone()))})}computeSingleOutputShape(e){const{dataFormat:t,filters:n,kernelSize:s,padding:i,strides:o,dilationRate:a}=this.cell,c=t==="channelsFirst",h=e[c?3:2],d=e[c?4:3],m=ai(h,s[0],i,o[0],a[0]),y=ai(d,s[1],i,o[1],a[1]),b=[...e.slice(0,2),...c?[n,m,y]:[m,y,n]];return b}}iN.className="ConvRNN2D";class Bp extends Dh{constructor(e){const{filters:t,kernelSize:n,strides:s,padding:i,dataFormat:o,dilationRate:a}=e;super(Object.assign({},e,{units:t}));this.filters=t,pn(this.filters,"filters"),this.kernelSize=qa(n,2,"kernelSize"),this.kernelSize.forEach(c=>pn(c,"kernelSize")),this.strides=qa(s||1,2,"strides"),this.strides.forEach(c=>pn(c,"strides")),this.padding=i||"valid",Ts(this.padding),this.dataFormat=o||"channelsLast",Gt(this.dataFormat),this.dilationRate=qa(a||1,2,"dilationRate"),this.dilationRate.forEach(c=>pn(c,"dilationRate"))}build(e){var t;e=St(e);const n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[n]}`);const s=e[n],i=4,o=this.kernelSize.concat([s,this.filters*i]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const a=this.kernelSize.concat([this.filters,this.filters*i]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let c;if(this.unitForgetBias){const h=this.biasInitializer,d=this.filters;c=new(t=class extends Us{apply(y,b){const w=h.apply([d]),L=Qs([d]),T=h.apply([d*2]);return Zb([w,L,T])}},t.className="CustomInit",t)}else c=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*i],null,c,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return ee(()=>{if(e.length!==3)throw new j(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training||!1,s=e[0],i=e[1],o=e[2],a=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Vr({ones:()=>On(s),rate:this.dropout,training:n,count:a}));const c=this.dropoutMask,h=(Ie,Se,Ee)=>!Se||!Se[Ee]?Ie:X(Se[Ee],Ie);let d=h(s,c,0),m=h(s,c,1),y=h(s,c,2),b=h(s,c,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Vr({ones:()=>On(i),rate:this.recurrentDropout,training:n,count:a}));const w=this.recurrentDropoutMask;let L=h(i,w,0),T=h(i,w,1),A=h(i,w,2),N=h(i,w,3);const E=3,[D,F,_,B]=ss(this.kernel.read(),a,E),[$,H,q,J]=this.useBias?ss(this.bias.read(),a):[null,null,null,null];d=this.inputConv(d,D,$,this.padding),m=this.inputConv(m,F,H,this.padding),y=this.inputConv(y,_,q,this.padding),b=this.inputConv(b,B,J,this.padding);const[re,ce,ue,he]=ss(this.recurrentKernel.read(),a,E);L=this.recurrentConv(L,re),T=this.recurrentConv(T,ce),A=this.recurrentConv(A,ue),N=this.recurrentConv(N,he);const de=this.recurrentActivation.apply(be(d,L)),le=this.recurrentActivation.apply(be(m,T)),ye=be(X(le,o),X(de,this.activation.apply(be(y,A)))),pe=X(this.recurrentActivation.apply(be(b,N)),this.activation.apply(ye));return[pe,pe,ye]})}getConfig(){const e=super.getConfig(),{units:t}=e,n=v3(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){const i=ji(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Di(i,n,this.dataFormat):i}recurrentConv(e,t){const n=1;return ji(e,t,n,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}Bp.className="ConvLSTM2DCell",me(Bp);class Vw extends iN{constructor(e){const t=new Bp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}}Vw.className="ConvLSTM2D",me(Vw);class Mp extends lt{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;const t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=je(e);if(0<this.rate&&this.rate<1){const s=t.training==null?!1:t.training,i=this.getNoiseShape(n),o=Th(()=>tv(n,this.rate,i,this.seed),()=>n,s);return o}return e})}getConfig(){const e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}}Mp.className="Dropout",me(Mp);class Yw extends Mp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}Yw.className="SpatialDropout1D",me(Yw);class Hw extends lt{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,pn(this.units,"units"),this.activation=Gr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=cn(e.kernelConstraint),this.biasConstraint=cn(e.biasConstraint),this.kernelRegularizer=_t(e.kernelRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=St(e);const t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=St(e);const t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=je(e),s=VA(this.activation.getClassName());let i;return s!=null?i=Ei(n,this.kernel.read(),s,this.bias?this.bias.read():null):(i=Ei(n,this.kernel.read()),this.bias!=null&&(i=Di(i,this.bias.read())),this.activation!=null&&(i=this.activation.apply(i))),i})}getConfig(){const e={units:this.units,activation:zr(this.activation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),biasInitializer:Vt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),biasConstraint:an(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}Hw.className="Dense",me(Hw);class qw extends lt{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=St(e);for(const t of e.slice(1))if(t==null)throw new j(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Ur(e,1)]}call(e,t){return ee(()=>{this.invokeCallHook(e,t);let n=je(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){const s=[0];for(let i=2;i<n.rank;++i)s.push(i);s.push(1),n=n.transpose(s)}return KP(n)})}getConfig(){const e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}qw.className="Flatten",me(qw);class jw extends lt{constructor(e){super(e);this.supportsMasking=!0,this.activation=Gr(e.activation)}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=je(e);return this.activation.apply(n)})}getConfig(){const e={activation:zr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}jw.className="Activation",me(jw);class Kw extends lt{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return ee(()=>(e=je(e),qP(e,this.n)))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}Kw.className="RepeatVector",me(Kw);class Xw extends lt{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){const n="Total size of new array must be unchanged.",s=t.slice();let i=1,o=null;for(let c=0;c<s.length;++c){const h=s[c];if(this.isUnknown(h))if(o===null)o=c;else throw new j("Can only specifiy one unknown dimension.");else i*=h}const a=Ur(e);if(o!==null){if(i===0||a%i!==0)throw new j(n);s[o]=a/i}else if(a!==i)throw new j(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=je(e),s=n.shape,i=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return n.reshape(i)})}getConfig(){const e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}}Xw.className="Reshape",me(Xw);class Jw extends lt{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);const t=si(1,e.dims.length+1);if(!ot(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new mn({ndim:this.dims.length+1})]}computeOutputShape(e){e=St(e);const t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Me(je(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}Jw.className="Permute",me(Jw);class Zw extends lt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){const n=je(e),s=-1;return Pl(kr(n,this.maskValue),s)}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=je(e),s=-1,i=!0,o=Pl(kr(n,this.maskValue),s,i),a=n.mul(o.asType(n.dtype));return a})}}Zw.className="Masking",me(Zw);class Qw extends lt{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(Nt(e.inputLength))}this.inputDim=e.inputDim,pn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,pn(this.outputDim,"outputDim"),this.embeddingsInitializer=Ft(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=_t(e.embeddingsRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.embeddingsConstraint=cn(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return ee(()=>this.maskZero?(e=je(e),kr(e,Qe(e))):null)}computeOutputShape(e){if(e=St(e),this.inputLength==null)return[...e,this.outputDim];const t=Nt(this.inputLength);if(t.length!==e.length-1)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){const i=t[s],o=e[s+1];if(i!=null&&o!=null&&i!==o)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);i==null&&(t[n]=o),n++}}return[e[0],...t,this.outputDim]}call(e,t){return ee(()=>{this.invokeCallHook(e,t);let n=je(e);n.dtype!=="int32"&&(n=Sh(n,"int32"));const s=ev(this.embeddings.read(),n.as1D());return s.reshape(St(this.computeOutputShape(n.shape)))})}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Vt(this.embeddingsInitializer),embeddingsRegularizer:It(this.embeddingsRegularizer),activityRegularizer:It(this.activityRegularizer),embeddingsConstraint:an(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}Qw.className="Embedding",me(Qw);class Fo extends lt{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new ze}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;const n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){const i=e[e.length-t.length+s],o=t[s];if(i==null||o==null||i<0||o<0)n.push(null);else if(i===1)n.push(o);else if(o===1)n.push(i);else{if(i!==o)throw new j("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(i)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[St(e)]),e=e,e.length<2)throw new j(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(const i of e)i!=null&&i[0]!==null&&t.push(i[0]);if(t=$r(t),t.length>1)throw new j(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let i=1;i<e.length;++i){const o=e[i]==null?null:e[i].slice(1);n=this.computeElementwiseOpOutputShape(n,o)}const s=e.map(i=>i.length);e.indexOf(null)===-1&&$r(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return ee(()=>{if(e=e,this.reshapeRequired){const n=[],s=e.map(i=>i.rank);if(s.indexOf(null)===-1){const i=Br(s);for(let o of e){const a=o.rank;for(let c=0;c<i-a;++c)o=Ih(o,1);n.push(o)}return this.mergeFunction(n)}else{let i=!1;for(const c of e){const h=c.rank;if(h==null){const d=c.shape,m=d[0],y=d.slice(1).concat([m]);let b=c.reshape([m].concat(Ur(d.slice(1))));b=Me(b,[1,0]),b=b.reshape(y),n.push(b),i=!0}else if(h>1){const d=si(1,h).concat([0]);n.push(Me(c,d)),i=!0}else n.push(c)}let o=this.mergeFunction(n);const a=o.rank;if(i){if(a==null){const c=o.shape,h=c.length,d=c[h-1],m=[d].concat(c.slice(0,c.length-1));o=Me(o.reshape([-1,d]),[1,0]).reshape(m)}else if(a>1){const c=[a-1].concat(si(0,a-1));o=Me(o,c)}}return o}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){const i=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,i)}let n=[];for(const s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=$r(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return ee(()=>{if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an Array");if(!Array.isArray(e))throw new j("`inputs` should be an Array");if(t.length!==e.length)throw new j(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Hn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ws(n,t[s]);return n})}}class kh extends Fo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return t})}}kh.className="Add",me(kh);function WQ(e){if(Array.isArray(e)){const t=new kh({});return t.apply(e)}else return new kh(e)}class Fh extends Fo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=X(t,e[n]);return t})}}Fh.className="Multiply",me(Fh);function $Q(e){if(Array.isArray(e)){const t=new Fh({});return t.apply(e)}else return new Fh(e)}class _h extends Fo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return X(1/e.length,t)})}}_h.className="Average",me(_h);function UQ(e){if(Array.isArray(e)){const t=new _h({});return t.apply(e)}else return new _h(e)}class Wh extends Fo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=_s(t,e[n]);return t})}}Wh.className="Maximum",me(Wh);function BQ(e){if(Array.isArray(e)){const t=new Wh({});return t.apply(e)}else return new Wh(e)}class $h extends Fo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Lo(t,e[n]);return t})}}$h.className="Minimum",me($h);function MQ(e){if(Array.isArray(e)){const t=new $h({});return t.apply(e)}else return new $h(e)}class Uh extends Fo{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new j("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(const s of e)if(s!=null){t=!1;break}if(t)return;const n=[];for(let s=0;s<e.length;++s){const i=e[s].slice();i.splice(this.axis,1);let o=!1;for(const a of n)if(ot(a,i)){o=!0;break}o||n.push(i)}if(n.length>1)throw new j("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return ee(()=>Zb(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new j("A `Concatenate` layer should be called on a list of inputs.");const t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(const i of t.slice(1)){if(n[s]==null||i[s]==null){n[s]=null;break}n[s]+=i[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new j("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new j(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return ee(()=>{let n=!0;if(t.forEach(o=>{if(o!=null){n=!1;return}}),n)return null;const s=[];for(let o=0;o<e.length;++o)t[o]==null?s.push(On(e[o]).asType("bool")):t[o].rank<e[o].rank?s.push(Hn(t[o],-1)):s.push(t[o]);const i=Mt(s,this.axis);return xd(i,-1,!1)})}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}Uh.className="Concatenate",me(Uh);function PQ(e){if(Array.isArray(e)){const t=new Uh({});return t.apply(e)}else return new Uh(e)}function Bh(e,t){for(;e<0;)e+=t;return e}function N3(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new ze("batchDot is not implemented for tensors of 4D or higher rank yet");if(k(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new ze("batchDot is not implemented for complex64-type Tensors yet.");const s=e.shape.length,i=t.shape.length;n==null&&(n=[s-1,i-2]);const o=n;return ee(()=>{let a;if(s>i){a=s-i;const h=[];for(let d=0;d<a;++d)h.push(1);t=t.reshape(t.shape.concat(h))}else if(i>s){a=i-s;const h=[];for(let d=0;d<a;++d)h.push(1);e=e.reshape(e.shape.concat(h))}else a=0;let c;if(e.shape.length===2&&t.shape.length===2)o[0]===o[1]?c=e.mul(t).sum(o[0]):c=e.transpose([1,0]).mul(t).sum(o[1]);else{const h=o[0]!==e.shape.length-1,d=o[1]===t.shape.length-1;c=e.matMul(t,h,d)}if(a>0){let h;s>i?h=s+i-3:h=s-1;const d=[];for(let m=h;m<h+a;++m)d.push(m);c=c.squeeze(d)}return c.shape.length===1&&(c=c.expandDims(1)),c})}class eL extends Fo{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const t=e[0],n=e[1];if(t.length>3||n.length>3)throw new ze("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new j(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new j(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((i,o)=>Bh(i,e[o].shape.length)):s=[Bh(this.axes,t.shape.length),Bh(this.axes,n.shape.length)],this.normalize&&(t=Tp(t,s[0]),n=Tp(n,s[1])),N3(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Bh(this.axes,e.length),Bh(this.axes,t.length)],n}computeOutputShape(e){k(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new ze("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);const i=t.concat(n);return i.length===1&&i.push(1),i}computeMask(e,t){return null}getConfig(){const e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}}eL.className="Dot",me(eL);class tL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=je(e),s=()=>up(n.shape,0,this.stddev).add(n),i=Th(s,()=>n,t.training||!1);return i})}}tL.className="GaussianNoise",me(tL);class nL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=je(e);if(this.rate>0&&this.rate<1){const s=()=>{const i=Math.sqrt(this.rate/(1-this.rate));return n.mul(up(n.shape,1,i))};return Th(s,()=>n,t.training||!1)}return n})}}nL.className="GaussianDropout",me(nL);class sL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||je(e).shape}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return ee(()=>{if(this.rate<1&&this.rate>0){const n=this._getNoiseShape(e),s=()=>{const i=je(e),o=1.6732632423543772,a=1.0507009873554805,c=-o*a;let h=Ki(To(n),this.rate);h=Sh(h,"float32");const d=((1-this.rate)*(1+this.rate*c**2))**-.5,m=-d*c*this.rate,y=i.mul(h).add(h.add(-1).mul(c));return y.mul(d).add(m)};return Th(s,()=>je(e),t.training||!1)}return e})}}sL.className="AlphaDropout",me(sL);function Mh(e,t,n,s,i,o=.001){let a;if(e.rank===2)a=CT(e,t,n,s,i,o);else if(e.rank===3)a=RT(e,t,n,s,i,o);else if(e.rank===4)a=OT(e,t,n,s,i,o);else throw new ze(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return a}function C3(e,t,n,s,i=.001){return ee(()=>{const o=Ud(e,s),a=o.mean,c=o.variance,h=Mh(e,a,c,n,t,i);return[h,a,c]})}function R3(e,t,n,s,i=.001){return ee(()=>{const o=Ud(e,s),a=o.mean,c=o.variance,h=[];for(const L of si(0,e.rank))s.indexOf(L)!==-1?h.push(1):h.push(e.shape[L]);const d=a.reshape(h),m=c.reshape(h),y=t==null?null:t.reshape(h),b=n==null?null:n.reshape(h),w=Mh(e,d,m,b,y,i);return[w,a,c]})}function O3(e,t,n,s,i=.001){return ot(s.slice().sort(),si(0,e.rank-1))?C3(e,t,n,s,i):R3(e,t,n,s,i)}class iL extends lt{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ft(e.betaInitializer||"zeros"),this.gammaInitializer=Ft(e.gammaInitializer||"ones"),this.movingMeanInitializer=Ft(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Ft(e.movingVarianceInitializer||"ones"),this.betaConstraint=cn(e.betaConstraint),this.gammaConstraint=cn(e.gammaConstraint),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer)}build(e){e=St(e);const t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new j(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new mn({ndim:e.length,axes:{[t]:n}})];const s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return ee(()=>{const n=t.training==null?!1:t.training,s=je(e),i=s.shape,o=i.length,a=si(0,o),c=this.axis>=0?this.axis:this.axis+o;a.splice(c,1);const h=Co(1,o);h[c]=i[c];const d=a.slice();d.sort();const m=!ot(d,si(0,o).slice(0,o-1)),y=()=>{if(m){const N=this.movingMean.read().reshape(h),E=this.movingVariance.read().reshape(h),D=this.center?this.beta.read().reshape(h):null,F=this.scale?this.gamma.read().reshape(h):null;return Mh(s,N,E,D,F,this.epsilon)}else return Mh(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return y();const[b,w,L]=O3(s,this.gamma.read(),this.beta.read(),a,this.epsilon),T=(N,E,D)=>{ee(()=>{const F=1-D,_=N.read(),B=_.sub(E).mul(F);N.write(_.sub(B))})},A=()=>{T(this.movingMean,w,this.momentum),T(this.movingVariance,L,this.momentum)};return A(),b})}getConfig(){const e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Vt(this.betaInitializer),gammaInitializer:Vt(this.gammaInitializer),movingMeanInitializer:Vt(this.movingMeanInitializer),movingVarianceInitializer:Vt(this.movingVarianceInitializer),betaRegularizer:It(this.betaRegularizer),gammaRegularizer:It(this.gammaRegularizer),betaConstraint:an(this.betaConstraint),gammaConstraint:an(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}iL.className="BatchNormalization",me(iL);class rL extends lt{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(const t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ft(e.betaInitializer||"zeros"),this.gammaInitializer=Ft(e.gammaInitializer||"ones"),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=St(e);const t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i<this.axis.length;++i)this.axis[i]<0&&(this.axis[i]+=t);for(const i of this.axis)if(i<0||i>=t)throw new Error(`Invalid axis: ${i}`);if(this.axis.length!==$r(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);const n=this.axis.map(i=>e[i]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){const n=je(e),s=n.shape,i=s.length;return ee(()=>{const o=!0;let{mean:a,variance:c}=Ud(n,this.axis,o);const h=Co(1,i);for(const L of this.axis)h[L]=s[L];const d=L=>L!=null&&L.shape.length!==i&&this.axis!==[i-1]?L.reshape(h):L;let m=d(this.gamma.read()),y=d(this.beta.read());const b=[],w=[];for(let L=0;L<i;++L)this.axis.indexOf(L)!==-1?(b.push(s[L]),w.push(1)):(b.push(1),w.push(s[L]));return a=a.tile(b),c=c.tile(b),m=m.tile(w),y=y.tile(w),Mh(n,a,c,y,m,this.epsilon)})}getConfig(){const e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Vt(this.betaInitializer),gammaInitializer:Vt(this.gammaInitializer),betaRegularizer:It(this.betaRegularizer),gammaRegularizer:It(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}}rL.className="LayerNormalization",me(rL);function zQ(e,t){return ee(()=>{if(e.rank!==3)throw new j(`temporalPadding expects input tensor to be 3-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[1,1]),t.length!==2)throw new j(`temporalPadding expects input padding pattern to be a length-2 array, but received a length-${t.length} array.`);const n=[[0,0],t,[0,0]];return Ci(e,n)})}function E3(e,t,n){return ee(()=>{if(e.rank!==4)throw new j(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new j("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ti()),n!=="channelsLast"&&n!=="channelsFirst")throw new j(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],Ci(e,s)})}class oL extends lt{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ti():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new j(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new j(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new j(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new mn({ndim:4})]}computeOutputShape(e){e=St(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return ee(()=>E3(je(e),this.padding,this.dataFormat))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}oL.className="ZeroPadding2D",me(oL);function Pp(e,t,n,s,i,o){return ee(()=>{Gt(i),qA(o),Ts(s),n==null&&(n=[1,1]),s==null&&(s="valid"),i==null&&(i=ti()),o==null&&(o="max"),e=kw(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=eh(e,t,n,c):a=Hl(e,t,n,c),i==="channelsFirst"&&(a=Me(a,[0,3,1,2])),a})}function rN(e,t,n,s,i,o){return ee(()=>{Gt(i),qA(o),Ts(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),i==null&&(i=ti()),o==null&&(o="max"),e=Zv(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=cb(e,t,n,c):a=jy(e,t,n,c),i==="channelsFirst"&&(a=Me(a,[0,4,1,2,3])),a})}class oN extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new j(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(pn(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new j(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);pn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Ts(this.padding),this.inputSpec=[new mn({ndim:3})]}computeOutputShape(e){e=St(e);const t=ai(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return ee(()=>{this.invokeCallHook(e,t),e=Ih(je(e),2);const n=this.poolingFunction(je(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Fr(n,[2])})}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class aL extends oN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ts(s),Pp(e,t,n,s,i,"max")}}aL.className="MaxPooling1D",me(aL);class cL extends oN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ts(s),Pp(e,t,n,s,i,"avg")}}cL.className="AveragePooling1D",me(cL);class aN extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new j(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];pn(this.poolSize,"poolSize"),pn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),Ts(this.padding),this.inputSpec=[new mn({ndim:4})]}computeOutputShape(e){e=St(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ai(t,this.poolSize[0],this.padding,this.strides[0]),n=ai(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return ee(()=>(this.invokeCallHook(e,t),this.poolingFunction(je(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class lL extends aN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ts(s),Pp(e,t,n,s,i,"max")}}lL.className="MaxPooling2D",me(lL);class hL extends aN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ts(s),Pp(e,t,n,s,i,"avg")}}hL.className="AveragePooling2D",me(hL);class cN extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new j(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];pn(this.poolSize,"poolSize"),pn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),Ts(this.padding),this.inputSpec=[new mn({ndim:5})]}computeOutputShape(e){e=St(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=ai(t,this.poolSize[0],this.padding,this.strides[0]),n=ai(n,this.poolSize[1],this.padding,this.strides[1]),s=ai(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return ee(()=>(this.invokeCallHook(e,t),this.poolingFunction(je(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class uL extends cN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ts(s),rN(e,t,n,s,i,"max")}}uL.className="MaxPooling3D",me(uL);class dL extends cN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ts(s),rN(e,t,n,s,i,"avg")}}dL.className="AveragePooling3D",me(dL);class lN extends lt{constructor(e){super(e);this.inputSpec=[new mn({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new ze}}class pL extends lN{constructor(e){super(e||{})}call(e,t){return ee(()=>{const n=je(e);return zt(n,1)})}}pL.className="GlobalAveragePooling1D",me(pL);class mL extends lN{constructor(e){super(e||{})}call(e,t){return ee(()=>{const n=je(e);return qn(n,1)})}}mL.className="GlobalMaxPooling1D",me(mL);class hN extends lt{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),this.inputSpec=[new mn({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new ze}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class fL extends hN{call(e,t){return ee(()=>{const n=je(e);return this.dataFormat==="channelsLast"?zt(n,[1,2]):zt(n,[2,3])})}}fL.className="GlobalAveragePooling2D",me(fL);class gL extends hN{call(e,t){return ee(()=>{const n=je(e);return this.dataFormat==="channelsLast"?qn(n,[1,2]):qn(n,[2,3])})}}gL.className="GlobalMaxPooling2D",me(gL);class uN extends lt{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){const e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){const s=t.layer,i=oi(s,n);delete t.layer;const o={layer:i};return Object.assign(o,t),new e(o)}}class yL extends uN{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=St(e),e.length<3)throw new j(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];const t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=St(e);const t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return ee(()=>{e=je(e);const n=(o,a)=>{const c=je(this.layer.call(o,t));return[c,[]]},s=sN(n,e,[],!1,null,null,!1,!0),i=s[1];return i})}}yL.className="TimeDistributed",me(yL);function D3(e){Ma(PP,"BidirectionalMergeMode",e)}const k3="concat";class bL extends uN{constructor(e){super(e);const t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=oi(n),t.goBackwards=!(t.goBackwards===!0);const s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=oi(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?k3:e.mergeMode,D3(this.mergeMode),e.weights)throw new ze("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){const t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,i;return this.returnState&&(i=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(i).concat(i.slice()):[n].concat(i).concat(i.slice()):jn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=nN(e,n,s,this.numConstants);if(e=i.inputs,n=i.initialState,s=i.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);const o=[],a=[];if(n!=null){const h=n.length;if(h%2>0)throw new j("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,o.push(...n);const d=n.map(m=>new mn({shape:m.shape}));this.forwardLayer.stateSpec=d.slice(0,h/2),this.backwardLayer.stateSpec=d.slice(h/2),a.push(...d)}if(s!=null)throw new ze("Support for constants in Bidirectional layers is not implemented yet.");const c=o[0]instanceof ri;for(const h of o)if(h instanceof ri!==c)throw new j("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(c){const h=[e].concat(o),d=this.inputSpec.concat(a),m=this.inputSpec;this.inputSpec=d;const y=super.apply(h,t);return this.inputSpec=m,y}else return super.apply(e,t)}call(e,t){return ee(()=>{const n=t.initialState;let s,i;if(n==null)s=this.forwardLayer.call(e,t),i=this.backwardLayer.call(e,t);else{const c=n.slice(0,n.length/2),h=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:c})),i=this.backwardLayer.call(e,Object.assign(t,{initialState:h}))}let o;this.returnState&&(Array.isArray(s)&&(o=s.slice(1).concat(i.slice(1))),s=s[0],i=i[0]),this.returnSequences&&(i=Is(i,1));let a;return this.mergeMode==="concat"?a=Zb([s,i]):this.mergeMode==="sum"?a=be(s,i):this.mergeMode==="ave"?a=X(.5,be(s,i)):this.mergeMode==="mul"?a=X(s,i):this.mergeMode==null&&(a=[s,i]),this.returnState?this.mergeMode==null?a.concat(o):[a].concat(o):a})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Oo(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Oo(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){const s=this.forwardLayer.states,i=s.map(o=>null);return Array.isArray(n)?n.concat(i).concat(i):[n].concat(i).concat(i)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){const e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){const n=oi(t.layer);if(delete t.layer,t.numConstants!=null)throw new ze("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const s=t;return s.layer=n,new e(s)}}bL.className="Bidirectional",me(bL);function F3(e){return new Ga(e)}function _3(e){return new Ow(e)}function W3(e){return new Nw(e)}function $3(e){return new Cw(e)}function U3(e){return new Rw(e)}function B3(e){return new Dw(e)}function M3(e){return new Ew(e)}function P3(e){return new _p(e)}function z3(e){return new Eh(e)}function G3(e){return new Ww(e)}function 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$V=Object.freeze({__proto__:null,json:WV});const 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Kn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Kn.className}}mp.className="GlorotNormal",me(mp);class fp extends Kn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Kn.className}}fp.className="HeNormal",me(fp);class gp extends Kn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Kn.className}}gp.className="HeUniform",me(gp);class yp extends Kn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Kn.className}}yp.className="LeCunNormal",me(yp);class bp extends Kn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Kn.className}}bp.className="LeCunNormal",me(bp);class aw extends Us{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new ze("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return ee(()=>{if(e.length<2)throw new ze("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);const n=e[0]>e[1]?[e[1],e[0]]:e,s=up(n,0,1,"float32");let i=AA.gramSchmidt(s);return e[0]>e[1]&&(i=i.transpose()),X(this.gain,i)})}getConfig(){return{gain:this.gain,seed:this.seed}}}aw.className="Orthogonal",me(aw);const nv={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function sv(e,t={}){return bh(e,ks.getMap().classNameMap,t,"initializer")}function Vt(e){return Gb(e)}function Ft(e){if(typeof e=="string"){const t=e in nv?nv[e]:e;if(t==="GlorotNormal")return new mp;if(t==="GlorotUniform")return new pp;if(t==="HeNormal")return new fp;if(t==="HeUniform")return new gp;if(t==="LeCunNormal")return new yp;if(t==="LeCunUniform")return new bp;{const n={};return n.className=t,n.config={},sv(n)}}else return e instanceof Us?e:sv(e)}function iz(){return new tw}function rz(){return new dp}function oz(e){return new nw(e)}function az(e){return new sw(e)}function cz(e){return new iw(e)}function lz(e){return new rw(e)}function hz(e){return new ow(e)}function uz(e){return new Kn(e)}function dz(e){return new pp(e)}function pz(e){return new mp(e)}function mz(e){return new fp(e)}function fz(e){return new gp(e)}function gz(e){return new yp(e)}function yz(e){return new bp(e)}function bz(e){return new aw(e)}var wz=Object.freeze({__proto__:null,zeros:iz,ones:rz,constant:oz,randomUniform:az,randomNormal:cz,truncatedNormal:lz,identity:hz,varianceScaling:uz,glorotUniform:dz,glorotNormal:pz,heNormal:mz,heUniform:fz,leCunNormal:gz,leCunUniform:yz,orthogonal:bz});let Lz=0;function iv(){return Lz++}const wp={};function Lp(e=""){return e in wp||(wp[e]=0),wp[e]+=1,e+wp[e].toString()}function cw(e){return Array.isArray(e)&&Array.isArray(e[0])}function Sp(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function je(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new j(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function 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e.write(be(e.read(),t))}function gQ(e,t){return e.write(Ce(e.read(),t))}function lw(e){return e.map(t=>t.read())}function hw(e){e.forEach(t=>{const n=t[0];n.write(t[1])})}function yQ(e,t){const n=t.map(i=>i.read()),s=ob(e,n);return t.map(i=>s.grads[i.name])}class mn{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}}class ri{constructor(e,t,n,s,i,o,a){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=i,this.outputTensorIndex=a,this.id=iv(),o!=null&&(this.originalName=KA(o),this.name=XA(this.originalName)),this.rank=t.length}}let Iz=0;class xp{constructor(e,t){this.callArgs=t,this.id=Iz++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(const n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){const e=[];for(const t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}}let xz=0;class lt extends go{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=xz++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){const n=this.getClassName();t=er(n)+"_"+Lp(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let i=null;e.batchSize!=null&&(i=e.batchSize),n=[i].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new ni(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new j(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return jn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return jn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Qi(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new Qi(`Layer ${this.name} is not connected, no input to return.`);return jn(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new Qi(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new Qi(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return jn(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=Nt(e),this.inputSpec==null||this.inputSpec.length===0)return;const t=Nt(this.inputSpec);if(e.length!==t.length)throw new j(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){const s=e[n],i=t[n];if(i==null)continue;const o=s.rank;if(i.ndim!=null&&o!==i.ndim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${i.ndim}, found ndim=${o}`);if(i.maxNDim!=null&&o>i.maxNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${i.maxNDim}, found ndim=${o}`);if(i.minNDim!=null&&o<i.minNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${i.minNDim}, found ndim=${o}.`);if(i.dtype!=null&&s.dtype!==i.dtype)throw new j(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${i.dtype}, found dtype=${s.dtype}.`);if(i.axes){const a=s.shape;for(const c in i.axes){const h=Number(c),d=i.axes[c],m=h>=0?a[h]:a[a.length+h];if(d!=null&&[d,null].indexOf(m)===-1)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected axis ${h} of input shape to have value ${d} but got shape ${a}.`)}}if(i.shape!=null)for(let a=0;a<i.shape.length;++a){const c=i.shape[a],h=s.shape[a];if(c!=null&&h!=null&&c!==h)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected shape=${i.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();const n=Nt(e);let s=!0;for(const o of n)if(!(o instanceof ri)){s=!1;break}let i=!0;for(const o of n)if(o instanceof ri){i=!1;break}if(s===i)throw new j("Arguments to apply() must be all SymbolicTensors or all Tensors");return Do(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);const o=[];for(const a of Nt(e))o.push(a.shape);this.build(jn(o)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&i&&(this._refCount=1)}if(this.assertInputCompatibility(e),i){let o=this.call(e,t);const a=Nt(o),c=[];for(let h of a)n.indexOf(h)!==-1&&(h=h.clone()),c.push(h);if(o=jn(c),this.activityRegularizer!=null)throw new ze("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}else{const o=Tz(e),a=this.computeOutputShape(o);let c;const h=Az(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?o[0]:o),a!=null&&a.length>0&&Array.isArray(a[0])?c=a.map((d,m)=>new ri(h,d,this,Nt(e),t,this.name,m)):c=new ri(h,a,this,Nt(e),t,this.name),this.addInboundNode(e,c,null,null,o,a,t),this._refCount++,this.activityRegularizer!=null)throw new ze("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return c}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape==null)return;if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Qi(`The layer ${this.name} has never been called and thus has no defined output shape.`);const e=[];for(const t of this.inboundNodes){const n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){const t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Qi(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new ni(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Ip(this.weights)}build(e){this.built=!0}getWeights(e=!1){return lw(e?this.trainableWeights:this.weights)}setWeights(e){ee(()=>{const t=this.weights;if(t.length!==e.length)throw new j(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;const n=[],s=lw(t);for(let i=0;i<s.length;++i){const o=s[i],a=t[i],c=e[i];if(!ot(o.shape,c.shape))throw new j(`Layer weight shape ${o.shape} not compatible with provided weight shape ${c.shape}`);n.push([a,c])}hw(n)})}addWeight(e,t,n,s,i,o,a){if(this._addedWeightNames.indexOf(e)!==-1)throw new j(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=Ft("zeros"));const c=s.apply(t,n),h=new ii(c,n,e,o,a);return c.dispose(),i!=null&&this.addLoss(()=>i.apply(h.read())),o==null&&(o=!0),o?this._trainableWeights.push(h):this._nonTrainableWeights.push(h),h}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){if(e==null||Array.isArray(e)&&e.length===0)return;e=Nt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e)}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,i,o,a=null){const c=Nt(e);t=Nt(t),n=Nt(n),s=Nt(s),i=Sp(i),o=Sp(o);const h=[],d=[],m=[];for(const y of c)h.push(y.sourceLayer),d.push(y.nodeIndex),m.push(y.tensorIndex);new xp({outboundLayer:this,inboundLayers:h,nodeIndices:d,tensorIndices:m,inputTensors:c,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:i,outputShapes:o},a);for(let y=0;y<t.length;y++)t[y].sourceLayer=this,t[y].nodeIndex=this.inboundNodes.length-1,t[y].tensorIndex=y}getConfig(){const e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount===0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}}function Tz(e){e=Nt(e);const t=[];for(const n of e)t.push(n.shape);return jn(t)}function Az(e){return"float32"}function ov(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{const s=t.inboundNodes[n];if(s.inboundLayers.length===0)return s.inputTensors;{const i=[];for(let o=0;o<s.inboundLayers.length;o++){const a=s.inputTensors[o],c=s.inboundLayers[o],h=s.nodeIndices[o],d=ov(a,c,h);for(const m of d)i.indexOf(m)===-1&&i.push(m)}return i}}}class Ya extends lt{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Lp("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new j("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new j("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new j("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");const n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];const s=new ri(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new xp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new j(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}}Ya.className="InputLayer",me(Ya);function av(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(e.batchShape!=null&&e.shape!=null)throw new j("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=e.batchShape;e.shape!=null&&t==null&&(t=[null].concat(e.shape));let n=e.dtype;n==null&&(n="float32");const s=new Ya({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}),i=s.inboundNodes[0].outputTensors;return i[0]}async function Mr(e){if(e==null)return;const t=[],n=[],s=[];for(const i in e){const o=e[i];if(typeof o!="number"){const a=o;t.push(a.data()),n.push(i),s.push(a)}}if(t.length>0){const i=await Promise.all(t);for(let o=0;o<i.length;++o)e[n[o]]=i[o][0];He(s)}}function cv(e){if(e==null)return;for(const t in e){const n=e[t];typeof n!="number"&&n.dispose()}}var lv;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(lv||(lv={}));const vz=125;class Ha{constructor(){this.validationData=null}setParams(e){this.params=e}async onEpochBegin(e,t){}async onEpochEnd(e,t){}async onBatchBegin(e,t){}async onBatchEnd(e,t){}async onTrainBegin(e){}async onTrainEnd(e){}setModel(e){}}class hv{constructor(e,t=10){e==null&&(e=[]),this.callbacks=e,this.queueLength=t}append(e){this.callbacks.push(e)}setParams(e){for(const t of this.callbacks)t.setParams(e)}setModel(e){for(const t of this.callbacks)t.setModel(e)}async onEpochBegin(e,t){t==null&&(t={});for(const n of this.callbacks)await n.onEpochBegin(e,t)}async onEpochEnd(e,t){t==null&&(t={});for(const n of this.callbacks)await n.onEpochEnd(e,t)}async onBatchBegin(e,t){t==null&&(t={});for(const n of this.callbacks)await n.onBatchBegin(e,t)}async onBatchEnd(e,t){t==null&&(t={});for(const n of this.callbacks)await n.onBatchEnd(e,t)}async onTrainBegin(e){e==null&&(e={});for(const t of this.callbacks)await t.onTrainBegin(e)}async onTrainEnd(e){e==null&&(e={});for(const t of this.callbacks)await t.onTrainEnd(e)}}class Nz extends Ha{constructor(){super()}async onEpochBegin(e){this.seen=0,this.totals={}}async onBatchEnd(e,t){t==null&&(t={});const n=t.size==null?0:t.size;this.seen+=n;for(const s in t){const i=t[s];if(typeof i=="number")this.totals.hasOwnProperty(s)||(this.totals[s]=0),this.totals[s]=this.totals[s]+i*n;else{let o;s in this.totals?o=this.totals[s]:this.totals[s]=0;const a=ee(()=>be(this.totals[s],X(i,n)));this.totals[s]=a,o!=null&&o.dispose()}}}async onEpochEnd(e,t){if(t!=null)for(const n of this.params.metrics){if(this.totals[n]==null)continue;typeof this.totals[n]=="number"?t[n]=this.totals[n]/this.seen:ee(()=>{const s=X(_e(1,this.seen),this.totals[n]);t[n]=s,this.totals[n].dispose(),Nn(t[n])})}}}class uv extends Ha{async onTrainBegin(e){this.epoch=[],this.history={}}async onEpochEnd(e,t){t==null&&(t={}),this.epoch.push(e);for(const n in t)this.history[n]==null&&(this.history[n]=[]),this.history[n].push(t[n])}async syncData(){const e=[],t=[],n=[];for(const i in this.history){const o=this.history[i];for(let a=0;a<o.length;++a)if(typeof o[a]!="number"){const c=o[a];e.push(c.data()),t.push(i),n.push(a)}}const s=await Promise.all(e);for(let i=0;i<s.length;++i){const o=this.history[t[i]][n[i]];o.dispose(),this.history[t[i]][n[i]]=s[i][0]}}}class dv extends Ha{constructor(e,t){super();if(this.currentEpoch=0,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=vz),this.yieldEvery==="never"&&e.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. 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s=0;s<e.sourceLayer.inboundNodes.length;++s)for(const i of e.sourceLayer.inboundNodes[s].outputTensors)if(i.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}class ki extends lt{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){const N=this.getClassName().toLowerCase();this.name=Lp(N)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],$r(this.inputs).length!==this.inputs.length)throw new j(`The list of inputs passed to the model is redundant. 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Found: ${this.outputs.map(N=>N.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(const N of this.outputs){const E=N.sourceLayer,D=N.nodeIndex,F=N.tensorIndex;this.outputLayers.push(E),this.outputLayersNodeIndices.push(D),this.outputLayersTensorIndices.push(F)}for(const N of this.inputs){const E=N.sourceLayer,D=N.nodeIndex,F=N.tensorIndex;xs(D===0,"input layer has >1 nodes"),xs(F===0,"input layer has >1 tensors"),this.inputLayers.push(E),this.inputLayersNodeIndices.push(D),this.inputLayersTensorIndices.push(F)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let N=0;N<this.inputLayers.length;N++){const E=this.inputLayers[N];if(!(E instanceof Ya))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${N} (0-based) originates from layer type ${E.getClassName()}.`);this.inputNames.push(E.name),this.feedInputShapes.push(E.batchInputShape),this.feedInputNames.push(E.name)}for(const N of this.outputLayers)this.outputNames.push(N.name);this.internalInputShapes=this.inputs.map(N=>N.shape),this.internalOutputShapes=this.outputs.map(N=>N.shape);const t={},n={},s={},i={},o={},a=[],c=(N,E,D,F,_,B)=>{(F==null||_==null||B==null)&&(F=N.sourceLayer,_=N.nodeIndex,B=N.tensorIndex);const $=F.inboundNodes[_];if(D.indexOf($)!==-1)throw new ni(`The tensor ${N.name} at layer "${F.name}" is part of a cycle.`);if(E.indexOf($)!==-1)return;this.containerNodes.add(ki.nodeKey(F,_)),F.id in o||(o[F.id]=Object.keys(o).length),D.indexOf($)===-1&&D.push($);const H=$.inboundLayers.length;for(let q=0;q<H;q++){const J=$.inputTensors[q],re=$.inboundLayers[q],ce=$.nodeIndices[q],ue=$.tensorIndices[q];c(J,E,D,re,ce,ue)}for(E.push($);D.indexOf($)>=0;)D.splice(D.indexOf($),1);a.push($)},h=[],d=[];for(const N of this.outputs)c(N,h,d);const m=a.slice().reverse();for(const N of m){n[N.id]=N,N.id in t||(t[N.id]=0);let E=t[N.id];const D=s[N.outboundLayer.id]==null?0:s[N.outboundLayer.id];E=Math.max(E,D),s[N.outboundLayer.id]=E,i[N.outboundLayer.id]=N.outboundLayer,t[N.id]=E;for(let F=0;F<N.inboundLayers.length;F++){const _=N.inboundLayers[F],B=N.nodeIndices[F],$=_.inboundNodes[B],H=t[$.id]==null?0:t[$.id];t[$.id]=Math.max(E+1,H),n[$.id]=$}}const y={};for(const N in t){const E=t[N];E in y||(y[E]=[]),y[E].push(n[N])}const b={};for(const N in s){const E=s[N];E in b||(b[E]=[]),b[E].push(i[N])}let w=Object.keys(b).map(N=>parseInt(N,10)).sort(cp);this.layers=[];for(const N of w){const E=b[N];E.sort((D,F)=>{const _=o[D.id],B=o[F.id];return _<B?-1:_>B?1:0});for(const D of E)D instanceof ki&&this.internalContainerRefs.push(D),this.layers.push(D)}this.layersByDepth=b,w=Object.keys(y).map(N=>parseInt(N,10)).sort(cp);const L=this.inputs.slice(),T=[];for(const N of w)for(const E of y[N]){const D=E.outboundLayer;if(D!=null){for(const F of E.inputTensors)if(L.indexOf(F)===-1)throw new ni(`Graph disconnected: cannot obtain value for tensor ${F} at layer "${D.name}". The following previous layers were accessed without issue: ${T}`);for(const F of E.outputTensors)L.push(F);T.push(D.name)}}this.nodesByDepth=y;const A=this.layers.map(N=>N.name);for(const N of A){const E=A.filter(D=>D===N).length;if(E!==1)throw new ni(`The name "${N}" is used ${E} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(A))}this.outboundNodes=[],this.inboundNodes=[],new xp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(N=>null),outputMasks:this.outputs.map(N=>null),inputShapes:this.inputs.map(N=>N.shape),outputShapes:this.outputs.map(N=>N.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();const e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(const t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(const t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new j("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(const t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){const n={};let s=0;for(const o of this.layers)for(const a of o.weights){if(n[a.originalName]!=null)throw new j(`Duplicate weight name: ${a.originalName}`);n[a.originalName]=a,s++}const i=[];for(const o in e){let a=o;if(n[o]==null){const c=o.split("/"),h=c.slice(0,-2).concat([c[c.length-1]]);a=h.join("/")}if(n[a]!=null)i.push([n[a],e[o]]);else if(t)throw new j(`Provided weight data has no target variable: ${o}`);delete n[a]}if(t){const o=[];for(const a in n)o.push(a);if(o.length>0)throw new j(`${o.length} of ${s} weights are not set: ${o}`)}hw(i)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${Dp}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){const n=bw(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return ee(()=>{e=Nt(e);const n=new Fo;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Nh(this.outputs,n,t)})}computeMask(e,t){return ee(()=>{e=Nt(e);let n;return t==null?n=Oo(null,e.length):n=Nt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){const t=Sp(e);if(t.length!==this.inputLayers.length)throw new j(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);const n={};for(let a=0;a<t.length;a++){const c=this.inputLayers[a],h=t[a],d=c.name+"_0_0";n[d]=h}const s=Object.keys(this.nodesByDepth).map(a=>parseInt(a,10)).sort(cp);if(s.length>1)for(const a of s){const c=this.nodesByDepth[a];for(const h of c){const d=h.outboundLayer;if(this.inputLayers.map(L=>L.id).indexOf(d.id)!==-1)continue;const m=[];for(let L=0;L<h.inboundLayers.length;L++){const T=h.inboundLayers[L],A=h.nodeIndices[L],N=h.tensorIndices[L],E=`${T.name}_${A}_${N}`,D=n[E];m.push(D)}const y=d.computeOutputShape(jn(m)),b=Sp(y),w=d.inboundNodes.indexOf(h);for(let L=0;L<b.length;L++){const T=`${d.name}_${w}_${L}`;n[T]=b[L]}}}const i=[],o=[];for(let a=0;a<this.outputLayers.length;a++){const c=this.outputLayers[a],h=this.outputLayersNodeIndices[a],d=this.outputLayersTensorIndices[a],m=`${c.name}_${h}_${d}`;o.push(m)}for(let a=0;a<o.length;a++){const c=o[a];xs(c in n),i.push(n[c])}return jn(i)}runInternalGraph(e,t){t==null&&(t=Oo(null,e.length));const n={};for(let c=0;c<this.inputs.length;++c){const h=this.inputs[c],d=e[c],m=t[c];n[h.id]=[d,m]}const s=Object.keys(this.nodesByDepth).map(c=>parseInt(c,10)).sort(cp);for(const c of s){const h=this.nodesByDepth[c];for(const d of h){const m=d.outboundLayer,y=d.inputTensors,b=d.outputTensors,w=new Array;for(const L of y)L.id in n&&w.push(n[L.id]);if(w.length===y.length){let L={},T,A,N,E;if(d.callArgs!=null&&(L=d.callArgs),w.length===1){const[D,F]=w[0];L.mask==null&&(L.mask=F),N=Nt(m.call(D,L)),E=Nt(m.computeMask(D,F)),T=[D],A=[F]}else T=w.map(D=>D[0]),A=w.map(D=>D[1]),L.mask==null&&(L.mask=A),N=Nt(m.call(T,L)),E=Nt(m.computeMask(T,A));if(m.activityRegularizer)throw new ze("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let D=0;D<b.length;++D){const F=b[D],_=N[D],B=E[D];n[F.id]=[_,B]}}}}const i=[],o=[],a=[];for(const c of this.outputs){xs(c.id in n,`Could not compute output ${c.name} : ${c.id}`);const[h,d]=n[c.id];a.push(h.shape),i.push(h),o.push(d)}return[i,o,a]}buildNodeConversionMap(e){const t={};let n;for(const s of this.layers){n=s instanceof ki?1:0;for(let i=0;i<s.inboundNodes.length;i++){const o=ki.nodeKey(s,i);this.containerNodes.has(o)&&(t[o]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new j(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new j("Provide either a layer name or layer index");for(const n of this.layers)if(n.name===e)return n;throw new j(`No such layer: ${e}`)}calculateLosses(){return ee(()=>{const e=[];for(const t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){const s=ki.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){const e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(const o of this.layers){const a=o.getClassName(),c=o.getConfig(),h=[];for(let m=0;m<o.inboundNodes.length;m++){const y=o.inboundNodes[m],b=ki.nodeKey(o,m);let w={};if(this.containerNodes.has(b)){if(y.callArgs)try{JSON.stringify(y.callArgs),w=y.callArgs}catch(L){console.warn(`Layer ${o.name} was passed non-serializable keyword arguments: ${y.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),w={}}if(y.inboundLayers.length>0){const L=[];for(let T=0;T<y.inboundLayers.length;T++){const A=y.inboundLayers[T],N=y.nodeIndices[T],E=y.tensorIndices[T],D=ki.nodeKey(A,N);let F=t[D];F==null&&(F=0),L.push([A.name,F,E,w])}h.push(L)}}}const d={};d.name=o.name,d.className=a,d.config=c,d.inboundNodes=h,n.push(d)}e.layers=n;const s=[];for(let o=0;o<this.inputLayers.length;o++){const a=this.inputLayers[o],c=this.inputLayersNodeIndices[o],h=ki.nodeKey(a,c);if(!this.containerNodes.has(h))continue;let d=t[h];d==null&&(d=0);const m=this.inputLayersTensorIndices[o];s.push([a.name,d,m])}e.inputLayers=s;const i=[];for(let o=0;o<this.outputLayers.length;o++){const a=this.outputLayers[o],c=this.outputLayersNodeIndices[o],h=ki.nodeKey(a,c);if(!this.containerNodes.has(h))continue;let d=t[h];d==null&&(d=0);const m=this.outputLayersTensorIndices[o];i.push([a.name,d,m])}return e.outputLayers=i,e}static fromConfig(e,t,n={},s=!1){const i={},o={};function a(T,A){T.name in o?o[T.name].push(A):o[T.name]=[A]}function c(T,A){const N=[];let E;for(const D of A){const F=D[0],_=D[1],B=D[2];if(E=D[3]==null?{}:D[3],!(F in i)){a(T,A);return}const $=i[F];if($.inboundNodes.length<=_){a(T,A);return}const H=$.inboundNodes[_];N.push(H.outputTensors[B])}N.length>0&&T.apply(jn(N),E)}function h(T){const A=T.name,N=oi(T,t.customObjects!=null?t.customObjects:{});N.setFastWeightInitDuringBuild(s),i[A]=N;const E=T.inboundNodes;E.forEach(D=>{if(!(D instanceof Array))throw new j(`Corrupted configuration, expected array for nodeData: ${D}`);a(N,D)})}const d=t.name,m=t.layers;for(const T of m)h(T);for(;!EP(o);)for(const T of m){const A=i[T.name];if(A.name in o){const N=o[A.name];delete o[A.name];for(const E of N)c(A,E)}}const y=[],b=[],w=t.inputLayers;for(const T of w){const A=T[0],N=T[1],E=T[2];xs(A in i);const D=i[A],F=D.inboundNodes[N].outputTensors;y.push(F[E])}const L=t.outputLayers;for(const T of L){const A=T[0],N=T[1],E=T[2];xs(A in i);const D=i[A],F=D.inboundNodes[N].outputTensors;b.push(F[E])}return new e({inputs:y,outputs:b,name:d})}get stateful(){if(this._stateful)throw new j("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(const e of this.layers)if(e.stateful)return!0;return!1}resetStates(){ee(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}}function Av(e,t,n){const s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(i=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){const i=[];return t.forEach(o=>{o in e?i.push(e[o]):i.push(null)}),i}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function vv(e,t){return Av(e,t,"classWeight")}function EQ(e,t){return Av(e,t,"sampleWeight")}async function Nv(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){const i=ee(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){const c=1;return e.argMax(c)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),o=Array.from(await i.data());He(i);const a=[];return o.forEach(c=>{if(n[c]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${c} exists in the data but not in classWeight`);a.push(n[c])}),ns(a,"float32")}else return null}function t3(e,t){return X(e,t)}const n3=32;function Cv(e,t){let n,s;const i=t;n=i.xs,s=i.ys,k(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);const o=Rv("input",e.inputNames,n),a=Rv("output",e.outputNames,s),c=o[0].shape[0];k(o.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${o.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),k(a.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${a.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let h=0;h<o.length;h++)k(o[h].shape[0]===c,()=>`Batch size mismatch: input ${e.inputNames[h]} has ${o[h].shape[0]}; expected  ${c} based on input ${e.inputNames[0]}.`);for(let h=0;h<a.length;h++)k(a[h].shape[0]===c,()=>`Batch size mismatch: output ${e.outputNames[h]} has ${a[h].shape[0]}; expected  ${c} based on input ${e.inputNames[0]}.`);return{xs:o,ys:a}}function Rv(e,t,n){if(n instanceof Q)return[n];if(Array.isArray(n))return k(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{const s=[];for(const i of t){if(n[i]==null)throw new j(`The feature data generated by the dataset lacks the required ${e} key '${i}'.`);s.push(n[i])}return s}}function s3(e){if(e.length===3)throw new ze("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function i3(e,t,n){const s=n.batchesPerEpoch!=null;if(k(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),k(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),k(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{const i=n.validationData!=null;let o,a;if(i)if(Ov(n.validationData))k(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{const A=s3(n.validationData);o=A.xs,a=A.ys}const c=e.makeTrainFunction(),h=e.getDedupedMetricsNames();let d;i?d=h.slice().concat(h.map(A=>"val_"+A)):d=h.slice();const m=pv(n.callbacks,n.yieldEvery),y=n.verbose==null?1:n.verbose,{callbackList:b,history:w}=mv(m,y,n.epochs,null,null,r3(t,n),null,i,d);b.setModel(e),e.history=w,await b.onTrainBegin(),e.stopTraining_=!1;let L=n.initialEpoch==null?0:n.initialEpoch,T=await t.iterator();for(;L<n.epochs;){const A={};await b.onEpochBegin(L);let N=0,E=0;for(s||(T=await t.iterator());s?N<n.batchesPerEpoch:!0;){const D=await T.next();if(s&&D.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${N} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(D.value!=null){const{xs:F,ys:_}=Cv(e,D.value),B={};B.batch=E,B.size=F[0].shape[0],await b.onBatchBegin(E,B);const $=[];if(n.classWeight!=null){const J=vv(n.classWeight,e.outputNames);for(let re=0;re<J.length;++re)$.push(await Nv(_[re],null,J[re]))}const H=F.concat(_).concat($),q=c(H);He(H);for(let J=0;J<h.length;++J){const re=h[J],ce=q[J];B[re]=ce,Nn(ce)}await b.onBatchEnd(E,B),cv(B),E++,N++}if(s?N>=n.batchesPerEpoch:D.done){if(i){let F;Ov(n.validationData)?F=Nt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):F=Nt(e.evaluate(o,a,{batchSize:n.validationBatchSize==null?n3:n.validationBatchSize,verbose:0}));for(let _=0;_<e.metricsNames.length;++_)A[`val_${e.metricsNames[_]}`]=F[_]}break}if(e.stopTraining_)break}if(await b.onEpochEnd(L,A),L++,e.stopTraining_)break}return await b.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function r3(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function Ov(e){return typeof e.iterator=="function"}function o3(e){return typeof e.next=="function"}async function a3(e,t,n){n=n||{};const s=n.batches!=null,i=e.testFunction;let o=[];if(n.verbose>0)throw new ze("Verbose mode is not implemented yet.");k(!s||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);const a=o3(t)?t:await t.iterator();let c=0,h=0;for(;s?h<n.batches:!0;){const d=await a.next();if(o=ee(()=>{if(d.value){const{xs:m,ys:y}=Cv(e,d.value),b=m.concat(y),w=ee(()=>i(b));if(He(b),h===0)for(let T=0;T<w.length;++T)o.push(Ne(0));const L=b[0].shape[0];for(let T=0;T<w.length;++T){const A=w[T],N=o[T];o[T]=ee(()=>be(o[T],X(L,A))),h>0&&He(N)}He(w),c+=L,++h}return o}),d.done){s&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). 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Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,i=!0,o){const[a,c]=this.standardizeUserDataXY(e,t,i,o);if(n!=null)throw new Error("sample weight is not supported yet.");let h=null;if(s!=null){const d=vv(s,this.outputNames);h=[];for(let m=0;m<d.length;++m)h.push(await Nv(c[m],null,d[m]))}return[a,c,h]}testLoop(e,t,n,s=0,i){return ee(()=>{const o=this.checkNumSamples(t,n,i,"steps"),a=[];if(s>0)throw new ze("Verbose mode is not implemented yet.");if(i!=null)throw new ze("steps mode in testLoop() is not implemented yet");{const c=Iw(o,n),h=ns(si(0,o));for(let d=0;d<c.length;++d){const m=c[d][0],y=c[d][1],b=ko(h,m,y-m),w=Sw(t,b),L=e(w);if(d===0)for(let T=0;T<L.length;++T)a.push(Ne(0));for(let T=0;T<L.length;++T){const A=L[T];a[T]=be(a[T],X(y-m,A))}}for(let d=0;d<a.length;++d)a[d]=_e(a[d],o)}return a})}getDedupedMetricsNames(){const e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){const s=e[n];let i=s;if(zA(e,s)>1){const o=zA(e.slice(0,n),s);i+=`_${o}`}t.push(i)}return t}makeTrainFunction(){return e=>{const t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),i=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),o=[],a=()=>{const m=[];for(let L=0;L<this.inputs.length;++L)m.push({key:this.inputs[L],value:n[L]});const y=new Fo(m),b=Nh(this.outputs,y,{training:!0});let w;for(let L=0;L<this.lossFunctions.length;++L){const T=this.lossFunctions[L];let A=T(s[L],b[L]);i[L]!=null&&(A=t3(A,i[L]));const N=zt(A);t.push(N),L===0?w=A:w=be(w,A)}for(let L=0;L<this.metricsTensors.length;++L){let T;if(this.outputs.length>1&&L<this.outputs.length)T=t[L];else{const A=this.metricsTensors[L][0],N=this.metricsTensors[L][1];T=zt(A(s[N],b[N]))}Nn(T),o.push(T)}return w=zt(w),this.calculateLosses().forEach(L=>{w=be(w,L)}),w},c=this.collectedTrainableWeights.map(m=>m.read()),h=!0,d=this.optimizer_.minimize(a,h,c);return[d].concat(o)}}makeTestFunction(){this.testFunction=e=>ee(()=>{const t=[];let n;const s=e.slice(0,this.inputs.length),i=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),o=[];for(let h=0;h<this.inputs.length;++h)o.push({key:this.inputs[h],value:s[h]});const a=new Fo(o),c=Nh(this.outputs,a);for(let h=0;h<this.lossFunctions.length;++h){const d=this.lossFunctions[h],m=zt(d(i[h],c[h]));h===0?n=m:n=be(n,m),t.push(n)}for(let h=0;h<this.metricsTensors.length;++h){const d=this.metricsTensors[h][0],m=this.metricsTensors[h][1],y=zt(d(i[m],c[m]));t.push(y)}return t})}async fit(e,t,n={}){return l3(this,e,t,n)}async fitDataset(e,t){return i3(this,e,t)}async trainOnBatch(e,t){const n=await this.standardizeUserData(e,t),s=n[0],i=n[1],o=this.makeTrainFunction(),a=o(s.concat(i)),c=[];for(const h of a){const d=await h.data();c.push(d[0])}return He(a),jn(c)}getNamedWeights(e){const t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,i=this.getWeights(n);for(let o=0;o<s.length;++o){if(n&&!s[o].trainable)continue;t.push({name:s[o].originalName,tensor:i[o]})}return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){const e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){const t=Sd().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Sd().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=er(this.loss);else if(Array.isArray(this.loss)){for(const t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>er(t))}else{const t=Object.keys(this.loss);e={};const n=this.loss;for(const s of t)if(typeof n[s]=="string")e[s]=er(n[s]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[er(Op(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>er(Op(e)));{const e={};for(const t in this.metrics)e[t]=er(Op(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");const t=vh(e.optimizer_config),n=oi(t);let s;if(typeof e.loss=="string")s=Eo(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(o=>Eo(o));else if(e.loss!=null){s={};for(const o in e.loss)s[o]=Eo(e.loss[o])}let i;if(Array.isArray(e.metrics))i=e.metrics.map(o=>Eo(o));else if(e.metrics!=null){i={};for(const o in e.metrics)i[o]=Eo(e.metrics[o])}this.compile({loss:s,metrics:i,optimizer:n})}async save(e,t){if(typeof e=="string"){const h=wy(e);if(h.length===0)throw new j(`Cannot find any save handlers for URL '${e}'`);if(h.length>1)throw new j(`Found more than one (${h.length}) save handlers for URL '${e}'`);e=h[0]}if(e.save==null)throw new j("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const n=await yy(this.getNamedWeights(t)),s=!1,i=null,o=this.toJSON(i,s),a={modelTopology:o,format:m3,generatedBy:`TensorFlow.js tfjs-layers v${Dp}`,convertedBy:null},c=t==null?!1:t.includeOptimizer;if(c&&this.optimizer!=null){a.trainingConfig=this.getTrainingConfig();const h="optimizer",{data:d,specs:m}=await yy(await this.optimizer.getWeights(),h);n.specs.push(...m),n.data=dd([n.data,d])}if(this.userDefinedMetadata!=null){const h=!0;Sv(this.userDefinedMetadata,this.name,h),a.userDefinedMetadata=this.userDefinedMetadata}return a.weightData=n.data,a.weightSpecs=n.specs,e.save(a)}setUserDefinedMetadata(e){Sv(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}}nr.className="Model",me(nr);class _v extends nr{}_v.className="Functional",me(_v);async function f3(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);const s=vh(n),i=oi(s,t);if(e.weightsManifest!=null){const o=await aT(e.weightsManifest,e.pathPrefix,i.weights.map(c=>c.originalName)),a={};for(const c of i.weights)a[c.originalName]=o[c.originalName];i.loadWeights(a),He(o)}return i}async function g3(e,t){if(t==null&&(t={}),typeof e=="string"){const n=Ly(e,t);if(n.length===0)n.push(fd(e,t));else if(n.length>1)throw new j(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return y3(e,void 0,t)}async function y3(e,t,n){if(n==null&&(n={}),e.load==null)throw new j("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const s=await e.load();let i=s.modelTopology;i.model_config!=null&&(i=i.model_config);const o=n.strict==null?!0:n.strict,a=s.weightData!=null&&s.weightSpecs!=null&&o,c=oi(vh(i),t,a),h=s.trainingConfig;if(h!=null&&c.loadTrainingConfig(h),s.userDefinedMetadata!=null&&c.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new j("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");const{modelWeights:d,optimizerWeights:m}=b3(s.weightData,s.weightSpecs);c.loadWeights(d,o),c.optimizer!=null&&m.length>0&&await c.optimizer.setWeights(m),He(d),He(m.map(y=>y.tensor))}return c}function b3(e,t){const n=ud(e,t),s={},i=[];return t.forEach(o=>{o.group==="optimizer"?i.push({name:o.name,tensor:n[o.name]}):s[o.name]=n[o.name]}),{modelWeights:s,optimizerWeights:i}}class ja extends nr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Lp("sequential_"),e.layers!=null)for(const t of e.layers)this.add(t)}checkShape(e){const t=e.inboundNodes[0].outputTensors[0].shape;if(t.some(n=>n<0))throw new j(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){const t=e instanceof ja||e instanceof nr;let n;if(t){if(n=e,n.outputs.length!==1)throw new j("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new j("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new j("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");const s=av({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new j(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new j("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=ov(this.outputs[0])}this.inboundNodes=[],new xp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Oo(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{const s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{const e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(St(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new nr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new ni("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new ni("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new ni("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new ni("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let i,o={};if(t instanceof Array){if(!(t[0].className!=null)||t[0].className==="Merge")throw new j("Legacy serialization format not supported yet.");i=t}else k(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),i=t.layers,delete t.layers,o=t;const a=new e(o);if(!(a instanceof ja))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(const c of i){const h=void 0,d=oi(c,h,s);s&&d.setFastWeightInitDuringBuild(!0),a.add(d)}return a}set stopTraining(e){if(this.model==null)throw new j("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new j("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){const e=[];for(const t of this.layers){const n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}}ja.className="Sequential",me(ja);function w3(e){return new nr(e)}function L3(e){return new ja(e)}function S3(e,t){return t==null&&(t={}),g3(e,t)}function Wv(e){return av(e)}function I3(e,t){Bs.registerCallbackConstructor(e,t)}class rs extends go{getConfig(){return{}}}class $v extends rs{apply(e,t=1){return XP(e,t)}}$v.className="elu",me($v);class Uv extends rs{apply(e){return Pd(e)}}Uv.className="selu",me(Uv);class Bv extends rs{apply(e){return Ri(e)}}Bv.className="relu",me(Bv);class Mv extends rs{apply(e){return ee(()=>Io(6,Ri(e)))}}Mv.className="relu6",me(Mv);class Pv extends rs{apply(e){return e}}Pv.className="linear",me(Pv);class zv extends rs{apply(e){return vi(e)}}zv.className="sigmoid",me(zv);class Gv extends rs{apply(e){return ZP(e)}}Gv.className="hardSigmoid",me(Gv);class Vv extends rs{apply(e){return Da(e)}}Vv.className="softplus",me(Vv);class Yv extends rs{apply(e){return JP(e)}}Yv.className="softsign",me(Yv);class Hv extends rs{apply(e){return Ca(e)}}Hv.className="tanh",me(Hv);class Tw extends rs{apply(e,t=-1){return No(e,t)}}Tw.className="softmax",me(Tw);class qv extends rs{apply(e,t=-1){return _d(e,t)}}qv.className="logSoftmax",me(qv);class jv extends rs{apply(e,t=1){return ee(()=>vi(e.mul(t)).mul(e))}}jv.className="swish",me(jv);function zr(e){return e.getClassName()}function Aw(e,t={}){return bh(e,ks.getMap().classNameMap,t,"activation")}function Gr(e){if(e==null){const t={};return t.className="linear",t.config={},Aw(t)}if(typeof e=="string"){const t={};return t.className=e,t.config={},Aw(t)}else return e instanceof rs?e:Aw(e)}function vw(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}class Kv extends go{}class Rh extends Kv{constructor(e){super();vw(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return ee(()=>{let t=ct([1]);return this.hasL1&&(t=be(t,Ue(X(this.l1,sn(e))))),this.hasL2&&(t=be(t,Ue(X(this.l2,xh(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}Rh.className="L1L2",me(Rh);function x3(e){return vw(e),new Rh({l1:e!=null?e.l1:null,l2:0})}function T3(e){return vw(e),new Rh({l2:e!=null?e.l2:null,l1:0})}const Xv={l1l2:"L1L2"};function It(e){return Gb(e)}function Jv(e,t={}){return bh(e,ks.getMap().classNameMap,t,"regularizer")}function _t(e){if(e==null)return null;if(typeof e=="string"){const t=e in Xv?Xv[e]:e,n={className:t,config:{}};return Jv(n)}else return e instanceof Kv?e:Jv(e)}class Nw extends lt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=je(e);let n=Ri(e);return this.maxValue!=null&&(n=Yn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}Nw.className="ReLU",me(Nw);class Cw extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=je(e);return Dd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}Cw.className="LeakyReLU",me(Cw);class Rw extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Ft(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=_t(e.alphaRegularizer),this.alphaConstraint=ln(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new j(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=St(e);const t=e.slice(1);if(this.sharedAxes!=null)for(const s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new mn({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=je(e),nh(e,this.alpha.read())}getConfig(){const e={alphaInitializer:Vt(this.alphaInitializer),alphaRegularizer:It(this.alphaRegularizer),alphaConstraint:cn(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}}Rw.className="PReLU",me(Rw);class Ow extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new ze(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=je(e);return So(n)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}Ow.className="ELU",me(Ow);class Ew extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){const n=je(e);return n.mul(Sh(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){const e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}}Ew.className="ThresholdedReLU",me(Ew);class Dw extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Tw().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){const n=je(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}Dw.className="Softmax",me(Dw);function Ka(e,t,n){if(typeof e=="number")return Oo(e,t);if(e.length!==t)throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){const i=e[s];if(!VP(i))throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${i}`)}return e}function ai(e,t,n,s,i=1){if(e==null)return e;const o=t+(t-1)*(i-1);let a;return n==="same"?a=e:a=e-o+1,Math.floor((a+s-1)/s)}function kp(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+Br([n-t,0]);else if(s==="same")e=e*t;else throw new j(`Unsupport padding mode: ${s}.`);return e}function kw(e,t){return ee(()=>(Gt(t),t==="channelsFirst"?Me(e,[0,2,3,1]):e))}function Zv(e,t){return ee(()=>(Gt(t),t==="channelsFirst"?Me(e,[0,2,3,4,1]):e))}function Qv(e,t,n,s=1,i="valid",o,a=1){return ee(()=>{if(o==null&&(o=ti()),Gt(o),e.shape.length!==3)throw new j(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new j(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new j(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(o==="channelsFirst"&&(e=Me(e,[0,2,1])),i==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let c=Nd(e,t,s,i==="same"?"same":"valid","NWC",a);return n!=null&&(c=Di(c,n)),c})}function DQ(e,t,n=1,s="valid",i,o=1){return ee(()=>(Gt(i),Qv(e,t,null,n,s,i,o)))}function kQ(e,t,n=[1,1],s="valid",i,o){return ee(()=>(Gt(i),Fw(e,t,null,n,s,i,o)))}function Fw(e,t,n,s=[1,1],i="valid",o,a,c=null){return ee(()=>{if(o==null&&(o=ti()),Gt(o),e.rank!==3&&e.rank!==4)throw new j(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new j(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let h=kw(e,o);if(i==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return h=vb({x:h,filter:t,strides:s,pad:i==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:n,activation:c}),o==="channelsFirst"&&(h=Me(h,[0,3,1,2])),h})}function FQ(e,t,n=[1,1,1],s="valid",i,o){return ee(()=>(Gt(i),eN(e,t,null,n,s,i,o)))}function eN(e,t,n,s=[1,1,1],i="valid",o,a){return ee(()=>{if(o==null&&(o=ti()),Gt(o),e.rank!==4&&e.rank!==5)throw new j(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new j(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let c=Zv(e,o);if(i==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return c=Zy(c,t,s,i==="same"?"same":"valid","NDHWC",a),n!=null&&(c=Di(c,n)),o==="channelsFirst"&&(c=Me(c,[0,4,1,2,3])),c})}class _w extends lt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",_w.verifyArgs(t),this.rank=e,pn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Ka(t.kernelSize,e,"kernelSize"),this.strides=Ka(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Ts(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Gt(this.dataFormat),this.activation=Gr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Ft(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=ln(t.biasConstraint),this.biasRegularizer=_t(t.biasRegularizer),this.activityRegularizer=_t(t.activityRegularizer),this.dilationRate=Ka(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new j(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new j(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new j(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(xs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Yb(e.kernelSize,"number",1,3))throw new j(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){const e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:zr(this.activation),useBias:this.useBias,biasInitializer:Vt(this.biasInitializer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),biasConstraint:cn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class Oh extends _w{constructor(e,t){super(e,t);this.kernel=null,Oh.verifyArgs(t),this.filters=t.filters,pn(this.filters,"filters"),this.kernelInitializer=Ft(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=ln(t.kernelConstraint),this.kernelRegularizer=_t(t.kernelRegularizer)}build(e){e=St(e);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[t]}`);const n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return ee(()=>{e=je(e);let n;const s=this.bias==null?null:this.bias.read(),i=VA(this.activation.getClassName());if(i!=null&&this.rank===2)n=Fw(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,i);else{if(this.rank===1)n=Qv(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Fw(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=eN(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=St(e);const t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let i=0;i<n.length;++i){const o=ai(n[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);t.push(o)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){const e={filters:this.filters,kernelInitializer:Vt(this.kernelInitializer),kernelRegularizer:It(this.kernelRegularizer),kernelConstraint:cn(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new j(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}}class Eh extends Oh{constructor(e){super(2,e);Eh.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Yb(e.kernelSize,"number",1,2))throw new j(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}Eh.className="Conv2D",me(Eh);class Fp extends Oh{constructor(e){super(3,e);Fp.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new j(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}Fp.className="Conv3D",me(Fp);class Ww extends Eh{constructor(e){super(e);if(this.inputSpec=[new mn({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new j(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=St(e),e.length!==4)throw new j("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new mn({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return ee(()=>{let n=je(e);if(n.shape.length!==4)throw new j(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);const s=n.shape,i=s[0];let o,a;this.dataFormat==="channelsFirst"?(o=2,a=3):(o=1,a=2);const c=s[o],h=s[a],d=this.kernelSize[0],m=this.kernelSize[1],y=this.strides[0],b=this.strides[1],w=kp(c,y,d,this.padding),L=kp(h,b,m,this.padding),T=[i,w,L,this.filters];this.dataFormat!=="channelsLast"&&(n=Me(n,[0,2,3,1]));let A=Cd(n,this.kernel.read(),T,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=Me(A,[0,3,1,2])),this.bias!=null&&(A=Di(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=St(e);const t=e.slice();let n,s,i;this.dataFormat==="channelsFirst"?(n=1,s=2,i=3):(n=3,s=1,i=2);const o=this.kernelSize[0],a=this.kernelSize[1],c=this.strides[0],h=this.strides[1];return t[n]=this.filters,t[s]=kp(t[s],c,o,this.padding),t[i]=kp(t[i],h,a,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}Ww.className="Conv2DTranspose",me(Ww);class tN extends Oh{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new j("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new j("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new j(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Ft(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=_t(t.depthwiseRegularizer),this.depthwiseConstraint=ln(t.depthwiseConstraint),this.pointwiseInitializer=Ft(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=_t(t.pointwiseRegularizer),this.pointwiseConstraint=ln(t.pointwiseConstraint)}build(e){if(e=St(e),e.length<this.rank+2)throw new j(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);const n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),i=[];for(let a=0;a<this.rank;++a)i.push(1);i.push(n*this.depthMultiplier,this.filters);const o=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,o,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",i,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,o,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,o,this.biasConstraint):this.bias=null,this.inputSpec=[new mn({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return ee(()=>{e=je(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Me(e,[0,2,3,1])),n=fb(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Di(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Me(n,[0,3,1,2])),n})}getConfig(){const e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Vt(this.depthwiseInitializer),e.pointwiseInitializer=Vt(this.pointwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.pointwiseRegularizer=It(this.pointwiseRegularizer),e.depthwiseConstraint=cn(this.depthwiseConstraint),e.pointwiseConstraint=cn(this.pointwiseConstraint),e}}tN.className="SeparableConv";class $w extends tN{constructor(e){super(2,e)}}$w.className="SeparableConv2D",me($w);class _p extends Oh{constructor(e){super(1,e);_p.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){const e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Yb(e.kernelSize,"number",1,1))throw new j(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}_p.className="Conv1D",me(_p);class Uw extends lt{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return ee(()=>{if(e=je(e),this.dataFormat==="channelsLast"){const n=hp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return hp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{const n=hp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return hp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){const e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}Uw.className="Cropping2D",me(Uw);class Bw extends lt{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){const t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{const t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return ee(()=>{let n=je(e);const s=n.shape;if(this.dataFormat==="channelsFirst"){n=Me(n,[0,2,3,1]);const i=this.size[0]*s[2],o=this.size[1]*s[3],a=n.resizeNearestNeighbor([i,o]);return Me(a,[0,3,1,2])}else{const i=this.size[0]*s[1],o=this.size[1]*s[2];return n.resizeNearestNeighbor([i,o])}})}getConfig(){const e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}Bw.className="UpSampling2D",me(Bw);function A3(e,t,n=[1,1],s="valid",i,o){return ee(()=>{i==null&&(i=ti()),Gt(i);let a=kw(e,i);if(e.rank!==4)throw new j(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new j(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return a=wo(a,t,n,s==="same"?"same":"valid","NHWC",o),i==="channelsFirst"&&(a=Me(a,[0,3,1,2])),a})}class Mw extends _w{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Ft(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=ln(e.depthwiseConstraint),this.depthwiseRegularizer=_t(e.depthwiseRegularizer)}build(e){if(e=St(e),e.length<4)throw new j(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);const n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{e=je(e);let n=A3(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Di(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=St(e);const t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,i=ai(t,this.kernelSize[0],this.padding,this.strides[0]),o=ai(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,i,o]:[e[0],i,o,s]}getConfig(){const e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Vt(this.depthwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.depthwiseConstraint=cn(this.depthwiseRegularizer),e}}Mw.className="DepthwiseConv2D",me(Mw);function nN(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new j("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function i(o){return o==null||Array.isArray(o)?o:[o]}return t=i(t),n=i(n),{inputs:e,initialState:t,constants:n}}function sN(e,t,n,s=!1,i,o,a=!1,c=!1){return ee(()=>{const h=t.shape.length;if(h<3)throw new j(`Input should be at least 3D, but is ${h}D.`);const d=[1,0].concat(si(2,h));if(t=Me(t,d),o!=null)throw new ze("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),i!=null&&(i=i.asType("bool").asType("float32"),i.rank===h-1&&(i=Hn(i,-1)),i=Me(i,d)),s&&(t=Is(t,0),i!=null&&(i=Is(i,0)));const m=[];let y,b=n;const w=t.shape[0],L=Oi(t);let T;i!=null&&(T=Oi(i));for(let N=0;N<w;++N){const E=L[N],D=ee(()=>e(E,b));if(i==null)y=D[0],b=D[1];else{const F=ee(()=>{const _=T[N],B=On(_).sub(_),$=D[0].mul(_).add(b[0].mul(B)),H=b.map((q,J)=>D[1][J].mul(_).add(q.mul(B)));return{output:$,newStates:H}});y=F.output,b=F.newStates}c&&m.push(y)}let A;if(c){const N=1;A=is(m,N)}return[y,A,b]})}class Fi extends lt{constructor(e){super(e);let t;if(e.cell==null)throw new j("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Up({cells:e.cell}):t=e.cell,t.stateSize==null)throw new j("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new mn({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return si(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){cw(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);const n=t[0];let s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){const i=[];for(const o of t)i.push([e[0],o]);return[s].concat(i)}else return s}computeMask(e,t){return ee(()=>{Array.isArray(t)&&(t=t[0]);const n=this.returnSequences?t:null;if(this.returnState){const s=this.states.map(i=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){const t=null;if(this.numConstants!=null)throw new ze("Constants support is not implemented in RNN yet.");cw(e)&&(e=e[0]),e=e;const n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new mn({shape:[n,null,...s]});const i=[e[0]].concat(e.slice(2));if(t!=null)throw new ze("Constants support is not implemented in RNN yet.");this.cell.build(i);let o;if(Array.isArray(this.cell.stateSize)?o=this.cell.stateSize:o=[this.cell.stateSize],this.stateSpec!=null){if(!ot(this.stateSpec.map(a=>a.shape[a.shape.length-1]),o))throw new j(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=o.map(a=>new mn({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){ee(()=>{if(!this.stateful)throw new Qi("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape[0];if(n==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>ct([n,s])):this.states_=[ct([n,this.cell.stateSize])];else if(e==null)He(this.states_),this.keptStates!=null&&(He(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>ct([n,s])):this.states_[0]=ct([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):He(this.states_);for(let s=0;s<this.states_.length;++s){const i=e[s],o=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,a=[n,o];if(!ot(i.shape,a))throw new j(`State ${s} is incompatible with layer ${this.name}: expected shape=${a}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Nn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=nN(e,n,s,this.numConstants);e=i.inputs,n=i.initialState,s=i.constants;let o=[],a=[];if(n!=null){t.initialState=n,o=o.concat(n),this.stateSpec=[];for(const h of n)this.stateSpec.push(new mn({shape:h.shape}));a=a.concat(this.stateSpec)}s!=null&&(t.constants=s,o=o.concat(s),this.numConstants=s.length);const c=o[0]instanceof ri;if(c){const h=[e].concat(o),d=this.inputSpec.concat(a),m=this.inputSpec;this.inputSpec=d;const y=super.apply(h,t);return this.inputSpec=m,y}else return super.apply(e,t)}call(e,t){return ee(()=>{const n=t==null?null:t.mask,s=t==null?null:t.training;let i=t==null?null:t.initialState;e=je(e),i==null&&(this.stateful?i=this.states_:i=this.getInitialState(e));const o=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(i.length!==o)throw new j(`RNN Layer has ${o} state(s) but was passed ${i.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const a={training:s},c=(w,L)=>{const T=this.cell.call([w].concat(L),a);return[T[0],T.slice(1)]},h=sN(c,e,i,this.goBackwards,n,null,this.unroll,this.returnSequences),d=h[0],m=h[1],y=h[2];this.stateful&&this.resetStates(y,s);const b=this.returnSequences?m:d;return this.returnState?[b].concat(y):b})}getInitialState(e){return ee(()=>{let t=ct(e.shape);return t=Ue(t,[1,2]),t=Ih(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Qb(t,[1,n]):t):this.cell.stateSize>1?[Qb(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){const e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);const n=this.cell.getConfig();return this.getClassName()===Fi.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){const s=t.cell,i=oi(s,n);return new e(Object.assign(t,{cell:i}))}}Fi.className="RNN",me(Fi);class Xa extends lt{}class Wp extends Xa{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,pn(this.units,"units"),this.activation=Gr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=ln(e.kernelConstraint),this.recurrentConstraint=ln(e.recurrentConstraint),this.biasConstraint=ln(e.biasConstraint),this.dropout=Va([1,Br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Va([1,Br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=St(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{if(e=e,e.length!==2)throw new j(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];const s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Vr({ones:()=>On(e),rate:this.dropout,training:s})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Vr({ones:()=>On(n),rate:this.recurrentDropout,training:s}));let i;const o=this.dropoutMask,a=this.recurrentDropoutMask;o!=null?i=Ei(X(e,o),this.kernel.read()):i=Ei(e,this.kernel.read()),this.bias!=null&&(i=Di(i,this.bias.read())),a!=null&&(n=X(n,a));let c=be(i,Ei(n,this.recurrentKernel.read()));return this.activation!=null&&(c=this.activation.apply(c)),[c,c]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:zr(this.activation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),recurrentInitializer:Vt(this.recurrentInitializer),biasInitializer:Vt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:cn(this.kernelConstraint),recurrentConstraint:cn(this.recurrentConstraint),biasConstraint:cn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}}Wp.className="SimpleRNNCell",me(Wp);class Pw extends Fi{constructor(e){e.cell=new Wp(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return new e(t)}}Pw.className="SimpleRNN",me(Pw);class $p extends Xa{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new j("GRUCell does not support reset_after parameter set to true.");this.units=e.units,pn(this.units,"units"),this.activation=Gr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Gr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=ln(e.kernelConstraint),this.recurrentConstraint=ln(e.recurrentConstraint),this.biasConstraint=ln(e.biasConstraint),this.dropout=Va([1,Br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Va([1,Br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=St(e);const t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{if(e=e,e.length!==2)throw new j(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training==null?!1:t.training;let s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Vr({ones:()=>On(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Vr({ones:()=>On(s),rate:this.recurrentDropout,training:n,count:3}));const i=this.dropoutMask,o=this.recurrentDropoutMask;let a,c,h;0<this.dropout&&this.dropout<1&&(e=X(e,i[0]));let d=Ei(e,this.kernel.read());this.useBias&&(d=Di(d,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,o[0]));const m=this.recurrentKernel.read(),[y,b]=ss(m,[2*this.units,this.units],m.rank-1),w=Ei(s,y),[L,T,A]=ss(d,3,d.rank-1),[N,E]=ss(w,2,w.rank-1);a=this.recurrentActivation.apply(be(L,N)),c=this.recurrentActivation.apply(be(T,E));const D=Ei(X(c,s),b);h=this.activation.apply(be(A,D));const F=be(X(a,s),X(be(1,Pt(a)),h));return[F,F]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:zr(this.activation),recurrentActivation:zr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),recurrentInitializer:Vt(this.recurrentInitializer),biasInitializer:Vt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:cn(this.kernelConstraint),recurrentConstraint:cn(this.recurrentConstraint),biasConstraint:cn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}}$p.className="GRUCell",me($p);class zw extends Fi{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new $p(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}zw.className="GRU",me(zw);class Dh extends Xa{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,pn(this.units,"units"),this.activation=Gr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Gr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=ln(e.kernelConstraint),this.recurrentConstraint=ln(e.recurrentConstraint),this.biasConstraint=ln(e.biasConstraint),this.dropout=Va([1,Br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Va([1,Br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=St(e);const n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){const i=this.biasInitializer,o=this.units;s=new(t=class extends Us{apply(c,h){const d=i.apply([o]),m=new dp().apply([o]),y=i.apply([o*2]);return QA(QA(d,m),y)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return ee(()=>{const n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new j(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1];const i=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Vr({ones:()=>On(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Vr({ones:()=>On(s),rate:this.recurrentDropout,training:n,count:4}));const o=this.dropoutMask,a=this.recurrentDropoutMask;let c,h,d,m;0<this.dropout&&this.dropout<1&&(e=X(e,o[0]));let y=Ei(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,a[0])),y=be(y,Ei(s,this.recurrentKernel.read())),this.useBias&&(y=Di(y,this.bias.read()));const[b,w,L,T]=ss(y,4,y.rank-1);c=this.recurrentActivation.apply(b),h=this.recurrentActivation.apply(w),d=be(X(h,i),X(c,this.activation.apply(L))),m=this.recurrentActivation.apply(T);const A=X(m,this.activation.apply(d));return[A,A,d]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:zr(this.activation),recurrentActivation:zr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),recurrentInitializer:Vt(this.recurrentInitializer),biasInitializer:Vt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:cn(this.kernelConstraint),recurrentConstraint:cn(this.recurrentConstraint),biasConstraint:cn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}}Dh.className="LSTMCell",me(Dh);class Gw extends Fi{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Dh(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}Gw.className="LSTM",me(Gw);class Up extends Xa{constructor(e){super(e);this.cells=e.cells}get stateSize(){const e=[];for(const t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return ee(()=>{e=e;let n=e.slice(1);const s=[];for(const a of this.cells.slice().reverse())Array.isArray(a.stateSize)?s.push(n.splice(0,a.stateSize.length)):s.push(n.splice(0,1));s.reverse();const i=[];let o;for(let a=0;a<this.cells.length;++a){const c=this.cells[a];n=s[a],a===0?o=[e[0]].concat(n):o=[o[0]].concat(n),o=c.call(o,t),i.push(o.slice(1))}n=[];for(const a of i.slice().reverse())n.push(...a);return[o[0]].concat(n)})}build(e){cw(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Do(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){const e=super.getConfig(),t=i=>({className:i.getClassName(),config:i.getConfig()}),n=this.cells.map(t),s={cells:n};return Object.assign({},e,s)}static fromConfig(e,t,n={}){const s=[];for(const i of t.cells)s.push(oi(i,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];const e=[];for(const t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){const e=[];for(const t of this.cells)e.push(...t.weights);return lw(e)}setWeights(e){const t=[];for(const n of this.cells){const s=n.weights.length,i=e.splice(s);for(let o=0;o<n.weights.length;++o)t.push([n.weights[o],i[o]])}hw(t)}}Up.className="StackedRNNCells",me(Up);function Vr(e){const{ones:t,rate:n,training:s=!1,count:i=1}=e,o=()=>tv(t(),n),a=()=>Th(o,t,s);if(!i||i<=1)return Nn(a().clone());const c=Array(i).fill(void 0).map(a);return c.map(h=>Nn(h.clone()))}var v3=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,s=Object.getOwnPropertySymbols(e);i<s.length;i++)t.indexOf(s[i])<0&&Object.prototype.propertyIsEnumerable.call(e,s[i])&&(n[s[i]]=e[s[i]]);return n};class _Q extends Xa{}class iN extends Fi{constructor(e){if(e.unroll)throw new ze("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new ze("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new mn({ndim:5})]}call(e,t){return ee(()=>{if(this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new j("ConvRNN2D cell does not support constants");const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return ee(()=>{const{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),i=[s[0],...s.slice(2)],o=ct(i);return Array.isArray(t)?Array(t.length).fill(o):[o]})}resetStates(e,t=!1){ee(()=>{if(!this.stateful)throw new Qi("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),i=[s[0],...s.slice(2)],o=n[0];if(o==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ct(i)):this.states_=[ct(i)];else if(e==null)He(this.states_),this.keptStates!=null&&(He(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ct(i)):this.states_[0]=ct(i);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):He(this.states_);for(let a=0;a<this.states_.length;++a){const c=e[a],h=i;if(!ot(c.shape,h))throw new j(`State ${a} is incompatible with layer ${this.name}: expected shape=${h}, received shape=${c.shape}`);this.states_[a]=c}}this.states_=this.states_.map(a=>Nn(a.clone()))})}computeSingleOutputShape(e){const{dataFormat:t,filters:n,kernelSize:s,padding:i,strides:o,dilationRate:a}=this.cell,c=t==="channelsFirst",h=e[c?3:2],d=e[c?4:3],m=ai(h,s[0],i,o[0],a[0]),y=ai(d,s[1],i,o[1],a[1]),b=[...e.slice(0,2),...c?[n,m,y]:[m,y,n]];return b}}iN.className="ConvRNN2D";class Bp extends Dh{constructor(e){const{filters:t,kernelSize:n,strides:s,padding:i,dataFormat:o,dilationRate:a}=e;super(Object.assign({},e,{units:t}));this.filters=t,pn(this.filters,"filters"),this.kernelSize=Ka(n,2,"kernelSize"),this.kernelSize.forEach(c=>pn(c,"kernelSize")),this.strides=Ka(s||1,2,"strides"),this.strides.forEach(c=>pn(c,"strides")),this.padding=i||"valid",Ts(this.padding),this.dataFormat=o||"channelsLast",Gt(this.dataFormat),this.dilationRate=Ka(a||1,2,"dilationRate"),this.dilationRate.forEach(c=>pn(c,"dilationRate"))}build(e){var t;e=St(e);const n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[n]}`);const s=e[n],i=4,o=this.kernelSize.concat([s,this.filters*i]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const a=this.kernelSize.concat([this.filters,this.filters*i]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let c;if(this.unitForgetBias){const h=this.biasInitializer,d=this.filters;c=new(t=class extends Us{apply(y,b){const w=h.apply([d]),L=Qs([d]),T=h.apply([d*2]);return Zb([w,L,T])}},t.className="CustomInit",t)}else c=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*i],null,c,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return ee(()=>{if(e.length!==3)throw new j(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training||!1,s=e[0],i=e[1],o=e[2],a=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Vr({ones:()=>On(s),rate:this.dropout,training:n,count:a}));const c=this.dropoutMask,h=(Ie,Se,Ee)=>!Se||!Se[Ee]?Ie:X(Se[Ee],Ie);let d=h(s,c,0),m=h(s,c,1),y=h(s,c,2),b=h(s,c,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Vr({ones:()=>On(i),rate:this.recurrentDropout,training:n,count:a}));const w=this.recurrentDropoutMask;let L=h(i,w,0),T=h(i,w,1),A=h(i,w,2),N=h(i,w,3);const E=3,[D,F,_,B]=ss(this.kernel.read(),a,E),[$,H,q,J]=this.useBias?ss(this.bias.read(),a):[null,null,null,null];d=this.inputConv(d,D,$,this.padding),m=this.inputConv(m,F,H,this.padding),y=this.inputConv(y,_,q,this.padding),b=this.inputConv(b,B,J,this.padding);const[re,ce,ue,he]=ss(this.recurrentKernel.read(),a,E);L=this.recurrentConv(L,re),T=this.recurrentConv(T,ce),A=this.recurrentConv(A,ue),N=this.recurrentConv(N,he);const de=this.recurrentActivation.apply(be(d,L)),le=this.recurrentActivation.apply(be(m,T)),ye=be(X(le,o),X(de,this.activation.apply(be(y,A)))),pe=X(this.recurrentActivation.apply(be(b,N)),this.activation.apply(ye));return[pe,pe,ye]})}getConfig(){const e=super.getConfig(),{units:t}=e,n=v3(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){const i=ji(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Di(i,n,this.dataFormat):i}recurrentConv(e,t){const n=1;return ji(e,t,n,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}Bp.className="ConvLSTM2DCell",me(Bp);class Vw extends iN{constructor(e){const t=new Bp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}}Vw.className="ConvLSTM2D",me(Vw);class Mp extends lt{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;const t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=je(e);if(0<this.rate&&this.rate<1){const s=t.training==null?!1:t.training,i=this.getNoiseShape(n),o=Th(()=>tv(n,this.rate,i,this.seed),()=>n,s);return o}return e})}getConfig(){const e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}}Mp.className="Dropout",me(Mp);class Yw extends Mp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}Yw.className="SpatialDropout1D",me(Yw);class Hw extends lt{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,pn(this.units,"units"),this.activation=Gr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=ln(e.kernelConstraint),this.biasConstraint=ln(e.biasConstraint),this.kernelRegularizer=_t(e.kernelRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=St(e);const t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=St(e);const t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=je(e),s=VA(this.activation.getClassName());let i;return s!=null?i=Ei(n,this.kernel.read(),s,this.bias?this.bias.read():null):(i=Ei(n,this.kernel.read()),this.bias!=null&&(i=Di(i,this.bias.read())),this.activation!=null&&(i=this.activation.apply(i))),i})}getConfig(){const e={units:this.units,activation:zr(this.activation),useBias:this.useBias,kernelInitializer:Vt(this.kernelInitializer),biasInitializer:Vt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:cn(this.kernelConstraint),biasConstraint:cn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}Hw.className="Dense",me(Hw);class qw extends lt{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=St(e);for(const t of e.slice(1))if(t==null)throw new j(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Ur(e,1)]}call(e,t){return ee(()=>{this.invokeCallHook(e,t);let n=je(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){const s=[0];for(let i=2;i<n.rank;++i)s.push(i);s.push(1),n=n.transpose(s)}return KP(n)})}getConfig(){const e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}qw.className="Flatten",me(qw);class jw extends lt{constructor(e){super(e);this.supportsMasking=!0,this.activation=Gr(e.activation)}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=je(e);return this.activation.apply(n)})}getConfig(){const e={activation:zr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}jw.className="Activation",me(jw);class Kw extends lt{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return ee(()=>(e=je(e),qP(e,this.n)))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}Kw.className="RepeatVector",me(Kw);class Xw extends lt{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){const n="Total size of new array must be unchanged.",s=t.slice();let i=1,o=null;for(let c=0;c<s.length;++c){const h=s[c];if(this.isUnknown(h))if(o===null)o=c;else throw new j("Can only specifiy one unknown dimension.");else i*=h}const a=Ur(e);if(o!==null){if(i===0||a%i!==0)throw new j(n);s[o]=a/i}else if(a!==i)throw new j(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=je(e),s=n.shape,i=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return n.reshape(i)})}getConfig(){const e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}}Xw.className="Reshape",me(Xw);class Jw extends lt{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);const t=si(1,e.dims.length+1);if(!ot(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new mn({ndim:this.dims.length+1})]}computeOutputShape(e){e=St(e);const t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Me(je(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}Jw.className="Permute",me(Jw);class Zw extends lt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){const n=je(e),s=-1;return Pl(kr(n,this.maskValue),s)}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=je(e),s=-1,i=!0,o=Pl(kr(n,this.maskValue),s,i),a=n.mul(o.asType(n.dtype));return a})}}Zw.className="Masking",me(Zw);class Qw extends lt{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(Nt(e.inputLength))}this.inputDim=e.inputDim,pn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,pn(this.outputDim,"outputDim"),this.embeddingsInitializer=Ft(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=_t(e.embeddingsRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.embeddingsConstraint=ln(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return ee(()=>this.maskZero?(e=je(e),kr(e,Qe(e))):null)}computeOutputShape(e){if(e=St(e),this.inputLength==null)return[...e,this.outputDim];const t=Nt(this.inputLength);if(t.length!==e.length-1)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){const i=t[s],o=e[s+1];if(i!=null&&o!=null&&i!==o)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);i==null&&(t[n]=o),n++}}return[e[0],...t,this.outputDim]}call(e,t){return ee(()=>{this.invokeCallHook(e,t);let n=je(e);n.dtype!=="int32"&&(n=Sh(n,"int32"));const s=ev(this.embeddings.read(),n.as1D());return s.reshape(St(this.computeOutputShape(n.shape)))})}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Vt(this.embeddingsInitializer),embeddingsRegularizer:It(this.embeddingsRegularizer),activityRegularizer:It(this.activityRegularizer),embeddingsConstraint:cn(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}Qw.className="Embedding",me(Qw);class Wo extends lt{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new ze}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;const n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){const i=e[e.length-t.length+s],o=t[s];if(i==null||o==null||i<0||o<0)n.push(null);else if(i===1)n.push(o);else if(o===1)n.push(i);else{if(i!==o)throw new j("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(i)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[St(e)]),e=e,e.length<2)throw new j(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(const i of e)i!=null&&i[0]!==null&&t.push(i[0]);if(t=$r(t),t.length>1)throw new j(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let i=1;i<e.length;++i){const o=e[i]==null?null:e[i].slice(1);n=this.computeElementwiseOpOutputShape(n,o)}const s=e.map(i=>i.length);e.indexOf(null)===-1&&$r(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return ee(()=>{if(e=e,this.reshapeRequired){const n=[],s=e.map(i=>i.rank);if(s.indexOf(null)===-1){const i=Br(s);for(let o of e){const a=o.rank;for(let c=0;c<i-a;++c)o=Ih(o,1);n.push(o)}return this.mergeFunction(n)}else{let i=!1;for(const c of e){const h=c.rank;if(h==null){const d=c.shape,m=d[0],y=d.slice(1).concat([m]);let b=c.reshape([m].concat(Ur(d.slice(1))));b=Me(b,[1,0]),b=b.reshape(y),n.push(b),i=!0}else if(h>1){const d=si(1,h).concat([0]);n.push(Me(c,d)),i=!0}else n.push(c)}let o=this.mergeFunction(n);const a=o.rank;if(i){if(a==null){const c=o.shape,h=c.length,d=c[h-1],m=[d].concat(c.slice(0,c.length-1));o=Me(o.reshape([-1,d]),[1,0]).reshape(m)}else if(a>1){const c=[a-1].concat(si(0,a-1));o=Me(o,c)}}return o}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){const i=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,i)}let n=[];for(const s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=$r(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return ee(()=>{if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an Array");if(!Array.isArray(e))throw new j("`inputs` should be an Array");if(t.length!==e.length)throw new j(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Hn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ws(n,t[s]);return n})}}class kh extends Wo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return t})}}kh.className="Add",me(kh);function WQ(e){if(Array.isArray(e)){const t=new kh({});return t.apply(e)}else return new kh(e)}class Fh extends Wo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=X(t,e[n]);return t})}}Fh.className="Multiply",me(Fh);function $Q(e){if(Array.isArray(e)){const t=new Fh({});return t.apply(e)}else return new Fh(e)}class _h extends Wo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return X(1/e.length,t)})}}_h.className="Average",me(_h);function UQ(e){if(Array.isArray(e)){const t=new _h({});return t.apply(e)}else return new _h(e)}class Wh extends Wo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=_s(t,e[n]);return t})}}Wh.className="Maximum",me(Wh);function BQ(e){if(Array.isArray(e)){const t=new Wh({});return t.apply(e)}else return new Wh(e)}class $h extends Wo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Io(t,e[n]);return t})}}$h.className="Minimum",me($h);function MQ(e){if(Array.isArray(e)){const t=new $h({});return t.apply(e)}else return new $h(e)}class Uh extends Wo{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new j("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(const s of e)if(s!=null){t=!1;break}if(t)return;const n=[];for(let s=0;s<e.length;++s){const i=e[s].slice();i.splice(this.axis,1);let o=!1;for(const a of n)if(ot(a,i)){o=!0;break}o||n.push(i)}if(n.length>1)throw new j("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return ee(()=>Zb(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new j("A `Concatenate` layer should be called on a list of inputs.");const t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(const i of t.slice(1)){if(n[s]==null||i[s]==null){n[s]=null;break}n[s]+=i[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new j("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new j(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return ee(()=>{let n=!0;if(t.forEach(o=>{if(o!=null){n=!1;return}}),n)return null;const s=[];for(let o=0;o<e.length;++o)t[o]==null?s.push(On(e[o]).asType("bool")):t[o].rank<e[o].rank?s.push(Hn(t[o],-1)):s.push(t[o]);const i=Mt(s,this.axis);return xd(i,-1,!1)})}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}Uh.className="Concatenate",me(Uh);function PQ(e){if(Array.isArray(e)){const t=new Uh({});return t.apply(e)}else return new Uh(e)}function Bh(e,t){for(;e<0;)e+=t;return e}function N3(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new ze("batchDot is not implemented for tensors of 4D or higher rank yet");if(k(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new ze("batchDot is not implemented for complex64-type Tensors yet.");const s=e.shape.length,i=t.shape.length;n==null&&(n=[s-1,i-2]);const o=n;return ee(()=>{let a;if(s>i){a=s-i;const h=[];for(let d=0;d<a;++d)h.push(1);t=t.reshape(t.shape.concat(h))}else if(i>s){a=i-s;const h=[];for(let d=0;d<a;++d)h.push(1);e=e.reshape(e.shape.concat(h))}else a=0;let c;if(e.shape.length===2&&t.shape.length===2)o[0]===o[1]?c=e.mul(t).sum(o[0]):c=e.transpose([1,0]).mul(t).sum(o[1]);else{const h=o[0]!==e.shape.length-1,d=o[1]===t.shape.length-1;c=e.matMul(t,h,d)}if(a>0){let h;s>i?h=s+i-3:h=s-1;const d=[];for(let m=h;m<h+a;++m)d.push(m);c=c.squeeze(d)}return c.shape.length===1&&(c=c.expandDims(1)),c})}class eL extends Wo{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const t=e[0],n=e[1];if(t.length>3||n.length>3)throw new ze("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new j(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new j(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((i,o)=>Bh(i,e[o].shape.length)):s=[Bh(this.axes,t.shape.length),Bh(this.axes,n.shape.length)],this.normalize&&(t=Tp(t,s[0]),n=Tp(n,s[1])),N3(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Bh(this.axes,e.length),Bh(this.axes,t.length)],n}computeOutputShape(e){k(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new ze("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);const i=t.concat(n);return i.length===1&&i.push(1),i}computeMask(e,t){return null}getConfig(){const e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}}eL.className="Dot",me(eL);class tL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=je(e),s=()=>up(n.shape,0,this.stddev).add(n),i=Th(s,()=>n,t.training||!1);return i})}}tL.className="GaussianNoise",me(tL);class nL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=je(e);if(this.rate>0&&this.rate<1){const s=()=>{const i=Math.sqrt(this.rate/(1-this.rate));return n.mul(up(n.shape,1,i))};return Th(s,()=>n,t.training||!1)}return n})}}nL.className="GaussianDropout",me(nL);class sL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||je(e).shape}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return ee(()=>{if(this.rate<1&&this.rate>0){const n=this._getNoiseShape(e),s=()=>{const i=je(e),o=1.6732632423543772,a=1.0507009873554805,c=-o*a;let h=Ki(vo(n),this.rate);h=Sh(h,"float32");const d=((1-this.rate)*(1+this.rate*c**2))**-.5,m=-d*c*this.rate,y=i.mul(h).add(h.add(-1).mul(c));return y.mul(d).add(m)};return Th(s,()=>je(e),t.training||!1)}return e})}}sL.className="AlphaDropout",me(sL);function Mh(e,t,n,s,i,o=.001){let a;if(e.rank===2)a=CT(e,t,n,s,i,o);else if(e.rank===3)a=RT(e,t,n,s,i,o);else if(e.rank===4)a=OT(e,t,n,s,i,o);else throw new ze(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return a}function C3(e,t,n,s,i=.001){return ee(()=>{const o=Ud(e,s),a=o.mean,c=o.variance,h=Mh(e,a,c,n,t,i);return[h,a,c]})}function R3(e,t,n,s,i=.001){return ee(()=>{const o=Ud(e,s),a=o.mean,c=o.variance,h=[];for(const L of si(0,e.rank))s.indexOf(L)!==-1?h.push(1):h.push(e.shape[L]);const d=a.reshape(h),m=c.reshape(h),y=t==null?null:t.reshape(h),b=n==null?null:n.reshape(h),w=Mh(e,d,m,b,y,i);return[w,a,c]})}function O3(e,t,n,s,i=.001){return ot(s.slice().sort(),si(0,e.rank-1))?C3(e,t,n,s,i):R3(e,t,n,s,i)}class iL extends lt{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ft(e.betaInitializer||"zeros"),this.gammaInitializer=Ft(e.gammaInitializer||"ones"),this.movingMeanInitializer=Ft(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Ft(e.movingVarianceInitializer||"ones"),this.betaConstraint=ln(e.betaConstraint),this.gammaConstraint=ln(e.gammaConstraint),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer)}build(e){e=St(e);const t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new j(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new mn({ndim:e.length,axes:{[t]:n}})];const s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return ee(()=>{const n=t.training==null?!1:t.training,s=je(e),i=s.shape,o=i.length,a=si(0,o),c=this.axis>=0?this.axis:this.axis+o;a.splice(c,1);const h=Oo(1,o);h[c]=i[c];const d=a.slice();d.sort();const m=!ot(d,si(0,o).slice(0,o-1)),y=()=>{if(m){const N=this.movingMean.read().reshape(h),E=this.movingVariance.read().reshape(h),D=this.center?this.beta.read().reshape(h):null,F=this.scale?this.gamma.read().reshape(h):null;return Mh(s,N,E,D,F,this.epsilon)}else return Mh(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return y();const[b,w,L]=O3(s,this.gamma.read(),this.beta.read(),a,this.epsilon),T=(N,E,D)=>{ee(()=>{const F=1-D,_=N.read(),B=_.sub(E).mul(F);N.write(_.sub(B))})},A=()=>{T(this.movingMean,w,this.momentum),T(this.movingVariance,L,this.momentum)};return A(),b})}getConfig(){const e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Vt(this.betaInitializer),gammaInitializer:Vt(this.gammaInitializer),movingMeanInitializer:Vt(this.movingMeanInitializer),movingVarianceInitializer:Vt(this.movingVarianceInitializer),betaRegularizer:It(this.betaRegularizer),gammaRegularizer:It(this.gammaRegularizer),betaConstraint:cn(this.betaConstraint),gammaConstraint:cn(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}iL.className="BatchNormalization",me(iL);class rL extends lt{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(const t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ft(e.betaInitializer||"zeros"),this.gammaInitializer=Ft(e.gammaInitializer||"ones"),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=St(e);const t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i<this.axis.length;++i)this.axis[i]<0&&(this.axis[i]+=t);for(const i of this.axis)if(i<0||i>=t)throw new Error(`Invalid axis: ${i}`);if(this.axis.length!==$r(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);const n=this.axis.map(i=>e[i]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){const n=je(e),s=n.shape,i=s.length;return ee(()=>{const o=!0;let{mean:a,variance:c}=Ud(n,this.axis,o);const h=Oo(1,i);for(const L of this.axis)h[L]=s[L];const d=L=>L!=null&&L.shape.length!==i&&this.axis!==[i-1]?L.reshape(h):L;let m=d(this.gamma.read()),y=d(this.beta.read());const b=[],w=[];for(let L=0;L<i;++L)this.axis.indexOf(L)!==-1?(b.push(s[L]),w.push(1)):(b.push(1),w.push(s[L]));return a=a.tile(b),c=c.tile(b),m=m.tile(w),y=y.tile(w),Mh(n,a,c,y,m,this.epsilon)})}getConfig(){const e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Vt(this.betaInitializer),gammaInitializer:Vt(this.gammaInitializer),betaRegularizer:It(this.betaRegularizer),gammaRegularizer:It(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}}rL.className="LayerNormalization",me(rL);function zQ(e,t){return ee(()=>{if(e.rank!==3)throw new j(`temporalPadding expects input tensor to be 3-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[1,1]),t.length!==2)throw new j(`temporalPadding expects input padding pattern to be a length-2 array, but received a length-${t.length} array.`);const n=[[0,0],t,[0,0]];return Ci(e,n)})}function E3(e,t,n){return ee(()=>{if(e.rank!==4)throw new j(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new j("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ti()),n!=="channelsLast"&&n!=="channelsFirst")throw new j(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],Ci(e,s)})}class oL extends lt{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ti():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new j(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new j(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new j(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new mn({ndim:4})]}computeOutputShape(e){e=St(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return ee(()=>E3(je(e),this.padding,this.dataFormat))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}oL.className="ZeroPadding2D",me(oL);function Pp(e,t,n,s,i,o){return ee(()=>{Gt(i),qA(o),Ts(s),n==null&&(n=[1,1]),s==null&&(s="valid"),i==null&&(i=ti()),o==null&&(o="max"),e=kw(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=eh(e,t,n,c):a=Hl(e,t,n,c),i==="channelsFirst"&&(a=Me(a,[0,3,1,2])),a})}function rN(e,t,n,s,i,o){return ee(()=>{Gt(i),qA(o),Ts(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),i==null&&(i=ti()),o==null&&(o="max"),e=Zv(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=cb(e,t,n,c):a=jy(e,t,n,c),i==="channelsFirst"&&(a=Me(a,[0,4,1,2,3])),a})}class oN extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new j(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(pn(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new j(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);pn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Ts(this.padding),this.inputSpec=[new mn({ndim:3})]}computeOutputShape(e){e=St(e);const t=ai(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return ee(()=>{this.invokeCallHook(e,t),e=Ih(je(e),2);const n=this.poolingFunction(je(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Fr(n,[2])})}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class aL extends oN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ts(s),Pp(e,t,n,s,i,"max")}}aL.className="MaxPooling1D",me(aL);class cL extends oN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ts(s),Pp(e,t,n,s,i,"avg")}}cL.className="AveragePooling1D",me(cL);class aN extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new j(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];pn(this.poolSize,"poolSize"),pn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),Ts(this.padding),this.inputSpec=[new mn({ndim:4})]}computeOutputShape(e){e=St(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ai(t,this.poolSize[0],this.padding,this.strides[0]),n=ai(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return ee(()=>(this.invokeCallHook(e,t),this.poolingFunction(je(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class lL extends aN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ts(s),Pp(e,t,n,s,i,"max")}}lL.className="MaxPooling2D",me(lL);class hL extends aN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ts(s),Pp(e,t,n,s,i,"avg")}}hL.className="AveragePooling2D",me(hL);class cN extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new j(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];pn(this.poolSize,"poolSize"),pn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),Ts(this.padding),this.inputSpec=[new mn({ndim:5})]}computeOutputShape(e){e=St(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=ai(t,this.poolSize[0],this.padding,this.strides[0]),n=ai(n,this.poolSize[1],this.padding,this.strides[1]),s=ai(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return ee(()=>(this.invokeCallHook(e,t),this.poolingFunction(je(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class uL extends cN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ts(s),rN(e,t,n,s,i,"max")}}uL.className="MaxPooling3D",me(uL);class dL extends cN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Gt(i),Ts(s),rN(e,t,n,s,i,"avg")}}dL.className="AveragePooling3D",me(dL);class lN extends lt{constructor(e){super(e);this.inputSpec=[new mn({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new ze}}class pL extends lN{constructor(e){super(e||{})}call(e,t){return ee(()=>{const n=je(e);return zt(n,1)})}}pL.className="GlobalAveragePooling1D",me(pL);class mL extends lN{constructor(e){super(e||{})}call(e,t){return ee(()=>{const n=je(e);return qn(n,1)})}}mL.className="GlobalMaxPooling1D",me(mL);class hN extends lt{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),this.inputSpec=[new mn({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new ze}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class fL extends hN{call(e,t){return ee(()=>{const n=je(e);return this.dataFormat==="channelsLast"?zt(n,[1,2]):zt(n,[2,3])})}}fL.className="GlobalAveragePooling2D",me(fL);class gL extends hN{call(e,t){return ee(()=>{const n=je(e);return this.dataFormat==="channelsLast"?qn(n,[1,2]):qn(n,[2,3])})}}gL.className="GlobalMaxPooling2D",me(gL);class uN extends lt{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){const e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){const s=t.layer,i=oi(s,n);delete t.layer;const o={layer:i};return Object.assign(o,t),new e(o)}}class yL extends uN{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=St(e),e.length<3)throw new j(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];const t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=St(e);const t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return ee(()=>{e=je(e);const n=(o,a)=>{const c=je(this.layer.call(o,t));return[c,[]]},s=sN(n,e,[],!1,null,null,!1,!0),i=s[1];return i})}}yL.className="TimeDistributed",me(yL);function D3(e){za(PP,"BidirectionalMergeMode",e)}const k3="concat";class bL extends uN{constructor(e){super(e);const t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=oi(n),t.goBackwards=!(t.goBackwards===!0);const s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=oi(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?k3:e.mergeMode,D3(this.mergeMode),e.weights)throw new ze("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){const t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,i;return this.returnState&&(i=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(i).concat(i.slice()):[n].concat(i).concat(i.slice()):jn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=nN(e,n,s,this.numConstants);if(e=i.inputs,n=i.initialState,s=i.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);const o=[],a=[];if(n!=null){const h=n.length;if(h%2>0)throw new j("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,o.push(...n);const d=n.map(m=>new mn({shape:m.shape}));this.forwardLayer.stateSpec=d.slice(0,h/2),this.backwardLayer.stateSpec=d.slice(h/2),a.push(...d)}if(s!=null)throw new ze("Support for constants in Bidirectional layers is not implemented yet.");const c=o[0]instanceof ri;for(const h of o)if(h instanceof ri!==c)throw new j("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(c){const h=[e].concat(o),d=this.inputSpec.concat(a),m=this.inputSpec;this.inputSpec=d;const y=super.apply(h,t);return this.inputSpec=m,y}else return super.apply(e,t)}call(e,t){return ee(()=>{const n=t.initialState;let s,i;if(n==null)s=this.forwardLayer.call(e,t),i=this.backwardLayer.call(e,t);else{const c=n.slice(0,n.length/2),h=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:c})),i=this.backwardLayer.call(e,Object.assign(t,{initialState:h}))}let o;this.returnState&&(Array.isArray(s)&&(o=s.slice(1).concat(i.slice(1))),s=s[0],i=i[0]),this.returnSequences&&(i=Is(i,1));let a;return this.mergeMode==="concat"?a=Zb([s,i]):this.mergeMode==="sum"?a=be(s,i):this.mergeMode==="ave"?a=X(.5,be(s,i)):this.mergeMode==="mul"?a=X(s,i):this.mergeMode==null&&(a=[s,i]),this.returnState?this.mergeMode==null?a.concat(o):[a].concat(o):a})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Do(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Do(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){const s=this.forwardLayer.states,i=s.map(o=>null);return Array.isArray(n)?n.concat(i).concat(i):[n].concat(i).concat(i)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){const e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){const n=oi(t.layer);if(delete t.layer,t.numConstants!=null)throw new ze("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const s=t;return s.layer=n,new e(s)}}bL.className="Bidirectional",me(bL);function F3(e){return new Ya(e)}function _3(e){return new Ow(e)}function W3(e){return new Nw(e)}function $3(e){return new Cw(e)}function U3(e){return new Rw(e)}function B3(e){return new Dw(e)}function M3(e){return new Ew(e)}function P3(e){return new _p(e)}function z3(e){return new Eh(e)}function G3(e){return new Ww(e)}function 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$V=Object.freeze({__proto__:null,json:WV});const 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n=e.name;return t!=null&&(n=t[n]),{name:n,dtype:e.type}}}function jV(e){const t=C().global;if(typeof t.atob!="undefined")return t.atob(e);if(typeof Buffer!="undefined")return new Buffer(e,"base64").toString();throw new Error("Unable to decode base64 in this environment. 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t.size=="number"?t.size:parseInt(t.size,10)):[]}function RL(e,t,n){const s=e[t];return s&&s.shape?TN(s.shape):n}function OL(e,t,n){const s=e[t];return s?((s.list.f&&s.list.f.length?s.list.f:s.list.i)||[]).map(i=>typeof i=="number"?i:parseInt(i,10)):n}function EL(e,t,n,s=!1){const i=e[t];return i&&i.list&&i.list.s?i.list.s.map(o=>IN(o,s)):n}function DL(e,t,n){const s=e[t];return s&&s.list&&s.list.shape?s.list.shape.map(i=>TN(i)):n}function kL(e,t,n){const s=e[t];return s&&s.list&&s.list.b?s.list.b:n}class KV{constructor(e,t,n){this.node=e,this.tensorMap=t,this.context=n,this.inputs=[],this.attrs={},this.inputs=e.inputNames.map(s=>this.getInput(s)),e.rawAttrs!=null&&(this.attrs=Object.keys(e.rawAttrs).reduce((s,i)=>(s[i]=this.getAttr(i),s),{}))}getInput(e){return Xn(e,this.tensorMap,this.context)}getAttr(e,t){const n=this.node.rawAttrs[e];if(n.tensor!=null)return Xn(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return AL(this.node.rawAttrs,e,t);if(n.s!=null)return xL(this.node.rawAttrs,e,t);if(n.b!=null)return TL(this.node.rawAttrs,e,t);if(n.shape!=null)return RL(this.node.rawAttrs,e,t);if(n.type!=null)return NL(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return OL(this.node.rawAttrs,e,t);if(n.list.s!=null)return EL(this.node.rawAttrs,e,t);if(n.list.shape!=null)return DL(this.node.rawAttrs,e,t);if(n.list.b!=null)return kL(this.node.rawAttrs,e,t);if(n.list.type!=null)return CL(this.node.rawAttrs,e,t)}return t}}const XV=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[be(R("a",e,t,n),R("b",e,t,n))];case"AddN":return[vT(R("tensors",e,t,n))];case"FloorMod":case"Mod":return[$d(R("a",e,t,n),R("b",e,t,n))];case"Mul":return[X(R("a",e,t,n),R("b",e,t,n))];case"RealDiv":case"Div":return[_e(R("a",e,t,n),R("b",e,t,n))];case"DivNoNan":return[tb(R("a",e,t,n),R("b",e,t,n))];case"FloorDiv":return[Id(R("a",e,t,n),R("b",e,t,n))];case"Sub":return[Ce(R("a",e,t,n),R("b",e,t,n))];case"Minimum":return[Io(R("a",e,t,n),R("b",e,t,n))];case"Maximum":return[_s(R("a",e,t,n),R("b",e,t,n))];case"Pow":return[ei(R("a",e,t,n),R("b",e,t,n))];case"SquaredDifference":return[ah(R("a",e,t,n),R("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},VQ="arithmetic";const JV=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[sn(R("x",e,t,n))];case"Acos":return[$y(R("x",e,t,n))];case"Acosh":return[Uy(R("x",e,t,n))];case"Asin":return[Py(R("x",e,t,n))];case"Asinh":return[zy(R("x",e,t,n))];case"Atan":return[Gy(R("x",e,t,n))];case"Atan2":return[Vy(R("x",e,t,n),R("y",e,t,n))];case"Atanh":return[Yy(R("x",e,t,n))];case"Ceil":return[Xy(R("x",e,t,n))];case"Complex":return[xi(R("real",e,t,n),R("imag",e,t,n))];case"Cos":return[Kl(R("x",e,t,n))];case"Cosh":return[Rd(R("x",e,t,n))];case"Elu":return[So(R("x",e,t,n))];case"Erf":return[nb(R("x",e,t,n))];case"Exp":return[Ls(R("x",e,t,n))];case"Expm1":return[sb(R("x",e,t,n))];case"Floor":return[Ra(R("x",e,t,n))];case"Log":return[ts(R("x",e,t,n))];case"Log1p":return[kd(R("x",e,t,n))];case"Imag":return[Ea(R("x",e,t,n))];case"Neg":return[Pt(R("x",e,t,n))];case"Reciprocal":return[db(R("x",e,t,n))];case"Real":return[xo(R("x",e,t,n))];case"Relu":return[Ri(R("x",e,t,n))];case"Round":return[mb(R("x",e,t,n))];case"Selu":return[Pd(R("x",e,t,n))];case"Sigmoid":return[vi(R("x",e,t,n))];case"Sin":return[zd(R("x",e,t,n))];case"Sign":return[gb(R("x",e,t,n))];case"Sinh":return[Gd(R("x",e,t,n))];case"Softplus":return[Da(R("x",e,t,n))];case"Sqrt":return[Ln(R("x",e,t,n))];case"Square":return[wt(R("x",e,t,n))];case"Tanh":return[Ca(R("x",e,t,n))];case"Tan":return[wb(R("x",e,t,n))];case"Relu6":case"ClipByValue":return[Yn(R("x",e,t,n),R("clipValueMin",e,t,n),R("clipValueMax",e,t,n))];case"Rsqrt":return[Md(Xn(e.inputNames[0],t,n))];case"Prod":return[Bd(R("x",e,t,n),R("axes",e,t,n))];case"LeakyRelu":return[Dd(R("x",e,t,n),R("alpha",e,t,n))];case"Prelu":return[nh(R("x",e,t,n),R("alpha",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},YQ="basic_math";function Ms(e,t,n=""){k(ZV(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function ZV(e,t){if(e.length!==t.length)return!1;for(let n=0;n<e.length;n++)if(e[n]!==-1&&t[n]!==-1&&e[n]!==t[n])return!1;return!0}class QV{constructor(e,t,n,s,i,o,a){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=s,this.identicalElementShapes=i,this.dynamicSize=o,this.clearAfterRead=a,this.tensors=[],this.closed_=!1,this.idTensor=Ne(0),Nn(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);const t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);const n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
           because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),Ms(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);n.tensor=t,Nn(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,s)=>this.write(n,t[s]))}gather(e,t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let s=0;s<this.size();s++)e.push(s)}if(e.length===0)return en([],[0].concat(this.elementShape));const n=this.readMany(e);return Ms(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),is(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return en([],[0].concat(this.elementShape));const t=[];for(let s=0;s<this.size();s++)t.push(s);const n=this.readMany(t);return Ms(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),Mt(n,0)}scatter(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);if(e.length!==t.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);const n=Math.max(...e);if(!this.dynamicSize&&n>=this.maxSize)throw new Error(`Max index must be < array size (${n}  vs. ${this.maxSize})`);this.writeMany(e,Oi(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0;const s=e.map(c=>(n+=c,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
           tensor.shape[0], but sum of lengths is
         ${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);const i=n===0?0:t.size/n,o=[];ee(()=>{t=K(t,[1,n,i]);for(let c=0;c<e.length;++c){const h=c===0?0:s[c-1],d=[0,h,0],m=[1,e[c],i];o[c]=K(nt(t,d,m),this.elementShape)}return o});const a=[];for(let c=0;c<e.length;c++)a[c]=c;this.writeMany(a,o)}}class Ph{constructor(e,t,n,s=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(i=>{if(n!==i.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${i.dtype}`);Ms(t,i.shape,"TensorList shape mismatch: "),Nn(i)}),this.idTensor=Ne(0),this.maxNumElements=s,Nn(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Ph([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);return Ms(e,this.elementShape,"TensorList shape mismatch: "),ee(()=>{const s=this.tensors.map(i=>K(i,e));return is(s,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");const n=this.tensors.pop();return Ms(n.shape,e,"TensorList shape mismatch: "),K(n,e)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Ms(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Nn(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);return Ms(this.tensors[e].shape,t,"TensorList shape mismatch: "),this.tensors[e]}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);Ms(this.elementShape,t.shape,"TensorList shape mismatch: "),Nn(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);return Ms(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size()),e.length===0?en([],[0].concat(this.elementShape)):ee(()=>{const s=e.map(i=>K(this.tensors[i],n));return is(s,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);return Ms(this.elementShape,t,"TensorList shape mismatch: "),this.size()===0?en([],[0].concat(this.elementShape)):ee(()=>{const n=this.tensors.map(s=>K(s,t));return Mt(n,0)})}}function eY(e,t,n){const s=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);const i=e.shape.slice(1);Ms(i,t,"TensorList shape mismatch: ");const o=Oi(e);return new Ph(o,t,s)}function tY(e,t,n){return new Ph([],e,t,n)}function nY(e,t,n,s){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);const i=Math.max(...t);if(s!=null&&s!==-1&&i>=s)throw new Error(`Max index must be < array size (${i}  vs. ${s})`);const o=new Ph([],n,e.dtype,s),a=Oi(e,0);return t.forEach((c,h)=>{o.setItem(c,a[h])}),o}function sY(e,t,n){let s=0;const i=t.map(h=>(s+=h,s));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
           tensor.shape[0], but sum of lengths is
-        ${s}, and tensor's shape is: ${e.shape}`);const o=s===0?0:e.size/s,a=ee(()=>{const h=[];e=K(e,[1,s,o]);for(let d=0;d<t.length;++d){const m=d===0?0:i[d-1],y=[0,m,0],b=[1,t[d],o];h[d]=K(nt(e,y,b),n)}return e.dispose(),h}),c=new Ph([],n,e.dtype,t.length);for(let h=0;h<a.length;h++)c.setItem(h,a[h]);return c}const iY=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{const s=R("thenBranch",e,t,n),i=R("elseBranch",e,t,n),o=R("cond",e,t,n),a=R("args",e,t,n),c=await o.data();return c[0]?n.functionMap[s].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap):n.functionMap[i].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{const s=R("body",e,t,n),i=R("cond",e,t,n),o=R("args",e,t,n),a=await n.functionMap[i].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap),c=o.map(m=>m.id);let h=await a[0].data();a.forEach(m=>{!m.kept&&c.indexOf(m.id)===-1&&m.dispose()});let d=o;for(;h[0];){const m=d;d=await n.functionMap[s].executeFunctionAsync(d,n.tensorArrayMap,n.tensorListMap);const y=d.map(w=>w.id);m.forEach(w=>{!w.kept&&c.indexOf(w.id)===-1&&y.indexOf(w.id)===-1&&w.dispose()});const b=await n.functionMap[i].executeFunctionAsync(d,n.tensorArrayMap,n.tensorListMap);h=await b[0].data(),b.forEach(w=>{!w.kept&&c.indexOf(w.id)===-1&&y.indexOf(w.id)===-1&&w.dispose()})}return d}case"LoopCond":{const s=R("pred",e,t,n);return[ir(s)]}case"Switch":{const s=R("pred",e,t,n);let i=R("data",e,t,n);return i.kept||(i=ir(i)),(await s.data())[0]?[void 0,i]:[i,void 0]}case"Merge":{const s=e.inputNames.find(i=>Xn(i,t,n)!==void 0);if(s){const i=Xn(s,t,n);return[ir(i)]}return}case"Enter":{const s=R("frameName",e,t,n),i=R("tensor",e,t,n);return n.enterFrame(s),[ir(i)]}case"Exit":{const s=R("tensor",e,t,n);return n.exitFrame(),[ir(s)]}case"NextIteration":{const s=R("tensor",e,t,n);return n.nextIteration(),[ir(s)]}case"TensorArrayV3":{const s=R("size",e,t,n),i=R("dtype",e,t,n),o=R("elementShape",e,t,n),a=R("dynamicSize",e,t,n),c=R("clearAfterRead",e,t,n),h=R("identicalElementShapes",e,t,n),d=R("name",e,t,n),m=new QV(d,i,s,o,h,a,c);return n.addTensorArray(m),[m.idTensor,Ne(1)]}case"TensorArrayWriteV3":{const s=R("tensorArrayId",e,t,n),i=R("index",e,t,n),o=R("tensor",e,t,n),a=n.getTensorArray(s.id);return a.write(i,o),[a.idTensor]}case"TensorArrayReadV3":{const s=R("tensorArrayId",e,t,n),i=R("index",e,t,n),o=n.getTensorArray(s.id);return[o.read(i)]}case"TensorArrayGatherV3":{const s=R("tensorArrayId",e,t,n),i=R("indices",e,t,n),o=R("dtype",e,t,n),a=n.getTensorArray(s.id);return[a.gather(i,o)]}case"TensorArrayScatterV3":{const s=R("tensorArrayId",e,t,n),i=R("indices",e,t,n),o=R("tensor",e,t,n),a=n.getTensorArray(s.id);return a.scatter(i,o),[a.idTensor]}case"TensorArrayConcatV3":{const s=R("tensorArrayId",e,t,n),i=n.getTensorArray(s.id),o=R("dtype",e,t,n);return[i.concat(o)]}case"TensorArraySplitV3":{const 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s=R("elementShape",e,t,n),i=R("elementDType",e,t,n),o=R("numElements",e,t,n),a=tY(s,i,o);return n.addTensorList(a),[a.idTensor]}case"TensorListGather":{const s=R("tensorListId",e,t,n),i=R("indices",e,t,n),o=R("elementShape",e,t,n),a=R("elementDType",e,t,n),c=n.getTensorList(s.id);return[c.gather(i,a,o)]}case"TensorListStack":{const s=R("tensorListId",e,t,n),i=R("elementShape",e,t,n),o=R("elementDType",e,t,n),a=R("numElements",e,t,n),c=n.getTensorList(s.id);return[c.stack(i,o,a)]}case"TensorListFromTensor":{const s=R("tensor",e,t,n),i=R("elementShape",e,t,n),o=R("elementDType",e,t,n),a=eY(s,i,o);return n.addTensorList(a),[a.idTensor]}case"TensorListConcat":{const s=R("tensorListId",e,t,n),i=n.getTensorList(s.id),o=R("dtype",e,t,n),a=R("elementShape",e,t,n);return[i.concat(o,a)]}case"TensorListPushBack":{const s=R("tensorListId",e,t,n),i=R("tensor",e,t,n),o=n.getTensorList(s.id);return o.pushBack(i),[o.idTensor]}case"TensorListPopBack":{const 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s=R("axis",e,t,n);return[Fr(R("x",e,t,n),s)]}case"Reshape":return[K(R("x",e,t,n),R("shape",e,t,n))];case"PadV2":case"Pad":return[Ci(R("x",e,t,n),R("padding",e,t,n),R("constantValue",e,t,n))];case"SpaceToBatchND":{const s=R("blockShape",e,t,n),i=R("paddings",e,t,n);return[th(R("x",e,t,n),s,i)]}case"BatchToSpaceND":{const s=R("blockShape",e,t,n),i=R("crops",e,t,n);return[ql(R("x",e,t,n),s,i)]}case"DepthToSpace":{const s=R("blockSize",e,t,n),i=R("dataFormat",e,t,n).toUpperCase();return[Qy(R("x",e,t,n),s,i)]}case"BroadcastTo":return[jl(R("x",e,t,n),R("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},ree="transformation";function vN(e,t,n){const s=((i,o,a)=>{switch(i.category){case"arithmetic":return ee(()=>XV(i,o,a));case"basic_math":return ee(()=>JV(i,o,a));case"control":return iY(i,o,a);case"convolution":return ee(()=>rY(i,o,a));case"creation":return ee(()=>oY(i,o,a));case"dynamic":return aY(i,o,a);case"evaluation":return ee(()=>cY(i,o,a));case"image":return ee(()=>hY(i,o,a));case"graph":return ee(()=>lY(i,o,a));case"logical":return ee(()=>uY(i,o,a));case"matrices":return ee(()=>dY(i,o,a));case"normalization":return ee(()=>pY(i,o,a));case"reduction":return ee(()=>mY(i,o,a));case"slice_join":return ee(()=>fY(i,o,a));case"spectral":return ee(()=>gY(i,o,a));case"transformation":return ee(()=>yY(i,o,a));case"custom":const c=LN(i.op);if(c&&c.customExecutor)return c.customExecutor(new KV(i,o,a));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return s instanceof Promise?s.then(i=>[].concat(i)):[].concat(s)}class NN{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){const e=[];for(let t=0;t<this.contexts.length-1;t++){const n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;const e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(const t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(const t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}}function CN(e,t,n,s){const i=new Set,o=[];let a=null,c=null;const h=new Set,d=Object.keys(e).map(b=>os(b)[0]);let m=[];s!=null&&(m=s.map(b=>os(b.name)[0]));const y=[...t];for(;y.length>0;){const b=y.pop();if((RN(b)||SY(b))&&(a==null&&(a=b,c=a.children.map(w=>w.name).filter(w=>i.has(w)))),i.add(b.name),n[b.name]!=null)continue;if(d.indexOf(b.name)!==-1)continue;if(m.indexOf(b.name)!==-1)continue;if(b.inputs.length===0){o.push(b.name);continue}b.inputs.forEach(w=>{if(h.has(w.name))return;h.add(w.name),y.push(w)})}return{inputs:e,outputs:t,usedNodes:i,missingInputs:o,dynamicNode:a,syncInputs:c}}function bY(e,t,n){const{usedNodes:s,inputs:i}=n,o=[],a=Object.keys(i).map(m=>os(m)[0]).map(m=>e.nodes[m]),c=e.initNodes;a.forEach(m=>{s.has(m.name)&&o.push(m)}),e.weights.forEach(m=>{s.has(m.name)&&o.push(m)}),c!=null&&c.forEach(m=>{s.has(m.name)&&o.push(m)});const h=new Set,d=[];for(;o.length>0;){const m=o.pop();h.add(m.name),t[m.name]||d.push(m),m.children.forEach(y=>{!h.has(y.name)&&s.has(y.name)&&y.inputs.every(b=>h.has(b.name))&&o.push(y)})}return d}const wY=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],LY=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"];function RN(e){return wY.indexOf(e.op)>=0}function SY(e){return LY.indexOf(e.op)>=0}class _L{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new _L(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){const t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{const t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){const n=e.map(i=>i.name).sort(),s=t.map(i=>i.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){const n=CN(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:i,syncInputs:o}=n;if(i!=null)throw new Error(`This execution contains the node '${i.name}', which has the dynamic op '${i.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${o}]`);if(s.length>0){const a=t.map(h=>h.name),c=Object.keys(e);throw new Error(`Cannot compute the outputs [${a}] from the provided inputs [${c}]. Missing the following inputs: [${s}]`)}return bY(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);const n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);const s=n.map(m=>this.graph.nodes[os(m)[0]]),i=t.map(m=>os(m)[0]);let o=i.map(m=>this.graph.nodes[m]);o.length===0&&(o=this._outputs);const a=this.getCompilationKey(s,o);let c=this.compiledMap.get(a);c==null&&(c=this.compile(e,o),this.compiledMap.set(a,c));const h={},d={};return ee(()=>{const m=new NN(this.weightMap,h,d,this.functionExecutorMap),y=Object.assign({},this.weightMap);Object.keys(e).forEach(L=>{const[T,A]=os(L),N=[];N[A]=e[L],y[T]=N});const b=this.getFrozenTensorIds(y),w={};for(let L=0;L<c.length;L++){const T=c[L];if(!y[T.name]){const A=vN(T,y,m);if(A instanceof Promise)throw new Error(`The execution of the op '${T.op}' returned a promise. Please use model.executeAsync() instead.`);y[T.name]=A,this.checkTensorForDisposal(T.name,T,y,m,b,i,w)}}return this.parent==null&&m.dispose(b),t.map(L=>Xn(L,y,m))})}getFrozenTensorIds(e){const t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,i,o,a){if(t.category==="control"||o.indexOf(e)!==-1)return;n[e].forEach(c=>{c!=null&&(a[c.id]=(a[c.id]||0)+t.children.length)}),t.inputs.forEach(c=>{if(c.category!=="control"){const h=fV(c.name,n,s);h!=null&&h.forEach(d=>{if(d&&!i.has(d.id)){const m=a[d.id];m===1?(d.dispose(),delete a[d.id]):m!=null&&a[d.id]--}})}})}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,s={},i={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));const o=new NN(this.weightMap,s,i,this.functionExecutorMap),a=await this.executeWithControlFlow(e,o,t,n),c=t.map(y=>Xn(y,a,o)),h=c.map(y=>y.id),d=Object.keys(e).map(y=>e[y].id),m=new Set([...h,...d,...this.weightIds]);return Object.keys(a).forEach(y=>{const b=a[y];b.forEach(w=>{w&&!w.isDisposed&&!m.has(w.id)&&w.dispose()})}),this.parent==null&&o.dispose(m),c}async executeFunctionAsync(e,t,n){const s=e.reduce((i,o,a)=>(i[this.inputs[a].name]=o,i),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){const i=Object.keys(e),o=i.map(E=>this.graph.nodes[os(E)[0]]),a=n.map(E=>os(E)[0]),c=a.map(E=>this.graph.nodes[E]),{usedNodes:h,missingInputs:d,dynamicNode:m,syncInputs:y}=CN(e,c,this.weightMap),b=[...o,...this.graph.weights].map(E=>({node:E,contexts:t.currentContext})),w=Object.assign({},this.weightMap);Object.keys(e).forEach(E=>{const[D,F]=os(E),_=[];_[F]=e[E],w[D]=_});const L={},T=this.getFrozenTensorIds(w),A={};for(;b.length>0;){const E=this.processStack(o,b,t,w,A,T,a,L,h);await Promise.all(E)}m==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");const N=c.filter(E=>!RN(E)&&!Xn(E.name,w,t)).map(E=>E.name);if(N.length>0){let E="";throw m!=null&&(E=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${y}]`),new Error(`Cannot compute the outputs [${N}] from the provided inputs [${i}]. Consider providing the following inputs: [${d}]. ${E}`)}return w}processStack(e,t,n,s,i,o,a,c,h){const d=[];for(;t.length>0;){const m=t.pop();n.currentContext=m.contexts;let y="";if(m.node.op==="Enter"&&R("isConstant",m.node,s,n)&&([y]=sr(m.node.name,n)),e.indexOf(m.node)===-1){const b=vN(m.node,s,n);y||([y]=sr(m.node.name,n));const w=n.currentContext;b instanceof Promise?d.push(b.then(L=>(s[y]=L,n.currentContext=w,this.checkTensorForDisposal(y,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,h),L))):(s[y]=b,this.checkTensorForDisposal(y,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,h))}else this.processChildNodes(m.node,t,n,s,i,h)}return d}processChildNodes(e,t,n,s,i,o){e.children.forEach(a=>{const[c]=sr(a.name,n);if(i[c]||!o.has(a.name))return;a.op==="Merge"?a.inputNames.some(h=>!!Xn(h,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a})):a.inputNames.every(h=>!!Xn(h,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a}))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{const n=e[t],[s]=os(t),i=this.graph.nodes[s];if(i.attrParams.shape&&i.attrParams.shape.value){const o=i.attrParams.shape.value,a=o.length===n.shape.length&&n.shape.every((c,h)=>o[h]===-1||o[h]===c);k(a,()=>`The shape of dict['${i.name}'] provided in model.execute(dict) must be [${o}], but was [${n.shape}]`)}i.attrParams.dtype&&i.attrParams.dtype.value&&k(n.dtype===i.attrParams.dtype.value,()=>`The dtype of dict['${i.name}'] provided in model.execute(dict) must be ${i.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){const t={};for(const n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){const s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){const t=Object.keys(e).filter(n=>{const[s]=os(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{if(this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null){const n=this._signature.outputs[t];return n.name}return t},{})}checkOutputs(e){e.forEach(t=>{const[n]=os(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}}const IY="?tfjs-format=file",xY="model.json";class ON{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={})}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){const e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=fd(e,this.loadOptions);else{const t=Ly(e,this.loadOptions);if(t.length===0)t.push(fd(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;const t=this.artifacts.modelTopology;let n={};this.artifacts.userDefinedMetadata!=null&&(n=this.artifacts.userDefinedMetadata.signature),this.version=`${t.versions.producer}.${t.versions.minConsumer}`;const s=ud(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new _L(SN.Instance.transformGraph(t,n)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),e.modelInitializer!=null){const i=SN.Instance.transformGraph(e.modelInitializer);this.initializer=new _L(i),this.initializer.weightMap=this.executor.weightMap,this.initializer.execute({},[])}return!0}async save(e,t){if(typeof e=="string"){const n=wy(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Q)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose()}}async function TY(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&(e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${xY}${IY}`));const n=new ON(e,t);return await n.load(),n}const EN="2.6.0";function AY(e,t){return Yp(e,t)}function Yp(e,t,n=new Map,s=new Set){if(e==null)return null;if(s.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(i.recurse)if(Ka(e)){const o=Array.isArray(e)?[]:{};s.add(e);for(const a in e){const c=e[a],h=Yp(c,t,n,s);o[a]=h}return s.delete(e),o}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,i.value),i.value}function vY(e,t=kN){return DN(e,t)}function DN(e,t,n=new Set){const s=e[0];if(n.has(s))throw new Error("Circular references are not supported.");const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(i.recurse)if(Ka(s)){const o=Array.isArray(s)?[]:{};n.add(s);for(const a in s){const c=e.map(d=>d[a]),h=DN(c,t,n);o[a]=h}return n.delete(s),o}else throw new Error(`Can't recurse into non-iterable type: ${s}`);else return i.value}function kN(e){return e===null?null:Ka(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function FN(e,t){const n=new Map;Yp(e,t,n);for(const i of Array.from(n.keys())){const o=n.get(i);if(o instanceof Promise){const a=await o;n.set(i,a)}}const s=Yp(e,t,n);return s}function Ka(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Q))}function NY(e){return e==null||CY(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Q||wn(e)}function CY(e){return e===null||typeof e!="object"&&typeof e!="function"}function RY(e){return AY(e,OY)}function OY(e){return e instanceof Q?{value:e.clone(),recurse:!1}:Ka(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class _N{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(const t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);const e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}}class WL extends _N{constructor(){super(WL.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){const e=this.capacity*2,t=new Array(e),n=this.length();for(let s=0;s<n;s++)t[s]=this.get(this.wrap(this.begin+s));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}}WL.INITIAL_CAPACITY=32;function WN(e){return new DY(e)}function oee(e){let t=e;return zh(()=>({value:t++,done:!1}))}function zh(e){return new kY(e)}function $N(e,t){return new BN(e,t)}function aee(e,t,n){return $N(zh(e).take(t),n)}function EY(e,t=Yr.FAIL){return new zY(e,t)}class fn{async toArray(){const e=[];let t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){const e=this.prefetch(100),t=[];let n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new MY(this,e)}filter(e){return new UY(this,e)}map(e){return new BY(this,e)}mapAsync(e){return new UN(this,e)}serialMapAsync(e){return new UN(this,e).serial()}flatmap(e){return new PY(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new $Y(this,e,t)}columnMajorBatch(e,t=!0,n=kN){const s=this.rowMajorBatch(e,t);return s.map(i=>vY(i,n))}concatenate(e,t){return new BN(WN([this,e]),t)}take(e){return e<0||e==null?this:new WY(this,e)}skip(e){return e<0||e==null?this:new _Y(this,e)}prefetch(e){return new MN(this,e)}shuffle(e,t){return new GY(this,e,t)}serial(){return new FY(this)}}class DY extends fn{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};const e=this.items[this.trav];return this.trav++,{value:RY(e),done:!1}}}class kY extends fn{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}}class FY extends fn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class _Y extends fn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){const e=await this.upstream.next();if(e.done)return e;He(e.value)}return this.upstream.next()}}class WY extends fn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class $Y extends fn{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){const e=[];for(;e.length<this.batchSize;){const t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class UY extends fn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){const e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;He(e.value)}}}class BY extends fn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=Hi(e.value),n=this.transform(e.value),s=Hi(n);for(const i of t)ld(i,s)||i.dispose();return{value:n,done:!1}}}class MY extends fn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}}class UN extends fn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=Hi(e.value),n=await this.transform(e.value),s=Hi(n);for(const i of t)ld(i,s)||i.dispose();return{value:n,done:!1}}}class $L extends fn{constructor(){super();this.outputQueue=new WL,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class PY extends $L{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=Hi(e.value),n=this.transform(e.value),s=Hi(n);this.outputQueue.pushAll(n);for(const i of t)ld(i,s)||i.dispose();return!0}}class BN extends fn{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){const e="TODO: fill in upstream of chained summaries";return`${e} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){const n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}var Yr;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Yr||(Yr={}));class zY extends fn{constructor(e,t=Yr.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){const e="TODO: fill in upstream of zip summaries";return`{${e}} -> Zip`}async nextState(e){await e;let t=0,n=0;function s(o){if(o instanceof fn){const a=o.next();return{value:a.then(c=>(t++,c.done&&n++,c.value)),recurse:!1}}else return{value:null,recurse:!0}}const i=await FN(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Yr.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Yr.SHORTEST:return{value:null,done:!0};case Yr.LONGEST:default:}return this.count++,{value:i,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class MN extends fn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new _N(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){const e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}}class GY extends MN{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=ka(n||Vn().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){const e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}}class Xa{constructor(){this.size=null}batch(e,t=!0){const n=this;k(e>0,()=>`batchSize needs to be positive, but it is
-      ${e}`);let s;return this.size===Infinity||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),as(async()=>(await n.iterator()).columnMajorBatch(e,t,HY),s)}concatenate(e){const t=this;let n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,as(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){const t=this;let n;return this.size===Infinity?n=Infinity:n=null,as(async()=>(await t.iterator()).filter(s=>ee(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return as(async()=>(await t.iterator()).map(n=>ee(()=>e(n))),this.size)}mapAsync(e){const t=this;return as(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const t=this;return as(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){const t=this;let n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,as(async()=>{const s=zh(async()=>({value:await t.iterator(),done:!1}));return $N(s.take(e))},n)}skip(e){const t=this;let n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,as(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const s=this,i=ka(t||Vn().toString());return as(async()=>{let o=i.int32();return n&&(o+=i.int32()),(await s.iterator()).shuffle(e,o.toString())},this.size)}take(e){const t=this;let n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,as(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}}Xa.MAX_BUFFER_SIZE=1e4;function as(e,t=null){return new class extends Xa{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function VY(e){return as(async()=>WN(e),e.length)}function YY(e){if(!Ka(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(const n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return as(async()=>{const n=await FN(e,s=>{if(s instanceof Xa)return{value:s.iterator(),recurse:!1};if(Ka(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return EY(n,Yr.SHORTEST)},t)}function HY(e){if(e===null)return null;const t=e[0];if(NY(t)){const n=qY(e);return{value:n,recurse:!1}}return{value:null,recurse:!0}}function qY(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Q?is(e):en(e)}class PN extends Xa{constructor(e){super();this.input=e}async iterator(){const e=await this.input.iterator(),t=e.decodeUTF8(),n=t.split(`
-`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s));return n}}const Hp='"',Gh=Symbol("out"),zN=Symbol("field"),qp=Symbol("quote"),UL=Symbol("quoteafterquote"),GN=Symbol("quoteinquote");class VN extends Xa{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new PN(e),t||(t={}),this.hasHeader=!(t.hasHeader===!1),this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){const e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&k(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);const t=this.fullColumnNames.reduce((s,i)=>(s[i]=s[i]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(k(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs)for(const s of Object.keys(this.columnConfigs)){const i=this.fullColumnNames.indexOf(s);if(i===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){const e=await this.base.iterator(),t=await e.next();if(t.done)throw new Error("No data was found for CSV parsing.");const n=t.value,s=this.parseRow(n,!1);return s}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){const t=this.parseRow(e),n={},s={};for(let i=0;i<this.fullColumnNames.length;i++){const o=this.fullColumnNames[i],a=this.columnConfigs?this.columnConfigs[o]:null;if(this.configuredColumnsOnly&&!a)continue;{const c=t[i];let h=null;if(c==="")if(a&&a.default!==void 0)h=a.default;else{if(a&&(a.required||a.isLabel))throw new Error(`Required column ${o} is empty in this line: ${e}`);h=void 0}else{const d=Number(c);if(isNaN(d))a&&a.dtype==="bool"?h=this.getBoolean(c):h=c;else if(!a||!a.dtype)h=d;else switch(a.dtype){case"float32":h=d;break;case"int32":h=Math.floor(d);break;case"bool":h=this.getBoolean(c);break;default:h=d}}a&&a.isLabel?s[o]=h:n[o]=h}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){const n=[];let s=0;const i=e.length;let o=Gh;for(let a=0;a<i;a++)switch(o){case Gh:switch(e.charAt(a)){case Hp:s=a+1,o=qp;break;case this.delimiter:if(s=a+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),o=Gh;break;default:o=zN,s=a;break}break;case zN:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a)),o=Gh,s=a+1;break;default:}break;case qp:switch(e.charAt(a)){case Hp:o=UL;break;default:}break;case UL:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a-1)),o=Gh,s=a+1;break;case Hp:o=qp;break;default:o=GN;break}break;case GN:switch(e.charAt(a)){case Hp:o=qp;break;default:}break;default:}if(o===UL?n.push(e.substring(s,i-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}}class YN extends fn{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;const t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=!(e.includeSpectrogram===!1),this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(C().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");const t=new YN(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");const e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);const t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize);return}async next(){if(this.isClosed)return{value:null,done:!0};let e,t;const n=await this.getAudioData();if(this.includeSpectrogram){const s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){const s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){const e=[],t=[];let n=0;return new Promise(s=>{const i=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(i),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){const t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,i)=>n.set(s,i*t)),n}getTensorFromAudioDataArray(e,t){const n=new Float32Array(we(t));return n.set(e,n.length-e.length),en(n,t)}}class HN extends fn{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=ns([0],"int32"),this.webcamConfig.centerCrop){const n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,i=(1-n)/2,o=(1-s)/2,a=i+n,c=s+o;this.cropBox=_r([o,i,c,a],[1,4])}else this.cropBox=_r([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(C().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}const n=new HN(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=uT(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return ee(()=>{const t=e.toFloat().expandDims(0);let n;n=Wr.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");const s=n.shape;return n.reshape(s.slice(1))})}async capture(){return(await this.next()).value}stop(){const e=this.stream.getTracks();e.forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class qN{}class jN extends fn{split(e){return new jY(this,e)}}class jY extends jN{constructor(e,t){super();this.upstream=e,this.impl=new KY(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class KY extends $L{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){const e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);const t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(const n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}}class XY extends fn{decodeUTF8(){return new JY(this)}}class JY extends jN{constructor(e){super();this.upstream=e,this.impl=new ZY(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class ZY extends $L{constructor(e){super();if(this.upstream=e,C().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{const{StringDecoder:t}=require("string_decoder");this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){const e=await this.upstream.next();let t;if(e.done)return!1;t=e.value;let n;return C().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}}class KN extends XY{constructor(e,t={}){super();this.file=e,this.options=t,k(e instanceof Uint8Array||(C().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};const e=new Promise((t,n)=>{const s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{const i=new FileReader;i.onload=a=>{let c=i.result;if(c instanceof ArrayBuffer&&(c=new Uint8Array(c)),!(c instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(c)},i.onabort=a=>n(new Error("Aborted")),i.onerror=a=>n(new Error(a.type));const o=this.file.slice(this.offset,s);i.readAsArrayBuffer(o)}this.offset=s});return{value:await e,done:!1}}}async function QY(e,t={}){let n,s;typeof e=="string"?n=e:(n=e.url,s=eH(e));const i=await $x(n,s);if(i.ok){const o=new Uint8Array(await i.arrayBuffer());return new KN(o,t)}else throw new Error(i.statusText)}const eH=e=>{const t={method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity};return t};function XN(e){return typeof e=="string"&&e.substr(0,7)==="file://"}class JN extends qN{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(XN(this.input)&&C().get("IS_NODE")){const e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new KN(this.input,this.options)}}class ZN extends qN{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return XN(this.url)?new JN(this.url,this.fileOptions).iterator():QY(this.url,this.fileOptions)}}function tH(e,t={}){return new VN(new ZN(e),t)}function nH(e){const t=zh(e);return as(async()=>t)}function sH(e){return as(async()=>{const t=await e();return zh(()=>t.next())})}async function iH(e,t){return HN.create(e,t)}async function rH(e){return YN.create(e)}const QN="2.6.0";var oH=Object.freeze({__proto__:null,array:VY,Dataset:Xa,zip:YY,CSVDataset:VN,TextLineDataset:PN,csv:tH,func:nH,generator:sH,microphone:rH,webcam:iH,FileDataSource:JN,URLDataSource:ZN,version_data:QN});function xe(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}const aH=tp,cH=Mb,lH=Pb,hH=zb,uH=jd;function BL(e,t,n,s){if(n==="linear")return e.linear(t);if(n==="relu")return e.relu(t);if(n==="elu")return wo(t);if(n==="relu6")return e.relu6(t);if(n==="prelu")return e.prelu(t,s);throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}class dH extends g{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new p(this,Fs())}write(e,t,n){this.firstUse&&(this.firstUse=!1,C().get("IS_NODE")&&Ba(`
+        ${s}, and tensor's shape is: ${e.shape}`);const o=s===0?0:e.size/s,a=ee(()=>{const h=[];e=K(e,[1,s,o]);for(let d=0;d<t.length;++d){const m=d===0?0:i[d-1],y=[0,m,0],b=[1,t[d],o];h[d]=K(nt(e,y,b),n)}return e.dispose(),h}),c=new Ph([],n,e.dtype,t.length);for(let h=0;h<a.length;h++)c.setItem(h,a[h]);return c}const iY=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{const s=R("thenBranch",e,t,n),i=R("elseBranch",e,t,n),o=R("cond",e,t,n),a=R("args",e,t,n),c=await o.data();return c[0]?n.functionMap[s].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap):n.functionMap[i].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{const s=R("body",e,t,n),i=R("cond",e,t,n),o=R("args",e,t,n),a=await n.functionMap[i].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap),c=o.map(m=>m.id);let h=await a[0].data();a.forEach(m=>{!m.kept&&c.indexOf(m.id)===-1&&m.dispose()});let d=o;for(;h[0];){const m=d;d=await n.functionMap[s].executeFunctionAsync(d,n.tensorArrayMap,n.tensorListMap);const y=d.map(w=>w.id);m.forEach(w=>{!w.kept&&c.indexOf(w.id)===-1&&y.indexOf(w.id)===-1&&w.dispose()});const b=await n.functionMap[i].executeFunctionAsync(d,n.tensorArrayMap,n.tensorListMap);h=await b[0].data(),b.forEach(w=>{!w.kept&&c.indexOf(w.id)===-1&&y.indexOf(w.id)===-1&&w.dispose()})}return d}case"LoopCond":{const s=R("pred",e,t,n);return[ir(s)]}case"Switch":{const s=R("pred",e,t,n);let i=R("data",e,t,n);return i.kept||(i=ir(i)),(await s.data())[0]?[void 0,i]:[i,void 0]}case"Merge":{const s=e.inputNames.find(i=>Xn(i,t,n)!==void 0);if(s){const i=Xn(s,t,n);return[ir(i)]}return}case"Enter":{const s=R("frameName",e,t,n),i=R("tensor",e,t,n);return n.enterFrame(s),[ir(i)]}case"Exit":{const s=R("tensor",e,t,n);return n.exitFrame(),[ir(s)]}case"NextIteration":{const s=R("tensor",e,t,n);return n.nextIteration(),[ir(s)]}case"TensorArrayV3":{const s=R("size",e,t,n),i=R("dtype",e,t,n),o=R("elementShape",e,t,n),a=R("dynamicSize",e,t,n),c=R("clearAfterRead",e,t,n),h=R("identicalElementShapes",e,t,n),d=R("name",e,t,n),m=new QV(d,i,s,o,h,a,c);return n.addTensorArray(m),[m.idTensor,Ne(1)]}case"TensorArrayWriteV3":{const s=R("tensorArrayId",e,t,n),i=R("index",e,t,n),o=R("tensor",e,t,n),a=n.getTensorArray(s.id);return a.write(i,o),[a.idTensor]}case"TensorArrayReadV3":{const s=R("tensorArrayId",e,t,n),i=R("index",e,t,n),o=n.getTensorArray(s.id);return[o.read(i)]}case"TensorArrayGatherV3":{const s=R("tensorArrayId",e,t,n),i=R("indices",e,t,n),o=R("dtype",e,t,n),a=n.getTensorArray(s.id);return[a.gather(i,o)]}case"TensorArrayScatterV3":{const s=R("tensorArrayId",e,t,n),i=R("indices",e,t,n),o=R("tensor",e,t,n),a=n.getTensorArray(s.id);return a.scatter(i,o),[a.idTensor]}case"TensorArrayConcatV3":{const s=R("tensorArrayId",e,t,n),i=n.getTensorArray(s.id),o=R("dtype",e,t,n);return[i.concat(o)]}case"TensorArraySplitV3":{const s=R("tensorArrayId",e,t,n),i=R("tensor",e,t,n),o=R("lengths",e,t,n),a=n.getTensorArray(s.id);return a.split(o,i),[a.idTensor]}case"TensorArraySizeV3":{const s=R("tensorArrayId",e,t,n),i=n.getTensorArray(s.id);return[Ne(i.size(),"int32")]}case"TensorArrayCloseV3":{const s=R("tensorArrayId",e,t,n),i=n.getTensorArray(s.id);return i.clearAndClose(),[i.idTensor]}case"TensorListSetItem":{const s=R("tensorListId",e,t,n),i=R("index",e,t,n),o=R("tensor",e,t,n),a=n.getTensorList(s.id);return a.setItem(i,o),[a.idTensor]}case"TensorListGetItem":{const s=R("tensorListId",e,t,n),i=R("index",e,t,n),o=R("elementShape",e,t,n),a=R("elementDType",e,t,n),c=n.getTensorList(s.id);return[c.getItem(i,o,a)]}case"TensorListScatterV2":case"TensorListScatter":{const s=R("indices",e,t,n),i=R("tensor",e,t,n),o=R("elementShape",e,t,n),a=R("numElements",e,t,n),c=nY(i,s,o,a);return n.addTensorList(c),[c.idTensor]}case"TensorListReserve":{const s=R("elementShape",e,t,n),i=R("elementDType",e,t,n),o=R("numElements",e,t,n),a=tY(s,i,o);return n.addTensorList(a),[a.idTensor]}case"TensorListGather":{const s=R("tensorListId",e,t,n),i=R("indices",e,t,n),o=R("elementShape",e,t,n),a=R("elementDType",e,t,n),c=n.getTensorList(s.id);return[c.gather(i,a,o)]}case"TensorListStack":{const s=R("tensorListId",e,t,n),i=R("elementShape",e,t,n),o=R("elementDType",e,t,n),a=R("numElements",e,t,n),c=n.getTensorList(s.id);return[c.stack(i,o,a)]}case"TensorListFromTensor":{const s=R("tensor",e,t,n),i=R("elementShape",e,t,n),o=R("elementDType",e,t,n),a=eY(s,i,o);return n.addTensorList(a),[a.idTensor]}case"TensorListConcat":{const s=R("tensorListId",e,t,n),i=n.getTensorList(s.id),o=R("dtype",e,t,n),a=R("elementShape",e,t,n);return[i.concat(o,a)]}case"TensorListPushBack":{const s=R("tensorListId",e,t,n),i=R("tensor",e,t,n),o=n.getTensorList(s.id);return o.pushBack(i),[o.idTensor]}case"TensorListPopBack":{const s=R("tensorListId",e,t,n),i=R("elementShape",e,t,n),o=R("elementDType",e,t,n),a=n.getTensorList(s.id);return[a.popBack(i,o)]}case"TensorListSplit":{const s=R("tensor",e,t,n),i=R("elementShape",e,t,n),o=R("lengths",e,t,n),a=sY(s,o,i);return n.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},HQ="control";function AN(e,t,n){const[s,i]=R("fusedOps",e,t,n),o=s==="biasadd",a=i==="prelu",c=s==="fusedbatchnorm",h=R("numArgs",e,t,n);if(o){if(a&&h!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&h!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(c)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");const d=R("strides",e,t,n),m=Vp(e,t,n),y=R("dataFormat",e,t,n).toUpperCase(),b=R("dilations",e,t,n),[w,L]=R("args",e,t,n);return{stride:d,pad:m,dataFormat:y,dilations:b,biasArg:w,preluArg:L,activationFunc:i}}const rY=(e,t,n)=>{switch(e.op){case"Conv1D":{const s=R("stride",e,t,n),i=R("pad",e,t,n),o=R("dataFormat",e,t,n).toUpperCase(),a=R("dilation",e,t,n);return[Nd(R("x",e,t,n),R("filter",e,t,n),s,i,o,a)]}case"Conv2D":{const s=R("strides",e,t,n),i=Vp(e,t,n),o=R("dataFormat",e,t,n).toUpperCase(),a=R("dilations",e,t,n);return[ji(R("x",e,t,n),R("filter",e,t,n),[s[1],s[2]],i,o,[a[1],a[2]])]}case"_FusedConv2D":{const{stride:s,pad:i,dataFormat:o,dilations:a,biasArg:c,preluArg:h,activationFunc:d}=AN(e,t,n);return[vb({x:R("x",e,t,n),filter:R("filter",e,t,n),strides:[s[1],s[2]],pad:i,dataFormat:o,dilations:[a[1],a[2]],bias:c,activation:d,preluActivationWeights:h})]}case"FusedDepthwiseConv2dNative":{const{stride:s,pad:i,dataFormat:o,dilations:a,biasArg:c,preluArg:h,activationFunc:d}=AN(e,t,n);return[bA({x:R("x",e,t,n),filter:R("filter",e,t,n),strides:[s[1],s[2]],pad:i,dataFormat:o,dilations:[a[1],a[2]],bias:c,activation:d,preluActivationWeights:h})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{const s=R("outputShape",e,t,n),i=R("strides",e,t,n),o=Vp(e,t,n);return[Cd(R("x",e,t,n),R("filter",e,t,n),s,[i[1],i[2]],o)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{const s=R("strides",e,t,n),i=Vp(e,t,n),o=R("dilations",e,t,n),a=R("dataFormat",e,t,n).toUpperCase();return[wo(R("input",e,t,n),R("filter",e,t,n),[s[1],s[2]],i,a,[o[1],o[2]])]}case"Conv3D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("dataFormat",e,t,n).toUpperCase(),a=R("dilations",e,t,n);return[Zy(R("x",e,t,n),R("filter",e,t,n),[s[1],s[2],s[3]],i,o,[a[1],a[2],a[3]])]}case"AvgPool":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[Hl(R("x",e,t,n),[o[1],o[2]],[s[1],s[2]],i)]}case"MaxPool":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[eh(R("x",e,t,n),[o[1],o[2]],[s[1],s[2]],i)]}case"MaxPoolWithArgmax":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n),a=R("includeBatchInIndex",e,t,n),{result:c,indexes:h}=VT(R("x",e,t,n),[o[1],o[2]],[s[1],s[2]],i,a);return[c,h]}case"AvgPool3D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[jy(R("x",e,t,n),[o[1],o[2],o[3]],[s[1],s[2],s[3]],i)]}case"MaxPool3D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[cb(R("x",e,t,n),[o[1],o[2],o[3]],[s[1],s[2],s[3]],i)]}case"Dilation2D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("dilations",e,t,n),a=s[1],c=s[2],h=o[1],d=o[2];return[eb(R("x",e,t,n),R("filter",e,t,n),[a,c],i,[h,d],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},qQ="convolution";const oY=(e,t,n)=>{switch(e.op){case"Fill":{const s=R("shape",e,t,n),i=R("dtype",e,t,n),o=R("value",e,t,n);return[Xl(s,o,i)]}case"LinSpace":{const s=R("start",e,t,n),i=R("stop",e,t,n),o=R("num",e,t,n);return[PT(s,i,o)]}case"Multinomial":{const s=R("logits",e,t,n),i=R("numSamples",e,t,n),o=R("seed",e,t,n);return[YT(s,i,o)]}case"OneHot":{const s=R("indices",e,t,n),i=R("depth",e,t,n),o=R("onValue",e,t,n),a=R("offValue",e,t,n);return[fo(s,i,o,a)]}case"Ones":return[Qs(R("shape",e,t,n),R("dtype",e,t,n))];case"OnesLike":return[On(R("x",e,t,n))];case"RandomUniform":return[vo(R("shape",e,t,n),R("minval",e,t,n),R("maxval",e,t,n),R("dtype",e,t,n))];case"Range":{const s=R("start",e,t,n),i=R("stop",e,t,n),o=R("step",e,t,n);return[sh(s,i,o,R("dtype",e,t,n))]}case"TruncatedNormal":{const s=R("shape",e,t,n),i=R("mean",e,t,n),o=R("stdDev",e,t,n),a=R("seed",e,t,n);return[ch(s,i,o,R("dtype",e,t,n),a)]}case"Zeros":return[ct(R("shape",e,t,n),R("dtype",e,t,n))];case"ZerosLike":return[Qe(R("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},jQ="creation";function FL(e,t,n){const s=R("boxes",e,t,n),i=R("scores",e,t,n),o=R("maxOutputSize",e,t,n),a=R("iouThreshold",e,t,n),c=R("scoreThreshold",e,t,n),h=R("softNmsSigma",e,t,n);return{boxes:s,scores:i,maxOutputSize:o,iouThreshold:a,scoreThreshold:c,softNmsSigma:h}}const aY=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{const{boxes:s,scores:i,maxOutputSize:o,iouThreshold:a,scoreThreshold:c,softNmsSigma:h}=FL(e,t,n),d=await Wr.nonMaxSuppressionWithScoreAsync(s,i,o,a,c,h);return[d.selectedIndices,d.selectedScores]}case"NonMaxSuppressionV4":{const{boxes:s,scores:i,maxOutputSize:o,iouThreshold:a,scoreThreshold:c}=FL(e,t,n),h=R("padToMaxOutputSize",e,t,n),d=await Wr.nonMaxSuppressionPaddedAsync(s,i,o,a,c,h);return[d.selectedIndices,d.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{const{boxes:s,scores:i,maxOutputSize:o,iouThreshold:a,scoreThreshold:c}=FL(e,t,n);return[await Wr.nonMaxSuppressionAsync(s,i,o,a,c)]}case"Where":{const s=ve(R("condition",e,t,n),"bool"),i=[await Ib(s)];return s.dispose(),i}case"ListDiff":return qT(R("x",e,t,n),R("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},KQ="dynamic";const cY=(e,t,n)=>{switch(e.op){case"TopKV2":{const s=R("x",e,t,n),i=R("k",e,t,n),o=R("sorted",e,t,n),a=Lb(s,i,o);return[a.values,a.indices]}case"Unique":{const s=R("x",e,t,n),i=qd(s);return[i.values,i.indices]}case"UniqueV2":{const s=R("x",e,t,n),i=R("axis",e,t,n),o=qd(s,i);return[o.values,o.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},XQ="evaluation";const lY=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":const s=R("default",e,t,n);return[Xn(e.name,t,n)||s];case"Placeholder":return[Xn(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{const d=R("x",e,t,n);return[ir(d)]}case"IdentityN":return R("x",e,t,n).map(d=>ir(d));case"Snapshot":const i=R("x",e,t,n);return[ir(i)];case"Shape":return[ns(R("x",e,t,n).shape,"int32")];case"ShapeN":return R("x",e,t,n).map(d=>ns(d.shape));case"Size":return[Ne(R("x",e,t,n).size,"int32")];case"Rank":return[Ne(R("x",e,t,n).rank,"int32")];case"NoOp":return[Ne(1)];case"Print":const o=R("x",e,t,n),a=R("data",e,t,n),c=R("message",e,t,n),h=R("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(c);for(let d=0;d<a.length;d++)console.log(Array.prototype.slice.call(a[d].dataSync()).slice(0,h));return[o];default:throw TypeError(`Node type ${e.op} is not implemented`)}},JQ="graph";const hY=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{const s=R("images",e,t,n),i=R("size",e,t,n),o=R("alignCorners",e,t,n);return[Wr.resizeBilinear(s,[i[0],i[1]],o)]}case"ResizeNearestNeighbor":{const s=R("images",e,t,n),i=R("size",e,t,n),o=R("alignCorners",e,t,n);return[Wr.resizeNearestNeighbor(s,[i[0],i[1]],o)]}case"CropAndResize":{const s=R("image",e,t,n),i=R("boxes",e,t,n),o=R("boxInd",e,t,n),a=R("cropSize",e,t,n),c=R("method",e,t,n),h=R("extrapolationValue",e,t,n);return[Wr.cropAndResize(s,i,o,a,c,h)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},ZQ="image";const uY=(e,t,n)=>{switch(e.op){case"Equal":return[Zs(R("a",e,t,n),R("b",e,t,n))];case"NotEqual":return[kr(R("a",e,t,n),R("b",e,t,n))];case"Greater":return[Ss(R("a",e,t,n),R("b",e,t,n))];case"GreaterEqual":return[Ki(R("a",e,t,n),R("b",e,t,n))];case"Less":return[Zl(R("a",e,t,n),R("b",e,t,n))];case"LessEqual":return[Dr(R("a",e,t,n),R("b",e,t,n))];case"LogicalAnd":return[Ws(R("a",e,t,n),R("b",e,t,n))];case"LogicalNot":return[Ql(R("a",e,t,n))];case"LogicalOr":return[Wd(R("a",e,t,n),R("b",e,t,n))];case"Select":case"SelectV2":return[_n(R("condition",e,t,n),R("a",e,t,n),R("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},QQ="logical";const dY=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[at(R("a",e,t,n),R("b",e,t,n),R("transposeA",e,t,n),R("transposeB",e,t,n))];case"Transpose":return[Me(R("x",e,t,n),R("perm",e,t,n))];case"_FusedMatMul":const[s,i]=R("fusedOps",e,t,n),o=s==="biasadd",a=i==="prelu",c=R("numArgs",e,t,n);if(o){if(a&&c!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&c!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}const[h,d]=R("args",e,t,n);return[ep({a:R("a",e,t,n),b:R("b",e,t,n),transposeA:R("transposeA",e,t,n),transposeB:R("transposeB",e,t,n),bias:h,activation:i,preluActivationWeights:d})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},eee="matrices";const pY=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[bo(R("x",e,t,n),R("mean",e,t,n),R("variance",e,t,n),R("offset",e,t,n),R("scale",e,t,n),R("epsilon",e,t,n))];case"FusedBatchNormV3":return[bo(R("x",e,t,n),R("mean",e,t,n),R("variance",e,t,n),R("offset",e,t,n),R("scale",e,t,n),R("epsilon",e,t,n))];case"LRN":return[rb(R("x",e,t,n),R("radius",e,t,n),R("bias",e,t,n),R("alpha",e,t,n),R("beta",e,t,n))];case"Softmax":return[No(R("x",e,t,n))];case"LogSoftmax":return[_d(R("x",e,t,n))];case"SparseToDense":return[xb(R("sparseIndices",e,t,n),R("outputShape",e,t,n),R("sparseValues",e,t,n),R("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},tee="normalization";const mY=(e,t,n)=>{switch(e.op){case"Max":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[qn(R("x",e,t,n),s,i)]}case"Mean":{const s=R("axis",e,t,n),i=R("keepDims",e,t,n);return[zt(R("x",e,t,n),s,i)]}case"Min":{const 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ss(o,i,s)}case"ScatterNd":{const s=R("indices",e,t,n),i=R("values",e,t,n),o=R("shape",e,t,n);return[dA(s,i,o)]}case"GatherNd":{const s=R("x",e,t,n),i=R("indices",e,t,n);return[pA(s,i)]}case"SparseToDense":{const s=R("sparseIndices",e,t,n),i=R("outputShape",e,t,n),o=R("sparseValues",e,t,n),a=R("defaultValue",e,t,n);return[xb(s,o,i,o.dtype===a.dtype?a:ve(a,o.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},see="slice_join";const gY=(e,t,n)=>{switch(e.op){case"FFT":return[rh(R("x",e,t,n))];case"IFFT":return[Wa(R("x",e,t,n))];case"RFFT":return[oh(R("x",e,t,n))];case"IRFFT":return[Hd(R("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},iee="spectral";const yY=(e,t,n)=>{switch(e.op){case"Cast":return[ve(R("x",e,t,n),R("dtype",e,t,n))];case"ExpandDims":{const s=R("axis",e,t,n);return[Hn(R("x",e,t,n),s)]}case"Squeeze":{const s=R("axis",e,t,n);return[Fr(R("x",e,t,n),s)]}case"Reshape":return[K(R("x",e,t,n),R("shape",e,t,n))];case"PadV2":case"Pad":return[Ci(R("x",e,t,n),R("padding",e,t,n),R("constantValue",e,t,n))];case"SpaceToBatchND":{const s=R("blockShape",e,t,n),i=R("paddings",e,t,n);return[th(R("x",e,t,n),s,i)]}case"BatchToSpaceND":{const s=R("blockShape",e,t,n),i=R("crops",e,t,n);return[ql(R("x",e,t,n),s,i)]}case"DepthToSpace":{const s=R("blockSize",e,t,n),i=R("dataFormat",e,t,n).toUpperCase();return[Qy(R("x",e,t,n),s,i)]}case"BroadcastTo":return[jl(R("x",e,t,n),R("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},ree="transformation";function vN(e,t,n){const s=((i,o,a)=>{switch(i.category){case"arithmetic":return ee(()=>XV(i,o,a));case"basic_math":return ee(()=>JV(i,o,a));case"control":return iY(i,o,a);case"convolution":return ee(()=>rY(i,o,a));case"creation":return ee(()=>oY(i,o,a));case"dynamic":return aY(i,o,a);case"evaluation":return ee(()=>cY(i,o,a));case"image":return ee(()=>hY(i,o,a));case"graph":return ee(()=>lY(i,o,a));case"logical":return ee(()=>uY(i,o,a));case"matrices":return ee(()=>dY(i,o,a));case"normalization":return ee(()=>pY(i,o,a));case"reduction":return ee(()=>mY(i,o,a));case"slice_join":return ee(()=>fY(i,o,a));case"spectral":return ee(()=>gY(i,o,a));case"transformation":return ee(()=>yY(i,o,a));case"custom":const c=LN(i.op);if(c&&c.customExecutor)return c.customExecutor(new KV(i,o,a));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return s instanceof Promise?s.then(i=>[].concat(i)):[].concat(s)}class NN{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){const e=[];for(let t=0;t<this.contexts.length-1;t++){const n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;const e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(const t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(const t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}}function CN(e,t,n,s){const i=new Set,o=[];let a=null,c=null;const h=new Set,d=Object.keys(e).map(b=>os(b)[0]);let m=[];s!=null&&(m=s.map(b=>os(b.name)[0]));const y=[...t];for(;y.length>0;){const b=y.pop();if((RN(b)||SY(b))&&(a==null&&(a=b,c=a.children.map(w=>w.name).filter(w=>i.has(w)))),i.add(b.name),n[b.name]!=null)continue;if(d.indexOf(b.name)!==-1)continue;if(m.indexOf(b.name)!==-1)continue;if(b.inputs.length===0){o.push(b.name);continue}b.inputs.forEach(w=>{if(h.has(w.name))return;h.add(w.name),y.push(w)})}return{inputs:e,outputs:t,usedNodes:i,missingInputs:o,dynamicNode:a,syncInputs:c}}function bY(e,t,n){const{usedNodes:s,inputs:i}=n,o=[],a=Object.keys(i).map(m=>os(m)[0]).map(m=>e.nodes[m]),c=e.initNodes;a.forEach(m=>{s.has(m.name)&&o.push(m)}),e.weights.forEach(m=>{s.has(m.name)&&o.push(m)}),c!=null&&c.forEach(m=>{s.has(m.name)&&o.push(m)});const h=new Set,d=[];for(;o.length>0;){const m=o.pop();h.add(m.name),t[m.name]||d.push(m),m.children.forEach(y=>{!h.has(y.name)&&s.has(y.name)&&y.inputs.every(b=>h.has(b.name))&&o.push(y)})}return d}const wY=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],LY=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"];function RN(e){return wY.indexOf(e.op)>=0}function SY(e){return LY.indexOf(e.op)>=0}class _L{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new _L(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){const t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{const t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){const n=e.map(i=>i.name).sort(),s=t.map(i=>i.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){const n=CN(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:i,syncInputs:o}=n;if(i!=null)throw new Error(`This execution contains the node '${i.name}', which has the dynamic op '${i.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${o}]`);if(s.length>0){const a=t.map(h=>h.name),c=Object.keys(e);throw new Error(`Cannot compute the outputs [${a}] from the provided inputs [${c}]. Missing the following inputs: [${s}]`)}return bY(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);const n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);const s=n.map(m=>this.graph.nodes[os(m)[0]]),i=t.map(m=>os(m)[0]);let o=i.map(m=>this.graph.nodes[m]);o.length===0&&(o=this._outputs);const a=this.getCompilationKey(s,o);let c=this.compiledMap.get(a);c==null&&(c=this.compile(e,o),this.compiledMap.set(a,c));const h={},d={};return ee(()=>{const m=new NN(this.weightMap,h,d,this.functionExecutorMap),y=Object.assign({},this.weightMap);Object.keys(e).forEach(L=>{const[T,A]=os(L),N=[];N[A]=e[L],y[T]=N});const b=this.getFrozenTensorIds(y),w={};for(let L=0;L<c.length;L++){const T=c[L];if(!y[T.name]){const A=vN(T,y,m);if(A instanceof Promise)throw new Error(`The execution of the op '${T.op}' returned a promise. Please use model.executeAsync() instead.`);y[T.name]=A,this.checkTensorForDisposal(T.name,T,y,m,b,i,w)}}return this.parent==null&&m.dispose(b),t.map(L=>Xn(L,y,m))})}getFrozenTensorIds(e){const t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,i,o,a){if(t.category==="control"||o.indexOf(e)!==-1)return;n[e].forEach(c=>{c!=null&&(a[c.id]=(a[c.id]||0)+t.children.length)}),t.inputs.forEach(c=>{if(c.category!=="control"){const h=fV(c.name,n,s);h!=null&&h.forEach(d=>{if(d&&!i.has(d.id)){const m=a[d.id];m===1?(d.dispose(),delete a[d.id]):m!=null&&a[d.id]--}})}})}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,s={},i={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));const o=new NN(this.weightMap,s,i,this.functionExecutorMap),a=await this.executeWithControlFlow(e,o,t,n),c=t.map(y=>Xn(y,a,o)),h=c.map(y=>y.id),d=Object.keys(e).map(y=>e[y].id),m=new Set([...h,...d,...this.weightIds]);return Object.keys(a).forEach(y=>{const b=a[y];b.forEach(w=>{w&&!w.isDisposed&&!m.has(w.id)&&w.dispose()})}),this.parent==null&&o.dispose(m),c}async executeFunctionAsync(e,t,n){const s=e.reduce((i,o,a)=>(i[this.inputs[a].name]=o,i),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){const i=Object.keys(e),o=i.map(E=>this.graph.nodes[os(E)[0]]),a=n.map(E=>os(E)[0]),c=a.map(E=>this.graph.nodes[E]),{usedNodes:h,missingInputs:d,dynamicNode:m,syncInputs:y}=CN(e,c,this.weightMap),b=[...o,...this.graph.weights].map(E=>({node:E,contexts:t.currentContext})),w=Object.assign({},this.weightMap);Object.keys(e).forEach(E=>{const[D,F]=os(E),_=[];_[F]=e[E],w[D]=_});const L={},T=this.getFrozenTensorIds(w),A={};for(;b.length>0;){const E=this.processStack(o,b,t,w,A,T,a,L,h);await Promise.all(E)}m==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");const N=c.filter(E=>!RN(E)&&!Xn(E.name,w,t)).map(E=>E.name);if(N.length>0){let E="";throw m!=null&&(E=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${y}]`),new Error(`Cannot compute the outputs [${N}] from the provided inputs [${i}]. Consider providing the following inputs: [${d}]. ${E}`)}return w}processStack(e,t,n,s,i,o,a,c,h){const d=[];for(;t.length>0;){const m=t.pop();n.currentContext=m.contexts;let y="";if(m.node.op==="Enter"&&R("isConstant",m.node,s,n)&&([y]=sr(m.node.name,n)),e.indexOf(m.node)===-1){const b=vN(m.node,s,n);y||([y]=sr(m.node.name,n));const w=n.currentContext;b instanceof Promise?d.push(b.then(L=>(s[y]=L,n.currentContext=w,this.checkTensorForDisposal(y,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,h),L))):(s[y]=b,this.checkTensorForDisposal(y,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,h))}else this.processChildNodes(m.node,t,n,s,i,h)}return d}processChildNodes(e,t,n,s,i,o){e.children.forEach(a=>{const[c]=sr(a.name,n);if(i[c]||!o.has(a.name))return;a.op==="Merge"?a.inputNames.some(h=>!!Xn(h,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a})):a.inputNames.every(h=>!!Xn(h,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a}))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{const n=e[t],[s]=os(t),i=this.graph.nodes[s];if(i.attrParams.shape&&i.attrParams.shape.value){const o=i.attrParams.shape.value,a=o.length===n.shape.length&&n.shape.every((c,h)=>o[h]===-1||o[h]===c);k(a,()=>`The shape of dict['${i.name}'] provided in model.execute(dict) must be [${o}], but was [${n.shape}]`)}i.attrParams.dtype&&i.attrParams.dtype.value&&k(n.dtype===i.attrParams.dtype.value,()=>`The dtype of dict['${i.name}'] provided in model.execute(dict) must be ${i.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){const t={};for(const n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){const s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){const t=Object.keys(e).filter(n=>{const[s]=os(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{if(this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null){const n=this._signature.outputs[t];return n.name}return t},{})}checkOutputs(e){e.forEach(t=>{const[n]=os(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}}const IY="?tfjs-format=file",xY="model.json";class ON{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={})}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){const e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=fd(e,this.loadOptions);else{const t=Ly(e,this.loadOptions);if(t.length===0)t.push(fd(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;const t=this.artifacts.modelTopology;let n={};this.artifacts.userDefinedMetadata!=null&&(n=this.artifacts.userDefinedMetadata.signature),this.version=`${t.versions.producer}.${t.versions.minConsumer}`;const s=ud(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new _L(SN.Instance.transformGraph(t,n)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),e.modelInitializer!=null){const i=SN.Instance.transformGraph(e.modelInitializer);this.initializer=new _L(i),this.initializer.weightMap=this.executor.weightMap,this.initializer.execute({},[])}return!0}async save(e,t){if(typeof e=="string"){const n=wy(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Q)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose()}}async function TY(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&(e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${xY}${IY}`));const n=new ON(e,t);return await n.load(),n}const EN="2.6.0";function AY(e,t){return Yp(e,t)}function Yp(e,t,n=new Map,s=new Set){if(e==null)return null;if(s.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(i.recurse)if(Ja(e)){const o=Array.isArray(e)?[]:{};s.add(e);for(const a in e){const c=e[a],h=Yp(c,t,n,s);o[a]=h}return s.delete(e),o}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,i.value),i.value}function vY(e,t=kN){return DN(e,t)}function DN(e,t,n=new Set){const s=e[0];if(n.has(s))throw new Error("Circular references are not supported.");const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(i.recurse)if(Ja(s)){const o=Array.isArray(s)?[]:{};n.add(s);for(const a in s){const c=e.map(d=>d[a]),h=DN(c,t,n);o[a]=h}return n.delete(s),o}else throw new Error(`Can't recurse into non-iterable type: ${s}`);else return i.value}function kN(e){return e===null?null:Ja(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function FN(e,t){const n=new Map;Yp(e,t,n);for(const i of Array.from(n.keys())){const o=n.get(i);if(o instanceof Promise){const a=await o;n.set(i,a)}}const s=Yp(e,t,n);return s}function Ja(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Q))}function NY(e){return e==null||CY(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Q||wn(e)}function CY(e){return e===null||typeof e!="object"&&typeof e!="function"}function RY(e){return AY(e,OY)}function OY(e){return e instanceof Q?{value:e.clone(),recurse:!1}:Ja(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class _N{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(const t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);const e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}}class WL extends _N{constructor(){super(WL.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){const e=this.capacity*2,t=new Array(e),n=this.length();for(let s=0;s<n;s++)t[s]=this.get(this.wrap(this.begin+s));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}}WL.INITIAL_CAPACITY=32;function WN(e){return new DY(e)}function oee(e){let t=e;return zh(()=>({value:t++,done:!1}))}function zh(e){return new kY(e)}function $N(e,t){return new BN(e,t)}function aee(e,t,n){return $N(zh(e).take(t),n)}function EY(e,t=Yr.FAIL){return new zY(e,t)}class fn{async toArray(){const e=[];let t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){const e=this.prefetch(100),t=[];let n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new MY(this,e)}filter(e){return new UY(this,e)}map(e){return new BY(this,e)}mapAsync(e){return new UN(this,e)}serialMapAsync(e){return new UN(this,e).serial()}flatmap(e){return new PY(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new $Y(this,e,t)}columnMajorBatch(e,t=!0,n=kN){const s=this.rowMajorBatch(e,t);return s.map(i=>vY(i,n))}concatenate(e,t){return new BN(WN([this,e]),t)}take(e){return e<0||e==null?this:new WY(this,e)}skip(e){return e<0||e==null?this:new _Y(this,e)}prefetch(e){return new MN(this,e)}shuffle(e,t){return new GY(this,e,t)}serial(){return new FY(this)}}class DY extends fn{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};const e=this.items[this.trav];return this.trav++,{value:RY(e),done:!1}}}class kY extends fn{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}}class FY extends fn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class _Y extends fn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){const e=await this.upstream.next();if(e.done)return e;He(e.value)}return this.upstream.next()}}class WY extends fn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class $Y extends fn{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){const e=[];for(;e.length<this.batchSize;){const t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class UY extends fn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){const e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;He(e.value)}}}class BY extends fn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=Hi(e.value),n=this.transform(e.value),s=Hi(n);for(const i of t)ld(i,s)||i.dispose();return{value:n,done:!1}}}class MY extends fn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}}class UN extends fn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=Hi(e.value),n=await this.transform(e.value),s=Hi(n);for(const i of t)ld(i,s)||i.dispose();return{value:n,done:!1}}}class $L extends fn{constructor(){super();this.outputQueue=new WL,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class PY extends $L{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=Hi(e.value),n=this.transform(e.value),s=Hi(n);this.outputQueue.pushAll(n);for(const i of t)ld(i,s)||i.dispose();return!0}}class BN extends fn{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){const e="TODO: fill in upstream of chained summaries";return`${e} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){const n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}var Yr;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Yr||(Yr={}));class zY extends fn{constructor(e,t=Yr.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){const e="TODO: fill in upstream of zip summaries";return`{${e}} -> Zip`}async nextState(e){await e;let t=0,n=0;function s(o){if(o instanceof fn){const a=o.next();return{value:a.then(c=>(t++,c.done&&n++,c.value)),recurse:!1}}else return{value:null,recurse:!0}}const i=await FN(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Yr.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Yr.SHORTEST:return{value:null,done:!0};case Yr.LONGEST:default:}return this.count++,{value:i,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class MN extends fn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new _N(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){const e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}}class GY extends MN{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=_a(n||Vn().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){const e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}}class Za{constructor(){this.size=null}batch(e,t=!0){const n=this;k(e>0,()=>`batchSize needs to be positive, but it is
+      ${e}`);let s;return this.size===Infinity||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),as(async()=>(await n.iterator()).columnMajorBatch(e,t,HY),s)}concatenate(e){const t=this;let n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,as(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){const t=this;let n;return this.size===Infinity?n=Infinity:n=null,as(async()=>(await t.iterator()).filter(s=>ee(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return as(async()=>(await t.iterator()).map(n=>ee(()=>e(n))),this.size)}mapAsync(e){const t=this;return as(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const t=this;return as(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){const t=this;let n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,as(async()=>{const s=zh(async()=>({value:await t.iterator(),done:!1}));return $N(s.take(e))},n)}skip(e){const t=this;let n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,as(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const s=this,i=_a(t||Vn().toString());return as(async()=>{let o=i.int32();return n&&(o+=i.int32()),(await s.iterator()).shuffle(e,o.toString())},this.size)}take(e){const t=this;let n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,as(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}}Za.MAX_BUFFER_SIZE=1e4;function as(e,t=null){return new class extends Za{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function VY(e){return as(async()=>WN(e),e.length)}function YY(e){if(!Ja(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(const n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return as(async()=>{const n=await FN(e,s=>{if(s instanceof Za)return{value:s.iterator(),recurse:!1};if(Ja(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return EY(n,Yr.SHORTEST)},t)}function HY(e){if(e===null)return null;const t=e[0];if(NY(t)){const n=qY(e);return{value:n,recurse:!1}}return{value:null,recurse:!0}}function qY(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Q?is(e):en(e)}class PN extends Za{constructor(e){super();this.input=e}async iterator(){const e=await this.input.iterator(),t=e.decodeUTF8(),n=t.split(`
+`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s));return n}}const Hp='"',Gh=Symbol("out"),zN=Symbol("field"),qp=Symbol("quote"),UL=Symbol("quoteafterquote"),GN=Symbol("quoteinquote");class VN extends Za{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new PN(e),t||(t={}),this.hasHeader=!(t.hasHeader===!1),this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){const e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&k(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);const t=this.fullColumnNames.reduce((s,i)=>(s[i]=s[i]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(k(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs)for(const s of Object.keys(this.columnConfigs)){const i=this.fullColumnNames.indexOf(s);if(i===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){const e=await this.base.iterator(),t=await e.next();if(t.done)throw new Error("No data was found for CSV parsing.");const n=t.value,s=this.parseRow(n,!1);return s}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){const t=this.parseRow(e),n={},s={};for(let i=0;i<this.fullColumnNames.length;i++){const o=this.fullColumnNames[i],a=this.columnConfigs?this.columnConfigs[o]:null;if(this.configuredColumnsOnly&&!a)continue;{const c=t[i];let h=null;if(c==="")if(a&&a.default!==void 0)h=a.default;else{if(a&&(a.required||a.isLabel))throw new Error(`Required column ${o} is empty in this line: ${e}`);h=void 0}else{const d=Number(c);if(isNaN(d))a&&a.dtype==="bool"?h=this.getBoolean(c):h=c;else if(!a||!a.dtype)h=d;else switch(a.dtype){case"float32":h=d;break;case"int32":h=Math.floor(d);break;case"bool":h=this.getBoolean(c);break;default:h=d}}a&&a.isLabel?s[o]=h:n[o]=h}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){const n=[];let s=0;const i=e.length;let o=Gh;for(let a=0;a<i;a++)switch(o){case Gh:switch(e.charAt(a)){case Hp:s=a+1,o=qp;break;case this.delimiter:if(s=a+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),o=Gh;break;default:o=zN,s=a;break}break;case zN:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a)),o=Gh,s=a+1;break;default:}break;case qp:switch(e.charAt(a)){case Hp:o=UL;break;default:}break;case UL:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a-1)),o=Gh,s=a+1;break;case Hp:o=qp;break;default:o=GN;break}break;case GN:switch(e.charAt(a)){case Hp:o=qp;break;default:}break;default:}if(o===UL?n.push(e.substring(s,i-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}}class YN extends fn{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;const t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=!(e.includeSpectrogram===!1),this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(C().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");const t=new YN(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");const e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);const t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize);return}async next(){if(this.isClosed)return{value:null,done:!0};let e,t;const n=await this.getAudioData();if(this.includeSpectrogram){const s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){const s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){const e=[],t=[];let n=0;return new Promise(s=>{const i=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(i),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){const t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,i)=>n.set(s,i*t)),n}getTensorFromAudioDataArray(e,t){const n=new Float32Array(we(t));return n.set(e,n.length-e.length),en(n,t)}}class HN extends fn{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=ns([0],"int32"),this.webcamConfig.centerCrop){const n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,i=(1-n)/2,o=(1-s)/2,a=i+n,c=s+o;this.cropBox=_r([o,i,c,a],[1,4])}else this.cropBox=_r([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(C().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}const n=new HN(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=uT(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return ee(()=>{const t=e.toFloat().expandDims(0);let n;n=Wr.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");const s=n.shape;return n.reshape(s.slice(1))})}async capture(){return(await this.next()).value}stop(){const e=this.stream.getTracks();e.forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class qN{}class jN extends fn{split(e){return new jY(this,e)}}class jY extends jN{constructor(e,t){super();this.upstream=e,this.impl=new KY(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class KY extends $L{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){const e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);const t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(const n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}}class XY extends fn{decodeUTF8(){return new JY(this)}}class JY extends jN{constructor(e){super();this.upstream=e,this.impl=new ZY(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class ZY extends $L{constructor(e){super();if(this.upstream=e,C().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{const{StringDecoder:t}=require("string_decoder");this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){const e=await this.upstream.next();let t;if(e.done)return!1;t=e.value;let n;return C().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}}class KN extends XY{constructor(e,t={}){super();this.file=e,this.options=t,k(e instanceof Uint8Array||(C().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};const e=new Promise((t,n)=>{const s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{const i=new FileReader;i.onload=a=>{let c=i.result;if(c instanceof ArrayBuffer&&(c=new Uint8Array(c)),!(c instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(c)},i.onabort=a=>n(new Error("Aborted")),i.onerror=a=>n(new Error(a.type));const o=this.file.slice(this.offset,s);i.readAsArrayBuffer(o)}this.offset=s});return{value:await e,done:!1}}}async function QY(e,t={}){let n,s;typeof e=="string"?n=e:(n=e.url,s=eH(e));const i=await $x(n,s);if(i.ok){const o=new Uint8Array(await i.arrayBuffer());return new KN(o,t)}else throw new Error(i.statusText)}const eH=e=>{const t={method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity};return t};function XN(e){return typeof e=="string"&&e.substr(0,7)==="file://"}class JN extends qN{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(XN(this.input)&&C().get("IS_NODE")){const e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new KN(this.input,this.options)}}class ZN extends 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 ============================
 Hi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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s=t.strideDepth,i=t.strideHeight,o=t.strideWidth,a=t.dilationDepth,c=t.dilationHeight,h=t.dilationWidth,d=t.effectiveFilterDepth,m=t.effectiveFilterHeight,y=t.effectiveFilterWidth,b=t.padInfo.front,w=t.padInfo.top,L=t.padInfo.left,T=n==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,A=this.readSync(e.dataId),N=Ze(t.outShape,e.dtype),E=N.values,D=t.outShape[1]*t.outShape[2]*t.outShape[3]*t.outShape[4],F=t.outShape[2]*t.outShape[3]*t.outShape[4],_=t.outShape[3]*t.outShape[4],B=t.outShape[4];for(let $=0;$<t.batchSize;++$){const H=$*D,q=$*e.strides[0];for(let J=0;J<t.inChannels;++J)for(let re=0;re<t.outDepth;++re){const ce=re*s-b;let ue=ce;for(;ue<0;)ue+=a;const he=Math.min(t.inDepth,d+ce),de=H+re*F;for(let le=0;le<t.outHeight;++le){const ye=le*i-w;let pe=ye;for(;pe<0;)pe+=c;const Ie=Math.min(t.inHeight,m+ye),Se=de+le*_;for(let Ee=0;Ee<t.outWidth;++Ee){const We=Ee*o-L;let Oe=We;for(;Oe<0;)Oe+=h;const $e=Math.min(t.inWidth,y+We),Ye=Se+Ee*B;let et=T,bt=0,Jt=0;for(let 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ue=0;for(let he=0;he<b;he+=d){const de=(J+he)/s;if(de<0||de>=n.outDepth||Math.floor(de)!==de)continue;for(let le=0;le<w;le+=m){const ye=(re+le)/i;if(ye<0||ye>=n.outHeight||Math.floor(ye)!==ye)continue;for(let pe=0;pe<L;pe+=y){const Ie=(ce+pe)/o;if(Ie<0||Ie>=n.outWidth||Math.floor(Ie)!==Ie)continue;const Se=F.get(_,de,ye,Ie,B);ue+=Se}}}E.set(ue*D,_,$,H,q,B)}return E.toTensor()}maxPool3d(e,t){return xe(e,"maxPool3d"),this.pool3d(e,t,"max").toFloat()}maxPool3dPositions(e,t){const n=Ze(t.outShape,"int32"),s=t.strideDepth,i=t.strideHeight,o=t.strideWidth,a=t.dilationDepth,c=t.dilationHeight,h=t.dilationWidth,d=t.effectiveFilterDepth,m=t.effectiveFilterHeight,y=t.effectiveFilterWidth,b=t.padInfo.front,w=t.padInfo.top,L=t.padInfo.left,T=this.bufferSync(e);for(let A=0;A<t.batchSize;++A)for(let N=0;N<t.inChannels;++N)for(let E=0;E<t.outDepth;++E){const D=E*s-b;let F=D;for(;F<0;)F+=a;const _=Math.min(t.inDepth,d+D);for(let B=0;B<t.outHeight;++B){const $=B*i-w;let H=$;for(;H<0;)H+=c;const q=Math.min(t.inHeight,m+$);for(let J=0;J<t.outWidth;++J){const re=J*o-L;let ce=re;for(;ce<0;)ce+=h;const ue=Math.min(t.inWidth,y+re);let he=Number.NEGATIVE_INFINITY,de=-1;for(let le=F;le<_;le+=a){const ye=le-D;for(let pe=H;pe<q;pe+=c){const Ie=pe-$;for(let Se=ce;Se<ue;Se+=h){const Ee=Se-re,We=T.get(A,le,pe,Se,N);We>=he&&(he=We,de=ye*m*y+Ie*m+Ee)}}}n.set(de,A,E,B,J,N)}}}return n.toTensor()}maxPool3dBackprop(e,t,n,s){xe([t,n],"maxPool3dBackprop");const i=this.maxPool3dPositions(t,s),o=s.strideDepth,a=s.strideHeight,c=s.strideWidth,h=s.dilationDepth,d=s.dilationHeight,m=s.dilationWidth,y=s.effectiveFilterDepth,b=s.effectiveFilterHeight,w=s.effectiveFilterWidth,L=y-1-s.padInfo.front,T=w-1-s.padInfo.left,A=b-1-s.padInfo.top,N=Ze(t.shape,"float32"),E=this.bufferSync(i),D=this.bufferSync(e);for(let F=0;F<s.batchSize;++F)for(let _=0;_<s.inChannels;++_)for(let B=0;B<s.inDepth;++B)for(let $=0;$<s.inHeight;++$)for(let H=0;H<s.inWidth;++H){const q=B-L,J=$-A,re=H-T;let ce=0;for(let 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yH={kernelName:_g,backendName:"cpu",kernelFunc:Vh};function Yh(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{dtype:o}=s;if(o==="complex64"){if(i.dtype==="complex64")return Ja({inputs:{x:i},backend:n});const a=ct(i.shape),c=Yh({inputs:{x:i},backend:n,attrs:{dtype:"float32"}}),h=li({inputs:{real:c,imag:a},backend:n});return a.dispose(),n.disposeIntermediateTensorInfo(c),h}if(i.dtype==="complex64"){const a=Vh({inputs:{input:i},backend:n}),c=Yh({inputs:{x:a},backend:n,attrs:{dtype:o}});return n.disposeIntermediateTensorInfo(a),c}if(!iy(i.dtype,o)){const a=Ja({inputs:{x:i},backend:n});return{dataId:a.dataId,shape:a.shape,dtype:o}}if(o==="int32"){const a=n.data.get(i.dataId).values,c=Int32Array.from(a);return n.makeTensorInfo(i.shape,"int32",c)}if(o==="bool"){const a=n.data.get(i.dataId).values,c=Tr([0],i.dtype),[h,d]=_o((m,y)=>m!==y?1:0)(i.shape,[],a,c,"bool");return n.makeTensorInfo(d,"bool",h)}throw new Error(`Error in Cast: failed to cast ${i.dtype} to ${o}`)}const 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pq={kernelName:Uu,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s,filter:i}=e,{strides:o,pad:a,dilations:c}=n,h=t,d=h.data.get(s.dataId).values,m=s.shape.length,y=h.data.get(i.dataId).values,b=i.shape.length,{batchSize:w,inHeight:L,inWidth:T,inChannels:A,outHeight:N,outWidth:E,padInfo:D,strideHeight:F,strideWidth:_,filterHeight:B,filterWidth:$,dilationHeight:H,dilationWidth:q,outShape:J}=Td(s.shape,i.shape,o,a,"NHWC",c),re=we(J),ce=J.length,ue=ao(s.dtype,re);for(let de=0;de<w;++de)for(let le=0;le<N;++le){const ye=le*F-D.top;for(let pe=0;pe<E;++pe){const Ie=pe*_-D.left;for(let Se=0;Se<A;++Se){let Ee=Number.MIN_SAFE_INTEGER;for(let Oe=0;Oe<B;++Oe){const $e=ye+Oe*H;if($e>=0&&$e<L)for(let Ye=0;Ye<$;++Ye){const et=Ie+Ye*q;if(et>=0&&et<T){const bt=Js([de,$e,et,Se],m,Ot(s.shape)),Jt=Js([Oe,Ye,Se],b,Ot(i.shape)),hn=d[bt]+y[Jt];hn>Ee&&(Ee=hn)}}}const We=Js([de,le,pe,Se],ce,Ot(J));ue[We]=Ee}}}const 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s=t.strideDepth,i=t.strideHeight,o=t.strideWidth,a=t.dilationDepth,c=t.dilationHeight,h=t.dilationWidth,d=t.effectiveFilterDepth,m=t.effectiveFilterHeight,y=t.effectiveFilterWidth,b=t.padInfo.front,w=t.padInfo.top,L=t.padInfo.left,T=n==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,A=this.readSync(e.dataId),N=Ze(t.outShape,e.dtype),E=N.values,D=t.outShape[1]*t.outShape[2]*t.outShape[3]*t.outShape[4],F=t.outShape[2]*t.outShape[3]*t.outShape[4],_=t.outShape[3]*t.outShape[4],B=t.outShape[4];for(let $=0;$<t.batchSize;++$){const H=$*D,q=$*e.strides[0];for(let J=0;J<t.inChannels;++J)for(let re=0;re<t.outDepth;++re){const ce=re*s-b;let ue=ce;for(;ue<0;)ue+=a;const he=Math.min(t.inDepth,d+ce),de=H+re*F;for(let le=0;le<t.outHeight;++le){const ye=le*i-w;let pe=ye;for(;pe<0;)pe+=c;const Ie=Math.min(t.inHeight,m+ye),Se=de+le*_;for(let Ee=0;Ee<t.outWidth;++Ee){const We=Ee*o-L;let Oe=We;for(;Oe<0;)Oe+=h;const $e=Math.min(t.inWidth,y+We),Ye=Se+Ee*B;let et=T,bt=0,Jt=0;for(let ls=ue;ls<he;ls+=a){const Yt=q+ls*e.strides[1];for(let $n=pe;$n<Ie;$n+=c){const hi=Yt+$n*e.strides[2];for(let vs=Oe;vs<$e;vs+=h){const hs=hi+vs*e.strides[3],zs=A[hs+J];if(n==="max"&&zs>et?et=zs:n==="avg"&&(bt+=zs,Jt++),isNaN(et))break}if(isNaN(et))break}if(isNaN(et))break}const un=Ye+J;E[un]=n==="avg"?bt/Jt:et}}}}return N.toTensor()}avgPool3d(e,t){return xe(e,"avgPool3d"),this.pool3d(e,t,"avg").toFloat()}avgPool3dBackprop(e,t,n){xe([e,t],"avgPool3dBackprop");const s=n.strideDepth,i=n.strideHeight,o=n.strideWidth,a=n.filterDepth,c=n.filterHeight,h=n.filterWidth,d=n.dilationDepth,m=n.dilationHeight,y=n.dilationWidth,b=n.effectiveFilterDepth,w=n.effectiveFilterHeight,L=n.effectiveFilterWidth,T=b-1-n.padInfo.front,A=L-1-n.padInfo.left,N=w-1-n.padInfo.top,E=Ze(t.shape,"float32"),D=1/(a*c*h),F=this.bufferSync(e);for(let _=0;_<n.batchSize;++_)for(let B=0;B<n.inChannels;++B)for(let $=0;$<n.inDepth;++$)for(let H=0;H<n.inHeight;++H)for(let q=0;q<n.inWidth;++q){const J=$-T,re=H-N,ce=q-A;let ue=0;for(let he=0;he<b;he+=d){const de=(J+he)/s;if(de<0||de>=n.outDepth||Math.floor(de)!==de)continue;for(let le=0;le<w;le+=m){const ye=(re+le)/i;if(ye<0||ye>=n.outHeight||Math.floor(ye)!==ye)continue;for(let pe=0;pe<L;pe+=y){const Ie=(ce+pe)/o;if(Ie<0||Ie>=n.outWidth||Math.floor(Ie)!==Ie)continue;const Se=F.get(_,de,ye,Ie,B);ue+=Se}}}E.set(ue*D,_,$,H,q,B)}return E.toTensor()}maxPool3d(e,t){return xe(e,"maxPool3d"),this.pool3d(e,t,"max").toFloat()}maxPool3dPositions(e,t){const n=Ze(t.outShape,"int32"),s=t.strideDepth,i=t.strideHeight,o=t.strideWidth,a=t.dilationDepth,c=t.dilationHeight,h=t.dilationWidth,d=t.effectiveFilterDepth,m=t.effectiveFilterHeight,y=t.effectiveFilterWidth,b=t.padInfo.front,w=t.padInfo.top,L=t.padInfo.left,T=this.bufferSync(e);for(let A=0;A<t.batchSize;++A)for(let N=0;N<t.inChannels;++N)for(let E=0;E<t.outDepth;++E){const D=E*s-b;let F=D;for(;F<0;)F+=a;const _=Math.min(t.inDepth,d+D);for(let B=0;B<t.outHeight;++B){const $=B*i-w;let H=$;for(;H<0;)H+=c;const q=Math.min(t.inHeight,m+$);for(let J=0;J<t.outWidth;++J){const re=J*o-L;let ce=re;for(;ce<0;)ce+=h;const ue=Math.min(t.inWidth,y+re);let he=Number.NEGATIVE_INFINITY,de=-1;for(let le=F;le<_;le+=a){const ye=le-D;for(let pe=H;pe<q;pe+=c){const Ie=pe-$;for(let Se=ce;Se<ue;Se+=h){const Ee=Se-re,We=T.get(A,le,pe,Se,N);We>=he&&(he=We,de=ye*m*y+Ie*m+Ee)}}}n.set(de,A,E,B,J,N)}}}return n.toTensor()}maxPool3dBackprop(e,t,n,s){xe([t,n],"maxPool3dBackprop");const i=this.maxPool3dPositions(t,s),o=s.strideDepth,a=s.strideHeight,c=s.strideWidth,h=s.dilationDepth,d=s.dilationHeight,m=s.dilationWidth,y=s.effectiveFilterDepth,b=s.effectiveFilterHeight,w=s.effectiveFilterWidth,L=y-1-s.padInfo.front,T=w-1-s.padInfo.left,A=b-1-s.padInfo.top,N=Ze(t.shape,"float32"),E=this.bufferSync(i),D=this.bufferSync(e);for(let F=0;F<s.batchSize;++F)for(let _=0;_<s.inChannels;++_)for(let B=0;B<s.inDepth;++B)for(let $=0;$<s.inHeight;++$)for(let H=0;H<s.inWidth;++H){const q=B-L,J=$-A,re=H-T;let ce=0;for(let ue=0;ue<y;ue+=h){const he=(q+ue)/o;if(he<0||he>=s.outDepth||Math.floor(he)!==he)continue;for(let de=0;de<b;de+=d){const le=(J+de)/a;if(le<0||le>=s.outHeight||Math.floor(le)!==le)continue;for(let ye=0;ye<w;ye+=m){const pe=(re+ye)/c;if(pe<0||pe>=s.outWidth||Math.floor(pe)!==pe)continue;const Ie=y*b*w-1-E.get(F,he,le,pe,_),Se=ue*b*w+de*w+ye,Ee=Ie===Se?1:0;if(Ee===0)continue;const We=D.get(F,he,le,pe,_);ce+=We*Ee}}}N.set(ce,F,B,$,H,_)}return N.toTensor()}resizeBilinear(e,t,n,s){xe(e,"resizeBilinear");const[i,o,a,c]=e.shape,h=this.readSync(e.dataId),d=new Float32Array(we([i,t,n,c])),m=[s&&t>1?o-1:o,s&&n>1?a-1:a],y=[s&&t>1?t-1:t,s&&n>1?n-1:n];let b=0;const w=m[0]/y[0],L=m[1]/y[1];for(let T=0;T<i;T++)for(let A=0;A<t;A++){const N=w*A,E=Math.floor(N),D=N-E,F=Math.min(o-1,Math.ceil(N)),_=T*e.strides[0]+E*e.strides[1],B=T*e.strides[0]+F*e.strides[1];for(let $=0;$<n;$++){const H=L*$,q=Math.floor(H),J=H-q,re=Math.min(a-1,Math.ceil(H)),ce=_+q*e.strides[2],ue=B+q*e.strides[2],he=_+re*e.strides[2],de=B+re*e.strides[2];for(let le=0;le<c;le++){const ye=h[ce+le],pe=h[ue+le],Ie=h[he+le],Se=h[de+le],Ee=ye+(Ie-ye)*J,We=pe+(Se-pe)*J,Oe=Ee+(We-Ee)*D;d[b++]=Oe}}}return en(d,[i,t,n,c])}resizeBilinearBackprop(e,t,n){xe([e,t],"resizeBilinearBackprop");const[s,i,o,a]=t.shape,[,c,h]=e.shape,d=new Float32Array(s*i*o*a),m=[n&&c>1?i-1:i,n&&h>1?o-1:o],y=[n&&c>1?c-1:c,n&&h>1?h-1:h],b=m[0]/y[0],w=m[1]/y[1],L=this.readSync(e.dataId);let T=0;for(let A=0;A<s;A++){const N=A*t.strides[0];for(let E=0;E<c;E++){const D=E*b,F=Math.floor(D),_=Math.min(Math.ceil(D),i-1),B=N+F*t.strides[1],$=N+_*t.strides[1],H=D-F,q=1-H;for(let J=0;J<h;J++){const re=J*w,ce=Math.floor(re),ue=Math.min(Math.ceil(re),o-1),he=re-ce,de=1-he,le=B+ce*t.strides[2],ye=B+ue*t.strides[2],pe=$+ce*t.strides[2],Ie=$+ue*t.strides[2],Se=q*de,Ee=q*he,We=H*de,Oe=H*he;for(let $e=0;$e<a;$e++){const 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$e=A[Oe];Oe=We+Se*N[2]+he*N[1]+q*N[0];const Ye=A[Oe];Oe=We+Ie*N[2]+de*N[1]+q*N[0];const et=A[Oe];Oe=We+Se*N[2]+de*N[1]+q*N[0];const bt=A[Oe],Jt=$e+(Ye-$e)*Ee,un=et+(bt-et)*Ee;Oe=We+ye*E[2]+ce*E[1]+D*E[0],w.values[Oe]=Jt+(un-Jt)*le}}}else for(let he=0;he<b;++he){const de=b>1?B*(h-1)+he*re:.5*(B+H)*(h-1);if(de<0||de>h-1){for(let pe=0;pe<d;pe++){const Ie=pe+he*E[2]+ce*E[1]+D*E[0];w.values[Ie]=o}continue}const le=Math.round(de),ye=Math.round(ue);for(let pe=0;pe<d;pe++){const Ie=pe+le*N[2]+ye*N[1]+q*N[0],Se=pe+he*E[2]+ce*E[1]+D*E[0];w.values[Se]=A[Ie]}}}}return w.toTensor()}sparseToDense(e,t,n,s){const{sliceRank:i,numUpdates:o,sliceSize:a,strides:c,outputSize:h}=va(t,e,n),d=!1;return this.scatter(e,t,n,h,a,o,i,c,s,d)}gatherND(e,t){const n=t.shape,s=n[n.length-1],[i,o,a,c]=gd(e,t);if(o===0)return en([],i,e.dtype);const h=new Ar([o,a],e.dtype),d=this.readSync(t.dataId),m=this.readSync(e.dataId);for(let y=0;y<o;y++){const b=[];let w=0;for(let L=0;L<s;L++){const T=d[y*s+L];w+=T*c[L],b.push(T)}if(w<0||w>=e.size/a)throw new Error(`Invalid indices: ${b} does not index into ${e.shape}`);for(let L=0;L<a;L++)h.values[y*a+L]=m[w*a+L]}return h.toTensor().reshape(i)}scatterND(e,t,n){const{sliceRank:s,numUpdates:i,sliceSize:o,strides:a,outputSize:c}=va(t,e,n),h=Ne(0),d=!0;return this.scatter(e,t,n,c,o,i,s,a,h,d)}fill(e,t,n){n=n||ba(t);const s=lo(n,we(e));return s.fill(t),Fs().makeTensor(s,e,n,this)}onesLike(e){if(e.dtype==="string")throw new Error("onesLike is not supported for string tensors");return this.fill(e.shape,1,e.dtype)}zerosLike(e){const t=lo(e.dtype,we(e.shape));return this.makeOutput(t,e.shape,e.dtype)}linspace(e,t,n){return Ub(e,t,n)}scatter(e,t,n,s,i,o,a,c,h,d){const m=[s/i,i],y=this.readSync(e.dataId),b=this.readSync(t.dataId);if(s===0)return en([],n,t.dtype);const w=new Ar(m,t.dtype);w.values.fill(this.readSync(h.dataId)[0]);for(let L=0;L<o;L++){const T=[];let A=0;for(let N=0;N<a;N++){const E=y[L*a+N];T.push(E),A+=E*c[N]}if(A<0||A>=s/i)throw new Error(`Invalid indices: ${T} does not index into ${n}`);for(let N=0;N<i;N++)d?w.values[A*i+N]+=b[L*i+N]:w.values[A*i+N]=t.rank===0?b[0]:b[L*i+N]}return w.toTensor().reshape(n)}}function e0(e){const t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}const pH=e=>{const{x:t}=e.inputs,n=e.backend;let s=new Float32Array(we(t.shape));if(t.dtype!=="complex64"){const i=n.data.get(t.dataId).values;s=e0(i)}else{const i=n.data.get(t.dataId),o=i.complexTensorInfos.real,a=i.complexTensorInfos.imag,c=n.data.get(o.dataId).values,h=n.data.get(a.dataId).values;for(let d=0;d<c.length;d++){const m=c[d],y=h[d];s[d]=Math.hypot(m,y)}}return n.makeOutput(s,t.shape,"float32")},mH={kernelName:ge,backendName:"cpu",kernelFunc:pH};function $o(e){return(t,n,s,i,o)=>{const a=tt(t,n),c=a.length,h=Ot(a),d=we(a),m=bn(o,d),y=t.length,b=n.length,w=Ot(t),L=Ot(n),T=Lo(t,a),A=Lo(n,a);if(T.length+A.length===0)for(let 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yH={kernelName:_g,backendName:"cpu",kernelFunc:Vh};function Yh(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{dtype:o}=s;if(o==="complex64"){if(i.dtype==="complex64")return Qa({inputs:{x:i},backend:n});const a=ct(i.shape),c=Yh({inputs:{x:i},backend:n,attrs:{dtype:"float32"}}),h=li({inputs:{real:c,imag:a},backend:n});return a.dispose(),n.disposeIntermediateTensorInfo(c),h}if(i.dtype==="complex64"){const a=Vh({inputs:{input:i},backend:n}),c=Yh({inputs:{x:a},backend:n,attrs:{dtype:o}});return n.disposeIntermediateTensorInfo(a),c}if(!iy(i.dtype,o)){const a=Qa({inputs:{x:i},backend:n});return{dataId:a.dataId,shape:a.shape,dtype:o}}if(o==="int32"){const a=n.data.get(i.dataId).values,c=Int32Array.from(a);return n.makeTensorInfo(i.shape,"int32",c)}if(o==="bool"){const a=n.data.get(i.dataId).values,c=Tr([0],i.dtype),[h,d]=$o((m,y)=>m!==y?1:0)(i.shape,[],a,c,"bool");return n.makeTensorInfo(d,"bool",h)}throw new Error(`Error in Cast: failed to cast ${i.dtype} to ${o}`)}const bH={kernelName:Jc,backendName:"cpu",kernelFunc:Yh};function ec(e,t,n,s){return n==null?({inputs:i,backend:o})=>{const{a,b:c}=i,h=o;xe([a,c],e);const d=h.data.get(a.dataId).values,m=h.data.get(c.dataId).values,y=s||a.dtype,[b,w]=t(a.shape,c.shape,d,m,y);return h.makeTensorInfo(w,y,b)}:({inputs:i,backend:o})=>{const{a,b:c}=i,h=o;if(a.dtype==="complex64"||c.dtype==="complex64"){const d=Yh({inputs:{x:a},backend:h,attrs:{dtype:"complex64"}}),m=h.data.get(d.dataId),y=m.complexTensorInfos.real,b=m.complexTensorInfos.imag,w=h.data.get(y.dataId).values,L=h.data.get(b.dataId).values,T=Yh({inputs:{x:c},backend:h,attrs:{dtype:"complex64"}}),A=h.data.get(T.dataId),N=A.complexTensorInfos.real,E=A.complexTensorInfos.imag,D=h.data.get(N.dataId).values,F=h.data.get(E.dataId).values,[_,B,$]=n(a.shape,c.shape,w,L,D,F),H=h.makeTensorInfo($,"float32",_),q=h.makeTensorInfo($,"float32",B),J=li({inputs:{real:H,imag:q},backend:h});return 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pq={kernelName:Uu,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s,filter:i}=e,{strides:o,pad:a,dilations:c}=n,h=t,d=h.data.get(s.dataId).values,m=s.shape.length,y=h.data.get(i.dataId).values,b=i.shape.length,{batchSize:w,inHeight:L,inWidth:T,inChannels:A,outHeight:N,outWidth:E,padInfo:D,strideHeight:F,strideWidth:_,filterHeight:B,filterWidth:$,dilationHeight:H,dilationWidth:q,outShape:J}=Td(s.shape,i.shape,o,a,"NHWC",c),re=we(J),ce=J.length,ue=lo(s.dtype,re);for(let de=0;de<w;++de)for(let le=0;le<N;++le){const ye=le*F-D.top;for(let pe=0;pe<E;++pe){const Ie=pe*_-D.left;for(let Se=0;Se<A;++Se){let Ee=Number.MIN_SAFE_INTEGER;for(let Oe=0;Oe<B;++Oe){const $e=ye+Oe*H;if($e>=0&&$e<L)for(let Ye=0;Ye<$;++Ye){const et=Ie+Ye*q;if(et>=0&&et<T){const bt=Js([de,$e,et,Se],m,Ot(s.shape)),Jt=Js([Oe,Ye,Se],b,Ot(i.shape)),un=d[bt]+y[Jt];un>Ee&&(Ee=un)}}}const We=Js([de,le,pe,Se],ce,Ot(J));ue[We]=Ee}}}const 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s=we(e);if(e.length<=1&&s<=n)return[1,s];if(e.length===2&&e[0]<=n&&e[1]<=n)return e;if(e.length===3&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){const i=ic(e);let o=2,a=2;return e.length&&([o,a]=rc(e)),s=i*(o/2)*(a/2),sd(s).map(c=>c*2)}return sd(s)}function Jp(e){return e%2===0}function Zp(e,t){if(e=e.slice(-2),t=t.slice(-2),ot(e,t))return!0;if(!e.length||!t.length)return!0;if(e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){const n=e.slice(-1)[0],s=t.slice(-1)[0];if(n===s)return!0;if(Jp(n)&&Jp(s)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&Jp(e[0])&&Jp(t[0])}let Qp,em;function bj(e){if(Qp==null){const t=_i(e);Qp=t.getParameter(t.MAX_TEXTURE_SIZE)}return Qp}function mee(){Qp=null}function fee(){em=null}function wj(e){if(em==null){const t=_i(e);em=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,em)}function Lj(e){if(e===0)return 0;let t;const n=_i(e);return Ps(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:Ps(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function Ps(e,t){const n=e.getExtension(t);return n!=null}function T0(e){try{const t=_i(e);if(t!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function Sj(e){if(e===0)return!1;const t=_i(e);if(e===1){if(!Ps(t,"OES_texture_float"))return!1}else if(!Ps(t,"EXT_color_buffer_float"))return!1;const n=JL(t);return n}function Ij(e){if(e===0)return!1;const t=_i(e);if(e===1){if(!Ps(t,"OES_texture_float"))return!1;if(!Ps(t,"WEBGL_color_buffer_float"))return!1}else{if(Ps(t,"EXT_color_buffer_float"))return JL(t);const s="EXT_color_buffer_half_float";if(Ps(t,s)){const i=t.getExtension(s);return xj(t,i)}return!1}const n=JL(t);return n}function JL(e){const t=qL(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);const s=1,i=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,s,i,0,t.textureFormatFloat,t.textureTypeFloat,null);const o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);const a=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(o),a}function xj(e,t){const n=qL(e,t),s=e.createTexture();e.bindTexture(e.TEXTURE_2D,s);const i=1,o=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,i,o,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);const a=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,a),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);const c=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(a),c}function Tj(e){if(e!==2)return!1;const t=_i(e),n=t.fenceSync!=null;return n}function Xh(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}const Ge=C();Ge.registerFlag("HAS_WEBGL",()=>Ge.getNumber("WEBGL_VERSION")>0),Ge.registerFlag("WEBGL_VERSION",()=>T0(2)?2:T0(1)?1:0),Ge.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1),Ge.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ge.get("WEBGL_VERSION")===2),Ge.registerFlag("WEBGL_CPU_FORWARD",()=>!0),Ge.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1),Ge.registerFlag("WEBGL_PACK",()=>Ge.getBool("HAS_WEBGL")),Ge.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ge.getBool("WEBGL_PACK")),Ge.registerFlag("WEBGL_PACK_CLIP",()=>Ge.getBool("WEBGL_PACK")),Ge.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1),Ge.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ge.getBool("WEBGL_PACK")),Ge.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ge.getBool("WEBGL_PACK")),Ge.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ge.getBool("WEBGL_PACK")),Ge.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ge.getBool("WEBGL_PACK")),Ge.registerFlag("WEBGL_PACK_REDUCE",()=>Ge.getBool("WEBGL_PACK")),Ge.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ge.getBool("WEBGL_PACK")),Ge.registerFlag("WEBGL_CONV_IM2COL",()=>Ge.getBool("WEBGL_PACK")),Ge.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>bj(Ge.getNumber("WEBGL_VERSION"))),Ge.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>wj(Ge.getNumber("WEBGL_VERSION"))),Ge.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{const e=Ge.getNumber("WEBGL_VERSION");return e===0?0:Lj(e)}),Ge.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ge.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Yx()),Ge.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>Sj(Ge.getNumber("WEBGL_VERSION"))),Ge.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ge.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ge.getBool("WEBGL_RENDER_FLOAT32_CAPABLE")),Ge.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>Ij(Ge.getNumber("WEBGL_VERSION"))),Ge.registerFlag("WEBGL_FENCE_API_ENABLED",()=>Tj(Ge.getNumber("WEBGL_VERSION"))),Ge.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>{const e=Ge.getBool("WEBGL_RENDER_FLOAT32_ENABLED");return e?4:0}),Ge.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});const{simpleAbsImpl:Aj,addImpl:vj,ceilImpl:Nj,expImpl:Cj,expm1Impl:Rj,floorImpl:Oj,logImpl:Ej,maxImpl:Dj,multiplyImpl:kj,rsqrtImpl:Fj,sliceImpl:_j,subImpl:Wj,transposeImpl:A0,uniqueImpl:$j}=UH;class Uj{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((i,o)=>`T${o}`);const n=[];this.variableNames.forEach(i=>{n.push(`float v${i} = get${i}AtOutCoords();`)});const s=this.variableNames.map(i=>`v${i}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -114,7 +114,7 @@ Hi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed thi
       ivec4 round(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:i,output:o,defineOutput:a,defineSpecialNaN:c,defineSpecialInf:h,defineRound:d}}function $o(e,t,n="index"){const s=Ot(t);return s.map((i,o)=>{const a=`int ${e[o]} = ${n} / ${i}`,c=o===s.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${i}`:`index -= ${e[o]} * ${i}`;return`${a}; ${c};`}).join("")}function tm(e){return e.length===1?`${e[0]}`:`vec${e.length}(${e.join(",")})`}function gee(e,t){if(e.length!==t.length)throw new Error(`Vectors to be dotted must be of the same length -got ${e.length} and ${t.length}`);const n=[],s=Math.floor(e.length/4),i=e.length%4;for(let o=0;o<s;o++){const a=e.slice(o*4,o*4+4),c=t.slice(o*4,o*4+4);n.push(`${tm(a)}, ${tm(c)}`)}if(i!==0){let o=e.slice(s*4),a=t.slice(s*4);o.length===1&&(o=o.map(c=>`float(${c})`),a=a.map(c=>`float(${c})`)),n.push(`${tm(o)}, ${tm(a)}`)}return n.map((o,a)=>`dot(${o})`).join("+")}function ZL(e){const t=Ot(e).map(n=>n.toString());return`
+    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:i,output:o,defineOutput:a,defineSpecialNaN:c,defineSpecialInf:h,defineRound:d}}function Bo(e,t,n="index"){const s=Ot(t);return s.map((i,o)=>{const a=`int ${e[o]} = ${n} / ${i}`,c=o===s.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${i}`:`index -= ${e[o]} * ${i}`;return`${a}; ${c};`}).join("")}function tm(e){return e.length===1?`${e[0]}`:`vec${e.length}(${e.join(",")})`}function gee(e,t){if(e.length!==t.length)throw new Error(`Vectors to be dotted must be of the same length -got ${e.length} and ${t.length}`);const n=[],s=Math.floor(e.length/4),i=e.length%4;for(let o=0;o<s;o++){const a=e.slice(o*4,o*4+4),c=t.slice(o*4,o*4+4);n.push(`${tm(a)}, ${tm(c)}`)}if(i!==0){let o=e.slice(s*4),a=t.slice(s*4);o.length===1&&(o=o.map(c=>`float(${c})`),a=a.map(c=>`float(${c})`)),n.push(`${tm(o)}, ${tm(a)}`)}return n.map((o,a)=>`dot(${o})`).join("+")}function ZL(e){const t=Ot(e).map(n=>n.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
   }
@@ -160,7 +160,7 @@ Hi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed thi
 `;const{getBroadcastDims:C0}=Bb;function zj(e,t,n,s){const i=[];e.forEach(L=>{const T=we(L.shapeInfo.logicalShape);L.shapeInfo.isUniform?i.push(`uniform float ${L.name}${T>1?`[${T}]`:""};`):(i.push(`uniform sampler2D ${L.name};`),i.push(`uniform int offset${L.name};`))});const o=i.join(`
 `),a=e.map(L=>Gj(L,t,s)).join(`
 `),c=t.texShape,h=Wn(),d=Hj(h);let m,y,b=Kj(h);t.isPacked?(m=Vj(t.logicalShape,c),y=jj(h)):(m=Yj(t.logicalShape,c),y=qj(h)),s&&(b+=Qj);const w=[b,d,y,o,m,a,n].join(`
-`);return w}function ic(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return uK(e);case 1:return pK(e);case 2:return fK(e);case 3:return yK(e);case 4:return wK(e);case 5:return LK(e);case 6:return SK(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function R0(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return hK(e);case 1:return dK(e);case 2:return mK(e);case 3:return gK(e);default:return bK(e)}}function Gj(e,t,n=!1){let s="";n?s+=R0(e):s+=ic(e);const i=e.shapeInfo.logicalShape,o=t.logicalShape;return i.length<=o.length&&(n?s+=IK(e,t):s+=xK(e,t)),s}function Vj(e,t){switch(e.length){case 0:return O0();case 1:return eK(e,t);case 2:return cK(e,t);case 3:return nK(e,t);default:return iK(e,t)}}function Yj(e,t){switch(e.length){case 0:return O0();case 1:return tK(e,t);case 2:return lK(e,t);case 3:return sK(e,t);case 4:return rK(e,t);case 5:return oK(e,t);case 6:return aK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function Hj(e){return`
+`);return w}function oc(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return uK(e);case 1:return pK(e);case 2:return fK(e);case 3:return yK(e);case 4:return wK(e);case 5:return LK(e);case 6:return SK(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function R0(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return hK(e);case 1:return dK(e);case 2:return mK(e);case 3:return gK(e);default:return bK(e)}}function Gj(e,t,n=!1){let s="";n?s+=R0(e):s+=oc(e);const i=e.shapeInfo.logicalShape,o=t.logicalShape;return i.length<=o.length&&(n?s+=IK(e,t):s+=xK(e,t)),s}function Vj(e,t){switch(e.length){case 0:return O0();case 1:return eK(e,t);case 2:return cK(e,t);case 3:return nK(e,t);default:return iK(e,t)}}function Yj(e,t){switch(e.length){case 0:return O0();case 1:return tK(e,t);case 2:return lK(e,t);case 3:return sK(e,t);case 4:return rK(e,t);case 5:return oK(e,t);case 6:return aK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function Hj(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
@@ -316,7 +316,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec3(b, r, c);
     }
-  `}function sK(e,t){const n=$o(["r","c","d"],e);return`
+  `}function sK(e,t){const n=Bo(["r","c","d"],e);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${t[0]}, ${t[1]}));
@@ -343,7 +343,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec${e.length}(${c});
     }
-  `}function rK(e,t){const n=$o(["r","c","d","d2"],e);return`
+  `}function rK(e,t){const n=Bo(["r","c","d","d2"],e);return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -351,7 +351,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec4(r, c, d, d2);
     }
-  `}function oK(e,t){const n=$o(["r","c","d","d2","d3"],e);return`
+  `}function oK(e,t){const n=Bo(["r","c","d","d2","d3"],e);return`
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                              ${t[1]}));
@@ -363,7 +363,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function aK(e,t){const n=$o(["r","c","d","d2","d3","d4"],e);return`
+  `}function aK(e,t){const n=Bo(["r","c","d","d2","d3","d4"],e);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -416,7 +416,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       int c = index - r * ${e[1]};
       return ivec2(r, c);
     }
-  `}function Uo(e){return`offset${e}`}function hK(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Wn();return`
+  `}function Mo(e){return`offset${e}`}function hK(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Wn();return`
     vec4 ${n}() {
       return ${s.texture2D}(${t}, halfCR);
     }
@@ -424,7 +424,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       float ${n}() {
         return sampleTexture(${t}, halfCR);
       }
-    `;const[o,a]=e.shapeInfo.texShape,c=Uo(t);return`
+    `;const[o,a]=e.shapeInfo.texShape,c=Mo(t);return`
     float ${n}() {
       vec2 uv = uvFromFlat(${o}, ${a}, ${c});
       return sampleTexture(${t}, uv);
@@ -437,13 +437,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     }
   `}function pK(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
       float ${n}(int index) {
-        ${rc(e)}
+        ${ac(e)}
       }
     `;const s=e.shapeInfo.texShape,i=s[0],o=s[1];if(o===1&&i===1)return`
       float ${n}(int index) {
         return sampleTexture(${t}, halfCR);
       }
-    `;const a=Uo(t);return o===1?`
+    `;const a=Mo(t);return o===1?`
       float ${n}(int index) {
         vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${i}.0);
         return sampleTexture(${t}, uv);
@@ -474,17 +474,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${b}.0, ${y}.0);
       return sampleTexture(${n}, uv);
     }
-  `}const{newShape:o,keptDims:a}=Sr(t),c=o;if(c.length<t.length){const y=oc(e,c),b=["row","col"];return`
-      ${ic(y)}
+  `}const{newShape:o,keptDims:a}=Sr(t),c=o;if(c.length<t.length){const y=cc(e,c),b=["row","col"];return`
+      ${oc(y)}
       float ${s}(int row, int col) {
-        return ${s}(${ac(b,a)});
+        return ${s}(${lc(b,a)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col) {
         int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
-        ${rc(e)}
+        ${ac(e)}
       }
-    `;const h=i[0],d=i[1],m=Uo(n);return d===1?`
+    `;const h=i[0],d=i[1],m=Mo(n);return d===1?`
     float ${s}(int row, int col) {
       float index = dot(vec3(row, col, ${m}), vec3(${t[1]}, 1, 1));
       vec2 uv = vec2(0.5, (index + 0.5) / ${h}.0);
@@ -503,10 +503,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     vec2 uv = uvFromFlat(${h}, ${d}, index);
     return sampleTexture(${n}, uv);
   }
-`}function gK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=e.shapeInfo.texShape,o=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)];if(t[0]===1){const y=t.slice(1),b=[1,2],w=oc(e,y),L=["b","row","col"];return`
+`}function gK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=e.shapeInfo.texShape,o=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)];if(t[0]===1){const y=t.slice(1),b=[1,2],w=cc(e,y),L=["b","row","col"];return`
         ${R0(w)}
         vec4 ${s}(int b, int row, int col) {
-          return ${s}(${ac(L,b)});
+          return ${s}(${lc(L,b)});
         }
       `}const a=o[0],c=o[1],h=Math.ceil(t[2]/2),d=h*Math.ceil(t[1]/2),m=Wn();return`
     vec4 ${s}(int b, int row, int col) {
@@ -514,16 +514,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${a}, ${c}, ${d}, ${h}, b, row, col);
       return ${m.texture2D}(${n}, uv);
     }
-  `}function yK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[1]*t[2],o=t[2],{newShape:a,keptDims:c}=Sr(t),h=a;if(h.length<t.length){const L=oc(e,h),T=["row","col","depth"];return`
-        ${ic(L)}
+  `}function yK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[1]*t[2],o=t[2],{newShape:a,keptDims:c}=Sr(t),h=a;if(h.length<t.length){const L=cc(e,h),T=["row","col","depth"];return`
+        ${oc(L)}
         float ${s}(int row, int col, int depth) {
-          return ${s}(${ac(T,c)});
+          return ${s}(${lc(T,c)});
         }
       `}if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
                           vec3(${i}, ${o}, 1)));
-        ${rc(e)}
+        ${ac(e)}
       }
     `;const d=e.shapeInfo.texShape,m=d[0],y=d[1],b=e.shapeInfo.flatOffset;if(y===i&&b==null)return`
         float ${s}(int row, int col, int depth) {
@@ -540,7 +540,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${y}.0, ${m}.0);
       return sampleTexture(${n}, uv);
     }
-  `;const w=Uo(n);return`
+  `;const w=Mo(n);return`
       float ${s}(int row, int col, int depth) {
         // Explicitly use integer operations as dot() only works on floats.
         int index = row * ${i} + col * ${o} + depth + ${w};
@@ -555,16 +555,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}, ${c});
       return ${w.texture2D}(${s}, uv);
     }
-  `}function wK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[3],o=t[2]*i,a=t[1]*o,{newShape:c,keptDims:h}=Sr(t);if(c.length<t.length){const L=oc(e,c),T=["row","col","depth","depth2"];return`
-      ${ic(L)}
+  `}function wK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[3],o=t[2]*i,a=t[1]*o,{newShape:c,keptDims:h}=Sr(t);if(c.length<t.length){const L=cc(e,c),T=["row","col","depth","depth2"];return`
+      ${oc(L)}
       float ${s}(int row, int col, int depth, int depth2) {
-        return ${s}(${ac(T,h)});
+        return ${s}(${lc(T,h)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
                           vec4(${a}, ${o}, ${i}, 1)));
-        ${rc(e)}
+        ${ac(e)}
       }
     `;const d=e.shapeInfo.flatOffset,m=e.shapeInfo.texShape,y=m[0],b=m[1];if(b===a&&d==null)return`
       float ${s}(int row, int col, int depth, int depth2) {
@@ -585,7 +585,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${b}.0, ${y}.0);
         return sampleTexture(${n}, uv);
       }
-    `;const w=Uo(n);return`
+    `;const w=Mo(n);return`
     float ${s}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${a} + col * ${o} +
@@ -593,10 +593,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${y}, ${b}, index + ${w});
       return sampleTexture(${n}, uv);
     }
-  `}function LK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[4],o=t[3]*i,a=t[2]*o,c=t[1]*a,{newShape:h,keptDims:d}=Sr(t);if(h.length<t.length){const T=oc(e,h),A=["row","col","depth","depth2","depth3"];return`
-      ${ic(T)}
+  `}function LK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[4],o=t[3]*i,a=t[2]*o,c=t[1]*a,{newShape:h,keptDims:d}=Sr(t);if(h.length<t.length){const T=cc(e,h),A=["row","col","depth","depth2","depth3"];return`
+      ${oc(T)}
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
-        return ${s}(${ac(A,d)});
+        return ${s}(${lc(A,d)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
@@ -604,7 +604,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           vec4(row, col, depth, depth2),
           vec4(${c}, ${a}, ${o}, ${i})) +
           depth3;
-        ${rc(e)}
+        ${ac(e)}
       }
     `;const m=e.shapeInfo.flatOffset,y=e.shapeInfo.texShape,b=y[0],w=y[1];if(w===c&&m==null)return`
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
@@ -626,7 +626,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${w}.0, ${b}.0);
         return sampleTexture(${n}, uv);
       }
-    `;const L=Uo(n);return`
+    `;const L=Mo(n);return`
     float ${s}(int row, int col, int depth, int depth2, int depth3) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${c} + col * ${a} + depth * ${o} +
@@ -634,11 +634,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${b}, ${w}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function SK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:i,keptDims:o}=Sr(t);if(i.length<t.length){const A=oc(e,i),N=["row","col","depth","depth2","depth3","depth4"];return`
-      ${ic(A)}
+  `}function SK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:i,keptDims:o}=Sr(t);if(i.length<t.length){const A=cc(e,i),N=["row","col","depth","depth2","depth3","depth4"];return`
+      ${oc(A)}
       float ${s}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${s}(${ac(N,o)});
+        return ${s}(${lc(N,o)});
       }
     `}const a=t[5],c=t[4]*a,h=t[3]*c,d=t[2]*h,m=t[1]*d;if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth,
@@ -649,7 +649,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           dot(
             vec2(depth3, depth4),
             vec2(${a}, 1)));
-        ${rc(e)}
+        ${ac(e)}
       }
     `;const y=e.shapeInfo.flatOffset,b=e.shapeInfo.texShape,w=b[0],L=b[1];if(L===m&&y==null)return`
       float ${s}(int row, int col, int depth,
@@ -675,7 +675,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${L}.0, ${w}.0);
         return sampleTexture(${n}, uv);
       }
-    `;const T=Uo(n);return`
+    `;const T=Mo(n);return`
     float ${s}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
       // Explicitly use integer operations as dot() only works on floats.
@@ -684,7 +684,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${w}, ${L}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function rc(e){const t=e.name,n=we(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
+  `}function ac(e){const t=e.name,n=we(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
     for (int i = 0; i < ${n}; i++) {
       if (i == index) {
         return ${t}[i];
@@ -715,7 +715,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${b}
       return get${s}(${L});
     }
-  `}function Et(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function oc(e,t){const n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function ac(e,t){return t.map(n=>e[n]).join(", ")}class TK{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);const i=e[e.length-1],o=Math.ceil(i/t);this.outputShape=e.slice(0,-1),o>1&&this.outputShape.push(o),s||this.variableNames.push("bestIndicesA");const a=this.outputShape,c=a.length,h=Et(c),d=cs("coords",c);let m,y;if(o===1){y=c+1;const $=Et(y);m=`
+  `}function Et(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function cc(e,t){const n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function lc(e,t){return t.map(n=>e[n]).join(", ")}class TK{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);const i=e[e.length-1],o=Math.ceil(i/t);this.outputShape=e.slice(0,-1),o>1&&this.outputShape.push(o),s||this.variableNames.push("bestIndicesA");const a=this.outputShape,c=a.length,h=Et(c),d=cs("coords",c);let m,y;if(o===1){y=c+1;const $=Et(y);m=`
         ${$} sourceLocR = ${$}(${d.join()}, 0);
         ++${d[c-1]};
         ${$} sourceLocG = ${$}(${d.join()}, 0);
@@ -915,7 +915,7 @@ return (round(mod(b, 2.0)) != 1) ?
 `,UK=k0+`
   return min(a, b);
 `,BK=`if (b == 0.0) return NAN;
-  return mod(a, b);`,MK="return (b >= 1.0) ? a : a * (b + 1.0);",_0="return (a < 0.) ? b * a : a;";class ln{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=tt(t,n),this.userCode=`
+  return mod(a, b);`,MK="return (b >= 1.0) ? a : a * (b + 1.0);",_0="return (a < 0.) ? b * a : a;";class hn{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=tt(t,n),this.userCode=`
       float binaryOperation(float a, float b) {
         ${e}
       }
@@ -1900,7 +1900,7 @@ return (round(mod(b, 2.0)) != 1) ?
       }
     `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}}function P0(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function z0(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}class m5{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=qh.DENSE;const t=Kh(e),n=Wn();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${$o(["r","c","d"],e)}
+        ${Bo(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -1921,7 +1921,7 @@ return (round(mod(b, 2.0)) != 1) ?
       }
     `}}class f5{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=qh.DENSE;const t=Kh(e),n=Wn();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${$o(["r","c","d"],e)}
+        ${Bo(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -2118,7 +2118,7 @@ return (round(mod(b, 2.0)) != 1) ?
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return tj(e,n)}function N5(e){const t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return aj(e,t)}function C5(e){const t=new Uint16Array([0,1,2,2,1,3]);return cj(e,t)}function Jh(e,t,n,s,i,o){hj(t,n);const a=lj(e),c=e.TEXTURE_2D;return Re(e,()=>e.bindTexture(c,a)),Re(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Re(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Re(e,()=>e.texParameteri(c,e.TEXTURE_MIN_FILTER,e.NEAREST)),Re(e,()=>e.texParameteri(c,e.TEXTURE_MAG_FILTER,e.NEAREST)),Re(e,()=>e.texImage2D(c,0,s,t,n,0,i,o,null)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null)),a}function Y0(e){return e.internalFormatFloat}function R5(e,t,n,s){const[i,o]=jh(t,n);return Jh(e,i,o,Y0(s),s.textureFormatFloat,e.FLOAT)}function H0(e){return e.internalFormatHalfFloat}function O5(e,t,n,s){const[i,o]=jh(t,n);return Jh(e,i,o,H0(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function q0(e){return e.downloadTextureFormat}function E5(e,t,n,s){const[i,o]=jh(t,n);return Jh(e,i,o,q0(s),e.RGBA,e.UNSIGNED_BYTE)}function j0(e){return e.internalFormatPackedFloat}function D5(e,t,n,s){const[i,o]=tc(t,n);return Jh(e,i,o,j0(s),e.RGBA,e.FLOAT)}function K0(e){return e.internalFormatPackedHalfFloat}function k5(e,t,n,s){const[i,o]=tc(t,n);return Jh(e,i,o,K0(s),e.RGBA,s.textureTypeHalfFloat)}function F5(e,t,n){const s=0,i=3*4,o=3*4+2*4;Re(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n));const a=S0(e,t,"clipSpacePos",n,3,o,s);return a&&S0(e,t,"uv",n,2,o,i)}function _5(e,t,n,s,i,o){Re(e,()=>e.bindTexture(e.TEXTURE_2D,t));let a,c,h;i instanceof Uint8Array?(a=new Uint8Array(n*s*4),c=e.UNSIGNED_BYTE,h=e.RGBA):(a=new Float32Array(n*s*4),c=e.FLOAT,h=o.internalFormatPackedFloat),a.set(i),Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,h,n,s,0,e.RGBA,c,a)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function W5(e,t,n){Re(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function $5(e,t,n,s){const i=e.createBuffer();Re(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,i));const o=4,a=4,c=o*a*t*n;return Re(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,c,e.STREAM_READ)),Re(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Re(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),i}function U5(e,t,n){const s=e,i=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,i),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),i}function B5(e,t,n,s){const[i,o]=jh(t,n),a=4,c=new Uint8Array(j4(t*n,a));return Re(e,()=>e.readPixels(0,0,i,o,s.downloadTextureFormat,e.UNSIGNED_BYTE,c)),new Float32Array(c.buffer)}function M5(e,t,n,s,i,o,a,c){const h=e,d=new Float32Array(K4(o,a));return h.bindBuffer(h.PIXEL_PACK_BUFFER,t),h.getBufferSubData(h.PIXEL_PACK_BUFFER,0,d),h.bindBuffer(h.PIXEL_PACK_BUFFER,null),d}function P5(e,t,n){const s=new Float32Array(t*n*4);return Re(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}class z5{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];const t=C().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Y4(t,e)):this.gl=_i(t);let n="WEBGL_color_buffer_float";const s="EXT_color_buffer_half_float";if(C().getNumber("WEBGL_VERSION")===1){const i="OES_texture_float",o="OES_texture_half_float";if(this.textureFloatExtension=Kp(this.gl,i),Ps(this.gl,o))this.textureHalfFloatExtension=Kp(this.gl,o);else if(C().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Ps(this.gl,s))this.colorBufferHalfFloatExtension=Kp(this.gl,s);else if(C().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Ps(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Ps(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=N5(this.gl),this.indexBuffer=C5(this.gl),this.framebuffer=uj(this.gl),this.textureConfig=qL(this.gl,this.textureHalfFloatExtension)}get debug(){return C().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");const e=this.gl;Re(e,()=>e.finish()),Re(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Re(e,()=>e.deleteFramebuffer(this.framebuffer)),Re(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Re(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Re(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),R5(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),O5(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),E5(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),W5(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),_5(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),k5(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),D5(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(I0(this.gl,this.framebuffer),this.outputTexture=null),Re(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>B5(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,i,o){return M5(this.gl,e,t,n,s,i,o,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return U5(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);const s=$5(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){const e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(C().getBool("WEBGL_FENCE_API_ENABLED")){const s=e,i=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{const o=s.clientWaitSync(i,0,0);return o===s.ALREADY_SIGNALED||o===s.CONDITION_SATISFIED},t=i}else C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>P5(this.gl,t,n))}createProgram(e){this.throwIfDisposed();const t=this.gl,n=nj(t,e),s=v5(t),i=rj(t);return Re(t,()=>t.attachShader(i,s)),Re(t,()=>t.attachShader(i,n)),oj(t,i),this.debug&&jL(t,i),this.vertexAttrsAreBound||(this.setProgram(i),this.vertexAttrsAreBound=F5(t,this.program,this.vertexBuffer)),i}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Re(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&jL(this.gl,this.program),Re(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?pj(this.gl,e,t):mj(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Re(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),fj(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();const[s,i]=tc(t,n);this.setOutputMatrixTextureDriver(e,s,i)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&jL(this.gl,this.program),Xp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;this.debug&&this.debugValidate(),Re(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Re(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Kp(this.gl,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const n=this.gl,s=this.getQueryTimerExtensionWebGL2(),i=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,i),i}const e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}const e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await sy(()=>this.disposed||this.isQueryAvailable(e,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){const n=this.gl,s=n.getQueryParameter(e,n.QUERY_RESULT);return s/1e6}else{const n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT);return s/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){const n=this.gl,s=this.getQueryTimerExtensionWebGL2(),i=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),i&&!this.disjoint}else{const n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){const e=G5(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){const{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;sy(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),KL(this.gl,e,this.framebuffer),this.debug&&Xp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(KL(this.gl,this.outputTexture,this.framebuffer),this.debug&&Xp(this.gl)):I0(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);const n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();const s=this.gl;KL(s,e,this.framebuffer),this.debug&&Xp(s),this.outputTexture=e,Re(s,()=>s.viewport(0,0,t,n)),Re(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Re(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}}function G5(e){let t=0;for(;t<e.length;++t){const n=e[t]();if(!n)break}return t-1}function V5(e,t,n,s){const i=t.userCode,o=n.map((w,L)=>{const T={logicalShape:w.shape,texShape:w.isUniform?null:w.texData.texShape,isUniform:w.isUniform,isPacked:w.isUniform?!1:w.texData.isPacked,flatOffset:null};return w.texData!=null&&w.texData.slice!=null&&w.texData.slice.flatOffset>0&&(T.flatOffset=w.texData.slice.flatOffset),{name:t.variableNames[L],shapeInfo:T}}),a=o.map(w=>w.shapeInfo),c={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},h=zj(o,c,i,t.packedInputs),d=e.createProgram(h);let m=null;const y=e.getUniformLocation(d,"NAN",!1);C().getNumber("WEBGL_VERSION")===1&&(m=e.getUniformLocation(d,"INFINITY",!1));const b={};for(let w=0;w<t.variableNames.length;w++){const L=t.variableNames[w],T=!1;b[L]=e.getUniformLocation(d,L,T),b[`offset${L}`]=e.getUniformLocation(d,`offset${L}`,T)}return{program:t,source:h,webGLProgram:d,uniformLocations:b,inShapeInfos:a,outShapeInfo:c,infLoc:m,nanLoc:y}}function X0(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{const i=n.logicalShape,o=t[s],a=o.shape;if(!ot(i,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${i} and ${a} must match`);if(n.isUniform&&o.isUniform)return;const c=n.texShape,h=o.isUniform?null:o.texData.texShape;if(!ot(c,h))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${c} and ${h} must match`)})}function Y5(e,t,n,s,i){X0(t.inShapeInfos,n),X0([t.outShapeInfo],[s]);const o=s.texData.texture,a=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(o,a[0],a[1]):e.setOutputMatrixTexture(o,a[0],a[1]),e.setProgram(t.webGLProgram),C().getNumber("WEBGL_VERSION")===1&&(t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity)),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((c,h)=>{const d=t.program.variableNames[h],m=t.uniformLocations[d],y=t.uniformLocations[`offset${d}`];if(m==null)return;if(c.isUniform){if(we(c.shape)<2)e.gl.uniform1f(m,c.uniformValues[0]);else{let b=c.uniformValues;b instanceof Float32Array||(b=new Float32Array(b)),e.gl.uniform1fv(m,b)}return}c.texData.slice!=null&&y!=null&&e.gl.uniform1i(y,c.texData.slice.flatOffset),e.setInputMatrixTexture(c.texData.texture,m,h)}),i!=null&&i(e,t.webGLProgram),e.executeProgram()}function H5(e,t,n){let s="";t.concat(n).forEach(a=>{const c=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0,h=a.isUniform?"uniform":a.texData.texShape;s+=`${a.shape}_${h}_${c}`});const i=e.userCode;let o=e.constructor.name;return o+="_"+s+"_"+i,o}class q5{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;const{filterWidth:s,inChannels:i,strideWidth:o,strideHeight:a,padInfo:c,outWidth:h,dilationWidth:d,dilationHeight:m,dataFormat:y}=n,{left:b,top:w}=c,L=i*s,T=Wn(),A=y==="channelsLast",N=A?0:1,E=A?1:2;let D="";for(let F=0;F<=1;F++)for(let _=0;_<=1;_++)D+=`
+    }`;return tj(e,n)}function N5(e){const t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return aj(e,t)}function C5(e){const t=new Uint16Array([0,1,2,2,1,3]);return cj(e,t)}function Jh(e,t,n,s,i,o){hj(t,n);const a=lj(e),c=e.TEXTURE_2D;return Re(e,()=>e.bindTexture(c,a)),Re(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Re(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Re(e,()=>e.texParameteri(c,e.TEXTURE_MIN_FILTER,e.NEAREST)),Re(e,()=>e.texParameteri(c,e.TEXTURE_MAG_FILTER,e.NEAREST)),Re(e,()=>e.texImage2D(c,0,s,t,n,0,i,o,null)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null)),a}function Y0(e){return e.internalFormatFloat}function R5(e,t,n,s){const[i,o]=jh(t,n);return Jh(e,i,o,Y0(s),s.textureFormatFloat,e.FLOAT)}function H0(e){return e.internalFormatHalfFloat}function O5(e,t,n,s){const[i,o]=jh(t,n);return Jh(e,i,o,H0(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function q0(e){return e.downloadTextureFormat}function E5(e,t,n,s){const[i,o]=jh(t,n);return Jh(e,i,o,q0(s),e.RGBA,e.UNSIGNED_BYTE)}function j0(e){return e.internalFormatPackedFloat}function D5(e,t,n,s){const[i,o]=sc(t,n);return Jh(e,i,o,j0(s),e.RGBA,e.FLOAT)}function K0(e){return e.internalFormatPackedHalfFloat}function k5(e,t,n,s){const[i,o]=sc(t,n);return Jh(e,i,o,K0(s),e.RGBA,s.textureTypeHalfFloat)}function F5(e,t,n){const s=0,i=3*4,o=3*4+2*4;Re(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n));const a=S0(e,t,"clipSpacePos",n,3,o,s);return a&&S0(e,t,"uv",n,2,o,i)}function _5(e,t,n,s,i,o){Re(e,()=>e.bindTexture(e.TEXTURE_2D,t));let a,c,h;i instanceof Uint8Array?(a=new Uint8Array(n*s*4),c=e.UNSIGNED_BYTE,h=e.RGBA):(a=new Float32Array(n*s*4),c=e.FLOAT,h=o.internalFormatPackedFloat),a.set(i),Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,h,n,s,0,e.RGBA,c,a)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function W5(e,t,n){Re(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function $5(e,t,n,s){const i=e.createBuffer();Re(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,i));const o=4,a=4,c=o*a*t*n;return Re(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,c,e.STREAM_READ)),Re(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Re(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),i}function U5(e,t,n){const s=e,i=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,i),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),i}function B5(e,t,n,s){const[i,o]=jh(t,n),a=4,c=new Uint8Array(j4(t*n,a));return Re(e,()=>e.readPixels(0,0,i,o,s.downloadTextureFormat,e.UNSIGNED_BYTE,c)),new Float32Array(c.buffer)}function M5(e,t,n,s,i,o,a,c){const h=e,d=new Float32Array(K4(o,a));return h.bindBuffer(h.PIXEL_PACK_BUFFER,t),h.getBufferSubData(h.PIXEL_PACK_BUFFER,0,d),h.bindBuffer(h.PIXEL_PACK_BUFFER,null),d}function P5(e,t,n){const s=new Float32Array(t*n*4);return Re(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}class z5{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];const t=C().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Y4(t,e)):this.gl=_i(t);let n="WEBGL_color_buffer_float";const s="EXT_color_buffer_half_float";if(C().getNumber("WEBGL_VERSION")===1){const i="OES_texture_float",o="OES_texture_half_float";if(this.textureFloatExtension=Kp(this.gl,i),Ps(this.gl,o))this.textureHalfFloatExtension=Kp(this.gl,o);else if(C().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Ps(this.gl,s))this.colorBufferHalfFloatExtension=Kp(this.gl,s);else if(C().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Ps(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Ps(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=N5(this.gl),this.indexBuffer=C5(this.gl),this.framebuffer=uj(this.gl),this.textureConfig=qL(this.gl,this.textureHalfFloatExtension)}get debug(){return C().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");const e=this.gl;Re(e,()=>e.finish()),Re(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Re(e,()=>e.deleteFramebuffer(this.framebuffer)),Re(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Re(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Re(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),R5(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),O5(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),E5(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),W5(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),_5(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),k5(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),D5(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(I0(this.gl,this.framebuffer),this.outputTexture=null),Re(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>B5(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,i,o){return M5(this.gl,e,t,n,s,i,o,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return U5(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);const s=$5(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){const e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(C().getBool("WEBGL_FENCE_API_ENABLED")){const s=e,i=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{const o=s.clientWaitSync(i,0,0);return o===s.ALREADY_SIGNALED||o===s.CONDITION_SATISFIED},t=i}else C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>P5(this.gl,t,n))}createProgram(e){this.throwIfDisposed();const t=this.gl,n=nj(t,e),s=v5(t),i=rj(t);return Re(t,()=>t.attachShader(i,s)),Re(t,()=>t.attachShader(i,n)),oj(t,i),this.debug&&jL(t,i),this.vertexAttrsAreBound||(this.setProgram(i),this.vertexAttrsAreBound=F5(t,this.program,this.vertexBuffer)),i}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Re(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&jL(this.gl,this.program),Re(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?pj(this.gl,e,t):mj(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Re(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),fj(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();const[s,i]=sc(t,n);this.setOutputMatrixTextureDriver(e,s,i)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&jL(this.gl,this.program),Xp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;this.debug&&this.debugValidate(),Re(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Re(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Kp(this.gl,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const n=this.gl,s=this.getQueryTimerExtensionWebGL2(),i=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,i),i}const e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}const e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await sy(()=>this.disposed||this.isQueryAvailable(e,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){const n=this.gl,s=n.getQueryParameter(e,n.QUERY_RESULT);return s/1e6}else{const n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT);return s/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){const n=this.gl,s=this.getQueryTimerExtensionWebGL2(),i=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),i&&!this.disjoint}else{const n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){const e=G5(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){const{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;sy(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),KL(this.gl,e,this.framebuffer),this.debug&&Xp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(KL(this.gl,this.outputTexture,this.framebuffer),this.debug&&Xp(this.gl)):I0(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);const n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();const s=this.gl;KL(s,e,this.framebuffer),this.debug&&Xp(s),this.outputTexture=e,Re(s,()=>s.viewport(0,0,t,n)),Re(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Re(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}}function G5(e){let t=0;for(;t<e.length;++t){const n=e[t]();if(!n)break}return t-1}function V5(e,t,n,s){const i=t.userCode,o=n.map((w,L)=>{const T={logicalShape:w.shape,texShape:w.isUniform?null:w.texData.texShape,isUniform:w.isUniform,isPacked:w.isUniform?!1:w.texData.isPacked,flatOffset:null};return w.texData!=null&&w.texData.slice!=null&&w.texData.slice.flatOffset>0&&(T.flatOffset=w.texData.slice.flatOffset),{name:t.variableNames[L],shapeInfo:T}}),a=o.map(w=>w.shapeInfo),c={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},h=zj(o,c,i,t.packedInputs),d=e.createProgram(h);let m=null;const y=e.getUniformLocation(d,"NAN",!1);C().getNumber("WEBGL_VERSION")===1&&(m=e.getUniformLocation(d,"INFINITY",!1));const b={};for(let w=0;w<t.variableNames.length;w++){const L=t.variableNames[w],T=!1;b[L]=e.getUniformLocation(d,L,T),b[`offset${L}`]=e.getUniformLocation(d,`offset${L}`,T)}return{program:t,source:h,webGLProgram:d,uniformLocations:b,inShapeInfos:a,outShapeInfo:c,infLoc:m,nanLoc:y}}function X0(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{const i=n.logicalShape,o=t[s],a=o.shape;if(!ot(i,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${i} and ${a} must match`);if(n.isUniform&&o.isUniform)return;const c=n.texShape,h=o.isUniform?null:o.texData.texShape;if(!ot(c,h))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${c} and ${h} must match`)})}function Y5(e,t,n,s,i){X0(t.inShapeInfos,n),X0([t.outShapeInfo],[s]);const o=s.texData.texture,a=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(o,a[0],a[1]):e.setOutputMatrixTexture(o,a[0],a[1]),e.setProgram(t.webGLProgram),C().getNumber("WEBGL_VERSION")===1&&(t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity)),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((c,h)=>{const d=t.program.variableNames[h],m=t.uniformLocations[d],y=t.uniformLocations[`offset${d}`];if(m==null)return;if(c.isUniform){if(we(c.shape)<2)e.gl.uniform1f(m,c.uniformValues[0]);else{let b=c.uniformValues;b instanceof Float32Array||(b=new Float32Array(b)),e.gl.uniform1fv(m,b)}return}c.texData.slice!=null&&y!=null&&e.gl.uniform1i(y,c.texData.slice.flatOffset),e.setInputMatrixTexture(c.texData.texture,m,h)}),i!=null&&i(e,t.webGLProgram),e.executeProgram()}function H5(e,t,n){let s="";t.concat(n).forEach(a=>{const c=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0,h=a.isUniform?"uniform":a.texData.texShape;s+=`${a.shape}_${h}_${c}`});const i=e.userCode;let o=e.constructor.name;return o+="_"+s+"_"+i,o}class q5{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;const{filterWidth:s,inChannels:i,strideWidth:o,strideHeight:a,padInfo:c,outWidth:h,dilationWidth:d,dilationHeight:m,dataFormat:y}=n,{left:b,top:w}=c,L=i*s,T=Wn(),A=y==="channelsLast",N=A?0:1,E=A?1:2;let D="";for(let F=0;F<=1;F++)for(let _=0;_<=1;_++)D+=`
           blockIndex = rc.y + ${_};
           pos = rc.x + ${F};
 
@@ -2966,7 +2966,7 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(result);
       }
-    `}}function c8(e){const t=$o(["r","c","d"],e);return`
+    `}}function c8(e){const t=Bo(["r","c","d"],e);return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
       ${t}
       return ivec3(r, c, d);
@@ -3478,7 +3478,7 @@ return (round(mod(b, 2.0)) != 1) ?
         ${o} coords = getOutputCoords();
         setOutput(getX(${a}));
       }
-    `}}class I8{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){const s=tC(t,n),i=nC(e,s,n);i in this.freeTextures||(this.freeTextures[i]=[]),i in this.usedTextures||(this.usedTextures[i]=[]);const o=eC(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[i].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=o,this.log();const c=this.freeTextures[i].shift();return this.usedTextures[i].push(c),c}let a;return s===Sn.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===Sn.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===Sn.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===Sn.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===Sn.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[i].push(a),this.numUsedTextures++,this._numBytesAllocated+=o,this.log(),a}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;const i=tC(n,s),o=nC(t,i,s);o in this.freeTextures||(this.freeTextures[o]=[]);const a=eC(t,i,this.gpgpu.gl,this.gpgpu.textureConfig,s),c=C().get("WEBGL_DELETE_TEXTURE_THRESHOLD");c!==-1&&this._numBytesAllocated>c?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=a):(this.freeTextures[o].push(e),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;const h=this.usedTextures[o],d=h.indexOf(e);if(d<0)throw new Error("Cannot release a texture that was never provided by this texture manager");h.splice(d,1),this.log()}log(){if(!this.logEnabled)return;const e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);const t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures==null)return;for(const e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(const e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}function x8(e,t){const n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function eC(e,t,n,s,i){const o=T8(t,s);let a;if(i){const[h,d]=tc(e[0],e[1]);a=h*d}else{const[h,d]=jh(e[0],e[1]);a=h*d}const c=x8(n,o);return a*c}function T8(e,t){switch(e){case Sn.PACKED_2X2_FLOAT32:return j0(t);case Sn.PACKED_2X2_FLOAT16:return K0(t);case Sn.UNPACKED_FLOAT32:return Y0(t);case Sn.UNPACKED_FLOAT16:return H0(t);case Sn.PACKED_4X1_UNSIGNED_BYTE:return q0(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function A8(e){return C().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Sn.PACKED_2X2_FLOAT32:Sn.UNPACKED_FLOAT32:e?Sn.PACKED_2X2_FLOAT16:Sn.UNPACKED_FLOAT16}function tC(e,t){if(e===As.UPLOAD)return Sn.PACKED_2X2_FLOAT32;if(e===As.RENDER||e==null)return A8(t);if(e===As.DOWNLOAD||e===As.PIXELS)return Sn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function nC(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}class v8{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let o=0;o<n.length;o++)n[o]=e[o]*t[o];this.outputShape=n,this.rank=n.length;const s=Et(this.rank),i=N8(e);this.userCode=`
+    `}}class I8{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){const s=tC(t,n),i=nC(e,s,n);i in this.freeTextures||(this.freeTextures[i]=[]),i in this.usedTextures||(this.usedTextures[i]=[]);const o=eC(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[i].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=o,this.log();const c=this.freeTextures[i].shift();return this.usedTextures[i].push(c),c}let a;return s===Sn.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===Sn.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===Sn.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===Sn.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===Sn.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[i].push(a),this.numUsedTextures++,this._numBytesAllocated+=o,this.log(),a}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;const i=tC(n,s),o=nC(t,i,s);o in this.freeTextures||(this.freeTextures[o]=[]);const a=eC(t,i,this.gpgpu.gl,this.gpgpu.textureConfig,s),c=C().get("WEBGL_DELETE_TEXTURE_THRESHOLD");c!==-1&&this._numBytesAllocated>c?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=a):(this.freeTextures[o].push(e),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;const h=this.usedTextures[o],d=h.indexOf(e);if(d<0)throw new Error("Cannot release a texture that was never provided by this texture manager");h.splice(d,1),this.log()}log(){if(!this.logEnabled)return;const e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);const t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures==null)return;for(const e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(const e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}function x8(e,t){const n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function eC(e,t,n,s,i){const o=T8(t,s);let a;if(i){const[h,d]=sc(e[0],e[1]);a=h*d}else{const[h,d]=jh(e[0],e[1]);a=h*d}const c=x8(n,o);return a*c}function T8(e,t){switch(e){case Sn.PACKED_2X2_FLOAT32:return j0(t);case Sn.PACKED_2X2_FLOAT16:return K0(t);case Sn.UNPACKED_FLOAT32:return Y0(t);case Sn.UNPACKED_FLOAT16:return H0(t);case Sn.PACKED_4X1_UNSIGNED_BYTE:return q0(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function A8(e){return C().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Sn.PACKED_2X2_FLOAT32:Sn.UNPACKED_FLOAT32:e?Sn.PACKED_2X2_FLOAT16:Sn.UNPACKED_FLOAT16}function tC(e,t){if(e===As.UPLOAD)return Sn.PACKED_2X2_FLOAT32;if(e===As.RENDER||e==null)return A8(t);if(e===As.DOWNLOAD||e===As.PIXELS)return Sn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function nC(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}class v8{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let o=0;o<n.length;o++)n[o]=e[o]*t[o];this.outputShape=n,this.rank=n.length;const s=Et(this.rank),i=N8(e);this.userCode=`
       void main() {
         ${s} resRC = getOutputCoords();
         setOutput(getA(${i}));
@@ -3641,7 +3641,7 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(getChannel(packedInput, ${a}));
       }
-    `}}const{segment_util:fC}=Bb,i6=Mb,r6=Pb,o6=zb,a6=jd,c6=1e-7,l6=1e-4,rm={};function h6(e){return e in rm||(rm[e]={}),rm[e]}function om(e,t=!1){if(e==="linear")return t?t6:C8;if(e==="relu")return t?dC:iC;if(e==="elu")return t?mC:oC;if(e==="relu6")return t?pC:rC;if(e==="prelu")return t?W0:_0;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}const u6=128,d6=600;function p6(){return C().global.screen==null?1024:C().global.screen.height*C().global.screen.width*window.devicePixelRatio*d6/1024/1024}const gC=1e3;class m6 extends g{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!C().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){const t=_i(C().getNumber("WEBGL_VERSION"));this.binaryCache=h6(C().getNumber("WEBGL_VERSION")),this.gpgpu=new z5(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new I8(this.gpgpu),this.numMBBeforeWarning=p6(),this.texData=new p(this,Fs())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((C().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||C().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");const s={};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:As.UPLOAD,refCount:1}),s}incRef(e){const t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){const t=this.texData.get(e);t.refCount--}}move(e,t,n,s){if(C().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:As.UPLOAD,refCount:1})}disposeIntermediateTensorInfo(e){const t=e.dataId;if(this.texData.has(t)){const n=this.texData.get(t);n.refCount--,n.refCount<1&&this.disposeData(t)}}readSync(e){const t=this.texData.get(e),{values:n,dtype:s,complexTensors:i,slice:o,shape:a,isPacked:c}=t;if(o!=null){let y;c?y=new Qh(a,im):y=new st(a,im);const b=this.runWebGLProgram(y,[{dataId:e,shape:a,dtype:s}],s),w=this.readSync(b.dataId);return this.disposeIntermediateTensorInfo(b),w}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;const h=this.activeTimers!=null;let d;h&&(d=Vn());let m;if(s==="complex64"){const y=i.real.dataSync(),b=i.imag.dataSync();m=Zi(y,b)}else m=this.getValuesFromTexture(e);return h&&(this.downloadWaitMs+=Vn()-d),this.convertAndCacheOnCPU(e,m)}async read(e){if(this.pendingRead.has(e)){const w=this.pendingRead.get(e);return new Promise(L=>w.push(L))}const t=this.texData.get(e),{values:n,shape:s,slice:i,dtype:o,complexTensors:a,isPacked:c}=t;if(i!=null){let w;c?w=new Qh(s,im):w=new st(s,im);const L=this.runWebGLProgram(w,[{dataId:e,shape:s,dtype:o}],o),T=this.read(L.dataId);return this.disposeIntermediateTensorInfo(L),T}if(n!=null)return this.convertAndCacheOnCPU(e);if(!C().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&C().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let h=null,d;if(o!=="complex64"&&C().get("WEBGL_BUFFER_SUPPORTED")){d=this.decode(e);const w=this.texData.get(d.dataId);h=this.gpgpu.createBufferFromTexture(w.texture,...Kh(s))}this.pendingRead.set(e,[]),o!=="complex64"&&await this.gpgpu.createAndWaitForFence();let m;if(o==="complex64"){const w=await Promise.all([a.real.data(),a.imag.data()]),L=w[0],T=w[1];m=Zi(L,T)}else if(h==null)m=this.getValuesFromTexture(e);else{const w=we(s);m=this.gpgpu.downloadFloat32MatrixFromBuffer(h,w)}d!=null&&this.disposeIntermediateTensorInfo(d);const y=this.convertAndCacheOnCPU(e,m),b=this.pendingRead.get(e);return this.pendingRead.delete(e),b.forEach(w=>w(y)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),y}checkNumericalProblems(e){if(e==null)return;for(let t=0;t<e.length;t++){const n=e[t];if(!Q4(n))throw C().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){const{shape:t,dtype:n,isPacked:s}=this.texData.get(e),i=we(t);if(C().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const y=this.decode(e),b=this.texData.get(y.dataId),w=this.gpgpu.downloadMatrixFromPackedTexture(b.texture,...Kh(t)).subarray(0,i);return this.disposeIntermediateTensorInfo(y),w}const o=C().getBool("WEBGL_PACK")&&s===!0,a=o?XL(t):t,c=o?new w5(a):new b5(a),h=this.runWebGLProgram(c,[{shape:a,dtype:n,dataId:e}],"float32"),d=this.texData.get(h.dataId),m=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(d.texture,d.texShape[0],d.texShape[1]).subarray(0,i);return this.disposeIntermediateTensorInfo(h),m}async time(e){const t=this.activeTimers,n=[];let s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();const i=Yi(this.activeTimers.map(c=>c.query)).filter(c=>c!=null),o=Yi(this.activeTimers.map(c=>c.name)).filter(c=>c!=null);this.activeTimers=t,s&&(this.programTimersStack=null);const a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const c=await Promise.all(i);a.kernelMs=Ox(c),a.getExtraProfileInfo=()=>c.map((h,d)=>({name:o[d],ms:h})).map(h=>`${h.name}: ${h.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:Vn(),endMs:null}}endTimer(e){return C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=Vn(),e)}async getQueryTime(e){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);const t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;this.releaseGPUData(e);const{complexTensors:t}=this.texData.get(e);t!=null&&(t.real.dispose(),t.imag.dispose()),this.texData.delete(e)}releaseGPUData(e){const{texture:t,dtype:n,texShape:s,usage:i,isPacked:o,slice:a}=this.texData.get(e),c=a&&a.origDataId||e,h=this.dataRefCount.get(c);h>1?this.dataRefCount.set(c,h-1):(this.dataRefCount.delete(c),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,i,o)));const d=this.texData.get(e);d.texture=null,d.texShape=null,d.isPacked=!1,d.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return C().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Fs().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=u6){const n=this.getCPUBackend();return!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(s=>this.texData.get(s.dataId).texture==null&&we(s.shape)<t)}getGPGPUContext(){return this.gpgpu}complex(e,t){const n=this.makeOutput(e.shape,"complex64"),s=this.texData.get(n.dataId);return s.complexTensors={real:Fs().keep(e.clone()),imag:Fs().keep(t.clone())},n}real(e){const t=this.texData.get(e.dataId);return t.complexTensors.real.clone()}imag(e){const t=this.texData.get(e.dataId);return t.complexTensors.imag.clone()}slice(e,t,n){if(this.shouldExecuteOnCPU([e])){const o=_j(this.texData.get(e.dataId).values,t,n,e.shape,e.dtype);return this.makeOutput(n,e.dtype,o)}if(we(n)===0)return en([],n,e.dtype);const{isPacked:s}=this.texData.get(e.dataId),i=Ey(e.shape,t,n);if(s||!i){const o=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new L8(n):new b8(n),a=o.getCustomSetupFunc(t);return this.compileAndRun(o,[e],null,a)}return this.uploadToGPU(e.dataId),this.shallowSlice(e,t,n)}shallowSlice(e,t,n){const s=this.texData.get(e.dataId),i=this.makeOutput(n,e.dtype),o=this.texData.get(i.dataId);Object.assign(o,s),o.shape=n,o.dtype=e.dtype;let a=Dy(t,e.strides);s.slice&&(a+=s.slice.flatOffset),o.slice={flatOffset:a,origDataId:s.slice&&s.slice.origDataId||e.dataId};const c=this.dataRefCount.get(o.slice.origDataId)||1;return this.dataRefCount.set(o.slice.origDataId,c+1),i}stridedSlice(e,t,n,s){const i=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.stridedSlice(e,t,n,s));if(i)return i;const o=bd(t,n,s);if(o.some(c=>c===0))return en([],o);const a=new S8(t,s,o);return this.compileAndRun(a,[e])}reverse(e,t){const n=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new f8(e.shape,t):new m8(e.shape,t);return this.compileAndRun(n,[e])}concat(e,t){if(e[0].dtype==="complex64"){const a=e.map(h=>So(h)),c=e.map(h=>Ra(h));return xi(this.concat(a,t),this.concat(c,t))}if(e.length===1)return e[0];if(e.length>C().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){const a=Math.floor(e.length/2),c=this.concat(e.slice(0,a),t),h=this.concat(e.slice(a),t);return this.concat([c,h],t)}if(C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].rank>1){const a=new r5(e.map(c=>c.shape),t);return this.compileAndRun(a,e)}const n=Or(e.map(a=>a.shape),t),s=e.map(a=>a.as2D(-1,we(a.shape.slice(t)))),i=new i5(s.map(a=>a.shape)),o=this.compileAndRun(i,s);return o.reshape(n)}neg(e){const t=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.neg(e));if(t)return t;if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,aC,e.dtype);const n=new st(e.shape,aC);return this.compileAndRun(n,[e])}batchMatMul(e,t,n,s){const i=n?e.shape[2]:e.shape[1],o=s?t.shape[1]:t.shape[2],a=n?e.shape[1]:e.shape[2],[c,,]=e.shape;if((i===1||o===1)&&a>gC){n&&(e=Me(e,[0,2,1])),s&&(t=Me(t,[0,2,1]));const m=o===1?e:e.as3D(c,a,1),y=o===1?2:1,b=o===1?t.as3D(c,1,a):t;return this.multiply(m,b).sum(y,!0)}const h=vn(e.dtype,t.dtype),d=new tS(e.shape,[c,i,o],n,s);return this.compileAndRun(d,[e,t],h)}fusedBatchMatMul({a:e,b:t,transposeA:n,transposeB:s,bias:i,activation:o,preluActivationWeights:a}){const c=n?e.shape[2]:e.shape[1],h=s?t.shape[1]:t.shape[2],[d,,]=e.shape,m=vn(e.dtype,t.dtype),y=i!=null,b=a!=null,w=o?om(o,!0):null,L=new tS(e.shape,[d,c,h],n,s,y,w,b),T=[e,t];return i&&T.push(i),a&&T.push(a),this.compileAndRun(L,T,m)}multiply(e,t){if(e.dtype==="complex64"){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),a=new D0(E0.REAL,e.shape,t.shape),c=new D0(E0.IMAG,e.shape,t.shape),h=[this.makeComplexComponentTensorInfo(e,i.complexTensors.real),this.makeComplexComponentTensorInfo(e,i.complexTensors.imag),this.makeComplexComponentTensorInfo(t,o.complexTensors.real),this.makeComplexComponentTensorInfo(t,o.complexTensors.imag)],d=this.compileAndRun(a,h),m=this.compileAndRun(c,h),y=this.complex(d,m);return d.dispose(),m.dispose(),y}const n=vn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=kj(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,F0,e.dtype);const s=new ln(F0,e.shape,t.shape);return this.compileAndRun(s,[e,t],e.dtype)}localResponseNormalization4D(e,t,n,s,i){const o=C().getBool("WEBGL_PACK_NORMALIZATION")?new X5(e.shape,t,n,s,i):new j5(e.shape,t,n,s,i);return this.compileAndRun(o,[e])}LRNGrad(e,t,n,s,i,o,a){const c=new K5(t.shape,s,i,o,a);return this.compileAndRun(c,[t,n,e])}tile(e,t){if(e.dtype==="string"){const s=this.readSync(e.dataId),i=s.map(a=>Dl(a)),o=Ze(e.shape,e.dtype,i);return r6(o,t)}const n=new v8(e.shape,t);return this.compileAndRun(n,[e])}pad(e,t,n){const s=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new a8(e.shape,t,n):new o8(e.shape,t,n);return this.compileAndRun(s,[e])}gather(e,t,n){const s=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.gather(e,t,n));if(s)return s;const i=new x5(e.shape,t.size,n);return this.compileAndRun(i,[e,t])}batchToSpaceND(e,t,n){k(e.rank<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");const s=t.reduce((d,m)=>d*m),i=fh(e.shape,t,s),o=gh(i.length,t.length),a=yh(e.shape,t,s),c=Rb(n,t.length),h=Ob(a,n,t.length);return Me(e.reshape(i),o).reshape(a).slice(c,h)}spaceToBatchND(e,t,n){k(e.rank<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");const s=t.reduce((m,y)=>m*y),i=[[0,0]];i.push(...n);for(let m=1+t.length;m<e.shape.length;++m)i.push([0,0]);const o=e.pad(i),a=fh(o.shape,t,s,!1),c=gh(a.length,t.length,!1),h=yh(o.shape,t,s,!1),d=Me(o.reshape(a),c);return K(d,h)}reduce(e,t,n){const s=e.shape[0],i=e.shape[1],o=Jl(i),a=Math.ceil(i/o),c={windowSize:o,inSize:i,batchSize:s,outSize:a},h=new J0(c,t),d=this.compileAndRun(h,[e],n);return d.shape[1]===1?d:this.reduce(d,t,n)}argReduce(e,t,n=null){let s=e.shape[0],i=e.shape[1];n!=null&&(s=n.shape[0],i=n.shape[1]);const o=Jl(i),a={windowSize:o,inSize:i,batchSize:s,outSize:Math.ceil(i/o)},c=new Mj(a,t,n==null),h=[e];n!=null&&h.push(n);const d=this.compileAndRun(c,h,"int32");return d.shape[1]===1?d:this.argReduce(e,t,d)}argReducePacked(e,t,n=null){const s=n!=null?n.shape:e.shape,i=s[s.length-1],o=Jl(i),a=new TK(s,o,t,n==null),c=n==null?[e]:[e,n],h=this.compileAndRun(a,c,"int32");return h.rank===e.rank?this.argReducePacked(e,t,h):h}sum(e,t){es("sum",t,e.rank);const[n,s]=Cn(e.shape,t),i=we(s),o=e.as2D(-1,i),a=cd(e.dtype);return this.reduce(o,"sum",a).reshape(n)}prod(e,t){const n=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.prod(e,t));if(n)return n;const[s,i]=Cn(e.shape,t),o=we(i),a=e.as2D(-1,o),c=cd(e.dtype);return this.reduce(a,"prod",c).reshape(s)}unsortedSegmentSum(e,t,n){let s=0;const i=kn([s],e.rank);let o=e;i!=null&&(o=Me(e,i),s=ws(1,e.rank)[0]);const a=fC.computeOutShape(o.shape,s,n),c=we([o.shape[s]]),h=o.as2D(-1,c),d=cd(e.dtype);let m=this.segOpCompute(h,"unsortedSegmentSum",t,d,n).reshape(a);return i!=null&&(m=Me(m,Ml(i))),m}segOpCompute(e,t,n,s,i){const o=e.shape[0],a=e.shape[1],c=fC.segOpComputeOptimalWindowSize(a,i),h={windowSize:c,inSize:a,batchSize:o,numSegments:i},d=new g8(h,t),m=this.compileAndRun(d,[e,n],s);return m.shape[1]===i?m:(n=sh(0,i).tile([a/c]),this.segOpCompute(m,t,n,s,i))}argMinMaxReduce(e,t,n){const s=[t];if(es("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.rank),!C().getBool("WEBGL_PACK_REDUCE")||e.rank<=2){const[i,o]=Cn(e.shape,s),a=we(o),c=e.as2D(-1,a);return this.argReduce(c,n).reshape(i)}return this.argReducePacked(e,n)}argMin(e,t){return this.argMinMaxReduce(e,t,"min")}argMax(e,t){return this.argMinMaxReduce(e,t,"max")}cumsum(e,t,n,s){if(t!==e.rank-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${e.rank-1} but got axis=${t}`);const i=e.shape[t];let o=e;for(let a=0;a<=Math.ceil(Math.log2(i))-1;a++){const c=new M0(e.shape,!1,s),h=c.getCustomSetupFunc(a),d=o;o=this.compileAndRun(c,[o],o.dtype,h),d.dispose()}if(n){const a=new M0(e.shape,n,s),c=o;o=this.compileAndRun(a,[o]),c.dispose()}return o}equal(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,VK,"bool");const n=new ln(RK,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}notEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,YK,"bool");const n=new ln(OK,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}less(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.less(e,t));if(n)return n;if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,HK,"bool");const s=new ln(EK,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}lessEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,qK,"bool");const n=new ln(DK,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}greater(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.greater(e,t));if(n)return n;if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,jK,"bool");const s=new ln(kK,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}greaterEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,KK,"bool");const n=new ln(FK,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}logicalNot(e){const t=new st(e.shape,Q8);return this.compileAndRun(t,[e])}logicalAnd(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,XK,"bool");const n=new ln(_K,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}logicalOr(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,JK,"bool");const n=new ln(WK,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}select(e,t,n){const s=new y8(e.rank,t.shape,t.rank);return this.compileAndRun(s,[e,t,n],vn(t.dtype,n.dtype))}where(e){Ba("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");const t=e.dataSync();return a6(e.shape,t)}topk(e,t,n){const s=e.dataSync();return o6(s,e.shape,e.dtype,t,n)}min(e,t){es("min",t,e.rank);const[n,s]=Cn(e.shape,t),i=we(s),o=e.as2D(-1,i);return this.reduce(o,"min",o.dtype).reshape(n)}minimum(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.minimum(e,t));if(n)return n;const s=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qr(QK,e.shape,t.shape):new ln(UK,e.shape,t.shape);return this.compileAndRun(s,[e,t])}mod(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qr(e5,e.shape,t.shape):new ln(BK,e.shape,t.shape);return this.compileAndRun(n,[e,t])}maximum(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.maximum(e,t));if(n)return n;const s=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qr(ZK,e.shape,t.shape):new ln($K,e.shape,t.shape);return this.compileAndRun(s,[e,t])}all(e,t){es("all",t,e.rank);const[n,s]=Cn(e.shape,t),i=we(s),o=e.as2D(-1,i);return this.reduce(o,"all",o.dtype).reshape(n)}any(e,t){es("any",t,e.rank);const[n,s]=Cn(e.shape,t),i=we(s),o=e.as2D(-1,i);return this.reduce(o,"any",o.dtype).reshape(n)}floorDiv(e,t){const n=NK,s="int32";if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,PK,s);const i=new ln(n,e.shape,t.shape);return this.compileAndRun(i,[e,t],s)}add(e,t){if(e.dtype==="complex64"&&t.dtype==="complex64")return this.complexSeparableBinaryOp(e,t,QL);const n=vn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=vj(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,QL,n);const s=new ln(QL,e.shape,t.shape);return this.compileAndRun(s,[e,t],n)}packedUnaryOp(e,t,n){const s=new Qh(e.shape,t);return this.compileAndRun(s,[e],n)}packedBinaryOp(e,t,n,s,i=!1){const o=new qr(n,e.shape,t.shape,i);return this.compileAndRun(o,[e,t],s)}complexSeparableBinaryOp(e,t,n){const s=this.texData.get(e.dataId),i=this.texData.get(t.dataId),[o,a]=[[s.complexTensors.real,i.complexTensors.real],[s.complexTensors.imag,i.complexTensors.imag]].map(h=>{const[d,m]=h,y=this.makeComplexComponentTensorInfo(e,d),b=this.makeComplexComponentTensorInfo(t,m),w=new ln(n,e.shape,t.shape);return this.compileAndRun(w,[y,b],vn(d.dtype,m.dtype))}),c=this.complex(o,a);return o.dispose(),a.dispose(),c}makeComplexComponentTensorInfo(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}addN(e){if(e.length===1)return e[0];if(e.length>C().get("WEBGL_MAX_TEXTURES_IN_SHADER")){const o=Math.floor(e.length/2),a=this.addN(e.slice(0,o)),c=this.addN(e.slice(o));return this.addN([a,c])}const t=e.map(o=>o.dtype).reduce((o,a)=>vn(o,a)),n=e.map(o=>o.shape),s=C().getBool("WEBGL_PACK"),i=s?new Bj(e[0].shape,n):new Uj(e[0].shape,n);return this.compileAndRun(i,e,t)}subtract(e,t){if(e.dtype==="complex64"&&t.dtype==="complex64")return this.complexSeparableBinaryOp(e,t,eS);const n=vn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=Wj(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,eS,e.dtype);const s=new ln(eS,e.shape,t.shape);return this.compileAndRun(s,[e,t],n)}pow(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS"),s=n?new qr(zK,e.shape,t.shape):new ln(CK,e.shape,t.shape),i=vn(e.dtype,t.dtype);return this.compileAndRun(s,[e,t],i)}ceil(e){if(this.shouldExecuteOnCPU([e])){const n=Nj(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,cC,e.dtype);const t=new st(e.shape,cC);return this.compileAndRun(t,[e])}floor(e){if(this.shouldExecuteOnCPU([e])){const n=Oj(this.texData.get(e.dataId).values,e.dtype);return 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this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,uC,e.dtype);const t=new st(e.shape,uC);return this.compileAndRun(t,[e])}softmax(e,t){const n=ft([t],e.shape),s=qn(e,n),i=Rn(s.shape,n),o=this.subtract(e,s.reshape(i)),a=this.exp(o),c=this.sum(a,n).reshape(i);return _e(a,c)}log(e){if(this.shouldExecuteOnCPU([e])){const n=Ej(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,n6,e.dtype);const t=new st(e.shape,W8);return this.compileAndRun(t,[e])}log1p(e){const t=new st(e.shape,$8);return this.compileAndRun(t,[e])}sqrt(e){const t=new st(e.shape,U8);return this.compileAndRun(t,[e])}rsqrt(e){if(this.shouldExecuteOnCPU([e])){const n=Fj(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}const t=new st(e.shape,B8);return this.compileAndRun(t,[e])}reciprocal(e){const t=new st(e.shape,Z8);return this.compileAndRun(t,[e])}relu(e){let t;return C().getBool("WEBGL_PACK")?t=new Qh(e.shape,dC):t=new st(e.shape,iC),this.compileAndRun(t,[e])}relu6(e){let t;return C().getBool("WEBGL_PACK")?t=new Qh(e.shape,pC):t=new st(e.shape,rC),this.compileAndRun(t,[e])}prelu(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qr(W0,e.shape,t.shape):new ln(_0,e.shape,t.shape);return this.compileAndRun(n,[e,t])}elu(e){if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,mC,e.dtype);const t=new st(e.shape,oC);return this.compileAndRun(t,[e])}eluDer(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qr(GK,e.shape,t.shape):new ln(MK,e.shape,t.shape);return this.compileAndRun(n,[e,t])}selu(e){const t=new st(e.shape,R8);return this.compileAndRun(t,[e])}int(e){const t=new st(e.shape,e6);return this.compileAndRun(t,[e],"int32")}clip(e,t,n){let s;C().getBool("WEBGL_PACK_CLIP")?s=new n5(e.shape):s=new t5(e.shape);const i=s.getCustomSetupFunc(t,n);return this.compileAndRun(s,[e],null,i)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){const n=Aj(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,sC,e.dtype);const t=new st(e.shape,sC);return this.compileAndRun(t,[e])}complexAbs(e){const t=this.texData.get(e.dataId),n=new s5(e.shape),s=[this.makeComplexComponentTensorInfo(e,t.complexTensors.real),this.makeComplexComponentTensorInfo(e,t.complexTensors.imag)];return this.compileAndRun(n,s)}sigmoid(e){const t=new st(e.shape,M8);return this.compileAndRun(t,[e])}softplus(e){const t=new st(e.shape,P8);return this.compileAndRun(t,[e])}asin(e){const t=new st(e.shape,z8);return this.compileAndRun(t,[e])}acos(e){const t=new st(e.shape,G8);return this.compileAndRun(t,[e])}atan(e){const t=new st(e.shape,V8);return this.compileAndRun(t,[e])}sinh(e){const t=new st(e.shape,Y8);return this.compileAndRun(t,[e])}cosh(e){const t=new st(e.shape,H8);return this.compileAndRun(t,[e])}tanh(e){const t=new st(e.shape,q8);return this.compileAndRun(t,[e])}asinh(e){const t=new st(e.shape,j8);return this.compileAndRun(t,[e])}acosh(e){const t=new st(e.shape,K8);return this.compileAndRun(t,[e])}atanh(e){const t=new st(e.shape,X8);return this.compileAndRun(t,[e])}erf(e){const t=new st(e.shape,J8);return this.compileAndRun(t,[e])}step(e,t){const n=new st(e.shape,O8(t));return this.compileAndRun(n,[e])}conv2dByMatMul(e,t,n,s,i,o){const a=e.shape,c=this.texData.get(e.dataId),h=n.inChannels,d=a[0]*a[1]*a[2],m=n.outChannels,y=n.dataFormat==="channelsLast",b=!1,w=!1,L=(d===1||m===1)&&h>gC,T=a[2]%2!==0&&!!c.isPacked;if(L||!C().getBool("WEBGL_LAZILY_UNPACK")||!C().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!T){const B=y?a[0]*a[1]*a[2]:a[0]*a[2]*a[3],$=K(e,[1,B,n.inChannels]),H=K(t,[1,n.inChannels,n.outChannels]),q=this.fusedBatchMatMul({a:$,b:H,transposeA:b,transposeB:w,bias:s,activation:i,preluActivationWeights:o});return K(q,n.outShape)}const A=y?a[0]*a[1]*(a[2]+1):a[0]*a[2]*(a[3]+1),N={dataId:e.dataId,shape:[1,A,n.inChannels],dtype:e.dtype},E=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k(Zp(c.shape,N.shape),()=>`packed reshape ${c.shape} to ${N.shape} isn't free`);const D=K(t,[1,n.inChannels,n.outChannels]),F=this.fusedBatchMatMul({a:N,b:D,transposeA:b,transposeB:w,bias:s,activation:i,preluActivationWeights:o}),_=this.texData.get(F.dataId);return k(_.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=E,_.shape=n.outShape,Fs().makeTensorFromDataId(F.dataId,n.outShape,F.dtype)}conv2dWithIm2Row(e,t,n,s,i,o){const{filterWidth:a,filterHeight:c,inChannels:h,outWidth:d,outHeight:m,dataFormat:y}=n,b=y==="channelsLast",w=a*c*h,L=m*d,T=[w,L],A=!0,N=!1,E=e.squeeze([0]),D=t.reshape([1,w,-1]),F=new q5(T,E.shape,n),_=this.compileAndRun(F,[E]).reshape([1,T[0],T[1]]),B=s!=null,$=o!=null,H=i?om(i,!0):null,q=new tS(_.shape,[1,L,n.outChannels],A,N,B,H,$),J=[_,D];s&&J.push(s),$&&J.push(o);const re=this.compileAndRun(q,J);return b?re.reshape([1,m,d,n.outChannels]):re.reshape([1,n.outChannels,m,d])}fusedConv2d({input:e,filter:t,convInfo:n,bias:s,activation:i,preluActivationWeights:o}){if(n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return this.conv2dByMatMul(e,t,n,s,i,o);if(C().getBool("WEBGL_CONV_IM2COL")&&e.shape[0]===1)return this.conv2dWithIm2Row(e,t,n,s,i,o);const a=s!=null,c=o!=null,h=i?om(i,!1):null,d=new $0(n,a,h,c),m=[e,t];return s&&m.push(s),o&&m.push(o),this.compileAndRun(d,m)}conv2d(e,t,n){if(n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return this.conv2dByMatMul(e,t,n);if(C().getBool("WEBGL_CONV_IM2COL")&&e.shape[0]===1)return this.conv2dWithIm2Row(e,t,n);const s=new $0(n);return this.compileAndRun(s,[e,t])}conv2dDerInput(e,t,n){const s=new a5(n);return this.compileAndRun(s,[e,t])}conv2dDerFilter(e,t,n){const s=new o5(n);return this.compileAndRun(s,[e,t])}fusedDepthwiseConv2D({input:e,filter:t,convInfo:n,bias:s,activation:i,preluActivationWeights:o}){const a=C().getBool("WEBGL_PACK_DEPTHWISECONV")&&n.strideWidth<=2&&n.outChannels/n.inChannels===1,c=i?om(i,a):null,h=[e,t],d=s!=null,m=o!=null;d&&h.push(s),m&&h.push(o);let y;return a?(y=new B0(n,d,c,m),this.compileAndRun(y,h)):(y=new U0(n,d,c,m),this.compileAndRun(y,h))}depthwiseConv2D(e,t,n){let s;return C().getBool("WEBGL_PACK_DEPTHWISECONV")&&n.strideWidth<=2&&n.outChannels/n.inChannels===1?(s=new B0(n),this.compileAndRun(s,[e,t])):(s=new U0(n),this.compileAndRun(s,[e,t]))}depthwiseConv2DDerInput(e,t,n){const s=new u5(n);return this.compileAndRun(s,[e,t])}depthwiseConv2DDerFilter(e,t,n){const s=new h5(n);return this.compileAndRun(s,[e,t])}conv3d(e,t,n){const s=new d5(n);return this.compileAndRun(s,[e,t])}conv3dDerInput(e,t,n){const s=new l5(n);return this.compileAndRun(s,[e,t])}conv3dDerFilter(e,t,n){const s=new c5(n);return this.compileAndRun(s,[e,t])}cast(e,t){return DA(e,t,this)}unstack(e,t){const n=e.shape[t],s=new Array(e.rank-1);let i=0;for(let h=0;h<e.rank;h++)h!==t&&(s[i++]=e.shape[h]);const o=new Array(e.rank).fill(0),a=e.shape.slice();a[t]=1;const c=new Array(n);for(let h=0;h<c.length;h++)o[t]=h,c[h]=this.slice(e,o,a).reshape(s);return c}avgPool3d(e,t){const n=new nS(t,"avg",!1);return this.compileAndRun(n,[e],"float32")}avgPool3dBackprop(e,t,n){const s=new vK(n);return this.compileAndRun(s,[e],t.dtype)}maxPool3d(e,t){const n=new nS(t,"max",!1);return this.compileAndRun(n,[e],"float32")}maxPool3dBackprop(e,t,n,s){const i=!0,o=new nS(s,"max",i),a=this.compileAndRun(o,[t]),c=new Z5(s),h=this.compileAndRun(c,[e,a],t.dtype);return a.dispose(),h}resizeBilinear(e,t,n,s){const i=C().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new 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this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){const{dataId:s}=this.makeTensorInfo(e,t,n);return Fs().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){const t=new s6(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new t8(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){const n=[nc(e.shape),...sc(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},i=[nc(t),...sc(t)],o=new Z0(i,n),a=!0,c=this.runWebGLProgram(o,[s],e.dtype,null,a);return{dataId:c.dataId,shape:t,dtype:c.dtype}}decode(e){const t=this.texData.get(e),{isPacked:n,shape:s,dtype:i}=t,o=XL(s);let a;n?a=new f5(o):a=new m5(o);const c=!0,h=this.runWebGLProgram(a,[{shape:o,dtype:i,dataId:e}],i,null,c);return{dtype:i,shape:s,dataId:h.dataId}}runWebGLProgram(e,t,n,s,i=!1){const o=this.makeTensorInfo(e.outputShape,n),a=this.texData.get(o.dataId);if(e.packedOutput&&(a.isPacked=!0),e.outPackingScheme===qh.DENSE){const L=Kh(e.outputShape);a.texShape=L.map(T=>T*2)}if(e.outTexUsage!=null&&(a.usage=e.outTexUsage),we(o.shape)===0)return a.values=bn(o.dtype,0),o;const c=[],h=t.map(L=>{if(L.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let T=this.texData.get(L.dataId);if(T.texture==null){if(!e.packedInputs&&we(L.shape)<=C().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:L.shape,texData:null,isUniform:!0,uniformValues:T.values};e.packedInputs&&(T.isPacked=!0,T.shape=L.shape)}else if(!!T.isPacked!==!!e.packedInputs)L=T.isPacked?this.unpackTensor(L):this.packTensor(L),c.push(L),T=this.texData.get(L.dataId);else if(T.isPacked&&!Zp(T.shape,L.shape)){const A=L,N=L.shape;L.shape=T.shape,L=this.packedReshape(L,N),c.push(L),T=this.texData.get(L.dataId),A.shape=N}return this.uploadToGPU(L.dataId),{shape:L.shape,texData:T,isUniform:!1}});this.uploadToGPU(o.dataId);const d={shape:o.shape,texData:a,isUniform:!1},m=H5(e,h,d),y=this.getAndSaveBinary(m,()=>V5(this.gpgpu,e,h,d)),b=this.activeTimers!=null;let w;if(b&&(w=this.startTimer()),Y5(this.gpgpu,y,h,d,s),c.forEach(L=>this.disposeIntermediateTensorInfo(L)),b&&(w=this.endTimer(w),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(w)})),!C().getBool("WEBGL_LAZILY_UNPACK")&&a.isPacked&&i===!1){const L=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),L}return o}compileAndRun(e,t,n,s,i=!1){n=n||t[0].dtype;const o=this.runWebGLProgram(e,t,n,s,i);return Fs().makeTensorFromDataId(o.dataId,o.shape,o.dtype)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){if(this.disposed)return;if(!C().getBool("IS_TEST")){const e=Object.keys(this.binaryCache);e.forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]})}this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=ee(()=>{if(!C().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=C().getBool("DEBUG");C().set("DEBUG",!1);const t=this.abs(Ne(1e-8)).dataSync()[0];if(C().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?c6:l6}uploadToGPU(e){const t=this.texData.get(e),{shape:n,dtype:s,values:i,texture:o,usage:a,isPacked:c}=t;if(o!=null)return;const h=this.activeTimers!=null;let d;h&&(d=Vn());let m=t.texShape;if(m==null&&(m=yj(n,c),t.texShape=m),i!=null){const y=XL(n);let b,w=m[1],L=m[0];const T=i instanceof Uint8Array;c?([w,L]=tc(m[0],m[1]),b=new S5(y,[L,w],T)):b=new L5(y,[L,w],T);const A=this.makeTensorInfo([L,w],s);T?this.texData.get(A.dataId).usage=As.PIXELS:this.texData.get(A.dataId).usage=As.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),w,L,i);const N=!0,E=this.runWebGLProgram(b,[A],s,null,N),D=this.texData.get(E.dataId);t.texture=D.texture,t.texShape=D.texShape,t.isPacked=D.isPacked,t.usage=D.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(E.dataId),t.values=null,h&&(this.uploadWaitMs+=Vn()-d)}else{const y=this.acquireTexture(m,a,s,c);t.texture=y}}convertAndCacheOnCPU(e,t){const n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=f6(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){const i=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${i} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*ry(t)}tryRunOnCpuOrThrow(e,t){if(this.shouldExecuteOnCPU(e))try{return t()}catch(n){if(C().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}}function f6(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){const n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}const g6="2.6.0";function y6(){C().set("WEBGL_FORCE_F16_TEXTURES",!0)}fy()&&Wy("webgl",()=>new m6,2);const bee={forceHalfFloat:y6},yC="if (isnan(x)) return x;",b6=`
+    `}}const{segment_util:fC}=Bb,i6=Mb,r6=Pb,o6=zb,a6=jd,c6=1e-7,l6=1e-4,rm={};function h6(e){return e in rm||(rm[e]={}),rm[e]}function om(e,t=!1){if(e==="linear")return t?t6:C8;if(e==="relu")return t?dC:iC;if(e==="elu")return t?mC:oC;if(e==="relu6")return t?pC:rC;if(e==="prelu")return t?W0:_0;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}const u6=128,d6=600;function p6(){return C().global.screen==null?1024:C().global.screen.height*C().global.screen.width*window.devicePixelRatio*d6/1024/1024}const gC=1e3;class m6 extends g{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!C().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){const t=_i(C().getNumber("WEBGL_VERSION"));this.binaryCache=h6(C().getNumber("WEBGL_VERSION")),this.gpgpu=new z5(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new I8(this.gpgpu),this.numMBBeforeWarning=p6(),this.texData=new p(this,Fs())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((C().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||C().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");const s={};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:As.UPLOAD,refCount:1}),s}incRef(e){const t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){const t=this.texData.get(e);t.refCount--}}move(e,t,n,s){if(C().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:As.UPLOAD,refCount:1})}disposeIntermediateTensorInfo(e){const t=e.dataId;if(this.texData.has(t)){const n=this.texData.get(t);n.refCount--,n.refCount<1&&this.disposeData(t)}}readSync(e){const t=this.texData.get(e),{values:n,dtype:s,complexTensors:i,slice:o,shape:a,isPacked:c}=t;if(o!=null){let y;c?y=new Qh(a,im):y=new st(a,im);const b=this.runWebGLProgram(y,[{dataId:e,shape:a,dtype:s}],s),w=this.readSync(b.dataId);return this.disposeIntermediateTensorInfo(b),w}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;const h=this.activeTimers!=null;let d;h&&(d=Vn());let m;if(s==="complex64"){const y=i.real.dataSync(),b=i.imag.dataSync();m=Zi(y,b)}else m=this.getValuesFromTexture(e);return h&&(this.downloadWaitMs+=Vn()-d),this.convertAndCacheOnCPU(e,m)}async read(e){if(this.pendingRead.has(e)){const w=this.pendingRead.get(e);return new Promise(L=>w.push(L))}const t=this.texData.get(e),{values:n,shape:s,slice:i,dtype:o,complexTensors:a,isPacked:c}=t;if(i!=null){let w;c?w=new Qh(s,im):w=new st(s,im);const L=this.runWebGLProgram(w,[{dataId:e,shape:s,dtype:o}],o),T=this.read(L.dataId);return this.disposeIntermediateTensorInfo(L),T}if(n!=null)return this.convertAndCacheOnCPU(e);if(!C().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&C().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let h=null,d;if(o!=="complex64"&&C().get("WEBGL_BUFFER_SUPPORTED")){d=this.decode(e);const w=this.texData.get(d.dataId);h=this.gpgpu.createBufferFromTexture(w.texture,...Kh(s))}this.pendingRead.set(e,[]),o!=="complex64"&&await this.gpgpu.createAndWaitForFence();let m;if(o==="complex64"){const w=await Promise.all([a.real.data(),a.imag.data()]),L=w[0],T=w[1];m=Zi(L,T)}else if(h==null)m=this.getValuesFromTexture(e);else{const w=we(s);m=this.gpgpu.downloadFloat32MatrixFromBuffer(h,w)}d!=null&&this.disposeIntermediateTensorInfo(d);const y=this.convertAndCacheOnCPU(e,m),b=this.pendingRead.get(e);return this.pendingRead.delete(e),b.forEach(w=>w(y)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),y}checkNumericalProblems(e){if(e==null)return;for(let t=0;t<e.length;t++){const n=e[t];if(!Q4(n))throw C().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){const{shape:t,dtype:n,isPacked:s}=this.texData.get(e),i=we(t);if(C().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const y=this.decode(e),b=this.texData.get(y.dataId),w=this.gpgpu.downloadMatrixFromPackedTexture(b.texture,...Kh(t)).subarray(0,i);return this.disposeIntermediateTensorInfo(y),w}const o=C().getBool("WEBGL_PACK")&&s===!0,a=o?XL(t):t,c=o?new w5(a):new b5(a),h=this.runWebGLProgram(c,[{shape:a,dtype:n,dataId:e}],"float32"),d=this.texData.get(h.dataId),m=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(d.texture,d.texShape[0],d.texShape[1]).subarray(0,i);return this.disposeIntermediateTensorInfo(h),m}async time(e){const t=this.activeTimers,n=[];let s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();const i=Yi(this.activeTimers.map(c=>c.query)).filter(c=>c!=null),o=Yi(this.activeTimers.map(c=>c.name)).filter(c=>c!=null);this.activeTimers=t,s&&(this.programTimersStack=null);const a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const c=await Promise.all(i);a.kernelMs=Ox(c),a.getExtraProfileInfo=()=>c.map((h,d)=>({name:o[d],ms:h})).map(h=>`${h.name}: ${h.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:Vn(),endMs:null}}endTimer(e){return C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=Vn(),e)}async getQueryTime(e){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);const t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;this.releaseGPUData(e);const{complexTensors:t}=this.texData.get(e);t!=null&&(t.real.dispose(),t.imag.dispose()),this.texData.delete(e)}releaseGPUData(e){const{texture:t,dtype:n,texShape:s,usage:i,isPacked:o,slice:a}=this.texData.get(e),c=a&&a.origDataId||e,h=this.dataRefCount.get(c);h>1?this.dataRefCount.set(c,h-1):(this.dataRefCount.delete(c),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,i,o)));const d=this.texData.get(e);d.texture=null,d.texShape=null,d.isPacked=!1,d.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return C().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Fs().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=u6){const n=this.getCPUBackend();return!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(s=>this.texData.get(s.dataId).texture==null&&we(s.shape)<t)}getGPGPUContext(){return this.gpgpu}complex(e,t){const n=this.makeOutput(e.shape,"complex64"),s=this.texData.get(n.dataId);return s.complexTensors={real:Fs().keep(e.clone()),imag:Fs().keep(t.clone())},n}real(e){const t=this.texData.get(e.dataId);return t.complexTensors.real.clone()}imag(e){const t=this.texData.get(e.dataId);return t.complexTensors.imag.clone()}slice(e,t,n){if(this.shouldExecuteOnCPU([e])){const o=_j(this.texData.get(e.dataId).values,t,n,e.shape,e.dtype);return this.makeOutput(n,e.dtype,o)}if(we(n)===0)return en([],n,e.dtype);const{isPacked:s}=this.texData.get(e.dataId),i=Ey(e.shape,t,n);if(s||!i){const o=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new L8(n):new b8(n),a=o.getCustomSetupFunc(t);return this.compileAndRun(o,[e],null,a)}return this.uploadToGPU(e.dataId),this.shallowSlice(e,t,n)}shallowSlice(e,t,n){const s=this.texData.get(e.dataId),i=this.makeOutput(n,e.dtype),o=this.texData.get(i.dataId);Object.assign(o,s),o.shape=n,o.dtype=e.dtype;let a=Dy(t,e.strides);s.slice&&(a+=s.slice.flatOffset),o.slice={flatOffset:a,origDataId:s.slice&&s.slice.origDataId||e.dataId};const c=this.dataRefCount.get(o.slice.origDataId)||1;return this.dataRefCount.set(o.slice.origDataId,c+1),i}stridedSlice(e,t,n,s){const i=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.stridedSlice(e,t,n,s));if(i)return i;const o=bd(t,n,s);if(o.some(c=>c===0))return en([],o);const a=new S8(t,s,o);return this.compileAndRun(a,[e])}reverse(e,t){const n=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new f8(e.shape,t):new m8(e.shape,t);return this.compileAndRun(n,[e])}concat(e,t){if(e[0].dtype==="complex64"){const a=e.map(h=>xo(h)),c=e.map(h=>Ea(h));return xi(this.concat(a,t),this.concat(c,t))}if(e.length===1)return e[0];if(e.length>C().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){const a=Math.floor(e.length/2),c=this.concat(e.slice(0,a),t),h=this.concat(e.slice(a),t);return this.concat([c,h],t)}if(C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].rank>1){const a=new r5(e.map(c=>c.shape),t);return this.compileAndRun(a,e)}const n=Or(e.map(a=>a.shape),t),s=e.map(a=>a.as2D(-1,we(a.shape.slice(t)))),i=new i5(s.map(a=>a.shape)),o=this.compileAndRun(i,s);return o.reshape(n)}neg(e){const t=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.neg(e));if(t)return t;if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,aC,e.dtype);const n=new st(e.shape,aC);return this.compileAndRun(n,[e])}batchMatMul(e,t,n,s){const i=n?e.shape[2]:e.shape[1],o=s?t.shape[1]:t.shape[2],a=n?e.shape[1]:e.shape[2],[c,,]=e.shape;if((i===1||o===1)&&a>gC){n&&(e=Me(e,[0,2,1])),s&&(t=Me(t,[0,2,1]));const m=o===1?e:e.as3D(c,a,1),y=o===1?2:1,b=o===1?t.as3D(c,1,a):t;return this.multiply(m,b).sum(y,!0)}const h=vn(e.dtype,t.dtype),d=new tS(e.shape,[c,i,o],n,s);return this.compileAndRun(d,[e,t],h)}fusedBatchMatMul({a:e,b:t,transposeA:n,transposeB:s,bias:i,activation:o,preluActivationWeights:a}){const c=n?e.shape[2]:e.shape[1],h=s?t.shape[1]:t.shape[2],[d,,]=e.shape,m=vn(e.dtype,t.dtype),y=i!=null,b=a!=null,w=o?om(o,!0):null,L=new tS(e.shape,[d,c,h],n,s,y,w,b),T=[e,t];return i&&T.push(i),a&&T.push(a),this.compileAndRun(L,T,m)}multiply(e,t){if(e.dtype==="complex64"){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),a=new D0(E0.REAL,e.shape,t.shape),c=new D0(E0.IMAG,e.shape,t.shape),h=[this.makeComplexComponentTensorInfo(e,i.complexTensors.real),this.makeComplexComponentTensorInfo(e,i.complexTensors.imag),this.makeComplexComponentTensorInfo(t,o.complexTensors.real),this.makeComplexComponentTensorInfo(t,o.complexTensors.imag)],d=this.compileAndRun(a,h),m=this.compileAndRun(c,h),y=this.complex(d,m);return d.dispose(),m.dispose(),y}const n=vn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=kj(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,F0,e.dtype);const s=new hn(F0,e.shape,t.shape);return this.compileAndRun(s,[e,t],e.dtype)}localResponseNormalization4D(e,t,n,s,i){const o=C().getBool("WEBGL_PACK_NORMALIZATION")?new X5(e.shape,t,n,s,i):new j5(e.shape,t,n,s,i);return this.compileAndRun(o,[e])}LRNGrad(e,t,n,s,i,o,a){const c=new K5(t.shape,s,i,o,a);return this.compileAndRun(c,[t,n,e])}tile(e,t){if(e.dtype==="string"){const s=this.readSync(e.dataId),i=s.map(a=>Dl(a)),o=Ze(e.shape,e.dtype,i);return r6(o,t)}const n=new v8(e.shape,t);return this.compileAndRun(n,[e])}pad(e,t,n){const s=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new a8(e.shape,t,n):new o8(e.shape,t,n);return this.compileAndRun(s,[e])}gather(e,t,n){const s=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.gather(e,t,n));if(s)return s;const i=new x5(e.shape,t.size,n);return this.compileAndRun(i,[e,t])}batchToSpaceND(e,t,n){k(e.rank<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");const s=t.reduce((d,m)=>d*m),i=fh(e.shape,t,s),o=gh(i.length,t.length),a=yh(e.shape,t,s),c=Rb(n,t.length),h=Ob(a,n,t.length);return Me(e.reshape(i),o).reshape(a).slice(c,h)}spaceToBatchND(e,t,n){k(e.rank<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");const s=t.reduce((m,y)=>m*y),i=[[0,0]];i.push(...n);for(let m=1+t.length;m<e.shape.length;++m)i.push([0,0]);const o=e.pad(i),a=fh(o.shape,t,s,!1),c=gh(a.length,t.length,!1),h=yh(o.shape,t,s,!1),d=Me(o.reshape(a),c);return K(d,h)}reduce(e,t,n){const s=e.shape[0],i=e.shape[1],o=Jl(i),a=Math.ceil(i/o),c={windowSize:o,inSize:i,batchSize:s,outSize:a},h=new J0(c,t),d=this.compileAndRun(h,[e],n);return 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a=0;a<=Math.ceil(Math.log2(i))-1;a++){const c=new M0(e.shape,!1,s),h=c.getCustomSetupFunc(a),d=o;o=this.compileAndRun(c,[o],o.dtype,h),d.dispose()}if(n){const a=new M0(e.shape,n,s),c=o;o=this.compileAndRun(a,[o]),c.dispose()}return o}equal(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,VK,"bool");const n=new hn(RK,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}notEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,YK,"bool");const n=new hn(OK,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}less(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.less(e,t));if(n)return n;if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,HK,"bool");const s=new hn(EK,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}lessEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,qK,"bool");const n=new hn(DK,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}greater(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.greater(e,t));if(n)return n;if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,jK,"bool");const s=new hn(kK,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}greaterEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,KK,"bool");const n=new hn(FK,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}logicalNot(e){const t=new st(e.shape,Q8);return this.compileAndRun(t,[e])}logicalAnd(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,XK,"bool");const n=new hn(_K,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}logicalOr(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,JK,"bool");const n=new hn(WK,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}select(e,t,n){const s=new y8(e.rank,t.shape,t.rank);return this.compileAndRun(s,[e,t,n],vn(t.dtype,n.dtype))}where(e){Pa("tf.where() in webgl locks the UI thread. 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this.compileAndRun(o,[e,t],s)}complexSeparableBinaryOp(e,t,n){const s=this.texData.get(e.dataId),i=this.texData.get(t.dataId),[o,a]=[[s.complexTensors.real,i.complexTensors.real],[s.complexTensors.imag,i.complexTensors.imag]].map(h=>{const[d,m]=h,y=this.makeComplexComponentTensorInfo(e,d),b=this.makeComplexComponentTensorInfo(t,m),w=new hn(n,e.shape,t.shape);return this.compileAndRun(w,[y,b],vn(d.dtype,m.dtype))}),c=this.complex(o,a);return o.dispose(),a.dispose(),c}makeComplexComponentTensorInfo(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}addN(e){if(e.length===1)return e[0];if(e.length>C().get("WEBGL_MAX_TEXTURES_IN_SHADER")){const o=Math.floor(e.length/2),a=this.addN(e.slice(0,o)),c=this.addN(e.slice(o));return this.addN([a,c])}const t=e.map(o=>o.dtype).reduce((o,a)=>vn(o,a)),n=e.map(o=>o.shape),s=C().getBool("WEBGL_PACK"),i=s?new Bj(e[0].shape,n):new Uj(e[0].shape,n);return this.compileAndRun(i,e,t)}subtract(e,t){if(e.dtype==="complex64"&&t.dtype==="complex64")return this.complexSeparableBinaryOp(e,t,eS);const n=vn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=Wj(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,eS,e.dtype);const s=new hn(eS,e.shape,t.shape);return this.compileAndRun(s,[e,t],n)}pow(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS"),s=n?new qr(zK,e.shape,t.shape):new hn(CK,e.shape,t.shape),i=vn(e.dtype,t.dtype);return this.compileAndRun(s,[e,t],i)}ceil(e){if(this.shouldExecuteOnCPU([e])){const n=Nj(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,cC,e.dtype);const t=new st(e.shape,cC);return this.compileAndRun(t,[e])}floor(e){if(this.shouldExecuteOnCPU([e])){const n=Oj(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,lC,e.dtype);const t=new st(e.shape,lC);return this.compileAndRun(t,[e])}sign(e){const t=new st(e.shape,E8);return this.compileAndRun(t,[e])}isNaN(e){const t=new st(e.shape,D8);return this.compileAndRun(t,[e],"bool")}isInf(e){const t=new st(e.shape,k8);return this.compileAndRun(t,[e],"bool")}isFinite(e){const t=new st(e.shape,F8);return this.compileAndRun(t,[e],"bool")}round(e){const t=new st(e.shape,_8);return this.compileAndRun(t,[e])}exp(e){if(this.shouldExecuteOnCPU([e])){const n=Cj(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,hC,e.dtype);const t=new st(e.shape,hC);return this.compileAndRun(t,[e])}expm1(e){if(this.shouldExecuteOnCPU([e])){const n=Rj(this.texData.get(e.dataId).values,e.dtype);return 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this.compileAndRun(t,[e])}relu(e){let t;return C().getBool("WEBGL_PACK")?t=new Qh(e.shape,dC):t=new st(e.shape,iC),this.compileAndRun(t,[e])}relu6(e){let t;return C().getBool("WEBGL_PACK")?t=new Qh(e.shape,pC):t=new st(e.shape,rC),this.compileAndRun(t,[e])}prelu(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qr(W0,e.shape,t.shape):new hn(_0,e.shape,t.shape);return this.compileAndRun(n,[e,t])}elu(e){if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,mC,e.dtype);const t=new st(e.shape,oC);return this.compileAndRun(t,[e])}eluDer(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qr(GK,e.shape,t.shape):new hn(MK,e.shape,t.shape);return this.compileAndRun(n,[e,t])}selu(e){const t=new st(e.shape,R8);return this.compileAndRun(t,[e])}int(e){const t=new st(e.shape,e6);return this.compileAndRun(t,[e],"int32")}clip(e,t,n){let s;C().getBool("WEBGL_PACK_CLIP")?s=new n5(e.shape):s=new t5(e.shape);const i=s.getCustomSetupFunc(t,n);return this.compileAndRun(s,[e],null,i)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){const n=Aj(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,sC,e.dtype);const t=new st(e.shape,sC);return this.compileAndRun(t,[e])}complexAbs(e){const t=this.texData.get(e.dataId),n=new s5(e.shape),s=[this.makeComplexComponentTensorInfo(e,t.complexTensors.real),this.makeComplexComponentTensorInfo(e,t.complexTensors.imag)];return this.compileAndRun(n,s)}sigmoid(e){const t=new st(e.shape,M8);return this.compileAndRun(t,[e])}softplus(e){const t=new st(e.shape,P8);return this.compileAndRun(t,[e])}asin(e){const t=new st(e.shape,z8);return this.compileAndRun(t,[e])}acos(e){const t=new st(e.shape,G8);return this.compileAndRun(t,[e])}atan(e){const t=new st(e.shape,V8);return this.compileAndRun(t,[e])}sinh(e){const t=new st(e.shape,Y8);return this.compileAndRun(t,[e])}cosh(e){const t=new st(e.shape,H8);return this.compileAndRun(t,[e])}tanh(e){const t=new st(e.shape,q8);return this.compileAndRun(t,[e])}asinh(e){const t=new st(e.shape,j8);return this.compileAndRun(t,[e])}acosh(e){const t=new st(e.shape,K8);return this.compileAndRun(t,[e])}atanh(e){const t=new st(e.shape,X8);return this.compileAndRun(t,[e])}erf(e){const t=new st(e.shape,J8);return this.compileAndRun(t,[e])}step(e,t){const n=new st(e.shape,O8(t));return this.compileAndRun(n,[e])}conv2dByMatMul(e,t,n,s,i,o){const a=e.shape,c=this.texData.get(e.dataId),h=n.inChannels,d=a[0]*a[1]*a[2],m=n.outChannels,y=n.dataFormat==="channelsLast",b=!1,w=!1,L=(d===1||m===1)&&h>gC,T=a[2]%2!==0&&!!c.isPacked;if(L||!C().getBool("WEBGL_LAZILY_UNPACK")||!C().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!T){const B=y?a[0]*a[1]*a[2]:a[0]*a[2]*a[3],$=K(e,[1,B,n.inChannels]),H=K(t,[1,n.inChannels,n.outChannels]),q=this.fusedBatchMatMul({a:$,b:H,transposeA:b,transposeB:w,bias:s,activation:i,preluActivationWeights:o});return K(q,n.outShape)}const A=y?a[0]*a[1]*(a[2]+1):a[0]*a[2]*(a[3]+1),N={dataId:e.dataId,shape:[1,A,n.inChannels],dtype:e.dtype},E=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k(Zp(c.shape,N.shape),()=>`packed reshape ${c.shape} to ${N.shape} isn't free`);const D=K(t,[1,n.inChannels,n.outChannels]),F=this.fusedBatchMatMul({a:N,b:D,transposeA:b,transposeB:w,bias:s,activation:i,preluActivationWeights:o}),_=this.texData.get(F.dataId);return k(_.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=E,_.shape=n.outShape,Fs().makeTensorFromDataId(F.dataId,n.outShape,F.dtype)}conv2dWithIm2Row(e,t,n,s,i,o){const{filterWidth:a,filterHeight:c,inChannels:h,outWidth:d,outHeight:m,dataFormat:y}=n,b=y==="channelsLast",w=a*c*h,L=m*d,T=[w,L],A=!0,N=!1,E=e.squeeze([0]),D=t.reshape([1,w,-1]),F=new q5(T,E.shape,n),_=this.compileAndRun(F,[E]).reshape([1,T[0],T[1]]),B=s!=null,$=o!=null,H=i?om(i,!0):null,q=new tS(_.shape,[1,L,n.outChannels],A,N,B,H,$),J=[_,D];s&&J.push(s),$&&J.push(o);const re=this.compileAndRun(q,J);return b?re.reshape([1,m,d,n.outChannels]):re.reshape([1,n.outChannels,m,d])}fusedConv2d({input:e,filter:t,convInfo:n,bias:s,activation:i,preluActivationWeights:o}){if(n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return this.conv2dByMatMul(e,t,n,s,i,o);if(C().getBool("WEBGL_CONV_IM2COL")&&e.shape[0]===1)return this.conv2dWithIm2Row(e,t,n,s,i,o);const a=s!=null,c=o!=null,h=i?om(i,!1):null,d=new $0(n,a,h,c),m=[e,t];return s&&m.push(s),o&&m.push(o),this.compileAndRun(d,m)}conv2d(e,t,n){if(n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return this.conv2dByMatMul(e,t,n);if(C().getBool("WEBGL_CONV_IM2COL")&&e.shape[0]===1)return this.conv2dWithIm2Row(e,t,n);const s=new $0(n);return this.compileAndRun(s,[e,t])}conv2dDerInput(e,t,n){const s=new a5(n);return this.compileAndRun(s,[e,t])}conv2dDerFilter(e,t,n){const s=new o5(n);return this.compileAndRun(s,[e,t])}fusedDepthwiseConv2D({input:e,filter:t,convInfo:n,bias:s,activation:i,preluActivationWeights:o}){const a=C().getBool("WEBGL_PACK_DEPTHWISECONV")&&n.strideWidth<=2&&n.outChannels/n.inChannels===1,c=i?om(i,a):null,h=[e,t],d=s!=null,m=o!=null;d&&h.push(s),m&&h.push(o);let y;return a?(y=new B0(n,d,c,m),this.compileAndRun(y,h)):(y=new U0(n,d,c,m),this.compileAndRun(y,h))}depthwiseConv2D(e,t,n){let s;return C().getBool("WEBGL_PACK_DEPTHWISECONV")&&n.strideWidth<=2&&n.outChannels/n.inChannels===1?(s=new B0(n),this.compileAndRun(s,[e,t])):(s=new U0(n),this.compileAndRun(s,[e,t]))}depthwiseConv2DDerInput(e,t,n){const s=new u5(n);return this.compileAndRun(s,[e,t])}depthwiseConv2DDerFilter(e,t,n){const s=new h5(n);return this.compileAndRun(s,[e,t])}conv3d(e,t,n){const s=new d5(n);return this.compileAndRun(s,[e,t])}conv3dDerInput(e,t,n){const s=new l5(n);return this.compileAndRun(s,[e,t])}conv3dDerFilter(e,t,n){const s=new c5(n);return this.compileAndRun(s,[e,t])}cast(e,t){return DA(e,t,this)}unstack(e,t){const n=e.shape[t],s=new Array(e.rank-1);let i=0;for(let h=0;h<e.rank;h++)h!==t&&(s[i++]=e.shape[h]);const o=new Array(e.rank).fill(0),a=e.shape.slice();a[t]=1;const c=new Array(n);for(let h=0;h<c.length;h++)o[t]=h,c[h]=this.slice(e,o,a).reshape(s);return c}avgPool3d(e,t){const n=new nS(t,"avg",!1);return this.compileAndRun(n,[e],"float32")}avgPool3dBackprop(e,t,n){const s=new vK(n);return this.compileAndRun(s,[e],t.dtype)}maxPool3d(e,t){const n=new nS(t,"max",!1);return this.compileAndRun(n,[e],"float32")}maxPool3dBackprop(e,t,n,s){const i=!0,o=new nS(s,"max",i),a=this.compileAndRun(o,[t]),c=new Z5(s),h=this.compileAndRun(c,[e,a],t.dtype);return a.dispose(),h}resizeBilinear(e,t,n,s){const i=C().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new u8(e.shape,t,n,s):new h8(e.shape,t,n,s);return this.compileAndRun(i,[e],"float32")}resizeBilinearBackprop(e,t,n){const s=new l8(e,t,n);return this.compileAndRun(s,[e])}resizeNearestNeighbor(e,t,n,s){const i=new p8(e.shape,t,n,s);return this.compileAndRun(i,[e])}resizeNearestNeighborBackprop(e,t,n){const s=new d8(e,t,n);return this.compileAndRun(s,[e])}multinomial(e,t,n,s){const i=t?e:No(e),o=i.shape[0],a=i.shape[1],c=new Q5(o,a,n),h=c.getCustomSetupFunc(s);return this.compileAndRun(c,[i],"int32",h)}oneHot(e,t,n,s){const i=new e8(e.size,t,n,s);return this.compileAndRun(i,[e])}diag(e){const t=new y5(e.size);return this.compileAndRun(t,[e])}cropAndResize(e,t,n,s,i,o){const a=new p5(e.shape,t.shape,s,i,o);return this.compileAndRun(a,[e,t,n],"float32")}depthToSpace(e,t,n){k(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`);const s=e.shape[0],i=n==="NHWC"?e.shape[1]:e.shape[2],o=n==="NHWC"?e.shape[2]:e.shape[3],a=n==="NHWC"?e.shape[3]:e.shape[1],c=i*t,h=o*t,d=a/(t*t),m=n==="NHWC"?[s,c,h,d]:[s,d,c,h],y=new g5(m,t,n);return this.compileAndRun(y,[e])}split(e,t,n){return i6(e,t,n)}scatterND(e,t,n){const{sliceRank:s,numUpdates:i,sliceSize:o,strides:a,outputSize:c}=va(t,e,n),h=[c/o,o],d=e.reshape([i,s]),m=t.reshape([i,o]);if(c===0)return kA(en([]),n);const y=Ne(0),b=new Q0(i,s,d.rank,m.rank,a,h),w=this.compileAndRun(b,[m,d,y]);return w.reshape(n)}sparseToDense(e,t,n,s){const{sliceRank:i,numUpdates:o,strides:a,outputSize:c}=va(t,e,n),h=!1,d=new Q0(o,i,e.rank,t.rank,a,[c,1],h),m=this.compileAndRun(d,[t,e,s]);return m.reshape(n)}fft(e){const t=!1;return this.fftImpl(e,t)}ifft(e){const t=!0;return this.fftImpl(e,t)}fftImpl(e,t){const n=this.texData.get(e.dataId),s=new V0(G0.REAL,e.shape,t),i=new V0(G0.IMAG,e.shape,t),o=[this.makeComplexComponentTensorInfo(e,n.complexTensors.real),this.makeComplexComponentTensorInfo(e,n.complexTensors.imag)],a=this.compileAndRun(s,o),c=this.compileAndRun(i,o),h=this.complex(a,c).as2D(e.shape[0],e.shape[1]);return a.dispose(),c.dispose(),h}gatherND(e,t){const n=t.shape,s=n[n.length-1],[i,o,a,c]=gd(e,t),h=t.reshape([o,s]),d=e.reshape([e.size/a,a]),m=new A5(s,c,[o,a]),y=this.compileAndRun(m,[d,h]);return y.reshape(i)}fill(e,t,n){if(n=n||ba(t),n==="string"){const s=lo(n,we(e));return s.fill(t),Fs().makeTensor(s,e,n,this)}else{const s=new I5(e,t),i=s.getCustomSetupFunc(t);return this.compileAndRun(s,[],n,i)}}onesLike(e){if(e.dtype==="string")throw new Error("onesLike is not supported under string dtype");return this.fill(e.shape,1,e.dtype)}zerosLike(e){return this.fill(e.shape,e.dtype==="string"?"":0,e.dtype)}linspace(e,t,n){return Ub(e,t,n)}makeTensorInfo(e,t,n){const s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){const{dataId:s}=this.makeTensorInfo(e,t,n);return Fs().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){const t=new s6(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new t8(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){const n=[ic(e.shape),...rc(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},i=[ic(t),...rc(t)],o=new Z0(i,n),a=!0,c=this.runWebGLProgram(o,[s],e.dtype,null,a);return{dataId:c.dataId,shape:t,dtype:c.dtype}}decode(e){const t=this.texData.get(e),{isPacked:n,shape:s,dtype:i}=t,o=XL(s);let a;n?a=new f5(o):a=new m5(o);const c=!0,h=this.runWebGLProgram(a,[{shape:o,dtype:i,dataId:e}],i,null,c);return{dtype:i,shape:s,dataId:h.dataId}}runWebGLProgram(e,t,n,s,i=!1){const o=this.makeTensorInfo(e.outputShape,n),a=this.texData.get(o.dataId);if(e.packedOutput&&(a.isPacked=!0),e.outPackingScheme===qh.DENSE){const L=Kh(e.outputShape);a.texShape=L.map(T=>T*2)}if(e.outTexUsage!=null&&(a.usage=e.outTexUsage),we(o.shape)===0)return a.values=bn(o.dtype,0),o;const c=[],h=t.map(L=>{if(L.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let T=this.texData.get(L.dataId);if(T.texture==null){if(!e.packedInputs&&we(L.shape)<=C().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:L.shape,texData:null,isUniform:!0,uniformValues:T.values};e.packedInputs&&(T.isPacked=!0,T.shape=L.shape)}else if(!!T.isPacked!==!!e.packedInputs)L=T.isPacked?this.unpackTensor(L):this.packTensor(L),c.push(L),T=this.texData.get(L.dataId);else if(T.isPacked&&!Zp(T.shape,L.shape)){const A=L,N=L.shape;L.shape=T.shape,L=this.packedReshape(L,N),c.push(L),T=this.texData.get(L.dataId),A.shape=N}return this.uploadToGPU(L.dataId),{shape:L.shape,texData:T,isUniform:!1}});this.uploadToGPU(o.dataId);const d={shape:o.shape,texData:a,isUniform:!1},m=H5(e,h,d),y=this.getAndSaveBinary(m,()=>V5(this.gpgpu,e,h,d)),b=this.activeTimers!=null;let w;if(b&&(w=this.startTimer()),Y5(this.gpgpu,y,h,d,s),c.forEach(L=>this.disposeIntermediateTensorInfo(L)),b&&(w=this.endTimer(w),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(w)})),!C().getBool("WEBGL_LAZILY_UNPACK")&&a.isPacked&&i===!1){const L=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),L}return o}compileAndRun(e,t,n,s,i=!1){n=n||t[0].dtype;const o=this.runWebGLProgram(e,t,n,s,i);return Fs().makeTensorFromDataId(o.dataId,o.shape,o.dtype)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){if(this.disposed)return;if(!C().getBool("IS_TEST")){const e=Object.keys(this.binaryCache);e.forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]})}this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=ee(()=>{if(!C().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=C().getBool("DEBUG");C().set("DEBUG",!1);const t=this.abs(Ne(1e-8)).dataSync()[0];if(C().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?c6:l6}uploadToGPU(e){const t=this.texData.get(e),{shape:n,dtype:s,values:i,texture:o,usage:a,isPacked:c}=t;if(o!=null)return;const h=this.activeTimers!=null;let d;h&&(d=Vn());let m=t.texShape;if(m==null&&(m=yj(n,c),t.texShape=m),i!=null){const y=XL(n);let b,w=m[1],L=m[0];const T=i instanceof Uint8Array;c?([w,L]=sc(m[0],m[1]),b=new S5(y,[L,w],T)):b=new L5(y,[L,w],T);const A=this.makeTensorInfo([L,w],s);T?this.texData.get(A.dataId).usage=As.PIXELS:this.texData.get(A.dataId).usage=As.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),w,L,i);const N=!0,E=this.runWebGLProgram(b,[A],s,null,N),D=this.texData.get(E.dataId);t.texture=D.texture,t.texShape=D.texShape,t.isPacked=D.isPacked,t.usage=D.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(E.dataId),t.values=null,h&&(this.uploadWaitMs+=Vn()-d)}else{const y=this.acquireTexture(m,a,s,c);t.texture=y}}convertAndCacheOnCPU(e,t){const n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=f6(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){const i=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${i} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*ry(t)}tryRunOnCpuOrThrow(e,t){if(this.shouldExecuteOnCPU(e))try{return t()}catch(n){if(C().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}}function f6(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){const n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}const g6="2.6.0";function y6(){C().set("WEBGL_FORCE_F16_TEXTURES",!0)}fy()&&Wy("webgl",()=>new m6,2);const bee={forceHalfFloat:y6},yC="if (isnan(x)) return x;",b6=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `,w6=`
@@ -3649,14 +3649,14 @@ return (round(mod(b, 2.0)) != 1) ?
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;function am(e){return({inputs:t,backend:n})=>{const{x:s}=t,i=n,o=new st(s.shape,e);return i.runWebGLProgram(o,[s],s.dtype)}}function iS(e,t,n,s){return({inputs:i,backend:o})=>{const{a,b:c}=i,h=o,d=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qr(t,a.shape,c.shape,!!n):new ln(e,a.shape,c.shape),m=s||a.dtype,y=h.runWebGLProgram(d,[a,c],m);return y}}const L6=b6+`
+`;function am(e){return({inputs:t,backend:n})=>{const{x:s}=t,i=n,o=new st(s.shape,e);return i.runWebGLProgram(o,[s],s.dtype)}}function iS(e,t,n,s){return({inputs:i,backend:o})=>{const{a,b:c}=i,h=o,d=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new qr(t,a.shape,c.shape,!!n):new hn(e,a.shape,c.shape),m=s||a.dtype,y=h.runWebGLProgram(d,[a,c],m);return y}}const L6=b6+`
   return atan(a, b);
 `,S6=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+w6+`
   return result;
-`,I6=iS(L6,S6),x6={kernelName:Li,backendName:"webgl",kernelFunc:I6};function rS(e){const{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}const T6={kernelName:al,backendName:"webgl",kernelFunc:rS};function A6(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;Xh(i,"avgPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:h}=s,d=1;k(sn(a,d),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${d}'`);const m=Fn(i.shape,o,a,d,c,h);if(m.filterWidth===1&&m.filterHeight===1&&ot(m.inShape,m.outShape))return rS({inputs:{x:i},backend:n});const y=new Zh(m,"avg",!1);return n.runWebGLProgram(y,[i],"float32")}const v6={kernelName:Xs,backendName:"webgl",kernelFunc:A6};function N6(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o}=t,a=o;Xh([i,o],"avgPoolBackprop");const{filterSize:c,strides:h,pad:d}=s,m=Fn(a.shape,c,h,1,d),y=new AK(m);return n.runWebGLProgram(y,[i],a.dtype)}const C6={kernelName:la,backendName:"webgl",kernelFunc:N6};class R6{constructor(e,t,n,s,i,o){this.outputShape=[],this.variableNames=["x","mean","variance"],tt(e,t),tt(e,n);let a="0.0";s!=null&&(tt(e,s),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let c="1.0";i!=null&&(tt(e,i),this.variableNames.push("scale"),c="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+`,I6=iS(L6,S6),x6={kernelName:Li,backendName:"webgl",kernelFunc:I6};function rS(e){const{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}const T6={kernelName:al,backendName:"webgl",kernelFunc:rS};function A6(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;Xh(i,"avgPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:h}=s,d=1;k(rn(a,d),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${d}'`);const m=Fn(i.shape,o,a,d,c,h);if(m.filterWidth===1&&m.filterHeight===1&&ot(m.inShape,m.outShape))return rS({inputs:{x:i},backend:n});const y=new Zh(m,"avg",!1);return n.runWebGLProgram(y,[i],"float32")}const v6={kernelName:Xs,backendName:"webgl",kernelFunc:A6};function N6(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o}=t,a=o;Xh([i,o],"avgPoolBackprop");const{filterSize:c,strides:h,pad:d}=s,m=Fn(a.shape,c,h,1,d),y=new AK(m);return n.runWebGLProgram(y,[i],a.dtype)}const C6={kernelName:ua,backendName:"webgl",kernelFunc:N6};class R6{constructor(e,t,n,s,i,o){this.outputShape=[],this.variableNames=["x","mean","variance"],tt(e,t),tt(e,n);let a="0.0";s!=null&&(tt(e,s),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let c="1.0";i!=null&&(tt(e,i),this.variableNames.push("scale"),c="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -3681,7 +3681,7 @@ return (round(mod(b, 2.0)) != 1) ?
       }
     `}}const E6=({inputs:e,backend:t,attrs:n})=>{const{x:s,mean:i,variance:o,offset:a,scale:c}=e;k(i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k(c==null||i.shape.length===c.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:h}=n;h==null&&(h=.001);const d=[s,i,o];let m=null;a!=null&&(m=a.shape,d.push(a));let y=null;c!=null&&(y=c.shape,d.push(c));const b=C().getBool("WEBGL_PACK_NORMALIZATION")?new O6(s.shape,i.shape,o.shape,m,y,h):new R6(s.shape,i.shape,o.shape,m,y,h),w=t.runWebGLProgram(b,d,d[0].dtype);return w},D6={kernelName:ol,backendName:"webgl",kernelFunc:E6};const k6=yC+`
   return cos(x);
-`,F6=am(k6),_6={kernelName:ha,backendName:"webgl",kernelFunc:F6};const W6=`
+`,F6=am(k6),_6={kernelName:da,backendName:"webgl",kernelFunc:F6};const W6=`
 if (a == b) {
   return 1.0;
 };
@@ -3703,7 +3703,7 @@ return a / b;`,$6=`
   }
 
   return result;
-`,U6=iS(W6,$6,!0),B6={kernelName:ua,backendName:"webgl",kernelFunc:U6};class M6{constructor(e){this.variableNames=["Image"],this.outputShape=[];const t=e[2];this.outputShape=e,this.userCode=`
+`,U6=iS(W6,$6,!0),B6={kernelName:pa,backendName:"webgl",kernelFunc:U6};class M6{constructor(e){this.variableNames=["Image"],this.outputShape=[];const t=e[2];this.outputShape=e,this.userCode=`
         void main() {
           ivec4 coords = getOutputCoords();
           int x = coords[2];
@@ -3773,7 +3773,7 @@ return a / b;`,$6=`
 
         ${t.output} = result;
       }
-    `}}const V6={kernelName:Qu,backendName:"webgl",kernelFunc:Y6};let cc;function Y6(e){const{inputs:t,backend:n,attrs:s}=e;let{pixels:i}=t;const{numChannels:o}=s,a=typeof HTMLVideoElement!="undefined"&&i instanceof HTMLVideoElement,c=typeof HTMLImageElement!="undefined"&&i instanceof HTMLImageElement,[h,d]=a?[i.videoWidth,i.videoHeight]:[i.width,i.height],m=[d,h],y=[d,h,o];(c||a)&&(cc==null&&(cc=document.createElement("canvas").getContext("2d")),cc.canvas.width=h,cc.canvas.height=d,cc.drawImage(i,0,0,h,d),i=cc.canvas);const b=n.makeTensorInfo(m,"int32");n.texData.get(b.dataId).usage=As.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(b.dataId),i);const w=C().getBool("WEBGL_PACK")?new G6(y):new z6(y),L=n.runWebGLProgram(w,[b],"int32");return n.disposeData(b.dataId),L}function H6(e){const t=[];for(;t.length===0||t[t.length-1].outSize!==1;){const n=t.length?t[t.length-1].outSize:e[1],s=Jl(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function q6(e,t,n,s){const i=H6(e.shape);let o=e;for(let a=0;a<i.length;a++){const{inSize:c,windowSize:h,outSize:d}=i[a],m=new J0({windowSize:h,inSize:c,batchSize:e.shape[0],outSize:d},n),y=o;o=s.runWebGLProgram(m,[o],t),y.dataId!==e.dataId&&s.disposeData(y.dataId)}return o}function j6(e,t,n){const s=[nc(e.shape),...sc(e.shape)],i={dtype:e.dtype,shape:s,dataId:e.dataId},o=[nc(t),...sc(t)],a=new Z0(o,s),c=!0,h=n.runWebGLProgram(a,[i],e.dtype,null,c);return{dataId:h.dataId,shape:t,dtype:h.dtype}}function oS(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{shape:o}=s,a=n,c=we(i.shape),h=id(o,c),d=we(h);k(c===d,()=>`The new shape (${h}) has ${d} elements and the old shape (${i.shape}) has ${c} elements. The new shape and old shape must have the same number of elements.`);const m=a.texData.get(i.dataId);return m.isPacked&&!Zp(i.shape,h)&&!(m.texture!==null&&Zp(m.shape,h))?j6(i,h,a):(a.incRef(i.dataId),{dataId:i.dataId,shape:h,dtype:i.dtype})}const K6={kernelName:yl,backendName:"webgl",kernelFunc:oS};function X6(e,t,n,s){const i=we(t),o=we(e.shape),a=o/i,c=oS({inputs:{x:e},attrs:{shape:[a,i]},backend:s}),h=q6(c,e.dtype,"max",s),d=oS({inputs:{x:h},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(c),s.disposeIntermediateTensorInfo(h),d}class J6{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let o=0;o<n.length;o++)n[o]=e[t[o]];this.outputShape=n,this.rank=n.length;const s=Et(this.rank),i=Z6(t);this.userCode=`
+    `}}const V6={kernelName:Qu,backendName:"webgl",kernelFunc:Y6};let hc;function Y6(e){const{inputs:t,backend:n,attrs:s}=e;let{pixels:i}=t;const{numChannels:o}=s,a=typeof HTMLVideoElement!="undefined"&&i instanceof HTMLVideoElement,c=typeof HTMLImageElement!="undefined"&&i instanceof HTMLImageElement,[h,d]=a?[i.videoWidth,i.videoHeight]:[i.width,i.height],m=[d,h],y=[d,h,o];(c||a)&&(hc==null&&(hc=document.createElement("canvas").getContext("2d")),hc.canvas.width=h,hc.canvas.height=d,hc.drawImage(i,0,0,h,d),i=hc.canvas);const b=n.makeTensorInfo(m,"int32");n.texData.get(b.dataId).usage=As.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(b.dataId),i);const w=C().getBool("WEBGL_PACK")?new G6(y):new z6(y),L=n.runWebGLProgram(w,[b],"int32");return n.disposeData(b.dataId),L}function H6(e){const t=[];for(;t.length===0||t[t.length-1].outSize!==1;){const n=t.length?t[t.length-1].outSize:e[1],s=Jl(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function q6(e,t,n,s){const i=H6(e.shape);let o=e;for(let a=0;a<i.length;a++){const{inSize:c,windowSize:h,outSize:d}=i[a],m=new J0({windowSize:h,inSize:c,batchSize:e.shape[0],outSize:d},n),y=o;o=s.runWebGLProgram(m,[o],t),y.dataId!==e.dataId&&s.disposeData(y.dataId)}return o}function j6(e,t,n){const s=[ic(e.shape),...rc(e.shape)],i={dtype:e.dtype,shape:s,dataId:e.dataId},o=[ic(t),...rc(t)],a=new Z0(o,s),c=!0,h=n.runWebGLProgram(a,[i],e.dtype,null,c);return{dataId:h.dataId,shape:t,dtype:h.dtype}}function oS(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{shape:o}=s,a=n,c=we(i.shape),h=id(o,c),d=we(h);k(c===d,()=>`The new shape (${h}) has ${d} elements and the old shape (${i.shape}) has ${c} elements. The new shape and old shape must have the same number of elements.`);const m=a.texData.get(i.dataId);return m.isPacked&&!Zp(i.shape,h)&&!(m.texture!==null&&Zp(m.shape,h))?j6(i,h,a):(a.incRef(i.dataId),{dataId:i.dataId,shape:h,dtype:i.dtype})}const K6={kernelName:yl,backendName:"webgl",kernelFunc:oS};function X6(e,t,n,s){const i=we(t),o=we(e.shape),a=o/i,c=oS({inputs:{x:e},attrs:{shape:[a,i]},backend:s}),h=q6(c,e.dtype,"max",s),d=oS({inputs:{x:h},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(c),s.disposeIntermediateTensorInfo(h),d}class J6{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let o=0;o<n.length;o++)n[o]=e[t[o]];this.outputShape=n,this.rank=n.length;const s=Et(this.rank),i=Z6(t);this.userCode=`
     void main() {
       ${s} resRC = getOutputCoords();
       setOutput(getA(${i}));
@@ -3795,7 +3795,7 @@ return a / b;`,$6=`
       }
       setOutput(result);
     }
-    `}}function bC(e,t,n){const s=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Q6(e.shape,t):new J6(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}const eX={kernelName:pl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{reductionIndices:i,keepDims:o}=t,a=n,c=s.shape.length,h=ft(i,s.shape);let d=h;const m=kn(d,c),y=m!=null,b=a.shouldExecuteOnCPU([s]);let w=s;if(y){if(b){const E=a.texData.get(w.dataId),D=E.values,F=new Array(c);for(let $=0;$<F.length;$++)F[$]=s.shape[m[$]];const _=A0(D,s.shape,s.dtype,m,F);w=a.makeTensorInfo(F,s.dtype);const B=a.texData.get(w.dataId);B.values=_}else w=bC(s,m,a);d=ws(d.length,c)}es("max",d,c);const[L,T]=Cn(w.shape,d);let A=L;o&&(A=Rn(L,h));let N;if(b){const E=a.texData.get(w.dataId),D=E.values,F=Dj(D,we(T),A,s.dtype);N=a.makeTensorInfo(A,s.dtype);const _=a.texData.get(N.dataId);_.values=F}else N=X6(w,T,A,a);return y&&a.disposeIntermediateTensorInfo(w),N}};function tX(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;Xh(i,"maxPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:h}=s,d=1;k(sn(a,d),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${d}'`);const m=Fn(i.shape,o,a,d,c,h);if(m.filterWidth===1&&m.filterHeight===1&&ot(m.inShape,m.outShape))return rS({inputs:{x:i},backend:n});const y=new Zh(m,"max",!1);return n.runWebGLProgram(y,[i],i.dtype)}const nX={kernelName:ml,backendName:"webgl",kernelFunc:tX};function sX(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o,output:a}=t,c=o;Xh([o,a],"maxPoolBackprop");const{filterSize:h,strides:d,pad:m,dimRoundingMode:y}=s,b=Fn(c.shape,h,d,1,m,y),w=!0,L=new Zh(b,"max",w),T=n.runWebGLProgram(L,[c],c.dtype),A=new J5(b),N=n.runWebGLProgram(A,[i,T],c.dtype);return n.disposeIntermediateTensorInfo(T),N}const iX={kernelName:Gu,backendName:"webgl",kernelFunc:sX};function rX(e,t,n,s){let i=new Zh(n,"max",!1);const o=s.runWebGLProgram(i,[e],"float32");i=new Zh(n,"max",!0,!0,t);const a=s.runWebGLProgram(i,[e],"float32");return[o,a]}const oX={kernelName:Vu,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{filterSize:i,strides:o,pad:a,includeBatchInIndex:c}=t,h=n;k(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);const d=[1,1];k(sn(o,d),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${d}'`);const m=Fn(s.shape,i,o,d,a),[y,b]=rX(s,c,m,h);return[y,b]}};const aX={kernelName:Og,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Ba("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c}=n,h=t,d=h.readSync(s.dataId),m=h.readSync(i.dataId),y=o,b=a,w=c;return tp(d,m,y,b,w)}};const cX=np,lX={kernelName:Hu,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Ba("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,padToMaxOutputSize:h}=n,d=t,m=d.readSync(s.dataId),y=d.readSync(i.dataId),{selectedIndices:b,validOutputs:w}=cX(m,y,o,a,c,h);return[b,w]}};const hX=sp,uX={kernelName:qu,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Ba("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,softNmsSigma:h}=n,d=t,m=d.readSync(s.dataId),y=d.readSync(i.dataId),b=o,w=a,L=c,T=h,{selectedIndices:A,selectedScores:N}=hX(m,y,b,w,L,T);return[A,N]}};class dX{constructor(e,t,n,s){this.variableNames=["Image"],this.outputShape=[];const i=e[1],o=e[2],a=Math.sin(t).toFixed(3),c=Math.cos(t).toFixed(3);this.outputShape=e;const[h,d]=Cb(s,i,o),m=h.toFixed(3),y=d.toFixed(3);let b="";typeof n=="number"?b=`float outputValue = ${n.toFixed(2)};`:b=`
+    `}}function bC(e,t,n){const s=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Q6(e.shape,t):new J6(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}const eX={kernelName:pl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{reductionIndices:i,keepDims:o}=t,a=n,c=s.shape.length,h=ft(i,s.shape);let d=h;const m=kn(d,c),y=m!=null,b=a.shouldExecuteOnCPU([s]);let w=s;if(y){if(b){const E=a.texData.get(w.dataId),D=E.values,F=new Array(c);for(let $=0;$<F.length;$++)F[$]=s.shape[m[$]];const _=A0(D,s.shape,s.dtype,m,F);w=a.makeTensorInfo(F,s.dtype);const B=a.texData.get(w.dataId);B.values=_}else w=bC(s,m,a);d=ws(d.length,c)}es("max",d,c);const[L,T]=Cn(w.shape,d);let A=L;o&&(A=Rn(L,h));let N;if(b){const E=a.texData.get(w.dataId),D=E.values,F=Dj(D,we(T),A,s.dtype);N=a.makeTensorInfo(A,s.dtype);const _=a.texData.get(N.dataId);_.values=F}else N=X6(w,T,A,a);return y&&a.disposeIntermediateTensorInfo(w),N}};function tX(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;Xh(i,"maxPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:h}=s,d=1;k(rn(a,d),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${d}'`);const m=Fn(i.shape,o,a,d,c,h);if(m.filterWidth===1&&m.filterHeight===1&&ot(m.inShape,m.outShape))return rS({inputs:{x:i},backend:n});const y=new Zh(m,"max",!1);return n.runWebGLProgram(y,[i],i.dtype)}const nX={kernelName:ml,backendName:"webgl",kernelFunc:tX};function sX(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o,output:a}=t,c=o;Xh([o,a],"maxPoolBackprop");const{filterSize:h,strides:d,pad:m,dimRoundingMode:y}=s,b=Fn(c.shape,h,d,1,m,y),w=!0,L=new Zh(b,"max",w),T=n.runWebGLProgram(L,[c],c.dtype),A=new J5(b),N=n.runWebGLProgram(A,[i,T],c.dtype);return n.disposeIntermediateTensorInfo(T),N}const iX={kernelName:Gu,backendName:"webgl",kernelFunc:sX};function rX(e,t,n,s){let i=new Zh(n,"max",!1);const o=s.runWebGLProgram(i,[e],"float32");i=new Zh(n,"max",!0,!0,t);const a=s.runWebGLProgram(i,[e],"float32");return[o,a]}const oX={kernelName:Vu,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{filterSize:i,strides:o,pad:a,includeBatchInIndex:c}=t,h=n;k(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);const d=[1,1];k(rn(o,d),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${d}'`);const m=Fn(s.shape,i,o,d,a),[y,b]=rX(s,c,m,h);return[y,b]}};const aX={kernelName:Og,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Pa("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c}=n,h=t,d=h.readSync(s.dataId),m=h.readSync(i.dataId),y=o,b=a,w=c;return tp(d,m,y,b,w)}};const cX=np,lX={kernelName:Hu,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Pa("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,padToMaxOutputSize:h}=n,d=t,m=d.readSync(s.dataId),y=d.readSync(i.dataId),{selectedIndices:b,validOutputs:w}=cX(m,y,o,a,c,h);return[b,w]}};const hX=sp,uX={kernelName:qu,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Pa("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,softNmsSigma:h}=n,d=t,m=d.readSync(s.dataId),y=d.readSync(i.dataId),b=o,w=a,L=c,T=h,{selectedIndices:A,selectedScores:N}=hX(m,y,b,w,L,T);return[A,N]}};class dX{constructor(e,t,n,s){this.variableNames=["Image"],this.outputShape=[];const i=e[1],o=e[2],a=Math.sin(t).toFixed(3),c=Math.cos(t).toFixed(3);this.outputShape=e;const[h,d]=Cb(s,i,o),m=h.toFixed(3),y=d.toFixed(3);let b="";typeof n=="number"?b=`float outputValue = ${n.toFixed(2)};`:b=`
         vec3 fill = vec3(${n.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         void main() {
@@ -3814,12 +3814,12 @@ return a / b;`,$6=`
         }
     `}}const pX={kernelName:ed,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{image:s}=e,{radians:i,fillValue:o,center:a}=t,c=n,h=new dX(s.shape,i,o,a),d=c.runWebGLProgram(h,[s],s.dtype);return d}};const mX=yC+`
   return sin(x);
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r=this.getRefPointsForAlignment(),[l,u,p]=r,g=O=>p.sub(O).magnitude(),f=(g(l)+g(u))/2,I=Math.floor(f/VX),S=Po(r),x=Math.floor(Math.max(0,S.x-zX*I)),v=Math.floor(Math.max(0,S.y-GX*I));return new su(x,v,Math.min(I,this.imageWidth+x),Math.min(I,this.imageHeight+v))}alignMinBbox(r){const l=fS(this.positions);return l.pad(l.width*r,l.height*r)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}}class YX extends Gs{getRefPointsForAlignment(){const r=this.positions;return[r[0],r[1],Po([r[3],r[4]])]}}class iu extends Gs{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return 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this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(r=>Array.from(r))}}static fromJSON(r){const l=r.descriptors.map(u=>new Float32Array(u));return new zo(r.label,l)}}class HX extends fm{static assertIsValidPredictedBox(r,l){if(fm.assertIsValidLabeledBox(r,l),!pc(r.score)||!pc(r.classScore))throw new Error(`${l} - expected properties score (${r.score}) and (${r.classScore}) to be a number between [0, 1]`)}constructor(r,l,u,p){super(r,l);this._score=u,this._classScore=p}get score(){return this._score}get classScore(){return this._classScore}}function Ui(r){return r.detection instanceof Ht}function Go(r,l){const u={detection:l};return Object.assign({},r,u)}function bS(){const r=window.fetch||function(){throw new Error("fetch - missing fetch implementation for browser environment")},l=function(){throw new Error("readFile - filesystem not available for browser 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this.text.length*r+2*l}getUpperLeft(r,l){const{anchorPosition:u}=this.options,p=u===Bi.BOTTOM_RIGHT||u===Bi.TOP_RIGHT,g=u===Bi.BOTTOM_LEFT||u===Bi.BOTTOM_RIGHT,f=this.measureWidth(r),I=this.measureHeight(),S=p?this.anchor.x-f:this.anchor.x,x=g?this.anchor.y-I:this.anchor.y;if(l){const{width:v,height:O}=l,C=Math.max(Math.min(S,v-f),0),U=Math.max(Math.min(x,O-I),0);return{x:C,y:U}}return{x:S,y:x}}draw(r){const l=Vo(r),u=Jn(l),{backgroundColor:p,fontColor:g,fontSize:f,fontStyle:I,padding:S}=this.options;u.font=`${f}px ${I}`;const x=this.measureWidth(u),v=this.measureHeight();u.fillStyle=p;const O=this.getUpperLeft(u,l);u.fillRect(O.x,O.y,x,v),u.fillStyle=g,this.text.forEach((C,U)=>{const G=S+O.x,ne=S+O.y+(U+1)*f;u.fillText(C,G,ne)})}}class NC{constructor(r={}){const{boxColor:l,lineWidth:u,label:p,drawLabelOptions:g}=r;this.boxColor=l||"rgba(0, 0, 255, 1)",this.lineWidth=u||2,this.label=p;const f={anchorPosition:Bi.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new 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r=this.getRefPointsForAlignment(),[l,u,p]=r,g=O=>p.sub(O).magnitude(),f=(g(l)+g(u))/2,I=Math.floor(f/VX),S=Go(r),x=Math.floor(Math.max(0,S.x-zX*I)),v=Math.floor(Math.max(0,S.y-GX*I));return new su(x,v,Math.min(I,this.imageWidth+x),Math.min(I,this.imageHeight+v))}alignMinBbox(r){const l=fS(this.positions);return l.pad(l.width*r,l.height*r)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}}class YX extends Gs{getRefPointsForAlignment(){const r=this.positions;return[r[0],r[1],Go([r[3],r[4]])]}}class iu extends Gs{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Go)}}class mm{constructor(r,l){this._label=r,this._distance=l}get label(){return this._label}get distance(){return this._distance}toString(r=!0){return`${this.label}${r?` (${zo(this.distance)})`:""}`}}class fm extends Ct{static assertIsValidLabeledBox(r,l){if(Ct.assertIsValidBox(r,l),!ui(r.label))throw new Error(`${l} - expected property label (${r.label}) to be a number`)}constructor(r,l){super(r);this._label=l}get label(){return this._label}}class Vo{constructor(r,l){if(!(typeof r=="string"))throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(l)||l.some(u=>!(u instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=r,this._descriptors=l}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(r=>Array.from(r))}}static fromJSON(r){const l=r.descriptors.map(u=>new Float32Array(u));return new Vo(r.label,l)}}class HX extends fm{static assertIsValidPredictedBox(r,l){if(fm.assertIsValidLabeledBox(r,l),!fc(r.score)||!fc(r.classScore))throw new Error(`${l} - expected properties score (${r.score}) and (${r.classScore}) to be a number between [0, 1]`)}constructor(r,l,u,p){super(r,l);this._score=u,this._classScore=p}get score(){return this._score}get classScore(){return this._classScore}}function Ui(r){return r.detection instanceof Ht}function Yo(r,l){const u={detection:l};return Object.assign({},r,u)}function bS(){const r=window.fetch||function(){throw new Error("fetch - missing fetch implementation for browser environment")},l=function(){throw new Error("readFile - filesystem not available for browser 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this.text.length*r+2*l}getUpperLeft(r,l){const{anchorPosition:u}=this.options,p=u===Bi.BOTTOM_RIGHT||u===Bi.TOP_RIGHT,g=u===Bi.BOTTOM_LEFT||u===Bi.BOTTOM_RIGHT,f=this.measureWidth(r),I=this.measureHeight(),S=p?this.anchor.x-f:this.anchor.x,x=g?this.anchor.y-I:this.anchor.y;if(l){const{width:v,height:O}=l,C=Math.max(Math.min(S,v-f),0),U=Math.max(Math.min(x,O-I),0);return{x:C,y:U}}return{x:S,y:x}}draw(r){const l=Ho(r),u=Jn(l),{backgroundColor:p,fontColor:g,fontSize:f,fontStyle:I,padding:S}=this.options;u.font=`${f}px ${I}`;const x=this.measureWidth(u),v=this.measureHeight();u.fillStyle=p;const O=this.getUpperLeft(u,l);u.fillRect(O.x,O.y,x,v),u.fillStyle=g,this.text.forEach((C,U)=>{const G=S+O.x,ne=S+O.y+(U+1)*f;u.fillText(C,G,ne)})}}class NC{constructor(r={}){const{boxColor:l,lineWidth:u,label:p,drawLabelOptions:g}=r;this.boxColor=l||"rgba(0, 0, 255, 1)",this.lineWidth=u||2,this.label=p;const f={anchorPosition:Bi.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new 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To get the original bytes, call tensor.bytes().")}return r}async bytes(){this.throwIfDisposed();const r=await Mi().read(this.dataId);return this.dtype==="string"?r:new Uint8Array(r.buffer)}dispose(){if(this.isDisposed)return;Mi().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(r=!1){return Ic.print(this,r)}clone(){return this.throwIfDisposed(),Ic.clone(this)}toString(r=!1){const l=this.dataSync();return gR(l,this.shape,this.dtype,r)}cast(r){return this.throwIfDisposed(),Ic.cast(this,r)}variable(r=!0,l,u){return this.throwIfDisposed(),Mi().makeVariable(this,r,l,u)}}Object.defineProperty(In,Symbol.hasInstance,{value:r=>!!r&&r.data!=null&&r.dataSync!=null&&r.throwIfDisposed!=null});class If extends In{constructor(r,l,u,p){super(r.shape,r.dtype,r.dataId,p);this.trainable=l,this.name=u}assign(r){if(r.dtype!==this.dtype)throw new Error(`dtype of the new value (${r.dtype}) and previous value (${this.dtype}) must match`);if(!Ho(r.shape,this.shape))throw new Error(`shape of the new value (${r.shape}) and previous value (${this.shape}) must match`);Mi().disposeTensor(this),this.dataId=r.dataId,Mi().incRef(this,null)}dispose(){Mi().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(If,Symbol.hasInstance,{value:r=>r instanceof In&&r.assign!=null&&r.assign instanceof Function});var IR;(function(r){r.R0="R0",r.R1="R1",r.R2="R2",r.R3="R3",r.R4="R4",r.R5="R5",r.R6="R6"})(IR||(IR={}));var MS;(function(r){r.float32="float32",r.int32="int32",r.bool="int32",r.complex64="complex64"})(MS||(MS={}));var PS;(function(r){r.float32="float32",r.int32="int32",r.bool="bool",r.complex64="complex64"})(PS||(PS={}));var zS;(function(r){r.float32="float32",r.int32="float32",r.bool="float32",r.complex64="complex64"})(zS||(zS={}));var GS;(function(r){r.float32="complex64",r.int32="complex64",r.bool="complex64",r.complex64="complex64"})(GS||(GS={}));const u7={float32:zS,int32:MS,bool:PS,complex64:GS};function xR(r,l){if(r==="string"||l==="string"){if(r==="string"&&l==="string")return"string";throw new Error(`Can not upcast ${r} with ${l}`)}return u7[r][l]}function mt(r,l){if(r.dtype===l.dtype)return[r,l];const u=xR(r.dtype,l.dtype);return[r.cast(u),l.cast(u)]}function xf(r){const l=[],u=new Set;return TR(r,l,u),l}function TR(r,l,u){if(r==null)return;if(r instanceof In){l.push(r);return}if(!d7(r))return;const p=r;for(const g in p){const f=p[g];u.has(f)||(u.add(f),TR(f,l,u))}}function d7(r){return Array.isArray(r)||typeof r=="object"}class AR{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const r in this.registeredVariables)this.registeredVariables[r].dispose()}}class pu{constructor(r){this.ENV=r,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new AR}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const r=this.getSortedBackends();for(let l=0;l<r.length;l++){const u=r[l],p=await this.initializeBackend(u).success;if(p){await this.setBackend(u);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){const{name:r,asyncInit:l}=this.initializeBackendsAndReturnBest();if(l)throw new Error(`The highest priority backend '${r}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(r)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(r){if(!(r in this.registry))if(r in this.registryFactory){const{asyncInit:l}=this.initializeBackend(r);if(l)return null}else return null;return this.registry[r]}findBackendFactory(r){return r in this.registryFactory?this.registryFactory[r].factory:null}registerBackend(r,l,u=1){return r in this.registryFactory?(console.warn(`${r} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[r]={factory:l,priority:u},!0)}async setBackend(r){if(this.registryFactory[r]==null)throw new Error(`Backend name '${r}' not found in registry`);if(this.backendName=r,this.registry[r]==null){this.backendInstance=null;const{success:l,asyncInit:u}=this.initializeBackend(r),p=u?await l:l;if(!p)return!1}return this.backendInstance=this.registry[r],this.setupRegisteredKernels(),this.profiler=new dR(this.backendInstance),!0}setupRegisteredKernels(){const r=FS(this.backendName);r.forEach(l=>{l.setupFunc!=null&&l.setupFunc(this.backendInstance)})}disposeRegisteredKernels(r){const l=FS(r);l.forEach(u=>{u.disposeFunc!=null&&u.disposeFunc(this.registry[r])})}initializeBackend(r){const l=this.registryFactory[r];if(l==null)throw new Error(`Cannot initialize backend ${r}, no registration found.`);try{const u=l.factory();if(u&&!(u instanceof CC)&&typeof u.then=="function"){const p=++this.pendingBackendInitId,g=u.then(f=>p<this.pendingBackendInitId?!1:(this.registry[r]=f,this.pendingBackendInit=null,!0)).catch(f=>(p<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${r} failed`),console.warn(f.stack||f.message)),!1));return this.pendingBackendInit=g,{success:g,asyncInit:!0}}else return this.registry[r]=u,{success:!0,asyncInit:!1}}catch(u){return console.warn(`Initialization of backend ${r} failed`),console.warn(u.stack||u.message),{success:!1,asyncInit:!1}}}removeBackend(r){if(!(r in this.registryFactory))throw new Error(`${r} backend not found in registry`);this.backendName===r&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,r in this.registry&&(this.disposeRegisteredKernels(r),this.registry[r].dispose(),delete this.registry[r]),delete this.registryFactory[r],this.backendName===r&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((r,l)=>this.registryFactory[l].priority-this.registryFactory[r].priority)}initializeBackendsAndReturnBest(){const r=this.getSortedBackends();for(let l=0;l<r.length;l++){const u=r[l],{success:p,asyncInit:g}=this.initializeBackend(u);if(g||p)return{name:u,asyncInit:g}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(r,l){const u=this.state.tensorInfo.get(l),p=u.backend,g=this.readSync(l);p.disposeData(l),u.backend=r,r.move(l,g,u.shape,u.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(r,l){let u=null;if(l==null){if(typeof r!="function")throw new Error("Please provide a function to tidy()");l=r}else{if(typeof r!="string"&&!(r instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof l!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");u=r}let p;return this.scopedRun(()=>this.startScope(u),()=>this.endScope(p),()=>(p=l(),p instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),p))}scopedRun(r,l,u){r();try{const p=u();return l(),p}catch(p){throw l(),p}}nextTensorId(){return pu.nextTensorId++}nextVariableId(){return pu.nextVariableId++}clone(r){const l=this.makeTensorFromDataId(r.dataId,r.shape,r.dtype),u={x:r},p=f=>({x:()=>{const I="float32",S={x:f},x={dtype:I};return Y.runKernelFunc(v=>v.cast(f,I),S,null,bc,x)}}),g=[];return this.addTapeNode(this.state.activeScope.name,u,[l],p,g,{}),l}runKernel(r,l,u,p,g){const f=null,I=null;return this.runKernelFunc(f,l,I,r,u,p,g)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(r,l,u){const p=this.backend.numDataIds();let g=0;u.forEach(S=>{g+=S.dtype==="complex64"?3:1});const f=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],I=p-l-g-f;if(I>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${I} data ids) after running '${r}'`)}runKernelFunc(r,l,u,p,g,f,I){let S,x=[];const v=this.isTapeOn();p==null&&(p=this.state.activeScope!=null?this.state.activeScope.name:"");const O=this.state.numBytes,C=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let U;const G=yf(p,this.backendName);let ne;if(G!=null)U=()=>{const oe=this.backend.numDataIds();ne=G.kernelFunc({inputs:l,attrs:g,backend:this.backend});const ge=Array.isArray(ne)?ne:[ne];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,oe,ge);const fe=ge.map(({dataId:Ae,shape:Te,dtype:Ve})=>this.makeTensorFromDataId(Ae,Te,Ve));if(v){let Ae=this.getTensorsForGradient(p,l,fe);if(Ae==null){I==null&&(I=[]);const Te=fe.filter((Ve,rt)=>I[rt]);Ae=(f||[]).slice().concat(Te)}x=this.saveTensorsForBackwardMode(Ae)}return fe};else{const oe=ge=>{if(!v)return;x=ge.map(fe=>this.keep(this.clone(fe)))};U=()=>{const ge=this.backend.numDataIds();ne=this.tidy(()=>r(this.backend,oe));const fe=Array.isArray(ne)?ne:[ne];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,ge,fe),fe}}let te;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?S=U():(te=this.profiler.profileKernel(p,l,()=>U()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(te),S=te.outputs)}),v&&this.addTapeNode(p,l,S,u,x,g),this.state.profiling&&this.state.activeProfile.kernels.push({name:p,bytesAdded:this.state.numBytes-O,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-C,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(oe=>l[oe]!=null?l[oe].shape:null),outputShapes:S.map(oe=>oe.shape),kernelTimeMs:te.timeMs,extraInfo:te.extraInfo}),Array.isArray(ne)?S:S[0]}saveTensorsForBackwardMode(r){const l=r.map(u=>this.keep(this.clone(u)));return l}getTensorsForGradient(r,l,u){const p=kS(r);if(p!=null){const g=p.inputsToSave||[],f=p.outputsToSave||[];let I;p.saveAllInputs?(Z(Array.isArray(l),()=>"saveAllInputs is true, expected inputs to be an array."),I=Object.keys(l).map(x=>l[x])):I=g.map(x=>l[x]);const S=u.filter((x,v)=>f[v]);return I.concat(S)}return null}makeTensor(r,l,u,p){if(r==null)throw new Error("Values passed to engine.makeTensor() are null");u=u||"float32",p=p||this.backend;let g=r;u==="string"&&au(r[0])&&(g=r.map(S=>uR(S)));const f=p.write(g,l,u),I=new In(l,u,f,this.nextTensorId());if(this.incRef(I,p),u==="string"){const S=this.state.tensorInfo.get(f),x=lR(g);this.state.numBytes+=x-S.bytes,S.bytes=x}return I}makeTensorFromDataId(r,l,u,p){u=u||"float32";const g=new In(l,u,r,this.nextTensorId());return this.incRef(g,p),g}makeVariable(r,l=!0,u,p){u=u||this.nextVariableId().toString(),p!=null&&p!==r.dtype&&(r=r.cast(p));const g=new If(r,l,u,this.nextTensorId());if(this.state.registeredVariables[g.name]!=null)throw new Error(`Variable with name ${g.name} was already registered`);return this.state.registeredVariables[g.name]=g,this.incRef(g,this.backend),g}incRef(r,l){const u=this.state.tensorInfo.has(r.dataId)?this.state.tensorInfo.get(r.dataId).refCount:0;if(this.state.numTensors++,r.dtype==="string"&&this.state.numStringTensors++,u===0){this.state.numDataBuffers++;let p=0;r.dtype!=="complex64"&&r.dtype!=="string"&&(p=r.size*cR(r.dtype)),this.state.tensorInfo.set(r.dataId,{backend:l||this.backend,dtype:r.dtype,shape:r.shape,bytes:p,refCount:0}),this.state.numBytes+=p}this.state.tensorInfo.get(r.dataId).refCount++,r instanceof If||this.track(r)}disposeTensor(r){if(!this.state.tensorInfo.has(r.dataId))return;this.state.numTensors--,r.dtype==="string"&&this.state.numStringTensors--;const l=this.state.tensorInfo.get(r.dataId),u=l.refCount;u<=1?(r.dtype!=="complex64"&&(this.state.numBytes-=l.bytes),this.state.numDataBuffers--,l.backend.disposeData(r.dataId),this.state.tensorInfo.delete(r.dataId)):this.state.tensorInfo.get(r.dataId).refCount--}disposeVariables(){for(const r in this.state.registeredVariables){const l=this.state.registeredVariables[r];this.disposeVariable(l)}}disposeVariable(r){this.disposeTensor(r),this.state.registeredVariables[r.name]!=null&&delete this.state.registeredVariables[r.name]}memory(){const r=this.backend.memory();return r.numTensors=this.state.numTensors,r.numDataBuffers=this.state.numDataBuffers,r.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(r.unreliable=!0,r.reasons==null&&(r.reasons=[]),r.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),r}async profile(r){this.state.profiling=!0;const l=this.state.numBytes,u=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await r(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(p=>p.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-l,this.state.activeProfile.newTensors=this.state.numTensors-u;for(const p of this.state.activeProfile.kernels)p.kernelTimeMs=await p.kernelTimeMs,p.extraInfo=await p.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(r,l,u,p,g,f){const I={id:this.state.nextTapeNodeId++,kernelName:r,inputs:l,outputs:u,saved:g},S=kS(r);S!=null&&(p=S.gradFunc),p!=null&&(I.gradient=x=>(x=x.map((v,O)=>{if(v==null){const C=u[O],U=qo(C.size,C.dtype);return this.makeTensor(U,C.shape,C.dtype)}return v}),p(x.length>1?x:x[0],g,f))),this.state.activeTape.push(I)}keep(r){return r.kept=!0,r}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(r){const l={track:[],name:"unnamed scope",id:this.state.nextScopeId++};r&&(l.name=r),this.state.scopeStack.push(l),this.state.activeScope=l}endScope(r){const l=xf(r),u=new Set(l.map(g=>g.id));for(let g=0;g<this.state.activeScope.track.length;g++){const f=this.state.activeScope.track[g];!f.kept&&!u.has(f.id)&&f.dispose()}const p=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],l.forEach(g=>{!g.kept&&g.scopeId===p.id&&this.track(g)})}gradients(r,l,u,p=!1){if(Z(l.length>0,()=>"gradients() received an empty list of xs."),u!=null&&u.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${u.dtype}'`);const g=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",r));Z(g instanceof In,()=>"The result y returned by f() must be a tensor.");const f=pR(this.state.activeTape,l,g);if(!p&&f.length===0&&l.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{const I={};I[g.id]=u==null?p7(g.shape):u,mR(I,f,x=>this.tidy(x),m7);const S=l.map(x=>I[x.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(x=>{for(const v of x.saved)v.dispose()}),this.state.activeTape=null),{value:g,grads:S}})}customGrad(r){return Z(_S(r),()=>"The f passed in customGrad(f) must be a function."),(...l)=>{Z(l.every(g=>g instanceof In),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let u;const p={};return l.forEach((g,f)=>{p[f]=g}),this.runKernelFunc((g,f)=>(u=r(...l,f),Z(u.value instanceof In,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),Z(_S(u.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),u.value),p,(g,f)=>{const I=u.gradFunc(g,f),S=Array.isArray(I)?I:[I];Z(S.length===l.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),Z(S.every(v=>v instanceof In),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");const x={};return S.forEach((v,O)=>{x[O]=()=>v}),x})}}readSync(r){const l=this.state.tensorInfo.get(r);return l.backend.readSync(r)}read(r){const l=this.state.tensorInfo.get(r);return l.backend.read(r)}async time(r){const l=$S(),u=await this.backend.time(r);return u.wallMs=$S()-l,u}track(r){return this.state.activeScope!=null&&(r.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(r)),r}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new AR;for(const r in this.registry)this.disposeRegisteredKernels(r),this.registry[r].dispose(),delete this.registry[r];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}}pu.nextTensorId=0;pu.nextVariableId=0;function p7(r){const l=wf(qt(r),"float32");return Y.makeTensor(l,r,"float32")}function VS(){const r=RS();if(r._tfengine==null){const l=new OC(r);r._tfengine=new pu(l)}return DC(r._tfengine.ENV),wR(()=>r._tfengine),r._tfengine}const Y=VS();function m7(r,l){const u={a:r,b:l};return Y.runKernelFunc((p,g)=>{const f=p.add(r,l);return g([r,l]),f},u,null,yc)}function vR(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}const hr=Cs();hr.registerFlag("DEBUG",()=>!1,r=>{r&&console.warn("Debugging mode is ON. 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Error(`param tensor has already been disposed for path ${l.path}`);l.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:r})=>Array.from(r.dataSync())).reduce((r,l)=>r.concat(l)))}async load(r){if(r instanceof Float32Array){this.extractWeights(r);return}await this.loadFromUri(r)}async loadFromUri(r){if(r&&typeof r!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);const l=await EI(r,this.getDefaultModelName());this.loadFromWeightMap(l)}async loadFromDisk(r){if(r&&typeof r!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);const{readFile:l}=gt.getEnv(),{manifestUri:u,modelBaseUri:p}=Mf(r,this.getDefaultModelName()),g=x=>Promise.all(x.map(v=>l(v).then(O=>O.buffer))),f=fr.io.weightsLoaderFactory(g),I=JSON.parse((await l(u)).toString()),S=await f(I,p);this.loadFromWeightMap(S)}loadFromWeightMap(r){const{paramMappings:l,params:u}=this.extractParamsFromWeigthMap(r);this._paramMappings=l,this._params=u}extractWeights(r){const{paramMappings:l,params:u}=this.extractParams(r);this._paramMappings=l,this._params=u}traversePropertyPath(r){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");const l=r.split("/").reduce((g,f)=>{if(!g.nextObj.hasOwnProperty(f))throw new Error(`traversePropertyPath - object does not have property ${f}, for path ${r}`);return{obj:g.nextObj,objProp:f,nextObj:g.nextObj[f]}},{nextObj:this.params}),{obj:u,objProp:p}=l;if(!u||!p||!(u[p]instanceof fr.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${r}`);return{obj:u,objProp:p}}}const Dc=Ke(Xe());function Qn(r,l,u){return Dc.tidy(()=>{let p=Dc.separableConv2d(r,l.depthwise_filter,l.pointwise_filter,u,"same");return p=Dc.add(p,l.bias),p})}const Dt=Ke(Xe());function Pf(r,l,u=!1){return Dt.tidy(()=>{const p=Dt.relu(u?Dt.add(Dt.conv2d(r,l.conv0.filters,[2,2],"same"),l.conv0.bias):Qn(r,l.conv0,[2,2])),g=Qn(p,l.conv1,[1,1]),f=Dt.relu(Dt.add(p,g)),I=Qn(f,l.conv2,[1,1]);return Dt.relu(Dt.add(p,Dt.add(g,I)))})}function Iu(r,l,u=!1,p=!0){return Dt.tidy(()=>{const g=Dt.relu(u?Dt.add(Dt.conv2d(r,l.conv0.filters,p?[2,2]:[1,1],"same"),l.conv0.bias):Qn(r,l.conv0,p?[2,2]:[1,1])),f=Qn(g,l.conv1,[1,1]),I=Dt.relu(Dt.add(g,f)),S=Qn(I,l.conv2,[1,1]),x=Dt.relu(Dt.add(g,Dt.add(f,S))),v=Qn(x,l.conv3,[1,1]);return Dt.relu(Dt.add(g,Dt.add(f,Dt.add(S,v))))})}const no=Ke(Xe());function na(r,l,u="same",p=!1){return no.tidy(()=>{const g=no.add(no.conv2d(r,l.filters,[1,1],u),l.bias);return p?no.relu(g):g})}function Pn(r,l){Object.keys(r).forEach(u=>{l.some(p=>p.originalPath===u)||r[u].dispose()})}const zf=Ke(Xe());function kc(r,l){return function(u,p,g,f){const I=zf.tensor4d(r(u*p*g*g),[g,g,u,p]),S=zf.tensor1d(r(p));return l.push({paramPath:`${f}/filters`},{paramPath:`${f}/bias`}),{filters:I,bias:S}}}const Gf=Ke(Xe());function Vf(r,l){return function(u,p,g){const f=Gf.tensor2d(r(u*p),[u,p]),I=Gf.tensor1d(r(p));return l.push({paramPath:`${g}/weights`},{paramPath:`${g}/bias`}),{weights:f,bias:I}}}class DI{constructor(r,l,u){this.depthwise_filter=r;this.pointwise_filter=l;this.bias=u}}const xu=Ke(Xe());function Fc(r,l){return function(u,p,g){const f=xu.tensor4d(r(3*3*u),[3,3,u,1]),I=xu.tensor4d(r(u*p),[1,1,u,p]),S=xu.tensor1d(r(p));return l.push({paramPath:`${g}/depthwise_filter`},{paramPath:`${g}/pointwise_filter`},{paramPath:`${g}/bias`}),new DI(f,I,S)}}function _c(r){return function(l){const u=r(`${l}/depthwise_filter`,4),p=r(`${l}/pointwise_filter`,4),g=r(`${l}/bias`,1);return new DI(u,p,g)}}function ms(r,l){return function(u,p,g){const f=r[u];if(!Bo(f,p))throw new Error(`expected weightMap[${u}] to be a Tensor${p}D, instead have ${f}`);return l.push({originalPath:u,paramPath:g||u}),f}}function zn(r){let l=r;function u(g){const f=l.slice(0,g);return l=l.slice(g),f}function p(){return l}return{extractWeights:u,getRemainingWeights:p}}function Yf(r,l){const u=kc(r,l),p=Fc(r,l);function g(I,S,x,v=!1){const O=v?u(I,S,3,`${x}/conv0`):p(I,S,`${x}/conv0`),C=p(S,S,`${x}/conv1`),U=p(S,S,`${x}/conv2`);return{conv0:O,conv1:C,conv2:U}}function f(I,S,x,v=!1){const{conv0:O,conv1:C,conv2:U}=g(I,S,x,v),G=p(S,S,`${x}/conv3`);return{conv0:O,conv1:C,conv2:U,conv3:G}}return{extractDenseBlock3Params:g,extractDenseBlock4Params:f}}function fE(r){const l=[],{extractWeights:u,getRemainingWeights:p}=zn(r),{extractDenseBlock4Params:g}=Yf(u,l),f=g(3,32,"dense0",!0),I=g(32,64,"dense1"),S=g(64,128,"dense2"),x=g(128,256,"dense3");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{dense0:f,dense1:I,dense2:S,dense3:x}}}function Hf(r){return function(l){const u=r(`${l}/filters`,4),p=r(`${l}/bias`,1);return{filters:u,bias:p}}}function qf(r,l){const u=ms(r,l),p=Hf(u),g=_c(u);function f(S,x=!1){const v=x?p(`${S}/conv0`):g(`${S}/conv0`),O=g(`${S}/conv1`),C=g(`${S}/conv2`);return{conv0:v,conv1:O,conv2:C}}function I(S,x=!1){const v=x?p(`${S}/conv0`):g(`${S}/conv0`),O=g(`${S}/conv1`),C=g(`${S}/conv2`),U=g(`${S}/conv3`);return{conv0:v,conv1:O,conv2:C,conv3:U}}return{extractDenseBlock3Params:f,extractDenseBlock4Params:I}}function gE(r){const l=[],{extractDenseBlock4Params:u}=qf(r,l),p={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2"),dense3:u("dense3")};return Pn(r,l),{params:p,paramMappings:l}}const Wc=Ke(Xe());class jf extends En{constructor(){super("FaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceFeatureExtractor - load model before inference");return Wc.tidy(()=>{const u=r.toBatchTensor(112,!0),p=[122.782,117.001,104.298],g=di(u,p).div(Wc.scalar(255));let f=Iu(g,l.dense0,!0);return f=Iu(f,l.dense1),f=Iu(f,l.dense2),f=Iu(f,l.dense3),f=Wc.avgPool(f,[7,7],[2,2],"valid"),f})}async forward(r){return this.forwardInput(await Rt(r))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(r){return gE(r)}extractParams(r){return fE(r)}}const $c=Ke(Xe());function Tu(r,l){return $c.tidy(()=>$c.add($c.matMul(r,l.weights),l.bias))}function yE(r,l,u){const p=[],{extractWeights:g,getRemainingWeights:f}=zn(r),I=Vf(g,p),S=I(l,u,"fc");if(f().length!==0)throw new Error(`weights remaing after extract: ${f().length}`);return{paramMappings:p,params:{fc:S}}}function bE(r){const l=[],u=ms(r,l);function p(f){const I=u(`${f}/weights`,2),S=u(`${f}/bias`,1);return{weights:I,bias:S}}const g={fc:p("fc")};return Pn(r,l),{params:g,paramMappings:l}}function Kf(r){const l={},u={};return Object.keys(r).forEach(p=>{const g=p.startsWith("fc")?u:l;g[p]=r[p]}),{featureExtractorMap:l,classifierMap:u}}const wE=Ke(Xe());class Xf extends En{constructor(r,l){super(r);this._faceFeatureExtractor=l}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return wE.tidy(()=>{const u=r instanceof to?this.faceFeatureExtractor.forwardInput(r):r;return Tu(u.as2D(u.shape[0],-1),l.fc)})}dispose(r=!0){this.faceFeatureExtractor.dispose(r),super.dispose(r)}loadClassifierParams(r){const{params:l,paramMappings:u}=this.extractClassifierParams(r);this._params=l,this._paramMappings=u}extractClassifierParams(r){return yE(r,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:u}=Kf(r);return this.faceFeatureExtractor.loadFromWeightMap(l),bE(u)}extractParams(r){const l=this.getClassifierChannelsIn(),u=this.getClassifierChannelsOut(),p=u*l+u,g=r.slice(0,r.length-p),f=r.slice(r.length-p);return this.faceFeatureExtractor.extractWeights(g),this.extractClassifierParams(f)}}const kI=["neutral","happy","sad","angry","fearful","disgusted","surprised"];class sa{constructor(r){if(r.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${r.length}`);kI.forEach((l,u)=>{this[l]=r[u]})}asSortedArray(){return kI.map(r=>({expression:r,probability:this[r]})).sort((r,l)=>l.probability-r.probability)}}const Uc=Ke(Xe());class FI extends Xf{constructor(r=new jf){super("FaceExpressionNet",r)}forwardInput(r){return Uc.tidy(()=>Uc.softmax(this.runNet(r)))}async forward(r){return this.forwardInput(await Rt(r))}async predictExpressions(r){const l=await Rt(r),u=await this.forwardInput(l),p=await Promise.all(Uc.unstack(u).map(async f=>{const I=await f.data();return f.dispose(),I}));u.dispose();const g=p.map(f=>new sa(f));return l.isBatchInput?g:g[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}}function _I(r){return r.expressions instanceof sa}function Jf(r,l){const u={expressions:l};return Object.assign({},r,u)}function B9(r,l,u=.1,p){const g=Array.isArray(l)?l:[l];g.forEach(f=>{const I=f instanceof sa?f:_I(f)?f.expressions:void 0;if(!I)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");const S=I.asSortedArray(),x=S.filter(C=>C.probability>u),v=Ui(f)?f.detection.box.bottomLeft:p||new Je(0,0),O=new fc(x.map(C=>`${C.expression} (${Mo(C.probability)})`),v);O.draw(r)})}function ia(r){return Ui(r)&&r.landmarks instanceof Gs&&r.unshiftedLandmarks instanceof Gs&&r.alignedRect instanceof Ht}function Bc(r,l){const{box:u}=r.detection,p=l.shiftBy(u.x,u.y),g=p.align(),{imageDims:f}=r.detection,I=new Ht(r.detection.score,g.rescale(f.reverse()),f),S={landmarks:p,unshiftedLandmarks:l,alignedRect:I};return Object.assign({},r,S)}class LE{constructor(r={}){const{drawLines:l=!0,drawPoints:u=!0,lineWidth:p,lineColor:g,pointSize:f,pointColor:I}=r;this.drawLines=l,this.drawPoints=u,this.lineWidth=p||1,this.pointSize=f||2,this.lineColor=g||"rgba(0, 255, 255, 1)",this.pointColor=I||"rgba(255, 0, 255, 1)"}}class SE{constructor(r,l={}){this.faceLandmarks=r,this.options=new LE(l)}draw(r){const l=Jn(r),{drawLines:u,drawPoints:p,lineWidth:g,lineColor:f,pointSize:I,pointColor:S}=this.options;if(u&&this.faceLandmarks instanceof iu&&(l.strokeStyle=f,l.lineWidth=g,cr(l,this.faceLandmarks.getJawOutline()),cr(l,this.faceLandmarks.getLeftEyeBrow()),cr(l,this.faceLandmarks.getRightEyeBrow()),cr(l,this.faceLandmarks.getNose()),cr(l,this.faceLandmarks.getLeftEye(),!0),cr(l,this.faceLandmarks.getRightEye(),!0),cr(l,this.faceLandmarks.getMouth(),!0)),p){l.strokeStyle=S,l.fillStyle=S;const x=v=>{l.beginPath(),l.arc(v.x,v.y,I,0,2*Math.PI),l.fill()};this.faceLandmarks.positions.forEach(x)}}}function M9(r,l){const u=Array.isArray(l)?l:[l];u.forEach(p=>{const g=p instanceof Gs?p:ia(p)?p.landmarks:void 0;if(!g)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new SE(g).draw(r)})}const WI={};hm(WI,{AnchorPosition:()=>Bi,DrawBox:()=>TS,DrawBoxOptions:()=>NC,DrawFaceLandmarks:()=>SE,DrawFaceLandmarksOptions:()=>LE,DrawTextField:()=>fc,DrawTextFieldOptions:()=>ym,drawContour:()=>cr,drawDetections:()=>KX,drawFaceExpressions:()=>B9,drawFaceLandmarks:()=>M9});function P9(r,l){const u=kc(r,l),p=Fc(r,l);function g(I,S,x){const v=p(I,S,`${x}/separable_conv0`),O=p(S,S,`${x}/separable_conv1`),C=u(I,S,1,`${x}/expansion_conv`);return{separable_conv0:v,separable_conv1:O,expansion_conv:C}}function f(I,S){const x=p(I,I,`${S}/separable_conv0`),v=p(I,I,`${S}/separable_conv1`),O=p(I,I,`${S}/separable_conv2`);return{separable_conv0:x,separable_conv1:v,separable_conv2:O}}return{extractConvParams:u,extractSeparableConvParams:p,extractReductionBlockParams:g,extractMainBlockParams:f}}function IE(r,l){const u=[],{extractWeights:p,getRemainingWeights:g}=zn(r),{extractConvParams:f,extractSeparableConvParams:I,extractReductionBlockParams:S,extractMainBlockParams:x}=P9(p,u),v=f(3,32,3,"entry_flow/conv_in"),O=S(32,64,"entry_flow/reduction_block_0"),C=S(64,128,"entry_flow/reduction_block_1"),U={conv_in:v,reduction_block_0:O,reduction_block_1:C},G={};Wi(l,0,1).forEach(ge=>{G[`main_block_${ge}`]=x(128,`middle_flow/main_block_${ge}`)});const ne=S(128,256,"exit_flow/reduction_block"),te=I(256,512,"exit_flow/separable_conv"),oe={reduction_block:ne,separable_conv:te};if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{paramMappings:u,params:{entry_flow:U,middle_flow:G,exit_flow:oe}}}function z9(r,l){const u=ms(r,l),p=Hf(u),g=_c(u);function f(S){const x=g(`${S}/separable_conv0`),v=g(`${S}/separable_conv1`),O=p(`${S}/expansion_conv`);return{separable_conv0:x,separable_conv1:v,expansion_conv:O}}function I(S){const x=g(`${S}/separable_conv0`),v=g(`${S}/separable_conv1`),O=g(`${S}/separable_conv2`);return{separable_conv0:x,separable_conv1:v,separable_conv2:O}}return{extractConvParams:p,extractSeparableConvParams:g,extractReductionBlockParams:f,extractMainBlockParams:I}}function xE(r,l){const u=[],{extractConvParams:p,extractSeparableConvParams:g,extractReductionBlockParams:f,extractMainBlockParams:I}=z9(r,u),S=p("entry_flow/conv_in"),x=f("entry_flow/reduction_block_0"),v=f("entry_flow/reduction_block_1"),O={conv_in:S,reduction_block_0:x,reduction_block_1:v},C={};Wi(l,0,1).forEach(te=>{C[`main_block_${te}`]=I(`middle_flow/main_block_${te}`)});const U=f("exit_flow/reduction_block"),G=g("exit_flow/separable_conv"),ne={reduction_block:U,separable_conv:G};return Pn(r,u),{params:{entry_flow:O,middle_flow:C,exit_flow:ne},paramMappings:u}}const dn=Ke(Xe());function TE(r,l,u){return dn.add(dn.conv2d(r,l.filters,u,"same"),l.bias)}function $I(r,l,u=!0){let p=u?dn.relu(r):r;return p=Qn(p,l.separable_conv0,[1,1]),p=Qn(dn.relu(p),l.separable_conv1,[1,1]),p=dn.maxPool(p,[3,3],[2,2],"same"),p=dn.add(p,TE(r,l.expansion_conv,[2,2])),p}function G9(r,l){let u=Qn(dn.relu(r),l.separable_conv0,[1,1]);return u=Qn(dn.relu(u),l.separable_conv1,[1,1]),u=Qn(dn.relu(u),l.separable_conv2,[1,1]),u=dn.add(u,r),u}class AE extends En{constructor(r){super("TinyXception");this._numMainBlocks=r}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyXception - load model before inference");return dn.tidy(()=>{const u=r.toBatchTensor(112,!0),p=[122.782,117.001,104.298],g=di(u,p).div(dn.scalar(256));let f=dn.relu(TE(g,l.entry_flow.conv_in,[2,2]));return f=$I(f,l.entry_flow.reduction_block_0,!1),f=$I(f,l.entry_flow.reduction_block_1),Wi(this._numMainBlocks,0,1).forEach(I=>{f=G9(f,l.middle_flow[`main_block_${I}`])}),f=$I(f,l.exit_flow.reduction_block),f=dn.relu(Qn(f,l.exit_flow.separable_conv,[1,1])),f})}async forward(r){return this.forwardInput(await Rt(r))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(r){return xE(r,this._numMainBlocks)}extractParams(r){return IE(r,this._numMainBlocks)}}function vE(r){const l=[],{extractWeights:u,getRemainingWeights:p}=zn(r),g=Vf(u,l),f=g(512,1,"fc/age"),I=g(512,2,"fc/gender");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{fc:{age:f,gender:I}}}}function NE(r){const l=[],u=ms(r,l);function p(f){const I=u(`${f}/weights`,2),S=u(`${f}/bias`,1);return{weights:I,bias:S}}const g={fc:{age:p("fc/age"),gender:p("fc/gender")}};return Pn(r,l),{params:g,paramMappings:l}}var gr;(function(r){r.FEMALE="female",r.MALE="male"})(gr||(gr={}));const Vi=Ke(Xe());class UI extends En{constructor(r=new AE(2)){super("AgeGenderNet");this._faceFeatureExtractor=r}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return Vi.tidy(()=>{const u=r instanceof to?this.faceFeatureExtractor.forwardInput(r):r,p=Vi.avgPool(u,[7,7],[2,2],"valid").as2D(u.shape[0],-1),g=Tu(p,l.fc.age).as1D(),f=Tu(p,l.fc.gender);return{age:g,gender:f}})}forwardInput(r){return Vi.tidy(()=>{const{age:l,gender:u}=this.runNet(r);return{age:l,gender:Vi.softmax(u)}})}async forward(r){return this.forwardInput(await Rt(r))}async predictAgeAndGender(r){const l=await Rt(r),u=await this.forwardInput(l),p=Vi.unstack(u.age),g=Vi.unstack(u.gender),f=p.map((S,x)=>({ageTensor:S,genderTensor:g[x]})),I=await Promise.all(f.map(async({ageTensor:S,genderTensor:x})=>{const v=(await S.data())[0],O=(await x.data())[0],C=O>.5,U=C?gr.MALE:gr.FEMALE,G=C?O:1-O;return S.dispose(),x.dispose(),{age:v,gender:U,genderProbability:G}}));return u.age.dispose(),u.gender.dispose(),l.isBatchInput?I:I[0]}getDefaultModelName(){return"age_gender_model"}dispose(r=!0){this.faceFeatureExtractor.dispose(r),super.dispose(r)}loadClassifierParams(r){const{params:l,paramMappings:u}=this.extractClassifierParams(r);this._params=l,this._paramMappings=u}extractClassifierParams(r){return vE(r)}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:u}=Kf(r);return this.faceFeatureExtractor.loadFromWeightMap(l),NE(u)}extractParams(r){const l=512*1+1+(512*2+2),u=r.slice(0,r.length-l),p=r.slice(r.length-l);return this.faceFeatureExtractor.extractWeights(u),this.extractClassifierParams(p)}}const fs=Ke(Xe());class Zf extends Xf{postProcess(r,l,u){const p=u.map(({width:f,height:I})=>{const S=l/Math.max(I,f);return{width:f*S,height:I*S}}),g=p.length;return fs.tidy(()=>{const f=(O,C)=>fs.stack([fs.fill([68],O),fs.fill([68],C)],1).as2D(1,136).as1D(),I=(O,C)=>{const{width:U,height:G}=p[O];return C(U,G)?Math.abs(U-G)/2:0},S=O=>I(O,(C,U)=>C<U),x=O=>I(O,(C,U)=>U<C),v=r.mul(fs.fill([g,136],l)).sub(fs.stack(Array.from(Array(g),(O,C)=>f(S(C),x(C))))).div(fs.stack(Array.from(Array(g),(O,C)=>f(p[C].width,p[C].height))));return v})}forwardInput(r){return fs.tidy(()=>{const l=this.runNet(r);return this.postProcess(l,r.inputSize,r.inputDimensions.map(([u,p])=>({height:u,width:p})))})}async forward(r){return this.forwardInput(await Rt(r))}async detectLandmarks(r){const l=await Rt(r),u=fs.tidy(()=>fs.unstack(this.forwardInput(l))),p=await Promise.all(u.map(async(g,f)=>{const I=Array.from(await g.data()),S=I.filter((v,O)=>dm(O)),x=I.filter((v,O)=>!dm(O));return new iu(Array(68).fill(0).map((v,O)=>new Je(S[O],x[O])),{height:l.getInputHeight(f),width:l.getInputWidth(f)})}));return u.forEach(g=>g.dispose()),l.isBatchInput?p:p[0]}getClassifierChannelsOut(){return 136}}class Au extends Zf{constructor(r=new jf){super("FaceLandmark68Net",r)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}}function CE(r){const l=[],{extractDenseBlock3Params:u}=qf(r,l),p={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2")};return Pn(r,l),{params:p,paramMappings:l}}function RE(r){const l=[],{extractWeights:u,getRemainingWeights:p}=zn(r),{extractDenseBlock3Params:g}=Yf(u,l),f=g(3,32,"dense0",!0),I=g(32,64,"dense1"),S=g(64,128,"dense2");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{dense0:f,dense1:I,dense2:S}}}const Mc=Ke(Xe());class OE extends En{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyFaceFeatureExtractor - load model before inference");return Mc.tidy(()=>{const u=r.toBatchTensor(112,!0),p=[122.782,117.001,104.298],g=di(u,p).div(Mc.scalar(255));let f=Pf(g,l.dense0,!0);return f=Pf(f,l.dense1),f=Pf(f,l.dense2),f=Mc.avgPool(f,[14,14],[2,2],"valid"),f})}async forward(r){return this.forwardInput(await Rt(r))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(r){return CE(r)}extractParams(r){return RE(r)}}class BI extends Zf{constructor(r=new OE){super("FaceLandmark68TinyNet",r)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}}class V9 extends Au{}const Qf=Ke(Xe());function EE(r,l){return Qf.add(Qf.mul(r,l.weights),l.biases)}const Pc=Ke(Xe());function MI(r,l,u,p,g="same"){const{filters:f,bias:I}=l.conv;let S=Pc.conv2d(r,f,u,g);return S=Pc.add(S,I),S=EE(S,l.scale),p?Pc.relu(S):S}function DE(r,l){return MI(r,l,[1,1],!0)}function PI(r,l){return MI(r,l,[1,1],!1)}function eg(r,l){return MI(r,l,[2,2],!0,"valid")}const gs=Ke(Xe());function Y9(r,l){function u(S,x,v){const O=r(S),C=O.length/(x*v*v);if(dS(C))throw new Error(`depth has to be an integer: ${C}, weights.length: ${O.length}, numFilters: ${x}, filterSize: ${v}`);return gs.tidy(()=>gs.transpose(gs.tensor4d(O,[x,C,v,v]),[2,3,1,0]))}function p(S,x,v,O){const C=u(S,x,v),U=gs.tensor1d(r(x));return l.push({paramPath:`${O}/filters`},{paramPath:`${O}/bias`}),{filters:C,bias:U}}function g(S,x){const v=gs.tensor1d(r(S)),O=gs.tensor1d(r(S));return l.push({paramPath:`${x}/weights`},{paramPath:`${x}/biases`}),{weights:v,biases:O}}function f(S,x,v,O){const C=p(S,x,v,`${O}/conv`),U=g(x,`${O}/scale`);return{conv:C,scale:U}}function I(S,x,v,O,C=!1){const U=f((C?.5:1)*S,x,v,`${O}/conv1`),G=f(S,x,v,`${O}/conv2`);return{conv1:U,conv2:G}}return{extractConvLayerParams:f,extractResidualLayerParams:I}}function kE(r){const{extractWeights:l,getRemainingWeights:u}=zn(r),p=[],{extractConvLayerParams:g,extractResidualLayerParams:f}=Y9(l,p),I=g(4704,32,7,"conv32_down"),S=f(9216,32,3,"conv32_1"),x=f(9216,32,3,"conv32_2"),v=f(9216,32,3,"conv32_3"),O=f(36864,64,3,"conv64_down",!0),C=f(36864,64,3,"conv64_1"),U=f(36864,64,3,"conv64_2"),G=f(36864,64,3,"conv64_3"),ne=f(147456,128,3,"conv128_down",!0),te=f(147456,128,3,"conv128_1"),oe=f(147456,128,3,"conv128_2"),ge=f(589824,256,3,"conv256_down",!0),fe=f(589824,256,3,"conv256_1"),Ae=f(589824,256,3,"conv256_2"),Te=f(589824,256,3,"conv256_down_out"),Ve=gs.tidy(()=>gs.transpose(gs.tensor2d(l(256*128),[128,256]),[1,0]));if(p.push({paramPath:"fc"}),u().length!==0)throw new Error(`weights remaing after extract: ${u().length}`);const rt={conv32_down:I,conv32_1:S,conv32_2:x,conv32_3:v,conv64_down:O,conv64_1:C,conv64_2:U,conv64_3:G,conv128_down:ne,conv128_1:te,conv128_2:oe,conv256_down:ge,conv256_1:fe,conv256_2:Ae,conv256_down_out:Te,fc:Ve};return{params:rt,paramMappings:p}}function H9(r,l){const u=ms(r,l);function p(I){const S=u(`${I}/scale/weights`,1),x=u(`${I}/scale/biases`,1);return{weights:S,biases:x}}function g(I){const S=u(`${I}/conv/filters`,4),x=u(`${I}/conv/bias`,1),v=p(I);return{conv:{filters:S,bias:x},scale:v}}function f(I){return{conv1:g(`${I}/conv1`),conv2:g(`${I}/conv2`)}}return{extractConvLayerParams:g,extractResidualLayerParams:f}}function FE(r){const l=[],{extractConvLayerParams:u,extractResidualLayerParams:p}=H9(r,l),g=u("conv32_down"),f=p("conv32_1"),I=p("conv32_2"),S=p("conv32_3"),x=p("conv64_down"),v=p("conv64_1"),O=p("conv64_2"),C=p("conv64_3"),U=p("conv128_down"),G=p("conv128_1"),ne=p("conv128_2"),te=p("conv256_down"),oe=p("conv256_1"),ge=p("conv256_2"),fe=p("conv256_down_out"),Ae=r.fc;if(l.push({originalPath:"fc",paramPath:"fc"}),!uS(Ae))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${Ae}`);const Te={conv32_down:g,conv32_1:f,conv32_2:I,conv32_3:S,conv64_down:x,conv64_1:v,conv64_2:O,conv64_3:C,conv128_down:U,conv128_1:G,conv128_2:ne,conv256_down:te,conv256_1:oe,conv256_2:ge,conv256_down_out:fe,fc:Ae};return Pn(r,l),{params:Te,paramMappings:l}}const Gn=Ke(Xe());function fi(r,l){let u=DE(r,l.conv1);return u=PI(u,l.conv2),u=Gn.add(u,r),u=Gn.relu(u),u}function vu(r,l){let u=eg(r,l.conv1);u=PI(u,l.conv2);let p=Gn.avgPool(r,2,2,"valid");const g=Gn.zeros(p.shape),f=p.shape[3]!==u.shape[3],I=p.shape[1]!==u.shape[1]||p.shape[2]!==u.shape[2];if(I){const S=[...u.shape];S[1]=1;const x=Gn.zeros(S);u=Gn.concat([u,x],1);const v=[...u.shape];v[2]=1;const O=Gn.zeros(v);u=Gn.concat([u,O],2)}return p=f?Gn.concat([p,g],3):p,u=Gn.add(p,u),u=Gn.relu(u),u}const Ds=Ke(Xe());class Nu extends En{constructor(){super("FaceRecognitionNet")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceRecognitionNet - load model before inference");return Ds.tidy(()=>{const u=Ds.cast(r.toBatchTensor(150,!0),"float32"),p=[122.782,117.001,104.298],g=di(u,p).div(Ds.scalar(256));let f=eg(g,l.conv32_down);f=Ds.maxPool(f,3,2,"valid"),f=fi(f,l.conv32_1),f=fi(f,l.conv32_2),f=fi(f,l.conv32_3),f=vu(f,l.conv64_down),f=fi(f,l.conv64_1),f=fi(f,l.conv64_2),f=fi(f,l.conv64_3),f=vu(f,l.conv128_down),f=fi(f,l.conv128_1),f=fi(f,l.conv128_2),f=vu(f,l.conv256_down),f=fi(f,l.conv256_1),f=fi(f,l.conv256_2),f=vu(f,l.conv256_down_out);const I=f.mean([1,2]),S=Ds.matMul(I,l.fc);return S})}async forward(r){return this.forwardInput(await Rt(r))}async computeFaceDescriptor(r){const l=await Rt(r),u=Ds.tidy(()=>Ds.unstack(this.forwardInput(l))),p=await Promise.all(u.map(g=>g.data()));return u.forEach(g=>g.dispose()),l.isBatchInput?p:p[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(r){return FE(r)}extractParams(r){return kE(r)}}function q9(r){const l=new Nu;return l.extractWeights(r),l}function tg(r,l){const u={descriptor:l};return Object.assign({},r,u)}function j9(r){return typeof r.age=="number"}function ng(r,l){const u={age:l};return Object.assign({},r,u)}function K9(r){return(r.gender===gr.MALE||r.gender===gr.FEMALE)&&pc(r.genderProbability)}function sg(r,l,u){const p={gender:l,genderProbability:u};return Object.assign({},r,p)}const gi=Ke(Xe());function X9(r,l){function u(x,v){const O=gi.tensor4d(r(3*3*x),[3,3,x,1]),C=gi.tensor1d(r(x)),U=gi.tensor1d(r(x)),G=gi.tensor1d(r(x)),ne=gi.tensor1d(r(x));return l.push({paramPath:`${v}/filters`},{paramPath:`${v}/batch_norm_scale`},{paramPath:`${v}/batch_norm_offset`},{paramPath:`${v}/batch_norm_mean`},{paramPath:`${v}/batch_norm_variance`}),{filters:O,batch_norm_scale:C,batch_norm_offset:U,batch_norm_mean:G,batch_norm_variance:ne}}function p(x,v,O,C,U){const G=gi.tensor4d(r(x*v*O*O),[O,O,x,v]),ne=gi.tensor1d(r(v));return l.push({paramPath:`${C}/filters`},{paramPath:`${C}/${U?"batch_norm_offset":"bias"}`}),{filters:G,bias:ne}}function g(x,v,O,C){const{filters:U,bias:G}=p(x,v,O,C,!0);return{filters:U,batch_norm_offset:G}}function f(x,v,O){const C=u(x,`${O}/depthwise_conv`),U=g(x,v,1,`${O}/pointwise_conv`);return{depthwise_conv:C,pointwise_conv:U}}function I(){const x=g(3,32,3,"mobilenetv1/conv_0"),v=f(32,64,"mobilenetv1/conv_1"),O=f(64,128,"mobilenetv1/conv_2"),C=f(128,128,"mobilenetv1/conv_3"),U=f(128,256,"mobilenetv1/conv_4"),G=f(256,256,"mobilenetv1/conv_5"),ne=f(256,512,"mobilenetv1/conv_6"),te=f(512,512,"mobilenetv1/conv_7"),oe=f(512,512,"mobilenetv1/conv_8"),ge=f(512,512,"mobilenetv1/conv_9"),fe=f(512,512,"mobilenetv1/conv_10"),Ae=f(512,512,"mobilenetv1/conv_11"),Te=f(512,1024,"mobilenetv1/conv_12"),Ve=f(1024,1024,"mobilenetv1/conv_13");return{conv_0:x,conv_1:v,conv_2:O,conv_3:C,conv_4:U,conv_5:G,conv_6:ne,conv_7:te,conv_8:oe,conv_9:ge,conv_10:fe,conv_11:Ae,conv_12:Te,conv_13:Ve}}function S(){const x=g(1024,256,1,"prediction_layer/conv_0"),v=g(256,512,3,"prediction_layer/conv_1"),O=g(512,128,1,"prediction_layer/conv_2"),C=g(128,256,3,"prediction_layer/conv_3"),U=g(256,128,1,"prediction_layer/conv_4"),G=g(128,256,3,"prediction_layer/conv_5"),ne=g(256,64,1,"prediction_layer/conv_6"),te=g(64,128,3,"prediction_layer/conv_7"),oe=p(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),ge=p(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),fe=p(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),Ae=p(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),Te=p(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),Ve=p(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),rt=p(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),vt=p(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),$t=p(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),Kt=p(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),Dn=p(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),Tn=p(128,18,1,"prediction_layer/box_predictor_5/class_predictor"),An={box_encoding_predictor:oe,class_predictor:ge},Ks={box_encoding_predictor:fe,class_predictor:Ae},Li={box_encoding_predictor:Te,class_predictor:Ve},Xs={box_encoding_predictor:rt,class_predictor:vt},la={box_encoding_predictor:$t,class_predictor:Kt},Xc={box_encoding_predictor:Dn,class_predictor:Tn};return{conv_0:x,conv_1:v,conv_2:O,conv_3:C,conv_4:U,conv_5:G,conv_6:ne,conv_7:te,box_predictor_0:An,box_predictor_1:Ks,box_predictor_2:Li,box_predictor_3:Xs,box_predictor_4:la,box_predictor_5:Xc}}return{extractMobilenetV1Params:I,extractPredictionLayerParams:S}}function _E(r){const l=[],{extractWeights:u,getRemainingWeights:p}=zn(r),{extractMobilenetV1Params:g,extractPredictionLayerParams:f}=X9(u,l),I=g(),S=f(),x=gi.tensor3d(u(5118*4),[1,5118,4]),v={extra_dim:x};if(l.push({paramPath:"output_layer/extra_dim"}),p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{params:{mobilenetv1:I,prediction_layer:S,output_layer:v},paramMappings:l}}function J9(r,l){const u=ms(r,l);function p(v,O,C){const U=u(`${v}/Conv2d_${O}_pointwise/weights`,4,`${C}/filters`),G=u(`${v}/Conv2d_${O}_pointwise/convolution_bn_offset`,1,`${C}/batch_norm_offset`);return{filters:U,batch_norm_offset:G}}function g(v){const O=`mobilenetv1/conv_${v}`,C=`MobilenetV1/Conv2d_${v}_depthwise`,U=`${O}/depthwise_conv`,G=`${O}/pointwise_conv`,ne=u(`${C}/depthwise_weights`,4,`${U}/filters`),te=u(`${C}/BatchNorm/gamma`,1,`${U}/batch_norm_scale`),oe=u(`${C}/BatchNorm/beta`,1,`${U}/batch_norm_offset`),ge=u(`${C}/BatchNorm/moving_mean`,1,`${U}/batch_norm_mean`),fe=u(`${C}/BatchNorm/moving_variance`,1,`${U}/batch_norm_variance`);return{depthwise_conv:{filters:ne,batch_norm_scale:te,batch_norm_offset:oe,batch_norm_mean:ge,batch_norm_variance:fe},pointwise_conv:p("MobilenetV1",v,G)}}function f(){return{conv_0:p("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:g(1),conv_2:g(2),conv_3:g(3),conv_4:g(4),conv_5:g(5),conv_6:g(6),conv_7:g(7),conv_8:g(8),conv_9:g(9),conv_10:g(10),conv_11:g(11),conv_12:g(12),conv_13:g(13)}}function I(v,O){const C=u(`${v}/weights`,4,`${O}/filters`),U=u(`${v}/biases`,1,`${O}/bias`);return{filters:C,bias:U}}function S(v){const O=I(`Prediction/BoxPredictor_${v}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${v}/box_encoding_predictor`),C=I(`Prediction/BoxPredictor_${v}/ClassPredictor`,`prediction_layer/box_predictor_${v}/class_predictor`);return{box_encoding_predictor:O,class_predictor:C}}function x(){return{conv_0:p("Prediction",0,"prediction_layer/conv_0"),conv_1:p("Prediction",1,"prediction_layer/conv_1"),conv_2:p("Prediction",2,"prediction_layer/conv_2"),conv_3:p("Prediction",3,"prediction_layer/conv_3"),conv_4:p("Prediction",4,"prediction_layer/conv_4"),conv_5:p("Prediction",5,"prediction_layer/conv_5"),conv_6:p("Prediction",6,"prediction_layer/conv_6"),conv_7:p("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:S(0),box_predictor_1:S(1),box_predictor_2:S(2),box_predictor_3:S(3),box_predictor_4:S(4),box_predictor_5:S(5)}}return{extractMobilenetV1Params:f,extractPredictionLayerParams:x}}function WE(r){const l=[],{extractMobilenetV1Params:u,extractPredictionLayerParams:p}=J9(r,l),g=r["Output/extra_dim"];if(l.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!lr(g))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${g}`);const f={mobilenetv1:u(),prediction_layer:p(),output_layer:{extra_dim:g}};return Pn(r,l),{params:f,paramMappings:l}}const so=Ke(Xe());function Hs(r,l,u){return so.tidy(()=>{let p=so.conv2d(r,l.filters,u,"same");return p=so.add(p,l.batch_norm_offset),so.clipByValue(p,0,6)})}const yr=Ke(Xe()),Z9=.0010000000474974513;function Q9(r,l,u){return yr.tidy(()=>{let p=yr.depthwiseConv2d(r,l.filters,u,"same");return p=yr.batchNorm(p,l.batch_norm_mean,l.batch_norm_variance,l.batch_norm_offset,l.batch_norm_scale,Z9),yr.clipByValue(p,0,6)})}function eZ(r){return[2,4,6,12].some(l=>l===r)?[2,2]:[1,1]}function $E(r,l){return yr.tidy(()=>{let u,p=Hs(r,l.conv_0,[2,2]);const g=[l.conv_1,l.conv_2,l.conv_3,l.conv_4,l.conv_5,l.conv_6,l.conv_7,l.conv_8,l.conv_9,l.conv_10,l.conv_11,l.conv_12,l.conv_13];if(g.forEach((f,I)=>{const S=I+1,x=eZ(S);p=Q9(p,f.depthwise_conv,x),p=Hs(p,f.pointwise_conv,[1,1]),S===11&&(u=p)}),u===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:p,conv11:u}})}function UE(r,l,u,p,g){const f=r.shape[0],I=Math.min(u,f),S=l.map((O,C)=>({score:O,boxIndex:C})).filter(O=>O.score>g).sort((O,C)=>C.score-O.score),x=O=>O<=p?1:0,v=[];return S.forEach(O=>{if(v.length>=I)return;const C=O.score;for(let U=v.length-1;U>=0;--U){const G=tZ(r,O.boxIndex,v[U]);if(G===0)continue;if(O.score*=x(G),O.score<=g)break}C===O.score&&v.push(O.boxIndex)}),v}function tZ(r,l,u){const p=r.arraySync(),g=Math.min(p[l][0],p[l][2]),f=Math.min(p[l][1],p[l][3]),I=Math.max(p[l][0],p[l][2]),S=Math.max(p[l][1],p[l][3]),x=Math.min(p[u][0],p[u][2]),v=Math.min(p[u][1],p[u][3]),O=Math.max(p[u][0],p[u][2]),C=Math.max(p[u][1],p[u][3]),U=(I-g)*(S-f),G=(O-x)*(C-v);if(U<=0||G<=0)return 0;const ne=Math.max(g,x),te=Math.max(f,v),oe=Math.min(I,O),ge=Math.min(S,C),fe=Math.max(oe-ne,0)*Math.max(ge-te,0);return fe/(U+G-fe)}const De=Ke(Xe());function nZ(r){const l=De.unstack(De.transpose(r,[1,0])),u=[De.sub(l[2],l[0]),De.sub(l[3],l[1])],p=[De.add(l[0],De.div(u[0],De.scalar(2))),De.add(l[1],De.div(u[1],De.scalar(2)))];return{sizes:u,centers:p}}function sZ(r,l){const{sizes:u,centers:p}=nZ(r),g=De.unstack(De.transpose(l,[1,0])),f=De.div(De.mul(De.exp(De.div(g[2],De.scalar(5))),u[0]),De.scalar(2)),I=De.add(De.mul(De.div(g[0],De.scalar(10)),u[0]),p[0]),S=De.div(De.mul(De.exp(De.div(g[3],De.scalar(5))),u[1]),De.scalar(2)),x=De.add(De.mul(De.div(g[1],De.scalar(10)),u[1]),p[1]);return De.transpose(De.stack([De.sub(I,f),De.sub(x,S),De.add(I,f),De.add(x,S)]),[1,0])}function BE(r,l,u){return De.tidy(()=>{const p=r.shape[0];let g=sZ(De.reshape(De.tile(u.extra_dim,[p,1,1]),[-1,4]),De.reshape(r,[-1,4]));g=De.reshape(g,[p,g.shape[0]/p,4]);const f=De.sigmoid(De.slice(l,[0,0,1],[-1,-1,-1]));let I=De.slice(f,[0,0,0],[-1,-1,1]);I=De.reshape(I,[p,I.shape[1]]);const S=De.unstack(g),x=De.unstack(I);return{boxes:S,scores:x}})}const Cu=Ke(Xe());function ra(r,l){return Cu.tidy(()=>{const u=r.shape[0],p=Cu.reshape(na(r,l.box_encoding_predictor),[u,-1,1,4]),g=Cu.reshape(na(r,l.class_predictor),[u,-1,3]);return{boxPredictionEncoding:p,classPrediction:g}})}const Ru=Ke(Xe());function ME(r,l,u){return Ru.tidy(()=>{const p=Hs(r,u.conv_0,[1,1]),g=Hs(p,u.conv_1,[2,2]),f=Hs(g,u.conv_2,[1,1]),I=Hs(f,u.conv_3,[2,2]),S=Hs(I,u.conv_4,[1,1]),x=Hs(S,u.conv_5,[2,2]),v=Hs(x,u.conv_6,[1,1]),O=Hs(v,u.conv_7,[2,2]),C=ra(l,u.box_predictor_0),U=ra(r,u.box_predictor_1),G=ra(g,u.box_predictor_2),ne=ra(I,u.box_predictor_3),te=ra(x,u.box_predictor_4),oe=ra(O,u.box_predictor_5),ge=Ru.concat([C.boxPredictionEncoding,U.boxPredictionEncoding,G.boxPredictionEncoding,ne.boxPredictionEncoding,te.boxPredictionEncoding,oe.boxPredictionEncoding],1),fe=Ru.concat([C.classPrediction,U.classPrediction,G.classPrediction,ne.classPrediction,te.classPrediction,oe.classPrediction],1);return{boxPredictions:ge,classPredictions:fe}})}class yi{constructor({minConfidence:r,maxResults:l}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=r||.5,this._maxResults=l||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}}const bi=Ke(Xe());class zc extends En{constructor(){super("SsdMobilenetv1")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("SsdMobilenetv1 - load model before inference");return bi.tidy(()=>{const u=bi.cast(r.toBatchTensor(512,!1),"float32"),p=bi.sub(bi.mul(u,bi.scalar(.007843137718737125)),bi.scalar(1)),g=$E(p,l.mobilenetv1),{boxPredictions:f,classPredictions:I}=ME(g.out,g.conv11,l.prediction_layer);return BE(f,I,l.output_layer)})}async forward(r){return this.forwardInput(await Rt(r))}async locateFaces(r,l={}){const{maxResults:u,minConfidence:p}=new yi(l),g=await Rt(r),{boxes:f,scores:I}=this.forwardInput(g),S=f[0],x=I[0];for(let fe=1;fe<f.length;fe++)f[fe].dispose(),I[fe].dispose();const v=Array.from(await x.data()),O=.5,C=UE(S,v,u,O,p),U=g.getReshapedInputDimensions(0),G=g.inputSize,ne=G/U.width,te=G/U.height,oe=S.arraySync(),ge=C.map(fe=>{const[Ae,Te]=[Math.max(0,oe[fe][0]),Math.min(1,oe[fe][2])].map(vt=>vt*te),[Ve,rt]=[Math.max(0,oe[fe][1]),Math.min(1,oe[fe][3])].map(vt=>vt*ne);return new Ht(v[fe],new su(Ve,Ae,rt-Ve,Te-Ae),{height:g.getInputHeight(0),width:g.getInputWidth(0)})});return S.dispose(),x.dispose(),ge}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(r){return WE(r)}extractParams(r){return _E(r)}}function PE(r){const l=new zc;return l.extractWeights(r),l}function iZ(r){return PE(r)}class rZ extends zc{}const zE=.4,GE=[new Je(.738768,.874946),new Je(2.42204,2.65704),new Je(4.30971,7.04493),new Je(10.246,4.59428),new Je(12.6868,11.8741)],VE=[new Je(1.603231,2.094468),new Je(6.041143,7.080126),new Je(2.882459,3.518061),new Je(4.266906,5.178857),new Je(9.041765,10.66308)],YE=[117.001,114.697,97.404],HE="tiny_yolov2_model",qE="tiny_yolov2_separable_conv_model";const ig=r=>typeof r=="number";function zI(r){if(!r)throw new Error(`invalid config: ${r}`);if(typeof r.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${r.withSeparableConvs}`);if(!ig(r.iouThreshold)||r.iouThreshold<0||r.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${r.iouThreshold}`);if(!Array.isArray(r.classes)||!r.classes.length||!r.classes.every(l=>typeof l=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(r.classes)}`);if(!Array.isArray(r.anchors)||!r.anchors.length||!r.anchors.map(l=>l||{}).every(l=>ig(l.x)&&ig(l.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(r.anchors)}`);if(r.meanRgb&&(!Array.isArray(r.meanRgb)||r.meanRgb.length!==3||!r.meanRgb.every(ig)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(r.meanRgb)}`)}const qs=Ke(Xe());function Gc(r){return qs.tidy(()=>{const l=qs.mul(r,qs.scalar(.10000000149011612));return qs.add(qs.relu(qs.sub(r,l)),l)})}const js=Ke(Xe());function br(r,l){return js.tidy(()=>{let u=js.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return u=js.conv2d(u,l.conv.filters,[1,1],"valid"),u=js.sub(u,l.bn.sub),u=js.mul(u,l.bn.truediv),u=js.add(u,l.conv.bias),Gc(u)})}const io=Ke(Xe());function wr(r,l){return io.tidy(()=>{let u=io.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return u=io.separableConv2d(u,l.depthwise_filter,l.pointwise_filter,[1,1],"valid"),u=io.add(u,l.bias),Gc(u)})}const GI=Ke(Xe());function oZ(r,l){const u=kc(r,l);function p(I,S){const x=GI.tensor1d(r(I)),v=GI.tensor1d(r(I));return l.push({paramPath:`${S}/sub`},{paramPath:`${S}/truediv`}),{sub:x,truediv:v}}function g(I,S,x){const v=u(I,S,3,`${x}/conv`),O=p(S,`${x}/bn`);return{conv:v,bn:O}}const f=Fc(r,l);return{extractConvParams:u,extractConvWithBatchNormParams:g,extractSeparableConvParams:f}}function jE(r,l,u,p){const{extractWeights:g,getRemainingWeights:f}=zn(r),I=[],{extractConvParams:S,extractConvWithBatchNormParams:x,extractSeparableConvParams:v}=oZ(g,I);let O;if(l.withSeparableConvs){const[C,U,G,ne,te,oe,ge,fe,Ae]=p,Te=l.isFirstLayerConv2d?S(C,U,3,"conv0"):v(C,U,"conv0"),Ve=v(U,G,"conv1"),rt=v(G,ne,"conv2"),vt=v(ne,te,"conv3"),$t=v(te,oe,"conv4"),Kt=v(oe,ge,"conv5"),Dn=fe?v(ge,fe,"conv6"):void 0,Tn=Ae?v(fe,Ae,"conv7"):void 0,An=S(Ae||fe||ge,5*u,1,"conv8");O={conv0:Te,conv1:Ve,conv2:rt,conv3:vt,conv4:$t,conv5:Kt,conv6:Dn,conv7:Tn,conv8:An}}else{const[C,U,G,ne,te,oe,ge,fe,Ae]=p,Te=x(C,U,"conv0"),Ve=x(U,G,"conv1"),rt=x(G,ne,"conv2"),vt=x(ne,te,"conv3"),$t=x(te,oe,"conv4"),Kt=x(oe,ge,"conv5"),Dn=x(ge,fe,"conv6"),Tn=x(fe,Ae,"conv7"),An=S(Ae,5*u,1,"conv8");O={conv0:Te,conv1:Ve,conv2:rt,conv3:vt,conv4:$t,conv5:Kt,conv6:Dn,conv7:Tn,conv8:An}}if(f().length!==0)throw new Error(`weights remaing after extract: ${f().length}`);return{params:O,paramMappings:I}}function aZ(r,l){const u=ms(r,l);function p(S){const x=u(`${S}/sub`,1),v=u(`${S}/truediv`,1);return{sub:x,truediv:v}}function g(S){const x=u(`${S}/filters`,4),v=u(`${S}/bias`,1);return{filters:x,bias:v}}function f(S){const x=g(`${S}/conv`),v=p(`${S}/bn`);return{conv:x,bn:v}}const I=_c(u);return{extractConvParams:g,extractConvWithBatchNormParams:f,extractSeparableConvParams:I}}function KE(r,l){const u=[],{extractConvParams:p,extractConvWithBatchNormParams:g,extractSeparableConvParams:f}=aZ(r,u);let I;if(l.withSeparableConvs){const S=l.filterSizes&&l.filterSizes.length||9;I={conv0:l.isFirstLayerConv2d?p("conv0"):f("conv0"),conv1:f("conv1"),conv2:f("conv2"),conv3:f("conv3"),conv4:f("conv4"),conv5:f("conv5"),conv6:S>7?f("conv6"):void 0,conv7:S>8?f("conv7"):void 0,conv8:p("conv8")}}else I={conv0:g("conv0"),conv1:g("conv1"),conv2:g("conv2"),conv3:g("conv3"),conv4:g("conv4"),conv5:g("conv5"),conv6:g("conv6"),conv7:g("conv7"),conv8:p("conv8")};return Pn(r,u),{params:I,paramMappings:u}}var VI;(function(r){r[r.XS=224]="XS",r[r.SM=320]="SM",r[r.MD=416]="MD",r[r.LG=608]="LG"})(VI||(VI={}));class Lr{constructor({inputSize:r,scoreThreshold:l}={}){this._name="TinyYolov2Options";if(this._inputSize=r||416,this._scoreThreshold=l||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}}const kt=Ke(Xe());class Vc extends En{constructor(r){super("TinyYolov2");zI(r),this._config=r}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(r,l){let u=br(r,l.conv0);return u=kt.maxPool(u,[2,2],[2,2],"same"),u=br(u,l.conv1),u=kt.maxPool(u,[2,2],[2,2],"same"),u=br(u,l.conv2),u=kt.maxPool(u,[2,2],[2,2],"same"),u=br(u,l.conv3),u=kt.maxPool(u,[2,2],[2,2],"same"),u=br(u,l.conv4),u=kt.maxPool(u,[2,2],[2,2],"same"),u=br(u,l.conv5),u=kt.maxPool(u,[2,2],[1,1],"same"),u=br(u,l.conv6),u=br(u,l.conv7),na(u,l.conv8,"valid",!1)}runMobilenet(r,l){let u=this.config.isFirstLayerConv2d?Gc(na(r,l.conv0,"valid",!1)):wr(r,l.conv0);return u=kt.maxPool(u,[2,2],[2,2],"same"),u=wr(u,l.conv1),u=kt.maxPool(u,[2,2],[2,2],"same"),u=wr(u,l.conv2),u=kt.maxPool(u,[2,2],[2,2],"same"),u=wr(u,l.conv3),u=kt.maxPool(u,[2,2],[2,2],"same"),u=wr(u,l.conv4),u=kt.maxPool(u,[2,2],[2,2],"same"),u=wr(u,l.conv5),u=kt.maxPool(u,[2,2],[1,1],"same"),u=l.conv6?wr(u,l.conv6):u,u=l.conv7?wr(u,l.conv7):u,na(u,l.conv8,"valid",!1)}forwardInput(r,l){const{params:u}=this;if(!u)throw new Error("TinyYolov2 - load model before inference");return kt.tidy(()=>{let p=kt.cast(r.toBatchTensor(l,!1),"float32");return p=this.config.meanRgb?di(p,this.config.meanRgb):p,p=p.div(kt.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(p,u):this.runTinyYolov2(p,u)})}async forward(r,l){return await this.forwardInput(await Rt(r),l)}async detect(r,l={}){const{inputSize:u,scoreThreshold:p}=new Lr(l),g=await Rt(r),f=await this.forwardInput(g,u),I=kt.tidy(()=>kt.unstack(f)[0].expandDims()),S={width:g.getInputWidth(0),height:g.getInputHeight(0)},x=await this.extractBoxes(I,g.getReshapedInputDimensions(0),p);f.dispose(),I.dispose();const v=x.map(te=>te.box),O=x.map(te=>te.score),C=x.map(te=>te.classScore),U=x.map(te=>this.config.classes[te.label]),G=gS(v.map(te=>te.rescale(u)),O,this.config.iouThreshold,!0),ne=G.map(te=>new mc(O[te],C[te],U[te],v[te],S));return ne}getDefaultModelName(){return""}extractParamsFromWeigthMap(r){return KE(r,this.config)}extractParams(r){const l=this.config.filterSizes||Vc.DEFAULT_FILTER_SIZES,u=l?l.length:void 0;if(u!==7&&u!==8&&u!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${u} filterSizes in config`);return jE(r,this.config,this.boxEncodingSize,l)}async extractBoxes(r,l,u){const{width:p,height:g}=l,f=Math.max(p,g),I=f/p,S=f/g,x=r.shape[1],v=this.config.anchors.length,[O,C,U]=kt.tidy(()=>{const oe=r.reshape([x,x,v,this.boxEncodingSize]),ge=oe.slice([0,0,0,0],[x,x,v,4]),fe=oe.slice([0,0,0,4],[x,x,v,1]),Ae=this.withClassScores?kt.softmax(oe.slice([0,0,0,5],[x,x,v,this.config.classes.length]),3):kt.scalar(0);return[ge,fe,Ae]}),G=[],ne=await C.array(),te=await O.array();for(let oe=0;oe<x;oe++)for(let ge=0;ge<x;ge++)for(let fe=0;fe<v;fe++){const Ae=nu(ne[oe][ge][fe][0]);if(!u||Ae>u){const Te=(ge+nu(te[oe][ge][fe][0]))/x*I,Ve=(oe+nu(te[oe][ge][fe][1]))/x*S,rt=Math.exp(te[oe][ge][fe][2])*this.config.anchors[fe].x/x*I,vt=Math.exp(te[oe][ge][fe][3])*this.config.anchors[fe].y/x*S,$t=Te-rt/2,Kt=Ve-vt/2,Dn={row:oe,col:ge,anchor:fe},{classScore:Tn,label:An}=this.withClassScores?await this.extractPredictedClass(U,Dn):{classScore:1,label:0};G.push({box:new tu($t,Kt,$t+rt,Kt+vt),score:Ae,classScore:Ae*Tn,label:An,...Dn})}}return O.dispose(),C.dispose(),U.dispose(),G}async extractPredictedClass(r,l){const{row:u,col:p,anchor:g}=l,f=await r.array();return Array(this.config.classes.length).fill(0).map((I,S)=>f[u][p][g][S]).map((I,S)=>({classScore:I,label:S})).reduce((I,S)=>I.classScore>S.classScore?I:S)}}Vc.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];class Ou extends Vc{constructor(r=!0){const l=Object.assign({},{withSeparableConvs:r,iouThreshold:zE,classes:["face"]},r?{anchors:VE,meanRgb:YE}:{anchors:GE,withClassScores:!0});super(l)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(r,l){const u=await this.detect(r,l);return u.map(p=>new Ht(p.score,p.relativeBox,{width:p.imageWidth,height:p.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?qE:HE}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}function cZ(r,l=!0){const u=new Ou(l);return u.extractWeights(r),u}class YI extends Lr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}}class wi{async then(r){return r(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}}const HI=Ke(Xe());async function oa(r,l,u,p,g=({alignedRect:f})=>f){const f=r.map(x=>ia(x)?g(x):x.detection),I=p||(l instanceof HI.Tensor?await Ec(l,f):await Oc(l,f)),S=await u(I);return I.forEach(x=>x instanceof HI.Tensor&&x.dispose()),S}async function Yc(r,l,u,p,g){return oa([r],l,async f=>u(f[0]),p,g)}const XE=.4,JE=[new Je(1.603231,2.094468),new Je(6.041143,7.080126),new Je(2.882459,3.518061),new Je(4.266906,5.178857),new Je(9.041765,10.66308)],ZE=[117.001,114.697,97.404];class Eu extends Vc{constructor(){const r={withSeparableConvs:!0,iouThreshold:XE,classes:["face"],anchors:JE,meanRgb:ZE,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(r)}get anchors(){return this.config.anchors}async locateFaces(r,l){const u=await this.detect(r,l);return u.map(p=>new Ht(p.score,p.relativeBox,{width:p.imageWidth,height:p.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}const pt={ssdMobilenetv1:new zc,tinyFaceDetector:new Eu,tinyYolov2:new Ou,faceLandmark68Net:new Au,faceLandmark68TinyNet:new BI,faceRecognitionNet:new Nu,faceExpressionNet:new FI,ageGenderNet:new UI},QE=(r,l)=>pt.ssdMobilenetv1.locateFaces(r,l),lZ=(r,l)=>pt.tinyFaceDetector.locateFaces(r,l),hZ=(r,l)=>pt.tinyYolov2.locateFaces(r,l),eD=r=>pt.faceLandmark68Net.detectLandmarks(r),uZ=r=>pt.faceLandmark68TinyNet.detectLandmarks(r),dZ=r=>pt.faceRecognitionNet.computeFaceDescriptor(r),pZ=r=>pt.faceExpressionNet.predictExpressions(r),mZ=r=>pt.ageGenderNet.predictAgeAndGender(r),tD=r=>pt.ssdMobilenetv1.load(r),fZ=r=>pt.tinyFaceDetector.load(r),gZ=r=>pt.tinyYolov2.load(r),yZ=r=>pt.faceLandmark68Net.load(r),bZ=r=>pt.faceLandmark68TinyNet.load(r),wZ=r=>pt.faceRecognitionNet.load(r),LZ=r=>pt.faceExpressionNet.load(r),SZ=r=>pt.ageGenderNet.load(r),IZ=tD,xZ=QE,TZ=eD;class nD extends wi{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.extractedFaces=u}}class Fu extends nD{async run(){const r=await this.parentTask,l=await oa(r,this.input,async u=>await Promise.all(u.map(p=>pt.faceExpressionNet.predictExpressions(p))),this.extractedFaces);return r.map((u,p)=>Jf(u,l[p]))}withAgeAndGender(){return new Du(this,this.input)}}class _u extends nD{async run(){const r=await this.parentTask;if(!r)return;const l=await Yc(r,this.input,u=>pt.faceExpressionNet.predictExpressions(u),this.extractedFaces);return Jf(r,l)}withAgeAndGender(){return new ku(this,this.input)}}class jc extends Fu{withAgeAndGender(){return new Hc(this,this.input)}withFaceDescriptors(){return new aa(this,this.input)}}class Kc extends _u{withAgeAndGender(){return new qc(this,this.input)}withFaceDescriptor(){return new ca(this,this.input)}}class sD extends wi{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.extractedFaces=u}}class Du extends sD{async run(){const r=await this.parentTask,l=await oa(r,this.input,async u=>await Promise.all(u.map(p=>pt.ageGenderNet.predictAgeAndGender(p))),this.extractedFaces);return r.map((u,p)=>{const{age:g,gender:f,genderProbability:I}=l[p];return ng(sg(u,f,I),g)})}withFaceExpressions(){return new Fu(this,this.input)}}class ku extends sD{async run(){const r=await this.parentTask;if(!r)return;const{age:l,gender:u,genderProbability:p}=await Yc(r,this.input,g=>pt.ageGenderNet.predictAgeAndGender(g),this.extractedFaces);return ng(sg(r,u,p),l)}withFaceExpressions(){return new _u(this,this.input)}}class Hc extends Du{withFaceExpressions(){return new jc(this,this.input)}withFaceDescriptors(){return new aa(this,this.input)}}class qc extends ku{withFaceExpressions(){return new Kc(this,this.input)}withFaceDescriptor(){return new ca(this,this.input)}}class qI extends wi{constructor(r,l){super();this.parentTask=r;this.input=l}}class aa extends qI{async run(){const r=await this.parentTask,l=await oa(r,this.input,u=>Promise.all(u.map(p=>pt.faceRecognitionNet.computeFaceDescriptor(p))),null,u=>u.landmarks.align(null,{useDlibAlignment:!0}));return l.map((u,p)=>tg(r[p],u))}withFaceExpressions(){return new jc(this,this.input)}withAgeAndGender(){return new Hc(this,this.input)}}class ca extends qI{async run(){const r=await this.parentTask;if(!r)return;const l=await Yc(r,this.input,u=>pt.faceRecognitionNet.computeFaceDescriptor(u),null,u=>u.landmarks.align(null,{useDlibAlignment:!0}));return tg(r,l)}withFaceExpressions(){return new Kc(this,this.input)}withAgeAndGender(){return new qc(this,this.input)}}const Wu=Ke(Xe());class jI extends wi{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.useTinyLandmarkNet=u}get landmarkNet(){return this.useTinyLandmarkNet?pt.faceLandmark68TinyNet:pt.faceLandmark68Net}}class KI extends jI{async run(){const r=await this.parentTask,l=r.map(g=>g.detection),u=this.input instanceof Wu.Tensor?await Ec(this.input,l):await Oc(this.input,l),p=await Promise.all(u.map(g=>this.landmarkNet.detectLandmarks(g)));return u.forEach(g=>g instanceof Wu.Tensor&&g.dispose()),r.map((g,f)=>Bc(g,p[f]))}withFaceExpressions(){return new jc(this,this.input)}withAgeAndGender(){return new Hc(this,this.input)}withFaceDescriptors(){return new aa(this,this.input)}}class XI extends jI{async run(){const r=await this.parentTask;if(!r)return;const{detection:l}=r,u=this.input instanceof Wu.Tensor?await Ec(this.input,[l]):await Oc(this.input,[l]),p=await this.landmarkNet.detectLandmarks(u[0]);return u.forEach(g=>g instanceof Wu.Tensor&&g.dispose()),Bc(r,p)}withFaceExpressions(){return new Kc(this,this.input)}withAgeAndGender(){return new qc(this,this.input)}withFaceDescriptor(){return new ca(this,this.input)}}class JI extends wi{constructor(r,l=new yi){super();this.input=r;this.options=l}}class rg extends JI{async run(){const{input:r,options:l}=this,u=l instanceof YI?p=>pt.tinyFaceDetector.locateFaces(p,l):l instanceof yi?p=>pt.ssdMobilenetv1.locateFaces(p,l):l instanceof Lr?p=>pt.tinyYolov2.locateFaces(p,l):null;if(!u)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return u(r)}runAndExtendWithFaceDetections(){return new Promise(async r=>{const l=await this.run();return r(l.map(u=>Go({},u)))})}withFaceLandmarks(r=!1){return new KI(this.runAndExtendWithFaceDetections(),this.input,r)}withFaceExpressions(){return new Fu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Du(this.runAndExtendWithFaceDetections(),this.input)}}class ZI extends JI{async run(){const r=await new rg(this.input,this.options);let l=r[0];return r.forEach(u=>{u.score>l.score&&(l=u)}),l}runAndExtendWithFaceDetection(){return new Promise(async r=>{const l=await this.run();return r(l?Go({},l):void 0)})}withFaceLandmarks(r=!1){return new XI(this.runAndExtendWithFaceDetection(),this.input,r)}withFaceExpressions(){return new _u(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ku(this.runAndExtendWithFaceDetection(),this.input)}}function AZ(r,l=new yi){return new ZI(r,l)}function og(r,l=new yi){return new rg(r,l)}async function iD(r,l){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await og(r,new yi(l?{minConfidence:l}:{})).withFaceLandmarks().withFaceDescriptors()}async function vZ(r,l={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await og(r,new Lr(l)).withFaceLandmarks().withFaceDescriptors()}const NZ=iD;function QI(r,l){if(r.length!==l.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");const u=Array.from(r),p=Array.from(l);return Math.sqrt(u.map((g,f)=>g-p[f]).reduce((g,f)=>g+Math.pow(f,2),0))}class rD{constructor(r,l=.6){this._distanceThreshold=l;const u=Array.isArray(r)?r:[r];if(!u.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let p=1;const g=()=>`person ${p++}`;this._labeledDescriptors=u.map(f=>{if(f instanceof zo)return f;if(f instanceof Float32Array)return new zo(g(),[f]);if(f.descriptor&&f.descriptor instanceof Float32Array)return new zo(g(),[f.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(r,l){return l.map(u=>QI(u,r)).reduce((u,p)=>u+p,0)/(l.length||1)}matchDescriptor(r){return this.labeledDescriptors.map(({descriptors:l,label:u})=>new mm(u,this.computeMeanDistance(r,l))).reduce((l,u)=>l.distance<u.distance?l:u)}findBestMatch(r){const l=this.matchDescriptor(r);return l.distance<this.distanceThreshold?l:new mm("unknown",l.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(r=>r.toJSON())}}static fromJSON(r){const l=r.labeledDescriptors.map(u=>zo.fromJSON(u));return new rD(l,r.distanceThreshold)}}function CZ(r){const l=new Eu;return l.extractWeights(r),l}function oD(r,l){const{width:u,height:p}=new us(l.width,l.height);if(u<=0||p<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:u,height:p})}`);if(Array.isArray(r))return r.map(g=>oD(g,{width:u,height:p}));if(ia(r)){const g=r.detection.forSize(u,p),f=r.unshiftedLandmarks.forSize(g.box.width,g.box.height);return Bc(Go(r,g),f)}return Ui(r)?Go(r,r.detection.forSize(u,p)):r instanceof Gs||r instanceof Ht?r.forSize(u,p):r}var aD="0.7.4";const RZ=Ke(Xe()),OZ=typeof process!="undefined",EZ=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",DZ={faceapi:aD,node:OZ,browser:EZ};export{UI as AgeGenderNet,tu as BoundingBox,Ct as Box,wi as ComposableTask,aa as ComputeAllFaceDescriptorsTask,qI as ComputeFaceDescriptorsTaskBase,ca as ComputeSingleFaceDescriptorTask,KI as DetectAllFaceLandmarksTask,rg as DetectAllFacesTask,jI as DetectFaceLandmarksTaskBase,JI as DetectFacesTaskBase,XI as DetectSingleFaceLandmarksTask,ZI as DetectSingleFaceTask,us as Dimensions,kI as FACE_EXPRESSION_LABELS,Ht as FaceDetection,rZ as FaceDetectionNet,FI as FaceExpressionNet,sa as FaceExpressions,Au as FaceLandmark68Net,BI as FaceLandmark68TinyNet,V9 as FaceLandmarkNet,Gs as FaceLandmarks,YX as FaceLandmarks5,iu as FaceLandmarks68,mm as FaceMatch,rD as FaceMatcher,Nu as FaceRecognitionNet,gr as Gender,fm as LabeledBox,zo as LabeledFaceDescriptors,to as NetInput,En as NeuralNetwork,mc as ObjectDetection,Je as Point,HX as PredictedBox,su as Rect,zc as SsdMobilenetv1,yi as SsdMobilenetv1Options,Eu as TinyFaceDetector,YI as TinyFaceDetectorOptions,Ou as TinyYolov2,Lr as TinyYolov2Options,VI as TinyYolov2SizeType,NZ as allFaces,iD as allFacesSsdMobilenetv1,vZ as allFacesTinyYolov2,AS as awaitMediaLoaded,vS as bufferToImage,dZ as computeFaceDescriptor,gc as createCanvas,ou as createCanvasFromMedia,iZ as createFaceDetectionNet,q9 as createFaceRecognitionNet,PE as createSsdMobilenetv1,CZ as createTinyFaceDetector,cZ as createTinyYolov2,og as detectAllFaces,eD as detectFaceLandmarks,uZ as detectFaceLandmarksTiny,TZ as detectLandmarks,AZ as detectSingleFace,WI as draw,gt as env,QI as euclideanDistance,ng as extendWithAge,tg as extendWithFaceDescriptor,Go as extendWithFaceDetection,Jf as extendWithFaceExpressions,Bc as extendWithFaceLandmarks,sg as extendWithGender,Ec as extractFaceTensors,Oc as extractFaces,W9 as fetchImage,OI as fetchJson,$9 as fetchNetWeights,ta as fetchOrThrow,Jn as getContext2dOrThrow,Yo as getMediaDimensions,NS as imageTensorToCanvas,RI as imageToSquare,PX as inverseSigmoid,mS as iou,wm as isMediaElement,ru as isMediaLoaded,j9 as isWithAge,Ui as isWithFaceDetection,_I as isWithFaceExpressions,ia as isWithFaceLandmarks,K9 as isWithGender,SZ as loadAgeGenderModel,IZ as loadFaceDetectionModel,LZ as loadFaceExpressionModel,yZ as loadFaceLandmarkModel,bZ as loadFaceLandmarkTinyModel,wZ as loadFaceRecognitionModel,tD as loadSsdMobilenetv1Model,fZ as loadTinyFaceDetectorModel,gZ as loadTinyYolov2Model,EI as loadWeightMap,xZ as locateFaces,U9 as matchDimensions,fS as minBbox,pt as nets,gS as nonMaxSuppression,di as normalize,yS as padToSquare,mZ as predictAgeAndGender,pZ as recognizeFaceExpressions,oD as resizeResults,Vo as resolveInput,MX as shuffleArray,nu as sigmoid,QE as ssdMobilenetv1,RZ as tf,lZ as tinyFaceDetector,hZ as tinyYolov2,Rt as toNetInput,hS as utils,zI as validateConfig,DZ as version};
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Error(`param tensor has already been disposed for path ${l.path}`);l.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:r})=>Array.from(r.dataSync())).reduce((r,l)=>r.concat(l)))}async load(r){if(r instanceof Float32Array){this.extractWeights(r);return}await this.loadFromUri(r)}async loadFromUri(r){if(r&&typeof r!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);const l=await EI(r,this.getDefaultModelName());this.loadFromWeightMap(l)}async loadFromDisk(r){if(r&&typeof r!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);const{readFile:l}=gt.getEnv(),{manifestUri:u,modelBaseUri:p}=Mf(r,this.getDefaultModelName()),g=x=>Promise.all(x.map(v=>l(v).then(O=>O.buffer))),f=fr.io.weightsLoaderFactory(g),I=JSON.parse((await l(u)).toString()),S=await f(I,p);this.loadFromWeightMap(S)}loadFromWeightMap(r){const{paramMappings:l,params:u}=this.extractParamsFromWeigthMap(r);this._paramMappings=l,this._params=u}extractWeights(r){const{paramMappings:l,params:u}=this.extractParams(r);this._paramMappings=l,this._params=u}traversePropertyPath(r){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");const l=r.split("/").reduce((g,f)=>{if(!g.nextObj.hasOwnProperty(f))throw new Error(`traversePropertyPath - object does not have property ${f}, for path ${r}`);return{obj:g.nextObj,objProp:f,nextObj:g.nextObj[f]}},{nextObj:this.params}),{obj:u,objProp:p}=l;if(!u||!p||!(u[p]instanceof fr.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${r}`);return{obj:u,objProp:p}}}const Fc=Ke(Xe());function Qn(r,l,u){return Fc.tidy(()=>{let p=Fc.separableConv2d(r,l.depthwise_filter,l.pointwise_filter,u,"same");return p=Fc.add(p,l.bias),p})}const Dt=Ke(Xe());function Pf(r,l,u=!1){return Dt.tidy(()=>{const p=Dt.relu(u?Dt.add(Dt.conv2d(r,l.conv0.filters,[2,2],"same"),l.conv0.bias):Qn(r,l.conv0,[2,2])),g=Qn(p,l.conv1,[1,1]),f=Dt.relu(Dt.add(p,g)),I=Qn(f,l.conv2,[1,1]);return Dt.relu(Dt.add(p,Dt.add(g,I)))})}function Iu(r,l,u=!1,p=!0){return Dt.tidy(()=>{const g=Dt.relu(u?Dt.add(Dt.conv2d(r,l.conv0.filters,p?[2,2]:[1,1],"same"),l.conv0.bias):Qn(r,l.conv0,p?[2,2]:[1,1])),f=Qn(g,l.conv1,[1,1]),I=Dt.relu(Dt.add(g,f)),S=Qn(I,l.conv2,[1,1]),x=Dt.relu(Dt.add(g,Dt.add(f,S))),v=Qn(x,l.conv3,[1,1]);return Dt.relu(Dt.add(g,Dt.add(f,Dt.add(S,v))))})}const no=Ke(Xe());function ia(r,l,u="same",p=!1){return no.tidy(()=>{const g=no.add(no.conv2d(r,l.filters,[1,1],u),l.bias);return p?no.relu(g):g})}function Pn(r,l){Object.keys(r).forEach(u=>{l.some(p=>p.originalPath===u)||r[u].dispose()})}const zf=Ke(Xe());function _c(r,l){return function(u,p,g,f){const I=zf.tensor4d(r(u*p*g*g),[g,g,u,p]),S=zf.tensor1d(r(p));return l.push({paramPath:`${f}/filters`},{paramPath:`${f}/bias`}),{filters:I,bias:S}}}const Gf=Ke(Xe());function Vf(r,l){return function(u,p,g){const f=Gf.tensor2d(r(u*p),[u,p]),I=Gf.tensor1d(r(p));return l.push({paramPath:`${g}/weights`},{paramPath:`${g}/bias`}),{weights:f,bias:I}}}class DI{constructor(r,l,u){this.depthwise_filter=r;this.pointwise_filter=l;this.bias=u}}const xu=Ke(Xe());function Wc(r,l){return function(u,p,g){const f=xu.tensor4d(r(3*3*u),[3,3,u,1]),I=xu.tensor4d(r(u*p),[1,1,u,p]),S=xu.tensor1d(r(p));return l.push({paramPath:`${g}/depthwise_filter`},{paramPath:`${g}/pointwise_filter`},{paramPath:`${g}/bias`}),new DI(f,I,S)}}function $c(r){return function(l){const u=r(`${l}/depthwise_filter`,4),p=r(`${l}/pointwise_filter`,4),g=r(`${l}/bias`,1);return new DI(u,p,g)}}function ms(r,l){return function(u,p,g){const f=r[u];if(!Po(f,p))throw new Error(`expected weightMap[${u}] to be a Tensor${p}D, instead have ${f}`);return l.push({originalPath:u,paramPath:g||u}),f}}function zn(r){let l=r;function u(g){const f=l.slice(0,g);return l=l.slice(g),f}function p(){return l}return{extractWeights:u,getRemainingWeights:p}}function Yf(r,l){const u=_c(r,l),p=Wc(r,l);function g(I,S,x,v=!1){const O=v?u(I,S,3,`${x}/conv0`):p(I,S,`${x}/conv0`),C=p(S,S,`${x}/conv1`),U=p(S,S,`${x}/conv2`);return{conv0:O,conv1:C,conv2:U}}function f(I,S,x,v=!1){const{conv0:O,conv1:C,conv2:U}=g(I,S,x,v),G=p(S,S,`${x}/conv3`);return{conv0:O,conv1:C,conv2:U,conv3:G}}return{extractDenseBlock3Params:g,extractDenseBlock4Params:f}}function fE(r){const l=[],{extractWeights:u,getRemainingWeights:p}=zn(r),{extractDenseBlock4Params:g}=Yf(u,l),f=g(3,32,"dense0",!0),I=g(32,64,"dense1"),S=g(64,128,"dense2"),x=g(128,256,"dense3");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{dense0:f,dense1:I,dense2:S,dense3:x}}}function Hf(r){return function(l){const u=r(`${l}/filters`,4),p=r(`${l}/bias`,1);return{filters:u,bias:p}}}function qf(r,l){const u=ms(r,l),p=Hf(u),g=$c(u);function f(S,x=!1){const v=x?p(`${S}/conv0`):g(`${S}/conv0`),O=g(`${S}/conv1`),C=g(`${S}/conv2`);return{conv0:v,conv1:O,conv2:C}}function I(S,x=!1){const v=x?p(`${S}/conv0`):g(`${S}/conv0`),O=g(`${S}/conv1`),C=g(`${S}/conv2`),U=g(`${S}/conv3`);return{conv0:v,conv1:O,conv2:C,conv3:U}}return{extractDenseBlock3Params:f,extractDenseBlock4Params:I}}function gE(r){const l=[],{extractDenseBlock4Params:u}=qf(r,l),p={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2"),dense3:u("dense3")};return Pn(r,l),{params:p,paramMappings:l}}const so=Ke(Xe());class jf extends En{constructor(){super("FaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceFeatureExtractor - load model before inference");return so.tidy(()=>{const u=so.cast(r.toBatchTensor(112,!0),"float32"),p=[122.782,117.001,104.298],g=di(u,p).div(so.scalar(255));let f=Iu(g,l.dense0,!0);return f=Iu(f,l.dense1),f=Iu(f,l.dense2),f=Iu(f,l.dense3),f=so.avgPool(f,[7,7],[2,2],"valid"),f})}async forward(r){return this.forwardInput(await Rt(r))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(r){return gE(r)}extractParams(r){return fE(r)}}const Uc=Ke(Xe());function Tu(r,l){return Uc.tidy(()=>Uc.add(Uc.matMul(r,l.weights),l.bias))}function yE(r,l,u){const p=[],{extractWeights:g,getRemainingWeights:f}=zn(r),I=Vf(g,p),S=I(l,u,"fc");if(f().length!==0)throw new Error(`weights remaing after extract: ${f().length}`);return{paramMappings:p,params:{fc:S}}}function bE(r){const l=[],u=ms(r,l);function p(f){const I=u(`${f}/weights`,2),S=u(`${f}/bias`,1);return{weights:I,bias:S}}const g={fc:p("fc")};return Pn(r,l),{params:g,paramMappings:l}}function Kf(r){const l={},u={};return Object.keys(r).forEach(p=>{const g=p.startsWith("fc")?u:l;g[p]=r[p]}),{featureExtractorMap:l,classifierMap:u}}const wE=Ke(Xe());class Xf extends En{constructor(r,l){super(r);this._faceFeatureExtractor=l}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return wE.tidy(()=>{const u=r instanceof to?this.faceFeatureExtractor.forwardInput(r):r;return Tu(u.as2D(u.shape[0],-1),l.fc)})}dispose(r=!0){this.faceFeatureExtractor.dispose(r),super.dispose(r)}loadClassifierParams(r){const{params:l,paramMappings:u}=this.extractClassifierParams(r);this._params=l,this._paramMappings=u}extractClassifierParams(r){return yE(r,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:u}=Kf(r);return this.faceFeatureExtractor.loadFromWeightMap(l),bE(u)}extractParams(r){const l=this.getClassifierChannelsIn(),u=this.getClassifierChannelsOut(),p=u*l+u,g=r.slice(0,r.length-p),f=r.slice(r.length-p);return this.faceFeatureExtractor.extractWeights(g),this.extractClassifierParams(f)}}const kI=["neutral","happy","sad","angry","fearful","disgusted","surprised"];class ra{constructor(r){if(r.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${r.length}`);kI.forEach((l,u)=>{this[l]=r[u]})}asSortedArray(){return kI.map(r=>({expression:r,probability:this[r]})).sort((r,l)=>l.probability-r.probability)}}const Bc=Ke(Xe());class FI extends Xf{constructor(r=new jf){super("FaceExpressionNet",r)}forwardInput(r){return Bc.tidy(()=>Bc.softmax(this.runNet(r)))}async forward(r){return this.forwardInput(await Rt(r))}async predictExpressions(r){const l=await Rt(r),u=await this.forwardInput(l),p=await Promise.all(Bc.unstack(u).map(async f=>{const I=await f.data();return f.dispose(),I}));u.dispose();const g=p.map(f=>new ra(f));return l.isBatchInput?g:g[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}}function _I(r){return r.expressions instanceof ra}function Jf(r,l){const u={expressions:l};return Object.assign({},r,u)}function B9(r,l,u=.1,p){const g=Array.isArray(l)?l:[l];g.forEach(f=>{const I=f instanceof ra?f:_I(f)?f.expressions:void 0;if(!I)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");const S=I.asSortedArray(),x=S.filter(C=>C.probability>u),v=Ui(f)?f.detection.box.bottomLeft:p||new Je(0,0),O=new yc(x.map(C=>`${C.expression} (${zo(C.probability)})`),v);O.draw(r)})}function oa(r){return Ui(r)&&r.landmarks instanceof Gs&&r.unshiftedLandmarks instanceof Gs&&r.alignedRect instanceof Ht}function Mc(r,l){const{box:u}=r.detection,p=l.shiftBy(u.x,u.y),g=p.align(),{imageDims:f}=r.detection,I=new Ht(r.detection.score,g.rescale(f.reverse()),f),S={landmarks:p,unshiftedLandmarks:l,alignedRect:I};return Object.assign({},r,S)}class LE{constructor(r={}){const{drawLines:l=!0,drawPoints:u=!0,lineWidth:p,lineColor:g,pointSize:f,pointColor:I}=r;this.drawLines=l,this.drawPoints=u,this.lineWidth=p||1,this.pointSize=f||2,this.lineColor=g||"rgba(0, 255, 255, 1)",this.pointColor=I||"rgba(255, 0, 255, 1)"}}class SE{constructor(r,l={}){this.faceLandmarks=r,this.options=new LE(l)}draw(r){const l=Jn(r),{drawLines:u,drawPoints:p,lineWidth:g,lineColor:f,pointSize:I,pointColor:S}=this.options;if(u&&this.faceLandmarks instanceof iu&&(l.strokeStyle=f,l.lineWidth=g,cr(l,this.faceLandmarks.getJawOutline()),cr(l,this.faceLandmarks.getLeftEyeBrow()),cr(l,this.faceLandmarks.getRightEyeBrow()),cr(l,this.faceLandmarks.getNose()),cr(l,this.faceLandmarks.getLeftEye(),!0),cr(l,this.faceLandmarks.getRightEye(),!0),cr(l,this.faceLandmarks.getMouth(),!0)),p){l.strokeStyle=S,l.fillStyle=S;const x=v=>{l.beginPath(),l.arc(v.x,v.y,I,0,2*Math.PI),l.fill()};this.faceLandmarks.positions.forEach(x)}}}function M9(r,l){const u=Array.isArray(l)?l:[l];u.forEach(p=>{const g=p instanceof Gs?p:oa(p)?p.landmarks:void 0;if(!g)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new SE(g).draw(r)})}const WI={};hm(WI,{AnchorPosition:()=>Bi,DrawBox:()=>TS,DrawBoxOptions:()=>NC,DrawFaceLandmarks:()=>SE,DrawFaceLandmarksOptions:()=>LE,DrawTextField:()=>yc,DrawTextFieldOptions:()=>ym,drawContour:()=>cr,drawDetections:()=>KX,drawFaceExpressions:()=>B9,drawFaceLandmarks:()=>M9});function P9(r,l){const u=_c(r,l),p=Wc(r,l);function g(I,S,x){const v=p(I,S,`${x}/separable_conv0`),O=p(S,S,`${x}/separable_conv1`),C=u(I,S,1,`${x}/expansion_conv`);return{separable_conv0:v,separable_conv1:O,expansion_conv:C}}function f(I,S){const x=p(I,I,`${S}/separable_conv0`),v=p(I,I,`${S}/separable_conv1`),O=p(I,I,`${S}/separable_conv2`);return{separable_conv0:x,separable_conv1:v,separable_conv2:O}}return{extractConvParams:u,extractSeparableConvParams:p,extractReductionBlockParams:g,extractMainBlockParams:f}}function IE(r,l){const u=[],{extractWeights:p,getRemainingWeights:g}=zn(r),{extractConvParams:f,extractSeparableConvParams:I,extractReductionBlockParams:S,extractMainBlockParams:x}=P9(p,u),v=f(3,32,3,"entry_flow/conv_in"),O=S(32,64,"entry_flow/reduction_block_0"),C=S(64,128,"entry_flow/reduction_block_1"),U={conv_in:v,reduction_block_0:O,reduction_block_1:C},G={};Wi(l,0,1).forEach(ge=>{G[`main_block_${ge}`]=x(128,`middle_flow/main_block_${ge}`)});const ne=S(128,256,"exit_flow/reduction_block"),te=I(256,512,"exit_flow/separable_conv"),oe={reduction_block:ne,separable_conv:te};if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{paramMappings:u,params:{entry_flow:U,middle_flow:G,exit_flow:oe}}}function z9(r,l){const u=ms(r,l),p=Hf(u),g=$c(u);function f(S){const x=g(`${S}/separable_conv0`),v=g(`${S}/separable_conv1`),O=p(`${S}/expansion_conv`);return{separable_conv0:x,separable_conv1:v,expansion_conv:O}}function I(S){const x=g(`${S}/separable_conv0`),v=g(`${S}/separable_conv1`),O=g(`${S}/separable_conv2`);return{separable_conv0:x,separable_conv1:v,separable_conv2:O}}return{extractConvParams:p,extractSeparableConvParams:g,extractReductionBlockParams:f,extractMainBlockParams:I}}function xE(r,l){const u=[],{extractConvParams:p,extractSeparableConvParams:g,extractReductionBlockParams:f,extractMainBlockParams:I}=z9(r,u),S=p("entry_flow/conv_in"),x=f("entry_flow/reduction_block_0"),v=f("entry_flow/reduction_block_1"),O={conv_in:S,reduction_block_0:x,reduction_block_1:v},C={};Wi(l,0,1).forEach(te=>{C[`main_block_${te}`]=I(`middle_flow/main_block_${te}`)});const U=f("exit_flow/reduction_block"),G=g("exit_flow/separable_conv"),ne={reduction_block:U,separable_conv:G};return Pn(r,u),{params:{entry_flow:O,middle_flow:C,exit_flow:ne},paramMappings:u}}const tn=Ke(Xe());function TE(r,l,u){return tn.add(tn.conv2d(r,l.filters,u,"same"),l.bias)}function $I(r,l,u=!0){let p=u?tn.relu(r):r;return p=Qn(p,l.separable_conv0,[1,1]),p=Qn(tn.relu(p),l.separable_conv1,[1,1]),p=tn.maxPool(p,[3,3],[2,2],"same"),p=tn.add(p,TE(r,l.expansion_conv,[2,2])),p}function G9(r,l){let u=Qn(tn.relu(r),l.separable_conv0,[1,1]);return u=Qn(tn.relu(u),l.separable_conv1,[1,1]),u=Qn(tn.relu(u),l.separable_conv2,[1,1]),u=tn.add(u,r),u}class AE extends En{constructor(r){super("TinyXception");this._numMainBlocks=r}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyXception - load model before inference");return tn.tidy(()=>{const u=tn.cast(r.toBatchTensor(112,!0),"float32"),p=[122.782,117.001,104.298],g=di(u,p).div(tn.scalar(256));let f=tn.relu(TE(g,l.entry_flow.conv_in,[2,2]));return f=$I(f,l.entry_flow.reduction_block_0,!1),f=$I(f,l.entry_flow.reduction_block_1),Wi(this._numMainBlocks,0,1).forEach(I=>{f=G9(f,l.middle_flow[`main_block_${I}`])}),f=$I(f,l.exit_flow.reduction_block),f=tn.relu(Qn(f,l.exit_flow.separable_conv,[1,1])),f})}async forward(r){return this.forwardInput(await Rt(r))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(r){return xE(r,this._numMainBlocks)}extractParams(r){return IE(r,this._numMainBlocks)}}function vE(r){const l=[],{extractWeights:u,getRemainingWeights:p}=zn(r),g=Vf(u,l),f=g(512,1,"fc/age"),I=g(512,2,"fc/gender");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{fc:{age:f,gender:I}}}}function NE(r){const l=[],u=ms(r,l);function p(f){const I=u(`${f}/weights`,2),S=u(`${f}/bias`,1);return{weights:I,bias:S}}const g={fc:{age:p("fc/age"),gender:p("fc/gender")}};return Pn(r,l),{params:g,paramMappings:l}}var gr;(function(r){r.FEMALE="female",r.MALE="male"})(gr||(gr={}));const Vi=Ke(Xe());class UI extends En{constructor(r=new AE(2)){super("AgeGenderNet");this._faceFeatureExtractor=r}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return Vi.tidy(()=>{const u=r instanceof to?this.faceFeatureExtractor.forwardInput(r):r,p=Vi.avgPool(u,[7,7],[2,2],"valid").as2D(u.shape[0],-1),g=Tu(p,l.fc.age).as1D(),f=Tu(p,l.fc.gender);return{age:g,gender:f}})}forwardInput(r){return Vi.tidy(()=>{const{age:l,gender:u}=this.runNet(r);return{age:l,gender:Vi.softmax(u)}})}async forward(r){return this.forwardInput(await Rt(r))}async predictAgeAndGender(r){const l=await Rt(r),u=await this.forwardInput(l),p=Vi.unstack(u.age),g=Vi.unstack(u.gender),f=p.map((S,x)=>({ageTensor:S,genderTensor:g[x]})),I=await Promise.all(f.map(async({ageTensor:S,genderTensor:x})=>{const v=(await S.data())[0],O=(await x.data())[0],C=O>.5,U=C?gr.MALE:gr.FEMALE,G=C?O:1-O;return S.dispose(),x.dispose(),{age:v,gender:U,genderProbability:G}}));return u.age.dispose(),u.gender.dispose(),l.isBatchInput?I:I[0]}getDefaultModelName(){return"age_gender_model"}dispose(r=!0){this.faceFeatureExtractor.dispose(r),super.dispose(r)}loadClassifierParams(r){const{params:l,paramMappings:u}=this.extractClassifierParams(r);this._params=l,this._paramMappings=u}extractClassifierParams(r){return vE(r)}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:u}=Kf(r);return this.faceFeatureExtractor.loadFromWeightMap(l),NE(u)}extractParams(r){const l=512*1+1+(512*2+2),u=r.slice(0,r.length-l),p=r.slice(r.length-l);return this.faceFeatureExtractor.extractWeights(u),this.extractClassifierParams(p)}}const fs=Ke(Xe());class Zf extends Xf{postProcess(r,l,u){const p=u.map(({width:f,height:I})=>{const S=l/Math.max(I,f);return{width:f*S,height:I*S}}),g=p.length;return fs.tidy(()=>{const f=(O,C)=>fs.stack([fs.fill([68],O,"float32"),fs.fill([68],C,"float32")],1).as2D(1,136).as1D(),I=(O,C)=>{const{width:U,height:G}=p[O];return C(U,G)?Math.abs(U-G)/2:0},S=O=>I(O,(C,U)=>C<U),x=O=>I(O,(C,U)=>U<C),v=r.mul(fs.fill([g,136],l,"float32")).sub(fs.stack(Array.from(Array(g),(O,C)=>f(S(C),x(C))))).div(fs.stack(Array.from(Array(g),(O,C)=>f(p[C].width,p[C].height))));return v})}forwardInput(r){return fs.tidy(()=>{const l=this.runNet(r);return this.postProcess(l,r.inputSize,r.inputDimensions.map(([u,p])=>({height:u,width:p})))})}async forward(r){return this.forwardInput(await Rt(r))}async detectLandmarks(r){const l=await Rt(r),u=fs.tidy(()=>fs.unstack(this.forwardInput(l))),p=await Promise.all(u.map(async(g,f)=>{const I=Array.from(await g.data()),S=I.filter((v,O)=>dm(O)),x=I.filter((v,O)=>!dm(O));return new iu(Array(68).fill(0).map((v,O)=>new Je(S[O],x[O])),{height:l.getInputHeight(f),width:l.getInputWidth(f)})}));return u.forEach(g=>g.dispose()),l.isBatchInput?p:p[0]}getClassifierChannelsOut(){return 136}}class Au extends Zf{constructor(r=new jf){super("FaceLandmark68Net",r)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}}function CE(r){const l=[],{extractDenseBlock3Params:u}=qf(r,l),p={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2")};return Pn(r,l),{params:p,paramMappings:l}}function RE(r){const l=[],{extractWeights:u,getRemainingWeights:p}=zn(r),{extractDenseBlock3Params:g}=Yf(u,l),f=g(3,32,"dense0",!0),I=g(32,64,"dense1"),S=g(64,128,"dense2");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{dense0:f,dense1:I,dense2:S}}}const io=Ke(Xe());class OE extends En{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyFaceFeatureExtractor - load model before inference");return io.tidy(()=>{const u=io.cast(r.toBatchTensor(112,!0),"float32"),p=[122.782,117.001,104.298],g=di(u,p).div(io.scalar(255));let f=Pf(g,l.dense0,!0);return f=Pf(f,l.dense1),f=Pf(f,l.dense2),f=io.avgPool(f,[14,14],[2,2],"valid"),f})}async forward(r){return this.forwardInput(await Rt(r))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(r){return CE(r)}extractParams(r){return RE(r)}}class BI extends Zf{constructor(r=new OE){super("FaceLandmark68TinyNet",r)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}}class V9 extends Au{}const Qf=Ke(Xe());function EE(r,l){return Qf.add(Qf.mul(r,l.weights),l.biases)}const Pc=Ke(Xe());function MI(r,l,u,p,g="same"){const{filters:f,bias:I}=l.conv;let S=Pc.conv2d(r,f,u,g);return S=Pc.add(S,I),S=EE(S,l.scale),p?Pc.relu(S):S}function DE(r,l){return MI(r,l,[1,1],!0)}function PI(r,l){return MI(r,l,[1,1],!1)}function eg(r,l){return MI(r,l,[2,2],!0,"valid")}const gs=Ke(Xe());function Y9(r,l){function u(S,x,v){const O=r(S),C=O.length/(x*v*v);if(dS(C))throw new Error(`depth has to be an integer: ${C}, weights.length: ${O.length}, numFilters: ${x}, filterSize: ${v}`);return gs.tidy(()=>gs.transpose(gs.tensor4d(O,[x,C,v,v]),[2,3,1,0]))}function p(S,x,v,O){const C=u(S,x,v),U=gs.tensor1d(r(x));return l.push({paramPath:`${O}/filters`},{paramPath:`${O}/bias`}),{filters:C,bias:U}}function g(S,x){const v=gs.tensor1d(r(S)),O=gs.tensor1d(r(S));return l.push({paramPath:`${x}/weights`},{paramPath:`${x}/biases`}),{weights:v,biases:O}}function f(S,x,v,O){const C=p(S,x,v,`${O}/conv`),U=g(x,`${O}/scale`);return{conv:C,scale:U}}function I(S,x,v,O,C=!1){const U=f((C?.5:1)*S,x,v,`${O}/conv1`),G=f(S,x,v,`${O}/conv2`);return{conv1:U,conv2:G}}return{extractConvLayerParams:f,extractResidualLayerParams:I}}function kE(r){const{extractWeights:l,getRemainingWeights:u}=zn(r),p=[],{extractConvLayerParams:g,extractResidualLayerParams:f}=Y9(l,p),I=g(4704,32,7,"conv32_down"),S=f(9216,32,3,"conv32_1"),x=f(9216,32,3,"conv32_2"),v=f(9216,32,3,"conv32_3"),O=f(36864,64,3,"conv64_down",!0),C=f(36864,64,3,"conv64_1"),U=f(36864,64,3,"conv64_2"),G=f(36864,64,3,"conv64_3"),ne=f(147456,128,3,"conv128_down",!0),te=f(147456,128,3,"conv128_1"),oe=f(147456,128,3,"conv128_2"),ge=f(589824,256,3,"conv256_down",!0),fe=f(589824,256,3,"conv256_1"),Ae=f(589824,256,3,"conv256_2"),Te=f(589824,256,3,"conv256_down_out"),Ve=gs.tidy(()=>gs.transpose(gs.tensor2d(l(256*128),[128,256]),[1,0]));if(p.push({paramPath:"fc"}),u().length!==0)throw new Error(`weights remaing after extract: ${u().length}`);const rt={conv32_down:I,conv32_1:S,conv32_2:x,conv32_3:v,conv64_down:O,conv64_1:C,conv64_2:U,conv64_3:G,conv128_down:ne,conv128_1:te,conv128_2:oe,conv256_down:ge,conv256_1:fe,conv256_2:Ae,conv256_down_out:Te,fc:Ve};return{params:rt,paramMappings:p}}function H9(r,l){const u=ms(r,l);function p(I){const S=u(`${I}/scale/weights`,1),x=u(`${I}/scale/biases`,1);return{weights:S,biases:x}}function g(I){const S=u(`${I}/conv/filters`,4),x=u(`${I}/conv/bias`,1),v=p(I);return{conv:{filters:S,bias:x},scale:v}}function f(I){return{conv1:g(`${I}/conv1`),conv2:g(`${I}/conv2`)}}return{extractConvLayerParams:g,extractResidualLayerParams:f}}function FE(r){const l=[],{extractConvLayerParams:u,extractResidualLayerParams:p}=H9(r,l),g=u("conv32_down"),f=p("conv32_1"),I=p("conv32_2"),S=p("conv32_3"),x=p("conv64_down"),v=p("conv64_1"),O=p("conv64_2"),C=p("conv64_3"),U=p("conv128_down"),G=p("conv128_1"),ne=p("conv128_2"),te=p("conv256_down"),oe=p("conv256_1"),ge=p("conv256_2"),fe=p("conv256_down_out"),Ae=r.fc;if(l.push({originalPath:"fc",paramPath:"fc"}),!uS(Ae))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${Ae}`);const Te={conv32_down:g,conv32_1:f,conv32_2:I,conv32_3:S,conv64_down:x,conv64_1:v,conv64_2:O,conv64_3:C,conv128_down:U,conv128_1:G,conv128_2:ne,conv256_down:te,conv256_1:oe,conv256_2:ge,conv256_down_out:fe,fc:Ae};return Pn(r,l),{params:Te,paramMappings:l}}const Gn=Ke(Xe());function fi(r,l){let u=DE(r,l.conv1);return u=PI(u,l.conv2),u=Gn.add(u,r),u=Gn.relu(u),u}function vu(r,l){let u=eg(r,l.conv1);u=PI(u,l.conv2);let p=Gn.avgPool(r,2,2,"valid");const g=Gn.zeros(p.shape),f=p.shape[3]!==u.shape[3],I=p.shape[1]!==u.shape[1]||p.shape[2]!==u.shape[2];if(I){const S=[...u.shape];S[1]=1;const x=Gn.zeros(S);u=Gn.concat([u,x],1);const v=[...u.shape];v[2]=1;const O=Gn.zeros(v);u=Gn.concat([u,O],2)}return p=f?Gn.concat([p,g],3):p,u=Gn.add(p,u),u=Gn.relu(u),u}const Ds=Ke(Xe());class Nu extends En{constructor(){super("FaceRecognitionNet")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceRecognitionNet - load model before inference");return Ds.tidy(()=>{const u=Ds.cast(r.toBatchTensor(150,!0),"float32"),p=[122.782,117.001,104.298],g=di(u,p).div(Ds.scalar(256));let f=eg(g,l.conv32_down);f=Ds.maxPool(f,3,2,"valid"),f=fi(f,l.conv32_1),f=fi(f,l.conv32_2),f=fi(f,l.conv32_3),f=vu(f,l.conv64_down),f=fi(f,l.conv64_1),f=fi(f,l.conv64_2),f=fi(f,l.conv64_3),f=vu(f,l.conv128_down),f=fi(f,l.conv128_1),f=fi(f,l.conv128_2),f=vu(f,l.conv256_down),f=fi(f,l.conv256_1),f=fi(f,l.conv256_2),f=vu(f,l.conv256_down_out);const I=f.mean([1,2]),S=Ds.matMul(I,l.fc);return S})}async forward(r){return this.forwardInput(await Rt(r))}async computeFaceDescriptor(r){const l=await Rt(r),u=Ds.tidy(()=>Ds.unstack(this.forwardInput(l))),p=await Promise.all(u.map(g=>g.data()));return u.forEach(g=>g.dispose()),l.isBatchInput?p:p[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(r){return FE(r)}extractParams(r){return kE(r)}}function q9(r){const l=new Nu;return l.extractWeights(r),l}function tg(r,l){const u={descriptor:l};return Object.assign({},r,u)}function j9(r){return typeof r.age=="number"}function ng(r,l){const u={age:l};return Object.assign({},r,u)}function K9(r){return(r.gender===gr.MALE||r.gender===gr.FEMALE)&&fc(r.genderProbability)}function sg(r,l,u){const p={gender:l,genderProbability:u};return Object.assign({},r,p)}const gi=Ke(Xe());function X9(r,l){function u(x,v){const O=gi.tensor4d(r(3*3*x),[3,3,x,1]),C=gi.tensor1d(r(x)),U=gi.tensor1d(r(x)),G=gi.tensor1d(r(x)),ne=gi.tensor1d(r(x));return l.push({paramPath:`${v}/filters`},{paramPath:`${v}/batch_norm_scale`},{paramPath:`${v}/batch_norm_offset`},{paramPath:`${v}/batch_norm_mean`},{paramPath:`${v}/batch_norm_variance`}),{filters:O,batch_norm_scale:C,batch_norm_offset:U,batch_norm_mean:G,batch_norm_variance:ne}}function p(x,v,O,C,U){const G=gi.tensor4d(r(x*v*O*O),[O,O,x,v]),ne=gi.tensor1d(r(v));return l.push({paramPath:`${C}/filters`},{paramPath:`${C}/${U?"batch_norm_offset":"bias"}`}),{filters:G,bias:ne}}function g(x,v,O,C){const{filters:U,bias:G}=p(x,v,O,C,!0);return{filters:U,batch_norm_offset:G}}function f(x,v,O){const C=u(x,`${O}/depthwise_conv`),U=g(x,v,1,`${O}/pointwise_conv`);return{depthwise_conv:C,pointwise_conv:U}}function I(){const x=g(3,32,3,"mobilenetv1/conv_0"),v=f(32,64,"mobilenetv1/conv_1"),O=f(64,128,"mobilenetv1/conv_2"),C=f(128,128,"mobilenetv1/conv_3"),U=f(128,256,"mobilenetv1/conv_4"),G=f(256,256,"mobilenetv1/conv_5"),ne=f(256,512,"mobilenetv1/conv_6"),te=f(512,512,"mobilenetv1/conv_7"),oe=f(512,512,"mobilenetv1/conv_8"),ge=f(512,512,"mobilenetv1/conv_9"),fe=f(512,512,"mobilenetv1/conv_10"),Ae=f(512,512,"mobilenetv1/conv_11"),Te=f(512,1024,"mobilenetv1/conv_12"),Ve=f(1024,1024,"mobilenetv1/conv_13");return{conv_0:x,conv_1:v,conv_2:O,conv_3:C,conv_4:U,conv_5:G,conv_6:ne,conv_7:te,conv_8:oe,conv_9:ge,conv_10:fe,conv_11:Ae,conv_12:Te,conv_13:Ve}}function S(){const x=g(1024,256,1,"prediction_layer/conv_0"),v=g(256,512,3,"prediction_layer/conv_1"),O=g(512,128,1,"prediction_layer/conv_2"),C=g(128,256,3,"prediction_layer/conv_3"),U=g(256,128,1,"prediction_layer/conv_4"),G=g(128,256,3,"prediction_layer/conv_5"),ne=g(256,64,1,"prediction_layer/conv_6"),te=g(64,128,3,"prediction_layer/conv_7"),oe=p(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),ge=p(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),fe=p(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),Ae=p(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),Te=p(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),Ve=p(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),rt=p(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),vt=p(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),$t=p(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),Kt=p(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),Dn=p(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),Tn=p(128,18,1,"prediction_layer/box_predictor_5/class_predictor"),An={box_encoding_predictor:oe,class_predictor:ge},Ks={box_encoding_predictor:fe,class_predictor:Ae},Li={box_encoding_predictor:Te,class_predictor:Ve},Xs={box_encoding_predictor:rt,class_predictor:vt},ua={box_encoding_predictor:$t,class_predictor:Kt},Xc={box_encoding_predictor:Dn,class_predictor:Tn};return{conv_0:x,conv_1:v,conv_2:O,conv_3:C,conv_4:U,conv_5:G,conv_6:ne,conv_7:te,box_predictor_0:An,box_predictor_1:Ks,box_predictor_2:Li,box_predictor_3:Xs,box_predictor_4:ua,box_predictor_5:Xc}}return{extractMobilenetV1Params:I,extractPredictionLayerParams:S}}function 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O=`mobilenetv1/conv_${v}`,C=`MobilenetV1/Conv2d_${v}_depthwise`,U=`${O}/depthwise_conv`,G=`${O}/pointwise_conv`,ne=u(`${C}/depthwise_weights`,4,`${U}/filters`),te=u(`${C}/BatchNorm/gamma`,1,`${U}/batch_norm_scale`),oe=u(`${C}/BatchNorm/beta`,1,`${U}/batch_norm_offset`),ge=u(`${C}/BatchNorm/moving_mean`,1,`${U}/batch_norm_mean`),fe=u(`${C}/BatchNorm/moving_variance`,1,`${U}/batch_norm_variance`);return{depthwise_conv:{filters:ne,batch_norm_scale:te,batch_norm_offset:oe,batch_norm_mean:ge,batch_norm_variance:fe},pointwise_conv:p("MobilenetV1",v,G)}}function f(){return{conv_0:p("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:g(1),conv_2:g(2),conv_3:g(3),conv_4:g(4),conv_5:g(5),conv_6:g(6),conv_7:g(7),conv_8:g(8),conv_9:g(9),conv_10:g(10),conv_11:g(11),conv_12:g(12),conv_13:g(13)}}function I(v,O){const C=u(`${v}/weights`,4,`${O}/filters`),U=u(`${v}/biases`,1,`${O}/bias`);return{filters:C,bias:U}}function S(v){const O=I(`Prediction/BoxPredictor_${v}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${v}/box_encoding_predictor`),C=I(`Prediction/BoxPredictor_${v}/ClassPredictor`,`prediction_layer/box_predictor_${v}/class_predictor`);return{box_encoding_predictor:O,class_predictor:C}}function x(){return{conv_0:p("Prediction",0,"prediction_layer/conv_0"),conv_1:p("Prediction",1,"prediction_layer/conv_1"),conv_2:p("Prediction",2,"prediction_layer/conv_2"),conv_3:p("Prediction",3,"prediction_layer/conv_3"),conv_4:p("Prediction",4,"prediction_layer/conv_4"),conv_5:p("Prediction",5,"prediction_layer/conv_5"),conv_6:p("Prediction",6,"prediction_layer/conv_6"),conv_7:p("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:S(0),box_predictor_1:S(1),box_predictor_2:S(2),box_predictor_3:S(3),box_predictor_4:S(4),box_predictor_5:S(5)}}return{extractMobilenetV1Params:f,extractPredictionLayerParams:x}}function WE(r){const 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p=r.arraySync(),g=Math.min(p[l][0],p[l][2]),f=Math.min(p[l][1],p[l][3]),I=Math.max(p[l][0],p[l][2]),S=Math.max(p[l][1],p[l][3]),x=Math.min(p[u][0],p[u][2]),v=Math.min(p[u][1],p[u][3]),O=Math.max(p[u][0],p[u][2]),C=Math.max(p[u][1],p[u][3]),U=(I-g)*(S-f),G=(O-x)*(C-v);if(U<=0||G<=0)return 0;const ne=Math.max(g,x),te=Math.max(f,v),oe=Math.min(I,O),ge=Math.min(S,C),fe=Math.max(oe-ne,0)*Math.max(ge-te,0);return fe/(U+G-fe)}const De=Ke(Xe());function nZ(r){const l=De.unstack(De.transpose(r,[1,0])),u=[De.sub(l[2],l[0]),De.sub(l[3],l[1])],p=[De.add(l[0],De.div(u[0],De.scalar(2))),De.add(l[1],De.div(u[1],De.scalar(2)))];return{sizes:u,centers:p}}function sZ(r,l){const{sizes:u,centers:p}=nZ(r),g=De.unstack(De.transpose(l,[1,0])),f=De.div(De.mul(De.exp(De.div(g[2],De.scalar(5))),u[0]),De.scalar(2)),I=De.add(De.mul(De.div(g[0],De.scalar(10)),u[0]),p[0]),S=De.div(De.mul(De.exp(De.div(g[3],De.scalar(5))),u[1]),De.scalar(2)),x=De.add(De.mul(De.div(g[1],De.scalar(10)),u[1]),p[1]);return De.transpose(De.stack([De.sub(I,f),De.sub(x,S),De.add(I,f),De.add(x,S)]),[1,0])}function BE(r,l,u){return De.tidy(()=>{const p=r.shape[0];let g=sZ(De.reshape(De.tile(u.extra_dim,[p,1,1]),[-1,4]),De.reshape(r,[-1,4]));g=De.reshape(g,[p,g.shape[0]/p,4]);const f=De.sigmoid(De.slice(l,[0,0,1],[-1,-1,-1]));let I=De.slice(f,[0,0,0],[-1,-1,1]);I=De.reshape(I,[p,I.shape[1]]);const S=De.unstack(g),x=De.unstack(I);return{boxes:S,scores:x}})}const Cu=Ke(Xe());function aa(r,l){return Cu.tidy(()=>{const u=r.shape[0],p=Cu.reshape(ia(r,l.box_encoding_predictor),[u,-1,1,4]),g=Cu.reshape(ia(r,l.class_predictor),[u,-1,3]);return{boxPredictionEncoding:p,classPrediction:g}})}const Ru=Ke(Xe());function ME(r,l,u){return Ru.tidy(()=>{const p=Hs(r,u.conv_0,[1,1]),g=Hs(p,u.conv_1,[2,2]),f=Hs(g,u.conv_2,[1,1]),I=Hs(f,u.conv_3,[2,2]),S=Hs(I,u.conv_4,[1,1]),x=Hs(S,u.conv_5,[2,2]),v=Hs(x,u.conv_6,[1,1]),O=Hs(v,u.conv_7,[2,2]),C=aa(l,u.box_predictor_0),U=aa(r,u.box_predictor_1),G=aa(g,u.box_predictor_2),ne=aa(I,u.box_predictor_3),te=aa(x,u.box_predictor_4),oe=aa(O,u.box_predictor_5),ge=Ru.concat([C.boxPredictionEncoding,U.boxPredictionEncoding,G.boxPredictionEncoding,ne.boxPredictionEncoding,te.boxPredictionEncoding,oe.boxPredictionEncoding],1),fe=Ru.concat([C.classPrediction,U.classPrediction,G.classPrediction,ne.classPrediction,te.classPrediction,oe.classPrediction],1);return{boxPredictions:ge,classPredictions:fe}})}class yi{constructor({minConfidence:r,maxResults:l}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=r||.5,this._maxResults=l||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}}const bi=Ke(Xe());class zc extends En{constructor(){super("SsdMobilenetv1")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("SsdMobilenetv1 - load model before inference");return bi.tidy(()=>{const u=bi.cast(r.toBatchTensor(512,!1),"float32"),p=bi.sub(bi.mul(u,bi.scalar(.007843137718737125)),bi.scalar(1)),g=$E(p,l.mobilenetv1),{boxPredictions:f,classPredictions:I}=ME(g.out,g.conv11,l.prediction_layer);return BE(f,I,l.output_layer)})}async forward(r){return this.forwardInput(await Rt(r))}async locateFaces(r,l={}){const{maxResults:u,minConfidence:p}=new yi(l),g=await Rt(r),{boxes:f,scores:I}=this.forwardInput(g),S=f[0],x=I[0];for(let fe=1;fe<f.length;fe++)f[fe].dispose(),I[fe].dispose();const v=Array.from(await x.data()),O=.5,C=UE(S,v,u,O,p),U=g.getReshapedInputDimensions(0),G=g.inputSize,ne=G/U.width,te=G/U.height,oe=S.arraySync(),ge=C.map(fe=>{const[Ae,Te]=[Math.max(0,oe[fe][0]),Math.min(1,oe[fe][2])].map(vt=>vt*te),[Ve,rt]=[Math.max(0,oe[fe][1]),Math.min(1,oe[fe][3])].map(vt=>vt*ne);return new Ht(v[fe],new su(Ve,Ae,rt-Ve,Te-Ae),{height:g.getInputHeight(0),width:g.getInputWidth(0)})});return S.dispose(),x.dispose(),ge}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(r){return WE(r)}extractParams(r){return _E(r)}}function PE(r){const l=new zc;return l.extractWeights(r),l}function iZ(r){return PE(r)}class rZ extends zc{}const zE=.4,GE=[new Je(.738768,.874946),new Je(2.42204,2.65704),new Je(4.30971,7.04493),new Je(10.246,4.59428),new Je(12.6868,11.8741)],VE=[new Je(1.603231,2.094468),new Je(6.041143,7.080126),new Je(2.882459,3.518061),new Je(4.266906,5.178857),new Je(9.041765,10.66308)],YE=[117.001,114.697,97.404],HE="tiny_yolov2_model",qE="tiny_yolov2_separable_conv_model";const ig=r=>typeof r=="number";function zI(r){if(!r)throw new Error(`invalid config: ${r}`);if(typeof r.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${r.withSeparableConvs}`);if(!ig(r.iouThreshold)||r.iouThreshold<0||r.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${r.iouThreshold}`);if(!Array.isArray(r.classes)||!r.classes.length||!r.classes.every(l=>typeof l=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(r.classes)}`);if(!Array.isArray(r.anchors)||!r.anchors.length||!r.anchors.map(l=>l||{}).every(l=>ig(l.x)&&ig(l.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(r.anchors)}`);if(r.meanRgb&&(!Array.isArray(r.meanRgb)||r.meanRgb.length!==3||!r.meanRgb.every(ig)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(r.meanRgb)}`)}const qs=Ke(Xe());function Gc(r){return qs.tidy(()=>{const l=qs.mul(r,qs.scalar(.10000000149011612));return qs.add(qs.relu(qs.sub(r,l)),l)})}const js=Ke(Xe());function br(r,l){return js.tidy(()=>{let u=js.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return u=js.conv2d(u,l.conv.filters,[1,1],"valid"),u=js.sub(u,l.bn.sub),u=js.mul(u,l.bn.truediv),u=js.add(u,l.conv.bias),Gc(u)})}const oo=Ke(Xe());function wr(r,l){return oo.tidy(()=>{let u=oo.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return u=oo.separableConv2d(u,l.depthwise_filter,l.pointwise_filter,[1,1],"valid"),u=oo.add(u,l.bias),Gc(u)})}const GI=Ke(Xe());function oZ(r,l){const u=_c(r,l);function p(I,S){const x=GI.tensor1d(r(I)),v=GI.tensor1d(r(I));return l.push({paramPath:`${S}/sub`},{paramPath:`${S}/truediv`}),{sub:x,truediv:v}}function g(I,S,x){const v=u(I,S,3,`${x}/conv`),O=p(S,`${x}/bn`);return{conv:v,bn:O}}const f=Wc(r,l);return{extractConvParams:u,extractConvWithBatchNormParams:g,extractSeparableConvParams:f}}function jE(r,l,u,p){const{extractWeights:g,getRemainingWeights:f}=zn(r),I=[],{extractConvParams:S,extractConvWithBatchNormParams:x,extractSeparableConvParams:v}=oZ(g,I);let O;if(l.withSeparableConvs){const[C,U,G,ne,te,oe,ge,fe,Ae]=p,Te=l.isFirstLayerConv2d?S(C,U,3,"conv0"):v(C,U,"conv0"),Ve=v(U,G,"conv1"),rt=v(G,ne,"conv2"),vt=v(ne,te,"conv3"),$t=v(te,oe,"conv4"),Kt=v(oe,ge,"conv5"),Dn=fe?v(ge,fe,"conv6"):void 0,Tn=Ae?v(fe,Ae,"conv7"):void 0,An=S(Ae||fe||ge,5*u,1,"conv8");O={conv0:Te,conv1:Ve,conv2:rt,conv3:vt,conv4:$t,conv5:Kt,conv6:Dn,conv7:Tn,conv8:An}}else{const[C,U,G,ne,te,oe,ge,fe,Ae]=p,Te=x(C,U,"conv0"),Ve=x(U,G,"conv1"),rt=x(G,ne,"conv2"),vt=x(ne,te,"conv3"),$t=x(te,oe,"conv4"),Kt=x(oe,ge,"conv5"),Dn=x(ge,fe,"conv6"),Tn=x(fe,Ae,"conv7"),An=S(Ae,5*u,1,"conv8");O={conv0:Te,conv1:Ve,conv2:rt,conv3:vt,conv4:$t,conv5:Kt,conv6:Dn,conv7:Tn,conv8:An}}if(f().length!==0)throw new Error(`weights remaing after extract: ${f().length}`);return{params:O,paramMappings:I}}function aZ(r,l){const u=ms(r,l);function p(S){const x=u(`${S}/sub`,1),v=u(`${S}/truediv`,1);return{sub:x,truediv:v}}function g(S){const x=u(`${S}/filters`,4),v=u(`${S}/bias`,1);return{filters:x,bias:v}}function f(S){const x=g(`${S}/conv`),v=p(`${S}/bn`);return{conv:x,bn:v}}const I=$c(u);return{extractConvParams:g,extractConvWithBatchNormParams:f,extractSeparableConvParams:I}}function KE(r,l){const u=[],{extractConvParams:p,extractConvWithBatchNormParams:g,extractSeparableConvParams:f}=aZ(r,u);let I;if(l.withSeparableConvs){const S=l.filterSizes&&l.filterSizes.length||9;I={conv0:l.isFirstLayerConv2d?p("conv0"):f("conv0"),conv1:f("conv1"),conv2:f("conv2"),conv3:f("conv3"),conv4:f("conv4"),conv5:f("conv5"),conv6:S>7?f("conv6"):void 0,conv7:S>8?f("conv7"):void 0,conv8:p("conv8")}}else I={conv0:g("conv0"),conv1:g("conv1"),conv2:g("conv2"),conv3:g("conv3"),conv4:g("conv4"),conv5:g("conv5"),conv6:g("conv6"),conv7:g("conv7"),conv8:p("conv8")};return Pn(r,u),{params:I,paramMappings:u}}var VI;(function(r){r[r.XS=224]="XS",r[r.SM=320]="SM",r[r.MD=416]="MD",r[r.LG=608]="LG"})(VI||(VI={}));class Lr{constructor({inputSize:r,scoreThreshold:l}={}){this._name="TinyYolov2Options";if(this._inputSize=r||416,this._scoreThreshold=l||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}}const kt=Ke(Xe());class Vc extends En{constructor(r){super("TinyYolov2");zI(r),this._config=r}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(r,l){let u=br(r,l.conv0);return u=kt.maxPool(u,[2,2],[2,2],"same"),u=br(u,l.conv1),u=kt.maxPool(u,[2,2],[2,2],"same"),u=br(u,l.conv2),u=kt.maxPool(u,[2,2],[2,2],"same"),u=br(u,l.conv3),u=kt.maxPool(u,[2,2],[2,2],"same"),u=br(u,l.conv4),u=kt.maxPool(u,[2,2],[2,2],"same"),u=br(u,l.conv5),u=kt.maxPool(u,[2,2],[1,1],"same"),u=br(u,l.conv6),u=br(u,l.conv7),ia(u,l.conv8,"valid",!1)}runMobilenet(r,l){let u=this.config.isFirstLayerConv2d?Gc(ia(r,l.conv0,"valid",!1)):wr(r,l.conv0);return u=kt.maxPool(u,[2,2],[2,2],"same"),u=wr(u,l.conv1),u=kt.maxPool(u,[2,2],[2,2],"same"),u=wr(u,l.conv2),u=kt.maxPool(u,[2,2],[2,2],"same"),u=wr(u,l.conv3),u=kt.maxPool(u,[2,2],[2,2],"same"),u=wr(u,l.conv4),u=kt.maxPool(u,[2,2],[2,2],"same"),u=wr(u,l.conv5),u=kt.maxPool(u,[2,2],[1,1],"same"),u=l.conv6?wr(u,l.conv6):u,u=l.conv7?wr(u,l.conv7):u,ia(u,l.conv8,"valid",!1)}forwardInput(r,l){const{params:u}=this;if(!u)throw new Error("TinyYolov2 - load model before inference");return kt.tidy(()=>{let p=kt.cast(r.toBatchTensor(l,!1),"float32");return p=this.config.meanRgb?di(p,this.config.meanRgb):p,p=p.div(kt.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(p,u):this.runTinyYolov2(p,u)})}async forward(r,l){return await this.forwardInput(await Rt(r),l)}async detect(r,l={}){const{inputSize:u,scoreThreshold:p}=new Lr(l),g=await Rt(r),f=await this.forwardInput(g,u),I=kt.tidy(()=>kt.unstack(f)[0].expandDims()),S={width:g.getInputWidth(0),height:g.getInputHeight(0)},x=await this.extractBoxes(I,g.getReshapedInputDimensions(0),p);f.dispose(),I.dispose();const v=x.map(te=>te.box),O=x.map(te=>te.score),C=x.map(te=>te.classScore),U=x.map(te=>this.config.classes[te.label]),G=gS(v.map(te=>te.rescale(u)),O,this.config.iouThreshold,!0),ne=G.map(te=>new gc(O[te],C[te],U[te],v[te],S));return ne}getDefaultModelName(){return""}extractParamsFromWeigthMap(r){return KE(r,this.config)}extractParams(r){const l=this.config.filterSizes||Vc.DEFAULT_FILTER_SIZES,u=l?l.length:void 0;if(u!==7&&u!==8&&u!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${u} filterSizes in config`);return jE(r,this.config,this.boxEncodingSize,l)}async extractBoxes(r,l,u){const{width:p,height:g}=l,f=Math.max(p,g),I=f/p,S=f/g,x=r.shape[1],v=this.config.anchors.length,[O,C,U]=kt.tidy(()=>{const oe=r.reshape([x,x,v,this.boxEncodingSize]),ge=oe.slice([0,0,0,0],[x,x,v,4]),fe=oe.slice([0,0,0,4],[x,x,v,1]),Ae=this.withClassScores?kt.softmax(oe.slice([0,0,0,5],[x,x,v,this.config.classes.length]),3):kt.scalar(0);return[ge,fe,Ae]}),G=[],ne=await C.array(),te=await O.array();for(let oe=0;oe<x;oe++)for(let ge=0;ge<x;ge++)for(let fe=0;fe<v;fe++){const Ae=nu(ne[oe][ge][fe][0]);if(!u||Ae>u){const Te=(ge+nu(te[oe][ge][fe][0]))/x*I,Ve=(oe+nu(te[oe][ge][fe][1]))/x*S,rt=Math.exp(te[oe][ge][fe][2])*this.config.anchors[fe].x/x*I,vt=Math.exp(te[oe][ge][fe][3])*this.config.anchors[fe].y/x*S,$t=Te-rt/2,Kt=Ve-vt/2,Dn={row:oe,col:ge,anchor:fe},{classScore:Tn,label:An}=this.withClassScores?await this.extractPredictedClass(U,Dn):{classScore:1,label:0};G.push({box:new tu($t,Kt,$t+rt,Kt+vt),score:Ae,classScore:Ae*Tn,label:An,...Dn})}}return O.dispose(),C.dispose(),U.dispose(),G}async extractPredictedClass(r,l){const{row:u,col:p,anchor:g}=l,f=await r.array();return Array(this.config.classes.length).fill(0).map((I,S)=>f[u][p][g][S]).map((I,S)=>({classScore:I,label:S})).reduce((I,S)=>I.classScore>S.classScore?I:S)}}Vc.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];class Ou extends Vc{constructor(r=!0){const l=Object.assign({},{withSeparableConvs:r,iouThreshold:zE,classes:["face"]},r?{anchors:VE,meanRgb:YE}:{anchors:GE,withClassScores:!0});super(l)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(r,l){const u=await this.detect(r,l);return u.map(p=>new Ht(p.score,p.relativeBox,{width:p.imageWidth,height:p.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?qE:HE}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}function cZ(r,l=!0){const u=new Ou(l);return u.extractWeights(r),u}class YI extends Lr{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}}class wi{async then(r){return r(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}}const HI=Ke(Xe());async function ca(r,l,u,p,g=({alignedRect:f})=>f){const f=r.map(x=>oa(x)?g(x):x.detection),I=p||(l instanceof HI.Tensor?await kc(l,f):await Dc(l,f)),S=await u(I);return I.forEach(x=>x instanceof HI.Tensor&&x.dispose()),S}async function Yc(r,l,u,p,g){return ca([r],l,async f=>u(f[0]),p,g)}const XE=.4,JE=[new Je(1.603231,2.094468),new Je(6.041143,7.080126),new Je(2.882459,3.518061),new Je(4.266906,5.178857),new Je(9.041765,10.66308)],ZE=[117.001,114.697,97.404];class Eu extends Vc{constructor(){const r={withSeparableConvs:!0,iouThreshold:XE,classes:["face"],anchors:JE,meanRgb:ZE,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(r)}get anchors(){return this.config.anchors}async locateFaces(r,l){const u=await this.detect(r,l);return u.map(p=>new Ht(p.score,p.relativeBox,{width:p.imageWidth,height:p.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}const pt={ssdMobilenetv1:new zc,tinyFaceDetector:new Eu,tinyYolov2:new Ou,faceLandmark68Net:new Au,faceLandmark68TinyNet:new BI,faceRecognitionNet:new Nu,faceExpressionNet:new FI,ageGenderNet:new UI},QE=(r,l)=>pt.ssdMobilenetv1.locateFaces(r,l),lZ=(r,l)=>pt.tinyFaceDetector.locateFaces(r,l),hZ=(r,l)=>pt.tinyYolov2.locateFaces(r,l),eD=r=>pt.faceLandmark68Net.detectLandmarks(r),uZ=r=>pt.faceLandmark68TinyNet.detectLandmarks(r),dZ=r=>pt.faceRecognitionNet.computeFaceDescriptor(r),pZ=r=>pt.faceExpressionNet.predictExpressions(r),mZ=r=>pt.ageGenderNet.predictAgeAndGender(r),tD=r=>pt.ssdMobilenetv1.load(r),fZ=r=>pt.tinyFaceDetector.load(r),gZ=r=>pt.tinyYolov2.load(r),yZ=r=>pt.faceLandmark68Net.load(r),bZ=r=>pt.faceLandmark68TinyNet.load(r),wZ=r=>pt.faceRecognitionNet.load(r),LZ=r=>pt.faceExpressionNet.load(r),SZ=r=>pt.ageGenderNet.load(r),IZ=tD,xZ=QE,TZ=eD;class nD extends wi{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.extractedFaces=u}}class Fu extends nD{async run(){const r=await this.parentTask,l=await ca(r,this.input,async u=>await Promise.all(u.map(p=>pt.faceExpressionNet.predictExpressions(p))),this.extractedFaces);return r.map((u,p)=>Jf(u,l[p]))}withAgeAndGender(){return new Du(this,this.input)}}class _u extends nD{async run(){const r=await this.parentTask;if(!r)return;const l=await Yc(r,this.input,u=>pt.faceExpressionNet.predictExpressions(u),this.extractedFaces);return Jf(r,l)}withAgeAndGender(){return new ku(this,this.input)}}class jc extends Fu{withAgeAndGender(){return new Hc(this,this.input)}withFaceDescriptors(){return new la(this,this.input)}}class Kc extends _u{withAgeAndGender(){return new qc(this,this.input)}withFaceDescriptor(){return new ha(this,this.input)}}class sD extends wi{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.extractedFaces=u}}class Du extends sD{async run(){const r=await this.parentTask,l=await ca(r,this.input,async u=>await Promise.all(u.map(p=>pt.ageGenderNet.predictAgeAndGender(p))),this.extractedFaces);return r.map((u,p)=>{const{age:g,gender:f,genderProbability:I}=l[p];return ng(sg(u,f,I),g)})}withFaceExpressions(){return new Fu(this,this.input)}}class ku extends sD{async run(){const r=await this.parentTask;if(!r)return;const{age:l,gender:u,genderProbability:p}=await Yc(r,this.input,g=>pt.ageGenderNet.predictAgeAndGender(g),this.extractedFaces);return ng(sg(r,u,p),l)}withFaceExpressions(){return new _u(this,this.input)}}class Hc extends Du{withFaceExpressions(){return new jc(this,this.input)}withFaceDescriptors(){return new la(this,this.input)}}class qc extends ku{withFaceExpressions(){return new Kc(this,this.input)}withFaceDescriptor(){return new ha(this,this.input)}}class qI extends wi{constructor(r,l){super();this.parentTask=r;this.input=l}}class la extends qI{async run(){const r=await this.parentTask,l=await ca(r,this.input,u=>Promise.all(u.map(p=>pt.faceRecognitionNet.computeFaceDescriptor(p))),null,u=>u.landmarks.align(null,{useDlibAlignment:!0}));return l.map((u,p)=>tg(r[p],u))}withFaceExpressions(){return new jc(this,this.input)}withAgeAndGender(){return new Hc(this,this.input)}}class ha extends qI{async run(){const r=await this.parentTask;if(!r)return;const l=await Yc(r,this.input,u=>pt.faceRecognitionNet.computeFaceDescriptor(u),null,u=>u.landmarks.align(null,{useDlibAlignment:!0}));return tg(r,l)}withFaceExpressions(){return new Kc(this,this.input)}withAgeAndGender(){return new qc(this,this.input)}}const Wu=Ke(Xe());class jI extends wi{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.useTinyLandmarkNet=u}get landmarkNet(){return this.useTinyLandmarkNet?pt.faceLandmark68TinyNet:pt.faceLandmark68Net}}class KI extends jI{async run(){const r=await this.parentTask,l=r.map(g=>g.detection),u=this.input instanceof Wu.Tensor?await kc(this.input,l):await Dc(this.input,l),p=await Promise.all(u.map(g=>this.landmarkNet.detectLandmarks(g)));return u.forEach(g=>g instanceof Wu.Tensor&&g.dispose()),r.map((g,f)=>Mc(g,p[f]))}withFaceExpressions(){return new jc(this,this.input)}withAgeAndGender(){return new Hc(this,this.input)}withFaceDescriptors(){return new la(this,this.input)}}class XI extends jI{async run(){const r=await this.parentTask;if(!r)return;const{detection:l}=r,u=this.input instanceof Wu.Tensor?await kc(this.input,[l]):await Dc(this.input,[l]),p=await this.landmarkNet.detectLandmarks(u[0]);return u.forEach(g=>g instanceof Wu.Tensor&&g.dispose()),Mc(r,p)}withFaceExpressions(){return new Kc(this,this.input)}withAgeAndGender(){return new qc(this,this.input)}withFaceDescriptor(){return new ha(this,this.input)}}class JI extends wi{constructor(r,l=new yi){super();this.input=r;this.options=l}}class rg extends JI{async run(){const{input:r,options:l}=this,u=l instanceof YI?p=>pt.tinyFaceDetector.locateFaces(p,l):l instanceof yi?p=>pt.ssdMobilenetv1.locateFaces(p,l):l instanceof Lr?p=>pt.tinyYolov2.locateFaces(p,l):null;if(!u)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return u(r)}runAndExtendWithFaceDetections(){return new Promise(async r=>{const l=await this.run();return r(l.map(u=>Yo({},u)))})}withFaceLandmarks(r=!1){return new KI(this.runAndExtendWithFaceDetections(),this.input,r)}withFaceExpressions(){return new Fu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Du(this.runAndExtendWithFaceDetections(),this.input)}}class ZI extends JI{async run(){const r=await new rg(this.input,this.options);let l=r[0];return r.forEach(u=>{u.score>l.score&&(l=u)}),l}runAndExtendWithFaceDetection(){return new Promise(async r=>{const l=await this.run();return r(l?Yo({},l):void 0)})}withFaceLandmarks(r=!1){return new XI(this.runAndExtendWithFaceDetection(),this.input,r)}withFaceExpressions(){return new _u(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ku(this.runAndExtendWithFaceDetection(),this.input)}}function AZ(r,l=new yi){return new ZI(r,l)}function og(r,l=new yi){return new rg(r,l)}async function iD(r,l){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await og(r,new yi(l?{minConfidence:l}:{})).withFaceLandmarks().withFaceDescriptors()}async function vZ(r,l={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await og(r,new Lr(l)).withFaceLandmarks().withFaceDescriptors()}const NZ=iD;function QI(r,l){if(r.length!==l.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");const u=Array.from(r),p=Array.from(l);return Math.sqrt(u.map((g,f)=>g-p[f]).reduce((g,f)=>g+Math.pow(f,2),0))}class rD{constructor(r,l=.6){this._distanceThreshold=l;const u=Array.isArray(r)?r:[r];if(!u.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let p=1;const g=()=>`person ${p++}`;this._labeledDescriptors=u.map(f=>{if(f instanceof Vo)return f;if(f instanceof Float32Array)return new Vo(g(),[f]);if(f.descriptor&&f.descriptor instanceof Float32Array)return new Vo(g(),[f.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(r,l){return l.map(u=>QI(u,r)).reduce((u,p)=>u+p,0)/(l.length||1)}matchDescriptor(r){return this.labeledDescriptors.map(({descriptors:l,label:u})=>new mm(u,this.computeMeanDistance(r,l))).reduce((l,u)=>l.distance<u.distance?l:u)}findBestMatch(r){const l=this.matchDescriptor(r);return l.distance<this.distanceThreshold?l:new mm("unknown",l.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(r=>r.toJSON())}}static fromJSON(r){const l=r.labeledDescriptors.map(u=>Vo.fromJSON(u));return new rD(l,r.distanceThreshold)}}function CZ(r){const l=new Eu;return l.extractWeights(r),l}function oD(r,l){const{width:u,height:p}=new us(l.width,l.height);if(u<=0||p<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:u,height:p})}`);if(Array.isArray(r))return r.map(g=>oD(g,{width:u,height:p}));if(oa(r)){const g=r.detection.forSize(u,p),f=r.unshiftedLandmarks.forSize(g.box.width,g.box.height);return Mc(Yo(r,g),f)}return Ui(r)?Yo(r,r.detection.forSize(u,p)):r instanceof Gs||r instanceof Ht?r.forSize(u,p):r}var aD="0.8.1";const RZ=Ke(Xe()),OZ=typeof process!="undefined",EZ=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",DZ={faceapi:aD,node:OZ,browser:EZ};export{UI as AgeGenderNet,tu as BoundingBox,Ct as Box,wi as ComposableTask,la as ComputeAllFaceDescriptorsTask,qI as ComputeFaceDescriptorsTaskBase,ha as ComputeSingleFaceDescriptorTask,KI as DetectAllFaceLandmarksTask,rg as DetectAllFacesTask,jI as DetectFaceLandmarksTaskBase,JI as DetectFacesTaskBase,XI as DetectSingleFaceLandmarksTask,ZI as DetectSingleFaceTask,us as Dimensions,kI as FACE_EXPRESSION_LABELS,Ht as FaceDetection,rZ as FaceDetectionNet,FI as FaceExpressionNet,ra as FaceExpressions,Au as FaceLandmark68Net,BI as FaceLandmark68TinyNet,V9 as FaceLandmarkNet,Gs as FaceLandmarks,YX as FaceLandmarks5,iu as FaceLandmarks68,mm as FaceMatch,rD as FaceMatcher,Nu as FaceRecognitionNet,gr as Gender,fm as LabeledBox,Vo as LabeledFaceDescriptors,to as NetInput,En as NeuralNetwork,gc as ObjectDetection,Je as Point,HX as PredictedBox,su as Rect,zc as SsdMobilenetv1,yi as SsdMobilenetv1Options,Eu as TinyFaceDetector,YI as TinyFaceDetectorOptions,Ou as TinyYolov2,Lr as TinyYolov2Options,VI as TinyYolov2SizeType,NZ as allFaces,iD as allFacesSsdMobilenetv1,vZ as allFacesTinyYolov2,AS as awaitMediaLoaded,vS as bufferToImage,dZ as computeFaceDescriptor,bc as createCanvas,ou as createCanvasFromMedia,iZ as createFaceDetectionNet,q9 as createFaceRecognitionNet,PE as createSsdMobilenetv1,CZ as createTinyFaceDetector,cZ as createTinyYolov2,og as detectAllFaces,eD as detectFaceLandmarks,uZ as detectFaceLandmarksTiny,TZ as detectLandmarks,AZ as detectSingleFace,WI as draw,gt as env,QI as euclideanDistance,ng as extendWithAge,tg as extendWithFaceDescriptor,Yo as extendWithFaceDetection,Jf as extendWithFaceExpressions,Mc as extendWithFaceLandmarks,sg as extendWithGender,kc as extractFaceTensors,Dc as extractFaces,W9 as fetchImage,OI as fetchJson,$9 as fetchNetWeights,sa as fetchOrThrow,Jn as getContext2dOrThrow,qo as getMediaDimensions,NS as imageTensorToCanvas,RI as imageToSquare,PX as inverseSigmoid,mS as iou,wm as isMediaElement,ru as isMediaLoaded,j9 as isWithAge,Ui as isWithFaceDetection,_I as isWithFaceExpressions,oa as isWithFaceLandmarks,K9 as isWithGender,SZ as loadAgeGenderModel,IZ as loadFaceDetectionModel,LZ as loadFaceExpressionModel,yZ as loadFaceLandmarkModel,bZ as loadFaceLandmarkTinyModel,wZ as loadFaceRecognitionModel,tD as loadSsdMobilenetv1Model,fZ as loadTinyFaceDetectorModel,gZ as loadTinyYolov2Model,EI as loadWeightMap,xZ as locateFaces,U9 as matchDimensions,fS as minBbox,pt as nets,gS as nonMaxSuppression,di as normalize,yS as padToSquare,mZ as predictAgeAndGender,pZ as recognizeFaceExpressions,oD as resizeResults,Ho as resolveInput,MX as shuffleArray,nu as sigmoid,QE as ssdMobilenetv1,RZ as tf,lZ as tinyFaceDetector,hZ as tinyYolov2,Rt as toNetInput,hS as utils,zI as validateConfig,DZ as version};
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/face-api.esm.js.map b/dist/face-api.esm.js.map
index 91e6cc7..c83dc34 100644
--- a/dist/face-api.esm.js.map
+++ b/dist/face-api.esm.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["node_modules/node-fetch/browser.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/backends/backend.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/environment.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/global_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/kernel_names.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/profiler.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/tape.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/tensor_format.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/tensor.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/types.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/tensor_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/engine.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/device_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/flags.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/operation.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/complex.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/types.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/model_management.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/platforms/platform_browser.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/platforms/platform_node.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/cast.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/clone.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/print.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/base_side_effects.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/progress.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/http.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/io.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/reshape.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/mat_mul.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/one_hot.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/math.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/tensor3d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/browser.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/serialization.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/test_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/version.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/globals.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/add.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/floorDiv.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/div.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/mul.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/abs.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/acos.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/acosh.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/add_n.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/all.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/any.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/arg_max.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/arg_min.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/asin.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/asinh.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/atan.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/atan2.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/atanh.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/concat.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/sigmoid.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/slice.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/tanh.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/basic_lstm_cell.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/batch_to_space_nd.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm2d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm3d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm4d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_to.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/ceil.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/clip_by_value.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/concat_1d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/concat_2d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/concat_3d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/concat_4d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/conv2d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/conv1d.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_input.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose.js", 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"node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/IsInf_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/IsNan_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Log1p_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Log_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/LogSoftmax_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/local_response_normalization_backprop.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/LRN_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/min_max_grad_util.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Max_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Maximum_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/max_pool_3d_backprop.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/MaxPool3D_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/ops/../../src/ops/max_pool_backprop.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/MaxPool_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Min_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Minimum_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Mod_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Multiply_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Negate_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/OneHot_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/OnesLike_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/PadV2_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Pow_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Prelu_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Reciprocal_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Relu6_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Relu_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Reshape_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/ResizeBilinear_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/ResizeNearestNeighbor_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Reverse_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Round_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Rsqrt_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/SelectV2_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Selu_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Sigmoid_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Sign_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Sin_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Sinh_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Slice_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Softmax_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Softplus_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/SpaceToBatchND_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/SplitV_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Sqrt_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Square_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/SquaredDifference_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Step_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Sub_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Sum_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Tan_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Tanh_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Tile_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Transpose_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/Unpack_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/UnsortedSegmentSum_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/gradients/../../src/gradients/ZerosLike_grad.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/register_all_gradients.ts", "node_modules/@tensorflow/tfjs-core/dist/../src/index.ts", "src/dom/imageToSquare.ts", "src/dom/NetInput.ts", "src/dom/toNetInput.ts", "src/dom/extractFaces.ts", "src/dom/extractFaceTensors.ts", "src/dom/fetchOrThrow.ts", "src/dom/fetchImage.ts", "src/dom/fetchJson.ts", "src/dom/fetchNetWeights.ts", "src/common/getModelUris.ts", "src/dom/loadWeightMap.ts", "src/dom/matchDimensions.ts", "src/NeuralNetwork.ts", "src/common/depthwiseSeparableConv.ts", "src/faceFeatureExtractor/denseBlock.ts", "src/common/convLayer.ts", "src/common/disposeUnusedWeightTensors.ts", "src/common/extractConvParamsFactory.ts", "src/common/extractFCParamsFactory.ts", "src/common/types.ts", "src/common/extractSeparableConvParamsFactory.ts", "src/common/extractWeightEntryFactory.ts", "src/common/extractWeightsFactory.ts", "src/faceFeatureExtractor/extractorsFactory.ts", "src/faceFeatureExtractor/extractParams.ts", "src/common/loadConvParamsFactory.ts", "src/faceFeatureExtractor/loadParamsFactory.ts", "src/faceFeatureExtractor/extractParamsFromWeigthMap.ts", "src/faceFeatureExtractor/FaceFeatureExtractor.ts", "src/common/fullyConnectedLayer.ts", "src/faceProcessor/extractParams.ts", "src/faceProcessor/extractParamsFromWeigthMap.ts", "src/faceProcessor/util.ts", "src/faceProcessor/FaceProcessor.ts", "src/faceExpressionNet/FaceExpressions.ts", "src/faceExpressionNet/FaceExpressionNet.ts", "src/factories/WithFaceExpressions.ts", "src/draw/drawFaceExpressions.ts", "src/factories/WithFaceLandmarks.ts", "src/draw/DrawFaceLandmarks.ts", "src/draw/index.ts", "src/xception/extractParams.ts", "src/xception/extractParamsFromWeigthMap.ts", "src/xception/TinyXception.ts", "src/ageGenderNet/extractParams.ts", "src/ageGenderNet/extractParamsFromWeigthMap.ts", "src/ageGenderNet/types.ts", "src/ageGenderNet/AgeGenderNet.ts", "src/faceLandmarkNet/FaceLandmark68NetBase.ts", "src/faceLandmarkNet/FaceLandmark68Net.ts", "src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "src/faceFeatureExtractor/extractParamsTiny.ts", "src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "src/faceLandmarkNet/index.ts", "src/faceRecognitionNet/scaleLayer.ts", "src/faceRecognitionNet/convLayer.ts", "src/faceRecognitionNet/extractParams.ts", "src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "src/faceRecognitionNet/residualLayer.ts", "src/faceRecognitionNet/FaceRecognitionNet.ts", "src/faceRecognitionNet/index.ts", "src/factories/WithFaceDescriptor.ts", "src/factories/WithAge.ts", "src/factories/WithGender.ts", "src/ssdMobilenetv1/extractParams.ts", "src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "src/ssdMobilenetv1/pointwiseConvLayer.ts", "src/ssdMobilenetv1/mobileNetV1.ts", "src/ssdMobilenetv1/nonMaxSuppression.ts", "src/ssdMobilenetv1/outputLayer.ts", "src/ssdMobilenetv1/boxPredictionLayer.ts", "src/ssdMobilenetv1/predictionLayer.ts", "src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "src/ssdMobilenetv1/SsdMobilenetv1.ts", "src/ssdMobilenetv1/index.ts", "src/tinyYolov2/const.ts", "src/tinyYolov2/config.ts", "src/tinyYolov2/leaky.ts", "src/tinyYolov2/convWithBatchNorm.ts", "src/tinyYolov2/depthwiseSeparableConv.ts", "src/tinyYolov2/extractParams.ts", "src/tinyYolov2/extractParamsFromWeigthMap.ts", "src/tinyYolov2/TinyYolov2Options.ts", "src/tinyYolov2/TinyYolov2Base.ts", "src/tinyYolov2/TinyYolov2.ts", "src/tinyYolov2/index.ts", "src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "src/globalApi/ComposableTask.ts", "src/globalApi/extractFacesAndComputeResults.ts", "src/tinyFaceDetector/const.ts", "src/tinyFaceDetector/TinyFaceDetector.ts", "src/globalApi/nets.ts", "src/globalApi/PredictFaceExpressionsTask.ts", "src/globalApi/PredictAgeAndGenderTask.ts", "src/globalApi/ComputeFaceDescriptorsTasks.ts", "src/globalApi/DetectFaceLandmarksTasks.ts", "src/globalApi/DetectFacesTasks.ts", "src/globalApi/detectFaces.ts", "src/globalApi/allFaces.ts", "src/euclideanDistance.ts", "src/globalApi/FaceMatcher.ts", "src/tinyFaceDetector/index.ts", "src/resizeResults.ts", "src/index.ts"],
-  "sourcesContent": ["\"use strict\";\n\n// ref: https://github.com/tc39/proposal-global\nvar getGlobal = function () {\n\t// the only reliable means to get the global object is\n\t// `Function('return this')()`\n\t// However, this causes CSP violations in Chrome apps.\n\tif (typeof self !== 'undefined') { return self; }\n\tif (typeof window !== 'undefined') { return window; }\n\tif (typeof global !== 'undefined') { return global; }\n\tthrow new Error('unable to locate global object');\n}\n\nvar global = getGlobal();\n\nmodule.exports = exports = global.fetch;\n\n// Needed for TypeScript and Webpack.\nif (global.fetch) {\n\texports.default = global.fetch.bind(global);\n}\n\nexports.Headers = global.Headers;\nexports.Request = global.Request;\nexports.Response = global.Response;", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage {\n    constructor(backend, dataMover) {\n        this.backend = backend;\n        this.dataMover = dataMover;\n        this.data = new WeakMap();\n        this.dataIdsCount = 0;\n    }\n    get(dataId) {\n        if (!this.data.has(dataId)) {\n            this.dataMover.moveData(this.backend, dataId);\n        }\n        return this.data.get(dataId);\n    }\n    set(dataId, value) {\n        this.dataIdsCount++;\n        this.data.set(dataId, value);\n    }\n    has(dataId) {\n        return this.data.has(dataId);\n    }\n    delete(dataId) {\n        this.dataIdsCount--;\n        return this.data.delete(dataId);\n    }\n    numDataIds() {\n        return this.dataIdsCount;\n    }\n}\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend {\n    time(f) {\n        return notYetImplemented('time');\n    }\n    read(dataId) {\n        return notYetImplemented('read');\n    }\n    readSync(dataId) {\n        return notYetImplemented('readSync');\n    }\n    numDataIds() {\n        return notYetImplemented('numDataIds');\n    }\n    disposeData(dataId) {\n        return notYetImplemented('disposeData');\n    }\n    write(values, shape, dtype) {\n        return notYetImplemented('write');\n    }\n    move(dataId, values, shape, dtype) {\n        return notYetImplemented('move');\n    }\n    memory() {\n        return notYetImplemented('memory');\n    }\n    /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n    floatPrecision() {\n        return notYetImplemented('floatPrecision');\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        return notYetImplemented('batchMatMul');\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedBatchMatMul');\n    }\n    slice(x, begin, size) {\n        return notYetImplemented('slice');\n    }\n    stridedSlice(x, begin, end, strides) {\n        return notYetImplemented('stridedSlice');\n    }\n    unstack(x, axis) {\n        return notYetImplemented('unstack');\n    }\n    reverse(a, axis) {\n        return notYetImplemented('reverse');\n    }\n    concat(tensors, axis) {\n        return notYetImplemented('concat');\n    }\n    neg(a) {\n        return notYetImplemented('neg');\n    }\n    add(a, b) {\n        return notYetImplemented('add');\n    }\n    addN(tensors) {\n        return notYetImplemented('addN');\n    }\n    subtract(a, b) {\n        return notYetImplemented('subtract');\n    }\n    multiply(a, b) {\n        return notYetImplemented('multiply');\n    }\n    realDivide(a, b) {\n        return notYetImplemented('realDivide');\n    }\n    floorDiv(a, b) {\n        return notYetImplemented('floorDiv');\n    }\n    sum(x, axes) {\n        return notYetImplemented('sum');\n    }\n    prod(x, axes) {\n        return notYetImplemented('prod');\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        return notYetImplemented('unsortedSegmentSum');\n    }\n    argMin(x, axis) {\n        return notYetImplemented('argMin');\n    }\n    argMax(x, axis) {\n        return notYetImplemented('argMax');\n    }\n    equal(a, b) {\n        return notYetImplemented('equal');\n    }\n    notEqual(a, b) {\n        return notYetImplemented('notEqual');\n    }\n    less(a, b) {\n        return notYetImplemented('less');\n    }\n    lessEqual(a, b) {\n        return notYetImplemented('lessEqual');\n    }\n    greater(a, b) {\n        return notYetImplemented('greater');\n    }\n    greaterEqual(a, b) {\n        return notYetImplemented('greaterEqual');\n    }\n    logicalNot(a) {\n        return notYetImplemented('logicalNot');\n    }\n    logicalAnd(a, b) {\n        return notYetImplemented('logicalAnd');\n    }\n    logicalOr(a, b) {\n        return notYetImplemented('logicalOr');\n    }\n    where(condition) {\n        return notYetImplemented('where');\n    }\n    select(condition, a, b) {\n        return notYetImplemented('select');\n    }\n    topk(x, k, sorted) {\n        return notYetImplemented('topk');\n    }\n    min(x, axes) {\n        return notYetImplemented('min');\n    }\n    minimum(a, b) {\n        return notYetImplemented('minimum');\n    }\n    mod(a, b) {\n        return notYetImplemented('mod');\n    }\n    max(x, axes) {\n        return notYetImplemented('max');\n    }\n    maximum(a, b) {\n        return notYetImplemented('maximum');\n    }\n    all(x, axes) {\n        return notYetImplemented('all');\n    }\n    any(x, axes) {\n        return notYetImplemented('any');\n    }\n    squaredDifference(a, b) {\n        return notYetImplemented('squaredDifference');\n    }\n    ceil(x) {\n        return notYetImplemented('ceil');\n    }\n    floor(x) {\n        return notYetImplemented('floor');\n    }\n    round(x) {\n        return notYetImplemented('round');\n    }\n    sign(x) {\n        return notYetImplemented('sign');\n    }\n    isNaN(x) {\n        return notYetImplemented('isNaN');\n    }\n    isInf(x) {\n        return notYetImplemented('isInf');\n    }\n    isFinite(x) {\n        return notYetImplemented('isFinite');\n    }\n    pow(a, b) {\n        return notYetImplemented('pow');\n    }\n    exp(x) {\n        return notYetImplemented('exp');\n    }\n    expm1(x) {\n        return notYetImplemented('expm1');\n    }\n    softmax(x, dim) {\n        return notYetImplemented('softmax');\n    }\n    log(x) {\n        return notYetImplemented('log');\n    }\n    log1p(x) {\n        return notYetImplemented('log1p');\n    }\n    sqrt(x) {\n        return notYetImplemented('sqrt');\n    }\n    rsqrt(x) {\n        return notYetImplemented('rsqrt');\n    }\n    square(x) {\n        return notYetImplemented('square');\n    }\n    reciprocal(x) {\n        return notYetImplemented('reciprocal');\n    }\n    relu(x) {\n        return notYetImplemented('relu');\n    }\n    relu6(x) {\n        return notYetImplemented('relu6');\n    }\n    prelu(x, a) {\n        return notYetImplemented('prelu');\n    }\n    elu(x) {\n        return notYetImplemented('elu');\n    }\n    eluDer(dy, y) {\n        return notYetImplemented('eluDer');\n    }\n    selu(x) {\n        return notYetImplemented('selu');\n    }\n    int(x) {\n        return notYetImplemented('int');\n    }\n    clip(x, min, max) {\n        return notYetImplemented('clip');\n    }\n    abs(x) {\n        return notYetImplemented('abs');\n    }\n    complexAbs(x) {\n        return notYetImplemented('complexAbs');\n    }\n    sigmoid(x) {\n        return notYetImplemented('sigmoid');\n    }\n    softplus(x) {\n        return notYetImplemented('softplus');\n    }\n    sin(x) {\n        return notYetImplemented('sin');\n    }\n    cos(x) {\n        return notYetImplemented('cos');\n    }\n    tan(x) {\n        return notYetImplemented('tan');\n    }\n    asin(x) {\n        return notYetImplemented('asin');\n    }\n    acos(x) {\n        return notYetImplemented('acos');\n    }\n    atan(x) {\n        return notYetImplemented('atan');\n    }\n    atan2(a, b) {\n        return notYetImplemented('atan2');\n    }\n    sinh(x) {\n        return notYetImplemented('sinh');\n    }\n    cosh(x) {\n        return notYetImplemented('cosh');\n    }\n    tanh(x) {\n        return notYetImplemented('tanh');\n    }\n    asinh(x) {\n        return notYetImplemented('asinh');\n    }\n    acosh(x) {\n        return notYetImplemented('acosh');\n    }\n    atanh(x) {\n        return notYetImplemented('atanh');\n    }\n    erf(x) {\n        return notYetImplemented('erf');\n    }\n    step(x, alpha) {\n        return notYetImplemented('step');\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedConv2d');\n    }\n    conv2d(x, filter, convInfo) {\n        return notYetImplemented('conv2d');\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        return notYetImplemented('conv2dDerInput');\n    }\n    conv2dDerFilter(x, dY, convInfo) {\n        return notYetImplemented('conv2dDerFilter');\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedDepthwiseConv2D');\n    }\n    depthwiseConv2D(input, filter, convInfo) {\n        return notYetImplemented('depthwiseConv2D');\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        return notYetImplemented('depthwiseConv2DDerInput');\n    }\n    depthwiseConv2DDerFilter(x, dY, convInfo) {\n        return notYetImplemented('depthwiseConv2DDerFilter');\n    }\n    conv3d(x, filter, convInfo) {\n        return notYetImplemented('conv3d');\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        return notYetImplemented('conv3dDerInput');\n    }\n    conv3dDerFilter(x, dY, convInfo) {\n        return notYetImplemented('conv3dDerFilter');\n    }\n    maxPool(x, convInfo) {\n        return notYetImplemented('maxPool');\n    }\n    maxPoolBackprop(dy, x, y, convInfo) {\n        return notYetImplemented('maxPoolBackprop');\n    }\n    avgPool(x, convInfo) {\n        return notYetImplemented('avgPool');\n    }\n    avgPoolBackprop(dy, x, convInfo) {\n        return notYetImplemented('avgPoolBackprop');\n    }\n    avgPool3d(x, convInfo) {\n        return notYetImplemented('avgPool3d');\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        return notYetImplemented('avgPool3dBackprop');\n    }\n    maxPool3d(x, convInfo) {\n        return notYetImplemented('maxPool3d');\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        return notYetImplemented('maxPool3dBackprop');\n    }\n    reshape(x, shape) {\n        return notYetImplemented('reshape');\n    }\n    cast(x, dtype) {\n        return notYetImplemented('cast');\n    }\n    tile(x, reps) {\n        return notYetImplemented('tile');\n    }\n    pad(x, paddings, constantValue) {\n        return notYetImplemented('pad');\n    }\n    transpose(x, perm) {\n        return notYetImplemented('transpose');\n    }\n    gather(x, indices, axis) {\n        return notYetImplemented('gather');\n    }\n    gatherND(x, indices) {\n        return notYetImplemented('gatherND');\n    }\n    scatterND(indices, updates, shape) {\n        return notYetImplemented('scatterND');\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        return notYetImplemented('batchToSpaceND');\n    }\n    spaceToBatchND(x, blockShape, paddings) {\n        return notYetImplemented('spaceToBatchND');\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        return notYetImplemented('resizeBilinear');\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        return notYetImplemented('resizeBilinearBackprop');\n    }\n    resizeNearestNeighbor(x, newHEight, newWidth, alignCorners) {\n        return notYetImplemented('resizeNearestNeighbor');\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        return notYetImplemented('resizeNearestNeighborBackprop');\n    }\n    batchNorm(x, mean, variance, offset, scale, varianceEpsilon) {\n        return notYetImplemented('batchNorm');\n    }\n    localResponseNormalization4D(x, radius, bias, alpha, beta) {\n        return notYetImplemented('localResponseNormalization4D');\n    }\n    LRNGrad(dy, inputImage, outputImage, radius, bias, alpha, beta) {\n        return notYetImplemented('LRNGrad');\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        return notYetImplemented('multinomial');\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        return notYetImplemented('oneHot');\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        return notYetImplemented('cumsum');\n    }\n    nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n        return notYetImplemented('nonMaxSuppression');\n    }\n    fft(x) {\n        return notYetImplemented('fft');\n    }\n    ifft(x) {\n        return notYetImplemented('ifft');\n    }\n    complex(real, imag) {\n        return notYetImplemented('complex');\n    }\n    real(input) {\n        return notYetImplemented('real');\n    }\n    imag(input) {\n        return notYetImplemented('imag');\n    }\n    cropAndResize(image, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        return notYetImplemented('cropAndResize');\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        return notYetImplemented('depthToSpace');\n    }\n    // Aligns with the \"SplitV\" kernel in TensorFlow.\n    split(value, sizeSplits, axis) {\n        return notYetImplemented('split');\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        return notYetImplemented('sparseToDense');\n    }\n    diag(x) {\n        return notYetImplemented('diag');\n    }\n    fill(shape, value, dtype) {\n        return notYetImplemented('fill');\n    }\n    onesLike(x) {\n        return notYetImplemented('onesLike');\n    }\n    zerosLike(x) {\n        return notYetImplemented('zerosLike');\n    }\n    linspace(start, stop, num) {\n        return notYetImplemented('linspace');\n    }\n    dispose() {\n        return notYetImplemented('dispose');\n    }\n}\nfunction notYetImplemented(kernelName) {\n    throw new Error(`'${kernelName}' not yet implemented or not found in the registry. ` +\n        `This kernel may not be supported by the tfjs backend you have chosen`);\n}\n//# sourceMappingURL=backend.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.\nconst TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';\n/**\n * The environment contains evaluated flags as well as the registered platform.\n * This is always used as a global singleton and can be retrieved with\n * `tf.env()`.\n *\n * @doc {heading: 'Environment'}\n */\nexport class Environment {\n    // tslint:disable-next-line: no-any\n    constructor(global) {\n        this.global = global;\n        this.flags = {};\n        this.flagRegistry = {};\n        this.urlFlags = {};\n        this.populateURLFlags();\n    }\n    setPlatform(platformName, platform) {\n        if (this.platform != null) {\n            console.warn(`Platform ${this.platformName} has already been set. ` +\n                `Overwriting the platform with ${platform}.`);\n        }\n        this.platformName = platformName;\n        this.platform = platform;\n    }\n    registerFlag(flagName, evaluationFn, setHook) {\n        this.flagRegistry[flagName] = { evaluationFn, setHook };\n        // Override the flag value from the URL. This has to happen here because the\n        // environment is initialized before flags get registered.\n        if (this.urlFlags[flagName] != null) {\n            const flagValue = this.urlFlags[flagName];\n            console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);\n            this.set(flagName, flagValue);\n        }\n    }\n    async getAsync(flagName) {\n        if (flagName in this.flags) {\n            return this.flags[flagName];\n        }\n        this.flags[flagName] = await this.evaluateFlag(flagName);\n        return this.flags[flagName];\n    }\n    get(flagName) {\n        if (flagName in this.flags) {\n            return this.flags[flagName];\n        }\n        const flagValue = this.evaluateFlag(flagName);\n        if (flagValue instanceof Promise) {\n            throw new Error(`Flag ${flagName} cannot be synchronously evaluated. ` +\n                `Please use getAsync() instead.`);\n        }\n        this.flags[flagName] = flagValue;\n        return this.flags[flagName];\n    }\n    getNumber(flagName) {\n        return this.get(flagName);\n    }\n    getBool(flagName) {\n        return this.get(flagName);\n    }\n    getFlags() {\n        return this.flags;\n    }\n    // For backwards compatibility.\n    get features() {\n        return this.flags;\n    }\n    set(flagName, value) {\n        if (this.flagRegistry[flagName] == null) {\n            throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);\n        }\n        this.flags[flagName] = value;\n        if (this.flagRegistry[flagName].setHook != null) {\n            this.flagRegistry[flagName].setHook(value);\n        }\n    }\n    evaluateFlag(flagName) {\n        if (this.flagRegistry[flagName] == null) {\n            throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);\n        }\n        return this.flagRegistry[flagName].evaluationFn();\n    }\n    setFlags(flags) {\n        this.flags = Object.assign({}, flags);\n    }\n    reset() {\n        this.flags = {};\n        this.urlFlags = {};\n        this.populateURLFlags();\n    }\n    populateURLFlags() {\n        if (typeof this.global === 'undefined' ||\n            typeof this.global.location === 'undefined' ||\n            typeof this.global.location.search === 'undefined') {\n            return;\n        }\n        const urlParams = getQueryParams(this.global.location.search);\n        if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n            const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');\n            keyValues.forEach(keyValue => {\n                const [key, value] = keyValue.split(':');\n                this.urlFlags[key] = parseValue(key, value);\n            });\n        }\n    }\n}\nexport function getQueryParams(queryString) {\n    const params = {};\n    queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n        decodeParam(params, t[0], t[1]);\n        return t.join('=');\n    });\n    return params;\n}\nfunction decodeParam(params, name, value) {\n    params[decodeURIComponent(name)] = decodeURIComponent(value || '');\n}\nfunction parseValue(flagName, value) {\n    value = value.toLowerCase();\n    if (value === 'true' || value === 'false') {\n        return value === 'true';\n    }\n    else if (`${+value}` === value) {\n        return +value;\n    }\n    throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);\n}\n/**\n * Returns the current environment (a global singleton).\n *\n * The environment object contains the evaluated feature values as well as the\n * active platform.\n *\n * @doc {heading: 'Environment'}\n */\nexport function env() {\n    return ENV;\n}\nexport let ENV = null;\nexport function setEnvironmentGlobal(environment) {\n    ENV = environment;\n}\n//# sourceMappingURL=environment.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Note that the identifier globalNameSpace is scoped to this module, but will\n// always resolve to the same global object regardless of how the module is\n// resolved.\n// tslint:disable-next-line:no-any\nlet globalNameSpace;\n// tslint:disable-next-line:no-any\nexport function getGlobalNamespace() {\n    if (globalNameSpace == null) {\n        // tslint:disable-next-line:no-any\n        let ns;\n        if (typeof (window) !== 'undefined') {\n            ns = window;\n        }\n        else if (typeof (global) !== 'undefined') {\n            ns = global;\n        }\n        else if (typeof (process) !== 'undefined') {\n            ns = process;\n        }\n        else if (typeof (self) !== 'undefined') {\n            ns = self;\n        }\n        else {\n            throw new Error('Could not find a global object');\n        }\n        globalNameSpace = ns;\n    }\n    return globalNameSpace;\n}\n// tslint:disable-next-line:no-any\nfunction getGlobalMap() {\n    const ns = getGlobalNamespace();\n    if (ns._tfGlobals == null) {\n        ns._tfGlobals = new Map();\n    }\n    return ns._tfGlobals;\n}\n/**\n * Returns a globally accessible 'singleton' object.\n *\n * @param key the name of the object\n * @param init a function to initialize to initialize this object\n *             the first time it is fetched.\n */\nexport function getGlobal(key, init) {\n    const globalMap = getGlobalMap();\n    if (globalMap.has(key)) {\n        return globalMap.get(key);\n    }\n    else {\n        const singleton = init();\n        globalMap.set(key, singleton);\n        return globalMap.get(key);\n    }\n}\n//# sourceMappingURL=global_util.js.map", "export const Abs = 'Abs';\nexport const Acos = 'Acos';\nexport const Acosh = 'Acosh';\nexport const Add = 'Add';\nexport const AddN = 'AddN';\nexport const All = 'All';\nexport const Any = 'Any';\nexport const ArgMax = 'ArgMax';\nexport const ArgMin = 'ArgMin';\nexport const Asin = 'Asin';\nexport const Asinh = 'Asinh';\nexport const Atan = 'Atan';\nexport const Atanh = 'Atanh';\nexport const Atan2 = 'Atan2';\nexport const AvgPool = 'AvgPool';\nexport const AvgPoolBackprop = 'AvgPoolBackprop';\nexport const AvgPool3D = 'AvgPool3D';\nexport const AvgPool3DBackprop = 'AvgPool3DBackprop';\nexport const BatchMatMul = 'BatchMatMul';\nexport const BatchToSpaceND = 'BatchToSpaceND';\nexport const BroadcastTo = 'BroadcastTo';\nexport const Cast = 'Cast';\nexport const Ceil = 'Ceil';\nexport const ClipByValue = 'ClipByValue';\nexport const Complex = 'Complex';\nexport const Concat = 'Concat';\nexport const Conv2D = 'Conv2D';\nexport const Conv2DBackpropFilter = 'Conv2DBackpropFilter';\nexport const Conv2DBackpropInput = 'Conv2DBackpropInput';\nexport const Conv3D = 'Conv3D';\nexport const Conv3DBackpropFilterV2 = 'Conv3DBackpropFilterV2';\nexport const Conv3DBackpropInputV2 = 'Conv3DBackpropInputV2';\nexport const Cos = 'Cos';\nexport const Cosh = 'Cosh';\nexport const Cumsum = 'Cumsum';\nexport const CropAndResize = 'CropAndResize';\nexport const DepthToSpace = 'DepthToSpace';\nexport const DepthwiseConv2dNative = 'DepthwiseConv2dNative';\nexport const DepthwiseConv2dNativeBackpropFilter = 'DepthwiseConv2dNativeBackpropFilter';\nexport const DepthwiseConv2dNativeBackpropInput = 'DepthwiseConv2dNativeBackpropInput';\nexport const Diag = 'Diag';\nexport const Dilation2D = 'Dilation2D';\nexport const Dilation2DBackpropInput = 'Dilation2DBackpropInput';\nexport const Dilation2DBackpropFilter = 'Dilation2DBackpropFilter';\nexport const Div = 'Div';\nexport const Elu = 'Elu';\nexport const EluGrad = 'EluGrad';\nexport const Erf = 'Erf';\nexport const Equal = 'Equal';\nexport const Exp = 'Exp';\nexport const Expm1 = 'Expm1';\nexport const FFT = 'FFT';\nexport const Fill = 'Fill';\nexport const FlipLeftRight = 'FlipLeftRight';\nexport const Floor = 'Floor';\nexport const FloorDiv = 'FloorDiv';\nexport const FusedBatchNorm = 'FusedBatchNorm';\nexport const GatherV2 = 'GatherV2';\nexport const GatherNd = 'GatherNd';\nexport const Greater = 'Greater';\nexport const GreaterEqual = 'GreaterEqual';\nexport const Identity = 'Identity';\nexport const IFFT = 'IFFT';\nexport const Imag = 'Imag';\nexport const IsFinite = 'IsFinite';\nexport const IsInf = 'IsInf';\nexport const IsNan = 'IsNan';\nexport const Less = 'Less';\nexport const LessEqual = 'LessEqual';\nexport const LinSpace = 'LinSpace';\nexport const Log = 'Log';\nexport const Log1p = 'Log1p';\nexport const LogicalAnd = 'LogicalAnd';\nexport const LogicalNot = 'LogicalNot';\nexport const LogicalOr = 'LogicalOr';\nexport const LogSoftmax = 'LogSoftmax';\nexport const LRN = 'LRN';\nexport const LRNBackprop = 'LRNBackprop';\nexport const Max = 'Max';\nexport const Maximum = 'Maximum';\nexport const MaxPool = 'MaxPool';\nexport const MaxPoolBackprop = 'MaxPoolBackprop';\nexport const MaxPool3D = 'MaxPool3D';\nexport const MaxPool3DBackprop = 'MaxPool3DBackprop';\nexport const MaxPoolWithArgmax = 'MaxPoolWithArgmax';\nexport const Mean = 'Mean';\nexport const Min = 'Min';\nexport const Minimum = 'Minimum';\nexport const Mod = 'Mod';\nexport const Multiply = 'Multiply';\nexport const Negate = 'Negate';\nexport const NotEqual = 'NotEqual';\nexport const NonMaxSuppressionV3 = 'NonMaxSuppressionV3';\nexport const NonMaxSuppressionV4 = 'NonMaxSuppressionV4';\nexport const NonMaxSuppressionV5 = 'NonMaxSuppressionV5';\nexport const OnesLike = 'OnesLike';\nexport const OneHot = 'OneHot';\nexport const PadV2 = 'PadV2';\nexport const Pool = 'Pool';\nexport const Pow = 'Pow';\nexport const Prelu = 'Prelu';\nexport const Prod = 'Prod';\nexport const Range = 'Range';\nexport const Real = 'Real';\nexport const Reciprocal = 'Reciprocal';\nexport const Relu = 'Relu';\nexport const Reshape = 'Reshape';\nexport const ResizeNearestNeighbor = 'ResizeNearestNeighbor';\nexport const ResizeNearestNeighborGrad = 'ResizeNearestNeighborGrad';\nexport const ResizeBilinear = 'ResizeBilinear';\nexport const ResizeBilinearGrad = 'ResizeBilinearGrad';\nexport const Relu6 = 'Relu6';\nexport const Reverse = 'Reverse';\nexport const Round = 'Round';\nexport const Rsqrt = 'Rsqrt';\nexport const ScatterNd = 'ScatterNd';\nexport const SelectV2 = 'SelectV2';\nexport const Selu = 'Selu';\nexport const Slice = 'Slice';\nexport const Sin = 'Sin';\nexport const Sinh = 'Sinh';\nexport const Sign = 'Sign';\nexport const Sigmoid = 'Sigmoid';\nexport const Softplus = 'Softplus';\nexport const Sqrt = 'Sqrt';\nexport const Sum = 'Sum';\nexport const SpaceToBatchND = 'SpaceToBatchND';\nexport const SplitV = 'SplitV';\nexport const Softmax = 'Softmax';\nexport const SquaredDifference = 'SquaredDifference';\nexport const Square = 'Square';\nexport const Sub = 'Sub';\nexport const SparseToDense = 'SparseToDense';\nexport const StridedSlice = 'StridedSlice';\nexport const Tan = 'Tan';\nexport const Tanh = 'Tanh';\nexport const Tile = 'Tile';\nexport const TopK = 'TopK';\nexport const Transpose = 'Transpose';\nexport const Unique = 'Unique';\nexport const Unpack = 'Unpack';\nexport const UnsortedSegmentSum = 'UnsortedSegmentSum';\nexport const ZerosLike = 'ZerosLike';\n/**\n * TensorFlow.js-only kernels\n */\nexport const Step = 'Step';\nexport const FromPixels = 'FromPixels';\nexport const RotateWithOffset = 'RotateWithOffset';\nexport const _FusedMatMul = '_FusedMatMul';\nexport const FusedConv2D = 'FusedConv2D';\nexport const FusedDepthwiseConv2D = 'FusedDepthwiseConv2D';\n//# sourceMappingURL=kernel_names.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nimport { getGlobal } from './global_util';\nconst kernelRegistry = getGlobal('kernelRegistry', () => new Map());\nconst gradRegistry = getGlobal('gradRegistry', () => new Map());\n/**\n * Returns the kernel function (code) associated with the provided names.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n */\nexport function getKernel(kernelName, backendName) {\n    const key = makeKey(kernelName, backendName);\n    return kernelRegistry.get(key);\n}\n/**\n * Returns the registered gradient info associated with the provided kernel.\n * @param kernelName The official TF kernel name.\n */\nexport function getGradient(kernelName) {\n    return gradRegistry.get(kernelName);\n}\nexport function getKernelsForBackend(backendName) {\n    const it = kernelRegistry.entries();\n    const result = [];\n    while (true) {\n        const { done, value } = it.next();\n        if (done) {\n            break;\n        }\n        const [key, config] = value;\n        const [backend,] = key.split('_');\n        if (backend === backendName) {\n            result.push(config);\n        }\n    }\n    return result;\n}\n/**\n * Registers the function (forward pass) for the kernel in a global registry.\n *\n * @param config A config object with the following properties:\n * - `kernelName` The official name of the kernel.\n * - `backendName` The official name of the backend.\n * - `kernelFunc` The function to run during the forward pass of the kernel.\n * - `setupFunc` Optional. Gets called once, after the backend initializes.\n * - `disposeFunc` Optional. Gets called once, right before the backend is\n * disposed.\n */\nexport function registerKernel(config) {\n    const { kernelName, backendName } = config;\n    const key = makeKey(kernelName, backendName);\n    if (kernelRegistry.has(key)) {\n        console.warn(`The kernel '${kernelName}' for backend ` +\n            `'${backendName}' is already registered`);\n    }\n    kernelRegistry.set(key, config);\n}\n/**\n * Registers a gradient function for a given kernel in the global registry,\n * to be used during the back-propagation of that kernel.\n *\n * @param config An object with the following properties:\n * - `kernelName` The name of the kernel that the gradient function is for.\n * - `gradFunc` The function to run during back-propagation.\n */\nexport function registerGradient(config) {\n    const { kernelName } = config;\n    if (gradRegistry.has(kernelName)) {\n        // TODO (yassogba) after 3.0 assess whether we need to keep this gated\n        // to debug mode.\n        if (env().getBool('DEBUG')) {\n            console.warn(`Overriding the gradient for '${kernelName}'`);\n        }\n    }\n    gradRegistry.set(kernelName, config);\n}\n/**\n * Removes the kernel function from the registry.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n *\n */\nexport function unregisterKernel(kernelName, backendName) {\n    const key = makeKey(kernelName, backendName);\n    if (!kernelRegistry.has(key)) {\n        throw new Error(`The kernel '${kernelName}' for backend ` +\n            `'${backendName}' is not registered`);\n    }\n    kernelRegistry.delete(key);\n}\n/** Removes the registered gradient from the global registry. */\nexport function unregisterGradient(kernelName) {\n    if (!gradRegistry.has(kernelName)) {\n        throw new Error(`The gradient '${kernelName}' for backend is not registered`);\n    }\n    gradRegistry.delete(kernelName);\n}\n/**\n * Finds kernels that have already been registered to a backend and re-registers\n * them for a new backend. Useful for registering custom backends.\n * @param registeredBackendName Already registered backend.\n * @param newBackendName New backend.\n */\nexport function copyRegisteredKernels(registeredBackendName, newBackendName) {\n    const kernels = getKernelsForBackend(registeredBackendName);\n    kernels.forEach(kernelConfig => {\n        const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName });\n        registerKernel(newKernelConfig);\n    });\n}\nfunction makeKey(kernelName, backendName) {\n    return `${backendName}_${kernelName}`;\n}\n//# sourceMappingURL=kernel_registry.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\n/**\n * Shuffles the array in-place using Fisher-Yates algorithm.\n *\n * ```js\n * const a = [1, 2, 3, 4, 5];\n * tf.util.shuffle(a);\n * console.log(a);\n * ```\n *\n * @param array The array to shuffle in-place.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\n// tslint:disable-next-line:no-any\nexport function shuffle(array) {\n    let counter = array.length;\n    let temp = 0;\n    let index = 0;\n    // While there are elements in the array\n    while (counter > 0) {\n        // Pick a random index\n        index = (Math.random() * counter) | 0;\n        // Decrease counter by 1\n        counter--;\n        // And swap the last element with it\n        temp = array[counter];\n        array[counter] = array[index];\n        array[index] = temp;\n    }\n}\n/** Clamps a value to a specified range. */\nexport function clamp(min, x, max) {\n    return Math.max(min, Math.min(x, max));\n}\nexport function nearestLargerEven(val) {\n    return val % 2 === 0 ? val : val + 1;\n}\nexport function sum(arr) {\n    let sum = 0;\n    for (let i = 0; i < arr.length; i++) {\n        sum += arr[i];\n    }\n    return sum;\n}\n/**\n * Returns a sample from a uniform [a, b) distribution.\n *\n * @param a The minimum support (inclusive).\n * @param b The maximum support (exclusive).\n * @return A pseudorandom number on the half-open interval [a,b).\n */\nexport function randUniform(a, b) {\n    const r = Math.random();\n    return (b * r) + (1 - r) * a;\n}\n/** Returns the squared Euclidean distance between two vectors. */\nexport function distSquared(a, b) {\n    let result = 0;\n    for (let i = 0; i < a.length; i++) {\n        const diff = Number(a[i]) - Number(b[i]);\n        result += diff * diff;\n    }\n    return result;\n}\n/**\n * Asserts that the expression is true. Otherwise throws an error with the\n * provided message.\n *\n * ```js\n * const x = 2;\n * tf.util.assert(x === 2, 'x is not 2');\n * ```\n *\n * @param expr The expression to assert (as a boolean).\n * @param msg A function that returns the message to report when throwing an\n *     error. We use a function for performance reasons.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function assert(expr, msg) {\n    if (!expr) {\n        throw new Error(typeof msg === 'string' ? msg : msg());\n    }\n}\nexport function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = '') {\n    assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nexport function assertNonNull(a) {\n    assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);\n}\n// NOTE: We explicitly type out what T extends instead of any so that\n// util.flatten on a nested array of number doesn't try to infer T as a\n// number[][], causing us to explicitly type util.flatten<number>().\n/**\n *  Flattens an arbitrarily nested array.\n *\n * ```js\n * const a = [[1, 2], [3, 4], [5, [6, [7]]]];\n * const flat = tf.util.flatten(a);\n * console.log(flat);\n * ```\n *\n *  @param arr The nested array to flatten.\n *  @param result The destination array which holds the elements.\n *  @param skipTypedArray If true, avoids flattening the typed arrays. Defaults\n *      to false.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function flatten(arr, result = [], skipTypedArray = false) {\n    if (result == null) {\n        result = [];\n    }\n    if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {\n        for (let i = 0; i < arr.length; ++i) {\n            flatten(arr[i], result, skipTypedArray);\n        }\n    }\n    else {\n        result.push(arr);\n    }\n    return result;\n}\n/**\n * Returns the size (number of elements) of the tensor given its shape.\n *\n * ```js\n * const shape = [3, 4, 2];\n * const size = tf.util.sizeFromShape(shape);\n * console.log(size);\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function sizeFromShape(shape) {\n    if (shape.length === 0) {\n        // Scalar.\n        return 1;\n    }\n    let size = shape[0];\n    for (let i = 1; i < shape.length; i++) {\n        size *= shape[i];\n    }\n    return size;\n}\nexport function isScalarShape(shape) {\n    return shape.length === 0;\n}\nexport function arraysEqual(n1, n2) {\n    if (n1 === n2) {\n        return true;\n    }\n    if (n1 == null || n2 == null) {\n        return false;\n    }\n    if (n1.length !== n2.length) {\n        return false;\n    }\n    for (let i = 0; i < n1.length; i++) {\n        if (n1[i] !== n2[i]) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function isInt(a) {\n    return a % 1 === 0;\n}\nexport function tanh(x) {\n    // tslint:disable-next-line:no-any\n    if (Math.tanh != null) {\n        // tslint:disable-next-line:no-any\n        return Math.tanh(x);\n    }\n    if (x === Infinity) {\n        return 1;\n    }\n    else if (x === -Infinity) {\n        return -1;\n    }\n    else {\n        const e2x = Math.exp(2 * x);\n        return (e2x - 1) / (e2x + 1);\n    }\n}\nexport function sizeToSquarishShape(size) {\n    const width = Math.ceil(Math.sqrt(size));\n    return [width, Math.ceil(size / width)];\n}\n/**\n * Creates a new array with randomized indicies to a given quantity.\n *\n * ```js\n * const randomTen = tf.util.createShuffledIndices(10);\n * console.log(randomTen);\n * ```\n *\n * @param number Quantity of how many shuffled indicies to create.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function createShuffledIndices(n) {\n    const shuffledIndices = new Uint32Array(n);\n    for (let i = 0; i < n; ++i) {\n        shuffledIndices[i] = i;\n    }\n    shuffle(shuffledIndices);\n    return shuffledIndices;\n}\nexport function rightPad(a, size) {\n    if (size <= a.length) {\n        return a;\n    }\n    return a + ' '.repeat(size - a.length);\n}\nexport function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {\n    return new Promise((resolve, reject) => {\n        let tryCount = 0;\n        const tryFn = () => {\n            if (checkFn()) {\n                resolve();\n                return;\n            }\n            tryCount++;\n            const nextBackoff = delayFn(tryCount);\n            if (maxCounter != null && tryCount >= maxCounter) {\n                reject();\n                return;\n            }\n            setTimeout(tryFn, nextBackoff);\n        };\n        tryFn();\n    });\n}\n/**\n * Given the full size of the array and a shape that may contain -1 as the\n * implicit dimension, returns the inferred shape where -1 is replaced.\n * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].\n *\n * @param shape The shape, which may contain -1 in some dimension.\n * @param size The full size (number of elements) of the array.\n * @return The inferred shape where -1 is replaced with the inferred size.\n */\nexport function inferFromImplicitShape(shape, size) {\n    let shapeProd = 1;\n    let implicitIdx = -1;\n    for (let i = 0; i < shape.length; ++i) {\n        if (shape[i] >= 0) {\n            shapeProd *= shape[i];\n        }\n        else if (shape[i] === -1) {\n            if (implicitIdx !== -1) {\n                throw Error(`Shapes can only have 1 implicit size. ` +\n                    `Found -1 at dim ${implicitIdx} and dim ${i}`);\n            }\n            implicitIdx = i;\n        }\n        else if (shape[i] < 0) {\n            throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n        }\n    }\n    if (implicitIdx === -1) {\n        if (size > 0 && size !== shapeProd) {\n            throw Error(`Size(${size}) must match the product of shape ${shape}`);\n        }\n        return shape;\n    }\n    if (shapeProd === 0) {\n        throw Error(`Cannot infer the missing size in [${shape}] when ` +\n            `there are 0 elements`);\n    }\n    if (size % shapeProd !== 0) {\n        throw Error(`The implicit shape can't be a fractional number. ` +\n            `Got ${size} / ${shapeProd}`);\n    }\n    const newShape = shape.slice();\n    newShape[implicitIdx] = size / shapeProd;\n    return newShape;\n}\nexport function parseAxisParam(axis, shape) {\n    const rank = shape.length;\n    // Normalize input\n    axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n    // Check for valid range\n    assert(axis.every(ax => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but ` +\n        `got axis ${axis}`);\n    // Check for only integers\n    assert(axis.every(ax => isInt(ax)), () => `All values in axis param must be integers but ` +\n        `got axis ${axis}`);\n    // Handle negative axis.\n    return axis.map(a => a < 0 ? rank + a : a);\n}\n/** Reduces the shape by removing all dimensions of shape 1. */\nexport function squeezeShape(shape, axis) {\n    const newShape = [];\n    const keptDims = [];\n    const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;\n    const axes = (axis == null || isEmptyArray) ?\n        null :\n        parseAxisParam(axis, shape).sort();\n    let j = 0;\n    for (let i = 0; i < shape.length; ++i) {\n        if (axes != null) {\n            if (axes[j] === i && shape[i] !== 1) {\n                throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n            }\n            if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {\n                newShape.push(shape[i]);\n                keptDims.push(i);\n            }\n            if (axes[j] <= i) {\n                j++;\n            }\n        }\n        if (shape[i] !== 1) {\n            newShape.push(shape[i]);\n            keptDims.push(i);\n        }\n    }\n    return { newShape, keptDims };\n}\nexport function getTypedArrayFromDType(dtype, size) {\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    return values;\n}\nexport function getArrayFromDType(dtype, size) {\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else if (dtype === 'string') {\n        values = new Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    return values;\n}\nexport function checkConversionForErrors(vals, dtype) {\n    for (let i = 0; i < vals.length; i++) {\n        const num = vals[i];\n        if (isNaN(num) || !isFinite(num)) {\n            throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n        }\n    }\n}\n/** Returns true if the dtype is valid. */\nexport function isValidDtype(dtype) {\n    return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' ||\n        dtype === 'int32' || dtype === 'string';\n}\n/**\n * Returns true if the new type can't encode the old type without loss of\n * precision.\n */\nexport function hasEncodingLoss(oldType, newType) {\n    if (newType === 'complex64') {\n        return false;\n    }\n    if (newType === 'float32' && oldType !== 'complex64') {\n        return false;\n    }\n    if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {\n        return false;\n    }\n    if (newType === 'bool' && oldType === 'bool') {\n        return false;\n    }\n    return true;\n}\nexport function isTypedArray(a) {\n    return a instanceof Float32Array || a instanceof Int32Array ||\n        a instanceof Uint8Array;\n}\nexport function bytesPerElement(dtype) {\n    if (dtype === 'float32' || dtype === 'int32') {\n        return 4;\n    }\n    else if (dtype === 'complex64') {\n        return 8;\n    }\n    else if (dtype === 'bool') {\n        return 1;\n    }\n    else {\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n}\n/**\n * Returns the approximate number of bytes allocated in the string array - 2\n * bytes per character. Computing the exact bytes for a native string in JS is\n * not possible since it depends on the encoding of the html page that serves\n * the website.\n */\nexport function bytesFromStringArray(arr) {\n    if (arr == null) {\n        return 0;\n    }\n    let bytes = 0;\n    arr.forEach(x => bytes += x.length);\n    return bytes;\n}\n/** Returns true if the value is a string. */\nexport function isString(value) {\n    return typeof value === 'string' || value instanceof String;\n}\nexport function isBoolean(value) {\n    return typeof value === 'boolean';\n}\nexport function isNumber(value) {\n    return typeof value === 'number';\n}\nexport function inferDtype(values) {\n    if (Array.isArray(values)) {\n        return inferDtype(values[0]);\n    }\n    if (values instanceof Float32Array) {\n        return 'float32';\n    }\n    else if (values instanceof Int32Array || values instanceof Uint8Array) {\n        return 'int32';\n    }\n    else if (isNumber(values)) {\n        return 'float32';\n    }\n    else if (isString(values)) {\n        return 'string';\n    }\n    else if (isBoolean(values)) {\n        return 'bool';\n    }\n    return 'float32';\n}\nexport function isFunction(f) {\n    return !!(f && f.constructor && f.call && f.apply);\n}\nexport function nearestDivisor(size, start) {\n    for (let i = start; i < size; ++i) {\n        if (size % i === 0) {\n            return i;\n        }\n    }\n    return size;\n}\nexport function computeStrides(shape) {\n    const rank = shape.length;\n    if (rank < 2) {\n        return [];\n    }\n    // Last dimension has implicit stride of 1, thus having D-1 (instead of D)\n    // strides.\n    const strides = new Array(rank - 1);\n    strides[rank - 2] = shape[rank - 1];\n    for (let i = rank - 3; i >= 0; --i) {\n        strides[i] = strides[i + 1] * shape[i + 1];\n    }\n    return strides;\n}\n/**\n * Create typed array for scalar value. Used for storing in `DataStorage`.\n */\nexport function createScalarValue(value, dtype) {\n    if (dtype === 'string') {\n        return encodeString(value);\n    }\n    return toTypedArray([value], dtype);\n}\nexport function toTypedArray(a, dtype) {\n    if (dtype === 'string') {\n        throw new Error('Cannot convert a string[] to a TypedArray');\n    }\n    if (Array.isArray(a)) {\n        a = flatten(a);\n    }\n    if (env().getBool('DEBUG')) {\n        checkConversionForErrors(a, dtype);\n    }\n    if (noConversionNeeded(a, dtype)) {\n        return a;\n    }\n    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n        return new Float32Array(a);\n    }\n    else if (dtype === 'int32') {\n        return new Int32Array(a);\n    }\n    else if (dtype === 'bool') {\n        const bool = new Uint8Array(a.length);\n        for (let i = 0; i < bool.length; ++i) {\n            if (Math.round(a[i]) !== 0) {\n                bool[i] = 1;\n            }\n        }\n        return bool;\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\nfunction createNestedArray(offset, shape, a) {\n    const ret = new Array();\n    if (shape.length === 1) {\n        const d = shape[0];\n        for (let i = 0; i < d; i++) {\n            ret[i] = a[offset + i];\n        }\n    }\n    else {\n        const d = shape[0];\n        const rest = shape.slice(1);\n        const len = rest.reduce((acc, c) => acc * c);\n        for (let i = 0; i < d; i++) {\n            ret[i] = createNestedArray(offset + i * len, rest, a);\n        }\n    }\n    return ret;\n}\n// Provide a nested array of TypedArray in given shape.\nexport function toNestedArray(shape, a) {\n    if (shape.length === 0) {\n        // Scalar type should return a single number.\n        return a[0];\n    }\n    const size = shape.reduce((acc, c) => acc * c);\n    if (size === 0) {\n        // A tensor with shape zero should be turned into empty list.\n        return [];\n    }\n    if (size !== a.length) {\n        throw new Error(`[${shape}] does not match the input size ${a.length}.`);\n    }\n    return createNestedArray(0, shape, a);\n}\nfunction noConversionNeeded(a, dtype) {\n    return (a instanceof Float32Array && dtype === 'float32') ||\n        (a instanceof Int32Array && dtype === 'int32') ||\n        (a instanceof Uint8Array && dtype === 'bool');\n}\nexport function makeOnesTypedArray(size, dtype) {\n    const array = makeZerosTypedArray(size, dtype);\n    for (let i = 0; i < array.length; i++) {\n        array[i] = 1;\n    }\n    return array;\n}\nexport function makeZerosTypedArray(size, dtype) {\n    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n        return new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        return new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        return new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\n/**\n * Make nested `TypedArray` filled with zeros.\n * @param shape The shape information for the nested array.\n * @param dtype dtype of the array element.\n */\nexport function makeZerosNestedTypedArray(shape, dtype) {\n    const size = shape.reduce((prev, curr) => prev * curr, 1);\n    if (dtype == null || dtype === 'float32') {\n        return toNestedArray(shape, new Float32Array(size));\n    }\n    else if (dtype === 'int32') {\n        return toNestedArray(shape, new Int32Array(size));\n    }\n    else if (dtype === 'bool') {\n        return toNestedArray(shape, new Uint8Array(size));\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\n/**\n * Returns the current high-resolution time in milliseconds relative to an\n * arbitrary time in the past. It works across different platforms (node.js,\n * browsers).\n *\n * ```js\n * console.log(tf.util.now());\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function now() {\n    return env().platform.now();\n}\nexport function assertNonNegativeIntegerDimensions(shape) {\n    shape.forEach(dimSize => {\n        assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got ` +\n            `shape [${shape}].`);\n    });\n}\n/**\n * Returns a platform-specific implementation of\n * [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n *\n * If `fetch` is defined on the global object (`window`, `process`, etc.),\n * `tf.util.fetch` returns that function.\n *\n * If not, `tf.util.fetch` returns a platform-specific solution.\n *\n * ```js\n * const resource = await tf.util.fetch('https://unpkg.com/@tensorflow/tfjs');\n * // handle response\n * ```\n *\n * @doc {heading: 'Util'}\n */\nexport function fetch(path, requestInits) {\n    return env().platform.fetch(path, requestInits);\n}\n/**\n * Encodes the provided string into bytes using the provided encoding scheme.\n *\n * @param s The string to encode.\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function encodeString(s, encoding = 'utf-8') {\n    encoding = encoding || 'utf-8';\n    return env().platform.encode(s, encoding);\n}\n/**\n * Decodes the provided bytes into a string using the provided encoding scheme.\n * @param bytes The bytes to decode.\n *\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function decodeString(bytes, encoding = 'utf-8') {\n    encoding = encoding || 'utf-8';\n    return env().platform.decode(bytes, encoding);\n}\n/**\n * Computes flat index for a given location (multidimentionsal index) in a\n * Tensor/multidimensional array.\n *\n * @param locs Location in the tensor.\n * @param rank Rank of the tensor.\n * @param strides Tensor strides.\n */\nexport function locToIndex(locs, rank, strides) {\n    if (rank === 0) {\n        return 0;\n    }\n    else if (rank === 1) {\n        return locs[0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n        index += strides[i] * locs[i];\n    }\n    return index;\n}\n/**\n * Computes the location (multidimensional index) in a tensor/multidimentional\n * array for a given flat index.\n *\n * @param index Index in flat array.\n * @param rank Rank of tensor.\n * @param strides Strides of tensor.\n */\nexport function indexToLoc(index, rank, strides) {\n    if (rank === 0) {\n        return [];\n    }\n    else if (rank === 1) {\n        return [index];\n    }\n    const locs = new Array(rank);\n    for (let i = 0; i < locs.length - 1; ++i) {\n        locs[i] = Math.floor(index / strides[i]);\n        index -= locs[i] * strides[i];\n    }\n    locs[locs.length - 1] = index;\n    return locs;\n}\n//# sourceMappingURL=util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from './util';\nexport class Profiler {\n    constructor(backendTimer, logger) {\n        this.backendTimer = backendTimer;\n        this.logger = logger;\n        if (logger == null) {\n            this.logger = new Logger();\n        }\n    }\n    profileKernel(kernelName, inputs, f) {\n        let outputs;\n        const holdResultWrapperFn = () => {\n            outputs = f();\n        };\n        const timer = this.backendTimer.time(holdResultWrapperFn);\n        for (let i = 0; i < outputs.length; i++) {\n            const output = outputs[i];\n            // Dangling promise here because we don't want to propagate up\n            // asynchronicity.\n            output.data().then(tensorVals => {\n                checkComputationForErrors(tensorVals, output.dtype, kernelName);\n            });\n        }\n        const kernelProfile = {\n            kernelName,\n            outputs,\n            inputs,\n            timeMs: timer.then(timing => timing.kernelMs),\n            extraInfo: timer.then(timing => timing.getExtraProfileInfo != null ?\n                timing.getExtraProfileInfo() :\n                '')\n        };\n        return kernelProfile;\n    }\n    logKernelProfile(kernelProfile) {\n        const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile;\n        outputs.forEach(result => {\n            Promise.all([result.data(), timeMs, extraInfo]).then(valueContainer => {\n                this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);\n            });\n        });\n    }\n}\nexport function checkComputationForErrors(vals, dtype, kernelName) {\n    if (dtype !== 'float32') {\n        // Only floating point computations will generate NaN values\n        return false;\n    }\n    for (let i = 0; i < vals.length; i++) {\n        const num = vals[i];\n        if (isNaN(num) || !isFinite(num)) {\n            // Throwing custom exception so behavior is testable.\n            console.warn(`Found ${num} in the result of '${kernelName}'`);\n            return true;\n        }\n    }\n    return false;\n}\nexport class Logger {\n    logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {\n        const time = typeof timeMs === 'number' ? util.rightPad(`${timeMs}ms`, 9) :\n            timeMs['error'];\n        const paddedName = util.rightPad(name, 25);\n        const rank = result.rank;\n        const size = result.size;\n        const shape = util.rightPad(result.shape.toString(), 14);\n        let inputShapesDescription = '';\n        for (const name in inputs) {\n            const input = inputs[name];\n            if (input != null) {\n                // The input might be a non-tensor (e.g HTMLImageElement), in which case\n                // we claim the output shape as input shape.\n                const inputShape = input.shape || result.shape;\n                const inputRank = inputShape.length;\n                inputShapesDescription +=\n                    `${name}: ${inputRank}D ${inputRank > 0 ? inputShape : ''} `;\n            }\n        }\n        console.log(`%c${paddedName}\\t%c${time}\\t%c${rank}D ${shape}\\t%c${size}\\t%c${inputShapesDescription}\\t%c${extraInfo}`, 'font-weight:bold', 'color:red', 'color:blue', 'color: orange', 'color: green', 'color: steelblue');\n    }\n}\n//# sourceMappingURL=profiler.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from './util';\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nexport function getFilteredNodesXToY(tape, xs, y) {\n    // Forward pass to compute all the nodes and Tensors that are transitively a\n    // function of x.\n    const tensorsFromX = {};\n    const nodesFromX = {};\n    for (let i = 0; i < xs.length; i++) {\n        tensorsFromX[xs[i].id] = true;\n    }\n    for (let i = 0; i < tape.length; i++) {\n        const node = tape[i];\n        const nodeInputs = node.inputs;\n        for (const inputName in nodeInputs) {\n            const input = nodeInputs[inputName];\n            let anyInputFromX = false;\n            for (let j = 0; j < xs.length; j++) {\n                if (tensorsFromX[input.id]) {\n                    node.outputs.forEach(output => tensorsFromX[output.id] = true);\n                    anyInputFromX = true;\n                    nodesFromX[node.id] = true;\n                    break;\n                }\n            }\n            if (anyInputFromX) {\n                break;\n            }\n        }\n    }\n    // Backward pass to find all of the nodes and Tensors that lead to y.\n    const tensorsLeadToY = {};\n    tensorsLeadToY[y.id] = true;\n    const nodesToY = {};\n    for (let i = tape.length - 1; i >= 0; i--) {\n        const node = tape[i];\n        const nodeInputs = node.inputs;\n        // If any of the outputs lead to y, mark all of the inputs as leading to y.\n        for (let j = 0; j < node.outputs.length; j++) {\n            if (tensorsLeadToY[node.outputs[j].id]) {\n                for (const inputName in nodeInputs) {\n                    tensorsLeadToY[nodeInputs[inputName].id] = true;\n                    nodesToY[node.id] = true;\n                }\n                break;\n            }\n        }\n    }\n    // Return the paths that come from x and lead to y.\n    const filteredTape = [];\n    for (let i = 0; i < tape.length; i++) {\n        const node = tape[i];\n        if (nodesFromX[node.id] && nodesToY[node.id]) {\n            // Prune the inputs from the node that aren't a function of x.\n            const prunedInputs = {};\n            for (const inputName in node.inputs) {\n                const nodeInput = node.inputs[inputName];\n                if (tensorsFromX[nodeInput.id]) {\n                    prunedInputs[inputName] = nodeInput;\n                }\n            }\n            // Copy the node and overwrite inputsAndArgs to the pruned version.\n            const prunedNode = Object.assign({}, node);\n            prunedNode.inputs = prunedInputs;\n            prunedNode.outputs = node.outputs;\n            filteredTape.push(prunedNode);\n        }\n    }\n    return filteredTape;\n}\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nexport function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy, add) {\n    // Walk the tape backward and keep a map of Tensor to its gradient.\n    for (let i = filteredTape.length - 1; i >= 0; i--) {\n        const node = filteredTape[i];\n        const dys = [];\n        node.outputs.forEach(o => {\n            const gradTensor = tensorAccumulatedGradientMap[o.id];\n            if (gradTensor != null) {\n                dys.push(gradTensor);\n            }\n            else {\n                // This particular output is not in the back-propagation subgraph, so it\n                // does not affect the final output, thus we put null for its dy.\n                dys.push(null);\n            }\n        });\n        if (node.gradient == null) {\n            throw new Error(`Cannot compute gradient: gradient function not found ` +\n                `for ${node.kernelName}.`);\n        }\n        // Backprop dy through this node and accumulate gradients over the inputs.\n        const inputGradients = node.gradient(dys);\n        for (const inputName in node.inputs) {\n            if (!(inputName in inputGradients)) {\n                throw new Error(`Cannot backprop through input ${inputName}. ` +\n                    `Available gradients found: ${Object.keys(inputGradients)}.`);\n            }\n            // Call the gradient function.\n            const dx = tidy(() => inputGradients[inputName]());\n            if (dx.dtype !== 'float32') {\n                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n                    `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n            }\n            const x = node.inputs[inputName];\n            if (!util.arraysEqual(dx.shape, x.shape)) {\n                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n                    `'${inputName}' has shape '${dx.shape}', which does not match ` +\n                    `the shape of the input '${x.shape}'`);\n            }\n            if (tensorAccumulatedGradientMap[x.id] == null) {\n                tensorAccumulatedGradientMap[x.id] = dx;\n            }\n            else {\n                const curGradient = tensorAccumulatedGradientMap[x.id];\n                tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);\n                curGradient.dispose();\n            }\n        }\n    }\n}\n//# sourceMappingURL=tape.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { computeStrides, isString, rightPad, sizeFromShape } from './util';\n// Maximum number of values before we decide to show ellipsis.\nconst FORMAT_LIMIT_NUM_VALS = 20;\n// Number of first and last values to show when displaying a, b,...,y, z.\nconst FORMAT_NUM_FIRST_LAST_VALS = 3;\n// Number of significant digits to show.\nconst FORMAT_NUM_SIG_DIGITS = 7;\nexport function tensorToString(vals, shape, dtype, verbose) {\n    const strides = computeStrides(shape);\n    const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n    const rank = shape.length;\n    const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n    const lines = ['Tensor'];\n    if (verbose) {\n        lines.push(`  dtype: ${dtype}`);\n        lines.push(`  rank: ${rank}`);\n        lines.push(`  shape: [${shape}]`);\n        lines.push(`  values:`);\n    }\n    lines.push(valsLines.map(l => '    ' + l).join('\\n'));\n    return lines.join('\\n');\n}\nfunction computeMaxSizePerColumn(vals, shape, dtype, strides) {\n    const n = sizeFromShape(shape);\n    const numCols = strides[strides.length - 1];\n    const padPerCol = new Array(numCols).fill(0);\n    const rank = shape.length;\n    const valuesOrTuples = dtype === 'complex64' ? createComplexTuples(vals) : vals;\n    if (rank > 1) {\n        for (let row = 0; row < n / numCols; row++) {\n            const offset = row * numCols;\n            for (let j = 0; j < numCols; j++) {\n                padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);\n            }\n        }\n    }\n    return padPerCol;\n}\nfunction valToString(val, pad, dtype) {\n    let valStr;\n    if (Array.isArray(val)) {\n        valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +\n            `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n    }\n    else if (isString(val)) {\n        valStr = `'${val}'`;\n    }\n    else if (dtype === 'bool') {\n        valStr = boolNumToString(val);\n    }\n    else {\n        valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n    }\n    return rightPad(valStr, pad);\n}\nfunction boolNumToString(v) {\n    return v === 0 ? 'false' : 'true';\n}\nfunction subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {\n    const storagePerElement = dtype === 'complex64' ? 2 : 1;\n    const size = shape[0];\n    const rank = shape.length;\n    if (rank === 0) {\n        if (dtype === 'complex64') {\n            const complexTuple = createComplexTuples(vals);\n            return [valToString(complexTuple[0], 0, dtype)];\n        }\n        if (dtype === 'bool') {\n            return [boolNumToString(vals[0])];\n        }\n        return [vals[0].toString()];\n    }\n    if (rank === 1) {\n        if (size > FORMAT_LIMIT_NUM_VALS) {\n            const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n            let firstVals = Array.from(vals.slice(0, firstValsSize));\n            let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));\n            if (dtype === 'complex64') {\n                firstVals = createComplexTuples(firstVals);\n                lastVals = createComplexTuples(lastVals);\n            }\n            return [\n                '[' +\n                    firstVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n                        .join(', ') +\n                    ', ..., ' +\n                    lastVals\n                        .map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype))\n                        .join(', ') +\n                    ']'\n            ];\n        }\n        const displayVals = dtype === 'complex64' ? createComplexTuples(vals) :\n            Array.from(vals);\n        return [\n            '[' +\n                displayVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n                    .join(', ') +\n                ']'\n        ];\n    }\n    // The array is rank 2 or more.\n    const subshape = shape.slice(1);\n    const substrides = strides.slice(1);\n    const stride = strides[0] * storagePerElement;\n    const lines = [];\n    if (size > FORMAT_LIMIT_NUM_VALS) {\n        for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false /* isLast */));\n        }\n        lines.push('...');\n        for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n        }\n    }\n    else {\n        for (let i = 0; i < size; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n        }\n    }\n    const sep = rank === 2 ? ',' : '';\n    lines[0] = '[' + lines[0] + sep;\n    for (let i = 1; i < lines.length - 1; i++) {\n        lines[i] = ' ' + lines[i] + sep;\n    }\n    let newLineSep = ',\\n';\n    for (let i = 2; i < rank; i++) {\n        newLineSep += '\\n';\n    }\n    lines[lines.length - 1] =\n        ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);\n    return lines;\n}\nfunction createComplexTuples(vals) {\n    const complexTuples = [];\n    for (let i = 0; i < vals.length; i += 2) {\n        complexTuples.push([vals[i], vals[i + 1]]);\n    }\n    return complexTuples;\n}\n//# sourceMappingURL=tensor_format.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensorToString } from './tensor_format';\nimport * as util from './util';\nimport { computeStrides, toNestedArray } from './util';\n/**\n * A mutable object, similar to `tf.Tensor`, that allows users to set values\n * at locations before converting to an immutable `tf.Tensor`.\n *\n * See `tf.buffer` for creating a tensor buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class TensorBuffer {\n    constructor(shape, dtype, values) {\n        this.dtype = dtype;\n        this.shape = shape.slice();\n        this.size = util.sizeFromShape(shape);\n        if (values != null) {\n            const n = values.length;\n            util.assert(n === this.size, () => `Length of values '${n}' does not match the size ` +\n                `inferred by the shape '${this.size}'.`);\n        }\n        if (dtype === 'complex64') {\n            throw new Error(`complex64 dtype TensorBuffers are not supported. Please create ` +\n                `a TensorBuffer for the real and imaginary parts separately and ` +\n                `call tf.complex(real, imag).`);\n        }\n        this.values = values || util.getArrayFromDType(dtype, this.size);\n        this.strides = computeStrides(shape);\n    }\n    /**\n     * Sets a value in the buffer at a given location.\n     *\n     * @param value The value to set.\n     * @param locs  The location indices.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    set(value, ...locs) {\n        if (locs.length === 0) {\n            locs = [0];\n        }\n        util.assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must ` +\n            `match the rank (${this.rank})`);\n        const index = this.locToIndex(locs);\n        this.values[index] = value;\n    }\n    /**\n     * Returns the value in the buffer at the provided location.\n     *\n     * @param locs The location indices.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    get(...locs) {\n        if (locs.length === 0) {\n            locs = [0];\n        }\n        let i = 0;\n        for (const loc of locs) {\n            if (loc < 0 || loc >= this.shape[i]) {\n                const msg = `Requested out of range element at ${locs}. ` +\n                    `  Buffer shape=${this.shape}`;\n                throw new Error(msg);\n            }\n            i++;\n        }\n        let index = locs[locs.length - 1];\n        for (let i = 0; i < locs.length - 1; ++i) {\n            index += this.strides[i] * locs[i];\n        }\n        return this.values[index];\n    }\n    locToIndex(locs) {\n        if (this.rank === 0) {\n            return 0;\n        }\n        else if (this.rank === 1) {\n            return locs[0];\n        }\n        let index = locs[locs.length - 1];\n        for (let i = 0; i < locs.length - 1; ++i) {\n            index += this.strides[i] * locs[i];\n        }\n        return index;\n    }\n    indexToLoc(index) {\n        if (this.rank === 0) {\n            return [];\n        }\n        else if (this.rank === 1) {\n            return [index];\n        }\n        const locs = new Array(this.shape.length);\n        for (let i = 0; i < locs.length - 1; ++i) {\n            locs[i] = Math.floor(index / this.strides[i]);\n            index -= locs[i] * this.strides[i];\n        }\n        locs[locs.length - 1] = index;\n        return locs;\n    }\n    get rank() {\n        return this.shape.length;\n    }\n    /**\n     * Creates an immutable `tf.Tensor` object from the buffer.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    toTensor() {\n        return trackerFn().makeTensor(this.values, this.shape, this.dtype);\n    }\n}\n// For tracking tensor creation and disposal.\nlet trackerFn = null;\n// Used by chaining methods to call into ops.\nlet opHandler = null;\n// Used to warn about deprecated methods.\nlet deprecationWarningFn = null;\n// This here so that we can use this method on dev branches and keep the\n// functionality at master.\n// tslint:disable-next-line:no-unused-expression\n[deprecationWarningFn];\n/**\n * An external consumer can register itself as the tensor tracker. This way\n * the Tensor class can notify the tracker for every tensor created and\n * disposed.\n */\nexport function setTensorTracker(fn) {\n    trackerFn = fn;\n}\n/**\n * An external consumer can register itself as the op handler. This way the\n * Tensor class can have chaining methods that call into ops via the op\n * handler.\n */\nexport function setOpHandler(handler) {\n    opHandler = handler;\n}\n/**\n * Sets the deprecation warning function to be used by this file. This way the\n * Tensor class can be a leaf but still use the environment.\n */\nexport function setDeprecationWarningFn(fn) {\n    deprecationWarningFn = fn;\n}\n/**\n * A `tf.Tensor` object represents an immutable, multidimensional array of\n * numbers that has a shape and a data type.\n *\n * See `tf.tensor` for details on how to create a `tf.Tensor`.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Tensor {\n    constructor(shape, dtype, dataId, id) {\n        /** Whether this tensor has been globally kept. */\n        this.kept = false;\n        this.isDisposedInternal = false;\n        this.shape = shape.slice();\n        this.dtype = dtype || 'float32';\n        this.size = util.sizeFromShape(shape);\n        this.strides = computeStrides(shape);\n        this.dataId = dataId;\n        this.id = id;\n        this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher');\n    }\n    get rank() {\n        return this.shape.length;\n    }\n    /**\n     * Returns a promise of `tf.TensorBuffer` that holds the underlying data.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async buffer() {\n        const vals = await this.data();\n        return opHandler.buffer(this.shape, this.dtype, vals);\n    }\n    /**\n     * Returns a `tf.TensorBuffer` that holds the underlying data.\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    bufferSync() {\n        return opHandler.buffer(this.shape, this.dtype, this.dataSync());\n    }\n    /**\n     * Returns the tensor data as a nested array. The transfer of data is done\n     * asynchronously.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async array() {\n        const vals = await this.data();\n        return toNestedArray(this.shape, vals);\n    }\n    /**\n     * Returns the tensor data as a nested array. The transfer of data is done\n     * synchronously.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    arraySync() {\n        return toNestedArray(this.shape, this.dataSync());\n    }\n    /**\n     * Asynchronously downloads the values from the `tf.Tensor`. Returns a\n     * promise of `TypedArray` that resolves when the computation has finished.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async data() {\n        this.throwIfDisposed();\n        const data = trackerFn().read(this.dataId);\n        if (this.dtype === 'string') {\n            const bytes = await data;\n            try {\n                return bytes.map(b => util.decodeString(b));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode the string bytes into utf-8. ' +\n                    'To get the original bytes, call tensor.bytes().');\n            }\n        }\n        return data;\n    }\n    /**\n     * Synchronously downloads the values from the `tf.Tensor`. This blocks the\n     * UI thread until the values are ready, which can cause performance issues.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    dataSync() {\n        this.throwIfDisposed();\n        const data = trackerFn().readSync(this.dataId);\n        if (this.dtype === 'string') {\n            try {\n                return data.map(b => util.decodeString(b));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode the string bytes into utf-8. ' +\n                    'To get the original bytes, call tensor.bytes().');\n            }\n        }\n        return data;\n    }\n    /** Returns the underlying bytes of the tensor's data. */\n    async bytes() {\n        this.throwIfDisposed();\n        const data = await trackerFn().read(this.dataId);\n        if (this.dtype === 'string') {\n            return data;\n        }\n        else {\n            return new Uint8Array(data.buffer);\n        }\n    }\n    /**\n     * Disposes `tf.Tensor` from memory.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    dispose() {\n        if (this.isDisposed) {\n            return;\n        }\n        trackerFn().disposeTensor(this);\n        this.isDisposedInternal = true;\n    }\n    get isDisposed() {\n        return this.isDisposedInternal;\n    }\n    throwIfDisposed() {\n        if (this.isDisposed) {\n            throw new Error(`Tensor is disposed.`);\n        }\n    }\n    /**\n     * Prints the `tf.Tensor`. See `tf.print` for details.\n     *\n     * @param verbose Whether to print verbose information about the tensor,\n     *    including dtype and size.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    print(verbose = false) {\n        return opHandler.print(this, verbose);\n    }\n    /**\n     * Returns a copy of the tensor. See `tf.clone` for details.\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    clone() {\n        this.throwIfDisposed();\n        return opHandler.clone(this);\n    }\n    /**\n     * Returns a human-readable description of the tensor. Useful for logging.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    toString(verbose = false) {\n        const vals = this.dataSync();\n        return tensorToString(vals, this.shape, this.dtype, verbose);\n    }\n    cast(dtype) {\n        this.throwIfDisposed();\n        return opHandler.cast(this, dtype);\n    }\n    variable(trainable = true, name, dtype) {\n        this.throwIfDisposed();\n        return trackerFn().makeVariable(this, trainable, name, dtype);\n    }\n}\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n    value: (instance) => {\n        // Implementation note: we should use properties of the object that will be\n        // defined before the constructor body has finished executing (methods).\n        // This is because when this code is transpiled by babel, babel will call\n        // classCallCheck before the constructor body is run.\n        // See https://github.com/tensorflow/tfjs/issues/3384 for backstory.\n        return !!instance && instance.data != null && instance.dataSync != null &&\n            instance.throwIfDisposed != null;\n    }\n});\n/**\n * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Variable extends Tensor {\n    constructor(initialValue, trainable, name, tensorId) {\n        super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);\n        this.trainable = trainable;\n        this.name = name;\n    }\n    /**\n     * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n     * the same shape and dtype as the old `tf.Tensor`.\n     *\n     * @param newValue New tensor to be assigned to this variable.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    assign(newValue) {\n        if (newValue.dtype !== this.dtype) {\n            throw new Error(`dtype of the new value (${newValue.dtype}) and ` +\n                `previous value (${this.dtype}) must match`);\n        }\n        if (!util.arraysEqual(newValue.shape, this.shape)) {\n            throw new Error(`shape of the new value (${newValue.shape}) and ` +\n                `previous value (${this.shape}) must match`);\n        }\n        trackerFn().disposeTensor(this);\n        this.dataId = newValue.dataId;\n        trackerFn().incRef(this, null /* backend */);\n    }\n    dispose() {\n        trackerFn().disposeVariable(this);\n        this.isDisposedInternal = true;\n    }\n}\nObject.defineProperty(Variable, Symbol.hasInstance, {\n    value: (instance) => {\n        return instance instanceof Tensor && instance.assign != null &&\n            instance.assign instanceof Function;\n    }\n});\n//# sourceMappingURL=tensor.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Rank;\n(function (Rank) {\n    Rank[\"R0\"] = \"R0\";\n    Rank[\"R1\"] = \"R1\";\n    Rank[\"R2\"] = \"R2\";\n    Rank[\"R3\"] = \"R3\";\n    Rank[\"R4\"] = \"R4\";\n    Rank[\"R5\"] = \"R5\";\n    Rank[\"R6\"] = \"R6\";\n})(Rank || (Rank = {}));\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nvar UpcastInt32AndMap;\n(function (UpcastInt32AndMap) {\n    UpcastInt32AndMap[\"float32\"] = \"float32\";\n    UpcastInt32AndMap[\"int32\"] = \"int32\";\n    UpcastInt32AndMap[\"bool\"] = \"int32\";\n    UpcastInt32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));\nvar UpcastBoolAndMap;\n(function (UpcastBoolAndMap) {\n    UpcastBoolAndMap[\"float32\"] = \"float32\";\n    UpcastBoolAndMap[\"int32\"] = \"int32\";\n    UpcastBoolAndMap[\"bool\"] = \"bool\";\n    UpcastBoolAndMap[\"complex64\"] = \"complex64\";\n})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));\nvar UpcastFloat32AndMap;\n(function (UpcastFloat32AndMap) {\n    UpcastFloat32AndMap[\"float32\"] = \"float32\";\n    UpcastFloat32AndMap[\"int32\"] = \"float32\";\n    UpcastFloat32AndMap[\"bool\"] = \"float32\";\n    UpcastFloat32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));\nvar UpcastComplex64AndMap;\n(function (UpcastComplex64AndMap) {\n    UpcastComplex64AndMap[\"float32\"] = \"complex64\";\n    UpcastComplex64AndMap[\"int32\"] = \"complex64\";\n    UpcastComplex64AndMap[\"bool\"] = \"complex64\";\n    UpcastComplex64AndMap[\"complex64\"] = \"complex64\";\n})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));\nconst upcastTypeMap = {\n    'float32': UpcastFloat32AndMap,\n    'int32': UpcastInt32AndMap,\n    'bool': UpcastBoolAndMap,\n    'complex64': UpcastComplex64AndMap\n};\nexport function upcastType(typeA, typeB) {\n    if (typeA === 'string' || typeB === 'string') {\n        if (typeA === 'string' && typeB === 'string') {\n            return 'string';\n        }\n        throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n    }\n    return upcastTypeMap[typeA][typeB];\n}\n/** Returns the output type after summation. */\nexport function sumOutType(type) {\n    return upcastType(type, 'int32');\n}\n//# sourceMappingURL=types.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from './tensor';\nimport { upcastType } from './types';\nimport { assert } from './util';\nexport function makeTypesMatch(a, b) {\n    if (a.dtype === b.dtype) {\n        return [a, b];\n    }\n    const dtype = upcastType(a.dtype, b.dtype);\n    return [a.cast(dtype), b.cast(dtype)];\n}\nexport function assertTypesMatch(a, b) {\n    assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and` +\n        ` second(${b.dtype}) input must match`);\n}\nexport function isTensorInList(tensor, tensorList) {\n    return tensorList.some(x => x.id === tensor.id);\n}\n/**\n * Extracts any `Tensor`s found within the provided object.\n *\n * @param container an object that may be a `Tensor` or may directly contain\n *   `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it\n *   is safe to pass any object here, except that `Promise`s are not\n *   supported.\n * @returns An array of `Tensors` found within the passed object. If the\n *   argument is simply a `Tensor', a list containing that `Tensor` is\n *   returned. If the object is not a `Tensor` or does not\n *   contain `Tensors`, an empty list is returned.\n */\nexport function getTensorsInContainer(result) {\n    const list = [];\n    const seen = new Set();\n    walkTensorContainer(result, list, seen);\n    return list;\n}\nfunction walkTensorContainer(container, list, seen) {\n    if (container == null) {\n        return;\n    }\n    if (container instanceof Tensor) {\n        list.push(container);\n        return;\n    }\n    if (!isIterable(container)) {\n        return;\n    }\n    // Iteration over keys works also for arrays.\n    const iterable = container;\n    for (const k in iterable) {\n        const val = iterable[k];\n        if (!seen.has(val)) {\n            seen.add(val);\n            walkTensorContainer(val, list, seen);\n        }\n    }\n}\n// tslint:disable-next-line:no-any\nfunction isIterable(obj) {\n    return Array.isArray(obj) || typeof obj === 'object';\n}\n//# sourceMappingURL=tensor_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { KernelBackend } from './backends/backend';\nimport { Environment, setEnvironmentGlobal } from './environment';\nimport { getGlobalNamespace } from './global_util';\nimport { Add, Cast } from './kernel_names';\nimport { getGradient, getKernel, getKernelsForBackend } from './kernel_registry';\nimport { Profiler } from './profiler';\nimport { backpropagateGradients, getFilteredNodesXToY } from './tape';\nimport { setTensorTracker, Tensor, Variable } from './tensor';\nimport { getTensorsInContainer } from './tensor_util';\nimport * as util from './util';\nimport { bytesFromStringArray, makeOnesTypedArray, now, sizeFromShape } from './util';\nclass EngineState {\n    constructor() {\n        // Public since optimizers will use it.\n        this.registeredVariables = {};\n        this.nextTapeNodeId = 0;\n        this.numBytes = 0;\n        this.numTensors = 0;\n        this.numStringTensors = 0;\n        this.numDataBuffers = 0;\n        // Number of nested tf.grad() statements when computing higher-order\n        // gradients. E.g. `1` for first-order gradients and `2` for second-order\n        // gradients. Used to track if the tape should be removed after a backprop.\n        this.gradientDepth = 0;\n        // Number of nested kernel calls. When kernel depth is greater than 1, we turn\n        // off the tape.\n        this.kernelDepth = 0;\n        this.scopeStack = [];\n        /**\n         * Keeps track of the number of data moves during a kernel execution. We\n         * maintain a stack since kernels can call other kernels, recursively.\n         */\n        this.numDataMovesStack = [];\n        this.nextScopeId = 0;\n        this.tensorInfo = new WeakMap();\n        this.profiling = false;\n        this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null };\n    }\n    dispose() {\n        for (const variableName in this.registeredVariables) {\n            this.registeredVariables[variableName].dispose();\n        }\n    }\n}\nexport class Engine {\n    constructor(ENV) {\n        this.ENV = ENV;\n        this.registry = {};\n        this.registryFactory = {};\n        this.pendingBackendInitId = 0;\n        this.state = new EngineState();\n    }\n    async ready() {\n        if (this.pendingBackendInit != null) {\n            return this.pendingBackendInit.then(() => { });\n        }\n        if (this.backendInstance != null) {\n            return;\n        }\n        const sortedBackends = this.getSortedBackends();\n        for (let i = 0; i < sortedBackends.length; i++) {\n            const backendName = sortedBackends[i];\n            const success = await this.initializeBackend(backendName).success;\n            if (success) {\n                await this.setBackend(backendName);\n                return;\n            }\n        }\n        throw new Error(`Could not initialize any backends, all backend initializations ` +\n            `failed.`);\n    }\n    get backend() {\n        if (this.pendingBackendInit != null) {\n            throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make ` +\n                `sure to await tf.ready() or await tf.setBackend() before calling ` +\n                `other methods`);\n        }\n        if (this.backendInstance == null) {\n            const { name, asyncInit } = this.initializeBackendsAndReturnBest();\n            if (asyncInit) {\n                throw new Error(`The highest priority backend '${name}' has not yet been ` +\n                    `initialized. Make sure to await tf.ready() or ` +\n                    `await tf.setBackend() before calling other methods`);\n            }\n            this.setBackend(name);\n        }\n        return this.backendInstance;\n    }\n    backendNames() {\n        return Object.keys(this.registryFactory);\n    }\n    findBackend(backendName) {\n        if (!(backendName in this.registry)) {\n            // If the backend hasn't been initialized but we have a registry entry for\n            // it, initialize it and return it.\n            if (backendName in this.registryFactory) {\n                const { asyncInit } = this.initializeBackend(backendName);\n                if (asyncInit) {\n                    // Backend is not ready yet.\n                    return null;\n                }\n            }\n            else {\n                return null;\n            }\n        }\n        return this.registry[backendName];\n    }\n    findBackendFactory(backendName) {\n        if (!(backendName in this.registryFactory)) {\n            return null;\n        }\n        return this.registryFactory[backendName].factory;\n    }\n    registerBackend(backendName, factory, priority = 1) {\n        if (backendName in this.registryFactory) {\n            console.warn(`${backendName} backend was already registered. ` +\n                `Reusing existing backend factory.`);\n            return false;\n        }\n        this.registryFactory[backendName] = { factory, priority };\n        return true;\n    }\n    async setBackend(backendName) {\n        if (this.registryFactory[backendName] == null) {\n            throw new Error(`Backend name '${backendName}' not found in registry`);\n        }\n        this.backendName = backendName;\n        if (this.registry[backendName] == null) {\n            this.backendInstance = null;\n            const { success, asyncInit } = this.initializeBackend(backendName);\n            const result = asyncInit ? await success : success;\n            if (!result) {\n                return false;\n            }\n        }\n        this.backendInstance = this.registry[backendName];\n        this.setupRegisteredKernels();\n        // Reset the profiler.\n        this.profiler = new Profiler(this.backendInstance);\n        return true;\n    }\n    setupRegisteredKernels() {\n        const kernels = getKernelsForBackend(this.backendName);\n        kernels.forEach(kernel => {\n            if (kernel.setupFunc != null) {\n                kernel.setupFunc(this.backendInstance);\n            }\n        });\n    }\n    disposeRegisteredKernels(backendName) {\n        const kernels = getKernelsForBackend(backendName);\n        kernels.forEach(kernel => {\n            if (kernel.disposeFunc != null) {\n                kernel.disposeFunc(this.registry[backendName]);\n            }\n        });\n    }\n    /**\n     * Initializes a backend by looking up the backend name in the factory\n     * registry and calling the factory method. Returns a boolean representing\n     * whether the initialization of the backend suceeded. Throws an error if\n     * there is no backend in the factory registry.\n     */\n    initializeBackend(backendName) {\n        const registryFactoryEntry = this.registryFactory[backendName];\n        if (registryFactoryEntry == null) {\n            throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);\n        }\n        try {\n            const backend = registryFactoryEntry.factory();\n            /* Test if the factory returns a promise.\n            Done in a more liberal way than\n            previous 'Promise.resolve(backend)===backend'\n            as we needed to account for custom Promise\n            implementations (e.g. Angular) */\n            if (backend && !(backend instanceof KernelBackend)\n                && typeof backend.then === 'function') {\n                const promiseId = ++this.pendingBackendInitId;\n                const success = backend\n                    .then(backendInstance => {\n                    // Outdated promise. Another backend was set in the meantime.\n                    if (promiseId < this.pendingBackendInitId) {\n                        return false;\n                    }\n                    this.registry[backendName] = backendInstance;\n                    this.pendingBackendInit = null;\n                    return true;\n                })\n                    .catch(err => {\n                    // Outdated promise. Another backend was set in the meantime.\n                    if (promiseId < this.pendingBackendInitId) {\n                        return false;\n                    }\n                    this.pendingBackendInit = null;\n                    console.warn(`Initialization of backend ${backendName} failed`);\n                    console.warn(err.stack || err.message);\n                    return false;\n                });\n                this.pendingBackendInit = success;\n                return { success, asyncInit: true };\n            }\n            else {\n                this.registry[backendName] = backend;\n                return { success: true, asyncInit: false };\n            }\n        }\n        catch (err) {\n            console.warn(`Initialization of backend ${backendName} failed`);\n            console.warn(err.stack || err.message);\n            return { success: false, asyncInit: false };\n        }\n    }\n    removeBackend(backendName) {\n        if (!(backendName in this.registryFactory)) {\n            throw new Error(`${backendName} backend not found in registry`);\n        }\n        if (this.backendName === backendName && this.pendingBackendInit != null) {\n            // There is a pending promise of the backend we want to remove. Make it\n            // obsolete.\n            this.pendingBackendInitId++;\n        }\n        if (backendName in this.registry) {\n            this.disposeRegisteredKernels(backendName);\n            this.registry[backendName].dispose();\n            delete this.registry[backendName];\n        }\n        delete this.registryFactory[backendName];\n        // Unset the backend if it is active.\n        if (this.backendName === backendName) {\n            this.pendingBackendInit = null;\n            this.backendName = null;\n            this.backendInstance = null;\n        }\n    }\n    getSortedBackends() {\n        if (Object.keys(this.registryFactory).length === 0) {\n            throw new Error('No backend found in registry.');\n        }\n        return Object.keys(this.registryFactory).sort((a, b) => {\n            // Highest priority comes first.\n            return this.registryFactory[b].priority -\n                this.registryFactory[a].priority;\n        });\n    }\n    initializeBackendsAndReturnBest() {\n        const sortedBackends = this.getSortedBackends();\n        for (let i = 0; i < sortedBackends.length; i++) {\n            const backendName = sortedBackends[i];\n            const { success, asyncInit } = this.initializeBackend(backendName);\n            if (asyncInit || success) {\n                return { name: backendName, asyncInit };\n            }\n        }\n        throw new Error(`Could not initialize any backends, all backend initializations ` +\n            `failed.`);\n    }\n    moveData(backend, dataId) {\n        const info = this.state.tensorInfo.get(dataId);\n        const srcBackend = info.backend;\n        const values = this.readSync(dataId);\n        // Delete the tensor from the old backend and move it to the new\n        // backend.\n        srcBackend.disposeData(dataId);\n        info.backend = backend;\n        backend.move(dataId, values, info.shape, info.dtype);\n        if (this.shouldCheckForMemLeaks()) {\n            // Track the number of moves during a kernel execution to correctly\n            // detect memory leaks.\n            this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;\n        }\n    }\n    tidy(nameOrFn, fn) {\n        let name = null;\n        if (fn == null) {\n            // Called with only 1 argument.\n            if (typeof nameOrFn !== 'function') {\n                throw new Error('Please provide a function to tidy()');\n            }\n            fn = nameOrFn;\n        }\n        else {\n            // Called with 2 arguments.\n            if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {\n                throw new Error('When calling with two arguments, the first argument ' +\n                    'to tidy() must be a string');\n            }\n            if (typeof fn !== 'function') {\n                throw new Error('When calling with two arguments, the 2nd argument ' +\n                    'to tidy() must be a function');\n            }\n            name = nameOrFn;\n            // TODO(nsthorat,smilkov): Do operation logging and performance\n            // profiling.\n        }\n        let result;\n        return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {\n            result = fn();\n            if (result instanceof Promise) {\n                console.error('Cannot return a Promise inside of tidy.');\n            }\n            return result;\n        });\n    }\n    scopedRun(start, end, f) {\n        start();\n        try {\n            const res = f();\n            end();\n            return res;\n        }\n        catch (ex) {\n            end();\n            throw ex;\n        }\n    }\n    nextTensorId() {\n        return Engine.nextTensorId++;\n    }\n    nextVariableId() {\n        return Engine.nextVariableId++;\n    }\n    /**\n     * This method is called instead of the public-facing tensor.clone() when\n     * saving a tensor for backwards pass. It makes sure to add the clone\n     * operation to the tape regardless of being called inside a kernel\n     * execution.\n     *\n     * This method will go away once all kernels are modularized since we won't\n     * need to turn off the tape inside runKernel().\n     */\n    clone(x) {\n        const y = this.makeTensorFromDataId(x.dataId, x.shape, x.dtype);\n        const inputs = { x };\n        const grad = (dy) => ({\n            x: () => {\n                const dtype = 'float32';\n                const gradInputs = { x: dy };\n                const attrs = { dtype };\n                return ENGINE.runKernelFunc(backend => backend.cast(dy, dtype), gradInputs, null /* grad */, Cast, attrs);\n            }\n        });\n        const saved = [];\n        this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {});\n        return y;\n    }\n    /**\n     * Execute a kernel with the given name and return the output tensor.\n     *\n     * @param kernelName The name of the kernel to execute.\n     * @param inputs A map of input names to tensors.\n     * @param attrs A map of attribute names to their values. An attribute is a\n     *     primitive (non-tensor) input to the kernel.\n     * @param inputsToSave A list of tensors, inputs to save for the backprop\n     *     computation.\n     * @param outputsToSave A list of booleans, specifying which output to save\n     *     for the backprop computation. These are booleans since the output\n     * tensors are not visible to the user.\n     */\n    runKernel(kernelName, inputs, attrs, inputsToSave, outputsToSave) {\n        const forwardFunc = null;\n        const backwardsFunc = null;\n        // Call runKernel as a stop-gap until we modularize all kernels.\n        // Once we modularize all kernels, we will remove the existing\n        // `runKernelFunc`.\n        return this.runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave);\n    }\n    shouldCheckForMemLeaks() {\n        return this.ENV.getBool('IS_TEST');\n    }\n    checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {\n        const numDataIdsAfter = this.backend.numDataIds();\n        // Count the number of data ids associated with the result of the kernel.\n        let numOutputDataIds = 0;\n        outInfos.forEach(info => {\n            // Complex numbers allocate 3 data ids, one for 'real', one for\n            // 'imaginary', and one for the container that holds the former two.\n            numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1);\n        });\n        // Account for the number of moves during kernel execution. A \"data move\"\n        // can happen in the middle of a kernel execution, placing a new (key,value)\n        // pair in the data storage. Since data moves have net zero effect (we\n        // always remove the data from the old backend), we have to cancel them out\n        // when detecting memory leaks.\n        const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];\n        const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;\n        if (dataIdsLeaked > 0) {\n            throw new Error(`Backend '${this.backendName}' has an internal memory leak ` +\n                `(${dataIdsLeaked} data ids) after running '${kernelName}'`);\n        }\n    }\n    /**\n     * @deprecated Use `runKernel` for newly added kernels. Keep using this method\n     *     only for kernels that are not yet fully modularized.\n     */\n    runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave) {\n        let outputs;\n        let saved = [];\n        const isTapeOn = this.isTapeOn();\n        if (kernelName == null) {\n            kernelName =\n                this.state.activeScope != null ? this.state.activeScope.name : '';\n        }\n        const startingBytecount = this.state.numBytes;\n        const startingNumTensors = this.state.numTensors;\n        if (this.shouldCheckForMemLeaks()) {\n            this.state.numDataMovesStack.push(0);\n        }\n        let kernelFunc;\n        const kernel = getKernel(kernelName, this.backendName);\n        let out;\n        if (kernel != null) {\n            kernelFunc = () => {\n                const numDataIdsBefore = this.backend.numDataIds();\n                out = kernel.kernelFunc({ inputs, attrs, backend: this.backend });\n                const outInfos = Array.isArray(out) ? out : [out];\n                if (this.shouldCheckForMemLeaks()) {\n                    this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);\n                }\n                const outTensors = outInfos.map(({ dataId, shape, dtype }) => this.makeTensorFromDataId(dataId, shape, dtype));\n                // Save the inputs and outputs.\n                // Do not save unless we are recording to the tape. Otherwise it would\n                // cause a mem leak since we would never run backprop, which disposes\n                // the kept tensors.\n                if (isTapeOn) {\n                    let tensorsToSave = this.getTensorsForGradient(kernelName, inputs, outTensors);\n                    if (tensorsToSave == null) {\n                        // Fallback for ops that call runKernelFunc and pass in\n                        // inputsToSave and outputsToSave. Currently this is the set of ops\n                        // with kernel support in the WASM backend. Once those ops and\n                        // respective gradients are modularised we can remove this path.\n                        if (outputsToSave == null) {\n                            outputsToSave = [];\n                        }\n                        const outsToSave = outTensors.filter((_, i) => outputsToSave[i]);\n                        tensorsToSave = (inputsToSave || []).slice().concat(outsToSave);\n                    }\n                    saved = this.saveTensorsForBackwardMode(tensorsToSave);\n                }\n                return outTensors;\n            };\n        }\n        else {\n            const saveFunc = (tensors) => {\n                // Do not save unless we are recording to the tape. Otherwise it would\n                // cause a mem leak since we would never run backprop, which disposes\n                // the kept tensors.\n                if (!isTapeOn) {\n                    return;\n                }\n                saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n            };\n            kernelFunc = () => {\n                const numDataIdsBefore = this.backend.numDataIds();\n                out = this.tidy(() => forwardFunc(this.backend, saveFunc));\n                const outs = (Array.isArray(out) ? out : [out]);\n                if (this.shouldCheckForMemLeaks()) {\n                    this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outs);\n                }\n                return outs;\n            };\n        }\n        // Stop recording to a tape when running a kernel.\n        let kernelProfile;\n        this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {\n            if (!this.ENV.getBool('DEBUG') && !this.state.profiling) {\n                outputs = kernelFunc();\n            }\n            else {\n                kernelProfile = this.profiler.profileKernel(kernelName, inputs, () => kernelFunc());\n                if (this.ENV.getBool('DEBUG')) {\n                    this.profiler.logKernelProfile(kernelProfile);\n                }\n                outputs = kernelProfile.outputs;\n            }\n        });\n        if (isTapeOn) {\n            this.addTapeNode(kernelName, inputs, outputs, backwardsFunc, saved, attrs);\n        }\n        if (this.state.profiling) {\n            this.state.activeProfile.kernels.push({\n                name: kernelName,\n                bytesAdded: this.state.numBytes - startingBytecount,\n                totalBytesSnapshot: this.state.numBytes,\n                tensorsAdded: this.state.numTensors - startingNumTensors,\n                totalTensorsSnapshot: this.state.numTensors,\n                inputShapes: Object.keys(inputs).map(key => inputs[key] != null ? inputs[key].shape : null),\n                outputShapes: outputs.map(item => item.shape),\n                kernelTimeMs: kernelProfile.timeMs,\n                extraInfo: kernelProfile.extraInfo\n            });\n        }\n        return (Array.isArray(out) ? outputs : outputs[0]);\n    }\n    /**\n     * Saves tensors used in forward mode for use in backward mode.\n     *\n     * @param tensors the list of tensors to save.\n     */\n    saveTensorsForBackwardMode(tensors) {\n        const saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n        return saved;\n    }\n    /**\n     * Returns a list of tensors to save for a given gradient calculation.\n     *\n     * Returns undefined if their is no registered gradient for this kernel in the\n     * gradient registry.\n     *\n     * @param kernelName name of kernel to look up gradient for.\n     * @param inputs a map of input tensors.\n     * @param outputs an array of output tensors from forward mode of kernel.\n     */\n    getTensorsForGradient(kernelName, inputs, outputs) {\n        const gradConfig = getGradient(kernelName);\n        if (gradConfig != null) {\n            const inputsToSave = gradConfig.inputsToSave || [];\n            const outputsToSave = gradConfig.outputsToSave || [];\n            // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs\n            // specified in inputsToSave will be saved.\n            let inputTensorsToSave;\n            if (gradConfig.saveAllInputs) {\n                util.assert(Array.isArray(inputs), () => 'saveAllInputs is true, expected inputs to be an array.');\n                inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);\n            }\n            else {\n                inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);\n            }\n            const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);\n            return inputTensorsToSave.concat(outputTensorsToSave);\n        }\n        // TODO(yassogba) throw exception here once all runkernelFunc calls with\n        // inputsToSave/outputsToSave are removed\n        return null;\n    }\n    /**\n     * Internal method used by public APIs for tensor creation. Makes a new\n     * tensor with the provided shape, dtype and values. It always\n     * creates a new data id and writes the values to the underlying backend.\n     */\n    makeTensor(values, shape, dtype, backend) {\n        if (values == null) {\n            throw new Error('Values passed to engine.makeTensor() are null');\n        }\n        dtype = dtype || 'float32';\n        backend = backend || this.backend;\n        let backendVals = values;\n        if (dtype === 'string' && util.isString(values[0])) {\n            backendVals = values.map(d => util.encodeString(d));\n        }\n        const dataId = backend.write(backendVals, shape, dtype);\n        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n        this.incRef(t, backend);\n        // Count bytes for string tensors.\n        if (dtype === 'string') {\n            const info = this.state.tensorInfo.get(dataId);\n            const newBytes = bytesFromStringArray(backendVals);\n            this.state.numBytes += newBytes - info.bytes;\n            info.bytes = newBytes;\n        }\n        return t;\n    }\n    /**\n     * Internal method used by backends. Makes a new tensor\n     * that is a wrapper around an existing data id. It doesn't create\n     * a new data id, only increments the ref count used in memory tracking.\n     */\n    makeTensorFromDataId(dataId, shape, dtype, backend) {\n        dtype = dtype || 'float32';\n        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n        this.incRef(t, backend);\n        return t;\n    }\n    makeVariable(initialValue, trainable = true, name, dtype) {\n        name = name || this.nextVariableId().toString();\n        if (dtype != null && dtype !== initialValue.dtype) {\n            initialValue = initialValue.cast(dtype);\n        }\n        const v = new Variable(initialValue, trainable, name, this.nextTensorId());\n        if (this.state.registeredVariables[v.name] != null) {\n            throw new Error(`Variable with name ${v.name} was already registered`);\n        }\n        this.state.registeredVariables[v.name] = v;\n        this.incRef(v, this.backend);\n        return v;\n    }\n    incRef(a, backend) {\n        const refCount = this.state.tensorInfo.has(a.dataId) ?\n            this.state.tensorInfo.get(a.dataId).refCount :\n            0;\n        this.state.numTensors++;\n        if (a.dtype === 'string') {\n            this.state.numStringTensors++;\n        }\n        if (refCount === 0) {\n            this.state.numDataBuffers++;\n            // Bytes for complex numbers are counted by their components. Bytes for\n            // string tensors are counted when writing values.\n            let bytes = 0;\n            if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n                bytes = a.size * util.bytesPerElement(a.dtype);\n            }\n            this.state.tensorInfo.set(a.dataId, {\n                backend: backend || this.backend,\n                dtype: a.dtype,\n                shape: a.shape,\n                bytes,\n                refCount: 0\n            });\n            this.state.numBytes += bytes;\n        }\n        this.state.tensorInfo.get(a.dataId).refCount++;\n        if (!(a instanceof Variable)) {\n            this.track(a);\n        }\n    }\n    disposeTensor(a) {\n        if (!this.state.tensorInfo.has(a.dataId)) {\n            return;\n        }\n        this.state.numTensors--;\n        if (a.dtype === 'string') {\n            this.state.numStringTensors--;\n        }\n        const info = this.state.tensorInfo.get(a.dataId);\n        const refCount = info.refCount;\n        if (refCount <= 1) {\n            // Don't count bytes for complex numbers as they are counted by their\n            // components.\n            if (a.dtype !== 'complex64') {\n                this.state.numBytes -= info.bytes;\n            }\n            this.state.numDataBuffers--;\n            info.backend.disposeData(a.dataId);\n            this.state.tensorInfo.delete(a.dataId);\n        }\n        else {\n            this.state.tensorInfo.get(a.dataId).refCount--;\n        }\n        // TODO(nsthorat): Construct an error and save the stack trace for\n        // debugging when in debug mode. Creating a stack trace is too expensive\n        // to do unconditionally.\n    }\n    disposeVariables() {\n        for (const varName in this.state.registeredVariables) {\n            const v = this.state.registeredVariables[varName];\n            this.disposeVariable(v);\n        }\n    }\n    disposeVariable(v) {\n        this.disposeTensor(v);\n        if (this.state.registeredVariables[v.name] != null) {\n            delete this.state.registeredVariables[v.name];\n        }\n    }\n    memory() {\n        const info = this.backend.memory();\n        info.numTensors = this.state.numTensors;\n        info.numDataBuffers = this.state.numDataBuffers;\n        info.numBytes = this.state.numBytes;\n        if (this.state.numStringTensors > 0) {\n            info.unreliable = true;\n            if (info.reasons == null) {\n                info.reasons = [];\n            }\n            info.reasons.push('Memory usage by string tensors is approximate ' +\n                '(2 bytes per character)');\n        }\n        return info;\n    }\n    async profile(query) {\n        this.state.profiling = true;\n        const startBytes = this.state.numBytes;\n        const startNumTensors = this.state.numTensors;\n        this.state.activeProfile.kernels = [];\n        this.state.activeProfile.result = await query();\n        this.state.profiling = false;\n        this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map(d => d.totalBytesSnapshot));\n        this.state.activeProfile.newBytes = this.state.numBytes - startBytes;\n        this.state.activeProfile.newTensors =\n            this.state.numTensors - startNumTensors;\n        for (const kernel of this.state.activeProfile.kernels) {\n            kernel.kernelTimeMs = await kernel.kernelTimeMs;\n            kernel.extraInfo = await kernel.extraInfo;\n        }\n        return this.state.activeProfile;\n    }\n    isTapeOn() {\n        return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;\n    }\n    addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {\n        const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved };\n        const gradConfig = getGradient(kernelName);\n        if (gradConfig != null) {\n            gradientsFunc = gradConfig.gradFunc;\n        }\n        if (gradientsFunc != null) {\n            tapeNode.gradient = (dys) => {\n                // TODO(smilkov): To optimize back-prop, pass dys that are not used in\n                // the backprop graph to the user as null instead of zeros\n                dys = dys.map((dy, i) => {\n                    if (dy == null) {\n                        const output = outputs[i];\n                        const vals = util.makeZerosTypedArray(output.size, output.dtype);\n                        return this.makeTensor(vals, output.shape, output.dtype);\n                    }\n                    return dy;\n                });\n                // Grad functions of ops with single outputs expect a dy, while ops\n                // with multiple outputs expect dys (array of dy).\n                return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);\n            };\n        }\n        this.state.activeTape.push(tapeNode);\n    }\n    keep(result) {\n        result.kept = true;\n        return result;\n    }\n    startTape() {\n        if (this.state.gradientDepth === 0) {\n            this.state.activeTape = [];\n        }\n        this.state.gradientDepth++;\n    }\n    endTape() {\n        this.state.gradientDepth--;\n    }\n    /**\n     * Start a scope. Use this with endScope() to achieve the same functionality\n     * as scope() without the need for a function closure.\n     */\n    startScope(name) {\n        const scopeInfo = {\n            track: [],\n            name: 'unnamed scope',\n            id: this.state.nextScopeId++\n        };\n        if (name) {\n            scopeInfo.name = name;\n        }\n        this.state.scopeStack.push(scopeInfo);\n        this.state.activeScope = scopeInfo;\n    }\n    /**\n     * End a scope. Use this with startScope() to achieve the same functionality\n     * as scope() without the need for a function closure.\n     */\n    endScope(result) {\n        const tensorsToTrackInParent = getTensorsInContainer(result);\n        const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map(t => t.id));\n        // Dispose the arrays tracked in this scope.\n        for (let i = 0; i < this.state.activeScope.track.length; i++) {\n            const tensor = this.state.activeScope.track[i];\n            if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) {\n                tensor.dispose();\n            }\n        }\n        const oldScope = this.state.scopeStack.pop();\n        this.state.activeScope = this.state.scopeStack.length === 0 ?\n            null :\n            this.state.scopeStack[this.state.scopeStack.length - 1];\n        // Track the current result in the parent scope.\n        tensorsToTrackInParent.forEach(tensor => {\n            // Only track the tensor if was allocated in the inner scope and is not\n            // globally kept.\n            if (!tensor.kept && tensor.scopeId === oldScope.id) {\n                this.track(tensor);\n            }\n        });\n    }\n    /**\n     * Returns gradients of `f` with respect to each of the `xs`. The gradients\n     * returned are of the same length as `xs`, but some might be null if `f`\n     * was not a function of that `x`. It also takes optional dy to multiply the\n     * gradient, which defaults to `1`.\n     */\n    gradients(f, xs, dy, allowNoGradients = false) {\n        util.assert(xs.length > 0, () => 'gradients() received an empty list of xs.');\n        if (dy != null && dy.dtype !== 'float32') {\n            throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n        }\n        const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy('forward', f));\n        util.assert(y instanceof Tensor, () => 'The result y returned by f() must be a tensor.');\n        // Filter out the nodes that don't connect x => y.\n        const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);\n        if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n            throw new Error('Cannot compute gradient of y=f(x) with respect to x. Make sure ' +\n                'that the f you passed encloses all operations that lead from x ' +\n                'to y.');\n        }\n        return this.tidy('backward', () => {\n            const accumulatedGradientMap = {};\n            accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;\n            // Backprop gradients through the filtered nodes.\n            backpropagateGradients(accumulatedGradientMap, filteredTape, \n            // Pass the tidy function to avoid circular dep with `tape.ts`.\n            f => this.tidy(f), \n            // Pass an add function to avoide a circular dep with `tape.ts`.\n            add);\n            const grads = xs.map(x => accumulatedGradientMap[x.id]);\n            if (this.state.gradientDepth === 0) {\n                // This means that we are not computing higher-order gradients\n                // and can clean up the tape.\n                this.state.activeTape.forEach(node => {\n                    for (const tensor of node.saved) {\n                        tensor.dispose();\n                    }\n                });\n                this.state.activeTape = null;\n            }\n            return { value: y, grads };\n        });\n    }\n    customGrad(f) {\n        util.assert(util.isFunction(f), () => 'The f passed in customGrad(f) must be a function.');\n        return (...inputs) => {\n            util.assert(inputs.every(t => t instanceof Tensor), () => 'The args passed in customGrad(f)(x1, x2,...) must all be ' +\n                'tensors');\n            let res;\n            const inputMap = {};\n            inputs.forEach((input, i) => {\n                inputMap[i] = input;\n            });\n            return this.runKernelFunc((_, save) => {\n                res = f(...[...inputs, save]);\n                util.assert(res.value instanceof Tensor, () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.value` is a tensor');\n                util.assert(util.isFunction(res.gradFunc), () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function.');\n                return res.value;\n            }, inputMap, (dy, saved) => {\n                const gradRes = res.gradFunc(dy, saved);\n                const grads = Array.isArray(gradRes) ? gradRes : [gradRes];\n                util.assert(grads.length === inputs.length, () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'the same number of tensors as inputs passed to f(...).');\n                util.assert(grads.every(t => t instanceof Tensor), () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'a list of only tensors.');\n                const gradMap = {};\n                grads.forEach((grad, i) => {\n                    gradMap[i] = () => grad;\n                });\n                return gradMap;\n            });\n        };\n    }\n    readSync(dataId) {\n        // Route the read to the correct backend.\n        const info = this.state.tensorInfo.get(dataId);\n        return info.backend.readSync(dataId);\n    }\n    read(dataId) {\n        // Route the read to the correct backend.\n        const info = this.state.tensorInfo.get(dataId);\n        return info.backend.read(dataId);\n    }\n    async time(query) {\n        const start = now();\n        const timingInfo = await this.backend.time(query);\n        timingInfo.wallMs = now() - start;\n        return timingInfo;\n    }\n    /**\n     * Tracks a Tensor in the current scope to be automatically cleaned up\n     * when the current scope ends, and returns the value.\n     *\n     * @param result The Tensor to track in the current scope.\n     */\n    track(result) {\n        if (this.state.activeScope != null) {\n            result.scopeId = this.state.activeScope.id;\n            this.state.activeScope.track.push(result);\n        }\n        return result;\n    }\n    get registeredVariables() {\n        return this.state.registeredVariables;\n    }\n    /**\n     * Resets the engine state. Removes all backends but does not remove\n     * registered backend factories.\n     */\n    reset() {\n        // Make any pending promise obsolete.\n        this.pendingBackendInitId++;\n        this.state.dispose();\n        this.ENV.reset();\n        this.state = new EngineState();\n        for (const backendName in this.registry) {\n            this.disposeRegisteredKernels(backendName);\n            this.registry[backendName].dispose();\n            delete this.registry[backendName];\n        }\n        this.backendName = null;\n        this.backendInstance = null;\n        this.pendingBackendInit = null;\n    }\n}\nEngine.nextTensorId = 0;\nEngine.nextVariableId = 0;\nfunction ones(shape) {\n    const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');\n    return ENGINE.makeTensor(values, shape, 'float32');\n}\nexport function getOrMakeEngine() {\n    const ns = getGlobalNamespace();\n    if (ns._tfengine == null) {\n        const environment = new Environment(ns);\n        ns._tfengine = new Engine(environment);\n    }\n    setEnvironmentGlobal(ns._tfengine.ENV);\n    // Tell the current tensor interface that the global engine is responsible\n    // for tracking.\n    setTensorTracker(() => ns._tfengine);\n    return ns._tfengine;\n}\nexport const ENGINE = getOrMakeEngine();\n/**\n * A implementation of the add op for use within engine and tape.\n *\n * This allows us to avoid a circular dependency between add.ts and engine.\n * It is exported to be available in tape tests.\n */\nexport function add(a, b) {\n    // We duplicate Add here to avoid a circular dependency with add.ts.\n    const inputs = { a, b };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.add(a, b);\n        save([a, b]);\n        return res;\n    }, inputs, null /* gradient */, Add);\n}\n//# sourceMappingURL=engine.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line:no-any\nfunction _isNavigatorDefined() {\n    return typeof navigator !== 'undefined' && navigator != null;\n}\nexport function isMobile() {\n    if (_isNavigatorDefined()) {\n        // tslint:disable-next-line:no-any\n        const a = navigator.userAgent || navigator.vendor || window.opera;\n        // tslint:disable-next-line:max-line-length\n        return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n            .test(a) ||\n            // tslint:disable-next-line:max-line-length\n            /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i\n                .test(a.substr(0, 4));\n    }\n    return false;\n}\nexport function isBrowser() {\n    return (typeof window !== 'undefined' && window.document != null) ||\n        //@ts-ignore\n        (typeof WorkerGlobalScope !== 'undefined');\n}\n//# sourceMappingURL=device_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './engine';\nimport * as device_util from './device_util';\nimport { env } from './environment';\nconst ENV = env();\n/**\n * This file contains environment-related flag registrations.\n */\n/** Whether to enable debug mode. */\nENV.registerFlag('DEBUG', () => false, debugValue => {\n    if (debugValue) {\n        console.warn('Debugging mode is ON. The output of every math call will ' +\n            'be downloaded to CPU and checked for NaNs. ' +\n            'This significantly impacts performance.');\n    }\n});\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_BROWSER', () => device_util.isBrowser());\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_NODE', () => (typeof process !== 'undefined') &&\n    (typeof process.versions !== 'undefined') &&\n    (typeof process.versions.node !== 'undefined'));\n/** Whether this browser is Chrome. */\nENV.registerFlag('IS_CHROME', () => typeof navigator !== 'undefined' && navigator != null &&\n    navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&\n    /Google Inc/.test(navigator.vendor));\n/**\n * True when the environment is \"production\" where we disable safety checks\n * to gain performance.\n */\nENV.registerFlag('PROD', () => false);\n/**\n * Whether to do sanity checks when inferring a shape from user-provided\n * values, used when creating a new tensor.\n */\nENV.registerFlag('TENSORLIKE_CHECK_SHAPE_CONSISTENCY', () => ENV.getBool('DEBUG'));\n/** Whether deprecation warnings are enabled. */\nENV.registerFlag('DEPRECATION_WARNINGS_ENABLED', () => true);\n/** True if running unit tests. */\nENV.registerFlag('IS_TEST', () => false);\n//# sourceMappingURL=flags.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { env } from './environment';\nimport { Tensor } from './tensor';\nimport { assert, flatten, inferDtype, isTypedArray, toTypedArray } from './util';\nexport function inferShape(val, dtype) {\n    let firstElem = val;\n    if (isTypedArray(val)) {\n        return dtype === 'string' ? [] : [val.length];\n    }\n    if (!Array.isArray(val)) {\n        return []; // Scalar.\n    }\n    const shape = [];\n    while (Array.isArray(firstElem) ||\n        isTypedArray(firstElem) && dtype !== 'string') {\n        shape.push(firstElem.length);\n        firstElem = firstElem[0];\n    }\n    if (Array.isArray(val) &&\n        env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {\n        deepAssertShapeConsistency(val, shape, []);\n    }\n    return shape;\n}\nfunction deepAssertShapeConsistency(val, shape, indices) {\n    indices = indices || [];\n    if (!(Array.isArray(val)) && !isTypedArray(val)) {\n        assert(shape.length === 0, () => `Element arr[${indices.join('][')}] is a primitive, ` +\n            `but should be an array/TypedArray of ${shape[0]} elements`);\n        return;\n    }\n    assert(shape.length > 0, () => `Element arr[${indices.join('][')}] should be a primitive, ` +\n        `but is an array of ${val.length} elements`);\n    assert(val.length === shape[0], () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +\n        `elements, but has ${val.length} elements`);\n    const subShape = shape.slice(1);\n    for (let i = 0; i < val.length; ++i) {\n        deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n    }\n}\nfunction assertDtype(expectedDtype, actualDType, argName, functionName) {\n    if (expectedDtype == null) {\n        return;\n    }\n    if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||\n        expectedDtype === 'numeric' && actualDType === 'string') {\n        throw new Error(`Argument '${argName}' passed to '${functionName}' must ` +\n            `be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n    }\n}\nexport function convertToTensor(x, argName, functionName, parseAsDtype = 'numeric') {\n    if (x instanceof Tensor) {\n        assertDtype(parseAsDtype, x.dtype, argName, functionName);\n        return x;\n    }\n    let inferredDtype = inferDtype(x);\n    // If the user expects a bool/int/float, use that info to update the\n    // inferredDtype when it is not a string.\n    if (inferredDtype !== 'string' &&\n        ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {\n        inferredDtype = parseAsDtype;\n    }\n    assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n    if ((x == null) ||\n        (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&\n            typeof x !== 'boolean' && typeof x !== 'string')) {\n        const type = x == null ? 'null' : x.constructor.name;\n        throw new Error(`Argument '${argName}' passed to '${functionName}' must be a ` +\n            `Tensor or TensorLike, but got '${type}'`);\n    }\n    const inferredShape = inferShape(x, inferredDtype);\n    if (!isTypedArray(x) && !Array.isArray(x)) {\n        x = [x];\n    }\n    const skipTypedArray = true;\n    const values = inferredDtype !== 'string' ?\n        toTypedArray(x, inferredDtype) :\n        flatten(x, [], skipTypedArray);\n    return ENGINE.makeTensor(values, inferredShape, inferredDtype);\n}\nexport function convertToTensorArray(arg, argName, functionName, parseAsDtype = 'numeric') {\n    if (!Array.isArray(arg)) {\n        throw new Error(`Argument ${argName} passed to ${functionName} must be a ` +\n            '`Tensor[]` or `TensorLike[]`');\n    }\n    const tensors = arg;\n    return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName), parseAsDtype);\n}\n//# sourceMappingURL=tensor_util_env.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nexport const OP_SCOPE_SUFFIX = '__op';\n/**\n * Used for wrapping functions that perform math operations on\n * Tensors. The function will be wrapped in a named scope that cleans all\n * memory usage after the function is done.\n */\nexport function op(f) {\n    const keys = Object.keys(f);\n    if (keys.length !== 1) {\n        throw new Error(`Please provide an object with a single key ` +\n            `(operation name) mapping to a function. Got an object with ` +\n            `${keys.length} keys.`);\n    }\n    let opName = keys[0];\n    const fn = f[opName];\n    // Strip the underscore from the end of the function name.\n    if (opName.endsWith('_')) {\n        opName = opName.substring(0, opName.length - 1);\n    }\n    // add an __op suffix to distinguish ops from kernels in tf.profile\n    opName = opName + OP_SCOPE_SUFFIX;\n    // tslint:disable-next-line:no-any\n    const f2 = (...args) => {\n        ENGINE.startScope(opName);\n        try {\n            const result = fn(...args);\n            if (result instanceof Promise) {\n                console.error('Cannot return a Promise inside of tidy.');\n            }\n            ENGINE.endScope(result);\n            return result;\n        }\n        catch (ex) {\n            ENGINE.endScope(null);\n            throw ex;\n        }\n    };\n    Object.defineProperty(f2, 'name', { value: opName, configurable: true });\n    // tslint:disable-next-line:no-any\n    return f2;\n}\n//# sourceMappingURL=operation.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Complex } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction complex_(real, imag) {\n    const $real = convertToTensor(real, 'real', 'complex');\n    const $imag = convertToTensor(imag, 'imag', 'complex');\n    util.assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n        `must match in call to tf.complex().`);\n    const forward = (backend) => {\n        return backend.complex($real, $imag);\n    };\n    const inputs = { real: $real, imag: $imag };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Complex);\n}\nexport const complex = op({ complex_ });\n//# sourceMappingURL=complex.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { assert, assertNonNegativeIntegerDimensions, flatten, inferDtype, isTypedArray, sizeFromShape, toTypedArray } from '../util';\n/** This is shared code across all tensor creation methods. */\nexport function makeTensor(values, shape, inferredShape, dtype) {\n    if (dtype == null) {\n        dtype = inferDtype(values);\n    }\n    if (dtype === 'complex64') {\n        throw new Error(`Cannot construct a complex64 tensor directly. ` +\n            `Please use tf.complex(real, imag).`);\n    }\n    if (!isTypedArray(values) && !Array.isArray(values) &&\n        typeof values !== 'number' && typeof values !== 'boolean' &&\n        typeof values !== 'string') {\n        throw new Error('values passed to tensor(values) must be a number/boolean/string or ' +\n            'an array of numbers/booleans/strings, or a TypedArray');\n    }\n    if (shape != null) {\n        assertNonNegativeIntegerDimensions(shape);\n        const providedSize = sizeFromShape(shape);\n        const inferredSize = sizeFromShape(inferredShape);\n        assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ` +\n            `${providedSize} values but has ${inferredSize}`);\n        for (let i = 0; i < inferredShape.length; ++i) {\n            const inferred = inferredShape[i];\n            const flatDimsDontMatch = i === inferredShape.length - 1 ?\n                inferred !== sizeFromShape(shape.slice(i)) :\n                true;\n            assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape ` +\n                `(${inferredShape}) does not match the provided ` +\n                `shape (${shape}). `);\n        }\n    }\n    if (!isTypedArray(values) && !Array.isArray(values)) {\n        values = [values];\n    }\n    shape = shape || inferredShape;\n    values = dtype !== 'string' ?\n        toTypedArray(values, dtype) :\n        flatten(values, [], true);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=tensor_ops_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`. If the values are strings,\n *     they will be encoded as utf-8 and kept as `Uint8Array[]`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor(values, shape, dtype) {\n    const inferredShape = inferShape(values, dtype);\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/* Type definitions for exporting and importing of models. */\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP = {\n    'float32': 4,\n    'float16': 2,\n    'int32': 4,\n    'uint16': 2,\n    'uint8': 1,\n    'bool': 1,\n    'complex64': 8\n};\n//# sourceMappingURL=types.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../ops/complex';\nimport { tensor } from '../ops/tensor';\nimport { sizeFromShape } from '../util';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/** Number of bytes reserved for the length of the string. (32bit integer). */\nconst NUM_BYTES_STRING_LENGTH = 4;\n/**\n * Encode a map from names to weight values as an ArrayBuffer, along with an\n * `Array` of `WeightsManifestEntry` as specification of the encoded weights.\n *\n * This function does not perform sharding.\n *\n * This function is the reverse of `decodeWeights`.\n *\n * @param tensors A map (\"dict\") from names to tensors.\n * @param group Group to which the weights belong (optional).\n * @returns A `Promise` of\n *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s\n *     concatenated.\n *   - An `Array` of `WeightManifestEntry`s, carrying information including\n *     tensor names, `dtype`s and shapes.\n * @throws Error: on unsupported tensor `dtype`.\n */\nexport async function encodeWeights(tensors, group) {\n    // TODO(adarob, cais): Support quantization.\n    const specs = [];\n    const dataPromises = [];\n    const names = Array.isArray(tensors) ?\n        tensors.map(tensor => tensor.name) :\n        Object.keys(tensors);\n    for (let i = 0; i < names.length; ++i) {\n        const name = names[i];\n        const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];\n        if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool' &&\n            t.dtype !== 'string' && t.dtype !== 'complex64') {\n            throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n        }\n        const spec = { name, shape: t.shape, dtype: t.dtype };\n        if (t.dtype === 'string') {\n            const utf8bytes = new Promise(async (resolve) => {\n                const vals = await t.bytes();\n                const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) +\n                    NUM_BYTES_STRING_LENGTH * vals.length;\n                const bytes = new Uint8Array(totalNumBytes);\n                let offset = 0;\n                for (let i = 0; i < vals.length; i++) {\n                    const val = vals[i];\n                    const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);\n                    bytes.set(bytesOfLength, offset);\n                    offset += NUM_BYTES_STRING_LENGTH;\n                    bytes.set(val, offset);\n                    offset += val.length;\n                }\n                resolve(bytes);\n            });\n            dataPromises.push(utf8bytes);\n        }\n        else {\n            dataPromises.push(t.data());\n        }\n        if (group != null) {\n            spec.group = group;\n        }\n        specs.push(spec);\n    }\n    const tensorValues = await Promise.all(dataPromises);\n    return { data: concatenateTypedArrays(tensorValues), specs };\n}\n/**\n * Decode flat ArrayBuffer as weights.\n *\n * This function does not handle sharding.\n *\n * This function is the reverse of `encodeWeights`.\n *\n * @param buffer A flat ArrayBuffer carrying the binary values of the tensors\n *   concatenated in the order specified in `specs`.\n * @param specs Specifications of the names, dtypes and shapes of the tensors\n *   whose value are encoded by `buffer`.\n * @return A map from tensor name to tensor value, with the names corresponding\n *   to names in `specs`.\n * @throws Error, if any of the tensors has unsupported dtype.\n */\nexport function decodeWeights(buffer, specs) {\n    // TODO(adarob, cais): Support quantization.\n    const out = {};\n    let float16Decode;\n    let offset = 0;\n    for (const spec of specs) {\n        const name = spec.name;\n        const dtype = spec.dtype;\n        const shape = spec.shape;\n        const size = sizeFromShape(shape);\n        let values;\n        if ('quantization' in spec) {\n            const quantization = spec.quantization;\n            if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n                if (!('min' in quantization && 'scale' in quantization)) {\n                    throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} ` +\n                        `doesn't have corresponding metadata min and scale.`);\n                }\n            }\n            else if (quantization.dtype === 'float16') {\n                if (dtype !== 'float32') {\n                    throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} ` +\n                        `which only supports weights of type float32 not ${dtype}.`);\n                }\n            }\n            else {\n                throw new Error(`Weight ${spec.name} has unknown ` +\n                    `quantization dtype ${quantization.dtype}. ` +\n                    `Supported quantization dtypes are: ` +\n                    `'uint8', 'uint16', and 'float16'.`);\n            }\n            const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n            const byteBuffer = buffer.slice(offset, offset + size * quantizationSizeFactor);\n            const quantizedArray = (quantization.dtype === 'uint8') ?\n                new Uint8Array(byteBuffer) :\n                new Uint16Array(byteBuffer);\n            if (dtype === 'float32') {\n                if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n                    values = new Float32Array(quantizedArray.length);\n                    for (let i = 0; i < quantizedArray.length; i++) {\n                        const v = quantizedArray[i];\n                        values[i] = v * quantization.scale + quantization.min;\n                    }\n                }\n                else if (quantization.dtype === 'float16') {\n                    if (float16Decode === undefined) {\n                        float16Decode = getFloat16Decoder();\n                    }\n                    values = float16Decode(quantizedArray);\n                }\n                else {\n                    throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n                        `for weight type float32.`);\n                }\n            }\n            else if (dtype === 'int32') {\n                if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {\n                    throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n                        `for weight type int32.`);\n                }\n                values = new Int32Array(quantizedArray.length);\n                for (let i = 0; i < quantizedArray.length; i++) {\n                    const v = quantizedArray[i];\n                    values[i] = Math.round(v * quantization.scale + quantization.min);\n                }\n            }\n            else {\n                throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n            }\n            offset += size * quantizationSizeFactor;\n        }\n        else if (dtype === 'string') {\n            const size = sizeFromShape(spec.shape);\n            values = [];\n            for (let i = 0; i < size; i++) {\n                const byteLength = new Uint32Array(buffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];\n                offset += NUM_BYTES_STRING_LENGTH;\n                const bytes = new Uint8Array(buffer.slice(offset, offset + byteLength));\n                values.push(bytes);\n                offset += byteLength;\n            }\n        }\n        else {\n            const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n            const byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);\n            if (dtype === 'float32') {\n                values = new Float32Array(byteBuffer);\n            }\n            else if (dtype === 'int32') {\n                values = new Int32Array(byteBuffer);\n            }\n            else if (dtype === 'bool') {\n                values = new Uint8Array(byteBuffer);\n            }\n            else if (dtype === 'complex64') {\n                values = new Float32Array(byteBuffer);\n                const real = new Float32Array(values.length / 2);\n                const image = new Float32Array(values.length / 2);\n                for (let i = 0; i < real.length; i++) {\n                    real[i] = values[i * 2];\n                    image[i] = values[i * 2 + 1];\n                }\n                const realTensor = tensor(real, shape, 'float32');\n                const imageTensor = tensor(image, shape, 'float32');\n                out[name] = complex(realTensor, imageTensor);\n            }\n            else {\n                throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n            }\n            offset += size * dtypeFactor;\n        }\n        if (dtype !== 'complex64') {\n            out[name] = tensor(values, shape, dtype);\n        }\n    }\n    return out;\n}\n/**\n * Concatenate TypedArrays into an ArrayBuffer.\n */\nexport function concatenateTypedArrays(xs) {\n    // TODO(adarob, cais): Support quantization.\n    if (xs === null) {\n        throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n    }\n    let totalByteLength = 0;\n    // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'\n    // can have a different byte length from that of the `TypedArray` itself,\n    // for example, when the `TypedArray` is created from an offset in an\n    // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match\n    // the `TypedArray` in byte length. If an element of `xs` does not show\n    // this property, a new `TypedArray` that satisfy this property will be\n    // constructed and pushed into `normalizedXs`.\n    const normalizedXs = [];\n    xs.forEach((x) => {\n        totalByteLength += x.byteLength;\n        // tslint:disable:no-any\n        normalizedXs.push(x.byteLength === x.buffer.byteLength ? x :\n            new x.constructor(x));\n        if (!(x instanceof Float32Array || x instanceof Int32Array ||\n            x instanceof Uint8Array)) {\n            throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n        }\n        // tslint:enable:no-any\n    });\n    const y = new Uint8Array(totalByteLength);\n    let offset = 0;\n    normalizedXs.forEach((x) => {\n        y.set(new Uint8Array(x.buffer), offset);\n        offset += x.byteLength;\n    });\n    return y.buffer;\n}\n// Use Buffer on Node.js instead of Blob/atob/btoa\nconst useNodeBuffer = typeof Buffer !== 'undefined' &&\n    (typeof Blob === 'undefined' || typeof atob === 'undefined' ||\n        typeof btoa === 'undefined');\n/**\n * Calculate the byte length of a JavaScript string.\n *\n * Note that a JavaScript string can contain wide characters, therefore the\n * length of the string is not necessarily equal to the byte length.\n *\n * @param str Input string.\n * @returns Byte length.\n */\nexport function stringByteLength(str) {\n    if (useNodeBuffer) {\n        return Buffer.byteLength(str);\n    }\n    return new Blob([str]).size;\n}\n/**\n * Encode an ArrayBuffer as a base64 encoded string.\n *\n * @param buffer `ArrayBuffer` to be converted.\n * @returns A string that base64-encodes `buffer`.\n */\nexport function arrayBufferToBase64String(buffer) {\n    if (useNodeBuffer) {\n        return Buffer.from(buffer).toString('base64');\n    }\n    const buf = new Uint8Array(buffer);\n    let s = '';\n    for (let i = 0, l = buf.length; i < l; i++) {\n        s += String.fromCharCode(buf[i]);\n    }\n    return btoa(s);\n}\n/**\n * Decode a base64 string as an ArrayBuffer.\n *\n * @param str Base64 string.\n * @returns Decoded `ArrayBuffer`.\n */\nexport function base64StringToArrayBuffer(str) {\n    if (useNodeBuffer) {\n        const buf = Buffer.from(str, 'base64');\n        return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n    }\n    const s = atob(str);\n    const buffer = new Uint8Array(s.length);\n    for (let i = 0; i < s.length; ++i) {\n        buffer.set([s.charCodeAt(i)], i);\n    }\n    return buffer.buffer;\n}\n/**\n * Concatenate a number of ArrayBuffers into one.\n *\n * @param buffers A number of array buffers to concatenate.\n * @returns Result of concatenating `buffers` in order.\n */\nexport function concatenateArrayBuffers(buffers) {\n    if (buffers.length === 1) {\n        return buffers[0];\n    }\n    let totalByteLength = 0;\n    buffers.forEach((buffer) => {\n        totalByteLength += buffer.byteLength;\n    });\n    const temp = new Uint8Array(totalByteLength);\n    let offset = 0;\n    buffers.forEach((buffer) => {\n        temp.set(new Uint8Array(buffer), offset);\n        offset += buffer.byteLength;\n    });\n    return temp.buffer;\n}\n/**\n * Get the basename of a path.\n *\n * Behaves in a way analogous to Linux's basename command.\n *\n * @param path\n */\nexport function basename(path) {\n    const SEPARATOR = '/';\n    path = path.trim();\n    while (path.endsWith(SEPARATOR)) {\n        path = path.slice(0, path.length - 1);\n    }\n    const items = path.split(SEPARATOR);\n    return items[items.length - 1];\n}\n/**\n * Populate ModelArtifactsInfo fields for a model with JSON topology.\n * @param modelArtifacts\n * @returns A ModelArtifactsInfo object.\n */\nexport function getModelArtifactsInfoForJSON(modelArtifacts) {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n        throw new Error('Expected JSON model topology, received ArrayBuffer.');\n    }\n    return {\n        dateSaved: new Date(),\n        modelTopologyType: 'JSON',\n        modelTopologyBytes: modelArtifacts.modelTopology == null ?\n            0 :\n            stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n        weightSpecsBytes: modelArtifacts.weightSpecs == null ?\n            0 :\n            stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n        weightDataBytes: modelArtifacts.weightData == null ?\n            0 :\n            modelArtifacts.weightData.byteLength,\n    };\n}\n/**\n * Computes mantisa table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 2048 mantissa lookup values.\n */\nfunction computeFloat16MantisaTable() {\n    const convertMantissa = (i) => {\n        let m = i << 13;\n        let e = 0;\n        while ((m & 0x00800000) === 0) {\n            e -= 0x00800000;\n            m <<= 1;\n        }\n        m &= ~0x00800000;\n        e += 0x38800000;\n        return m | e;\n    };\n    const mantisaTable = new Uint32Array(2048);\n    mantisaTable[0] = 0;\n    for (let i = 1; i < 1024; i++) {\n        mantisaTable[i] = convertMantissa(i);\n    }\n    for (let i = 1024; i < 2048; i++) {\n        mantisaTable[i] = 0x38000000 + ((i - 1024) << 13);\n    }\n    return mantisaTable;\n}\n/**\n * Computes exponent table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 64 exponent lookup values.\n */\nfunction computeFloat16ExponentTable() {\n    const exponentTable = new Uint32Array(64);\n    exponentTable[0] = 0;\n    exponentTable[31] = 0x47800000;\n    exponentTable[32] = 0x80000000;\n    exponentTable[63] = 0xc7800000;\n    for (let i = 1; i < 31; i++) {\n        exponentTable[i] = i << 23;\n    }\n    for (let i = 33; i < 63; i++) {\n        exponentTable[i] = 0x80000000 + ((i - 32) << 23);\n    }\n    return exponentTable;\n}\n/**\n * Computes offset table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 6d offset values.\n */\nfunction computeFloat16OffsetTable() {\n    const offsetTable = new Uint32Array(64);\n    for (let i = 0; i < 64; i++) {\n        offsetTable[i] = 1024;\n    }\n    offsetTable[0] = offsetTable[32] = 0;\n    return offsetTable;\n}\n/**\n * Retrieve a Float16 decoder which will decode a ByteArray of Float16 values\n * to a Float32Array.\n *\n * @returns Function (buffer: Uint16Array) => Float32Array which decodes\n *          the Uint16Array of Float16 bytes to a Float32Array.\n */\nexport function getFloat16Decoder() {\n    // Algorithm is based off of\n    // http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n    // Cache lookup tables\n    const mantisaTable = computeFloat16MantisaTable();\n    const exponentTable = computeFloat16ExponentTable();\n    const offsetTable = computeFloat16OffsetTable();\n    return (quantizedArray) => {\n        const buffer = new ArrayBuffer(4 * quantizedArray.length);\n        const bufferUint32View = new Uint32Array(buffer);\n        for (let index = 0; index < quantizedArray.length; index++) {\n            const float16Bits = quantizedArray[index];\n            const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 0x3ff)] +\n                exponentTable[float16Bits >> 10];\n            bufferUint32View[index] = float32Bits;\n        }\n        return new Float32Array(buffer);\n    };\n}\n//# sourceMappingURL=io_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class IORouterRegistry {\n    constructor() {\n        this.saveRouters = [];\n        this.loadRouters = [];\n    }\n    static getInstance() {\n        if (IORouterRegistry.instance == null) {\n            IORouterRegistry.instance = new IORouterRegistry();\n        }\n        return IORouterRegistry.instance;\n    }\n    /**\n     * Register a save-handler router.\n     *\n     * @param saveRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `save` method defined or `null`.\n     */\n    static registerSaveRouter(saveRouter) {\n        IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n    }\n    /**\n     * Register a load-handler router.\n     *\n     * @param loadRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `load` method defined or `null`.\n     */\n    static registerLoadRouter(loadRouter) {\n        IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n    }\n    /**\n     * Look up IOHandler for saving, given a URL-like string.\n     *\n     * @param url\n     * @returns If only one match is found, an instance of IOHandler with the\n     * `save` method defined. If no match is found, `null`.\n     * @throws Error, if more than one match is found.\n     */\n    static getSaveHandlers(url) {\n        return IORouterRegistry.getHandlers(url, 'save');\n    }\n    /**\n     * Look up IOHandler for loading, given a URL-like string.\n     *\n     * @param url\n     * @param loadOptions Optional, custom load options.\n     * @returns All valid handlers for `url`, given the currently registered\n     *   handler routers.\n     */\n    static getLoadHandlers(url, loadOptions) {\n        return IORouterRegistry.getHandlers(url, 'load', loadOptions);\n    }\n    static getHandlers(url, handlerType, loadOptions) {\n        const validHandlers = [];\n        const routers = handlerType === 'load' ?\n            IORouterRegistry.getInstance().loadRouters :\n            IORouterRegistry.getInstance().saveRouters;\n        routers.forEach(router => {\n            const handler = router(url, loadOptions);\n            if (handler !== null) {\n                validHandlers.push(handler);\n            }\n        });\n        return validHandlers;\n    }\n}\nexport const registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);\nexport const registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);\nexport const getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);\nexport const getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);\n//# sourceMappingURL=router_registry.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DATABASE_NAME = 'tensorflowjs';\nconst DATABASE_VERSION = 1;\n// Model data and ModelArtifactsInfo (metadata) are stored in two separate\n// stores for efficient access of the list of stored models and their metadata.\n// 1. The object store for model data: topology, weights and weight manifests.\nconst MODEL_STORE_NAME = 'models_store';\n// 2. The object store for ModelArtifactsInfo, including meta-information such\n//    as the type of topology (JSON vs binary), byte size of the topology, byte\n//    size of the weights, etc.\nconst INFO_STORE_NAME = 'model_info_store';\n/**\n * Delete the entire database for tensorflow.js, including the models store.\n */\nexport async function deleteDatabase() {\n    const idbFactory = getIndexedDBFactory();\n    return new Promise((resolve, reject) => {\n        const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);\n        deleteRequest.onsuccess = () => resolve();\n        deleteRequest.onerror = error => reject(error);\n    });\n}\nfunction getIndexedDBFactory() {\n    if (!env().getBool('IS_BROWSER')) {\n        // TODO(cais): Add more info about what IOHandler subtypes are available.\n        //   Maybe point to a doc page on the web and/or automatically determine\n        //   the available IOHandlers and print them in the error message.\n        throw new Error('Failed to obtain IndexedDB factory because the current environment' +\n            'is not a web browser.');\n    }\n    // tslint:disable-next-line:no-any\n    const theWindow = typeof window === 'undefined' ? self : window;\n    const factory = theWindow.indexedDB || theWindow.mozIndexedDB ||\n        theWindow.webkitIndexedDB || theWindow.msIndexedDB ||\n        theWindow.shimIndexedDB;\n    if (factory == null) {\n        throw new Error('The current browser does not appear to support IndexedDB.');\n    }\n    return factory;\n}\nfunction setUpDatabase(openRequest) {\n    const db = openRequest.result;\n    db.createObjectStore(MODEL_STORE_NAME, { keyPath: 'modelPath' });\n    db.createObjectStore(INFO_STORE_NAME, { keyPath: 'modelPath' });\n}\n/**\n * IOHandler subclass: Browser IndexedDB.\n *\n * See the doc string of `browserIndexedDB` for more details.\n */\nexport class BrowserIndexedDB {\n    constructor(modelPath) {\n        this.indexedDB = getIndexedDBFactory();\n        if (modelPath == null || !modelPath) {\n            throw new Error('For IndexedDB, modelPath must not be null, undefined or empty.');\n        }\n        this.modelPath = modelPath;\n    }\n    async save(modelArtifacts) {\n        // TODO(cais): Support saving GraphDef models.\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        return this.databaseAction(this.modelPath, modelArtifacts);\n    }\n    async load() {\n        return this.databaseAction(this.modelPath);\n    }\n    /**\n     * Perform database action to put model artifacts into or read model artifacts\n     * from IndexedDB object store.\n     *\n     * Whether the action is put or get depends on whether `modelArtifacts` is\n     * specified. If it is specified, the action will be put; otherwise the action\n     * will be get.\n     *\n     * @param modelPath A unique string path for the model.\n     * @param modelArtifacts If specified, it will be the model artifacts to be\n     *   stored in IndexedDB.\n     * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`\n     *   of `ModelArtifacts`, if the action is get.\n     */\n    databaseAction(modelPath, modelArtifacts) {\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                if (modelArtifacts == null) {\n                    // Read model out from object store.\n                    const modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');\n                    const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                    const getRequest = modelStore.get(this.modelPath);\n                    getRequest.onsuccess = () => {\n                        if (getRequest.result == null) {\n                            db.close();\n                            return reject(new Error(`Cannot find model with path '${this.modelPath}' ` +\n                                `in IndexedDB.`));\n                        }\n                        else {\n                            resolve(getRequest.result.modelArtifacts);\n                        }\n                    };\n                    getRequest.onerror = error => {\n                        db.close();\n                        return reject(getRequest.error);\n                    };\n                    modelTx.oncomplete = () => db.close();\n                }\n                else {\n                    // Put model into object store.\n                    const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n                    // First, put ModelArtifactsInfo into info store.\n                    const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n                    let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                    const putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo });\n                    let modelTx;\n                    putInfoRequest.onsuccess = () => {\n                        // Second, put model data into model store.\n                        modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n                        const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                        const putModelRequest = modelStore.put({\n                            modelPath: this.modelPath,\n                            modelArtifacts,\n                            modelArtifactsInfo\n                        });\n                        putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo });\n                        putModelRequest.onerror = error => {\n                            // If the put-model request fails, roll back the info entry as\n                            // well.\n                            infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                            const deleteInfoRequest = infoStore.delete(this.modelPath);\n                            deleteInfoRequest.onsuccess = () => {\n                                db.close();\n                                return reject(putModelRequest.error);\n                            };\n                            deleteInfoRequest.onerror = error => {\n                                db.close();\n                                return reject(putModelRequest.error);\n                            };\n                        };\n                    };\n                    putInfoRequest.onerror = error => {\n                        db.close();\n                        return reject(putInfoRequest.error);\n                    };\n                    infoTx.oncomplete = () => {\n                        if (modelTx == null) {\n                            db.close();\n                        }\n                        else {\n                            modelTx.oncomplete = () => db.close();\n                        }\n                    };\n                }\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n}\nBrowserIndexedDB.URL_SCHEME = 'indexeddb://';\nexport const indexedDBRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n            return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\n/**\n * Creates a browser IndexedDB IOHandler for saving and loading models.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save('indexeddb://MyModel'));\n * console.log(saveResult);\n * ```\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),\n *   which can be used with, e.g., `tf.Model.save`.\n */\nexport function browserIndexedDB(modelPath) {\n    return new BrowserIndexedDB(modelPath);\n}\nfunction maybeStripScheme(key) {\n    return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?\n        key.slice(BrowserIndexedDB.URL_SCHEME.length) :\n        key;\n}\nexport class BrowserIndexedDBManager {\n    constructor() {\n        this.indexedDB = getIndexedDBFactory();\n    }\n    async listModels() {\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                const tx = db.transaction(INFO_STORE_NAME, 'readonly');\n                const store = tx.objectStore(INFO_STORE_NAME);\n                // tslint:disable:max-line-length\n                // Need to cast `store` as `any` here because TypeScript's DOM\n                // library does not have the `getAll()` method even though the\n                // method is supported in the latest version of most mainstream\n                // browsers:\n                // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll\n                // tslint:enable:max-line-length\n                // tslint:disable-next-line:no-any\n                const getAllInfoRequest = store.getAll();\n                getAllInfoRequest.onsuccess = () => {\n                    const out = {};\n                    for (const item of getAllInfoRequest.result) {\n                        out[item.modelPath] = item.modelArtifactsInfo;\n                    }\n                    resolve(out);\n                };\n                getAllInfoRequest.onerror = error => {\n                    db.close();\n                    return reject(getAllInfoRequest.error);\n                };\n                tx.oncomplete = () => db.close();\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n    async removeModel(path) {\n        path = maybeStripScheme(path);\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n                const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                const getInfoRequest = infoStore.get(path);\n                let modelTx;\n                getInfoRequest.onsuccess = () => {\n                    if (getInfoRequest.result == null) {\n                        db.close();\n                        return reject(new Error(`Cannot find model with path '${path}' ` +\n                            `in IndexedDB.`));\n                    }\n                    else {\n                        // First, delete the entry in the info store.\n                        const deleteInfoRequest = infoStore.delete(path);\n                        const deleteModelData = () => {\n                            // Second, delete the entry in the model store.\n                            modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n                            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                            const deleteModelRequest = modelStore.delete(path);\n                            deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo);\n                            deleteModelRequest.onerror = error => reject(getInfoRequest.error);\n                        };\n                        // Proceed with deleting model data regardless of whether deletion\n                        // of info data succeeds or not.\n                        deleteInfoRequest.onsuccess = deleteModelData;\n                        deleteInfoRequest.onerror = error => {\n                            deleteModelData();\n                            db.close();\n                            return reject(getInfoRequest.error);\n                        };\n                    }\n                };\n                getInfoRequest.onerror = error => {\n                    db.close();\n                    return reject(getInfoRequest.error);\n                };\n                infoTx.oncomplete = () => {\n                    if (modelTx == null) {\n                        db.close();\n                    }\n                    else {\n                        modelTx.oncomplete = () => db.close();\n                    }\n                };\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n}\n//# sourceMappingURL=indexed_db.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { arrayBufferToBase64String, base64StringToArrayBuffer, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst PATH_SEPARATOR = '/';\nconst PATH_PREFIX = 'tensorflowjs_models';\nconst INFO_SUFFIX = 'info';\nconst MODEL_TOPOLOGY_SUFFIX = 'model_topology';\nconst WEIGHT_SPECS_SUFFIX = 'weight_specs';\nconst WEIGHT_DATA_SUFFIX = 'weight_data';\nconst MODEL_METADATA_SUFFIX = 'model_metadata';\n/**\n * Purge all tensorflow.js-saved model artifacts from local storage.\n *\n * @returns Paths of the models purged.\n */\nexport function purgeLocalStorageArtifacts() {\n    if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n        typeof window.localStorage === 'undefined') {\n        throw new Error('purgeLocalStorageModels() cannot proceed because local storage is ' +\n            'unavailable in the current environment.');\n    }\n    const LS = window.localStorage;\n    const purgedModelPaths = [];\n    for (let i = 0; i < LS.length; ++i) {\n        const key = LS.key(i);\n        const prefix = PATH_PREFIX + PATH_SEPARATOR;\n        if (key.startsWith(prefix) && key.length > prefix.length) {\n            LS.removeItem(key);\n            const modelName = getModelPathFromKey(key);\n            if (purgedModelPaths.indexOf(modelName) === -1) {\n                purgedModelPaths.push(modelName);\n            }\n        }\n    }\n    return purgedModelPaths;\n}\nfunction getModelKeys(path) {\n    return {\n        info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n        topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n        weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n        weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),\n        modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)\n    };\n}\n/**\n * Get model path from a local-storage key.\n *\n * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'\n *\n * @param key\n */\nfunction getModelPathFromKey(key) {\n    const items = key.split(PATH_SEPARATOR);\n    if (items.length < 3) {\n        throw new Error(`Invalid key format: ${key}`);\n    }\n    return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\nfunction maybeStripScheme(key) {\n    return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?\n        key.slice(BrowserLocalStorage.URL_SCHEME.length) :\n        key;\n}\n/**\n * IOHandler subclass: Browser Local Storage.\n *\n * See the doc string to `browserLocalStorage` for more details.\n */\nexport class BrowserLocalStorage {\n    constructor(modelPath) {\n        if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n            typeof window.localStorage === 'undefined') {\n            // TODO(cais): Add more info about what IOHandler subtypes are\n            // available.\n            //   Maybe point to a doc page on the web and/or automatically determine\n            //   the available IOHandlers and print them in the error message.\n            throw new Error('The current environment does not support local storage.');\n        }\n        this.LS = window.localStorage;\n        if (modelPath == null || !modelPath) {\n            throw new Error('For local storage, modelPath must not be null, undefined or empty.');\n        }\n        this.modelPath = modelPath;\n        this.keys = getModelKeys(this.modelPath);\n    }\n    /**\n     * Save model artifacts to browser local storage.\n     *\n     * See the documentation to `browserLocalStorage` for details on the saved\n     * artifacts.\n     *\n     * @param modelArtifacts The model artifacts to be stored.\n     * @returns An instance of SaveResult.\n     */\n    async save(modelArtifacts) {\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        else {\n            const topology = JSON.stringify(modelArtifacts.modelTopology);\n            const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n            const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n            try {\n                this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n                this.LS.setItem(this.keys.topology, topology);\n                this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n                this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData));\n                this.LS.setItem(this.keys.modelMetadata, JSON.stringify({\n                    format: modelArtifacts.format,\n                    generatedBy: modelArtifacts.generatedBy,\n                    convertedBy: modelArtifacts.convertedBy,\n                    userDefinedMetadata: modelArtifacts.userDefinedMetadata\n                }));\n                return { modelArtifactsInfo };\n            }\n            catch (err) {\n                // If saving failed, clean up all items saved so far.\n                this.LS.removeItem(this.keys.info);\n                this.LS.removeItem(this.keys.topology);\n                this.LS.removeItem(this.keys.weightSpecs);\n                this.LS.removeItem(this.keys.weightData);\n                this.LS.removeItem(this.keys.modelMetadata);\n                throw new Error(`Failed to save model '${this.modelPath}' to local storage: ` +\n                    `size quota being exceeded is a possible cause of this failure: ` +\n                    `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` +\n                    `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` +\n                    `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n            }\n        }\n    }\n    /**\n     * Load a model from local storage.\n     *\n     * See the documentation to `browserLocalStorage` for details on the saved\n     * artifacts.\n     *\n     * @returns The loaded model (if loading succeeds).\n     */\n    async load() {\n        const info = JSON.parse(this.LS.getItem(this.keys.info));\n        if (info == null) {\n            throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);\n        }\n        if (info.modelTopologyType !== 'JSON') {\n            throw new Error('BrowserLocalStorage does not support loading non-JSON model ' +\n                'topology yet.');\n        }\n        const out = {};\n        // Load topology.\n        const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n        if (topology == null) {\n            throw new Error(`In local storage, the topology of model '${this.modelPath}' ` +\n                `is missing.`);\n        }\n        out.modelTopology = topology;\n        // Load weight specs.\n        const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n        if (weightSpecs == null) {\n            throw new Error(`In local storage, the weight specs of model '${this.modelPath}' ` +\n                `are missing.`);\n        }\n        out.weightSpecs = weightSpecs;\n        // Load meta-data fields.\n        const metadataString = this.LS.getItem(this.keys.modelMetadata);\n        if (metadataString != null) {\n            const metadata = JSON.parse(metadataString);\n            out.format = metadata['format'];\n            out.generatedBy = metadata['generatedBy'];\n            out.convertedBy = metadata['convertedBy'];\n            out.userDefinedMetadata = metadata['userDefinedMetadata'];\n        }\n        // Load weight data.\n        const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n        if (weightDataBase64 == null) {\n            throw new Error(`In local storage, the binary weight values of model ` +\n                `'${this.modelPath}' are missing.`);\n        }\n        out.weightData = base64StringToArrayBuffer(weightDataBase64);\n        return out;\n    }\n}\nBrowserLocalStorage.URL_SCHEME = 'localstorage://';\nexport const localStorageRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n            return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\n/**\n * Factory function for local storage IOHandler.\n *\n * This `IOHandler` supports both `save` and `load`.\n *\n * For each model's saved artifacts, four items are saved to local storage.\n *   - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the\n *     model, such as date saved, type of the topology, size in bytes, etc.\n *   - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-\n *     style models, this is a stringized JSON.\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the\n *     model, can be used to decode the saved binary weight values (see\n *     item below).\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary\n *     weight values, stored as a base64-encoded string.\n *\n * Saving may throw an `Error` if the total size of the artifacts exceed the\n * browser-specific quota.\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `IOHandler`, which can be used with, e.g.,\n *   `tf.Model.save`.\n */\nexport function browserLocalStorage(modelPath) {\n    return new BrowserLocalStorage(modelPath);\n}\nexport class BrowserLocalStorageManager {\n    constructor() {\n        assert(env().getBool('IS_BROWSER'), () => 'Current environment is not a web browser');\n        assert(typeof window === 'undefined' ||\n            typeof window.localStorage !== 'undefined', () => 'Current browser does not appear to support localStorage');\n        this.LS = window.localStorage;\n    }\n    async listModels() {\n        const out = {};\n        const prefix = PATH_PREFIX + PATH_SEPARATOR;\n        const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n        for (let i = 0; i < this.LS.length; ++i) {\n            const key = this.LS.key(i);\n            if (key.startsWith(prefix) && key.endsWith(suffix)) {\n                const modelPath = getModelPathFromKey(key);\n                out[modelPath] = JSON.parse(this.LS.getItem(key));\n            }\n        }\n        return out;\n    }\n    async removeModel(path) {\n        path = maybeStripScheme(path);\n        const keys = getModelKeys(path);\n        if (this.LS.getItem(keys.info) == null) {\n            throw new Error(`Cannot find model at path '${path}'`);\n        }\n        const info = JSON.parse(this.LS.getItem(keys.info));\n        this.LS.removeItem(keys.info);\n        this.LS.removeItem(keys.topology);\n        this.LS.removeItem(keys.weightSpecs);\n        this.LS.removeItem(keys.weightData);\n        return info;\n    }\n}\n//# sourceMappingURL=local_storage.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Classes and functions for model management across multiple storage mediums.\n *\n * Supported client actions:\n * - Listing models on all registered storage mediums.\n * - Remove model by URL from any registered storage mediums, by using URL\n *   string.\n * - Moving or copying model from one path to another in the same medium or from\n *   one medium to another, by using URL strings.\n */\nimport { assert } from '../util';\nimport { IORouterRegistry } from './router_registry';\nconst URL_SCHEME_SUFFIX = '://';\nexport class ModelStoreManagerRegistry {\n    constructor() {\n        this.managers = {};\n    }\n    static getInstance() {\n        if (ModelStoreManagerRegistry.instance == null) {\n            ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n        }\n        return ModelStoreManagerRegistry.instance;\n    }\n    /**\n     * Register a save-handler router.\n     *\n     * @param saveRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `save` method defined or `null`.\n     */\n    static registerManager(scheme, manager) {\n        assert(scheme != null, () => 'scheme must not be undefined or null.');\n        if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n            scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n        }\n        assert(scheme.length > 0, () => 'scheme must not be an empty string.');\n        const registry = ModelStoreManagerRegistry.getInstance();\n        assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);\n        registry.managers[scheme] = manager;\n    }\n    static getManager(scheme) {\n        const manager = this.getInstance().managers[scheme];\n        if (manager == null) {\n            throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n        }\n        return manager;\n    }\n    static getSchemes() {\n        return Object.keys(this.getInstance().managers);\n    }\n}\n/**\n * Helper method for parsing a URL string into a scheme and a path.\n *\n * @param url E.g., 'localstorage://my-model'\n * @returns A dictionary with two fields: scheme and path.\n *   Scheme: e.g., 'localstorage' in the example above.\n *   Path: e.g., 'my-model' in the example above.\n */\nfunction parseURL(url) {\n    if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n        throw new Error(`The url string provided does not contain a scheme. ` +\n            `Supported schemes are: ` +\n            `${ModelStoreManagerRegistry.getSchemes().join(',')}`);\n    }\n    return {\n        scheme: url.split(URL_SCHEME_SUFFIX)[0],\n        path: url.split(URL_SCHEME_SUFFIX)[1],\n    };\n}\nasync function cloneModelInternal(sourceURL, destURL, deleteSource = false) {\n    assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);\n    const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n    assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);\n    assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n        `load handlers for source URL ${sourceURL}.`);\n    const loadHandler = loadHandlers[0];\n    const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n    assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination ` +\n        `URL ${destURL}.`);\n    assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n        `save handlers for destination URL ${destURL}.`);\n    const saveHandler = saveHandlers[0];\n    const sourceScheme = parseURL(sourceURL).scheme;\n    const sourcePath = parseURL(sourceURL).path;\n    const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n    const modelArtifacts = await loadHandler.load();\n    // If moving within the same storage medium, remove the old model as soon as\n    // the loading is done. Without doing this, it is possible that the combined\n    // size of the two models will cause the cloning to fail.\n    if (deleteSource && sameMedium) {\n        await ModelStoreManagerRegistry.getManager(sourceScheme)\n            .removeModel(sourcePath);\n    }\n    const saveResult = await saveHandler.save(modelArtifacts);\n    // If moving between mediums, the deletion is done after the save succeeds.\n    // This guards against the case in which saving to the destination medium\n    // fails.\n    if (deleteSource && !sameMedium) {\n        await ModelStoreManagerRegistry.getManager(sourceScheme)\n            .removeModel(sourcePath);\n    }\n    return saveResult.modelArtifactsInfo;\n}\n/**\n * List all models stored in registered storage mediums.\n *\n * For a web browser environment, the registered mediums are Local Storage and\n * IndexedDB.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @returns A `Promise` of a dictionary mapping URLs of existing models to\n * their model artifacts info. URLs include medium-specific schemes, e.g.,\n *   'indexeddb://my/model/1'. Model artifacts info include type of the\n * model's topology, byte sizes of the topology, weights, etc.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function listModels() {\n    const schemes = ModelStoreManagerRegistry.getSchemes();\n    const out = {};\n    for (const scheme of schemes) {\n        const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();\n        for (const path in schemeOut) {\n            const url = scheme + URL_SCHEME_SUFFIX + path;\n            out[url] = schemeOut[path];\n        }\n    }\n    return out;\n}\n/**\n * Remove a model specified by URL from a reigstered storage medium.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @param url A URL to a stored model, with a scheme prefix, e.g.,\n *   'localstorage://my-model-1', 'indexeddb://my/model/2'.\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n *   is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function removeModel(url) {\n    const schemeAndPath = parseURL(url);\n    const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n    return manager.removeModel(schemeAndPath.path);\n}\n/**\n * Copy a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Copying within a storage medium, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Copying between two storage mediums, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Copy the model, from Local Storage to IndexedDB.\n * await tf.io.copyModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove both models.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of copying.\n * @param destURL Destination URL of copying.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function copyModel(sourceURL, destURL) {\n    const deleteSource = false;\n    return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n/**\n * Move a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Moving within a storage medium, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Moving between two storage mediums, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Move the model, from Local Storage to IndexedDB.\n * await tf.io.moveModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove the moved model.\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of moving.\n * @param destURL Destination URL of moving.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function moveModel(sourceURL, destURL) {\n    const deleteSource = true;\n    return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\nexport { moveModel, copyModel, removeModel, listModels };\n//# sourceMappingURL=model_management.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { BrowserIndexedDB, BrowserIndexedDBManager } from '../io/indexed_db';\nimport { BrowserLocalStorage, BrowserLocalStorageManager } from '../io/local_storage';\nimport { ModelStoreManagerRegistry } from '../io/model_management';\nexport class PlatformBrowser {\n    fetch(path, init) {\n        return fetch(path, init);\n    }\n    now() {\n        return performance.now();\n    }\n    encode(text, encoding) {\n        if (encoding !== 'utf-8' && encoding !== 'utf8') {\n            throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);\n        }\n        if (this.textEncoder == null) {\n            this.textEncoder = new TextEncoder();\n        }\n        return this.textEncoder.encode(text);\n    }\n    decode(bytes, encoding) {\n        return new TextDecoder(encoding).decode(bytes);\n    }\n}\nif (env().get('IS_BROWSER')) {\n    env().setPlatform('browser', new PlatformBrowser());\n    // Register LocalStorage IOHandler\n    try {\n        ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n    }\n    catch (err) {\n    }\n    // Register IndexedDB IOHandler\n    try {\n        ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n    }\n    catch (err) {\n    }\n}\n//# sourceMappingURL=platform_browser.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\n// We are wrapping this within an object so it can be stubbed by Jasmine.\nexport const getNodeFetch = {\n    // tslint:disable-next-line:no-require-imports\n    importFetch: () => require('node-fetch')\n};\nlet systemFetch;\n// These getters and setters are for testing so we don't export a mutable\n// variable.\nexport function resetSystemFetch() {\n    systemFetch = null;\n}\nexport function setSystemFetch(fetchFn) {\n    systemFetch = fetchFn;\n}\nexport function getSystemFetch() {\n    return systemFetch;\n}\nexport class PlatformNode {\n    constructor() {\n        // tslint:disable-next-line:no-require-imports\n        this.util = require('util');\n        // According to the spec, the built-in encoder can do only UTF-8 encoding.\n        // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder\n        this.textEncoder = new this.util.TextEncoder();\n    }\n    fetch(path, requestInits) {\n        if (env().global.fetch != null) {\n            return env().global.fetch(path, requestInits);\n        }\n        if (systemFetch == null) {\n            systemFetch = getNodeFetch.importFetch();\n        }\n        return systemFetch(path, requestInits);\n    }\n    now() {\n        const time = process.hrtime();\n        return time[0] * 1000 + time[1] / 1000000;\n    }\n    encode(text, encoding) {\n        if (encoding !== 'utf-8' && encoding !== 'utf8') {\n            throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);\n        }\n        return this.textEncoder.encode(text);\n    }\n    decode(bytes, encoding) {\n        if (bytes.length === 0) {\n            return '';\n        }\n        return new this.util.TextDecoder(encoding).decode(bytes);\n    }\n}\nif (env().get('IS_NODE')) {\n    env().setPlatform('node', new PlatformNode());\n}\n//# sourceMappingURL=platform_node.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport * as util from '../util';\n/**\n * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.\n *\n * The values are stored in CPU as `TypedArray`. Fill the buffer using\n * `buffer.set()`, or by modifying directly `buffer.values`.\n *\n * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with\n * those values.\n *\n * ```js\n * // Create a buffer and set values at particular indices.\n * const buffer = tf.buffer([2, 2]);\n * buffer.set(3, 0, 0);\n * buffer.set(5, 1, 0);\n *\n * // Convert the buffer back to a tensor.\n * buffer.toTensor().print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The dtype of the buffer. Defaults to 'float32'.\n * @param values The values of the buffer as `TypedArray`. Defaults to\n * zeros.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function buffer(shape, dtype = 'float32', values) {\n    dtype = dtype || 'float32';\n    util.assertNonNegativeIntegerDimensions(shape);\n    return new TensorBuffer(shape, dtype, values);\n}\n//# sourceMappingURL=buffer.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cast } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Casts a `tf.Tensor` to a new dtype.\n *\n * ```js\n * const x = tf.tensor1d([1.5, 2.5, 3]);\n * tf.cast(x, 'int32').print();\n * ```\n * @param x The input tensor to be casted.\n * @param dtype The dtype to cast the input tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction cast_(x, dtype) {\n    const $x = convertToTensor(x, 'x', 'cast');\n    // Sanity checks.\n    if (!util.isValidDtype(dtype)) {\n        throw new Error(`Failed to cast to unknown dtype ${dtype}`);\n    }\n    if (dtype === 'string' && $x.dtype !== 'string' ||\n        dtype !== 'string' && $x.dtype === 'string') {\n        throw new Error('Only strings can be casted to strings');\n    }\n    const inputs = { x: $x };\n    const attrs = { dtype };\n    return ENGINE.runKernelFunc(backend => backend.cast($x, dtype), inputs, null /* grad */, Cast, attrs);\n}\nexport const cast = op({ cast_ });\n//# sourceMappingURL=cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Identity } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction clone_(x) {\n    const $x = convertToTensor(x, 'x', 'clone', null);\n    const forward = () => ENGINE.makeTensorFromDataId($x.dataId, $x.shape, $x.dtype);\n    const inputs = { x: $x };\n    // Note this op is called tf.identity in python. Hence the kernel name used\n    // here.\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Identity);\n}\nexport const clone = op({ clone_ });\n//# sourceMappingURL=clone.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function print(x, verbose = false) {\n    console.log(x.toString(verbose));\n}\n//# sourceMappingURL=print.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Required side effectful code for tfjs-core\n// Set up Engine and ENV\nimport { getOrMakeEngine } from './engine';\ngetOrMakeEngine();\n// Register backend-agnostic flags.\nimport './flags';\n// Register platforms\nimport './platforms/platform_browser';\nimport './platforms/platform_node';\n// Set up OpHandler\nimport { buffer } from './ops/buffer';\nimport { cast } from './ops/cast';\nimport { clone } from './ops/clone';\nimport { print } from './ops/print';\nimport { setOpHandler } from './tensor';\nconst opHandler = {\n    buffer,\n    cast,\n    clone,\n    print\n};\nsetOpHandler(opHandler);\n//# sourceMappingURL=base_side_effects.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandlers related to files, such as browser-triggered file downloads,\n * user-selected files in browser.\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { basename, concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DEFAULT_FILE_NAME_PREFIX = 'model';\nconst DEFAULT_JSON_EXTENSION_NAME = '.json';\nconst DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';\nfunction defer(f) {\n    return new Promise(resolve => setTimeout(resolve)).then(f);\n}\nexport class BrowserDownloads {\n    constructor(fileNamePrefix) {\n        if (!env().getBool('IS_BROWSER')) {\n            // TODO(cais): Provide info on what IOHandlers are available under the\n            //   current environment.\n            throw new Error('browserDownloads() cannot proceed because the current environment ' +\n                'is not a browser.');\n        }\n        if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n            fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n        }\n        if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n            fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n        }\n        this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n        this.weightDataFileName =\n            fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n    }\n    async save(modelArtifacts) {\n        if (typeof (document) === 'undefined') {\n            throw new Error('Browser downloads are not supported in ' +\n                'this environment since `document` is not present');\n        }\n        const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: 'application/octet-stream' }));\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserDownloads.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        else {\n            const weightsManifest = [{\n                    paths: ['./' + this.weightDataFileName],\n                    weights: modelArtifacts.weightSpecs\n                }];\n            const modelTopologyAndWeightManifest = {\n                modelTopology: modelArtifacts.modelTopology,\n                format: modelArtifacts.format,\n                generatedBy: modelArtifacts.generatedBy,\n                convertedBy: modelArtifacts.convertedBy,\n                weightsManifest\n            };\n            const modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: 'application/json' }));\n            // If anchor elements are not provided, create them without attaching them\n            // to parents, so that the downloaded file names can be controlled.\n            const jsonAnchor = this.jsonAnchor == null ? document.createElement('a') :\n                this.jsonAnchor;\n            jsonAnchor.download = this.modelTopologyFileName;\n            jsonAnchor.href = modelTopologyAndWeightManifestURL;\n            // Trigger downloads by evoking a click event on the download anchors.\n            // When multiple downloads are started synchronously, Firefox will only\n            // save the last one.\n            await defer(() => jsonAnchor.dispatchEvent(new MouseEvent('click')));\n            if (modelArtifacts.weightData != null) {\n                const weightDataAnchor = this.weightDataAnchor == null ?\n                    document.createElement('a') :\n                    this.weightDataAnchor;\n                weightDataAnchor.download = this.weightDataFileName;\n                weightDataAnchor.href = weightsURL;\n                await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent('click')));\n            }\n            return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) };\n        }\n    }\n}\nBrowserDownloads.URL_SCHEME = 'downloads://';\nclass BrowserFiles {\n    constructor(files) {\n        if (files == null || files.length < 1) {\n            throw new Error(`When calling browserFiles, at least 1 file is required, ` +\n                `but received ${files}`);\n        }\n        this.files = files;\n    }\n    async load() {\n        const jsonFile = this.files[0];\n        const weightFiles = this.files.slice(1);\n        return new Promise((resolve, reject) => {\n            const jsonReader = new FileReader();\n            jsonReader.onload = (event) => {\n                // tslint:disable-next-line:no-any\n                const modelJSON = JSON.parse(event.target.result);\n                const modelTopology = modelJSON.modelTopology;\n                if (modelTopology == null) {\n                    reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));\n                    return;\n                }\n                if (weightFiles.length === 0) {\n                    resolve({ modelTopology });\n                }\n                const weightsManifest = modelJSON.weightsManifest;\n                if (weightsManifest == null) {\n                    reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));\n                    return;\n                }\n                let pathToFile;\n                try {\n                    pathToFile =\n                        this.checkManifestAndWeightFiles(weightsManifest, weightFiles);\n                }\n                catch (err) {\n                    reject(err);\n                    return;\n                }\n                const weightSpecs = [];\n                const paths = [];\n                const perFileBuffers = [];\n                weightsManifest.forEach(weightsGroup => {\n                    weightsGroup.paths.forEach(path => {\n                        paths.push(path);\n                        perFileBuffers.push(null);\n                    });\n                    weightSpecs.push(...weightsGroup.weights);\n                });\n                weightsManifest.forEach(weightsGroup => {\n                    weightsGroup.paths.forEach(path => {\n                        const weightFileReader = new FileReader();\n                        weightFileReader.onload = (event) => {\n                            // tslint:disable-next-line:no-any\n                            const weightData = event.target.result;\n                            const index = paths.indexOf(path);\n                            perFileBuffers[index] = weightData;\n                            if (perFileBuffers.indexOf(null) === -1) {\n                                resolve({\n                                    modelTopology,\n                                    weightSpecs,\n                                    weightData: concatenateArrayBuffers(perFileBuffers),\n                                    format: modelJSON.format,\n                                    generatedBy: modelJSON.generatedBy,\n                                    convertedBy: modelJSON.convertedBy,\n                                    userDefinedMetadata: modelJSON.userDefinedMetadata\n                                });\n                            }\n                        };\n                        weightFileReader.onerror = error => reject(`Failed to weights data from file of path '${path}'.`);\n                        weightFileReader.readAsArrayBuffer(pathToFile[path]);\n                    });\n                });\n            };\n            jsonReader.onerror = error => reject(`Failed to read model topology and weights manifest JSON ` +\n                `from file '${jsonFile.name}'. BrowserFiles supports loading ` +\n                `Keras-style tf.Model artifacts only.`);\n            jsonReader.readAsText(jsonFile);\n        });\n    }\n    /**\n     * Check the compatibility between weights manifest and weight files.\n     */\n    checkManifestAndWeightFiles(manifest, files) {\n        const basenames = [];\n        const fileNames = files.map(file => basename(file.name));\n        const pathToFile = {};\n        for (const group of manifest) {\n            group.paths.forEach(path => {\n                const pathBasename = basename(path);\n                if (basenames.indexOf(pathBasename) !== -1) {\n                    throw new Error(`Duplicate file basename found in weights manifest: ` +\n                        `'${pathBasename}'`);\n                }\n                basenames.push(pathBasename);\n                if (fileNames.indexOf(pathBasename) === -1) {\n                    throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);\n                }\n                else {\n                    pathToFile[path] = files[fileNames.indexOf(pathBasename)];\n                }\n            });\n        }\n        if (basenames.length !== files.length) {\n            throw new Error(`Mismatch in the number of files in weights manifest ` +\n                `(${basenames.length}) and the number of weight files provided ` +\n                `(${files.length}).`);\n        }\n        return pathToFile;\n    }\n}\nexport const browserDownloadsRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n            return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\n/**\n * Creates an IOHandler that triggers file downloads from the browser.\n *\n * The returned `IOHandler` instance can be used as model exporting methods such\n * as `tf.Model.save` and supports only saving.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * const saveResult = await model.save('downloads://mymodel');\n * // This will trigger downloading of two files:\n * //   'mymodel.json' and 'mymodel.weights.bin'.\n * console.log(saveResult);\n * ```\n *\n * @param fileNamePrefix Prefix name of the files to be downloaded. For use with\n *   `tf.Model`, `fileNamePrefix` should follow either of the following two\n *   formats:\n *   1. `null` or `undefined`, in which case the default file\n *      names will be used:\n *      - 'model.json' for the JSON file containing the model topology and\n *        weights manifest.\n *      - 'model.weights.bin' for the binary file containing the binary weight\n *        values.\n *   2. A single string or an Array of a single string, as the file name prefix.\n *      For example, if `'foo'` is provided, the downloaded JSON\n *      file and binary weights file will be named 'foo.json' and\n *      'foo.weights.bin', respectively.\n * @param config Additional configuration for triggering downloads.\n * @returns An instance of `BrowserDownloads` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserDownloads(fileNamePrefix = 'model') {\n    return new BrowserDownloads(fileNamePrefix);\n}\n/**\n * Creates an IOHandler that loads model artifacts from user-selected files.\n *\n * This method can be used for loading from files such as user-selected files\n * in the browser.\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * // Note: This code snippet won't run properly without the actual file input\n * //   elements in the HTML DOM.\n *\n * // Suppose there are two HTML file input (`<input type=\"file\" ...>`)\n * // elements.\n * const uploadJSONInput = document.getElementById('upload-json');\n * const uploadWeightsInput = document.getElementById('upload-weights');\n * const model = await tf.loadLayersModel(tf.io.browserFiles(\n *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));\n * ```\n *\n * @param files `File`s to load from. Currently, this function supports only\n *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for\n *   which an `Array` of `File`s is expected (in that order):\n *   - A JSON file containing the model topology and weight manifest.\n *   - Optionally, One or more binary files containing the binary weights.\n *     These files must have names that match the paths in the `weightsManifest`\n *     contained by the aforementioned JSON file, or errors will be thrown\n *     during loading. These weights files have the same format as the ones\n *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`\n *     Python PIP package. If no weights files are provided, only the model\n *     topology will be loaded from the JSON file above.\n * @returns An instance of `Files` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserFiles(files) {\n    return new BrowserFiles(files);\n}\n//# sourceMappingURL=browser_files.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../util';\n/**\n * Monitor Promise.all progress, fire onProgress callback function.\n *\n * @param promises Promise list going to be monitored\n * @param onProgress Callback function. Fired when a promise resolved.\n * @param startFraction Optional fraction start. Default to 0.\n * @param endFraction Optional fraction end. Default to 1.\n */\nexport function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {\n    checkPromises(promises);\n    startFraction = startFraction == null ? 0 : startFraction;\n    endFraction = endFraction == null ? 1 : endFraction;\n    checkFraction(startFraction, endFraction);\n    let resolvedPromise = 0;\n    const registerMonitor = (promise) => {\n        promise.then(value => {\n            const fraction = startFraction +\n                ++resolvedPromise / promises.length * (endFraction - startFraction);\n            // pass fraction as parameter to callback function.\n            onProgress(fraction);\n            return value;\n        });\n        return promise;\n    };\n    function checkPromises(promises) {\n        assert(promises != null && Array.isArray(promises) && promises.length > 0, () => 'promises must be a none empty array');\n    }\n    function checkFraction(startFraction, endFraction) {\n        assert(startFraction >= 0 && startFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n            `got startFraction ${startFraction}`);\n        assert(endFraction >= 0 && endFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n            `got endFraction ${endFraction}`);\n        assert(endFraction >= startFraction, () => `startFraction must be no more than endFraction, but ` +\n            `got startFraction ${startFraction} and endFraction ` +\n            `${endFraction}`);\n    }\n    return Promise.all(promises.map(registerMonitor));\n}\n//# sourceMappingURL=progress.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\nimport * as util from '../util';\nimport { decodeWeights } from './io_utils';\nimport { monitorPromisesProgress } from './progress';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/**\n * Reads binary weights data from a number of URLs.\n *\n * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.\n * @param requestOptions RequestInit (options) for the HTTP requests.\n * @param fetchFunc Optional overriding value for the `window.fetch` function.\n * @param onProgress Optional, progress callback function, fired periodically\n *   before the load is completed.\n * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same\n *   length as `fetchURLs`.\n */\nexport async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {\n    if (loadOptions == null) {\n        loadOptions = {};\n    }\n    const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch :\n        loadOptions.fetchFunc;\n    // Create the requests for all of the weights in parallel.\n    const requests = fetchURLs.map(fetchURL => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true }));\n    const fetchStartFraction = 0;\n    const fetchEndFraction = 0.5;\n    const responses = loadOptions.onProgress == null ?\n        await Promise.all(requests) :\n        await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);\n    const bufferPromises = responses.map(response => response.arrayBuffer());\n    const bufferStartFraction = 0.5;\n    const bufferEndFraction = 1;\n    const buffers = loadOptions.onProgress == null ?\n        await Promise.all(bufferPromises) :\n        await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);\n    return buffers;\n}\n/**\n * Reads a weights manifest JSON configuration, fetches the weights and\n * returns them as `Tensor`s.\n *\n * @param manifest The weights manifest JSON.\n * @param filePathPrefix The path prefix for filenames given in the manifest.\n *     Defaults to the empty string.\n * @param weightNames The names of the weights to be fetched.\n */\nexport async function loadWeights(manifest, filePathPrefix = '', weightNames, requestInit) {\n    // TODO(nsthorat): Groups are currently fetched atomically. If you need a\n    // single weight from a group, the whole group will be fetched. At a future\n    // date, we should support fetching only the individual shards within a\n    // group that are needed to reconstruct the requested weight.\n    // TODO(cais): Use `decodeWeights` for implementation.\n    const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit });\n    const loadWeights = weightsLoaderFactory(fetchWeights);\n    return loadWeights(manifest, filePathPrefix, weightNames);\n}\n/**\n * Creates a function, which reads a weights manifest JSON configuration,\n * fetches the weight files using the specified function and returns them as\n * `Tensor`s.\n *\n * ```js\n * // example for creating a nodejs weight loader, which reads the weight files\n * // from disk using fs.readFileSync\n *\n * import * as fs from 'fs'\n *\n * const fetchWeightsFromDisk = (filePaths: string[]) =>\n *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)\n *\n * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n *\n * const manifest = JSON.parse(\n *   fs.readFileSync('./my_model-weights_manifest').toString()\n * )\n * const weightMap = await loadWeights(manifest, './')\n * ```\n * @param fetchWeightsFunction The function used for fetching the weight files.\n * @returns Weight loading function.\n */\nexport function weightsLoaderFactory(fetchWeightsFunction) {\n    return async (manifest, filePathPrefix = '', weightNames) => {\n        // Collect all the groups, weights, and their relative offsets to be\n        // fetched.\n        const groupIndicesToFetchMap = manifest.map(() => false);\n        const groupWeightsToFetch = {};\n        const weightsFound = weightNames != null ? weightNames.map(() => false) : [];\n        const allManifestWeightNames = [];\n        manifest.forEach((manifestGroupConfig, groupIndex) => {\n            let groupOffset = 0;\n            manifestGroupConfig.weights.forEach(weightsEntry => {\n                const rawDtype = ('quantization' in weightsEntry) ?\n                    weightsEntry.quantization.dtype :\n                    weightsEntry.dtype;\n                const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *\n                    util.sizeFromShape(weightsEntry.shape);\n                const enqueueWeightsForFetchingFn = () => {\n                    groupIndicesToFetchMap[groupIndex] = true;\n                    if (groupWeightsToFetch[groupIndex] == null) {\n                        groupWeightsToFetch[groupIndex] = [];\n                    }\n                    groupWeightsToFetch[groupIndex].push({\n                        manifestEntry: weightsEntry,\n                        groupOffset,\n                        sizeBytes: weightsBytes\n                    });\n                };\n                if (weightNames != null) {\n                    weightNames.forEach((weightName, weightIndex) => {\n                        if (weightName === weightsEntry.name) {\n                            enqueueWeightsForFetchingFn();\n                            weightsFound[weightIndex] = true;\n                        }\n                    });\n                }\n                else {\n                    enqueueWeightsForFetchingFn();\n                }\n                allManifestWeightNames.push(weightsEntry.name);\n                groupOffset += weightsBytes;\n            });\n        });\n        if (!weightsFound.every(found => found)) {\n            const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n            throw new Error(`Could not find weights in manifest with names: ` +\n                `${weightsNotFound.join(', ')}. \\n` +\n                `Manifest JSON has weights with names: ` +\n                `${allManifestWeightNames.join(', ')}.`);\n        }\n        // Convert the one-hot boolean groupId => shouldFetch map to a list of group\n        // IDs.\n        const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n            if (shouldFetch) {\n                accumulator.push(i);\n            }\n            return accumulator;\n        }, []);\n        const fetchUrls = [];\n        groupIndicesToFetch.forEach(i => {\n            manifest[i].paths.forEach(filepath => {\n                const fetchUrl = filePathPrefix +\n                    (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;\n                fetchUrls.push(fetchUrl);\n            });\n        });\n        const buffers = await fetchWeightsFunction(fetchUrls);\n        const weightsTensorMap = {};\n        let bufferIndexOffset = 0;\n        groupIndicesToFetch.forEach(i => {\n            const numBuffers = manifest[i].paths.length;\n            let groupBytes = 0;\n            for (let i = 0; i < numBuffers; i++) {\n                groupBytes += buffers[bufferIndexOffset + i].byteLength;\n            }\n            // Create a buffer for the whole group.\n            const groupBuffer = new ArrayBuffer(groupBytes);\n            const groupByteBuffer = new Uint8Array(groupBuffer);\n            let groupBufferOffset = 0;\n            for (let i = 0; i < numBuffers; i++) {\n                const buffer = new Uint8Array(buffers[bufferIndexOffset + i]);\n                groupByteBuffer.set(buffer, groupBufferOffset);\n                groupBufferOffset += buffer.byteLength;\n            }\n            const weightsEntries = groupWeightsToFetch[i];\n            weightsEntries.forEach(weightsEntry => {\n                const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);\n                const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n                for (const name in nameToTensorMap) {\n                    weightsTensorMap[name] = nameToTensorMap[name];\n                }\n            });\n            bufferIndexOffset += numBuffers;\n        });\n        return weightsTensorMap;\n    };\n}\n//# sourceMappingURL=weights_loader.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandler implementations based on HTTP requests in the web browser.\n *\n * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n */\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nimport { loadWeightsAsArrayBuffer } from './weights_loader';\nconst OCTET_STREAM_MIME_TYPE = 'application/octet-stream';\nconst JSON_TYPE = 'application/json';\nexport class HTTPRequest {\n    constructor(path, loadOptions) {\n        this.DEFAULT_METHOD = 'POST';\n        if (loadOptions == null) {\n            loadOptions = {};\n        }\n        this.weightPathPrefix = loadOptions.weightPathPrefix;\n        this.onProgress = loadOptions.onProgress;\n        this.weightUrlConverter = loadOptions.weightUrlConverter;\n        if (loadOptions.fetchFunc != null) {\n            assert(typeof loadOptions.fetchFunc === 'function', () => 'Must pass a function that matches the signature of ' +\n                '`fetch` (see ' +\n                'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');\n            this.fetch = loadOptions.fetchFunc;\n        }\n        else {\n            this.fetch = env().platform.fetch;\n        }\n        assert(path != null && path.length > 0, () => 'URL path for http must not be null, undefined or ' +\n            'empty.');\n        if (Array.isArray(path)) {\n            assert(path.length === 2, () => 'URL paths for http must have a length of 2, ' +\n                `(actual length is ${path.length}).`);\n        }\n        this.path = path;\n        if (loadOptions.requestInit != null &&\n            loadOptions.requestInit.body != null) {\n            throw new Error('requestInit is expected to have no pre-existing body, but has one.');\n        }\n        this.requestInit = loadOptions.requestInit || {};\n    }\n    async save(modelArtifacts) {\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserHTTPRequest.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        const init = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);\n        init.body = new FormData();\n        const weightsManifest = [{\n                paths: ['./model.weights.bin'],\n                weights: modelArtifacts.weightSpecs,\n            }];\n        const modelTopologyAndWeightManifest = {\n            modelTopology: modelArtifacts.modelTopology,\n            format: modelArtifacts.format,\n            generatedBy: modelArtifacts.generatedBy,\n            convertedBy: modelArtifacts.convertedBy,\n            userDefinedMetadata: modelArtifacts.userDefinedMetadata,\n            weightsManifest\n        };\n        init.body.append('model.json', new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), 'model.json');\n        if (modelArtifacts.weightData != null) {\n            init.body.append('model.weights.bin', new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), 'model.weights.bin');\n        }\n        const response = await this.fetch(this.path, init);\n        if (response.ok) {\n            return {\n                modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n                responses: [response],\n            };\n        }\n        else {\n            throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ` +\n                `${response.status}.`);\n        }\n    }\n    /**\n     * Load model artifacts via HTTP request(s).\n     *\n     * See the documentation to `tf.io.http` for details on the saved\n     * artifacts.\n     *\n     * @returns The loaded model artifacts (if loading succeeds).\n     */\n    async load() {\n        const modelConfigRequest = await this.fetch(this.path, this.requestInit);\n        if (!modelConfigRequest.ok) {\n            throw new Error(`Request to ${this.path} failed with status code ` +\n                `${modelConfigRequest.status}. Please verify this URL points to ` +\n                `the model JSON of the model to load.`);\n        }\n        let modelConfig;\n        try {\n            modelConfig = await modelConfigRequest.json();\n        }\n        catch (e) {\n            let message = `Failed to parse model JSON of response from ${this.path}.`;\n            // TODO(nsthorat): Remove this after some time when we're comfortable that\n            // .pb files are mostly gone.\n            if (this.path.endsWith('.pb')) {\n                message += ' Your path contains a .pb file extension. ' +\n                    'Support for .pb models have been removed in TensorFlow.js 1.0 ' +\n                    'in favor of .json models. You can re-convert your Python ' +\n                    'TensorFlow model using the TensorFlow.js 1.0 conversion scripts ' +\n                    'or you can convert your.pb models with the \\'pb2json\\'' +\n                    'NPM script in the tensorflow/tfjs-converter repository.';\n            }\n            else {\n                message += ' Please make sure the server is serving valid ' +\n                    'JSON for this request.';\n            }\n            throw new Error(message);\n        }\n        const modelTopology = modelConfig.modelTopology;\n        const weightsManifest = modelConfig.weightsManifest;\n        const generatedBy = modelConfig.generatedBy;\n        const convertedBy = modelConfig.convertedBy;\n        const format = modelConfig.format;\n        const userDefinedMetadata = modelConfig.userDefinedMetadata;\n        // We do not allow both modelTopology and weightsManifest to be missing.\n        if (modelTopology == null && weightsManifest == null) {\n            throw new Error(`The JSON from HTTP path ${this.path} contains neither model ` +\n                `topology or manifest for weights.`);\n        }\n        let weightSpecs;\n        let weightData;\n        if (weightsManifest != null) {\n            const results = await this.loadWeights(weightsManifest);\n            [weightSpecs, weightData] = results;\n        }\n        const artifacts = {\n            modelTopology,\n            weightSpecs,\n            weightData,\n            userDefinedMetadata,\n            generatedBy,\n            convertedBy,\n            format\n        };\n        const initializer = modelConfig.modelInitializer;\n        if (initializer) {\n            artifacts.modelInitializer = initializer;\n        }\n        return artifacts;\n    }\n    async loadWeights(weightsManifest) {\n        const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n        const [prefix, suffix] = parseUrl(weightPath);\n        const pathPrefix = this.weightPathPrefix || prefix;\n        const weightSpecs = [];\n        for (const entry of weightsManifest) {\n            weightSpecs.push(...entry.weights);\n        }\n        const fetchURLs = [];\n        const urlPromises = [];\n        for (const weightsGroup of weightsManifest) {\n            for (const path of weightsGroup.paths) {\n                if (this.weightUrlConverter != null) {\n                    urlPromises.push(this.weightUrlConverter(path));\n                }\n                else {\n                    fetchURLs.push(pathPrefix + path + suffix);\n                }\n            }\n        }\n        if (this.weightUrlConverter) {\n            fetchURLs.push(...await Promise.all(urlPromises));\n        }\n        const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {\n            requestInit: this.requestInit,\n            fetchFunc: this.fetch,\n            onProgress: this.onProgress\n        });\n        return [weightSpecs, concatenateArrayBuffers(buffers)];\n    }\n}\nHTTPRequest.URL_SCHEME_REGEX = /^https?:\\/\\//;\n/**\n * Extract the prefix and suffix of the url, where the prefix is the path before\n * the last file, and suffix is the search params after the last file.\n * ```\n * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'\n * [prefix, suffix] = parseUrl(url)\n * // prefix = 'http://tfhub.dev/model/1/'\n * // suffix = '?tfjs-format=file'\n * ```\n * @param url the model url to be parsed.\n */\nexport function parseUrl(url) {\n    const lastSlash = url.lastIndexOf('/');\n    const lastSearchParam = url.lastIndexOf('?');\n    const prefix = url.substring(0, lastSlash);\n    const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';\n    return [prefix + '/', suffix];\n}\nexport function isHTTPScheme(url) {\n    return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;\n}\nexport const httpRouter = (url, loadOptions) => {\n    if (typeof fetch === 'undefined' &&\n        (loadOptions == null || loadOptions.fetchFunc == null)) {\n        // `http` uses `fetch` or `node-fetch`, if one wants to use it in\n        // an environment that is not the browser or node they have to setup a\n        // global fetch polyfill.\n        return null;\n    }\n    else {\n        let isHTTP = true;\n        if (Array.isArray(url)) {\n            isHTTP = url.every(urlItem => isHTTPScheme(urlItem));\n        }\n        else {\n            isHTTP = isHTTPScheme(url);\n        }\n        if (isHTTP) {\n            return http(url, loadOptions);\n        }\n    }\n    return null;\n};\nIORouterRegistry.registerSaveRouter(httpRouter);\nIORouterRegistry.registerLoadRouter(httpRouter);\n/**\n * Creates an IOHandler subtype that sends model artifacts to HTTP server.\n *\n * An HTTP request of the `multipart/form-data` mime type will be sent to the\n * `path` URL. The form data includes artifacts that represent the topology\n * and/or weights of the model. In the case of Keras-style `tf.Model`, two\n * blobs (files) exist in form-data:\n *   - A JSON file consisting of `modelTopology` and `weightsManifest`.\n *   - A binary weights file consisting of the concatenated weight values.\n * These files are in the same format as the one generated by\n * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).\n *\n * The following code snippet exemplifies the client-side code that uses this\n * function:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save(tf.io.http(\n *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));\n * console.log(saveResult);\n * ```\n *\n * If the default `POST` method is to be used, without any custom parameters\n * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:\n *\n * ```js\n * const saveResult = await model.save('http://model-server:5000/upload');\n * ```\n *\n * The following GitHub Gist\n * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864\n * implements a server based on [flask](https://github.com/pallets/flask) that\n * can receive the request. Upon receiving the model artifacts via the requst,\n * this particular server reconsistutes instances of [Keras\n * Models](https://keras.io/models/model/) in memory.\n *\n *\n * @param path A URL path to the model.\n *   Can be an absolute HTTP path (e.g.,\n *   'http://localhost:8000/model-upload)') or a relative path (e.g.,\n *   './model-upload').\n * @param requestInit Request configurations to be used when sending\n *    HTTP request to server using `fetch`. It can contain fields such as\n *    `method`, `credentials`, `headers`, `mode`, etc. See\n *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request\n *    for more information. `requestInit` must not have a body, because the\n * body will be set by TensorFlow.js. File blobs representing the model\n * topology (filename: 'model.json') and the weights of the model (filename:\n * 'model.weights.bin') will be appended to the body. If `requestInit` has a\n * `body`, an Error will be thrown.\n * @param loadOptions Optional configuration for the loading. It includes the\n *   following fields:\n *   - weightPathPrefix Optional, this specifies the path prefix for weight\n *     files, by default this is calculated from the path param.\n *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,\n *     the `fetch` from node-fetch can be used here.\n *   - onProgress Optional, progress callback function, fired periodically\n *     before the load is completed.\n * @returns An instance of `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function http(path, loadOptions) {\n    return new HTTPRequest(path, loadOptions);\n}\n/**\n * Deprecated. Use `tf.io.http`.\n * @param path\n * @param loadOptions\n */\nexport function browserHTTPRequest(path, loadOptions) {\n    return http(path, loadOptions);\n}\n//# sourceMappingURL=http.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nclass PassthroughLoader {\n    constructor(modelArtifacts) {\n        this.modelArtifacts = modelArtifacts;\n    }\n    async load() {\n        return this.modelArtifacts;\n    }\n}\nclass PassthroughSaver {\n    constructor(saveHandler) {\n        this.saveHandler = saveHandler;\n    }\n    async save(modelArtifacts) {\n        return this.saveHandler(modelArtifacts);\n    }\n}\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadLayersModel(tf.io.fromMemory(\n *     modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelArtifacts a object containing model topology (i.e., parsed from\n *   the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n *   names, shapes, types, and quantization of the weight data.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n *   concatenated in the order described by the weightSpecs.\n * @param trainingConfig Model training configuration. Optional.\n *\n * @returns A passthrough `IOHandler` that simply loads the provided data.\n */\nexport function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {\n    if (arguments.length === 1) {\n        const isModelArtifacts = modelArtifacts.modelTopology != null ||\n            modelArtifacts.weightSpecs != null;\n        if (isModelArtifacts) {\n            return new PassthroughLoader(modelArtifacts);\n        }\n        else {\n            // Legacy support: with only modelTopology.\n            // TODO(cais): Remove this deprecated API.\n            console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n                'The argument should be of type ModelArtifacts. ' +\n                'The multi-argument signature of tf.io.fromMemory() has been ' +\n                'deprecated and will be removed in a future release.');\n            return new PassthroughLoader({ modelTopology: modelArtifacts });\n        }\n    }\n    else {\n        // Legacy support.\n        // TODO(cais): Remove this deprecated API.\n        console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n            'The argument should be of type ModelArtifacts. ' +\n            'The multi-argument signature of tf.io.fromMemory() has been ' +\n            'deprecated and will be removed in a future release.');\n        return new PassthroughLoader({\n            modelTopology: modelArtifacts,\n            weightSpecs,\n            weightData,\n            trainingConfig\n        });\n    }\n}\n/**\n * Creates an IOHandler that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n *   // ... do something with the artifacts ...\n *   return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n *     `SaveResult`.\n */\nexport function withSaveHandler(saveHandler) {\n    return new PassthroughSaver(saveHandler);\n}\n//# sourceMappingURL=passthrough.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Importing local_storage and indexed_db is necessary for the routers to be\n// registered.\nimport './indexed_db';\nimport './local_storage';\nimport { browserFiles } from './browser_files';\nimport { browserHTTPRequest, http, isHTTPScheme } from './http';\nimport { concatenateArrayBuffers, decodeWeights, encodeWeights, getModelArtifactsInfoForJSON } from './io_utils';\nimport { fromMemory, withSaveHandler } from './passthrough';\nimport { getLoadHandlers, getSaveHandlers, registerLoadRouter, registerSaveRouter } from './router_registry';\nimport { loadWeights, weightsLoaderFactory } from './weights_loader';\nexport { copyModel, listModels, moveModel, removeModel } from './model_management';\nexport { browserFiles, browserHTTPRequest, concatenateArrayBuffers, decodeWeights, encodeWeights, fromMemory, getLoadHandlers, getModelArtifactsInfoForJSON, getSaveHandlers, http, isHTTPScheme, loadWeights, registerLoadRouter, registerSaveRouter, weightsLoaderFactory, withSaveHandler };\n//# sourceMappingURL=io.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reshape } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Reshapes a `tf.Tensor` to a given shape.\n *\n * Given an input tensor, returns a new tensor with the same values as the\n * input tensor with shape `shape`.\n *\n * If one component of shape is the special value -1, the size of that\n * dimension is computed so that the total size remains constant. In\n * particular, a shape of [-1] flattens into 1-D. At most one component of\n * shape can be -1.\n *\n * If shape is 1-D or higher, then the operation returns a tensor with shape\n * shape filled with the values of tensor. In this case, the number of\n * elements implied by shape must be the same as the number of elements in\n * tensor.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.reshape([2, 2]).print();\n * ```\n *\n * @param x The input tensor to be reshaped.\n * @param shape An array of integers defining the output tensor shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction reshape_(x, shape) {\n    const $x = convertToTensor(x, 'x', 'reshape', null);\n    const inputs = { x: $x };\n    const attrs = { shape };\n    const forward = (backend, save) => {\n        shape = util.inferFromImplicitShape(shape, $x.size);\n        util.assert($x.size === util.sizeFromShape(shape), () => 'new shape and old shape must have the same number of elements.');\n        save([$x]);\n        return backend.reshape($x, shape);\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Reshape, attrs);\n}\nexport const reshape = op({ reshape_ });\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchMatMul } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the dot product of two matrices, A * B. These must be matrices.\n *\n * ```js\n * const a = tf.tensor2d([1, 2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.matMul(b).print();  // or tf.matMul(a, b)\n * ```\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction matMul_(a, b, transposeA = false, transposeB = false) {\n    let $a = convertToTensor(a, 'a', 'matMul');\n    let $b = convertToTensor(b, 'b', 'matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    util.assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in matMul: inputs must have the same rank of at least 2, ` +\n        `got ranks ${$a.rank} and ${$b.rank}.`);\n    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n    const outerDimsA = $a.shape.slice(0, -2);\n    const outerDimsB = $b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n    util.assert(util.arraysEqual(outerDimsA, outerDimsB), () => `Error in matMul: outer dimensions (${outerDimsA}) and (` +\n        `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} must match.`);\n    util.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (` +\n        `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} and transposeA=${transposeA}` +\n        ` and transposeB=${transposeB} must match.`);\n    const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n    const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n        reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n    const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n        reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n    const forward = (backend, save) => {\n        save([a3D, b3D]);\n        return backend.batchMatMul(a3D, b3D, transposeA, transposeB);\n    };\n    const inputs = { a: a3D, b: b3D };\n    const attrs = { transposeA, transposeB };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, BatchMatMul, attrs);\n    return reshape(res, outShape);\n}\nexport const matMul = op({ matMul_ });\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OneHot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank\n * `R+1` with the last axis of size `depth`.\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor` of indices with dtype `int32`.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n *     not match the location.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction oneHot_(indices, depth, onValue = 1, offValue = 0) {\n    if (depth < 2) {\n        throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n    }\n    const $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');\n    const outShape = [...$indices.shape, depth];\n    const forward = (backend, save) => {\n        save([$indices]);\n        return reshape(backend.oneHot(reshape($indices, [$indices.size]), depth, onValue, offValue), outShape);\n    };\n    const inputs = { indices: $indices };\n    const attrs = { depth, onValue, offValue };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, OneHot, attrs);\n}\nexport const oneHot = op({ oneHot_ });\n//# sourceMappingURL=one_hot.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Transpose } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print();  // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction transpose_(x, perm) {\n    const $x = convertToTensor(x, 'x', 'transpose');\n    if (perm == null) {\n        perm = $x.shape.map((s, i) => i).reverse();\n    }\n    util.assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n        `must match length of perm ${perm}.`);\n    perm.forEach(axis => {\n        util.assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n            ` but got ${perm}`);\n    });\n    if ($x.rank <= 1) {\n        return $x.clone();\n    }\n    const inputs = { x: $x };\n    const attrs = { perm };\n    return ENGINE.runKernelFunc(backend => backend.transpose($x, perm), inputs, null /* gradient */, Transpose, attrs);\n}\nexport const transpose = op({ transpose_ });\n//# sourceMappingURL=transpose.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { matMul } from './mat_mul';\nimport { oneHot } from './one_hot';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Computes the confusion matrix from true labels and predicted labels.\n *\n * ```js\n * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n * const numClasses = 3;\n * const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n * out.print();\n * // Expected output matrix:\n * // [[2, 0, 0],\n * //  [0, 1, 1],\n * //  [0, 0, 1]]\n * ```\n *\n * @param labels The target labels, assumed to be 0-based integers\n *   for the classes. The shape is `[numExamples]`, where\n *   `numExamples` is the number of examples included.\n * @param predictions The predicted classes, assumed to be\n *   0-based integers for the classes. Must have the same shape as `labels`.\n * @param numClasses Number of all classes, as an integer.\n *   Its value must be larger than the largest element in `labels` and\n *   `predictions`.\n * @returns The confusion matrix as a int32-type 2D tensor. The value at\n *   row `r` and column `c` is the number of times examples of actual class\n *   `r` were predicted as class `c`.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nexport function confusionMatrix_(labels, predictions, numClasses) {\n    const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');\n    const $predictions = convertToTensor(predictions, 'predictions', 'confusionMatrix');\n    util.assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, ` +\n        `but got ${numClasses}`);\n    util.assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n    util.assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, ` +\n        `but got ${$predictions.rank}`);\n    util.assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ` +\n        `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +\n        `Labels and predictions should have the same number of elements.`);\n    util.assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ` +\n        `${numClasses}`);\n    // TODO(cais): In the future, if oneHot supports tensors inputs for\n    //   `numClasses`, `confusionMatrix` can make `numClasses` optional.\n    const oneHotLabels = oneHot(cast($labels, 'int32'), numClasses);\n    const oneHotPredictions = oneHot(cast($predictions, 'int32'), numClasses);\n    const oneHotLabelsT = transpose(oneHotLabels);\n    return cast(matMul(oneHotLabelsT, oneHotPredictions), 'int32');\n}\nexport const confusionMatrix = op({ confusionMatrix_ });\n//# sourceMappingURL=confusion_matrix.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Exports under the tf.math.* namespace.\n */\nimport { confusionMatrix } from './ops/confusion_matrix';\nexport { confusionMatrix };\n//# sourceMappingURL=math.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor3d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided,  it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor3d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 3) {\n        throw new Error('tensor3d() requires shape to have three numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n        throw new Error('tensor3d() requires values to be number[][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor3d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor3d.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FromPixels } from '../kernel_names';\nimport { getKernel } from '../kernel_registry';\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { tensor3d } from './tensor3d';\nlet fromPixels2DContext;\n/**\n * Creates a `tf.Tensor` from an image.\n *\n * ```js\n * const image = new ImageData(1, 1);\n * image.data[0] = 100;\n * image.data[1] = 150;\n * image.data[2] = 200;\n * image.data[3] = 255;\n *\n * tf.browser.fromPixels(image).print();\n * ```\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel. You can also pass in an image\n * object with following attributes:\n * `{data: Uint8Array; width: number; height: number}`\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n *\n * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}\n */\nfunction fromPixels_(pixels, numChannels = 3) {\n    // Sanity checks.\n    if (numChannels > 4) {\n        throw new Error('Cannot construct Tensor with more than 4 channels from pixels.');\n    }\n    if (pixels == null) {\n        throw new Error('pixels passed to tf.browser.fromPixels() can not be null');\n    }\n    let isPixelData = false;\n    let isImageData = false;\n    let isVideo = false;\n    let isImage = false;\n    let isCanvasLike = false;\n    if (pixels.data instanceof Uint8Array) {\n        isPixelData = true;\n    }\n    else if (typeof (ImageData) !== 'undefined' && pixels instanceof ImageData) {\n        isImageData = true;\n    }\n    else if (typeof (HTMLVideoElement) !== 'undefined' &&\n        pixels instanceof HTMLVideoElement) {\n        isVideo = true;\n    }\n    else if (typeof (HTMLImageElement) !== 'undefined' &&\n        pixels instanceof HTMLImageElement) {\n        isImage = true;\n        // tslint:disable-next-line: no-any\n    }\n    else if (pixels.getContext != null) {\n        isCanvasLike = true;\n    }\n    else {\n        throw new Error('pixels passed to tf.browser.fromPixels() must be either an ' +\n            `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData ` +\n            `in browser, or OffscreenCanvas, ImageData in webworker` +\n            ` or {data: Uint32Array, width: number, height: number}, ` +\n            `but was ${pixels.constructor.name}`);\n    }\n    if (isVideo) {\n        const HAVE_CURRENT_DATA_READY_STATE = 2;\n        if (isVideo &&\n            pixels.readyState <\n                HAVE_CURRENT_DATA_READY_STATE) {\n            throw new Error('The video element has not loaded data yet. Please wait for ' +\n                '`loadeddata` event on the <video> element.');\n        }\n    }\n    // If the current backend has 'FromPixels' registered, it has a more\n    // efficient way of handling pixel uploads, so we call that.\n    const kernel = getKernel(FromPixels, ENGINE.backendName);\n    if (kernel != null) {\n        const inputs = { pixels };\n        const attrs = { numChannels };\n        return ENGINE.runKernel(FromPixels, inputs, attrs);\n    }\n    const [width, height] = isVideo ?\n        [\n            pixels.videoWidth,\n            pixels.videoHeight\n        ] :\n        [pixels.width, pixels.height];\n    let vals;\n    if (isCanvasLike) {\n        vals =\n            // tslint:disable-next-line:no-any\n            pixels.getContext('2d').getImageData(0, 0, width, height).data;\n    }\n    else if (isImageData || isPixelData) {\n        vals = pixels.data;\n    }\n    else if (isImage || isVideo) {\n        if (fromPixels2DContext == null) {\n            fromPixels2DContext = document.createElement('canvas').getContext('2d');\n        }\n        fromPixels2DContext.canvas.width = width;\n        fromPixels2DContext.canvas.height = height;\n        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n        vals = fromPixels2DContext.getImageData(0, 0, width, height).data;\n    }\n    let values;\n    if (numChannels === 4) {\n        values = new Int32Array(vals);\n    }\n    else {\n        const numPixels = width * height;\n        values = new Int32Array(numPixels * numChannels);\n        for (let i = 0; i < numPixels; i++) {\n            for (let channel = 0; channel < numChannels; ++channel) {\n                values[i * numChannels + channel] = vals[i * 4 + channel];\n            }\n        }\n    }\n    const outShape = [height, width, numChannels];\n    return tensor3d(values, outShape, 'int32');\n}\n/**\n * Draws a `tf.Tensor` of pixel values to a byte array or optionally a\n * canvas.\n *\n * When the dtype of the input is 'float32', we assume values in the range\n * [0-1]. Otherwise, when input is 'int32', we assume values in the range\n * [0-255].\n *\n * Returns a promise that resolves when the canvas has been drawn to.\n *\n * @param img A rank-2 or rank-3 tensor. If rank-2, draws grayscale. If\n *     rank-3, must have depth of 1, 3 or 4. When depth of 1, draws\n * grayscale. When depth of 3, we draw with the first three components of\n * the depth dimension corresponding to r, g, b and alpha = 1. When depth of\n * 4, all four components of the depth dimension correspond to r, g, b, a.\n * @param canvas The canvas to draw to.\n *\n * @doc {heading: 'Browser', namespace: 'browser'}\n */\nexport async function toPixels(img, canvas) {\n    let $img = convertToTensor(img, 'img', 'toPixels');\n    if (!(img instanceof Tensor)) {\n        // Assume int32 if user passed a native array.\n        const originalImgTensor = $img;\n        $img = cast(originalImgTensor, 'int32');\n        originalImgTensor.dispose();\n    }\n    if ($img.rank !== 2 && $img.rank !== 3) {\n        throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);\n    }\n    const [height, width] = $img.shape.slice(0, 2);\n    const depth = $img.rank === 2 ? 1 : $img.shape[2];\n    if (depth > 4 || depth === 2) {\n        throw new Error(`toPixels only supports depth of size ` +\n            `1, 3 or 4 but got ${depth}`);\n    }\n    if ($img.dtype !== 'float32' && $img.dtype !== 'int32') {\n        throw new Error(`Unsupported type for toPixels: ${$img.dtype}.` +\n            ` Please use float32 or int32 tensors.`);\n    }\n    const data = await $img.data();\n    const multiplier = $img.dtype === 'float32' ? 255 : 1;\n    const bytes = new Uint8ClampedArray(width * height * 4);\n    for (let i = 0; i < height * width; ++i) {\n        const rgba = [0, 0, 0, 255];\n        for (let d = 0; d < depth; d++) {\n            const value = data[i * depth + d];\n            if ($img.dtype === 'float32') {\n                if (value < 0 || value > 1) {\n                    throw new Error(`Tensor values for a float32 Tensor must be in the ` +\n                        `range [0 - 1] but encountered ${value}.`);\n                }\n            }\n            else if ($img.dtype === 'int32') {\n                if (value < 0 || value > 255) {\n                    throw new Error(`Tensor values for a int32 Tensor must be in the ` +\n                        `range [0 - 255] but encountered ${value}.`);\n                }\n            }\n            if (depth === 1) {\n                rgba[0] = value * multiplier;\n                rgba[1] = value * multiplier;\n                rgba[2] = value * multiplier;\n            }\n            else {\n                rgba[d] = value * multiplier;\n            }\n        }\n        const j = i * 4;\n        bytes[j + 0] = Math.round(rgba[0]);\n        bytes[j + 1] = Math.round(rgba[1]);\n        bytes[j + 2] = Math.round(rgba[2]);\n        bytes[j + 3] = Math.round(rgba[3]);\n    }\n    if (canvas != null) {\n        canvas.width = width;\n        canvas.height = height;\n        const ctx = canvas.getContext('2d');\n        const imageData = new ImageData(bytes, width, height);\n        ctx.putImageData(imageData, 0, 0);\n    }\n    if ($img !== img) {\n        $img.dispose();\n    }\n    return bytes;\n}\nexport const fromPixels = op({ fromPixels_ });\n//# sourceMappingURL=browser.js.map", "import { computeStrides } from '../util';\n/**\n * Validate gather nd inputs.\n *\n * @param tensor The tensor contains the source values.\n * @param indices The tensor contains the indices to slice the source.\n *\n * @returns [resultShape, numUpdates, sliceSize, strides]\n */\nexport function prepareAndValidate(tensor, indices) {\n    if (tensor.rank < 1) {\n        throw new Error('tf.gatherND() expects the input to be rank 1 or higher,' +\n            ` but the rank was ${tensor.rank}.`);\n    }\n    if (indices.rank < 1) {\n        throw new Error('tf.gatherND() expects the indices to be rank 1 or higher,' +\n            ` but the rank was ${indices.rank}.`);\n    }\n    if (indices.dtype !== 'int32') {\n        throw new Error('tf.gatherND() expects the indices to be int32 type,' +\n            ` but the dtype was ${indices.dtype}.`);\n    }\n    if (indices.shape[indices.rank - 1] > tensor.rank) {\n        throw new Error('index innermost dimension length must be <= tensor rank; saw: ' +\n            `${indices.shape[indices.rank - 1]} vs. ${tensor.rank}`);\n    }\n    if (tensor.size === 0) {\n        throw new Error('Requested more than 0 entries, but input is empty.' +\n            ` Input shape: ${tensor.shape}.`);\n    }\n    const indicesShape = indices.shape;\n    const sliceRank = indicesShape[indicesShape.length - 1];\n    // The result shape is\n    //   indices.shape[:-1] + params.shape[indices.shape[-1]:]\n    let nResult = 1;\n    for (let i = 0; i < indicesShape.length - 1; ++i) {\n        nResult *= indicesShape[i];\n    }\n    const inputShape = tensor.shape;\n    const resultShape = indicesShape.slice();\n    resultShape.pop();\n    let sliceSize = 1;\n    for (let i = sliceRank; i < tensor.rank; ++i) {\n        sliceSize *= inputShape[i];\n        resultShape.push(inputShape[i]);\n    }\n    const strides = [...computeStrides(tensor.shape).map(stride => stride / sliceSize),\n        1].slice(0, sliceRank);\n    return [resultShape, nResult, sliceSize, strides];\n}\n//# sourceMappingURL=gather_nd_util.js.map", "import { computeStrides, sizeFromShape } from '../util';\n/**\n * Check whether updates.shape = indices.shape[:batchDim] +\n * shape[sliceDim:]\n *\n * @param x The input tensor.\n */\nexport function validateUpdateShape(shape, indices, updates) {\n    const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n    const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;\n    const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +\n        `shape[sliceDim:], got updates.shape: ${updates.shape}` +\n        `, indices.shape: ${indices.shape}, shape: ${shape}` +\n        `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n    if (updates.rank < batchDim) {\n        throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n    }\n    if (shape.length < sliceDim + (updates.rank - batchDim)) {\n        throw new Error(shapeError +\n            ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n    }\n    if (updates.rank !== batchDim + shape.length - sliceDim) {\n        throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n    }\n    for (let d = 0; d < batchDim; ++d) {\n        if (updates.shape[d] !== indices.shape[d]) {\n            throw new Error(shapeError +\n                ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);\n        }\n    }\n    for (let d = 0; d < updates.rank - batchDim; ++d) {\n        if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n            throw new Error(shapeError +\n                ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);\n        }\n    }\n}\n/**\n * Validate scatter nd inputs.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n */\nexport function validateInput(updates, indices, shape) {\n    if (indices.rank < 1) {\n        throw new Error('tf.scatterND() expects the indices to be rank 1 or higher,' +\n            ` but the rank was ${indices.rank}.`);\n    }\n    if (updates.rank < 1) {\n        throw new Error('tf.scatterND() expects the updates to be rank 1 or higher,' +\n            ` but the rank was ${updates.rank}.`);\n    }\n    if (indices.dtype !== 'int32') {\n        throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);\n    }\n    if (shape.length < 1) {\n        throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);\n    }\n    if (shape.length === 0) {\n        if (indices.size === 0) {\n            throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);\n        }\n        if (updates.size === 0) {\n            throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);\n        }\n    }\n    validateUpdateShape(shape, indices, updates);\n}\n/**\n * Calculate the shape information for the output.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n *\n * @returns ScatterShapeInfo\n */\nexport function calculateShapes(updates, indices, shape) {\n    // Calculate the number of dimensions in indices\n    const indicesRank = indices.shape.length;\n    const sliceRank = (indicesRank > 1) ? indices.shape[indicesRank - 1] : 1;\n    // Calculate the number of elements that make up each slice of our updated\n    // tensor. This allows us to work with flattened tensors and copy over whole\n    // slices at a time.\n    const totalNd = shape.length;\n    let sliceSize = 1;\n    for (let i = sliceRank; i < totalNd; ++i) {\n        sliceSize *= shape[i];\n    }\n    const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;\n    const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;\n    const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];\n    const outputSize = sizeFromShape(shape);\n    return { sliceRank, numUpdates, sliceSize, strides, outputSize };\n}\n//# sourceMappingURL=scatter_nd_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nexport function assertParamsValid(input, begin, size) {\n    const inputRank = input.shape.length;\n    util.assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must ` +\n        `match the rank of the array (${inputRank}).`);\n    util.assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must ` +\n        `match the rank of the array (${inputRank}).`);\n    for (let i = 0; i < inputRank; ++i) {\n        util.assert(begin[i] + size[i] <= input.shape[i], () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] ` +\n            `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${input.shape[i]})`);\n    }\n}\n/** Converts a binary mask to an array of axes. Used in stridedSlice(). */\nexport function maskToAxes(mask) {\n    const axes = [];\n    let axis = 0;\n    while (mask > 0) {\n        if (mask & 1) {\n            axes.push(axis);\n        }\n        mask /= 2;\n        axis++;\n    }\n    return axes;\n}\n/** Computes the output shape given the strided slice params. */\nexport function computeOutShape(begin, end, strides) {\n    const size = [];\n    for (let axis = 0; axis < begin.length; axis++) {\n        size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);\n    }\n    return size;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stride value. Otherwise, insert.\nexport function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {\n    const newStrides = [...strides];\n    for (let i = newStrides.length; i < inputShape.length; i++) {\n        newStrides.push(1);\n    }\n    for (let i = 0; i < numElidedAxes; i++) {\n        if (i === 0) {\n            newStrides[ellipsisInsertionIndex] = 1;\n        }\n        else {\n            newStrides.splice(ellipsisInsertionIndex, 0 /* num elements to delete */, 1 /* element to add */);\n            newStrides.pop();\n        }\n    }\n    return newStrides;\n}\nfunction unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {\n    if (normalizedAxis <= ellipsisInsertionIndex) {\n        return normalizedAxis;\n    }\n    return normalizedAxis - (numElidedAxes - 1);\n}\nfunction getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {\n    const elidedAxes = [];\n    for (let i = 0; i < numElidedAxes; i++) {\n        elidedAxes.push(ellipsisInsertionIndex + i);\n    }\n    return elidedAxes;\n}\n// Normalize the start, end and strides.\nexport function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {\n    const inputRank = inputShape.length;\n    let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);\n    if (ellipsisAxes.length && numInterpolatedAxes > 0) {\n        const fullIndex = ellipsisAxes[0];\n        // The ellipsis applies to the masked index as well as any dimensions\n        // that are interpolated.\n        const numElidedAxes = numInterpolatedAxes + 1;\n        normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);\n        normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);\n        normalizedStrides =\n            stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);\n    }\n    else {\n        for (let axis = 0; axis < inputRank; axis++) {\n            normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);\n            normalizedEnd[axis] =\n                stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);\n            normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);\n        }\n    }\n    return {\n        begin: normalizedBegin,\n        end: normalizedEnd,\n        strides: normalizedStrides\n    };\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current start value. Otherwise, insert.\nexport function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {\n    const newIndices = [...inputShape];\n    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n    for (let axis = 0; axis < newIndices.length; axis++) {\n        if (elidedAxes.indexOf(axis) > -1) {\n            newIndices[axis] = 0;\n        }\n        else {\n            const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n            let originalValue = originalBegin[originalAxis];\n            if (beginMask & 1 << originalAxis) {\n                originalValue = 0;\n            }\n            newIndices[axis] = originalValue;\n        }\n    }\n    return newIndices;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stop value. Otherwise, insert.\nexport function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {\n    const newIndices = [...inputShape];\n    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n    for (let axis = 0; axis < newIndices.length; axis++) {\n        if (elidedAxes.indexOf(axis) > -1) {\n            newIndices[axis] = Number.MAX_SAFE_INTEGER;\n        }\n        else {\n            const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n            let originalValue = originalEnd[originalAxis];\n            if (endMask & 1 << originalAxis) {\n                originalValue = Number.MAX_SAFE_INTEGER;\n            }\n            newIndices[axis] = originalValue;\n        }\n    }\n    for (let i = 0; i < newIndices.length; i++) {\n        // Handle negative indices\n        const axisSize = inputShape[i];\n        if (newIndices[i] < 0) {\n            newIndices[i] += axisSize;\n        }\n        newIndices[i] = util.clamp(0, newIndices[i], inputShape[i]);\n    }\n    return newIndices;\n}\nexport function stridesForAxis(strides, axis, ellipsisMask) {\n    let stride = strides[axis];\n    if (ellipsisMask & (1 << axis) || stride == null) {\n        stride = 1;\n    }\n    return stride;\n}\nexport function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {\n    // Begin with the specified index\n    let start = startIndices[axis];\n    const stride = strides[axis] || 1;\n    // Check the axis bit from right of masked axes, or the begin index is not set\n    // for the axis.\n    if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {\n        if (stride > 0) {\n            // Forward iteration - use the first element. These values will get\n            // clamped below (Note: We could have set them to 0 and axis_size-1, but\n            // use lowest() and max() to maintain symmetry with StopForAxis())\n            start = Number.MIN_SAFE_INTEGER;\n        }\n        else {\n            // Backward iteration - use the last element.\n            start = Number.MAX_SAFE_INTEGER;\n        }\n    }\n    // Handle negative indices\n    const axisSize = inputShape[axis];\n    if (start < 0) {\n        start += axisSize;\n    }\n    // Clamping\n    start = util.clamp(0, start, axisSize - 1);\n    return start;\n}\nexport function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {\n    // Begin with the specified index\n    let stop = stopIndices[axis];\n    const stride = strides[axis] || 1;\n    // Check the axis bit from right of masked axes, or if the stop index is not\n    // set for this axis.\n    if (endMask & (1 << axis) || ellipsisMask & (1 << axis) || stop == null) {\n        if (stride > 0) {\n            // Forward iteration - use the last element. These values will get\n            // clamped below\n            stop = Number.MAX_SAFE_INTEGER;\n        }\n        else {\n            // Backward iteration - use the first element.\n            stop = Number.MIN_SAFE_INTEGER;\n        }\n    }\n    // Handle negative indices\n    const axisSize = inputShape[axis];\n    if (stop < 0) {\n        stop += axisSize;\n    }\n    // Clamping\n    // Because the end index points one past the last element, we need slightly\n    // different clamping ranges depending on the direction.\n    if (stride > 0) {\n        // Forward iteration\n        stop = util.clamp(0, stop, axisSize);\n    }\n    else {\n        // Backward iteration\n        stop = util.clamp(-1, stop, axisSize - 1);\n    }\n    return stop;\n}\n/**\n * Returns true if the slice occupies a continous set of elements in the\n * 'flat' space.\n */\nexport function isSliceContinous(shape, begin, size) {\n    // Index of the first axis that has size > 1.\n    let firstNonOneAxis = size.length;\n    for (let i = 0; i < size.length; i++) {\n        if (size[i] > 1) {\n            firstNonOneAxis = i;\n            break;\n        }\n    }\n    for (let i = firstNonOneAxis + 1; i < size.length; i++) {\n        if (begin[i] > 0 || size[i] !== shape[i]) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function computeFlatOffset(begin, strides) {\n    let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;\n    for (let i = 0; i < begin.length - 1; i++) {\n        flatOffset += begin[i] * strides[i];\n    }\n    return flatOffset;\n}\nexport function parseSliceParams(x, begin, size) {\n    // The following logic allows for more ergonomic calls.\n    let begin_;\n    const xRank = x.shape.length;\n    if (typeof begin === 'number') {\n        begin_ = [begin, ...new Array(xRank - 1).fill(0)];\n    }\n    else if (begin.length < xRank) {\n        begin_ = begin.concat(new Array(xRank - begin.length).fill(0));\n    }\n    else {\n        begin_ = begin.slice();\n    }\n    begin_.forEach(d => {\n        util.assert(d !== -1, () => 'slice() does not support negative begin indexing.');\n    });\n    let size_;\n    if (size == null) {\n        size_ = new Array(xRank).fill(-1);\n    }\n    else if (typeof size === 'number') {\n        size_ = [size, ...new Array(xRank - 1).fill(-1)];\n    }\n    else if (size.length < xRank) {\n        size_ = size.concat(new Array(xRank - size.length).fill(-1));\n    }\n    else {\n        size_ = size;\n    }\n    size_ = size_.map((d, i) => {\n        if (d >= 0) {\n            return d;\n        }\n        else {\n            util.assert(d === -1, () => `Negative size values should be exactly -1 but got ` +\n                `${d} for the slice() size at index ${i}.`);\n            return x.shape[i] - begin_[i];\n        }\n    });\n    return [begin_, size_];\n}\n//# sourceMappingURL=slice_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from './util';\n/**\n * Serializable defines the serialization contract.\n *\n * TFJS requires serializable classes to return their className when asked\n * to avoid issues with minification.\n */\nexport class Serializable {\n    /**\n     * Return the class name for this class to use in serialization contexts.\n     *\n     * Generally speaking this will be the same thing that constructor.name\n     * would have returned.  However, the class name needs to be robust\n     * against minification for serialization/deserialization to work properly.\n     *\n     * There's also places such as initializers.VarianceScaling, where\n     * implementation details between different languages led to different\n     * class hierarchies and a non-leaf node is used for serialization purposes.\n     */\n    getClassName() {\n        return this.constructor\n            .className;\n    }\n    /**\n     * Creates an instance of T from a ConfigDict.\n     *\n     * This works for most descendants of serializable.  A few need to\n     * provide special handling.\n     * @param cls A Constructor for the class to instantiate.\n     * @param config The Configuration for the object.\n     */\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/**\n * Maps string keys to class constructors.\n *\n * Used during (de)serialization from the cross-language JSON format, which\n * requires the class name in the serialization format matches the class\n * names as used in Python, should it exist.\n */\nexport class SerializationMap {\n    constructor() {\n        this.classNameMap = {};\n    }\n    /**\n     * Returns the singleton instance of the map.\n     */\n    static getMap() {\n        if (SerializationMap.instance == null) {\n            SerializationMap.instance = new SerializationMap();\n        }\n        return SerializationMap.instance;\n    }\n    /**\n     * Registers the class as serializable.\n     */\n    static register(cls) {\n        SerializationMap.getMap().classNameMap[cls.className] =\n            [cls, cls.fromConfig];\n    }\n}\n/**\n * Register a class with the serialization map of TensorFlow.js.\n *\n * This is often used for registering custom Layers, so they can be\n * serialized and deserialized.\n *\n * Example:\n *\n * ```js\n * class MyCustomLayer extends tf.layers.Layer {\n *   static className = 'MyCustomLayer';\n *\n *   constructor(config) {\n *     super(config);\n *   }\n * }\n * tf.serialization.registerClass(MyCustomLayer);\n * ```\n *\n * @param cls The class to be registered. It must have a public static member\n *   called `className` defined and the value must be a non-empty string.\n *\n * @doc {heading: 'Models', subheading: 'Serialization', ignoreCI: true}\n */\nexport function registerClass(cls) {\n    assert(cls.className != null, () => `Class being registered does not have the static className ` +\n        `property defined.`);\n    assert(typeof cls.className === 'string', () => `className is required to be a string, but got type ` +\n        typeof cls.className);\n    assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, ` +\n        `which is disallowed.`);\n    SerializationMap.register(cls);\n}\n//# sourceMappingURL=serialization.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { inferShape } from './tensor_util_env';\nimport { arraysEqual, flatten, isString, isTypedArray } from './util';\nconst TEST_EPSILON_FLOAT32 = 1e-3;\nexport const TEST_EPSILON_FLOAT16 = 1e-1;\nexport function expectArraysClose(actual, expected, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon));\n}\nexport function testEpsilon() {\n    return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :\n        TEST_EPSILON_FLOAT16;\n}\nfunction expectArraysPredicate(actual, expected, predicate) {\n    let checkClassType = true;\n    if (isTypedArray(actual) || isTypedArray(expected)) {\n        checkClassType = false;\n    }\n    if (isTypedArray(actual) && isTypedArray(expected)) {\n        checkClassType = true;\n    }\n    if (checkClassType) {\n        const aType = actual.constructor.name;\n        const bType = expected.constructor.name;\n        if (aType !== bType) {\n            throw new Error(`Arrays are of different type. Actual: ${aType}. ` +\n                `Expected: ${bType}`);\n        }\n    }\n    if (Array.isArray(actual) && Array.isArray(expected)) {\n        const actualShape = inferShape(actual);\n        const expectedShape = inferShape(expected);\n        if (!arraysEqual(actualShape, expectedShape)) {\n            throw new Error(`Arrays have different shapes. ` +\n                `Actual: [${actualShape}]. Expected: [${expectedShape}]`);\n        }\n    }\n    const actualFlat = isTypedArray(actual) ? actual : flatten(actual);\n    const expectedFlat = isTypedArray(expected) ?\n        expected :\n        flatten(expected);\n    if (actualFlat.length !== expectedFlat.length) {\n        throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs ` +\n            `expected: ${expectedFlat.length}.\\n` +\n            `Actual:   ${actualFlat}.\\n` +\n            `Expected: ${expectedFlat}.`);\n    }\n    for (let i = 0; i < expectedFlat.length; ++i) {\n        const a = actualFlat[i];\n        const e = expectedFlat[i];\n        if (!predicate(a, e)) {\n            throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\\n` +\n                `Actual:   ${actualFlat}.\\n` +\n                `Expected: ${expectedFlat}.`);\n        }\n    }\n}\nexport function expectPromiseToFail(fn, done) {\n    fn().then(() => done.fail(), () => done());\n}\nexport function expectArraysEqual(actual, expected) {\n    const exp = typeof expected === 'string' || typeof expected === 'number' ||\n        typeof expected === 'boolean' ?\n        [expected] :\n        expected;\n    if (isString(actual) || isString(actual[0]) ||\n        isString(expected) || isString(expected[0])) {\n        // tslint:disable-next-line: triple-equals\n        return expectArraysPredicate(actual, exp, (a, b) => a == b);\n    }\n    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));\n}\nexport function expectNumbersClose(a, e, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    if (!areClose(a, e, epsilon)) {\n        throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n    }\n}\nfunction areClose(a, e, epsilon) {\n    if (!isFinite(a) && !isFinite(e)) {\n        return true;\n    }\n    if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {\n        return false;\n    }\n    return true;\n}\nexport function expectValuesInRange(actual, low, high) {\n    for (let i = 0; i < actual.length; i++) {\n        if (actual[i] < low || actual[i] > high) {\n            throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);\n        }\n    }\n}\nexport function expectArrayBuffersEqual(actual, expected) {\n    // Safari & Jasmine don't like comparing ArrayBuffers directly. Wrapping in\n    // a Float32Array solves this issue.\n    expect(new Float32Array(actual)).toEqual(new Float32Array(expected));\n}\n//# sourceMappingURL=test_util.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { env } from './environment';\nimport { setDeprecationWarningFn } from './tensor';\nimport { getTensorsInContainer } from './tensor_util';\n/**\n * Enables production mode which disables correctness checks in favor of\n * performance.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableProdMode() {\n    env().set('PROD', true);\n}\n/**\n * Enables debug mode which will log information about all executed kernels:\n * the elapsed time of the kernel execution, as well as the rank, shape, and\n * size of the output tensor.\n *\n * Debug mode will significantly slow down your application as it will\n * download the result of every operation to the CPU. This should not be used in\n * production. Debug mode does not affect the timing information of the kernel\n * execution as we do not measure download time in the kernel execution time.\n *\n * See also: `tf.profile`, `tf.memory`.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableDebugMode() {\n    env().set('DEBUG', true);\n}\n/** Globally disables deprecation warnings */\nexport function disableDeprecationWarnings() {\n    env().set('DEPRECATION_WARNINGS_ENABLED', false);\n    console.warn(`TensorFlow.js deprecation warnings have been disabled.`);\n}\n/** Warn users about deprecated functionality. */\nexport function deprecationWarn(msg) {\n    if (env().getBool('DEPRECATION_WARNINGS_ENABLED')) {\n        console.warn(msg + ' You can disable deprecation warnings with ' +\n            'tf.disableDeprecationWarnings().');\n    }\n}\nsetDeprecationWarningFn(deprecationWarn);\n/**\n * Dispose all variables kept in backend engine.\n *\n * @doc {heading: 'Environment'}\n */\nexport function disposeVariables() {\n    ENGINE.disposeVariables();\n}\n/**\n * It returns the global engine that keeps track of all tensors and backends.\n *\n * @doc {heading: 'Environment'}\n */\nexport function engine() {\n    return ENGINE;\n}\n/**\n * Returns memory info at the current time in the program. The result is an\n * object with the following properties:\n *\n * - `numBytes`: Number of bytes allocated (undisposed) at this time.\n * - `numTensors`: Number of unique tensors allocated.\n * - `numDataBuffers`: Number of unique data buffers allocated\n *   (undisposed) at this time, which is \u2264 the number of tensors\n *   (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same\n *   data buffer with `a`).\n * - `unreliable`: True if the memory usage is unreliable. See `reasons` when\n *    `unreliable` is true.\n * - `reasons`: `string[]`, reasons why the memory is unreliable, present if\n *    `unreliable` is true.\n *\n * WebGL Properties:\n * - `numBytesInGPU`: Number of bytes allocated (undisposed) in the GPU only at\n *     this time.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function memory() {\n    return ENGINE.memory();\n}\n/**\n * Executes the provided function `f()` and returns a promise that resolves\n * with information about the function's memory use:\n * - `newBytes`: the number of new bytes allocated\n * - `newTensors`: the number of new tensors created\n * - `peakBytes`: the peak number of bytes allocated\n * - `kernels`: an array of objects for each kernel involved that reports\n * their input and output shapes, number of bytes used, and number of new\n * tensors created.\n *\n * ```js\n * const profile = await tf.profile(() => {\n *   const x = tf.tensor1d([1, 2, 3]);\n *   let x2 = x.square();\n *   x2.dispose();\n *   x2 = x.square();\n *   x2.dispose();\n *   return x;\n * });\n *\n * console.log(`newBytes: ${profile.newBytes}`);\n * console.log(`newTensors: ${profile.newTensors}`);\n * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>\n * k.totalBytesSnapshot)}`);\n * ```\n *\n *\n * @doc {heading: 'Performance', subheading: 'Profile'}\n */\nexport function profile(f) {\n    return ENGINE.profile(f);\n}\n/**\n * Executes the provided function `fn` and after it is executed, cleans up all\n * intermediate tensors allocated by `fn` except those returned by `fn`.\n * `fn` must not return a Promise (async functions not allowed). The returned\n * result can be a complex object.\n *\n * Using this method helps avoid memory leaks. In general, wrap calls to\n * operations in `tf.tidy` for automatic memory cleanup.\n *\n * NOTE: Variables do *not* get cleaned up when inside a tidy(). If you want to\n * dispose variables, please use `tf.disposeVariables` or call dispose()\n * directly on variables.\n *\n * ```js\n * // y = 2 ^ 2 + 1\n * const y = tf.tidy(() => {\n *   // a, b, and one will be cleaned up when the tidy ends.\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *   const b = a.square();\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * y.print();\n * ```\n *\n * @param nameOrFn The name of the closure, or the function to execute.\n *     If a name is provided, the 2nd argument should be the function.\n *     If debug mode is on, the timing and the memory usage of the function\n *     will be tracked and displayed on the console using the provided name.\n * @param fn The function to execute.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function tidy(nameOrFn, fn) {\n    return ENGINE.tidy(nameOrFn, fn);\n}\n/**\n * Disposes any `tf.Tensor`s found within the provided object.\n *\n * @param container an object that may be a `tf.Tensor` or may directly\n *     contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If\n *     the object is not a `tf.Tensor` or does not contain `Tensors`, nothing\n *     happens. In general it is safe to pass any object here, except that\n *     `Promise`s are not supported.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function dispose(container) {\n    const tensors = getTensorsInContainer(container);\n    tensors.forEach(tensor => tensor.dispose());\n}\n/**\n * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed\n * automatically.\n *\n * ```js\n * let b;\n * const y = tf.tidy(() => {\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *\n *   // b will not be cleaned up by the tidy. a and one will be cleaned up\n *   // when the tidy ends.\n *   b = tf.keep(a.square());\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * console.log('y:');\n * y.print();\n * console.log('b:');\n * b.print();\n * ```\n *\n * @param result The tensor to keep from being disposed.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function keep(result) {\n    return ENGINE.keep(result);\n}\n/**\n * Executes `f()` and returns a promise that resolves with timing\n * information.\n *\n * The result is an object with the following properties:\n *\n * - `wallMs`: Wall execution time.\n * - `kernelMs`: Kernel execution time, ignoring data transfer. If using the\n * WebGL backend and the query timer extension is not available, this will\n * return an error object.\n * - On `WebGL` The following additional properties exist:\n *   - `uploadWaitMs`: CPU blocking time on texture uploads.\n *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).\n *\n * ```js\n * const x = tf.randomNormal([20, 20]);\n * const time = await tf.time(() => x.matMul(x));\n *\n * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);\n * ```\n *\n * @param f The function to execute and time.\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nexport function time(f) {\n    return ENGINE.time(f);\n}\n/**\n * Sets the backend (cpu, webgl, wasm, etc) responsible for creating tensors and\n * executing operations on those tensors. Returns a promise that resolves\n * to a boolean if the backend initialization was successful.\n *\n * Note this disposes the current backend, if any, as well as any tensors\n * associated with it. A new backend is initialized, even if it is of the\n * same type as the previous one.\n *\n * @param backendName The name of the backend. Currently supports\n *     `'webgl'|'cpu'` in the browser, `'tensorflow'` under node.js\n *     (requires tfjs-node), and `'wasm'` (requires tfjs-backend-wasm).\n *\n * @doc {heading: 'Backends'}\n */\nexport function setBackend(backendName) {\n    return ENGINE.setBackend(backendName);\n}\n/**\n * Returns a promise that resolves when the currently selected backend (or the\n * highest priority one) has initialized. Await this promise when you are using\n * a backend that has async initialization.\n *\n * @doc {heading: 'Backends'}\n */\nexport function ready() {\n    return ENGINE.ready();\n}\n/**\n * Returns the current backend name (cpu, webgl, etc). The backend is\n * responsible for creating tensors and executing operations on those tensors.\n *\n * @doc {heading: 'Backends'}\n */\nexport function getBackend() {\n    return ENGINE.backendName;\n}\n/**\n * Removes a backend and the registered factory.\n *\n * @doc {heading: 'Backends'}\n */\nexport function removeBackend(name) {\n    ENGINE.removeBackend(name);\n}\n/**\n * Finds the backend registered under the provided name. Returns null if the\n * name is not in the registry, or the registration hasn't finished yet.\n */\nexport function findBackend(name) {\n    return ENGINE.findBackend(name);\n}\n/**\n * Finds the backend factory registered under the provided name. Returns a\n * function that produces a new backend when called. Returns null if the name\n * is not in the registry.\n */\nexport function findBackendFactory(name) {\n    return ENGINE.findBackendFactory(name);\n}\n/**\n * Registers a global backend. The registration should happen when importing\n * a module file (e.g. when importing `backend_webgl.ts`), and is used for\n * modular builds (e.g. custom tfjs bundle with only webgl support).\n *\n * @param factory The backend factory function. When called, it should\n * return a backend instance, or a promise of an instance.\n * @param priority The priority of the backend (higher = more important).\n *     In case multiple backends are registered, the priority is used to find\n *     the best backend. Defaults to 1.\n * @return False if there is already a registered backend under this name, true\n *     if not.\n *\n * @doc {heading: 'Backends'}\n */\nexport function registerBackend(name, factory, priority = 1) {\n    return ENGINE.registerBackend(name, factory, priority);\n}\n/**\n * Gets the current backend. If no backends have been initialized, this will\n * attempt to initialize the best backend. Will throw an error if the highest\n * priority backend has async initialization, in which case, you should call\n * 'await tf.ready()' before running other code.\n *\n * @doc {heading: 'Backends'}\n */\nexport function backend() {\n    return ENGINE.backend;\n}\n/**\n * Sets the global platform.\n *\n * @param platformName The name of this platform.\n * @param platform A platform implementation.\n */\nexport function setPlatform(platformName, platform) {\n    env().setPlatform(platformName, platform);\n}\n//# sourceMappingURL=globals.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Add } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n *\n * ```js\n * // Broadcast add a with b.\n * const a = tf.scalar(5);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n * @param a The first `tf.Tensor` to add.\n * @param b The second `tf.Tensor` to add. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction add_(a, b) {\n    let $a = convertToTensor(a, 'a', 'add');\n    let $b = convertToTensor(b, 'b', 'add');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.add($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Add);\n}\nexport const add = op({ add_ });\n//# sourceMappingURL=add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FloorDiv } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n * The result is rounded with floor function.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.floorDiv(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.floorDiv(b).print();  // or tf.floorDiv(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction floorDiv_(a, b) {\n    let $a = convertToTensor(a, 'a', 'floorDiv');\n    let $b = convertToTensor(b, 'b', 'floorDiv');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.floorDiv($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, FloorDiv);\n}\nexport const floorDiv = op({ floorDiv_ });\n//# sourceMappingURL=floorDiv.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Div } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { floorDiv } from './floorDiv';\nimport { op } from './operation';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction div_(a, b) {\n    let $a = convertToTensor(a, 'a', 'div');\n    let $b = convertToTensor(b, 'b', 'div');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'int32' && $b.dtype === 'int32') {\n        return floorDiv($a, $b);\n    }\n    const forward = (backend, save) => {\n        const res = backend.realDivide($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = {};\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Div, attrs);\n}\nexport const div = op({ div_ });\n//# sourceMappingURL=div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Multiply } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.\n *\n * We also expose `tf.mulStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([2, 3, 4, 5]);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n *\n * ```js\n * // Broadcast mul a with b.\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.scalar(5);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n * @param a The first tensor to multiply.\n * @param b The second tensor to multiply. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mul_(a, b) {\n    let $a = convertToTensor(a, 'a', 'mul');\n    let $b = convertToTensor(b, 'b', 'mul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.multiply($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Multiply);\n}\nexport const mul = op({ mul_ });\n//# sourceMappingURL=mul.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Abs } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes absolute value element-wise: `abs(x)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.abs().print();  // or tf.abs(x)\n * ```\n * @param x The input `tf.Tensor`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction abs_(x) {\n    const $x = convertToTensor(x, 'x', 'abs');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'complex64') {\n            return backend.complexAbs($x);\n        }\n        return backend.abs($x);\n    }, inputs, null /* grad */, Abs);\n}\nexport const abs = op({ abs_ });\n//# sourceMappingURL=abs.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.acos().print();  // or tf.acos(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acos_(x) {\n    const $x = convertToTensor(x, 'x', 'acos');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.acos($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Acos);\n}\nexport const acos = op({ acos_ });\n//# sourceMappingURL=acos.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:\n * `acosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([10, 1, 3, 5.7]);\n *\n * x.acosh().print();  // or tf.acosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acosh_(x) {\n    const $x = convertToTensor(x, 'x', 'acosh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.acosh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Acosh);\n}\nexport const acosh = op({ acosh_ });\n//# sourceMappingURL=acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AddN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n *\n * tf.addN([a, b, c]).print();\n * ```\n * @param tensors A list of tensors with the same shape and dtype.\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction addN_(tensors) {\n    util.assert(Array.isArray(tensors), () => 'The argument passed to tf.addN() must be a list of tensors');\n    util.assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ` +\n        `${tensors.length}`);\n    const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));\n    const firstTensor = $tensors[0];\n    $tensors.forEach(t => {\n        if (t.dtype !== firstTensor.dtype) {\n            throw new Error('All tensors passed to tf.addN() must have the same dtype');\n        }\n    });\n    $tensors.forEach(t => {\n        if (!util.arraysEqual(t.shape, firstTensor.shape)) {\n            throw new Error('All tensors passed to tf.addN() must have the same shape');\n        }\n    });\n    const forward = (backend, save) => {\n        const res = backend.addN($tensors);\n        save($tensors);\n        return res;\n    };\n    const inputs = $tensors;\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, AddN);\n}\nexport const addN = op({ addN_ });\n//# sourceMappingURL=add_n.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n * Returns true if the axis specifies the inner most dimensions of the\n * array.\n */\nexport function axesAreInnerMostDims(axes, rank) {\n    for (let i = 0; i < axes.length; ++i) {\n        if (axes[axes.length - i - 1] !== rank - 1 - i) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function combineLocations(outputLoc, reduceLoc, axes) {\n    const rank = outputLoc.length + reduceLoc.length;\n    const loc = [];\n    let outIdx = 0;\n    let reduceIdx = 0;\n    for (let dim = 0; dim < rank; dim++) {\n        if (axes.indexOf(dim) === -1) {\n            loc.push(outputLoc[outIdx++]);\n        }\n        else {\n            loc.push(reduceLoc[reduceIdx++]);\n        }\n    }\n    return loc;\n}\nexport function computeOutAndReduceShapes(aShape, axes) {\n    const outShape = [];\n    const rank = aShape.length;\n    for (let dim = 0; dim < rank; dim++) {\n        if (axes.indexOf(dim) === -1) {\n            outShape.push(aShape[dim]);\n        }\n    }\n    const reduceShape = axes.map(dim => aShape[dim]);\n    return [outShape, reduceShape];\n}\nexport function expandShapeToKeepDim(shape, axes) {\n    const reduceSubShape = axes.map(x => 1);\n    return combineLocations(shape, reduceSubShape, axes);\n}\nexport function assertAxesAreInnerMostDims(msg, axes, rank) {\n    util.assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. ` +\n        `Got axes ${axes} and rank-${rank} input.`);\n}\n/**\n * Returns the axes permutation to be used with `tf.transpose`, if such\n * permutation is necessary. Otherwise it returns null. This method is used by\n * operations that operate only on inner-most axes.\n */\nexport function getAxesPermutation(axes, rank) {\n    if (axesAreInnerMostDims(axes, rank)) {\n        return null;\n    }\n    const result = [];\n    for (let i = 0; i < rank; ++i) {\n        if (axes.indexOf(i) === -1) {\n            result.push(i);\n        }\n    }\n    axes.forEach(axis => result.push(axis));\n    return result;\n}\n/** Returns the axes permutation that undoes the original permutation. */\nexport function getUndoAxesPermutation(axes) {\n    return axes.map((axis, i) => [i, axis])\n        .sort((a, b) => a[1] - b[1])\n        .map(x => x[0]);\n}\nexport function getInnerMostAxes(numAxes, rank) {\n    const res = [];\n    for (let i = rank - numAxes; i < rank; ++i) {\n        res.push(i);\n    }\n    return res;\n}\n//# sourceMappingURL=axis_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { All } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the logical and of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.all().print();  // or tf.all(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.all(axis).print();  // or tf.all(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction all_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'all', 'bool');\n    const forward = (backend) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = getInnerMostAxes(axes.length, $x.rank);\n        }\n        const res = backend.all($x, axes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(res.shape, origAxes);\n            return reshape(res, newShape);\n        }\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, All, attrs);\n}\nexport const all = op({ all_ });\n//# sourceMappingURL=all.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Any } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the logical or of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.any().print();  // or tf.any(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.any(axis).print();  // or tf.any(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction any_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'any', 'bool');\n    const forward = (backend) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = getInnerMostAxes(axes.length, $x.rank);\n        }\n        const res = backend.any($x, axes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(res.shape, origAxes);\n            return reshape(res, newShape);\n        }\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Any, attrs);\n}\n// tslint:disable-next-line:variable-name\nexport const any = op({ any_ });\n//# sourceMappingURL=any.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Returns the indices of the maximum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMax().print();  // or tf.argMax(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMax(axis).print();  // or tf.argMax(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMax_(x, axis = 0) {\n    let $x = convertToTensor(x, 'x', 'argMax');\n    const forward = (backend, save) => {\n        save([$x]);\n        let axes = util.parseAxisParam(axis, $x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        return backend.argMax($x, axes[0]);\n    };\n    const inputs = { x: $x };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ArgMax, attrs);\n}\nexport const argMax = op({ argMax_ });\n//# sourceMappingURL=arg_max.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Returns the indices of the minimum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMin().print();  // or tf.argMin(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMin(axis).print();  // or tf.argMin(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMin_(x, axis = 0) {\n    let $x = convertToTensor(x, 'x', 'argMin');\n    const forward = (backend, save) => {\n        save([$x]);\n        if (axis == null) {\n            axis = 0;\n        }\n        let axes = util.parseAxisParam(axis, $x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        return backend.argMin($x, axes[0]);\n    };\n    const inputs = { x: $x };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ArgMin, attrs);\n}\nexport const argMin = op({ argMin_ });\n//# sourceMappingURL=arg_min.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asin().print();  // or tf.asin(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asin_(x) {\n    const $x = convertToTensor(x, 'x', 'asin');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.asin($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Asin);\n}\nexport const asin = op({ asin_ });\n//# sourceMappingURL=asin.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:\n * `asinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asinh().print();  // or tf.asinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asinh_(x) {\n    const $x = convertToTensor(x, 'x', 'asinh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.asinh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Asinh);\n}\nexport const asinh = op({ asinh_ });\n//# sourceMappingURL=asinh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.atan().print();  // or tf.atan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan_(x) {\n    const $x = convertToTensor(x, 'x', 'atan');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.atan($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Atan);\n}\nexport const atan = op({ atan_ });\n//# sourceMappingURL=atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan2 } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);\n * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);\n *\n * tf.atan2(a, b).print()\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan2_(a, b) {\n    let $a = convertToTensor(a, 'a', 'atan2');\n    let $b = convertToTensor(b, 'b', 'atan2');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.atan2($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Atan2);\n}\nexport const atan2 = op({ atan2_ });\n//# sourceMappingURL=atan2.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:\n * `atanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.atanh().print();  // or tf.atanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atanh_(x) {\n    const $x = convertToTensor(x, 'x', 'atanh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.atanh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Atanh);\n}\nexport const atanh = op({ atanh_ });\n//# sourceMappingURL=atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n *\n * @param inputShape Input tensor shape is of the following dimensions:\n *     `[batch, height, width, inChannels]`.\n * @param filterShape The filter shape is of the following dimensions:\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat The data format of the input and output data.\n *     Defaults to 'NHWC'.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`.\n *     Defaults to `[1, 1]`. If `dilations` is a single number, then\n *     `dilationHeight == dilationWidth`.\n */\nexport function computeDilation2DInfo(inputShape, filterShape, strides, pad, dataFormat = 'NHWC', dilations) {\n    // `computerConv2DInfo` require filterShape to be in the dimension of:\n    // `[filterHeight, filterWidth, depth, outDepth]`, dilation2d doesn't have\n    // outDepth, it should have the same depth as the input.\n    // Input shape: [batch, height, width, inChannels]\n    const inputChannels = inputShape[3];\n    const $filterShape = [...filterShape, inputChannels];\n    const $dataFormat = convertConv2DDataFormat(dataFormat);\n    return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad, null /* roundingMode */, null /* depthWise */, $dataFormat);\n}\nexport function computePool2DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'channelsLast') {\n    const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n    let filterShape;\n    if (dataFormat === 'channelsLast') {\n        filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n    }\n    else if (dataFormat === 'channelsFirst') {\n        filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    return computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, false, dataFormat);\n}\n/**\n * Computes the information for a forward pass of a pooling3D operation.\n */\nexport function computePool3DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'NDHWC') {\n    const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);\n    let filterShape;\n    let $dataFormat;\n    if (dataFormat === 'NDHWC') {\n        $dataFormat = 'channelsLast';\n        filterShape =\n            [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];\n    }\n    else if (dataFormat === 'NCDHW') {\n        $dataFormat = 'channelsFirst';\n        filterShape =\n            [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    return computeConv3DInfo(inShape, filterShape, strides, dilations, pad, false, $dataFormat, roundingMode);\n}\n/**\n * Computes the information for a forward pass of a convolution/pooling\n * operation.\n */\nexport function computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, depthwise = false, dataFormat = 'channelsLast') {\n    let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n    if (dataFormat === 'channelsLast') {\n        [batchSize, inHeight, inWidth, inChannels] = inShape;\n    }\n    else if (dataFormat === 'channelsFirst') {\n        [batchSize, inChannels, inHeight, inWidth] = inShape;\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    const [filterHeight, filterWidth, , filterChannels] = filterShape;\n    const [strideHeight, strideWidth] = parseTupleParam(strides);\n    const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n    const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);\n    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n    let outShape;\n    if (dataFormat === 'channelsFirst') {\n        outShape = [batchSize, outChannels, outHeight, outWidth];\n    }\n    else if (dataFormat === 'channelsLast') {\n        outShape = [batchSize, outHeight, outWidth, outChannels];\n    }\n    return {\n        batchSize,\n        dataFormat,\n        inHeight,\n        inWidth,\n        inChannels,\n        outHeight,\n        outWidth,\n        outChannels,\n        padInfo,\n        strideHeight,\n        strideWidth,\n        filterHeight,\n        filterWidth,\n        effectiveFilterHeight,\n        effectiveFilterWidth,\n        dilationHeight,\n        dilationWidth,\n        inShape,\n        outShape,\n        filterShape\n    };\n}\n/**\n * Computes the information for a forward pass of a 3D convolution/pooling\n * operation.\n */\nexport function computeConv3DInfo(inShape, filterShape, strides, dilations, pad, depthwise = false, dataFormat = 'channelsLast', roundingMode) {\n    let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];\n    if (dataFormat === 'channelsLast') {\n        [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n    }\n    else if (dataFormat === 'channelsFirst') {\n        [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;\n    const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n    const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);\n    const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);\n    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n    const { padInfo, outDepth, outHeight, outWidth } = get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);\n    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n    let outShape;\n    if (dataFormat === 'channelsFirst') {\n        outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n    }\n    else if (dataFormat === 'channelsLast') {\n        outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n    }\n    return {\n        batchSize,\n        dataFormat,\n        inDepth,\n        inHeight,\n        inWidth,\n        inChannels,\n        outDepth,\n        outHeight,\n        outWidth,\n        outChannels,\n        padInfo,\n        strideDepth,\n        strideHeight,\n        strideWidth,\n        filterDepth,\n        filterHeight,\n        filterWidth,\n        effectiveFilterDepth,\n        effectiveFilterHeight,\n        effectiveFilterWidth,\n        dilationDepth,\n        dilationHeight,\n        dilationWidth,\n        inShape,\n        outShape,\n        filterShape\n    };\n}\nfunction computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {\n    if (zeroPad == null) {\n        zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n    }\n    const inputRows = inShape[0];\n    const inputCols = inShape[1];\n    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    return [outputRows, outputCols];\n}\nfunction computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {\n    if (zeroPad == null) {\n        zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n    }\n    const inputDepth = inShape[0];\n    const inputRows = inShape[1];\n    const inputCols = inShape[2];\n    const outputDepths = conditionalRound((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputDepths), () => `The output # of depths (${outputDepths}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    return [outputDepths, outputRows, outputCols, outChannels];\n}\nexport function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {\n    const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n    return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\nfunction parseTupleParam(param) {\n    if (typeof param === 'number') {\n        return [param, param, param];\n    }\n    if (param.length === 2) {\n        return [param[0], param[1], 1];\n    }\n    return param;\n}\nfunction parse3TupleParam(param) {\n    return typeof param === 'number' ? [param, param, param] : param;\n}\n/* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d\n * Atrous convolution is equivalent to standard convolution with upsampled\n * filters with effective_filter_height =\n * filter_height + (filter_height - 1) * (dilation - 1)\n * and effective_filter_width =\n * filter_width + (filter_width - 1) * (dilation - 1),\n * produced by inserting dilation - 1 zeros along consecutive elements across\n * the filters' spatial dimensions.\n * When there is a dilation, this converts a filter dimension to the\n * effective filter dimension, so it can be used in a standard convolution.\n */\nfunction getEffectiveFilterSize(filterSize, dilation) {\n    if (dilation <= 1) {\n        return filterSize;\n    }\n    return filterSize + (filterSize - 1) * (dilation - 1);\n}\nfunction getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {\n    let padInfo;\n    let outHeight;\n    let outWidth;\n    if (typeof pad === 'number') {\n        const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n        padInfo = { top: pad, bottom: pad, left: pad, right: pad, type: padType };\n        const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad, roundingMode);\n        outHeight = outShape[0];\n        outWidth = outShape[1];\n    }\n    else if (pad === 'same') {\n        outHeight = Math.ceil(inHeight / strideHeight);\n        outWidth = Math.ceil(inWidth / strideWidth);\n        const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);\n        const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);\n        const top = Math.floor(padAlongHeight / 2);\n        const bottom = padAlongHeight - top;\n        const left = Math.floor(padAlongWidth / 2);\n        const right = padAlongWidth - left;\n        padInfo = { top, bottom, left, right, type: 'SAME' };\n    }\n    else if (pad === 'valid') {\n        padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: 'VALID' };\n        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n    }\n    else if (typeof pad === 'object') {\n        const top = dataFormat === 'channelsLast' ? pad[1][0] : pad[2][0];\n        const bottom = dataFormat === 'channelsLast' ? pad[1][1] : pad[2][1];\n        const left = dataFormat === 'channelsLast' ? pad[2][0] : pad[3][0];\n        const right = dataFormat === 'channelsLast' ? pad[2][1] : pad[3][1];\n        const padType = (top === 0 && bottom === 0 && left === 0 && right === 0) ?\n            'VALID' :\n            'EXPLICIT';\n        padInfo = { top, bottom, left, right, type: padType };\n        outHeight = conditionalRound((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);\n        outWidth = conditionalRound((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);\n    }\n    else {\n        throw Error(`Unknown padding parameter: ${pad}`);\n    }\n    return { padInfo, outHeight, outWidth };\n}\nfunction get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {\n    let padInfo;\n    let outDepth;\n    let outHeight;\n    let outWidth;\n    if (typeof pad === 'number') {\n        const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n        padInfo = {\n            top: pad,\n            bottom: pad,\n            left: pad,\n            right: pad,\n            front: pad,\n            back: pad,\n            type: padType\n        };\n        const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad, roundingMode);\n        outDepth = outShape[0];\n        outHeight = outShape[1];\n        outWidth = outShape[2];\n    }\n    else if (pad === 'same') {\n        outDepth = Math.ceil(inDepth / strideDepth);\n        outHeight = Math.ceil(inHeight / strideHeight);\n        outWidth = Math.ceil(inWidth / strideWidth);\n        const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n        const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;\n        const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n        const front = Math.floor(padAlongDepth / 2);\n        const back = padAlongDepth - front;\n        const top = Math.floor(padAlongHeight / 2);\n        const bottom = padAlongHeight - top;\n        const left = Math.floor(padAlongWidth / 2);\n        const right = padAlongWidth - left;\n        padInfo = { top, bottom, left, right, front, back, type: 'SAME' };\n    }\n    else if (pad === 'valid') {\n        padInfo = {\n            top: 0,\n            bottom: 0,\n            left: 0,\n            right: 0,\n            front: 0,\n            back: 0,\n            type: 'VALID'\n        };\n        outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);\n        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n    }\n    else {\n        throw Error(`Unknown padding parameter: ${pad}`);\n    }\n    return { padInfo, outDepth, outHeight, outWidth };\n}\n/**\n * Rounds a value depending on the rounding mode\n * @param value\n * @param roundingMode\n */\nfunction conditionalRound(value, roundingMode) {\n    if (!roundingMode) {\n        return value;\n    }\n    switch (roundingMode) {\n        case 'round':\n            // used for Caffe Conv\n            return Math.round(value);\n        case 'ceil':\n            // used for Caffe Pool\n            return Math.ceil(value);\n        case 'floor':\n            return Math.floor(value);\n        default:\n            throw new Error(`Unknown roundingMode ${roundingMode}`);\n    }\n}\nexport function tupleValuesAreOne(param) {\n    const [dimA, dimB, dimC] = parseTupleParam(param);\n    return dimA === 1 && dimB === 1 && dimC === 1;\n}\nexport function eitherStridesOrDilationsAreOne(strides, dilations) {\n    return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n/**\n * Convert Conv2D dataFormat from 'NHWC'|'NCHW' to\n *    'channelsLast'|'channelsFirst'\n * @param dataFormat in 'NHWC'|'NCHW' mode\n * @return dataFormat in 'channelsLast'|'channelsFirst' mode\n * @throws unknown dataFormat\n */\nexport function convertConv2DDataFormat(dataFormat) {\n    if (dataFormat === 'NHWC') {\n        return 'channelsLast';\n    }\n    else if (dataFormat === 'NCHW') {\n        return 'channelsFirst';\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n}\n//# sourceMappingURL=conv_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction avgPool_(x, filterSize, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'avgPool', 'float32');\n    const dilations = 1;\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in avgPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        save([x4D]);\n        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n            util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n            return x4D.clone();\n        }\n        return backend.avgPool(x4D, convInfo);\n    };\n    const inputs = { x: x4D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool, attrs);\n    res = cast(res, $x.dtype);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const avgPool = op({ avgPool_ });\n//# sourceMappingURL=avg_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { deprecationWarn } from '../globals';\nimport { AvgPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D average pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.avgPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates:\n *     `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction avgPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat = 'NDHWC', dilations) {\n    if (dilations == null) {\n        dilations = [1, 1, 1];\n    }\n    else {\n        deprecationWarn('dilations is deprecated, this field will be gone in ' +\n            'v3.0.0.');\n    }\n    const $x = convertToTensor(x, 'x', 'avgPool3d', 'float32');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in avgPool3d: Only NDHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool3d: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in avgPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1, 1];\n        }\n        const convInfo = conv_util.computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);\n        save([x5D]);\n        return backend.avgPool3d(x5D, convInfo);\n    };\n    const inputs = { x: x5D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat, dilations };\n    let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool3D, attrs);\n    res = cast(res, x5D.dtype);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const avgPool3d = op({ avgPool3d_ });\n//# sourceMappingURL=avg_pool_3d.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nexport function assertParamsConsistent(shapes, axis) {\n    const rank = shapes[0].length;\n    shapes.forEach((shape, i) => {\n        util.assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same ` +\n            `as the rank of the rest (${rank})`);\n    });\n    util.assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);\n    const firstShape = shapes[0];\n    shapes.forEach((shape, i) => {\n        for (let r = 0; r < rank; r++) {\n            util.assert((r === axis) || (shape[r] === firstShape[r]), () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) ` +\n                `does not match the shape of the rest (${firstShape}) ` +\n                `along the non-concatenated axis ${i}.`);\n        }\n    });\n}\nexport function computeOutShape(shapes, axis) {\n    const outputShape = shapes[0].slice();\n    for (let i = 1; i < shapes.length; i++) {\n        outputShape[axis] += shapes[i][axis];\n    }\n    return outputShape;\n}\n//# sourceMappingURL=concat_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Concat } from '../kernel_names';\nimport { convertToTensorArray } from '../tensor_util_env';\nimport { assert, parseAxisParam, sizeFromShape } from '../util';\nimport { assertParamsConsistent, computeOutShape } from './concat_util';\nimport { op } from './operation';\nimport { tensor } from './tensor';\n/**\n * Concatenates a list of `tf.Tensor`s along a given axis.\n *\n * The tensors ranks and types must match, and their sizes must match in all\n * dimensions except `axis`.\n *\n * Also available are stricter rank-specific methods that assert that\n * `tensors` are of the given rank:\n *   - `tf.concat1d`\n *   - `tf.concat2d`\n *   - `tf.concat3d`\n *   - `tf.concat4d`\n *\n * Except `tf.concat1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * a.concat(b).print();  // or a.concat(b)\n * ```\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.concat([a, b, c]).print();\n * ```\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [10, 20]]);\n * const b = tf.tensor2d([[3, 4], [30, 40]]);\n * const axis = 1;\n * tf.concat([a, b], axis).print();\n * ```\n * @param tensors A list of tensors to concatenate.\n * @param axis The axis to concate along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction concat_(tensors, axis = 0) {\n    assert(tensors.length >= 1, () => 'Pass at least one tensor to concat');\n    let $tensors = convertToTensorArray(tensors, 'tensors', 'concat');\n    if ($tensors[0].dtype === 'complex64') {\n        $tensors.forEach(tensor => {\n            if (tensor.dtype !== 'complex64') {\n                throw new Error(`Cannot concatenate complex64 tensors with a tensor\n          with dtype ${tensor.dtype}. `);\n            }\n        });\n    }\n    const forward = (backend, save) => {\n        const $axis = parseAxisParam(axis, $tensors[0].shape)[0];\n        const outShape = computeOutShape($tensors.map(t => t.shape), $axis);\n        if (sizeFromShape(outShape) === 0) {\n            return tensor([], outShape);\n        }\n        // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n        $tensors = $tensors.filter(t => t.size > 0);\n        if ($tensors.length === 1) {\n            return $tensors[0];\n        }\n        const shapes = $tensors.map(t => t.shape);\n        assertParamsConsistent(shapes, $axis);\n        const res = backend.concat($tensors, $axis);\n        save($tensors);\n        return res;\n    };\n    const inputs = $tensors;\n    const attr = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Concat, attr);\n}\nexport const concat = op({ concat_ });\n//# sourceMappingURL=concat.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sigmoid } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sigmoid element-wise, `1 / (1 + exp(-x))`\n *\n * ```js\n * const x = tf.tensor1d([0, -1, 2, -3]);\n *\n * x.sigmoid().print();  // or tf.sigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sigmoid_(x) {\n    const $x = convertToTensor(x, 'x', 'sigmoid');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sigmoid($x);\n        save([res]);\n        return res;\n    }, inputs, null /* grad */, Sigmoid);\n}\nexport const sigmoid = op({ sigmoid_ });\n//# sourceMappingURL=sigmoid.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Slice } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport * as slice_util from './slice_util';\n/**\n * Extracts a slice from a `tf.Tensor` starting at coordinates `begin`\n * and is of size `size`.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `x` is of the given rank:\n *   - `tf.slice1d`\n *   - `tf.slice2d`\n *   - `tf.slice3d`\n *   - `tf.slice4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.slice([1], [2]).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * x.slice([1, 0], [1, 2]).print();\n * ```\n * @param x The input `tf.Tensor` to slice from.\n * @param begin The coordinates to start the slice from. The length can be\n *     less than the rank of x - the rest of the axes will have implicit 0 as\n *     start. Can also be a single number, in which case it specifies the\n *     first axis.\n * @param size The size of the slice. The length can be less than the rank of\n *     x - the rest of the axes will have implicit -1. A value of -1 requests\n *     the rest of the dimensions in the axis. Can also be a single number,\n *     in which case it specifies the size of the first axis.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction slice_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice');\n    if ($x.rank === 0) {\n        throw new Error('Slicing scalar is not possible');\n    }\n    const forward = (backend, save) => {\n        const [begin_, size_] = slice_util.parseSliceParams($x, begin, size);\n        slice_util.assertParamsValid($x, begin_, size_);\n        save([$x]);\n        return backend.slice($x, begin_, size_);\n    };\n    const inputs = { x: $x };\n    const attrs = { begin, size };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Slice, attrs);\n}\nexport const slice = op({ slice_ });\n//# sourceMappingURL=slice.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, 70]);\n *\n * x.tanh().print();  // or tf.tanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tanh_(x) {\n    const $x = convertToTensor(x, 'x', 'tanh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const y = backend.tanh($x);\n        save([y]);\n        return y;\n    }, inputs, null /* grad */, Tanh);\n}\nexport const tanh = op({ tanh_ });\n//# sourceMappingURL=tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { add } from './add';\nimport { concat } from './concat';\nimport { matMul } from './mat_mul';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { slice } from './slice';\nimport { tanh } from './tanh';\n/**\n * Computes the next state and output of a BasicLSTMCell.\n *\n * Returns `[newC, newH]`.\n *\n * Derived from tf.contrib.rnn.BasicLSTMCell.\n *\n * @param forgetBias Forget bias for the cell.\n * @param lstmKernel The weights for the cell.\n * @param lstmBias The bias for the cell.\n * @param data The input to the cell.\n * @param c Previous cell state.\n * @param h Previous cell output.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {\n    const $forgetBias = convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');\n    const $lstmKernel = convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');\n    const $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');\n    const $data = convertToTensor(data, 'data', 'basicLSTMCell');\n    const $c = convertToTensor(c, 'c', 'basicLSTMCell');\n    const $h = convertToTensor(h, 'h', 'basicLSTMCell');\n    const combined = concat([$data, $h], 1);\n    const weighted = matMul(combined, $lstmKernel);\n    const res = add(weighted, $lstmBias);\n    // i = input_gate, j = new_input, f = forget_gate, o = output_gate\n    const batchSize = res.shape[0];\n    const sliceCols = res.shape[1] / 4;\n    const sliceSize = [batchSize, sliceCols];\n    const i = slice(res, [0, 0], sliceSize);\n    const j = slice(res, [0, sliceCols], sliceSize);\n    const f = slice(res, [0, sliceCols * 2], sliceSize);\n    const o = slice(res, [0, sliceCols * 3], sliceSize);\n    const newC = add(mul(sigmoid(i), tanh(j)), mul($c, sigmoid(add($forgetBias, f))));\n    const newH = mul(tanh(newC), sigmoid(o));\n    return [newC, newH];\n}\nexport const basicLSTMCell = op({ basicLSTMCell_ });\n//# sourceMappingURL=basic_lstm_cell.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchToSpaceND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation reshapes the \"batch\" dimension 0 into `M + 1` dimensions of\n * shape `blockShape + [batch]`, interleaves these blocks back into the grid\n * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with\n * the same rank as the input. The spatial dimensions of this intermediate\n * result are then optionally cropped according to `crops` to produce the\n * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise\n * description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n * const blockShape = [2, 2];\n * const crops = [[0, 0], [0, 0]];\n *\n * x.batchToSpaceND(blockShape, crops).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param crops A 2-D array.  Must have shape `[M, 2]`, all values must be >= 0.\n * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input\n * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required\n * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,\n * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,\n * x.shape[N-1]]`\n *\n * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /\n * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`\n * according to `crops` to produce the output of shape: `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],\n * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -\n * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction batchToSpaceND_(x, blockShape, crops) {\n    const $x = convertToTensor(x, 'x', 'batchToSpaceND');\n    const prod = blockShape.reduce((a, b) => a * b);\n    util.assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);\n    util.assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);\n    util.assert($x.shape[0] % prod === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of ` +\n        `the elements of blockShape ${blockShape.join(' * ')} === ${prod}`);\n    const forward = backend => {\n        return backend.batchToSpaceND($x, blockShape, crops);\n    };\n    const inputs = { x: $x };\n    const attrs = { blockShape, crops };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, BatchToSpaceND, attrs);\n}\nexport const batchToSpaceND = op({ batchToSpaceND_ });\n//# sourceMappingURL=batch_to_space_nd.js.map", "import { reshape } from './reshape';\nexport function xAs4D(x) {\n    let x4D;\n    if (x.rank === 0 || x.rank === 1) {\n        x4D = reshape(x, [1, 1, 1, x.size]);\n    }\n    else if (x.rank === 2) {\n        x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n    }\n    else if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    else {\n        x4D = x;\n    }\n    return x4D;\n}\n//# sourceMappingURL=batchnorm_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FusedBatchNorm } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { xAs4D } from './batchnorm_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Batch normalization.\n *\n * As described in\n * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).\n *\n * Mean, variance, scale, and offset can be of two shapes:\n *   - The same shape as the input.\n *   - In the common case, the depth dimension is the last dimension of x, so\n *     the values would be an `tf.Tensor1D` of shape [depth].\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that parameters passed are of given rank\n *   - `tf.batchNorm2d`\n *   - `tf.batchNorm3d`\n *   - `tf.batchNorm4d`\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction batchNorm_(x, mean, variance, offset, scale, varianceEpsilon) {\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($mean.rank === $variance.rank, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert($offset == null || $mean.rank === $offset.rank, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert($scale == null || $mean.rank === $scale.rank, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    const x4D = xAs4D($x);\n    const forward = (backend, save) => {\n        save([x4D, $mean, $variance, $scale]);\n        return backend.batchNorm(x4D, as1DOr4D($mean), as1DOr4D($variance), as1DOr4D($offset), as1DOr4D($scale), varianceEpsilon);\n    };\n    const inputs = {\n        x: x4D,\n        scale: $scale,\n        offset: $offset,\n        mean: $mean,\n        variance: $variance\n    };\n    const attrs = { varianceEpsilon };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, FusedBatchNorm, attrs);\n    return reshape(res, $x.shape);\n}\nfunction as1DOr4D(x) {\n    if (x == null) {\n        return null;\n    }\n    if (x.rank === 0) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [x.size]);\n    }\n    else if (x.rank === 1) {\n        return x;\n    }\n    else if (x.rank === 2) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n    }\n    else if (x.rank === 3) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    return x;\n}\nexport const batchNorm = op({ batchNorm_ });\n//# sourceMappingURL=batchnorm.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 2D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm2d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm2d = op({ batchNorm2d_ });\n//# sourceMappingURL=batchnorm2d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 3D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm3d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm3d = op({ batchNorm3d_ });\n//# sourceMappingURL=batchnorm3d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 4D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm4d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm4d = op({ batchNorm4d_ });\n//# sourceMappingURL=batchnorm4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BroadcastTo } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { clone } from './clone';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Broadcast an array to a compatible shape NumPy-style.\n *\n * The tensor's shape is compared to the broadcast shape from end to beginning.\n * Ones are prepended to the tensor's shape until is has the same length as\n * the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is\n * already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then\n * the input tensor is tiled N times along that axis (using tf.tile).\n *\n * @param input The tensor that is to be broadcasted.\n * @param shape The input is to be broadcast to this shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction broadcastTo_(x, shape) {\n    let input = convertToTensor(x, 'broadcastTo', 'x');\n    const xShape = input.shape;\n    if (shape.some(d => !(d > 0) || d % 1 !== 0)) {\n        throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);\n    }\n    if (shape.length < input.rank) {\n        throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input.rank}.`);\n    }\n    if (shape.length > input.rank) {\n        const newShape = input.shape.slice();\n        while (newShape.length < shape.length) {\n            newShape.unshift(1);\n        }\n        input = reshape(input, newShape);\n    }\n    const inputShape = input.shape;\n    const reps = Array.from(shape);\n    for (let i = shape.length - 1; i >= 0; i--) {\n        if (inputShape[i] === shape[i]) {\n            reps[i] = 1;\n        }\n        else if (input.shape[i] !== 1) {\n            throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);\n        }\n    }\n    const axes = reps.map((n, i) => n > 1 ? i : -1).filter(i => i >= 0);\n    if (axes.length === 0) {\n        return clone(input);\n    }\n    const forward = (backend) => backend.tile(input, reps);\n    const inputs = { x: input };\n    const attrs = { shape, inputShape };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, BroadcastTo, attrs);\n}\nexport const broadcastTo = op({ broadcastTo_ });\n//# sourceMappingURL=broadcast_to.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Ceil } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.ceil().print();  // or tf.ceil(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction ceil_(x) {\n    const $x = convertToTensor(x, 'x', 'ceil');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.ceil($x), inputs, null /* grad */, Ceil);\n}\nexport const ceil = op({ ceil_ });\n//# sourceMappingURL=ceil.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ClipByValue } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)\n * ```\n * @param x The input tensor.\n * @param clipValueMin Lower-bound of range to be clipped to.\n * @param clipValueMax Upper-bound of range to be clipped to.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction clipByValue_(x, clipValueMin, clipValueMax) {\n    const $x = convertToTensor(x, 'x', 'clipByValue');\n    util.assert((clipValueMin <= clipValueMax), () => `Error in clip: min (${clipValueMin}) must be ` +\n        `less than or equal to max (${clipValueMax}).`);\n    const inputs = { x: $x };\n    const attrs = { clipValueMin, clipValueMax };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.clip($x, clipValueMin, clipValueMax);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, ClipByValue, attrs);\n}\nexport const clipByValue = op({ clipByValue_ });\n//# sourceMappingURL=clip_by_value.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(3) = |r1, g1, b1|\n * B: shape(2) = |r2, g2|\n * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @return The concatenated array.\n */\nfunction concat1d_(tensors) {\n    return concat(tensors, 0 /* axis */);\n}\nexport const concat1d = op({ concat1d_ });\n//# sourceMappingURL=concat_1d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *\n * B: shape(2, 3) = | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * C = tf.concat2d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *                  | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * if axis = 1:\n * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |\n *                   | r2, g2, b2, r4, g4, b4 |\n *\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concatenate along.\n * @return The concatenated array.\n */\nfunction concat2d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat2d = op({ concat2d_ });\n//# sourceMappingURL=concat_2d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor3D`s along an axis.\n * See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *\n * B: shape(2, 1, 3) = | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * C = tf.concat3d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *                     | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * if axis = 1:\n * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |\n *                     | r2, g2, b2, r4, g4, b4 |\n *\n * if axis = 2:\n * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |\n *                      | r2, g2, b2, r4, g4, b4 |\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat3d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat3d = op({ concat3d_ });\n//# sourceMappingURL=concat_3d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor4D`s along an axis.\n * See `concat` for details.\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat4d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat4d = op({ concat4d_ });\n//# sourceMappingURL=concat_4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 2D convolution over the input x.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv2d');\n    const $filter = convertToTensor(filter, 'filter', 'conv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n    util.assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match ` +\n        `input depth for filter ${$filter.shape[2]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const forward = (backend, save) => {\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        const res = backend.conv2d(x4D, $filter, convInfo);\n        save([x4D, $filter]);\n        return res;\n    };\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv2D, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const conv2d = op({ conv2d_ });\n//# sourceMappingURL=conv2d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 1D convolution over the input x.\n *\n * @param x The input tensor, of rank 3 or rank 2, of shape\n *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.\n * @param filter The filter, rank 3, of shape\n *     `[filterWidth, inDepth, outDepth]`.\n * @param stride The number of entries by which the filter is moved right at\n *     each step.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from \"NWC\", \"NCW\". Defaults to \"NWC\",\n *     the data is stored in the order of [batch, in_width, in_channels]. Only\n *     \"NWC\" is currently supported.\n * @param dilation The dilation rate in which we sample input values in\n *     atrous convolution. Defaults to `1`. If it is greater than 1, then\n *     stride must be `1`.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv1d_(x, filter, stride, pad, dataFormat = 'NWC', dilation = 1, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv1d');\n    const $filter = convertToTensor(filter, 'filter', 'conv1d');\n    let x3D = $x;\n    let reshapedTo3D = false;\n    if ($x.rank === 2) {\n        reshapedTo3D = true;\n        x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);\n    }\n    util.assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n    util.assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv1d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    util.assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +\n        `input depth for filter ${$filter.shape[1]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(stride, dilation), () => 'Error in conv1D: Either stride or dilation must be 1. ' +\n        `Got stride ${stride} and dilation '${dilation}'`);\n    util.assert(dataFormat === 'NWC', () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);\n    const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);\n    const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);\n    const strides = [1, stride];\n    const dilations = [1, dilation];\n    const conv2dDataFormat = 'NHWC';\n    const res = conv2d(input4D, filter4D, strides, pad, conv2dDataFormat, dilations, dimRoundingMode);\n    if (reshapedTo3D) {\n        return reshape(res, [res.shape[2], res.shape[3]]);\n    }\n    return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);\n}\nexport const conv1d = op({ conv1d_ });\n//# sourceMappingURL=conv1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropInput } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 2D convolution.\n *\n * @param xShape The shape of the input: [batch, height, width, inDepth].\n * If length of 3, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 4 or rank 3 of shape\n *   `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction conv2DBackpropInput_(xShape, dy, filter, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n    util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n        `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n    let xShape4D = xShape;\n    let dy4D = dy;\n    let reshapedTo4D = false;\n    if (dy.rank === 3) {\n        reshapedTo4D = true;\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n        xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n    }\n    util.assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ` +\n        `${xShape4D.length}.`);\n    util.assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got ` +\n        `rank ${dy4D.rank}`);\n    util.assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got ` +\n        `rank ${filter.rank}`);\n    const inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1];\n    const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n    util.assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must ` +\n        `match input depth for filter ${filter.shape[2]}.`);\n    util.assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must ` +\n        `match output depth for filter ${filter.shape[3]}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2dDerInput: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const dilations = 1;\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(xShape4D, filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        const res = backend.conv2dDerInput(dy4D, filter, convInfo);\n        save([dy4D, filter]);\n        return res;\n    };\n    const inputs = { dy: dy4D, filter };\n    const attrs = { strides, pad, dataFormat, dimRoundingMode, inputShape: xShape4D };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv2DBackpropInput, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const conv2DBackpropInput = op({ conv2DBackpropInput_ });\n//# sourceMappingURL=conv2d_backprop_input.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport { conv2DBackpropInput } from './conv2d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 2D convolution of an image, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 4 or rank 3, of shape\n *   `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 4 or rank 3:\n *     `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.\n * @param strides The strides of the original convolution:\n *     `[strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n * @param dimRoundingMode The rounding mode used when computing output\n *    dimensions if pad is a number. If none is provided, it will not round\n *    and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2dTranspose_(x, filter, outputShape, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv2dTranspose');\n    const $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');\n    return conv2DBackpropInput(outputShape, $x, $filter, strides, pad, 'NHWC', dimRoundingMode);\n}\nexport const conv2dTranspose = op({ conv2dTranspose_ });\n//# sourceMappingURL=conv2d_transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { eitherStridesOrDilationsAreOne } from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 3D convolution over the input x.\n *\n * @param x The input tensor, of rank 5 or rank 4, of shape\n *     `[batch, depth, height, width, channels]`. If rank 4,\n * batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.\n *      inChannels must match between input and filter.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationDepth, dilationHeight,\n *     dilationWidth]` in which we sample input values across the height\n *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.\n *     If `dilations` is a single number, then\n *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater\n *     than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3d_(x, filter, strides, pad, dataFormat = 'NDHWC', dilations = [1, 1, 1]) {\n    const $x = convertToTensor(x, 'x', 'conv3d');\n    const $filter = convertToTensor(filter, 'filter', 'conv3d');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n    util.assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +\n        `input depth for filter ${$filter.shape[3]}.`);\n    util.assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv3D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computeConv3DInfo(x5D.shape, $filter.shape, strides, dilations, pad);\n        const res = backend.conv3d(x5D, $filter, convInfo);\n        save([x5D, $filter]);\n        return res;\n    };\n    const inputs = { x: x5D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv3D, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const conv3d = op({ conv3d_ });\n//# sourceMappingURL=conv3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3DBackpropInputV2 } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 3D convolution.\n *\n * @param xShape The shape of the input: [batch, depth, height, width,\n * in_channels]. If length of 4, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 5 or rank 4 of shape\n *   `[batch, outDepth, outHeight, outWidth, in_channels]`.\n * If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n */\nfunction conv3DBackpropInput_(xShape, dy, filter, strides, pad) {\n    util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n        `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n    let xShape5D = xShape;\n    let dy5D = dy;\n    let reshapedTo5D = false;\n    if (dy.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n        xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n    }\n    const inDepth = xShape5D[4];\n    const outDepth = dy5D.shape[4];\n    util.assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ` +\n        `${xShape5D.length}.`);\n    util.assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got ` +\n        `rank ${dy5D.rank}`);\n    util.assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got ` +\n        `rank ${filter.rank}`);\n    util.assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must ` +\n        `match input depth for filter ${filter.shape[3]}.`);\n    util.assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must ` +\n        `match output depth for filter ${filter.shape[4]}.`);\n    const forward = backend => {\n        const dilations = 1;\n        const convInfo = conv_util.computeConv3DInfo(xShape5D, filter.shape, strides, dilations, pad);\n        return backend.conv3dDerInput(dy5D, filter, convInfo);\n    };\n    const inputs = { dy: dy5D };\n    const attrs = { pad };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropInputV2, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const conv3DBackpropInput = op({ conv3DBackpropInput_ });\n//# sourceMappingURL=conv3d_backprop_input.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport { conv3DBackpropInput } from './conv3d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 3D convolution of a volume, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 5 or rank 4, of shape\n *   `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[depth, filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 5 or rank 4:\n *     `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is\n *    assumed.\n * @param strides The strides of the original convolution:\n *     `[strideDepth, strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3dTranspose_(x, filter, outputShape, strides, pad) {\n    const $x = convertToTensor(x, 'x', 'conv3dTranspose');\n    const $filter = convertToTensor(filter, 'filter', 'conv3dTranspose');\n    return conv3DBackpropInput(outputShape, $x, $filter, strides, pad);\n}\nexport const conv3dTranspose = op({ conv3dTranspose_ });\n//# sourceMappingURL=conv3d_transpose.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.cos().print();  // or tf.cos(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cos_(x) {\n    const $x = convertToTensor(x, 'x', 'cos');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.cos($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Cos);\n}\nexport const cos = op({ cos_ });\n//# sourceMappingURL=cos.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.cosh().print();  // or tf.cosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cosh_(x) {\n    const $x = convertToTensor(x, 'x', 'cosh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.cosh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Cosh);\n}\nexport const cosh = op({ cosh_ });\n//# sourceMappingURL=cosh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cumsum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { getAxesPermutation, getInnerMostAxes, getUndoAxesPermutation } from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Computes the cumulative sum of a `tf.Tensor` along `axis`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4]);\n * x.cumsum().print();\n * ```\n * ```js\n * const x = tf.tensor([[1, 2], [3, 4]]);\n * x.cumsum().print();\n * ```\n *\n * @param x The input tensor to be summed.\n * @param axis The axis along which to sum. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative sum. Optional.\n *     Defaults to false. If set to true then the sum of each tensor entry\n *     does not include its own value, but only the values previous to it\n *     along the specified axis.\n * @param reverse Whether to sum in the opposite direction. Optional.\n *     Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Scan'}\n */\nfunction cumsum_(x, axis = 0, exclusive = false, reverse = false) {\n    const $x = convertToTensor(x, 'x', 'cumsum');\n    const forward = (backend, save) => {\n        const permutation = getAxesPermutation([axis], $x.rank);\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n        }\n        const permutedAxis = getInnerMostAxes(1, $x.rank)[0];\n        let value = backend.cumsum(permutedX, permutedAxis, exclusive, reverse);\n        save([$x]);\n        if (permutation != null) {\n            const reversePermutation = getUndoAxesPermutation(permutation);\n            value = transpose(value, reversePermutation);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, exclusive, reverse };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Cumsum, attrs);\n}\nexport const cumsum = op({ cumsum_ });\n//# sourceMappingURL=cumsum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthToSpace } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Rearranges data from depth into blocks of spatial data. More specifically,\n * this op outputs a copy of the input tensor where values from the `depth`\n * dimension are moved in spatial blocks to the `height` and `width` dimensions.\n * The attr `blockSize` indicates the input block size and how the data is\n * moved.\n *\n *  - Chunks of data of size `blockSize * blockSize` from depth are rearranged\n * into non-overlapping blocks of size `blockSize x blockSize`\n *\n *  - The width the output tensor is `inputWidth * blockSize`, whereas the\n * height is `inputHeight * blockSize`\n *\n *  - The Y, X coordinates within each block of the output image are determined\n * by the high order component of the input channel index\n *\n *  - The depth of the input tensor must be divisible by `blockSize *\n * blockSize`\n *\n * The `dataFormat` attr specifies the layout of the input and output tensors\n * with the following options: \"NHWC\": [ `batch, height, width, channels` ]\n * \"NCHW\": [ `batch, channels, height, width` ]\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);\n * const blockSize = 2;\n * const dataFormat = \"NHWC\";\n *\n * tf.depthToSpace(x, blockSize, dataFormat).print();\n * ```\n *\n * @param x The input tensor of rank 4\n * @param blockSIze  An `int` that is `>= 2`. The size of the spatial block\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to \"NHWC\"\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction depthToSpace_(x, blockSize, dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'depthToSpace');\n    const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];\n    const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];\n    const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];\n    util.assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputHeight} and ${blockSize}  for depthToSpace with input shape\n    ${$x.shape}`);\n    util.assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputWidth} and ${blockSize} for depthToSpace with input shape\n        ${$x.shape}`);\n    util.assert((inputDepth % (blockSize * blockSize) === 0), () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);\n    const forward = backend => backend.depthToSpace($x, blockSize, dataFormat);\n    const inputs = { x: $x };\n    const attrs = { blockSize, dataFormat };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, DepthToSpace, attrs);\n}\nexport const depthToSpace = op({ depthToSpace_ });\n//# sourceMappingURL=depth_to_space.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNative } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Depthwise 2D convolution.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction depthwiseConv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n    const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels ` +\n        `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n        `filter ${$filter.shape[2]}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1];\n        }\n        util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +\n            `1. Got strides ${strides} and dilations '${dilations}'`);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n        const res = backend.depthwiseConv2D(x4D, $filter, convInfo);\n        save([x4D, $filter]);\n        return res;\n    };\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, DepthwiseConv2dNative, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const depthwiseConv2d = op({ depthwiseConv2d_ });\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Diag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Returns a diagonal tensor with a given diagonal values.\n *\n * Given a diagonal, this operation returns a tensor with the diagonal and\n * everything else padded with zeros.\n *\n * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor\n * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * tf.diag(x).print()\n * ```\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4, 5, 6, 6, 8], [4, 2])\n *\n * tf.diag(x).print()\n * ```\n * @param x The input tensor.\n */\nfunction diag_(x) {\n    const $x = convertToTensor(x, 'x', 'diag');\n    const forward = backend => {\n        const flat = reshape($x, [$x.size]);\n        const result = backend.diag(flat);\n        const outShape = [...x.shape, ...x.shape];\n        return reshape(result, outShape);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Diag);\n}\nexport const diag = op({ diag_ });\n//# sourceMappingURL=diag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Dilation2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the grayscale dilation over the input `x`.\n *\n * @param x The input tensor, rank 3 or rank 4 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter tensor, rank 3, of shape\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat Specify the data format of the input and output data.\n *      Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the\n *      default format \"NHWC\", the data is stored in the order of: [batch,\n *      height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations`\n *     is a single number, then `dilationHeight == dilationWidth`. If it is\n *     greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction dilation2d_(x, filter, strides, pad, dilations = [1, 1], dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'dilation2d');\n    const $filter = convertToTensor(filter, 'filter', 'dilation2d');\n    util.assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ` +\n        `${$x.rank}.`);\n    util.assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(dataFormat === 'NHWC', () => `Error in dilation2d: Only NHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n        reshapedTo4D = true;\n    }\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dilations };\n    const res = ENGINE.runKernel(Dilation2D, inputs, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const dilation2d = op({ dilation2d_ });\n//# sourceMappingURL=dilation2d.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the dimensions in the input shape that are broadcasted to\n * produce the provided output shape.\n *\n * The returned dimensions are 0-indexed and sorted. An example:\n * inShape = [4, 1, 3]\n * outShape = [5, 4, 3, 3]\n * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.\n */\nexport function getBroadcastDims(inShape, outShape) {\n    const inRank = inShape.length;\n    const dims = [];\n    for (let i = 0; i < inRank; i++) {\n        const dim = inRank - 1 - i;\n        const a = inShape[dim] || 1;\n        const b = outShape[outShape.length - 1 - i] || 1;\n        if (b > 1 && a === 1) {\n            dims.unshift(dim);\n        }\n    }\n    return dims;\n}\n/**\n * Returns the axes in the output space that should be reduced to produce\n * the input space.\n */\nexport function getReductionAxes(inShape, outShape) {\n    const result = [];\n    for (let i = 0; i < outShape.length; i++) {\n        const inDim = inShape[inShape.length - i - 1];\n        const outAxis = outShape.length - i - 1;\n        const outDim = outShape[outAxis];\n        if (inDim == null || (inDim === 1 && outDim > 1)) {\n            result.unshift(outAxis);\n        }\n    }\n    return result;\n}\nexport function assertAndGetBroadcastShape(shapeA, shapeB) {\n    const result = [];\n    const l = Math.max(shapeA.length, shapeB.length);\n    for (let i = 0; i < l; i++) {\n        let a = shapeA[shapeA.length - i - 1];\n        if (a == null) {\n            a = 1;\n        }\n        let b = shapeB[shapeB.length - i - 1];\n        if (b == null) {\n            b = 1;\n        }\n        if (a === 1) {\n            result.unshift(b);\n        }\n        else if (b === 1) {\n            result.unshift(a);\n        }\n        else if (a !== b) {\n            const errMsg = `Operands could not be broadcast together with shapes ` +\n                `${shapeA} and ${shapeB}.`;\n            throw Error(errMsg);\n        }\n        else {\n            result.unshift(a);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=broadcast_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Equal } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a == b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.equal(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction equal_(a, b) {\n    let $a = convertToTensor(a, 'a', 'equal');\n    let $b = convertToTensor(b, 'b', 'equal');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.equal($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null, Equal);\n}\nexport const equal = op({ equal_ });\n//# sourceMappingURL=equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SelectV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, assertShapesMatch } from '../util';\nimport { broadcastTo } from './broadcast_to';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the elements, either `a` or `b` depending on the `condition`.\n *\n * If the condition is true, select from `a`, otherwise select from `b`.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const a = tf.tensor1d([1 , 2, 3]);\n * const b = tf.tensor1d([-1, -2, -3]);\n *\n * a.where(cond, b).print();\n * ```\n *\n * @param condition The input condition. Must be of dtype bool.\n * @param a If `condition` is rank 1, `a` may have a higher rank but\n *     its first dimension must match the size of `condition`.\n * @param b A tensor with the same dtype as `a` and with shape that is\n *     compatible with `a`.\n * @return A tensor with same dtype as `a` and `b`, and shape that is\n *     broadcastable from `a` and `b`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction where_(condition, a, b) {\n    const $a = convertToTensor(a, 'a', 'where');\n    const $b = convertToTensor(b, 'b', 'where');\n    const $condition = convertToTensor(condition, 'condition', 'where', 'bool');\n    // TODO: move this logic to forward function when the broadcastTo op is\n    // implemented in WASM.\n    // Find the broadcastable shape for $a and $b.\n    const broadcastShape = assertAndGetBroadcastShape($a.shape, $b.shape);\n    const $broadcastedA = broadcastTo($a, broadcastShape);\n    const $broadcastedB = broadcastTo($b, broadcastShape);\n    if ($condition.rank === 1) {\n        // If condition rank is 1, then the first dimension must match the size of\n        // condition.\n        assert($condition.shape[0] === $a.shape[0], () => 'The first dimension of `a` must match the size of `condition`.');\n    }\n    if ($condition.rank !== 1) {\n        // A must have the same shape as condition.\n        assertShapesMatch($condition.shape, $broadcastedB.shape, 'Error in where: ');\n    }\n    const forward = (backend, save) => {\n        const res = backend.select($condition, $broadcastedA, $broadcastedB);\n        save([$condition]);\n        return res;\n    };\n    const inputs = {\n        condition: $condition,\n        t: $broadcastedA,\n        e: $broadcastedB\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, SelectV2);\n}\nexport const where = op({ where_ });\n//# sourceMappingURL=where.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ZerosLike } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.zerosLike(x).print();\n * ```\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction zerosLike_(x) {\n    const $x = convertToTensor(x, 'x', 'zerosLike');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.zerosLike($x), inputs, null /* grad */, ZerosLike);\n}\nexport const zerosLike = op({ zerosLike_ });\n//# sourceMappingURL=zeros_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { div } from './div';\nimport { equal } from './equal';\nimport { op } from './operation';\nimport { where } from './where';\nimport { zerosLike } from './zeros_like';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0\n * if denominator is 0.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n * const c = tf.tensor1d([0, 0, 0, 0]);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n * const c = tf.scalar(0);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction divNoNan_(a, b) {\n    // TODO: Make this into its own kernel.\n    let $a = convertToTensor(a, 'a', 'div');\n    let $b = convertToTensor(b, 'b', 'div');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const divResult = div($a, $b);\n    const zeros = zerosLike(divResult);\n    const bEqualsZero = equal($b, zeros);\n    return where(bEqualsZero, zeros, divResult);\n}\nexport const divNoNan = op({ divNoNan_ });\n//# sourceMappingURL=div_no_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor2d([[1, 2], [3, 4]]);\n * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n *\n * a.dot(b).print();  // or tf.dot(a, b)\n * b.dot(a).print();\n * b.dot(c).print();\n * ```\n * @param t1 The first tensor in the dot operation.\n * @param t2 The second tensor in the dot operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction dot_(t1, t2) {\n    const $t1 = convertToTensor(t1, 't1', 'dot');\n    const $t2 = convertToTensor(t2, 't2', 'dot');\n    util.assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ` +\n        `${$t1.rank} and ${$t2.rank}.`);\n    const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);\n    const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);\n    util.assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ` +\n        `${t1Inner} and ${t2Inner}.`);\n    if ($t1.rank === 1 && $t2.rank === 1) {\n        const t12D = reshape($t1, [1, -1]);\n        const t22D = reshape($t2, [-1, 1]);\n        const t1t2 = matMul(t12D, t22D);\n        return reshape(t1t2, []);\n    }\n    else if ($t1.rank === 1 && $t2.rank === 2) {\n        const t12D = reshape($t1, [1, -1]);\n        const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n        const t1t2 = matMul(t12D, t22D);\n        return reshape(t1t2, [t1t2.size]);\n    }\n    else if ($t1.rank === 2 && $t2.rank === 1) {\n        const t22D = reshape($t2, [-1, 1]);\n        const t1t2 = matMul($t1, t22D);\n        return reshape(t1t2, [t1t2.size]);\n    }\n    else {\n        const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n        const t1t2 = matMul($t1, t22D);\n        return t1t2;\n    }\n}\nexport const dot = op({ dot_ });\n//# sourceMappingURL=dot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Elu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential linear element-wise: `x > 0 ? e ^ x - 1 : 0`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 1, -3, 2]);\n *\n * x.elu().print();  // or tf.elu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction elu_(x) {\n    const $x = convertToTensor(x, 'x', 'elu');\n    const forward = (backend, save) => {\n        const y = backend.elu($x);\n        save([y]);\n        return y;\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Elu);\n}\nexport const elu = op({ elu_ });\n//# sourceMappingURL=elu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Erf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes gause error function of the input `tf.Tensor` element-wise:\n * `erf(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.erf().print(); // or tf.erf(x);\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction erf_(x) {\n    let $x = convertToTensor(x, 'x', 'erf');\n    util.assert($x.dtype === 'int32' || $x.dtype === 'float32', () => 'Input dtype must be `int32` or `float32`.');\n    if ($x.dtype === 'int32') {\n        $x = cast($x, 'float32');\n    }\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.erf($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Erf);\n}\nexport const erf = op({ erf_ });\n//# sourceMappingURL=erf.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Exp } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.exp().print();  // or tf.exp(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction exp_(x) {\n    const $x = convertToTensor(x, 'x', 'exp');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.exp($x);\n        save([res]);\n        return res;\n    }, inputs, null /* grad */, Exp);\n}\nexport const exp = op({ exp_ });\n//# sourceMappingURL=exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n * into the tensor's shape.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const axis = 1;\n * x.expandDims(axis).print();\n * ```\n *\n * @param x The input tensor whose dimensions to be expanded.\n * @param axis The dimension index at which to insert shape of `1`. Defaults\n *     to 0 (the first dimension).\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction expandDims_(x, axis = 0) {\n    const parseAs = null;\n    const $x = convertToTensor(x, 'x', 'expandDims', parseAs);\n    util.assert(axis <= $x.rank, () => 'Axis must be <= rank of the tensor');\n    const newShape = $x.shape.slice();\n    if (axis < 0) {\n        // Negative value is counted from the tail of rank.\n        util.assert(-($x.rank + 1) <= axis, () => `Axis must be in the interval [${-($x.rank + 1)}, ${$x.rank}]`);\n        axis = $x.rank + axis + 1;\n    }\n    newShape.splice(axis, 0, 1);\n    return reshape($x, newShape);\n}\nexport const expandDims = op({ expandDims_ });\n//# sourceMappingURL=expand_dims.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Expm1 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential of the input `tf.Tensor` minus one element-wise.\n * `e ^ x - 1`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.expm1().print();  // or tf.expm1(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction expm1_(x) {\n    const $x = convertToTensor(x, 'x', 'expm1');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.expm1($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Expm1);\n}\nexport const expm1 = op({ expm1_ });\n//# sourceMappingURL=expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tile } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Construct a tensor by repeating it the number of times given by reps.\n *\n * This operation creates a new tensor by replicating `input` `reps`\n * times. The output tensor's i'th dimension has `input.shape[i] *\n * reps[i]` elements, and the values of `input` are replicated\n * `reps[i]` times along the i'th dimension. For example, tiling\n * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n *\n * a.tile([2]).print();    // or a.tile([2])\n * ```\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.tile([1, 2]).print();  // or a.tile([1, 2])\n * ```\n * @param x The tensor to tile.\n * @param reps Determines the number of replications per dimension.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction tile_(x, reps) {\n    const parseAs = null;\n    const $x = convertToTensor(x, 'x', 'tile', parseAs);\n    util.assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n        `must match length of reps ${reps}.`);\n    const forward = (backend, save) => {\n        const res = backend.tile($x, reps);\n        save([$x]);\n        return res;\n    };\n    const inputsToSave = [$x];\n    const inputs = { x: $x };\n    const attrs = { reps };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Tile, attrs, inputsToSave);\n}\nexport const tile = op({ tile_ });\n//# sourceMappingURL=tile.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { expandDims } from './expand_dims';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { tile } from './tile';\n/**\n * Create an identity matrix.\n *\n * @param numRows Number of rows.\n * @param numColumns Number of columns. Defaults to `numRows`.\n * @param batchShape If provided, will add the batch shape to the beginning\n *   of the shape of the returned `tf.Tensor` by repeating the identity\n *   matrix.\n * @param dtype Data type.\n * @returns Identity matrix of the specified size and data type, possibly\n *   with batch repetition if `batchShape` is specified.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction eye_(numRows, numColumns, batchShape, dtype = 'float32') {\n    if (numColumns == null) {\n        numColumns = numRows;\n    }\n    const buff = buffer([numRows, numColumns], dtype);\n    const n = numRows <= numColumns ? numRows : numColumns;\n    for (let i = 0; i < n; ++i) {\n        buff.set(1, i, i);\n    }\n    const out = reshape(buff.toTensor(), [numRows, numColumns]);\n    if (batchShape == null) {\n        return out;\n    }\n    else {\n        if (batchShape.length === 1) {\n            return tile(expandDims(out, 0), [batchShape[0], 1, 1]);\n        }\n        else if (batchShape.length === 2) {\n            // tslint:disable-next-line:no-unnecessary-type-assertion\n            return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);\n        }\n        else if (batchShape.length === 3) {\n            // tslint:disable-next-line:no-unnecessary-type-assertion\n            return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [\n                batchShape[0], batchShape[1], batchShape[2], 1, 1\n            ]);\n        }\n        else {\n            throw new Error(`eye() currently supports only 1D and 2D ` +\n                // tslint:disable-next-line:no-any\n                `batchShapes, but received ${batchShape.length}D.`);\n        }\n    }\n}\nexport const eye = op({ eye_ });\n//# sourceMappingURL=eye.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Fill } from '../kernel_names';\n/**\n * Creates a `tf.Tensor` filled with a scalar value.\n *\n * ```js\n * tf.fill([2, 2], 4).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param value The scalar value to fill the tensor with.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction fill(shape, value, dtype) {\n    const attrs = { shape, value, dtype };\n    return ENGINE.runKernelFunc(backend => backend.fill(shape, value, dtype), {}, null, Fill, attrs);\n}\nexport { fill };\n//# sourceMappingURL=fill.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Floor } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.floor().print();  // or tf.floor(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction floor_(x) {\n    const $x = convertToTensor(x, 'x', 'floor');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.floor($x), inputs, null /* grad */, Floor);\n}\nexport const floor = op({ floor_ });\n//# sourceMappingURL=floor.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inputs of size above this threshold will be parallelized by calling multiple\n * shader programs.\n */\nimport { nearestDivisor } from '../util';\nexport const PARALLELIZE_THRESHOLD = 30;\nexport function computeOptimalWindowSize(inSize) {\n    if (inSize <= PARALLELIZE_THRESHOLD) {\n        return inSize;\n    }\n    return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n}\n//# sourceMappingURL=reduce_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nearestDivisor } from '../util';\nimport { PARALLELIZE_THRESHOLD } from './reduce_util';\nexport function segOpComputeOptimalWindowSize(inSize, numSegments) {\n    let done = false;\n    let res;\n    if (inSize <= PARALLELIZE_THRESHOLD) {\n        res = inSize;\n        done = true;\n    }\n    else {\n        res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n    }\n    while (!done) {\n        if (res > numSegments || res === inSize) {\n            done = true;\n        }\n        else {\n            res = nearestDivisor(inSize, res + 1);\n        }\n    }\n    return res;\n}\nexport function computeOutShape(aShape, axis, numSegments) {\n    const outShape = [];\n    const rank = aShape.length;\n    for (let dim = 0; dim < rank; dim++) {\n        if (dim !== axis) {\n            outShape.push(aShape[dim]);\n        }\n        else {\n            outShape.push(numSegments);\n        }\n    }\n    return outShape;\n}\nexport function collectGatherOpShapeInfo(x, indices, axis) {\n    const dimSize = x.shape[axis];\n    const outputShape = [];\n    let batchSize = 1;\n    let sliceSize = 1;\n    for (let i = 0; i < axis; i++) {\n        outputShape.push(x.shape[i]);\n        batchSize *= x.shape[i];\n    }\n    for (let i = 0; i < indices.rank; i++) {\n        outputShape.push(indices.shape[i]);\n    }\n    for (let i = axis + 1; i < x.rank; i++) {\n        outputShape.push(x.shape[i]);\n        sliceSize *= x.shape[i];\n    }\n    return { batchSize, sliceSize, dimSize, outputShape };\n}\n//# sourceMappingURL=segment_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { collectGatherOpShapeInfo } from './segment_util';\n/**\n * Gather slices from tensor `x`'s axis `axis` according to `indices`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const indices = tf.tensor1d([1, 3, 3], 'int32');\n *\n * x.gather(indices).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const indices = tf.tensor1d([1, 1, 0], 'int32');\n *\n * x.gather(indices).print();\n * ```\n * @param x The input tensor whose slices to be gathered.\n * @param indices The indices of the values to extract.\n * @param axis The axis over which to select values. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction gather_(x, indices, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'gather');\n    const $indices = convertToTensor(indices, 'indices', 'gather', 'int32');\n    const inputs = { x: $x, indices: $indices };\n    const attrs = { axis };\n    const forward = (backend, save) => {\n        const parsedAxis = parseAxisParam(axis, $x.shape)[0];\n        const shapeInfo = collectGatherOpShapeInfo($x, $indices, parsedAxis);\n        const res = backend.gather($x, reshape($indices, [$indices.size]), parsedAxis);\n        save([$x, $indices]);\n        return reshape(res, shapeInfo.outputShape);\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, GatherV2, attrs);\n}\nexport const gather = op({ gather_ });\n//# sourceMappingURL=gather.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Greater } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a > b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greater(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greater_(a, b) {\n    let $a = convertToTensor(a, 'a', 'greater');\n    let $b = convertToTensor(b, 'b', 'greater');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.greater($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Greater);\n}\nexport const greater = op({ greater_ });\n//# sourceMappingURL=greater.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GreaterEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a >= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greaterEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greaterEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'greaterEqual');\n    let $b = convertToTensor(b, 'b', 'greaterEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.greaterEqual($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, GreaterEqual);\n}\nexport const greaterEqual = op({ greaterEqual_ });\n//# sourceMappingURL=greater_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Imag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the imaginary part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the imaginary part of each element in input considered as a complex number.\n * If input is real, a tensor of all zeros is returned.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.imag(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction imag_(input) {\n    const $input = convertToTensor(input, 'input', 'imag');\n    const forward = (backend) => {\n        return backend.imag($input);\n    };\n    const inputs = { input: $input };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Imag);\n}\nexport const imag = op({ imag_ });\n//# sourceMappingURL=imag.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsFinite } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are finite.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isFinite().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isFinite_(x) {\n    const $x = convertToTensor(x, 'x', 'isFinite');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.isFinite($x), inputs, null /* grad */, IsFinite);\n}\nexport const isFinite = op({ isFinite_ });\n//# sourceMappingURL=is_finite.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsInf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are Infinity or -Infinity.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isInf().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isInf_(x) {\n    const $x = convertToTensor(x, 'x', 'isInf');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.isInf($x), inputs, null /* grad */, IsInf);\n}\nexport const isInf = op({ isInf_ });\n//# sourceMappingURL=is_inf.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsNan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * RReturns which elements of x are NaN.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isNaN().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isNaN_(x) {\n    const $x = convertToTensor(x, 'x', 'isNaN');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.isNaN($x), inputs, null /* grad */, IsNan);\n}\nexport const isNaN = op({ isNaN_ });\n//# sourceMappingURL=is_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Maximum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.maximumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * ```js\n * // Broadcast maximum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction maximum_(a, b) {\n    let $a = convertToTensor(a, 'a', 'maximum');\n    let $b = convertToTensor(b, 'b', 'maximum');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'bool') {\n        $a = cast($a, 'int32');\n        $b = cast($b, 'int32');\n    }\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.maximum($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Maximum);\n}\nexport const maximum = op({ maximum_ });\n//# sourceMappingURL=maximum.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { isTypedArray } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.scalar` as it makes the code more readable.\n *\n * ```js\n * tf.scalar(3.14).print();\n * ```\n *\n * @param value The value of the scalar.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function scalar(value, dtype) {\n    if (((isTypedArray(value) && dtype !== 'string') || Array.isArray(value)) &&\n        dtype !== 'complex64') {\n        throw new Error('Error creating a new Scalar: value must be a primitive ' +\n            '(number|boolean|string)');\n    }\n    if (dtype === 'string' && isTypedArray(value) &&\n        !(value instanceof Uint8Array)) {\n        throw new Error('When making a scalar from encoded string, ' +\n            'the value must be `Uint8Array`.');\n    }\n    const shape = [];\n    const inferredShape = [];\n    return makeTensor(value, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=scalar.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { maximum } from './maximum';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { scalar } from './scalar';\n/**\n * Computes leaky rectified linear element-wise.\n *\n * See\n * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](\n *     http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.leakyRelu(0.1).print();  // or tf.leakyRelu(x, 0.1)\n * ```\n * @param x The input tensor.\n * @param alpha The scaling factor for negative values, defaults to 0.2.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction leakyRelu_(x, alpha = 0.2) {\n    const $x = convertToTensor(x, 'x', 'leakyRelu');\n    return maximum(mul(scalar(alpha), $x), $x);\n}\nexport const leakyRelu = op({ leakyRelu_ });\n//# sourceMappingURL=leaky_relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Less } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a < b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.less(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction less_(a, b) {\n    let $a = convertToTensor(a, 'a', 'less');\n    let $b = convertToTensor(b, 'b', 'less');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.less($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Less);\n}\nexport const less = op({ less_ });\n//# sourceMappingURL=less.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LessEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a <= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.lessEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction lessEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'lessEqual');\n    let $b = convertToTensor(b, 'b', 'lessEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.lessEqual($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LessEqual);\n}\nexport const lessEqual = op({ lessEqual_ });\n//# sourceMappingURL=less_equal.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LinSpace } from '../kernel_names';\n/**\n * Return an evenly spaced sequence of numbers over the given interval.\n *\n * ```js\n * tf.linspace(0, 9, 10).print();\n * ```\n * @param start The start value of the sequence.\n * @param stop The end value of the sequence.\n * @param num The number of values to generate.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function linspace(start, stop, num) {\n    if (num <= 0) {\n        throw new Error('The number of values should be positive.');\n    }\n    const attrs = { start, stop, num };\n    return ENGINE.runKernelFunc(backend => backend.linspace(start, stop, num), {} /* inputs */, null /* grad */, LinSpace, attrs);\n}\n//# sourceMappingURL=linspace.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LRN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Normalizes the activation of a local neighborhood across or within\n * channels.\n *\n * @param x The input tensor. The 4-D input tensor is treated as a 3-D array\n *     of 1D vectors (along the last dimension), and each vector is\n *     normalized independently.\n * @param depthRadius The number of adjacent channels in the 1D normalization\n *     window.\n * @param bias A constant bias term for the basis.\n * @param alpha A scale factor, usually positive.\n * @param beta An exponent.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n    const $x = convertToTensor(x, 'x', 'localResponseNormalization');\n    util.assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${$x.rank}.`);\n    util.assert(util.isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an ` +\n        `integer but got depthRadius ${depthRadius}.`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    const forward = (backend, save) => {\n        const y = backend.localResponseNormalization4D(x4D, depthRadius, bias, alpha, beta);\n        save([x4D, y]);\n        return y;\n    };\n    const inputs = { x: x4D };\n    const attrs = { depthRadius, bias, alpha, beta };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, LRN, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    else {\n        return res;\n    }\n}\nexport const localResponseNormalization = op({ localResponseNormalization_ });\n//# sourceMappingURL=local_response_normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E]);\n *\n * x.log().print();  // or tf.log(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log_(x) {\n    const $x = convertToTensor(x, 'x', 'log');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.log($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Log);\n}\nexport const log = op({ log_ });\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log1p } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` plus one\n * element-wise: `ln(1 + x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E - 1]);\n *\n * x.log1p().print();  // or tf.log1p(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log1p_(x) {\n    const $x = convertToTensor(x, 'x', 'log1p');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.log1p($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Log1p);\n}\nexport const log1p = op({ log1p_ });\n//# sourceMappingURL=log1p.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { Tensor, Variable } from './tensor';\nimport { convertToTensor, convertToTensorArray } from './tensor_util_env';\nimport * as util from './util';\n/**\n * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the\n * gradient of `f(x)` with respect to `x`.\n *\n * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to\n * `x` is computed instead. `f(x)` must take a single tensor `x` and return a\n * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.grad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * g(x).print();\n * ```\n *\n * ```js\n * // f(x) = x ^ 3\n * const f = x => x.pow(tf.scalar(3, 'int32'));\n * // f'(x) = 3x ^ 2\n * const g = tf.grad(f);\n * // f''(x) = 6x\n * const gg = tf.grad(g);\n *\n * const x = tf.tensor1d([2, 3]);\n * gg(x).print();\n * ```\n *\n * @param f The function f(x), to compute gradient for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grad(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in grad(f) must be a function');\n    return (x, dy) => {\n        // x can be of any dtype, thus null as the last argument.\n        const $x = convertToTensor(x, 'x', 'tf.grad', null);\n        const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grad') : null;\n        return ENGINE.tidy(() => {\n            const { value, grads } = ENGINE.gradients(() => f($x), [$x], $dy);\n            if ($dy != null) {\n                util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grad(f)(x, dy) must match the shape ' +\n                    'returned by f(x)');\n            }\n            checkGrads(grads);\n            return grads[0];\n        });\n    };\n}\n/**\n * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,\n * which gives an array of gradients of `f()` with respect to each input\n * [`x1`,`x2`,...].\n *\n * If `dy` is passed when calling `g()`, the gradient of\n * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.\n * The provided `f` must take one or more tensors and return a single tensor\n * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df / da = b, df / db = a\n * const g = tf.grads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const [da, db] = g([a, b]);\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @param f The function `f(x1, x2,...)` to compute gradients for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grads(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in grads(f) must be a function');\n    return (args, dy) => {\n        util.assert(Array.isArray(args), () => 'The args passed in grads(f)(args) must be an array ' +\n            'of `Tensor`s or `TensorLike`s');\n        // args can be of any dtype, thus null as the last argument.\n        const $args = convertToTensorArray(args, 'args', 'tf.grads', null);\n        const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grads') : null;\n        return ENGINE.tidy(() => {\n            const { value, grads } = ENGINE.gradients(() => f(...$args), $args, $dy);\n            if ($dy != null) {\n                util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grads(f)([x1,...], dy) must ' +\n                    'match the shape returned by f([x1,...])');\n            }\n            checkGrads(grads);\n            return grads;\n        });\n    };\n}\n/**\n * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.valueAndGrad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * const {value, grad} = g(x);\n *\n * console.log('value');\n * value.print();\n * console.log('grad');\n * grad.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrad(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in valueAndGrad(f) must be a function');\n    return (x, dy) => {\n        util.assert(x instanceof Tensor, () => 'The x passed in valueAndGrad(f)(x) must be a tensor');\n        util.assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrad(f)(x, dy) must be a tensor');\n        const { grads, value } = ENGINE.gradients(() => f(x), [x], dy);\n        checkGrads(grads);\n        return { grad: grads[0], value };\n    };\n}\n/**\n * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df/da = b, df/db = a\n * const g = tf.valueAndGrads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const {value, grads} = g([a, b]);\n *\n * const [da, db] = grads;\n *\n * console.log('value');\n * value.print();\n *\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrads(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in valueAndGrads(f) must be a function');\n    return (args, dy) => {\n        util.assert(Array.isArray(args) && args.every(arg => arg instanceof Tensor), () => 'The args passed in valueAndGrads(f)(args) must be array of ' +\n            'tensors');\n        util.assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrads(f)(args, dy) must be a tensor');\n        const res = ENGINE.gradients(() => f(...args), args, dy);\n        if (dy != null) {\n            util.assertShapesMatch(res.value.shape, dy.shape, 'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +\n                'match the shape returned by f([x1,...])');\n        }\n        checkGrads(res.grads);\n        return res;\n    };\n}\n/**\n * Computes and returns the gradient of f(x) with respect to the list of\n * trainable variables provided by `varList`. If no list is provided, it\n * defaults to all trainable variables.\n *\n * ```js\n * const a = tf.variable(tf.tensor1d([3, 4]));\n * const b = tf.variable(tf.tensor1d([5, 6]));\n * const x = tf.tensor1d([1, 2]);\n *\n * // f(a, b) = a * x ^ 2 + b * x\n * const f = () => a.mul(x.square()).add(b.mul(x)).sum();\n * // df/da = x ^ 2, df/db = x\n * const {value, grads} = tf.variableGrads(f);\n *\n * Object.keys(grads).forEach(varName => grads[varName].print());\n * ```\n *\n * @param f The function to execute. f() should return a scalar.\n * @param varList The list of variables to compute the gradients with respect\n *     to. Defaults to all trainable variables.\n * @returns An object with the following keys and values:\n *   - `value`: The value of the function `f`.\n *   - `grads`: A map from the names of the variables to the gradients.\n *     If the `varList` argument is provided explicitly and contains a subset of\n *     non-trainable variables, this map in the return value will contain keys\n *     that map the names of the non-trainable variables to `null`.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction variableGrads(f, varList) {\n    util.assert(util.isFunction(f), () => 'The f passed in variableGrads(f) must be a function');\n    util.assert(varList == null ||\n        Array.isArray(varList) && varList.every(v => v instanceof Variable), () => 'The varList passed in variableGrads(f, varList) must be an array ' +\n        'of variables');\n    const specifiedVarList = varList != null;\n    if (!specifiedVarList) {\n        // Get all of the trainable variables.\n        varList = [];\n        for (const varName in ENGINE.registeredVariables) {\n            varList.push(ENGINE.registeredVariables[varName]);\n        }\n    }\n    const specifiedNonTrainable = specifiedVarList ? varList.filter(variable => !variable.trainable) : null;\n    // Prune non-trainable variables.\n    const originalVarCount = varList.length;\n    varList = varList.filter(variable => variable.trainable);\n    util.assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to ` +\n        `be trainable, but none of the ${originalVarCount} variables is ` +\n        `trainable.`);\n    const allowNoGradients = true;\n    const { value, grads } = ENGINE.gradients(f, varList, null, allowNoGradients);\n    util.assert(grads.some(g => g != null), () => 'Cannot find a connection between any variable and the result of ' +\n        'the loss function y=f(x). Please make sure the operations that ' +\n        'use variables are inside the function f passed to minimize().');\n    util.assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it ` +\n        `returned a rank-${value.rank} tensor`);\n    const namedGrads = {};\n    varList.forEach((v, i) => {\n        if (grads[i] != null) {\n            namedGrads[v.name] = grads[i];\n        }\n    });\n    if (specifiedNonTrainable != null) {\n        // If varList is explicitly provided and contains non-trainable values,\n        // add them to the returned gradients with `null` values.\n        specifiedNonTrainable.forEach(v => namedGrads[v.name] = null);\n    }\n    return { value, grads: namedGrads };\n}\n/**\n * Overrides the gradient computation of a function `f`.\n *\n * Takes a function\n * `f(...inputs, save) => {value: Tensor, gradFunc: (dy, saved) => Tensor[]}`\n * and returns another function `g(...inputs)` which takes the same inputs as\n * `f`. When called, `g` returns `f().value`. In backward mode, custom gradients\n * with respect to each input of `f` are computed using `f().gradFunc`.\n *\n * The `save` function passsed to `f` should be used for saving tensors needed\n * in the gradient. And the `saved` passed to the `gradFunc` is a\n * `NamedTensorMap`, which contains those saved tensor.\n *\n * ```js\n * const customOp = tf.customGrad((x, save) => {\n *   // Save x to make sure it's available later for the gradient.\n *   save([x]);\n *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);\n *   return {\n *     value: x.square(),\n *     // Note `saved.x` which points to the `x` we saved earlier.\n *     gradFunc: (dy, saved) => [dy.mul(saved[0].abs())]\n *   };\n * });\n *\n * const x = tf.tensor1d([-1, -2, 3]);\n * const dx = tf.grad(x => customOp(x));\n *\n * console.log(`f(x):`);\n * customOp(x).print();\n * console.log(`f'(x):`);\n * dx(x).print();\n * ```\n *\n * @param f The function to evaluate in forward mode, which should return\n *     `{value: Tensor, gradFunc: (dy, saved) => Tensor[]}`, where `gradFunc`\n *     returns the custom gradients of `f` with respect to its inputs.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction customGrad(f) {\n    return ENGINE.customGrad(f);\n}\nfunction checkGrads(grads) {\n    const numNullGradients = grads.filter(g => g == null).length;\n    if (numNullGradients > 0) {\n        throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.`);\n    }\n}\nexport { customGrad, variableGrads, valueAndGrad, valueAndGrads, grad, grads, };\n//# sourceMappingURL=gradients.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Negate } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes `-1 * x` element-wise.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);\n *\n * x.neg().print();  // or tf.neg(x)\n * ```\n *\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction neg_(x) {\n    const $x = convertToTensor(x, 'x', 'neg');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.neg($x), inputs, null /* grad */, Negate);\n}\nexport const neg = op({ neg_ });\n//# sourceMappingURL=neg.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softplus } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.softplus().print();  // or tf.softplus(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction softplus_(x) {\n    const $x = convertToTensor(x, 'x', 'softplus');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.softplus($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Softplus);\n}\nexport const softplus = op({ softplus_ });\n//# sourceMappingURL=softplus.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../gradients';\nimport { convertToTensor } from '../tensor_util_env';\nimport { mul } from './mul';\nimport { neg } from './neg';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { softplus } from './softplus';\n/**\n * Computes log sigmoid of the input `tf.Tensor` element-wise:\n * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.logSigmoid().print();  // or tf.logSigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction logSigmoid_(x) {\n    const $x = convertToTensor(x, 'x', 'logSigmoid');\n    // Use a custom gradient to maintain previous implementation.\n    // There is no LogSigmoid kernel in TF so we can't use engine.runKernel\n    // directly\n    const customOp = customGrad((x) => {\n        // TODO(yassogba) we can remove the chained softplus call here only\n        // after backends have modualrized softplus at which point we can call\n        // engine runKernel(..., Sotfplus, ...) directly.\n        const value = neg(softplus(neg(x)));\n        const gradFunc = (dy) => {\n            const derX = mul(dy, sigmoid(neg(x)));\n            return derX;\n        };\n        return { value, gradFunc };\n    });\n    return customOp($x);\n}\nexport const logSigmoid = op({ logSigmoid_ });\n//# sourceMappingURL=log_sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Max } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the maximum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.max().print();  // or tf.max(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.max(axis).print();  // or tf.max(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction max_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'max');\n    const forward = (backend, save) => {\n        const origAxes = util.parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let maxInput = $x;\n        if (permutedAxes != null) {\n            maxInput = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, maxInput.rank);\n        }\n        const y = backend.max(maxInput, axes);\n        if (permutedAxes != null) {\n            maxInput.dispose();\n        }\n        let res = y;\n        if (keepDims) {\n            const expandedShape = axis_util.expandShapeToKeepDim(res.shape, util.parseAxisParam(axis, $x.shape));\n            res = reshape(res, expandedShape);\n            y.dispose();\n        }\n        save([$x, res]);\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { reductionIndices: axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Max, attrs);\n}\nexport const max = op({ max_ });\n//# sourceMappingURL=max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sub } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n *\n * ```js\n * // Broadcast subtract a with b.\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.scalar(5);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n * @param a The first `tf.Tensor` to subtract from.\n * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction sub_(a, b) {\n    let $a = convertToTensor(a, 'a', 'sub');\n    let $b = convertToTensor(b, 'b', 'sub');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.subtract($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Sub);\n}\nexport const sub = op({ sub_ });\n//# sourceMappingURL=sub.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the sum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If axes has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.sum().print();  // or tf.sum(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.sum(axis).print();  // or tf.sum(x, axis)\n * ```\n *\n * @param x The input tensor to compute the sum over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction sum_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'sum');\n    if ($x.dtype === 'bool') {\n        $x = cast($x, 'int32');\n    }\n    const forward = (backend, save) => {\n        save([$x]);\n        const axes = parseAxisParam(axis, $x.shape);\n        const permutation = getAxesPermutation(axes, $x.rank);\n        let reductionAxes = axes;\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n        }\n        let value = backend.sum(permutedX, reductionAxes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(value.shape, axes);\n            value = reshape(value, newShape);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Sum, attrs);\n}\nexport const sum = op({ sum_ });\n//# sourceMappingURL=sum.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogSoftmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { op } from './operation';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log softmax.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param axis The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction logSoftmax_(logits, axis = -1) {\n    const $logits = convertToTensor(logits, 'logits', 'logSoftmax');\n    if (axis === -1) {\n        axis = $logits.rank - 1;\n    }\n    if (axis !== $logits.rank - 1) {\n        throw Error('Log Softmax along a non-last dimension is not yet supported. ' +\n            `Logits was rank ${$logits.rank} and axis was ${axis}`);\n    }\n    const forward = (backend, save) => {\n        const keepDims = true;\n        const xMax = max(logits, axis, true);\n        const shifted = sub(logits, xMax);\n        const value = sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis, keepDims)));\n        save([value]);\n        return value;\n    };\n    const inputs = { logits: $logits };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LogSoftmax, attrs);\n}\nexport const logSoftmax = op({ logSoftmax_ });\n//# sourceMappingURL=log_softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { add } from './add';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log(sum(exp(elements across the reduction dimensions)).\n *\n * Reduces the input along the dimensions given in `axis`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.logSumExp().print();  // or tf.logSumExp(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.logSumExp(axis).print();  // or tf.logSumExp(a, axis)\n * ```\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. If null (the default),\n *     reduces all dimensions.\n * @param keepDims If true, retains reduced dimensions with length\n *     of 1. Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction logSumExp_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'logSumExp');\n    const axes = parseAxisParam(axis, $x.shape);\n    const xMax = max($x, axes, true /* keepDims */);\n    const a = sub($x, xMax);\n    const b = exp(a);\n    const c = sum(b, axes);\n    const d = log(c);\n    const res = add(reshape(xMax, d.shape), d);\n    if (keepDims) {\n        const newShape = expandShapeToKeepDim(res.shape, axes);\n        return reshape(res, newShape);\n    }\n    return res;\n}\nexport const logSumExp = op({ logSumExp_ });\n//# sourceMappingURL=log_sum_exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalAnd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a AND b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalAnd(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalAnd_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(backend => backend.logicalAnd($a, $b), inputs, null /* grad */, LogicalAnd);\n}\nexport const logicalAnd = op({ logicalAnd_ });\n//# sourceMappingURL=logical_and.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalNot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the truth value of `NOT x` element-wise.\n *\n * ```js\n * const a = tf.tensor1d([false, true], 'bool');\n *\n * a.logicalNot().print();\n * ```\n *\n * @param x The input tensor. Must be of dtype 'bool'.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalNot_(x) {\n    const $x = convertToTensor(x, 'x', 'logicalNot', 'bool');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.logicalNot($x), inputs, null /* grad */, LogicalNot);\n}\nexport const logicalNot = op({ logicalNot_ });\n//# sourceMappingURL=logical_not.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalOr } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a OR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalOr(b).print();\n * ```\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalOr_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalOr', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalOr', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(backend => backend.logicalOr($a, $b), inputs, null /* grad */, LogicalOr);\n}\nexport const logicalOr = op({ logicalOr_ });\n//# sourceMappingURL=logical_or.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { logicalAnd } from './logical_and';\nimport { logicalNot } from './logical_not';\nimport { logicalOr } from './logical_or';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalXor(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalXor_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalXor', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalXor', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    // x ^ y = (x | y) & ~(x & y)\n    return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));\n}\nexport const logicalXor = op({ logicalXor_ });\n//# sourceMappingURL=logical_xor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction maxPool_(x, filterSize, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'maxPool');\n    const dilations = 1;\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        let y;\n        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n            util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n            y = x4D.clone();\n        }\n        else {\n            y = backend.maxPool(x4D, convInfo);\n        }\n        save([x4D, y]);\n        return y;\n    };\n    const inputs = { x: x4D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const maxPool = op({ maxPool_ });\n//# sourceMappingURL=max_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { deprecationWarn } from '../globals';\nimport { MaxPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D max pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.maxPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPool3d_(x, filterSize = [1, 1, 1], strides, pad, dimRoundingMode, dataFormat = 'NDHWC', dilations) {\n    if (dilations == null) {\n        dilations = [1, 1, 1];\n    }\n    else {\n        deprecationWarn('dilations is deprecated, this field will be gone in ' +\n            'v3.0.0.');\n    }\n    const $x = convertToTensor(x, 'x', 'maxPool3d');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in maxPool3d: Only NDHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool3d: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1, 1];\n        }\n        const convInfo = conv_util.computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);\n        const y = backend.maxPool3d(x5D, convInfo);\n        save([x5D, y]);\n        return y;\n    };\n    const inputs = { x: x5D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat, dilations };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool3D, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const maxPool3d = op({ maxPool3d_ });\n//# sourceMappingURL=max_pool_3d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPoolWithArgmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the 2D max pooling of an image with Argmax index.\n * The indices in argmax are flattened, so that a maximum value at position `[b,\n * y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if\n * include_batch_in_index is False; `((b * height + y) * width + x) * channels\n * +c` if include_batch_in_index is True.\n *\n * The indices returned are always in `[0, height) x [0, width)` before\n * flattening.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param includeBatchIndex Defaults to False. Whether to include batch\n *    dimension in flattened index of argmax.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPoolWithArgmax_(x, filterSize, strides, pad, includeBatchInIndex = false) {\n    const $x = convertToTensor(x, 'x', 'maxPoolWithArgmax');\n    const inputs = { x: $x };\n    const attrs = { filterSize, strides, pad, includeBatchInIndex };\n    const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);\n    return { result: result[0], indexes: result[1] };\n}\nexport const maxPoolWithArgmax = op({ maxPoolWithArgmax_ });\n//# sourceMappingURL=max_pool_with_argmax.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeZerosTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\n/**\n * Creates a `tf.Tensor` with all elements set to 0.\n *\n * ```js\n * tf.zeros([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Can\n *     be 'float32', 'int32' or 'bool'. Defaults to 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function zeros(shape, dtype = 'float32') {\n    if (dtype === 'complex64') {\n        const real = zeros(shape, 'float32');\n        const imag = zeros(shape, 'float32');\n        return complex(real, imag);\n    }\n    const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=zeros.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeOnesTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\nimport { zeros } from './zeros';\n/**\n * Creates a `tf.Tensor` with all elements set to 1.\n *\n * ```js\n * tf.ones([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n *     'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function ones(shape, dtype = 'float32') {\n    if (dtype === 'complex64') {\n        const real = ones(shape, 'float32');\n        const imag = zeros(shape, 'float32');\n        return complex(real, imag);\n    }\n    const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=ones.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../gradients';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam, sizeFromShape } from '../util';\nimport { computeOutAndReduceShapes } from './axis_util';\nimport { cast } from './cast';\nimport { div } from './div';\nimport { mul } from './mul';\nimport { ones } from './ones';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { scalar } from './scalar';\nimport { sum } from './sum';\n/**\n * Computes the mean of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is\n * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with\n * a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.mean().print();  // or tf.mean(a)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.mean(axis).print();  // or tf.mean(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction mean_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'mean');\n    const axes = parseAxisParam(axis, $x.shape);\n    const shapes = computeOutAndReduceShapes($x.shape, axes);\n    const reduceShape = shapes[1];\n    const reduceSize = sizeFromShape(reduceShape);\n    // Use a custom gradient to bypass 2 gradient backprops since mean is used\n    // extremely often.\n    const customOp = customGrad((x) => {\n        const reduceSizeScalar = scalar(reduceSize);\n        // Cast if needed.\n        const xReduce = reduceSizeScalar.dtype === x.dtype ?\n            x :\n            cast(x, reduceSizeScalar.dtype);\n        const res = div(xReduce, reduceSizeScalar);\n        const value = sum(res, axis, keepDims);\n        const gradFunc = (dy) => {\n            const expandedDyShape = x.shape.slice();\n            axes.forEach(axis => {\n                expandedDyShape[axis] = 1;\n            });\n            const expandedDy = reshape(dy, expandedDyShape);\n            const derX = div(mul(expandedDy, ones(x.shape, 'float32')), reduceSize);\n            return derX;\n        };\n        return { value, gradFunc };\n    });\n    return customOp($x);\n}\nexport const mean = op({ mean_ });\n//# sourceMappingURL=mean.js.map", "import { ENGINE } from '../engine';\nimport { Min } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the minimum value from the input.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axes`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axes` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.min().print();  // or tf.min(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.min(axis).print();  // or tf.min(x, axis)\n * ```\n *\n * @param x The input Tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction min_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'min');\n    const forward = (backend, save) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let minInput = $x;\n        if (permutedAxes != null) {\n            minInput = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        const y = backend.min(minInput, axes);\n        if (permutedAxes != null) {\n            minInput.dispose();\n        }\n        let res = y;\n        if (keepDims) {\n            const expandedShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n            res = reshape(y, expandedShape);\n            y.dispose();\n        }\n        save([$x, res]);\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Min, attrs);\n}\nexport const min = op({ min_ });\n//# sourceMappingURL=min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Minimum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `minimumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * ```js\n * // Broadcast minimum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction minimum_(a, b) {\n    let $a = convertToTensor(a, 'a', 'minimum');\n    let $b = convertToTensor(b, 'b', 'minimum');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'bool') {\n        $a = cast($a, 'int32');\n        $b = cast($b, 'int32');\n    }\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.minimum($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Minimum);\n}\nexport const minimum = op({ minimum_ });\n//# sourceMappingURL=minimum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Mod } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the mod of a and b element-wise.\n * `floor(x / y) * y + mod(x, y) = x`\n * Supports broadcasting.\n *\n * We also expose `tf.modStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * ```js\n * // Broadcast a mod b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mod_(a, b) {\n    let $a = convertToTensor(a, 'a', 'mod');\n    let $b = convertToTensor(b, 'b', 'mod');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.mod($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Mod);\n}\nexport const mod = op({ mod_ });\n//# sourceMappingURL=mod.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes square of `x` element-wise: `x ^ 2`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);\n *\n * x.square().print();  // or tf.square(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction square_(x) {\n    const $x = convertToTensor(x, 'x', 'square');\n    const attrs = {};\n    const inputsToSave = [$x];\n    const outputsToSave = [];\n    return ENGINE.runKernelFunc((backend, save) => {\n        save([$x]);\n        return backend.square($x);\n    }, { x: $x }, null /* grad */, 'Square', attrs, inputsToSave, outputsToSave);\n}\nexport const square = op({ square_ });\n//# sourceMappingURL=square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { cast } from './cast';\nimport { mean } from './mean';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { square } from './square';\nimport { sub } from './sub';\n/**\n * Calculates the mean and variance of `x`. The mean and variance are\n * calculated by aggregating the contents of `x` across `axes`. If `x` is\n * 1-D and `axes = [0]` this is just the mean and variance of a vector.\n *\n * @param x The input tensor.\n * @param axis The dimension(s) along with to compute mean and\n *     variance. By default it reduces all dimensions.\n * @param keepDims If true, the moments have the same dimensionality as the\n *     input.\n * @return An object with two keys: `mean` and `variance`.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction moments_(x, axis = null, keepDims = false) {\n    x = convertToTensor(x, 'x', 'moments');\n    const axes = parseAxisParam(axis, x.shape);\n    const xMean = mean(x, axes, keepDims);\n    let keepDimsShape = xMean.shape;\n    if (!keepDims) {\n        keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);\n    }\n    const devSquared = square(sub(cast(x, 'float32'), reshape(xMean, keepDimsShape)));\n    const variance = mean(devSquared, axes, keepDims);\n    return { mean: xMean, variance };\n}\nexport const moments = op({ moments_ });\n//# sourceMappingURL=moments.js.map", "import { convertToTensor, convertToTensorArray } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the next states and outputs of a stack of LSTMCells.\n *\n * Each cell output is used as input to the next cell.\n *\n * Returns `[cellState, cellOutput]`.\n *\n * Derived from tf.contrib.rn.MultiRNNCell.\n *\n * @param lstmCells Array of LSTMCell functions.\n * @param data The input to the cell.\n * @param c Array of previous cell states.\n * @param h Array of previous cell outputs.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction multiRNNCell_(lstmCells, data, c, h) {\n    const $data = convertToTensor(data, 'data', 'multiRNNCell');\n    const $c = convertToTensorArray(c, 'c', 'multiRNNCell');\n    const $h = convertToTensorArray(h, 'h', 'multiRNNCell');\n    let input = $data;\n    const newStates = [];\n    for (let i = 0; i < lstmCells.length; i++) {\n        const output = lstmCells[i](input, $c[i], $h[i]);\n        newStates.push(output[0]);\n        newStates.push(output[1]);\n        input = output[1];\n    }\n    const newC = [];\n    const newH = [];\n    for (let i = 0; i < newStates.length; i += 2) {\n        newC.push(newStates[i]);\n        newH.push(newStates[i + 1]);\n    }\n    return [newC, newH];\n}\nexport const multiRNNCell = op({ multiRNNCell_ });\n//# sourceMappingURL=multi_rnn_cell.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Creates a `tf.Tensor` with values drawn from a multinomial distribution.\n *\n * ```js\n * const probs = tf.tensor([.75, .25]);\n * tf.multinomial(probs, 3).print();\n * ```\n *\n * @param logits 1D array with unnormalized log-probabilities, or\n *     2D array of shape `[batchSize, numOutcomes]`. See the `normalized`\n *     parameter.\n * @param numSamples Number of samples to draw for each row slice.\n * @param seed The seed number.\n * @param normalized Whether the provided `logits` are normalized true\n *     probabilities (sum to 1). Defaults to false.\n * @return 1D array of shape `[numSamples]`, or 2D array of shape\n *     `[batchSize, numSamples]`, depending on the rank of the input.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction multinomial_(logits, numSamples, seed, normalized = false) {\n    const $logits = convertToTensor(logits, 'logits', 'multinomial');\n    const numOutcomes = $logits.size;\n    const origRank = $logits.rank;\n    if (numOutcomes < 2) {\n        throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ` +\n            `${numOutcomes}.`);\n    }\n    if (origRank > 2) {\n        throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n    }\n    seed = seed || Math.random();\n    const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;\n    const res = ENGINE.runKernelFunc(backend => backend.multinomial(logits2D, normalized, numSamples, seed), { logits2D });\n    // tslint:disable-next-line:no-unnecessary-type-assertion\n    return origRank === 1 ? reshape(res, [res.size]) : res;\n}\nexport const multinomial = op({ multinomial_ });\n//# sourceMappingURL=multinomial.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { NotEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a != b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([0, 2, 3]);\n *\n * a.notEqual(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction notEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'notEqual');\n    let $b = convertToTensor(b, 'b', 'notEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend) => backend.notEqual($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, NotEqual);\n}\nexport const notEqual = op({ notEqual_ });\n//# sourceMappingURL=not_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Real } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the real part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the real part of each element in input considered as a complex number.\n *\n * If the input is real, it simply makes a clone.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.real(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction real_(input) {\n    const $input = convertToTensor(input, 'input', 'real');\n    const forward = (backend) => {\n        return backend.real($input);\n    };\n    const inputs = { input: $input };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Real);\n}\nexport const real = op({ real_ });\n//# sourceMappingURL=real.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OnesLike } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { complex } from './complex';\nimport { imag } from './imag';\nimport { op } from './operation';\nimport { real } from './real';\nimport { zerosLike } from './zeros_like';\n/**\n * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.onesLike(x).print();\n * ```\n * @param x A tensor.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction onesLike_(x) {\n    const $x = convertToTensor(x, 'x', 'onesLike');\n    const forward = (backend, save) => {\n        if ($x.dtype === 'complex64') {\n            const r = onesLike(real($x));\n            const i = zerosLike(imag($x));\n            return complex(r, i);\n        }\n        return backend.onesLike($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, OnesLike);\n}\nexport const onesLike = op({ onesLike_ });\n//# sourceMappingURL=ones_like.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the outer product of two vectors, `v1` and `v2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([3, 4, 5]);\n *\n * tf.outerProduct(a, b).print();\n * ```\n * @param v1 The first vector in the outer product operation.\n * @param v2 The second vector in the outer product operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction outerProduct_(v1, v2) {\n    const $v1 = convertToTensor(v1, 'v1', 'outerProduct');\n    const $v2 = convertToTensor(v2, 'v2', 'outerProduct');\n    util.assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ` +\n        `${$v1.rank} and ${$v2.rank}.`);\n    const v12D = reshape($v1, [-1, 1]);\n    const v22D = reshape($v2, [1, -1]);\n    return matMul(v12D, v22D);\n}\nexport const outerProduct = op({ outerProduct_ });\n//# sourceMappingURL=outer_product.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { PadV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Pads a `tf.Tensor` with a given value and paddings.\n *\n * This operation currently only implements the `CONSTANT` mode.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `paddings` is of given length.\n *   - `tf.pad1d`\n *   - `tf.pad2d`\n *   - `tf.pad3d`\n *   - `tf.pad4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.pad([[1, 2]]).print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * @param constantValue The pad value to use. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction pad_(x, paddings, constantValue = 0) {\n    const $x = convertToTensor(x, 'x', 'pad');\n    if ($x.rank === 0) {\n        throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');\n    }\n    const forward = (backend, save) => {\n        save([$x]);\n        return backend.pad($x, paddings, constantValue);\n    };\n    const attrs = { paddings, constantValue };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, PadV2, attrs);\n}\nexport const pad = op({ pad_ });\n//# sourceMappingURL=pad.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.\n */\nfunction pad1d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 2, () => 'Invalid number of paddings. Must be length of 2.');\n    return pad(x, [paddings], constantValue);\n}\nexport const pad1d = op({ pad1d_ });\n//# sourceMappingURL=pad1d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.\n */\nfunction pad2d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 2 && paddings[0].length === 2 &&\n        paddings[1].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad2d = op({ pad2d_ });\n//# sourceMappingURL=pad2d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.\n */\nfunction pad3d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 3 && paddings[0].length === 2 &&\n        paddings[1].length === 2 && paddings[2].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad3d = op({ pad3d_ });\n//# sourceMappingURL=pad3d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.\n */\nfunction pad4d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 4 && paddings[0].length === 2 &&\n        paddings[1].length === 2 && paddings[2].length === 2 &&\n        paddings[3].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad4d = op({ pad4d_ });\n//# sourceMappingURL=pad4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SpaceToBatchND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation divides \"spatial\" dimensions `[1, ..., M]` of the input into\n * a grid of blocks of shape `blockShape`, and interleaves these blocks with\n * the \"batch\" dimension (0) such that in the output, the spatial\n * dimensions `[1, ..., M]` correspond to the position within the grid,\n * and the batch dimension combines both the position within a spatial block\n * and the original batch position. Prior to division into blocks,\n * the spatial dimensions of the input are optionally zero padded\n * according to `paddings`. See below for a precise description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n * const blockShape = [2, 2];\n * const paddings = [[0, 0], [0, 0]];\n *\n * x.spaceToBatchND(blockShape, paddings).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=\n *     0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad\n * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It\n * is required that\n * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input\n * according to `paddings` to produce `padded` of shape paddedShape.\n *\n * 2. Reshape `padded` to `reshapedPadded` of shape:\n * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`\n *\n * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`\n * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the\n * batch dimension, producing an output tensor of shape:\n * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction spaceToBatchND_(x, blockShape, paddings) {\n    const $x = convertToTensor(x, 'x', 'spaceToBatchND');\n    util.assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);\n    util.assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);\n    util.assert($x.shape.reduce((a, b, i) => {\n        if (i > 0 && i <= blockShape.length) {\n            return a &&\n                ((b + paddings[i - 1][0] + paddings[i - 1][1]) %\n                    blockShape[i - 1] ===\n                    0);\n        }\n        return a;\n    }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);\n    const forward = backend => backend.spaceToBatchND($x, blockShape, paddings);\n    const inputs = { x: $x };\n    const attrs = { blockShape, paddings };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, SpaceToBatchND, attrs);\n}\nexport const spaceToBatchND = op({ spaceToBatchND_ });\n//# sourceMappingURL=space_to_batch_nd.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { avgPool } from './avg_pool';\nimport { batchToSpaceND } from './batch_to_space_nd';\nimport * as conv_util from './conv_util';\nimport { maxPool } from './max_pool';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { spaceToBatchND } from './space_to_batch_nd';\n/**\n * Performs an N-D pooling operation\n *\n * @param input The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param windowShape The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param poolingType The type of pooling, either 'max' or 'avg'.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction pool_(input, windowShape, poolingType, pad, dilations, strides) {\n    if (dilations == null) {\n        dilations = [1, 1];\n    }\n    if (strides == null) {\n        strides = 1;\n    }\n    if (pad === 0) {\n        pad = 'valid';\n    }\n    const $x = convertToTensor(input, 'x', 'maxPool');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in pool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = conv_util.computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad);\n    const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];\n    // The following implementation does batchToSpace(pool(spaceToBatch(x)))\n    // whenever dilation > 1 since the TF kernels do not support dilation > 1.\n    // tslint:disable-next-line:max-line-length\n    // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037\n    let basePadding;\n    if (pad === 'same') {\n        basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);\n    }\n    else {\n        basePadding = [[0, 0], [0, 0]];\n    }\n    const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n    const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n    const convertedPad = isDilationOne ? pad : 'valid';\n    const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n    const forwardOp = poolingType === 'avg' ?\n        () => avgPool(convertedX, windowShape, strides, convertedPad) :\n        () => maxPool(convertedX, windowShape, strides, convertedPad);\n    const y = forwardOp();\n    const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\n// Helper function to compute crops and paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184\nfunction requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {\n    const padStart = basePadding.map(b => b[0]);\n    const origPadEnd = basePadding.map(b => b[1]);\n    const fullInputShape = inputShape.concat(padStart, origPadEnd);\n    const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n    const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n    const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n    const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n    return [paddings, crops];\n}\n// Helper function to compute base paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524\nfunction withSpaceToBatchBasePaddings(filterShape, dilation) {\n    // Spatial dimensions of the filters and the upsampled filters in which we\n    // introduce (rate - 1) zeros between consecutive filter values.\n    const dilatedFilterShape = filterShape.map((s, i) => {\n        return s + (s - 1) * (dilation[i] - 1);\n    });\n    const padExtraShape = dilatedFilterShape.map(s => s - 1);\n    // When padding is odd, we pad more at end, following the same\n    // convention as conv2d.\n    const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));\n    const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n    return padExtraShape.map((_, i) => {\n        return [padExtraStart[i], padExtraEnd[i]];\n    });\n}\nexport const pool = op({ pool_ });\n//# sourceMappingURL=pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Pow } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the power of one `tf.Tensor` to another. Supports broadcasting.\n *\n * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for\n * corresponding elements in x and y. The result's dtype will be the upcasted\n * type of the `base` and `exp` dtypes.\n *\n * ```js\n * const a = tf.tensor([[2, 3], [4, 5]])\n * const b = tf.tensor([[1, 2], [3, 0]]).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n *\n * ```js\n * const a = tf.tensor([[1, 2], [3, 4]])\n * const b = tf.tensor(2).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n * We also expose `powStrict` which has the same signature as this op and\n * asserts that `base` and `exp` are the same shape (does not broadcast).\n *\n * @param base The base `tf.Tensor` to pow element-wise.\n * @param exp The exponent `tf.Tensor` to pow element-wise.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction pow_(base, exp) {\n    let $base = convertToTensor(base, 'base', 'pow');\n    let $exp = convertToTensor(exp, 'exp', 'pow');\n    [$base, $exp] = makeTypesMatch($base, $exp);\n    const inputs = { a: $base, b: $exp };\n    const forward = (backend, save) => {\n        const y = backend.pow($base, $exp);\n        save([$base, $exp, y]);\n        return y;\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Pow);\n}\nexport const pow = op({ pow_ });\n//# sourceMappingURL=pow.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prelu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes leaky rectified linear element-wise with parametric alphas.\n *\n * `x < 0 ? alpha * x : f(x) = x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n * const alpha = tf.scalar(0.1);\n *\n * x.prelu(alpha).print();  // or tf.prelu(x, alpha)\n * ```\n * @param x The input tensor.\n * @param alpha Scaling factor for negative values.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction prelu_(x, alpha) {\n    const $x = convertToTensor(x, 'x', 'prelu');\n    const $alpha = convertToTensor(alpha, 'alpha', 'prelu');\n    const forward = (backend, save) => {\n        const res = backend.prelu($x, $alpha);\n        save([$x, $alpha]);\n        return res;\n    };\n    const inputs = { x: $x, alpha: $alpha };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Prelu);\n}\nexport const prelu = op({ prelu_ });\n//# sourceMappingURL=prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prod } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the product of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.prod().print();  // or tf.prod(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.prod(axis).print();  // or tf.prod(x, axis)\n * ```\n *\n * @param x The input tensor to compute the product over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction prod_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'prod');\n    const forward = (backend) => {\n        if ($x.dtype === 'bool') {\n            $x = cast($x, 'int32');\n        }\n        const axes = parseAxisParam(axis, $x.shape);\n        const permutation = getAxesPermutation(axes, $x.rank);\n        let reductionAxes = axes;\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n        }\n        let value = backend.prod(permutedX, reductionAxes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(value.shape, axes);\n            value = reshape(value, newShape);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Prod, attrs);\n}\nexport const prod = op({ prod_ });\n//# sourceMappingURL=prod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { sizeFromShape } from '../util';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with values sampled from a random number generator\n * function defined by the user.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param randFunction A random number generator function which is called\n * for each element in the output tensor.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\nfunction rand_(shape, randFunction, dtype) {\n    const size = sizeFromShape(shape);\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    for (let i = 0; i < size; i++) {\n        values[i] = randFunction();\n    }\n    return ENGINE.makeTensor(values, shape, dtype);\n}\nexport const rand = op({ rand_ });\n//# sourceMappingURL=rand.js.map", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as seedrandom from 'seedrandom';\nimport { expectNumbersClose, testEpsilon } from '../test_util';\n// https://en.wikipedia.org/wiki/Marsaglia_polar_method\nexport class MPRandGauss {\n    constructor(mean, stdDeviation, dtype, truncated, seed) {\n        this.mean = mean;\n        this.stdDev = stdDeviation;\n        this.dtype = dtype;\n        this.nextVal = NaN;\n        this.truncated = truncated;\n        if (this.truncated) {\n            this.upper = this.mean + this.stdDev * 2;\n            this.lower = this.mean - this.stdDev * 2;\n        }\n        const seedValue = seed ? seed : Math.random();\n        this.random = seedrandom.alea(seedValue.toString());\n    }\n    /** Returns next sample from a Gaussian distribution. */\n    nextValue() {\n        if (!isNaN(this.nextVal)) {\n            const value = this.nextVal;\n            this.nextVal = NaN;\n            return value;\n        }\n        let resultX, resultY;\n        let isValid = false;\n        while (!isValid) {\n            let v1, v2, s;\n            do {\n                v1 = 2 * this.random() - 1;\n                v2 = 2 * this.random() - 1;\n                s = v1 * v1 + v2 * v2;\n            } while (s >= 1 || s === 0);\n            const mul = Math.sqrt(-2.0 * Math.log(s) / s);\n            resultX = this.mean + this.stdDev * v1 * mul;\n            resultY = this.mean + this.stdDev * v2 * mul;\n            if (!this.truncated || this.isValidTruncated(resultX)) {\n                isValid = true;\n            }\n        }\n        if (!this.truncated || this.isValidTruncated(resultY)) {\n            this.nextVal = this.convertValue(resultY);\n        }\n        return this.convertValue(resultX);\n    }\n    /** Handles proper rounding for non-floating-point numbers. */\n    convertValue(value) {\n        if (this.dtype == null || this.dtype === 'float32') {\n            return value;\n        }\n        return Math.round(value);\n    }\n    /** Returns true if less than 2-standard-deviations from the mean. */\n    isValidTruncated(value) {\n        return value <= this.upper && value >= this.lower;\n    }\n}\n// Marsaglia, George, and Wai Wan Tsang. 2000. \"A Simple Method for Generating\n// Gamma Variables.\"\nexport class RandGamma {\n    constructor(alpha, beta, dtype, seed) {\n        this.alpha = alpha;\n        this.beta = 1 / beta; // convert rate to scale parameter\n        this.dtype = dtype;\n        const seedValue = seed ? seed : Math.random();\n        this.randu = seedrandom.alea(seedValue.toString());\n        this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());\n        if (alpha < 1) {\n            this.d = alpha + (2 / 3);\n        }\n        else {\n            this.d = alpha - (1 / 3);\n        }\n        this.c = 1 / Math.sqrt(9 * this.d);\n    }\n    /** Returns next sample from a gamma distribution. */\n    nextValue() {\n        let x2, v0, v1, x, u, v;\n        while (true) {\n            do {\n                x = this.randn.nextValue();\n                v = 1 + (this.c * x);\n            } while (v <= 0);\n            v *= v * v;\n            x2 = x * x;\n            v0 = 1 - (0.331 * x2 * x2);\n            v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v)));\n            u = this.randu();\n            if (u < v0 || Math.log(u) < v1) {\n                break;\n            }\n        }\n        v = (1 / this.beta) * this.d * v;\n        if (this.alpha < 1) {\n            v *= Math.pow(this.randu(), 1 / this.alpha);\n        }\n        return this.convertValue(v);\n    }\n    /** Handles proper rounding for non-floating-point numbers. */\n    convertValue(value) {\n        if (this.dtype === 'float32') {\n            return value;\n        }\n        return Math.round(value);\n    }\n}\nexport class UniformRandom {\n    constructor(min = 0, max = 1, dtype, seed) {\n        /** Handles proper rounding for non floating point numbers. */\n        this.canReturnFloat = () => (this.dtype == null || this.dtype === 'float32');\n        this.min = min;\n        this.range = max - min;\n        this.dtype = dtype;\n        if (seed == null) {\n            seed = Math.random();\n        }\n        if (typeof seed === 'number') {\n            seed = seed.toString();\n        }\n        if (!this.canReturnFloat() && this.range <= 1) {\n            throw new Error(`The difference between ${min} - ${max} <= 1 and dtype is not float`);\n        }\n        this.random = seedrandom.alea(seed);\n    }\n    convertValue(value) {\n        if (this.canReturnFloat()) {\n            return value;\n        }\n        return Math.round(value);\n    }\n    nextValue() {\n        return this.convertValue(this.min + this.range * this.random());\n    }\n}\nexport function jarqueBeraNormalityTest(values) {\n    // https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test\n    const n = values.length;\n    const s = skewness(values);\n    const k = kurtosis(values);\n    const jb = n / 6 * (Math.pow(s, 2) + 0.25 * Math.pow(k - 3, 2));\n    // JB test requires 2-degress of freedom from Chi-Square @ 0.95:\n    // http://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm\n    const CHI_SQUARE_2DEG = 5.991;\n    if (jb > CHI_SQUARE_2DEG) {\n        throw new Error(`Invalid p-value for JB: ${jb}`);\n    }\n}\nexport function expectArrayInMeanStdRange(actual, expectedMean, expectedStdDev, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    const actualMean = mean(actual);\n    expectNumbersClose(actualMean, expectedMean, epsilon);\n    expectNumbersClose(standardDeviation(actual, actualMean), expectedStdDev, epsilon);\n}\nfunction mean(values) {\n    let sum = 0;\n    for (let i = 0; i < values.length; i++) {\n        sum += values[i];\n    }\n    return sum / values.length;\n}\nfunction standardDeviation(values, mean) {\n    let squareDiffSum = 0;\n    for (let i = 0; i < values.length; i++) {\n        const diff = values[i] - mean;\n        squareDiffSum += diff * diff;\n    }\n    return Math.sqrt(squareDiffSum / values.length);\n}\nfunction kurtosis(values) {\n    // https://en.wikipedia.org/wiki/Kurtosis\n    const valuesMean = mean(values);\n    const n = values.length;\n    let sum2 = 0;\n    let sum4 = 0;\n    for (let i = 0; i < n; i++) {\n        const v = values[i] - valuesMean;\n        sum2 += Math.pow(v, 2);\n        sum4 += Math.pow(v, 4);\n    }\n    return (1 / n) * sum4 / Math.pow((1 / n) * sum2, 2);\n}\nfunction skewness(values) {\n    // https://en.wikipedia.org/wiki/Skewness\n    const valuesMean = mean(values);\n    const n = values.length;\n    let sum2 = 0;\n    let sum3 = 0;\n    for (let i = 0; i < n; i++) {\n        const v = values[i] - valuesMean;\n        sum2 += Math.pow(v, 2);\n        sum3 += Math.pow(v, 3);\n    }\n    return (1 / n) * sum3 / Math.pow((1 / (n - 1)) * sum2, 3 / 2);\n}\n//# sourceMappingURL=rand_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { RandGamma } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a gamma distribution.\n *\n * ```js\n * tf.randomGamma([2, 2], 1).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param alpha The shape parameter of the gamma distribution.\n * @param beta The inverse scale parameter of the gamma distribution. Defaults\n *     to 1.\n * @param dtype The data type of the output. Defaults to float32.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomGamma_(shape, alpha, beta = 1, dtype = 'float32', seed) {\n    if (beta == null) {\n        beta = 1;\n    }\n    if (dtype == null) {\n        dtype = 'float32';\n    }\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new Error(`Unsupported data type ${dtype}`);\n    }\n    const rgamma = new RandGamma(alpha, beta, dtype, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = rgamma.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomGamma = op({ randomGamma_ });\n//# sourceMappingURL=random_gamma.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a normal distribution.\n *\n * ```js\n * tf.randomNormal([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n    if (dtype != null && dtype === 'bool') {\n        throw new Error(`Unsupported data type ${dtype}`);\n    }\n    const randGauss = new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = randGauss.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomNormal = op({ randomNormal_ });\n//# sourceMappingURL=random_normal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { UniformRandom } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a uniform distribution.\n *\n * The generated values follow a uniform distribution in the range [minval,\n * maxval). The lower bound minval is included in the range, while the upper\n * bound maxval is excluded.\n *\n * ```js\n * tf.randomUniform([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param minval The lower bound on the range of random values to generate.\n *   Defaults to 0.\n * @param maxval The upper bound on the range of random values to generate.\n *   Defaults to 1.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomUniform_(shape, minval = 0, maxval = 1, dtype = 'float32', seed) {\n    const res = buffer(shape, dtype);\n    const random = new UniformRandom(minval, maxval, null, seed);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = random.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomUniform = op({ randomUniform_ });\n//# sourceMappingURL=random_uniform.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor1d` as it makes the code more readable.\n *\n * ```js\n * tf.tensor1d([1, 2, 3]).print();\n * ```\n *\n * @param values The values of the tensor. Can be array of numbers,\n *     or a `TypedArray`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor1d(values, dtype) {\n    assertNonNull(values);\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 1) {\n        throw new Error('tensor1d() requires values to be a flat/TypedArray');\n    }\n    const shape = null;\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor1d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Range } from '../kernel_names';\nimport { makeZerosTypedArray } from '../util';\nimport { tensor1d } from './tensor1d';\nimport { zeros } from './zeros';\n/**\n * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.\n *\n * The tensor is a is half-open interval meaning it includes start, but\n * excludes stop. Decrementing ranges and negative step values are also\n * supported.sv\n *\n *\n * ```js\n * tf.range(0, 9, 2).print();\n * ```\n *\n * @param start An integer start value\n * @param stop An integer stop value\n * @param step An integer increment (will default to 1 or -1)\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function range(start, stop, step = 1, dtype = 'float32') {\n    if (step === 0) {\n        throw new Error('Cannot have a step of zero');\n    }\n    const forward = () => {\n        const sameStartStop = start === stop;\n        const increasingRangeNegativeStep = start < stop && step < 0;\n        const decreasingRangePositiveStep = stop < start && step > 1;\n        if (sameStartStop || increasingRangeNegativeStep ||\n            decreasingRangePositiveStep) {\n            return zeros([0], dtype);\n        }\n        const numElements = Math.abs(Math.ceil((stop - start) / step));\n        const values = makeZerosTypedArray(numElements, dtype);\n        if (stop < start && step === 1) {\n            // Auto adjust the step's sign if it hasn't been set\n            // (or was set to 1)\n            step = -1;\n        }\n        values[0] = start;\n        for (let i = 1; i < values.length; i++) {\n            values[i] = values[i - 1] + step;\n        }\n        return tensor1d(values, dtype);\n    };\n    const attrs = { start, stop, step, dtype };\n    return ENGINE.runKernelFunc(forward, {} /* inputs */, null /* grad */, Range, attrs);\n}\n//# sourceMappingURL=range.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reciprocal } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of x element-wise: `1 / x`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, 2]);\n *\n * x.reciprocal().print();  // or tf.reciprocal(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction reciprocal_(x) {\n    const $x = convertToTensor(x, 'x', 'reciprocal');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.reciprocal($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Reciprocal);\n}\nexport const reciprocal = op({ reciprocal_ });\n//# sourceMappingURL=reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { cast } from './cast';\n/**\n * Computes rectified linear element-wise: `max(x, 0)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.relu().print();  // or tf.relu(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu_(x) {\n    const $x = convertToTensor(x, 'x', 'relu');\n    const forward = (backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'bool') {\n            return cast($x, 'int32');\n        }\n        return backend.relu($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Relu);\n}\nexport const relu = op({ relu_ });\n//# sourceMappingURL=relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu6 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes rectified linear 6 element-wise: `min(max(x, 0), 6)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 8]);\n *\n * x.relu6().print();  // or tf.relu6(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu6_(x) {\n    const $x = convertToTensor(x, 'x', 'relu6');\n    const forward = (backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'bool') {\n            return cast($x, 'int32');\n        }\n        return backend.relu6($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Relu6);\n}\nexport const relu6 = op({ relu6_ });\n//# sourceMappingURL=relu6.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reverse } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { clone } from './clone';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Reverses a `tf.Tensor` along a specified axis.\n *\n * Also available are stricter rank-specific methods that assert that `x` is\n * of the given rank:\n *   - `tf.reverse1d`\n *   - `tf.reverse2d`\n *   - `tf.reverse3d`\n *   - `tf.reverse4d`\n *\n * Except `tf.reverse1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.reverse().print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.reverse(axis).print();\n * ```\n * @param x The input tensor to be reversed.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction reverse_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    const forward = (backend) => {\n        const axes = parseAxisParam(axis, $x.shape);\n        if ($x.rank === 0) {\n            return clone($x);\n        }\n        const res = backend.reverse($x, axes);\n        return reshape(res, $x.shape);\n    };\n    const inputs = { x: $x };\n    const attrs = { dims: axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Reverse, attrs);\n}\nexport const reverse = op({ reverse_ });\n//# sourceMappingURL=reverse.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor1D`.\n *\n * @param x The input tensor.\n */\nfunction reverse1d_(x) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);\n    return reverse($x, 0);\n}\nexport const reverse1d = op({ reverse1d_ });\n//# sourceMappingURL=reverse_1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor2D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse2d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse2d = op({ reverse2d_ });\n//# sourceMappingURL=reverse_2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor3D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse3d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse3d = op({ reverse3d_ });\n//# sourceMappingURL=reverse_3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor4D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse4d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse4d = op({ reverse4d_ });\n//# sourceMappingURL=reverse_4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Round } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes round of input `tf.Tensor` element-wise: `round(x)`.\n * It implements banker's rounding.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.round().print();  // or tf.round(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction round_(x) {\n    const $x = convertToTensor(x, 'x', 'round');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.round($x), inputs, null /* grad */, Round);\n}\nexport const round = op({ round_ });\n//# sourceMappingURL=round.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Rsqrt } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of square root of the input `tf.Tensor` element-wise:\n * `y = 1 / sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.rsqrt().print();  // or tf.rsqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction rsqrt_(x) {\n    const $x = convertToTensor(x, 'x', 'rsqrt');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.rsqrt($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Rsqrt);\n}\nexport const rsqrt = op({ rsqrt_ });\n//# sourceMappingURL=rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Selu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes scaled exponential linear element-wise.\n *\n * `x < 0 ? scale * alpha * (exp(x) - 1) : x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.selu().print();  // or tf.selu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction selu_(x) {\n    const $x = convertToTensor(x, 'x', 'selu');\n    const forward = (backend, save) => {\n        const res = backend.selu($x);\n        save([$x]);\n        return res;\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Selu);\n}\nexport const selu = op({ selu_ });\n//# sourceMappingURL=selu.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport { depthwiseConv2d } from './depthwise_conv2d';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * 2-D convolution with separable filters.\n *\n * Performs a depthwise convolution that acts separately on channels followed\n * by a pointwise convolution that mixes channels. Note that this is\n * separability between dimensions [1, 2] and 3, not spatial separability\n * between dimensions 1 and 2.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is\n *     the filter used in the first step.\n * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape\n *     `[1, 1, inChannels * channelMultiplier, outChannels]`. This is\n *     the filter used in the second step.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation = [1, 1], dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'separableConv2d');\n    const $depthwiseFilter = convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');\n    const $pointwiseFilter = convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    if (dataFormat === 'NCHW') {\n        throw new Error('separableConv2d currently does not support dataFormat NCHW; only ' +\n            'NHWC is supported');\n    }\n    util.assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but ` +\n        `got rank ${$depthwiseFilter.rank}.`);\n    util.assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but ` +\n        `got rank ${$depthwiseFilter.rank}.`);\n    util.assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter ` +\n        ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n    util.assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise ` +\n        `filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n    const inChannels = $depthwiseFilter.shape[2];\n    const channelMultiplier = $depthwiseFilter.shape[3];\n    util.assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter ` +\n        `must be ${inChannels * channelMultiplier}, ` +\n        `but got ${$pointwiseFilter.shape[2]}.`);\n    const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);\n    const pointwiseStride = 1;\n    const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const separableConv2d = op({ separableConv2d_ });\n//# sourceMappingURL=separable_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\n/**\n * Computes the difference between two lists of numbers.\n *\n * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`\n * that represents all values that are in `x` but not in `y`. The returned\n * Tensor `out` is sorted in the same order that the numbers appear in `x`\n * (duplicates are preserved). This operation also returns a Tensor indices that\n * represents the position of each out element in `x`. In other words:\n *\n * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`\n *\n * ```js\n * const x = [1, 2, 3, 4, 5, 6];\n * const y = [1, 3, 5];\n *\n * const [out, indices] = await tf.setdiff1dAsync(x, y);\n * out.print(); // [2, 4, 6]\n * indices.print(); // [1, 3, 5]\n * ```\n *\n * @param x 1-D Tensor. Values to keep.\n * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the\n *     output.\n * @returns Promise of Tensor tuple [out, indices].\n *  out: Tensor with the same type as x.\n *  indices: A Tensor of type int32.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nasync function setdiff1dAsync_(x, y) {\n    const $x = convertToTensor(x, 'x', 'setdiff1d');\n    const $y = convertToTensor(y, 'y', 'setdiff1d');\n    util.assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);\n    util.assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);\n    util.assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);\n    const xVals = await $x.data();\n    const yVals = await $y.data();\n    const ySet = new Set(yVals);\n    let outputSize = 0;\n    for (let i = 0; i < xVals.length; i++) {\n        if (!ySet.has(xVals[i])) {\n            outputSize++;\n        }\n    }\n    const buffer = new TensorBuffer([outputSize], $x.dtype);\n    const indices = new TensorBuffer([outputSize], 'int32');\n    for (let i = 0, p = 0; i < xVals.length; i++) {\n        if (!ySet.has(xVals[i])) {\n            buffer.values[p] = xVals[i];\n            indices.values[p] = i;\n            p++;\n        }\n    }\n    return [buffer.toTensor(), indices.toTensor()];\n}\nexport const setdiff1dAsync = setdiff1dAsync_;\n//# sourceMappingURL=setdiff1d_async.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sign } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns an element-wise indication of the sign of a number.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);\n *\n * x.sign().print();  // or tf.sign(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sign_(x) {\n    const $x = convertToTensor(x, 'x', 'sign');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.sign($x), inputs, null /* grad */, Sign);\n}\nexport const sign = op({ sign_ });\n//# sourceMappingURL=sign.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sin of the input Tensor element-wise: `sin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.sin().print();  // or tf.sin(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sin_(x) {\n    const $x = convertToTensor(x, 'x', 'sin');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sin($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sin);\n}\nexport const sin = op({ sin_ });\n//# sourceMappingURL=sin.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.sinh().print();  // or tf.sinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sinh_(x) {\n    const $x = convertToTensor(x, 'x', 'sinh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sinh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sinh);\n}\nexport const sinh = op({ sinh_ });\n//# sourceMappingURL=sinh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 1D slice from 1D array starting at coordinates `begin` and is\n * of length `size`. See `slice` for details.\n */\nfunction slice1d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice1d');\n    util.assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, [begin], [size]);\n}\nexport const slice1d = op({ slice1d_ });\n//# sourceMappingURL=slice1d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 2D slice from a 2D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice2d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice2d');\n    util.assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice2d = op({ slice2d_ });\n//# sourceMappingURL=slice2d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 3D slice from a 3D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice3d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice3d');\n    util.assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice3d = op({ slice3d_ });\n//# sourceMappingURL=slice3d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 4D slice from a 4D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice4d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice4d');\n    util.assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice4d = op({ slice4d_ });\n//# sourceMappingURL=slice4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the softmax normalized vector given the logits.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction softmax_(logits, dim = -1) {\n    const $logits = convertToTensor(logits, 'logits', 'softmax', 'float32');\n    if (dim === -1) {\n        dim = $logits.rank - 1;\n    }\n    if (dim !== $logits.rank - 1) {\n        throw Error('Softmax along a non-last dimension is not yet supported. ' +\n            `Logits was rank ${$logits.rank} and dim was ${dim}`);\n    }\n    const inputs = { logits: $logits };\n    const attrs = { dim };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const y = backend.softmax($logits, dim);\n        save([y]);\n        return y;\n    }, inputs, null /* grad */, Softmax, attrs);\n}\nexport const softmax = op({ softmax_ });\n//# sourceMappingURL=softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\n/**\n * Fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the inner-most\n * dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.fft().print();  // tf.spectral.fft(x).print();\n * ```\n * @param input The complex input to compute an fft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction fft_(input) {\n    assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.fft() must be complex64 ` +\n        `but got ${input.dtype}.`);\n    const inputs = { input };\n    return ENGINE.runKernelFunc(backend => {\n        // Collapse all outer dimensions to a single batch dimension.\n        const innerDimensionSize = input.shape[input.shape.length - 1];\n        const batch = input.size / innerDimensionSize;\n        const input2D = input.as2D(batch, innerDimensionSize);\n        const result = backend.fft(input2D);\n        return result.reshape(input.shape);\n    }, inputs, null /* gradient */, FFT);\n}\nexport const fft = op({ fft_ });\n//# sourceMappingURL=fft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { IFFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Inverse fast Fourier transform.\n *\n * Computes the inverse 1-dimensional discrete Fourier transform over the\n * inner-most dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.ifft().print();  // tf.spectral.ifft(x).print();\n * ```\n * @param input The complex input to compute an ifft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction ifft_(input) {\n    assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.ifft() must be complex64 ` +\n        `but got ${input.dtype}.`);\n    const inputs = { input };\n    return ENGINE.runKernelFunc(backend => {\n        // Collapse all outer dimensions to a single batch dimension.\n        const innerDimensionSize = input.shape[input.shape.length - 1];\n        const batch = input.size / innerDimensionSize;\n        const input2D = reshape(input, [batch, innerDimensionSize]);\n        const result = backend.ifft(input2D);\n        return reshape(result, input.shape);\n    }, inputs, null /* gradient */, IFFT);\n}\nexport const ifft = op({ ifft_ });\n//# sourceMappingURL=ifft.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { reverse } from '../reverse';\nimport { scalar } from '../scalar';\nimport { slice } from '../slice';\nimport { ifft } from './ifft';\n/**\n * Inversed real value input fast Fourier transform.\n *\n * Computes the 1-dimensional inversed discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([0, 0, 0]);\n * const x = tf.complex(real, imag);\n *\n * x.irfft().print();\n * ```\n * @param input The real value input to compute an irfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction irfft_(input) {\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n    let ret;\n    if (innerDimensionSize <= 2) {\n        const complexInput = reshape(input, [batch, innerDimensionSize]);\n        ret = ifft(complexInput);\n    }\n    else {\n        // The length of unique components of the DFT of a real-valued signal\n        // is 2 * (input_len - 1)\n        const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n        const realInput = reshape(real(input), [batch, innerDimensionSize]);\n        const imagInput = reshape(imag(input), [batch, innerDimensionSize]);\n        const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);\n        const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));\n        const r = concat([realInput, realConjugate], 1);\n        const i = concat([imagInput, imagConjugate], 1);\n        const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);\n        ret = ifft(complexInput);\n    }\n    ret = real(ret);\n    // reshape the result if the input is 3D tensor.\n    if (input.rank === 3 && input.shape[0] !== 0) {\n        const temp = ret;\n        const batch = input.shape[0];\n        ret = reshape(ret, [batch, ret.shape[0] / batch, ret.shape[1]]);\n        temp.dispose();\n    }\n    return ret;\n}\nexport const irfft = op({ irfft_ });\n//# sourceMappingURL=irfft.js.map", "import { assert } from '../util';\n/**\n * Prepare the split size array. When the input is a number, the axis is evenly\n * divided among the split size. When the input contains the negative value, the\n * rest of the axis is allocated toward that.\n */\nexport function prepareSplitSize(x, numOrSizeSplits, axis = 0) {\n    let splitSizes = [];\n    if (typeof (numOrSizeSplits) === 'number') {\n        assert(x.shape[axis] % numOrSizeSplits === 0, () => 'Number of splits must evenly divide the axis.');\n        splitSizes =\n            new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);\n    }\n    else {\n        const numOfNegs = numOrSizeSplits.reduce((count, value) => {\n            if (value === -1) {\n                count += 1;\n            }\n            return count;\n        }, 0);\n        assert(numOfNegs <= 1, () => 'There should be only one negative value in split array.');\n        const negIndex = numOrSizeSplits.indexOf(-1);\n        // Allow the number of split array to be -1, which indicates the rest\n        // of dimension is allocated to that split.\n        if (negIndex !== -1) {\n            const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);\n            numOrSizeSplits[negIndex] = x.shape[axis] - total;\n        }\n        assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => 'The sum of sizes must match the size of the axis dimension.');\n        splitSizes = numOrSizeSplits;\n    }\n    return splitSizes;\n}\n//# sourceMappingURL=split_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SplitV } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { op } from './operation';\nimport { prepareSplitSize } from './split_util';\n/**\n * Splits a `tf.Tensor` into sub tensors.\n *\n * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`\n * into `numOrSizeSplits` smaller tensors.\n * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.\n *\n * If `numOrSizeSplits` is a number array, splits `x` into\n * `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the\n * same size as `x` except along dimension `axis` where the size is\n * `numOrSizeSplits[i]`.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);\n * const [a, b] = tf.split(x, 2, 1);\n * a.print();\n * b.print();\n *\n * const [c, d, e] = tf.split(x, [1, 2, 1], 1);\n * c.print();\n * d.print();\n * e.print();\n * ```\n *\n * @param x The input tensor to split.\n * @param numOrSizeSplits Either an integer indicating the number of\n * splits along the axis or an array of integers containing the sizes of\n * each output tensor along the axis. If a number then it must evenly divide\n * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.\n * Can contain one -1 indicating that dimension is to be inferred.\n * @param axis The dimension along which to split. Defaults to 0 (the first\n * dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction split_(x, numOrSizeSplits, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'split');\n    const forward = (backend, _) => {\n        const $axis = parseAxisParam(axis, $x.shape)[0];\n        const splitSizes = prepareSplitSize($x, numOrSizeSplits, $axis);\n        return backend.split($x, splitSizes, $axis);\n    };\n    const inputs = { x: $x };\n    const attr = { numOrSizeSplits, axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, SplitV, attr);\n}\nexport const split = op({ split_ });\n//# sourceMappingURL=split.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../../util';\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { split } from '../split';\nimport { zeros } from '../zeros';\nimport { zerosLike } from '../zeros_like';\nimport { fft } from './fft';\n/**\n * Real value input fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * real.rfft().print();\n * ```\n * @param input The real value input to compute an rfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction rfft_(input, fftLength) {\n    assert(input.dtype === 'float32', () => `The dtype for rfft() must be real value but got ${input.dtype}`);\n    let innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n    let adjustedInput;\n    if (fftLength != null && fftLength < innerDimensionSize) {\n        // Need to crop\n        const begin = input.shape.map(v => 0);\n        const size = input.shape.map(v => v);\n        size[input.shape.length - 1] = fftLength;\n        adjustedInput = slice(input, begin, size);\n        innerDimensionSize = fftLength;\n    }\n    else if (fftLength != null && fftLength > innerDimensionSize) {\n        // Need to pad with zeros\n        const zerosShape = input.shape.map(v => v);\n        zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;\n        adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);\n        innerDimensionSize = fftLength;\n    }\n    else {\n        adjustedInput = input;\n    }\n    // Complement the input with zero imaginary numbers.\n    const zerosInput = zerosLike(adjustedInput);\n    const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);\n    const ret = fft(complexInput);\n    // Exclude complex conjugations. These conjugations are put symmetrically.\n    const half = Math.floor(innerDimensionSize / 2) + 1;\n    const realValues = real(ret);\n    const imagValues = imag(ret);\n    const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);\n    const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);\n    const outputShape = adjustedInput.shape.slice();\n    outputShape[adjustedInput.shape.length - 1] = half;\n    return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);\n}\nexport const rfft = op({ rfft_ });\n//# sourceMappingURL=rfft.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sqrt } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.sqrt().print();  // or tf.sqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sqrt_(x) {\n    const $x = convertToTensor(x, 'x', 'sqrt');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sqrt($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sqrt);\n}\nexport const sqrt = op({ sqrt_ });\n//# sourceMappingURL=sqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SquaredDifference } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns (a - b) * (a - b) element-wise.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * ```js\n * // Broadcast squared difference  a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction squaredDifference_(a, b) {\n    let $a = convertToTensor(a, 'a', 'squaredDifference');\n    let $b = convertToTensor(b, 'b', 'squaredDifference');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.squaredDifference($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = {};\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, SquaredDifference, attrs);\n}\nexport const squaredDifference = op({ squaredDifference_ });\n//# sourceMappingURL=squared_difference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { squeezeShape } from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Removes dimensions of size 1 from the shape of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);\n * x.squeeze().print();\n * ```\n *\n * @param x The input tensor to be squeezed.\n * @param axis An optional list of numbers. If specified, only\n *     squeezes the dimensions listed. The dimension index starts at 0. It\n * is an error to squeeze a dimension that is not 1.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction squeeze_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'squeeze');\n    return reshape($x, squeezeShape($x.shape, axis).newShape);\n}\nexport const squeeze = op({ squeeze_ });\n//# sourceMappingURL=squeeze.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensorArray } from '../tensor_util_env';\nimport * as util from '../util';\nimport { concat } from './concat';\nimport { expandDims } from './expand_dims';\nimport { op } from './operation';\n/**\n * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.stack([a, b, c]).print();\n * ```\n *\n * @param tensors A list of tensor objects with the same shape and dtype.\n * @param axis The axis to stack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction stack_(tensors, axis = 0) {\n    const $tensors = convertToTensorArray(tensors, 'tensors', 'stack');\n    util.assert($tensors.length >= 1, () => 'Pass at least one tensor to tf.stack');\n    if ($tensors.length === 1) {\n        return expandDims($tensors[0], axis);\n    }\n    const rank = $tensors[0].rank;\n    const shape = $tensors[0].shape;\n    const dtype = $tensors[0].dtype;\n    util.assert(axis <= rank, () => 'Axis must be <= rank of the tensor');\n    $tensors.forEach(t => {\n        util.assertShapesMatch(shape, t.shape, 'All tensors passed to stack must have matching shapes');\n        util.assert(dtype === t.dtype, () => 'All tensors passed to stack must have matching dtypes');\n    });\n    const expandedTensors = $tensors.map(t => expandDims(t, axis));\n    // Stack exists in the TensorFlow C++ API\n    // (https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/stack) but not\n    // in\n    // https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/core/ops/ops.pbtxt.\n    // Therefore we are treating it like a high-level op rather than\n    // creating a dedicated stack kernel.\n    return concat(expandedTensors, axis);\n}\nexport const stack = op({ stack_ });\n//# sourceMappingURL=stack.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Step } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha * x`\n *\n * ```js\n * const x = tf.tensor1d([0, 2, -1, -3]);\n *\n * x.step(.5).print();  // or tf.step(x, .5)\n * ```\n * @param x The input tensor.\n * @param alpha The gradient when input is negative.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction step_(x, alpha = 0.0) {\n    const $x = convertToTensor(x, 'x', 'step');\n    const inputs = { x: $x };\n    const attrs = { alpha };\n    return ENGINE.runKernelFunc(backend => backend.step($x, alpha), inputs, null /* grad */, Step, attrs);\n}\nexport const step = op({ step_ });\n//# sourceMappingURL=step.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { StridedSlice } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { slice } from './slice';\nimport { computeOutShape, getNormalizedAxes, maskToAxes } from './slice_util';\n/**\n * Extracts a strided slice of a tensor.\n *\n * Roughly speaking, this op extracts a slice of size (end-begin)/stride from\n * the given input tensor (x). Starting at the location specified by begin the\n * slice continues by adding stride to the index until all dimensions are not\n * less than end. Note that a stride can be negative, which causes a reverse\n * slice.\n *\n * ```js\n * const t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],\n *    [3, 2, 3]);\n * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print()  // [[[3, 3, 3]]]\n * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print()  // [[[3, 3, 3],\n *                                                     // [4, 4, 4]]]\n * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],\n *                                                     // [3, 3, 3]]]\n * ```\n *\n * @param x The tensor to stride slice.\n * @param begin The coordinates to start the slice from.\n * @param end: The coordinates to end the slice at.\n * @param strides: The size of the slice.\n * @param beginMask: If the ith bit of beginMask is set, begin[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param endMask: If the ith bit of endMask is set, end[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param shrinkAxisMask: a bitmask where bit i implies that\n * the ith specification should shrink the dimensionality. begin and end must\n * imply a slice of size 1 in the dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {\n    let $x = convertToTensor(x, 'x', 'stridedSlice');\n    const forward = (backend) => {\n        if (strides == null) {\n            strides = new Array(begin.length);\n        }\n        const ellipsisAxes = maskToAxes(ellipsisMask);\n        if (ellipsisAxes.length > 1) {\n            throw new Error('Multiple ellipses in slice is not allowed.');\n        }\n        if (ellipsisMask !== 0 && newAxisMask !== 0) {\n            throw new Error('Using both ellipsisMask and newAxisMask is not yet supported.');\n        }\n        if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {\n            throw new Error('Using both ellipsisMask and shrinkAxisMask is not yet supported.');\n        }\n        const numInterpolatedAxes = $x.rank - begin.length;\n        // Expand the dims of x based on the newAxisMask.\n        const expandAxes = maskToAxes(newAxisMask);\n        const newShape = $x.shape.slice();\n        expandAxes.forEach(axis => {\n            begin[axis] = 0;\n            end[axis] = 1;\n            newShape.splice(axis, 0, 1);\n        });\n        $x = reshape($x, newShape);\n        const { begin: normalizedBegin, end: normalizedEnd, strides: normalizedStrides } = getNormalizedAxes($x.shape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask);\n        begin = normalizedBegin;\n        end = normalizedEnd;\n        strides = normalizedStrides;\n        const shrinkAxes = maskToAxes(shrinkAxisMask);\n        // Adjust the ends based on the shrink mask.\n        shrinkAxes.forEach(axis => {\n            end[axis] = begin[axis] + 1;\n            strides[axis] = 1;\n        });\n        // Figure out the output shape.\n        const size = computeOutShape(begin, end, strides);\n        // Remove the axes based on shrinkMask.\n        const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);\n        const nonStrided = strides.every(v => v === 1);\n        if (nonStrided) {\n            return reshape(slice($x, begin, size), outShape);\n        }\n        const res = backend.stridedSlice($x, begin, end, strides);\n        return reshape(res, outShape);\n    };\n    const inputs = { x: $x };\n    const attrs = {\n        begin,\n        end,\n        strides,\n        beginMask,\n        endMask,\n        ellipsisMask,\n        newAxisMask,\n        shrinkAxisMask\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, StridedSlice, attrs);\n}\nexport const stridedSlice = op({ stridedSlice_ });\n//# sourceMappingURL=strided_slice.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.tan().print();  // or tf.tan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tan_(x) {\n    const $x = convertToTensor(x, 'x', 'tan');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.tan($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Tan);\n}\nexport const tan = op({ tan_ });\n//# sourceMappingURL=tan.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor2d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor2d([[1, 2], [3, 4]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor2d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 2) {\n        throw new Error('tensor2d() requires shape to have two numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n        throw new Error('tensor2d() requires values to be number[][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor2d() requires shape to be provided when `values` ' +\n            'are a flat/TypedArray');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor2d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor4d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor4d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 4) {\n        throw new Error('tensor4d() requires shape to have four numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n        throw new Error('tensor4d() requires values to be number[][][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor4d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor5d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor5d([[[[[1], [2]], [[3], [4]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor5d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 5) {\n        throw new Error('tensor5d() requires shape to have five numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n        throw new Error('tensor5d() requires values to be ' +\n            'number[][][][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor5d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor5d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor6d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor6d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 6) {\n        throw new Error('tensor6d() requires shape to have six numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n        throw new Error('tensor6d() requires values to be number[][][][][][] or ' +\n            'flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor6d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    shape = shape ||\n        inferredShape;\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor6d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { TopK } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Finds the values and indices of the `k` largest entries along the last\n * dimension.\n *\n * If the input is a vector (rank=1), finds the k largest entries in the vector\n * and outputs their values and indices as vectors. Thus values[j] is the j-th\n * largest entry in input, and its index is indices[j].\n * For higher rank inputs, computes the top k entries along the last dimension.\n *\n * If two elements are equal, the lower-index element appears first.\n *\n * ```js\n * const a = tf.tensor2d([[1, 5], [4, 3]]);\n * const {values, indices} = tf.topk(a);\n * values.print();\n * indices.print();\n * ```\n * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.\n * @param k Number of top elements to look for along the last dimension.\n * @param sorted If true, the resulting `k` elements will be sorted by the\n *     values in descending order.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction topk_(x, k = 1, sorted = true) {\n    const $x = convertToTensor(x, 'x', 'topk');\n    if ($x.rank === 0) {\n        throw new Error('topk() expects the input to be of rank 1 or higher');\n    }\n    const lastDim = $x.shape[$x.shape.length - 1];\n    if (k > lastDim) {\n        throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) ` +\n            `but got ${k}`);\n    }\n    const inputs = { x: $x };\n    const attrs = { k, sorted };\n    const [values, indices] = ENGINE.runKernelFunc(b => b.topk($x, k, sorted), inputs, null /* grad */, TopK, attrs);\n    return { values, indices };\n}\nexport const topk = op({ topk_ });\n//# sourceMappingURL=topk.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a truncated normal\n * distribution.\n *\n * ```js\n * tf.truncatedNormal([2, 2]).print();\n * ```\n *\n * The generated values follow a normal distribution with specified mean and\n * standard deviation, except that values whose magnitude is more than 2\n * standard deviations from the mean are dropped and re-picked.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output tensor.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction truncatedNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n    if (dtype != null && dtype === 'bool') {\n        throw new Error(`Unsupported data type $ { dtype }`);\n    }\n    const randGauss = new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = randGauss.nextValue();\n    }\n    return res.toTensor();\n}\nexport const truncatedNormal = op({ truncatedNormal_ });\n//# sourceMappingURL=truncated_normal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unique } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert } from '../util';\nimport { op } from './operation';\n/**\n * Finds unique elements along an axis of a tensor.\n *\n * It returns a tensor `values` containing all of the unique elements along the\n * `axis` of the given tensor `x` in the same order that they occur along the\n * `axis` in `x`; `x` does not need to be sorted. It also returns a tensor\n * `indices` the same size as the number of the elements in `x` along the `axis`\n * dimension. It contains the index in the unique output `values`.\n *\n * ```js\n * // A 1-D tensor\n * const a = tf.tensor1d([1, 1, 2, 4, 4, 4, 7, 8, 8]);\n * const {values, indices} = tf.unique(a);\n * values.print();   // [1, 2, 4, 7, 8,]\n * indices.print();  // [0, 0, 1, 2, 2, 2, 3, 4, 4]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=0\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 0)\n * values.print();   // [[1, 0, 0],\n *                   //  [2, 0, 0]]\n * indices.print();  // [0, 0, 1]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=1\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 1)\n * values.print();   // [[1, 0],\n *                   //  [1, 0],\n *                   //  [2, 0]]\n * indices.print();  // [0, 1, 1]\n * ```\n * @param x A tensor (int32, string, bool).\n * @param axis The axis of the tensor to find the unique elements.\n * @returns [uniqueElements, indices] (see above for details)\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction unique_(x, axis = 0) {\n    // x can be of any dtype, thus null as the last argument.\n    const $x = convertToTensor(x, 'x', 'unique', null);\n    assert($x.rank > 0, () => 'The input tensor must be at least 1D');\n    const inputs = { x: $x };\n    const attrs = { axis };\n    const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs);\n    return { values, indices };\n}\nexport const unique = op({ unique_ });\n//# sourceMappingURL=unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { UnsortedSegmentSum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, isInt } from '../util';\nimport { op } from './operation';\n/**\n * Computes the sum along segments of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');\n * const numSegments = 3;\n *\n * x.unsortedSegmentSum(segmentIds, numSegments).print()\n * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)\n * ```\n * @param x The `tf.Tensor` that will be summed along its segments.\n * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s\n * dimension along the `axis`.  Maps each element of `x` to a segment.\n * @param numSegments The number of distinct `segmentIds`.\n *\n * @doc {heading: 'Operations', subheading: 'Segment'}\n */\nfunction unsortedSegmentSum_(x, segmentIds, numSegments) {\n    const $x = convertToTensor(x, 'x', 'unsortedSegmentSum');\n    const $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');\n    assert(isInt(numSegments), () => 'numSegments must be of dtype int');\n    const inputs = { x: $x, segmentIds: $segmentIds };\n    const attrs = { numSegments };\n    const forward = (backend, save) => {\n        const res = backend.unsortedSegmentSum($x, $segmentIds, numSegments);\n        save([$segmentIds]);\n        return res;\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, UnsortedSegmentSum, attrs);\n}\nexport const unsortedSegmentSum = op({ unsortedSegmentSum_ });\n//# sourceMappingURL=unsorted_segment_sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unpack } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * tf.unstack(a).forEach(tensor => tensor.print());\n * ```\n *\n * @param x A tensor object.\n * @param axis The axis to unstack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction unstack_(x, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'unstack');\n    util.assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);\n    if (axis < 0) {\n        axis += $x.shape.length;\n    }\n    const inputs = { value: $x };\n    const attrs = { axis };\n    const forward = (backend) => backend.unstack($x, axis);\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Unpack, attrs);\n}\nexport const unstack = op({ unstack_ });\n//# sourceMappingURL=unstack.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\n/**\n * Creates a new variable with the provided initial value.\n * ```js\n * const x = tf.variable(tf.tensor([1, 2, 3]));\n * x.assign(tf.tensor([4, 5, 6]));\n *\n * x.print();\n * ```\n *\n * @param initialValue Initial value for the tensor.\n * @param trainable If true, optimizers are allowed to update it.\n * @param name Name of the variable. Defaults to a unique id.\n * @param dtype If set, initialValue will be converted to the given type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function variable(initialValue, trainable = true, name, dtype) {\n    return ENGINE.makeVariable(initialValue, trainable, name, dtype);\n}\n//# sourceMappingURL=variable.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the Where kernel shared between cpu and webgl */\nimport { buffer } from '../ops/buffer';\nexport function whereImpl(condShape, condVals) {\n    const indices = [];\n    for (let i = 0; i < condVals.length; i++) {\n        if (condVals[i]) {\n            indices.push(i);\n        }\n    }\n    const inBuffer = buffer(condShape, 'int32');\n    const out = buffer([indices.length, condShape.length], 'int32');\n    for (let i = 0; i < indices.length; i++) {\n        const loc = inBuffer.indexToLoc(indices[i]);\n        const offset = i * condShape.length;\n        out.values.set(loc, offset);\n    }\n    return out.toTensor();\n}\n//# sourceMappingURL=where_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { whereImpl } from '../backends/where_impl';\nimport { convertToTensor } from '../tensor_util_env';\n/**\n * Returns the coordinates of true elements of condition.\n *\n * The coordinates are returned in a 2-D tensor where the first dimension (rows)\n * represents the number of true elements, and the second dimension (columns)\n * represents the coordinates of the true elements. Keep in mind, the shape of\n * the output tensor can vary depending on how many true values there are in\n * input. Indices are output in row-major order. The resulting tensor has the\n * shape `[numTrueElems, condition.rank]`.\n *\n * This is analogous to calling the python `tf.where(cond)` without an x or y.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const result = await tf.whereAsync(cond);\n * result.print();\n * ```\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nasync function whereAsync_(condition) {\n    const $condition = convertToTensor(condition, 'condition', 'whereAsync', 'bool');\n    const vals = await $condition.data();\n    const res = whereImpl($condition.shape, vals);\n    if (condition !== $condition) {\n        $condition.dispose();\n    }\n    return res;\n}\nexport const whereAsync = whereAsync_;\n//# sourceMappingURL=where_async.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { gather } from './gather';\nimport { reshape } from './reshape';\nimport { squeeze } from './squeeze';\nimport { whereAsync } from './where_async';\n/**\n * Apply boolean mask to tensor.\n *\n * ```js\n * const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);\n * const mask = tf.tensor1d([1, 0, 1], 'bool');\n * const result = await tf.booleanMaskAsync(tensor, mask);\n * result.print();\n * ```\n *\n * @param tensor N-D tensor.\n * @param mask K-D boolean tensor, K <= N and K must be known statically.\n * @param axis A 0-D int Tensor representing the axis in tensor to mask from.\n *     By default, axis is 0 which will mask from the first dimension.\n *     Otherwise K + axis <= N.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nasync function booleanMaskAsync_(tensor, mask, axis) {\n    const $tensor = convertToTensor(tensor, 'tensor', 'boolMask');\n    const $mask = convertToTensor(mask, 'mask', 'boolMask', 'bool');\n    const axisFrom = axis == null ? 0 : axis;\n    const maskDim = $mask.rank;\n    const tensorShape = $tensor.shape;\n    util.assert(maskDim > 0, () => 'mask cannot be scalar');\n    util.assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);\n    let leadingSize = 1;\n    for (let i = axisFrom; i < axisFrom + maskDim; i++) {\n        leadingSize *= tensorShape[i];\n    }\n    const targetTensorShape = tensorShape.slice(0, axisFrom)\n        .concat([leadingSize], tensorShape.slice(axisFrom + maskDim));\n    const reshapedTensor = reshape($tensor, targetTensorShape);\n    const reshapedMask = reshape($mask, [-1]);\n    const positivePositions = await whereAsync(reshapedMask);\n    const indices = squeeze(positivePositions, [1]);\n    const res = gather(reshapedTensor, indices, axisFrom);\n    // Ensure no memory leak.\n    if (tensor !== $tensor) {\n        $tensor.dispose();\n    }\n    if (mask !== $mask) {\n        $mask.dispose();\n    }\n    indices.dispose();\n    reshapedTensor.dispose();\n    reshapedMask.dispose();\n    positivePositions.dispose();\n    return res;\n}\nexport const booleanMaskAsync = booleanMaskAsync_;\n//# sourceMappingURL=boolean_mask.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../globals';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertShapesMatch } from '../util';\nimport { equal } from './equal';\nimport { greater } from './greater';\nimport { greaterEqual } from './greater_equal';\nimport { less } from './less';\nimport { lessEqual } from './less_equal';\nimport { notEqual } from './not_equal';\nimport { op } from './operation';\n/**\n * @deprecated\n * Strict version of `tf.notEqual` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction notEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'notEqualStrict');\n    const $b = convertToTensor(b, 'b', 'notEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in notEqualStrict: ');\n    return notEqual($a, $b);\n}\n/**\n * @deprecated\n * Strict version of `tf.less` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction lessStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'lessStrict');\n    const $b = convertToTensor(b, 'b', 'lessStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in lessStrict: ');\n    return less($a, $b);\n}\nfunction equalStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'equalStrict');\n    const $b = convertToTensor(b, 'b', 'equalStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in equalStrict: ');\n    return equal($a, $b);\n}\nfunction lessEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'lessEqualStrict');\n    const $b = convertToTensor(b, 'b', 'lessEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in lessEqualStrict: ');\n    return lessEqual($a, $b);\n}\nfunction greaterStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'greaterStrict');\n    const $b = convertToTensor(b, 'b', 'greaterStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in greaterStrict: ');\n    return greater($a, $b);\n}\nfunction greaterEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'greaterEqualStrict');\n    const $b = convertToTensor(b, 'b', 'greaterEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in greaterEqualStrict: ');\n    return greaterEqual($a, $b);\n}\nexport const equalStrict = op({ equalStrict_ });\nexport const greaterEqualStrict = op({ greaterEqualStrict_ });\nexport const greaterStrict = op({ greaterStrict_ });\nexport const lessEqualStrict = op({ lessEqualStrict_ });\nexport const lessStrict = op({ lessStrict_ });\nexport const notEqualStrict = op({ notEqualStrict_ });\n//# sourceMappingURL=compare.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../globals';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { maximum } from './maximum';\nimport { minimum } from './minimum';\nimport { mod } from './mod';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { squaredDifference } from './squared_difference';\nimport { sub } from './sub';\n/**\n * @deprecated\n * Adds two `tf.Tensor`s element-wise, A + B.\n *\n * Inputs must be the same shape. For broadcasting support, use add() instead.\n *\n * @param a The first Tensor to add element-wise.\n * @param b The second Tensor to add element-wise.\n */\nfunction addStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'addStrict');\n    const $b = convertToTensor(b, 'b', 'addStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in addStrict: ');\n    return add($a, $b);\n}\n/**\n * @deprecated\n * Subtracts two `tf.Tensor`s element-wise, A - B. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.sub` instead.\n *\n * @param a The first Tensor to subtract element-wise.\n * @param b The second Tensor to subtract element-wise.\n */\nfunction subStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'subStrict');\n    const $b = convertToTensor(b, 'b', 'subStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in subStrict: ');\n    return sub($a, $b);\n}\n/**\n * @deprecated\n * Computes the power of one `tf.Tensor` to another. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.pow` instead.\n *\n * @param base The base tensor to pow element-wise.\n * @param exp The exponent tensor to pow element-wise.\n */\nfunction powStrict_(base, exp) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    util.assertShapesMatch(base.shape, exp.shape, 'Error in powStrict: ');\n    return pow(base, exp);\n}\n/**\n * @deprecated\n * Multiplies two `tf.Tensor`s element-wise, A * B.\n *\n * Inputs must be the same shape. For broadcasting support, use `tf.mul`.\n *\n * @param a The first tensor to multiply.\n * @param b The first tensor to multiply. Must have the same\n *    dtype as `a`.\n */\nfunction mulStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'mul');\n    const $b = convertToTensor(b, 'b', 'mul');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in multiplyStrict: ');\n    return mul($a, $b);\n}\n/**\n * @deprecated\n * Divides two `tf.Tensor`s element-wise, A / B. Inputs must\n * be the same shape.\n *\n * @param a The first tensor as the numerator for element-wise division.\n * @param b The second tensor as the denominator for element-wise division.\n */\nfunction divStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'div');\n    const $b = convertToTensor(b, 'b', 'div');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in divideStrict: ');\n    return div($a, $b);\n}\n/**\n * @deprecated\n * Returns the mod of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use mod().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction modStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'modStrict');\n    const $b = convertToTensor(b, 'b', 'modStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in modStrict: ');\n    return mod($a, $b);\n}\n/**\n * @deprecated\n * Returns the min of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use minimum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction minimumStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'minimumStrict');\n    const $b = convertToTensor(b, 'b', 'minimumStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in minimumStrict: ');\n    return minimum($a, $b);\n}\n/**\n * @deprecated\n * Returns the max of a and b (`a > b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use maximum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction maximumStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'maximumStrict');\n    const $b = convertToTensor(b, 'b', 'maximumStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in maximumStrict: ');\n    return maximum($a, $b);\n}\n/**\n * @deprecated\n * Returns (a - b) * (a - b) element-wise.\n *\n * Inputs must be the same shape. For broadcasting support, use\n * `tf.squaredDifference` instead.\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\nfunction squaredDifferenceStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'squaredDifferenceStrict');\n    const $b = convertToTensor(b, 'b', 'squaredDifferenceStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in squaredDifferenceStrict: ');\n    return squaredDifference($a, $b);\n}\nexport const addStrict = op({ addStrict_ });\nexport const divStrict = op({ divStrict_ });\nexport const maximumStrict = op({ maximumStrict_ });\nexport const minimumStrict = op({ minimumStrict_ });\nexport const modStrict = op({ modStrict_ });\nexport const mulStrict = op({ mulStrict_ });\nexport const powStrict = op({ powStrict_ });\nexport const squaredDifferenceStrict = op({ squaredDifferenceStrict_ });\nexport const subStrict = op({ subStrict_ });\n//# sourceMappingURL=binary_ops.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { abs } from './abs';\nimport * as axis_util from './axis_util';\nimport { max } from './max';\nimport { min } from './min';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { reshape } from './reshape';\nimport { scalar } from './scalar';\nimport { sqrt } from './sqrt';\nimport { square } from './square';\nimport { sum } from './sum';\n/**\n * Computes the norm of scalar, vectors, and matrices.\n * This function can compute several different vector norms (the 1-norm, the\n * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)\n * and matrix norms (Frobenius, 1-norm, and inf-norm).\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.norm().print();  // or tf.norm(x)\n * ```\n *\n * @param x The input array.\n * @param ord Optional. Order of the norm. Supported norm types are\n * following:\n *\n *  | ord        | norm for matrices         | norm for vectors\n *  |------------|---------------------------|---------------------\n *  |'euclidean' |Frobenius norm             |2-norm\n *  |'fro'       |Frobenius norm\t           |\n *  |Infinity    |max(sum(abs(x), axis=1))   |max(abs(x))\n *  |-Infinity   |min(sum(abs(x), axis=1))   |min(abs(x))\n *  |1           |max(sum(abs(x), axis=0))   |sum(abs(x))\n *  |2           |                           |sum(abs(x)^2)^1/2*\n *\n * @param axis Optional. If axis is null (the default), the input is\n * considered a vector and a single vector norm is computed over the entire\n * set of values in the Tensor, i.e. norm(x, ord) is equivalent\n * to norm(x.reshape([-1]), ord). If axis is a integer, the input\n * is considered a batch of vectors, and axis determines the axis in x\n * over which to compute vector norms. If axis is a 2-tuple of integer it is\n * considered a batch of matrices and axis determines the axes in NDArray\n * over which to compute a matrix norm.\n * @param keepDims Optional. If true, the norm have the same dimensionality\n * as the input.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction norm_(x, ord = 'euclidean', axis = null, keepDims = false) {\n    x = convertToTensor(x, 'x', 'norm');\n    const norm = normImpl(x, ord, axis);\n    let keepDimsShape = norm.shape;\n    if (keepDims) {\n        const axes = parseAxisParam(axis, x.shape);\n        keepDimsShape = axis_util.expandShapeToKeepDim(norm.shape, axes);\n    }\n    return reshape(norm, keepDimsShape);\n}\nfunction normImpl(x, p, axis = null) {\n    if (x.rank === 0) {\n        return abs(x);\n    }\n    // consider vector when no axis is specified\n    if (x.rank !== 1 && axis === null) {\n        return normImpl(reshape(x, [-1]), p, axis);\n    }\n    // vector\n    if (x.rank === 1 || typeof axis === 'number' ||\n        Array.isArray(axis) && axis.length === 1) {\n        if (p === 1) {\n            return sum(abs(x), axis);\n        }\n        if (p === Infinity) {\n            return max(abs(x), axis);\n        }\n        if (p === -Infinity) {\n            return min(abs(x), axis);\n        }\n        if (p === 'euclidean' || p === 2) {\n            // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2\n            return sqrt(sum(pow(abs(x), scalar(2, 'int32')), axis));\n        }\n        throw new Error(`Error in norm: invalid ord value: ${p}`);\n    }\n    // matrix (assumption axis[0] < axis[1])\n    if (Array.isArray(axis) && axis.length === 2) {\n        if (p === 1) {\n            return max(sum(abs(x), axis[0]), axis[1] - 1);\n        }\n        if (p === Infinity) {\n            return max(sum(abs(x), axis[1]), axis[0]);\n        }\n        if (p === -Infinity) {\n            return min(sum(abs(x), axis[1]), axis[0]);\n        }\n        if (p === 'fro' || p === 'euclidean') {\n            // norm(x) = sqrt(sum(pow(x, 2)))\n            return sqrt(sum(square(x), axis));\n        }\n        throw new Error(`Error in norm: invalid ord value: ${p}`);\n    }\n    throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\nexport const norm = op({ norm_ });\n//# sourceMappingURL=norm.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assertTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { scalar } from './scalar';\nimport { sub } from './sub';\n/**\n * Compute the moving average of a variable.\n *\n * Without zeroDebias, the moving average operation is defined by:\n *   `v += delta`\n * where\n *   `delta = (1 - decay) * (x - v)`\n *\n * With zeroDebias (default), the `delta` term is scaled to debias the\n * effect of the (assumed) zero-initialization of `v`.\n *   `delta /= (1 - decay ^ step)`\n *\n * For more details on the zero-debiasing algorithm, see:\n *   https://arxiv.org/abs/1412.6980\n *\n * Note that this function is completely stateless and does not keep track of\n * step count. The step count needs to be maintained by the caller and passed\n * in as `step`.\n *\n * @param v The current moving average value.\n * @param x New input value, must have the same shape and dtype as `v`.\n * @param decay The decay factor. Typical values are 0.95 and 0.99.\n * @param step Step count.\n * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).\n * @returns The new moving average value.\n *\n * @doc {heading: 'Operations', subheading: 'Moving Average'}\n */\nfunction movingAverage_(v, x, decay, step, zeroDebias = true) {\n    const $v = convertToTensor(v, 'v', 'movingAverage');\n    const $x = convertToTensor(x, 'x', 'movingAverage');\n    const $decay = convertToTensor(decay, 'decay', 'movingAverage');\n    assertTypesMatch($v, $x);\n    util.assert(util.arraysEqual($v.shape, $x.shape), () => 'Shape mismatch in v and x');\n    const one = scalar(1);\n    const oneMinusDecay = sub(one, $decay);\n    let update = mul(sub($x, $v), oneMinusDecay);\n    if (zeroDebias) {\n        util.assert(step != null, () => 'When using zeroDebias: true, step is required.');\n        const $step = convertToTensor(step, 'step', 'movingAverage');\n        update = div(update, sub(one, pow($decay, $step)));\n    }\n    return add($v, update);\n}\nexport const movingAverage = op({ movingAverage_ });\n//# sourceMappingURL=moving_average.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ScatterNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport * as scatter_nd_util from './scatter_nd_util';\n/**\n * Creates a new tensor by applying sparse updates to individual\n * values or slices within a zero tensor of the given shape tensor according to\n * indices. This operator is the inverse of the `tf.gatherND` operator which\n * extracts values or slices from a given tensor.\n *\n * ```js\n * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32');\n * const updates = tf.tensor1d([9, 10, 11, 12]);\n * const shape = [8];\n * tf.scatterND(indices, updates, shape).print() //[0, 11, 0, 10, 9, 0, 0, 12]\n * ```\n *\n * @param indices The tensor contains the indices into the output tensor.\n * @param updates The tensor contains the value for the indices.\n * @param shape: The shape of the output tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction scatterND_(indices, updates, shape) {\n    const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');\n    const $updates = convertToTensor(updates, 'updates', 'scatterND');\n    scatter_nd_util.validateInput($updates, $indices, shape);\n    const forward = (backend) => {\n        return backend.scatterND($indices, $updates, shape);\n    };\n    const inputs = { indices: $indices, updates: $updates };\n    const attrs = { shape };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ScatterNd, attrs);\n}\nexport const scatterND = op({ scatterND_ });\n//# sourceMappingURL=scatter_nd.js.map", "/**\n * Validate sparseToDense inputs.\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape number[]. Shape of the dense output tensor.\n * @param validateIndices boolean. indice validation is not supported, error\n * will be thrown if it is set.\n */\nexport function validateInput(sparseIndices, sparseValues, outputShape, defaultValues) {\n    if (sparseIndices.dtype !== 'int32') {\n        throw new Error('tf.sparseToDense() expects the indices to be int32 type,' +\n            ` but the dtype was ${sparseIndices.dtype}.`);\n    }\n    if (sparseIndices.rank > 2) {\n        throw new Error('sparseIndices should be a scalar, vector, or matrix,' +\n            ` but got shape ${sparseIndices.shape}.`);\n    }\n    const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n    const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n    if (outputShape.length !== numDims) {\n        throw new Error('outputShape has incorrect number of elements:,' +\n            ` ${outputShape.length}, should be: ${numDims}.`);\n    }\n    const numValues = sparseValues.size;\n    if (!(sparseValues.rank === 0 ||\n        sparseValues.rank === 1 && numValues === numElems)) {\n        throw new Error('sparseValues has incorrect shape ' +\n            `${sparseValues.shape}, should be [] or [${numElems}]`);\n    }\n    if (sparseValues.dtype !== defaultValues.dtype) {\n        throw new Error('sparseValues.dtype must match defaultValues.dtype');\n    }\n}\n//# sourceMappingURL=sparse_to_dense_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SparseToDense } from '../kernel_names';\nimport * as sparse_to_dense from '../ops/sparse_to_dense_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Converts a sparse representation into a dense tensor.\n *\n * Builds an array dense with shape outputShape such that:\n *\n * // If sparseIndices is scalar\n * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)\n *\n * // If sparseIndices is a vector, then for each i\n * dense[sparseIndices[i]] = sparseValues[i]\n *\n * // If sparseIndices is an n by d matrix, then for each i in [0, n)\n * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]\n * All other values in dense are set to defaultValue. If sparseValues is a\n * scalar, all sparse indices are set to this single value.\n *\n * If indices are repeated the final value is summed over all values for those\n * indices.\n *\n * ```js\n * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');\n * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');\n * const shape = [8];\n * tf.sparseToDense(indices, values, shape).print();\n * ```\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape Shape of the dense output tensor. the type is inferred.\n * @param defaultValue Scalar. Value to set for indices not specified in\n * sparseIndices. Defaults to zero.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {\n    const $sparseIndices = convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');\n    const $sparseValues = convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');\n    const $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);\n    sparse_to_dense.validateInput($sparseIndices, $sparseValues, outputShape, $defaultValue);\n    const inputs = {\n        sparseIndices: $sparseIndices,\n        sparseValues: $sparseValues,\n        defaultValue: $defaultValue\n    };\n    const attrs = { outputShape };\n    return ENGINE.runKernelFunc(backend => backend.sparseToDense($sparseIndices, $sparseValues, outputShape, $defaultValue), inputs, null /* grad */, SparseToDense, attrs);\n}\nexport const sparseToDense = op({ sparseToDense_ });\n//# sourceMappingURL=sparse_to_dense.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Gather slices from input tensor into a Tensor with shape specified by\n * `indices`.\n *\n * `indices` is an K-dimensional integer tensor, best thought of as a\n * (K-1)-dimensional tensor of indices into input, where each element defines a\n * slice of input:\n * output[\\\\(i_0, ..., i_{K-2}\\\\)] = input[indices[\\\\(i_0, ..., i_{K-2}\\\\)]]\n *\n * Whereas in `tf.gather`, `indices` defines slices into the first dimension of\n * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions\n * of input, where N = indices.shape[-1].\n *\n * The last dimension of indices can be at most the rank of input:\n * indices.shape[-1] <= input.rank\n *\n * The last dimension of `indices` corresponds to elements\n * (if indices.shape[-1] == input.rank) or slices\n * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of\n * input.\n * The output tensor has shape\n * indices.shape[:-1] + input.shape[indices.shape[-1]:]\n *\n * Note that on CPU, if an out of bound index is found, an error is returned. On\n * GPU, if an out of bound index is found, a 0 is stored in the corresponding\n * output value.\n *\n * ```js\n * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');\n * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);\n * tf.gatherND(input, indices).print() // [10, 11]\n * ```\n *\n * @param x The tensor from which to gather values.\n * @param indices Index tensor, must be of type int32.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction gatherND_(x, indices) {\n    const $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');\n    const $x = convertToTensor(x, 'x', 'gatherND');\n    const forward = (backend) => {\n        return backend.gatherND($x, $indices);\n    };\n    const inputs = { params: $x, indices: $indices };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, GatherNd);\n}\nexport const gatherND = op({ gatherND_ });\n//# sourceMappingURL=gather_nd.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n * Normalize noise shape based on provided tensor and noise shape.\n *\n * @param x Tensor.\n * @param noiseShape The shape for the randomly generated keep/drop flags, as\n *   an array of numbers. Optional.\n * @returns Normalized noise shape.\n */\nexport function getNoiseShape(x, noiseShape) {\n    if (noiseShape == null) {\n        return x.shape.slice();\n    }\n    if (util.arraysEqual(x.shape, noiseShape)) {\n        return noiseShape;\n    }\n    if (x.shape.length === noiseShape.length) {\n        const newDimension = [];\n        for (let i = 0; i < x.shape.length; i++) {\n            if (noiseShape[i] == null && x.shape[i] != null) {\n                newDimension.push(x.shape[i]);\n            }\n            else {\n                newDimension.push(noiseShape[i]);\n            }\n        }\n        return newDimension;\n    }\n    return noiseShape;\n}\n//# sourceMappingURL=dropout_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { getNoiseShape } from './dropout_util';\nimport { floor } from './floor';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { randomUniform } from './random_uniform';\n/**\n * Computes dropout.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 2, 1]);\n * const rate = 0.75;\n * const output = tf.dropout(x, rate);\n * output.print();\n * ```\n *\n * @param x A floating point Tensor or TensorLike.\n * @param rate A float in the range [0, 1). The probability that each element\n *   of x is discarded.\n * @param noiseShape An array of numbers of type int32, representing the\n * shape for randomly generated keep/drop flags. If the noiseShape has null\n * value, it will be automatically replaced with the x's relative dimension\n * size. Optional.\n * @param seed Used to create random seeds. Optional.\n * @returns A Tensor of the same shape of x.\n *\n * @doc {heading: 'Operations', subheading: 'Dropout'}\n */\nfunction dropout_(x, rate, noiseShape, seed) {\n    const $x = convertToTensor(x, 'x', 'dropout');\n    util.assert($x.dtype === 'float32', () => `x has to be a floating point tensor since it's going to be ` +\n        `scaled, but got a ${$x.dtype} tensor instead.`);\n    util.assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);\n    if (rate === 0) {\n        return x instanceof Tensor ? $x.clone() : $x;\n    }\n    const $noiseShape = getNoiseShape($x, noiseShape);\n    const keepProb = 1 - rate;\n    const multiplier = div(floor(add(randomUniform($noiseShape, 0, 1, 'float32', seed), keepProb)), keepProb);\n    return mul($x, multiplier);\n}\nexport const dropout = op({ dropout_ });\n//# sourceMappingURL=dropout.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensor1d } from './tensor1d';\nexport function enclosingPowerOfTwo(value) {\n    // Return 2**N for integer N such that 2**N >= value.\n    return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2.0))));\n}\nexport function cosineWindow(windowLength, a, b) {\n    const even = 1 - windowLength % 2;\n    const newValues = new Float32Array(windowLength);\n    for (let i = 0; i < windowLength; ++i) {\n        const cosArg = (2.0 * Math.PI * i) / (windowLength + even - 1);\n        newValues[i] = a - b * Math.cos(cosArg);\n    }\n    return tensor1d(newValues, 'float32');\n}\n//# sourceMappingURL=signal_ops_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, assertShapesMatch, getTypedArrayFromDType } from '../util';\nimport { tensor } from './tensor';\n/**\n * Returns whether the targets are in the top K predictions.\n *\n * ```js\n * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);\n * const targets = tf.tensor1d([2, 0]);\n * const precision = await tf.inTopKAsync(predictions, targets);\n * precision.print();\n * ```\n * @param predictions 2-D or higher `tf.Tensor` with last dimension being\n *     at least `k`.\n * @param targets 1-D or higher `tf.Tensor`.\n * @param k Optional Number of top elements to look at for computing precision,\n *     default to 1.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nasync function inTopKAsync_(predictions, targets, k = 1) {\n    const $predictions = convertToTensor(predictions, 'predictions', 'inTopK');\n    const $targets = convertToTensor(targets, 'targets', 'inTopK');\n    assert($predictions.rank > 1, () => 'inTopK() expects the predictions to be of rank 2 or higher, ' +\n        `but got ${$predictions.rank}`);\n    assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than ` +\n        `targets rank, but got predictions rank ` +\n        `${$predictions.rank} and targets rank ${$targets.rank}`);\n    assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, ` +\n        'except the last dimension.');\n    const lastDim = $predictions.shape[$predictions.shape.length - 1];\n    assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last ` +\n        `dimension (${lastDim}), but got ${k}`);\n    const predictionsVals = await $predictions.data();\n    const targetsVals = await $targets.data();\n    // Reshape predictionsVals into a 2d tensor [batch, lastDim]\n    // and look up topK along lastDim.\n    const [batch, size] = [predictionsVals.length / lastDim, lastDim];\n    const precision = getTypedArrayFromDType('bool', batch);\n    for (let b = 0; b < batch; b++) {\n        const offset = b * size;\n        const vals = predictionsVals.subarray(offset, offset + size);\n        const valAndInd = [];\n        for (let i = 0; i < vals.length; i++) {\n            valAndInd.push({ value: vals[i], index: i });\n        }\n        valAndInd.sort((a, b) => b.value - a.value);\n        precision[b] = 0;\n        for (let i = 0; i < k; i++) {\n            if (valAndInd[i].index === targetsVals[b]) {\n                precision[b] = 1;\n                break;\n            }\n        }\n    }\n    if (predictions !== $predictions) {\n        $predictions.dispose();\n    }\n    if (targets !== $targets) {\n        $targets.dispose();\n    }\n    // Output precision has the same shape as targets.\n    return tensor(precision, $targets.shape, 'bool');\n}\nexport const inTopKAsync = inTopKAsync_;\n//# sourceMappingURL=in_top_k.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropFilter } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the filter of a 2D convolution.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.\n * @param dy The dy image, of rank 4 or rank 3, of shape\n *     [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.\n * @param filterShape The shape of the filter, length 4,\n *     [filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction conv2DBackpropFilter_(x, dy, filterShape, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n    let x4D = x;\n    if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    let dy4D = dy;\n    if (dy4D.rank === 3) {\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ` +\n        `${x4D.shape}.`);\n    util.assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ` +\n        `${dy4D.shape}.`);\n    util.assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ` +\n        `${filterShape}.`);\n    const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n    const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n    util.assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must ` +\n        `match input depth in filter (${filterShape[2]}.`);\n    util.assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must ` +\n        `match output depth for filter (${filterShape[3]}).`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2dDerFilter: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const dilations = 1;\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, filterShape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        return backend.conv2dDerFilter(x4D, dy4D, convInfo);\n    };\n    const inputs = { x: x4D, dy: dy4D };\n    const attrs = { strides, pad, dataFormat, dimRoundingMode };\n    return ENGINE.runKernelFunc(forward, inputs, null, Conv2DBackpropFilter, attrs);\n}\nexport const conv2DBackpropFilter = op({ conv2DBackpropFilter_ });\n//# sourceMappingURL=conv2d_backprop_filter.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as broadcast_util from './broadcast_util';\nimport { elu } from './elu';\nimport { mul } from './mul';\nimport { prelu } from './prelu';\nimport { relu } from './relu';\nimport { relu6 } from './relu6';\nimport { reshape } from './reshape';\nimport { step } from './step';\nimport { sum } from './sum';\n// Returns gradient for fused activation.\nexport function getFusedDyActivation(dy, y, activation) {\n    if (activation == null || activation === 'linear') {\n        return dy;\n    }\n    if (activation === 'relu') {\n        return mul(dy, step(y));\n    }\n    throw new Error(`Cannot compute gradient for fused activation ${activation}.`);\n}\n// Returns gradient for fused bias.\nexport function getFusedBiasGradient(bias, dyActivation) {\n    let res = dyActivation;\n    const reduceAxes = broadcast_util.getReductionAxes(bias.shape, dyActivation.shape);\n    if (reduceAxes.length > 0) {\n        res = sum(res, reduceAxes);\n    }\n    return reshape(res, bias.shape);\n}\nexport function applyActivation(x, activation, preluActivationWeights) {\n    if (activation === 'linear') {\n        return x;\n    }\n    else if (activation === 'relu') {\n        return relu(x);\n    }\n    else if (activation === 'elu') {\n        return elu(x);\n    }\n    else if (activation === 'relu6') {\n        return relu6(x);\n    }\n    else if (activation === 'prelu') {\n        return prelu(x, preluActivationWeights);\n    }\n    throw new Error(`Unknown fused activation ${activation}.`);\n}\n// Whether we should call fused ops.\nexport const shouldFuse = (gradientDepth, activation) => {\n    const gradientMode = gradientDepth > 0;\n    return !gradientMode || activation === 'linear';\n};\n//# sourceMappingURL=fused_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { conv2d as unfusedConv2d } from '../conv2d';\nimport { conv2DBackpropFilter } from '../conv2d_backprop_filter';\nimport { conv2DBackpropInput } from '../conv2d_backprop_input';\nimport * as conv_util from '../conv_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes a 2D convolution over the input x, optionally fused with adding a\n * bias and applying an activation.\n *\n * ```js\n * const inputDepth = 2;\n * const inShape = [2, 2, 2, inputDepth];\n * const outputDepth = 2;\n * const fSize = 1;\n * const pad = 0;\n * const strides = 1;\n *\n * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n * 16], inShape);\n * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,\n * outputDepth]);\n *\n * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',\n * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();\n * ```\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid` output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`) to be\n *     applied\n *      after biasAdd.\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights }) {\n    activation = activation || 'linear';\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    const $x = convertToTensor(x, 'x', 'conv2d');\n    const $filter = convertToTensor(filter, 'filter', 'conv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ` +\n        `${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in fused conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match ` +\n        `input depth for filter ${$filter.shape[2]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    util.assert(dataFormat === 'NHWC', () => `Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);\n    const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n        [$bias] = makeTypesMatch($bias, $x);\n        broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused conv2d');\n    }\n    const grad = (dy, saved) => {\n        const [$filter, x4D, y, $bias] = saved;\n        const dyActivation = getFusedDyActivation(dy, y, activation);\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused conv2D: ' +\n            `dilation rates greater than 1 ` +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n        const xDer = conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad);\n        const filterDer = conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad);\n        const der = [xDer, filterDer];\n        if ($bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            der.push(biasDer);\n        }\n        return der;\n    };\n    const forward = (backend) => {\n        const res = backend.fusedConv2d({\n            input: x4D,\n            filter: $filter,\n            convInfo,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return res;\n    };\n    const inputs = {\n        x: x4D,\n        filter: $filter,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((x4D, filter, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedConv2D, attrs);\n            save([filter, x4D, res]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOp(x4D, $filter);\n    }\n    else {\n        const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedConv2D, attrs);\n            save([filter, x4D, res, bias]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOpWithBias(x4D, $filter, $bias);\n    }\n}\nexport const conv2d = op({ fusedConv2d_ });\n//# sourceMappingURL=conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropFilter } from '../kernel_names';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, convInfo) {\n    let x4D = x;\n    if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    let dy4D = dy;\n    if (dy4D.rank === 3) {\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    const forward = backend => backend.depthwiseConv2DDerFilter(x4D, dy4D, convInfo);\n    const inputs = { x: x4D, dy: dy4D };\n    return ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropFilter);\n}\nexport const depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ });\n//# sourceMappingURL=depthwise_conv2d_native_backprop_filter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropInput } from '../kernel_names';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, convInfo) {\n    let dy4D = dy;\n    let reshapedTo4D = false;\n    if (dy.rank === 3) {\n        reshapedTo4D = true;\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    const forward = backend => backend.depthwiseConv2DDerInput(dy4D, filter, convInfo);\n    const inputs = { dy: dy4D };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropInput);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ });\n//# sourceMappingURL=depthwise_conv2d_native_backprop_input.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedDepthwiseConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport * as conv_util from '../conv_util';\nimport { depthwiseConv2d as unfusedDepthwiseConv2d } from '../depthwise_conv2d';\nimport { depthwiseConv2dNativeBackpropFilter } from '../depthwise_conv2d_native_backprop_filter';\nimport { depthwiseConv2dNativeBackpropInput } from '../depthwise_conv2d_native_backprop_input';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes depthwise 2D convolution, optionally fused with adding a\n * bias and applying an activation.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`).\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedDepthwiseConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights }) {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedDepthwiseConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n    const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, ` +\n        `but got rank ${$filter.rank}.`);\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels ` +\n        `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n        `filter ${$filter.shape[2]}.`);\n    if (dilations == null) {\n        dilations = [1, 1];\n    }\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in fused depthwiseConv2d: Either strides or dilations must ' +\n        `be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in fused depthwiseConv2d: pad must be an integer when ` +\n            `using dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n        [$bias] = makeTypesMatch($bias, $x);\n        broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');\n    }\n    const grad = (dy, saved) => {\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused depthwiseConv2d: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${dilations}'`);\n        const [$filter, x4D, y, bias] = saved;\n        const dyActivation = getFusedDyActivation(dy, y, activation);\n        const xDer = depthwiseConv2dNativeBackpropInput(x4D.shape, dyActivation, $filter, convInfo);\n        const filterDer = depthwiseConv2dNativeBackpropFilter(x4D, dyActivation, $filter.shape, convInfo);\n        if (bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            return [xDer, filterDer, biasDer];\n        }\n        return [xDer, filterDer];\n    };\n    const forward = (backend) => {\n        const res = backend.fusedDepthwiseConv2D({\n            input: x4D,\n            filter: $filter,\n            convInfo,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return res;\n    };\n    const inputs = {\n        x: x4D,\n        filter: $filter,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((x4D, filter, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedDepthwiseConv2D, attrs);\n            save([filter, x4D, res]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOp(x4D, $filter);\n    }\n    else {\n        const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedDepthwiseConv2D, attrs);\n            save([filter, x4D, res, bias]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOpWithBias(x4D, $filter, $bias);\n    }\n}\nexport const depthwiseConv2d = op({ fusedDepthwiseConv2d_ });\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { _FusedMatMul } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { matMul as unfusedMatMul } from '../mat_mul';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes the dot product of two matrices with optional activation and bias.\n *\n * ```js\n * const a = tf.tensor2d([-1, -2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const bias = tf.tensor2d([1, 2], [1, 2]);\n *\n * tf.fused.matMul({a, b, bias, activation: 'relu'}).print();\n * ```\n *\n * @param obj An object with the following properties:\n * - `a` First matrix in dot product operation.\n * - `b` Second matrix in dot product operation.\n * - `transposeA` If true, `a` is transposed before multiplication.\n * - `transposeB` If true, `b` is transposed before multiplication.\n * - `bias` Matrix to be added to the result.\n * - `activation` Name of activation kernel (defaults to `linear`).\n * - `preluActivationWeights` Tensor of prelu weights.\n */\nfunction fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation = 'linear', preluActivationWeights }) {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedMatMul(a, b, transposeA, transposeB);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    let $a = convertToTensor(a, 'a', 'fused matMul');\n    let $b = convertToTensor(b, 'b', 'fused matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n    const outerDimsA = $a.shape.slice(0, -2);\n    const outerDimsB = $b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n    util.assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in fused matMul: inputs must have the same rank of at least ` +\n        `2, got ranks ${$a.rank} and ${$b.rank}.`);\n    util.assert(util.arraysEqual(outerDimsA, outerDimsB), () => `Error in fused matMul: outer dimensions (${outerDimsA}) and (` +\n        `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} must match.`);\n    util.assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (` +\n        `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} and transposeA=${transposeA}` +\n        ` and transposeB=${transposeB} must match.`);\n    const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n    const a3D = transposeA ?\n        reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n        reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n    const b3D = transposeB ?\n        reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n        reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused matMul');\n        [$bias] = makeTypesMatch($bias, $a);\n        broadcast_util.assertAndGetBroadcastShape(outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused matMul');\n    }\n    const grad = (dy, saved) => {\n        const [a3D, b3D, y, $bias] = saved;\n        // we reshape dy because the result of the forward is not\n        // necessarily going to be a 3d tensor due to a reshape done at the end of\n        // the customOp.\n        const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation);\n        let aDer;\n        let bDer;\n        if (!transposeA && !transposeB) {\n            aDer = unfusedMatMul(dyActivation, b3D, false, true);\n            bDer = unfusedMatMul(a3D, dyActivation, true, false);\n        }\n        else if (!transposeA && transposeB) {\n            aDer = unfusedMatMul(dyActivation, b3D, false, false);\n            bDer = unfusedMatMul(dyActivation, a3D, true, false);\n        }\n        else if (transposeA && !transposeB) {\n            aDer = unfusedMatMul(b3D, dyActivation, false, true);\n            bDer = unfusedMatMul(a3D, dyActivation, false, false);\n        }\n        else {\n            aDer = unfusedMatMul(b3D, dyActivation, true, true);\n            bDer = unfusedMatMul(dyActivation, a3D, true, true);\n        }\n        if (bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            return [aDer, bDer, biasDer];\n        }\n        else {\n            return [aDer, bDer];\n        }\n    };\n    const forward = (backend) => {\n        const y = backend.fusedBatchMatMul({\n            a: a3D,\n            b: b3D,\n            transposeA,\n            transposeB,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return y;\n    };\n    const inputs = {\n        a: a3D,\n        b: b3D,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { transposeA, transposeB, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((a3D, b3D, save) => {\n            const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, _FusedMatMul, attrs);\n            save([a3D, b3D, res]);\n            return { value: reshape(res, outShape), gradFunc: grad };\n        });\n        return customOp(a3D, b3D);\n    }\n    else {\n        const customOpWithBias = customGrad((a3D, b3D, $bias, save) => {\n            const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, _FusedMatMul, attrs);\n            save([a3D, b3D, res, $bias]);\n            return { value: reshape(res, outShape), gradFunc: grad };\n        });\n        return customOpWithBias(a3D, b3D, $bias);\n    }\n}\nexport const matMul = op({ fusedMatMul_ });\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2d } from './fused/conv2d';\nimport { depthwiseConv2d } from './fused/depthwise_conv2d';\nimport { matMul } from './fused/mat_mul';\nexport { conv2d, depthwiseConv2d, matMul };\n//# sourceMappingURL=fused_ops.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a hamming window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hammingWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hammingWindow_(windowLength) {\n    return cosineWindow(windowLength, 0.54, 0.46);\n}\nexport const hammingWindow = op({ hammingWindow_ });\n//# sourceMappingURL=hamming_window.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a Hann window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hannWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hannWindow_(windowLength) {\n    return cosineWindow(windowLength, 0.5, 0.5);\n}\nexport const hannWindow = op({ hannWindow_ });\n//# sourceMappingURL=hann_window.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../concat';\nimport { fill } from '../fill';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { tensor2d } from '../tensor2d';\n/**\n * Expands input into frames of frameLength.\n * Slides a window size with frameStep.\n *\n * ```js\n * tf.signal.frame([1, 2, 3], 2, 1).print();\n * ```\n * @param signal The input tensor to be expanded\n * @param frameLength Length of each frame\n * @param frameStep The frame hop size in samples.\n * @param padEnd Whether to pad the end of signal with padValue.\n * @param padValue An number to use where the input signal does\n *     not exist when padEnd is True.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction frame_(signal, frameLength, frameStep, padEnd = false, padValue = 0) {\n    let start = 0;\n    const output = [];\n    while (start + frameLength <= signal.size) {\n        output.push(slice(signal, start, frameLength));\n        start += frameStep;\n    }\n    if (padEnd) {\n        while (start < signal.size) {\n            const padLen = (start + frameLength) - signal.size;\n            const pad = concat([\n                slice(signal, start, frameLength - padLen), fill([padLen], padValue)\n            ]);\n            output.push(pad);\n            start += frameStep;\n        }\n    }\n    if (output.length === 0) {\n        return tensor2d([], [0, frameLength]);\n    }\n    return reshape(concat(output), [output.length, frameLength]);\n}\nexport const frame = op({ frame_ });\n//# sourceMappingURL=frame.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../concat';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { enclosingPowerOfTwo } from '../signal_ops_util';\nimport { slice } from '../slice';\nimport { rfft } from '../spectral/rfft';\nimport { frame } from './frame';\nimport { hannWindow } from './hann_window';\n/**\n * Computes the Short-time Fourier Transform of signals\n * See: https://en.wikipedia.org/wiki/Short-time_Fourier_transform\n *\n * ```js\n * const input = tf.tensor1d([1, 1, 1, 1, 1])\n * tf.signal.stft(input, 3, 1).print();\n * ```\n * @param signal 1-dimensional real value tensor.\n * @param frameLength The window length of samples.\n * @param frameStep The number of samples to step.\n * @param fftLength The size of the FFT to apply.\n * @param windowFn A callable that takes a window length and returns 1-d tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction stft_(signal, frameLength, frameStep, fftLength, windowFn = hannWindow) {\n    if (fftLength == null) {\n        fftLength = enclosingPowerOfTwo(frameLength);\n    }\n    const framedSignal = frame(signal, frameLength, frameStep);\n    const windowedSignal = mul(framedSignal, windowFn(frameLength));\n    const output = [];\n    for (let i = 0; i < framedSignal.shape[0]; i++) {\n        output.push(rfft(slice(windowedSignal, [i, 0], [1, frameLength]), fftLength));\n    }\n    return concat(output);\n}\nexport const stft = op({ stft_ });\n//# sourceMappingURL=stft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { CropAndResize } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Extracts crops from the input image tensor and resizes them using bilinear\n * sampling or nearest neighbor sampling (possibly with aspect ratio change)\n * to a common output size specified by crop_size.\n *\n * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,\n *     where imageHeight and imageWidth must be positive, specifying the\n *     batch of images from which to take crops\n * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized\n *     coordinates of the box in the boxInd[i]'th image in the batch\n * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range\n *     `[0, batch)` that specifies the image that the `i`-th box refers to.\n * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`\n *     specifying the size to which all crops are resized to.\n * @param method Optional string from `'bilinear' | 'nearest'`,\n *     defaults to bilinear, which specifies the sampling method for resizing\n * @param extrapolationValue A threshold for deciding when to remove boxes based\n *     on score. Defaults to 0.\n * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction cropAndResize_(image, boxes, boxInd, cropSize, method, extrapolationValue) {\n    const $image = convertToTensor(image, 'image', 'cropAndResize');\n    const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');\n    const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');\n    method = method || 'bilinear';\n    extrapolationValue = extrapolationValue || 0;\n    const numBoxes = $boxes.shape[0];\n    util.assert($image.rank === 4, () => 'Error in cropAndResize: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    util.assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +\n        `but had shape ${$boxes.shape}.`);\n    util.assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +\n        `but had shape ${$boxes.shape}.`);\n    util.assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got ` +\n        `length ${cropSize.length}.`);\n    util.assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);\n    util.assert(method === 'bilinear' || method === 'nearest', () => `method must be bilinear or nearest, but was ${method}`);\n    const forward = (backend) => backend.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);\n    const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };\n    const attrs = { method, extrapolationValue, cropSize };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, CropAndResize, attrs);\n    return res;\n}\nexport const cropAndResize = op({ cropAndResize_ });\n//# sourceMappingURL=crop_and_resize.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FlipLeftRight } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Flips the image left to right. Currently available in the CPU, WebGL, and\n * WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction flipLeftRight_(image) {\n    const $image = convertToTensor(image, 'image', 'flipLeftRight', 'float32');\n    util.assert($image.rank === 4, () => 'Error in flipLeftRight: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    const inputs = { image: $image };\n    const res = ENGINE.runKernel(FlipLeftRight, inputs, {});\n    return res;\n}\nexport const flipLeftRight = op({ flipLeftRight_ });\n//# sourceMappingURL=flip_left_right.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { RotateWithOffset } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Rotates the input image tensor counter-clockwise with an optional offset\n * center of rotation. Currently available in the CPU, WebGL, and WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n * @param radians The amount of rotation.\n * @param fillValue The value to fill in the empty space leftover\n *     after rotation. Can be either a single grayscale value (0-255), or an\n *     array of three numbers `[red, green, blue]` specifying the red, green,\n *     and blue channels. Defaults to `0` (black).\n * @param center The center of rotation. Can be either a single value (0-1), or\n *     an array of two numbers `[centerX, centerY]`. Defaults to `0.5` (rotates\n *     the image around its center).\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction rotateWithOffset_(image, radians, fillValue = 0, center = 0.5) {\n    const $image = convertToTensor(image, 'image', 'rotateWithOffset', 'float32');\n    util.assert($image.rank === 4, () => 'Error in rotateWithOffset: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    const inputs = { image: $image };\n    const attrs = { radians, fillValue, center };\n    const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);\n    return res;\n}\nexport const rotateWithOffset = op({ rotateWithOffset_ });\n//# sourceMappingURL=rotate_with_offset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nfunction nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n    if (iouThreshold == null) {\n        iouThreshold = 0.5;\n    }\n    if (scoreThreshold == null) {\n        scoreThreshold = Number.NEGATIVE_INFINITY;\n    }\n    if (softNmsSigma == null) {\n        softNmsSigma = 0.0;\n    }\n    const numBoxes = boxes.shape[0];\n    maxOutputSize = Math.min(maxOutputSize, numBoxes);\n    util.assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n    util.assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n    util.assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n    util.assert(scores.rank === 1, () => 'scores must be a 1D tensor');\n    util.assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, ` +\n        `but was ${scores.shape[0]}`);\n    util.assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);\n    return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n}\nexport { nonMaxSuppSanityCheck };\n//# sourceMappingURL=nonmax_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV3 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\nfunction nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n    maxOutputSize = inputs.maxOutputSize;\n    iouThreshold = inputs.iouThreshold;\n    scoreThreshold = inputs.scoreThreshold;\n    const attrs = { maxOutputSize, iouThreshold, scoreThreshold };\n    return ENGINE.runKernelFunc(b => b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold), { boxes: $boxes, scores: $scores }, null /* grad */, NonMaxSuppressionV3, attrs);\n}\nexport const nonMaxSuppression = op({ nonMaxSuppression_ });\n//# sourceMappingURL=non_max_suppression.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inserts a value into a sorted array. This method allows duplicate, meaning it\n * allows inserting duplicate value, in which case, the element will be inserted\n * at the lowest index of the value.\n * @param arr The array to modify.\n * @param element The element to insert.\n * @param comparator Optional. If no comparator is specified, elements are\n * compared using array_util.defaultComparator, which is suitable for Strings\n * and Numbers in ascending arrays. If the array contains multiple instances of\n * the target value, the left-most instance will be returned. To provide a\n * comparator, it should take 2 arguments to compare and return a negative,\n * zero, or a positive number.\n */\nexport function binaryInsert(arr, element, comparator) {\n    const index = binarySearch(arr, element, comparator);\n    const insertionPoint = index < 0 ? -(index + 1) : index;\n    arr.splice(insertionPoint, 0, element);\n}\n/**\n * Searches the array for the target using binary search, returns the index\n * of the found element, or position to insert if element not found. If no\n * comparator is specified, elements are compared using array_\n * util.defaultComparator, which is suitable for Strings and Numbers in\n * ascending arrays. If the array contains multiple instances of the target\n * value, the left-most instance will be returned.\n * @param arr The array to be searched in.\n * @param target The target to be searched for.\n * @param comparator Should take 2 arguments to compare and return a negative,\n *    zero, or a positive number.\n * @return Lowest index of the target value if found, otherwise the insertion\n *    point where the target should be inserted, in the form of\n *    (-insertionPoint - 1).\n */\nexport function binarySearch(arr, target, comparator) {\n    return binarySearch_(arr, target, comparator || defaultComparator);\n}\n/**\n * Compares its two arguments for order.\n * @param a The first element to be compared.\n * @param b The second element to be compared.\n * @return A negative number, zero, or a positive number as the first\n *     argument is less than, equal to, or greater than the second.\n */\nfunction defaultComparator(a, b) {\n    return a > b ? 1 : a < b ? -1 : 0;\n}\nfunction binarySearch_(arr, target, comparator) {\n    let left = 0;\n    let right = arr.length;\n    let middle = 0;\n    let found = false;\n    while (left < right) {\n        middle = left + ((right - left) >>> 1);\n        const compareResult = comparator(target, arr[middle]);\n        if (compareResult > 0) {\n            left = middle + 1;\n        }\n        else {\n            right = middle;\n            // If compareResult is 0, the value is found. We record it is found,\n            // and then keep looking because there may be duplicate.\n            found = !compareResult;\n        }\n    }\n    return found ? left : -left - 1;\n}\n//# sourceMappingURL=array_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Implementation of the NonMaxSuppression kernel shared between webgl and cpu.\n */\nimport { scalar } from '../ops/scalar';\nimport { tensor1d } from '../ops/tensor1d';\nimport { binaryInsert } from './array_util';\nexport function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */)\n        .selectedIndices;\n}\nexport function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */, false /* returnScoresTensor */, padToMaxOutputSize /* padToMaxOutputSize */, true\n    /* returnValidOutputs */ );\n}\nexport function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true /* returnScoresTensor */);\n}\nfunction nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {\n    // The list is sorted in ascending order, so that we can always pop the\n    // candidate with the largest score in O(1) time.\n    const candidates = [];\n    for (let i = 0; i < scores.length; i++) {\n        if (scores[i] > scoreThreshold) {\n            candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 });\n        }\n    }\n    candidates.sort(ascendingComparator);\n    // If softNmsSigma is 0, the outcome of this algorithm is exactly same as\n    // before.\n    const scale = softNmsSigma > 0 ? (-0.5 / softNmsSigma) : 0.0;\n    const selectedIndices = [];\n    const selectedScores = [];\n    while (selectedIndices.length < maxOutputSize && candidates.length > 0) {\n        const candidate = candidates.pop();\n        const { score: originalScore, boxIndex, suppressBeginIndex } = candidate;\n        if (originalScore < scoreThreshold) {\n            break;\n        }\n        // Overlapping boxes are likely to have similar scores, therefore we\n        // iterate through the previously selected boxes backwards in order to\n        // see if candidate's score should be suppressed. We use\n        // suppressBeginIndex to track and ensure a candidate can be suppressed\n        // by a selected box no more than once. Also, if the overlap exceeds\n        // iouThreshold, we simply ignore the candidate.\n        let ignoreCandidate = false;\n        for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {\n            const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);\n            if (iou >= iouThreshold) {\n                ignoreCandidate = true;\n                break;\n            }\n            candidate.score =\n                candidate.score * suppressWeight(iouThreshold, scale, iou);\n            if (candidate.score <= scoreThreshold) {\n                break;\n            }\n        }\n        // At this point, if `candidate.score` has not dropped below\n        // `scoreThreshold`, then we know that we went through all of the\n        // previous selections and can safely update `suppressBeginIndex` to the\n        // end of the selected array. Then we can re-insert the candidate with\n        // the updated score and suppressBeginIndex back in the candidate list.\n        // If on the other hand, `candidate.score` has dropped below the score\n        // threshold, we will not add it back to the candidates list.\n        candidate.suppressBeginIndex = selectedIndices.length;\n        if (!ignoreCandidate) {\n            // Candidate has passed all the tests, and is not suppressed, so\n            // select the candidate.\n            if (candidate.score === originalScore) {\n                selectedIndices.push(boxIndex);\n                selectedScores.push(candidate.score);\n            }\n            else if (candidate.score > scoreThreshold) {\n                // Candidate's score is suppressed but is still high enough to be\n                // considered, so add back to the candidates list.\n                binaryInsert(candidates, candidate, ascendingComparator);\n            }\n        }\n    }\n    // NonMaxSuppressionV4 feature: padding output to maxOutputSize.\n    const validOutputs = selectedIndices.length;\n    const elemsToPad = maxOutputSize - validOutputs;\n    if (padToMaxOutputSize && elemsToPad > 0) {\n        selectedIndices.push(...new Array(elemsToPad).fill(0));\n        selectedScores.push(...new Array(elemsToPad).fill(0.0));\n    }\n    const result = { selectedIndices: tensor1d(selectedIndices, 'int32') };\n    if (returnScoresTensor) {\n        result['selectedScores'] = tensor1d(selectedScores, 'float32');\n    }\n    if (returnValidOutputs) {\n        result['validOutputs'] = scalar(validOutputs, 'int32');\n    }\n    return result;\n}\nfunction intersectionOverUnion(boxes, i, j) {\n    const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n    const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n    const yminI = Math.min(iCoord[0], iCoord[2]);\n    const xminI = Math.min(iCoord[1], iCoord[3]);\n    const ymaxI = Math.max(iCoord[0], iCoord[2]);\n    const xmaxI = Math.max(iCoord[1], iCoord[3]);\n    const yminJ = Math.min(jCoord[0], jCoord[2]);\n    const xminJ = Math.min(jCoord[1], jCoord[3]);\n    const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n    const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n    const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n    const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n    if (areaI <= 0 || areaJ <= 0) {\n        return 0.0;\n    }\n    const intersectionYmin = Math.max(yminI, yminJ);\n    const intersectionXmin = Math.max(xminI, xminJ);\n    const intersectionYmax = Math.min(ymaxI, ymaxJ);\n    const intersectionXmax = Math.min(xmaxI, xmaxJ);\n    const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *\n        Math.max(intersectionXmax - intersectionXmin, 0.0);\n    return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n// A Gaussian penalty function, this method always returns values in [0, 1].\n// The weight is a function of similarity, the more overlap two boxes are, the\n// smaller the weight is, meaning highly overlapping boxe will be significantly\n// penalized. On the other hand, a non-overlapping box will not be penalized.\nfunction suppressWeight(iouThreshold, scale, iou) {\n    const weight = Math.exp(scale * iou * iou);\n    return iou <= iouThreshold ? weight : 0.0;\n}\nfunction ascendingComparator(c1, c2) {\n    // For objects with same scores, we make the object with the larger index go\n    // first. In an array that pops from the end, this means that the object with\n    // the smaller index will be popped first. This ensures the same output as\n    // the TensorFlow python version.\n    return (c1.score - c2.score) ||\n        ((c1.score === c2.score) && (c2.boxIndex - c1.boxIndex));\n}\n//# sourceMappingURL=non_max_suppression_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV3Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This is the async version of `nonMaxSuppression`\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n    maxOutputSize = inputs.maxOutputSize;\n    iouThreshold = inputs.iouThreshold;\n    scoreThreshold = inputs.scoreThreshold;\n    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n    const boxesVals = boxesAndScores[0];\n    const scoresVals = boxesAndScores[1];\n    // We call a cpu based impl directly with the typedarray data  here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\n//# sourceMappingURL=non_max_suppression_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV5 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    maxOutputSize = params.maxOutputSize;\n    iouThreshold = params.iouThreshold;\n    scoreThreshold = params.scoreThreshold;\n    softNmsSigma = params.softNmsSigma;\n    const inputs = { boxes: $boxes, scores: $scores };\n    const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n    const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);\n    return { selectedIndices: result[0], selectedScores: result[1] };\n}\nexport const nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ });\n//# sourceMappingURL=non_max_suppression_with_score.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV5Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    maxOutputSize = params.maxOutputSize;\n    iouThreshold = params.iouThreshold;\n    scoreThreshold = params.scoreThreshold;\n    softNmsSigma = params.softNmsSigma;\n    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n    const boxesVals = boxesAndScores[0];\n    const scoresVals = boxesAndScores[1];\n    // We call a cpu based impl directly with the typedarray data  here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;\n//# sourceMappingURL=non_max_suppression_with_score_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV4 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n    const $maxOutputSize = params.maxOutputSize;\n    const $iouThreshold = params.iouThreshold;\n    const $scoreThreshold = params.scoreThreshold;\n    const inputs = { boxes: $boxes, scores: $scores };\n    const attrs = {\n        maxOutputSize: $maxOutputSize,\n        iouThreshold: $iouThreshold,\n        scoreThreshold: $scoreThreshold,\n        padToMaxOutputSize\n    };\n    const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);\n    return { selectedIndices: result[0], validOutputs: result[1] };\n}\nexport const nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ });\n//# sourceMappingURL=non_max_suppression_padded.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV4Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n    const $maxOutputSize = params.maxOutputSize;\n    const $iouThreshold = params.iouThreshold;\n    const $scoreThreshold = params.scoreThreshold;\n    const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);\n    // We call a cpu based impl directly with the typedarray data here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;\n//# sourceMappingURL=non_max_suppression_padded_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeBilinear } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Bilinear resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeBilinear_(images, size, alignCorners = false) {\n    const $images = convertToTensor(images, 'images', 'resizeBilinear');\n    util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got ` +\n        `rank ${$images.rank}.`);\n    util.assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ` +\n        `${size}.`);\n    let batchImages = $images;\n    let reshapedTo4D = false;\n    if ($images.rank === 3) {\n        reshapedTo4D = true;\n        batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n    }\n    const [newHeight, newWidth] = size;\n    const forward = (backend, save) => {\n        save([batchImages]);\n        return backend.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);\n    };\n    const inputs = { images: batchImages };\n    const attrs = { alignCorners, size };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, ResizeBilinear, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const resizeBilinear = op({ resizeBilinear_ });\n//# sourceMappingURL=resize_bilinear.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeNearestNeighbor } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * NearestNeighbor resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeNearestNeighbor_(images, size, alignCorners = false) {\n    const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');\n    util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` +\n        `rank ${$images.rank}.`);\n    util.assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ` +\n        `${size}.`);\n    util.assert($images.dtype === 'float32' || $images.dtype === 'int32', () => '`images` must have `int32` or `float32` as dtype');\n    let batchImages = $images;\n    let reshapedTo4D = false;\n    if ($images.rank === 3) {\n        reshapedTo4D = true;\n        batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n    }\n    const [newHeight, newWidth] = size;\n    const inputs = { images: batchImages };\n    const attrs = { alignCorners, size };\n    const forward = (backend, save) => {\n        save([batchImages]);\n        return backend.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);\n    };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, ResizeNearestNeighbor, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const resizeNearestNeighbor = op({ resizeNearestNeighbor_ });\n//# sourceMappingURL=resize_nearest_neighbor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assert } from '../../util';\nimport { greaterEqual } from '../greater_equal';\nimport { lessEqual } from '../less_equal';\nimport { logicalAnd } from '../logical_and';\nimport { op } from '../operation';\nimport { range } from '../range';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\nimport { zeros } from '../zeros';\n/**\n * Copy a tensor setting everything outside a central band in each innermost\n * matrix to zero.\n *\n * The band part is computed as follows: Assume input has `k` dimensions\n * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where\n * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.\n * The indicator function\n * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower))`\n * `&& (num_upper < 0 || (n-m) <= num_upper)`\n *\n * ```js\n * const x = tf.tensor2d([[ 0,  1,  2, 3],\n *                        [-1,  0,  1, 2],\n *                        [-2, -1,  0, 1],\n *                        [-3, -2, -1, 0]]);\n * let y = tf.linalg.bandPart(x, 1, -1);\n * y.print(); // [[ 0,  1,  2, 3],\n *            //  [-1,  0,  1, 2],\n *            //  [ 0, -1,  0, 1],\n *            //  [ 0, 0 , -1, 0]]\n * let z = tf.linalg.bandPart(x, 2, 1);\n * z.print(); // [[ 0,  1,  0, 0],\n *            //  [-1,  0,  1, 0],\n *            //  [-2, -1,  0, 1],\n *            //  [ 0, -2, -1, 0]]\n * ```\n *\n * @param x Rank `k` tensor\n * @param numLower Number of subdiagonals to keep.\n *   If negative, keep entire lower triangle.\n * @param numUpper Number of subdiagonals to keep.\n *   If negative, keep entire upper triangle.\n * @returns Rank `k` tensor of the same shape as input.\n *   The extracted banded tensor.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction bandPart_(a, numLower, numUpper) {\n    assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);\n    assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);\n    const $a = convertToTensor(a, 'a', 'bandPart');\n    assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);\n    const shape = $a.shape;\n    const [M, N] = $a.shape.slice(-2);\n    if (!(numLower <= M)) {\n        throw new Error(`bandPart(): numLower (${numLower})` +\n            ` must not be greater than the number of rows (${M}).`);\n    }\n    if (!(numUpper <= N)) {\n        throw new Error(`bandPart(): numUpper (${numUpper})` +\n            ` must not be greater than the number of columns (${N}).`);\n    }\n    if (numLower < 0) {\n        numLower = M;\n    }\n    if (numUpper < 0) {\n        numUpper = N;\n    }\n    const i = reshape(range(0, M, 1, 'int32'), [-1, 1]);\n    const j = range(0, N, 1, 'int32');\n    const ij = sub(i, j);\n    const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, 'int32')), greaterEqual(ij, scalar(-numUpper, 'int32')));\n    const zero = zeros([M, N], $a.dtype);\n    return reshape(stack(unstack(reshape($a, [-1, M, N]))\n        .map(mat => where(inBand, mat, zero))), shape);\n}\nexport const bandPart = op({ bandPart_ });\n//# sourceMappingURL=band_part.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { assert } from '../../util';\nimport { div } from '../div';\nimport { mul } from '../mul';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { split } from '../split';\nimport { squeeze } from '../squeeze';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\n/**\n * Gram-Schmidt orthogonalization.\n *\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * let y = tf.linalg.gramSchmidt(x);\n * y.print();\n * console.log('Othogonalized:');\n * y.dot(y.transpose()).print();  // should be nearly the identity matrix.\n * console.log('First row direction maintained:');\n * const data = await y.array();\n * console.log(data[0][1] / data[0][0]);  // should be nearly 2.\n * ```\n *\n * @param xs The vectors to be orthogonalized, in one of the two following\n *   formats:\n *   - An Array of `tf.Tensor1D`.\n *   - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows\n *     of `xs`.\n *   In each case, all the vectors must have the same length and the length\n *   must be greater than or equal to the number of vectors.\n * @returns The orthogonalized and normalized vectors or matrix.\n *   Orthogonalization means that the vectors or the rows of the matrix\n *   are orthogonal (zero inner products). Normalization means that each\n *   vector or each row of the matrix has an L2 norm that equals `1`.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction gramSchmidt_(xs) {\n    let inputIsTensor2D;\n    if (Array.isArray(xs)) {\n        inputIsTensor2D = false;\n        assert(xs != null && xs.length > 0, () => 'Gram-Schmidt process: input must not be null, undefined, or ' +\n            'empty');\n        const dim = xs[0].shape[0];\n        for (let i = 1; i < xs.length; ++i) {\n            assert(xs[i].shape[0] === dim, () => 'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +\n                `(${xs[i].shape[0]} vs. ${dim})`);\n        }\n    }\n    else {\n        inputIsTensor2D = true;\n        xs = split(xs, xs.shape[0], 0).map(x => squeeze(x, [0]));\n    }\n    assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds ` +\n        `number of dimensions (${xs[0].shape[0]}).`);\n    const ys = [];\n    const xs1d = xs;\n    for (let i = 0; i < xs.length; ++i) {\n        ys.push(ENGINE.tidy(() => {\n            let x = xs1d[i];\n            if (i > 0) {\n                for (let j = 0; j < i; ++j) {\n                    const proj = mul(sum(mul(ys[j], x)), ys[j]);\n                    x = sub(x, proj);\n                }\n            }\n            return div(x, norm(x, 'euclidean'));\n        }));\n    }\n    if (inputIsTensor2D) {\n        return stack(ys, 0);\n    }\n    else {\n        return ys;\n    }\n}\nexport const gramSchmidt = op({ gramSchmidt_ });\n//# sourceMappingURL=gram_schmidt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { dispose } from '../../globals';\nimport { assert } from '../../util';\nimport { clone } from '../clone';\nimport { concat } from '../concat';\nimport { div } from '../div';\nimport { eye } from '../eye';\nimport { greater } from '../greater';\nimport { matMul } from '../mat_mul';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { tensor2d } from '../tensor2d';\nimport { transpose } from '../transpose';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\n/**\n * Compute QR decomposition of m-by-n matrix using Householder transformation.\n *\n * Implementation based on\n *   [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]\n * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [3, 4]]);\n * let [q, r] = tf.linalg.qr(a);\n * console.log('Q');\n * q.print();\n * console.log('R');\n * r.print();\n * console.log('Orthogonalized');\n * q.dot(q.transpose()).print()  // should be nearly the identity matrix.\n * console.log('Reconstructed');\n * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];\n * ```\n *\n * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose\n *   it has the shape `[..., M, N]`.\n * @param fullMatrices An optional boolean parameter. Defaults to `false`.\n *   If `true`, compute full-sized `Q`. If `false` (the default),\n *   compute only the leading N columns of `Q` and `R`.\n * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,\n *   i.e., its columns all have unit norm and are mutually orthogonal.\n *   If `M >= N`,\n *     If `fullMatrices` is `false` (default),\n *       - `Q` has a shape of `[..., M, N]`,\n *       - `R` has a shape of `[..., N, N]`.\n *     If `fullMatrices` is `true` (default),\n *       - `Q` has a shape of `[..., M, M]`,\n *       - `R` has a shape of `[..., M, N]`.\n *   If `M < N`,\n *     - `Q` has a shape of `[..., M, M]`,\n *     - `R` has a shape of `[..., M, N]`.\n * @throws If the rank of `x` is less than 2.\n *\n * @doc {heading:'Operations',\n *       subheading:'Linear Algebra',\n *       namespace:'linalg'}\n */\nfunction qr_(x, fullMatrices = false) {\n    assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);\n    if (x.rank === 2) {\n        return qr2d(x, fullMatrices);\n    }\n    else {\n        // Rank > 2.\n        // TODO(cais): Below we split the input into individual 2D tensors,\n        //   perform QR decomposition on them and then stack the results back\n        //   together. We should explore whether this can be parallelized.\n        const outerDimsProd = x.shape.slice(0, x.shape.length - 2)\n            .reduce((value, prev) => value * prev);\n        const x2ds = unstack(reshape(x, [\n            outerDimsProd, x.shape[x.shape.length - 2],\n            x.shape[x.shape.length - 1]\n        ]), 0);\n        const q2ds = [];\n        const r2ds = [];\n        x2ds.forEach(x2d => {\n            const [q2d, r2d] = qr2d(x2d, fullMatrices);\n            q2ds.push(q2d);\n            r2ds.push(r2d);\n        });\n        const q = reshape(stack(q2ds, 0), x.shape);\n        const r = reshape(stack(r2ds, 0), x.shape);\n        return [q, r];\n    }\n}\nfunction qr2d(x, fullMatrices = false) {\n    return ENGINE.tidy(() => {\n        assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);\n        const m = x.shape[0];\n        const n = x.shape[1];\n        let q = eye(m); // Orthogonal transform so far.\n        let r = clone(x); // Transformed matrix so far.\n        const one2D = tensor2d([[1]], [1, 1]);\n        let w = clone(one2D);\n        const iters = m >= n ? n : m;\n        for (let j = 0; j < iters; ++j) {\n            // This tidy within the for-loop ensures we clean up temporary\n            // tensors as soon as they are no longer needed.\n            const rTemp = r;\n            const wTemp = w;\n            const qTemp = q;\n            [w, r, q] = ENGINE.tidy(() => {\n                // Find H = I - tau * w * w', to put zeros below R(j, j).\n                const rjEnd1 = slice(r, [j, j], [m - j, 1]);\n                const normX = norm(rjEnd1);\n                const rjj = slice(r, [j, j], [1, 1]);\n                // The sign() function returns 0 on 0, which causes division by zero.\n                const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));\n                const u1 = sub(rjj, mul(s, normX));\n                const wPre = div(rjEnd1, u1);\n                if (wPre.shape[0] === 1) {\n                    w = clone(one2D);\n                }\n                else {\n                    w = concat([\n                        one2D,\n                        slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])\n                    ], 0);\n                }\n                const tau = neg(div(matMul(s, u1), normX));\n                // -- R := HR, Q := QH.\n                const rjEndAll = slice(r, [j, 0], [m - j, n]);\n                const tauTimesW = mul(tau, w);\n                const wT = transpose(w);\n                if (j === 0) {\n                    r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n                }\n                else {\n                    const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n                    r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);\n                }\n                const tawTimesWT = transpose(tauTimesW);\n                const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);\n                if (j === 0) {\n                    q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n                }\n                else {\n                    const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n                    q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);\n                }\n                return [w, r, q];\n            });\n            dispose([rTemp, wTemp, qTemp]);\n        }\n        if (!fullMatrices && m > n) {\n            q = slice(q, [0, 0], [m, n]);\n            r = slice(r, [0, 0], [n, n]);\n        }\n        return [q, r];\n    });\n}\nexport const qr = op({ qr_ });\n//# sourceMappingURL=qr.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Reduction;\n(function (Reduction) {\n    Reduction[Reduction[\"NONE\"] = 0] = \"NONE\";\n    Reduction[Reduction[\"MEAN\"] = 1] = \"MEAN\";\n    Reduction[Reduction[\"SUM\"] = 2] = \"SUM\";\n    Reduction[Reduction[\"SUM_BY_NONZERO_WEIGHTS\"] = 3] = \"SUM_BY_NONZERO_WEIGHTS\";\n})(Reduction || (Reduction = {}));\n//# sourceMappingURL=loss_ops_utils.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { Reduction } from '../loss_ops_utils';\nimport { mean } from '../mean';\nimport { mul } from '../mul';\nimport { notEqual } from '../not_equal';\nimport { ones } from '../ones';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sum } from '../sum';\n/**\n * Computes the weighted loss between two tensors.\n *\n * @param losses Tensor of shape `[batch_size, d1, ... dN]`.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `losses`, and must be broadcastable to `losses` (i.e., all\n *    dimensions must be either `1`, or the same as the corresponding\n *    `losses` dimension).\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction computeWeightedLoss_(losses, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');\n    }\n    const weightedLoss = ($weights == null) ? $losses : mul($losses, $weights);\n    if (reduction === Reduction.NONE) {\n        return weightedLoss;\n    }\n    if (reduction === Reduction.SUM) {\n        return sum(weightedLoss);\n    }\n    if (reduction === Reduction.MEAN) {\n        if ($weights == null) {\n            return mean(weightedLoss);\n        }\n        else {\n            const broadcastFactor = $losses.size / $weights.size;\n            const result = div(sum(weightedLoss), sum($weights));\n            return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) :\n                result;\n        }\n    }\n    if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n        if ($weights == null) {\n            return div(sum(weightedLoss), scalar($losses.size));\n        }\n        else {\n            const broadcastedWeights = mul($weights, ones($losses.shape));\n            const numNonZeros = cast(sum(notEqual(broadcastedWeights, scalar(0))), 'float32');\n            return div(sum(weightedLoss), numNonZeros);\n        }\n    }\n    throw Error(`Unknown reduction: ${reduction}`);\n}\nexport const computeWeightedLoss = op({ computeWeightedLoss_ });\n//# sourceMappingURL=compute_weighted_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the absolute difference loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'absoluteDifference');\n    const $predictions = convertToTensor(predictions, 'predictions', 'absoluteDifference');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'absoluteDifference');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in absoluteDifference: ');\n    const losses = abs(sub($labels, $predictions));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const absoluteDifference = op({ absoluteDifference_ });\n//# sourceMappingURL=absolute_difference.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the cosine distance loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param axis The dimension along which the cosine distance is computed.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'cosineDistance');\n    const $predictions = convertToTensor(predictions, 'predictions', 'cosineDistance');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'cosineDistance');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in cosineDistance: ');\n    const one = scalar(1);\n    const losses = sub(one, sum(mul($labels, $predictions), axis, true));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const cosineDistance = op({ cosineDistance_ });\n//# sourceMappingURL=cosine_distance.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the Hinge loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $labels = convertToTensor(labels, 'labels', 'hingeLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'hingeLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');\n    const one = scalar(1);\n    // Convert binary labels to (-1, 1)\n    $labels = sub(mul(scalar(2), $labels), one);\n    const losses = relu(sub(one, mul($labels, $predictions)));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const hingeLoss = op({ hingeLoss_ });\n//# sourceMappingURL=hinge_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { Reduction } from '../loss_ops_utils';\nimport { minimum } from '../minimum';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { square } from '../square';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the huber loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param delta Point where huber loss changes from quadratic to linear.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`.\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction huberLoss_(labels, predictions, weights, delta = 1.0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'huberLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'huberLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');\n    const deltaScalar = scalar(delta);\n    const error = abs(sub($predictions, $labels));\n    const quadratic = minimum(error, deltaScalar);\n    const linear = sub(error, quadratic);\n    const losses = add(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const huberLoss = op({ huberLoss_ });\n//# sourceMappingURL=huber_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { log } from '../log';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the log loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param epsilon A small increment to avoid taking log of zero\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction logLoss_(labels, predictions, weights, epsilon = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'logLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'logLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'logLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');\n    const one = scalar(1);\n    const epsilonScalar = scalar(epsilon);\n    const l1 = neg(mul($labels, log(add($predictions, epsilonScalar))));\n    const l2 = mul(sub(one, $labels), log(add(sub(one, $predictions), epsilonScalar)));\n    const losses = sub(l1, l2);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const logLoss = op({ logLoss_ });\n//# sourceMappingURL=log_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { squaredDifference } from '../squared_difference';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the mean squared error between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'meanSquaredError');\n    const $predictions = convertToTensor(predictions, 'predictions', 'meanSquaredError');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'meanSquaredError');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in meanSquaredError: ');\n    const losses = squaredDifference($labels, $predictions);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const meanSquaredError = op({ meanSquaredError_ });\n//# sourceMappingURL=mean_squared_error.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { exp } from '../exp';\nimport { log1p } from '../log1p';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\nfunction sigmoidCrossEntropyWithLogits_(labels, logits) {\n    const $labels = convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');\n    const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');\n    assertShapesMatch($labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');\n    /**\n     * Implementation Details:\n     *\n     * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n     *     z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n     *   = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n     *   = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n     *   = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n     *   = (1 - z) * x + log(1 + exp(-x))\n     *   = x - x * z + log(1 + exp(-x))\n     *\n     *   For x < 0, to avoid overflow in exp(-x), we reformulate the above\n     *     x - x * z + log(1 + exp(-x))\n     *   = log(exp(x)) - x * z + log(1 + exp(-x))\n     *   = - x * z + log(1 + exp(x))\n     *\n     * Hence, to ensure stability and avoid overflow, the implementation uses\n     * this equivalent formulation:\n     *     max(x, 0) - x * z + log(1 + exp(-abs(x)))\n     */\n    const maxOutput = relu($logits);\n    const outputXTarget = mul($logits, $labels);\n    const sigmoidOutput = log1p(exp(neg(abs($logits))));\n    return add(sub(maxOutput, outputXTarget), sigmoidOutput);\n}\n/**\n * Computes the sigmoid cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)\n *                         + 0.5 * labelSmoothing\n *\n * @param multiClassLabels The ground truth output tensor of shape\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $multiClassLabels = convertToTensor(multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');\n    const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');\n    }\n    assertShapesMatch($multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');\n    if (labelSmoothing > 0) {\n        const labelSmoothingScalar = scalar(labelSmoothing);\n        const one = scalar(1);\n        const half = scalar(0.5);\n        $multiClassLabels =\n            add(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));\n    }\n    const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ });\n//# sourceMappingURL=sigmoid_cross_entropy.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../../gradients';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { expandShapeToKeepDim } from '../axis_util';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { exp } from '../exp';\nimport { logSumExp } from '../log_sum_exp';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes softmax cross entropy between logits and labels.\n *\n * Measures the probability error in discrete classification tasks in which\n * the classes are mutually exclusive (each entry is in exactly one class).\n * For example, each CIFAR-10 image is labeled with one and only one label: an\n * image can be a dog or a truck, but not both.\n *\n * `NOTE`: While the classes are mutually exclusive, their probabilities need\n * not be. All that is required is that each row of labels is a valid\n * probability distribution. If they are not, the computation of the gradient\n * will be incorrect.\n *\n * `WARNING`: This op expects unscaled logits, since it performs a softmax on\n * logits internally for efficiency. Do not call this op with the output of\n * softmax, as it will produce incorrect results.\n *\n * logits and labels must have the same shape, e.g. [batch_size, num_classes]\n * and the same dtype.\n * @param labels The labels array.\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\nfunction softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {\n    if (dim === -1) {\n        dim = logits.rank - 1;\n    }\n    if (dim !== logits.rank - 1) {\n        throw Error(`Softmax cross entropy along a non-last dimension is not yet ` +\n            `supported. Labels / logits was rank ${logits.rank} ` +\n            `and dim was ${dim}`);\n    }\n    // Use a custom gradient for numerical stability.\n    const customOp = customGrad((labels, logits, save) => {\n        // Reference:\n        //   1. http://cs231n.github.io/linear-classify/#softmax\n        //   2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/\n        const keepDims = true;\n        const lse = logSumExp(logits, [dim], keepDims);\n        const logResult = sub(cast(logits, 'float32'), lse);\n        save([labels, logResult]);\n        const costVector = neg(mul(logResult, labels));\n        const value = sum(costVector, [dim]);\n        const gradFunc = (dy, saved) => {\n            const [labels, logResult] = saved;\n            const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n            return [\n                mul(reshape(dy, dyShape), sub(cast(labels, 'float32'), exp(logResult))),\n                mul(reshape(dy, dyShape), sub(exp(logResult), cast(labels, 'float32'))),\n            ];\n        };\n        return { value, gradFunc };\n    });\n    return customOp(labels, logits);\n}\n/**\n * Computes the softmax cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newOnehotLabels = onehotLabels * (1 - labelSmoothing)\n *                         + labelSmoothing / numClasses\n *\n * @param onehotLabels One hot encoded labels\n *    [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or 1, and must be\n *    broadcastable to `loss`  of shape [batch_size]\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $onehotLabels = convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');\n    const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');\n    }\n    assertShapesMatch($onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');\n    if (labelSmoothing > 0) {\n        const labelSmoothingScalar = scalar(labelSmoothing);\n        const one = scalar(1);\n        const numClasses = scalar($onehotLabels.shape[1]);\n        $onehotLabels =\n            add(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));\n    }\n    const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const softmaxCrossEntropy = op({ softmaxCrossEntropy_ });\n//# sourceMappingURL=softmax_cross_entropy.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Modularized ops.\nexport { abs } from './abs';\nexport { acos } from './acos';\nexport { acosh } from './acosh';\nexport { add } from './add';\nexport { addN } from './add_n';\nexport { all } from './all';\nexport { any } from './any';\nexport { argMax } from './arg_max';\nexport { argMin } from './arg_min';\nexport { asin } from './asin';\nexport { asinh } from './asinh';\nexport { atan } from './atan';\nexport { atan2 } from './atan2';\nexport { atanh } from './atanh';\nexport { avgPool } from './avg_pool';\nexport { avgPool3d } from './avg_pool_3d';\nexport { basicLSTMCell } from './basic_lstm_cell';\nexport { batchToSpaceND } from './batch_to_space_nd';\nexport { batchNorm } from './batchnorm';\nexport { batchNorm2d } from './batchnorm2d';\nexport { batchNorm3d } from './batchnorm3d';\nexport { batchNorm4d } from './batchnorm4d';\nexport { broadcastTo } from './broadcast_to';\nexport { buffer } from './buffer';\nexport { cast } from './cast';\nexport { ceil } from './ceil';\nexport { clipByValue } from './clip_by_value';\nexport { clone } from './clone';\nexport { complex } from './complex';\nexport { concat } from './concat';\nexport { concat1d } from './concat_1d';\nexport { concat2d } from './concat_2d';\nexport { concat3d } from './concat_3d';\nexport { concat4d } from './concat_4d';\nexport { conv1d } from './conv1d';\nexport { conv2d } from './conv2d';\nexport { conv2dTranspose } from './conv2d_transpose';\nexport { conv3d } from './conv3d';\nexport { conv3dTranspose } from './conv3d_transpose';\nexport { cos } from './cos';\nexport { cosh } from './cosh';\nexport { cumsum } from './cumsum';\nexport { depthToSpace } from './depth_to_space';\nexport { depthwiseConv2d } from './depthwise_conv2d';\nexport { diag } from './diag';\nexport { dilation2d } from './dilation2d';\nexport { div } from './div';\nexport { divNoNan } from './div_no_nan';\nexport { dot } from './dot';\nexport { elu } from './elu';\nexport { equal } from './equal';\nexport { erf } from './erf';\nexport { exp } from './exp';\nexport { expandDims } from './expand_dims';\nexport { expm1 } from './expm1';\nexport { eye } from './eye';\nexport { fill } from './fill';\nexport { floor } from './floor';\nexport { floorDiv } from './floorDiv';\nexport { gather } from './gather';\nexport { greater } from './greater';\nexport { greaterEqual } from './greater_equal';\nexport { imag } from './imag';\nexport { isFinite } from './is_finite';\nexport { isInf } from './is_inf';\nexport { isNaN } from './is_nan';\nexport { leakyRelu } from './leaky_relu';\nexport { less } from './less';\nexport { lessEqual } from './less_equal';\nexport { linspace } from './linspace';\nexport { localResponseNormalization } from './local_response_normalization';\nexport { log } from './log';\nexport { log1p } from './log1p';\nexport { logSigmoid } from './log_sigmoid';\nexport { logSoftmax } from './log_softmax';\nexport { logSumExp } from './log_sum_exp';\nexport { logicalAnd } from './logical_and';\nexport { logicalNot } from './logical_not';\nexport { logicalOr } from './logical_or';\nexport { logicalXor } from './logical_xor';\nexport { matMul } from './mat_mul';\nexport { max } from './max';\nexport { maxPool } from './max_pool';\nexport { maxPool3d } from './max_pool_3d';\nexport { maxPoolWithArgmax } from './max_pool_with_argmax';\nexport { maximum } from './maximum';\nexport { mean } from './mean';\nexport { min } from './min';\nexport { minimum } from './minimum';\nexport { mod } from './mod';\nexport { moments } from './moments';\nexport { mul } from './mul';\nexport { multiRNNCell } from './multi_rnn_cell';\nexport { multinomial } from './multinomial';\nexport { neg } from './neg';\nexport { notEqual } from './not_equal';\nexport { oneHot } from './one_hot';\nexport { ones } from './ones';\nexport { onesLike } from './ones_like';\nexport { outerProduct } from './outer_product';\nexport { pad } from './pad';\nexport { pad1d } from './pad1d';\nexport { pad2d } from './pad2d';\nexport { pad3d } from './pad3d';\nexport { pad4d } from './pad4d';\nexport { pool } from './pool';\nexport { pow } from './pow';\nexport { prelu } from './prelu';\nexport { print } from './print';\nexport { prod } from './prod';\nexport { rand } from './rand';\nexport { randomGamma } from './random_gamma';\nexport { randomNormal } from './random_normal';\nexport { randomUniform } from './random_uniform';\nexport { range } from './range';\nexport { real } from './real';\nexport { reciprocal } from './reciprocal';\nexport { relu } from './relu';\nexport { relu6 } from './relu6';\nexport { reshape } from './reshape';\nexport { reverse } from './reverse';\nexport { reverse1d } from './reverse_1d';\nexport { reverse2d } from './reverse_2d';\nexport { reverse3d } from './reverse_3d';\nexport { reverse4d } from './reverse_4d';\nexport { round } from './round';\nexport { rsqrt } from './rsqrt';\nexport { scalar } from './scalar';\nexport { selu } from './selu';\nexport { separableConv2d } from './separable_conv2d';\nexport { setdiff1dAsync } from './setdiff1d_async';\nexport { sigmoid } from './sigmoid';\nexport { sign } from './sign';\nexport { sin } from './sin';\nexport { sinh } from './sinh';\nexport { slice } from './slice';\nexport { slice1d } from './slice1d';\nexport { slice2d } from './slice2d';\nexport { slice3d } from './slice3d';\nexport { slice4d } from './slice4d';\nexport { softmax } from './softmax';\nexport { softplus } from './softplus';\nexport { spaceToBatchND } from './space_to_batch_nd';\nexport { fft } from './spectral/fft';\nexport { ifft } from './spectral/ifft';\nexport { irfft } from './spectral/irfft';\nexport { rfft } from './spectral/rfft';\nexport { split } from './split';\nexport { sqrt } from './sqrt';\nexport { square } from './square';\nexport { squaredDifference } from './squared_difference';\nexport { squeeze } from './squeeze';\nexport { stack } from './stack';\nexport { step } from './step';\nexport { stridedSlice } from './strided_slice';\nexport { sub } from './sub';\nexport { sum } from './sum';\nexport { tan } from './tan';\nexport { tanh } from './tanh';\nexport { tensor } from './tensor';\nexport { tensor1d } from './tensor1d';\nexport { tensor2d } from './tensor2d';\nexport { tensor3d } from './tensor3d';\nexport { tensor4d } from './tensor4d';\nexport { tensor5d } from './tensor5d';\nexport { tensor6d } from './tensor6d';\nexport { tile } from './tile';\nexport { topk } from './topk';\nexport { truncatedNormal } from './truncated_normal';\nexport { unique } from './unique';\nexport { unsortedSegmentSum } from './unsorted_segment_sum';\nexport { unstack } from './unstack';\nexport { variable } from './variable';\nexport { where } from './where';\nexport { whereAsync } from './where_async';\nexport { zeros } from './zeros';\nexport { zerosLike } from './zeros_like';\nexport * from './boolean_mask';\nexport * from './compare';\nexport * from './binary_ops';\nexport * from './transpose';\nexport * from './norm';\nexport * from './moving_average';\nexport * from './scatter_nd';\nexport * from './sparse_to_dense';\nexport * from './gather_nd';\nexport * from './dropout';\nexport * from './signal_ops_util';\nexport * from './in_top_k';\nexport { op, OP_SCOPE_SUFFIX } from './operation';\nimport { rfft } from './spectral/rfft';\nimport { fft } from './spectral/fft';\nimport { ifft } from './spectral/ifft';\nimport { irfft } from './spectral/irfft';\nconst spectral = {\n    fft,\n    ifft,\n    rfft,\n    irfft\n};\nimport * as fused from './fused_ops';\nimport { hammingWindow } from './signal/hamming_window';\nimport { hannWindow } from './signal/hann_window';\nimport { frame } from './signal/frame';\nimport { stft } from './signal/stft';\nconst signal = {\n    hammingWindow,\n    hannWindow,\n    frame,\n    stft,\n};\n// Image Ops namespace\nimport { cropAndResize } from './image/crop_and_resize';\nimport { flipLeftRight } from './image/flip_left_right';\nimport { rotateWithOffset } from './image/rotate_with_offset';\nimport { nonMaxSuppression } from './image/non_max_suppression';\nimport { nonMaxSuppressionAsync } from './image/non_max_suppression_async';\nimport { nonMaxSuppressionWithScore } from './image/non_max_suppression_with_score';\nimport { nonMaxSuppressionWithScoreAsync } from './image/non_max_suppression_with_score_async';\nimport { nonMaxSuppressionPadded } from './image/non_max_suppression_padded';\nimport { nonMaxSuppressionPaddedAsync } from './image/non_max_suppression_padded_async';\nimport { resizeBilinear } from './image/resize_bilinear';\nimport { resizeNearestNeighbor } from './image/resize_nearest_neighbor';\nconst image = {\n    flipLeftRight,\n    resizeNearestNeighbor,\n    resizeBilinear,\n    rotateWithOffset,\n    cropAndResize,\n    nonMaxSuppression,\n    nonMaxSuppressionAsync,\n    nonMaxSuppressionWithScore,\n    nonMaxSuppressionWithScoreAsync,\n    nonMaxSuppressionPadded,\n    nonMaxSuppressionPaddedAsync\n};\n// linalg namespace\nimport { bandPart } from './linalg/band_part';\nimport { gramSchmidt } from './linalg/gram_schmidt';\nimport { qr } from './linalg/qr';\nconst linalg = {\n    bandPart,\n    gramSchmidt,\n    qr\n};\n// losses namespace;\nimport { absoluteDifference } from './losses/absolute_difference';\nimport { computeWeightedLoss } from './losses/compute_weighted_loss';\nimport { cosineDistance } from './losses/cosine_distance';\nimport { hingeLoss } from './losses/hinge_loss';\nimport { huberLoss } from './losses/huber_loss';\nimport { logLoss } from './losses/log_loss';\nimport { meanSquaredError } from './losses/mean_squared_error';\nimport { sigmoidCrossEntropy } from './losses/sigmoid_cross_entropy';\nimport { softmaxCrossEntropy } from './losses/softmax_cross_entropy';\nconst losses = {\n    absoluteDifference,\n    computeWeightedLoss,\n    cosineDistance,\n    hingeLoss,\n    huberLoss,\n    logLoss,\n    meanSquaredError,\n    sigmoidCrossEntropy,\n    softmaxCrossEntropy\n};\n// Second level exports.\nexport { image, linalg, losses, spectral, fused, signal };\n//# sourceMappingURL=ops.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dispose } from '../globals';\nimport { variableGrads } from '../gradients';\nimport { scalar } from '../ops/ops';\nimport { Serializable } from '../serialization';\n/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */\nexport class Optimizer extends Serializable {\n    /**\n     * Executes `f()` and minimizes the scalar output of `f()` by computing\n     * gradients of y with respect to the list of trainable variables provided by\n     * `varList`. If no list is provided, it defaults to all trainable variables.\n     *\n     * @param f The function to execute and whose output to minimize.\n     * @param returnCost Whether to return the scalar cost value produced by\n     * executing `f()`.\n     * @param varList An optional list of variables to update. If specified, only\n     * the trainable variables in varList will be updated by minimize. Defaults to\n     * all trainable variables.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers'}\n     */\n    minimize(f, returnCost = false, varList) {\n        const { value, grads } = this.computeGradients(f, varList);\n        if (varList != null) {\n            const gradArray = varList.map(v => ({ name: v.name, tensor: grads[v.name] }));\n            this.applyGradients(gradArray);\n        }\n        else {\n            this.applyGradients(grads);\n        }\n        // Dispose gradients.\n        dispose(grads);\n        if (returnCost) {\n            return value;\n        }\n        else {\n            value.dispose();\n            return null;\n        }\n    }\n    /**\n     * The number of iterations that this optimizer instance has been invoked for.\n     */\n    get iterations() {\n        if (this.iterations_ == null) {\n            this.iterations_ = 0;\n        }\n        return this.iterations_;\n    }\n    incrementIterations() {\n        this.iterations_ = this.iterations + 1;\n    }\n    /**\n     * Executes f() and computes the gradient of the scalar output of f() with\n     * respect to the list of trainable variables provided by `varList`. If no\n     * list is provided, it defaults to all trainable variables.\n     *\n     * @param f The function to execute and whose output to use for computing\n     * gradients with respect to variables.\n     * @param varList An optional list of variables to compute gradients with\n     * respect to. If specified, only the trainable variables in varList will have\n     * gradients computed with respect to. Defaults to all trainable variables.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers'}\n     */\n    computeGradients(f, varList) {\n        return variableGrads(f, varList);\n    }\n    /**\n     * Dispose the variables (if any) owned by this optimizer instance.\n     */\n    dispose() {\n        if (this.iterations_ != null) {\n            dispose(this.iterations_);\n        }\n    }\n    async saveIterations() {\n        if (this.iterations_ == null) {\n            this.iterations_ = 0;\n        }\n        return {\n            name: 'iter',\n            // TODO(cais): Use 'int64' type when available.\n            tensor: scalar(this.iterations_, 'int32')\n        };\n    }\n    async getWeights() {\n        throw new Error('getWeights() is not implemented for this optimizer yet.');\n    }\n    async setWeights(weightValues) {\n        throw new Error(`setWeights() is not implemented for this optimizer class ` +\n            `${this.getClassName()}`);\n    }\n    /**\n     * Extract the first element of the weight values and set it\n     * as the iterations counter variable of this instance of optimizer.\n     *\n     * @param weightValues\n     * @returns Weight values with the first element consumed and excluded.\n     */\n    async extractIterations(weightValues) {\n        this.iterations_ = (await weightValues[0].tensor.data())[0];\n        return weightValues.slice(1);\n    }\n}\nObject.defineProperty(Optimizer, Symbol.hasInstance, {\n    value: (instance) => {\n        return instance.minimize != null && instance.computeGradients != null &&\n            instance.applyGradients != null;\n    }\n});\n//# sourceMappingURL=optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/ops';\nimport { square } from '../ops/square';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class AdadeltaOptimizer extends Optimizer {\n    constructor(learningRate, rho, epsilon = null) {\n        super();\n        this.learningRate = learningRate;\n        this.rho = rho;\n        this.epsilon = epsilon;\n        this.accumulatedGrads = [];\n        this.accumulatedUpdates = [];\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            const trainable = false;\n            if (this.accumulatedGrads[i] == null) {\n                this.accumulatedGrads[i] = {\n                    originalName: `${name}/accum_grad`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedUpdates[i] == null) {\n                this.accumulatedUpdates[i] = {\n                    originalName: `${name}/accum_var`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedGrad = this.accumulatedGrads[i].variable;\n            const accumulatedUpdate = this.accumulatedUpdates[i].variable;\n            tidy(() => {\n                const newAccumulatedGrad = add(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));\n                const updates = mul(div(sqrt(add(accumulatedUpdate, this.epsilon)), sqrt(add(accumulatedGrad, this.epsilon))), gradient);\n                const newAccumulatedUpdate = add(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));\n                accumulatedGrad.assign(newAccumulatedGrad);\n                accumulatedUpdate.assign(newAccumulatedUpdate);\n                const newValue = add(mul(updates, -this.learningRate), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedUpdates != null) {\n            dispose(this.accumulatedGrads.map(v => v.variable));\n            dispose(this.accumulatedUpdates.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const variableCount = weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedGrads =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedUpdates =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'rho': this.rho,\n            'epsilon': this.epsilon\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['rho'], config['epsilon']);\n    }\n}\n/** @nocollapse */\nAdadeltaOptimizer.className = 'Adadelta'; // Name matters for Python compatibility.\nregisterClass(AdadeltaOptimizer);\n//# sourceMappingURL=adadelta_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { fill } from '../ops/fill';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class AdagradOptimizer extends Optimizer {\n    constructor(learningRate, initialAccumulatorValue = 0.1) {\n        super();\n        this.learningRate = learningRate;\n        this.initialAccumulatorValue = initialAccumulatorValue;\n        this.accumulatedGrads = [];\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            if (this.accumulatedGrads[i] == null) {\n                const trainable = false;\n                this.accumulatedGrads[i] = {\n                    originalName: `${name}/accumulator`,\n                    variable: tidy(() => fill(value.shape, this.initialAccumulatorValue)\n                        .variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedGrad = this.accumulatedGrads[i].variable;\n            tidy(() => {\n                const newAccumulatedGrad = add(accumulatedGrad, square(gradient));\n                accumulatedGrad.assign(newAccumulatedGrad);\n                const newValue = add(mul(div(gradient, sqrt(add(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedGrads != null) {\n            dispose(this.accumulatedGrads.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        return [await this.saveIterations()].concat(this.accumulatedGrads.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const trainable = false;\n        this.accumulatedGrads = weightValues.map(v => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'initialAccumulatorValue': this.initialAccumulatorValue,\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['initialAccumulatorValue']);\n    }\n}\n/** @nocollapse */\nAdagradOptimizer.className = 'Adagrad'; // Note: Name matters for Python compatibility.\nregisterClass(AdagradOptimizer);\n//# sourceMappingURL=adagrad_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { pow } from '../ops/pow';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\nexport class AdamOptimizer extends Optimizer {\n    constructor(learningRate, beta1, beta2, epsilon = null) {\n        super();\n        this.learningRate = learningRate;\n        this.beta1 = beta1;\n        this.beta2 = beta2;\n        this.epsilon = epsilon;\n        this.accumulatedFirstMoment = [];\n        this.accumulatedSecondMoment = [];\n        tidy(() => {\n            // accB* will be updated by batch.\n            this.accBeta1 = scalar(beta1).variable();\n            this.accBeta2 = scalar(beta2).variable();\n        });\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const varNames = Array.isArray(variableGradients) ?\n            variableGradients.map(v => v.name) :\n            Object.keys(variableGradients);\n        tidy(() => {\n            const oneMinusAccBeta1 = sub(1, this.accBeta1);\n            const oneMinusAccBeta2 = sub(1, this.accBeta2);\n            varNames.forEach((name, i) => {\n                const value = ENGINE.registeredVariables[name];\n                const trainable = false;\n                if (this.accumulatedFirstMoment[i] == null) {\n                    this.accumulatedFirstMoment[i] = {\n                        originalName: `${name}/m`,\n                        variable: tidy(() => zerosLike(value).variable(trainable))\n                    };\n                }\n                if (this.accumulatedSecondMoment[i] == null) {\n                    this.accumulatedSecondMoment[i] = {\n                        originalName: `${name}/v`,\n                        variable: tidy(() => zerosLike(value).variable(trainable))\n                    };\n                }\n                const gradient = Array.isArray(variableGradients) ?\n                    variableGradients[i].tensor :\n                    variableGradients[name];\n                if (gradient == null) {\n                    return;\n                }\n                const firstMoment = this.accumulatedFirstMoment[i].variable;\n                const secondMoment = this.accumulatedSecondMoment[i].variable;\n                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n                const newSecondMoment = add(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));\n                const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);\n                const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);\n                firstMoment.assign(newFirstMoment);\n                secondMoment.assign(newSecondMoment);\n                const newValue = add(mul(div(biasCorrectedFirstMoment, add(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);\n                value.assign(newValue);\n            });\n            this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n            this.accBeta2.assign(mul(this.accBeta2, this.beta2));\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.accBeta1.dispose();\n        this.accBeta2.dispose();\n        if (this.accumulatedFirstMoment != null) {\n            dispose(this.accumulatedFirstMoment.map(v => v.variable));\n        }\n        if (this.accumulatedSecondMoment != null) {\n            dispose(this.accumulatedSecondMoment.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        tidy(() => {\n            this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));\n            this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));\n        });\n        const variableCount = weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedFirstMoment =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedSecondMoment =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'beta1': this.beta1,\n            'beta2': this.beta2,\n            'epsilon': this.epsilon,\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon']);\n    }\n}\n/** @nocollapse */\nAdamOptimizer.className = 'Adam'; // Note: Name matters for Python compatibility.\nregisterClass(AdamOptimizer);\n//# sourceMappingURL=adam_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { abs } from '../ops/abs';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { maximum } from '../ops/maximum';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\nexport class AdamaxOptimizer extends Optimizer {\n    constructor(learningRate, beta1, beta2, epsilon = null, decay = 0.0) {\n        super();\n        this.learningRate = learningRate;\n        this.beta1 = beta1;\n        this.beta2 = beta2;\n        this.epsilon = epsilon;\n        this.decay = decay;\n        this.accumulatedFirstMoment = [];\n        this.accumulatedWeightedInfNorm = [];\n        tidy(() => {\n            this.iteration = scalar(0).variable();\n            this.accBeta1 = scalar(beta1).variable();\n        });\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        tidy(() => {\n            const oneMinusAccBeta1 = sub(1, this.accBeta1);\n            const lr = div(-this.learningRate, add(mul(this.iteration, this.decay), 1));\n            variableNames.forEach((name, i) => {\n                const value = ENGINE.registeredVariables[name];\n                const trainable = false;\n                if (this.accumulatedFirstMoment[i] == null) {\n                    this.accumulatedFirstMoment[i] = {\n                        originalName: `${name}/m`,\n                        variable: zerosLike(value).variable(trainable)\n                    };\n                }\n                if (this.accumulatedWeightedInfNorm[i] == null) {\n                    this.accumulatedWeightedInfNorm[i] = {\n                        originalName: `${name}/v`,\n                        variable: zerosLike(value).variable(trainable)\n                    };\n                }\n                const gradient = Array.isArray(variableGradients) ?\n                    variableGradients[i].tensor :\n                    variableGradients[name];\n                if (gradient == null) {\n                    return;\n                }\n                const firstMoment = this.accumulatedFirstMoment[i].variable;\n                const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;\n                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n                const ut0 = mul(weightedInfNorm, this.beta2);\n                const ut1 = abs(gradient);\n                const newWeightedInfNorm = maximum(ut0, ut1);\n                firstMoment.assign(newFirstMoment);\n                weightedInfNorm.assign(newWeightedInfNorm);\n                const newValue = add(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add(newWeightedInfNorm, this.epsilon))), value);\n                value.assign(newValue);\n            });\n            this.iteration.assign(add(this.iteration, 1));\n            this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.accBeta1.dispose();\n        this.iteration.dispose();\n        if (this.accumulatedFirstMoment != null) {\n            dispose(this.accumulatedFirstMoment.map(v => v.variable));\n        }\n        if (this.accumulatedWeightedInfNorm != null) {\n            dispose(this.accumulatedWeightedInfNorm.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        throw new Error('getWeights() is not implemented for Adamax yet.');\n    }\n    async setWeights(weightValues) {\n        throw new Error('setWeights() is not implemented for Adamax yet.');\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'beta1': this.beta1,\n            'beta2': this.beta2,\n            'epsilon': this.epsilon,\n            'decay': this.decay\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon'], config['decay']);\n    }\n}\n/** @nocollapse */\nAdamaxOptimizer.className = 'Adamax'; // Note: Name matters for Python compatbility.\nregisterClass(AdamaxOptimizer);\n//# sourceMappingURL=adamax_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { keep, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class SGDOptimizer extends Optimizer {\n    constructor(learningRate) {\n        super();\n        this.learningRate = learningRate;\n        this.setLearningRate(learningRate);\n    }\n    applyGradients(variableGradients) {\n        const varNames = Array.isArray(variableGradients) ?\n            variableGradients.map(v => v.name) :\n            Object.keys(variableGradients);\n        varNames.forEach((name, i) => {\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const value = ENGINE.registeredVariables[name];\n            tidy(() => {\n                const newValue = add(mul(this.c, gradient), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    /**\n     * Sets the learning rate of the optimizer.\n     */\n    setLearningRate(learningRate) {\n        this.learningRate = learningRate;\n        if (this.c != null) {\n            this.c.dispose();\n        }\n        this.c = keep(scalar(-learningRate));\n    }\n    dispose() {\n        this.c.dispose();\n    }\n    async getWeights() {\n        return [await this.saveIterations()];\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        if (weightValues.length !== 0) {\n            throw new Error('SGD optimizer does not have settable weights.');\n        }\n    }\n    getConfig() {\n        return { 'learningRate': this.learningRate };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate']);\n    }\n}\n/** @nocollapse */\nSGDOptimizer.className = 'SGD'; // Note: Name matters for Python compatibility.\nregisterClass(SGDOptimizer);\n//# sourceMappingURL=sgd_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { SGDOptimizer } from './sgd_optimizer';\n/** @doclink Optimizer */\nexport class MomentumOptimizer extends SGDOptimizer {\n    constructor(learningRate, momentum, useNesterov = false) {\n        super(learningRate);\n        this.learningRate = learningRate;\n        this.momentum = momentum;\n        this.useNesterov = useNesterov;\n        this.accumulations = [];\n        this.m = scalar(this.momentum);\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            if (this.accumulations[i] == null) {\n                const trainable = false;\n                this.accumulations[i] = {\n                    originalName: `${name}/momentum`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const accumulation = this.accumulations[i].variable;\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            tidy(() => {\n                let newValue;\n                const newAccumulation = add(mul(this.m, accumulation), gradient);\n                if (this.useNesterov) {\n                    newValue = add(mul(this.c, add(gradient, mul(newAccumulation, this.m))), value);\n                }\n                else {\n                    newValue = add(mul(this.c, newAccumulation), value);\n                }\n                accumulation.assign(newAccumulation);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.m.dispose();\n        if (this.accumulations != null) {\n            dispose(this.accumulations.map(v => v.variable));\n        }\n    }\n    /**\n     * Sets the momentum of the optimizer.\n     *\n     * @param momentum\n     */\n    setMomentum(momentum) {\n        this.momentum = momentum;\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        return [await this.saveIterations()].concat(this.accumulations.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const trainable = false;\n        this.accumulations = weightValues.map(v => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'momentum': this.momentum,\n            'useNesterov': this.useNesterov\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['momentum'], config['useNesterov']);\n    }\n}\n/** @nocollapse */\nMomentumOptimizer.className = 'Momentum'; // Name matters for Python compatibility.\nregisterClass(MomentumOptimizer);\n//# sourceMappingURL=momentum_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class RMSPropOptimizer extends Optimizer {\n    constructor(learningRate, decay = 0.9, momentum = 0.0, epsilon = null, centered = false) {\n        super();\n        this.learningRate = learningRate;\n        this.decay = decay;\n        this.momentum = momentum;\n        this.epsilon = epsilon;\n        this.accumulatedMeanSquares = [];\n        this.accumulatedMoments = [];\n        this.accumulatedMeanGrads = [];\n        this.centered = centered;\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n        if (learningRate == null) {\n            throw new Error(`learningRate for RMSPropOptimizer must be defined.`);\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            const trainable = false;\n            if (this.accumulatedMeanSquares[i] == null) {\n                this.accumulatedMeanSquares[i] = {\n                    originalName: `${name}/rms`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedMoments[i] == null) {\n                this.accumulatedMoments[i] = {\n                    originalName: `${name}/momentum`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedMeanGrads[i] == null && this.centered) {\n                this.accumulatedMeanGrads[i] = {\n                    originalName: `${name}/mg`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;\n            const accumulatedMoments = this.accumulatedMoments[i].variable;\n            tidy(() => {\n                const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n                if (this.centered) {\n                    const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;\n                    // Centered gradient\n                    const newAccumulatedMeanGrad = add(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));\n                    const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add(square(newAccumulatedMeanGrad), this.epsilon))));\n                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), gradContribution);\n                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n                    accumulatedMeanGrad.assign(newAccumulatedMeanGrad);\n                    accumulatedMoments.assign(newAccumulatedMoments);\n                    const newValue = sub(value, newAccumulatedMoments);\n                    value.assign(newValue);\n                }\n                else {\n                    // Plain gradient\n                    const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add(newAccumulatedMeanSquare, this.epsilon))));\n                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n                    accumulatedMoments.assign(newAccumulatedMoments);\n                    const newValue = sub(value, newAccumulatedMoments);\n                    value.assign(newValue);\n                }\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedMeanSquares != null) {\n            dispose(this.accumulatedMeanSquares.map(v => v.variable));\n        }\n        if (this.accumulatedMeanGrads != null && this.centered) {\n            dispose(this.accumulatedMeanGrads.map(v => v.variable));\n        }\n        if (this.accumulatedMoments != null) {\n            dispose(this.accumulatedMoments.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];\n        if (this.centered) {\n            variables.push(...this.accumulatedMeanGrads);\n        }\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedMeanSquares =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedMoments =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        if (this.centered) {\n            this.accumulatedMeanGrads =\n                weightValues.slice(variableCount * 2, variableCount * 3)\n                    .map(v => ({\n                    originalName: v.name,\n                    variable: v.tensor.variable(trainable)\n                }));\n        }\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'decay': this.decay,\n            'momentum': this.momentum,\n            'epsilon': this.epsilon,\n            'centered': this.centered\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['decay'], config['momentum'], config['epsilon'], config['centered']);\n    }\n}\n/** @nocollapse */\nRMSPropOptimizer.className = 'RMSProp'; // Note: Name matters for Python compatibility.\nregisterClass(RMSPropOptimizer);\n//# sourceMappingURL=rmsprop_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AdadeltaOptimizer } from './adadelta_optimizer';\nimport { AdagradOptimizer } from './adagrad_optimizer';\nimport { AdamOptimizer } from './adam_optimizer';\nimport { AdamaxOptimizer } from './adamax_optimizer';\nimport { MomentumOptimizer } from './momentum_optimizer';\nimport { RMSPropOptimizer } from './rmsprop_optimizer';\nimport { SGDOptimizer } from './sgd_optimizer';\nexport class OptimizerConstructors {\n    /**\n     * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.\n     *\n     * ```js\n     * // Fit a quadratic function by learning the coefficients a, b, c.\n     * const xs = tf.tensor1d([0, 1, 2, 3]);\n     * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);\n     *\n     * const a = tf.scalar(Math.random()).variable();\n     * const b = tf.scalar(Math.random()).variable();\n     * const c = tf.scalar(Math.random()).variable();\n     *\n     * // y = a * x^2 + b * x + c.\n     * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);\n     * const loss = (pred, label) => pred.sub(label).square().mean();\n     *\n     * const learningRate = 0.01;\n     * const optimizer = tf.train.sgd(learningRate);\n     *\n     * // Train the model.\n     * for (let i = 0; i < 10; i++) {\n     *   optimizer.minimize(() => loss(f(xs), ys));\n     * }\n     *\n     * // Make predictions.\n     * console.log(\n     *     `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);\n     * const preds = f(xs).dataSync();\n     * preds.forEach((pred, i) => {\n     *   console.log(`x: ${i}, pred: ${pred}`);\n     * });\n     * ```\n     *\n     * @param learningRate The learning rate to use for the SGD algorithm.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static sgd(learningRate) {\n        return new SGDOptimizer(learningRate);\n    }\n    /**\n     * Constructs a `tf.MomentumOptimizer` that uses momentum gradient\n     * descent.\n     *\n     * See\n     * [http://proceedings.mlr.press/v28/sutskever13.pdf](\n     * http://proceedings.mlr.press/v28/sutskever13.pdf)\n     *\n     * @param learningRate The learning rate to use for the Momentum gradient\n     * descent algorithm.\n     * @param momentum The momentum to use for the momentum gradient descent\n     * algorithm.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static momentum(learningRate, momentum, useNesterov = false) {\n        return new MomentumOptimizer(learningRate, momentum, useNesterov);\n    }\n    /**\n     * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient\n     * descent. This implementation uses plain momentum and is not centered\n     * version of RMSProp.\n     *\n     * See\n     * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](\n     * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\n     *\n     * @param learningRate The learning rate to use for the RMSProp gradient\n     * descent algorithm.\n     * @param decay The discounting factor for the history/coming gradient.\n     * @param momentum The momentum to use for the RMSProp gradient descent\n     * algorithm.\n     * @param epsilon Small value to avoid zero denominator.\n     * @param centered If true, gradients are normalized by the estimated\n     * variance of the gradient.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static rmsprop(learningRate, decay = .9, momentum = 0.0, epsilon = null, centered = false) {\n        return new RMSPropOptimizer(learningRate, decay, momentum, epsilon, centered);\n    }\n    /**\n     * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.\n     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n     *\n     * @param learningRate The learning rate to use for the Adam gradient\n     * descent algorithm.\n     * @param beta1 The exponential decay rate for the 1st moment estimates.\n     * @param beta2 The exponential decay rate for the 2nd moment estimates.\n     * @param epsilon A small constant for numerical stability.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adam(learningRate = 0.001, beta1 = 0.9, beta2 = 0.999, epsilon = null) {\n        return new AdamOptimizer(learningRate, beta1, beta2, epsilon);\n    }\n    /**\n     * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.\n     * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)\n     *\n     * @param learningRate The learning rate to use for the Adadelta gradient\n     * descent algorithm.\n     * @param rho The learning rate decay over each update.\n     * @param epsilon A constant epsilon used to better condition the grad\n     * update.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adadelta(learningRate = .001, rho = .95, epsilon = null) {\n        return new AdadeltaOptimizer(learningRate, rho, epsilon);\n    }\n    /**\n     * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.\n     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n     *\n     * @param learningRate The learning rate to use for the Adamax gradient\n     * descent algorithm.\n     * @param beta1 The exponential decay rate for the 1st moment estimates.\n     * @param beta2 The exponential decay rate for the 2nd moment estimates.\n     * @param epsilon A small constant for numerical stability.\n     * @param decay The learning rate decay over each update.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adamax(learningRate = 0.002, beta1 = 0.9, beta2 = 0.999, epsilon = null, decay = 0.0) {\n        return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);\n    }\n    /**\n     * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.\n     * See\n     * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](\n     * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\n     * or\n     * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](\n     * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)\n     *\n     * @param learningRate The learning rate to use for the Adagrad gradient\n     * descent algorithm.\n     * @param initialAccumulatorValue Starting value for the accumulators, must be\n     * positive.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adagrad(learningRate, initialAccumulatorValue = 0.1) {\n        return new AdagradOptimizer(learningRate, initialAccumulatorValue);\n    }\n}\n//# sourceMappingURL=optimizer_constructors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// So typings can propagate.\nimport { AdadeltaOptimizer } from './optimizers/adadelta_optimizer';\nimport { AdagradOptimizer } from './optimizers/adagrad_optimizer';\nimport { AdamOptimizer } from './optimizers/adam_optimizer';\nimport { AdamaxOptimizer } from './optimizers/adamax_optimizer';\nimport { MomentumOptimizer } from './optimizers/momentum_optimizer';\nimport { OptimizerConstructors } from './optimizers/optimizer_constructors';\nimport { RMSPropOptimizer } from './optimizers/rmsprop_optimizer';\nimport { SGDOptimizer } from './optimizers/sgd_optimizer';\n// tslint:disable-next-line:no-unused-expression\n[MomentumOptimizer, SGDOptimizer, AdadeltaOptimizer, AdagradOptimizer,\n    RMSPropOptimizer, AdamaxOptimizer, AdamOptimizer];\nexport const train = {\n    sgd: OptimizerConstructors.sgd,\n    momentum: OptimizerConstructors.momentum,\n    adadelta: OptimizerConstructors.adadelta,\n    adagrad: OptimizerConstructors.adagrad,\n    rmsprop: OptimizerConstructors.rmsprop,\n    adamax: OptimizerConstructors.adamax,\n    adam: OptimizerConstructors.adam\n};\n//# sourceMappingURL=train.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst delayCallback = (() => {\n    if (typeof requestAnimationFrame !== 'undefined') {\n        return requestAnimationFrame;\n    }\n    else if (typeof setImmediate !== 'undefined') {\n        return setImmediate;\n    }\n    return (f) => f(); // no delays\n})();\n/**\n * Returns a promise that resolve when a requestAnimationFrame has completed.\n *\n * On Node.js this uses setImmediate instead of requestAnimationFrame.\n *\n * This is simply a sugar method so that users can do the following:\n * `await tf.nextFrame();`\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nfunction nextFrame() {\n    return new Promise(resolve => delayCallback(() => resolve()));\n}\nexport { nextFrame };\n//# sourceMappingURL=browser_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Returns the image center in pixels.\nexport function getImageCenter(center, imageHeight, imageWidth) {\n    const centerX = imageWidth * (typeof center === 'number' ? center : center[0]);\n    const centerY = imageHeight * (typeof center === 'number' ? center : center[1]);\n    return [centerX, centerY];\n}\n//# sourceMappingURL=rotate_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Gets the new shape of the input Tensor after it's been reshaped\n * to:\n * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),\n * inputShape[1], ..., inputShape[N-1]]\n *\n * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshaped(inputShape, blockShape, prod, batchToSpace = true) {\n    let reshaped = [];\n    if (batchToSpace) {\n        reshaped = reshaped.concat(blockShape.slice(0));\n        reshaped.push(inputShape[0] / prod);\n        reshaped = reshaped.concat(inputShape.slice(1));\n    }\n    else {\n        reshaped = reshaped.concat(inputShape[0]);\n        const spatialLength = blockShape.length;\n        for (let i = 0; i < spatialLength; ++i) {\n            reshaped =\n                reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);\n        }\n        reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n    }\n    return reshaped;\n}\n/**\n * Gets the permutation that will transpose the dimensions of the\n * reshaped tensor to shape:\n *\n * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,\n * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {\n    const permuted = [];\n    if (batchToSpace) {\n        permuted.push(blockShapeRank);\n        for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {\n            if (i <= 2 * blockShapeRank) {\n                permuted.push(i);\n                permuted.push(i - (blockShapeRank + 1));\n            }\n            else {\n                permuted.push(i);\n            }\n        }\n    }\n    else {\n        const permutedBeforeBatch = [];\n        const permutedAfterBatch = [];\n        for (let i = 1; i < reshapedRank; ++i) {\n            if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {\n                permutedAfterBatch.push(i);\n            }\n            else {\n                permutedBeforeBatch.push(i);\n            }\n        }\n        permuted.push(...permutedBeforeBatch);\n        permuted.push(0);\n        permuted.push(...permutedAfterBatch);\n    }\n    return permuted;\n}\n/**\n * Gets the shape of the reshaped and permuted input Tensor before any cropping\n * is applied.  The new shape will be:\n *\n * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,\n * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshapedPermuted(inputShape, blockShape, prod, batchToSpace = true) {\n    const reshapedPermuted = [];\n    if (batchToSpace) {\n        reshapedPermuted.push(inputShape[0] / prod);\n    }\n    else {\n        reshapedPermuted.push(inputShape[0] * prod);\n    }\n    for (let i = 1; i < inputShape.length; ++i) {\n        if (i <= blockShape.length) {\n            if (batchToSpace) {\n                reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);\n            }\n            else {\n                reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);\n            }\n        }\n        else {\n            reshapedPermuted.push(inputShape[i]);\n        }\n    }\n    return reshapedPermuted;\n}\n/**\n * Converts the crops argument into the beginning coordinates of a slice\n * operation.\n */\nexport function getSliceBeginCoords(crops, blockShape) {\n    const sliceBeginCoords = [0];\n    for (let i = 0; i < blockShape; ++i) {\n        sliceBeginCoords.push(crops[i][0]);\n    }\n    return sliceBeginCoords;\n}\n/**\n * Converts the crops argument into the size of a slice operation.  When\n * combined with getSliceBeginCoords this function allows the reshaped and\n * permuted Tensor to be cropped to its final output shape of:\n *\n * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,\n * inputShape[M] * blockShape[M-1] -crops[M-1,0] -\n * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getSliceSize(uncroppedShape, crops, blockShape) {\n    const sliceSize = uncroppedShape.slice(0, 1);\n    for (let i = 0; i < blockShape; ++i) {\n        sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);\n    }\n    return sliceSize;\n}\n//# sourceMappingURL=array_ops_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const SELU_SCALEALPHA = 1.7580993408473768599402175208123;\nexport const SELU_SCALE = 1.0507009873554804934193349852946;\n//# sourceMappingURL=selu_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const ERF_P = 0.3275911;\nexport const ERF_A1 = 0.254829592;\nexport const ERF_A2 = -0.284496736;\nexport const ERF_A3 = 1.421413741;\nexport const ERF_A4 = -1.453152027;\nexport const ERF_A5 = 1.061405429;\n//# sourceMappingURL=erf_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nexport function warn(...msg) {\n    if (!env().getBool('IS_TEST')) {\n        console.warn(...msg);\n    }\n}\nexport function log(...msg) {\n    if (!env().getBool('IS_TEST')) {\n        console.log(...msg);\n    }\n}\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Merges real and imaginary Float32Arrays into a single complex Float32Array.\n *\n * The memory layout is interleaved as follows:\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n * complex: [r0, i0, r1, i1, r2, i2]\n *\n * This is the inverse of splitRealAndImagArrays.\n *\n * @param real The real values of the complex tensor values.\n * @param imag The imag values of the complex tensor values.\n * @returns A complex tensor as a Float32Array with merged values.\n */\nexport function mergeRealAndImagArrays(real, imag) {\n    if (real.length !== imag.length) {\n        throw new Error(`Cannot merge real and imag arrays of different lengths. real:` +\n            `${real.length}, imag: ${imag.length}.`);\n    }\n    const result = new Float32Array(real.length * 2);\n    for (let i = 0; i < result.length; i += 2) {\n        result[i] = real[i / 2];\n        result[i + 1] = imag[i / 2];\n    }\n    return result;\n}\n/**\n * Splits a complex Float32Array into real and imag parts.\n *\n * The memory layout is interleaved as follows:\n * complex: [r0, i0, r1, i1, r2, i2]\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n *\n * This is the inverse of mergeRealAndImagArrays.\n *\n * @param complex The complex tensor values.\n * @returns An object with real and imag Float32Array components of the complex\n *     tensor.\n */\nexport function splitRealAndImagArrays(complex) {\n    const real = new Float32Array(complex.length / 2);\n    const imag = new Float32Array(complex.length / 2);\n    for (let i = 0; i < complex.length; i += 2) {\n        real[i / 2] = complex[i];\n        imag[i / 2] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Extracts even indexed complex values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithEvenIndex(complex) {\n    const len = Math.ceil(complex.length / 4);\n    const real = new Float32Array(len);\n    const imag = new Float32Array(len);\n    for (let i = 0; i < complex.length; i += 4) {\n        real[Math.floor(i / 4)] = complex[i];\n        imag[Math.floor(i / 4)] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Extracts odd indexed comple values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithOddIndex(complex) {\n    const len = Math.floor(complex.length / 4);\n    const real = new Float32Array(len);\n    const imag = new Float32Array(len);\n    for (let i = 2; i < complex.length; i += 4) {\n        real[Math.floor(i / 4)] = complex[i];\n        imag[Math.floor(i / 4)] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Get the map representing a complex value in the given array.\n * @param complex The complex tensor values.\n * @param index An index of the target complex value.\n */\nexport function getComplexWithIndex(complex, index) {\n    const real = complex[index * 2];\n    const imag = complex[index * 2 + 1];\n    return { real, imag };\n}\n/**\n * Insert a given complex value into the TypedArray.\n * @param data The array in which the complex value is inserted.\n * @param c The complex value to be inserted.\n * @param index An index of the target complex value.\n */\nexport function assignToTypedArray(data, real, imag, index) {\n    data[index * 2] = real;\n    data[index * 2 + 1] = imag;\n}\n/**\n * Make the list of exponent terms used by FFT.\n */\nexport function exponents(n, inverse) {\n    const real = new Float32Array(n / 2);\n    const imag = new Float32Array(n / 2);\n    for (let i = 0; i < Math.ceil(n / 2); i++) {\n        const x = (inverse ? 2 : -2) * Math.PI * (i / n);\n        real[i] = Math.cos(x);\n        imag[i] = Math.sin(x);\n    }\n    return { real, imag };\n}\n/**\n * Make the exponent term used by FFT.\n */\nexport function exponent(k, n, inverse) {\n    const x = (inverse ? 2 : -2) * Math.PI * (k / n);\n    const real = Math.cos(x);\n    const imag = Math.sin(x);\n    return { real, imag };\n}\n//# sourceMappingURL=complex_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { cast } from '../ops/cast';\nimport { scalar } from '../ops/scalar';\nimport { tensor1d } from '../ops/tensor1d';\nimport { zeros } from '../ops/zeros';\nimport { hasEncodingLoss, makeZerosTypedArray } from '../util';\n// Utilities needed by backend consumers of tf-core.\nexport * from '../ops/axis_util';\nexport * from '../ops/broadcast_util';\nexport * from '../ops/concat_util';\nexport * from '../ops/conv_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/reduce_util';\nimport * as slice_util from '../ops/slice_util';\nexport { slice_util };\nexport { upcastType } from '../types';\nexport * from '../ops/rotate_util';\nexport * from '../ops/array_ops_util';\nexport * from '../ops/gather_nd_util';\nexport * from '../ops/scatter_nd_util';\nexport * from '../ops/selu_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/erf_util';\nexport * from '../log';\nexport * from '../backends/complex_util';\nexport * from '../ops/split_util';\nimport * as segment_util from '../ops/segment_util';\nexport { segment_util };\nexport function castTensor(x, dtype, backend) {\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return x.clone();\n        }\n        const zerosTensor = zeros(x.shape);\n        const floatX = cast(x, 'float32');\n        const result = backend.complex(floatX, zerosTensor);\n        zerosTensor.dispose();\n        floatX.dispose();\n        return result;\n    }\n    if (!hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        return ENGINE.makeTensorFromDataId(x.dataId, x.shape, dtype);\n    }\n    if (x.dtype === 'complex64') {\n        const real = backend.real(x);\n        const result = cast(real, dtype);\n        real.dispose();\n        return result;\n    }\n    if (dtype === 'int32') {\n        return backend.int(x);\n    }\n    else if (dtype === 'bool') {\n        const zero = scalar(0, x.dtype);\n        const result = backend.notEqual(x, zero);\n        zero.dispose();\n        return result;\n    }\n    else {\n        throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n    }\n}\nexport function reshapeTensor(x, shape) {\n    return ENGINE.makeTensorFromDataId(x.dataId, shape, x.dtype);\n}\nexport function linspaceImpl(start, stop, num) {\n    const step = (stop - start) / (num - 1);\n    const values = makeZerosTypedArray(num, 'float32');\n    values[0] = start;\n    for (let i = 1; i < values.length; i++) {\n        values[i] = values[i - 1] + step;\n    }\n    return tensor1d(values, 'float32');\n}\n//# sourceMappingURL=backend_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { slice } from '../ops/slice';\n// TODO(annxingyuan): Use this helper in WASM Split kernel once intermediate\n// kernels have been modularized in WebGL and CPU\n// https://github.com/tensorflow/tfjs/issues/2822.\n/** Shared implementation of the split kernel across WebGL and CPU. */\nexport function split(x, sizeSplits, axis) {\n    const begin = new Array(x.rank).fill(0);\n    const size = x.shape.slice();\n    return sizeSplits.map(s => {\n        const sliceSize = [...size];\n        sliceSize[axis] = s;\n        const sliceT = slice(x, begin, sliceSize);\n        begin[axis] += s;\n        return sliceT;\n    });\n}\n//# sourceMappingURL=split_shared.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * An implementation of the tile kernel shared between webgl and cpu for string\n * tensors only.\n */\nimport { buffer } from '../ops/buffer';\nexport function tile(xBuf, reps) {\n    const newShape = new Array(xBuf.rank);\n    for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = xBuf.shape[i] * reps[i];\n    }\n    const result = buffer(newShape, xBuf.dtype);\n    for (let i = 0; i < result.values.length; ++i) {\n        const newLoc = result.indexToLoc(i);\n        const originalLoc = new Array(xBuf.rank);\n        for (let j = 0; j < originalLoc.length; j++) {\n            originalLoc[j] = newLoc[j] % xBuf.shape[j];\n        }\n        const originalIndex = xBuf.locToIndex(originalLoc);\n        result.values[i] = xBuf.values[originalIndex];\n    }\n    return result.toTensor();\n}\n//# sourceMappingURL=tile_impl.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the TopK kernel shared between webgl and cpu. */\nimport { tensor } from '../ops/tensor';\nimport { getTypedArrayFromDType } from '../util';\nexport function topkImpl(x, xShape, xDtype, k, sorted) {\n    // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.\n    const lastDim = xShape[xShape.length - 1];\n    const [batch, size] = [x.length / lastDim, lastDim];\n    const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);\n    const allTopKIndices = getTypedArrayFromDType('int32', batch * k);\n    for (let b = 0; b < batch; b++) {\n        const offset = b * size;\n        const vals = x.subarray(offset, offset + size);\n        const valAndInd = [];\n        for (let i = 0; i < vals.length; i++) {\n            valAndInd.push({ value: vals[i], index: i });\n        }\n        valAndInd.sort((a, b) => b.value - a.value);\n        const outOffset = b * k;\n        const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n        const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n        for (let i = 0; i < k; i++) {\n            topKVals[i] = valAndInd[i].value;\n            topKIndices[i] = valAndInd[i].index;\n        }\n    }\n    // Reshape back to the original input shape, except that the last\n    // dimension is k.\n    const outputShape = xShape.slice();\n    outputShape[outputShape.length - 1] = k;\n    return [\n        tensor(allTopKVals, outputShape, xDtype),\n        tensor(allTopKIndices, outputShape, 'int32')\n    ];\n}\n//# sourceMappingURL=topk_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { nonMaxSuppressionV3Impl, nonMaxSuppressionV4Impl, nonMaxSuppressionV5Impl } from './non_max_suppression_impl';\nexport { split } from './split_shared';\nexport { tile } from './tile_impl';\nexport { topkImpl } from './topk_impl';\nexport { whereImpl } from './where_impl';\n//# sourceMappingURL=kernel_impls.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// base.ts is tfjs-core without auto registration of things like flags,\n// gradients, chained ops or the opHandler. See base_side_effects.ts for parts\n// tfjs core that are required side effects.\n/**\n * @fileoverview\n * @suppress {partialAlias} Optimization disabled due to passing the module\n * object into a function below:\n *\n *   import * as ops from './ops/ops';\n *   setOpHandler(ops);\n */\n// Serialization.\nimport * as io from './io/io';\nimport * as math from './math';\nimport * as browser from './ops/browser';\nimport * as gather_util from './ops/gather_nd_util';\nimport * as scatter_util from './ops/scatter_nd_util';\nimport * as slice_util from './ops/slice_util';\nimport * as serialization from './serialization';\nimport * as tensor_util from './tensor_util';\nimport * as test_util from './test_util';\nimport * as util from './util';\nimport { version } from './version';\n// Optimizers.\nexport { AdadeltaOptimizer } from './optimizers/adadelta_optimizer';\nexport { AdagradOptimizer } from './optimizers/adagrad_optimizer';\nexport { AdamOptimizer } from './optimizers/adam_optimizer';\nexport { AdamaxOptimizer } from './optimizers/adamax_optimizer';\nexport { MomentumOptimizer } from './optimizers/momentum_optimizer';\nexport { Optimizer } from './optimizers/optimizer';\nexport { RMSPropOptimizer } from './optimizers/rmsprop_optimizer';\nexport { SGDOptimizer } from './optimizers/sgd_optimizer';\nexport { Tensor, TensorBuffer, Variable } from './tensor';\nexport { Rank, sumOutType, upcastType } from './types';\nexport * from './ops/ops';\nexport { Reduction } from './ops/loss_ops_utils';\nexport * from './train';\nexport * from './globals';\nexport * from './kernel_registry';\nexport { customGrad, grad, grads, valueAndGrad, valueAndGrads, variableGrads } from './gradients';\nexport { Environment, env, ENV } from './environment';\nexport { version as version_core };\n// Top-level method exports.\nexport { nextFrame } from './browser_util';\n// Second level exports.\nimport * as backend_util from './backends/backend_util';\nimport * as device_util from './device_util';\nexport { browser, io, math, serialization, test_util, util, backend_util, tensor_util, slice_util, gather_util, scatter_util, device_util };\nimport * as kernel_impls from './backends/kernel_impls';\nexport { kernel_impls };\n// Backend specific.\nexport { KernelBackend, DataStorage } from './backends/backend';\n// Export all kernel names / info.\nexport * from './kernel_names';\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const absGradConfig = {\n    kernelName: Abs,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, step(cast(x, 'float32'), -1)) };\n    }\n};\n//# sourceMappingURL=Abs_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acos } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { neg } from '../ops/neg';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const acosGradConfig = {\n    kernelName: Acos,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = square(cast(x, 'float32'));\n                const b = sqrt(sub(scalar(1), a));\n                return neg(div(dy, b));\n            }\n        };\n    }\n};\n//# sourceMappingURL=Acos_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acosh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const acoshGradConfig = {\n    kernelName: Acosh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = sqrt(sub(square(cast(x, 'float32')), 1));\n                return div(dy, a);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Acosh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const addGradConfig = {\n    kernelName: Add,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Add_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AddN } from '../kernel_names';\nexport const addNGradConfig = {\n    kernelName: AddN,\n    saveAllInputs: true,\n    gradFunc: (dy, saved) => {\n        const ders = {};\n        saved.forEach((_, i) => {\n            ders[i] = () => dy.clone();\n        });\n        return ders;\n    }\n};\n//# sourceMappingURL=AddN_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ArgMax } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const argMaxGradConfig = {\n    kernelName: ArgMax,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => zerosLike(x) };\n    }\n};\n//# sourceMappingURL=ArgMax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ArgMin } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const argMinGradConfig = {\n    kernelName: ArgMin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => zerosLike(x) };\n    }\n};\n//# sourceMappingURL=ArgMin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asin } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const asinGradConfig = {\n    kernelName: Asin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, 'float32'))))) };\n    }\n};\n//# sourceMappingURL=Asin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asinh } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nexport const asinhGradConfig = {\n    kernelName: Asinh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = sqrt(add(scalar(1), square(cast(x, 'float32'))));\n                return div(dy, a);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Asinh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan2 } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const atan2GradConfig = {\n    kernelName: Atan2,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const d = add(square(a), square(b));\n            let res = mul(dy, div(b, d));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            const d = add(square(a), square(b));\n            let res = neg(mul(dy, div(a, d)));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Atan2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nexport const atanGradConfig = {\n    kernelName: Atan,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, add(square(cast(x, 'float32')), 1)) };\n    }\n};\n//# sourceMappingURL=Atan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atanh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { scalar } from '../ops/scalar';\nexport const atanhGradConfig = {\n    kernelName: Atanh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, sub(scalar(1), square(cast(x, 'float32')))) };\n    }\n};\n//# sourceMappingURL=Atanh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool3DBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of a 3d avg pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0. The dilation\n *     rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction avgPool3dBackprop_(dy, input, filterSize, strides, dilations = [1, 1, 1], pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'avgPool3dBackprop');\n    const $input = convertToTensor(input, 'input', 'avgPool3dBackprop');\n    let dy5D = $dy;\n    let input5D = $input;\n    let reshapedTo5D = false;\n    if ($input.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n        input5D = reshape($input, [\n            1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n        ]);\n    }\n    util.assert(dy5D.rank === 5, () => `Error in avgPool3dBackprop: dy must be rank 5 but got rank ` +\n        `${dy5D.rank}.`);\n    util.assert(input5D.rank === 5, () => `Error in avgPool3dBackprop: input must be rank 5 but got rank ` +\n        `${input5D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool3dBackprop: Either strides or dilations ' +\n        `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n        return backend.avgPool3dBackprop(dy5D, input5D, convInfo);\n    };\n    const inputs = { dy: dy5D, input: input5D };\n    const attrs = { filterSize, strides, dilations, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool3DBackprop, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const avgPool3dBackprop = op({ avgPool3dBackprop_ });\n//# sourceMappingURL=avg_pool_3d_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool3D } from '../kernel_names';\nimport { avgPool3dBackprop } from '../ops/avg_pool_3d_backprop';\nexport const avgPool3DGradConfig = {\n    kernelName: AvgPool3D,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { filterSize, strides, dilations, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1, 1] : dilations;\n        return {\n            x: () => avgPool3dBackprop(dy, x, filterSize, strides, $dilations, pad, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=AvgPool3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPoolBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of an 2D avg pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The input image, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction avgPoolBackprop_(dy, input, filterSize, strides, pad) {\n    const $dy = convertToTensor(dy, 'dy', 'avgPoolBackprop');\n    const $input = convertToTensor(input, 'input', 'avgPoolBackprop');\n    util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n    let input4D = $input;\n    let dy4D = $dy;\n    let reshapedTo4D = false;\n    if ($input.rank === 3) {\n        reshapedTo4D = true;\n        input4D =\n            reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);\n        dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);\n    }\n    util.assert(dy4D.rank === 4, () => `Error in avgPoolBackprop: dy must be rank 4 but got rank ` +\n        `${dy4D.rank}.`);\n    util.assert(input4D.rank === 4, () => `Error in avgPoolBackprop: input must be rank 4 but got rank ` +\n        `${input4D.rank}.`);\n    const forward = backend => {\n        const convInfo = conv_util.computePool2DInfo(input4D.shape, filterSize, strides, 1 /* dilations */, pad);\n        return backend.avgPoolBackprop(dy4D, input4D, convInfo);\n    };\n    const inputs = { dy: dy4D, input: input4D };\n    const attrs = { filterSize, strides, pad };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, AvgPoolBackprop, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const avgPoolBackprop = op({ avgPoolBackprop_ });\n//# sourceMappingURL=avg_pool_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool } from '../kernel_names';\nimport { avgPoolBackprop } from '../ops/avg_pool_backprop';\nexport const avgPoolGradConfig = {\n    kernelName: AvgPool,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { filterSize, strides, pad } = attrs;\n        return {\n            x: () => avgPoolBackprop(dy, x, filterSize, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=AvgPool_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchMatMul } from '../kernel_names';\nimport { matMul } from '../ops/mat_mul';\nexport const batchMatMulGradConfig = {\n    kernelName: BatchMatMul,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved, attrs) => {\n        const [a, b] = saved;\n        const { transposeA, transposeB } = attrs;\n        if (!transposeA && !transposeB) {\n            return {\n                a: () => matMul(dy, b, false, true),\n                b: () => matMul(a, dy, true, false)\n            };\n        }\n        else if (!transposeA && transposeB) {\n            return {\n                a: () => matMul(dy, b, false, false),\n                b: () => matMul(dy, a, true, false)\n            };\n        }\n        else if (transposeA && !transposeB) {\n            return {\n                a: () => matMul(b, dy, false, true),\n                b: () => matMul(a, dy, false, false)\n            };\n        }\n        else {\n            return {\n                a: () => matMul(b, dy, true, true),\n                b: () => matMul(dy, a, true, true)\n            };\n        }\n    }\n};\n//# sourceMappingURL=BatchMatMul_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchToSpaceND } from '../kernel_names';\nimport { spaceToBatchND } from '../ops/space_to_batch_nd';\nexport const batchToSpaceNDGradConfig = {\n    kernelName: BatchToSpaceND,\n    gradFunc: (dy, saved, attrs) => {\n        const { blockShape, crops } = attrs;\n        return { x: () => spaceToBatchND(dy, blockShape, crops) };\n    }\n};\n//# sourceMappingURL=BatchToSpaceND_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BroadcastTo } from '../kernel_names';\nimport { sum } from '../ops/sum';\nexport const broadcastToGradConfig = {\n    kernelName: BroadcastTo,\n    gradFunc: (dy, saved, attrs) => {\n        const broadCastToAttrs = attrs;\n        const inputShape = broadCastToAttrs.inputShape;\n        const outputShape = broadCastToAttrs.shape;\n        const reps = Array.from(outputShape);\n        for (let i = inputShape.length - 1; i >= 0; i--) {\n            if (inputShape[i] === outputShape[i]) {\n                reps[i] = 1;\n            }\n            else if (inputShape[i] !== 1) {\n                throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);\n            }\n        }\n        const axes = [];\n        for (let i = 0; i < reps.length; i++) {\n            if (reps[i] > 1) {\n                axes.push(i);\n            }\n        }\n        return { x: () => sum(dy, axes, true /* keepDims */) };\n    }\n};\n//# sourceMappingURL=BroadcastTo_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cast } from '../kernel_names';\nexport const castGradConfig = {\n    kernelName: Cast,\n    gradFunc: (dy) => {\n        return { x: () => dy.clone() };\n    }\n};\n//# sourceMappingURL=Cast_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const ceilGradConfig = {\n    kernelName: Ceil,\n    gradFunc: (dy) => {\n        // TODO(manrajgrover): Return null for gradients when backprop supports it.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Ceil_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ClipByValue } from '../kernel_names';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { lessEqual } from '../ops/less_equal';\nimport { logicalAnd } from '../ops/logical_and';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const clipByValueGradConfig = {\n    kernelName: ClipByValue,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { clipValueMin, clipValueMax } = attrs;\n        return {\n            x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy)),\n        };\n    }\n};\n//# sourceMappingURL=ClipByValue_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Concat } from '../kernel_names';\nimport { split } from '../ops/split';\nimport { parseAxisParam } from '../util';\nexport const concatGradConfig = {\n    kernelName: Concat,\n    saveAllInputs: true,\n    gradFunc: (dy, saved, attrs) => {\n        const shapes = saved.map(t => t.shape);\n        const { axis } = attrs;\n        const $axis = parseAxisParam(axis, saved[0].shape)[0];\n        const sizeSplits = shapes.map(s => s[$axis]);\n        const derTensors = split(dy, sizeSplits, $axis);\n        return derTensors.map(t => () => t);\n    }\n};\n//# sourceMappingURL=Concat_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv2D } from '../kernel_names';\nimport { conv2DBackpropFilter } from '../ops/conv2d_backprop_filter';\nimport { conv2DBackpropInput } from '../ops/conv2d_backprop_input';\nimport * as conv_util from '../ops/conv_util';\nimport * as util from '../util';\nexport const conv2DGradConfig = {\n    kernelName: Conv2D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x4D, $filter] = saved;\n        const { dilations, strides, pad, dataFormat } = attrs;\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of conv2D: dilation rates greater than 1 ' +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n        return {\n            x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad, dataFormat),\n            filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad, dataFormat)\n        };\n    }\n};\n//# sourceMappingURL=Conv2D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv2DBackpropInput } from '../kernel_names';\nimport { conv2d } from '../ops/conv2d';\nimport { conv2DBackpropFilter } from '../ops/conv2d_backprop_filter';\nexport const conv2DBackpropInputGradConfig = {\n    kernelName: Conv2DBackpropInput,\n    inputsToSave: ['dy', 'filter'],\n    gradFunc: (ddx, saved, attrs) => {\n        const [dy, filter] = saved;\n        const { strides, pad, dataFormat, dimRoundingMode } = attrs;\n        return {\n            dy: () => conv2d(ddx, filter, strides, pad, dataFormat, 1 /* dilations */, dimRoundingMode),\n            filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad, dataFormat, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=Conv2DBackpropInput_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3DBackpropFilterV2 } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the filter of a 3D convolution.\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     [batch, depth, height, width, inChannels]. If rank 4, batch of 1 is\n *     assumed.\n * @param dy The dy image, of rank 5 or rank 4, of shape\n *     [batch, depth, height, width, outDepth]. If rank 4, batch of 1 is\n *     assumed.\n * @param filterShape The shape of the filter, length 5,\n *     [filterDepth, filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideDepth, strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction conv3DBackpropFilter_(x, dy, filterShape, strides, pad) {\n    let x5D = x;\n    if (x.rank === 4) {\n        x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);\n    }\n    let dy5D = dy;\n    if (dy5D.rank === 4) {\n        dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ` +\n        `${x5D.shape}.`);\n    util.assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ` +\n        `${dy5D.shape}.`);\n    util.assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ` +\n        `${filterShape}.`);\n    util.assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must ` +\n        `match input depth in filter (${filterShape[3]}.`);\n    util.assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must ` +\n        `match output depth for filter (${filterShape[4]}).`);\n    const forward = backend => {\n        const dilations = 1;\n        const convInfo = conv_util.computeConv3DInfo(x5D.shape, filterShape, strides, dilations, pad);\n        return backend.conv3dDerFilter(x5D, dy5D, convInfo);\n    };\n    const inputs = { x: x5D, y: dy5D };\n    const attrs = { strides, pad };\n    return ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropFilterV2, attrs);\n}\nexport const conv3DBackpropFilter = op({ conv3DBackpropFilter_ });\n//# sourceMappingURL=conv3d_backprop_filter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv3D } from '../kernel_names';\nimport { conv3DBackpropFilter } from '../ops/conv3d_backprop_filter';\nimport { conv3DBackpropInput } from '../ops/conv3d_backprop_input';\nimport { tupleValuesAreOne } from '../ops/conv_util';\nimport * as util from '../util';\nexport const conv3DGradConfig = {\n    kernelName: Conv3D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const { dilations, strides, pad } = attrs;\n        util.assert(tupleValuesAreOne(dilations), () => 'Error in gradient of conv3D: dilation rates greater than 1 are ' +\n            `not yet supported in gradients. Got dilations '${dilations}'`);\n        const [x5D, $filter] = saved;\n        return {\n            x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad),\n            filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=Conv3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { sin } from '../ops/sin';\nexport const cosGradConfig = {\n    kernelName: Cos,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(neg(sin(cast(x, 'float32'))), dy) };\n    }\n};\n//# sourceMappingURL=Cos_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cosh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { sinh } from '../ops/sinh';\nexport const coshGradConfig = {\n    kernelName: Cosh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(sinh(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Cosh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cumsum } from '../kernel_names';\nimport { getAxesPermutation } from '../ops/axis_util';\nimport { cumsum } from '../ops/cumsum';\nimport { transpose } from '../ops/transpose';\nexport const cumsumGradConfig = {\n    kernelName: Cumsum,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { axis, exclusive, reverse } = attrs;\n        return {\n            x: () => {\n                const permutation = getAxesPermutation([axis], x.rank);\n                let out = cumsum(dy, axis, exclusive, !reverse);\n                if (permutation != null) {\n                    out = transpose(out, permutation);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Cumsum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { DepthwiseConv2dNative } from '../kernel_names';\nimport * as conv_util from '../ops/conv_util';\nimport { depthwiseConv2dNativeBackpropFilter } from '../ops/depthwise_conv2d_native_backprop_filter';\nimport { depthwiseConv2dNativeBackpropInput } from '../ops/depthwise_conv2d_native_backprop_input';\nimport * as util from '../util';\nexport const depthwiseConv2dNativeGradConfig = {\n    kernelName: DepthwiseConv2dNative,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const { dilations, strides, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1] : dilations;\n        util.assert(conv_util.tupleValuesAreOne($dilations), () => 'Error in gradient of depthwiseConv2dNative: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${$dilations}'`);\n        const [x, filter] = saved;\n        util.assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be ` +\n            `rank 4, but got rank ${x.rank}.`);\n        util.assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be ` +\n            `rank 4, but got rank ${filter.rank}.`);\n        util.assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input ` +\n            `channels (${x.shape[3]}) must match the inChannels dimension ` +\n            `in filter ${filter.shape[2]}.`);\n        util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, $dilations), () => 'Error in gradient of depthwiseConv2d: Either strides or ' +\n            `dilations must be  1. Got strides ${strides} and dilations ` +\n            `'${$dilations}'.`);\n        if (dimRoundingMode != null) {\n            util.assert(util.isInt(pad), () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n                `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n        }\n        const convInfo = conv_util.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad, dimRoundingMode, true /* depthwise */);\n        return {\n            x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, convInfo),\n            filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, convInfo),\n        };\n    }\n};\n//# sourceMappingURL=DepthwiseConv2dNative_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput } from '../kernel_names';\nexport const dilation2dGradConfig = {\n    kernelName: Dilation2D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, filter] = saved;\n        const inputInputs = { x, filter, dy };\n        const filterInputs = { x, filter, dy };\n        return {\n            x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),\n            filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)\n        };\n    }\n};\n//# sourceMappingURL=Dilation2D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const divGradConfig = {\n    kernelName: Div,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = div(dy, cast(b, 'float32'));\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            let res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = reshape(sum(res, reduceAxes), b.shape);\n            }\n            const tmp = square(b);\n            return neg(div(res, cast(tmp, 'float32')));\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Div_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Elu, EluGrad } from '../kernel_names';\nexport const eluGradConfig = {\n    kernelName: Elu,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        const backPropKernelFunc = (backend) => {\n            return backend.eluDer(dy, y);\n        };\n        const inputs = { dy, y };\n        return {\n            x: () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* grad */, EluGrad)\n        };\n    }\n};\n//# sourceMappingURL=Elu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Erf } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { square } from '../ops/square';\nexport const erfGradConfig = {\n    kernelName: Erf,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));\n        return { x: () => mul(dy, a) };\n    }\n};\n//# sourceMappingURL=Erf_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '../kernel_names';\nimport { mul } from '../ops/mul';\nexport const expGradConfig = {\n    kernelName: Exp,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(dy, y) };\n    }\n};\n//# sourceMappingURL=Exp_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nexport const expm1GradConfig = {\n    kernelName: Expm1,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, exp(x)) };\n    }\n};\n//# sourceMappingURL=Expm1_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const floorGradConfig = {\n    kernelName: Floor,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Floor_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FloorDiv } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const floorDivGradConfig = {\n    kernelName: FloorDiv,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = div(dy, cast(b, 'float32'));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            let res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = reshape(sum(res, reduceAxes), b.shape);\n            }\n            const tmp = square(b);\n            return neg(div(res, cast(tmp, 'float32')));\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=FloorDiv_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedBatchNorm } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { getReductionAxes } from '../ops/broadcast_util';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { rsqrt } from '../ops/rsqrt';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nimport { tile } from '../ops/tile';\nexport const fusedBatchNormGradConfig = {\n    kernelName: FusedBatchNorm,\n    inputsToSave: ['x', 'mean', 'variance', 'scale'],\n    gradFunc: (dy, saved, attrs) => {\n        const { varianceEpsilon } = attrs;\n        const [x, mean, variance, scale] = saved;\n        const scaleValue = scale == null ? scalar(1) : scale;\n        const reductionAxes = getReductionAxes(mean.shape, x.shape);\n        const tileShape = [];\n        if (mean.rank === 1) {\n            for (let i = 0; i < x.shape.length - 1; ++i) {\n                tileShape.push(x.shape[i]);\n            }\n            tileShape.push(1);\n        }\n        const xMinusMean = sub(x, mean);\n        const dyTimesScaleValue = mul(dy, scaleValue);\n        const oneOverSqrtVariance = rsqrt(add(variance, scalar(varianceEpsilon)));\n        const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));\n        const derX = () => {\n            if (mean.rank === 1) {\n                return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean.shape[0]]), tileShape)), scaleValue), x.shape);\n            }\n            else {\n                return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);\n            }\n        };\n        const derMean = () => {\n            let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);\n            if (mean.rank === 1) {\n                meanDer = sum(meanDer, reductionAxes);\n            }\n            return reshape(meanDer, mean.shape);\n        };\n        const derVariance = () => {\n            let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);\n            if (mean.rank === 1) {\n                varianceDer = sum(varianceDer, reductionAxes);\n            }\n            return reshape(varianceDer, mean.shape);\n        };\n        const derScale = () => {\n            const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);\n            let scaleDer = mul(dy, xMinusMean2TimesRsqrt);\n            if (mean.rank === 1) {\n                scaleDer = sum(scaleDer, reductionAxes);\n            }\n            return reshape(scaleDer, mean.shape);\n        };\n        const derOffset = () => {\n            let offsetDer = dy;\n            if (mean.rank === 1) {\n                offsetDer = sum(offsetDer, reductionAxes);\n            }\n            return reshape(offsetDer, mean.shape);\n        };\n        return {\n            x: derX,\n            mean: derMean,\n            variance: derVariance,\n            scale: derScale,\n            offset: derOffset\n        };\n    }\n};\n//# sourceMappingURL=FusedBatchNorm_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GatherV2 } from '../kernel_names';\nimport { getUndoAxesPermutation } from '../ops/axis_util';\nimport { reshape } from '../ops/reshape';\nimport { transpose } from '../ops/transpose';\nimport { unsortedSegmentSum } from '../ops/unsorted_segment_sum';\nimport { parseAxisParam } from '../util';\nexport const gatherGradConfig = {\n    kernelName: GatherV2,\n    inputsToSave: ['x', 'indices'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, indices] = saved;\n        const { axis } = attrs;\n        const parsedAxis = parseAxisParam(axis, x.shape)[0];\n        const derX = () => {\n            const paramsShape = x.shape;\n            const indicesSize = indices.size;\n            const outerShape = paramsShape.slice(0, parsedAxis);\n            const outerDims = outerShape.length;\n            const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);\n            const innerDims = innerShape.length;\n            const outerAxesIndices = arrayRange(0, outerDims);\n            const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);\n            const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);\n            const values = reshape(dy, valuesShape);\n            const reshapedIndices = reshape(indices, [indicesSize]);\n            const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);\n            const valuesTranspose = transpose(values, transposeDims);\n            let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);\n            const invertTransposeDims = getUndoAxesPermutation(transposeDims);\n            paramsGrad = transpose(paramsGrad, invertTransposeDims);\n            return paramsGrad;\n        };\n        return { x: derX, indices: () => indices };\n    }\n};\nfunction arrayRange(start, stop) {\n    const result = [];\n    for (let i = start; i < stop; ++i) {\n        result.push(i);\n    }\n    return result;\n}\nfunction arrayConcat(arrays) {\n    const result = [];\n    for (let i = 0; i < arrays.length; ++i) {\n        for (let j = 0; j < arrays[i].length; ++j) {\n            result.push(arrays[i][j]);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=GatherV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GreaterEqual } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const greaterEqualGradConfig = {\n    kernelName: GreaterEqual,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        return { a: () => zerosLike(a), b: () => zerosLike(b) };\n    }\n};\n//# sourceMappingURL=GreaterEqual_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '../kernel_names';\nimport { cast } from '../ops/cast';\nexport const identityGradConfig = {\n    kernelName: Identity,\n    gradFunc: (dy) => {\n        return { x: () => cast(dy, 'float32') };\n    }\n};\n//# sourceMappingURL=Identity_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsFinite } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isFiniteGradConfig = {\n    kernelName: IsFinite,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsFinite_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsInf } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isInfGradConfig = {\n    kernelName: IsInf,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsInf_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsNan } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isNanGradConfig = {\n    kernelName: IsNan,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsNan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log1p } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nexport const log1pGradConfig = {\n    kernelName: Log1p,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, add(x, 1)) };\n    }\n};\n//# sourceMappingURL=Log1p_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nexport const logGradConfig = {\n    kernelName: Log,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, cast(x, 'float32')) };\n    }\n};\n//# sourceMappingURL=Log_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LogSoftmax } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nexport const logSoftmaxGradConfig = {\n    kernelName: LogSoftmax,\n    inputsToSave: [],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [value] = saved;\n        const { axis } = attrs;\n        return {\n            logits: () => {\n                const keepDims = true;\n                const softmax = exp(value);\n                return sub(dy, mul(sum(dy, axis, keepDims), softmax));\n            }\n        };\n    }\n};\n//# sourceMappingURL=LogSoftmax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LRNBackprop } from '../kernel_names';\nimport { op } from './operation';\nfunction localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n    const forward = backend => backend.LRNGrad(dy, x, y, depthRadius, bias, alpha, beta);\n    const inputs = { x, y, dy };\n    const attrs = { depthRadius, bias, alpha, beta };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LRNBackprop, attrs);\n}\nexport const localResponseNormalizationBackprop = op({ localResponseNormalizationBackprop_ });\n//# sourceMappingURL=local_response_normalization_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LRN } from '../kernel_names';\nimport { localResponseNormalizationBackprop } from '../ops/local_response_normalization_backprop';\nexport const lrnGradConfig = {\n    kernelName: LRN,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { depthRadius, bias, alpha, beta } = attrs;\n        return {\n            x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)\n        };\n    }\n};\n//# sourceMappingURL=LRN_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as axis_util from '../ops/axis_util';\nimport { cast } from '../ops/cast';\nimport { equal } from '../ops/equal';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { transpose } from '../ops/transpose';\n/**\n * Gradient helper function for the min and max operations.\n */\nexport function gradForMinAndMax(dy, y, xOrig, origAxes, permutedAxes) {\n    if (y.rank < xOrig.rank) {\n        y = reshape(y, axis_util.expandShapeToKeepDim(y.shape, origAxes));\n    }\n    if (dy.rank < xOrig.rank) {\n        dy = reshape(dy, axis_util.expandShapeToKeepDim(dy.shape, origAxes));\n    }\n    return {\n        x: () => {\n            const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));\n            return permutedAxes == null ? dx : transpose(dx, permutedAxes);\n        }\n    };\n}\n//# sourceMappingURL=min_max_grad_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nimport * as util from '../util';\nimport { gradForMinAndMax } from './min_max_grad_util';\nexport const maxGradConfig = {\n    kernelName: Max,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const maxAttrs = attrs;\n        const { reductionIndices } = maxAttrs;\n        const [x, y] = saved;\n        const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(origAxes, x.rank);\n        const maxGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);\n        return {\n            x: () => {\n                let out = maxGrad['x']();\n                if (permutedAxes != null) {\n                    out = transpose(out);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Max_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Maximum } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { less } from '../ops/less';\nimport { mul } from '../ops/mul';\nexport const maximumGradConfig = {\n    kernelName: Maximum,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const derA = () => mul(dy, cast(greaterEqual(a, b), 'float32'));\n        const derB = () => mul(dy, cast(less(a, b), 'float32'));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Maximum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool3DBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of a 3d max pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank 4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param output The original output image, of rank 5 of shape\n *     [batchSize, outDepth, outHeight, outWidth, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPool3dBackprop_(dy, input, output, filterSize, strides, dilations = [1, 1, 1], pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'maxPool3dBackprop');\n    const $input = convertToTensor(input, 'input', 'maxPool3dBackprop');\n    const $output = convertToTensor(output, 'output', 'maxPool3dBackprop');\n    let dy5D = $dy;\n    let input5D = $input;\n    let output5D = $output;\n    let reshapedTo5D = false;\n    if ($input.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n        input5D = reshape($input, [\n            1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n        ]);\n        output5D = reshape($output, [\n            1, $output.shape[0], $output.shape[1], $output.shape[2], $output.shape[3]\n        ]);\n    }\n    util.assert(dy5D.rank === 5, () => `Error in maxPool3dBackprop: dy must be rank 5 but got rank ` +\n        `${dy5D.rank}.`);\n    util.assert(input5D.rank === 5, () => `Error in maxPool3dBackprop: input must be rank 5 but got rank ` +\n        `${input5D.rank}.`);\n    util.assert(output5D.rank === 5, () => `Error in maxPool3dBackprop: output must be rank 5 but got rank ` +\n        `${output5D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool3dBackprop: Either strides or dilations ' +\n        `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n        return backend.maxPool3dBackprop(dy5D, input5D, output5D, convInfo);\n    };\n    const inputs = { dy: dy5D, input: input5D, output: output5D };\n    const attrs = { filterSize, strides, dilations, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool3DBackprop, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const maxPool3dBackprop = op({ maxPool3dBackprop_ });\n//# sourceMappingURL=max_pool_3d_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPool3D } from '../kernel_names';\nimport { maxPool3dBackprop } from '../ops/max_pool_3d_backprop';\nexport const maxPool3DGradConfig = {\n    kernelName: MaxPool3D,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { filterSize, strides, dilations, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1, 1] : dilations;\n        return {\n            x: () => maxPool3dBackprop(dy, x, y, filterSize, strides, $dilations, pad, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=MaxPool3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPoolBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\n/**\n * Computes the backprop of a 2D max pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The original input image, of rank 4, of shape\n *     [batchSize, height, width, channels].\n * @param output The original output image, of rank 4, of shape\n *     [batchSize, outHeight, outWidth, channels].\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPoolBackprop_(dy, input, output, filterSize, strides, pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'maxPoolBackprop');\n    const $input = convertToTensor(input, 'input', 'maxPoolBackprop');\n    const $output = convertToTensor(output, 'output', 'maxPoolBackprop');\n    util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy ` +\n        `(${$dy.rank})`);\n    util.assert($dy.rank === 4, () => `Error in maxPoolBackprop: dy must be rank 4 but got rank ` +\n        `${$dy.rank}.`);\n    util.assert($input.rank === 4, () => `Error in maxPoolBackprop: input must be rank 4 but got rank ` +\n        `${$input.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPoolBackprop: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool2DInfo($input.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        return backend.maxPoolBackprop($dy, $input, $output, convInfo);\n    };\n    const inputs = { dy: $dy, input: $input, output: $output };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    return ENGINE.runKernelFunc(forward, inputs, null, MaxPoolBackprop, attrs);\n}\nexport const maxPoolBackprop = op({ maxPoolBackprop_ });\n//# sourceMappingURL=max_pool_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPool } from '../kernel_names';\nimport { maxPoolBackprop } from '../ops/max_pool_backprop';\nexport const maxPoolGradConfig = {\n    kernelName: MaxPool,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { filterSize, strides, pad } = attrs;\n        return {\n            x: () => maxPoolBackprop(dy, x, y, filterSize, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=MaxPool_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Min } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nimport * as util from '../util';\nimport { gradForMinAndMax } from './min_max_grad_util';\nexport const minGradConfig = {\n    kernelName: Min,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const minAttrs = attrs;\n        const { axis } = minAttrs;\n        const [x, y] = saved;\n        const origAxes = util.parseAxisParam(axis, x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(origAxes, x.rank);\n        const minGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);\n        return {\n            x: () => {\n                let out = minGrad['x']();\n                if (permutedAxes != null) {\n                    out = transpose(out);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Min_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Minimum } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { greater } from '../ops/greater';\nimport { lessEqual } from '../ops/less_equal';\nimport { mul } from '../ops/mul';\nexport const minimumGradConfig = {\n    kernelName: Minimum,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const derA = () => mul(dy, cast(lessEqual(a, b), 'float32'));\n        const derB = () => mul(dy, cast(greater(a, b), 'float32'));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Minimum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Mod } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { div } from '../ops/div';\nimport { floor } from '../ops/floor';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const modGradConfig = {\n    kernelName: Mod,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(dy, reduceAxes), a.shape);\n            }\n            return dy;\n        };\n        const derB = () => {\n            const res = mul(dy, neg(floor(div(a, b))));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), b.shape);\n            }\n            return res;\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Mod_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const multiplyGradConfig = {\n    kernelName: Multiply,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = mul(dy, cast(b, 'float32'));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            const res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), b.shape);\n            }\n            return res;\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Multiply_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Negate } from '../kernel_names';\nimport { neg } from '../ops/neg';\nexport const negateGradConfig = {\n    kernelName: Negate,\n    gradFunc: (dy) => {\n        return { x: () => neg(dy) };\n    }\n};\n//# sourceMappingURL=Negate_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { OneHot } from '../kernel_names';\nimport { zeros } from '../ops/zeros';\nexport const oneHotGradConfig = {\n    kernelName: OneHot,\n    inputsToSave: ['indices'],\n    gradFunc: (dy, saved) => {\n        const indices = saved[0];\n        return { indices: () => zeros(indices.shape, 'float32') };\n    }\n};\n//# sourceMappingURL=OneHot_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { OnesLike } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const onesLikeGradConfig = {\n    kernelName: OnesLike,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=OnesLike_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { PadV2 } from '../kernel_names';\nimport { slice } from '../ops/slice';\nexport const padV2GradConfig = {\n    kernelName: PadV2,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        // Pad introduces values around the original tensor, so the gradient\n        // slices the original shape out of the gradient.\n        const x = saved[0];\n        const { paddings } = attrs;\n        const begin = paddings.map(p => p[0]);\n        return { x: () => slice(dy, begin, x.shape) };\n    }\n};\n//# sourceMappingURL=PadV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Pow } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { greater } from '../ops/greater';\nimport { log } from '../ops/log';\nimport { mul } from '../ops/mul';\nimport { pow } from '../ops/pow';\nimport { reshape } from '../ops/reshape';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const powGradConfig = {\n    kernelName: Pow,\n    inputsToSave: ['a', 'b'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [a, b, y] = saved;\n        const base = a;\n        const exp = b;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(base.shape, exp.shape);\n        const derBase = () => {\n            const expFloat = cast(exp, 'float32');\n            let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));\n            const reduceAxes = broadcast_util.getReductionAxes(base.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, base.shape);\n        };\n        const derExp = () => {\n            const condition = greater(base, 0);\n            const logBase = where(condition, log(base), zerosLike(base));\n            let res = mul(dy, mul(y, logBase));\n            const reduceAxes = broadcast_util.getReductionAxes(exp.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, exp.shape);\n        };\n        return { a: derBase, b: derExp };\n    }\n};\n//# sourceMappingURL=Pow_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Prelu } from '../kernel_names';\nimport { getReductionAxes } from '../ops/broadcast_util';\nimport { greater } from '../ops/greater';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const preluGradConfig = {\n    kernelName: Prelu,\n    inputsToSave: ['x', 'alpha'],\n    gradFunc: (dy, saved) => {\n        const [x, alpha] = saved;\n        const mask = greater(x, 0);\n        return {\n            x: () => where(mask, dy, mul(dy, alpha)),\n            alpha: () => {\n                let res = where(mask, zerosLike(dy), mul(dy, x));\n                const reduceAxes = getReductionAxes(alpha.shape, dy.shape);\n                if (reduceAxes.length > 0) {\n                    res = sum(res, reduceAxes);\n                }\n                return reshape(res, alpha.shape);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Prelu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reciprocal } from '../kernel_names';\nimport { div } from '../ops/div';\nimport { neg } from '../ops/neg';\nimport { square } from '../ops/square';\nexport const reciprocalGradConfig = {\n    kernelName: Reciprocal,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, neg(square(x))) };\n    }\n};\n//# sourceMappingURL=Reciprocal_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu6 } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { lessEqual } from '../ops/less_equal';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const relu6GradConfig = {\n    kernelName: Relu6,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        const mask = mul(lessEqual(x, 6), step(x));\n        return { x: () => mul(dy, cast(mask, 'float32')) };\n    }\n};\n//# sourceMappingURL=Relu6_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const reluGradConfig = {\n    kernelName: Relu,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, cast(step(x), 'float32')) };\n    }\n};\n//# sourceMappingURL=Relu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape } from '../kernel_names';\nimport { reshape } from '../ops/reshape';\nexport const reshapeGradConfig = {\n    kernelName: Reshape,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => reshape(dy, x.shape) };\n    }\n};\n//# sourceMappingURL=Reshape_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ResizeBilinear, ResizeBilinearGrad } from '../kernel_names';\nexport const resizeBilinearGradConfig = {\n    kernelName: ResizeBilinear,\n    inputsToSave: ['images'],\n    gradFunc: (dy, saved, attrs) => {\n        const [images] = saved;\n        const backPropKernelFunc = (backend) => {\n            const { alignCorners } = attrs;\n            return backend.resizeBilinearBackprop(dy, images, alignCorners);\n        };\n        const inputs = { images };\n        const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* gradient */, ResizeBilinearGrad, attrs);\n        return { images: imagesDer };\n    }\n};\n//# sourceMappingURL=ResizeBilinear_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ResizeNearestNeighbor, ResizeNearestNeighborGrad } from '../kernel_names';\nexport const resizeNearestNeighborGradConfig = {\n    kernelName: ResizeNearestNeighbor,\n    inputsToSave: ['images'],\n    gradFunc: (dy, saved, attrs) => {\n        const [images] = saved;\n        const backPropKernelFunc = (backend) => {\n            const { alignCorners } = attrs;\n            return backend.resizeNearestNeighborBackprop(dy, images, alignCorners);\n        };\n        const inputs = { images };\n        const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* gradient */, ResizeNearestNeighborGrad, attrs);\n        return { images: imagesDer };\n    }\n};\n//# sourceMappingURL=ResizeNearestNeighbor_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reverse } from '../kernel_names';\nimport { reverse } from '../ops/reverse';\nimport { parseAxisParam } from '../util';\nexport const reverseGradConfig = {\n    kernelName: Reverse,\n    gradFunc: (dy, saved, attrs) => {\n        const { dims } = attrs;\n        const axes = parseAxisParam(dims, dy.shape);\n        return { x: () => reverse(dy, axes) };\n    }\n};\n//# sourceMappingURL=Reverse_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Round } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const roundGradConfig = {\n    kernelName: Round,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Round_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '../kernel_names';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { pow } from '../ops/pow';\nexport const rsqrtGradConfig = {\n    kernelName: Rsqrt,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => neg(div(dy, mul(pow(x, 1.5), 2))) };\n    }\n};\n//# sourceMappingURL=Rsqrt_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SelectV2 } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { logicalNot } from '../ops/logical_not';\nimport { mul } from '../ops/mul';\nimport { zerosLike } from '../ops/zeros_like';\nexport const selectV2PoolGradConfig = {\n    kernelName: SelectV2,\n    inputsToSave: ['condition'],\n    gradFunc: (dy, saved) => {\n        const [condition] = saved;\n        return {\n            // TODO(julianoks): Return null for condition gradient\n            // when backprop supports it.\n            condition: () => cast(zerosLike(condition), 'float32'),\n            t: () => mul(dy, cast(condition, dy.dtype)),\n            e: () => mul(dy, cast(logicalNot(condition), dy.dtype))\n        };\n    }\n};\n//# sourceMappingURL=SelectV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Selu } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { exp } from '../ops/exp';\nimport { greater } from '../ops/greater';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { SELU_SCALE, SELU_SCALEALPHA } from '../ops/selu_util';\nimport { where } from '../ops/where';\nexport const seluGradConfig = {\n    kernelName: Selu,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const mask = greater(x, scalar(0));\n                const scaleAlpha = scalar(SELU_SCALEALPHA);\n                const scale = scalar(SELU_SCALE);\n                const greaterThanZeroDer = mul(dy, scale);\n                const lessEqualZeroDer = mul(mul(dy, scaleAlpha), exp(cast(x, 'float32')));\n                return where(mask, greaterThanZeroDer, lessEqualZeroDer);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Selu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sigmoid } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nexport const sigmoidGradConfig = {\n    kernelName: Sigmoid,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(dy, mul(y, sub(scalar(1), y))) };\n    }\n};\n//# sourceMappingURL=Sigmoid_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sign } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const signGradConfig = {\n    kernelName: Sign,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Sign_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { cos } from '../ops/cos';\nimport { mul } from '../ops/mul';\nexport const sinGradConfig = {\n    kernelName: Sin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(cos(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Sin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sinh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { cosh } from '../ops/cosh';\nimport { mul } from '../ops/mul';\nexport const sinhGradConfig = {\n    kernelName: Sinh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(cosh(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Sinh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Slice } from '../kernel_names';\nimport { pad } from '../ops/pad';\nimport { parseSliceParams } from '../ops/slice_util';\nexport const sliceGradConfig = {\n    kernelName: Slice,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { begin, size } = attrs;\n        const inputShape = x.shape;\n        const [begin_, size_] = parseSliceParams(x, begin, size);\n        // Create an Nx2 padding where the first column represents how many\n        // zeros are prepended (at start) for each dimension, and the second\n        // column indicates how many zeros are appended (at end).\n        // The number of zeros to append is the shape of the input\n        // elementwise-subtracted by both the begin vector and sizes vector.\n        const paddings = [];\n        for (let i = 0; i < dy.rank; i++) {\n            paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);\n        }\n        return { x: () => pad(dy, paddings) };\n    }\n};\n//# sourceMappingURL=Slice_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softmax } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nexport const softmaxGradConfig = {\n    kernelName: Softmax,\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [y] = saved;\n        const { dim } = attrs;\n        const keepDims = true;\n        const dyTimesY = mul(dy, y);\n        return {\n            logits: () => sub(dyTimesY, mul(sum(dyTimesY, [dim], keepDims), y))\n        };\n    }\n};\n//# sourceMappingURL=Softmax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softplus } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { sigmoid } from '../ops/sigmoid';\nexport const softplusGradConfig = {\n    kernelName: Softplus,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, sigmoid(x)) };\n    }\n};\n//# sourceMappingURL=Softplus_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SpaceToBatchND } from '../kernel_names';\nimport { batchToSpaceND } from '../ops/batch_to_space_nd';\nexport const spaceToBatchNDGradConfig = {\n    kernelName: SpaceToBatchND,\n    gradFunc: (dy, saved, attrs) => {\n        const { blockShape, paddings } = attrs;\n        return { x: () => batchToSpaceND(dy, blockShape, paddings) };\n    }\n};\n//# sourceMappingURL=SpaceToBatchND_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SplitV } from '../kernel_names';\nimport { concat } from '../ops/concat';\nexport const splitVGradConfig = {\n    kernelName: SplitV,\n    gradFunc: (dy, saved, attrs) => {\n        const { axis } = attrs;\n        return { x: () => concat(dy, axis) };\n    }\n};\n//# sourceMappingURL=SplitV_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sqrt } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nexport const sqrtGradConfig = {\n    kernelName: Sqrt,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, mul(sqrt(cast(x, 'float32')), 2)) };\n    }\n};\n//# sourceMappingURL=Sqrt_grad.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nexport const squareGradConfig = {\n    kernelName: Square,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, mul(cast(x, 'float32'), 2)) };\n    }\n};\n//# sourceMappingURL=Square_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nexport const squaredDifferenceGradConfig = {\n    kernelName: SquaredDifference,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const two = scalar(2);\n        const derA = () => mul(dy, mul(two, sub(a, b)));\n        const derB = () => mul(dy, mul(two, sub(b, a)));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=SquaredDifference_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Step } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const stepGradConfig = {\n    kernelName: Step,\n    gradFunc: (dy) => {\n        // TODO(manrajgrover): Return null for gradients when backprop supports\n        // it.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Step_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const subGradConfig = {\n    kernelName: Sub,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(neg(res), b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Sub_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sum } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { ones } from '../ops/ones';\nimport { reshape } from '../ops/reshape';\nimport { parseAxisParam } from '../util';\nexport const sumGradConfig = {\n    kernelName: Sum,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const expandedDyShape = x.shape.slice();\n        const { axis } = attrs;\n        const axes = parseAxisParam(axis, x.shape);\n        axes.forEach(axis => {\n            expandedDyShape[axis] = 1;\n        });\n        const expandedDy = reshape(dy, expandedDyShape);\n        const derX = mul(expandedDy, ones(x.shape, 'float32'));\n        return { x: () => derX };\n    }\n};\n//# sourceMappingURL=Sum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '../kernel_names';\nimport { cos } from '../ops/cos';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nexport const tanGradConfig = {\n    kernelName: Tan,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, square(cos(x))) };\n    }\n};\n//# sourceMappingURL=Tan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tanh } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const tanhGradConfig = {\n    kernelName: Tanh,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(sub(scalar(1), square(y)), dy) };\n    }\n};\n//# sourceMappingURL=Tanh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tile } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { slice } from '../ops/slice';\nimport { zerosLike } from '../ops/zeros_like';\nexport const tileGradConfig = {\n    kernelName: Tile,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { reps } = attrs;\n        const derX = () => {\n            let xGrad = zerosLike(x);\n            // TODO(cais): Maybe reduce memory footprint by avoiding repeated\n            // slicing.\n            if (x.rank === 1) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    xGrad = add(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));\n                }\n            }\n            else if (x.rank === 2) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        xGrad = add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [\n                            x.shape[0], x.shape[1]\n                        ]));\n                    }\n                }\n            }\n            else if (x.rank === 3) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        for (let k = 0; k < reps[2]; ++k) {\n                            xGrad =\n                                add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));\n                        }\n                    }\n                }\n            }\n            else if (x.rank === 4) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        for (let k = 0; k < reps[2]; ++k) {\n                            for (let l = 0; l < reps[3]; ++l) {\n                                xGrad =\n                                    add(xGrad, slice(dy, [\n                                        i * x.shape[0], j * x.shape[1], k * x.shape[2],\n                                        l * x.shape[3]\n                                    ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));\n                            }\n                        }\n                    }\n                }\n            }\n            else {\n                throw new Error(`Gradient for tile operation is not implemented for rank-` +\n                    `${x.rank} tensors yet.`);\n            }\n            return xGrad;\n        };\n        return { x: derX };\n    },\n};\n//# sourceMappingURL=Tile_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nexport const transposeGradConfig = {\n    kernelName: Transpose,\n    gradFunc: (dy, saved, attrs) => {\n        const transposeAttrs = attrs;\n        const { perm } = transposeAttrs;\n        const undoPerm = axis_util.getUndoAxesPermutation(perm);\n        return { x: () => transpose(dy, undoPerm) };\n    }\n};\n//# sourceMappingURL=Transpose_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unpack } from '../kernel_names';\nimport { stack } from '../ops/stack';\nexport const unpackGradConfig = {\n    kernelName: Unpack,\n    gradFunc: (dy, saved, attrs) => {\n        const unpackAttrs = attrs;\n        const { axis } = unpackAttrs;\n        return { value: () => stack(dy, axis) };\n    }\n};\n//# sourceMappingURL=Unpack_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { UnsortedSegmentSum } from '../kernel_names';\nimport { expandDims } from '../ops/expand_dims';\nimport { gather } from '../ops/gather';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { logicalAnd } from '../ops/logical_and';\nimport { maximum } from '../ops/maximum';\nimport { ones } from '../ops/ones';\nimport { scalar } from '../ops/scalar';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const unsortedSegmentSumGradConfig = {\n    kernelName: UnsortedSegmentSum,\n    inputsToSave: ['segmentIds'],\n    gradFunc: (dy, saved) => {\n        const [segmentIds] = saved;\n        const derX = () => {\n            return gatherDropNegatives(dy, segmentIds);\n        };\n        return { x: derX };\n    }\n};\nfunction gatherDropNegatives(x, indices) {\n    // Helper function for unsorted segment ops. Gathers params for\n    // positive segment ids and gathers 0 for inputs with negative segment id.\n    // Mirrors _GatherDropNegatives from tensorflow/python/ops/math_grad.py\n    const zeroClippedIndices = maximum(indices, zerosLike(indices));\n    const gathered = gather(x, zeroClippedIndices);\n    let isPositive = greaterEqual(indices, scalar(0, 'int32'));\n    const numIters = gathered.rank - isPositive.rank;\n    for (let i = 0; i < numIters; ++i) {\n        isPositive = expandDims(isPositive, i + 1);\n    }\n    isPositive = logicalAnd(isPositive, ones(gathered.shape, 'bool'));\n    const zeroSlice = zerosLike(gathered);\n    return where(isPositive, gathered, zeroSlice);\n}\n//# sourceMappingURL=UnsortedSegmentSum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ZerosLike } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const zerosLikeGradConfig = {\n    kernelName: ZerosLike,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=ZerosLike_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { absGradConfig } from './gradients/Abs_grad';\nimport { acosGradConfig } from './gradients/Acos_grad';\nimport { acoshGradConfig } from './gradients/Acosh_grad';\nimport { addGradConfig } from './gradients/Add_grad';\nimport { addNGradConfig } from './gradients/AddN_grad';\nimport { argMaxGradConfig } from './gradients/ArgMax_grad';\nimport { argMinGradConfig } from './gradients/ArgMin_grad';\nimport { asinGradConfig } from './gradients/Asin_grad';\nimport { asinhGradConfig } from './gradients/Asinh_grad';\nimport { atan2GradConfig } from './gradients/Atan2_grad';\nimport { atanGradConfig } from './gradients/Atan_grad';\nimport { atanhGradConfig } from './gradients/Atanh_grad';\nimport { avgPool3DGradConfig } from './gradients/AvgPool3D_grad';\nimport { avgPoolGradConfig } from './gradients/AvgPool_grad';\nimport { batchMatMulGradConfig } from './gradients/BatchMatMul_grad';\nimport { batchToSpaceNDGradConfig } from './gradients/BatchToSpaceND_grad';\nimport { broadcastToGradConfig } from './gradients/BroadcastTo_grad';\nimport { castGradConfig } from './gradients/Cast_grad';\nimport { ceilGradConfig } from './gradients/Ceil_grad';\nimport { clipByValueGradConfig } from './gradients/ClipByValue_grad';\nimport { concatGradConfig } from './gradients/Concat_grad';\nimport { conv2DGradConfig } from './gradients/Conv2D_grad';\nimport { conv2DBackpropInputGradConfig } from './gradients/Conv2DBackpropInput_grad';\nimport { conv3DGradConfig } from './gradients/Conv3D_grad';\nimport { cosGradConfig } from './gradients/Cos_grad';\nimport { coshGradConfig } from './gradients/Cosh_grad';\nimport { cumsumGradConfig } from './gradients/Cumsum_grad';\nimport { depthwiseConv2dNativeGradConfig } from './gradients/DepthwiseConv2dNative_grad';\nimport { dilation2dGradConfig } from './gradients/Dilation2D_grad';\nimport { divGradConfig } from './gradients/Div_grad';\nimport { eluGradConfig } from './gradients/Elu_grad';\nimport { erfGradConfig } from './gradients/Erf_grad';\nimport { expGradConfig } from './gradients/Exp_grad';\nimport { expm1GradConfig } from './gradients/Expm1_grad';\nimport { floorGradConfig } from './gradients/Floor_grad';\nimport { floorDivGradConfig } from './gradients/FloorDiv_grad';\nimport { fusedBatchNormGradConfig } from './gradients/FusedBatchNorm_grad';\nimport { gatherGradConfig } from './gradients/GatherV2_grad';\nimport { greaterEqualGradConfig } from './gradients/GreaterEqual_grad';\nimport { identityGradConfig } from './gradients/Identity_grad';\nimport { isFiniteGradConfig } from './gradients/IsFinite_grad';\nimport { isInfGradConfig } from './gradients/IsInf_grad';\nimport { isNanGradConfig } from './gradients/IsNan_grad';\nimport { log1pGradConfig } from './gradients/Log1p_grad';\nimport { logGradConfig } from './gradients/Log_grad';\nimport { logSoftmaxGradConfig } from './gradients/LogSoftmax_grad';\nimport { lrnGradConfig } from './gradients/LRN_grad';\nimport { maxGradConfig } from './gradients/Max_grad';\nimport { maximumGradConfig } from './gradients/Maximum_grad';\nimport { maxPool3DGradConfig } from './gradients/MaxPool3D_grad';\nimport { maxPoolGradConfig } from './gradients/MaxPool_grad';\nimport { minGradConfig } from './gradients/Min_grad';\nimport { minimumGradConfig } from './gradients/Minimum_grad';\nimport { modGradConfig } from './gradients/Mod_grad';\nimport { multiplyGradConfig } from './gradients/Multiply_grad';\nimport { negateGradConfig } from './gradients/Negate_grad';\nimport { oneHotGradConfig } from './gradients/OneHot_grad';\nimport { onesLikeGradConfig } from './gradients/OnesLike_grad';\nimport { padV2GradConfig } from './gradients/PadV2_grad';\nimport { powGradConfig } from './gradients/Pow_grad';\nimport { preluGradConfig } from './gradients/Prelu_grad';\nimport { reciprocalGradConfig } from './gradients/Reciprocal_grad';\nimport { relu6GradConfig } from './gradients/Relu6_grad';\nimport { reluGradConfig } from './gradients/Relu_grad';\nimport { reshapeGradConfig } from './gradients/Reshape_grad';\nimport { resizeBilinearGradConfig } from './gradients/ResizeBilinear_grad';\nimport { resizeNearestNeighborGradConfig } from './gradients/ResizeNearestNeighbor_grad';\nimport { reverseGradConfig } from './gradients/Reverse_grad';\nimport { roundGradConfig } from './gradients/Round_grad';\nimport { rsqrtGradConfig } from './gradients/Rsqrt_grad';\nimport { selectV2PoolGradConfig } from './gradients/SelectV2_grad';\nimport { seluGradConfig } from './gradients/Selu_grad';\nimport { sigmoidGradConfig } from './gradients/Sigmoid_grad';\nimport { signGradConfig } from './gradients/Sign_grad';\nimport { sinGradConfig } from './gradients/Sin_grad';\nimport { sinhGradConfig } from './gradients/Sinh_grad';\nimport { sliceGradConfig } from './gradients/Slice_grad';\nimport { softmaxGradConfig } from './gradients/Softmax_grad';\nimport { softplusGradConfig } from './gradients/Softplus_grad';\nimport { spaceToBatchNDGradConfig } from './gradients/SpaceToBatchND_grad';\nimport { splitVGradConfig } from './gradients/SplitV_grad';\nimport { sqrtGradConfig } from './gradients/Sqrt_grad';\nimport { squareGradConfig } from './gradients/Square_grad';\nimport { squaredDifferenceGradConfig } from './gradients/SquaredDifference_grad';\nimport { stepGradConfig } from './gradients/Step_grad';\nimport { subGradConfig } from './gradients/Sub_grad';\nimport { sumGradConfig } from './gradients/Sum_grad';\nimport { tanGradConfig } from './gradients/Tan_grad';\nimport { tanhGradConfig } from './gradients/Tanh_grad';\nimport { tileGradConfig } from './gradients/Tile_grad';\nimport { transposeGradConfig } from './gradients/Transpose_grad';\nimport { unpackGradConfig } from './gradients/Unpack_grad';\nimport { unsortedSegmentSumGradConfig } from './gradients/UnsortedSegmentSum_grad';\nimport { zerosLikeGradConfig } from './gradients/ZerosLike_grad';\nimport { registerGradient } from './kernel_registry';\n// Export all kernel configs here so that the package can auto register them\nconst gradConfigs = [\n    absGradConfig,\n    acosGradConfig,\n    acoshGradConfig,\n    addGradConfig,\n    addNGradConfig,\n    argMaxGradConfig,\n    argMinGradConfig,\n    asinGradConfig,\n    asinhGradConfig,\n    atan2GradConfig,\n    atanGradConfig,\n    atanhGradConfig,\n    avgPool3DGradConfig,\n    avgPoolGradConfig,\n    batchMatMulGradConfig,\n    batchToSpaceNDGradConfig,\n    broadcastToGradConfig,\n    castGradConfig,\n    ceilGradConfig,\n    clipByValueGradConfig,\n    concatGradConfig,\n    conv2DBackpropInputGradConfig,\n    conv2DGradConfig,\n    conv3DGradConfig,\n    cosGradConfig,\n    coshGradConfig,\n    cumsumGradConfig,\n    depthwiseConv2dNativeGradConfig,\n    dilation2dGradConfig,\n    divGradConfig,\n    eluGradConfig,\n    erfGradConfig,\n    expGradConfig,\n    expm1GradConfig,\n    floorDivGradConfig,\n    floorGradConfig,\n    fusedBatchNormGradConfig,\n    gatherGradConfig,\n    greaterEqualGradConfig,\n    identityGradConfig,\n    isFiniteGradConfig,\n    isInfGradConfig,\n    isNanGradConfig,\n    log1pGradConfig,\n    logGradConfig,\n    logSoftmaxGradConfig,\n    lrnGradConfig,\n    maxGradConfig,\n    maxGradConfig,\n    maximumGradConfig,\n    maxPool3DGradConfig,\n    maxPoolGradConfig,\n    minGradConfig,\n    minimumGradConfig,\n    modGradConfig,\n    multiplyGradConfig,\n    negateGradConfig,\n    oneHotGradConfig,\n    onesLikeGradConfig,\n    padV2GradConfig,\n    padV2GradConfig,\n    powGradConfig,\n    preluGradConfig,\n    reciprocalGradConfig,\n    relu6GradConfig,\n    reluGradConfig,\n    reshapeGradConfig,\n    resizeBilinearGradConfig,\n    resizeNearestNeighborGradConfig,\n    reverseGradConfig,\n    roundGradConfig,\n    rsqrtGradConfig,\n    selectV2PoolGradConfig,\n    seluGradConfig,\n    sigmoidGradConfig,\n    signGradConfig,\n    sinGradConfig,\n    sinhGradConfig,\n    sliceGradConfig,\n    softmaxGradConfig,\n    softplusGradConfig,\n    spaceToBatchNDGradConfig,\n    spaceToBatchNDGradConfig,\n    splitVGradConfig,\n    splitVGradConfig,\n    sqrtGradConfig,\n    squaredDifferenceGradConfig,\n    squareGradConfig,\n    stepGradConfig,\n    subGradConfig,\n    sumGradConfig,\n    tanGradConfig,\n    tanhGradConfig,\n    tileGradConfig,\n    transposeGradConfig,\n    unpackGradConfig,\n    unsortedSegmentSumGradConfig,\n    zerosLikeGradConfig\n];\nfor (const gradientConfig of gradConfigs) {\n    registerGradient(gradientConfig);\n}\n//# sourceMappingURL=register_all_gradients.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { abs } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.abs = function () {\n    this.throwIfDisposed();\n    return abs(this);\n};\n//# sourceMappingURL=abs.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { acos } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.acos = function () {\n    this.throwIfDisposed();\n    return acos(this);\n};\n//# sourceMappingURL=acos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { acosh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.acosh = function () {\n    this.throwIfDisposed();\n    return acosh(this);\n};\n//# sourceMappingURL=acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { addStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.addStrict = function (x) {\n    this.throwIfDisposed();\n    return addStrict(this, x);\n};\n//# sourceMappingURL=add_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { add } from '../../ops/add';\nimport { Tensor } from '../../tensor';\nTensor.prototype.add = function (b) {\n    this.throwIfDisposed();\n    return add(this, b);\n};\n//# sourceMappingURL=add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { all } from '../../ops/all';\nimport { Tensor } from '../../tensor';\nTensor.prototype.all = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return all(this, axis, keepDims);\n};\n//# sourceMappingURL=all.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { any } from '../../ops/any';\nimport { Tensor } from '../../tensor';\nTensor.prototype.any = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return any(this, axis, keepDims);\n};\n//# sourceMappingURL=any.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { argMax } from '../../ops/arg_max';\nimport { Tensor } from '../../tensor';\nTensor.prototype.argMax = function (axis) {\n    this.throwIfDisposed();\n    return argMax(this, axis);\n};\n//# sourceMappingURL=arg_max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { argMin } from '../../ops/arg_min';\nimport { Tensor } from '../../tensor';\nTensor.prototype.argMin = function (axis) {\n    this.throwIfDisposed();\n    return argMin(this, axis);\n};\n//# sourceMappingURL=arg_min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\nimport { assert } from '../../util';\n/** Converts a size-1 `tf.Tensor` to a `tf.Scalar`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asScalar = function () {\n    this.throwIfDisposed();\n    assert(this.size === 1, () => 'The array must have only 1 element.');\n    return reshape(this, []);\n};\n//# sourceMappingURL=as_scalar.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * Casts a `tf.Tensor` to a specified dtype.\n *\n * @param dtype Data-type to cast the tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asType = function (dtype) {\n    this.throwIfDisposed();\n    return cast(this, dtype);\n};\n//# sourceMappingURL=as_type.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/** Converts a `tf.Tensor` to a `tf.Tensor1D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as1D = function () {\n    this.throwIfDisposed();\n    return reshape(this, [this.size]);\n};\n//# sourceMappingURL=as1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor2D`.\n *\n * @param rows Number of rows in `tf.Tensor2D`.\n * @param columns Number of columns in `tf.Tensor2D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as2D = function (rows, columns) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns]);\n};\n//# sourceMappingURL=as2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor3D`.\n *\n * @param rows Number of rows in `tf.Tensor3D`.\n * @param columns Number of columns in `tf.Tensor3D`.\n * @param depth Depth of `tf.Tensor3D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as3D = function (rows, columns, depth) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth]);\n};\n//# sourceMappingURL=as3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor4D`.\n *\n * @param rows Number of rows in `tf.Tensor4D`.\n * @param columns Number of columns in `tf.Tensor4D`.\n * @param depth Depth of `tf.Tensor4D`.\n * @param depth2 4th dimension of `tf.Tensor4D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as4D = function (rows, columns, depth, depth2) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth, depth2]);\n};\n//# sourceMappingURL=as4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor5D`.\n *\n * @param rows Number of rows in `tf.Tensor5D`.\n * @param columns Number of columns in `tf.Tensor5D`.\n * @param depth Depth of `tf.Tensor5D`.\n * @param depth2 4th dimension of `tf.Tensor5D`.\n * @param depth3 5th dimension of 'tf.Tensor5D'\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as5D = function (rows, columns, depth, depth2, depth3) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth, depth2, depth3]);\n};\n//# sourceMappingURL=as5d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { asin } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.asin = function () {\n    this.throwIfDisposed();\n    return asin(this);\n};\n//# sourceMappingURL=asin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { asinh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.asinh = function () {\n    this.throwIfDisposed();\n    return asinh(this);\n};\n//# sourceMappingURL=asinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { atan } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atan = function () {\n    this.throwIfDisposed();\n    return atan(this);\n};\n//# sourceMappingURL=atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { atan2 } from '../../ops/atan2';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atan2 = function (b) {\n    this.throwIfDisposed();\n    return atan2(this, b);\n};\n//# sourceMappingURL=atan2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { atanh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atanh = function () {\n    this.throwIfDisposed();\n    return atanh(this);\n};\n//# sourceMappingURL=atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { avgPool } from '../../ops/avg_pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.avgPool = function (filterSize, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return avgPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=avg_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { batchToSpaceND } from '../../ops/batch_to_space_nd';\nimport { Tensor } from '../../tensor';\nTensor.prototype.batchToSpaceND = function (blockShape, crops) {\n    this.throwIfDisposed();\n    return batchToSpaceND(this, blockShape, crops);\n};\n//# sourceMappingURL=batch_to_space_nd.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { batchNorm } from '../../ops/batchnorm';\nimport { Tensor } from '../../tensor';\nTensor.prototype.batchNorm = function (mean, variance, offset, scale, varianceEpsilon) {\n    this.throwIfDisposed();\n    return batchNorm(this, mean, variance, offset, scale, varianceEpsilon);\n};\n//# sourceMappingURL=batchnorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { broadcastTo } from '../../ops/broadcast_to';\nimport { Tensor } from '../../tensor';\nTensor.prototype.broadcastTo = function (shape) {\n    this.throwIfDisposed();\n    return broadcastTo(this, shape);\n};\n//# sourceMappingURL=broadcast_to.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cast = function (dtype) {\n    this.throwIfDisposed();\n    return cast(this, dtype);\n};\n//# sourceMappingURL=cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { ceil } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.ceil = function () {\n    this.throwIfDisposed();\n    return ceil(this);\n};\n//# sourceMappingURL=ceil.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { clipByValue } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.clipByValue = function (min, max) {\n    this.throwIfDisposed();\n    return clipByValue(this, min, max);\n};\n//# sourceMappingURL=clip_by_value.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../../ops/concat';\nimport { Tensor } from '../../tensor';\nTensor.prototype.concat = function (x, axis) {\n    this.throwIfDisposed();\n    if (x instanceof Tensor) {\n        x = [x];\n    }\n    return concat([this, ...x], axis);\n};\n//# sourceMappingURL=concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv1d } from '../../ops/conv1d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv1d = function (filter, stride, pad, dataFormat, dilation, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv1d(this, filter, stride, pad, dataFormat, dilation, dimRoundingMode);\n};\n//# sourceMappingURL=conv1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2dTranspose } from '../../ops/conv2d_transpose';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv2dTranspose = function (filter, outputShape, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv2dTranspose(this, filter, outputShape, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=conv2d_transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2d } from '../../ops/conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cos } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cos = function () {\n    this.throwIfDisposed();\n    return cos(this);\n};\n//# sourceMappingURL=cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cosh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cosh = function () {\n    this.throwIfDisposed();\n    return cosh(this);\n};\n//# sourceMappingURL=cosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { cumsum } from '../../ops/cumsum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cumsum = function (axis, exclusive, reverse) {\n    this.throwIfDisposed();\n    return cumsum(this, axis, exclusive, reverse);\n};\n//# sourceMappingURL=cumsum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { depthToSpace } from '../../ops/depth_to_space';\nimport { Tensor } from '../../tensor';\nTensor.prototype.depthToSpace = function (blockSize, dataFormat) {\n    this.throwIfDisposed();\n    return depthToSpace(this, blockSize, dataFormat);\n};\n//# sourceMappingURL=depth_to_space.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../../globals';\nimport { depthwiseConv2d } from '../../ops/depthwise_conv2d';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated Use `depthwiseConv2d` instead.\n */\nTensor.prototype.depthwiseConv2D = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    deprecationWarn('depthwiseConv2D is deprecated, use depthwiseConv2d instead');\n    this.throwIfDisposed();\n    return depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=depthwise_conv2D_deprecated.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { depthwiseConv2d } from '../../ops/depthwise_conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.depthwiseConv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    this.throwIfDisposed();\n    return depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dilation2d } from '../../ops/dilation2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.dilation2d = function (filter, strides, pad, dilations, dataFormat) {\n    this.throwIfDisposed();\n    return dilation2d(this, filter, strides, pad, dilations, dataFormat);\n};\n//# sourceMappingURL=dilation2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { divNoNan } from '../../ops/div_no_nan';\nimport { Tensor } from '../../tensor';\nTensor.prototype.divNoNan = function (b) {\n    this.throwIfDisposed();\n    return divNoNan(this, b);\n};\n//# sourceMappingURL=div_no_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { divStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.divStrict = function (x) {\n    this.throwIfDisposed();\n    return divStrict(this, x);\n};\n//# sourceMappingURL=div_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { div } from '../../ops/div';\nimport { Tensor } from '../../tensor';\nTensor.prototype.div = function (b) {\n    this.throwIfDisposed();\n    return div(this, b);\n};\n//# sourceMappingURL=div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dot } from '../../ops/dot';\nimport { Tensor } from '../../tensor';\nTensor.prototype.dot = function (b) {\n    this.throwIfDisposed();\n    return dot(this, b);\n};\n//# sourceMappingURL=dot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { elu } from '../../ops/elu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.elu = function () {\n    this.throwIfDisposed();\n    return elu(this);\n};\n//# sourceMappingURL=elu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { equalStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.equalStrict = function (x) {\n    this.throwIfDisposed();\n    return equalStrict(this, x);\n};\n//# sourceMappingURL=equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { equal } from '../../ops/equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.equal = function (b) {\n    this.throwIfDisposed();\n    return equal(this, b);\n};\n//# sourceMappingURL=equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { erf } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.erf = function () {\n    this.throwIfDisposed();\n    return erf(this);\n};\n//# sourceMappingURL=erf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { exp } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.exp = function () {\n    this.throwIfDisposed();\n    return exp(this);\n};\n//# sourceMappingURL=exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { expandDims } from '../../ops/expand_dims';\nimport { Tensor } from '../../tensor';\nTensor.prototype.expandDims = function (axis) {\n    this.throwIfDisposed();\n    return expandDims(this, axis);\n};\n//# sourceMappingURL=expand_dims.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { expm1 } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.expm1 = function () {\n    this.throwIfDisposed();\n    return expm1(this);\n};\n//# sourceMappingURL=expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { fft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.fft = function () {\n    this.throwIfDisposed();\n    return fft(this);\n};\n//# sourceMappingURL=fft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/** Flatten a Tensor to a 1D array.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.flatten = function () {\n    this.throwIfDisposed();\n    return reshape(this, [this.size]);\n};\n//# sourceMappingURL=flatten.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { floor } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.floor = function () {\n    this.throwIfDisposed();\n    return floor(this);\n};\n//# sourceMappingURL=floor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { floorDiv } from '../../ops/floorDiv';\nimport { Tensor } from '../../tensor';\nTensor.prototype.floorDiv = function (b) {\n    this.throwIfDisposed();\n    return floorDiv(this, b);\n};\n//# sourceMappingURL=floorDiv.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { gather } from '../../ops/gather';\nimport { Tensor } from '../../tensor';\nTensor.prototype.gather = function (indices, axis) {\n    this.throwIfDisposed();\n    return gather(this, indices, axis);\n};\n//# sourceMappingURL=gather.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { greaterEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return greaterEqualStrict(this, x);\n};\n//# sourceMappingURL=greater_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { greaterEqual } from '../../ops/greater_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.greaterEqual = function (b) {\n    this.throwIfDisposed();\n    return greaterEqual(this, b);\n};\n//# sourceMappingURL=greater_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { greaterStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterStrict = function (x) {\n    this.throwIfDisposed();\n    return greaterStrict(this, x);\n};\n//# sourceMappingURL=greater_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { greater } from '../../ops/greater';\nimport { Tensor } from '../../tensor';\nTensor.prototype.greater = function (b) {\n    this.throwIfDisposed();\n    return greater(this, b);\n};\n//# sourceMappingURL=greater.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { ifft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.ifft = function () {\n    this.throwIfDisposed();\n    return ifft(this);\n};\n//# sourceMappingURL=ifft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { irfft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.irfft = function () {\n    this.throwIfDisposed();\n    return irfft(this);\n};\n//# sourceMappingURL=irfft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isFinite } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isFinite = function () {\n    this.throwIfDisposed();\n    return isFinite(this);\n};\n//# sourceMappingURL=is_finite.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isInf } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isInf = function () {\n    this.throwIfDisposed();\n    return isInf(this);\n};\n//# sourceMappingURL=is_inf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isNaN } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isNaN = function () {\n    this.throwIfDisposed();\n    return isNaN(this);\n};\n//# sourceMappingURL=is_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { leakyRelu } from '../../ops/leaky_relu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.leakyRelu = function (alpha) {\n    this.throwIfDisposed();\n    return leakyRelu(this, alpha);\n};\n//# sourceMappingURL=leaky_relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { lessEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.lessEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return lessEqualStrict(this, x);\n};\n//# sourceMappingURL=less_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { lessEqual } from '../../ops/less_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.lessEqual = function (b) {\n    this.throwIfDisposed();\n    return lessEqual(this, b);\n};\n//# sourceMappingURL=less_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { lessStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.lessStrict = function (x) {\n    this.throwIfDisposed();\n    return lessStrict(this, x);\n};\n//# sourceMappingURL=less_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { less } from '../../ops/less';\nimport { Tensor } from '../../tensor';\nTensor.prototype.less = function (b) {\n    this.throwIfDisposed();\n    return less(this, b);\n};\n//# sourceMappingURL=less.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { localResponseNormalization } from '../../ops/local_response_normalization';\nimport { Tensor } from '../../tensor';\nTensor.prototype.localResponseNormalization = function (depthRadius, bias, alpha, beta) {\n    this.throwIfDisposed();\n    return localResponseNormalization(this, depthRadius, bias, alpha, beta);\n};\n//# sourceMappingURL=local_response_normalization.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { logSigmoid } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSigmoid = function () {\n    this.throwIfDisposed();\n    return logSigmoid(this);\n};\n//# sourceMappingURL=log_sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { logSoftmax } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSoftmax = function (axis) {\n    this.throwIfDisposed();\n    return logSoftmax(this, axis);\n};\n//# sourceMappingURL=log_softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logSumExp } from '../../ops/log_sum_exp';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSumExp = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return logSumExp(this, axis, keepDims);\n};\n//# sourceMappingURL=log_sum_exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { log } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.log = function () {\n    this.throwIfDisposed();\n    return log(this);\n};\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { log1p } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.log1p = function () {\n    this.throwIfDisposed();\n    return log1p(this);\n};\n//# sourceMappingURL=log1p.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalAnd } from '../../ops/logical_and';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalAnd = function (b) {\n    this.throwIfDisposed();\n    return logicalAnd(this, b);\n};\n//# sourceMappingURL=logical_and.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalNot } from '../../ops/logical_not';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalNot = function () {\n    this.throwIfDisposed();\n    return logicalNot(this);\n};\n//# sourceMappingURL=logical_not.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalOr } from '../../ops/logical_or';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalOr = function (b) {\n    this.throwIfDisposed();\n    return logicalOr(this, b);\n};\n//# sourceMappingURL=logical_or.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalXor } from '../../ops/logical_xor';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalXor = function (b) {\n    this.throwIfDisposed();\n    return logicalXor(this, b);\n};\n//# sourceMappingURL=logical_xor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { matMul } from '../../ops/mat_mul';\nimport { Tensor } from '../../tensor';\nTensor.prototype.matMul = function (b, transposeA, transposeB) {\n    this.throwIfDisposed();\n    return matMul(this, b, transposeA, transposeB);\n};\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { maxPool } from '../../ops/max_pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.maxPool = function (filterSize, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return maxPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=max_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { max } from '../../ops/max';\nimport { Tensor } from '../../tensor';\nTensor.prototype.max = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return max(this, axis, keepDims);\n};\n//# sourceMappingURL=max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { maximumStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.maximumStrict = function (x) {\n    this.throwIfDisposed();\n    return maximumStrict(this, x);\n};\n//# sourceMappingURL=maximum_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { maximum } from '../../ops/maximum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.maximum = function (b) {\n    this.throwIfDisposed();\n    return maximum(this, b);\n};\n//# sourceMappingURL=maximum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mean } from '../../ops/mean';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mean = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return mean(this, axis, keepDims);\n};\n//# sourceMappingURL=mean.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { min } from '../../ops/min';\nimport { Tensor } from '../../tensor';\nTensor.prototype.min = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return min(this, axis, keepDims);\n};\n//# sourceMappingURL=min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { minimumStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.minimumStrict = function (x) {\n    this.throwIfDisposed();\n    return minimumStrict(this, x);\n};\n//# sourceMappingURL=minimum_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { minimum } from '../../ops/minimum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.minimum = function (b) {\n    this.throwIfDisposed();\n    return minimum(this, b);\n};\n//# sourceMappingURL=minimum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { modStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.modStrict = function (x) {\n    this.throwIfDisposed();\n    return modStrict(this, x);\n};\n//# sourceMappingURL=mod_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mod } from '../../ops/mod';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mod = function (b) {\n    this.throwIfDisposed();\n    return mod(this, b);\n};\n//# sourceMappingURL=mod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { mulStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.mulStrict = function (x) {\n    this.throwIfDisposed();\n    return mulStrict(this, x);\n};\n//# sourceMappingURL=mul_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mul } from '../../ops/mul';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mul = function (b) {\n    this.throwIfDisposed();\n    return mul(this, b);\n};\n//# sourceMappingURL=mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { neg } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.neg = function () {\n    this.throwIfDisposed();\n    return neg(this);\n};\n//# sourceMappingURL=neg.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { norm } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.norm = function (ord, axis, keepDims) {\n    this.throwIfDisposed();\n    return norm(this, ord, axis, keepDims);\n};\n//# sourceMappingURL=norm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { notEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.notEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return notEqualStrict(this, x);\n};\n//# sourceMappingURL=not_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { notEqual } from '../../ops/not_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.notEqual = function (b) {\n    this.throwIfDisposed();\n    return notEqual(this, b);\n};\n//# sourceMappingURL=not_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { oneHot } from '../../ops/one_hot';\nimport { Tensor } from '../../tensor';\nTensor.prototype.oneHot = function (depth, onValue = 1, offValue = 0) {\n    this.throwIfDisposed();\n    return oneHot(this, depth, onValue, offValue);\n};\n//# sourceMappingURL=one_hot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { onesLike } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.onesLike = function () {\n    this.throwIfDisposed();\n    return onesLike(this);\n};\n//# sourceMappingURL=ones_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pad } from '../../ops/pad';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pad = function (paddings, constantValue) {\n    this.throwIfDisposed();\n    return pad(this, paddings, constantValue);\n};\n//# sourceMappingURL=pad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pool } from '../../ops/pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pool = function (windowShape, poolingType, padding, dilationRate, strides) {\n    this.throwIfDisposed();\n    return pool(this, windowShape, poolingType, padding, dilationRate, strides);\n};\n//# sourceMappingURL=pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { powStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.powStrict = function (exp) {\n    this.throwIfDisposed();\n    return powStrict(this, exp);\n};\n//# sourceMappingURL=pow_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pow } from '../../ops/pow';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pow = function (exp) {\n    this.throwIfDisposed();\n    return pow(this, exp);\n};\n//# sourceMappingURL=pow.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { prelu } from '../../ops/prelu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.prelu = function (alpha) {\n    this.throwIfDisposed();\n    return prelu(this, alpha);\n};\n//# sourceMappingURL=prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { prod } from '../../ops/prod';\nimport { Tensor } from '../../tensor';\nTensor.prototype.prod = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return prod(this, axis, keepDims);\n};\n//# sourceMappingURL=prod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { reciprocal } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reciprocal = function () {\n    this.throwIfDisposed();\n    return reciprocal(this);\n};\n//# sourceMappingURL=reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { relu } from '../../ops/relu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.relu = function () {\n    this.throwIfDisposed();\n    return relu(this);\n};\n//# sourceMappingURL=relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { relu6 } from '../../ops/relu6';\nimport { Tensor } from '../../tensor';\nTensor.prototype.relu6 = function () {\n    this.throwIfDisposed();\n    return relu6(this);\n};\n//# sourceMappingURL=relu6.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Reshapes the tensor into the shape of the provided tensor.\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.reshapeAs = function (x) {\n    this.throwIfDisposed();\n    return reshape(this, x.shape);\n};\n//# sourceMappingURL=reshape_as.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reshape = function (shape) {\n    this.throwIfDisposed();\n    return reshape(this, shape);\n};\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { resizeBilinear } from '../../ops/image/resize_bilinear';\nimport { Tensor } from '../../tensor';\nTensor.prototype.resizeBilinear = function (newShape2D, alignCorners) {\n    this.throwIfDisposed();\n    return resizeBilinear(this, newShape2D, alignCorners);\n};\n//# sourceMappingURL=resize_bilinear.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { resizeNearestNeighbor } from '../../ops/image/resize_nearest_neighbor';\nimport { Tensor } from '../../tensor';\nTensor.prototype.resizeNearestNeighbor = function (newShape2D, alignCorners) {\n    this.throwIfDisposed();\n    return resizeNearestNeighbor(this, newShape2D, alignCorners);\n};\n//# sourceMappingURL=resize_nearest_neighbor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reverse } from '../../ops/reverse';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reverse = function (axis) {\n    this.throwIfDisposed();\n    return reverse(this, axis);\n};\n//# sourceMappingURL=reverse.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { rfft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.rfft = function () {\n    this.throwIfDisposed();\n    return rfft(this);\n};\n//# sourceMappingURL=rfft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { round } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.round = function () {\n    this.throwIfDisposed();\n    return round(this);\n};\n//# sourceMappingURL=round.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { rsqrt } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.rsqrt = function () {\n    this.throwIfDisposed();\n    return rsqrt(this);\n};\n//# sourceMappingURL=rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { selu } from '../../ops/selu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.selu = function () {\n    this.throwIfDisposed();\n    return selu(this);\n};\n//# sourceMappingURL=selu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { separableConv2d } from '../../ops/separable_conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.separableConv2d = function (depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat) {\n    this.throwIfDisposed();\n    return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat);\n};\n//# sourceMappingURL=separable_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sigmoid } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sigmoid = function () {\n    this.throwIfDisposed();\n    return sigmoid(this);\n};\n//# sourceMappingURL=sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sign } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sign = function () {\n    this.throwIfDisposed();\n    return sign(this);\n};\n//# sourceMappingURL=sign.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sin } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sin = function () {\n    this.throwIfDisposed();\n    return sin(this);\n};\n//# sourceMappingURL=sin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sinh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sinh = function () {\n    this.throwIfDisposed();\n    return sinh(this);\n};\n//# sourceMappingURL=sinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { slice } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.slice = function (begin, size) {\n    this.throwIfDisposed();\n    return slice(this, begin, size);\n};\n//# sourceMappingURL=slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { softmax } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.softmax = function (dim) {\n    this.throwIfDisposed();\n    return softmax(this, dim);\n};\n//# sourceMappingURL=softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { softplus } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.softplus = function () {\n    this.throwIfDisposed();\n    return softplus(this);\n};\n//# sourceMappingURL=softplus.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { spaceToBatchND } from '../../ops/space_to_batch_nd';\nimport { Tensor } from '../../tensor';\nTensor.prototype.spaceToBatchND = function (blockShape, paddings) {\n    this.throwIfDisposed();\n    return spaceToBatchND(this, blockShape, paddings);\n};\n//# sourceMappingURL=space_to_batch_nd.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { split } from '../../ops/split';\nimport { Tensor } from '../../tensor';\nTensor.prototype.split = function (numOrSizeSplits, axis) {\n    this.throwIfDisposed();\n    return split(this, numOrSizeSplits, axis);\n};\n//# sourceMappingURL=split.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sqrt } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sqrt = function () {\n    this.throwIfDisposed();\n    return sqrt(this);\n};\n//# sourceMappingURL=sqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { square } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.square = function () {\n    this.throwIfDisposed();\n    return square(this);\n};\n//# sourceMappingURL=square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { squaredDifference } from '../../ops/squared_difference';\nimport { Tensor } from '../../tensor';\nTensor.prototype.squaredDifference = function (b) {\n    this.throwIfDisposed();\n    return squaredDifference(this, b);\n};\n//# sourceMappingURL=squared_difference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { squaredDifferenceStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.squaredDifferenceStrict = function (x) {\n    this.throwIfDisposed();\n    return squaredDifferenceStrict(this, x);\n};\n//# sourceMappingURL=squared_difference_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { squeeze } from '../../ops/squeeze';\nimport { Tensor } from '../../tensor';\nTensor.prototype.squeeze = function (axis) {\n    this.throwIfDisposed();\n    return squeeze(this, axis);\n};\n//# sourceMappingURL=squeeze.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { stack } from '../../ops/stack';\nimport { Tensor } from '../../tensor';\nTensor.prototype.stack = function (x, axis) {\n    this.throwIfDisposed();\n    const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];\n    return stack(tensorsToBeStacked, axis);\n};\n//# sourceMappingURL=stack.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { step } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.step = function (alpha) {\n    this.throwIfDisposed();\n    return step(this, alpha);\n};\n//# sourceMappingURL=step.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { stridedSlice } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.stridedSlice = function (begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n    this.throwIfDisposed();\n    return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n};\n//# sourceMappingURL=strided_slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { subStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.subStrict = function (x) {\n    this.throwIfDisposed();\n    return subStrict(this, x);\n};\n//# sourceMappingURL=sub_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { sub } from '../../ops/sub';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sub = function (b) {\n    this.throwIfDisposed();\n    return sub(this, b);\n};\n//# sourceMappingURL=sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { sum } from '../../ops/sum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sum = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return sum(this, axis, keepDims);\n};\n//# sourceMappingURL=sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { tan } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tan = function () {\n    this.throwIfDisposed();\n    return tan(this);\n};\n//# sourceMappingURL=tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { tanh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tanh = function () {\n    this.throwIfDisposed();\n    return tanh(this);\n};\n//# sourceMappingURL=tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tile } from '../../ops/tile';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tile = function (reps) {\n    this.throwIfDisposed();\n    return tile(this, reps);\n};\n//# sourceMappingURL=tile.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `bool`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toBool = function () {\n    this.throwIfDisposed();\n    return cast(this, 'bool');\n};\n//# sourceMappingURL=to_bool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `float32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toFloat = function () {\n    this.throwIfDisposed();\n    return cast(this, 'float32');\n};\n//# sourceMappingURL=to_float.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `int32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toInt = function () {\n    this.throwIfDisposed();\n    return cast(this, 'int32');\n};\n//# sourceMappingURL=to_int.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { topk } from '../../ops/topk';\nimport { Tensor } from '../../tensor';\nTensor.prototype.topk = function (k, sorted) {\n    this.throwIfDisposed();\n    return topk(this, k, sorted);\n};\n//# sourceMappingURL=topk.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { transpose } from '../../ops/transpose';\nimport { Tensor } from '../../tensor';\nTensor.prototype.transpose = function (perm) {\n    this.throwIfDisposed();\n    return transpose(this, perm);\n};\n//# sourceMappingURL=transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unique } from '../../ops/unique';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unique = function (axis) {\n    this.throwIfDisposed();\n    return unique(this, axis);\n};\n//# sourceMappingURL=unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unsortedSegmentSum } from '../../ops/unsorted_segment_sum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unsortedSegmentSum = function (segmentIds, numSegments) {\n    this.throwIfDisposed();\n    return unsortedSegmentSum(this, segmentIds, numSegments);\n};\n//# sourceMappingURL=unsorted_segment_sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unstack } from '../../ops/unstack';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unstack = function (axis) {\n    this.throwIfDisposed();\n    return unstack(this, axis);\n};\n//# sourceMappingURL=unstack.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { where } from '../../ops/where';\nimport { Tensor } from '../../tensor';\nTensor.prototype.where = function (condition, x) {\n    this.throwIfDisposed();\n    return where(condition, this, x);\n};\n//# sourceMappingURL=where.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { zerosLike } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.zerosLike = function () {\n    this.throwIfDisposed();\n    return zerosLike(this);\n};\n//# sourceMappingURL=zeros_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './abs';\nimport './acos';\nimport './acosh';\nimport './add_strict';\nimport './add';\nimport './all';\nimport './any';\nimport './arg_max';\nimport './arg_min';\nimport './as_scalar';\nimport './as_type';\nimport './as1d';\nimport './as2d';\nimport './as3d';\nimport './as4d';\nimport './as5d';\nimport './asin';\nimport './asinh';\nimport './atan';\nimport './atan2';\nimport './atanh';\nimport './avg_pool';\nimport './batch_to_space_nd';\nimport './batchnorm';\nimport './broadcast_to';\nimport './cast';\nimport './ceil';\nimport './clip_by_value';\nimport './concat';\nimport './conv1d';\nimport './conv2d_transpose';\nimport './conv2d';\nimport './cos';\nimport './cosh';\nimport './cumsum';\nimport './depth_to_space';\nimport './depthwise_conv2D_deprecated';\nimport './depthwise_conv2d';\nimport './dilation2d';\nimport './div_no_nan';\nimport './div_strict';\nimport './div';\nimport './dot';\nimport './elu';\nimport './equal_strict';\nimport './equal';\nimport './erf';\nimport './exp';\nimport './expand_dims';\nimport './expm1';\nimport './fft';\nimport './flatten';\nimport './floor';\nimport './floorDiv';\nimport './gather';\nimport './greater_equal_strict';\nimport './greater_equal';\nimport './greater_strict';\nimport './greater';\nimport './ifft';\nimport './irfft';\nimport './is_finite';\nimport './is_inf';\nimport './is_nan';\nimport './leaky_relu';\nimport './less_equal_strict';\nimport './less_equal';\nimport './less_strict';\nimport './less';\nimport './local_response_normalization';\nimport './log_sigmoid';\nimport './log_softmax';\nimport './log_sum_exp';\nimport './log';\nimport './log1p';\nimport './logical_and';\nimport './logical_not';\nimport './logical_or';\nimport './logical_xor';\nimport './mat_mul';\nimport './max_pool';\nimport './max';\nimport './maximum_strict';\nimport './maximum';\nimport './mean';\nimport './min';\nimport './minimum_strict';\nimport './minimum';\nimport './mod_strict';\nimport './mod';\nimport './mul_strict';\nimport './mul';\nimport './neg';\nimport './norm';\nimport './not_equal_strict';\nimport './not_equal';\nimport './one_hot';\nimport './ones_like';\nimport './pad';\nimport './pool';\nimport './pow_strict';\nimport './pow';\nimport './prelu';\nimport './prod';\nimport './reciprocal';\nimport './relu';\nimport './relu6';\nimport './reshape_as';\nimport './reshape';\nimport './resize_bilinear';\nimport './resize_nearest_neighbor';\nimport './reverse';\nimport './rfft';\nimport './round';\nimport './rsqrt';\nimport './selu';\nimport './separable_conv2d';\nimport './sigmoid';\nimport './sign';\nimport './sin';\nimport './sinh';\nimport './slice';\nimport './softmax';\nimport './softplus';\nimport './space_to_batch_nd';\nimport './split';\nimport './sqrt';\nimport './square';\nimport './squared_difference';\nimport './squared_difference_strict';\nimport './squeeze';\nimport './stack';\nimport './step';\nimport './strided_slice';\nimport './sub_strict';\nimport './sub';\nimport './sum';\nimport './tan';\nimport './tanh';\nimport './tile';\nimport './to_bool';\nimport './to_float';\nimport './to_int';\nimport './topk';\nimport './transpose';\nimport './unique';\nimport './unsorted_segment_sum';\nimport './unstack';\nimport './where';\nimport './zeros_like';\n//# sourceMappingURL=register_all_chained_ops.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Required side effectful code.\nimport './base_side_effects';\n// All exports from this package should be in base.\nexport * from './base';\n// Register all the gradients.\nimport './register_all_gradients';\n// Import all op chainers and add type info to Tensor.\nimport './public/chained_ops/register_all_chained_ops';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { backend } from '@tensorflow/tfjs-core';\nlet _epsilon;\n/**\n * Returns the value of the fuzz factor used in numeric expressions.\n */\nexport function epsilon() {\n    if (_epsilon == null) {\n        _epsilon = backend().epsilon();\n    }\n    return _epsilon;\n}\n/**\n * Sets the value of the fuzz factor used in numeric expressions.\n * @param e New value of epsilon.\n */\nexport function setEpsilon(e) {\n    _epsilon = e;\n}\n/**\n * Returns the default image data format convention.\n */\nexport function imageDataFormat() {\n    return 'channelsLast';\n}\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Explicit error types.\n *\n * See the following link for more information about why the code includes\n * calls to setPrototypeOf:\n *\n * https://github.com/Microsoft/TypeScript-wiki/blob/master/Breaking-Changes.md#extending-built-ins-like-error-array-and-map-may-no-longer-work\n */\n// tslint:enable\n/**\n * Equivalent of Python's AttributeError.\n */\nexport class AttributeError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, AttributeError.prototype);\n    }\n}\n/**\n * Equivalent of Python's RuntimeError.\n */\nexport class RuntimeError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, RuntimeError.prototype);\n    }\n}\n/**\n * Equivalent of Python's ValueError.\n */\nexport class ValueError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, ValueError.prototype);\n    }\n}\n/**\n * Equivalent of Python's NotImplementedError.\n */\nexport class NotImplementedError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, NotImplementedError.prototype);\n    }\n}\n/**\n * Equivalent of Python's AssertionError.\n */\nexport class AssertionError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, AssertionError.prototype);\n    }\n}\n/**\n * Equivalent of Python's IndexError.\n */\nexport class IndexError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, IndexError.prototype);\n    }\n}\n//# sourceMappingURL=errors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: utils/generic_utils.py */\nimport { util } from '@tensorflow/tfjs-core';\nimport { AssertionError, ValueError } from '../errors';\n// tslint:enable\n/**\n * If `value` is an Array, equivalent to Python's `value * numValues`.\n * If `value` is not an Array, equivalent to Python's `[value] * numValues`\n */\n// tslint:disable-next-line:no-any\nexport function pyListRepeat(value, numValues) {\n    if (Array.isArray(value)) {\n        // tslint:disable-next-line:no-any\n        let newArray = [];\n        for (let i = 0; i < numValues; i++) {\n            newArray = newArray.concat(value);\n        }\n        return newArray;\n    }\n    else {\n        const newArray = new Array(numValues);\n        newArray.fill(value);\n        return newArray;\n    }\n}\nexport function assert(val, message) {\n    if (!val) {\n        throw new AssertionError(message);\n    }\n}\n/**\n * Count the number of elements of the `array` that are equal to `reference`.\n */\nexport function count(array, refernce) {\n    let counter = 0;\n    for (const item of array) {\n        if (item === refernce) {\n            counter++;\n        }\n    }\n    return counter;\n}\n/**\n * If an array is of length 1, just return the first element. Otherwise, return\n * the full array.\n * @param tensors\n */\nexport function singletonOrArray(xs) {\n    if (xs.length === 1) {\n        return xs[0];\n    }\n    return xs;\n}\n/**\n * Normalizes a list/tensor into a list.\n *\n * If a tensor is passed, we return\n * a list of size 1 containing the tensor.\n *\n * @param x target object to be normalized.\n */\n// tslint:disable-next-line:no-any\nexport function toList(x) {\n    if (Array.isArray(x)) {\n        return x;\n    }\n    return [x];\n}\n/**\n * Generate a UID for a list\n */\n// tslint:disable-next-line:no-any\nexport function objectListUid(objs) {\n    const objectList = toList(objs);\n    let retVal = '';\n    for (const obj of objectList) {\n        if (obj.id == null) {\n            throw new ValueError(`Object ${obj} passed to objectListUid without an id`);\n        }\n        if (retVal !== '') {\n            retVal = retVal + ', ';\n        }\n        retVal = `${retVal}${Math.abs(obj.id)}`;\n    }\n    return retVal;\n}\n/**\n * Converts string to snake-case.\n * @param name\n */\nexport function toSnakeCase(name) {\n    const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, '$1_$2');\n    const insecure = intermediate.replace(/([a-z])([A-Z])/g, '$1_$2').toLowerCase();\n    /*\n     If the class is private the name starts with \"_\" which is not secure\n     for creating scopes. We prefix the name with \"private\" in this case.\n     */\n    if (insecure[0] !== '_') {\n        return insecure;\n    }\n    return 'private' + insecure;\n}\nexport function toCamelCase(identifier) {\n    // quick return for empty string or single character strings\n    if (identifier.length <= 1) {\n        return identifier;\n    }\n    // Check for the underscore indicating snake_case\n    if (identifier.indexOf('_') === -1) {\n        return identifier;\n    }\n    return identifier.replace(/[_]+(\\w|$)/g, (m, p1) => p1.toUpperCase());\n}\n// tslint:disable-next-line:no-any\nlet _GLOBAL_CUSTOM_OBJECTS = {};\nexport function serializeKerasObject(instance) {\n    if (instance === null || instance === undefined) {\n        return null;\n    }\n    const dict = {};\n    dict['className'] = instance.getClassName();\n    dict['config'] = instance.getConfig();\n    return dict;\n}\n/**\n * Replace ndarray-style scalar objects in serialization objects with numbers.\n *\n * Background: In some versions of tf.keras, certain scalar values in the HDF5\n * model save file can be serialized as: `{'type': 'ndarray', 'value': num}`,\n * where in `num` is a plain number. This method converts such serialization\n * to a `number`.\n *\n * @param config The keras-format serialization object to be processed\n *   (in place).\n */\nfunction convertNDArrayScalarsInConfig(config) {\n    if (config == null || typeof config !== 'object') {\n        return;\n    }\n    else if (Array.isArray(config)) {\n        config.forEach(configItem => convertNDArrayScalarsInConfig(configItem));\n    }\n    else {\n        const fields = Object.keys(config);\n        for (const field of fields) {\n            const value = config[field];\n            if (value != null && typeof value === 'object') {\n                if (!Array.isArray(value) && value['type'] === 'ndarray' &&\n                    typeof value['value'] === 'number') {\n                    config[field] = value['value'];\n                }\n                else {\n                    convertNDArrayScalarsInConfig(value);\n                }\n            }\n        }\n    }\n}\n/**\n * Deserialize a saved Keras Object\n * @param identifier either a string ID or a saved Keras dictionary\n * @param moduleObjects a list of Python class names to object constructors\n * @param customObjects a list of Python class names to object constructors\n * @param printableModuleName debug text for the object being reconstituted\n * @param fastWeightInit Optional flag to use fast weight initialization\n *   during deserialization. This is applicable to cases in which\n *   the initialization will be immediately overwritten by loaded weight\n *   values. Default: `false`.\n * @returns a TensorFlow.js Layers object\n */\n// tslint:disable:no-any\nexport function deserializeKerasObject(identifier, moduleObjects = {}, customObjects = {}, printableModuleName = 'object', fastWeightInit = false) {\n    // tslint:enable\n    if (typeof identifier === 'string') {\n        const functionName = identifier;\n        let fn;\n        if (functionName in customObjects) {\n            fn = customObjects[functionName];\n        }\n        else if (functionName in _GLOBAL_CUSTOM_OBJECTS) {\n            fn = _GLOBAL_CUSTOM_OBJECTS[functionName];\n        }\n        else {\n            fn = moduleObjects[functionName];\n            if (fn == null) {\n                throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. ` +\n                    `This may be due to one of the following reasons:\\n` +\n                    `1. The ${printableModuleName} is defined in Python, in which ` +\n                    `case it needs to be ported to TensorFlow.js or your JavaScript ` +\n                    `code.\\n` +\n                    `2. The custom ${printableModuleName} is defined in JavaScript, ` +\n                    `but is not registered properly with ` +\n                    `tf.serialization.registerClass().`);\n                // TODO(cais): Add link to tutorial page on custom layers.\n            }\n        }\n        return fn;\n    }\n    else {\n        // In this case we are dealing with a Keras config dictionary.\n        const config = identifier;\n        if (config['className'] == null || config['config'] == null) {\n            throw new ValueError(`${printableModuleName}: Improper config format: ` +\n                `${JSON.stringify(config)}.\\n` +\n                `'className' and 'config' must set.`);\n        }\n        const className = config['className'];\n        let cls, fromConfig;\n        if (className in customObjects) {\n            [cls, fromConfig] = customObjects[className];\n        }\n        else if (className in _GLOBAL_CUSTOM_OBJECTS) {\n            [cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS['className'];\n        }\n        else if (className in moduleObjects) {\n            [cls, fromConfig] = moduleObjects[className];\n        }\n        if (cls == null) {\n            throw new ValueError(`Unknown ${printableModuleName}: ${className}. ` +\n                `This may be due to one of the following reasons:\\n` +\n                `1. The ${printableModuleName} is defined in Python, in which ` +\n                `case it needs to be ported to TensorFlow.js or your JavaScript ` +\n                `code.\\n` +\n                `2. The custom ${printableModuleName} is defined in JavaScript, ` +\n                `but is not registered properly with ` +\n                `tf.serialization.registerClass().`);\n            // TODO(cais): Add link to tutorial page on custom layers.\n        }\n        if (fromConfig != null) {\n            // Porting notes: Instead of checking to see whether fromConfig accepts\n            // customObjects, we create a customObjects dictionary and tack it on to\n            // config['config'] as config['config'].customObjects. Objects can use it,\n            // if they want.\n            // tslint:disable-next-line:no-any\n            const customObjectsCombined = {};\n            for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) {\n                customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key];\n            }\n            for (const key of Object.keys(customObjects)) {\n                customObjectsCombined[key] = customObjects[key];\n            }\n            // Add the customObjects to config\n            const nestedConfig = config['config'];\n            nestedConfig['customObjects'] = customObjectsCombined;\n            const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n            for (const key of Object.keys(customObjects)) {\n                _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n            }\n            convertNDArrayScalarsInConfig(config['config']);\n            const returnObj = fromConfig(cls, config['config'], customObjects, fastWeightInit);\n            _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n            return returnObj;\n        }\n        else {\n            // Then `cls` may be a function returning a class.\n            // In this case by convention `config` holds\n            // the kwargs of the function.\n            const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n            for (const key of Object.keys(customObjects)) {\n                _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n            }\n            // In python this is **config['config'], for tfjs-layers we require\n            // classes that use this fall-through construction method to take\n            // a config interface that mimics the expansion of named parameters.\n            const returnObj = new cls(config['config']);\n            _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n            return returnObj;\n        }\n    }\n}\n/**\n * Compares two numbers for sorting.\n * @param a\n * @param b\n */\nexport function numberCompare(a, b) {\n    return (a < b) ? -1 : ((a > b) ? 1 : 0);\n}\n/**\n * Comparison of two numbers for reverse sorting.\n * @param a\n * @param b\n */\nexport function reverseNumberCompare(a, b) {\n    return -1 * numberCompare(a, b);\n}\n/**\n * Convert a string into the corresponding DType.\n * @param dtype\n * @returns An instance of DType.\n */\nexport function stringToDType(dtype) {\n    switch (dtype) {\n        case 'float32':\n            return 'float32';\n        default:\n            throw new ValueError(`Invalid dtype: ${dtype}`);\n    }\n}\n/**\n * Test the element-by-element equality of two Arrays of strings.\n * @param xs First array of strings.\n * @param ys Second array of strings.\n * @returns Wether the two arrays are all equal, element by element.\n */\nexport function stringsEqual(xs, ys) {\n    if (xs == null || ys == null) {\n        return xs === ys;\n    }\n    if (xs.length !== ys.length) {\n        return false;\n    }\n    for (let i = 0; i < xs.length; ++i) {\n        if (xs[i] !== ys[i]) {\n            return false;\n        }\n    }\n    return true;\n}\n/**\n * Get the unique elements of an array.\n * @param xs Array.\n * @returns An Array consisting of the unique elements in `xs`.\n */\nexport function unique(xs) {\n    if (xs == null) {\n        return xs;\n    }\n    const out = [];\n    // TODO(cais): Maybe improve performance by sorting.\n    for (const x of xs) {\n        if (out.indexOf(x) === -1) {\n            out.push(x);\n        }\n    }\n    return out;\n}\n/**\n * Determine if an Object is empty (i.e., does not have own properties).\n * @param obj Object\n * @returns Whether the Object is empty.\n * @throws ValueError: If object is `null` or `undefined`.\n */\nexport function isObjectEmpty(obj) {\n    if (obj == null) {\n        throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);\n    }\n    for (const key in obj) {\n        if (obj.hasOwnProperty(key)) {\n            return false;\n        }\n    }\n    return true;\n}\n/**\n * Helper function used to build type union/enum run-time checkers.\n * @param values The list of allowed values.\n * @param label A string name for the type\n * @param value The value to test.\n * @throws ValueError: If the value is not in values nor `undefined`/`null`.\n */\nexport function checkStringTypeUnionValue(values, label, value) {\n    if (value == null) {\n        return;\n    }\n    if (values.indexOf(value) < 0) {\n        throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`);\n    }\n}\n/**\n * Helper function for verifying the types of inputs.\n *\n * Ensures that the elements of `x` are all of type `expectedType`.\n * Also verifies that the length of `x` is within bounds.\n *\n * @param x Object to test.\n * @param expectedType The string expected type of all of the elements in the\n * Array.\n * @param minLength Return false if x.length is less than this.\n * @param maxLength Return false if x.length is greater than this.\n * @returns true if and only if `x` is an `Array<expectedType>` with\n * length >= `minLength` and <= `maxLength`.\n */\n// tslint:disable:no-any\nexport function checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) {\n    assert(minLength >= 0);\n    assert(maxLength >= minLength);\n    return (Array.isArray(x) && x.length >= minLength && x.length <= maxLength &&\n        x.every(e => typeof e === expectedType));\n}\n// tslint:enable:no-any\n/**\n * Assert that a value or an array of value are positive integer.\n *\n * @param value The value being asserted on. May be a single number or an array\n *   of numbers.\n * @param name Name of the value, used to make the error message.\n */\nexport function assertPositiveInteger(value, name) {\n    if (Array.isArray(value)) {\n        util.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`);\n        value.forEach((v, i) => assertPositiveInteger(v, `element ${i + 1} of ${name}`));\n    }\n    else {\n        util.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ` +\n            `${formatAsFriendlyString(value)}.`);\n    }\n}\n/**\n * Format a value into a display-friendly, human-readable fashion.\n *\n * - `null` is formatted as `'null'`\n * - Strings are formated with flanking pair of quotes.\n * - Arrays are formatted with flanking pair of square brackets.\n *\n * @param value The value to display.\n * @return Formatted string.\n */\n// tslint:disable-next-line:no-any\nexport function formatAsFriendlyString(value) {\n    if (value === null) {\n        return 'null';\n    }\n    else if (Array.isArray(value)) {\n        return '[' + value.map(v => formatAsFriendlyString(v)).join(',') + ']';\n    }\n    else if (typeof value === 'string') {\n        return `\"${value}\"`;\n    }\n    else {\n        return `${value}`;\n    }\n}\n/**\n * Returns a function `f2` (decorator) which wraps the original function\n * `f`. `f2` guarantees that `f` can be called at most once\n * every `waitMs` ms. If `f2` is called more often, it will return\n * the last returned result of `f`.\n *\n * @param f The original function `f` to wrap.\n * @param waitMs The time between two consecutive calls to `f` in ms.\n */\nexport function debounce(f, waitMs) {\n    let lastTime = util.now();\n    let lastResult;\n    const f2 = (...args) => {\n        const now = util.now();\n        if (now - lastTime < waitMs) {\n            return lastResult;\n        }\n        lastTime = now;\n        lastResult = f(...args);\n        return lastResult;\n    };\n    return f2;\n}\n/**\n * Returns the fusable activation given a layers identifier.\n *\n * @param activationName The layers identifier string.\n * @return The name of the fusable activation.\n */\nexport function mapActivationToFusedKernel(activationName) {\n    if (activationName === 'relu') {\n        return 'relu';\n    }\n    if (activationName === 'linear') {\n        return 'linear';\n    }\n    if (activationName === 'elu') {\n        return 'elu';\n    }\n    return null;\n}\n/**\n * Returns the cartesian product of sets of values.\n * This works the same as itertools.product in Python.\n *\n * Example:\n *\n * filters = [128, 256, 512]\n * paddings = ['same', 'valid']\n *\n * product = [ [128, 'same'], [128, 'valid'], [256, 'same'], [256, 'valid'],\n * [512, 'same'], [512, 'valid']]\n *\n * @param arrayOfValues List/array of values.\n * @return The cartesian product.\n */\nexport function getCartesianProductOfValues(...arrayOfValues) {\n    assert(arrayOfValues.length > 0, 'arrayOfValues is empty');\n    for (const values of arrayOfValues) {\n        assert(Array.isArray(values), 'one of the values is not an array');\n        assert(values.length > 0, 'one of the values is empty');\n    }\n    return arrayOfValues.reduce((products, values) => {\n        if (products.length === 0) {\n            return values.map(value => [value]);\n        }\n        return values\n            .map(value => {\n            return products.map((prevValue) => [...prevValue, value]);\n        })\n            .reduce((flattenedProduct, unflattenedProduct) => {\n            return flattenedProduct.concat(unflattenedProduct);\n        }, []);\n    }, []);\n}\n//# sourceMappingURL=generic_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/contraints.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport { deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\n/**\n * Helper function used by many of the Constraints to find the L2Norms.\n */\nfunction calcL2Norms(w, axis) {\n    return tidy(() => tfc.sqrt(tfc.sum(tfc.mul(w, w), axis, true)));\n}\n/**\n * Base class for functions that impose constraints on weight values\n *\n * @doc {\n *   heading: 'Constraints',\n *   subheading: 'Classes',\n *   namespace: 'constraints'\n * }\n */\nexport class Constraint extends serialization.Serializable {\n    getConfig() {\n        return {};\n    }\n}\nexport class MaxNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultMaxValue = 2;\n        this.defaultAxis = 0;\n        this.maxValue =\n            args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => {\n            const norms = calcL2Norms(w, this.axis);\n            const desired = tfc.clipByValue(norms, 0, this.maxValue);\n            return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));\n        });\n    }\n    getConfig() {\n        return { maxValue: this.maxValue, axis: this.axis };\n    }\n}\n/** @nocollapse */\nMaxNorm.className = 'MaxNorm';\nserialization.registerClass(MaxNorm);\nexport class UnitNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultAxis = 0;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => tfc.div(w, tfc.add(epsilon(), calcL2Norms(w, this.axis))));\n    }\n    getConfig() {\n        return { axis: this.axis };\n    }\n}\n/** @nocollapse */\nUnitNorm.className = 'UnitNorm';\nserialization.registerClass(UnitNorm);\nexport class NonNeg extends Constraint {\n    apply(w) {\n        return tfc.relu(w);\n    }\n}\n/** @nocollapse */\nNonNeg.className = 'NonNeg';\nserialization.registerClass(NonNeg);\nexport class MinMaxNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultMinValue = 0.0;\n        this.defaultMaxValue = 1.0;\n        this.defaultRate = 1.0;\n        this.defaultAxis = 0;\n        this.minValue =\n            args.minValue != null ? args.minValue : this.defaultMinValue;\n        this.maxValue =\n            args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n        this.rate = args.rate != null ? args.rate : this.defaultRate;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => {\n            const norms = calcL2Norms(w, this.axis);\n            const desired = tfc.add(tfc.mul(this.rate, tfc.clipByValue(norms, this.minValue, this.maxValue)), tfc.mul(1.0 - this.rate, norms));\n            return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));\n        });\n    }\n    getConfig() {\n        return {\n            minValue: this.minValue,\n            maxValue: this.maxValue,\n            rate: this.rate,\n            axis: this.axis\n        };\n    }\n}\n/** @nocollapse */\nMinMaxNorm.className = 'MinMaxNorm';\nserialization.registerClass(MinMaxNorm);\n// Maps the JavaScript-like identifier keys to the corresponding registry\n// symbols.\nexport const CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'maxNorm': 'MaxNorm',\n    'minMaxNorm': 'MinMaxNorm',\n    'nonNeg': 'NonNeg',\n    'unitNorm': 'UnitNorm'\n};\nexport function serializeConstraint(constraint) {\n    return serializeKerasObject(constraint);\n}\nexport function deserializeConstraint(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'constraint');\n}\nexport function getConstraint(identifier) {\n    if (identifier == null) {\n        return null;\n    }\n    if (typeof identifier === 'string') {\n        const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        const config = { className, config: {} };\n        return deserializeConstraint(config);\n    }\n    else if (identifier instanceof Constraint) {\n        return identifier;\n    }\n    else {\n        return deserializeConstraint(identifier);\n    }\n}\n//# sourceMappingURL=constraints.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// tslint:disable-next-line:max-line-length\nimport { MaxNorm, MinMaxNorm, NonNeg, UnitNorm } from './constraints';\n/**\n * MaxNorm weight constraint.\n *\n * Constrains the weights incident to each hidden unit\n * to have a norm less than or equal to a desired value.\n *\n * References\n *       - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting\n * Srivastava, Hinton, et al.\n * 2014](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)\n *\n * @doc {heading: 'Constraints',namespace: 'constraints'}\n */\nexport function maxNorm(args) {\n    return new MaxNorm(args);\n}\n/**\n * Constrains the weights incident to each hidden unit to have unit norm.\n *\n * @doc {heading: 'Constraints', namespace: 'constraints'}\n */\nexport function unitNorm(args) {\n    return new UnitNorm(args);\n}\n/**\n * Constains the weight to be non-negative.\n *\n * @doc {heading: 'Constraints', namespace: 'constraints'}\n */\nexport function nonNeg() {\n    return new NonNeg();\n}\n/** @doc {heading: 'Constraints', namespace: 'constraints'} */\nexport function minMaxNorm(config) {\n    return new MinMaxNorm(config);\n}\n//# sourceMappingURL=exports_constraints.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport const VALID_DATA_FORMAT_VALUES = ['channelsFirst', 'channelsLast'];\nexport const VALID_PADDING_MODE_VALUES = ['valid', 'same', 'causal'];\nexport const VALID_POOL_MODE_VALUES = ['max', 'avg'];\nexport const VALID_BIDIRECTIONAL_MERGE_MODES = ['sum', 'mul', 'concat', 'ave'];\nexport const VALID_SAMPLE_WEIGHT_MODES = ['temporal'];\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Common functions for TensorFlow.js Layers.\n */\nimport { VALID_DATA_FORMAT_VALUES, VALID_PADDING_MODE_VALUES, VALID_POOL_MODE_VALUES } from './keras_format/common';\nimport { checkStringTypeUnionValue } from './utils/generic_utils';\n// A map from the requested scoped name of a Tensor to the number of Tensors\n// wanting that name so far.  This allows enforcing name uniqueness by appending\n// an incrementing index, e.g. scope/name, scope/name_1, scope/name_2, etc.\nconst nameMap = new Map();\nexport function checkDataFormat(value) {\n    checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, 'DataFormat', value);\n}\nexport function checkPaddingMode(value) {\n    checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, 'PaddingMode', value);\n}\nexport function checkPoolMode(value) {\n    checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, 'PoolMode', value);\n}\nconst _nameScopeStack = [];\nconst _nameScopeDivider = '/';\n/**\n * Enter namescope, which can be nested.\n */\nexport function nameScope(name, fn) {\n    _nameScopeStack.push(name);\n    try {\n        const val = fn();\n        _nameScopeStack.pop();\n        return val;\n    }\n    catch (e) {\n        _nameScopeStack.pop();\n        throw e;\n    }\n}\n/**\n * Get the current namescope as a flat, concatenated string.\n */\nfunction currentNameScopePrefix() {\n    if (_nameScopeStack.length === 0) {\n        return '';\n    }\n    else {\n        return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider;\n    }\n}\n/**\n * Get the name a Tensor (or Variable) would have if not uniqueified.\n * @param tensorName\n * @return Scoped name string.\n */\nexport function getScopedTensorName(tensorName) {\n    if (!isValidTensorName(tensorName)) {\n        throw new Error('Not a valid tensor name: \\'' + tensorName + '\\'');\n    }\n    return currentNameScopePrefix() + tensorName;\n}\n/**\n * Get unique names for Tensors and Variables.\n * @param scopedName The fully-qualified name of the Tensor, i.e. as produced by\n *  `getScopedTensorName()`.\n * @return A unique version of the given fully scoped name.\n *   If this is the first time that the scoped name is seen in this session,\n *   then the given `scopedName` is returned unaltered.  If the same name is\n *   seen again (producing a collision), an incrementing suffix is added to the\n *   end of the name, so it takes the form 'scope/name_1', 'scope/name_2', etc.\n */\nexport function getUniqueTensorName(scopedName) {\n    if (!isValidTensorName(scopedName)) {\n        throw new Error('Not a valid tensor name: \\'' + scopedName + '\\'');\n    }\n    if (!nameMap.has(scopedName)) {\n        nameMap.set(scopedName, 0);\n    }\n    const index = nameMap.get(scopedName);\n    nameMap.set(scopedName, nameMap.get(scopedName) + 1);\n    if (index > 0) {\n        const result = `${scopedName}_${index}`;\n        // Mark the composed name as used in case someone wants\n        // to call getUniqueTensorName(\"name_1\").\n        nameMap.set(result, 1);\n        return result;\n    }\n    else {\n        return scopedName;\n    }\n}\nconst tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\\._\\/]*$/);\n/**\n * Determine whether a string is a valid tensor name.\n * @param name\n * @returns A Boolean indicating whether `name` is a valid tensor name.\n */\nexport function isValidTensorName(name) {\n    return !!name.match(tensorNameRegex);\n}\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Math utility functions.\n *\n * This file contains some frequently used math function that operates on\n * number[] or Float32Array and return a number. Many of these functions are\n * not-so-thick wrappers around TF.js Core functions. But they offer the\n * convenience of\n * 1) not having to convert the inputs into Tensors,\n * 2) not having to convert the returned Tensors to numbers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { scalar, tensor1d } from '@tensorflow/tfjs-core';\nimport { ValueError } from '../errors';\n/**\n * Determine if a number is an integer.\n */\nexport function isInteger(x) {\n    return x === parseInt(x.toString(), 10);\n}\n/**\n * Calculate the product of an array of numbers.\n * @param array The array to calculate the product over.\n * @param begin Beginning index, inclusive.\n * @param end Ending index, exclusive.\n * @return The product.\n */\nexport function arrayProd(array, begin, end) {\n    if (begin == null) {\n        begin = 0;\n    }\n    if (end == null) {\n        end = array.length;\n    }\n    let prod = 1;\n    for (let i = begin; i < end; ++i) {\n        prod *= array[i];\n    }\n    return prod;\n}\n/**\n * A helper function transforms the two input types to an instance of Tensor1D,\n * so the return value can be fed directly into various TF.js Core functions.\n * @param array\n */\nfunction toArray1D(array) {\n    array = Array.isArray(array) ? new Float32Array(array) : array;\n    return tensor1d(array);\n}\n/**\n * Compute minimum value.\n * @param array\n * @return minimum value.\n */\nexport function min(array) {\n    return tfc.min(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute maximum value.\n * @param array\n * @return maximum value\n */\nexport function max(array) {\n    return tfc.max(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute sum of array.\n * @param array\n * @return The sum.\n */\nexport function sum(array) {\n    return tfc.sum(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute mean of array.\n * @param array\n * @return The mean.\n */\nexport function mean(array) {\n    return sum(array) / array.length;\n}\n/**\n * Compute variance of array.\n * @param array\n * @return The variance.\n */\nexport function variance(array) {\n    const demeaned = tfc.sub(toArray1D(array), scalar(mean(array)));\n    const sumSquare = tfc.sum(tfc.mul(demeaned, demeaned)).dataSync()[0];\n    return sumSquare / array.length;\n}\n/**\n * Compute median of array.\n * @param array\n * @return The median value.\n */\nexport function median(array) {\n    const arraySorted = array.slice().sort((a, b) => a - b);\n    const lowIdx = Math.floor((arraySorted.length - 1) / 2);\n    const highIdx = Math.ceil((arraySorted.length - 1) / 2);\n    if (lowIdx === highIdx) {\n        return arraySorted[lowIdx];\n    }\n    return (arraySorted[lowIdx] + arraySorted[highIdx]) / 2;\n}\n/**\n * Generate an array of integers in [begin, end).\n * @param begin Beginning integer, inclusive.\n * @param end Ending integer, exclusive.\n * @returns Range array.\n * @throws ValueError, iff `end` < `begin`.\n */\nexport function range(begin, end) {\n    if (end < begin) {\n        throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);\n    }\n    const out = [];\n    for (let i = begin; i < end; ++i) {\n        out.push(i);\n    }\n    return out;\n}\n//# sourceMappingURL=math_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * deeplearn.js backend.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { onesLike as coreOnesLike, scalar, tensor1d, tidy, where, zerosLike as coreZerosLike } from '@tensorflow/tfjs-core';\nimport { checkDataFormat } from '../common';\nimport { NotImplementedError, ValueError } from '../errors';\nimport * as math_utils from '../utils/math_utils';\nimport { imageDataFormat } from './common';\n// tslint:enable\n/* Setting and getting backend from deeplearn.js. */\n// Default deeplearn.js backend is WebGL (GPU).\nlet backend = 'webgl';\nexport function setBackend(requestedBackend) {\n    tfc.setBackend(requestedBackend);\n    backend = requestedBackend;\n}\nexport function getBackend() {\n    return backend;\n}\n/**\n * Indicates whether the backend is operating symbolically.\n *\n * This function will be used to determine how to interpret user code. If\n * it returns true, calls to the backend construct a symbolic graph; if\n * it returns false, calls to the backend execute immediately.\n */\nexport function isBackendSymbolic() {\n    return false;\n}\n/**\n * Get the number of elements in a Tensor.\n * @param x The Tensor.\n * @return Number of elements in `x`.\n */\nexport function countParams(x) {\n    const shape = x.shape;\n    if (shape.length > 0) {\n        return shape.reduce((a, b) => a * b);\n    }\n    else {\n        // Scalar.\n        return 1;\n    }\n}\n/**\n * Casts a tensor to a different dtype and returns it.\n * @param x Input tensor.\n * @param dtype String: 'float32'|'int32'|'bool'.\n * @returns Tensor of the specified `dtype`.\n */\nexport function cast(x, dtype) {\n    return x.asType(dtype);\n}\n/**\n * Adds a 1-sized dimension at index \"axis\".\n * @param x Input tensor.\n * @param axis Position where to add the new axis.\n * @returns Result of the dimension expansion.\n */\nexport function expandDims(x, axis = -1) {\n    const outShape = x.shape.slice();\n    if (axis < 0) {\n        axis = outShape.length + axis + 1;\n    }\n    outShape.splice(axis, 0, 1);\n    return x.reshape(outShape);\n}\n/**\n * Repeats a 2D tensor.\n *\n * If `x` has shape `[samples, dim]` and `n` is 2, for example, the output\n * will have shape `[samples, 2, dim]`.\n *\n * @param x Input tensor.\n * @param n Integer, number of times to repeat.\n * @returns The result of the repeat operation.\n * @throws ValueError: If input tensor is not 2D.\n */\nexport function repeat(x, n) {\n    return tidy(() => {\n        if (x.shape.length !== 2) {\n            throw new ValueError(`repeat() expects a rank-2 tensor, but received a ` +\n                `rank-${x.shape.length} tensor.`);\n        }\n        const y = expandDims(x, 1);\n        return tile(y, [1, n, 1]);\n    });\n}\n/**\n * Flatten a Tensor into 1D.\n * @param x Input tensor.\n * @return The result of the flattening `x`.\n */\nexport function flatten(x) {\n    const newShape = [math_utils.arrayProd(x.shape)];\n    return x.reshape(newShape);\n}\n/**\n * Turn a nD tensor into a 2D tensor with same 0th dimension.\n * In other words, it flattens each data samples of a batch.\n *\n * @param x The tensor to flatten. The rank of this tensor is required to be 2\n *   or higher.\n * @return The result of the flattening.\n */\nexport function batchFlatten(x) {\n    if (x.rank <= 1) {\n        throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);\n    }\n    const newShape = [x.shape[0], math_utils.arrayProd(x.shape, 1)];\n    return x.reshape(newShape);\n}\n/**\n * Do slicing along the first axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size size of the slice along the first axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongFirstAxis(array, start, size) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                return tfc.slice2d(array, [start, 0], [size, array.shape[1]]);\n            case 3:\n                return tfc.slice3d(array, [start, 0, 0], [size, array.shape[1], array.shape[2]]);\n            case 4:\n                return tfc.slice4d(array, [start, 0, 0, 0], [size, array.shape[1], array.shape[2], array.shape[3]]);\n            case 5:\n                return tfc.slice(array, [start, 0, 0, 0, 0], [\n                    size, array.shape[1], array.shape[2], array.shape[3], array.shape[4]\n                ]);\n            case 6:\n                return tfc.slice(array, [start, 0, 0, 0, 0, 0], [\n                    size, array.shape[1], array.shape[2], array.shape[3], array.shape[4],\n                    array.shape[5]\n                ]);\n            default:\n                throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Do slicing along the last axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size size of the slice along the last axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongLastAxis(array, start, size) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                return tfc.slice2d(array, [0, start], [array.shape[0], size]);\n            case 3:\n                return tfc.slice3d(array, [0, 0, start], [array.shape[0], array.shape[1], size]);\n            case 4:\n                return tfc.slice4d(array, [0, 0, 0, start], [array.shape[0], array.shape[1], array.shape[2], size]);\n            default:\n                throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Do slicing along the sepcified axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size of the slice along the chosen axis.\n * @param choose an axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongAxis(array, start, size, axis) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            case 3:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return tfc.slice3d(array, [0, start, 0], [array.shape[0], size, array.shape[2]]);\n                    case 3:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            case 4:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return tfc.slice4d(array, [0, start, 0, 0], [array.shape[0], size, array.shape[2], array.shape[3]]);\n                    case 3:\n                        return tfc.slice4d(array, [0, 0, start, 0], [array.shape[0], array.shape[1], size, array.shape[3]]);\n                    case 4:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            default:\n                throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Concatenates a list of tensors alongside the specified axis.\n * @param tensors `Array` of tensors to concatenate.\n * @param axis Concatenation axis.\n * @returns The result of the concatenation.\n */\nexport function concatenate(tensors, axis = -1) {\n    let rank;\n    if (axis < 0) {\n        rank = tensors[0].rank;\n        if (rank !== 0) {\n            axis = rank;\n        }\n        else {\n            axis = 0;\n        }\n    }\n    if (axis === tensors[0].rank) {\n        // Porting Note: This is necessary because tfc.concat() requires axis to be\n        //   in the interval [-rank, rank).\n        axis = -1;\n    }\n    // Porting Note: Sparse concat is not supported yet.\n    return tfc.concat(tensors, axis);\n}\n/**\n * Concatenate two arrays along the first dimension.\n * @param a The 1st `tf.Tensor` to concatenate.\n * @param b The 2nd `tf.Tensor` to concatenate.\n * @returns Result of the concatenation.\n * @throws ValueError: If `a` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function concatAlongFirstAxis(a, b) {\n    switch (a.rank) {\n        case 1:\n            return tfc.concat1d([a, b]);\n        case 2:\n            return tfc.concat2d([a, b], 0);\n        case 3:\n            return tfc.concat3d([a, b], 0);\n        case 4:\n            return tfc.concat4d([a, b], 0);\n        default:\n            throw new ValueError(`concatAlongFirstAxis() received an unsupported ` +\n                `tensor rank: ${a.rank}`);\n    }\n}\n/**\n * Creates a tensor by tiling `x` by `n`.\n * @param x A tensor.\n * @param n An Array of integers or a single integer. If an Array, the length\n *   must be the same as the number of dimensions in `x`. If a single integer,\n *   it will be treated as an Array of length 1.\n */\nexport function tile(x, n) {\n    if (!Array.isArray(n)) {\n        n = [n];\n    }\n    if (x.rank !== n.length) {\n        throw new ValueError(`The length of input n (${n.length}) does not match ` +\n            `the number of dimensions in input x (${x.rank})`);\n    }\n    return tfc.tile(x, n);\n}\n/* Creation of random tensors. */\n/**\n * Get a tensor with normal distribution of values.\n *\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @return The normal tensor.\n */\nexport function randomNormal(shape, mean = 0.0, stddev = 1.0, dtype, seed) {\n    return tfc.randomNormal(shape, mean, stddev, dtype, seed);\n}\n/* Linear Algebra */\n/**\n * Multiply two tensors and returns the result as a tensor.\n *\n * For 2D tensors, this is equivalent to matrix multiplication (matMul).\n * For tensors of higher ranks, it follows the Theano behavior,\n * (e.g. `(2, 3) * (4, 3, 5) -> (2, 4, 5)`).  From the Theano documentation:\n *\n * For N dimensions it is a sum product over the last axis of x and the\n * second-to-last of y:\n *\n * @param a A tensor of at least rank 2.\n * @param b A tensor of at least rank 2.\n * @param activation (optional) A string identifying the activation\n *   function.\n * @return Result of the dot operation.\n */\nexport function dot(a, b, activation, bias) {\n    if ((a.rank < 2) || (b.rank < 2)) {\n        throw new NotImplementedError(`dot requires both inputs to be rank >= 2` +\n            ` but got x shape = ${a.shape} and y shape = ${b.shape}`);\n    }\n    if (b.rank >= 3) {\n        const xLastDim = a.shape.slice(-1)[0];\n        const ySecondLastDim = b.shape.slice(-2)[0];\n        if (xLastDim !== ySecondLastDim) {\n            throw new NotImplementedError(`If rank y >= 3, then the second last dim` +\n                ` of y must equal the last dim of x but got x shape = ${a.shape} and ` +\n                ` y shape = ${b.shape}`);\n        }\n    }\n    // Handle basic 2D x 2D case.\n    if ((a.rank === 2) && (b.rank === 2)) {\n        const transposeA = false;\n        const transposeB = false;\n        // tfc.fused.matMul only fuses certain activation functions. Unsupported\n        // activation functions are treated as 'linear' activations, which is\n        // equivalent to a no-op.\n        return tfc.fused.matMul({\n            a,\n            b: b,\n            transposeA,\n            transposeB,\n            bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n            activation\n        });\n    }\n    else {\n        // Reshape x into the analogous 2D Tensor.\n        const aFirstDims = a.shape.slice(); // Holds all but the last dim of x.\n        const aLastDim = aFirstDims.pop();\n        a = a.reshape([-1, aLastDim]);\n        // Reshape y into the analogous 2D Tensor, and keep track of the\n        // required dimensions to reproduce the output shape.\n        const bShape = b.shape.slice();\n        const bLastDim = bShape.pop();\n        const ySecondLastDim = bShape.pop();\n        const yOtherDims = [...bShape, bLastDim];\n        // permutation should be like [r-2, 0, 1, 2, ... r-4, r-3, r-1]\n        // where r is the rank of y.\n        const perm = Array.from({ length: b.rank }, (_, i) => {\n            if (i === 0) {\n                return b.rank - 2;\n            }\n            else if (i <= b.rank - 2) {\n                return i - 1;\n            }\n            return i;\n        });\n        b = b.transpose(perm).reshape([ySecondLastDim, -1]);\n        // Multiply x and y as 2D Tensors, and then reshape back to original.\n        const outputShape = [...aFirstDims, ...yOtherDims];\n        const transposeA = false;\n        const transposeB = false;\n        return tfc.fused\n            .matMul({\n            a,\n            b,\n            transposeA,\n            transposeB,\n            bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n            activation\n        })\n            .reshape(outputShape);\n    }\n}\n/**\n * Compute the sign Tensor of an input Tensor.\n *\n * Elements of the input `tf.Tensor` that are === 0 are mapped to 0.\n * Elements of the input `tf.Tensor` that are > 0 are mapped to 1.\n * Elements of the input `tf.Tensor` that are < 0 are mapped to -1.\n *\n * @param x Input `tf.Tensor`.\n * @return The sign `tf.Tensor`.\n */\nexport function sign(x) {\n    // TODO(cais): Move to the core.\n    return tidy(() => {\n        const zerosLikeX = coreZerosLike(x);\n        const onesLikeX = coreOnesLike(x);\n        return where(tfc.equal(x, zerosLikeX), zerosLikeX, where(tfc.greater(x, coreZerosLike(x)), onesLikeX, tfc.mul(-1, onesLikeX)));\n    });\n}\n/**\n * Computes the one-hot representation of an integer tensor.\n * @param indices nD integer tensor of shape\n *   `(batch_size, dim1, dim2, ... dim(n-1))`\n * @param numClasses Integer, number of classes to consider.\n * @returns (n + 1)D one hot representation of the input\n *   with shape `(batch_size, dim1, dim2, ... dim(n-1), num_classes)`\n */\nexport function oneHot(indices, numClasses) {\n    return tidy(() => {\n        if (indices.rank !== 1) {\n            throw new Error('Only 1D one-hot tensors are supported in the ' +\n                'deeplearn backend, at present.');\n        }\n        indices = indices.toInt();\n        return tfc.oneHot(indices, numClasses).toFloat();\n    });\n}\n/* Elementary math functions. */\n/**\n * Retrieves the elements of indices `indices` in the tensor `reference`.\n * @param reference A tensor.\n * @param indices An integer tensor of indices or an `Array` of integers.\n * @param axis Axis along which to perform the gather operation.\n * @returns The result of the gathering as a tensor.\n */\nexport function gather(reference, indices, axis) {\n    return tidy(() => {\n        if (Array.isArray(indices)) {\n            indices = tensor1d(indices, 'int32');\n        }\n        else {\n            indices = indices.toInt();\n        }\n        return tfc.gather(reference, indices, axis);\n    });\n}\n/**\n * Element-wise square.\n * @param x Input tensor.\n * @return element-wise x^2\n */\nexport function square(x) {\n    return tfc.mul(x, x);\n}\n/**\n * Element-wise exponentiation.\n *\n * Porting Note: In PyKeras, `a` (the exponent) is a Python integer, which\n *   takes advatnage of the backend's (e.g., TensorFlow's) automatic\n * conversion to tensor. Here we allow `a` to be either a number or a tensor.\n *\n * @param x The base tensor.\n * @param a The exponent, tensor or number. If a number, it is rounded to the\n *   nearest integer and converted to a tensor.\n * @returns A tensor of the same shape as `x`.\n */\nexport function pow(x, a) {\n    return tidy(() => {\n        if (typeof (a) === 'number') {\n            a = scalar(Math.round(a), 'int32');\n        }\n        if (a.dtype !== 'int32') {\n            throw new NotImplementedError(`Non-int32 dtype (${a.dtype}) is not supported by pow() yet`);\n        }\n        return tfc.pow(x, a);\n    });\n}\n/**\n * Reshapes bias tensor according to rank of x.\n */\nfunction reshapeBias(xRank, bias, dataFormat) {\n    const biasShape = bias.shape;\n    if (bias.rank !== 1 && bias.rank !== xRank) {\n        throw new ValueError(`Unexpected bias dimensions: ${bias.rank}` +\n            `; expected it to be 1 or ${xRank}`);\n    }\n    if (xRank === 5) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1, 1, 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, 1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank === 4) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1, 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[2], biasShape[0], biasShape[1]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank === 3) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[1], biasShape[0]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank < 3) {\n        return bias;\n    }\n    throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);\n}\n/* Neural-network operations. */\n/**\n * Add a bias to a tensor.\n *\n * @param x The tensor to add the bias to.\n * @param bias The bias to add to `x`. Must be 1D or the same rank as `x`.\n * @return Result of the bias adding.\n * @throws ValueError: If the rank of `bias` is incorrect.\n */\nexport function biasAdd(x, bias, dataFormat) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        return x.add(reshapeBias(x.rank, bias, dataFormat));\n    });\n}\n/**\n * Exponential linear unit (ELU).\n * @param x A tensor or variable to compute the activation function for.\n * @param alpha: A scalar, a scaling factor for the negative section.\n * @return Output of the ELU operation.\n */\nexport function elu(x, alpha = 1) {\n    // TODO(cais): Add support for alpha values other than 1.\n    if (alpha !== 1) {\n        throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented ` +\n            `yet.`);\n    }\n    return tfc.elu(x);\n}\n/**\n * Softsign of a tensor.\n *\n * Defined as x / (abs(x) + 1), element-wise.\n *\n * @param x: Input.\n * @returns Output.\n */\nexport function softsign(x) {\n    return tidy(() => tfc.div(x, tfc.abs(x).add(1)));\n}\n/**\n * Sets entries in `x` to zero at random, while scaling the entire tensor.\n *\n * @param x input tensor.\n * @param level fraction of the entries in the tensor that will be set to 0.\n * @param noiseShape shape of randomly generated keep/drop flags, must be\n *   broadcastable to the shape of `x`. Optional.\n * @param seed random seed to ensure determinism. Optional.\n * @returns Result of the dropout operation.\n */\nexport function dropout(x, level, noiseShape, seed) {\n    return tidy(() => tfc.dropout(x, level, noiseShape, seed));\n}\n/**\n * Element-wise, segment-wise linear approximation of sigmoid.\n *\n * Returns `0.` if `x < -2.5`, `1.` if `x > 2.5`.\n * In `-2.5 <= x <= 2.5`, returns `0.2 * x + 0.5`.\n *\n * @param x Input tensor.\n * @returns Output tensor.\n */\nexport function hardSigmoid(x) {\n    return tidy(() => {\n        const y = tfc.add(.5, tfc.mul(.2, x));\n        return tfc.clipByValue(y, 0, 1);\n    });\n}\n/**\n * Invoke `x` in the training phase, and `alt` otherwise.\n *\n * Porting Note: We do not create placeholder tensors for the `training`\n * boolean flag here, because there is no such thing in the TF.js imperative\n * backend.\n *\n * @param x The function to invoke iff `training` is `true`.\n * @param alt The function to invoke iff `training` is `false`.\n * @param training Boolean flag for whether training phase is active.\n * @returns The return value of `x()` if `training` is `true`, or the return\n *   value of `alt()` if `training` is `false`.\n */\nexport function inTrainPhase(x, alt, training = false) {\n    return training ? x() : alt();\n}\n//# sourceMappingURL=tfjs_backend.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport const VALID_FAN_MODE_VALUES = ['fanIn', 'fanOut', 'fanAvg'];\nexport const VALID_DISTRIBUTION_VALUES = ['normal', 'uniform', 'truncatedNormal'];\n// We can't easily extract a string[] from the string union type, but we can\n// recapitulate the list, enforcing at compile time that the values are valid\n// and that we have the right number of them.\n/**\n * A string array of valid Initializer class names.\n *\n * This is guaranteed to match the `InitializerClassName` union type.\n */\nexport const initializerClassNames = [\n    'Zeros', 'Ones', 'Constant', 'RandomNormal', 'RandomUniform',\n    'TruncatedNormal', 'VarianceScaling', 'Orthogonal', 'Identity'\n];\n//# sourceMappingURL=initializer_config.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { eye, linalg, mul, ones, randomUniform, scalar, serialization, tidy, truncatedNormal, zeros } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { checkDataFormat } from './common';\nimport { NotImplementedError, ValueError } from './errors';\nimport { VALID_DISTRIBUTION_VALUES, VALID_FAN_MODE_VALUES } from './keras_format/initializer_config';\nimport { checkStringTypeUnionValue, deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\nimport { arrayProd } from './utils/math_utils';\nexport function checkFanMode(value) {\n    checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, 'FanMode', value);\n}\nexport function checkDistribution(value) {\n    checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, 'Distribution', value);\n}\n/**\n * Initializer base class.\n *\n * @doc {\n *   heading: 'Initializers', subheading: 'Classes', namespace: 'initializers'}\n */\nexport class Initializer extends serialization.Serializable {\n    fromConfigUsesCustomObjects() {\n        return false;\n    }\n    getConfig() {\n        return {};\n    }\n}\nexport class Zeros extends Initializer {\n    apply(shape, dtype) {\n        return zeros(shape, dtype);\n    }\n}\n/** @nocollapse */\nZeros.className = 'Zeros';\nserialization.registerClass(Zeros);\nexport class Ones extends Initializer {\n    apply(shape, dtype) {\n        return ones(shape, dtype);\n    }\n}\n/** @nocollapse */\nOnes.className = 'Ones';\nserialization.registerClass(Ones);\nexport class Constant extends Initializer {\n    constructor(args) {\n        super();\n        if (typeof args !== 'object') {\n            throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);\n        }\n        if (args.value === undefined) {\n            throw new ValueError(`config must have value set but got ${args}`);\n        }\n        this.value = args.value;\n    }\n    apply(shape, dtype) {\n        return tidy(() => mul(scalar(this.value), ones(shape, dtype)));\n    }\n    getConfig() {\n        return {\n            value: this.value,\n        };\n    }\n}\n/** @nocollapse */\nConstant.className = 'Constant';\nserialization.registerClass(Constant);\nexport class RandomUniform extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MINVAL = -0.05;\n        this.DEFAULT_MAXVAL = 0.05;\n        this.minval = args.minval || this.DEFAULT_MINVAL;\n        this.maxval = args.maxval || this.DEFAULT_MAXVAL;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        return randomUniform(shape, this.minval, this.maxval, dtype);\n    }\n    getConfig() {\n        return { minval: this.minval, maxval: this.maxval, seed: this.seed };\n    }\n}\n/** @nocollapse */\nRandomUniform.className = 'RandomUniform';\nserialization.registerClass(RandomUniform);\nexport class RandomNormal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MEAN = 0.;\n        this.DEFAULT_STDDEV = 0.05;\n        this.mean = args.mean || this.DEFAULT_MEAN;\n        this.stddev = args.stddev || this.DEFAULT_STDDEV;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        dtype = dtype || 'float32';\n        if (dtype !== 'float32' && dtype !== 'int32') {\n            throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n        }\n        return K.randomNormal(shape, this.mean, this.stddev, dtype, this.seed);\n    }\n    getConfig() {\n        return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n    }\n}\n/** @nocollapse */\nRandomNormal.className = 'RandomNormal';\nserialization.registerClass(RandomNormal);\nexport class TruncatedNormal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MEAN = 0.;\n        this.DEFAULT_STDDEV = 0.05;\n        this.mean = args.mean || this.DEFAULT_MEAN;\n        this.stddev = args.stddev || this.DEFAULT_STDDEV;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        dtype = dtype || 'float32';\n        if (dtype !== 'float32' && dtype !== 'int32') {\n            throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);\n        }\n        return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed);\n    }\n    getConfig() {\n        return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n    }\n}\n/** @nocollapse */\nTruncatedNormal.className = 'TruncatedNormal';\nserialization.registerClass(TruncatedNormal);\nexport class Identity extends Initializer {\n    constructor(args) {\n        super();\n        this.gain = args.gain != null ? args.gain : 1.0;\n    }\n    apply(shape, dtype) {\n        return tidy(() => {\n            if (shape.length !== 2 || shape[0] !== shape[1]) {\n                throw new ValueError('Identity matrix initializer can only be used for' +\n                    ' 2D square matrices.');\n            }\n            else {\n                return mul(this.gain, eye(shape[0]));\n            }\n        });\n    }\n    getConfig() {\n        return { gain: this.gain };\n    }\n}\n/** @nocollapse */\nIdentity.className = 'Identity';\nserialization.registerClass(Identity);\n/**\n * Computes the number of input and output units for a weight shape.\n * @param shape Shape of weight.\n * @param dataFormat data format to use for convolution kernels.\n *   Note that all kernels in Keras are standardized on the\n *   CHANNEL_LAST ordering (even when inputs are set to CHANNEL_FIRST).\n * @return An length-2 array: fanIn, fanOut.\n */\nfunction computeFans(shape, dataFormat = 'channelsLast') {\n    let fanIn;\n    let fanOut;\n    checkDataFormat(dataFormat);\n    if (shape.length === 2) {\n        fanIn = shape[0];\n        fanOut = shape[1];\n    }\n    else if ([3, 4, 5].indexOf(shape.length) !== -1) {\n        if (dataFormat === 'channelsFirst') {\n            const receptiveFieldSize = arrayProd(shape, 2);\n            fanIn = shape[1] * receptiveFieldSize;\n            fanOut = shape[0] * receptiveFieldSize;\n        }\n        else if (dataFormat === 'channelsLast') {\n            const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2);\n            fanIn = shape[shape.length - 2] * receptiveFieldSize;\n            fanOut = shape[shape.length - 1] * receptiveFieldSize;\n        }\n    }\n    else {\n        const shapeProd = arrayProd(shape);\n        fanIn = Math.sqrt(shapeProd);\n        fanOut = Math.sqrt(shapeProd);\n    }\n    return [fanIn, fanOut];\n}\nexport class VarianceScaling extends Initializer {\n    /**\n     * Constructor of VarianceScaling.\n     * @throws ValueError for invalid value in scale.\n     */\n    constructor(args) {\n        super();\n        if (args.scale < 0.0) {\n            throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);\n        }\n        this.scale = args.scale == null ? 1.0 : args.scale;\n        this.mode = args.mode == null ? 'fanIn' : args.mode;\n        checkFanMode(this.mode);\n        this.distribution =\n            args.distribution == null ? 'normal' : args.distribution;\n        checkDistribution(this.distribution);\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        const fans = computeFans(shape);\n        const fanIn = fans[0];\n        const fanOut = fans[1];\n        let scale = this.scale;\n        if (this.mode === 'fanIn') {\n            scale /= Math.max(1, fanIn);\n        }\n        else if (this.mode === 'fanOut') {\n            scale /= Math.max(1, fanOut);\n        }\n        else {\n            scale /= Math.max(1, (fanIn + fanOut) / 2);\n        }\n        if (this.distribution === 'normal') {\n            const stddev = Math.sqrt(scale);\n            dtype = dtype || 'float32';\n            if (dtype !== 'float32' && dtype !== 'int32') {\n                throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);\n            }\n            return truncatedNormal(shape, 0, stddev, dtype, this.seed);\n        }\n        else {\n            const limit = Math.sqrt(3 * scale);\n            return randomUniform(shape, -limit, limit, dtype);\n        }\n    }\n    getConfig() {\n        return {\n            scale: this.scale,\n            mode: this.mode,\n            distribution: this.distribution,\n            seed: this.seed\n        };\n    }\n}\n/** @nocollapse */\nVarianceScaling.className = 'VarianceScaling';\nserialization.registerClass(VarianceScaling);\nexport class GlorotUniform extends VarianceScaling {\n    /**\n     * Constructor of GlorotUniform\n     * @param scale\n     * @param mode\n     * @param distribution\n     * @param seed\n     */\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanAvg',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, GlorotUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nGlorotUniform.className = 'GlorotUniform';\nserialization.registerClass(GlorotUniform);\nexport class GlorotNormal extends VarianceScaling {\n    /**\n     * Constructor of GlorotNormal.\n     * @param scale\n     * @param mode\n     * @param distribution\n     * @param seed\n     */\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanAvg',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, GlorotNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nGlorotNormal.className = 'GlorotNormal';\nserialization.registerClass(GlorotNormal);\nexport class HeNormal extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 2.0,\n            mode: 'fanIn',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, HeNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nHeNormal.className = 'HeNormal';\nserialization.registerClass(HeNormal);\nexport class HeUniform extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 2.0,\n            mode: 'fanIn',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, HeUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nHeUniform.className = 'HeUniform';\nserialization.registerClass(HeUniform);\nexport class LeCunNormal extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanIn',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, LeCunNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nLeCunNormal.className = 'LeCunNormal';\nserialization.registerClass(LeCunNormal);\nexport class LeCunUniform extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanIn',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, LeCunUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nLeCunUniform.className = 'LeCunNormal';\nserialization.registerClass(LeCunUniform);\nexport class Orthogonal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_GAIN = 1;\n        this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain;\n        this.seed = args.seed;\n        if (this.seed != null) {\n            throw new NotImplementedError('Random seed is not implemented for Orthogonal Initializer yet.');\n        }\n    }\n    apply(shape, dtype) {\n        return tidy(() => {\n            if (shape.length < 2) {\n                throw new NotImplementedError('Shape must be at least 2D.');\n            }\n            if (shape[0] * shape[1] > 2000) {\n                console.warn(`Orthogonal initializer is being called on a matrix with more ` +\n                    `than 2000 (${shape[0] * shape[1]}) elements: ` +\n                    `Slowness may result.`);\n            }\n            // TODO(cais): Add seed support.\n            const normalizedShape = shape[0] > shape[1] ? [shape[1], shape[0]] : shape;\n            const a = K.randomNormal(normalizedShape, 0, 1, 'float32');\n            let q = linalg.gramSchmidt(a);\n            if (shape[0] > shape[1]) {\n                q = q.transpose();\n            }\n            return mul(this.gain, q);\n        });\n    }\n    getConfig() {\n        return {\n            gain: this.gain,\n            seed: this.seed,\n        };\n    }\n}\n/** @nocollapse */\nOrthogonal.className = 'Orthogonal';\nserialization.registerClass(Orthogonal);\n// Maps the JavaScript-like identifier keys to the corresponding registry\n// symbols.\nexport const INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'constant': 'Constant',\n    'glorotNormal': 'GlorotNormal',\n    'glorotUniform': 'GlorotUniform',\n    'heNormal': 'HeNormal',\n    'heUniform': 'HeUniform',\n    'identity': 'Identity',\n    'leCunNormal': 'LeCunNormal',\n    'leCunUniform': 'LeCunUniform',\n    'ones': 'Ones',\n    'orthogonal': 'Orthogonal',\n    'randomNormal': 'RandomNormal',\n    'randomUniform': 'RandomUniform',\n    'truncatedNormal': 'TruncatedNormal',\n    'varianceScaling': 'VarianceScaling',\n    'zeros': 'Zeros'\n};\nfunction deserializeInitializer(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'initializer');\n}\nexport function serializeInitializer(initializer) {\n    return serializeKerasObject(initializer);\n}\nexport function getInitializer(identifier) {\n    if (typeof identifier === 'string') {\n        const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        /* We have four 'helper' classes for common initializers that\n        all get serialized as 'VarianceScaling' and shouldn't go through\n        the deserializeInitializer pathway. */\n        if (className === 'GlorotNormal') {\n            return new GlorotNormal();\n        }\n        else if (className === 'GlorotUniform') {\n            return new GlorotUniform();\n        }\n        else if (className === 'HeNormal') {\n            return new HeNormal();\n        }\n        else if (className === 'HeUniform') {\n            return new HeUniform();\n        }\n        else if (className === 'LeCunNormal') {\n            return new LeCunNormal();\n        }\n        else if (className === 'LeCunUniform') {\n            return new LeCunUniform();\n        }\n        else {\n            const config = {};\n            config['className'] = className;\n            config['config'] = {};\n            return deserializeInitializer(config);\n        }\n    }\n    else if (identifier instanceof Initializer) {\n        return identifier;\n    }\n    else {\n        return deserializeInitializer(identifier);\n    }\n}\n//# sourceMappingURL=initializers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// tslint:disable-next-line:max-line-length\nimport { Constant, GlorotNormal, GlorotUniform, HeNormal, HeUniform, Identity, LeCunNormal, LeCunUniform, Ones, Orthogonal, RandomNormal, RandomUniform, TruncatedNormal, VarianceScaling, Zeros } from './initializers';\n/**\n * Initializer that generates tensors initialized to 0.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function zeros() {\n    return new Zeros();\n}\n/**\n * Initializer that generates tensors initialized to 1.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function ones() {\n    return new Ones();\n}\n/**\n * Initializer that generates values initialized to some constant.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function constant(args) {\n    return new Constant(args);\n}\n/**\n * Initializer that generates random values initialized to a uniform\n * distribution.\n *\n * Values will be distributed uniformly between the configured minval and\n * maxval.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function randomUniform(args) {\n    return new RandomUniform(args);\n}\n/**\n * Initializer that generates random values initialized to a normal\n * distribution.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function randomNormal(args) {\n    return new RandomNormal(args);\n}\n/**\n * Initializer that generates random values initialized to a truncated normal.\n * distribution.\n *\n * These values are similar to values from a `RandomNormal` except that values\n * more than two standard deviations from the mean are discarded and re-drawn.\n * This is the recommended initializer for neural network weights and filters.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function truncatedNormal(args) {\n    return new TruncatedNormal(args);\n}\n/**\n * Initializer that generates the identity matrix.\n * Only use for square 2D matrices.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function identity(args) {\n    return new Identity(args);\n}\n/**\n * Initializer capable of adapting its scale to the shape of weights.\n * With distribution=NORMAL, samples are drawn from a truncated normal\n * distribution centered on zero, with `stddev = sqrt(scale / n)` where n is:\n *   - number of input units in the weight tensor, if mode = FAN_IN.\n *   - number of output units, if mode = FAN_OUT.\n *   - average of the numbers of input and output units, if mode = FAN_AVG.\n * With distribution=UNIFORM,\n * samples are drawn from a uniform distribution\n * within [-limit, limit], with `limit = sqrt(3 * scale / n)`.\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function varianceScaling(config) {\n    return new VarianceScaling(config);\n}\n/**\n * Glorot uniform initializer, also called Xavier uniform initializer.\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotUniform(args) {\n    return new GlorotUniform(args);\n}\n/**\n * Glorot normal initializer, also called Xavier normal initializer.\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor.\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotNormal(args) {\n    return new GlorotNormal(args);\n}\n/**\n * He normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function heNormal(args) {\n    return new HeNormal(args);\n}\n/**\n * He uniform initializer.\n *\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / fan_in)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function heUniform(args) {\n    return new HeUniform(args);\n}\n/**\n * LeCun normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(1 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * References:\n *   [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n *   [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunNormal(args) {\n    return new LeCunNormal(args);\n}\n/**\n * LeCun uniform initializer.\n *\n * It draws samples from a uniform distribution in the interval\n * `[-limit, limit]` with `limit = sqrt(3 / fanIn)`,\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunUniform(args) {\n    return new LeCunUniform(args);\n}\n/**\n * Initializer that generates a random orthogonal matrix.\n *\n * Reference:\n * [Saxe et al., http://arxiv.org/abs/1312.6120](http://arxiv.org/abs/1312.6120)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function orthogonal(args) {\n    return new Orthogonal(args);\n}\n//# sourceMappingURL=exports_initializers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Utilities related to persistent state in the backend.\n */\n/**\n * An ID to track `tf.SymbolicTensor`s and derived classes.\n * Required in different places in engine/topology.ts to identify unique\n * tensors.\n */\nlet _nextUniqueTensorId = 0;\nexport function getNextUniqueTensorId() {\n    return _nextUniqueTensorId++;\n}\nconst _uidPrefixes = {};\n/**\n * Provides a unique UID given a string prefix.\n *\n * @param prefix\n */\nexport function getUid(prefix = '') {\n    if (!(prefix in _uidPrefixes)) {\n        _uidPrefixes[prefix] = 0;\n    }\n    _uidPrefixes[prefix] += 1;\n    return prefix + _uidPrefixes[prefix].toString();\n}\n//# sourceMappingURL=state.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { ValueError } from '../errors';\n// tslint:enable\n/**\n * Determine whether the input is an Array of Shapes.\n */\nexport function isArrayOfShapes(x) {\n    return Array.isArray(x) && Array.isArray(x[0]);\n}\n/**\n * Special case of normalizing shapes to lists.\n *\n * @param x A shape or list of shapes to normalize into a list of Shapes.\n * @return A list of Shapes.\n */\nexport function normalizeShapeList(x) {\n    if (x.length === 0) {\n        return [];\n    }\n    if (!Array.isArray(x[0])) {\n        return [x];\n    }\n    return x;\n}\n/**\n * Helper function to obtain exactly one Tensor.\n * @param xs: A single `tf.Tensor` or an `Array` of `tf.Tensor`s.\n * @return A single `tf.Tensor`. If `xs` is an `Array`, return the first one.\n * @throws ValueError: If `xs` is an `Array` and its length is not 1.\n */\nexport function getExactlyOneTensor(xs) {\n    let x;\n    if (Array.isArray(xs)) {\n        if (xs.length !== 1) {\n            throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);\n        }\n        x = xs[0];\n    }\n    else {\n        x = xs;\n    }\n    return x;\n}\n/**\n * Helper function to obtain exactly on instance of Shape.\n *\n * @param shapes Input single `Shape` or Array of `Shape`s.\n * @returns If input is a single `Shape`, return it unchanged. If the input is\n *   an `Array` containing exactly one instance of `Shape`, return the instance.\n *   Otherwise, throw a `ValueError`.\n * @throws ValueError: If input is an `Array` of `Shape`s, and its length is not\n *   1.\n */\nexport function getExactlyOneShape(shapes) {\n    if (Array.isArray(shapes) && Array.isArray(shapes[0])) {\n        if (shapes.length === 1) {\n            shapes = shapes;\n            return shapes[0];\n        }\n        else {\n            throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`);\n        }\n    }\n    else {\n        return shapes;\n    }\n}\n//# sourceMappingURL=types_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Count the elements in an Array of LayerVariables.\n *\n * @param weights: The LayerVariables of which the constituent numbers are to\n *   be counted.\n * @returns A count of the elements in all the LayerVariables\n */\nexport function countParamsInWeights(weights) {\n    let count = 0;\n    for (const weight of weights) {\n        if (weight.shape.length === 0) {\n            count += 1;\n        }\n        else {\n            count += weight.shape.reduce((a, b) => a * b);\n        }\n    }\n    return count;\n}\n//# sourceMappingURL=variable_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { variableGrads } from '@tensorflow/tfjs-core';\nimport { getNextUniqueTensorId } from './backend/state';\nimport { getScopedTensorName, getUniqueTensorName } from './common';\nimport { NotImplementedError } from './errors';\nconst DEFAULT_VARIABLE_NAME_PREFIX = 'Variable';\n/**\n * A `tf.layers.LayerVariable` is similar to a `tf.Tensor` in that it has a\n * dtype and shape, but its value is mutable.  The value is itself represented\n * as a`tf.Tensor`, and can be read with the `read()` method and updated with\n * the `write()` method.\n */\nexport class LayerVariable {\n    /**\n     * Construct Variable from a `tf.Tensor`.\n     *\n     * If not explicitly named, the Variable will be given a name with the\n     * prefix 'Variable'. Variable names are unique. In the case of name\n     * collision, suffixies '_<num>' will be added to the name.\n     *\n     * @param val Initial value of the Variable.\n     * @param name Name of the variable. If `null` or `undefined` is provided, it\n     *   will default a name with the prefix 'Variable'.\n     * @param constraint Optional, projection function to be applied to the\n     * variable after optimize updates\n     * @throws ValueError if `name` is `null` or `undefined`.\n     */\n    constructor(val, dtype = 'float32', name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true, constraint = null) {\n        this.dtype = dtype == null ? 'float32' : dtype;\n        this.shape = val.shape;\n        this.id = getNextUniqueTensorId();\n        name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name;\n        this.originalName = getScopedTensorName(name);\n        this.name = getUniqueTensorName(this.originalName);\n        this.trainable_ = trainable;\n        this.constraint = constraint;\n        this.val = tfc.variable(val, this.trainable_, this.name, this.dtype);\n    }\n    /**\n     * Get a snapshot of the Variable's value.\n     *\n     * The returned value is a snapshot of the Variable's value at the time of\n     * the invocation. Future mutations in the value of the tensor will only\n     * be reflected by future calls to this method.\n     */\n    read() {\n        this.assertNotDisposed();\n        return this.val;\n    }\n    /**\n     * Update the value of the Variable.\n     *\n     * @param newVal: The new value to update to. Must be consistent with the\n     *   dtype and shape of the Variable.\n     * @return This Variable.\n     */\n    write(newVal) {\n        // TODO(cais): Once  TF.js Core supports Tensor.dtype, check dtype match.\n        this.assertNotDisposed();\n        checkShapesMatch(this.val, newVal);\n        // Skip updating if this is the exact same tensor.\n        if (this.val.id !== newVal.id) {\n            this.val.assign(newVal);\n            if (this.constraint != null) {\n                this.val.assign(this.constraint.apply(this.val));\n            }\n        }\n        return this;\n    }\n    /**\n     * Dispose this LayersVariable instance from memory.\n     */\n    dispose() {\n        this.assertNotDisposed();\n        this.val.dispose();\n    }\n    assertNotDisposed() {\n        if (this.val.isDisposed) {\n            throw new Error(`LayersVariable ${this.name} is already disposed.`);\n        }\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this.trainable_ = trainable;\n        this.val.trainable = trainable;\n    }\n}\nfunction checkShapesMatch(x, y) {\n    if (x.shape.toString() !== y.shape.toString()) {\n        throw new Error('Shape mismatch: ' + JSON.stringify(x.shape) + ' vs. ' +\n            JSON.stringify(y.shape));\n    }\n}\n/**\n * Create a Variable.\n * @param x The initial value of the `Variable`.\n * @param dtype optional, the type of the variable.\n * @param name optional, the name of the variable, default provided by\n * Variable.\n * @param constraint optional, a constraint to be applied after every update.\n * @return The newly instantiated `Variable`.\n */\nexport function variable(x, dtype, name, constraint) {\n    return new LayerVariable(x, dtype, name, true, constraint);\n}\n/**\n * Instantiates an all-zeros Variable and returns it.\n *\n * @param shape Shape of the tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return An all-zero Variable.\n */\nexport function zerosVariable(shape, dtype, name) {\n    // TODO(cais): Implement logic for dtype.\n    return new LayerVariable(tfc.zeros(shape), dtype, name);\n}\n/**\n * Instantiates an all-zeros tensor of the same shape as another tensor.\n *\n * @param x The other tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return A newly instantiated Variable.\n */\nexport function zerosLike(x, dtype, name) {\n    return new LayerVariable(tfc.zerosLike(x), dtype, name);\n}\n/**\n * Instantiates an all-ones tensor and returns it.\n *\n * @param shape Shape of the tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return An all-ones Variable.\n */\nexport function onesVariable(shape, dtype, name) {\n    // TODO(cais): Implement logic for dtype.\n    const allocated = tfc.ones(shape);\n    return new LayerVariable(allocated, dtype, name);\n}\n/**\n * Instantiates an all-ones tensor of the same shape as another tensor.\n *\n * @param x The other tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return A newly instantiated Variable.\n */\nexport function onesLike(x, dtype, name) {\n    const allocated = tfc.onesLike(x);\n    return new LayerVariable(allocated, dtype, name);\n}\n/**\n * Instantiate an identity matrix and returns it, as a Variable\n *\n * @param size Number of rows/columns.\n * @param dtype Data type of returned Variable.\n * @param name Name of returned Variable.\n * @return A Variable, an identity matrix.\n */\nexport function eyeVariable(size, dtype, name) {\n    return new LayerVariable(tfc.eye(size), dtype, name);\n}\n/**\n * Get a Variable with uniform distribution of values.\n * @param shape Shape of the tensor.\n * @param minval Lower bound of the uniform distribution.\n * @param maxval Upper bound of the uniform distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The uniform-random Variable.\n */\nexport function randomUniformVariable(shape, minval, maxval, dtype, seed, name = 'randomUniform') {\n    return new LayerVariable(tfc.randomUniform(shape, minval, maxval, dtype), dtype, name);\n}\n/**\n * Get a Variable with truncated-normal distribution of values.\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The truncated-normal-random Variable.\n */\nexport function truncatedNormalVariable(shape, mean = 0.0, stddev = 1.0, dtype, seed, name = 'truncatedNormal') {\n    // TODO(cais): Implement logic for dtype and seed once they are supported\n    // by deeplearn.js.\n    dtype = dtype || 'float32';\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n    }\n    return new LayerVariable(tfc.truncatedNormal(shape, mean, stddev, dtype, seed), dtype, name);\n}\n/**\n * Get a Variable with normal distribution of values.\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The truncated-normal-random Variable.\n */\nexport function randomNormalVariable(shape, mean = 0.0, stddev = 1.0, dtype, seed, name = 'randomNormal') {\n    dtype = dtype || 'float32';\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new NotImplementedError(`randomNormalVariable does not support dType ${dtype}.`);\n    }\n    return new LayerVariable(tfc.randomNormal(shape, mean, stddev, dtype, seed), dtype, name);\n}\n/**\n * Update the value of a Variable.\n * @param x The Variable to be updated.\n * @param xNew The new value to update to.\n * @return The Variable updated.\n */\nexport function update(x, xNew) {\n    return x.write(xNew);\n}\n/**\n * Update the value of a Variable by adding an increment.\n * @param x The Variable to be updated.\n * @param increment The incrment to add to `x`.\n * @return The Variable updated.\n */\nexport function updateAdd(x, increment) {\n    return x.write(tfc.add(x.read(), increment));\n}\n/**\n * Update the value of a Variable by subtracting a decrement.\n * @param x The Variable to be updated.\n * @param decrement The decrement to subtract from `x`.\n * @return The Variable updated.\n */\nexport function updateSub(x, decrement) {\n    return x.write(tfc.sub(x.read(), decrement));\n}\n/**\n * Get the values of an array of Variables.\n *\n * @param tensors An `Array` of `Variable`s to get the values of.\n * @return The values of the inputs, as an `Array` of`tf.Tensor`s.\n */\nexport function batchGetValue(xs) {\n    return xs.map(x => x.read());\n}\n/**\n * Update the value of multiple Variables at once.\n *\n * @param variablesAndValues An `Array`, each element is of type\n *   [Variable, Tensor]. The first item is the\n *   `Variable` of which the value is to be updated. The second item\n *   carries the new value.\n */\nexport function batchSetValue(variablesAndValues) {\n    variablesAndValues.forEach(variableAndValue => {\n        const variable = variableAndValue[0];\n        variable.write(variableAndValue[1]);\n    });\n}\n/**\n * Returns the gradients of `variables` w.r.t. the return value of `lossFn`.\n * @param lossFn A function which returns a Scalar to be used as the function\n *   value (i.e., numerator) for differentiation.\n * @param variables List of variables to be used as the independent variables\n *   (i.e., denominator) for differentiation.\n * @returns An Array of gradients tensors.\n */\nexport function gradients(lossFn, variables) {\n    // TODO(cais): The return type signature can be simplified if deeplearn makes\n    //   the corresponding type public.\n    const variableList = variables.map(variable => variable.read());\n    const valudAndGrads = variableGrads(lossFn, variableList);\n    return variables.map(variable => valudAndGrads.grads[variable.name]);\n}\n//# sourceMappingURL=variables.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/engine/topology.py */\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getNextUniqueTensorId, getUid } from '../backend/state';\nimport { getScopedTensorName, getUniqueTensorName, nameScope } from '../common';\nimport { AttributeError, NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { getInitializer } from '../initializers';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as types_utils from '../utils/types_utils';\nimport * as variable_utils from '../utils/variable_utils';\nimport { batchGetValue, batchSetValue, LayerVariable } from '../variables';\n/**\n * Specifies the ndim, dtype and shape of every input to a layer.\n *\n * Every layer should expose (if appropriate) an `inputSpec` attribute:\n * a list of instances of InputSpec (one per input tensor).\n *\n * A null entry in a shape is compatible with any dimension,\n * a null shape is compatible with any shape.\n */\nexport class InputSpec {\n    constructor(args) {\n        this.dtype = args.dtype;\n        this.shape = args.shape;\n        /*\n          TODO(michaelterry): Could throw error if ndim and shape are both defined\n            (then backport).\n        */\n        if (args.shape != null) {\n            this.ndim = args.shape.length;\n        }\n        else {\n            this.ndim = args.ndim;\n        }\n        this.maxNDim = args.maxNDim;\n        this.minNDim = args.minNDim;\n        this.axes = args.axes || {};\n    }\n}\n/**\n * `tf.SymbolicTensor` is a placeholder for a Tensor without any concrete value.\n *\n * They are most often encountered when building a graph of `Layer`s for a\n * a `tf.LayersModel` and the input data's shape, but not values are known.\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\nexport class SymbolicTensor {\n    /**\n     *\n     * @param dtype\n     * @param shape\n     * @param sourceLayer The Layer that produced this symbolic tensor.\n     * @param inputs The inputs passed to sourceLayer's __call__() method.\n     * @param nodeIndex\n     * @param tensorIndex\n     * @param callArgs The keyword arguments passed to the __call__() method.\n     * @param name\n     * @param outputTensorIndex The index of this tensor in the list of outputs\n     *   returned by apply().\n     */\n    constructor(dtype, shape, sourceLayer, inputs, callArgs, name, outputTensorIndex) {\n        this.dtype = dtype;\n        this.shape = shape;\n        this.sourceLayer = sourceLayer;\n        this.inputs = inputs;\n        this.callArgs = callArgs;\n        this.outputTensorIndex = outputTensorIndex;\n        this.id = getNextUniqueTensorId();\n        if (name != null) {\n            this.originalName = getScopedTensorName(name);\n            this.name = getUniqueTensorName(this.originalName);\n        }\n        this.rank = shape.length;\n    }\n}\nlet _nextNodeID = 0;\n/**\n * A `Node` describes the connectivity between two layers.\n *\n * Each time a layer is connected to some new input,\n * a node is added to `layer.inboundNodes`.\n *\n * Each time the output of a layer is used by another layer,\n * a node is added to `layer.outboundNodes`.\n *\n * `nodeIndices` and `tensorIndices` are basically fine-grained coordinates\n * describing the origin of the `inputTensors`, verifying the following:\n *\n * `inputTensors[i] ==\n * inboundLayers[i].inboundNodes[nodeIndices[i]].outputTensors[\n *   tensorIndices[i]]`\n *\n * A node from layer A to layer B is added to:\n *     A.outboundNodes\n *     B.inboundNodes\n */\nexport class Node {\n    constructor(args, \n    // TODO(michaelterry): Define actual type for this.\n    callArgs) {\n        this.callArgs = callArgs;\n        this.id = _nextNodeID++;\n        /*\n          Layer instance (NOT a list).\n          this is the layer that takes a list of input tensors\n          and turns them into a list of output tensors.\n          the current node will be added to\n          the inboundNodes of outboundLayer.\n        */\n        this.outboundLayer = args.outboundLayer;\n        /*\n            The following 3 properties describe where\n            the input tensors come from: which layers,\n            and for each layer, which node and which\n            tensor output of each node.\n        */\n        // List of layer instances.\n        this.inboundLayers = args.inboundLayers;\n        // List of integers, 1:1 mapping with inboundLayers.\n        this.nodeIndices = args.nodeIndices;\n        // List of integers, 1:1 mapping with inboundLayers.\n        this.tensorIndices = args.tensorIndices;\n        /*\n            Following 2 properties:\n            tensor inputs and outputs of outboundLayer.\n        */\n        // List of tensors. 1:1 mapping with inboundLayers.\n        this.inputTensors = args.inputTensors;\n        // List of tensors, created by outboundLayer.call().\n        this.outputTensors = args.outputTensors;\n        /*\n            Following 2 properties: input and output masks.\n            List of tensors, 1:1 mapping with inputTensor.\n        */\n        this.inputMasks = args.inputMasks;\n        // List of tensors, created by outboundLayer.computeMask().\n        this.outputMasks = args.outputMasks;\n        // Following 2 properties: input and output shapes.\n        // List of shape tuples, shapes of inputTensors.\n        this.inputShapes = args.inputShapes;\n        // List of shape tuples, shapes of outputTensors.\n        this.outputShapes = args.outputShapes;\n        // Add nodes to all layers involved.\n        for (const layer of args.inboundLayers) {\n            if (layer != null) {\n                layer.outboundNodes.push(this);\n            }\n        }\n        args.outboundLayer.inboundNodes.push(this);\n    }\n    getConfig() {\n        const inboundNames = [];\n        for (const layer of this.inboundLayers) {\n            if (layer != null) {\n                inboundNames.push(layer.name);\n            }\n            else {\n                inboundNames.push(null);\n            }\n        }\n        return {\n            outboundLayer: this.outboundLayer ? this.outboundLayer.name : null,\n            inboundLayers: inboundNames,\n            nodeIndices: this.nodeIndices,\n            tensorIndices: this.tensorIndices\n        };\n    }\n}\nlet _nextLayerID = 0;\n/**\n * A layer is a grouping of operations and weights that can be composed to\n * create a `tf.LayersModel`.\n *\n * Layers are constructed by using the functions under the\n * [tf.layers](#Layers-Basic) namespace.\n *\n * @doc {heading: 'Layers', subheading: 'Classes', namespace: 'layers'}\n */\nexport class Layer extends serialization.Serializable {\n    constructor(args = {}) {\n        super();\n        this._callHook = null;\n        this._addedWeightNames = [];\n        // Porting Notes: PyKeras does not have this property in this base Layer\n        //   class. Instead lets Layer subclass set it dynamically and checks the\n        //   value with `hasattr`. In tfjs-layers, we let this be a member of this\n        //   base class.\n        this._stateful = false;\n        this.id = _nextLayerID++;\n        this.activityRegularizer = null;\n        this.inputSpec = null;\n        this.supportsMasking = false;\n        // These properties will be set upon call of this.build()\n        this._trainableWeights = [];\n        this._nonTrainableWeights = [];\n        this._losses = [];\n        this._updates = [];\n        this._built = false;\n        /*\n          These lists will be filled via successive calls\n          to this.addInboundNode().\n         */\n        this.inboundNodes = [];\n        this.outboundNodes = [];\n        let name = args.name;\n        if (!name) {\n            const prefix = this.getClassName();\n            name = generic_utils.toSnakeCase(prefix) + '_' + getUid(prefix);\n        }\n        this.name = name;\n        this.trainable_ = args.trainable == null ? true : args.trainable;\n        if (args.inputShape != null || args.batchInputShape != null) {\n            /*\n              In this case we will later create an input layer\n              to insert before the current layer\n             */\n            let batchInputShape;\n            if (args.batchInputShape != null) {\n                batchInputShape = args.batchInputShape;\n            }\n            else if (args.inputShape != null) {\n                let batchSize = null;\n                if (args.batchSize != null) {\n                    batchSize = args.batchSize;\n                }\n                batchInputShape = [batchSize].concat(args.inputShape);\n            }\n            this.batchInputShape = batchInputShape;\n            // Set dtype.\n            let dtype = args.dtype;\n            if (dtype == null) {\n                dtype = args.inputDType;\n            }\n            if (dtype == null) {\n                dtype = 'float32';\n            }\n            this.dtype = dtype;\n        }\n        if (args.weights != null) {\n            this.initialWeights = args.weights;\n        }\n        else {\n            this.initialWeights = null;\n        }\n        // The value of `_refCount` is initialized to null. When the layer is used\n        // in a symbolic way for the first time, it will be set to 1.\n        this._refCount = null;\n        this.fastWeightInitDuringBuild = false;\n    }\n    /**\n     * Converts a layer and its index to a unique (immutable type) name.\n     * This function is used internally with `this.containerNodes`.\n     * @param layer The layer.\n     * @param nodeIndex The layer's position (e.g. via enumerate) in a list of\n     *   nodes.\n     *\n     * @returns The unique name.\n     */\n    static nodeKey(layer, nodeIndex) {\n        return layer.name + '_ib-' + nodeIndex.toString();\n    }\n    /**\n     * Returns this.inboundNode at index nodeIndex.\n     *\n     * Porting note: This is a replacement for _get_node_attribute_at_index()\n     * @param nodeIndex\n     * @param attrName The name of the attribute related to request for this node.\n     */\n    getNodeAtIndex(nodeIndex, attrName) {\n        if (this.inboundNodes.length === 0) {\n            throw new RuntimeError('The layer has never been called ' +\n                `and thus has no defined ${attrName}.`);\n        }\n        if (this.inboundNodes.length <= nodeIndex) {\n            throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, ` +\n                `but the layer has only ${this.inboundNodes.length} inbound nodes.`);\n        }\n        return this.inboundNodes[nodeIndex];\n    }\n    /**\n     * Retrieves the input tensor(s) of a layer at a given node.\n     *\n     * @param nodeIndex Integer, index of the node from which to retrieve the\n     *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n     *   was called.\n     *\n     * @return A tensor (or list of tensors if the layer has multiple inputs).\n     */\n    getInputAt(nodeIndex) {\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(nodeIndex, 'input').inputTensors);\n    }\n    /**\n     * Retrieves the output tensor(s) of a layer at a given node.\n     *\n     * @param nodeIndex Integer, index of the node from which to retrieve the\n     *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n     *   was called.\n     *\n     * @return A tensor (or list of tensors if the layer has multiple outputs).\n     */\n    getOutputAt(nodeIndex) {\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(nodeIndex, 'output').outputTensors);\n    }\n    // Properties\n    /**\n     * Retrieves the input tensor(s) of a layer.\n     *\n     * Only applicable if the layer has exactly one inbound node,\n     * i.e. if it is connected to one incoming layer.\n     *\n     * @return Input tensor or list of input tensors.\n     *\n     * @exception AttributeError if the layer is connected to more than one\n     *   incoming layers.\n     */\n    get input() {\n        if (this.inboundNodes.length > 1) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has multiple inbound nodes, ' +\n                'hence the notion of \"layer input\" ' +\n                'is ill-defined. ' +\n                'Use `getInputAt(nodeIndex)` instead.');\n        }\n        else if (this.inboundNodes.length === 0) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' is not connected, no input to return.');\n        }\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(0, 'input').inputTensors);\n    }\n    /**\n     * Retrieves the output tensor(s) of a layer.\n     *\n     * Only applicable if the layer has exactly one inbound node,\n     * i.e. if it is connected to one incoming layer.\n     *\n     * @return Output tensor or list of output tensors.\n     *\n     * @exception AttributeError if the layer is connected to more than one\n     *   incoming layers.\n     */\n    get output() {\n        if (this.inboundNodes.length === 0) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has no inbound nodes.');\n        }\n        if (this.inboundNodes.length > 1) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has multiple inbound nodes, ' +\n                'hence the notion of \"layer output\" ' +\n                'is ill-defined. ' +\n                'Use `getOutputAt(nodeIndex)` instead.');\n        }\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(0, 'output').outputTensors);\n    }\n    get losses() {\n        return this._losses;\n    }\n    /**\n     * Retrieves the Layer's current loss values.\n     *\n     * Used for regularizers during training.\n     */\n    calculateLosses() {\n        // Porting Node: This is an augmentation to Layer.loss in PyKeras.\n        //   In PyKeras, Layer.loss returns symbolic tensors. Here a concrete\n        //   Tensor (specifically Scalar) values are returned. This is due to the\n        //   imperative backend.\n        return this.losses.map(lossFn => lossFn());\n    }\n    get updates() {\n        return this._updates;\n    }\n    get built() {\n        return this._built;\n    }\n    set built(built) {\n        this._built = built;\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this._trainableWeights.forEach(w => w.trainable = trainable);\n        this.trainable_ = trainable;\n    }\n    get trainableWeights() {\n        if (this.trainable_) {\n            return this._trainableWeights.filter(w => w.trainable);\n        }\n        else {\n            return [];\n        }\n    }\n    set trainableWeights(weights) {\n        this._trainableWeights = weights;\n    }\n    get nonTrainableWeights() {\n        if (this.trainable) {\n            return this._trainableWeights.filter(w => !w.trainable)\n                .concat(this._nonTrainableWeights);\n        }\n        else {\n            return this._trainableWeights.concat(this._nonTrainableWeights);\n        }\n    }\n    set nonTrainableWeights(weights) {\n        this._nonTrainableWeights = weights;\n    }\n    /**\n     * The concatenation of the lists trainableWeights and nonTrainableWeights\n     * (in this order).\n     */\n    get weights() {\n        return this.trainableWeights.concat(this.nonTrainableWeights);\n    }\n    get stateful() {\n        return this._stateful;\n    }\n    /**\n     * Reset the states of the layer.\n     *\n     * This method of the base Layer class is essentially a no-op.\n     * Subclasses that are stateful (e.g., stateful RNNs) should override this\n     * method.\n     */\n    resetStates() {\n        if (!this.stateful) {\n            throw new Error('Cannot call the resetStates() method of a non-stateful Layer ' +\n                'object.');\n        }\n    }\n    /**\n     * Checks compatibility between the layer and provided inputs.\n     *\n     * This checks that the tensor(s) `input`\n     * verify the input assumptions of the layer\n     * (if any). If not, exceptions are raised.\n     *\n     * @param inputs Input tensor or list of input tensors.\n     *\n     * @exception ValueError in case of mismatch between\n     *   the provided inputs and the expectations of the layer.\n     */\n    assertInputCompatibility(inputs) {\n        inputs = generic_utils.toList(inputs);\n        if (this.inputSpec == null || this.inputSpec.length === 0) {\n            return;\n        }\n        const inputSpec = generic_utils.toList(this.inputSpec);\n        if (inputs.length !== inputSpec.length) {\n            throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, ` +\n                `but it received ${inputs.length} input tensors. ` +\n                `Input received: ${inputs}`);\n        }\n        for (let inputIndex = 0; inputIndex < inputs.length; inputIndex++) {\n            const x = inputs[inputIndex];\n            const spec = inputSpec[inputIndex];\n            if (spec == null) {\n                continue;\n            }\n            // Check ndim.\n            const ndim = x.rank;\n            if (spec.ndim != null) {\n                if (ndim !== spec.ndim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: ` +\n                        `expected ndim=${spec.ndim}, found ndim=${ndim}`);\n                }\n            }\n            if (spec.maxNDim != null) {\n                if (ndim > spec.maxNDim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}` +\n                        `: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);\n                }\n            }\n            if (spec.minNDim != null) {\n                if (ndim < spec.minNDim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}` +\n                        `: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);\n                }\n            }\n            // Check dtype.\n            if (spec.dtype != null) {\n                if (x.dtype !== spec.dtype) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} ` +\n                        `: expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);\n                }\n            }\n            // Check specific shape axes.\n            if (spec.axes) {\n                const xShape = x.shape;\n                for (const key in spec.axes) {\n                    const axis = Number(key);\n                    const value = spec.axes[key];\n                    // Perform Python-style slicing in case axis < 0;\n                    // TODO(cais): Use https://github.com/alvivi/typescript-underscore to\n                    // ensure type safety through Underscore calls.\n                    const xShapeAtAxis = axis >= 0 ? xShape[axis] : xShape[xShape.length + axis];\n                    if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) {\n                        throw new ValueError(`Input ${inputIndex} is incompatible with layer ` +\n                            `${this.name}: expected axis ${axis} of input shape to ` +\n                            `have value ${value} but got shape ${xShape}.`);\n                    }\n                }\n            }\n            // Check shape.\n            if (spec.shape != null) {\n                for (let i = 0; i < spec.shape.length; ++i) {\n                    const specDim = spec.shape[i];\n                    const dim = x.shape[i];\n                    if (specDim != null && dim != null) {\n                        if (specDim !== dim) {\n                            throw new ValueError(`Input ${inputIndex} is incompatible with layer ` +\n                                `${this.name}: expected shape=${spec.shape}, ` +\n                                `found shape=${x.shape}.`);\n                        }\n                    }\n                }\n            }\n        }\n    }\n    /**\n     * This is where the layer's logic lives.\n     *\n     * @param inputs Input tensor, or list/tuple of input tensors.\n     * @param kwargs Additional keyword arguments.\n     *\n     * @return A tensor or list/tuple of tensors.\n     */\n    call(inputs, kwargs) {\n        return inputs;\n    }\n    invokeCallHook(inputs, kwargs) {\n        if (this._callHook != null) {\n            this._callHook(inputs, kwargs);\n        }\n    }\n    /**\n     * Set call hook.\n     * This is currently used for testing only.\n     * @param callHook\n     */\n    setCallHook(callHook) {\n        this._callHook = callHook;\n    }\n    /**\n     * Clear call hook.\n     * This is currently used for testing only.\n     */\n    clearCallHook() {\n        this._callHook = null;\n    }\n    /**\n     * Builds or executes a `Layer's logic.\n     *\n     * When called with `tf.Tensor`(s), execute the `Layer`s computation and\n     * return Tensor(s). For example:\n     *\n     * ```js\n     * const denseLayer = tf.layers.dense({\n     *   units: 1,\n     *   kernelInitializer: 'zeros',\n     *   useBias: false\n     * });\n     *\n     * // Invoke the layer's apply() method with a `tf.Tensor` (with concrete\n     * // numeric values).\n     * const input = tf.ones([2, 2]);\n     * const output = denseLayer.apply(input);\n     *\n     * // The output's value is expected to be [[0], [0]], due to the fact that\n     * // the dense layer has a kernel initialized to all-zeros and does not have\n     * // a bias.\n     * output.print();\n     * ```\n     *\n     * When called with `tf.SymbolicTensor`(s), this will prepare the layer for\n     * future execution.  This entails internal book-keeping on shapes of\n     * expected Tensors, wiring layers together, and initializing weights.\n     *\n     * Calling `apply` with `tf.SymbolicTensor`s are typically used during the\n     * building of non-`tf.Sequential` models. For example:\n     *\n     * ```js\n     * const flattenLayer = tf.layers.flatten();\n     * const denseLayer = tf.layers.dense({units: 1});\n     *\n     * // Use tf.layers.input() to obtain a SymbolicTensor as input to apply().\n     * const input = tf.input({shape: [2, 2]});\n     * const output1 = flattenLayer.apply(input);\n     *\n     * // output1.shape is [null, 4]. The first dimension is the undetermined\n     * // batch size. The second dimension comes from flattening the [2, 2]\n     * // shape.\n     * console.log(JSON.stringify(output1.shape));\n     *\n     * // The output SymbolicTensor of the flatten layer can be used to call\n     * // the apply() of the dense layer:\n     * const output2 = denseLayer.apply(output1);\n     *\n     * // output2.shape is [null, 1]. The first dimension is the undetermined\n     * // batch size. The second dimension matches the number of units of the\n     * // dense layer.\n     * console.log(JSON.stringify(output2.shape));\n     *\n     * // The input and output and be used to construct a model that consists\n     * // of the flatten and dense layers.\n     * const model = tf.model({inputs: input, outputs: output2});\n     * ```\n     *\n     * @param inputs a `tf.Tensor` or `tf.SymbolicTensor` or an Array of them.\n     * @param kwargs Additional keyword arguments to be passed to `call()`.\n     *\n     * @return Output of the layer's `call` method.\n     *\n     * @exception ValueError error in case the layer is missing shape information\n     *   for its `build` call.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    // Porting Note: This is a replacement for __call__() in Python.\n    apply(inputs, kwargs) {\n        kwargs = kwargs || {};\n        this.assertNotDisposed();\n        // Ensure inputs are all the same type.\n        const inputsList = generic_utils.toList(inputs);\n        let allAreSymbolic = true;\n        for (const input of inputsList) {\n            if (!(input instanceof SymbolicTensor)) {\n                allAreSymbolic = false;\n                break;\n            }\n        }\n        let noneAreSymbolic = true;\n        for (const input of inputsList) {\n            if (input instanceof SymbolicTensor) {\n                noneAreSymbolic = false;\n                break;\n            }\n        }\n        if (allAreSymbolic === noneAreSymbolic) {\n            throw new ValueError('Arguments to apply() must be all ' +\n                'SymbolicTensors or all Tensors');\n        }\n        // TODO(michaelterry): nameScope() may not be necessary.\n        return nameScope(this.name, () => {\n            // Handle laying building (weight creating, input spec locking).\n            if (!this.built) {\n                /*\n                  Throw exceptions in case the input is not compatible\n                  with the inputSpec specified in the layer constructor.\n                 */\n                this.assertInputCompatibility(inputs);\n                // Collect input shapes to build layer.\n                const inputShapes = [];\n                for (const xElem of generic_utils.toList(inputs)) {\n                    inputShapes.push(xElem.shape);\n                }\n                this.build(generic_utils.singletonOrArray(inputShapes));\n                this.built = true;\n                // Load weights that were specified at layer instantiation.\n                if (this.initialWeights) {\n                    this.setWeights(this.initialWeights);\n                }\n                if (this._refCount === null && noneAreSymbolic) {\n                    // The first use of this layer is a non-symbolic call, set ref count\n                    // to 1 so the Layer can be properly disposed if its dispose() method\n                    // is called.\n                    this._refCount = 1;\n                }\n            }\n            /*\n              Throw exceptions in case the input is not compatible\n              with the inputSpec set at build time.\n            */\n            this.assertInputCompatibility(inputs);\n            // Handle mask propagation.\n            // TODO(michaelterry): Mask propagation not currently implemented.\n            // Actually call the layer, collecting output(s), mask(s), and shape(s).\n            if (noneAreSymbolic) {\n                let output = this.call(inputs, kwargs);\n                // TODO(michaelterry): Compute the outputMask\n                // If the layer returns tensors from its inputs, unmodified,\n                // we copy them to avoid loss of tensor metadata.\n                const outputList = generic_utils.toList(output);\n                const outputListCopy = [];\n                // TODO(michaelterry): This copying may not be necessary given our eager\n                // backend.\n                for (let x of outputList) {\n                    if (inputsList.indexOf(x) !== -1) {\n                        x = x.clone();\n                    }\n                    outputListCopy.push(x);\n                }\n                output = generic_utils.singletonOrArray(outputListCopy);\n                if (this.activityRegularizer != null) {\n                    throw new NotImplementedError('Layer invocation in the presence of activity ' +\n                        'regularizer(s) is not supported yet.');\n                }\n                // TODO(michaelterry): Call addInboundNode()?\n                return output;\n            }\n            else {\n                const inputShape = collectInputShape(inputs);\n                const outputShape = this.computeOutputShape(inputShape);\n                let output;\n                const outputDType = guessOutputDType(inputs);\n                this.warnOnIncompatibleInputShape(Array.isArray(inputs) ? inputShape[0] :\n                    inputShape);\n                if (outputShape != null && outputShape.length > 0 &&\n                    Array.isArray(outputShape[0])) {\n                    // We have multiple output shapes. Create multiple output tensors.\n                    output = outputShape\n                        .map((shape, index) => new SymbolicTensor(outputDType, shape, this, generic_utils.toList(inputs), kwargs, this.name, index));\n                }\n                else {\n                    output = new SymbolicTensor(outputDType, outputShape, this, generic_utils.toList(inputs), kwargs, this.name);\n                }\n                /*\n                  Add an inbound node to the layer, so that it keeps track\n                  of the call and of all new variables created during the call.\n                  This also updates the layer history of the output tensor(s).\n                  If the input tensor(s) had no previous history,\n                  this does nothing.\n                */\n                this.addInboundNode(inputs, output, null, null, inputShape, outputShape, kwargs);\n                this._refCount++;\n                if (this.activityRegularizer != null) {\n                    throw new NotImplementedError('Layer invocation in the presence of activity ' +\n                        'regularizer(s) is not supported yet.');\n                }\n                return output;\n            }\n        });\n    }\n    /**\n     * Check compatibility between input shape and this layer's batchInputShape.\n     *\n     * Print warning if any incompatibility is found.\n     *\n     * @param inputShape Input shape to be checked.\n     */\n    warnOnIncompatibleInputShape(inputShape) {\n        if (this.batchInputShape == null) {\n            return;\n        }\n        else if (inputShape.length !== this.batchInputShape.length) {\n            console.warn(`The rank of the input tensor provided (shape: ` +\n                `${JSON.stringify(inputShape)}) does not match that of the ` +\n                `batchInputShape (${JSON.stringify(this.batchInputShape)}) ` +\n                `of the layer ${this.name}`);\n        }\n        else {\n            let dimMismatch = false;\n            this.batchInputShape.forEach((dimension, i) => {\n                if (dimension != null && inputShape[i] != null &&\n                    inputShape[i] !== dimension) {\n                    dimMismatch = true;\n                }\n            });\n            if (dimMismatch) {\n                console.warn(`The shape of the input tensor ` +\n                    `(${JSON.stringify(inputShape)}) does not ` +\n                    `match the expectation of layer ${this.name}: ` +\n                    `${JSON.stringify(this.batchInputShape)}`);\n            }\n        }\n    }\n    /**\n     * Retrieves the output shape(s) of a layer.\n     *\n     * Only applicable if the layer has only one inbound node, or if all inbound\n     * nodes have the same output shape.\n     *\n     * @returns Output shape or shapes.\n     * @throws AttributeError: if the layer is connected to more than one incoming\n     *   nodes.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    get outputShape() {\n        if (this.inboundNodes == null || this.inboundNodes.length === 0) {\n            throw new AttributeError(`The layer ${this.name} has never been called and thus has no ` +\n                `defined output shape.`);\n        }\n        const allOutputShapes = [];\n        for (const node of this.inboundNodes) {\n            const shapeString = JSON.stringify(node.outputShapes);\n            if (allOutputShapes.indexOf(shapeString) === -1) {\n                allOutputShapes.push(shapeString);\n            }\n        }\n        if (allOutputShapes.length === 1) {\n            const outputShapes = this.inboundNodes[0].outputShapes;\n            if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) &&\n                outputShapes.length === 1) {\n                return outputShapes[0];\n            }\n            else {\n                return outputShapes;\n            }\n        }\n        else {\n            throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different ` +\n                `output shapes. Hence the notion of \"output shape\" is ill-defined ` +\n                `for the layer.`);\n            // TODO(cais): Implement getOutputShapeAt().\n        }\n    }\n    /**\n     * Counts the total number of numbers (e.g., float32, int32) in the\n     * weights.\n     *\n     * @returns An integer count.\n     * @throws RuntimeError: If the layer is not built yet (in which case its\n     *   weights are not defined yet.)\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    countParams() {\n        if (!this.built) {\n            throw new RuntimeError(`You tried to call countParams() on ${this.name}, ` +\n                `but the layer is not built yet. Build it first by calling ` +\n                `build(batchInputShape).`);\n        }\n        return variable_utils.countParamsInWeights(this.weights);\n    }\n    /**\n     * Creates the layer weights.\n     *\n     * Must be implemented on all layers that have weights.\n     *\n     * Called when apply() is called to construct the weights.\n     *\n     * @param inputShape A `Shape` or array of `Shape` (unused).\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    build(inputShape) {\n        this.built = true;\n    }\n    /**\n     * Returns the current values of the weights of the layer.\n     *\n     * @param trainableOnly Whether to get the values of only trainable weights.\n     * @returns Weight values as an `Array` of `tf.Tensor`s.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    getWeights(trainableOnly = false) {\n        return batchGetValue(trainableOnly ? this.trainableWeights : this.weights);\n    }\n    /**\n     * Sets the weights of the layer, from Tensors.\n     *\n     * @param weights a list of Tensors. The number of arrays and their shape\n     *   must match number of the dimensions of the weights of the layer (i.e.\n     *   it should match the output of `getWeights`).\n     *\n     * @exception ValueError If the provided weights list does not match the\n     *   layer's specifications.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    setWeights(weights) {\n        tidy(() => {\n            const params = this.weights;\n            if (params.length !== weights.length) {\n                // TODO(cais): Restore the following and use `providedWeights`, instead\n                // of `weights` in the error message, once the deeplearn.js bug is\n                // fixed: https://github.com/PAIR-code/deeplearnjs/issues/498 const\n                // providedWeights = JSON.stringify(weights).substr(0, 50);\n                throw new ValueError(`You called setWeights(weights) on layer \"${this.name}\" ` +\n                    `with a weight list of length ${weights.length}, ` +\n                    `but the layer was expecting ${params.length} weights. ` +\n                    `Provided weights: ${weights}...`);\n            }\n            if (params.length === 0) {\n                return;\n            }\n            const weightValueTuples = [];\n            const paramValues = batchGetValue(params);\n            for (let i = 0; i < paramValues.length; ++i) {\n                const pv = paramValues[i];\n                const p = params[i];\n                const w = weights[i];\n                if (!util.arraysEqual(pv.shape, w.shape)) {\n                    throw new ValueError(`Layer weight shape ${pv.shape} ` +\n                        `not compatible with provided weight shape ${w.shape}`);\n                }\n                weightValueTuples.push([p, w]);\n            }\n            batchSetValue(weightValueTuples);\n        });\n    }\n    /**\n     * Adds a weight variable to the layer.\n     *\n     * @param name Name of the new weight variable.\n     * @param shape The shape of the weight.\n     * @param dtype The dtype of the weight.\n     * @param initializer An initializer instance.\n     * @param regularizer A regularizer instance.\n     * @param trainable Whether the weight should be trained via backprop or not\n     *   (assuming that the layer itself is also trainable).\n     * @param constraint An optional trainable.\n     * @return The created weight variable.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint) {\n        // Reject duplicate weight names.\n        if (this._addedWeightNames.indexOf(name) !== -1) {\n            throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);\n        }\n        this._addedWeightNames.push(name);\n        if (dtype == null) {\n            dtype = 'float32';\n        }\n        if (this.fastWeightInitDuringBuild) {\n            initializer = getInitializer('zeros');\n        }\n        const initValue = initializer.apply(shape, dtype);\n        const weight = new LayerVariable(initValue, dtype, name, trainable, constraint);\n        initValue.dispose();\n        // Request backend not to dispose the weights of the model on scope() exit.\n        if (regularizer != null) {\n            this.addLoss(() => regularizer.apply(weight.read()));\n        }\n        if (trainable == null) {\n            trainable = true;\n        }\n        if (trainable) {\n            this._trainableWeights.push(weight);\n        }\n        else {\n            this._nonTrainableWeights.push(weight);\n        }\n        return weight;\n    }\n    /**\n     * Set the fast-weight-initialization flag.\n     *\n     * In cases where the initialized weight values will be immediately\n     * overwritten by loaded weight values during model loading, setting\n     * the flag to `true` saves unnecessary calls to potentially expensive\n     * initializers and speeds up the loading process.\n     *\n     * @param value Target value of the flag.\n     */\n    setFastWeightInitDuringBuild(value) {\n        this.fastWeightInitDuringBuild = value;\n    }\n    /**\n     * Add losses to the layer.\n     *\n     * The loss may potentionally be conditional on some inputs tensors,\n     * for instance activity losses are conditional on the layer's inputs.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    addLoss(losses) {\n        if (losses == null || Array.isArray(losses) && losses.length === 0) {\n            return;\n        }\n        // Update this.losses\n        losses = generic_utils.toList(losses);\n        if (this._losses !== undefined && this._losses !== null) {\n            this.losses.push(...losses);\n        }\n    }\n    /**\n     * Computes the output shape of the layer.\n     *\n     * Assumes that the layer will be built to match that input shape provided.\n     *\n     * @param inputShape A shape (tuple of integers) or a list of shape tuples\n     *   (one per output tensor of the layer). Shape tuples can include null for\n     *   free dimensions, instead of an integer.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    /**\n     * Computes an output mask tensor.\n     *\n     * @param inputs Tensor or list of tensors.\n     * @param mask Tensor or list of tensors.\n     *\n     * @return null or a tensor (or list of tensors, one per output tensor of the\n     * layer).\n     */\n    computeMask(inputs, mask) {\n        if (!this.supportsMasking) {\n            if (mask != null) {\n                if (Array.isArray(mask)) {\n                    mask.forEach(maskElement => {\n                        if (maskElement != null) {\n                            throw new TypeError(`Layer ${this.name} does not support masking, ` +\n                                'but was passed an inputMask.');\n                        }\n                    });\n                }\n                else {\n                    throw new TypeError(`Layer ${this.name} does not support masking, ` +\n                        'but was passed an inputMask.');\n                }\n            }\n            // masking not explicitly supported: return null as mask\n            return null;\n        }\n        // if masking is explictly supported, by default\n        // carry over the input mask\n        return mask;\n    }\n    /**\n     * Internal method to create an inbound node for the layer.\n     *\n     * @param inputTensors List of input tensors.\n     * @param outputTensors List of output tensors.\n     * @param inputMasks List of input masks (a mask can be a tensor, or null).\n     * @param outputMasks List of output masks (a mask can be a tensor, or null).\n     * @param inputShapes List of input shape tuples.\n     * @param outputShapes List of output shape tuples.\n     * @param kwargs Dictionary of keyword arguments that were passed to the\n     *   `call` method of the layer at the call that created the node.\n     */\n    addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) {\n        const inputTensorList = generic_utils.toList(inputTensors);\n        outputTensors = generic_utils.toList(outputTensors);\n        inputMasks = generic_utils.toList(inputMasks);\n        outputMasks = generic_utils.toList(outputMasks);\n        inputShapes = types_utils.normalizeShapeList(inputShapes);\n        outputShapes = types_utils.normalizeShapeList(outputShapes);\n        // Collect input tensor(s) coordinates.\n        const inboundLayers = [];\n        const nodeIndices = [];\n        const tensorIndices = [];\n        for (const x of inputTensorList) {\n            /*\n             * TODO(michaelterry): Keras adds this value to tensors; it's not\n             * clear whether we'll use this or not.\n             */\n            inboundLayers.push(x.sourceLayer);\n            nodeIndices.push(x.nodeIndex);\n            tensorIndices.push(x.tensorIndex);\n        }\n        // Create node, add it to inbound nodes.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers,\n            nodeIndices,\n            tensorIndices,\n            inputTensors: inputTensorList,\n            outputTensors,\n            inputMasks,\n            outputMasks,\n            inputShapes,\n            outputShapes\n        }, kwargs);\n        // Update tensor history\n        for (let i = 0; i < outputTensors.length; i++) {\n            // TODO(michaelterry: _uses_learning_phase not tracked.\n            outputTensors[i].sourceLayer = this;\n            outputTensors[i].nodeIndex = this.inboundNodes.length - 1;\n            outputTensors[i].tensorIndex = i;\n        }\n    }\n    /**\n     * Returns the config of the layer.\n     *\n     * A layer config is a TS dictionary (serializable)\n     * containing the configuration of a layer.\n     * The same layer can be reinstantiated later\n     * (without its trained weights) from this configuration.\n     *\n     * The config of a layer does not include connectivity\n     * information, nor the layer class name.  These are handled\n     * by 'Container' (one layer of abstraction above).\n     *\n     * Porting Note: The TS dictionary follows TS naming standrds for\n     * keys, and uses tfjs-layers type-safe Enums.  Serialization methods\n     * should use a helper function to convert to the pythonic storage\n     * standard. (see serialization_utils.convertTsToPythonic)\n     *\n     * @returns TS dictionary of configuration.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    getConfig() {\n        const config = { name: this.name, trainable: this.trainable };\n        if (this.batchInputShape != null) {\n            config['batchInputShape'] = this.batchInputShape;\n        }\n        if (this.dtype != null) {\n            config['dtype'] = this.dtype;\n        }\n        return config;\n    }\n    /**\n     * Dispose the weight variables that this Layer instance holds.\n     *\n     * @returns {number} Number of disposed variables.\n     */\n    disposeWeights() {\n        this.weights.forEach(weight => weight.dispose());\n        return this.weights.length;\n    }\n    assertNotDisposed() {\n        if (this._refCount === 0) {\n            throw new Error(`Layer '${this.name}' is already disposed.`);\n        }\n    }\n    /**\n     * Attempt to dispose layer's weights.\n     *\n     * This method decrease the reference count of the Layer object by 1.\n     *\n     * A Layer is reference-counted. Its reference count is incremented by 1\n     * the first item its `apply()` method is called and when it becomes a part\n     * of a new `Node` (through calling the `apply()`) method on a\n     * `tf.SymbolicTensor`).\n     *\n     * If the reference count of a Layer becomes 0, all the weights will be\n     * disposed and the underlying memory (e.g., the textures allocated in WebGL)\n     * will be freed.\n     *\n     * Note: If the reference count is greater than 0 after the decrement, the\n     * weights of the Layer will *not* be disposed.\n     *\n     * After a Layer is disposed, it cannot be used in calls such as `apply()`,\n     * `getWeights()` or `setWeights()` anymore.\n     *\n     * @returns A DisposeResult Object with the following fields:\n     *   - refCountAfterDispose: The reference count of the Container after this\n     *     `dispose()` call.\n     *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n     *     during this `dispose()` call.\n     * @throws {Error} If the layer is not built yet, or if the layer has already\n     *   been disposed.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    dispose() {\n        if (!this.built) {\n            throw new Error(`Cannot dispose Layer ${this.name} because it has not been ` +\n                `built yet.`);\n        }\n        if (this._refCount === null) {\n            throw new Error(`Cannot dispose Layer ${this.name} because it has not been used ` +\n                `yet.`);\n        }\n        this.assertNotDisposed();\n        let numDisposedVariables = 0;\n        if (--this._refCount === 0) {\n            numDisposedVariables = this.disposeWeights();\n        }\n        return { refCountAfterDispose: this._refCount, numDisposedVariables };\n    }\n}\n/**\n * Collects the input shape(s) of a list of `tf.Tensor`s or\n * `tf.SymbolicTensor`s.\n *\n * TODO(michaelterry): Update PyKeras docs (backport).\n *\n * @param inputTensors List of input tensors (or single input tensor).\n *\n * @return List of shape tuples (or single tuple), one tuple per input.\n */\nfunction collectInputShape(inputTensors) {\n    inputTensors =\n        generic_utils.toList(inputTensors);\n    const shapes = [];\n    for (const x of inputTensors) {\n        shapes.push(x.shape);\n    }\n    return generic_utils.singletonOrArray(shapes);\n}\n/**\n * Guesses output dtype based on inputs.\n *\n * At present, just returns 'float32' for any input.\n *\n * @param inputTensors List of input tensors (or single input tensor).\n *\n * @return The guessed DType. At present, always returns 'float32'.\n */\nfunction guessOutputDType(inputTensors) {\n    return 'float32';\n}\n/**\n * Returns the list of input tensors necessary to compute `tensor`.\n *\n * Output will always be a list of tensors (potentially with 1 element).\n *\n * @param tensor The tensor to start from.\n * @param layer Origin layer of the tensor.\n * @param nodeIndex Origin node index of the tensor.\n *\n * @return Array of input tensors.\n */\nexport function getSourceInputs(tensor, layer, nodeIndex) {\n    if (layer == null || (nodeIndex != null && nodeIndex > 0)) {\n        layer = tensor.sourceLayer;\n        nodeIndex = tensor.nodeIndex;\n    }\n    if (layer.inboundNodes.length === 0) {\n        return [tensor];\n    }\n    else {\n        const node = layer.inboundNodes[nodeIndex];\n        if (node.inboundLayers.length === 0) {\n            return node.inputTensors;\n        }\n        else {\n            const sourceTensors = [];\n            for (let i = 0; i < node.inboundLayers.length; i++) {\n                const x = node.inputTensors[i];\n                const layer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const previousSources = getSourceInputs(x, layer, nodeIndex);\n                // Avoid input redundancy.\n                for (const x of previousSources) {\n                    if (sourceTensors.indexOf(x) === -1) {\n                        sourceTensors.push(x);\n                    }\n                }\n            }\n            return sourceTensors;\n        }\n    }\n}\n//# sourceMappingURL=topology.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { serialization } from '@tensorflow/tfjs-core';\nimport { getUid } from '../backend/state';\nimport { ValueError } from '../errors';\nimport { Layer, Node, SymbolicTensor } from './topology';\nexport class InputLayer extends Layer {\n    constructor(args) {\n        super({\n            dtype: args.dtype,\n            name: args.name != null ? args.name : getUid('input').toString()\n        });\n        // Normalize config.batchSize and config.sparse\n        if (args.batchSize == null) {\n            args.batchSize = null;\n        }\n        if (args.sparse == null) {\n            args.sparse = false;\n        }\n        this.trainable = false;\n        this.built = true;\n        this.sparse = args.sparse;\n        if (args.inputShape != null && args.batchInputShape != null) {\n            throw new ValueError('Only provide the inputShape OR ' +\n                'batchInputShape argument to inputLayer, not both at the same time.');\n        }\n        let batchInputShape = args.batchInputShape;\n        if (batchInputShape == null) {\n            if (args.inputShape == null) {\n                throw new ValueError('An InputLayer should be passed either a ' +\n                    '`batchInputShape` or an `inputShape`.');\n            }\n            else {\n                batchInputShape = [args.batchSize].concat(args.inputShape);\n            }\n        }\n        else {\n            // TODO(michaelterry): Backport to PyKeras\n            if (args.batchSize != null) {\n                throw new ValueError('Cannot specify batchSize if batchInputShape is ' +\n                    'specified when creating an InputLayer.');\n            }\n        }\n        const dtype = args.dtype || 'float32';\n        this.batchInputShape = batchInputShape;\n        this.dtype = dtype;\n        // TODO(michaelterry): Backport this to PyKeras?\n        this.inputSpec = [{ shape: batchInputShape }];\n        const inputTensor = new SymbolicTensor(this.dtype, this.batchInputShape, this, [], {}, this.name);\n        inputTensor.nodeIndex = 0;\n        inputTensor.tensorIndex = 0;\n        // Create an input node to add to this.outboundNode.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers: [],\n            nodeIndices: [],\n            tensorIndices: [],\n            inputTensors: [inputTensor],\n            outputTensors: [inputTensor],\n            inputMasks: [null],\n            outputMasks: [null],\n            inputShapes: [batchInputShape],\n            outputShapes: [batchInputShape]\n        });\n    }\n    apply(inputs, kwargs) {\n        throw new ValueError('Cannot pass any input to an ' +\n            `InputLayer's apply() method. InputLayer name: ${this.name}`);\n    }\n    dispose() {\n        // dispose() for InputLayer is overridden as no-op.\n        return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 };\n    }\n    getConfig() {\n        return {\n            batchInputShape: this.batchInputShape,\n            dtype: this.dtype,\n            sparse: this.sparse,\n            name: this.name\n        };\n    }\n}\n/** @nocollapse */\nInputLayer.className = 'InputLayer';\nserialization.registerClass(InputLayer);\nexport function Input(config) {\n    if (config.batchShape == null && config.shape == null) {\n        throw new Error('Please provide to Input either a `shape`' +\n            ' or a `batchShape` argument. Note that ' +\n            '`shape` does not include the batch ' +\n            'dimension.');\n    }\n    if (config.batchShape != null && config.shape != null) {\n        // TODO(michaelterry): Backport to PyKeras.\n        throw new ValueError('Please provide either a `shape` or `batchShape` ' +\n            'argument to Input, but not both.');\n    }\n    let batchShape = config.batchShape;\n    if (config.shape != null && batchShape == null) {\n        batchShape = [null].concat(config.shape);\n    }\n    let dtype = config.dtype;\n    if (dtype == null) {\n        dtype = 'float32';\n    }\n    const inputLayer = new InputLayer({\n        batchInputShape: batchShape,\n        name: config.name,\n        dtype,\n        sparse: config.sparse\n    });\n    const outputs = inputLayer.inboundNodes[0].outputTensors;\n    return outputs[0];\n}\n//# sourceMappingURL=input_layer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { dispose } from '@tensorflow/tfjs-core';\n/**\n * Turn any Scalar values in a Logs object into actual number values.\n *\n * @param logs The `Logs` object to be resolved in place.\n */\nexport async function resolveScalarsInLogs(logs) {\n    if (logs == null) {\n        return;\n    }\n    const promises = [];\n    const keys = [];\n    const scalarsToDispose = [];\n    for (const key in logs) {\n        const value = logs[key];\n        if (typeof value !== 'number') {\n            const valueScalar = value;\n            promises.push(valueScalar.data());\n            keys.push(key);\n            scalarsToDispose.push(valueScalar);\n        }\n    }\n    if (promises.length > 0) {\n        const values = await Promise.all(promises);\n        for (let i = 0; i < values.length; ++i) {\n            logs[keys[i]] = values[i][0];\n        }\n        // Dispose the original scalar tensors.\n        dispose(scalarsToDispose);\n    }\n}\n/**\n * Dispose all Tensors in an UnresolvedLogs object.\n *\n * @param logs An `UnresolvedLogs` object potentially containing `tf.Tensor`s in\n *   places where the values can be `tf.Tensor` or `number`.\n */\nexport function disposeTensorsInLogs(logs) {\n    if (logs == null) {\n        return;\n    }\n    for (const key in logs) {\n        const value = logs[key];\n        if (typeof value !== 'number') {\n            value.dispose();\n        }\n    }\n}\n//# sourceMappingURL=logs.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/callbacks.py */\nimport { add, div, keep, mul, nextFrame, tidy, util } from '@tensorflow/tfjs-core';\nimport { ValueError } from './errors';\nimport { resolveScalarsInLogs } from './logs';\nimport * as generic_utils from './utils/generic_utils';\n/** Verbosity logging level when fitting a model. */\nexport var ModelLoggingVerbosity;\n(function (ModelLoggingVerbosity) {\n    ModelLoggingVerbosity[ModelLoggingVerbosity[\"SILENT\"] = 0] = \"SILENT\";\n    ModelLoggingVerbosity[ModelLoggingVerbosity[\"VERBOSE\"] = 1] = \"VERBOSE\";\n})(ModelLoggingVerbosity || (ModelLoggingVerbosity = {}));\n/** How often to yield to the main thread when training (in ms). */\nexport const DEFAULT_YIELD_EVERY_MS = 125;\n/**\n * Abstract base class used to build new callbacks.\n *\n * The `logs` dictionary that callback methods take as argument will contain\n * keys for quantities relevant to the current batch or epoch.\n *\n * Currently, the `.fit()` method of the `Sequential` model class\n * will include the following quantities in the `logs` that\n * it passes to its callbacks:\n *\n * onEpochEnd: Logs include `acc` and `loss`, and optionally include `valLoss`\n *   (if validation is enabled in `fit`), and `valAcc` (if validation and\n *   accuracy monitoring are enabled).\n * onBatchBegin: Logs include `size`, the number of samples in the current\n *   batch.\n * onBatchEnd: Logs include `loss`, and optionally `acc` (if accuracy monitoring\n *   is enabled).\n */\nexport class BaseCallback {\n    constructor() {\n        // TODO(michaelterry): This type is a best guess.\n        this.validationData = null;\n    }\n    setParams(params) {\n        this.params = params;\n    }\n    async onEpochBegin(epoch, logs) { }\n    async onEpochEnd(epoch, logs) { }\n    async onBatchBegin(batch, logs) { }\n    async onBatchEnd(batch, logs) { }\n    async onTrainBegin(logs) { }\n    async onTrainEnd(logs) { }\n    // LayersModel needs to call Callback.setModel(), but cannot actually depend\n    // on Callback because that creates a cyclic dependency.  Providing this no-op\n    // method on BaseCallback breaks the cycle: this way LayersModel can depend on\n    // BaseCallback but not on Callback.  The argument is typed as `Container`\n    // (the superclass of LayersModel) to avoid recapitulating the cycle. Callback\n    // overrides this method and enforces that the argument is really a\n    // LayersModel.\n    setModel(model) {\n        // Do nothing. Use Callback instead of BaseCallback to track the model.\n    }\n}\n/**\n * Container abstracting a list of callbacks.\n */\nexport class CallbackList {\n    // TODO(cais): When the need arises, uncomment the following lines and\n    // implement the queue for time values.\n    // private deltaTBatch: number;\n    // private deltaTsBatchBegin: Array<number>;\n    // private deltaTsBatchEnd: Array<number>;\n    /**\n     * Constructor of CallbackList.\n     * @param callbacks Array of `Callback` instances.\n     * @param queueLength Queue length for keeping running statistics over\n     *   callback execution time.\n     */\n    constructor(callbacks, queueLength = 10) {\n        // TODO(cais): Make use of queueLength when implementing the queue for time\n        // values.\n        if (callbacks == null) {\n            callbacks = [];\n        }\n        this.callbacks = callbacks;\n        this.queueLength = queueLength;\n    }\n    append(callback) {\n        this.callbacks.push(callback);\n    }\n    setParams(params) {\n        for (const callback of this.callbacks) {\n            callback.setParams(params);\n        }\n    }\n    setModel(model) {\n        for (const callback of this.callbacks) {\n            callback.setModel(model);\n        }\n    }\n    /**\n     * Called at the start of an epoch.\n     * @param epoch Index of epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onEpochBegin(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onEpochBegin(epoch, logs);\n        }\n    }\n    /**\n     * Called at the end of an epoch.\n     * @param epoch Index of epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onEpochEnd(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onEpochEnd(epoch, logs);\n        }\n    }\n    /**\n     * Called  right before processing a batch.\n     * @param batch Index of batch within the current epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onBatchBegin(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onBatchBegin(batch, logs);\n        }\n    }\n    /**\n     * Called at the end of a batch.\n     * @param batch Index of batch within the current epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onBatchEnd(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onBatchEnd(batch, logs);\n        }\n    }\n    /**\n     * Called at the beginning of training.\n     * @param logs Dictionary of logs.\n     */\n    async onTrainBegin(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onTrainBegin(logs);\n        }\n    }\n    /**\n     * Called at the end of training.\n     * @param logs Dictionary of logs.\n     */\n    async onTrainEnd(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onTrainEnd(logs);\n        }\n    }\n}\n/**\n * Callback that accumulates epoch averages of metrics.\n *\n * This callback is automatically applied to every LayersModel.\n */\nexport class BaseLogger extends BaseCallback {\n    constructor() {\n        super();\n    }\n    async onEpochBegin(epoch) {\n        this.seen = 0;\n        this.totals = {};\n    }\n    async onBatchEnd(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        const batchSize = logs['size'] == null ? 0 : logs['size'];\n        this.seen += batchSize;\n        for (const key in logs) {\n            const value = logs[key];\n            if (typeof value === 'number') {\n                if (!this.totals.hasOwnProperty(key)) {\n                    this.totals[key] = 0;\n                }\n                this.totals[key] = this.totals[key] + value * batchSize;\n            }\n            else {\n                let oldTotalsToDispose;\n                if (key in this.totals) {\n                    oldTotalsToDispose = this.totals[key];\n                }\n                else {\n                    this.totals[key] = 0;\n                }\n                const total = tidy(() => add((this.totals[key]), mul(value, batchSize)));\n                this.totals[key] = total;\n                if (oldTotalsToDispose != null) {\n                    oldTotalsToDispose.dispose();\n                }\n            }\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        if (logs != null) {\n            for (const key of this.params['metrics']) {\n                if (this.totals[key] == null) {\n                    continue;\n                }\n                if (typeof this.totals[key] === 'number') {\n                    logs[key] = this.totals[key] / this.seen;\n                }\n                else {\n                    tidy(() => {\n                        const log = mul(div(1, this.seen), this.totals[key]);\n                        logs[key] = log;\n                        this.totals[key].dispose();\n                        keep(logs[key]);\n                    });\n                }\n            }\n        }\n    }\n}\n/**\n * Callback that records events into a `History` object. This callback is\n * automatically applied to every TF.js Layers model. The `History` object\n * gets returned by the `fit` method of models.\n */\nexport class History extends BaseCallback {\n    async onTrainBegin(logs) {\n        this.epoch = [];\n        this.history = {};\n    }\n    async onEpochEnd(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        this.epoch.push(epoch);\n        for (const key in logs) {\n            if (this.history[key] == null) {\n                this.history[key] = [];\n            }\n            this.history[key].push(logs[key]);\n        }\n    }\n    /**\n     * Await the values of all losses and metrics.\n     */\n    async syncData() {\n        const promises = [];\n        const keys = [];\n        const indices = [];\n        for (const key in this.history) {\n            const valueArray = this.history[key];\n            for (let i = 0; i < valueArray.length; ++i) {\n                if (typeof valueArray[i] !== 'number') {\n                    const valueScalar = valueArray[i];\n                    promises.push(valueScalar.data());\n                    keys.push(key);\n                    indices.push(i);\n                }\n            }\n        }\n        const values = await Promise.all(promises);\n        for (let n = 0; n < values.length; ++n) {\n            const tensorToDispose = this.history[keys[n]][indices[n]];\n            tensorToDispose.dispose();\n            this.history[keys[n]][indices[n]] = values[n][0];\n        }\n    }\n}\n/**\n * Custom callback for training.\n */\nexport class CustomCallback extends BaseCallback {\n    constructor(args, yieldEvery) {\n        super();\n        this.currentEpoch = 0;\n        this.yieldEvery = yieldEvery || 'auto';\n        if (this.yieldEvery === 'auto') {\n            this.yieldEvery = DEFAULT_YIELD_EVERY_MS;\n        }\n        if (this.yieldEvery === 'never' && args.onYield != null) {\n            throw new Error('yieldEvery is `never` but you provided an `onYield` callback. ' +\n                'Either change `yieldEvery` or remove the callback');\n        }\n        if (util.isNumber(this.yieldEvery)) {\n            // Decorate `maybeWait` so it will be called at most once every\n            // `yieldEvery` ms.\n            this.maybeWait = generic_utils.debounce(this.maybeWait.bind(this), this.yieldEvery);\n        }\n        this.trainBegin = args.onTrainBegin;\n        this.trainEnd = args.onTrainEnd;\n        this.epochBegin = args.onEpochBegin;\n        this.epochEnd = args.onEpochEnd;\n        this.batchBegin = args.onBatchBegin;\n        this.batchEnd = args.onBatchEnd;\n        this.yield = args.onYield;\n    }\n    async maybeWait(epoch, batch, logs) {\n        const ps = [];\n        if (this.yield != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.yield(epoch, batch, logs));\n        }\n        ps.push(nextFrame());\n        await Promise.all(ps);\n    }\n    async onEpochBegin(epoch, logs) {\n        this.currentEpoch = epoch;\n        if (this.epochBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.epochBegin(epoch, logs);\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        const ps = [];\n        if (this.epochEnd != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.epochEnd(epoch, logs));\n        }\n        if (this.yieldEvery === 'epoch') {\n            ps.push(nextFrame());\n        }\n        await Promise.all(ps);\n    }\n    async onBatchBegin(batch, logs) {\n        if (this.batchBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.batchBegin(batch, logs);\n        }\n    }\n    async onBatchEnd(batch, logs) {\n        const ps = [];\n        if (this.batchEnd != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.batchEnd(batch, logs));\n        }\n        if (this.yieldEvery === 'batch') {\n            ps.push(nextFrame());\n        }\n        else if (util.isNumber(this.yieldEvery)) {\n            ps.push(this.maybeWait(this.currentEpoch, batch, logs));\n        }\n        await Promise.all(ps);\n    }\n    async onTrainBegin(logs) {\n        if (this.trainBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.trainBegin(logs);\n        }\n    }\n    async onTrainEnd(logs) {\n        if (this.trainEnd != null) {\n            await resolveScalarsInLogs(logs);\n            await this.trainEnd(logs);\n        }\n    }\n}\n/**\n * Standardize callbacks or configurations of them to an Array of callbacks.\n */\nexport function standardizeCallbacks(callbacks, yieldEvery) {\n    if (callbacks == null) {\n        callbacks = {};\n    }\n    if (callbacks instanceof BaseCallback) {\n        return [callbacks];\n    }\n    if (Array.isArray(callbacks) && callbacks[0] instanceof BaseCallback) {\n        return callbacks;\n    }\n    // Convert custom callback configs to custom callback objects.\n    const callbackConfigs = generic_utils.toList(callbacks);\n    return callbackConfigs.map(callbackConfig => new CustomCallback(callbackConfig, yieldEvery));\n}\n/**\n * A global registry for callback constructors to be used during\n * LayersModel.fit().\n */\nexport class CallbackConstructorRegistry {\n    /**\n     * Blocks public access to constructor.\n     */\n    constructor() { }\n    /**\n     * Register a tf.LayersModel.fit() callback constructor.\n     *\n     * The registered callback constructor will be used to instantiate\n     * callbacks for every tf.LayersModel.fit() call afterwards.\n     *\n     * @param verbosityLevel Level of verbosity at which the `callbackConstructor`\n     *   is to be reigstered.\n     * @param callbackConstructor A no-arg constructor for `tf.Callback`.\n     * @throws Error, if the same callbackConstructor has been registered before,\n     *   either at the same or a different `verbosityLevel`.\n     */\n    static registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n        util.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, ` +\n            `but got ${verbosityLevel}`);\n        CallbackConstructorRegistry.checkForDuplicate(callbackConstructor);\n        if (CallbackConstructorRegistry.constructors[verbosityLevel] == null) {\n            CallbackConstructorRegistry.constructors[verbosityLevel] = [];\n        }\n        CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor);\n    }\n    static checkForDuplicate(callbackConstructor) {\n        for (const levelName in CallbackConstructorRegistry.constructors) {\n            const constructors = CallbackConstructorRegistry.constructors[+levelName];\n            constructors.forEach(ctor => {\n                if (ctor === callbackConstructor) {\n                    throw new ValueError('Duplicate callback constructor.');\n                }\n            });\n        }\n    }\n    /**\n     * Clear all registered callback constructors.\n     */\n    static clear() {\n        CallbackConstructorRegistry.constructors = {};\n    }\n    /**\n     * Create callbacks using the registered callback constructors.\n     *\n     * Given `verbosityLevel`, all constructors registered at that level or above\n     * will be called and the instantiated callbacks will be used.\n     *\n     * @param verbosityLevel: Level of verbosity.\n     */\n    static createCallbacks(verbosityLevel) {\n        const constructors = [];\n        for (const levelName in CallbackConstructorRegistry.constructors) {\n            const level = +levelName;\n            if (verbosityLevel >= level) {\n                constructors.push(...CallbackConstructorRegistry.constructors[level]);\n            }\n        }\n        return constructors.map(ctor => new ctor());\n    }\n}\nCallbackConstructorRegistry.constructors = {};\nexport function configureCallbacks(callbacks, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) {\n    const history = new History();\n    const actualCallbacks = [\n        new BaseLogger(), ...CallbackConstructorRegistry.createCallbacks(verbose)\n    ];\n    if (callbacks != null) {\n        actualCallbacks.push(...callbacks);\n    }\n    actualCallbacks.push(history);\n    const callbackList = new CallbackList(actualCallbacks);\n    // TODO(cais): Figure out when this LayersModel instance can have a\n    // dynamically\n    //   set property called 'callback_model' as in PyKeras.\n    callbackList.setParams({\n        epochs,\n        initialEpoch,\n        samples: numTrainSamples,\n        steps: stepsPerEpoch,\n        batchSize,\n        verbose,\n        doValidation,\n        metrics: callbackMetrics,\n    });\n    return { callbackList, history };\n}\n//# sourceMappingURL=base_callbacks.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source layers/__init__.py */\nimport { serialization } from '@tensorflow/tfjs-core';\nimport { deserializeKerasObject } from '../utils/generic_utils';\n/**\n * Instantiate a layer from a config dictionary.\n * @param config dict of the form {class_name: str, config: dict}\n * @param customObjects dict mapping class names (or function names)\n *   of custom (non-Keras) objects to class/functions\n * @param fastWeightInit Optional flag to use fast weight initialization\n *   during deserialization. This is applicable to cases in which\n *   the initialization will be immediately overwritten by loaded weight\n *   values. Default: `false`.\n * @returns Layer instance (may be LayersModel, Sequential, Layer...)\n */\nexport function deserialize(config, customObjects = {}, fastWeightInit = false) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'layer', fastWeightInit);\n}\n//# sourceMappingURL=serialization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source: losses.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { tidy, util } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport * as K from './backend/tfjs_backend';\nimport { ValueError } from './errors';\n/**\n * Normalizes a tensor wrt the L2 norm alongside the specified axis.\n * @param x\n * @param axis Axis along which to perform normalization.\n */\nexport function l2Normalize(x, axis) {\n    return tidy(() => {\n        if (x.dtype !== 'float32') {\n            x = x.asType('float32');\n        }\n        const squareSum = tfc.sum(K.square(x), axis, true);\n        const epsilonTensor = tfc.fill(squareSum.shape, epsilon());\n        const norm = tfc.sqrt(tfc.maximum(squareSum, epsilonTensor));\n        return tfc.div(x, norm);\n    });\n}\nexport function meanSquaredError(yTrue, yPred) {\n    return tidy(() => tfc.mean(K.square(tfc.sub(yPred, yTrue)), -1));\n}\nexport function meanAbsoluteError(yTrue, yPred) {\n    return tidy(() => tfc.mean(tfc.abs(tfc.sub(yPred, yTrue)), -1));\n}\nexport function meanAbsolutePercentageError(yTrue, yPred) {\n    return tidy(() => {\n        const diff = tfc.sub(yTrue, yPred);\n        const clippedTrue = tfc.clipByValue(tfc.abs(yTrue), epsilon(), Number.MAX_VALUE);\n        const absResult = tfc.abs(tfc.div(diff, clippedTrue));\n        return tfc.mul(100, tfc.mean(absResult, -1));\n    });\n}\nexport function meanSquaredLogarithmicError(yTrue, yPred) {\n    return tidy(() => {\n        const clippedPred = tfc.clipByValue(yPred, epsilon(), Number.MAX_VALUE);\n        const firstLog = tfc.log(tfc.add(1, clippedPred));\n        const clippedTrue = tfc.clipByValue(yTrue, epsilon(), Number.MAX_VALUE);\n        const secondLog = tfc.log(tfc.add(1, clippedTrue));\n        return tfc.mean(K.square(tfc.sub(firstLog, secondLog)), -1);\n    });\n}\nexport function squaredHinge(yTrue, yPred) {\n    return tidy(() => {\n        const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n        return tfc.mean(K.square(maxResult), -1);\n    });\n}\nexport function hinge(yTrue, yPred) {\n    return tidy(() => {\n        const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n        return tfc.mean(maxResult, -1);\n    });\n}\nexport function categoricalHinge(yTrue, yPred) {\n    return tidy(() => {\n        const pos = tfc.sum(tfc.mul(yTrue, yPred), -1);\n        const neg = tfc.max(tfc.mul(tfc.sub(1, yTrue), yPred), -1);\n        return tfc.maximum(0, tfc.add(1, tfc.sub(neg, pos)));\n    });\n}\n/**\n * Logarithm of the hyperbolic cosine of the prediction error.\n *\n * `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and\n * to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly\n * like the mean squared error, but will not be so strongly affected by the\n * occasional wildly incorrect prediction.\n */\nexport function logcosh(yTrue, yPred) {\n    return tidy(() => {\n        const log2 = Math.log(2);\n        const predictionDiff = tfc.sub(yPred, yTrue);\n        const logcoshResult = tfc.sub(tfc.add(predictionDiff, tfc.softplus(tfc.mul(-2, predictionDiff))), log2);\n        return tfc.mean(logcoshResult, -1);\n    });\n}\nexport function categoricalCrossentropy(target, output, fromLogits = false) {\n    return tidy(() => {\n        if (fromLogits) {\n            output = tfc.softmax(output);\n        }\n        else {\n            // scale preds so that the class probabilities of each sample sum to 1.\n            const outputSum = tfc.sum(output, output.shape.length - 1, true);\n            output = tfc.div(output, outputSum);\n        }\n        output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n        return tfc.neg(tfc.sum(tfc.mul(target.toFloat(), tfc.log(output)), output.shape.length - 1));\n    });\n}\n/**\n * Categorical crossentropy with integer targets.\n *\n * @param target An integer tensor.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n */\nexport function sparseCategoricalCrossentropy(target, output, fromLogits = false) {\n    return tidy(() => {\n        const flatTarget = tfc.floor(K.flatten(target)).toInt();\n        output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n        const outputShape = output.shape;\n        const oneHotTarget = tfc.oneHot(flatTarget, outputShape[outputShape.length - 1])\n            .reshape(outputShape);\n        return categoricalCrossentropy(oneHotTarget, output, fromLogits);\n    });\n}\n/**\n * From TensorFlow's implementation in nn_impl.py:\n *\n * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n *      z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n *    = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n *    = (1 - z) * x + log(1 + exp(-x))\n *    = x - x * z + log(1 + exp(-x))\n * For x < 0, to avoid overflow in exp(-x), we reformulate the above\n *      x - x * z + log(1 + exp(-x))\n *    = log(exp(x)) - x * z + log(1 + exp(-x))\n *    = - x * z + log(1 + exp(x))\n * Hence, to ensure stability and avoid overflow, the implementation uses this\n * equivalent formulation\n *    max(x, 0) - x * z + log(1 + exp(-abs(x)))\n *\n * @param labels The labels.\n * @param logits The logits.\n */\nexport function sigmoidCrossEntropyWithLogits(labels, logits) {\n    if (!util.arraysEqual(labels.shape, logits.shape)) {\n        throw new ValueError(`logits and labels must have the same shape, but got shapes ` +\n            `${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);\n    }\n    return tidy(() => {\n        // The logistic loss formula from above is\n        //   x - x * z + log(1 + exp(-x))\n        // For x < 0, a more numerically stable formula is\n        //   -x * z + log(1 + exp(x))\n        // Note that these two expressions can be combined into the following:\n        //   max(x, 0) - x * z + log(1 + exp(-abs(x)))\n        const reluLogits = logits.relu();\n        const negAbsLogits = logits.abs().neg();\n        return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p());\n    });\n}\nexport function binaryCrossentropy(yTrue, yPred) {\n    return tidy(() => {\n        let y;\n        y = tfc.clipByValue(yPred, epsilon(), 1 - epsilon());\n        y = tfc.log(tfc.div(y, tfc.sub(1, y)));\n        return tfc.mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);\n    });\n}\nexport function kullbackLeiblerDivergence(yTrue, yPred) {\n    return tidy(() => {\n        const clippedTrue = tfc.clipByValue(yTrue, epsilon(), 1);\n        const clippedPred = tfc.clipByValue(yPred, epsilon(), 1);\n        return tfc.sum(tfc.mul(yTrue, tfc.log(tfc.div(clippedTrue, clippedPred))), -1);\n    });\n}\nexport function poisson(yTrue, yPred) {\n    return tidy(() => {\n        const logPred = tfc.log(tfc.add(epsilon(), yPred));\n        return tfc.mean(tfc.sub(yPred, tfc.mul(yTrue, logPred)), -1);\n    });\n}\nexport function cosineProximity(yTrue, yPred) {\n    return tidy(() => {\n        const trueNormalized = l2Normalize(yTrue, -1);\n        const predNormalized = l2Normalize(yPred, -1);\n        const trueXPred = tfc.mul(trueNormalized, predNormalized);\n        return tfc.neg(tfc.sum(trueXPred, -1));\n    });\n}\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const msle = meanSquaredLogarithmicError;\nexport const MSLE = meanSquaredLogarithmicError;\nexport const kld = kullbackLeiblerDivergence;\nexport const KLD = kullbackLeiblerDivergence;\nexport const cosine = cosineProximity;\n// TODO(michaelterry): Add deserialize() function.\nexport const lossesMap = {\n    meanSquaredError,\n    meanAbsoluteError,\n    meanAbsolutePercentageError,\n    meanSquaredLogarithmicError,\n    squaredHinge,\n    hinge,\n    categoricalHinge,\n    logcosh,\n    categoricalCrossentropy,\n    sparseCategoricalCrossentropy,\n    binaryCrossentropy,\n    kullbackLeiblerDivergence,\n    poisson,\n    cosineProximity\n};\n// Porting note: This diverges from the PyKeras implementation and may need to\n// change based on (de)serialization requirements.\nexport function get(identifierOrFn) {\n    if (typeof identifierOrFn === 'string') {\n        if (identifierOrFn in lossesMap) {\n            return lossesMap[identifierOrFn];\n        }\n        let errMsg = `Unknown loss ${identifierOrFn}`;\n        if (identifierOrFn.toLowerCase().includes('softmaxcrossentropy')) {\n            errMsg = `Unknown loss ${identifierOrFn}. ` +\n                'Use \"categoricalCrossentropy\" as the string name for ' +\n                'tf.losses.softmaxCrossEntropy';\n        }\n        throw new ValueError(errMsg);\n    }\n    else {\n        return identifierOrFn;\n    }\n}\n//# sourceMappingURL=losses.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Built-in metrics.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { tidy } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { NotImplementedError, ValueError } from './errors';\nimport { categoricalCrossentropy as categoricalCrossentropyLoss, cosineProximity, meanAbsoluteError, meanAbsolutePercentageError, meanSquaredError, sparseCategoricalCrossentropy as sparseCategoricalCrossentropyLoss } from './losses';\nimport { binaryCrossentropy as lossBinaryCrossentropy } from './losses';\nimport { lossesMap } from './losses';\nimport * as util from './utils/generic_utils';\nexport function binaryAccuracy(yTrue, yPred) {\n    return tidy(() => {\n        const threshold = tfc.mul(.5, tfc.onesLike(yPred));\n        const yPredThresholded = K.cast(tfc.greater(yPred, threshold), yTrue.dtype);\n        return tfc.mean(tfc.equal(yTrue, yPredThresholded), -1);\n    });\n}\nexport function categoricalAccuracy(yTrue, yPred) {\n    return tidy(() => K.cast(tfc.equal(tfc.argMax(yTrue, -1), tfc.argMax(yPred, -1)), 'float32'));\n}\nfunction truePositives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(1), yPred.equal(1)).sum().cast('float32');\n    });\n}\nfunction falseNegatives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(1), yPred.equal(0)).sum().cast('float32');\n    });\n}\nfunction falsePositives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(0), yPred.equal(1)).sum().cast('float32');\n    });\n}\nexport function precision(yTrue, yPred) {\n    return tidy(() => {\n        const tp = truePositives(yTrue, yPred);\n        const fp = falsePositives(yTrue, yPred);\n        const denominator = tp.add(fp);\n        return tfc.where(tfc.greater(denominator, 0), tp.div(denominator), 0)\n            .cast('float32');\n    });\n}\nexport function recall(yTrue, yPred) {\n    return tidy(() => {\n        const tp = truePositives(yTrue, yPred);\n        const fn = falseNegatives(yTrue, yPred);\n        const denominator = tp.add(fn);\n        return tfc.where(tfc.greater(denominator, 0), tp.div(denominator), 0)\n            .cast('float32');\n    });\n}\nexport function binaryCrossentropy(yTrue, yPred) {\n    return lossBinaryCrossentropy(yTrue, yPred);\n}\nexport function sparseCategoricalAccuracy(yTrue, yPred) {\n    if (yTrue.rank === yPred.rank) {\n        yTrue = yTrue.squeeze([yTrue.rank - 1]);\n    }\n    yPred = yPred.argMax(-1);\n    if (yPred.dtype !== yTrue.dtype) {\n        yPred = yPred.asType(yTrue.dtype);\n    }\n    return tfc.equal(yTrue, yPred).asType('float32');\n}\nexport function topKCategoricalAccuracy(yTrue, yPred) {\n    throw new NotImplementedError();\n}\nexport function sparseTopKCategoricalAccuracy(yTrue, yPred) {\n    throw new NotImplementedError();\n}\n// Aliases.\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const categoricalCrossentropy = categoricalCrossentropyLoss;\nexport const cosine = cosineProximity;\nexport const sparseCategoricalCrossentropy = sparseCategoricalCrossentropyLoss;\n// TODO(cais, nielsene): Add serialize().\nexport const metricsMap = {\n    binaryAccuracy,\n    categoricalAccuracy,\n    precision,\n    categoricalCrossentropy,\n    sparseCategoricalCrossentropy,\n    mse,\n    MSE,\n    mae,\n    MAE,\n    mape,\n    MAPE,\n    cosine\n};\nexport function get(identifier) {\n    if (typeof identifier === 'string' && identifier in metricsMap) {\n        return metricsMap[identifier];\n    }\n    else if (typeof identifier !== 'string' && identifier != null) {\n        return identifier;\n    }\n    else {\n        throw new ValueError(`Unknown metric ${identifier}`);\n    }\n}\n/**\n * Get the shortcut function name.\n *\n * If the fn name is a string,\n *   directly return the string name.\n * If the function is included in metricsMap or lossesMap,\n *   return key of the map.\n *   - If the function relative to multiple keys,\n *     return the first found key as the function name.\n *   - If the function exists in both lossesMap and metricsMap,\n *     search lossesMap first.\n * If the function is not included in metricsMap or lossesMap,\n *   return the function name.\n *\n * @param fn loss function, metric function, or short cut name.\n * @returns Loss or Metric name in string.\n */\nexport function getLossOrMetricName(fn) {\n    util.assert(fn !== null, `Unknown LossOrMetricFn ${fn}`);\n    if (typeof fn === 'string') {\n        return fn;\n    }\n    else {\n        let fnName;\n        for (const key of Object.keys(lossesMap)) {\n            if (lossesMap[key] === fn) {\n                fnName = key;\n                break;\n            }\n        }\n        if (fnName !== undefined) {\n            return fnName;\n        }\n        for (const key of Object.keys(metricsMap)) {\n            if (metricsMap[key] === fn) {\n                fnName = key;\n                break;\n            }\n        }\n        if (fnName !== undefined) {\n            return fnName;\n        }\n        return fn.name;\n    }\n}\n//# sourceMappingURL=metrics.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Optimizers.\n */\nimport { train } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport { ValueError } from './errors';\n// Add (de)serialize()\n// Porting note: This diverges from the PyKeras implementation and may need to\n// change based on (de)serialization requirements.\nexport function getOptimizer(identifier) {\n    const optimizerMap = {\n        'Adagrad': () => train.adagrad(0.01),\n        'Adadelta': () => train.adadelta(1, 0.95, epsilon()),\n        'Adam': () => train.adam(0.001, 0.9, 0.999, epsilon()),\n        'Adamax': () => train.adamax(0.002, 0.9, 0.999, epsilon(), 0),\n        'RMSProp': () => train.rmsprop(0.001, 0.9, 0, epsilon()),\n        'SGD': () => train.sgd(0.01)\n    };\n    optimizerMap['adagrad'] = optimizerMap['Adagrad'];\n    optimizerMap['adadelta'] = optimizerMap['Adadelta'];\n    optimizerMap['adam'] = optimizerMap['Adam'];\n    optimizerMap['adamax'] = optimizerMap['Adamax'];\n    optimizerMap['rmsprop'] = optimizerMap['RMSProp'];\n    optimizerMap['sgd'] = optimizerMap['SGD'];\n    if (identifier in optimizerMap) {\n        return optimizerMap[identifier]();\n    }\n    throw new ValueError(`Unknown Optimizer ${identifier}`);\n}\n//# sourceMappingURL=optimizers.js.map", "/**\n * @license\n * Copyright 2019 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/** Utility functions related to user-defined metadata. */\n// Maximum recommended serialized size for user-defined metadata.\n// Beyond this limit, a warning message will be printed during model loading and\n// saving.\nexport const MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024;\n/**\n * Check validity of user-defined metadata.\n *\n * @param userDefinedMetadata\n * @param modelName Name of the model that the user-defined metadata belongs to.\n *   Used during construction of error messages.\n * @param checkSize Whether to check the size of the metadata is under\n *   recommended limit. Default: `false`. If `true`, will try stringify the\n *   JSON object and print a console warning if the serialzied size is above the\n *   limit.\n * @throws Error if `userDefinedMetadata` is not a plain JSON object.\n */\nexport function checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) {\n    if (userDefinedMetadata == null ||\n        typeof userDefinedMetadata !== 'object' ||\n        Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype ||\n        !plainObjectCheck(userDefinedMetadata)) {\n        throw new Error('User-defined metadata is expected to be a JSON object, but is not.');\n    }\n    if (checkSize) {\n        const out = JSON.stringify(userDefinedMetadata);\n        if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) {\n            console.warn(`User-defined metadata of model \"${modelName}\" is too large in ` +\n                `size (length=${out.length} when serialized). It is not ` +\n                `recommended to store such large objects in user-defined metadata. ` +\n                `Please make sure its serialized length is <= ` +\n                `${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`);\n        }\n    }\n}\n/**\n * Check if an input is plain JSON object or any valid subfield of it.\n *\n * @param x The input to be checked.\n * @param assertObject Whether to assert `x` is a JSON object, i.e., reject\n *   cases of arrays and primitives.\n * @return Returns `true` if and only if `x` is a plain JSON object,\n *   a JSON-valid primitive including string, number, boolean and null,\n *   or an array of the said types.\n */\n// tslint:disable-next-line:no-any\nexport function plainObjectCheck(x) {\n    if (x === null) {\n        // Note: typeof `null` is 'object', and `null` is valid in JSON.\n        return true;\n    }\n    else if (typeof x === 'object') {\n        if (Object.getPrototypeOf(x) === Object.prototype) {\n            // `x` is a JavaScript object and its prototype is Object.\n            const keys = Object.keys(x);\n            for (const key of keys) {\n                if (typeof key !== 'string') {\n                    // JSON keys must be strings.\n                    return false;\n                }\n                if (!plainObjectCheck(x[key])) { // Recursive call.\n                    return false;\n                }\n            }\n            return true;\n        }\n        else {\n            // `x` is a JavaScript object but its prototype is not Object.\n            if (Array.isArray(x)) {\n                // `x` is a JavaScript array.\n                for (const item of x) {\n                    if (!plainObjectCheck(item)) { // Recursive call.\n                        return false;\n                    }\n                }\n                return true;\n            }\n            else {\n                // `x` is a JavaScript object and its prototype is not Object,\n                // and it's not an Array. I.e., it's a complex object such as\n                // `Error` and `Date`.\n                return false;\n            }\n        }\n    }\n    else {\n        // `x` is not a JavaScript object or `null`.\n        const xType = typeof x;\n        return xType === 'string' || xType === 'number' || xType === 'boolean';\n    }\n}\n//# sourceMappingURL=user_defined_metadata.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { countParamsInWeights } from './variable_utils';\n/**\n * Print the summary of a LayersModel object.\n *\n * @param model tf.LayersModel instance.\n * @param lineLength Total length of printed lines. Set this to adapt to the\n *   display to different terminal or console sizes.\n * @param positions Relative or absolute positions of log elements in each\n *   line. Each number corresponds to right-most (i.e., ending) position of a\n *   column.\n *   If not provided, defaults to `[0.45, 0.85, 1]` for sequential-like\n *   models and `[0.33, 0.55, 0.67, 1]` for non-sequential like models.\n * @param printFn Print function to use.\n *   It will be called on each line of the summary. You can provide a custom\n *   function in order to capture the string summary. Defaults to `console.log`.\n */\nexport function printSummary(model, lineLength, positions, \n// tslint:disable-next-line:no-any\nprintFn = console.log) {\n    const sequentialLike = isModelSequentialLike(model);\n    // Header names for different log elements.\n    const toDisplay = ['Layer (type)', 'Output shape', 'Param #'];\n    if (sequentialLike) {\n        lineLength = lineLength || 65;\n        positions = positions || [0.45, 0.85, 1];\n    }\n    else {\n        lineLength = lineLength || 98;\n        positions = positions || [0.33, 0.55, 0.67, 1];\n        // Header names for different log elements.\n    }\n    if (positions[positions.length - 1] <= 1) {\n        // `positions` is relative. Convert it to absolute positioning.\n        positions = positions.map(p => Math.floor(lineLength * p));\n    }\n    let relevantNodes;\n    if (!sequentialLike) {\n        toDisplay.push('Receives inputs');\n        relevantNodes = [];\n        for (const depth in model.nodesByDepth) {\n            relevantNodes.push(...model.nodesByDepth[depth]);\n        }\n    }\n    printFn('_'.repeat(lineLength));\n    printRow(toDisplay, positions, printFn);\n    printFn('='.repeat(lineLength));\n    const layers = model.layers;\n    for (let i = 0; i < layers.length; ++i) {\n        if (sequentialLike) {\n            printLayerSummary(layers[i], positions, printFn);\n        }\n        else {\n            printLayerSummaryWithConnections(layers[i], positions, relevantNodes, printFn);\n        }\n        printFn((i === layers.length - 1 ? '=' : '_').repeat(lineLength));\n    }\n    // tslint:disable-next-line:no-any\n    model.checkTrainableWeightsConsistency();\n    const trainableCount = countTrainableParams(model);\n    const nonTrainableCount = countParamsInWeights(model.nonTrainableWeights);\n    printFn(`Total params: ${trainableCount + nonTrainableCount}`);\n    printFn(`Trainable params: ${trainableCount}`);\n    printFn(`Non-trainable params: ${nonTrainableCount}`);\n    printFn('_'.repeat(lineLength));\n}\nfunction countTrainableParams(model) {\n    let trainableCount;\n    // tslint:disable:no-any\n    if (model.collectedTrainableWeights != null) {\n        trainableCount =\n            countParamsInWeights(model.collectedTrainableWeights);\n    }\n    else {\n        trainableCount = countParamsInWeights(model.trainableWeights);\n    }\n    // tslint:enable:no-any\n    return trainableCount;\n}\nfunction isModelSequentialLike(model) {\n    let sequentialLike = true;\n    const nodesByDepth = [];\n    const nodes = [];\n    for (const depth in model.nodesByDepth) {\n        nodesByDepth.push(model.nodesByDepth[depth]);\n    }\n    for (const depthNodes of nodesByDepth) {\n        if (depthNodes.length > 1 ||\n            depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) {\n            sequentialLike = false;\n            break;\n        }\n        nodes.push(...depthNodes);\n    }\n    if (sequentialLike) {\n        // Search for shared layers.\n        for (const layer of model.layers) {\n            let flag = false;\n            for (const node of layer.inboundNodes) {\n                if (nodes.indexOf(node) !== -1) {\n                    if (flag) {\n                        sequentialLike = false;\n                        break;\n                    }\n                    else {\n                        flag = true;\n                    }\n                }\n            }\n            if (!sequentialLike) {\n                break;\n            }\n        }\n    }\n    return sequentialLike;\n}\nfunction printRow(fields, positions, \n// tslint:disable-next-line:no-any\nprintFn = console.log) {\n    let line = '';\n    for (let i = 0; i < fields.length; ++i) {\n        if (i > 0) {\n            line = line.slice(0, line.length - 1) + ' ';\n        }\n        line += fields[i];\n        line = line.slice(0, positions[i]);\n        line += ' '.repeat(positions[i] - line.length);\n    }\n    printFn(line);\n}\n/**\n * Prints a summary for a single Layer, without connectivity information.\n *\n * @param layer: Layer instance to print.\n */\nfunction printLayerSummary(layer, positions, \n// tslint:disable-next-line:no-any\nprintFn) {\n    let outputShape;\n    try {\n        outputShape = JSON.stringify(layer.outputShape);\n    }\n    catch (err) {\n        outputShape = 'multiple';\n    }\n    const name = layer.name;\n    const className = layer.getClassName();\n    const fields = [`${name} (${className})`, outputShape, layer.countParams().toString()];\n    printRow(fields, positions, printFn);\n}\n/**\n * Prints a summary for a single Layer, with connectivity information.\n */\nfunction printLayerSummaryWithConnections(layer, positions, relevantNodes, \n// tslint:disable-next-line:no-any\nprintFn) {\n    let outputShape;\n    try {\n        outputShape = JSON.stringify(layer.outputShape);\n    }\n    catch (err) {\n        outputShape = 'multiple';\n    }\n    const connections = [];\n    for (const node of layer.inboundNodes) {\n        if (relevantNodes != null && relevantNodes.length > 0 &&\n            relevantNodes.indexOf(node) === -1) {\n            continue;\n        }\n        for (let i = 0; i < node.inboundLayers.length; ++i) {\n            const inboundLayer = node.inboundLayers[i].name;\n            const inboundLayerIndex = node.nodeIndices[i];\n            const inboundTensorIndex = node.tensorIndices[i];\n            connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);\n        }\n    }\n    const name = layer.name;\n    const className = layer.getClassName();\n    const firstConnection = connections.length === 0 ? '' : connections[0];\n    const fields = [\n        `${name} (${className})`, outputShape, layer.countParams().toString(),\n        firstConnection\n    ];\n    printRow(fields, positions, printFn);\n    for (let i = 1; i < connections.length; ++i) {\n        printRow(['', '', '', connections[i]], positions, printFn);\n    }\n}\n//# sourceMappingURL=layer_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as generic_utils from '../utils/generic_utils';\n// tslint:enable\n/**\n * Test whether a value in an array is the name of a LayersModel or Layer.\n * @param key The key name that the value is found under. Note that the key\n *   may not be at the level immediately above the value, if the value is in a\n *   nested array.\n * @param index Index of the value in the Array that it is found in.\n * @param value The value object.\n * @returns A boolean indicating whether value is a name.\n */\nfunction isArrayItemInputOrOutputName(key, index, value) {\n    return (key === 'inboundNodes' || key === 'outputLayers' ||\n        key === 'inputLayers') &&\n        index === 0 && typeof value === 'string';\n}\n/**\n * Convert a Pythonic config object to TypeScript config object.\n * @param pythonicConfig The config object to convert.\n * @param key Optional key name of the object being converted.\n * @returns Result of the conversion.\n */\nexport function convertPythonicToTs(pythonicConfig, key) {\n    if (pythonicConfig === null) {\n        return null;\n    }\n    else if (typeof pythonicConfig === 'string') {\n        return generic_utils.toCamelCase(pythonicConfig);\n    }\n    else if ((typeof pythonicConfig === 'number') ||\n        (typeof pythonicConfig === 'boolean')) {\n        return pythonicConfig;\n    }\n    else if (pythonicConfig instanceof Array) {\n        const tsArray = [];\n        const arrayLength = pythonicConfig.length;\n        for (let i = 0; i < arrayLength; ++i) {\n            const item = pythonicConfig[i];\n            if (isArrayItemInputOrOutputName(key, i, item)) {\n                tsArray.push(item);\n            }\n            else {\n                tsArray.push(convertPythonicToTs(item, key));\n            }\n        }\n        return tsArray;\n    }\n    else {\n        const tsDict = {};\n        for (const pythonicKey of Object.keys(pythonicConfig)) {\n            const pythonicValue = pythonicConfig[pythonicKey];\n            if (pythonicKey === 'name' && typeof pythonicValue === 'string') {\n                // Special case the 'name' key with a string value. Name values, such as\n                // the names of LayersModel and Layer instances, should not undergo the\n                // camel-case conversion.\n                tsDict[pythonicKey] = pythonicValue;\n            }\n            else {\n                const tsKey = generic_utils.toCamelCase(pythonicKey);\n                tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey);\n            }\n        }\n        return tsDict;\n    }\n}\n/**\n * Convert a TypeScript config object to Python config object.\n * @param tsConfig The config object to convert.\n * @param key Optional key name of the object being converted.\n * @returns Result of the conversion.\n */\nexport function convertTsToPythonic(tsConfig, key) {\n    if (tsConfig === null || tsConfig === undefined) {\n        return null;\n    }\n    else if (typeof tsConfig === 'string') {\n        return generic_utils.toSnakeCase(tsConfig);\n    }\n    else if ((typeof tsConfig === 'number') || (typeof tsConfig === 'boolean')) {\n        return tsConfig;\n    }\n    else if (tsConfig instanceof Array) {\n        const pyArray = [];\n        const arrayLength = tsConfig.length;\n        for (let i = 0; i < arrayLength; ++i) {\n            const item = tsConfig[i];\n            if (isArrayItemInputOrOutputName(key, i, item)) {\n                pyArray.push(item);\n            }\n            else {\n                pyArray.push(convertTsToPythonic(item, key));\n            }\n        }\n        return pyArray;\n    }\n    else {\n        const pyDict = {};\n        for (const tsKey of Object.keys(tsConfig)) {\n            const tsValue = tsConfig[tsKey];\n            const pyKey = generic_utils.toSnakeCase(tsKey);\n            if ((tsKey === 'name' || tsKey === 'className') &&\n                typeof tsValue === 'string') {\n                // Special case the 'name' key with a string value. Name values, such as\n                // the names of LayersModel and Layer instances, should not undergo the\n                // snake-case conversion.\n                pyDict[pyKey] = tsValue;\n            }\n            else {\n                pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey);\n            }\n        }\n        return pyDict;\n    }\n}\n//# sourceMappingURL=serialization_utils.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Executor: Evaluates SymbolicTensor based on feeds.\n */\nimport { cast, dispose, memory, util } from '@tensorflow/tfjs-core';\nimport { ValueError } from '../errors';\nimport { toList } from '../utils/generic_utils';\nimport { InputLayer } from './input_layer';\nimport { SymbolicTensor } from './topology';\n/**\n * Helper function to check the dtype and shape compatibility of a feed value.\n */\nfunction assertFeedCompatibility(key, val) {\n    // Check dtype compatibility.\n    if (key.dtype == null || key.dtype === val.dtype) {\n        //  a.  If types match, return val tensor as is.\n        return val;\n    }\n    try {\n        //  b. Attempt to convert to expected type.\n        return cast(val, key.dtype);\n    }\n    catch (err) {\n        //  c. If conversion fails, return helpful error.\n        throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype ` +\n            `of the key '${key.name}' (${key.dtype}).`);\n    }\n}\n/**\n * FeedDict: A mapping from unique SymbolicTensors to feed values for them.\n * A feed value is a concrete value represented as an `Tensor`.\n */\nexport class FeedDict {\n    /**\n     * Constructor, optionally does copy-construction.\n     * @param feeds An Array of `Feed`s, or another `FeedDict`, in which case\n     *   copy-construction will be performed.\n     */\n    constructor(feeds) {\n        this.id2Value = {};\n        this.id2Mask = {};\n        this.name2Id = {};\n        if (feeds instanceof FeedDict) {\n            for (const id in feeds.id2Value) {\n                this.id2Value[id] = feeds.id2Value[id];\n                if (id in feeds.id2Mask) {\n                    this.id2Mask[id] = feeds.id2Mask[id];\n                }\n            }\n        }\n        else {\n            if (feeds == null) {\n                return;\n            }\n            for (const feed of feeds) {\n                this.add(feed.key, feed.value);\n            }\n        }\n    }\n    /**\n     * Add a key-value pair to the FeedDict.\n     *\n     * @param key The key of the feed.\n     * @param value The value of the tensor feed.\n     * @param mask The value of the mask feed (optional).\n     * @returns This `FeedDict`.\n     * @throws ValueError: If the key `SymbolicTensor` already exists in the\n     *   `FeedDict`.\n     */\n    add(key, value, mask) {\n        if (this.id2Value[key.id] == null) {\n            this.id2Value[key.id] = assertFeedCompatibility(key, value);\n            this.name2Id[key.name] = key.id;\n            if (mask != null) {\n                this.id2Mask[key.id] = mask;\n            }\n        }\n        else {\n            throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);\n        }\n        return this;\n    }\n    /**\n     * Add a Feed to the FeedDict.\n     * @param feed The new `Feed` to add.\n     * @returns This `FeedDict`.\n     */\n    addFeed(feed) {\n        this.add(feed.key, feed.value);\n    }\n    /**\n     * Probe whether a key already exists in the FeedDict.\n     * @param key\n     */\n    hasKey(key) {\n        return this.id2Value[key.id] != null;\n    }\n    /**\n     * Get all the SymbolicTensor available in this FeedDict.\n     */\n    names() {\n        return Object.keys(this.name2Id);\n    }\n    /**\n     * Get the feed value for given key.\n     * @param key The SymbolicTensor, or its name (as a string), of which the\n     *     value is sought.\n     * @returns If `key` exists, the corresponding feed value.\n     * @throws ValueError: If `key` does not exist in this `FeedDict`.\n     */\n    getValue(key) {\n        if (key instanceof SymbolicTensor) {\n            if (this.id2Value[key.id] == null) {\n                throw new ValueError(`Nonexistent key: ${key.name}`);\n            }\n            else {\n                return this.id2Value[key.id];\n            }\n        }\n        else {\n            const id = this.name2Id[key];\n            if (id == null) {\n                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n            }\n            return this.id2Value[id];\n        }\n    }\n    /**\n     * Get the feed mask for given key.\n     * @param key The SymbolicTensor, or its name (as a string), of which the\n     *     value is sought.\n     * @returns If `key` exists, the corresponding feed mask.\n     * @throws ValueError: If `key` does not exist in this `FeedDict`.\n     */\n    getMask(key) {\n        if (key instanceof SymbolicTensor) {\n            if (this.id2Value[key.id] == null) {\n                throw new ValueError(`Nonexistent key: ${key.name}`);\n            }\n            else {\n                return this.id2Mask[key.id];\n            }\n        }\n        else {\n            const id = this.name2Id[key];\n            if (id == null) {\n                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n            }\n            return this.id2Mask[id];\n        }\n    }\n    /** Dispose all mask Tensors held by this object. */\n    disposeMasks() {\n        if (this.id2Mask != null) {\n            dispose(this.id2Mask);\n        }\n    }\n}\n// Cache for topologically sorted SymbolicTensors for given execution\n// targets (i.e., fetches).\nconst cachedSorted = {};\n// Cache for recipient count maps for given execution targets (i.e., fetches).\nconst cachedRecipientCounts = {};\n/**\n * Execute a SymbolicTensor by using concrete feed values.\n *\n * A `SymbolicTensor` object is a node in a computation graph of TF.js\n * Layers. The object is backed by a source layer and input\n * `SymbolicTensor`s to the source layer. This method evaluates\n * the `call()` method of the source layer, using concrete values of the\n * inputs obtained from either\n * * `feedDict`, if the input key exists in `feedDict`, or else,\n * * a recursive call to `execute()` itself.\n *\n * @param x: The `SymbolicTensor` to execute.\n * @param feedDict: The feed values, as base condition of the recursion.\n *   execution.\n * @param kwargs: Optional keyword arguments.\n * @param probe: A probe object (of interface `ExecutionProbe`) used for\n *   testing memory footprint of `execute` calls.\n * @returns Result of the execution.\n * @throws ValueError: If any `SymbolicTensor`s from `InputLayer`s\n *   encountered during the execution lacks a feed value in `feedDict`.\n */\nexport function execute(fetches, feedDict, kwargs, probe) {\n    const training = kwargs == null ? false : kwargs['training'];\n    const arrayFetches = Array.isArray(fetches);\n    const fetchArray = arrayFetches ? fetches : [fetches];\n    const outputNames = fetchArray.map(t => t.name);\n    const finalOutputs = [];\n    const feedNames = feedDict.names();\n    for (const outputName of outputNames) {\n        if (feedNames.indexOf(outputName) !== -1) {\n            finalOutputs.push(feedDict.getValue(outputName));\n        }\n        else {\n            finalOutputs.push(null);\n        }\n    }\n    if (probe != null) {\n        // For optional probing of memory footprint during execution.\n        probe.maxNumTensors = -Infinity;\n        probe.minNumTensors = Infinity;\n    }\n    // Check cache.\n    const fetchAndFeedKey = outputNames.join(',') + '|' + feedDict.names().join(',');\n    let sorted;\n    let recipientCounts;\n    if (cachedSorted[fetchAndFeedKey] == null) {\n        // Cache doesn't contain the desired combination of fetches. Compute\n        // topological sort for the combination for the first time.\n        const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);\n        sorted = out.sorted;\n        recipientCounts = out.recipientCounts;\n        // Store results in cache for future use.\n        cachedSorted[fetchAndFeedKey] = sorted;\n        cachedRecipientCounts[fetchAndFeedKey] = recipientCounts;\n    }\n    sorted = cachedSorted[fetchAndFeedKey];\n    recipientCounts = {};\n    if (!training) {\n        Object.assign(recipientCounts, cachedRecipientCounts[fetchAndFeedKey]);\n    }\n    const internalFeedDict = new FeedDict(feedDict);\n    // Start iterative execution on the topologically-sorted SymbolicTensors.\n    for (let i = 0; i < sorted.length; ++i) {\n        if (probe != null) {\n            // For optional probing of memory usage during execution.\n            const numTensors = memory().numTensors;\n            if (numTensors > probe.maxNumTensors) {\n                probe.maxNumTensors = numTensors;\n            }\n            if (numTensors < probe.minNumTensors) {\n                probe.minNumTensors = numTensors;\n            }\n        }\n        const symbolic = sorted[i];\n        const srcLayer = symbolic.sourceLayer;\n        if (srcLayer instanceof InputLayer) {\n            continue;\n        }\n        const inputValues = [];\n        const inputMasks = [];\n        const tensorsToDispose = [];\n        let maskExists = false;\n        for (const input of symbolic.inputs) {\n            const value = internalFeedDict.getValue(input);\n            const mask = internalFeedDict.getMask(input);\n            inputValues.push(value);\n            inputMasks.push(mask);\n            if (mask != null) {\n                maskExists = true;\n            }\n            if (!training) {\n                recipientCounts[input.name]--;\n                if (recipientCounts[input.name] === 0 && !feedDict.hasKey(input) &&\n                    outputNames.indexOf(input.name) === -1 && !value.isDisposed &&\n                    input.sourceLayer.stateful !== true) {\n                    tensorsToDispose.push(value);\n                }\n            }\n        }\n        if (maskExists) {\n            kwargs = kwargs || {};\n            kwargs['mask'] = inputMasks[0];\n        }\n        const outputTensors = toList(srcLayer.apply(inputValues, kwargs));\n        let outputMask = null;\n        if (srcLayer.supportsMasking) {\n            outputMask = srcLayer.computeMask(inputValues, inputMasks);\n        }\n        const layerOutputs = getNodeOutputs(symbolic);\n        const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];\n        for (let i = 0; i < outputSymbolicTensors.length; ++i) {\n            if (!internalFeedDict.hasKey(outputSymbolicTensors[i])) {\n                internalFeedDict.add(outputSymbolicTensors[i], outputTensors[i], Array.isArray(outputMask) ? outputMask[0] : outputMask);\n            }\n            const index = outputNames.indexOf(outputSymbolicTensors[i].name);\n            if (index !== -1) {\n                finalOutputs[index] = outputTensors[i];\n            }\n        }\n        if (!training) {\n            // Clean up Tensors that are no longer needed.\n            dispose(tensorsToDispose);\n        }\n    }\n    // NOTE(cais): Unlike intermediate tensors, we don't discard mask\n    // tensors as we go, because these tensors are sometimes passed over a\n    // series of mutliple layers, i.e., not obeying the immediate input\n    // relations in the graph. If this becomes a memory-usage concern,\n    // we can improve this in the future.\n    internalFeedDict.disposeMasks();\n    return arrayFetches ? finalOutputs : finalOutputs[0];\n}\n/**\n * Sort the `SymbolicTensor`s topologically, for an array of fetches.\n *\n * This function calls getTopologicalSortAndRecipientCountsForOneFetch and\n * merges their results.\n *\n * @param fetch The array of fetches requested. Must be a non-empty array.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientCounts: Recipient counts for all SymbolicTensors in `sorted`.\n */\nfunction getTopologicalSortAndRecipientCounts(fetches, feedDict) {\n    util.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`);\n    let finalSorted = [];\n    let finalRecipientMap = {};\n    if (fetches.length === 1) {\n        // Special-casing 1 fetch for efficiency.\n        const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);\n        finalSorted = out.sorted;\n        finalRecipientMap = out.recipientMap;\n    }\n    else {\n        const visited = new Set();\n        for (const fetch of fetches) {\n            const { sorted, recipientMap } = getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict);\n            // Merge sorted SymbolicTensor Arrays.\n            for (const symbolicTensor of sorted) {\n                if (!visited.has(symbolicTensor.name)) {\n                    finalSorted.push(symbolicTensor);\n                    visited.add(symbolicTensor.name);\n                }\n            }\n            // Merge recipient maps.\n            for (const name in recipientMap) {\n                if (finalRecipientMap[name] == null) {\n                    finalRecipientMap[name] = new Set();\n                }\n                recipientMap[name].forEach(recipient => finalRecipientMap[name].add(recipient));\n            }\n        }\n    }\n    return {\n        sorted: finalSorted,\n        recipientCounts: recipientMap2Counts(finalRecipientMap)\n    };\n}\nfunction recipientMap2Counts(recipientMap) {\n    const recipientCounts = {};\n    for (const name in recipientMap) {\n        recipientCounts[name] = recipientMap[name].size;\n    }\n    return recipientCounts;\n}\n/**\n * Sort the `SymbolicTensor`s topologically, for a single fetch.\n *\n * This helper function processes the upstream SymbolicTensors of a single\n * fetch.\n *\n * @param fetch The single fetch requested.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientMap: Recipient names for all SymbolicTensors in `sorted`.\n */\nexport function getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict) {\n    const visited = new Set();\n    const sorted = [];\n    const recipientMap = {};\n    // Put keys of the feedDict into visited first, so they don't have to be\n    // walked. This is needed in case where there are feeds for intermediate\n    // SymbolicTensors of the graph.\n    for (const key of feedDict.names()) {\n        visited.add(key);\n    }\n    const stack = [];\n    const marks = [];\n    // Initial population of stack and marks.\n    stack.push(fetch);\n    while (stack.length > 0) {\n        const top = stack[stack.length - 1];\n        if (visited.has(top.name)) {\n            stack.pop();\n            continue;\n        }\n        const topIsMarked = marks[marks.length - 1] === stack.length - 1;\n        if (top.inputs.length === 0 || topIsMarked) {\n            // Input SymbolicTensor or all children have been visited.\n            stack.pop();\n            sorted.push(top);\n            visited.add(top.name);\n            if (topIsMarked) {\n                marks.pop();\n            }\n        }\n        else {\n            // A non-input SymbolicTensor whose upstream SymbolicTensors haven't\n            // been visited yet. Push them onto the stack.\n            marks.push(stack.length - 1);\n            for (const input of top.inputs) {\n                // Increment the recipient count. Note that this needs to happen\n                // regardless of whether the SymbolicTensor has been visited before.\n                if (recipientMap[input.name] == null) {\n                    recipientMap[input.name] = new Set();\n                }\n                recipientMap[input.name].add(top.name);\n                if (visited.has(input.name)) {\n                    continue; // Avoid repeated visits to the same SymbolicTensor.\n                }\n                stack.push(input);\n            }\n        }\n    }\n    return { sorted, recipientMap };\n}\n/**\n * Get the symbolic output tensors of the node to which a given fetch belongs.\n * @param fetch The fetched symbolic tensor.\n * @returns The Array of symbolic tensors output by the node to which `fetch`\n *   belongs.\n */\nfunction getNodeOutputs(fetch) {\n    let layerOutputs;\n    if (fetch.sourceLayer.inboundNodes.length === 1) {\n        layerOutputs = fetch.sourceLayer.output;\n    }\n    else {\n        let nodeIndex = null;\n        for (let i = 0; i < fetch.sourceLayer.inboundNodes.length; ++i) {\n            for (const outputTensor of fetch.sourceLayer.inboundNodes[i]\n                .outputTensors) {\n                if (outputTensor.id === fetch.id) {\n                    nodeIndex = i;\n                    break;\n                }\n            }\n        }\n        layerOutputs = fetch.sourceLayer.getOutputAt(nodeIndex);\n    }\n    return layerOutputs;\n}\n//# sourceMappingURL=executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/engine/topology.py */\nimport { tidy } from '@tensorflow/tfjs-core';\nimport { getUid } from '../backend/state';\nimport { NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { deserialize as deserializeLayer } from '../layers/serialization';\nimport * as generic_utils from '../utils/generic_utils';\nimport { convertTsToPythonic } from '../utils/serialization_utils';\nimport * as types_utils from '../utils/types_utils';\nimport { batchSetValue } from '../variables';\nimport { version as layersVersion } from '../version';\nimport { execute, FeedDict } from './executor';\nimport { InputLayer } from './input_layer';\nimport { Layer, Node } from './topology';\n/**\n * A Container is a directed acyclic graph of layers.\n *\n * It is the topological form of a \"model\". A LayersModel\n * is simply a Container with added training routines.\n *\n */\nexport class Container extends Layer {\n    constructor(args) {\n        // No args passed to super's constructor.\n        super({});\n        this.containerNodes = new Set();\n        this.name = args.name;\n        if (this.name == null) {\n            const prefix = this.getClassName().toLowerCase();\n            this.name = getUid(prefix);\n        }\n        this.supportsMasking = false;\n        this.trainable_ = true;\n        // TODO(michaelterry): Initialize perInputLosses/Updates here.\n        // Container-specific properties.\n        if (Array.isArray(args.inputs)) {\n            this.inputs = args.inputs.slice();\n        }\n        else {\n            this.inputs = [args.inputs];\n        }\n        if (Array.isArray(args.outputs)) {\n            this.outputs = args.outputs.slice();\n        }\n        else {\n            this.outputs = [args.outputs];\n        }\n        // Check for redundancy in inputs.\n        if (generic_utils.unique(this.inputs).length !== this.inputs.length) {\n            throw new ValueError('The list of inputs passed to the model is ' +\n                'redundant. All inputs should only appear once. Found: ' +\n                `${this.inputs.map(x => x.name)}`);\n        }\n        // Check for redundancy in outputs.\n        if (generic_utils.unique(this.outputs).length !== this.outputs.length) {\n            console.warn('The list of outputs passed to the model is redundant. ' +\n                'All outputs should only appear once. Found: ' +\n                `${this.outputs.map(x => x.name)}`);\n        }\n        /*\n          List of initial layers (1 to 1 mapping with this.inputs, hence the same\n          layer might appear twice)\n        */\n        this.inputLayers = [];\n        this.inputLayersNodeIndices = [];\n        this.inputLayersTensorIndices = [];\n        /*\n          List of layers (1 to 1 mapping with this.outputs, hence the same layer\n          might appear twice)\n        */\n        this.outputLayers = [];\n        this.outputLayersNodeIndices = [];\n        this.outputLayersTensorIndices = [];\n        /*\n          All layers in order of horizontal graph traversal. Entries are unique.\n          Includes input and output layers.\n        */\n        this.layers = [];\n        /*\n          References to container layers that were constructed internally. We need\n          these to properly dispose of tensors from nested containers.\n        */\n        this.internalContainerRefs = [];\n        // TODO(michaelterry): Determine if caching still needed with eager\n        // backend.\n        /*\n          This is for performance optimization when calling the Container on new\n          inputs. Every time the Container is called on a set on input tensors,\n          we compute the output tensors, output masks and output shapes in one pass,\n          then cache them here. When one of these outputs is queried later,\n          we retrieve it from there instead of recomputing it.\n        */\n        // this.outputTensorCache = {};\n        // this.outputShapeCache = {};\n        // Build this.outputLayers:\n        for (const x of this.outputs) {\n            const layer = x.sourceLayer;\n            const nodeIndex = x.nodeIndex;\n            const tensorIndex = x.tensorIndex;\n            this.outputLayers.push(layer);\n            this.outputLayersNodeIndices.push(nodeIndex);\n            this.outputLayersTensorIndices.push(tensorIndex);\n        }\n        // TODO(michaelterry): Add output mask cache code.\n        // Build this.inputLayers:\n        for (const x of this.inputs) {\n            const layer = x.sourceLayer;\n            const nodeIndex = x.nodeIndex;\n            const tensorIndex = x.tensorIndex;\n            /*\n              It's supposed to be an input layer, so only one node\n              and one tensor output.\n            */\n            generic_utils.assert(nodeIndex === 0, 'input layer has >1 nodes');\n            generic_utils.assert(tensorIndex === 0, 'input layer has >1 tensors');\n            this.inputLayers.push(layer);\n            this.inputLayersNodeIndices.push(nodeIndex);\n            this.inputLayersTensorIndices.push(tensorIndex);\n        }\n        // Build this.inputNames and this.outputNames.\n        this.inputNames = [];\n        this.outputNames = [];\n        this.feedInputShapes = [];\n        this.feedInputNames = [];\n        this.feedOutputNames = [];\n        for (let i = 0; i < this.inputLayers.length; i++) {\n            const layer = this.inputLayers[i];\n            // Check that layer is an InputLayer.\n            if (!(layer instanceof InputLayer)) {\n                throw new TypeError('Input layers to a LayersModel must be InputLayer objects. ' +\n                    `Received inputs: ${args.inputs}. ` +\n                    `Input ${i} (0-based) originates ` +\n                    `from layer type ${layer.getClassName()}.`);\n            }\n            this.inputNames.push(layer.name);\n            this.feedInputShapes.push(layer.batchInputShape);\n            this.feedInputNames.push(layer.name);\n        }\n        for (const layer of this.outputLayers) {\n            this.outputNames.push(layer.name);\n        }\n        this.internalInputShapes = this.inputs.map(x => x.shape);\n        this.internalOutputShapes = this.outputs.map(x => x.shape);\n        /*\n          Container_nodes: set of nodes included in the graph (not all nodes\n          included in the layers are relevant to the current graph).\n        */\n        // ids of all nodes relevant to the Container:\n        const nodesDepths = {};\n        // To recover nodes from their ID.\n        const nodeIDToNode = {};\n        const layersDepths = {};\n        // To layers from their ID.\n        const layerIDToLayer = {};\n        const layerIndices = {};\n        const nodesInDecreasingDepth = [];\n        /**\n         * Builds a map of the graph of layers.\n         *\n         * This recursively updates the map `layerIndices`,\n         * the list `nodesInDecreasingDepth` and the set `containerNodes`.\n         *\n         * @param tensor Some tensor in a graph.\n         * @param finishedNodes Set of nodes whose subgraphs have been traversed\n         *         completely. Useful to prevent duplicated work.\n         * @param nodesInProgress Set of nodes that are currently active on the\n         *         recursion stack. Useful to detect cycles.\n         * @param layer Layer from which `tensor` comes from. If not provided,\n         *   will be obtained from tensor.sourceLayer.\n         * @param nodeIndex Node index from which `tensor` comes from.\n         * @param tensorIndex TensorIndex from which `tensor` comes from.\n         *\n         * @exception RuntimeError if a cycle is detected.\n         */\n        const buildMapOfGraph = (tensor, finishedNodes, nodesInProgress, layer, nodeIndex, tensorIndex) => {\n            if (layer == null || nodeIndex == null || tensorIndex == null) {\n                layer = tensor.sourceLayer;\n                nodeIndex = tensor.nodeIndex;\n                tensorIndex = tensor.tensorIndex;\n            }\n            const node = layer.inboundNodes[nodeIndex];\n            // Prevent cycles.\n            if (nodesInProgress.indexOf(node) !== -1) {\n                throw new RuntimeError(`The tensor ${tensor.name} at layer \"${layer.name}\" ` +\n                    'is part of a cycle.');\n            }\n            // Don't repeat work for shared subgraphs\n            if (finishedNodes.indexOf(node) !== -1) {\n                return;\n            }\n            // Update containerNodes.\n            this.containerNodes.add(Container.nodeKey(layer, nodeIndex));\n            // Store the traversal order for layer sorting.\n            if (!(layer.id in layerIndices)) {\n                layerIndices[layer.id] = Object.keys(layerIndices).length;\n            }\n            if (nodesInProgress.indexOf(node) === -1) {\n                nodesInProgress.push(node);\n            }\n            // Propagate to all previous tensors connected to this node.\n            const numInboundLayers = node.inboundLayers.length;\n            for (let i = 0; i < numInboundLayers; i++) {\n                const x = node.inputTensors[i];\n                const layer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const tensorIndex = node.tensorIndices[i];\n                buildMapOfGraph(x, finishedNodes, nodesInProgress, layer, nodeIndex, tensorIndex);\n            }\n            finishedNodes.push(node);\n            while (nodesInProgress.indexOf(node) >= 0) {\n                nodesInProgress.splice(nodesInProgress.indexOf(node), 1);\n            }\n            nodesInDecreasingDepth.push(node);\n        };\n        const finishedNodes = [];\n        const nodesInProgress = [];\n        for (const x of this.outputs) {\n            buildMapOfGraph(x, finishedNodes, nodesInProgress);\n        }\n        const reversedNodesInDecreasingDepth = nodesInDecreasingDepth.slice().reverse();\n        for (const node of reversedNodesInDecreasingDepth) {\n            nodeIDToNode[node.id] = node;\n            // If the depth is not set, the node has no outbound nodes (depth 0).\n            if (!(node.id in nodesDepths)) {\n                nodesDepths[node.id] = 0;\n            }\n            let depth = nodesDepths[node.id];\n            // Update the depth of the corresponding layer\n            const previousDepth = (layersDepths[node.outboundLayer.id] == null ?\n                0 :\n                layersDepths[node.outboundLayer.id]);\n            /*\n              If we've seen this layer before at a higher depth, we should use that\n              depth instead of the node depth.  This is necessary for shared layers\n              that have inputs at different depth levels in the graph.\n            */\n            depth = Math.max(depth, previousDepth);\n            layersDepths[node.outboundLayer.id] = depth;\n            layerIDToLayer[node.outboundLayer.id] = node.outboundLayer;\n            nodesDepths[node.id] = depth;\n            // Update the depth of inbound nodes.\n            for (let i = 0; i < node.inboundLayers.length; i++) {\n                const inboundLayer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const inboundNode = inboundLayer.inboundNodes[nodeIndex];\n                const previousDepth = (nodesDepths[inboundNode.id] == null ? 0 :\n                    nodesDepths[inboundNode.id]);\n                nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth);\n                nodeIDToNode[inboundNode.id] = inboundNode;\n            }\n        }\n        // Build a dict {depth: list of nodes with this depth}\n        const nodesByDepth = {};\n        for (const nodeID in nodesDepths) {\n            const depth = nodesDepths[nodeID];\n            if (!(depth in nodesByDepth)) {\n                nodesByDepth[depth] = [];\n            }\n            nodesByDepth[depth].push(nodeIDToNode[nodeID]);\n        }\n        // Build a dict {depth: list of layers with this depth}\n        const layersByDepth = {};\n        for (const layerID in layersDepths) {\n            const depth = layersDepths[layerID];\n            if (!(depth in layersByDepth)) {\n                layersByDepth[depth] = [];\n            }\n            layersByDepth[depth].push(layerIDToLayer[layerID]);\n        }\n        // Get sorted list of layer depths.\n        let depthKeys = Object.keys(layersByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Set this.layers and this.layersByDepth.\n        this.layers = [];\n        for (const depth of depthKeys) {\n            const layersForDepth = layersByDepth[depth];\n            // Container.layers needs to have a deterministic order:\n            // here we order them by traversal order.\n            layersForDepth.sort((a, b) => {\n                const aIndex = layerIndices[a.id];\n                const bIndex = layerIndices[b.id];\n                if (aIndex < bIndex) {\n                    return -1;\n                }\n                if (aIndex > bIndex) {\n                    return 1;\n                }\n                return 0;\n            });\n            for (const layer of layersForDepth) {\n                if (layer instanceof Container) {\n                    this.internalContainerRefs.push(layer);\n                }\n                this.layers.push(layer);\n            }\n        }\n        this.layersByDepth = layersByDepth;\n        // Get sorted list of node depths;\n        depthKeys = Object.keys(nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Check that all tensors required are computable.\n        // computable_tensors: all tensors in the graph\n        // that can be computed from the inputs provided.\n        const computableTensors = this.inputs.slice();\n        // To provide a better error msg.\n        const layersWithCompleteInput = [];\n        for (const depth of depthKeys) {\n            for (const node of nodesByDepth[depth]) {\n                const layer = node.outboundLayer;\n                if (layer != null) {\n                    for (const x of node.inputTensors) {\n                        if (computableTensors.indexOf(x) === -1) {\n                            throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x}` +\n                                ` at layer \"${layer.name}\". ` +\n                                'The following previous layers were accessed without ' +\n                                `issue: ${layersWithCompleteInput}`);\n                        }\n                    }\n                    for (const x of node.outputTensors) {\n                        computableTensors.push(x);\n                    }\n                    layersWithCompleteInput.push(layer.name);\n                }\n            }\n        }\n        // Set this.containerNodes and this.nodesByDepth.\n        this.nodesByDepth = nodesByDepth;\n        // Ensure name unicity, which will be crucial for serialization\n        // (since serialized nodes refer to layers by their name).\n        const allNames = this.layers.map(x => x.name);\n        for (const name of allNames) {\n            const numOccurrences = allNames.filter(x => x === name).length;\n            if (numOccurrences !== 1) {\n                throw new RuntimeError(`The name \"${name}\" is used ${numOccurrences} times ` +\n                    'in the model. All layer names should be unique. Layer names: ' +\n                    JSON.stringify(allNames));\n            }\n        }\n        // Layer parameters.\n        // The new container starts with a single inbound node\n        // for its inputs, and no outbound nodes.\n        // Will be appended to by future calls to apply().\n        this.outboundNodes = [];\n        // Will be appended to below, and by future calls to apply().\n        this.inboundNodes = [];\n        // Create the node linking internal inputs to internal outputs.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers: [],\n            nodeIndices: [],\n            tensorIndices: [],\n            inputTensors: this.inputs,\n            outputTensors: this.outputs,\n            inputMasks: this.inputs.map(x => null),\n            outputMasks: this.outputs.map(x => null),\n            inputShapes: this.inputs.map(x => x.shape),\n            outputShapes: this.outputs.map(x => x.shape)\n        });\n        this.built = true;\n        this._refCount = 1; // The ref count of a container always start at 1.\n    }\n    assertNotDisposed() {\n        if (this._refCount === 0) {\n            throw new Error(`Container '${this.name}' is already disposed.`);\n        }\n    }\n    /**\n     * Attempt to dispose a LayersModel's weights.\n     *\n     * This method decrease the reference count of the LayersModel object by 1.\n     *\n     * A LayersModel is reference-counted. Its reference count is incremented by 1\n     * when it is first constructed and when it is used as a Layer of another\n     * LayersModel.\n     *\n     * If the reference count of a LayersModel becomes 0, the `dispose` method of\n     * all its constituent `Layer`s will be called.\n     *\n     * Note: If the reference count is greater than 0 after the decrement, the\n     * `dispose` method of its constituent `Layer`s will *not* be called.\n     *\n     * After a LayersModel is disposed, it cannot be used in calls such as\n     * 'predict`, `evaluate` or `fit` anymore.\n     *\n     * @returns A DisposeResult Object with the following fields:\n     *   - refCountAfterDispose: The reference count of the LayersModel after this\n     *     `dispose()` call.\n     *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n     *     during this `dispose()` call.\n     * @throws {Error} If the layer is not built yet, or if the LayersModel has\n     *   already been disposed.\n     */\n    dispose() {\n        this.assertNotDisposed();\n        const result = { refCountAfterDispose: null, numDisposedVariables: 0 };\n        if (--this._refCount === 0) {\n            for (const layer of this.layers) {\n                result.numDisposedVariables += layer.dispose().numDisposedVariables;\n            }\n            // Call dispose on each internally created container layer again to ensure\n            // their refCounts hit zero and their tensors are subsequently deleted.\n            for (const container of this.internalContainerRefs) {\n                result.numDisposedVariables += container.dispose().numDisposedVariables;\n            }\n        }\n        result.refCountAfterDispose = this._refCount;\n        return result;\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this.layers.forEach(layer => {\n            // tslint:disable-next-line:no-any\n            layer._trainableWeights\n                .forEach(w => w.trainable = trainable);\n        });\n        this.trainable_ = trainable;\n    }\n    get trainableWeights() {\n        // Porting Note: This check below is to prevent errors where the\n        //   _trainableWeights inherited from the parent class (Layer) gets\n        //   inadvertently used.\n        if (this._trainableWeights.length > 0) {\n            throw new ValueError('Container instance unexpectedly contains _trainableWeights.' +\n                'The trainable weights of a Container are a union of the ' +\n                'trainable weights of its consituent Layers. Its own ' +\n                '_trainableWeights must remain an empty Array.');\n        }\n        if (!this.trainable) {\n            return [];\n        }\n        let weights = [];\n        for (const layer of this.layers) {\n            weights = weights.concat(layer.trainableWeights);\n        }\n        return weights;\n    }\n    get nonTrainableWeights() {\n        const weights = [];\n        for (const layer of this.layers) {\n            weights.push(...layer.nonTrainableWeights);\n        }\n        if (!this.trainable) {\n            const trainableWeights = [];\n            for (const layer of this.layers) {\n                trainableWeights.push(...layer.trainableWeights);\n            }\n            return trainableWeights.concat(weights);\n        }\n        return weights;\n    }\n    get weights() {\n        return this.trainableWeights.concat(this.nonTrainableWeights);\n    }\n    /**\n     * Loads all layer weights from a JSON object.\n     *\n     * Porting Note: HDF5 weight files cannot be directly loaded in JavaScript /\n     *   TypeScript. The utility script at `scripts/pykeras.py` offers means\n     *   to convert them into JSON strings compatible with this method.\n     * Porting Note: TensorFlow.js Layers supports only loading by name currently.\n     *\n     * @param weights A JSON mapping weight names to weight values as nested\n     *   arrays of numbers, or a `NamedTensorMap`, i.e., a JSON mapping weight\n     *   names to `tf.Tensor` objects.\n     * @param strict Require that the provided weights exactly match those\n     *   required by the container.  Default: `true`.  Passing `false` means that\n     *   extra weights and missing weights will be silently ignored.\n     */\n    loadWeights(weights, strict = true) {\n        const nameToWeight = {};\n        let totalWeightsCount = 0;\n        for (const layer of this.layers) {\n            for (const weight of layer.weights) {\n                if (nameToWeight[weight.originalName] != null) {\n                    throw new ValueError(`Duplicate weight name: ${weight.originalName}`);\n                }\n                nameToWeight[weight.originalName] = weight;\n                totalWeightsCount++;\n            }\n        }\n        const weightValueTuples = [];\n        for (const name in weights) {\n            // TF 2.2.0 added cell name to the weight name in the format of\n            // layer_name/cell_name/weight_name, we need to remove\n            // the inner cell name.\n            let validatedName = name;\n            if (nameToWeight[name] == null) {\n                const tokens = name.split('/');\n                const shortenNameArray = tokens.slice(0, -2).concat([tokens[tokens.length - 1]]);\n                validatedName = shortenNameArray.join('/');\n            }\n            if (nameToWeight[validatedName] != null) {\n                weightValueTuples.push([nameToWeight[validatedName], weights[name]]);\n            }\n            else if (strict) {\n                throw new ValueError(`Provided weight data has no target variable: ${name}`);\n            }\n            delete nameToWeight[validatedName];\n        }\n        if (strict) {\n            // Check that all weights are set.\n            const unsetNames = [];\n            for (const name in nameToWeight) {\n                unsetNames.push(name);\n            }\n            if (unsetNames.length > 0) {\n                throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ` +\n                    `${unsetNames}`);\n            }\n        }\n        batchSetValue(weightValueTuples);\n    }\n    /**\n     * Util shared between different serialization methods.\n     * @returns LayersModel config with Keras version information added.\n     */\n    updatedConfig() {\n        const theConfig = this.getConfig();\n        const modelConfig = {};\n        modelConfig['className'] = this.getClassName();\n        modelConfig['config'] = theConfig;\n        modelConfig['kerasVersion'] = `tfjs-layers ${layersVersion}`;\n        // TODO(nielsene): Replace something like K.backend() once\n        // possible.\n        modelConfig['backend'] = 'TensorFlow.js';\n        return modelConfig;\n    }\n    /**\n     * Returns a JSON string containing the network configuration.\n     *\n     * To load a network from a JSON save file, use\n     * models.modelFromJSON(jsonString);\n     * @param extraJsonArgs Unused in tfjs-layers, maintained for PyKeras\n     * @param returnString Whether the return value should be stringified\n     *    (default: `true`).\n     * @returns a JSON string if `returnString` (default), or a JSON object if\n     *   `!returnString`.\n     */\n    // tslint:disable-next-line:no-any\n    toJSON(unused, returnString = true) {\n        const modelConfig = convertTsToPythonic(this.updatedConfig());\n        return returnString ? JSON.stringify(modelConfig) : modelConfig;\n    }\n    /**\n     * Call the model on new inputs.\n     *\n     * In this case `call` just reapplies all ops in the graph to the new inputs\n     * (e.g. build a new computational graph from the provided inputs).\n     *\n     * @param inputs A tensor or list of tensors.\n     * @param mask A mask or list of masks. A mask can be either a tensor or null\n     *   (no mask).\n     *\n     * @return A tensor if there is a single output, or a list of tensors if there\n     *   are more than one outputs.\n     */\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = generic_utils.toList(inputs);\n            const feedDict = new FeedDict();\n            for (let i = 0; i < this.inputs.length; ++i) {\n                feedDict.add(this.inputs[i], inputs[i]);\n            }\n            return execute(this.outputs, feedDict, kwargs);\n        });\n    }\n    /**\n     * Computes an output mask tensor.\n     *\n     * @param inputs Tensor or list of tensors.\n     * @param mask Tensor or list of tensors.\n     *\n     * @return null or a tensor (or list of tensors, one per output tensor of the\n     * layer).\n     */\n    computeMask(inputs, mask) {\n        return tidy(() => {\n            inputs = generic_utils.toList(inputs);\n            let masks;\n            if (mask == null) {\n                masks = generic_utils.pyListRepeat(null, inputs.length);\n            }\n            else {\n                masks = generic_utils.toList(mask);\n            }\n            // TODO(michaelterry): Add support for mask caching.\n            return this.runInternalGraph(inputs, masks)[1];\n        });\n    }\n    /**\n     * Computes the output shape of the layer.\n     *\n     * Assumes that the layer will be built to match that input shape provided.\n     *\n     * @param inputShape A shape (tuple of integers) or a list of shape tuples\n     *   (one per output tensor of the layer). Shape tuples can include null for\n     *   free dimensions, instead of an integer.\n     */\n    computeOutputShape(inputShape) {\n        const inputShapes = types_utils.normalizeShapeList(inputShape);\n        if (inputShapes.length !== this.inputLayers.length) {\n            throw new ValueError(`Invalid inputShape argument ${inputShape}: ` +\n                `model has ${this.inputLayers.length} tensor inputs.`);\n        }\n        // TODO(michaelterry): Add caching\n        const layersToOutputShapes = {};\n        for (let i = 0; i < inputShapes.length; i++) {\n            const layer = this.inputLayers[i];\n            const inputShape = inputShapes[i];\n            // It's an input layer: computeOutputShape is identity,\n            // and there is only one node and one tensor output.\n            const shapeKey = layer.name + '_0_0';\n            layersToOutputShapes[shapeKey] = inputShape;\n        }\n        const depthKeys = Object.keys(this.nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Iterate over nodes, by depth level.\n        if (depthKeys.length > 1) {\n            for (const depth of depthKeys) {\n                const nodes = this.nodesByDepth[depth];\n                for (const node of nodes) {\n                    // This is always a single layer, never a list.\n                    const layer = node.outboundLayer;\n                    if (this.inputLayers.map(x => x.id).indexOf(layer.id) !== -1) {\n                        // We've already covered the input layers a few lines above.\n                        continue;\n                    }\n                    // Potentially redundant list, same size of node.inputTensors.\n                    const inputShapes = [];\n                    for (let j = 0; j < node.inboundLayers.length; j++) {\n                        const inboundLayer = node.inboundLayers[j];\n                        const nodeIndex = node.nodeIndices[j];\n                        const tensorIndex = node.tensorIndices[j];\n                        const shapeKey = `${inboundLayer.name}_${nodeIndex}_${tensorIndex}`;\n                        const inputShape = layersToOutputShapes[shapeKey];\n                        inputShapes.push(inputShape);\n                    }\n                    const outputShape = layer.computeOutputShape(generic_utils.singletonOrArray(inputShapes));\n                    const outputShapes = types_utils.normalizeShapeList(outputShape);\n                    const nodeIndex = layer.inboundNodes.indexOf(node);\n                    for (let j = 0; j < outputShapes.length; j++) {\n                        const shapeKey = `${layer.name}_${nodeIndex}_${j}`;\n                        layersToOutputShapes[shapeKey] = outputShapes[j];\n                    }\n                }\n            }\n        }\n        // Read final output shapes from layersToOutputShapes.\n        const outputShapes = [];\n        const outputShapeKeys = [];\n        for (let i = 0; i < this.outputLayers.length; i++) {\n            const layer = this.outputLayers[i];\n            const nodeIndex = this.outputLayersNodeIndices[i];\n            const tensorIndex = this.outputLayersTensorIndices[i];\n            const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;\n            outputShapeKeys.push(shapeKey);\n        }\n        for (let i = 0; i < outputShapeKeys.length; i++) {\n            const key = outputShapeKeys[i];\n            generic_utils.assert(key in layersToOutputShapes);\n            outputShapes.push(layersToOutputShapes[key]);\n        }\n        // TODO(michaelterry): Update cache\n        return generic_utils.singletonOrArray(outputShapes);\n    }\n    /**\n     * Computes output tensors for new inputs.\n     *\n     * Note:\n     *   - Expects `inputs` to be a list (potentially with 1 element).\n     *\n     * @param inputs List of tensors\n     * @param masks List of masks (tensors or null).\n     * @return Three lists: outputTensors, outputMasks, outputShapes\n     */\n    runInternalGraph(inputs, masks) {\n        if (masks == null) {\n            masks = generic_utils.pyListRepeat(null, inputs.length);\n        }\n        // Dictionary mapping reference tensors to tuples\n        // (computed tensor, compute mask)\n        // we assume a 1:1 mapping from tensor to mask\n        // TODO: raise exception when a `.computeMask()` call\n        // does not return a list the same size as `call`\n        const tensorMap = {};\n        for (let i = 0; i < this.inputs.length; ++i) {\n            const x = this.inputs[i];\n            const y = inputs[i];\n            const mask = masks[i];\n            tensorMap[x.id] = [y, mask];\n        }\n        const depthKeys = Object.keys(this.nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        for (const depth of depthKeys) {\n            const nodes = this.nodesByDepth[depth];\n            for (const node of nodes) {\n                // This is always a single layer, never a list.\n                const layer = node.outboundLayer;\n                const referenceInputTensors = node.inputTensors;\n                const referenceOutputTensors = node.outputTensors;\n                // If all previous input tensors are available in tensorMap,\n                // then call node.inboundLayer on them.\n                // List of tuples [input, mask]:\n                const computedData = new Array();\n                for (const x of referenceInputTensors) {\n                    if (x.id in tensorMap) {\n                        computedData.push(tensorMap[x.id]);\n                    }\n                }\n                if (computedData.length === referenceInputTensors.length) {\n                    // TODO(michaelterry): Add K.name_scope here, if we need it.\n                    let kwargs = {};\n                    let computedTensors;\n                    let computedMasks;\n                    let outputTensors;\n                    let outputMasks;\n                    // call layer\n                    if (node.callArgs != null) {\n                        kwargs = node.callArgs;\n                    }\n                    if (computedData.length === 1) {\n                        const [computedTensor, computedMask] = computedData[0];\n                        if (kwargs['mask'] == null) {\n                            kwargs['mask'] = computedMask;\n                        }\n                        outputTensors =\n                            generic_utils.toList(layer.call(computedTensor, kwargs));\n                        outputMasks = generic_utils.toList(layer.computeMask(computedTensor, computedMask));\n                        computedTensors = [computedTensor];\n                        computedMasks = [computedMask];\n                    }\n                    else {\n                        computedTensors = computedData.map(x => x[0]);\n                        computedMasks = computedData.map(x => x[1]);\n                        if (kwargs['mask'] == null) {\n                            kwargs['mask'] = computedMasks;\n                        }\n                        outputTensors =\n                            generic_utils.toList(layer.call(computedTensors, kwargs));\n                        outputMasks = generic_utils.toList(layer.computeMask(computedTensors, computedMasks));\n                    }\n                    if (layer.activityRegularizer) {\n                        throw new NotImplementedError('LayersModel invocation with concrete Tensor value(s) in the ' +\n                            'presence of activity regularizer(s) is not supported yet.');\n                    }\n                    // TODO(michaelterry): Add model updates and losses\n                    // Update tensor map.\n                    for (let i = 0; i < referenceOutputTensors.length; ++i) {\n                        const x = referenceOutputTensors[i];\n                        const y = outputTensors[i];\n                        const mask = outputMasks[i];\n                        tensorMap[x.id] = [y, mask];\n                    }\n                }\n            }\n        }\n        const outputTensors = [];\n        const outputMasks = [];\n        const outputShapes = [];\n        for (const x of this.outputs) {\n            generic_utils.assert(x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`);\n            const [tensor, mask] = tensorMap[x.id];\n            outputShapes.push(tensor.shape);\n            outputTensors.push(tensor);\n            outputMasks.push(mask);\n        }\n        // TODO(michaelterry): Add support for caches.\n        return [outputTensors, outputMasks, outputShapes];\n    }\n    /**\n     * Builds a map of internal node keys to node ordering.\n     * Used in serializaion a node orderings may change as unused nodes are\n     * dropped. Porting Note:  This helper method was pulled out of getConfig to\n     * improve readability.\n     * @param layers An array of Layers in the model.\n     * @returns Map of Node Keys to index order within the layer.\n     */\n    buildNodeConversionMap(layers) {\n        const nodeConversionMap = {};\n        let keptNodes;\n        for (const layer of this.layers) {\n            keptNodes = layer instanceof Container ? 1 : 0;\n            for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n                const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n                if (this.containerNodes.has(nodeKey)) {\n                    // i.e. we mark it to be saved\n                    nodeConversionMap[nodeKey] = keptNodes;\n                    keptNodes += 1;\n                }\n            }\n        }\n        return nodeConversionMap;\n    }\n    /**\n     * Retrieves a layer based on either its name (unique) or index.\n     *\n     * Indices are based on order of horizontal graph traversal (bottom-up).\n     *\n     * If both `name` and `index` are specified, `index` takes precedence.\n     *\n     * @param name Name of layer.\n     * @param index Index of layer.\n     * @returns A Layer instance.\n     * @throws ValueError: In case of invalid layer name or index.\n     *\n     * @doc {\n     *    heading: 'Layers',\n     *    subheading: 'Classes',\n     *    namespace: 'layers',\n     *    subclasses: ['LayersModel']\n     * }\n     */\n    getLayer(name, index) {\n        if (index != null) {\n            if (this.layers.length <= index) {\n                throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only ` +\n                    `has ${this.layers.length} layer(s).`);\n            }\n            else {\n                return this.layers[index];\n            }\n        }\n        else {\n            if (name == null) {\n                throw new ValueError('Provide either a layer name or layer index');\n            }\n        }\n        for (const layer of this.layers) {\n            if (layer.name === name) {\n                return layer;\n            }\n        }\n        throw new ValueError(`No such layer: ${name}`);\n    }\n    /**\n     * Retrieves the Container's current loss values.\n     *\n     * Used for regularizers during training.\n     */\n    calculateLosses() {\n        // Porting Node: This is an augmentation to Container.loss in PyKeras.\n        //   In PyKeras, Container.loss returns symbolic tensors. Here a concrete\n        //   Tensor (specifically Scalar) values are returned. This is due to the\n        //   imperative backend.\n        return tidy(() => {\n            const losses = [];\n            for (const layer of this.layers) {\n                for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length; ++nodeIndex) {\n                    const nodeKey = Container.nodeKey(layer, nodeIndex);\n                    if (this.containerNodes.has(nodeKey)) {\n                        losses.push(...layer.calculateLosses());\n                    }\n                }\n            }\n            // TODO(cais): Add any unconditional model-level losses?\n            return losses;\n        });\n    }\n    getConfig() {\n        const config = { name: this.name };\n        // Build a map from layer unique name (self._node_key)\n        // to the index of the nodes that are saved in the config.\n        // Only nodes in container_nodes are saved.\n        const nodeConversionMap = this.buildNodeConversionMap(this.layers);\n        // Serialize and save the layers in layerConfigs\n        const layerConfigs = [];\n        for (const layer of this.layers) {\n            const layerClassName = layer.getClassName();\n            const layerConfig = layer.getConfig();\n            const filteredInboundNodes = [];\n            for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n                const node = layer.inboundNodes[originalNodeIndex];\n                const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n                let kwargs = {};\n                if (this.containerNodes.has(nodeKey)) {\n                    // The node is relevant to the model:\n                    // add to filteredInboundNodes.\n                    if (node.callArgs) {\n                        try {\n                            JSON.stringify(node.callArgs);\n                            kwargs = node.callArgs;\n                        }\n                        catch (err) {\n                            console.warn(`Layer ${layer.name} was passed ` +\n                                `non-serializable keyword arguments: ` +\n                                `${node.callArgs}. They will not be included ` +\n                                `in the serialized model (and thus will be ` +\n                                `missing at deserialization time).`);\n                            kwargs = {};\n                        }\n                    }\n                    if (node.inboundLayers.length > 0) {\n                        const nodeData = [];\n                        for (let i = 0; i < node.inboundLayers.length; i++) {\n                            const inboundLayer = node.inboundLayers[i];\n                            const nodeIndex = node.nodeIndices[i];\n                            const tensorIndex = node.tensorIndices[i];\n                            const nodeKey = Container.nodeKey(inboundLayer, nodeIndex);\n                            let newNodeIndex = nodeConversionMap[nodeKey];\n                            if (newNodeIndex == null) {\n                                newNodeIndex = 0;\n                            }\n                            nodeData.push([inboundLayer.name, newNodeIndex, tensorIndex, kwargs]);\n                        }\n                        filteredInboundNodes.push(nodeData);\n                    }\n                }\n            }\n            const dict = {};\n            dict['name'] = layer.name;\n            dict['className'] = layerClassName;\n            dict['config'] = layerConfig;\n            dict['inboundNodes'] = filteredInboundNodes;\n            layerConfigs.push(dict);\n        }\n        config['layers'] = layerConfigs;\n        // Gather info about inputs and outputs\n        const modelInputs = [];\n        for (let i = 0; i < this.inputLayers.length; i++) {\n            const layer = this.inputLayers[i];\n            const nodeIndex = this.inputLayersNodeIndices[i];\n            const nodeKey = Container.nodeKey(layer, nodeIndex);\n            if (!this.containerNodes.has(nodeKey)) {\n                continue;\n            }\n            let newNodeIndex = nodeConversionMap[nodeKey];\n            if (newNodeIndex === null || newNodeIndex === undefined) {\n                newNodeIndex = 0;\n            }\n            const tensorIndex = this.inputLayersTensorIndices[i];\n            modelInputs.push([layer.name, newNodeIndex, tensorIndex]);\n        }\n        config['inputLayers'] = modelInputs;\n        const modelOutputs = [];\n        for (let i = 0; i < this.outputLayers.length; i++) {\n            const layer = this.outputLayers[i];\n            const nodeIndex = this.outputLayersNodeIndices[i];\n            const nodeKey = Container.nodeKey(layer, nodeIndex);\n            if (!this.containerNodes.has(nodeKey)) {\n                continue;\n            }\n            let newNodeIndex = nodeConversionMap[nodeKey];\n            if (newNodeIndex === null || newNodeIndex === undefined) {\n                newNodeIndex = 0;\n            }\n            const tensorIndex = this.outputLayersTensorIndices[i];\n            modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);\n        }\n        config['outputLayers'] = modelOutputs;\n        return config;\n    }\n    /**\n     * Instantiates a LayersModel from its config (output of `get_config()`).\n     * @param cls the class to create\n     * @param config LayersModel config dictionary.\n     * @param customObjects An optional dictionary of custom objects.\n     * @param fastWeightInit Optional flag to use fast weight initialization\n     *   during deserialization. This is applicable to cases in which\n     *   the initialization will be immediately overwritten by loaded weight\n     *   values. Default: `false`.\n     * @returns A LayersModel instance.\n     * @throws ValueError: In case of improperly formatted config dict.\n     */\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n        // Layer instances created during\n        // the graph reconstruction process\n        const createdLayers = {};\n        // Dictionary mapping layer instances to\n        // node data that specifies a layer call.\n        // It acts as a queue that maintains any unprocessed\n        // layer call until it becomes possible to process it\n        // (i.e. until the input tensors to the call all exist).\n        const unprocessedNodes = {};\n        function addUnprocessedNode(layer, nodeData) {\n            if (!(layer.name in unprocessedNodes)) {\n                unprocessedNodes[layer.name] = [nodeData];\n            }\n            else {\n                unprocessedNodes[layer.name].push(nodeData);\n            }\n        }\n        function processNode(layer, nodeData) {\n            const inputTensors = [];\n            let kwargs;\n            for (const inputData of nodeData) {\n                const inboundLayerName = inputData[0];\n                const inboundNodeIndex = inputData[1];\n                const inboundTensorIndex = inputData[2];\n                kwargs = inputData[3] == null ?\n                    {} :\n                    inputData[3];\n                if (!(inboundLayerName in createdLayers)) {\n                    addUnprocessedNode(layer, nodeData);\n                    return;\n                }\n                const inboundLayer = createdLayers[inboundLayerName];\n                if (inboundLayer.inboundNodes.length <= inboundNodeIndex) {\n                    addUnprocessedNode(layer, nodeData);\n                    return;\n                }\n                const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex];\n                inputTensors.push(inboundNode.outputTensors[inboundTensorIndex]);\n            }\n            // Call layer on its inputs, thus creating the node\n            // and building the layer if needed.\n            // Note: This has Eager vs Graph Implications.\n            if (inputTensors.length > 0) {\n                layer.apply(generic_utils.singletonOrArray(inputTensors), kwargs); // was ** kwargs\n            }\n        }\n        /**\n         * Deserialize a layer, then call it on appropriate inputs.\n         * @param layerData: layer config dict.\n         * @throws ValueError: In case of improperly formatted `layer_data`\n         * dict.\n         */\n        function processLayer(layerData) {\n            const layerName = layerData['name'];\n            // Instantiate layer.\n            const layer = deserializeLayer(layerData, config['customObjects'] != null ?\n                config['customObjects'] :\n                {});\n            layer.setFastWeightInitDuringBuild(fastWeightInit);\n            createdLayers[layerName] = layer;\n            // Gather layer inputs.\n            const inboundNodesData = layerData['inboundNodes'];\n            inboundNodesData.forEach(nodeData => {\n                if (!(nodeData instanceof Array)) {\n                    throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);\n                }\n                // We don't process nodes (i.e. make layer calls)\n                // on the fly because the inbound node may not yet exist,\n                // in case of layer shared at different topological depths\n                // (e.g.a model such as A(B(A(B(x)))))\n                addUnprocessedNode(layer, nodeData);\n            });\n        }\n        // First, we create all layers and enqueue nodes to be processed.\n        const name = config['name'];\n        const layersFromConfig = config['layers'];\n        for (const layerData of layersFromConfig) {\n            processLayer(layerData);\n        }\n        // Then we process nodes in order of layer depth.\n        // Nodes that cannot yet be processed(if the inbound node\n        // does not yet exist) are re - enqueued, and the process\n        // is repeated until all nodes are processed.\n        while (!generic_utils.isObjectEmpty(unprocessedNodes)) {\n            for (const layerData of layersFromConfig) {\n                const layer = createdLayers[layerData['name']];\n                if (layer.name in unprocessedNodes) {\n                    const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name];\n                    delete unprocessedNodes[layer.name];\n                    for (const nodeData of currentUnprocessedNodesForLayer) {\n                        processNode(layer, nodeData);\n                    }\n                }\n            }\n        }\n        const inputTensors = [];\n        const outputTensors = [];\n        const inputLayersFromConfig = config['inputLayers'];\n        for (const layerData of inputLayersFromConfig) {\n            const layerName = layerData[0];\n            const nodeIndex = layerData[1];\n            const tensorIndex = layerData[2];\n            generic_utils.assert(layerName in createdLayers);\n            const layer = createdLayers[layerName];\n            const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n            inputTensors.push(layerOutputTensors[tensorIndex]);\n        }\n        const outputLayersFromConfig = config['outputLayers'];\n        for (const layerData of outputLayersFromConfig) {\n            const layerName = layerData[0];\n            const nodeIndex = layerData[1];\n            const tensorIndex = layerData[2];\n            generic_utils.assert(layerName in createdLayers);\n            const layer = createdLayers[layerName];\n            const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n            outputTensors.push(layerOutputTensors[tensorIndex]);\n        }\n        return new cls({ inputs: inputTensors, outputs: outputTensors, name });\n    }\n    /**\n     * Determine whether the container is stateful.\n     *\n     * Porting Note: this is the equivalent of the stateful @property of\n     *   the Container class in PyKeras.\n     */\n    get stateful() {\n        // Porting Note: This check is to prevent inadvertent setting of the\n        //   _stateful property of the Container instance.\n        if (this._stateful) {\n            throw new ValueError('Container instance unexpectedly has _stateful = true. The ' +\n                'statefulness of a Container is determined by the Layers it ' +\n                'contains. Its _stateful property must remain the default false.');\n        }\n        for (const layer of this.layers) {\n            if (layer.stateful) {\n                return true;\n            }\n        }\n        return false;\n    }\n    /**\n     * Reset the state of all stateful constituent layers (if any).\n     *\n     * Examples of stateful layers include RNN layers whose `stateful` property\n     * is set as `true`.\n     */\n    resetStates() {\n        tidy(() => {\n            this.layers.forEach(layer => {\n                // tslint:disable:no-any\n                if (layer.stateful) {\n                    layer.resetStates();\n                }\n                // tslint:enable:no-any\n            });\n        });\n    }\n}\n//# sourceMappingURL=container.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { dispose, mul, tensor1d, tidy } from '@tensorflow/tfjs-core';\nfunction standardizeSampleOrClassWeights(xWeight, outputNames, weightType) {\n    const numOutputs = outputNames.length;\n    if (xWeight == null || (Array.isArray(xWeight) && xWeight.length === 0)) {\n        return outputNames.map(name => null);\n    }\n    if (numOutputs === 1) {\n        if (Array.isArray(xWeight) && xWeight.length === 1) {\n            return xWeight;\n        }\n        else if (typeof xWeight === 'object' && outputNames[0] in xWeight) {\n            return [xWeight[outputNames[0]]];\n        }\n        else {\n            return [xWeight];\n        }\n    }\n    if (Array.isArray(xWeight)) {\n        if (xWeight.length !== numOutputs) {\n            throw new Error(`Provided ${weightType} is an array of ${xWeight.length} ` +\n                `element(s), but the model has ${numOutputs} outputs. ` +\n                `Make sure a set of weights is provided for each model output.`);\n        }\n        return xWeight;\n    }\n    else if (typeof xWeight === 'object' && Object.keys(xWeight).length > 0 &&\n        typeof xWeight[Object.keys(xWeight)[0]] ===\n            'object') {\n        const output = [];\n        outputNames.forEach(outputName => {\n            if (outputName in xWeight) {\n                output.push(xWeight[outputName]);\n            }\n            else {\n                output.push(null);\n            }\n        });\n        return output;\n    }\n    else {\n        throw new Error(`The model has multiple (${numOutputs}) outputs, ` +\n            `so ${weightType} must be either an array with ` +\n            `${numOutputs} elements or an object with ${outputNames} keys. ` +\n            `Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);\n    }\n}\n/**\n * Standardize class weighting objects.\n *\n * This function takes a single class-weighting object, an array of them,\n * or a map from output name to class-weighting object. It compares it to the\n * output name(s) of the model, base on which it outputs an array of\n * class-weighting objects of which the length matches the number of outputs.\n *\n * @param classWeight Input class-weighting object(s).\n * @param outputNames All output name(s) of the model.\n * @return An array of class-weighting objects. The length of the array matches\n *   the model's number of outputs.\n */\nexport function standardizeClassWeights(classWeight, outputNames) {\n    return standardizeSampleOrClassWeights(classWeight, outputNames, 'classWeight');\n}\nexport function standardizeSampleWeights(classWeight, outputNames) {\n    return standardizeSampleOrClassWeights(classWeight, outputNames, 'sampleWeight');\n}\n/**\n * Standardize by-sample and/or by-class weights for training.\n *\n * Note that this function operates on one model output at a time. For a model\n * with multiple outputs, you must call this function multiple times.\n *\n * @param y The target tensor that the by-sample and/or by-class weight is for.\n *     The values of y are assumed to encode the classes, either directly\n *     as an integer index, or as one-hot encoding.\n * @param sampleWeight By-sample weights.\n * @param classWeight By-class weights: an object mapping class indices\n *     (integers) to a weight (float) to apply to the model's loss for the\n *     samples from this class during training. This can be useful to tell the\n *     model to \"pay more attention\" to samples from an under-represented class.\n * @param sampleWeightMode The mode for the sample weights.\n * @return A Promise of weight tensor, of which the size of the first dimension\n *     matches that of `y`.\n */\nexport async function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) {\n    if (sampleWeight != null || sampleWeightMode != null) {\n        // TODO(cais): Once 'temporal' mode is implemented, document it in the doc\n        // string.\n        throw new Error('Support sampleWeight is not implemented yet');\n    }\n    if (classWeight != null) {\n        // Apply class weights per sample.\n        const yClasses = tidy(() => {\n            if (y.shape.length === 1) {\n                // Assume class indices.\n                return y.clone();\n            }\n            else if (y.shape.length === 2) {\n                if (y.shape[1] > 1) {\n                    // Assume one-hot encoding of classes.\n                    const axis = 1;\n                    return y.argMax(axis);\n                }\n                else if (y.shape[1] === 1) {\n                    // Class index.\n                    return y.reshape([y.shape[0]]);\n                }\n                else {\n                    throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) ` +\n                        `during handling of class weights. The size is expected to be ` +\n                        `>= 1.`);\n                }\n            }\n            else {\n                throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during ` +\n                    `handling of class weights. The rank is expected to be 1 or 2.`);\n            }\n        });\n        const yClassIndices = Array.from(await yClasses.data());\n        dispose(yClasses);\n        const classSampleWeight = [];\n        yClassIndices.forEach(classIndex => {\n            if (classWeight[classIndex] == null) {\n                throw new Error(`classWeight must contain all classes in the training data. ` +\n                    `The class ${classIndex} exists in the data but not in ` +\n                    `classWeight`);\n            }\n            else {\n                classSampleWeight.push(classWeight[classIndex]);\n            }\n        });\n        return tensor1d(classSampleWeight, 'float32');\n    }\n    else {\n        return null;\n    }\n}\n/**\n * Apply per-sample weights on the loss values from a number of samples.\n *\n * @param losses Loss tensor of shape `[batchSize]`.\n * @param sampleWeights Per-sample weight tensor of shape `[batchSize]`.\n * @returns Tensor of the same shape as`losses`.\n */\nexport function computeWeightedLoss(losses, sampleWeights) {\n    return mul(losses, sampleWeights);\n}\n//# sourceMappingURL=training_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Interfaces and methods for training models using TensorFlow.js datasets.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { scalar } from '@tensorflow/tfjs-core';\nimport { configureCallbacks, standardizeCallbacks } from '../base_callbacks';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { disposeTensorsInLogs } from '../logs';\nimport { singletonOrArray, toList } from '../utils/generic_utils';\nimport { standardizeClassWeights, standardizeWeights } from './training_utils';\n// Default batch size used during tensor-based validation.\nconst DEFAULT_VALIDATION_BATCH_SIZE = 32;\n/**\n * Standardize the output of a dataset iterator for use by\n * LayersModel.fitDataset().\n *\n * @param model: A `tf.LayersModel` object.\n * @param iteratorOut The output of a dataset iterator. It is required to be\n *   an object of the form `{xs: TensorOrArrayOrMap, ys:\n * TensorOrArrayOrMap}`, where `TensorOrArrayOrMap` is a single `tf.Tensor`,\n * a `tf.Tensor[]`, or a flat map from string names to `tf.Tensor`s.\n * @returns A flat array of `tf.Tensor` objects: the input `tf.Tensor`s\n *   followed by the target `tf.Tensor`s.  When `tf.Tensor`s are provided\n *   as a map, the order in the resulting array is taken from the `inputNames`\n *   and `outputNames` of the model.\n */\nfunction standardizeDataIteratorOutput(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, iteratorOut) {\n    let xs;\n    let ys;\n    const iteratorOutObj = iteratorOut;\n    xs = iteratorOutObj['xs'];\n    ys = iteratorOutObj['ys'];\n    tfc.util.assert(xs != null && ys != null, () => 'A Dataset iterator for fitDataset() is expected to generate ' +\n        'objects of the form `{xs: xVal, ys: yVal}`, where the two ' +\n        'values may be `tf.Tensor`, an array of Tensors, or a map of ' +\n        'string to Tensor.  The provided Dataset instead generates ' +\n        `${iteratorOut}`);\n    const flattenedXs = flattenTensorOrArrayOrMap('input', model.inputNames, xs);\n    const flattenedYs = flattenTensorOrArrayOrMap('output', model.outputNames, ys);\n    const batchSize = flattenedXs[0].shape[0];\n    tfc.util.assert(flattenedXs.length === model.inputs.length, () => `LayersModel has ${model.inputs.length} inputs, but the dataset ` +\n        `provides ${flattenedXs.length} inputs.  (Expected input keys: ` +\n        `${JSON.stringify(model.inputNames)})`);\n    tfc.util.assert(flattenedYs.length === model.outputs.length, () => `LayersModel has ${model.outputs.length} outputs, but the dataset ` +\n        `provides ${flattenedYs.length} outputs.  (Expected output keys: ` +\n        `${JSON.stringify(model.outputNames)})`);\n    for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {\n        tfc.util.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ` +\n            `${model.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; ` +\n            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);\n    }\n    for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {\n        tfc.util.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ` +\n            `${model.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; ` +\n            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);\n    }\n    return { xs: flattenedXs, ys: flattenedYs };\n}\nfunction flattenTensorOrArrayOrMap(inputOrOutput, names, values) {\n    if (values instanceof tfc.Tensor) {\n        return [values];\n    }\n    else if (Array.isArray(values)) {\n        tfc.util.assert(values.length === names.length, () => `Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`);\n        return values;\n    }\n    else {\n        const result = [];\n        // Check that all the required keys are available.\n        for (const name of names) {\n            if (values[name] == null) {\n                throw new ValueError(`The feature data generated by the dataset lacks the required ` +\n                    `${inputOrOutput} key '${name}'.`);\n            }\n            result.push(values[name]);\n        }\n        return result;\n    }\n}\nfunction standardizeTensorValidationData(data) {\n    if (data.length === 3) {\n        throw new NotImplementedError('Validation with sample weights is not implemented yet.');\n    }\n    return { xs: data[0], ys: data[1] };\n}\nexport async function fitDataset(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, dataset, args) {\n    const hasBatchesPerEpoch = args.batchesPerEpoch != null;\n    tfc.util.assert(model.optimizer != null, () => 'You must compile a model before training/testing. Use ' +\n        'LayersModel.compile(modelCompileConfig).');\n    tfc.util.assert(args != null, () => `For fitDataset(), the 2nd argument (config) is required, ` +\n        `but it is not provided in this call.`);\n    tfc.util.assert(args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs), () => `For fitDataset(), config.epochs is expected to be a positive ` +\n        `integer, but got ${args.epochs}`);\n    tfc.util.assert(!hasBatchesPerEpoch ||\n        (args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch)), () => `For fitDataset(), config.batchesPerEpoch is expected to be a ` +\n        `positive integer if specified, but got ${args.batchesPerEpoch}`);\n    tfc.util.assert(\n    // tslint:disable-next-line:no-any\n    args['validationSplit'] == null, () => '`validationSplit` is not supported by `fitDataset()`. ' +\n        'Use validationData instead.');\n    if (model.isTraining) {\n        throw new Error('Cannot start training because another fit() call is ongoing.');\n    }\n    model.isTraining = true;\n    try {\n        const doValidation = args.validationData != null;\n        let valXs;\n        let valYs;\n        if (doValidation) {\n            if (isDatasetObject(args.validationData)) {\n                tfc.util.assert(args.validationBatches == null ||\n                    (args.validationBatches > 0 &&\n                        Number.isInteger(args.validationBatches)), () => `For fitDataset() with dataset-based validation, ` +\n                    `config.validationBatches is expected not to be provided, ` +\n                    `or to be a positive integer, ` +\n                    `but got ${args.validationBatches}`);\n            }\n            else {\n                const validationData = standardizeTensorValidationData(args.validationData);\n                valXs = validationData.xs;\n                valYs = validationData.ys;\n            }\n        }\n        const trainFunction = model.makeTrainFunction();\n        const outLabels = model.getDedupedMetricsNames();\n        let callbackMetrics;\n        if (doValidation) {\n            callbackMetrics =\n                outLabels.slice().concat(outLabels.map(n => 'val_' + n));\n        }\n        else {\n            callbackMetrics = outLabels.slice();\n        }\n        const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);\n        const verbose = args.verbose == null ? 1 : args.verbose;\n        const { callbackList, history } = configureCallbacks(callbacks, verbose, args.epochs, null, null, getStepsPerEpoch(dataset, args), null, // Batch size determined by the dataset itself.\n        doValidation, callbackMetrics);\n        callbackList.setModel(model);\n        model.history = history;\n        await callbackList.onTrainBegin();\n        model.stopTraining_ = false;\n        let epoch = args.initialEpoch == null ? 0 : args.initialEpoch;\n        let dataIterator = await dataset.iterator();\n        while (epoch < args.epochs) {\n            const epochLogs = {};\n            await callbackList.onEpochBegin(epoch);\n            let stepsDone = 0;\n            let batchIndex = 0;\n            if (!hasBatchesPerEpoch) {\n                dataIterator = await dataset.iterator();\n            }\n            while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) {\n                const iteratorOut = await dataIterator.next();\n                // If `batchesPerEpoch` is specified, the dataset should not be\n                // exhausted until all epoches are done.\n                if (hasBatchesPerEpoch && iteratorOut.done) {\n                    console.warn('You provided `batchesPerEpoch` as ' +\n                        `${args.batchesPerEpoch}, ` +\n                        'but your dataset iterator ran out of data after ' +\n                        `${stepsDone} batches; ` +\n                        'interrupting training. Make sure that your ' +\n                        'dataset can generate at least `batchesPerEpoch * epochs` ' +\n                        'batches (in this case, ' +\n                        `${args.batchesPerEpoch * args.epochs} batches). ` +\n                        'You may need to use the repeat() function when building ' +\n                        'your dataset.');\n                    break;\n                }\n                if (iteratorOut.value != null) {\n                    const { xs, ys } = standardizeDataIteratorOutput(model, iteratorOut.value);\n                    const batchLogs = {};\n                    batchLogs['batch'] = batchIndex;\n                    batchLogs['size'] = xs[0].shape[0];\n                    await callbackList.onBatchBegin(batchIndex, batchLogs);\n                    const sampleWeights = [];\n                    if (args.classWeight != null) {\n                        const standardClassWeights = standardizeClassWeights(args.classWeight, model.outputNames);\n                        for (let i = 0; i < standardClassWeights.length; ++i) {\n                            sampleWeights.push(await standardizeWeights(ys[i], null, standardClassWeights[i]));\n                        }\n                    }\n                    // Train on batch.\n                    const ins = xs.concat(ys).concat(sampleWeights);\n                    const outs = trainFunction(ins);\n                    tfc.dispose(ins);\n                    for (let i = 0; i < outLabels.length; ++i) {\n                        const label = outLabels[i];\n                        const out = outs[i];\n                        batchLogs[label] = out;\n                        tfc.keep(out);\n                    }\n                    await callbackList.onBatchEnd(batchIndex, batchLogs);\n                    disposeTensorsInLogs(batchLogs);\n                    batchIndex++;\n                    stepsDone++;\n                }\n                if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch :\n                    iteratorOut.done) {\n                    // Epoch finished. Perform validation.\n                    if (doValidation) {\n                        let valOuts;\n                        if (isDatasetObject(args.validationData)) {\n                            valOuts = toList(await model.evaluateDataset(args.validationData, { batches: args.validationBatches }));\n                        }\n                        else {\n                            valOuts = toList(model.evaluate(valXs, valYs, {\n                                batchSize: args.validationBatchSize == null ?\n                                    DEFAULT_VALIDATION_BATCH_SIZE :\n                                    args.validationBatchSize,\n                                verbose: 0\n                            }));\n                        }\n                        for (let i = 0; i < model.metricsNames.length; ++i) {\n                            epochLogs[`val_${model.metricsNames[i]}`] = valOuts[i];\n                        }\n                    }\n                    // Call `break` to exit one epoch lopp after validation is done. If\n                    // config.batchesPerEpoch is specified, an epoch while loop will\n                    // stop when `stepsDone >= config.batchesPerEpoch`. When\n                    // config.batchesPerEpoch is not provided, the following `break` is\n                    // required to exit the while lopp after dataset is exhausted.\n                    break;\n                }\n                if (model.stopTraining_) {\n                    break;\n                }\n            }\n            await callbackList.onEpochEnd(epoch, epochLogs);\n            epoch++;\n            if (model.stopTraining_) {\n                break;\n            }\n        }\n        await callbackList.onTrainEnd();\n        await model.history.syncData();\n        return model.history;\n    }\n    finally {\n        model.isTraining = false;\n    }\n}\n/** Helper function that determines number of steps (batches) per epoch. */\nfunction getStepsPerEpoch(dataset, args) {\n    // Attempt to determine # of batches in an epoch.\n    let stepsPerEpoch = null;\n    if (args.batchesPerEpoch != null) {\n        stepsPerEpoch = args.batchesPerEpoch;\n    }\n    else if (Number.isFinite(dataset.size)) {\n        stepsPerEpoch = dataset.size;\n    }\n    return stepsPerEpoch;\n}\n// Check if provided object is a Dataset object by checking its .iterator\n// element.\nfunction isDatasetObject(dataset) {\n    return (typeof dataset.iterator === 'function');\n}\n// Check if provided object is a LazyIterator object by checking it's .next\n// element.\nfunction isLazyIteratorObject(iterator) {\n    return (typeof iterator.next === 'function');\n}\nexport async function evaluateDataset(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, dataset, args) {\n    args = args || {};\n    const hasBatches = args.batches != null;\n    const f = model.testFunction;\n    let outs = [];\n    if (args.verbose > 0) {\n        throw new NotImplementedError('Verbose mode is not implemented yet.');\n    }\n    tfc.util.assert(!hasBatches || (args.batches > 0 && Number.isInteger(args.batches)), () => 'Test loop expects `batches` to be a positive integer, but ' +\n        `received ${JSON.stringify(args.batches)}`);\n    const dataIterator = isLazyIteratorObject(dataset) ?\n        dataset :\n        await dataset.iterator();\n    // Keeps track of number of examples used in this evaluation.\n    let numExamples = 0;\n    let batch = 0;\n    while (hasBatches ? batch < args.batches : true) {\n        const iteratorOut = await dataIterator.next();\n        outs = tfc.tidy(() => {\n            if (iteratorOut.value) {\n                // TODO(cais): Once real dataset is available, use\n                //   `map(x => standardizeDataIteratorOutput(model, x).map(f)`.\n                const { xs, ys } = standardizeDataIteratorOutput(model, iteratorOut.value);\n                const xsAndYs = xs.concat(ys);\n                const batchOuts = tfc.tidy(() => f(xsAndYs));\n                tfc.dispose(xsAndYs);\n                if (batch === 0) {\n                    for (let i = 0; i < batchOuts.length; ++i) {\n                        outs.push(scalar(0));\n                    }\n                }\n                const batchSize = xsAndYs[0].shape[0];\n                for (let i = 0; i < batchOuts.length; ++i) {\n                    const batchOut = batchOuts[i];\n                    const oldScalar = outs[i];\n                    outs[i] =\n                        tfc.tidy(() => tfc.add(outs[i], tfc.mul(batchSize, batchOut)));\n                    if (batch > 0) {\n                        tfc.dispose(oldScalar);\n                    }\n                }\n                tfc.dispose(batchOuts);\n                numExamples += batchSize;\n                ++batch;\n            }\n            return outs;\n        });\n        if (iteratorOut.done) {\n            if (hasBatches) {\n                console.warn('Your dataset iterator ran out of data during evaluateDataset(). ' +\n                    'Interrupting evalution. Make sure that your ' +\n                    'dataset can generate at least `batches` ' +\n                    `batches (in this case, ${args.batches} batches). ` +\n                    'You may need to use the repeat() function when building ' +\n                    'your dataset.');\n            }\n            break;\n        }\n    }\n    for (let i = 0; i < outs.length; ++i) {\n        const oldScalar = outs[i];\n        outs[i] = tfc.div(outs[i], numExamples);\n        tfc.dispose(oldScalar);\n    }\n    return singletonOrArray(outs);\n}\n//# sourceMappingURL=training_dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Interfaces and methods for training models using tf.Tensor objects.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { Tensor, tensor1d, util } from '@tensorflow/tfjs-core';\nimport { expandDims, gather, sliceAlongFirstAxis } from '../backend/tfjs_backend';\nimport { configureCallbacks, standardizeCallbacks } from '../base_callbacks';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { disposeTensorsInLogs } from '../logs';\nimport { range } from '../utils/math_utils';\nexport function checkBatchSize(batchSize) {\n    tfc.util.assert(batchSize > 0 && Number.isInteger(batchSize), () => `batchSize is required to be a positive integer, but got ${batchSize}`);\n}\n/**\n * Slice a Tensor or an Array of Tensors, by start and stop indices.\n *\n * Porting Note: The `_slice_arrays` function in PyKeras is covered by this\n *   function and `sliceArraysByIndices()` together.\n *\n * @param arrays: the input.\n * @param start: the starting index (inclusive).\n * @param stop: the stopping index (exclusive).\n * @returns The result of the slicing. If `arrays` is an `Array` of\n *   `tf.Tensor`s, the slicing will be applied to all elements of the `Array`\n *   in the same way.\n */\nexport function sliceArrays(arrays, start, stop) {\n    if (arrays == null) {\n        return [null];\n    }\n    else if (Array.isArray(arrays)) {\n        return arrays.map(array => sliceAlongFirstAxis(array, start, stop - start));\n    }\n    else { // Tensor.\n        return sliceAlongFirstAxis(arrays, start, stop - start);\n    }\n}\n/**\n * Slice a Tensor or an Array of Tensors, by random-order indices.\n *\n * Porting Note: The `_slice_arrays` function in PyKeras is covered by this\n *   function and `sliceArrays()` together.\n *\n * @param arrays The input `tf.Tensor` or `Array` of `tf.Tensor`s to slice.\n *   If an `Array` of `tf.Tensor`s, all `tf.Tensor`s will be sliced in the\n *   same fashion.\n * @param indices The indices to use for slicing along the first (batch)\n *   dimension.\n * @returns Result(s) of the slicing.\n */\nexport function sliceArraysByIndices(arrays, indices) {\n    return tfc.tidy(() => {\n        if (arrays == null) {\n            return null;\n        }\n        else if (Array.isArray(arrays)) {\n            return arrays.map(array => sliceArraysByIndices(array, indices));\n        }\n        else {\n            // TODO(cais): indices should be a pre-constructed Tensor1D to avoid\n            //   tensor1d() calls.\n            return gather(arrays, indices.dtype === 'int32' ? indices : indices.toInt());\n        }\n    });\n}\n/**\n * Returns a list of batch indices (tuples of indices).\n * @param size: Integer, total size of the data to slice into batches.\n * @param batchSize: Integer, batch size.\n * @returns An Array of [batchStart, batchEnd] tuples. batchStart is\n *   inclusive; batchEnd is exclusive. I.e., each batch consists of indices x\n *   that satisfy batchStart <= x < batchEnd.\n */\nexport function makeBatches(size, batchSize) {\n    const output = [];\n    let batchStart = 0;\n    let batchEnd = null;\n    while (batchStart < size) {\n        batchEnd = batchStart + batchSize;\n        if (batchEnd >= size) {\n            batchEnd = size;\n        }\n        output.push([batchStart, batchEnd]);\n        batchStart = batchEnd;\n    }\n    return output;\n}\n/**\n * Abstract fit function for `f(ins)`.\n * @param f A Function returning a list of tensors. For training, this\n *   function is expected to perform the updates to the variables.\n * @param ins List of tensors to be fed to `f`.\n * @param outLabels List of strings, display names of the outputs of `f`.\n * @param batchSize Integer batch size or `== null` if unknown. Default : 32.\n * @param epochs Number of times to iterate over the data. Default : 1.\n * @param verbose Verbosity mode: 0, 1, or 2. Default: 1.\n * @param callbacks List of callbacks to be called during training.\n * @param valF Function to call for validation.\n * @param valIns List of tensors to be fed to `valF`.\n * @param shuffle Whether to shuffle the data at the beginning of every\n * epoch. Default : true.\n * @param callbackMetrics List of strings, the display names of the metrics\n *   passed to the callbacks. They should be the concatenation of the\n *   display names of the outputs of `f` and the list of display names\n *   of the outputs of `valF`.\n * @param initialEpoch Epoch at which to start training (useful for\n *   resuming a previous training run). Default : 0.\n * @param stepsPerEpoch Total number of steps (batches on samples) before\n *   declaring one epoch finished and starting the next epoch. Ignored with\n *   the default value of `undefined` or `null`.\n * @param validationSteps Number of steps to run validation for (only if\n *   doing validation from data tensors). Not applicable for tfjs-layers.\n * @returns A `History` object.\n */\nasync function fitLoop(\n// Type `model` as `any` here to avoid circular dependency w/ training.ts.\n// tslint:disable-next-line:no-any\nmodel, f, ins, outLabels, batchSize, epochs, verbose, callbacks, valF, valIns, shuffle, callbackMetrics, initialEpoch, stepsPerEpoch, validationSteps) {\n    if (batchSize == null) {\n        batchSize = 32;\n    }\n    if (epochs == null) {\n        epochs = 1;\n    }\n    if (shuffle == null) {\n        shuffle = true;\n    }\n    if (initialEpoch == null) {\n        initialEpoch = 0;\n    }\n    // TODO(cais): Change const to let below when implementing validation.\n    let doValidation = false;\n    if (valF != null && valIns != null) {\n        doValidation = true;\n        // TODO(cais): verbose message.\n    }\n    if (validationSteps != null) {\n        doValidation = true;\n        if (stepsPerEpoch == null) {\n            throw new ValueError('Can only use `validationSteps` when doing step-wise training, ' +\n                'i.e., `stepsPerEpoch` must be set.');\n        }\n    }\n    const numTrainSamples = model.checkNumSamples(ins, batchSize, stepsPerEpoch, 'steps_per_epoch');\n    let indexArray;\n    if (numTrainSamples != null) {\n        indexArray = range(0, numTrainSamples);\n    }\n    if (verbose == null) {\n        verbose = 1;\n    }\n    const { callbackList, history } = configureCallbacks(callbacks, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics);\n    callbackList.setModel(model);\n    model.history = history;\n    await callbackList.onTrainBegin();\n    model.stopTraining_ = false;\n    // TODO(cais): Take care of callbacks.validation_data as in PyKeras.\n    // TODO(cais): Pre-convert feeds for performance as in PyKeras.\n    for (let epoch = initialEpoch; epoch < epochs; ++epoch) {\n        await callbackList.onEpochBegin(epoch);\n        const epochLogs = {};\n        if (stepsPerEpoch != null) {\n            throw new NotImplementedError('stepsPerEpoch mode is not implemented yet.');\n        }\n        else {\n            if (shuffle === 'batch') {\n                throw new NotImplementedError('batch shuffling is not implemneted yet');\n            }\n            else if (shuffle) {\n                util.shuffle(indexArray);\n            }\n            // Convert the potentially shuffled indices to Tensor1D, to avoid the\n            // cost of repeated creation of Array1Ds later on.\n            const epochIndexArray1D = tensor1d(indexArray);\n            const batches = makeBatches(numTrainSamples, batchSize);\n            for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                const batchLogs = {};\n                await callbackList.onBatchBegin(batchIndex, batchLogs);\n                tfc.tidy(() => {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    const batchIds = sliceAlongFirstAxis(epochIndexArray1D, batchStart, batchEnd - batchStart);\n                    batchLogs['batch'] = batchIndex;\n                    batchLogs['size'] = batchEnd - batchStart;\n                    // TODO(cais): In ins, train flag can be a number, instead of an\n                    //   Tensor? Do we need to handle this in tfjs-layers?\n                    const insBatch = sliceArraysByIndices(ins, batchIds);\n                    const outs = f(insBatch);\n                    for (let i = 0; i < outLabels.length; ++i) {\n                        const label = outLabels[i];\n                        const out = outs[i];\n                        batchLogs[label] = out;\n                        tfc.keep(out);\n                        // TODO(cais): Use scope() to avoid ownership.\n                    }\n                    if (batchIndex === batches.length - 1) { // Last batch.\n                        if (doValidation) {\n                            const valOuts = model.testLoop(valF, valIns, batchSize);\n                            // Porting Notes: In tfjs-layers, valOuts is always an Array.\n                            for (let i = 0; i < outLabels.length; ++i) {\n                                const label = outLabels[i];\n                                const out = valOuts[i];\n                                tfc.keep(out);\n                                // TODO(cais): Use scope() to avoid ownership.\n                                epochLogs['val_' + label] = out;\n                            }\n                        }\n                    }\n                });\n                await callbackList.onBatchEnd(batchIndex, batchLogs);\n                disposeTensorsInLogs(batchLogs);\n                if (model.stopTraining_) {\n                    break;\n                }\n                // TODO(cais): return outs as list of Tensor.\n            }\n            epochIndexArray1D.dispose();\n        }\n        // TODO(cais): Run validation at the end of the epoch.\n        await callbackList.onEpochEnd(epoch, epochLogs);\n        if (model.stopTraining_) {\n            break;\n        }\n    }\n    await callbackList.onTrainEnd();\n    await model.history.syncData();\n    return model.history;\n}\nexport async function fitTensors(\n// Type `model` as `any` here to avoid circular dependency w/ training.ts.\n// tslint:disable-next-line:no-any\nmodel, x, y, args = {}) {\n    if (model.isTraining) {\n        throw new Error('Cannot start training because another fit() call is ongoing.');\n    }\n    model.isTraining = true;\n    let inputs;\n    let targets;\n    let inputValX;\n    let inputValY;\n    let valX;\n    let valY;\n    let sampleWeights;\n    try {\n        const batchSize = args.batchSize == null ? 32 : args.batchSize;\n        checkBatchSize(batchSize);\n        // Validate user data.\n        // TODO(cais): Support sampleWeight.\n        const checkBatchAxis = false;\n        const standardizedOuts = await model.standardizeUserData(x, y, args.sampleWeight, args.classWeight, checkBatchAxis, batchSize);\n        inputs = standardizedOuts[0];\n        targets = standardizedOuts[1];\n        sampleWeights = standardizedOuts[2];\n        // Prepare validation data.\n        let doValidation = false;\n        let valIns;\n        if (args.validationData != null && args.validationData.length > 0) {\n            doValidation = true;\n            if (args.validationData.length === 2) {\n                // config.validationData consists of valX and valY.\n                inputValX = args.validationData[0];\n                inputValY = args.validationData[1];\n            }\n            else if (args.validationData.length === 3) {\n                throw new NotImplementedError('validationData including sample weights is not supported yet.');\n            }\n            else {\n                throw new ValueError(`When passing validation data, it must contain 2 (valX, valY) ` +\n                    `or 3 (valX, valY, valSampleWeight) items; ` +\n                    `${args.validationData} is invalid.`);\n            }\n            const checkBatchAxis = true;\n            const valStandardized = await model.standardizeUserData(inputValX, inputValY, null, /** Unused sample weights. */ null, /** Unused class weights. */ checkBatchAxis, batchSize);\n            valX = valStandardized[0];\n            valY = valStandardized[1];\n            valIns = valX.concat(valY);\n            // TODO(cais): Add useLearningPhase data properly.\n        }\n        else if (args.validationSplit != null && args.validationSplit > 0 &&\n            args.validationSplit < 1) {\n            doValidation = true;\n            // Porting Note: In tfjs-layers, inputs[0] is always a Tensor.\n            const splitAt = Math.floor(inputs[0].shape[0] * (1 - args.validationSplit));\n            const originalBatchSize = inputs[0].shape[0];\n            valX = sliceArrays(inputs, splitAt, originalBatchSize);\n            inputs = sliceArrays(inputs, 0, splitAt);\n            valY = sliceArrays(targets, splitAt, originalBatchSize);\n            targets = sliceArrays(targets, 0, splitAt);\n            // TODO(cais): Once sampleWeights becomes available, slice it to get\n            //   valSampleWeights.\n            valIns = valX.concat(valY);\n            // TODO(cais): Add useLearningPhase data properly.\n        }\n        else if (args.validationSteps != null) {\n            doValidation = true;\n            // TODO(cais): Add useLearningPhase.\n        }\n        const ins = inputs.concat(targets).concat(sampleWeights);\n        model.checkTrainableWeightsConsistency();\n        // TODO(cais): Handle use_learning_phase and learning_phase?\n        // Porting Note: Here we see a key deviation of tfjs-layers from\n        // Keras.\n        //  Due to the imperative nature of tfjs-layers' backend (tfjs-core),\n        //  we do not construct symbolic computation graphs to embody the\n        //  training process. Instead, we define a function that performs the\n        //  training action. In PyKeras, the data (inputs and targets) are fed\n        //  through graph placeholders. In tfjs-layers, the data are fed as\n        //  function arguments. Since the function are defined below in the\n        //  scope, we don't have equivalents of PyKeras's\n        //  `_make_train_funciton`.\n        const trainFunction = model.makeTrainFunction();\n        const outLabels = model.getDedupedMetricsNames();\n        let valFunction;\n        let callbackMetrics;\n        if (doValidation) {\n            model.makeTestFunction();\n            valFunction = model.testFunction;\n            callbackMetrics =\n                outLabels.slice().concat(outLabels.map(n => 'val_' + n));\n        }\n        else {\n            valFunction = null;\n            valIns = [];\n            callbackMetrics = outLabels.slice();\n        }\n        const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);\n        const out = await fitLoop(model, trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null);\n        return out;\n    }\n    finally {\n        model.isTraining = false;\n        // Memory clean up.\n        disposeNewTensors(inputs, x);\n        disposeNewTensors(targets, y);\n        disposeNewTensors(valX, inputValX);\n        disposeNewTensors(valY, inputValY);\n        if (sampleWeights != null) {\n            tfc.dispose(sampleWeights);\n        }\n    }\n    // TODO(cais): Add value to outLabels.\n}\n/**\n * Ensure tensors all have a rank of at least 2.\n *\n * If a tensor has a rank of 1, it is dimension-expanded to rank 2.\n * If any tensor has a rank of 0 (i.e., is a scalar), an error will be thrown.\n */\nexport function ensureTensorsRank2OrHigher(tensors) {\n    const outs = [];\n    if (tensors instanceof Tensor) {\n        tensors = [tensors];\n    }\n    // Make Tensors at least 2D.\n    for (let i = 0; i < tensors.length; ++i) {\n        const tensor = tensors[i];\n        if (tensor.rank === 1) {\n            outs.push(expandDims(tensor, 1));\n        }\n        else if (tensor.rank === 0) {\n            throw new Error('Expected tensor to be at least 1D, but received a 0D tensor ' +\n                '(scalar).');\n        }\n        else {\n            outs.push(tensor);\n        }\n    }\n    return outs;\n}\n/**\n * Compare a set of tensors with a reference (old) set, discard the ones\n * in the new set that are not present in the reference set.\n *\n * This method is used for memory clenaup during calls such as\n * LayersModel.fit().\n *\n * @param tensors New set which may contain Tensors not present in\n *   `refTensors`.\n * @param refTensors Reference Tensor set.\n */\n// TODO(cais, kangyizhang): Deduplicate with tfjs-data.\nexport function disposeNewTensors(tensors, refTensors) {\n    if (tensors == null) {\n        return;\n    }\n    const oldTensorIds = [];\n    if (refTensors instanceof Tensor) {\n        oldTensorIds.push(refTensors.id);\n    }\n    else if (Array.isArray(refTensors)) {\n        refTensors.forEach(t => oldTensorIds.push(t.id));\n    }\n    else if (refTensors != null) {\n        // `oldTensors` is a map from string name to Tensor.\n        for (const name in refTensors) {\n            const oldTensor = refTensors[name];\n            oldTensorIds.push(oldTensor.id);\n        }\n    }\n    const tensorsToDispose = [];\n    if (tensors instanceof Tensor) {\n        if (oldTensorIds.indexOf(tensors.id) === -1) {\n            tensorsToDispose.push(tensors);\n        }\n    }\n    else if (Array.isArray(tensors)) {\n        tensors.forEach(t => {\n            if (oldTensorIds.indexOf(t.id) === -1) {\n                tensorsToDispose.push(t);\n            }\n        });\n    }\n    else if (tensors != null) {\n        // `oldTensors` is a map from string name to Tensor.\n        for (const name in tensors) {\n            const tensor = tensors[name];\n            if (oldTensorIds.indexOf(tensor.id) === -1) {\n                tensorsToDispose.push(tensor);\n            }\n        }\n    }\n    tensorsToDispose.forEach(t => {\n        if (!t.isDisposed) {\n            t.dispose();\n        }\n    });\n}\n//# sourceMappingURL=training_tensors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source: engine/training.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { io, Optimizer, scalar, serialization, Tensor, tensor1d, util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { deserialize } from '../layers/serialization';\nimport * as losses from '../losses';\nimport * as Metrics from '../metrics';\nimport * as optimizers from '../optimizers';\nimport { checkUserDefinedMetadata } from '../user_defined_metadata';\nimport { count, pyListRepeat, singletonOrArray, toCamelCase, toSnakeCase, unique } from '../utils/generic_utils';\nimport { printSummary } from '../utils/layer_utils';\nimport { range } from '../utils/math_utils';\nimport { convertPythonicToTs } from '../utils/serialization_utils';\nimport { version } from '../version';\nimport { Container } from './container';\nimport { execute, FeedDict } from './executor';\nimport { evaluateDataset, fitDataset } from './training_dataset';\nimport { checkBatchSize, disposeNewTensors, ensureTensorsRank2OrHigher, fitTensors, makeBatches, sliceArrays, sliceArraysByIndices } from './training_tensors';\nimport { computeWeightedLoss, standardizeClassWeights, standardizeWeights } from './training_utils';\n/**\n * Helper function for polymorphic input data: 1. singleton Tensor.\n */\nexport function isDataTensor(x) {\n    return x instanceof Tensor;\n}\n/**\n * Helper function for polymorphic input data: 2. Array of Tensor.\n */\nexport function isDataArray(x) {\n    return Array.isArray(x);\n}\n/**\n * Helper function for polymorphic input data: 3. \"dict\" of Tensor.\n */\nexport function isDataDict(x) {\n    return !isDataTensor(x) && !isDataArray(x);\n}\n/**\n * Normalizes inputs and targets provided by users.\n * @param data User-provided input data (polymorphic).\n * @param names An Array of expected Tensor names.\n * @param shapes Optional Array of expected Tensor shapes.\n * @param checkBatchAxis Whether to check that the batch axis of the arrays\n *   match  the expected value found in `shapes`.\n * @param exceptionPrefix String prefix used for exception formatting.\n * @returns List of standardized input Tensors (one Tensor per model input).\n * @throws ValueError: in case of improperly formatted user data.\n */\nexport function standardizeInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = '') {\n    if (names == null || names.length === 0) {\n        // Check for the case where the model expected no data, but some data got\n        // sent.\n        if (data != null) {\n            let gotUnexpectedData = false;\n            if (isDataArray(data) && data.length > 0) {\n                gotUnexpectedData = true;\n            }\n            else if (isDataDict(data)) {\n                for (const key in data) {\n                    if (data.hasOwnProperty(key)) {\n                        gotUnexpectedData = true;\n                        break;\n                    }\n                }\n            }\n            else {\n                // `data` is a singleton Tensor in this case.\n                gotUnexpectedData = true;\n            }\n            if (gotUnexpectedData) {\n                throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, ` +\n                    `but got ${data}`);\n            }\n        }\n        return [];\n    }\n    if (data == null) {\n        return names.map(name => null);\n    }\n    let arrays;\n    if (isDataDict(data)) {\n        data = data;\n        arrays = [];\n        for (const name of names) {\n            if (data[name] == null) {\n                throw new ValueError(`No data provided for \"${name}\". Need data for each key in: ` +\n                    `${names}`);\n            }\n            arrays.push(data[name]);\n        }\n    }\n    else if (isDataArray(data)) {\n        data = data;\n        if (data.length !== names.length) {\n            throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of ` +\n                `Tensors that you are passing to your model is not the size the ` +\n                `model expected. Expected to see ${names.length} Tensor(s), but ` +\n                `instead got the following list of Tensor(s): ${data}`);\n        }\n        arrays = data;\n    }\n    else {\n        data = data;\n        if (names.length > 1) {\n            throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), ` +\n                `but only received one Tensor. Found: Tensor with shape ${data.shape}`);\n        }\n        arrays = [data];\n    }\n    arrays = ensureTensorsRank2OrHigher(arrays);\n    // Check shape compatibility.\n    if (shapes != null) {\n        for (let i = 0; i < names.length; ++i) {\n            if (shapes[i] == null) {\n                continue;\n            }\n            const array = arrays[i];\n            if (array.shape.length !== shapes[i].length) {\n                throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                    `to have ${shapes[i].length} dimension(s). but got array with ` +\n                    `shape ${array.shape}`);\n            }\n            for (let j = 0; j < shapes[i].length; ++j) {\n                if (j === 0 && !checkBatchAxis) {\n                    // Skip the first (batch) axis.\n                    continue;\n                }\n                const dim = array.shape[j];\n                const refDim = shapes[i][j];\n                if (refDim != null && refDim >= 0 && dim !== refDim) {\n                    throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                        `to have shape [${shapes[i]}], but got array with shape ` +\n                        `[${array.shape}].`);\n                }\n            }\n        }\n    }\n    return arrays;\n}\n/**\n * User input validation for Tensors.\n * @param inputs `Array` of `tf.Tensor`s for inputs.\n * @param targets `Array` of `tf.Tensor`s for targets.\n * @param weights Optional `Array` of `tf.Tensor`s for sample weights.\n * @throws ValueError: in case of incorrectly formatted data.\n */\nexport function checkArrayLengths(inputs, targets, weights) {\n    const setX = unique(inputs.map(input => input.shape[0]));\n    setX.sort();\n    const setY = unique(targets.map(target => target.shape[0]));\n    setY.sort();\n    // TODO(cais): Check `weights` as well.\n    if (setX.length > 1) {\n        throw new ValueError(`All input Tensors (x) should have the same number of samples. ` +\n            `Got array shapes: ` +\n            `${JSON.stringify(inputs.map(input => input.shape))}`);\n    }\n    if (setY.length > 1) {\n        throw new ValueError(`All target Tensors (y) should have the same number of samples. ` +\n            `Got array shapes: ` +\n            `${JSON.stringify(targets.map(target => target.shape))}`);\n    }\n    if (setX.length > 0 && setY.length > 0 && !util.arraysEqual(setX, setY)) {\n        throw new ValueError(`Input Tensors should have the same number of samples as target ` +\n            `Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target ` +\n            `sample(s).`);\n    }\n}\n/**\n * Validation on the compatibility of targes and loss functions.\n *\n * This helps prevent users from using loss functions incorrectly.\n *\n * @param targets `Array` of `tf.Tensor`s of targets.\n * @param lossFns `Array` of loss functions.\n * @param outputShapes `Array` of shapes of model outputs.\n */\nfunction checkLossAndTargetCompatibility(targets, lossFns, outputShapes) {\n    // TODO(cais): Dedicated test coverage?\n    const keyLosses = [\n        losses.meanSquaredError, losses.binaryCrossentropy,\n        losses.categoricalCrossentropy\n    ];\n    for (let i = 0; i < targets.length; ++i) {\n        const y = targets[i];\n        const loss = lossFns[i];\n        const shape = outputShapes[i];\n        if (loss == null) {\n            continue;\n        }\n        if (loss === losses.categoricalCrossentropy) {\n            if (y.shape[y.shape.length - 1] === 1) {\n                throw new ValueError(`You are passing a target array of shape ${y.shape} while using ` +\n                    `a loss 'categorical_crossentropy'. 'categorical_crossentropy'` +\n                    `expects targets to be binary matrices (1s and 0s) of shape ` +\n                    `[samples, classes].`);\n                // TODO(cais): Example code in error message.\n            }\n        }\n        if (keyLosses.indexOf(loss) !== -1) {\n            const slicedYShape = y.shape.slice(1);\n            const slicedShape = shape.slice(1);\n            for (let j = 0; j < slicedYShape.length; ++j) {\n                const targetDim = slicedYShape[j];\n                const outDim = slicedShape[j];\n                if (outDim != null && targetDim !== outDim) {\n                    throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an ` +\n                        `output of shape ${shape}, while using a loss function that ` +\n                        `expects targets to have the same shape as the output.`);\n                }\n            }\n        }\n    }\n}\n/**\n * Check inputs provided by the user.\n *\n * Porting Note: This corresponds to _standardize_input_data() in Python\n *   Keras. Because of the strong typing in TF.js, we do not need to convert\n *   the data. Specifically:\n *   1) in PyKeras, `data` can be `DataFrame` instances from pandas, for\n *      example. We don't need to worry about that here because there is no\n *      widely popular javascript/typesdcript equivalent of pandas (so far).\n *      If one becomes available in the future, we can add support.\n *   2) in PyKeras, inputs can be Python dict. But here we are stipulating\n * that the data is either a single `tf.Tensor` or an Array of `tf.Tensor`s. We\n * may add support for `Object` data inputs in the future when the need\n * arises.\n *\n * Instead, we perform basic checks for number of parameters and shapes.\n *\n * @param data: The input data.\n * @param names: Name for the inputs, from the model.\n * @param shapes: Expected shapes for the input data, from the model.\n * @param checkBatchAxis: Whether the size along the batch axis (i.e., the\n *   first dimension) will be checked for matching.\n * @param exceptionPrefix: Execption prefix message, used in generating error\n *   messages.\n * @throws ValueError: on incorrect number of inputs or mismatches in shapes.\n */\nfunction checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = '') {\n    let arrays;\n    if (Array.isArray(data)) {\n        if (data.length !== names.length) {\n            throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of ` +\n                `Tensors that you are passing to your model is not the size the ` +\n                `the model expected. Expected to see ${names.length} Tensor(s),` +\n                ` but instead got ${data.length} Tensors(s).`);\n        }\n        arrays = data;\n    }\n    else {\n        if (names.length > 1) {\n            throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, ` +\n                `but only received one Tensor. Found: array with shape ` +\n                `${JSON.stringify(data.shape)}.`);\n        }\n        arrays = [data];\n    }\n    if (shapes != null) {\n        for (let i = 0; i < names.length; ++i) {\n            if (shapes[i] == null) {\n                continue;\n            }\n            const array = arrays[i];\n            if (array.shape.length !== shapes[i].length) {\n                throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                    `to have ${shapes[i].length} dimension(s), but got array with ` +\n                    `shape ${JSON.stringify(array.shape)}`);\n            }\n            for (let j = 0; j < shapes[i].length; ++j) {\n                if (j === 0 && !checkBatchAxis) {\n                    continue;\n                }\n                const dim = array.shape[j];\n                const refDim = shapes[i][j];\n                if (refDim != null) {\n                    if (refDim !== dim) {\n                        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ` +\n                            `${names[i]} to have shape ${JSON.stringify(shapes[i])} but ` +\n                            `got array with shape ${JSON.stringify(array.shape)}.`);\n                    }\n                }\n            }\n        }\n    }\n}\n/**\n * Maps metric functions to model outputs.\n * @param metrics An shortcut strings name, metric function, `Array` or dict\n *   (`Object`) of metric functions.\n * @param outputNames An `Array` of the names of model outputs.\n * @returns An `Array` (one entry per model output) of `Array` of metric\n *   functions. For instance, if the model has 2 outputs, and for the first\n *   output we want to compute `binaryAccuracy` and `binaryCrossentropy`,\n *   and just `binaryAccuracy` for the second output, the `Array` would look\n *   like:\n *     `[[binaryAccuracy, binaryCrossentropy],  [binaryAccuracy]]`\n * @throws TypeError: incompatible metrics format.\n */\nexport function collectMetrics(metrics, outputNames) {\n    if (metrics == null || Array.isArray(metrics) && metrics.length === 0) {\n        return outputNames.map(name => []);\n    }\n    let wrappedMetrics;\n    if (typeof metrics === 'string' || typeof metrics === 'function') {\n        wrappedMetrics = [metrics];\n    }\n    else if (Array.isArray(metrics) || typeof metrics === 'object') {\n        wrappedMetrics = metrics;\n    }\n    else {\n        throw new TypeError('Type of metrics argument not understood. Expected an string,' +\n            `function, Array, or Object, found: ${metrics}`);\n    }\n    if (Array.isArray(wrappedMetrics)) {\n        // We then apply all metrics to all outputs.\n        return outputNames.map(name => wrappedMetrics);\n    }\n    else {\n        // In this case, metrics is a dict.\n        const nestedMetrics = [];\n        for (const name of outputNames) {\n            let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : [];\n            if (!Array.isArray(outputMetrics)) {\n                outputMetrics = [outputMetrics];\n            }\n            nestedMetrics.push(outputMetrics);\n        }\n        return nestedMetrics;\n    }\n}\nconst LAYERS_MODEL_FORMAT_NAME = 'layers-model';\n/**\n * A `tf.LayersModel` is a directed, acyclic graph of `tf.Layer`s plus methods\n * for training, evaluation, prediction and saving.\n *\n * `tf.LayersModel` is the basic unit of training, inference and evaluation in\n * TensorFlow.js. To create a `tf.LayersModel`, use `tf.LayersModel`.\n *\n * See also:\n *   `tf.Sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class LayersModel extends Container {\n    constructor(args) {\n        super(args);\n        this.isTraining = false;\n    }\n    /**\n     * Print a text summary of the model's layers.\n     *\n     * The summary includes\n     * - Name and type of all layers that comprise the model.\n     * - Output shape(s) of the layers\n     * - Number of weight parameters of each layer\n     * - If the model has non-sequential-like topology, the inputs each layer\n     *   receives\n     * - The total number of trainable and non-trainable parameters of the model.\n     *\n     * ```js\n     * const input1 = tf.input({shape: [10]});\n     * const input2 = tf.input({shape: [20]});\n     * const dense1 = tf.layers.dense({units: 4}).apply(input1);\n     * const dense2 = tf.layers.dense({units: 8}).apply(input2);\n     * const concat = tf.layers.concatenate().apply([dense1, dense2]);\n     * const output =\n     *     tf.layers.dense({units: 3, activation: 'softmax'}).apply(concat);\n     *\n     * const model = tf.model({inputs: [input1, input2], outputs: output});\n     * model.summary();\n     * ```\n     *\n     * @param lineLength Custom line length, in number of characters.\n     * @param positions Custom widths of each of the columns, as either\n     *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n     *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n     *   right-most (i.e., ending) position of a column.\n     * @param printFn Custom print function. Can be used to replace the default\n     *   `console.log`. For example, you can use `x => {}` to mute the printed\n     *   messages in the console.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    summary(lineLength, positions, printFn = console.log) {\n        if (!this.built) {\n            throw new ValueError(`This model has never been called, thus its weights have not been ` +\n                `created yet. So no summary can be displayed. Build the model ` +\n                `first (e.g., by calling it on some test data).`);\n        }\n        printSummary(this, lineLength, positions, printFn);\n    }\n    /**\n     * Configures and prepares the model for training and evaluation.  Compiling\n     * outfits the model with an optimizer, loss, and/or metrics.  Calling `fit`\n     * or `evaluate` on an un-compiled model will throw an error.\n     *\n     * @param args a `ModelCompileArgs` specifying the loss, optimizer, and\n     * metrics to be used for fitting and evaluating this model.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    compile(args) {\n        if (args.loss == null) {\n            args.loss = [];\n        }\n        this.loss = args.loss;\n        if (typeof args.optimizer === 'string') {\n            this.optimizer_ = optimizers.getOptimizer(args.optimizer);\n            this.isOptimizerOwned = true;\n        }\n        else {\n            if (!(args.optimizer instanceof Optimizer)) {\n                throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`);\n            }\n            this.optimizer_ = args.optimizer;\n            this.isOptimizerOwned = false;\n        }\n        // TODO(cais): Add lossWeights.\n        // TODO(cais): Add sampleWeightMode.\n        // Prepare loss functions.\n        let lossFunctions = [];\n        if (!Array.isArray(args.loss) && typeof args.loss !== 'string' &&\n            typeof args.loss !== 'function') {\n            args.loss = args.loss;\n            for (const name in args.loss) {\n                if (this.outputNames.indexOf(name) === -1) {\n                    throw new ValueError(`Unknown entry in loss dictionary: \"${name}\". ` +\n                        `Only expected the following keys: ${this.outputNames}`);\n                }\n            }\n            for (const name of this.outputNames) {\n                if (args.loss[name] == null) {\n                    console.warn(`Output \"${name}\" is missing from loss dictionary. We assume ` +\n                        `this was done on purpose, and we will not be expecting data ` +\n                        `to be passed to ${name} during training`);\n                }\n                lossFunctions.push(losses.get(args.loss[name]));\n            }\n        }\n        else if (Array.isArray(args.loss)) {\n            if (args.loss.length !== this.outputs.length) {\n                throw new ValueError(`When passing an Array as loss, it should have one entry per ` +\n                    `model output. The model has ${this.outputs.length} output(s), ` +\n                    `but you passed loss=${args.loss}.`);\n            }\n            const theLosses = args.loss;\n            lossFunctions = theLosses.map(l => losses.get(l));\n        }\n        else {\n            const lossFunction = losses.get(args.loss);\n            this.outputs.forEach(_ => {\n                lossFunctions.push(lossFunction);\n            });\n        }\n        this.lossFunctions = lossFunctions;\n        this.feedOutputNames = [];\n        this.feedOutputShapes = [];\n        this.feedLossFns = [];\n        for (let i = 0; i < this.outputs.length; ++i) {\n            // TODO(cais): Logic for skipping target(s).\n            const shape = this.internalOutputShapes[i];\n            const name = this.outputNames[i];\n            this.feedOutputNames.push(name);\n            this.feedOutputShapes.push(shape);\n            this.feedLossFns.push(this.lossFunctions[i]);\n        }\n        // TODO(cais): Add logic for output masks.\n        // TODO(cais): Add logic for sample weights.\n        const skipTargetIndices = [];\n        // Prepare metrics.\n        this.metrics = args.metrics;\n        // TODO(cais): Add weightedMetrics.\n        this.metricsNames = ['loss'];\n        this.metricsTensors = [];\n        // Compute total loss.\n        // Porting Note: In PyKeras, metrics_tensors are symbolic tensor objects.\n        //   Here, metricsTensors are TypeScript functions. This difference is due\n        //   to the difference in symbolic/imperative property of the backends.\n        nameScope('loss', () => {\n            for (let i = 0; i < this.outputs.length; ++i) {\n                if (skipTargetIndices.indexOf(i) !== -1) {\n                    continue;\n                }\n                // TODO(cais): Add weightedLoss, sampleWeight and mask.\n                //   The following line should be weightedLoss\n                const weightedLoss = this.lossFunctions[i];\n                if (this.outputs.length > 1) {\n                    this.metricsTensors.push([weightedLoss, i]);\n                    this.metricsNames.push(this.outputNames[i] + '_loss');\n                }\n            }\n            // Porting Note: Due to the imperative nature of the backend, we calculate\n            //   the regularizer penalties in the totalLossFunction, instead of here.\n        });\n        const nestedMetrics = collectMetrics(args.metrics, this.outputNames);\n        // TODO(cais): Add nestedWeightedMetrics.\n        /**\n         * Helper function used in loop below.\n         */\n        const appendMetric = (outputIndex, metricName, metricTensor) => {\n            if (this.outputNames.length > 1) {\n                metricName = this.outputNames[outputIndex] + '_' + metricName;\n            }\n            this.metricsNames.push(metricName);\n            this.metricsTensors.push([metricTensor, outputIndex]);\n        };\n        nameScope('metric', () => {\n            for (let i = 0; i < this.outputs.length; ++i) {\n                if (skipTargetIndices.indexOf(i) !== -1) {\n                    continue;\n                }\n                const outputMetrics = nestedMetrics[i];\n                // TODO(cais): Add weights and outputWeightedMetrics.\n                // TODO(cais): Add optional arg `weights` to the following function.\n                const handleMetrics = (metrics) => {\n                    const metricNamePrefix = '';\n                    let metricName;\n                    let accFn;\n                    let weightedMetricFn;\n                    //  TODO(cais): Use 'weights_' for weighted metrics.\n                    for (const metric of metrics) {\n                        if (typeof metric === 'string' &&\n                            ['accuracy', 'acc', 'crossentropy', 'ce'].indexOf(metric) !==\n                                -1) {\n                            const outputShape = this.internalOutputShapes[i];\n                            if (outputShape[outputShape.length - 1] === 1 ||\n                                this.lossFunctions[i] === losses.binaryCrossentropy) {\n                                // case: binary accuracy/crossentropy.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.binaryAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.binaryCrossentropy;\n                                }\n                            }\n                            else if (this.lossFunctions[i] ===\n                                losses.sparseCategoricalCrossentropy) {\n                                // case: categorical accuracy / crossentropy with sparse\n                                // targets.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.sparseCategoricalAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.sparseCategoricalCrossentropy;\n                                }\n                            }\n                            else {\n                                // case: categorical accuracy / crossentropy.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.categoricalAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.categoricalCrossentropy;\n                                }\n                            }\n                            let suffix;\n                            if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                suffix = 'acc';\n                            }\n                            else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                suffix = 'ce';\n                            }\n                            // TODO(cais): Add weighting actually.\n                            weightedMetricFn = accFn;\n                            metricName = metricNamePrefix + suffix;\n                        }\n                        else {\n                            const metricFn = Metrics.get(metric);\n                            // TODO(cais): Add weighting actually.\n                            weightedMetricFn = metricFn;\n                            metricName =\n                                metricNamePrefix + Metrics.getLossOrMetricName(metric);\n                        }\n                        // TODO(cais): Add weighting and masking to metricResult.\n                        let metricResult;\n                        nameScope(metricName, () => {\n                            metricResult = weightedMetricFn;\n                        });\n                        appendMetric(i, metricName, metricResult);\n                    }\n                };\n                handleMetrics(outputMetrics);\n                // TODO(cais): Call handleMetrics with weights.\n            }\n        });\n        // Porting Notes: Given the imperative backend of tfjs-core,\n        //   there is no need for constructing the symbolic graph and placeholders.\n        this.collectedTrainableWeights = this.trainableWeights;\n    }\n    /**\n     * Check trainable weights count consistency.\n     *\n     * This will raise a warning if `this.trainableWeights` and\n     * `this.collectedTrainableWeights` are inconsistent (i.e., have different\n     * numbers of parameters).\n     * Inconsistency will typically arise when one modifies `model.trainable`\n     * without calling `model.compile()` again.\n     */\n    checkTrainableWeightsConsistency() {\n        if (this.collectedTrainableWeights == null) {\n            return;\n        }\n        if (this.trainableWeights.length !==\n            this.collectedTrainableWeights.length) {\n            console.warn('Discrepancy between trainableweights and collected trainable ' +\n                'weights. Did you set `model.trainable` without calling ' +\n                '`model.compile()` afterwards?');\n        }\n    }\n    /**\n     * Returns the loss value & metrics values for the model in test mode.\n     *\n     * Loss and metrics are specified during `compile()`, which needs to happen\n     * before calls to `evaluate()`.\n     *\n     * Computation is done in batches.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const result = model.evaluate(\n     *     tf.ones([8, 10]), tf.ones([8, 1]), {batchSize: 4});\n     * result.print();\n     * ```\n     *\n     * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple inputs.\n     * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple outputs.\n     * @param args A `ModelEvaluateArgs`, containing optional fields.\n     *\n     * @return `Scalar` test loss (if the model has a single output and no\n     *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n     *   and/or metrics). The attribute `model.metricsNames`\n     *   will give you the display labels for the scalar outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    evaluate(x, y, args = {}) {\n        const batchSize = args.batchSize == null ? 32 : args.batchSize;\n        checkBatchSize(batchSize);\n        // TODO(cais): Standardize `config.sampleWeights` as well.\n        // Validate user data.\n        const checkBatchAxis = true;\n        const standardizedOuts = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n        try {\n            // TODO(cais): If uses `useLearningPhase`, set the corresponding element\n            // of the input to 0.\n            const ins = standardizedOuts[0].concat(standardizedOuts[1]);\n            this.makeTestFunction();\n            const f = this.testFunction;\n            const testOuts = this.testLoop(f, ins, batchSize, args.verbose, args.steps);\n            return singletonOrArray(testOuts);\n        }\n        finally {\n            disposeNewTensors(standardizedOuts[0], x);\n            disposeNewTensors(standardizedOuts[1], y);\n        }\n    }\n    // TODO(cais): Add code snippet below once real dataset objects are\n    //   available.\n    /**\n     * Evaluate model using a dataset object.\n     *\n     * Note: Unlike `evaluate()`, this method is asynchronous (`async`);\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for evaluation. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs. Of the two items in the array, the\n     *   first is the input feature(s) and the second is the output target(s).\n     * @param args A configuration object for the dataset-based evaluation.\n     * @returns Loss and metric values as an Array of `Scalar` objects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async evaluateDataset(dataset, args) {\n        this.makeTestFunction();\n        return evaluateDataset(this, dataset, args);\n    }\n    /**\n     * Get number of samples provided for training, evaluation or prediction.\n     *\n     * @param ins Input `tf.Tensor`.\n     * @param batchSize Integer batch size, optional.\n     * @param steps Total number of steps (batches of samples) before\n     * declaring loop finished. Optional.\n     * @param stepsName The public API's parameter name for `steps`.\n     * @returns Number of samples provided.\n     */\n    checkNumSamples(ins, batchSize, steps, stepsName = 'steps') {\n        let numSamples;\n        if (steps != null) {\n            numSamples = null;\n            if (batchSize != null) {\n                throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.` +\n                    `Got batchSize = ${batchSize}`);\n            }\n        }\n        else if (ins != null) {\n            if (Array.isArray(ins)) {\n                numSamples = ins[0].shape[0];\n            }\n            else {\n                numSamples = ins.shape[0];\n            }\n        }\n        else {\n            throw new ValueError(`Either the input data should have a defined shape, or ` +\n                `${stepsName} shoud be specified.`);\n        }\n        return numSamples;\n    }\n    /**\n     * Execute internal tensors of the model with input data feed.\n     * @param inputs Input data feed. Must match the inputs of the model.\n     * @param outputs Names of the output tensors to be fetched. Must match\n     *   names of the SymbolicTensors that belong to the graph.\n     * @returns Fetched values for `outputs`.\n     */\n    execute(inputs, outputs) {\n        if (Array.isArray(outputs) && outputs.length === 0) {\n            throw new ValueError('`outputs` is an empty Array, which is not allowed.');\n        }\n        const outputsIsArray = Array.isArray(outputs);\n        const outputNames = (outputsIsArray ? outputs : [outputs]);\n        const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames);\n        // Format the input into a FeedDict.\n        const feedDict = new FeedDict();\n        if (inputs instanceof Tensor) {\n            inputs = [inputs];\n        }\n        if (Array.isArray(inputs)) {\n            if (inputs.length !== this.inputs.length) {\n                throw new ValueError(`The number of inputs provided (${inputs.length}) ` +\n                    `does not match the number of inputs of this model ` +\n                    `(${this.inputs.length}).`);\n            }\n            for (let i = 0; i < this.inputs.length; ++i) {\n                feedDict.add(this.inputs[i], inputs[i]);\n            }\n        }\n        else {\n            for (const input of this.inputs) {\n                const tensorValue = inputs[input.name];\n                if (tensorValue == null) {\n                    throw new ValueError(`No value is provided for the model's input ${input.name}`);\n                }\n                feedDict.add(input, tensorValue);\n            }\n        }\n        // Run execution.\n        const executeOutputs = execute(outputSymbolicTensors, feedDict);\n        return outputsIsArray ? executeOutputs : executeOutputs[0];\n    }\n    /**\n     * Retrieve the model's internal symbolic tensors from symbolic-tensor names.\n     */\n    retrieveSymbolicTensors(symbolicTensorNames) {\n        const outputSymbolicTensors = pyListRepeat(null, symbolicTensorNames.length);\n        let outputsRemaining = symbolicTensorNames.length;\n        for (const layer of this.layers) {\n            const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output];\n            const layerOutputNames = layerOutputs.map(output => output.name);\n            for (let i = 0; i < symbolicTensorNames.length; ++i) {\n                const index = layerOutputNames.indexOf(symbolicTensorNames[i]);\n                if (index !== -1) {\n                    outputSymbolicTensors[i] = layerOutputs[index];\n                    outputsRemaining--;\n                }\n                if (outputsRemaining === 0) {\n                    break;\n                }\n            }\n            if (outputsRemaining === 0) {\n                break;\n            }\n        }\n        if (outputsRemaining > 0) {\n            const remainingNames = [];\n            outputSymbolicTensors.forEach((tensor, i) => {\n                if (tensor == null) {\n                    remainingNames.push(symbolicTensorNames[i]);\n                }\n            });\n            throw new ValueError(`Cannot find SymbolicTensors for output name(s): ` +\n                `${JSON.stringify(remainingNames)}`);\n        }\n        return outputSymbolicTensors;\n    }\n    /**\n     * Helper method to loop over some data in batches.\n     *\n     * Porting Note: Not using the functional approach in the Python equivalent\n     *   due to the imperative backend.\n     * Porting Note: Does not support step mode currently.\n     *\n     * @param ins: input data\n     * @param batchSize: integer batch size.\n     * @param verbose: verbosity model\n     * @returns: Predictions as `tf.Tensor` (if a single output) or an `Array` of\n     *   `tf.Tensor` (if multipe outputs).\n     */\n    predictLoop(ins, batchSize = 32, verbose = false) {\n        return tfc.tidy(() => {\n            const numSamples = this.checkNumSamples(ins);\n            if (verbose) {\n                throw new NotImplementedError('Verbose predictLoop() is not implemented yet.');\n            }\n            // Sample-based predictions.\n            // Porting Note: Tensor currently does not support sliced assignments as\n            //   in numpy, e.g., x[1:3] = y. Therefore we use concatenation while\n            //   iterating over the batches.\n            const batches = makeBatches(numSamples, batchSize);\n            const outsBatches = this.outputs.map(output => []);\n            // TODO(cais): Can the scope() be pushed down inside the for loop?\n            for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                const batchOuts = tfc.tidy(() => {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    // TODO(cais): Take care of the case of the last element is a flag for\n                    //   training/test.\n                    const insBatch = sliceArrays(ins, batchStart, batchEnd);\n                    // Construct the feeds for execute();\n                    const feeds = [];\n                    if (Array.isArray(insBatch)) {\n                        for (let i = 0; i < insBatch.length; ++i) {\n                            feeds.push({ key: this.inputs[i], value: insBatch[i] });\n                        }\n                    }\n                    else {\n                        feeds.push({ key: this.inputs[0], value: insBatch });\n                    }\n                    const feedDict = new FeedDict(feeds);\n                    return execute(this.outputs, feedDict);\n                });\n                batchOuts.forEach((batchOut, i) => outsBatches[i].push(batchOut));\n            }\n            return singletonOrArray(outsBatches.map(batches => tfc.concat(batches, 0)));\n        });\n    }\n    /**\n     * Generates output predictions for the input samples.\n     *\n     * Computation is done in batches.\n     *\n     * Note: the \"step\" mode of predict() is currently not supported.\n     *   This is because the TensorFlow.js core backend is imperative only.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predict(tf.ones([8, 10]), {batchSize: 4}).print();\n     * ```\n     *\n     * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n     *   the model has multiple inputs.\n     * @param args A `ModelPredictArgs` object containing optional fields.\n     *\n     * @return Prediction results as a `tf.Tensor`(s).\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and the model's expectations, or in case a stateful model receives a\n     *   number of samples that is not a multiple of the batch size.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(x, args = {}) {\n        const xsRank2OrHigher = ensureTensorsRank2OrHigher(x);\n        checkInputData(xsRank2OrHigher, this.inputNames, this.feedInputShapes, false);\n        try {\n            // TODO(cais): Take care of stateful models.\n            //   if (this.stateful) ...\n            // TODO(cais): Take care of the learning_phase boolean flag.\n            //   if (this.useLearningPhase) ...\n            const batchSize = args.batchSize == null ? 32 : args.batchSize;\n            checkBatchSize(batchSize);\n            return this.predictLoop(xsRank2OrHigher, batchSize);\n        }\n        finally {\n            disposeNewTensors(xsRank2OrHigher, x);\n        }\n    }\n    /**\n     * Returns predictions for a single batch of samples.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predictOnBatch(tf.ones([8, 10])).print();\n     * ```\n     * @param x: Input samples, as a Tensor (for models with exactly one\n     *   input) or an array of Tensors (for models with more than one input).\n     * @return Tensor(s) of predictions\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predictOnBatch(x) {\n        checkInputData(x, this.inputNames, this.feedInputShapes, true);\n        // TODO(cais): Take care of the learning_phase boolean flag.\n        //   if (this.useLearningPhase) ...\n        const batchSize = (Array.isArray(x) ? x[0] : x).shape[0];\n        return this.predictLoop(x, batchSize);\n    }\n    standardizeUserDataXY(x, y, checkBatchAxis = true, batchSize) {\n        // TODO(cais): Add sampleWeight, classWeight\n        if (this.optimizer_ == null) {\n            throw new RuntimeError('You must compile a model before training/testing. Use ' +\n                'LayersModel.compile(modelCompileArgs).');\n        }\n        const outputShapes = [];\n        for (let i = 0; i < this.feedOutputShapes.length; ++i) {\n            const outputShape = this.feedOutputShapes[i];\n            const lossFn = this.feedLossFns[i];\n            if (lossFn === losses.sparseCategoricalCrossentropy) {\n                outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1]));\n            }\n            else {\n                // Porting Note: Because of strong typing `lossFn` must be a function.\n                outputShapes.push(outputShape);\n            }\n        }\n        x = standardizeInputData(x, this.feedInputNames, this.feedInputShapes, false, 'input');\n        y = standardizeInputData(y, this.feedOutputNames, outputShapes, false, 'target');\n        // TODO(cais): Standardize sampleWeights & classWeights.\n        checkArrayLengths(x, y, null);\n        // TODO(cais): Check sampleWeights as well.\n        checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes);\n        if (this.stateful && batchSize != null && batchSize > 0) {\n            if (x[0].shape[0] % batchSize !== 0) {\n                throw new ValueError(`In a stateful network, you should only pass inputs with a ` +\n                    `number of samples that is divisible by the batch size ` +\n                    `${batchSize}. Found: ${x[0].shape[0]} sample(s).`);\n            }\n        }\n        return [x, y];\n    }\n    async standardizeUserData(x, y, sampleWeight, classWeight, checkBatchAxis = true, batchSize) {\n        const [standardXs, standardYs] = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n        // TODO(cais): Handle sampleWeights.\n        if (sampleWeight != null) {\n            throw new Error('sample weight is not supported yet.');\n        }\n        let standardSampleWeights = null;\n        if (classWeight != null) {\n            const classWeights = standardizeClassWeights(classWeight, this.outputNames);\n            standardSampleWeights = [];\n            for (let i = 0; i < classWeights.length; ++i) {\n                standardSampleWeights.push(await standardizeWeights(standardYs[i], null, classWeights[i]));\n            }\n        }\n        // TODO(cais): Deal with the case of model.stateful == true.\n        return [standardXs, standardYs, standardSampleWeights];\n    }\n    /**\n     * Loop over some test data in batches.\n     * @param f A Function returning a list of tensors.\n     * @param ins Array of tensors to be fed to `f`.\n     * @param batchSize Integer batch size or `null` / `undefined`.\n     * @param verbose verbosity mode.\n     * @param steps Total number of steps (batches of samples) before\n     * declaring test finished. Ignored with the default value of `null` /\n     * `undefined`.\n     * @returns Array of Scalars.\n     */\n    testLoop(f, ins, batchSize, verbose = 0, steps) {\n        return tfc.tidy(() => {\n            const numSamples = this.checkNumSamples(ins, batchSize, steps, 'steps');\n            const outs = [];\n            if (verbose > 0) {\n                throw new NotImplementedError('Verbose mode is not implemented yet.');\n            }\n            // TODO(cais): Use `indicesForConversionToDense' to prevent slow down.\n            if (steps != null) {\n                throw new NotImplementedError('steps mode in testLoop() is not implemented yet');\n            }\n            else {\n                const batches = makeBatches(numSamples, batchSize);\n                const indexArray = tensor1d(range(0, numSamples));\n                for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    const batchIds = K.sliceAlongFirstAxis(indexArray, batchStart, batchEnd - batchStart);\n                    // TODO(cais): In ins, train flag can be a number, instead of an\n                    //   Tensor? Do we need to handle this in tfjs-layers?\n                    const insBatch = sliceArraysByIndices(ins, batchIds);\n                    const batchOuts = f(insBatch);\n                    if (batchIndex === 0) {\n                        for (let i = 0; i < batchOuts.length; ++i) {\n                            outs.push(scalar(0));\n                        }\n                    }\n                    for (let i = 0; i < batchOuts.length; ++i) {\n                        const batchOut = batchOuts[i];\n                        outs[i] =\n                            tfc.add(outs[i], tfc.mul(batchEnd - batchStart, batchOut));\n                    }\n                }\n                for (let i = 0; i < outs.length; ++i) {\n                    outs[i] = tfc.div(outs[i], numSamples);\n                }\n            }\n            return outs;\n        });\n    }\n    getDedupedMetricsNames() {\n        const outLabels = this.metricsNames;\n        // Rename duplicated metrics names (can happen with an output layer\n        // shared among multiple dataflows).\n        const dedupedOutLabels = [];\n        for (let i = 0; i < outLabels.length; ++i) {\n            const label = outLabels[i];\n            let newLabel = label;\n            if (count(outLabels, label) > 1) {\n                const dupIndex = count(outLabels.slice(0, i), label);\n                newLabel += `_${dupIndex}`;\n            }\n            dedupedOutLabels.push(newLabel);\n        }\n        return dedupedOutLabels;\n    }\n    /**\n     * Creates a function that performs the following actions:\n     *\n     * 1. computes the losses\n     * 2. sums them to get the total loss\n     * 3. call the optimizer computes the gradients of the LayersModel's\n     *    trainable weights w.r.t. the total loss and update the variables\n     * 4. calculates the metrics\n     * 5. returns the values of the losses and metrics.\n     */\n    makeTrainFunction() {\n        return (data) => {\n            const lossValues = [];\n            const inputs = data.slice(0, this.inputs.length);\n            const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n            const sampleWeights = data.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2);\n            const metricsValues = [];\n            // Create a function that computes the total loss based on the\n            // inputs. This function is used for obtaining gradients through\n            // backprop.\n            const totalLossFunction = () => {\n                const feeds = [];\n                for (let i = 0; i < this.inputs.length; ++i) {\n                    feeds.push({ key: this.inputs[i], value: inputs[i] });\n                }\n                const feedDict = new FeedDict(feeds);\n                const outputs = execute(this.outputs, feedDict, { 'training': true });\n                // TODO(cais): Take care of the case of multiple outputs from a\n                //   single layer?\n                let totalLoss;\n                for (let i = 0; i < this.lossFunctions.length; ++i) {\n                    const lossFunction = this.lossFunctions[i];\n                    let loss = lossFunction(targets[i], outputs[i]);\n                    if (sampleWeights[i] != null) {\n                        loss = computeWeightedLoss(loss, sampleWeights[i]);\n                    }\n                    // TODO(cais): push Scalar instead.\n                    const meanLoss = tfc.mean(loss);\n                    // TODO(cais): Use a scope() instead, to avoid ownership.\n                    lossValues.push(meanLoss);\n                    if (i === 0) {\n                        totalLoss = loss;\n                    }\n                    else {\n                        totalLoss = tfc.add(totalLoss, loss);\n                    }\n                }\n                // Compute the metrics.\n                // TODO(cais): These should probably be calculated outside\n                //   totalLossFunction to benefit speed?\n                for (let i = 0; i < this.metricsTensors.length; ++i) {\n                    let weightedMetric;\n                    if (this.outputs.length > 1 && i < this.outputs.length) {\n                        weightedMetric = lossValues[i];\n                    }\n                    else {\n                        const metric = this.metricsTensors[i][0];\n                        const outputIndex = this.metricsTensors[i][1];\n                        weightedMetric =\n                            tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));\n                    }\n                    tfc.keep(weightedMetric);\n                    // TODO(cais): Use a scope() instead, to avoid ownership.\n                    metricsValues.push(weightedMetric);\n                }\n                totalLoss = tfc.mean(totalLoss);\n                // Add regularizer penalties.\n                this.calculateLosses().forEach(regularizerLoss => {\n                    totalLoss = tfc.add(totalLoss, regularizerLoss);\n                });\n                return totalLoss;\n            };\n            const variables = this.collectedTrainableWeights.map(param => param.read());\n            const returnCost = true;\n            const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables);\n            return [totalLossValue].concat(metricsValues);\n        };\n    }\n    /**\n     * Create a function which, when invoked with an array of `tf.Tensor`s as a\n     * batch of inputs, returns the prespecified loss and metrics of the model\n     * under the batch of input data.\n     */\n    makeTestFunction() {\n        this.testFunction = (data) => {\n            return tfc.tidy(() => {\n                const valOutputs = [];\n                let totalLoss;\n                const inputs = data.slice(0, this.inputs.length);\n                const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n                const feeds = [];\n                for (let i = 0; i < this.inputs.length; ++i) {\n                    feeds.push({ key: this.inputs[i], value: inputs[i] });\n                }\n                const feedDict = new FeedDict(feeds);\n                const outputs = execute(this.outputs, feedDict);\n                // Compute total loss.\n                for (let i = 0; i < this.lossFunctions.length; ++i) {\n                    const lossFunction = this.lossFunctions[i];\n                    // TODO(cais): Add sample weighting and replace the simple\n                    // averaging.\n                    const loss = tfc.mean(lossFunction(targets[i], outputs[i]));\n                    if (i === 0) {\n                        totalLoss = loss;\n                    }\n                    else {\n                        totalLoss = tfc.add(totalLoss, loss);\n                    }\n                    valOutputs.push(totalLoss);\n                }\n                // Compute the metrics.\n                for (let i = 0; i < this.metricsTensors.length; ++i) {\n                    const metric = this.metricsTensors[i][0];\n                    const outputIndex = this.metricsTensors[i][1];\n                    // TODO(cais): Replace K.mean() with a proper weighting function.\n                    const meanMetric = tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));\n                    valOutputs.push(meanMetric);\n                }\n                return valOutputs;\n            });\n        };\n    }\n    /**\n     * Trains the model for a fixed number of epochs (iterations on a\n     * dataset).\n     *\n     * ```js\n     * const model = tf.sequential({\n     *     layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * for (let i = 1; i < 5 ; ++i) {\n     *   const h = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *       batchSize: 4,\n     *       epochs: 3\n     *   });\n     *   console.log(\"Loss after Epoch \" + i + \" : \" + h.history.loss[0]);\n     * }\n     * ```\n     *\n     * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n     * model has multiple inputs. If all inputs in the model are named, you\n     * can also pass a dictionary mapping input names to `tf.Tensor`s.\n     * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n     * the model has multiple outputs. If all outputs in the model are named,\n     * you can also pass a dictionary mapping output names to `tf.Tensor`s.\n     * @param args A `ModelFitArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @exception ValueError In case of mismatch between the provided input\n     * data and what the model expects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fit(x, y, args = {}) {\n        return fitTensors(this, x, y, args);\n    }\n    // TODO(cais): Add code snippet below when it's possible to instantiate\n    //   actual dataset objects.\n    /**\n     * Trains the model using a dataset object.\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for training. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs.\n     *   Of the two items in the array, the first is the input feature(s) and\n     *   the second is the output target(s).\n     * @param args A `ModelFitDatasetArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fitDataset(dataset, args) {\n        return fitDataset(this, dataset, args);\n    }\n    /**\n     * Runs a single gradient update on a single batch of data.\n     *\n     * This method differs from `fit()` and `fitDataset()` in the following\n     * regards:\n     *   - It operates on exactly one batch of data.\n     *   - It returns only the loss and matric values, instead of\n     *     returning the batch-by-batch loss and metric values.\n     *   - It doesn't support fine-grained options such as verbosity and\n     *     callbacks.\n     *\n     * @param x Input data. It could be one of the following:\n     *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n     *     multiple inputs).\n     *   - An Object mapping input names to corresponding `tf.Tensor` (if the\n     *     model has named inputs).\n     * @param y Target darta. It could be either a `tf.Tensor` a multiple\n     *   `tf.Tensor`s. It should be consistent with `x`.\n     * @returns Training loss or losses (in case the model has\n     *   multiple outputs), along with metrics (if any), as numbers.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async trainOnBatch(x, y) {\n        // TODO(cais): Support sampleWeight and classWeight.\n        // TODO(cais): Support Dataset objects.\n        const standardizeOut = await this.standardizeUserData(x, y);\n        const inputs = standardizeOut[0];\n        const targets = standardizeOut[1];\n        const trainFunction = this.makeTrainFunction();\n        const losses = trainFunction(inputs.concat(targets));\n        const lossValues = [];\n        for (const loss of losses) {\n            const v = await loss.data();\n            lossValues.push(v[0]);\n        }\n        tfc.dispose(losses);\n        return singletonOrArray(lossValues);\n    }\n    /**\n     * Extract weight values of the model.\n     *\n     * @param config: An instance of `io.SaveConfig`, which specifies\n     * model-saving options such as whether only trainable weights are to be\n     * saved.\n     * @returns A `NamedTensorMap` mapping original weight names (i.e.,\n     *   non-uniqueified weight names) to their values.\n     */\n    getNamedWeights(config) {\n        const namedWeights = [];\n        const trainableOnly = config != null && config.trainableOnly;\n        const weights = trainableOnly ? this.trainableWeights : this.weights;\n        const weightValues = this.getWeights(trainableOnly);\n        for (let i = 0; i < weights.length; ++i) {\n            if (trainableOnly && !weights[i].trainable) {\n                // Optionally skip non-trainable weights.\n                continue;\n            }\n            namedWeights.push({ name: weights[i].originalName, tensor: weightValues[i] });\n        }\n        return namedWeights;\n    }\n    /**\n     * Setter used for force stopping of LayersModel.fit() (i.e., training).\n     *\n     * Example:\n     *\n     * ```js\n     * const input = tf.input({shape: [10]});\n     * const output = tf.layers.dense({units: 1}).apply(input);\n     * const model = tf.model({inputs: [input], outputs: [output]});\n     * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n     * const xs = tf.ones([8, 10]);\n     * const ys = tf.zeros([8, 1]);\n     *\n     * const history = await model.fit(xs, ys, {\n     *   epochs: 10,\n     *   callbacks: {\n     *     onEpochEnd: async (epoch, logs) => {\n     *       if (epoch === 2) {\n     *         model.stopTraining = true;\n     *       }\n     *     }\n     *   }\n     * });\n     *\n     * // There should be only 3 values in the loss array, instead of 10\n     * values,\n     * // due to the stopping after 3 epochs.\n     * console.log(history.history.loss);\n     * ```\n     */\n    set stopTraining(stop) {\n        this.stopTraining_ = stop;\n    }\n    get stopTraining() {\n        return this.stopTraining_;\n    }\n    get optimizer() {\n        return this.optimizer_;\n    }\n    set optimizer(optimizer) {\n        if (this.optimizer_ !== optimizer) {\n            this.optimizer_ = optimizer;\n            this.isOptimizerOwned = false;\n        }\n    }\n    dispose() {\n        const result = super.dispose();\n        if (result.refCountAfterDispose === 0 && this.optimizer != null &&\n            this.isOptimizerOwned) {\n            const numTensorsBeforeOptmizerDisposal = tfc.memory().numTensors;\n            this.optimizer_.dispose();\n            result.numDisposedVariables +=\n                numTensorsBeforeOptmizerDisposal - tfc.memory().numTensors;\n        }\n        return result;\n    }\n    getLossIdentifiers() {\n        let lossNames;\n        if (typeof this.loss === 'string') {\n            lossNames = toSnakeCase(this.loss);\n        }\n        else if (Array.isArray(this.loss)) {\n            for (const loss of this.loss) {\n                if (typeof loss !== 'string') {\n                    throw new Error('Serialization of non-string loss is not supported.');\n                }\n            }\n            lossNames = this.loss.map(name => toSnakeCase(name));\n        }\n        else {\n            const outputNames = Object.keys(this.loss);\n            lossNames = {};\n            const losses = this.loss;\n            for (const outputName of outputNames) {\n                if (typeof losses[outputName] === 'string') {\n                    lossNames[outputName] =\n                        toSnakeCase(losses[outputName]);\n                }\n                else {\n                    throw new Error('Serialization of non-string loss is not supported.');\n                }\n            }\n        }\n        return lossNames;\n    }\n    getMetricIdentifiers() {\n        if (typeof this.metrics === 'string' ||\n            typeof this.metrics === 'function') {\n            return [toSnakeCase(Metrics.getLossOrMetricName(this.metrics))];\n        }\n        else if (Array.isArray(this.metrics)) {\n            return this.metrics.map(metric => toSnakeCase(Metrics.getLossOrMetricName(metric)));\n        }\n        else {\n            const metricsIdentifiers = {};\n            for (const key in this.metrics) {\n                metricsIdentifiers[key] =\n                    toSnakeCase(Metrics.getLossOrMetricName(this.metrics[key]));\n            }\n            return metricsIdentifiers;\n        }\n    }\n    getTrainingConfig() {\n        return {\n            loss: this.getLossIdentifiers(),\n            metrics: this.getMetricIdentifiers(),\n            optimizer_config: {\n                class_name: this.optimizer.getClassName(),\n                config: this.optimizer.getConfig()\n            }\n        };\n        // TODO(cais): Add weight_metrics when they are supported.\n        // TODO(cais): Add sample_weight_mode when it's supported.\n        // TODO(cais): Add loss_weights when it's supported.\n    }\n    loadTrainingConfig(trainingConfig) {\n        if (trainingConfig.weighted_metrics != null) {\n            throw new Error('Loading weight_metrics is not supported yet.');\n        }\n        if (trainingConfig.loss_weights != null) {\n            throw new Error('Loading loss_weights is not supported yet.');\n        }\n        if (trainingConfig.sample_weight_mode != null) {\n            throw new Error('Loading sample_weight_mode is not supported yet.');\n        }\n        const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config);\n        const optimizer = deserialize(tsConfig);\n        let loss;\n        if (typeof trainingConfig.loss === 'string') {\n            loss = toCamelCase(trainingConfig.loss);\n        }\n        else if (Array.isArray(trainingConfig.loss)) {\n            loss = trainingConfig.loss.map(lossEntry => toCamelCase(lossEntry));\n        }\n        else if (trainingConfig.loss != null) {\n            loss = {};\n            for (const key in trainingConfig.loss) {\n                loss[key] = toCamelCase(trainingConfig.loss[key]);\n            }\n        }\n        let metrics;\n        if (Array.isArray(trainingConfig.metrics)) {\n            metrics = trainingConfig.metrics.map(metric => toCamelCase(metric));\n        }\n        else if (trainingConfig.metrics != null) {\n            metrics = {};\n            for (const key in trainingConfig.metrics) {\n                metrics[key] = toCamelCase(trainingConfig.metrics[key]);\n            }\n        }\n        this.compile({ loss, metrics, optimizer });\n    }\n    /**\n     * Save the configuration and/or weights of the LayersModel.\n     *\n     * An `IOHandler` is an object that has a `save` method of the proper\n     * signature defined. The `save` method manages the storing or\n     * transmission of serialized data (\"artifacts\") that represent the\n     * model's topology and weights onto or via a specific medium, such as\n     * file downloads, local storage, IndexedDB in the web browser and HTTP\n     * requests to a server. TensorFlow.js provides `IOHandler`\n     * implementations for a number of frequently used saving mediums, such as\n     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n     * for more details.\n     *\n     * This method also allows you to refer to certain types of `IOHandler`s\n     * as URL-like string shortcuts, such as 'localstorage://' and\n     * 'indexeddb://'.\n     *\n     * Example 1: Save `model`'s topology and weights to browser [local\n     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n     * then load it back.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * console.log('Prediction from original model:');\n     * model.predict(tf.ones([1, 3])).print();\n     *\n     * const saveResults = await model.save('localstorage://my-model-1');\n     *\n     * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * loadedModel.predict(tf.ones([1, 3])).print();\n     * ```\n     *\n     * Example 2. Saving `model`'s topology and weights to browser\n     * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n     * then load it back.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * console.log('Prediction from original model:');\n     * model.predict(tf.ones([1, 3])).print();\n     *\n     * const saveResults = await model.save('indexeddb://my-model-1');\n     *\n     * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * loadedModel.predict(tf.ones([1, 3])).print();\n     * ```\n     *\n     * Example 3. Saving `model`'s topology and weights as two files\n     * (`my-model-1.json` and `my-model-1.weights.bin`) downloaded from\n     * browser.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * const saveResults = await model.save('downloads://my-model-1');\n     * ```\n     *\n     * Example 4. Send  `model`'s topology and weights to an HTTP server.\n     * See the documentation of `tf.io.http` for more details\n     * including specifying request parameters and implementation of the\n     * server.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * const saveResults = await model.save('http://my-server/model/upload');\n     * ```\n     *\n     * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n     * scheme-based string shortcut for `IOHandler`.\n     * @param config Options for saving the model.\n     * @returns A `Promise` of `SaveResult`, which summarizes the result of\n     * the saving, such as byte sizes of the saved artifacts for the model's\n     *   topology and weight values.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async save(handlerOrURL, config) {\n        if (typeof handlerOrURL === 'string') {\n            const handlers = io.getSaveHandlers(handlerOrURL);\n            if (handlers.length === 0) {\n                throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n            }\n            else if (handlers.length > 1) {\n                throw new ValueError(`Found more than one (${handlers.length}) save handlers for ` +\n                    `URL '${handlerOrURL}'`);\n            }\n            handlerOrURL = handlers[0];\n        }\n        if (handlerOrURL.save == null) {\n            throw new ValueError('LayersModel.save() cannot proceed because the IOHandler ' +\n                'provided does not have the `save` attribute defined.');\n        }\n        const weightDataAndSpecs = await io.encodeWeights(this.getNamedWeights(config));\n        const returnString = false;\n        const unusedArg = null;\n        const modelConfig = this.toJSON(unusedArg, returnString);\n        const modelArtifacts = {\n            modelTopology: modelConfig,\n            format: LAYERS_MODEL_FORMAT_NAME,\n            generatedBy: `TensorFlow.js tfjs-layers v${version}`,\n            convertedBy: null,\n        };\n        const includeOptimizer = config == null ? false : config.includeOptimizer;\n        if (includeOptimizer && this.optimizer != null) {\n            modelArtifacts.trainingConfig = this.getTrainingConfig();\n            const weightType = 'optimizer';\n            const { data: optimizerWeightData, specs: optimizerWeightSpecs } = await io.encodeWeights(await this.optimizer.getWeights(), weightType);\n            weightDataAndSpecs.specs.push(...optimizerWeightSpecs);\n            weightDataAndSpecs.data = io.concatenateArrayBuffers([weightDataAndSpecs.data, optimizerWeightData]);\n        }\n        if (this.userDefinedMetadata != null) {\n            // Check serialized size of user-defined metadata.\n            const checkSize = true;\n            checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize);\n            modelArtifacts.userDefinedMetadata = this.userDefinedMetadata;\n        }\n        modelArtifacts.weightData = weightDataAndSpecs.data;\n        modelArtifacts.weightSpecs = weightDataAndSpecs.specs;\n        return handlerOrURL.save(modelArtifacts);\n    }\n    /**\n     * Set user-defined metadata.\n     *\n     * The set metadata will be serialized together with the topology\n     * and weights of the model during `save()` calls.\n     *\n     * @param setUserDefinedMetadata\n     */\n    setUserDefinedMetadata(userDefinedMetadata) {\n        checkUserDefinedMetadata(userDefinedMetadata, this.name);\n        this.userDefinedMetadata = userDefinedMetadata;\n    }\n    /**\n     * Get user-defined metadata.\n     *\n     * The metadata is supplied via one of the two routes:\n     *   1. By calling `setUserDefinedMetadata()`.\n     *   2. Loaded during model loading (if the model is constructed\n     *      via `tf.loadLayersModel()`.)\n     *\n     * If no user-defined metadata is available from either of the\n     * two routes, this function will return `undefined`.\n     */\n    getUserDefinedMetadata() {\n        return this.userDefinedMetadata;\n    }\n}\n// The class name is 'Model' rather than 'LayersModel' for backwards\n// compatibility since this class name shows up in the serialization format.\n/** @nocollapse */\nLayersModel.className = 'Model';\nserialization.registerClass(LayersModel);\n/**\n * A `tf.Functional` is an alias to `tf.LayersModel`.\n *\n * See also:\n *   `tf.LayersModel`, `tf.Sequential`, `tf.loadLayersModel`.\n */\n/** @doc {heading: 'Models', subheading: 'Classes'} */\nexport class Functional extends LayersModel {\n}\nFunctional.className = 'Functional';\nserialization.registerClass(Functional);\n//# sourceMappingURL=training.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source keras/models.py */\nimport { dispose, io, serialization, util } from '@tensorflow/tfjs-core';\nimport { getUid } from './backend/state';\nimport { Input } from './engine/input_layer';\nimport { getSourceInputs, Node } from './engine/topology';\nimport { LayersModel } from './engine/training';\nimport { NotImplementedError, RuntimeError, ValueError } from './errors';\nimport { deserialize } from './layers/serialization';\nimport * as generic_utils from './utils/generic_utils';\nimport { convertPythonicToTs } from './utils/serialization_utils';\nimport { getExactlyOneShape } from './utils/types_utils';\n/**\n * Parses a JSON model configuration file and returns a model instance.\n *\n * ```js\n * // This example shows how to serialize a model using `toJSON()` and\n * // deserialize it as another model using `tf.models.modelFromJSON()`.\n * // Note: this example serializes and deserializes only the topology\n * // of the model; the weights of the loaded model will be different\n * // from those of the the original model, due to random weight\n * // initialization.\n * // To load the topology and weights of a model, use `tf.loadLayersModel()`.\n * const model1 = tf.sequential();\n * model1.add(tf.layers.repeatVector({inputShape: [2], n: 4}));\n * // Serialize `model1` as a JSON object.\n * const model1JSON = model1.toJSON(null, false);\n * model1.summary();\n *\n * const model2 = await tf.models.modelFromJSON(model1JSON);\n * model2.summary();\n * ```\n *\n *  @param modelAndWeightsConfig JSON object or string encoding a model and\n *       weights configuration. It can also be only the topology JSON of the\n *       model, in which case the weights will not be loaded.\n *  @param custom_objects Optional dictionary mapping names\n *       (strings) to custom classes or functions to be\n *       considered during deserialization.\n * @returns A TensorFlow.js Layers `tf.LayersModel` instance (uncompiled).\n */\nexport async function modelFromJSON(modelAndWeightsConfig, customObjects) {\n    if (!('modelTopology' in modelAndWeightsConfig)) {\n        modelAndWeightsConfig = { modelTopology: modelAndWeightsConfig };\n    }\n    modelAndWeightsConfig = modelAndWeightsConfig;\n    let modelTopology = modelAndWeightsConfig.modelTopology;\n    if (modelTopology['model_config'] != null) {\n        // If the model-topology JSON contains a 'model_config' field, then it is\n        // a full model JSON (e.g., from `keras.Model.save()`), which contains\n        // not only the model's architecture in its 'model_config' field, but\n        // additional information such as the model's optimizer. We use only the\n        // 'model_config' field currently.\n        modelTopology = modelTopology['model_config'];\n    }\n    const tsConfig = convertPythonicToTs(modelTopology);\n    const model = deserialize(tsConfig, customObjects);\n    if (modelAndWeightsConfig.weightsManifest != null) {\n        // Load the weight values keyed by the original tensor names in the model\n        // file that was loaded.  These should match the keys of the weight\n        // manifest.\n        const weightValues = await io.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model.weights.map(weight => weight.originalName));\n        // Map the weights to the unique tensor names generated during model loading\n        const uniqueWeightValues = {};\n        for (const weight of model.weights) {\n            uniqueWeightValues[weight.originalName] =\n                weightValues[weight.originalName];\n        }\n        model.loadWeights(uniqueWeightValues);\n        // Dispose temporary weight values.\n        dispose(weightValues);\n    }\n    return model;\n}\n/**\n * Load a model, including its topology and optionally weights.  See the\n * Tutorial named \"How to import a Keras Model\" for usage examples.\n *\n * Example 1: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 2. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Load a model from user-selected files from HTML\n * [file input\n * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).\n *\n * ```js\n * // Note: this code snippet will not work without the HTML elements in the\n * //   page\n * const jsonUpload = document.getElementById('json-upload');\n * const weightsUpload = document.getElementById('weights-upload');\n *\n * const model = await tf.loadLayersModel(\n *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));\n * ```\n *\n * Example 4. Load a model from an HTTP server.\n *\n * ```js\n * const model = await\n *     tf.loadLayersModel('https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');\n * model.summary();\n * ```\n *\n * @param pathOrIOHandler Can be either of the two formats\n *   1. A string path to the `ModelAndWeightsConfig` JSON describing\n *      the model in the canonical TensorFlow.js format. This path will be\n *      interpreted as a relative HTTP path, to which `fetch` will be used to\n *      request the model topology and weight manifest JSON.\n *      The content of the JSON file is assumed to be a JSON object with the\n *      following fields and values:\n *      - 'modelTopology': A JSON object that can be either of:\n *        1. a model architecture JSON consistent with the format of the return\n *            value of `keras.Model.to_json()`\n *        2. a full model JSON in the format of `keras.models.save_model()`.\n *      - 'weightsManifest': A TensorFlow.js weights manifest.\n *      See the Python converter function `save_model()` for more details.\n *      It is also assumed that model weights can be accessed from relative\n *      paths described by the `paths` fields in weights manifest.\n *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`\n *      method.\n * @param options Optional configuration arguments for the model loading,\n *   including:\n *   - `strict`: Require that the provided weights exactly match those required\n *     by the layers.  Default true.  Passing false means that both extra\n *     weights and missing weights will be silently ignored.\n *   - `onProgress`: A progress callback of the form:\n *     `(fraction: number) => void`. This callback can be used to monitor the\n *     model-loading process.\n * @returns A `Promise` of `tf.LayersModel`, with the topology and weights\n *     loaded.\n */\nexport async function loadLayersModelInternal(pathOrIOHandler, options) {\n    if (options == null) {\n        options = {};\n    }\n    if (typeof pathOrIOHandler === 'string') {\n        const handlers = io.getLoadHandlers(pathOrIOHandler, options);\n        if (handlers.length === 0) {\n            // For backward compatibility: if no load handler can be found,\n            // assume it is a relative http path.\n            // TODO(cais): Reformat the args into a single `LoadOptions` once the core\n            // is refactored.\n            handlers.push(io.browserHTTPRequest(pathOrIOHandler, options));\n        }\n        else if (handlers.length > 1) {\n            throw new ValueError(`Found more than one (${handlers.length}) load handlers for ` +\n                `URL '${pathOrIOHandler}'`);\n        }\n        pathOrIOHandler = handlers[0];\n    }\n    return loadLayersModelFromIOHandler(pathOrIOHandler, undefined, options);\n}\n/**\n * Load a model and optionally its weights, using an IOHandler object.\n *\n * @param handler The instance of `IOHandler` to be used during the model\n *   loading.\n * @param customObjects Any optional custom objects to be used during model\n *   loading.\n * @param strict Whether the weight loading will be done in strict mode.\n *   Default: `true`.\n */\nexport async function loadLayersModelFromIOHandler(handler, customObjects, options) {\n    if (options == null) {\n        options = {};\n    }\n    if (handler.load == null) {\n        throw new ValueError('Cannot proceed with model loading because the IOHandler provided ' +\n            'does not have the `load` method implemented.');\n    }\n    const artifacts = await handler.load();\n    let modelTopology = artifacts.modelTopology;\n    if (modelTopology['model_config'] != null) {\n        modelTopology = modelTopology['model_config'];\n    }\n    const strict = options.strict == null ? true : options.strict;\n    // If weights are provided and the weight-loading mode is strict, use\n    // fast weight initialization. This skips costly initializers such as\n    // 'orthogonal' and saves unnecessary computation in cases where\n    // the initialized weight values will immediately be overwritten by\n    // loaded weight values.\n    const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict;\n    const model = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit);\n    const trainingConfig = artifacts.trainingConfig;\n    if (trainingConfig != null) {\n        model.loadTrainingConfig(trainingConfig);\n    }\n    if (artifacts.userDefinedMetadata != null) {\n        model.setUserDefinedMetadata(artifacts.userDefinedMetadata);\n    }\n    // If weightData is present, load the weights into the model.\n    if (artifacts.weightData != null) {\n        // Loading weights requires weightSpecs.\n        if (artifacts.weightSpecs == null) {\n            throw new ValueError('LayersModel artifacts contains weight data, but not weight specs. ' +\n                'Therefore loading of weights cannot proceed.');\n        }\n        const { modelWeights, optimizerWeights } = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs);\n        model.loadWeights(modelWeights, strict);\n        if (model.optimizer != null && optimizerWeights.length > 0) {\n            await model.optimizer.setWeights(optimizerWeights);\n        }\n        // Dispose temporary weight values.\n        dispose(modelWeights);\n        dispose(optimizerWeights.map(w => w.tensor));\n    }\n    return model;\n}\nfunction decodeModelAndOptimizerWeights(buffer, specs) {\n    const name2Tensor = io.decodeWeights(buffer, specs);\n    const modelWeights = {};\n    const optimizerWeights = [];\n    specs.forEach(spec => {\n        if (spec.group === 'optimizer') {\n            optimizerWeights.push({ name: spec.name, tensor: name2Tensor[spec.name] });\n        }\n        else {\n            modelWeights[spec.name] = name2Tensor[spec.name];\n        }\n    });\n    return { modelWeights, optimizerWeights };\n}\n/**\n * A model with a stack of layers, feeding linearly from one to the next.\n *\n * `tf.sequential` is a factory function that creates an instance of\n * `tf.Sequential`.\n *\n * ```js\n *  // Define a model for linear regression.\n *  const model = tf.sequential();\n *  model.add(tf.layers.dense({units: 1, inputShape: [1]}));\n *\n *  // Prepare the model for training: Specify the loss and the optimizer.\n *  model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n *\n *  // Generate some synthetic data for training.\n *  const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);\n *  const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);\n *\n *  // Train the model using the data then do inference on a data point the\n *  // model hasn't seen:\n *  await model.fit(xs, ys);\n *  model.predict(tf.tensor2d([5], [1, 1])).print();\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class Sequential extends LayersModel {\n    constructor(args) {\n        super({ inputs: [], outputs: [] });\n        args = args || {};\n        this.trainable = true;\n        this.built = false;\n        // Set model name.\n        this.name = (args.name != null) ? args.name : getUid('sequential_');\n        // Add to the model any layers passed to the constructor.\n        if (args.layers != null) {\n            for (const layer of args.layers) {\n                this.add(layer);\n            }\n        }\n    }\n    // Helper function to Sequential.add  Throws if the new output shape will be\n    // invalid.\n    checkShape(layer) {\n        const shape = layer.inboundNodes[0].outputTensors[0].shape;\n        if (shape.some(x => x < 0)) {\n            throw new ValueError('Negative dimension size caused by adding layer ' +\n                `${layer.name} with input shape [` +\n                `${layer.inboundNodes[0].inputTensors[0].shape}]`);\n        }\n    }\n    /**\n     * Adds a layer instance on top of the layer stack.\n     *\n     * ```js\n     *  const model = tf.sequential();\n     *  model.add(tf.layers.dense({units: 8, inputShape: [1]}));\n     *  model.add(tf.layers.dense({units: 4, activation: 'relu6'}));\n     *  model.add(tf.layers.dense({units: 1, activation: 'relu6'}));\n     *  // Note that the untrained model is random at this point.\n     *  model.predict(tf.randomNormal([10, 1])).print();\n     * ```\n     * @param layer Layer instance.\n     *\n     * @exception ValueError In case the `layer` argument does not know its\n     * input shape.\n     * @exception ValueError In case the `layer` argument has multiple output\n     *   tensors, or is already connected somewhere else (forbidden in\n     *   `Sequential` models).\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    add(layer) {\n        const isLayerModelInstance = layer instanceof Sequential || layer instanceof LayersModel;\n        let modelLayer;\n        if (isLayerModelInstance) {\n            modelLayer = layer;\n            if (modelLayer.outputs.length !== 1) {\n                throw new ValueError('All layers in a Sequential model ' +\n                    'should have a single output tensor. ' +\n                    'For multi-output layers, ' +\n                    'use the functional API.');\n            }\n            if (modelLayer.inputs.length !== 1) {\n                throw new ValueError('All layers in a Sequential model ' +\n                    'should have a single input tensor. ' +\n                    'For multi-input layers, ' +\n                    'use the functional API.');\n            }\n        }\n        if (this.outputs.length === 0) {\n            // first layer in model: check that it is an input layer\n            if (layer.inboundNodes.length === 0) {\n                // create an input layer\n                if (layer.batchInputShape == null) {\n                    throw new ValueError('The first layer in a Sequential model must ' +\n                        'get an `inputShape` or `batchInputShape` argument.');\n                }\n                // Instantiate the input layer.\n                const x = Input({\n                    batchShape: layer.batchInputShape,\n                    dtype: layer.dtype,\n                    name: layer.name + '_input'\n                });\n                // This will build the current layer and create the node connecting\n                // the current layer to the input layer we just created.\n                layer.apply(x);\n            }\n            if (isLayerModelInstance) {\n                this.outputs = modelLayer.outputs;\n                this.inputs = modelLayer.inputs;\n            }\n            else {\n                if (layer.inboundNodes.length !== 1) {\n                    throw new ValueError('A layer added to a Sequential model must not already be ' +\n                        `connected somewhere else. LayersModel received layer ${layer.name} ` +\n                        `which has ${layer.inboundNodes.length} pre-existing inbound ` +\n                        'connections.');\n                }\n                if (layer.inboundNodes[0].outputTensors.length !== 1) {\n                    throw new ValueError('All layers in a Sequential model ' +\n                        'should have a single output tensor. ' +\n                        'For multi-output layers, ' +\n                        'use the functional API.');\n                }\n                this.checkShape(layer);\n                this.outputs = [layer.inboundNodes[0].outputTensors[0]];\n                this.inputs = getSourceInputs(this.outputs[0]);\n            }\n            this.inboundNodes = [];\n            // We create an input node, which we will keep updated\n            // as we add more layers.\n            // (This call has side effects.)\n            // tslint:disable-next-line:no-unused-expression\n            new Node({\n                outboundLayer: this,\n                inboundLayers: [],\n                nodeIndices: [],\n                tensorIndices: [],\n                inputTensors: this.inputs,\n                outputTensors: this.outputs,\n                // no model-level masking for now\n                inputMasks: generic_utils.pyListRepeat(null, this.inputs.length),\n                outputMasks: [null],\n                inputShapes: this.inputs.map(x => x.shape),\n                outputShapes: this.outputs[0].shape\n            });\n        }\n        else {\n            const outputTensor = layer.apply(this.outputs[0]);\n            if (Array.isArray(outputTensor)) {\n                throw new TypeError('All layers in a Sequential model ' +\n                    'should have a single output tensor. ' +\n                    'For multi-output layers, ' +\n                    'use the functional API.');\n            }\n            this.checkShape(layer);\n            this.outputs = [outputTensor];\n            // update self.inbound_nodes\n            this.inboundNodes[0].outputTensors = this.outputs;\n            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n        }\n        this.layers.push(layer);\n        this.built = false;\n    }\n    /**\n     * Removes the last layer in the model.\n     *\n     * @exception TypeError if there are no layers in the model.\n     */\n    pop() {\n        if (this.layers.length === 0) {\n            throw new TypeError('There are no layers in the model.');\n        }\n        this.layers.pop();\n        if (this.layers.length === 0) {\n            this.outputs = [];\n            this.inboundNodes = [];\n            this.outboundNodes = [];\n        }\n        else {\n            const lastLayerIndex = this.layers.length - 1;\n            this.layers[lastLayerIndex].outboundNodes = [];\n            this.outputs = [this.layers[lastLayerIndex].output];\n            // update self.inbound_nodes\n            this.inboundNodes[0].outputTensors = this.outputs;\n            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n        }\n    }\n    call(inputs, kwargs) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.call(inputs, kwargs);\n    }\n    build(inputShape) {\n        // Call `getExactlyOneShape` without using its return value,\n        // to verify that exactly one input shape is provided.\n        getExactlyOneShape(inputShape);\n        if (this.inputs.length === 0 || this.outputs.length === 0) {\n            throw new TypeError('Sequential model cannot be built: model is empty.' +\n                ' Add some layers first.');\n        }\n        // actually create the model\n        this.model = new LayersModel({\n            inputs: this.inputs,\n            outputs: this.outputs[0],\n            name: this.name + '_model'\n        });\n        this.model.trainable = this.trainable;\n        // mirror model attributes\n        this.supportsMasking = this.model.supportsMasking;\n        // TODO(michaelterry): Add caches\n        this.inputLayers = this.model.inputLayers;\n        this.inputLayersNodeIndices = this.model.inputLayersNodeIndices;\n        this.inputLayersTensorIndices = this.model.inputLayersTensorIndices;\n        this.outputLayers = this.model.outputLayers;\n        this.outputLayersNodeIndices = this.model.outputLayersNodeIndices;\n        this.outputLayersTensorIndices = this.model.outputLayersTensorIndices;\n        this.nodesByDepth = this.model.nodesByDepth;\n        this.containerNodes = this.model.containerNodes;\n        this.outputNames = this.model.outputNames;\n        this.inputNames = this.model.inputNames;\n        // TODO(michaelterry): Add feedInputNames, feedInputs, if needed.\n        // TODO(michaelterry): Add callbackModel if needed.\n        this.built = true;\n    }\n    countParams() {\n        if (!this.built) {\n            this.build();\n        }\n        return super.countParams();\n    }\n    /**\n     * Print a text summary of the Sequential model's layers.\n     *\n     * The summary includes\n     * - Name and type of all layers that comprise the model.\n     * - Output shape(s) of the layers\n     * - Number of weight parameters of each layer\n     * - The total number of trainable and non-trainable parameters of the\n     * model.\n     *\n     * ```js\n     * const model = tf.sequential();\n     * model.add(\n     *     tf.layers.dense({units: 100, inputShape: [10], activation: 'relu'}));\n     * model.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));\n     *\n     * model.summary();\n     * ```\n     *\n     * @param lineLength Custom line length, in number of characters.\n     * @param positions Custom widths of each of the columns, as either\n     *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n     *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n     *   right-most (i.e., ending) position of a column.\n     * @param printFn Custom print function. Can be used to replace the default\n     *   `console.log`. For example, you can use `x => {}` to mute the printed\n     *   messages in the console.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    summary(lineLength, positions, printFn = console.log) {\n        if (!this.built) {\n            this.build();\n        }\n        super.summary(lineLength, positions, printFn);\n    }\n    /**\n     * Sets the weights of the model.\n     *\n     * @param weights Should be a list of Tensors with shapes and types matching\n     *   the output of `model.getWeights()`.\n     */\n    setWeights(weights) {\n        if (this.model == null) {\n            this.build();\n        }\n        this.model.setWeights(weights);\n    }\n    /**\n     * Returns the loss value & metrics values for the model in test mode.\n     *\n     * Loss and metrics are specified during `compile()`, which needs to happen\n     * before calls to `evaluate()`.\n     *\n     * Computation is done in batches.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const result = model.evaluate(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *   batchSize: 4,\n     * });\n     * result.print();\n     * ```\n     *\n     * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple inputs.\n     * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple outputs.\n     * @param args A `ModelEvaluateConfig`, containing optional fields.\n     *\n     * @return `Scalar` test loss (if the model has a single output and no\n     *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n     *   and/or metrics). The attribute `model.metricsNames`\n     *   will give you the display labels for the scalar outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    evaluate(x, y, args = {}) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before being used.');\n        }\n        return this.model.evaluate(x, y, args);\n    }\n    // TODO(cais): Add code snippet below once real dataset objects are\n    //   available.\n    /**\n     * Evaluate model using a dataset object.\n     *\n     * Note: Unlike `evaluate()`, this method is asynchronous (`async`);\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for evaluation. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs. Of the two items in the array, the\n     *   first is the input feature(s) and the second is the output target(s).\n     * @param args A configuration object for the dataset-based evaluation.\n     * @returns Loss and metric values as an Array of `Scalar` objects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async evaluateDataset(dataset, args) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before being used.');\n        }\n        return this.model.evaluateDataset(dataset, args);\n    }\n    /**\n     * Generates output predictions for the input samples.\n     *\n     * Computation is done in batches.\n     *\n     * Note: the \"step\" mode of predict() is currently not supported.\n     *   This is because the TensorFow.js core backend is imperative only.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predict(tf.ones([2, 10])).print();\n     * ```\n     *\n     * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n     *   the model has multiple inputs.\n     * @param conifg A `ModelPredictConfig` object containing optional fields.\n     *\n     * @return `tf.Tensor`(s) of predictions.\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and the model's expectations, or in case a stateful model receives a\n     *   number of samples that is not a multiple of the batch size.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(x, args = {}) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.predict(x, args);\n    }\n    /**\n     * Returns predictions for a single batch of samples.\n     *\n     * @param x: Input samples, as a Tensor, or list of Tensors (if the model\n     *   has multiple inputs).\n     * @return Tensor(s) of predictions\n     */\n    predictOnBatch(x) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.predictOnBatch(x);\n    }\n    /**\n     * See `LayersModel.compile`.\n     *\n     * @param args\n     */\n    compile(args) {\n        this.build();\n        this.model.compile(args);\n        this.optimizer_ = this.model.optimizer;\n        // tslint:disable-next-line:no-any\n        this.isOptimizerOwned = this.model.isOptimizerOwned;\n        this.loss = this.model.loss;\n        this.metrics = this.model.metrics;\n        // TODO(cais): Add this.lossWeights, this.sampleWeightMode,\n        //   this.weightedMetrics, this.targets.\n        this.metricsTensors = this.model.metricsTensors;\n        this.metricsNames = this.model.metricsNames;\n        // TODO(cais): Add sampleWeights.\n    }\n    get optimizer() {\n        return this.model == null ? undefined : this.model.optimizer;\n    }\n    set optimizer(optimizer) {\n        this.model.optimizer = optimizer;\n    }\n    /**\n     * Trains the model for a fixed number of epochs (iterations on a dataset).\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const history = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *   batchSize: 4,\n     *   epochs: 3\n     * });\n     * console.log(history.history.loss[0]);\n     * ```\n     *\n     * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n     * model has multiple inputs. If all inputs in the model are named, you can\n     * also pass a dictionary mapping input names to `tf.Tensor`s.\n     * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n     * the model has multiple outputs. If all outputs in the model are named, you\n     *  can also pass a dictionary mapping output names to `tf.Tensor`s.\n     * @param args  A `ModelFitConfig`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and what the model expects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fit(x, y, args = {}) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before ' +\n                'being used.');\n        }\n        return this.model.fit(x, y, args);\n    }\n    /**\n     * Trains the model using a dataset object.\n     *\n     * ```js\n     * const xArray = [\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     * ];\n     * const yArray = [1, 1, 1, 1];\n     * // Create a dataset from the JavaScript array.\n     * const xDataset = tf.data.array(xArray);\n     * const yDataset = tf.data.array(yArray);\n     * // Zip combines the `x` and `y` Datasets into a single Dataset, the\n     * // iterator of which will return an object containing of two tensors,\n     * // corresponding to `x` and `y`.  The call to `batch(4)` will bundle\n     * // four such samples into a single object, with the same keys now pointing\n     * // to tensors that hold 4 examples, organized along the batch dimension.\n     * // The call to `shuffle(4)` causes each iteration through the dataset to\n     * // happen in a different order.  The size of the shuffle window is 4.\n     * const xyDataset = tf.data.zip({xs: xDataset, ys: yDataset})\n     *     .batch(4)\n     *     .shuffle(4);\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [9]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const history = await model.fitDataset(xyDataset, {\n     *   epochs: 4,\n     *   callbacks: {onEpochEnd: (epoch, logs) => console.log(logs.loss)}\n     * });\n     * ```\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected to\n     *   generate a dataset iterator object, the `next()` method of which is\n     *   expected to produce data batches for evaluation. The return value of the\n     *   `next()` call ought to contain a boolean `done` field and a `value`\n     *   field.\n     *\n     *   The `value` field is expected to be an object of with fields\n     *   `xs` and `ys`, which point to the feature tensor and the target tensor,\n     *   respectively. This case is for models with exactly one input and one\n     *   output (e.g.. a sequential model). For example:\n     *   ```js\n     *   {value: {xs: xsTensor, ys: ysTensor}, done: false}\n     *   ```\n     *\n     *   If the model has multiple inputs, the `xs` field of `value` should\n     *   be an object mapping input names to their respective feature tensors.\n     *   For example:\n     *   ```js\n     *   {\n     *     value: {\n     *       xs: {\n     *         input_1: xsTensor1,\n     *         input_2: xsTensor2\n     *       },\n     *       ys: ysTensor\n     *     },\n     *     done: false\n     *   }\n     *   ```\n     *   If the model has multiple outputs, the `ys` field of `value` should\n     *   be an object mapping output names to their respective target tensors.\n     *   For example:\n     *   ```js\n     *   {\n     *     value: {\n     *       xs: xsTensor,\n     *       ys: {\n     *         output_1: ysTensor1,\n     *         output_2: ysTensor2\n     *       },\n     *     },\n     *     done: false\n     *   }\n     *   ```\n     * @param args A `ModelFitDatasetArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async fitDataset(dataset, args) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before ' +\n                'being used.');\n        }\n        return this.model.fitDataset(dataset, args);\n    }\n    /**\n     * Runs a single gradient update on a single batch of data.\n     *\n     * This method differs from `fit()` and `fitDataset()` in the following\n     * regards:\n     *   - It operates on exactly one batch of data.\n     *   - It returns only the loss and matric values, instead of\n     *     returning the batch-by-batch loss and metric values.\n     *   - It doesn't support fine-grained options such as verbosity and\n     *     callbacks.\n     *\n     * @param x Input data. It could be one of the following:\n     *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n     *     multiple inputs).\n     *   - An Object mapping input names to corresponding `tf.Tensor` (if the\n     *     model has named inputs).\n     * @param y Target darta. It could be either a `tf.Tensor` a multiple\n     *   `tf.Tensor`s. It should be consistent with `x`.\n     * @returns Training loss or losses (in case the model has\n     *   multiple outputs), along with metrics (if any), as numbers.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async trainOnBatch(x, y) {\n        return this.model.trainOnBatch(x, y);\n    }\n    /* See parent class for JsDoc */\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n        let configArray;\n        let extraModelConfig = {};\n        if (config instanceof Array) {\n            if (!(config[0].className != null) ||\n                config[0]['className'] === 'Merge') {\n                throw new ValueError('Legacy serialization format not supported yet.');\n            }\n            configArray = config;\n        }\n        else {\n            util.assert(config['layers'] != null, () => `When the config data for a Sequential model is not an Array, ` +\n                `it must be an Object that contains the 'layers' field.`);\n            configArray = config['layers'];\n            delete config['layers'];\n            extraModelConfig = config;\n        }\n        const model = new cls(extraModelConfig);\n        if (!(model instanceof Sequential)) {\n            throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model}`);\n        }\n        for (const conf of configArray) {\n            const customObjects = undefined;\n            const layer = deserialize(conf, customObjects, fastWeightInit);\n            if (fastWeightInit) {\n                layer.setFastWeightInitDuringBuild(true);\n            }\n            model.add(layer);\n        }\n        return model;\n    }\n    /**\n     * Setter used for force stopping of LayersModel.fit() (i.e., training).\n     *\n     * Example:\n     *\n     * ```js\n     * const model = tf.sequential();\n     * model.add(tf.layers.dense({units: 1, inputShape: [10]}));\n     * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n     * const xs = tf.ones([8, 10]);\n     * const ys = tf.zeros([8, 1]);\n     *\n     * const history = await model.fit(xs, ys, {\n     *   epochs: 10,\n     *   callbacks: {\n     *     onEpochEnd: async (epoch, logs) => {\n     *       if (epoch === 2) {\n     *         model.stopTraining = true;\n     *       }\n     *     }\n     *   }\n     * });\n     *\n     * // There should be only 3 values in the loss array, instead of 10 values,\n     * // due to the stopping after 3 epochs.\n     * console.log(history.history.loss);\n     * ```\n     */\n    set stopTraining(stop) {\n        // TODO(cais): When refactoring to remove the composition pattern happens,\n        // remove this method overriding.\n        if (this.model == null) {\n            throw new ValueError('Cannot set the stopTraining property of a sequential model before ' +\n                'it is compiled.');\n        }\n        this.model.stopTraining = stop;\n    }\n    get stopTraining() {\n        if (this.model == null) {\n            throw new ValueError('Cannot get the stopTraining property of a sequential model before ' +\n                'it is compiled.');\n        }\n        return this.model.stopTraining;\n    }\n    // TODO(cais): Override get trainableWeights() here\n    // tslint:disable-next-line:no-any\n    getConfig() {\n        // NOTE(cais): We override the return type of getConfig() to `any` here,\n        //   because the `Sequential` class is a special case among `Container`\n        //   subtypes in that its getConfig() method returns an Array (not a\n        //   dict).\n        const layers = [];\n        for (const layer of this.layers) {\n            const dict = {};\n            dict['className'] = layer.getClassName();\n            dict['config'] = layer.getConfig();\n            layers.push(dict);\n        }\n        return { name: this.name, layers };\n    }\n}\n/** @nocollapse */\nSequential.className = 'Sequential';\nserialization.registerClass(Sequential);\n//# sourceMappingURL=models.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { CallbackConstructorRegistry } from './base_callbacks';\nimport { Input, } from './engine/input_layer';\nimport { LayersModel } from './engine/training';\nimport { loadLayersModelInternal, Sequential } from './models';\n// TODO(cais): Add doc string to all the public static functions in this\n//   class; include exectuable JavaScript code snippets where applicable\n//   (b/74074458).\n// LayersModel and related factory methods.\n/**\n * A model is a data structure that consists of `Layers` and defines inputs\n * and outputs.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.model` is more generic, supporting an arbitrary graph (without\n * cycles) of layers. `tf.sequential` is less generic and supports only a linear\n * stack of layers.\n *\n * When creating a `tf.LayersModel`, specify its input(s) and output(s). Layers\n * are used to wire input(s) to output(s).\n *\n * For example, the following code snippet defines a model consisting of\n * two `dense` layers, with 10 and 4 units, respectively.\n *\n * ```js\n * // Define input, which has a size of 5 (not including batch dimension).\n * const input = tf.input({shape: [5]});\n *\n * // First dense layer uses relu activation.\n * const denseLayer1 = tf.layers.dense({units: 10, activation: 'relu'});\n * // Second dense layer uses softmax activation.\n * const denseLayer2 = tf.layers.dense({units: 4, activation: 'softmax'});\n *\n * // Obtain the output symbolic tensor by applying the layers on the input.\n * const output = denseLayer2.apply(denseLayer1.apply(input));\n *\n * // Create the model based on the inputs.\n * const model = tf.model({inputs: input, outputs: output});\n *\n * // The model can be used for training, evaluation and prediction.\n * // For example, the following line runs prediction with the model on\n * // some fake data.\n * model.predict(tf.ones([2, 5])).print();\n * ```\n * See also:\n *   `tf.sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function model(args) {\n    return new LayersModel(args);\n}\n/**\n * Creates a `tf.Sequential` model.  A sequential model is any model where the\n * outputs of one layer are the inputs to the next layer, i.e. the model\n * topology is a simple 'stack' of layers, with no branching or skipping.\n *\n * This means that the first layer passed to a `tf.Sequential` model should have\n * a defined input shape. What that means is that it should have received an\n * `inputShape` or `batchInputShape` argument, or for some type of layers\n * (recurrent, Dense...) an `inputDim` argument.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.sequential` is less generic, supporting only a linear stack of layers.\n * `tf.model` is more generic and supports an arbitrary graph (without\n * cycles) of layers.\n *\n * Examples:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have an input shape defined.\n * model.add(tf.layers.dense({units: 32, inputShape: [50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output, which equals\n * // `[null, 4]`. The 1st dimension is the undetermined batch dimension; the\n * // 2nd is the output size of the model's last layer.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * It is also possible to specify a batch size (with potentially undetermined\n * batch dimension, denoted by \"null\") for the first layer using the\n * `batchInputShape` key. The following example is equivalent to the above:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have a defined input shape\n * model.add(tf.layers.dense({units: 32, batchInputShape: [null, 50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * You can also use an `Array` of already-constructed `Layer`s to create\n * a `tf.Sequential` model:\n *\n * ```js\n * const model = tf.sequential({\n *   layers: [tf.layers.dense({units: 32, inputShape: [50]}),\n *            tf.layers.dense({units: 4})]\n * });\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function sequential(config) {\n    return new Sequential(config);\n}\n/**\n * Load a model composed of Layer objects, including its topology and optionally\n * weights. See the Tutorial named \"How to import a Keras Model\" for usage\n * examples.\n *\n * This method is applicable to:\n *\n * 1. Models created with the `tf.layers.*`, `tf.sequential`, and\n * `tf.model` APIs of TensorFlow.js and later saved with the\n * `tf.LayersModel.save` method.\n * 2. Models converted from Keras or TensorFlow tf.keras using the\n * [tensorflowjs_converter](https://github.com/tensorflow/tfjs/tree/master/tfjs-converter).\n *\n * This mode is *not* applicable to TensorFlow `SavedModel`s or their converted\n * forms. For those models, use `tf.loadGraphModel`.\n *\n * Example 1. Load a model from an HTTP server.\n *\n * ```js\n * const model = await tf.loadLayersModel(\n *     'https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');\n * model.summary();\n * ```\n *\n * Example 2: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 4. Load a model from user-selected files from HTML\n * [file input\n * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).\n *\n * ```js\n * // Note: this code snippet will not work without the HTML elements in the\n * //   page\n * const jsonUpload = document.getElementById('json-upload');\n * const weightsUpload = document.getElementById('weights-upload');\n *\n * const model = await tf.loadLayersModel(\n *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));\n * ```\n *\n * @param pathOrIOHandler Can be either of the two formats\n *   1. A string path to the `ModelAndWeightsConfig` JSON describing\n *      the model in the canonical TensorFlow.js format. For file://\n *      (tfjs-node-only), http:// and https:// schemas, the path can be\n *      either absolute or relative.\n *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`\n *      method.\n * @param options Optional configuration arguments for the model loading,\n *   including:\n *   - `strict`: Require that the provided weights exactly match those required\n *     by the layers.  Default true.  Passing false means that both extra\n *     weights and missing weights will be silently ignored.\n *   - `onProgress`: A function of the signature `(fraction: number) => void',\n *     that can be used as the progress callback for the model loading.\n * @returns A `Promise` of `tf.LayersModel`, with the topology and weights\n *     loaded.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport function loadLayersModel(pathOrIOHandler, options) {\n    if (options == null) {\n        options = {};\n    }\n    return loadLayersModelInternal(pathOrIOHandler, options);\n}\n/**\n * Used to instantiate an input to a model as a `tf.SymbolicTensor`.\n *\n * Users should call the `input` factory function for\n * consistency with other generator functions.\n *\n * Example:\n *\n * ```js\n * // Defines a simple logistic regression model with 32 dimensional input\n * // and 3 dimensional output.\n * const x = tf.input({shape: [32]});\n * const y = tf.layers.dense({units: 3, activation: 'softmax'}).apply(x);\n * const model = tf.model({inputs: x, outputs: y});\n * model.predict(tf.ones([2, 32])).print();\n * ```\n *\n * Note: `input` is only necessary when using `model`. When using\n * `sequential`, specify `inputShape` for the first layer or use `inputLayer`\n * as the first layer.\n *\n * @doc {heading: 'Models', subheading: 'Inputs'}\n */\nexport function input(config) {\n    return Input(config);\n}\nexport function registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n    CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);\n}\n//# sourceMappingURL=exports.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// Layer activation functions\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { deserializeKerasObject } from './utils/generic_utils';\n/**\n * Base class for Activations.\n *\n * Special note: due to cross-language compatibility reasons, the\n * static readonly className field in this family of classes must be set to\n * the initialLowerCamelCase name of the activation.\n */\nexport class Activation extends serialization.Serializable {\n    getConfig() {\n        return {};\n    }\n}\n/**\n * Exponential linear unit (ELU).\n * Reference: https://arxiv.org/abs/1511.07289\n */\nexport class Elu extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x: Input.\n     * @param alpha: Scaling factor the negative section.\n     * @return Output of the ELU activation.\n     */\n    apply(x, alpha = 1) {\n        return K.elu(x, alpha);\n    }\n}\n/** @nocollapse */\nElu.className = 'elu';\nserialization.registerClass(Elu);\n/**\n * Scaled Exponential Linear Unit. (Klambauer et al., 2017).\n * Reference: Self-Normalizing Neural Networks, https://arxiv.org/abs/1706.02515\n * Notes:\n *   - To be used together with the initialization \"lecunNormal\".\n *   - To be used together with the dropout variant \"AlphaDropout\".\n */\nexport class Selu extends Activation {\n    apply(x) {\n        return tfc.selu(x);\n    }\n}\n/** @nocollapse */\nSelu.className = 'selu';\nserialization.registerClass(Selu);\n/**\n *  Rectified linear unit\n */\nexport class Relu extends Activation {\n    apply(x) {\n        return tfc.relu(x);\n    }\n}\n/** @nocollapse */\nRelu.className = 'relu';\nserialization.registerClass(Relu);\n/**\n * Rectified linear unit activation maxing out at 6.0.\n */\nexport class Relu6 extends Activation {\n    apply(x) {\n        return tidy(() => tfc.minimum(6.0, tfc.relu(x)));\n    }\n}\n/** @nocollapse */\nRelu6.className = 'relu6';\nserialization.registerClass(Relu6);\n//* Linear activation (no-op) */\nexport class Linear extends Activation {\n    apply(x) {\n        return x;\n    }\n}\n/** @nocollapse */\nLinear.className = 'linear';\nserialization.registerClass(Linear);\n/**\n * Sigmoid activation function.\n */\nexport class Sigmoid extends Activation {\n    apply(x) {\n        return tfc.sigmoid(x);\n    }\n}\n/** @nocollapse */\nSigmoid.className = 'sigmoid';\nserialization.registerClass(Sigmoid);\n/**\n * Segment-wise linear approximation of sigmoid.\n */\nexport class HardSigmoid extends Activation {\n    apply(x) {\n        return K.hardSigmoid(x);\n    }\n}\n/** @nocollapse */\nHardSigmoid.className = 'hardSigmoid';\nserialization.registerClass(HardSigmoid);\n/**\n * Softplus activation function.\n */\nexport class Softplus extends Activation {\n    apply(x) {\n        return tfc.softplus(x);\n    }\n}\n/** @nocollapse */\nSoftplus.className = 'softplus';\nserialization.registerClass(Softplus);\n/**\n * Softsign activation function.\n */\nexport class Softsign extends Activation {\n    apply(x) {\n        return K.softsign(x);\n    }\n}\n/** @nocollapse */\nSoftsign.className = 'softsign';\nserialization.registerClass(Softsign);\n/**\n * Hyperbolic tangent function.\n */\nexport class Tanh extends Activation {\n    apply(x) {\n        return tfc.tanh(x);\n    }\n}\n/** @nocollapse */\nTanh.className = 'tanh';\nserialization.registerClass(Tanh);\n/**\n * Softmax activation function\n */\nexport class Softmax extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x Tensor.\n     * @param axis Integer, axis along which the softmax normalization is applied.\n     * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n     * an error.\n     *\n     * @returns a Tensor of the same shape as x\n     *\n     * @throws ValueError: In case `dim(x) < 2`.\n     */\n    apply(x, axis = (-1)) {\n        return tfc.softmax(x, axis);\n    }\n}\n/** @nocollapse */\nSoftmax.className = 'softmax';\nserialization.registerClass(Softmax);\n/**\n * Log softmax activation function\n */\nexport class LogSoftmax extends Activation {\n    /**\n     * Calculate the activation function of log softmax:\n     * log( exp(x_i) / sum(exp(x)) )\n     *\n     * @param x Tensor.\n     * @param axis Integer, axis along which the softmax normalization is applied.\n     * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n     * an error.\n     *\n     * @returns a Tensor of the same shape as x\n     *\n     * @throws ValueError: In case `dim(x) < 2`.\n     */\n    apply(x, axis = (-1)) {\n        return tfc.logSoftmax(x, axis);\n    }\n}\n/** @nocollapse */\nLogSoftmax.className = 'logSoftmax';\nserialization.registerClass(LogSoftmax);\n/**\n * Swish activation function\n */\nexport class Swish extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x Tensor.\n     * @param alpha Scaling factor for the sigmoid function.\n     * @returns a Tensor of the same shape as x\n     */\n    apply(x, alpha = 1) {\n        return tidy(() => tfc.sigmoid(x.mul(alpha)).mul(x));\n    }\n}\n/** @nocollapse */\nSwish.className = 'swish';\nserialization.registerClass(Swish);\nexport function serializeActivation(activation) {\n    return activation.getClassName();\n}\nexport function deserializeActivation(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'activation');\n}\nexport function getActivation(identifier) {\n    if (identifier == null) {\n        const config = {};\n        config['className'] = 'linear';\n        config['config'] = {};\n        return deserializeActivation(config);\n    }\n    if (typeof identifier === 'string') {\n        const config = {};\n        config['className'] = identifier;\n        config['config'] = {};\n        return deserializeActivation(config);\n    }\n    else if (identifier instanceof Activation) {\n        return identifier;\n    }\n    else {\n        return deserializeActivation(identifier);\n    }\n}\n//# sourceMappingURL=activations.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* original source: keras/regularizers.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { abs, add, serialization, sum, tidy, zeros } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\nfunction assertObjectArgs(args) {\n    if (args != null && typeof args !== 'object') {\n        throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an ` +\n            `object, but received: ${args}`);\n    }\n}\n/**\n * Regularizer base class.\n */\nexport class Regularizer extends serialization.Serializable {\n}\nexport class L1L2 extends Regularizer {\n    constructor(args) {\n        super();\n        assertObjectArgs(args);\n        this.l1 = args == null || args.l1 == null ? 0.01 : args.l1;\n        this.l2 = args == null || args.l2 == null ? 0.01 : args.l2;\n        this.hasL1 = this.l1 !== 0;\n        this.hasL2 = this.l2 !== 0;\n    }\n    /**\n     * Porting note: Renamed from __call__.\n     * @param x Variable of which to calculate the regularization score.\n     */\n    apply(x) {\n        return tidy(() => {\n            let regularization = zeros([1]);\n            if (this.hasL1) {\n                regularization = add(regularization, sum(tfc.mul(this.l1, abs(x))));\n            }\n            if (this.hasL2) {\n                regularization =\n                    add(regularization, sum(tfc.mul(this.l2, K.square(x))));\n            }\n            return regularization.asScalar();\n        });\n    }\n    getConfig() {\n        return { 'l1': this.l1, 'l2': this.l2 };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls({ l1: config['l1'], l2: config['l2'] });\n    }\n}\n/** @nocollapse */\nL1L2.className = 'L1L2';\nserialization.registerClass(L1L2);\nexport function l1(args) {\n    assertObjectArgs(args);\n    return new L1L2({ l1: args != null ? args.l1 : null, l2: 0 });\n}\nexport function l2(args) {\n    assertObjectArgs(args);\n    return new L1L2({ l2: args != null ? args.l2 : null, l1: 0 });\n}\n// Maps the JavaScript-like identifier keys to the corresponding keras symbols.\nexport const REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'l1l2': 'L1L2'\n};\nexport function serializeRegularizer(constraint) {\n    return serializeKerasObject(constraint);\n}\nexport function deserializeRegularizer(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'regularizer');\n}\nexport function getRegularizer(identifier) {\n    if (identifier == null) {\n        return null;\n    }\n    if (typeof identifier === 'string') {\n        const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        const config = { className, config: {} };\n        return deserializeRegularizer(config);\n    }\n    else if (identifier instanceof Regularizer) {\n        return identifier;\n    }\n    else {\n        return deserializeRegularizer(identifier);\n    }\n}\n//# sourceMappingURL=regularizers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n *  Advanced activation layers.\n */\nimport { clipByValue, elu, leakyRelu, prelu, relu, serialization } from '@tensorflow/tfjs-core';\nimport { Softmax as softmaxActivation } from '../activations';\nimport { cast } from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class ReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.supportsMasking = true;\n        if (args != null) {\n            this.maxValue = args.maxValue;\n        }\n    }\n    call(inputs, kwargs) {\n        inputs = getExactlyOneTensor(inputs);\n        let output = relu(inputs);\n        if (this.maxValue != null) {\n            output = clipByValue(output, 0, this.maxValue);\n        }\n        return output;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { maxValue: this.maxValue };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nReLU.className = 'ReLU';\nserialization.registerClass(ReLU);\nexport class LeakyReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA = 0.3;\n        if (args == null) {\n            args = {};\n        }\n        this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return leakyRelu(x, this.alpha);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { alpha: this.alpha };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nLeakyReLU.className = 'LeakyReLU';\nserialization.registerClass(LeakyReLU);\nexport class PReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA_INITIALIZER = 'zeros';\n        if (args == null) {\n            args = {};\n        }\n        this.supportsMasking = true;\n        this.alphaInitializer =\n            getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER);\n        this.alphaRegularizer = getRegularizer(args.alphaRegularizer);\n        this.alphaConstraint = getConstraint(args.alphaConstraint);\n        if (args.sharedAxes == null) {\n            this.sharedAxes = null;\n        }\n        else if (Array.isArray(args.sharedAxes)) {\n            this.sharedAxes = args.sharedAxes;\n        }\n        else if (typeof args.sharedAxes === 'number') {\n            this.sharedAxes = [args.sharedAxes];\n        }\n        else {\n            throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, ` +\n                `but got ${args.sharedAxes}`);\n        }\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const paramShape = inputShape.slice(1);\n        if (this.sharedAxes != null) {\n            for (const i of this.sharedAxes) {\n                paramShape[i - 1] = 1;\n            }\n        }\n        this.alpha = this.addWeight('alpha', paramShape, 'float32', this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);\n        // Set input spec.\n        const axes = {};\n        if (this.sharedAxes != null) {\n            for (let i = 1; i < inputShape.length; ++i) {\n                axes[i] = inputShape[i];\n            }\n        }\n        this.inputSpec = [new InputSpec({\n                ndim: inputShape.length,\n                axes,\n            })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        inputs = getExactlyOneTensor(inputs);\n        return prelu(inputs, this.alpha.read());\n    }\n    getConfig() {\n        const config = {\n            alphaInitializer: serializeInitializer(this.alphaInitializer),\n            alphaRegularizer: serializeRegularizer(this.alphaRegularizer),\n            alphaConstraint: serializeConstraint(this.alphaConstraint),\n            sharedAxes: this.sharedAxes\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nPReLU.className = 'PReLU';\nserialization.registerClass(PReLU);\nexport class ELU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) {\n            throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ` +\n                `ELU layer yet.`);\n        }\n        this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return elu(x);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { alpha: this.alpha };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nELU.className = 'ELU';\nserialization.registerClass(ELU);\nexport class ThresholdedReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_THETA = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return x.mul(cast(x.greater(this.theta), 'float32'));\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { theta: this.theta };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nThresholdedReLU.className = 'ThresholdedReLU';\nserialization.registerClass(ThresholdedReLU);\nexport class Softmax extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_AXIS = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        this.softmax = new softmaxActivation().apply;\n        this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return this.softmax(x, this.axis);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { axis: this.axis };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nSoftmax.className = 'Softmax';\nserialization.registerClass(Softmax);\n//# sourceMappingURL=advanced_activations.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { ValueError } from '../errors';\nimport { pyListRepeat } from './generic_utils';\nimport { isInteger, max } from './math_utils';\n/**\n * Transforms a single number of array of numbers into an array of numbers.\n * @param value\n * @param n: The size of the tuple to be returned.\n * @param name: Name of the parameter, used for generating error messages.\n * @returns An array of numbers.\n */\nexport function normalizeArray(value, n, name) {\n    if (typeof value === 'number') {\n        return pyListRepeat(value, n);\n    }\n    else {\n        if (value.length !== n) {\n            throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers.` +\n                ` Received: ${value.length} elements.`);\n        }\n        for (let i = 0; i < n; ++i) {\n            const singleValue = value[i];\n            if (!isInteger(singleValue)) {\n                throw new ValueError(`The ${name} argument must be an integer or tuple of ${n}` +\n                    ` integers. Received: ${JSON.stringify(value)} including a` +\n                    ` non-integer number ${singleValue}`);\n            }\n        }\n        return value;\n    }\n}\n/**\n * Determines output length of a convolution given input length.\n * @param inputLength\n * @param filterSize\n * @param padding\n * @param stride\n * @param dilation: dilation rate.\n */\nexport function convOutputLength(inputLength, filterSize, padding, stride, dilation = 1) {\n    if (inputLength == null) {\n        return inputLength;\n    }\n    const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1);\n    let outputLength;\n    if (padding === 'same') {\n        outputLength = inputLength;\n    }\n    else { // VALID\n        outputLength = inputLength - dilatedFilterSize + 1;\n    }\n    return Math.floor((outputLength + stride - 1) / stride);\n}\nexport function deconvLength(dimSize, strideSize, kernelSize, padding) {\n    if (dimSize == null) {\n        return null;\n    }\n    if (padding === 'valid') {\n        dimSize = dimSize * strideSize + max([kernelSize - strideSize, 0]);\n    }\n    else if (padding === 'same') {\n        dimSize = dimSize * strideSize;\n    }\n    else {\n        throw new ValueError(`Unsupport padding mode: ${padding}.`);\n    }\n    return dimSize;\n}\n//# sourceMappingURL=conv_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Convolutional Layers\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { convOutputLength, deconvLength, normalizeArray } from '../utils/conv_utils';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Transpose and cast the input before the conv2d.\n * @param x Input image tensor.\n * @param dataFormat\n */\nexport function preprocessConv2DInput(x, dataFormat) {\n    // TODO(cais): Cast type to float32 if not.\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        if (dataFormat === 'channelsFirst') {\n            return tfc.transpose(x, [0, 2, 3, 1]); // NCHW -> NHWC.\n        }\n        else {\n            return x;\n        }\n    });\n}\n/**\n * Transpose and cast the input before the conv3d.\n * @param x Input image tensor.\n * @param dataFormat\n */\nexport function preprocessConv3DInput(x, dataFormat) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        if (dataFormat === 'channelsFirst') {\n            return tfc.transpose(x, [0, 2, 3, 4, 1]); // NCDHW -> NDHWC.\n        }\n        else {\n            return x;\n        }\n    });\n}\n/**\n * 1D-convolution with bias added.\n *\n * Porting Note: This function does not exist in the Python Keras backend.\n *   It is exactly the same as `conv2d`, except the added `bias`.\n *\n * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.\n * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.\n * @param bias Bias, rank-3, of shape `[outDepth]`.\n * @param strides\n * @param padding Padding mode.\n * @param dataFormat Data format.\n * @param dilationRate\n * @returns The result of the 1D convolution.\n * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.\n */\nexport function conv1dWithBias(x, kernel, bias, strides = 1, padding = 'valid', dataFormat, dilationRate = 1) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        // Check the ranks of x, kernel and bias.\n        if (x.shape.length !== 3) {\n            throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ` +\n                `${x.shape.length} instead.`);\n        }\n        if (kernel.shape.length !== 3) {\n            throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ` +\n                `${kernel.shape.length} instead`);\n        }\n        if (bias != null && bias.shape.length !== 1) {\n            throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ` +\n                `${kernel.shape.length} instead`);\n        }\n        // TODO(cais): Support CAUSAL padding mode.\n        if (dataFormat === 'channelsFirst') {\n            x = tfc.transpose(x, [0, 2, 1]); // NCW -> NWC.\n        }\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv1dWithBias is not ' +\n                'implemented yet.');\n        }\n        let y = tfc.conv1d(x, kernel, strides, padding === 'same' ? 'same' : 'valid', 'NWC', dilationRate);\n        if (bias != null) {\n            y = K.biasAdd(y, bias);\n        }\n        return y;\n    });\n}\n/**\n * 1D-convolution.\n *\n * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.\n * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.s\n * @param strides\n * @param padding Padding mode.\n * @param dataFormat Data format.\n * @param dilationRate\n * @returns The result of the 1D convolution.\n * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.\n */\nexport function conv1d(x, kernel, strides = 1, padding = 'valid', dataFormat, dilationRate = 1) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv1dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 2D Convolution\n * @param x\n * @param kernel kernel of the convolution.\n * @param strides strides array.\n * @param padding padding mode. Default to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param dilationRate dilation rate array.\n * @returns Result of the 2D pooling.\n */\nexport function conv2d(x, kernel, strides = [1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv2dWithBiasActivation(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 2D Convolution with an added bias and optional activation.\n * Note: This function does not exist in the Python Keras Backend. This function\n * is exactly the same as `conv2d`, except the added `bias`.\n */\nexport function conv2dWithBiasActivation(x, kernel, bias, strides = [1, 1], padding = 'valid', dataFormat, dilationRate, activation = null) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        if (x.rank !== 3 && x.rank !== 4) {\n            throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, ` +\n                `but received ${x.rank}.`);\n        }\n        if (kernel.rank !== 3 && kernel.rank !== 4) {\n            throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, ` +\n                `but received ${x.rank}.`);\n        }\n        let y = preprocessConv2DInput(x, dataFormat);\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv1dWithBias is not ' +\n                'implemented yet.');\n        }\n        y = tfc.fused.conv2d({\n            x: y,\n            filter: kernel,\n            strides: strides,\n            pad: padding === 'same' ? 'same' : 'valid',\n            dilations: dilationRate,\n            dataFormat: 'NHWC',\n            bias,\n            activation\n        });\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]);\n        }\n        return y;\n    });\n}\n/**\n * 3D Convolution.\n * @param x\n * @param kernel kernel of the convolution.\n * @param strides strides array.\n * @param padding padding mode. Default to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param dilationRate dilation rate array.\n * @returns Result of the 3D convolution.\n */\nexport function conv3d(x, kernel, strides = [1, 1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv3dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 3D Convolution with an added bias.\n * Note: This function does not exist in the Python Keras Backend. This function\n * is exactly the same as `conv3d`, except the added `bias`.\n */\nexport function conv3dWithBias(x, kernel, bias, strides = [1, 1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        if (x.rank !== 4 && x.rank !== 5) {\n            throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ` +\n                `${x.rank}.`);\n        }\n        if (kernel.rank !== 4 && kernel.rank !== 5) {\n            throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ` +\n                `${x.rank}.`);\n        }\n        let y = preprocessConv3DInput(x, dataFormat);\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv3dWithBias is not ' +\n                'implemented yet.');\n        }\n        y = tfc.conv3d(y, kernel, strides, padding === 'same' ? 'same' : 'valid', 'NDHWC', dilationRate);\n        if (bias != null) {\n            y = K.biasAdd(y, bias);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 4, 1, 2, 3]);\n        }\n        return y;\n    });\n}\n/**\n * Abstract convolution layer.\n */\nexport class BaseConv extends Layer {\n    constructor(rank, args) {\n        super(args);\n        this.bias = null;\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        BaseConv.verifyArgs(args);\n        this.rank = rank;\n        generic_utils.assertPositiveInteger(this.rank, 'rank');\n        if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) {\n            throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is ` +\n                `not implemented yet.`);\n        }\n        this.kernelSize = normalizeArray(args.kernelSize, rank, 'kernelSize');\n        this.strides = normalizeArray(args.strides == null ? 1 : args.strides, rank, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        checkPaddingMode(this.padding);\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        this.activation = getActivation(args.activation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.dilationRate = normalizeArray(args.dilationRate == null ? 1 : args.dilationRate, rank, 'dilationRate');\n        if (this.rank === 1 &&\n            (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) {\n            throw new ValueError(`dilationRate must be a number or an array of a single number ` +\n                `for 1D convolution, but received ` +\n                `${JSON.stringify(this.dilationRate)}`);\n        }\n        else if (this.rank === 2) {\n            if (typeof this.dilationRate === 'number') {\n                this.dilationRate = [this.dilationRate, this.dilationRate];\n            }\n            else if (this.dilationRate.length !== 2) {\n                throw new ValueError(`dilationRate must be a number or array of two numbers for 2D ` +\n                    `convolution, but received ${JSON.stringify(this.dilationRate)}`);\n            }\n        }\n        else if (this.rank === 3) {\n            if (typeof this.dilationRate === 'number') {\n                this.dilationRate =\n                    [this.dilationRate, this.dilationRate, this.dilationRate];\n            }\n            else if (this.dilationRate.length !== 3) {\n                throw new ValueError(`dilationRate must be a number or array of three numbers for 3D ` +\n                    `convolution, but received ${JSON.stringify(this.dilationRate)}`);\n            }\n        }\n    }\n    static verifyArgs(args) {\n        // Check config.kernelSize type and shape.\n        generic_utils.assert('kernelSize' in args, `required key 'kernelSize' not in config`);\n        if (typeof args.kernelSize !== 'number' &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 3)) {\n            throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with ` +\n                `length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n    getConfig() {\n        const config = {\n            kernelSize: this.kernelSize,\n            strides: this.strides,\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n            dilationRate: this.dilationRate,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            biasConstraint: serializeConstraint(this.biasConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/**\n * Abstract nD convolution layer.  Ancestor of convolution layers which reduce\n * across channels, i.e., Conv1D and Conv2D, but not DepthwiseConv2D.\n */\nexport class Conv extends BaseConv {\n    constructor(rank, args) {\n        super(rank, args);\n        this.kernel = null;\n        Conv.verifyArgs(args);\n        this.filters = args.filters;\n        generic_utils.assertPositiveInteger(this.filters, 'filters');\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError(`The channel dimension of the input should be defined. ` +\n                `Found ${inputShape[channelAxis]}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const kernelShape = this.kernelSize.concat([inputDim, this.filters]);\n        this.kernel = this.addWeight('kernel', kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        this.inputSpec = [{ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } }];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let outputs;\n            const biasValue = this.bias == null ? null : this.bias.read();\n            const fusedActivationName = generic_utils.mapActivationToFusedKernel(this.activation.getClassName());\n            if (fusedActivationName != null && this.rank === 2) {\n                outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate, fusedActivationName);\n            }\n            else {\n                if (this.rank === 1) {\n                    outputs = conv1dWithBias(inputs, this.kernel.read(), biasValue, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);\n                }\n                else if (this.rank === 2) {\n                    // TODO(cais): Move up to constructor.\n                    outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n                }\n                else if (this.rank === 3) {\n                    outputs = conv3dWithBias(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n                }\n                else {\n                    throw new NotImplementedError('convolutions greater than 3D are not implemented yet.');\n                }\n                if (this.activation != null) {\n                    outputs = this.activation.apply(outputs);\n                }\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const newSpace = [];\n        const space = (this.dataFormat === 'channelsLast') ?\n            inputShape.slice(1, inputShape.length - 1) :\n            inputShape.slice(2);\n        for (let i = 0; i < space.length; ++i) {\n            const newDim = convOutputLength(space[i], this.kernelSize[i], this.padding, this.strides[i], typeof this.dilationRate === 'number' ? this.dilationRate :\n                this.dilationRate[i]);\n            newSpace.push(newDim);\n        }\n        let outputShape = [inputShape[0]];\n        if (this.dataFormat === 'channelsLast') {\n            outputShape = outputShape.concat(newSpace);\n            outputShape.push(this.filters);\n        }\n        else {\n            outputShape.push(this.filters);\n            outputShape = outputShape.concat(newSpace);\n        }\n        return outputShape;\n    }\n    getConfig() {\n        const config = {\n            filters: this.filters,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    static verifyArgs(args) {\n        // Check config.filters type, shape, and value.\n        if (!('filters' in args) || typeof args.filters !== 'number' ||\n            args.filters < 1) {\n            throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 ` +\n                `but got ${JSON.stringify(args.filters)}`);\n        }\n    }\n}\nexport class Conv2D extends Conv {\n    constructor(args) {\n        super(2, args);\n        Conv2D.verifyArgs(args);\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if ((typeof args.kernelSize !== 'number') &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 2)) {\n            throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with ` +\n                `length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n}\n/** @nocollapse */\nConv2D.className = 'Conv2D';\nserialization.registerClass(Conv2D);\nexport class Conv3D extends Conv {\n    constructor(args) {\n        super(3, args);\n        Conv3D.verifyArgs(args);\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if (typeof args.kernelSize !== 'number') {\n            if (!(Array.isArray(args.kernelSize) &&\n                (args.kernelSize.length === 1 || args.kernelSize.length === 3))) {\n                throw new ValueError(`Conv3D expects config.kernelSize to be number or` +\n                    ` [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`);\n            }\n        }\n    }\n}\n/** @nocollapse */\nConv3D.className = 'Conv3D';\nserialization.registerClass(Conv3D);\nexport class Conv2DTranspose extends Conv2D {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n        if (this.padding !== 'same' && this.padding !== 'valid') {\n            throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' ` +\n                `and 'valid', but received padding mode ${this.padding}`);\n        }\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length !== 4) {\n            throw new ValueError('Input should have rank 4; Received input shape: ' +\n                JSON.stringify(inputShape));\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError('The channel dimension of the inputs should be defined. ' +\n                'Found `None`.');\n        }\n        const inputDim = inputShape[channelAxis];\n        const kernelShape = this.kernelSize.concat([this.filters, inputDim]);\n        this.kernel = this.addWeight('kernel', kernelShape, 'float32', this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], 'float32', this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        // Set input spec.\n        this.inputSpec =\n            [new InputSpec({ ndim: 4, axes: { [channelAxis]: inputDim } })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            let input = getExactlyOneTensor(inputs);\n            if (input.shape.length !== 4) {\n                throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but ` +\n                    `received a tensor of rank-${input.shape.length}`);\n            }\n            const inputShape = input.shape;\n            const batchSize = inputShape[0];\n            let hAxis;\n            let wAxis;\n            if (this.dataFormat === 'channelsFirst') {\n                hAxis = 2;\n                wAxis = 3;\n            }\n            else {\n                hAxis = 1;\n                wAxis = 2;\n            }\n            const height = inputShape[hAxis];\n            const width = inputShape[wAxis];\n            const kernelH = this.kernelSize[0];\n            const kernelW = this.kernelSize[1];\n            const strideH = this.strides[0];\n            const strideW = this.strides[1];\n            // Infer the dynamic output shape.\n            const outHeight = deconvLength(height, strideH, kernelH, this.padding);\n            const outWidth = deconvLength(width, strideW, kernelW, this.padding);\n            // Porting Note: We don't branch based on `this.dataFormat` here,\n            // because\n            //   the tjfs-core function `conv2dTranspose` called below always\n            //   assumes channelsLast.\n            const outputShape = [batchSize, outHeight, outWidth, this.filters];\n            if (this.dataFormat !== 'channelsLast') {\n                input = tfc.transpose(input, [0, 2, 3, 1]);\n            }\n            let outputs = tfc.conv2dTranspose(input, this.kernel.read(), outputShape, this.strides, this.padding);\n            if (this.dataFormat !== 'channelsLast') {\n                outputs = tfc.transpose(outputs, [0, 3, 1, 2]);\n            }\n            if (this.bias != null) {\n                outputs =\n                    K.biasAdd(outputs, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                outputs = this.activation.apply(outputs);\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        let channelAxis;\n        let heightAxis;\n        let widthAxis;\n        if (this.dataFormat === 'channelsFirst') {\n            channelAxis = 1;\n            heightAxis = 2;\n            widthAxis = 3;\n        }\n        else {\n            channelAxis = 3;\n            heightAxis = 1;\n            widthAxis = 2;\n        }\n        const kernelH = this.kernelSize[0];\n        const kernelW = this.kernelSize[1];\n        const strideH = this.strides[0];\n        const strideW = this.strides[1];\n        outputShape[channelAxis] = this.filters;\n        outputShape[heightAxis] =\n            deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);\n        outputShape[widthAxis] =\n            deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);\n        return outputShape;\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['dilationRate'];\n        return config;\n    }\n}\n/** @nocollapse */\nConv2DTranspose.className = 'Conv2DTranspose';\nserialization.registerClass(Conv2DTranspose);\nexport class SeparableConv extends Conv {\n    constructor(rank, config) {\n        super(rank, config);\n        this.DEFAULT_DEPTHWISE_INITIALIZER = 'glorotUniform';\n        this.DEFAULT_POINTWISE_INITIALIZER = 'glorotUniform';\n        this.depthwiseKernel = null;\n        this.pointwiseKernel = null;\n        if (config.filters == null) {\n            throw new ValueError('The `filters` configuration field is required by SeparableConv, ' +\n                'but is unspecified.');\n        }\n        if (config.kernelInitializer != null || config.kernelRegularizer != null ||\n            config.kernelConstraint != null) {\n            throw new ValueError('Fields kernelInitializer, kernelRegularizer and kernelConstraint ' +\n                'are invalid for SeparableConv2D. Use depthwiseInitializer, ' +\n                'depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, ' +\n                'pointwiseRegularizer and pointwiseConstraint instead.');\n        }\n        if (config.padding != null && config.padding !== 'same' &&\n            config.padding !== 'valid') {\n            throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: ` +\n                `'same' and 'valid', but received ${JSON.stringify(config.padding)}`);\n        }\n        this.depthMultiplier =\n            config.depthMultiplier == null ? 1 : config.depthMultiplier;\n        this.depthwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER);\n        this.depthwiseRegularizer = getRegularizer(config.depthwiseRegularizer);\n        this.depthwiseConstraint = getConstraint(config.depthwiseConstraint);\n        this.pointwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER);\n        this.pointwiseRegularizer = getRegularizer(config.pointwiseRegularizer);\n        this.pointwiseConstraint = getConstraint(config.pointwiseConstraint);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < this.rank + 2) {\n            throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ` +\n                `${this.rank + 2}, but received input shape: ` +\n                `${JSON.stringify(inputShape)}`);\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n            throw new ValueError(`The channel dimension of the inputs should be defined, ` +\n                `but found ${JSON.stringify(inputShape[channelAxis])}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]);\n        const pointwiseKernelShape = [];\n        for (let i = 0; i < this.rank; ++i) {\n            pointwiseKernelShape.push(1);\n        }\n        pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);\n        const trainable = true;\n        this.depthwiseKernel = this.addWeight('depthwise_kernel', depthwiseKernelShape, 'float32', this.depthwiseInitializer, this.depthwiseRegularizer, trainable, this.depthwiseConstraint);\n        this.pointwiseKernel = this.addWeight('pointwise_kernel', pointwiseKernelShape, 'float32', this.pointwiseInitializer, this.pointwiseRegularizer, trainable, this.pointwiseConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], 'float32', this.biasInitializer, this.biasRegularizer, trainable, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.inputSpec =\n            [new InputSpec({ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let output;\n            if (this.rank === 1) {\n                throw new NotImplementedError('1D separable convolution is not implemented yet.');\n            }\n            else if (this.rank === 2) {\n                if (this.dataFormat === 'channelsFirst') {\n                    inputs = tfc.transpose(inputs, [0, 2, 3, 1]); // NCHW -> NHWC.\n                }\n                output = tfc.separableConv2d(inputs, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, 'NHWC');\n            }\n            if (this.useBias) {\n                output = K.biasAdd(output, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                output = this.activation.apply(output);\n            }\n            if (this.dataFormat === 'channelsFirst') {\n                output = tfc.transpose(output, [0, 3, 1, 2]); // NHWC -> NCHW.\n            }\n            return output;\n        });\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        delete config['kernelInitializer'];\n        delete config['kernelRegularizer'];\n        delete config['kernelConstraint'];\n        config['depthwiseInitializer'] =\n            serializeInitializer(this.depthwiseInitializer);\n        config['pointwiseInitializer'] =\n            serializeInitializer(this.pointwiseInitializer);\n        config['depthwiseRegularizer'] =\n            serializeRegularizer(this.depthwiseRegularizer);\n        config['pointwiseRegularizer'] =\n            serializeRegularizer(this.pointwiseRegularizer);\n        config['depthwiseConstraint'] =\n            serializeConstraint(this.depthwiseConstraint);\n        config['pointwiseConstraint'] =\n            serializeConstraint(this.pointwiseConstraint);\n        return config;\n    }\n}\n/** @nocollapse */\nSeparableConv.className = 'SeparableConv';\nexport class SeparableConv2D extends SeparableConv {\n    constructor(args) {\n        super(2, args);\n    }\n}\n/** @nocollapse */\nSeparableConv2D.className = 'SeparableConv2D';\nserialization.registerClass(SeparableConv2D);\nexport class Conv1D extends Conv {\n    constructor(args) {\n        super(1, args);\n        Conv1D.verifyArgs(args);\n        this.inputSpec = [{ ndim: 3 }];\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        delete config['dataFormat'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if (typeof args.kernelSize !== 'number' &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 1)) {\n            throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with ` +\n                `length 1, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n}\n/** @nocollapse */\nConv1D.className = 'Conv1D';\nserialization.registerClass(Conv1D);\nexport class Cropping2D extends Layer {\n    constructor(args) {\n        super(args);\n        if (typeof args.cropping === 'number') {\n            this.cropping =\n                [[args.cropping, args.cropping], [args.cropping, args.cropping]];\n        }\n        else if (typeof args.cropping[0] === 'number') {\n            this.cropping = [\n                [args.cropping[0], args.cropping[0]],\n                [args.cropping[1], args.cropping[1]]\n            ];\n        }\n        else {\n            this.cropping = args.cropping;\n        }\n        this.dataFormat =\n            args.dataFormat === undefined ? 'channelsLast' : args.dataFormat;\n        this.inputSpec = [{ ndim: 4 }];\n    }\n    computeOutputShape(inputShape) {\n        if (this.dataFormat === 'channelsFirst') {\n            return [\n                inputShape[0], inputShape[1],\n                inputShape[2] - this.cropping[0][0] - this.cropping[0][1],\n                inputShape[3] - this.cropping[1][0] - this.cropping[1][1]\n            ];\n        }\n        else {\n            return [\n                inputShape[0],\n                inputShape[1] - this.cropping[0][0] - this.cropping[0][1],\n                inputShape[2] - this.cropping[1][0] - this.cropping[1][1], inputShape[3]\n            ];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                const hSliced = K.sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);\n                return K.sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);\n            }\n            else {\n                const hSliced = K.sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);\n                return K.sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);\n            }\n        });\n    }\n    getConfig() {\n        const config = { cropping: this.cropping, dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nCropping2D.className = 'Cropping2D';\nserialization.registerClass(Cropping2D);\nexport class UpSampling2D extends Layer {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_SIZE = [2, 2];\n        this.inputSpec = [{ ndim: 4 }];\n        this.size = args.size == null ? this.DEFAULT_SIZE : args.size;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n    }\n    computeOutputShape(inputShape) {\n        if (this.dataFormat === 'channelsFirst') {\n            const height = inputShape[2] == null ? null : this.size[0] * inputShape[2];\n            const width = inputShape[3] == null ? null : this.size[1] * inputShape[3];\n            return [inputShape[0], inputShape[1], height, width];\n        }\n        else {\n            const height = inputShape[1] == null ? null : this.size[0] * inputShape[1];\n            const width = inputShape[2] == null ? null : this.size[1] * inputShape[2];\n            return [inputShape[0], height, width, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            let input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            if (this.dataFormat === 'channelsFirst') {\n                input = tfc.transpose(input, [0, 2, 3, 1]);\n                const height = this.size[0] * inputShape[2];\n                const width = this.size[1] * inputShape[3];\n                const resized = input.resizeNearestNeighbor([height, width]);\n                return tfc.transpose(resized, [0, 3, 1, 2]);\n            }\n            else {\n                const height = this.size[0] * inputShape[1];\n                const width = this.size[1] * inputShape[2];\n                return input.resizeNearestNeighbor([height, width]);\n            }\n        });\n    }\n    getConfig() {\n        const config = { size: this.size, dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nUpSampling2D.className = 'UpSampling2D';\nserialization.registerClass(UpSampling2D);\n//# sourceMappingURL=convolutional.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Depthwise Convolutional Layers\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { convOutputLength } from '../utils/conv_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { BaseConv, preprocessConv2DInput } from './convolutional';\n/**\n * 2D convolution with separable filters.\n * @param x Input tensor.\n * @param depthwiseKernel Convolution kernel for depthwise convolution.\n * @param strides Strides (Array of two integers).\n * @param padding Padding model.\n * @param dataFormat Data format.\n * @param dilationRate Array of two integers, dilation rates for the separable\n *   convolution.\n * @returns Output tensor.\n * @throws ValueError If depthwiseKernel is not a 4D array.\n */\nexport function depthwiseConv2d(x, depthwiseKernel, strides = [1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        let y = preprocessConv2DInput(x, dataFormat);\n        if (x.rank !== 4) {\n            throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ` +\n                `${x.rank}-D`);\n        }\n        if (depthwiseKernel.rank !== 4) {\n            throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ` +\n                `${depthwiseKernel.rank}-D`);\n        }\n        y = tfc.depthwiseConv2d(y, depthwiseKernel, strides, padding === 'same' ? 'same' : 'valid', 'NHWC', dilationRate);\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]);\n        }\n        return y;\n    });\n}\nexport class DepthwiseConv2D extends BaseConv {\n    constructor(args) {\n        super(2, args);\n        this.depthwiseKernel = null;\n        this.depthMultiplier =\n            args.depthMultiplier == null ? 1 : args.depthMultiplier;\n        this.depthwiseInitializer = getInitializer(args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.depthwiseConstraint = getConstraint(args.depthwiseConstraint);\n        this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < 4) {\n            throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. ` +\n                `Received input shape: ${JSON.stringify(inputShape)}.`);\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : 3;\n        if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n            throw new ValueError('The channel dimension of the inputs to DepthwiseConv2D should ' +\n                `be defined, but is not (${inputShape[channelAxis]}).`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const depthwiseKernelShape = [\n            this.kernelSize[0], this.kernelSize[1], inputDim, this.depthMultiplier\n        ];\n        this.depthwiseKernel = this.addWeight('depthwise_kernel', depthwiseKernelShape, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [inputDim * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let outputs = depthwiseConv2d(inputs, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);\n            // TODO(cais): Add support for dilation.\n            if (this.useBias) {\n                outputs = K.biasAdd(outputs, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                outputs = this.activation.apply(outputs);\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const rows = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        const cols = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        const outFilters = this.dataFormat === 'channelsFirst' ?\n            inputShape[1] * this.depthMultiplier :\n            inputShape[3] * this.depthMultiplier;\n        const outRows = convOutputLength(rows, this.kernelSize[0], this.padding, this.strides[0]);\n        const outCols = convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], outFilters, outRows, outCols];\n        }\n        else {\n            // In this case, assume 'channelsLast'.\n            return [inputShape[0], outRows, outCols, outFilters];\n        }\n    }\n    getConfig() {\n        const config = super.getConfig();\n        config['depthMultiplier'] = this.depthMultiplier;\n        config['depthwiseInitializer'] =\n            serializeInitializer(this.depthwiseInitializer);\n        config['depthwiseRegularizer'] =\n            serializeRegularizer(this.depthwiseRegularizer);\n        config['depthwiseConstraint'] =\n            serializeConstraint(this.depthwiseRegularizer);\n        return config;\n    }\n}\n/** @nocollapse */\nDepthwiseConv2D.className = 'DepthwiseConv2D';\nserialization.registerClass(DepthwiseConv2D);\n//# sourceMappingURL=convolutional_depthwise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Recurrent Neural Network Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, SymbolicTensor } from '../engine/topology';\nimport { Layer } from '../engine/topology';\nimport { AttributeError, NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, Initializer, Ones, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor, isArrayOfShapes } from '../utils/types_utils';\nimport { batchGetValue, batchSetValue } from '../variables';\nimport { deserialize } from './serialization';\n/**\n * Standardize `apply()` args to a single list of tensor inputs.\n *\n * When running a model loaded from file, the input tensors `initialState` and\n * `constants` are passed to `RNN.apply()` as part of `inputs` instead of the\n * dedicated kwargs fields. `inputs` consists of\n * `[inputs, initialState0, initialState1, ..., constant0, constant1]` in this\n * case.\n * This method makes sure that arguments are\n * separated and that `initialState` and `constants` are `Array`s of tensors\n * (or None).\n *\n * @param inputs Tensor or `Array` of  tensors.\n * @param initialState Tensor or `Array` of tensors or `null`/`undefined`.\n * @param constants Tensor or `Array` of tensors or `null`/`undefined`.\n * @returns An object consisting of\n *   inputs: A tensor.\n *   initialState: `Array` of tensors or `null`.\n *   constants: `Array` of tensors or `null`.\n * @throws ValueError, if `inputs` is an `Array` but either `initialState` or\n *   `constants` is provided.\n */\nexport function standardizeArgs(inputs, initialState, constants, numConstants) {\n    if (Array.isArray(inputs)) {\n        if (initialState != null || constants != null) {\n            throw new ValueError('When inputs is an array, neither initialState or constants ' +\n                'should be provided');\n        }\n        if (numConstants != null) {\n            constants = inputs.slice(inputs.length - numConstants, inputs.length);\n            inputs = inputs.slice(0, inputs.length - numConstants);\n        }\n        if (inputs.length > 1) {\n            initialState = inputs.slice(1, inputs.length);\n        }\n        inputs = inputs[0];\n    }\n    function toListOrNull(x) {\n        if (x == null || Array.isArray(x)) {\n            return x;\n        }\n        else {\n            return [x];\n        }\n    }\n    initialState = toListOrNull(initialState);\n    constants = toListOrNull(constants);\n    return { inputs, initialState, constants };\n}\n/**\n * Iterates over the time dimension of a tensor.\n *\n * @param stepFunction RNN step function.\n *   Parameters:\n *     inputs: tensor with shape `[samples, ...]` (no time dimension),\n *       representing input for the batch of samples at a certain time step.\n *     states: an Array of tensors.\n *   Returns:\n *     outputs: tensor with shape `[samples, outputDim]` (no time dimension).\n *     newStates: list of tensors, same length and shapes as `states`. The first\n *       state in the list must be the output tensor at the previous timestep.\n * @param inputs Tensor of temporal data of shape `[samples, time, ...]` (at\n *   least 3D).\n * @param initialStates Tensor with shape `[samples, outputDim]` (no time\n *   dimension), containing the initial values of the states used in the step\n *   function.\n * @param goBackwards If `true`, do the iteration over the time dimension in\n *   reverse order and return the reversed sequence.\n * @param mask Binary tensor with shape `[sample, time, 1]`, with a zero for\n *   every element that is masked.\n * @param constants An Array of constant values passed at each step.\n * @param unroll Whether to unroll the RNN or to use a symbolic loop. *Not*\n *   applicable to this imperative deeplearn.js backend. Its value is ignored.\n * @param needPerStepOutputs Whether the per-step outputs are to be\n *   concatenated into a single tensor and returned (as the second return\n *   value). Default: `false`. This arg is included so that the relatively\n *   expensive concatenation of the stepwise outputs can be omitted unless\n *   the stepwise outputs need to be kept (e.g., for an LSTM layer of which\n *   `returnSequence` is `true`.)\n * @returns An Array: `[lastOutput, outputs, newStates]`.\n *   lastOutput: the lastest output of the RNN, of shape `[samples, ...]`.\n *   outputs: tensor with shape `[samples, time, ...]` where each entry\n *     `output[s, t]` is the output of the step function at time `t` for sample\n *     `s`. This return value is provided if and only if the\n *     `needPerStepOutputs` is set as `true`. If it is set as `false`, this\n *     return value will be `undefined`.\n *   newStates: Array of tensors, latest states returned by the step function,\n *      of shape `(samples, ...)`.\n * @throws ValueError If input dimension is less than 3.\n *\n * TODO(nielsene): This needs to be tidy-ed.\n */\nexport function rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, constants, unroll = false, needPerStepOutputs = false) {\n    return tfc.tidy(() => {\n        const ndim = inputs.shape.length;\n        if (ndim < 3) {\n            throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);\n        }\n        // Transpose to time-major, i.e., from [batch, time, ...] to [time, batch,\n        // ...].\n        const axes = [1, 0].concat(math_utils.range(2, ndim));\n        inputs = tfc.transpose(inputs, axes);\n        if (constants != null) {\n            throw new NotImplementedError('The rnn() functoin of the deeplearn.js backend does not support ' +\n                'constants yet.');\n        }\n        // Porting Note: the unroll option is ignored by the imperative backend.\n        if (unroll) {\n            console.warn('Backend rnn(): the unroll = true option is not applicable to the ' +\n                'imperative deeplearn.js backend.');\n        }\n        if (mask != null) {\n            mask = mask.asType('bool').asType('float32');\n            if (mask.rank === ndim - 1) {\n                mask = tfc.expandDims(mask, -1);\n            }\n            mask = tfc.transpose(mask, axes);\n        }\n        if (goBackwards) {\n            inputs = tfc.reverse(inputs, 0);\n            if (mask != null) {\n                mask = tfc.reverse(mask, 0);\n            }\n        }\n        // Porting Note: PyKeras with TensorFlow backend uses a symbolic loop\n        //   (tf.while_loop). But for the imperative deeplearn.js backend, we just\n        //   use the usual TypeScript control flow to iterate over the time steps in\n        //   the inputs.\n        // Porting Note: PyKeras patches a \"_use_learning_phase\" attribute to\n        // outputs.\n        //   This is not idiomatic in TypeScript. The info regarding whether we are\n        //   in a learning (i.e., training) phase for RNN is passed in a different\n        //   way.\n        const perStepOutputs = [];\n        let lastOutput;\n        let states = initialStates;\n        const timeSteps = inputs.shape[0];\n        const perStepInputs = tfc.unstack(inputs);\n        let perStepMasks;\n        if (mask != null) {\n            perStepMasks = tfc.unstack(mask);\n        }\n        for (let t = 0; t < timeSteps; ++t) {\n            const currentInput = perStepInputs[t];\n            const stepOutputs = tfc.tidy(() => stepFunction(currentInput, states));\n            if (mask == null) {\n                lastOutput = stepOutputs[0];\n                states = stepOutputs[1];\n            }\n            else {\n                const maskedOutputs = tfc.tidy(() => {\n                    const stepMask = perStepMasks[t];\n                    const negStepMask = tfc.onesLike(stepMask).sub(stepMask);\n                    // TODO(cais): Would tfc.where() be better for performance?\n                    const output = stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask));\n                    const newStates = states.map((state, i) => {\n                        return stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask));\n                    });\n                    return { output, newStates };\n                });\n                lastOutput = maskedOutputs.output;\n                states = maskedOutputs.newStates;\n            }\n            if (needPerStepOutputs) {\n                perStepOutputs.push(lastOutput);\n            }\n        }\n        let outputs;\n        if (needPerStepOutputs) {\n            const axis = 1;\n            outputs = tfc.stack(perStepOutputs, axis);\n        }\n        return [lastOutput, outputs, states];\n    });\n}\nexport class RNN extends Layer {\n    constructor(args) {\n        super(args);\n        let cell;\n        if (args.cell == null) {\n            throw new ValueError('cell property is missing for the constructor of RNN.');\n        }\n        else if (Array.isArray(args.cell)) {\n            cell = new StackedRNNCells({ cells: args.cell });\n        }\n        else {\n            cell = args.cell;\n        }\n        if (cell.stateSize == null) {\n            throw new ValueError('The RNN cell should have an attribute `stateSize` (tuple of ' +\n                'integers, one integer per RNN state).');\n        }\n        this.cell = cell;\n        this.returnSequences =\n            args.returnSequences == null ? false : args.returnSequences;\n        this.returnState = args.returnState == null ? false : args.returnState;\n        this.goBackwards = args.goBackwards == null ? false : args.goBackwards;\n        this._stateful = args.stateful == null ? false : args.stateful;\n        this.unroll = args.unroll == null ? false : args.unroll;\n        this.supportsMasking = true;\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n        this.stateSpec = null;\n        this.states_ = null;\n        // TODO(cais): Add constantsSpec and numConstants.\n        this.numConstants = null;\n        // TODO(cais): Look into the use of initial_state in the kwargs of the\n        //   constructor.\n        this.keptStates = [];\n    }\n    // Porting Note: This is the equivalent of `RNN.states` property getter in\n    //   PyKeras.\n    getStates() {\n        if (this.states_ == null) {\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            return math_utils.range(0, numStates).map(x => null);\n        }\n        else {\n            return this.states_;\n        }\n    }\n    // Porting Note: This is the equivalent of the `RNN.states` property setter in\n    //   PyKeras.\n    setStates(states) {\n        this.states_ = states;\n    }\n    computeOutputShape(inputShape) {\n        if (isArrayOfShapes(inputShape)) {\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        // TODO(cais): Remove the casting once stacked RNN cells become supported.\n        let stateSize = this.cell.stateSize;\n        if (!Array.isArray(stateSize)) {\n            stateSize = [stateSize];\n        }\n        const outputDim = stateSize[0];\n        let outputShape;\n        if (this.returnSequences) {\n            outputShape = [inputShape[0], inputShape[1], outputDim];\n        }\n        else {\n            outputShape = [inputShape[0], outputDim];\n        }\n        if (this.returnState) {\n            const stateShape = [];\n            for (const dim of stateSize) {\n                stateShape.push([inputShape[0], dim]);\n            }\n            return [outputShape].concat(stateShape);\n        }\n        else {\n            return outputShape;\n        }\n    }\n    computeMask(inputs, mask) {\n        return tfc.tidy(() => {\n            if (Array.isArray(mask)) {\n                mask = mask[0];\n            }\n            const outputMask = this.returnSequences ? mask : null;\n            if (this.returnState) {\n                const stateMask = this.states.map(s => null);\n                return [outputMask].concat(stateMask);\n            }\n            else {\n                return outputMask;\n            }\n        });\n    }\n    /**\n     * Get the current state tensors of the RNN.\n     *\n     * If the state hasn't been set, return an array of `null`s of the correct\n     * length.\n     */\n    get states() {\n        if (this.states_ == null) {\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            const output = [];\n            for (let i = 0; i < numStates; ++i) {\n                output.push(null);\n            }\n            return output;\n        }\n        else {\n            return this.states_;\n        }\n    }\n    set states(s) {\n        this.states_ = s;\n    }\n    build(inputShape) {\n        // Note inputShape will be an Array of Shapes of initial states and\n        // constants if these are passed in apply().\n        const constantShape = null;\n        if (this.numConstants != null) {\n            throw new NotImplementedError('Constants support is not implemented in RNN yet.');\n        }\n        if (isArrayOfShapes(inputShape)) {\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        const batchSize = this.stateful ? inputShape[0] : null;\n        const inputDim = inputShape.slice(2);\n        this.inputSpec[0] = new InputSpec({ shape: [batchSize, null, ...inputDim] });\n        // Allow cell (if RNNCell Layer) to build before we set or validate\n        // stateSpec.\n        const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        if (constantShape != null) {\n            throw new NotImplementedError('Constants support is not implemented in RNN yet.');\n        }\n        else {\n            this.cell.build(stepInputShape);\n        }\n        // Set or validate stateSpec.\n        let stateSize;\n        if (Array.isArray(this.cell.stateSize)) {\n            stateSize = this.cell.stateSize;\n        }\n        else {\n            stateSize = [this.cell.stateSize];\n        }\n        if (this.stateSpec != null) {\n            if (!util.arraysEqual(this.stateSpec.map(spec => spec.shape[spec.shape.length - 1]), stateSize)) {\n                throw new ValueError(`An initialState was passed that is not compatible with ` +\n                    `cell.stateSize. Received stateSpec=${this.stateSpec}; ` +\n                    `However cell.stateSize is ${this.cell.stateSize}`);\n            }\n        }\n        else {\n            this.stateSpec =\n                stateSize.map(dim => new InputSpec({ shape: [null, dim] }));\n        }\n        if (this.stateful) {\n            this.resetStates();\n        }\n    }\n    /**\n     * Reset the state tensors of the RNN.\n     *\n     * If the `states` argument is `undefined` or `null`, will set the\n     * state tensor(s) of the RNN to all-zero tensors of the appropriate\n     * shape(s).\n     *\n     * If `states` is provided, will set the state tensors of the RNN to its\n     * value.\n     *\n     * @param states Optional externally-provided initial states.\n     * @param training Whether this call is done during training. For stateful\n     *   RNNs, this affects whether the old states are kept or discarded. In\n     *   particular, if `training` is `true`, the old states will be kept so\n     *   that subsequent backpropgataion through time (BPTT) may work properly.\n     *   Else, the old states will be discarded.\n     */\n    resetStates(states, training = false) {\n        tidy(() => {\n            if (!this.stateful) {\n                throw new AttributeError('Cannot call resetStates() on an RNN Layer that is not stateful.');\n            }\n            const batchSize = this.inputSpec[0].shape[0];\n            if (batchSize == null) {\n                throw new ValueError('If an RNN is stateful, it needs to know its batch size. Specify ' +\n                    'the batch size of your input tensors: \\n' +\n                    '- If using a Sequential model, specify the batch size by ' +\n                    'passing a `batchInputShape` option to your first layer.\\n' +\n                    '- If using the functional API, specify the batch size by ' +\n                    'passing a `batchShape` option to your Input layer.');\n            }\n            // Initialize state if null.\n            if (this.states_ == null) {\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ =\n                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n                }\n                else {\n                    this.states_ = [tfc.zeros([batchSize, this.cell.stateSize])];\n                }\n            }\n            else if (states == null) {\n                // Dispose old state tensors.\n                tfc.dispose(this.states_);\n                // For stateful RNNs, fully dispose kept old states.\n                if (this.keptStates != null) {\n                    tfc.dispose(this.keptStates);\n                    this.keptStates = [];\n                }\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ =\n                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n                }\n                else {\n                    this.states_[0] = tfc.zeros([batchSize, this.cell.stateSize]);\n                }\n            }\n            else {\n                if (!Array.isArray(states)) {\n                    states = [states];\n                }\n                if (states.length !== this.states_.length) {\n                    throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), ` +\n                        `but it received ${states.length} state value(s). Input ` +\n                        `received: ${states}`);\n                }\n                if (training === true) {\n                    // Store old state tensors for complete disposal later, i.e., during\n                    // the next no-arg call to this method. We do not dispose the old\n                    // states immediately because that BPTT (among other things) require\n                    // them.\n                    this.keptStates.push(this.states_.slice());\n                }\n                else {\n                    tfc.dispose(this.states_);\n                }\n                for (let index = 0; index < this.states_.length; ++index) {\n                    const value = states[index];\n                    const dim = Array.isArray(this.cell.stateSize) ?\n                        this.cell.stateSize[index] :\n                        this.cell.stateSize;\n                    const expectedShape = [batchSize, dim];\n                    if (!util.arraysEqual(value.shape, expectedShape)) {\n                        throw new ValueError(`State ${index} is incompatible with layer ${this.name}: ` +\n                            `expected shape=${expectedShape}, received shape=${value.shape}`);\n                    }\n                    this.states_[index] = value;\n                }\n            }\n            this.states_ = this.states_.map(state => tfc.keep(state.clone()));\n        });\n    }\n    apply(inputs, kwargs) {\n        // TODO(cais): Figure out whether initialState is in kwargs or inputs.\n        let initialState = kwargs == null ? null : kwargs['initialState'];\n        let constants = kwargs == null ? null : kwargs['constants'];\n        if (kwargs == null) {\n            kwargs = {};\n        }\n        const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n        inputs = standardized.inputs;\n        initialState = standardized.initialState;\n        constants = standardized.constants;\n        // If any of `initial_state` or `constants` are specified and are\n        // `tf.SymbolicTensor`s, then add them to the inputs and temporarily modify\n        // the input_spec to include them.\n        let additionalInputs = [];\n        let additionalSpecs = [];\n        if (initialState != null) {\n            kwargs['initialState'] = initialState;\n            additionalInputs = additionalInputs.concat(initialState);\n            this.stateSpec = [];\n            for (const state of initialState) {\n                this.stateSpec.push(new InputSpec({ shape: state.shape }));\n            }\n            // TODO(cais): Use the following instead.\n            // this.stateSpec = initialState.map(state => new InputSpec({shape:\n            // state.shape}));\n            additionalSpecs = additionalSpecs.concat(this.stateSpec);\n        }\n        if (constants != null) {\n            kwargs['constants'] = constants;\n            additionalInputs = additionalInputs.concat(constants);\n            // TODO(cais): Add this.constantsSpec.\n            this.numConstants = constants.length;\n        }\n        const isTensor = additionalInputs[0] instanceof SymbolicTensor;\n        if (isTensor) {\n            // Compute full input spec, including state and constants.\n            const fullInput = [inputs].concat(additionalInputs);\n            const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n            // Perform the call with temporarily replaced inputSpec.\n            const originalInputSpec = this.inputSpec;\n            this.inputSpec = fullInputSpec;\n            const output = super.apply(fullInput, kwargs);\n            this.inputSpec = originalInputSpec;\n            return output;\n        }\n        else {\n            return super.apply(inputs, kwargs);\n        }\n    }\n    // tslint:disable-next-line:no-any\n    call(inputs, kwargs) {\n        // Input shape: `[samples, time (padded with zeros), input_dim]`.\n        // Note that the .build() method of subclasses **must** define\n        // this.inputSpec and this.stateSpec owith complete input shapes.\n        return tidy(() => {\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            let initialState = kwargs == null ? null : kwargs['initialState'];\n            inputs = getExactlyOneTensor(inputs);\n            if (initialState == null) {\n                if (this.stateful) {\n                    initialState = this.states_;\n                }\n                else {\n                    initialState = this.getInitialState(inputs);\n                }\n            }\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            if (initialState.length !== numStates) {\n                throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ` +\n                    `${initialState.length} initial state(s).`);\n            }\n            if (this.unroll) {\n                console.warn('Ignoring unroll = true for RNN layer, due to imperative backend.');\n            }\n            const cellCallKwargs = { training };\n            // TODO(cais): Add support for constants.\n            const step = (inputs, states) => {\n                // `inputs` and `states` are concatenated to form a single `Array` of\n                // `tf.Tensor`s as the input to `cell.call()`.\n                const outputs = this.cell.call([inputs].concat(states), cellCallKwargs);\n                // Marshall the return value into output and new states.\n                return [outputs[0], outputs.slice(1)];\n            };\n            // TODO(cais): Add support for constants.\n            const rnnOutputs = rnn(step, inputs, initialState, this.goBackwards, mask, null, this.unroll, this.returnSequences);\n            const lastOutput = rnnOutputs[0];\n            const outputs = rnnOutputs[1];\n            const states = rnnOutputs[2];\n            if (this.stateful) {\n                this.resetStates(states, training);\n            }\n            const output = this.returnSequences ? outputs : lastOutput;\n            // TODO(cais): Porperty set learning phase flag.\n            if (this.returnState) {\n                return [output].concat(states);\n            }\n            else {\n                return output;\n            }\n        });\n    }\n    getInitialState(inputs) {\n        return tidy(() => {\n            // Build an all-zero tensor of shape [samples, outputDim].\n            // [Samples, timeSteps, inputDim].\n            let initialState = tfc.zeros(inputs.shape);\n            // [Samples].\n            initialState = tfc.sum(initialState, [1, 2]);\n            initialState = K.expandDims(initialState); // [Samples, 1].\n            if (Array.isArray(this.cell.stateSize)) {\n                return this.cell.stateSize.map(dim => dim > 1 ? K.tile(initialState, [1, dim]) : initialState);\n            }\n            else {\n                return this.cell.stateSize > 1 ?\n                    [K.tile(initialState, [1, this.cell.stateSize])] :\n                    [initialState];\n            }\n        });\n    }\n    get trainableWeights() {\n        if (!this.trainable) {\n            return [];\n        }\n        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n        return this.cell.trainableWeights;\n    }\n    get nonTrainableWeights() {\n        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n        if (!this.trainable) {\n            return this.cell.weights;\n        }\n        return this.cell.nonTrainableWeights;\n    }\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.cell != null) {\n            this.cell.setFastWeightInitDuringBuild(value);\n        }\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            returnSequences: this.returnSequences,\n            returnState: this.returnState,\n            goBackwards: this.goBackwards,\n            stateful: this.stateful,\n            unroll: this.unroll,\n        };\n        if (this.numConstants != null) {\n            config['numConstants'] = this.numConstants;\n        }\n        const cellConfig = this.cell.getConfig();\n        if (this.getClassName() === RNN.className) {\n            config['cell'] = {\n                'className': this.cell.getClassName(),\n                'config': cellConfig,\n            };\n        }\n        // this order is necessary, to prevent cell name from replacing layer name\n        return Object.assign({}, cellConfig, baseConfig, config);\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const cellConfig = config['cell'];\n        const cell = deserialize(cellConfig, customObjects);\n        return new cls(Object.assign(config, { cell }));\n    }\n}\n/** @nocollapse */\nRNN.className = 'RNN';\nserialization.registerClass(RNN);\n// Porting Note: This is a common parent class for RNN cells. There is no\n// equivalent of this in PyKeras. Having a common parent class forgoes the\n//  need for `has_attr(cell, ...)` checks or its TypeScript equivalent.\n/**\n * An RNNCell layer.\n *\n * @doc {heading: 'Layers', subheading: 'Classes'}\n */\nexport class RNNCell extends Layer {\n}\nexport class SimpleRNNCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        this.units = args.units;\n        assertPositiveInteger(this.units, `units`);\n        this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.stateSize = this.units;\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        // TODO(cais): Use regularizer.\n        this.kernel = this.addWeight('kernel', [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.built = true;\n    }\n    // Porting Note: PyKeras' equivalent of this method takes two tensor inputs:\n    //   `inputs` and `states`. Here, the two tensors are combined into an\n    //   `Tensor[]` Array as the first input argument.\n    //   Similarly, PyKeras' equivalent of this method returns two values:\n    //    `output` and `[output]`. Here the two are combined into one length-2\n    //    `Tensor[]`, consisting of `output` repeated.\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (inputs.length !== 2) {\n                throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);\n            }\n            let prevOutput = inputs[1];\n            inputs = inputs[0];\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(prevOutput),\n                    rate: this.recurrentDropout,\n                    training\n                });\n            }\n            let h;\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            if (dpMask != null) {\n                h = K.dot(tfc.mul(inputs, dpMask), this.kernel.read());\n            }\n            else {\n                h = K.dot(inputs, this.kernel.read());\n            }\n            if (this.bias != null) {\n                h = K.biasAdd(h, this.bias.read());\n            }\n            if (recDpMask != null) {\n                prevOutput = tfc.mul(prevOutput, recDpMask);\n            }\n            let output = tfc.add(h, K.dot(prevOutput, this.recurrentKernel.read()));\n            if (this.activation != null) {\n                output = this.activation.apply(output);\n            }\n            // TODO(cais): Properly set learning phase on output tensor?\n            return [output, output];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nSimpleRNNCell.className = 'SimpleRNNCell';\nserialization.registerClass(SimpleRNNCell);\nexport class SimpleRNN extends RNN {\n    constructor(args) {\n        args.cell = new SimpleRNNCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nSimpleRNN.className = 'SimpleRNN';\nserialization.registerClass(SimpleRNN);\nexport class GRUCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        if (args.resetAfter) {\n            throw new ValueError(`GRUCell does not support reset_after parameter set to true.`);\n        }\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :\n            args.activation);\n        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.implementation = args.implementation;\n        this.stateSize = this.units;\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const inputDim = inputShape[inputShape.length - 1];\n        this.kernel = this.addWeight('kernel', [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n        //   of the weights and bias in the call() method, at execution time.\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (inputs.length !== 2) {\n                throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            let hTMinus1 = inputs[1]; // Previous memory state.\n            inputs = inputs[0];\n            // Note: For superior performance, TensorFlow.js always uses\n            // implementation 2, regardless of the actual value of\n            // config.implementation.\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training,\n                    count: 3\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: 3\n                });\n            }\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            let z;\n            let r;\n            let hh;\n            if (0 < this.dropout && this.dropout < 1) {\n                inputs = tfc.mul(inputs, dpMask[0]);\n            }\n            let matrixX = K.dot(inputs, this.kernel.read());\n            if (this.useBias) {\n                matrixX = K.biasAdd(matrixX, this.bias.read());\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n            }\n            const recurrentKernelValue = this.recurrentKernel.read();\n            const [rk1, rk2] = tfc.split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1);\n            const matrixInner = K.dot(hTMinus1, rk1);\n            const [xZ, xR, xH] = tfc.split(matrixX, 3, matrixX.rank - 1);\n            const [recurrentZ, recurrentR] = tfc.split(matrixInner, 2, matrixInner.rank - 1);\n            z = this.recurrentActivation.apply(tfc.add(xZ, recurrentZ));\n            r = this.recurrentActivation.apply(tfc.add(xR, recurrentR));\n            const recurrentH = K.dot(tfc.mul(r, hTMinus1), rk2);\n            hh = this.activation.apply(tfc.add(xH, recurrentH));\n            const h = tfc.add(tfc.mul(z, hTMinus1), tfc.mul(tfc.add(1, tfc.neg(z)), hh));\n            // TODO(cais): Add use_learning_phase flag properly.\n            return [h, h];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            recurrentActivation: serializeActivation(this.recurrentActivation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n            implementation: this.implementation,\n            resetAfter: false\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nGRUCell.className = 'GRUCell';\nserialization.registerClass(GRUCell);\nexport class GRU extends RNN {\n    constructor(args) {\n        if (args.implementation === 0) {\n            console.warn('`implementation=0` has been deprecated, and now defaults to ' +\n                '`implementation=1`. Please update your layer call.');\n        }\n        args.cell = new GRUCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        if (config['implmentation'] === 0) {\n            config['implementation'] = 1;\n        }\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nGRU.className = 'GRU';\nserialization.registerClass(GRU);\nexport class LSTMCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :\n            args.activation);\n        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.unitForgetBias = args.unitForgetBias;\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.implementation = args.implementation;\n        this.stateSize = [this.units, this.units];\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        var _a;\n        inputShape = getExactlyOneShape(inputShape);\n        const inputDim = inputShape[inputShape.length - 1];\n        this.kernel = this.addWeight('kernel', [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        let biasInitializer;\n        if (this.useBias) {\n            if (this.unitForgetBias) {\n                const capturedBiasInit = this.biasInitializer;\n                const capturedUnits = this.units;\n                biasInitializer = new (_a = class CustomInit extends Initializer {\n                        apply(shape, dtype) {\n                            // TODO(cais): More informative variable names?\n                            const bI = capturedBiasInit.apply([capturedUnits]);\n                            const bF = (new Ones()).apply([capturedUnits]);\n                            const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);\n                            return K.concatAlongFirstAxis(K.concatAlongFirstAxis(bI, bF), bCAndH);\n                        }\n                    },\n                    /** @nocollapse */\n                    _a.className = 'CustomInit',\n                    _a)();\n            }\n            else {\n                biasInitializer = this.biasInitializer;\n            }\n            this.bias = this.addWeight('bias', [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n        //   of the weights and bias in the call() method, at execution time.\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            inputs = inputs;\n            if (inputs.length !== 3) {\n                throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            let hTMinus1 = inputs[1]; // Previous memory state.\n            const cTMinus1 = inputs[2]; // Previous carry state.\n            inputs = inputs[0];\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training,\n                    count: 4\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: 4\n                });\n            }\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            // Note: For superior performance, TensorFlow.js always uses\n            // implementation 2 regardless of the actual value of\n            // config.implementation.\n            let i;\n            let f;\n            let c;\n            let o;\n            if (0 < this.dropout && this.dropout < 1) {\n                inputs = tfc.mul(inputs, dpMask[0]);\n            }\n            let z = K.dot(inputs, this.kernel.read());\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n            }\n            z = tfc.add(z, K.dot(hTMinus1, this.recurrentKernel.read()));\n            if (this.useBias) {\n                z = K.biasAdd(z, this.bias.read());\n            }\n            const [z0, z1, z2, z3] = tfc.split(z, 4, z.rank - 1);\n            i = this.recurrentActivation.apply(z0);\n            f = this.recurrentActivation.apply(z1);\n            c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(z2)));\n            o = this.recurrentActivation.apply(z3);\n            const h = tfc.mul(o, this.activation.apply(c));\n            // TODO(cais): Add use_learning_phase flag properly.\n            return [h, h, c];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            recurrentActivation: serializeActivation(this.recurrentActivation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            unitForgetBias: this.unitForgetBias,\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n            implementation: this.implementation,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nLSTMCell.className = 'LSTMCell';\nserialization.registerClass(LSTMCell);\nexport class LSTM extends RNN {\n    constructor(args) {\n        if (args.implementation === 0) {\n            console.warn('`implementation=0` has been deprecated, and now defaults to ' +\n                '`implementation=1`. Please update your layer call.');\n        }\n        args.cell = new LSTMCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        if (config['implmentation'] === 0) {\n            config['implementation'] = 1;\n        }\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nLSTM.className = 'LSTM';\nserialization.registerClass(LSTM);\nexport class StackedRNNCells extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.cells = args.cells;\n    }\n    get stateSize() {\n        // States are a flat list in reverse order of the cell stack.\n        // This allows perserving the requirement `stack.statesize[0] ===\n        // outputDim`. E.g., states of a 2-layer LSTM would be `[h2, c2, h1, c1]`,\n        // assuming one LSTM has states `[h, c]`.\n        const stateSize = [];\n        for (const cell of this.cells.slice().reverse()) {\n            if (Array.isArray(cell.stateSize)) {\n                stateSize.push(...cell.stateSize);\n            }\n            else {\n                stateSize.push(cell.stateSize);\n            }\n        }\n        return stateSize;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            let states = inputs.slice(1);\n            // Recover per-cell states.\n            const nestedStates = [];\n            for (const cell of this.cells.slice().reverse()) {\n                if (Array.isArray(cell.stateSize)) {\n                    nestedStates.push(states.splice(0, cell.stateSize.length));\n                }\n                else {\n                    nestedStates.push(states.splice(0, 1));\n                }\n            }\n            nestedStates.reverse();\n            // Call the cells in order and store the returned states.\n            const newNestedStates = [];\n            let callInputs;\n            for (let i = 0; i < this.cells.length; ++i) {\n                const cell = this.cells[i];\n                states = nestedStates[i];\n                // TODO(cais): Take care of constants.\n                if (i === 0) {\n                    callInputs = [inputs[0]].concat(states);\n                }\n                else {\n                    callInputs = [callInputs[0]].concat(states);\n                }\n                callInputs = cell.call(callInputs, kwargs);\n                newNestedStates.push(callInputs.slice(1));\n            }\n            // Format the new states as a flat list in reverse cell order.\n            states = [];\n            for (const cellStates of newNestedStates.slice().reverse()) {\n                states.push(...cellStates);\n            }\n            return [callInputs[0]].concat(states);\n        });\n    }\n    build(inputShape) {\n        if (isArrayOfShapes(inputShape)) {\n            // TODO(cais): Take care of input constants.\n            // const constantShape = inputShape.slice(1);\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        let outputDim;\n        this.cells.forEach((cell, i) => {\n            nameScope(`RNNCell_${i}`, () => {\n                // TODO(cais): Take care of input constants.\n                cell.build(inputShape);\n                if (Array.isArray(cell.stateSize)) {\n                    outputDim = cell.stateSize[0];\n                }\n                else {\n                    outputDim = cell.stateSize;\n                }\n                inputShape = [inputShape[0], outputDim];\n            });\n        });\n        this.built = true;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const getCellConfig = (cell) => {\n            return {\n                'className': cell.getClassName(),\n                'config': cell.getConfig(),\n            };\n        };\n        const cellConfigs = this.cells.map(getCellConfig);\n        const config = { 'cells': cellConfigs };\n        return Object.assign({}, baseConfig, config);\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const cells = [];\n        for (const cellConfig of config['cells']) {\n            cells.push(deserialize(cellConfig, customObjects));\n        }\n        return new cls({ cells });\n    }\n    get trainableWeights() {\n        if (!this.trainable) {\n            return [];\n        }\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.trainableWeights);\n        }\n        return weights;\n    }\n    get nonTrainableWeights() {\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.nonTrainableWeights);\n        }\n        if (!this.trainable) {\n            const trainableWeights = [];\n            for (const cell of this.cells) {\n                trainableWeights.push(...cell.trainableWeights);\n            }\n            return trainableWeights.concat(weights);\n        }\n        return weights;\n    }\n    /**\n     * Retrieve the weights of a the model.\n     *\n     * @returns A flat `Array` of `tf.Tensor`s.\n     */\n    getWeights() {\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.weights);\n        }\n        return batchGetValue(weights);\n    }\n    /**\n     * Set the weights of the model.\n     *\n     * @param weights An `Array` of `tf.Tensor`s with shapes and types matching\n     *     the output of `getWeights()`.\n     */\n    setWeights(weights) {\n        const tuples = [];\n        for (const cell of this.cells) {\n            const numParams = cell.weights.length;\n            const inputWeights = weights.splice(numParams);\n            for (let i = 0; i < cell.weights.length; ++i) {\n                tuples.push([cell.weights[i], inputWeights[i]]);\n            }\n        }\n        batchSetValue(tuples);\n    }\n}\n/** @nocollapse */\nStackedRNNCells.className = 'StackedRNNCells';\nserialization.registerClass(StackedRNNCells);\nexport function generateDropoutMask(args) {\n    const { ones, rate, training = false, count = 1 } = args;\n    const droppedInputs = () => K.dropout(ones(), rate);\n    const createMask = () => K.inTrainPhase(droppedInputs, ones, training);\n    // just in case count is provided with null or undefined\n    if (!count || count <= 1) {\n        return tfc.keep(createMask().clone());\n    }\n    const masks = Array(count).fill(undefined).map(createMask);\n    return masks.map(m => tfc.keep(m.clone()));\n}\n//# sourceMappingURL=recurrent.js.map", "/**\n * @license\n * Copyright 2020 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nvar __rest = (this && this.__rest) || function (s, e) {\n    var t = {};\n    for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\n        t[p] = s[p];\n    if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\n        for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\n            if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\n                t[p[i]] = s[p[i]];\n        }\n    return t;\n};\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode } from '../common';\nimport { InputSpec } from '../engine/topology';\nimport { AttributeError, NotImplementedError, ValueError } from '../errors';\nimport { Initializer } from '../initializers';\nimport { convOutputLength, normalizeArray } from '../utils/conv_utils';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport { getExactlyOneShape } from '../utils/types_utils';\nimport { generateDropoutMask, LSTMCell, RNN, RNNCell } from './recurrent';\nclass ConvRNN2DCell extends RNNCell {\n}\n/**\n * Base class for convolutional-recurrent layers.\n */\nclass ConvRNN2D extends RNN {\n    constructor(args) {\n        if (args.unroll) {\n            throw new NotImplementedError('Unrolling is not possible with convolutional RNNs.');\n        }\n        if (Array.isArray(args.cell)) {\n            throw new NotImplementedError('It is not possible at the moment to stack convolutional cells.');\n        }\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 5 })];\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            if (kwargs && kwargs['constants']) {\n                throw new ValueError('ConvRNN2D cell does not support constants');\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    computeOutputShape(inputShape) {\n        let outShape = this.computeSingleOutputShape(inputShape);\n        if (!this.returnSequences) {\n            outShape = [outShape[0], ...outShape.slice(2)];\n        }\n        if (this.returnState) {\n            outShape =\n                [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])];\n        }\n        return outShape;\n    }\n    getInitialState(inputs) {\n        return tfc.tidy(() => {\n            const { stateSize } = this.cell;\n            const inputShape = inputs.shape;\n            const outputShape = this.computeSingleOutputShape(inputShape);\n            const stateShape = [outputShape[0], ...outputShape.slice(2)];\n            const initialState = tfc.zeros(stateShape);\n            if (Array.isArray(stateSize)) {\n                return Array(stateSize.length).fill(initialState);\n            }\n            return [initialState];\n        });\n    }\n    resetStates(states, training = false) {\n        tfc.tidy(() => {\n            if (!this.stateful) {\n                throw new AttributeError('Cannot call resetStates() on an RNN Layer that is not stateful.');\n            }\n            const inputShape = this.inputSpec[0].shape;\n            const outputShape = this.computeSingleOutputShape(inputShape);\n            const stateShape = [outputShape[0], ...outputShape.slice(2)];\n            const batchSize = inputShape[0];\n            if (batchSize == null) {\n                throw new ValueError('If an RNN is stateful, it needs to know its batch size. Specify ' +\n                    'the batch size of your input tensors: \\n' +\n                    '- If using a Sequential model, specify the batch size by ' +\n                    'passing a `batchInputShape` option to your first layer.\\n' +\n                    '- If using the functional API, specify the batch size by ' +\n                    'passing a `batchShape` option to your Input layer.');\n            }\n            // Initialize state if null.\n            if (this.getStates() == null) {\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));\n                }\n                else {\n                    this.states_ = [tfc.zeros(stateShape)];\n                }\n            }\n            else if (states == null) {\n                // Dispose old state tensors.\n                tfc.dispose(this.states_);\n                // For stateful RNNs, fully dispose kept old states.\n                if (this.keptStates != null) {\n                    tfc.dispose(this.keptStates);\n                    this.keptStates = [];\n                }\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));\n                }\n                else {\n                    this.states_[0] = tfc.zeros(stateShape);\n                }\n            }\n            else {\n                if (!Array.isArray(states)) {\n                    states = [states];\n                }\n                if (states.length !== this.states_.length) {\n                    throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), ` +\n                        `but it received ${states.length} state value(s). Input ` +\n                        `received: ${states}`);\n                }\n                if (training) {\n                    // Store old state tensors for complete disposal later, i.e., during\n                    // the next no-arg call to this method. We do not dispose the old\n                    // states immediately because that BPTT (among other things) require\n                    // them.\n                    this.keptStates.push(this.states_.slice());\n                }\n                else {\n                    tfc.dispose(this.states_);\n                }\n                for (let index = 0; index < this.states_.length; ++index) {\n                    const value = states[index];\n                    const expectedShape = stateShape;\n                    if (!util.arraysEqual(value.shape, expectedShape)) {\n                        throw new ValueError(`State ${index} is incompatible with layer ${this.name}: ` +\n                            `expected shape=${expectedShape}, received shape=${value.shape}`);\n                    }\n                    this.states_[index] = value;\n                }\n            }\n            this.states_ = this.states_.map(state => tfc.keep(state.clone()));\n        });\n    }\n    computeSingleOutputShape(inputShape) {\n        const { dataFormat, filters, kernelSize, padding, strides, dilationRate } = this.cell;\n        const isChannelsFirst = dataFormat === 'channelsFirst';\n        const h = inputShape[isChannelsFirst ? 3 : 2];\n        const w = inputShape[isChannelsFirst ? 4 : 3];\n        const hOut = convOutputLength(h, kernelSize[0], padding, strides[0], dilationRate[0]);\n        const wOut = convOutputLength(w, kernelSize[1], padding, strides[1], dilationRate[1]);\n        const outShape = [\n            ...inputShape.slice(0, 2),\n            ...(isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters])\n        ];\n        return outShape;\n    }\n}\n/** @nocollapse */\nConvRNN2D.className = 'ConvRNN2D';\nexport class ConvLSTM2DCell extends LSTMCell {\n    constructor(args) {\n        const { filters, kernelSize, strides, padding, dataFormat, dilationRate, } = args;\n        super(Object.assign({}, args, { units: filters }));\n        this.filters = filters;\n        assertPositiveInteger(this.filters, 'filters');\n        this.kernelSize = normalizeArray(kernelSize, 2, 'kernelSize');\n        this.kernelSize.forEach(size => assertPositiveInteger(size, 'kernelSize'));\n        this.strides = normalizeArray(strides || 1, 2, 'strides');\n        this.strides.forEach(stride => assertPositiveInteger(stride, 'strides'));\n        this.padding = padding || 'valid';\n        checkPaddingMode(this.padding);\n        this.dataFormat = dataFormat || 'channelsLast';\n        checkDataFormat(this.dataFormat);\n        this.dilationRate = normalizeArray(dilationRate || 1, 2, 'dilationRate');\n        this.dilationRate.forEach(rate => assertPositiveInteger(rate, 'dilationRate'));\n    }\n    build(inputShape) {\n        var _a;\n        inputShape = getExactlyOneShape(inputShape);\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError(`The channel dimension of the input should be defined. ` +\n                `Found ${inputShape[channelAxis]}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const numOfKernels = 4;\n        const kernelShape = this.kernelSize.concat([inputDim, this.filters * numOfKernels]);\n        this.kernel = this.addWeight('kernel', kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        const recurrentKernelShape = this.kernelSize.concat([this.filters, this.filters * numOfKernels]);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', recurrentKernelShape, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            let biasInitializer;\n            if (this.unitForgetBias) {\n                const init = this.biasInitializer;\n                const filters = this.filters;\n                biasInitializer = new (_a = class CustomInit extends Initializer {\n                        apply(shape, dtype) {\n                            const biasI = init.apply([filters]);\n                            const biasF = tfc.ones([filters]);\n                            const biasCAndO = init.apply([filters * 2]);\n                            return K.concatenate([biasI, biasF, biasCAndO]);\n                        }\n                    },\n                    /** @nocollapse */\n                    _a.className = 'CustomInit',\n                    _a)();\n            }\n            else {\n                biasInitializer = this.biasInitializer;\n            }\n            this.bias = this.addWeight('bias', [this.filters * numOfKernels], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            if (inputs.length !== 3) {\n                throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            const training = kwargs['training'] || false;\n            const x = inputs[0]; // Current input\n            const hTMinus1 = inputs[1]; // Previous memory state.\n            const cTMinus1 = inputs[2]; // Previous carry state.\n            const numOfKernels = 4;\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(x),\n                    rate: this.dropout,\n                    training,\n                    count: numOfKernels\n                });\n            }\n            const dropoutMask = this.dropoutMask;\n            const applyDropout = (x, mask, index) => {\n                if (!mask || !mask[index]) {\n                    return x;\n                }\n                return tfc.mul(mask[index], x);\n            };\n            let xI = applyDropout(x, dropoutMask, 0);\n            let xF = applyDropout(x, dropoutMask, 1);\n            let xC = applyDropout(x, dropoutMask, 2);\n            let xO = applyDropout(x, dropoutMask, 3);\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: numOfKernels\n                });\n            }\n            const recDropoutMask = this.recurrentDropoutMask;\n            let hI = applyDropout(hTMinus1, recDropoutMask, 0);\n            let hF = applyDropout(hTMinus1, recDropoutMask, 1);\n            let hC = applyDropout(hTMinus1, recDropoutMask, 2);\n            let hO = applyDropout(hTMinus1, recDropoutMask, 3);\n            const kernelChannelAxis = 3;\n            const [kernelI, kernelF, kernelC, kernelO] = tfc.split(this.kernel.read(), numOfKernels, kernelChannelAxis);\n            const [biasI, biasF, biasC, biasO] = this.useBias ?\n                tfc.split(this.bias.read(), numOfKernels) :\n                [null, null, null, null];\n            xI = this.inputConv(xI, kernelI, biasI, this.padding);\n            xF = this.inputConv(xF, kernelF, biasF, this.padding);\n            xC = this.inputConv(xC, kernelC, biasC, this.padding);\n            xO = this.inputConv(xO, kernelO, biasO, this.padding);\n            const [recKernelI, recKernelF, recKernelC, recKernelO] = tfc.split(this.recurrentKernel.read(), numOfKernels, kernelChannelAxis);\n            hI = this.recurrentConv(hI, recKernelI);\n            hF = this.recurrentConv(hF, recKernelF);\n            hC = this.recurrentConv(hC, recKernelC);\n            hO = this.recurrentConv(hO, recKernelO);\n            const i = this.recurrentActivation.apply(tfc.add(xI, hI));\n            const f = this.recurrentActivation.apply(tfc.add(xF, hF));\n            const c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(tfc.add(xC, hC))));\n            const h = tfc.mul(this.recurrentActivation.apply(tfc.add(xO, hO)), this.activation.apply(c));\n            return [h, h, c];\n        });\n    }\n    getConfig() {\n        const _a = super.getConfig(), { 'units': _ } = _a, baseConfig = __rest(_a, ['units']);\n        const config = {\n            filters: this.filters,\n            kernelSize: this.kernelSize,\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n            dilationRate: this.dilationRate,\n            strides: this.strides,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n    inputConv(x, w, b, padding) {\n        const out = tfc.conv2d(x, w, this.strides, (padding || 'valid'), this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC', this.dilationRate);\n        if (b) {\n            return K.biasAdd(out, b, this.dataFormat);\n        }\n        return out;\n    }\n    recurrentConv(x, w) {\n        const strides = 1;\n        return tfc.conv2d(x, w, strides, 'same', this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC');\n    }\n}\n/** @nocollapse */\nConvLSTM2DCell.className = 'ConvLSTM2DCell';\ntfc.serialization.registerClass(ConvLSTM2DCell);\nexport class ConvLSTM2D extends ConvRNN2D {\n    constructor(args) {\n        const cell = new ConvLSTM2DCell(args);\n        super(Object.assign({}, args, { cell }));\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nConvLSTM2D.className = 'ConvLSTM2D';\ntfc.serialization.registerClass(ConvLSTM2D);\n//# sourceMappingURL=convolutional_recurrent.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Basic Layers.\n */\nimport { any, notEqual, serialization, tidy, transpose, util } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport * as K from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { assertPositiveInteger, mapActivationToFusedKernel } from '../utils/generic_utils';\nimport { arrayProd, range } from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class Dropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.rate = Math.max(Math.min(args.rate, 1), 0);\n        // So that the scalar doesn't get tidied up between executions.\n        this.noiseShape = args.noiseShape;\n        this.seed = args.seed;\n        this.supportsMasking = true;\n    }\n    getNoiseShape(input) {\n        if (this.noiseShape == null) {\n            return this.noiseShape;\n        }\n        const inputShape = input.shape;\n        const noiseShape = [];\n        for (let i = 0; i < this.noiseShape.length; ++i) {\n            noiseShape.push(this.noiseShape[i] == null ? inputShape[i] : this.noiseShape[i]);\n        }\n        return noiseShape;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            if (0 < this.rate && this.rate < 1) {\n                const training = kwargs['training'] == null ? false : kwargs['training'];\n                const noiseShape = this.getNoiseShape(input);\n                const output = K.inTrainPhase(() => K.dropout(input, this.rate, noiseShape, this.seed), () => input, training);\n                return output;\n            }\n            return inputs;\n        });\n    }\n    getConfig() {\n        const config = {\n            rate: this.rate,\n            noiseShape: this.noiseShape,\n            seed: this.seed,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    dispose() {\n        return super.dispose();\n    }\n}\n/** @nocollapse */\nDropout.className = 'Dropout';\nserialization.registerClass(Dropout);\nexport class SpatialDropout1D extends Dropout {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [{ ndim: 3 }];\n    }\n    getNoiseShape(input) {\n        const inputShape = input.shape;\n        return [inputShape[0], 1, inputShape[2]];\n    }\n}\n/** @nocollapse */\nSpatialDropout1D.className = 'SpatialDropout1D';\nserialization.registerClass(SpatialDropout1D);\nexport class Dense extends Layer {\n    constructor(args) {\n        super(args);\n        // Default activation: Linear (none).\n        this.activation = null;\n        this.useBias = true;\n        this.kernel = null;\n        this.bias = null;\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        if (args.batchInputShape == null && args.inputShape == null &&\n            args.inputDim != null) {\n            // This logic is copied from Layer's constructor, since we can't\n            // do exactly what the Python constructor does for Dense().\n            let batchSize = null;\n            if (args.batchSize != null) {\n                batchSize = args.batchSize;\n            }\n            this.batchInputShape = [batchSize, args.inputDim];\n        }\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation);\n        if (args.useBias != null) {\n            this.useBias = args.useBias;\n        }\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.supportsMasking = true;\n        this.inputSpec = [{ minNDim: 2 }];\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const inputLastDim = inputShape[inputShape.length - 1];\n        if (this.kernel == null) {\n            this.kernel = this.addWeight('kernel', [inputLastDim, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n            if (this.useBias) {\n                this.bias = this.addWeight('bias', [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n            }\n        }\n        this.inputSpec = [{ minNDim: 2, axes: { [-1]: inputLastDim } }];\n        this.built = true;\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        outputShape[outputShape.length - 1] = this.units;\n        return outputShape;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Dense layer accepts only a single input.\n            const input = getExactlyOneTensor(inputs);\n            const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());\n            let output;\n            if (fusedActivationName != null) {\n                output = K.dot(input, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null);\n            }\n            else {\n                output = K.dot(input, this.kernel.read());\n                if (this.bias != null) {\n                    output = K.biasAdd(output, this.bias.read());\n                }\n                if (this.activation != null) {\n                    output = this.activation.apply(output);\n                }\n            }\n            return output;\n        });\n    }\n    getConfig() {\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nDense.className = 'Dense';\nserialization.registerClass(Dense);\nexport class Flatten extends Layer {\n    constructor(args) {\n        args = args || {};\n        super(args);\n        this.inputSpec = [{ minNDim: 3 }];\n        this.dataFormat = args.dataFormat;\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        for (const dim of inputShape.slice(1)) {\n            if (dim == null) {\n                throw new ValueError(`The shape of the input to \"Flatten\" is not fully defined ` +\n                    `(got ${inputShape.slice(1)}). Make sure to pass a complete ` +\n                    `\"input_shape\" or \"batch_input_shape\" argument to the first ` +\n                    `layer in your model.`);\n            }\n        }\n        return [inputShape[0], arrayProd(inputShape, 1)];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            let input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsFirst' && input.rank > 1) {\n                const permutation = [0];\n                for (let i = 2; i < input.rank; ++i) {\n                    permutation.push(i);\n                }\n                permutation.push(1);\n                input = input.transpose(permutation);\n            }\n            return K.batchFlatten(input);\n        });\n    }\n    getConfig() {\n        const config = {};\n        if (this.dataFormat != null) {\n            config['dataFormat'] = this.dataFormat;\n        }\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nFlatten.className = 'Flatten';\nserialization.registerClass(Flatten);\nexport class Activation extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.activation = getActivation(args.activation);\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            return this.activation.apply(input);\n        });\n    }\n    getConfig() {\n        const config = { activation: serializeActivation(this.activation) };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nActivation.className = 'Activation';\nserialization.registerClass(Activation);\nexport class RepeatVector extends Layer {\n    constructor(args) {\n        super(args);\n        this.n = args.n;\n        this.inputSpec = [{ ndim: 2 }];\n    }\n    computeOutputShape(inputShape) {\n        return [inputShape[0], this.n, inputShape[1]];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            return K.repeat(inputs, this.n);\n        });\n    }\n    getConfig() {\n        const config = {\n            n: this.n,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nRepeatVector.className = 'RepeatVector';\nserialization.registerClass(RepeatVector);\nexport class Reshape extends Layer {\n    constructor(args) {\n        super(args);\n        this.targetShape = args.targetShape;\n        // Make sure that all unknown dimensions are represented as `null`.\n        for (let i = 0; i < this.targetShape.length; ++i) {\n            if (this.isUnknown(this.targetShape[i])) {\n                this.targetShape[i] = null;\n            }\n        }\n    }\n    isUnknown(dim) {\n        return dim < 0 || dim == null;\n    }\n    /**\n     * Finds and replaces a missing dimension in output shape.\n     *\n     * This is a near direct port of the internal Numpy function\n     * `_fix_unknown_dimension` in `numpy/core/src/multiarray/shape.c`.\n     *\n     * @param inputShape: Original shape of array begin reshape.\n     * @param outputShape: Target shape of the array, with at most a single\n     * `null` or negative number, which indicates an underdetermined dimension\n     * that should be derived from `inputShape` and the known dimensions of\n     *   `outputShape`.\n     * @returns: The output shape with `null` replaced with its computed value.\n     * @throws: ValueError: If `inputShape` and `outputShape` do not match.\n     */\n    fixUnknownDimension(inputShape, outputShape) {\n        const errorMsg = 'Total size of new array must be unchanged.';\n        const finalShape = outputShape.slice();\n        let known = 1;\n        let unknown = null;\n        for (let i = 0; i < finalShape.length; ++i) {\n            const dim = finalShape[i];\n            if (this.isUnknown(dim)) {\n                if (unknown === null) {\n                    unknown = i;\n                }\n                else {\n                    throw new ValueError('Can only specifiy one unknown dimension.');\n                }\n            }\n            else {\n                known *= dim;\n            }\n        }\n        const originalSize = arrayProd(inputShape);\n        if (unknown !== null) {\n            if (known === 0 || originalSize % known !== 0) {\n                throw new ValueError(errorMsg);\n            }\n            finalShape[unknown] = originalSize / known;\n        }\n        else if (originalSize !== known) {\n            throw new ValueError(errorMsg);\n        }\n        return finalShape;\n    }\n    computeOutputShape(inputShape) {\n        let anyUnknownDims = false;\n        for (let i = 0; i < inputShape.length; ++i) {\n            if (this.isUnknown(inputShape[i])) {\n                anyUnknownDims = true;\n                break;\n            }\n        }\n        if (anyUnknownDims) {\n            return inputShape.slice(0, 1).concat(this.targetShape);\n        }\n        else {\n            return inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            const outputShape = inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n            return input.reshape(outputShape);\n        });\n    }\n    getConfig() {\n        const config = {\n            targetShape: this.targetShape,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nReshape.className = 'Reshape';\nserialization.registerClass(Reshape);\nexport class Permute extends Layer {\n    constructor(args) {\n        super(args);\n        if (args.dims == null) {\n            throw new Error('Required configuration field `dims` is missing during Permute ' +\n                'constructor call.');\n        }\n        if (!Array.isArray(args.dims)) {\n            throw new Error('Permute constructor requires `dims` to be an Array, but received ' +\n                `${args.dims} instead.`);\n        }\n        // Check the validity of the permutation indices.\n        const expectedSortedIndices = range(1, args.dims.length + 1);\n        if (!util.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) {\n            throw new Error('Invalid permutation `dims`: ' + JSON.stringify(args.dims) +\n                ' `dims` must contain consecutive integers starting from 1.');\n        }\n        this.dims = args.dims;\n        this.dimsIncludingBatch = [0].concat(this.dims);\n        this.inputSpec = [new InputSpec({ ndim: this.dims.length + 1 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        this.dims.forEach((dim, i) => {\n            outputShape[i + 1] = inputShape[dim];\n        });\n        return outputShape;\n    }\n    call(inputs, kwargs) {\n        return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch);\n    }\n    getConfig() {\n        const config = {\n            dims: this.dims,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nPermute.className = 'Permute';\nserialization.registerClass(Permute);\nexport class Masking extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.supportsMasking = true;\n        if (args != null) {\n            this.maskValue = args.maskValue == null ? 0 : args.maskValue;\n        }\n        else {\n            this.maskValue = 0;\n        }\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { maskValue: this.maskValue };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    computeMask(inputs, mask) {\n        const input = getExactlyOneTensor(inputs);\n        const axis = -1;\n        return any(notEqual(input, this.maskValue), axis);\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const axis = -1;\n            const keepDims = true;\n            const booleanMask = any(notEqual(input, this.maskValue), axis, keepDims);\n            const output = input.mul(booleanMask.asType(input.dtype));\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMasking.className = 'Masking';\nserialization.registerClass(Masking);\n//# sourceMappingURL=core.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Embedding Layer.\n *\n * Original source: keras/constraints.py\n */\nimport { notEqual, serialization, tidy, zerosLike } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class Embedding extends Layer {\n    constructor(args) {\n        super(args);\n        this.embeddings = null;\n        this.DEFAULT_EMBEDDINGS_INITIALIZER = 'randomUniform';\n        if (args.batchInputShape == null && args.inputShape == null) {\n            // Porting Note: This logic is copied from Layer's constructor, since we\n            // can't do exactly what the Python constructor does for Embedding().\n            // Specifically, the super constructor can not be called after the\n            // mutation of the `config` argument.\n            let batchSize = null;\n            if (args.batchSize != null) {\n                batchSize = args.batchSize;\n            }\n            if (args.inputLength == null) {\n                // Fix super-constructor to what it would have done if\n                // 'config.inputShape' were (None, )\n                this.batchInputShape = [batchSize, null];\n            }\n            else {\n                // Fix super-constructor to what it would have done if\n                // 'config.inputShape' were (config.inputLength, )\n                this.batchInputShape =\n                    [batchSize].concat(generic_utils.toList(args.inputLength));\n            }\n        }\n        this.inputDim = args.inputDim;\n        generic_utils.assertPositiveInteger(this.inputDim, 'inputDim');\n        this.outputDim = args.outputDim;\n        generic_utils.assertPositiveInteger(this.outputDim, 'outputDim');\n        this.embeddingsInitializer = getInitializer(args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER);\n        this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.embeddingsConstraint = getConstraint(args.embeddingsConstraint);\n        this.maskZero = args.maskZero;\n        this.supportsMasking = args.maskZero;\n        this.inputLength = args.inputLength;\n    }\n    build(inputShape) {\n        this.embeddings = this.addWeight('embeddings', [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint);\n        this.built = true;\n    }\n    // Override warnOnIncompatibleInputShape because an embedding layer allows\n    // the input to have varying ranks.\n    warnOnIncompatibleInputShape(inputShape) { }\n    computeMask(inputs, mask) {\n        return tidy(() => {\n            if (!this.maskZero) {\n                return null;\n            }\n            else {\n                inputs = getExactlyOneTensor(inputs);\n                return notEqual(inputs, zerosLike(inputs));\n            }\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (this.inputLength == null) {\n            return [...inputShape, this.outputDim];\n        }\n        // inputLength can be an array if input is 3D or higher.\n        const inLens = generic_utils.toList(this.inputLength);\n        if (inLens.length !== inputShape.length - 1) {\n            throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received ` +\n                `input shape has shape ${inputShape}`);\n        }\n        else {\n            let i = 0;\n            for (let k = 0; k < inLens.length; ++k) {\n                const s1 = inLens[k];\n                const s2 = inputShape[k + 1];\n                if ((s1 != null) && (s2 != null) && (s1 !== s2)) {\n                    throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received ` +\n                        `input shape has shape ${inputShape}`);\n                }\n                else if (s1 == null) {\n                    inLens[i] = s2;\n                }\n                i++;\n            }\n        }\n        return [inputShape[0], ...inLens, this.outputDim];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Embedding layer accepts only a single input.\n            let input = getExactlyOneTensor(inputs);\n            if (input.dtype !== 'int32') {\n                input = K.cast(input, 'int32');\n            }\n            const output = K.gather(this.embeddings.read(), input.as1D());\n            return output.reshape(getExactlyOneShape(this.computeOutputShape(input.shape)));\n        });\n    }\n    getConfig() {\n        const config = {\n            inputDim: this.inputDim,\n            outputDim: this.outputDim,\n            embeddingsInitializer: serializeInitializer(this.embeddingsInitializer),\n            embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            embeddingsConstraint: serializeConstraint(this.embeddingsConstraint),\n            maskZero: this.maskZero,\n            inputLength: this.inputLength\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nEmbedding.className = 'Embedding';\nserialization.registerClass(Embedding);\n//# sourceMappingURL=embeddings.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Merge Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { l2Normalize } from '../losses';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as mathUtils from '../utils/math_utils';\nimport { getExactlyOneShape } from '../utils/types_utils';\n/**\n * Generic Merge layer for element-wise merge functions.\n *\n * Used to implement `Sum`, `Average`, `Concatenate`, etc.\n */\nexport class Merge extends Layer {\n    constructor(args) {\n        super(args || {});\n        this.supportsMasking = true;\n    }\n    /**\n     * Logic for merging multiple tensors, to be overridden by subclasses.\n     * @param inputs\n     */\n    mergeFunction(inputs) {\n        throw new NotImplementedError();\n    }\n    /**\n     * Computes the shape of the result of an elementwise operation.\n     *\n     * @param shape1: Shape of the first tensor.\n     * @param shape2: Shape of the second tensor.\n     * @returns Expected output shape when an elementwise operation is carried\n     *   out on 2 tensors with shapes `shape1` and `shape2`.\n     * @throws ValueError: If `shape1` and `shape2` are not compatible for\n     *   element-wise operations.\n     */\n    computeElementwiseOpOutputShape(shape1, shape2) {\n        if (shape1 == null || shape2 == null) {\n            return null;\n        }\n        else if (shape1.length < shape2.length) {\n            return this.computeElementwiseOpOutputShape(shape2, shape1);\n        }\n        else if (shape2.length === 0) {\n            return shape1;\n        }\n        const outputShape = shape1.slice(0, shape1.length - shape2.length);\n        for (let k = 0; k < shape2.length; ++k) {\n            const i = shape1[shape1.length - shape2.length + k];\n            const j = shape2[k];\n            if (i == null || j == null || i < 0 || j < 0) {\n                outputShape.push(null);\n            }\n            else if (i === 1) {\n                outputShape.push(j);\n            }\n            else if (j === 1) {\n                outputShape.push(i);\n            }\n            else {\n                if (i !== j) {\n                    throw new ValueError('Operands could not be broadcast together with shapes ' +\n                        JSON.stringify(shape1) + ' ' + JSON.stringify(shape2));\n                }\n                outputShape.push(i);\n            }\n        }\n        return outputShape;\n    }\n    build(inputShape) {\n        // Used purely for shape validation.\n        if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) {\n            // Make sure that inputShape is an Array of shape.\n            inputShape = [getExactlyOneShape(inputShape)];\n        }\n        inputShape = inputShape;\n        if (inputShape.length < 2) {\n            throw new ValueError('A merge layer should be called on an Array of at least 2 inputs.' +\n                ` Got ${inputShape.length} input(s).`);\n        }\n        // Make sure that there is at most one unique batch size among the input\n        // shapes.\n        let batchSizes = [];\n        for (const shape of inputShape) {\n            if (shape != null && shape[0] !== null) {\n                batchSizes.push(shape[0]);\n            }\n        }\n        batchSizes = generic_utils.unique(batchSizes);\n        if (batchSizes.length > 1) {\n            throw new ValueError(`Can not merge tensors with different batch sizes. ` +\n                `Got tensors with shapes: ${JSON.stringify(inputShape)}.`);\n        }\n        let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1);\n        for (let i = 1; i < inputShape.length; ++i) {\n            const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n            outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n        }\n        // If the inputs have different ranks, we have to reshape them to make them\n        // broadcastable.\n        const allRanks = inputShape.map(shape => shape.length);\n        if (inputShape.indexOf(null) === -1 &&\n            generic_utils.unique(allRanks).length === 1) {\n            this.reshapeRequired = false;\n        }\n        else {\n            this.reshapeRequired = true;\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (this.reshapeRequired) {\n                const reshapedInputs = [];\n                const inputDims = inputs.map(input => input.rank);\n                if (inputDims.indexOf(null) === -1) {\n                    // If ranks of all inputs are available, we simply expand each of them\n                    // at axis=1 until all of them have the same rank.\n                    const maxNDim = mathUtils.max(inputDims);\n                    for (let x of inputs) {\n                        const xNDim = x.rank;\n                        for (let k = 0; k < maxNDim - xNDim; ++k) {\n                            x = K.expandDims(x, 1);\n                        }\n                        reshapedInputs.push(x);\n                    }\n                    return this.mergeFunction(reshapedInputs);\n                }\n                else {\n                    // Transpose all inputs so that batch size is the last dimension.\n                    // [batchSize, dim1, dim2, ...] -> [dim1, dim2, ..., batchSize]\n                    let transposed = false;\n                    for (const x of inputs) {\n                        const xNDim = x.rank;\n                        if (xNDim == null) {\n                            const xShape = x.shape;\n                            const batchSize = xShape[0];\n                            const newShape = xShape.slice(1).concat([batchSize]);\n                            let xTransposed = x.reshape([batchSize].concat(mathUtils.arrayProd(xShape.slice(1))));\n                            xTransposed = tfc.transpose(xTransposed, [1, 0]);\n                            xTransposed = xTransposed.reshape(newShape);\n                            reshapedInputs.push(xTransposed);\n                            transposed = true;\n                        }\n                        else if (xNDim > 1) {\n                            const dims = mathUtils.range(1, xNDim).concat([0]);\n                            reshapedInputs.push(tfc.transpose(x, dims));\n                            transposed = true;\n                        }\n                        else {\n                            // We don't transpose inputs if they are 1D vectors or scalars.\n                            reshapedInputs.push(x);\n                        }\n                    }\n                    let y = this.mergeFunction(reshapedInputs);\n                    const yNDim = y.rank;\n                    if (transposed) {\n                        // If inputs have been transposed, we have to transpose the output\n                        // too.\n                        if (yNDim == null) {\n                            const yShape = y.shape;\n                            const yNDim = yShape.length;\n                            const batchSize = yShape[yNDim - 1];\n                            const newShape = [batchSize].concat(yShape.slice(0, yShape.length - 1));\n                            y = tfc.transpose(y.reshape([-1, batchSize]), [1, 0])\n                                .reshape(newShape);\n                        }\n                        else if (yNDim > 1) {\n                            const dims = [yNDim - 1].concat(mathUtils.range(0, yNDim - 1));\n                            y = tfc.transpose(y, dims);\n                        }\n                    }\n                    return y;\n                }\n            }\n            else {\n                return this.mergeFunction(inputs);\n            }\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = inputShape;\n        let outputShape;\n        if (inputShape[0] == null) {\n            outputShape = null;\n        }\n        else {\n            outputShape = inputShape[0].slice(1);\n        }\n        for (let i = 1; i < inputShape.length; ++i) {\n            const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n            outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n        }\n        let batchSizes = [];\n        for (const shape of inputShape) {\n            if (shape != null && shape[0] !== null) {\n                batchSizes.push(shape[0]);\n            }\n        }\n        batchSizes = generic_utils.unique(batchSizes);\n        if (batchSizes.length === 1) {\n            outputShape = batchSizes.concat(outputShape);\n        }\n        else {\n            outputShape = [null].concat(outputShape);\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        return tfc.tidy(() => {\n            if (mask == null) {\n                return null;\n            }\n            if (!Array.isArray(mask)) {\n                throw new ValueError('`mask` should be an Array');\n            }\n            if (!Array.isArray(inputs)) {\n                throw new ValueError('`inputs` should be an Array');\n            }\n            if (mask.length !== inputs.length) {\n                throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same ` +\n                    `length, but have different lengths ` +\n                    `(${inputs.length} vs ${mask.length})`);\n            }\n            if (mask.every(m => m == null)) {\n                return null;\n            }\n            mask = mask.map(m => m == null ? m : tfc.expandDims(m, 0));\n            let output = mask[0];\n            for (let i = 1; i < mask.length - 1; ++i) {\n                output = tfc.logicalAnd(output, mask[i]);\n            }\n            return output;\n        });\n    }\n}\nexport class Add extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.add(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nAdd.className = 'Add';\nserialization.registerClass(Add);\n/**\n * Calculate the element-wise sum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Add` layer, by using no input argument\n *    or a single configuration argument. The resultant `Add` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const addLayer = tf.layers.add();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = addLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.add([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.add([input1, input2]).print();\n * // Gives [[11, 22], [33, 44]].\n *\n */\nexport function add(config) {\n    if (Array.isArray(config)) {\n        const layer = new Add({});\n        return layer.apply(config);\n    }\n    else {\n        return new Add(config);\n    }\n}\nexport class Multiply extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.mul(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMultiply.className = 'Multiply';\nserialization.registerClass(Multiply);\n/**\n * Calculate the element-wise product of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Multiply` layer, by using no input argument\n *    or a single configuration argument. The resultant `Multiply` layer can\n *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const multiplyLayer = tf.layers.multiply();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = multiplyLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.multiply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.multiply([input1, input2]).print();\n * // Gives [[10, 40], [90, 160]].\n *\n */\nexport function multiply(config) {\n    if (Array.isArray(config)) {\n        const layer = new Multiply({});\n        return layer.apply(config);\n    }\n    else {\n        return new Multiply(config);\n    }\n}\nexport class Average extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.add(output, inputs[i]);\n            }\n            return tfc.mul(1 / inputs.length, output);\n        });\n    }\n}\n/** @nocollapse */\nAverage.className = 'Average';\nserialization.registerClass(Average);\n/**\n * Calculate the element-wise arithmetic mean of inputs, which all have the same\n * shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Average` layer, by using no input argument\n *    or a single configuration argument. The resultant `Average` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const averageLayer = tf.layers.average();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = averageLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.average([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.average([input1, input2]).print();\n * // Gives [[5.5, 11], [16.5, 22]].\n *\n */\nexport function average(config) {\n    if (Array.isArray(config)) {\n        const layer = new Average({});\n        return layer.apply(config);\n    }\n    else {\n        return new Average(config);\n    }\n}\nexport class Maximum extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0];\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.maximum(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMaximum.className = 'Maximum';\nserialization.registerClass(Maximum);\n/**\n * Calculate the element-wise maximum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Maximum` layer, by using no input argument\n *    or a single configuration argument. The resultant `Maximum` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const maximumLayer = tf.layers.maximum();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = maximumLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.maximum([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);\n * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);\n * tf.layers.maximum([input1, input2]).print();\n * // Gives [[10, 20], [30, 40]].\n *\n */\nexport function maximum(config) {\n    if (Array.isArray(config)) {\n        const layer = new Maximum({});\n        return layer.apply(config);\n    }\n    else {\n        return new Maximum(config);\n    }\n}\nexport class Minimum extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0];\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.minimum(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMinimum.className = 'Minimum';\nserialization.registerClass(Minimum);\n/**\n * Calculate the element-wise minimum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Minimum` layer, by using no input argument\n *    or a single configuration argument. The resultant `Minimum` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const minimumLayer = tf.layers.minimum();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = minimumLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.minimum([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);\n * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);\n * tf.layers.minimum([input1, input2]).print();\n * // Gives [[1, 2], [3, 4]].\n *\n */\nexport function minimum(config) {\n    if (Array.isArray(config)) {\n        const layer = new Minimum({});\n        return layer.apply(config);\n    }\n    else {\n        return new Minimum(config);\n    }\n}\nexport class Concatenate extends Merge {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_AXIS = -1;\n        if (args == null) {\n            args = {};\n        }\n        this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n        this.supportsMasking = true;\n        this.reshapeRequired = false;\n    }\n    build(inputShape) {\n        // Used purely for shape validation.]\n        if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) ||\n            inputShape.length === 1) {\n            throw new ValueError('A `Concatenate` layer should be called on a list of at least 2 ' +\n                'inputs');\n        }\n        inputShape = inputShape;\n        let allNoneShape = true;\n        for (const shape of inputShape) {\n            if (shape != null) {\n                allNoneShape = false;\n                break;\n            }\n        }\n        if (allNoneShape) {\n            return;\n        }\n        const shapeSet = [];\n        for (let i = 0; i < inputShape.length; ++i) {\n            const shapeWithoutConcatAxis = inputShape[i].slice();\n            shapeWithoutConcatAxis.splice(this.axis, 1);\n            let exists = false;\n            for (const shape of shapeSet) {\n                if (util.arraysEqual(shape, shapeWithoutConcatAxis)) {\n                    exists = true;\n                    break;\n                }\n            }\n            if (!exists) {\n                shapeSet.push(shapeWithoutConcatAxis);\n            }\n        }\n        if (shapeSet.length > 1) {\n            throw new ValueError('A `Concatenate` layer requires inputs with matching shapes ' +\n                'except for the concat axis. Got input shapes: ' +\n                JSON.stringify(inputShape));\n        }\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            return K.concatenate(inputs, this.axis);\n        });\n    }\n    computeOutputShape(inputShape) {\n        if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) {\n            throw new ValueError('A `Concatenate` layer should be called on a list of inputs.');\n        }\n        const inputShapes = inputShape;\n        const outputShape = inputShapes[0].slice();\n        const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis;\n        // Porting Note: the line above is because TypeScript doesn't support\n        //   negative indices.\n        for (const shape of inputShapes.slice(1)) {\n            if (outputShape[axis] == null || shape[axis] == null) {\n                outputShape[axis] = null;\n                break;\n            }\n            outputShape[axis] += shape[axis];\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        if (mask == null) {\n            return null;\n        }\n        if (!Array.isArray(mask)) {\n            throw new ValueError('`mask` should be an array for Concatenate');\n        }\n        if (!Array.isArray(inputs)) {\n            throw new ValueError('`inputs` should be an array for Concatenate');\n        }\n        if (mask.length !== inputs.length) {\n            throw new ValueError(`Mismatch in the length of mask (${mask.length}) ` +\n                `and the legnth of inputs (${inputs.length})`);\n        }\n        return tfc.tidy(() => {\n            let allNullMasks = true;\n            mask.forEach(m => {\n                if (m != null) {\n                    allNullMasks = false;\n                    return;\n                }\n            });\n            if (allNullMasks) {\n                return null;\n            }\n            const outputMasks = [];\n            for (let i = 0; i < inputs.length; ++i) {\n                if (mask[i] == null) {\n                    // Input is unmasked. Append all 1's to masks.\n                    outputMasks.push(tfc.onesLike(inputs[i]).asType('bool'));\n                }\n                else if (mask[i].rank < inputs[i].rank) {\n                    // Mask is smaller than the input, expand it.\n                    outputMasks.push(tfc.expandDims(mask[i], -1));\n                }\n                else {\n                    outputMasks.push(mask[i]);\n                }\n            }\n            const concatenatedMasks = tfc.concat(outputMasks, this.axis);\n            return tfc.all(concatenatedMasks, -1, false);\n        });\n    }\n    getConfig() {\n        const config = {\n            'axis': this.axis,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nConcatenate.className = 'Concatenate';\nserialization.registerClass(Concatenate);\n/**\n * Concatenate an `Array` of inputs.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Concatenate` layer, by using no input argument\n *    or a single configuration argument. The resultant `Concatenate` layer can\n *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const concatLayer = tf.layers.concatenate();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 3]});\n * const input2 = tf.input({shape: [2, 4]});\n * const output = concatLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 7], with the first dimension as the undetermined batch\n * // dimension and the last dimension as the result of concatenating the\n * // last dimensions of the two inputs.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 3]});\n * const input2 = tf.input({shape: [2, 4]});\n * const output = tf.layers.concatenate([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension and the last dimension as the result of concatenating the\n * // last dimensions of the two inputs.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);\n * const input2 = tf.tensor2d([[10, 20], [30, 40]], [2, 2]);\n * tf.layers.concatenate([input1, input2]).print();\n * // Gives [[1, 2, 10, 20], [3, 4, 30, 40]].\n *\n */\nexport function concatenate(config) {\n    if (Array.isArray(config)) {\n        const layer = new Concatenate({});\n        return layer.apply(config);\n    }\n    else {\n        return new Concatenate(config);\n    }\n}\n/**\n * Interpretable potentially negative axis index.\n *\n * For example, given axis = -1, and dim = 3, this function will return 2.\n *\n * @param axis The axis index, may be a positive, zero or negative integer.\n * @param dim Total number of dimensions, a positive integer.\n * @returns A non-negative axis index equivalent to the input `axis`.\n */\nfunction interpretAxis(axis, dim) {\n    while (axis < 0) {\n        axis += dim;\n    }\n    return axis;\n}\nfunction batchDot(x, y, axes) {\n    if (x.shape.length > 3 || y.shape.length > 3) {\n        throw new NotImplementedError('batchDot is not implemented for tensors of 4D or higher rank yet');\n    }\n    tfc.util.assert(x.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, ` +\n        `but got ${x.shape.length}`);\n    tfc.util.assert(x.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, ` +\n        `but got ${y.shape.length}`);\n    if (typeof axes === 'number') {\n        axes = [axes, axes];\n    }\n    if (x.dtype === 'complex64' || y.dtype === 'complex64') {\n        throw new NotImplementedError('batchDot is not implemented for complex64-type Tensors yet.');\n    }\n    const xNDim = x.shape.length;\n    const yNDim = y.shape.length;\n    if (axes == null) {\n        // Behave like batchMatmul by default.\n        axes = [xNDim - 1, yNDim - 2];\n    }\n    const axesArray = axes;\n    return tfc.tidy(() => {\n        let diff;\n        if (xNDim > yNDim) {\n            diff = xNDim - yNDim;\n            const diffShape = [];\n            for (let i = 0; i < diff; ++i) {\n                diffShape.push(1);\n            }\n            y = y.reshape(y.shape.concat(diffShape));\n        }\n        else if (yNDim > xNDim) {\n            diff = yNDim - xNDim;\n            const diffShape = [];\n            for (let i = 0; i < diff; ++i) {\n                diffShape.push(1);\n            }\n            x = x.reshape(x.shape.concat(diffShape));\n        }\n        else {\n            diff = 0;\n        }\n        let out;\n        if (x.shape.length === 2 && y.shape.length === 2) {\n            if (axesArray[0] === axesArray[1]) {\n                out = x.mul(y).sum(axesArray[0]);\n            }\n            else {\n                out = x.transpose([1, 0]).mul(y).sum(axesArray[1]);\n            }\n        }\n        else {\n            const adjX = axesArray[0] !== x.shape.length - 1;\n            const adjY = axesArray[1] === y.shape.length - 1;\n            out = x.matMul(y, adjX, adjY);\n        }\n        if (diff > 0) {\n            let idx;\n            if (xNDim > yNDim) {\n                idx = xNDim + yNDim - 3;\n            }\n            else {\n                idx = xNDim - 1;\n            }\n            const squeezeAxes = [];\n            for (let i = idx; i < idx + diff; ++i) {\n                squeezeAxes.push(i);\n            }\n            out = out.squeeze(squeezeAxes);\n        }\n        if (out.shape.length === 1) {\n            out = out.expandDims(1);\n        }\n        return out;\n    });\n}\nexport class Dot extends Merge {\n    constructor(args) {\n        super(args);\n        this.axes = args.axes;\n        this.normalize = args.normalize == null ? false : args.normalize;\n        this.supportsMasking = true;\n        this.reshapeRequired = false;\n    }\n    build(inputShape) {\n        tfc.util.assert(Array.isArray(inputShape) && inputShape.length === 2 &&\n            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');\n        const shape1 = inputShape[0];\n        const shape2 = inputShape[1];\n        if (shape1.length > 3 || shape2.length > 3) {\n            throw new NotImplementedError('Dot layer does not support tensors of 4D or higher rank yet.');\n        }\n        const axes = this.interpretAxes(shape1, shape2);\n        if (shape1[axes[0]] !== shape2[axes[1]]) {\n            throw new ValueError(`Dimension incompatibility: ` +\n                `${shape1[axes[0]]} !== ${shape2[axes[1]]}`);\n        }\n    }\n    mergeFunction(inputs) {\n        if (inputs.length !== 2) {\n            throw new ValueError('A `Dot` layer must be called on exactly 2 inputs, ' +\n                `but received ${inputs.length} input(s).`);\n        }\n        let x1 = inputs[0];\n        let x2 = inputs[1];\n        let axes;\n        if (!Array.isArray(this.axes)) {\n            axes = [\n                interpretAxis(this.axes, x1.shape.length),\n                interpretAxis(this.axes, x2.shape.length)\n            ];\n        }\n        else {\n            axes = this.axes.map((axis, i) => interpretAxis(axis, inputs[i].shape.length));\n        }\n        if (this.normalize) {\n            x1 = l2Normalize(x1, axes[0]);\n            x2 = l2Normalize(x2, axes[1]);\n        }\n        return batchDot(x1, x2, axes);\n    }\n    interpretAxes(shape1, shape2) {\n        let axes;\n        if (!Array.isArray(this.axes)) {\n            // `this.axes` is a single integer.\n            axes = [\n                interpretAxis(this.axes, shape1.length),\n                interpretAxis(this.axes, shape2.length)\n            ];\n        }\n        else {\n            // `this.axes` is an Array of integers.\n            axes = this.axes;\n        }\n        return axes;\n    }\n    computeOutputShape(inputShape) {\n        tfc.util.assert(Array.isArray(inputShape) && inputShape.length === 2 &&\n            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');\n        const shape1 = inputShape[0].slice();\n        const shape2 = inputShape[1].slice();\n        if (shape1.length > 3 || shape2.length > 3) {\n            throw new NotImplementedError('Dot layer does not support tensors of 4D or higher rank yet.');\n        }\n        const axes = this.interpretAxes(shape1, shape2);\n        shape1.splice(axes[0], 1);\n        shape2.splice(axes[1], 1);\n        shape2.splice(0, 1);\n        const outputShape = shape1.concat(shape2);\n        if (outputShape.length === 1) {\n            outputShape.push(1);\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        return null;\n    }\n    getConfig() {\n        const config = {\n            'axes': this.axes,\n            'normalize': this.normalize\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nDot.className = 'Dot';\nserialization.registerClass(Dot);\n// TODO(cais): Add functional interfaces for the merge layers.\n//# sourceMappingURL=merge.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Noise Layers.\n */\nimport { greaterEqual, randomUniform, serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { Layer } from '../engine/topology';\nimport { getExactlyOneTensor } from '../utils/types_utils';\nexport class GaussianNoise extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.stddev = args.stddev;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { stddev: this.stddev };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const noised = () => K.randomNormal(input.shape, 0, this.stddev).add(input);\n            const output = K.inTrainPhase(noised, () => input, kwargs['training'] || false);\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nGaussianNoise.className = 'GaussianNoise';\nserialization.registerClass(GaussianNoise);\nexport class GaussianDropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.rate = args.rate;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { rate: this.rate };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            if (this.rate > 0 && this.rate < 1) {\n                const noised = () => {\n                    const stddev = Math.sqrt(this.rate / (1 - this.rate));\n                    return input.mul(K.randomNormal(input.shape, 1, stddev));\n                };\n                return K.inTrainPhase(noised, () => input, kwargs['training'] || false);\n            }\n            return input;\n        });\n    }\n}\n/** @nocollapse */\nGaussianDropout.className = 'GaussianDropout';\nserialization.registerClass(GaussianDropout);\n/**\n * Applies Alpha Dropout to the input.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs\n * to their original values, in order to ensure the self-normalizing property\n * even after this dropout.\n * Alpha Dropout fits well to Scaled Exponential Linear Units\n * by randomly setting activations to the negative saturation value.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the\n *     shape for randomly generated keep/drop flags.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n */\nexport class AlphaDropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.rate = args.rate;\n        this.noiseShape = args.noiseShape;\n    }\n    _getNoiseShape(inputs) {\n        return this.noiseShape || getExactlyOneTensor(inputs).shape;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { rate: this.rate };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.rate < 1 && this.rate > 0) {\n                const noiseShape = this._getNoiseShape(inputs);\n                const droppedInputs = () => {\n                    const input = getExactlyOneTensor(inputs);\n                    const alpha = 1.6732632423543772848170429916717;\n                    const scale = 1.0507009873554804934193349852946;\n                    const alphaP = -alpha * scale;\n                    let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate);\n                    keptIdx = K.cast(keptIdx, 'float32'); // get default dtype.\n                    // Get affine transformation params.\n                    const a = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5;\n                    const b = -a * alphaP * this.rate;\n                    // Apply mask.\n                    const x = input.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP));\n                    return x.mul(a).add(b);\n                };\n                return K.inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs['training'] || false);\n            }\n            return inputs;\n        });\n    }\n}\n/** @nocollapse */\nAlphaDropout.className = 'AlphaDropout';\nserialization.registerClass(AlphaDropout);\n//# sourceMappingURL=noise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Normalization layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { moments, serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Applies batch normalization on x given mean, var, beta and gamma.\n *\n * I.e. returns:\n *   `output = (x - mean) / (sqrt(var) + epsilon) * gamma + beta`\n *\n * @param x Input tensor.\n * @param mean Mean of batch.\n * @param variance Variance of batch.\n * @param beta Tensor with which to center the input.\n * @param gamma Tensor by which to scale the input.\n * @param epsilon Fuzz factor.\n * @returns The result of the batch normalization.\n */\nexport function batchNormalization(x, mean, variance, beta, gamma, epsilon = 1e-3) {\n    let out;\n    if (x.rank === 2) {\n        out = tfc.batchNorm2d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else if (x.rank === 3) {\n        // TODO(cais): Check rank; give proper error message.\n        out = tfc.batchNorm3d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else if (x.rank === 4) {\n        out = tfc.batchNorm4d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else {\n        throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} ` +\n            `yet`);\n    }\n    return out;\n}\n/**\n * Non-broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    return tidy(() => {\n        const meanAndVariance = tfc.moments(x, reductionAxes);\n        const mean = meanAndVariance.mean;\n        const variance = meanAndVariance.variance;\n        const normed = batchNormalization(x, mean, variance, beta, gamma, epsilon);\n        return [normed, mean, variance];\n    });\n}\n/**\n * Broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    return tidy(() => {\n        const meanAndVariance = tfc.moments(x, reductionAxes);\n        const mean = meanAndVariance.mean;\n        const variance = meanAndVariance.variance;\n        const targetShape = [];\n        for (const axis of math_utils.range(0, x.rank)) {\n            if (reductionAxes.indexOf(axis) !== -1) {\n                targetShape.push(1);\n            }\n            else {\n                targetShape.push(x.shape[axis]);\n            }\n        }\n        const broadcastMean = mean.reshape(targetShape);\n        const broadcastVariance = variance.reshape(targetShape);\n        const broadcastGamma = gamma == null ? null : gamma.reshape(targetShape);\n        const broadcastBeta = beta == null ? null : beta.reshape(targetShape);\n        const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon);\n        return [normed, mean, variance];\n    });\n}\n/**\n * Batch normalization for use in training (not inference).\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nexport function normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    if (util.arraysEqual(reductionAxes.slice().sort(), math_utils.range(0, x.rank - 1))) {\n        return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);\n    }\n    else {\n        return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);\n    }\n}\nexport class BatchNormalization extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.supportsMasking = true;\n        this.axis = args.axis == null ? -1 : args.axis;\n        this.momentum = args.momentum == null ? 0.99 : args.momentum;\n        this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n        this.center = args.center == null ? true : args.center;\n        this.scale = args.scale == null ? true : args.scale;\n        this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n        this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n        this.movingMeanInitializer =\n            getInitializer(args.movingMeanInitializer || 'zeros');\n        this.movingVarianceInitializer =\n            getInitializer(args.movingVarianceInitializer || 'ones');\n        this.betaConstraint = getConstraint(args.betaConstraint);\n        this.gammaConstraint = getConstraint(args.gammaConstraint);\n        this.betaRegularizer = getRegularizer(args.betaRegularizer);\n        this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const axis = this.axis >= 0 ? this.axis : (this.axis + inputShape.length);\n        const dim = inputShape[axis];\n        if (dim == null) {\n            throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but ` +\n                `the layer received an input with shape ` +\n                `${JSON.stringify(inputShape)}.`);\n        }\n        this.inputSpec =\n            [new InputSpec({ ndim: inputShape.length, axes: { [axis]: dim } })];\n        const shape = [dim];\n        if (this.scale) {\n            this.gamma = this.addWeight('gamma', shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint);\n        }\n        if (this.center) {\n            this.beta = this.addWeight('beta', shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint);\n        }\n        this.movingMean = this.addWeight('moving_mean', shape, null, this.movingMeanInitializer, null, false);\n        this.movingVariance = this.addWeight('moving_variance', shape, null, this.movingVarianceInitializer, null, false);\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            const input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            const ndim = inputShape.length;\n            const reductionAxes = math_utils.range(0, ndim);\n            const axis = this.axis >= 0 ? this.axis : (this.axis + ndim);\n            reductionAxes.splice(axis, 1);\n            const broadcastShape = generic_utils.pyListRepeat(1, ndim);\n            broadcastShape[axis] = inputShape[axis];\n            const sortedReductionAxes = reductionAxes.slice();\n            sortedReductionAxes.sort();\n            const needsBroadcasting = !util.arraysEqual(sortedReductionAxes, math_utils.range(0, ndim).slice(0, ndim - 1));\n            const normalizeInference = () => {\n                if (needsBroadcasting) {\n                    const broadcastMovingMean = this.movingMean.read().reshape(broadcastShape);\n                    const broadcastMovingVariance = this.movingVariance.read().reshape(broadcastShape);\n                    const broadcastBeta = this.center ? this.beta.read().reshape(broadcastShape) : null;\n                    const broadcastGamma = this.scale ? this.gamma.read().reshape(broadcastShape) : null;\n                    return batchNormalization(input, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon);\n                }\n                else {\n                    return batchNormalization(input, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);\n                }\n            };\n            if (!training) {\n                return normalizeInference();\n            }\n            const [normedTraining, mean, variance] = normalizeBatchInTraining(input, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon);\n            const doMovingAverage = (variable, value, momentum) => {\n                tfc.tidy(() => {\n                    const decay = 1 - momentum;\n                    const origValue = variable.read();\n                    const updateDelta = origValue.sub(value).mul(decay);\n                    variable.write(origValue.sub(updateDelta));\n                });\n            };\n            // Perform updates to moving mean and moving variance for training.\n            // Porting Note: In PyKeras, these updates to `movingMean` and\n            //   `movingAverage` are done as a deferred Graph, added to the `Layer`'s\n            //   `update`s using the `add_update()` method. Here we do it imperatively\n            //   and encapsulate the updates in a function that is invoked\n            //   immediately.\n            const updateMovingMeanAndVariance = () => {\n                doMovingAverage(this.movingMean, mean, this.momentum);\n                doMovingAverage(this.movingVariance, variance, this.momentum);\n            };\n            updateMovingMeanAndVariance();\n            return normedTraining;\n        });\n    }\n    getConfig() {\n        const config = {\n            axis: this.axis,\n            momentum: this.momentum,\n            epsilon: this.epsilon,\n            center: this.center,\n            scale: this.scale,\n            betaInitializer: serializeInitializer(this.betaInitializer),\n            gammaInitializer: serializeInitializer(this.gammaInitializer),\n            movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),\n            movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer),\n            betaRegularizer: serializeRegularizer(this.betaRegularizer),\n            gammaRegularizer: serializeRegularizer(this.gammaRegularizer),\n            betaConstraint: serializeConstraint(this.betaConstraint),\n            gammaConstraint: serializeConstraint(this.gammaConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nBatchNormalization.className = 'BatchNormalization';\nserialization.registerClass(BatchNormalization);\nexport class LayerNormalization extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.axis = args.axis == null ? -1 : args.axis;\n        if (typeof this.axis === 'number') {\n            if (!Number.isInteger(this.axis)) {\n                throw new Error(`Expected axis to be an integer, but received ${this.axis}`);\n            }\n        }\n        else if (Array.isArray(this.axis)) {\n            for (const axis of this.axis) {\n                if (!Number.isInteger(axis)) {\n                    throw new Error(`Expected axis to be an array of integers, ` +\n                        `but received ${JSON.stringify(this.axis)}`);\n                }\n            }\n        }\n        else {\n            throw new Error(`Expected axis to be an integer or an array of integers, ` +\n                `but received ${JSON.stringify(this.axis)}`);\n        }\n        this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n        this.center = args.center == null ? true : args.center;\n        this.scale = args.scale == null ? true : args.scale;\n        this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n        this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n        this.betaRegularizer = getRegularizer(args.betaRegularizer);\n        this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n        this.supportsMasking = true;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const nDims = inputShape.length;\n        // Convert axis to array and resolve negatives.\n        if (typeof this.axis === 'number') {\n            this.axis = [this.axis];\n        }\n        for (let i = 0; i < this.axis.length; ++i) {\n            if (this.axis[i] < 0) {\n                this.axis[i] += nDims;\n            }\n        }\n        // Further validate axes.\n        for (const axis of this.axis) {\n            if (axis < 0 || axis >= nDims) {\n                throw new Error(`Invalid axis: ${axis}`);\n            }\n        }\n        if (this.axis.length !== generic_utils.unique(this.axis).length) {\n            throw new Error(`Found duplicate axes in: ${this.axis}`);\n        }\n        const paramShape = this.axis.map(axis => inputShape[axis]);\n        const trainable = true;\n        if (this.scale) {\n            this.gamma = this.addWeight('gamma', paramShape, 'float32', this.gammaInitializer, this.gammaRegularizer, trainable);\n        }\n        else {\n            this.gamma = null;\n        }\n        if (this.center) {\n            this.beta = this.addWeight('beta', paramShape, 'float32', this.betaInitializer, this.betaRegularizer, trainable);\n        }\n        else {\n            this.beta = null;\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        const input = getExactlyOneTensor(inputs);\n        const inputShape = input.shape;\n        const nDims = inputShape.length;\n        return tidy(() => {\n            const keepDims = true;\n            let { mean, variance } = moments(input, this.axis, keepDims);\n            const broadcastShape = generic_utils.pyListRepeat(1, nDims);\n            for (const dim of this.axis) {\n                broadcastShape[dim] = inputShape[dim];\n            }\n            const broadcast = (v) => {\n                if (v != null && v.shape.length !== nDims &&\n                    this.axis !== [nDims - 1]) {\n                    return v.reshape(broadcastShape);\n                }\n                else {\n                    return v;\n                }\n            };\n            let scale = broadcast(this.gamma.read());\n            let offset = broadcast(this.beta.read());\n            // TODO(https://github.com/tensorflow/tfjs/issues/2120): The tiling below\n            // is a workaround for the limitation of core's batchNormalization?d don't\n            // support broadcasting in their gradients. In addition, the tiling is\n            // necessary to ensure correctness on the browser CPU backend regardless\n            // of forward or backward computation. Remove this workaround once the\n            // limitation is addressed. See .\n            const momentsTiling = [];\n            const scaleOffsetTiling = [];\n            for (let i = 0; i < nDims; ++i) {\n                if (this.axis.indexOf(i) !== -1) {\n                    momentsTiling.push(inputShape[i]);\n                    scaleOffsetTiling.push(1);\n                }\n                else {\n                    momentsTiling.push(1);\n                    scaleOffsetTiling.push(inputShape[i]);\n                }\n            }\n            mean = mean.tile(momentsTiling);\n            variance = variance.tile(momentsTiling);\n            scale = scale.tile(scaleOffsetTiling);\n            offset = offset.tile(scaleOffsetTiling);\n            return batchNormalization(input, mean, variance, offset, scale, this.epsilon);\n        });\n    }\n    getConfig() {\n        const config = {\n            axis: this.axis,\n            epsilon: this.epsilon,\n            center: this.center,\n            scale: this.scale,\n            betaInitializer: serializeInitializer(this.betaInitializer),\n            gammaInitializer: serializeInitializer(this.gammaInitializer),\n            betaRegularizer: serializeRegularizer(this.betaRegularizer),\n            gammaRegularizer: serializeRegularizer(this.gammaRegularizer)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nLayerNormalization.className = 'LayerNormalization';\nserialization.registerClass(LayerNormalization);\n//# sourceMappingURL=normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Padding Layers.\n */\n// Porting Note: In Python Keras, the padding layers are in convolutional.py,\n//   but we decided to put them in a separate file (padding.ts) for clarity.\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Pads the middle dimension of a 3D tensor.\n *\n * @param x Input `tf.Tensor` to be padded.\n * @param padding `Array` of 2 integers, how many zeros to add at the start and\n *   end of the middle dimension (i.e., dimension 1).\n * @return A padded 3D `tf.Tensor`.\n */\nexport function temporalPadding(x, padding) {\n    return tidy(() => {\n        if (x.rank !== 3) {\n            throw new ValueError(`temporalPadding expects input tensor to be 3-D, but received a ` +\n                `${x.rank}-D tensor.`);\n        }\n        if (padding == null) {\n            padding = [1, 1];\n        }\n        if (padding.length !== 2) {\n            throw new ValueError(`temporalPadding expects input padding pattern to be a length-2 ` +\n                `array, but received a length-${padding.length} array.`);\n        }\n        const pattern = [[0, 0], padding, [0, 0]];\n        return tfc.pad(x, pattern);\n    });\n}\n/**\n * Pads the 2nd and 3rd dimensions of a 4D tensor.\n *\n * @param x Input `tf.Tensor` to be padded.\n * @param padding `Array` of two `Array`s, each of which is an `Array` of two\n *   integers. The amount of padding at the beginning and end of the 2nd and 3rd\n *   dimensions, respectively.\n * @param dataFormat 'channelsLast' (default) or 'channelsFirst'.\n * @return Padded 4D `tf.Tensor`.\n */\nexport function spatial2dPadding(x, padding, dataFormat) {\n    return tidy(() => {\n        if (x.rank !== 4) {\n            throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ` +\n                `${x.rank}-D tensor.`);\n        }\n        if (padding == null) {\n            padding = [[1, 1], [1, 1]];\n        }\n        if (padding.length !== 2 || padding[0].length !== 2 ||\n            padding[1].length !== 2) {\n            throw new ValueError('spatial2dPadding expects `padding` to be an Array of two Arrays, ' +\n                'each of which is an Array of two integers.');\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (dataFormat !== 'channelsLast' && dataFormat !== 'channelsFirst') {\n            throw new ValueError(`Unknown data format: ${dataFormat}. ` +\n                `Supported data formats are 'channelsLast' and 'channelsFirst.`);\n        }\n        let pattern;\n        if (dataFormat === 'channelsFirst') {\n            pattern = [[0, 0], [0, 0], padding[0], padding[1]];\n        }\n        else {\n            pattern = [[0, 0], padding[0], padding[1], [0, 0]];\n        }\n        return tfc.pad(x, pattern);\n    });\n}\nexport class ZeroPadding2D extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.dataFormat =\n            args.dataFormat == null ? imageDataFormat() : args.dataFormat;\n        // TODO(cais): Maybe refactor the following logic surrounding `padding`\n        //   into a helper method.\n        if (args.padding == null) {\n            this.padding = [[1, 1], [1, 1]];\n        }\n        else if (typeof args.padding === 'number') {\n            this.padding =\n                [[args.padding, args.padding], [args.padding, args.padding]];\n        }\n        else {\n            args.padding = args.padding;\n            if (args.padding.length !== 2) {\n                throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but ` +\n                    `received a length-${args.padding.length} array.`);\n            }\n            let heightPadding;\n            let widthPadding;\n            if (typeof args.padding[0] === 'number') {\n                heightPadding = [args.padding[0], args.padding[0]];\n                widthPadding = [args.padding[1], args.padding[1]];\n            }\n            else {\n                args.padding = args.padding;\n                if (args.padding[0].length !== 2) {\n                    throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, ` +\n                        `but received a length-${args.padding[0].length} array.`);\n                }\n                heightPadding = args.padding[0];\n                if (args.padding[1].length !== 2) {\n                    throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, ` +\n                        `but received a length-${args.padding[1].length} array.`);\n                }\n                widthPadding = args.padding[1];\n            }\n            this.padding = [heightPadding, widthPadding];\n        }\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let rows;\n        let cols;\n        if (this.dataFormat === 'channelsFirst') {\n            if (inputShape[2] != null && inputShape[2] >= 0) {\n                rows = inputShape[2] + this.padding[0][0] + this.padding[0][1];\n            }\n            else {\n                rows = null;\n            }\n            if (inputShape[3] != null && inputShape[3] >= 0) {\n                cols = inputShape[3] + this.padding[1][0] + this.padding[1][1];\n            }\n            else {\n                cols = null;\n            }\n            return [inputShape[0], inputShape[1], rows, cols];\n        }\n        else {\n            if (inputShape[1] != null && inputShape[1] >= 0) {\n                rows = inputShape[1] + this.padding[0][0] + this.padding[0][1];\n            }\n            else {\n                rows = null;\n            }\n            if (inputShape[2] != null && inputShape[2] >= 0) {\n                cols = inputShape[2] + this.padding[1][0] + this.padding[1][1];\n            }\n            else {\n                cols = null;\n            }\n            return [inputShape[0], rows, cols, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => spatial2dPadding(getExactlyOneTensor(inputs), this.padding, this.dataFormat));\n    }\n    getConfig() {\n        const config = {\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nZeroPadding2D.className = 'ZeroPadding2D';\nserialization.registerClass(ZeroPadding2D);\n//# sourceMappingURL=padding.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Pooling Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode, checkPoolMode } from '../common';\nimport { InputSpec } from '../engine/topology';\nimport { Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { convOutputLength } from '../utils/conv_utils';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { preprocessConv2DInput, preprocessConv3DInput } from './convolutional';\n/**\n * 2D pooling.\n * @param x\n * @param poolSize\n * @param stridesdes strides. Defaults to [1, 1].\n * @param padding padding. Defaults to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param poolMode Mode of pooling. Defaults to 'max'.\n * @returns Result of the 2D pooling.\n */\nexport function pool2d(x, poolSize, strides, padding, dataFormat, poolMode) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        checkPoolMode(poolMode);\n        checkPaddingMode(padding);\n        if (strides == null) {\n            strides = [1, 1];\n        }\n        if (padding == null) {\n            padding = 'valid';\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (poolMode == null) {\n            poolMode = 'max';\n        }\n        // TODO(cais): Remove the preprocessing step once deeplearn.js supports\n        // dataFormat as an input argument.\n        x = preprocessConv2DInput(x, dataFormat); // x is NHWC after preprocessing.\n        let y;\n        const paddingString = (padding === 'same') ? 'same' : 'valid';\n        if (poolMode === 'max') {\n            // TODO(cais): Rank check?\n            y = tfc.maxPool(x, poolSize, strides, paddingString);\n        }\n        else { // 'avg'\n            // TODO(cais): Check the dtype and rank of x and give clear error message\n            //   if those are incorrect.\n            y = tfc.avgPool(\n            // TODO(cais): Rank check?\n            x, poolSize, strides, paddingString);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]); // NHWC -> NCHW.\n        }\n        return y;\n    });\n}\n/**\n * 3D pooling.\n * @param x\n * @param poolSize. Default to [1, 1, 1].\n * @param strides strides. Defaults to [1, 1, 1].\n * @param padding padding. Defaults to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param poolMode Mode of pooling. Defaults to 'max'.\n * @returns Result of the 3D pooling.\n */\nexport function pool3d(x, poolSize, strides, padding, dataFormat, poolMode) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        checkPoolMode(poolMode);\n        checkPaddingMode(padding);\n        if (strides == null) {\n            strides = [1, 1, 1];\n        }\n        if (padding == null) {\n            padding = 'valid';\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (poolMode == null) {\n            poolMode = 'max';\n        }\n        // x is NDHWC after preprocessing.\n        x = preprocessConv3DInput(x, dataFormat);\n        let y;\n        const paddingString = (padding === 'same') ? 'same' : 'valid';\n        if (poolMode === 'max') {\n            y = tfc.maxPool3d(x, poolSize, strides, paddingString);\n        }\n        else { // 'avg'\n            y = tfc.avgPool3d(x, poolSize, strides, paddingString);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 4, 1, 2, 3]); // NDHWC -> NCDHW.\n        }\n        return y;\n    });\n}\n/**\n * Abstract class for different pooling 1D layers.\n */\nexport class Pooling1D extends Layer {\n    /**\n     *\n     * @param args Parameters for the Pooling layer.\n     *\n     * config.poolSize defaults to 2.\n     */\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = 2;\n        }\n        super(args);\n        if (typeof args.poolSize === 'number') {\n            this.poolSize = [args.poolSize];\n        }\n        else if (Array.isArray(args.poolSize) &&\n            args.poolSize.length === 1 &&\n            typeof args.poolSize[0] === 'number') {\n            this.poolSize = args.poolSize;\n        }\n        else {\n            throw new ValueError(`poolSize for 1D convolutional layer must be a number or an ` +\n                `Array of a single number, but received ` +\n                `${JSON.stringify(args.poolSize)}`);\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else {\n            if (typeof args.strides === 'number') {\n                this.strides = [args.strides];\n            }\n            else if (Array.isArray(args.strides) &&\n                args.strides.length === 1 &&\n                typeof args.strides[0] === 'number') {\n                this.strides = args.strides;\n            }\n            else {\n                throw new ValueError(`strides for 1D convolutional layer must be a number or an ` +\n                    `Array of a single number, but received ` +\n                    `${JSON.stringify(args.strides)}`);\n            }\n        }\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const length = convOutputLength(inputShape[1], this.poolSize[0], this.padding, this.strides[0]);\n        return [inputShape[0], length, inputShape[2]];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Add dummy last dimension.\n            inputs = K.expandDims(getExactlyOneTensor(inputs), 2);\n            const output = this.poolingFunction(getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, 'channelsLast');\n            // Remove dummy last dimension.\n            return tfc.squeeze(output, [2]);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling1D extends Pooling1D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling1D.className = 'MaxPooling1D';\nserialization.registerClass(MaxPooling1D);\nexport class AveragePooling1D extends Pooling1D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling1D.className = 'AveragePooling1D';\nserialization.registerClass(AveragePooling1D);\n/**\n * Abstract class for different pooling 2D layers.\n */\nexport class Pooling2D extends Layer {\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = [2, 2];\n        }\n        super(args);\n        this.poolSize = Array.isArray(args.poolSize) ?\n            args.poolSize :\n            [args.poolSize, args.poolSize];\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else if (Array.isArray(args.strides)) {\n            if (args.strides.length !== 2) {\n                throw new ValueError(`If the strides property of a 2D pooling layer is an Array, ` +\n                    `it is expected to have a length of 2, but received length ` +\n                    `${args.strides.length}.`);\n            }\n            this.strides = args.strides;\n        }\n        else {\n            // `config.strides` is a number.\n            this.strides = [args.strides, args.strides];\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let rows = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        let cols = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        rows =\n            convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]);\n        cols =\n            convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], inputShape[1], rows, cols];\n        }\n        else {\n            return [inputShape[0], rows, cols, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n            dataFormat: this.dataFormat\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling2D extends Pooling2D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling2D.className = 'MaxPooling2D';\nserialization.registerClass(MaxPooling2D);\nexport class AveragePooling2D extends Pooling2D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling2D.className = 'AveragePooling2D';\nserialization.registerClass(AveragePooling2D);\n/**\n * Abstract class for different pooling 3D layers.\n */\nexport class Pooling3D extends Layer {\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = [2, 2, 2];\n        }\n        super(args);\n        this.poolSize = Array.isArray(args.poolSize) ?\n            args.poolSize :\n            [args.poolSize, args.poolSize, args.poolSize];\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else if (Array.isArray(args.strides)) {\n            if (args.strides.length !== 3) {\n                throw new ValueError(`If the strides property of a 3D pooling layer is an Array, ` +\n                    `it is expected to have a length of 3, but received length ` +\n                    `${args.strides.length}.`);\n            }\n            this.strides = args.strides;\n        }\n        else {\n            // `config.strides` is a number.\n            this.strides = [args.strides, args.strides, args.strides];\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 5 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let depths = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        let rows = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        let cols = this.dataFormat === 'channelsFirst' ? inputShape[4] : inputShape[3];\n        depths = convOutputLength(depths, this.poolSize[0], this.padding, this.strides[0]);\n        rows =\n            convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]);\n        cols =\n            convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], inputShape[1], depths, rows, cols];\n        }\n        else {\n            return [inputShape[0], depths, rows, cols, inputShape[4]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n            dataFormat: this.dataFormat\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling3D extends Pooling3D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool3d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling3D.className = 'MaxPooling3D';\nserialization.registerClass(MaxPooling3D);\nexport class AveragePooling3D extends Pooling3D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool3d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling3D.className = 'AveragePooling3D';\nserialization.registerClass(AveragePooling3D);\n/**\n * Abstract class for different global pooling 1D layers.\n */\nexport class GlobalPooling1D extends Layer {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n    }\n    computeOutputShape(inputShape) {\n        return [inputShape[0], inputShape[2]];\n    }\n    call(inputs, kwargs) {\n        throw new NotImplementedError();\n    }\n}\nexport class GlobalAveragePooling1D extends GlobalPooling1D {\n    constructor(args) {\n        super(args || {});\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            return tfc.mean(input, 1);\n        });\n    }\n}\n/** @nocollapse */\nGlobalAveragePooling1D.className = 'GlobalAveragePooling1D';\nserialization.registerClass(GlobalAveragePooling1D);\nexport class GlobalMaxPooling1D extends GlobalPooling1D {\n    constructor(args) {\n        super(args || {});\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            return tfc.max(input, 1);\n        });\n    }\n}\n/** @nocollapse */\nGlobalMaxPooling1D.className = 'GlobalMaxPooling1D';\nserialization.registerClass(GlobalMaxPooling1D);\n/**\n * Abstract class for different global pooling 2D layers.\n */\nexport class GlobalPooling2D extends Layer {\n    constructor(args) {\n        super(args);\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = inputShape;\n        if (this.dataFormat === 'channelsLast') {\n            return [inputShape[0], inputShape[3]];\n        }\n        else {\n            return [inputShape[0], inputShape[1]];\n        }\n    }\n    call(inputs, kwargs) {\n        throw new NotImplementedError();\n    }\n    getConfig() {\n        const config = { dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class GlobalAveragePooling2D extends GlobalPooling2D {\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                return tfc.mean(input, [1, 2]);\n            }\n            else {\n                return tfc.mean(input, [2, 3]);\n            }\n        });\n    }\n}\n/** @nocollapse */\nGlobalAveragePooling2D.className = 'GlobalAveragePooling2D';\nserialization.registerClass(GlobalAveragePooling2D);\nexport class GlobalMaxPooling2D extends GlobalPooling2D {\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                return tfc.max(input, [1, 2]);\n            }\n            else {\n                return tfc.max(input, [2, 3]);\n            }\n        });\n    }\n}\n/** @nocollapse */\nGlobalMaxPooling2D.className = 'GlobalMaxPooling2D';\nserialization.registerClass(GlobalMaxPooling2D);\n//# sourceMappingURL=pooling.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Layers that augment the functionality of a base layer.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { InputSpec, Layer, SymbolicTensor } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { VALID_BIDIRECTIONAL_MERGE_MODES } from '../keras_format/common';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { rnn, standardizeArgs } from './recurrent';\nimport { deserialize } from './serialization';\n/**\n * Abstract wrapper base class.\n *\n * Wrappers take another layer and augment it in various ways.\n * Do not use this class as a layer, it is only an abstract base class.\n * Two usable wrappers are the `TimeDistributed` and `Bidirectional` wrappers.\n */\nexport class Wrapper extends Layer {\n    constructor(args) {\n        // Porting Note: In PyKeras, `self.layer` is set prior to the calling\n        //   `super()`. But we can't do that here due to TypeScript's restriction.\n        //   See: https://github.com/Microsoft/TypeScript/issues/8277\n        //   As a result, we have to add checks in `get trainable()` and\n        //   `set trainable()` below in order to prevent using `this.layer` when\n        //   its value is `undefined`. The super constructor does use the getter\n        //   and the setter of `this.layer`.\n        super(args);\n        this.layer = args.layer;\n    }\n    build(inputShape) {\n        this.built = true;\n    }\n    // TODO(cais): Implement activityRegularizer getter.\n    get trainable() {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        if (this.layer != null) {\n            return this.layer.trainable;\n        }\n        else {\n            return false;\n        }\n    }\n    set trainable(value) {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        if (this.layer != null) {\n            this.layer.trainable = value;\n        }\n    }\n    get trainableWeights() {\n        return this.layer.trainableWeights;\n    }\n    // TODO(cais): Implement setter for trainableWeights.\n    get nonTrainableWeights() {\n        return this.layer.nonTrainableWeights;\n    }\n    // TODO(cais): Implement setter for nonTrainableWeights.\n    get updates() {\n        // tslint:disable-next-line:no-any\n        return this.layer._updates;\n    }\n    // TODO(cais): Implement getUpdatesFor().\n    get losses() {\n        return this.layer.losses;\n    }\n    // TODO(cais): Implement getLossesFor().\n    getWeights() {\n        return this.layer.getWeights();\n    }\n    setWeights(weights) {\n        this.layer.setWeights(weights);\n    }\n    getConfig() {\n        const config = {\n            'layer': {\n                'className': this.layer.getClassName(),\n                'config': this.layer.getConfig(),\n            }\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.layer != null) {\n            this.layer.setFastWeightInitDuringBuild(value);\n        }\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const layerConfig = config['layer'];\n        const layer = deserialize(layerConfig, customObjects);\n        delete config['layer'];\n        const newConfig = { layer };\n        Object.assign(newConfig, config);\n        return new cls(newConfig);\n    }\n}\nexport class TimeDistributed extends Wrapper {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < 3) {\n            throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received ` +\n                `input shape ${JSON.stringify(inputShape)}`);\n        }\n        this.inputSpec = [{ shape: inputShape }];\n        const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        if (!this.layer.built) {\n            this.layer.build(childInputShape);\n            this.layer.built = true;\n        }\n        super.build(inputShape);\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        const childOutputShape = this.layer.computeOutputShape(childInputShape);\n        const timesteps = inputShape[1];\n        return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1));\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            // TODO(cais): Add 'training' and 'useLearningPhase' to kwargs.\n            inputs = getExactlyOneTensor(inputs);\n            // Porting Note: In tfjs-layers, `inputs` are always concrete tensor\n            // values. Hence the inputs can't have an undetermined first (batch)\n            // dimension, which is why we always use the K.rnn approach here.\n            const step = (inputs, states) => {\n                // TODO(cais): Add useLearningPhase.\n                // NOTE(cais): `layer.call` may return a length-1 array of Tensor in\n                //   some cases (e.g., `layer` is a `Sequential` instance), which is\n                //   why `getExactlyOneTensor` is used below.\n                const output = getExactlyOneTensor(this.layer.call(inputs, kwargs));\n                return [output, []];\n            };\n            const rnnOutputs = rnn(step, inputs, [], false /* goBackwards */, null /* mask */, null /* constants */, false /* unroll */, true /* needPerStepOutputs */);\n            const y = rnnOutputs[1];\n            // TODO(cais): Add activity regularization.\n            // TODO(cais): Add useLearningPhase.\n            return y;\n        });\n    }\n}\n/** @nocollapse */\nTimeDistributed.className = 'TimeDistributed';\nserialization.registerClass(TimeDistributed);\nexport function checkBidirectionalMergeMode(value) {\n    generic_utils.checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, 'BidirectionalMergeMode', value);\n}\nconst DEFAULT_BIDIRECTIONAL_MERGE_MODE = 'concat';\nexport class Bidirectional extends Wrapper {\n    constructor(args) {\n        super(args);\n        // Note: When creating `this.forwardLayer`, the original Layer object\n        //   (`config.layer`) ought to be cloned. This is why we call\n        //   `getConfig()` followed by `deserialize()`. Without this cloning,\n        //   the layer names saved during serialization will incorrectly contain\n        //   the 'forward_' prefix. In Python Keras, this is done using\n        //   `copy.copy` (shallow copy), which does not have a simple equivalent\n        //   in JavaScript. JavaScript's `Object.assign()` does not copy\n        //   methods.\n        const layerConfig = args.layer.getConfig();\n        const forwDict = {};\n        forwDict['className'] = args.layer.getClassName();\n        forwDict['config'] = layerConfig;\n        this.forwardLayer = deserialize(forwDict);\n        layerConfig['goBackwards'] =\n            layerConfig['goBackwards'] === true ? false : true;\n        const backDict = {};\n        backDict['className'] = args.layer.getClassName();\n        backDict['config'] = layerConfig;\n        this.backwardLayer = deserialize(backDict);\n        this.forwardLayer.name = 'forward_' + this.forwardLayer.name;\n        this.backwardLayer.name = 'backward_' + this.backwardLayer.name;\n        this.mergeMode = args.mergeMode === undefined ?\n            DEFAULT_BIDIRECTIONAL_MERGE_MODE :\n            args.mergeMode;\n        checkBidirectionalMergeMode(this.mergeMode);\n        if (args.weights) {\n            throw new NotImplementedError('weights support is not implemented for Bidirectional layer yet.');\n        }\n        this._stateful = args.layer.stateful;\n        this.returnSequences = args.layer.returnSequences;\n        this.returnState = args.layer.returnState;\n        this.supportsMasking = true;\n        this._trainable = true;\n        this.inputSpec = args.layer.inputSpec;\n        this.numConstants = null;\n    }\n    get trainable() {\n        return this._trainable;\n    }\n    set trainable(value) {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        this._trainable = value;\n        if (this.forwardLayer != null) {\n            this.forwardLayer.trainable = value;\n        }\n        if (this.backwardLayer != null) {\n            this.backwardLayer.trainable = value;\n        }\n    }\n    getWeights() {\n        return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights());\n    }\n    setWeights(weights) {\n        const numWeights = weights.length;\n        const numeightsOver2 = Math.floor(numWeights / 2);\n        this.forwardLayer.setWeights(weights.slice(0, numeightsOver2));\n        this.backwardLayer.setWeights(weights.slice(numeightsOver2));\n    }\n    computeOutputShape(inputShape) {\n        let layerShapes = this.forwardLayer.computeOutputShape(inputShape);\n        if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) {\n            layerShapes = [layerShapes];\n        }\n        layerShapes = layerShapes;\n        let outputShape;\n        let outputShapes;\n        let stateShape;\n        if (this.returnState) {\n            stateShape = layerShapes.slice(1);\n            outputShape = layerShapes[0];\n        }\n        else {\n            outputShape = layerShapes[0];\n        }\n        outputShape = outputShape;\n        if (this.mergeMode === 'concat') {\n            outputShape[outputShape.length - 1] *= 2;\n            outputShapes = [outputShape];\n        }\n        else if (this.mergeMode == null) {\n            outputShapes = [outputShape, outputShape.slice()];\n        }\n        else {\n            outputShapes = [outputShape];\n        }\n        if (this.returnState) {\n            if (this.mergeMode == null) {\n                return outputShapes.concat(stateShape).concat(stateShape.slice());\n            }\n            return [outputShape].concat(stateShape).concat(stateShape.slice());\n        }\n        return generic_utils.singletonOrArray(outputShapes);\n    }\n    apply(inputs, kwargs) {\n        let initialState = kwargs == null ? null : kwargs['initialState'];\n        let constants = kwargs == null ? null : kwargs['constants'];\n        if (kwargs == null) {\n            kwargs = {};\n        }\n        const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n        inputs = standardized.inputs;\n        initialState = standardized.initialState;\n        constants = standardized.constants;\n        if (Array.isArray(inputs)) {\n            initialState = inputs.slice(1);\n            inputs = inputs[0];\n        }\n        if ((initialState == null || initialState.length === 0) &&\n            constants == null) {\n            return super.apply(inputs, kwargs);\n        }\n        const additionalInputs = [];\n        const additionalSpecs = [];\n        if (initialState != null) {\n            const numStates = initialState.length;\n            if (numStates % 2 > 0) {\n                throw new ValueError('When passing `initialState` to a Bidrectional RNN, ' +\n                    'the state should be an Array containing the states of ' +\n                    'the underlying RNNs.');\n            }\n            kwargs['initialState'] = initialState;\n            additionalInputs.push(...initialState);\n            const stateSpecs = initialState\n                .map(state => new InputSpec({ shape: state.shape }));\n            this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2);\n            this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2);\n            additionalSpecs.push(...stateSpecs);\n        }\n        if (constants != null) {\n            throw new NotImplementedError('Support for constants in Bidirectional layers is not ' +\n                'implemented yet.');\n        }\n        const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor;\n        for (const tensor of additionalInputs) {\n            if (tensor instanceof SymbolicTensor !== isSymbolicTensor) {\n                throw new ValueError('The initial state of a Bidirectional layer cannot be ' +\n                    'specified as a mix of symbolic and non-symbolic tensors');\n            }\n        }\n        if (isSymbolicTensor) {\n            // Compute the full input and specs, including the states.\n            const fullInput = [inputs].concat(additionalInputs);\n            const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n            // Perform the call temporarily and replace inputSpec.\n            // Note: with initial states symbolic calls and non-symbolic calls to\n            // this method differ in how the initial states are passed. For\n            // symbolic calls, the initial states are passed in the first arg, as\n            // an Array of SymbolicTensors; for non-symbolic calls, they are\n            // passed in the second arg as a part of the kwargs. Hence the need to\n            // temporarily modify inputSpec here.\n            // TODO(cais): Make refactoring so that this hacky code below is no\n            // longer needed.\n            const originalInputSpec = this.inputSpec;\n            this.inputSpec = fullInputSpec;\n            const output = super.apply(fullInput, kwargs);\n            this.inputSpec = originalInputSpec;\n            return output;\n        }\n        else {\n            return super.apply(inputs, kwargs);\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const initialState = kwargs['initialState'];\n            let y;\n            let yRev;\n            if (initialState == null) {\n                y = this.forwardLayer.call(inputs, kwargs);\n                yRev = this.backwardLayer.call(inputs, kwargs);\n            }\n            else {\n                const forwardState = initialState.slice(0, initialState.length / 2);\n                const backwardState = initialState.slice(initialState.length / 2);\n                y = this.forwardLayer.call(inputs, Object.assign(kwargs, { initialState: forwardState }));\n                yRev = this.backwardLayer.call(inputs, Object.assign(kwargs, { initialState: backwardState }));\n            }\n            let states;\n            if (this.returnState) {\n                if (Array.isArray(y)) {\n                    states = y.slice(1).concat(yRev.slice(1));\n                }\n                else {\n                }\n                y = y[0];\n                yRev = yRev[0];\n            }\n            if (this.returnSequences) {\n                yRev = tfc.reverse(yRev, 1);\n            }\n            let output;\n            if (this.mergeMode === 'concat') {\n                output = K.concatenate([y, yRev]);\n            }\n            else if (this.mergeMode === 'sum') {\n                output = tfc.add(y, yRev);\n            }\n            else if (this.mergeMode === 'ave') {\n                output = tfc.mul(.5, tfc.add(y, yRev));\n            }\n            else if (this.mergeMode === 'mul') {\n                output = tfc.mul(y, yRev);\n            }\n            else if (this.mergeMode == null) {\n                output = [y, yRev];\n            }\n            // TODO(cais): Properly set learning phase.\n            if (this.returnState) {\n                if (this.mergeMode == null) {\n                    return output.concat(states);\n                }\n                return [output].concat(states);\n            }\n            return output;\n        });\n    }\n    resetStates(states) {\n        this.forwardLayer.resetStates();\n        this.backwardLayer.resetStates();\n    }\n    build(inputShape) {\n        nameScope(this.forwardLayer.name, () => {\n            this.forwardLayer.build(inputShape);\n        });\n        nameScope(this.backwardLayer.name, () => {\n            this.backwardLayer.build(inputShape);\n        });\n        this.built = true;\n    }\n    computeMask(inputs, mask) {\n        if (Array.isArray(mask)) {\n            mask = mask[0];\n        }\n        let outputMask;\n        if (this.returnSequences) {\n            if (this.mergeMode == null) {\n                outputMask = [mask, mask];\n            }\n            else {\n                outputMask = mask;\n            }\n        }\n        else {\n            if (this.mergeMode == null) {\n                outputMask = [null, null];\n            }\n            else {\n                outputMask = null;\n            }\n        }\n        if (this.returnState) {\n            const states = this.forwardLayer.states;\n            const stateMask = states.map(state => null);\n            if (Array.isArray(outputMask)) {\n                return outputMask.concat(stateMask).concat(stateMask);\n            }\n            else {\n                return [outputMask].concat(stateMask).concat(stateMask);\n            }\n        }\n        else {\n            return outputMask;\n        }\n    }\n    get trainableWeights() {\n        return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights);\n    }\n    get nonTrainableWeights() {\n        return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights);\n    }\n    // TODO(cais): Implement constraints().\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.forwardLayer != null) {\n            this.forwardLayer.setFastWeightInitDuringBuild(value);\n        }\n        if (this.backwardLayer != null) {\n            this.backwardLayer.setFastWeightInitDuringBuild(value);\n        }\n    }\n    getConfig() {\n        const config = {\n            'mergeMode': this.mergeMode,\n        };\n        // TODO(cais): Add logic for `numConstants` once the property is added.\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        const rnnLayer = deserialize(config['layer']);\n        delete config['layer'];\n        // TODO(cais): Add logic for `numConstants` once the property is added.\n        if (config['numConstants'] != null) {\n            throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants ` +\n                `present is not supported yet.`);\n        }\n        // tslint:disable-next-line:no-any\n        const newConfig = config;\n        newConfig['layer'] = rnnLayer;\n        return new cls(newConfig);\n    }\n}\n/** @nocollapse */\nBidirectional.className = 'Bidirectional';\nserialization.registerClass(Bidirectional);\n//# sourceMappingURL=wrappers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { InputLayer } from './engine/input_layer';\nimport { Layer } from './engine/topology';\nimport { input } from './exports';\nimport { ELU, LeakyReLU, PReLU, ReLU, Softmax, ThresholdedReLU } from './layers/advanced_activations';\nimport { Conv1D, Conv2D, Conv2DTranspose, Conv3D, Cropping2D, SeparableConv2D, UpSampling2D } from './layers/convolutional';\nimport { DepthwiseConv2D } from './layers/convolutional_depthwise';\nimport { ConvLSTM2D, ConvLSTM2DCell } from './layers/convolutional_recurrent';\nimport { Activation, Dense, Dropout, Flatten, Masking, Permute, RepeatVector, Reshape, SpatialDropout1D } from './layers/core';\nimport { Embedding } from './layers/embeddings';\nimport { Add, Average, Concatenate, Dot, Maximum, Minimum, Multiply } from './layers/merge';\nimport { AlphaDropout, GaussianDropout, GaussianNoise } from './layers/noise';\nimport { BatchNormalization, LayerNormalization } from './layers/normalization';\nimport { ZeroPadding2D } from './layers/padding';\nimport { AveragePooling1D, AveragePooling2D, AveragePooling3D, GlobalAveragePooling1D, GlobalAveragePooling2D, GlobalMaxPooling1D, GlobalMaxPooling2D, MaxPooling1D, MaxPooling2D, MaxPooling3D } from './layers/pooling';\nimport { GRU, GRUCell, LSTM, LSTMCell, RNN, RNNCell, SimpleRNN, SimpleRNNCell, StackedRNNCells } from './layers/recurrent';\nimport { Bidirectional, TimeDistributed } from './layers/wrappers';\n// TODO(cais): Add doc string to all the public static functions in this\n//   class; include exectuable JavaScript code snippets where applicable\n//   (b/74074458).\n// Input Layer.\n/**\n * An input layer is an entry point into a `tf.LayersModel`.\n *\n * `InputLayer` is generated automatically for `tf.Sequential`` models by\n * specifying the `inputshape` or `batchInputShape` for the first layer.  It\n * should not be specified explicitly. However, it can be useful sometimes,\n * e.g., when constructing a sequential model from a subset of another\n * sequential model's layers. Like the code snippet below shows.\n *\n * ```js\n * // Define a model which simply adds two inputs.\n * const model1 = tf.sequential();\n * model1.add(tf.layers.dense({inputShape: [4], units: 3, activation: 'relu'}));\n * model1.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));\n * model1.summary();\n * model1.predict(tf.zeros([1, 4])).print();\n *\n * // Construct another model, reusing the second layer of `model1` while\n * // not using the first layer of `model1`. Note that you cannot add the second\n * // layer of `model` directly as the first layer of the new sequential model,\n * // because doing so will lead to an error related to the fact that the layer\n * // is not an input layer. Instead, you need to create an `inputLayer` and add\n * // it to the new sequential model before adding the reused layer.\n * const model2 = tf.sequential();\n * // Use an inputShape that matches the input shape of `model1`'s second\n * // layer.\n * model2.add(tf.layers.inputLayer({inputShape: [3]}));\n * model2.add(model1.layers[1]);\n * model2.summary();\n * model2.predict(tf.zeros([1, 3])).print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Inputs', namespace: 'layers'}\n */\nexport function inputLayer(args) {\n    return new InputLayer(args);\n}\n// Advanced Activation Layers.\n/**\n * Exponetial Linear Unit (ELU).\n *\n * It follows:\n * `f(x) =  alpha * (exp(x) - 1.) for x < 0`,\n * `f(x) = x for x >= 0`.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * References:\n *   - [Fast and Accurate Deep Network Learning by Exponential Linear Units\n * (ELUs)](https://arxiv.org/abs/1511.07289v1)\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function elu(args) {\n    return new ELU(args);\n}\n/**\n * Rectified Linear Unit activation function.\n *\n * Input shape:\n *   Arbitrary. Use the config field `inputShape` (Array of integers, does\n *   not include the sample axis) when using this layer as the first layer\n *   in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function reLU(args) {\n    return new ReLU(args);\n}\n/**\n * Leaky version of a rectified linear unit.\n *\n * It allows a small gradient when the unit is not active:\n * `f(x) = alpha * x for x < 0.`\n * `f(x) = x for x >= 0.`\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function leakyReLU(args) {\n    return new LeakyReLU(args);\n}\n/**\n * Parameterized version of a leaky rectified linear unit.\n *\n * It follows\n * `f(x) = alpha * x for x < 0.`\n * `f(x) = x for x >= 0.`\n * wherein `alpha` is a trainable weight.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function prelu(args) {\n    return new PReLU(args);\n}\n/**\n * Softmax activation layer.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function softmax(args) {\n    return new Softmax(args);\n}\n/**\n * Thresholded Rectified Linear Unit.\n *\n * It follows:\n * `f(x) = x for x > theta`,\n * `f(x) = 0 otherwise`.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * References:\n *   - [Zero-Bias Autoencoders and the Benefits of Co-Adapting\n * Features](http://arxiv.org/abs/1402.3337)\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function thresholdedReLU(args) {\n    return new ThresholdedReLU(args);\n}\n// Convolutional Layers.\n/**\n * 1D convolution layer (e.g., temporal convolution).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input over a single spatial (or temporal) dimension\n * to produce a tensor of outputs.\n *\n * If `use_bias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model, provide an\n * `inputShape` argument `Array` or `null`.\n *\n * For example, `inputShape` would be:\n * - `[10, 128]` for sequences of 10 vectors of 128-dimensional vectors\n * - `[null, 128]` for variable-length sequences of 128-dimensional vectors.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional',  namespace: 'layers'}\n */\nexport function conv1d(args) {\n    return new Conv1D(args);\n}\n/**\n * 2D convolution layer (e.g. spatial convolution over images).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input to produce a tensor of outputs.\n *\n * If `useBias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model,\n * provide the keyword argument `inputShape`\n * (Array of integers, does not include the sample axis),\n * e.g. `inputShape=[128, 128, 3]` for 128x128 RGB pictures\n * in `dataFormat='channelsLast'`.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv2d(args) {\n    return new Conv2D(args);\n}\n/**\n * Transposed convolutional layer (sometimes called Deconvolution).\n *\n * The need for transposed convolutions generally arises\n * from the desire to use a transformation going in the opposite direction of\n * a normal convolution, i.e., from something that has the shape of the output\n * of some convolution to something that has the shape of its input while\n * maintaining a connectivity pattern that is compatible with said\n * convolution.\n *\n * When using this layer as the first layer in a model, provide the\n * configuration `inputShape` (`Array` of integers, does not include the\n * sample axis), e.g., `inputShape: [128, 128, 3]` for 128x128 RGB pictures in\n * `dataFormat: 'channelsLast'`.\n *\n * Input shape:\n *   4D tensor with shape:\n *   `[batch, channels, rows, cols]` if `dataFormat` is `'channelsFirst'`.\n *   or 4D tensor with shape\n *   `[batch, rows, cols, channels]` if `dataFormat` is `'channelsLast`.\n *\n * Output shape:\n *   4D tensor with shape:\n *   `[batch, filters, newRows, newCols]` if `dataFormat` is\n * `'channelsFirst'`. or 4D tensor with shape:\n *   `[batch, newRows, newCols, filters]` if `dataFormat` is `'channelsLast'`.\n *\n * References:\n *   - [A guide to convolution arithmetic for deep\n * learning](https://arxiv.org/abs/1603.07285v1)\n *   - [Deconvolutional\n * Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv2dTranspose(args) {\n    return new Conv2DTranspose(args);\n}\n/**\n * 3D convolution layer (e.g. spatial convolution over volumes).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input to produce a tensor of outputs.\n *\n * If `useBias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model,\n * provide the keyword argument `inputShape`\n * (Array of integers, does not include the sample axis),\n * e.g. `inputShape=[128, 128, 128, 1]` for 128x128x128 grayscale volumes\n * in `dataFormat='channelsLast'`.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv3d(args) {\n    return new Conv3D(args);\n}\n/**\n * Depthwise separable 2D convolution.\n *\n * Separable convolution consists of first performing\n * a depthwise spatial convolution\n * (which acts on each input channel separately)\n * followed by a pointwise convolution which mixes together the resulting\n * output channels. The `depthMultiplier` argument controls how many\n * output channels are generated per input channel in the depthwise step.\n *\n * Intuitively, separable convolutions can be understood as\n * a way to factorize a convolution kernel into two smaller kernels,\n * or as an extreme version of an Inception block.\n *\n * Input shape:\n *   4D tensor with shape:\n *     `[batch, channels, rows, cols]` if data_format='channelsFirst'\n *   or 4D tensor with shape:\n *     `[batch, rows, cols, channels]` if data_format='channelsLast'.\n *\n * Output shape:\n *   4D tensor with shape:\n *     `[batch, filters, newRows, newCols]` if data_format='channelsFirst'\n *   or 4D tensor with shape:\n *     `[batch, newRows, newCols, filters]` if data_format='channelsLast'.\n *     `rows` and `cols` values might have changed due to padding.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function separableConv2d(args) {\n    return new SeparableConv2D(args);\n}\n/**\n * Cropping layer for 2D input (e.g., image).\n *\n * This layer can crop an input\n * at the top, bottom, left and right side of an image tensor.\n *\n * Input shape:\n *   4D tensor with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, rows, cols, channels]`\n *   - If `data_format` is `\"channels_first\"`:\n *     `[batch, channels, rows, cols]`.\n *\n * Output shape:\n *   4D with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, croppedRows, croppedCols, channels]`\n *    - If `dataFormat` is `\"channelsFirst\"`:\n *     `[batch, channels, croppedRows, croppedCols]`.\n *\n * Examples\n * ```js\n *\n * const model = tf.sequential();\n * model.add(tf.layers.cropping2D({cropping:[[2, 2], [2, 2]],\n *                                inputShape: [128, 128, 3]}));\n * //now output shape is [batch, 124, 124, 3]\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function cropping2D(args) {\n    return new Cropping2D(args);\n}\n/**\n * Upsampling layer for 2D inputs.\n *\n * Repeats the rows and columns of the data\n * by size[0] and size[1] respectively.\n *\n *\n * Input shape:\n *    4D tensor with shape:\n *     - If `dataFormat` is `\"channelsLast\"`:\n *         `[batch, rows, cols, channels]`\n *     - If `dataFormat` is `\"channelsFirst\"`:\n *        `[batch, channels, rows, cols]`\n *\n * Output shape:\n *     4D tensor with shape:\n *     - If `dataFormat` is `\"channelsLast\"`:\n *        `[batch, upsampledRows, upsampledCols, channels]`\n *     - If `dataFormat` is `\"channelsFirst\"`:\n *         `[batch, channels, upsampledRows, upsampledCols]`\n *\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function upSampling2d(args) {\n    return new UpSampling2D(args);\n}\n// Convolutional(depthwise) Layers.\n/**\n * Depthwise separable 2D convolution.\n *\n * Depthwise Separable convolutions consists in performing just the first step\n * in a depthwise spatial convolution (which acts on each input channel\n * separately). The `depthMultplier` argument controls how many output channels\n * are generated per input channel in the depthwise step.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function depthwiseConv2d(args) {\n    return new DepthwiseConv2D(args);\n}\n// Basic Layers.\n/**\n * Applies an activation function to an output.\n *\n * This layer applies element-wise activation function.  Other layers, notably\n * `dense` can also apply activation functions.  Use this isolated activation\n * function to extract the values before and after the\n * activation. For instance:\n *\n * ```js\n * const input = tf.input({shape: [5]});\n * const denseLayer = tf.layers.dense({units: 1});\n * const activationLayer = tf.layers.activation({activation: 'relu6'});\n *\n * // Obtain the output symbolic tensors by applying the layers in order.\n * const denseOutput = denseLayer.apply(input);\n * const activationOutput = activationLayer.apply(denseOutput);\n *\n * // Create the model based on the inputs.\n * const model = tf.model({\n *     inputs: input,\n *     outputs: [denseOutput, activationOutput]\n * });\n *\n * // Collect both outputs and print separately.\n * const [denseOut, activationOut] = model.predict(tf.randomNormal([6, 5]));\n * denseOut.print();\n * activationOut.print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function activation(args) {\n    return new Activation(args);\n}\n/**\n * Creates a dense (fully connected) layer.\n *\n * This layer implements the operation:\n *   `output = activation(dot(input, kernel) + bias)`\n *\n * `activation` is the element-wise activation function\n *   passed as the `activation` argument.\n *\n * `kernel` is a weights matrix created by the layer.\n *\n * `bias` is a bias vector created by the layer (only applicable if `useBias`\n * is `true`).\n *\n * **Input shape:**\n *\n *   nD `tf.Tensor` with shape: `(batchSize, ..., inputDim)`.\n *\n *   The most common situation would be\n *   a 2D input with shape `(batchSize, inputDim)`.\n *\n * **Output shape:**\n *\n *   nD tensor with shape: `(batchSize, ..., units)`.\n *\n *   For instance, for a 2D input with shape `(batchSize, inputDim)`,\n *   the output would have shape `(batchSize, units)`.\n *\n * Note: if the input to the layer has a rank greater than 2, then it is\n * flattened prior to the initial dot product with the kernel.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function dense(args) {\n    return new Dense(args);\n}\n/**\n * Applies\n * [dropout](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf) to\n * the input.\n *\n * Dropout consists in randomly setting a fraction `rate` of input units to 0 at\n * each update during training time, which helps prevent overfitting.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function dropout(args) {\n    return new Dropout(args);\n}\n/**\n * Spatial 1D version of Dropout.\n *\n * This Layer type performs the same function as the Dropout layer, but it drops\n * entire 1D feature maps instead of individual elements. For example, if an\n * input example consists of 3 timesteps and the feature map for each timestep\n * has a size of 4, a `spatialDropout1d` layer may zero out the feature maps\n * of the 1st timesteps and 2nd timesteps completely while sparing all feature\n * elements of the 3rd timestep.\n *\n * If adjacent frames (timesteps) are strongly correlated (as is normally the\n * case in early convolution layers), regular dropout will not regularize the\n * activation and will otherwise just result in merely an effective learning\n * rate decrease. In this case, `spatialDropout1d` will help promote\n * independence among feature maps and should be used instead.\n *\n * **Arguments:**\n *   rate: A floating-point number >=0 and <=1. Fraction of the input elements\n *     to drop.\n *\n * **Input shape:**\n *   3D tensor with shape `(samples, timesteps, channels)`.\n *\n * **Output shape:**\n *   Same as the input shape.\n *\n * References:\n *   - [Efficient Object Localization Using Convolutional\n *      Networks](https://arxiv.org/abs/1411.4280)\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function spatialDropout1d(args) {\n    return new SpatialDropout1D(args);\n}\n/**\n * Flattens the input. Does not affect the batch size.\n *\n * A `Flatten` layer flattens each batch in its inputs to 1D (making the output\n * 2D).\n *\n * For example:\n *\n * ```js\n * const input = tf.input({shape: [4, 3]});\n * const flattenLayer = tf.layers.flatten();\n * // Inspect the inferred output shape of the flatten layer, which\n * // equals `[null, 12]`. The 2nd dimension is 4 * 3, i.e., the result of the\n * // flattening. (The 1st dimension is the undermined batch size.)\n * console.log(JSON.stringify(flattenLayer.apply(input).shape));\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function flatten(args) {\n    return new Flatten(args);\n}\n/**\n * Repeats the input n times in a new dimension.\n *\n * ```js\n *  const model = tf.sequential();\n *  model.add(tf.layers.repeatVector({n: 4, inputShape: [2]}));\n *  const x = tf.tensor2d([[10, 20]]);\n *  // Use the model to do inference on a data point the model hasn't see\n *  model.predict(x).print();\n *  // output shape is now [batch, 2, 4]\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function repeatVector(args) {\n    return new RepeatVector(args);\n}\n/**\n * Reshapes an input to a certain shape.\n *\n * ```js\n * const input = tf.input({shape: [4, 3]});\n * const reshapeLayer = tf.layers.reshape({targetShape: [2, 6]});\n * // Inspect the inferred output shape of the Reshape layer, which\n * // equals `[null, 2, 6]`. (The 1st dimension is the undermined batch size.)\n * console.log(JSON.stringify(reshapeLayer.apply(input).shape));\n * ```\n *\n * Input shape:\n *   Arbitrary, although all dimensions in the input shape must be fixed.\n *   Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n *\n * Output shape:\n *   [batchSize, targetShape[0], targetShape[1], ...,\n *    targetShape[targetShape.length - 1]].\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function reshape(args) {\n    return new Reshape(args);\n}\n/**\n * Permutes the dimensions of the input according to a given pattern.\n *\n * Useful for, e.g., connecting RNNs and convnets together.\n *\n * Example:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.permute({\n *   dims: [2, 1],\n *   inputShape: [10, 64]\n * }));\n * console.log(model.outputShape);\n * // Now model's output shape is [null, 64, 10], where null is the\n * // unpermuted sample (batch) dimension.\n * ```\n *\n * Input shape:\n *   Arbitrary. Use the configuration field `inputShape` when using this\n *   layer as the first layer in a model.\n *\n * Output shape:\n *   Same rank as the input shape, but with the dimensions re-ordered (i.e.,\n *   permuted) according to the `dims` configuration of this layer.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function permute(args) {\n    return new Permute(args);\n}\n/**\n * Maps positive integers (indices) into dense vectors of fixed size.\n * eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]\n *\n * **Input shape:** 2D tensor with shape: `[batchSize, sequenceLength]`.\n *\n * **Output shape:** 3D tensor with shape: `[batchSize, sequenceLength,\n * outputDim]`.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function embedding(args) {\n    return new Embedding(args);\n}\n// Merge Layers.\n/**\n * Layer that performs element-wise addition on an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape, and returns a\n * single tensor (also of the same shape). The inputs are specified as an\n * `Array` when the `apply` method of the `Add` layer instance is called. For\n * example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const addLayer = tf.layers.add();\n * const sum = addLayer.apply([input1, input2]);\n * console.log(JSON.stringify(sum.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function add(args) {\n    return new Add(args);\n}\n/**\n * Layer that performs element-wise averaging on an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape, and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const averageLayer = tf.layers.average();\n * const average = averageLayer.apply([input1, input2]);\n * console.log(JSON.stringify(average.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function average(args) {\n    return new Average(args);\n}\n/**\n * Layer that concatenates an `Array` of inputs.\n *\n * It takes a list of tensors, all of the same shape except for the\n * concatenation axis, and returns a single tensor, the concatenation\n * of all inputs. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 3]});\n * const concatLayer = tf.layers.concatenate();\n * const output = concatLayer.apply([input1, input2]);\n * console.log(JSON.stringify(output.shape));\n * // You get [null, 2, 5], with the first dimension as the undetermined batch\n * // dimension. The last dimension (5) is the result of concatenating the\n * // last dimensions of the inputs (2 and 3).\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function concatenate(args) {\n    return new Concatenate(args);\n}\n/**\n * Layer that computes the element-wise maximum an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const maxLayer = tf.layers.maximum();\n * const max = maxLayer.apply([input1, input2]);\n * console.log(JSON.stringify(max.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function maximum(args) {\n    return new Maximum(args);\n}\n/**\n * Layer that computes the element-wise minimum of an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const minLayer = tf.layers.minimum();\n * const min = minLayer.apply([input1, input2]);\n * console.log(JSON.stringify(min.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function minimum(args) {\n    return new Minimum(args);\n}\n/**\n * Layer that multiplies (element-wise) an `Array` of inputs.\n *\n * It takes as input an Array of tensors, all of the same\n * shape, and returns a single tensor (also of the same shape).\n * For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const input3 = tf.input({shape: [2, 2]});\n * const multiplyLayer = tf.layers.multiply();\n * const product = multiplyLayer.apply([input1, input2, input3]);\n * console.log(product.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function multiply(args) {\n    return new Multiply(args);\n}\n/**\n * Layer that computes a dot product between samples in two tensors.\n *\n * E.g., if applied to a list of two tensors `a` and `b` both of shape\n * `[batchSize, n]`, the output will be a tensor of shape `[batchSize, 1]`,\n * where each entry at index `[i, 0]` will be the dot product between\n * `a[i, :]` and `b[i, :]`.\n *\n * Example:\n *\n * ```js\n * const dotLayer = tf.layers.dot({axes: -1});\n * const x1 = tf.tensor2d([[10, 20], [30, 40]]);\n * const x2 = tf.tensor2d([[-1, -2], [-3, -4]]);\n *\n * // Invoke the layer's apply() method in eager (imperative) mode.\n * const y = dotLayer.apply([x1, x2]);\n * y.print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function dot(args) {\n    return new Dot(args);\n}\n// Normalization Layers.\n/**\n * Batch normalization layer (Ioffe and Szegedy, 2014).\n *\n * Normalize the activations of the previous layer at each batch,\n * i.e. applies a transformation that maintains the mean activation\n * close to 0 and the activation standard deviation close to 1.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape` (Array of integers, does\n *   not include the sample axis) when calling the constructor of this class,\n *   if this layer is used as a first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Batch Normalization: Accelerating Deep Network Training by Reducing\n * Internal Covariate Shift](https://arxiv.org/abs/1502.03167)\n *\n * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}\n */\nexport function batchNormalization(args) {\n    return new BatchNormalization(args);\n}\n/**\n * Layer-normalization layer (Ba et al., 2016).\n *\n * Normalizes the activations of the previous layer for each given example in a\n * batch independently, instead of across a batch like in `batchNormalization`.\n * In other words, this layer applies a transformation that maintanis the mean\n * activation within each example close to0 and activation variance close to 1.\n *\n * Input shape:\n *   Arbitrary. Use the argument `inputShape` when using this layer as the first\n *   layer in a model.\n *\n * Output shape:\n *   Same as input.\n *\n * References:\n *   - [Layer Normalization](https://arxiv.org/abs/1607.06450)\n *\n * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}\n */\nexport function layerNormalization(args) {\n    return new LayerNormalization(args);\n}\n// Padding Layers.\n/**\n * Zero-padding layer for 2D input (e.g., image).\n *\n * This layer can add rows and columns of zeros\n * at the top, bottom, left and right side of an image tensor.\n *\n * Input shape:\n *   4D tensor with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, rows, cols, channels]`\n *   - If `data_format` is `\"channels_first\"`:\n *     `[batch, channels, rows, cols]`.\n *\n * Output shape:\n *   4D with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, paddedRows, paddedCols, channels]`\n *    - If `dataFormat` is `\"channelsFirst\"`:\n *     `[batch, channels, paddedRows, paddedCols]`.\n *\n * @doc {heading: 'Layers', subheading: 'Padding', namespace: 'layers'}\n */\nexport function zeroPadding2d(args) {\n    return new ZeroPadding2D(args);\n}\n// Pooling Layers.\n/**\n * Average pooling operation for spatial data.\n *\n * Input shape: `[batchSize, inLength, channels]`\n *\n * Output shape: `[batchSize, pooledLength, channels]`\n *\n * `tf.avgPool1d` is an alias.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling1d(args) {\n    return new AveragePooling1D(args);\n}\nexport function avgPool1d(args) {\n    return averagePooling1d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling1d(args) {\n    return averagePooling1d(args);\n}\n/**\n * Average pooling operation for spatial data.\n *\n * Input shape:\n *  - If `dataFormat === CHANNEL_LAST`:\n *      4D tensor with shape:\n *      `[batchSize, rows, cols, channels]`\n *  - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *      `[batchSize, channels, rows, cols]`\n *\n * Output shape\n *  - If `dataFormat === CHANNEL_LAST`:\n *      4D tensor with shape:\n *      `[batchSize, pooleRows, pooledCols, channels]`\n *  - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *      `[batchSize, channels, pooleRows, pooledCols]`\n *\n * `tf.avgPool2d` is an alias.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling2d(args) {\n    return new AveragePooling2D(args);\n}\nexport function avgPool2d(args) {\n    return averagePooling2d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling2d(args) {\n    return averagePooling2d(args);\n}\n/**\n * Average pooling operation for 3D data.\n *\n * Input shape\n *   - If `dataFormat === channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, depths, rows, cols, channels]`\n *   - If `dataFormat === channelsFirst`:\n *      4D tensor with shape:\n *       `[batchSize, channels, depths, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`\n *   - If `dataFormat=channelsFirst`:\n *       5D tensor with shape:\n *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling3d(args) {\n    return new AveragePooling3D(args);\n}\nexport function avgPool3d(args) {\n    return averagePooling3d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling3d(args) {\n    return averagePooling3d(args);\n}\n/**\n * Global average pooling operation for temporal data.\n *\n * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.\n *\n * Output Shape:2D tensor with shape: `[batchSize, features]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalAveragePooling1d(args) {\n    return new GlobalAveragePooling1D(args);\n}\n/**\n * Global average pooling operation for spatial data.\n *\n * Input shape:\n *   - If `dataFormat` is `CHANNEL_LAST`:\n *       4D tensor with shape: `[batchSize, rows, cols, channels]`.\n *   - If `dataFormat` is `CHANNEL_FIRST`:\n *       4D tensor with shape: `[batchSize, channels, rows, cols]`.\n *\n * Output shape:\n *   2D tensor with shape: `[batchSize, channels]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalAveragePooling2d(args) {\n    return new GlobalAveragePooling2D(args);\n}\n/**\n * Global max pooling operation for temporal data.\n *\n * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.\n *\n * Output Shape:2D tensor with shape: `[batchSize, features]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalMaxPooling1d(args) {\n    return new GlobalMaxPooling1D(args);\n}\n/**\n * Global max pooling operation for spatial data.\n *\n * Input shape:\n *   - If `dataFormat` is `CHANNEL_LAST`:\n *       4D tensor with shape: `[batchSize, rows, cols, channels]`.\n *   - If `dataFormat` is `CHANNEL_FIRST`:\n *       4D tensor with shape: `[batchSize, channels, rows, cols]`.\n *\n * Output shape:\n *   2D tensor with shape: `[batchSize, channels]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalMaxPooling2d(args) {\n    return new GlobalMaxPooling2D(args);\n}\n/**\n * Max pooling operation for temporal data.\n *\n * Input shape:  `[batchSize, inLength, channels]`\n *\n * Output shape: `[batchSize, pooledLength, channels]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling1d(args) {\n    return new MaxPooling1D(args);\n}\n/**\n * Max pooling operation for spatial data.\n *\n * Input shape\n *   - If `dataFormat === CHANNEL_LAST`:\n *       4D tensor with shape:\n *       `[batchSize, rows, cols, channels]`\n *   - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *       `[batchSize, channels, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=CHANNEL_LAST`:\n *       4D tensor with shape:\n *       `[batchSize, pooleRows, pooledCols, channels]`\n *   - If `dataFormat=CHANNEL_FIRST`:\n *       4D tensor with shape:\n *       `[batchSize, channels, pooleRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling2d(args) {\n    return new MaxPooling2D(args);\n}\n/**\n * Max pooling operation for 3D data.\n *\n * Input shape\n *   - If `dataFormat === channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, depths, rows, cols, channels]`\n *   - If `dataFormat === channelsFirst`:\n *      5D tensor with shape:\n *       `[batchSize, channels, depths, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`\n *   - If `dataFormat=channelsFirst`:\n *       5D tensor with shape:\n *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling3d(args) {\n    return new MaxPooling3D(args);\n}\n// Recurrent Layers.\n/**\n * Gated Recurrent Unit - Cho et al. 2014.\n *\n * This is an `RNN` layer consisting of one `GRUCell`. However, unlike\n * the underlying `GRUCell`, the `apply` method of `SimpleRNN` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const rnn = tf.layers.gru({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `GRUCell`'s number of units.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function gru(args) {\n    return new GRU(args);\n}\n/**\n * Cell class for `GRU`.\n *\n * `GRUCell` is distinct from the `RNN` subclass `GRU` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `GRU` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.gruCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `GRUCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.gruCell({units: 4}),\n *   tf.layers.gruCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `gruCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `GRUCell`, use the\n * `tf.layers.gru`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function gruCell(args) {\n    return new GRUCell(args);\n}\n/**\n * Long-Short Term Memory layer - Hochreiter 1997.\n *\n * This is an `RNN` layer consisting of one `LSTMCell`. However, unlike\n * the underlying `LSTMCell`, the `apply` method of `LSTM` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const lstm = tf.layers.lstm({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = lstm.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `LSTMCell`'s number of units.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function lstm(args) {\n    return new LSTM(args);\n}\n/**\n * Cell class for `LSTM`.\n *\n * `LSTMCell` is distinct from the `RNN` subclass `LSTM` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `LSTM` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.lstmCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `LSTMCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.lstmCell({units: 4}),\n *   tf.layers.lstmCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `lstmCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `LSTMCell`, use the\n * `tf.layers.lstm`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function lstmCell(args) {\n    return new LSTMCell(args);\n}\n/**\n * Fully-connected RNN where the output is to be fed back to input.\n *\n * This is an `RNN` layer consisting of one `SimpleRNNCell`. However, unlike\n * the underlying `SimpleRNNCell`, the `apply` method of `SimpleRNN` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const rnn = tf.layers.simpleRNN({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `SimpleRNNCell`'s number of units.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function simpleRNN(args) {\n    return new SimpleRNN(args);\n}\n/**\n * Cell class for `SimpleRNN`.\n *\n * `SimpleRNNCell` is distinct from the `RNN` subclass `SimpleRNN` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `SimpleRNN` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.simpleRNNCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `SimpleRNNCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.simpleRNNCell({units: 4}),\n *   tf.layers.simpleRNNCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `SimpleRNNCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `SimpleRNNCell`, use the\n * `tf.layers.simpleRNN`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function simpleRNNCell(args) {\n    return new SimpleRNNCell(args);\n}\n/**\n * Convolutional LSTM layer - Xingjian Shi 2015.\n *\n * This is an `ConvRNN2D` layer consisting of one `ConvLSTM2DCell`. However,\n * unlike the underlying `ConvLSTM2DCell`, the `apply` method of `ConvLSTM2D`\n * operates on a sequence of inputs. The shape of the input (not including the\n * first, batch dimension) needs to be 4-D, with the first dimension being time\n * steps. For example:\n *\n * ```js\n * const filters = 3;\n * const kernelSize = 3;\n *\n * const batchSize = 4;\n * const sequenceLength = 2;\n * const size = 5;\n * const channels = 3;\n *\n * const inputShape = [batchSize, sequenceLength, size, size, channels];\n * const input = tf.ones(inputShape);\n *\n * const layer = tf.layers.convLstm2d({filters, kernelSize});\n *\n * const output = layer.apply(input);\n * ```\n */\n/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */\nexport function convLstm2d(args) {\n    return new ConvLSTM2D(args);\n}\n/**\n * Cell class for `ConvLSTM2D`.\n *\n * `ConvLSTM2DCell` is distinct from the `ConvRNN2D` subclass `ConvLSTM2D` in\n * that its `call` method takes the input data of only a single time step and\n * returns the cell's output at the time step, while `ConvLSTM2D` takes the\n * input data over a number of time steps. For example:\n *\n * ```js\n * const filters = 3;\n * const kernelSize = 3;\n *\n * const sequenceLength = 1;\n * const size = 5;\n * const channels = 3;\n *\n * const inputShape = [sequenceLength, size, size, channels];\n * const input = tf.ones(inputShape);\n *\n * const cell = tf.layers.convLstm2dCell({filters, kernelSize});\n *\n * cell.build(input.shape);\n *\n * const outputSize = size - kernelSize + 1;\n * const outShape = [sequenceLength, outputSize, outputSize, filters];\n *\n * const initialH = tf.zeros(outShape);\n * const initialC = tf.zeros(outShape);\n *\n * const [o, h, c] = cell.call([input, initialH, initialC], {});\n * ```\n */\n/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */\nexport function convLstm2dCell(args) {\n    return new ConvLSTM2DCell(args);\n}\n/**\n * Base class for recurrent layers.\n *\n * Input shape:\n *   3D tensor with shape `[batchSize, timeSteps, inputDim]`.\n *\n * Output shape:\n *   - if `returnState`, an Array of tensors (i.e., `tf.Tensor`s). The first\n *     tensor is the output. The remaining tensors are the states at the\n *     last time step, each with shape `[batchSize, units]`.\n *   - if `returnSequences`, the output will have shape\n *     `[batchSize, timeSteps, units]`.\n *   - else, the output will have shape `[batchSize, units]`.\n *\n * Masking:\n *   This layer supports masking for input data with a variable number\n *   of timesteps. To introduce masks to your data,\n *   use an embedding layer with the `mask_zero` parameter\n *   set to `True`.\n *\n * Notes on using statefulness in RNNs:\n *   You can set RNN layers to be 'stateful', which means that the states\n *   computed for the samples in one batch will be reused as initial states\n *   for the samples in the next batch. This assumes a one-to-one mapping\n *   between samples in different successive batches.\n *\n *   To enable statefulness:\n *     - specify `stateful: true` in the layer constructor.\n *     - specify a fixed batch size for your model, by passing\n *       if sequential model:\n *         `batchInputShape=[...]` to the first layer in your model.\n *       else for functional model with 1 or more Input layers:\n *         `batchShape=[...]` to all the first layers in your model.\n *       This is the expected shape of your inputs *including the batch size*.\n *       It should be a tuple of integers, e.g. `(32, 10, 100)`.\n *     - specify `shuffle=False` when calling fit().\n *\n *   To reset the states of your model, call `.resetStates()` on either\n *   a specific layer, or on your entire model.\n *\n * Note on specifying the initial state of RNNs\n *   You can specify the initial state of RNN layers symbolically by\n *   calling them with the option `initialState`. The value of\n *   `initialState` should be a tensor or list of tensors representing\n *   the initial state of the RNN layer.\n *\n *   You can specify the initial state of RNN layers numerically by\n *   calling `resetStates` with the keyword argument `states`. The value of\n *   `states` should be a numpy array or list of numpy arrays representing\n *   the initial state of the RNN layer.\n *\n * Note on passing external constants to RNNs\n *   You can pass \"external\" constants to the cell using the `constants`\n *   keyword argument of `RNN.call` method. This requires that the `cell.call`\n *   method accepts the same keyword argument `constants`. Such constants\n *   can be used to conditon the cell transformation on additional static inputs\n *   (not changing over time), a.k.a an attention mechanism.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function rnn(args) {\n    return new RNN(args);\n}\n/**\n * Wrapper allowing a stack of RNN cells to behave as a single cell.\n *\n * Used to implement efficient stacked RNNs.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function stackedRNNCells(args) {\n    return new StackedRNNCells(args);\n}\n// Wrapper Layers.\n/** @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'} */\nexport function bidirectional(args) {\n    return new Bidirectional(args);\n}\n/**\n * This wrapper applies a layer to every temporal slice of an input.\n *\n * The input should be at least 3D,  and the dimension of the index `1` will be\n * considered to be the temporal dimension.\n *\n * Consider a batch of 32 samples, where each sample is a sequence of 10 vectors\n * of 16 dimensions. The batch input shape of the layer is then `[32,  10,\n * 16]`, and the `inputShape`, not including the sample dimension, is\n * `[10, 16]`.\n *\n * You can then use `TimeDistributed` to apply a `Dense` layer to each of the 10\n * timesteps, independently:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.timeDistributed({\n *   layer: tf.layers.dense({units: 8}),\n *   inputShape: [10, 16],\n * }));\n *\n * // Now model.outputShape = [null, 10, 8].\n * // The output will then have shape `[32, 10, 8]`.\n *\n * // In subsequent layers, there is no need for `inputShape`:\n * model.add(tf.layers.timeDistributed({layer: tf.layers.dense({units: 32})}));\n * console.log(JSON.stringify(model.outputs[0].shape));\n * // Now model.outputShape = [null, 10, 32].\n * ```\n *\n * The output will then have shape `[32, 10, 32]`.\n *\n * `TimeDistributed` can be used with arbitrary layers, not just `Dense`, for\n * instance a `Conv2D` layer.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.timeDistributed({\n *   layer: tf.layers.conv2d({filters: 64, kernelSize: [3, 3]}),\n *   inputShape: [10, 299, 299, 3],\n * }));\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'}\n */\nexport function timeDistributed(args) {\n    return new TimeDistributed(args);\n}\n// Aliases for pooling.\nexport const globalMaxPool1d = globalMaxPooling1d;\nexport const globalMaxPool2d = globalMaxPooling2d;\nexport const maxPool1d = maxPooling1d;\nexport const maxPool2d = maxPooling2d;\nexport { Layer, RNN, RNNCell, input /* alias for tf.input */ };\n/**\n * Apply additive zero-centered Gaussian noise.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * This is useful to mitigate overfitting\n * (you could see it as a form of random data augmentation).\n * Gaussian Noise (GS) is a natural choice as corruption process\n * for real valued inputs.\n *\n * # Arguments\n *     stddev: float, standard deviation of the noise distribution.\n *\n * # Input shape\n *         Arbitrary. Use the keyword argument `input_shape`\n *         (tuple of integers, does not include the samples axis)\n *         when using this layer as the first layer in a model.\n *\n * # Output shape\n *         Same shape as input.\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function gaussianNoise(args) {\n    return new GaussianNoise(args);\n}\n/**\n * Apply multiplicative 1-centered Gaussian noise.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](\n *      http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function gaussianDropout(args) {\n    return new GaussianDropout(args);\n}\n/**\n * Applies Alpha Dropout to the input.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs\n * to their original values, in order to ensure the self-normalizing property\n * even after this dropout.\n * Alpha Dropout fits well to Scaled Exponential Linear Units\n * by randomly setting activations to the negative saturation value.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the\n *     shape for randomly generated keep/drop flags.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function alphaDropout(args) {\n    return new AlphaDropout(args);\n}\n/**\n * Masks a sequence by using a mask value to skip timesteps.\n *\n * If all features for a given sample timestep are equal to `mask_value`,\n * then the sample timestep will be masked (skipped) in all downstream layers\n * (as long as they support masking).\n *\n * If any downstream layer does not support masking yet receives such\n * an input mask, an exception will be raised.\n *\n * Arguments:\n *   - `maskValue`: Either None or mask value to skip.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * @doc {heading: 'Layers', subheading: 'Mask', namespace: 'layers'}\n */\nexport function masking(args) {\n    return new Masking(args);\n}\n//# sourceMappingURL=exports_layers.js.map", "import * as losses from './losses';\nimport * as metrics from './metrics';\n/**\n * Binary accuracy metric function.\n *\n * `yTrue` and `yPred` can have 0-1 values. Example:\n * ```js\n * const x = tf.tensor2d([[1, 1, 1, 1], [0, 0, 0, 0]], [2, 4]);\n * const y = tf.tensor2d([[1, 0, 1, 0], [0, 0, 0, 1]], [2, 4]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * `yTrue` and `yPred` can also have floating-number values between 0 and 1, in\n * which case the values will be thresholded at 0.5 to yield 0-1 values (i.e.,\n * a value >= 0.5 and <= 1.0 is interpreted as 1.\n * )\n * Example:\n * ```js\n * const x = tf.tensor1d([1, 1, 1, 1, 0, 0, 0, 0]);\n * const y = tf.tensor1d([0.2, 0.4, 0.6, 0.8, 0.2, 0.3, 0.4, 0.7]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryAccuracy(yTrue, yPred) {\n    return metrics.binaryAccuracy(yTrue, yPred);\n}\n/**\n * Binary crossentropy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0], [1], [1], [1]]);\n * const y = tf.tensor2d([[0], [0], [0.5], [1]]);\n * const crossentropy = tf.metrics.binaryCrossentropy(x, y);\n * crossentropy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction, probabilities for the `1` case.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryCrossentropy(yTrue, yPred) {\n    return metrics.binaryCrossentropy(yTrue, yPred);\n}\n/**\n * Sparse categorical accuracy metric function.\n *\n * Example:\n * ```js\n *\n * const yTrue = tf.tensor1d([1, 1, 2, 2, 0]);\n * const yPred = tf.tensor2d(\n *      [[0, 1, 0], [1, 0, 0], [0, 0.4, 0.6], [0, 0.6, 0.4], [0.7, 0.3, 0]]);\n * const crossentropy = tf.metrics.sparseCategoricalAccuracy(yTrue, yPred);\n * crossentropy.print();\n * ```\n *\n * @param yTrue True labels: indices.\n * @param yPred Predicted probabilities or logits.\n * @returns Accuracy tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function sparseCategoricalAccuracy(yTrue, yPred) {\n    return metrics.sparseCategoricalAccuracy(yTrue, yPred);\n}\n/**\n * Categorical accuracy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0, 0, 0, 1], [0, 0, 0, 1]]);\n * const y = tf.tensor2d([[0.1, 0.8, 0.05, 0.05], [0.1, 0.05, 0.05, 0.8]]);\n * const accuracy = tf.metrics.categoricalAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth: one-hot encoding of categories.\n * @param yPred Binary Tensor of prediction: probabilities or logits for the\n *   same categories as in `yTrue`.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalAccuracy(yTrue, yPred) {\n    return metrics.categoricalAccuracy(yTrue, yPred);\n}\n/**\n * Categorical crossentropy between an output tensor and a target tensor.\n *\n * @param target A tensor of the same shape as `output`.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalCrossentropy(yTrue, yPred) {\n    return metrics.categoricalCrossentropy(yTrue, yPred);\n}\n/**\n * Computes the precision of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const precision = tf.metrics.precision(x, y);\n * precision.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to be contain only 0-1 values.\n * @param yPred The predicted values. Expected to be contain only 0-1 values.\n * @return Precision Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function precision(yTrue, yPred) {\n    return metrics.precision(yTrue, yPred);\n}\n/**\n * Computes the recall of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const recall = tf.metrics.recall(x, y);\n * recall.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to be contain only 0-1 values.\n * @param yPred The predicted values. Expected to be contain only 0-1 values.\n * @return Recall Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function recall(yTrue, yPred) {\n    return metrics.recall(yTrue, yPred);\n}\n/**\n * Loss or metric function: Cosine proximity.\n *\n * Mathematically, cosine proximity is defined as:\n *   `-sum(l2Normalize(yTrue) * l2Normalize(yPred))`,\n * wherein `l2Normalize()` normalizes the L2 norm of the input to 1 and `*`\n * represents element-wise multiplication.\n *\n * ```js\n * const yTrue = tf.tensor2d([[1, 0], [1, 0]]);\n * const yPred = tf.tensor2d([[1 / Math.sqrt(2), 1 / Math.sqrt(2)], [0, 1]]);\n * const proximity = tf.metrics.cosineProximity(yTrue, yPred);\n * proximity.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Cosine proximity Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function cosineProximity(yTrue, yPred) {\n    return losses.cosineProximity(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean absolute error.\n *\n * Mathematically, mean absolute error is defined as:\n *   `mean(abs(yPred - yTrue))`,\n * wherein the `mean` is applied over feature dimensions.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [0, 0], [2, 3]]);\n * const yPred = tf.tensor2d([[0, 1], [0, 1], [-2, -3]]);\n * const mse = tf.metrics.meanAbsoluteError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsoluteError(yTrue, yPred) {\n    return losses.meanAbsoluteError(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean absolute percentage error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [10, 20]]);\n * const yPred = tf.tensor2d([[0, 1], [11, 24]]);\n * const mse = tf.metrics.meanAbsolutePercentageError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MAPE`, `tf.metrics.mape`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute percentage error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsolutePercentageError(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\nexport function MAPE(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\nexport function mape(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean squared error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [3, 4]]);\n * const yPred = tf.tensor2d([[0, 1], [-3, -4]]);\n * const mse = tf.metrics.meanSquaredError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MSE`, `tf.metrics.mse`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean squared error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanSquaredError(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\nexport function MSE(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\nexport function mse(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\n//# sourceMappingURL=exports_metrics.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport { modelFromJSON } from './models';\n//# sourceMappingURL=exports_models.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as regularizers from './regularizers';\n// tslint:disable-next-line:max-line-length\nimport { L1L2 } from './regularizers';\n/**\n * Regularizer for L1 and L2 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l1 * abs(x)) + sum(l2 * x^2)\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l1l2(config) {\n    return new L1L2(config);\n}\n/**\n * Regularizer for L1 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l1 * abs(x))\n * @param args l1 config.\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l1(config) {\n    return regularizers.l1(config);\n}\n/**\n * Regularizer for L2 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l2 * x^2)\n * @param args l2 config.\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l2(config) {\n    return regularizers.l2(config);\n}\n//# sourceMappingURL=exports_regularizers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/callbacks.py */\nimport { BaseCallback } from './base_callbacks';\nimport { LayersModel } from './engine/training';\nimport { NotImplementedError } from './errors';\nimport { resolveScalarsInLogs } from './logs';\nexport class Callback extends BaseCallback {\n    constructor() {\n        super(...arguments);\n        /** Instance of `keras.models.Model`. Reference of the model being trained. */\n        this.model = null;\n    }\n    setModel(model) {\n        if (!(model instanceof LayersModel)) {\n            throw new Error('model must be a LayersModel, not some other Container');\n        }\n        this.model = model;\n    }\n}\nfunction less(currVal, prevVal) {\n    return currVal < prevVal;\n}\nfunction greater(currVal, prevVal) {\n    return currVal > prevVal;\n}\n/**\n * A Callback that stops training when a monitored quantity has stopped\n * improving.\n */\nexport class EarlyStopping extends Callback {\n    constructor(args) {\n        super();\n        if (args == null) {\n            args = {};\n        }\n        if (args.restoreBestWeights) {\n            throw new NotImplementedError('restoreBestWeights = True is not implemented in EarlyStopping yet.');\n        }\n        this.monitor = args.monitor || 'val_loss';\n        this.minDelta = Math.abs(args.minDelta || 0);\n        this.patience = args.patience || 0;\n        this.verbose = args.verbose || 0;\n        this.mode = args.mode || 'auto';\n        this.baseline = args.baseline;\n        if (['auto', 'min', 'max'].indexOf(this.mode) === -1) {\n            console.warn(`EarlyStopping mode '${this.mode}' is invalid. ` +\n                `Falling back to mode 'auto'.`);\n            this.mode = 'auto';\n        }\n        if (this.mode === 'min') {\n            this.monitorFunc = less;\n        }\n        else if (this.mode === 'max') {\n            this.monitorFunc = greater;\n        }\n        else {\n            // For mode === 'auto'.\n            if (this.monitor.indexOf('acc') !== -1) {\n                this.monitorFunc = greater;\n            }\n            else {\n                this.monitorFunc = less;\n            }\n        }\n        if (this.monitorFunc === less) {\n            this.minDelta *= -1;\n        }\n    }\n    async onTrainBegin(logs) {\n        this.wait = 0;\n        this.stoppedEpoch = 0;\n        if (this.baseline != null) {\n            this.best = this.baseline;\n        }\n        else {\n            this.best = this.monitorFunc === less ? Infinity : -Infinity;\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        await resolveScalarsInLogs(logs);\n        const current = this.getMonitorValue(logs);\n        if (current == null) {\n            return;\n        }\n        if (this.monitorFunc(current - this.minDelta, this.best)) {\n            this.best = current;\n            this.wait = 0;\n            // TODO(cais): Logic for restoreBestWeights.\n        }\n        else {\n            this.wait++;\n            if (this.wait >= this.patience) {\n                this.stoppedEpoch = epoch;\n                this.model.stopTraining = true;\n            }\n            // TODO(cais): Logic for restoreBestWeights.\n        }\n    }\n    async onTrainEnd(logs) {\n        if (this.stoppedEpoch > 0 && this.verbose) {\n            console.log(`Epoch ${this.stoppedEpoch}: early stopping.`);\n        }\n    }\n    getMonitorValue(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        const monitorValue = logs[this.monitor];\n        if (monitorValue == null) {\n            console.warn(`Metric for EarlyStopping ${this.monitor} is not available. ` +\n                `Available metrics are: ${Object.keys(logs)}`);\n        }\n        return monitorValue;\n    }\n}\n/**\n * Factory function for a Callback that stops training when a monitored\n * quantity has stopped improving.\n *\n * Early stopping is a type of regularization, and protects model against\n * overfitting.\n *\n * The following example based on fake data illustrates how this callback\n * can be used during `tf.LayersModel.fit()`:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense({\n *   units: 3,\n *   activation: 'softmax',\n *   kernelInitializer: 'ones',\n *   inputShape: [2]\n * }));\n * const xs = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const ys = tf.tensor2d([[1, 0, 0], [0, 1, 0]], [2, 3]);\n * const xsVal = tf.tensor2d([4, 3, 2, 1], [2, 2]);\n * const ysVal = tf.tensor2d([[0, 0, 1], [0, 1, 0]], [2, 3]);\n * model.compile(\n *     {loss: 'categoricalCrossentropy', optimizer: 'sgd', metrics: ['acc']});\n *\n * // Without the EarlyStopping callback, the val_acc value would be:\n * //   0.5, 0.5, 0.5, 0.5, ...\n * // With val_acc being monitored, training should stop after the 2nd epoch.\n * const history = await model.fit(xs, ys, {\n *   epochs: 10,\n *   validationData: [xsVal, ysVal],\n *   callbacks: tf.callbacks.earlyStopping({monitor: 'val_acc'})\n * });\n *\n * // Expect to see a length-2 array.\n * console.log(history.history.val_acc);\n * ```\n *\n * @doc {\n *   heading: 'Callbacks',\n *   namespace: 'callbacks'\n * }\n */\nexport function earlyStopping(args) {\n    return new EarlyStopping(args);\n}\nexport const callbacks = { earlyStopping };\n//# sourceMappingURL=callbacks.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// This file lists all exports of TensorFlow.js Layers\nimport * as constraints from './exports_constraints';\nimport * as initializers from './exports_initializers';\nimport * as layers from './exports_layers';\nimport * as metrics from './exports_metrics';\nimport * as models from './exports_models';\nimport * as regularizers from './exports_regularizers';\nexport { CallbackList, CustomCallback, History } from './base_callbacks';\nexport { Callback, callbacks, EarlyStopping } from './callbacks';\nexport { InputSpec, SymbolicTensor } from './engine/topology';\nexport { LayersModel } from './engine/training';\nexport { input, loadLayersModel, model, registerCallbackConstructor, sequential } from './exports';\nexport { RNN } from './layers/recurrent';\nexport { Sequential } from './models';\nexport { LayerVariable } from './variables';\nexport { version as version_layers } from './version';\nexport { constraints, initializers, layers, metrics, models, regularizers };\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/** DataType enum. */\nexport var DataType;\n(function (DataType) {\n    DataType[DataType[\"DT_INVALID\"] = 0] = \"DT_INVALID\";\n    DataType[DataType[\"DT_FLOAT\"] = 1] = \"DT_FLOAT\";\n    DataType[DataType[\"DT_DOUBLE\"] = 2] = \"DT_DOUBLE\";\n    DataType[DataType[\"DT_INT32\"] = 3] = \"DT_INT32\";\n    DataType[DataType[\"DT_UINT8\"] = 4] = \"DT_UINT8\";\n    DataType[DataType[\"DT_INT16\"] = 5] = \"DT_INT16\";\n    DataType[DataType[\"DT_INT8\"] = 6] = \"DT_INT8\";\n    DataType[DataType[\"DT_STRING\"] = 7] = \"DT_STRING\";\n    DataType[DataType[\"DT_COMPLEX64\"] = 8] = \"DT_COMPLEX64\";\n    DataType[DataType[\"DT_INT64\"] = 9] = \"DT_INT64\";\n    DataType[DataType[\"DT_BOOL\"] = 10] = \"DT_BOOL\";\n    DataType[DataType[\"DT_QINT8\"] = 11] = \"DT_QINT8\";\n    DataType[DataType[\"DT_QUINT8\"] = 12] = \"DT_QUINT8\";\n    DataType[DataType[\"DT_QINT32\"] = 13] = \"DT_QINT32\";\n    DataType[DataType[\"DT_BFLOAT16\"] = 14] = \"DT_BFLOAT16\";\n    DataType[DataType[\"DT_FLOAT_REF\"] = 101] = \"DT_FLOAT_REF\";\n    DataType[DataType[\"DT_DOUBLE_REF\"] = 102] = \"DT_DOUBLE_REF\";\n    DataType[DataType[\"DT_INT32_REF\"] = 103] = \"DT_INT32_REF\";\n    DataType[DataType[\"DT_UINT8_REF\"] = 104] = \"DT_UINT8_REF\";\n    DataType[DataType[\"DT_INT16_REF\"] = 105] = \"DT_INT16_REF\";\n    DataType[DataType[\"DT_INT8_REF\"] = 106] = \"DT_INT8_REF\";\n    DataType[DataType[\"DT_STRING_REF\"] = 107] = \"DT_STRING_REF\";\n    DataType[DataType[\"DT_COMPLEX64_REF\"] = 108] = \"DT_COMPLEX64_REF\";\n    DataType[DataType[\"DT_INT64_REF\"] = 109] = \"DT_INT64_REF\";\n    DataType[DataType[\"DT_BOOL_REF\"] = 110] = \"DT_BOOL_REF\";\n    DataType[DataType[\"DT_QINT8_REF\"] = 111] = \"DT_QINT8_REF\";\n    DataType[DataType[\"DT_QUINT8_REF\"] = 112] = \"DT_QUINT8_REF\";\n    DataType[DataType[\"DT_QINT32_REF\"] = 113] = \"DT_QINT32_REF\";\n    DataType[DataType[\"DT_BFLOAT16_REF\"] = 114] = \"DT_BFLOAT16_REF\";\n})(DataType || (DataType = {}));\nexport var SaverDef;\n(function (SaverDef) {\n    /** CheckpointFormatVersion enum. */\n    let CheckpointFormatVersion;\n    (function (CheckpointFormatVersion) {\n        CheckpointFormatVersion[CheckpointFormatVersion[\"LEGACY\"] = 0] = \"LEGACY\";\n        CheckpointFormatVersion[CheckpointFormatVersion[\"V1\"] = 1] = \"V1\";\n        CheckpointFormatVersion[CheckpointFormatVersion[\"V2\"] = 2] = \"V2\";\n    })(CheckpointFormatVersion = SaverDef.CheckpointFormatVersion || (SaverDef.CheckpointFormatVersion = {}));\n})(SaverDef || (SaverDef = {}));\n//# sourceMappingURL=compiled_api.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst CUSTOM_OPS = {};\n/**\n * Register an Op for graph model executor. This allow you to register\n * TensorFlow custom op or override existing op.\n *\n * Here is an example of registering a new MatMul Op.\n * ```js\n * const customMatmul = (node) =>\n *    tf.matMul(\n *        node.inputs[0], node.inputs[1],\n *        node.attrs['transpose_a'], node.attrs['transpose_b']);\n *\n * tf.registerOp('MatMul', customMatmul);\n * ```\n * The inputs and attrs of the node object is based on the TensorFlow op\n * registry.\n *\n * @param name The Tensorflow Op name.\n * @param opFunc An op function which is called with the current graph node\n * during execution and needs to return a tensor or a list of tensors. The node\n * has the following attributes:\n *    - attr: A map from attribute name to its value\n *    - inputs: A list of input tensors\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function registerOp(name, opFunc) {\n    const opMapper = {\n        tfOpName: name,\n        category: 'custom',\n        inputs: [],\n        attrs: [],\n        customExecutor: opFunc\n    };\n    CUSTOM_OPS[name] = opMapper;\n}\n/**\n * Retrieve the OpMapper object for the registered op.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function getRegisteredOp(name) {\n    return CUSTOM_OPS[name];\n}\n/**\n * Deregister the Op for graph model executor.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function deregisterOp(name) {\n    delete CUSTOM_OPS[name];\n}\n//# sourceMappingURL=register.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { clone, util } from '@tensorflow/tfjs-core';\nexport function getParamValue(paramName, node, tensorMap, context) {\n    const inputParam = node.inputParams[paramName];\n    if (inputParam && inputParam.inputIndexStart !== undefined) {\n        const start = inputParam.inputIndexStart;\n        const end = inputParam.inputIndexEnd === 0 ?\n            undefined :\n            (inputParam.inputIndexEnd === undefined ? start + 1 :\n                inputParam.inputIndexEnd);\n        if (inputParam.type === 'tensor') {\n            return getTensor(node.inputNames[inputParam.inputIndexStart], tensorMap, context);\n        }\n        if (inputParam.type === 'tensors') {\n            const inputs = node.inputNames.slice(start, end);\n            return inputs.map(name => getTensor(name, tensorMap, context));\n        }\n        const tensor = getTensor(node.inputNames.slice(start)[0], tensorMap, context);\n        const data = tensor.dataSync();\n        return inputParam.type === 'number' ?\n            data[0] :\n            util.toNestedArray(tensor.shape, data);\n    }\n    const attrParam = node.attrParams[paramName];\n    return attrParam && attrParam.value;\n}\n/**\n * Retrieve the tensor based on input name by extracting the node name and\n * output index information.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n */\nexport function getTensor(name, tensorsMap, context) {\n    const [nodeName, index] = parseNodeName(name);\n    const contextId = context.currentContextIds.find(contextId => {\n        return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId)];\n    });\n    return contextId !== undefined ?\n        tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] :\n        undefined;\n}\n/**\n * Retrieve the tensors based on input name for current context.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n */\nexport function getTensorsForCurrentContenxt(name, tensorsMap, context) {\n    return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];\n}\n/**\n * Returns the node name and index from the Node input name.\n * @param inputName The input name of the node, in format of\n * node_name:output_index, i.e. MatMul:0, if the output_index is not set, it is\n * default to 0.\n */\nexport function getNodeNameAndIndex(inputName, context) {\n    const [nodeName, index] = parseNodeName(inputName);\n    return [\n        getNodeNameWithContextId(nodeName, context && context.currentContextId),\n        index\n    ];\n}\nfunction getNodeNameWithContextId(name, contextId) {\n    return !!contextId ? `${name}-${contextId}` : name;\n}\nexport function parseNodeName(name) {\n    const parts = name.split(':');\n    if (parts.length === 1) {\n        return [name, 0];\n    }\n    const nodeName = parts[0];\n    return [nodeName, Number(parts[parts.length - 1])];\n}\nexport function split(arr, size) {\n    const res = [];\n    for (let i = 0; i < arr.length; i += size) {\n        res.push(arr.slice(i, i + size));\n    }\n    return res;\n}\nexport function getPadding(node, tensorMap, context) {\n    let pad = getParamValue('pad', node, tensorMap, context);\n    if (pad === 'explicit') {\n        // This is 1d array, we need to convert it to 2d array\n        pad = getParamValue('explicitPaddings', node, tensorMap, context);\n        const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];\n        for (let i = 0; i < 4; i++) {\n            explicitPadding[i][0] = pad[i * 2];\n            explicitPadding[i][1] = pad[i * 2 + 1];\n        }\n        return explicitPadding;\n    }\n    return pad;\n}\n/**\n *  Reuse the tensor if it is marked as keep, otherwise clone the tensor to\n *  avoid disposal. This is important for TensorArray and TensorList ops, since\n *  internally they use a tensor as the id for TensorArray and TensorList, and\n * to simplify lookup, they also use Tensor.id as the key to the internal map.\n * These id tensors have been marked as kept in the backend, we need avoid clone\n * them in order to create new Tensor.id.\n * @param tensor\n */\nexport function cloneTensor(tensor) {\n    return tensor.kept ? tensor : clone(tensor);\n}\n//# sourceMappingURL=utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Add',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AddV2',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AddN',\n        'category': 'arithmetic',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'BiasAdd',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sub',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'RealDiv',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Div',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'DivNoNan',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'FloorDiv',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Mul',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Maximum',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Minimum',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Pow',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'SquaredDifference',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Mod',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'FloorMod',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    }\n];\n//# sourceMappingURL=arithmetic.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Abs',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Acos',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Asin',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atan',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atan2',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'y', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Ceil',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ClipByValue',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'clip_value_min', 'name': 'clipValueMin', 'type': 'number' },\n            { 'tfName': 'clip_value_max', 'name': 'clipValueMax', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Complex',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'real', 'type': 'tensor' },\n            { 'start': 1, 'name': 'imag', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ComplexAbs',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Cos',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Cosh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Elu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Exp',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Floor',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Log',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Imag',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'outputType',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Neg',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Real',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'outputType',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Prelu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'alpha', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Relu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Relu6',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'clipValueMin',\n                'name': 'clipValueMin',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'clipValueMax',\n                'name': 'clipValueMax',\n                'type': 'number',\n                'defaultValue': 6\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Selu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sigmoid',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sin',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sinh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sqrt',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Rsqrt',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Square',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Tan',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Tanh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sign',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Round',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Expm1',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Log1p',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Reciprocal',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Softplus',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Asinh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Acosh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atanh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Erf',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Prod',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axes', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'keep_dims',\n                'name': 'keepDims',\n                'type': 'bool',\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LeakyRelu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'alpha',\n                'name': 'alpha',\n                'type': 'number',\n                'defaultValue': 0.2\n            },\n            {\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=basic_math.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'LoopCond',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'name': 'pred', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Switch',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'data', 'type': 'tensor' },\n            { 'start': 1, 'name': 'pred', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Merge',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Enter',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'frame_name', 'name': 'frameName', 'type': 'string' },\n            { 'tfName': 'is_constant', 'name': 'isConstant', 'type': 'bool' }\n        ]\n    },\n    {\n        'tfOpName': 'Exit',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'NextIteration',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'size', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },\n            { 'tfName': 'dynamic_size', 'name': 'dynamicSize', 'type': 'bool' },\n            { 'tfName': 'clear_after_read', 'name': 'clearAfterRead', 'type': 'bool' },\n            {\n                'tfName': 'identical_element_shapes',\n                'name': 'identicalElementShapes',\n                'type': 'bool'\n            },\n            { 'tfName': 'tensor_array_name', 'name': 'name', 'type': 'string' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayWriteV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayReadV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{\n                'tfName': 'dtype',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    },\n    {\n        'tfOpName': 'TensorArrayGatherV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayScatterV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorArrayConcatV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }, {\n                'tfName': 'element_shape_except0',\n                'name': 'elementShapeExcept0',\n                'type': 'shape',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArraySplitV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 2, 'name': 'lengths', 'type': 'number[]' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorArraySizeV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'flowIn', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayCloseV3',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'StatelessIf',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'cond', 'type': 'tensor' },\n            { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }\n        ],\n        'attrs': [\n            { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },\n            { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'If',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'cond', 'type': 'tensor' },\n            { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }\n        ],\n        'attrs': [\n            { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },\n            { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'StatelessWhile',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },\n            { 'tfName': 'body', 'name': 'body', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'While',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },\n            { 'tfName': 'body', 'name': 'body', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListScatter',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' }\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListScatterV2',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 3, 'name': 'numElements', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListGather',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListGetItem',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListSetItem',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListReserve',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 1, 'name': 'numElements', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListFromTensor',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' }\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListStack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' },\n            { 'tfName': 'num_elements', 'name': 'numElements', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListSplit',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 2, 'name': 'lengths', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListConcat',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListPopBack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListPushBack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n];\n//# sourceMappingURL=control.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'AvgPool',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPool',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPoolWithArgmax',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' }, {\n                'tfName': 'include_batch_in_index',\n                'name': 'includeBatchInIndex',\n                'type': 'bool'\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AvgPool3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPool3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Conv1D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'stride', 'name': 'stride', 'type': 'number' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NWC'\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'dilation',\n                'name': 'dilation',\n                'type': 'number',\n                'defaultValue': 1\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Conv2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            { 'tfName': 'useCudnnOnGpu', 'name': 'useCudnnOnGpu', 'type': 'bool' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': '_FusedConv2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'use_cudnn_on_gpu',\n                'name': 'useCudnnOnGpu',\n                'type': 'bool',\n                'defaultValue': true\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'dilations',\n                'name': 'dilations',\n                'type': 'number[]',\n                'defaultValue': [1, 1, 1, 1]\n            },\n            {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.0001\n            },\n        ]\n    },\n    {\n        'tfOpName': 'Conv2DBackpropInput',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 2, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 0, 'name': 'outputShape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n        ]\n    },\n    {\n        'tfOpName': 'DepthwiseConv2d',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'input', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'DepthwiseConv2dNative',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'input', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'FusedDepthwiseConv2dNative',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'dilations',\n                'name': 'dilations',\n                'type': 'number[]',\n                'defaultValue': [1, 1, 1, 1]\n            },\n            {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Conv3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ],\n    },\n    {\n        'tfOpName': 'Dilation2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'rates', 'name': 'dilations', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }\n        ]\n    }\n];\n//# sourceMappingURL=convolution.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Fill',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n            { 'start': 1, 'name': 'value', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'LinSpace',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'start', 'type': 'number' },\n            { 'start': 1, 'name': 'stop', 'type': 'number' },\n            { 'start': 2, 'name': 'num', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'OneHot',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'depth', 'type': 'number' },\n            { 'start': 2, 'name': 'onValue', 'type': 'number', 'defaultValue': 1 },\n            { 'start': 3, 'name': 'offValue', 'type': 'number', 'defaultValue': 0 },\n        ],\n        'attrs': [\n            {\n                'tfName': 'axis',\n                'name': 'axis',\n                'type': 'number',\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Ones',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'OnesLike',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'RandomUniform',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'minval',\n                'name': 'minval',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'maxval',\n                'name': 'maxval',\n                'type': 'number',\n                'defaultValue': 1\n            },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'seed2',\n                'name': 'seed2',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Range',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'start', 'type': 'number' },\n            { 'start': 1, 'name': 'stop', 'type': 'number' },\n            { 'start': 2, 'name': 'step', 'type': 'number', 'defaultValue': 0 },\n        ],\n        'attrs': [{ 'tfName': 'Tidx', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TruncatedNormal',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'means',\n                'name': 'mean',\n                'type': 'number',\n                'defaultValue': 0.0\n            },\n            {\n                'tfName': 'stddev',\n                'name': 'stdDev',\n                'type': 'number',\n                'defaultValue': 1.0\n            },\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number' }, {\n                'tfName': 'seed2',\n                'name': 'seed2',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Zeros',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'ZerosLike',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'Multinomial',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'logits', 'type': 'tensor' },\n            { 'start': 1, 'name': 'numSamples', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number' },\n            { 'tfName': 'seed2', 'name': 'seed2', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'output_dtype', 'name': 'output_dtype', 'type': 'dtype' }\n        ]\n    }\n];\n//# sourceMappingURL=creation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'NonMaxSuppressionV2',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV3',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV4',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'T_threshold',\n                'name': 'threshold',\n                'type': 'dtype',\n                'notSupported': true\n            },\n            {\n                'tfName': 'pad_to_max_output_size',\n                'name': 'padToMaxOutputSize',\n                'type': 'bool'\n            }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV5',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' },\n            { 'start': 5, 'name': 'softNmsSigma', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Where',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ListDiff',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'y', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=dynamic.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'TopKV2',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'k', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'sorted', 'name': 'sorted', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Unique',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n    },\n    {\n        'tfOpName': 'UniqueV2',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' },\n        ],\n    },\n];\n//# sourceMappingURL=evaluation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'PlaceholderWithDefault',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'default', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'Placeholder',\n        'category': 'graph',\n        'attrs': [\n            { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    { 'tfOpName': 'Const', 'category': 'graph' }, {\n        'tfOpName': 'Identity',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'IdentityN',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Snapshot',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Rank',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Size',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Shape',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'ShapeN',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Print',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'data', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'message', 'name': 'message', 'type': 'string' }, {\n                'tfName': 'first_n',\n                'name': 'firstN',\n                'type': 'number',\n                'notSupported': true\n            },\n            {\n                'tfName': 'summarize',\n                'name': 'summarize',\n                'type': 'number',\n                'defaultValue': 3\n            }\n        ]\n    },\n    { 'tfOpName': 'NoOp', 'category': 'graph', 'inputs': [] }, {\n        'tfOpName': 'StopGradient',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'FakeQuantWithMinMaxVars',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'min', 'name': 'min', 'type': 'number' },\n            { 'tfName': 'max', 'name': 'max', 'type': 'number' }\n        ]\n    }\n];\n//# sourceMappingURL=graph.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'ResizeBilinear',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'images', 'type': 'tensor' },\n            { 'start': 1, 'name': 'size', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ResizeNearestNeighbor',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'images', 'type': 'tensor' },\n            { 'start': 1, 'name': 'size', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'CropAndResize',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'image', 'type': 'tensor' },\n            { 'start': 1, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 2, 'name': 'boxInd', 'type': 'tensor' },\n            { 'start': 3, 'name': 'cropSize', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'method', 'name': 'method', 'type': 'string' }, {\n                'tfName': 'extrapolation_value',\n                'name': 'extrapolationValue',\n                'type': 'number'\n            }\n        ]\n    }\n];\n//# sourceMappingURL=image.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Equal',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'NotEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Greater',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'GreaterEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Less',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LessEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalAnd',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalNot',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalOr',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Select',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n            { 'start': 1, 'name': 'a', 'type': 'tensor' },\n            { 'start': 2, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'SelectV2',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n            { 'start': 1, 'name': 'a', 'type': 'tensor' },\n            { 'start': 2, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=logical.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': '_FusedMatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' }, {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.0001\n            },\n            {\n                'tfName': 'transpose_a',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'transpose_b',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'transpose_a',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'transpose_b',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'BatchMatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'adj_x',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'adj_y',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'BatchMatMulV2',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'adj_x',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'adj_y',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Transpose',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'perm', 'type': 'number[]' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=matrices.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'FusedBatchNorm',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'FusedBatchNormV2',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'FusedBatchNormV3',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'LRN',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'depth_radius',\n                'name': 'radius',\n                'type': 'number',\n                'defaultValue': 5\n            },\n            { 'tfName': 'bias', 'name': 'bias', 'type': 'number', 'defaultValue': 1.0 },\n            {\n                'tfName': 'alpha',\n                'name': 'alpha',\n                'type': 'number',\n                'defaultValue': 1.0\n            },\n            {\n                'tfName': 'beta',\n                'name': 'beta',\n                'type': 'number',\n                'defaultValue': 0.5\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Softmax',\n        'category': 'normalization',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'LogSoftmax',\n        'category': 'normalization',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'SparseToDense',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },\n            { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'defaultValue': true,\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Max',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Mean',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Min',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Sum',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'All',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Any',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'ArgMax',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'ArgMin',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Prod',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Cumsum',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'exclusive', 'name': 'exclusive', 'type': 'bool' },\n            { 'tfName': 'reverse', 'name': 'reverse', 'type': 'bool' }\n        ]\n    }\n];\n//# sourceMappingURL=reduction.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'ConcatV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'end': -1, 'name': 'tensors', 'type': 'tensors' },\n            { 'start': -1, 'name': 'axis', 'type': 'number' }\n        ],\n        'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]\n    },\n    {\n        'tfOpName': 'Concat',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 1, 'end': 0, 'name': 'tensors', 'type': 'tensors' },\n            { 'start': 0, 'name': 'axis', 'type': 'number' }\n        ],\n        'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]\n    },\n    {\n        'tfOpName': 'GatherV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'Gather',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Reverse',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'dims', 'type': 'bool', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ReverseV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Slice',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'begin', 'type': 'number[]' },\n            { 'start': 2, 'name': 'size', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'StridedSlice',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'begin', 'type': 'number[]' },\n            { 'start': 2, 'name': 'end', 'type': 'number[]' },\n            { 'start': 3, 'name': 'strides', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'begin_mask',\n                'name': 'beginMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'end_mask',\n                'name': 'endMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'new_axis_mask',\n                'name': 'newAxisMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'ellipsis_mask',\n                'name': 'ellipsisMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'shrink_axis_mask',\n                'name': 'shrinkAxisMask',\n                'type': 'number',\n                'defaultValue': 0\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Pack',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'Unpack',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'num',\n                'name': 'num',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Tile',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'reps', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Split',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'axis', 'type': 'number', 'defaultValue': 0 },\n            { 'start': 1, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'num_split',\n                'name': 'numOrSizeSplits',\n                'type': 'number',\n                'defaultValue': 1\n            }]\n    },\n    {\n        'tfOpName': 'SplitV',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'numOrSizeSplits', 'type': 'number[]' },\n            { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'ScatterNd',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'values', 'type': 'tensor' },\n            { 'start': 2, 'name': 'shape', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'GatherNd',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'SparseToDense',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },\n            { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'defaultValue': false,\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=slice_join.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'FFT',\n        'category': 'spectral',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'IFFT',\n        'category': 'spectral',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'RFFT',\n        'category': 'spectral',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' }, {\n                'start': 1,\n                'name': 'fft_length',\n                'type': 'number',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'IRFFT',\n        'category': 'spectral',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' }, {\n                'start': 1,\n                'name': 'fft_length',\n                'type': 'number',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=spectral.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Cast',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'SrcT',\n                'name': 'sdtype',\n                'type': 'dtype',\n                'notSupported': true\n            },\n            { 'tfName': 'DstT', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'ExpandDims',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Pad',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' },\n        ],\n        'attrs': [{\n                'tfName': 'constant_value',\n                'name': 'constantValue',\n                'type': 'number',\n                'defaultValue': 0\n            }]\n    },\n    {\n        'tfOpName': 'PadV2',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' }, {\n                'start': 2,\n                'name': 'constantValue',\n                'type': 'number',\n                'defaultValue': 0\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Reshape',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'shape', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Squeeze',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'axis',\n                'tfDeprecatedName': 'squeeze_dims',\n                'name': 'axis',\n                'type': 'number[]'\n            }]\n    },\n    {\n        'tfOpName': 'SpaceToBatchND',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'paddings', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'BatchToSpaceND',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'crops', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'DepthToSpace',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'block_size', 'name': 'blockSize', 'type': 'number' },\n            { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string' }\n        ]\n    },\n    {\n        'tfOpName': 'BroadcastTo',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': []\n    }\n];\n//# sourceMappingURL=transformation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport * as tensorflow from '../data/compiled_api';\nimport { getRegisteredOp } from './custom_op/register';\nimport { getNodeNameAndIndex } from './executors/utils';\nimport * as arithmetic from './op_list/arithmetic';\nimport * as basicMath from './op_list/basic_math';\nimport * as control from './op_list/control';\nimport * as convolution from './op_list/convolution';\nimport * as creation from './op_list/creation';\nimport * as dynamic from './op_list/dynamic';\nimport * as evaluation from './op_list/evaluation';\nimport * as graph from './op_list/graph';\nimport * as image from './op_list/image';\nimport * as logical from './op_list/logical';\nimport * as matrices from './op_list/matrices';\nimport * as normalization from './op_list/normalization';\nimport * as reduction from './op_list/reduction';\nimport * as sliceJoin from './op_list/slice_join';\nimport * as spectral from './op_list/spectral';\nimport * as transformation from './op_list/transformation';\nexport class OperationMapper {\n    // Singleton instance for the mapper\n    static get Instance() {\n        return this._instance || (this._instance = new this());\n    }\n    // Loads the op mapping from the JSON file.\n    constructor() {\n        const ops = [\n            arithmetic, basicMath, control, convolution, creation, dynamic,\n            evaluation, logical, image, graph, matrices, normalization, reduction,\n            sliceJoin, spectral, transformation\n        ];\n        const mappersJson = [].concat(...ops.map(op => op.json));\n        this.opMappers = mappersJson.reduce((map, mapper) => {\n            map[mapper.tfOpName] = mapper;\n            return map;\n        }, {});\n    }\n    // Converts the model inference graph from Tensorflow GraphDef to local\n    // representation for TensorFlow.js API\n    transformGraph(graph, signature = {}) {\n        const tfNodes = graph.node;\n        const placeholders = [];\n        const weights = [];\n        const initNodes = [];\n        const nodes = tfNodes.reduce((map, node) => {\n            map[node.name] = this.mapNode(node);\n            if (node.op.startsWith('Placeholder')) {\n                placeholders.push(map[node.name]);\n            }\n            else if (node.op === 'Const') {\n                weights.push(map[node.name]);\n            }\n            else if (node.input == null || node.input.length === 0) {\n                initNodes.push(map[node.name]);\n            }\n            return map;\n        }, {});\n        let inputs = [];\n        const outputs = [];\n        let inputNodeNameToKey = {};\n        let outputNodeNameToKey = {};\n        if (signature != null) {\n            inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);\n            outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);\n        }\n        const allNodes = Object.keys(nodes);\n        allNodes.forEach(key => {\n            const node = nodes[key];\n            node.inputNames.forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                node.inputs.push(nodes[nodeName]);\n                nodes[nodeName].children.push(node);\n            });\n        });\n        // if signature has not outputs set, add any node that does not have\n        // outputs.\n        if (Object.keys(outputNodeNameToKey).length === 0) {\n            allNodes.forEach(key => {\n                const node = nodes[key];\n                if (node.children.length === 0) {\n                    outputs.push(node);\n                }\n            });\n        }\n        else {\n            Object.keys(outputNodeNameToKey).forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                const node = nodes[nodeName];\n                if (node != null) {\n                    node.signatureKey = outputNodeNameToKey[name];\n                    outputs.push(node);\n                }\n            });\n        }\n        if (Object.keys(inputNodeNameToKey).length > 0) {\n            Object.keys(inputNodeNameToKey).forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                const node = nodes[nodeName];\n                if (node) {\n                    node.signatureKey = inputNodeNameToKey[name];\n                    inputs.push(node);\n                }\n            });\n        }\n        else {\n            inputs = placeholders;\n        }\n        let functions = {};\n        if (graph.library != null && graph.library.function != null) {\n            functions = graph.library.function.reduce((functions, func) => {\n                functions[func.signature.name] = this.mapFunction(func);\n                return functions;\n            }, {});\n        }\n        const result = { nodes, inputs, outputs, weights, placeholders, signature, functions };\n        if (initNodes.length > 0) {\n            result.initNodes = initNodes;\n        }\n        return result;\n    }\n    mapSignatureEntries(entries) {\n        return Object.keys(entries || {})\n            .reduce((prev, curr) => {\n            prev[entries[curr].name] = curr;\n            return prev;\n        }, {});\n    }\n    mapNode(node) {\n        // Unsupported ops will cause an error at run-time (not parse time), since\n        // they may not be used by the actual execution subgraph.\n        const mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {};\n        if (node.attr == null) {\n            node.attr = {};\n        }\n        const newNode = {\n            name: node.name,\n            op: node.op,\n            category: mapper.category,\n            inputNames: (node.input ||\n                []).map(input => input.startsWith('^') ? input.substr(1) : input),\n            inputs: [],\n            children: [],\n            inputParams: {},\n            attrParams: {},\n            rawAttrs: node.attr\n        };\n        if (mapper.inputs != null) {\n            newNode.inputParams =\n                mapper.inputs.reduce((map, param) => {\n                    map[param.name] = {\n                        type: param.type,\n                        inputIndexStart: param.start,\n                        inputIndexEnd: param.end\n                    };\n                    return map;\n                }, {});\n        }\n        if (mapper.attrs != null) {\n            newNode.attrParams =\n                mapper.attrs.reduce((map, param) => {\n                    const type = param.type;\n                    let value = undefined;\n                    switch (param.type) {\n                        case 'string':\n                            value = getStringParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'string[]':\n                            value = getStringArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'number':\n                            value = getNumberParam(node.attr, param.tfName, (param.defaultValue || 0));\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'number[]':\n                            value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'bool':\n                            value = getBoolParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'bool[]':\n                            value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'shape':\n                            value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'shape[]':\n                            value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'dtype':\n                            value = getDtypeParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'dtype[]':\n                            value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'func':\n                            value = getFuncParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'tensor':\n                        case 'tensors':\n                            break;\n                        default:\n                            throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`);\n                    }\n                    map[param.name] = { value, type };\n                    return map;\n                }, {});\n        }\n        return newNode;\n    }\n    // map the TFunctionDef to TFJS graph object\n    mapFunction(functionDef) {\n        const tfNodes = functionDef.nodeDef;\n        const placeholders = [];\n        const weights = [];\n        let nodes = {};\n        if (tfNodes != null) {\n            nodes = tfNodes.reduce((map, node) => {\n                map[node.name] = this.mapNode(node);\n                if (node.op === 'Const') {\n                    weights.push(map[node.name]);\n                }\n                return map;\n            }, {});\n        }\n        const inputs = [];\n        const outputs = [];\n        functionDef.signature.inputArg.forEach(arg => {\n            const [nodeName,] = getNodeNameAndIndex(arg.name);\n            const node = {\n                name: nodeName,\n                op: 'Placeholder',\n                inputs: [],\n                inputNames: [],\n                category: 'graph',\n                inputParams: {},\n                attrParams: { dtype: { value: parseDtypeParam(arg.type), type: 'dtype' } },\n                children: []\n            };\n            node.signatureKey = arg.name;\n            inputs.push(node);\n            nodes[nodeName] = node;\n        });\n        const allNodes = Object.keys(nodes);\n        allNodes.forEach(key => {\n            const node = nodes[key];\n            node.inputNames.forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                node.inputs.push(nodes[nodeName]);\n                nodes[nodeName].children.push(node);\n            });\n        });\n        const returnNodeMap = functionDef.ret;\n        functionDef.signature.outputArg.forEach(output => {\n            const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);\n            const node = nodes[nodeName];\n            if (node != null) {\n                node.defaultOutput = index;\n                outputs.push(node);\n            }\n        });\n        const signature = this.mapArgsToSignature(functionDef);\n        return { nodes, inputs, outputs, weights, placeholders, signature };\n    }\n    mapArgsToSignature(functionDef) {\n        return {\n            methodName: functionDef.signature.name,\n            inputs: functionDef.signature.inputArg.reduce((map, arg) => {\n                map[arg.name] = this.mapArgToTensorInfo(arg);\n                return map;\n            }, {}),\n            outputs: functionDef.signature.outputArg.reduce((map, arg) => {\n                map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);\n                return map;\n            }, {}),\n        };\n    }\n    mapArgToTensorInfo(arg, nameMap) {\n        let name = arg.name;\n        if (nameMap != null) {\n            name = nameMap[name];\n        }\n        return { name, dtype: arg.type };\n    }\n}\nexport function decodeBase64(text) {\n    const global = env().global;\n    if (typeof global.atob !== 'undefined') {\n        return global.atob(text);\n    }\n    else if (typeof Buffer !== 'undefined') {\n        return new Buffer(text, 'base64').toString();\n    }\n    else {\n        throw new Error('Unable to decode base64 in this environment. ' +\n            'Missing built-in atob() or Buffer()');\n    }\n}\nexport function parseStringParam(s, keepCase) {\n    const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);\n    return keepCase ? value : value.toLowerCase();\n}\nexport function getStringParam(attrs, name, def, keepCase = false) {\n    const param = attrs[name];\n    if (param != null) {\n        return parseStringParam(param.s, keepCase);\n    }\n    return def;\n}\nexport function getBoolParam(attrs, name, def) {\n    const param = attrs[name];\n    return param ? param.b : def;\n}\nexport function getNumberParam(attrs, name, def) {\n    const param = attrs[name] || {};\n    const value = param['i'] != null ? param['i'] : (param['f'] != null ? param['f'] : def);\n    return (typeof value === 'number') ? value : parseInt(value, 10);\n}\nexport function parseDtypeParam(value) {\n    if (typeof (value) === 'string') {\n        // tslint:disable-next-line:no-any\n        value = tensorflow.DataType[value];\n    }\n    switch (value) {\n        case tensorflow.DataType.DT_FLOAT:\n            return 'float32';\n        case tensorflow.DataType.DT_INT32:\n        case tensorflow.DataType.DT_INT64:\n        case tensorflow.DataType.DT_INT8:\n        case tensorflow.DataType.DT_UINT8:\n            return 'int32';\n        case tensorflow.DataType.DT_BOOL:\n            return 'bool';\n        case tensorflow.DataType.DT_DOUBLE:\n            return 'float32';\n        case tensorflow.DataType.DT_STRING:\n            return 'string';\n        default:\n            // Unknown dtype error will happen at runtime (instead of parse time),\n            // since these nodes might not be used by the actual subgraph execution.\n            return null;\n    }\n}\nexport function getFuncParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.func) {\n        return param.func.name;\n    }\n    return def;\n}\nexport function getDtypeParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.type) {\n        return parseDtypeParam(param.type);\n    }\n    return def;\n}\nexport function getDtypeArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.type) {\n        return param.list.type.map(v => parseDtypeParam(v));\n    }\n    return def;\n}\nexport function parseTensorShapeParam(shape) {\n    if (shape.unknownRank) {\n        return undefined;\n    }\n    if (shape.dim != null) {\n        return shape.dim.map(dim => (typeof dim.size === 'number') ? dim.size : parseInt(dim.size, 10));\n    }\n    return [];\n}\nexport function getTensorShapeParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.shape) {\n        return parseTensorShapeParam(param.shape);\n    }\n    return def;\n}\nexport function getNumericArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param) {\n        return ((param.list.f && param.list.f.length ? param.list.f :\n            param.list.i) ||\n            [])\n            .map(v => (typeof v === 'number') ? v : parseInt(v, 10));\n    }\n    return def;\n}\nexport function getStringArrayParam(attrs, name, def, keepCase = false) {\n    const param = attrs[name];\n    if (param && param.list && param.list.s) {\n        return param.list.s.map((v) => {\n            return parseStringParam(v, keepCase);\n        });\n    }\n    return def;\n}\nexport function getTensorShapeArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.shape) {\n        return param.list.shape.map((v) => {\n            return parseTensorShapeParam(v);\n        });\n    }\n    return def;\n}\nexport function getBoolArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.b) {\n        return param.list.b;\n    }\n    return def;\n}\n//# sourceMappingURL=operation_mapper.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getTensor } from '../executors/utils';\nimport { getBoolArrayParam, getBoolParam, getDtypeArrayParam, getDtypeParam, getNumberParam, getNumericArrayParam, getStringArrayParam, getStringParam, getTensorShapeArrayParam, getTensorShapeParam } from '../operation_mapper';\n/**\n * Helper class for lookup inputs and params for nodes in the model graph.\n */\nexport class NodeValueImpl {\n    constructor(node, tensorMap, context) {\n        this.node = node;\n        this.tensorMap = tensorMap;\n        this.context = context;\n        this.inputs = [];\n        this.attrs = {};\n        this.inputs = node.inputNames.map(name => this.getInput(name));\n        if (node.rawAttrs != null) {\n            this.attrs = Object.keys(node.rawAttrs)\n                .reduce((attrs, key) => {\n                attrs[key] = this.getAttr(key);\n                return attrs;\n            }, {});\n        }\n    }\n    /**\n     * Return the value of the attribute or input param.\n     * @param name String: name of attribute or input param.\n     */\n    getInput(name) {\n        return getTensor(name, this.tensorMap, this.context);\n    }\n    /**\n     * Return the value of the attribute or input param.\n     * @param name String: name of attribute or input param.\n     */\n    getAttr(name, defaultValue) {\n        const value = this.node.rawAttrs[name];\n        if (value.tensor != null) {\n            return getTensor(name, this.tensorMap, this.context);\n        }\n        if (value.i != null || value.f != null) {\n            return getNumberParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.s != null) {\n            return getStringParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.b != null) {\n            return getBoolParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.shape != null) {\n            return getTensorShapeParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.type != null) {\n            return getDtypeParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.list != null) {\n            if (value.list.i != null || value.list.f != null) {\n                return getNumericArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.s != null) {\n                return getStringArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.shape != null) {\n                return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.b != null) {\n                return getBoolArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.type != null) {\n                return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n        }\n        return defaultValue;\n    }\n}\n//# sourceMappingURL=node_value_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This file exports ops used by the converters executors. By default it\n * re-exports all ops. In a custom build this is aliased to a file that will\n * only exports ops for a given model.json.\n */\nexport * from './ops';\n//# sourceMappingURL=ops_for_converter.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'BiasAdd':\n        case 'AddV2':\n        case 'Add': {\n            return [tfOps.add(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'AddN': {\n            return [tfOps.addN(getParamValue('tensors', node, tensorMap, context))];\n        }\n        case 'FloorMod':\n        case 'Mod':\n            return [tfOps.mod(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        case 'Mul':\n            return [tfOps.mul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        case 'RealDiv':\n        case 'Div': {\n            return [tfOps.div(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'DivNoNan': {\n            return [tfOps.divNoNan(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'FloorDiv': {\n            return [tfOps.floorDiv(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Sub': {\n            return [tfOps.sub(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Minimum': {\n            return [tfOps.minimum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Maximum': {\n            return [tfOps.maximum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Pow': {\n            return [tfOps.pow(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'SquaredDifference': {\n            return [tfOps.squaredDifference(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'arithmetic';\n//# sourceMappingURL=arithmetic_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Abs':\n        case 'ComplexAbs':\n            return [tfOps.abs(getParamValue('x', node, tensorMap, context))];\n        case 'Acos':\n            return [tfOps.acos(getParamValue('x', node, tensorMap, context))];\n        case 'Acosh':\n            return [tfOps.acosh(getParamValue('x', node, tensorMap, context))];\n        case 'Asin':\n            return [tfOps.asin(getParamValue('x', node, tensorMap, context))];\n        case 'Asinh':\n            return [tfOps.asinh(getParamValue('x', node, tensorMap, context))];\n        case 'Atan':\n            return [tfOps.atan(getParamValue('x', node, tensorMap, context))];\n        case 'Atan2':\n            return [tfOps.atan2(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context))];\n        case 'Atanh':\n            return [tfOps.atanh(getParamValue('x', node, tensorMap, context))];\n        case 'Ceil':\n            return [tfOps.ceil(getParamValue('x', node, tensorMap, context))];\n        case 'Complex':\n            return [tfOps.complex(getParamValue('real', node, tensorMap, context), getParamValue('imag', node, tensorMap, context))];\n        case 'Cos':\n            return [tfOps.cos(getParamValue('x', node, tensorMap, context))];\n        case 'Cosh':\n            return [tfOps.cosh(getParamValue('x', node, tensorMap, context))];\n        case 'Elu':\n            return [tfOps.elu(getParamValue('x', node, tensorMap, context))];\n        case 'Erf':\n            return [tfOps.erf(getParamValue('x', node, tensorMap, context))];\n        case 'Exp':\n            return [tfOps.exp(getParamValue('x', node, tensorMap, context))];\n        case 'Expm1': {\n            return [tfOps.expm1(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Floor':\n            return [tfOps.floor(getParamValue('x', node, tensorMap, context))];\n        case 'Log':\n            return [tfOps.log(getParamValue('x', node, tensorMap, context))];\n        case 'Log1p': {\n            return [tfOps.log1p(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Imag':\n            return [tfOps.imag(getParamValue('x', node, tensorMap, context))];\n        case 'Neg':\n            return [tfOps.neg(getParamValue('x', node, tensorMap, context))];\n        case 'Reciprocal': {\n            return [tfOps.reciprocal(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Real':\n            return [tfOps.real(getParamValue('x', node, tensorMap, context))];\n        case 'Relu':\n            return [tfOps.relu(getParamValue('x', node, tensorMap, context))];\n        case 'Round': {\n            return [tfOps.round(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Selu':\n            return [tfOps.selu(getParamValue('x', node, tensorMap, context))];\n        case 'Sigmoid':\n            return [tfOps.sigmoid(getParamValue('x', node, tensorMap, context))];\n        case 'Sin':\n            return [tfOps.sin(getParamValue('x', node, tensorMap, context))];\n        case 'Sign': {\n            return [tfOps.sign(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Sinh': {\n            return [tfOps.sinh(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Softplus': {\n            return [tfOps.softplus(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Sqrt': {\n            return [tfOps.sqrt(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Square': {\n            return [tfOps.square(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Tanh': {\n            return [tfOps.tanh(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Tan':\n            return [tfOps.tan(getParamValue('x', node, tensorMap, context))];\n        case 'Relu6':\n        case 'ClipByValue':\n            return [tfOps.clipByValue(getParamValue('x', node, tensorMap, context), getParamValue('clipValueMin', node, tensorMap, context), getParamValue('clipValueMax', node, tensorMap, context))];\n        case 'Rsqrt':\n            return [tfOps.rsqrt(getTensor(node.inputNames[0], tensorMap, context))];\n        case 'Prod':\n            return [tfOps.prod(getParamValue('x', node, tensorMap, context), getParamValue('axes', node, tensorMap, context))];\n        case 'LeakyRelu':\n            return [tfOps.leakyRelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n        case 'Prelu':\n            return [tfOps.prelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'basic_math';\n//# sourceMappingURL=basic_math_executor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This differs from util.assertShapesMatch in that it allows values of\n * negative one, an undefined size of a dimensinon, in a shape to match\n * anything.\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = '') {\n    util.assert(shapesEqualAllowUndefinedSize(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nexport function shapesEqualAllowUndefinedSize(n1, n2) {\n    if (n1.length !== n2.length) {\n        return false;\n    }\n    for (let i = 0; i < n1.length; i++) {\n        if (n1[i] !== -1 && n2[i] !== -1 && n1[i] !== n2[i]) {\n            return false;\n        }\n    }\n    return true;\n}\n//# sourceMappingURL=tensor_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize } from './tensor_utils';\n/**\n * The TensorArray object keeps an array of Tensors.  It\n * allows reading from the array and writing to the array.\n */\nexport class TensorArray {\n    constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) {\n        this.name = name;\n        this.dtype = dtype;\n        this.maxSize = maxSize;\n        this.elementShape = elementShape;\n        this.identicalElementShapes = identicalElementShapes;\n        this.dynamicSize = dynamicSize;\n        this.clearAfterRead = clearAfterRead;\n        this.tensors = [];\n        this.closed_ = false;\n        this.idTensor = scalar(0);\n        keep(this.idTensor);\n    }\n    get id() {\n        return this.idTensor.id;\n    }\n    get closed() {\n        return this.closed_;\n    }\n    /**\n     * Dispose the tensors and idTensor and mark the TensoryArray as closed.\n     */\n    clearAndClose(keepIds) {\n        this.tensors.forEach(tensor => {\n            if (keepIds == null || !keepIds.has(tensor.tensor.id)) {\n                tensor.tensor.dispose();\n            }\n        });\n        this.tensors = [];\n        this.closed_ = true;\n        this.idTensor.dispose();\n    }\n    size() {\n        return this.tensors.length;\n    }\n    /**\n     * Read the value at location index in the TensorArray.\n     * @param index Number the index to read from.\n     */\n    read(index) {\n        if (this.closed_) {\n            throw new Error(`TensorArray ${this.name} has already been closed.`);\n        }\n        if (index < 0 || index >= this.size()) {\n            throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);\n        }\n        const tensorWithState = this.tensors[index];\n        if (tensorWithState.cleared) {\n            throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read ` +\n                `(perhaps try setting clear_after_read = false?).`);\n        }\n        if (this.clearAfterRead) {\n            tensorWithState.cleared = true;\n        }\n        tensorWithState.read = true;\n        return tensorWithState.tensor;\n    }\n    /**\n     * Helper method to read multiple tensors from the specified indices.\n     */\n    readMany(indices) {\n        return indices.map(index => this.read(index));\n    }\n    /**\n     * Write value into the index of the TensorArray.\n     * @param index number the index to write to.\n     * @param tensor\n     */\n    write(index, tensor) {\n        if (this.closed_) {\n            throw new Error(`TensorArray ${this.name} has already been closed.`);\n        }\n        if (index < 0 || !this.dynamicSize && index >= this.maxSize) {\n            throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);\n        }\n        const t = this.tensors[index] || {};\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},\n          because the value dtype is ${tensor.dtype}, but TensorArray dtype is ${this.dtype}.`);\n        }\n        // Set the shape for the first time write to unknow shape tensor array\n        if (this.size() === 0 &&\n            (this.elementShape == null || this.elementShape.length === 0)) {\n            this.elementShape = tensor.shape;\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`);\n        if (t.read) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);\n        }\n        if (t.written) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);\n        }\n        t.tensor = tensor;\n        keep(tensor);\n        t.written = true;\n        this.tensors[index] = t;\n    }\n    /**\n     * Helper method to write multiple tensors to the specified indices.\n     */\n    writeMany(indices, tensors) {\n        if (indices.length !== tensors.length) {\n            throw new Error(`TensorArray ${this.name}: could not write multiple tensors,` +\n                `because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);\n        }\n        indices.forEach((i, index) => this.write(i, tensors[index]));\n    }\n    /**\n     * Return selected values in the TensorArray as a packed Tensor. All of\n     * selected values must have been written and their shapes must all match.\n     * @param [indices] number[] Optional. Taking values in [0, max_value). If the\n     *    TensorArray is not dynamic, max_value=size(). If not specified returns\n     *    all tensors in the original order.\n     * @param [dtype]\n     */\n    gather(indices, dtype) {\n        if (!!dtype && dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);\n        }\n        if (!indices) {\n            indices = [];\n            for (let i = 0; i < this.size(); i++) {\n                indices.push(i);\n            }\n        }\n        else {\n            indices = indices.slice(0, this.size());\n        }\n        if (indices.length === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        // Read all the PersistentTensors into a vector to keep track of\n        // their memory.\n        const tensors = this.readMany(indices);\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, 'TensorArray shape mismatch: ');\n        return stack(tensors, 0);\n    }\n    /**\n     * Return the values in the TensorArray as a concatenated Tensor.\n     */\n    concat(dtype) {\n        if (!!dtype && dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);\n        }\n        if (this.size() === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        const indices = [];\n        for (let i = 0; i < this.size(); i++) {\n            indices.push(i);\n        }\n        // Collect all the tensors from the tensors array.\n        const tensors = this.readMany(indices);\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);\n        return concat(tensors, 0);\n    }\n    /**\n     * Scatter the values of a Tensor in specific indices of a TensorArray.\n     * @param indices nummber[] values in [0, max_value). If the\n     *    TensorArray is not dynamic, max_value=size().\n     * @param tensor Tensor input tensor.\n     */\n    scatter(indices, tensor) {\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n        }\n        if (indices.length !== tensor.shape[0]) {\n            throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n        }\n        const maxIndex = Math.max(...indices);\n        if (!this.dynamicSize && maxIndex >= this.maxSize) {\n            throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);\n        }\n        this.writeMany(indices, unstack(tensor, 0));\n    }\n    /**\n     * Split the values of a Tensor into the TensorArray.\n     * @param length number[] with the lengths to use when splitting value along\n     *    its first dimension.\n     * @param tensor Tensor, the tensor to split.\n     */\n    split(length, tensor) {\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n        }\n        let totalLength = 0;\n        const cumulativeLengths = length.map(len => {\n            totalLength += len;\n            return totalLength;\n        });\n        if (totalLength !== tensor.shape[0]) {\n            throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n        }\n        if (!this.dynamicSize && length.length !== this.maxSize) {\n            throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), ` +\n                'and the TensorArray is not marked as dynamically resizeable');\n        }\n        const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n        const tensors = [];\n        tidy(() => {\n            tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n            for (let i = 0; i < length.length; ++i) {\n                const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n                const indices = [0, previousLength, 0];\n                const sizes = [1, length[i], elementPerRow];\n                tensors[i] = reshape(slice(tensor, indices, sizes), this.elementShape);\n            }\n            return tensors;\n        });\n        const indices = [];\n        for (let i = 0; i < length.length; i++) {\n            indices[i] = i;\n        }\n        this.writeMany(indices, tensors);\n    }\n}\n//# sourceMappingURL=tensor_array.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize } from './tensor_utils';\n/**\n * TensorList stores a container of `tf.Tensor` objects, which are accessible\n * via tensors field.\n *\n * In order to get a copy of the underlying list, use the copy method:\n * ```\n *    TensorList b = a.copy();\n *    b.tensors().pushBack(t);  // This does not modify a.tensors().\n * ```\n *\n * Note that this is not a deep copy: the memory locations of the underlying\n * tensors will still point to the same locations of the corresponding tensors\n * in the original.\n */\nexport class TensorList {\n    /**\n     *\n     * @param tensors list of tensors\n     * @param elementShape shape of each tensor\n     * @param elementDtype data type of each tensor\n     * @param maxNumElements The maximum allowed size of `tensors`. Defaults to -1\n     *   meaning that the size of `tensors` is unbounded.\n     */\n    constructor(tensors, elementShape, elementDtype, maxNumElements = -1) {\n        this.tensors = tensors;\n        this.elementShape = elementShape;\n        this.elementDtype = elementDtype;\n        if (tensors != null) {\n            tensors.forEach(tensor => {\n                if (elementDtype !== tensor.dtype) {\n                    throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor.dtype}`);\n                }\n                assertShapesMatchAllowUndefinedSize(elementShape, tensor.shape, 'TensorList shape mismatch: ');\n                keep(tensor);\n            });\n        }\n        this.idTensor = scalar(0);\n        this.maxNumElements = maxNumElements;\n        keep(this.idTensor);\n    }\n    get id() {\n        return this.idTensor.id;\n    }\n    /**\n     * Get a new TensorList containing a copy of the underlying tensor container.\n     */\n    copy() {\n        return new TensorList([...this.tensors], this.elementShape, this.elementDtype);\n    }\n    /**\n     * Dispose the tensors and idTensor and clear the tensor list.\n     */\n    clearAndClose(keepIds) {\n        this.tensors.forEach(tensor => {\n            if (keepIds == null || !keepIds.has(tensor.id)) {\n                tensor.dispose();\n            }\n        });\n        this.tensors.length = 0;\n        this.idTensor.dispose();\n    }\n    /**\n     * The size of the tensors in the tensor list.\n     */\n    size() {\n        return this.tensors.length;\n    }\n    /**\n     * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1)\n     * tf.Tensor.\n     * @param elementShape shape of each tensor\n     * @param elementDtype data type of each tensor\n     * @param numElements the number of elements to stack\n     */\n    stack(elementShape, elementDtype, numElements = -1) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (numElements !== -1 && this.tensors.length !== numElements) {\n            throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);\n        }\n        assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, 'TensorList shape mismatch: ');\n        return tidy(() => {\n            const reshapedTensors = this.tensors.map(tensor => reshape(tensor, elementShape));\n            return stack(reshapedTensors, 0);\n        });\n    }\n    /**\n     * Pop a tensor from the end of the list.\n     * @param elementShape shape of the tensor\n     * @param elementDtype data type of the tensor\n     */\n    popBack(elementShape, elementDtype) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (this.size() === 0) {\n            throw new Error('Trying to pop from an empty list.');\n        }\n        const tensor = this.tensors.pop();\n        assertShapesMatchAllowUndefinedSize(tensor.shape, elementShape, 'TensorList shape mismatch: ');\n        return reshape(tensor, elementShape);\n    }\n    /**\n     * Push a tensor to the end of the list.\n     * @param tensor Tensor to be pushed.\n     */\n    pushBack(tensor) {\n        if (tensor.dtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(tensor.shape, this.elementShape, 'TensorList shape mismatch: ');\n        if (this.maxNumElements === this.size()) {\n            throw new Error(`Trying to push element into a full list.`);\n        }\n        keep(tensor);\n        this.tensors.push(tensor);\n    }\n    /**\n     * Update the size of the list.\n     * @param size the new size of the list.\n     */\n    resize(size) {\n        if (size < 0) {\n            throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);\n        }\n        if (this.maxNumElements !== -1 && size > this.maxNumElements) {\n            throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);\n        }\n        this.tensors.length = size;\n    }\n    /**\n     * Retrieve the element at the provided index\n     * @param elementShape shape of the tensor\n     * @param elementDtype dtype of the tensor\n     * @param elementIndex index of the tensor\n     */\n    getItem(elementIndex, elementShape, elementDtype) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (elementIndex < 0 || elementIndex > this.tensors.length) {\n            throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);\n        }\n        if (this.tensors[elementIndex] == null) {\n            throw new Error(`element at index ${elementIndex} is null.`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, 'TensorList shape mismatch: ');\n        return this.tensors[elementIndex];\n    }\n    /**\n     * Set the tensor at the index\n     * @param elementIndex index of the tensor\n     * @param tensor the tensor to be inserted into the list\n     */\n    setItem(elementIndex, tensor) {\n        if (tensor.dtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n        }\n        if (elementIndex < 0 ||\n            this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {\n            throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, 'TensorList shape mismatch: ');\n        keep(tensor);\n        this.tensors[elementIndex] = tensor;\n    }\n    /**\n     * Return selected values in the TensorList as a stacked Tensor. All of\n     * selected values must have been written and their shapes must all match.\n     * @param indices indices of tensors to gather\n     * @param elementDtype output tensor dtype\n     * @param elementShape output tensor element shape\n     */\n    gather(indices, elementDtype, elementShape) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n        // When indices is greater than the size of the list, indices beyond the\n        // size of the list are ignored.\n        indices = indices.slice(0, this.size());\n        if (indices.length === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        return tidy(() => {\n            const tensors = indices.map(i => reshape(this.tensors[i], elementShape));\n            return stack(tensors, 0);\n        });\n    }\n    /**\n     * Return the values in the TensorList as a concatenated Tensor.\n     * @param elementDtype output tensor dtype\n     * @param elementShape output tensor element shape\n     */\n    concat(elementDtype, elementShape) {\n        if (!!elementDtype && elementDtype !== this.elementDtype) {\n            throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n        if (this.size() === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        return tidy(() => {\n            const tensors = this.tensors.map(t => reshape(t, elementShape));\n            return concat(tensors, 0);\n        });\n    }\n}\n/**\n * Creates a TensorList which, when stacked, has the value of tensor.\n * @param tensor from tensor\n * @param elementShape output tensor element shape\n */\nexport function fromTensor(tensor, elementShape, elementDtype) {\n    const dtype = tensor.dtype;\n    if (tensor.shape.length < 1) {\n        throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor.shape}`);\n    }\n    if (tensor.dtype !== elementDtype) {\n        throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${elementDtype}`);\n    }\n    const outputShape = tensor.shape.slice(1);\n    assertShapesMatchAllowUndefinedSize(outputShape, elementShape, 'TensorList shape mismatch: ');\n    const tensorList = unstack(tensor);\n    return new TensorList(tensorList, elementShape, dtype);\n}\n/**\n * Return a TensorList of the given size with empty elements.\n * @param elementShape the shape of the future elements of the list\n * @param elementDtype the desired type of elements in the list\n * @param numElements the number of elements to reserve\n */\nexport function reserve(elementShape, elementDtype, numElements) {\n    return new TensorList([], elementShape, elementDtype, numElements);\n}\n/**\n * Put tensors at specific indices of a stacked tensor into a TensorList.\n * @param indices list of indices on how to scatter the tensor.\n * @param tensor input tensor.\n * @param elementShape the shape of the future elements of the list\n * @param numElements the number of elements to scatter\n */\nexport function scatter(tensor, indices, elementShape, numElements) {\n    if (indices.length !== tensor.shape[0]) {\n        throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n    }\n    const maxIndex = Math.max(...indices);\n    if (numElements != null && numElements !== -1 && maxIndex >= numElements) {\n        throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);\n    }\n    const list = new TensorList([], elementShape, tensor.dtype, numElements);\n    const tensors = unstack(tensor, 0);\n    indices.forEach((value, index) => {\n        list.setItem(value, tensors[index]);\n    });\n    return list;\n}\n/**\n * Split the values of a Tensor into a TensorList.\n * @param length the lengths to use when splitting value along\n *    its first dimension.\n * @param tensor the tensor to split.\n * @param elementShape the shape of the future elements of the list\n */\nexport function split(tensor, length, elementShape) {\n    let totalLength = 0;\n    const cumulativeLengths = length.map(len => {\n        totalLength += len;\n        return totalLength;\n    });\n    if (totalLength !== tensor.shape[0]) {\n        throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n    }\n    const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n    const tensors = tidy(() => {\n        const tensors = [];\n        tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n        for (let i = 0; i < length.length; ++i) {\n            const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n            const indices = [0, previousLength, 0];\n            const sizes = [1, length[i], elementPerRow];\n            tensors[i] = reshape(slice(tensor, indices, sizes), elementShape);\n        }\n        tensor.dispose();\n        return tensors;\n    });\n    const list = new TensorList([], elementShape, tensor.dtype, length.length);\n    for (let i = 0; i < tensors.length; i++) {\n        list.setItem(i, tensors[i]);\n    }\n    return list;\n}\n//# sourceMappingURL=tensor_list.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { scalar } from '@tensorflow/tfjs-core';\nimport { TensorArray } from '../../executor/tensor_array';\nimport { fromTensor, reserve, scatter, split } from '../../executor/tensor_list';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = async (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'If':\n        case 'StatelessIf': {\n            const thenFunc = getParamValue('thenBranch', node, tensorMap, context);\n            const elseFunc = getParamValue('elseBranch', node, tensorMap, context);\n            const cond = getParamValue('cond', node, tensorMap, context);\n            const args = getParamValue('args', node, tensorMap, context);\n            const condValue = await cond.data();\n            if (condValue[0]) {\n                return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n            }\n            else {\n                return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n            }\n        }\n        case 'While':\n        case 'StatelessWhile': {\n            const bodyFunc = getParamValue('body', node, tensorMap, context);\n            const condFunc = getParamValue('cond', node, tensorMap, context);\n            const args = getParamValue('args', node, tensorMap, context);\n            // Calculate the condition of the loop\n            const condResult = (await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap));\n            const argIds = args.map(tensor => tensor.id);\n            let condValue = await condResult[0].data();\n            // Dispose the intermediate tensors for condition function\n            condResult.forEach(tensor => {\n                if (!tensor.kept && argIds.indexOf(tensor.id) === -1) {\n                    tensor.dispose();\n                }\n            });\n            let result = args;\n            while (condValue[0]) {\n                // Record the previous result for intermediate tensor tracking\n                const origResult = result;\n                // Execution the body of the loop\n                result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);\n                const resultIds = result.map(tensor => tensor.id);\n                // Dispose the intermediate tensor for body function that is not global\n                // kept, not input/output of the body function\n                origResult.forEach(tensor => {\n                    if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n                        resultIds.indexOf(tensor.id) === -1) {\n                        tensor.dispose();\n                    }\n                });\n                // Recalcuate the condition of the loop using the latest results.\n                const condResult = (await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap));\n                condValue = await condResult[0].data();\n                // Dispose the intermediate tensors for condition function\n                condResult.forEach(tensor => {\n                    if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n                        resultIds.indexOf(tensor.id) === -1) {\n                        tensor.dispose();\n                    }\n                });\n            }\n            return result;\n        }\n        case 'LoopCond': {\n            const pred = getParamValue('pred', node, tensorMap, context);\n            return [cloneTensor(pred)];\n        }\n        case 'Switch': {\n            const pred = getParamValue('pred', node, tensorMap, context);\n            let data = getParamValue('data', node, tensorMap, context);\n            if (!data.kept) {\n                data = cloneTensor(data);\n            }\n            // Outputs nodes :0 => false, :1 => true\n            return (await pred.data())[0] ? [undefined, data] : [data, undefined];\n        }\n        case 'Merge': {\n            const inputName = node.inputNames.find(name => getTensor(name, tensorMap, context) !== undefined);\n            if (inputName) {\n                const data = getTensor(inputName, tensorMap, context);\n                return [cloneTensor(data)];\n            }\n            return undefined;\n        }\n        case 'Enter': {\n            const frameId = getParamValue('frameName', node, tensorMap, context);\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.enterFrame(frameId);\n            return [cloneTensor(data)];\n        }\n        case 'Exit': {\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.exitFrame();\n            return [cloneTensor(data)];\n        }\n        case 'NextIteration': {\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.nextIteration();\n            return [cloneTensor(data)];\n        }\n        case 'TensorArrayV3': {\n            const size = getParamValue('size', node, tensorMap, context);\n            const dtype = getParamValue('dtype', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const dynamicSize = getParamValue('dynamicSize', node, tensorMap, context);\n            const clearAfterRead = getParamValue('clearAfterRead', node, tensorMap, context);\n            const identicalElementShapes = getParamValue('identicalElementShapes', node, tensorMap, context);\n            const name = getParamValue('name', node, tensorMap, context);\n            const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);\n            context.addTensorArray(tensorArray);\n            return [tensorArray.idTensor, scalar(1.0)];\n        }\n        case 'TensorArrayWriteV3': {\n            const id = getParamValue('tensorArrayId', node, tensorMap, context);\n            const index = getParamValue('index', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const writeTensorArray = context.getTensorArray(id.id);\n            writeTensorArray.write(index, writeTensor);\n            return [writeTensorArray.idTensor];\n        }\n        case 'TensorArrayReadV3': {\n            const readId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const readIndex = getParamValue('index', node, tensorMap, context);\n            const readTensorArray = context.getTensorArray(readId.id);\n            return [readTensorArray.read(readIndex)];\n        }\n        case 'TensorArrayGatherV3': {\n            const gatherId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const gatherIndices = getParamValue('indices', node, tensorMap, context);\n            const gatherDtype = getParamValue('dtype', node, tensorMap, context);\n            const gatherTensorArray = context.getTensorArray(gatherId.id);\n            return [gatherTensorArray.gather(gatherIndices, gatherDtype)];\n        }\n        case 'TensorArrayScatterV3': {\n            const scatterId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const scatterIndices = getParamValue('indices', node, tensorMap, context);\n            const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n            const scatterTensorArray = context.getTensorArray(scatterId.id);\n            scatterTensorArray.scatter(scatterIndices, scatterTensor);\n            return [scatterTensorArray.idTensor];\n        }\n        case 'TensorArrayConcatV3': {\n            const concatId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const concatTensorArray = context.getTensorArray(concatId.id);\n            const concatDtype = getParamValue('dtype', node, tensorMap, context);\n            return [concatTensorArray.concat(concatDtype)];\n        }\n        case 'TensorArraySplitV3': {\n            const splitId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const splitTensor = getParamValue('tensor', node, tensorMap, context);\n            const lengths = getParamValue('lengths', node, tensorMap, context);\n            const splitTensorArray = context.getTensorArray(splitId.id);\n            splitTensorArray.split(lengths, splitTensor);\n            return [splitTensorArray.idTensor];\n        }\n        case 'TensorArraySizeV3': {\n            const sizeId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const sizeTensorArray = context.getTensorArray(sizeId.id);\n            return [scalar(sizeTensorArray.size(), 'int32')];\n        }\n        case 'TensorArrayCloseV3': {\n            const closeId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const closeTensorArray = context.getTensorArray(closeId.id);\n            closeTensorArray.clearAndClose();\n            return [closeTensorArray.idTensor];\n        }\n        case 'TensorListSetItem': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const index = getParamValue('index', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            tensorList.setItem(index, writeTensor);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListGetItem': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const readIndex = getParamValue('index', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDType = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.getItem(readIndex, elementShape, elementDType)];\n        }\n        case 'TensorListScatterV2':\n        case 'TensorListScatter': {\n            const scatterIndices = getParamValue('indices', node, tensorMap, context);\n            const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListReserve': {\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = reserve(elementShape, elementDtype, numElements);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListGather': {\n            const gatherId = getParamValue('tensorListId', node, tensorMap, context);\n            const gatherIndices = getParamValue('indices', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(gatherId.id);\n            return [tensorList.gather(gatherIndices, elementDtype, elementShape)];\n        }\n        case 'TensorListStack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.stack(elementShape, elementDtype, numElements)];\n        }\n        case 'TensorListFromTensor': {\n            const tensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = fromTensor(tensor, elementShape, elementDtype);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListConcat': {\n            const concatId = getParamValue('tensorListId', node, tensorMap, context);\n            const tensorList = context.getTensorList(concatId.id);\n            const concatDtype = getParamValue('dtype', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            return [tensorList.concat(concatDtype, elementShape)];\n        }\n        case 'TensorListPushBack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            tensorList.pushBack(writeTensor);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListPopBack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDType = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.popBack(elementShape, elementDType)];\n        }\n        case 'TensorListSplit': {\n            const splitTensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const lengths = getParamValue('lengths', node, tensorMap, context);\n            const tensorList = split(splitTensor, lengths, elementShape);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'control';\n//# sourceMappingURL=control_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getPadding, getParamValue } from './utils';\nfunction fusedConvAndDepthWiseParams(node, tensorMap, context) {\n    const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n    const isBiasAdd = extraOp === 'biasadd';\n    const isPrelu = activationFunc === 'prelu';\n    const isBatchNorm = extraOp === 'fusedbatchnorm';\n    const numArgs = getParamValue('numArgs', node, tensorMap, context);\n    if (isBiasAdd) {\n        if (isPrelu && numArgs !== 2) {\n            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu ' +\n                'must have two extra arguments: bias and alpha.');\n        }\n        if (!isPrelu && numArgs !== 1) {\n            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd must have ' +\n                'one extra argument: bias.');\n        }\n    }\n    if (isBatchNorm) {\n        throw new Error('FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.');\n    }\n    const stride = getParamValue('strides', node, tensorMap, context);\n    const pad = getPadding(node, tensorMap, context);\n    const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n        .toUpperCase();\n    const dilations = getParamValue('dilations', node, tensorMap, context);\n    const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n    return {\n        stride,\n        pad,\n        dataFormat,\n        dilations,\n        biasArg,\n        preluArg,\n        activationFunc\n    };\n}\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Conv1D': {\n            const stride = getParamValue('stride', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilation = getParamValue('dilation', node, tensorMap, context);\n            return [tfOps.conv1d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), stride, pad, dataFormat, dilation)];\n        }\n        case 'Conv2D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            return [tfOps.conv2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n        }\n        case '_FusedConv2D': {\n            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n            return [tfOps.fused.conv2d({\n                    x: getParamValue('x', node, tensorMap, context),\n                    filter: getParamValue('filter', node, tensorMap, context),\n                    strides: [stride[1], stride[2]],\n                    pad: pad,\n                    dataFormat: dataFormat,\n                    dilations: [dilations[1], dilations[2]],\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        }\n        case 'FusedDepthwiseConv2dNative': {\n            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n            return [tfOps.fused.depthwiseConv2d({\n                    x: getParamValue('x', node, tensorMap, context),\n                    filter: getParamValue('filter', node, tensorMap, context),\n                    strides: [stride[1], stride[2]],\n                    pad: pad,\n                    dataFormat: dataFormat,\n                    dilations: [dilations[1], dilations[2]],\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        }\n        case 'Conv2DBackpropInput':\n        case 'Conv2dTranspose': {\n            const shape = getParamValue('outputShape', node, tensorMap, context);\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            return [tfOps.conv2dTranspose(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), shape, [stride[1], stride[2]], pad)];\n        }\n        case 'DepthwiseConv2dNative':\n        case 'DepthwiseConv2d': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            return [tfOps.depthwiseConv2d(getParamValue('input', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n        }\n        case 'Conv3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            return [tfOps.conv3d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2], stride[3]], pad, dataFormat, [dilations[1], dilations[2], dilations[3]])];\n        }\n        case 'AvgPool': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.avgPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n        }\n        case 'MaxPool': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.maxPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n        }\n        case 'MaxPoolWithArgmax': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            const includeBatchInIndex = getParamValue('includeBatchInIndex', node, tensorMap, context);\n            const { result, indexes } = tfOps.maxPoolWithArgmax(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad, includeBatchInIndex);\n            return [result, indexes];\n        }\n        case 'AvgPool3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.avgPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n        }\n        case 'MaxPool3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.maxPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n        }\n        case 'Dilation2D': {\n            const strides = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            // strides: [1, stride_height, stride_width, 1].\n            const strideHeight = strides[1];\n            const strideWidth = strides[2];\n            // dilations: [1, dilation_height, dilation_width, 1].\n            const dilationHeight = dilations[1];\n            const dilationWidth = dilations[2];\n            return [tfOps.dilation2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [strideHeight, strideWidth], pad, [dilationHeight, dilationWidth], 'NHWC' /* dataFormat */)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'convolution';\n//# sourceMappingURL=convolution_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Fill': {\n            const shape = getParamValue('shape', node, tensorMap, context);\n            const dtype = getParamValue('dtype', node, tensorMap, context);\n            const value = getParamValue('value', node, tensorMap, context);\n            return [tfOps.fill(shape, value, dtype)];\n        }\n        case 'LinSpace': {\n            const start = getParamValue('start', node, tensorMap, context);\n            const stop = getParamValue('stop', node, tensorMap, context);\n            const num = getParamValue('num', node, tensorMap, context);\n            return [tfOps.linspace(start, stop, num)];\n        }\n        case 'Multinomial': {\n            const logits = getParamValue('logits', node, tensorMap, context);\n            const numSamples = getParamValue('numSamples', node, tensorMap, context);\n            const seed = getParamValue('seed', node, tensorMap, context);\n            return [tfOps.multinomial(logits, numSamples, seed)];\n        }\n        case 'OneHot': {\n            const indices = getParamValue('indices', node, tensorMap, context);\n            const depth = getParamValue('depth', node, tensorMap, context);\n            const onValue = getParamValue('onValue', node, tensorMap, context);\n            const offValue = getParamValue('offValue', node, tensorMap, context);\n            return [tfOps.oneHot(indices, depth, onValue, offValue)];\n        }\n        case 'Ones': {\n            return [tfOps.ones(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'OnesLike': {\n            return [tfOps.onesLike(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'RandomUniform': {\n            return [tfOps.randomUniform(\n                // tslint:disable-next-line:no-any\n                getParamValue('shape', node, tensorMap, context), getParamValue('minval', node, tensorMap, context), getParamValue('maxval', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'Range': {\n            const start = getParamValue('start', node, tensorMap, context);\n            const stop = getParamValue('stop', node, tensorMap, context);\n            const step = getParamValue('step', node, tensorMap, context);\n            return [tfOps.range(start, stop, step, getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'TruncatedNormal': {\n            const shape = getParamValue('shape', node, tensorMap, context);\n            const mean = getParamValue('mean', node, tensorMap, context);\n            const stdDev = getParamValue('stdDev', node, tensorMap, context);\n            const seed = getParamValue('seed', node, tensorMap, context);\n            return [tfOps.truncatedNormal(shape, mean, stdDev, getParamValue('dtype', node, tensorMap, context), seed)];\n        }\n        case 'Zeros': {\n            return [tfOps.zeros(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'ZerosLike': {\n            return [tfOps.zerosLike(getParamValue('x', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'creation';\n//# sourceMappingURL=creation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nfunction nmsParams(node, tensorMap, context) {\n    const boxes = getParamValue('boxes', node, tensorMap, context);\n    const scores = getParamValue('scores', node, tensorMap, context);\n    const maxOutputSize = getParamValue('maxOutputSize', node, tensorMap, context);\n    const iouThreshold = getParamValue('iouThreshold', node, tensorMap, context);\n    const scoreThreshold = getParamValue('scoreThreshold', node, tensorMap, context);\n    const softNmsSigma = getParamValue('softNmsSigma', node, tensorMap, context);\n    return {\n        boxes,\n        scores,\n        maxOutputSize,\n        iouThreshold,\n        scoreThreshold,\n        softNmsSigma\n    };\n}\nexport const executeOp = async (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'NonMaxSuppressionV5': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node, tensorMap, context);\n            const result = await tfOps.image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n            return [result.selectedIndices, result.selectedScores];\n        }\n        case 'NonMaxSuppressionV4': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n            const padToMaxOutputSize = getParamValue('padToMaxOutputSize', node, tensorMap, context);\n            const result = await tfOps.image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n            return [result.selectedIndices, result.validOutputs];\n        }\n        case 'NonMaxSuppressionV3':\n        case 'NonMaxSuppressionV2': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n            return [await tfOps.image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];\n        }\n        case 'Where': {\n            const condition = tfOps.cast(getParamValue('condition', node, tensorMap, context), 'bool');\n            const result = [await tfOps.whereAsync(condition)];\n            condition.dispose();\n            return result;\n        }\n        case 'ListDiff': {\n            return tfOps.setdiff1dAsync(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context));\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'dynamic';\n//# sourceMappingURL=dynamic_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'TopKV2': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const k = getParamValue('k', node, tensorMap, context);\n            const sorted = getParamValue('sorted', node, tensorMap, context);\n            const result = tfOps.topk(x, k, sorted);\n            return [result.values, result.indices];\n        }\n        case 'Unique': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const result = tfOps.unique(x);\n            return [result.values, result.indices];\n        }\n        case 'UniqueV2': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const result = tfOps.unique(x, axis);\n            return [result.values, result.indices];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'evaluation';\n//# sourceMappingURL=evaluation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Const': {\n            return tensorMap[node.name];\n        }\n        case 'PlaceholderWithDefault':\n            const def = getParamValue('default', node, tensorMap, context);\n            return [getTensor(node.name, tensorMap, context) || def];\n        case 'Placeholder':\n            return [getTensor(node.name, tensorMap, context)];\n        case 'Identity':\n        case 'StopGradient':\n        case 'FakeQuantWithMinMaxVars': { // This op is currently ignored.\n            const data = getParamValue('x', node, tensorMap, context);\n            return [cloneTensor(data)];\n        }\n        case 'IdentityN':\n            return getParamValue('x', node, tensorMap, context)\n                .map((t) => cloneTensor(t));\n        case 'Snapshot':\n            const snapshot = getParamValue('x', node, tensorMap, context);\n            return [cloneTensor(snapshot)];\n        case 'Shape':\n            return [tfOps.tensor1d(getParamValue('x', node, tensorMap, context).shape, 'int32')];\n        case 'ShapeN':\n            return getParamValue('x', node, tensorMap, context)\n                .map((t) => tfOps.tensor1d(t.shape));\n        case 'Size':\n            return [tfOps.scalar(getParamValue('x', node, tensorMap, context).size, 'int32')];\n        case 'Rank':\n            return [tfOps.scalar(getParamValue('x', node, tensorMap, context).rank, 'int32')];\n        case 'NoOp':\n            return [tfOps.scalar(1)];\n        case 'Print':\n            const input = getParamValue('x', node, tensorMap, context);\n            const data = getParamValue('data', node, tensorMap, context);\n            const message = getParamValue('message', node, tensorMap, context);\n            const summarize = getParamValue('summarize', node, tensorMap, context);\n            console.warn('The graph has a tf.print() operation,' +\n                'usually used for debugging, which slows down performance.');\n            console.log(message);\n            for (let i = 0; i < data.length; i++) {\n                console.log(Array.prototype.slice.call(data[i].dataSync())\n                    .slice(0, summarize));\n            }\n            return [input];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'graph';\n//# sourceMappingURL=graph_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'ResizeBilinear': {\n            const images = getParamValue('images', node, tensorMap, context);\n            const size = getParamValue('size', node, tensorMap, context);\n            const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n            return [tfOps.image.resizeBilinear(images, [size[0], size[1]], alignCorners)];\n        }\n        case 'ResizeNearestNeighbor': {\n            const images = getParamValue('images', node, tensorMap, context);\n            const size = getParamValue('size', node, tensorMap, context);\n            const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n            return [tfOps.image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners)];\n        }\n        case 'CropAndResize': {\n            const image = getParamValue('image', node, tensorMap, context);\n            const boxes = getParamValue('boxes', node, tensorMap, context);\n            const boxInd = getParamValue('boxInd', node, tensorMap, context);\n            const cropSize = getParamValue('cropSize', node, tensorMap, context);\n            const method = getParamValue('method', node, tensorMap, context);\n            const extrapolationValue = getParamValue('extrapolationValue', node, tensorMap, context);\n            return [tfOps.image.cropAndResize(image, boxes, boxInd, cropSize, method, extrapolationValue)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'image';\n//# sourceMappingURL=image_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Equal': {\n            return [tfOps.equal(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'NotEqual': {\n            return [tfOps.notEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Greater': {\n            return [tfOps.greater(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'GreaterEqual': {\n            return [tfOps.greaterEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Less': {\n            return [tfOps.less(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LessEqual': {\n            return [tfOps.lessEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LogicalAnd': {\n            return [tfOps.logicalAnd(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LogicalNot': {\n            return [tfOps.logicalNot(getParamValue('a', node, tensorMap, context))];\n        }\n        case 'LogicalOr': {\n            return [tfOps.logicalOr(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Select':\n        case 'SelectV2': {\n            return [tfOps.where(getParamValue('condition', node, tensorMap, context), getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'logical';\n//# sourceMappingURL=logical_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'BatchMatMul':\n        case 'BatchMatMulV2':\n        case 'MatMul':\n            return [tfOps.matMul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context), getParamValue('transposeA', node, tensorMap, context), getParamValue('transposeB', node, tensorMap, context))];\n        case 'Transpose':\n            return [tfOps.transpose(getParamValue('x', node, tensorMap, context), getParamValue('perm', node, tensorMap, context))];\n        case '_FusedMatMul':\n            const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n            const isBiasAdd = extraOp === 'biasadd';\n            const isPrelu = activationFunc === 'prelu';\n            const numArgs = getParamValue('numArgs', node, tensorMap, context);\n            if (isBiasAdd) {\n                if (isPrelu && numArgs !== 2) {\n                    throw new Error('Fused MatMul with BiasAdd and Prelu must have two ' +\n                        'extra arguments: bias and alpha.');\n                }\n                if (!isPrelu && numArgs !== 1) {\n                    throw new Error('Fused MatMul with BiasAdd must have one extra argument: bias.');\n                }\n            }\n            const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n            return [tfOps.fused.matMul({\n                    a: getParamValue('a', node, tensorMap, context),\n                    b: getParamValue('b', node, tensorMap, context),\n                    transposeA: getParamValue('transposeA', node, tensorMap, context),\n                    transposeB: getParamValue('transposeB', node, tensorMap, context),\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'matrices';\n//# sourceMappingURL=matrices_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'FusedBatchNorm':\n        case 'FusedBatchNormV2': {\n            return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n        }\n        case 'FusedBatchNormV3': {\n            return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n        }\n        case 'LRN': {\n            return [tfOps.localResponseNormalization(getParamValue('x', node, tensorMap, context), getParamValue('radius', node, tensorMap, context), getParamValue('bias', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context), getParamValue('beta', node, tensorMap, context))];\n        }\n        case 'Softmax': {\n            return [tfOps.softmax(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'LogSoftmax': {\n            return [tfOps.logSoftmax(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'SparseToDense': {\n            return [tfOps.sparseToDense(getParamValue('sparseIndices', node, tensorMap, context), getParamValue('outputShape', node, tensorMap, context), getParamValue('sparseValues', node, tensorMap, context), getParamValue('defaultValue', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'normalization';\n//# sourceMappingURL=normalization_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Max': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.max(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Mean': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.mean(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Min': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.min(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Sum': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.sum(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'All': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.all(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Any': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.any(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'ArgMax': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.argMax(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'ArgMin': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.argMin(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Prod': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.prod(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Cumsum': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const exclusive = getParamValue('exclusive', node, tensorMap, context);\n            const reverse = getParamValue('reverse', node, tensorMap, context);\n            return [tfOps.cumsum(getParamValue('x', node, tensorMap, context), axis, exclusive, reverse)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'reduction';\n//# sourceMappingURL=reduction_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tidy, util } from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'ConcatV2':\n        case 'Concat': {\n            const n = getParamValue('n', node, tensorMap, context);\n            const axis = getParamValue('axis', node, tensorMap, context);\n            let inputs = getParamValue('tensors', node, tensorMap, context);\n            inputs = inputs.slice(0, n);\n            return [tfOps.concat(inputs, axis)];\n        }\n        case 'GatherV2':\n        case 'Gather': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const input = getParamValue('x', node, tensorMap, context);\n            const indices = getParamValue('indices', node, tensorMap, context);\n            return [tfOps.gather(input, tfOps.cast(indices, 'int32'), axis)];\n        }\n        case 'ReverseV2':\n        case 'Reverse': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const input = getParamValue('x', node, tensorMap, context);\n            return [tfOps.reverse(input, axis)];\n        }\n        case 'Slice': {\n            // tslint:disable-next-line:no-any\n            const begin = getParamValue('begin', node, tensorMap, context);\n            // tslint:disable-next-line:no-any\n            const size = getParamValue('size', node, tensorMap, context);\n            return [tfOps.slice(getParamValue('x', node, tensorMap, context), begin, size)];\n        }\n        case 'StridedSlice': {\n            const begin = getParamValue('begin', node, tensorMap, context);\n            const end = getParamValue('end', node, tensorMap, context);\n            const strides = getParamValue('strides', node, tensorMap, context);\n            const beginMask = getParamValue('beginMask', node, tensorMap, context);\n            const endMask = getParamValue('endMask', node, tensorMap, context);\n            const ellipsisMask = getParamValue('ellipsisMask', node, tensorMap, context);\n            const newAxisMask = getParamValue('newAxisMask', node, tensorMap, context);\n            const shrinkAxisMask = getParamValue('shrinkAxisMask', node, tensorMap, context);\n            const tensor = getParamValue('x', node, tensorMap, context);\n            return [tfOps.stridedSlice(tensor, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)];\n        }\n        case 'Pack': {\n            return tidy(() => {\n                const axis = getParamValue('axis', node, tensorMap, context);\n                const tensors = getParamValue('tensors', node, tensorMap, context);\n                // Reshape the tensors to the first tensor's shape if they don't\n                // match.\n                const shape = tensors[0].shape;\n                const squeezedShape = tfOps.squeeze(tensors[0]).shape;\n                const mapped = tensors.map(tensor => {\n                    const sameShape = util.arraysEqual(tensor.shape, shape);\n                    if (!sameShape &&\n                        !util.arraysEqual(tfOps.squeeze(tensor).shape, squeezedShape)) {\n                        throw new Error('the input tensors shape does not match');\n                    }\n                    return sameShape ? tensor : tfOps.reshape(tensor, shape);\n                });\n                return [tfOps.stack(mapped, axis)];\n            });\n        }\n        case 'Unpack': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const tensor = getParamValue('tensor', node, tensorMap, context);\n            return tfOps.unstack(tensor, axis);\n        }\n        case 'Tile': {\n            const reps = getParamValue('reps', node, tensorMap, context);\n            return [tfOps.tile(getParamValue('x', node, tensorMap, context), reps)];\n        }\n        case 'Split':\n        case 'SplitV': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const numOrSizeSplits = getParamValue('numOrSizeSplits', node, tensorMap, context);\n            const tensor = getParamValue('x', node, tensorMap, context);\n            return tfOps.split(tensor, numOrSizeSplits, axis);\n        }\n        case 'ScatterNd': {\n            const indices = getParamValue('indices', node, tensorMap, context);\n            const values = getParamValue('values', node, tensorMap, context);\n            const shape = getParamValue('shape', node, tensorMap, context);\n            return [tfOps.scatterND(indices, values, shape)];\n        }\n        case 'GatherNd': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const indices = getParamValue('indices', node, tensorMap, context);\n            return [tfOps.gatherND(x, indices)];\n        }\n        case 'SparseToDense': {\n            const indices = getParamValue('sparseIndices', node, tensorMap, context);\n            const shape = getParamValue('outputShape', node, tensorMap, context);\n            const sparseValues = getParamValue('sparseValues', node, tensorMap, context);\n            const defaultValue = getParamValue('defaultValue', node, tensorMap, context);\n            return [tfOps.sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ?\n                    defaultValue :\n                    tfOps.cast(defaultValue, sparseValues.dtype))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'slice_join';\n//# sourceMappingURL=slice_join_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'FFT': {\n            return [tfOps.fft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'IFFT': {\n            return [tfOps.ifft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'RFFT': {\n            return [tfOps.rfft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'IRFFT': {\n            return [tfOps.irfft(getParamValue('x', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'spectral';\n//# sourceMappingURL=spectral_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Cast': {\n            return [tfOps.cast(getParamValue('x', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'ExpandDims': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.expandDims(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Squeeze': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.squeeze(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Reshape': {\n            return [tfOps.reshape(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n        }\n        case 'PadV2':\n        case 'Pad': {\n            return [tfOps.pad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('constantValue', node, tensorMap, context))];\n        }\n        case 'SpaceToBatchND': {\n            const blockShape = getParamValue('blockShape', node, tensorMap, context);\n            const paddings = getParamValue('paddings', node, tensorMap, context);\n            return [tfOps.spaceToBatchND(getParamValue('x', node, tensorMap, context), blockShape, paddings)];\n        }\n        case 'BatchToSpaceND': {\n            const blockShape = getParamValue('blockShape', node, tensorMap, context);\n            const crops = getParamValue('crops', node, tensorMap, context);\n            return [tfOps.batchToSpaceND(getParamValue('x', node, tensorMap, context), blockShape, crops)];\n        }\n        case 'DepthToSpace': {\n            const blockSize = getParamValue('blockSize', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context).toUpperCase();\n            return [tfOps.depthToSpace(getParamValue('x', node, tensorMap, context), blockSize, dataFormat)];\n        }\n        case 'BroadcastTo': {\n            return [tfOps.broadcastTo(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'transformation';\n//# sourceMappingURL=transformation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { NodeValueImpl } from './custom_op/node_value_impl';\nimport { getRegisteredOp } from './custom_op/register';\nimport * as arithmetic from './executors/arithmetic_executor';\nimport * as basicMath from './executors/basic_math_executor';\nimport * as control from './executors/control_executor';\nimport * as convolution from './executors/convolution_executor';\nimport * as creation from './executors/creation_executor';\nimport * as dynamic from './executors/dynamic_executor';\nimport * as evaluation from './executors/evaluation_executor';\nimport * as graph from './executors/graph_executor';\nimport * as image from './executors/image_executor';\nimport * as logical from './executors/logical_executor';\nimport * as matrices from './executors/matrices_executor';\nimport * as normalization from './executors/normalization_executor';\nimport * as reduction from './executors/reduction_executor';\nimport * as sliceJoin from './executors/slice_join_executor';\nimport * as spectral from './executors/spectral_executor';\nimport * as transformation from './executors/transformation_executor';\n/**\n * Executes the op defined by the node object.\n * @param node\n * @param tensorMap contains tensors for executed nodes and weights\n */\nexport function executeOp(node, tensorMap, context) {\n    const value = ((node, tensorMap, context) => {\n        switch (node.category) {\n            case 'arithmetic':\n                return tfc.tidy(() => arithmetic.executeOp(node, tensorMap, context));\n            case 'basic_math':\n                return tfc.tidy(() => basicMath.executeOp(node, tensorMap, context));\n            case 'control':\n                return control.executeOp(node, tensorMap, context);\n            case 'convolution':\n                return tfc.tidy(() => convolution.executeOp(node, tensorMap, context));\n            case 'creation':\n                return tfc.tidy(() => creation.executeOp(node, tensorMap, context));\n            case 'dynamic':\n                return dynamic.executeOp(node, tensorMap, context);\n            case 'evaluation':\n                return tfc.tidy(() => evaluation.executeOp(node, tensorMap, context));\n            case 'image':\n                return tfc.tidy(() => image.executeOp(node, tensorMap, context));\n            case 'graph':\n                return tfc.tidy(() => graph.executeOp(node, tensorMap, context));\n            case 'logical':\n                return tfc.tidy(() => logical.executeOp(node, tensorMap, context));\n            case 'matrices':\n                return tfc.tidy(() => matrices.executeOp(node, tensorMap, context));\n            case 'normalization':\n                return tfc.tidy(() => normalization.executeOp(node, tensorMap, context));\n            case 'reduction':\n                return tfc.tidy(() => reduction.executeOp(node, tensorMap, context));\n            case 'slice_join':\n                return tfc.tidy(() => sliceJoin.executeOp(node, tensorMap, context));\n            case 'spectral':\n                return tfc.tidy(() => spectral.executeOp(node, tensorMap, context));\n            case 'transformation':\n                return tfc.tidy(() => transformation.executeOp(node, tensorMap, context));\n            case 'custom':\n                const opMapper = getRegisteredOp(node.op);\n                if (opMapper && opMapper.customExecutor) {\n                    return opMapper.customExecutor(new NodeValueImpl(node, tensorMap, context));\n                }\n                else {\n                    throw TypeError(`Custom op ${node.op} is not registered.`);\n                }\n            default:\n                throw TypeError(`Unknown op '${node.op}'. File an issue at ` +\n                    `https://github.com/tensorflow/tfjs/issues so we can add it` +\n                    `, or register a custom execution with tf.registerOp()`);\n        }\n    })(node, tensorMap, context);\n    if (value instanceof Promise) {\n        return value.then((data) => [].concat(data));\n    }\n    return [].concat(value);\n}\n//# sourceMappingURL=operation_executor.js.map", "/**\n * ExecutionContext captures the runtime environment of the node. It keeps\n * track of the current frame and iteration for the control flow ops.\n *\n * For example, typical Dynamic RNN model may contain loops, for which\n * TensorFlow will generate graphs with Enter/Exit nodes to control the\n * current execution frame, and NextIteration Nodes for iteration id increment.\n * For model with branch logic, TensorFLow will generate Switch/Merge ops.\n */\nexport class ExecutionContext {\n    constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}) {\n        this.weightMap = weightMap;\n        this.tensorArrayMap = tensorArrayMap;\n        this.tensorListMap = tensorListMap;\n        this.functionMap = functionMap;\n        this.rootContext = { id: 0, frameName: '', iterationId: 0 };\n        this.contexts = [this.rootContext];\n        this.lastId = 0;\n        this.generateCurrentContextIds();\n    }\n    newFrame(id, frameName) {\n        return { id, frameName, iterationId: 0 };\n    }\n    /**\n     * Set the current context\n     * @param contexts: ExecutionContextInfo[] the current path of execution\n     * frames\n     */\n    set currentContext(contexts) {\n        if (this.contexts !== contexts) {\n            this.contexts = contexts;\n            this.generateCurrentContextIds();\n        }\n    }\n    get currentContext() {\n        return this.contexts;\n    }\n    /**\n     * Returns the current context in string format.\n     */\n    get currentContextId() {\n        return this._currentContextIds[0];\n    }\n    /**\n     * Returns the current context and all parent contexts in string format.\n     * This allow access to the nodes in the current and parent frames.\n     */\n    get currentContextIds() {\n        return this._currentContextIds;\n    }\n    generateCurrentContextIds() {\n        const names = [];\n        for (let i = 0; i < this.contexts.length - 1; i++) {\n            const contexts = this.contexts.slice(0, this.contexts.length - i);\n            names.push(this.contextIdforContexts(contexts));\n        }\n        names.push('');\n        this._currentContextIds = names;\n    }\n    contextIdforContexts(contexts) {\n        return contexts ?\n            contexts\n                .map(context => (context.id === 0 && context.iterationId === 0) ?\n                '' :\n                `${context.frameName}-${context.iterationId}`)\n                .join('/') :\n            '';\n    }\n    /**\n     * Enter a new frame, a new context is pushed on the current context list.\n     * @param frameId new frame id\n     */\n    enterFrame(frameId) {\n        if (this.contexts) {\n            this.lastId++;\n            this.contexts = this.contexts.slice();\n            this.contexts.push(this.newFrame(this.lastId, frameId));\n            this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));\n        }\n    }\n    /**\n     * Exit the current frame, the last context is removed from the current\n     * context list.\n     */\n    exitFrame() {\n        if (this.contexts && this.contexts.length > 1) {\n            this.contexts = this.contexts.slice();\n            this.contexts.splice(-1);\n            this.currentContextIds.shift();\n        }\n        else {\n            throw new Error('Cannot exit frame, the context is empty');\n        }\n    }\n    /**\n     * Enter the next iteration of a loop, the iteration id of last context is\n     * increased.\n     */\n    nextIteration() {\n        if (this.contexts && this.contexts.length > 0) {\n            this.contexts = this.contexts.slice();\n            this.lastId++;\n            const context = Object.assign({}, this.contexts[this.contexts.length - 1]);\n            context.iterationId += 1;\n            context.id = this.lastId;\n            this.contexts.splice(-1, 1, context);\n            this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));\n        }\n        else {\n            throw new Error('Cannot increase frame iteration, the context is empty');\n        }\n    }\n    getWeight(name) {\n        return this.weightMap[name];\n    }\n    addTensorArray(tensorArray) {\n        this.tensorArrayMap[tensorArray.id] = tensorArray;\n    }\n    getTensorArray(id) {\n        return this.tensorArrayMap[id];\n    }\n    addTensorList(tensorList) {\n        this.tensorListMap[tensorList.id] = tensorList;\n    }\n    getTensorList(id) {\n        return this.tensorListMap[id];\n    }\n    dispose(keepIds) {\n        for (const key in this.tensorArrayMap) {\n            this.tensorArrayMap[key].clearAndClose(keepIds);\n        }\n        for (const key in this.tensorListMap) {\n            this.tensorListMap[key].clearAndClose(keepIds);\n        }\n    }\n}\n//# sourceMappingURL=execution_context.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { parseNodeName } from '../operations/executors/utils';\n/**\n * Given graph inputs and desired outputs, find the minimal set of nodes\n * to execute in order to compute the outputs. In addition return other useful\n * info such:\n * - Missing inputs needed to compute the output.\n * - Whether the subgraph contains dynamic ops (control flow, dynamic shape).\n * - Alternative inputs in order to avoid async (dynamic op) execution.\n */\nexport function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {\n    const usedNodes = new Set();\n    const missingInputs = [];\n    let dynamicNode = null;\n    let syncInputs = null;\n    // Start with the outputs, going backwards and find all the nodes that are\n    // needed to compute those outputs.\n    const seen = new Set();\n    const inputNodeNames = Object.keys(inputs).map(name => parseNodeName(name)[0]);\n    let initNodeNames = [];\n    if (initNodes != null) {\n        initNodeNames = initNodes.map(node => parseNodeName(node.name)[0]);\n    }\n    const frontier = [...outputs];\n    while (frontier.length > 0) {\n        const node = frontier.pop();\n        if (isControlFlow(node) || isDynamicShape(node)) {\n            if (dynamicNode == null) {\n                dynamicNode = node;\n                syncInputs = dynamicNode.children.map(child => child.name)\n                    .filter(name => usedNodes.has(name));\n            }\n        }\n        usedNodes.add(node.name);\n        // Weights are dead end since we already have their values.\n        if (weightMap[node.name] != null) {\n            continue;\n        }\n        // This node is a dead end since it's one of the user-provided inputs.\n        if (inputNodeNames.indexOf(node.name) !== -1) {\n            continue;\n        }\n        // This node is a dead end since it doesn't have any inputs.\n        if (initNodeNames.indexOf(node.name) !== -1) {\n            continue;\n        }\n        if (node.inputs.length === 0) {\n            missingInputs.push(node.name);\n            continue;\n        }\n        node.inputs.forEach(input => {\n            // Don't add to the frontier if it is already there.\n            if (seen.has(input.name)) {\n                return;\n            }\n            seen.add(input.name);\n            frontier.push(input);\n        });\n    }\n    return { inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs };\n}\n/**\n * Given the execution info, return a list of nodes in topological order that\n * need to be executed to compute the output.\n */\nexport function getNodesInTopologicalOrder(graph, weightMap, executionInfo) {\n    const { usedNodes, inputs } = executionInfo;\n    const frontier = [];\n    const inputNodes = Object.keys(inputs)\n        .map(name => parseNodeName(name)[0])\n        .map(name => graph.nodes[name]);\n    const initNodes = graph.initNodes;\n    inputNodes.forEach(input => {\n        if (usedNodes.has(input.name)) {\n            frontier.push(input);\n        }\n    });\n    graph.weights.forEach(weight => {\n        if (usedNodes.has(weight.name)) {\n            frontier.push(weight);\n        }\n    });\n    if (initNodes != null) {\n        initNodes.forEach(node => {\n            if (usedNodes.has(node.name)) {\n                frontier.push(node);\n            }\n        });\n    }\n    const seen = new Set();\n    const orderedNodes = [];\n    while (frontier.length > 0) {\n        const node = frontier.pop();\n        seen.add(node.name);\n        if (!weightMap[node.name]) {\n            orderedNodes.push(node);\n        }\n        node.children.forEach(child => {\n            if (!seen.has(child.name) && usedNodes.has(child.name) &&\n                child.inputs.every(input => seen.has(input.name))) {\n                frontier.push(child);\n            }\n        });\n    }\n    return orderedNodes;\n}\nconst CONTROL_FLOW_OPS = [\n    'Switch', 'Merge', 'Enter', 'Exit', 'NextIteration', 'StatelessIf',\n    'StatelessWhile', 'if', 'While'\n];\nconst DYNAMIC_SHAPE_OPS = [\n    'NonMaxSuppressionV2', 'NonMaxSuppressionV3', 'NonMaxSuppressionV5', 'Where'\n];\nexport function isControlFlow(node) {\n    return CONTROL_FLOW_OPS.indexOf(node.op) >= 0;\n}\nexport function isDynamicShape(node) {\n    return DYNAMIC_SHAPE_OPS.indexOf(node.op) >= 0;\n}\n//# sourceMappingURL=model_analysis.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tidy, util } from '@tensorflow/tfjs-core';\nimport { getNodeNameAndIndex, getParamValue, getTensor, getTensorsForCurrentContenxt, parseNodeName } from '../operations/executors/utils';\nimport { executeOp } from '../operations/operation_executor';\nimport { ExecutionContext } from './execution_context';\nimport { getExecutionSubgraph, getNodesInTopologicalOrder, isControlFlow } from './model_analysis';\nexport class GraphExecutor {\n    /**\n     *\n     * @param graph Graph the model or function graph to be executed.\n     * @param parent When building function exector you need to set the parent\n     * executor. Since the weights and function executor maps are set at parant\n     * level, that function executor can access the function maps and weight maps\n     * through the parent.\n     */\n    constructor(graph, parent) {\n        this.graph = graph;\n        this.parent = parent;\n        this.compiledMap = new Map();\n        this._weightMap = {};\n        this.SEPERATOR = ',';\n        this._functions = {};\n        this._functionExecutorMap = {};\n        this._outputs = graph.outputs;\n        this._inputs = graph.inputs;\n        this._initNodes = graph.initNodes;\n        this._signature = graph.signature;\n        this._functions = graph.functions;\n        // create sub-graph executors\n        if (graph.functions != null) {\n            Object.keys(graph.functions).forEach(name => {\n                this._functionExecutorMap[name] =\n                    new GraphExecutor(graph.functions[name], this);\n            });\n        }\n    }\n    get weightIds() {\n        return this.parent ? this.parent.weightIds : this._weightIds;\n    }\n    get functionExecutorMap() {\n        return this.parent ? this.parent.functionExecutorMap :\n            this._functionExecutorMap;\n    }\n    get weightMap() {\n        return this.parent ? this.parent.weightMap : this._weightMap;\n    }\n    set weightMap(weightMap) {\n        const weightIds = Object.keys(weightMap).map(key => weightMap[key].map(tensor => tensor.id));\n        this._weightIds = [].concat(...weightIds);\n        this._weightMap = weightMap;\n    }\n    get inputs() {\n        return this._inputs.map(node => {\n            return {\n                name: node.name,\n                shape: node.attrParams['shape'] ?\n                    node.attrParams['shape'].value :\n                    undefined,\n                dtype: node.attrParams['dtype'] ?\n                    node.attrParams['dtype'].value :\n                    undefined\n            };\n        });\n    }\n    get outputs() {\n        return this._outputs.map(node => {\n            return {\n                name: node.name,\n                shape: node.attrParams['shape'] ?\n                    node.attrParams['shape'].value :\n                    undefined,\n                dtype: node.attrParams['dtype'] ?\n                    node.attrParams['dtype'].value :\n                    undefined\n            };\n        });\n    }\n    get inputNodes() {\n        return this._inputs.map(node => node.signatureKey || node.name);\n    }\n    get outputNodes() {\n        return this._outputs.map((node) => {\n            const name = node.signatureKey || node.name;\n            return node.defaultOutput ? (`${name}:${node.defaultOutput}`) : name;\n        });\n    }\n    get functions() {\n        return Object.keys(this._functions).reduce((map, key) => {\n            map[key] = this._functions[key].signature;\n            return map;\n        }, {});\n    }\n    getCompilationKey(inputs, outputs) {\n        const sortedInputs = inputs.map(node => node.name).sort();\n        const sortedOutputs = outputs.map(node => node.name).sort();\n        return sortedInputs.join(this.SEPERATOR) + '--' +\n            sortedOutputs.join(this.SEPERATOR);\n    }\n    /**\n     * Compiles the inference graph and returns the minimal set of nodes that are\n     * required for execution, in the correct execution order.\n     */\n    compile(inputs, outputs) {\n        const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);\n        const { missingInputs, dynamicNode, syncInputs } = executionInfo;\n        if (dynamicNode != null) {\n            throw new Error(`This execution contains the node '${dynamicNode.name}', which has ` +\n                `the dynamic op '${dynamicNode.op}'. Please use ` +\n                `model.executeAsync() instead. Alternatively, to avoid the ` +\n                `dynamic ops, specify the inputs [${syncInputs}]`);\n        }\n        if (missingInputs.length > 0) {\n            const outNames = outputs.map(n => n.name);\n            const inNames = Object.keys(inputs);\n            throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs ` +\n                `[${inNames}]. Missing the following inputs: [${missingInputs}]`);\n        }\n        return getNodesInTopologicalOrder(this.graph, this.weightMap, executionInfo);\n    }\n    /**\n     * Executes the inference for given input tensors.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     */\n    execute(inputs, outputs) {\n        inputs = this.mapInputs(inputs);\n        const names = Object.keys(inputs).sort();\n        this.checkInputs(inputs);\n        this.checkInputShapeAndType(inputs);\n        outputs = this.mapOutputs(outputs);\n        this.checkOutputs(outputs);\n        const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n        const outputNodeNames = outputs.map(name => parseNodeName(name)[0]);\n        let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n        // If no outputs are specified, then use the default outputs of the model.\n        if (outputNodes.length === 0) {\n            outputNodes = this._outputs;\n        }\n        const compilationKey = this.getCompilationKey(inputNodes, outputNodes);\n        // Do nothing if the compiled graph cache contains the input.\n        let orderedNodes = this.compiledMap.get(compilationKey);\n        if (orderedNodes == null) {\n            orderedNodes = this.compile(inputs, outputNodes);\n            this.compiledMap.set(compilationKey, orderedNodes);\n        }\n        const tensorArrayMap = {};\n        const tensorListMap = {};\n        return tidy(() => {\n            const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n            const tensorsMap = Object.assign({}, this.weightMap);\n            Object.keys(inputs).forEach(name => {\n                const [nodeName, index] = parseNodeName(name);\n                const tensors = [];\n                tensors[index] = inputs[name];\n                tensorsMap[nodeName] = tensors;\n            });\n            const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n            const intermediateTensorConsumerCount = {};\n            for (let i = 0; i < orderedNodes.length; i++) {\n                const node = orderedNodes[i];\n                if (!tensorsMap[node.name]) {\n                    const tensors = executeOp(node, tensorsMap, context);\n                    if (tensors instanceof Promise) {\n                        throw new Error(`The execution of the op '${node.op}' returned a promise. ` +\n                            `Please use model.executeAsync() instead.`);\n                    }\n                    tensorsMap[node.name] = tensors;\n                    this.checkTensorForDisposal(node.name, node, tensorsMap, context, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount);\n                }\n            }\n            // dispose the context for the root executor\n            if (this.parent == null) {\n                context.dispose(tensorsToKeep);\n            }\n            return outputs.map(name => getTensor(name, tensorsMap, context));\n        });\n    }\n    getFrozenTensorIds(tensorMap) {\n        const ids = [].concat.apply([], Object.keys(tensorMap)\n            .map(key => tensorMap[key])\n            .map(tensors => tensors.map(tensor => tensor.id)));\n        return new Set(ids);\n    }\n    checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount) {\n        // Skip output nodes and any control flow nodes, since its dependency is\n        // tricky to track correctly.\n        if (node.category === 'control' || outputNames.indexOf(nodeName) !== -1) {\n            return;\n        }\n        tensorMap[nodeName].forEach(tensor => {\n            if (tensor != null) {\n                intermediateTensorConsumerCount[tensor.id] =\n                    (intermediateTensorConsumerCount[tensor.id] || 0) +\n                        node.children.length;\n            }\n        });\n        node.inputs.forEach(input => {\n            // Skip any control flow nodes, since its dependency is tricky to track\n            // correctly.\n            if (input.category !== 'control') {\n                const tensors = getTensorsForCurrentContenxt(input.name, tensorMap, context);\n                if (tensors != null) {\n                    tensors.forEach(tensor => {\n                        if (tensor && !tensorsToKeep.has(tensor.id)) {\n                            const count = intermediateTensorConsumerCount[tensor.id];\n                            if (count === 1) {\n                                tensor.dispose();\n                                delete intermediateTensorConsumerCount[tensor.id];\n                            }\n                            else if (count != null) {\n                                // only intermediate nodes has count set, inputs and weights are\n                                // not.\n                                intermediateTensorConsumerCount[tensor.id]--;\n                            }\n                        }\n                    });\n                }\n            }\n        });\n    }\n    /**\n     * Executes the inference for given input tensors in Async fashion.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     */\n    async executeAsync(inputs, outputs) {\n        return this._executeAsync(inputs, outputs);\n    }\n    /**\n     * Executes the inference for given input tensors in Async fashion.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     * @param isFunctionExecution Flag for executing a function.\n     * @param tensorArrayMap Optional, global TensorArray map by id. Used for\n     * function execution.\n     * @param tensorArrayMap Optinal global TensorList map by id. Used for\n     * function execution.\n     */\n    async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {\n        if (!isFunctionExecution) {\n            inputs = this.mapInputs(inputs);\n            this.checkInputs(inputs);\n            this.checkInputShapeAndType(inputs);\n            outputs = this.mapOutputs(outputs);\n            this.checkOutputs(outputs);\n        }\n        const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n        // Graph with control flow op requires runtime evaluation of the execution\n        // order, while without control flow the execution order is pre-determined\n        // in the compile method.\n        const tensorMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);\n        const results = outputs.map(name => getTensor(name, tensorMap, context));\n        // dispose all the intermediate tensors\n        const outputIds = results.map(t => t.id);\n        const inputIds = Object.keys(inputs).map(name => inputs[name].id);\n        const keepIds = new Set([...outputIds, ...inputIds, ...this.weightIds]);\n        Object.keys(tensorMap).forEach(key => {\n            const tensorArray = tensorMap[key];\n            tensorArray.forEach(tensor => {\n                if (tensor && !tensor.isDisposed && !keepIds.has(tensor.id)) {\n                    tensor.dispose();\n                }\n            });\n        });\n        // dispose the context for the root executor\n        if (this.parent == null) {\n            context.dispose(keepIds);\n        }\n        return results;\n    }\n    async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {\n        const mappedInputs = inputs.reduce((map, tensor, index) => {\n            map[this.inputs[index].name] = tensor;\n            return map;\n        }, {});\n        return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);\n    }\n    /**\n     * When there are control flow nodes in the graph, the graph execution use\n     * ExecutionContext to keep track of the frames and loop iterators.\n     * @param inputs placeholder tensors for the graph.\n     * @param context the execution context object for current execution.\n     * @param isFunctionExecution Flag for executing a function.\n     */\n    async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {\n        const names = Object.keys(inputs);\n        const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n        const outputNodeNames = outputNames.map(name => parseNodeName(name)[0]);\n        const outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n        const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes, this.weightMap);\n        const stack = [...inputNodes, ...this.graph.weights].map(node => {\n            return { node, contexts: context.currentContext };\n        });\n        const tensorsMap = Object.assign({}, this.weightMap);\n        Object.keys(inputs).forEach(name => {\n            const [nodeName, index] = parseNodeName(name);\n            const tensors = [];\n            tensors[index] = inputs[name];\n            tensorsMap[nodeName] = tensors;\n        });\n        const intermediateTensorConsumerCount = {};\n        const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n        const added = {};\n        while (stack.length > 0) {\n            const promises = this.processStack(inputNodes, stack, context, tensorsMap, added, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount, usedNodes);\n            await Promise.all(promises);\n        }\n        if (dynamicNode == null && !isFunctionExecution) {\n            console.warn(`This model execution did not contain any nodes with control flow ` +\n                `or dynamic output shapes. You can use model.execute() instead.`);\n        }\n        const missingOutputs = outputNodes\n            .filter(node => !isControlFlow(node) &&\n            !getTensor(node.name, tensorsMap, context))\n            .map(node => node.name);\n        if (missingOutputs.length > 0) {\n            let alternativeMsg = '';\n            if (dynamicNode != null) {\n                alternativeMsg =\n                    `Alternatively, to avoid the dynamic ops, use model.execute() ` +\n                        `and specify the inputs [${syncInputs}]`;\n            }\n            throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided ` +\n                `inputs [${names}]. Consider providing the following inputs: ` +\n                `[${missingInputs}]. ${alternativeMsg}`);\n        }\n        return tensorsMap;\n    }\n    processStack(inputNodes, stack, context, tensorMap, added, tensorsToKeep, outputNames, intermediateTensorConsumerCount, usedNodes) {\n        const promises = [];\n        while (stack.length > 0) {\n            const item = stack.pop();\n            context.currentContext = item.contexts;\n            let nodeName = '';\n            // The tensor of the Enter op with isConstant set should be set\n            // in the parent scope, so it will be available as constant for the\n            // whole loop.\n            if (item.node.op === 'Enter' &&\n                getParamValue('isConstant', item.node, tensorMap, context)) {\n                [nodeName] = getNodeNameAndIndex(item.node.name, context);\n            }\n            // only process nodes that are not provided as input nodes.\n            if (inputNodes.indexOf(item.node) === -1) {\n                const tensors = executeOp(item.node, tensorMap, context);\n                if (!nodeName) {\n                    [nodeName] = getNodeNameAndIndex(item.node.name, context);\n                }\n                const currentContext = context.currentContext;\n                if (tensors instanceof Promise) {\n                    promises.push(tensors.then(t => {\n                        tensorMap[nodeName] = t;\n                        context.currentContext = currentContext;\n                        this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n                        this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n                        return t;\n                    }));\n                }\n                else {\n                    tensorMap[nodeName] = tensors;\n                    this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n                    this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n                }\n            }\n            else {\n                this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n            }\n        }\n        return promises;\n    }\n    processChildNodes(node, stack, context, tensorMap, added, usedNodes) {\n        node.children.forEach((childNode) => {\n            const [nodeName,] = getNodeNameAndIndex(childNode.name, context);\n            if (added[nodeName] || !usedNodes.has(childNode.name)) {\n                return;\n            }\n            // Merge op can be pushed if any of its inputs has value.\n            if (childNode.op === 'Merge') {\n                if (childNode.inputNames.some(name => {\n                    return !!getTensor(name, tensorMap, context);\n                })) {\n                    added[nodeName] = true;\n                    stack.push({ contexts: context.currentContext, node: childNode });\n                }\n            }\n            else // Otherwise all inputs must to have value.\n             if (childNode.inputNames.every(name => {\n                return !!getTensor(name, tensorMap, context);\n            })) {\n                added[nodeName] = true;\n                stack.push({ contexts: context.currentContext, node: childNode });\n            }\n        });\n    }\n    /**\n     * Releases the memory used by the weight tensors.\n     */\n    dispose() {\n        Object.keys(this.weightMap)\n            .forEach(key => this.weightMap[key].forEach(tensor => tensor.dispose()));\n    }\n    checkInputShapeAndType(inputs) {\n        Object.keys(inputs).forEach(name => {\n            const input = inputs[name];\n            const [nodeName,] = parseNodeName(name);\n            const node = this.graph.nodes[nodeName];\n            if (node.attrParams['shape'] && node.attrParams['shape'].value) {\n                const shape = node.attrParams['shape'].value;\n                const match = shape.length === input.shape.length &&\n                    input.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim);\n                util.assert(match, () => `The shape of dict['${node.name}'] provided in ` +\n                    `model.execute(dict) must be [${shape}], but was ` +\n                    `[${input.shape}]`);\n            }\n            if (node.attrParams['dtype'] && node.attrParams['dtype'].value) {\n                util.assert(input.dtype === node.attrParams['dtype'].value, () => `The dtype of dict['${node.name}'] provided in ` +\n                    `model.execute(dict) must be ` +\n                    `${node.attrParams['dtype'].value}, but was ${input.dtype}`);\n            }\n        });\n    }\n    mapInputs(inputs) {\n        const result = {};\n        for (const inputName in inputs) {\n            if (this._signature != null && this._signature.inputs != null &&\n                this._signature.inputs[inputName] != null) {\n                const tensor = this._signature.inputs[inputName];\n                result[tensor.name] = inputs[inputName];\n            }\n            else {\n                result[inputName] = inputs[inputName];\n            }\n        }\n        return result;\n    }\n    checkInputs(inputs) {\n        const notInGraph = Object.keys(inputs).filter(name => {\n            const [nodeName] = parseNodeName(name);\n            return this.graph.nodes[nodeName] == null;\n        });\n        if (notInGraph.length > 0) {\n            throw new Error(`The dict provided in model.execute(dict) has ` +\n                `keys: [${notInGraph}] that are not part of graph`);\n        }\n    }\n    mapOutputs(outputs) {\n        return outputs.map(name => {\n            if (this._signature != null && this._signature.outputs != null &&\n                this._signature.outputs[name] != null) {\n                const tensor = this._signature.outputs[name];\n                return tensor.name;\n            }\n            return name;\n        }, {});\n    }\n    checkOutputs(outputs) {\n        outputs.forEach(name => {\n            const [normalizedName] = parseNodeName(name);\n            if (!this.graph.nodes[normalizedName]) {\n                throw new Error(`The output '${name}' is not found in the graph`);\n            }\n        });\n    }\n}\n//# sourceMappingURL=graph_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { io, Tensor } from '@tensorflow/tfjs-core';\nimport { OperationMapper } from '../operations/operation_mapper';\nimport { GraphExecutor } from './graph_executor';\nexport const TFHUB_SEARCH_PARAM = '?tfjs-format=file';\nexport const DEFAULT_MODEL_NAME = 'model.json';\n/**\n * A `tf.GraphModel` is a directed, acyclic graph built from a\n * SavedModel GraphDef and allows inference execution.\n *\n * A `tf.GraphModel` can only be created by loading from a model converted from\n * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using\n * the command line converter tool and loaded via `tf.loadGraphModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class GraphModel {\n    /**\n     * @param modelUrl url for the model, or an `io.IOHandler`.\n     * @param weightManifestUrl url for the weight file generated by\n     * scripts/convert.py script.\n     * @param requestOption options for Request, which allows to send credentials\n     * and custom headers.\n     * @param onProgress Optional, progress callback function, fired periodically\n     * before the load is completed.\n     */\n    constructor(modelUrl, loadOptions = {}) {\n        this.modelUrl = modelUrl;\n        this.loadOptions = loadOptions;\n        this.version = 'n/a';\n        if (loadOptions == null) {\n            this.loadOptions = {};\n        }\n    }\n    // Returns the version information for the tensorflow model GraphDef.\n    get modelVersion() {\n        return this.version;\n    }\n    get inputNodes() {\n        return this.executor.inputNodes;\n    }\n    get outputNodes() {\n        return this.executor.outputNodes;\n    }\n    get inputs() {\n        return this.executor.inputs;\n    }\n    get outputs() {\n        return this.executor.outputs;\n    }\n    get weights() {\n        return this.executor.weightMap;\n    }\n    findIOHandler() {\n        const path = this.modelUrl;\n        if (path.load != null) {\n            // Path is an IO Handler.\n            this.handler = path;\n        }\n        else if (this.loadOptions.requestInit != null) {\n            this.handler = io.browserHTTPRequest(path, this.loadOptions);\n        }\n        else {\n            const handlers = io.getLoadHandlers(path, this.loadOptions);\n            if (handlers.length === 0) {\n                // For backward compatibility: if no load handler can be found,\n                // assume it is a relative http path.\n                handlers.push(io.browserHTTPRequest(path, this.loadOptions));\n            }\n            else if (handlers.length > 1) {\n                throw new Error(`Found more than one (${handlers.length}) load handlers for ` +\n                    `URL '${[path]}'`);\n            }\n            this.handler = handlers[0];\n        }\n    }\n    /**\n     * Loads the model and weight files, construct the in memory weight map and\n     * compile the inference graph.\n     */\n    async load() {\n        this.findIOHandler();\n        if (this.handler.load == null) {\n            throw new Error('Cannot proceed with model loading because the IOHandler provided ' +\n                'does not have the `load` method implemented.');\n        }\n        const artifacts = await this.handler.load();\n        return this.loadSync(artifacts);\n    }\n    /**\n     * Synchronously construct the in memory weight map and\n     * compile the inference graph. Also initialize hashtable if any.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    loadSync(artifacts) {\n        this.artifacts = artifacts;\n        const graph = this.artifacts.modelTopology;\n        let signature = {};\n        if (this.artifacts.userDefinedMetadata != null) {\n            signature = // tslint:disable-next-line:no-any\n                this.artifacts.userDefinedMetadata.signature;\n        }\n        this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;\n        const weightMap = io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);\n        this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, signature));\n        this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);\n        if (artifacts.modelInitializer != null) {\n            const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer);\n            this.initializer = new GraphExecutor(initializer);\n            this.initializer.weightMap = this.executor.weightMap;\n            this.initializer.execute({}, []);\n        }\n        return true;\n    }\n    /**\n     * Save the configuration and/or weights of the GraphModel.\n     *\n     * An `IOHandler` is an object that has a `save` method of the proper\n     * signature defined. The `save` method manages the storing or\n     * transmission of serialized data (\"artifacts\") that represent the\n     * model's topology and weights onto or via a specific medium, such as\n     * file downloads, local storage, IndexedDB in the web browser and HTTP\n     * requests to a server. TensorFlow.js provides `IOHandler`\n     * implementations for a number of frequently used saving mediums, such as\n     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n     * for more details.\n     *\n     * This method also allows you to refer to certain types of `IOHandler`s\n     * as URL-like string shortcuts, such as 'localstorage://' and\n     * 'indexeddb://'.\n     *\n     * Example 1: Save `model`'s topology and weights to browser [local\n     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n     * then load it back.\n     *\n     * ```js\n     * const modelUrl =\n     *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n     * const model = await tf.loadGraphModel(modelUrl);\n     * const zeros = tf.zeros([1, 224, 224, 3]);\n     * model.predict(zeros).print();\n     *\n     * const saveResults = await model.save('localstorage://my-model-1');\n     *\n     * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * model.predict(zeros).print();\n     * ```\n     *\n     * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n     * scheme-based string shortcut for `IOHandler`.\n     * @param config Options for saving the model.\n     * @returns A `Promise` of `SaveResult`, which summarizes the result of\n     * the saving, such as byte sizes of the saved artifacts for the model's\n     *   topology and weight values.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async save(handlerOrURL, config) {\n        if (typeof handlerOrURL === 'string') {\n            const handlers = io.getSaveHandlers(handlerOrURL);\n            if (handlers.length === 0) {\n                throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n            }\n            else if (handlers.length > 1) {\n                throw new Error(`Found more than one (${handlers.length}) save handlers for ` +\n                    `URL '${handlerOrURL}'`);\n            }\n            handlerOrURL = handlers[0];\n        }\n        if (handlerOrURL.save == null) {\n            throw new Error('GraphModel.save() cannot proceed because the IOHandler ' +\n                'provided does not have the `save` attribute defined.');\n        }\n        return handlerOrURL.save(this.artifacts);\n    }\n    /**\n     * Execute the inference for the input tensors.\n     *\n     * @param input The input tensors, when there is single input for the model,\n     * inputs param should be a `tf.Tensor`. For models with mutliple inputs,\n     * inputs params should be in either `tf.Tensor`[] if the input order is\n     * fixed, or otherwise NamedTensorMap format.\n     *\n     * For model with multiple inputs, we recommend you use NamedTensorMap as the\n     * input type, if you use `tf.Tensor`[], the order of the array needs to\n     * follow the\n     * order of inputNodes array. @see {@link GraphModel.inputNodes}\n     *\n     * You can also feed any intermediate nodes using the NamedTensorMap as the\n     * input type. For example, given the graph\n     *    InputNode => Intermediate => OutputNode,\n     * you can execute the subgraph Intermediate => OutputNode by calling\n     *    model.execute('IntermediateNode' : tf.tensor(...));\n     *\n     * This is useful for models that uses tf.dynamic_rnn, where the intermediate\n     * state needs to be fed manually.\n     *\n     * For batch inference execution, the tensors for each input need to be\n     * concatenated together. For example with mobilenet, the required input shape\n     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].\n     * If we are provide a batched data of 100 images, the input tensor should be\n     * in the shape of [100, 244, 244, 3].\n     *\n     * @param config Prediction configuration for specifying the batch size and\n     * output node names. Currently the batch size option is ignored for graph\n     * model.\n     *\n     * @returns Inference result tensors. The output would be single `tf.Tensor`\n     * if model has single output node, otherwise Tensor[] or NamedTensorMap[]\n     * will be returned for model with multiple outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(inputs, config) {\n        return this.execute(inputs, this.outputNodes);\n    }\n    normalizeInputs(inputs) {\n        if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {\n            // The input is already a NamedTensorMap.\n            return inputs;\n        }\n        inputs = Array.isArray(inputs) ? inputs : [inputs];\n        if (inputs.length !== this.inputNodes.length) {\n            throw new Error('Input tensor count mismatch,' +\n                `the graph model has ${this.inputNodes.length} placeholders, ` +\n                `while there are ${inputs.length} input tensors.`);\n        }\n        return this.inputNodes.reduce((map, inputName, i) => {\n            map[inputName] = inputs[i];\n            return map;\n        }, {});\n    }\n    normalizeOutputs(outputs) {\n        outputs = outputs || this.outputNodes;\n        return !Array.isArray(outputs) ? [outputs] : outputs;\n    }\n    /**\n     * Executes inference for the model for given input tensors.\n     * @param inputs tensor, tensor array or tensor map of the inputs for the\n     * model, keyed by the input node names.\n     * @param outputs output node name from the Tensorflow model, if no\n     * outputs are specified, the default outputs of the model would be used.\n     * You can inspect intermediate nodes of the model by adding them to the\n     * outputs array.\n     *\n     * @returns A single tensor if provided with a single output or no outputs\n     * are provided and there is only one default output, otherwise return a\n     * tensor array. The order of the tensor array is the same as the outputs\n     * if provided, otherwise the order of outputNodes attribute of the model.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    execute(inputs, outputs) {\n        inputs = this.normalizeInputs(inputs);\n        outputs = this.normalizeOutputs(outputs);\n        const result = this.executor.execute(inputs, outputs);\n        return result.length > 1 ? result : result[0];\n    }\n    /**\n     * Executes inference for the model for given input tensors in async\n     * fashion, use this method when your model contains control flow ops.\n     * @param inputs tensor, tensor array or tensor map of the inputs for the\n     * model, keyed by the input node names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     *\n     * @returns A Promise of single tensor if provided with a single output or\n     * no outputs are provided and there is only one default output, otherwise\n     * return a tensor map.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async executeAsync(inputs, outputs) {\n        inputs = this.normalizeInputs(inputs);\n        outputs = this.normalizeOutputs(outputs);\n        const result = await this.executor.executeAsync(inputs, outputs);\n        return result.length > 1 ? result : result[0];\n    }\n    convertTensorMapToTensorsMap(map) {\n        return Object.keys(map).reduce((newMap, key) => {\n            newMap[key] = [map[key]];\n            return newMap;\n        }, {});\n    }\n    /**\n     * Releases the memory used by the weight tensors.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    dispose() {\n        this.executor.dispose();\n        if (this.initializer) {\n            this.initializer.dispose();\n        }\n    }\n}\n/**\n * Load a graph model given a URL to the model definition.\n *\n * Example of loading MobileNetV2 from a URL and making a prediction with a\n * zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n * const model = await tf.loadGraphModel(modelUrl);\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n *\n * Example of loading MobileNetV2 from a TF Hub URL and making a prediction with\n * a zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';\n * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n * @param modelUrl The url or an `io.IOHandler` that loads the model.\n * @param options Options for the HTTP request, which allows to send credentials\n *    and custom headers.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport async function loadGraphModel(modelUrl, options = {}) {\n    if (modelUrl == null) {\n        throw new Error('modelUrl in loadGraphModel() cannot be null. Please provide a url ' +\n            'or an IOHandler that loads the model');\n    }\n    if (options == null) {\n        options = {};\n    }\n    if (options.fromTFHub) {\n        if (modelUrl.load == null) {\n            if (!modelUrl.endsWith('/')) {\n                modelUrl = modelUrl + '/';\n            }\n            modelUrl = `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;\n        }\n    }\n    const model = new GraphModel(modelUrl, options);\n    await model.load();\n    return model;\n}\n//# sourceMappingURL=graph_model.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { GraphModel, loadGraphModel } from './executor/graph_model';\nexport { deregisterOp, registerOp } from './operations/custom_op/register';\nexport { version as version_converter } from './version';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\n/**\n * Apply a mapping function to a nested structure in a recursive manner.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapResult`.  The `DeepMapResult` either provides a\n *   replacement value for that node (i.e., replacing the subtree), or indicates\n *   that the node should be processed recursively.\n */\nexport function deepMap(input, mapFn) {\n    return deepMapInternal(input, mapFn);\n}\n/**\n * @param seen: A Map of known object mappings (i.e., memoized results of\n *   `mapFn()`)\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepMapInternal(input, mapFn, seen = new Map(), containedIn = new Set()) {\n    if (input == null) {\n        return null;\n    }\n    if (containedIn.has(input)) {\n        throw new Error('Circular references are not supported.');\n    }\n    if (seen.has(input)) {\n        return seen.get(input);\n    }\n    const result = mapFn(input);\n    if (result.recurse && result.value !== null) {\n        throw new Error('A deep map function may not return both a value and recurse=true.');\n    }\n    if (!result.recurse) {\n        seen.set(input, result.value);\n        return result.value;\n    }\n    else if (isIterable(input)) {\n        // tslint:disable-next-line:no-any\n        const mappedIterable = Array.isArray(input) ? [] : {};\n        containedIn.add(input);\n        for (const k in input) {\n            const child = input[k];\n            const childResult = deepMapInternal(child, mapFn, seen, containedIn);\n            mappedIterable[k] = childResult;\n        }\n        containedIn.delete(input);\n        return mappedIterable;\n    }\n    else {\n        throw new Error(`Can't recurse into non-iterable type: ${input}`);\n    }\n}\n// TODO(soergel, kangyizhang) Reconsider naming of deepZip() to avoid confusion\n// with zip()\n/**\n * Zip nested structures together in a recursive manner.\n *\n * This has the effect of transposing or pivoting data, e.g. converting it from\n * a row-major representation to a column-major representation.\n *\n * For example, `deepZip([{a: 1, b: 2}, {a: 3, b: 4}])` returns\n * `{a: [1, 3], b: [2, 4]}`.\n *\n * The inputs should all have the same nested structure (i.e., of arrays and\n * dicts).  The result is a single object with the same nested structure, where\n * the leaves are arrays collecting the values of the inputs at that location\n * (or, optionally, the result of a custom function applied to those arrays).\n *\n * @param inputs: An array of the objects to zip together.\n * @param zipFn: (optional) A function that expects an array of elements at a\n *   single node of the object tree, and returns a `DeepMapResult`.  The\n *   `DeepMapResult` either provides a result value for that node (i.e.,\n *   representing the subtree), or indicates that the node should be processed\n *   recursively.  The default zipFn recurses as far as possible and places\n *   arrays at the leaves.\n */\nexport function deepZip(inputs, zipFn = zipToList) {\n    return deepZipInternal(inputs, zipFn);\n}\n/**\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepZipInternal(inputs, zipFn, containedIn = new Set()) {\n    // The recursion follows the structure of input 0; it's assumed that all the\n    // other inputs have the same structure.\n    const input = inputs[0];\n    if (containedIn.has(input)) {\n        throw new Error('Circular references are not supported.');\n    }\n    const result = zipFn(inputs);\n    if (result.recurse && result.value !== null) {\n        throw new Error('A deep zip function may not return both a value and recurse=true.');\n    }\n    if (!result.recurse) {\n        return result.value;\n    }\n    else if (isIterable(input)) {\n        // tslint:disable-next-line:no-any\n        const mappedIterable = Array.isArray(input) ? [] : {};\n        containedIn.add(input);\n        for (const k in input) {\n            const children = inputs.map(x => x[k]);\n            const childResult = deepZipInternal(children, zipFn, containedIn);\n            mappedIterable[k] = childResult;\n        }\n        containedIn.delete(input);\n        return mappedIterable;\n    }\n    else {\n        throw new Error(`Can't recurse into non-iterable type: ${input}`);\n    }\n}\n// tslint:disable-next-line:no-any\nexport function zipToList(x) {\n    if (x === null) {\n        return null;\n    }\n    // TODO(soergel): validate array type?\n    if (isIterable(x[0])) {\n        return { value: null, recurse: true };\n    }\n    else {\n        return { value: x, recurse: false };\n    }\n}\n/**\n * Apply an async mapping function to a nested structure in a recursive manner.\n *\n * This first creates a nested structure of Promises, and then awaits all of\n * those, resulting in a single Promise for a resolved nested structure.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapAsyncResult`.  The `DeepMapAsyncResult` either provides\n *   a `Promise` for a replacement value for that node (i.e., replacing the\n *   subtree), or indicates that the node should be processed recursively.  Note\n *   that the decision whether or not to recurse must be made immediately; only\n *   the mapped value may be promised.\n */\nexport async function deepMapAndAwaitAll(input, mapFn) {\n    const seen = new Map();\n    // First do a normal deepMap, collecting Promises in 'seen' as a side effect.\n    deepMapInternal(input, mapFn, seen);\n    // Replace the Promises in 'seen' in place.\n    // Note TypeScript provides no async map iteration, and regular map iteration\n    // is broken too, so sadly we have to do Array.from() to make it work.\n    // (There's no advantage to Promise.all(), and that would be tricky anyway.)\n    for (const key of Array.from(seen.keys())) {\n        const value = seen.get(key);\n        if (value instanceof Promise) {\n            const mappedValue = await value;\n            seen.set(key, mappedValue);\n        }\n    }\n    // Normal deepMap again, this time filling in the resolved values.\n    // It's unfortunate that we have to do two passes.\n    // TODO(soergel): test performance and think harder about a fast solution.\n    const result = deepMapInternal(input, mapFn, seen);\n    return result;\n}\n/**\n * Determine whether the argument is iterable.\n *\n * @returns true if the argument is an array or any non-Tensor object.\n */\n// tslint:disable-next-line:no-any\nexport function isIterable(obj) {\n    return obj != null && (!ArrayBuffer.isView(obj)) &&\n        (Array.isArray(obj) ||\n            (typeof obj === 'object' && !(obj instanceof tf.Tensor)));\n}\n/**\n * Determine whether the argument can be converted to Tensor.\n *\n * Tensors, primitives, arrays, and TypedArrays all qualify; anything else does\n * not.\n *\n * @returns true if the argument can be converted to Tensor.\n */\n// tslint:disable-next-line:no-any\nexport function canTensorify(obj) {\n    return obj == null || isPrimitive(obj) || Array.isArray(obj) ||\n        (typeof obj === 'object' && (obj instanceof tf.Tensor)) ||\n        tf.util.isTypedArray(obj);\n}\n/**\n * Returns true if the given `value` is a primitive type. Otherwise returns\n * false. This is equivalant to node util.isPrimitive\n */\nfunction isPrimitive(value) {\n    return (value === null ||\n        (typeof value !== 'object' && typeof value !== 'function'));\n}\n//# sourceMappingURL=deep_map.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { deepMap, isIterable } from './deep_map';\nexport function deepClone(container) {\n    return deepMap(container, cloneIfTensor);\n}\n// tslint:disable-next-line: no-any\nfunction cloneIfTensor(item) {\n    if (item instanceof tf.Tensor) {\n        return ({ value: item.clone(), recurse: false });\n    }\n    else if (isIterable(item)) {\n        return { value: null, recurse: true };\n    }\n    else {\n        return { value: item, recurse: false };\n    }\n}\n//# sourceMappingURL=deep_clone.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * A ring buffer, providing O(1) FIFO, LIFO, and related operations.\n */\nexport class RingBuffer {\n    /**\n     * Constructs a `RingBuffer`.\n     * @param capacity The number of items that the buffer can accomodate.\n     */\n    constructor(capacity) {\n        this.capacity = capacity;\n        // Note we store the indices in the range 0 <= index < 2*capacity.\n        // This allows us to distinguish the full from the empty case.\n        // See https://www.snellman.net/blog/archive/2016-12-13-ring-buffers/\n        this.begin = 0; // inclusive\n        this.end = 0; // exclusive\n        if (capacity == null) {\n            throw new RangeError('Can\\'t create a ring buffer of unknown capacity.');\n        }\n        if (capacity < 1) {\n            throw new RangeError('Can\\'t create ring buffer of capacity < 1.');\n        }\n        this.data = new Array(capacity);\n        this.doubledCapacity = 2 * capacity;\n    }\n    /**\n     * Map any index into the range 0 <= index < 2*capacity.\n     */\n    wrap(index) {\n        // don't trust % on negative numbers\n        while (index < 0) {\n            index += this.doubledCapacity;\n        }\n        return index % this.doubledCapacity;\n    }\n    get(index) {\n        if (index < 0) {\n            throw new RangeError('Can\\'t get item at a negative index.');\n        }\n        return this.data[index % this.capacity];\n    }\n    set(index, value) {\n        if (index < 0) {\n            throw new RangeError('Can\\'t set item at a negative index.');\n        }\n        this.data[index % this.capacity] = value;\n    }\n    /**\n     * Returns the current number of items in the buffer.\n     */\n    length() {\n        let length = this.end - this.begin;\n        if (length < 0) {\n            length = this.doubledCapacity + length;\n        }\n        return length;\n    }\n    /**\n     * Reports whether the buffer is full.\n     * @returns true if the number of items in the buffer equals its capacity, and\n     *   false otherwise.\n     */\n    isFull() {\n        return this.length() === this.capacity;\n    }\n    /**\n     * Reports whether the buffer is empty.\n     * @returns true if the number of items in the buffer equals zero, and\n     *   false otherwise.\n     */\n    isEmpty() {\n        return this.length() === 0;\n    }\n    /**\n     * Adds an item to the end of the buffer.\n     */\n    push(value) {\n        if (this.isFull()) {\n            throw new RangeError('Ring buffer is full.');\n        }\n        this.set(this.end, value);\n        this.end = this.wrap(this.end + 1);\n    }\n    /**\n     * Adds many items to the end of the buffer, in order.\n     */\n    pushAll(values) {\n        for (const value of values) {\n            this.push(value);\n        }\n    }\n    /**\n     * Removes and returns the last item in the buffer.\n     */\n    pop() {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        this.end = this.wrap(this.end - 1);\n        const result = this.get(this.end);\n        this.set(this.end, undefined);\n        return result;\n    }\n    /**\n     * Adds an item to the beginning of the buffer.\n     */\n    unshift(value) {\n        if (this.isFull()) {\n            throw new RangeError('Ring buffer is full.');\n        }\n        this.begin = this.wrap(this.begin - 1);\n        this.set(this.begin, value);\n    }\n    /**\n     * Removes and returns the first item in the buffer.\n     */\n    shift() {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        const result = this.get(this.begin);\n        this.set(this.begin, undefined);\n        this.begin = this.wrap(this.begin + 1);\n        return result;\n    }\n    /**\n     * Removes and returns a specific item in the buffer, and moves the last item\n     * to the vacated slot.  This is useful for implementing a shuffling stream.\n     * Note that this operation necessarily scrambles the original order.\n     *\n     * @param relativeIndex: the index of the item to remove, relative to the\n     *   first item in the buffer (e.g., hiding the ring nature of the underlying\n     *   storage).\n     */\n    shuffleExcise(relativeIndex) {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        const index = this.wrap(this.begin + relativeIndex);\n        const result = this.get(index);\n        this.set(index, this.pop());\n        return result;\n    }\n}\n//# sourceMappingURL=ring_buffer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { RingBuffer } from './ring_buffer';\nexport class GrowingRingBuffer extends RingBuffer {\n    /**\n     * Constructs a `GrowingRingBuffer`.\n     */\n    constructor() {\n        super(GrowingRingBuffer.INITIAL_CAPACITY);\n    }\n    isFull() {\n        return false;\n    }\n    push(value) {\n        if (super.isFull()) {\n            this.expand();\n        }\n        super.push(value);\n    }\n    unshift(value) {\n        if (super.isFull()) {\n            this.expand();\n        }\n        super.unshift(value);\n    }\n    /**\n     * Doubles the capacity of the buffer.\n     */\n    expand() {\n        const newCapacity = this.capacity * 2;\n        const newData = new Array(newCapacity);\n        const len = this.length();\n        // Rotate the buffer to start at index 0 again, since we can't just\n        // allocate more space at the end.\n        for (let i = 0; i < len; i++) {\n            newData[i] = this.get(this.wrap(this.begin + i));\n        }\n        this.data = newData;\n        this.capacity = newCapacity;\n        this.doubledCapacity = 2 * this.capacity;\n        this.begin = 0;\n        this.end = len;\n    }\n}\nGrowingRingBuffer.INITIAL_CAPACITY = 32;\n//# sourceMappingURL=growing_ring_buffer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport * as seedrandom from 'seedrandom';\nimport { deepClone } from '../util/deep_clone';\nimport { deepMapAndAwaitAll, deepZip, zipToList } from '../util/deep_map';\nimport { GrowingRingBuffer } from '../util/growing_ring_buffer';\nimport { RingBuffer } from '../util/ring_buffer';\n// Here we implement a simple asynchronous iterator.\n// This lets us avoid using either third-party stream libraries or\n// recent TypeScript language support requiring polyfills.\n/**\n * Create a `LazyIterator` from an array of items.\n */\nexport function iteratorFromItems(items) {\n    return new ArrayIterator(items);\n}\n/**\n * Create a `LazyIterator` of incrementing integers.\n */\nexport function iteratorFromIncrementing(start) {\n    let i = start;\n    return iteratorFromFunction(() => ({ value: i++, done: false }));\n}\n/**\n * Create a `LazyIterator` from a function.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const iter = tf.data.iteratorFromFunction(func);\n * await iter.forEachAsync(e => console.log(e));\n * ```\n *\n * @param func A function that produces data on each call.\n */\nexport function iteratorFromFunction(func) {\n    return new FunctionCallIterator(func);\n}\n/**\n * Create a `LazyIterator` by concatenating underlying streams, which are\n * themselves provided as a stream.\n *\n * This can also be thought of as a \"stream flatten\" operation.\n *\n * @param baseIterators A stream of streams to be concatenated.\n * @param baseErrorHandler An optional function that can intercept `Error`s\n *   raised during a `next()` call on the base stream.  This function can decide\n *   whether the error should be propagated, whether the error should be\n *   ignored, or whether the base stream should be terminated.\n */\nexport function iteratorFromConcatenated(baseIterators, baseErrorHandler) {\n    return new ChainedIterator(baseIterators, baseErrorHandler);\n}\n/**\n * Create a `LazyIterator` by concatenating streams produced by calling a\n * stream-generating function a given number of times.\n *\n * Since a `LazyIterator` is read-once, it cannot be repeated, but this\n * function can be used to achieve a similar effect:\n *\n *   LazyIterator.ofConcatenatedFunction(() => new MyIterator(), 6);\n *\n * @param iteratorFunc: A function that produces a new stream on each call.\n * @param count: The number of times to call the function.\n * @param baseErrorHandler An optional function that can intercept `Error`s\n *   raised during a `next()` call on the base stream.  This function can decide\n *   whether the error should be propagated, whether the error should be\n *   ignored, or whether the base stream should be terminated.\n */\nexport function iteratorFromConcatenatedFunction(iteratorFunc, count, baseErrorHandler) {\n    return iteratorFromConcatenated(iteratorFromFunction(iteratorFunc).take(count), baseErrorHandler);\n}\n/**\n * Create a `LazyIterator` by zipping together an array, dict, or nested\n * structure of `LazyIterator`s (and perhaps additional constants).\n *\n * The underlying streams must provide elements in a consistent order such\n * that they correspond.\n *\n * Typically, the underlying streams should have the same number of\n * elements. If they do not, the behavior is determined by the\n * `mismatchMode` argument.\n *\n * The nested structure of the `iterators` argument determines the\n * structure of elements in the resulting iterator.\n *\n * @param iterators: An array or object containing LazyIterators at the\n * leaves.\n * @param mismatchMode: Determines what to do when one underlying iterator\n * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)\n * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`\n * causes the zipped iterator to terminate with the furst underlying\n * streams, so elements remaining on the longer streams are ignored.\n * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling\n * in nulls for the exhausted streams, until all streams are exhausted.\n */\nexport function iteratorFromZipped(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n    return new ZipIterator(iterators, mismatchMode);\n}\n/**\n * An asynchronous iterator, providing lazy access to a potentially\n * unbounded stream of elements.\n *\n * Iterator can be obtained from a dataset:\n * `const iter = await dataset.iterator();`\n */\nexport class LazyIterator {\n    /**\n     * Collect all remaining elements of a bounded stream into an array.\n     * Obviously this will succeed only for small streams that fit in memory.\n     * Useful for testing.\n     *\n     * @returns A Promise for an array of stream elements, which will resolve\n     *   when the stream is exhausted.\n     */\n    async toArray() {\n        const result = [];\n        let x = await this.next();\n        while (!x.done) {\n            result.push(x.value);\n            x = await this.next();\n        }\n        return result;\n    }\n    /**\n     * Collect all elements of this dataset into an array with prefetching 100\n     * elements. This is useful for testing, because the prefetch changes the\n     * order in which the Promises are resolved along the processing pipeline.\n     * This may help expose bugs where results are dependent on the order of\n     * Promise resolution rather than on the logical order of the stream (i.e.,\n     * due to hidden mutable state).\n     *\n     * @returns A Promise for an array of stream elements, which will resolve\n     *   when the stream is exhausted.\n     */\n    async toArrayForTest() {\n        const stream = this.prefetch(100);\n        const result = [];\n        let x = await stream.next();\n        while (!x.done) {\n            result.push(x.value);\n            x = await stream.next();\n        }\n        return result;\n    }\n    /**\n     * Draw items from the stream until it is exhausted.\n     *\n     * This can be useful when the stream has side effects but no output.  In\n     * that case, calling this function guarantees that the stream will be\n     * fully processed.\n     */\n    async resolveFully() {\n        let x = await this.next();\n        while (!x.done) {\n            x = await this.next();\n        }\n    }\n    /**\n     * Draw items from the stream until it is exhausted, or a predicate fails.\n     *\n     * This can be useful when the stream has side effects but no output.  In\n     * that case, calling this function guarantees that the stream will be\n     * fully processed.\n     */\n    async resolveWhile(predicate) {\n        let x = await this.next();\n        let shouldContinue = predicate(x.value);\n        while ((!x.done) && shouldContinue) {\n            x = await this.next();\n            shouldContinue = predicate(x.value);\n        }\n    }\n    /**\n     * Handles errors thrown on this stream using a provided handler function.\n     *\n     * @param handler A function that handles any `Error` thrown during a `next()`\n     *   call and returns true if the stream should continue (dropping the failed\n     *   call) or false if the stream should quietly terminate.  If the handler\n     *   itself throws (or rethrows) an `Error`, that will be propagated.\n     *\n     * @returns A `LazyIterator` of elements passed through from upstream,\n     *   possibly filtering or terminating on upstream `next()` calls that\n     *   throw an `Error`.\n     */\n    handleErrors(handler) {\n        return new ErrorHandlingLazyIterator(this, handler);\n    }\n    // TODO(soergel): Implement reduce() etc.\n    /**\n     * Filters this stream according to `predicate`.\n     *\n     * @param predicate A function mapping a stream element to a boolean or a\n     * `Promise` for one.\n     *\n     * @returns A `LazyIterator` of elements for which the predicate was true.\n     */\n    filter(predicate) {\n        return new FilterIterator(this, predicate);\n    }\n    /**\n     * Maps this stream through a 1-to-1 transform.\n     *\n     * @param transform A function mapping a stream element to a transformed\n     *   element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    map(transform) {\n        return new MapIterator(this, transform);\n    }\n    /**\n     * Maps this stream through an async 1-to-1 transform.\n     *\n     * @param transform A function mapping a stream element to a `Promise` for a\n     *   transformed stream element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    mapAsync(transform) {\n        return new AsyncMapIterator(this, transform);\n    }\n    /**\n     * Maps this stream through a 1-to-1 transform, forcing serial execution.\n     *\n     * @param transform A function mapping a stream element to a transformed\n     *   element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    serialMapAsync(transform) {\n        return new AsyncMapIterator(this, transform).serial();\n    }\n    /**\n     * Maps this stream through a 1-to-many transform.\n     *\n     * @param transform A function mapping a stream element to an array of\n     *   transformed elements.\n     *\n     * @returns A `DataStream` of transformed elements.\n     */\n    flatmap(transform) {\n        return new FlatmapIterator(this, transform);\n    }\n    /**\n     * Apply a function to every element of the stream.\n     *\n     * @param f A function to apply to each stream element.\n     */\n    async forEachAsync(f) {\n        return this.map(f).resolveFully();\n    }\n    /**\n     * Apply a function to every element of the stream, forcing serial execution.\n     *\n     * @param f A function to apply to each stream element.  Should return 'true'\n     *   to indicate that the stream should continue, or 'false' to cause it to\n     *   terminate.\n     */\n    async serialForEach(f) {\n        return this.serialMapAsync(f).resolveWhile(x => (x === true));\n    }\n    /**\n     * Groups elements into batches, represented as arrays of elements.\n     *\n     * We can think of the elements of this iterator as 'rows' (even if they are\n     * nested structures).  By the same token, consecutive values for a given\n     * key within the elements form a 'column'.  This matches the usual sense of\n     * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n     *\n     * Thus, \"Row-major\" means that the resulting batch is simply a collection of\n     * rows: `[row1, row2, row3, ...]`.  This is contrast to the column-major\n     * form, which is needed for vectorized computation.\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @returns A `LazyIterator` of batches of elements, represented as arrays\n     *   of the original element type.\n     */\n    rowMajorBatch(batchSize, smallLastBatch = true) {\n        return new RowMajorBatchIterator(this, batchSize, smallLastBatch);\n    }\n    /**\n     * Groups elements into batches, represented in column-major form.\n     *\n     * We can think of the elements of this iterator as 'rows' (even if they are\n     * nested structures).  By the same token, consecutive values for a given\n     * key within the elements form a 'column'.  This matches the usual sense of\n     * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n     *\n     * Thus, \"column-major\" means that the resulting batch is a (potentially\n     * nested) structure representing the columns.  Each column entry, then,\n     * contains a collection of the values found in that column for a range of\n     * input elements.  This representation allows for vectorized computation, in\n     * contrast to the row-major form.\n     *\n     * The inputs should all have the same nested structure (i.e., of arrays and\n     * dicts).  The result is a single object with the same nested structure,\n     * where the leaves are arrays collecting the values of the inputs at that\n     * location (or, optionally, the result of a custom function applied to those\n     * arrays).\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @param zipFn: (optional) A function that expects an array of elements at a\n     *   single node of the object tree, and returns a `DeepMapResult`.  The\n     *   `DeepMapResult` either provides a result value for that node (i.e.,\n     *   representing the subtree), or indicates that the node should be processed\n     *   recursively.  The default zipFn recurses as far as possible and places\n     *   arrays at the leaves.\n     * @returns A `LazyIterator` of batches of elements, represented as an object\n     *   with collections at the leaves.\n     */\n    columnMajorBatch(batchSize, smallLastBatch = true, \n    // tslint:disable-next-line:no-any\n    zipFn = zipToList) {\n        // First collect the desired number of input elements as a row-major batch.\n        const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch);\n        // Now 'rotate' or 'pivot' the data, collecting all values from each column\n        // in the batch (i.e., for each key within the elements) into an array.\n        return rowBatches.map(x => deepZip(x, zipFn));\n    }\n    /**\n     * Concatenate this `LazyIterator` with another.\n     *\n     * @param iterator A `LazyIterator` to be concatenated onto this one.\n     * @param baseErrorHandler An optional function that can intercept `Error`s\n     *   raised during a `next()` call on the base stream.  This function can\n     *   decide whether the error should be propagated, whether the error should\n     *   be ignored, or whether the base stream should be terminated.\n     * @returns A `LazyIterator`.\n     */\n    concatenate(iterator, baseErrorHandler) {\n        return new ChainedIterator(iteratorFromItems([this, iterator]), baseErrorHandler);\n    }\n    /**\n     * Limits this stream to return at most `count` items.\n     *\n     * @param count The maximum number of items to provide from the stream. If\n     * a negative or undefined value is given, the entire stream is returned\n     *   unaltered.\n     */\n    take(count) {\n        if (count < 0 || count == null) {\n            return this;\n        }\n        return new TakeIterator(this, count);\n    }\n    /**\n     * Skips the first `count` items in this stream.\n     *\n     * @param count The number of items to skip.  If a negative or undefined\n     * value is given, the entire stream is returned unaltered.\n     */\n    skip(count) {\n        if (count < 0 || count == null) {\n            return this;\n        }\n        return new SkipIterator(this, count);\n    }\n    /**\n     * Prefetch the first `bufferSize` items in this stream.\n     *\n     * Note this prefetches Promises, but makes no guarantees about when those\n     * Promises resolve.\n     *\n     * @param bufferSize: An integer specifying the number of elements to be\n     *   prefetched.\n     */\n    prefetch(bufferSize) {\n        return new PrefetchIterator(this, bufferSize);\n    }\n    // TODO(soergel): deep sharded shuffle, where supported\n    /**\n     * Randomly shuffles the elements of this stream.\n     *\n     * @param bufferSize: An integer specifying the number of elements from\n     * this stream from which the new stream will sample.\n     * @param seed: (Optional.) An integer specifying the random seed that\n     * will be used to create the distribution.\n     */\n    shuffle(windowSize, seed) {\n        return new ShuffleIterator(this, windowSize, seed);\n    }\n    /**\n     * Force an iterator to execute serially: each next() call will await the\n     * prior one, so that they cannot execute concurrently.\n     */\n    serial() {\n        return new SerialIterator(this);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on LazyIterator.  Unfortunately they can't be placed in separate files,\n// due to resulting trouble with circular imports.\n// ============================================================================\n// Iterators that just extend LazyIterator directly\n// ============================================================================\nclass ArrayIterator extends LazyIterator {\n    constructor(items) {\n        super();\n        this.items = items;\n        this.trav = 0;\n    }\n    summary() {\n        return `Array of ${this.items.length} items`;\n    }\n    async next() {\n        if (this.trav >= this.items.length) {\n            return { value: null, done: true };\n        }\n        const item = this.items[this.trav];\n        this.trav++;\n        return { value: deepClone(item), done: false };\n    }\n}\nclass FunctionCallIterator extends LazyIterator {\n    constructor(nextFn) {\n        super();\n        this.nextFn = nextFn;\n    }\n    summary() {\n        return `Function call`;\n    }\n    async next() {\n        try {\n            return this.nextFn();\n        }\n        catch (e) {\n            // Modify the error message but leave the stack trace intact\n            e.message =\n                `Error thrown while iterating through a dataset: ${e.message}`;\n            throw e;\n        }\n    }\n}\nclass SerialIterator extends LazyIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Serial`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        return this.upstream.next();\n    }\n}\nclass SkipIterator extends LazyIterator {\n    constructor(upstream, maxCount) {\n        super();\n        this.upstream = upstream;\n        this.maxCount = maxCount;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.count = 0;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Skip`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        // TODO(soergel): consider tradeoffs of reading in parallel, eg.\n        // collecting next() promises in an Array and then waiting for\n        // Promise.all() of those. Benefit: pseudo-parallel execution.  Drawback:\n        // maybe delayed GC.\n        while (this.count++ < this.maxCount) {\n            const skipped = await this.upstream.next();\n            // short-circuit if upstream is already empty\n            if (skipped.done) {\n                return skipped;\n            }\n            tf.dispose(skipped.value);\n        }\n        return this.upstream.next();\n    }\n}\nclass TakeIterator extends LazyIterator {\n    constructor(upstream, maxCount) {\n        super();\n        this.upstream = upstream;\n        this.maxCount = maxCount;\n        this.count = 0;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Take`;\n    }\n    async next() {\n        if (this.count++ >= this.maxCount) {\n            return { value: null, done: true };\n        }\n        return this.upstream.next();\n    }\n}\n// Note this batch just groups items into row-wise element arrays.\n// Rotating these to a column-wise representation happens only at the dataset\n// level.\nclass RowMajorBatchIterator extends LazyIterator {\n    constructor(upstream, batchSize, enableSmallLastBatch = true) {\n        super();\n        this.upstream = upstream;\n        this.batchSize = batchSize;\n        this.enableSmallLastBatch = enableSmallLastBatch;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> RowMajorBatch`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        const batch = [];\n        while (batch.length < this.batchSize) {\n            const item = await this.upstream.next();\n            if (item.done) {\n                if (this.enableSmallLastBatch && batch.length > 0) {\n                    return { value: batch, done: false };\n                }\n                return { value: null, done: true };\n            }\n            batch.push(item.value);\n        }\n        return { value: batch, done: false };\n    }\n}\nclass FilterIterator extends LazyIterator {\n    constructor(upstream, predicate) {\n        super();\n        this.upstream = upstream;\n        this.predicate = predicate;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Filter`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        while (true) {\n            const item = await this.upstream.next();\n            if (item.done || this.predicate(item.value)) {\n                return item;\n            }\n            tf.dispose(item.value);\n        }\n    }\n}\nclass MapIterator extends LazyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Map`;\n    }\n    async next() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return { value: null, done: true };\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // That's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying\n        // any intermediate Tensors.  Here we are concerned only about the\n        // inputs.\n        const mapped = this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mapped);\n        // TODO(soergel) faster intersection\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return { value: mapped, done: false };\n    }\n}\nclass ErrorHandlingLazyIterator extends LazyIterator {\n    constructor(upstream, handler) {\n        super();\n        this.upstream = upstream;\n        this.handler = handler;\n        this.count = 0;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> handleErrors`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        while (true) {\n            try {\n                return await this.upstream.next();\n            }\n            catch (e) {\n                if (!this.handler(e)) {\n                    return { value: null, done: true };\n                }\n                // If the handler returns true, loop and fetch the next upstream item.\n                // If the upstream iterator throws an endless stream of errors, and if\n                // the handler says to ignore them, then we loop forever here.  That is\n                // the correct behavior-- it's up to the handler to decide when to stop.\n            }\n        }\n    }\n}\nclass AsyncMapIterator extends LazyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> AsyncMap`;\n    }\n    async next() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return { value: null, done: true };\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // That's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying\n        // any intermediate Tensors.  Here we are concerned only about the\n        // inputs.\n        const mapped = await this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mapped);\n        // TODO(soergel) faster intersection\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return { value: mapped, done: false };\n    }\n}\n// Iterators that maintain a queue of pending items\n// ============================================================================\n/**\n * A base class for transforming streams that operate by maintaining an\n * output queue of elements that are ready to return via next().  This is\n * commonly required when the transformation is 1-to-many:  A call to next()\n * may trigger a call to the underlying stream, which will produce many\n * mapped elements of this stream-- of which we need to return only one, so\n * we have to queue the rest.\n */\nexport class OneToManyIterator extends LazyIterator {\n    constructor() {\n        super();\n        this.outputQueue = new GrowingRingBuffer();\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        // Fetch so that the queue contains at least one item if possible.\n        // If the upstream source is exhausted, AND there are no items left in\n        // the output queue, then this stream is also exhausted.\n        while (this.outputQueue.length() === 0) {\n            // TODO(soergel): consider parallel reads.\n            if (!await this.pump()) {\n                return { value: null, done: true };\n            }\n        }\n        return { value: this.outputQueue.shift(), done: false };\n    }\n}\nclass FlatmapIterator extends OneToManyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Flatmap`;\n    }\n    async pump() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return false;\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // that's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying any\n        // intermediate Tensors.  Here we are concerned only about the inputs.\n        const mappedArray = this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mappedArray);\n        this.outputQueue.pushAll(mappedArray);\n        // TODO(soergel) faster intersection, and deduplicate outputTensors\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return true;\n    }\n}\n/**\n * Provides a `LazyIterator` that concatenates a stream of underlying\n * streams.\n *\n * Doing this in a concurrency-safe way requires some trickery.  In\n * particular, we want this stream to return the elements from the\n * underlying streams in the correct order according to when next() was\n * called, even if the resulting Promises resolve in a different order.\n */\nexport class ChainedIterator extends LazyIterator {\n    constructor(iterators, baseErrorHandler) {\n        super();\n        this.baseErrorHandler = baseErrorHandler;\n        // Strict Promise execution order:\n        // a next() call may not even begin until the previous one completes.\n        this.lastRead = null;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.iterator = null;\n        this.moreIterators = iterators;\n    }\n    summary() {\n        const upstreamSummaries = 'TODO: fill in upstream of chained summaries';\n        return `${upstreamSummaries} -> Chained`;\n    }\n    async next() {\n        this.lastRead = this.readFromChain(this.lastRead);\n        return this.lastRead;\n    }\n    async readFromChain(lastRead) {\n        // Must await on the previous read since the previous read may have advanced\n        // the stream of streams, from which we need to read.\n        // This is unfortunate since we can't parallelize reads. Which means\n        // prefetching of chained streams is a no-op.\n        // One solution is to prefetch immediately upstream of this.\n        await lastRead;\n        if (this.iterator == null) {\n            const iteratorResult = await this.moreIterators.next();\n            if (iteratorResult.done) {\n                // No more streams to stream from.\n                return { value: null, done: true };\n            }\n            this.iterator = iteratorResult.value;\n            if (this.baseErrorHandler != null) {\n                this.iterator = this.iterator.handleErrors(this.baseErrorHandler);\n            }\n        }\n        const itemResult = await this.iterator.next();\n        if (itemResult.done) {\n            this.iterator = null;\n            return this.readFromChain(lastRead);\n        }\n        return itemResult;\n    }\n}\nexport var ZipMismatchMode;\n(function (ZipMismatchMode) {\n    ZipMismatchMode[ZipMismatchMode[\"FAIL\"] = 0] = \"FAIL\";\n    ZipMismatchMode[ZipMismatchMode[\"SHORTEST\"] = 1] = \"SHORTEST\";\n    ZipMismatchMode[ZipMismatchMode[\"LONGEST\"] = 2] = \"LONGEST\"; // use nulls for exhausted streams; use up the longest stream.\n})(ZipMismatchMode || (ZipMismatchMode = {}));\n/**\n * Provides a `LazyIterator` that zips together an array, dict, or nested\n * structure of `LazyIterator`s (and perhaps additional constants).\n *\n * The underlying streams must provide elements in a consistent order such\n * that they correspond.\n *\n * Typically, the underlying streams should have the same number of\n * elements. If they do not, the behavior is determined by the\n * `mismatchMode` argument.\n *\n * The nested structure of the `iterators` argument determines the\n * structure of elements in the resulting iterator.\n *\n * Doing this in a concurrency-safe way requires some trickery.  In\n * particular, we want this stream to return the elements from the\n * underlying streams in the correct order according to when next() was\n * called, even if the resulting Promises resolve in a different order.\n *\n * @param iterators: An array or object containing LazyIterators at the\n * leaves.\n * @param mismatchMode: Determines what to do when one underlying iterator\n * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)\n * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`\n * causes the zipped iterator to terminate with the furst underlying\n * streams, so elements remaining on the longer streams are ignored.\n * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling\n * in nulls for the exhausted streams, until all streams are exhausted.\n */\nclass ZipIterator extends LazyIterator {\n    constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n        super();\n        this.iterators = iterators;\n        this.mismatchMode = mismatchMode;\n        this.count = 0;\n        this.currentPromise = null;\n    }\n    summary() {\n        const upstreamSummaries = 'TODO: fill in upstream of zip summaries';\n        return `{${upstreamSummaries}} -> Zip`;\n    }\n    async nextState(afterState) {\n        // This chaining ensures that the underlying next() are not even called\n        // before the previous ones have resolved.\n        await afterState;\n        // Collect underlying iterator \"done\" signals as a side effect in\n        // getNext()\n        let numIterators = 0;\n        let iteratorsDone = 0;\n        function getNext(container) {\n            if (container instanceof LazyIterator) {\n                const result = container.next();\n                return {\n                    value: result.then(x => {\n                        numIterators++;\n                        if (x.done) {\n                            iteratorsDone++;\n                        }\n                        return x.value;\n                    }),\n                    recurse: false\n                };\n            }\n            else {\n                return { value: null, recurse: true };\n            }\n        }\n        const mapped = await deepMapAndAwaitAll(this.iterators, getNext);\n        if (numIterators === iteratorsDone) {\n            // The streams have all ended.\n            return { value: null, done: true };\n        }\n        if (iteratorsDone > 0) {\n            switch (this.mismatchMode) {\n                case ZipMismatchMode.FAIL:\n                    throw new Error('Zipped streams should have the same length. ' +\n                        `Mismatched at element ${this.count}.`);\n                case ZipMismatchMode.SHORTEST:\n                    return { value: null, done: true };\n                case ZipMismatchMode.LONGEST:\n                default:\n                // Continue.  The exhausted streams already produced value: null.\n            }\n        }\n        this.count++;\n        return { value: mapped, done: false };\n    }\n    async next() {\n        this.currentPromise = this.nextState(this.currentPromise);\n        return this.currentPromise;\n    }\n}\n// Iterators that maintain a ring buffer of pending promises\n// ============================================================================\n/**\n * A stream that prefetches a given number of items from an upstream source,\n * returning them in FIFO order.\n *\n * Note this prefetches Promises, but makes no guarantees about when those\n * Promises resolve.\n */\nexport class PrefetchIterator extends LazyIterator {\n    constructor(upstream, bufferSize) {\n        super();\n        this.upstream = upstream;\n        this.bufferSize = bufferSize;\n        this.buffer = new RingBuffer(bufferSize);\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Prefetch`;\n    }\n    /**\n     * Refill the prefetch buffer.  Returns only after the buffer is full, or\n     * the upstream source is exhausted.\n     */\n    refill() {\n        while (!this.buffer.isFull()) {\n            const v = this.upstream.next();\n            this.buffer.push(v);\n        }\n    }\n    next() {\n        this.refill();\n        // This shift will never throw an error because the buffer is always\n        // full after a refill. If the stream is exhausted, the buffer will be\n        // full of Promises that will resolve to the end-of-stream signal.\n        return this.buffer.shift();\n    }\n}\n/**\n * A stream that performs a sliding-window random shuffle on an upstream\n * source. This is like a `PrefetchIterator` except that the items are\n * returned in randomized order.  Mixing naturally improves as the buffer\n * size increases.\n */\nexport class ShuffleIterator extends PrefetchIterator {\n    constructor(upstream, windowSize, seed) {\n        super(upstream, windowSize);\n        this.upstream = upstream;\n        this.windowSize = windowSize;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.upstreamExhausted = false;\n        this.random = seedrandom.alea(seed || tf.util.now().toString());\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    randomInt(max) {\n        return Math.floor(this.random() * max);\n    }\n    chooseIndex() {\n        return this.randomInt(this.buffer.length());\n    }\n    async serialNext() {\n        // TODO(soergel): consider performance\n        if (!this.upstreamExhausted) {\n            this.refill();\n        }\n        while (!this.buffer.isEmpty()) {\n            const chosenIndex = this.chooseIndex();\n            const result = await this.buffer.shuffleExcise(chosenIndex);\n            if (result.done) {\n                this.upstreamExhausted = true;\n            }\n            else {\n                this.refill();\n                return result;\n            }\n        }\n        return { value: null, done: true };\n    }\n}\n//# sourceMappingURL=lazy_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport * as seedrandom from 'seedrandom';\nimport { iteratorFromConcatenated, iteratorFromFunction, iteratorFromItems, iteratorFromZipped, ZipMismatchMode } from './iterators/lazy_iterator';\nimport { canTensorify, deepMapAndAwaitAll, isIterable } from './util/deep_map';\n// TODO(soergel): consider vectorized operations within the pipeline.\n/**\n * Represents a potentially large list of independent data elements (typically\n * 'samples' or 'examples').\n *\n * A 'data example' may be a primitive, an array, a map from string keys to\n * values, or any nested structure of these.\n *\n * A `Dataset` represents an ordered collection of elements, together with a\n * chain of transformations to be performed on those elements. Each\n * transformation is a method of `Dataset` that returns another `Dataset`, so\n * these may be chained, e.g.\n * `const processedDataset = rawDataset.filter(...).map(...).batch(...)`.\n *\n * Data loading and transformation is done in a lazy, streaming fashion.  The\n * dataset may be iterated over multiple times; each iteration starts the data\n * loading anew and recapitulates the transformations.\n *\n * A `Dataset` is typically processed as a stream of unbatched examples --i.e.,\n * its transformations are applied one example at a time. Batching produces a\n * new `Dataset` where each element is a batch. Batching should usually come\n * last in a pipeline, because data transformations are easier to express on a\n * per-example basis than on a per-batch basis.\n *\n * The following code examples are calling `await dataset.forEachAsync(...)` to\n * iterate once over the entire dataset in order to print out the data.\n *\n * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}\n */\nexport class Dataset {\n    constructor() {\n        this.size = null;\n    }\n    // TODO(soergel): Make Datasets report whether repeated iterator() calls\n    // produce the same result (e.g., reading from a file) or different results\n    // (e.g., from the webcam).  Currently we don't make this distinction but it\n    // could be important for the user to know.\n    // abstract isDeterministic(): boolean;\n    /**\n     * Groups elements into batches.\n     *\n     * It is assumed that each of the incoming dataset elements has the same\n     * structure-- i.e. the same set of keys at each location in an object\n     * hierarchy.  For each key, the resulting `Dataset` provides a batched\n     * element collecting all of the incoming values for that key.\n     *\n     *  * Incoming primitives are grouped into a 1-D Tensor.\n     *  * Incoming Tensors are grouped into a new Tensor where the 0'th axis is\n     *    the batch dimension.\n     *  * Incoming arrays are converted to Tensor and then batched.\n     *  * A nested array is interpreted as an n-D Tensor, so the batched result\n     *    has n+1 dimensions.\n     *  * An array that cannot be converted to Tensor produces an error.\n     *\n     * If an array should not be batched as a unit, it should first be converted\n     * to an object with integer keys.\n     *\n     * Here are a few examples:\n     *\n     * Batch a dataset of numbers:\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8]).batch(4);\n     * await a.forEachAsync(e => e.print());\n     * ```\n     *\n     * Batch a dataset of arrays:\n     * ```js\n     * const b = tf.data.array([[1], [2], [3], [4], [5], [6], [7], [8]]).batch(4);\n     * await b.forEachAsync(e => e.print());\n     * ```\n     *\n     * Batch a dataset of objects:\n     * ```js\n     * const c = tf.data.array([{a: 1, b: 11}, {a: 2, b: 12}, {a: 3, b: 13},\n     *   {a: 4, b: 14}, {a: 5, b: 15}, {a: 6, b: 16}, {a: 7, b: 17},\n     *   {a: 8, b: 18}]).batch(4);\n     * await c.forEachAsync(e => {\n     *   console.log('{');\n     *   for(var key in e) {\n     *     console.log(key+':');\n     *     e[key].print();\n     *   }\n     *   console.log('}');\n     * })\n     * ```\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @returns A `Dataset`, from which a stream of batches can be obtained.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    batch(batchSize, smallLastBatch = true) {\n        const base = this;\n        tf.util.assert(batchSize > 0, () => `batchSize needs to be positive, but it is\n      ${batchSize}`);\n        let size;\n        if (this.size === Infinity || this.size == null) {\n            // If the size of this dataset is infinity or null, the new size keeps the\n            // same.\n            size = this.size;\n        }\n        else if (smallLastBatch) {\n            // If the size of this dataset is known and include small last batch, the\n            // new size is full batch count plus last batch.\n            size = Math.ceil(this.size / batchSize);\n        }\n        else {\n            // If the size of this dataset is known and not include small last batch,\n            // the new size is full batch count.\n            size = Math.floor(this.size / batchSize);\n        }\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator())\n                .columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat);\n        }, size);\n    }\n    /**\n     * Concatenates this `Dataset` with another.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]);\n     * const b = tf.data.array([4, 5, 6]);\n     * const c = a.concatenate(b);\n     * await c.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param dataset A `Dataset` to be concatenated onto this one.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    concatenate(dataset) {\n        const base = this;\n        let size;\n        if (this.size === Infinity || dataset.size === Infinity) {\n            // If the size of any of these two dataset is infinity, new size is\n            // infinity.\n            size = Infinity;\n        }\n        else if (this.size != null && dataset.size != null) {\n            // If the size of both datasets are known and not infinity, new size is\n            // sum the size of these two datasets.\n            size = this.size + dataset.size;\n        }\n        else {\n            // If neither of these two datasets has infinite size and any of these two\n            // datasets' size is null, the new size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).concatenate(await dataset.iterator()), size);\n    }\n    /**\n     * Filters this dataset according to `predicate`.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n     *   .filter(x => x%2 === 0);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param predicate A function mapping a dataset element to a boolean or a\n     * `Promise` for one.\n     *\n     * @returns A `Dataset` of elements for which the predicate was true.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    filter(predicate) {\n        const base = this;\n        let size;\n        if (this.size === Infinity) {\n            // If the size of this dataset is infinity, new size is infinity\n            size = Infinity;\n        }\n        else {\n            // If this dataset has limited elements, new size is null because it might\n            // exhausted randomly.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).filter(x => tf.tidy(() => predicate(x)));\n        }, size);\n    }\n    /**\n     * Apply a function to every element of the dataset.\n     *\n     * After the function is applied to a dataset element, any Tensors contained\n     * within that element are disposed.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param f A function to apply to each dataset element.\n     * @returns A `Promise` that resolves after all elements have been processed.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async forEachAsync(f) {\n        return (await this.iterator()).forEachAsync(f);\n    }\n    /**\n     * Maps this dataset through a 1-to-1 transform.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]).map(x => x*x);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param transform A function mapping a dataset element to a transformed\n     *   dataset element.\n     *\n     * @returns A `Dataset` of transformed elements.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    map(transform) {\n        const base = this;\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).map(x => tf.tidy(() => transform(x)));\n        }, this.size);\n    }\n    /**\n     * Maps this dataset through an async 1-to-1 transform.\n     *\n     * ```js\n     * const a =\n     *  tf.data.array([1, 2, 3]).mapAsync(x => new Promise(function(resolve){\n     *    setTimeout(() => {\n     *      resolve(x * x);\n     *    }, Math.random()*1000 + 500);\n     *  }));\n     * console.log(await a.toArray());\n     * ```\n     *\n     * @param transform A function mapping a dataset element to a `Promise` for a\n     *   transformed dataset element.  This transform is responsible for disposing\n     *   any intermediate `Tensor`s, i.e. by wrapping its computation in\n     *   `tf.tidy()`; that cannot be automated here (as it is in the synchronous\n     *   `map()` case).\n     *\n     * @returns A `Dataset` of transformed elements.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    mapAsync(transform) {\n        const base = this;\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).mapAsync(transform);\n        }, this.size);\n    }\n    /**\n     *  Creates a `Dataset` that prefetches elements from this dataset.\n     *\n     * @param bufferSize: An integer specifying the number of elements to be\n     *   prefetched.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    prefetch(bufferSize) {\n        if (bufferSize == null) {\n            throw new RangeError('`Dataset.prefetch()` requires bufferSize to be specified.');\n        }\n        const base = this;\n        return datasetFromIteratorFn(async () => (await base.iterator()).prefetch(bufferSize), this.size);\n    }\n    /**\n     * Repeats this dataset `count` times.\n     *\n     * NOTE: If this dataset is a function of global state (e.g. a random number\n     * generator), then different repetitions may produce different elements.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]).repeat(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: (Optional) An integer, representing the number of times\n     *   the dataset should be repeated. The default behavior (if `count` is\n     *   `undefined` or negative) is for the dataset be repeated indefinitely.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    repeat(count) {\n        const base = this;\n        let size;\n        if (this.size != null && count > 0) {\n            // If this dataset has size and count is positive, new size is current\n            // size multiply count. This also covers the case that current size is\n            // infinity.\n            size = this.size * count;\n        }\n        else if (count === 0) {\n            // If count is 0, new size is 0.\n            size = 0;\n        }\n        else if (this.size != null && (count === undefined || count < 0)) {\n            // If this dataset has size and count is undefined or negative, the\n            // dataset will be repeated indefinitely and new size is infinity.\n            size = Infinity;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => {\n            const iteratorIterator = iteratorFromFunction(async () => ({ value: await base.iterator(), done: false }));\n            return iteratorFromConcatenated(iteratorIterator.take(count));\n        }, size);\n    }\n    /**\n     * Creates a `Dataset` that skips `count` initial elements from this dataset.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).skip(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: The number of elements of this dataset that should be skipped\n     *   to form the new dataset.  If `count` is greater than the size of this\n     *   dataset, the new dataset will contain no elements.  If `count`\n     *   is `undefined` or negative, skips the entire dataset.\n     *\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    skip(count) {\n        const base = this;\n        let size;\n        if (this.size != null && count >= 0 && this.size >= count) {\n            // If the size of this dataset is greater than count, the new dataset's\n            // size is current size minus skipped size.This also covers the case that\n            // current size is infinity.\n            size = this.size - count;\n        }\n        else if (this.size != null &&\n            (this.size < count || count === undefined || count < 0)) {\n            // If the size of this dataset is smaller than count, or count is\n            // undefined or negative, skips the entire dataset and the new size is 0.\n            size = 0;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).skip(count), size);\n    }\n    /**\n     * Pseudorandomly shuffles the elements of this dataset. This is done in a\n     * streaming manner, by sampling from a given number of prefetched elements.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).shuffle(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param bufferSize: An integer specifying the number of elements from this\n     *   dataset from which the new dataset will sample.\n     * @param seed: (Optional) An integer specifying the random seed that will\n     *   be used to create the distribution.\n     * @param reshuffleEachIteration: (Optional) A boolean, which if true\n     *   indicates that the dataset should be pseudorandomly reshuffled each time\n     *   it is iterated over. If false, elements will be returned in the same\n     *   shuffled order on each iteration. (Defaults to `true`.)\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    shuffle(bufferSize, seed, reshuffleEachIteration = true) {\n        if (bufferSize == null || bufferSize < 0) {\n            if (this.size == null) {\n                throw new RangeError('`Dataset.shuffle()` requires bufferSize to be specified.');\n            }\n            else {\n                throw new RangeError('`Dataset.shuffle()` requires bufferSize to be specified.  ' +\n                    'If your data fits in main memory (for regular JS objects), ' +\n                    'and/or GPU memory (for `tf.Tensor`s), consider setting ' +\n                    `bufferSize to the dataset size (${this.size} elements)`);\n            }\n        }\n        const base = this;\n        const random = seedrandom.alea(seed || tf.util.now().toString());\n        return datasetFromIteratorFn(async () => {\n            let seed2 = random.int32();\n            if (reshuffleEachIteration) {\n                seed2 += random.int32();\n            }\n            return (await base.iterator()).shuffle(bufferSize, seed2.toString());\n        }, this.size);\n    }\n    /**\n     * Creates a `Dataset` with at most `count` initial elements from this\n     * dataset.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).take(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: The number of elements of this dataset that should be taken\n     *   to form the new dataset.  If `count` is `undefined` or negative, or if\n     *   `count` is greater than the size of this dataset, the new dataset will\n     *   contain all elements of this dataset.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    take(count) {\n        const base = this;\n        let size;\n        if (this.size != null && this.size > count) {\n            // If the size of this dataset is greater than count, the new dataset's\n            // size is count.\n            size = count;\n        }\n        else if (this.size != null && this.size <= count) {\n            // If the size of this dataset is equal or smaller than count, the new\n            // dataset's size is the size of this dataset.\n            size = this.size;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).take(count), size);\n    }\n    /**\n     * Collect all elements of this dataset into an array.\n     *\n     * Obviously this will succeed only for small datasets that fit in memory.\n     * Useful for testing and generally should be avoided if possible.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]);\n     * console.log(await a.toArray());\n     * ```\n     *\n     * @returns A Promise for an array of elements, which will resolve\n     *   when a new stream has been obtained and fully consumed.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async toArray() {\n        if (this.size === Infinity) {\n            throw new Error('Can not convert infinite data stream to array.');\n        }\n        return (await this.iterator()).toArray();\n    }\n    /**\n     * Collect all elements of this dataset into an array with prefetching 100\n     * elements. This is useful for testing, because the prefetch changes the\n     * order in which the Promises are resolved along the processing pipeline.\n     * This may help expose bugs where results are dependent on the order of\n     * Promise resolution rather than on the logical order of the stream (i.e.,\n     * due to hidden mutable state).\n     *\n     * @returns A Promise for an array of elements, which will resolve\n     *   when a new stream has been obtained and fully consumed.\n     */\n    async toArrayForTest() {\n        if (this.size === Infinity) {\n            throw new Error('Can not convert infinite data stream to array.');\n        }\n        return (await this.iterator()).toArrayForTest();\n    }\n}\n// TODO(soergel): deep sharded shuffle, where supported\nDataset.MAX_BUFFER_SIZE = 10000;\n/**\n * Create a `Dataset` defined by a provided iterator() function.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const iter = tf.data.iteratorFromFunction(func);\n * const ds = tf.data.datasetFromIteratorFn(iter);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n */\nexport function datasetFromIteratorFn(iteratorFn, size = null) {\n    return new class extends Dataset {\n        constructor() {\n            super(...arguments);\n            this.size = size;\n        }\n        /*\n         * Provide a new stream of elements.  Note this will also start new streams\n         * from any underlying `Dataset`s.\n         */\n        async iterator() {\n            return iteratorFn();\n        }\n    }();\n}\n/**\n * Create a `Dataset` from an array of elements.\n *\n * Create a Dataset from an array of objects:\n * ```js\n * const a = tf.data.array([{'item': 1}, {'item': 2}, {'item': 3}]);\n * await a.forEachAsync(e => console.log(e));\n * ```\n *\n * Create a Dataset from an array of numbers:\n * ```js\n * const a = tf.data.array([4, 5, 6]);\n * await a.forEachAsync(e => console.log(e));\n * ```\n * @param items An array of elements that will be parsed as items in a dataset.\n *\n * @doc {heading: 'Data', subheading: 'Creation', namespace: 'data'}\n */\nexport function array(items) {\n    return datasetFromIteratorFn(async () => iteratorFromItems(items), items.length);\n}\n/**\n * Create a `Dataset` by zipping together an array, dict, or nested\n * structure of `Dataset`s (and perhaps additional constants).\n * The underlying datasets must provide elements in a consistent order such that\n * they correspond.\n *\n * The number of elements in the resulting dataset is the same as the size of\n * the smallest dataset in datasets.\n *\n * The nested structure of the `datasets` argument determines the\n * structure of elements in the resulting iterator.\n *\n * Note this means that, given an array of two datasets that produce dict\n * elements, the result is a dataset that produces elements that are arrays\n * of two dicts:\n *\n * Zip an array of datasets:\n * ```js\n * console.log('Zip two datasets of objects:');\n * const ds1 = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);\n * const ds2 = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);\n * const ds3 = tf.data.zip([ds1, ds2]);\n * await ds3.forEachAsync(e => console.log(JSON.stringify(e)));\n *\n * // If the goal is to merge the dicts in order to produce elements like\n * // {a: ..., b: ...}, this requires a second step such as:\n * console.log('Merge the objects:');\n * const ds4 = ds3.map(x => {return {a: x[0].a, b: x[1].b}});\n * await ds4.forEachAsync(e => console.log(e));\n * ```\n *\n * Zip a dict of datasets:\n * ```js\n * const a = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);\n * const b = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);\n * const c = tf.data.zip({c: a, d: b});\n * await c.forEachAsync(e => console.log(JSON.stringify(e)));\n * ```\n *\n * @doc {heading: 'Data', subheading: 'Operations', namespace: 'data'}\n */\nexport function zip(datasets) {\n    // manually type-check the argument for JS users\n    if (!isIterable(datasets)) {\n        throw new Error('The argument to zip() must be an object or array.');\n    }\n    let size;\n    if (Array.isArray(datasets)) {\n        for (let i = 0; i < datasets.length; i++) {\n            size = size == null ? datasets[i].size :\n                Math.min(size, datasets[i].size);\n        }\n    }\n    else if (datasets instanceof Object) {\n        for (const ds in datasets) {\n            size = size == null ? datasets[ds].size :\n                Math.min(size, datasets[ds].size);\n        }\n    }\n    return datasetFromIteratorFn(async () => {\n        const streams = await deepMapAndAwaitAll(datasets, d => {\n            if (d instanceof Dataset) {\n                return { value: d.iterator(), recurse: false };\n            }\n            else if (isIterable(d)) {\n                return { value: null, recurse: true };\n            }\n            else {\n                throw new Error('Leaves of the structure passed to zip() must be Datasets, ' +\n                    'not primitives.');\n            }\n        });\n        return iteratorFromZipped(streams, ZipMismatchMode.SHORTEST);\n    }, size);\n}\n/**\n * A zip function for use with deepZip, passed via the columnMajorBatch call.\n *\n * Accepts an array of identically-structured nested elements and either batches\n * them (if they are primitives, numeric arrays, or Tensors) or requests\n * recursion (if not).\n */\n// tslint:disable-next-line:no-any\nfunction deepBatchConcat(rows) {\n    if (rows === null) {\n        return null;\n    }\n    // use the first item to decide whether to recurse or batch here.\n    const exampleRow = rows[0];\n    if (canTensorify(exampleRow)) {\n        // rows is an array of primitives, Tensors, or arrays.  Batch them.\n        const value = batchConcat(rows);\n        return { value, recurse: false };\n    }\n    // the example row is an object, so recurse into it.\n    return { value: null, recurse: true };\n}\n/**\n * Assembles a list of same-shaped numbers, number arrays, or Tensors\n * into a single new Tensor where axis 0 is the batch dimension.\n */\nfunction batchConcat(arrays) {\n    if (arrays.length === 0) {\n        // We can't return an empty Tensor because we don't know the element shape.\n        throw new Error('Can\\'t make a batch of zero elements.');\n    }\n    if (arrays[0] instanceof tf.Tensor) {\n        // Input is an array of Tensors\n        return tf.stack(arrays);\n    }\n    else {\n        // Input is a possibly-nested array of numbers.\n        return tf.tensor(arrays);\n    }\n}\n//# sourceMappingURL=dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { Dataset } from '../dataset';\n/**\n * Represents a potentially large collection of text lines.\n *\n * The results are not batched.\n */\nexport class TextLineDataset extends Dataset {\n    /**\n     * Create a `TextLineDataset`.\n     *\n     * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n     */\n    constructor(input) {\n        super();\n        this.input = input;\n    }\n    async iterator() {\n        const inputIterator = await this.input.iterator();\n        const utf8Iterator = inputIterator.decodeUTF8();\n        const lineIterator = utf8Iterator.split('\\n').map(line => {\n            // Windows/DOS format text file has extra line breaker at the end of line.\n            if (line.endsWith('\\r')) {\n                line = line.slice(0, -1);\n            }\n            return line;\n        });\n        return lineIterator;\n    }\n}\n//# sourceMappingURL=text_line_dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { Dataset } from '../dataset';\nimport { TextLineDataset } from './text_line_dataset';\nconst CODE_QUOTE = '\"';\nconst STATE_OUT = Symbol('out');\nconst STATE_FIELD = Symbol('field');\nconst STATE_QUOTE = Symbol('quote');\nconst STATE_QUOTE_AFTER_QUOTE = Symbol('quoteafterquote');\nconst STATE_WITHIN_QUOTE_IN_QUOTE = Symbol('quoteinquote');\n/**\n * Represents a potentially large collection of delimited text records.\n *\n * The produced `TensorContainer`s each contain one key-value pair for\n * every column of the table.  When a field is empty in the incoming data, the\n * resulting value is `undefined`, or throw error if it is required.  Values\n * that can be parsed as numbers are emitted as type `number`, other values\n * are parsed as `string`.\n *\n * The results are not batched.\n *\n * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}\n */\nexport class CSVDataset extends Dataset {\n    /**\n     * Create a `CSVDataset`.\n     *\n     * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n     * @param csvConfig (Optional) A CSVConfig object that contains configurations\n     *     of reading and decoding from CSV file(s).\n     *\n     *     hasHeader: (Optional) A boolean value that indicates whether the first\n     *     row of provided CSV file is a header line with column names, and should\n     *     not be included in the data. Defaults to `true`.\n     *\n     *     columnNames: (Optional) A list of strings that corresponds to\n     *     the CSV column names, in order. If provided, it ignores the column\n     *     names inferred from the header row. If not provided, infers the column\n     *     names from the first row of the records. If hasHeader is false and\n     *     columnNames is not provided, this method throws an error.\n     *\n     *     columnConfigs: (Optional) A dictionary whose key is column names, value\n     *     is an object stating if this column is required, column's data type,\n     *     default value, and if this column is label. If provided, keys must\n     *     correspond to names provided in columnNames or inferred from the file\n     *     header lines. If isLabel is true any column, returns an array of two\n     *     items: the first item is a dict of features key/value pairs, the second\n     *     item is a dict of labels key/value pairs. If no feature is marked as\n     *     label, returns a dict of features only.\n     *\n     *     configuredColumnsOnly (Optional) If true, only columns provided in\n     *     columnConfigs will be parsed and provided during iteration.\n     *\n     *     delimiter (Optional) The string used to parse each line of the input\n     *     file. Defaults to `,`.\n     */\n    constructor(input, csvConfig) {\n        super();\n        this.input = input;\n        this.hasHeader = true;\n        this.fullColumnNames = null;\n        this.columnNamesValidated = false;\n        this.columnConfigs = null;\n        this.configuredColumnsOnly = false;\n        this.delimiter = ',';\n        this.delimWhitespace = false;\n        this.base = new TextLineDataset(input);\n        if (!csvConfig) {\n            csvConfig = {};\n        }\n        this.hasHeader = csvConfig.hasHeader === false ? false : true;\n        this.fullColumnNames = csvConfig.columnNames;\n        this.columnConfigs = csvConfig.columnConfigs;\n        this.configuredColumnsOnly = csvConfig.configuredColumnsOnly;\n        if (csvConfig.delimWhitespace) {\n            util.assert(csvConfig.delimiter == null, () => 'Delimiter should not be provided when delimWhitespace is true.');\n            this.delimWhitespace = true;\n            this.delimiter = ' ';\n        }\n        else {\n            this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : ',';\n        }\n    }\n    /**\n     * Returns column names of the csv dataset. If `configuredColumnsOnly` is\n     * true, return column names in `columnConfigs`. If `configuredColumnsOnly` is\n     * false and `columnNames` is provided, `columnNames`. If\n     * `configuredColumnsOnly` is false and `columnNames` is not provided, return\n     * all column names parsed from the csv file. For example usage please go to\n     * `tf.data.csv`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async columnNames() {\n        if (!this.columnNamesValidated) {\n            await this.setColumnNames();\n        }\n        return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) :\n            this.fullColumnNames;\n    }\n    /* 1) If `columnNames` is provided as string[], use this string[] as output\n     * keys in corresponding order. The length must match the number of inferred\n     * columns if `hasHeader` is true .\n     * 2) If `columnNames` is not provided, parse header line as `columnNames` if\n     * hasHeader is true. If `hasHeader` is false, throw an error.\n     * 3) If `columnConfigs` is provided, all the keys in `columnConfigs` must\n     * exist in parsed `columnNames`.\n     */\n    async setColumnNames() {\n        const columnNamesFromFile = await this.maybeReadHeaderLine();\n        if (!this.fullColumnNames && !columnNamesFromFile) {\n            // Throw an error if columnNames is not provided and no header line.\n            throw new Error('Column names must be provided if there is no header line.');\n        }\n        else if (this.fullColumnNames && columnNamesFromFile) {\n            // Check provided columnNames match header line.\n            util.assert(columnNamesFromFile.length === this.fullColumnNames.length, () => 'The length of provided columnNames (' +\n                this.fullColumnNames.length.toString() +\n                ') does not match the length of the header line read from ' +\n                'file (' + columnNamesFromFile.length.toString() + ').');\n        }\n        if (!this.fullColumnNames) {\n            this.fullColumnNames = columnNamesFromFile;\n        }\n        // Check if there are duplicate column names.\n        const counts = this.fullColumnNames.reduce((countAcc, name) => {\n            countAcc[name] = (countAcc[name] + 1) || 1;\n            return countAcc;\n        }, {});\n        const duplicateNames = Object.keys(counts).filter((name) => (counts[name] > 1));\n        util.assert(duplicateNames.length === 0, () => 'Duplicate column names found: ' + duplicateNames.toString());\n        // Check if keys in columnConfigs match columnNames.\n        if (this.columnConfigs) {\n            for (const key of Object.keys(this.columnConfigs)) {\n                const index = this.fullColumnNames.indexOf(key);\n                if (index === -1) {\n                    throw new Error('The key \"' + key +\n                        '\" provided in columnConfigs does not match any of the column ' +\n                        'names (' + this.fullColumnNames.toString() + ').');\n                }\n            }\n        }\n        this.columnNamesValidated = true;\n    }\n    async maybeReadHeaderLine() {\n        if (this.hasHeader) {\n            const iter = await this.base.iterator();\n            const firstElement = await iter.next();\n            if (firstElement.done) {\n                throw new Error('No data was found for CSV parsing.');\n            }\n            const firstLine = firstElement.value;\n            const headers = this.parseRow(firstLine, false);\n            return headers;\n        }\n        else {\n            return null;\n        }\n    }\n    async iterator() {\n        if (!this.columnNamesValidated) {\n            await this.setColumnNames();\n        }\n        let lines = await this.base.iterator();\n        if (this.hasHeader) {\n            // We previously read the first line to get the columnNames.\n            // Now that we're providing data, skip it.\n            lines = lines.skip(1);\n        }\n        return lines.map(x => this.makeDataElement(x));\n    }\n    makeDataElement(line) {\n        const values = this.parseRow(line);\n        const features = {};\n        const labels = {};\n        for (let i = 0; i < this.fullColumnNames.length; i++) {\n            const key = this.fullColumnNames[i];\n            const config = this.columnConfigs ? this.columnConfigs[key] : null;\n            if (this.configuredColumnsOnly && !config) {\n                // This column is not selected.\n                continue;\n            }\n            else {\n                const value = values[i];\n                let parsedValue = null;\n                if (value === '') {\n                    // If default value is provided, use it. If default value is not\n                    // provided, set as undefined.\n                    if (config && config.default !== undefined) {\n                        parsedValue = config.default;\n                    }\n                    else if (config && (config.required || config.isLabel)) {\n                        throw new Error(`Required column ${key} is empty in this line: ${line}`);\n                    }\n                    else {\n                        parsedValue = undefined;\n                    }\n                }\n                else {\n                    // A value is present, so parse it based on type\n                    const valueAsNum = Number(value);\n                    if (isNaN(valueAsNum)) {\n                        // The value is a string and this column is declared as boolean\n                        // in config, parse it as boolean.\n                        if (config && config.dtype === 'bool') {\n                            parsedValue = this.getBoolean(value);\n                        }\n                        else {\n                            // Set value as string\n                            parsedValue = value;\n                        }\n                    }\n                    else if (!config || !config.dtype) {\n                        // If this value is a number and no type config is provided, return\n                        // it as number.\n                        parsedValue = valueAsNum;\n                    }\n                    else {\n                        // If this value is a number and data type is provided, parse it\n                        // according to provided data type.\n                        switch (config.dtype) {\n                            case 'float32':\n                                parsedValue = valueAsNum;\n                                break;\n                            case 'int32':\n                                parsedValue = Math.floor(valueAsNum);\n                                break;\n                            case 'bool':\n                                parsedValue = this.getBoolean(value);\n                                break;\n                            default:\n                                parsedValue = valueAsNum;\n                        }\n                    }\n                }\n                // Check if this column is label.\n                (config && config.isLabel) ? labels[key] = parsedValue :\n                    features[key] = parsedValue;\n            }\n        }\n        // If label exists, return an object of features and labels as {xs:features,\n        // ys:labels}, otherwise return features only.\n        if (Object.keys(labels).length === 0) {\n            return features;\n        }\n        else {\n            return { xs: features, ys: labels };\n        }\n    }\n    getBoolean(value) {\n        if (value === '1' || value.toLowerCase() === 'true') {\n            return 1;\n        }\n        else {\n            return 0;\n        }\n    }\n    // adapted from https://beta.observablehq.com/@mbostock/streaming-csv\n    parseRow(line, validateElementCount = true) {\n        const result = [];\n        let readOffset = 0;\n        const readLength = line.length;\n        let currentState = STATE_OUT;\n        // Goes through the line to parse quote.\n        for (let i = 0; i < readLength; i++) {\n            switch (currentState) {\n                // Before enter a new field\n                case STATE_OUT:\n                    switch (line.charAt(i)) {\n                        // Enter a quoted field\n                        case CODE_QUOTE:\n                            readOffset = i + 1;\n                            currentState = STATE_QUOTE;\n                            break;\n                        // Read an empty field\n                        case this.delimiter:\n                            readOffset = i + 1;\n                            // If delimiter is white space and configured to collapse\n                            // multiple white spaces, ignore this white space.\n                            if (this.delimiter === ' ' && this.delimWhitespace) {\n                                break;\n                            }\n                            result.push('');\n                            currentState = STATE_OUT;\n                            break;\n                        // Enter an unquoted field\n                        default:\n                            currentState = STATE_FIELD;\n                            readOffset = i;\n                            break;\n                    }\n                    break;\n                // In an unquoted field\n                case STATE_FIELD:\n                    switch (line.charAt(i)) {\n                        // Exit an unquoted field, add it to result\n                        case this.delimiter:\n                            result.push(line.substring(readOffset, i));\n                            currentState = STATE_OUT;\n                            readOffset = i + 1;\n                            break;\n                        default:\n                    }\n                    break;\n                // In a quoted field\n                case STATE_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Read a quote after a quote\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE_AFTER_QUOTE;\n                            break;\n                        default:\n                    }\n                    break;\n                // This state means it's right after a second quote in a field\n                case STATE_QUOTE_AFTER_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Finished a quoted field\n                        case this.delimiter:\n                            result.push(line.substring(readOffset, i - 1));\n                            currentState = STATE_OUT;\n                            readOffset = i + 1;\n                            break;\n                        // Finished a quoted part in a quoted field\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE;\n                            break;\n                        // In a quoted part in a quoted field\n                        default:\n                            currentState = STATE_WITHIN_QUOTE_IN_QUOTE;\n                            break;\n                    }\n                    break;\n                case STATE_WITHIN_QUOTE_IN_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Exit a quoted part in a quoted field\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE;\n                            break;\n                        default:\n                    }\n                    break;\n                default:\n            }\n        }\n        // Adds last item based on if it is quoted.\n        if (currentState === STATE_QUOTE_AFTER_QUOTE) {\n            result.push(line.substring(readOffset, readLength - 1));\n        }\n        else {\n            result.push(line.substring(readOffset));\n        }\n        // Check if each row has the same number of elements as column names.\n        if (validateElementCount && result.length !== this.fullColumnNames.length) {\n            throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);\n        }\n        return result;\n    }\n}\n// TODO(soergel): add more basic datasets for parity with tf.data\n// tf.data.FixedLengthRecordDataset()\n// tf.data.TFRecordDataset()\n//# sourceMappingURL=csv_dataset.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env, tensor, util } from '@tensorflow/tfjs-core';\nimport { LazyIterator } from './lazy_iterator';\n/**\n * Provide a stream of tensors from microphone audio stream. The tensors are\n * representing audio data as frequency-domain spectrogram generated with\n * browser's native FFT. Tensors representing time-domain waveform is available\n * based on configuration. Only works in browser environment.\n */\nexport class MicrophoneIterator extends LazyIterator {\n    constructor(microphoneConfig) {\n        super();\n        this.microphoneConfig = microphoneConfig;\n        this.isClosed = false;\n        this.fftSize = microphoneConfig.fftSize || 1024;\n        const fftSizeLog2 = Math.log2(this.fftSize);\n        if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 ||\n            !Number.isInteger(fftSizeLog2)) {\n            throw new Error(`Invalid fftSize: it must be a power of 2 between ` +\n                `2 to 4 and 2 to 14, but got ${this.fftSize}`);\n        }\n        this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43;\n        this.sampleRateHz = microphoneConfig.sampleRateHz;\n        this.columnTruncateLength =\n            microphoneConfig.columnTruncateLength || this.fftSize;\n        this.audioTrackConstraints = microphoneConfig.audioTrackConstraints;\n        this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0;\n        this.includeSpectrogram =\n            microphoneConfig.includeSpectrogram === false ? false : true;\n        this.includeWaveform =\n            microphoneConfig.includeWaveform === true ? true : false;\n        if (!this.includeSpectrogram && !this.includeWaveform) {\n            throw new Error('Both includeSpectrogram and includeWaveform are false. ' +\n                'At least one type of data should be returned.');\n        }\n    }\n    summary() {\n        return `microphone`;\n    }\n    // Construct a MicrophoneIterator and start the audio stream.\n    static async create(microphoneConfig = {}) {\n        if (env().get('IS_NODE')) {\n            throw new Error('microphone API is only supported in browser environment.');\n        }\n        const microphoneIterator = new MicrophoneIterator(microphoneConfig);\n        // Call async function start() to initialize the audio stream.\n        await microphoneIterator.start();\n        return microphoneIterator;\n    }\n    // Start the audio stream and FFT.\n    async start() {\n        try {\n            this.stream = await navigator.mediaDevices.getUserMedia({\n                audio: this.audioTrackConstraints == null ? true :\n                    this.audioTrackConstraints,\n                video: false\n            });\n        }\n        catch (e) {\n            throw new Error(`Error thrown while initializing video stream: ${e.message}`);\n        }\n        if (!this.stream) {\n            throw new Error('Could not obtain audio from microphone.');\n        }\n        const ctxConstructor = \n        // tslint:disable-next-line:no-any\n        window.AudioContext || window.webkitAudioContext;\n        this.audioContext = new ctxConstructor();\n        if (!this.sampleRateHz) {\n            // If sample rate is not provided, use the available sample rate on\n            // device.\n            this.sampleRateHz = this.audioContext.sampleRate;\n        }\n        else if (this.audioContext.sampleRate !== this.sampleRateHz) {\n            throw new Error(`Mismatch in sampling rate: ` +\n                `Expected: ${this.sampleRateHz}; ` +\n                `Actual: ${this.audioContext.sampleRate}`);\n        }\n        const streamSource = this.audioContext.createMediaStreamSource(this.stream);\n        this.analyser = this.audioContext.createAnalyser();\n        this.analyser.fftSize = this.fftSize * 2;\n        this.analyser.smoothingTimeConstant = this.smoothingTimeConstant;\n        streamSource.connect(this.analyser);\n        this.freqData = new Float32Array(this.fftSize);\n        this.timeData = new Float32Array(this.fftSize);\n        return;\n    }\n    async next() {\n        if (this.isClosed) {\n            return { value: null, done: true };\n        }\n        let spectrogramTensor;\n        let waveformTensor;\n        const audioDataQueue = await this.getAudioData();\n        if (this.includeSpectrogram) {\n            const freqData = this.flattenQueue(audioDataQueue.freqDataQueue);\n            spectrogramTensor = this.getTensorFromAudioDataArray(freqData, [this.numFrames, this.columnTruncateLength, 1]);\n        }\n        if (this.includeWaveform) {\n            const timeData = this.flattenQueue(audioDataQueue.timeDataQueue);\n            waveformTensor = this.getTensorFromAudioDataArray(timeData, [this.numFrames * this.fftSize, 1]);\n        }\n        return {\n            value: { 'spectrogram': spectrogramTensor, 'waveform': waveformTensor },\n            done: false\n        };\n    }\n    // Capture one result from the audio stream, and extract the value from\n    // iterator.next() result.\n    async capture() {\n        return (await this.next()).value;\n    }\n    async getAudioData() {\n        const freqDataQueue = [];\n        const timeDataQueue = [];\n        let currentFrames = 0;\n        return new Promise(resolve => {\n            const intervalID = setInterval(() => {\n                if (this.includeSpectrogram) {\n                    this.analyser.getFloatFrequencyData(this.freqData);\n                    // If the audio stream is initializing, return empty queue.\n                    if (this.freqData[0] === -Infinity) {\n                        resolve({ freqDataQueue, timeDataQueue });\n                    }\n                    freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength));\n                }\n                if (this.includeWaveform) {\n                    this.analyser.getFloatTimeDomainData(this.timeData);\n                    timeDataQueue.push(this.timeData.slice());\n                }\n                // Clean interval and return when all frames have been collected\n                if (++currentFrames === this.numFrames) {\n                    clearInterval(intervalID);\n                    resolve({ freqDataQueue, timeDataQueue });\n                }\n            }, this.fftSize / this.sampleRateHz * 1e3);\n        });\n    }\n    // Stop the audio stream and pause the iterator.\n    stop() {\n        if (!this.isClosed) {\n            this.isClosed = true;\n            this.analyser.disconnect();\n            this.audioContext.close();\n            if (this.stream != null && this.stream.getTracks().length > 0) {\n                this.stream.getTracks()[0].stop();\n            }\n        }\n    }\n    // Override toArray() function to prevent collecting.\n    toArray() {\n        throw new Error('Can not convert infinite audio stream to array.');\n    }\n    // Return audio sampling rate in Hz\n    getSampleRate() {\n        return this.sampleRateHz;\n    }\n    flattenQueue(queue) {\n        const frameSize = queue[0].length;\n        const freqData = new Float32Array(queue.length * frameSize);\n        queue.forEach((data, i) => freqData.set(data, i * frameSize));\n        return freqData;\n    }\n    getTensorFromAudioDataArray(freqData, shape) {\n        const vals = new Float32Array(util.sizeFromShape(shape));\n        // If the data is less than the output shape, the rest is padded with zeros.\n        vals.set(freqData, vals.length - freqData.length);\n        return tensor(vals, shape);\n    }\n}\n//# sourceMappingURL=microphone_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { browser, env, image, tensor1d, tensor2d, tidy, util } from '@tensorflow/tfjs-core';\nimport { LazyIterator } from './lazy_iterator';\n/**\n * Provide a stream of image tensors from webcam video stream. Only works in\n * browser environment.\n */\nexport class WebcamIterator extends LazyIterator {\n    constructor(webcamVideoElement, webcamConfig) {\n        super();\n        this.webcamVideoElement = webcamVideoElement;\n        this.webcamConfig = webcamConfig;\n        this.isClosed = true;\n        this.resize = false;\n        if (this.needToResize()) {\n            this.resize = true;\n            this.cropSize =\n                [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth];\n            this.cropBoxInd = tensor1d([0], 'int32');\n            if (this.webcamConfig.centerCrop) {\n                // Calculate the box based on resizing shape.\n                const widthCroppingRatio = this.webcamConfig.resizeWidth * 1.0 / this.webcamVideoElement.width;\n                const heightCroppingRatio = this.webcamConfig.resizeHeight * 1.0 /\n                    this.webcamVideoElement.height;\n                const widthCropStart = (1 - widthCroppingRatio) / 2;\n                const heightCropStart = (1 - heightCroppingRatio) / 2;\n                const widthCropEnd = widthCropStart + widthCroppingRatio;\n                const heightCropEnd = heightCroppingRatio + heightCropStart;\n                this.cropBox = tensor2d([heightCropStart, widthCropStart, heightCropEnd, widthCropEnd], [1, 4]);\n            }\n            else {\n                this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]);\n            }\n        }\n    }\n    summary() {\n        return `webcam`;\n    }\n    // Construct a WebcamIterator and start it's video stream.\n    static async create(webcamVideoElement, webcamConfig = {}) {\n        if (env().get('IS_NODE')) {\n            throw new Error('tf.data.webcam is only supported in browser environment.');\n        }\n        if (!webcamVideoElement) {\n            // If webcam video element is not provided, create a hidden video element\n            // with provided width and height.\n            webcamVideoElement = document.createElement('video');\n            if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) {\n                throw new Error('Please provide webcam video element, or resizeWidth and ' +\n                    'resizeHeight to create a hidden video element.');\n            }\n            webcamVideoElement.width = webcamConfig.resizeWidth;\n            webcamVideoElement.height = webcamConfig.resizeHeight;\n        }\n        const webcamIterator = new WebcamIterator(webcamVideoElement, webcamConfig);\n        // Call async function to initialize the video stream.\n        await webcamIterator.start();\n        return webcamIterator;\n    }\n    // Async function to start video stream.\n    async start() {\n        if (this.webcamConfig.facingMode) {\n            util.assert((this.webcamConfig.facingMode === 'user') ||\n                (this.webcamConfig.facingMode === 'environment'), () => `Invalid webcam facing mode: ${this.webcamConfig.facingMode}. ` +\n                `Please provide 'user' or 'environment'`);\n        }\n        try {\n            this.stream = await navigator.mediaDevices.getUserMedia({\n                video: {\n                    deviceId: this.webcamConfig.deviceId,\n                    facingMode: this.webcamConfig.facingMode ?\n                        this.webcamConfig.facingMode :\n                        'user',\n                    width: this.webcamVideoElement.width,\n                    height: this.webcamVideoElement.height\n                }\n            });\n        }\n        catch (e) {\n            // Modify the error message but leave the stack trace intact\n            e.message = `Error thrown while initializing video stream: ${e.message}`;\n            throw e;\n        }\n        if (!this.stream) {\n            throw new Error('Could not obtain video from webcam.');\n        }\n        // Older browsers may not have srcObject\n        try {\n            this.webcamVideoElement.srcObject = this.stream;\n        }\n        catch (error) {\n            console.log(error);\n            this.webcamVideoElement.src = window.URL.createObjectURL(this.stream);\n        }\n        // Start the webcam video stream\n        this.webcamVideoElement.play();\n        this.isClosed = false;\n        return new Promise(resolve => {\n            // Add event listener to make sure the webcam has been fully initialized.\n            this.webcamVideoElement.onloadedmetadata = () => {\n                resolve();\n            };\n        });\n    }\n    async next() {\n        if (this.isClosed) {\n            return { value: null, done: true };\n        }\n        let img;\n        try {\n            img = browser.fromPixels(this.webcamVideoElement);\n        }\n        catch (e) {\n            throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`);\n        }\n        if (this.resize) {\n            try {\n                return { value: this.cropAndResizeFrame(img), done: false };\n            }\n            catch (e) {\n                throw new Error(`Error thrown cropping the video: ${e.message}`);\n            }\n            finally {\n                img.dispose();\n            }\n        }\n        else {\n            return { value: img, done: false };\n        }\n    }\n    needToResize() {\n        // If resizeWidth and resizeHeight are provided, and different from the\n        // width and height of original HTMLVideoElement, then resizing and cropping\n        // is required.\n        if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight &&\n            (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth ||\n                this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) {\n            return true;\n        }\n        return false;\n    }\n    // Cropping and resizing each frame based on config\n    cropAndResizeFrame(img) {\n        return tidy(() => {\n            const expandedImage = img.toFloat().expandDims(0);\n            let resizedImage;\n            resizedImage = image.cropAndResize(expandedImage, this.cropBox, this.cropBoxInd, this.cropSize, 'bilinear');\n            // Extract image from batch cropping.\n            const shape = resizedImage.shape;\n            return resizedImage.reshape(shape.slice(1));\n        });\n    }\n    // Capture one frame from the video stream, and extract the value from\n    // iterator.next() result.\n    async capture() {\n        return (await this.next()).value;\n    }\n    // Stop the video stream and pause webcam iterator.\n    stop() {\n        const tracks = this.stream.getTracks();\n        tracks.forEach(track => track.stop());\n        try {\n            this.webcamVideoElement.srcObject = null;\n        }\n        catch (error) {\n            console.log(error);\n            this.webcamVideoElement.src = null;\n        }\n        this.isClosed = true;\n    }\n    // Override toArray() function to prevent collecting.\n    toArray() {\n        throw new Error('Can not convert infinite video stream to array.');\n    }\n}\n//# sourceMappingURL=webcam_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * Represents a data source readable as a stream of binary data chunks.\n *\n * Because `Dataset`s can be read repeatedly (via `Dataset.iterator()`), this\n * provides a means to repeatedly create streams from the underlying data\n * sources.\n */\nexport class DataSource {\n}\n// TODO(soergel): consider convenience factory functions here\n// in combination with chainable source->dataset above, e.g.:\n// tf.data.url(...).asCsvDataset().shuffle().batch()\n//# sourceMappingURL=datasource.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nexport class StringIterator extends LazyIterator {\n    /**\n     * Splits a string stream on a given separator.\n     *\n     * It is assumed that the incoming chunk boundaries have no semantic meaning,\n     * so conceptually the incoming stream is treated simply as the concatenation\n     * of its elements.\n     *\n     * The outgoing stream provides chunks corresponding to the results of the\n     * standard string split() operation (even if such a chunk spanned incoming\n     * chunks).  The separators are not included.\n     *\n     * A typical usage is to split a text file (represented as a stream with\n     * arbitrary chunk boundaries) into lines.\n     *\n     * @param upstream A readable stream of strings that can be treated as\n     *   concatenated.\n     * @param separator A character to split on.\n     */\n    split(separator) {\n        return new SplitIterator(this, separator);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on StringIterator.  Unfortunately they can't be placed in separate files, due\n// to resulting trouble with circular imports.\n// ============================================================================\n// We wanted multiple inheritance, e.g.\n//   class SplitIterator extends QueueIterator<string>, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\nclass SplitIterator extends StringIterator {\n    constructor(upstream, separator) {\n        super();\n        this.upstream = upstream;\n        this.impl = new SplitIteratorImpl(upstream, separator);\n    }\n    summary() {\n        return this.impl.summary();\n    }\n    async next() {\n        return this.impl.next();\n    }\n}\nclass SplitIteratorImpl extends OneToManyIterator {\n    constructor(upstream, separator) {\n        super();\n        this.upstream = upstream;\n        this.separator = separator;\n        // A partial string at the end of an upstream chunk\n        this.carryover = '';\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Split('${this.separator}')`;\n    }\n    async pump() {\n        const chunkResult = await this.upstream.next();\n        if (chunkResult.done) {\n            if (this.carryover === '') {\n                return false;\n            }\n            // Pretend that the pump succeeded in order to emit the small last batch.\n            // The next pump() call will actually fail.\n            this.outputQueue.push(this.carryover);\n            this.carryover = '';\n            return true;\n        }\n        const lines = chunkResult.value.split(this.separator);\n        // Note the behavior: \" ab \".split(' ') === ['', 'ab', '']\n        // Thus the carryover may be '' if the separator falls on a chunk\n        // boundary; this produces the correct result.\n        lines[0] = this.carryover + lines[0];\n        for (const line of lines.slice(0, -1)) {\n            this.outputQueue.push(line);\n        }\n        this.carryover = lines[lines.length - 1];\n        return true;\n    }\n}\n//# sourceMappingURL=string_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nimport { StringIterator } from './string_iterator';\nexport class ByteChunkIterator extends LazyIterator {\n    /**\n     * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n     *\n     * The byte arrays producetd from the ByteChunkIterator on which this is\n     * called will be interpreted as concatenated.  No assumptions are made about\n     * the boundaries of the incoming chunks, so a multi-byte UTF8 encoding of a\n     * character may span the boundary between chunks.  This naturally happens,\n     * for instance, when reading fixed-size byte arrays from a file.\n     */\n    decodeUTF8() {\n        return new Utf8Iterator(this);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on ByteChunkIterator.  Unfortunately they can't be placed in separate files,\n// due to resulting trouble with circular imports.\n// ============================================================================\n// We wanted multiple inheritance, e.g.\n//   class Utf8Iterator extends QueueIterator<string>, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\nclass Utf8Iterator extends StringIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        this.impl = new Utf8IteratorImpl(upstream);\n    }\n    summary() {\n        return this.impl.summary();\n    }\n    async next() {\n        return this.impl.next();\n    }\n}\n/**\n * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n *\n * This is tricky because the incoming byte array boundaries may disrupt a\n * multi-byte UTF8 character. Thus any incomplete character data at the end of\n * a chunk must be carried over and prepended to the next chunk before\n * decoding. Luckily with native decoder, TextDecoder in browser and\n * string_decoder in node, byte array boundaries are handled automatically.\n *\n * In the context of an input pipeline for machine learning, UTF8 decoding is\n * needed to parse text files containing training examples or prediction\n * requests (e.g., formatted as CSV or JSON). We cannot use the built-in\n * decoding provided by FileReader.readAsText() because here we are in a\n * streaming context, which FileReader does not support.\n *\n * @param upstream A `LazyIterator` of `Uint8Arrays` containing UTF8-encoded\n *   text, which should be interpreted as concatenated.  No assumptions are\n *   made about the boundaries of the incoming chunks, so a multi-byte UTF8\n *   encoding of a character may span the boundary between chunks.  This\n *   naturally happens, for instance, when reading fixed-size byte arrays from a\n *   file.\n */\nclass Utf8IteratorImpl extends OneToManyIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        if (env().get('IS_BROWSER')) {\n            this.decoder = new TextDecoder('utf-8');\n        }\n        else {\n            // tslint:disable-next-line:no-require-imports\n            const { StringDecoder } = require('string_decoder');\n            this.decoder = new StringDecoder('utf8');\n        }\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Utf8`;\n    }\n    async pump() {\n        const chunkResult = await this.upstream.next();\n        let chunk;\n        if (chunkResult.done) {\n            return false;\n        }\n        else {\n            chunk = chunkResult.value;\n        }\n        let text;\n        if (env().get('IS_BROWSER')) {\n            text = this.decoder.decode(chunk, { stream: true });\n        }\n        else {\n            text = this.decoder.write(Buffer.from(chunk.buffer));\n        }\n        this.outputQueue.push(text);\n        return true;\n    }\n}\n//# sourceMappingURL=byte_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n// inspired by https://github.com/maxogden/filereader-stream\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { ByteChunkIterator } from './byte_chunk_iterator';\n/**\n * Provide a stream of chunks from a File, Blob, or Uint8Array.\n * @param file The source File, Blob or Uint8Array.\n * @param options Optional settings controlling file reading.\n * @returns a lazy Iterator of Uint8Arrays containing sequential chunks of the\n *   input File, Blob or Uint8Array.\n */\nexport class FileChunkIterator extends ByteChunkIterator {\n    constructor(file, options = {}) {\n        super();\n        this.file = file;\n        this.options = options;\n        util.assert((file instanceof Uint8Array) ||\n            (env().get('IS_BROWSER') ?\n                (file instanceof File || file instanceof Blob) :\n                false), () => 'FileChunkIterator only supports File, Blob and Uint8Array ' +\n            'right now.');\n        this.offset = options.offset || 0;\n        // default 1MB chunk has tolerable perf on large files\n        this.chunkSize = options.chunkSize || 1024 * 1024;\n    }\n    summary() {\n        return `FileChunks ${this.file}`;\n    }\n    async next() {\n        if (this.offset >= ((this.file instanceof Uint8Array) ?\n            this.file.byteLength :\n            this.file.size)) {\n            return { value: null, done: true };\n        }\n        const chunk = new Promise((resolve, reject) => {\n            const end = this.offset + this.chunkSize;\n            if (this.file instanceof Uint8Array) {\n                // Note if end > this.uint8Array.byteLength, we just get a small last\n                // chunk.\n                resolve(new Uint8Array(this.file.slice(this.offset, end)));\n            }\n            else {\n                // This branch assumes that this.file type is File or Blob, which\n                // means it is in the browser environment.\n                // TODO(soergel): is this a performance issue?\n                const fileReader = new FileReader();\n                fileReader.onload = (event) => {\n                    let data = fileReader.result;\n                    // Not sure we can trust the return type of\n                    // FileReader.readAsArrayBuffer See e.g.\n                    // https://github.com/node-file-api/FileReader/issues/2\n                    if (data instanceof ArrayBuffer) {\n                        data = new Uint8Array(data);\n                    }\n                    if (!(data instanceof Uint8Array)) {\n                        return reject(new TypeError('FileReader returned unknown type.'));\n                    }\n                    resolve(data);\n                };\n                fileReader.onabort = (event) => {\n                    return reject(new Error('Aborted'));\n                };\n                fileReader.onerror = (event) => {\n                    return reject(new Error(event.type));\n                };\n                // TODO(soergel): better handle onabort, onerror\n                // Note if end > this.file.size, we just get a small last chunk.\n                const slice = this.file.slice(this.offset, end);\n                // We can't use readAsText here (even if we know the file is text)\n                // because the slice boundary may fall within a multi-byte character.\n                fileReader.readAsArrayBuffer(slice);\n            }\n            this.offset = end;\n        });\n        return { value: (await chunk), done: false };\n    }\n}\n//# sourceMappingURL=file_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { FileChunkIterator } from './file_chunk_iterator';\n/**\n * Provide a stream of chunks from a URL.\n *\n * Note this class first downloads the entire file into memory before providing\n * the first element from the stream.  This is because the Fetch API does not\n * yet reliably provide a reader stream for the response body.\n */\nexport async function urlChunkIterator(url, options = {}) {\n    let urlString;\n    let requestInit;\n    if ((typeof url) === 'string') {\n        urlString = url;\n    }\n    else {\n        urlString = url.url;\n        requestInit = getRequestInitFromRequest(url);\n    }\n    const response = await util.fetch(urlString, requestInit);\n    if (response.ok) {\n        const uint8Array = new Uint8Array(await response.arrayBuffer());\n        return new FileChunkIterator(uint8Array, options);\n    }\n    else {\n        throw new Error(response.statusText);\n    }\n}\n// Generate RequestInit from Request to match tf.util.fetch signature.\nconst getRequestInitFromRequest = (request) => {\n    const init = {\n        method: request.method,\n        headers: request.headers,\n        body: request.body,\n        mode: request.mode,\n        credentials: request.credentials,\n        cache: request.cache,\n        redirect: request.redirect,\n        referrer: request.referrer,\n        integrity: request.integrity,\n    };\n    return init;\n};\n//# sourceMappingURL=url_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n// Skip tslint any type check cause this method is aiming to check type of\n// input.\n// tslint:disable-next-line:no-any\nexport function isLocalPath(source) {\n    return (typeof source === 'string') && source.substr(0, 7) === 'file://';\n}\n//# sourceMappingURL=source_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { DataSource } from '../datasource';\nimport { FileChunkIterator } from '../iterators/file_chunk_iterator';\nimport { isLocalPath } from '../util/source_util';\n/**\n * Represents a file, blob, or Uint8Array readable as a stream of binary data\n * chunks.\n */\nexport class FileDataSource extends DataSource {\n    /**\n     * Create a `FileDataSource`.\n     *\n     * @param input Local file path, or `File`/`Blob`/`Uint8Array` object to\n     *     read. Local file only works in node environment.\n     * @param options Options passed to the underlying `FileChunkIterator`s,\n     *   such as {chunksize: 1024}.\n     */\n    constructor(input, options = {}) {\n        super();\n        this.input = input;\n        this.options = options;\n    }\n    async iterator() {\n        if (isLocalPath(this.input) && env().get('IS_NODE')) {\n            // tslint:disable-next-line:no-require-imports\n            const fs = require('fs');\n            this.input = fs.readFileSync(this.input.substr(7));\n        }\n        // TODO(kangyizhang): Add LocalFileChunkIterator to split local streaming\n        // with file in browser.\n        return new FileChunkIterator(this.input, this.options);\n    }\n}\n//# sourceMappingURL=file_data_source.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { DataSource } from '../datasource';\nimport { urlChunkIterator } from '../iterators/url_chunk_iterator';\nimport { isLocalPath } from '../util/source_util';\nimport { FileDataSource } from './file_data_source';\n/*\n * Represents a URL readable as a stream of binary data chunks.\n */\nexport class URLDataSource extends DataSource {\n    /**\n     * Create a `URLDataSource`.\n     *\n     * @param url A source URL string, or a `Request` object.\n     * @param options Options passed to the underlying `FileChunkIterator`s,\n     *   such as {chunksize: 1024}.\n     */\n    constructor(url, fileOptions = {}) {\n        super();\n        this.url = url;\n        this.fileOptions = fileOptions;\n    }\n    // TODO(soergel): provide appropriate caching options.  Currently this\n    // will download the URL anew for each call to iterator().  Since we have\n    // to treat the downloaded file as a blob/buffer anyway, we may as well retain\n    // it-- but that raises GC issues.  Also we may want a persistent disk cache.\n    async iterator() {\n        if (isLocalPath(this.url)) {\n            return (new FileDataSource(this.url, this.fileOptions))\n                .iterator();\n        }\n        else {\n            return urlChunkIterator(this.url, this.fileOptions);\n        }\n    }\n}\n//# sourceMappingURL=url_data_source.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { datasetFromIteratorFn } from './dataset';\nimport { CSVDataset } from './datasets/csv_dataset';\nimport { iteratorFromFunction } from './iterators/lazy_iterator';\nimport { MicrophoneIterator } from './iterators/microphone_iterator';\nimport { WebcamIterator } from './iterators/webcam_iterator';\nimport { URLDataSource } from './sources/url_data_source';\n/**\n * Create a `CSVDataset` by reading and decoding CSV file(s) from provided URL\n * or local path if it's in Node environment.\n *\n * Note: If isLabel in columnConfigs is `true` for at least one column, the\n * element in returned `CSVDataset` will be an object of\n * `{xs:features, ys:labels}`: xs is a dict of features key/value pairs, ys\n * is a dict of labels key/value pairs. If no column is marked as label,\n * returns a dict of features only.\n *\n * ```js\n * const csvUrl =\n * 'https://storage.googleapis.com/tfjs-examples/multivariate-linear-regression/data/boston-housing-train.csv';\n *\n * async function run() {\n *   // We want to predict the column \"medv\", which represents a median value of\n *   // a home (in $1000s), so we mark it as a label.\n *   const csvDataset = tf.data.csv(\n *     csvUrl, {\n *       columnConfigs: {\n *         medv: {\n *           isLabel: true\n *         }\n *       }\n *     });\n *\n *   // Number of features is the number of column names minus one for the label\n *   // column.\n *   const numOfFeatures = (await csvDataset.columnNames()).length - 1;\n *\n *   // Prepare the Dataset for training.\n *   const flattenedDataset =\n *     csvDataset\n *     .map(({xs, ys}) =>\n *       {\n *         // Convert xs(features) and ys(labels) from object form (keyed by\n *         // column name) to array form.\n *         return {xs:Object.values(xs), ys:Object.values(ys)};\n *       })\n *     .batch(10);\n *\n *   // Define the model.\n *   const model = tf.sequential();\n *   model.add(tf.layers.dense({\n *     inputShape: [numOfFeatures],\n *     units: 1\n *   }));\n *   model.compile({\n *     optimizer: tf.train.sgd(0.000001),\n *     loss: 'meanSquaredError'\n *   });\n *\n *   // Fit the model using the prepared Dataset\n *   return model.fitDataset(flattenedDataset, {\n *     epochs: 10,\n *     callbacks: {\n *       onEpochEnd: async (epoch, logs) => {\n *         console.log(epoch + ':' + logs.loss);\n *       }\n *     }\n *   });\n * }\n *\n * await run();\n * ```\n *\n * @param source URL or local path to get CSV file. If it's a local path, it\n * must have prefix `file://` and it only works in node environment.\n * @param csvConfig (Optional) A CSVConfig object that contains configurations\n *     of reading and decoding from CSV file(s).\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   configParamIndices: [1]\n *  }\n */\nexport function csv(source, csvConfig = {}) {\n    return new CSVDataset(new URLDataSource(source), csvConfig);\n}\n/**\n * Create a `Dataset` that produces each element by calling a provided function.\n *\n * Note that repeated iterations over this `Dataset` may produce different\n * results, because the function will be called anew for each element of each\n * iteration.\n *\n * Also, beware that the sequence of calls to this function may be out of order\n * in time with respect to the logical order of the Dataset. This is due to the\n * asynchronous lazy nature of stream processing, and depends on downstream\n * transformations (e.g. .shuffle()). If the provided function is pure, this is\n * no problem, but if it is a closure over a mutable state (e.g., a traversal\n * pointer), then the order of the produced elements may be scrambled.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const ds = tf.data.func(func);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * @param f A function that produces one data element on each call.\n */\nexport function func(f) {\n    const iter = iteratorFromFunction(f);\n    return datasetFromIteratorFn(async () => iter);\n}\n/**\n * Create a `Dataset` that produces each element from provided JavaScript\n * generator, which is a function*\n * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions),\n * or a function that returns an\n * iterator\n * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions).\n *\n * The returned iterator should have `.next()` function that returns element in\n * format of `{value: TensorContainer, done:boolean}`.\n *\n * Example of creating a dataset from an iterator factory:\n * ```js\n * function makeIterator() {\n *   const numElements = 10;\n *   let index = 0;\n *\n *   const iterator = {\n *     next: () => {\n *       let result;\n *       if (index < numElements) {\n *         result = {value: index, done: false};\n *         index++;\n *         return result;\n *       }\n *       return {value: index, done: true};\n *     }\n *   };\n *   return iterator;\n * }\n * const ds = tf.data.generator(makeIterator);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * Example of creating a dataset from a generator:\n * ```js\n * function* dataGenerator() {\n *   const numElements = 10;\n *   let index = 0;\n *   while (index < numElements) {\n *     const x = index;\n *     index++;\n *     yield x;\n *   }\n * }\n *\n * const ds = tf.data.generator(dataGenerator);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * @param generator A Javascript generator function that returns a JavaScript\n *     iterator.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   configParamIndices: [1]\n *  }\n */\nexport function generator(generator) {\n    return datasetFromIteratorFn(async () => {\n        const gen = await generator();\n        return iteratorFromFunction(() => gen.next());\n    });\n}\n/**\n * Create an iterator that generate `Tensor`s from webcam video stream. This API\n * only works in Browser environment when the device has webcam.\n *\n * Note: this code snippet only works when the device has a webcam. It will\n * request permission to open the webcam when running.\n * ```js\n * const videoElement = document.createElement('video');\n * videoElement.width = 100;\n * videoElement.height = 100;\n * const cam = await tf.data.webcam(videoElement);\n * const img = await cam.capture();\n * img.print();\n * cam.stop();\n * ```\n *\n * @param webcamVideoElement A `HTMLVideoElement` used to play video from\n *     webcam. If this element is not provided, a hidden `HTMLVideoElement` will\n *     be created. In that case, `resizeWidth` and `resizeHeight` must be\n *     provided to set the generated tensor shape.\n * @param webcamConfig A `WebcamConfig` object that contains configurations of\n *     reading and manipulating data from webcam video stream.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   ignoreCI: true\n *  }\n */\nexport async function webcam(webcamVideoElement, webcamConfig) {\n    return WebcamIterator.create(webcamVideoElement, webcamConfig);\n}\n/**\n * Create an iterator that generate frequency-domain spectrogram `Tensor`s from\n * microphone audio stream with browser's native FFT. This API only works in\n * browser environment when the device has microphone.\n *\n * Note: this code snippet only works when the device has a microphone. It will\n * request permission to open the microphone when running.\n * ```js\n * const mic = await tf.data.microphone({\n *   fftSize: 1024,\n *   columnTruncateLength: 232,\n *   numFramesPerSpectrogram: 43,\n *   sampleRateHz:44100,\n *   includeSpectrogram: true,\n *   includeWaveform: true\n * });\n * const audioData = await mic.capture();\n * const spectrogramTensor = audioData.spectrogram;\n * spectrogramTensor.print();\n * const waveformTensor = audioData.waveform;\n * waveformTensor.print();\n * mic.stop();\n * ```\n *\n * @param microphoneConfig A `MicrophoneConfig` object that contains\n *     configurations of reading audio data from microphone.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   ignoreCI: true\n *  }\n */\nexport async function microphone(microphoneConfig) {\n    return MicrophoneIterator.create(microphoneConfig);\n}\n//# sourceMappingURL=readers.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { array, Dataset, zip } from './dataset';\nexport { CSVDataset } from './datasets/csv_dataset';\nexport { TextLineDataset } from './datasets/text_line_dataset';\nexport { csv, func, generator, microphone, webcam } from './readers';\nexport { FileDataSource } from './sources/file_data_source';\nexport { URLDataSource } from './sources/url_data_source';\nexport { version as version_data } from './version';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function assertNotComplex(tensor, opName) {\n    if (!Array.isArray(tensor)) {\n        tensor = [tensor];\n    }\n    tensor.forEach(t => {\n        if (t != null) {\n            util.assert(t.dtype !== 'complex64', () => `${opName} does not support complex64 tensors in the CPU backend.`);\n        }\n    });\n}\n//# sourceMappingURL=cpu_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { backend_util, DataStorage, engine, env, kernel_impls, KernelBackend, max, slice_util, TensorBuffer, upcastType, util } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV3Impl = kernel_impls.nonMaxSuppressionV3Impl;\nconst split = kernel_impls.split;\nconst tile = kernel_impls.tile;\nconst topkImpl = kernel_impls.topkImpl;\nconst whereImpl = kernel_impls.whereImpl;\nimport * as seedrandom from 'seedrandom';\nimport { assertNotComplex } from './cpu_util';\nfunction mapActivation(backend, x, activation, preluActivationWeights) {\n    if (activation === 'linear') {\n        return backend.linear(x);\n    }\n    else if (activation === 'relu') {\n        return backend.relu(x);\n    }\n    else if (activation === 'elu') {\n        return tf.elu(x);\n    }\n    else if (activation === 'relu6') {\n        return backend.relu6(x);\n    }\n    else if (activation === 'prelu') {\n        return backend.prelu(x, preluActivationWeights);\n    }\n    throw new Error(`Activation ${activation} has not been implemented for the CPU backend.`);\n}\nexport class MathBackendCPU extends KernelBackend {\n    constructor() {\n        super();\n        this.blockSize = 48;\n        this.firstUse = true;\n        this.data = new DataStorage(this, engine());\n    }\n    write(values, shape, dtype) {\n        if (this.firstUse) {\n            this.firstUse = false;\n            if (env().get('IS_NODE')) {\n                backend_util.warn('\\n============================\\n' +\n                    'Hi there \uD83D\uDC4B. Looks like you are running TensorFlow.js in ' +\n                    'Node.js. To speed things up dramatically, install our node ' +\n                    'backend, which binds to TensorFlow C++, by running ' +\n                    'npm i @tensorflow/tfjs-node, ' +\n                    'or npm i @tensorflow/tfjs-node-gpu if you have CUDA. ' +\n                    'Then call require(\\'@tensorflow/tfjs-node\\'); (-gpu ' +\n                    'suffix for CUDA) at the start of your program. ' +\n                    'Visit https://github.com/tensorflow/tfjs-node for more details.' +\n                    '\\n============================');\n            }\n        }\n        const dataId = {};\n        this.data.set(dataId, { values, dtype, refCount: 1 });\n        return dataId;\n    }\n    /**\n     * Create a data bucket in cpu backend.\n     * @param shape Shape of the `TensorInfo`.\n     * @param dtype DType of the `TensorInfo`.\n     * @param values The value of the `TensorInfo` stored as a flattened array.\n     */\n    makeTensorInfo(shape, dtype, values) {\n        const outId = this.write(values, shape, dtype);\n        return { dataId: outId, shape, dtype };\n    }\n    /** Increase refCount of a `TensorData`. */\n    incRef(dataId) {\n        const tensorData = this.data.get(dataId);\n        tensorData.refCount++;\n    }\n    /** Decrease refCount of a `TensorData`. */\n    decRef(dataId) {\n        if (this.data.has(dataId)) {\n            const tensorData = this.data.get(dataId);\n            tensorData.refCount--;\n        }\n    }\n    move(dataId, values, shape, dtype) {\n        this.data.set(dataId, { values, dtype, refCount: 1 });\n    }\n    numDataIds() {\n        return this.data.numDataIds();\n    }\n    async read(dataId) {\n        return this.readSync(dataId);\n    }\n    readSync(dataId) {\n        const { dtype, complexTensorInfos } = this.data.get(dataId);\n        if (dtype === 'complex64') {\n            const realValues = this.readSync(complexTensorInfos.real.dataId);\n            const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n            return backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        return this.data.get(dataId).values;\n    }\n    bufferSync(t) {\n        const data = this.readSync(t.dataId);\n        let decodedData = data;\n        if (t.dtype === 'string') {\n            try {\n                // Decode the bytes into string.\n                decodedData = data.map(d => util.decodeString(d));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode encoded string bytes into utf-8');\n            }\n        }\n        return tf.buffer(t.shape, t.dtype, decodedData);\n    }\n    makeOutput(values, shape, dtype) {\n        const dataId = this.write(values, shape, dtype);\n        return engine().makeTensorFromDataId(dataId, shape, dtype, this);\n    }\n    disposeData(dataId) {\n        if (this.data.has(dataId)) {\n            const { complexTensorInfos } = this.data.get(dataId);\n            if (complexTensorInfos != null) {\n                this.disposeData(complexTensorInfos.real.dataId);\n                this.disposeData(complexTensorInfos.imag.dataId);\n            }\n            this.data.delete(dataId);\n        }\n    }\n    disposeIntermediateTensorInfo(tensorInfo) {\n        const dataId = tensorInfo.dataId;\n        if (this.data.has(dataId)) {\n            const tensorData = this.data.get(dataId);\n            tensorData.refCount--;\n            if (tensorData.refCount < 1) {\n                this.disposeData(dataId);\n            }\n        }\n    }\n    async time(f) {\n        const start = util.now();\n        f();\n        const kernelMs = util.now() - start;\n        return { kernelMs };\n    }\n    memory() {\n        return {\n            // Unreliable due to automatic gc. The numbers above are cumulative.\n            unreliable: true,\n            reasons: ['The reported memory is an upper bound. Due to automatic garbage ' +\n                    'collection, the true allocated memory may be less.']\n        };\n    }\n    stridedSlice(x, begin, end, strides) {\n        assertNotComplex(x, 'stridedSlice');\n        const outShape = slice_util.computeOutShape(begin, end, strides);\n        if (outShape.some(axis => axis === 0)) {\n            return tf.tensor([], outShape);\n        }\n        const buffer = tf.buffer(outShape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < buffer.size; i++) {\n            const loc = buffer.indexToLoc(i);\n            const newLoc = new Array(loc.length);\n            for (let j = 0; j < newLoc.length; j++) {\n                newLoc[j] = loc[j] * strides[j] + begin[j];\n            }\n            buffer.set(xBuf.get(...newLoc), ...loc);\n        }\n        return buffer.toTensor();\n    }\n    diag(x) {\n        const xVals = this.readSync(x.dataId);\n        const buffer = tf.buffer([x.size, x.size], x.dtype);\n        const vals = buffer.values;\n        for (let i = 0; i < xVals.length; i++) {\n            vals[i * x.size + i] = xVals[i];\n        }\n        return buffer.toTensor();\n    }\n    unstack(x, axis) {\n        const num = x.shape[axis];\n        const outShape = new Array(x.rank - 1);\n        let outIndex = 0;\n        for (let i = 0; i < x.rank; i++) {\n            if (i !== axis) {\n                outShape[outIndex++] = x.shape[i];\n            }\n        }\n        const begin = new Array(x.rank).fill(0);\n        const size = x.shape.slice();\n        size[axis] = 1;\n        const res = new Array(num);\n        for (let i = 0; i < res.length; i++) {\n            begin[axis] = i;\n            res[i] = tf.slice(x, begin, size).reshape(outShape);\n        }\n        return res;\n    }\n    reverse(x, axis) {\n        assertNotComplex(x, 'reverse');\n        const buffer = tf.buffer(x.shape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < buffer.size; i++) {\n            const outLoc = buffer.indexToLoc(i);\n            const inLoc = outLoc.slice();\n            axis.forEach(ax => inLoc[ax] = x.shape[ax] - 1 - inLoc[ax]);\n            buffer.set(xBuf.get(...inLoc), ...outLoc);\n        }\n        return buffer.toTensor();\n    }\n    neg(x) {\n        assertNotComplex(x, 'neg');\n        // TODO(lina128): Use mul directly once neg is modularized.\n        return tf.mul(tf.scalar(-1), x);\n    }\n    addN(tensors) {\n        assertNotComplex(tensors, 'addN');\n        const vals = tensors.map(t => this.readSync(t.dataId));\n        const result = tf.buffer(tensors[0].shape, tensors[0].dtype);\n        const resultVals = result.values;\n        for (let i = 0; i < tensors.length; i++) {\n            const currVals = vals[i];\n            for (let j = 0; j < resultVals.length; j++) {\n                resultVals[j] += currVals[j];\n            }\n        }\n        return result.toTensor();\n    }\n    softmax(logits, dim) {\n        const axes = util.parseAxisParam([dim], logits.shape);\n        // TODO(annxingyuan): Call maxImpl rather than op as part of softmax kernel\n        // modularization.\n        const maxLogit = max(logits, axes);\n        const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n        // TODO(lina128): Use sub directly once softmax is modularized.\n        const a = tf.sub(logits, maxLogit.reshape(expandedShape));\n        const b = tf.exp(a);\n        const sumExp = this.sum(b, axes).reshape(expandedShape);\n        // TODO(annxingyuan): Call divImpl rather than op as part of softmax\n        // kernel modularization.\n        return tf.div(b, sumExp);\n    }\n    pow(a, b) {\n        assertNotComplex([a, b], 'pow');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aValue, bValue) => Math.pow(aValue, bValue));\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        assertNotComplex([a, b], 'matMul');\n        const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n        const leftDim = transposeA ? a.shape[2] : a.shape[1];\n        const rightDim = transposeB ? b.shape[1] : b.shape[2];\n        const batchDim = a.shape[0];\n        const aValues = this.readSync(a.dataId);\n        const bValues = this.readSync(b.dataId);\n        const [aBatch, aOuterStep, aInnerStep] = transposeA ?\n            [a.strides[0], 1, a.strides[1]] :\n            [a.strides[0], a.strides[1], 1];\n        const [bInnerStep, bOuterStep, bBatch] = transposeB ?\n            [1, b.strides[1], b.strides[0]] :\n            [b.strides[1], 1, b.strides[0]];\n        const size = leftDim * rightDim;\n        const result = tf.buffer([batchDim, leftDim, rightDim], a.dtype);\n        const resVals = result.values;\n        const blockSize = this.blockSize;\n        for (let b = 0; b < batchDim; b++) {\n            for (let i0 = 0; i0 < leftDim; i0 += blockSize) {\n                for (let j0 = 0; j0 < rightDim; j0 += blockSize) {\n                    for (let k0 = 0; k0 < sharedDim; k0 += blockSize) {\n                        // for when blockSize doesn't evenly divide the input\n                        const iBlock = Math.min(i0 + blockSize, leftDim);\n                        const jBlock = Math.min(j0 + blockSize, rightDim);\n                        const kBlock = Math.min(k0 + blockSize, sharedDim);\n                        for (let i = i0; i < iBlock; i++) {\n                            for (let j = j0; j < jBlock; j++) {\n                                let sum = 0.0;\n                                for (let k = k0; k < kBlock; k++) {\n                                    sum += aValues[b * aBatch + i * aOuterStep + k * aInnerStep] *\n                                        bValues[k * bInnerStep + j * bOuterStep + b * bBatch];\n                                }\n                                resVals[b * size + (i * rightDim + j)] += sum;\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return result.toTensor();\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        let result = this.batchMatMul(a, b, transposeA, transposeB);\n        if (bias) {\n            // TODO(lina128): Use add directly once fusedBatchMatMul is modularized.\n            result = tf.add(result, bias);\n        }\n        if (activation) {\n            result =\n                mapActivation(this, result, activation, preluActivationWeights);\n        }\n        return result;\n    }\n    floorDiv(a, b) {\n        assertNotComplex([a, b], 'floorDiv');\n        const op = (a, b) => Math.floor(a / b);\n        const outputDtype = 'int32';\n        return this.broadcastedBinaryOp(a, b, outputDtype, op);\n    }\n    sum(x, axes) {\n        assertNotComplex(x, 'sum');\n        backend_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(outShape, resultDtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let sum = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                sum += aVals[offset + j];\n            }\n            vals[i] = sum;\n        }\n        return result;\n    }\n    prod(x, axes) {\n        assertNotComplex(x, 'sum');\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(outShape, resultDtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let prod = 1;\n            for (let j = 0; j < reduceSize; ++j) {\n                prod *= aVals[offset + j];\n            }\n            vals[i] = prod;\n        }\n        return result;\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        assertNotComplex(x, 'unsortedSegmentSum');\n        const res = [];\n        // Reshape the segment id's so that they can be broadcast with\n        // x. The new shape should be [segmentIds.shape, 1, ..., 1]\n        const numIters = x.rank - segmentIds.rank;\n        for (let i = 0; i < numIters; ++i) {\n            segmentIds = segmentIds.expandDims(i + 1);\n        }\n        for (let i = 0; i < numSegments; ++i) {\n            const segmentId = tf.scalar(i, 'int32');\n            const mask = tf.equal(segmentId, segmentIds).asType('float32');\n            const sum = mask.mul(x).sum(0);\n            res.push(sum);\n        }\n        return tf.stack(res);\n    }\n    argMin(x, axis) {\n        assertNotComplex(x, 'argMin');\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('argMin', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, 'int32');\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let min = aVals[offset];\n            let minIndex = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value < min) {\n                    min = value;\n                    minIndex = j;\n                }\n            }\n            vals[i] = minIndex;\n        }\n        return result;\n    }\n    argMax(x, axis) {\n        assertNotComplex(x, 'argMax');\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('argMax', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, 'int32');\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let max = aVals[offset];\n            let maxIndex = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value > max) {\n                    max = value;\n                    maxIndex = j;\n                }\n            }\n            vals[i] = maxIndex;\n        }\n        return result;\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        assertNotComplex(x, 'cumsum');\n        if (axis !== x.rank - 1) {\n            throw new Error(`backend.cumsum in CPU expects an inner-most axis=${x.rank - 1} ` +\n                `but got axis=${axis}`);\n        }\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(x.shape, resultDtype);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        const finalDim = x.shape[x.rank - 1];\n        const indexAdjuster = reverse ?\n            (i, j) => i + finalDim - j - 1 :\n            (i, j) => i + j;\n        for (let i = 0; i < aVals.length; i += finalDim) {\n            for (let j = 0; j < finalDim; j++) {\n                const idx = indexAdjuster(i, j);\n                if (j === 0) {\n                    vals[idx] = exclusive ? 0 : aVals[idx];\n                }\n                else {\n                    const prevIdx = indexAdjuster(i, j - 1);\n                    vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :\n                        aVals[idx] + vals[prevIdx];\n                }\n            }\n        }\n        return result;\n    }\n    equal(a, b) {\n        assertNotComplex([a, b], 'equal');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal === bVal) ? 1 : 0;\n        });\n    }\n    notEqual(a, b) {\n        assertNotComplex([a, b], 'notEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal !== bVal) ? 1 : 0;\n        });\n    }\n    less(a, b) {\n        assertNotComplex([a, b], 'less');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal < bVal) ? 1 : 0;\n        });\n    }\n    lessEqual(a, b) {\n        assertNotComplex([a, b], 'lessEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal <= bVal) ? 1 : 0;\n        });\n    }\n    greater(a, b) {\n        assertNotComplex([a, b], 'greater');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal > bVal) ? 1 : 0;\n        });\n    }\n    greaterEqual(a, b) {\n        assertNotComplex([a, b], 'greaterEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal >= bVal) ? 1 : 0;\n        });\n    }\n    logicalAnd(a, b) {\n        assertNotComplex([a, b], 'logicalAnd');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return aVal && bVal;\n        });\n    }\n    logicalOr(a, b) {\n        assertNotComplex([a, b], 'logicalOr');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return aVal || bVal;\n        });\n    }\n    select(condition, a, b) {\n        assertNotComplex([condition, a, b], 'select');\n        const values = this.readSync(condition.dataId);\n        const aValues = this.readSync(a.dataId);\n        const bValues = this.readSync(b.dataId);\n        const result = tf.zeros(a.shape, upcastType(a.dtype, b.dtype));\n        const newValues = this.readSync(result.dataId);\n        let index = 0;\n        const offset = condition.rank === 0 || condition.rank > 1 || a.rank === 1 ?\n            1 :\n            util.sizeFromShape(a.shape.slice(1));\n        for (let i = 0; i < values.length; i++) {\n            for (let j = 0; j < offset; j++) {\n                if (values[i] === 1) {\n                    newValues[index++] = aValues[i];\n                }\n                else {\n                    newValues[index++] = bValues[i];\n                }\n            }\n        }\n        return result;\n    }\n    where(condition) {\n        assertNotComplex([condition], 'where');\n        const condVals = this.readSync(condition.dataId);\n        return whereImpl(condition.shape, condVals);\n    }\n    topk(x, k, sorted) {\n        assertNotComplex(x, 'topk');\n        const xVals = this.readSync(x.dataId);\n        return topkImpl(xVals, x.shape, x.dtype, k, sorted);\n    }\n    min(x, axes) {\n        assertNotComplex(x, 'min');\n        backend_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let min = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value < min) {\n                    min = value;\n                }\n            }\n            vals[i] = min;\n        }\n        return result;\n    }\n    minimum(a, b) {\n        assertNotComplex([a, b], 'minimum');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => Math.min(aVal, bVal));\n    }\n    mod(a, b) {\n        assertNotComplex([a, b], 'mod');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n            const rem = aVal % bVal;\n            if ((aVal < 0 && bVal < 0) || (aVal >= 0 && bVal >= 0)) {\n                return rem;\n            }\n            else {\n                return (rem + bVal) % bVal;\n            }\n        });\n    }\n    maximum(a, b) {\n        assertNotComplex([a, b], 'maximum');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => Math.max(aVal, bVal));\n    }\n    all(x, axes) {\n        assertNotComplex(x, 'all');\n        backend_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let all = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                all = all && value;\n            }\n            vals[i] = all;\n        }\n        return result;\n    }\n    any(x, axes) {\n        assertNotComplex(x, 'any');\n        backend_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let anyVal = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                anyVal = anyVal || value;\n            }\n            vals[i] = anyVal;\n        }\n        return result;\n    }\n    squaredDifference(a, b) {\n        assertNotComplex([a, b], 'squaredDifference');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n            const diff = aVal - bVal;\n            return diff * diff;\n        });\n    }\n    linear(x) {\n        return x;\n    }\n    relu(x) {\n        assertNotComplex(x, 'relu');\n        const res = tf.zeros(x.shape, x.dtype);\n        const resVals = this.readSync(res.dataId);\n        const inVals = this.readSync(x.dataId);\n        for (let i = 0; i < inVals.length; ++i) {\n            resVals[i] = Math.max(0, inVals[i]);\n        }\n        return res;\n    }\n    relu6(x) {\n        assertNotComplex(x, 'relu');\n        const res = tf.zeros(x.shape, x.dtype);\n        const resVals = this.readSync(res.dataId);\n        const inVals = this.readSync(x.dataId);\n        for (let i = 0; i < inVals.length; ++i) {\n            resVals[i] = Math.min(Math.max(0, inVals[i]), 6);\n        }\n        return res;\n    }\n    prelu(x, a) {\n        assertNotComplex([x, a], 'prelu');\n        return this.broadcastedBinaryOp(x, a, x.dtype, (xValue, aValue) => xValue < 0 ? aValue * xValue : xValue);\n    }\n    eluDer(dy, y) {\n        assertNotComplex([dy, y], 'eluDer');\n        const resultValues = new Float32Array(y.size);\n        const values = this.readSync(y.dataId);\n        const dyValues = this.readSync(dy.dataId);\n        for (let i = 0; i < values.length; ++i) {\n            const v = values[i];\n            if (v >= 1) {\n                resultValues[i] = dyValues[i];\n            }\n            else {\n                resultValues[i] = dyValues[i] * (v + 1);\n            }\n        }\n        return this.makeOutput(resultValues, y.shape, 'float32');\n    }\n    atan2(a, b) {\n        assertNotComplex([a, b], 'atan2');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aValue, bValue) => Math.atan2(aValue, bValue));\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        let result = this.conv2d(input, filter, convInfo);\n        if (bias) {\n            // TODO(lina128): Use add directly once fusedConv2d is modularized.\n            result = tf.add(result, bias);\n        }\n        if (activation) {\n            result =\n                mapActivation(this, result, activation, preluActivationWeights);\n        }\n        return result;\n    }\n    conv2d(x, filter, convInfo) {\n        assertNotComplex([x, filter], 'conv2d');\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const padLeft = convInfo.padInfo.left;\n        const padTop = convInfo.padInfo.top;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const y = tf.buffer(convInfo.outShape, x.dtype);\n        const xBatchStride = x.strides[0];\n        const xRowStride = isChannelsLast ? x.strides[1] : x.strides[2];\n        const xColStride = isChannelsLast ? x.strides[2] : 1;\n        const xChannelStride = isChannelsLast ? 1 : x.strides[1];\n        const yBatchStride = y.strides[0];\n        const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];\n        const yColStride = isChannelsLast ? y.strides[2] : 1;\n        const yChannelStride = isChannelsLast ? 1 : y.strides[1];\n        const xVals = this.readSync(x.dataId);\n        const wVals = this.readSync(filter.dataId);\n        const yVals = y.values;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n            const xOffset1 = b * xBatchStride;\n            const yOffset1 = b * yBatchStride;\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const yOffset2 = yOffset1 + yR * yRowStride;\n                const xRCorner = yR * convInfo.strideHeight - padTop;\n                for (let wR = 0; wR < filterHeight; wR++) {\n                    const xR = xRCorner + wR * dilationHeight;\n                    if (xR < 0 || xR >= convInfo.inHeight) {\n                        continue;\n                    }\n                    const wOffset1 = wR * filter.strides[0];\n                    const xOffset2 = xOffset1 + xR * xRowStride;\n                    for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                        const yOffset3 = yOffset2 + yC * yColStride;\n                        const xCCorner = yC * convInfo.strideWidth - padLeft;\n                        for (let wC = 0; wC < filterWidth; wC++) {\n                            const xC = xCCorner + wC * dilationWidth;\n                            if (xC < 0 || xC >= convInfo.inWidth) {\n                                continue;\n                            }\n                            const wOffset2 = wOffset1 + wC * filter.strides[1];\n                            const xOffset3 = xOffset2 + xC * xColStride;\n                            let wOffset3 = wOffset2;\n                            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                const xVal = xVals[xOffset3 + d1 * xChannelStride];\n                                for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                                    yVals[yOffset3 + d2 * yChannelStride] +=\n                                        xVal * wVals[wOffset3 + d2];\n                                }\n                                wOffset3 += convInfo.outChannels;\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return y.toTensor();\n    }\n    conv3d(x, filter, convInfo) {\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const padFront = convInfo.padInfo.front;\n        const padLeft = convInfo.padInfo.left;\n        const padTop = convInfo.padInfo.top;\n        const y = tf.buffer(convInfo.outShape, x.dtype);\n        const xVals = this.readSync(x.dataId);\n        const wVals = this.readSync(filter.dataId);\n        const yVals = y.values;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n            const xOffset1 = b * x.strides[0];\n            const yOffset1 = b * y.strides[0];\n            for (let yF = 0; yF < convInfo.outDepth; ++yF) {\n                const yOffset2 = yOffset1 + yF * y.strides[1];\n                const xFCorner = yF * convInfo.strideDepth - padFront;\n                for (let wF = 0; wF < filterDepth; wF++) {\n                    const xF = xFCorner + wF * dilationDepth;\n                    if (xF < 0 || xF >= convInfo.inDepth) {\n                        continue;\n                    }\n                    const wOffset1 = wF * filter.strides[0];\n                    const xOffset2 = xOffset1 + xF * x.strides[1];\n                    for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                        const yOffset3 = yOffset2 + yR * y.strides[2];\n                        const xRCorner = yR * convInfo.strideHeight - padTop;\n                        for (let wR = 0; wR < filterHeight; wR++) {\n                            const xR = xRCorner + wR * dilationHeight;\n                            if (xR < 0 || xR >= convInfo.inHeight) {\n                                continue;\n                            }\n                            const wOffset2 = wOffset1 + wR * filter.strides[1];\n                            const xOffset3 = xOffset2 + xR * x.strides[2];\n                            for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                                const yOffset4 = yOffset3 + yC * convInfo.outChannels;\n                                const xCCorner = yC * convInfo.strideWidth - padLeft;\n                                for (let wC = 0; wC < filterWidth; wC++) {\n                                    const xC = xCCorner + wC * dilationWidth;\n                                    if (xC < 0 || xC >= convInfo.inWidth) {\n                                        continue;\n                                    }\n                                    const wOffset3 = wOffset2 + wC * filter.strides[2];\n                                    const xOffset4 = xOffset3 + xC * convInfo.inChannels;\n                                    let wOffset4 = wOffset3;\n                                    for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                        const xVal = xVals[xOffset4 + d1];\n                                        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                                            yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];\n                                        }\n                                        wOffset4 += convInfo.outChannels;\n                                    }\n                                }\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return y.toTensor();\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        assertNotComplex([dy, filter], 'conv2dDerInput');\n        const dx = tf.buffer(convInfo.inShape, 'float32');\n        const dxValues = dx.values;\n        const dyValues = this.readSync(dy.dataId);\n        const fltValues = this.readSync(filter.dataId);\n        const [fltS0, fltS1, fltS2] = filter.strides;\n        const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth, dataFormat } = convInfo;\n        const topPad = filterHeight - 1 - convInfo.padInfo.top;\n        const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const xBatchStride = dx.strides[0];\n        const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];\n        const xColStride = isChannelsLast ? dx.strides[2] : 1;\n        const xChannelStride = isChannelsLast ? 1 : dx.strides[1];\n        const yBatchStride = dy.strides[0];\n        const yRowStride = isChannelsLast ? dy.strides[1] : dy.strides[2];\n        const yColStride = isChannelsLast ? dy.strides[2] : 1;\n        const yChannelStride = isChannelsLast ? 1 : dy.strides[1];\n        for (let b = 0; b < batchSize; ++b) {\n            for (let d1 = 0; d1 < inChannels; ++d1) {\n                for (let xR = 0; xR < inHeight; ++xR) {\n                    const xRCorner = xR - topPad;\n                    const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                    const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                    for (let xC = 0; xC < inWidth; ++xC) {\n                        const xCCorner = xC - leftPad;\n                        const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                        const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                        let dotProd = 0;\n                        for (let yR = xRMin; yR < yRMax; ++yR) {\n                            const wR = yR * strideHeight - xRCorner;\n                            for (let yC = xCMin; yC < yCMax; ++yC) {\n                                const wC = yC * strideWidth - xCCorner;\n                                const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;\n                                const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n                                for (let d2 = 0; d2 < outChannels; ++d2) {\n                                    const pixel = dyValues[dyOffset + yChannelStride * d2];\n                                    const weight = fltValues[fltOffset + d2];\n                                    dotProd += pixel * weight;\n                                }\n                            }\n                        }\n                        const dxOffset = xBatchStride * b + xRowStride * xR +\n                            xColStride * xC + xChannelStride * d1;\n                        dxValues[dxOffset] = dotProd;\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        const dx = tf.buffer(convInfo.inShape, 'float32');\n        const dxValues = dx.values;\n        const [dxS0, dxS1, dxS2, dxS3] = dx.strides;\n        const dyValues = this.readSync(dy.dataId);\n        const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n        const fltValues = this.readSync(filter.dataId);\n        const [fltS0, fltS1, fltS2, fltS3] = filter.strides;\n        const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo;\n        const frontPad = filterDepth - 1 - convInfo.padInfo.front;\n        const topPad = filterHeight - 1 - convInfo.padInfo.top;\n        const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let d1 = 0; d1 < inChannels; ++d1) {\n                // Frames of depth\n                for (let xF = 0; xF < inDepth; ++xF) {\n                    const xFCorner = xF - frontPad;\n                    const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));\n                    const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);\n                    // Rows as per standard 2d matrix notation\n                    for (let xR = 0; xR < inHeight; ++xR) {\n                        const xRCorner = xR - topPad;\n                        const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                        const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                        // Columns as per standard 2d matrix notation\n                        for (let xC = 0; xC < inWidth; ++xC) {\n                            const xCCorner = xC - leftPad;\n                            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                            const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                            let dotProd = 0;\n                            for (let yF = xFMin; yF < yFMax; ++yF) {\n                                const wF = yF * strideDepth - xFCorner;\n                                for (let yR = xRMin; yR < yRMax; ++yR) {\n                                    const wR = yR * strideHeight - xRCorner;\n                                    for (let yC = xCMin; yC < yCMax; ++yC) {\n                                        const wC = yC * strideWidth - xCCorner;\n                                        const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;\n                                        const fltOffset = fltS0 * (filterDepth - 1 - wF) +\n                                            fltS1 * (filterHeight - 1 - wR) +\n                                            fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;\n                                        for (let d2 = 0; d2 < outChannels; ++d2) {\n                                            const pixel = dyValues[dyOffset + d2];\n                                            const weight = fltValues[fltOffset + d2];\n                                            dotProd += pixel * weight;\n                                        }\n                                    }\n                                }\n                            }\n                            dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =\n                                dotProd;\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    conv2dDerFilter(x, dy, convInfo) {\n        assertNotComplex([x, dy], 'conv2dDerFilter');\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const dW = tf.buffer(convInfo.filterShape, 'float32');\n        const leftPad = convInfo.padInfo.left;\n        const topPad = convInfo.padInfo.top;\n        const xBuf = this.bufferSync(x);\n        const dyBuf = this.bufferSync(dy);\n        for (let wR = 0; wR < filterHeight; ++wR) {\n            const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n            const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n            for (let wC = 0; wC < filterWidth; ++wC) {\n                const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                    for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                        // Need to convolve.\n                        let dotProd = 0;\n                        for (let b = 0; b < convInfo.batchSize; ++b) {\n                            for (let yR = yRMin; yR < yRMax; ++yR) {\n                                const xR = wR + yR * strideHeight - topPad;\n                                for (let yC = yCMin; yC < yCMax; ++yC) {\n                                    const xC = wC + yC * strideWidth - leftPad;\n                                    if (isChannelsLast) {\n                                        dotProd +=\n                                            xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n                                    }\n                                    else {\n                                        dotProd +=\n                                            xBuf.get(b, d1, xR, xC) * dyBuf.get(b, d2, yR, yC);\n                                    }\n                                }\n                            }\n                        }\n                        dW.set(dotProd, wR, wC, d1, d2);\n                    }\n                }\n            }\n        }\n        return dW.toTensor();\n    }\n    conv3dDerFilter(x, dy, convInfo) {\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dw = tf.buffer(convInfo.filterShape, 'float32');\n        const dwValues = dw.values;\n        const [dwS0, dwS1, dwS2, dwS3] = dw.strides;\n        const dyValues = this.readSync(dy.dataId);\n        const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n        const xValues = this.readSync(x.dataId);\n        const [xS0, xS1, xS2, xS3] = x.strides;\n        const frontPad = convInfo.padInfo.front;\n        const leftPad = convInfo.padInfo.left;\n        const topPad = convInfo.padInfo.top;\n        for (let wF = 0; wF < filterDepth; ++wF) {\n            const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));\n            const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);\n            const wOffset1 = wF * dwS0;\n            for (let wR = 0; wR < filterHeight; ++wR) {\n                const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n                const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n                const wOffset2 = wR * dwS1 + wOffset1;\n                for (let wC = 0; wC < filterWidth; ++wC) {\n                    const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                    const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                    const wOffset3 = wC * dwS2 + wOffset2;\n                    for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                        const wOffset4 = d1 * dwS3 + wOffset3;\n                        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                            let dotProd = 0;\n                            for (let b = 0; b < convInfo.batchSize; ++b) {\n                                const xOffset1 = b * xS0;\n                                const yOffset1 = b * dyS0;\n                                for (let yF = yFMin; yF < yFMax; ++yF) {\n                                    const xF = wF + yF * strideDepth - frontPad;\n                                    const xOffset2 = xF * xS1 + xOffset1;\n                                    const yOffset2 = yF * dyS1 + yOffset1;\n                                    for (let yR = yRMin; yR < yRMax; ++yR) {\n                                        const xR = wR + yR * strideHeight - topPad;\n                                        const xOffset3 = xR * xS2 + xOffset2;\n                                        const yOffset3 = yR * dyS2 + yOffset2;\n                                        for (let yC = yCMin; yC < yCMax; ++yC) {\n                                            const xC = wC + yC * strideWidth - leftPad;\n                                            const xOffset4 = xC * xS3 + xOffset3;\n                                            const yOffset4 = yC * dyS3 + yOffset3;\n                                            dotProd +=\n                                                xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];\n                                        }\n                                    }\n                                }\n                            }\n                            dwValues[wOffset4 + d2] = dotProd;\n                        }\n                    }\n                }\n            }\n        }\n        return dw.toTensor();\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        let result = this.depthwiseConv2D(input, filter, convInfo);\n        if (bias) {\n            // TODO(lina128): Use add directly once fusedDepthwiseConv2D is\n            // modularized.\n            result = tf.add(result, bias);\n        }\n        if (activation) {\n            result =\n                mapActivation(this, result, activation, preluActivationWeights);\n        }\n        return result;\n    }\n    depthwiseConv2D(x, filter, convInfo) {\n        assertNotComplex([x, filter], 'depthwiseConv2D');\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const padLeft = convInfo.padInfo.left;\n        const padTop = convInfo.padInfo.top;\n        const chMul = convInfo.outChannels / convInfo.inChannels;\n        const y = tf.buffer(convInfo.outShape, x.dtype);\n        const xVals = this.readSync(x.dataId);\n        const wVals = this.readSync(filter.dataId);\n        const yVals = y.values;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n            const xOffset1 = b * x.strides[0];\n            const yOffset1 = b * y.strides[0];\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const yOffset2 = yOffset1 + yR * y.strides[1];\n                const xRCorner = yR * convInfo.strideHeight - padLeft;\n                for (let wR = 0; wR < filterHeight; ++wR) {\n                    const xR = xRCorner + wR * dilationHeight;\n                    if (xR < 0 || xR >= convInfo.inHeight) {\n                        continue;\n                    }\n                    const wOffset1 = wR * filter.strides[0];\n                    const xOffset2 = xOffset1 + xR * x.strides[1];\n                    for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                        const yOffset3 = yOffset2 + yC * y.strides[2];\n                        const xCCorner = yC * convInfo.strideWidth - padTop;\n                        for (let wC = 0; wC < filterWidth; ++wC) {\n                            const xC = xCCorner + wC * dilationWidth;\n                            if (xC < 0 || xC >= convInfo.inWidth) {\n                                continue;\n                            }\n                            const wOffset2 = wOffset1 + wC * filter.strides[1];\n                            const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n                            let yOffset4 = yOffset3;\n                            let wOffset3 = wOffset2;\n                            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                const xVal = xVals[xOffset3 + d1];\n                                for (let q = 0; q < chMul; ++q) {\n                                    yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];\n                                }\n                                yOffset4 += chMul;\n                                wOffset3 += chMul;\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return y.toTensor();\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        assertNotComplex([dy, filter], 'depthwiseConv2DDerInput');\n        const dx = tf.buffer(convInfo.inShape, 'float32');\n        const dxValues = dx.values;\n        const [dxS0, dxS1, dxS2] = dx.strides;\n        const dyValues = this.readSync(dy.dataId);\n        const [dyS0, dyS1, dyS2] = dy.strides;\n        const fltValues = this.readSync(filter.dataId);\n        const [fltS0, fltS1, fltS2] = filter.strides;\n        const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n        const topPad = filterHeight - 1 - convInfo.padInfo.top;\n        const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n        const chMul = outChannels / inChannels;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let d1 = 0; d1 < inChannels; ++d1) {\n                for (let xR = 0; xR < inHeight; ++xR) {\n                    const xRCorner = xR - topPad;\n                    const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                    const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                    for (let xC = 0; xC < inWidth; ++xC) {\n                        const xCCorner = xC - leftPad;\n                        const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                        const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                        let dotProd = 0;\n                        for (let yR = xRMin; yR < yRMax; ++yR) {\n                            const wR = yR * strideHeight - xRCorner;\n                            for (let yC = xCMin; yC < yCMax; ++yC) {\n                                const wC = yC * strideWidth - xCCorner;\n                                const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n                                const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n                                for (let dm = 0; dm < chMul; ++dm) {\n                                    const d2 = d1 * chMul + dm;\n                                    const pixel = dyValues[dyOffset + d2];\n                                    const weight = fltValues[fltOffset + dm];\n                                    dotProd += pixel * weight;\n                                }\n                            }\n                        }\n                        dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    depthwiseConv2DDerFilter(x, dy, convInfo) {\n        assertNotComplex([x, dy], 'depthwiseConv2DDerFilter');\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dW = tf.buffer(convInfo.filterShape, 'float32');\n        const leftPad = convInfo.padInfo.left;\n        const topPad = convInfo.padInfo.top;\n        const chMul = convInfo.outChannels / convInfo.inChannels;\n        const xBuf = this.bufferSync(x);\n        const dyBuf = this.bufferSync(dy);\n        for (let wR = 0; wR < filterHeight; ++wR) {\n            const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n            const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n            for (let wC = 0; wC < filterWidth; ++wC) {\n                const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                    const d1 = Math.trunc(d2 / chMul);\n                    const dm = d2 % chMul;\n                    let dotProd = 0;\n                    for (let b = 0; b < convInfo.batchSize; ++b) {\n                        for (let yR = yRMin; yR < yRMax; ++yR) {\n                            const xR = wR + yR * strideHeight - topPad;\n                            for (let yC = yCMin; yC < yCMax; ++yC) {\n                                const xC = wC + yC * strideWidth - leftPad;\n                                dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n                            }\n                        }\n                    }\n                    dW.set(dotProd, wR, wC, d1, dm);\n                }\n            }\n        }\n        return dW.toTensor();\n    }\n    tile(x, reps) {\n        assertNotComplex(x, 'tile');\n        return tile(this.bufferSync(x), reps);\n    }\n    gather(x, indices, axis) {\n        assertNotComplex([x, indices], 'gather');\n        const newShape = x.shape.slice();\n        const indicesValues = this.readSync(indices.dataId);\n        newShape[axis] = indicesValues.length;\n        const result = tf.buffer(newShape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < result.size; ++i) {\n            const newLoc = result.indexToLoc(i);\n            const originalLoc = newLoc.slice();\n            originalLoc[axis] = indicesValues[newLoc[axis]];\n            const originalIndex = xBuf.locToIndex(originalLoc);\n            result.values[i] = xBuf.values[originalIndex];\n        }\n        return result.toTensor();\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        assertNotComplex([x], 'batchToSpaceND');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n        const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n        const reshapedPermuted = backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n        const sliceBeginCoords = backend_util.getSliceBeginCoords(crops, blockShape.length);\n        const sliceSize = backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n        return tf.transpose(x.reshape(reshaped), permuted)\n            .reshape(reshapedPermuted)\n            .slice(sliceBeginCoords, sliceSize);\n    }\n    pool3d(x, convInfo, poolType) {\n        assertNotComplex(x, 'pool3d');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n            Number.POSITIVE_INFINITY);\n        const xValues = this.readSync(x.dataId);\n        const output = tf.buffer(convInfo.outShape, x.dtype);\n        const outputVals = output.values;\n        const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] *\n            convInfo.outShape[3] * convInfo.outShape[4];\n        const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];\n        const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];\n        const outputColStrides = convInfo.outShape[4];\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            const outputBatchOffset = batch * outputBatchStrides;\n            const inputBatchOffset = batch * x.strides[0];\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n                    const xDepthCorner = yDepth * strideDepth - padFront;\n                    let xDepthMin = xDepthCorner;\n                    while (xDepthMin < 0) {\n                        xDepthMin += dilationDepth;\n                    }\n                    const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n                    const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides;\n                    for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n                        const xRowCorner = yRow * strideHeight - padTop;\n                        let xRowMin = xRowCorner;\n                        while (xRowMin < 0) {\n                            xRowMin += dilationHeight;\n                        }\n                        const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n                        const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;\n                        for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n                            const xColCorner = yCol * strideWidth - padLeft;\n                            let xColMin = xColCorner;\n                            while (xColMin < 0) {\n                                xColMin += dilationWidth;\n                            }\n                            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n                            // Shader code begins\n                            const outputColOffset = outputRowOffset + yCol * outputColStrides;\n                            let minMaxValue = initialValue;\n                            let avgValue = 0;\n                            let count = 0;\n                            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n                                const xDepthOffset = inputBatchOffset + xDepth * x.strides[1];\n                                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                                    const xRowOffset = xDepthOffset + xRow * x.strides[2];\n                                    for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                                        const xColOffset = xRowOffset + xCol * x.strides[3];\n                                        const pixel = xValues[xColOffset + channel];\n                                        if ((poolType === 'max' && pixel > minMaxValue)) {\n                                            minMaxValue = pixel;\n                                        }\n                                        else if (poolType === 'avg') {\n                                            avgValue += pixel;\n                                            count++;\n                                        }\n                                        if (isNaN(minMaxValue)) {\n                                            break;\n                                        }\n                                    }\n                                    if (isNaN(minMaxValue)) {\n                                        break;\n                                    }\n                                }\n                                if (isNaN(minMaxValue)) {\n                                    break;\n                                }\n                            }\n                            const outputOffset = outputColOffset + channel;\n                            outputVals[outputOffset] =\n                                poolType === 'avg' ? avgValue / count : minMaxValue;\n                        }\n                    }\n                }\n            }\n        }\n        return output.toTensor();\n    }\n    avgPool3d(x, convInfo) {\n        assertNotComplex(x, 'avgPool3d');\n        return this.pool3d(x, convInfo, 'avg').toFloat();\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        assertNotComplex([dy, x], 'avgPool3dBackprop');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const dx = tf.buffer(x.shape, 'float32');\n        const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n        const dyBuf = this.bufferSync(dy);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n                    for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n                        for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n                            // Shader code begins.\n                            const dyDepthCorner = dxDepth - padFront;\n                            const dyRowCorner = dxRow - padTop;\n                            const dyColCorner = dxCol - padLeft;\n                            let dotProd = 0;\n                            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n                                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n                                    Math.floor(dyDepth) !== dyDepth) {\n                                    continue;\n                                }\n                                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                                    const dyRow = (dyRowCorner + wRow) / strideHeight;\n                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n                                        Math.floor(dyRow) !== dyRow) {\n                                        continue;\n                                    }\n                                    for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                                        const dyCol = (dyColCorner + wCol) / strideWidth;\n                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n                                            Math.floor(dyCol) !== dyCol) {\n                                            continue;\n                                        }\n                                        const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        dotProd += pixel;\n                                    }\n                                }\n                            }\n                            dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    maxPool3d(x, convInfo) {\n        assertNotComplex(x, 'maxPool3d');\n        return this.pool3d(x, convInfo, 'max').toFloat();\n    }\n    maxPool3dPositions(x, convInfo) {\n        const maxPositions = tf.buffer(convInfo.outShape, 'int32');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const xBuf = this.bufferSync(x);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n                    const xDepthCorner = yDepth * strideDepth - padFront;\n                    let xDepthMin = xDepthCorner;\n                    while (xDepthMin < 0) {\n                        xDepthMin += dilationDepth;\n                    }\n                    const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n                    for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n                        const xRowCorner = yRow * strideHeight - padTop;\n                        let xRowMin = xRowCorner;\n                        while (xRowMin < 0) {\n                            xRowMin += dilationHeight;\n                        }\n                        const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n                        for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n                            const xColCorner = yCol * strideWidth - padLeft;\n                            let xColMin = xColCorner;\n                            while (xColMin < 0) {\n                                xColMin += dilationWidth;\n                            }\n                            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n                            // Shader code begins\n                            let maxValue = Number.NEGATIVE_INFINITY;\n                            let maxPosition = -1;\n                            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n                                const wDepth = xDepth - xDepthCorner;\n                                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                                    const wRow = xRow - xRowCorner;\n                                    for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                                        const wCol = xCol - xColCorner;\n                                        const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);\n                                        if (pixel >= maxValue) {\n                                            maxValue = pixel;\n                                            maxPosition = wDepth * effectiveFilterHeight *\n                                                effectiveFilterWidth +\n                                                wRow * effectiveFilterHeight + wCol;\n                                        }\n                                    }\n                                }\n                            }\n                            maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return maxPositions.toTensor();\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        assertNotComplex([x, y], 'maxPool3dBackprop');\n        const maxPositions = this.maxPool3dPositions(x, convInfo);\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const dx = tf.buffer(x.shape, 'float32');\n        const maxPosBuf = this.bufferSync(maxPositions);\n        const dyBuf = this.bufferSync(dy);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n                    for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n                        for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n                            // Shader code begins\n                            const dyDepthCorner = dxDepth - padFront;\n                            const dyRowCorner = dxRow - padTop;\n                            const dyColCorner = dxCol - padLeft;\n                            let dotProd = 0;\n                            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n                                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n                                    Math.floor(dyDepth) !== dyDepth) {\n                                    continue;\n                                }\n                                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                                    const dyRow = (dyRowCorner + wRow) / strideHeight;\n                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n                                        Math.floor(dyRow) !== dyRow) {\n                                        continue;\n                                    }\n                                    for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                                        const dyCol = (dyColCorner + wCol) / strideWidth;\n                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n                                            Math.floor(dyCol) !== dyCol) {\n                                            continue;\n                                        }\n                                        const maxPos = effectiveFilterDepth *\n                                            effectiveFilterHeight * effectiveFilterWidth -\n                                            1 -\n                                            maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth +\n                                            wRow * effectiveFilterWidth + wCol;\n                                        const mask = maxPos === curPos ? 1 : 0;\n                                        if (mask === 0) {\n                                            continue;\n                                        }\n                                        const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        dotProd += pixel * mask;\n                                    }\n                                }\n                            }\n                            dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        assertNotComplex(x, 'resizeBilinear');\n        const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n        const xValues = this.readSync(x.dataId);\n        const result = new Float32Array(util.sizeFromShape([batch, newHeight, newWidth, numChannels]));\n        const effectiveInputSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutputSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        let outputIdx = 0;\n        const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n        const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n        for (let b = 0; b < batch; b++) {\n            for (let r = 0; r < newHeight; r++) {\n                const sourceFracRow = effectiveRowSizeRatio * r;\n                const sourceRowFloor = Math.floor(sourceFracRow);\n                const rowFrac = sourceFracRow - sourceRowFloor;\n                const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));\n                const topRowOffset = b * x.strides[0] + sourceRowFloor * x.strides[1];\n                const botRowOffset = b * x.strides[0] + sourceRowCeil * x.strides[1];\n                for (let c = 0; c < newWidth; c++) {\n                    const sourceFracCol = effectiveColSizeRatio * c;\n                    const sourceColFloor = Math.floor(sourceFracCol);\n                    const colFrac = sourceFracCol - sourceColFloor;\n                    const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));\n                    const topLeftOffest = topRowOffset + sourceColFloor * x.strides[2];\n                    const botLeftOffset = botRowOffset + sourceColFloor * x.strides[2];\n                    const topRightOffset = topRowOffset + sourceColCeil * x.strides[2];\n                    const botRightOffest = botRowOffset + sourceColCeil * x.strides[2];\n                    for (let d = 0; d < numChannels; d++) {\n                        // Begin shader.\n                        // Compute the fractional index of the source.\n                        const topLeft = xValues[topLeftOffest + d];\n                        const bottomLeft = xValues[botLeftOffset + d];\n                        const topRight = xValues[topRightOffset + d];\n                        const bottomRight = xValues[botRightOffest + d];\n                        const top = topLeft + (topRight - topLeft) * colFrac;\n                        const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;\n                        const newValue = top + (bottom - top) * rowFrac;\n                        result[outputIdx++] = newValue;\n                    }\n                }\n            }\n        }\n        return tf.tensor(result, [batch, newHeight, newWidth, numChannels]);\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        assertNotComplex([dy, x], 'resizeBilinearBackprop');\n        const [batch, xHeight, xWidth, depth] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        const output = new Float32Array(batch * xHeight * xWidth * depth);\n        // In the backwards pass, we want to find the pixels that were generated\n        // for each pixel in the input image the forward pass and add the\n        // corresponding coefficient from dy to the gradient (with some\n        // interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/3039375c86a5bbc9610c7725dcaa95d635f87ba2/tensorflow/core/kernels/resize_bilinear_op.cc#L275\n        const dyValues = this.readSync(dy.dataId);\n        let offset = 0;\n        for (let b = 0; b < batch; b++) {\n            const bOffset = b * x.strides[0];\n            for (let r = 0; r < yHeight; r++) {\n                const dxR = r * heightScale;\n                const topDxRIndex = Math.floor(dxR);\n                const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);\n                const topDxROffset = bOffset + topDxRIndex * x.strides[1];\n                const bottomDxROffset = bOffset + bottomDxRIndex * x.strides[1];\n                const dxRLerp = dxR - topDxRIndex;\n                const inverseDxRLerp = 1.0 - dxRLerp;\n                for (let c = 0; c < yWidth; c++) {\n                    const dxC = c * widthScale;\n                    const leftDxCIndex = Math.floor(dxC);\n                    const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);\n                    const dxCLerp = dxC - leftDxCIndex;\n                    const inverseDxCLerp = 1.0 - dxCLerp;\n                    const topLeftRCOffset = topDxROffset + leftDxCIndex * x.strides[2];\n                    const topRightRCOffset = topDxROffset + rightDxCIndex * x.strides[2];\n                    const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * x.strides[2];\n                    const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * x.strides[2];\n                    const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;\n                    const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;\n                    const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;\n                    const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;\n                    for (let d = 0; d < depth; d++) {\n                        const dyVal = dyValues[offset++];\n                        output[topLeftRCOffset + d] +=\n                            dyVal * inverseDxRLerpTimesInverseDxCLerp;\n                        output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;\n                        output[bottomLeftRCOffset + d] +=\n                            dyVal * dxRLerpTimesInverseDxCLerp;\n                        output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(output, [batch, xWidth, xHeight, depth], x.dtype);\n    }\n    resizeNearestNeighbor(x, newHeight, newWidth, alignCorners) {\n        assertNotComplex(x, 'resizeNearestNeighbor');\n        const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n        const xValues = this.readSync(x.dataId);\n        const output = new Float32Array(batch * newHeight * newWidth * numChannels);\n        const effectiveInputSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutputSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n        const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n        let outputOffset = 0;\n        for (let b = 0; b < batch; b++) {\n            const batchOffset = b * x.strides[0];\n            for (let r = 0; r < newHeight; r++) {\n                const sourceFracRow = effectiveRowSizeRatio * r;\n                const sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) :\n                    Math.floor(sourceFracRow));\n                const rowOffset = batchOffset + sourceNearestRow * x.strides[1];\n                for (let c = 0; c < newWidth; c++) {\n                    const sourceFracCol = effectiveColSizeRatio * c;\n                    const sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) :\n                        Math.floor(sourceFracCol));\n                    const colOffset = rowOffset + sourceNearestCol * x.strides[2];\n                    for (let d = 0; d < numChannels; d++) {\n                        // Begin shader.\n                        // Compute the fractional index of the source.\n                        const newVal = xValues[colOffset + d];\n                        output[outputOffset++] = newVal;\n                    }\n                }\n            }\n        }\n        return tf.tensor(output, [batch, newHeight, newWidth, numChannels], x.dtype);\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        assertNotComplex([dy, x], 'resizeNearestNeighborBackprop');\n        const [batch, xHeight, xWidth, depth] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        const output = new Float32Array(batch * xHeight * xWidth * depth);\n        const dyValues = this.readSync(dy.dataId);\n        // In the backwards pass, we want to find the pixels that were generated\n        // for each pixel in the input image the forward pass\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        // Loop over the output space.\n        for (let b = 0; b < batch; b++) {\n            const batchOffset = b * x.strides[0];\n            for (let r = 0; r < xHeight; r++) {\n                const rowOffset = batchOffset + r * x.strides[1];\n                // Compute bounds for where in dy we will look\n                const startRLerp = Math.floor(r * invHeightScale);\n                const startDyR = Math.floor(startRLerp - (winHeight / 2));\n                for (let c = 0; c < xWidth; c++) {\n                    const colOffset = rowOffset + c * x.strides[2];\n                    // Compute bounds for where in dy we will look\n                    const startCLerp = Math.floor(c * invWidthScale);\n                    const startDyC = Math.floor(startCLerp - (winWidth / 2));\n                    for (let d = 0; d < depth; d++) {\n                        let accum = 0;\n                        // loop over dy\n                        for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {\n                            const dyR = dyRIndex + startDyR;\n                            // Guard against the window exceeding the bounds of dy\n                            if (dyR < 0 || dyR >= yHeight) {\n                                continue;\n                            }\n                            const dyROffset = batchOffset + dyR * dy.strides[1];\n                            const sourceFracRow = dyR * heightScale;\n                            const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) :\n                                Math.floor(sourceFracRow));\n                            if (r !== sourceNearestRow) {\n                                continue;\n                            }\n                            for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {\n                                const dyC = dyCIndex + startDyC;\n                                // Guard against the window exceeding the bounds of dy\n                                if (dyC < 0 || dyC >= yWidth) {\n                                    continue;\n                                }\n                                const dyCOffset = dyROffset + dyC * dy.strides[2];\n                                const sourceFracCol = dyC * widthScale;\n                                const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) :\n                                    Math.floor(sourceFracCol));\n                                if (c === sourceNearestCol) {\n                                    accum += dyValues[dyCOffset + d];\n                                }\n                            }\n                        }\n                        output[colOffset + d] = accum;\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(output, x.shape, x.dtype);\n    }\n    localResponseNormalization4D(x, depthRadius, bias, alpha, beta) {\n        assertNotComplex(x, 'localResponseNormalization4D');\n        const channels = x.shape[3];\n        const maxD = channels - 1;\n        const xValues = this.readSync(x.dataId);\n        const size = x.size;\n        const result = new Float32Array(size);\n        function sumAcrossChannels(offset) {\n            const currentChannel = offset % channels;\n            let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n            const endSumOffset = offset - currentChannel +\n                Math.min(currentChannel + depthRadius, maxD);\n            let sum = 0.0;\n            for (; beginSumOffset <= endSumOffset; beginSumOffset++) {\n                const z = xValues[beginSumOffset];\n                sum += z * z;\n            }\n            return sum;\n        }\n        for (let offset = 0; offset < size; offset++) {\n            const sum = sumAcrossChannels(offset);\n            const val = xValues[offset] * Math.pow(bias + alpha * sum, -beta);\n            result[offset] = val;\n        }\n        return tf.tensor4d(result, x.shape);\n    }\n    LRNGrad(dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {\n        assertNotComplex(dy, 'LRNGrad');\n        const channels = dy.shape[3];\n        const dyValues = this.readSync(dy.dataId);\n        const inputImageValues = this.readSync(inputImage.dataId);\n        const outputImageValues = this.readSync(outputImage.dataId);\n        const result = new Float32Array(dy.size);\n        const size = dy.size;\n        for (let offset = 0; offset < size; offset++) {\n            const currentChannel = offset % channels;\n            const depthBegin = (offset - currentChannel) + Math.max(0, currentChannel - depthRadius);\n            const depthEnd = (offset - currentChannel) +\n                Math.min(channels, currentChannel + depthRadius + 1);\n            let norm = 0;\n            for (let k = depthBegin; k < depthEnd; k++) {\n                norm += Math.pow(inputImageValues[k], 2);\n            }\n            norm = alpha * norm + bias;\n            for (let k = depthBegin; k < depthEnd; k++) {\n                let dyi = -2 * alpha * beta * inputImageValues[k] *\n                    outputImageValues[offset] / norm;\n                if (offset === k) {\n                    dyi += Math.pow(norm, -beta);\n                }\n                dyi *= dyValues[offset];\n                result[k] += dyi;\n            }\n        }\n        return tf.tensor4d(result, dy.shape);\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        assertNotComplex(logits, 'multinomial');\n        const probabilities = normalized ? logits : tf.softmax(logits);\n        const batchSize = probabilities.shape[0];\n        const numEvents = probabilities.shape[1];\n        const res = tf.zeros([batchSize, numSamples], 'int32');\n        const resVals = this.readSync(res.dataId);\n        const probVals = this.readSync(probabilities.dataId);\n        for (let b = 0; b < batchSize; ++b) {\n            const offset = b * numEvents;\n            // The cdf won't include the last event. It will be implicit if no other\n            // event happened.\n            const cdf = new Float32Array(numEvents - 1);\n            cdf[0] = probVals[offset];\n            for (let event = 1; event < cdf.length; ++event) {\n                cdf[event] = cdf[event - 1] + probVals[offset + event];\n            }\n            const random = seedrandom.alea(seed.toString());\n            const outOffset = b * numSamples;\n            for (let sampleId = 0; sampleId < numSamples; ++sampleId) {\n                const r = random();\n                // Assume last event happened by default.\n                resVals[outOffset + sampleId] = cdf.length;\n                for (let event = 0; event < cdf.length; event++) {\n                    if (r < cdf[event]) {\n                        resVals[outOffset + sampleId] = event;\n                        break;\n                    }\n                }\n            }\n        }\n        return res;\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        assertNotComplex(indices, 'oneHot');\n        const res = new Float32Array(indices.size * depth);\n        res.fill(offValue);\n        const indicesVal = this.readSync(indices.dataId);\n        for (let event = 0; event < indices.size; ++event) {\n            if (indicesVal[event] >= 0 && indicesVal[event] < depth) {\n                res[event * depth + indicesVal[event]] = onValue;\n            }\n        }\n        return tf.tensor2d(res, [indices.size, depth], 'int32');\n    }\n    nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n        assertNotComplex(boxes, 'nonMaxSuppression');\n        const boxesVals = this.readSync(boxes.dataId);\n        const scoresVals = this.readSync(scores.dataId);\n        return nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        util.assert(dataFormat === 'NHWC', () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`);\n        util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n        const batchSize = x.shape[0];\n        const inputHeight = x.shape[1];\n        const inputWidth = x.shape[2];\n        const inputDepth = x.shape[3];\n        const outputHeight = inputHeight * blockSize;\n        const outputWidth = inputWidth * blockSize;\n        const outputDepth = inputDepth / (blockSize * blockSize);\n        const xValues = this.readSync(x.dataId);\n        const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);\n        let outputIdx = 0;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let h = 0; h < outputHeight; ++h) {\n                const inH = Math.floor(h / blockSize);\n                const offsetH = (h % blockSize);\n                for (let w = 0; w < outputWidth; ++w) {\n                    const inW = Math.floor(w / blockSize);\n                    const offsetW = (w % blockSize);\n                    const offsetD = (offsetH * blockSize + offsetW) * outputDepth;\n                    for (let d = 0; d < outputDepth; ++d) {\n                        const inD = d + offsetD;\n                        const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));\n                        result[outputIdx++] = xValues[inputIdx];\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(result, [batchSize, outputHeight, outputWidth, outputDepth]);\n    }\n    broadcastedBinaryOp(a, b, dtype, op) {\n        const newShape = backend_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const result = tf.buffer(newShape, dtype);\n        const aVals = this.readSync(a.dataId);\n        const bVals = this.readSync(b.dataId);\n        const aBroadcastDims = backend_util.getBroadcastDims(a.shape, newShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(b.shape, newShape);\n        const resVals = result.values;\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < resVals.length; ++i) {\n                resVals[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n            }\n        }\n        else {\n            const aBuf = this.bufferSync(a);\n            const bBuf = this.bufferSync(b);\n            for (let i = 0; i < resVals.length; ++i) {\n                const loc = result.indexToLoc(i);\n                const aLoc = loc.slice(-a.rank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = aBuf.locToIndex(aLoc);\n                const bLoc = loc.slice(-b.rank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = bBuf.locToIndex(bLoc);\n                resVals[i] = op(aVals[aIndex], bVals[bIndex]);\n            }\n        }\n        return result.toTensor();\n    }\n    split(x, sizeSplits, axis) {\n        return split(x, sizeSplits, axis);\n    }\n    dispose() { }\n    floatPrecision() {\n        return 32;\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return super.epsilon();\n    }\n    cropAndResize(images, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        const [batch, imageHeight, imageWidth, numChannels] = images.shape;\n        const numBoxes = boxes.shape[0];\n        const [cropHeight, cropWidth] = cropSize;\n        const output = tf.buffer([numBoxes, cropHeight, cropWidth, numChannels], 'float32');\n        const boxVals = this.readSync(boxes.dataId);\n        const boxIndVals = this.readSync(boxIndex.dataId);\n        const imageVals = this.readSync(images.dataId);\n        const inStride = images.strides; // to calculate flat indexes into image\n        const outStride = output.strides; // to calculate flat indexes into output\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc\n        for (let b = 0; b < numBoxes; b++) {\n            const startInd = b * 4;\n            const y1 = boxVals[startInd];\n            const x1 = boxVals[startInd + 1];\n            const y2 = boxVals[startInd + 2];\n            const x2 = boxVals[startInd + 3];\n            const bInd = boxIndVals[b];\n            if (bInd >= batch) {\n                continue;\n            }\n            const heightScale = (cropHeight > 1) ?\n                (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) :\n                0;\n            const widthScale = (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;\n            for (let y = 0; y < cropHeight; y++) {\n                const yInd = (cropHeight > 1) ?\n                    y1 * (imageHeight - 1) + y * (heightScale) :\n                    0.5 * (y1 + y2) * (imageHeight - 1);\n                if (yInd < 0 || yInd > imageHeight - 1) {\n                    for (let x = 0; x < cropWidth; x++) {\n                        for (let c = 0; c < numChannels; c++) {\n                            const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[ind] = extrapolationValue;\n                        }\n                    }\n                    continue;\n                }\n                if (method === 'bilinear') {\n                    const topInd = Math.floor(yInd);\n                    const bottomInd = Math.ceil(yInd);\n                    const yLerp = yInd - topInd;\n                    for (let x = 0; x < cropWidth; x++) {\n                        const xInd = (cropWidth > 1) ?\n                            x1 * (imageWidth - 1) + x * widthScale :\n                            0.5 * (x1 + x2) * (imageWidth - 1);\n                        if (xInd < 0 || xInd > imageWidth - 1) {\n                            for (let c = 0; c < numChannels; c++) {\n                                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                                output.values[ind] = extrapolationValue;\n                            }\n                            continue;\n                        }\n                        const leftInd = Math.floor(xInd);\n                        const rightInd = Math.ceil(xInd);\n                        const xLerp = xInd - leftInd;\n                        for (let c = 0; c < numChannels; c++) {\n                            let ind = c + leftInd * inStride[2] + topInd * inStride[1] +\n                                bInd * inStride[0];\n                            const topLeft = imageVals[ind];\n                            ind = c + rightInd * inStride[2] + topInd * inStride[1] +\n                                bInd * inStride[0];\n                            const topRight = imageVals[ind];\n                            ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +\n                                bInd * inStride[0];\n                            const bottomLeft = imageVals[ind];\n                            ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +\n                                bInd * inStride[0];\n                            const bottomRight = imageVals[ind];\n                            const top = topLeft + (topRight - topLeft) * xLerp;\n                            const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;\n                            ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[ind] = top + ((bottom - top) * yLerp);\n                        }\n                    }\n                }\n                else { // method == \"nearest\"\n                    for (let x = 0; x < cropWidth; ++x) {\n                        const xInd = (cropWidth > 1) ?\n                            x1 * (imageWidth - 1) + x * widthScale :\n                            0.5 * (x1 + x2) * (imageWidth - 1);\n                        if (xInd < 0 || xInd > imageWidth - 1) {\n                            for (let c = 0; c < numChannels; c++) {\n                                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                                output.values[ind] = extrapolationValue;\n                            }\n                            continue;\n                        }\n                        const closestX = Math.round(xInd);\n                        const closestY = Math.round(yInd);\n                        for (let c = 0; c < numChannels; c++) {\n                            const inInd = c + closestX * inStride[2] +\n                                closestY * inStride[1] + bInd * inStride[0];\n                            const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[outInd] = imageVals[inInd];\n                        }\n                    }\n                }\n            }\n        }\n        return output.toTensor();\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n        const sumDupeIndices = false;\n        return this.scatter(sparseIndices, sparseValues, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n    }\n    gatherND(x, indices) {\n        const indicesShape = indices.shape;\n        const sliceRank = indicesShape[indicesShape.length - 1];\n        const [resultShape, numSlices, sliceSize, strides] = backend_util.prepareAndValidate(x, indices);\n        if (numSlices === 0) {\n            return tf.tensor([], resultShape, x.dtype);\n        }\n        const buffer = new TensorBuffer([numSlices, sliceSize], x.dtype);\n        const indicesData = this.readSync(indices.dataId);\n        const xData = this.readSync(x.dataId);\n        for (let i = 0; i < numSlices; i++) {\n            const index = [];\n            let flattenIndex = 0;\n            for (let j = 0; j < sliceRank; j++) {\n                const dim = indicesData[i * sliceRank + j];\n                flattenIndex += dim * strides[j];\n                index.push(dim);\n            }\n            if (flattenIndex < 0 || flattenIndex >= x.size / sliceSize) {\n                throw new Error(`Invalid indices: ${index} does not index into ${x.shape}`);\n            }\n            for (let k = 0; k < sliceSize; k++) {\n                buffer.values[i * sliceSize + k] = xData[flattenIndex * sliceSize + k];\n            }\n        }\n        return buffer.toTensor().reshape(resultShape);\n    }\n    scatterND(indices, updates, shape) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(updates, indices, shape);\n        const defaultValue = tf.scalar(0);\n        const sumDupeIndices = true;\n        return this.scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n    }\n    fill(shape, value, dtype) {\n        dtype = dtype || util.inferDtype(value);\n        const values = util.getArrayFromDType(dtype, util.sizeFromShape(shape));\n        values.fill(value);\n        return engine().makeTensor(values, shape, dtype, this);\n    }\n    onesLike(x) {\n        if (x.dtype === 'string') {\n            throw new Error('onesLike is not supported for string tensors');\n        }\n        else {\n            return this.fill(x.shape, 1, x.dtype);\n        }\n    }\n    zerosLike(x) {\n        const values = util.getArrayFromDType(x.dtype, util.sizeFromShape(x.shape));\n        return this.makeOutput(values, x.shape, x.dtype);\n    }\n    linspace(start, stop, num) {\n        return backend_util.linspaceImpl(start, stop, num);\n    }\n    scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {\n        const flattenShape = [outputSize / sliceSize, sliceSize];\n        const indicesData = this.readSync(indices.dataId);\n        const updatesData = this.readSync(updates.dataId);\n        if (outputSize === 0) {\n            return tf.tensor([], shape, updates.dtype);\n        }\n        const buffer = new TensorBuffer(flattenShape, updates.dtype);\n        buffer.values.fill(this.readSync(defaultValue.dataId)[0]);\n        for (let i = 0; i < numUpdates; i++) {\n            const index = [];\n            let flattenIndex = 0;\n            for (let j = 0; j < sliceRank; j++) {\n                const dim = indicesData[i * sliceRank + j];\n                index.push(dim);\n                flattenIndex += dim * strides[j];\n            }\n            if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {\n                throw new Error(`Invalid indices: ${index} does not index into ${shape}`);\n            }\n            for (let k = 0; k < sliceSize; k++) {\n                if (sumDupeIndices) {\n                    buffer.values[flattenIndex * sliceSize + k] +=\n                        updatesData[i * sliceSize + k];\n                }\n                else {\n                    buffer.values[flattenIndex * sliceSize + k] = updates.rank === 0 ?\n                        updatesData[0] :\n                        updatesData[i * sliceSize + k];\n                }\n            }\n        }\n        return buffer.toTensor().reshape(shape);\n    }\n}\n//# sourceMappingURL=backend_cpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs, util } from '@tensorflow/tfjs-core';\nexport function simpleAbsImpl(vals) {\n    const resultValues = new Float32Array(vals.length);\n    for (let i = 0; i < vals.length; ++i) {\n        resultValues[i] = Math.abs(vals[i]);\n    }\n    return resultValues;\n}\nexport const absKernelFunc = (args) => {\n    const { x } = args.inputs;\n    const cpuBackend = args.backend;\n    let resultValues = new Float32Array(util.sizeFromShape(x.shape));\n    if (x.dtype !== 'complex64') {\n        const values = cpuBackend.data.get(x.dataId).values;\n        resultValues = simpleAbsImpl(values);\n    }\n    else {\n        const complexVals = cpuBackend.data.get(x.dataId);\n        const real = complexVals.complexTensorInfos.real;\n        const imag = complexVals.complexTensorInfos.imag;\n        const realVals = cpuBackend.data.get(real.dataId).values;\n        const imagVals = cpuBackend.data.get(imag.dataId).values;\n        for (let i = 0; i < realVals.length; i++) {\n            const real = realVals[i];\n            const imag = imagVals[i];\n            resultValues[i] = Math.hypot(real, imag);\n        }\n    }\n    return cpuBackend.makeOutput(resultValues, x.shape, 'float32');\n};\nexport const absConfig = {\n    kernelName: Abs,\n    backendName: 'cpu',\n    kernelFunc: absKernelFunc,\n};\n//# sourceMappingURL=Abs.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for binary ops. Supports broadcast.\n */\nexport function createSimpleBinaryKernelImpl(op) {\n    return (aShape, bShape, aVals, bVals, dtype) => {\n        const newShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const resultRank = newShape.length;\n        const resultStrides = util.computeStrides(newShape);\n        const resultSize = util.sizeFromShape(newShape);\n        const result = util.getTypedArrayFromDType(dtype, resultSize);\n        const aRank = aShape.length;\n        const bRank = bShape.length;\n        const aStrides = util.computeStrides(aShape);\n        const bStrides = util.computeStrides(bShape);\n        const aBroadcastDims = backend_util.getBroadcastDims(aShape, newShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(bShape, newShape);\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < result.length; ++i) {\n                result[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n            }\n        }\n        else {\n            for (let i = 0; i < result.length; ++i) {\n                const loc = util.indexToLoc(i, resultRank, resultStrides);\n                const aLoc = loc.slice(-aRank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n                const bLoc = loc.slice(-bRank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n                result[i] = op(aVals[aIndex], bVals[bIndex]);\n            }\n        }\n        return [result, newShape];\n    };\n}\n//# sourceMappingURL=binary_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Complex } from '@tensorflow/tfjs-core';\nexport function complex(args) {\n    const { inputs, backend } = args;\n    const { real, imag } = inputs;\n    const realVals = backend.data.get(real.dataId).values;\n    const imagVals = backend.data.get(imag.dataId).values;\n    const complexInfo = backend.makeTensorInfo(real.shape, 'complex64');\n    const complex = backend.data.get(complexInfo.dataId);\n    // The complex tensor owns the underlying real and imag tensorInfos, only the\n    // complex tensor tracks refCount, when complexData is disposed the\n    // underlying tensorData will be disposed.\n    complex.complexTensorInfos = {\n        real: backend.makeTensorInfo(real.shape, 'float32', realVals),\n        imag: backend.makeTensorInfo(imag.shape, 'float32', imagVals)\n    };\n    return complexInfo;\n}\nexport const complexConfig = {\n    kernelName: Complex,\n    backendName: 'cpu',\n    kernelFunc: complex\n};\n//# sourceMappingURL=Complex.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '@tensorflow/tfjs-core';\nexport function identity(args) {\n    const { inputs, backend } = args;\n    const { x } = inputs;\n    backend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nexport const identityConfig = {\n    kernelName: Identity,\n    backendName: 'cpu',\n    kernelFunc: identity\n};\n//# sourceMappingURL=Identity.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Real } from '@tensorflow/tfjs-core';\nexport function real(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const real = backend.data.get(input.dataId).complexTensorInfos.real;\n    const realVal = backend.data.get(real.dataId).values;\n    // When complex tensor is disposed, its underlying parts will be disposed too.\n    // Make new tensor out of the real value of the complex. This makes sure the\n    // value is still accessible even if complex tensor is disposed.\n    return backend.makeTensorInfo(real.shape, real.dtype, realVal);\n}\nexport const realConfig = {\n    kernelName: Real,\n    backendName: 'cpu',\n    kernelFunc: real\n};\n//# sourceMappingURL=Real.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { Cast, util } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { complex } from './Complex';\nimport { identity } from './Identity';\nimport { real } from './Real';\nexport function cast(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { dtype } = attrs;\n    // Casting to complex64.\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return identity({ inputs: { x }, backend });\n        }\n        // TODO(lina128): Import kernel function once zeros is modularized.\n        const zerosTensor = tf.zeros(x.shape);\n        const floatX = cast({ inputs: { x }, backend, attrs: { dtype: 'float32' } });\n        const result = complex({ inputs: { real: floatX, imag: zerosTensor }, backend });\n        zerosTensor.dispose();\n        backend.disposeIntermediateTensorInfo(floatX);\n        return result;\n    }\n    // Casting from complex64\n    if (x.dtype === 'complex64') {\n        const realPart = real({ inputs: { input: x }, backend });\n        const result = cast({ inputs: { x: realPart }, backend, attrs: { dtype } });\n        backend.disposeIntermediateTensorInfo(realPart);\n        return result;\n    }\n    if (!util.hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        const result = identity({ inputs: { x }, backend });\n        return { dataId: result.dataId, shape: result.shape, dtype };\n    }\n    if (dtype === 'int32') {\n        const values = backend.data.get(x.dataId).values;\n        const resultValues = Int32Array.from(values);\n        return backend.makeTensorInfo(x.shape, 'int32', resultValues);\n    }\n    if (dtype === 'bool') {\n        // This is essentially the result of notEqual(x, 0). We avoid using\n        // kernel notEqual to avoid circular dependency, i.e. binary_utils ->\n        // cast -> notEqual -> binary_utils.\n        const xVals = backend.data.get(x.dataId).values;\n        const zero = util.toTypedArray([0], x.dtype);\n        const [resultData, resultShape] = createSimpleBinaryKernelImpl((a, b) => (a !== b) ? 1 : 0)(x.shape, [], xVals, zero, 'bool');\n        return backend.makeTensorInfo(resultShape, 'bool', resultData);\n    }\n    throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nexport const castConfig = {\n    kernelName: Cast,\n    backendName: 'cpu',\n    kernelFunc: cast\n};\n//# sourceMappingURL=Cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { cast } from '../kernels/Cast';\nimport { complex } from '../kernels/Complex';\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param name Kernel name.\n * @param binaryKernelImpl A `SimpleBinaryKernelImpl` for the kernel.\n * @param binaryKernelComplexImpl Optional. If exists, represents a\n *     `ComplexBinaryKernelImpl` for the kernel, will be used when input dtype\n *     is `complex64`.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc(name, simpleImpl, complexImpl, dtype) {\n    if (complexImpl == null) {\n        return ({ inputs, backend }) => {\n            const { a, b } = inputs;\n            const cpuBackend = backend;\n            assertNotComplex([a, b], name);\n            const aVals = cpuBackend.data.get(a.dataId).values;\n            const bVals = cpuBackend.data.get(b.dataId).values;\n            const $dtype = dtype || a.dtype;\n            const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n            return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n        };\n    }\n    return ({ inputs, backend }) => {\n        const { a, b } = inputs;\n        const cpuBackend = backend;\n        if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n            const $aComplex = cast({ inputs: { x: a }, backend: cpuBackend, attrs: { dtype: 'complex64' } });\n            const $aComplexVals = cpuBackend.data.get($aComplex.dataId);\n            const aReal = $aComplexVals.complexTensorInfos.real;\n            const aImag = $aComplexVals.complexTensorInfos.imag;\n            const aRealVals = cpuBackend.data.get(aReal.dataId).values;\n            const aImagVals = cpuBackend.data.get(aImag.dataId).values;\n            const $bComplex = cast({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: 'complex64' } });\n            const $bComplexVals = cpuBackend.data.get($bComplex.dataId);\n            const bReal = $bComplexVals.complexTensorInfos.real;\n            const bImag = $bComplexVals.complexTensorInfos.imag;\n            const bRealVals = cpuBackend.data.get(bReal.dataId).values;\n            const bImagVals = cpuBackend.data.get(bImag.dataId).values;\n            const [resultRealData, resultImagData, resultShape] = complexImpl(a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);\n            const resultReal = cpuBackend.makeTensorInfo(resultShape, 'float32', resultRealData);\n            const resultImag = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImagData);\n            const result = complex({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend });\n            cpuBackend.disposeIntermediateTensorInfo($aComplex);\n            cpuBackend.disposeIntermediateTensorInfo($bComplex);\n            cpuBackend.disposeIntermediateTensorInfo(resultReal);\n            cpuBackend.disposeIntermediateTensorInfo(resultImag);\n            return result;\n        }\n        else {\n            const aVals = cpuBackend.data.get(a.dataId).values;\n            const bVals = cpuBackend.data.get(b.dataId).values;\n            const $dtype = dtype || a.dtype;\n            const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n            return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n        }\n    };\n}\n/**\n * Template that creates the complex type implementation for binary ops.\n * Supports broadcast.\n */\nexport function createComplexBinaryKernelImpl(op) {\n    return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) => {\n        const resultShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const resultSize = util.sizeFromShape(resultShape);\n        const resultRank = resultShape.length;\n        const resultStrides = util.computeStrides(resultShape);\n        const resultRealVals = util.getTypedArrayFromDType('float32', resultSize);\n        const resultImagVals = util.getTypedArrayFromDType('float32', resultSize);\n        const aBroadcastDims = backend_util.getBroadcastDims(aShape, resultShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(bShape, resultShape);\n        const aVals = backend_util.mergeRealAndImagArrays(aRealVals, aImagVals);\n        const bVals = backend_util.mergeRealAndImagArrays(bRealVals, bImagVals);\n        const aRank = aShape.length;\n        const aStrides = util.computeStrides(aShape);\n        const bRank = bShape.length;\n        const bStrides = util.computeStrides(bShape);\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < resultRealVals.length; i++) {\n                const aIdx = i % aVals.length;\n                const bIdx = i % bVals.length;\n                const result = op(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);\n                resultRealVals[i] = result.real;\n                resultImagVals[i] = result.imag;\n            }\n        }\n        else {\n            for (let i = 0; i < resultRealVals.length; i++) {\n                const loc = util.indexToLoc(i, resultRank, resultStrides);\n                const aLoc = loc.slice(-aRank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n                const bLoc = loc.slice(-bRank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n                const opResult = op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);\n                resultRealVals[i] = opResult.real;\n                resultImagVals[i] = opResult.imag;\n            }\n        }\n        return [resultRealVals, resultImagVals, resultShape];\n    };\n}\n//# sourceMappingURL=kernel_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const addImpl = createSimpleBinaryKernelImpl(((a, b) => a + b));\nexport const addComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal + bReal, imag: aImag + bImag };\n}));\nexport const add = binaryKernelFunc(Add, addImpl, addComplexImpl);\nexport const addConfig = {\n    kernelName: Add,\n    backendName: 'cpu',\n    kernelFunc: add\n};\n//# sourceMappingURL=Add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for unary op.\n */\nexport function createSimpleUnaryImpl(op) {\n    return (values, dtype, attrs) => {\n        const newValues = util.getTypedArrayFromDType(dtype, values.length);\n        for (let i = 0; i < values.length; ++i) {\n            newValues[i] = op(values[i], attrs);\n        }\n        return newValues;\n    };\n}\n//# sourceMappingURL=unary_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param name Kernel name.\n * @param op A `SimpleUnaryOperation` for the kernel.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the input. This is mainly used in certain\n *     kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFunc(name, op, dtype) {\n    return ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        assertNotComplex(x, name);\n        if (x.dtype === 'string' || dtype === 'string') {\n            throw new Error('unaryKernelFunc does not support string input/output');\n        }\n        const cpuBackend = backend;\n        const values = cpuBackend.data.get(x.dataId).values;\n        const xSize = util.sizeFromShape(x.shape);\n        const $dtype = dtype || x.dtype;\n        const newValues = util.getArrayFromDType($dtype, xSize);\n        for (let i = 0; i < xSize; ++i) {\n            newValues[i] = op(values[i], attrs);\n        }\n        return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n    };\n}\n/**\n * Template that creates a `KernelFunc` for unary ops from the given\n * `SimpleUnaryImpl`..\n * @param name Kernel name.\n * @param unaryImpl A `SimpleUnaryImpl` that implements the op.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the input. This is mainly used in certain\n *     kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFuncFromImpl(name, unaryImpl, dtype) {\n    return ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        assertNotComplex(x, name);\n        if (x.dtype === 'string' || dtype === 'string') {\n            throw new Error('unaryKernelFunc does not support string input/output');\n        }\n        const cpuBackend = backend;\n        const values = cpuBackend.data.get(x.dataId).values;\n        const $dtype = dtype || x.dtype;\n        const newValues = unaryImpl(values, $dtype, attrs);\n        return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n    };\n}\n//# sourceMappingURL=unary_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));\nexport const ceilKernelFunc = unaryKernelFuncFromImpl(Ceil, ceilImpl);\nexport const ceilConfig = {\n    kernelName: Ceil,\n    backendName: 'cpu',\n    kernelFunc: ceilKernelFunc,\n};\n//# sourceMappingURL=Ceil.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));\nexport const expKernelFunc = unaryKernelFuncFromImpl(Exp, expImpl);\nexport const expConfig = {\n    kernelName: Exp,\n    backendName: 'cpu',\n    kernelFunc: expKernelFunc,\n};\n//# sourceMappingURL=Exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));\nexport const expm1KernelFunc = unaryKernelFuncFromImpl(Expm1, expm1Impl);\nexport const expm1Config = {\n    kernelName: Expm1,\n    backendName: 'cpu',\n    kernelFunc: expm1KernelFunc,\n};\n//# sourceMappingURL=Expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));\nexport const floorKernelFunc = unaryKernelFuncFromImpl(Floor, floorImpl);\nexport const floorConfig = {\n    kernelName: Floor,\n    backendName: 'cpu',\n    kernelFunc: floorKernelFunc,\n};\n//# sourceMappingURL=Floor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));\nexport const logKernelFunc = unaryKernelFuncFromImpl(Log, logImpl);\nexport const logConfig = {\n    kernelName: Log,\n    backendName: 'cpu',\n    kernelFunc: logKernelFunc,\n};\n//# sourceMappingURL=Log.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function maxImpl(aVals, reduceSize, outShape, dtype) {\n    const vals = util.getTypedArrayFromDType(dtype, util.sizeFromShape(outShape));\n    for (let i = 0; i < vals.length; ++i) {\n        const offset = i * reduceSize;\n        let max = aVals[offset];\n        for (let j = 0; j < reduceSize; ++j) {\n            const value = aVals[offset + j];\n            if (value > max) {\n                max = value;\n            }\n        }\n        vals[i] = max;\n    }\n    return vals;\n}\n//# sourceMappingURL=Max_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const multiplyImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue * bValue));\nexport const multiplyComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return {\n        real: aReal * bReal - aImag * bImag,\n        imag: aReal * bImag + aImag * bReal\n    };\n}));\nexport const multiply = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);\nexport const multiplyConfig = {\n    kernelName: Multiply,\n    backendName: 'cpu',\n    kernelFunc: multiply\n};\n//# sourceMappingURL=Multiply.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));\nexport const rsqrtKernelFunc = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);\nexport const rsqrtConfig = {\n    kernelName: Rsqrt,\n    backendName: 'cpu',\n    kernelFunc: rsqrtKernelFunc,\n};\n//# sourceMappingURL=Rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Slice, slice_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function sliceImpl(vals, begin, size, shape, dtype) {\n    const isContinous = slice_util.isSliceContinous(shape, begin, size);\n    const length = util.sizeFromShape(size);\n    const xStrides = util.computeStrides(shape);\n    if (isContinous) {\n        const flatOffset = slice_util.computeFlatOffset(begin, xStrides);\n        return vals.subarray(flatOffset, flatOffset + length);\n    }\n    const outVals = util.getTypedArrayFromDType(dtype, length);\n    for (let i = 0; i < length; ++i) {\n        const rank = size.length;\n        const strides = util.computeStrides(size);\n        const loc = util.indexToLoc(i, rank, strides);\n        const xLoc = loc.map((idx, j) => idx + begin[j]);\n        const xIndex = util.locToIndex(xLoc, shape.length, xStrides);\n        outVals[i] = vals[xIndex];\n    }\n    return outVals;\n}\nexport function slice(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { begin, size } = attrs;\n    assertNotComplex(x, 'slice');\n    const [$begin, $size] = slice_util.parseSliceParams(x, begin, size);\n    slice_util.assertParamsValid(x, $begin, $size);\n    const vals = backend.data.get(x.dataId).values;\n    const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);\n    return backend.makeTensorInfo($size, x.dtype, outVals);\n}\nexport const sliceConfig = {\n    kernelName: Slice,\n    backendName: 'cpu',\n    kernelFunc: slice\n};\n//# sourceMappingURL=Slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const subImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue - bValue));\nexport const subComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal - bReal, imag: aImag - bImag };\n}));\nexport const sub = binaryKernelFunc(Sub, subImpl, subComplexImpl);\nexport const subConfig = {\n    kernelName: Sub,\n    backendName: 'cpu',\n    kernelFunc: sub\n};\n//# sourceMappingURL=Sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function transposeImpl(xVals, xShape, dtype, perm, newShape) {\n    const xRank = xShape.length;\n    const xSize = util.sizeFromShape(xShape);\n    const xStrides = util.computeStrides(xShape);\n    const newStrides = util.computeStrides(newShape);\n    const result = util.getTypedArrayFromDType(dtype, util.sizeFromShape(newShape));\n    for (let i = 0; i < xSize; ++i) {\n        const loc = util.indexToLoc(i, xRank, xStrides);\n        // Permute location.\n        const newLoc = new Array(loc.length);\n        for (let i = 0; i < newLoc.length; i++) {\n            newLoc[i] = loc[perm[i]];\n        }\n        const newIndex = util.locToIndex(newLoc, xRank, newStrides);\n        result[newIndex] = xVals[i];\n    }\n    return result;\n}\n//# sourceMappingURL=Transpose_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer, util } from '@tensorflow/tfjs-core';\nexport function uniqueImpl(values, axis, shape, dtype) {\n    // Normalize and validate axis.\n    const $axis = util.parseAxisParam(axis, shape)[0];\n    // Calculate the new shape that is suitable for extracting data along the\n    // given axis.\n    //\n    // The rank is 3.\n    // The size of the 1st dimension is the size of all the axes < the given axis.\n    // The size of the 2nd dimension is the same as the size of the given axis.\n    // The size of the 3rd dimension is the size of all the axes > the given axis.\n    //\n    // For example, for a 4D tensor with shape=[2, 3, 5, 4] and axis=2, the\n    // newShape would be: [2*3, 5, 4].\n    //\n    // Note that this is not the final output shape. This will be the shape for an\n    // intermediate TensorBuffer (see inputBuffer below) to allow us to extract\n    // values along the given axis. To demonstrate how it works, consider the\n    // following example:\n    //\n    // Input: a 3D tensor, with shape [1, 2, 3]\n    // [\n    //   [\n    //      [1,2,3],\n    //      [4,5,6]\n    //   ]\n    // ]\n    // Axis: 2 (the last axis).\n    // Along axis 2, we expect to extract 3 tensors: [1,4], [2,5], [3,6].\n    //\n    // For this example, newShape would be: [2, 3, 1], where 2 is calculated from\n    // 1*2. The re-shaped data would look like:\n    //\n    // [\n    //   [\n    //     [1], [2], [3]\n    //   ],\n    //   [\n    //     [4], [5], [6]\n    //   ]\n    // ]\n    //\n    // Then, we can construct a 3-level nested loop by the following dimension\n    // order to extract the values along the axis (dimension1):\n    // i: dimension1       // 0,1,2 (newShape[1])\n    //   m: dimension0     // 0,1   (newShape[0])\n    //     n: dimension2   // 0     (newShape[2])\n    //\n    //                       m, i, n\n    //                      ---------\n    // Iteration 0: data at [0, 0, 0] => \"1\"\n    // Iteration 1: data at [1, 0, 0] => \"4\"\n    // We got [1,4].\n    // Iteration 2: data at [0, 1, 0] => \"2\"\n    // Iteration 3: data at [1, 1, 0] => \"5\"\n    // We got [2,5].\n    // Iteration 4: data at [0, 2, 0] => \"3\"\n    // Iteration 5: data at [1, 2, 0] => \"6\"\n    // We got [3,6].\n    const newShape = [1, shape[0], 1];\n    for (let i = 0; i < $axis; i++) {\n        newShape[0] *= shape[i];\n    }\n    newShape[1] = shape[$axis];\n    for (let i = $axis + 1; i < shape.length; i++) {\n        newShape[2] *= shape[i];\n    }\n    // A map from unique elements (their string representations) to their values\n    // in \"indices\" (below).\n    const uniqueElements = {};\n    // The indices of each unique element in the original tensor along the given\n    // axis. It is 1D and has the same size as the given axis.\n    const indices = new Int32Array(shape[$axis]);\n    // Create a buffer so we can easily extract value at a given location.\n    const inputBuffer = new TensorBuffer(newShape, dtype, values);\n    // The indices along the given axis that have unique elements. This is a\n    // de-duped version of \"indices\" above.\n    const uniqueIndices = [];\n    const is1DTensor = newShape[0] === 1 && newShape[2] === 1;\n    for (let i = 0; i < shape[$axis]; i++) {\n        // Extract values along the axis.\n        let element;\n        if (is1DTensor) {\n            // Fast path for 1D tensor input.\n            element = values[i].toString();\n        }\n        else {\n            const axisValues = [];\n            for (let m = 0; m < newShape[0]; m++) {\n                for (let n = 0; n < newShape[2]; n++) {\n                    axisValues.push(inputBuffer.get(m, i, n));\n                }\n            }\n            element = axisValues.join(',');\n        }\n        // Dedup and update various indices.\n        if (uniqueElements[element] !== undefined) {\n            indices[i] = uniqueElements[element];\n        }\n        else {\n            const uniqueIndex = Object.keys(uniqueElements).length;\n            uniqueElements[element] = uniqueIndex;\n            indices[i] = uniqueIndex;\n            uniqueIndices.push(i);\n        }\n    }\n    // Now we know where each of the unique elements are located along the axis\n    // (uniqueIndices). Extract them from input buffer and store them in the\n    // output buffer.\n    const outputTmpShape = newShape.slice();\n    outputTmpShape[1] = Object.keys(uniqueElements).length;\n    const outputBuffer = new TensorBuffer(outputTmpShape, dtype);\n    uniqueIndices.forEach((uniqueElementIndex, i) => {\n        for (let m = 0; m < newShape[0]; m++) {\n            for (let n = 0; n < newShape[2]; n++) {\n                outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);\n            }\n        }\n    });\n    // The output shape can be calculated from the input shape with the size of\n    // the given axis replaced by the number of unique elements along that axis.\n    const outputShape = shape.slice();\n    outputShape[$axis] = outputTmpShape[1];\n    return {\n        outputValues: outputBuffer.values,\n        outputShape,\n        indices,\n    };\n}\n//# sourceMappingURL=Unique_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Shared kernel impls for use in other backends.\nexport { simpleAbsImpl } from './kernels/Abs';\nexport { addImpl } from './kernels/Add';\nexport { ceilImpl } from './kernels/Ceil';\nexport { expImpl } from './kernels/Exp';\nexport { expm1Impl } from './kernels/Expm1';\nexport { floorImpl } from './kernels/Floor';\nexport { logImpl } from './kernels/Log';\nexport { maxImpl } from './kernels/Max_impl';\nexport { multiplyImpl } from './kernels/Multiply';\nexport { rsqrtImpl } from './kernels/Rsqrt';\nexport { sliceImpl } from './kernels/Slice';\nexport { subImpl } from './kernels/Sub';\nexport { transposeImpl } from './kernels/Transpose_impl';\nexport { uniqueImpl } from './kernels/Unique_impl';\n//# sourceMappingURL=shared.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/*\n * base.ts contains all the exports from tfjs-backend-cpu\n * without auto-kernel registration\n */\nimport { registerBackend } from '@tensorflow/tfjs-core';\nimport { MathBackendCPU } from './backend_cpu';\nimport * as shared from './shared';\nexport { MathBackendCPU } from './backend_cpu';\nexport { version as version_cpu } from './version';\nexport { shared };\n// Side effects for default initialization of MathBackendCPU\nregisterBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acos } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const acosKernelFunc = unaryKernelFunc(Acos, (xi) => Math.acos(xi));\nexport const acosConfig = {\n    kernelName: Acos,\n    backendName: 'cpu',\n    kernelFunc: acosKernelFunc,\n};\n//# sourceMappingURL=Acos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acosh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const acoshKernelFunc = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi));\nexport const acoshConfig = {\n    kernelName: Acosh,\n    backendName: 'cpu',\n    kernelFunc: acoshKernelFunc,\n};\n//# sourceMappingURL=Acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asin } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const asinKernelFunc = unaryKernelFunc(Asin, (xi) => Math.asin(xi));\nexport const asinConfig = {\n    kernelName: Asin,\n    backendName: 'cpu',\n    kernelFunc: asinKernelFunc,\n};\n//# sourceMappingURL=Asin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asinh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const asinhKernelFunc = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi));\nexport const asinhConfig = {\n    kernelName: Asinh,\n    backendName: 'cpu',\n    kernelFunc: asinhKernelFunc,\n};\n//# sourceMappingURL=Asinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const atanKernelFunc = unaryKernelFunc(Atan, (xi) => Math.atan(xi));\nexport const atanConfig = {\n    kernelName: Atan,\n    backendName: 'cpu',\n    kernelFunc: atanKernelFunc,\n};\n//# sourceMappingURL=Atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atanh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const atanhKernelFunc = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi));\nexport const atanhConfig = {\n    kernelName: Atanh,\n    backendName: 'cpu',\n    kernelFunc: atanhKernelFunc,\n};\n//# sourceMappingURL=Atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from '@tensorflow/tfjs-core';\nexport function pool(xValues, xShape, dtype, strides, convInfo, poolType) {\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n        Number.POSITIVE_INFINITY);\n    const output = buffer(convInfo.outShape, dtype);\n    const outputVals = output.values;\n    const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];\n    const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];\n    const outputColStrides = convInfo.outShape[3];\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        const outputBatchOffset = b * outputBatchStrides;\n        const inputBatchOffset = b * strides[0];\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const xRCorner = yR * strideHeight - padTop;\n                const xRMin = Math.max(0, xRCorner);\n                const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n                const outputRowOffset = outputBatchOffset + yR * outputRowStrides;\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const xCCorner = yC * strideWidth - padLeft;\n                    const xCMin = Math.max(0, xCCorner);\n                    const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n                    let minMaxValue = initialValue;\n                    let avgValue = 0;\n                    let count = 0;\n                    for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n                        const xROffset = inputBatchOffset + xR * strides[1];\n                        for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                            const xCOffset = xROffset + xC * strides[2];\n                            const pixel = xValues[xCOffset + d];\n                            if ((poolType === 'max' && pixel > minMaxValue)) {\n                                minMaxValue = pixel;\n                            }\n                            else if (poolType === 'avg') {\n                                avgValue += pixel;\n                                count++;\n                            }\n                        }\n                        if (isNaN(minMaxValue)) {\n                            break;\n                        }\n                    }\n                    const outputOffset = outputRowOffset + yC * outputColStrides + d;\n                    outputVals[outputOffset] =\n                        poolType === 'avg' ? avgValue / count : minMaxValue;\n                }\n            }\n        }\n    }\n    return output;\n}\nexport function maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) {\n    const maxPositions = buffer(convInfo.outShape, 'int32');\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const xBuf = buffer(xShape, dtype, xValues);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const xRCorner = yR * strideHeight - padTop;\n                let xRMin = xRCorner;\n                while (xRMin < 0) {\n                    xRMin += dilationHeight;\n                }\n                // const xRMin = Math.max(0, xRCorner);\n                const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const xCCorner = yC * strideWidth - padLeft;\n                    let xCMin = xCCorner;\n                    while (xCMin < 0) {\n                        xCMin += dilationWidth;\n                    }\n                    const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n                    let maxValue = Number.NEGATIVE_INFINITY;\n                    let maxPosition = -1;\n                    for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n                        const wR = xR - xRCorner;\n                        for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                            const wC = xC - xCCorner;\n                            const pixel = xBuf.get(b, xR, xC, d);\n                            if (pixel > maxValue) {\n                                maxValue = pixel;\n                                if (flattenPositions) {\n                                    maxPosition = includeBatchInIndex ?\n                                        ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) *\n                                            convInfo.inChannels +\n                                            d :\n                                        (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;\n                                }\n                                else {\n                                    maxPosition = wR * effectiveFilterWidth + wC;\n                                }\n                            }\n                        }\n                    }\n                    maxPositions.set(maxPosition, b, yR, yC, d);\n                }\n            }\n        }\n    }\n    return maxPositions;\n}\n//# sourceMappingURL=pool_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool, backend_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { pool } from '../utils/pool_utils';\nimport { identity } from './Identity';\nexport function avgPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'avgPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    let res;\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        res = identity({ inputs: { x }, backend });\n    }\n    else {\n        const xValues = backend.data.get(x.dataId).values;\n        const strides = util.computeStrides(x.shape);\n        const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'avg');\n        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);\n    }\n    return res;\n}\nexport const avgPoolConfig = {\n    kernelName: AvgPool,\n    backendName: 'cpu',\n    kernelFunc: avgPool\n};\n//# sourceMappingURL=AvgPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPoolBackprop, backend_util, buffer } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function avgPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input } = inputs;\n    const x = input;\n    assertNotComplex([dy, input], 'avgPoolBackprop');\n    const { filterSize, strides, pad } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = buffer(x.shape, 'float32');\n    const avgMultiplier = 1 / (filterHeight * filterWidth);\n    const dyData = backend.data.get(dy.dataId).values;\n    const dyBuf = buffer(dy.shape, 'float32', dyData);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n                for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n                    // Shader code begins.\n                    const dyRCorner = dxR - padTop;\n                    const dyCCorner = dxC - padLeft;\n                    let dotProd = 0;\n                    for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n                        const dyR = (dyRCorner + wR) / strideHeight;\n                        if (dyR < 0 || dyR >= convInfo.outHeight ||\n                            Math.floor(dyR) !== dyR) {\n                            continue;\n                        }\n                        for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                            const dyC = (dyCCorner + wC) / strideWidth;\n                            if (dyC < 0 || dyC >= convInfo.outWidth ||\n                                Math.floor(dyC) !== dyC) {\n                                continue;\n                            }\n                            const pixel = dyBuf.get(b, dyR, dyC, d);\n                            dotProd += pixel;\n                        }\n                    }\n                    dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const avgPoolBackpropConfig = {\n    kernelName: AvgPoolBackprop,\n    backendName: 'cpu',\n    kernelFunc: avgPoolBackprop\n};\n//# sourceMappingURL=AvgPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedBatchNorm, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function batchNormKernelFunc(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, scale, offset, mean, variance } = inputs;\n    util.assert(mean.shape.length === variance.shape.length, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert(offset == null || mean.shape.length === offset.shape.length, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert(scale == null || mean.shape.length === scale.shape.length, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    assertNotComplex([x, mean, variance, scale, offset], 'batchNorm');\n    let { varianceEpsilon } = attrs;\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const xVals = backend.data.get(x.dataId).values;\n    const mVals = backend.data.get(mean.dataId).values;\n    const varVals = backend.data.get(variance.dataId).values;\n    const sVals = scale ? backend.data.get(scale.dataId).values :\n        new Float32Array([1]);\n    const offVals = offset ?\n        backend.data.get(offset.dataId).values :\n        new Float32Array([0]);\n    const outVals = new Float32Array(xVals.length);\n    const offValsLength = offVals.length;\n    const sValsLength = sVals.length;\n    const varValsLength = varVals.length;\n    const mValsLength = mVals.length;\n    let offi = 0;\n    let mi = 0;\n    let si = 0;\n    let vi = 0;\n    for (let i = 0; i < xVals.length; ++i) {\n        outVals[i] = offVals[offi++] +\n            (xVals[i] - mVals[mi++]) * sVals[si++] /\n                Math.sqrt(varVals[vi++] + varianceEpsilon);\n        if (offi >= offValsLength) {\n            offi = 0;\n        }\n        if (mi >= mValsLength) {\n            mi = 0;\n        }\n        if (si >= sValsLength) {\n            si = 0;\n        }\n        if (vi >= varValsLength) {\n            vi = 0;\n        }\n    }\n    return backend.makeTensorInfo(x.shape, x.dtype, outVals);\n}\nexport const batchNormConfig = {\n    kernelName: FusedBatchNorm,\n    backendName: 'cpu',\n    kernelFunc: batchNormKernelFunc,\n};\n//# sourceMappingURL=BatchNorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ClipByValue } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const clipKernelFunc = unaryKernelFunc(ClipByValue, (xi, attrs) => {\n    const clipAttrs = attrs;\n    if (xi > clipAttrs.clipValueMax) {\n        return clipAttrs.clipValueMax;\n    }\n    return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;\n});\nexport const clipConfig = {\n    kernelName: ClipByValue,\n    backendName: 'cpu',\n    kernelFunc: clipKernelFunc,\n};\n//# sourceMappingURL=Clip.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Imag } from '@tensorflow/tfjs-core';\nexport function imag(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const imag = backend.data.get(input.dataId).complexTensorInfos.imag;\n    const imagVal = backend.data.get(imag.dataId).values;\n    // When complex tensor is disposed, its underlying parts will be disposed too.\n    // Make new tensor out of the imag value of the complex. This makes sure the\n    // value is still accessible even if complex tensor is disposed.\n    return backend.makeTensorInfo(imag.shape, imag.dtype, imagVal);\n}\nexport const imagConfig = {\n    kernelName: Imag,\n    backendName: 'cpu',\n    kernelFunc: imag\n};\n//# sourceMappingURL=Imag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape, util } from '@tensorflow/tfjs-core';\nexport function reshape(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { shape } = attrs;\n    const xSize = util.sizeFromShape(x.shape);\n    const $shape = util.inferFromImplicitShape(shape, xSize);\n    const $xSize = util.sizeFromShape($shape);\n    util.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n        `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n        `shape must have the same number of elements.`);\n    backend.incRef(x.dataId);\n    const xData = backend.data.get(x.dataId);\n    if (xData.complexTensorInfos != null) {\n        const real = xData.complexTensorInfos.real;\n        const imag = xData.complexTensorInfos.imag;\n        real.shape = $shape;\n        imag.shape = $shape;\n    }\n    return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nexport const reshapeConfig = {\n    kernelName: Reshape,\n    backendName: 'cpu',\n    kernelFunc: reshape\n};\n//# sourceMappingURL=Reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Concat, util } from '@tensorflow/tfjs-core';\nimport { complex } from './Complex';\nimport { imag } from './Imag';\nimport { real } from './Real';\nimport { reshape } from './Reshape';\nexport function concat(args) {\n    const { inputs, backend, attrs } = args;\n    const { axis } = attrs;\n    const $axis = util.parseAxisParam(axis, inputs[0].shape)[0];\n    let outShape = backend_util.computeOutShape(inputs.map(t => t.shape), $axis);\n    if (util.sizeFromShape(outShape) === 0) {\n        return backend.makeTensorInfo(outShape, inputs[0].dtype, []);\n    }\n    // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n    const $inputs = inputs.filter(t => util.sizeFromShape(t.shape) > 0);\n    if ($inputs.length === 1) {\n        return $inputs[0];\n    }\n    const shapes = $inputs.map(t => t.shape);\n    backend_util.assertParamsConsistent(shapes, $axis);\n    if ($inputs[0].dtype === 'complex64') {\n        const reals = $inputs.map((t) => real({ inputs: { input: t }, backend }));\n        const imags = $inputs.map((t) => imag({ inputs: { input: t }, backend }));\n        const realConcated = concat({ inputs: reals, backend, attrs: { axis } });\n        const imagConcated = concat({ inputs: imags, backend, attrs: { axis } });\n        const result = complex({ inputs: { real: realConcated, imag: imagConcated }, backend });\n        reals.forEach(r => backend.disposeIntermediateTensorInfo(r));\n        imags.forEach(i => backend.disposeIntermediateTensorInfo(i));\n        backend.disposeIntermediateTensorInfo(realConcated);\n        backend.disposeIntermediateTensorInfo(imagConcated);\n        return result;\n    }\n    // Any concat of n-dimensional tensors across any axis can be reduced to\n    // a concatenation of two-dimensional tensors across the axis 1 by first\n    // partitioning the axes of the original tensors into those less than the\n    // axis to be concatenated and the rest. Then reshape the tensors\n    // into a two-dimensional tensor by collapsing these two sets of axes and\n    // concatenate the resulting matrices across the axis 1, finally reshaping\n    // the result to have the proper shape.\n    const inputs2D = $inputs.map(t => {\n        const innerSize = util.sizeFromShape(t.shape.slice($axis));\n        const shape = [-1, innerSize];\n        return reshape({ inputs: { x: t }, backend, attrs: { shape } });\n    });\n    // Concats 2d tensors along axis=1.\n    outShape =\n        backend_util.computeOutShape(inputs2D.map(t => t.shape), 1 /* axis */);\n    const outVals = util.getTypedArrayFromDType($inputs[0].dtype, util.sizeFromShape(outShape));\n    if (inputs2D[0].shape[0] === 1) {\n        // Use built-in TypedArray.set() method for speed.\n        let offset = 0;\n        inputs2D.forEach(t => {\n            const val = backend.data.get(t.dataId).values;\n            const size = util.sizeFromShape(t.shape);\n            outVals.set(val, offset);\n            offset += size;\n        });\n    }\n    else {\n        let colOffset = 0;\n        inputs2D.forEach(t => {\n            const tVals = backend.data.get(t.dataId).values;\n            let tIdx = 0;\n            for (let row = 0; row < t.shape[0]; ++row) {\n                const resIdx = row * outShape[1] + colOffset;\n                for (let col = 0; col < t.shape[1]; ++col) {\n                    outVals[resIdx + col] = tVals[tIdx++];\n                }\n            }\n            colOffset += t.shape[1];\n        });\n    }\n    const finalOutShape = backend_util.computeOutShape($inputs.map(t => t.shape), $axis);\n    const outInfo = backend.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);\n    inputs2D.forEach(t => backend.disposeIntermediateTensorInfo(t));\n    return outInfo;\n}\nexport const concatConfig = {\n    kernelName: Concat,\n    backendName: 'cpu',\n    kernelFunc: concat\n};\n//# sourceMappingURL=Concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const cosKernelFunc = unaryKernelFunc(Cos, (xi) => Math.cos(xi));\nexport const cosConfig = {\n    kernelName: Cos,\n    backendName: 'cpu',\n    kernelFunc: cosKernelFunc,\n};\n//# sourceMappingURL=Cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cosh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const coshKernelFunc = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi));\nexport const coshConfig = {\n    kernelName: Cosh,\n    backendName: 'cpu',\n    kernelFunc: coshKernelFunc,\n};\n//# sourceMappingURL=Cosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2D, util } from '@tensorflow/tfjs-core';\nexport const dilation2dConfig = {\n    kernelName: Dilation2D,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const xVals = cpuBackend.data.get(x.dataId).values;\n        const xRank = x.shape.length;\n        const filterVals = cpuBackend.data.get(filter.dataId).values;\n        const filterRank = filter.shape.length;\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        const outSize = util.sizeFromShape(outShape);\n        const outRank = outShape.length;\n        const outputVals = util.getArrayFromDType(x.dtype, outSize);\n        // Upsampling the input by fill in `dilation size - 1` values between each\n        // input value.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const xIndex = util.locToIndex([b, hIn, wIn, d], xRank, util.computeStrides(x.shape));\n                                        const filterIndex = util.locToIndex([h, w, d], filterRank, util.computeStrides(filter.shape));\n                                        const val = xVals[xIndex] + filterVals[filterIndex];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        const outputIndex = util.locToIndex([b, hOut, wOut, d], outRank, util.computeStrides(outShape));\n                        outputVals[outputIndex] = curVal;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(outputVals, x.dtype), outShape, x.dtype);\n        return { dataId, shape: outShape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2DBackpropFilter, util } from '@tensorflow/tfjs-core';\nexport const dilation2dBackpropFilterConfig = {\n    kernelName: Dilation2DBackpropFilter,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter, dy } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const $x = util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n        const $filter = util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        util.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy ` +\n            `must have the same rank as output ${outShape.length}, but got ` +\n            `${dy.rank}`);\n        const $dy = util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n        // The computed filter gradients has the same dimensions as the filter:\n        // [filterHeight, filterWidth, depth]\n        const gradients = util.makeZerosNestedTypedArray(filter.shape, filter.dtype);\n        // In the case of multiple argmax branches, we only back-propagate along the\n        // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n        // similarly to the max-pooling backward routines.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        let hMax = 0;\n                        let wMax = 0;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                            hMax = h;\n                                            wMax = w;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);\n        return { dataId, shape: filter.shape, dtype: filter.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2DBackpropFilter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2DBackpropInput, util } from '@tensorflow/tfjs-core';\nexport const dilation2dBackpropInputConfig = {\n    kernelName: Dilation2DBackpropInput,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter, dy } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const $x = util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n        const $filter = util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        util.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy ` +\n            `must have the same rank as output ${outShape.length}, but got ` +\n            `${dy.rank}`);\n        const $dy = util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n        // The computed gradients has the same dimensions as the input:\n        // [batch, inputHeight, inputCols, inChannel]\n        const gradients = util.makeZerosNestedTypedArray(x.shape, x.dtype);\n        // In the case of multiple argmax branches, we only back-propagate along the\n        // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n        // similarly to the max-pooling backward routines.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        let hInMax = (hBeg < 0) ? 0 : hBeg;\n                        let wInMax = (wBeg < 0) ? 0 : wBeg;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                            hInMax = hIn;\n                                            wInMax = wIn;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(gradients, x.dtype), x.shape, x.dtype);\n        return { dataId, shape: x.shape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2DBackpropInput.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const divImpl = createSimpleBinaryKernelImpl((a, b) => a / b);\nexport const div = binaryKernelFunc(Div, divImpl);\nexport const divConfig = {\n    kernelName: Div,\n    backendName: 'cpu',\n    kernelFunc: div\n};\n//# sourceMappingURL=Div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Elu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const eluKernelFunc = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : (Math.exp(xi) - 1));\nexport const eluConfig = {\n    kernelName: Elu,\n    backendName: 'cpu',\n    kernelFunc: eluKernelFunc,\n};\n//# sourceMappingURL=Elu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Erf } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nconst p = backend_util.ERF_P;\nconst a1 = backend_util.ERF_A1;\nconst a2 = backend_util.ERF_A2;\nconst a3 = backend_util.ERF_A3;\nconst a4 = backend_util.ERF_A4;\nconst a5 = backend_util.ERF_A5;\nexport const erfKernelFunc = unaryKernelFunc(Erf, (xi) => {\n    const sign = Math.sign(xi);\n    const v = Math.abs(xi);\n    const t = 1.0 / (1.0 + p * v);\n    return sign *\n        (1.0 -\n            (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t *\n                Math.exp(-v * v));\n});\nexport const erfConfig = {\n    kernelName: Erf,\n    backendName: 'cpu',\n    kernelFunc: erfKernelFunc,\n};\n//# sourceMappingURL=Erf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { add } from '../kernels/Add';\nimport { complex } from '../kernels/Complex';\nimport { concat } from '../kernels/Concat';\nimport { divConfig } from '../kernels/Div';\nimport { identity } from '../kernels/Identity';\nimport { imag } from '../kernels/Imag';\nimport { multiply } from '../kernels/Multiply';\nimport { real } from '../kernels/Real';\nimport { slice } from '../kernels/Slice';\nimport { sub } from '../kernels/Sub';\n/**\n * Calculate FFT of inner most elements of batch tensor.\n */\nexport function fftBatch(input, inverse, cpuBackend) {\n    const inputShape = input.shape;\n    const batch = inputShape[0];\n    const innerDim = inputShape[1];\n    const inputVals = cpuBackend.data.get(input.dataId);\n    const real2D = inputVals.complexTensorInfos.real;\n    const imag2D = inputVals.complexTensorInfos.imag;\n    // Collects real and imaginary values separately.\n    const resultShape = [batch, innerDim];\n    const resultSize = util.sizeFromShape(resultShape);\n    const resultReal = util.getTypedArrayFromDType('float32', resultSize);\n    const resultImag = util.getTypedArrayFromDType('float32', resultSize);\n    for (let b = 0; b < batch; b++) {\n        // TODO: Support slice ops for complex type.\n        const r = slice({\n            inputs: { x: real2D },\n            backend: cpuBackend,\n            attrs: { begin: [b, 0], size: [1, innerDim] }\n        });\n        const i = slice({\n            inputs: { x: imag2D },\n            backend: cpuBackend,\n            attrs: { begin: [b, 0], size: [1, innerDim] }\n        });\n        const input = complex({ inputs: { real: r, imag: i }, backend: cpuBackend });\n        // Run FFT by batch element.\n        const { real, imag } = fftImpl(input, inverse, cpuBackend);\n        const res = backend_util.mergeRealAndImagArrays(real, imag);\n        for (let d = 0; d < innerDim; d++) {\n            const c = backend_util.getComplexWithIndex(res, d);\n            resultReal[b * innerDim + d] = c.real;\n            resultImag[b * innerDim + d] = c.imag;\n        }\n        cpuBackend.disposeIntermediateTensorInfo(r);\n        cpuBackend.disposeIntermediateTensorInfo(i);\n        cpuBackend.disposeIntermediateTensorInfo(input);\n    }\n    const $realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultReal);\n    const $imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImag);\n    const result = complex({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend });\n    cpuBackend.disposeIntermediateTensorInfo($realInfo);\n    cpuBackend.disposeIntermediateTensorInfo($imagInfo);\n    return result;\n}\nexport function fftImpl(input, inverse, cpuBackend) {\n    const inputSize = util.sizeFromShape(input.shape);\n    const inputVals = cpuBackend.data.get(input.dataId);\n    const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values;\n    const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;\n    if (isExponentOf2(inputSize)) {\n        const result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);\n        const resultShape = [input.shape[0], input.shape[1]];\n        if (inverse) {\n            const realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.real);\n            const imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.imag);\n            const sizeInfo = cpuBackend.makeTensorInfo([], 'float32', util.createScalarValue(inputSize, 'float32'));\n            const sizeInfoCopy = identity({ inputs: { x: sizeInfo }, backend: cpuBackend });\n            const divRealInfo = divConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend });\n            const divImagInfo = divConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend });\n            const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values;\n            const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values;\n            cpuBackend.disposeIntermediateTensorInfo(realInfo);\n            cpuBackend.disposeIntermediateTensorInfo(imagInfo);\n            cpuBackend.disposeIntermediateTensorInfo(sizeInfo);\n            cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);\n            cpuBackend.disposeIntermediateTensorInfo(divRealInfo);\n            cpuBackend.disposeIntermediateTensorInfo(divImagInfo);\n            return { real: divRealVals, imag: divImagVals };\n        }\n        return result;\n    }\n    else {\n        const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n        const rawOutput = fourierTransformByMatmul(data, inputSize, inverse);\n        return backend_util.splitRealAndImagArrays(rawOutput);\n    }\n}\nfunction isExponentOf2(size) {\n    return (size & size - 1) === 0;\n}\n// FFT using Cooley-Tukey algorithm on radix 2 dimensional input.\nfunction fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {\n    if (size === 1) {\n        return { real: realVals, imag: imagVals };\n    }\n    const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n    const half = size / 2;\n    const evenComplex = backend_util.complexWithEvenIndex(data);\n    const evenRealVals = evenComplex.real;\n    const evenImagVals = evenComplex.imag;\n    const evenShape = [evenRealVals.length];\n    const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenRealVals);\n    const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenImagVals);\n    const evenTensorInfo = complex({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend });\n    const oddComplex = backend_util.complexWithOddIndex(data);\n    const oddRealVals = oddComplex.real;\n    const oddImagVals = oddComplex.imag;\n    const oddShape = [oddRealVals.length];\n    const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddRealVals);\n    const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddImagVals);\n    const oddTensorInfo = complex({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend });\n    // Recursive call for half part of original input.\n    const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);\n    const $evenRealVals = $evenComplex.real;\n    const $evenImagVals = $evenComplex.imag;\n    const $evenShape = [$evenRealVals.length];\n    const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenRealVals);\n    const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenImagVals);\n    const $evenTensorInfo = complex({\n        inputs: { real: $evenRealInfo, imag: $evenImagInfo },\n        backend: cpuBackend\n    });\n    const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);\n    const $oddRealVals = $oddComplex.real;\n    const $oddImagVals = $oddComplex.imag;\n    const $oddShape = [$oddRealVals.length];\n    const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddRealVals);\n    const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddImagVals);\n    const $oddTensorInfo = complex({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend });\n    const e = backend_util.exponents(size, inverse);\n    const eShape = [e.real.length];\n    const eRealInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.real);\n    const eImagInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.imag);\n    const complexInfo = complex({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend });\n    const exponentInfo = multiply({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend });\n    const addPart = add({\n        inputs: { a: $evenTensorInfo, b: exponentInfo },\n        backend: cpuBackend\n    });\n    const subPart = sub({\n        inputs: { a: $evenTensorInfo, b: exponentInfo },\n        backend: cpuBackend\n    });\n    const addPartReal = real({ inputs: { input: addPart }, backend: cpuBackend });\n    const subPartReal = real({ inputs: { input: subPart }, backend: cpuBackend });\n    const addPartImag = imag({ inputs: { input: addPart }, backend: cpuBackend });\n    const subPartImag = imag({ inputs: { input: subPart }, backend: cpuBackend });\n    const $real = concat({\n        inputs: [addPartReal, subPartReal],\n        backend: cpuBackend,\n        attrs: { axis: 0 }\n    });\n    const $imag = concat({\n        inputs: [addPartImag, subPartImag],\n        backend: cpuBackend,\n        attrs: { axis: 0 }\n    });\n    const $realVals = cpuBackend.data.get($real.dataId).values;\n    const $imagVals = cpuBackend.data.get($imag.dataId).values;\n    cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo(eRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(eImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(complexInfo);\n    cpuBackend.disposeIntermediateTensorInfo(exponentInfo);\n    cpuBackend.disposeIntermediateTensorInfo(addPart);\n    cpuBackend.disposeIntermediateTensorInfo(subPart);\n    cpuBackend.disposeIntermediateTensorInfo(addPartReal);\n    cpuBackend.disposeIntermediateTensorInfo(addPartImag);\n    cpuBackend.disposeIntermediateTensorInfo(subPartReal);\n    cpuBackend.disposeIntermediateTensorInfo(subPartImag);\n    cpuBackend.disposeIntermediateTensorInfo($real);\n    cpuBackend.disposeIntermediateTensorInfo($imag);\n    return { real: $realVals, imag: $imagVals };\n}\n// Calculate fourier transform by multplying sinusoid matrix.\nfunction fourierTransformByMatmul(data, size, inverse) {\n    const ret = new Float32Array(size * 2);\n    // TODO: Use matmul instead once it supports complex64 type.\n    for (let r = 0; r < size; r++) {\n        let real = 0.0;\n        let imag = 0.0;\n        for (let c = 0; c < size; c++) {\n            const e = backend_util.exponent(r * c, size, inverse);\n            const term = backend_util.getComplexWithIndex(data, c);\n            real += term.real * e.real - term.imag * e.imag;\n            imag += term.real * e.imag + term.imag * e.real;\n        }\n        if (inverse) {\n            real /= size;\n            imag /= size;\n        }\n        backend_util.assignToTypedArray(ret, real, imag, r);\n    }\n    return ret;\n}\n//# sourceMappingURL=fft_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FFT, util } from '@tensorflow/tfjs-core';\nimport { fftBatch } from '../utils/fft_utils';\nimport { reshape } from './Reshape';\nexport function fft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputSize = util.sizeFromShape(input.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({\n        inputs: { x: input },\n        backend,\n        attrs: { shape: [batch, innerDimensionSize] }\n    });\n    const result = fftBatch(input2D, false, backend);\n    const resultReshaped = reshape({ inputs: { x: result }, backend, attrs: { shape: input.shape } });\n    backend.disposeIntermediateTensorInfo(input2D);\n    backend.disposeIntermediateTensorInfo(result);\n    return resultReshaped;\n}\nexport const fftConfig = {\n    kernelName: FFT,\n    backendName: 'cpu',\n    kernelFunc: fft\n};\n//# sourceMappingURL=FFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FlipLeftRight, util } from '@tensorflow/tfjs-core';\nexport const flipLeftRightConfig = {\n    kernelName: FlipLeftRight,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const cpuBackend = backend;\n        const output = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(image.shape));\n        const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n        const imageVals = cpuBackend.data.get(image.dataId).values;\n        for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n            for (let row = 0; row < imageHeight; row++) {\n                const rowOffset = row * (imageWidth * numChannels);\n                for (let col = 0; col < imageWidth; col++) {\n                    const colOffset = col * numChannels;\n                    for (let channel = 0; channel < numChannels; channel++) {\n                        const coords = [batch, row, col, channel];\n                        const x = coords[2];\n                        const coordX = Math.round(imageWidth - x);\n                        const outIdx = batchOffset + rowOffset + colOffset + channel;\n                        let outputValue = imageVals[outIdx];\n                        // If the coordinate position falls within the image boundaries...\n                        if (coordX >= 0 && coordX < imageWidth) {\n                            // set the output to the image value at the coordinate position.\n                            const rotatedColOffset = coordX * numChannels;\n                            const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel;\n                            outputValue = imageVals[imageIdx];\n                        }\n                        output[outIdx] = outputValue;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(output, image.shape, image.dtype);\n        return { dataId, shape: image.shape, dtype: image.dtype };\n    }\n};\n//# sourceMappingURL=FlipLeftRight.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IFFT, util } from '@tensorflow/tfjs-core';\nimport { fftBatch } from '../utils/fft_utils';\nimport { reshape } from './Reshape';\nexport function ifft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputSize = util.sizeFromShape(input.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({\n        inputs: { x: input },\n        backend,\n        attrs: { shape: [batch, innerDimensionSize] }\n    });\n    const result = fftBatch(input2D, true, backend);\n    const resultReshaped = reshape({ inputs: { x: result }, backend, attrs: { shape: input.shape } });\n    backend.disposeIntermediateTensorInfo(input2D);\n    backend.disposeIntermediateTensorInfo(result);\n    return resultReshaped;\n}\nexport const ifftConfig = {\n    kernelName: IFFT,\n    backendName: 'cpu',\n    kernelFunc: ifft\n};\n//# sourceMappingURL=IFFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsFinite } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isFiniteKernelFunc = unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, 'bool');\nexport const isFiniteConfig = {\n    kernelName: IsFinite,\n    backendName: 'cpu',\n    kernelFunc: isFiniteKernelFunc,\n};\n//# sourceMappingURL=IsFinite.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsInf } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isInfKernelFunc = unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, 'bool');\nexport const isInfConfig = {\n    kernelName: IsInf,\n    backendName: 'cpu',\n    kernelFunc: isInfKernelFunc,\n};\n//# sourceMappingURL=IsInf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsNan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isNaNKernelFunc = unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, 'bool');\nexport const isNaNConfig = {\n    kernelName: IsNan,\n    backendName: 'cpu',\n    kernelFunc: isNaNKernelFunc,\n};\n//# sourceMappingURL=IsNaN.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log1p } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const log1pKernelFunc = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi));\nexport const log1pConfig = {\n    kernelName: Log1p,\n    backendName: 'cpu',\n    kernelFunc: log1pKernelFunc,\n};\n//# sourceMappingURL=Log1p.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LogicalNot } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const logicalNotKernelFunc = unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, 'bool');\nexport const logicalNotConfig = {\n    kernelName: LogicalNot,\n    backendName: 'cpu',\n    kernelFunc: logicalNotKernelFunc,\n};\n//# sourceMappingURL=LogicalNot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '@tensorflow/tfjs-core';\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxImpl } from './Max_impl';\nimport { transposeImpl } from './Transpose_impl';\nexport const maxConfig = {\n    kernelName: Max,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { reductionIndices, keepDims } = attrs;\n        const cpuBackend = backend;\n        let xShape = x.shape;\n        const xRank = xShape.length;\n        const origAxes = util.parseAxisParam(reductionIndices, xShape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        let xVals = cpuBackend.data.get(x.dataId).values;\n        if (permutedAxes != null) {\n            const newShape = new Array(xRank);\n            for (let i = 0; i < newShape.length; i++) {\n                newShape[i] = xShape[permutedAxes[i]];\n            }\n            xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n            xShape = newShape;\n        }\n        assertNotComplex(x, 'max');\n        backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n        const [maxOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(xShape, axes);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);\n        const dataId = cpuBackend.write(result, maxOutShape, x.dtype);\n        let outShape = maxOutShape;\n        if (keepDims) {\n            // reshape\n            const newShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n            outShape = newShape;\n        }\n        return { dataId, shape: outShape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPool, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { pool } from '../utils/pool_utils';\nimport { identity } from './Identity';\nexport function maxPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'maxPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    let res;\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        res = identity({ inputs: { x }, backend });\n    }\n    else {\n        const xValues = backend.data.get(x.dataId).values;\n        const strides = util.computeStrides(x.shape);\n        const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'max');\n        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);\n    }\n    return res;\n}\nexport const maxPoolConfig = {\n    kernelName: MaxPool,\n    backendName: 'cpu',\n    kernelFunc: maxPool\n};\n//# sourceMappingURL=MaxPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, buffer, MaxPoolBackprop } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxPoolPositions } from '../utils/pool_utils';\nexport function maxPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input, output } = inputs;\n    const x = input;\n    assertNotComplex([input, output], 'maxPoolBackprop');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n    const xValues = backend.data.get(x.dataId).values;\n    const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = buffer(x.shape, 'float32');\n    const dyData = backend.data.get(dy.dataId).values;\n    const dyBuf = buffer(dy.shape, 'float32', dyData);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n                for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n                    // Shader code begins.\n                    const dyRCorner = dxR - padTop;\n                    const dyCCorner = dxC - padLeft;\n                    let dotProd = 0;\n                    for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n                        const dyR = (dyRCorner + wR) / strideHeight;\n                        if (dyR < 0 || dyR >= convInfo.outHeight ||\n                            Math.floor(dyR) !== dyR) {\n                            continue;\n                        }\n                        for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                            const dyC = (dyCCorner + wC) / strideWidth;\n                            if (dyC < 0 || dyC >= convInfo.outWidth ||\n                                Math.floor(dyC) !== dyC) {\n                                continue;\n                            }\n                            const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 -\n                                maxPosBuf.get(b, dyR, dyC, d);\n                            const curPos = wR * effectiveFilterWidth + wC;\n                            const mask = maxPos === curPos ? 1 : 0;\n                            if (mask === 0) {\n                                continue;\n                            }\n                            const pixel = dyBuf.get(b, dyR, dyC, d);\n                            dotProd += pixel * mask;\n                        }\n                    }\n                    dx.set(dotProd, b, dxR, dxC, d);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const maxPoolBackpropConfig = {\n    kernelName: MaxPoolBackprop,\n    backendName: 'cpu',\n    kernelFunc: maxPoolBackprop\n};\n//# sourceMappingURL=MaxPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { maxPoolPositions, pool } from '../utils/pool_utils';\nexport function maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) {\n    const strides = util.computeStrides(xShape);\n    const maxPools = pool(xValues, xShape, dtype, strides, convInfo, 'max');\n    const maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex);\n    return [maxPools.values, maxPositions.values];\n}\n//# sourceMappingURL=MaxPoolWithArgmax_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPoolWithArgmax } from '@tensorflow/tfjs-core';\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxPoolWithArgmaxImpl } from './MaxPoolWithArgmax_impl';\nexport const maxPoolWithArgmaxConfig = {\n    kernelName: MaxPoolWithArgmax,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { filterSize, strides, pad, includeBatchInIndex } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(x, 'MaxPoolWithArgmax');\n        const values = cpuBackend.data.get(x.dataId).values;\n        const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad);\n        const [pooled, indexes] = maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo);\n        const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype);\n        const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype);\n        return [\n            { dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype },\n            { dataId: indexesDataId, shape: convInfo.outShape, dtype: 'int32' }\n        ];\n    }\n};\n//# sourceMappingURL=MaxPoolWithArgmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NonMaxSuppressionV4 } from '@tensorflow/tfjs-core';\nimport { kernel_impls } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\nimport { assertNotComplex } from '../cpu_util';\nexport const nonMaxSuppressionV4Config = {\n    kernelName: NonMaxSuppressionV4,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(boxes, 'NonMaxSuppressionPadded');\n        const boxesVals = cpuBackend.data.get(boxes.dataId).values;\n        const scoresVals = cpuBackend.data.get(scores.dataId).values;\n        const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n        return [selectedIndices, validOutputs];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV4.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NonMaxSuppressionV5 } from '@tensorflow/tfjs-core';\nimport { kernel_impls } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nimport { assertNotComplex } from '../cpu_util';\nexport const nonMaxSuppressionV5Config = {\n    kernelName: NonMaxSuppressionV5,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(boxes, 'NonMaxSuppressionWithScore');\n        const boxesVals = cpuBackend.data.get(boxes.dataId).values;\n        const scoresVals = cpuBackend.data.get(scores.dataId).values;\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        const softNmsSigmaVal = softNmsSigma;\n        const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n        return [selectedIndices, selectedScores];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV5.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NotEqual } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const notEqualImpl = createSimpleBinaryKernelImpl(((a, b) => (a !== b) ? 1 : 0));\nexport const notEqual = binaryKernelFunc(NotEqual, notEqualImpl, null /* complexOp */, 'bool');\nexport const notEqualConfig = {\n    kernelName: NotEqual,\n    backendName: 'cpu',\n    kernelFunc: notEqual\n};\n//# sourceMappingURL=NotEqual.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { PadV2, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function padV2(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { paddings, constantValue } = attrs;\n    assertNotComplex(x, 'pad');\n    const outShape = paddings.map((p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);\n    const start = paddings.map(p => p[0]);\n    const xVals = backend.data.get(x.dataId).values;\n    const xSize = util.sizeFromShape(x.shape);\n    const xRank = x.shape.length;\n    const xStrides = util.computeStrides(x.shape);\n    const resultSize = util.sizeFromShape(outShape);\n    const resultRank = outShape.length;\n    const resultStrides = util.computeStrides(outShape);\n    const resVals = util.getTypedArrayFromDType(x.dtype, resultSize);\n    if (constantValue !== 0) {\n        resVals.fill(constantValue);\n    }\n    for (let i = 0; i < xSize; i++) {\n        const coords = util.indexToLoc(i, xRank, xStrides);\n        const outCoords = coords.map((c, i) => c + start[i]);\n        const outIndex = util.locToIndex(outCoords, resultRank, resultStrides);\n        resVals[outIndex] = xVals[i];\n    }\n    const outId = backend.write(resVals, outShape, x.dtype);\n    return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nexport const padV2Config = {\n    kernelName: PadV2,\n    backendName: 'cpu',\n    kernelFunc: padV2\n};\n//# sourceMappingURL=PadV2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reciprocal } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const reciprocalKernelFunc = unaryKernelFunc(Reciprocal, (xi) => 1 / xi);\nexport const reciprocalConfig = {\n    kernelName: Reciprocal,\n    backendName: 'cpu',\n    kernelFunc: reciprocalKernelFunc,\n};\n//# sourceMappingURL=Reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, RotateWithOffset, util } from '@tensorflow/tfjs-core';\nexport const rotateWithOffsetConfig = {\n    kernelName: RotateWithOffset,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const { radians, fillValue, center } = attrs;\n        const cpuBackend = backend;\n        const output = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(image.shape));\n        const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n        const [centerX, centerY] = backend_util.getImageCenter(center, imageHeight, imageWidth);\n        const fullOpacityValue = 255;\n        const sinFactor = Math.sin(radians);\n        const cosFactor = Math.cos(radians);\n        const imageVals = cpuBackend.data.get(image.dataId).values;\n        for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n            for (let row = 0; row < imageHeight; row++) {\n                const rowOffset = row * (imageWidth * numChannels);\n                for (let col = 0; col < imageWidth; col++) {\n                    const colOffset = col * numChannels;\n                    for (let channel = 0; channel < numChannels; channel++) {\n                        const coords = [batch, row, col, channel];\n                        const x = coords[2];\n                        const y = coords[1];\n                        // coordX/coordY are the result of rotating and translating x/y.\n                        let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;\n                        let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;\n                        coordX = Math.round(coordX + centerX);\n                        coordY = Math.round(coordY + centerY);\n                        let outputValue = fillValue;\n                        if (typeof fillValue !== 'number') {\n                            if (channel === 3) {\n                                outputValue = fullOpacityValue;\n                            }\n                            else {\n                                outputValue = fillValue[channel];\n                            }\n                        }\n                        // If the coordinate position falls within the image boundaries...\n                        if (coordX >= 0 && coordX < imageWidth && coordY >= 0 &&\n                            coordY < imageHeight) {\n                            // set the output to the image value at the coordinate position.\n                            const rotatedRowOffset = coordY * (imageWidth * numChannels);\n                            const rotatedColOffset = coordX * numChannels;\n                            const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;\n                            outputValue = imageVals[imageIdx];\n                        }\n                        const outIdx = batchOffset + rowOffset + colOffset + channel;\n                        output[outIdx] = outputValue;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(output, image.shape, image.dtype);\n        return { dataId, shape: image.shape, dtype: image.dtype };\n    }\n};\n//# sourceMappingURL=RotateWithOffset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Round } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const roundKernelFunc = unaryKernelFunc(Round, (xi) => {\n    // The algorithm is based on banker's rounding.\n    const base = Math.floor(xi);\n    if (xi - base < 0.5) {\n        return Math.floor(xi);\n    }\n    else if (xi - base > 0.5) {\n        return Math.ceil(xi);\n    }\n    else {\n        if (base % 2.0 === 0.0) {\n            return base;\n        }\n        else {\n            return base + 1.0;\n        }\n    }\n});\nexport const roundConfig = {\n    kernelName: Round,\n    backendName: 'cpu',\n    kernelFunc: roundKernelFunc,\n};\n//# sourceMappingURL=Round.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Selu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nconst scaleAlpha = backend_util.SELU_SCALEALPHA;\nconst scale = backend_util.SELU_SCALE;\nexport const seluKernelFunc = unaryKernelFunc(Selu, (xi) => {\n    if (xi >= 0) {\n        return scale * xi;\n    }\n    else {\n        return scaleAlpha * (Math.exp(xi) - 1);\n    }\n});\nexport const seluConfig = {\n    kernelName: Selu,\n    backendName: 'cpu',\n    kernelFunc: seluKernelFunc,\n};\n//# sourceMappingURL=Selu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sigmoid } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sigmoidKernelFunc = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));\nexport const sigmoidConfig = {\n    kernelName: Sigmoid,\n    backendName: 'cpu',\n    kernelFunc: sigmoidKernelFunc,\n};\n//# sourceMappingURL=Sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sign } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const signKernelFunc = unaryKernelFunc(Sign, (xi) => {\n    if (xi < 0) {\n        return -1;\n    }\n    else if (xi > 0) {\n        return 1;\n    }\n    else {\n        return 0;\n    }\n});\nexport const signConfig = {\n    kernelName: Sign,\n    backendName: 'cpu',\n    kernelFunc: signKernelFunc,\n};\n//# sourceMappingURL=Sign.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sinKernelFunc = unaryKernelFunc(Sin, (xi) => Math.sin(xi));\nexport const sinConfig = {\n    kernelName: Sin,\n    backendName: 'cpu',\n    kernelFunc: sinKernelFunc,\n};\n//# sourceMappingURL=Sin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sinh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sinhKernelFunc = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi));\nexport const sinhConfig = {\n    kernelName: Sinh,\n    backendName: 'cpu',\n    kernelFunc: sinhKernelFunc,\n};\n//# sourceMappingURL=Sinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softplus } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\n// mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n// epsilon is the difference between 1.0 and the next representable float.\n// For a single precision 32 bit float this should be 2^-23, see:\n// https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\nconst epsilon = 1.1920928955078125e-7;\nconst threshold = Math.log(epsilon) + 2.0;\nexport const softplusKernelFunc = unaryKernelFunc(Softplus, (xi) => {\n    // Value above which exp(x) may overflow, but softplus(x) == x\n    // is within machine epsilon.\n    const tooLarge = xi > -threshold;\n    // Value below which exp(x) may underflow, but softplus(x) == exp(x)\n    // is within machine epsilon.\n    const tooSmall = xi < threshold;\n    const expX = Math.exp(xi);\n    let result;\n    if (tooSmall) {\n        result = expX;\n    }\n    else if (tooLarge) {\n        result = xi;\n    }\n    else {\n        result = Math.log(1.0 + expX);\n    }\n    return result;\n});\nexport const softplusConfig = {\n    kernelName: Softplus,\n    backendName: 'cpu',\n    kernelFunc: softplusKernelFunc,\n};\n//# sourceMappingURL=Softplus.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { transposeImpl } from './Transpose_impl';\nexport function transpose(args) {\n    const { inputs, attrs, backend } = args;\n    const { x } = inputs;\n    const { perm } = attrs;\n    assertNotComplex(x, 'transpose');\n    const xRank = x.shape.length;\n    const newShape = new Array(xRank);\n    for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = x.shape[perm[i]];\n    }\n    const values = backend.data.get(x.dataId).values;\n    const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);\n    const dataId = backend.write(result, newShape, x.dtype);\n    return { dataId, shape: newShape, dtype: x.dtype };\n}\nexport const transposeConfig = {\n    kernelName: Transpose,\n    backendName: 'cpu',\n    kernelFunc: transpose\n};\n//# sourceMappingURL=Transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, SpaceToBatchND, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { padV2Config } from './PadV2';\nimport { reshape } from './Reshape';\nimport { transpose } from './Transpose';\nexport function spaceToBatchND(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { blockShape, paddings } = attrs;\n    assertNotComplex([x], 'spaceToBatchND');\n    const prod = util.sizeFromShape(blockShape);\n    const completePaddings = [[0, 0]];\n    completePaddings.push(...paddings);\n    for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n        completePaddings.push([0, 0]);\n    }\n    const paddedX = padV2Config.kernelFunc({\n        inputs: { x },\n        backend,\n        attrs: { paddings: completePaddings, constantValue: 0 }\n    });\n    const reshapedPaddedShape = backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n    const permutedReshapedPaddedPermutation = backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n    const flattenShape = backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n    const reshapeInputs = { x: paddedX };\n    const reshapeAttrs = { shape: reshapedPaddedShape };\n    const paddedXReshaped = reshape({ inputs: reshapeInputs, backend, attrs: reshapeAttrs });\n    const transposeInputs = { x: paddedXReshaped };\n    const transposeAttrs = { perm: permutedReshapedPaddedPermutation };\n    const paddedXT = transpose({ inputs: transposeInputs, backend, attrs: transposeAttrs });\n    const resultReshapeInputs = { x: paddedXT };\n    const resultReshapeAttrs = { shape: flattenShape };\n    const result = reshape({ inputs: resultReshapeInputs, backend, attrs: resultReshapeAttrs });\n    backend.disposeIntermediateTensorInfo(paddedX);\n    backend.disposeIntermediateTensorInfo(paddedXReshaped);\n    backend.disposeIntermediateTensorInfo(paddedXT);\n    return result;\n}\nexport const spaceToBatchNDConfig = {\n    kernelName: SpaceToBatchND,\n    backendName: 'cpu',\n    kernelFunc: spaceToBatchND\n};\n//# sourceMappingURL=SpaceToBatchND.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sqrt } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sqrtKernelFunc = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));\nexport const sqrtConfig = {\n    kernelName: Sqrt,\n    backendName: 'cpu',\n    kernelFunc: sqrtKernelFunc,\n};\n//# sourceMappingURL=Sqrt.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport const squareConfig = {\n    kernelName: Square,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend }) => {\n        const { x } = inputs;\n        const cpuBackend = backend;\n        assertNotComplex(x, 'square');\n        const values = cpuBackend.data.get(x.dataId).values;\n        const newValues = new Float32Array(values.length);\n        for (let i = 0; i < values.length; ++i) {\n            const value = values[i];\n            newValues[i] = value * value;\n        }\n        const dataId = cpuBackend.write(newValues, x.shape, x.dtype);\n        return { dataId, shape: x.shape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const squaredDifferenceImpl = createSimpleBinaryKernelImpl(((a, b) => {\n    const diff = a - b;\n    return diff * diff;\n}));\nexport const squaredDifference = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);\nexport const squaredDifferenceConfig = {\n    kernelName: SquaredDifference,\n    backendName: 'cpu',\n    kernelFunc: squaredDifference\n};\n//# sourceMappingURL=SquaredDifference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Step } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const stepKernelFunc = unaryKernelFunc(Step, (xi, attrs) => {\n    const stepAttrs = attrs;\n    if (isNaN(xi)) {\n        return NaN;\n    }\n    else {\n        return xi > 0 ? 1 : stepAttrs.alpha;\n    }\n});\nexport const stepConfig = {\n    kernelName: Step,\n    backendName: 'cpu',\n    kernelFunc: stepKernelFunc,\n};\n//# sourceMappingURL=Step.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const tanKernelFunc = unaryKernelFunc(Tan, (xi) => Math.tan(xi));\nexport const tanConfig = {\n    kernelName: Tan,\n    backendName: 'cpu',\n    kernelFunc: tanKernelFunc,\n};\n//# sourceMappingURL=Tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tanh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const tanhKernelFunc = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi));\nexport const tanhConfig = {\n    kernelName: Tanh,\n    backendName: 'cpu',\n    kernelFunc: tanhKernelFunc,\n};\n//# sourceMappingURL=Tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unique } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { uniqueImpl } from './Unique_impl';\nexport function unique(args) {\n    const { inputs, attrs, backend } = args;\n    const { axis } = attrs;\n    const { x } = inputs;\n    assertNotComplex(x, 'unique');\n    const values = backend.data.get(x.dataId).values;\n    const { outputValues, outputShape, indices } = uniqueImpl(values, axis, x.shape, x.dtype);\n    return [\n        backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n        backend.makeTensorInfo([indices.length], 'int32', indices),\n    ];\n}\nexport const uniqueConfig = {\n    kernelName: Unique,\n    backendName: 'cpu',\n    kernelFunc: unique,\n};\n//# sourceMappingURL=Unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// We explicitly import the modular kernels so they get registered in the\n// global registry when we compile the library. A modular build would replace\n// the contents of this file and import only the kernels that are needed.\nimport { registerKernel } from '@tensorflow/tfjs-core';\nimport { absConfig } from './kernels/Abs';\nimport { acosConfig } from './kernels/Acos';\nimport { acoshConfig } from './kernels/Acosh';\nimport { addConfig } from './kernels/Add';\nimport { asinConfig } from './kernels/Asin';\nimport { asinhConfig } from './kernels/Asinh';\nimport { atanConfig } from './kernels/Atan';\nimport { atanhConfig } from './kernels/Atanh';\nimport { avgPoolConfig } from './kernels/AvgPool';\nimport { avgPoolBackpropConfig } from './kernels/AvgPoolBackprop';\nimport { batchNormConfig } from './kernels/BatchNorm';\nimport { castConfig } from './kernels/Cast';\nimport { ceilConfig } from './kernels/Ceil';\nimport { clipConfig } from './kernels/Clip';\nimport { complexConfig } from './kernels/Complex';\nimport { concatConfig } from './kernels/Concat';\nimport { cosConfig } from './kernels/Cos';\nimport { coshConfig } from './kernels/Cosh';\nimport { dilation2dConfig } from './kernels/Dilation2D';\nimport { dilation2dBackpropFilterConfig } from './kernels/Dilation2DBackpropFilter';\nimport { dilation2dBackpropInputConfig } from './kernels/Dilation2DBackpropInput';\nimport { divConfig } from './kernels/Div';\nimport { eluConfig } from './kernels/Elu';\nimport { erfConfig } from './kernels/Erf';\nimport { expConfig } from './kernels/Exp';\nimport { expm1Config } from './kernels/Expm1';\nimport { fftConfig } from './kernels/FFT';\nimport { flipLeftRightConfig } from './kernels/FlipLeftRight';\nimport { floorConfig } from './kernels/Floor';\nimport { identityConfig } from './kernels/Identity';\nimport { ifftConfig } from './kernels/IFFT';\nimport { imagConfig } from './kernels/Imag';\nimport { isFiniteConfig } from './kernels/IsFinite';\nimport { isInfConfig } from './kernels/IsInf';\nimport { isNaNConfig } from './kernels/IsNaN';\nimport { logConfig } from './kernels/Log';\nimport { log1pConfig } from './kernels/Log1p';\nimport { logicalNotConfig } from './kernels/LogicalNot';\nimport { maxConfig } from './kernels/Max';\nimport { maxPoolConfig } from './kernels/MaxPool';\nimport { maxPoolBackpropConfig } from './kernels/MaxPoolBackprop';\nimport { maxPoolWithArgmaxConfig } from './kernels/MaxPoolWithArgmax';\nimport { multiplyConfig } from './kernels/Multiply';\nimport { nonMaxSuppressionV4Config } from './kernels/NonMaxSuppressionV4';\nimport { nonMaxSuppressionV5Config } from './kernels/NonMaxSuppressionV5';\nimport { notEqualConfig } from './kernels/NotEqual';\nimport { padV2Config } from './kernels/PadV2';\nimport { realConfig } from './kernels/Real';\nimport { reciprocalConfig } from './kernels/Reciprocal';\nimport { reshapeConfig } from './kernels/Reshape';\nimport { rotateWithOffsetConfig } from './kernels/RotateWithOffset';\nimport { roundConfig } from './kernels/Round';\nimport { rsqrtConfig } from './kernels/Rsqrt';\nimport { seluConfig } from './kernels/Selu';\nimport { sigmoidConfig } from './kernels/Sigmoid';\nimport { signConfig } from './kernels/Sign';\nimport { sinConfig } from './kernels/Sin';\nimport { sinhConfig } from './kernels/Sinh';\nimport { sliceConfig } from './kernels/Slice';\nimport { softplusConfig } from './kernels/Softplus';\nimport { spaceToBatchNDConfig } from './kernels/SpaceToBatchND';\nimport { sqrtConfig } from './kernels/Sqrt';\nimport { squareConfig } from './kernels/Square';\nimport { squaredDifferenceConfig } from './kernels/SquaredDifference';\nimport { stepConfig } from './kernels/Step';\nimport { subConfig } from './kernels/Sub';\nimport { tanConfig } from './kernels/Tan';\nimport { tanhConfig } from './kernels/Tanh';\nimport { transposeConfig } from './kernels/Transpose';\nimport { uniqueConfig } from './kernels/Unique';\n// List all kernel configs here\nconst kernelConfigs = [\n    absConfig,\n    acosConfig,\n    acoshConfig,\n    addConfig,\n    asinConfig,\n    asinhConfig,\n    atanConfig,\n    atanhConfig,\n    avgPoolConfig,\n    avgPoolBackpropConfig,\n    batchNormConfig,\n    castConfig,\n    ceilConfig,\n    clipConfig,\n    complexConfig,\n    concatConfig,\n    cosConfig,\n    coshConfig,\n    dilation2dConfig,\n    dilation2dBackpropInputConfig,\n    dilation2dBackpropFilterConfig,\n    divConfig,\n    eluConfig,\n    erfConfig,\n    expConfig,\n    expm1Config,\n    fftConfig,\n    flipLeftRightConfig,\n    floorConfig,\n    identityConfig,\n    ifftConfig,\n    imagConfig,\n    isFiniteConfig,\n    isInfConfig,\n    isNaNConfig,\n    logConfig,\n    log1pConfig,\n    logicalNotConfig,\n    maxPoolConfig,\n    maxPoolBackpropConfig,\n    maxPoolWithArgmaxConfig,\n    maxConfig,\n    multiplyConfig,\n    nonMaxSuppressionV4Config,\n    nonMaxSuppressionV5Config,\n    notEqualConfig,\n    padV2Config,\n    realConfig,\n    reciprocalConfig,\n    reshapeConfig,\n    rotateWithOffsetConfig,\n    roundConfig,\n    rsqrtConfig,\n    seluConfig,\n    sigmoidConfig,\n    signConfig,\n    sinConfig,\n    sinhConfig,\n    sliceConfig,\n    softplusConfig,\n    spaceToBatchNDConfig,\n    sqrtConfig,\n    squareConfig,\n    squaredDifferenceConfig,\n    stepConfig,\n    subConfig,\n    tanConfig,\n    tanhConfig,\n    transposeConfig,\n    uniqueConfig,\n];\nfor (const kernelConfig of kernelConfigs) {\n    registerKernel(kernelConfig);\n}\n//# sourceMappingURL=register_all_kernels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// All exports from this package should be in base.\nexport * from './base';\nimport './register_all_kernels';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst contexts = {};\nconst WEBGL_ATTRIBUTES = {\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: true\n};\nexport function setWebGLContext(webGLVersion, gl) {\n    contexts[webGLVersion] = gl;\n}\nexport function getWebGLContext(webGLVersion) {\n    if (!(webGLVersion in contexts)) {\n        const newCtx = getWebGLRenderingContext(webGLVersion);\n        if (newCtx !== null) {\n            contexts[webGLVersion] = newCtx;\n        }\n        else {\n            console.log('Could not get context for WebGL version', webGLVersion);\n            return null;\n        }\n    }\n    const gl = contexts[webGLVersion];\n    if (gl.isContextLost()) {\n        delete contexts[webGLVersion];\n        return getWebGLContext(webGLVersion);\n    }\n    gl.disable(gl.DEPTH_TEST);\n    gl.disable(gl.STENCIL_TEST);\n    gl.disable(gl.BLEND);\n    gl.disable(gl.DITHER);\n    gl.disable(gl.POLYGON_OFFSET_FILL);\n    gl.disable(gl.SAMPLE_COVERAGE);\n    gl.enable(gl.SCISSOR_TEST);\n    gl.enable(gl.CULL_FACE);\n    gl.cullFace(gl.BACK);\n    return contexts[webGLVersion];\n}\nfunction createCanvas(webGLVersion) {\n    if (typeof OffscreenCanvas !== 'undefined' && webGLVersion === 2) {\n        return new OffscreenCanvas(300, 150);\n    }\n    else if (typeof document !== 'undefined') {\n        return document.createElement('canvas');\n    }\n    else {\n        throw new Error('Cannot create a canvas in this context');\n    }\n}\nfunction getWebGLRenderingContext(webGLVersion) {\n    if (webGLVersion !== 1 && webGLVersion !== 2) {\n        throw new Error('Cannot get WebGL rendering context, WebGL is disabled.');\n    }\n    const canvas = createCanvas(webGLVersion);\n    canvas.addEventListener('webglcontextlost', (ev) => {\n        ev.preventDefault();\n        delete contexts[webGLVersion];\n    }, false);\n    if (webGLVersion === 1) {\n        return (canvas.getContext('webgl', WEBGL_ATTRIBUTES) ||\n            canvas.getContext('experimental-webgl', WEBGL_ATTRIBUTES));\n    }\n    return canvas.getContext('webgl2', WEBGL_ATTRIBUTES);\n}\n//# sourceMappingURL=canvas_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nexport var PackingScheme;\n(function (PackingScheme) {\n    /**\n     * All values in a single texel are densely packed without any constraints.\n     *\n     * This is how the shader encodes a tensor with shape = [2, 3, 4]\n     * (indices are [batch, row, col]).\n     *\n     * 000|001   010|011   020|021\n     * -------   -------   -------\n     * 002|003   012|013   022|023\n     *\n     * 100|101   110|111   120|121\n     * -------   -------   -------\n     * 102|103   112|113   122|123\n     *\n     */\n    PackingScheme[PackingScheme[\"DENSE\"] = 0] = \"DENSE\";\n    /**\n     * Single texels contain only values from the same batch, and from adjacent\n     * rows and columns.\n     *\n     * This is how the shader encodes a tensor with shape = [2, 3, 5]\n     * (indices are [batch, row, col]).\n     *\n     * 000|001   002|003   004|xxx   020|021   022|023   024|xxx\n     * -------   -------   -------   -------   -------   -------\n     * 010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n     *\n     * 100|101   102|103   104|xxx   120|121   122|123   124|xxx\n     * -------   -------   -------   -------   -------   -------\n     * 110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n     *\n     */\n    PackingScheme[PackingScheme[\"SHARED_BATCH\"] = 1] = \"SHARED_BATCH\";\n})(PackingScheme || (PackingScheme = {}));\nexport var TextureUsage;\n(function (TextureUsage) {\n    TextureUsage[TextureUsage[\"RENDER\"] = 0] = \"RENDER\";\n    TextureUsage[TextureUsage[\"UPLOAD\"] = 1] = \"UPLOAD\";\n    TextureUsage[TextureUsage[\"PIXELS\"] = 2] = \"PIXELS\";\n    TextureUsage[TextureUsage[\"DOWNLOAD\"] = 3] = \"DOWNLOAD\";\n})(TextureUsage || (TextureUsage = {}));\nexport var PhysicalTextureType;\n(function (PhysicalTextureType) {\n    PhysicalTextureType[PhysicalTextureType[\"UNPACKED_FLOAT16\"] = 0] = \"UNPACKED_FLOAT16\";\n    PhysicalTextureType[PhysicalTextureType[\"UNPACKED_FLOAT32\"] = 1] = \"UNPACKED_FLOAT32\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_4X1_UNSIGNED_BYTE\"] = 2] = \"PACKED_4X1_UNSIGNED_BYTE\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_2X2_FLOAT32\"] = 3] = \"PACKED_2X2_FLOAT32\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_2X2_FLOAT16\"] = 4] = \"PACKED_2X2_FLOAT16\";\n})(PhysicalTextureType || (PhysicalTextureType = {}));\nexport function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {\n    return [columns, rows];\n}\nexport function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {\n    return matrixSize * channelsPerTexture;\n}\nexport function getColorMatrixTextureShapeWidthHeight(rows, columns) {\n    return [columns * 4, rows];\n}\n/**\n * Get shape for densely packed RGBA texture.\n */\nexport function getDenseTexShape(shape) {\n    const size = util.sizeFromShape(shape);\n    const texelsNeeded = Math.ceil(size / 4);\n    return util.sizeToSquarishShape(texelsNeeded);\n}\nexport function getMatrixSizeFromUnpackedArraySize(unpackedSize, channelsPerTexture) {\n    if (unpackedSize % channelsPerTexture !== 0) {\n        throw new Error(`unpackedSize (${unpackedSize}) must be a multiple of ` +\n            `${channelsPerTexture}`);\n    }\n    return unpackedSize / channelsPerTexture;\n}\nexport function decodeMatrixFromUnpackedColorRGBAArray(unpackedArray, matrix, channels) {\n    const requiredSize = unpackedArray.length * channels / 4;\n    if (matrix.length < requiredSize) {\n        throw new Error(`matrix length (${matrix.length}) must be >= ${requiredSize}`);\n    }\n    let dst = 0;\n    for (let src = 0; src < unpackedArray.length; src += 4) {\n        for (let c = 0; c < channels; c++) {\n            matrix[dst++] = unpackedArray[src + c];\n        }\n    }\n}\nexport function getPackedMatrixTextureShapeWidthHeight(rows, columns) {\n    return [\n        Math.max(1, Math.ceil(columns / 2)), Math.max(1, Math.ceil(rows / 2))\n    ];\n}\nexport function getPackedRGBAArraySizeFromMatrixShape(rows, columns) {\n    const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return w * h * 4;\n}\nexport function getTextureConfig(\n// tslint:disable-next-line:no-any\ngl, textureHalfFloatExtension) {\n    // tslint:disable-next-line:no-any\n    const glany = gl;\n    let internalFormatFloat;\n    let internalFormatHalfFloat;\n    let internalFormatPackedHalfFloat;\n    let internalFormatPackedFloat;\n    let textureFormatFloat;\n    let downloadTextureFormat;\n    let downloadUnpackNumChannels;\n    let defaultNumChannels;\n    let textureTypeHalfFloat;\n    let textureTypeFloat;\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        internalFormatFloat = glany.R32F;\n        internalFormatHalfFloat = glany.R16F;\n        internalFormatPackedHalfFloat = glany.RGBA16F;\n        internalFormatPackedFloat = glany.RGBA32F;\n        textureFormatFloat = glany.RED;\n        downloadUnpackNumChannels = 4;\n        defaultNumChannels = 1;\n        textureTypeHalfFloat = glany.HALF_FLOAT;\n        textureTypeFloat = glany.FLOAT;\n    }\n    else {\n        internalFormatFloat = gl.RGBA;\n        internalFormatHalfFloat = gl.RGBA;\n        internalFormatPackedHalfFloat = gl.RGBA;\n        internalFormatPackedFloat = glany.RGBA;\n        textureFormatFloat = gl.RGBA;\n        downloadUnpackNumChannels = 4;\n        defaultNumChannels = 4;\n        textureTypeHalfFloat = textureHalfFloatExtension != null ?\n            textureHalfFloatExtension.HALF_FLOAT_OES :\n            null;\n        textureTypeFloat = gl.FLOAT;\n    }\n    downloadTextureFormat = gl.RGBA;\n    return {\n        internalFormatFloat,\n        internalFormatHalfFloat,\n        internalFormatPackedHalfFloat,\n        internalFormatPackedFloat,\n        textureFormatFloat,\n        downloadTextureFormat,\n        downloadUnpackNumChannels,\n        defaultNumChannels,\n        textureTypeHalfFloat,\n        textureTypeFloat\n    };\n}\n//# sourceMappingURL=tex_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { getWebGLContext } from './canvas_util';\nimport { getTextureConfig } from './tex_util';\nexport function callAndCheck(gl, func) {\n    const returnValue = func();\n    if (env().getBool('DEBUG')) {\n        checkWebGLError(gl);\n    }\n    return returnValue;\n}\nfunction checkWebGLError(gl) {\n    const error = gl.getError();\n    if (error !== gl.NO_ERROR) {\n        throw new Error('WebGL Error: ' + getWebGLErrorMessage(gl, error));\n    }\n}\n// https://en.wikipedia.org/wiki/Half-precision_floating-point_format\nconst MIN_FLOAT16 = 5.96e-8;\nconst MAX_FLOAT16 = 65504;\nexport function canBeRepresented(num) {\n    if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED') || num === 0 ||\n        (MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16)) {\n        return true;\n    }\n    return false;\n}\nexport function getWebGLErrorMessage(gl, status) {\n    switch (status) {\n        case gl.NO_ERROR:\n            return 'NO_ERROR';\n        case gl.INVALID_ENUM:\n            return 'INVALID_ENUM';\n        case gl.INVALID_VALUE:\n            return 'INVALID_VALUE';\n        case gl.INVALID_OPERATION:\n            return 'INVALID_OPERATION';\n        case gl.INVALID_FRAMEBUFFER_OPERATION:\n            return 'INVALID_FRAMEBUFFER_OPERATION';\n        case gl.OUT_OF_MEMORY:\n            return 'OUT_OF_MEMORY';\n        case gl.CONTEXT_LOST_WEBGL:\n            return 'CONTEXT_LOST_WEBGL';\n        default:\n            return `Unknown error code ${status}`;\n    }\n}\nexport function getExtensionOrThrow(gl, extensionName) {\n    return throwIfNull(gl, () => gl.getExtension(extensionName), 'Extension \"' + extensionName + '\" not supported on this browser.');\n}\nexport function createVertexShader(gl, vertexShaderSource) {\n    const vertexShader = throwIfNull(gl, () => gl.createShader(gl.VERTEX_SHADER), 'Unable to create vertex WebGLShader.');\n    callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));\n    callAndCheck(gl, () => gl.compileShader(vertexShader));\n    if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {\n        console.log(gl.getShaderInfoLog(vertexShader));\n        throw new Error('Failed to compile vertex shader.');\n    }\n    return vertexShader;\n}\nexport function createFragmentShader(gl, fragmentShaderSource) {\n    const fragmentShader = throwIfNull(gl, () => gl.createShader(gl.FRAGMENT_SHADER), 'Unable to create fragment WebGLShader.');\n    callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));\n    callAndCheck(gl, () => gl.compileShader(fragmentShader));\n    if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {\n        logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));\n        throw new Error('Failed to compile fragment shader.');\n    }\n    return fragmentShader;\n}\nconst lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;\nfunction logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {\n    const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);\n    if (lineNumberRegexResult == null) {\n        console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);\n        console.log(shaderSource);\n        return;\n    }\n    const lineNumber = +lineNumberRegexResult[1];\n    const shaderLines = shaderSource.split('\\n');\n    const pad = shaderLines.length.toString().length + 2;\n    const linesWithLineNumbers = shaderLines.map((line, lineNumber) => util.rightPad((lineNumber + 1).toString(), pad) + line);\n    let maxLineLength = 0;\n    for (let i = 0; i < linesWithLineNumbers.length; i++) {\n        maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);\n    }\n    const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);\n    const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);\n    const afterErrorLines = linesWithLineNumbers.slice(lineNumber);\n    console.log(beforeErrorLines.join('\\n'));\n    console.log(shaderInfoLog.split('\\n')[0]);\n    console.log(`%c ${util.rightPad(errorLine[0], maxLineLength)}`, 'border:1px solid red; background-color:#e3d2d2; color:#a61717');\n    console.log(afterErrorLines.join('\\n'));\n}\nexport function createProgram(gl) {\n    return throwIfNull(gl, () => gl.createProgram(), 'Unable to create WebGLProgram.');\n}\nexport function linkProgram(gl, program) {\n    callAndCheck(gl, () => gl.linkProgram(program));\n    if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {\n        console.log(gl.getProgramInfoLog(program));\n        throw new Error('Failed to link vertex and fragment shaders.');\n    }\n}\nexport function validateProgram(gl, program) {\n    callAndCheck(gl, () => gl.validateProgram(program));\n    if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {\n        console.log(gl.getProgramInfoLog(program));\n        throw new Error('Shader program validation failed.');\n    }\n}\nexport function createStaticVertexBuffer(gl, data) {\n    const buffer = throwIfNull(gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));\n    return buffer;\n}\nexport function createStaticIndexBuffer(gl, data) {\n    const buffer = throwIfNull(gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n    callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));\n    return buffer;\n}\nexport function getNumChannels() {\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        return 1;\n    }\n    return 4;\n}\nexport function createTexture(gl) {\n    return throwIfNull(gl, () => gl.createTexture(), 'Unable to create WebGLTexture.');\n}\nexport function validateTextureSize(width, height) {\n    const maxTextureSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n    if ((width <= 0) || (height <= 0)) {\n        const requested = `[${width}x${height}]`;\n        throw new Error('Requested texture size ' + requested + ' is invalid.');\n    }\n    if ((width > maxTextureSize) || (height > maxTextureSize)) {\n        const requested = `[${width}x${height}]`;\n        const max = `[${maxTextureSize}x${maxTextureSize}]`;\n        throw new Error('Requested texture size ' + requested +\n            ' greater than WebGL maximum on this browser / GPU ' + max + '.');\n    }\n}\nexport function createFramebuffer(gl) {\n    return throwIfNull(gl, () => gl.createFramebuffer(), 'Unable to create WebGLFramebuffer.');\n}\nexport function bindVertexBufferToProgramAttribute(gl, program, attribute, buffer, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {\n    const loc = gl.getAttribLocation(program, attribute);\n    if (loc === -1) {\n        // The GPU compiler decided to strip out this attribute because it's unused,\n        // thus no need to bind.\n        return false;\n    }\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes));\n    callAndCheck(gl, () => gl.enableVertexAttribArray(loc));\n    return true;\n}\nexport function bindTextureUnit(gl, texture, textureUnit) {\n    validateTextureUnit(gl, textureUnit);\n    callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n}\nexport function unbindTextureUnit(gl, textureUnit) {\n    validateTextureUnit(gl, textureUnit);\n    callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function getProgramUniformLocationOrThrow(gl, program, uniformName) {\n    return throwIfNull(gl, () => gl.getUniformLocation(program, uniformName), 'uniform \"' + uniformName + '\" not present in program.');\n}\nexport function getProgramUniformLocation(gl, program, uniformName) {\n    return gl.getUniformLocation(program, uniformName);\n}\nexport function bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) {\n    callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));\n    callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));\n}\nexport function bindCanvasToFramebuffer(gl) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n    callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));\n    callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));\n}\nexport function bindColorTextureToFramebuffer(gl, texture, framebuffer) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));\n}\nexport function unbindColorTextureFromFramebuffer(gl, framebuffer) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));\n}\nexport function validateFramebuffer(gl) {\n    const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);\n    if (status !== gl.FRAMEBUFFER_COMPLETE) {\n        throw new Error('Error binding framebuffer: ' + getFramebufferErrorMessage(gl, status));\n    }\n}\nexport function getFramebufferErrorMessage(gl, status) {\n    switch (status) {\n        case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:\n            return 'FRAMEBUFFER_INCOMPLETE_ATTACHMENT';\n        case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:\n            return 'FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT';\n        case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:\n            return 'FRAMEBUFFER_INCOMPLETE_DIMENSIONS';\n        case gl.FRAMEBUFFER_UNSUPPORTED:\n            return 'FRAMEBUFFER_UNSUPPORTED';\n        default:\n            return `unknown error ${status}`;\n    }\n}\nfunction throwIfNull(gl, returnTOrNull, failureMessage) {\n    const tOrNull = callAndCheck(gl, () => returnTOrNull());\n    if (tOrNull == null) {\n        throw new Error(failureMessage);\n    }\n    return tOrNull;\n}\nfunction validateTextureUnit(gl, textureUnit) {\n    const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;\n    const glTextureUnit = textureUnit + gl.TEXTURE0;\n    if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {\n        const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;\n        throw new Error(`textureUnit must be in ${textureUnitRange}.`);\n    }\n}\nexport function getBatchDim(shape, dimsToSkip = 2) {\n    return util.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));\n}\nexport function getRowsCols(shape) {\n    if (shape.length === 0) {\n        throw Error('Cannot get rows and columns of an empty shape array.');\n    }\n    return [\n        shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]\n    ];\n}\nexport function getShapeAs3D(shape) {\n    let shapeAs3D = [1, 1, 1];\n    const isScalar = shape.length === 0 || (shape.length === 1 && shape[0] === 1);\n    if (!isScalar) {\n        shapeAs3D =\n            [getBatchDim(shape), ...getRowsCols(shape)];\n    }\n    return shapeAs3D;\n}\nexport function getTextureShapeFromLogicalShape(logShape, isPacked = false) {\n    let maxTexSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n    if (isPacked) {\n        maxTexSize = maxTexSize * 2;\n        // This logic ensures we accurately count the number of packed texels needed\n        // to accommodate the tensor. We can only pack values in the same texel if\n        // they are from adjacent pairs of rows/cols within the same batch. So if a\n        // tensor has 3 rows, we pretend it has 4 rows in order to account for the\n        // fact that the texels containing the third row are half empty.\n        logShape = logShape.map((d, i) => i >= logShape.length - 2 ?\n            util.nearestLargerEven(logShape[i]) :\n            logShape[i]);\n        // Packed texture height is at least 2 (the channel height of a single\n        // texel).\n        if (logShape.length === 1) {\n            logShape = [2, logShape[0]];\n        }\n    }\n    // If logical shape is 2, we don't squeeze, since we want to match physical.\n    if (logShape.length !== 2) {\n        const squeezeResult = util.squeezeShape(logShape);\n        logShape = squeezeResult.newShape;\n    }\n    let size = util.sizeFromShape(logShape);\n    if (logShape.length <= 1 && size <= maxTexSize) {\n        return [1, size];\n    }\n    else if (logShape.length === 2 && logShape[0] <= maxTexSize &&\n        logShape[1] <= maxTexSize) {\n        return logShape;\n    }\n    else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize &&\n        logShape[2] <= maxTexSize) {\n        return [logShape[0] * logShape[1], logShape[2]];\n    }\n    else if (logShape.length === 3 && logShape[0] <= maxTexSize &&\n        logShape[1] * logShape[2] <= maxTexSize) {\n        return [logShape[0], logShape[1] * logShape[2]];\n    }\n    else if (logShape.length === 4 &&\n        logShape[0] * logShape[1] * logShape[2] <= maxTexSize &&\n        logShape[3] <= maxTexSize) {\n        return [logShape[0] * logShape[1] * logShape[2], logShape[3]];\n    }\n    else if (logShape.length === 4 && logShape[0] <= maxTexSize &&\n        logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {\n        return [logShape[0], logShape[1] * logShape[2] * logShape[3]];\n    }\n    else {\n        if (isPacked) {\n            // For packed textures size equals the number of channels required to\n            // accommodate the texture data. However in order to squarify such that\n            // inner dimensions stay even, we rewrite size to equal the number of\n            // texels. Then in the return statement we rehydrate the squarified\n            // dimensions to channel units.\n            const batchDim = getBatchDim(logShape);\n            let rows = 2, cols = 2;\n            if (logShape.length) {\n                [rows, cols] = getRowsCols(logShape);\n            }\n            size = batchDim * (rows / 2) * (cols / 2);\n            return util.sizeToSquarishShape(size).map(d => d * 2);\n        }\n        return util.sizeToSquarishShape(size);\n    }\n}\nfunction isEven(n) {\n    return n % 2 === 0;\n}\n/**\n * This determines whether reshaping a packed texture requires rearranging\n * the data within the texture, assuming 2x2 packing.\n */\nexport function isReshapeFree(shape1, shape2) {\n    shape1 = shape1.slice(-2);\n    shape2 = shape2.slice(-2);\n    if (util.arraysEqual(shape1, shape2)) {\n        return true;\n    }\n    if (!shape1.length || !shape2.length) { // One of the shapes is a scalar.\n        return true;\n    }\n    if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 ||\n        shape2[1] === 0) {\n        return true;\n    }\n    if (shape1.length !== shape2.length) { // One of the shapes is a vector.\n        const shape1Cols = shape1.slice(-1)[0];\n        const shape2Cols = shape2.slice(-1)[0];\n        if (shape1Cols === shape2Cols) {\n            return true;\n        }\n        if (isEven(shape1Cols) && isEven(shape2Cols) &&\n            (shape1[0] === 1 || shape2[0] === 1)) {\n            return true;\n        }\n    }\n    return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);\n}\n// We cache webgl params because the environment gets reset between\n// unit tests and we don't want to constantly query the WebGLContext for\n// MAX_TEXTURE_SIZE.\nlet MAX_TEXTURE_SIZE;\nlet MAX_TEXTURES_IN_SHADER;\nexport function getWebGLMaxTextureSize(webGLVersion) {\n    if (MAX_TEXTURE_SIZE == null) {\n        const gl = getWebGLContext(webGLVersion);\n        MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n    }\n    return MAX_TEXTURE_SIZE;\n}\nexport function resetMaxTextureSize() {\n    MAX_TEXTURE_SIZE = null;\n}\nexport function resetMaxTexturesInShader() {\n    MAX_TEXTURES_IN_SHADER = null;\n}\nexport function getMaxTexturesInShader(webGLVersion) {\n    if (MAX_TEXTURES_IN_SHADER == null) {\n        const gl = getWebGLContext(webGLVersion);\n        MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);\n    }\n    // We cap at 16 to avoid spurious runtime \"memory exhausted\" error.\n    return Math.min(16, MAX_TEXTURES_IN_SHADER);\n}\nexport function getWebGLDisjointQueryTimerVersion(webGLVersion) {\n    if (webGLVersion === 0) {\n        return 0;\n    }\n    let queryTimerVersion;\n    const gl = getWebGLContext(webGLVersion);\n    if (hasExtension(gl, 'EXT_disjoint_timer_query_webgl2') &&\n        webGLVersion === 2) {\n        queryTimerVersion = 2;\n    }\n    else if (hasExtension(gl, 'EXT_disjoint_timer_query')) {\n        queryTimerVersion = 1;\n    }\n    else {\n        queryTimerVersion = 0;\n    }\n    return queryTimerVersion;\n}\nexport function hasExtension(gl, extensionName) {\n    const ext = gl.getExtension(extensionName);\n    return ext != null;\n}\nexport function isWebGLVersionEnabled(webGLVersion) {\n    try {\n        const gl = getWebGLContext(webGLVersion);\n        if (gl != null) {\n            return true;\n        }\n    }\n    catch (e) {\n        console.log('Error when getting WebGL context: ', e);\n        return false;\n    }\n    return false;\n}\nexport function isCapableOfRenderingToFloatTexture(webGLVersion) {\n    if (webGLVersion === 0) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    if (webGLVersion === 1) {\n        if (!hasExtension(gl, 'OES_texture_float')) {\n            return false;\n        }\n    }\n    else {\n        if (!hasExtension(gl, 'EXT_color_buffer_float')) {\n            return false;\n        }\n    }\n    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n    return isFrameBufferComplete;\n}\n/**\n * Check if we can download values from a float/half-float texture.\n *\n * Note that for performance reasons we use binding a texture to a framebuffer\n * as a proxy for ability to download float values later using readPixels. The\n * texture params of this texture will not match those in readPixels exactly\n * but if we are unable to bind some kind of float texture to the frameBuffer\n * then we definitely will not be able to read float values from it.\n */\nexport function isDownloadFloatTextureEnabled(webGLVersion) {\n    if (webGLVersion === 0) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    if (webGLVersion === 1) {\n        if (!hasExtension(gl, 'OES_texture_float')) {\n            return false;\n        }\n        if (!hasExtension(gl, 'WEBGL_color_buffer_float')) {\n            return false;\n        }\n    }\n    else {\n        if (hasExtension(gl, 'EXT_color_buffer_float')) {\n            return createFloatTextureAndBindToFramebuffer(gl);\n        }\n        const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n        if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {\n            const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n            return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);\n        }\n        return false;\n    }\n    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n    return isFrameBufferComplete;\n}\nfunction createFloatTextureAndBindToFramebuffer(gl) {\n    const texConfig = getTextureConfig(gl);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    const width = 1;\n    const height = 1;\n    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);\n    const frameBuffer = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    gl.deleteTexture(texture);\n    gl.deleteFramebuffer(frameBuffer);\n    return isFrameBufferComplete;\n}\nfunction createHalfFloatTextureAndBindToFramebuffer(\n// tslint:disable-next-line:no-any\ngl, textureHalfFloatExtension) {\n    const texConfig = getTextureConfig(gl, textureHalfFloatExtension);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    const width = 1;\n    const height = 1;\n    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);\n    const frameBuffer = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    gl.deleteTexture(texture);\n    gl.deleteFramebuffer(frameBuffer);\n    return isFrameBufferComplete;\n}\nexport function isWebGLFenceEnabled(webGLVersion) {\n    if (webGLVersion !== 2) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    // tslint:disable-next-line:no-any\n    const isEnabled = gl.fenceSync != null;\n    return isEnabled;\n}\nexport function assertNotComplex(tensor, opName) {\n    if (!Array.isArray(tensor)) {\n        tensor = [tensor];\n    }\n    tensor.forEach(t => {\n        if (t != null) {\n            util.assert(t.dtype !== 'complex64', () => `${opName} does not support complex64 tensors ` +\n                'in the WebGL backend.');\n        }\n    });\n}\n//# sourceMappingURL=webgl_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { device_util, env } from '@tensorflow/tfjs-core';\nimport { getMaxTexturesInShader, getWebGLDisjointQueryTimerVersion, getWebGLMaxTextureSize, isCapableOfRenderingToFloatTexture, isDownloadFloatTextureEnabled, isWebGLFenceEnabled, isWebGLVersionEnabled } from './webgl_util';\nconst ENV = env();\n/**\n * This file contains WebGL-specific flag registrations.\n */\n/**\n * True if WebGL is supported.\n */\nENV.registerFlag('HAS_WEBGL', () => ENV.getNumber('WEBGL_VERSION') > 0);\n/** 0: No WebGL, 1: WebGL 1.0, 2: WebGL 2.0. */\nENV.registerFlag('WEBGL_VERSION', () => {\n    if (isWebGLVersionEnabled(2)) {\n        return 2;\n    }\n    else if (isWebGLVersionEnabled(1)) {\n        return 1;\n    }\n    return 0;\n});\n/** Whether to check for numerical representation problems. */\nENV.registerFlag('WEBGL_CHECK_NUMERICAL_PROBLEMS', () => false);\nENV.registerFlag('WEBGL_BUFFER_SUPPORTED', () => ENV.get('WEBGL_VERSION') === 2);\n/** Whether the WebGL backend will sometimes forward ops to the CPU. */\nENV.registerFlag('WEBGL_CPU_FORWARD', () => true);\n/** Whether the WebGL backend will always use f16 textures for rendering. */\nENV.registerFlag('WEBGL_FORCE_F16_TEXTURES', () => false);\n/** Whether to turn all packing related flags on. */\nENV.registerFlag('WEBGL_PACK', () => ENV.getBool('HAS_WEBGL'));\n/** Whether we will pack the batchnormalization op. */\nENV.registerFlag('WEBGL_PACK_NORMALIZATION', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack the clip op. */\nENV.registerFlag('WEBGL_PACK_CLIP', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack the depthwise conv op. */\n// TODO: https://github.com/tensorflow/tfjs/issues/1679\nENV.registerFlag('WEBGL_PACK_DEPTHWISECONV', () => false);\n/** Whether we will pack binary ops. */\nENV.registerFlag('WEBGL_PACK_BINARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack unary ops. */\nENV.registerFlag('WEBGL_PACK_UNARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack array ops. */\nENV.registerFlag('WEBGL_PACK_ARRAY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack image ops. */\nENV.registerFlag('WEBGL_PACK_IMAGE_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack reduce ops. */\nENV.registerFlag('WEBGL_PACK_REDUCE', () => ENV.getBool('WEBGL_PACK'));\n/** Whether packed WebGL kernels lazily unpack their outputs. */\nENV.registerFlag('WEBGL_LAZILY_UNPACK', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will use the im2col algorithm to speed up convolutions. */\nENV.registerFlag('WEBGL_CONV_IM2COL', () => ENV.getBool('WEBGL_PACK'));\n/** The maximum texture dimension. */\nENV.registerFlag('WEBGL_MAX_TEXTURE_SIZE', () => getWebGLMaxTextureSize(ENV.getNumber('WEBGL_VERSION')));\n/** The maximum texture dimension. */\nENV.registerFlag('WEBGL_MAX_TEXTURES_IN_SHADER', () => getMaxTexturesInShader(ENV.getNumber('WEBGL_VERSION')));\n/**\n * The disjoint_query_timer extension version.\n * 0: disabled, 1: EXT_disjoint_timer_query, 2:\n * EXT_disjoint_timer_query_webgl2.\n * In Firefox with WebGL 2.0,\n * EXT_disjoint_timer_query_webgl2 is not available, so we must use the\n * WebGL 1.0 extension.\n */\nENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', () => {\n    const webGLVersion = ENV.getNumber('WEBGL_VERSION');\n    if (webGLVersion === 0) {\n        return 0;\n    }\n    return getWebGLDisjointQueryTimerVersion(webGLVersion);\n});\n/**\n * Whether the timer object from the disjoint_query_timer extension gives\n * timing information that is reliable.\n */\nENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', () => ENV.getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&\n    !device_util.isMobile());\n/**\n * Whether the device is physically capable of rendering to float32 textures.\n */\nENV.registerFlag('WEBGL_RENDER_FLOAT32_CAPABLE', () => isCapableOfRenderingToFloatTexture(ENV.getNumber('WEBGL_VERSION')));\n/**\n * Whether rendering to float32 textures is enabled. If disabled, renders to\n * float16 textures.\n */\nENV.registerFlag('WEBGL_RENDER_FLOAT32_ENABLED', () => {\n    return ENV.getBool('WEBGL_FORCE_F16_TEXTURES') ?\n        false :\n        ENV.getBool('WEBGL_RENDER_FLOAT32_CAPABLE');\n});\n/**\n * Whether downloading float textures is enabled (16 or 32 bit). If disabled,\n * uses IEEE 754 encoding of the float32 values to 4 uint8 when downloading.\n */\nENV.registerFlag('WEBGL_DOWNLOAD_FLOAT_ENABLED', () => isDownloadFloatTextureEnabled(ENV.getNumber('WEBGL_VERSION')));\n/** Whether the fence API is available. */\nENV.registerFlag('WEBGL_FENCE_API_ENABLED', () => isWebGLFenceEnabled(ENV.getNumber('WEBGL_VERSION')));\n/**\n * Tensors with size <= than this will be uploaded as uniforms, not textures.\n */\nENV.registerFlag('WEBGL_SIZE_UPLOAD_UNIFORM', () => {\n    // Use uniform uploads only when 32bit floats are supported. In\n    // 16bit\n    // environments there are problems with comparing a 16bit texture value\n    // with a 32bit uniform value.\n    const useUniforms = ENV.getBool('WEBGL_RENDER_FLOAT32_ENABLED');\n    return useUniforms ? 4 : 0;\n});\n/**\n * If the total number of bytes allocated on the GPU is greater than this\n * number, we will aggressively delete textures upon disposal with\n * gl.deleteMatrixTexture, rather than making them available for reuse.\n *\n * Default value -1 indicates that we will never aggressively delete textures.\n */\nENV.registerFlag('WEBGL_DELETE_TEXTURE_THRESHOLD', () => {\n    return -1;\n}, threshold => {\n    if (threshold < 0 && threshold !== -1) {\n        throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never ` +\n            `delete) or at least 0, but got ${threshold}.`);\n    }\n});\n//# sourceMappingURL=flags_webgl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Import shared functionality from tfjs-backend-cpu without triggering\n// side effects.\n// tslint:disable-next-line: no-imports-from-dist\nimport * as shared from '@tensorflow/tfjs-backend-cpu/dist/shared';\nconst { simpleAbsImpl: simpleAbsImplCPU, addImpl: addImplCPU, ceilImpl: ceilImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, multiplyImpl: multiplyImplCPU, rsqrtImpl: rsqrtImplCPU, sliceImpl: sliceImplCPU, subImpl: subImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU, } = shared;\nexport { simpleAbsImplCPU, addImplCPU, ceilImplCPU, expImplCPU, expm1ImplCPU, logImplCPU, multiplyImplCPU, sliceImplCPU, subImplCPU, floorImplCPU, maxImplCPU, rsqrtImplCPU, transposeImplCPU, uniqueImplCPU, };\n//# sourceMappingURL=shared.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AddNProgram {\n    constructor(outputShape, shapes) {\n        this.outputShape = [];\n        this.outputShape = outputShape;\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const snippets = [];\n        // Get target elements from every input tensor.\n        this.variableNames.forEach(variable => {\n            snippets.push(`float v${variable} = get${variable}AtOutCoords();`);\n        });\n        // Calculate the sum of all elements.\n        const operation = this.variableNames\n            .map(variable => {\n            return `v${variable}`;\n        })\n            .join(' + ');\n        this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        float result = ${operation};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=addn_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AddNPackedProgram {\n    constructor(outputShape, shapes) {\n        this.outputShape = [];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const snippets = [];\n        // Get target elements from every input tensor.\n        this.variableNames.forEach(variable => {\n            snippets.push(`vec4 v${variable} = get${variable}AtOutCoords();`);\n        });\n        // Calculate the sum of all elements.\n        const operation = this.variableNames\n            .map(variable => {\n            return `v${variable}`;\n        })\n            .join(' + ');\n        this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        vec4 result = ${operation};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=addn_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ArgMinMaxProgram {\n    constructor(reduceInfo, op, firstPass) {\n        this.variableNames = ['A'];\n        const { windowSize, batchSize, outSize } = reduceInfo;\n        if (!firstPass) {\n            this.variableNames.push('bestIndicesA');\n        }\n        this.outputShape = [batchSize, outSize];\n        const compOp = (op === 'max') ? '>' : '<';\n        const indexSnippet = firstPass ?\n            'inOffset + i;' :\n            'round(getBestIndicesA(batch, inOffset + i));';\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          int inIdx = ${indexSnippet};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${compOp} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=argminmax_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport function getVecChannels(name, rank) {\n    return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank).map(d => `${name}.${d}`);\n}\nexport function getChannels(name, rank) {\n    if (rank === 1) {\n        return [name];\n    }\n    return getVecChannels(name, rank);\n}\nexport function getSourceCoords(rank, dims) {\n    if (rank === 1) {\n        return 'rc';\n    }\n    let coords = '';\n    for (let i = 0; i < rank; i++) {\n        coords += dims[i];\n        if (i < rank - 1) {\n            coords += ',';\n        }\n    }\n    return coords;\n}\n//# sourceMappingURL=packing_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nexport function getGlslDifferences() {\n    let version;\n    let attribute;\n    let varyingVs;\n    let varyingFs;\n    let texture2D;\n    let output;\n    let defineOutput;\n    let defineSpecialNaN;\n    let defineSpecialInf;\n    let defineRound;\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        version = '#version 300 es';\n        attribute = 'in';\n        varyingVs = 'out';\n        varyingFs = 'in';\n        texture2D = 'texture';\n        output = 'outputColor';\n        defineOutput = 'out vec4 outputColor;';\n        // Use custom isnan definition to work across differences between\n        // implementations on various platforms. While this should happen in ANGLE\n        // we still see differences between android and windows (on chrome) when\n        // using isnan directly.\n        defineSpecialNaN = `\n      bool isnan_custom(float val) {\n        return (val > 0.0 || val < 0.0) ? false : val != 0.0;\n      }\n\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan_custom(val.x),\n          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n      }\n\n      #define isnan(value) isnan_custom(value)\n    `;\n        // In webgl 2 we do not need to specify a custom isinf so there is no\n        // need for a special INFINITY constant.\n        defineSpecialInf = ``;\n        defineRound = `\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n    }\n    else {\n        version = '';\n        attribute = 'attribute';\n        varyingVs = 'varying';\n        varyingFs = 'varying';\n        texture2D = 'texture2D';\n        output = 'gl_FragColor';\n        defineOutput = '';\n        // WebGL1 has no built in isnan so we define one here.\n        defineSpecialNaN = `\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    `;\n        defineSpecialInf = `\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    `;\n        defineRound = `\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n    }\n    return {\n        version,\n        attribute,\n        varyingVs,\n        varyingFs,\n        texture2D,\n        output,\n        defineOutput,\n        defineSpecialNaN,\n        defineSpecialInf,\n        defineRound\n    };\n}\n//# sourceMappingURL=glsl_version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Produces GLSL code that derives logical coordinates from a flat\n * index. The code performs integer division with each stride and decrements\n * the index until the index equals the final dimension coordinate.\n */\nexport function getLogicalCoordinatesFromFlatIndex(coords, shape, index = 'index') {\n    const strides = util.computeStrides(shape);\n    return strides\n        .map((stride, i) => {\n        const line1 = `int ${coords[i]} = ${index} / ${stride}`;\n        const line2 = i === strides.length - 1 ?\n            `int ${coords[i + 1]} = ${index} - ${coords[i]} * ${stride}` :\n            `index -= ${coords[i]} * ${stride}`;\n        return `${line1}; ${line2};`;\n    })\n        .join('');\n}\nfunction buildVec(x) {\n    if (x.length === 1) {\n        return `${x[0]}`;\n    }\n    return `vec${x.length}(${x.join(',')})`;\n}\n/**\n * Produces GLSL code that computes the dot product of the input x and y\n * vectors. Handles splitting inputs into increments of vec4s when necessary.\n */\nexport function dotify(x, y) {\n    if (x.length !== y.length) {\n        throw new Error(`Vectors to be dotted must be of the same length -` +\n            `got ${x.length} and ${y.length}`);\n    }\n    const slices = [];\n    const nearestVec4 = Math.floor(x.length / 4);\n    const nearestVec4Remainder = x.length % 4;\n    for (let i = 0; i < nearestVec4; i++) {\n        const xSlice = x.slice(i * 4, i * 4 + 4);\n        const ySlice = y.slice(i * 4, i * 4 + 4);\n        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n    }\n    if (nearestVec4Remainder !== 0) {\n        let xSlice = x.slice(nearestVec4 * 4);\n        let ySlice = y.slice(nearestVec4 * 4);\n        if (xSlice.length === 1) {\n            xSlice = xSlice.map(d => `float(${d})`);\n            ySlice = ySlice.map(d => `float(${d})`);\n        }\n        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n    }\n    return slices.map((d, i) => `dot(${d})`).join('+');\n}\n/**\n * Produces GLSL that computes the flat index from 3D coordinates.\n */\nexport function getFlatIndexFrom3D(shape) {\n    const strides = util.computeStrides(shape).map(d => d.toString());\n    return `\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;\n  }\n`;\n}\nexport const ENCODE_FLOAT_SNIPPET = `\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`;\n//# sourceMappingURL=shader_compiler_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nconst { getBroadcastDims } = backend_util;\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nexport function makeShader(inputsInfo, outputShape, userCode, usesPackedTextures) {\n    const prefixSnippets = [];\n    inputsInfo.forEach(x => {\n        const size = util.sizeFromShape(x.shapeInfo.logicalShape);\n        // Snippet when we decided to upload the values as uniform.\n        if (x.shapeInfo.isUniform) {\n            prefixSnippets.push(`uniform float ${x.name}${size > 1 ? `[${size}]` : ''};`);\n        }\n        else {\n            prefixSnippets.push(`uniform sampler2D ${x.name};`);\n            prefixSnippets.push(`uniform int offset${x.name};`);\n        }\n    });\n    const inputPrefixSnippet = prefixSnippets.join('\\n');\n    const inputSamplingSnippet = inputsInfo\n        .map(x => getInputSamplingSnippet(x, outputShape, usesPackedTextures))\n        .join('\\n');\n    const outTexShape = outputShape.texShape;\n    const glsl = getGlslDifferences();\n    const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);\n    let outputSamplingSnippet;\n    let floatTextureSetOutputSnippet;\n    let shaderPrefix = getShaderPrefix(glsl);\n    if (outputShape.isPacked) {\n        outputSamplingSnippet =\n            getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n        floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);\n    }\n    else {\n        outputSamplingSnippet =\n            getOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n        floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);\n    }\n    if (usesPackedTextures) {\n        shaderPrefix += SHADER_PACKED_PREFIX;\n    }\n    const source = [\n        shaderPrefix, floatTextureSampleSnippet, floatTextureSetOutputSnippet,\n        inputPrefixSnippet, outputSamplingSnippet, inputSamplingSnippet, userCode\n    ].join('\\n');\n    return source;\n}\nfunction getSamplerFromInInfo(inInfo) {\n    const shape = inInfo.shapeInfo.logicalShape;\n    switch (shape.length) {\n        case 0:\n            return getSamplerScalar(inInfo);\n        case 1:\n            return getSampler1D(inInfo);\n        case 2:\n            return getSampler2D(inInfo);\n        case 3:\n            return getSampler3D(inInfo);\n        case 4:\n            return getSampler4D(inInfo);\n        case 5:\n            return getSampler5D(inInfo);\n        case 6:\n            return getSampler6D(inInfo);\n        default:\n            throw new Error(`${shape.length}-D input sampling` +\n                ` is not yet supported`);\n    }\n}\nfunction getPackedSamplerFromInInfo(inInfo) {\n    const shape = inInfo.shapeInfo.logicalShape;\n    switch (shape.length) {\n        case 0:\n            return getPackedSamplerScalar(inInfo);\n        case 1:\n            return getPackedSampler1D(inInfo);\n        case 2:\n            return getPackedSampler2D(inInfo);\n        case 3:\n            return getPackedSampler3D(inInfo);\n        default:\n            return getPackedSamplerND(inInfo);\n    }\n}\nfunction getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false) {\n    let res = '';\n    if (usesPackedTextures) {\n        res += getPackedSamplerFromInInfo(inInfo);\n    }\n    else {\n        res += getSamplerFromInInfo(inInfo);\n    }\n    const inShape = inInfo.shapeInfo.logicalShape;\n    const outShape = outShapeInfo.logicalShape;\n    if (inShape.length <= outShape.length) {\n        if (usesPackedTextures) {\n            res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);\n        }\n        else {\n            res += getSamplerAtOutputCoords(inInfo, outShapeInfo);\n        }\n    }\n    return res;\n}\nfunction getPackedOutputSamplingSnippet(outShape, outTexShape) {\n    switch (outShape.length) {\n        case 0:\n            return getOutputScalarCoords();\n        case 1:\n            return getOutputPacked1DCoords(outShape, outTexShape);\n        case 2:\n            return getOutputPacked2DCoords(outShape, outTexShape);\n        case 3:\n            return getOutputPacked3DCoords(outShape, outTexShape);\n        default:\n            return getOutputPackedNDCoords(outShape, outTexShape);\n    }\n}\nfunction getOutputSamplingSnippet(outShape, outTexShape) {\n    switch (outShape.length) {\n        case 0:\n            return getOutputScalarCoords();\n        case 1:\n            return getOutput1DCoords(outShape, outTexShape);\n        case 2:\n            return getOutput2DCoords(outShape, outTexShape);\n        case 3:\n            return getOutput3DCoords(outShape, outTexShape);\n        case 4:\n            return getOutput4DCoords(outShape, outTexShape);\n        case 5:\n            return getOutput5DCoords(outShape, outTexShape);\n        case 6:\n            return getOutput6DCoords(outShape, outTexShape);\n        default:\n            throw new Error(`${outShape.length}-D output sampling is not yet supported`);\n    }\n}\nfunction getFloatTextureSampleSnippet(glsl) {\n    return `\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${glsl.texture2D}(textureSampler, uv).r;\n    }\n  `;\n}\nfunction getFloatTextureSetRSnippet(glsl) {\n    return `\n    void setOutput(float val) {\n      ${glsl.output} = vec4(val, 0, 0, 0);\n    }\n  `;\n}\nfunction getFloatTextureSetRGBASnippet(glsl) {\n    return `\n    void setOutput(vec4 val) {\n      ${glsl.output} = val;\n    }\n  `;\n}\nfunction getShaderPrefix(glsl) {\n    const SHADER_PREFIX = `${glsl.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${glsl.varyingFs} vec2 resultUV;\n    ${glsl.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${glsl.defineSpecialNaN}\n    ${glsl.defineSpecialInf}\n    ${glsl.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${SAMPLE_1D_SNIPPET}\n    ${SAMPLE_2D_SNIPPET}\n    ${SAMPLE_3D_SNIPPET}\n  `;\n    return SHADER_PREFIX;\n}\nconst SAMPLE_1D_SNIPPET = `\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SAMPLE_2D_SNIPPET = `\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SAMPLE_3D_SNIPPET = `\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SHADER_PACKED_PREFIX = `\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;\nfunction getOutputScalarCoords() {\n    return `\n    int getOutputCoords() {\n      return 0;\n    }\n  `;\n}\nfunction getOutputPacked1DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (packedTexShape[0] === 1) {\n        return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);\n      }\n    `;\n    }\n    if (packedTexShape[1] === 1) {\n        return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);\n      }\n    `;\n    }\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);\n    }\n  `;\n}\nfunction getOutput1DCoords(shape, texShape) {\n    if (texShape[0] === 1) {\n        return `\n      int getOutputCoords() {\n        return int(resultUV.x * ${texShape[1]}.0);\n      }\n    `;\n    }\n    if (texShape[1] === 1) {\n        return `\n      int getOutputCoords() {\n        return int(resultUV.y * ${texShape[0]}.0);\n      }\n    `;\n    }\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      return resTexRC.x * ${texShape[1]} + resTexRC.y;\n    }\n  `;\n}\nfunction getOutputPacked3DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texelsInLogicalRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;\n}\nfunction getOutput3DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\nfunction getOutputPackedNDCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);\n    let texelsInBatchN = texelsInBatch;\n    let batches = ``;\n    let coords = 'b, r, c';\n    for (let b = 2; b < shape.length - 1; b++) {\n        texelsInBatchN *= shape[shape.length - b - 1];\n        batches = `\n      int b${b} = index / ${texelsInBatchN};\n      index -= b${b} * ${texelsInBatchN};\n    ` + batches;\n        coords = `b${b}, ` + coords;\n    }\n    return `\n    ivec${shape.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      ${batches}\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec${shape.length}(${coords});\n    }\n  `;\n}\nfunction getOutput4DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2'], shape);\n    return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec4(r, c, d, d2);\n    }\n  `;\n}\nfunction getOutput5DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3'], shape);\n    return `\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},\n                             ${texShape[1]}));\n\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `;\n}\nfunction getOutput6DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3', 'd4'], shape);\n    return `\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `;\n}\nfunction getOutputPacked2DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (util.arraysEqual(shape, texShape)) {\n        return `\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      }\n    `;\n    }\n    // texels needed to accommodate a logical row\n    const texelsInLogicalRow = Math.ceil(shape[1] / 2);\n    /**\n     * getOutputCoords\n     *\n     * resTexRC: The rows and columns of the texels. If you move over one\n     * texel to the right in the packed texture, you are moving over one column\n     * (not two).\n     *\n     * index: The texel index\n     */\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getOutput2DCoords(shape, texShape) {\n    if (util.arraysEqual(shape, texShape)) {\n        return `\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));\n      }\n    `;\n    }\n    if (shape[1] === 1) {\n        return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;\n    }\n    if (shape[0] === 1) {\n        return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;\n    }\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      int r = index / ${shape[1]};\n      int c = index - r * ${shape[1]};\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getFlatOffsetUniformName(texName) {\n    return `offset${texName}`;\n}\nfunction getPackedSamplerScalar(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}() {\n      return ${glsl.texture2D}(${texName}, halfCR);\n    }\n  `;\n}\nfunction getSamplerScalar(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    if (inputInfo.shapeInfo.isUniform) {\n        return `float ${funcName}() {return ${texName};}`;\n    }\n    const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;\n    if (texNumR === 1 && texNumC === 1) {\n        return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n    }\n    const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}() {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler1D(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${packedTexShape[0]}, ${packedTexShape[1]}, index);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler1D(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int index) {\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texShape = inputInfo.shapeInfo.texShape;\n    const tNumR = texShape[0];\n    const tNumC = texShape[1];\n    if (tNumC === 1 && tNumR === 1) {\n        return `\n      float ${funcName}(int index) {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    if (tNumC === 1) {\n        return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (tNumR === 1) {\n        return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n    float ${funcName}(int index) {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler2D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const glsl = getGlslDifferences();\n    if (texShape != null && util.arraysEqual(shape, texShape)) {\n        return `\n      vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n\n        return ${glsl.texture2D}(${texName}, uv);\n      }\n    `;\n    }\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const valuesPerRow = Math.ceil(shape[1] / 2);\n    return `\n    vec4 ${funcName}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler2D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    if (texShape != null && util.arraysEqual(shape, texShape)) {\n        const texNumR = texShape[0];\n        const texNumC = texShape[1];\n        return `\n    float ${funcName}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['row', 'col'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const offset = getFlatOffsetUniformName(texName);\n    if (texNumC === 1) {\n        // index is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    if (texNumR === 1) {\n        // index is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    return `\n  float ${funcName}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${shape[1]} + col + ${offset};\n    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n    return sampleTexture(${texName}, uv);\n  }\n`;\n}\nfunction getPackedSampler3D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (shape[0] === 1) {\n        const squeezedShape = shape.slice(1);\n        const keptDims = [1, 2];\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['b', 'row', 'col'];\n        return `\n        ${getPackedSamplerFromInInfo(newInputInfo)}\n        vec4 ${funcName}(int b, int row, int col) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n    }\n    const texNumR = packedTexShape[0];\n    const texNumC = packedTexShape[1];\n    const valuesPerRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler3D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride0 = shape[1] * shape[2];\n    const stride1 = shape[2];\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['row', 'col', 'depth'];\n        return `\n        ${getSamplerFromInInfo(newInputInfo)}\n        float ${funcName}(int row, int col, int depth) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${stride0}, ${stride1}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n        float ${funcName}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${texNumC}.0, ${texNumR}.0);\n          return sampleTexture(${texName}, uv);\n        }\n      `;\n    }\n    if (texNumC === stride1 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n      float ${funcName}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};\n        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n        return sampleTexture(${texName}, uv);\n      }\n  `;\n}\nfunction getPackedSamplerND(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const rank = shape.length;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texNumR = packedTexShape[0];\n    const texNumC = packedTexShape[1];\n    const valuesPerRow = Math.ceil(shape[rank - 1] / 2);\n    let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);\n    let params = `int b, int row, int col`;\n    let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;\n    for (let b = 2; b < rank - 1; b++) {\n        params = `int b${b}, ` + params;\n        texelsInBatch *= shape[rank - b - 1];\n        index = `b${b} * ${texelsInBatch} + ` + index;\n    }\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(${params}) {\n      int index = ${index};\n      int texR = index / ${texNumC};\n      int texC = index - texR * ${texNumC};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler4D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride2 = shape[3];\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${stride1}, ${stride2}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride2 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} +\n          depth * ${stride2} + depth2;\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler5D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride3 = shape[4];\n    const stride2 = shape[3] * stride3;\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2', 'depth3'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          depth3;\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${stride1}, ${stride2}, ${stride3}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride3 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3]},\n               ${shape[2] * shape[3]}, ${shape[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler6D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    const stride4 = shape[5];\n    const stride3 = shape[4] * stride4;\n    const stride2 = shape[3] * stride3;\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${stride4}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride4 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},\n               ${shape[2] * shape[3] * shape[4]},\n               ${shape[3] * shape[4]},\n               ${shape[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getUniformSampler(inputInfo) {\n    const texName = inputInfo.name;\n    const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n    if (inSize < 2) {\n        return `return ${texName};`;\n    }\n    return `\n    for (int i = 0; i < ${inSize}; i++) {\n      if (i == index) {\n        return ${texName}[i];\n      }\n    }\n  `;\n}\nfunction getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n    const texName = inputInfo.name;\n    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n    const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n    const inRank = inputInfo.shapeInfo.logicalShape.length;\n    const outRank = outShapeInfo.logicalShape.length;\n    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n    const type = getCoordsDataType(outRank);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet;\n    const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n    if (inRank === 0) {\n        coordsSnippet = '';\n    }\n    else if (outRank < 2 && broadcastDims.length >= 1) {\n        coordsSnippet = 'coords = 0;';\n    }\n    else {\n        coordsSnippet =\n            broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n                .join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n        unpackedCoordsSnippet = 'coords';\n    }\n    else {\n        unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n            .map((s, i) => `coords.${fields[i + rankDiff]}`)\n            .join(', ');\n    }\n    let output = `return outputValue;`;\n    const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n    const isInputScalar = inSize === 1;\n    const outSize = util.sizeFromShape(outShapeInfo.logicalShape);\n    const isOutputScalar = outSize === 1;\n    if (inRank === 1 && !isInputScalar && !isOutputScalar) {\n        output = `\n      return vec4(outputValue.xy, outputValue.xy);\n    `;\n    }\n    else if (isInputScalar && !isOutputScalar) {\n        if (outRank === 1) {\n            output = `\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `;\n        }\n        else {\n            output = `\n        return vec4(outputValue.x);\n      `;\n        }\n    }\n    else if (broadcastDims.length) {\n        const rows = inRank - 2;\n        const cols = inRank - 1;\n        if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {\n            output = `return vec4(outputValue.x);`;\n        }\n        else if (broadcastDims.indexOf(rows) > -1) {\n            output = `return vec4(outputValue.x, outputValue.y, ` +\n                `outputValue.x, outputValue.y);`;\n        }\n        else if (broadcastDims.indexOf(cols) > -1) {\n            output = `return vec4(outputValue.xx, outputValue.zz);`;\n        }\n    }\n    return `\n    vec4 ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});\n      ${output}\n    }\n  `;\n}\nfunction getSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n    const texName = inputInfo.name;\n    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n    const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n    const outTexShape = outShapeInfo.texShape;\n    const inTexShape = inputInfo.shapeInfo.texShape;\n    const inRank = inputInfo.shapeInfo.logicalShape.length;\n    const outRank = outShapeInfo.logicalShape.length;\n    if (!inputInfo.shapeInfo.isUniform && inRank === outRank &&\n        inputInfo.shapeInfo.flatOffset == null &&\n        util.arraysEqual(inTexShape, outTexShape)) {\n        return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, resultUV);\n      }\n    `;\n    }\n    const type = getCoordsDataType(outRank);\n    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet;\n    const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n    if (inRank === 0) {\n        coordsSnippet = '';\n    }\n    else if (outRank < 2 && broadcastDims.length >= 1) {\n        coordsSnippet = 'coords = 0;';\n    }\n    else {\n        coordsSnippet =\n            broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n                .join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n        unpackedCoordsSnippet = 'coords';\n    }\n    else {\n        unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n            .map((s, i) => `coords.${fields[i + rankDiff]}`)\n            .join(', ');\n    }\n    return `\n    float ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      return get${texFuncSnippet}(${unpackedCoordsSnippet});\n    }\n  `;\n}\nexport function getCoordsDataType(rank) {\n    if (rank <= 1) {\n        return 'int';\n    }\n    else if (rank === 2) {\n        return 'ivec2';\n    }\n    else if (rank === 3) {\n        return 'ivec3';\n    }\n    else if (rank === 4) {\n        return 'ivec4';\n    }\n    else if (rank === 5) {\n        return 'ivec5';\n    }\n    else if (rank === 6) {\n        return 'ivec6';\n    }\n    else {\n        throw Error(`GPU for rank ${rank} is not yet supported`);\n    }\n}\n/** Returns a new input info (a copy) that has a squeezed logical shape. */\nfunction squeezeInputInfo(inInfo, squeezedShape) {\n    // Deep copy.\n    const newInputInfo = JSON.parse(JSON.stringify(inInfo));\n    newInputInfo.shapeInfo.logicalShape = squeezedShape;\n    return newInputInfo;\n}\nfunction getSqueezedParams(params, keptDims) {\n    return keptDims.map(d => params[d]).join(', ');\n}\n//# sourceMappingURL=shader_compiler.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ArgMinMaxPackedProgram {\n    constructor(shape, windowSize, op, firstPass) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        util.assert(shape.length > 2, () => `Packed arg${op.charAt(0).toUpperCase() +\n            op.slice(1)} supports only inputs with rank above 2.`);\n        const inSize = shape[shape.length - 1];\n        const outSize = Math.ceil(inSize / windowSize);\n        this.outputShape = shape.slice(0, -1);\n        if (outSize > 1) {\n            this.outputShape.push(outSize);\n        }\n        if (!firstPass) {\n            this.variableNames.push('bestIndicesA');\n        }\n        const outShape = this.outputShape;\n        const rank = outShape.length;\n        const dtype = getCoordsDataType(rank);\n        const coords = getChannels('coords', rank);\n        let sourceLocSetup;\n        let sourceRank;\n        if (outSize === 1) {\n            sourceRank = rank + 1;\n            const sourceLocDType = getCoordsDataType(sourceRank);\n            sourceLocSetup = `\n        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 1]};\n        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 2]};\n        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 1]};\n        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 2]};`;\n        }\n        else {\n            sourceRank = rank;\n            sourceLocSetup = `\n        ${dtype} sourceLocR = coords;\n        ++${coords[rank - 1]};\n        ${dtype} sourceLocG = coords;\n        ++${coords[rank - 2]};\n        ${dtype} sourceLocA = coords;\n        --${coords[rank - 1]};\n        ${dtype} sourceLocB = coords;\n        --${coords[rank - 2]};`;\n        }\n        const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, sourceRank);\n        const inChannel = '.' + channels[sourceRank - 1]; // e.g. \".b\" for rank 3.\n        const intChannels = channels.map(x => 'int ' + x);\n        const srcRCoords = getChannels('sourceLocR', sourceRank - 1).concat('inIdx.r');\n        const srcGCoords = getChannels('sourceLocG', sourceRank - 1).concat('inIdx.g');\n        const srcBCoords = getChannels('sourceLocB', sourceRank - 1).concat('inIdx.b');\n        const srcACoords = getChannels('sourceLocA', sourceRank - 1).concat('inIdx.a');\n        const compOp = (op === 'max') ? 'greaterThan' : 'lessThan';\n        const fetchCandidateIdx = firstPass ? '' : `\n          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),\n                             getBestIndicesAChannel(${srcGCoords.join()}),\n                             getBestIndicesAChannel(${srcBCoords.join()}),\n                             getBestIndicesAChannel(${srcACoords.join()})));`;\n        const fetchValue = `vec4(\n            getAChannel(${srcRCoords.join()}),\n            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,\n            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;\n        const getBestIndicesAChannelSnippet = firstPass ? '' : `\n      float getBestIndicesAChannel(${intChannels.join()}) {\n        return getChannel(getBestIndicesA(${channels.join()}),\n                                          vec2(${channels.slice(-2).join()}));\n      }`;\n        this.userCode = `\n      float getAChannel(${intChannels.join()}) {\n        return getChannel(getA(${channels.join()}),\n                               vec2(${channels.slice(-2).join()}));\n      }\n      ${getBestIndicesAChannelSnippet}\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        bool hasNextCol = ${coords[rank - 1]} < ${outShape[rank - 1] - 1};\n        bool hasNextRow = ${coords[rank - 2]} < ${outShape[rank - 2] - 1};\n        ${sourceLocSetup}\n        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},\n          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${fetchValue};\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          inIdx = srcIdx;\n          ${fetchCandidateIdx}\n          vec4 candidate = ${fetchValue};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=argminmax_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AvgPool2DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const avgMultiplier = 1 / (filterHeight * filterWidth);\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth};\n            wC+= ${dilationWidth}) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class AvgPool3DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy'];\n        this.outputShape = convInfo.inShape;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=avg_pool_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\n// (Ar + Ai)(Br + Bi) =\n// ArBr + ArBi + AiBr + AiBi = ArBr - AB + ArBi + AiBr\n// Yr = ArBr - AB\n// Yi = ArBi + AiBr\nexport const COMPLEX_MULTIPLY = {\n    REAL: 'return areal * breal - aimag * bimag;',\n    IMAG: 'return areal * bimag + aimag * breal;'\n};\nexport class BinaryOpComplexProgram {\n    constructor(op, aShape, bShape) {\n        this.variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        this.userCode = `\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${op}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_complex_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nconst CHECK_NAN_SNIPPET = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nexport const ADD = 'return a + b;';\nexport const SUB = 'return a - b;';\nexport const MUL = 'return a * b;';\n// We use native integer division to deal with floating point imprecision. Since\n// we implement floor division and glsl implements truncated division, we\n// correct for this by subtracting 1 from result when the result is negative and\n// there is a remainder.\nexport const INT_DIV = `\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`;\nexport const POW = `\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nif (b == 0.0) {\n  return 1.0;\n}\nreturn (round(mod(b, 2.0)) != 1) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`;\nexport const SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport const EQUAL = `return float(a == b);`;\nexport const NOT_EQUAL = `return float(a != b);`;\nexport const LESS = `return float(a < b);`;\nexport const LESS_EQUAL = `return float(a <= b);`;\nexport const GREATER = `return float(a > b);`;\nexport const GREATER_EQUAL = `return float(a >= b);`;\nexport const LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;\nexport const LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;\nexport const MAX = CHECK_NAN_SNIPPET + `\n  return max(a, b);\n`;\nexport const MIN = CHECK_NAN_SNIPPET + `\n  return min(a, b);\n`;\nexport const MOD = `if (b == 0.0) return NAN;\n  return mod(a, b);`;\nexport const ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;\nexport const PRELU = `return (a < 0.) ? b * a : a;`;\nexport class BinaryOpProgram {\n    constructor(op, aShape, bShape) {\n        this.variableNames = ['A', 'B'];\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        this.userCode = `\n      float binaryOperation(float a, float b) {\n        ${op}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nconst CHECK_NAN_SNIPPET = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\nexport const INT_DIV = `\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n`;\nexport const POW = `\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const PRELU = `\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;\nexport const ELU_DER = `\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`;\nexport const EQUAL = `\n  return vec4(equal(a, b));\n`;\nexport const NOT_EQUAL = `\n  return vec4(notEqual(a, b));\n`;\nexport const LESS = `\n  return vec4(lessThan(a, b));\n`;\nexport const LESS_EQUAL = `\n  return vec4(lessThanEqual(a, b));\n`;\nexport const GREATER = `\n  return vec4(greaterThan(a, b));\n`;\nexport const GREATER_EQUAL = `\n  return vec4(greaterThanEqual(a, b));\n`;\nexport const LOGICAL_AND = `\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`;\nexport const LOGICAL_OR = `\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`;\nexport const MAX = `\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const MIN = `\n  vec4 result = vec4(min(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const MOD = `\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport class BinaryOpPackedProgram {\n    constructor(op, aShape, bShape, checkOutOfBounds = false) {\n        this.variableNames = ['A', 'B'];\n        this.supportsBroadcasting = true;\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const rank = this.outputShape.length;\n        let checkOutOfBoundsString = '';\n        if (checkOutOfBounds) {\n            if (rank === 0 || util.sizeFromShape(this.outputShape) === 1) {\n                checkOutOfBoundsString = `\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;\n            }\n            else {\n                const dtype = getCoordsDataType(rank);\n                checkOutOfBoundsString = `\n          ${dtype} coords = getOutputCoords();\n        `;\n                if (rank === 1) {\n                    checkOutOfBoundsString += `\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;\n                }\n                else {\n                    const channels = getChannels('coords', rank);\n                    checkOutOfBoundsString += `\n            bool nextRowOutOfBounds =\n              (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};\n            bool nextColOutOfBounds =\n              (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `;\n                }\n            }\n        }\n        this.userCode = `\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${op}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${checkOutOfBoundsString}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ClipProgram {\n    constructor(aShape) {\n        this.variableNames = ['A'];\n        this.outputShape = aShape;\n        this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    `;\n    }\n    getCustomSetupFunc(min, max) {\n        return (gpgpu, webGLProgram) => {\n            if (this.minLoc == null) {\n                this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n                this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n            }\n            gpgpu.gl.uniform1f(this.minLoc, min);\n            gpgpu.gl.uniform1f(this.maxLoc, max);\n        };\n    }\n}\n//# sourceMappingURL=clip_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ClipPackedProgram {\n    constructor(aShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = aShape;\n        this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    `;\n    }\n    getCustomSetupFunc(min, max) {\n        return (gpgpu, webGLProgram) => {\n            if (this.minLoc == null) {\n                this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n                this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n            }\n            gpgpu.gl.uniform1f(this.minLoc, min);\n            gpgpu.gl.uniform1f(this.maxLoc, max);\n        };\n    }\n}\n//# sourceMappingURL=clip_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ComplexAbsProgram {\n    constructor(shape) {\n        this.variableNames = ['real', 'imag'];\n        this.outputShape = shape;\n        this.userCode = `\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `;\n    }\n}\n//# sourceMappingURL=complex_abs_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class ConcatProgram {\n    // Concats 2d tensors along axis=1. See comments in MathBackendWebGL.concat().\n    constructor(shapes) {\n        this.outputShape = [];\n        this.outputShape = backend_util.computeOutShape(shapes, 1 /* axis */);\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const offsets = new Array(shapes.length - 1);\n        offsets[0] = shapes[0][1];\n        for (let i = 1; i < offsets.length; i++) {\n            offsets[i] = offsets[i - 1] + shapes[i][1];\n        }\n        const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];\n        for (let i = 1; i < offsets.length; i++) {\n            const shift = offsets[i - 1];\n            snippets.push(`else if (yC < ${offsets[i]}) ` +\n                `setOutput(getT${i}(yR, yC-${shift}));`);\n        }\n        const lastIndex = offsets.length;\n        const lastShift = offsets[offsets.length - 1];\n        snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${snippets.join('\\n        ')}\n      }\n    `;\n    }\n}\n//# sourceMappingURL=concat_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ConcatPackedProgram {\n    constructor(shapes, axis) {\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = [];\n        this.outputShape = backend_util.computeOutShape(shapes, axis);\n        const shape = this.outputShape;\n        const rank = shape.length;\n        const dtype = getCoordsDataType(rank);\n        const coords = getChannels('coords', rank);\n        const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank);\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const offsets = new Array(shapes.length - 1);\n        offsets[0] = shapes[0][axis];\n        for (let i = 1; i < offsets.length; i++) {\n            offsets[i] = offsets[i - 1] + shapes[i][axis];\n        }\n        const channel = channels[axis];\n        const lastChannels = channels.slice(-2);\n        const allChannels = channels.join();\n        let getValueSnippet = `if (${channel} < ${offsets[0]}) {\n        return getChannel(\n            getT0(${allChannels}), vec2(${lastChannels.join()}));\n        }`;\n        for (let i = 1; i < offsets.length; i++) {\n            const shift = offsets[i - 1];\n            // Note: the >= comparison below may seem unnecessary given the check\n            // above but is needed to workaround branch execution issues on some\n            // devices. It makes all the conditions exclusive without relying on\n            // execution order.\n            getValueSnippet += `\n        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i - 1]}) {\n          return getChannel(\n            getT${i}(${shiftedChannels(channels, channel, shift)}),\n            vec2(${shiftedChannels(lastChannels, channel, shift)}));\n        }`;\n        }\n        const lastIndex = offsets.length;\n        const shift = offsets[offsets.length - 1];\n        getValueSnippet += `\n        return getChannel(\n          getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),\n          vec2(${shiftedChannels(lastChannels, channel, shift)}));`;\n        this.userCode = `\n      float getValue(${channels.map(x => 'int ' + x)}) {\n        ${getValueSnippet}\n      }\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${coords}), 0., 0., 0.);\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} + 1;\n        if (${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.g = getValue(${coords});\n        }\n\n        ${coords[rank - 2]} = ${coords[rank - 2]} + 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]}) {\n          result.a = getValue(${coords});\n        }\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} - 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]} &&\n            ${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.b = getValue(${coords});\n        }\n        setOutput(result);\n      }\n    `;\n    }\n}\n/**\n * Return an expression for coordinates into a vector where a given channel\n * will be offset by [shift].\n *\n * @param channels the channels to consider\n * @param channel the channel we want shifted\n * @param shift  the amount to subtract from the channel.\n *\n * @returns a string of the form 'x, y-[shift], z' where any one channel can\n * have the shift applied.\n */\nfunction shiftedChannels(channels, channel, shift) {\n    const channelIdx = channels.indexOf(channel);\n    const res = channels.map((c, idx) => {\n        if (idx === channelIdx) {\n            return `${c} - ${shift}`;\n        }\n        else {\n            return c;\n        }\n    });\n    return res.join();\n}\n//# sourceMappingURL=concat_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Conv2DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              if (${isChannelsLast}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv2DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        const rowDim = isChannelsLast ? 1 : 2;\n        const colDim = isChannelsLast ? 2 : 3;\n        const channelDim = isChannelsLast ? 3 : 1;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${channelDim}];\n\n        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n\n              if (${isChannelsLast}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv3DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.userCode = `\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {\n            int xF = wF + yF * ${strideDepth} - ${padFront};\n\n            if (xF < 0 || xF >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n              int xR = wR + yR * ${strideHeight} - ${padTop};\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n                int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv3DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padFront = filterDepth - 1 - convInfo.padInfo.front;\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;\n\n          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${filterDepth} - 1 - wF;\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${filterHeight} - 1 - wR;\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${filterWidth} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthwiseConv2DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${channelMul} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class DepthwiseConv2DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${channelMul}; dm++) {\n              int d2 = d1 * ${channelMul} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_backprop_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Conv2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivationWeights = false) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n        const inputDepthVec4Remainder = convInfo.inChannels % 4;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const rowDim = isChannelsLast ? 1 : 2;\n        const colDim = isChannelsLast ? 2 : 3;\n        const channelDim = isChannelsLast ? 3 : 1;\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivationWeights) {\n                activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivationWeights) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${channelDim}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${convInfo.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${inputDepthVec4Remainder === 1}) {\n\n              if (${isChannelsLast}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              }\n\n            } else if (${inputDepthVec4Remainder === 2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${inputDepthVec4Remainder === 3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\nexport class Conv3DProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n        const inputDepthVec4Remainder = convInfo.inChannels % 4;\n        this.userCode = `\n      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          int xF = xFCorner + wF * ${dilationDepth};\n\n          if (xF < 0 || xF >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            int xR = xRCorner + wR * ${dilationHeight};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${inputDepthVec4Remainder === 1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);\n              } else if (${inputDepthVec4Remainder === 2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${inputDepthVec4Remainder === 3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthwiseConv2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const xNumRows = convInfo.inHeight;\n        const xNumCols = convInfo.inWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${xNumRows}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${xNumCols}) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport class DepthwiseConvPacked2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['x', 'W'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = convInfo.outShape;\n        const xNumRows = convInfo.inHeight;\n        const xNumCols = convInfo.inWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const texelsAcross = filterWidth;\n        let mainLoop = `int xR; int xC; int xCOffset;`;\n        for (let r = 0; r < filterHeight; r++) {\n            for (let c = 0; c < filterWidth; c++) {\n                mainLoop += `\n          vec4 xTexelR${r}C${c * 2} = vec4(0.);\n          vec4 wR${r}C${c} = vec4(0.);\n          vec4 xR${r}C${c} = vec4(0.);`;\n            }\n        }\n        /**\n         * This vectorized implementation works by gathering the values needed for\n         * each output channel's dot product into vec4's and then multiplying them\n         * all together (this happens in the final double for-loop below). Most of\n         * the main loop consists of constructing these vec4's with the minimum\n         * number of texture2D calls, which means making use of all four returned\n         * values from a texture2D call at once.\n         */\n        for (let r = 0; r < filterHeight; r++) {\n            for (let texelC = 0; texelC < texelsAcross; texelC++) {\n                const c = texelC * 2;\n                mainLoop += `\n          xR = xRCorner + ${r * dilationHeight};\n          xC = xCCorner + ${c * dilationWidth};\n        `;\n                if (strideWidth === 1) {\n                    if (c < filterWidth) {\n                        // If padding is odd, the outer texels have to be composed.\n                        if (padLeft % 2 === 1) {\n                            // TODO: Ensure vec4 previous does not result in redundant sample,\n                            // and avoid setting xTexelRC's that exceed the boundary in the\n                            // first place rather than resetting them to vec4(0)).\n                            // To compute xCOffset:\n                            // - If padding is odd, we must add 1 to ensure we ask for an\n                            // even-numbered row.\n                            // - We subtract 2 to access the previous texel.\n                            mainLoop += `\n                xCOffset = xC + 1;\n                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= ${xNumCols}) {\n                    xTexelR${r}C${c}.zw = vec2(0.);\n                  }\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xCOffset = xC + 1 - 2;\n                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  vec4 previous = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= ${xNumCols}) {\n                    previous.zw = vec2(0.);\n                  }\n\n                  xR${r}C${c} = vec4(previous.zw, xTexelR${r}C${c}.xy);\n                } else {\n                  xR${r}C${c} = vec4(0, 0, xTexelR${r}C${c}.xy);\n                }\n              `;\n                        }\n                        else {\n                            // Padding is even, so xRC corresponds to a single texel.\n                            mainLoop += `\n                if(xR >= 0 && xR < ${xNumRows} && xC >= 0 && xC < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xR${r}C${c} = xTexelR${r}C${c};\n              `;\n                        }\n                        if (c + 1 < filterWidth) {\n                            // If dilation is even, the second entry should match the first\n                            // (either both are composed or both are single samples). But if\n                            // dilation is odd, then the second entry should be the opposite\n                            // of the first (if the first is composed, the second is a single\n                            // sample, and vice versa.)\n                            const nextTexelOffset = padLeft % 2 === 0 ?\n                                util.nearestLargerEven(dilationWidth) :\n                                dilationWidth;\n                            if ((dilationWidth % 2 === 0 && padLeft % 2 === 1) ||\n                                (dilationWidth % 2 !== 0 && padLeft % 2 !== 1)) {\n                                mainLoop += `\n                  xCOffset = xC + ${padLeft % 2} + ${nextTexelOffset};\n\n                  if(xR >= 0 && xR < ${xNumRows} &&\n                    xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                  }\n                `;\n                                // If dilation > 1 then the xRC's will not be able to share any\n                                // values, so each xRC will require two unique calls to getX.\n                                if (dilationWidth > 1) {\n                                    mainLoop += `\n                    xCOffset -= 2;\n                    if(xR >= 0 && xR < ${xNumRows} &&\n                      xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                      xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n                    } else {\n                      xTexelR${r}C${c} = vec4(0.);\n                    }\n                  `;\n                                }\n                                mainLoop += `\n                  xR${r}C${c + 1} = vec4(\n                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.xy);\n                `;\n                            }\n                            else {\n                                mainLoop += `\n                  xCOffset = xC + ${nextTexelOffset};\n\n                  if(xR >= 0 && xR < ${xNumRows} &&\n                    xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                  }\n\n                  xR${r}C${c + 1} = xTexelR${r}C${c + 2};\n                `;\n                            }\n                        }\n                    }\n                }\n                else { // stride > 1\n                    if (c < filterWidth) {\n                        mainLoop += `\n              if(xR >= 0 && xR < ${xNumRows}) {\n            `;\n                        // Depending on whether padLeft is even or odd, we want either the\n                        // xy or zw channels from X texels for xR${r}C${c}. If padLeft is\n                        // even, xR${r}C${c + 1} is simply the zw channels of texels we've\n                        // already sampled. But if padLeft is odd, xR${r}C{$c + 1}.zw will\n                        // need to come from the xy channels of a new texel, hence the `vec4\n                        // final` initialized below.\n                        if (padLeft % 2 === 1) {\n                            mainLoop += `\n                xCOffset = xC + 1 - ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < ${xNumCols}) {\n                  xTexelR${r}C${c + 2} = getX(batch, xR, xC + 1, d1);\n                } else {\n                  xTexelR${r}C${c + 2} = vec4(0.);\n                }\n\n                xR${r}C${c} = vec4(\n                  xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n              `;\n                            if (c + 1 < filterWidth) {\n                                mainLoop += `\n                  vec4 final = vec4(0.);\n                  xCOffset = xC + 1 + ${strideWidth};\n                  if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xR${r}C${c + 1} = vec4(xTexelR${r}C${c + 2}.xy, final.xy);\n                `;\n                            }\n                        }\n                        else {\n                            mainLoop += `\n                if(xC >= 0 && xC < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xCOffset = xC + ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${r}C${c + 2} = vec4(0.);\n                }\n\n                xR${r}C${c} = vec4(\n                  xTexelR${r}C${c}.xy, xTexelR${r}C${c + 2}.xy);\n              `;\n                            if (c + 1 < filterWidth) {\n                                mainLoop += `\n                  xR${r}C${c + 1} = vec4(\n                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n                `;\n                            }\n                        }\n                        mainLoop += `}`;\n                    }\n                }\n                if (c < filterWidth) {\n                    mainLoop += `\n            vec4 wTexelR${r}C${c} = getW(${r}, ${c}, d1, q);\n            wR${r}C${c} = vec4(wTexelR${r}C${c}.xz, wTexelR${r}C${c}.xz);\n          `;\n                    if (c + 1 < filterWidth) {\n                        mainLoop += `\n              vec4 wTexelR${r}C${c + 1} = getW(${r}, ${c + 1}, d1, q);\n              wR${r}C${c + 1} =\n                vec4(wTexelR${r}C${c + 1}.xz, wTexelR${r}C${c + 1}.xz);`;\n                    }\n                }\n            }\n        }\n        for (let r = 0; r < filterHeight; r++) {\n            for (let c = 0; c < filterWidth; c++) {\n                mainLoop += `dotProd += xR${r}C${c} * wR${r}C${c};`;\n            }\n        }\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 dotProd = vec4(0.);\n\n        ${mainLoop}\n\n        vec4 result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_packed_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class CropAndResizeProgram {\n    constructor(imageShape, boxShape, cropSize, method, extrapolationValue) {\n        this.variableNames = ['Image', 'Boxes', 'BoxInd'];\n        this.outputShape = [];\n        const [batch, imageHeight, imageWidth, depth] = imageShape;\n        const [numBoxes,] = boxShape;\n        const [cropHeight, cropWidth] = cropSize;\n        this.outputShape = [numBoxes, cropHeight, cropWidth, depth];\n        const methodId = method === 'bilinear' ? 1 : 0;\n        const [inputHeightFloat, inputWidthFloat] = [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];\n        const [heightRatio, heightScale, inY] = cropHeight > 1 ?\n            [\n                `${(imageHeight - 1) / (cropHeight - 1)}`,\n                '(y2-y1) * height_ratio',\n                `y1*${inputHeightFloat} + float(y)*(height_scale)`,\n            ] :\n            [\n                '0.0',\n                '0.0',\n                `0.5 * (y1+y2) * ${inputHeightFloat}`,\n            ];\n        const [widthRatio, widthScale, inX] = cropWidth > 1 ?\n            [\n                `${(imageWidth - 1) / (cropWidth - 1)}`,\n                '(x2-x1) * width_ratio',\n                `x1*${inputWidthFloat} + float(x)*(width_scale)`,\n            ] :\n            [\n                '0.0',\n                '0.0',\n                `0.5 * (x1+x2) * ${inputWidthFloat}`,\n            ];\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc\n        this.userCode = `\n      const float height_ratio = float(${heightRatio});\n      const float width_ratio = float(${widthRatio});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${batch}) {\n          return;\n        }\n\n        float height_scale = ${heightScale};\n        float width_scale = ${widthScale};\n\n        float in_y = ${inY};\n        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n        float in_x = ${inX};\n        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${methodId} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=crop_and_resize_gpu.js.map", "import { getCoordsDataType } from './shader_compiler';\nexport class CumSumProgram {\n    constructor(shape, exclusive, reverse) {\n        this.variableNames = ['x'];\n        this.outputShape = shape;\n        const rank = shape.length;\n        const val = exclusive ? '0.0' : `getX(${getCoords(rank, 'coords')})`;\n        const length = shape[shape.length - 1];\n        let condition = '';\n        let idxString = '';\n        // When exclusive is set, the cumsum op becomes roll op that copies the\n        // value from the previous index based on the direction specified by the\n        // reverse flag.\n        if (exclusive) {\n            condition = reverse ? `end != ${length - 1}` : 'end != 0';\n            idxString = reverse ? 'end + 1' : 'end - 1';\n        }\n        else {\n            condition = reverse ? `end + pow2 < ${length}` : 'end >= pow2';\n            idxString = (reverse ? 'end + pow2' : 'end - pow2');\n        }\n        this.userCode = `\n      uniform float index;\n      void main() {\n        ${getCoordsDataType(rank)} coords = getOutputCoords();\n        int end = ${getFinalCoord(rank, 'coords')};\n        float val = ${val};\n        int pow2 = int(pow(2.0, index));\n        if (${condition}) {\n          int idx = ${idxString};\n          ${getFinalCoord(rank, 'coords')} = idx;\n          val += getX(${getCoords(rank, 'coords')});\n        }\n        setOutput(val);\n      }\n    `;\n    }\n    getCustomSetupFunc(index) {\n        return (gpgpu, webGLProgram) => {\n            if (this.index == null) {\n                this.index = gpgpu.getUniformLocation(webGLProgram, 'index');\n            }\n            gpgpu.gl.uniform1f(this.index, index);\n        };\n    }\n}\nfunction getCoords(rank, name) {\n    if (rank === 1) {\n        return `${name}`;\n    }\n    else if (rank === 2) {\n        return `${name}.x, ${name}.y`;\n    }\n    else if (rank === 3) {\n        return `${name}.x, ${name}.y, ${name}.z`;\n    }\n    else if (rank === 4) {\n        return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n    }\n    else {\n        throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n    }\n}\nfunction getFinalCoord(rank, name) {\n    if (rank === 1) {\n        return `${name}`;\n    }\n    else if (rank === 2) {\n        return `${name}.y`;\n    }\n    else if (rank === 3) {\n        return `${name}.z`;\n    }\n    else if (rank === 4) {\n        return `${name}.w`;\n    }\n    else {\n        throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n    }\n}\n//# sourceMappingURL=cumsum_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nimport { getDenseTexShape, PackingScheme } from './tex_util';\nexport class DecodeMatrixProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        this.outPackingScheme = PackingScheme.DENSE;\n        const texShape = getDenseTexShape(outputShape);\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=decode_matrix_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nimport { getDenseTexShape, PackingScheme } from './tex_util';\nexport class DecodeMatrixPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outPackingScheme = PackingScheme.DENSE;\n        const texShape = getDenseTexShape(outputShape);\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=decode_matrix_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthToSpaceProgram {\n    constructor(outputShape, blockSize, dataFormat) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        this.outputShape = outputShape;\n        this.blockSize = blockSize;\n        this.dataFormat = dataFormat;\n        this.userCode = `\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${blockSize};\n      int offset_h = imod(h, ${blockSize});\n      int in_w = w / ${blockSize};\n      int offset_w = imod(w, ${blockSize});\n      int offset_d = (offset_h * ${blockSize} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `;\n    }\n    getHeightCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[1]`;\n        }\n        else {\n            return `coords[2]`;\n        }\n    }\n    getWidthCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[2]`;\n        }\n        else {\n            return `coords[3]`;\n        }\n    }\n    getDepthCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[3]`;\n        }\n        else {\n            return `coords[1]`;\n        }\n    }\n    getOutputDepthSize() {\n        if (this.dataFormat === 'NHWC') {\n            return this.outputShape[3];\n        }\n        else {\n            return this.outputShape[1];\n        }\n    }\n    getInputSamplingString() {\n        if (this.dataFormat === 'NHWC') {\n            return `getX(b, in_h, in_w, in_d)`;\n        }\n        else {\n            return `getX(b, in_d, in_h, in_w)`;\n        }\n    }\n}\n//# sourceMappingURL=depth_to_space_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DiagProgram {\n    constructor(size) {\n        this.variableNames = ['X'];\n        this.outputShape = [size, size];\n        this.userCode = `\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=diag_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport { ENCODE_FLOAT_SNIPPET } from './shader_compiler_util';\nimport { TextureUsage } from './tex_util';\nexport class EncodeFloatProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.outTexUsage = TextureUsage.DOWNLOAD;\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_float_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport { ENCODE_FLOAT_SNIPPET } from './shader_compiler_util';\nimport { TextureUsage } from './tex_util';\nexport class EncodeFloatPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = false;\n        this.outTexUsage = TextureUsage.DOWNLOAD;\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_float_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nexport class EncodeMatrixProgram {\n    constructor(outputShape, texShape, inputIsUnsignedByte = false) {\n        this.variableNames = ['A'];\n        const glsl = getGlslDifferences();\n        const [height, width] = texShape;\n        this.outputShape = outputShape;\n        let output = `result`;\n        if (inputIsUnsignedByte) {\n            output = `floor(result * 255. + 0.5)`;\n        }\n        this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / ${width};\n        int c = imod(flatIndex, ${width});\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n        vec4 values = ${glsl.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${glsl.output} = vec4(${output}, 0., 0., 0.);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_matrix_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\n/*\nThis is how the shader encodes a tensor with shape = [2, 3, 5]\n(indices are [batch, row, col]).\n\n000|001   002|003   004|xxx   020|021   022|023   024|xxx\n-------   -------   -------   -------   -------   -------\n010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\n100|101   102|103   104|xxx   120|121   122|123   124|xxx\n-------   -------   -------   -------   -------   -------\n110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\nSingle texels contain only values from the same batch, and from adjacent rows\nand columns.\n */\nexport class EncodeMatrixPackedProgram {\n    constructor(outputShape, texShape, inputIsUnsignedByte = false) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        const glsl = getGlslDifferences();\n        const [height, width] = texShape;\n        this.outputShape = outputShape;\n        let mainLoop = '';\n        let output = 'result';\n        if (inputIsUnsignedByte) {\n            output = 'floor(result * 255. + 0.5)';\n        }\n        for (let row = 0; row <= 1; row++) {\n            for (let col = 0; col <= 1; col++) {\n                const channel = row * 2 + col;\n                mainLoop += `\n          localCoords = coords;\n          if(localCoords[2] + ${col} < ${outputShape[2]}) {\n            localCoords[2] += ${col};\n            if(localCoords[1] + ${row} < ${outputShape[1]}) {\n              localCoords[1] += ${row};\n\n              flatIndex = getFlatIndex(localCoords);\n              offset = imod(flatIndex, 4);\n\n              flatIndex = idiv(flatIndex, 4, 1.);\n\n              r = flatIndex / ${width};\n              c = imod(flatIndex, ${width});\n              uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n              values = ${glsl.texture2D}(A, uv);\n\n              if(offset == 0) {\n                result[${channel}] = values[0];\n              } else if(offset == 1) {\n                result[${channel}] = values[1];\n              } else if(offset == 2) {\n                result[${channel}] = values[2];\n              } else {\n                result[${channel}] = values[3];\n              }\n            }\n          }\n        `;\n            }\n        }\n        this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        vec4 result = vec4(0.);\n        int flatIndex, r, c, offset;\n        ivec3 localCoords;\n        vec2 uv;\n        vec4 values;\n\n        ${mainLoop}\n\n        ${glsl.output} = ${output};\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_matrix_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const COMPLEX_FFT = {\n    REAL: 'return real * expR - imag * expI;',\n    IMAG: 'return real * expI + imag * expR;'\n};\nexport class FFTProgram {\n    constructor(op, inputShape, inverse) {\n        this.variableNames = ['real', 'imag'];\n        const innerDim = inputShape[1];\n        this.outputShape = inputShape;\n        const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;\n        const resultDenominator = inverse ? `${innerDim}.0` : '1.0';\n        this.userCode = `\n      const float exponentMultiplier = ${exponentMultiplierSnippet};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${op}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${innerDim});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${innerDim}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=fft_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FillProgram {\n    constructor(shape, value) {\n        this.outputShape = [];\n        this.variableNames = ['x'];\n        this.outputShape = shape;\n        this.userCode = `\n      uniform float value;\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    `;\n    }\n    getCustomSetupFunc(value) {\n        return (gpgpu, webGLProgram) => {\n            if (this.valueLoc == null) {\n                this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'value');\n            }\n            gpgpu.gl.uniform1f(this.valueLoc, value);\n        };\n    }\n}\n//# sourceMappingURL=fill_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class GatherProgram {\n    constructor(aShape, indicesLength, axis) {\n        this.variableNames = ['A', 'indices'];\n        const outputShape = aShape.slice();\n        outputShape[axis] = indicesLength;\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const sourceCoords = getSourceCoords(aShape, axis);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n    }\n}\nfunction getSourceCoords(aShape, axis) {\n    const rank = aShape.length;\n    if (rank > 4) {\n        throw Error(`Gather for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n        return `int(getIndices(resRC))`;\n    }\n    const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n    const sourceCoords = [];\n    for (let i = 0; i < aShape.length; i++) {\n        if (i === axis) {\n            sourceCoords.push(`int(getIndices(${currentCoords[i]}))`);\n        }\n        else {\n            sourceCoords.push(`${currentCoords[i]}`);\n        }\n    }\n    return sourceCoords.join();\n}\n//# sourceMappingURL=gather_gpu.js.map", "import { getCoordsDataType } from './shader_compiler';\nexport class GatherNDProgram {\n    constructor(sliceDim, strides, shape) {\n        this.sliceDim = sliceDim;\n        this.strides = strides;\n        this.variableNames = ['x', 'indices'];\n        this.outputShape = shape;\n        const stridesType = getCoordsDataType(strides.length);\n        const dtype = getCoordsDataType(shape.length);\n        const strideString = this.sliceDim > 1 ? 'strides[j]' : 'strides';\n        this.userCode = `\n        ${stridesType} strides = ${stridesType}(${this.strides});\n         void main() {\n          ${dtype} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${strideString};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `;\n    }\n}\n//# sourceMappingURL=gather_nd_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\nexport function createVertexShader(gl) {\n    const glsl = getGlslDifferences();\n    const vertexShaderSource = `${glsl.version}\n    precision highp float;\n    ${glsl.attribute} vec3 clipSpacePos;\n    ${glsl.attribute} vec2 uv;\n    ${glsl.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;\n    return webgl_util.createVertexShader(gl, vertexShaderSource);\n}\nexport function createVertexBuffer(gl) {\n    // [x y z u v] * [upper-left, lower-left, upper-right, lower-right]\n    const vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);\n    return webgl_util.createStaticVertexBuffer(gl, vertexArray);\n}\nexport function createIndexBuffer(gl) {\n    // OpenGL (and WebGL) have \"CCW == front\" winding\n    const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);\n    return webgl_util.createStaticIndexBuffer(gl, triangleVertexIndices);\n}\nfunction createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {\n    webgl_util.validateTextureSize(width, height);\n    const texture = webgl_util.createTexture(gl);\n    const tex2d = gl.TEXTURE_2D;\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(tex2d, texture));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));\n    webgl_util.callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null));\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n    return texture;\n}\nexport function getInternalFormatForFloat32MatrixTexture(textureConfig) {\n    return textureConfig.internalFormatFloat;\n}\nexport function createFloat32MatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT);\n}\nexport function getInternalFormatForFloat16MatrixTexture(textureConfig) {\n    return textureConfig.internalFormatHalfFloat;\n}\nexport function createFloat16MatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);\n}\nexport function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) {\n    return textureConfig.downloadTextureFormat;\n}\nexport function createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE);\n}\nexport function getInternalFormatForPackedMatrixTexture(textureConfig) {\n    return textureConfig.internalFormatPackedFloat;\n}\nexport function createPackedMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT);\n}\nexport function getInternalFormatForFloat16PackedMatrixTexture(textureConfig) {\n    return textureConfig.internalFormatPackedHalfFloat;\n}\nexport function createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat);\n}\nexport function bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {\n    const posOffset = 0; // x is the first buffer element\n    const uvOffset = 3 * 4; // uv comes after [x y z]\n    const stride = (3 * 4) + (2 * 4); // xyz + uv, each entry is 4-byte float.\n    webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));\n    const success = webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'clipSpacePos', vertexBuffer, 3, stride, posOffset);\n    return success &&\n        webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'uv', vertexBuffer, 2, stride, uvOffset);\n}\nexport function uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) {\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n    let dataForUpload, texelDataType, internalFormat;\n    if (data instanceof Uint8Array) {\n        dataForUpload = new Uint8Array(width * height * 4);\n        texelDataType = gl.UNSIGNED_BYTE;\n        internalFormat = gl.RGBA;\n    }\n    else {\n        dataForUpload = new Float32Array(width * height * 4);\n        texelDataType = gl.FLOAT;\n        internalFormat = textureConfig.internalFormatPackedFloat;\n    }\n    dataForUpload.set(data);\n    webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload));\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function uploadPixelDataToTexture(gl, texture, pixels) {\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n    if (pixels.data instanceof Uint8Array) {\n        webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));\n    }\n    else {\n        webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n    }\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function createBufferFromOutputTexture(gl2, rows, columns, textureConfig) {\n    // Create and bind the buffer.\n    const buffer = gl2.createBuffer();\n    webgl_util.callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer));\n    // Initialize the buffer to the size of the texture in bytes.\n    const bytesPerFloat = 4;\n    const valuesPerTexel = 4;\n    const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;\n    webgl_util.callAndCheck(gl2, () => gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));\n    // Enqueue a command on the GPU command queue to copy of texture into the\n    // buffer.\n    webgl_util.callAndCheck(gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));\n    webgl_util.callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));\n    return buffer;\n}\nexport function downloadFloat32MatrixFromBuffer(gl, buffer, size) {\n    const gl2 = gl;\n    const downloadTarget = new Float32Array(size);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n    return downloadTarget;\n}\nexport function downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {\n    const [w, h] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    const numChannels = 4;\n    const downloadTarget = new Uint8Array(tex_util.getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));\n    webgl_util.callAndCheck(gl, () => gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget));\n    // By wrapping the buffer in a Float32Array, we use native browser IEEE 754\n    // decoding of the 4 bytes that back each 32 bit float.\n    return new Float32Array(downloadTarget.buffer);\n}\nexport function downloadPackedMatrixFromBuffer(gl, buffer, batch, rows, cols, physicalRows, physicalCols, textureConfig) {\n    const gl2 = gl;\n    const downloadTarget = new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n    return downloadTarget;\n}\nexport function downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {\n    const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);\n    webgl_util.callAndCheck(gl, () => gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));\n    return packedRGBA;\n}\n//# sourceMappingURL=gpgpu_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { getWebGLContext, setWebGLContext } from './canvas_util';\nimport * as gpgpu_util from './gpgpu_util';\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\nexport class GPGPUContext {\n    constructor(gl) {\n        this.outputTexture = null;\n        this.program = null;\n        this.disposed = false;\n        this.vertexAttrsAreBound = false;\n        this.itemsToPoll = [];\n        const glVersion = env().getNumber('WEBGL_VERSION');\n        if (gl != null) {\n            this.gl = gl;\n            setWebGLContext(glVersion, gl);\n        }\n        else {\n            this.gl = getWebGLContext(glVersion);\n        }\n        // WebGL 2.0 enables texture floats without an extension.\n        let COLOR_BUFFER_FLOAT = 'WEBGL_color_buffer_float';\n        const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n        if (env().getNumber('WEBGL_VERSION') === 1) {\n            const TEXTURE_FLOAT = 'OES_texture_float';\n            const TEXTURE_HALF_FLOAT = 'OES_texture_half_float';\n            this.textureFloatExtension =\n                webgl_util.getExtensionOrThrow(this.gl, TEXTURE_FLOAT);\n            if (webgl_util.hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {\n                this.textureHalfFloatExtension =\n                    webgl_util.getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);\n            }\n            else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n                throw new Error('GL context does not support half float textures, yet the ' +\n                    'environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n            }\n            this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);\n            if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n                this.colorBufferHalfFloatExtension =\n                    webgl_util.getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);\n            }\n            else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n                throw new Error('GL context does not support color renderable half floats, yet ' +\n                    'the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n            }\n        }\n        else {\n            COLOR_BUFFER_FLOAT = 'EXT_color_buffer_float';\n            if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {\n                this.colorBufferFloatExtension =\n                    this.gl.getExtension(COLOR_BUFFER_FLOAT);\n            }\n            else if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n                this.colorBufferHalfFloatExtension =\n                    this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n            }\n            else {\n                throw new Error('GL context does not support color renderable floats');\n            }\n        }\n        this.vertexBuffer = gpgpu_util.createVertexBuffer(this.gl);\n        this.indexBuffer = gpgpu_util.createIndexBuffer(this.gl);\n        this.framebuffer = webgl_util.createFramebuffer(this.gl);\n        this.textureConfig =\n            tex_util.getTextureConfig(this.gl, this.textureHalfFloatExtension);\n    }\n    get debug() {\n        return env().getBool('DEBUG');\n    }\n    dispose() {\n        if (this.disposed) {\n            return;\n        }\n        if (this.program != null) {\n            console.warn('Disposing a GPGPUContext that still has a bound WebGLProgram.' +\n                ' This is probably a resource leak, delete the program with ' +\n                'GPGPUContext.deleteProgram before disposing.');\n        }\n        if (this.outputTexture != null) {\n            console.warn('Disposing a GPGPUContext that still has a bound output matrix ' +\n                'texture.  This is probably a resource leak, delete the output ' +\n                'matrix texture with GPGPUContext.deleteMatrixTexture before ' +\n                'disposing.');\n        }\n        const gl = this.gl;\n        webgl_util.callAndCheck(gl, () => gl.finish());\n        webgl_util.callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));\n        webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));\n        this.disposed = true;\n    }\n    createFloat32MatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createFloat16MatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createUnsignedBytesMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    uploadPixelDataToTexture(texture, pixels) {\n        this.throwIfDisposed();\n        gpgpu_util.uploadPixelDataToTexture(this.gl, texture, pixels);\n    }\n    uploadDenseMatrixToTexture(texture, width, height, data) {\n        this.throwIfDisposed();\n        gpgpu_util.uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig);\n    }\n    createFloat16PackedMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createPackedMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    deleteMatrixTexture(texture) {\n        this.throwIfDisposed();\n        if (this.outputTexture === texture) {\n            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n            this.outputTexture = null;\n        }\n        webgl_util.callAndCheck(this.gl, () => this.gl.deleteTexture(texture));\n    }\n    downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) {\n        return this.downloadMatrixDriver(texture, () => gpgpu_util.downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig));\n    }\n    downloadPackedMatrixFromBuffer(buffer, batch, rows, columns, physicalRows, physicalCols) {\n        return gpgpu_util.downloadPackedMatrixFromBuffer(this.gl, buffer, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);\n    }\n    downloadFloat32MatrixFromBuffer(buffer, size) {\n        return gpgpu_util.downloadFloat32MatrixFromBuffer(this.gl, buffer, size);\n    }\n    createBufferFromTexture(texture, rows, columns) {\n        this.bindTextureToFrameBuffer(texture);\n        const result = gpgpu_util.createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig);\n        this.unbindTextureToFrameBuffer();\n        return result;\n    }\n    createAndWaitForFence() {\n        const fenceContext = this.createFence(this.gl);\n        return this.pollFence(fenceContext);\n    }\n    createFence(gl) {\n        let query;\n        let isFencePassed;\n        if (env().getBool('WEBGL_FENCE_API_ENABLED')) {\n            const gl2 = gl;\n            const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);\n            gl.flush();\n            isFencePassed = () => {\n                const status = gl2.clientWaitSync(sync, 0, 0);\n                return status === gl2.ALREADY_SIGNALED ||\n                    status === gl2.CONDITION_SATISFIED;\n            };\n            query = sync;\n        }\n        else if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n            query = this.beginQuery();\n            this.endQuery();\n            isFencePassed = () => this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n        }\n        else {\n            // If we have no way to fence, return true immediately. This will fire in\n            // WebGL 1.0 when there is no disjoint query timer. In this case, because\n            // the fence passes immediately, we'll immediately ask for a download of\n            // the texture, which will cause the UI thread to hang.\n            isFencePassed = () => true;\n        }\n        return { query, isFencePassed };\n    }\n    downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {\n        return this.downloadMatrixDriver(texture, () => gpgpu_util.downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));\n    }\n    createProgram(fragmentShaderSource) {\n        this.throwIfDisposed();\n        const gl = this.gl;\n        const fragmentShader = webgl_util.createFragmentShader(gl, fragmentShaderSource);\n        const vertexShader = gpgpu_util.createVertexShader(gl);\n        const program = webgl_util.createProgram(gl);\n        webgl_util.callAndCheck(gl, () => gl.attachShader(program, vertexShader));\n        webgl_util.callAndCheck(gl, () => gl.attachShader(program, fragmentShader));\n        webgl_util.linkProgram(gl, program);\n        if (this.debug) {\n            webgl_util.validateProgram(gl, program);\n        }\n        if (!this.vertexAttrsAreBound) {\n            this.setProgram(program);\n            this.vertexAttrsAreBound = gpgpu_util.bindVertexProgramAttributeStreams(gl, this.program, this.vertexBuffer);\n        }\n        return program;\n    }\n    deleteProgram(program) {\n        this.throwIfDisposed();\n        if (program === this.program) {\n            this.program = null;\n        }\n        if (program != null) {\n            webgl_util.callAndCheck(this.gl, () => this.gl.deleteProgram(program));\n        }\n    }\n    setProgram(program) {\n        this.throwIfDisposed();\n        this.program = program;\n        if ((this.program != null) && this.debug) {\n            webgl_util.validateProgram(this.gl, this.program);\n        }\n        webgl_util.callAndCheck(this.gl, () => this.gl.useProgram(program));\n    }\n    getUniformLocation(program, uniformName, shouldThrow = true) {\n        this.throwIfDisposed();\n        if (shouldThrow) {\n            return webgl_util.getProgramUniformLocationOrThrow(this.gl, program, uniformName);\n        }\n        else {\n            return webgl_util.getProgramUniformLocation(this.gl, program, uniformName);\n        }\n    }\n    getAttributeLocation(program, attribute) {\n        this.throwIfDisposed();\n        return webgl_util.callAndCheck(this.gl, () => this.gl.getAttribLocation(program, attribute));\n    }\n    getUniformLocationNoThrow(program, uniformName) {\n        this.throwIfDisposed();\n        return this.gl.getUniformLocation(program, uniformName);\n    }\n    setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) {\n        this.throwIfDisposed();\n        this.throwIfNoProgram();\n        webgl_util.bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit);\n    }\n    setOutputMatrixTexture(outputMatrixTexture, rows, columns) {\n        this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);\n    }\n    setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) {\n        this.throwIfDisposed();\n        const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n        this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);\n    }\n    setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n        this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);\n    }\n    setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n        throw new Error('setOutputPackedMatrixWriteRegion not implemented.');\n    }\n    debugValidate() {\n        if (this.program != null) {\n            webgl_util.validateProgram(this.gl, this.program);\n        }\n        webgl_util.validateFramebuffer(this.gl);\n    }\n    executeProgram() {\n        this.throwIfDisposed();\n        this.throwIfNoProgram();\n        const gl = this.gl;\n        if (this.debug) {\n            this.debugValidate();\n        }\n        webgl_util.callAndCheck(gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));\n    }\n    blockUntilAllProgramsCompleted() {\n        this.throwIfDisposed();\n        webgl_util.callAndCheck(this.gl, () => this.gl.finish());\n    }\n    getQueryTimerExtension() {\n        if (this.disjointQueryTimerExtension == null) {\n            this.disjointQueryTimerExtension =\n                webgl_util.getExtensionOrThrow(this.gl, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2 ?\n                    'EXT_disjoint_timer_query_webgl2' :\n                    'EXT_disjoint_timer_query');\n        }\n        return this.disjointQueryTimerExtension;\n    }\n    getQueryTimerExtensionWebGL2() {\n        return this.getQueryTimerExtension();\n    }\n    getQueryTimerExtensionWebGL1() {\n        return this.getQueryTimerExtension();\n    }\n    beginQuery() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            const query = gl2.createQuery();\n            gl2.beginQuery(ext.TIME_ELAPSED_EXT, query);\n            return query;\n        }\n        const ext = this.getQueryTimerExtensionWebGL1();\n        const query = ext.createQueryEXT();\n        ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);\n        return query;\n    }\n    endQuery() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            gl2.endQuery(ext.TIME_ELAPSED_EXT);\n            return;\n        }\n        const ext = this.getQueryTimerExtensionWebGL1();\n        ext.endQueryEXT(ext.TIME_ELAPSED_EXT);\n    }\n    async waitForQueryAndGetTime(query) {\n        await util.repeatedTry(() => this.disposed || // while testing contexts are created / disposed\n            // in rapid succession, so without this check we\n            // may poll for the query timer indefinitely\n            this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')));\n        return this.getQueryTime(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n    }\n    getQueryTime(query, queryTimerVersion) {\n        if (queryTimerVersion === 0) {\n            return null;\n        }\n        if (queryTimerVersion === 2) {\n            const gl2 = this.gl;\n            const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);\n            // Return milliseconds.\n            return timeElapsedNanos / 1000000;\n        }\n        else {\n            const ext = this.getQueryTimerExtensionWebGL1();\n            const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);\n            // Return milliseconds.\n            return timeElapsedNanos / 1000000;\n        }\n    }\n    isQueryAvailable(query, queryTimerVersion) {\n        if (queryTimerVersion === 0) {\n            return true;\n        }\n        if (queryTimerVersion === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);\n            if (this.disjoint == null) {\n                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n            }\n            return available && !this.disjoint;\n        }\n        else {\n            const ext = this.getQueryTimerExtensionWebGL1();\n            const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);\n            if (this.disjoint == null) {\n                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n            }\n            return available && !this.disjoint;\n        }\n    }\n    pollFence(fenceContext) {\n        return new Promise(resolve => {\n            this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());\n        });\n    }\n    pollItems() {\n        // Find the last query that has finished.\n        const index = linearSearchLastTrue(this.itemsToPoll.map(x => x.isDoneFn));\n        for (let i = 0; i <= index; ++i) {\n            const { resolveFn } = this.itemsToPoll[i];\n            resolveFn();\n        }\n        this.itemsToPoll = this.itemsToPoll.slice(index + 1);\n    }\n    addItemToPoll(isDoneFn, resolveFn) {\n        this.itemsToPoll.push({ isDoneFn, resolveFn });\n        if (this.itemsToPoll.length > 1) {\n            // We already have a running loop that polls.\n            return;\n        }\n        // Start a new loop that polls.\n        util.repeatedTry(() => {\n            this.pollItems();\n            // End the loop if no more items to poll.\n            return this.itemsToPoll.length === 0;\n        });\n    }\n    bindTextureToFrameBuffer(texture) {\n        this.throwIfDisposed();\n        webgl_util.bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);\n        if (this.debug) {\n            webgl_util.validateFramebuffer(this.gl);\n        }\n    }\n    unbindTextureToFrameBuffer() {\n        if (this.outputTexture != null) {\n            webgl_util.bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);\n            if (this.debug) {\n                webgl_util.validateFramebuffer(this.gl);\n            }\n        }\n        else {\n            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n        }\n    }\n    downloadMatrixDriver(texture, downloadAndDecode) {\n        this.bindTextureToFrameBuffer(texture);\n        const result = downloadAndDecode();\n        this.unbindTextureToFrameBuffer();\n        return result;\n    }\n    setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) {\n        this.throwIfDisposed();\n        const gl = this.gl;\n        webgl_util.bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);\n        if (this.debug) {\n            webgl_util.validateFramebuffer(gl);\n        }\n        this.outputTexture = outputMatrixTextureMaybePacked;\n        webgl_util.callAndCheck(gl, () => gl.viewport(0, 0, width, height));\n        webgl_util.callAndCheck(gl, () => gl.scissor(0, 0, width, height));\n    }\n    setOutputMatrixWriteRegionDriver(x, y, width, height) {\n        this.throwIfDisposed();\n        webgl_util.callAndCheck(this.gl, () => this.gl.scissor(x, y, width, height));\n    }\n    throwIfDisposed() {\n        if (this.disposed) {\n            throw new Error('Attempted to use disposed GPGPUContext.');\n        }\n    }\n    throwIfNoProgram() {\n        if (this.program == null) {\n            throw new Error('No GPU program is currently set.');\n        }\n    }\n}\n/**\n * Finds the index of the last true element using linear search.\n * Note: We can't do binary search because Chrome expects us to explicitly\n * test all fences before download:\n * https://github.com/tensorflow/tfjs/issues/1145\n */\nexport function linearSearchLastTrue(arr) {\n    let i = 0;\n    for (; i < arr.length; ++i) {\n        const isDone = arr[i]();\n        if (!isDone) {\n            break;\n        }\n    }\n    return i - 1;\n}\n//# sourceMappingURL=gpgpu_context.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport * as shader_compiler from './shader_compiler';\nexport function compileProgram(gpgpu, program, inputs, output) {\n    const userCode = program.userCode;\n    const inputInfos = inputs.map((input, i) => {\n        const shapeInfo = {\n            logicalShape: input.shape,\n            texShape: input.isUniform ? null : input.texData.texShape,\n            isUniform: input.isUniform,\n            isPacked: input.isUniform ? false : input.texData.isPacked,\n            flatOffset: null\n        };\n        if (input.texData != null && input.texData.slice != null &&\n            input.texData.slice.flatOffset > 0) {\n            shapeInfo.flatOffset = input.texData.slice.flatOffset;\n        }\n        return { name: program.variableNames[i], shapeInfo };\n    });\n    const inShapeInfos = inputInfos.map(x => x.shapeInfo);\n    const outShapeInfo = {\n        logicalShape: output.shape,\n        texShape: output.texData.texShape,\n        isUniform: false,\n        isPacked: output.texData.isPacked,\n        flatOffset: null\n    };\n    const source = shader_compiler.makeShader(inputInfos, outShapeInfo, userCode, program.packedInputs);\n    const webGLProgram = gpgpu.createProgram(source);\n    // Add special uniforms (NAN, INFINITY)\n    let infLoc = null;\n    const nanLoc = gpgpu.getUniformLocation(webGLProgram, 'NAN', false);\n    if (env().getNumber('WEBGL_VERSION') === 1) {\n        infLoc = gpgpu.getUniformLocation(webGLProgram, 'INFINITY', false);\n    }\n    // Add user-defined uniforms\n    const uniformLocations = {};\n    for (let i = 0; i < program.variableNames.length; i++) {\n        const varName = program.variableNames[i];\n        const shouldThrow = false;\n        uniformLocations[varName] =\n            gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);\n        uniformLocations[`offset${varName}`] =\n            gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);\n    }\n    return {\n        program,\n        source,\n        webGLProgram,\n        uniformLocations,\n        inShapeInfos,\n        outShapeInfo,\n        infLoc,\n        nanLoc,\n    };\n}\nfunction validateBinaryAndProgram(shapeInfos, inputs) {\n    if (shapeInfos.length !== inputs.length) {\n        throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but ` +\n            `was executed with ${inputs.length} inputs`);\n    }\n    shapeInfos.forEach((s, i) => {\n        const shapeA = s.logicalShape;\n        const input = inputs[i];\n        const shapeB = input.shape;\n        if (!util.arraysEqual(shapeA, shapeB)) {\n            throw Error(`Binary was compiled with different shapes than ` +\n                `the current args. Shapes ${shapeA} and ${shapeB} must match`);\n        }\n        // The input is uploaded as uniform.\n        if (s.isUniform && input.isUniform) {\n            return;\n        }\n        const texShapeA = s.texShape;\n        const texShapeB = input.isUniform ? null : input.texData.texShape;\n        if (!util.arraysEqual(texShapeA, texShapeB)) {\n            throw Error(`Binary was compiled with different texture shapes than the` +\n                ` current args. Shape ${texShapeA} and ${texShapeB} must match`);\n        }\n    });\n}\nexport function runProgram(gpgpu, binary, inputs, output, customSetup) {\n    validateBinaryAndProgram(binary.inShapeInfos, inputs);\n    validateBinaryAndProgram([binary.outShapeInfo], [output]);\n    const outTex = output.texData.texture;\n    const outTexShape = output.texData.texShape;\n    if (output.texData.isPacked) {\n        gpgpu.setOutputPackedMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n    }\n    else {\n        gpgpu.setOutputMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n    }\n    gpgpu.setProgram(binary.webGLProgram);\n    // Set special uniforms (NAN, INFINITY)\n    if (env().getNumber('WEBGL_VERSION') === 1) {\n        if (binary.infLoc !== null) {\n            gpgpu.gl.uniform1f(binary.infLoc, Infinity);\n        }\n    }\n    if (binary.nanLoc !== null) {\n        gpgpu.gl.uniform1f(binary.nanLoc, NaN);\n    }\n    // Set user-defined inputs\n    inputs.forEach((input, i) => {\n        const varName = binary.program.variableNames[i];\n        const varLoc = binary.uniformLocations[varName];\n        const varOffsetLoc = binary.uniformLocations[`offset${varName}`];\n        if (varLoc == null) {\n            // The compiler inferred that this variable is not used in this shader.\n            return;\n        }\n        if (input.isUniform) {\n            // Upload the values of the tensor as uniform.\n            if (util.sizeFromShape(input.shape) < 2) {\n                gpgpu.gl.uniform1f(varLoc, input.uniformValues[0]);\n            }\n            else {\n                let vals = input.uniformValues;\n                if (!(vals instanceof Float32Array)) {\n                    vals = new Float32Array(vals);\n                }\n                gpgpu.gl.uniform1fv(varLoc, vals);\n            }\n            return;\n        }\n        // If the input was sliced, upload the flat offset index.\n        if (input.texData.slice != null && varOffsetLoc != null) {\n            gpgpu.gl.uniform1i(varOffsetLoc, input.texData.slice.flatOffset);\n        }\n        gpgpu.setInputMatrixTexture(input.texData.texture, varLoc, i);\n    });\n    if (customSetup != null) {\n        customSetup(gpgpu, binary.webGLProgram);\n    }\n    gpgpu.executeProgram();\n}\nexport function makeShaderKey(program, inputs, output) {\n    let keyInputs = '';\n    inputs.concat(output).forEach(x => {\n        const hasOffset = x.texData != null && x.texData.slice != null &&\n            x.texData.slice.flatOffset > 0;\n        const texShape = x.isUniform ? 'uniform' : x.texData.texShape;\n        keyInputs += `${x.shape}_${texShape}_${hasOffset}`;\n    });\n    const keyUserCode = program.userCode;\n    let key = program.constructor.name;\n    // Fast string concat. See https://jsperf.com/string-concatenation/14.\n    key += '_' + keyInputs + '_' + keyUserCode;\n    return key;\n}\n//# sourceMappingURL=gpgpu_math.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nexport class Im2ColPackedProgram {\n    constructor(outputShape, inputShape, convInfo) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        const { filterWidth, inChannels, strideWidth, strideHeight, padInfo, outWidth, dilationWidth, dilationHeight, dataFormat } = convInfo;\n        const { left, top } = padInfo;\n        const itemsPerBlockRow = inChannels * filterWidth;\n        const glsl = getGlslDifferences();\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const rowDim = isChannelsLast ? 0 : 1;\n        const colDim = isChannelsLast ? 1 : 2;\n        let unrolled = ``;\n        for (let row = 0; row <= 1; row++) {\n            for (let col = 0; col <= 1; col++) {\n                unrolled += `\n          blockIndex = rc.y + ${col};\n          pos = rc.x + ${row};\n\n          if(blockIndex < ${outputShape[1]} && pos < ${outputShape[0]}) {\n            offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${top};\n            d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});\n\n            if(d0 < ${inputShape[rowDim]} && d0 >= 0) {\n\n              offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${strideWidth}. - ${left}.);\n              d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${itemsPerBlockRow}.) / ${inChannels}.));\n\n              if(d1 < ${inputShape[colDim]} && d1 >= 0) {\n\n                ch = int(mod(float(pos), ${inChannels}.));\n\n                if (${isChannelsLast}) {\n                  innerDims = vec2(d1, ch);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;\n            }\n        }\n        this.userCode = `\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${unrolled}\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=im2col_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNProgram {\n    constructor(xShape, radius, bias, alpha, beta) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        const rad = radius;\n        const maxD = xShape[3] - 1;\n        this.outputShape = xShape;\n        // optimize pow(bias + alpha * sum, -beta)\n        // src: https://github.com/tensorflow/tensorflow/..\n        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n        // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n        let powOperator;\n        const basis = `float(${bias}) + float(${alpha}) * sum`;\n        if (beta === 0.5) {\n            powOperator = `inversesqrt(${basis})`;\n        }\n        else if (beta === 1.0) {\n            powOperator = `1.0/(${basis})`;\n        }\n        else {\n            powOperator = `exp(log(${basis}) * float(-${beta}));`;\n        }\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${rad}; j <= ${rad}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${maxD}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${powOperator};\n        setOutput(val);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNGradProgram {\n    constructor(inputShape, depthRadius, bias, alpha, beta) {\n        this.variableNames = ['inputImage', 'outputImage', 'dy'];\n        this.outputShape = [];\n        this.outputShape = inputShape;\n        this.depth = inputShape[3];\n        this.depthRadius = depthRadius;\n        this.bias = bias;\n        this.alpha = alpha;\n        this.beta = beta;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${depthRadius})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${depthRadius} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${alpha}) * norm + float(${bias});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${alpha})\n                * float(${beta})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${beta});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_grad_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNPackedProgram {\n    constructor(xShape, radius, bias, alpha, beta) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const rad = radius;\n        const maxD = xShape[3] - 1;\n        this.outputShape = xShape;\n        // optimize pow(bias + alpha * sum, -beta)\n        // src: https://github.com/tensorflow/tensorflow/..\n        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n        // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n        let powOperator;\n        const basis = `float(${bias}) + float(${alpha}) * sum`;\n        if (beta === 0.5) {\n            powOperator = `inversesqrt(${basis})`;\n        }\n        else if (beta === 1.0) {\n            powOperator = `1.0/(${basis})`;\n        }\n        else {\n            powOperator = `exp(log(${basis}) * float(-${beta}));`;\n        }\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${rad};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${rad}; j <= ${rad}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${powOperator};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MaxPool2DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'maxPos'];\n        this.outputShape = convInfo.inShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n          wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${effectiveFilterWidth} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class MaxPool3DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'maxPos'];\n        this.outputShape = convInfo.inShape;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n           wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${lastIndex} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                  wR * ${effectiveFilterWidth} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=max_pool_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MatMulPackedProgram {\n    constructor(aShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['matrixA', 'matrixB'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        const sharedDim = transposeA ? aShape[1] : aShape[2];\n        const sharedDimensionPacked = Math.ceil(sharedDim / 2);\n        const aSample = transposeA ? 'i * 2, rc.y' : 'rc.y, i * 2';\n        const bSample = transposeB ? 'rc.z, i * 2' : 'i * 2, rc.z';\n        const aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];\n        const bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const float sharedDimension = ${sharedDimensionPacked}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${sharedDimensionPacked}; i++) {\n          vec4 a = getMatrixA(rc.x, ${aSample});\n          vec4 b = getMatrixB(rc.x, ${bSample});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${aSwizzle[0]} * ${bSwizzle[0]});\n          result += (${aSwizzle[1]} * ${bSwizzle[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${addBiasSnippet}\n\n        ${applyActivationSnippet}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=mulmat_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MultinomialProgram {\n    constructor(batchSize, numOutcomes, numSamples) {\n        this.variableNames = ['probs'];\n        this.outputShape = [batchSize, numSamples];\n        this.userCode = `\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < ${numOutcomes - 1}; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(${numOutcomes - 1}));\n      }\n    `;\n    }\n    getCustomSetupFunc(seed) {\n        return (gpgpu, webGLProgram) => {\n            if (this.seedLoc == null) {\n                this.seedLoc = gpgpu.getUniformLocation(webGLProgram, 'seed');\n            }\n            gpgpu.gl.uniform1f(this.seedLoc, seed);\n        };\n    }\n}\n//# sourceMappingURL=multinomial_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class OneHotProgram {\n    constructor(numIndices, depth, onValue, offValue) {\n        this.variableNames = ['indices'];\n        this.outputShape = [numIndices, depth];\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${offValue}), float(${onValue}),\n                      float(index == coords.y)));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=onehot_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class PackProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        // Only input / output 3D tensors.\n        this.outputShape = outputShape;\n        const rank = outputShape.length;\n        if (rank === 0) {\n            this.userCode = `\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;\n        }\n        else {\n            const channels = getChannels('rc', rank);\n            const dtype = getCoordsDataType(rank);\n            const outOfBoundsCondition = getOutOfBoundsCondition(rank, outputShape, channels);\n            const setup = getSetup(rank, outputShape[outputShape.length - 1], outputShape[outputShape.length - 2], channels);\n            const output = getOutput(outputShape, channels);\n            this.userCode = `\n        void main() {\n          ${dtype} rc = getOutputCoords();\n\n          if(${outOfBoundsCondition}) {\n            setOutput(vec4(0));\n          } else {\n            ${setup}\n\n            setOutput(vec4(${output}));\n          }\n        }\n      `;\n        }\n    }\n}\nfunction getSourceCoordsArr(rank, dims) {\n    const coords = [];\n    for (let row = 0; row <= 1; row++) {\n        for (let col = 0; col <= 1; col++) {\n            let coord = `${row === 0 ? 'r' : 'rp1'}, ${col === 0 ? 'c' : 'cp1'}`;\n            for (let d = 2; d < rank; d++) {\n                coord = `${dims[dims.length - 1 - d]},` + coord;\n            }\n            coords.push(coord);\n        }\n    }\n    return coords;\n}\nfunction getOutOfBoundsCondition(rank, shape, dims) {\n    if (rank === 1) {\n        return `rc > ${shape[0]}`;\n    }\n    let cond = '';\n    for (let i = rank - 2; i < rank; i++) {\n        cond += `${dims[i]} >= ${shape[i]}`;\n        if (i < rank - 1) {\n            cond += '||';\n        }\n    }\n    return cond;\n}\nfunction getSetup(rank, cols, rows, dims) {\n    if (rank === 1) {\n        return '';\n    }\n    const innerDims = dims.slice(-2);\n    return `\n    int r = ${innerDims[0]};\n    int c = ${innerDims[1]};\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= ${cols};\n    bool rEdge = rp1 >= ${rows};\n  `;\n}\nfunction getOutput(shape, dims) {\n    const rank = shape.length;\n    const sourceCoords = getSourceCoordsArr(rank, dims);\n    if (rank === 1) {\n        return `getA(rc),\n            rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),\n            0, 0`;\n    }\n    return `getA(${sourceCoords[0]}),\n          cEdge ? 0. : getA(${sourceCoords[1]}),\n          rEdge ? 0. : getA(${sourceCoords[2]}),\n          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;\n}\n//# sourceMappingURL=pack_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class PadProgram {\n    constructor(xShape, paddings, constantValue) {\n        this.variableNames = ['x'];\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const type = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const unpackedCoords = ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);\n        if (rank === 1) {\n            this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float(${constantValue}));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `;\n            return;\n        }\n        this.userCode = `\n      ${type} start = ${type}(${start});\n      ${type} end = ${type}(${end});\n\n      void main() {\n        ${type} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float(${constantValue}));\n        } else {\n          ${type} coords = outC - start;\n          setOutput(getX(${unpackedCoords}));\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pad_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class PadPackedProgram {\n    constructor(xShape, paddings, constantValue) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const dtype = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const coords = getChannels('rc', rank);\n        const source = getChannels('source', rank);\n        const cLimit = `${coords[rank - 1]} < ${this.outputShape[rank - 1]}`;\n        const innerDims = rank === 1 ? 'source' : `vec2(${source.slice(-2).join()})`;\n        const componentSetup = [\n            `${dtype} rc = outputLoc;`, `${coords[rank - 1]} += 1;\n       if(${cLimit}) {\n      `,\n            rank === 1 ? '' : `}\n       rc = outputLoc;\n       ${coords[rank - 2]} += 1;\n       if(${coords[rank - 2]} < ${this.outputShape[rank - 2]}) {`,\n            rank === 1 ? '' : `  ${coords[rank - 1]} += 1;\n         if(${cLimit}) {`\n        ];\n        const paddingArea = rank === 1 ?\n            'rc < start || rc >= end' :\n            'any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))';\n        let mainLoop = '';\n        for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {\n            mainLoop += `\n        ${componentSetup[i]}\n        if (${paddingArea}) {\n          result[${i}] = float(${constantValue});\n        } else {\n          ${dtype} source = rc - start;\n          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n        }\n        mainLoop += (rank === 1 ? `} ` : `}}`);\n        this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pad_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Pool2DProgram {\n    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n        this.variableNames = ['x'];\n        if (poolType === 'avg' && computePositions) {\n            throw new Error('Cannot compute positions for average pool.');\n        }\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.outputShape = convInfo.outShape;\n        const isAvgPool = poolType === 'avg';\n        const batchFlattenPositionStr = `((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n        const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n        let initializationValue = '0.0';\n        if (!isAvgPool) {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n        }\n        if (computePositions) {\n            const compareOp = '>=';\n            this.userCode = `\n        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n        const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${compareOp} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${flattenPositions ? (includeBatchInIndex ? batchFlattenPositionStr :\n                flattenPositionStr) :\n                `wR * ${effectiveFilterWidth} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n            return;\n        }\n        const compareOp = 'max';\n        let returnValue = `${poolType}(${poolType}(${poolType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (poolType === 'avg') {\n            returnValue = `avgValue / count`;\n        }\n        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n        const filterWidthVec4Remainder = filterWidth % 4;\n        const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)\n            );\n\n            ${updateSnippet}\n          }\n\n          int xC = xCCorner + ${filterWidthNearestVec4};\n          if (${filterWidthVec4Remainder === 1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              initializationValue\n            );\n\n            ${updateSnippet}\n          }\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\nexport class Pool3DProgram {\n    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n        this.variableNames = ['x'];\n        if (poolType === 'avg' && computePositions) {\n            throw new Error('Cannot compute positions for average pool.');\n        }\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.outputShape = convInfo.outShape;\n        const isAvgPool = poolType === 'avg';\n        let initializationValue = '0.0';\n        if (!isAvgPool) {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n        }\n        if (computePositions) {\n            const compareOp = '>=';\n            this.userCode = `\n        const ivec3 strides =\n            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${effectiveFilterDepth};\n              wD += ${dilationDepth}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${effectiveFilterHeight};\n                wR += ${dilationHeight}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${effectiveFilterWidth};\n                  wC += ${dilationWidth}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${compareOp} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${flattenPositions ?\n                (includeBatchInIndex ?\n                    `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` :\n                    `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch`) :\n                `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                      wR * ${effectiveFilterWidth} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n            return;\n        }\n        const compareOp = 'max';\n        let returnValue = `${poolType}(${poolType}(${poolType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (poolType === 'avg') {\n            returnValue = `avgValue / count`;\n        }\n        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n        const filterWidthVec4Remainder = filterWidth % 4;\n        const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        this.userCode = `\n      const ivec3 strides =\n        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)\n              );\n\n              ${updateSnippet}\n            }\n\n            int xC = xCCorner + ${filterWidthNearestVec4};\n            if (${filterWidthVec4Remainder === 1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                initializationValue\n              );\n\n              ${updateSnippet}\n            }\n          }\n          setOutput(${returnValue});\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pool_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ReduceProgram {\n    constructor(reduceInfo, reduceType) {\n        this.variableNames = ['x'];\n        const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n        this.outputShape = [batchSize, outSize];\n        let initializationValue = '0.0';\n        let compareOp = ``;\n        if (reduceType === 'prod') {\n            initializationValue = '1.0';\n        }\n        else if (reduceType === 'min') {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '1.0 / 1e-20';\n            compareOp = `min`;\n        }\n        else if (reduceType === 'max') {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n            compareOp = `max`;\n        }\n        let returnValue = `${reduceType}(${reduceType}(${reduceType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (reduceType === 'sum') {\n            returnValue = `sumValue`;\n        }\n        else if (reduceType === 'prod') {\n            returnValue = `prodValue`;\n        }\n        else if (reduceType === 'all') {\n            returnValue = `allValue`;\n        }\n        else if (reduceType === 'any') {\n            returnValue = `anyValue`;\n        }\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        let updateSnippet = `\n      if (${reduceType === 'sum'}) {\n        sumValue += dot(values, ones);\n      } else if (${reduceType === 'prod'}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        let vecType = `vec4`;\n        if (reduceType === 'all') {\n            initializationValue = '1.0';\n            updateSnippet = `\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `;\n            vecType = `bvec4`;\n        }\n        else if (reduceType === 'any') {\n            initializationValue = '0.0';\n            updateSnippet = `\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `;\n            vecType = `bvec4`;\n        }\n        let checkOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n        }\n        this.userCode = `\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        vec4 minMaxValue = vec4(${initializationValue});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\n//# sourceMappingURL=reduce_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as shader_util from './shader_compiler_util';\nexport class ReshapePackedProgram {\n    constructor(outputShape, inputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        let mainLoop = ``;\n        for (let i = 0; i < 4; i++) {\n            let thisRC = `thisRC = rc;`;\n            if (i % 2 === 1) {\n                thisRC += `thisRC.z += 1;`;\n            }\n            if (i > 1) {\n                thisRC += `thisRC.y += 1;`;\n            }\n            mainLoop += `\n        ${thisRC}\n        ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ''}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${i}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${i > 0 ? '}' : ''}\n      `;\n        }\n        this.userCode = `\n      ${getReshapedInputCoords(inputShape)}\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${outputShape[1]};\n        int cols = ${outputShape[2]};\n\n        ${mainLoop}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\nfunction getReshapedInputCoords(shape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n    return `\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\n//# sourceMappingURL=reshape_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearBackpropProgram {\n    constructor(dy, x, alignCorners) {\n        this.variableNames = ['dy'];\n        this.outputShape = [];\n        this.outputShape = x.shape;\n        const [, xHeight, xWidth,] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        // In the backwards pass, we want to find the pixels that were generated for\n        // each pixel in the input image the forward pass and add the corresponding\n        // coefficient from dy to the gradient (with some interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearPackedProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        this.userCode = `\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,\n                                     ${oldWidth}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${depth - 1};\n        bool hasNextRow = coords.z < ${newWidth - 1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeNearestNeigborBackpropProgram {\n    constructor(dy, x, alignCorners) {\n        this.variableNames = ['dy'];\n        this.outputShape = [];\n        this.outputShape = x.shape;\n        const [, xHeight, xWidth,] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        // In the backwards pass, we want to find the pixels that were generated for\n        // each pixel in the input image the forward pass and add the corresponding\n        // coefficient from dy to the gradient (with some interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${effectiveXSize[0]}) *\n                (float(dyR) / float(${effectiveYSize[0]}));\n\n            float sourceFracCol =\n                float(${effectiveXSize[1]}) *\n                  (float(dyC) / float(${effectiveYSize[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${xHeight}) - 1),\n                ${alignCorners} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${xWidth}) - 1),\n                ${alignCorners} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_nearest_neighbor_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeNearestNeighborProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        // When align corners is false, we rounds the value with floor.\n        const roundBase = alignCorners ? '0.5' : '0.0';\n        this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_nearest_neighbor_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class ReverseProgram {\n    constructor(xShape, axis) {\n        this.variableNames = ['x'];\n        const rank = xShape.length;\n        if (rank > 4) {\n            throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n        }\n        this.outputShape = xShape;\n        if (rank === 1) {\n            this.userCode = `\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${xShape[0]} - coord - 1));\n        }\n      `;\n            return;\n        }\n        const getInCoord = (i) => {\n            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n                return `${xShape[i]} - coords[${i}] - 1`;\n            }\n            return `coords[${i}]`;\n        };\n        const inCoords = xShape.map((_, i) => getInCoord(i)).join(',');\n        const type = getCoordsDataType(rank);\n        this.userCode = `\n      void main() {\n        ${type} coords = getOutputCoords();\n        setOutput(getX(${inCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=reverse_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ReversePackedProgram {\n    constructor(xShape, axis) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const rank = xShape.length;\n        if (rank > 4) {\n            throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n        }\n        this.outputShape = xShape;\n        const channels = getChannels('rc', rank);\n        const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;\n        const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;\n        const type = getCoordsDataType(rank);\n        if (rank === 1) {\n            this.userCode = `\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${xShape[0]} - rc - 1),\n            ${xShape[0]} - rc - 1);\n          if(${nextColumn}){\n              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),\n                ${xShape[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `;\n        }\n        else {\n            this.userCode = `\n        void main() {\n          ${type} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${getR(channels.slice())};\n          if(${nextColumn}){\n            result.g = ${getG(channels.slice())};\n          }\n          if(${nextRow}) {\n            result.b = ${getB(channels.slice())};\n            if(${nextColumn}) {\n              result.a = ${getA(channels.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `;\n        }\n        function getR(channels) {\n            return getChannel(channels);\n        }\n        function getG(channels) {\n            channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getB(channels) {\n            channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getA(channels) {\n            channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n            channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getChannel(channels) {\n            const inCoordsArray = xShape.map((_, i) => getInCoord(i, channels));\n            const inCoords = inCoordsArray.join(',');\n            const innerDims = inCoordsArray.slice(-2).join(',');\n            return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;\n        }\n        function getInCoord(i, channels1) {\n            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n                return `${xShape[i]} - ${channels1[i]} - 1`;\n            }\n            else {\n                return `${channels1[i]}`;\n            }\n        }\n    }\n}\n//# sourceMappingURL=reverse_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class ScatterProgram {\n    constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true) {\n        this.variableNames = ['updates', 'indices', 'defaultValue'];\n        this.outputShape = shape;\n        const stridesType = getCoordsDataType(strides.length);\n        const dtype = getCoordsDataType(shape.length);\n        let indicesString = '';\n        if (indicesRank === 1) {\n            indicesString = 'i';\n        }\n        else if (indicesRank === 2) {\n            indicesString = 'i, j';\n        }\n        const indicesSnippet = `getIndices(${indicesString})`;\n        let updatesString = '';\n        if (updatesRank === 1) {\n            updatesString = 'i';\n        }\n        else if (updatesRank === 2) {\n            updatesString = 'i, coords[1]';\n        }\n        const updatesSnippet = `getUpdates(${updatesString})`;\n        const strideString = sliceDim > 1 ? 'strides[j]' : 'strides';\n        this.userCode = `\n        ${stridesType} strides = ${stridesType}(${strides});\n\n        void main() {\n          ${dtype} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${updateSize}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${sliceDim}; j++) {\n              int index = round(${indicesSnippet});\n              flattenedIndex += index * ${strideString};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${updatesSnippet};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `;\n    }\n}\n//# sourceMappingURL=scatter_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class SegmentOpProgram {\n    constructor(segOpInfo, segOpType) {\n        this.variableNames = ['x', 'segmentIds'];\n        const windowSize = segOpInfo.windowSize;\n        const batchSize = segOpInfo.batchSize;\n        const inSize = segOpInfo.inSize;\n        const numSegments = segOpInfo.numSegments;\n        const outSize = numSegments * Math.ceil(inSize / windowSize);\n        this.outputShape = [batchSize, outSize];\n        const initializationValue = '0.0';\n        const returnValue = `sumValue`;\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        const updateSnippet = `\n        sumValue += dot(values, segFilter);\n    `;\n        let checkValueOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkValueOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n        }\n        let checkSegmentIdOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkSegmentIdOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return -1.0;\n        }\n      `;\n        }\n        this.userCode = `\n      const float initializationValue = ${initializationValue};\n\n      float getValue(int batch, int inIdx) {\n        ${checkValueOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${checkSegmentIdOutOfBounds}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${numSegments})) * float(${windowSize}));\n        int currentSeg = int(mod(float(outIdx), float(${numSegments})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\n//# sourceMappingURL=segment_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class SelectProgram {\n    constructor(cRank, shape, rank) {\n        this.variableNames = ['c', 'a', 'b'];\n        this.outputShape = shape;\n        let cCoords;\n        let abCoords;\n        if (rank > 4) {\n            throw Error(`Where for rank ${rank} is not yet supported`);\n        }\n        if (rank === 1) {\n            abCoords = `resRC`;\n            cCoords = `resRC`;\n        }\n        else {\n            const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n            const cCoordVars = [];\n            const abCoordVars = [];\n            for (let i = 0; i < shape.length; i++) {\n                abCoordVars.push(`${currentCoords[i]}`);\n                if (i < cRank) {\n                    cCoordVars.push(`${currentCoords[i]}`);\n                }\n            }\n            cCoords = cCoordVars.join();\n            abCoords = abCoordVars.join();\n        }\n        const dtype = getCoordsDataType(rank);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        float cVal = getC(${cCoords});\n        if (cVal >= 1.0) {\n          setOutput(getA(${abCoords}));\n        } else {\n          setOutput(getB(${abCoords}));\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=select_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class SliceProgram {\n    constructor(destSize) {\n        this.variableNames = ['source'];\n        this.outputShape = destSize;\n        this.rank = destSize.length;\n        const dtype = getCoordsDataType(this.rank);\n        const uniformPart = `uniform int start[${this.rank}];`;\n        const sourceCoords = getCoords(this.rank);\n        let body;\n        const coordSum = destSize.map((_, i) => {\n            return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`;\n        });\n        body = `\n        ${dtype} sourceLoc;\n        ${dtype} coords = getOutputCoords();\n        ${coordSum.join('\\n')}\n      `;\n        this.userCode = `\n      ${uniformPart}\n      void main() {\n        ${body}\n        setOutput(getSource(${sourceCoords}));\n      }\n    `;\n    }\n    getCustomSetupFunc(start) {\n        if (start.length !== this.rank) {\n            throw Error(`The rank (${this.rank}) of the program must match the ` +\n                `length of start (${start.length})`);\n        }\n        return (gpgpu, webGLProgram) => {\n            if (this.startLoc == null) {\n                this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n                if (this.startLoc == null) {\n                    // This means the compiler has optimized and realized it doesn't need\n                    // the uniform.\n                    return;\n                }\n            }\n            gpgpu.gl.uniform1iv(this.startLoc, start);\n        };\n    }\n}\nconst coords = ['x', 'y', 'z', 'w', 'u', 'v'];\nfunction getCoords(rank) {\n    if (rank === 1) {\n        return 'sourceLoc';\n    }\n    else if (rank <= 6) {\n        return coords.slice(0, rank).map(x => 'sourceLoc.' + x).join(',');\n    }\n    else {\n        throw Error(`Slicing for rank ${rank} is not yet supported`);\n    }\n}\n//# sourceMappingURL=slice_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class SlicePackedProgram {\n    constructor(destSize) {\n        this.variableNames = ['source'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = destSize;\n        this.rank = destSize.length;\n        const dtype = getCoordsDataType(this.rank);\n        const coords = getChannels('coords', this.rank);\n        const sourceLoc = getChannels('sourceLoc', this.rank);\n        const innerDims = this.rank === 1 ? 'sourceLoc' : `vec2(${sourceLoc.slice(-2).join()})`;\n        const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;\n        const upperRow = `\n      result.x = ${getChannel};\n      if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n        ++${sourceLoc[this.rank - 1]};\n        result.y = ${getChannel};\n        --${sourceLoc[this.rank - 1]};\n      }\n    `;\n        const lowerRow = this.rank === 1 ? '' : `\n      --${coords[this.rank - 1]};\n      if (++${coords[this.rank - 2]} < ${destSize[this.rank - 2]}) {\n        ++${sourceLoc[this.rank - 2]};\n        result.z = ${getChannel};\n        if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n          ++${sourceLoc[this.rank - 1]};\n          result.w = ${getChannel};\n        }\n      }\n    `;\n        const sourceLocSetup = this.rank <= 4 ?\n            `sourceLoc = coords +\n            ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` :\n            destSize.map((_, i) => `${sourceLoc[i]} = ${coords[i]} + start[${i}];`)\n                .join('\\n');\n        this.userCode = `\n      uniform int start[${this.rank}];\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        ${dtype} sourceLoc;\n        ${sourceLocSetup}\n        vec4 result = vec4(0.);\n        ${upperRow}\n        ${lowerRow}\n        setOutput(result);\n      }\n    `;\n    }\n    getCustomSetupFunc(start) {\n        if (start.length !== this.rank) {\n            throw Error(`The rank (${this.rank}) of the program must match the ` +\n                `length of start (${start.length})`);\n        }\n        return (gpgpu, webGLProgram) => {\n            if (this.startLoc == null) {\n                this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n                if (this.startLoc == null) {\n                    // This means the compiler has optimized and realized it doesn't need\n                    // the uniform.\n                    return;\n                }\n            }\n            gpgpu.gl.uniform1iv(this.startLoc, start);\n        };\n    }\n}\n//# sourceMappingURL=slice_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class StridedSliceProgram {\n    constructor(begin, strides, size) {\n        this.variableNames = ['x'];\n        this.outputShape = size;\n        const rank = size.length;\n        const inputDtype = getCoordsDataType(size.length);\n        const dtype = getCoordsDataType(size.length);\n        let newCoords = '';\n        if (rank === 1) {\n            newCoords = 'coords * strides + begin';\n        }\n        else {\n            let outputAxis = 0;\n            newCoords =\n                size.map((_, i) => {\n                    outputAxis++;\n                    return size.length === 1 ?\n                        `coords * strides[${i}] + begin[${i}]` :\n                        `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;\n                })\n                    .join(',');\n        }\n        this.userCode = `\n      ${inputDtype} begin = ${inputDtype}(${begin});\n      ${inputDtype} strides = ${inputDtype}(${strides});\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        setOutput(getX(${newCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=strided_slice_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { getInternalFormatForFloat16MatrixTexture, getInternalFormatForFloat16PackedMatrixTexture, getInternalFormatForFloat32MatrixTexture, getInternalFormatForPackedMatrixTexture, getInternalFormatForUnsignedBytesMatrixTexture } from './gpgpu_util';\nimport { getPackedMatrixTextureShapeWidthHeight, getUnpackedMatrixTextureShapeWidthHeight, PhysicalTextureType, TextureUsage } from './tex_util';\nexport class TextureManager {\n    constructor(gpgpu) {\n        this.gpgpu = gpgpu;\n        this.numUsedTextures = 0;\n        this.numFreeTextures = 0;\n        this._numBytesAllocated = 0;\n        this._numBytesFree = 0; // How many bytes that have been allocated\n        // are available for reuse.\n        this.freeTextures = {};\n        this.logEnabled = false;\n        this.usedTextures = {};\n    }\n    acquireTexture(shapeRC, usage, isPacked) {\n        const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);\n        const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);\n        if (!(shapeKey in this.freeTextures)) {\n            this.freeTextures[shapeKey] = [];\n        }\n        if (!(shapeKey in this.usedTextures)) {\n            this.usedTextures[shapeKey] = [];\n        }\n        const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n        if (this.freeTextures[shapeKey].length > 0) {\n            this.numFreeTextures--;\n            this.numUsedTextures++;\n            this._numBytesFree -= texBytes;\n            this.log();\n            const newTexture = this.freeTextures[shapeKey].shift();\n            this.usedTextures[shapeKey].push(newTexture);\n            return newTexture;\n        }\n        let newTexture;\n        if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {\n            newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {\n            newTexture =\n                this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {\n            newTexture =\n                this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {\n            newTexture =\n                this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {\n            newTexture =\n                this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        this.usedTextures[shapeKey].push(newTexture);\n        this.numUsedTextures++;\n        this._numBytesAllocated += texBytes;\n        this.log();\n        return newTexture;\n    }\n    releaseTexture(texture, shape, logicalTexType, isPacked) {\n        if (this.freeTextures == null) {\n            // Already disposed.\n            return;\n        }\n        const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);\n        const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);\n        if (!(shapeKey in this.freeTextures)) {\n            this.freeTextures[shapeKey] = [];\n        }\n        const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n        const deleteTexThreshold = env().get('WEBGL_DELETE_TEXTURE_THRESHOLD');\n        if (deleteTexThreshold !== -1 &&\n            this._numBytesAllocated > deleteTexThreshold) {\n            this.gpgpu.deleteMatrixTexture(texture);\n            this._numBytesAllocated -= texBytes;\n        }\n        else {\n            this.freeTextures[shapeKey].push(texture);\n            this.numFreeTextures++;\n            this._numBytesFree += texBytes;\n        }\n        this.numUsedTextures--;\n        const texList = this.usedTextures[shapeKey];\n        const texIndex = texList.indexOf(texture);\n        if (texIndex < 0) {\n            throw new Error('Cannot release a texture that was never provided by this ' +\n                'texture manager');\n        }\n        texList.splice(texIndex, 1);\n        this.log();\n    }\n    log() {\n        if (!this.logEnabled) {\n            return;\n        }\n        const total = this.numFreeTextures + this.numUsedTextures;\n        console.log('Free/Used', `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`);\n        const freeRatio = this._numBytesFree / this._numBytesAllocated;\n        console.log(`Bytes allocated: ${this._numBytesAllocated}`);\n        console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`);\n    }\n    get numBytesAllocated() {\n        return this._numBytesAllocated;\n    }\n    get numBytesFree() {\n        return this._numBytesFree;\n    }\n    getNumUsedTextures() {\n        return this.numUsedTextures;\n    }\n    getNumFreeTextures() {\n        return this.numFreeTextures;\n    }\n    dispose() {\n        if (this.freeTextures == null) {\n            // Already disposed.\n            return;\n        }\n        for (const texShape in this.freeTextures) {\n            this.freeTextures[texShape].forEach(tex => {\n                this.gpgpu.deleteMatrixTexture(tex);\n            });\n        }\n        for (const texShape in this.usedTextures) {\n            this.usedTextures[texShape].forEach(tex => {\n                this.gpgpu.deleteMatrixTexture(tex);\n            });\n        }\n        this.freeTextures = null;\n        this.usedTextures = null;\n        this.numUsedTextures = 0;\n        this.numFreeTextures = 0;\n        this._numBytesAllocated = 0;\n        this._numBytesFree = 0;\n    }\n}\nfunction numBytesForInternalFormat(gl, internalFormat) {\n    // tslint:disable-next-line:no-any\n    const glany = gl;\n    if (internalFormat === glany.R32F) {\n        return 4;\n    }\n    else if (internalFormat === glany.R16F) {\n        return 2;\n    }\n    else if (internalFormat === glany.RGBA32F) {\n        return 16;\n    }\n    else if (internalFormat === gl.RGBA) {\n        return 16;\n    }\n    else if (internalFormat === glany.RGBA16F) {\n        return 8;\n    }\n    throw new Error(`Unknown internal format ${internalFormat}`);\n}\nexport function computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) {\n    // It is not possible to infer packed status from the texture type because\n    // depending on the textureConfig, different  texture types may resolve to the\n    // same internal format (e.g. in WebGL1, the internal format for\n    // UNPACKED_FLOAT16 textures is gl.RGBA). Therefore we pass in `isPacked`\n    // explicitly.\n    const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig);\n    let numElements;\n    if (isPacked) {\n        const [packedWidth, packedHeight] = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n        numElements = packedWidth * packedHeight;\n    }\n    else {\n        const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n        numElements = width * height;\n    }\n    const bytesPerElement = numBytesForInternalFormat(gl, internalFormat);\n    return numElements * bytesPerElement;\n}\nfunction internalFormatForPhysicalTexType(physicalTexType, textureConfig) {\n    switch (physicalTexType) {\n        case PhysicalTextureType.PACKED_2X2_FLOAT32:\n            return getInternalFormatForPackedMatrixTexture(textureConfig);\n        case PhysicalTextureType.PACKED_2X2_FLOAT16:\n            return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);\n        case PhysicalTextureType.UNPACKED_FLOAT32:\n            return getInternalFormatForFloat32MatrixTexture(textureConfig);\n        case PhysicalTextureType.UNPACKED_FLOAT16:\n            return getInternalFormatForFloat16MatrixTexture(textureConfig);\n        case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:\n            return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);\n        default:\n            throw new Error(`Unknown physical texture type ${physicalTexType}`);\n    }\n}\nfunction getPhysicalTextureForRendering(isPacked) {\n    if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED')) {\n        if (isPacked) {\n            return PhysicalTextureType.PACKED_2X2_FLOAT32;\n        }\n        return PhysicalTextureType.UNPACKED_FLOAT32;\n    }\n    if (isPacked) {\n        return PhysicalTextureType.PACKED_2X2_FLOAT16;\n    }\n    return PhysicalTextureType.UNPACKED_FLOAT16;\n}\nfunction getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {\n    if (logicalTexType === TextureUsage.UPLOAD) {\n        return PhysicalTextureType.PACKED_2X2_FLOAT32;\n    }\n    else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {\n        return getPhysicalTextureForRendering(isPacked);\n    }\n    else if (logicalTexType === TextureUsage.DOWNLOAD ||\n        logicalTexType === TextureUsage.PIXELS) {\n        return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;\n    }\n    throw new Error(`Unknown logical texture type ${logicalTexType}`);\n}\nfunction getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {\n    return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;\n}\n//# sourceMappingURL=texture_manager.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class TileProgram {\n    constructor(aShape, reps) {\n        this.variableNames = ['A'];\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[i] * reps[i];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const sourceCoords = getSourceCoords(aShape);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n    }\n}\nfunction getSourceCoords(aShape) {\n    const rank = aShape.length;\n    if (rank > 5) {\n        throw Error(`Tile for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n        return `imod(resRC, ${aShape[0]})`;\n    }\n    const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u'];\n    const sourceCoords = [];\n    for (let i = 0; i < aShape.length; i++) {\n        sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);\n    }\n    return sourceCoords.join();\n}\n//# sourceMappingURL=tile_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class UnaryOpProgram {\n    constructor(aShape, opSnippet) {\n        this.variableNames = ['A'];\n        this.outputShape = aShape;\n        this.userCode = `\n      float unaryOperation(float x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n    }\n}\nconst CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;\nexport const LINEAR = `return x;`;\nexport const ABS = `return abs(x);`;\nexport const RELU = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\nexport const RELU6 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`;\nexport const ELU = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\nexport const SELU = `\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${backend_util.SELU_SCALEALPHA};\n  float scale = ${backend_util.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`;\nexport function STEP(alpha = 0.0) {\n    return CHECK_NAN_SNIPPET + `\n    return x > 0.0 ? 1.0 : float(${alpha});\n  `;\n}\nexport const NEG = `return -x;`;\nexport const CEIL = `return ceil(x);`;\nexport const FLOOR = `return floor(x);`;\nexport const SIGN = `\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n`;\nexport const IS_NAN = `return float(isnan(x));`;\nexport const IS_INF = `return float(isinf(x));`;\nexport const IS_FINITE = `return float(!isnan(x) && !isinf(x));`;\nexport const ROUND = `\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`;\nexport const EXP = `return exp(x);`;\nexport const EXPM1 = `return exp(x) - 1.0;`;\nexport const LOG = `if (x < 0.0) return NAN;\n  return log(x);`;\nexport const LOG1P = `return log(1.0 + x);`;\nexport const SQRT = `return sqrt(x);`;\nexport const RSQRT = `return inversesqrt(x);`;\nexport const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n/**\n * mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n *\n * epsilon is the difference between 1.0 and the next representable\n * float. For a single precision 32 bit float this should be 2^-23, see:\n * https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\n *\n * too_large = (x > -threshold) is value above which exp(x) may overflow\n * but softplus(x) == x is within machine epsilon\n *\n * too_small = (x < threshold) is value below which exp(x) may underflow,\n * but softplus(x) == exp(x) is within machine epsilon.\n */\nexport const SOFTPLUS = `\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`;\nexport const ASIN = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`;\nexport const ACOS = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`;\nexport const ATAN = CHECK_NAN_SNIPPET + `\n  return atan(x);\n`;\nexport const SINH = `\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`;\nexport const COSH = `\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`;\nexport const TANH = `\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`;\nexport const ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`;\nexport const ACOSH = CHECK_NAN_SNIPPET + `\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));`;\nexport const ATANH = CHECK_NAN_SNIPPET + `\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;\nexport const ERF = `\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = ${backend_util.ERF_P};\n  float a1 = ${backend_util.ERF_A1};\n  float a2 = ${backend_util.ERF_A2};\n  float a3 = ${backend_util.ERF_A3};\n  float a4 = ${backend_util.ERF_A4};\n  float a5 = ${backend_util.ERF_A5};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`;\nexport const RECIPROCAL = `return 1.0 / x;`;\nexport const LOGICAL_NOT = `return float(!(x >= 1.0));`;\nexport const TO_INT = `return float(int(x));`;\nexport const CLONE = 'return x;';\n//# sourceMappingURL=unaryop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const LINEAR = `return x;`;\nexport const LOG = `\n  vec4 result = log(x);\n  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\n  result.r = isNaN.r == 1.0 ? NAN : result.r;\n  result.g = isNaN.g == 1.0 ? NAN : result.g;\n  result.b = isNaN.b == 1.0 ? NAN : result.b;\n  result.a = isNaN.a == 1.0 ? NAN : result.a;\n\n  return result;\n`;\nexport const RELU = `\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nexport const RELU6 = `\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nexport const ELU = `\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`;\nexport class UnaryOpPackedProgram {\n    constructor(aShape, opSnippet) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = aShape;\n        this.userCode = `\n      vec4 unaryOperation(vec4 x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=unaryop_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels, getSourceCoords } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class UnpackProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = false;\n        this.outputShape = outputShape;\n        const rank = outputShape.length;\n        const channels = getChannels('rc', rank);\n        const dtype = getCoordsDataType(rank);\n        const sourceCoords = getSourceCoords(rank, channels);\n        const innerDims = channels.slice(-2);\n        const coords = rank <= 1 ? 'rc' : `vec2(${innerDims.join(',')})`;\n        this.userCode = `\n      void main() {\n        ${dtype} rc = getOutputCoords();\n        vec4 packedInput = getA(${sourceCoords});\n\n        setOutput(getChannel(packedInput, ${coords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=unpack_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Import webgl flags.\nimport './flags_webgl';\nimport * as tf from '@tensorflow/tfjs-core';\nimport { complex, div, engine, env, imag, max, range, real, reshape, scalar, softmax, tensor, tidy, transpose } from '@tensorflow/tfjs-core';\nimport { backend_util, buffer, kernel_impls, slice_util, util } from '@tensorflow/tfjs-core';\nimport { DataStorage, KernelBackend, upcastType } from '@tensorflow/tfjs-core';\nimport { addImplCPU, ceilImplCPU, expImplCPU, expm1ImplCPU, floorImplCPU, logImplCPU, multiplyImplCPU, rsqrtImplCPU, simpleAbsImplCPU, sliceImplCPU, subImplCPU } from './kernel_utils/shared';\nconst { segment_util } = backend_util;\nconst split = kernel_impls.split;\nconst tile = kernel_impls.tile;\nconst topkImpl = kernel_impls.topkImpl;\nconst whereImpl = kernel_impls.whereImpl;\nimport { AddNProgram } from './addn_gpu';\nimport { AddNPackedProgram } from './addn_packed_gpu';\nimport { ArgMinMaxProgram } from './argminmax_gpu';\nimport { ArgMinMaxPackedProgram } from './argminmax_packed_gpu';\nimport { AvgPool3DBackpropProgram } from './avg_pool_backprop_gpu';\nimport * as binaryop_complex_gpu from './binaryop_complex_gpu';\nimport { BinaryOpComplexProgram } from './binaryop_complex_gpu';\nimport * as binaryop_gpu from './binaryop_gpu';\nimport { BinaryOpProgram } from './binaryop_gpu';\nimport * as binaryop_packed_gpu from './binaryop_packed_gpu';\nimport { BinaryOpPackedProgram } from './binaryop_packed_gpu';\nimport { getWebGLContext } from './canvas_util';\nimport { ClipProgram } from './clip_gpu';\nimport { ClipPackedProgram } from './clip_packed_gpu';\nimport { ComplexAbsProgram } from './complex_abs_gpu';\nimport { ConcatProgram } from './concat_gpu';\nimport { ConcatPackedProgram } from './concat_packed_gpu';\nimport { Conv2DDerFilterProgram, Conv2DDerInputProgram, Conv3DDerFilterProgram, Conv3DDerInputProgram } from './conv_backprop_gpu';\nimport { DepthwiseConv2DDerFilterProgram, DepthwiseConv2DDerInputProgram } from './conv_backprop_gpu_depthwise';\nimport { Conv2DProgram, Conv3DProgram } from './conv_gpu';\nimport { DepthwiseConv2DProgram } from './conv_gpu_depthwise';\nimport { DepthwiseConvPacked2DProgram } from './conv_packed_gpu_depthwise';\nimport { CropAndResizeProgram } from './crop_and_resize_gpu';\nimport { CumSumProgram } from './cumsum_gpu';\nimport { DecodeMatrixProgram } from './decode_matrix_gpu';\nimport { DecodeMatrixPackedProgram } from './decode_matrix_packed_gpu';\nimport { DepthToSpaceProgram } from './depth_to_space_gpu';\nimport { DiagProgram } from './diag_gpu';\nimport { EncodeFloatProgram } from './encode_float_gpu';\nimport { EncodeFloatPackedProgram } from './encode_float_packed_gpu';\nimport { EncodeMatrixProgram } from './encode_matrix_gpu';\nimport { EncodeMatrixPackedProgram } from './encode_matrix_packed_gpu';\nimport * as fft_gpu from './fft_gpu';\nimport { FFTProgram } from './fft_gpu';\nimport { FillProgram } from './fill_gpu';\nimport { GatherProgram } from './gather_gpu';\nimport { GatherNDProgram } from './gather_nd_gpu';\nimport { GPGPUContext } from './gpgpu_context';\nimport * as gpgpu_math from './gpgpu_math';\nimport { Im2ColPackedProgram } from './im2col_packed_gpu';\nimport { LRNProgram } from './lrn_gpu';\nimport { LRNGradProgram } from './lrn_grad_gpu';\nimport { LRNPackedProgram } from './lrn_packed_gpu';\nimport { MaxPool3DBackpropProgram } from './max_pool_backprop_gpu';\nimport { MatMulPackedProgram } from './mulmat_packed_gpu';\nimport { MultinomialProgram } from './multinomial_gpu';\nimport { OneHotProgram } from './onehot_gpu';\nimport { PackProgram } from './pack_gpu';\nimport { PadProgram } from './pad_gpu';\nimport { PadPackedProgram } from './pad_packed_gpu';\nimport { Pool3DProgram } from './pool_gpu';\nimport { ReduceProgram } from './reduce_gpu';\nimport { ReshapePackedProgram } from './reshape_packed_gpu';\nimport { ResizeBilinearBackpropProgram } from './resize_bilinear_backprop_gpu';\nimport { ResizeBilinearProgram } from './resize_bilinear_gpu';\nimport { ResizeBilinearPackedProgram } from './resize_bilinear_packed_gpu';\nimport { ResizeNearestNeigborBackpropProgram } from './resize_nearest_neighbor_backprop_gpu';\nimport { ResizeNearestNeighborProgram } from './resize_nearest_neighbor_gpu';\nimport { ReverseProgram } from './reverse_gpu';\nimport { ReversePackedProgram } from './reverse_packed_gpu';\nimport { ScatterProgram } from './scatter_gpu';\nimport { SegmentOpProgram } from './segment_gpu';\nimport { SelectProgram } from './select_gpu';\nimport { SliceProgram } from './slice_gpu';\nimport { SlicePackedProgram } from './slice_packed_gpu';\nimport { StridedSliceProgram } from './strided_slice_gpu';\nimport * as tex_util from './tex_util';\nimport { TextureUsage } from './tex_util';\nimport { TextureManager } from './texture_manager';\nimport { TileProgram } from './tile_gpu';\nimport * as unary_op from './unaryop_gpu';\nimport { UnaryOpProgram } from './unaryop_gpu';\nimport * as unary_packed_op from './unaryop_packed_gpu';\nimport { UnaryOpPackedProgram } from './unaryop_packed_gpu';\nimport { UnpackProgram } from './unpack_gpu';\nimport * as webgl_util from './webgl_util';\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\nconst binaryCaches = {};\nexport function getBinaryCache(webGLVersion) {\n    if (webGLVersion in binaryCaches) {\n        return binaryCaches[webGLVersion];\n    }\n    binaryCaches[webGLVersion] = {};\n    return binaryCaches[webGLVersion];\n}\nfunction mapActivationToShaderProgram(activation, packed = false) {\n    if (activation === 'linear') {\n        if (packed) {\n            return unary_packed_op.LINEAR;\n        }\n        return unary_op.LINEAR;\n    }\n    else if (activation === 'relu') {\n        if (packed) {\n            return unary_packed_op.RELU;\n        }\n        return unary_op.RELU;\n    }\n    else if (activation === 'elu') {\n        if (packed) {\n            return unary_packed_op.ELU;\n        }\n        return unary_op.ELU;\n    }\n    else if (activation === 'relu6') {\n        if (packed) {\n            return unary_packed_op.RELU6;\n        }\n        return unary_op.RELU6;\n    }\n    else if (activation === 'prelu') {\n        if (packed) {\n            return binaryop_packed_gpu.PRELU;\n        }\n        return binaryop_gpu.PRELU;\n    }\n    throw new Error(`Activation ${activation} has not been implemented for the WebGL backend.`);\n}\n// Empirically determined constant used to determine size threshold for handing\n// off execution to the CPU.\nconst CPU_HANDOFF_SIZE_THRESHOLD = 128;\n// Empirically determined constant used to decide the number of MB on GPU\n// before we warn about high memory use. The MB are this constant * screen area\n// * dpi / 1024 / 1024.\nconst BEFORE_PAGING_CONSTANT = 600;\nfunction numMBBeforeWarning() {\n    if (env().global.screen == null) {\n        return 1024; // 1 GB.\n    }\n    return (env().global.screen.height * env().global.screen.width *\n        window.devicePixelRatio) *\n        BEFORE_PAGING_CONSTANT / 1024 / 1024;\n}\n// Empirically determined minimal shared dimension in matmul before we forward\n// to a.mul(b).sum() in order to take advantage of GPU parallelism. See\n// https://github.com/tensorflow/tfjs-core/pull/1379 for benchmarks.\nexport const MATMUL_SHARED_DIM_THRESHOLD = 1000;\nexport class MathBackendWebGL extends KernelBackend {\n    constructor(gpgpu) {\n        super();\n        // Maps data ids that have a pending read operation, to list of subscribers.\n        this.pendingRead = new WeakMap();\n        // List of data ids that are scheduled for disposal, but are waiting on a\n        // pending read operation.\n        this.pendingDisposal = new WeakSet();\n        // Used to count the number of 'shallow' sliced tensors that point to the\n        // same data id.\n        this.dataRefCount = new WeakMap();\n        this.numBytesInGPU = 0;\n        // Accumulated time spent (including blocking) in uploading data to webgl.\n        this.uploadWaitMs = 0;\n        // Accumulated time spent (including blocking in downloading data from webgl.\n        this.downloadWaitMs = 0;\n        this.warnedAboutMemory = false;\n        this.warnedAboutCPUBackend = false;\n        this.pendingDeletes = 0;\n        this.disposed = false;\n        if (!env().getBool('HAS_WEBGL')) {\n            throw new Error('WebGL is not supported on this device');\n        }\n        if (gpgpu == null) {\n            const gl = getWebGLContext(env().getNumber('WEBGL_VERSION'));\n            this.binaryCache = getBinaryCache(env().getNumber('WEBGL_VERSION'));\n            this.gpgpu = new GPGPUContext(gl);\n            this.canvas = gl.canvas;\n            this.gpgpuCreatedLocally = true;\n        }\n        else {\n            this.gpgpu = gpgpu;\n            this.binaryCache = {};\n            this.gpgpuCreatedLocally = false;\n            this.canvas = gpgpu.gl.canvas;\n        }\n        this.textureManager = new TextureManager(this.gpgpu);\n        this.numMBBeforeWarning = numMBBeforeWarning();\n        this.texData = new DataStorage(this, engine());\n    }\n    numDataIds() {\n        return this.texData.numDataIds() +\n            (this.cpuBackend ? this.cpuBackend.numDataIds() : 0) -\n            this.pendingDeletes;\n    }\n    write(values, shape, dtype) {\n        if (env().getBool('WEBGL_CHECK_NUMERICAL_PROBLEMS') ||\n            env().getBool('DEBUG')) {\n            this.checkNumericalProblems(values);\n        }\n        if (dtype === 'complex64' && values != null) {\n            throw new Error(`Cannot write to a complex64 dtype. ` +\n                `Please use tf.complex(real, imag).`);\n        }\n        const dataId = {};\n        this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });\n        return dataId;\n    }\n    /** Increase refCount of a `TextureData`. */\n    incRef(dataId) {\n        const texData = this.texData.get(dataId);\n        texData.refCount++;\n    }\n    /** Decrease refCount of a `TextureData`. */\n    decRef(dataId) {\n        if (this.texData.has(dataId)) {\n            const texData = this.texData.get(dataId);\n            texData.refCount--;\n        }\n    }\n    move(dataId, values, shape, dtype) {\n        if (env().getBool('DEBUG')) {\n            this.checkNumericalProblems(values);\n        }\n        if (dtype === 'complex64') {\n            throw new Error(`Cannot write to a complex64 dtype. ` +\n                `Please use tf.complex(real, imag).`);\n        }\n        this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });\n    }\n    disposeIntermediateTensorInfo(tensorInfo) {\n        const dataId = tensorInfo.dataId;\n        if (this.texData.has(dataId)) {\n            const textureData = this.texData.get(dataId);\n            textureData.refCount--;\n            if (textureData.refCount < 1) {\n                this.disposeData(dataId);\n            }\n        }\n    }\n    readSync(dataId) {\n        const texData = this.texData.get(dataId);\n        const { values, dtype, complexTensors, slice, shape, isPacked } = texData;\n        // The presence of `slice` indicates this tensor is a shallow slice of a\n        // different tensor, and is using that original tensor's texture. Run\n        // `clone` in order to copy that texture and read from it.\n        if (slice != null) {\n            let program;\n            if (isPacked) {\n                program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n            }\n            else {\n                program = new UnaryOpProgram(shape, unary_op.CLONE);\n            }\n            const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n            const data = this.readSync(res.dataId);\n            this.disposeIntermediateTensorInfo(res);\n            return data;\n        }\n        if (values != null) {\n            return this.convertAndCacheOnCPU(dataId);\n        }\n        if (dtype === 'string') {\n            return values;\n        }\n        const shouldTimeProgram = this.activeTimers != null;\n        let start;\n        if (shouldTimeProgram) {\n            start = util.now();\n        }\n        let result;\n        if (dtype === 'complex64') {\n            const realValues = complexTensors.real.dataSync();\n            const imagValues = complexTensors.imag.dataSync();\n            result = backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        else {\n            result = this.getValuesFromTexture(dataId);\n        }\n        if (shouldTimeProgram) {\n            this.downloadWaitMs += util.now() - start;\n        }\n        return this.convertAndCacheOnCPU(dataId, result);\n    }\n    async read(dataId) {\n        if (this.pendingRead.has(dataId)) {\n            const subscribers = this.pendingRead.get(dataId);\n            return new Promise(resolve => subscribers.push(resolve));\n        }\n        const texData = this.texData.get(dataId);\n        const { values, shape, slice, dtype, complexTensors, isPacked } = texData;\n        // The presence of `slice` indicates this tensor is a shallow slice of a\n        // different tensor, and is using that original tensor's texture. Run\n        // `clone` in order to copy that texture and read from it.\n        if (slice != null) {\n            let program;\n            if (isPacked) {\n                program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n            }\n            else {\n                program = new UnaryOpProgram(shape, unary_op.CLONE);\n            }\n            const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n            const data = this.read(res.dataId);\n            this.disposeIntermediateTensorInfo(res);\n            return data;\n        }\n        if (values != null) {\n            return this.convertAndCacheOnCPU(dataId);\n        }\n        if (!env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED') &&\n            env().getNumber('WEBGL_VERSION') === 2) {\n            throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and ` +\n                `WEBGL_VERSION=2 not yet supported.`);\n        }\n        let buffer = null;\n        let tmpDownloadTarget;\n        if (dtype !== 'complex64' && env().get('WEBGL_BUFFER_SUPPORTED')) {\n            // Possibly copy the texture into a buffer before inserting a fence.\n            tmpDownloadTarget = this.decode(dataId);\n            const tmpData = this.texData.get(tmpDownloadTarget.dataId);\n            buffer = this.gpgpu.createBufferFromTexture(tmpData.texture, ...tex_util.getDenseTexShape(shape));\n        }\n        this.pendingRead.set(dataId, []);\n        if (dtype !== 'complex64') {\n            // Create a fence and wait for it to resolve.\n            await this.gpgpu.createAndWaitForFence();\n        }\n        // Download the values from the GPU.\n        let vals;\n        if (dtype === 'complex64') {\n            const ps = await Promise.all([complexTensors.real.data(), complexTensors.imag.data()]);\n            const realValues = ps[0];\n            const imagValues = ps[1];\n            vals = backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        else if (buffer == null) {\n            vals = this.getValuesFromTexture(dataId);\n        }\n        else {\n            const size = util.sizeFromShape(shape);\n            vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer, size);\n        }\n        if (tmpDownloadTarget != null) {\n            this.disposeIntermediateTensorInfo(tmpDownloadTarget);\n        }\n        const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);\n        const subscribers = this.pendingRead.get(dataId);\n        this.pendingRead.delete(dataId);\n        // Notify all pending reads.\n        subscribers.forEach(resolve => resolve(dTypeVals));\n        if (this.pendingDisposal.has(dataId)) {\n            this.pendingDisposal.delete(dataId);\n            this.disposeData(dataId);\n            this.pendingDeletes--;\n        }\n        return dTypeVals;\n    }\n    checkNumericalProblems(values) {\n        if (values == null) {\n            return;\n        }\n        for (let i = 0; i < values.length; i++) {\n            const num = values[i];\n            if (!webgl_util.canBeRepresented(num)) {\n                if (env().getBool('WEBGL_RENDER_FLOAT32_CAPABLE')) {\n                    throw Error(`The value ${num} cannot be represented with your ` +\n                        `current settings. Consider enabling float32 rendering: ` +\n                        `'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);\n                }\n                throw Error(`The value ${num} cannot be represented on this device.`);\n            }\n        }\n    }\n    getValuesFromTexture(dataId) {\n        const { shape, dtype, isPacked } = this.texData.get(dataId);\n        const size = util.sizeFromShape(shape);\n        if (env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {\n            const tmpTarget = this.decode(dataId);\n            const tmpData = this.texData.get(tmpTarget.dataId);\n            const vals = this.gpgpu\n                .downloadMatrixFromPackedTexture(tmpData.texture, ...tex_util.getDenseTexShape(shape))\n                .subarray(0, size);\n            this.disposeIntermediateTensorInfo(tmpTarget);\n            return vals;\n        }\n        const shouldUsePackedProgram = env().getBool('WEBGL_PACK') && isPacked === true;\n        const outputShape = shouldUsePackedProgram ? webgl_util.getShapeAs3D(shape) : shape;\n        const program = shouldUsePackedProgram ?\n            new EncodeFloatPackedProgram(outputShape) :\n            new EncodeFloatProgram(outputShape);\n        const output = this.runWebGLProgram(program, [{ shape: outputShape, dtype, dataId }], 'float32');\n        const tmpData = this.texData.get(output.dataId);\n        const vals = this.gpgpu\n            .downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture, tmpData.texShape[0], tmpData.texShape[1])\n            .subarray(0, size);\n        this.disposeIntermediateTensorInfo(output);\n        return vals;\n    }\n    async time(f) {\n        const oldActiveTimers = this.activeTimers;\n        const newActiveTimers = [];\n        let outerMostTime = false;\n        if (this.programTimersStack == null) {\n            this.programTimersStack = newActiveTimers;\n            outerMostTime = true;\n        }\n        else {\n            this.activeTimers.push(newActiveTimers);\n        }\n        this.activeTimers = newActiveTimers;\n        f();\n        // needing to split these up because util.flatten only accepts certain types\n        const flattenedActiveTimerQueries = util.flatten(this.activeTimers.map((d) => d.query))\n            .filter(d => d != null);\n        const flattenedActiveTimerNames = util.flatten(this.activeTimers.map((d) => d.name))\n            .filter(d => d != null);\n        this.activeTimers = oldActiveTimers;\n        if (outerMostTime) {\n            this.programTimersStack = null;\n        }\n        const res = {\n            uploadWaitMs: this.uploadWaitMs,\n            downloadWaitMs: this.downloadWaitMs,\n            kernelMs: null,\n            wallMs: null // will be filled by the engine\n        };\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n            res['kernelMs'] = util.sum(kernelMs);\n            res['getExtraProfileInfo'] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d }))\n                .map(d => `${d.name}: ${d.ms}`)\n                .join(', ');\n        }\n        else {\n            res['kernelMs'] = {\n                error: 'WebGL query timers are not supported in this environment.'\n            };\n        }\n        this.uploadWaitMs = 0;\n        this.downloadWaitMs = 0;\n        return res;\n    }\n    memory() {\n        return {\n            unreliable: false,\n            numBytesInGPU: this.numBytesInGPU,\n            numBytesInGPUAllocated: this.textureManager.numBytesAllocated,\n            numBytesInGPUFree: this.textureManager.numBytesFree\n        };\n    }\n    startTimer() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            return this.gpgpu.beginQuery();\n        }\n        return { startMs: util.now(), endMs: null };\n    }\n    endTimer(query) {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            this.gpgpu.endQuery();\n            return query;\n        }\n        query.endMs = util.now();\n        return query;\n    }\n    async getQueryTime(query) {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            return this.gpgpu.waitForQueryAndGetTime(query);\n        }\n        const timerQuery = query;\n        return timerQuery.endMs - timerQuery.startMs;\n    }\n    disposeData(dataId) {\n        if (this.pendingDisposal.has(dataId)) {\n            return;\n        }\n        if (this.pendingRead.has(dataId)) {\n            this.pendingDisposal.add(dataId);\n            this.pendingDeletes++;\n            return;\n        }\n        // No-op if already disposed.\n        if (!this.texData.has(dataId)) {\n            return;\n        }\n        this.releaseGPUData(dataId);\n        const { complexTensors } = this.texData.get(dataId);\n        if (complexTensors != null) {\n            complexTensors.real.dispose();\n            complexTensors.imag.dispose();\n        }\n        this.texData.delete(dataId);\n    }\n    releaseGPUData(dataId) {\n        const { texture, dtype, texShape, usage, isPacked, slice } = this.texData.get(dataId);\n        const key = slice && slice.origDataId || dataId;\n        const refCount = this.dataRefCount.get(key);\n        if (refCount > 1) {\n            this.dataRefCount.set(key, refCount - 1);\n        }\n        else {\n            this.dataRefCount.delete(key);\n            if (texture != null) {\n                this.numBytesInGPU -= this.computeBytes(texShape, dtype);\n                this.textureManager.releaseTexture(texture, texShape, usage, isPacked);\n            }\n        }\n        const texData = this.texData.get(dataId);\n        texData.texture = null;\n        texData.texShape = null;\n        texData.isPacked = false;\n        texData.slice = null;\n    }\n    getTexture(dataId) {\n        this.uploadToGPU(dataId);\n        return this.texData.get(dataId).texture;\n    }\n    /**\n     * Returns internal information for the specific data bucket. Used in unit\n     * tests.\n     */\n    getDataInfo(dataId) {\n        return this.texData.get(dataId);\n    }\n    getCPUBackend() {\n        if (!env().getBool('WEBGL_CPU_FORWARD')) {\n            return null;\n        }\n        if (this.cpuBackend == null) {\n            this.cpuBackend = engine().findBackend('cpu');\n        }\n        return this.cpuBackend;\n    }\n    /*\n    Tests whether all the inputs to an op are small and on the CPU. This heuristic\n    determines when it would be faster to execute a kernel on the CPU. WebGL\n    kernels opt into running this check and forwarding when appropriate.\n    TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more\n    sustainable strategy for optimizing backend execution of ops.\n     */\n    shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) {\n        const cpuBackend = this.getCPUBackend();\n        if (!this.warnedAboutCPUBackend && cpuBackend == null) {\n            console.warn('Your application contains ops that are small enough to be ' +\n                'executed on the CPU backend, however the CPU backend cannot ' +\n                'be found. Consider importing the CPU backend ' +\n                '(@tensorflow/tfjs-backend-cpu) for better performance.');\n            this.warnedAboutCPUBackend = true;\n        }\n        return cpuBackend != null &&\n            inputs.every(input => this.texData.get(input.dataId).texture == null &&\n                util.sizeFromShape(input.shape) < sizeThreshold);\n    }\n    getGPGPUContext() {\n        return this.gpgpu;\n    }\n    complex(real, imag) {\n        const result = this.makeOutput(real.shape, 'complex64');\n        const resultData = this.texData.get(result.dataId);\n        // The backend owns the reference to the underlying real and imaginary\n        // clones. These will explicitly get disposed when the complex tensor is\n        // disposed.\n        resultData.complexTensors = {\n            real: engine().keep(real.clone()),\n            imag: engine().keep(imag.clone())\n        };\n        return result;\n    }\n    real(input) {\n        const resultData = this.texData.get(input.dataId);\n        return resultData.complexTensors.real.clone();\n    }\n    imag(input) {\n        const resultData = this.texData.get(input.dataId);\n        return resultData.complexTensors.imag.clone();\n    }\n    slice(x, begin, size) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = sliceImplCPU(this.texData.get(x.dataId).values, begin, size, x.shape, x.dtype);\n            return this.makeOutput(size, x.dtype, outValues);\n        }\n        // Short-circuit computation if the slice is zero-sized.\n        if (util.sizeFromShape(size) === 0) {\n            return tensor([], size, x.dtype);\n        }\n        const { isPacked } = this.texData.get(x.dataId);\n        const isContinous = slice_util.isSliceContinous(x.shape, begin, size);\n        if (isPacked || !isContinous) {\n            const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n                new SlicePackedProgram(size) :\n                new SliceProgram(size);\n            const customSetup = program.getCustomSetupFunc(begin);\n            return this.compileAndRun(program, [x], null, customSetup);\n        }\n        this.uploadToGPU(x.dataId);\n        return this.shallowSlice(x, begin, size);\n    }\n    shallowSlice(x, begin, size) {\n        const xTexData = this.texData.get(x.dataId);\n        const t = this.makeOutput(size, x.dtype);\n        const newTexData = this.texData.get(t.dataId);\n        // Copy texture data from the original tensor.\n        Object.assign(newTexData, xTexData);\n        newTexData.shape = size;\n        newTexData.dtype = x.dtype;\n        let flatOffset = slice_util.computeFlatOffset(begin, x.strides);\n        if (xTexData.slice) {\n            // We are slicing an already sliced tensor, so we have to accumulate\n            // the offset.\n            flatOffset += xTexData.slice.flatOffset;\n        }\n        newTexData.slice = {\n            flatOffset,\n            // Point to the original dataId, which is used to do ref counting.\n            origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId\n        };\n        // Increase the ref count for that data bucket.\n        const refCount = this.dataRefCount.get(newTexData.slice.origDataId) || 1;\n        this.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);\n        return t;\n    }\n    stridedSlice(x, begin, end, strides) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.stridedSlice(x, begin, end, strides));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const outShape = slice_util.computeOutShape(begin, end, strides);\n        if (outShape.some(axis => axis === 0)) {\n            return tensor([], outShape);\n        }\n        const program = new StridedSliceProgram(begin, strides, outShape);\n        return this.compileAndRun(program, [x]);\n    }\n    reverse(x, axis) {\n        const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n            new ReversePackedProgram(x.shape, axis) :\n            new ReverseProgram(x.shape, axis);\n        return this.compileAndRun(program, [x]);\n    }\n    concat(tensors, axis) {\n        if (tensors[0].dtype === 'complex64') {\n            const reals = tensors.map((t) => real(t));\n            const imags = tensors.map((t) => imag(t));\n            return complex(this.concat(reals, axis), this.concat(imags, axis));\n        }\n        if (tensors.length === 1) {\n            return tensors[0];\n        }\n        if (tensors.length > env().getNumber('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n            const midIndex = Math.floor(tensors.length / 2);\n            const leftSide = this.concat(tensors.slice(0, midIndex), axis);\n            const rightSide = this.concat(tensors.slice(midIndex), axis);\n            return this.concat([leftSide, rightSide], axis);\n        }\n        if (env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') && tensors[0].rank > 1) {\n            const program = new ConcatPackedProgram(tensors.map(t => t.shape), axis);\n            return this.compileAndRun(program, tensors);\n        }\n        // Any concat of n-dimensional tensors across any axis can be reduced to\n        // a concatenation of two-dimensional tensors across the axis 1 by first\n        // partitioning the axes of the original tensors into those less than the\n        // axis to be concatenated and the rest. Then reshape the tensors\n        // into a two-dimensional tensor by collapsing these two sets of axes and\n        // concatenate the resulting matrices across the axis 1, finally reshaping\n        // the result to have the proper shape.\n        const outShape = backend_util.computeOutShape(tensors.map(t => t.shape), axis);\n        const tensors2D = tensors.map(t => t.as2D(-1, util.sizeFromShape(t.shape.slice(axis))));\n        const program = new ConcatProgram(tensors2D.map(t => t.shape));\n        const res = this.compileAndRun(program, tensors2D);\n        return res.reshape(outShape);\n    }\n    neg(x) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.neg(x));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.NEG, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.NEG);\n        return this.compileAndRun(program, [x]);\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n        const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n        const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n        const [batch, ,] = a.shape;\n        // Since the matrices are vectors, it is faster to call mul().sum()\n        // because sum() is O(sqrt(N)) due to divide-and-conquer.\n        if ((outerShapeA === 1 || outerShapeB === 1) &&\n            sharedDim > MATMUL_SHARED_DIM_THRESHOLD) {\n            if (transposeA) {\n                a = transpose(a, [0, 2, 1]);\n            }\n            if (transposeB) {\n                b = transpose(b, [0, 2, 1]);\n            }\n            const a3D = outerShapeB === 1 ? a : a.as3D(batch, sharedDim, 1);\n            const axis = outerShapeB === 1 ? 2 : 1;\n            const b3D = outerShapeB === 1 ? b.as3D(batch, 1, sharedDim) : b;\n            return this.multiply(a3D, b3D).sum(axis, true /* keepDims */);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        const program = new MatMulPackedProgram(a.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n        const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n        const [batch, ,] = a.shape;\n        const dtype = upcastType(a.dtype, b.dtype);\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;\n        const program = new MatMulPackedProgram(a.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [a, b];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (preluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        return this.compileAndRun(program, inputs, dtype);\n    }\n    multiply(a, b) {\n        if (a.dtype === 'complex64') {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const realProgram = new BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.REAL, a.shape, b.shape);\n            const imagProgram = new BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);\n            const inputs = [\n                this.makeComplexComponentTensorInfo(a, aData.complexTensors.real),\n                this.makeComplexComponentTensorInfo(a, aData.complexTensors.imag),\n                this.makeComplexComponentTensorInfo(b, bData.complexTensors.real),\n                this.makeComplexComponentTensorInfo(b, bData.complexTensors.imag)\n            ];\n            const real = this.compileAndRun(realProgram, inputs);\n            const imag = this.compileAndRun(imagProgram, inputs);\n            const complex = this.complex(real, imag);\n            real.dispose();\n            imag.dispose();\n            return complex;\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        if (this.shouldExecuteOnCPU([a, b])) {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const [outValues, outShape] = multiplyImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n            return this.makeOutput(outShape, dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_gpu.MUL, a.dtype);\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.MUL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], a.dtype);\n    }\n    localResponseNormalization4D(x, radius, bias, alpha, beta) {\n        const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n            new LRNPackedProgram(x.shape, radius, bias, alpha, beta) :\n            new LRNProgram(x.shape, radius, bias, alpha, beta);\n        return this.compileAndRun(program, [x]);\n    }\n    LRNGrad(dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {\n        const program = new LRNGradProgram(inputImage.shape, depthRadius, bias, alpha, beta);\n        return this.compileAndRun(program, [inputImage, outputImage, dy]);\n    }\n    tile(x, reps) {\n        if (x.dtype === 'string') {\n            const data = this.readSync(x.dataId);\n            const decodedData = data.map(d => util.decodeString(d));\n            const buf = buffer(x.shape, x.dtype, decodedData);\n            return tile(buf, reps);\n        }\n        const program = new TileProgram(x.shape, reps);\n        return this.compileAndRun(program, [x]);\n    }\n    pad(x, paddings, constantValue) {\n        const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n            new PadPackedProgram(x.shape, paddings, constantValue) :\n            new PadProgram(x.shape, paddings, constantValue);\n        return this.compileAndRun(program, [x]);\n    }\n    gather(x, indices, axis) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x, indices], () => this.cpuBackend.gather(x, indices, axis));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = new GatherProgram(x.shape, indices.size, axis);\n        return this.compileAndRun(program, [x, indices]);\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        util.assert(x.rank <= 4, () => 'batchToSpaceND for rank > 4 with a WebGL backend not ' +\n            'implemented yet');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n        const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n        const reshapedPermuted = backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n        const sliceBeginCoords = backend_util.getSliceBeginCoords(crops, blockShape.length);\n        const sliceSize = backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n        return transpose(x.reshape(reshaped), permuted)\n            .reshape(reshapedPermuted)\n            .slice(sliceBeginCoords, sliceSize);\n    }\n    spaceToBatchND(x, blockShape, paddings) {\n        util.assert(x.rank <= 4, () => 'spaceToBatchND for rank > 4 with a WebGL backend not ' +\n            'implemented yet');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const completePaddings = [[0, 0]];\n        completePaddings.push(...paddings);\n        for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n            completePaddings.push([0, 0]);\n        }\n        const paddedX = x.pad(completePaddings);\n        const reshapedPaddedShape = backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n        const permutedReshapedPaddedPermutation = backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n        const flattenShape = backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n        const paddedXT = transpose(paddedX.reshape(reshapedPaddedShape), permutedReshapedPaddedPermutation);\n        return reshape(paddedXT, flattenShape);\n    }\n    reduce(x, reduceType, dtype) {\n        const batchSize = x.shape[0];\n        const inSize = x.shape[1];\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const outSize = Math.ceil(inSize / windowSize);\n        const reduceInfo = { windowSize, inSize, batchSize, outSize };\n        const program = new ReduceProgram(reduceInfo, reduceType);\n        const output = this.compileAndRun(program, [x], dtype);\n        // No need to run another GPGPU program.\n        if (output.shape[1] === 1) {\n            return output;\n        }\n        return this.reduce(output, reduceType, dtype);\n    }\n    argReduce(x, reduceType, bestIndicesA = null) {\n        let batchSize = x.shape[0];\n        let inSize = x.shape[1];\n        if (bestIndicesA != null) {\n            batchSize = bestIndicesA.shape[0];\n            inSize = bestIndicesA.shape[1];\n        }\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const reduceInfo = {\n            windowSize,\n            inSize,\n            batchSize,\n            outSize: Math.ceil(inSize / windowSize)\n        };\n        const program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);\n        const inputs = [x];\n        if (bestIndicesA != null) {\n            inputs.push(bestIndicesA);\n        }\n        const output = this.compileAndRun(program, inputs, 'int32');\n        // No need to run another GPGPU program.\n        if (output.shape[1] === 1) {\n            return output;\n        }\n        return this.argReduce(x, reduceType, output);\n    }\n    argReducePacked(x, reduceType, bestIndicesA = null) {\n        const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;\n        const inSize = inShape[inShape.length - 1];\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null);\n        const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];\n        const output = this.compileAndRun(program, inputs, 'int32');\n        if (output.rank === x.rank) {\n            return this.argReducePacked(x, reduceType, output);\n        }\n        return output;\n    }\n    sum(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        return this.reduce(a2D, 'sum', outputDType).reshape(outShape);\n    }\n    prod(x, axes) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.prod(x, axes));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        return this.reduce(a2D, 'prod', outputDType).reshape(outShape);\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        let axis = 0;\n        const permutation = backend_util.getAxesPermutation([axis], x.rank);\n        let permutedX = x;\n        if (permutation != null) {\n            permutedX = transpose(x, permutation);\n            axis = backend_util.getInnerMostAxes(1, x.rank)[0];\n        }\n        const outShape = segment_util.computeOutShape(permutedX.shape, axis, numSegments);\n        const inSize = util.sizeFromShape([permutedX.shape[axis]]);\n        const a2D = permutedX.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        let result = this.segOpCompute(a2D, 'unsortedSegmentSum', segmentIds, outputDType, numSegments)\n            .reshape(outShape);\n        if (permutation != null) {\n            result =\n                transpose(result, backend_util.getUndoAxesPermutation(permutation));\n        }\n        return result;\n    }\n    segOpCompute(x, segOpType, segmentIds, dtype, numSegments) {\n        const batchSize = x.shape[0];\n        const inSize = x.shape[1];\n        const windowSize = segment_util.segOpComputeOptimalWindowSize(inSize, numSegments);\n        const segOpInfo = { windowSize, inSize, batchSize, numSegments };\n        const program = new SegmentOpProgram(segOpInfo, segOpType);\n        const output = this.compileAndRun(program, [x, segmentIds], dtype);\n        // No need to run another GPGPU program.\n        if (output.shape[1] === numSegments) {\n            return output;\n        }\n        segmentIds = range(0, numSegments).tile([inSize / windowSize]);\n        return this.segOpCompute(output, segOpType, segmentIds, dtype, numSegments);\n    }\n    argMinMaxReduce(x, axis, reduceType) {\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('arg' + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.rank);\n        if (!env().getBool('WEBGL_PACK_REDUCE') || x.rank <= 2) {\n            const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n            const inSize = util.sizeFromShape(reduceShape);\n            const a2D = x.as2D(-1, inSize);\n            return this.argReduce(a2D, reduceType).reshape(outShape);\n        }\n        return this.argReducePacked(x, reduceType);\n    }\n    argMin(x, axis) {\n        return this.argMinMaxReduce(x, axis, 'min');\n    }\n    argMax(x, axis) {\n        return this.argMinMaxReduce(x, axis, 'max');\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        if (axis !== x.rank - 1) {\n            throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.rank - 1} ` +\n                `but got axis=${axis}`);\n        }\n        const size = x.shape[axis];\n        let result = x;\n        // Use cumsum parallel algorithm, ref:\n        // https://developer.nvidia.com/gpugems/gpugems3/part-vi-gpu-computing/chapter-39-parallel-prefix-sum-scan-cuda\n        for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {\n            const program = new CumSumProgram(x.shape, false, reverse);\n            const customSetup = program.getCustomSetupFunc(i);\n            const prevResult = result;\n            result = this.compileAndRun(program, [result], result.dtype, customSetup);\n            prevResult.dispose();\n        }\n        // For exclusive cumsum, shift the end result in the direction of sum and\n        // add 0 to the front index.\n        if (exclusive) {\n            const program = new CumSumProgram(x.shape, exclusive, reverse);\n            const prevResult = result;\n            result = this.compileAndRun(program, [result]);\n            prevResult.dispose();\n        }\n        return result;\n    }\n    equal(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    notEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.NOT_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.NOT_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    less(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.less(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LESS, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LESS, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    lessEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LESS_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LESS_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    greater(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.greater(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.GREATER, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.GREATER, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    greaterEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.GREATER_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.GREATER_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    logicalNot(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.LOGICAL_NOT);\n        return this.compileAndRun(program, [x]);\n    }\n    logicalAnd(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LOGICAL_AND, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LOGICAL_AND, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    logicalOr(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LOGICAL_OR, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LOGICAL_OR, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    select(condition, a, b) {\n        const program = new SelectProgram(condition.rank, a.shape, a.rank);\n        return this.compileAndRun(program, [condition, a, b], upcastType(a.dtype, b.dtype));\n    }\n    where(condition) {\n        backend_util.warn('tf.where() in webgl locks the UI thread. ' +\n            'Call tf.whereAsync() instead');\n        const condVals = condition.dataSync();\n        return whereImpl(condition.shape, condVals);\n    }\n    topk(x, k, sorted) {\n        const xVals = x.dataSync();\n        return topkImpl(xVals, x.shape, x.dtype, k, sorted);\n    }\n    min(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'min', a2D.dtype).reshape(outShape);\n    }\n    minimum(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.minimum(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MIN, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MIN, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    mod(a, b) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MOD, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MOD, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    maximum(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.maximum(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MAX, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MAX, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    all(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'all', a2D.dtype).reshape(outShape);\n    }\n    any(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'any', a2D.dtype).reshape(outShape);\n    }\n    floorDiv(a, b) {\n        const op = binaryop_gpu.INT_DIV;\n        const outputDtype = 'int32';\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.INT_DIV, outputDtype);\n        }\n        const program = new BinaryOpProgram(op, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], outputDtype);\n    }\n    add(a, b) {\n        if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n            return this.complexSeparableBinaryOp(a, b, binaryop_gpu.ADD);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        if (this.shouldExecuteOnCPU([a, b])) {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const [outValues, outShape] = addImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n            return this.makeOutput(outShape, dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_gpu.ADD, dtype);\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.ADD, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    packedUnaryOp(x, op, dtype) {\n        const program = new UnaryOpPackedProgram(x.shape, op);\n        return this.compileAndRun(program, [x], dtype);\n    }\n    packedBinaryOp(a, b, op, dtype, checkOutOfBounds = false) {\n        const program = new BinaryOpPackedProgram(op, a.shape, b.shape, checkOutOfBounds);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    /**\n     * Computes a complex binary operation that can be decomposed into a simple\n     * binary operation on both the real and imagary parts.\n     */\n    complexSeparableBinaryOp(a, b, op) {\n        const aData = this.texData.get(a.dataId);\n        const bData = this.texData.get(b.dataId);\n        const [real, imag] = [\n            [aData.complexTensors.real, bData.complexTensors.real],\n            [aData.complexTensors.imag, bData.complexTensors.imag]\n        ].map(complexParts => {\n            const [aPart, bPart] = complexParts;\n            const aHandle = this.makeComplexComponentTensorInfo(a, aPart);\n            const bHandle = this.makeComplexComponentTensorInfo(b, bPart);\n            const program = new BinaryOpProgram(op, a.shape, b.shape);\n            return this.compileAndRun(program, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));\n        });\n        const complex = this.complex(real, imag);\n        real.dispose();\n        imag.dispose();\n        return complex;\n    }\n    // Returns a TensorInfo with the complex shape and the dataId of the\n    // underlying part. We need to do this because a reshaped complex tensor is\n    // not reflected in its parts.\n    makeComplexComponentTensorInfo(complexTensor, complexPart) {\n        return {\n            dataId: complexPart.dataId,\n            dtype: complexPart.dtype,\n            shape: complexTensor.shape\n        };\n    }\n    addN(tensors) {\n        if (tensors.length === 1) {\n            return tensors[0];\n        }\n        // Limit the number of uploaded textures for optimization.\n        if (tensors.length > env().get('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n            const midIndex = Math.floor(tensors.length / 2);\n            const leftSide = this.addN(tensors.slice(0, midIndex));\n            const rightSide = this.addN(tensors.slice(midIndex));\n            return this.addN([leftSide, rightSide]);\n        }\n        const dtype = tensors.map(t => t.dtype).reduce((d1, d2) => upcastType(d1, d2));\n        const shapes = tensors.map(t => t.shape);\n        // We can make sure shapes are identical in op level.\n        const usePackedOp = env().getBool('WEBGL_PACK');\n        const program = usePackedOp ?\n            new AddNPackedProgram(tensors[0].shape, shapes) :\n            new AddNProgram(tensors[0].shape, shapes);\n        return this.compileAndRun(program, tensors, dtype);\n    }\n    subtract(a, b) {\n        if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n            return this.complexSeparableBinaryOp(a, b, binaryop_gpu.SUB);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        if (this.shouldExecuteOnCPU([a, b])) {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const [outValues, outShape] = subImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n            return this.makeOutput(outShape, dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_gpu.SUB, a.dtype);\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.SUB, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    pow(a, b) {\n        const usePackedOp = env().getBool('WEBGL_PACK_BINARY_OPERATIONS');\n        const program = usePackedOp ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.POW, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.POW, a.shape, b.shape);\n        const dtype = upcastType(a.dtype, b.dtype);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    ceil(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = ceilImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.CEIL, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.CEIL);\n        return this.compileAndRun(program, [x]);\n    }\n    floor(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = floorImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.FLOOR, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.FLOOR);\n        return this.compileAndRun(program, [x]);\n    }\n    sign(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SIGN);\n        return this.compileAndRun(program, [x]);\n    }\n    isNaN(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_NAN);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    isInf(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_INF);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    isFinite(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_FINITE);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    round(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ROUND);\n        return this.compileAndRun(program, [x]);\n    }\n    exp(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = expImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.EXP, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.EXP);\n        return this.compileAndRun(program, [x]);\n    }\n    expm1(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = expm1ImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.EXPM1, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.EXPM1);\n        return this.compileAndRun(program, [x]);\n    }\n    softmax(logits, dim) {\n        const axes = util.parseAxisParam([dim], logits.shape);\n        // TODO(annxingyuan): Call maxImpl rather than op as part of softmax kernel\n        // modularization.\n        const maxLogit = max(logits, axes);\n        const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n        const a = this.subtract(logits, maxLogit.reshape(expandedShape));\n        const b = this.exp(a);\n        const sumExp = this.sum(b, axes).reshape(expandedShape);\n        // TODO(annxingyuan): Call divImpl rather than op as part of softmax kernel\n        // modularization.\n        return div(b, sumExp);\n    }\n    log(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = logImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_packed_op.LOG, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.LOG);\n        return this.compileAndRun(program, [x]);\n    }\n    log1p(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.LOG1P);\n        return this.compileAndRun(program, [x]);\n    }\n    sqrt(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SQRT);\n        return this.compileAndRun(program, [x]);\n    }\n    rsqrt(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = rsqrtImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.RSQRT);\n        return this.compileAndRun(program, [x]);\n    }\n    reciprocal(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.RECIPROCAL);\n        return this.compileAndRun(program, [x]);\n    }\n    relu(x) {\n        let program;\n        if (env().getBool('WEBGL_PACK')) {\n            program = new UnaryOpPackedProgram(x.shape, unary_packed_op.RELU);\n        }\n        else {\n            program = new UnaryOpProgram(x.shape, unary_op.RELU);\n        }\n        return this.compileAndRun(program, [x]);\n    }\n    relu6(x) {\n        let program;\n        if (env().getBool('WEBGL_PACK')) {\n            program = new UnaryOpPackedProgram(x.shape, unary_packed_op.RELU6);\n        }\n        else {\n            program = new UnaryOpProgram(x.shape, unary_op.RELU6);\n        }\n        return this.compileAndRun(program, [x]);\n    }\n    prelu(x, alpha) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.PRELU, x.shape, alpha.shape) :\n            new BinaryOpProgram(binaryop_gpu.PRELU, x.shape, alpha.shape);\n        return this.compileAndRun(program, [x, alpha]);\n    }\n    elu(x) {\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_packed_op.ELU, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.ELU);\n        return this.compileAndRun(program, [x]);\n    }\n    eluDer(dy, y) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.ELU_DER, dy.shape, y.shape) :\n            new BinaryOpProgram(binaryop_gpu.ELU_DER, dy.shape, y.shape);\n        return this.compileAndRun(program, [dy, y]);\n    }\n    selu(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SELU);\n        return this.compileAndRun(program, [x]);\n    }\n    int(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.TO_INT);\n        return this.compileAndRun(program, [x], 'int32');\n    }\n    clip(x, min, max) {\n        let program;\n        if (env().getBool('WEBGL_PACK_CLIP')) {\n            program = new ClipPackedProgram(x.shape);\n        }\n        else {\n            program = new ClipProgram(x.shape);\n        }\n        const customSetup = program.getCustomSetupFunc(min, max);\n        return this.compileAndRun(program, [x], null, customSetup);\n    }\n    abs(x) {\n        // TODO: handle cases when x is complex.\n        if (this.shouldExecuteOnCPU([x]) && x.dtype !== 'complex64') {\n            const outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.ABS, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.ABS);\n        return this.compileAndRun(program, [x]);\n    }\n    complexAbs(x) {\n        const xData = this.texData.get(x.dataId);\n        const program = new ComplexAbsProgram(x.shape);\n        const inputs = [\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.real),\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.imag),\n        ];\n        return this.compileAndRun(program, inputs);\n    }\n    sigmoid(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SIGMOID);\n        return this.compileAndRun(program, [x]);\n    }\n    softplus(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SOFTPLUS);\n        return this.compileAndRun(program, [x]);\n    }\n    asin(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ASIN);\n        return this.compileAndRun(program, [x]);\n    }\n    acos(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ACOS);\n        return this.compileAndRun(program, [x]);\n    }\n    atan(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ATAN);\n        return this.compileAndRun(program, [x]);\n    }\n    sinh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SINH);\n        return this.compileAndRun(program, [x]);\n    }\n    cosh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.COSH);\n        return this.compileAndRun(program, [x]);\n    }\n    tanh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.TANH);\n        return this.compileAndRun(program, [x]);\n    }\n    asinh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ASINH);\n        return this.compileAndRun(program, [x]);\n    }\n    acosh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ACOSH);\n        return this.compileAndRun(program, [x]);\n    }\n    atanh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ATANH);\n        return this.compileAndRun(program, [x]);\n    }\n    erf(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ERF);\n        return this.compileAndRun(program, [x]);\n    }\n    step(x, alpha) {\n        const program = new UnaryOpProgram(x.shape, unary_op.STEP(alpha));\n        return this.compileAndRun(program, [x]);\n    }\n    conv2dByMatMul(x, filter, convInfo, bias, activation, preluActivationWeights) {\n        // Reshapes conv2D input to 2D tensors, uses matMul and then reshape the\n        // result from 2D to 4D.\n        const xShape = x.shape;\n        const xTexData = this.texData.get(x.dataId);\n        const sharedMatMulDim = convInfo.inChannels;\n        const outerShapeX = xShape[0] * xShape[1] * xShape[2];\n        const outerShapeFilter = convInfo.outChannels;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const transposeA = false;\n        const transposeB = false;\n        // TODO: Once reduction ops are packed, batchMatMul will always be packed\n        // and we can remove this condition.\n        const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) &&\n            sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;\n        const reshapeWillBeExpensive = xShape[2] % 2 !== 0 && !!xTexData.isPacked;\n        if (batchMatMulWillBeUnpacked || !env().getBool('WEBGL_LAZILY_UNPACK') ||\n            !env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ||\n            !reshapeWillBeExpensive) {\n            const targetShape = isChannelsLast ? xShape[0] * xShape[1] * xShape[2] :\n                xShape[0] * xShape[2] * xShape[3];\n            const xReshaped = reshape(x, [1, targetShape, convInfo.inChannels]);\n            const filterReshaped = reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);\n            const result = this.fusedBatchMatMul({\n                a: xReshaped,\n                b: filterReshaped,\n                transposeA,\n                transposeB,\n                bias,\n                activation,\n                preluActivationWeights\n            });\n            return reshape(result, convInfo.outShape);\n        }\n        // Following optimization is specific to packed |x| with odd row count\n        // (For example, in channelLast mode, 'row count' refers to x.shape[2]):\n        // we avoid expensive packed 2x2 reshape by padding row count to next,\n        // even number. When x.shape[2] is odd, the result of packed batchMatMul is\n        // the same (has the same texture layout and and values in the texture) as\n        // it is for even x.shape[2] + 1. We make the odd-rows tensor to look like\n        // even-rows tensor before the operation and, after the batchMatMul,\n        // fix the even-rows result to have odd number of rows.\n        const targetShape = isChannelsLast ?\n            xShape[0] * xShape[1] * (xShape[2] + 1) :\n            xShape[0] * xShape[2] * (xShape[3] + 1);\n        const xReshaped = {\n            dataId: x.dataId,\n            shape: [1, targetShape, convInfo.inChannels],\n            dtype: x.dtype\n        };\n        // xTexData.shape gets referenced from GPGPUBinary.inShapeInfos.\n        // Decrementing row count, after batchMatMul->...->compileProgram leads to\n        // invalid row count within the reference in GPGPUBinary.inShapeInfos.\n        // Alternative fix would be to provide a copy to GPGPUBinary.inShapeInfos\n        // in compileProgram method, but that would affect compilation of all\n        // programs - instead, provide a copy here, with even row count, before\n        // calling batchMatMul->...->compileProgram and after that, the original\n        // xTexData.shape is restored.\n        const originalXTexDataShape = xTexData.shape;\n        xTexData.shape = xTexData.shape.slice();\n        xTexData.shape[xTexData.shape.length - 2]++;\n        util.assert(webgl_util.isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);\n        const filterReshaped = reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);\n        const pointwiseConv = this.fusedBatchMatMul({\n            a: xReshaped,\n            b: filterReshaped,\n            transposeA,\n            transposeB,\n            bias,\n            activation,\n            preluActivationWeights\n        });\n        const pointwiseConvTexData = this.texData.get(pointwiseConv.dataId);\n        util.assert(pointwiseConvTexData.isPacked, () => 'batchMatMul result is expected to be packed');\n        // Restore the input shape to original.\n        xTexData.shape = originalXTexDataShape;\n        // Set the output shape - there is no need for expensive reshape as data\n        // layout is already correct.\n        pointwiseConvTexData.shape = convInfo.outShape;\n        return engine().makeTensorFromDataId(pointwiseConv.dataId, convInfo.outShape, pointwiseConv.dtype);\n    }\n    conv2dWithIm2Row(x, filter, convInfo, bias, activation, preluActivationWeights) {\n        // Rearranges conv2d input so each block to be convolved over forms the\n        // column of a new matrix with shape [filterWidth * filterHeight *\n        // inChannels, outHeight * outWidth]. The filter is also rearranged so each\n        // output channel forms a row of a new matrix with shape [outChannels,\n        // filterWidth * filterHeight * inChannels]. The convolution is then\n        // computed by multiplying these matrices and reshaping the result.\n        const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo;\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const sharedDim = filterWidth * filterHeight * inChannels;\n        const numCols = outHeight * outWidth;\n        const x2ColShape = [sharedDim, numCols];\n        const transposeA = true;\n        const transposeB = false;\n        const xSqueezed = x.squeeze([0]);\n        const w2Row = filter.reshape([1, sharedDim, -1]);\n        const im2ColProgram = new Im2ColPackedProgram(x2ColShape, xSqueezed.shape, convInfo);\n        const im2Col = this.compileAndRun(im2ColProgram, [xSqueezed]).reshape([\n            1, x2ColShape[0], x2ColShape[1]\n        ]);\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;\n        const matmulProgram = new MatMulPackedProgram(im2Col.shape, [1, numCols, convInfo.outChannels], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [im2Col, w2Row];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (hasPreluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        const product = this.compileAndRun(matmulProgram, inputs);\n        if (isChannelsLast) {\n            return product.reshape([1, outHeight, outWidth, convInfo.outChannels]);\n        }\n        else {\n            return product.reshape([1, convInfo.outChannels, outHeight, outWidth]);\n        }\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n            (convInfo.padInfo.type === 'SAME' ||\n                convInfo.padInfo.type === 'VALID')) {\n            return this.conv2dByMatMul(input, filter, convInfo, bias, activation, preluActivationWeights);\n        }\n        if (env().getBool('WEBGL_CONV_IM2COL') && input.shape[0] === 1) {\n            return this.conv2dWithIm2Row(input, filter, convInfo, bias, activation, preluActivationWeights);\n        }\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, false) : null;\n        const program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [input, filter];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (preluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        return this.compileAndRun(program, inputs);\n    }\n    conv2d(x, filter, convInfo) {\n        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n            (convInfo.padInfo.type === 'SAME' ||\n                convInfo.padInfo.type === 'VALID')) {\n            return this.conv2dByMatMul(x, filter, convInfo);\n        }\n        if (env().getBool('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {\n            return this.conv2dWithIm2Row(x, filter, convInfo);\n        }\n        const program = new Conv2DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        const program = new Conv2DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    conv2dDerFilter(x, dy, convInfo) {\n        const program = new Conv2DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        const shouldPackDepthwiseConv = env().getBool('WEBGL_PACK_DEPTHWISECONV') &&\n            convInfo.strideWidth <= 2 &&\n            convInfo.outChannels / convInfo.inChannels === 1;\n        const fusedActivation = activation ?\n            mapActivationToShaderProgram(activation, shouldPackDepthwiseConv) :\n            null;\n        const inputs = [input, filter];\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        if (hasBias) {\n            inputs.push(bias);\n        }\n        if (hasPreluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        let program;\n        if (shouldPackDepthwiseConv) {\n            program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n            return this.compileAndRun(program, inputs);\n        }\n        program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n        return this.compileAndRun(program, inputs);\n    }\n    depthwiseConv2D(x, filter, convInfo) {\n        let program;\n        if (env().getBool('WEBGL_PACK_DEPTHWISECONV') &&\n            convInfo.strideWidth <= 2 &&\n            convInfo.outChannels / convInfo.inChannels === 1) {\n            program = new DepthwiseConvPacked2DProgram(convInfo);\n            return this.compileAndRun(program, [x, filter]);\n        }\n        program = new DepthwiseConv2DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        const program = new DepthwiseConv2DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    depthwiseConv2DDerFilter(x, dy, convInfo) {\n        const program = new DepthwiseConv2DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    conv3d(x, filter, convInfo) {\n        const program = new Conv3DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        const program = new Conv3DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    conv3dDerFilter(x, dy, convInfo) {\n        const program = new Conv3DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    cast(x, dtype) {\n        return backend_util.castTensor(x, dtype, this);\n    }\n    unstack(x, axis) {\n        const num = x.shape[axis];\n        const outShape = new Array(x.rank - 1);\n        let outIndex = 0;\n        for (let i = 0; i < x.rank; i++) {\n            if (i !== axis) {\n                outShape[outIndex++] = x.shape[i];\n            }\n        }\n        const begin = new Array(x.rank).fill(0);\n        const size = x.shape.slice();\n        size[axis] = 1;\n        const res = new Array(num);\n        for (let i = 0; i < res.length; i++) {\n            begin[axis] = i;\n            res[i] = this.slice(x, begin, size).reshape(outShape);\n        }\n        return res;\n    }\n    avgPool3d(x, convInfo) {\n        const program = new Pool3DProgram(convInfo, 'avg', false);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        const avgPool3dBackpropProgram = new AvgPool3DBackpropProgram(convInfo);\n        return this.compileAndRun(avgPool3dBackpropProgram, [dy], x.dtype);\n    }\n    maxPool3d(x, convInfo) {\n        const program = new Pool3DProgram(convInfo, 'max', false);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        const getPositions = true;\n        const maxPool3dPositionsProgram = new Pool3DProgram(convInfo, 'max', getPositions);\n        const maxPool3dPositions = this.compileAndRun(maxPool3dPositionsProgram, [x]);\n        const maxPool3dBackPropProgram = new MaxPool3DBackpropProgram(convInfo);\n        const result = this.compileAndRun(maxPool3dBackPropProgram, [dy, maxPool3dPositions], x.dtype);\n        maxPool3dPositions.dispose();\n        return result;\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        const program = env().getBool('WEBGL_PACK_IMAGE_OPERATIONS') ?\n            new ResizeBilinearPackedProgram(x.shape, newHeight, newWidth, alignCorners) :\n            new ResizeBilinearProgram(x.shape, newHeight, newWidth, alignCorners);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        const program = new ResizeBilinearBackpropProgram(dy, x, alignCorners);\n        return this.compileAndRun(program, [dy]);\n    }\n    resizeNearestNeighbor(x, newHeight, newWidth, alignCorners) {\n        const program = new ResizeNearestNeighborProgram(x.shape, newHeight, newWidth, alignCorners);\n        return this.compileAndRun(program, [x]);\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        const program = new ResizeNearestNeigborBackpropProgram(dy, x, alignCorners);\n        return this.compileAndRun(program, [dy]);\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        const probs = normalized ? logits : softmax(logits);\n        const batchSize = probs.shape[0];\n        const numOutcomes = probs.shape[1];\n        const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);\n        const customSetup = program.getCustomSetupFunc(seed);\n        return this.compileAndRun(program, [probs], 'int32', customSetup);\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        const program = new OneHotProgram(indices.size, depth, onValue, offValue);\n        return this.compileAndRun(program, [indices]);\n    }\n    diag(x) {\n        const program = new DiagProgram(x.size);\n        return this.compileAndRun(program, [x]);\n    }\n    cropAndResize(image, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        const program = new CropAndResizeProgram(image.shape, boxes.shape, cropSize, method, extrapolationValue);\n        return this.compileAndRun(program, [image, boxes, boxIndex], 'float32');\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n        const batchSize = x.shape[0];\n        const inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];\n        const inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];\n        const inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];\n        const outputHeight = inputHeight * blockSize;\n        const outputWidth = inputWidth * blockSize;\n        const outputDepth = inputDepth / (blockSize * blockSize);\n        const outputShape = (dataFormat === 'NHWC') ?\n            [batchSize, outputHeight, outputWidth, outputDepth] :\n            [batchSize, outputDepth, outputHeight, outputWidth];\n        const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);\n        return this.compileAndRun(program, [x]);\n    }\n    split(x, sizeSplits, axis) {\n        return split(x, sizeSplits, axis);\n    }\n    scatterND(indices, updates, shape) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(updates, indices, shape);\n        const flattenShape = [outputSize / sliceSize, sliceSize];\n        const flattenIndices = indices.reshape([numUpdates, sliceRank]);\n        const flattenX = updates.reshape([numUpdates, sliceSize]);\n        if (outputSize === 0) {\n            return backend_util.reshapeTensor(tensor([]), shape);\n        }\n        const defaultValue = scalar(0);\n        const program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.rank, flattenX.rank, strides, flattenShape);\n        const res = this.compileAndRun(program, [flattenX, flattenIndices, defaultValue]);\n        return res.reshape(shape);\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        const { sliceRank, numUpdates, strides, outputSize } = backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n        const sumDupeIndices = false;\n        const program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.rank, sparseValues.rank, strides, [outputSize, 1], sumDupeIndices);\n        const res = this.compileAndRun(program, [sparseValues, sparseIndices, defaultValue]);\n        return res.reshape(outputShape);\n    }\n    fft(x) {\n        const inverse = false;\n        return this.fftImpl(x, inverse);\n    }\n    ifft(x) {\n        const inverse = true;\n        return this.fftImpl(x, inverse);\n    }\n    fftImpl(x, inverse) {\n        const xData = this.texData.get(x.dataId);\n        const realProgram = new FFTProgram(fft_gpu.COMPLEX_FFT.REAL, x.shape, inverse);\n        const imagProgram = new FFTProgram(fft_gpu.COMPLEX_FFT.IMAG, x.shape, inverse);\n        const inputs = [\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.real),\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.imag),\n        ];\n        const real = this.compileAndRun(realProgram, inputs);\n        const imag = this.compileAndRun(imagProgram, inputs);\n        const complex = this.complex(real, imag).as2D(x.shape[0], x.shape[1]);\n        real.dispose();\n        imag.dispose();\n        return complex;\n    }\n    gatherND(x, indices) {\n        const indicesShape = indices.shape;\n        const sliceRank = indicesShape[indicesShape.length - 1];\n        const [resultShape, numSlices, sliceSize, strides] = backend_util.prepareAndValidate(x, indices);\n        const flattenIndices = indices.reshape([numSlices, sliceRank]);\n        const flattenX = x.reshape([x.size / sliceSize, sliceSize]);\n        const program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);\n        const res = this.compileAndRun(program, [flattenX, flattenIndices]);\n        return res.reshape(resultShape);\n    }\n    fill(shape, value, dtype) {\n        dtype = dtype || util.inferDtype(value);\n        if (dtype === 'string') {\n            // String type should be handled in CPU memory.\n            const values = util.getArrayFromDType(dtype, util.sizeFromShape(shape));\n            values.fill(value);\n            return engine().makeTensor(values, shape, dtype, this);\n        }\n        else {\n            const program = new FillProgram(shape, value);\n            const customSetup = program.getCustomSetupFunc(value);\n            return this.compileAndRun(program, [], dtype, customSetup);\n        }\n    }\n    onesLike(x) {\n        if (x.dtype === 'string') {\n            throw new Error('onesLike is not supported under string dtype');\n        }\n        else {\n            // TODO(cais, smilkov): Add WebGL shader for onesLike:\n            //   https://github.com/tensorflow/tfjs/issues/1293\n            return this.fill(x.shape, 1, x.dtype);\n        }\n    }\n    zerosLike(x) {\n        return this.fill(x.shape, x.dtype === 'string' ? '' : 0, x.dtype);\n    }\n    linspace(start, stop, num) {\n        // TODO: Use CPU implementation due to the precision problem in Safari.\n        return backend_util.linspaceImpl(start, stop, num);\n    }\n    makeTensorInfo(shape, dtype, values) {\n        const dataId = this.write(values, shape, dtype);\n        this.texData.get(dataId).usage = null;\n        return { dataId, shape, dtype };\n    }\n    makeOutput(shape, dtype, values) {\n        const { dataId } = this.makeTensorInfo(shape, dtype, values);\n        return engine().makeTensorFromDataId(dataId, shape, dtype, this);\n    }\n    unpackTensor(input) {\n        const program = new UnpackProgram(input.shape);\n        return this.runWebGLProgram(program, [input], input.dtype);\n    }\n    packTensor(input) {\n        const program = new PackProgram(input.shape);\n        const preventEagerUnpackingOutput = true;\n        return this.runWebGLProgram(program, [input], input.dtype, null /* customSetup */, preventEagerUnpackingOutput);\n    }\n    packedReshape(input, afterShape) {\n        const input3DShape = [\n            webgl_util.getBatchDim(input.shape),\n            ...webgl_util.getRowsCols(input.shape)\n        ];\n        const input3D = {\n            dtype: input.dtype,\n            shape: input3DShape,\n            dataId: input.dataId\n        };\n        const afterShapeAs3D = [\n            webgl_util.getBatchDim(afterShape), ...webgl_util.getRowsCols(afterShape)\n        ];\n        const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n        const preventEagerUnpackingOfOutput = true;\n        const output = this.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n        return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n    }\n    decode(dataId) {\n        const texData = this.texData.get(dataId);\n        const { isPacked, shape, dtype } = texData;\n        const shapeAs3D = webgl_util.getShapeAs3D(shape);\n        let program;\n        if (isPacked) {\n            program = new DecodeMatrixPackedProgram(shapeAs3D);\n        }\n        else {\n            program = new DecodeMatrixProgram(shapeAs3D);\n        }\n        const preventEagerUnpackingOfOutput = true;\n        const out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype, dataId }], dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n        return { dtype, shape, dataId: out.dataId };\n    }\n    runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {\n        const output = this.makeTensorInfo(program.outputShape, outputDtype);\n        const outData = this.texData.get(output.dataId);\n        if (program.packedOutput) {\n            outData.isPacked = true;\n        }\n        if (program.outPackingScheme === tex_util.PackingScheme.DENSE) {\n            const texelShape = tex_util.getDenseTexShape(program.outputShape);\n            // For a densely packed output, we explicitly set texShape\n            // so it doesn't get assigned later according to our typical packing\n            // scheme wherein a single texel can only contain values from adjacent\n            // rows/cols.\n            outData.texShape = texelShape.map(d => d * 2);\n        }\n        if (program.outTexUsage != null) {\n            outData.usage = program.outTexUsage;\n        }\n        if (util.sizeFromShape(output.shape) === 0) {\n            // Short-circuit the computation since the result is empty (has 0 in its\n            // shape).\n            outData.values =\n                util.getTypedArrayFromDType(output.dtype, 0);\n            return output;\n        }\n        const dataToDispose = [];\n        const inputsData = inputs.map(input => {\n            if (input.dtype === 'complex64') {\n                throw new Error(`GPGPUProgram does not support complex64 input. For complex64 ` +\n                    `dtypes, please separate the program into real and imaginary ` +\n                    `parts.`);\n            }\n            let texData = this.texData.get(input.dataId);\n            if (texData.texture == null) {\n                if (!program.packedInputs &&\n                    util.sizeFromShape(input.shape) <=\n                        env().getNumber('WEBGL_SIZE_UPLOAD_UNIFORM')) {\n                    // Upload small tensors that live on the CPU as uniforms, not as\n                    // textures. Do this only when the environment supports 32bit floats\n                    // due to problems when comparing 16bit floats with 32bit floats.\n                    // TODO(https://github.com/tensorflow/tfjs/issues/821): Make it\n                    // possible for packed shaders to sample from uniforms.\n                    return {\n                        shape: input.shape,\n                        texData: null,\n                        isUniform: true,\n                        uniformValues: texData.values\n                    };\n                }\n                // This ensures that if a packed program's inputs have not yet been\n                // uploaded to the GPU, they get uploaded as packed right off the bat.\n                if (program.packedInputs) {\n                    texData.isPacked = true;\n                    texData.shape = input.shape;\n                }\n            }\n            else if (!!texData.isPacked !== !!program.packedInputs) {\n                input = texData.isPacked ? this.unpackTensor(input) :\n                    this.packTensor(input);\n                dataToDispose.push(input);\n                texData = this.texData.get(input.dataId);\n            }\n            else if (texData.isPacked &&\n                !webgl_util.isReshapeFree(texData.shape, input.shape)) {\n                // This is a special case where a texture exists for a tensor\n                // but the shapes are incompatible (due to packing constraints) because\n                // the tensor did not have a chance to go through the packed reshape\n                // shader. This only happens when we reshape the *same* tensor to form\n                // *distinct* inputs to an op, e.g. dotting a vector with itself. This\n                // case will disappear once packed uploading is the default.\n                const savedInput = input;\n                const targetShape = input.shape;\n                input.shape = texData.shape;\n                input = this.packedReshape(input, targetShape);\n                dataToDispose.push(input);\n                texData = this.texData.get(input.dataId);\n                savedInput.shape = targetShape;\n            }\n            this.uploadToGPU(input.dataId);\n            return { shape: input.shape, texData, isUniform: false };\n        });\n        this.uploadToGPU(output.dataId);\n        const outputData = { shape: output.shape, texData: outData, isUniform: false };\n        const key = gpgpu_math.makeShaderKey(program, inputsData, outputData);\n        const binary = this.getAndSaveBinary(key, () => {\n            return gpgpu_math.compileProgram(this.gpgpu, program, inputsData, outputData);\n        });\n        const shouldTimeProgram = this.activeTimers != null;\n        let query;\n        if (shouldTimeProgram) {\n            query = this.startTimer();\n        }\n        gpgpu_math.runProgram(this.gpgpu, binary, inputsData, outputData, customSetup);\n        dataToDispose.forEach(info => this.disposeIntermediateTensorInfo(info));\n        if (shouldTimeProgram) {\n            query = this.endTimer(query);\n            this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });\n        }\n        if (!env().getBool('WEBGL_LAZILY_UNPACK') && outData.isPacked &&\n            preventEagerUnpackingOfOutput === false) {\n            const unpacked = this.unpackTensor(output);\n            this.disposeIntermediateTensorInfo(output);\n            return unpacked;\n        }\n        return output;\n    }\n    compileAndRun(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {\n        outputDtype = outputDtype || inputs[0].dtype;\n        const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput);\n        return engine().makeTensorFromDataId(outInfo.dataId, outInfo.shape, outInfo.dtype);\n    }\n    getAndSaveBinary(key, getBinary) {\n        if (!(key in this.binaryCache)) {\n            this.binaryCache[key] = getBinary();\n        }\n        return this.binaryCache[key];\n    }\n    getTextureManager() {\n        return this.textureManager;\n    }\n    dispose() {\n        if (this.disposed) {\n            return;\n        }\n        // Avoid disposing the compiled webgl programs during unit testing because\n        // it slows down test execution.\n        if (!env().getBool('IS_TEST')) {\n            const allKeys = Object.keys(this.binaryCache);\n            allKeys.forEach(key => {\n                this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);\n                delete this.binaryCache[key];\n            });\n        }\n        this.textureManager.dispose();\n        if (this.canvas != null &&\n            (typeof (HTMLCanvasElement) !== 'undefined' &&\n                this.canvas instanceof HTMLCanvasElement)) {\n            this.canvas.remove();\n        }\n        else {\n            this.canvas = null;\n        }\n        if (this.gpgpuCreatedLocally) {\n            this.gpgpu.program = null;\n            this.gpgpu.dispose();\n        }\n        this.disposed = true;\n    }\n    floatPrecision() {\n        if (this.floatPrecisionValue == null) {\n            this.floatPrecisionValue = tidy(() => {\n                if (!env().get('WEBGL_RENDER_FLOAT32_ENABLED')) {\n                    // Momentarily switching DEBUG flag to false so we don't throw an\n                    // error trying to upload a small value.\n                    const debugFlag = env().getBool('DEBUG');\n                    env().set('DEBUG', false);\n                    const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];\n                    env().set('DEBUG', debugFlag);\n                    if (underflowCheckValue > 0) {\n                        return 32;\n                    }\n                }\n                return 16;\n            });\n        }\n        return this.floatPrecisionValue;\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n    }\n    uploadToGPU(dataId) {\n        const texData = this.texData.get(dataId);\n        const { shape, dtype, values, texture, usage, isPacked } = texData;\n        if (texture != null) {\n            // Array is already on GPU. No-op.\n            return;\n        }\n        const shouldTimeProgram = this.activeTimers != null;\n        let start;\n        if (shouldTimeProgram) {\n            start = util.now();\n        }\n        let texShape = texData.texShape;\n        if (texShape == null) {\n            texShape = webgl_util.getTextureShapeFromLogicalShape(shape, isPacked);\n            texData.texShape = texShape;\n        }\n        if (values != null) {\n            const shapeAs3D = webgl_util.getShapeAs3D(shape);\n            let program;\n            let width = texShape[1], height = texShape[0];\n            const isByteArray = values instanceof Uint8Array;\n            if (isPacked) {\n                [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]);\n                program = new EncodeMatrixPackedProgram(shapeAs3D, [height, width], isByteArray);\n            }\n            else {\n                program =\n                    new EncodeMatrixProgram(shapeAs3D, [height, width], isByteArray);\n            }\n            const tempDenseInputHandle = this.makeTensorInfo([height, width], dtype);\n            if (isByteArray) {\n                this.texData.get(tempDenseInputHandle.dataId).usage =\n                    TextureUsage.PIXELS;\n            }\n            else {\n                this.texData.get(tempDenseInputHandle.dataId).usage =\n                    TextureUsage.UPLOAD;\n            }\n            this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);\n            // We want the output to remain packed regardless of the value of\n            // WEBGL_PACK.\n            const preventEagerUnpacking = true;\n            const encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, null, preventEagerUnpacking);\n            // Have the original texture assume the identity of the encoded output.\n            const outputTexData = this.texData.get(encodedOutputTarget.dataId);\n            texData.texture = outputTexData.texture;\n            texData.texShape = outputTexData.texShape;\n            texData.isPacked = outputTexData.isPacked;\n            texData.usage = outputTexData.usage;\n            this.disposeIntermediateTensorInfo(tempDenseInputHandle);\n            this.texData.delete(encodedOutputTarget.dataId);\n            // Once uploaded, don't store the values on cpu.\n            texData.values = null;\n            if (shouldTimeProgram) {\n                this.uploadWaitMs += util.now() - start;\n            }\n        }\n        else {\n            const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);\n            texData.texture = newTexture;\n        }\n    }\n    convertAndCacheOnCPU(dataId, float32Values) {\n        const texData = this.texData.get(dataId);\n        const { dtype } = texData;\n        this.releaseGPUData(dataId);\n        if (float32Values != null) {\n            texData.values = float32ToTypedArray(float32Values, dtype);\n        }\n        return texData.values;\n    }\n    acquireTexture(texShape, texType, dtype, isPacked) {\n        this.numBytesInGPU += this.computeBytes(texShape, dtype);\n        if (!this.warnedAboutMemory &&\n            this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {\n            const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);\n            this.warnedAboutMemory = true;\n            console.warn(`High memory usage in GPU: ${mb} MB, ` +\n                `most likely due to a memory leak`);\n        }\n        return this.textureManager.acquireTexture(texShape, texType, isPacked);\n    }\n    computeBytes(shape, dtype) {\n        return shape[0] * shape[1] * util.bytesPerElement(dtype);\n    }\n    tryRunOnCpuOrThrow(inputs, fn) {\n        if (this.shouldExecuteOnCPU(inputs)) {\n            try {\n                return fn();\n            }\n            catch (e) {\n                if (env().getBool('IS_TEST')) {\n                    throw new Error('CPU forwarding failed');\n                }\n            }\n        }\n        return null;\n    }\n}\nfunction float32ToTypedArray(a, dtype) {\n    if (dtype === 'float32' || dtype === 'complex64') {\n        return a;\n    }\n    else if (dtype === 'int32' || dtype === 'bool') {\n        const result = (dtype === 'int32') ? new Int32Array(a.length) :\n            new Uint8Array(a.length);\n        for (let i = 0; i < result.length; ++i) {\n            result[i] = Math.round(a[i]);\n        }\n        return result;\n    }\n    else {\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n}\n//# sourceMappingURL=backend_webgl.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport * as gpgpu_util from './gpgpu_util';\nimport * as webgl_util from './webgl_util';\nexport { MathBackendWebGL } from './backend_webgl';\nexport { setWebGLContext } from './canvas_util';\nexport { GPGPUContext } from './gpgpu_context';\n// WebGL specific utils.\nexport { gpgpu_util, webgl_util };\n/**\n * Enforce use of half precision textures if available on the platform.\n *\n * @doc {heading: 'Environment', namespace: 'webgl'}\n */\nexport function forceHalfFloat() {\n    env().set('WEBGL_FORCE_F16_TEXTURES', true);\n}\n//# sourceMappingURL=webgl.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// base.ts is the webgl backend without auto kernel registration.\nimport { device_util, registerBackend } from '@tensorflow/tfjs-core';\nimport { MathBackendWebGL } from './backend_webgl';\nexport { version as version_webgl } from './version';\nif (device_util.isBrowser()) {\n    registerBackend('webgl', () => new MathBackendWebGL(), 2 /* priority */);\n}\n// Export webgl utilities\nexport * from './webgl';\n// Export forceHalfFlost under webgl namespace for the union bundle.\nimport { forceHalfFloat } from './webgl';\nexport const webgl = { forceHalfFloat };\n//# sourceMappingURL=base.js.map", "import { env } from '@tensorflow/tfjs-core';\nimport { BinaryOpProgram } from '../binaryop_gpu';\nimport { BinaryOpPackedProgram } from '../binaryop_packed_gpu';\nimport { UnaryOpProgram } from '../unaryop_gpu';\nexport const CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;\nexport const CHECK_NAN_SNIPPET_BINARY = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nexport const CHECK_NAN_SNIPPET_BINARY_PACKED = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param opSnippets Op snippet to create `UnaryOpProgram`.\n */\nexport function unaryKernelFunc(opSnippet) {\n    return ({ inputs, backend }) => {\n        const { x } = inputs;\n        const webglBackend = backend;\n        const program = new UnaryOpProgram(x.shape, opSnippet);\n        return webglBackend.runWebGLProgram(program, [x], x.dtype);\n    };\n}\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param opSnippet Op snippet to create `BinaryOpProgram`.\n * @param packedOpSnippet Op snippet to create `BinaryOpPackedProgram`.\n * @param checkOutOfBoundsForPackedProgram Whether to set checkOutOfBounds=true\n *     when creating BinaryOpPackedProgram.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc(opSnippet, packedOpSnippet, checkOutOfBoundsForPackedProgram, dtype) {\n    // TODO(jingjin): handle complex64.\n    return ({ inputs, backend }) => {\n        const { a, b } = inputs;\n        const webglBackend = backend;\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(packedOpSnippet, a.shape, b.shape, !!checkOutOfBoundsForPackedProgram) :\n            new BinaryOpProgram(opSnippet, a.shape, b.shape);\n        const $dtype = dtype || a.dtype;\n        const output = webglBackend.runWebGLProgram(program, [a, b], $dtype);\n        return output;\n    };\n}\n//# sourceMappingURL=kernel_funcs_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan2 } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc, CHECK_NAN_SNIPPET_BINARY, CHECK_NAN_SNIPPET_BINARY_PACKED } from '../kernel_utils/kernel_funcs_utils';\nconst ATAN2 = CHECK_NAN_SNIPPET_BINARY + `\n  return atan(a, b);\n`;\nconst ATAN2_PACKED = `\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET_BINARY_PACKED + `\n  return result;\n`;\nexport const atan2KernelFunc = binaryKernelFunc(ATAN2, ATAN2_PACKED);\nexport const atan2Config = {\n    kernelName: Atan2,\n    backendName: 'webgl',\n    kernelFunc: atan2KernelFunc,\n};\n//# sourceMappingURL=Atan2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '@tensorflow/tfjs-core';\nexport function identity(args) {\n    const { inputs, backend } = args;\n    const { x } = inputs;\n    backend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nexport const identityConfig = {\n    kernelName: Identity,\n    backendName: 'webgl',\n    kernelFunc: identity\n};\n//# sourceMappingURL=Identity.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool, backend_util, util } from '@tensorflow/tfjs-core';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nimport { identity } from './Identity';\nexport function avgPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'avgPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        return identity({ inputs: { x }, backend });\n    }\n    const avgPoolProgram = new Pool2DProgram(convInfo, 'avg', false);\n    return backend.runWebGLProgram(avgPoolProgram, [x], 'float32');\n}\nexport const avgPoolConfig = {\n    kernelName: AvgPool,\n    backendName: 'webgl',\n    kernelFunc: avgPool\n};\n//# sourceMappingURL=AvgPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPoolBackprop, backend_util } from '@tensorflow/tfjs-core';\nimport { AvgPool2DBackpropProgram } from '../avg_pool_backprop_gpu';\nimport { assertNotComplex } from '../webgl_util';\nexport function avgPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input } = inputs;\n    const x = input;\n    assertNotComplex([dy, input], 'avgPoolBackprop');\n    const { filterSize, strides, pad } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);\n    const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);\n    return backend.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);\n}\nexport const avgPoolBackpropConfig = {\n    kernelName: AvgPoolBackprop,\n    backendName: 'webgl',\n    kernelFunc: avgPoolBackprop\n};\n//# sourceMappingURL=AvgPoolBackprop.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class BatchNormProgram {\n    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n        this.outputShape = [];\n        this.variableNames = ['x', 'mean', 'variance'];\n        backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n        backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n        let offsetSnippet = '0.0';\n        if (offsetShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n            this.variableNames.push('offset');\n            offsetSnippet = 'getOffsetAtOutCoords()';\n        }\n        let scaleSnippet = '1.0';\n        if (scaleShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n            this.variableNames.push('scale');\n            scaleSnippet = 'getScaleAtOutCoords()';\n        }\n        this.outputShape = xShape;\n        this.userCode = `\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${offsetSnippet};\n        float scale = ${scaleSnippet};\n        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=batchnorm_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class BatchNormPackedProgram {\n    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.variableNames = ['x', 'mean', 'variance'];\n        backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n        backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n        let offsetSnippet = 'vec4(0.0)';\n        if (offsetShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n            this.variableNames.push('offset');\n            offsetSnippet = 'getOffsetAtOutCoords()';\n        }\n        let scaleSnippet = 'vec4(1.0)';\n        if (scaleShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n            this.variableNames.push('scale');\n            scaleSnippet = 'getScaleAtOutCoords()';\n        }\n        this.outputShape = xShape;\n        this.userCode = `\n      void main() {\n        vec4 offset = ${offsetSnippet};\n        vec4 scale = ${scaleSnippet};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=batchnorm_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, FusedBatchNorm, util } from '@tensorflow/tfjs-core';\nimport { BatchNormProgram } from '../batchnorm_gpu';\nimport { BatchNormPackedProgram } from '../batchnorm_packed_gpu';\nexport const batchNormKernelFunc = ({ inputs, backend, attrs }) => {\n    const { x, mean, variance, offset, scale } = inputs;\n    util.assert(mean.shape.length === variance.shape.length, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert(offset == null || mean.shape.length === offset.shape.length, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert(scale == null || mean.shape.length === scale.shape.length, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    let { varianceEpsilon } = attrs;\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const finalInputs = [x, mean, variance];\n    let offsetShape = null;\n    if (offset != null) {\n        offsetShape = offset.shape;\n        finalInputs.push(offset);\n    }\n    let scaleShape = null;\n    if (scale != null) {\n        scaleShape = scale.shape;\n        finalInputs.push(scale);\n    }\n    const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n        new BatchNormPackedProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) :\n        new BatchNormProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);\n    const output = backend.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);\n    return output;\n};\nexport const batchNormConfig = {\n    kernelName: FusedBatchNorm,\n    backendName: 'webgl',\n    kernelFunc: batchNormKernelFunc,\n};\n//# sourceMappingURL=BatchNorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '@tensorflow/tfjs-core';\nimport { CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst COS = CHECK_NAN_SNIPPET_UNARY + `\n  return cos(x);\n`;\nexport const cosKernelFunc = unaryKernelFunc(COS);\nexport const cosConfig = {\n    kernelName: Cos,\n    backendName: 'webgl',\n    kernelFunc: cosKernelFunc,\n};\n//# sourceMappingURL=Cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\n// Without the equality check div produces 0.9999 for a = b, which when\n// floored can cause errors.\nconst DIV = `\nif (a == b) {\n  return 1.0;\n};\nreturn a / b;`;\n// We do the same as in ./binaryop_gpu, with vec4 and ivec4.\n// On Linux, the vectorized implementation produces NaNs when a and b are 0.\nconst DIV_PACKED = `\n  // vec4 one = vec4(equal(a, b));\n  // return one + (vec4(1.0) - one) * a / b;\n  vec4 result = a / b;\n  if(a.x == b.x) {\n    result.x = 1.;\n  }\n  if(a.y == b.y) {\n    result.y = 1.;\n  }\n  if(a.z == b.z) {\n    result.z = 1.;\n  }\n  if(a.w == b.w) {\n    result.w = 1.;\n  }\n\n  return result;\n`;\nexport const divKernelFunc = binaryKernelFunc(DIV, DIV_PACKED, true /* checkOutOfBoundsForPackedProgram */);\nexport const divConfig = {\n    kernelName: Div,\n    backendName: 'webgl',\n    kernelFunc: divKernelFunc,\n};\n//# sourceMappingURL=Div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FlipLeftRightProgram {\n    constructor(imageShape) {\n        this.variableNames = ['Image'];\n        this.outputShape = [];\n        const imageWidth = imageShape[2];\n        this.outputShape = imageShape;\n        this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${imageWidth} - x;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${imageWidth}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n    }\n}\n//# sourceMappingURL=flip_left_right_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FlipLeftRight } from '@tensorflow/tfjs-core';\nimport { FlipLeftRightProgram } from '../flip_left_right_gpu';\nexport const flipLeftRightConfig = {\n    kernelName: FlipLeftRight,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend }) => {\n        const { image } = inputs;\n        const webglBackend = backend;\n        const program = new FlipLeftRightProgram(image.shape);\n        const output = webglBackend.runWebGLProgram(program, [image], image.dtype);\n        return output;\n    }\n};\n//# sourceMappingURL=FlipLeftRight.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from '../../glsl_version';\nexport class FromPixelsProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        const glsl = getGlslDifferences();\n        const [height, width,] = outputShape;\n        this.outputShape = outputShape;\n        this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);\n\n        vec4 values = ${glsl.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=from_pixels_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from '../../glsl_version';\nexport class FromPixelsPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        const glsl = getGlslDifferences();\n        const [height, width,] = outputShape;\n        this.outputShape = outputShape;\n        this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(${width}.0, ${height}.0);\n            vec4 values = ${glsl.texture2D}(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=from_pixels_packed_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { FromPixels } from '@tensorflow/tfjs-core';\nimport { TextureUsage } from '../tex_util';\nimport { FromPixelsProgram } from './FromPixels_utils/from_pixels_gpu';\nimport { FromPixelsPackedProgram } from './FromPixels_utils/from_pixels_packed_gpu';\nexport const fromPixelsConfig = {\n    kernelName: FromPixels,\n    backendName: 'webgl',\n    kernelFunc: fromPixels,\n};\nlet fromPixels2DContext;\nfunction fromPixels(args) {\n    const { inputs, backend, attrs } = args;\n    let { pixels } = inputs;\n    const { numChannels } = attrs;\n    const isVideo = typeof (HTMLVideoElement) !== 'undefined' &&\n        pixels instanceof HTMLVideoElement;\n    const isImage = typeof (HTMLImageElement) !== 'undefined' &&\n        pixels instanceof HTMLImageElement;\n    const [width, height] = isVideo ?\n        [\n            pixels.videoWidth,\n            pixels.videoHeight\n        ] :\n        [pixels.width, pixels.height];\n    const texShape = [height, width];\n    const outShape = [height, width, numChannels];\n    if (isImage || isVideo) {\n        if (fromPixels2DContext == null) {\n            fromPixels2DContext = document.createElement('canvas').getContext('2d');\n        }\n        fromPixels2DContext.canvas.width = width;\n        fromPixels2DContext.canvas.height = height;\n        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n        pixels = fromPixels2DContext.canvas;\n    }\n    const tempPixelHandle = backend.makeTensorInfo(texShape, 'int32');\n    // This is a byte texture with pixels.\n    backend.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;\n    backend.gpgpu.uploadPixelDataToTexture(backend.getTexture(tempPixelHandle.dataId), pixels);\n    const program = env().getBool('WEBGL_PACK') ?\n        new FromPixelsPackedProgram(outShape) :\n        new FromPixelsProgram(outShape);\n    const res = backend.runWebGLProgram(program, [tempPixelHandle], 'int32');\n    backend.disposeData(tempPixelHandle.dataId);\n    return res;\n}\n//# sourceMappingURL=FromPixels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { ReduceProgram } from '../reduce_gpu';\n// Returns an array of configuration objects that describe each stage of the\n// reduction.\nfunction getReductionStages(inShape) {\n    const stages = [];\n    while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {\n        const outSize = stages.length ? stages[stages.length - 1].outSize : inShape[1];\n        const windowSize = backend_util.computeOptimalWindowSize(outSize);\n        stages.push({\n            inSize: outSize,\n            windowSize,\n            outSize: Math.ceil(outSize / windowSize)\n        });\n    }\n    return stages;\n}\nexport function reduce(x, dtype, reductionType, backend) {\n    const reductionStages = getReductionStages(x.shape);\n    let result = x;\n    for (let i = 0; i < reductionStages.length; i++) {\n        const { inSize, windowSize, outSize } = reductionStages[i];\n        const program = new ReduceProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, reductionType);\n        const previousResult = result;\n        result = backend.runWebGLProgram(program, [result], dtype);\n        if (previousResult.dataId !== x.dataId) {\n            backend.disposeData(previousResult.dataId);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=reduce.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ReshapePackedProgram } from '../reshape_packed_gpu';\nimport { getBatchDim, getRowsCols } from '../webgl_util';\nexport function packedReshape(input, afterShape, backend) {\n    const input3DShape = [getBatchDim(input.shape),\n        ...getRowsCols(input.shape)];\n    const input3D = {\n        dtype: input.dtype,\n        shape: input3DShape,\n        dataId: input.dataId\n    };\n    const afterShapeAs3D = [getBatchDim(afterShape),\n        ...getRowsCols(afterShape)];\n    const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n    const preventEagerUnpackingOfOutput = true;\n    const output = backend.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n    return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n}\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape, util } from '@tensorflow/tfjs-core';\nimport { packedReshape } from '../kernel_utils/reshape';\nimport { isReshapeFree } from '../webgl_util';\nexport function reshape(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { shape } = attrs;\n    const webglBackend = backend;\n    const xSize = util.sizeFromShape(x.shape);\n    const $shape = util.inferFromImplicitShape(shape, xSize);\n    const $xSize = util.sizeFromShape($shape);\n    util.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n        `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n        `shape must have the same number of elements.`);\n    const xTexData = webglBackend.texData.get(x.dataId);\n    if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) &&\n        !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {\n        return packedReshape(x, $shape, webglBackend);\n    }\n    webglBackend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nexport const reshapeConfig = {\n    kernelName: Reshape,\n    backendName: 'webgl',\n    kernelFunc: reshape\n};\n//# sourceMappingURL=Reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { reduce } from '../kernel_utils/reduce';\nimport { reshape } from '../kernels/Reshape';\nexport function maxImpl(x, reduceShape, outShape, backend) {\n    const inSize = util.sizeFromShape(reduceShape);\n    const xSize = util.sizeFromShape(x.shape);\n    const batchSize = xSize / inSize;\n    const reshapedInput = reshape({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend });\n    const reduced = reduce(reshapedInput, x.dtype, 'max', backend);\n    const reshapedOutput = reshape({ inputs: { x: reduced }, attrs: { shape: outShape }, backend });\n    backend.disposeIntermediateTensorInfo(reshapedInput);\n    backend.disposeIntermediateTensorInfo(reduced);\n    return reshapedOutput;\n}\n//# sourceMappingURL=Max_impl.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class TransposeProgram {\n    constructor(aShape, newDim) {\n        this.variableNames = ['A'];\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[newDim[i]];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const switched = getSwitchedCoords(newDim);\n        this.userCode = `\n    void main() {\n      ${dtype} resRC = getOutputCoords();\n      setOutput(getA(${switched}));\n    }\n    `;\n    }\n}\nfunction getSwitchedCoords(newDim) {\n    const rank = newDim.length;\n    if (rank > 6) {\n        throw Error(`Transpose for rank ${rank} is not yet supported`);\n    }\n    const originalOrder = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u', 'resRC.v'];\n    const switchedCoords = new Array(rank);\n    for (let i = 0; i < newDim.length; i++) {\n        switchedCoords[newDim[i]] = originalOrder[i];\n    }\n    return switchedCoords.join();\n}\n//# sourceMappingURL=transpose_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getVecChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class TransposePackedProgram {\n    constructor(aShape, newDim) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[newDim[i]];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        if (this.rank > 6) {\n            throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);\n        }\n        const dtype = getCoordsDataType(this.rank);\n        const outputOrder = getVecChannels('rc', this.rank);\n        const switchedOrder = new Array(this.rank);\n        for (let i = 0; i < newDim.length; i++) {\n            switchedOrder[newDim[i]] = outputOrder[i];\n        }\n        const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;\n        const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;\n        const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;\n        this.userCode = `\n    void main() {\n      ${dtype} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${getc};\n      if(${nextColumn}) {\n        result[1] = ${getc};\n      }\n      --${outputOrder[this.rank - 1]};\n      if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {\n        result[2] = ${getc};\n        if(${nextColumn}) {\n          result[3] = ${getc};\n        }\n      }\n      setOutput(result);\n    }\n    `;\n    }\n}\n//# sourceMappingURL=transpose_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { transposeImplCPU } from '../kernel_utils/shared';\nimport { TransposeProgram } from '../transpose_gpu';\nimport { TransposePackedProgram } from '../transpose_packed_gpu';\nexport function transposeImpl(x, perm, backend) {\n    const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n        new TransposePackedProgram(x.shape, perm) :\n        new TransposeProgram(x.shape, perm);\n    return backend.runWebGLProgram(program, [x], x.dtype);\n}\nexport { transposeImplCPU };\n//# sourceMappingURL=Transpose_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '@tensorflow/tfjs-core';\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { maxImplCPU } from '../kernel_utils/shared';\nimport { maxImpl } from './Max_impl';\nimport { transposeImpl, transposeImplCPU } from './Transpose_impl';\nexport const maxConfig = {\n    kernelName: Max,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { reductionIndices, keepDims } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        const maxInputIsTransposed = permutedAxes != null;\n        const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);\n        let maxInput = x;\n        if (maxInputIsTransposed) {\n            if (shouldExecuteOnCPU) {\n                const xTexData = webglBackend.texData.get(maxInput.dataId);\n                const values = xTexData.values;\n                const newShape = new Array(xRank);\n                for (let i = 0; i < newShape.length; i++) {\n                    newShape[i] = x.shape[permutedAxes[i]];\n                }\n                const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n                maxInput = webglBackend.makeTensorInfo(newShape, x.dtype);\n                const maxInputData = webglBackend.texData.get(maxInput.dataId);\n                maxInputData.values = maxInputValues;\n            }\n            else {\n                maxInput = transposeImpl(x, permutedAxes, webglBackend);\n            }\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n        }\n        backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n        const [maxOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(maxInput.shape, axes);\n        let outShape = maxOutShape;\n        if (keepDims) {\n            // rather than reshape at the end, set the target shape here.\n            outShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n        }\n        let out;\n        if (shouldExecuteOnCPU) {\n            const xTexData = webglBackend.texData.get(maxInput.dataId);\n            const values = xTexData.values;\n            const outValues = maxImplCPU(values, util.sizeFromShape(reduceShape), outShape, x.dtype);\n            out = webglBackend.makeTensorInfo(outShape, x.dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n        }\n        else {\n            out = maxImpl(maxInput, reduceShape, outShape, webglBackend);\n        }\n        if (maxInputIsTransposed) {\n            webglBackend.disposeIntermediateTensorInfo(maxInput);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPool, util } from '@tensorflow/tfjs-core';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nimport { identity } from './Identity';\nexport function maxPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'maxPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        return identity({ inputs: { x }, backend });\n    }\n    const maxPoolProgram = new Pool2DProgram(convInfo, 'max', false);\n    return backend.runWebGLProgram(maxPoolProgram, [x], x.dtype);\n}\nexport const maxPoolConfig = {\n    kernelName: MaxPool,\n    backendName: 'webgl',\n    kernelFunc: maxPool\n};\n//# sourceMappingURL=MaxPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPoolBackprop } from '@tensorflow/tfjs-core';\nimport { MaxPool2DBackpropProgram } from '../max_pool_backprop_gpu';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nexport function maxPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input, output } = inputs;\n    const x = input;\n    assertNotComplex([input, output], 'maxPoolBackprop');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n    const getPositions = true;\n    const maxPoolPositionsProgram = new Pool2DProgram(convInfo, 'max', getPositions);\n    const maxPoolPositions = backend.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);\n    const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);\n    const result = backend.runWebGLProgram(maxPoolBackPropProgram, [dy, maxPoolPositions], x.dtype);\n    backend.disposeIntermediateTensorInfo(maxPoolPositions);\n    return result;\n}\nexport const maxPoolBackpropConfig = {\n    kernelName: MaxPoolBackprop,\n    backendName: 'webgl',\n    kernelFunc: maxPoolBackprop\n};\n//# sourceMappingURL=MaxPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Pool2DProgram } from '../pool_gpu';\nexport function maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, backend) {\n    let program = new Pool2DProgram(convInfo, 'max', false);\n    const poolOutput = backend.runWebGLProgram(program, [x], 'float32');\n    program = new Pool2DProgram(convInfo, 'max', true, true, includeBatchInIndex);\n    const indexOutput = backend.runWebGLProgram(program, [x], 'float32');\n    return [poolOutput, indexOutput];\n}\n//# sourceMappingURL=MaxPoolWithArgmax_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPoolWithArgmax } from '@tensorflow/tfjs-core';\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { maxPoolWithArgmaxImpl } from './MaxPoolWithArgmax_impl';\nexport const maxPoolWithArgmaxConfig = {\n    kernelName: MaxPoolWithArgmax,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { filterSize, strides, pad, includeBatchInIndex } = attrs;\n        const webglBackend = backend;\n        util.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);\n        const dilations = [1, 1];\n        util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n            `Got strides ${strides} and dilations '${dilations}'`);\n        const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad);\n        const [result, indexes] = maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, webglBackend);\n        return [result, indexes];\n    }\n};\n//# sourceMappingURL=MaxPoolWithArgmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls, NonMaxSuppressionV3 } from '@tensorflow/tfjs-core';\nexport const nonMaxSuppressionV3Config = {\n    kernelName: NonMaxSuppressionV3,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        return kernel_impls.nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal);\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV3.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls, NonMaxSuppressionV4 } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\nexport const nonMaxSuppressionV4Config = {\n    kernelName: NonMaxSuppressionV4,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n        return [selectedIndices, validOutputs];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV4.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls } from '@tensorflow/tfjs-core';\nimport { NonMaxSuppressionV5 } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nexport const nonMaxSuppressionV5Config = {\n    kernelName: NonMaxSuppressionV5,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        const softNmsSigmaVal = softNmsSigma;\n        const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n        return [selectedIndices, selectedScores];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV5.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class RotateProgram {\n    constructor(imageShape, radians, fillValue, center) {\n        this.variableNames = ['Image'];\n        this.outputShape = [];\n        const imageHeight = imageShape[1];\n        const imageWidth = imageShape[2];\n        const sinFactor = Math.sin(radians).toFixed(3);\n        const cosFactor = Math.cos(radians).toFixed(3);\n        this.outputShape = imageShape;\n        const [centerX, centerY] = backend_util.getImageCenter(center, imageHeight, imageWidth);\n        const centerXString = centerX.toFixed(3);\n        const centerYString = centerY.toFixed(3);\n        let fillSnippet = '';\n        if (typeof fillValue === 'number') {\n            fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;\n        }\n        else {\n            fillSnippet = `\n        vec3 fill = vec3(${fillValue.join(',')});\n        float outputValue = fill[coords[3]];`;\n        }\n        this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - ${centerXString}) * ${cosFactor} - (float(y) - ${centerYString}) * ${sinFactor};\n          float coordYFloat = (float(x) - ${centerXString}) * ${sinFactor} + (float(y) - ${centerYString}) * ${cosFactor};\n          int coordX = int(round(coordXFloat + ${centerXString}));\n          int coordY = int(round(coordYFloat + ${centerYString}));\n          ${fillSnippet}\n          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n    }\n}\n//# sourceMappingURL=rotate_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { RotateWithOffset } from '@tensorflow/tfjs-core';\nimport { RotateProgram } from '../rotate_gpu';\nexport const rotateWithOffsetConfig = {\n    kernelName: RotateWithOffset,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const { radians, fillValue, center } = attrs;\n        const webglBackend = backend;\n        const program = new RotateProgram(image.shape, radians, fillValue, center);\n        const output = webglBackend.runWebGLProgram(program, [image], image.dtype);\n        return output;\n    }\n};\n//# sourceMappingURL=RotateWithOffset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '@tensorflow/tfjs-core';\nimport { CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SIN = CHECK_NAN_SNIPPET_UNARY + `\n  return sin(x);\n`;\nexport const sinKernelFunc = unaryKernelFunc(SIN);\nexport const sinConfig = {\n    kernelName: Sin,\n    backendName: 'webgl',\n    kernelFunc: sinKernelFunc,\n};\n//# sourceMappingURL=Sin.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SQUARE = `return x * x;`;\nexport const squareKernelFunc = unaryKernelFunc(SQUARE);\nexport const squareConfig = {\n    kernelName: Square,\n    backendName: 'webgl',\n    kernelFunc: squareKernelFunc,\n};\n//# sourceMappingURL=Square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport const squaredDifferenceKernelFunc = binaryKernelFunc(SQUARED_DIFFERENCE, SQUARED_DIFFERENCE);\nexport const squaredDifferenceConfig = {\n    kernelName: SquaredDifference,\n    backendName: 'webgl',\n    kernelFunc: squaredDifferenceKernelFunc,\n};\n//# sourceMappingURL=SquaredDifference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst TAN = `return tan(x);`;\nexport const tanKernelFunc = unaryKernelFunc(TAN);\nexport const tanConfig = {\n    kernelName: Tan,\n    backendName: 'webgl',\n    kernelFunc: tanKernelFunc,\n};\n//# sourceMappingURL=Tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '@tensorflow/tfjs-core';\nimport { transposeImpl } from './Transpose_impl';\nimport { transposeImplCPU as cpuTranspose } from './Transpose_impl';\nexport const transposeConfig = {\n    kernelName: Transpose,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { perm } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const newShape = new Array(xRank);\n        for (let i = 0; i < newShape.length; i++) {\n            newShape[i] = x.shape[perm[i]];\n        }\n        let out;\n        if (webglBackend.shouldExecuteOnCPU([x])) {\n            const xTexData = webglBackend.texData.get(x.dataId);\n            const values = xTexData.values;\n            const outValues = cpuTranspose(values, x.shape, x.dtype, perm, newShape);\n            out = webglBackend.makeTensorInfo(newShape, x.dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n        }\n        else {\n            out = transposeImpl(x, perm, webglBackend);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unique } from '@tensorflow/tfjs-core';\nimport { uniqueImplCPU } from '../kernel_utils/shared';\nimport { assertNotComplex } from '../webgl_util';\nexport function unique(args) {\n    const { inputs, attrs, backend } = args;\n    const { axis } = attrs;\n    const { x } = inputs;\n    assertNotComplex(x, 'unique');\n    // For now, always forward calculation to the CPU backend.\n    console.warn('WARNING: ', 'UI might be locked temporarily as data is being downloaded');\n    const values = backend.readSync(x.dataId);\n    const { outputValues, outputShape, indices } = uniqueImplCPU(values, axis, x.shape, x.dtype);\n    return [\n        backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n        backend.makeTensorInfo([indices.length], 'int32', indices),\n    ];\n}\nexport const uniqueConfig = {\n    kernelName: Unique,\n    backendName: 'webgl',\n    kernelFunc: unique,\n};\n//# sourceMappingURL=Unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { registerKernel } from '@tensorflow/tfjs-core';\nimport { atan2Config } from './kernels/Atan2';\nimport { avgPoolConfig } from './kernels/AvgPool';\nimport { avgPoolBackpropConfig } from './kernels/AvgPoolBackprop';\nimport { batchNormConfig } from './kernels/BatchNorm';\nimport { cosConfig } from './kernels/Cos';\nimport { divConfig } from './kernels/Div';\nimport { flipLeftRightConfig } from './kernels/FlipLeftRight';\nimport { fromPixelsConfig } from './kernels/FromPixels';\nimport { identityConfig } from './kernels/Identity';\nimport { maxConfig } from './kernels/Max';\nimport { maxPoolConfig } from './kernels/MaxPool';\nimport { maxPoolBackpropConfig } from './kernels/MaxPoolBackprop';\nimport { maxPoolWithArgmaxConfig } from './kernels/MaxPoolWithArgmax';\nimport { nonMaxSuppressionV3Config } from './kernels/NonMaxSuppressionV3';\nimport { nonMaxSuppressionV4Config } from './kernels/NonMaxSuppressionV4';\nimport { nonMaxSuppressionV5Config } from './kernels/NonMaxSuppressionV5';\nimport { reshapeConfig } from './kernels/Reshape';\nimport { rotateWithOffsetConfig } from './kernels/RotateWithOffset';\nimport { sinConfig } from './kernels/Sin';\nimport { squareConfig } from './kernels/Square';\nimport { squaredDifferenceConfig } from './kernels/SquaredDifference';\nimport { tanConfig } from './kernels/Tan';\nimport { transposeConfig } from './kernels/Transpose';\nimport { uniqueConfig } from './kernels/Unique';\n// List all kernel configs here\nconst kernelConfigs = [\n    atan2Config,\n    avgPoolConfig,\n    avgPoolBackpropConfig,\n    batchNormConfig,\n    cosConfig,\n    divConfig,\n    flipLeftRightConfig,\n    fromPixelsConfig,\n    identityConfig,\n    maxConfig,\n    maxPoolConfig,\n    maxPoolBackpropConfig,\n    maxPoolWithArgmaxConfig,\n    nonMaxSuppressionV3Config,\n    nonMaxSuppressionV4Config,\n    nonMaxSuppressionV5Config,\n    reshapeConfig,\n    rotateWithOffsetConfig,\n    sinConfig,\n    squareConfig,\n    squaredDifferenceConfig,\n    tanConfig,\n    transposeConfig,\n    uniqueConfig,\n];\nfor (const kernelConfig of kernelConfigs) {\n    registerKernel(kernelConfig);\n}\n//# sourceMappingURL=register_all_kernels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// All exports from this package should be in base.\nexport * from './base';\nimport './register_all_kernels';\n//# sourceMappingURL=index.js.map", "/** @license See the LICENSE file. */\n\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport {version};\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport * from '@tensorflow/tfjs-core';\nexport * from '@tensorflow/tfjs-layers';\nexport * from '@tensorflow/tfjs-converter';\n\n// Export data api as tf.data\nimport * as data from '@tensorflow/tfjs-data';\nexport {data};\n\n// Import and register backends.\nimport '@tensorflow/tfjs-backend-cpu';\nimport '@tensorflow/tfjs-backend-webgl';\n\n// Import versions of all sub-packages.\nimport {version_core} from '@tensorflow/tfjs-core';\nimport {version_cpu} from '@tensorflow/tfjs-backend-cpu';\nimport {version_webgl} from '@tensorflow/tfjs-backend-webgl';\nimport {version_data} from '@tensorflow/tfjs-data';\nimport {version_layers} from '@tensorflow/tfjs-layers';\nimport {version_converter} from '@tensorflow/tfjs-converter';\nimport {version as version_union} from './version';\n\nexport const version = {\n  'tfjs-core': version_core,\n  'tfjs-backend-cpu': version_cpu,\n  'tfjs-backend-webgl': version_webgl,\n  'tfjs-data': version_data,\n  'tfjs-layers': version_layers,\n  'tfjs-converter': version_converter,\n  'tfjs': version_union\n};\n", "export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r)\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g)\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b)\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0)\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = input.toBatchTensor(112, true)\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = input.toBatchTensor(112, true)\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([\n          tf.fill([68], fillX),\n          tf.fill([68], fillY)\n        ], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = input.toBatchTensor(112, true)\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n"],
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+  "sourcesContent": ["\"use strict\";\n\n// ref: https://github.com/tc39/proposal-global\nvar getGlobal = function () {\n\t// the only reliable means to get the global object is\n\t// `Function('return this')()`\n\t// However, this causes CSP violations in Chrome apps.\n\tif (typeof self !== 'undefined') { return self; }\n\tif (typeof window !== 'undefined') { return window; }\n\tif (typeof global !== 'undefined') { return global; }\n\tthrow new Error('unable to locate global object');\n}\n\nvar global = getGlobal();\n\nmodule.exports = exports = global.fetch;\n\n// Needed for TypeScript and Webpack.\nif (global.fetch) {\n\texports.default = global.fetch.bind(global);\n}\n\nexports.Headers = global.Headers;\nexports.Request = global.Request;\nexports.Response = global.Response;", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage {\n    constructor(backend, dataMover) {\n        this.backend = backend;\n        this.dataMover = dataMover;\n        this.data = new WeakMap();\n        this.dataIdsCount = 0;\n    }\n    get(dataId) {\n        if (!this.data.has(dataId)) {\n            this.dataMover.moveData(this.backend, dataId);\n        }\n        return this.data.get(dataId);\n    }\n    set(dataId, value) {\n        this.dataIdsCount++;\n        this.data.set(dataId, value);\n    }\n    has(dataId) {\n        return this.data.has(dataId);\n    }\n    delete(dataId) {\n        this.dataIdsCount--;\n        return this.data.delete(dataId);\n    }\n    numDataIds() {\n        return this.dataIdsCount;\n    }\n}\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend {\n    time(f) {\n        return notYetImplemented('time');\n    }\n    read(dataId) {\n        return notYetImplemented('read');\n    }\n    readSync(dataId) {\n        return notYetImplemented('readSync');\n    }\n    numDataIds() {\n        return notYetImplemented('numDataIds');\n    }\n    disposeData(dataId) {\n        return notYetImplemented('disposeData');\n    }\n    write(values, shape, dtype) {\n        return notYetImplemented('write');\n    }\n    move(dataId, values, shape, dtype) {\n        return notYetImplemented('move');\n    }\n    memory() {\n        return notYetImplemented('memory');\n    }\n    /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n    floatPrecision() {\n        return notYetImplemented('floatPrecision');\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        return notYetImplemented('batchMatMul');\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedBatchMatMul');\n    }\n    slice(x, begin, size) {\n        return notYetImplemented('slice');\n    }\n    stridedSlice(x, begin, end, strides) {\n        return notYetImplemented('stridedSlice');\n    }\n    unstack(x, axis) {\n        return notYetImplemented('unstack');\n    }\n    reverse(a, axis) {\n        return notYetImplemented('reverse');\n    }\n    concat(tensors, axis) {\n        return notYetImplemented('concat');\n    }\n    neg(a) {\n        return notYetImplemented('neg');\n    }\n    add(a, b) {\n        return notYetImplemented('add');\n    }\n    addN(tensors) {\n        return notYetImplemented('addN');\n    }\n    subtract(a, b) {\n        return notYetImplemented('subtract');\n    }\n    multiply(a, b) {\n        return notYetImplemented('multiply');\n    }\n    realDivide(a, b) {\n        return notYetImplemented('realDivide');\n    }\n    floorDiv(a, b) {\n        return notYetImplemented('floorDiv');\n    }\n    sum(x, axes) {\n        return notYetImplemented('sum');\n    }\n    prod(x, axes) {\n        return notYetImplemented('prod');\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        return notYetImplemented('unsortedSegmentSum');\n    }\n    argMin(x, axis) {\n        return notYetImplemented('argMin');\n    }\n    argMax(x, axis) {\n        return notYetImplemented('argMax');\n    }\n    equal(a, b) {\n        return notYetImplemented('equal');\n    }\n    notEqual(a, b) {\n        return notYetImplemented('notEqual');\n    }\n    less(a, b) {\n        return notYetImplemented('less');\n    }\n    lessEqual(a, b) {\n        return notYetImplemented('lessEqual');\n    }\n    greater(a, b) {\n        return notYetImplemented('greater');\n    }\n    greaterEqual(a, b) {\n        return notYetImplemented('greaterEqual');\n    }\n    logicalNot(a) {\n        return notYetImplemented('logicalNot');\n    }\n    logicalAnd(a, b) {\n        return notYetImplemented('logicalAnd');\n    }\n    logicalOr(a, b) {\n        return notYetImplemented('logicalOr');\n    }\n    where(condition) {\n        return notYetImplemented('where');\n    }\n    select(condition, a, b) {\n        return notYetImplemented('select');\n    }\n    topk(x, k, sorted) {\n        return notYetImplemented('topk');\n    }\n    min(x, axes) {\n        return notYetImplemented('min');\n    }\n    minimum(a, b) {\n        return notYetImplemented('minimum');\n    }\n    mod(a, b) {\n        return notYetImplemented('mod');\n    }\n    max(x, axes) {\n        return notYetImplemented('max');\n    }\n    maximum(a, b) {\n        return notYetImplemented('maximum');\n    }\n    all(x, axes) {\n        return notYetImplemented('all');\n    }\n    any(x, axes) {\n        return notYetImplemented('any');\n    }\n    squaredDifference(a, b) {\n        return notYetImplemented('squaredDifference');\n    }\n    ceil(x) {\n        return notYetImplemented('ceil');\n    }\n    floor(x) {\n        return notYetImplemented('floor');\n    }\n    round(x) {\n        return notYetImplemented('round');\n    }\n    sign(x) {\n        return notYetImplemented('sign');\n    }\n    isNaN(x) {\n        return notYetImplemented('isNaN');\n    }\n    isInf(x) {\n        return notYetImplemented('isInf');\n    }\n    isFinite(x) {\n        return notYetImplemented('isFinite');\n    }\n    pow(a, b) {\n        return notYetImplemented('pow');\n    }\n    exp(x) {\n        return notYetImplemented('exp');\n    }\n    expm1(x) {\n        return notYetImplemented('expm1');\n    }\n    softmax(x, dim) {\n        return notYetImplemented('softmax');\n    }\n    log(x) {\n        return notYetImplemented('log');\n    }\n    log1p(x) {\n        return notYetImplemented('log1p');\n    }\n    sqrt(x) {\n        return notYetImplemented('sqrt');\n    }\n    rsqrt(x) {\n        return notYetImplemented('rsqrt');\n    }\n    square(x) {\n        return notYetImplemented('square');\n    }\n    reciprocal(x) {\n        return notYetImplemented('reciprocal');\n    }\n    relu(x) {\n        return notYetImplemented('relu');\n    }\n    relu6(x) {\n        return notYetImplemented('relu6');\n    }\n    prelu(x, a) {\n        return notYetImplemented('prelu');\n    }\n    elu(x) {\n        return notYetImplemented('elu');\n    }\n    eluDer(dy, y) {\n        return notYetImplemented('eluDer');\n    }\n    selu(x) {\n        return notYetImplemented('selu');\n    }\n    int(x) {\n        return notYetImplemented('int');\n    }\n    clip(x, min, max) {\n        return notYetImplemented('clip');\n    }\n    abs(x) {\n        return notYetImplemented('abs');\n    }\n    complexAbs(x) {\n        return notYetImplemented('complexAbs');\n    }\n    sigmoid(x) {\n        return notYetImplemented('sigmoid');\n    }\n    softplus(x) {\n        return notYetImplemented('softplus');\n    }\n    sin(x) {\n        return notYetImplemented('sin');\n    }\n    cos(x) {\n        return notYetImplemented('cos');\n    }\n    tan(x) {\n        return notYetImplemented('tan');\n    }\n    asin(x) {\n        return notYetImplemented('asin');\n    }\n    acos(x) {\n        return notYetImplemented('acos');\n    }\n    atan(x) {\n        return notYetImplemented('atan');\n    }\n    atan2(a, b) {\n        return notYetImplemented('atan2');\n    }\n    sinh(x) {\n        return notYetImplemented('sinh');\n    }\n    cosh(x) {\n        return notYetImplemented('cosh');\n    }\n    tanh(x) {\n        return notYetImplemented('tanh');\n    }\n    asinh(x) {\n        return notYetImplemented('asinh');\n    }\n    acosh(x) {\n        return notYetImplemented('acosh');\n    }\n    atanh(x) {\n        return notYetImplemented('atanh');\n    }\n    erf(x) {\n        return notYetImplemented('erf');\n    }\n    step(x, alpha) {\n        return notYetImplemented('step');\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedConv2d');\n    }\n    conv2d(x, filter, convInfo) {\n        return notYetImplemented('conv2d');\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        return notYetImplemented('conv2dDerInput');\n    }\n    conv2dDerFilter(x, dY, convInfo) {\n        return notYetImplemented('conv2dDerFilter');\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedDepthwiseConv2D');\n    }\n    depthwiseConv2D(input, filter, convInfo) {\n        return notYetImplemented('depthwiseConv2D');\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        return notYetImplemented('depthwiseConv2DDerInput');\n    }\n    depthwiseConv2DDerFilter(x, dY, convInfo) {\n        return notYetImplemented('depthwiseConv2DDerFilter');\n    }\n    conv3d(x, filter, convInfo) {\n        return notYetImplemented('conv3d');\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        return notYetImplemented('conv3dDerInput');\n    }\n    conv3dDerFilter(x, dY, convInfo) {\n        return notYetImplemented('conv3dDerFilter');\n    }\n    maxPool(x, convInfo) {\n        return notYetImplemented('maxPool');\n    }\n    maxPoolBackprop(dy, x, y, convInfo) {\n        return notYetImplemented('maxPoolBackprop');\n    }\n    avgPool(x, convInfo) {\n        return notYetImplemented('avgPool');\n    }\n    avgPoolBackprop(dy, x, convInfo) {\n        return notYetImplemented('avgPoolBackprop');\n    }\n    avgPool3d(x, convInfo) {\n        return notYetImplemented('avgPool3d');\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        return notYetImplemented('avgPool3dBackprop');\n    }\n    maxPool3d(x, convInfo) {\n        return notYetImplemented('maxPool3d');\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        return notYetImplemented('maxPool3dBackprop');\n    }\n    reshape(x, shape) {\n        return notYetImplemented('reshape');\n    }\n    cast(x, dtype) {\n        return notYetImplemented('cast');\n    }\n    tile(x, reps) {\n        return notYetImplemented('tile');\n    }\n    pad(x, paddings, constantValue) {\n        return notYetImplemented('pad');\n    }\n    transpose(x, perm) {\n        return notYetImplemented('transpose');\n    }\n    gather(x, indices, axis) {\n        return notYetImplemented('gather');\n    }\n    gatherND(x, indices) {\n        return notYetImplemented('gatherND');\n    }\n    scatterND(indices, updates, shape) {\n        return notYetImplemented('scatterND');\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        return notYetImplemented('batchToSpaceND');\n    }\n    spaceToBatchND(x, blockShape, paddings) {\n        return notYetImplemented('spaceToBatchND');\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        return notYetImplemented('resizeBilinear');\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        return notYetImplemented('resizeBilinearBackprop');\n    }\n    resizeNearestNeighbor(x, newHEight, newWidth, alignCorners) {\n        return notYetImplemented('resizeNearestNeighbor');\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        return notYetImplemented('resizeNearestNeighborBackprop');\n    }\n    batchNorm(x, mean, variance, offset, scale, varianceEpsilon) {\n        return notYetImplemented('batchNorm');\n    }\n    localResponseNormalization4D(x, radius, bias, alpha, beta) {\n        return notYetImplemented('localResponseNormalization4D');\n    }\n    LRNGrad(dy, inputImage, outputImage, radius, bias, alpha, beta) {\n        return notYetImplemented('LRNGrad');\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        return notYetImplemented('multinomial');\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        return notYetImplemented('oneHot');\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        return notYetImplemented('cumsum');\n    }\n    nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n        return notYetImplemented('nonMaxSuppression');\n    }\n    fft(x) {\n        return notYetImplemented('fft');\n    }\n    ifft(x) {\n        return notYetImplemented('ifft');\n    }\n    complex(real, imag) {\n        return notYetImplemented('complex');\n    }\n    real(input) {\n        return notYetImplemented('real');\n    }\n    imag(input) {\n        return notYetImplemented('imag');\n    }\n    cropAndResize(image, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        return notYetImplemented('cropAndResize');\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        return notYetImplemented('depthToSpace');\n    }\n    // Aligns with the \"SplitV\" kernel in TensorFlow.\n    split(value, sizeSplits, axis) {\n        return notYetImplemented('split');\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        return notYetImplemented('sparseToDense');\n    }\n    diag(x) {\n        return notYetImplemented('diag');\n    }\n    fill(shape, value, dtype) {\n        return notYetImplemented('fill');\n    }\n    onesLike(x) {\n        return notYetImplemented('onesLike');\n    }\n    zerosLike(x) {\n        return notYetImplemented('zerosLike');\n    }\n    linspace(start, stop, num) {\n        return notYetImplemented('linspace');\n    }\n    dispose() {\n        return notYetImplemented('dispose');\n    }\n}\nfunction notYetImplemented(kernelName) {\n    throw new Error(`'${kernelName}' not yet implemented or not found in the registry. ` +\n        `This kernel may not be supported by the tfjs backend you have chosen`);\n}\n//# sourceMappingURL=backend.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.\nconst TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';\n/**\n * The environment contains evaluated flags as well as the registered platform.\n * This is always used as a global singleton and can be retrieved with\n * `tf.env()`.\n *\n * @doc {heading: 'Environment'}\n */\nexport class Environment {\n    // tslint:disable-next-line: no-any\n    constructor(global) {\n        this.global = global;\n        this.flags = {};\n        this.flagRegistry = {};\n        this.urlFlags = {};\n        this.populateURLFlags();\n    }\n    setPlatform(platformName, platform) {\n        if (this.platform != null) {\n            console.warn(`Platform ${this.platformName} has already been set. ` +\n                `Overwriting the platform with ${platform}.`);\n        }\n        this.platformName = platformName;\n        this.platform = platform;\n    }\n    registerFlag(flagName, evaluationFn, setHook) {\n        this.flagRegistry[flagName] = { evaluationFn, setHook };\n        // Override the flag value from the URL. This has to happen here because the\n        // environment is initialized before flags get registered.\n        if (this.urlFlags[flagName] != null) {\n            const flagValue = this.urlFlags[flagName];\n            console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);\n            this.set(flagName, flagValue);\n        }\n    }\n    async getAsync(flagName) {\n        if (flagName in this.flags) {\n            return this.flags[flagName];\n        }\n        this.flags[flagName] = await this.evaluateFlag(flagName);\n        return this.flags[flagName];\n    }\n    get(flagName) {\n        if (flagName in this.flags) {\n            return this.flags[flagName];\n        }\n        const flagValue = this.evaluateFlag(flagName);\n        if (flagValue instanceof Promise) {\n            throw new Error(`Flag ${flagName} cannot be synchronously evaluated. ` +\n                `Please use getAsync() instead.`);\n        }\n        this.flags[flagName] = flagValue;\n        return this.flags[flagName];\n    }\n    getNumber(flagName) {\n        return this.get(flagName);\n    }\n    getBool(flagName) {\n        return this.get(flagName);\n    }\n    getFlags() {\n        return this.flags;\n    }\n    // For backwards compatibility.\n    get features() {\n        return this.flags;\n    }\n    set(flagName, value) {\n        if (this.flagRegistry[flagName] == null) {\n            throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);\n        }\n        this.flags[flagName] = value;\n        if (this.flagRegistry[flagName].setHook != null) {\n            this.flagRegistry[flagName].setHook(value);\n        }\n    }\n    evaluateFlag(flagName) {\n        if (this.flagRegistry[flagName] == null) {\n            throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);\n        }\n        return this.flagRegistry[flagName].evaluationFn();\n    }\n    setFlags(flags) {\n        this.flags = Object.assign({}, flags);\n    }\n    reset() {\n        this.flags = {};\n        this.urlFlags = {};\n        this.populateURLFlags();\n    }\n    populateURLFlags() {\n        if (typeof this.global === 'undefined' ||\n            typeof this.global.location === 'undefined' ||\n            typeof this.global.location.search === 'undefined') {\n            return;\n        }\n        const urlParams = getQueryParams(this.global.location.search);\n        if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n            const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');\n            keyValues.forEach(keyValue => {\n                const [key, value] = keyValue.split(':');\n                this.urlFlags[key] = parseValue(key, value);\n            });\n        }\n    }\n}\nexport function getQueryParams(queryString) {\n    const params = {};\n    queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n        decodeParam(params, t[0], t[1]);\n        return t.join('=');\n    });\n    return params;\n}\nfunction decodeParam(params, name, value) {\n    params[decodeURIComponent(name)] = decodeURIComponent(value || '');\n}\nfunction parseValue(flagName, value) {\n    value = value.toLowerCase();\n    if (value === 'true' || value === 'false') {\n        return value === 'true';\n    }\n    else if (`${+value}` === value) {\n        return +value;\n    }\n    throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);\n}\n/**\n * Returns the current environment (a global singleton).\n *\n * The environment object contains the evaluated feature values as well as the\n * active platform.\n *\n * @doc {heading: 'Environment'}\n */\nexport function env() {\n    return ENV;\n}\nexport let ENV = null;\nexport function setEnvironmentGlobal(environment) {\n    ENV = environment;\n}\n//# sourceMappingURL=environment.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Note that the identifier globalNameSpace is scoped to this module, but will\n// always resolve to the same global object regardless of how the module is\n// resolved.\n// tslint:disable-next-line:no-any\nlet globalNameSpace;\n// tslint:disable-next-line:no-any\nexport function getGlobalNamespace() {\n    if (globalNameSpace == null) {\n        // tslint:disable-next-line:no-any\n        let ns;\n        if (typeof (window) !== 'undefined') {\n            ns = window;\n        }\n        else if (typeof (global) !== 'undefined') {\n            ns = global;\n        }\n        else if (typeof (process) !== 'undefined') {\n            ns = process;\n        }\n        else if (typeof (self) !== 'undefined') {\n            ns = self;\n        }\n        else {\n            throw new Error('Could not find a global object');\n        }\n        globalNameSpace = ns;\n    }\n    return globalNameSpace;\n}\n// tslint:disable-next-line:no-any\nfunction getGlobalMap() {\n    const ns = getGlobalNamespace();\n    if (ns._tfGlobals == null) {\n        ns._tfGlobals = new Map();\n    }\n    return ns._tfGlobals;\n}\n/**\n * Returns a globally accessible 'singleton' object.\n *\n * @param key the name of the object\n * @param init a function to initialize to initialize this object\n *             the first time it is fetched.\n */\nexport function getGlobal(key, init) {\n    const globalMap = getGlobalMap();\n    if (globalMap.has(key)) {\n        return globalMap.get(key);\n    }\n    else {\n        const singleton = init();\n        globalMap.set(key, singleton);\n        return globalMap.get(key);\n    }\n}\n//# sourceMappingURL=global_util.js.map", "export const Abs = 'Abs';\nexport const Acos = 'Acos';\nexport const Acosh = 'Acosh';\nexport const Add = 'Add';\nexport const AddN = 'AddN';\nexport const All = 'All';\nexport const Any = 'Any';\nexport const ArgMax = 'ArgMax';\nexport const ArgMin = 'ArgMin';\nexport const Asin = 'Asin';\nexport const Asinh = 'Asinh';\nexport const Atan = 'Atan';\nexport const Atanh = 'Atanh';\nexport const Atan2 = 'Atan2';\nexport const AvgPool = 'AvgPool';\nexport const AvgPoolBackprop = 'AvgPoolBackprop';\nexport const AvgPool3D = 'AvgPool3D';\nexport const AvgPool3DBackprop = 'AvgPool3DBackprop';\nexport const BatchMatMul = 'BatchMatMul';\nexport const BatchToSpaceND = 'BatchToSpaceND';\nexport const BroadcastTo = 'BroadcastTo';\nexport const Cast = 'Cast';\nexport const Ceil = 'Ceil';\nexport const ClipByValue = 'ClipByValue';\nexport const Complex = 'Complex';\nexport const Concat = 'Concat';\nexport const Conv2D = 'Conv2D';\nexport const Conv2DBackpropFilter = 'Conv2DBackpropFilter';\nexport const Conv2DBackpropInput = 'Conv2DBackpropInput';\nexport const Conv3D = 'Conv3D';\nexport const Conv3DBackpropFilterV2 = 'Conv3DBackpropFilterV2';\nexport const Conv3DBackpropInputV2 = 'Conv3DBackpropInputV2';\nexport const Cos = 'Cos';\nexport const Cosh = 'Cosh';\nexport const Cumsum = 'Cumsum';\nexport const CropAndResize = 'CropAndResize';\nexport const DepthToSpace = 'DepthToSpace';\nexport const DepthwiseConv2dNative = 'DepthwiseConv2dNative';\nexport const DepthwiseConv2dNativeBackpropFilter = 'DepthwiseConv2dNativeBackpropFilter';\nexport const DepthwiseConv2dNativeBackpropInput = 'DepthwiseConv2dNativeBackpropInput';\nexport const Diag = 'Diag';\nexport const Dilation2D = 'Dilation2D';\nexport const Dilation2DBackpropInput = 'Dilation2DBackpropInput';\nexport const Dilation2DBackpropFilter = 'Dilation2DBackpropFilter';\nexport const Div = 'Div';\nexport const Elu = 'Elu';\nexport const EluGrad = 'EluGrad';\nexport const Erf = 'Erf';\nexport const Equal = 'Equal';\nexport const Exp = 'Exp';\nexport const Expm1 = 'Expm1';\nexport const FFT = 'FFT';\nexport const Fill = 'Fill';\nexport const FlipLeftRight = 'FlipLeftRight';\nexport const Floor = 'Floor';\nexport const FloorDiv = 'FloorDiv';\nexport const FusedBatchNorm = 'FusedBatchNorm';\nexport const GatherV2 = 'GatherV2';\nexport const GatherNd = 'GatherNd';\nexport const Greater = 'Greater';\nexport const GreaterEqual = 'GreaterEqual';\nexport const Identity = 'Identity';\nexport const IFFT = 'IFFT';\nexport const Imag = 'Imag';\nexport const IsFinite = 'IsFinite';\nexport const IsInf = 'IsInf';\nexport const IsNan = 'IsNan';\nexport const Less = 'Less';\nexport const LessEqual = 'LessEqual';\nexport const LinSpace = 'LinSpace';\nexport const Log = 'Log';\nexport const Log1p = 'Log1p';\nexport const LogicalAnd = 'LogicalAnd';\nexport const LogicalNot = 'LogicalNot';\nexport const LogicalOr = 'LogicalOr';\nexport const LogSoftmax = 'LogSoftmax';\nexport const LRN = 'LRN';\nexport const LRNBackprop = 'LRNBackprop';\nexport const Max = 'Max';\nexport const Maximum = 'Maximum';\nexport const MaxPool = 'MaxPool';\nexport const MaxPoolBackprop = 'MaxPoolBackprop';\nexport const MaxPool3D = 'MaxPool3D';\nexport const MaxPool3DBackprop = 'MaxPool3DBackprop';\nexport const MaxPoolWithArgmax = 'MaxPoolWithArgmax';\nexport const Mean = 'Mean';\nexport const Min = 'Min';\nexport const Minimum = 'Minimum';\nexport const Mod = 'Mod';\nexport const Multiply = 'Multiply';\nexport const Negate = 'Negate';\nexport const NotEqual = 'NotEqual';\nexport const NonMaxSuppressionV3 = 'NonMaxSuppressionV3';\nexport const NonMaxSuppressionV4 = 'NonMaxSuppressionV4';\nexport const NonMaxSuppressionV5 = 'NonMaxSuppressionV5';\nexport const OnesLike = 'OnesLike';\nexport const OneHot = 'OneHot';\nexport const PadV2 = 'PadV2';\nexport const Pool = 'Pool';\nexport const Pow = 'Pow';\nexport const Prelu = 'Prelu';\nexport const Prod = 'Prod';\nexport const Range = 'Range';\nexport const Real = 'Real';\nexport const Reciprocal = 'Reciprocal';\nexport const Relu = 'Relu';\nexport const Reshape = 'Reshape';\nexport const ResizeNearestNeighbor = 'ResizeNearestNeighbor';\nexport const ResizeNearestNeighborGrad = 'ResizeNearestNeighborGrad';\nexport const ResizeBilinear = 'ResizeBilinear';\nexport const ResizeBilinearGrad = 'ResizeBilinearGrad';\nexport const Relu6 = 'Relu6';\nexport const Reverse = 'Reverse';\nexport const Round = 'Round';\nexport const Rsqrt = 'Rsqrt';\nexport const ScatterNd = 'ScatterNd';\nexport const SelectV2 = 'SelectV2';\nexport const Selu = 'Selu';\nexport const Slice = 'Slice';\nexport const Sin = 'Sin';\nexport const Sinh = 'Sinh';\nexport const Sign = 'Sign';\nexport const Sigmoid = 'Sigmoid';\nexport const Softplus = 'Softplus';\nexport const Sqrt = 'Sqrt';\nexport const Sum = 'Sum';\nexport const SpaceToBatchND = 'SpaceToBatchND';\nexport const SplitV = 'SplitV';\nexport const Softmax = 'Softmax';\nexport const SquaredDifference = 'SquaredDifference';\nexport const Square = 'Square';\nexport const Sub = 'Sub';\nexport const SparseToDense = 'SparseToDense';\nexport const StridedSlice = 'StridedSlice';\nexport const Tan = 'Tan';\nexport const Tanh = 'Tanh';\nexport const Tile = 'Tile';\nexport const TopK = 'TopK';\nexport const Transpose = 'Transpose';\nexport const Unique = 'Unique';\nexport const Unpack = 'Unpack';\nexport const UnsortedSegmentSum = 'UnsortedSegmentSum';\nexport const ZerosLike = 'ZerosLike';\n/**\n * TensorFlow.js-only kernels\n */\nexport const Step = 'Step';\nexport const FromPixels = 'FromPixels';\nexport const RotateWithOffset = 'RotateWithOffset';\nexport const _FusedMatMul = '_FusedMatMul';\nexport const FusedConv2D = 'FusedConv2D';\nexport const FusedDepthwiseConv2D = 'FusedDepthwiseConv2D';\n//# sourceMappingURL=kernel_names.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nimport { getGlobal } from './global_util';\nconst kernelRegistry = getGlobal('kernelRegistry', () => new Map());\nconst gradRegistry = getGlobal('gradRegistry', () => new Map());\n/**\n * Returns the kernel function (code) associated with the provided names.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n */\nexport function getKernel(kernelName, backendName) {\n    const key = makeKey(kernelName, backendName);\n    return kernelRegistry.get(key);\n}\n/**\n * Returns the registered gradient info associated with the provided kernel.\n * @param kernelName The official TF kernel name.\n */\nexport function getGradient(kernelName) {\n    return gradRegistry.get(kernelName);\n}\nexport function getKernelsForBackend(backendName) {\n    const it = kernelRegistry.entries();\n    const result = [];\n    while (true) {\n        const { done, value } = it.next();\n        if (done) {\n            break;\n        }\n        const [key, config] = value;\n        const [backend,] = key.split('_');\n        if (backend === backendName) {\n            result.push(config);\n        }\n    }\n    return result;\n}\n/**\n * Registers the function (forward pass) for the kernel in a global registry.\n *\n * @param config A config object with the following properties:\n * - `kernelName` The official name of the kernel.\n * - `backendName` The official name of the backend.\n * - `kernelFunc` The function to run during the forward pass of the kernel.\n * - `setupFunc` Optional. Gets called once, after the backend initializes.\n * - `disposeFunc` Optional. Gets called once, right before the backend is\n * disposed.\n */\nexport function registerKernel(config) {\n    const { kernelName, backendName } = config;\n    const key = makeKey(kernelName, backendName);\n    if (kernelRegistry.has(key)) {\n        console.warn(`The kernel '${kernelName}' for backend ` +\n            `'${backendName}' is already registered`);\n    }\n    kernelRegistry.set(key, config);\n}\n/**\n * Registers a gradient function for a given kernel in the global registry,\n * to be used during the back-propagation of that kernel.\n *\n * @param config An object with the following properties:\n * - `kernelName` The name of the kernel that the gradient function is for.\n * - `gradFunc` The function to run during back-propagation.\n */\nexport function registerGradient(config) {\n    const { kernelName } = config;\n    if (gradRegistry.has(kernelName)) {\n        // TODO (yassogba) after 3.0 assess whether we need to keep this gated\n        // to debug mode.\n        if (env().getBool('DEBUG')) {\n            console.warn(`Overriding the gradient for '${kernelName}'`);\n        }\n    }\n    gradRegistry.set(kernelName, config);\n}\n/**\n * Removes the kernel function from the registry.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n *\n */\nexport function unregisterKernel(kernelName, backendName) {\n    const key = makeKey(kernelName, backendName);\n    if (!kernelRegistry.has(key)) {\n        throw new Error(`The kernel '${kernelName}' for backend ` +\n            `'${backendName}' is not registered`);\n    }\n    kernelRegistry.delete(key);\n}\n/** Removes the registered gradient from the global registry. */\nexport function unregisterGradient(kernelName) {\n    if (!gradRegistry.has(kernelName)) {\n        throw new Error(`The gradient '${kernelName}' for backend is not registered`);\n    }\n    gradRegistry.delete(kernelName);\n}\n/**\n * Finds kernels that have already been registered to a backend and re-registers\n * them for a new backend. Useful for registering custom backends.\n * @param registeredBackendName Already registered backend.\n * @param newBackendName New backend.\n */\nexport function copyRegisteredKernels(registeredBackendName, newBackendName) {\n    const kernels = getKernelsForBackend(registeredBackendName);\n    kernels.forEach(kernelConfig => {\n        const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName });\n        registerKernel(newKernelConfig);\n    });\n}\nfunction makeKey(kernelName, backendName) {\n    return `${backendName}_${kernelName}`;\n}\n//# sourceMappingURL=kernel_registry.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\n/**\n * Shuffles the array in-place using Fisher-Yates algorithm.\n *\n * ```js\n * const a = [1, 2, 3, 4, 5];\n * tf.util.shuffle(a);\n * console.log(a);\n * ```\n *\n * @param array The array to shuffle in-place.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\n// tslint:disable-next-line:no-any\nexport function shuffle(array) {\n    let counter = array.length;\n    let temp = 0;\n    let index = 0;\n    // While there are elements in the array\n    while (counter > 0) {\n        // Pick a random index\n        index = (Math.random() * counter) | 0;\n        // Decrease counter by 1\n        counter--;\n        // And swap the last element with it\n        temp = array[counter];\n        array[counter] = array[index];\n        array[index] = temp;\n    }\n}\n/** Clamps a value to a specified range. */\nexport function clamp(min, x, max) {\n    return Math.max(min, Math.min(x, max));\n}\nexport function nearestLargerEven(val) {\n    return val % 2 === 0 ? val : val + 1;\n}\nexport function sum(arr) {\n    let sum = 0;\n    for (let i = 0; i < arr.length; i++) {\n        sum += arr[i];\n    }\n    return sum;\n}\n/**\n * Returns a sample from a uniform [a, b) distribution.\n *\n * @param a The minimum support (inclusive).\n * @param b The maximum support (exclusive).\n * @return A pseudorandom number on the half-open interval [a,b).\n */\nexport function randUniform(a, b) {\n    const r = Math.random();\n    return (b * r) + (1 - r) * a;\n}\n/** Returns the squared Euclidean distance between two vectors. */\nexport function distSquared(a, b) {\n    let result = 0;\n    for (let i = 0; i < a.length; i++) {\n        const diff = Number(a[i]) - Number(b[i]);\n        result += diff * diff;\n    }\n    return result;\n}\n/**\n * Asserts that the expression is true. Otherwise throws an error with the\n * provided message.\n *\n * ```js\n * const x = 2;\n * tf.util.assert(x === 2, 'x is not 2');\n * ```\n *\n * @param expr The expression to assert (as a boolean).\n * @param msg A function that returns the message to report when throwing an\n *     error. We use a function for performance reasons.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function assert(expr, msg) {\n    if (!expr) {\n        throw new Error(typeof msg === 'string' ? msg : msg());\n    }\n}\nexport function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = '') {\n    assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nexport function assertNonNull(a) {\n    assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);\n}\n// NOTE: We explicitly type out what T extends instead of any so that\n// util.flatten on a nested array of number doesn't try to infer T as a\n// number[][], causing us to explicitly type util.flatten<number>().\n/**\n *  Flattens an arbitrarily nested array.\n *\n * ```js\n * const a = [[1, 2], [3, 4], [5, [6, [7]]]];\n * const flat = tf.util.flatten(a);\n * console.log(flat);\n * ```\n *\n *  @param arr The nested array to flatten.\n *  @param result The destination array which holds the elements.\n *  @param skipTypedArray If true, avoids flattening the typed arrays. Defaults\n *      to false.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function flatten(arr, result = [], skipTypedArray = false) {\n    if (result == null) {\n        result = [];\n    }\n    if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {\n        for (let i = 0; i < arr.length; ++i) {\n            flatten(arr[i], result, skipTypedArray);\n        }\n    }\n    else {\n        result.push(arr);\n    }\n    return result;\n}\n/**\n * Returns the size (number of elements) of the tensor given its shape.\n *\n * ```js\n * const shape = [3, 4, 2];\n * const size = tf.util.sizeFromShape(shape);\n * console.log(size);\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function sizeFromShape(shape) {\n    if (shape.length === 0) {\n        // Scalar.\n        return 1;\n    }\n    let size = shape[0];\n    for (let i = 1; i < shape.length; i++) {\n        size *= shape[i];\n    }\n    return size;\n}\nexport function isScalarShape(shape) {\n    return shape.length === 0;\n}\nexport function arraysEqual(n1, n2) {\n    if (n1 === n2) {\n        return true;\n    }\n    if (n1 == null || n2 == null) {\n        return false;\n    }\n    if (n1.length !== n2.length) {\n        return false;\n    }\n    for (let i = 0; i < n1.length; i++) {\n        if (n1[i] !== n2[i]) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function isInt(a) {\n    return a % 1 === 0;\n}\nexport function tanh(x) {\n    // tslint:disable-next-line:no-any\n    if (Math.tanh != null) {\n        // tslint:disable-next-line:no-any\n        return Math.tanh(x);\n    }\n    if (x === Infinity) {\n        return 1;\n    }\n    else if (x === -Infinity) {\n        return -1;\n    }\n    else {\n        const e2x = Math.exp(2 * x);\n        return (e2x - 1) / (e2x + 1);\n    }\n}\nexport function sizeToSquarishShape(size) {\n    const width = Math.ceil(Math.sqrt(size));\n    return [width, Math.ceil(size / width)];\n}\n/**\n * Creates a new array with randomized indicies to a given quantity.\n *\n * ```js\n * const randomTen = tf.util.createShuffledIndices(10);\n * console.log(randomTen);\n * ```\n *\n * @param number Quantity of how many shuffled indicies to create.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function createShuffledIndices(n) {\n    const shuffledIndices = new Uint32Array(n);\n    for (let i = 0; i < n; ++i) {\n        shuffledIndices[i] = i;\n    }\n    shuffle(shuffledIndices);\n    return shuffledIndices;\n}\nexport function rightPad(a, size) {\n    if (size <= a.length) {\n        return a;\n    }\n    return a + ' '.repeat(size - a.length);\n}\nexport function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {\n    return new Promise((resolve, reject) => {\n        let tryCount = 0;\n        const tryFn = () => {\n            if (checkFn()) {\n                resolve();\n                return;\n            }\n            tryCount++;\n            const nextBackoff = delayFn(tryCount);\n            if (maxCounter != null && tryCount >= maxCounter) {\n                reject();\n                return;\n            }\n            setTimeout(tryFn, nextBackoff);\n        };\n        tryFn();\n    });\n}\n/**\n * Given the full size of the array and a shape that may contain -1 as the\n * implicit dimension, returns the inferred shape where -1 is replaced.\n * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].\n *\n * @param shape The shape, which may contain -1 in some dimension.\n * @param size The full size (number of elements) of the array.\n * @return The inferred shape where -1 is replaced with the inferred size.\n */\nexport function inferFromImplicitShape(shape, size) {\n    let shapeProd = 1;\n    let implicitIdx = -1;\n    for (let i = 0; i < shape.length; ++i) {\n        if (shape[i] >= 0) {\n            shapeProd *= shape[i];\n        }\n        else if (shape[i] === -1) {\n            if (implicitIdx !== -1) {\n                throw Error(`Shapes can only have 1 implicit size. ` +\n                    `Found -1 at dim ${implicitIdx} and dim ${i}`);\n            }\n            implicitIdx = i;\n        }\n        else if (shape[i] < 0) {\n            throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n        }\n    }\n    if (implicitIdx === -1) {\n        if (size > 0 && size !== shapeProd) {\n            throw Error(`Size(${size}) must match the product of shape ${shape}`);\n        }\n        return shape;\n    }\n    if (shapeProd === 0) {\n        throw Error(`Cannot infer the missing size in [${shape}] when ` +\n            `there are 0 elements`);\n    }\n    if (size % shapeProd !== 0) {\n        throw Error(`The implicit shape can't be a fractional number. ` +\n            `Got ${size} / ${shapeProd}`);\n    }\n    const newShape = shape.slice();\n    newShape[implicitIdx] = size / shapeProd;\n    return newShape;\n}\nexport function parseAxisParam(axis, shape) {\n    const rank = shape.length;\n    // Normalize input\n    axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n    // Check for valid range\n    assert(axis.every(ax => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but ` +\n        `got axis ${axis}`);\n    // Check for only integers\n    assert(axis.every(ax => isInt(ax)), () => `All values in axis param must be integers but ` +\n        `got axis ${axis}`);\n    // Handle negative axis.\n    return axis.map(a => a < 0 ? rank + a : a);\n}\n/** Reduces the shape by removing all dimensions of shape 1. */\nexport function squeezeShape(shape, axis) {\n    const newShape = [];\n    const keptDims = [];\n    const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;\n    const axes = (axis == null || isEmptyArray) ?\n        null :\n        parseAxisParam(axis, shape).sort();\n    let j = 0;\n    for (let i = 0; i < shape.length; ++i) {\n        if (axes != null) {\n            if (axes[j] === i && shape[i] !== 1) {\n                throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n            }\n            if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {\n                newShape.push(shape[i]);\n                keptDims.push(i);\n            }\n            if (axes[j] <= i) {\n                j++;\n            }\n        }\n        if (shape[i] !== 1) {\n            newShape.push(shape[i]);\n            keptDims.push(i);\n        }\n    }\n    return { newShape, keptDims };\n}\nexport function getTypedArrayFromDType(dtype, size) {\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    return values;\n}\nexport function getArrayFromDType(dtype, size) {\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else if (dtype === 'string') {\n        values = new Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    return values;\n}\nexport function checkConversionForErrors(vals, dtype) {\n    for (let i = 0; i < vals.length; i++) {\n        const num = vals[i];\n        if (isNaN(num) || !isFinite(num)) {\n            throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n        }\n    }\n}\n/** Returns true if the dtype is valid. */\nexport function isValidDtype(dtype) {\n    return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' ||\n        dtype === 'int32' || dtype === 'string';\n}\n/**\n * Returns true if the new type can't encode the old type without loss of\n * precision.\n */\nexport function hasEncodingLoss(oldType, newType) {\n    if (newType === 'complex64') {\n        return false;\n    }\n    if (newType === 'float32' && oldType !== 'complex64') {\n        return false;\n    }\n    if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {\n        return false;\n    }\n    if (newType === 'bool' && oldType === 'bool') {\n        return false;\n    }\n    return true;\n}\nexport function isTypedArray(a) {\n    return a instanceof Float32Array || a instanceof Int32Array ||\n        a instanceof Uint8Array;\n}\nexport function bytesPerElement(dtype) {\n    if (dtype === 'float32' || dtype === 'int32') {\n        return 4;\n    }\n    else if (dtype === 'complex64') {\n        return 8;\n    }\n    else if (dtype === 'bool') {\n        return 1;\n    }\n    else {\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n}\n/**\n * Returns the approximate number of bytes allocated in the string array - 2\n * bytes per character. Computing the exact bytes for a native string in JS is\n * not possible since it depends on the encoding of the html page that serves\n * the website.\n */\nexport function bytesFromStringArray(arr) {\n    if (arr == null) {\n        return 0;\n    }\n    let bytes = 0;\n    arr.forEach(x => bytes += x.length);\n    return bytes;\n}\n/** Returns true if the value is a string. */\nexport function isString(value) {\n    return typeof value === 'string' || value instanceof String;\n}\nexport function isBoolean(value) {\n    return typeof value === 'boolean';\n}\nexport function isNumber(value) {\n    return typeof value === 'number';\n}\nexport function inferDtype(values) {\n    if (Array.isArray(values)) {\n        return inferDtype(values[0]);\n    }\n    if (values instanceof Float32Array) {\n        return 'float32';\n    }\n    else if (values instanceof Int32Array || values instanceof Uint8Array) {\n        return 'int32';\n    }\n    else if (isNumber(values)) {\n        return 'float32';\n    }\n    else if (isString(values)) {\n        return 'string';\n    }\n    else if (isBoolean(values)) {\n        return 'bool';\n    }\n    return 'float32';\n}\nexport function isFunction(f) {\n    return !!(f && f.constructor && f.call && f.apply);\n}\nexport function nearestDivisor(size, start) {\n    for (let i = start; i < size; ++i) {\n        if (size % i === 0) {\n            return i;\n        }\n    }\n    return size;\n}\nexport function computeStrides(shape) {\n    const rank = shape.length;\n    if (rank < 2) {\n        return [];\n    }\n    // Last dimension has implicit stride of 1, thus having D-1 (instead of D)\n    // strides.\n    const strides = new Array(rank - 1);\n    strides[rank - 2] = shape[rank - 1];\n    for (let i = rank - 3; i >= 0; --i) {\n        strides[i] = strides[i + 1] * shape[i + 1];\n    }\n    return strides;\n}\n/**\n * Create typed array for scalar value. Used for storing in `DataStorage`.\n */\nexport function createScalarValue(value, dtype) {\n    if (dtype === 'string') {\n        return encodeString(value);\n    }\n    return toTypedArray([value], dtype);\n}\nexport function toTypedArray(a, dtype) {\n    if (dtype === 'string') {\n        throw new Error('Cannot convert a string[] to a TypedArray');\n    }\n    if (Array.isArray(a)) {\n        a = flatten(a);\n    }\n    if (env().getBool('DEBUG')) {\n        checkConversionForErrors(a, dtype);\n    }\n    if (noConversionNeeded(a, dtype)) {\n        return a;\n    }\n    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n        return new Float32Array(a);\n    }\n    else if (dtype === 'int32') {\n        return new Int32Array(a);\n    }\n    else if (dtype === 'bool') {\n        const bool = new Uint8Array(a.length);\n        for (let i = 0; i < bool.length; ++i) {\n            if (Math.round(a[i]) !== 0) {\n                bool[i] = 1;\n            }\n        }\n        return bool;\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\nfunction createNestedArray(offset, shape, a) {\n    const ret = new Array();\n    if (shape.length === 1) {\n        const d = shape[0];\n        for (let i = 0; i < d; i++) {\n            ret[i] = a[offset + i];\n        }\n    }\n    else {\n        const d = shape[0];\n        const rest = shape.slice(1);\n        const len = rest.reduce((acc, c) => acc * c);\n        for (let i = 0; i < d; i++) {\n            ret[i] = createNestedArray(offset + i * len, rest, a);\n        }\n    }\n    return ret;\n}\n// Provide a nested array of TypedArray in given shape.\nexport function toNestedArray(shape, a) {\n    if (shape.length === 0) {\n        // Scalar type should return a single number.\n        return a[0];\n    }\n    const size = shape.reduce((acc, c) => acc * c);\n    if (size === 0) {\n        // A tensor with shape zero should be turned into empty list.\n        return [];\n    }\n    if (size !== a.length) {\n        throw new Error(`[${shape}] does not match the input size ${a.length}.`);\n    }\n    return createNestedArray(0, shape, a);\n}\nfunction noConversionNeeded(a, dtype) {\n    return (a instanceof Float32Array && dtype === 'float32') ||\n        (a instanceof Int32Array && dtype === 'int32') ||\n        (a instanceof Uint8Array && dtype === 'bool');\n}\nexport function makeOnesTypedArray(size, dtype) {\n    const array = makeZerosTypedArray(size, dtype);\n    for (let i = 0; i < array.length; i++) {\n        array[i] = 1;\n    }\n    return array;\n}\nexport function makeZerosTypedArray(size, dtype) {\n    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n        return new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        return new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        return new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\n/**\n * Make nested `TypedArray` filled with zeros.\n * @param shape The shape information for the nested array.\n * @param dtype dtype of the array element.\n */\nexport function makeZerosNestedTypedArray(shape, dtype) {\n    const size = shape.reduce((prev, curr) => prev * curr, 1);\n    if (dtype == null || dtype === 'float32') {\n        return toNestedArray(shape, new Float32Array(size));\n    }\n    else if (dtype === 'int32') {\n        return toNestedArray(shape, new Int32Array(size));\n    }\n    else if (dtype === 'bool') {\n        return toNestedArray(shape, new Uint8Array(size));\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\n/**\n * Returns the current high-resolution time in milliseconds relative to an\n * arbitrary time in the past. It works across different platforms (node.js,\n * browsers).\n *\n * ```js\n * console.log(tf.util.now());\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function now() {\n    return env().platform.now();\n}\nexport function assertNonNegativeIntegerDimensions(shape) {\n    shape.forEach(dimSize => {\n        assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got ` +\n            `shape [${shape}].`);\n    });\n}\n/**\n * Returns a platform-specific implementation of\n * [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n *\n * If `fetch` is defined on the global object (`window`, `process`, etc.),\n * `tf.util.fetch` returns that function.\n *\n * If not, `tf.util.fetch` returns a platform-specific solution.\n *\n * ```js\n * const resource = await tf.util.fetch('https://unpkg.com/@tensorflow/tfjs');\n * // handle response\n * ```\n *\n * @doc {heading: 'Util'}\n */\nexport function fetch(path, requestInits) {\n    return env().platform.fetch(path, requestInits);\n}\n/**\n * Encodes the provided string into bytes using the provided encoding scheme.\n *\n * @param s The string to encode.\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function encodeString(s, encoding = 'utf-8') {\n    encoding = encoding || 'utf-8';\n    return env().platform.encode(s, encoding);\n}\n/**\n * Decodes the provided bytes into a string using the provided encoding scheme.\n * @param bytes The bytes to decode.\n *\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function decodeString(bytes, encoding = 'utf-8') {\n    encoding = encoding || 'utf-8';\n    return env().platform.decode(bytes, encoding);\n}\n/**\n * Computes flat index for a given location (multidimentionsal index) in a\n * Tensor/multidimensional array.\n *\n * @param locs Location in the tensor.\n * @param rank Rank of the tensor.\n * @param strides Tensor strides.\n */\nexport function locToIndex(locs, rank, strides) {\n    if (rank === 0) {\n        return 0;\n    }\n    else if (rank === 1) {\n        return locs[0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n        index += strides[i] * locs[i];\n    }\n    return index;\n}\n/**\n * Computes the location (multidimensional index) in a tensor/multidimentional\n * array for a given flat index.\n *\n * @param index Index in flat array.\n * @param rank Rank of tensor.\n * @param strides Strides of tensor.\n */\nexport function indexToLoc(index, rank, strides) {\n    if (rank === 0) {\n        return [];\n    }\n    else if (rank === 1) {\n        return [index];\n    }\n    const locs = new Array(rank);\n    for (let i = 0; i < locs.length - 1; ++i) {\n        locs[i] = Math.floor(index / strides[i]);\n        index -= locs[i] * strides[i];\n    }\n    locs[locs.length - 1] = index;\n    return locs;\n}\n//# sourceMappingURL=util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from './util';\nexport class Profiler {\n    constructor(backendTimer, logger) {\n        this.backendTimer = backendTimer;\n        this.logger = logger;\n        if (logger == null) {\n            this.logger = new Logger();\n        }\n    }\n    profileKernel(kernelName, inputs, f) {\n        let outputs;\n        const holdResultWrapperFn = () => {\n            outputs = f();\n        };\n        const timer = this.backendTimer.time(holdResultWrapperFn);\n        for (let i = 0; i < outputs.length; i++) {\n            const output = outputs[i];\n            // Dangling promise here because we don't want to propagate up\n            // asynchronicity.\n            output.data().then(tensorVals => {\n                checkComputationForErrors(tensorVals, output.dtype, kernelName);\n            });\n        }\n        const kernelProfile = {\n            kernelName,\n            outputs,\n            inputs,\n            timeMs: timer.then(timing => timing.kernelMs),\n            extraInfo: timer.then(timing => timing.getExtraProfileInfo != null ?\n                timing.getExtraProfileInfo() :\n                '')\n        };\n        return kernelProfile;\n    }\n    logKernelProfile(kernelProfile) {\n        const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile;\n        outputs.forEach(result => {\n            Promise.all([result.data(), timeMs, extraInfo]).then(valueContainer => {\n                this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);\n            });\n        });\n    }\n}\nexport function checkComputationForErrors(vals, dtype, kernelName) {\n    if (dtype !== 'float32') {\n        // Only floating point computations will generate NaN values\n        return false;\n    }\n    for (let i = 0; i < vals.length; i++) {\n        const num = vals[i];\n        if (isNaN(num) || !isFinite(num)) {\n            // Throwing custom exception so behavior is testable.\n            console.warn(`Found ${num} in the result of '${kernelName}'`);\n            return true;\n        }\n    }\n    return false;\n}\nexport class Logger {\n    logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {\n        const time = typeof timeMs === 'number' ? util.rightPad(`${timeMs}ms`, 9) :\n            timeMs['error'];\n        const paddedName = util.rightPad(name, 25);\n        const rank = result.rank;\n        const size = result.size;\n        const shape = util.rightPad(result.shape.toString(), 14);\n        let inputShapesDescription = '';\n        for (const name in inputs) {\n            const input = inputs[name];\n            if (input != null) {\n                // The input might be a non-tensor (e.g HTMLImageElement), in which case\n                // we claim the output shape as input shape.\n                const inputShape = input.shape || result.shape;\n                const inputRank = inputShape.length;\n                inputShapesDescription +=\n                    `${name}: ${inputRank}D ${inputRank > 0 ? inputShape : ''} `;\n            }\n        }\n        console.log(`%c${paddedName}\\t%c${time}\\t%c${rank}D ${shape}\\t%c${size}\\t%c${inputShapesDescription}\\t%c${extraInfo}`, 'font-weight:bold', 'color:red', 'color:blue', 'color: orange', 'color: green', 'color: steelblue');\n    }\n}\n//# sourceMappingURL=profiler.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from './util';\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nexport function getFilteredNodesXToY(tape, xs, y) {\n    // Forward pass to compute all the nodes and Tensors that are transitively a\n    // function of x.\n    const tensorsFromX = {};\n    const nodesFromX = {};\n    for (let i = 0; i < xs.length; i++) {\n        tensorsFromX[xs[i].id] = true;\n    }\n    for (let i = 0; i < tape.length; i++) {\n        const node = tape[i];\n        const nodeInputs = node.inputs;\n        for (const inputName in nodeInputs) {\n            const input = nodeInputs[inputName];\n            let anyInputFromX = false;\n            for (let j = 0; j < xs.length; j++) {\n                if (tensorsFromX[input.id]) {\n                    node.outputs.forEach(output => tensorsFromX[output.id] = true);\n                    anyInputFromX = true;\n                    nodesFromX[node.id] = true;\n                    break;\n                }\n            }\n            if (anyInputFromX) {\n                break;\n            }\n        }\n    }\n    // Backward pass to find all of the nodes and Tensors that lead to y.\n    const tensorsLeadToY = {};\n    tensorsLeadToY[y.id] = true;\n    const nodesToY = {};\n    for (let i = tape.length - 1; i >= 0; i--) {\n        const node = tape[i];\n        const nodeInputs = node.inputs;\n        // If any of the outputs lead to y, mark all of the inputs as leading to y.\n        for (let j = 0; j < node.outputs.length; j++) {\n            if (tensorsLeadToY[node.outputs[j].id]) {\n                for (const inputName in nodeInputs) {\n                    tensorsLeadToY[nodeInputs[inputName].id] = true;\n                    nodesToY[node.id] = true;\n                }\n                break;\n            }\n        }\n    }\n    // Return the paths that come from x and lead to y.\n    const filteredTape = [];\n    for (let i = 0; i < tape.length; i++) {\n        const node = tape[i];\n        if (nodesFromX[node.id] && nodesToY[node.id]) {\n            // Prune the inputs from the node that aren't a function of x.\n            const prunedInputs = {};\n            for (const inputName in node.inputs) {\n                const nodeInput = node.inputs[inputName];\n                if (tensorsFromX[nodeInput.id]) {\n                    prunedInputs[inputName] = nodeInput;\n                }\n            }\n            // Copy the node and overwrite inputsAndArgs to the pruned version.\n            const prunedNode = Object.assign({}, node);\n            prunedNode.inputs = prunedInputs;\n            prunedNode.outputs = node.outputs;\n            filteredTape.push(prunedNode);\n        }\n    }\n    return filteredTape;\n}\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nexport function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy, add) {\n    // Walk the tape backward and keep a map of Tensor to its gradient.\n    for (let i = filteredTape.length - 1; i >= 0; i--) {\n        const node = filteredTape[i];\n        const dys = [];\n        node.outputs.forEach(o => {\n            const gradTensor = tensorAccumulatedGradientMap[o.id];\n            if (gradTensor != null) {\n                dys.push(gradTensor);\n            }\n            else {\n                // This particular output is not in the back-propagation subgraph, so it\n                // does not affect the final output, thus we put null for its dy.\n                dys.push(null);\n            }\n        });\n        if (node.gradient == null) {\n            throw new Error(`Cannot compute gradient: gradient function not found ` +\n                `for ${node.kernelName}.`);\n        }\n        // Backprop dy through this node and accumulate gradients over the inputs.\n        const inputGradients = node.gradient(dys);\n        for (const inputName in node.inputs) {\n            if (!(inputName in inputGradients)) {\n                throw new Error(`Cannot backprop through input ${inputName}. ` +\n                    `Available gradients found: ${Object.keys(inputGradients)}.`);\n            }\n            // Call the gradient function.\n            const dx = tidy(() => inputGradients[inputName]());\n            if (dx.dtype !== 'float32') {\n                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n                    `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n            }\n            const x = node.inputs[inputName];\n            if (!util.arraysEqual(dx.shape, x.shape)) {\n                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n                    `'${inputName}' has shape '${dx.shape}', which does not match ` +\n                    `the shape of the input '${x.shape}'`);\n            }\n            if (tensorAccumulatedGradientMap[x.id] == null) {\n                tensorAccumulatedGradientMap[x.id] = dx;\n            }\n            else {\n                const curGradient = tensorAccumulatedGradientMap[x.id];\n                tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);\n                curGradient.dispose();\n            }\n        }\n    }\n}\n//# sourceMappingURL=tape.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { computeStrides, isString, rightPad, sizeFromShape } from './util';\n// Maximum number of values before we decide to show ellipsis.\nconst FORMAT_LIMIT_NUM_VALS = 20;\n// Number of first and last values to show when displaying a, b,...,y, z.\nconst FORMAT_NUM_FIRST_LAST_VALS = 3;\n// Number of significant digits to show.\nconst FORMAT_NUM_SIG_DIGITS = 7;\nexport function tensorToString(vals, shape, dtype, verbose) {\n    const strides = computeStrides(shape);\n    const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n    const rank = shape.length;\n    const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n    const lines = ['Tensor'];\n    if (verbose) {\n        lines.push(`  dtype: ${dtype}`);\n        lines.push(`  rank: ${rank}`);\n        lines.push(`  shape: [${shape}]`);\n        lines.push(`  values:`);\n    }\n    lines.push(valsLines.map(l => '    ' + l).join('\\n'));\n    return lines.join('\\n');\n}\nfunction computeMaxSizePerColumn(vals, shape, dtype, strides) {\n    const n = sizeFromShape(shape);\n    const numCols = strides[strides.length - 1];\n    const padPerCol = new Array(numCols).fill(0);\n    const rank = shape.length;\n    const valuesOrTuples = dtype === 'complex64' ? createComplexTuples(vals) : vals;\n    if (rank > 1) {\n        for (let row = 0; row < n / numCols; row++) {\n            const offset = row * numCols;\n            for (let j = 0; j < numCols; j++) {\n                padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);\n            }\n        }\n    }\n    return padPerCol;\n}\nfunction valToString(val, pad, dtype) {\n    let valStr;\n    if (Array.isArray(val)) {\n        valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +\n            `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n    }\n    else if (isString(val)) {\n        valStr = `'${val}'`;\n    }\n    else if (dtype === 'bool') {\n        valStr = boolNumToString(val);\n    }\n    else {\n        valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n    }\n    return rightPad(valStr, pad);\n}\nfunction boolNumToString(v) {\n    return v === 0 ? 'false' : 'true';\n}\nfunction subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {\n    const storagePerElement = dtype === 'complex64' ? 2 : 1;\n    const size = shape[0];\n    const rank = shape.length;\n    if (rank === 0) {\n        if (dtype === 'complex64') {\n            const complexTuple = createComplexTuples(vals);\n            return [valToString(complexTuple[0], 0, dtype)];\n        }\n        if (dtype === 'bool') {\n            return [boolNumToString(vals[0])];\n        }\n        return [vals[0].toString()];\n    }\n    if (rank === 1) {\n        if (size > FORMAT_LIMIT_NUM_VALS) {\n            const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n            let firstVals = Array.from(vals.slice(0, firstValsSize));\n            let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));\n            if (dtype === 'complex64') {\n                firstVals = createComplexTuples(firstVals);\n                lastVals = createComplexTuples(lastVals);\n            }\n            return [\n                '[' +\n                    firstVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n                        .join(', ') +\n                    ', ..., ' +\n                    lastVals\n                        .map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype))\n                        .join(', ') +\n                    ']'\n            ];\n        }\n        const displayVals = dtype === 'complex64' ? createComplexTuples(vals) :\n            Array.from(vals);\n        return [\n            '[' +\n                displayVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n                    .join(', ') +\n                ']'\n        ];\n    }\n    // The array is rank 2 or more.\n    const subshape = shape.slice(1);\n    const substrides = strides.slice(1);\n    const stride = strides[0] * storagePerElement;\n    const lines = [];\n    if (size > FORMAT_LIMIT_NUM_VALS) {\n        for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false /* isLast */));\n        }\n        lines.push('...');\n        for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n        }\n    }\n    else {\n        for (let i = 0; i < size; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n        }\n    }\n    const sep = rank === 2 ? ',' : '';\n    lines[0] = '[' + lines[0] + sep;\n    for (let i = 1; i < lines.length - 1; i++) {\n        lines[i] = ' ' + lines[i] + sep;\n    }\n    let newLineSep = ',\\n';\n    for (let i = 2; i < rank; i++) {\n        newLineSep += '\\n';\n    }\n    lines[lines.length - 1] =\n        ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);\n    return lines;\n}\nfunction createComplexTuples(vals) {\n    const complexTuples = [];\n    for (let i = 0; i < vals.length; i += 2) {\n        complexTuples.push([vals[i], vals[i + 1]]);\n    }\n    return complexTuples;\n}\n//# sourceMappingURL=tensor_format.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensorToString } from './tensor_format';\nimport * as util from './util';\nimport { computeStrides, toNestedArray } from './util';\n/**\n * A mutable object, similar to `tf.Tensor`, that allows users to set values\n * at locations before converting to an immutable `tf.Tensor`.\n *\n * See `tf.buffer` for creating a tensor buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class TensorBuffer {\n    constructor(shape, dtype, values) {\n        this.dtype = dtype;\n        this.shape = shape.slice();\n        this.size = util.sizeFromShape(shape);\n        if (values != null) {\n            const n = values.length;\n            util.assert(n === this.size, () => `Length of values '${n}' does not match the size ` +\n                `inferred by the shape '${this.size}'.`);\n        }\n        if (dtype === 'complex64') {\n            throw new Error(`complex64 dtype TensorBuffers are not supported. Please create ` +\n                `a TensorBuffer for the real and imaginary parts separately and ` +\n                `call tf.complex(real, imag).`);\n        }\n        this.values = values || util.getArrayFromDType(dtype, this.size);\n        this.strides = computeStrides(shape);\n    }\n    /**\n     * Sets a value in the buffer at a given location.\n     *\n     * @param value The value to set.\n     * @param locs  The location indices.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    set(value, ...locs) {\n        if (locs.length === 0) {\n            locs = [0];\n        }\n        util.assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must ` +\n            `match the rank (${this.rank})`);\n        const index = this.locToIndex(locs);\n        this.values[index] = value;\n    }\n    /**\n     * Returns the value in the buffer at the provided location.\n     *\n     * @param locs The location indices.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    get(...locs) {\n        if (locs.length === 0) {\n            locs = [0];\n        }\n        let i = 0;\n        for (const loc of locs) {\n            if (loc < 0 || loc >= this.shape[i]) {\n                const msg = `Requested out of range element at ${locs}. ` +\n                    `  Buffer shape=${this.shape}`;\n                throw new Error(msg);\n            }\n            i++;\n        }\n        let index = locs[locs.length - 1];\n        for (let i = 0; i < locs.length - 1; ++i) {\n            index += this.strides[i] * locs[i];\n        }\n        return this.values[index];\n    }\n    locToIndex(locs) {\n        if (this.rank === 0) {\n            return 0;\n        }\n        else if (this.rank === 1) {\n            return locs[0];\n        }\n        let index = locs[locs.length - 1];\n        for (let i = 0; i < locs.length - 1; ++i) {\n            index += this.strides[i] * locs[i];\n        }\n        return index;\n    }\n    indexToLoc(index) {\n        if (this.rank === 0) {\n            return [];\n        }\n        else if (this.rank === 1) {\n            return [index];\n        }\n        const locs = new Array(this.shape.length);\n        for (let i = 0; i < locs.length - 1; ++i) {\n            locs[i] = Math.floor(index / this.strides[i]);\n            index -= locs[i] * this.strides[i];\n        }\n        locs[locs.length - 1] = index;\n        return locs;\n    }\n    get rank() {\n        return this.shape.length;\n    }\n    /**\n     * Creates an immutable `tf.Tensor` object from the buffer.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    toTensor() {\n        return trackerFn().makeTensor(this.values, this.shape, this.dtype);\n    }\n}\n// For tracking tensor creation and disposal.\nlet trackerFn = null;\n// Used by chaining methods to call into ops.\nlet opHandler = null;\n// Used to warn about deprecated methods.\nlet deprecationWarningFn = null;\n// This here so that we can use this method on dev branches and keep the\n// functionality at master.\n// tslint:disable-next-line:no-unused-expression\n[deprecationWarningFn];\n/**\n * An external consumer can register itself as the tensor tracker. This way\n * the Tensor class can notify the tracker for every tensor created and\n * disposed.\n */\nexport function setTensorTracker(fn) {\n    trackerFn = fn;\n}\n/**\n * An external consumer can register itself as the op handler. This way the\n * Tensor class can have chaining methods that call into ops via the op\n * handler.\n */\nexport function setOpHandler(handler) {\n    opHandler = handler;\n}\n/**\n * Sets the deprecation warning function to be used by this file. This way the\n * Tensor class can be a leaf but still use the environment.\n */\nexport function setDeprecationWarningFn(fn) {\n    deprecationWarningFn = fn;\n}\n/**\n * A `tf.Tensor` object represents an immutable, multidimensional array of\n * numbers that has a shape and a data type.\n *\n * See `tf.tensor` for details on how to create a `tf.Tensor`.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Tensor {\n    constructor(shape, dtype, dataId, id) {\n        /** Whether this tensor has been globally kept. */\n        this.kept = false;\n        this.isDisposedInternal = false;\n        this.shape = shape.slice();\n        this.dtype = dtype || 'float32';\n        this.size = util.sizeFromShape(shape);\n        this.strides = computeStrides(shape);\n        this.dataId = dataId;\n        this.id = id;\n        this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher');\n    }\n    get rank() {\n        return this.shape.length;\n    }\n    /**\n     * Returns a promise of `tf.TensorBuffer` that holds the underlying data.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async buffer() {\n        const vals = await this.data();\n        return opHandler.buffer(this.shape, this.dtype, vals);\n    }\n    /**\n     * Returns a `tf.TensorBuffer` that holds the underlying data.\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    bufferSync() {\n        return opHandler.buffer(this.shape, this.dtype, this.dataSync());\n    }\n    /**\n     * Returns the tensor data as a nested array. The transfer of data is done\n     * asynchronously.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async array() {\n        const vals = await this.data();\n        return toNestedArray(this.shape, vals);\n    }\n    /**\n     * Returns the tensor data as a nested array. The transfer of data is done\n     * synchronously.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    arraySync() {\n        return toNestedArray(this.shape, this.dataSync());\n    }\n    /**\n     * Asynchronously downloads the values from the `tf.Tensor`. Returns a\n     * promise of `TypedArray` that resolves when the computation has finished.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async data() {\n        this.throwIfDisposed();\n        const data = trackerFn().read(this.dataId);\n        if (this.dtype === 'string') {\n            const bytes = await data;\n            try {\n                return bytes.map(b => util.decodeString(b));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode the string bytes into utf-8. ' +\n                    'To get the original bytes, call tensor.bytes().');\n            }\n        }\n        return data;\n    }\n    /**\n     * Synchronously downloads the values from the `tf.Tensor`. This blocks the\n     * UI thread until the values are ready, which can cause performance issues.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    dataSync() {\n        this.throwIfDisposed();\n        const data = trackerFn().readSync(this.dataId);\n        if (this.dtype === 'string') {\n            try {\n                return data.map(b => util.decodeString(b));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode the string bytes into utf-8. ' +\n                    'To get the original bytes, call tensor.bytes().');\n            }\n        }\n        return data;\n    }\n    /** Returns the underlying bytes of the tensor's data. */\n    async bytes() {\n        this.throwIfDisposed();\n        const data = await trackerFn().read(this.dataId);\n        if (this.dtype === 'string') {\n            return data;\n        }\n        else {\n            return new Uint8Array(data.buffer);\n        }\n    }\n    /**\n     * Disposes `tf.Tensor` from memory.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    dispose() {\n        if (this.isDisposed) {\n            return;\n        }\n        trackerFn().disposeTensor(this);\n        this.isDisposedInternal = true;\n    }\n    get isDisposed() {\n        return this.isDisposedInternal;\n    }\n    throwIfDisposed() {\n        if (this.isDisposed) {\n            throw new Error(`Tensor is disposed.`);\n        }\n    }\n    /**\n     * Prints the `tf.Tensor`. See `tf.print` for details.\n     *\n     * @param verbose Whether to print verbose information about the tensor,\n     *    including dtype and size.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    print(verbose = false) {\n        return opHandler.print(this, verbose);\n    }\n    /**\n     * Returns a copy of the tensor. See `tf.clone` for details.\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    clone() {\n        this.throwIfDisposed();\n        return opHandler.clone(this);\n    }\n    /**\n     * Returns a human-readable description of the tensor. Useful for logging.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    toString(verbose = false) {\n        const vals = this.dataSync();\n        return tensorToString(vals, this.shape, this.dtype, verbose);\n    }\n    cast(dtype) {\n        this.throwIfDisposed();\n        return opHandler.cast(this, dtype);\n    }\n    variable(trainable = true, name, dtype) {\n        this.throwIfDisposed();\n        return trackerFn().makeVariable(this, trainable, name, dtype);\n    }\n}\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n    value: (instance) => {\n        // Implementation note: we should use properties of the object that will be\n        // defined before the constructor body has finished executing (methods).\n        // This is because when this code is transpiled by babel, babel will call\n        // classCallCheck before the constructor body is run.\n        // See https://github.com/tensorflow/tfjs/issues/3384 for backstory.\n        return !!instance && instance.data != null && instance.dataSync != null &&\n            instance.throwIfDisposed != null;\n    }\n});\n/**\n * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Variable extends Tensor {\n    constructor(initialValue, trainable, name, tensorId) {\n        super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);\n        this.trainable = trainable;\n        this.name = name;\n    }\n    /**\n     * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n     * the same shape and dtype as the old `tf.Tensor`.\n     *\n     * @param newValue New tensor to be assigned to this variable.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    assign(newValue) {\n        if (newValue.dtype !== this.dtype) {\n            throw new Error(`dtype of the new value (${newValue.dtype}) and ` +\n                `previous value (${this.dtype}) must match`);\n        }\n        if (!util.arraysEqual(newValue.shape, this.shape)) {\n            throw new Error(`shape of the new value (${newValue.shape}) and ` +\n                `previous value (${this.shape}) must match`);\n        }\n        trackerFn().disposeTensor(this);\n        this.dataId = newValue.dataId;\n        trackerFn().incRef(this, null /* backend */);\n    }\n    dispose() {\n        trackerFn().disposeVariable(this);\n        this.isDisposedInternal = true;\n    }\n}\nObject.defineProperty(Variable, Symbol.hasInstance, {\n    value: (instance) => {\n        return instance instanceof Tensor && instance.assign != null &&\n            instance.assign instanceof Function;\n    }\n});\n//# sourceMappingURL=tensor.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Rank;\n(function (Rank) {\n    Rank[\"R0\"] = \"R0\";\n    Rank[\"R1\"] = \"R1\";\n    Rank[\"R2\"] = \"R2\";\n    Rank[\"R3\"] = \"R3\";\n    Rank[\"R4\"] = \"R4\";\n    Rank[\"R5\"] = \"R5\";\n    Rank[\"R6\"] = \"R6\";\n})(Rank || (Rank = {}));\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nvar UpcastInt32AndMap;\n(function (UpcastInt32AndMap) {\n    UpcastInt32AndMap[\"float32\"] = \"float32\";\n    UpcastInt32AndMap[\"int32\"] = \"int32\";\n    UpcastInt32AndMap[\"bool\"] = \"int32\";\n    UpcastInt32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));\nvar UpcastBoolAndMap;\n(function (UpcastBoolAndMap) {\n    UpcastBoolAndMap[\"float32\"] = \"float32\";\n    UpcastBoolAndMap[\"int32\"] = \"int32\";\n    UpcastBoolAndMap[\"bool\"] = \"bool\";\n    UpcastBoolAndMap[\"complex64\"] = \"complex64\";\n})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));\nvar UpcastFloat32AndMap;\n(function (UpcastFloat32AndMap) {\n    UpcastFloat32AndMap[\"float32\"] = \"float32\";\n    UpcastFloat32AndMap[\"int32\"] = \"float32\";\n    UpcastFloat32AndMap[\"bool\"] = \"float32\";\n    UpcastFloat32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));\nvar UpcastComplex64AndMap;\n(function (UpcastComplex64AndMap) {\n    UpcastComplex64AndMap[\"float32\"] = \"complex64\";\n    UpcastComplex64AndMap[\"int32\"] = \"complex64\";\n    UpcastComplex64AndMap[\"bool\"] = \"complex64\";\n    UpcastComplex64AndMap[\"complex64\"] = \"complex64\";\n})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));\nconst upcastTypeMap = {\n    'float32': UpcastFloat32AndMap,\n    'int32': UpcastInt32AndMap,\n    'bool': UpcastBoolAndMap,\n    'complex64': UpcastComplex64AndMap\n};\nexport function upcastType(typeA, typeB) {\n    if (typeA === 'string' || typeB === 'string') {\n        if (typeA === 'string' && typeB === 'string') {\n            return 'string';\n        }\n        throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n    }\n    return upcastTypeMap[typeA][typeB];\n}\n/** Returns the output type after summation. */\nexport function sumOutType(type) {\n    return upcastType(type, 'int32');\n}\n//# sourceMappingURL=types.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from './tensor';\nimport { upcastType } from './types';\nimport { assert } from './util';\nexport function makeTypesMatch(a, b) {\n    if (a.dtype === b.dtype) {\n        return [a, b];\n    }\n    const dtype = upcastType(a.dtype, b.dtype);\n    return [a.cast(dtype), b.cast(dtype)];\n}\nexport function assertTypesMatch(a, b) {\n    assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and` +\n        ` second(${b.dtype}) input must match`);\n}\nexport function isTensorInList(tensor, tensorList) {\n    return tensorList.some(x => x.id === tensor.id);\n}\n/**\n * Extracts any `Tensor`s found within the provided object.\n *\n * @param container an object that may be a `Tensor` or may directly contain\n *   `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it\n *   is safe to pass any object here, except that `Promise`s are not\n *   supported.\n * @returns An array of `Tensors` found within the passed object. If the\n *   argument is simply a `Tensor', a list containing that `Tensor` is\n *   returned. If the object is not a `Tensor` or does not\n *   contain `Tensors`, an empty list is returned.\n */\nexport function getTensorsInContainer(result) {\n    const list = [];\n    const seen = new Set();\n    walkTensorContainer(result, list, seen);\n    return list;\n}\nfunction walkTensorContainer(container, list, seen) {\n    if (container == null) {\n        return;\n    }\n    if (container instanceof Tensor) {\n        list.push(container);\n        return;\n    }\n    if (!isIterable(container)) {\n        return;\n    }\n    // Iteration over keys works also for arrays.\n    const iterable = container;\n    for (const k in iterable) {\n        const val = iterable[k];\n        if (!seen.has(val)) {\n            seen.add(val);\n            walkTensorContainer(val, list, seen);\n        }\n    }\n}\n// tslint:disable-next-line:no-any\nfunction isIterable(obj) {\n    return Array.isArray(obj) || typeof obj === 'object';\n}\n//# sourceMappingURL=tensor_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { KernelBackend } from './backends/backend';\nimport { Environment, setEnvironmentGlobal } from './environment';\nimport { getGlobalNamespace } from './global_util';\nimport { Add, Cast } from './kernel_names';\nimport { getGradient, getKernel, getKernelsForBackend } from './kernel_registry';\nimport { Profiler } from './profiler';\nimport { backpropagateGradients, getFilteredNodesXToY } from './tape';\nimport { setTensorTracker, Tensor, Variable } from './tensor';\nimport { getTensorsInContainer } from './tensor_util';\nimport * as util from './util';\nimport { bytesFromStringArray, makeOnesTypedArray, now, sizeFromShape } from './util';\nclass EngineState {\n    constructor() {\n        // Public since optimizers will use it.\n        this.registeredVariables = {};\n        this.nextTapeNodeId = 0;\n        this.numBytes = 0;\n        this.numTensors = 0;\n        this.numStringTensors = 0;\n        this.numDataBuffers = 0;\n        // Number of nested tf.grad() statements when computing higher-order\n        // gradients. E.g. `1` for first-order gradients and `2` for second-order\n        // gradients. Used to track if the tape should be removed after a backprop.\n        this.gradientDepth = 0;\n        // Number of nested kernel calls. When kernel depth is greater than 1, we turn\n        // off the tape.\n        this.kernelDepth = 0;\n        this.scopeStack = [];\n        /**\n         * Keeps track of the number of data moves during a kernel execution. We\n         * maintain a stack since kernels can call other kernels, recursively.\n         */\n        this.numDataMovesStack = [];\n        this.nextScopeId = 0;\n        this.tensorInfo = new WeakMap();\n        this.profiling = false;\n        this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null };\n    }\n    dispose() {\n        for (const variableName in this.registeredVariables) {\n            this.registeredVariables[variableName].dispose();\n        }\n    }\n}\nexport class Engine {\n    constructor(ENV) {\n        this.ENV = ENV;\n        this.registry = {};\n        this.registryFactory = {};\n        this.pendingBackendInitId = 0;\n        this.state = new EngineState();\n    }\n    async ready() {\n        if (this.pendingBackendInit != null) {\n            return this.pendingBackendInit.then(() => { });\n        }\n        if (this.backendInstance != null) {\n            return;\n        }\n        const sortedBackends = this.getSortedBackends();\n        for (let i = 0; i < sortedBackends.length; i++) {\n            const backendName = sortedBackends[i];\n            const success = await this.initializeBackend(backendName).success;\n            if (success) {\n                await this.setBackend(backendName);\n                return;\n            }\n        }\n        throw new Error(`Could not initialize any backends, all backend initializations ` +\n            `failed.`);\n    }\n    get backend() {\n        if (this.pendingBackendInit != null) {\n            throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make ` +\n                `sure to await tf.ready() or await tf.setBackend() before calling ` +\n                `other methods`);\n        }\n        if (this.backendInstance == null) {\n            const { name, asyncInit } = this.initializeBackendsAndReturnBest();\n            if (asyncInit) {\n                throw new Error(`The highest priority backend '${name}' has not yet been ` +\n                    `initialized. Make sure to await tf.ready() or ` +\n                    `await tf.setBackend() before calling other methods`);\n            }\n            this.setBackend(name);\n        }\n        return this.backendInstance;\n    }\n    backendNames() {\n        return Object.keys(this.registryFactory);\n    }\n    findBackend(backendName) {\n        if (!(backendName in this.registry)) {\n            // If the backend hasn't been initialized but we have a registry entry for\n            // it, initialize it and return it.\n            if (backendName in this.registryFactory) {\n                const { asyncInit } = this.initializeBackend(backendName);\n                if (asyncInit) {\n                    // Backend is not ready yet.\n                    return null;\n                }\n            }\n            else {\n                return null;\n            }\n        }\n        return this.registry[backendName];\n    }\n    findBackendFactory(backendName) {\n        if (!(backendName in this.registryFactory)) {\n            return null;\n        }\n        return this.registryFactory[backendName].factory;\n    }\n    registerBackend(backendName, factory, priority = 1) {\n        if (backendName in this.registryFactory) {\n            console.warn(`${backendName} backend was already registered. ` +\n                `Reusing existing backend factory.`);\n            return false;\n        }\n        this.registryFactory[backendName] = { factory, priority };\n        return true;\n    }\n    async setBackend(backendName) {\n        if (this.registryFactory[backendName] == null) {\n            throw new Error(`Backend name '${backendName}' not found in registry`);\n        }\n        this.backendName = backendName;\n        if (this.registry[backendName] == null) {\n            this.backendInstance = null;\n            const { success, asyncInit } = this.initializeBackend(backendName);\n            const result = asyncInit ? await success : success;\n            if (!result) {\n                return false;\n            }\n        }\n        this.backendInstance = this.registry[backendName];\n        this.setupRegisteredKernels();\n        // Reset the profiler.\n        this.profiler = new Profiler(this.backendInstance);\n        return true;\n    }\n    setupRegisteredKernels() {\n        const kernels = getKernelsForBackend(this.backendName);\n        kernels.forEach(kernel => {\n            if (kernel.setupFunc != null) {\n                kernel.setupFunc(this.backendInstance);\n            }\n        });\n    }\n    disposeRegisteredKernels(backendName) {\n        const kernels = getKernelsForBackend(backendName);\n        kernels.forEach(kernel => {\n            if (kernel.disposeFunc != null) {\n                kernel.disposeFunc(this.registry[backendName]);\n            }\n        });\n    }\n    /**\n     * Initializes a backend by looking up the backend name in the factory\n     * registry and calling the factory method. Returns a boolean representing\n     * whether the initialization of the backend suceeded. Throws an error if\n     * there is no backend in the factory registry.\n     */\n    initializeBackend(backendName) {\n        const registryFactoryEntry = this.registryFactory[backendName];\n        if (registryFactoryEntry == null) {\n            throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);\n        }\n        try {\n            const backend = registryFactoryEntry.factory();\n            /* Test if the factory returns a promise.\n            Done in a more liberal way than\n            previous 'Promise.resolve(backend)===backend'\n            as we needed to account for custom Promise\n            implementations (e.g. Angular) */\n            if (backend && !(backend instanceof KernelBackend)\n                && typeof backend.then === 'function') {\n                const promiseId = ++this.pendingBackendInitId;\n                const success = backend\n                    .then(backendInstance => {\n                    // Outdated promise. Another backend was set in the meantime.\n                    if (promiseId < this.pendingBackendInitId) {\n                        return false;\n                    }\n                    this.registry[backendName] = backendInstance;\n                    this.pendingBackendInit = null;\n                    return true;\n                })\n                    .catch(err => {\n                    // Outdated promise. Another backend was set in the meantime.\n                    if (promiseId < this.pendingBackendInitId) {\n                        return false;\n                    }\n                    this.pendingBackendInit = null;\n                    console.warn(`Initialization of backend ${backendName} failed`);\n                    console.warn(err.stack || err.message);\n                    return false;\n                });\n                this.pendingBackendInit = success;\n                return { success, asyncInit: true };\n            }\n            else {\n                this.registry[backendName] = backend;\n                return { success: true, asyncInit: false };\n            }\n        }\n        catch (err) {\n            console.warn(`Initialization of backend ${backendName} failed`);\n            console.warn(err.stack || err.message);\n            return { success: false, asyncInit: false };\n        }\n    }\n    removeBackend(backendName) {\n        if (!(backendName in this.registryFactory)) {\n            throw new Error(`${backendName} backend not found in registry`);\n        }\n        if (this.backendName === backendName && this.pendingBackendInit != null) {\n            // There is a pending promise of the backend we want to remove. Make it\n            // obsolete.\n            this.pendingBackendInitId++;\n        }\n        if (backendName in this.registry) {\n            this.disposeRegisteredKernels(backendName);\n            this.registry[backendName].dispose();\n            delete this.registry[backendName];\n        }\n        delete this.registryFactory[backendName];\n        // Unset the backend if it is active.\n        if (this.backendName === backendName) {\n            this.pendingBackendInit = null;\n            this.backendName = null;\n            this.backendInstance = null;\n        }\n    }\n    getSortedBackends() {\n        if (Object.keys(this.registryFactory).length === 0) {\n            throw new Error('No backend found in registry.');\n        }\n        return Object.keys(this.registryFactory).sort((a, b) => {\n            // Highest priority comes first.\n            return this.registryFactory[b].priority -\n                this.registryFactory[a].priority;\n        });\n    }\n    initializeBackendsAndReturnBest() {\n        const sortedBackends = this.getSortedBackends();\n        for (let i = 0; i < sortedBackends.length; i++) {\n            const backendName = sortedBackends[i];\n            const { success, asyncInit } = this.initializeBackend(backendName);\n            if (asyncInit || success) {\n                return { name: backendName, asyncInit };\n            }\n        }\n        throw new Error(`Could not initialize any backends, all backend initializations ` +\n            `failed.`);\n    }\n    moveData(backend, dataId) {\n        const info = this.state.tensorInfo.get(dataId);\n        const srcBackend = info.backend;\n        const values = this.readSync(dataId);\n        // Delete the tensor from the old backend and move it to the new\n        // backend.\n        srcBackend.disposeData(dataId);\n        info.backend = backend;\n        backend.move(dataId, values, info.shape, info.dtype);\n        if (this.shouldCheckForMemLeaks()) {\n            // Track the number of moves during a kernel execution to correctly\n            // detect memory leaks.\n            this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;\n        }\n    }\n    tidy(nameOrFn, fn) {\n        let name = null;\n        if (fn == null) {\n            // Called with only 1 argument.\n            if (typeof nameOrFn !== 'function') {\n                throw new Error('Please provide a function to tidy()');\n            }\n            fn = nameOrFn;\n        }\n        else {\n            // Called with 2 arguments.\n            if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {\n                throw new Error('When calling with two arguments, the first argument ' +\n                    'to tidy() must be a string');\n            }\n            if (typeof fn !== 'function') {\n                throw new Error('When calling with two arguments, the 2nd argument ' +\n                    'to tidy() must be a function');\n            }\n            name = nameOrFn;\n            // TODO(nsthorat,smilkov): Do operation logging and performance\n            // profiling.\n        }\n        let result;\n        return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {\n            result = fn();\n            if (result instanceof Promise) {\n                console.error('Cannot return a Promise inside of tidy.');\n            }\n            return result;\n        });\n    }\n    scopedRun(start, end, f) {\n        start();\n        try {\n            const res = f();\n            end();\n            return res;\n        }\n        catch (ex) {\n            end();\n            throw ex;\n        }\n    }\n    nextTensorId() {\n        return Engine.nextTensorId++;\n    }\n    nextVariableId() {\n        return Engine.nextVariableId++;\n    }\n    /**\n     * This method is called instead of the public-facing tensor.clone() when\n     * saving a tensor for backwards pass. It makes sure to add the clone\n     * operation to the tape regardless of being called inside a kernel\n     * execution.\n     *\n     * This method will go away once all kernels are modularized since we won't\n     * need to turn off the tape inside runKernel().\n     */\n    clone(x) {\n        const y = this.makeTensorFromDataId(x.dataId, x.shape, x.dtype);\n        const inputs = { x };\n        const grad = (dy) => ({\n            x: () => {\n                const dtype = 'float32';\n                const gradInputs = { x: dy };\n                const attrs = { dtype };\n                return ENGINE.runKernelFunc(backend => backend.cast(dy, dtype), gradInputs, null /* grad */, Cast, attrs);\n            }\n        });\n        const saved = [];\n        this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {});\n        return y;\n    }\n    /**\n     * Execute a kernel with the given name and return the output tensor.\n     *\n     * @param kernelName The name of the kernel to execute.\n     * @param inputs A map of input names to tensors.\n     * @param attrs A map of attribute names to their values. An attribute is a\n     *     primitive (non-tensor) input to the kernel.\n     * @param inputsToSave A list of tensors, inputs to save for the backprop\n     *     computation.\n     * @param outputsToSave A list of booleans, specifying which output to save\n     *     for the backprop computation. These are booleans since the output\n     * tensors are not visible to the user.\n     */\n    runKernel(kernelName, inputs, attrs, inputsToSave, outputsToSave) {\n        const forwardFunc = null;\n        const backwardsFunc = null;\n        // Call runKernel as a stop-gap until we modularize all kernels.\n        // Once we modularize all kernels, we will remove the existing\n        // `runKernelFunc`.\n        return this.runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave);\n    }\n    shouldCheckForMemLeaks() {\n        return this.ENV.getBool('IS_TEST');\n    }\n    checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {\n        const numDataIdsAfter = this.backend.numDataIds();\n        // Count the number of data ids associated with the result of the kernel.\n        let numOutputDataIds = 0;\n        outInfos.forEach(info => {\n            // Complex numbers allocate 3 data ids, one for 'real', one for\n            // 'imaginary', and one for the container that holds the former two.\n            numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1);\n        });\n        // Account for the number of moves during kernel execution. A \"data move\"\n        // can happen in the middle of a kernel execution, placing a new (key,value)\n        // pair in the data storage. Since data moves have net zero effect (we\n        // always remove the data from the old backend), we have to cancel them out\n        // when detecting memory leaks.\n        const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];\n        const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;\n        if (dataIdsLeaked > 0) {\n            throw new Error(`Backend '${this.backendName}' has an internal memory leak ` +\n                `(${dataIdsLeaked} data ids) after running '${kernelName}'`);\n        }\n    }\n    /**\n     * @deprecated Use `runKernel` for newly added kernels. Keep using this method\n     *     only for kernels that are not yet fully modularized.\n     */\n    runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave) {\n        let outputs;\n        let saved = [];\n        const isTapeOn = this.isTapeOn();\n        if (kernelName == null) {\n            kernelName =\n                this.state.activeScope != null ? this.state.activeScope.name : '';\n        }\n        const startingBytecount = this.state.numBytes;\n        const startingNumTensors = this.state.numTensors;\n        if (this.shouldCheckForMemLeaks()) {\n            this.state.numDataMovesStack.push(0);\n        }\n        let kernelFunc;\n        const kernel = getKernel(kernelName, this.backendName);\n        let out;\n        if (kernel != null) {\n            kernelFunc = () => {\n                const numDataIdsBefore = this.backend.numDataIds();\n                out = kernel.kernelFunc({ inputs, attrs, backend: this.backend });\n                const outInfos = Array.isArray(out) ? out : [out];\n                if (this.shouldCheckForMemLeaks()) {\n                    this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);\n                }\n                const outTensors = outInfos.map(({ dataId, shape, dtype }) => this.makeTensorFromDataId(dataId, shape, dtype));\n                // Save the inputs and outputs.\n                // Do not save unless we are recording to the tape. Otherwise it would\n                // cause a mem leak since we would never run backprop, which disposes\n                // the kept tensors.\n                if (isTapeOn) {\n                    let tensorsToSave = this.getTensorsForGradient(kernelName, inputs, outTensors);\n                    if (tensorsToSave == null) {\n                        // Fallback for ops that call runKernelFunc and pass in\n                        // inputsToSave and outputsToSave. Currently this is the set of ops\n                        // with kernel support in the WASM backend. Once those ops and\n                        // respective gradients are modularised we can remove this path.\n                        if (outputsToSave == null) {\n                            outputsToSave = [];\n                        }\n                        const outsToSave = outTensors.filter((_, i) => outputsToSave[i]);\n                        tensorsToSave = (inputsToSave || []).slice().concat(outsToSave);\n                    }\n                    saved = this.saveTensorsForBackwardMode(tensorsToSave);\n                }\n                return outTensors;\n            };\n        }\n        else {\n            const saveFunc = (tensors) => {\n                // Do not save unless we are recording to the tape. Otherwise it would\n                // cause a mem leak since we would never run backprop, which disposes\n                // the kept tensors.\n                if (!isTapeOn) {\n                    return;\n                }\n                saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n            };\n            kernelFunc = () => {\n                const numDataIdsBefore = this.backend.numDataIds();\n                out = this.tidy(() => forwardFunc(this.backend, saveFunc));\n                const outs = (Array.isArray(out) ? out : [out]);\n                if (this.shouldCheckForMemLeaks()) {\n                    this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outs);\n                }\n                return outs;\n            };\n        }\n        // Stop recording to a tape when running a kernel.\n        let kernelProfile;\n        this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {\n            if (!this.ENV.getBool('DEBUG') && !this.state.profiling) {\n                outputs = kernelFunc();\n            }\n            else {\n                kernelProfile = this.profiler.profileKernel(kernelName, inputs, () => kernelFunc());\n                if (this.ENV.getBool('DEBUG')) {\n                    this.profiler.logKernelProfile(kernelProfile);\n                }\n                outputs = kernelProfile.outputs;\n            }\n        });\n        if (isTapeOn) {\n            this.addTapeNode(kernelName, inputs, outputs, backwardsFunc, saved, attrs);\n        }\n        if (this.state.profiling) {\n            this.state.activeProfile.kernels.push({\n                name: kernelName,\n                bytesAdded: this.state.numBytes - startingBytecount,\n                totalBytesSnapshot: this.state.numBytes,\n                tensorsAdded: this.state.numTensors - startingNumTensors,\n                totalTensorsSnapshot: this.state.numTensors,\n                inputShapes: Object.keys(inputs).map(key => inputs[key] != null ? inputs[key].shape : null),\n                outputShapes: outputs.map(item => item.shape),\n                kernelTimeMs: kernelProfile.timeMs,\n                extraInfo: kernelProfile.extraInfo\n            });\n        }\n        return (Array.isArray(out) ? outputs : outputs[0]);\n    }\n    /**\n     * Saves tensors used in forward mode for use in backward mode.\n     *\n     * @param tensors the list of tensors to save.\n     */\n    saveTensorsForBackwardMode(tensors) {\n        const saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n        return saved;\n    }\n    /**\n     * Returns a list of tensors to save for a given gradient calculation.\n     *\n     * Returns undefined if their is no registered gradient for this kernel in the\n     * gradient registry.\n     *\n     * @param kernelName name of kernel to look up gradient for.\n     * @param inputs a map of input tensors.\n     * @param outputs an array of output tensors from forward mode of kernel.\n     */\n    getTensorsForGradient(kernelName, inputs, outputs) {\n        const gradConfig = getGradient(kernelName);\n        if (gradConfig != null) {\n            const inputsToSave = gradConfig.inputsToSave || [];\n            const outputsToSave = gradConfig.outputsToSave || [];\n            // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs\n            // specified in inputsToSave will be saved.\n            let inputTensorsToSave;\n            if (gradConfig.saveAllInputs) {\n                util.assert(Array.isArray(inputs), () => 'saveAllInputs is true, expected inputs to be an array.');\n                inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);\n            }\n            else {\n                inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);\n            }\n            const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);\n            return inputTensorsToSave.concat(outputTensorsToSave);\n        }\n        // TODO(yassogba) throw exception here once all runkernelFunc calls with\n        // inputsToSave/outputsToSave are removed\n        return null;\n    }\n    /**\n     * Internal method used by public APIs for tensor creation. Makes a new\n     * tensor with the provided shape, dtype and values. It always\n     * creates a new data id and writes the values to the underlying backend.\n     */\n    makeTensor(values, shape, dtype, backend) {\n        if (values == null) {\n            throw new Error('Values passed to engine.makeTensor() are null');\n        }\n        dtype = dtype || 'float32';\n        backend = backend || this.backend;\n        let backendVals = values;\n        if (dtype === 'string' && util.isString(values[0])) {\n            backendVals = values.map(d => util.encodeString(d));\n        }\n        const dataId = backend.write(backendVals, shape, dtype);\n        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n        this.incRef(t, backend);\n        // Count bytes for string tensors.\n        if (dtype === 'string') {\n            const info = this.state.tensorInfo.get(dataId);\n            const newBytes = bytesFromStringArray(backendVals);\n            this.state.numBytes += newBytes - info.bytes;\n            info.bytes = newBytes;\n        }\n        return t;\n    }\n    /**\n     * Internal method used by backends. Makes a new tensor\n     * that is a wrapper around an existing data id. It doesn't create\n     * a new data id, only increments the ref count used in memory tracking.\n     */\n    makeTensorFromDataId(dataId, shape, dtype, backend) {\n        dtype = dtype || 'float32';\n        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n        this.incRef(t, backend);\n        return t;\n    }\n    makeVariable(initialValue, trainable = true, name, dtype) {\n        name = name || this.nextVariableId().toString();\n        if (dtype != null && dtype !== initialValue.dtype) {\n            initialValue = initialValue.cast(dtype);\n        }\n        const v = new Variable(initialValue, trainable, name, this.nextTensorId());\n        if (this.state.registeredVariables[v.name] != null) {\n            throw new Error(`Variable with name ${v.name} was already registered`);\n        }\n        this.state.registeredVariables[v.name] = v;\n        this.incRef(v, this.backend);\n        return v;\n    }\n    incRef(a, backend) {\n        const refCount = this.state.tensorInfo.has(a.dataId) ?\n            this.state.tensorInfo.get(a.dataId).refCount :\n            0;\n        this.state.numTensors++;\n        if (a.dtype === 'string') {\n            this.state.numStringTensors++;\n        }\n        if (refCount === 0) {\n            this.state.numDataBuffers++;\n            // Bytes for complex numbers are counted by their components. Bytes for\n            // string tensors are counted when writing values.\n            let bytes = 0;\n            if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n                bytes = a.size * util.bytesPerElement(a.dtype);\n            }\n            this.state.tensorInfo.set(a.dataId, {\n                backend: backend || this.backend,\n                dtype: a.dtype,\n                shape: a.shape,\n                bytes,\n                refCount: 0\n            });\n            this.state.numBytes += bytes;\n        }\n        this.state.tensorInfo.get(a.dataId).refCount++;\n        if (!(a instanceof Variable)) {\n            this.track(a);\n        }\n    }\n    disposeTensor(a) {\n        if (!this.state.tensorInfo.has(a.dataId)) {\n            return;\n        }\n        this.state.numTensors--;\n        if (a.dtype === 'string') {\n            this.state.numStringTensors--;\n        }\n        const info = this.state.tensorInfo.get(a.dataId);\n        const refCount = info.refCount;\n        if (refCount <= 1) {\n            // Don't count bytes for complex numbers as they are counted by their\n            // components.\n            if (a.dtype !== 'complex64') {\n                this.state.numBytes -= info.bytes;\n            }\n            this.state.numDataBuffers--;\n            info.backend.disposeData(a.dataId);\n            this.state.tensorInfo.delete(a.dataId);\n        }\n        else {\n            this.state.tensorInfo.get(a.dataId).refCount--;\n        }\n        // TODO(nsthorat): Construct an error and save the stack trace for\n        // debugging when in debug mode. Creating a stack trace is too expensive\n        // to do unconditionally.\n    }\n    disposeVariables() {\n        for (const varName in this.state.registeredVariables) {\n            const v = this.state.registeredVariables[varName];\n            this.disposeVariable(v);\n        }\n    }\n    disposeVariable(v) {\n        this.disposeTensor(v);\n        if (this.state.registeredVariables[v.name] != null) {\n            delete this.state.registeredVariables[v.name];\n        }\n    }\n    memory() {\n        const info = this.backend.memory();\n        info.numTensors = this.state.numTensors;\n        info.numDataBuffers = this.state.numDataBuffers;\n        info.numBytes = this.state.numBytes;\n        if (this.state.numStringTensors > 0) {\n            info.unreliable = true;\n            if (info.reasons == null) {\n                info.reasons = [];\n            }\n            info.reasons.push('Memory usage by string tensors is approximate ' +\n                '(2 bytes per character)');\n        }\n        return info;\n    }\n    async profile(query) {\n        this.state.profiling = true;\n        const startBytes = this.state.numBytes;\n        const startNumTensors = this.state.numTensors;\n        this.state.activeProfile.kernels = [];\n        this.state.activeProfile.result = await query();\n        this.state.profiling = false;\n        this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map(d => d.totalBytesSnapshot));\n        this.state.activeProfile.newBytes = this.state.numBytes - startBytes;\n        this.state.activeProfile.newTensors =\n            this.state.numTensors - startNumTensors;\n        for (const kernel of this.state.activeProfile.kernels) {\n            kernel.kernelTimeMs = await kernel.kernelTimeMs;\n            kernel.extraInfo = await kernel.extraInfo;\n        }\n        return this.state.activeProfile;\n    }\n    isTapeOn() {\n        return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;\n    }\n    addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {\n        const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved };\n        const gradConfig = getGradient(kernelName);\n        if (gradConfig != null) {\n            gradientsFunc = gradConfig.gradFunc;\n        }\n        if (gradientsFunc != null) {\n            tapeNode.gradient = (dys) => {\n                // TODO(smilkov): To optimize back-prop, pass dys that are not used in\n                // the backprop graph to the user as null instead of zeros\n                dys = dys.map((dy, i) => {\n                    if (dy == null) {\n                        const output = outputs[i];\n                        const vals = util.makeZerosTypedArray(output.size, output.dtype);\n                        return this.makeTensor(vals, output.shape, output.dtype);\n                    }\n                    return dy;\n                });\n                // Grad functions of ops with single outputs expect a dy, while ops\n                // with multiple outputs expect dys (array of dy).\n                return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);\n            };\n        }\n        this.state.activeTape.push(tapeNode);\n    }\n    keep(result) {\n        result.kept = true;\n        return result;\n    }\n    startTape() {\n        if (this.state.gradientDepth === 0) {\n            this.state.activeTape = [];\n        }\n        this.state.gradientDepth++;\n    }\n    endTape() {\n        this.state.gradientDepth--;\n    }\n    /**\n     * Start a scope. Use this with endScope() to achieve the same functionality\n     * as scope() without the need for a function closure.\n     */\n    startScope(name) {\n        const scopeInfo = {\n            track: [],\n            name: 'unnamed scope',\n            id: this.state.nextScopeId++\n        };\n        if (name) {\n            scopeInfo.name = name;\n        }\n        this.state.scopeStack.push(scopeInfo);\n        this.state.activeScope = scopeInfo;\n    }\n    /**\n     * End a scope. Use this with startScope() to achieve the same functionality\n     * as scope() without the need for a function closure.\n     */\n    endScope(result) {\n        const tensorsToTrackInParent = getTensorsInContainer(result);\n        const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map(t => t.id));\n        // Dispose the arrays tracked in this scope.\n        for (let i = 0; i < this.state.activeScope.track.length; i++) {\n            const tensor = this.state.activeScope.track[i];\n            if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) {\n                tensor.dispose();\n            }\n        }\n        const oldScope = this.state.scopeStack.pop();\n        this.state.activeScope = this.state.scopeStack.length === 0 ?\n            null :\n            this.state.scopeStack[this.state.scopeStack.length - 1];\n        // Track the current result in the parent scope.\n        tensorsToTrackInParent.forEach(tensor => {\n            // Only track the tensor if was allocated in the inner scope and is not\n            // globally kept.\n            if (!tensor.kept && tensor.scopeId === oldScope.id) {\n                this.track(tensor);\n            }\n        });\n    }\n    /**\n     * Returns gradients of `f` with respect to each of the `xs`. The gradients\n     * returned are of the same length as `xs`, but some might be null if `f`\n     * was not a function of that `x`. It also takes optional dy to multiply the\n     * gradient, which defaults to `1`.\n     */\n    gradients(f, xs, dy, allowNoGradients = false) {\n        util.assert(xs.length > 0, () => 'gradients() received an empty list of xs.');\n        if (dy != null && dy.dtype !== 'float32') {\n            throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n        }\n        const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy('forward', f));\n        util.assert(y instanceof Tensor, () => 'The result y returned by f() must be a tensor.');\n        // Filter out the nodes that don't connect x => y.\n        const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);\n        if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n            throw new Error('Cannot compute gradient of y=f(x) with respect to x. Make sure ' +\n                'that the f you passed encloses all operations that lead from x ' +\n                'to y.');\n        }\n        return this.tidy('backward', () => {\n            const accumulatedGradientMap = {};\n            accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;\n            // Backprop gradients through the filtered nodes.\n            backpropagateGradients(accumulatedGradientMap, filteredTape, \n            // Pass the tidy function to avoid circular dep with `tape.ts`.\n            f => this.tidy(f), \n            // Pass an add function to avoide a circular dep with `tape.ts`.\n            add);\n            const grads = xs.map(x => accumulatedGradientMap[x.id]);\n            if (this.state.gradientDepth === 0) {\n                // This means that we are not computing higher-order gradients\n                // and can clean up the tape.\n                this.state.activeTape.forEach(node => {\n                    for (const tensor of node.saved) {\n                        tensor.dispose();\n                    }\n                });\n                this.state.activeTape = null;\n            }\n            return { value: y, grads };\n        });\n    }\n    customGrad(f) {\n        util.assert(util.isFunction(f), () => 'The f passed in customGrad(f) must be a function.');\n        return (...inputs) => {\n            util.assert(inputs.every(t => t instanceof Tensor), () => 'The args passed in customGrad(f)(x1, x2,...) must all be ' +\n                'tensors');\n            let res;\n            const inputMap = {};\n            inputs.forEach((input, i) => {\n                inputMap[i] = input;\n            });\n            return this.runKernelFunc((_, save) => {\n                res = f(...[...inputs, save]);\n                util.assert(res.value instanceof Tensor, () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.value` is a tensor');\n                util.assert(util.isFunction(res.gradFunc), () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function.');\n                return res.value;\n            }, inputMap, (dy, saved) => {\n                const gradRes = res.gradFunc(dy, saved);\n                const grads = Array.isArray(gradRes) ? gradRes : [gradRes];\n                util.assert(grads.length === inputs.length, () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'the same number of tensors as inputs passed to f(...).');\n                util.assert(grads.every(t => t instanceof Tensor), () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'a list of only tensors.');\n                const gradMap = {};\n                grads.forEach((grad, i) => {\n                    gradMap[i] = () => grad;\n                });\n                return gradMap;\n            });\n        };\n    }\n    readSync(dataId) {\n        // Route the read to the correct backend.\n        const info = this.state.tensorInfo.get(dataId);\n        return info.backend.readSync(dataId);\n    }\n    read(dataId) {\n        // Route the read to the correct backend.\n        const info = this.state.tensorInfo.get(dataId);\n        return info.backend.read(dataId);\n    }\n    async time(query) {\n        const start = now();\n        const timingInfo = await this.backend.time(query);\n        timingInfo.wallMs = now() - start;\n        return timingInfo;\n    }\n    /**\n     * Tracks a Tensor in the current scope to be automatically cleaned up\n     * when the current scope ends, and returns the value.\n     *\n     * @param result The Tensor to track in the current scope.\n     */\n    track(result) {\n        if (this.state.activeScope != null) {\n            result.scopeId = this.state.activeScope.id;\n            this.state.activeScope.track.push(result);\n        }\n        return result;\n    }\n    get registeredVariables() {\n        return this.state.registeredVariables;\n    }\n    /**\n     * Resets the engine state. Removes all backends but does not remove\n     * registered backend factories.\n     */\n    reset() {\n        // Make any pending promise obsolete.\n        this.pendingBackendInitId++;\n        this.state.dispose();\n        this.ENV.reset();\n        this.state = new EngineState();\n        for (const backendName in this.registry) {\n            this.disposeRegisteredKernels(backendName);\n            this.registry[backendName].dispose();\n            delete this.registry[backendName];\n        }\n        this.backendName = null;\n        this.backendInstance = null;\n        this.pendingBackendInit = null;\n    }\n}\nEngine.nextTensorId = 0;\nEngine.nextVariableId = 0;\nfunction ones(shape) {\n    const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');\n    return ENGINE.makeTensor(values, shape, 'float32');\n}\nexport function getOrMakeEngine() {\n    const ns = getGlobalNamespace();\n    if (ns._tfengine == null) {\n        const environment = new Environment(ns);\n        ns._tfengine = new Engine(environment);\n    }\n    setEnvironmentGlobal(ns._tfengine.ENV);\n    // Tell the current tensor interface that the global engine is responsible\n    // for tracking.\n    setTensorTracker(() => ns._tfengine);\n    return ns._tfengine;\n}\nexport const ENGINE = getOrMakeEngine();\n/**\n * A implementation of the add op for use within engine and tape.\n *\n * This allows us to avoid a circular dependency between add.ts and engine.\n * It is exported to be available in tape tests.\n */\nexport function add(a, b) {\n    // We duplicate Add here to avoid a circular dependency with add.ts.\n    const inputs = { a, b };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.add(a, b);\n        save([a, b]);\n        return res;\n    }, inputs, null /* gradient */, Add);\n}\n//# sourceMappingURL=engine.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line:no-any\nfunction _isNavigatorDefined() {\n    return typeof navigator !== 'undefined' && navigator != null;\n}\nexport function isMobile() {\n    if (_isNavigatorDefined()) {\n        // tslint:disable-next-line:no-any\n        const a = navigator.userAgent || navigator.vendor || window.opera;\n        // tslint:disable-next-line:max-line-length\n        return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n            .test(a) ||\n            // tslint:disable-next-line:max-line-length\n            /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i\n                .test(a.substr(0, 4));\n    }\n    return false;\n}\nexport function isBrowser() {\n    return (typeof window !== 'undefined' && window.document != null) ||\n        //@ts-ignore\n        (typeof WorkerGlobalScope !== 'undefined');\n}\n//# sourceMappingURL=device_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './engine';\nimport * as device_util from './device_util';\nimport { env } from './environment';\nconst ENV = env();\n/**\n * This file contains environment-related flag registrations.\n */\n/** Whether to enable debug mode. */\nENV.registerFlag('DEBUG', () => false, debugValue => {\n    if (debugValue) {\n        console.warn('Debugging mode is ON. The output of every math call will ' +\n            'be downloaded to CPU and checked for NaNs. ' +\n            'This significantly impacts performance.');\n    }\n});\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_BROWSER', () => device_util.isBrowser());\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_NODE', () => (typeof process !== 'undefined') &&\n    (typeof process.versions !== 'undefined') &&\n    (typeof process.versions.node !== 'undefined'));\n/** Whether this browser is Chrome. */\nENV.registerFlag('IS_CHROME', () => typeof navigator !== 'undefined' && navigator != null &&\n    navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&\n    /Google Inc/.test(navigator.vendor));\n/**\n * True when the environment is \"production\" where we disable safety checks\n * to gain performance.\n */\nENV.registerFlag('PROD', () => false);\n/**\n * Whether to do sanity checks when inferring a shape from user-provided\n * values, used when creating a new tensor.\n */\nENV.registerFlag('TENSORLIKE_CHECK_SHAPE_CONSISTENCY', () => ENV.getBool('DEBUG'));\n/** Whether deprecation warnings are enabled. */\nENV.registerFlag('DEPRECATION_WARNINGS_ENABLED', () => true);\n/** True if running unit tests. */\nENV.registerFlag('IS_TEST', () => false);\n//# sourceMappingURL=flags.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { env } from './environment';\nimport { Tensor } from './tensor';\nimport { assert, flatten, inferDtype, isTypedArray, toTypedArray } from './util';\nexport function inferShape(val, dtype) {\n    let firstElem = val;\n    if (isTypedArray(val)) {\n        return dtype === 'string' ? [] : [val.length];\n    }\n    if (!Array.isArray(val)) {\n        return []; // Scalar.\n    }\n    const shape = [];\n    while (Array.isArray(firstElem) ||\n        isTypedArray(firstElem) && dtype !== 'string') {\n        shape.push(firstElem.length);\n        firstElem = firstElem[0];\n    }\n    if (Array.isArray(val) &&\n        env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {\n        deepAssertShapeConsistency(val, shape, []);\n    }\n    return shape;\n}\nfunction deepAssertShapeConsistency(val, shape, indices) {\n    indices = indices || [];\n    if (!(Array.isArray(val)) && !isTypedArray(val)) {\n        assert(shape.length === 0, () => `Element arr[${indices.join('][')}] is a primitive, ` +\n            `but should be an array/TypedArray of ${shape[0]} elements`);\n        return;\n    }\n    assert(shape.length > 0, () => `Element arr[${indices.join('][')}] should be a primitive, ` +\n        `but is an array of ${val.length} elements`);\n    assert(val.length === shape[0], () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +\n        `elements, but has ${val.length} elements`);\n    const subShape = shape.slice(1);\n    for (let i = 0; i < val.length; ++i) {\n        deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n    }\n}\nfunction assertDtype(expectedDtype, actualDType, argName, functionName) {\n    if (expectedDtype == null) {\n        return;\n    }\n    if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||\n        expectedDtype === 'numeric' && actualDType === 'string') {\n        throw new Error(`Argument '${argName}' passed to '${functionName}' must ` +\n            `be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n    }\n}\nexport function convertToTensor(x, argName, functionName, parseAsDtype = 'numeric') {\n    if (x instanceof Tensor) {\n        assertDtype(parseAsDtype, x.dtype, argName, functionName);\n        return x;\n    }\n    let inferredDtype = inferDtype(x);\n    // If the user expects a bool/int/float, use that info to update the\n    // inferredDtype when it is not a string.\n    if (inferredDtype !== 'string' &&\n        ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {\n        inferredDtype = parseAsDtype;\n    }\n    assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n    if ((x == null) ||\n        (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&\n            typeof x !== 'boolean' && typeof x !== 'string')) {\n        const type = x == null ? 'null' : x.constructor.name;\n        throw new Error(`Argument '${argName}' passed to '${functionName}' must be a ` +\n            `Tensor or TensorLike, but got '${type}'`);\n    }\n    const inferredShape = inferShape(x, inferredDtype);\n    if (!isTypedArray(x) && !Array.isArray(x)) {\n        x = [x];\n    }\n    const skipTypedArray = true;\n    const values = inferredDtype !== 'string' ?\n        toTypedArray(x, inferredDtype) :\n        flatten(x, [], skipTypedArray);\n    return ENGINE.makeTensor(values, inferredShape, inferredDtype);\n}\nexport function convertToTensorArray(arg, argName, functionName, parseAsDtype = 'numeric') {\n    if (!Array.isArray(arg)) {\n        throw new Error(`Argument ${argName} passed to ${functionName} must be a ` +\n            '`Tensor[]` or `TensorLike[]`');\n    }\n    const tensors = arg;\n    return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName), parseAsDtype);\n}\n//# sourceMappingURL=tensor_util_env.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nexport const OP_SCOPE_SUFFIX = '__op';\n/**\n * Used for wrapping functions that perform math operations on\n * Tensors. The function will be wrapped in a named scope that cleans all\n * memory usage after the function is done.\n */\nexport function op(f) {\n    const keys = Object.keys(f);\n    if (keys.length !== 1) {\n        throw new Error(`Please provide an object with a single key ` +\n            `(operation name) mapping to a function. Got an object with ` +\n            `${keys.length} keys.`);\n    }\n    let opName = keys[0];\n    const fn = f[opName];\n    // Strip the underscore from the end of the function name.\n    if (opName.endsWith('_')) {\n        opName = opName.substring(0, opName.length - 1);\n    }\n    // add an __op suffix to distinguish ops from kernels in tf.profile\n    opName = opName + OP_SCOPE_SUFFIX;\n    // tslint:disable-next-line:no-any\n    const f2 = (...args) => {\n        ENGINE.startScope(opName);\n        try {\n            const result = fn(...args);\n            if (result instanceof Promise) {\n                console.error('Cannot return a Promise inside of tidy.');\n            }\n            ENGINE.endScope(result);\n            return result;\n        }\n        catch (ex) {\n            ENGINE.endScope(null);\n            throw ex;\n        }\n    };\n    Object.defineProperty(f2, 'name', { value: opName, configurable: true });\n    // tslint:disable-next-line:no-any\n    return f2;\n}\n//# sourceMappingURL=operation.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Complex } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction complex_(real, imag) {\n    const $real = convertToTensor(real, 'real', 'complex');\n    const $imag = convertToTensor(imag, 'imag', 'complex');\n    util.assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n        `must match in call to tf.complex().`);\n    const forward = (backend) => {\n        return backend.complex($real, $imag);\n    };\n    const inputs = { real: $real, imag: $imag };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Complex);\n}\nexport const complex = op({ complex_ });\n//# sourceMappingURL=complex.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { assert, assertNonNegativeIntegerDimensions, flatten, inferDtype, isTypedArray, sizeFromShape, toTypedArray } from '../util';\n/** This is shared code across all tensor creation methods. */\nexport function makeTensor(values, shape, inferredShape, dtype) {\n    if (dtype == null) {\n        dtype = inferDtype(values);\n    }\n    if (dtype === 'complex64') {\n        throw new Error(`Cannot construct a complex64 tensor directly. ` +\n            `Please use tf.complex(real, imag).`);\n    }\n    if (!isTypedArray(values) && !Array.isArray(values) &&\n        typeof values !== 'number' && typeof values !== 'boolean' &&\n        typeof values !== 'string') {\n        throw new Error('values passed to tensor(values) must be a number/boolean/string or ' +\n            'an array of numbers/booleans/strings, or a TypedArray');\n    }\n    if (shape != null) {\n        assertNonNegativeIntegerDimensions(shape);\n        const providedSize = sizeFromShape(shape);\n        const inferredSize = sizeFromShape(inferredShape);\n        assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ` +\n            `${providedSize} values but has ${inferredSize}`);\n        for (let i = 0; i < inferredShape.length; ++i) {\n            const inferred = inferredShape[i];\n            const flatDimsDontMatch = i === inferredShape.length - 1 ?\n                inferred !== sizeFromShape(shape.slice(i)) :\n                true;\n            assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape ` +\n                `(${inferredShape}) does not match the provided ` +\n                `shape (${shape}). `);\n        }\n    }\n    if (!isTypedArray(values) && !Array.isArray(values)) {\n        values = [values];\n    }\n    shape = shape || inferredShape;\n    values = dtype !== 'string' ?\n        toTypedArray(values, dtype) :\n        flatten(values, [], true);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=tensor_ops_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`. If the values are strings,\n *     they will be encoded as utf-8 and kept as `Uint8Array[]`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor(values, shape, dtype) {\n    const inferredShape = inferShape(values, dtype);\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/* Type definitions for exporting and importing of models. */\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP = {\n    'float32': 4,\n    'float16': 2,\n    'int32': 4,\n    'uint16': 2,\n    'uint8': 1,\n    'bool': 1,\n    'complex64': 8\n};\n//# sourceMappingURL=types.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../ops/complex';\nimport { tensor } from '../ops/tensor';\nimport { sizeFromShape } from '../util';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/** Number of bytes reserved for the length of the string. (32bit integer). */\nconst NUM_BYTES_STRING_LENGTH = 4;\n/**\n * Encode a map from names to weight values as an ArrayBuffer, along with an\n * `Array` of `WeightsManifestEntry` as specification of the encoded weights.\n *\n * This function does not perform sharding.\n *\n * This function is the reverse of `decodeWeights`.\n *\n * @param tensors A map (\"dict\") from names to tensors.\n * @param group Group to which the weights belong (optional).\n * @returns A `Promise` of\n *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s\n *     concatenated.\n *   - An `Array` of `WeightManifestEntry`s, carrying information including\n *     tensor names, `dtype`s and shapes.\n * @throws Error: on unsupported tensor `dtype`.\n */\nexport async function encodeWeights(tensors, group) {\n    // TODO(adarob, cais): Support quantization.\n    const specs = [];\n    const dataPromises = [];\n    const names = Array.isArray(tensors) ?\n        tensors.map(tensor => tensor.name) :\n        Object.keys(tensors);\n    for (let i = 0; i < names.length; ++i) {\n        const name = names[i];\n        const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];\n        if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool' &&\n            t.dtype !== 'string' && t.dtype !== 'complex64') {\n            throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n        }\n        const spec = { name, shape: t.shape, dtype: t.dtype };\n        if (t.dtype === 'string') {\n            const utf8bytes = new Promise(async (resolve) => {\n                const vals = await t.bytes();\n                const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) +\n                    NUM_BYTES_STRING_LENGTH * vals.length;\n                const bytes = new Uint8Array(totalNumBytes);\n                let offset = 0;\n                for (let i = 0; i < vals.length; i++) {\n                    const val = vals[i];\n                    const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);\n                    bytes.set(bytesOfLength, offset);\n                    offset += NUM_BYTES_STRING_LENGTH;\n                    bytes.set(val, offset);\n                    offset += val.length;\n                }\n                resolve(bytes);\n            });\n            dataPromises.push(utf8bytes);\n        }\n        else {\n            dataPromises.push(t.data());\n        }\n        if (group != null) {\n            spec.group = group;\n        }\n        specs.push(spec);\n    }\n    const tensorValues = await Promise.all(dataPromises);\n    return { data: concatenateTypedArrays(tensorValues), specs };\n}\n/**\n * Decode flat ArrayBuffer as weights.\n *\n * This function does not handle sharding.\n *\n * This function is the reverse of `encodeWeights`.\n *\n * @param buffer A flat ArrayBuffer carrying the binary values of the tensors\n *   concatenated in the order specified in `specs`.\n * @param specs Specifications of the names, dtypes and shapes of the tensors\n *   whose value are encoded by `buffer`.\n * @return A map from tensor name to tensor value, with the names corresponding\n *   to names in `specs`.\n * @throws Error, if any of the tensors has unsupported dtype.\n */\nexport function decodeWeights(buffer, specs) {\n    // TODO(adarob, cais): Support quantization.\n    const out = {};\n    let float16Decode;\n    let offset = 0;\n    for (const spec of specs) {\n        const name = spec.name;\n        const dtype = spec.dtype;\n        const shape = spec.shape;\n        const size = sizeFromShape(shape);\n        let values;\n        if ('quantization' in spec) {\n            const quantization = spec.quantization;\n            if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n                if (!('min' in quantization && 'scale' in quantization)) {\n                    throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} ` +\n                        `doesn't have corresponding metadata min and scale.`);\n                }\n            }\n            else if (quantization.dtype === 'float16') {\n                if (dtype !== 'float32') {\n                    throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} ` +\n                        `which only supports weights of type float32 not ${dtype}.`);\n                }\n            }\n            else {\n                throw new Error(`Weight ${spec.name} has unknown ` +\n                    `quantization dtype ${quantization.dtype}. ` +\n                    `Supported quantization dtypes are: ` +\n                    `'uint8', 'uint16', and 'float16'.`);\n            }\n            const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n            const byteBuffer = buffer.slice(offset, offset + size * quantizationSizeFactor);\n            const quantizedArray = (quantization.dtype === 'uint8') ?\n                new Uint8Array(byteBuffer) :\n                new Uint16Array(byteBuffer);\n            if (dtype === 'float32') {\n                if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n                    values = new Float32Array(quantizedArray.length);\n                    for (let i = 0; i < quantizedArray.length; i++) {\n                        const v = quantizedArray[i];\n                        values[i] = v * quantization.scale + quantization.min;\n                    }\n                }\n                else if (quantization.dtype === 'float16') {\n                    if (float16Decode === undefined) {\n                        float16Decode = getFloat16Decoder();\n                    }\n                    values = float16Decode(quantizedArray);\n                }\n                else {\n                    throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n                        `for weight type float32.`);\n                }\n            }\n            else if (dtype === 'int32') {\n                if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {\n                    throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n                        `for weight type int32.`);\n                }\n                values = new Int32Array(quantizedArray.length);\n                for (let i = 0; i < quantizedArray.length; i++) {\n                    const v = quantizedArray[i];\n                    values[i] = Math.round(v * quantization.scale + quantization.min);\n                }\n            }\n            else {\n                throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n            }\n            offset += size * quantizationSizeFactor;\n        }\n        else if (dtype === 'string') {\n            const size = sizeFromShape(spec.shape);\n            values = [];\n            for (let i = 0; i < size; i++) {\n                const byteLength = new Uint32Array(buffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];\n                offset += NUM_BYTES_STRING_LENGTH;\n                const bytes = new Uint8Array(buffer.slice(offset, offset + byteLength));\n                values.push(bytes);\n                offset += byteLength;\n            }\n        }\n        else {\n            const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n            const byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);\n            if (dtype === 'float32') {\n                values = new Float32Array(byteBuffer);\n            }\n            else if (dtype === 'int32') {\n                values = new Int32Array(byteBuffer);\n            }\n            else if (dtype === 'bool') {\n                values = new Uint8Array(byteBuffer);\n            }\n            else if (dtype === 'complex64') {\n                values = new Float32Array(byteBuffer);\n                const real = new Float32Array(values.length / 2);\n                const image = new Float32Array(values.length / 2);\n                for (let i = 0; i < real.length; i++) {\n                    real[i] = values[i * 2];\n                    image[i] = values[i * 2 + 1];\n                }\n                const realTensor = tensor(real, shape, 'float32');\n                const imageTensor = tensor(image, shape, 'float32');\n                out[name] = complex(realTensor, imageTensor);\n            }\n            else {\n                throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n            }\n            offset += size * dtypeFactor;\n        }\n        if (dtype !== 'complex64') {\n            out[name] = tensor(values, shape, dtype);\n        }\n    }\n    return out;\n}\n/**\n * Concatenate TypedArrays into an ArrayBuffer.\n */\nexport function concatenateTypedArrays(xs) {\n    // TODO(adarob, cais): Support quantization.\n    if (xs === null) {\n        throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n    }\n    let totalByteLength = 0;\n    // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'\n    // can have a different byte length from that of the `TypedArray` itself,\n    // for example, when the `TypedArray` is created from an offset in an\n    // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match\n    // the `TypedArray` in byte length. If an element of `xs` does not show\n    // this property, a new `TypedArray` that satisfy this property will be\n    // constructed and pushed into `normalizedXs`.\n    const normalizedXs = [];\n    xs.forEach((x) => {\n        totalByteLength += x.byteLength;\n        // tslint:disable:no-any\n        normalizedXs.push(x.byteLength === x.buffer.byteLength ? x :\n            new x.constructor(x));\n        if (!(x instanceof Float32Array || x instanceof Int32Array ||\n            x instanceof Uint8Array)) {\n            throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n        }\n        // tslint:enable:no-any\n    });\n    const y = new Uint8Array(totalByteLength);\n    let offset = 0;\n    normalizedXs.forEach((x) => {\n        y.set(new Uint8Array(x.buffer), offset);\n        offset += x.byteLength;\n    });\n    return y.buffer;\n}\n// Use Buffer on Node.js instead of Blob/atob/btoa\nconst useNodeBuffer = typeof Buffer !== 'undefined' &&\n    (typeof Blob === 'undefined' || typeof atob === 'undefined' ||\n        typeof btoa === 'undefined');\n/**\n * Calculate the byte length of a JavaScript string.\n *\n * Note that a JavaScript string can contain wide characters, therefore the\n * length of the string is not necessarily equal to the byte length.\n *\n * @param str Input string.\n * @returns Byte length.\n */\nexport function stringByteLength(str) {\n    if (useNodeBuffer) {\n        return Buffer.byteLength(str);\n    }\n    return new Blob([str]).size;\n}\n/**\n * Encode an ArrayBuffer as a base64 encoded string.\n *\n * @param buffer `ArrayBuffer` to be converted.\n * @returns A string that base64-encodes `buffer`.\n */\nexport function arrayBufferToBase64String(buffer) {\n    if (useNodeBuffer) {\n        return Buffer.from(buffer).toString('base64');\n    }\n    const buf = new Uint8Array(buffer);\n    let s = '';\n    for (let i = 0, l = buf.length; i < l; i++) {\n        s += String.fromCharCode(buf[i]);\n    }\n    return btoa(s);\n}\n/**\n * Decode a base64 string as an ArrayBuffer.\n *\n * @param str Base64 string.\n * @returns Decoded `ArrayBuffer`.\n */\nexport function base64StringToArrayBuffer(str) {\n    if (useNodeBuffer) {\n        const buf = Buffer.from(str, 'base64');\n        return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n    }\n    const s = atob(str);\n    const buffer = new Uint8Array(s.length);\n    for (let i = 0; i < s.length; ++i) {\n        buffer.set([s.charCodeAt(i)], i);\n    }\n    return buffer.buffer;\n}\n/**\n * Concatenate a number of ArrayBuffers into one.\n *\n * @param buffers A number of array buffers to concatenate.\n * @returns Result of concatenating `buffers` in order.\n */\nexport function concatenateArrayBuffers(buffers) {\n    if (buffers.length === 1) {\n        return buffers[0];\n    }\n    let totalByteLength = 0;\n    buffers.forEach((buffer) => {\n        totalByteLength += buffer.byteLength;\n    });\n    const temp = new Uint8Array(totalByteLength);\n    let offset = 0;\n    buffers.forEach((buffer) => {\n        temp.set(new Uint8Array(buffer), offset);\n        offset += buffer.byteLength;\n    });\n    return temp.buffer;\n}\n/**\n * Get the basename of a path.\n *\n * Behaves in a way analogous to Linux's basename command.\n *\n * @param path\n */\nexport function basename(path) {\n    const SEPARATOR = '/';\n    path = path.trim();\n    while (path.endsWith(SEPARATOR)) {\n        path = path.slice(0, path.length - 1);\n    }\n    const items = path.split(SEPARATOR);\n    return items[items.length - 1];\n}\n/**\n * Populate ModelArtifactsInfo fields for a model with JSON topology.\n * @param modelArtifacts\n * @returns A ModelArtifactsInfo object.\n */\nexport function getModelArtifactsInfoForJSON(modelArtifacts) {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n        throw new Error('Expected JSON model topology, received ArrayBuffer.');\n    }\n    return {\n        dateSaved: new Date(),\n        modelTopologyType: 'JSON',\n        modelTopologyBytes: modelArtifacts.modelTopology == null ?\n            0 :\n            stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n        weightSpecsBytes: modelArtifacts.weightSpecs == null ?\n            0 :\n            stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n        weightDataBytes: modelArtifacts.weightData == null ?\n            0 :\n            modelArtifacts.weightData.byteLength,\n    };\n}\n/**\n * Computes mantisa table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 2048 mantissa lookup values.\n */\nfunction computeFloat16MantisaTable() {\n    const convertMantissa = (i) => {\n        let m = i << 13;\n        let e = 0;\n        while ((m & 0x00800000) === 0) {\n            e -= 0x00800000;\n            m <<= 1;\n        }\n        m &= ~0x00800000;\n        e += 0x38800000;\n        return m | e;\n    };\n    const mantisaTable = new Uint32Array(2048);\n    mantisaTable[0] = 0;\n    for (let i = 1; i < 1024; i++) {\n        mantisaTable[i] = convertMantissa(i);\n    }\n    for (let i = 1024; i < 2048; i++) {\n        mantisaTable[i] = 0x38000000 + ((i - 1024) << 13);\n    }\n    return mantisaTable;\n}\n/**\n * Computes exponent table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 64 exponent lookup values.\n */\nfunction computeFloat16ExponentTable() {\n    const exponentTable = new Uint32Array(64);\n    exponentTable[0] = 0;\n    exponentTable[31] = 0x47800000;\n    exponentTable[32] = 0x80000000;\n    exponentTable[63] = 0xc7800000;\n    for (let i = 1; i < 31; i++) {\n        exponentTable[i] = i << 23;\n    }\n    for (let i = 33; i < 63; i++) {\n        exponentTable[i] = 0x80000000 + ((i - 32) << 23);\n    }\n    return exponentTable;\n}\n/**\n * Computes offset table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 6d offset values.\n */\nfunction computeFloat16OffsetTable() {\n    const offsetTable = new Uint32Array(64);\n    for (let i = 0; i < 64; i++) {\n        offsetTable[i] = 1024;\n    }\n    offsetTable[0] = offsetTable[32] = 0;\n    return offsetTable;\n}\n/**\n * Retrieve a Float16 decoder which will decode a ByteArray of Float16 values\n * to a Float32Array.\n *\n * @returns Function (buffer: Uint16Array) => Float32Array which decodes\n *          the Uint16Array of Float16 bytes to a Float32Array.\n */\nexport function getFloat16Decoder() {\n    // Algorithm is based off of\n    // http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n    // Cache lookup tables\n    const mantisaTable = computeFloat16MantisaTable();\n    const exponentTable = computeFloat16ExponentTable();\n    const offsetTable = computeFloat16OffsetTable();\n    return (quantizedArray) => {\n        const buffer = new ArrayBuffer(4 * quantizedArray.length);\n        const bufferUint32View = new Uint32Array(buffer);\n        for (let index = 0; index < quantizedArray.length; index++) {\n            const float16Bits = quantizedArray[index];\n            const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 0x3ff)] +\n                exponentTable[float16Bits >> 10];\n            bufferUint32View[index] = float32Bits;\n        }\n        return new Float32Array(buffer);\n    };\n}\n//# sourceMappingURL=io_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class IORouterRegistry {\n    constructor() {\n        this.saveRouters = [];\n        this.loadRouters = [];\n    }\n    static getInstance() {\n        if (IORouterRegistry.instance == null) {\n            IORouterRegistry.instance = new IORouterRegistry();\n        }\n        return IORouterRegistry.instance;\n    }\n    /**\n     * Register a save-handler router.\n     *\n     * @param saveRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `save` method defined or `null`.\n     */\n    static registerSaveRouter(saveRouter) {\n        IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n    }\n    /**\n     * Register a load-handler router.\n     *\n     * @param loadRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `load` method defined or `null`.\n     */\n    static registerLoadRouter(loadRouter) {\n        IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n    }\n    /**\n     * Look up IOHandler for saving, given a URL-like string.\n     *\n     * @param url\n     * @returns If only one match is found, an instance of IOHandler with the\n     * `save` method defined. If no match is found, `null`.\n     * @throws Error, if more than one match is found.\n     */\n    static getSaveHandlers(url) {\n        return IORouterRegistry.getHandlers(url, 'save');\n    }\n    /**\n     * Look up IOHandler for loading, given a URL-like string.\n     *\n     * @param url\n     * @param loadOptions Optional, custom load options.\n     * @returns All valid handlers for `url`, given the currently registered\n     *   handler routers.\n     */\n    static getLoadHandlers(url, loadOptions) {\n        return IORouterRegistry.getHandlers(url, 'load', loadOptions);\n    }\n    static getHandlers(url, handlerType, loadOptions) {\n        const validHandlers = [];\n        const routers = handlerType === 'load' ?\n            IORouterRegistry.getInstance().loadRouters :\n            IORouterRegistry.getInstance().saveRouters;\n        routers.forEach(router => {\n            const handler = router(url, loadOptions);\n            if (handler !== null) {\n                validHandlers.push(handler);\n            }\n        });\n        return validHandlers;\n    }\n}\nexport const registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);\nexport const registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);\nexport const getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);\nexport const getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);\n//# sourceMappingURL=router_registry.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DATABASE_NAME = 'tensorflowjs';\nconst DATABASE_VERSION = 1;\n// Model data and ModelArtifactsInfo (metadata) are stored in two separate\n// stores for efficient access of the list of stored models and their metadata.\n// 1. The object store for model data: topology, weights and weight manifests.\nconst MODEL_STORE_NAME = 'models_store';\n// 2. The object store for ModelArtifactsInfo, including meta-information such\n//    as the type of topology (JSON vs binary), byte size of the topology, byte\n//    size of the weights, etc.\nconst INFO_STORE_NAME = 'model_info_store';\n/**\n * Delete the entire database for tensorflow.js, including the models store.\n */\nexport async function deleteDatabase() {\n    const idbFactory = getIndexedDBFactory();\n    return new Promise((resolve, reject) => {\n        const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);\n        deleteRequest.onsuccess = () => resolve();\n        deleteRequest.onerror = error => reject(error);\n    });\n}\nfunction getIndexedDBFactory() {\n    if (!env().getBool('IS_BROWSER')) {\n        // TODO(cais): Add more info about what IOHandler subtypes are available.\n        //   Maybe point to a doc page on the web and/or automatically determine\n        //   the available IOHandlers and print them in the error message.\n        throw new Error('Failed to obtain IndexedDB factory because the current environment' +\n            'is not a web browser.');\n    }\n    // tslint:disable-next-line:no-any\n    const theWindow = typeof window === 'undefined' ? self : window;\n    const factory = theWindow.indexedDB || theWindow.mozIndexedDB ||\n        theWindow.webkitIndexedDB || theWindow.msIndexedDB ||\n        theWindow.shimIndexedDB;\n    if (factory == null) {\n        throw new Error('The current browser does not appear to support IndexedDB.');\n    }\n    return factory;\n}\nfunction setUpDatabase(openRequest) {\n    const db = openRequest.result;\n    db.createObjectStore(MODEL_STORE_NAME, { keyPath: 'modelPath' });\n    db.createObjectStore(INFO_STORE_NAME, { keyPath: 'modelPath' });\n}\n/**\n * IOHandler subclass: Browser IndexedDB.\n *\n * See the doc string of `browserIndexedDB` for more details.\n */\nexport class BrowserIndexedDB {\n    constructor(modelPath) {\n        this.indexedDB = getIndexedDBFactory();\n        if (modelPath == null || !modelPath) {\n            throw new Error('For IndexedDB, modelPath must not be null, undefined or empty.');\n        }\n        this.modelPath = modelPath;\n    }\n    async save(modelArtifacts) {\n        // TODO(cais): Support saving GraphDef models.\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        return this.databaseAction(this.modelPath, modelArtifacts);\n    }\n    async load() {\n        return this.databaseAction(this.modelPath);\n    }\n    /**\n     * Perform database action to put model artifacts into or read model artifacts\n     * from IndexedDB object store.\n     *\n     * Whether the action is put or get depends on whether `modelArtifacts` is\n     * specified. If it is specified, the action will be put; otherwise the action\n     * will be get.\n     *\n     * @param modelPath A unique string path for the model.\n     * @param modelArtifacts If specified, it will be the model artifacts to be\n     *   stored in IndexedDB.\n     * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`\n     *   of `ModelArtifacts`, if the action is get.\n     */\n    databaseAction(modelPath, modelArtifacts) {\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                if (modelArtifacts == null) {\n                    // Read model out from object store.\n                    const modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');\n                    const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                    const getRequest = modelStore.get(this.modelPath);\n                    getRequest.onsuccess = () => {\n                        if (getRequest.result == null) {\n                            db.close();\n                            return reject(new Error(`Cannot find model with path '${this.modelPath}' ` +\n                                `in IndexedDB.`));\n                        }\n                        else {\n                            resolve(getRequest.result.modelArtifacts);\n                        }\n                    };\n                    getRequest.onerror = error => {\n                        db.close();\n                        return reject(getRequest.error);\n                    };\n                    modelTx.oncomplete = () => db.close();\n                }\n                else {\n                    // Put model into object store.\n                    const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n                    // First, put ModelArtifactsInfo into info store.\n                    const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n                    let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                    const putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo });\n                    let modelTx;\n                    putInfoRequest.onsuccess = () => {\n                        // Second, put model data into model store.\n                        modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n                        const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                        const putModelRequest = modelStore.put({\n                            modelPath: this.modelPath,\n                            modelArtifacts,\n                            modelArtifactsInfo\n                        });\n                        putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo });\n                        putModelRequest.onerror = error => {\n                            // If the put-model request fails, roll back the info entry as\n                            // well.\n                            infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                            const deleteInfoRequest = infoStore.delete(this.modelPath);\n                            deleteInfoRequest.onsuccess = () => {\n                                db.close();\n                                return reject(putModelRequest.error);\n                            };\n                            deleteInfoRequest.onerror = error => {\n                                db.close();\n                                return reject(putModelRequest.error);\n                            };\n                        };\n                    };\n                    putInfoRequest.onerror = error => {\n                        db.close();\n                        return reject(putInfoRequest.error);\n                    };\n                    infoTx.oncomplete = () => {\n                        if (modelTx == null) {\n                            db.close();\n                        }\n                        else {\n                            modelTx.oncomplete = () => db.close();\n                        }\n                    };\n                }\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n}\nBrowserIndexedDB.URL_SCHEME = 'indexeddb://';\nexport const indexedDBRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n            return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\n/**\n * Creates a browser IndexedDB IOHandler for saving and loading models.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save('indexeddb://MyModel'));\n * console.log(saveResult);\n * ```\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),\n *   which can be used with, e.g., `tf.Model.save`.\n */\nexport function browserIndexedDB(modelPath) {\n    return new BrowserIndexedDB(modelPath);\n}\nfunction maybeStripScheme(key) {\n    return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?\n        key.slice(BrowserIndexedDB.URL_SCHEME.length) :\n        key;\n}\nexport class BrowserIndexedDBManager {\n    constructor() {\n        this.indexedDB = getIndexedDBFactory();\n    }\n    async listModels() {\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                const tx = db.transaction(INFO_STORE_NAME, 'readonly');\n                const store = tx.objectStore(INFO_STORE_NAME);\n                // tslint:disable:max-line-length\n                // Need to cast `store` as `any` here because TypeScript's DOM\n                // library does not have the `getAll()` method even though the\n                // method is supported in the latest version of most mainstream\n                // browsers:\n                // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll\n                // tslint:enable:max-line-length\n                // tslint:disable-next-line:no-any\n                const getAllInfoRequest = store.getAll();\n                getAllInfoRequest.onsuccess = () => {\n                    const out = {};\n                    for (const item of getAllInfoRequest.result) {\n                        out[item.modelPath] = item.modelArtifactsInfo;\n                    }\n                    resolve(out);\n                };\n                getAllInfoRequest.onerror = error => {\n                    db.close();\n                    return reject(getAllInfoRequest.error);\n                };\n                tx.oncomplete = () => db.close();\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n    async removeModel(path) {\n        path = maybeStripScheme(path);\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n                const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                const getInfoRequest = infoStore.get(path);\n                let modelTx;\n                getInfoRequest.onsuccess = () => {\n                    if (getInfoRequest.result == null) {\n                        db.close();\n                        return reject(new Error(`Cannot find model with path '${path}' ` +\n                            `in IndexedDB.`));\n                    }\n                    else {\n                        // First, delete the entry in the info store.\n                        const deleteInfoRequest = infoStore.delete(path);\n                        const deleteModelData = () => {\n                            // Second, delete the entry in the model store.\n                            modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n                            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                            const deleteModelRequest = modelStore.delete(path);\n                            deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo);\n                            deleteModelRequest.onerror = error => reject(getInfoRequest.error);\n                        };\n                        // Proceed with deleting model data regardless of whether deletion\n                        // of info data succeeds or not.\n                        deleteInfoRequest.onsuccess = deleteModelData;\n                        deleteInfoRequest.onerror = error => {\n                            deleteModelData();\n                            db.close();\n                            return reject(getInfoRequest.error);\n                        };\n                    }\n                };\n                getInfoRequest.onerror = error => {\n                    db.close();\n                    return reject(getInfoRequest.error);\n                };\n                infoTx.oncomplete = () => {\n                    if (modelTx == null) {\n                        db.close();\n                    }\n                    else {\n                        modelTx.oncomplete = () => db.close();\n                    }\n                };\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n}\n//# sourceMappingURL=indexed_db.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { arrayBufferToBase64String, base64StringToArrayBuffer, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst PATH_SEPARATOR = '/';\nconst PATH_PREFIX = 'tensorflowjs_models';\nconst INFO_SUFFIX = 'info';\nconst MODEL_TOPOLOGY_SUFFIX = 'model_topology';\nconst WEIGHT_SPECS_SUFFIX = 'weight_specs';\nconst WEIGHT_DATA_SUFFIX = 'weight_data';\nconst MODEL_METADATA_SUFFIX = 'model_metadata';\n/**\n * Purge all tensorflow.js-saved model artifacts from local storage.\n *\n * @returns Paths of the models purged.\n */\nexport function purgeLocalStorageArtifacts() {\n    if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n        typeof window.localStorage === 'undefined') {\n        throw new Error('purgeLocalStorageModels() cannot proceed because local storage is ' +\n            'unavailable in the current environment.');\n    }\n    const LS = window.localStorage;\n    const purgedModelPaths = [];\n    for (let i = 0; i < LS.length; ++i) {\n        const key = LS.key(i);\n        const prefix = PATH_PREFIX + PATH_SEPARATOR;\n        if (key.startsWith(prefix) && key.length > prefix.length) {\n            LS.removeItem(key);\n            const modelName = getModelPathFromKey(key);\n            if (purgedModelPaths.indexOf(modelName) === -1) {\n                purgedModelPaths.push(modelName);\n            }\n        }\n    }\n    return purgedModelPaths;\n}\nfunction getModelKeys(path) {\n    return {\n        info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n        topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n        weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n        weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),\n        modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)\n    };\n}\n/**\n * Get model path from a local-storage key.\n *\n * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'\n *\n * @param key\n */\nfunction getModelPathFromKey(key) {\n    const items = key.split(PATH_SEPARATOR);\n    if (items.length < 3) {\n        throw new Error(`Invalid key format: ${key}`);\n    }\n    return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\nfunction maybeStripScheme(key) {\n    return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?\n        key.slice(BrowserLocalStorage.URL_SCHEME.length) :\n        key;\n}\n/**\n * IOHandler subclass: Browser Local Storage.\n *\n * See the doc string to `browserLocalStorage` for more details.\n */\nexport class BrowserLocalStorage {\n    constructor(modelPath) {\n        if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n            typeof window.localStorage === 'undefined') {\n            // TODO(cais): Add more info about what IOHandler subtypes are\n            // available.\n            //   Maybe point to a doc page on the web and/or automatically determine\n            //   the available IOHandlers and print them in the error message.\n            throw new Error('The current environment does not support local storage.');\n        }\n        this.LS = window.localStorage;\n        if (modelPath == null || !modelPath) {\n            throw new Error('For local storage, modelPath must not be null, undefined or empty.');\n        }\n        this.modelPath = modelPath;\n        this.keys = getModelKeys(this.modelPath);\n    }\n    /**\n     * Save model artifacts to browser local storage.\n     *\n     * See the documentation to `browserLocalStorage` for details on the saved\n     * artifacts.\n     *\n     * @param modelArtifacts The model artifacts to be stored.\n     * @returns An instance of SaveResult.\n     */\n    async save(modelArtifacts) {\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        else {\n            const topology = JSON.stringify(modelArtifacts.modelTopology);\n            const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n            const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n            try {\n                this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n                this.LS.setItem(this.keys.topology, topology);\n                this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n                this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData));\n                this.LS.setItem(this.keys.modelMetadata, JSON.stringify({\n                    format: modelArtifacts.format,\n                    generatedBy: modelArtifacts.generatedBy,\n                    convertedBy: modelArtifacts.convertedBy,\n                    userDefinedMetadata: modelArtifacts.userDefinedMetadata\n                }));\n                return { modelArtifactsInfo };\n            }\n            catch (err) {\n                // If saving failed, clean up all items saved so far.\n                this.LS.removeItem(this.keys.info);\n                this.LS.removeItem(this.keys.topology);\n                this.LS.removeItem(this.keys.weightSpecs);\n                this.LS.removeItem(this.keys.weightData);\n                this.LS.removeItem(this.keys.modelMetadata);\n                throw new Error(`Failed to save model '${this.modelPath}' to local storage: ` +\n                    `size quota being exceeded is a possible cause of this failure: ` +\n                    `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` +\n                    `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` +\n                    `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n            }\n        }\n    }\n    /**\n     * Load a model from local storage.\n     *\n     * See the documentation to `browserLocalStorage` for details on the saved\n     * artifacts.\n     *\n     * @returns The loaded model (if loading succeeds).\n     */\n    async load() {\n        const info = JSON.parse(this.LS.getItem(this.keys.info));\n        if (info == null) {\n            throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);\n        }\n        if (info.modelTopologyType !== 'JSON') {\n            throw new Error('BrowserLocalStorage does not support loading non-JSON model ' +\n                'topology yet.');\n        }\n        const out = {};\n        // Load topology.\n        const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n        if (topology == null) {\n            throw new Error(`In local storage, the topology of model '${this.modelPath}' ` +\n                `is missing.`);\n        }\n        out.modelTopology = topology;\n        // Load weight specs.\n        const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n        if (weightSpecs == null) {\n            throw new Error(`In local storage, the weight specs of model '${this.modelPath}' ` +\n                `are missing.`);\n        }\n        out.weightSpecs = weightSpecs;\n        // Load meta-data fields.\n        const metadataString = this.LS.getItem(this.keys.modelMetadata);\n        if (metadataString != null) {\n            const metadata = JSON.parse(metadataString);\n            out.format = metadata['format'];\n            out.generatedBy = metadata['generatedBy'];\n            out.convertedBy = metadata['convertedBy'];\n            out.userDefinedMetadata = metadata['userDefinedMetadata'];\n        }\n        // Load weight data.\n        const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n        if (weightDataBase64 == null) {\n            throw new Error(`In local storage, the binary weight values of model ` +\n                `'${this.modelPath}' are missing.`);\n        }\n        out.weightData = base64StringToArrayBuffer(weightDataBase64);\n        return out;\n    }\n}\nBrowserLocalStorage.URL_SCHEME = 'localstorage://';\nexport const localStorageRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n            return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\n/**\n * Factory function for local storage IOHandler.\n *\n * This `IOHandler` supports both `save` and `load`.\n *\n * For each model's saved artifacts, four items are saved to local storage.\n *   - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the\n *     model, such as date saved, type of the topology, size in bytes, etc.\n *   - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-\n *     style models, this is a stringized JSON.\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the\n *     model, can be used to decode the saved binary weight values (see\n *     item below).\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary\n *     weight values, stored as a base64-encoded string.\n *\n * Saving may throw an `Error` if the total size of the artifacts exceed the\n * browser-specific quota.\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `IOHandler`, which can be used with, e.g.,\n *   `tf.Model.save`.\n */\nexport function browserLocalStorage(modelPath) {\n    return new BrowserLocalStorage(modelPath);\n}\nexport class BrowserLocalStorageManager {\n    constructor() {\n        assert(env().getBool('IS_BROWSER'), () => 'Current environment is not a web browser');\n        assert(typeof window === 'undefined' ||\n            typeof window.localStorage !== 'undefined', () => 'Current browser does not appear to support localStorage');\n        this.LS = window.localStorage;\n    }\n    async listModels() {\n        const out = {};\n        const prefix = PATH_PREFIX + PATH_SEPARATOR;\n        const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n        for (let i = 0; i < this.LS.length; ++i) {\n            const key = this.LS.key(i);\n            if (key.startsWith(prefix) && key.endsWith(suffix)) {\n                const modelPath = getModelPathFromKey(key);\n                out[modelPath] = JSON.parse(this.LS.getItem(key));\n            }\n        }\n        return out;\n    }\n    async removeModel(path) {\n        path = maybeStripScheme(path);\n        const keys = getModelKeys(path);\n        if (this.LS.getItem(keys.info) == null) {\n            throw new Error(`Cannot find model at path '${path}'`);\n        }\n        const info = JSON.parse(this.LS.getItem(keys.info));\n        this.LS.removeItem(keys.info);\n        this.LS.removeItem(keys.topology);\n        this.LS.removeItem(keys.weightSpecs);\n        this.LS.removeItem(keys.weightData);\n        return info;\n    }\n}\n//# sourceMappingURL=local_storage.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Classes and functions for model management across multiple storage mediums.\n *\n * Supported client actions:\n * - Listing models on all registered storage mediums.\n * - Remove model by URL from any registered storage mediums, by using URL\n *   string.\n * - Moving or copying model from one path to another in the same medium or from\n *   one medium to another, by using URL strings.\n */\nimport { assert } from '../util';\nimport { IORouterRegistry } from './router_registry';\nconst URL_SCHEME_SUFFIX = '://';\nexport class ModelStoreManagerRegistry {\n    constructor() {\n        this.managers = {};\n    }\n    static getInstance() {\n        if (ModelStoreManagerRegistry.instance == null) {\n            ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n        }\n        return ModelStoreManagerRegistry.instance;\n    }\n    /**\n     * Register a save-handler router.\n     *\n     * @param saveRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `save` method defined or `null`.\n     */\n    static registerManager(scheme, manager) {\n        assert(scheme != null, () => 'scheme must not be undefined or null.');\n        if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n            scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n        }\n        assert(scheme.length > 0, () => 'scheme must not be an empty string.');\n        const registry = ModelStoreManagerRegistry.getInstance();\n        assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);\n        registry.managers[scheme] = manager;\n    }\n    static getManager(scheme) {\n        const manager = this.getInstance().managers[scheme];\n        if (manager == null) {\n            throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n        }\n        return manager;\n    }\n    static getSchemes() {\n        return Object.keys(this.getInstance().managers);\n    }\n}\n/**\n * Helper method for parsing a URL string into a scheme and a path.\n *\n * @param url E.g., 'localstorage://my-model'\n * @returns A dictionary with two fields: scheme and path.\n *   Scheme: e.g., 'localstorage' in the example above.\n *   Path: e.g., 'my-model' in the example above.\n */\nfunction parseURL(url) {\n    if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n        throw new Error(`The url string provided does not contain a scheme. ` +\n            `Supported schemes are: ` +\n            `${ModelStoreManagerRegistry.getSchemes().join(',')}`);\n    }\n    return {\n        scheme: url.split(URL_SCHEME_SUFFIX)[0],\n        path: url.split(URL_SCHEME_SUFFIX)[1],\n    };\n}\nasync function cloneModelInternal(sourceURL, destURL, deleteSource = false) {\n    assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);\n    const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n    assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);\n    assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n        `load handlers for source URL ${sourceURL}.`);\n    const loadHandler = loadHandlers[0];\n    const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n    assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination ` +\n        `URL ${destURL}.`);\n    assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n        `save handlers for destination URL ${destURL}.`);\n    const saveHandler = saveHandlers[0];\n    const sourceScheme = parseURL(sourceURL).scheme;\n    const sourcePath = parseURL(sourceURL).path;\n    const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n    const modelArtifacts = await loadHandler.load();\n    // If moving within the same storage medium, remove the old model as soon as\n    // the loading is done. Without doing this, it is possible that the combined\n    // size of the two models will cause the cloning to fail.\n    if (deleteSource && sameMedium) {\n        await ModelStoreManagerRegistry.getManager(sourceScheme)\n            .removeModel(sourcePath);\n    }\n    const saveResult = await saveHandler.save(modelArtifacts);\n    // If moving between mediums, the deletion is done after the save succeeds.\n    // This guards against the case in which saving to the destination medium\n    // fails.\n    if (deleteSource && !sameMedium) {\n        await ModelStoreManagerRegistry.getManager(sourceScheme)\n            .removeModel(sourcePath);\n    }\n    return saveResult.modelArtifactsInfo;\n}\n/**\n * List all models stored in registered storage mediums.\n *\n * For a web browser environment, the registered mediums are Local Storage and\n * IndexedDB.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @returns A `Promise` of a dictionary mapping URLs of existing models to\n * their model artifacts info. URLs include medium-specific schemes, e.g.,\n *   'indexeddb://my/model/1'. Model artifacts info include type of the\n * model's topology, byte sizes of the topology, weights, etc.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function listModels() {\n    const schemes = ModelStoreManagerRegistry.getSchemes();\n    const out = {};\n    for (const scheme of schemes) {\n        const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();\n        for (const path in schemeOut) {\n            const url = scheme + URL_SCHEME_SUFFIX + path;\n            out[url] = schemeOut[path];\n        }\n    }\n    return out;\n}\n/**\n * Remove a model specified by URL from a reigstered storage medium.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @param url A URL to a stored model, with a scheme prefix, e.g.,\n *   'localstorage://my-model-1', 'indexeddb://my/model/2'.\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n *   is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function removeModel(url) {\n    const schemeAndPath = parseURL(url);\n    const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n    return manager.removeModel(schemeAndPath.path);\n}\n/**\n * Copy a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Copying within a storage medium, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Copying between two storage mediums, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Copy the model, from Local Storage to IndexedDB.\n * await tf.io.copyModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove both models.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of copying.\n * @param destURL Destination URL of copying.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function copyModel(sourceURL, destURL) {\n    const deleteSource = false;\n    return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n/**\n * Move a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Moving within a storage medium, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Moving between two storage mediums, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Move the model, from Local Storage to IndexedDB.\n * await tf.io.moveModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove the moved model.\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of moving.\n * @param destURL Destination URL of moving.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function moveModel(sourceURL, destURL) {\n    const deleteSource = true;\n    return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\nexport { moveModel, copyModel, removeModel, listModels };\n//# sourceMappingURL=model_management.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { BrowserIndexedDB, BrowserIndexedDBManager } from '../io/indexed_db';\nimport { BrowserLocalStorage, BrowserLocalStorageManager } from '../io/local_storage';\nimport { ModelStoreManagerRegistry } from '../io/model_management';\nexport class PlatformBrowser {\n    fetch(path, init) {\n        return fetch(path, init);\n    }\n    now() {\n        return performance.now();\n    }\n    encode(text, encoding) {\n        if (encoding !== 'utf-8' && encoding !== 'utf8') {\n            throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);\n        }\n        if (this.textEncoder == null) {\n            this.textEncoder = new TextEncoder();\n        }\n        return this.textEncoder.encode(text);\n    }\n    decode(bytes, encoding) {\n        return new TextDecoder(encoding).decode(bytes);\n    }\n}\nif (env().get('IS_BROWSER')) {\n    env().setPlatform('browser', new PlatformBrowser());\n    // Register LocalStorage IOHandler\n    try {\n        ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n    }\n    catch (err) {\n    }\n    // Register IndexedDB IOHandler\n    try {\n        ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n    }\n    catch (err) {\n    }\n}\n//# sourceMappingURL=platform_browser.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\n// We are wrapping this within an object so it can be stubbed by Jasmine.\nexport const getNodeFetch = {\n    // tslint:disable-next-line:no-require-imports\n    importFetch: () => require('node-fetch')\n};\nlet systemFetch;\n// These getters and setters are for testing so we don't export a mutable\n// variable.\nexport function resetSystemFetch() {\n    systemFetch = null;\n}\nexport function setSystemFetch(fetchFn) {\n    systemFetch = fetchFn;\n}\nexport function getSystemFetch() {\n    return systemFetch;\n}\nexport class PlatformNode {\n    constructor() {\n        // tslint:disable-next-line:no-require-imports\n        this.util = require('util');\n        // According to the spec, the built-in encoder can do only UTF-8 encoding.\n        // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder\n        this.textEncoder = new this.util.TextEncoder();\n    }\n    fetch(path, requestInits) {\n        if (env().global.fetch != null) {\n            return env().global.fetch(path, requestInits);\n        }\n        if (systemFetch == null) {\n            systemFetch = getNodeFetch.importFetch();\n        }\n        return systemFetch(path, requestInits);\n    }\n    now() {\n        const time = process.hrtime();\n        return time[0] * 1000 + time[1] / 1000000;\n    }\n    encode(text, encoding) {\n        if (encoding !== 'utf-8' && encoding !== 'utf8') {\n            throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);\n        }\n        return this.textEncoder.encode(text);\n    }\n    decode(bytes, encoding) {\n        if (bytes.length === 0) {\n            return '';\n        }\n        return new this.util.TextDecoder(encoding).decode(bytes);\n    }\n}\nif (env().get('IS_NODE')) {\n    env().setPlatform('node', new PlatformNode());\n}\n//# sourceMappingURL=platform_node.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport * as util from '../util';\n/**\n * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.\n *\n * The values are stored in CPU as `TypedArray`. Fill the buffer using\n * `buffer.set()`, or by modifying directly `buffer.values`.\n *\n * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with\n * those values.\n *\n * ```js\n * // Create a buffer and set values at particular indices.\n * const buffer = tf.buffer([2, 2]);\n * buffer.set(3, 0, 0);\n * buffer.set(5, 1, 0);\n *\n * // Convert the buffer back to a tensor.\n * buffer.toTensor().print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The dtype of the buffer. Defaults to 'float32'.\n * @param values The values of the buffer as `TypedArray`. Defaults to\n * zeros.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function buffer(shape, dtype = 'float32', values) {\n    dtype = dtype || 'float32';\n    util.assertNonNegativeIntegerDimensions(shape);\n    return new TensorBuffer(shape, dtype, values);\n}\n//# sourceMappingURL=buffer.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cast } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Casts a `tf.Tensor` to a new dtype.\n *\n * ```js\n * const x = tf.tensor1d([1.5, 2.5, 3]);\n * tf.cast(x, 'int32').print();\n * ```\n * @param x The input tensor to be casted.\n * @param dtype The dtype to cast the input tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction cast_(x, dtype) {\n    const $x = convertToTensor(x, 'x', 'cast');\n    // Sanity checks.\n    if (!util.isValidDtype(dtype)) {\n        throw new Error(`Failed to cast to unknown dtype ${dtype}`);\n    }\n    if (dtype === 'string' && $x.dtype !== 'string' ||\n        dtype !== 'string' && $x.dtype === 'string') {\n        throw new Error('Only strings can be casted to strings');\n    }\n    const inputs = { x: $x };\n    const attrs = { dtype };\n    return ENGINE.runKernelFunc(backend => backend.cast($x, dtype), inputs, null /* grad */, Cast, attrs);\n}\nexport const cast = op({ cast_ });\n//# sourceMappingURL=cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Identity } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction clone_(x) {\n    const $x = convertToTensor(x, 'x', 'clone', null);\n    const forward = () => ENGINE.makeTensorFromDataId($x.dataId, $x.shape, $x.dtype);\n    const inputs = { x: $x };\n    // Note this op is called tf.identity in python. Hence the kernel name used\n    // here.\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Identity);\n}\nexport const clone = op({ clone_ });\n//# sourceMappingURL=clone.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function print(x, verbose = false) {\n    console.log(x.toString(verbose));\n}\n//# sourceMappingURL=print.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Required side effectful code for tfjs-core\n// Set up Engine and ENV\nimport { getOrMakeEngine } from './engine';\ngetOrMakeEngine();\n// Register backend-agnostic flags.\nimport './flags';\n// Register platforms\nimport './platforms/platform_browser';\nimport './platforms/platform_node';\n// Set up OpHandler\nimport { buffer } from './ops/buffer';\nimport { cast } from './ops/cast';\nimport { clone } from './ops/clone';\nimport { print } from './ops/print';\nimport { setOpHandler } from './tensor';\nconst opHandler = {\n    buffer,\n    cast,\n    clone,\n    print\n};\nsetOpHandler(opHandler);\n//# sourceMappingURL=base_side_effects.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandlers related to files, such as browser-triggered file downloads,\n * user-selected files in browser.\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { basename, concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DEFAULT_FILE_NAME_PREFIX = 'model';\nconst DEFAULT_JSON_EXTENSION_NAME = '.json';\nconst DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';\nfunction defer(f) {\n    return new Promise(resolve => setTimeout(resolve)).then(f);\n}\nexport class BrowserDownloads {\n    constructor(fileNamePrefix) {\n        if (!env().getBool('IS_BROWSER')) {\n            // TODO(cais): Provide info on what IOHandlers are available under the\n            //   current environment.\n            throw new Error('browserDownloads() cannot proceed because the current environment ' +\n                'is not a browser.');\n        }\n        if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n            fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n        }\n        if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n            fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n        }\n        this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n        this.weightDataFileName =\n            fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n    }\n    async save(modelArtifacts) {\n        if (typeof (document) === 'undefined') {\n            throw new Error('Browser downloads are not supported in ' +\n                'this environment since `document` is not present');\n        }\n        const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: 'application/octet-stream' }));\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserDownloads.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        else {\n            const weightsManifest = [{\n                    paths: ['./' + this.weightDataFileName],\n                    weights: modelArtifacts.weightSpecs\n                }];\n            const modelTopologyAndWeightManifest = {\n                modelTopology: modelArtifacts.modelTopology,\n                format: modelArtifacts.format,\n                generatedBy: modelArtifacts.generatedBy,\n                convertedBy: modelArtifacts.convertedBy,\n                weightsManifest\n            };\n            const modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: 'application/json' }));\n            // If anchor elements are not provided, create them without attaching them\n            // to parents, so that the downloaded file names can be controlled.\n            const jsonAnchor = this.jsonAnchor == null ? document.createElement('a') :\n                this.jsonAnchor;\n            jsonAnchor.download = this.modelTopologyFileName;\n            jsonAnchor.href = modelTopologyAndWeightManifestURL;\n            // Trigger downloads by evoking a click event on the download anchors.\n            // When multiple downloads are started synchronously, Firefox will only\n            // save the last one.\n            await defer(() => jsonAnchor.dispatchEvent(new MouseEvent('click')));\n            if (modelArtifacts.weightData != null) {\n                const weightDataAnchor = this.weightDataAnchor == null ?\n                    document.createElement('a') :\n                    this.weightDataAnchor;\n                weightDataAnchor.download = this.weightDataFileName;\n                weightDataAnchor.href = weightsURL;\n                await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent('click')));\n            }\n            return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) };\n        }\n    }\n}\nBrowserDownloads.URL_SCHEME = 'downloads://';\nclass BrowserFiles {\n    constructor(files) {\n        if (files == null || files.length < 1) {\n            throw new Error(`When calling browserFiles, at least 1 file is required, ` +\n                `but received ${files}`);\n        }\n        this.files = files;\n    }\n    async load() {\n        const jsonFile = this.files[0];\n        const weightFiles = this.files.slice(1);\n        return new Promise((resolve, reject) => {\n            const jsonReader = new FileReader();\n            jsonReader.onload = (event) => {\n                // tslint:disable-next-line:no-any\n                const modelJSON = JSON.parse(event.target.result);\n                const modelTopology = modelJSON.modelTopology;\n                if (modelTopology == null) {\n                    reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));\n                    return;\n                }\n                if (weightFiles.length === 0) {\n                    resolve({ modelTopology });\n                }\n                const weightsManifest = modelJSON.weightsManifest;\n                if (weightsManifest == null) {\n                    reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));\n                    return;\n                }\n                let pathToFile;\n                try {\n                    pathToFile =\n                        this.checkManifestAndWeightFiles(weightsManifest, weightFiles);\n                }\n                catch (err) {\n                    reject(err);\n                    return;\n                }\n                const weightSpecs = [];\n                const paths = [];\n                const perFileBuffers = [];\n                weightsManifest.forEach(weightsGroup => {\n                    weightsGroup.paths.forEach(path => {\n                        paths.push(path);\n                        perFileBuffers.push(null);\n                    });\n                    weightSpecs.push(...weightsGroup.weights);\n                });\n                weightsManifest.forEach(weightsGroup => {\n                    weightsGroup.paths.forEach(path => {\n                        const weightFileReader = new FileReader();\n                        weightFileReader.onload = (event) => {\n                            // tslint:disable-next-line:no-any\n                            const weightData = event.target.result;\n                            const index = paths.indexOf(path);\n                            perFileBuffers[index] = weightData;\n                            if (perFileBuffers.indexOf(null) === -1) {\n                                resolve({\n                                    modelTopology,\n                                    weightSpecs,\n                                    weightData: concatenateArrayBuffers(perFileBuffers),\n                                    format: modelJSON.format,\n                                    generatedBy: modelJSON.generatedBy,\n                                    convertedBy: modelJSON.convertedBy,\n                                    userDefinedMetadata: modelJSON.userDefinedMetadata\n                                });\n                            }\n                        };\n                        weightFileReader.onerror = error => reject(`Failed to weights data from file of path '${path}'.`);\n                        weightFileReader.readAsArrayBuffer(pathToFile[path]);\n                    });\n                });\n            };\n            jsonReader.onerror = error => reject(`Failed to read model topology and weights manifest JSON ` +\n                `from file '${jsonFile.name}'. BrowserFiles supports loading ` +\n                `Keras-style tf.Model artifacts only.`);\n            jsonReader.readAsText(jsonFile);\n        });\n    }\n    /**\n     * Check the compatibility between weights manifest and weight files.\n     */\n    checkManifestAndWeightFiles(manifest, files) {\n        const basenames = [];\n        const fileNames = files.map(file => basename(file.name));\n        const pathToFile = {};\n        for (const group of manifest) {\n            group.paths.forEach(path => {\n                const pathBasename = basename(path);\n                if (basenames.indexOf(pathBasename) !== -1) {\n                    throw new Error(`Duplicate file basename found in weights manifest: ` +\n                        `'${pathBasename}'`);\n                }\n                basenames.push(pathBasename);\n                if (fileNames.indexOf(pathBasename) === -1) {\n                    throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);\n                }\n                else {\n                    pathToFile[path] = files[fileNames.indexOf(pathBasename)];\n                }\n            });\n        }\n        if (basenames.length !== files.length) {\n            throw new Error(`Mismatch in the number of files in weights manifest ` +\n                `(${basenames.length}) and the number of weight files provided ` +\n                `(${files.length}).`);\n        }\n        return pathToFile;\n    }\n}\nexport const browserDownloadsRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n            return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\n/**\n * Creates an IOHandler that triggers file downloads from the browser.\n *\n * The returned `IOHandler` instance can be used as model exporting methods such\n * as `tf.Model.save` and supports only saving.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * const saveResult = await model.save('downloads://mymodel');\n * // This will trigger downloading of two files:\n * //   'mymodel.json' and 'mymodel.weights.bin'.\n * console.log(saveResult);\n * ```\n *\n * @param fileNamePrefix Prefix name of the files to be downloaded. For use with\n *   `tf.Model`, `fileNamePrefix` should follow either of the following two\n *   formats:\n *   1. `null` or `undefined`, in which case the default file\n *      names will be used:\n *      - 'model.json' for the JSON file containing the model topology and\n *        weights manifest.\n *      - 'model.weights.bin' for the binary file containing the binary weight\n *        values.\n *   2. A single string or an Array of a single string, as the file name prefix.\n *      For example, if `'foo'` is provided, the downloaded JSON\n *      file and binary weights file will be named 'foo.json' and\n *      'foo.weights.bin', respectively.\n * @param config Additional configuration for triggering downloads.\n * @returns An instance of `BrowserDownloads` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserDownloads(fileNamePrefix = 'model') {\n    return new BrowserDownloads(fileNamePrefix);\n}\n/**\n * Creates an IOHandler that loads model artifacts from user-selected files.\n *\n * This method can be used for loading from files such as user-selected files\n * in the browser.\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * // Note: This code snippet won't run properly without the actual file input\n * //   elements in the HTML DOM.\n *\n * // Suppose there are two HTML file input (`<input type=\"file\" ...>`)\n * // elements.\n * const uploadJSONInput = document.getElementById('upload-json');\n * const uploadWeightsInput = document.getElementById('upload-weights');\n * const model = await tf.loadLayersModel(tf.io.browserFiles(\n *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));\n * ```\n *\n * @param files `File`s to load from. Currently, this function supports only\n *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for\n *   which an `Array` of `File`s is expected (in that order):\n *   - A JSON file containing the model topology and weight manifest.\n *   - Optionally, One or more binary files containing the binary weights.\n *     These files must have names that match the paths in the `weightsManifest`\n *     contained by the aforementioned JSON file, or errors will be thrown\n *     during loading. These weights files have the same format as the ones\n *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`\n *     Python PIP package. If no weights files are provided, only the model\n *     topology will be loaded from the JSON file above.\n * @returns An instance of `Files` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserFiles(files) {\n    return new BrowserFiles(files);\n}\n//# sourceMappingURL=browser_files.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../util';\n/**\n * Monitor Promise.all progress, fire onProgress callback function.\n *\n * @param promises Promise list going to be monitored\n * @param onProgress Callback function. Fired when a promise resolved.\n * @param startFraction Optional fraction start. Default to 0.\n * @param endFraction Optional fraction end. Default to 1.\n */\nexport function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {\n    checkPromises(promises);\n    startFraction = startFraction == null ? 0 : startFraction;\n    endFraction = endFraction == null ? 1 : endFraction;\n    checkFraction(startFraction, endFraction);\n    let resolvedPromise = 0;\n    const registerMonitor = (promise) => {\n        promise.then(value => {\n            const fraction = startFraction +\n                ++resolvedPromise / promises.length * (endFraction - startFraction);\n            // pass fraction as parameter to callback function.\n            onProgress(fraction);\n            return value;\n        });\n        return promise;\n    };\n    function checkPromises(promises) {\n        assert(promises != null && Array.isArray(promises) && promises.length > 0, () => 'promises must be a none empty array');\n    }\n    function checkFraction(startFraction, endFraction) {\n        assert(startFraction >= 0 && startFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n            `got startFraction ${startFraction}`);\n        assert(endFraction >= 0 && endFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n            `got endFraction ${endFraction}`);\n        assert(endFraction >= startFraction, () => `startFraction must be no more than endFraction, but ` +\n            `got startFraction ${startFraction} and endFraction ` +\n            `${endFraction}`);\n    }\n    return Promise.all(promises.map(registerMonitor));\n}\n//# sourceMappingURL=progress.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\nimport * as util from '../util';\nimport { decodeWeights } from './io_utils';\nimport { monitorPromisesProgress } from './progress';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/**\n * Reads binary weights data from a number of URLs.\n *\n * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.\n * @param requestOptions RequestInit (options) for the HTTP requests.\n * @param fetchFunc Optional overriding value for the `window.fetch` function.\n * @param onProgress Optional, progress callback function, fired periodically\n *   before the load is completed.\n * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same\n *   length as `fetchURLs`.\n */\nexport async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {\n    if (loadOptions == null) {\n        loadOptions = {};\n    }\n    const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch :\n        loadOptions.fetchFunc;\n    // Create the requests for all of the weights in parallel.\n    const requests = fetchURLs.map(fetchURL => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true }));\n    const fetchStartFraction = 0;\n    const fetchEndFraction = 0.5;\n    const responses = loadOptions.onProgress == null ?\n        await Promise.all(requests) :\n        await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);\n    const bufferPromises = responses.map(response => response.arrayBuffer());\n    const bufferStartFraction = 0.5;\n    const bufferEndFraction = 1;\n    const buffers = loadOptions.onProgress == null ?\n        await Promise.all(bufferPromises) :\n        await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);\n    return buffers;\n}\n/**\n * Reads a weights manifest JSON configuration, fetches the weights and\n * returns them as `Tensor`s.\n *\n * @param manifest The weights manifest JSON.\n * @param filePathPrefix The path prefix for filenames given in the manifest.\n *     Defaults to the empty string.\n * @param weightNames The names of the weights to be fetched.\n */\nexport async function loadWeights(manifest, filePathPrefix = '', weightNames, requestInit) {\n    // TODO(nsthorat): Groups are currently fetched atomically. If you need a\n    // single weight from a group, the whole group will be fetched. At a future\n    // date, we should support fetching only the individual shards within a\n    // group that are needed to reconstruct the requested weight.\n    // TODO(cais): Use `decodeWeights` for implementation.\n    const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit });\n    const loadWeights = weightsLoaderFactory(fetchWeights);\n    return loadWeights(manifest, filePathPrefix, weightNames);\n}\n/**\n * Creates a function, which reads a weights manifest JSON configuration,\n * fetches the weight files using the specified function and returns them as\n * `Tensor`s.\n *\n * ```js\n * // example for creating a nodejs weight loader, which reads the weight files\n * // from disk using fs.readFileSync\n *\n * import * as fs from 'fs'\n *\n * const fetchWeightsFromDisk = (filePaths: string[]) =>\n *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)\n *\n * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n *\n * const manifest = JSON.parse(\n *   fs.readFileSync('./my_model-weights_manifest').toString()\n * )\n * const weightMap = await loadWeights(manifest, './')\n * ```\n * @param fetchWeightsFunction The function used for fetching the weight files.\n * @returns Weight loading function.\n */\nexport function weightsLoaderFactory(fetchWeightsFunction) {\n    return async (manifest, filePathPrefix = '', weightNames) => {\n        // Collect all the groups, weights, and their relative offsets to be\n        // fetched.\n        const groupIndicesToFetchMap = manifest.map(() => false);\n        const groupWeightsToFetch = {};\n        const weightsFound = weightNames != null ? weightNames.map(() => false) : [];\n        const allManifestWeightNames = [];\n        manifest.forEach((manifestGroupConfig, groupIndex) => {\n            let groupOffset = 0;\n            manifestGroupConfig.weights.forEach(weightsEntry => {\n                const rawDtype = ('quantization' in weightsEntry) ?\n                    weightsEntry.quantization.dtype :\n                    weightsEntry.dtype;\n                const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *\n                    util.sizeFromShape(weightsEntry.shape);\n                const enqueueWeightsForFetchingFn = () => {\n                    groupIndicesToFetchMap[groupIndex] = true;\n                    if (groupWeightsToFetch[groupIndex] == null) {\n                        groupWeightsToFetch[groupIndex] = [];\n                    }\n                    groupWeightsToFetch[groupIndex].push({\n                        manifestEntry: weightsEntry,\n                        groupOffset,\n                        sizeBytes: weightsBytes\n                    });\n                };\n                if (weightNames != null) {\n                    weightNames.forEach((weightName, weightIndex) => {\n                        if (weightName === weightsEntry.name) {\n                            enqueueWeightsForFetchingFn();\n                            weightsFound[weightIndex] = true;\n                        }\n                    });\n                }\n                else {\n                    enqueueWeightsForFetchingFn();\n                }\n                allManifestWeightNames.push(weightsEntry.name);\n                groupOffset += weightsBytes;\n            });\n        });\n        if (!weightsFound.every(found => found)) {\n            const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n            throw new Error(`Could not find weights in manifest with names: ` +\n                `${weightsNotFound.join(', ')}. \\n` +\n                `Manifest JSON has weights with names: ` +\n                `${allManifestWeightNames.join(', ')}.`);\n        }\n        // Convert the one-hot boolean groupId => shouldFetch map to a list of group\n        // IDs.\n        const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n            if (shouldFetch) {\n                accumulator.push(i);\n            }\n            return accumulator;\n        }, []);\n        const fetchUrls = [];\n        groupIndicesToFetch.forEach(i => {\n            manifest[i].paths.forEach(filepath => {\n                const fetchUrl = filePathPrefix +\n                    (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;\n                fetchUrls.push(fetchUrl);\n            });\n        });\n        const buffers = await fetchWeightsFunction(fetchUrls);\n        const weightsTensorMap = {};\n        let bufferIndexOffset = 0;\n        groupIndicesToFetch.forEach(i => {\n            const numBuffers = manifest[i].paths.length;\n            let groupBytes = 0;\n            for (let i = 0; i < numBuffers; i++) {\n                groupBytes += buffers[bufferIndexOffset + i].byteLength;\n            }\n            // Create a buffer for the whole group.\n            const groupBuffer = new ArrayBuffer(groupBytes);\n            const groupByteBuffer = new Uint8Array(groupBuffer);\n            let groupBufferOffset = 0;\n            for (let i = 0; i < numBuffers; i++) {\n                const buffer = new Uint8Array(buffers[bufferIndexOffset + i]);\n                groupByteBuffer.set(buffer, groupBufferOffset);\n                groupBufferOffset += buffer.byteLength;\n            }\n            const weightsEntries = groupWeightsToFetch[i];\n            weightsEntries.forEach(weightsEntry => {\n                const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);\n                const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n                for (const name in nameToTensorMap) {\n                    weightsTensorMap[name] = nameToTensorMap[name];\n                }\n            });\n            bufferIndexOffset += numBuffers;\n        });\n        return weightsTensorMap;\n    };\n}\n//# sourceMappingURL=weights_loader.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandler implementations based on HTTP requests in the web browser.\n *\n * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n */\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nimport { loadWeightsAsArrayBuffer } from './weights_loader';\nconst OCTET_STREAM_MIME_TYPE = 'application/octet-stream';\nconst JSON_TYPE = 'application/json';\nexport class HTTPRequest {\n    constructor(path, loadOptions) {\n        this.DEFAULT_METHOD = 'POST';\n        if (loadOptions == null) {\n            loadOptions = {};\n        }\n        this.weightPathPrefix = loadOptions.weightPathPrefix;\n        this.onProgress = loadOptions.onProgress;\n        this.weightUrlConverter = loadOptions.weightUrlConverter;\n        if (loadOptions.fetchFunc != null) {\n            assert(typeof loadOptions.fetchFunc === 'function', () => 'Must pass a function that matches the signature of ' +\n                '`fetch` (see ' +\n                'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');\n            this.fetch = loadOptions.fetchFunc;\n        }\n        else {\n            this.fetch = env().platform.fetch;\n        }\n        assert(path != null && path.length > 0, () => 'URL path for http must not be null, undefined or ' +\n            'empty.');\n        if (Array.isArray(path)) {\n            assert(path.length === 2, () => 'URL paths for http must have a length of 2, ' +\n                `(actual length is ${path.length}).`);\n        }\n        this.path = path;\n        if (loadOptions.requestInit != null &&\n            loadOptions.requestInit.body != null) {\n            throw new Error('requestInit is expected to have no pre-existing body, but has one.');\n        }\n        this.requestInit = loadOptions.requestInit || {};\n    }\n    async save(modelArtifacts) {\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserHTTPRequest.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        const init = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);\n        init.body = new FormData();\n        const weightsManifest = [{\n                paths: ['./model.weights.bin'],\n                weights: modelArtifacts.weightSpecs,\n            }];\n        const modelTopologyAndWeightManifest = {\n            modelTopology: modelArtifacts.modelTopology,\n            format: modelArtifacts.format,\n            generatedBy: modelArtifacts.generatedBy,\n            convertedBy: modelArtifacts.convertedBy,\n            userDefinedMetadata: modelArtifacts.userDefinedMetadata,\n            weightsManifest\n        };\n        init.body.append('model.json', new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), 'model.json');\n        if (modelArtifacts.weightData != null) {\n            init.body.append('model.weights.bin', new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), 'model.weights.bin');\n        }\n        const response = await this.fetch(this.path, init);\n        if (response.ok) {\n            return {\n                modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n                responses: [response],\n            };\n        }\n        else {\n            throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ` +\n                `${response.status}.`);\n        }\n    }\n    /**\n     * Load model artifacts via HTTP request(s).\n     *\n     * See the documentation to `tf.io.http` for details on the saved\n     * artifacts.\n     *\n     * @returns The loaded model artifacts (if loading succeeds).\n     */\n    async load() {\n        const modelConfigRequest = await this.fetch(this.path, this.requestInit);\n        if (!modelConfigRequest.ok) {\n            throw new Error(`Request to ${this.path} failed with status code ` +\n                `${modelConfigRequest.status}. Please verify this URL points to ` +\n                `the model JSON of the model to load.`);\n        }\n        let modelConfig;\n        try {\n            modelConfig = await modelConfigRequest.json();\n        }\n        catch (e) {\n            let message = `Failed to parse model JSON of response from ${this.path}.`;\n            // TODO(nsthorat): Remove this after some time when we're comfortable that\n            // .pb files are mostly gone.\n            if (this.path.endsWith('.pb')) {\n                message += ' Your path contains a .pb file extension. ' +\n                    'Support for .pb models have been removed in TensorFlow.js 1.0 ' +\n                    'in favor of .json models. You can re-convert your Python ' +\n                    'TensorFlow model using the TensorFlow.js 1.0 conversion scripts ' +\n                    'or you can convert your.pb models with the \\'pb2json\\'' +\n                    'NPM script in the tensorflow/tfjs-converter repository.';\n            }\n            else {\n                message += ' Please make sure the server is serving valid ' +\n                    'JSON for this request.';\n            }\n            throw new Error(message);\n        }\n        const modelTopology = modelConfig.modelTopology;\n        const weightsManifest = modelConfig.weightsManifest;\n        const generatedBy = modelConfig.generatedBy;\n        const convertedBy = modelConfig.convertedBy;\n        const format = modelConfig.format;\n        const userDefinedMetadata = modelConfig.userDefinedMetadata;\n        // We do not allow both modelTopology and weightsManifest to be missing.\n        if (modelTopology == null && weightsManifest == null) {\n            throw new Error(`The JSON from HTTP path ${this.path} contains neither model ` +\n                `topology or manifest for weights.`);\n        }\n        let weightSpecs;\n        let weightData;\n        if (weightsManifest != null) {\n            const results = await this.loadWeights(weightsManifest);\n            [weightSpecs, weightData] = results;\n        }\n        const artifacts = {\n            modelTopology,\n            weightSpecs,\n            weightData,\n            userDefinedMetadata,\n            generatedBy,\n            convertedBy,\n            format\n        };\n        const initializer = modelConfig.modelInitializer;\n        if (initializer) {\n            artifacts.modelInitializer = initializer;\n        }\n        return artifacts;\n    }\n    async loadWeights(weightsManifest) {\n        const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n        const [prefix, suffix] = parseUrl(weightPath);\n        const pathPrefix = this.weightPathPrefix || prefix;\n        const weightSpecs = [];\n        for (const entry of weightsManifest) {\n            weightSpecs.push(...entry.weights);\n        }\n        const fetchURLs = [];\n        const urlPromises = [];\n        for (const weightsGroup of weightsManifest) {\n            for (const path of weightsGroup.paths) {\n                if (this.weightUrlConverter != null) {\n                    urlPromises.push(this.weightUrlConverter(path));\n                }\n                else {\n                    fetchURLs.push(pathPrefix + path + suffix);\n                }\n            }\n        }\n        if (this.weightUrlConverter) {\n            fetchURLs.push(...await Promise.all(urlPromises));\n        }\n        const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {\n            requestInit: this.requestInit,\n            fetchFunc: this.fetch,\n            onProgress: this.onProgress\n        });\n        return [weightSpecs, concatenateArrayBuffers(buffers)];\n    }\n}\nHTTPRequest.URL_SCHEME_REGEX = /^https?:\\/\\//;\n/**\n * Extract the prefix and suffix of the url, where the prefix is the path before\n * the last file, and suffix is the search params after the last file.\n * ```\n * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'\n * [prefix, suffix] = parseUrl(url)\n * // prefix = 'http://tfhub.dev/model/1/'\n * // suffix = '?tfjs-format=file'\n * ```\n * @param url the model url to be parsed.\n */\nexport function parseUrl(url) {\n    const lastSlash = url.lastIndexOf('/');\n    const lastSearchParam = url.lastIndexOf('?');\n    const prefix = url.substring(0, lastSlash);\n    const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';\n    return [prefix + '/', suffix];\n}\nexport function isHTTPScheme(url) {\n    return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;\n}\nexport const httpRouter = (url, loadOptions) => {\n    if (typeof fetch === 'undefined' &&\n        (loadOptions == null || loadOptions.fetchFunc == null)) {\n        // `http` uses `fetch` or `node-fetch`, if one wants to use it in\n        // an environment that is not the browser or node they have to setup a\n        // global fetch polyfill.\n        return null;\n    }\n    else {\n        let isHTTP = true;\n        if (Array.isArray(url)) {\n            isHTTP = url.every(urlItem => isHTTPScheme(urlItem));\n        }\n        else {\n            isHTTP = isHTTPScheme(url);\n        }\n        if (isHTTP) {\n            return http(url, loadOptions);\n        }\n    }\n    return null;\n};\nIORouterRegistry.registerSaveRouter(httpRouter);\nIORouterRegistry.registerLoadRouter(httpRouter);\n/**\n * Creates an IOHandler subtype that sends model artifacts to HTTP server.\n *\n * An HTTP request of the `multipart/form-data` mime type will be sent to the\n * `path` URL. The form data includes artifacts that represent the topology\n * and/or weights of the model. In the case of Keras-style `tf.Model`, two\n * blobs (files) exist in form-data:\n *   - A JSON file consisting of `modelTopology` and `weightsManifest`.\n *   - A binary weights file consisting of the concatenated weight values.\n * These files are in the same format as the one generated by\n * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).\n *\n * The following code snippet exemplifies the client-side code that uses this\n * function:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save(tf.io.http(\n *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));\n * console.log(saveResult);\n * ```\n *\n * If the default `POST` method is to be used, without any custom parameters\n * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:\n *\n * ```js\n * const saveResult = await model.save('http://model-server:5000/upload');\n * ```\n *\n * The following GitHub Gist\n * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864\n * implements a server based on [flask](https://github.com/pallets/flask) that\n * can receive the request. Upon receiving the model artifacts via the requst,\n * this particular server reconsistutes instances of [Keras\n * Models](https://keras.io/models/model/) in memory.\n *\n *\n * @param path A URL path to the model.\n *   Can be an absolute HTTP path (e.g.,\n *   'http://localhost:8000/model-upload)') or a relative path (e.g.,\n *   './model-upload').\n * @param requestInit Request configurations to be used when sending\n *    HTTP request to server using `fetch`. It can contain fields such as\n *    `method`, `credentials`, `headers`, `mode`, etc. See\n *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request\n *    for more information. `requestInit` must not have a body, because the\n * body will be set by TensorFlow.js. File blobs representing the model\n * topology (filename: 'model.json') and the weights of the model (filename:\n * 'model.weights.bin') will be appended to the body. If `requestInit` has a\n * `body`, an Error will be thrown.\n * @param loadOptions Optional configuration for the loading. It includes the\n *   following fields:\n *   - weightPathPrefix Optional, this specifies the path prefix for weight\n *     files, by default this is calculated from the path param.\n *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,\n *     the `fetch` from node-fetch can be used here.\n *   - onProgress Optional, progress callback function, fired periodically\n *     before the load is completed.\n * @returns An instance of `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function http(path, loadOptions) {\n    return new HTTPRequest(path, loadOptions);\n}\n/**\n * Deprecated. Use `tf.io.http`.\n * @param path\n * @param loadOptions\n */\nexport function browserHTTPRequest(path, loadOptions) {\n    return http(path, loadOptions);\n}\n//# sourceMappingURL=http.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nclass PassthroughLoader {\n    constructor(modelArtifacts) {\n        this.modelArtifacts = modelArtifacts;\n    }\n    async load() {\n        return this.modelArtifacts;\n    }\n}\nclass PassthroughSaver {\n    constructor(saveHandler) {\n        this.saveHandler = saveHandler;\n    }\n    async save(modelArtifacts) {\n        return this.saveHandler(modelArtifacts);\n    }\n}\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadLayersModel(tf.io.fromMemory(\n *     modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelArtifacts a object containing model topology (i.e., parsed from\n *   the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n *   names, shapes, types, and quantization of the weight data.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n *   concatenated in the order described by the weightSpecs.\n * @param trainingConfig Model training configuration. Optional.\n *\n * @returns A passthrough `IOHandler` that simply loads the provided data.\n */\nexport function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {\n    if (arguments.length === 1) {\n        const isModelArtifacts = modelArtifacts.modelTopology != null ||\n            modelArtifacts.weightSpecs != null;\n        if (isModelArtifacts) {\n            return new PassthroughLoader(modelArtifacts);\n        }\n        else {\n            // Legacy support: with only modelTopology.\n            // TODO(cais): Remove this deprecated API.\n            console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n                'The argument should be of type ModelArtifacts. ' +\n                'The multi-argument signature of tf.io.fromMemory() has been ' +\n                'deprecated and will be removed in a future release.');\n            return new PassthroughLoader({ modelTopology: modelArtifacts });\n        }\n    }\n    else {\n        // Legacy support.\n        // TODO(cais): Remove this deprecated API.\n        console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n            'The argument should be of type ModelArtifacts. ' +\n            'The multi-argument signature of tf.io.fromMemory() has been ' +\n            'deprecated and will be removed in a future release.');\n        return new PassthroughLoader({\n            modelTopology: modelArtifacts,\n            weightSpecs,\n            weightData,\n            trainingConfig\n        });\n    }\n}\n/**\n * Creates an IOHandler that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n *   // ... do something with the artifacts ...\n *   return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n *     `SaveResult`.\n */\nexport function withSaveHandler(saveHandler) {\n    return new PassthroughSaver(saveHandler);\n}\n//# sourceMappingURL=passthrough.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Importing local_storage and indexed_db is necessary for the routers to be\n// registered.\nimport './indexed_db';\nimport './local_storage';\nimport { browserFiles } from './browser_files';\nimport { browserHTTPRequest, http, isHTTPScheme } from './http';\nimport { concatenateArrayBuffers, decodeWeights, encodeWeights, getModelArtifactsInfoForJSON } from './io_utils';\nimport { fromMemory, withSaveHandler } from './passthrough';\nimport { getLoadHandlers, getSaveHandlers, registerLoadRouter, registerSaveRouter } from './router_registry';\nimport { loadWeights, weightsLoaderFactory } from './weights_loader';\nexport { copyModel, listModels, moveModel, removeModel } from './model_management';\nexport { browserFiles, browserHTTPRequest, concatenateArrayBuffers, decodeWeights, encodeWeights, fromMemory, getLoadHandlers, getModelArtifactsInfoForJSON, getSaveHandlers, http, isHTTPScheme, loadWeights, registerLoadRouter, registerSaveRouter, weightsLoaderFactory, withSaveHandler };\n//# sourceMappingURL=io.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reshape } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Reshapes a `tf.Tensor` to a given shape.\n *\n * Given an input tensor, returns a new tensor with the same values as the\n * input tensor with shape `shape`.\n *\n * If one component of shape is the special value -1, the size of that\n * dimension is computed so that the total size remains constant. In\n * particular, a shape of [-1] flattens into 1-D. At most one component of\n * shape can be -1.\n *\n * If shape is 1-D or higher, then the operation returns a tensor with shape\n * shape filled with the values of tensor. In this case, the number of\n * elements implied by shape must be the same as the number of elements in\n * tensor.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.reshape([2, 2]).print();\n * ```\n *\n * @param x The input tensor to be reshaped.\n * @param shape An array of integers defining the output tensor shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction reshape_(x, shape) {\n    const $x = convertToTensor(x, 'x', 'reshape', null);\n    const inputs = { x: $x };\n    const attrs = { shape };\n    const forward = (backend, save) => {\n        shape = util.inferFromImplicitShape(shape, $x.size);\n        util.assert($x.size === util.sizeFromShape(shape), () => 'new shape and old shape must have the same number of elements.');\n        save([$x]);\n        return backend.reshape($x, shape);\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Reshape, attrs);\n}\nexport const reshape = op({ reshape_ });\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchMatMul } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the dot product of two matrices, A * B. These must be matrices.\n *\n * ```js\n * const a = tf.tensor2d([1, 2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.matMul(b).print();  // or tf.matMul(a, b)\n * ```\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction matMul_(a, b, transposeA = false, transposeB = false) {\n    let $a = convertToTensor(a, 'a', 'matMul');\n    let $b = convertToTensor(b, 'b', 'matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    util.assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in matMul: inputs must have the same rank of at least 2, ` +\n        `got ranks ${$a.rank} and ${$b.rank}.`);\n    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n    const outerDimsA = $a.shape.slice(0, -2);\n    const outerDimsB = $b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n    util.assert(util.arraysEqual(outerDimsA, outerDimsB), () => `Error in matMul: outer dimensions (${outerDimsA}) and (` +\n        `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} must match.`);\n    util.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (` +\n        `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} and transposeA=${transposeA}` +\n        ` and transposeB=${transposeB} must match.`);\n    const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n    const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n        reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n    const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n        reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n    const forward = (backend, save) => {\n        save([a3D, b3D]);\n        return backend.batchMatMul(a3D, b3D, transposeA, transposeB);\n    };\n    const inputs = { a: a3D, b: b3D };\n    const attrs = { transposeA, transposeB };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, BatchMatMul, attrs);\n    return reshape(res, outShape);\n}\nexport const matMul = op({ matMul_ });\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OneHot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank\n * `R+1` with the last axis of size `depth`.\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor` of indices with dtype `int32`.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n *     not match the location.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction oneHot_(indices, depth, onValue = 1, offValue = 0) {\n    if (depth < 2) {\n        throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n    }\n    const $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');\n    const outShape = [...$indices.shape, depth];\n    const forward = (backend, save) => {\n        save([$indices]);\n        return reshape(backend.oneHot(reshape($indices, [$indices.size]), depth, onValue, offValue), outShape);\n    };\n    const inputs = { indices: $indices };\n    const attrs = { depth, onValue, offValue };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, OneHot, attrs);\n}\nexport const oneHot = op({ oneHot_ });\n//# sourceMappingURL=one_hot.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Transpose } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print();  // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction transpose_(x, perm) {\n    const $x = convertToTensor(x, 'x', 'transpose');\n    if (perm == null) {\n        perm = $x.shape.map((s, i) => i).reverse();\n    }\n    util.assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n        `must match length of perm ${perm}.`);\n    perm.forEach(axis => {\n        util.assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n            ` but got ${perm}`);\n    });\n    if ($x.rank <= 1) {\n        return $x.clone();\n    }\n    const inputs = { x: $x };\n    const attrs = { perm };\n    return ENGINE.runKernelFunc(backend => backend.transpose($x, perm), inputs, null /* gradient */, Transpose, attrs);\n}\nexport const transpose = op({ transpose_ });\n//# sourceMappingURL=transpose.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { matMul } from './mat_mul';\nimport { oneHot } from './one_hot';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Computes the confusion matrix from true labels and predicted labels.\n *\n * ```js\n * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n * const numClasses = 3;\n * const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n * out.print();\n * // Expected output matrix:\n * // [[2, 0, 0],\n * //  [0, 1, 1],\n * //  [0, 0, 1]]\n * ```\n *\n * @param labels The target labels, assumed to be 0-based integers\n *   for the classes. The shape is `[numExamples]`, where\n *   `numExamples` is the number of examples included.\n * @param predictions The predicted classes, assumed to be\n *   0-based integers for the classes. Must have the same shape as `labels`.\n * @param numClasses Number of all classes, as an integer.\n *   Its value must be larger than the largest element in `labels` and\n *   `predictions`.\n * @returns The confusion matrix as a int32-type 2D tensor. The value at\n *   row `r` and column `c` is the number of times examples of actual class\n *   `r` were predicted as class `c`.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nexport function confusionMatrix_(labels, predictions, numClasses) {\n    const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');\n    const $predictions = convertToTensor(predictions, 'predictions', 'confusionMatrix');\n    util.assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, ` +\n        `but got ${numClasses}`);\n    util.assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n    util.assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, ` +\n        `but got ${$predictions.rank}`);\n    util.assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ` +\n        `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +\n        `Labels and predictions should have the same number of elements.`);\n    util.assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ` +\n        `${numClasses}`);\n    // TODO(cais): In the future, if oneHot supports tensors inputs for\n    //   `numClasses`, `confusionMatrix` can make `numClasses` optional.\n    const oneHotLabels = oneHot(cast($labels, 'int32'), numClasses);\n    const oneHotPredictions = oneHot(cast($predictions, 'int32'), numClasses);\n    const oneHotLabelsT = transpose(oneHotLabels);\n    return cast(matMul(oneHotLabelsT, oneHotPredictions), 'int32');\n}\nexport const confusionMatrix = op({ confusionMatrix_ });\n//# sourceMappingURL=confusion_matrix.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Exports under the tf.math.* namespace.\n */\nimport { confusionMatrix } from './ops/confusion_matrix';\nexport { confusionMatrix };\n//# sourceMappingURL=math.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor3d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided,  it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor3d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 3) {\n        throw new Error('tensor3d() requires shape to have three numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n        throw new Error('tensor3d() requires values to be number[][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor3d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor3d.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FromPixels } from '../kernel_names';\nimport { getKernel } from '../kernel_registry';\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { tensor3d } from './tensor3d';\nlet fromPixels2DContext;\n/**\n * Creates a `tf.Tensor` from an image.\n *\n * ```js\n * const image = new ImageData(1, 1);\n * image.data[0] = 100;\n * image.data[1] = 150;\n * image.data[2] = 200;\n * image.data[3] = 255;\n *\n * tf.browser.fromPixels(image).print();\n * ```\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel. You can also pass in an image\n * object with following attributes:\n * `{data: Uint8Array; width: number; height: number}`\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n *\n * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}\n */\nfunction fromPixels_(pixels, numChannels = 3) {\n    // Sanity checks.\n    if (numChannels > 4) {\n        throw new Error('Cannot construct Tensor with more than 4 channels from pixels.');\n    }\n    if (pixels == null) {\n        throw new Error('pixels passed to tf.browser.fromPixels() can not be null');\n    }\n    let isPixelData = false;\n    let isImageData = false;\n    let isVideo = false;\n    let isImage = false;\n    let isCanvasLike = false;\n    if (pixels.data instanceof Uint8Array) {\n        isPixelData = true;\n    }\n    else if (typeof (ImageData) !== 'undefined' && pixels instanceof ImageData) {\n        isImageData = true;\n    }\n    else if (typeof (HTMLVideoElement) !== 'undefined' &&\n        pixels instanceof HTMLVideoElement) {\n        isVideo = true;\n    }\n    else if (typeof (HTMLImageElement) !== 'undefined' &&\n        pixels instanceof HTMLImageElement) {\n        isImage = true;\n        // tslint:disable-next-line: no-any\n    }\n    else if (pixels.getContext != null) {\n        isCanvasLike = true;\n    }\n    else {\n        throw new Error('pixels passed to tf.browser.fromPixels() must be either an ' +\n            `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData ` +\n            `in browser, or OffscreenCanvas, ImageData in webworker` +\n            ` or {data: Uint32Array, width: number, height: number}, ` +\n            `but was ${pixels.constructor.name}`);\n    }\n    if (isVideo) {\n        const HAVE_CURRENT_DATA_READY_STATE = 2;\n        if (isVideo &&\n            pixels.readyState <\n                HAVE_CURRENT_DATA_READY_STATE) {\n            throw new Error('The video element has not loaded data yet. Please wait for ' +\n                '`loadeddata` event on the <video> element.');\n        }\n    }\n    // If the current backend has 'FromPixels' registered, it has a more\n    // efficient way of handling pixel uploads, so we call that.\n    const kernel = getKernel(FromPixels, ENGINE.backendName);\n    if (kernel != null) {\n        const inputs = { pixels };\n        const attrs = { numChannels };\n        return ENGINE.runKernel(FromPixels, inputs, attrs);\n    }\n    const [width, height] = isVideo ?\n        [\n            pixels.videoWidth,\n            pixels.videoHeight\n        ] :\n        [pixels.width, pixels.height];\n    let vals;\n    if (isCanvasLike) {\n        vals =\n            // tslint:disable-next-line:no-any\n            pixels.getContext('2d').getImageData(0, 0, width, height).data;\n    }\n    else if (isImageData || isPixelData) {\n        vals = pixels.data;\n    }\n    else if (isImage || isVideo) {\n        if (fromPixels2DContext == null) {\n            fromPixels2DContext = document.createElement('canvas').getContext('2d');\n        }\n        fromPixels2DContext.canvas.width = width;\n        fromPixels2DContext.canvas.height = height;\n        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n        vals = fromPixels2DContext.getImageData(0, 0, width, height).data;\n    }\n    let values;\n    if (numChannels === 4) {\n        values = new Int32Array(vals);\n    }\n    else {\n        const numPixels = width * height;\n        values = new Int32Array(numPixels * numChannels);\n        for (let i = 0; i < numPixels; i++) {\n            for (let channel = 0; channel < numChannels; ++channel) {\n                values[i * numChannels + channel] = vals[i * 4 + channel];\n            }\n        }\n    }\n    const outShape = [height, width, numChannels];\n    return tensor3d(values, outShape, 'int32');\n}\n/**\n * Draws a `tf.Tensor` of pixel values to a byte array or optionally a\n * canvas.\n *\n * When the dtype of the input is 'float32', we assume values in the range\n * [0-1]. Otherwise, when input is 'int32', we assume values in the range\n * [0-255].\n *\n * Returns a promise that resolves when the canvas has been drawn to.\n *\n * @param img A rank-2 or rank-3 tensor. If rank-2, draws grayscale. If\n *     rank-3, must have depth of 1, 3 or 4. When depth of 1, draws\n * grayscale. When depth of 3, we draw with the first three components of\n * the depth dimension corresponding to r, g, b and alpha = 1. When depth of\n * 4, all four components of the depth dimension correspond to r, g, b, a.\n * @param canvas The canvas to draw to.\n *\n * @doc {heading: 'Browser', namespace: 'browser'}\n */\nexport async function toPixels(img, canvas) {\n    let $img = convertToTensor(img, 'img', 'toPixels');\n    if (!(img instanceof Tensor)) {\n        // Assume int32 if user passed a native array.\n        const originalImgTensor = $img;\n        $img = cast(originalImgTensor, 'int32');\n        originalImgTensor.dispose();\n    }\n    if ($img.rank !== 2 && $img.rank !== 3) {\n        throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);\n    }\n    const [height, width] = $img.shape.slice(0, 2);\n    const depth = $img.rank === 2 ? 1 : $img.shape[2];\n    if (depth > 4 || depth === 2) {\n        throw new Error(`toPixels only supports depth of size ` +\n            `1, 3 or 4 but got ${depth}`);\n    }\n    if ($img.dtype !== 'float32' && $img.dtype !== 'int32') {\n        throw new Error(`Unsupported type for toPixels: ${$img.dtype}.` +\n            ` Please use float32 or int32 tensors.`);\n    }\n    const data = await $img.data();\n    const multiplier = $img.dtype === 'float32' ? 255 : 1;\n    const bytes = new Uint8ClampedArray(width * height * 4);\n    for (let i = 0; i < height * width; ++i) {\n        const rgba = [0, 0, 0, 255];\n        for (let d = 0; d < depth; d++) {\n            const value = data[i * depth + d];\n            if ($img.dtype === 'float32') {\n                if (value < 0 || value > 1) {\n                    throw new Error(`Tensor values for a float32 Tensor must be in the ` +\n                        `range [0 - 1] but encountered ${value}.`);\n                }\n            }\n            else if ($img.dtype === 'int32') {\n                if (value < 0 || value > 255) {\n                    throw new Error(`Tensor values for a int32 Tensor must be in the ` +\n                        `range [0 - 255] but encountered ${value}.`);\n                }\n            }\n            if (depth === 1) {\n                rgba[0] = value * multiplier;\n                rgba[1] = value * multiplier;\n                rgba[2] = value * multiplier;\n            }\n            else {\n                rgba[d] = value * multiplier;\n            }\n        }\n        const j = i * 4;\n        bytes[j + 0] = Math.round(rgba[0]);\n        bytes[j + 1] = Math.round(rgba[1]);\n        bytes[j + 2] = Math.round(rgba[2]);\n        bytes[j + 3] = Math.round(rgba[3]);\n    }\n    if (canvas != null) {\n        canvas.width = width;\n        canvas.height = height;\n        const ctx = canvas.getContext('2d');\n        const imageData = new ImageData(bytes, width, height);\n        ctx.putImageData(imageData, 0, 0);\n    }\n    if ($img !== img) {\n        $img.dispose();\n    }\n    return bytes;\n}\nexport const fromPixels = op({ fromPixels_ });\n//# sourceMappingURL=browser.js.map", "import { computeStrides } from '../util';\n/**\n * Validate gather nd inputs.\n *\n * @param tensor The tensor contains the source values.\n * @param indices The tensor contains the indices to slice the source.\n *\n * @returns [resultShape, numUpdates, sliceSize, strides]\n */\nexport function prepareAndValidate(tensor, indices) {\n    if (tensor.rank < 1) {\n        throw new Error('tf.gatherND() expects the input to be rank 1 or higher,' +\n            ` but the rank was ${tensor.rank}.`);\n    }\n    if (indices.rank < 1) {\n        throw new Error('tf.gatherND() expects the indices to be rank 1 or higher,' +\n            ` but the rank was ${indices.rank}.`);\n    }\n    if (indices.dtype !== 'int32') {\n        throw new Error('tf.gatherND() expects the indices to be int32 type,' +\n            ` but the dtype was ${indices.dtype}.`);\n    }\n    if (indices.shape[indices.rank - 1] > tensor.rank) {\n        throw new Error('index innermost dimension length must be <= tensor rank; saw: ' +\n            `${indices.shape[indices.rank - 1]} vs. ${tensor.rank}`);\n    }\n    if (tensor.size === 0) {\n        throw new Error('Requested more than 0 entries, but input is empty.' +\n            ` Input shape: ${tensor.shape}.`);\n    }\n    const indicesShape = indices.shape;\n    const sliceRank = indicesShape[indicesShape.length - 1];\n    // The result shape is\n    //   indices.shape[:-1] + params.shape[indices.shape[-1]:]\n    let nResult = 1;\n    for (let i = 0; i < indicesShape.length - 1; ++i) {\n        nResult *= indicesShape[i];\n    }\n    const inputShape = tensor.shape;\n    const resultShape = indicesShape.slice();\n    resultShape.pop();\n    let sliceSize = 1;\n    for (let i = sliceRank; i < tensor.rank; ++i) {\n        sliceSize *= inputShape[i];\n        resultShape.push(inputShape[i]);\n    }\n    const strides = [...computeStrides(tensor.shape).map(stride => stride / sliceSize),\n        1].slice(0, sliceRank);\n    return [resultShape, nResult, sliceSize, strides];\n}\n//# sourceMappingURL=gather_nd_util.js.map", "import { computeStrides, sizeFromShape } from '../util';\n/**\n * Check whether updates.shape = indices.shape[:batchDim] +\n * shape[sliceDim:]\n *\n * @param x The input tensor.\n */\nexport function validateUpdateShape(shape, indices, updates) {\n    const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n    const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;\n    const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +\n        `shape[sliceDim:], got updates.shape: ${updates.shape}` +\n        `, indices.shape: ${indices.shape}, shape: ${shape}` +\n        `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n    if (updates.rank < batchDim) {\n        throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n    }\n    if (shape.length < sliceDim + (updates.rank - batchDim)) {\n        throw new Error(shapeError +\n            ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n    }\n    if (updates.rank !== batchDim + shape.length - sliceDim) {\n        throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n    }\n    for (let d = 0; d < batchDim; ++d) {\n        if (updates.shape[d] !== indices.shape[d]) {\n            throw new Error(shapeError +\n                ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);\n        }\n    }\n    for (let d = 0; d < updates.rank - batchDim; ++d) {\n        if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n            throw new Error(shapeError +\n                ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);\n        }\n    }\n}\n/**\n * Validate scatter nd inputs.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n */\nexport function validateInput(updates, indices, shape) {\n    if (indices.rank < 1) {\n        throw new Error('tf.scatterND() expects the indices to be rank 1 or higher,' +\n            ` but the rank was ${indices.rank}.`);\n    }\n    if (updates.rank < 1) {\n        throw new Error('tf.scatterND() expects the updates to be rank 1 or higher,' +\n            ` but the rank was ${updates.rank}.`);\n    }\n    if (indices.dtype !== 'int32') {\n        throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);\n    }\n    if (shape.length < 1) {\n        throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);\n    }\n    if (shape.length === 0) {\n        if (indices.size === 0) {\n            throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);\n        }\n        if (updates.size === 0) {\n            throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);\n        }\n    }\n    validateUpdateShape(shape, indices, updates);\n}\n/**\n * Calculate the shape information for the output.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n *\n * @returns ScatterShapeInfo\n */\nexport function calculateShapes(updates, indices, shape) {\n    // Calculate the number of dimensions in indices\n    const indicesRank = indices.shape.length;\n    const sliceRank = (indicesRank > 1) ? indices.shape[indicesRank - 1] : 1;\n    // Calculate the number of elements that make up each slice of our updated\n    // tensor. This allows us to work with flattened tensors and copy over whole\n    // slices at a time.\n    const totalNd = shape.length;\n    let sliceSize = 1;\n    for (let i = sliceRank; i < totalNd; ++i) {\n        sliceSize *= shape[i];\n    }\n    const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;\n    const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;\n    const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];\n    const outputSize = sizeFromShape(shape);\n    return { sliceRank, numUpdates, sliceSize, strides, outputSize };\n}\n//# sourceMappingURL=scatter_nd_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nexport function assertParamsValid(input, begin, size) {\n    const inputRank = input.shape.length;\n    util.assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must ` +\n        `match the rank of the array (${inputRank}).`);\n    util.assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must ` +\n        `match the rank of the array (${inputRank}).`);\n    for (let i = 0; i < inputRank; ++i) {\n        util.assert(begin[i] + size[i] <= input.shape[i], () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] ` +\n            `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${input.shape[i]})`);\n    }\n}\n/** Converts a binary mask to an array of axes. Used in stridedSlice(). */\nexport function maskToAxes(mask) {\n    const axes = [];\n    let axis = 0;\n    while (mask > 0) {\n        if (mask & 1) {\n            axes.push(axis);\n        }\n        mask /= 2;\n        axis++;\n    }\n    return axes;\n}\n/** Computes the output shape given the strided slice params. */\nexport function computeOutShape(begin, end, strides) {\n    const size = [];\n    for (let axis = 0; axis < begin.length; axis++) {\n        size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);\n    }\n    return size;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stride value. Otherwise, insert.\nexport function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {\n    const newStrides = [...strides];\n    for (let i = newStrides.length; i < inputShape.length; i++) {\n        newStrides.push(1);\n    }\n    for (let i = 0; i < numElidedAxes; i++) {\n        if (i === 0) {\n            newStrides[ellipsisInsertionIndex] = 1;\n        }\n        else {\n            newStrides.splice(ellipsisInsertionIndex, 0 /* num elements to delete */, 1 /* element to add */);\n            newStrides.pop();\n        }\n    }\n    return newStrides;\n}\nfunction unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {\n    if (normalizedAxis <= ellipsisInsertionIndex) {\n        return normalizedAxis;\n    }\n    return normalizedAxis - (numElidedAxes - 1);\n}\nfunction getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {\n    const elidedAxes = [];\n    for (let i = 0; i < numElidedAxes; i++) {\n        elidedAxes.push(ellipsisInsertionIndex + i);\n    }\n    return elidedAxes;\n}\n// Normalize the start, end and strides.\nexport function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {\n    const inputRank = inputShape.length;\n    let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);\n    if (ellipsisAxes.length && numInterpolatedAxes > 0) {\n        const fullIndex = ellipsisAxes[0];\n        // The ellipsis applies to the masked index as well as any dimensions\n        // that are interpolated.\n        const numElidedAxes = numInterpolatedAxes + 1;\n        normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);\n        normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);\n        normalizedStrides =\n            stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);\n    }\n    else {\n        for (let axis = 0; axis < inputRank; axis++) {\n            normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);\n            normalizedEnd[axis] =\n                stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);\n            normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);\n        }\n    }\n    return {\n        begin: normalizedBegin,\n        end: normalizedEnd,\n        strides: normalizedStrides\n    };\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current start value. Otherwise, insert.\nexport function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {\n    const newIndices = [...inputShape];\n    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n    for (let axis = 0; axis < newIndices.length; axis++) {\n        if (elidedAxes.indexOf(axis) > -1) {\n            newIndices[axis] = 0;\n        }\n        else {\n            const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n            let originalValue = originalBegin[originalAxis];\n            if (beginMask & 1 << originalAxis) {\n                originalValue = 0;\n            }\n            newIndices[axis] = originalValue;\n        }\n    }\n    return newIndices;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stop value. Otherwise, insert.\nexport function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {\n    const newIndices = [...inputShape];\n    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n    for (let axis = 0; axis < newIndices.length; axis++) {\n        if (elidedAxes.indexOf(axis) > -1) {\n            newIndices[axis] = Number.MAX_SAFE_INTEGER;\n        }\n        else {\n            const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n            let originalValue = originalEnd[originalAxis];\n            if (endMask & 1 << originalAxis) {\n                originalValue = Number.MAX_SAFE_INTEGER;\n            }\n            newIndices[axis] = originalValue;\n        }\n    }\n    for (let i = 0; i < newIndices.length; i++) {\n        // Handle negative indices\n        const axisSize = inputShape[i];\n        if (newIndices[i] < 0) {\n            newIndices[i] += axisSize;\n        }\n        newIndices[i] = util.clamp(0, newIndices[i], inputShape[i]);\n    }\n    return newIndices;\n}\nexport function stridesForAxis(strides, axis, ellipsisMask) {\n    let stride = strides[axis];\n    if (ellipsisMask & (1 << axis) || stride == null) {\n        stride = 1;\n    }\n    return stride;\n}\nexport function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {\n    // Begin with the specified index\n    let start = startIndices[axis];\n    const stride = strides[axis] || 1;\n    // Check the axis bit from right of masked axes, or the begin index is not set\n    // for the axis.\n    if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {\n        if (stride > 0) {\n            // Forward iteration - use the first element. These values will get\n            // clamped below (Note: We could have set them to 0 and axis_size-1, but\n            // use lowest() and max() to maintain symmetry with StopForAxis())\n            start = Number.MIN_SAFE_INTEGER;\n        }\n        else {\n            // Backward iteration - use the last element.\n            start = Number.MAX_SAFE_INTEGER;\n        }\n    }\n    // Handle negative indices\n    const axisSize = inputShape[axis];\n    if (start < 0) {\n        start += axisSize;\n    }\n    // Clamping\n    start = util.clamp(0, start, axisSize - 1);\n    return start;\n}\nexport function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {\n    // Begin with the specified index\n    let stop = stopIndices[axis];\n    const stride = strides[axis] || 1;\n    // Check the axis bit from right of masked axes, or if the stop index is not\n    // set for this axis.\n    if (endMask & (1 << axis) || ellipsisMask & (1 << axis) || stop == null) {\n        if (stride > 0) {\n            // Forward iteration - use the last element. These values will get\n            // clamped below\n            stop = Number.MAX_SAFE_INTEGER;\n        }\n        else {\n            // Backward iteration - use the first element.\n            stop = Number.MIN_SAFE_INTEGER;\n        }\n    }\n    // Handle negative indices\n    const axisSize = inputShape[axis];\n    if (stop < 0) {\n        stop += axisSize;\n    }\n    // Clamping\n    // Because the end index points one past the last element, we need slightly\n    // different clamping ranges depending on the direction.\n    if (stride > 0) {\n        // Forward iteration\n        stop = util.clamp(0, stop, axisSize);\n    }\n    else {\n        // Backward iteration\n        stop = util.clamp(-1, stop, axisSize - 1);\n    }\n    return stop;\n}\n/**\n * Returns true if the slice occupies a continous set of elements in the\n * 'flat' space.\n */\nexport function isSliceContinous(shape, begin, size) {\n    // Index of the first axis that has size > 1.\n    let firstNonOneAxis = size.length;\n    for (let i = 0; i < size.length; i++) {\n        if (size[i] > 1) {\n            firstNonOneAxis = i;\n            break;\n        }\n    }\n    for (let i = firstNonOneAxis + 1; i < size.length; i++) {\n        if (begin[i] > 0 || size[i] !== shape[i]) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function computeFlatOffset(begin, strides) {\n    let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;\n    for (let i = 0; i < begin.length - 1; i++) {\n        flatOffset += begin[i] * strides[i];\n    }\n    return flatOffset;\n}\nexport function parseSliceParams(x, begin, size) {\n    // The following logic allows for more ergonomic calls.\n    let begin_;\n    const xRank = x.shape.length;\n    if (typeof begin === 'number') {\n        begin_ = [begin, ...new Array(xRank - 1).fill(0)];\n    }\n    else if (begin.length < xRank) {\n        begin_ = begin.concat(new Array(xRank - begin.length).fill(0));\n    }\n    else {\n        begin_ = begin.slice();\n    }\n    begin_.forEach(d => {\n        util.assert(d !== -1, () => 'slice() does not support negative begin indexing.');\n    });\n    let size_;\n    if (size == null) {\n        size_ = new Array(xRank).fill(-1);\n    }\n    else if (typeof size === 'number') {\n        size_ = [size, ...new Array(xRank - 1).fill(-1)];\n    }\n    else if (size.length < xRank) {\n        size_ = size.concat(new Array(xRank - size.length).fill(-1));\n    }\n    else {\n        size_ = size;\n    }\n    size_ = size_.map((d, i) => {\n        if (d >= 0) {\n            return d;\n        }\n        else {\n            util.assert(d === -1, () => `Negative size values should be exactly -1 but got ` +\n                `${d} for the slice() size at index ${i}.`);\n            return x.shape[i] - begin_[i];\n        }\n    });\n    return [begin_, size_];\n}\n//# sourceMappingURL=slice_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from './util';\n/**\n * Serializable defines the serialization contract.\n *\n * TFJS requires serializable classes to return their className when asked\n * to avoid issues with minification.\n */\nexport class Serializable {\n    /**\n     * Return the class name for this class to use in serialization contexts.\n     *\n     * Generally speaking this will be the same thing that constructor.name\n     * would have returned.  However, the class name needs to be robust\n     * against minification for serialization/deserialization to work properly.\n     *\n     * There's also places such as initializers.VarianceScaling, where\n     * implementation details between different languages led to different\n     * class hierarchies and a non-leaf node is used for serialization purposes.\n     */\n    getClassName() {\n        return this.constructor\n            .className;\n    }\n    /**\n     * Creates an instance of T from a ConfigDict.\n     *\n     * This works for most descendants of serializable.  A few need to\n     * provide special handling.\n     * @param cls A Constructor for the class to instantiate.\n     * @param config The Configuration for the object.\n     */\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/**\n * Maps string keys to class constructors.\n *\n * Used during (de)serialization from the cross-language JSON format, which\n * requires the class name in the serialization format matches the class\n * names as used in Python, should it exist.\n */\nexport class SerializationMap {\n    constructor() {\n        this.classNameMap = {};\n    }\n    /**\n     * Returns the singleton instance of the map.\n     */\n    static getMap() {\n        if (SerializationMap.instance == null) {\n            SerializationMap.instance = new SerializationMap();\n        }\n        return SerializationMap.instance;\n    }\n    /**\n     * Registers the class as serializable.\n     */\n    static register(cls) {\n        SerializationMap.getMap().classNameMap[cls.className] =\n            [cls, cls.fromConfig];\n    }\n}\n/**\n * Register a class with the serialization map of TensorFlow.js.\n *\n * This is often used for registering custom Layers, so they can be\n * serialized and deserialized.\n *\n * Example:\n *\n * ```js\n * class MyCustomLayer extends tf.layers.Layer {\n *   static className = 'MyCustomLayer';\n *\n *   constructor(config) {\n *     super(config);\n *   }\n * }\n * tf.serialization.registerClass(MyCustomLayer);\n * ```\n *\n * @param cls The class to be registered. It must have a public static member\n *   called `className` defined and the value must be a non-empty string.\n *\n * @doc {heading: 'Models', subheading: 'Serialization', ignoreCI: true}\n */\nexport function registerClass(cls) {\n    assert(cls.className != null, () => `Class being registered does not have the static className ` +\n        `property defined.`);\n    assert(typeof cls.className === 'string', () => `className is required to be a string, but got type ` +\n        typeof cls.className);\n    assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, ` +\n        `which is disallowed.`);\n    SerializationMap.register(cls);\n}\n//# sourceMappingURL=serialization.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { inferShape } from './tensor_util_env';\nimport { arraysEqual, flatten, isString, isTypedArray } from './util';\nconst TEST_EPSILON_FLOAT32 = 1e-3;\nexport const TEST_EPSILON_FLOAT16 = 1e-1;\nexport function expectArraysClose(actual, expected, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon));\n}\nexport function testEpsilon() {\n    return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :\n        TEST_EPSILON_FLOAT16;\n}\nfunction expectArraysPredicate(actual, expected, predicate) {\n    let checkClassType = true;\n    if (isTypedArray(actual) || isTypedArray(expected)) {\n        checkClassType = false;\n    }\n    if (isTypedArray(actual) && isTypedArray(expected)) {\n        checkClassType = true;\n    }\n    if (checkClassType) {\n        const aType = actual.constructor.name;\n        const bType = expected.constructor.name;\n        if (aType !== bType) {\n            throw new Error(`Arrays are of different type. Actual: ${aType}. ` +\n                `Expected: ${bType}`);\n        }\n    }\n    if (Array.isArray(actual) && Array.isArray(expected)) {\n        const actualShape = inferShape(actual);\n        const expectedShape = inferShape(expected);\n        if (!arraysEqual(actualShape, expectedShape)) {\n            throw new Error(`Arrays have different shapes. ` +\n                `Actual: [${actualShape}]. Expected: [${expectedShape}]`);\n        }\n    }\n    const actualFlat = isTypedArray(actual) ? actual : flatten(actual);\n    const expectedFlat = isTypedArray(expected) ?\n        expected :\n        flatten(expected);\n    if (actualFlat.length !== expectedFlat.length) {\n        throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs ` +\n            `expected: ${expectedFlat.length}.\\n` +\n            `Actual:   ${actualFlat}.\\n` +\n            `Expected: ${expectedFlat}.`);\n    }\n    for (let i = 0; i < expectedFlat.length; ++i) {\n        const a = actualFlat[i];\n        const e = expectedFlat[i];\n        if (!predicate(a, e)) {\n            throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\\n` +\n                `Actual:   ${actualFlat}.\\n` +\n                `Expected: ${expectedFlat}.`);\n        }\n    }\n}\nexport function expectPromiseToFail(fn, done) {\n    fn().then(() => done.fail(), () => done());\n}\nexport function expectArraysEqual(actual, expected) {\n    const exp = typeof expected === 'string' || typeof expected === 'number' ||\n        typeof expected === 'boolean' ?\n        [expected] :\n        expected;\n    if (isString(actual) || isString(actual[0]) ||\n        isString(expected) || isString(expected[0])) {\n        // tslint:disable-next-line: triple-equals\n        return expectArraysPredicate(actual, exp, (a, b) => a == b);\n    }\n    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));\n}\nexport function expectNumbersClose(a, e, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    if (!areClose(a, e, epsilon)) {\n        throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n    }\n}\nfunction areClose(a, e, epsilon) {\n    if (!isFinite(a) && !isFinite(e)) {\n        return true;\n    }\n    if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {\n        return false;\n    }\n    return true;\n}\nexport function expectValuesInRange(actual, low, high) {\n    for (let i = 0; i < actual.length; i++) {\n        if (actual[i] < low || actual[i] > high) {\n            throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);\n        }\n    }\n}\nexport function expectArrayBuffersEqual(actual, expected) {\n    // Safari & Jasmine don't like comparing ArrayBuffers directly. Wrapping in\n    // a Float32Array solves this issue.\n    expect(new Float32Array(actual)).toEqual(new Float32Array(expected));\n}\n//# sourceMappingURL=test_util.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { env } from './environment';\nimport { setDeprecationWarningFn } from './tensor';\nimport { getTensorsInContainer } from './tensor_util';\n/**\n * Enables production mode which disables correctness checks in favor of\n * performance.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableProdMode() {\n    env().set('PROD', true);\n}\n/**\n * Enables debug mode which will log information about all executed kernels:\n * the elapsed time of the kernel execution, as well as the rank, shape, and\n * size of the output tensor.\n *\n * Debug mode will significantly slow down your application as it will\n * download the result of every operation to the CPU. This should not be used in\n * production. Debug mode does not affect the timing information of the kernel\n * execution as we do not measure download time in the kernel execution time.\n *\n * See also: `tf.profile`, `tf.memory`.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableDebugMode() {\n    env().set('DEBUG', true);\n}\n/** Globally disables deprecation warnings */\nexport function disableDeprecationWarnings() {\n    env().set('DEPRECATION_WARNINGS_ENABLED', false);\n    console.warn(`TensorFlow.js deprecation warnings have been disabled.`);\n}\n/** Warn users about deprecated functionality. */\nexport function deprecationWarn(msg) {\n    if (env().getBool('DEPRECATION_WARNINGS_ENABLED')) {\n        console.warn(msg + ' You can disable deprecation warnings with ' +\n            'tf.disableDeprecationWarnings().');\n    }\n}\nsetDeprecationWarningFn(deprecationWarn);\n/**\n * Dispose all variables kept in backend engine.\n *\n * @doc {heading: 'Environment'}\n */\nexport function disposeVariables() {\n    ENGINE.disposeVariables();\n}\n/**\n * It returns the global engine that keeps track of all tensors and backends.\n *\n * @doc {heading: 'Environment'}\n */\nexport function engine() {\n    return ENGINE;\n}\n/**\n * Returns memory info at the current time in the program. The result is an\n * object with the following properties:\n *\n * - `numBytes`: Number of bytes allocated (undisposed) at this time.\n * - `numTensors`: Number of unique tensors allocated.\n * - `numDataBuffers`: Number of unique data buffers allocated\n *   (undisposed) at this time, which is \u2264 the number of tensors\n *   (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same\n *   data buffer with `a`).\n * - `unreliable`: True if the memory usage is unreliable. See `reasons` when\n *    `unreliable` is true.\n * - `reasons`: `string[]`, reasons why the memory is unreliable, present if\n *    `unreliable` is true.\n *\n * WebGL Properties:\n * - `numBytesInGPU`: Number of bytes allocated (undisposed) in the GPU only at\n *     this time.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function memory() {\n    return ENGINE.memory();\n}\n/**\n * Executes the provided function `f()` and returns a promise that resolves\n * with information about the function's memory use:\n * - `newBytes`: the number of new bytes allocated\n * - `newTensors`: the number of new tensors created\n * - `peakBytes`: the peak number of bytes allocated\n * - `kernels`: an array of objects for each kernel involved that reports\n * their input and output shapes, number of bytes used, and number of new\n * tensors created.\n *\n * ```js\n * const profile = await tf.profile(() => {\n *   const x = tf.tensor1d([1, 2, 3]);\n *   let x2 = x.square();\n *   x2.dispose();\n *   x2 = x.square();\n *   x2.dispose();\n *   return x;\n * });\n *\n * console.log(`newBytes: ${profile.newBytes}`);\n * console.log(`newTensors: ${profile.newTensors}`);\n * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>\n * k.totalBytesSnapshot)}`);\n * ```\n *\n *\n * @doc {heading: 'Performance', subheading: 'Profile'}\n */\nexport function profile(f) {\n    return ENGINE.profile(f);\n}\n/**\n * Executes the provided function `fn` and after it is executed, cleans up all\n * intermediate tensors allocated by `fn` except those returned by `fn`.\n * `fn` must not return a Promise (async functions not allowed). The returned\n * result can be a complex object.\n *\n * Using this method helps avoid memory leaks. In general, wrap calls to\n * operations in `tf.tidy` for automatic memory cleanup.\n *\n * NOTE: Variables do *not* get cleaned up when inside a tidy(). If you want to\n * dispose variables, please use `tf.disposeVariables` or call dispose()\n * directly on variables.\n *\n * ```js\n * // y = 2 ^ 2 + 1\n * const y = tf.tidy(() => {\n *   // a, b, and one will be cleaned up when the tidy ends.\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *   const b = a.square();\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * y.print();\n * ```\n *\n * @param nameOrFn The name of the closure, or the function to execute.\n *     If a name is provided, the 2nd argument should be the function.\n *     If debug mode is on, the timing and the memory usage of the function\n *     will be tracked and displayed on the console using the provided name.\n * @param fn The function to execute.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function tidy(nameOrFn, fn) {\n    return ENGINE.tidy(nameOrFn, fn);\n}\n/**\n * Disposes any `tf.Tensor`s found within the provided object.\n *\n * @param container an object that may be a `tf.Tensor` or may directly\n *     contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If\n *     the object is not a `tf.Tensor` or does not contain `Tensors`, nothing\n *     happens. In general it is safe to pass any object here, except that\n *     `Promise`s are not supported.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function dispose(container) {\n    const tensors = getTensorsInContainer(container);\n    tensors.forEach(tensor => tensor.dispose());\n}\n/**\n * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed\n * automatically.\n *\n * ```js\n * let b;\n * const y = tf.tidy(() => {\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *\n *   // b will not be cleaned up by the tidy. a and one will be cleaned up\n *   // when the tidy ends.\n *   b = tf.keep(a.square());\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * console.log('y:');\n * y.print();\n * console.log('b:');\n * b.print();\n * ```\n *\n * @param result The tensor to keep from being disposed.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function keep(result) {\n    return ENGINE.keep(result);\n}\n/**\n * Executes `f()` and returns a promise that resolves with timing\n * information.\n *\n * The result is an object with the following properties:\n *\n * - `wallMs`: Wall execution time.\n * - `kernelMs`: Kernel execution time, ignoring data transfer. If using the\n * WebGL backend and the query timer extension is not available, this will\n * return an error object.\n * - On `WebGL` The following additional properties exist:\n *   - `uploadWaitMs`: CPU blocking time on texture uploads.\n *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).\n *\n * ```js\n * const x = tf.randomNormal([20, 20]);\n * const time = await tf.time(() => x.matMul(x));\n *\n * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);\n * ```\n *\n * @param f The function to execute and time.\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nexport function time(f) {\n    return ENGINE.time(f);\n}\n/**\n * Sets the backend (cpu, webgl, wasm, etc) responsible for creating tensors and\n * executing operations on those tensors. Returns a promise that resolves\n * to a boolean if the backend initialization was successful.\n *\n * Note this disposes the current backend, if any, as well as any tensors\n * associated with it. A new backend is initialized, even if it is of the\n * same type as the previous one.\n *\n * @param backendName The name of the backend. Currently supports\n *     `'webgl'|'cpu'` in the browser, `'tensorflow'` under node.js\n *     (requires tfjs-node), and `'wasm'` (requires tfjs-backend-wasm).\n *\n * @doc {heading: 'Backends'}\n */\nexport function setBackend(backendName) {\n    return ENGINE.setBackend(backendName);\n}\n/**\n * Returns a promise that resolves when the currently selected backend (or the\n * highest priority one) has initialized. Await this promise when you are using\n * a backend that has async initialization.\n *\n * @doc {heading: 'Backends'}\n */\nexport function ready() {\n    return ENGINE.ready();\n}\n/**\n * Returns the current backend name (cpu, webgl, etc). The backend is\n * responsible for creating tensors and executing operations on those tensors.\n *\n * @doc {heading: 'Backends'}\n */\nexport function getBackend() {\n    return ENGINE.backendName;\n}\n/**\n * Removes a backend and the registered factory.\n *\n * @doc {heading: 'Backends'}\n */\nexport function removeBackend(name) {\n    ENGINE.removeBackend(name);\n}\n/**\n * Finds the backend registered under the provided name. Returns null if the\n * name is not in the registry, or the registration hasn't finished yet.\n */\nexport function findBackend(name) {\n    return ENGINE.findBackend(name);\n}\n/**\n * Finds the backend factory registered under the provided name. Returns a\n * function that produces a new backend when called. Returns null if the name\n * is not in the registry.\n */\nexport function findBackendFactory(name) {\n    return ENGINE.findBackendFactory(name);\n}\n/**\n * Registers a global backend. The registration should happen when importing\n * a module file (e.g. when importing `backend_webgl.ts`), and is used for\n * modular builds (e.g. custom tfjs bundle with only webgl support).\n *\n * @param factory The backend factory function. When called, it should\n * return a backend instance, or a promise of an instance.\n * @param priority The priority of the backend (higher = more important).\n *     In case multiple backends are registered, the priority is used to find\n *     the best backend. Defaults to 1.\n * @return False if there is already a registered backend under this name, true\n *     if not.\n *\n * @doc {heading: 'Backends'}\n */\nexport function registerBackend(name, factory, priority = 1) {\n    return ENGINE.registerBackend(name, factory, priority);\n}\n/**\n * Gets the current backend. If no backends have been initialized, this will\n * attempt to initialize the best backend. Will throw an error if the highest\n * priority backend has async initialization, in which case, you should call\n * 'await tf.ready()' before running other code.\n *\n * @doc {heading: 'Backends'}\n */\nexport function backend() {\n    return ENGINE.backend;\n}\n/**\n * Sets the global platform.\n *\n * @param platformName The name of this platform.\n * @param platform A platform implementation.\n */\nexport function setPlatform(platformName, platform) {\n    env().setPlatform(platformName, platform);\n}\n//# sourceMappingURL=globals.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Add } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n *\n * ```js\n * // Broadcast add a with b.\n * const a = tf.scalar(5);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n * @param a The first `tf.Tensor` to add.\n * @param b The second `tf.Tensor` to add. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction add_(a, b) {\n    let $a = convertToTensor(a, 'a', 'add');\n    let $b = convertToTensor(b, 'b', 'add');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.add($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Add);\n}\nexport const add = op({ add_ });\n//# sourceMappingURL=add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FloorDiv } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n * The result is rounded with floor function.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.floorDiv(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.floorDiv(b).print();  // or tf.floorDiv(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction floorDiv_(a, b) {\n    let $a = convertToTensor(a, 'a', 'floorDiv');\n    let $b = convertToTensor(b, 'b', 'floorDiv');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.floorDiv($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, FloorDiv);\n}\nexport const floorDiv = op({ floorDiv_ });\n//# sourceMappingURL=floorDiv.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Div } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { floorDiv } from './floorDiv';\nimport { op } from './operation';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction div_(a, b) {\n    let $a = convertToTensor(a, 'a', 'div');\n    let $b = convertToTensor(b, 'b', 'div');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'int32' && $b.dtype === 'int32') {\n        return floorDiv($a, $b);\n    }\n    const forward = (backend, save) => {\n        const res = backend.realDivide($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = {};\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Div, attrs);\n}\nexport const div = op({ div_ });\n//# sourceMappingURL=div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Multiply } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.\n *\n * We also expose `tf.mulStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([2, 3, 4, 5]);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n *\n * ```js\n * // Broadcast mul a with b.\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.scalar(5);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n * @param a The first tensor to multiply.\n * @param b The second tensor to multiply. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mul_(a, b) {\n    let $a = convertToTensor(a, 'a', 'mul');\n    let $b = convertToTensor(b, 'b', 'mul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.multiply($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Multiply);\n}\nexport const mul = op({ mul_ });\n//# sourceMappingURL=mul.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Abs } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes absolute value element-wise: `abs(x)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.abs().print();  // or tf.abs(x)\n * ```\n * @param x The input `tf.Tensor`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction abs_(x) {\n    const $x = convertToTensor(x, 'x', 'abs');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'complex64') {\n            return backend.complexAbs($x);\n        }\n        return backend.abs($x);\n    }, inputs, null /* grad */, Abs);\n}\nexport const abs = op({ abs_ });\n//# sourceMappingURL=abs.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.acos().print();  // or tf.acos(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acos_(x) {\n    const $x = convertToTensor(x, 'x', 'acos');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.acos($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Acos);\n}\nexport const acos = op({ acos_ });\n//# sourceMappingURL=acos.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:\n * `acosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([10, 1, 3, 5.7]);\n *\n * x.acosh().print();  // or tf.acosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acosh_(x) {\n    const $x = convertToTensor(x, 'x', 'acosh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.acosh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Acosh);\n}\nexport const acosh = op({ acosh_ });\n//# sourceMappingURL=acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AddN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n *\n * tf.addN([a, b, c]).print();\n * ```\n * @param tensors A list of tensors with the same shape and dtype.\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction addN_(tensors) {\n    util.assert(Array.isArray(tensors), () => 'The argument passed to tf.addN() must be a list of tensors');\n    util.assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ` +\n        `${tensors.length}`);\n    const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));\n    const firstTensor = $tensors[0];\n    $tensors.forEach(t => {\n        if (t.dtype !== firstTensor.dtype) {\n            throw new Error('All tensors passed to tf.addN() must have the same dtype');\n        }\n    });\n    $tensors.forEach(t => {\n        if (!util.arraysEqual(t.shape, firstTensor.shape)) {\n            throw new Error('All tensors passed to tf.addN() must have the same shape');\n        }\n    });\n    const forward = (backend, save) => {\n        const res = backend.addN($tensors);\n        save($tensors);\n        return res;\n    };\n    const inputs = $tensors;\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, AddN);\n}\nexport const addN = op({ addN_ });\n//# sourceMappingURL=add_n.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n * Returns true if the axis specifies the inner most dimensions of the\n * array.\n */\nexport function axesAreInnerMostDims(axes, rank) {\n    for (let i = 0; i < axes.length; ++i) {\n        if (axes[axes.length - i - 1] !== rank - 1 - i) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function combineLocations(outputLoc, reduceLoc, axes) {\n    const rank = outputLoc.length + reduceLoc.length;\n    const loc = [];\n    let outIdx = 0;\n    let reduceIdx = 0;\n    for (let dim = 0; dim < rank; dim++) {\n        if (axes.indexOf(dim) === -1) {\n            loc.push(outputLoc[outIdx++]);\n        }\n        else {\n            loc.push(reduceLoc[reduceIdx++]);\n        }\n    }\n    return loc;\n}\nexport function computeOutAndReduceShapes(aShape, axes) {\n    const outShape = [];\n    const rank = aShape.length;\n    for (let dim = 0; dim < rank; dim++) {\n        if (axes.indexOf(dim) === -1) {\n            outShape.push(aShape[dim]);\n        }\n    }\n    const reduceShape = axes.map(dim => aShape[dim]);\n    return [outShape, reduceShape];\n}\nexport function expandShapeToKeepDim(shape, axes) {\n    const reduceSubShape = axes.map(x => 1);\n    return combineLocations(shape, reduceSubShape, axes);\n}\nexport function assertAxesAreInnerMostDims(msg, axes, rank) {\n    util.assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. ` +\n        `Got axes ${axes} and rank-${rank} input.`);\n}\n/**\n * Returns the axes permutation to be used with `tf.transpose`, if such\n * permutation is necessary. Otherwise it returns null. This method is used by\n * operations that operate only on inner-most axes.\n */\nexport function getAxesPermutation(axes, rank) {\n    if (axesAreInnerMostDims(axes, rank)) {\n        return null;\n    }\n    const result = [];\n    for (let i = 0; i < rank; ++i) {\n        if (axes.indexOf(i) === -1) {\n            result.push(i);\n        }\n    }\n    axes.forEach(axis => result.push(axis));\n    return result;\n}\n/** Returns the axes permutation that undoes the original permutation. */\nexport function getUndoAxesPermutation(axes) {\n    return axes.map((axis, i) => [i, axis])\n        .sort((a, b) => a[1] - b[1])\n        .map(x => x[0]);\n}\nexport function getInnerMostAxes(numAxes, rank) {\n    const res = [];\n    for (let i = rank - numAxes; i < rank; ++i) {\n        res.push(i);\n    }\n    return res;\n}\n//# sourceMappingURL=axis_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { All } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the logical and of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.all().print();  // or tf.all(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.all(axis).print();  // or tf.all(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction all_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'all', 'bool');\n    const forward = (backend) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = getInnerMostAxes(axes.length, $x.rank);\n        }\n        const res = backend.all($x, axes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(res.shape, origAxes);\n            return reshape(res, newShape);\n        }\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, All, attrs);\n}\nexport const all = op({ all_ });\n//# sourceMappingURL=all.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Any } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the logical or of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.any().print();  // or tf.any(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.any(axis).print();  // or tf.any(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction any_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'any', 'bool');\n    const forward = (backend) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = getInnerMostAxes(axes.length, $x.rank);\n        }\n        const res = backend.any($x, axes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(res.shape, origAxes);\n            return reshape(res, newShape);\n        }\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Any, attrs);\n}\n// tslint:disable-next-line:variable-name\nexport const any = op({ any_ });\n//# sourceMappingURL=any.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Returns the indices of the maximum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMax().print();  // or tf.argMax(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMax(axis).print();  // or tf.argMax(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMax_(x, axis = 0) {\n    let $x = convertToTensor(x, 'x', 'argMax');\n    const forward = (backend, save) => {\n        save([$x]);\n        let axes = util.parseAxisParam(axis, $x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        return backend.argMax($x, axes[0]);\n    };\n    const inputs = { x: $x };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ArgMax, attrs);\n}\nexport const argMax = op({ argMax_ });\n//# sourceMappingURL=arg_max.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Returns the indices of the minimum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMin().print();  // or tf.argMin(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMin(axis).print();  // or tf.argMin(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMin_(x, axis = 0) {\n    let $x = convertToTensor(x, 'x', 'argMin');\n    const forward = (backend, save) => {\n        save([$x]);\n        if (axis == null) {\n            axis = 0;\n        }\n        let axes = util.parseAxisParam(axis, $x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        return backend.argMin($x, axes[0]);\n    };\n    const inputs = { x: $x };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ArgMin, attrs);\n}\nexport const argMin = op({ argMin_ });\n//# sourceMappingURL=arg_min.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asin().print();  // or tf.asin(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asin_(x) {\n    const $x = convertToTensor(x, 'x', 'asin');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.asin($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Asin);\n}\nexport const asin = op({ asin_ });\n//# sourceMappingURL=asin.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:\n * `asinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asinh().print();  // or tf.asinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asinh_(x) {\n    const $x = convertToTensor(x, 'x', 'asinh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.asinh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Asinh);\n}\nexport const asinh = op({ asinh_ });\n//# sourceMappingURL=asinh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.atan().print();  // or tf.atan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan_(x) {\n    const $x = convertToTensor(x, 'x', 'atan');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.atan($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Atan);\n}\nexport const atan = op({ atan_ });\n//# sourceMappingURL=atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan2 } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);\n * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);\n *\n * tf.atan2(a, b).print()\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan2_(a, b) {\n    let $a = convertToTensor(a, 'a', 'atan2');\n    let $b = convertToTensor(b, 'b', 'atan2');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.atan2($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Atan2);\n}\nexport const atan2 = op({ atan2_ });\n//# sourceMappingURL=atan2.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:\n * `atanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.atanh().print();  // or tf.atanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atanh_(x) {\n    const $x = convertToTensor(x, 'x', 'atanh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.atanh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Atanh);\n}\nexport const atanh = op({ atanh_ });\n//# sourceMappingURL=atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n *\n * @param inputShape Input tensor shape is of the following dimensions:\n *     `[batch, height, width, inChannels]`.\n * @param filterShape The filter shape is of the following dimensions:\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat The data format of the input and output data.\n *     Defaults to 'NHWC'.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`.\n *     Defaults to `[1, 1]`. If `dilations` is a single number, then\n *     `dilationHeight == dilationWidth`.\n */\nexport function computeDilation2DInfo(inputShape, filterShape, strides, pad, dataFormat = 'NHWC', dilations) {\n    // `computerConv2DInfo` require filterShape to be in the dimension of:\n    // `[filterHeight, filterWidth, depth, outDepth]`, dilation2d doesn't have\n    // outDepth, it should have the same depth as the input.\n    // Input shape: [batch, height, width, inChannels]\n    const inputChannels = inputShape[3];\n    const $filterShape = [...filterShape, inputChannels];\n    const $dataFormat = convertConv2DDataFormat(dataFormat);\n    return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad, null /* roundingMode */, null /* depthWise */, $dataFormat);\n}\nexport function computePool2DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'channelsLast') {\n    const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n    let filterShape;\n    if (dataFormat === 'channelsLast') {\n        filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n    }\n    else if (dataFormat === 'channelsFirst') {\n        filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    return computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, false, dataFormat);\n}\n/**\n * Computes the information for a forward pass of a pooling3D operation.\n */\nexport function computePool3DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'NDHWC') {\n    const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);\n    let filterShape;\n    let $dataFormat;\n    if (dataFormat === 'NDHWC') {\n        $dataFormat = 'channelsLast';\n        filterShape =\n            [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];\n    }\n    else if (dataFormat === 'NCDHW') {\n        $dataFormat = 'channelsFirst';\n        filterShape =\n            [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    return computeConv3DInfo(inShape, filterShape, strides, dilations, pad, false, $dataFormat, roundingMode);\n}\n/**\n * Computes the information for a forward pass of a convolution/pooling\n * operation.\n */\nexport function computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, depthwise = false, dataFormat = 'channelsLast') {\n    let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n    if (dataFormat === 'channelsLast') {\n        [batchSize, inHeight, inWidth, inChannels] = inShape;\n    }\n    else if (dataFormat === 'channelsFirst') {\n        [batchSize, inChannels, inHeight, inWidth] = inShape;\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    const [filterHeight, filterWidth, , filterChannels] = filterShape;\n    const [strideHeight, strideWidth] = parseTupleParam(strides);\n    const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n    const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);\n    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n    let outShape;\n    if (dataFormat === 'channelsFirst') {\n        outShape = [batchSize, outChannels, outHeight, outWidth];\n    }\n    else if (dataFormat === 'channelsLast') {\n        outShape = [batchSize, outHeight, outWidth, outChannels];\n    }\n    return {\n        batchSize,\n        dataFormat,\n        inHeight,\n        inWidth,\n        inChannels,\n        outHeight,\n        outWidth,\n        outChannels,\n        padInfo,\n        strideHeight,\n        strideWidth,\n        filterHeight,\n        filterWidth,\n        effectiveFilterHeight,\n        effectiveFilterWidth,\n        dilationHeight,\n        dilationWidth,\n        inShape,\n        outShape,\n        filterShape\n    };\n}\n/**\n * Computes the information for a forward pass of a 3D convolution/pooling\n * operation.\n */\nexport function computeConv3DInfo(inShape, filterShape, strides, dilations, pad, depthwise = false, dataFormat = 'channelsLast', roundingMode) {\n    let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];\n    if (dataFormat === 'channelsLast') {\n        [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n    }\n    else if (dataFormat === 'channelsFirst') {\n        [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;\n    const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n    const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);\n    const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);\n    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n    const { padInfo, outDepth, outHeight, outWidth } = get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);\n    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n    let outShape;\n    if (dataFormat === 'channelsFirst') {\n        outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n    }\n    else if (dataFormat === 'channelsLast') {\n        outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n    }\n    return {\n        batchSize,\n        dataFormat,\n        inDepth,\n        inHeight,\n        inWidth,\n        inChannels,\n        outDepth,\n        outHeight,\n        outWidth,\n        outChannels,\n        padInfo,\n        strideDepth,\n        strideHeight,\n        strideWidth,\n        filterDepth,\n        filterHeight,\n        filterWidth,\n        effectiveFilterDepth,\n        effectiveFilterHeight,\n        effectiveFilterWidth,\n        dilationDepth,\n        dilationHeight,\n        dilationWidth,\n        inShape,\n        outShape,\n        filterShape\n    };\n}\nfunction computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {\n    if (zeroPad == null) {\n        zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n    }\n    const inputRows = inShape[0];\n    const inputCols = inShape[1];\n    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    return [outputRows, outputCols];\n}\nfunction computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {\n    if (zeroPad == null) {\n        zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n    }\n    const inputDepth = inShape[0];\n    const inputRows = inShape[1];\n    const inputCols = inShape[2];\n    const outputDepths = conditionalRound((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputDepths), () => `The output # of depths (${outputDepths}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    return [outputDepths, outputRows, outputCols, outChannels];\n}\nexport function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {\n    const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n    return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\nfunction parseTupleParam(param) {\n    if (typeof param === 'number') {\n        return [param, param, param];\n    }\n    if (param.length === 2) {\n        return [param[0], param[1], 1];\n    }\n    return param;\n}\nfunction parse3TupleParam(param) {\n    return typeof param === 'number' ? [param, param, param] : param;\n}\n/* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d\n * Atrous convolution is equivalent to standard convolution with upsampled\n * filters with effective_filter_height =\n * filter_height + (filter_height - 1) * (dilation - 1)\n * and effective_filter_width =\n * filter_width + (filter_width - 1) * (dilation - 1),\n * produced by inserting dilation - 1 zeros along consecutive elements across\n * the filters' spatial dimensions.\n * When there is a dilation, this converts a filter dimension to the\n * effective filter dimension, so it can be used in a standard convolution.\n */\nfunction getEffectiveFilterSize(filterSize, dilation) {\n    if (dilation <= 1) {\n        return filterSize;\n    }\n    return filterSize + (filterSize - 1) * (dilation - 1);\n}\nfunction getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {\n    let padInfo;\n    let outHeight;\n    let outWidth;\n    if (typeof pad === 'number') {\n        const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n        padInfo = { top: pad, bottom: pad, left: pad, right: pad, type: padType };\n        const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad, roundingMode);\n        outHeight = outShape[0];\n        outWidth = outShape[1];\n    }\n    else if (pad === 'same') {\n        outHeight = Math.ceil(inHeight / strideHeight);\n        outWidth = Math.ceil(inWidth / strideWidth);\n        const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);\n        const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);\n        const top = Math.floor(padAlongHeight / 2);\n        const bottom = padAlongHeight - top;\n        const left = Math.floor(padAlongWidth / 2);\n        const right = padAlongWidth - left;\n        padInfo = { top, bottom, left, right, type: 'SAME' };\n    }\n    else if (pad === 'valid') {\n        padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: 'VALID' };\n        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n    }\n    else if (typeof pad === 'object') {\n        const top = dataFormat === 'channelsLast' ? pad[1][0] : pad[2][0];\n        const bottom = dataFormat === 'channelsLast' ? pad[1][1] : pad[2][1];\n        const left = dataFormat === 'channelsLast' ? pad[2][0] : pad[3][0];\n        const right = dataFormat === 'channelsLast' ? pad[2][1] : pad[3][1];\n        const padType = (top === 0 && bottom === 0 && left === 0 && right === 0) ?\n            'VALID' :\n            'EXPLICIT';\n        padInfo = { top, bottom, left, right, type: padType };\n        outHeight = conditionalRound((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);\n        outWidth = conditionalRound((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);\n    }\n    else {\n        throw Error(`Unknown padding parameter: ${pad}`);\n    }\n    return { padInfo, outHeight, outWidth };\n}\nfunction get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {\n    let padInfo;\n    let outDepth;\n    let outHeight;\n    let outWidth;\n    if (typeof pad === 'number') {\n        const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n        padInfo = {\n            top: pad,\n            bottom: pad,\n            left: pad,\n            right: pad,\n            front: pad,\n            back: pad,\n            type: padType\n        };\n        const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad, roundingMode);\n        outDepth = outShape[0];\n        outHeight = outShape[1];\n        outWidth = outShape[2];\n    }\n    else if (pad === 'same') {\n        outDepth = Math.ceil(inDepth / strideDepth);\n        outHeight = Math.ceil(inHeight / strideHeight);\n        outWidth = Math.ceil(inWidth / strideWidth);\n        const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n        const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;\n        const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n        const front = Math.floor(padAlongDepth / 2);\n        const back = padAlongDepth - front;\n        const top = Math.floor(padAlongHeight / 2);\n        const bottom = padAlongHeight - top;\n        const left = Math.floor(padAlongWidth / 2);\n        const right = padAlongWidth - left;\n        padInfo = { top, bottom, left, right, front, back, type: 'SAME' };\n    }\n    else if (pad === 'valid') {\n        padInfo = {\n            top: 0,\n            bottom: 0,\n            left: 0,\n            right: 0,\n            front: 0,\n            back: 0,\n            type: 'VALID'\n        };\n        outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);\n        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n    }\n    else {\n        throw Error(`Unknown padding parameter: ${pad}`);\n    }\n    return { padInfo, outDepth, outHeight, outWidth };\n}\n/**\n * Rounds a value depending on the rounding mode\n * @param value\n * @param roundingMode\n */\nfunction conditionalRound(value, roundingMode) {\n    if (!roundingMode) {\n        return value;\n    }\n    switch (roundingMode) {\n        case 'round':\n            // used for Caffe Conv\n            return Math.round(value);\n        case 'ceil':\n            // used for Caffe Pool\n            return Math.ceil(value);\n        case 'floor':\n            return Math.floor(value);\n        default:\n            throw new Error(`Unknown roundingMode ${roundingMode}`);\n    }\n}\nexport function tupleValuesAreOne(param) {\n    const [dimA, dimB, dimC] = parseTupleParam(param);\n    return dimA === 1 && dimB === 1 && dimC === 1;\n}\nexport function eitherStridesOrDilationsAreOne(strides, dilations) {\n    return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n/**\n * Convert Conv2D dataFormat from 'NHWC'|'NCHW' to\n *    'channelsLast'|'channelsFirst'\n * @param dataFormat in 'NHWC'|'NCHW' mode\n * @return dataFormat in 'channelsLast'|'channelsFirst' mode\n * @throws unknown dataFormat\n */\nexport function convertConv2DDataFormat(dataFormat) {\n    if (dataFormat === 'NHWC') {\n        return 'channelsLast';\n    }\n    else if (dataFormat === 'NCHW') {\n        return 'channelsFirst';\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n}\n//# sourceMappingURL=conv_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction avgPool_(x, filterSize, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'avgPool', 'float32');\n    const dilations = 1;\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in avgPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        save([x4D]);\n        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n            util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n            return x4D.clone();\n        }\n        return backend.avgPool(x4D, convInfo);\n    };\n    const inputs = { x: x4D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool, attrs);\n    res = cast(res, $x.dtype);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const avgPool = op({ avgPool_ });\n//# sourceMappingURL=avg_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { deprecationWarn } from '../globals';\nimport { AvgPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D average pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.avgPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates:\n *     `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction avgPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat = 'NDHWC', dilations) {\n    if (dilations == null) {\n        dilations = [1, 1, 1];\n    }\n    else {\n        deprecationWarn('dilations is deprecated, this field will be gone in ' +\n            'v3.0.0.');\n    }\n    const $x = convertToTensor(x, 'x', 'avgPool3d', 'float32');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in avgPool3d: Only NDHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool3d: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in avgPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1, 1];\n        }\n        const convInfo = conv_util.computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);\n        save([x5D]);\n        return backend.avgPool3d(x5D, convInfo);\n    };\n    const inputs = { x: x5D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat, dilations };\n    let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool3D, attrs);\n    res = cast(res, x5D.dtype);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const avgPool3d = op({ avgPool3d_ });\n//# sourceMappingURL=avg_pool_3d.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nexport function assertParamsConsistent(shapes, axis) {\n    const rank = shapes[0].length;\n    shapes.forEach((shape, i) => {\n        util.assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same ` +\n            `as the rank of the rest (${rank})`);\n    });\n    util.assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);\n    const firstShape = shapes[0];\n    shapes.forEach((shape, i) => {\n        for (let r = 0; r < rank; r++) {\n            util.assert((r === axis) || (shape[r] === firstShape[r]), () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) ` +\n                `does not match the shape of the rest (${firstShape}) ` +\n                `along the non-concatenated axis ${i}.`);\n        }\n    });\n}\nexport function computeOutShape(shapes, axis) {\n    const outputShape = shapes[0].slice();\n    for (let i = 1; i < shapes.length; i++) {\n        outputShape[axis] += shapes[i][axis];\n    }\n    return outputShape;\n}\n//# sourceMappingURL=concat_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Concat } from '../kernel_names';\nimport { convertToTensorArray } from '../tensor_util_env';\nimport { assert, parseAxisParam, sizeFromShape } from '../util';\nimport { assertParamsConsistent, computeOutShape } from './concat_util';\nimport { op } from './operation';\nimport { tensor } from './tensor';\n/**\n * Concatenates a list of `tf.Tensor`s along a given axis.\n *\n * The tensors ranks and types must match, and their sizes must match in all\n * dimensions except `axis`.\n *\n * Also available are stricter rank-specific methods that assert that\n * `tensors` are of the given rank:\n *   - `tf.concat1d`\n *   - `tf.concat2d`\n *   - `tf.concat3d`\n *   - `tf.concat4d`\n *\n * Except `tf.concat1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * a.concat(b).print();  // or a.concat(b)\n * ```\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.concat([a, b, c]).print();\n * ```\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [10, 20]]);\n * const b = tf.tensor2d([[3, 4], [30, 40]]);\n * const axis = 1;\n * tf.concat([a, b], axis).print();\n * ```\n * @param tensors A list of tensors to concatenate.\n * @param axis The axis to concate along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction concat_(tensors, axis = 0) {\n    assert(tensors.length >= 1, () => 'Pass at least one tensor to concat');\n    let $tensors = convertToTensorArray(tensors, 'tensors', 'concat');\n    if ($tensors[0].dtype === 'complex64') {\n        $tensors.forEach(tensor => {\n            if (tensor.dtype !== 'complex64') {\n                throw new Error(`Cannot concatenate complex64 tensors with a tensor\n          with dtype ${tensor.dtype}. `);\n            }\n        });\n    }\n    const forward = (backend, save) => {\n        const $axis = parseAxisParam(axis, $tensors[0].shape)[0];\n        const outShape = computeOutShape($tensors.map(t => t.shape), $axis);\n        if (sizeFromShape(outShape) === 0) {\n            return tensor([], outShape);\n        }\n        // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n        $tensors = $tensors.filter(t => t.size > 0);\n        if ($tensors.length === 1) {\n            return $tensors[0];\n        }\n        const shapes = $tensors.map(t => t.shape);\n        assertParamsConsistent(shapes, $axis);\n        const res = backend.concat($tensors, $axis);\n        save($tensors);\n        return res;\n    };\n    const inputs = $tensors;\n    const attr = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Concat, attr);\n}\nexport const concat = op({ concat_ });\n//# sourceMappingURL=concat.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sigmoid } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sigmoid element-wise, `1 / (1 + exp(-x))`\n *\n * ```js\n * const x = tf.tensor1d([0, -1, 2, -3]);\n *\n * x.sigmoid().print();  // or tf.sigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sigmoid_(x) {\n    const $x = convertToTensor(x, 'x', 'sigmoid');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sigmoid($x);\n        save([res]);\n        return res;\n    }, inputs, null /* grad */, Sigmoid);\n}\nexport const sigmoid = op({ sigmoid_ });\n//# sourceMappingURL=sigmoid.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Slice } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport * as slice_util from './slice_util';\n/**\n * Extracts a slice from a `tf.Tensor` starting at coordinates `begin`\n * and is of size `size`.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `x` is of the given rank:\n *   - `tf.slice1d`\n *   - `tf.slice2d`\n *   - `tf.slice3d`\n *   - `tf.slice4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.slice([1], [2]).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * x.slice([1, 0], [1, 2]).print();\n * ```\n * @param x The input `tf.Tensor` to slice from.\n * @param begin The coordinates to start the slice from. The length can be\n *     less than the rank of x - the rest of the axes will have implicit 0 as\n *     start. Can also be a single number, in which case it specifies the\n *     first axis.\n * @param size The size of the slice. The length can be less than the rank of\n *     x - the rest of the axes will have implicit -1. A value of -1 requests\n *     the rest of the dimensions in the axis. Can also be a single number,\n *     in which case it specifies the size of the first axis.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction slice_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice');\n    if ($x.rank === 0) {\n        throw new Error('Slicing scalar is not possible');\n    }\n    const forward = (backend, save) => {\n        const [begin_, size_] = slice_util.parseSliceParams($x, begin, size);\n        slice_util.assertParamsValid($x, begin_, size_);\n        save([$x]);\n        return backend.slice($x, begin_, size_);\n    };\n    const inputs = { x: $x };\n    const attrs = { begin, size };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Slice, attrs);\n}\nexport const slice = op({ slice_ });\n//# sourceMappingURL=slice.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, 70]);\n *\n * x.tanh().print();  // or tf.tanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tanh_(x) {\n    const $x = convertToTensor(x, 'x', 'tanh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const y = backend.tanh($x);\n        save([y]);\n        return y;\n    }, inputs, null /* grad */, Tanh);\n}\nexport const tanh = op({ tanh_ });\n//# sourceMappingURL=tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { add } from './add';\nimport { concat } from './concat';\nimport { matMul } from './mat_mul';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { slice } from './slice';\nimport { tanh } from './tanh';\n/**\n * Computes the next state and output of a BasicLSTMCell.\n *\n * Returns `[newC, newH]`.\n *\n * Derived from tf.contrib.rnn.BasicLSTMCell.\n *\n * @param forgetBias Forget bias for the cell.\n * @param lstmKernel The weights for the cell.\n * @param lstmBias The bias for the cell.\n * @param data The input to the cell.\n * @param c Previous cell state.\n * @param h Previous cell output.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {\n    const $forgetBias = convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');\n    const $lstmKernel = convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');\n    const $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');\n    const $data = convertToTensor(data, 'data', 'basicLSTMCell');\n    const $c = convertToTensor(c, 'c', 'basicLSTMCell');\n    const $h = convertToTensor(h, 'h', 'basicLSTMCell');\n    const combined = concat([$data, $h], 1);\n    const weighted = matMul(combined, $lstmKernel);\n    const res = add(weighted, $lstmBias);\n    // i = input_gate, j = new_input, f = forget_gate, o = output_gate\n    const batchSize = res.shape[0];\n    const sliceCols = res.shape[1] / 4;\n    const sliceSize = [batchSize, sliceCols];\n    const i = slice(res, [0, 0], sliceSize);\n    const j = slice(res, [0, sliceCols], sliceSize);\n    const f = slice(res, [0, sliceCols * 2], sliceSize);\n    const o = slice(res, [0, sliceCols * 3], sliceSize);\n    const newC = add(mul(sigmoid(i), tanh(j)), mul($c, sigmoid(add($forgetBias, f))));\n    const newH = mul(tanh(newC), sigmoid(o));\n    return [newC, newH];\n}\nexport const basicLSTMCell = op({ basicLSTMCell_ });\n//# sourceMappingURL=basic_lstm_cell.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchToSpaceND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation reshapes the \"batch\" dimension 0 into `M + 1` dimensions of\n * shape `blockShape + [batch]`, interleaves these blocks back into the grid\n * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with\n * the same rank as the input. The spatial dimensions of this intermediate\n * result are then optionally cropped according to `crops` to produce the\n * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise\n * description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n * const blockShape = [2, 2];\n * const crops = [[0, 0], [0, 0]];\n *\n * x.batchToSpaceND(blockShape, crops).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param crops A 2-D array.  Must have shape `[M, 2]`, all values must be >= 0.\n * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input\n * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required\n * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,\n * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,\n * x.shape[N-1]]`\n *\n * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /\n * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`\n * according to `crops` to produce the output of shape: `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],\n * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -\n * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction batchToSpaceND_(x, blockShape, crops) {\n    const $x = convertToTensor(x, 'x', 'batchToSpaceND');\n    const prod = blockShape.reduce((a, b) => a * b);\n    util.assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);\n    util.assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);\n    util.assert($x.shape[0] % prod === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of ` +\n        `the elements of blockShape ${blockShape.join(' * ')} === ${prod}`);\n    const forward = backend => {\n        return backend.batchToSpaceND($x, blockShape, crops);\n    };\n    const inputs = { x: $x };\n    const attrs = { blockShape, crops };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, BatchToSpaceND, attrs);\n}\nexport const batchToSpaceND = op({ batchToSpaceND_ });\n//# sourceMappingURL=batch_to_space_nd.js.map", "import { reshape } from './reshape';\nexport function xAs4D(x) {\n    let x4D;\n    if (x.rank === 0 || x.rank === 1) {\n        x4D = reshape(x, [1, 1, 1, x.size]);\n    }\n    else if (x.rank === 2) {\n        x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n    }\n    else if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    else {\n        x4D = x;\n    }\n    return x4D;\n}\n//# sourceMappingURL=batchnorm_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FusedBatchNorm } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { xAs4D } from './batchnorm_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Batch normalization.\n *\n * As described in\n * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).\n *\n * Mean, variance, scale, and offset can be of two shapes:\n *   - The same shape as the input.\n *   - In the common case, the depth dimension is the last dimension of x, so\n *     the values would be an `tf.Tensor1D` of shape [depth].\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that parameters passed are of given rank\n *   - `tf.batchNorm2d`\n *   - `tf.batchNorm3d`\n *   - `tf.batchNorm4d`\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction batchNorm_(x, mean, variance, offset, scale, varianceEpsilon) {\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($mean.rank === $variance.rank, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert($offset == null || $mean.rank === $offset.rank, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert($scale == null || $mean.rank === $scale.rank, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    const x4D = xAs4D($x);\n    const forward = (backend, save) => {\n        save([x4D, $mean, $variance, $scale]);\n        return backend.batchNorm(x4D, as1DOr4D($mean), as1DOr4D($variance), as1DOr4D($offset), as1DOr4D($scale), varianceEpsilon);\n    };\n    const inputs = {\n        x: x4D,\n        scale: $scale,\n        offset: $offset,\n        mean: $mean,\n        variance: $variance\n    };\n    const attrs = { varianceEpsilon };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, FusedBatchNorm, attrs);\n    return reshape(res, $x.shape);\n}\nfunction as1DOr4D(x) {\n    if (x == null) {\n        return null;\n    }\n    if (x.rank === 0) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [x.size]);\n    }\n    else if (x.rank === 1) {\n        return x;\n    }\n    else if (x.rank === 2) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n    }\n    else if (x.rank === 3) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    return x;\n}\nexport const batchNorm = op({ batchNorm_ });\n//# sourceMappingURL=batchnorm.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 2D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm2d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm2d = op({ batchNorm2d_ });\n//# sourceMappingURL=batchnorm2d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 3D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm3d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm3d = op({ batchNorm3d_ });\n//# sourceMappingURL=batchnorm3d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 4D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm4d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm4d = op({ batchNorm4d_ });\n//# sourceMappingURL=batchnorm4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BroadcastTo } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { clone } from './clone';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Broadcast an array to a compatible shape NumPy-style.\n *\n * The tensor's shape is compared to the broadcast shape from end to beginning.\n * Ones are prepended to the tensor's shape until is has the same length as\n * the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is\n * already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then\n * the input tensor is tiled N times along that axis (using tf.tile).\n *\n * @param input The tensor that is to be broadcasted.\n * @param shape The input is to be broadcast to this shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction broadcastTo_(x, shape) {\n    let input = convertToTensor(x, 'broadcastTo', 'x');\n    const xShape = input.shape;\n    if (shape.some(d => !(d > 0) || d % 1 !== 0)) {\n        throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);\n    }\n    if (shape.length < input.rank) {\n        throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input.rank}.`);\n    }\n    if (shape.length > input.rank) {\n        const newShape = input.shape.slice();\n        while (newShape.length < shape.length) {\n            newShape.unshift(1);\n        }\n        input = reshape(input, newShape);\n    }\n    const inputShape = input.shape;\n    const reps = Array.from(shape);\n    for (let i = shape.length - 1; i >= 0; i--) {\n        if (inputShape[i] === shape[i]) {\n            reps[i] = 1;\n        }\n        else if (input.shape[i] !== 1) {\n            throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);\n        }\n    }\n    const axes = reps.map((n, i) => n > 1 ? i : -1).filter(i => i >= 0);\n    if (axes.length === 0) {\n        return clone(input);\n    }\n    const forward = (backend) => backend.tile(input, reps);\n    const inputs = { x: input };\n    const attrs = { shape, inputShape };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, BroadcastTo, attrs);\n}\nexport const broadcastTo = op({ broadcastTo_ });\n//# sourceMappingURL=broadcast_to.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Ceil } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.ceil().print();  // or tf.ceil(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction ceil_(x) {\n    const $x = convertToTensor(x, 'x', 'ceil');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.ceil($x), inputs, null /* grad */, Ceil);\n}\nexport const ceil = op({ ceil_ });\n//# sourceMappingURL=ceil.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ClipByValue } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)\n * ```\n * @param x The input tensor.\n * @param clipValueMin Lower-bound of range to be clipped to.\n * @param clipValueMax Upper-bound of range to be clipped to.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction clipByValue_(x, clipValueMin, clipValueMax) {\n    const $x = convertToTensor(x, 'x', 'clipByValue');\n    util.assert((clipValueMin <= clipValueMax), () => `Error in clip: min (${clipValueMin}) must be ` +\n        `less than or equal to max (${clipValueMax}).`);\n    const inputs = { x: $x };\n    const attrs = { clipValueMin, clipValueMax };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.clip($x, clipValueMin, clipValueMax);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, ClipByValue, attrs);\n}\nexport const clipByValue = op({ clipByValue_ });\n//# sourceMappingURL=clip_by_value.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(3) = |r1, g1, b1|\n * B: shape(2) = |r2, g2|\n * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @return The concatenated array.\n */\nfunction concat1d_(tensors) {\n    return concat(tensors, 0 /* axis */);\n}\nexport const concat1d = op({ concat1d_ });\n//# sourceMappingURL=concat_1d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *\n * B: shape(2, 3) = | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * C = tf.concat2d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *                  | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * if axis = 1:\n * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |\n *                   | r2, g2, b2, r4, g4, b4 |\n *\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concatenate along.\n * @return The concatenated array.\n */\nfunction concat2d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat2d = op({ concat2d_ });\n//# sourceMappingURL=concat_2d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor3D`s along an axis.\n * See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *\n * B: shape(2, 1, 3) = | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * C = tf.concat3d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *                     | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * if axis = 1:\n * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |\n *                     | r2, g2, b2, r4, g4, b4 |\n *\n * if axis = 2:\n * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |\n *                      | r2, g2, b2, r4, g4, b4 |\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat3d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat3d = op({ concat3d_ });\n//# sourceMappingURL=concat_3d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor4D`s along an axis.\n * See `concat` for details.\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat4d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat4d = op({ concat4d_ });\n//# sourceMappingURL=concat_4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 2D convolution over the input x.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv2d');\n    const $filter = convertToTensor(filter, 'filter', 'conv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n    util.assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match ` +\n        `input depth for filter ${$filter.shape[2]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const forward = (backend, save) => {\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        const res = backend.conv2d(x4D, $filter, convInfo);\n        save([x4D, $filter]);\n        return res;\n    };\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv2D, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const conv2d = op({ conv2d_ });\n//# sourceMappingURL=conv2d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 1D convolution over the input x.\n *\n * @param x The input tensor, of rank 3 or rank 2, of shape\n *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.\n * @param filter The filter, rank 3, of shape\n *     `[filterWidth, inDepth, outDepth]`.\n * @param stride The number of entries by which the filter is moved right at\n *     each step.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from \"NWC\", \"NCW\". Defaults to \"NWC\",\n *     the data is stored in the order of [batch, in_width, in_channels]. Only\n *     \"NWC\" is currently supported.\n * @param dilation The dilation rate in which we sample input values in\n *     atrous convolution. Defaults to `1`. If it is greater than 1, then\n *     stride must be `1`.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv1d_(x, filter, stride, pad, dataFormat = 'NWC', dilation = 1, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv1d');\n    const $filter = convertToTensor(filter, 'filter', 'conv1d');\n    let x3D = $x;\n    let reshapedTo3D = false;\n    if ($x.rank === 2) {\n        reshapedTo3D = true;\n        x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);\n    }\n    util.assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n    util.assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv1d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    util.assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +\n        `input depth for filter ${$filter.shape[1]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(stride, dilation), () => 'Error in conv1D: Either stride or dilation must be 1. ' +\n        `Got stride ${stride} and dilation '${dilation}'`);\n    util.assert(dataFormat === 'NWC', () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);\n    const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);\n    const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);\n    const strides = [1, stride];\n    const dilations = [1, dilation];\n    const conv2dDataFormat = 'NHWC';\n    const res = conv2d(input4D, filter4D, strides, pad, conv2dDataFormat, dilations, dimRoundingMode);\n    if (reshapedTo3D) {\n        return reshape(res, [res.shape[2], res.shape[3]]);\n    }\n    return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);\n}\nexport const conv1d = op({ conv1d_ });\n//# sourceMappingURL=conv1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropInput } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 2D convolution.\n *\n * @param xShape The shape of the input: [batch, height, width, inDepth].\n * If length of 3, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 4 or rank 3 of shape\n *   `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction conv2DBackpropInput_(xShape, dy, filter, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n    util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n        `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n    let xShape4D = xShape;\n    let dy4D = dy;\n    let reshapedTo4D = false;\n    if (dy.rank === 3) {\n        reshapedTo4D = true;\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n        xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n    }\n    util.assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ` +\n        `${xShape4D.length}.`);\n    util.assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got ` +\n        `rank ${dy4D.rank}`);\n    util.assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got ` +\n        `rank ${filter.rank}`);\n    const inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1];\n    const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n    util.assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must ` +\n        `match input depth for filter ${filter.shape[2]}.`);\n    util.assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must ` +\n        `match output depth for filter ${filter.shape[3]}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2dDerInput: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const dilations = 1;\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(xShape4D, filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        const res = backend.conv2dDerInput(dy4D, filter, convInfo);\n        save([dy4D, filter]);\n        return res;\n    };\n    const inputs = { dy: dy4D, filter };\n    const attrs = { strides, pad, dataFormat, dimRoundingMode, inputShape: xShape4D };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv2DBackpropInput, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const conv2DBackpropInput = op({ conv2DBackpropInput_ });\n//# sourceMappingURL=conv2d_backprop_input.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport { conv2DBackpropInput } from './conv2d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 2D convolution of an image, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 4 or rank 3, of shape\n *   `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 4 or rank 3:\n *     `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.\n * @param strides The strides of the original convolution:\n *     `[strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n * @param dimRoundingMode The rounding mode used when computing output\n *    dimensions if pad is a number. If none is provided, it will not round\n *    and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2dTranspose_(x, filter, outputShape, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv2dTranspose');\n    const $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');\n    return conv2DBackpropInput(outputShape, $x, $filter, strides, pad, 'NHWC', dimRoundingMode);\n}\nexport const conv2dTranspose = op({ conv2dTranspose_ });\n//# sourceMappingURL=conv2d_transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { eitherStridesOrDilationsAreOne } from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 3D convolution over the input x.\n *\n * @param x The input tensor, of rank 5 or rank 4, of shape\n *     `[batch, depth, height, width, channels]`. If rank 4,\n * batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.\n *      inChannels must match between input and filter.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationDepth, dilationHeight,\n *     dilationWidth]` in which we sample input values across the height\n *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.\n *     If `dilations` is a single number, then\n *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater\n *     than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3d_(x, filter, strides, pad, dataFormat = 'NDHWC', dilations = [1, 1, 1]) {\n    const $x = convertToTensor(x, 'x', 'conv3d');\n    const $filter = convertToTensor(filter, 'filter', 'conv3d');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n    util.assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +\n        `input depth for filter ${$filter.shape[3]}.`);\n    util.assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv3D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computeConv3DInfo(x5D.shape, $filter.shape, strides, dilations, pad);\n        const res = backend.conv3d(x5D, $filter, convInfo);\n        save([x5D, $filter]);\n        return res;\n    };\n    const inputs = { x: x5D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv3D, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const conv3d = op({ conv3d_ });\n//# sourceMappingURL=conv3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3DBackpropInputV2 } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 3D convolution.\n *\n * @param xShape The shape of the input: [batch, depth, height, width,\n * in_channels]. If length of 4, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 5 or rank 4 of shape\n *   `[batch, outDepth, outHeight, outWidth, in_channels]`.\n * If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n */\nfunction conv3DBackpropInput_(xShape, dy, filter, strides, pad) {\n    util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n        `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n    let xShape5D = xShape;\n    let dy5D = dy;\n    let reshapedTo5D = false;\n    if (dy.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n        xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n    }\n    const inDepth = xShape5D[4];\n    const outDepth = dy5D.shape[4];\n    util.assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ` +\n        `${xShape5D.length}.`);\n    util.assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got ` +\n        `rank ${dy5D.rank}`);\n    util.assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got ` +\n        `rank ${filter.rank}`);\n    util.assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must ` +\n        `match input depth for filter ${filter.shape[3]}.`);\n    util.assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must ` +\n        `match output depth for filter ${filter.shape[4]}.`);\n    const forward = backend => {\n        const dilations = 1;\n        const convInfo = conv_util.computeConv3DInfo(xShape5D, filter.shape, strides, dilations, pad);\n        return backend.conv3dDerInput(dy5D, filter, convInfo);\n    };\n    const inputs = { dy: dy5D };\n    const attrs = { pad };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropInputV2, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const conv3DBackpropInput = op({ conv3DBackpropInput_ });\n//# sourceMappingURL=conv3d_backprop_input.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport { conv3DBackpropInput } from './conv3d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 3D convolution of a volume, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 5 or rank 4, of shape\n *   `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[depth, filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 5 or rank 4:\n *     `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is\n *    assumed.\n * @param strides The strides of the original convolution:\n *     `[strideDepth, strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3dTranspose_(x, filter, outputShape, strides, pad) {\n    const $x = convertToTensor(x, 'x', 'conv3dTranspose');\n    const $filter = convertToTensor(filter, 'filter', 'conv3dTranspose');\n    return conv3DBackpropInput(outputShape, $x, $filter, strides, pad);\n}\nexport const conv3dTranspose = op({ conv3dTranspose_ });\n//# sourceMappingURL=conv3d_transpose.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.cos().print();  // or tf.cos(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cos_(x) {\n    const $x = convertToTensor(x, 'x', 'cos');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.cos($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Cos);\n}\nexport const cos = op({ cos_ });\n//# sourceMappingURL=cos.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.cosh().print();  // or tf.cosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cosh_(x) {\n    const $x = convertToTensor(x, 'x', 'cosh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.cosh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Cosh);\n}\nexport const cosh = op({ cosh_ });\n//# sourceMappingURL=cosh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cumsum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { getAxesPermutation, getInnerMostAxes, getUndoAxesPermutation } from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Computes the cumulative sum of a `tf.Tensor` along `axis`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4]);\n * x.cumsum().print();\n * ```\n * ```js\n * const x = tf.tensor([[1, 2], [3, 4]]);\n * x.cumsum().print();\n * ```\n *\n * @param x The input tensor to be summed.\n * @param axis The axis along which to sum. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative sum. Optional.\n *     Defaults to false. If set to true then the sum of each tensor entry\n *     does not include its own value, but only the values previous to it\n *     along the specified axis.\n * @param reverse Whether to sum in the opposite direction. Optional.\n *     Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Scan'}\n */\nfunction cumsum_(x, axis = 0, exclusive = false, reverse = false) {\n    const $x = convertToTensor(x, 'x', 'cumsum');\n    const forward = (backend, save) => {\n        const permutation = getAxesPermutation([axis], $x.rank);\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n        }\n        const permutedAxis = getInnerMostAxes(1, $x.rank)[0];\n        let value = backend.cumsum(permutedX, permutedAxis, exclusive, reverse);\n        save([$x]);\n        if (permutation != null) {\n            const reversePermutation = getUndoAxesPermutation(permutation);\n            value = transpose(value, reversePermutation);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, exclusive, reverse };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Cumsum, attrs);\n}\nexport const cumsum = op({ cumsum_ });\n//# sourceMappingURL=cumsum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthToSpace } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Rearranges data from depth into blocks of spatial data. More specifically,\n * this op outputs a copy of the input tensor where values from the `depth`\n * dimension are moved in spatial blocks to the `height` and `width` dimensions.\n * The attr `blockSize` indicates the input block size and how the data is\n * moved.\n *\n *  - Chunks of data of size `blockSize * blockSize` from depth are rearranged\n * into non-overlapping blocks of size `blockSize x blockSize`\n *\n *  - The width the output tensor is `inputWidth * blockSize`, whereas the\n * height is `inputHeight * blockSize`\n *\n *  - The Y, X coordinates within each block of the output image are determined\n * by the high order component of the input channel index\n *\n *  - The depth of the input tensor must be divisible by `blockSize *\n * blockSize`\n *\n * The `dataFormat` attr specifies the layout of the input and output tensors\n * with the following options: \"NHWC\": [ `batch, height, width, channels` ]\n * \"NCHW\": [ `batch, channels, height, width` ]\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);\n * const blockSize = 2;\n * const dataFormat = \"NHWC\";\n *\n * tf.depthToSpace(x, blockSize, dataFormat).print();\n * ```\n *\n * @param x The input tensor of rank 4\n * @param blockSIze  An `int` that is `>= 2`. The size of the spatial block\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to \"NHWC\"\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction depthToSpace_(x, blockSize, dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'depthToSpace');\n    const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];\n    const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];\n    const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];\n    util.assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputHeight} and ${blockSize}  for depthToSpace with input shape\n    ${$x.shape}`);\n    util.assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputWidth} and ${blockSize} for depthToSpace with input shape\n        ${$x.shape}`);\n    util.assert((inputDepth % (blockSize * blockSize) === 0), () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);\n    const forward = backend => backend.depthToSpace($x, blockSize, dataFormat);\n    const inputs = { x: $x };\n    const attrs = { blockSize, dataFormat };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, DepthToSpace, attrs);\n}\nexport const depthToSpace = op({ depthToSpace_ });\n//# sourceMappingURL=depth_to_space.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNative } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Depthwise 2D convolution.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction depthwiseConv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n    const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels ` +\n        `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n        `filter ${$filter.shape[2]}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1];\n        }\n        util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +\n            `1. Got strides ${strides} and dilations '${dilations}'`);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n        const res = backend.depthwiseConv2D(x4D, $filter, convInfo);\n        save([x4D, $filter]);\n        return res;\n    };\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, DepthwiseConv2dNative, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const depthwiseConv2d = op({ depthwiseConv2d_ });\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Diag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Returns a diagonal tensor with a given diagonal values.\n *\n * Given a diagonal, this operation returns a tensor with the diagonal and\n * everything else padded with zeros.\n *\n * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor\n * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * tf.diag(x).print()\n * ```\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4, 5, 6, 6, 8], [4, 2])\n *\n * tf.diag(x).print()\n * ```\n * @param x The input tensor.\n */\nfunction diag_(x) {\n    const $x = convertToTensor(x, 'x', 'diag');\n    const forward = backend => {\n        const flat = reshape($x, [$x.size]);\n        const result = backend.diag(flat);\n        const outShape = [...x.shape, ...x.shape];\n        return reshape(result, outShape);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Diag);\n}\nexport const diag = op({ diag_ });\n//# sourceMappingURL=diag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Dilation2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the grayscale dilation over the input `x`.\n *\n * @param x The input tensor, rank 3 or rank 4 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter tensor, rank 3, of shape\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat Specify the data format of the input and output data.\n *      Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the\n *      default format \"NHWC\", the data is stored in the order of: [batch,\n *      height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations`\n *     is a single number, then `dilationHeight == dilationWidth`. If it is\n *     greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction dilation2d_(x, filter, strides, pad, dilations = [1, 1], dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'dilation2d');\n    const $filter = convertToTensor(filter, 'filter', 'dilation2d');\n    util.assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ` +\n        `${$x.rank}.`);\n    util.assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(dataFormat === 'NHWC', () => `Error in dilation2d: Only NHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n        reshapedTo4D = true;\n    }\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dilations };\n    const res = ENGINE.runKernel(Dilation2D, inputs, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const dilation2d = op({ dilation2d_ });\n//# sourceMappingURL=dilation2d.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the dimensions in the input shape that are broadcasted to\n * produce the provided output shape.\n *\n * The returned dimensions are 0-indexed and sorted. An example:\n * inShape = [4, 1, 3]\n * outShape = [5, 4, 3, 3]\n * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.\n */\nexport function getBroadcastDims(inShape, outShape) {\n    const inRank = inShape.length;\n    const dims = [];\n    for (let i = 0; i < inRank; i++) {\n        const dim = inRank - 1 - i;\n        const a = inShape[dim] || 1;\n        const b = outShape[outShape.length - 1 - i] || 1;\n        if (b > 1 && a === 1) {\n            dims.unshift(dim);\n        }\n    }\n    return dims;\n}\n/**\n * Returns the axes in the output space that should be reduced to produce\n * the input space.\n */\nexport function getReductionAxes(inShape, outShape) {\n    const result = [];\n    for (let i = 0; i < outShape.length; i++) {\n        const inDim = inShape[inShape.length - i - 1];\n        const outAxis = outShape.length - i - 1;\n        const outDim = outShape[outAxis];\n        if (inDim == null || (inDim === 1 && outDim > 1)) {\n            result.unshift(outAxis);\n        }\n    }\n    return result;\n}\nexport function assertAndGetBroadcastShape(shapeA, shapeB) {\n    const result = [];\n    const l = Math.max(shapeA.length, shapeB.length);\n    for (let i = 0; i < l; i++) {\n        let a = shapeA[shapeA.length - i - 1];\n        if (a == null) {\n            a = 1;\n        }\n        let b = shapeB[shapeB.length - i - 1];\n        if (b == null) {\n            b = 1;\n        }\n        if (a === 1) {\n            result.unshift(b);\n        }\n        else if (b === 1) {\n            result.unshift(a);\n        }\n        else if (a !== b) {\n            const errMsg = `Operands could not be broadcast together with shapes ` +\n                `${shapeA} and ${shapeB}.`;\n            throw Error(errMsg);\n        }\n        else {\n            result.unshift(a);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=broadcast_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Equal } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a == b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.equal(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction equal_(a, b) {\n    let $a = convertToTensor(a, 'a', 'equal');\n    let $b = convertToTensor(b, 'b', 'equal');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.equal($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null, Equal);\n}\nexport const equal = op({ equal_ });\n//# sourceMappingURL=equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SelectV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, assertShapesMatch } from '../util';\nimport { broadcastTo } from './broadcast_to';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the elements, either `a` or `b` depending on the `condition`.\n *\n * If the condition is true, select from `a`, otherwise select from `b`.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const a = tf.tensor1d([1 , 2, 3]);\n * const b = tf.tensor1d([-1, -2, -3]);\n *\n * a.where(cond, b).print();\n * ```\n *\n * @param condition The input condition. Must be of dtype bool.\n * @param a If `condition` is rank 1, `a` may have a higher rank but\n *     its first dimension must match the size of `condition`.\n * @param b A tensor with the same dtype as `a` and with shape that is\n *     compatible with `a`.\n * @return A tensor with same dtype as `a` and `b`, and shape that is\n *     broadcastable from `a` and `b`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction where_(condition, a, b) {\n    const $a = convertToTensor(a, 'a', 'where');\n    const $b = convertToTensor(b, 'b', 'where');\n    const $condition = convertToTensor(condition, 'condition', 'where', 'bool');\n    // TODO: move this logic to forward function when the broadcastTo op is\n    // implemented in WASM.\n    // Find the broadcastable shape for $a and $b.\n    const broadcastShape = assertAndGetBroadcastShape($a.shape, $b.shape);\n    const $broadcastedA = broadcastTo($a, broadcastShape);\n    const $broadcastedB = broadcastTo($b, broadcastShape);\n    if ($condition.rank === 1) {\n        // If condition rank is 1, then the first dimension must match the size of\n        // condition.\n        assert($condition.shape[0] === $a.shape[0], () => 'The first dimension of `a` must match the size of `condition`.');\n    }\n    if ($condition.rank !== 1) {\n        // A must have the same shape as condition.\n        assertShapesMatch($condition.shape, $broadcastedB.shape, 'Error in where: ');\n    }\n    const forward = (backend, save) => {\n        const res = backend.select($condition, $broadcastedA, $broadcastedB);\n        save([$condition]);\n        return res;\n    };\n    const inputs = {\n        condition: $condition,\n        t: $broadcastedA,\n        e: $broadcastedB\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, SelectV2);\n}\nexport const where = op({ where_ });\n//# sourceMappingURL=where.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ZerosLike } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.zerosLike(x).print();\n * ```\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction zerosLike_(x) {\n    const $x = convertToTensor(x, 'x', 'zerosLike');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.zerosLike($x), inputs, null /* grad */, ZerosLike);\n}\nexport const zerosLike = op({ zerosLike_ });\n//# sourceMappingURL=zeros_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { div } from './div';\nimport { equal } from './equal';\nimport { op } from './operation';\nimport { where } from './where';\nimport { zerosLike } from './zeros_like';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0\n * if denominator is 0.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n * const c = tf.tensor1d([0, 0, 0, 0]);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n * const c = tf.scalar(0);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction divNoNan_(a, b) {\n    // TODO: Make this into its own kernel.\n    let $a = convertToTensor(a, 'a', 'div');\n    let $b = convertToTensor(b, 'b', 'div');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const divResult = div($a, $b);\n    const zeros = zerosLike(divResult);\n    const bEqualsZero = equal($b, zeros);\n    return where(bEqualsZero, zeros, divResult);\n}\nexport const divNoNan = op({ divNoNan_ });\n//# sourceMappingURL=div_no_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor2d([[1, 2], [3, 4]]);\n * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n *\n * a.dot(b).print();  // or tf.dot(a, b)\n * b.dot(a).print();\n * b.dot(c).print();\n * ```\n * @param t1 The first tensor in the dot operation.\n * @param t2 The second tensor in the dot operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction dot_(t1, t2) {\n    const $t1 = convertToTensor(t1, 't1', 'dot');\n    const $t2 = convertToTensor(t2, 't2', 'dot');\n    util.assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ` +\n        `${$t1.rank} and ${$t2.rank}.`);\n    const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);\n    const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);\n    util.assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ` +\n        `${t1Inner} and ${t2Inner}.`);\n    if ($t1.rank === 1 && $t2.rank === 1) {\n        const t12D = reshape($t1, [1, -1]);\n        const t22D = reshape($t2, [-1, 1]);\n        const t1t2 = matMul(t12D, t22D);\n        return reshape(t1t2, []);\n    }\n    else if ($t1.rank === 1 && $t2.rank === 2) {\n        const t12D = reshape($t1, [1, -1]);\n        const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n        const t1t2 = matMul(t12D, t22D);\n        return reshape(t1t2, [t1t2.size]);\n    }\n    else if ($t1.rank === 2 && $t2.rank === 1) {\n        const t22D = reshape($t2, [-1, 1]);\n        const t1t2 = matMul($t1, t22D);\n        return reshape(t1t2, [t1t2.size]);\n    }\n    else {\n        const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n        const t1t2 = matMul($t1, t22D);\n        return t1t2;\n    }\n}\nexport const dot = op({ dot_ });\n//# sourceMappingURL=dot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Elu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential linear element-wise: `x > 0 ? e ^ x - 1 : 0`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 1, -3, 2]);\n *\n * x.elu().print();  // or tf.elu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction elu_(x) {\n    const $x = convertToTensor(x, 'x', 'elu');\n    const forward = (backend, save) => {\n        const y = backend.elu($x);\n        save([y]);\n        return y;\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Elu);\n}\nexport const elu = op({ elu_ });\n//# sourceMappingURL=elu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Erf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes gause error function of the input `tf.Tensor` element-wise:\n * `erf(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.erf().print(); // or tf.erf(x);\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction erf_(x) {\n    let $x = convertToTensor(x, 'x', 'erf');\n    util.assert($x.dtype === 'int32' || $x.dtype === 'float32', () => 'Input dtype must be `int32` or `float32`.');\n    if ($x.dtype === 'int32') {\n        $x = cast($x, 'float32');\n    }\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.erf($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Erf);\n}\nexport const erf = op({ erf_ });\n//# sourceMappingURL=erf.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Exp } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.exp().print();  // or tf.exp(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction exp_(x) {\n    const $x = convertToTensor(x, 'x', 'exp');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.exp($x);\n        save([res]);\n        return res;\n    }, inputs, null /* grad */, Exp);\n}\nexport const exp = op({ exp_ });\n//# sourceMappingURL=exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n * into the tensor's shape.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const axis = 1;\n * x.expandDims(axis).print();\n * ```\n *\n * @param x The input tensor whose dimensions to be expanded.\n * @param axis The dimension index at which to insert shape of `1`. Defaults\n *     to 0 (the first dimension).\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction expandDims_(x, axis = 0) {\n    const parseAs = null;\n    const $x = convertToTensor(x, 'x', 'expandDims', parseAs);\n    util.assert(axis <= $x.rank, () => 'Axis must be <= rank of the tensor');\n    const newShape = $x.shape.slice();\n    if (axis < 0) {\n        // Negative value is counted from the tail of rank.\n        util.assert(-($x.rank + 1) <= axis, () => `Axis must be in the interval [${-($x.rank + 1)}, ${$x.rank}]`);\n        axis = $x.rank + axis + 1;\n    }\n    newShape.splice(axis, 0, 1);\n    return reshape($x, newShape);\n}\nexport const expandDims = op({ expandDims_ });\n//# sourceMappingURL=expand_dims.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Expm1 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential of the input `tf.Tensor` minus one element-wise.\n * `e ^ x - 1`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.expm1().print();  // or tf.expm1(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction expm1_(x) {\n    const $x = convertToTensor(x, 'x', 'expm1');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.expm1($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Expm1);\n}\nexport const expm1 = op({ expm1_ });\n//# sourceMappingURL=expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tile } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Construct a tensor by repeating it the number of times given by reps.\n *\n * This operation creates a new tensor by replicating `input` `reps`\n * times. The output tensor's i'th dimension has `input.shape[i] *\n * reps[i]` elements, and the values of `input` are replicated\n * `reps[i]` times along the i'th dimension. For example, tiling\n * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n *\n * a.tile([2]).print();    // or a.tile([2])\n * ```\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.tile([1, 2]).print();  // or a.tile([1, 2])\n * ```\n * @param x The tensor to tile.\n * @param reps Determines the number of replications per dimension.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction tile_(x, reps) {\n    const parseAs = null;\n    const $x = convertToTensor(x, 'x', 'tile', parseAs);\n    util.assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n        `must match length of reps ${reps}.`);\n    const forward = (backend, save) => {\n        const res = backend.tile($x, reps);\n        save([$x]);\n        return res;\n    };\n    const inputsToSave = [$x];\n    const inputs = { x: $x };\n    const attrs = { reps };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Tile, attrs, inputsToSave);\n}\nexport const tile = op({ tile_ });\n//# sourceMappingURL=tile.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { expandDims } from './expand_dims';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { tile } from './tile';\n/**\n * Create an identity matrix.\n *\n * @param numRows Number of rows.\n * @param numColumns Number of columns. Defaults to `numRows`.\n * @param batchShape If provided, will add the batch shape to the beginning\n *   of the shape of the returned `tf.Tensor` by repeating the identity\n *   matrix.\n * @param dtype Data type.\n * @returns Identity matrix of the specified size and data type, possibly\n *   with batch repetition if `batchShape` is specified.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction eye_(numRows, numColumns, batchShape, dtype = 'float32') {\n    if (numColumns == null) {\n        numColumns = numRows;\n    }\n    const buff = buffer([numRows, numColumns], dtype);\n    const n = numRows <= numColumns ? numRows : numColumns;\n    for (let i = 0; i < n; ++i) {\n        buff.set(1, i, i);\n    }\n    const out = reshape(buff.toTensor(), [numRows, numColumns]);\n    if (batchShape == null) {\n        return out;\n    }\n    else {\n        if (batchShape.length === 1) {\n            return tile(expandDims(out, 0), [batchShape[0], 1, 1]);\n        }\n        else if (batchShape.length === 2) {\n            // tslint:disable-next-line:no-unnecessary-type-assertion\n            return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);\n        }\n        else if (batchShape.length === 3) {\n            // tslint:disable-next-line:no-unnecessary-type-assertion\n            return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [\n                batchShape[0], batchShape[1], batchShape[2], 1, 1\n            ]);\n        }\n        else {\n            throw new Error(`eye() currently supports only 1D and 2D ` +\n                // tslint:disable-next-line:no-any\n                `batchShapes, but received ${batchShape.length}D.`);\n        }\n    }\n}\nexport const eye = op({ eye_ });\n//# sourceMappingURL=eye.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Fill } from '../kernel_names';\n/**\n * Creates a `tf.Tensor` filled with a scalar value.\n *\n * ```js\n * tf.fill([2, 2], 4).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param value The scalar value to fill the tensor with.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction fill(shape, value, dtype) {\n    const attrs = { shape, value, dtype };\n    return ENGINE.runKernelFunc(backend => backend.fill(shape, value, dtype), {}, null, Fill, attrs);\n}\nexport { fill };\n//# sourceMappingURL=fill.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Floor } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.floor().print();  // or tf.floor(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction floor_(x) {\n    const $x = convertToTensor(x, 'x', 'floor');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.floor($x), inputs, null /* grad */, Floor);\n}\nexport const floor = op({ floor_ });\n//# sourceMappingURL=floor.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inputs of size above this threshold will be parallelized by calling multiple\n * shader programs.\n */\nimport { nearestDivisor } from '../util';\nexport const PARALLELIZE_THRESHOLD = 30;\nexport function computeOptimalWindowSize(inSize) {\n    if (inSize <= PARALLELIZE_THRESHOLD) {\n        return inSize;\n    }\n    return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n}\n//# sourceMappingURL=reduce_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nearestDivisor } from '../util';\nimport { PARALLELIZE_THRESHOLD } from './reduce_util';\nexport function segOpComputeOptimalWindowSize(inSize, numSegments) {\n    let done = false;\n    let res;\n    if (inSize <= PARALLELIZE_THRESHOLD) {\n        res = inSize;\n        done = true;\n    }\n    else {\n        res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n    }\n    while (!done) {\n        if (res > numSegments || res === inSize) {\n            done = true;\n        }\n        else {\n            res = nearestDivisor(inSize, res + 1);\n        }\n    }\n    return res;\n}\nexport function computeOutShape(aShape, axis, numSegments) {\n    const outShape = [];\n    const rank = aShape.length;\n    for (let dim = 0; dim < rank; dim++) {\n        if (dim !== axis) {\n            outShape.push(aShape[dim]);\n        }\n        else {\n            outShape.push(numSegments);\n        }\n    }\n    return outShape;\n}\nexport function collectGatherOpShapeInfo(x, indices, axis) {\n    const dimSize = x.shape[axis];\n    const outputShape = [];\n    let batchSize = 1;\n    let sliceSize = 1;\n    for (let i = 0; i < axis; i++) {\n        outputShape.push(x.shape[i]);\n        batchSize *= x.shape[i];\n    }\n    for (let i = 0; i < indices.rank; i++) {\n        outputShape.push(indices.shape[i]);\n    }\n    for (let i = axis + 1; i < x.rank; i++) {\n        outputShape.push(x.shape[i]);\n        sliceSize *= x.shape[i];\n    }\n    return { batchSize, sliceSize, dimSize, outputShape };\n}\n//# sourceMappingURL=segment_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { collectGatherOpShapeInfo } from './segment_util';\n/**\n * Gather slices from tensor `x`'s axis `axis` according to `indices`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const indices = tf.tensor1d([1, 3, 3], 'int32');\n *\n * x.gather(indices).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const indices = tf.tensor1d([1, 1, 0], 'int32');\n *\n * x.gather(indices).print();\n * ```\n * @param x The input tensor whose slices to be gathered.\n * @param indices The indices of the values to extract.\n * @param axis The axis over which to select values. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction gather_(x, indices, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'gather');\n    const $indices = convertToTensor(indices, 'indices', 'gather', 'int32');\n    const inputs = { x: $x, indices: $indices };\n    const attrs = { axis };\n    const forward = (backend, save) => {\n        const parsedAxis = parseAxisParam(axis, $x.shape)[0];\n        const shapeInfo = collectGatherOpShapeInfo($x, $indices, parsedAxis);\n        const res = backend.gather($x, reshape($indices, [$indices.size]), parsedAxis);\n        save([$x, $indices]);\n        return reshape(res, shapeInfo.outputShape);\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, GatherV2, attrs);\n}\nexport const gather = op({ gather_ });\n//# sourceMappingURL=gather.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Greater } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a > b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greater(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greater_(a, b) {\n    let $a = convertToTensor(a, 'a', 'greater');\n    let $b = convertToTensor(b, 'b', 'greater');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.greater($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Greater);\n}\nexport const greater = op({ greater_ });\n//# sourceMappingURL=greater.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GreaterEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a >= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greaterEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greaterEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'greaterEqual');\n    let $b = convertToTensor(b, 'b', 'greaterEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.greaterEqual($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, GreaterEqual);\n}\nexport const greaterEqual = op({ greaterEqual_ });\n//# sourceMappingURL=greater_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Imag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the imaginary part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the imaginary part of each element in input considered as a complex number.\n * If input is real, a tensor of all zeros is returned.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.imag(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction imag_(input) {\n    const $input = convertToTensor(input, 'input', 'imag');\n    const forward = (backend) => {\n        return backend.imag($input);\n    };\n    const inputs = { input: $input };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Imag);\n}\nexport const imag = op({ imag_ });\n//# sourceMappingURL=imag.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsFinite } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are finite.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isFinite().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isFinite_(x) {\n    const $x = convertToTensor(x, 'x', 'isFinite');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.isFinite($x), inputs, null /* grad */, IsFinite);\n}\nexport const isFinite = op({ isFinite_ });\n//# sourceMappingURL=is_finite.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsInf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are Infinity or -Infinity.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isInf().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isInf_(x) {\n    const $x = convertToTensor(x, 'x', 'isInf');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.isInf($x), inputs, null /* grad */, IsInf);\n}\nexport const isInf = op({ isInf_ });\n//# sourceMappingURL=is_inf.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsNan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * RReturns which elements of x are NaN.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isNaN().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isNaN_(x) {\n    const $x = convertToTensor(x, 'x', 'isNaN');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.isNaN($x), inputs, null /* grad */, IsNan);\n}\nexport const isNaN = op({ isNaN_ });\n//# sourceMappingURL=is_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Maximum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.maximumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * ```js\n * // Broadcast maximum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction maximum_(a, b) {\n    let $a = convertToTensor(a, 'a', 'maximum');\n    let $b = convertToTensor(b, 'b', 'maximum');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'bool') {\n        $a = cast($a, 'int32');\n        $b = cast($b, 'int32');\n    }\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.maximum($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Maximum);\n}\nexport const maximum = op({ maximum_ });\n//# sourceMappingURL=maximum.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { isTypedArray } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.scalar` as it makes the code more readable.\n *\n * ```js\n * tf.scalar(3.14).print();\n * ```\n *\n * @param value The value of the scalar.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function scalar(value, dtype) {\n    if (((isTypedArray(value) && dtype !== 'string') || Array.isArray(value)) &&\n        dtype !== 'complex64') {\n        throw new Error('Error creating a new Scalar: value must be a primitive ' +\n            '(number|boolean|string)');\n    }\n    if (dtype === 'string' && isTypedArray(value) &&\n        !(value instanceof Uint8Array)) {\n        throw new Error('When making a scalar from encoded string, ' +\n            'the value must be `Uint8Array`.');\n    }\n    const shape = [];\n    const inferredShape = [];\n    return makeTensor(value, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=scalar.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { maximum } from './maximum';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { scalar } from './scalar';\n/**\n * Computes leaky rectified linear element-wise.\n *\n * See\n * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](\n *     http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.leakyRelu(0.1).print();  // or tf.leakyRelu(x, 0.1)\n * ```\n * @param x The input tensor.\n * @param alpha The scaling factor for negative values, defaults to 0.2.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction leakyRelu_(x, alpha = 0.2) {\n    const $x = convertToTensor(x, 'x', 'leakyRelu');\n    return maximum(mul(scalar(alpha), $x), $x);\n}\nexport const leakyRelu = op({ leakyRelu_ });\n//# sourceMappingURL=leaky_relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Less } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a < b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.less(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction less_(a, b) {\n    let $a = convertToTensor(a, 'a', 'less');\n    let $b = convertToTensor(b, 'b', 'less');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.less($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Less);\n}\nexport const less = op({ less_ });\n//# sourceMappingURL=less.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LessEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a <= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.lessEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction lessEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'lessEqual');\n    let $b = convertToTensor(b, 'b', 'lessEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.lessEqual($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LessEqual);\n}\nexport const lessEqual = op({ lessEqual_ });\n//# sourceMappingURL=less_equal.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LinSpace } from '../kernel_names';\n/**\n * Return an evenly spaced sequence of numbers over the given interval.\n *\n * ```js\n * tf.linspace(0, 9, 10).print();\n * ```\n * @param start The start value of the sequence.\n * @param stop The end value of the sequence.\n * @param num The number of values to generate.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function linspace(start, stop, num) {\n    if (num <= 0) {\n        throw new Error('The number of values should be positive.');\n    }\n    const attrs = { start, stop, num };\n    return ENGINE.runKernelFunc(backend => backend.linspace(start, stop, num), {} /* inputs */, null /* grad */, LinSpace, attrs);\n}\n//# sourceMappingURL=linspace.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LRN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Normalizes the activation of a local neighborhood across or within\n * channels.\n *\n * @param x The input tensor. The 4-D input tensor is treated as a 3-D array\n *     of 1D vectors (along the last dimension), and each vector is\n *     normalized independently.\n * @param depthRadius The number of adjacent channels in the 1D normalization\n *     window.\n * @param bias A constant bias term for the basis.\n * @param alpha A scale factor, usually positive.\n * @param beta An exponent.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n    const $x = convertToTensor(x, 'x', 'localResponseNormalization');\n    util.assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${$x.rank}.`);\n    util.assert(util.isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an ` +\n        `integer but got depthRadius ${depthRadius}.`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    const forward = (backend, save) => {\n        const y = backend.localResponseNormalization4D(x4D, depthRadius, bias, alpha, beta);\n        save([x4D, y]);\n        return y;\n    };\n    const inputs = { x: x4D };\n    const attrs = { depthRadius, bias, alpha, beta };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, LRN, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    else {\n        return res;\n    }\n}\nexport const localResponseNormalization = op({ localResponseNormalization_ });\n//# sourceMappingURL=local_response_normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E]);\n *\n * x.log().print();  // or tf.log(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log_(x) {\n    const $x = convertToTensor(x, 'x', 'log');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.log($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Log);\n}\nexport const log = op({ log_ });\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log1p } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` plus one\n * element-wise: `ln(1 + x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E - 1]);\n *\n * x.log1p().print();  // or tf.log1p(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log1p_(x) {\n    const $x = convertToTensor(x, 'x', 'log1p');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.log1p($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Log1p);\n}\nexport const log1p = op({ log1p_ });\n//# sourceMappingURL=log1p.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { Tensor, Variable } from './tensor';\nimport { convertToTensor, convertToTensorArray } from './tensor_util_env';\nimport * as util from './util';\n/**\n * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the\n * gradient of `f(x)` with respect to `x`.\n *\n * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to\n * `x` is computed instead. `f(x)` must take a single tensor `x` and return a\n * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.grad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * g(x).print();\n * ```\n *\n * ```js\n * // f(x) = x ^ 3\n * const f = x => x.pow(tf.scalar(3, 'int32'));\n * // f'(x) = 3x ^ 2\n * const g = tf.grad(f);\n * // f''(x) = 6x\n * const gg = tf.grad(g);\n *\n * const x = tf.tensor1d([2, 3]);\n * gg(x).print();\n * ```\n *\n * @param f The function f(x), to compute gradient for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grad(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in grad(f) must be a function');\n    return (x, dy) => {\n        // x can be of any dtype, thus null as the last argument.\n        const $x = convertToTensor(x, 'x', 'tf.grad', null);\n        const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grad') : null;\n        return ENGINE.tidy(() => {\n            const { value, grads } = ENGINE.gradients(() => f($x), [$x], $dy);\n            if ($dy != null) {\n                util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grad(f)(x, dy) must match the shape ' +\n                    'returned by f(x)');\n            }\n            checkGrads(grads);\n            return grads[0];\n        });\n    };\n}\n/**\n * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,\n * which gives an array of gradients of `f()` with respect to each input\n * [`x1`,`x2`,...].\n *\n * If `dy` is passed when calling `g()`, the gradient of\n * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.\n * The provided `f` must take one or more tensors and return a single tensor\n * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df / da = b, df / db = a\n * const g = tf.grads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const [da, db] = g([a, b]);\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @param f The function `f(x1, x2,...)` to compute gradients for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grads(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in grads(f) must be a function');\n    return (args, dy) => {\n        util.assert(Array.isArray(args), () => 'The args passed in grads(f)(args) must be an array ' +\n            'of `Tensor`s or `TensorLike`s');\n        // args can be of any dtype, thus null as the last argument.\n        const $args = convertToTensorArray(args, 'args', 'tf.grads', null);\n        const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grads') : null;\n        return ENGINE.tidy(() => {\n            const { value, grads } = ENGINE.gradients(() => f(...$args), $args, $dy);\n            if ($dy != null) {\n                util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grads(f)([x1,...], dy) must ' +\n                    'match the shape returned by f([x1,...])');\n            }\n            checkGrads(grads);\n            return grads;\n        });\n    };\n}\n/**\n * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.valueAndGrad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * const {value, grad} = g(x);\n *\n * console.log('value');\n * value.print();\n * console.log('grad');\n * grad.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrad(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in valueAndGrad(f) must be a function');\n    return (x, dy) => {\n        util.assert(x instanceof Tensor, () => 'The x passed in valueAndGrad(f)(x) must be a tensor');\n        util.assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrad(f)(x, dy) must be a tensor');\n        const { grads, value } = ENGINE.gradients(() => f(x), [x], dy);\n        checkGrads(grads);\n        return { grad: grads[0], value };\n    };\n}\n/**\n * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df/da = b, df/db = a\n * const g = tf.valueAndGrads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const {value, grads} = g([a, b]);\n *\n * const [da, db] = grads;\n *\n * console.log('value');\n * value.print();\n *\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrads(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in valueAndGrads(f) must be a function');\n    return (args, dy) => {\n        util.assert(Array.isArray(args) && args.every(arg => arg instanceof Tensor), () => 'The args passed in valueAndGrads(f)(args) must be array of ' +\n            'tensors');\n        util.assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrads(f)(args, dy) must be a tensor');\n        const res = ENGINE.gradients(() => f(...args), args, dy);\n        if (dy != null) {\n            util.assertShapesMatch(res.value.shape, dy.shape, 'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +\n                'match the shape returned by f([x1,...])');\n        }\n        checkGrads(res.grads);\n        return res;\n    };\n}\n/**\n * Computes and returns the gradient of f(x) with respect to the list of\n * trainable variables provided by `varList`. If no list is provided, it\n * defaults to all trainable variables.\n *\n * ```js\n * const a = tf.variable(tf.tensor1d([3, 4]));\n * const b = tf.variable(tf.tensor1d([5, 6]));\n * const x = tf.tensor1d([1, 2]);\n *\n * // f(a, b) = a * x ^ 2 + b * x\n * const f = () => a.mul(x.square()).add(b.mul(x)).sum();\n * // df/da = x ^ 2, df/db = x\n * const {value, grads} = tf.variableGrads(f);\n *\n * Object.keys(grads).forEach(varName => grads[varName].print());\n * ```\n *\n * @param f The function to execute. f() should return a scalar.\n * @param varList The list of variables to compute the gradients with respect\n *     to. Defaults to all trainable variables.\n * @returns An object with the following keys and values:\n *   - `value`: The value of the function `f`.\n *   - `grads`: A map from the names of the variables to the gradients.\n *     If the `varList` argument is provided explicitly and contains a subset of\n *     non-trainable variables, this map in the return value will contain keys\n *     that map the names of the non-trainable variables to `null`.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction variableGrads(f, varList) {\n    util.assert(util.isFunction(f), () => 'The f passed in variableGrads(f) must be a function');\n    util.assert(varList == null ||\n        Array.isArray(varList) && varList.every(v => v instanceof Variable), () => 'The varList passed in variableGrads(f, varList) must be an array ' +\n        'of variables');\n    const specifiedVarList = varList != null;\n    if (!specifiedVarList) {\n        // Get all of the trainable variables.\n        varList = [];\n        for (const varName in ENGINE.registeredVariables) {\n            varList.push(ENGINE.registeredVariables[varName]);\n        }\n    }\n    const specifiedNonTrainable = specifiedVarList ? varList.filter(variable => !variable.trainable) : null;\n    // Prune non-trainable variables.\n    const originalVarCount = varList.length;\n    varList = varList.filter(variable => variable.trainable);\n    util.assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to ` +\n        `be trainable, but none of the ${originalVarCount} variables is ` +\n        `trainable.`);\n    const allowNoGradients = true;\n    const { value, grads } = ENGINE.gradients(f, varList, null, allowNoGradients);\n    util.assert(grads.some(g => g != null), () => 'Cannot find a connection between any variable and the result of ' +\n        'the loss function y=f(x). Please make sure the operations that ' +\n        'use variables are inside the function f passed to minimize().');\n    util.assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it ` +\n        `returned a rank-${value.rank} tensor`);\n    const namedGrads = {};\n    varList.forEach((v, i) => {\n        if (grads[i] != null) {\n            namedGrads[v.name] = grads[i];\n        }\n    });\n    if (specifiedNonTrainable != null) {\n        // If varList is explicitly provided and contains non-trainable values,\n        // add them to the returned gradients with `null` values.\n        specifiedNonTrainable.forEach(v => namedGrads[v.name] = null);\n    }\n    return { value, grads: namedGrads };\n}\n/**\n * Overrides the gradient computation of a function `f`.\n *\n * Takes a function\n * `f(...inputs, save) => {value: Tensor, gradFunc: (dy, saved) => Tensor[]}`\n * and returns another function `g(...inputs)` which takes the same inputs as\n * `f`. When called, `g` returns `f().value`. In backward mode, custom gradients\n * with respect to each input of `f` are computed using `f().gradFunc`.\n *\n * The `save` function passsed to `f` should be used for saving tensors needed\n * in the gradient. And the `saved` passed to the `gradFunc` is a\n * `NamedTensorMap`, which contains those saved tensor.\n *\n * ```js\n * const customOp = tf.customGrad((x, save) => {\n *   // Save x to make sure it's available later for the gradient.\n *   save([x]);\n *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);\n *   return {\n *     value: x.square(),\n *     // Note `saved.x` which points to the `x` we saved earlier.\n *     gradFunc: (dy, saved) => [dy.mul(saved[0].abs())]\n *   };\n * });\n *\n * const x = tf.tensor1d([-1, -2, 3]);\n * const dx = tf.grad(x => customOp(x));\n *\n * console.log(`f(x):`);\n * customOp(x).print();\n * console.log(`f'(x):`);\n * dx(x).print();\n * ```\n *\n * @param f The function to evaluate in forward mode, which should return\n *     `{value: Tensor, gradFunc: (dy, saved) => Tensor[]}`, where `gradFunc`\n *     returns the custom gradients of `f` with respect to its inputs.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction customGrad(f) {\n    return ENGINE.customGrad(f);\n}\nfunction checkGrads(grads) {\n    const numNullGradients = grads.filter(g => g == null).length;\n    if (numNullGradients > 0) {\n        throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.`);\n    }\n}\nexport { customGrad, variableGrads, valueAndGrad, valueAndGrads, grad, grads, };\n//# sourceMappingURL=gradients.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Negate } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes `-1 * x` element-wise.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);\n *\n * x.neg().print();  // or tf.neg(x)\n * ```\n *\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction neg_(x) {\n    const $x = convertToTensor(x, 'x', 'neg');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.neg($x), inputs, null /* grad */, Negate);\n}\nexport const neg = op({ neg_ });\n//# sourceMappingURL=neg.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softplus } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.softplus().print();  // or tf.softplus(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction softplus_(x) {\n    const $x = convertToTensor(x, 'x', 'softplus');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.softplus($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Softplus);\n}\nexport const softplus = op({ softplus_ });\n//# sourceMappingURL=softplus.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../gradients';\nimport { convertToTensor } from '../tensor_util_env';\nimport { mul } from './mul';\nimport { neg } from './neg';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { softplus } from './softplus';\n/**\n * Computes log sigmoid of the input `tf.Tensor` element-wise:\n * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.logSigmoid().print();  // or tf.logSigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction logSigmoid_(x) {\n    const $x = convertToTensor(x, 'x', 'logSigmoid');\n    // Use a custom gradient to maintain previous implementation.\n    // There is no LogSigmoid kernel in TF so we can't use engine.runKernel\n    // directly\n    const customOp = customGrad((x) => {\n        // TODO(yassogba) we can remove the chained softplus call here only\n        // after backends have modualrized softplus at which point we can call\n        // engine runKernel(..., Sotfplus, ...) directly.\n        const value = neg(softplus(neg(x)));\n        const gradFunc = (dy) => {\n            const derX = mul(dy, sigmoid(neg(x)));\n            return derX;\n        };\n        return { value, gradFunc };\n    });\n    return customOp($x);\n}\nexport const logSigmoid = op({ logSigmoid_ });\n//# sourceMappingURL=log_sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Max } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the maximum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.max().print();  // or tf.max(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.max(axis).print();  // or tf.max(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction max_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'max');\n    const forward = (backend, save) => {\n        const origAxes = util.parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let maxInput = $x;\n        if (permutedAxes != null) {\n            maxInput = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, maxInput.rank);\n        }\n        const y = backend.max(maxInput, axes);\n        if (permutedAxes != null) {\n            maxInput.dispose();\n        }\n        let res = y;\n        if (keepDims) {\n            const expandedShape = axis_util.expandShapeToKeepDim(res.shape, util.parseAxisParam(axis, $x.shape));\n            res = reshape(res, expandedShape);\n            y.dispose();\n        }\n        save([$x, res]);\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { reductionIndices: axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Max, attrs);\n}\nexport const max = op({ max_ });\n//# sourceMappingURL=max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sub } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n *\n * ```js\n * // Broadcast subtract a with b.\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.scalar(5);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n * @param a The first `tf.Tensor` to subtract from.\n * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction sub_(a, b) {\n    let $a = convertToTensor(a, 'a', 'sub');\n    let $b = convertToTensor(b, 'b', 'sub');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.subtract($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Sub);\n}\nexport const sub = op({ sub_ });\n//# sourceMappingURL=sub.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the sum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If axes has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.sum().print();  // or tf.sum(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.sum(axis).print();  // or tf.sum(x, axis)\n * ```\n *\n * @param x The input tensor to compute the sum over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction sum_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'sum');\n    if ($x.dtype === 'bool') {\n        $x = cast($x, 'int32');\n    }\n    const forward = (backend, save) => {\n        save([$x]);\n        const axes = parseAxisParam(axis, $x.shape);\n        const permutation = getAxesPermutation(axes, $x.rank);\n        let reductionAxes = axes;\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n        }\n        let value = backend.sum(permutedX, reductionAxes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(value.shape, axes);\n            value = reshape(value, newShape);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Sum, attrs);\n}\nexport const sum = op({ sum_ });\n//# sourceMappingURL=sum.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogSoftmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { op } from './operation';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log softmax.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param axis The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction logSoftmax_(logits, axis = -1) {\n    const $logits = convertToTensor(logits, 'logits', 'logSoftmax');\n    if (axis === -1) {\n        axis = $logits.rank - 1;\n    }\n    if (axis !== $logits.rank - 1) {\n        throw Error('Log Softmax along a non-last dimension is not yet supported. ' +\n            `Logits was rank ${$logits.rank} and axis was ${axis}`);\n    }\n    const forward = (backend, save) => {\n        const keepDims = true;\n        const xMax = max(logits, axis, true);\n        const shifted = sub(logits, xMax);\n        const value = sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis, keepDims)));\n        save([value]);\n        return value;\n    };\n    const inputs = { logits: $logits };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LogSoftmax, attrs);\n}\nexport const logSoftmax = op({ logSoftmax_ });\n//# sourceMappingURL=log_softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { add } from './add';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log(sum(exp(elements across the reduction dimensions)).\n *\n * Reduces the input along the dimensions given in `axis`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.logSumExp().print();  // or tf.logSumExp(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.logSumExp(axis).print();  // or tf.logSumExp(a, axis)\n * ```\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. If null (the default),\n *     reduces all dimensions.\n * @param keepDims If true, retains reduced dimensions with length\n *     of 1. Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction logSumExp_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'logSumExp');\n    const axes = parseAxisParam(axis, $x.shape);\n    const xMax = max($x, axes, true /* keepDims */);\n    const a = sub($x, xMax);\n    const b = exp(a);\n    const c = sum(b, axes);\n    const d = log(c);\n    const res = add(reshape(xMax, d.shape), d);\n    if (keepDims) {\n        const newShape = expandShapeToKeepDim(res.shape, axes);\n        return reshape(res, newShape);\n    }\n    return res;\n}\nexport const logSumExp = op({ logSumExp_ });\n//# sourceMappingURL=log_sum_exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalAnd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a AND b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalAnd(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalAnd_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(backend => backend.logicalAnd($a, $b), inputs, null /* grad */, LogicalAnd);\n}\nexport const logicalAnd = op({ logicalAnd_ });\n//# sourceMappingURL=logical_and.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalNot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the truth value of `NOT x` element-wise.\n *\n * ```js\n * const a = tf.tensor1d([false, true], 'bool');\n *\n * a.logicalNot().print();\n * ```\n *\n * @param x The input tensor. Must be of dtype 'bool'.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalNot_(x) {\n    const $x = convertToTensor(x, 'x', 'logicalNot', 'bool');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.logicalNot($x), inputs, null /* grad */, LogicalNot);\n}\nexport const logicalNot = op({ logicalNot_ });\n//# sourceMappingURL=logical_not.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalOr } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a OR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalOr(b).print();\n * ```\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalOr_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalOr', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalOr', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(backend => backend.logicalOr($a, $b), inputs, null /* grad */, LogicalOr);\n}\nexport const logicalOr = op({ logicalOr_ });\n//# sourceMappingURL=logical_or.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { logicalAnd } from './logical_and';\nimport { logicalNot } from './logical_not';\nimport { logicalOr } from './logical_or';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalXor(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalXor_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalXor', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalXor', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    // x ^ y = (x | y) & ~(x & y)\n    return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));\n}\nexport const logicalXor = op({ logicalXor_ });\n//# sourceMappingURL=logical_xor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction maxPool_(x, filterSize, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'maxPool');\n    const dilations = 1;\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        let y;\n        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n            util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n            y = x4D.clone();\n        }\n        else {\n            y = backend.maxPool(x4D, convInfo);\n        }\n        save([x4D, y]);\n        return y;\n    };\n    const inputs = { x: x4D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const maxPool = op({ maxPool_ });\n//# sourceMappingURL=max_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { deprecationWarn } from '../globals';\nimport { MaxPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D max pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.maxPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPool3d_(x, filterSize = [1, 1, 1], strides, pad, dimRoundingMode, dataFormat = 'NDHWC', dilations) {\n    if (dilations == null) {\n        dilations = [1, 1, 1];\n    }\n    else {\n        deprecationWarn('dilations is deprecated, this field will be gone in ' +\n            'v3.0.0.');\n    }\n    const $x = convertToTensor(x, 'x', 'maxPool3d');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in maxPool3d: Only NDHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool3d: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1, 1];\n        }\n        const convInfo = conv_util.computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);\n        const y = backend.maxPool3d(x5D, convInfo);\n        save([x5D, y]);\n        return y;\n    };\n    const inputs = { x: x5D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat, dilations };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool3D, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const maxPool3d = op({ maxPool3d_ });\n//# sourceMappingURL=max_pool_3d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPoolWithArgmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the 2D max pooling of an image with Argmax index.\n * The indices in argmax are flattened, so that a maximum value at position `[b,\n * y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if\n * include_batch_in_index is False; `((b * height + y) * width + x) * channels\n * +c` if include_batch_in_index is True.\n *\n * The indices returned are always in `[0, height) x [0, width)` before\n * flattening.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param includeBatchIndex Defaults to False. Whether to include batch\n *    dimension in flattened index of argmax.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPoolWithArgmax_(x, filterSize, strides, pad, includeBatchInIndex = false) {\n    const $x = convertToTensor(x, 'x', 'maxPoolWithArgmax');\n    const inputs = { x: $x };\n    const attrs = { filterSize, strides, pad, includeBatchInIndex };\n    const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);\n    return { result: result[0], indexes: result[1] };\n}\nexport const maxPoolWithArgmax = op({ maxPoolWithArgmax_ });\n//# sourceMappingURL=max_pool_with_argmax.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeZerosTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\n/**\n * Creates a `tf.Tensor` with all elements set to 0.\n *\n * ```js\n * tf.zeros([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Can\n *     be 'float32', 'int32' or 'bool'. Defaults to 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function zeros(shape, dtype = 'float32') {\n    if (dtype === 'complex64') {\n        const real = zeros(shape, 'float32');\n        const imag = zeros(shape, 'float32');\n        return complex(real, imag);\n    }\n    const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=zeros.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeOnesTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\nimport { zeros } from './zeros';\n/**\n * Creates a `tf.Tensor` with all elements set to 1.\n *\n * ```js\n * tf.ones([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n *     'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function ones(shape, dtype = 'float32') {\n    if (dtype === 'complex64') {\n        const real = ones(shape, 'float32');\n        const imag = zeros(shape, 'float32');\n        return complex(real, imag);\n    }\n    const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=ones.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../gradients';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam, sizeFromShape } from '../util';\nimport { computeOutAndReduceShapes } from './axis_util';\nimport { cast } from './cast';\nimport { div } from './div';\nimport { mul } from './mul';\nimport { ones } from './ones';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { scalar } from './scalar';\nimport { sum } from './sum';\n/**\n * Computes the mean of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is\n * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with\n * a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.mean().print();  // or tf.mean(a)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.mean(axis).print();  // or tf.mean(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction mean_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'mean');\n    const axes = parseAxisParam(axis, $x.shape);\n    const shapes = computeOutAndReduceShapes($x.shape, axes);\n    const reduceShape = shapes[1];\n    const reduceSize = sizeFromShape(reduceShape);\n    // Use a custom gradient to bypass 2 gradient backprops since mean is used\n    // extremely often.\n    const customOp = customGrad((x) => {\n        const reduceSizeScalar = scalar(reduceSize);\n        // Cast if needed.\n        const xReduce = reduceSizeScalar.dtype === x.dtype ?\n            x :\n            cast(x, reduceSizeScalar.dtype);\n        const res = div(xReduce, reduceSizeScalar);\n        const value = sum(res, axis, keepDims);\n        const gradFunc = (dy) => {\n            const expandedDyShape = x.shape.slice();\n            axes.forEach(axis => {\n                expandedDyShape[axis] = 1;\n            });\n            const expandedDy = reshape(dy, expandedDyShape);\n            const derX = div(mul(expandedDy, ones(x.shape, 'float32')), reduceSize);\n            return derX;\n        };\n        return { value, gradFunc };\n    });\n    return customOp($x);\n}\nexport const mean = op({ mean_ });\n//# sourceMappingURL=mean.js.map", "import { ENGINE } from '../engine';\nimport { Min } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the minimum value from the input.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axes`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axes` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.min().print();  // or tf.min(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.min(axis).print();  // or tf.min(x, axis)\n * ```\n *\n * @param x The input Tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction min_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'min');\n    const forward = (backend, save) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let minInput = $x;\n        if (permutedAxes != null) {\n            minInput = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        const y = backend.min(minInput, axes);\n        if (permutedAxes != null) {\n            minInput.dispose();\n        }\n        let res = y;\n        if (keepDims) {\n            const expandedShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n            res = reshape(y, expandedShape);\n            y.dispose();\n        }\n        save([$x, res]);\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Min, attrs);\n}\nexport const min = op({ min_ });\n//# sourceMappingURL=min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Minimum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `minimumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * ```js\n * // Broadcast minimum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction minimum_(a, b) {\n    let $a = convertToTensor(a, 'a', 'minimum');\n    let $b = convertToTensor(b, 'b', 'minimum');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'bool') {\n        $a = cast($a, 'int32');\n        $b = cast($b, 'int32');\n    }\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.minimum($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Minimum);\n}\nexport const minimum = op({ minimum_ });\n//# sourceMappingURL=minimum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Mod } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the mod of a and b element-wise.\n * `floor(x / y) * y + mod(x, y) = x`\n * Supports broadcasting.\n *\n * We also expose `tf.modStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * ```js\n * // Broadcast a mod b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mod_(a, b) {\n    let $a = convertToTensor(a, 'a', 'mod');\n    let $b = convertToTensor(b, 'b', 'mod');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.mod($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Mod);\n}\nexport const mod = op({ mod_ });\n//# sourceMappingURL=mod.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes square of `x` element-wise: `x ^ 2`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);\n *\n * x.square().print();  // or tf.square(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction square_(x) {\n    const $x = convertToTensor(x, 'x', 'square');\n    const attrs = {};\n    const inputsToSave = [$x];\n    const outputsToSave = [];\n    return ENGINE.runKernelFunc((backend, save) => {\n        save([$x]);\n        return backend.square($x);\n    }, { x: $x }, null /* grad */, 'Square', attrs, inputsToSave, outputsToSave);\n}\nexport const square = op({ square_ });\n//# sourceMappingURL=square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { cast } from './cast';\nimport { mean } from './mean';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { square } from './square';\nimport { sub } from './sub';\n/**\n * Calculates the mean and variance of `x`. The mean and variance are\n * calculated by aggregating the contents of `x` across `axes`. If `x` is\n * 1-D and `axes = [0]` this is just the mean and variance of a vector.\n *\n * @param x The input tensor.\n * @param axis The dimension(s) along with to compute mean and\n *     variance. By default it reduces all dimensions.\n * @param keepDims If true, the moments have the same dimensionality as the\n *     input.\n * @return An object with two keys: `mean` and `variance`.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction moments_(x, axis = null, keepDims = false) {\n    x = convertToTensor(x, 'x', 'moments');\n    const axes = parseAxisParam(axis, x.shape);\n    const xMean = mean(x, axes, keepDims);\n    let keepDimsShape = xMean.shape;\n    if (!keepDims) {\n        keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);\n    }\n    const devSquared = square(sub(cast(x, 'float32'), reshape(xMean, keepDimsShape)));\n    const variance = mean(devSquared, axes, keepDims);\n    return { mean: xMean, variance };\n}\nexport const moments = op({ moments_ });\n//# sourceMappingURL=moments.js.map", "import { convertToTensor, convertToTensorArray } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the next states and outputs of a stack of LSTMCells.\n *\n * Each cell output is used as input to the next cell.\n *\n * Returns `[cellState, cellOutput]`.\n *\n * Derived from tf.contrib.rn.MultiRNNCell.\n *\n * @param lstmCells Array of LSTMCell functions.\n * @param data The input to the cell.\n * @param c Array of previous cell states.\n * @param h Array of previous cell outputs.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction multiRNNCell_(lstmCells, data, c, h) {\n    const $data = convertToTensor(data, 'data', 'multiRNNCell');\n    const $c = convertToTensorArray(c, 'c', 'multiRNNCell');\n    const $h = convertToTensorArray(h, 'h', 'multiRNNCell');\n    let input = $data;\n    const newStates = [];\n    for (let i = 0; i < lstmCells.length; i++) {\n        const output = lstmCells[i](input, $c[i], $h[i]);\n        newStates.push(output[0]);\n        newStates.push(output[1]);\n        input = output[1];\n    }\n    const newC = [];\n    const newH = [];\n    for (let i = 0; i < newStates.length; i += 2) {\n        newC.push(newStates[i]);\n        newH.push(newStates[i + 1]);\n    }\n    return [newC, newH];\n}\nexport const multiRNNCell = op({ multiRNNCell_ });\n//# sourceMappingURL=multi_rnn_cell.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Creates a `tf.Tensor` with values drawn from a multinomial distribution.\n *\n * ```js\n * const probs = tf.tensor([.75, .25]);\n * tf.multinomial(probs, 3).print();\n * ```\n *\n * @param logits 1D array with unnormalized log-probabilities, or\n *     2D array of shape `[batchSize, numOutcomes]`. See the `normalized`\n *     parameter.\n * @param numSamples Number of samples to draw for each row slice.\n * @param seed The seed number.\n * @param normalized Whether the provided `logits` are normalized true\n *     probabilities (sum to 1). Defaults to false.\n * @return 1D array of shape `[numSamples]`, or 2D array of shape\n *     `[batchSize, numSamples]`, depending on the rank of the input.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction multinomial_(logits, numSamples, seed, normalized = false) {\n    const $logits = convertToTensor(logits, 'logits', 'multinomial');\n    const numOutcomes = $logits.size;\n    const origRank = $logits.rank;\n    if (numOutcomes < 2) {\n        throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ` +\n            `${numOutcomes}.`);\n    }\n    if (origRank > 2) {\n        throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n    }\n    seed = seed || Math.random();\n    const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;\n    const res = ENGINE.runKernelFunc(backend => backend.multinomial(logits2D, normalized, numSamples, seed), { logits2D });\n    // tslint:disable-next-line:no-unnecessary-type-assertion\n    return origRank === 1 ? reshape(res, [res.size]) : res;\n}\nexport const multinomial = op({ multinomial_ });\n//# sourceMappingURL=multinomial.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { NotEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a != b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([0, 2, 3]);\n *\n * a.notEqual(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction notEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'notEqual');\n    let $b = convertToTensor(b, 'b', 'notEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend) => backend.notEqual($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, NotEqual);\n}\nexport const notEqual = op({ notEqual_ });\n//# sourceMappingURL=not_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Real } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the real part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the real part of each element in input considered as a complex number.\n *\n * If the input is real, it simply makes a clone.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.real(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction real_(input) {\n    const $input = convertToTensor(input, 'input', 'real');\n    const forward = (backend) => {\n        return backend.real($input);\n    };\n    const inputs = { input: $input };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Real);\n}\nexport const real = op({ real_ });\n//# sourceMappingURL=real.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OnesLike } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { complex } from './complex';\nimport { imag } from './imag';\nimport { op } from './operation';\nimport { real } from './real';\nimport { zerosLike } from './zeros_like';\n/**\n * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.onesLike(x).print();\n * ```\n * @param x A tensor.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction onesLike_(x) {\n    const $x = convertToTensor(x, 'x', 'onesLike');\n    const forward = (backend, save) => {\n        if ($x.dtype === 'complex64') {\n            const r = onesLike(real($x));\n            const i = zerosLike(imag($x));\n            return complex(r, i);\n        }\n        return backend.onesLike($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, OnesLike);\n}\nexport const onesLike = op({ onesLike_ });\n//# sourceMappingURL=ones_like.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the outer product of two vectors, `v1` and `v2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([3, 4, 5]);\n *\n * tf.outerProduct(a, b).print();\n * ```\n * @param v1 The first vector in the outer product operation.\n * @param v2 The second vector in the outer product operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction outerProduct_(v1, v2) {\n    const $v1 = convertToTensor(v1, 'v1', 'outerProduct');\n    const $v2 = convertToTensor(v2, 'v2', 'outerProduct');\n    util.assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ` +\n        `${$v1.rank} and ${$v2.rank}.`);\n    const v12D = reshape($v1, [-1, 1]);\n    const v22D = reshape($v2, [1, -1]);\n    return matMul(v12D, v22D);\n}\nexport const outerProduct = op({ outerProduct_ });\n//# sourceMappingURL=outer_product.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { PadV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Pads a `tf.Tensor` with a given value and paddings.\n *\n * This operation currently only implements the `CONSTANT` mode.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `paddings` is of given length.\n *   - `tf.pad1d`\n *   - `tf.pad2d`\n *   - `tf.pad3d`\n *   - `tf.pad4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.pad([[1, 2]]).print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * @param constantValue The pad value to use. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction pad_(x, paddings, constantValue = 0) {\n    const $x = convertToTensor(x, 'x', 'pad');\n    if ($x.rank === 0) {\n        throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');\n    }\n    const forward = (backend, save) => {\n        save([$x]);\n        return backend.pad($x, paddings, constantValue);\n    };\n    const attrs = { paddings, constantValue };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, PadV2, attrs);\n}\nexport const pad = op({ pad_ });\n//# sourceMappingURL=pad.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.\n */\nfunction pad1d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 2, () => 'Invalid number of paddings. Must be length of 2.');\n    return pad(x, [paddings], constantValue);\n}\nexport const pad1d = op({ pad1d_ });\n//# sourceMappingURL=pad1d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.\n */\nfunction pad2d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 2 && paddings[0].length === 2 &&\n        paddings[1].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad2d = op({ pad2d_ });\n//# sourceMappingURL=pad2d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.\n */\nfunction pad3d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 3 && paddings[0].length === 2 &&\n        paddings[1].length === 2 && paddings[2].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad3d = op({ pad3d_ });\n//# sourceMappingURL=pad3d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.\n */\nfunction pad4d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 4 && paddings[0].length === 2 &&\n        paddings[1].length === 2 && paddings[2].length === 2 &&\n        paddings[3].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad4d = op({ pad4d_ });\n//# sourceMappingURL=pad4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SpaceToBatchND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation divides \"spatial\" dimensions `[1, ..., M]` of the input into\n * a grid of blocks of shape `blockShape`, and interleaves these blocks with\n * the \"batch\" dimension (0) such that in the output, the spatial\n * dimensions `[1, ..., M]` correspond to the position within the grid,\n * and the batch dimension combines both the position within a spatial block\n * and the original batch position. Prior to division into blocks,\n * the spatial dimensions of the input are optionally zero padded\n * according to `paddings`. See below for a precise description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n * const blockShape = [2, 2];\n * const paddings = [[0, 0], [0, 0]];\n *\n * x.spaceToBatchND(blockShape, paddings).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=\n *     0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad\n * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It\n * is required that\n * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input\n * according to `paddings` to produce `padded` of shape paddedShape.\n *\n * 2. Reshape `padded` to `reshapedPadded` of shape:\n * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`\n *\n * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`\n * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the\n * batch dimension, producing an output tensor of shape:\n * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction spaceToBatchND_(x, blockShape, paddings) {\n    const $x = convertToTensor(x, 'x', 'spaceToBatchND');\n    util.assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);\n    util.assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);\n    util.assert($x.shape.reduce((a, b, i) => {\n        if (i > 0 && i <= blockShape.length) {\n            return a &&\n                ((b + paddings[i - 1][0] + paddings[i - 1][1]) %\n                    blockShape[i - 1] ===\n                    0);\n        }\n        return a;\n    }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);\n    const forward = backend => backend.spaceToBatchND($x, blockShape, paddings);\n    const inputs = { x: $x };\n    const attrs = { blockShape, paddings };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, SpaceToBatchND, attrs);\n}\nexport const spaceToBatchND = op({ spaceToBatchND_ });\n//# sourceMappingURL=space_to_batch_nd.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { avgPool } from './avg_pool';\nimport { batchToSpaceND } from './batch_to_space_nd';\nimport * as conv_util from './conv_util';\nimport { maxPool } from './max_pool';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { spaceToBatchND } from './space_to_batch_nd';\n/**\n * Performs an N-D pooling operation\n *\n * @param input The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param windowShape The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param poolingType The type of pooling, either 'max' or 'avg'.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction pool_(input, windowShape, poolingType, pad, dilations, strides) {\n    if (dilations == null) {\n        dilations = [1, 1];\n    }\n    if (strides == null) {\n        strides = 1;\n    }\n    if (pad === 0) {\n        pad = 'valid';\n    }\n    const $x = convertToTensor(input, 'x', 'maxPool');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in pool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = conv_util.computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad);\n    const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];\n    // The following implementation does batchToSpace(pool(spaceToBatch(x)))\n    // whenever dilation > 1 since the TF kernels do not support dilation > 1.\n    // tslint:disable-next-line:max-line-length\n    // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037\n    let basePadding;\n    if (pad === 'same') {\n        basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);\n    }\n    else {\n        basePadding = [[0, 0], [0, 0]];\n    }\n    const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n    const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n    const convertedPad = isDilationOne ? pad : 'valid';\n    const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n    const forwardOp = poolingType === 'avg' ?\n        () => avgPool(convertedX, windowShape, strides, convertedPad) :\n        () => maxPool(convertedX, windowShape, strides, convertedPad);\n    const y = forwardOp();\n    const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\n// Helper function to compute crops and paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184\nfunction requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {\n    const padStart = basePadding.map(b => b[0]);\n    const origPadEnd = basePadding.map(b => b[1]);\n    const fullInputShape = inputShape.concat(padStart, origPadEnd);\n    const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n    const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n    const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n    const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n    return [paddings, crops];\n}\n// Helper function to compute base paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524\nfunction withSpaceToBatchBasePaddings(filterShape, dilation) {\n    // Spatial dimensions of the filters and the upsampled filters in which we\n    // introduce (rate - 1) zeros between consecutive filter values.\n    const dilatedFilterShape = filterShape.map((s, i) => {\n        return s + (s - 1) * (dilation[i] - 1);\n    });\n    const padExtraShape = dilatedFilterShape.map(s => s - 1);\n    // When padding is odd, we pad more at end, following the same\n    // convention as conv2d.\n    const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));\n    const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n    return padExtraShape.map((_, i) => {\n        return [padExtraStart[i], padExtraEnd[i]];\n    });\n}\nexport const pool = op({ pool_ });\n//# sourceMappingURL=pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Pow } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the power of one `tf.Tensor` to another. Supports broadcasting.\n *\n * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for\n * corresponding elements in x and y. The result's dtype will be the upcasted\n * type of the `base` and `exp` dtypes.\n *\n * ```js\n * const a = tf.tensor([[2, 3], [4, 5]])\n * const b = tf.tensor([[1, 2], [3, 0]]).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n *\n * ```js\n * const a = tf.tensor([[1, 2], [3, 4]])\n * const b = tf.tensor(2).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n * We also expose `powStrict` which has the same signature as this op and\n * asserts that `base` and `exp` are the same shape (does not broadcast).\n *\n * @param base The base `tf.Tensor` to pow element-wise.\n * @param exp The exponent `tf.Tensor` to pow element-wise.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction pow_(base, exp) {\n    let $base = convertToTensor(base, 'base', 'pow');\n    let $exp = convertToTensor(exp, 'exp', 'pow');\n    [$base, $exp] = makeTypesMatch($base, $exp);\n    const inputs = { a: $base, b: $exp };\n    const forward = (backend, save) => {\n        const y = backend.pow($base, $exp);\n        save([$base, $exp, y]);\n        return y;\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Pow);\n}\nexport const pow = op({ pow_ });\n//# sourceMappingURL=pow.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prelu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes leaky rectified linear element-wise with parametric alphas.\n *\n * `x < 0 ? alpha * x : f(x) = x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n * const alpha = tf.scalar(0.1);\n *\n * x.prelu(alpha).print();  // or tf.prelu(x, alpha)\n * ```\n * @param x The input tensor.\n * @param alpha Scaling factor for negative values.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction prelu_(x, alpha) {\n    const $x = convertToTensor(x, 'x', 'prelu');\n    const $alpha = convertToTensor(alpha, 'alpha', 'prelu');\n    const forward = (backend, save) => {\n        const res = backend.prelu($x, $alpha);\n        save([$x, $alpha]);\n        return res;\n    };\n    const inputs = { x: $x, alpha: $alpha };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Prelu);\n}\nexport const prelu = op({ prelu_ });\n//# sourceMappingURL=prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prod } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the product of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.prod().print();  // or tf.prod(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.prod(axis).print();  // or tf.prod(x, axis)\n * ```\n *\n * @param x The input tensor to compute the product over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction prod_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'prod');\n    const forward = (backend) => {\n        if ($x.dtype === 'bool') {\n            $x = cast($x, 'int32');\n        }\n        const axes = parseAxisParam(axis, $x.shape);\n        const permutation = getAxesPermutation(axes, $x.rank);\n        let reductionAxes = axes;\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n        }\n        let value = backend.prod(permutedX, reductionAxes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(value.shape, axes);\n            value = reshape(value, newShape);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Prod, attrs);\n}\nexport const prod = op({ prod_ });\n//# sourceMappingURL=prod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { sizeFromShape } from '../util';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with values sampled from a random number generator\n * function defined by the user.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param randFunction A random number generator function which is called\n * for each element in the output tensor.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\nfunction rand_(shape, randFunction, dtype) {\n    const size = sizeFromShape(shape);\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    for (let i = 0; i < size; i++) {\n        values[i] = randFunction();\n    }\n    return ENGINE.makeTensor(values, shape, dtype);\n}\nexport const rand = op({ rand_ });\n//# sourceMappingURL=rand.js.map", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as seedrandom from 'seedrandom';\nimport { expectNumbersClose, testEpsilon } from '../test_util';\n// https://en.wikipedia.org/wiki/Marsaglia_polar_method\nexport class MPRandGauss {\n    constructor(mean, stdDeviation, dtype, truncated, seed) {\n        this.mean = mean;\n        this.stdDev = stdDeviation;\n        this.dtype = dtype;\n        this.nextVal = NaN;\n        this.truncated = truncated;\n        if (this.truncated) {\n            this.upper = this.mean + this.stdDev * 2;\n            this.lower = this.mean - this.stdDev * 2;\n        }\n        const seedValue = seed ? seed : Math.random();\n        this.random = seedrandom.alea(seedValue.toString());\n    }\n    /** Returns next sample from a Gaussian distribution. */\n    nextValue() {\n        if (!isNaN(this.nextVal)) {\n            const value = this.nextVal;\n            this.nextVal = NaN;\n            return value;\n        }\n        let resultX, resultY;\n        let isValid = false;\n        while (!isValid) {\n            let v1, v2, s;\n            do {\n                v1 = 2 * this.random() - 1;\n                v2 = 2 * this.random() - 1;\n                s = v1 * v1 + v2 * v2;\n            } while (s >= 1 || s === 0);\n            const mul = Math.sqrt(-2.0 * Math.log(s) / s);\n            resultX = this.mean + this.stdDev * v1 * mul;\n            resultY = this.mean + this.stdDev * v2 * mul;\n            if (!this.truncated || this.isValidTruncated(resultX)) {\n                isValid = true;\n            }\n        }\n        if (!this.truncated || this.isValidTruncated(resultY)) {\n            this.nextVal = this.convertValue(resultY);\n        }\n        return this.convertValue(resultX);\n    }\n    /** Handles proper rounding for non-floating-point numbers. */\n    convertValue(value) {\n        if (this.dtype == null || this.dtype === 'float32') {\n            return value;\n        }\n        return Math.round(value);\n    }\n    /** Returns true if less than 2-standard-deviations from the mean. */\n    isValidTruncated(value) {\n        return value <= this.upper && value >= this.lower;\n    }\n}\n// Marsaglia, George, and Wai Wan Tsang. 2000. \"A Simple Method for Generating\n// Gamma Variables.\"\nexport class RandGamma {\n    constructor(alpha, beta, dtype, seed) {\n        this.alpha = alpha;\n        this.beta = 1 / beta; // convert rate to scale parameter\n        this.dtype = dtype;\n        const seedValue = seed ? seed : Math.random();\n        this.randu = seedrandom.alea(seedValue.toString());\n        this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());\n        if (alpha < 1) {\n            this.d = alpha + (2 / 3);\n        }\n        else {\n            this.d = alpha - (1 / 3);\n        }\n        this.c = 1 / Math.sqrt(9 * this.d);\n    }\n    /** Returns next sample from a gamma distribution. */\n    nextValue() {\n        let x2, v0, v1, x, u, v;\n        while (true) {\n            do {\n                x = this.randn.nextValue();\n                v = 1 + (this.c * x);\n            } while (v <= 0);\n            v *= v * v;\n            x2 = x * x;\n            v0 = 1 - (0.331 * x2 * x2);\n            v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v)));\n            u = this.randu();\n            if (u < v0 || Math.log(u) < v1) {\n                break;\n            }\n        }\n        v = (1 / this.beta) * this.d * v;\n        if (this.alpha < 1) {\n            v *= Math.pow(this.randu(), 1 / this.alpha);\n        }\n        return this.convertValue(v);\n    }\n    /** Handles proper rounding for non-floating-point numbers. */\n    convertValue(value) {\n        if (this.dtype === 'float32') {\n            return value;\n        }\n        return Math.round(value);\n    }\n}\nexport class UniformRandom {\n    constructor(min = 0, max = 1, dtype, seed) {\n        /** Handles proper rounding for non floating point numbers. */\n        this.canReturnFloat = () => (this.dtype == null || this.dtype === 'float32');\n        this.min = min;\n        this.range = max - min;\n        this.dtype = dtype;\n        if (seed == null) {\n            seed = Math.random();\n        }\n        if (typeof seed === 'number') {\n            seed = seed.toString();\n        }\n        if (!this.canReturnFloat() && this.range <= 1) {\n            throw new Error(`The difference between ${min} - ${max} <= 1 and dtype is not float`);\n        }\n        this.random = seedrandom.alea(seed);\n    }\n    convertValue(value) {\n        if (this.canReturnFloat()) {\n            return value;\n        }\n        return Math.round(value);\n    }\n    nextValue() {\n        return this.convertValue(this.min + this.range * this.random());\n    }\n}\nexport function jarqueBeraNormalityTest(values) {\n    // https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test\n    const n = values.length;\n    const s = skewness(values);\n    const k = kurtosis(values);\n    const jb = n / 6 * (Math.pow(s, 2) + 0.25 * Math.pow(k - 3, 2));\n    // JB test requires 2-degress of freedom from Chi-Square @ 0.95:\n    // http://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm\n    const CHI_SQUARE_2DEG = 5.991;\n    if (jb > CHI_SQUARE_2DEG) {\n        throw new Error(`Invalid p-value for JB: ${jb}`);\n    }\n}\nexport function expectArrayInMeanStdRange(actual, expectedMean, expectedStdDev, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    const actualMean = mean(actual);\n    expectNumbersClose(actualMean, expectedMean, epsilon);\n    expectNumbersClose(standardDeviation(actual, actualMean), expectedStdDev, epsilon);\n}\nfunction mean(values) {\n    let sum = 0;\n    for (let i = 0; i < values.length; i++) {\n        sum += values[i];\n    }\n    return sum / values.length;\n}\nfunction standardDeviation(values, mean) {\n    let squareDiffSum = 0;\n    for (let i = 0; i < values.length; i++) {\n        const diff = values[i] - mean;\n        squareDiffSum += diff * diff;\n    }\n    return Math.sqrt(squareDiffSum / values.length);\n}\nfunction kurtosis(values) {\n    // https://en.wikipedia.org/wiki/Kurtosis\n    const valuesMean = mean(values);\n    const n = values.length;\n    let sum2 = 0;\n    let sum4 = 0;\n    for (let i = 0; i < n; i++) {\n        const v = values[i] - valuesMean;\n        sum2 += Math.pow(v, 2);\n        sum4 += Math.pow(v, 4);\n    }\n    return (1 / n) * sum4 / Math.pow((1 / n) * sum2, 2);\n}\nfunction skewness(values) {\n    // https://en.wikipedia.org/wiki/Skewness\n    const valuesMean = mean(values);\n    const n = values.length;\n    let sum2 = 0;\n    let sum3 = 0;\n    for (let i = 0; i < n; i++) {\n        const v = values[i] - valuesMean;\n        sum2 += Math.pow(v, 2);\n        sum3 += Math.pow(v, 3);\n    }\n    return (1 / n) * sum3 / Math.pow((1 / (n - 1)) * sum2, 3 / 2);\n}\n//# sourceMappingURL=rand_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { RandGamma } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a gamma distribution.\n *\n * ```js\n * tf.randomGamma([2, 2], 1).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param alpha The shape parameter of the gamma distribution.\n * @param beta The inverse scale parameter of the gamma distribution. Defaults\n *     to 1.\n * @param dtype The data type of the output. Defaults to float32.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomGamma_(shape, alpha, beta = 1, dtype = 'float32', seed) {\n    if (beta == null) {\n        beta = 1;\n    }\n    if (dtype == null) {\n        dtype = 'float32';\n    }\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new Error(`Unsupported data type ${dtype}`);\n    }\n    const rgamma = new RandGamma(alpha, beta, dtype, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = rgamma.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomGamma = op({ randomGamma_ });\n//# sourceMappingURL=random_gamma.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a normal distribution.\n *\n * ```js\n * tf.randomNormal([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n    if (dtype != null && dtype === 'bool') {\n        throw new Error(`Unsupported data type ${dtype}`);\n    }\n    const randGauss = new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = randGauss.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomNormal = op({ randomNormal_ });\n//# sourceMappingURL=random_normal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { UniformRandom } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a uniform distribution.\n *\n * The generated values follow a uniform distribution in the range [minval,\n * maxval). The lower bound minval is included in the range, while the upper\n * bound maxval is excluded.\n *\n * ```js\n * tf.randomUniform([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param minval The lower bound on the range of random values to generate.\n *   Defaults to 0.\n * @param maxval The upper bound on the range of random values to generate.\n *   Defaults to 1.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomUniform_(shape, minval = 0, maxval = 1, dtype = 'float32', seed) {\n    const res = buffer(shape, dtype);\n    const random = new UniformRandom(minval, maxval, null, seed);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = random.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomUniform = op({ randomUniform_ });\n//# sourceMappingURL=random_uniform.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor1d` as it makes the code more readable.\n *\n * ```js\n * tf.tensor1d([1, 2, 3]).print();\n * ```\n *\n * @param values The values of the tensor. Can be array of numbers,\n *     or a `TypedArray`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor1d(values, dtype) {\n    assertNonNull(values);\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 1) {\n        throw new Error('tensor1d() requires values to be a flat/TypedArray');\n    }\n    const shape = null;\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor1d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Range } from '../kernel_names';\nimport { makeZerosTypedArray } from '../util';\nimport { tensor1d } from './tensor1d';\nimport { zeros } from './zeros';\n/**\n * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.\n *\n * The tensor is a is half-open interval meaning it includes start, but\n * excludes stop. Decrementing ranges and negative step values are also\n * supported.sv\n *\n *\n * ```js\n * tf.range(0, 9, 2).print();\n * ```\n *\n * @param start An integer start value\n * @param stop An integer stop value\n * @param step An integer increment (will default to 1 or -1)\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function range(start, stop, step = 1, dtype = 'float32') {\n    if (step === 0) {\n        throw new Error('Cannot have a step of zero');\n    }\n    const forward = () => {\n        const sameStartStop = start === stop;\n        const increasingRangeNegativeStep = start < stop && step < 0;\n        const decreasingRangePositiveStep = stop < start && step > 1;\n        if (sameStartStop || increasingRangeNegativeStep ||\n            decreasingRangePositiveStep) {\n            return zeros([0], dtype);\n        }\n        const numElements = Math.abs(Math.ceil((stop - start) / step));\n        const values = makeZerosTypedArray(numElements, dtype);\n        if (stop < start && step === 1) {\n            // Auto adjust the step's sign if it hasn't been set\n            // (or was set to 1)\n            step = -1;\n        }\n        values[0] = start;\n        for (let i = 1; i < values.length; i++) {\n            values[i] = values[i - 1] + step;\n        }\n        return tensor1d(values, dtype);\n    };\n    const attrs = { start, stop, step, dtype };\n    return ENGINE.runKernelFunc(forward, {} /* inputs */, null /* grad */, Range, attrs);\n}\n//# sourceMappingURL=range.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reciprocal } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of x element-wise: `1 / x`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, 2]);\n *\n * x.reciprocal().print();  // or tf.reciprocal(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction reciprocal_(x) {\n    const $x = convertToTensor(x, 'x', 'reciprocal');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.reciprocal($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Reciprocal);\n}\nexport const reciprocal = op({ reciprocal_ });\n//# sourceMappingURL=reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { cast } from './cast';\n/**\n * Computes rectified linear element-wise: `max(x, 0)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.relu().print();  // or tf.relu(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu_(x) {\n    const $x = convertToTensor(x, 'x', 'relu');\n    const forward = (backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'bool') {\n            return cast($x, 'int32');\n        }\n        return backend.relu($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Relu);\n}\nexport const relu = op({ relu_ });\n//# sourceMappingURL=relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu6 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes rectified linear 6 element-wise: `min(max(x, 0), 6)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 8]);\n *\n * x.relu6().print();  // or tf.relu6(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu6_(x) {\n    const $x = convertToTensor(x, 'x', 'relu6');\n    const forward = (backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'bool') {\n            return cast($x, 'int32');\n        }\n        return backend.relu6($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Relu6);\n}\nexport const relu6 = op({ relu6_ });\n//# sourceMappingURL=relu6.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reverse } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { clone } from './clone';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Reverses a `tf.Tensor` along a specified axis.\n *\n * Also available are stricter rank-specific methods that assert that `x` is\n * of the given rank:\n *   - `tf.reverse1d`\n *   - `tf.reverse2d`\n *   - `tf.reverse3d`\n *   - `tf.reverse4d`\n *\n * Except `tf.reverse1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.reverse().print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.reverse(axis).print();\n * ```\n * @param x The input tensor to be reversed.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction reverse_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    const forward = (backend) => {\n        const axes = parseAxisParam(axis, $x.shape);\n        if ($x.rank === 0) {\n            return clone($x);\n        }\n        const res = backend.reverse($x, axes);\n        return reshape(res, $x.shape);\n    };\n    const inputs = { x: $x };\n    const attrs = { dims: axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Reverse, attrs);\n}\nexport const reverse = op({ reverse_ });\n//# sourceMappingURL=reverse.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor1D`.\n *\n * @param x The input tensor.\n */\nfunction reverse1d_(x) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);\n    return reverse($x, 0);\n}\nexport const reverse1d = op({ reverse1d_ });\n//# sourceMappingURL=reverse_1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor2D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse2d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse2d = op({ reverse2d_ });\n//# sourceMappingURL=reverse_2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor3D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse3d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse3d = op({ reverse3d_ });\n//# sourceMappingURL=reverse_3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor4D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse4d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse4d = op({ reverse4d_ });\n//# sourceMappingURL=reverse_4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Round } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes round of input `tf.Tensor` element-wise: `round(x)`.\n * It implements banker's rounding.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.round().print();  // or tf.round(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction round_(x) {\n    const $x = convertToTensor(x, 'x', 'round');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.round($x), inputs, null /* grad */, Round);\n}\nexport const round = op({ round_ });\n//# sourceMappingURL=round.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Rsqrt } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of square root of the input `tf.Tensor` element-wise:\n * `y = 1 / sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.rsqrt().print();  // or tf.rsqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction rsqrt_(x) {\n    const $x = convertToTensor(x, 'x', 'rsqrt');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.rsqrt($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Rsqrt);\n}\nexport const rsqrt = op({ rsqrt_ });\n//# sourceMappingURL=rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Selu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes scaled exponential linear element-wise.\n *\n * `x < 0 ? scale * alpha * (exp(x) - 1) : x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.selu().print();  // or tf.selu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction selu_(x) {\n    const $x = convertToTensor(x, 'x', 'selu');\n    const forward = (backend, save) => {\n        const res = backend.selu($x);\n        save([$x]);\n        return res;\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Selu);\n}\nexport const selu = op({ selu_ });\n//# sourceMappingURL=selu.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport { depthwiseConv2d } from './depthwise_conv2d';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * 2-D convolution with separable filters.\n *\n * Performs a depthwise convolution that acts separately on channels followed\n * by a pointwise convolution that mixes channels. Note that this is\n * separability between dimensions [1, 2] and 3, not spatial separability\n * between dimensions 1 and 2.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is\n *     the filter used in the first step.\n * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape\n *     `[1, 1, inChannels * channelMultiplier, outChannels]`. This is\n *     the filter used in the second step.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation = [1, 1], dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'separableConv2d');\n    const $depthwiseFilter = convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');\n    const $pointwiseFilter = convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    if (dataFormat === 'NCHW') {\n        throw new Error('separableConv2d currently does not support dataFormat NCHW; only ' +\n            'NHWC is supported');\n    }\n    util.assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but ` +\n        `got rank ${$depthwiseFilter.rank}.`);\n    util.assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but ` +\n        `got rank ${$depthwiseFilter.rank}.`);\n    util.assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter ` +\n        ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n    util.assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise ` +\n        `filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n    const inChannels = $depthwiseFilter.shape[2];\n    const channelMultiplier = $depthwiseFilter.shape[3];\n    util.assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter ` +\n        `must be ${inChannels * channelMultiplier}, ` +\n        `but got ${$pointwiseFilter.shape[2]}.`);\n    const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);\n    const pointwiseStride = 1;\n    const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const separableConv2d = op({ separableConv2d_ });\n//# sourceMappingURL=separable_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\n/**\n * Computes the difference between two lists of numbers.\n *\n * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`\n * that represents all values that are in `x` but not in `y`. The returned\n * Tensor `out` is sorted in the same order that the numbers appear in `x`\n * (duplicates are preserved). This operation also returns a Tensor indices that\n * represents the position of each out element in `x`. In other words:\n *\n * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`\n *\n * ```js\n * const x = [1, 2, 3, 4, 5, 6];\n * const y = [1, 3, 5];\n *\n * const [out, indices] = await tf.setdiff1dAsync(x, y);\n * out.print(); // [2, 4, 6]\n * indices.print(); // [1, 3, 5]\n * ```\n *\n * @param x 1-D Tensor. Values to keep.\n * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the\n *     output.\n * @returns Promise of Tensor tuple [out, indices].\n *  out: Tensor with the same type as x.\n *  indices: A Tensor of type int32.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nasync function setdiff1dAsync_(x, y) {\n    const $x = convertToTensor(x, 'x', 'setdiff1d');\n    const $y = convertToTensor(y, 'y', 'setdiff1d');\n    util.assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);\n    util.assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);\n    util.assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);\n    const xVals = await $x.data();\n    const yVals = await $y.data();\n    const ySet = new Set(yVals);\n    let outputSize = 0;\n    for (let i = 0; i < xVals.length; i++) {\n        if (!ySet.has(xVals[i])) {\n            outputSize++;\n        }\n    }\n    const buffer = new TensorBuffer([outputSize], $x.dtype);\n    const indices = new TensorBuffer([outputSize], 'int32');\n    for (let i = 0, p = 0; i < xVals.length; i++) {\n        if (!ySet.has(xVals[i])) {\n            buffer.values[p] = xVals[i];\n            indices.values[p] = i;\n            p++;\n        }\n    }\n    return [buffer.toTensor(), indices.toTensor()];\n}\nexport const setdiff1dAsync = setdiff1dAsync_;\n//# sourceMappingURL=setdiff1d_async.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sign } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns an element-wise indication of the sign of a number.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);\n *\n * x.sign().print();  // or tf.sign(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sign_(x) {\n    const $x = convertToTensor(x, 'x', 'sign');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.sign($x), inputs, null /* grad */, Sign);\n}\nexport const sign = op({ sign_ });\n//# sourceMappingURL=sign.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sin of the input Tensor element-wise: `sin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.sin().print();  // or tf.sin(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sin_(x) {\n    const $x = convertToTensor(x, 'x', 'sin');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sin($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sin);\n}\nexport const sin = op({ sin_ });\n//# sourceMappingURL=sin.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.sinh().print();  // or tf.sinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sinh_(x) {\n    const $x = convertToTensor(x, 'x', 'sinh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sinh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sinh);\n}\nexport const sinh = op({ sinh_ });\n//# sourceMappingURL=sinh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 1D slice from 1D array starting at coordinates `begin` and is\n * of length `size`. See `slice` for details.\n */\nfunction slice1d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice1d');\n    util.assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, [begin], [size]);\n}\nexport const slice1d = op({ slice1d_ });\n//# sourceMappingURL=slice1d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 2D slice from a 2D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice2d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice2d');\n    util.assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice2d = op({ slice2d_ });\n//# sourceMappingURL=slice2d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 3D slice from a 3D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice3d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice3d');\n    util.assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice3d = op({ slice3d_ });\n//# sourceMappingURL=slice3d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 4D slice from a 4D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice4d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice4d');\n    util.assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice4d = op({ slice4d_ });\n//# sourceMappingURL=slice4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the softmax normalized vector given the logits.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction softmax_(logits, dim = -1) {\n    const $logits = convertToTensor(logits, 'logits', 'softmax', 'float32');\n    if (dim === -1) {\n        dim = $logits.rank - 1;\n    }\n    if (dim !== $logits.rank - 1) {\n        throw Error('Softmax along a non-last dimension is not yet supported. ' +\n            `Logits was rank ${$logits.rank} and dim was ${dim}`);\n    }\n    const inputs = { logits: $logits };\n    const attrs = { dim };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const y = backend.softmax($logits, dim);\n        save([y]);\n        return y;\n    }, inputs, null /* grad */, Softmax, attrs);\n}\nexport const softmax = op({ softmax_ });\n//# sourceMappingURL=softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\n/**\n * Fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the inner-most\n * dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.fft().print();  // tf.spectral.fft(x).print();\n * ```\n * @param input The complex input to compute an fft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction fft_(input) {\n    assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.fft() must be complex64 ` +\n        `but got ${input.dtype}.`);\n    const inputs = { input };\n    return ENGINE.runKernelFunc(backend => {\n        // Collapse all outer dimensions to a single batch dimension.\n        const innerDimensionSize = input.shape[input.shape.length - 1];\n        const batch = input.size / innerDimensionSize;\n        const input2D = input.as2D(batch, innerDimensionSize);\n        const result = backend.fft(input2D);\n        return result.reshape(input.shape);\n    }, inputs, null /* gradient */, FFT);\n}\nexport const fft = op({ fft_ });\n//# sourceMappingURL=fft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { IFFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Inverse fast Fourier transform.\n *\n * Computes the inverse 1-dimensional discrete Fourier transform over the\n * inner-most dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.ifft().print();  // tf.spectral.ifft(x).print();\n * ```\n * @param input The complex input to compute an ifft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction ifft_(input) {\n    assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.ifft() must be complex64 ` +\n        `but got ${input.dtype}.`);\n    const inputs = { input };\n    return ENGINE.runKernelFunc(backend => {\n        // Collapse all outer dimensions to a single batch dimension.\n        const innerDimensionSize = input.shape[input.shape.length - 1];\n        const batch = input.size / innerDimensionSize;\n        const input2D = reshape(input, [batch, innerDimensionSize]);\n        const result = backend.ifft(input2D);\n        return reshape(result, input.shape);\n    }, inputs, null /* gradient */, IFFT);\n}\nexport const ifft = op({ ifft_ });\n//# sourceMappingURL=ifft.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { reverse } from '../reverse';\nimport { scalar } from '../scalar';\nimport { slice } from '../slice';\nimport { ifft } from './ifft';\n/**\n * Inversed real value input fast Fourier transform.\n *\n * Computes the 1-dimensional inversed discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([0, 0, 0]);\n * const x = tf.complex(real, imag);\n *\n * x.irfft().print();\n * ```\n * @param input The real value input to compute an irfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction irfft_(input) {\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n    let ret;\n    if (innerDimensionSize <= 2) {\n        const complexInput = reshape(input, [batch, innerDimensionSize]);\n        ret = ifft(complexInput);\n    }\n    else {\n        // The length of unique components of the DFT of a real-valued signal\n        // is 2 * (input_len - 1)\n        const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n        const realInput = reshape(real(input), [batch, innerDimensionSize]);\n        const imagInput = reshape(imag(input), [batch, innerDimensionSize]);\n        const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);\n        const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));\n        const r = concat([realInput, realConjugate], 1);\n        const i = concat([imagInput, imagConjugate], 1);\n        const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);\n        ret = ifft(complexInput);\n    }\n    ret = real(ret);\n    // reshape the result if the input is 3D tensor.\n    if (input.rank === 3 && input.shape[0] !== 0) {\n        const temp = ret;\n        const batch = input.shape[0];\n        ret = reshape(ret, [batch, ret.shape[0] / batch, ret.shape[1]]);\n        temp.dispose();\n    }\n    return ret;\n}\nexport const irfft = op({ irfft_ });\n//# sourceMappingURL=irfft.js.map", "import { assert } from '../util';\n/**\n * Prepare the split size array. When the input is a number, the axis is evenly\n * divided among the split size. When the input contains the negative value, the\n * rest of the axis is allocated toward that.\n */\nexport function prepareSplitSize(x, numOrSizeSplits, axis = 0) {\n    let splitSizes = [];\n    if (typeof (numOrSizeSplits) === 'number') {\n        assert(x.shape[axis] % numOrSizeSplits === 0, () => 'Number of splits must evenly divide the axis.');\n        splitSizes =\n            new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);\n    }\n    else {\n        const numOfNegs = numOrSizeSplits.reduce((count, value) => {\n            if (value === -1) {\n                count += 1;\n            }\n            return count;\n        }, 0);\n        assert(numOfNegs <= 1, () => 'There should be only one negative value in split array.');\n        const negIndex = numOrSizeSplits.indexOf(-1);\n        // Allow the number of split array to be -1, which indicates the rest\n        // of dimension is allocated to that split.\n        if (negIndex !== -1) {\n            const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);\n            numOrSizeSplits[negIndex] = x.shape[axis] - total;\n        }\n        assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => 'The sum of sizes must match the size of the axis dimension.');\n        splitSizes = numOrSizeSplits;\n    }\n    return splitSizes;\n}\n//# sourceMappingURL=split_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SplitV } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { op } from './operation';\nimport { prepareSplitSize } from './split_util';\n/**\n * Splits a `tf.Tensor` into sub tensors.\n *\n * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`\n * into `numOrSizeSplits` smaller tensors.\n * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.\n *\n * If `numOrSizeSplits` is a number array, splits `x` into\n * `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the\n * same size as `x` except along dimension `axis` where the size is\n * `numOrSizeSplits[i]`.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);\n * const [a, b] = tf.split(x, 2, 1);\n * a.print();\n * b.print();\n *\n * const [c, d, e] = tf.split(x, [1, 2, 1], 1);\n * c.print();\n * d.print();\n * e.print();\n * ```\n *\n * @param x The input tensor to split.\n * @param numOrSizeSplits Either an integer indicating the number of\n * splits along the axis or an array of integers containing the sizes of\n * each output tensor along the axis. If a number then it must evenly divide\n * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.\n * Can contain one -1 indicating that dimension is to be inferred.\n * @param axis The dimension along which to split. Defaults to 0 (the first\n * dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction split_(x, numOrSizeSplits, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'split');\n    const forward = (backend, _) => {\n        const $axis = parseAxisParam(axis, $x.shape)[0];\n        const splitSizes = prepareSplitSize($x, numOrSizeSplits, $axis);\n        return backend.split($x, splitSizes, $axis);\n    };\n    const inputs = { x: $x };\n    const attr = { numOrSizeSplits, axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, SplitV, attr);\n}\nexport const split = op({ split_ });\n//# sourceMappingURL=split.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../../util';\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { split } from '../split';\nimport { zeros } from '../zeros';\nimport { zerosLike } from '../zeros_like';\nimport { fft } from './fft';\n/**\n * Real value input fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * real.rfft().print();\n * ```\n * @param input The real value input to compute an rfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction rfft_(input, fftLength) {\n    assert(input.dtype === 'float32', () => `The dtype for rfft() must be real value but got ${input.dtype}`);\n    let innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n    let adjustedInput;\n    if (fftLength != null && fftLength < innerDimensionSize) {\n        // Need to crop\n        const begin = input.shape.map(v => 0);\n        const size = input.shape.map(v => v);\n        size[input.shape.length - 1] = fftLength;\n        adjustedInput = slice(input, begin, size);\n        innerDimensionSize = fftLength;\n    }\n    else if (fftLength != null && fftLength > innerDimensionSize) {\n        // Need to pad with zeros\n        const zerosShape = input.shape.map(v => v);\n        zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;\n        adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);\n        innerDimensionSize = fftLength;\n    }\n    else {\n        adjustedInput = input;\n    }\n    // Complement the input with zero imaginary numbers.\n    const zerosInput = zerosLike(adjustedInput);\n    const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);\n    const ret = fft(complexInput);\n    // Exclude complex conjugations. These conjugations are put symmetrically.\n    const half = Math.floor(innerDimensionSize / 2) + 1;\n    const realValues = real(ret);\n    const imagValues = imag(ret);\n    const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);\n    const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);\n    const outputShape = adjustedInput.shape.slice();\n    outputShape[adjustedInput.shape.length - 1] = half;\n    return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);\n}\nexport const rfft = op({ rfft_ });\n//# sourceMappingURL=rfft.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sqrt } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.sqrt().print();  // or tf.sqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sqrt_(x) {\n    const $x = convertToTensor(x, 'x', 'sqrt');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sqrt($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sqrt);\n}\nexport const sqrt = op({ sqrt_ });\n//# sourceMappingURL=sqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SquaredDifference } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns (a - b) * (a - b) element-wise.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * ```js\n * // Broadcast squared difference  a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction squaredDifference_(a, b) {\n    let $a = convertToTensor(a, 'a', 'squaredDifference');\n    let $b = convertToTensor(b, 'b', 'squaredDifference');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.squaredDifference($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = {};\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, SquaredDifference, attrs);\n}\nexport const squaredDifference = op({ squaredDifference_ });\n//# sourceMappingURL=squared_difference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { squeezeShape } from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Removes dimensions of size 1 from the shape of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);\n * x.squeeze().print();\n * ```\n *\n * @param x The input tensor to be squeezed.\n * @param axis An optional list of numbers. If specified, only\n *     squeezes the dimensions listed. The dimension index starts at 0. It\n * is an error to squeeze a dimension that is not 1.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction squeeze_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'squeeze');\n    return reshape($x, squeezeShape($x.shape, axis).newShape);\n}\nexport const squeeze = op({ squeeze_ });\n//# sourceMappingURL=squeeze.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensorArray } from '../tensor_util_env';\nimport * as util from '../util';\nimport { concat } from './concat';\nimport { expandDims } from './expand_dims';\nimport { op } from './operation';\n/**\n * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.stack([a, b, c]).print();\n * ```\n *\n * @param tensors A list of tensor objects with the same shape and dtype.\n * @param axis The axis to stack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction stack_(tensors, axis = 0) {\n    const $tensors = convertToTensorArray(tensors, 'tensors', 'stack');\n    util.assert($tensors.length >= 1, () => 'Pass at least one tensor to tf.stack');\n    if ($tensors.length === 1) {\n        return expandDims($tensors[0], axis);\n    }\n    const rank = $tensors[0].rank;\n    const shape = $tensors[0].shape;\n    const dtype = $tensors[0].dtype;\n    util.assert(axis <= rank, () => 'Axis must be <= rank of the tensor');\n    $tensors.forEach(t => {\n        util.assertShapesMatch(shape, t.shape, 'All tensors passed to stack must have matching shapes');\n        util.assert(dtype === t.dtype, () => 'All tensors passed to stack must have matching dtypes');\n    });\n    const expandedTensors = $tensors.map(t => expandDims(t, axis));\n    // Stack exists in the TensorFlow C++ API\n    // (https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/stack) but not\n    // in\n    // https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/core/ops/ops.pbtxt.\n    // Therefore we are treating it like a high-level op rather than\n    // creating a dedicated stack kernel.\n    return concat(expandedTensors, axis);\n}\nexport const stack = op({ stack_ });\n//# sourceMappingURL=stack.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Step } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha * x`\n *\n * ```js\n * const x = tf.tensor1d([0, 2, -1, -3]);\n *\n * x.step(.5).print();  // or tf.step(x, .5)\n * ```\n * @param x The input tensor.\n * @param alpha The gradient when input is negative.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction step_(x, alpha = 0.0) {\n    const $x = convertToTensor(x, 'x', 'step');\n    const inputs = { x: $x };\n    const attrs = { alpha };\n    return ENGINE.runKernelFunc(backend => backend.step($x, alpha), inputs, null /* grad */, Step, attrs);\n}\nexport const step = op({ step_ });\n//# sourceMappingURL=step.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { StridedSlice } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { slice } from './slice';\nimport { computeOutShape, getNormalizedAxes, maskToAxes } from './slice_util';\n/**\n * Extracts a strided slice of a tensor.\n *\n * Roughly speaking, this op extracts a slice of size (end-begin)/stride from\n * the given input tensor (x). Starting at the location specified by begin the\n * slice continues by adding stride to the index until all dimensions are not\n * less than end. Note that a stride can be negative, which causes a reverse\n * slice.\n *\n * ```js\n * const t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],\n *    [3, 2, 3]);\n * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print()  // [[[3, 3, 3]]]\n * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print()  // [[[3, 3, 3],\n *                                                     // [4, 4, 4]]]\n * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],\n *                                                     // [3, 3, 3]]]\n * ```\n *\n * @param x The tensor to stride slice.\n * @param begin The coordinates to start the slice from.\n * @param end: The coordinates to end the slice at.\n * @param strides: The size of the slice.\n * @param beginMask: If the ith bit of beginMask is set, begin[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param endMask: If the ith bit of endMask is set, end[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param shrinkAxisMask: a bitmask where bit i implies that\n * the ith specification should shrink the dimensionality. begin and end must\n * imply a slice of size 1 in the dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {\n    let $x = convertToTensor(x, 'x', 'stridedSlice');\n    const forward = (backend) => {\n        if (strides == null) {\n            strides = new Array(begin.length);\n        }\n        const ellipsisAxes = maskToAxes(ellipsisMask);\n        if (ellipsisAxes.length > 1) {\n            throw new Error('Multiple ellipses in slice is not allowed.');\n        }\n        if (ellipsisMask !== 0 && newAxisMask !== 0) {\n            throw new Error('Using both ellipsisMask and newAxisMask is not yet supported.');\n        }\n        if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {\n            throw new Error('Using both ellipsisMask and shrinkAxisMask is not yet supported.');\n        }\n        const numInterpolatedAxes = $x.rank - begin.length;\n        // Expand the dims of x based on the newAxisMask.\n        const expandAxes = maskToAxes(newAxisMask);\n        const newShape = $x.shape.slice();\n        expandAxes.forEach(axis => {\n            begin[axis] = 0;\n            end[axis] = 1;\n            newShape.splice(axis, 0, 1);\n        });\n        $x = reshape($x, newShape);\n        const { begin: normalizedBegin, end: normalizedEnd, strides: normalizedStrides } = getNormalizedAxes($x.shape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask);\n        begin = normalizedBegin;\n        end = normalizedEnd;\n        strides = normalizedStrides;\n        const shrinkAxes = maskToAxes(shrinkAxisMask);\n        // Adjust the ends based on the shrink mask.\n        shrinkAxes.forEach(axis => {\n            end[axis] = begin[axis] + 1;\n            strides[axis] = 1;\n        });\n        // Figure out the output shape.\n        const size = computeOutShape(begin, end, strides);\n        // Remove the axes based on shrinkMask.\n        const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);\n        const nonStrided = strides.every(v => v === 1);\n        if (nonStrided) {\n            return reshape(slice($x, begin, size), outShape);\n        }\n        const res = backend.stridedSlice($x, begin, end, strides);\n        return reshape(res, outShape);\n    };\n    const inputs = { x: $x };\n    const attrs = {\n        begin,\n        end,\n        strides,\n        beginMask,\n        endMask,\n        ellipsisMask,\n        newAxisMask,\n        shrinkAxisMask\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, StridedSlice, attrs);\n}\nexport const stridedSlice = op({ stridedSlice_ });\n//# sourceMappingURL=strided_slice.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.tan().print();  // or tf.tan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tan_(x) {\n    const $x = convertToTensor(x, 'x', 'tan');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.tan($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Tan);\n}\nexport const tan = op({ tan_ });\n//# sourceMappingURL=tan.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor2d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor2d([[1, 2], [3, 4]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor2d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 2) {\n        throw new Error('tensor2d() requires shape to have two numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n        throw new Error('tensor2d() requires values to be number[][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor2d() requires shape to be provided when `values` ' +\n            'are a flat/TypedArray');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor2d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor4d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor4d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 4) {\n        throw new Error('tensor4d() requires shape to have four numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n        throw new Error('tensor4d() requires values to be number[][][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor4d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor5d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor5d([[[[[1], [2]], [[3], [4]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor5d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 5) {\n        throw new Error('tensor5d() requires shape to have five numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n        throw new Error('tensor5d() requires values to be ' +\n            'number[][][][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor5d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor5d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor6d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor6d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 6) {\n        throw new Error('tensor6d() requires shape to have six numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n        throw new Error('tensor6d() requires values to be number[][][][][][] or ' +\n            'flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor6d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    shape = shape ||\n        inferredShape;\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor6d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { TopK } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Finds the values and indices of the `k` largest entries along the last\n * dimension.\n *\n * If the input is a vector (rank=1), finds the k largest entries in the vector\n * and outputs their values and indices as vectors. Thus values[j] is the j-th\n * largest entry in input, and its index is indices[j].\n * For higher rank inputs, computes the top k entries along the last dimension.\n *\n * If two elements are equal, the lower-index element appears first.\n *\n * ```js\n * const a = tf.tensor2d([[1, 5], [4, 3]]);\n * const {values, indices} = tf.topk(a);\n * values.print();\n * indices.print();\n * ```\n * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.\n * @param k Number of top elements to look for along the last dimension.\n * @param sorted If true, the resulting `k` elements will be sorted by the\n *     values in descending order.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction topk_(x, k = 1, sorted = true) {\n    const $x = convertToTensor(x, 'x', 'topk');\n    if ($x.rank === 0) {\n        throw new Error('topk() expects the input to be of rank 1 or higher');\n    }\n    const lastDim = $x.shape[$x.shape.length - 1];\n    if (k > lastDim) {\n        throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) ` +\n            `but got ${k}`);\n    }\n    const inputs = { x: $x };\n    const attrs = { k, sorted };\n    const [values, indices] = ENGINE.runKernelFunc(b => b.topk($x, k, sorted), inputs, null /* grad */, TopK, attrs);\n    return { values, indices };\n}\nexport const topk = op({ topk_ });\n//# sourceMappingURL=topk.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a truncated normal\n * distribution.\n *\n * ```js\n * tf.truncatedNormal([2, 2]).print();\n * ```\n *\n * The generated values follow a normal distribution with specified mean and\n * standard deviation, except that values whose magnitude is more than 2\n * standard deviations from the mean are dropped and re-picked.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output tensor.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction truncatedNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n    if (dtype != null && dtype === 'bool') {\n        throw new Error(`Unsupported data type $ { dtype }`);\n    }\n    const randGauss = new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = randGauss.nextValue();\n    }\n    return res.toTensor();\n}\nexport const truncatedNormal = op({ truncatedNormal_ });\n//# sourceMappingURL=truncated_normal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unique } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert } from '../util';\nimport { op } from './operation';\n/**\n * Finds unique elements along an axis of a tensor.\n *\n * It returns a tensor `values` containing all of the unique elements along the\n * `axis` of the given tensor `x` in the same order that they occur along the\n * `axis` in `x`; `x` does not need to be sorted. It also returns a tensor\n * `indices` the same size as the number of the elements in `x` along the `axis`\n * dimension. It contains the index in the unique output `values`.\n *\n * ```js\n * // A 1-D tensor\n * const a = tf.tensor1d([1, 1, 2, 4, 4, 4, 7, 8, 8]);\n * const {values, indices} = tf.unique(a);\n * values.print();   // [1, 2, 4, 7, 8,]\n * indices.print();  // [0, 0, 1, 2, 2, 2, 3, 4, 4]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=0\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 0)\n * values.print();   // [[1, 0, 0],\n *                   //  [2, 0, 0]]\n * indices.print();  // [0, 0, 1]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=1\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 1)\n * values.print();   // [[1, 0],\n *                   //  [1, 0],\n *                   //  [2, 0]]\n * indices.print();  // [0, 1, 1]\n * ```\n * @param x A tensor (int32, string, bool).\n * @param axis The axis of the tensor to find the unique elements.\n * @returns [uniqueElements, indices] (see above for details)\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction unique_(x, axis = 0) {\n    // x can be of any dtype, thus null as the last argument.\n    const $x = convertToTensor(x, 'x', 'unique', null);\n    assert($x.rank > 0, () => 'The input tensor must be at least 1D');\n    const inputs = { x: $x };\n    const attrs = { axis };\n    const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs);\n    return { values, indices };\n}\nexport const unique = op({ unique_ });\n//# sourceMappingURL=unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { UnsortedSegmentSum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, isInt } from '../util';\nimport { op } from './operation';\n/**\n * Computes the sum along segments of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');\n * const numSegments = 3;\n *\n * x.unsortedSegmentSum(segmentIds, numSegments).print()\n * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)\n * ```\n * @param x The `tf.Tensor` that will be summed along its segments.\n * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s\n * dimension along the `axis`.  Maps each element of `x` to a segment.\n * @param numSegments The number of distinct `segmentIds`.\n *\n * @doc {heading: 'Operations', subheading: 'Segment'}\n */\nfunction unsortedSegmentSum_(x, segmentIds, numSegments) {\n    const $x = convertToTensor(x, 'x', 'unsortedSegmentSum');\n    const $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');\n    assert(isInt(numSegments), () => 'numSegments must be of dtype int');\n    const inputs = { x: $x, segmentIds: $segmentIds };\n    const attrs = { numSegments };\n    const forward = (backend, save) => {\n        const res = backend.unsortedSegmentSum($x, $segmentIds, numSegments);\n        save([$segmentIds]);\n        return res;\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, UnsortedSegmentSum, attrs);\n}\nexport const unsortedSegmentSum = op({ unsortedSegmentSum_ });\n//# sourceMappingURL=unsorted_segment_sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unpack } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * tf.unstack(a).forEach(tensor => tensor.print());\n * ```\n *\n * @param x A tensor object.\n * @param axis The axis to unstack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction unstack_(x, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'unstack');\n    util.assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);\n    if (axis < 0) {\n        axis += $x.shape.length;\n    }\n    const inputs = { value: $x };\n    const attrs = { axis };\n    const forward = (backend) => backend.unstack($x, axis);\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Unpack, attrs);\n}\nexport const unstack = op({ unstack_ });\n//# sourceMappingURL=unstack.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\n/**\n * Creates a new variable with the provided initial value.\n * ```js\n * const x = tf.variable(tf.tensor([1, 2, 3]));\n * x.assign(tf.tensor([4, 5, 6]));\n *\n * x.print();\n * ```\n *\n * @param initialValue Initial value for the tensor.\n * @param trainable If true, optimizers are allowed to update it.\n * @param name Name of the variable. Defaults to a unique id.\n * @param dtype If set, initialValue will be converted to the given type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function variable(initialValue, trainable = true, name, dtype) {\n    return ENGINE.makeVariable(initialValue, trainable, name, dtype);\n}\n//# sourceMappingURL=variable.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the Where kernel shared between cpu and webgl */\nimport { buffer } from '../ops/buffer';\nexport function whereImpl(condShape, condVals) {\n    const indices = [];\n    for (let i = 0; i < condVals.length; i++) {\n        if (condVals[i]) {\n            indices.push(i);\n        }\n    }\n    const inBuffer = buffer(condShape, 'int32');\n    const out = buffer([indices.length, condShape.length], 'int32');\n    for (let i = 0; i < indices.length; i++) {\n        const loc = inBuffer.indexToLoc(indices[i]);\n        const offset = i * condShape.length;\n        out.values.set(loc, offset);\n    }\n    return out.toTensor();\n}\n//# sourceMappingURL=where_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { whereImpl } from '../backends/where_impl';\nimport { convertToTensor } from '../tensor_util_env';\n/**\n * Returns the coordinates of true elements of condition.\n *\n * The coordinates are returned in a 2-D tensor where the first dimension (rows)\n * represents the number of true elements, and the second dimension (columns)\n * represents the coordinates of the true elements. Keep in mind, the shape of\n * the output tensor can vary depending on how many true values there are in\n * input. Indices are output in row-major order. The resulting tensor has the\n * shape `[numTrueElems, condition.rank]`.\n *\n * This is analogous to calling the python `tf.where(cond)` without an x or y.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const result = await tf.whereAsync(cond);\n * result.print();\n * ```\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nasync function whereAsync_(condition) {\n    const $condition = convertToTensor(condition, 'condition', 'whereAsync', 'bool');\n    const vals = await $condition.data();\n    const res = whereImpl($condition.shape, vals);\n    if (condition !== $condition) {\n        $condition.dispose();\n    }\n    return res;\n}\nexport const whereAsync = whereAsync_;\n//# sourceMappingURL=where_async.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { gather } from './gather';\nimport { reshape } from './reshape';\nimport { squeeze } from './squeeze';\nimport { whereAsync } from './where_async';\n/**\n * Apply boolean mask to tensor.\n *\n * ```js\n * const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);\n * const mask = tf.tensor1d([1, 0, 1], 'bool');\n * const result = await tf.booleanMaskAsync(tensor, mask);\n * result.print();\n * ```\n *\n * @param tensor N-D tensor.\n * @param mask K-D boolean tensor, K <= N and K must be known statically.\n * @param axis A 0-D int Tensor representing the axis in tensor to mask from.\n *     By default, axis is 0 which will mask from the first dimension.\n *     Otherwise K + axis <= N.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nasync function booleanMaskAsync_(tensor, mask, axis) {\n    const $tensor = convertToTensor(tensor, 'tensor', 'boolMask');\n    const $mask = convertToTensor(mask, 'mask', 'boolMask', 'bool');\n    const axisFrom = axis == null ? 0 : axis;\n    const maskDim = $mask.rank;\n    const tensorShape = $tensor.shape;\n    util.assert(maskDim > 0, () => 'mask cannot be scalar');\n    util.assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);\n    let leadingSize = 1;\n    for (let i = axisFrom; i < axisFrom + maskDim; i++) {\n        leadingSize *= tensorShape[i];\n    }\n    const targetTensorShape = tensorShape.slice(0, axisFrom)\n        .concat([leadingSize], tensorShape.slice(axisFrom + maskDim));\n    const reshapedTensor = reshape($tensor, targetTensorShape);\n    const reshapedMask = reshape($mask, [-1]);\n    const positivePositions = await whereAsync(reshapedMask);\n    const indices = squeeze(positivePositions, [1]);\n    const res = gather(reshapedTensor, indices, axisFrom);\n    // Ensure no memory leak.\n    if (tensor !== $tensor) {\n        $tensor.dispose();\n    }\n    if (mask !== $mask) {\n        $mask.dispose();\n    }\n    indices.dispose();\n    reshapedTensor.dispose();\n    reshapedMask.dispose();\n    positivePositions.dispose();\n    return res;\n}\nexport const booleanMaskAsync = booleanMaskAsync_;\n//# sourceMappingURL=boolean_mask.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../globals';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertShapesMatch } from '../util';\nimport { equal } from './equal';\nimport { greater } from './greater';\nimport { greaterEqual } from './greater_equal';\nimport { less } from './less';\nimport { lessEqual } from './less_equal';\nimport { notEqual } from './not_equal';\nimport { op } from './operation';\n/**\n * @deprecated\n * Strict version of `tf.notEqual` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction notEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'notEqualStrict');\n    const $b = convertToTensor(b, 'b', 'notEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in notEqualStrict: ');\n    return notEqual($a, $b);\n}\n/**\n * @deprecated\n * Strict version of `tf.less` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction lessStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'lessStrict');\n    const $b = convertToTensor(b, 'b', 'lessStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in lessStrict: ');\n    return less($a, $b);\n}\nfunction equalStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'equalStrict');\n    const $b = convertToTensor(b, 'b', 'equalStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in equalStrict: ');\n    return equal($a, $b);\n}\nfunction lessEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'lessEqualStrict');\n    const $b = convertToTensor(b, 'b', 'lessEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in lessEqualStrict: ');\n    return lessEqual($a, $b);\n}\nfunction greaterStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'greaterStrict');\n    const $b = convertToTensor(b, 'b', 'greaterStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in greaterStrict: ');\n    return greater($a, $b);\n}\nfunction greaterEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'greaterEqualStrict');\n    const $b = convertToTensor(b, 'b', 'greaterEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in greaterEqualStrict: ');\n    return greaterEqual($a, $b);\n}\nexport const equalStrict = op({ equalStrict_ });\nexport const greaterEqualStrict = op({ greaterEqualStrict_ });\nexport const greaterStrict = op({ greaterStrict_ });\nexport const lessEqualStrict = op({ lessEqualStrict_ });\nexport const lessStrict = op({ lessStrict_ });\nexport const notEqualStrict = op({ notEqualStrict_ });\n//# sourceMappingURL=compare.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../globals';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { maximum } from './maximum';\nimport { minimum } from './minimum';\nimport { mod } from './mod';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { squaredDifference } from './squared_difference';\nimport { sub } from './sub';\n/**\n * @deprecated\n * Adds two `tf.Tensor`s element-wise, A + B.\n *\n * Inputs must be the same shape. For broadcasting support, use add() instead.\n *\n * @param a The first Tensor to add element-wise.\n * @param b The second Tensor to add element-wise.\n */\nfunction addStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'addStrict');\n    const $b = convertToTensor(b, 'b', 'addStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in addStrict: ');\n    return add($a, $b);\n}\n/**\n * @deprecated\n * Subtracts two `tf.Tensor`s element-wise, A - B. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.sub` instead.\n *\n * @param a The first Tensor to subtract element-wise.\n * @param b The second Tensor to subtract element-wise.\n */\nfunction subStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'subStrict');\n    const $b = convertToTensor(b, 'b', 'subStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in subStrict: ');\n    return sub($a, $b);\n}\n/**\n * @deprecated\n * Computes the power of one `tf.Tensor` to another. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.pow` instead.\n *\n * @param base The base tensor to pow element-wise.\n * @param exp The exponent tensor to pow element-wise.\n */\nfunction powStrict_(base, exp) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    util.assertShapesMatch(base.shape, exp.shape, 'Error in powStrict: ');\n    return pow(base, exp);\n}\n/**\n * @deprecated\n * Multiplies two `tf.Tensor`s element-wise, A * B.\n *\n * Inputs must be the same shape. For broadcasting support, use `tf.mul`.\n *\n * @param a The first tensor to multiply.\n * @param b The first tensor to multiply. Must have the same\n *    dtype as `a`.\n */\nfunction mulStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'mul');\n    const $b = convertToTensor(b, 'b', 'mul');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in multiplyStrict: ');\n    return mul($a, $b);\n}\n/**\n * @deprecated\n * Divides two `tf.Tensor`s element-wise, A / B. Inputs must\n * be the same shape.\n *\n * @param a The first tensor as the numerator for element-wise division.\n * @param b The second tensor as the denominator for element-wise division.\n */\nfunction divStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'div');\n    const $b = convertToTensor(b, 'b', 'div');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in divideStrict: ');\n    return div($a, $b);\n}\n/**\n * @deprecated\n * Returns the mod of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use mod().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction modStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'modStrict');\n    const $b = convertToTensor(b, 'b', 'modStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in modStrict: ');\n    return mod($a, $b);\n}\n/**\n * @deprecated\n * Returns the min of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use minimum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction minimumStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'minimumStrict');\n    const $b = convertToTensor(b, 'b', 'minimumStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in minimumStrict: ');\n    return minimum($a, $b);\n}\n/**\n * @deprecated\n * Returns the max of a and b (`a > b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use maximum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction maximumStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'maximumStrict');\n    const $b = convertToTensor(b, 'b', 'maximumStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in maximumStrict: ');\n    return maximum($a, $b);\n}\n/**\n * @deprecated\n * Returns (a - b) * (a - b) element-wise.\n *\n * Inputs must be the same shape. For broadcasting support, use\n * `tf.squaredDifference` instead.\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\nfunction squaredDifferenceStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'squaredDifferenceStrict');\n    const $b = convertToTensor(b, 'b', 'squaredDifferenceStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in squaredDifferenceStrict: ');\n    return squaredDifference($a, $b);\n}\nexport const addStrict = op({ addStrict_ });\nexport const divStrict = op({ divStrict_ });\nexport const maximumStrict = op({ maximumStrict_ });\nexport const minimumStrict = op({ minimumStrict_ });\nexport const modStrict = op({ modStrict_ });\nexport const mulStrict = op({ mulStrict_ });\nexport const powStrict = op({ powStrict_ });\nexport const squaredDifferenceStrict = op({ squaredDifferenceStrict_ });\nexport const subStrict = op({ subStrict_ });\n//# sourceMappingURL=binary_ops.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { abs } from './abs';\nimport * as axis_util from './axis_util';\nimport { max } from './max';\nimport { min } from './min';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { reshape } from './reshape';\nimport { scalar } from './scalar';\nimport { sqrt } from './sqrt';\nimport { square } from './square';\nimport { sum } from './sum';\n/**\n * Computes the norm of scalar, vectors, and matrices.\n * This function can compute several different vector norms (the 1-norm, the\n * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)\n * and matrix norms (Frobenius, 1-norm, and inf-norm).\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.norm().print();  // or tf.norm(x)\n * ```\n *\n * @param x The input array.\n * @param ord Optional. Order of the norm. Supported norm types are\n * following:\n *\n *  | ord        | norm for matrices         | norm for vectors\n *  |------------|---------------------------|---------------------\n *  |'euclidean' |Frobenius norm             |2-norm\n *  |'fro'       |Frobenius norm\t           |\n *  |Infinity    |max(sum(abs(x), axis=1))   |max(abs(x))\n *  |-Infinity   |min(sum(abs(x), axis=1))   |min(abs(x))\n *  |1           |max(sum(abs(x), axis=0))   |sum(abs(x))\n *  |2           |                           |sum(abs(x)^2)^1/2*\n *\n * @param axis Optional. If axis is null (the default), the input is\n * considered a vector and a single vector norm is computed over the entire\n * set of values in the Tensor, i.e. norm(x, ord) is equivalent\n * to norm(x.reshape([-1]), ord). If axis is a integer, the input\n * is considered a batch of vectors, and axis determines the axis in x\n * over which to compute vector norms. If axis is a 2-tuple of integer it is\n * considered a batch of matrices and axis determines the axes in NDArray\n * over which to compute a matrix norm.\n * @param keepDims Optional. If true, the norm have the same dimensionality\n * as the input.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction norm_(x, ord = 'euclidean', axis = null, keepDims = false) {\n    x = convertToTensor(x, 'x', 'norm');\n    const norm = normImpl(x, ord, axis);\n    let keepDimsShape = norm.shape;\n    if (keepDims) {\n        const axes = parseAxisParam(axis, x.shape);\n        keepDimsShape = axis_util.expandShapeToKeepDim(norm.shape, axes);\n    }\n    return reshape(norm, keepDimsShape);\n}\nfunction normImpl(x, p, axis = null) {\n    if (x.rank === 0) {\n        return abs(x);\n    }\n    // consider vector when no axis is specified\n    if (x.rank !== 1 && axis === null) {\n        return normImpl(reshape(x, [-1]), p, axis);\n    }\n    // vector\n    if (x.rank === 1 || typeof axis === 'number' ||\n        Array.isArray(axis) && axis.length === 1) {\n        if (p === 1) {\n            return sum(abs(x), axis);\n        }\n        if (p === Infinity) {\n            return max(abs(x), axis);\n        }\n        if (p === -Infinity) {\n            return min(abs(x), axis);\n        }\n        if (p === 'euclidean' || p === 2) {\n            // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2\n            return sqrt(sum(pow(abs(x), scalar(2, 'int32')), axis));\n        }\n        throw new Error(`Error in norm: invalid ord value: ${p}`);\n    }\n    // matrix (assumption axis[0] < axis[1])\n    if (Array.isArray(axis) && axis.length === 2) {\n        if (p === 1) {\n            return max(sum(abs(x), axis[0]), axis[1] - 1);\n        }\n        if (p === Infinity) {\n            return max(sum(abs(x), axis[1]), axis[0]);\n        }\n        if (p === -Infinity) {\n            return min(sum(abs(x), axis[1]), axis[0]);\n        }\n        if (p === 'fro' || p === 'euclidean') {\n            // norm(x) = sqrt(sum(pow(x, 2)))\n            return sqrt(sum(square(x), axis));\n        }\n        throw new Error(`Error in norm: invalid ord value: ${p}`);\n    }\n    throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\nexport const norm = op({ norm_ });\n//# sourceMappingURL=norm.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assertTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { scalar } from './scalar';\nimport { sub } from './sub';\n/**\n * Compute the moving average of a variable.\n *\n * Without zeroDebias, the moving average operation is defined by:\n *   `v += delta`\n * where\n *   `delta = (1 - decay) * (x - v)`\n *\n * With zeroDebias (default), the `delta` term is scaled to debias the\n * effect of the (assumed) zero-initialization of `v`.\n *   `delta /= (1 - decay ^ step)`\n *\n * For more details on the zero-debiasing algorithm, see:\n *   https://arxiv.org/abs/1412.6980\n *\n * Note that this function is completely stateless and does not keep track of\n * step count. The step count needs to be maintained by the caller and passed\n * in as `step`.\n *\n * @param v The current moving average value.\n * @param x New input value, must have the same shape and dtype as `v`.\n * @param decay The decay factor. Typical values are 0.95 and 0.99.\n * @param step Step count.\n * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).\n * @returns The new moving average value.\n *\n * @doc {heading: 'Operations', subheading: 'Moving Average'}\n */\nfunction movingAverage_(v, x, decay, step, zeroDebias = true) {\n    const $v = convertToTensor(v, 'v', 'movingAverage');\n    const $x = convertToTensor(x, 'x', 'movingAverage');\n    const $decay = convertToTensor(decay, 'decay', 'movingAverage');\n    assertTypesMatch($v, $x);\n    util.assert(util.arraysEqual($v.shape, $x.shape), () => 'Shape mismatch in v and x');\n    const one = scalar(1);\n    const oneMinusDecay = sub(one, $decay);\n    let update = mul(sub($x, $v), oneMinusDecay);\n    if (zeroDebias) {\n        util.assert(step != null, () => 'When using zeroDebias: true, step is required.');\n        const $step = convertToTensor(step, 'step', 'movingAverage');\n        update = div(update, sub(one, pow($decay, $step)));\n    }\n    return add($v, update);\n}\nexport const movingAverage = op({ movingAverage_ });\n//# sourceMappingURL=moving_average.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ScatterNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport * as scatter_nd_util from './scatter_nd_util';\n/**\n * Creates a new tensor by applying sparse updates to individual\n * values or slices within a zero tensor of the given shape tensor according to\n * indices. This operator is the inverse of the `tf.gatherND` operator which\n * extracts values or slices from a given tensor.\n *\n * ```js\n * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32');\n * const updates = tf.tensor1d([9, 10, 11, 12]);\n * const shape = [8];\n * tf.scatterND(indices, updates, shape).print() //[0, 11, 0, 10, 9, 0, 0, 12]\n * ```\n *\n * @param indices The tensor contains the indices into the output tensor.\n * @param updates The tensor contains the value for the indices.\n * @param shape: The shape of the output tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction scatterND_(indices, updates, shape) {\n    const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');\n    const $updates = convertToTensor(updates, 'updates', 'scatterND');\n    scatter_nd_util.validateInput($updates, $indices, shape);\n    const forward = (backend) => {\n        return backend.scatterND($indices, $updates, shape);\n    };\n    const inputs = { indices: $indices, updates: $updates };\n    const attrs = { shape };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ScatterNd, attrs);\n}\nexport const scatterND = op({ scatterND_ });\n//# sourceMappingURL=scatter_nd.js.map", "/**\n * Validate sparseToDense inputs.\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape number[]. Shape of the dense output tensor.\n * @param validateIndices boolean. indice validation is not supported, error\n * will be thrown if it is set.\n */\nexport function validateInput(sparseIndices, sparseValues, outputShape, defaultValues) {\n    if (sparseIndices.dtype !== 'int32') {\n        throw new Error('tf.sparseToDense() expects the indices to be int32 type,' +\n            ` but the dtype was ${sparseIndices.dtype}.`);\n    }\n    if (sparseIndices.rank > 2) {\n        throw new Error('sparseIndices should be a scalar, vector, or matrix,' +\n            ` but got shape ${sparseIndices.shape}.`);\n    }\n    const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n    const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n    if (outputShape.length !== numDims) {\n        throw new Error('outputShape has incorrect number of elements:,' +\n            ` ${outputShape.length}, should be: ${numDims}.`);\n    }\n    const numValues = sparseValues.size;\n    if (!(sparseValues.rank === 0 ||\n        sparseValues.rank === 1 && numValues === numElems)) {\n        throw new Error('sparseValues has incorrect shape ' +\n            `${sparseValues.shape}, should be [] or [${numElems}]`);\n    }\n    if (sparseValues.dtype !== defaultValues.dtype) {\n        throw new Error('sparseValues.dtype must match defaultValues.dtype');\n    }\n}\n//# sourceMappingURL=sparse_to_dense_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SparseToDense } from '../kernel_names';\nimport * as sparse_to_dense from '../ops/sparse_to_dense_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Converts a sparse representation into a dense tensor.\n *\n * Builds an array dense with shape outputShape such that:\n *\n * // If sparseIndices is scalar\n * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)\n *\n * // If sparseIndices is a vector, then for each i\n * dense[sparseIndices[i]] = sparseValues[i]\n *\n * // If sparseIndices is an n by d matrix, then for each i in [0, n)\n * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]\n * All other values in dense are set to defaultValue. If sparseValues is a\n * scalar, all sparse indices are set to this single value.\n *\n * If indices are repeated the final value is summed over all values for those\n * indices.\n *\n * ```js\n * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');\n * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');\n * const shape = [8];\n * tf.sparseToDense(indices, values, shape).print();\n * ```\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape Shape of the dense output tensor. the type is inferred.\n * @param defaultValue Scalar. Value to set for indices not specified in\n * sparseIndices. Defaults to zero.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {\n    const $sparseIndices = convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');\n    const $sparseValues = convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');\n    const $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);\n    sparse_to_dense.validateInput($sparseIndices, $sparseValues, outputShape, $defaultValue);\n    const inputs = {\n        sparseIndices: $sparseIndices,\n        sparseValues: $sparseValues,\n        defaultValue: $defaultValue\n    };\n    const attrs = { outputShape };\n    return ENGINE.runKernelFunc(backend => backend.sparseToDense($sparseIndices, $sparseValues, outputShape, $defaultValue), inputs, null /* grad */, SparseToDense, attrs);\n}\nexport const sparseToDense = op({ sparseToDense_ });\n//# sourceMappingURL=sparse_to_dense.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Gather slices from input tensor into a Tensor with shape specified by\n * `indices`.\n *\n * `indices` is an K-dimensional integer tensor, best thought of as a\n * (K-1)-dimensional tensor of indices into input, where each element defines a\n * slice of input:\n * output[\\\\(i_0, ..., i_{K-2}\\\\)] = input[indices[\\\\(i_0, ..., i_{K-2}\\\\)]]\n *\n * Whereas in `tf.gather`, `indices` defines slices into the first dimension of\n * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions\n * of input, where N = indices.shape[-1].\n *\n * The last dimension of indices can be at most the rank of input:\n * indices.shape[-1] <= input.rank\n *\n * The last dimension of `indices` corresponds to elements\n * (if indices.shape[-1] == input.rank) or slices\n * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of\n * input.\n * The output tensor has shape\n * indices.shape[:-1] + input.shape[indices.shape[-1]:]\n *\n * Note that on CPU, if an out of bound index is found, an error is returned. On\n * GPU, if an out of bound index is found, a 0 is stored in the corresponding\n * output value.\n *\n * ```js\n * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');\n * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);\n * tf.gatherND(input, indices).print() // [10, 11]\n * ```\n *\n * @param x The tensor from which to gather values.\n * @param indices Index tensor, must be of type int32.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction gatherND_(x, indices) {\n    const $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');\n    const $x = convertToTensor(x, 'x', 'gatherND');\n    const forward = (backend) => {\n        return backend.gatherND($x, $indices);\n    };\n    const inputs = { params: $x, indices: $indices };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, GatherNd);\n}\nexport const gatherND = op({ gatherND_ });\n//# sourceMappingURL=gather_nd.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n * Normalize noise shape based on provided tensor and noise shape.\n *\n * @param x Tensor.\n * @param noiseShape The shape for the randomly generated keep/drop flags, as\n *   an array of numbers. Optional.\n * @returns Normalized noise shape.\n */\nexport function getNoiseShape(x, noiseShape) {\n    if (noiseShape == null) {\n        return x.shape.slice();\n    }\n    if (util.arraysEqual(x.shape, noiseShape)) {\n        return noiseShape;\n    }\n    if (x.shape.length === noiseShape.length) {\n        const newDimension = [];\n        for (let i = 0; i < x.shape.length; i++) {\n            if (noiseShape[i] == null && x.shape[i] != null) {\n                newDimension.push(x.shape[i]);\n            }\n            else {\n                newDimension.push(noiseShape[i]);\n            }\n        }\n        return newDimension;\n    }\n    return noiseShape;\n}\n//# sourceMappingURL=dropout_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { getNoiseShape } from './dropout_util';\nimport { floor } from './floor';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { randomUniform } from './random_uniform';\n/**\n * Computes dropout.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 2, 1]);\n * const rate = 0.75;\n * const output = tf.dropout(x, rate);\n * output.print();\n * ```\n *\n * @param x A floating point Tensor or TensorLike.\n * @param rate A float in the range [0, 1). The probability that each element\n *   of x is discarded.\n * @param noiseShape An array of numbers of type int32, representing the\n * shape for randomly generated keep/drop flags. If the noiseShape has null\n * value, it will be automatically replaced with the x's relative dimension\n * size. Optional.\n * @param seed Used to create random seeds. Optional.\n * @returns A Tensor of the same shape of x.\n *\n * @doc {heading: 'Operations', subheading: 'Dropout'}\n */\nfunction dropout_(x, rate, noiseShape, seed) {\n    const $x = convertToTensor(x, 'x', 'dropout');\n    util.assert($x.dtype === 'float32', () => `x has to be a floating point tensor since it's going to be ` +\n        `scaled, but got a ${$x.dtype} tensor instead.`);\n    util.assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);\n    if (rate === 0) {\n        return x instanceof Tensor ? $x.clone() : $x;\n    }\n    const $noiseShape = getNoiseShape($x, noiseShape);\n    const keepProb = 1 - rate;\n    const multiplier = div(floor(add(randomUniform($noiseShape, 0, 1, 'float32', seed), keepProb)), keepProb);\n    return mul($x, multiplier);\n}\nexport const dropout = op({ dropout_ });\n//# sourceMappingURL=dropout.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensor1d } from './tensor1d';\nexport function enclosingPowerOfTwo(value) {\n    // Return 2**N for integer N such that 2**N >= value.\n    return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2.0))));\n}\nexport function cosineWindow(windowLength, a, b) {\n    const even = 1 - windowLength % 2;\n    const newValues = new Float32Array(windowLength);\n    for (let i = 0; i < windowLength; ++i) {\n        const cosArg = (2.0 * Math.PI * i) / (windowLength + even - 1);\n        newValues[i] = a - b * Math.cos(cosArg);\n    }\n    return tensor1d(newValues, 'float32');\n}\n//# sourceMappingURL=signal_ops_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, assertShapesMatch, getTypedArrayFromDType } from '../util';\nimport { tensor } from './tensor';\n/**\n * Returns whether the targets are in the top K predictions.\n *\n * ```js\n * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);\n * const targets = tf.tensor1d([2, 0]);\n * const precision = await tf.inTopKAsync(predictions, targets);\n * precision.print();\n * ```\n * @param predictions 2-D or higher `tf.Tensor` with last dimension being\n *     at least `k`.\n * @param targets 1-D or higher `tf.Tensor`.\n * @param k Optional Number of top elements to look at for computing precision,\n *     default to 1.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nasync function inTopKAsync_(predictions, targets, k = 1) {\n    const $predictions = convertToTensor(predictions, 'predictions', 'inTopK');\n    const $targets = convertToTensor(targets, 'targets', 'inTopK');\n    assert($predictions.rank > 1, () => 'inTopK() expects the predictions to be of rank 2 or higher, ' +\n        `but got ${$predictions.rank}`);\n    assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than ` +\n        `targets rank, but got predictions rank ` +\n        `${$predictions.rank} and targets rank ${$targets.rank}`);\n    assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, ` +\n        'except the last dimension.');\n    const lastDim = $predictions.shape[$predictions.shape.length - 1];\n    assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last ` +\n        `dimension (${lastDim}), but got ${k}`);\n    const predictionsVals = await $predictions.data();\n    const targetsVals = await $targets.data();\n    // Reshape predictionsVals into a 2d tensor [batch, lastDim]\n    // and look up topK along lastDim.\n    const [batch, size] = [predictionsVals.length / lastDim, lastDim];\n    const precision = getTypedArrayFromDType('bool', batch);\n    for (let b = 0; b < batch; b++) {\n        const offset = b * size;\n        const vals = predictionsVals.subarray(offset, offset + size);\n        const valAndInd = [];\n        for (let i = 0; i < vals.length; i++) {\n            valAndInd.push({ value: vals[i], index: i });\n        }\n        valAndInd.sort((a, b) => b.value - a.value);\n        precision[b] = 0;\n        for (let i = 0; i < k; i++) {\n            if (valAndInd[i].index === targetsVals[b]) {\n                precision[b] = 1;\n                break;\n            }\n        }\n    }\n    if (predictions !== $predictions) {\n        $predictions.dispose();\n    }\n    if (targets !== $targets) {\n        $targets.dispose();\n    }\n    // Output precision has the same shape as targets.\n    return tensor(precision, $targets.shape, 'bool');\n}\nexport const inTopKAsync = inTopKAsync_;\n//# sourceMappingURL=in_top_k.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropFilter } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the filter of a 2D convolution.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.\n * @param dy The dy image, of rank 4 or rank 3, of shape\n *     [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.\n * @param filterShape The shape of the filter, length 4,\n *     [filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction conv2DBackpropFilter_(x, dy, filterShape, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n    let x4D = x;\n    if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    let dy4D = dy;\n    if (dy4D.rank === 3) {\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ` +\n        `${x4D.shape}.`);\n    util.assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ` +\n        `${dy4D.shape}.`);\n    util.assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ` +\n        `${filterShape}.`);\n    const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n    const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n    util.assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must ` +\n        `match input depth in filter (${filterShape[2]}.`);\n    util.assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must ` +\n        `match output depth for filter (${filterShape[3]}).`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2dDerFilter: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const dilations = 1;\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, filterShape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        return backend.conv2dDerFilter(x4D, dy4D, convInfo);\n    };\n    const inputs = { x: x4D, dy: dy4D };\n    const attrs = { strides, pad, dataFormat, dimRoundingMode };\n    return ENGINE.runKernelFunc(forward, inputs, null, Conv2DBackpropFilter, attrs);\n}\nexport const conv2DBackpropFilter = op({ conv2DBackpropFilter_ });\n//# sourceMappingURL=conv2d_backprop_filter.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as broadcast_util from './broadcast_util';\nimport { elu } from './elu';\nimport { mul } from './mul';\nimport { prelu } from './prelu';\nimport { relu } from './relu';\nimport { relu6 } from './relu6';\nimport { reshape } from './reshape';\nimport { step } from './step';\nimport { sum } from './sum';\n// Returns gradient for fused activation.\nexport function getFusedDyActivation(dy, y, activation) {\n    if (activation == null || activation === 'linear') {\n        return dy;\n    }\n    if (activation === 'relu') {\n        return mul(dy, step(y));\n    }\n    throw new Error(`Cannot compute gradient for fused activation ${activation}.`);\n}\n// Returns gradient for fused bias.\nexport function getFusedBiasGradient(bias, dyActivation) {\n    let res = dyActivation;\n    const reduceAxes = broadcast_util.getReductionAxes(bias.shape, dyActivation.shape);\n    if (reduceAxes.length > 0) {\n        res = sum(res, reduceAxes);\n    }\n    return reshape(res, bias.shape);\n}\nexport function applyActivation(x, activation, preluActivationWeights) {\n    if (activation === 'linear') {\n        return x;\n    }\n    else if (activation === 'relu') {\n        return relu(x);\n    }\n    else if (activation === 'elu') {\n        return elu(x);\n    }\n    else if (activation === 'relu6') {\n        return relu6(x);\n    }\n    else if (activation === 'prelu') {\n        return prelu(x, preluActivationWeights);\n    }\n    throw new Error(`Unknown fused activation ${activation}.`);\n}\n// Whether we should call fused ops.\nexport const shouldFuse = (gradientDepth, activation) => {\n    const gradientMode = gradientDepth > 0;\n    return !gradientMode || activation === 'linear';\n};\n//# sourceMappingURL=fused_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { conv2d as unfusedConv2d } from '../conv2d';\nimport { conv2DBackpropFilter } from '../conv2d_backprop_filter';\nimport { conv2DBackpropInput } from '../conv2d_backprop_input';\nimport * as conv_util from '../conv_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes a 2D convolution over the input x, optionally fused with adding a\n * bias and applying an activation.\n *\n * ```js\n * const inputDepth = 2;\n * const inShape = [2, 2, 2, inputDepth];\n * const outputDepth = 2;\n * const fSize = 1;\n * const pad = 0;\n * const strides = 1;\n *\n * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n * 16], inShape);\n * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,\n * outputDepth]);\n *\n * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',\n * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();\n * ```\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid` output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`) to be\n *     applied\n *      after biasAdd.\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights }) {\n    activation = activation || 'linear';\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    const $x = convertToTensor(x, 'x', 'conv2d');\n    const $filter = convertToTensor(filter, 'filter', 'conv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ` +\n        `${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in fused conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match ` +\n        `input depth for filter ${$filter.shape[2]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    util.assert(dataFormat === 'NHWC', () => `Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);\n    const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n        [$bias] = makeTypesMatch($bias, $x);\n        broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused conv2d');\n    }\n    const grad = (dy, saved) => {\n        const [$filter, x4D, y, $bias] = saved;\n        const dyActivation = getFusedDyActivation(dy, y, activation);\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused conv2D: ' +\n            `dilation rates greater than 1 ` +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n        const xDer = conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad);\n        const filterDer = conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad);\n        const der = [xDer, filterDer];\n        if ($bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            der.push(biasDer);\n        }\n        return der;\n    };\n    const forward = (backend) => {\n        const res = backend.fusedConv2d({\n            input: x4D,\n            filter: $filter,\n            convInfo,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return res;\n    };\n    const inputs = {\n        x: x4D,\n        filter: $filter,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((x4D, filter, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedConv2D, attrs);\n            save([filter, x4D, res]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOp(x4D, $filter);\n    }\n    else {\n        const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedConv2D, attrs);\n            save([filter, x4D, res, bias]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOpWithBias(x4D, $filter, $bias);\n    }\n}\nexport const conv2d = op({ fusedConv2d_ });\n//# sourceMappingURL=conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropFilter } from '../kernel_names';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, convInfo) {\n    let x4D = x;\n    if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    let dy4D = dy;\n    if (dy4D.rank === 3) {\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    const forward = backend => backend.depthwiseConv2DDerFilter(x4D, dy4D, convInfo);\n    const inputs = { x: x4D, dy: dy4D };\n    return ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropFilter);\n}\nexport const depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ });\n//# sourceMappingURL=depthwise_conv2d_native_backprop_filter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropInput } from '../kernel_names';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, convInfo) {\n    let dy4D = dy;\n    let reshapedTo4D = false;\n    if (dy.rank === 3) {\n        reshapedTo4D = true;\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    const forward = backend => backend.depthwiseConv2DDerInput(dy4D, filter, convInfo);\n    const inputs = { dy: dy4D };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropInput);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ });\n//# sourceMappingURL=depthwise_conv2d_native_backprop_input.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedDepthwiseConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport * as conv_util from '../conv_util';\nimport { depthwiseConv2d as unfusedDepthwiseConv2d } from '../depthwise_conv2d';\nimport { depthwiseConv2dNativeBackpropFilter } from '../depthwise_conv2d_native_backprop_filter';\nimport { depthwiseConv2dNativeBackpropInput } from '../depthwise_conv2d_native_backprop_input';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes depthwise 2D convolution, optionally fused with adding a\n * bias and applying an activation.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`).\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedDepthwiseConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights }) {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedDepthwiseConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n    const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, ` +\n        `but got rank ${$filter.rank}.`);\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels ` +\n        `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n        `filter ${$filter.shape[2]}.`);\n    if (dilations == null) {\n        dilations = [1, 1];\n    }\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in fused depthwiseConv2d: Either strides or dilations must ' +\n        `be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in fused depthwiseConv2d: pad must be an integer when ` +\n            `using dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n        [$bias] = makeTypesMatch($bias, $x);\n        broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');\n    }\n    const grad = (dy, saved) => {\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused depthwiseConv2d: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${dilations}'`);\n        const [$filter, x4D, y, bias] = saved;\n        const dyActivation = getFusedDyActivation(dy, y, activation);\n        const xDer = depthwiseConv2dNativeBackpropInput(x4D.shape, dyActivation, $filter, convInfo);\n        const filterDer = depthwiseConv2dNativeBackpropFilter(x4D, dyActivation, $filter.shape, convInfo);\n        if (bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            return [xDer, filterDer, biasDer];\n        }\n        return [xDer, filterDer];\n    };\n    const forward = (backend) => {\n        const res = backend.fusedDepthwiseConv2D({\n            input: x4D,\n            filter: $filter,\n            convInfo,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return res;\n    };\n    const inputs = {\n        x: x4D,\n        filter: $filter,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((x4D, filter, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedDepthwiseConv2D, attrs);\n            save([filter, x4D, res]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOp(x4D, $filter);\n    }\n    else {\n        const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedDepthwiseConv2D, attrs);\n            save([filter, x4D, res, bias]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOpWithBias(x4D, $filter, $bias);\n    }\n}\nexport const depthwiseConv2d = op({ fusedDepthwiseConv2d_ });\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { _FusedMatMul } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { matMul as unfusedMatMul } from '../mat_mul';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes the dot product of two matrices with optional activation and bias.\n *\n * ```js\n * const a = tf.tensor2d([-1, -2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const bias = tf.tensor2d([1, 2], [1, 2]);\n *\n * tf.fused.matMul({a, b, bias, activation: 'relu'}).print();\n * ```\n *\n * @param obj An object with the following properties:\n * - `a` First matrix in dot product operation.\n * - `b` Second matrix in dot product operation.\n * - `transposeA` If true, `a` is transposed before multiplication.\n * - `transposeB` If true, `b` is transposed before multiplication.\n * - `bias` Matrix to be added to the result.\n * - `activation` Name of activation kernel (defaults to `linear`).\n * - `preluActivationWeights` Tensor of prelu weights.\n */\nfunction fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation = 'linear', preluActivationWeights }) {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedMatMul(a, b, transposeA, transposeB);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    let $a = convertToTensor(a, 'a', 'fused matMul');\n    let $b = convertToTensor(b, 'b', 'fused matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n    const outerDimsA = $a.shape.slice(0, -2);\n    const outerDimsB = $b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n    util.assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in fused matMul: inputs must have the same rank of at least ` +\n        `2, got ranks ${$a.rank} and ${$b.rank}.`);\n    util.assert(util.arraysEqual(outerDimsA, outerDimsB), () => `Error in fused matMul: outer dimensions (${outerDimsA}) and (` +\n        `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} must match.`);\n    util.assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (` +\n        `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} and transposeA=${transposeA}` +\n        ` and transposeB=${transposeB} must match.`);\n    const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n    const a3D = transposeA ?\n        reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n        reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n    const b3D = transposeB ?\n        reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n        reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused matMul');\n        [$bias] = makeTypesMatch($bias, $a);\n        broadcast_util.assertAndGetBroadcastShape(outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused matMul');\n    }\n    const grad = (dy, saved) => {\n        const [a3D, b3D, y, $bias] = saved;\n        // we reshape dy because the result of the forward is not\n        // necessarily going to be a 3d tensor due to a reshape done at the end of\n        // the customOp.\n        const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation);\n        let aDer;\n        let bDer;\n        if (!transposeA && !transposeB) {\n            aDer = unfusedMatMul(dyActivation, b3D, false, true);\n            bDer = unfusedMatMul(a3D, dyActivation, true, false);\n        }\n        else if (!transposeA && transposeB) {\n            aDer = unfusedMatMul(dyActivation, b3D, false, false);\n            bDer = unfusedMatMul(dyActivation, a3D, true, false);\n        }\n        else if (transposeA && !transposeB) {\n            aDer = unfusedMatMul(b3D, dyActivation, false, true);\n            bDer = unfusedMatMul(a3D, dyActivation, false, false);\n        }\n        else {\n            aDer = unfusedMatMul(b3D, dyActivation, true, true);\n            bDer = unfusedMatMul(dyActivation, a3D, true, true);\n        }\n        if (bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            return [aDer, bDer, biasDer];\n        }\n        else {\n            return [aDer, bDer];\n        }\n    };\n    const forward = (backend) => {\n        const y = backend.fusedBatchMatMul({\n            a: a3D,\n            b: b3D,\n            transposeA,\n            transposeB,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return y;\n    };\n    const inputs = {\n        a: a3D,\n        b: b3D,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { transposeA, transposeB, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((a3D, b3D, save) => {\n            const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, _FusedMatMul, attrs);\n            save([a3D, b3D, res]);\n            return { value: reshape(res, outShape), gradFunc: grad };\n        });\n        return customOp(a3D, b3D);\n    }\n    else {\n        const customOpWithBias = customGrad((a3D, b3D, $bias, save) => {\n            const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, _FusedMatMul, attrs);\n            save([a3D, b3D, res, $bias]);\n            return { value: reshape(res, outShape), gradFunc: grad };\n        });\n        return customOpWithBias(a3D, b3D, $bias);\n    }\n}\nexport const matMul = op({ fusedMatMul_ });\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2d } from './fused/conv2d';\nimport { depthwiseConv2d } from './fused/depthwise_conv2d';\nimport { matMul } from './fused/mat_mul';\nexport { conv2d, depthwiseConv2d, matMul };\n//# sourceMappingURL=fused_ops.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a hamming window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hammingWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hammingWindow_(windowLength) {\n    return cosineWindow(windowLength, 0.54, 0.46);\n}\nexport const hammingWindow = op({ hammingWindow_ });\n//# sourceMappingURL=hamming_window.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a Hann window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hannWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hannWindow_(windowLength) {\n    return cosineWindow(windowLength, 0.5, 0.5);\n}\nexport const hannWindow = op({ hannWindow_ });\n//# sourceMappingURL=hann_window.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../concat';\nimport { fill } from '../fill';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { tensor2d } from '../tensor2d';\n/**\n * Expands input into frames of frameLength.\n * Slides a window size with frameStep.\n *\n * ```js\n * tf.signal.frame([1, 2, 3], 2, 1).print();\n * ```\n * @param signal The input tensor to be expanded\n * @param frameLength Length of each frame\n * @param frameStep The frame hop size in samples.\n * @param padEnd Whether to pad the end of signal with padValue.\n * @param padValue An number to use where the input signal does\n *     not exist when padEnd is True.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction frame_(signal, frameLength, frameStep, padEnd = false, padValue = 0) {\n    let start = 0;\n    const output = [];\n    while (start + frameLength <= signal.size) {\n        output.push(slice(signal, start, frameLength));\n        start += frameStep;\n    }\n    if (padEnd) {\n        while (start < signal.size) {\n            const padLen = (start + frameLength) - signal.size;\n            const pad = concat([\n                slice(signal, start, frameLength - padLen), fill([padLen], padValue)\n            ]);\n            output.push(pad);\n            start += frameStep;\n        }\n    }\n    if (output.length === 0) {\n        return tensor2d([], [0, frameLength]);\n    }\n    return reshape(concat(output), [output.length, frameLength]);\n}\nexport const frame = op({ frame_ });\n//# sourceMappingURL=frame.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../concat';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { enclosingPowerOfTwo } from '../signal_ops_util';\nimport { slice } from '../slice';\nimport { rfft } from '../spectral/rfft';\nimport { frame } from './frame';\nimport { hannWindow } from './hann_window';\n/**\n * Computes the Short-time Fourier Transform of signals\n * See: https://en.wikipedia.org/wiki/Short-time_Fourier_transform\n *\n * ```js\n * const input = tf.tensor1d([1, 1, 1, 1, 1])\n * tf.signal.stft(input, 3, 1).print();\n * ```\n * @param signal 1-dimensional real value tensor.\n * @param frameLength The window length of samples.\n * @param frameStep The number of samples to step.\n * @param fftLength The size of the FFT to apply.\n * @param windowFn A callable that takes a window length and returns 1-d tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction stft_(signal, frameLength, frameStep, fftLength, windowFn = hannWindow) {\n    if (fftLength == null) {\n        fftLength = enclosingPowerOfTwo(frameLength);\n    }\n    const framedSignal = frame(signal, frameLength, frameStep);\n    const windowedSignal = mul(framedSignal, windowFn(frameLength));\n    const output = [];\n    for (let i = 0; i < framedSignal.shape[0]; i++) {\n        output.push(rfft(slice(windowedSignal, [i, 0], [1, frameLength]), fftLength));\n    }\n    return concat(output);\n}\nexport const stft = op({ stft_ });\n//# sourceMappingURL=stft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { CropAndResize } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Extracts crops from the input image tensor and resizes them using bilinear\n * sampling or nearest neighbor sampling (possibly with aspect ratio change)\n * to a common output size specified by crop_size.\n *\n * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,\n *     where imageHeight and imageWidth must be positive, specifying the\n *     batch of images from which to take crops\n * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized\n *     coordinates of the box in the boxInd[i]'th image in the batch\n * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range\n *     `[0, batch)` that specifies the image that the `i`-th box refers to.\n * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`\n *     specifying the size to which all crops are resized to.\n * @param method Optional string from `'bilinear' | 'nearest'`,\n *     defaults to bilinear, which specifies the sampling method for resizing\n * @param extrapolationValue A threshold for deciding when to remove boxes based\n *     on score. Defaults to 0.\n * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction cropAndResize_(image, boxes, boxInd, cropSize, method, extrapolationValue) {\n    const $image = convertToTensor(image, 'image', 'cropAndResize');\n    const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');\n    const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');\n    method = method || 'bilinear';\n    extrapolationValue = extrapolationValue || 0;\n    const numBoxes = $boxes.shape[0];\n    util.assert($image.rank === 4, () => 'Error in cropAndResize: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    util.assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +\n        `but had shape ${$boxes.shape}.`);\n    util.assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +\n        `but had shape ${$boxes.shape}.`);\n    util.assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got ` +\n        `length ${cropSize.length}.`);\n    util.assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);\n    util.assert(method === 'bilinear' || method === 'nearest', () => `method must be bilinear or nearest, but was ${method}`);\n    const forward = (backend) => backend.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);\n    const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };\n    const attrs = { method, extrapolationValue, cropSize };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, CropAndResize, attrs);\n    return res;\n}\nexport const cropAndResize = op({ cropAndResize_ });\n//# sourceMappingURL=crop_and_resize.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FlipLeftRight } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Flips the image left to right. Currently available in the CPU, WebGL, and\n * WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction flipLeftRight_(image) {\n    const $image = convertToTensor(image, 'image', 'flipLeftRight', 'float32');\n    util.assert($image.rank === 4, () => 'Error in flipLeftRight: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    const inputs = { image: $image };\n    const res = ENGINE.runKernel(FlipLeftRight, inputs, {});\n    return res;\n}\nexport const flipLeftRight = op({ flipLeftRight_ });\n//# sourceMappingURL=flip_left_right.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { RotateWithOffset } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Rotates the input image tensor counter-clockwise with an optional offset\n * center of rotation. Currently available in the CPU, WebGL, and WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n * @param radians The amount of rotation.\n * @param fillValue The value to fill in the empty space leftover\n *     after rotation. Can be either a single grayscale value (0-255), or an\n *     array of three numbers `[red, green, blue]` specifying the red, green,\n *     and blue channels. Defaults to `0` (black).\n * @param center The center of rotation. Can be either a single value (0-1), or\n *     an array of two numbers `[centerX, centerY]`. Defaults to `0.5` (rotates\n *     the image around its center).\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction rotateWithOffset_(image, radians, fillValue = 0, center = 0.5) {\n    const $image = convertToTensor(image, 'image', 'rotateWithOffset', 'float32');\n    util.assert($image.rank === 4, () => 'Error in rotateWithOffset: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    const inputs = { image: $image };\n    const attrs = { radians, fillValue, center };\n    const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);\n    return res;\n}\nexport const rotateWithOffset = op({ rotateWithOffset_ });\n//# sourceMappingURL=rotate_with_offset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nfunction nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n    if (iouThreshold == null) {\n        iouThreshold = 0.5;\n    }\n    if (scoreThreshold == null) {\n        scoreThreshold = Number.NEGATIVE_INFINITY;\n    }\n    if (softNmsSigma == null) {\n        softNmsSigma = 0.0;\n    }\n    const numBoxes = boxes.shape[0];\n    maxOutputSize = Math.min(maxOutputSize, numBoxes);\n    util.assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n    util.assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n    util.assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n    util.assert(scores.rank === 1, () => 'scores must be a 1D tensor');\n    util.assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, ` +\n        `but was ${scores.shape[0]}`);\n    util.assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);\n    return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n}\nexport { nonMaxSuppSanityCheck };\n//# sourceMappingURL=nonmax_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV3 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\nfunction nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n    maxOutputSize = inputs.maxOutputSize;\n    iouThreshold = inputs.iouThreshold;\n    scoreThreshold = inputs.scoreThreshold;\n    const attrs = { maxOutputSize, iouThreshold, scoreThreshold };\n    return ENGINE.runKernelFunc(b => b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold), { boxes: $boxes, scores: $scores }, null /* grad */, NonMaxSuppressionV3, attrs);\n}\nexport const nonMaxSuppression = op({ nonMaxSuppression_ });\n//# sourceMappingURL=non_max_suppression.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inserts a value into a sorted array. This method allows duplicate, meaning it\n * allows inserting duplicate value, in which case, the element will be inserted\n * at the lowest index of the value.\n * @param arr The array to modify.\n * @param element The element to insert.\n * @param comparator Optional. If no comparator is specified, elements are\n * compared using array_util.defaultComparator, which is suitable for Strings\n * and Numbers in ascending arrays. If the array contains multiple instances of\n * the target value, the left-most instance will be returned. To provide a\n * comparator, it should take 2 arguments to compare and return a negative,\n * zero, or a positive number.\n */\nexport function binaryInsert(arr, element, comparator) {\n    const index = binarySearch(arr, element, comparator);\n    const insertionPoint = index < 0 ? -(index + 1) : index;\n    arr.splice(insertionPoint, 0, element);\n}\n/**\n * Searches the array for the target using binary search, returns the index\n * of the found element, or position to insert if element not found. If no\n * comparator is specified, elements are compared using array_\n * util.defaultComparator, which is suitable for Strings and Numbers in\n * ascending arrays. If the array contains multiple instances of the target\n * value, the left-most instance will be returned.\n * @param arr The array to be searched in.\n * @param target The target to be searched for.\n * @param comparator Should take 2 arguments to compare and return a negative,\n *    zero, or a positive number.\n * @return Lowest index of the target value if found, otherwise the insertion\n *    point where the target should be inserted, in the form of\n *    (-insertionPoint - 1).\n */\nexport function binarySearch(arr, target, comparator) {\n    return binarySearch_(arr, target, comparator || defaultComparator);\n}\n/**\n * Compares its two arguments for order.\n * @param a The first element to be compared.\n * @param b The second element to be compared.\n * @return A negative number, zero, or a positive number as the first\n *     argument is less than, equal to, or greater than the second.\n */\nfunction defaultComparator(a, b) {\n    return a > b ? 1 : a < b ? -1 : 0;\n}\nfunction binarySearch_(arr, target, comparator) {\n    let left = 0;\n    let right = arr.length;\n    let middle = 0;\n    let found = false;\n    while (left < right) {\n        middle = left + ((right - left) >>> 1);\n        const compareResult = comparator(target, arr[middle]);\n        if (compareResult > 0) {\n            left = middle + 1;\n        }\n        else {\n            right = middle;\n            // If compareResult is 0, the value is found. We record it is found,\n            // and then keep looking because there may be duplicate.\n            found = !compareResult;\n        }\n    }\n    return found ? left : -left - 1;\n}\n//# sourceMappingURL=array_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Implementation of the NonMaxSuppression kernel shared between webgl and cpu.\n */\nimport { scalar } from '../ops/scalar';\nimport { tensor1d } from '../ops/tensor1d';\nimport { binaryInsert } from './array_util';\nexport function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */)\n        .selectedIndices;\n}\nexport function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */, false /* returnScoresTensor */, padToMaxOutputSize /* padToMaxOutputSize */, true\n    /* returnValidOutputs */ );\n}\nexport function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true /* returnScoresTensor */);\n}\nfunction nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {\n    // The list is sorted in ascending order, so that we can always pop the\n    // candidate with the largest score in O(1) time.\n    const candidates = [];\n    for (let i = 0; i < scores.length; i++) {\n        if (scores[i] > scoreThreshold) {\n            candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 });\n        }\n    }\n    candidates.sort(ascendingComparator);\n    // If softNmsSigma is 0, the outcome of this algorithm is exactly same as\n    // before.\n    const scale = softNmsSigma > 0 ? (-0.5 / softNmsSigma) : 0.0;\n    const selectedIndices = [];\n    const selectedScores = [];\n    while (selectedIndices.length < maxOutputSize && candidates.length > 0) {\n        const candidate = candidates.pop();\n        const { score: originalScore, boxIndex, suppressBeginIndex } = candidate;\n        if (originalScore < scoreThreshold) {\n            break;\n        }\n        // Overlapping boxes are likely to have similar scores, therefore we\n        // iterate through the previously selected boxes backwards in order to\n        // see if candidate's score should be suppressed. We use\n        // suppressBeginIndex to track and ensure a candidate can be suppressed\n        // by a selected box no more than once. Also, if the overlap exceeds\n        // iouThreshold, we simply ignore the candidate.\n        let ignoreCandidate = false;\n        for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {\n            const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);\n            if (iou >= iouThreshold) {\n                ignoreCandidate = true;\n                break;\n            }\n            candidate.score =\n                candidate.score * suppressWeight(iouThreshold, scale, iou);\n            if (candidate.score <= scoreThreshold) {\n                break;\n            }\n        }\n        // At this point, if `candidate.score` has not dropped below\n        // `scoreThreshold`, then we know that we went through all of the\n        // previous selections and can safely update `suppressBeginIndex` to the\n        // end of the selected array. Then we can re-insert the candidate with\n        // the updated score and suppressBeginIndex back in the candidate list.\n        // If on the other hand, `candidate.score` has dropped below the score\n        // threshold, we will not add it back to the candidates list.\n        candidate.suppressBeginIndex = selectedIndices.length;\n        if (!ignoreCandidate) {\n            // Candidate has passed all the tests, and is not suppressed, so\n            // select the candidate.\n            if (candidate.score === originalScore) {\n                selectedIndices.push(boxIndex);\n                selectedScores.push(candidate.score);\n            }\n            else if (candidate.score > scoreThreshold) {\n                // Candidate's score is suppressed but is still high enough to be\n                // considered, so add back to the candidates list.\n                binaryInsert(candidates, candidate, ascendingComparator);\n            }\n        }\n    }\n    // NonMaxSuppressionV4 feature: padding output to maxOutputSize.\n    const validOutputs = selectedIndices.length;\n    const elemsToPad = maxOutputSize - validOutputs;\n    if (padToMaxOutputSize && elemsToPad > 0) {\n        selectedIndices.push(...new Array(elemsToPad).fill(0));\n        selectedScores.push(...new Array(elemsToPad).fill(0.0));\n    }\n    const result = { selectedIndices: tensor1d(selectedIndices, 'int32') };\n    if (returnScoresTensor) {\n        result['selectedScores'] = tensor1d(selectedScores, 'float32');\n    }\n    if (returnValidOutputs) {\n        result['validOutputs'] = scalar(validOutputs, 'int32');\n    }\n    return result;\n}\nfunction intersectionOverUnion(boxes, i, j) {\n    const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n    const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n    const yminI = Math.min(iCoord[0], iCoord[2]);\n    const xminI = Math.min(iCoord[1], iCoord[3]);\n    const ymaxI = Math.max(iCoord[0], iCoord[2]);\n    const xmaxI = Math.max(iCoord[1], iCoord[3]);\n    const yminJ = Math.min(jCoord[0], jCoord[2]);\n    const xminJ = Math.min(jCoord[1], jCoord[3]);\n    const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n    const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n    const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n    const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n    if (areaI <= 0 || areaJ <= 0) {\n        return 0.0;\n    }\n    const intersectionYmin = Math.max(yminI, yminJ);\n    const intersectionXmin = Math.max(xminI, xminJ);\n    const intersectionYmax = Math.min(ymaxI, ymaxJ);\n    const intersectionXmax = Math.min(xmaxI, xmaxJ);\n    const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *\n        Math.max(intersectionXmax - intersectionXmin, 0.0);\n    return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n// A Gaussian penalty function, this method always returns values in [0, 1].\n// The weight is a function of similarity, the more overlap two boxes are, the\n// smaller the weight is, meaning highly overlapping boxe will be significantly\n// penalized. On the other hand, a non-overlapping box will not be penalized.\nfunction suppressWeight(iouThreshold, scale, iou) {\n    const weight = Math.exp(scale * iou * iou);\n    return iou <= iouThreshold ? weight : 0.0;\n}\nfunction ascendingComparator(c1, c2) {\n    // For objects with same scores, we make the object with the larger index go\n    // first. In an array that pops from the end, this means that the object with\n    // the smaller index will be popped first. This ensures the same output as\n    // the TensorFlow python version.\n    return (c1.score - c2.score) ||\n        ((c1.score === c2.score) && (c2.boxIndex - c1.boxIndex));\n}\n//# sourceMappingURL=non_max_suppression_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV3Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This is the async version of `nonMaxSuppression`\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n    maxOutputSize = inputs.maxOutputSize;\n    iouThreshold = inputs.iouThreshold;\n    scoreThreshold = inputs.scoreThreshold;\n    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n    const boxesVals = boxesAndScores[0];\n    const scoresVals = boxesAndScores[1];\n    // We call a cpu based impl directly with the typedarray data  here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\n//# sourceMappingURL=non_max_suppression_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV5 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    maxOutputSize = params.maxOutputSize;\n    iouThreshold = params.iouThreshold;\n    scoreThreshold = params.scoreThreshold;\n    softNmsSigma = params.softNmsSigma;\n    const inputs = { boxes: $boxes, scores: $scores };\n    const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n    const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);\n    return { selectedIndices: result[0], selectedScores: result[1] };\n}\nexport const nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ });\n//# sourceMappingURL=non_max_suppression_with_score.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV5Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    maxOutputSize = params.maxOutputSize;\n    iouThreshold = params.iouThreshold;\n    scoreThreshold = params.scoreThreshold;\n    softNmsSigma = params.softNmsSigma;\n    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n    const boxesVals = boxesAndScores[0];\n    const scoresVals = boxesAndScores[1];\n    // We call a cpu based impl directly with the typedarray data  here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;\n//# sourceMappingURL=non_max_suppression_with_score_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV4 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n    const $maxOutputSize = params.maxOutputSize;\n    const $iouThreshold = params.iouThreshold;\n    const $scoreThreshold = params.scoreThreshold;\n    const inputs = { boxes: $boxes, scores: $scores };\n    const attrs = {\n        maxOutputSize: $maxOutputSize,\n        iouThreshold: $iouThreshold,\n        scoreThreshold: $scoreThreshold,\n        padToMaxOutputSize\n    };\n    const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);\n    return { selectedIndices: result[0], validOutputs: result[1] };\n}\nexport const nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ });\n//# sourceMappingURL=non_max_suppression_padded.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV4Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n    const $maxOutputSize = params.maxOutputSize;\n    const $iouThreshold = params.iouThreshold;\n    const $scoreThreshold = params.scoreThreshold;\n    const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);\n    // We call a cpu based impl directly with the typedarray data here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;\n//# sourceMappingURL=non_max_suppression_padded_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeBilinear } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Bilinear resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeBilinear_(images, size, alignCorners = false) {\n    const $images = convertToTensor(images, 'images', 'resizeBilinear');\n    util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got ` +\n        `rank ${$images.rank}.`);\n    util.assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ` +\n        `${size}.`);\n    let batchImages = $images;\n    let reshapedTo4D = false;\n    if ($images.rank === 3) {\n        reshapedTo4D = true;\n        batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n    }\n    const [newHeight, newWidth] = size;\n    const forward = (backend, save) => {\n        save([batchImages]);\n        return backend.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);\n    };\n    const inputs = { images: batchImages };\n    const attrs = { alignCorners, size };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, ResizeBilinear, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const resizeBilinear = op({ resizeBilinear_ });\n//# sourceMappingURL=resize_bilinear.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeNearestNeighbor } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * NearestNeighbor resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeNearestNeighbor_(images, size, alignCorners = false) {\n    const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');\n    util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` +\n        `rank ${$images.rank}.`);\n    util.assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ` +\n        `${size}.`);\n    util.assert($images.dtype === 'float32' || $images.dtype === 'int32', () => '`images` must have `int32` or `float32` as dtype');\n    let batchImages = $images;\n    let reshapedTo4D = false;\n    if ($images.rank === 3) {\n        reshapedTo4D = true;\n        batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n    }\n    const [newHeight, newWidth] = size;\n    const inputs = { images: batchImages };\n    const attrs = { alignCorners, size };\n    const forward = (backend, save) => {\n        save([batchImages]);\n        return backend.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);\n    };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, ResizeNearestNeighbor, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const resizeNearestNeighbor = op({ resizeNearestNeighbor_ });\n//# sourceMappingURL=resize_nearest_neighbor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assert } from '../../util';\nimport { greaterEqual } from '../greater_equal';\nimport { lessEqual } from '../less_equal';\nimport { logicalAnd } from '../logical_and';\nimport { op } from '../operation';\nimport { range } from '../range';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\nimport { zeros } from '../zeros';\n/**\n * Copy a tensor setting everything outside a central band in each innermost\n * matrix to zero.\n *\n * The band part is computed as follows: Assume input has `k` dimensions\n * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where\n * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.\n * The indicator function\n * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower))`\n * `&& (num_upper < 0 || (n-m) <= num_upper)`\n *\n * ```js\n * const x = tf.tensor2d([[ 0,  1,  2, 3],\n *                        [-1,  0,  1, 2],\n *                        [-2, -1,  0, 1],\n *                        [-3, -2, -1, 0]]);\n * let y = tf.linalg.bandPart(x, 1, -1);\n * y.print(); // [[ 0,  1,  2, 3],\n *            //  [-1,  0,  1, 2],\n *            //  [ 0, -1,  0, 1],\n *            //  [ 0, 0 , -1, 0]]\n * let z = tf.linalg.bandPart(x, 2, 1);\n * z.print(); // [[ 0,  1,  0, 0],\n *            //  [-1,  0,  1, 0],\n *            //  [-2, -1,  0, 1],\n *            //  [ 0, -2, -1, 0]]\n * ```\n *\n * @param x Rank `k` tensor\n * @param numLower Number of subdiagonals to keep.\n *   If negative, keep entire lower triangle.\n * @param numUpper Number of subdiagonals to keep.\n *   If negative, keep entire upper triangle.\n * @returns Rank `k` tensor of the same shape as input.\n *   The extracted banded tensor.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction bandPart_(a, numLower, numUpper) {\n    assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);\n    assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);\n    const $a = convertToTensor(a, 'a', 'bandPart');\n    assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);\n    const shape = $a.shape;\n    const [M, N] = $a.shape.slice(-2);\n    if (!(numLower <= M)) {\n        throw new Error(`bandPart(): numLower (${numLower})` +\n            ` must not be greater than the number of rows (${M}).`);\n    }\n    if (!(numUpper <= N)) {\n        throw new Error(`bandPart(): numUpper (${numUpper})` +\n            ` must not be greater than the number of columns (${N}).`);\n    }\n    if (numLower < 0) {\n        numLower = M;\n    }\n    if (numUpper < 0) {\n        numUpper = N;\n    }\n    const i = reshape(range(0, M, 1, 'int32'), [-1, 1]);\n    const j = range(0, N, 1, 'int32');\n    const ij = sub(i, j);\n    const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, 'int32')), greaterEqual(ij, scalar(-numUpper, 'int32')));\n    const zero = zeros([M, N], $a.dtype);\n    return reshape(stack(unstack(reshape($a, [-1, M, N]))\n        .map(mat => where(inBand, mat, zero))), shape);\n}\nexport const bandPart = op({ bandPart_ });\n//# sourceMappingURL=band_part.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { assert } from '../../util';\nimport { div } from '../div';\nimport { mul } from '../mul';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { split } from '../split';\nimport { squeeze } from '../squeeze';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\n/**\n * Gram-Schmidt orthogonalization.\n *\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * let y = tf.linalg.gramSchmidt(x);\n * y.print();\n * console.log('Othogonalized:');\n * y.dot(y.transpose()).print();  // should be nearly the identity matrix.\n * console.log('First row direction maintained:');\n * const data = await y.array();\n * console.log(data[0][1] / data[0][0]);  // should be nearly 2.\n * ```\n *\n * @param xs The vectors to be orthogonalized, in one of the two following\n *   formats:\n *   - An Array of `tf.Tensor1D`.\n *   - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows\n *     of `xs`.\n *   In each case, all the vectors must have the same length and the length\n *   must be greater than or equal to the number of vectors.\n * @returns The orthogonalized and normalized vectors or matrix.\n *   Orthogonalization means that the vectors or the rows of the matrix\n *   are orthogonal (zero inner products). Normalization means that each\n *   vector or each row of the matrix has an L2 norm that equals `1`.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction gramSchmidt_(xs) {\n    let inputIsTensor2D;\n    if (Array.isArray(xs)) {\n        inputIsTensor2D = false;\n        assert(xs != null && xs.length > 0, () => 'Gram-Schmidt process: input must not be null, undefined, or ' +\n            'empty');\n        const dim = xs[0].shape[0];\n        for (let i = 1; i < xs.length; ++i) {\n            assert(xs[i].shape[0] === dim, () => 'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +\n                `(${xs[i].shape[0]} vs. ${dim})`);\n        }\n    }\n    else {\n        inputIsTensor2D = true;\n        xs = split(xs, xs.shape[0], 0).map(x => squeeze(x, [0]));\n    }\n    assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds ` +\n        `number of dimensions (${xs[0].shape[0]}).`);\n    const ys = [];\n    const xs1d = xs;\n    for (let i = 0; i < xs.length; ++i) {\n        ys.push(ENGINE.tidy(() => {\n            let x = xs1d[i];\n            if (i > 0) {\n                for (let j = 0; j < i; ++j) {\n                    const proj = mul(sum(mul(ys[j], x)), ys[j]);\n                    x = sub(x, proj);\n                }\n            }\n            return div(x, norm(x, 'euclidean'));\n        }));\n    }\n    if (inputIsTensor2D) {\n        return stack(ys, 0);\n    }\n    else {\n        return ys;\n    }\n}\nexport const gramSchmidt = op({ gramSchmidt_ });\n//# sourceMappingURL=gram_schmidt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { dispose } from '../../globals';\nimport { assert } from '../../util';\nimport { clone } from '../clone';\nimport { concat } from '../concat';\nimport { div } from '../div';\nimport { eye } from '../eye';\nimport { greater } from '../greater';\nimport { matMul } from '../mat_mul';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { tensor2d } from '../tensor2d';\nimport { transpose } from '../transpose';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\n/**\n * Compute QR decomposition of m-by-n matrix using Householder transformation.\n *\n * Implementation based on\n *   [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]\n * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [3, 4]]);\n * let [q, r] = tf.linalg.qr(a);\n * console.log('Q');\n * q.print();\n * console.log('R');\n * r.print();\n * console.log('Orthogonalized');\n * q.dot(q.transpose()).print()  // should be nearly the identity matrix.\n * console.log('Reconstructed');\n * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];\n * ```\n *\n * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose\n *   it has the shape `[..., M, N]`.\n * @param fullMatrices An optional boolean parameter. Defaults to `false`.\n *   If `true`, compute full-sized `Q`. If `false` (the default),\n *   compute only the leading N columns of `Q` and `R`.\n * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,\n *   i.e., its columns all have unit norm and are mutually orthogonal.\n *   If `M >= N`,\n *     If `fullMatrices` is `false` (default),\n *       - `Q` has a shape of `[..., M, N]`,\n *       - `R` has a shape of `[..., N, N]`.\n *     If `fullMatrices` is `true` (default),\n *       - `Q` has a shape of `[..., M, M]`,\n *       - `R` has a shape of `[..., M, N]`.\n *   If `M < N`,\n *     - `Q` has a shape of `[..., M, M]`,\n *     - `R` has a shape of `[..., M, N]`.\n * @throws If the rank of `x` is less than 2.\n *\n * @doc {heading:'Operations',\n *       subheading:'Linear Algebra',\n *       namespace:'linalg'}\n */\nfunction qr_(x, fullMatrices = false) {\n    assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);\n    if (x.rank === 2) {\n        return qr2d(x, fullMatrices);\n    }\n    else {\n        // Rank > 2.\n        // TODO(cais): Below we split the input into individual 2D tensors,\n        //   perform QR decomposition on them and then stack the results back\n        //   together. We should explore whether this can be parallelized.\n        const outerDimsProd = x.shape.slice(0, x.shape.length - 2)\n            .reduce((value, prev) => value * prev);\n        const x2ds = unstack(reshape(x, [\n            outerDimsProd, x.shape[x.shape.length - 2],\n            x.shape[x.shape.length - 1]\n        ]), 0);\n        const q2ds = [];\n        const r2ds = [];\n        x2ds.forEach(x2d => {\n            const [q2d, r2d] = qr2d(x2d, fullMatrices);\n            q2ds.push(q2d);\n            r2ds.push(r2d);\n        });\n        const q = reshape(stack(q2ds, 0), x.shape);\n        const r = reshape(stack(r2ds, 0), x.shape);\n        return [q, r];\n    }\n}\nfunction qr2d(x, fullMatrices = false) {\n    return ENGINE.tidy(() => {\n        assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);\n        const m = x.shape[0];\n        const n = x.shape[1];\n        let q = eye(m); // Orthogonal transform so far.\n        let r = clone(x); // Transformed matrix so far.\n        const one2D = tensor2d([[1]], [1, 1]);\n        let w = clone(one2D);\n        const iters = m >= n ? n : m;\n        for (let j = 0; j < iters; ++j) {\n            // This tidy within the for-loop ensures we clean up temporary\n            // tensors as soon as they are no longer needed.\n            const rTemp = r;\n            const wTemp = w;\n            const qTemp = q;\n            [w, r, q] = ENGINE.tidy(() => {\n                // Find H = I - tau * w * w', to put zeros below R(j, j).\n                const rjEnd1 = slice(r, [j, j], [m - j, 1]);\n                const normX = norm(rjEnd1);\n                const rjj = slice(r, [j, j], [1, 1]);\n                // The sign() function returns 0 on 0, which causes division by zero.\n                const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));\n                const u1 = sub(rjj, mul(s, normX));\n                const wPre = div(rjEnd1, u1);\n                if (wPre.shape[0] === 1) {\n                    w = clone(one2D);\n                }\n                else {\n                    w = concat([\n                        one2D,\n                        slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])\n                    ], 0);\n                }\n                const tau = neg(div(matMul(s, u1), normX));\n                // -- R := HR, Q := QH.\n                const rjEndAll = slice(r, [j, 0], [m - j, n]);\n                const tauTimesW = mul(tau, w);\n                const wT = transpose(w);\n                if (j === 0) {\n                    r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n                }\n                else {\n                    const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n                    r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);\n                }\n                const tawTimesWT = transpose(tauTimesW);\n                const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);\n                if (j === 0) {\n                    q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n                }\n                else {\n                    const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n                    q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);\n                }\n                return [w, r, q];\n            });\n            dispose([rTemp, wTemp, qTemp]);\n        }\n        if (!fullMatrices && m > n) {\n            q = slice(q, [0, 0], [m, n]);\n            r = slice(r, [0, 0], [n, n]);\n        }\n        return [q, r];\n    });\n}\nexport const qr = op({ qr_ });\n//# sourceMappingURL=qr.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Reduction;\n(function (Reduction) {\n    Reduction[Reduction[\"NONE\"] = 0] = \"NONE\";\n    Reduction[Reduction[\"MEAN\"] = 1] = \"MEAN\";\n    Reduction[Reduction[\"SUM\"] = 2] = \"SUM\";\n    Reduction[Reduction[\"SUM_BY_NONZERO_WEIGHTS\"] = 3] = \"SUM_BY_NONZERO_WEIGHTS\";\n})(Reduction || (Reduction = {}));\n//# sourceMappingURL=loss_ops_utils.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { Reduction } from '../loss_ops_utils';\nimport { mean } from '../mean';\nimport { mul } from '../mul';\nimport { notEqual } from '../not_equal';\nimport { ones } from '../ones';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sum } from '../sum';\n/**\n * Computes the weighted loss between two tensors.\n *\n * @param losses Tensor of shape `[batch_size, d1, ... dN]`.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `losses`, and must be broadcastable to `losses` (i.e., all\n *    dimensions must be either `1`, or the same as the corresponding\n *    `losses` dimension).\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction computeWeightedLoss_(losses, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');\n    }\n    const weightedLoss = ($weights == null) ? $losses : mul($losses, $weights);\n    if (reduction === Reduction.NONE) {\n        return weightedLoss;\n    }\n    if (reduction === Reduction.SUM) {\n        return sum(weightedLoss);\n    }\n    if (reduction === Reduction.MEAN) {\n        if ($weights == null) {\n            return mean(weightedLoss);\n        }\n        else {\n            const broadcastFactor = $losses.size / $weights.size;\n            const result = div(sum(weightedLoss), sum($weights));\n            return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) :\n                result;\n        }\n    }\n    if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n        if ($weights == null) {\n            return div(sum(weightedLoss), scalar($losses.size));\n        }\n        else {\n            const broadcastedWeights = mul($weights, ones($losses.shape));\n            const numNonZeros = cast(sum(notEqual(broadcastedWeights, scalar(0))), 'float32');\n            return div(sum(weightedLoss), numNonZeros);\n        }\n    }\n    throw Error(`Unknown reduction: ${reduction}`);\n}\nexport const computeWeightedLoss = op({ computeWeightedLoss_ });\n//# sourceMappingURL=compute_weighted_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the absolute difference loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'absoluteDifference');\n    const $predictions = convertToTensor(predictions, 'predictions', 'absoluteDifference');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'absoluteDifference');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in absoluteDifference: ');\n    const losses = abs(sub($labels, $predictions));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const absoluteDifference = op({ absoluteDifference_ });\n//# sourceMappingURL=absolute_difference.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the cosine distance loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param axis The dimension along which the cosine distance is computed.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'cosineDistance');\n    const $predictions = convertToTensor(predictions, 'predictions', 'cosineDistance');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'cosineDistance');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in cosineDistance: ');\n    const one = scalar(1);\n    const losses = sub(one, sum(mul($labels, $predictions), axis, true));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const cosineDistance = op({ cosineDistance_ });\n//# sourceMappingURL=cosine_distance.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the Hinge loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $labels = convertToTensor(labels, 'labels', 'hingeLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'hingeLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');\n    const one = scalar(1);\n    // Convert binary labels to (-1, 1)\n    $labels = sub(mul(scalar(2), $labels), one);\n    const losses = relu(sub(one, mul($labels, $predictions)));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const hingeLoss = op({ hingeLoss_ });\n//# sourceMappingURL=hinge_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { Reduction } from '../loss_ops_utils';\nimport { minimum } from '../minimum';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { square } from '../square';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the huber loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param delta Point where huber loss changes from quadratic to linear.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`.\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction huberLoss_(labels, predictions, weights, delta = 1.0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'huberLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'huberLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');\n    const deltaScalar = scalar(delta);\n    const error = abs(sub($predictions, $labels));\n    const quadratic = minimum(error, deltaScalar);\n    const linear = sub(error, quadratic);\n    const losses = add(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const huberLoss = op({ huberLoss_ });\n//# sourceMappingURL=huber_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { log } from '../log';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the log loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param epsilon A small increment to avoid taking log of zero\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction logLoss_(labels, predictions, weights, epsilon = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'logLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'logLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'logLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');\n    const one = scalar(1);\n    const epsilonScalar = scalar(epsilon);\n    const l1 = neg(mul($labels, log(add($predictions, epsilonScalar))));\n    const l2 = mul(sub(one, $labels), log(add(sub(one, $predictions), epsilonScalar)));\n    const losses = sub(l1, l2);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const logLoss = op({ logLoss_ });\n//# sourceMappingURL=log_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { squaredDifference } from '../squared_difference';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the mean squared error between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'meanSquaredError');\n    const $predictions = convertToTensor(predictions, 'predictions', 'meanSquaredError');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'meanSquaredError');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in meanSquaredError: ');\n    const losses = squaredDifference($labels, $predictions);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const meanSquaredError = op({ meanSquaredError_ });\n//# sourceMappingURL=mean_squared_error.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { exp } from '../exp';\nimport { log1p } from '../log1p';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\nfunction sigmoidCrossEntropyWithLogits_(labels, logits) {\n    const $labels = convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');\n    const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');\n    assertShapesMatch($labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');\n    /**\n     * Implementation Details:\n     *\n     * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n     *     z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n     *   = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n     *   = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n     *   = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n     *   = (1 - z) * x + log(1 + exp(-x))\n     *   = x - x * z + log(1 + exp(-x))\n     *\n     *   For x < 0, to avoid overflow in exp(-x), we reformulate the above\n     *     x - x * z + log(1 + exp(-x))\n     *   = log(exp(x)) - x * z + log(1 + exp(-x))\n     *   = - x * z + log(1 + exp(x))\n     *\n     * Hence, to ensure stability and avoid overflow, the implementation uses\n     * this equivalent formulation:\n     *     max(x, 0) - x * z + log(1 + exp(-abs(x)))\n     */\n    const maxOutput = relu($logits);\n    const outputXTarget = mul($logits, $labels);\n    const sigmoidOutput = log1p(exp(neg(abs($logits))));\n    return add(sub(maxOutput, outputXTarget), sigmoidOutput);\n}\n/**\n * Computes the sigmoid cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)\n *                         + 0.5 * labelSmoothing\n *\n * @param multiClassLabels The ground truth output tensor of shape\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $multiClassLabels = convertToTensor(multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');\n    const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');\n    }\n    assertShapesMatch($multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');\n    if (labelSmoothing > 0) {\n        const labelSmoothingScalar = scalar(labelSmoothing);\n        const one = scalar(1);\n        const half = scalar(0.5);\n        $multiClassLabels =\n            add(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));\n    }\n    const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ });\n//# sourceMappingURL=sigmoid_cross_entropy.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../../gradients';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { expandShapeToKeepDim } from '../axis_util';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { exp } from '../exp';\nimport { logSumExp } from '../log_sum_exp';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes softmax cross entropy between logits and labels.\n *\n * Measures the probability error in discrete classification tasks in which\n * the classes are mutually exclusive (each entry is in exactly one class).\n * For example, each CIFAR-10 image is labeled with one and only one label: an\n * image can be a dog or a truck, but not both.\n *\n * `NOTE`: While the classes are mutually exclusive, their probabilities need\n * not be. All that is required is that each row of labels is a valid\n * probability distribution. If they are not, the computation of the gradient\n * will be incorrect.\n *\n * `WARNING`: This op expects unscaled logits, since it performs a softmax on\n * logits internally for efficiency. Do not call this op with the output of\n * softmax, as it will produce incorrect results.\n *\n * logits and labels must have the same shape, e.g. [batch_size, num_classes]\n * and the same dtype.\n * @param labels The labels array.\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\nfunction softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {\n    if (dim === -1) {\n        dim = logits.rank - 1;\n    }\n    if (dim !== logits.rank - 1) {\n        throw Error(`Softmax cross entropy along a non-last dimension is not yet ` +\n            `supported. Labels / logits was rank ${logits.rank} ` +\n            `and dim was ${dim}`);\n    }\n    // Use a custom gradient for numerical stability.\n    const customOp = customGrad((labels, logits, save) => {\n        // Reference:\n        //   1. http://cs231n.github.io/linear-classify/#softmax\n        //   2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/\n        const keepDims = true;\n        const lse = logSumExp(logits, [dim], keepDims);\n        const logResult = sub(cast(logits, 'float32'), lse);\n        save([labels, logResult]);\n        const costVector = neg(mul(logResult, labels));\n        const value = sum(costVector, [dim]);\n        const gradFunc = (dy, saved) => {\n            const [labels, logResult] = saved;\n            const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n            return [\n                mul(reshape(dy, dyShape), sub(cast(labels, 'float32'), exp(logResult))),\n                mul(reshape(dy, dyShape), sub(exp(logResult), cast(labels, 'float32'))),\n            ];\n        };\n        return { value, gradFunc };\n    });\n    return customOp(labels, logits);\n}\n/**\n * Computes the softmax cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newOnehotLabels = onehotLabels * (1 - labelSmoothing)\n *                         + labelSmoothing / numClasses\n *\n * @param onehotLabels One hot encoded labels\n *    [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or 1, and must be\n *    broadcastable to `loss`  of shape [batch_size]\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $onehotLabels = convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');\n    const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');\n    }\n    assertShapesMatch($onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');\n    if (labelSmoothing > 0) {\n        const labelSmoothingScalar = scalar(labelSmoothing);\n        const one = scalar(1);\n        const numClasses = scalar($onehotLabels.shape[1]);\n        $onehotLabels =\n            add(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));\n    }\n    const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const softmaxCrossEntropy = op({ softmaxCrossEntropy_ });\n//# sourceMappingURL=softmax_cross_entropy.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Modularized ops.\nexport { abs } from './abs';\nexport { acos } from './acos';\nexport { acosh } from './acosh';\nexport { add } from './add';\nexport { addN } from './add_n';\nexport { all } from './all';\nexport { any } from './any';\nexport { argMax } from './arg_max';\nexport { argMin } from './arg_min';\nexport { asin } from './asin';\nexport { asinh } from './asinh';\nexport { atan } from './atan';\nexport { atan2 } from './atan2';\nexport { atanh } from './atanh';\nexport { avgPool } from './avg_pool';\nexport { avgPool3d } from './avg_pool_3d';\nexport { basicLSTMCell } from './basic_lstm_cell';\nexport { batchToSpaceND } from './batch_to_space_nd';\nexport { batchNorm } from './batchnorm';\nexport { batchNorm2d } from './batchnorm2d';\nexport { batchNorm3d } from './batchnorm3d';\nexport { batchNorm4d } from './batchnorm4d';\nexport { broadcastTo } from './broadcast_to';\nexport { buffer } from './buffer';\nexport { cast } from './cast';\nexport { ceil } from './ceil';\nexport { clipByValue } from './clip_by_value';\nexport { clone } from './clone';\nexport { complex } from './complex';\nexport { concat } from './concat';\nexport { concat1d } from './concat_1d';\nexport { concat2d } from './concat_2d';\nexport { concat3d } from './concat_3d';\nexport { concat4d } from './concat_4d';\nexport { conv1d } from './conv1d';\nexport { conv2d } from './conv2d';\nexport { conv2dTranspose } from './conv2d_transpose';\nexport { conv3d } from './conv3d';\nexport { conv3dTranspose } from './conv3d_transpose';\nexport { cos } from './cos';\nexport { cosh } from './cosh';\nexport { cumsum } from './cumsum';\nexport { depthToSpace } from './depth_to_space';\nexport { depthwiseConv2d } from './depthwise_conv2d';\nexport { diag } from './diag';\nexport { dilation2d } from './dilation2d';\nexport { div } from './div';\nexport { divNoNan } from './div_no_nan';\nexport { dot } from './dot';\nexport { elu } from './elu';\nexport { equal } from './equal';\nexport { erf } from './erf';\nexport { exp } from './exp';\nexport { expandDims } from './expand_dims';\nexport { expm1 } from './expm1';\nexport { eye } from './eye';\nexport { fill } from './fill';\nexport { floor } from './floor';\nexport { floorDiv } from './floorDiv';\nexport { gather } from './gather';\nexport { greater } from './greater';\nexport { greaterEqual } from './greater_equal';\nexport { imag } from './imag';\nexport { isFinite } from './is_finite';\nexport { isInf } from './is_inf';\nexport { isNaN } from './is_nan';\nexport { leakyRelu } from './leaky_relu';\nexport { less } from './less';\nexport { lessEqual } from './less_equal';\nexport { linspace } from './linspace';\nexport { localResponseNormalization } from './local_response_normalization';\nexport { log } from './log';\nexport { log1p } from './log1p';\nexport { logSigmoid } from './log_sigmoid';\nexport { logSoftmax } from './log_softmax';\nexport { logSumExp } from './log_sum_exp';\nexport { logicalAnd } from './logical_and';\nexport { logicalNot } from './logical_not';\nexport { logicalOr } from './logical_or';\nexport { logicalXor } from './logical_xor';\nexport { matMul } from './mat_mul';\nexport { max } from './max';\nexport { maxPool } from './max_pool';\nexport { maxPool3d } from './max_pool_3d';\nexport { maxPoolWithArgmax } from './max_pool_with_argmax';\nexport { maximum } from './maximum';\nexport { mean } from './mean';\nexport { min } from './min';\nexport { minimum } from './minimum';\nexport { mod } from './mod';\nexport { moments } from './moments';\nexport { mul } from './mul';\nexport { multiRNNCell } from './multi_rnn_cell';\nexport { multinomial } from './multinomial';\nexport { neg } from './neg';\nexport { notEqual } from './not_equal';\nexport { oneHot } from './one_hot';\nexport { ones } from './ones';\nexport { onesLike } from './ones_like';\nexport { outerProduct } from './outer_product';\nexport { pad } from './pad';\nexport { pad1d } from './pad1d';\nexport { pad2d } from './pad2d';\nexport { pad3d } from './pad3d';\nexport { pad4d } from './pad4d';\nexport { pool } from './pool';\nexport { pow } from './pow';\nexport { prelu } from './prelu';\nexport { print } from './print';\nexport { prod } from './prod';\nexport { rand } from './rand';\nexport { randomGamma } from './random_gamma';\nexport { randomNormal } from './random_normal';\nexport { randomUniform } from './random_uniform';\nexport { range } from './range';\nexport { real } from './real';\nexport { reciprocal } from './reciprocal';\nexport { relu } from './relu';\nexport { relu6 } from './relu6';\nexport { reshape } from './reshape';\nexport { reverse } from './reverse';\nexport { reverse1d } from './reverse_1d';\nexport { reverse2d } from './reverse_2d';\nexport { reverse3d } from './reverse_3d';\nexport { reverse4d } from './reverse_4d';\nexport { round } from './round';\nexport { rsqrt } from './rsqrt';\nexport { scalar } from './scalar';\nexport { selu } from './selu';\nexport { separableConv2d } from './separable_conv2d';\nexport { setdiff1dAsync } from './setdiff1d_async';\nexport { sigmoid } from './sigmoid';\nexport { sign } from './sign';\nexport { sin } from './sin';\nexport { sinh } from './sinh';\nexport { slice } from './slice';\nexport { slice1d } from './slice1d';\nexport { slice2d } from './slice2d';\nexport { slice3d } from './slice3d';\nexport { slice4d } from './slice4d';\nexport { softmax } from './softmax';\nexport { softplus } from './softplus';\nexport { spaceToBatchND } from './space_to_batch_nd';\nexport { fft } from './spectral/fft';\nexport { ifft } from './spectral/ifft';\nexport { irfft } from './spectral/irfft';\nexport { rfft } from './spectral/rfft';\nexport { split } from './split';\nexport { sqrt } from './sqrt';\nexport { square } from './square';\nexport { squaredDifference } from './squared_difference';\nexport { squeeze } from './squeeze';\nexport { stack } from './stack';\nexport { step } from './step';\nexport { stridedSlice } from './strided_slice';\nexport { sub } from './sub';\nexport { sum } from './sum';\nexport { tan } from './tan';\nexport { tanh } from './tanh';\nexport { tensor } from './tensor';\nexport { tensor1d } from './tensor1d';\nexport { tensor2d } from './tensor2d';\nexport { tensor3d } from './tensor3d';\nexport { tensor4d } from './tensor4d';\nexport { tensor5d } from './tensor5d';\nexport { tensor6d } from './tensor6d';\nexport { tile } from './tile';\nexport { topk } from './topk';\nexport { truncatedNormal } from './truncated_normal';\nexport { unique } from './unique';\nexport { unsortedSegmentSum } from './unsorted_segment_sum';\nexport { unstack } from './unstack';\nexport { variable } from './variable';\nexport { where } from './where';\nexport { whereAsync } from './where_async';\nexport { zeros } from './zeros';\nexport { zerosLike } from './zeros_like';\nexport * from './boolean_mask';\nexport * from './compare';\nexport * from './binary_ops';\nexport * from './transpose';\nexport * from './norm';\nexport * from './moving_average';\nexport * from './scatter_nd';\nexport * from './sparse_to_dense';\nexport * from './gather_nd';\nexport * from './dropout';\nexport * from './signal_ops_util';\nexport * from './in_top_k';\nexport { op, OP_SCOPE_SUFFIX } from './operation';\nimport { rfft } from './spectral/rfft';\nimport { fft } from './spectral/fft';\nimport { ifft } from './spectral/ifft';\nimport { irfft } from './spectral/irfft';\nconst spectral = {\n    fft,\n    ifft,\n    rfft,\n    irfft\n};\nimport * as fused from './fused_ops';\nimport { hammingWindow } from './signal/hamming_window';\nimport { hannWindow } from './signal/hann_window';\nimport { frame } from './signal/frame';\nimport { stft } from './signal/stft';\nconst signal = {\n    hammingWindow,\n    hannWindow,\n    frame,\n    stft,\n};\n// Image Ops namespace\nimport { cropAndResize } from './image/crop_and_resize';\nimport { flipLeftRight } from './image/flip_left_right';\nimport { rotateWithOffset } from './image/rotate_with_offset';\nimport { nonMaxSuppression } from './image/non_max_suppression';\nimport { nonMaxSuppressionAsync } from './image/non_max_suppression_async';\nimport { nonMaxSuppressionWithScore } from './image/non_max_suppression_with_score';\nimport { nonMaxSuppressionWithScoreAsync } from './image/non_max_suppression_with_score_async';\nimport { nonMaxSuppressionPadded } from './image/non_max_suppression_padded';\nimport { nonMaxSuppressionPaddedAsync } from './image/non_max_suppression_padded_async';\nimport { resizeBilinear } from './image/resize_bilinear';\nimport { resizeNearestNeighbor } from './image/resize_nearest_neighbor';\nconst image = {\n    flipLeftRight,\n    resizeNearestNeighbor,\n    resizeBilinear,\n    rotateWithOffset,\n    cropAndResize,\n    nonMaxSuppression,\n    nonMaxSuppressionAsync,\n    nonMaxSuppressionWithScore,\n    nonMaxSuppressionWithScoreAsync,\n    nonMaxSuppressionPadded,\n    nonMaxSuppressionPaddedAsync\n};\n// linalg namespace\nimport { bandPart } from './linalg/band_part';\nimport { gramSchmidt } from './linalg/gram_schmidt';\nimport { qr } from './linalg/qr';\nconst linalg = {\n    bandPart,\n    gramSchmidt,\n    qr\n};\n// losses namespace;\nimport { absoluteDifference } from './losses/absolute_difference';\nimport { computeWeightedLoss } from './losses/compute_weighted_loss';\nimport { cosineDistance } from './losses/cosine_distance';\nimport { hingeLoss } from './losses/hinge_loss';\nimport { huberLoss } from './losses/huber_loss';\nimport { logLoss } from './losses/log_loss';\nimport { meanSquaredError } from './losses/mean_squared_error';\nimport { sigmoidCrossEntropy } from './losses/sigmoid_cross_entropy';\nimport { softmaxCrossEntropy } from './losses/softmax_cross_entropy';\nconst losses = {\n    absoluteDifference,\n    computeWeightedLoss,\n    cosineDistance,\n    hingeLoss,\n    huberLoss,\n    logLoss,\n    meanSquaredError,\n    sigmoidCrossEntropy,\n    softmaxCrossEntropy\n};\n// Second level exports.\nexport { image, linalg, losses, spectral, fused, signal };\n//# sourceMappingURL=ops.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dispose } from '../globals';\nimport { variableGrads } from '../gradients';\nimport { scalar } from '../ops/ops';\nimport { Serializable } from '../serialization';\n/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */\nexport class Optimizer extends Serializable {\n    /**\n     * Executes `f()` and minimizes the scalar output of `f()` by computing\n     * gradients of y with respect to the list of trainable variables provided by\n     * `varList`. If no list is provided, it defaults to all trainable variables.\n     *\n     * @param f The function to execute and whose output to minimize.\n     * @param returnCost Whether to return the scalar cost value produced by\n     * executing `f()`.\n     * @param varList An optional list of variables to update. If specified, only\n     * the trainable variables in varList will be updated by minimize. Defaults to\n     * all trainable variables.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers'}\n     */\n    minimize(f, returnCost = false, varList) {\n        const { value, grads } = this.computeGradients(f, varList);\n        if (varList != null) {\n            const gradArray = varList.map(v => ({ name: v.name, tensor: grads[v.name] }));\n            this.applyGradients(gradArray);\n        }\n        else {\n            this.applyGradients(grads);\n        }\n        // Dispose gradients.\n        dispose(grads);\n        if (returnCost) {\n            return value;\n        }\n        else {\n            value.dispose();\n            return null;\n        }\n    }\n    /**\n     * The number of iterations that this optimizer instance has been invoked for.\n     */\n    get iterations() {\n        if (this.iterations_ == null) {\n            this.iterations_ = 0;\n        }\n        return this.iterations_;\n    }\n    incrementIterations() {\n        this.iterations_ = this.iterations + 1;\n    }\n    /**\n     * Executes f() and computes the gradient of the scalar output of f() with\n     * respect to the list of trainable variables provided by `varList`. If no\n     * list is provided, it defaults to all trainable variables.\n     *\n     * @param f The function to execute and whose output to use for computing\n     * gradients with respect to variables.\n     * @param varList An optional list of variables to compute gradients with\n     * respect to. If specified, only the trainable variables in varList will have\n     * gradients computed with respect to. Defaults to all trainable variables.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers'}\n     */\n    computeGradients(f, varList) {\n        return variableGrads(f, varList);\n    }\n    /**\n     * Dispose the variables (if any) owned by this optimizer instance.\n     */\n    dispose() {\n        if (this.iterations_ != null) {\n            dispose(this.iterations_);\n        }\n    }\n    async saveIterations() {\n        if (this.iterations_ == null) {\n            this.iterations_ = 0;\n        }\n        return {\n            name: 'iter',\n            // TODO(cais): Use 'int64' type when available.\n            tensor: scalar(this.iterations_, 'int32')\n        };\n    }\n    async getWeights() {\n        throw new Error('getWeights() is not implemented for this optimizer yet.');\n    }\n    async setWeights(weightValues) {\n        throw new Error(`setWeights() is not implemented for this optimizer class ` +\n            `${this.getClassName()}`);\n    }\n    /**\n     * Extract the first element of the weight values and set it\n     * as the iterations counter variable of this instance of optimizer.\n     *\n     * @param weightValues\n     * @returns Weight values with the first element consumed and excluded.\n     */\n    async extractIterations(weightValues) {\n        this.iterations_ = (await weightValues[0].tensor.data())[0];\n        return weightValues.slice(1);\n    }\n}\nObject.defineProperty(Optimizer, Symbol.hasInstance, {\n    value: (instance) => {\n        return instance.minimize != null && instance.computeGradients != null &&\n            instance.applyGradients != null;\n    }\n});\n//# sourceMappingURL=optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/ops';\nimport { square } from '../ops/square';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class AdadeltaOptimizer extends Optimizer {\n    constructor(learningRate, rho, epsilon = null) {\n        super();\n        this.learningRate = learningRate;\n        this.rho = rho;\n        this.epsilon = epsilon;\n        this.accumulatedGrads = [];\n        this.accumulatedUpdates = [];\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            const trainable = false;\n            if (this.accumulatedGrads[i] == null) {\n                this.accumulatedGrads[i] = {\n                    originalName: `${name}/accum_grad`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedUpdates[i] == null) {\n                this.accumulatedUpdates[i] = {\n                    originalName: `${name}/accum_var`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedGrad = this.accumulatedGrads[i].variable;\n            const accumulatedUpdate = this.accumulatedUpdates[i].variable;\n            tidy(() => {\n                const newAccumulatedGrad = add(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));\n                const updates = mul(div(sqrt(add(accumulatedUpdate, this.epsilon)), sqrt(add(accumulatedGrad, this.epsilon))), gradient);\n                const newAccumulatedUpdate = add(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));\n                accumulatedGrad.assign(newAccumulatedGrad);\n                accumulatedUpdate.assign(newAccumulatedUpdate);\n                const newValue = add(mul(updates, -this.learningRate), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedUpdates != null) {\n            dispose(this.accumulatedGrads.map(v => v.variable));\n            dispose(this.accumulatedUpdates.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const variableCount = weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedGrads =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedUpdates =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'rho': this.rho,\n            'epsilon': this.epsilon\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['rho'], config['epsilon']);\n    }\n}\n/** @nocollapse */\nAdadeltaOptimizer.className = 'Adadelta'; // Name matters for Python compatibility.\nregisterClass(AdadeltaOptimizer);\n//# sourceMappingURL=adadelta_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { fill } from '../ops/fill';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class AdagradOptimizer extends Optimizer {\n    constructor(learningRate, initialAccumulatorValue = 0.1) {\n        super();\n        this.learningRate = learningRate;\n        this.initialAccumulatorValue = initialAccumulatorValue;\n        this.accumulatedGrads = [];\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            if (this.accumulatedGrads[i] == null) {\n                const trainable = false;\n                this.accumulatedGrads[i] = {\n                    originalName: `${name}/accumulator`,\n                    variable: tidy(() => fill(value.shape, this.initialAccumulatorValue)\n                        .variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedGrad = this.accumulatedGrads[i].variable;\n            tidy(() => {\n                const newAccumulatedGrad = add(accumulatedGrad, square(gradient));\n                accumulatedGrad.assign(newAccumulatedGrad);\n                const newValue = add(mul(div(gradient, sqrt(add(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedGrads != null) {\n            dispose(this.accumulatedGrads.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        return [await this.saveIterations()].concat(this.accumulatedGrads.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const trainable = false;\n        this.accumulatedGrads = weightValues.map(v => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'initialAccumulatorValue': this.initialAccumulatorValue,\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['initialAccumulatorValue']);\n    }\n}\n/** @nocollapse */\nAdagradOptimizer.className = 'Adagrad'; // Note: Name matters for Python compatibility.\nregisterClass(AdagradOptimizer);\n//# sourceMappingURL=adagrad_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { pow } from '../ops/pow';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\nexport class AdamOptimizer extends Optimizer {\n    constructor(learningRate, beta1, beta2, epsilon = null) {\n        super();\n        this.learningRate = learningRate;\n        this.beta1 = beta1;\n        this.beta2 = beta2;\n        this.epsilon = epsilon;\n        this.accumulatedFirstMoment = [];\n        this.accumulatedSecondMoment = [];\n        tidy(() => {\n            // accB* will be updated by batch.\n            this.accBeta1 = scalar(beta1).variable();\n            this.accBeta2 = scalar(beta2).variable();\n        });\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const varNames = Array.isArray(variableGradients) ?\n            variableGradients.map(v => v.name) :\n            Object.keys(variableGradients);\n        tidy(() => {\n            const oneMinusAccBeta1 = sub(1, this.accBeta1);\n            const oneMinusAccBeta2 = sub(1, this.accBeta2);\n            varNames.forEach((name, i) => {\n                const value = ENGINE.registeredVariables[name];\n                const trainable = false;\n                if (this.accumulatedFirstMoment[i] == null) {\n                    this.accumulatedFirstMoment[i] = {\n                        originalName: `${name}/m`,\n                        variable: tidy(() => zerosLike(value).variable(trainable))\n                    };\n                }\n                if (this.accumulatedSecondMoment[i] == null) {\n                    this.accumulatedSecondMoment[i] = {\n                        originalName: `${name}/v`,\n                        variable: tidy(() => zerosLike(value).variable(trainable))\n                    };\n                }\n                const gradient = Array.isArray(variableGradients) ?\n                    variableGradients[i].tensor :\n                    variableGradients[name];\n                if (gradient == null) {\n                    return;\n                }\n                const firstMoment = this.accumulatedFirstMoment[i].variable;\n                const secondMoment = this.accumulatedSecondMoment[i].variable;\n                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n                const newSecondMoment = add(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));\n                const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);\n                const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);\n                firstMoment.assign(newFirstMoment);\n                secondMoment.assign(newSecondMoment);\n                const newValue = add(mul(div(biasCorrectedFirstMoment, add(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);\n                value.assign(newValue);\n            });\n            this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n            this.accBeta2.assign(mul(this.accBeta2, this.beta2));\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.accBeta1.dispose();\n        this.accBeta2.dispose();\n        if (this.accumulatedFirstMoment != null) {\n            dispose(this.accumulatedFirstMoment.map(v => v.variable));\n        }\n        if (this.accumulatedSecondMoment != null) {\n            dispose(this.accumulatedSecondMoment.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        tidy(() => {\n            this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));\n            this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));\n        });\n        const variableCount = weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedFirstMoment =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedSecondMoment =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'beta1': this.beta1,\n            'beta2': this.beta2,\n            'epsilon': this.epsilon,\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon']);\n    }\n}\n/** @nocollapse */\nAdamOptimizer.className = 'Adam'; // Note: Name matters for Python compatibility.\nregisterClass(AdamOptimizer);\n//# sourceMappingURL=adam_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { abs } from '../ops/abs';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { maximum } from '../ops/maximum';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\nexport class AdamaxOptimizer extends Optimizer {\n    constructor(learningRate, beta1, beta2, epsilon = null, decay = 0.0) {\n        super();\n        this.learningRate = learningRate;\n        this.beta1 = beta1;\n        this.beta2 = beta2;\n        this.epsilon = epsilon;\n        this.decay = decay;\n        this.accumulatedFirstMoment = [];\n        this.accumulatedWeightedInfNorm = [];\n        tidy(() => {\n            this.iteration = scalar(0).variable();\n            this.accBeta1 = scalar(beta1).variable();\n        });\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        tidy(() => {\n            const oneMinusAccBeta1 = sub(1, this.accBeta1);\n            const lr = div(-this.learningRate, add(mul(this.iteration, this.decay), 1));\n            variableNames.forEach((name, i) => {\n                const value = ENGINE.registeredVariables[name];\n                const trainable = false;\n                if (this.accumulatedFirstMoment[i] == null) {\n                    this.accumulatedFirstMoment[i] = {\n                        originalName: `${name}/m`,\n                        variable: zerosLike(value).variable(trainable)\n                    };\n                }\n                if (this.accumulatedWeightedInfNorm[i] == null) {\n                    this.accumulatedWeightedInfNorm[i] = {\n                        originalName: `${name}/v`,\n                        variable: zerosLike(value).variable(trainable)\n                    };\n                }\n                const gradient = Array.isArray(variableGradients) ?\n                    variableGradients[i].tensor :\n                    variableGradients[name];\n                if (gradient == null) {\n                    return;\n                }\n                const firstMoment = this.accumulatedFirstMoment[i].variable;\n                const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;\n                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n                const ut0 = mul(weightedInfNorm, this.beta2);\n                const ut1 = abs(gradient);\n                const newWeightedInfNorm = maximum(ut0, ut1);\n                firstMoment.assign(newFirstMoment);\n                weightedInfNorm.assign(newWeightedInfNorm);\n                const newValue = add(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add(newWeightedInfNorm, this.epsilon))), value);\n                value.assign(newValue);\n            });\n            this.iteration.assign(add(this.iteration, 1));\n            this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.accBeta1.dispose();\n        this.iteration.dispose();\n        if (this.accumulatedFirstMoment != null) {\n            dispose(this.accumulatedFirstMoment.map(v => v.variable));\n        }\n        if (this.accumulatedWeightedInfNorm != null) {\n            dispose(this.accumulatedWeightedInfNorm.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        throw new Error('getWeights() is not implemented for Adamax yet.');\n    }\n    async setWeights(weightValues) {\n        throw new Error('setWeights() is not implemented for Adamax yet.');\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'beta1': this.beta1,\n            'beta2': this.beta2,\n            'epsilon': this.epsilon,\n            'decay': this.decay\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon'], config['decay']);\n    }\n}\n/** @nocollapse */\nAdamaxOptimizer.className = 'Adamax'; // Note: Name matters for Python compatbility.\nregisterClass(AdamaxOptimizer);\n//# sourceMappingURL=adamax_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { keep, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class SGDOptimizer extends Optimizer {\n    constructor(learningRate) {\n        super();\n        this.learningRate = learningRate;\n        this.setLearningRate(learningRate);\n    }\n    applyGradients(variableGradients) {\n        const varNames = Array.isArray(variableGradients) ?\n            variableGradients.map(v => v.name) :\n            Object.keys(variableGradients);\n        varNames.forEach((name, i) => {\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const value = ENGINE.registeredVariables[name];\n            tidy(() => {\n                const newValue = add(mul(this.c, gradient), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    /**\n     * Sets the learning rate of the optimizer.\n     */\n    setLearningRate(learningRate) {\n        this.learningRate = learningRate;\n        if (this.c != null) {\n            this.c.dispose();\n        }\n        this.c = keep(scalar(-learningRate));\n    }\n    dispose() {\n        this.c.dispose();\n    }\n    async getWeights() {\n        return [await this.saveIterations()];\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        if (weightValues.length !== 0) {\n            throw new Error('SGD optimizer does not have settable weights.');\n        }\n    }\n    getConfig() {\n        return { 'learningRate': this.learningRate };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate']);\n    }\n}\n/** @nocollapse */\nSGDOptimizer.className = 'SGD'; // Note: Name matters for Python compatibility.\nregisterClass(SGDOptimizer);\n//# sourceMappingURL=sgd_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { SGDOptimizer } from './sgd_optimizer';\n/** @doclink Optimizer */\nexport class MomentumOptimizer extends SGDOptimizer {\n    constructor(learningRate, momentum, useNesterov = false) {\n        super(learningRate);\n        this.learningRate = learningRate;\n        this.momentum = momentum;\n        this.useNesterov = useNesterov;\n        this.accumulations = [];\n        this.m = scalar(this.momentum);\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            if (this.accumulations[i] == null) {\n                const trainable = false;\n                this.accumulations[i] = {\n                    originalName: `${name}/momentum`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const accumulation = this.accumulations[i].variable;\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            tidy(() => {\n                let newValue;\n                const newAccumulation = add(mul(this.m, accumulation), gradient);\n                if (this.useNesterov) {\n                    newValue = add(mul(this.c, add(gradient, mul(newAccumulation, this.m))), value);\n                }\n                else {\n                    newValue = add(mul(this.c, newAccumulation), value);\n                }\n                accumulation.assign(newAccumulation);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.m.dispose();\n        if (this.accumulations != null) {\n            dispose(this.accumulations.map(v => v.variable));\n        }\n    }\n    /**\n     * Sets the momentum of the optimizer.\n     *\n     * @param momentum\n     */\n    setMomentum(momentum) {\n        this.momentum = momentum;\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        return [await this.saveIterations()].concat(this.accumulations.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const trainable = false;\n        this.accumulations = weightValues.map(v => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'momentum': this.momentum,\n            'useNesterov': this.useNesterov\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['momentum'], config['useNesterov']);\n    }\n}\n/** @nocollapse */\nMomentumOptimizer.className = 'Momentum'; // Name matters for Python compatibility.\nregisterClass(MomentumOptimizer);\n//# sourceMappingURL=momentum_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class RMSPropOptimizer extends Optimizer {\n    constructor(learningRate, decay = 0.9, momentum = 0.0, epsilon = null, centered = false) {\n        super();\n        this.learningRate = learningRate;\n        this.decay = decay;\n        this.momentum = momentum;\n        this.epsilon = epsilon;\n        this.accumulatedMeanSquares = [];\n        this.accumulatedMoments = [];\n        this.accumulatedMeanGrads = [];\n        this.centered = centered;\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n        if (learningRate == null) {\n            throw new Error(`learningRate for RMSPropOptimizer must be defined.`);\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            const trainable = false;\n            if (this.accumulatedMeanSquares[i] == null) {\n                this.accumulatedMeanSquares[i] = {\n                    originalName: `${name}/rms`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedMoments[i] == null) {\n                this.accumulatedMoments[i] = {\n                    originalName: `${name}/momentum`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedMeanGrads[i] == null && this.centered) {\n                this.accumulatedMeanGrads[i] = {\n                    originalName: `${name}/mg`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;\n            const accumulatedMoments = this.accumulatedMoments[i].variable;\n            tidy(() => {\n                const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n                if (this.centered) {\n                    const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;\n                    // Centered gradient\n                    const newAccumulatedMeanGrad = add(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));\n                    const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add(square(newAccumulatedMeanGrad), this.epsilon))));\n                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), gradContribution);\n                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n                    accumulatedMeanGrad.assign(newAccumulatedMeanGrad);\n                    accumulatedMoments.assign(newAccumulatedMoments);\n                    const newValue = sub(value, newAccumulatedMoments);\n                    value.assign(newValue);\n                }\n                else {\n                    // Plain gradient\n                    const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add(newAccumulatedMeanSquare, this.epsilon))));\n                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n                    accumulatedMoments.assign(newAccumulatedMoments);\n                    const newValue = sub(value, newAccumulatedMoments);\n                    value.assign(newValue);\n                }\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedMeanSquares != null) {\n            dispose(this.accumulatedMeanSquares.map(v => v.variable));\n        }\n        if (this.accumulatedMeanGrads != null && this.centered) {\n            dispose(this.accumulatedMeanGrads.map(v => v.variable));\n        }\n        if (this.accumulatedMoments != null) {\n            dispose(this.accumulatedMoments.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];\n        if (this.centered) {\n            variables.push(...this.accumulatedMeanGrads);\n        }\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedMeanSquares =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedMoments =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        if (this.centered) {\n            this.accumulatedMeanGrads =\n                weightValues.slice(variableCount * 2, variableCount * 3)\n                    .map(v => ({\n                    originalName: v.name,\n                    variable: v.tensor.variable(trainable)\n                }));\n        }\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'decay': this.decay,\n            'momentum': this.momentum,\n            'epsilon': this.epsilon,\n            'centered': this.centered\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['decay'], config['momentum'], config['epsilon'], config['centered']);\n    }\n}\n/** @nocollapse */\nRMSPropOptimizer.className = 'RMSProp'; // Note: Name matters for Python compatibility.\nregisterClass(RMSPropOptimizer);\n//# sourceMappingURL=rmsprop_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AdadeltaOptimizer } from './adadelta_optimizer';\nimport { AdagradOptimizer } from './adagrad_optimizer';\nimport { AdamOptimizer } from './adam_optimizer';\nimport { AdamaxOptimizer } from './adamax_optimizer';\nimport { MomentumOptimizer } from './momentum_optimizer';\nimport { RMSPropOptimizer } from './rmsprop_optimizer';\nimport { SGDOptimizer } from './sgd_optimizer';\nexport class OptimizerConstructors {\n    /**\n     * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.\n     *\n     * ```js\n     * // Fit a quadratic function by learning the coefficients a, b, c.\n     * const xs = tf.tensor1d([0, 1, 2, 3]);\n     * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);\n     *\n     * const a = tf.scalar(Math.random()).variable();\n     * const b = tf.scalar(Math.random()).variable();\n     * const c = tf.scalar(Math.random()).variable();\n     *\n     * // y = a * x^2 + b * x + c.\n     * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);\n     * const loss = (pred, label) => pred.sub(label).square().mean();\n     *\n     * const learningRate = 0.01;\n     * const optimizer = tf.train.sgd(learningRate);\n     *\n     * // Train the model.\n     * for (let i = 0; i < 10; i++) {\n     *   optimizer.minimize(() => loss(f(xs), ys));\n     * }\n     *\n     * // Make predictions.\n     * console.log(\n     *     `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);\n     * const preds = f(xs).dataSync();\n     * preds.forEach((pred, i) => {\n     *   console.log(`x: ${i}, pred: ${pred}`);\n     * });\n     * ```\n     *\n     * @param learningRate The learning rate to use for the SGD algorithm.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static sgd(learningRate) {\n        return new SGDOptimizer(learningRate);\n    }\n    /**\n     * Constructs a `tf.MomentumOptimizer` that uses momentum gradient\n     * descent.\n     *\n     * See\n     * [http://proceedings.mlr.press/v28/sutskever13.pdf](\n     * http://proceedings.mlr.press/v28/sutskever13.pdf)\n     *\n     * @param learningRate The learning rate to use for the Momentum gradient\n     * descent algorithm.\n     * @param momentum The momentum to use for the momentum gradient descent\n     * algorithm.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static momentum(learningRate, momentum, useNesterov = false) {\n        return new MomentumOptimizer(learningRate, momentum, useNesterov);\n    }\n    /**\n     * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient\n     * descent. This implementation uses plain momentum and is not centered\n     * version of RMSProp.\n     *\n     * See\n     * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](\n     * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\n     *\n     * @param learningRate The learning rate to use for the RMSProp gradient\n     * descent algorithm.\n     * @param decay The discounting factor for the history/coming gradient.\n     * @param momentum The momentum to use for the RMSProp gradient descent\n     * algorithm.\n     * @param epsilon Small value to avoid zero denominator.\n     * @param centered If true, gradients are normalized by the estimated\n     * variance of the gradient.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static rmsprop(learningRate, decay = .9, momentum = 0.0, epsilon = null, centered = false) {\n        return new RMSPropOptimizer(learningRate, decay, momentum, epsilon, centered);\n    }\n    /**\n     * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.\n     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n     *\n     * @param learningRate The learning rate to use for the Adam gradient\n     * descent algorithm.\n     * @param beta1 The exponential decay rate for the 1st moment estimates.\n     * @param beta2 The exponential decay rate for the 2nd moment estimates.\n     * @param epsilon A small constant for numerical stability.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adam(learningRate = 0.001, beta1 = 0.9, beta2 = 0.999, epsilon = null) {\n        return new AdamOptimizer(learningRate, beta1, beta2, epsilon);\n    }\n    /**\n     * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.\n     * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)\n     *\n     * @param learningRate The learning rate to use for the Adadelta gradient\n     * descent algorithm.\n     * @param rho The learning rate decay over each update.\n     * @param epsilon A constant epsilon used to better condition the grad\n     * update.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adadelta(learningRate = .001, rho = .95, epsilon = null) {\n        return new AdadeltaOptimizer(learningRate, rho, epsilon);\n    }\n    /**\n     * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.\n     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n     *\n     * @param learningRate The learning rate to use for the Adamax gradient\n     * descent algorithm.\n     * @param beta1 The exponential decay rate for the 1st moment estimates.\n     * @param beta2 The exponential decay rate for the 2nd moment estimates.\n     * @param epsilon A small constant for numerical stability.\n     * @param decay The learning rate decay over each update.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adamax(learningRate = 0.002, beta1 = 0.9, beta2 = 0.999, epsilon = null, decay = 0.0) {\n        return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);\n    }\n    /**\n     * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.\n     * See\n     * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](\n     * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\n     * or\n     * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](\n     * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)\n     *\n     * @param learningRate The learning rate to use for the Adagrad gradient\n     * descent algorithm.\n     * @param initialAccumulatorValue Starting value for the accumulators, must be\n     * positive.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adagrad(learningRate, initialAccumulatorValue = 0.1) {\n        return new AdagradOptimizer(learningRate, initialAccumulatorValue);\n    }\n}\n//# sourceMappingURL=optimizer_constructors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// So typings can propagate.\nimport { AdadeltaOptimizer } from './optimizers/adadelta_optimizer';\nimport { AdagradOptimizer } from './optimizers/adagrad_optimizer';\nimport { AdamOptimizer } from './optimizers/adam_optimizer';\nimport { AdamaxOptimizer } from './optimizers/adamax_optimizer';\nimport { MomentumOptimizer } from './optimizers/momentum_optimizer';\nimport { OptimizerConstructors } from './optimizers/optimizer_constructors';\nimport { RMSPropOptimizer } from './optimizers/rmsprop_optimizer';\nimport { SGDOptimizer } from './optimizers/sgd_optimizer';\n// tslint:disable-next-line:no-unused-expression\n[MomentumOptimizer, SGDOptimizer, AdadeltaOptimizer, AdagradOptimizer,\n    RMSPropOptimizer, AdamaxOptimizer, AdamOptimizer];\nexport const train = {\n    sgd: OptimizerConstructors.sgd,\n    momentum: OptimizerConstructors.momentum,\n    adadelta: OptimizerConstructors.adadelta,\n    adagrad: OptimizerConstructors.adagrad,\n    rmsprop: OptimizerConstructors.rmsprop,\n    adamax: OptimizerConstructors.adamax,\n    adam: OptimizerConstructors.adam\n};\n//# sourceMappingURL=train.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst delayCallback = (() => {\n    if (typeof requestAnimationFrame !== 'undefined') {\n        return requestAnimationFrame;\n    }\n    else if (typeof setImmediate !== 'undefined') {\n        return setImmediate;\n    }\n    return (f) => f(); // no delays\n})();\n/**\n * Returns a promise that resolve when a requestAnimationFrame has completed.\n *\n * On Node.js this uses setImmediate instead of requestAnimationFrame.\n *\n * This is simply a sugar method so that users can do the following:\n * `await tf.nextFrame();`\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nfunction nextFrame() {\n    return new Promise(resolve => delayCallback(() => resolve()));\n}\nexport { nextFrame };\n//# sourceMappingURL=browser_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Returns the image center in pixels.\nexport function getImageCenter(center, imageHeight, imageWidth) {\n    const centerX = imageWidth * (typeof center === 'number' ? center : center[0]);\n    const centerY = imageHeight * (typeof center === 'number' ? center : center[1]);\n    return [centerX, centerY];\n}\n//# sourceMappingURL=rotate_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Gets the new shape of the input Tensor after it's been reshaped\n * to:\n * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),\n * inputShape[1], ..., inputShape[N-1]]\n *\n * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshaped(inputShape, blockShape, prod, batchToSpace = true) {\n    let reshaped = [];\n    if (batchToSpace) {\n        reshaped = reshaped.concat(blockShape.slice(0));\n        reshaped.push(inputShape[0] / prod);\n        reshaped = reshaped.concat(inputShape.slice(1));\n    }\n    else {\n        reshaped = reshaped.concat(inputShape[0]);\n        const spatialLength = blockShape.length;\n        for (let i = 0; i < spatialLength; ++i) {\n            reshaped =\n                reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);\n        }\n        reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n    }\n    return reshaped;\n}\n/**\n * Gets the permutation that will transpose the dimensions of the\n * reshaped tensor to shape:\n *\n * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,\n * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {\n    const permuted = [];\n    if (batchToSpace) {\n        permuted.push(blockShapeRank);\n        for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {\n            if (i <= 2 * blockShapeRank) {\n                permuted.push(i);\n                permuted.push(i - (blockShapeRank + 1));\n            }\n            else {\n                permuted.push(i);\n            }\n        }\n    }\n    else {\n        const permutedBeforeBatch = [];\n        const permutedAfterBatch = [];\n        for (let i = 1; i < reshapedRank; ++i) {\n            if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {\n                permutedAfterBatch.push(i);\n            }\n            else {\n                permutedBeforeBatch.push(i);\n            }\n        }\n        permuted.push(...permutedBeforeBatch);\n        permuted.push(0);\n        permuted.push(...permutedAfterBatch);\n    }\n    return permuted;\n}\n/**\n * Gets the shape of the reshaped and permuted input Tensor before any cropping\n * is applied.  The new shape will be:\n *\n * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,\n * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshapedPermuted(inputShape, blockShape, prod, batchToSpace = true) {\n    const reshapedPermuted = [];\n    if (batchToSpace) {\n        reshapedPermuted.push(inputShape[0] / prod);\n    }\n    else {\n        reshapedPermuted.push(inputShape[0] * prod);\n    }\n    for (let i = 1; i < inputShape.length; ++i) {\n        if (i <= blockShape.length) {\n            if (batchToSpace) {\n                reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);\n            }\n            else {\n                reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);\n            }\n        }\n        else {\n            reshapedPermuted.push(inputShape[i]);\n        }\n    }\n    return reshapedPermuted;\n}\n/**\n * Converts the crops argument into the beginning coordinates of a slice\n * operation.\n */\nexport function getSliceBeginCoords(crops, blockShape) {\n    const sliceBeginCoords = [0];\n    for (let i = 0; i < blockShape; ++i) {\n        sliceBeginCoords.push(crops[i][0]);\n    }\n    return sliceBeginCoords;\n}\n/**\n * Converts the crops argument into the size of a slice operation.  When\n * combined with getSliceBeginCoords this function allows the reshaped and\n * permuted Tensor to be cropped to its final output shape of:\n *\n * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,\n * inputShape[M] * blockShape[M-1] -crops[M-1,0] -\n * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getSliceSize(uncroppedShape, crops, blockShape) {\n    const sliceSize = uncroppedShape.slice(0, 1);\n    for (let i = 0; i < blockShape; ++i) {\n        sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);\n    }\n    return sliceSize;\n}\n//# sourceMappingURL=array_ops_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const SELU_SCALEALPHA = 1.7580993408473768599402175208123;\nexport const SELU_SCALE = 1.0507009873554804934193349852946;\n//# sourceMappingURL=selu_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const ERF_P = 0.3275911;\nexport const ERF_A1 = 0.254829592;\nexport const ERF_A2 = -0.284496736;\nexport const ERF_A3 = 1.421413741;\nexport const ERF_A4 = -1.453152027;\nexport const ERF_A5 = 1.061405429;\n//# sourceMappingURL=erf_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nexport function warn(...msg) {\n    if (!env().getBool('IS_TEST')) {\n        console.warn(...msg);\n    }\n}\nexport function log(...msg) {\n    if (!env().getBool('IS_TEST')) {\n        console.log(...msg);\n    }\n}\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Merges real and imaginary Float32Arrays into a single complex Float32Array.\n *\n * The memory layout is interleaved as follows:\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n * complex: [r0, i0, r1, i1, r2, i2]\n *\n * This is the inverse of splitRealAndImagArrays.\n *\n * @param real The real values of the complex tensor values.\n * @param imag The imag values of the complex tensor values.\n * @returns A complex tensor as a Float32Array with merged values.\n */\nexport function mergeRealAndImagArrays(real, imag) {\n    if (real.length !== imag.length) {\n        throw new Error(`Cannot merge real and imag arrays of different lengths. real:` +\n            `${real.length}, imag: ${imag.length}.`);\n    }\n    const result = new Float32Array(real.length * 2);\n    for (let i = 0; i < result.length; i += 2) {\n        result[i] = real[i / 2];\n        result[i + 1] = imag[i / 2];\n    }\n    return result;\n}\n/**\n * Splits a complex Float32Array into real and imag parts.\n *\n * The memory layout is interleaved as follows:\n * complex: [r0, i0, r1, i1, r2, i2]\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n *\n * This is the inverse of mergeRealAndImagArrays.\n *\n * @param complex The complex tensor values.\n * @returns An object with real and imag Float32Array components of the complex\n *     tensor.\n */\nexport function splitRealAndImagArrays(complex) {\n    const real = new Float32Array(complex.length / 2);\n    const imag = new Float32Array(complex.length / 2);\n    for (let i = 0; i < complex.length; i += 2) {\n        real[i / 2] = complex[i];\n        imag[i / 2] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Extracts even indexed complex values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithEvenIndex(complex) {\n    const len = Math.ceil(complex.length / 4);\n    const real = new Float32Array(len);\n    const imag = new Float32Array(len);\n    for (let i = 0; i < complex.length; i += 4) {\n        real[Math.floor(i / 4)] = complex[i];\n        imag[Math.floor(i / 4)] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Extracts odd indexed comple values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithOddIndex(complex) {\n    const len = Math.floor(complex.length / 4);\n    const real = new Float32Array(len);\n    const imag = new Float32Array(len);\n    for (let i = 2; i < complex.length; i += 4) {\n        real[Math.floor(i / 4)] = complex[i];\n        imag[Math.floor(i / 4)] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Get the map representing a complex value in the given array.\n * @param complex The complex tensor values.\n * @param index An index of the target complex value.\n */\nexport function getComplexWithIndex(complex, index) {\n    const real = complex[index * 2];\n    const imag = complex[index * 2 + 1];\n    return { real, imag };\n}\n/**\n * Insert a given complex value into the TypedArray.\n * @param data The array in which the complex value is inserted.\n * @param c The complex value to be inserted.\n * @param index An index of the target complex value.\n */\nexport function assignToTypedArray(data, real, imag, index) {\n    data[index * 2] = real;\n    data[index * 2 + 1] = imag;\n}\n/**\n * Make the list of exponent terms used by FFT.\n */\nexport function exponents(n, inverse) {\n    const real = new Float32Array(n / 2);\n    const imag = new Float32Array(n / 2);\n    for (let i = 0; i < Math.ceil(n / 2); i++) {\n        const x = (inverse ? 2 : -2) * Math.PI * (i / n);\n        real[i] = Math.cos(x);\n        imag[i] = Math.sin(x);\n    }\n    return { real, imag };\n}\n/**\n * Make the exponent term used by FFT.\n */\nexport function exponent(k, n, inverse) {\n    const x = (inverse ? 2 : -2) * Math.PI * (k / n);\n    const real = Math.cos(x);\n    const imag = Math.sin(x);\n    return { real, imag };\n}\n//# sourceMappingURL=complex_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { cast } from '../ops/cast';\nimport { scalar } from '../ops/scalar';\nimport { tensor1d } from '../ops/tensor1d';\nimport { zeros } from '../ops/zeros';\nimport { hasEncodingLoss, makeZerosTypedArray } from '../util';\n// Utilities needed by backend consumers of tf-core.\nexport * from '../ops/axis_util';\nexport * from '../ops/broadcast_util';\nexport * from '../ops/concat_util';\nexport * from '../ops/conv_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/reduce_util';\nimport * as slice_util from '../ops/slice_util';\nexport { slice_util };\nexport { upcastType } from '../types';\nexport * from '../ops/rotate_util';\nexport * from '../ops/array_ops_util';\nexport * from '../ops/gather_nd_util';\nexport * from '../ops/scatter_nd_util';\nexport * from '../ops/selu_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/erf_util';\nexport * from '../log';\nexport * from '../backends/complex_util';\nexport * from '../ops/split_util';\nimport * as segment_util from '../ops/segment_util';\nexport { segment_util };\nexport function castTensor(x, dtype, backend) {\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return x.clone();\n        }\n        const zerosTensor = zeros(x.shape);\n        const floatX = cast(x, 'float32');\n        const result = backend.complex(floatX, zerosTensor);\n        zerosTensor.dispose();\n        floatX.dispose();\n        return result;\n    }\n    if (!hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        return ENGINE.makeTensorFromDataId(x.dataId, x.shape, dtype);\n    }\n    if (x.dtype === 'complex64') {\n        const real = backend.real(x);\n        const result = cast(real, dtype);\n        real.dispose();\n        return result;\n    }\n    if (dtype === 'int32') {\n        return backend.int(x);\n    }\n    else if (dtype === 'bool') {\n        const zero = scalar(0, x.dtype);\n        const result = backend.notEqual(x, zero);\n        zero.dispose();\n        return result;\n    }\n    else {\n        throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n    }\n}\nexport function reshapeTensor(x, shape) {\n    return ENGINE.makeTensorFromDataId(x.dataId, shape, x.dtype);\n}\nexport function linspaceImpl(start, stop, num) {\n    const step = (stop - start) / (num - 1);\n    const values = makeZerosTypedArray(num, 'float32');\n    values[0] = start;\n    for (let i = 1; i < values.length; i++) {\n        values[i] = values[i - 1] + step;\n    }\n    return tensor1d(values, 'float32');\n}\n//# sourceMappingURL=backend_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { slice } from '../ops/slice';\n// TODO(annxingyuan): Use this helper in WASM Split kernel once intermediate\n// kernels have been modularized in WebGL and CPU\n// https://github.com/tensorflow/tfjs/issues/2822.\n/** Shared implementation of the split kernel across WebGL and CPU. */\nexport function split(x, sizeSplits, axis) {\n    const begin = new Array(x.rank).fill(0);\n    const size = x.shape.slice();\n    return sizeSplits.map(s => {\n        const sliceSize = [...size];\n        sliceSize[axis] = s;\n        const sliceT = slice(x, begin, sliceSize);\n        begin[axis] += s;\n        return sliceT;\n    });\n}\n//# sourceMappingURL=split_shared.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * An implementation of the tile kernel shared between webgl and cpu for string\n * tensors only.\n */\nimport { buffer } from '../ops/buffer';\nexport function tile(xBuf, reps) {\n    const newShape = new Array(xBuf.rank);\n    for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = xBuf.shape[i] * reps[i];\n    }\n    const result = buffer(newShape, xBuf.dtype);\n    for (let i = 0; i < result.values.length; ++i) {\n        const newLoc = result.indexToLoc(i);\n        const originalLoc = new Array(xBuf.rank);\n        for (let j = 0; j < originalLoc.length; j++) {\n            originalLoc[j] = newLoc[j] % xBuf.shape[j];\n        }\n        const originalIndex = xBuf.locToIndex(originalLoc);\n        result.values[i] = xBuf.values[originalIndex];\n    }\n    return result.toTensor();\n}\n//# sourceMappingURL=tile_impl.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the TopK kernel shared between webgl and cpu. */\nimport { tensor } from '../ops/tensor';\nimport { getTypedArrayFromDType } from '../util';\nexport function topkImpl(x, xShape, xDtype, k, sorted) {\n    // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.\n    const lastDim = xShape[xShape.length - 1];\n    const [batch, size] = [x.length / lastDim, lastDim];\n    const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);\n    const allTopKIndices = getTypedArrayFromDType('int32', batch * k);\n    for (let b = 0; b < batch; b++) {\n        const offset = b * size;\n        const vals = x.subarray(offset, offset + size);\n        const valAndInd = [];\n        for (let i = 0; i < vals.length; i++) {\n            valAndInd.push({ value: vals[i], index: i });\n        }\n        valAndInd.sort((a, b) => b.value - a.value);\n        const outOffset = b * k;\n        const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n        const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n        for (let i = 0; i < k; i++) {\n            topKVals[i] = valAndInd[i].value;\n            topKIndices[i] = valAndInd[i].index;\n        }\n    }\n    // Reshape back to the original input shape, except that the last\n    // dimension is k.\n    const outputShape = xShape.slice();\n    outputShape[outputShape.length - 1] = k;\n    return [\n        tensor(allTopKVals, outputShape, xDtype),\n        tensor(allTopKIndices, outputShape, 'int32')\n    ];\n}\n//# sourceMappingURL=topk_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { nonMaxSuppressionV3Impl, nonMaxSuppressionV4Impl, nonMaxSuppressionV5Impl } from './non_max_suppression_impl';\nexport { split } from './split_shared';\nexport { tile } from './tile_impl';\nexport { topkImpl } from './topk_impl';\nexport { whereImpl } from './where_impl';\n//# sourceMappingURL=kernel_impls.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// base.ts is tfjs-core without auto registration of things like flags,\n// gradients, chained ops or the opHandler. See base_side_effects.ts for parts\n// tfjs core that are required side effects.\n/**\n * @fileoverview\n * @suppress {partialAlias} Optimization disabled due to passing the module\n * object into a function below:\n *\n *   import * as ops from './ops/ops';\n *   setOpHandler(ops);\n */\n// Serialization.\nimport * as io from './io/io';\nimport * as math from './math';\nimport * as browser from './ops/browser';\nimport * as gather_util from './ops/gather_nd_util';\nimport * as scatter_util from './ops/scatter_nd_util';\nimport * as slice_util from './ops/slice_util';\nimport * as serialization from './serialization';\nimport * as tensor_util from './tensor_util';\nimport * as test_util from './test_util';\nimport * as util from './util';\nimport { version } from './version';\n// Optimizers.\nexport { AdadeltaOptimizer } from './optimizers/adadelta_optimizer';\nexport { AdagradOptimizer } from './optimizers/adagrad_optimizer';\nexport { AdamOptimizer } from './optimizers/adam_optimizer';\nexport { AdamaxOptimizer } from './optimizers/adamax_optimizer';\nexport { MomentumOptimizer } from './optimizers/momentum_optimizer';\nexport { Optimizer } from './optimizers/optimizer';\nexport { RMSPropOptimizer } from './optimizers/rmsprop_optimizer';\nexport { SGDOptimizer } from './optimizers/sgd_optimizer';\nexport { Tensor, TensorBuffer, Variable } from './tensor';\nexport { Rank, sumOutType, upcastType } from './types';\nexport * from './ops/ops';\nexport { Reduction } from './ops/loss_ops_utils';\nexport * from './train';\nexport * from './globals';\nexport * from './kernel_registry';\nexport { customGrad, grad, grads, valueAndGrad, valueAndGrads, variableGrads } from './gradients';\nexport { Environment, env, ENV } from './environment';\nexport { version as version_core };\n// Top-level method exports.\nexport { nextFrame } from './browser_util';\n// Second level exports.\nimport * as backend_util from './backends/backend_util';\nimport * as device_util from './device_util';\nexport { browser, io, math, serialization, test_util, util, backend_util, tensor_util, slice_util, gather_util, scatter_util, device_util };\nimport * as kernel_impls from './backends/kernel_impls';\nexport { kernel_impls };\n// Backend specific.\nexport { KernelBackend, DataStorage } from './backends/backend';\n// Export all kernel names / info.\nexport * from './kernel_names';\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const absGradConfig = {\n    kernelName: Abs,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, step(cast(x, 'float32'), -1)) };\n    }\n};\n//# sourceMappingURL=Abs_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acos } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { neg } from '../ops/neg';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const acosGradConfig = {\n    kernelName: Acos,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = square(cast(x, 'float32'));\n                const b = sqrt(sub(scalar(1), a));\n                return neg(div(dy, b));\n            }\n        };\n    }\n};\n//# sourceMappingURL=Acos_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acosh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const acoshGradConfig = {\n    kernelName: Acosh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = sqrt(sub(square(cast(x, 'float32')), 1));\n                return div(dy, a);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Acosh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const addGradConfig = {\n    kernelName: Add,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Add_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AddN } from '../kernel_names';\nexport const addNGradConfig = {\n    kernelName: AddN,\n    saveAllInputs: true,\n    gradFunc: (dy, saved) => {\n        const ders = {};\n        saved.forEach((_, i) => {\n            ders[i] = () => dy.clone();\n        });\n        return ders;\n    }\n};\n//# sourceMappingURL=AddN_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ArgMax } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const argMaxGradConfig = {\n    kernelName: ArgMax,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => zerosLike(x) };\n    }\n};\n//# sourceMappingURL=ArgMax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ArgMin } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const argMinGradConfig = {\n    kernelName: ArgMin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => zerosLike(x) };\n    }\n};\n//# sourceMappingURL=ArgMin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asin } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const asinGradConfig = {\n    kernelName: Asin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, 'float32'))))) };\n    }\n};\n//# sourceMappingURL=Asin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asinh } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nexport const asinhGradConfig = {\n    kernelName: Asinh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = sqrt(add(scalar(1), square(cast(x, 'float32'))));\n                return div(dy, a);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Asinh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan2 } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const atan2GradConfig = {\n    kernelName: Atan2,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const d = add(square(a), square(b));\n            let res = mul(dy, div(b, d));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            const d = add(square(a), square(b));\n            let res = neg(mul(dy, div(a, d)));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Atan2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nexport const atanGradConfig = {\n    kernelName: Atan,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, add(square(cast(x, 'float32')), 1)) };\n    }\n};\n//# sourceMappingURL=Atan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atanh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { scalar } from '../ops/scalar';\nexport const atanhGradConfig = {\n    kernelName: Atanh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, sub(scalar(1), square(cast(x, 'float32')))) };\n    }\n};\n//# sourceMappingURL=Atanh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool3DBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of a 3d avg pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0. The dilation\n *     rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction avgPool3dBackprop_(dy, input, filterSize, strides, dilations = [1, 1, 1], pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'avgPool3dBackprop');\n    const $input = convertToTensor(input, 'input', 'avgPool3dBackprop');\n    let dy5D = $dy;\n    let input5D = $input;\n    let reshapedTo5D = false;\n    if ($input.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n        input5D = reshape($input, [\n            1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n        ]);\n    }\n    util.assert(dy5D.rank === 5, () => `Error in avgPool3dBackprop: dy must be rank 5 but got rank ` +\n        `${dy5D.rank}.`);\n    util.assert(input5D.rank === 5, () => `Error in avgPool3dBackprop: input must be rank 5 but got rank ` +\n        `${input5D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool3dBackprop: Either strides or dilations ' +\n        `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n        return backend.avgPool3dBackprop(dy5D, input5D, convInfo);\n    };\n    const inputs = { dy: dy5D, input: input5D };\n    const attrs = { filterSize, strides, dilations, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool3DBackprop, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const avgPool3dBackprop = op({ avgPool3dBackprop_ });\n//# sourceMappingURL=avg_pool_3d_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool3D } from '../kernel_names';\nimport { avgPool3dBackprop } from '../ops/avg_pool_3d_backprop';\nexport const avgPool3DGradConfig = {\n    kernelName: AvgPool3D,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { filterSize, strides, dilations, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1, 1] : dilations;\n        return {\n            x: () => avgPool3dBackprop(dy, x, filterSize, strides, $dilations, pad, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=AvgPool3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPoolBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of an 2D avg pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The input image, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction avgPoolBackprop_(dy, input, filterSize, strides, pad) {\n    const $dy = convertToTensor(dy, 'dy', 'avgPoolBackprop');\n    const $input = convertToTensor(input, 'input', 'avgPoolBackprop');\n    util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n    let input4D = $input;\n    let dy4D = $dy;\n    let reshapedTo4D = false;\n    if ($input.rank === 3) {\n        reshapedTo4D = true;\n        input4D =\n            reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);\n        dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);\n    }\n    util.assert(dy4D.rank === 4, () => `Error in avgPoolBackprop: dy must be rank 4 but got rank ` +\n        `${dy4D.rank}.`);\n    util.assert(input4D.rank === 4, () => `Error in avgPoolBackprop: input must be rank 4 but got rank ` +\n        `${input4D.rank}.`);\n    const forward = backend => {\n        const convInfo = conv_util.computePool2DInfo(input4D.shape, filterSize, strides, 1 /* dilations */, pad);\n        return backend.avgPoolBackprop(dy4D, input4D, convInfo);\n    };\n    const inputs = { dy: dy4D, input: input4D };\n    const attrs = { filterSize, strides, pad };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, AvgPoolBackprop, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const avgPoolBackprop = op({ avgPoolBackprop_ });\n//# sourceMappingURL=avg_pool_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool } from '../kernel_names';\nimport { avgPoolBackprop } from '../ops/avg_pool_backprop';\nexport const avgPoolGradConfig = {\n    kernelName: AvgPool,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { filterSize, strides, pad } = attrs;\n        return {\n            x: () => avgPoolBackprop(dy, x, filterSize, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=AvgPool_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchMatMul } from '../kernel_names';\nimport { matMul } from '../ops/mat_mul';\nexport const batchMatMulGradConfig = {\n    kernelName: BatchMatMul,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved, attrs) => {\n        const [a, b] = saved;\n        const { transposeA, transposeB } = attrs;\n        if (!transposeA && !transposeB) {\n            return {\n                a: () => matMul(dy, b, false, true),\n                b: () => matMul(a, dy, true, false)\n            };\n        }\n        else if (!transposeA && transposeB) {\n            return {\n                a: () => matMul(dy, b, false, false),\n                b: () => matMul(dy, a, true, false)\n            };\n        }\n        else if (transposeA && !transposeB) {\n            return {\n                a: () => matMul(b, dy, false, true),\n                b: () => matMul(a, dy, false, false)\n            };\n        }\n        else {\n            return {\n                a: () => matMul(b, dy, true, true),\n                b: () => matMul(dy, a, true, true)\n            };\n        }\n    }\n};\n//# sourceMappingURL=BatchMatMul_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchToSpaceND } from '../kernel_names';\nimport { spaceToBatchND } from '../ops/space_to_batch_nd';\nexport const batchToSpaceNDGradConfig = {\n    kernelName: BatchToSpaceND,\n    gradFunc: (dy, saved, attrs) => {\n        const { blockShape, crops } = attrs;\n        return { x: () => spaceToBatchND(dy, blockShape, crops) };\n    }\n};\n//# sourceMappingURL=BatchToSpaceND_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BroadcastTo } from '../kernel_names';\nimport { sum } from '../ops/sum';\nexport const broadcastToGradConfig = {\n    kernelName: BroadcastTo,\n    gradFunc: (dy, saved, attrs) => {\n        const broadCastToAttrs = attrs;\n        const inputShape = broadCastToAttrs.inputShape;\n        const outputShape = broadCastToAttrs.shape;\n        const reps = Array.from(outputShape);\n        for (let i = inputShape.length - 1; i >= 0; i--) {\n            if (inputShape[i] === outputShape[i]) {\n                reps[i] = 1;\n            }\n            else if (inputShape[i] !== 1) {\n                throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);\n            }\n        }\n        const axes = [];\n        for (let i = 0; i < reps.length; i++) {\n            if (reps[i] > 1) {\n                axes.push(i);\n            }\n        }\n        return { x: () => sum(dy, axes, true /* keepDims */) };\n    }\n};\n//# sourceMappingURL=BroadcastTo_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cast } from '../kernel_names';\nexport const castGradConfig = {\n    kernelName: Cast,\n    gradFunc: (dy) => {\n        return { x: () => dy.clone() };\n    }\n};\n//# sourceMappingURL=Cast_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const ceilGradConfig = {\n    kernelName: Ceil,\n    gradFunc: (dy) => {\n        // TODO(manrajgrover): Return null for gradients when backprop supports it.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Ceil_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ClipByValue } from '../kernel_names';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { lessEqual } from '../ops/less_equal';\nimport { logicalAnd } from '../ops/logical_and';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const clipByValueGradConfig = {\n    kernelName: ClipByValue,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { clipValueMin, clipValueMax } = attrs;\n        return {\n            x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy)),\n        };\n    }\n};\n//# sourceMappingURL=ClipByValue_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Concat } from '../kernel_names';\nimport { split } from '../ops/split';\nimport { parseAxisParam } from '../util';\nexport const concatGradConfig = {\n    kernelName: Concat,\n    saveAllInputs: true,\n    gradFunc: (dy, saved, attrs) => {\n        const shapes = saved.map(t => t.shape);\n        const { axis } = attrs;\n        const $axis = parseAxisParam(axis, saved[0].shape)[0];\n        const sizeSplits = shapes.map(s => s[$axis]);\n        const derTensors = split(dy, sizeSplits, $axis);\n        return derTensors.map(t => () => t);\n    }\n};\n//# sourceMappingURL=Concat_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv2D } from '../kernel_names';\nimport { conv2DBackpropFilter } from '../ops/conv2d_backprop_filter';\nimport { conv2DBackpropInput } from '../ops/conv2d_backprop_input';\nimport * as conv_util from '../ops/conv_util';\nimport * as util from '../util';\nexport const conv2DGradConfig = {\n    kernelName: Conv2D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x4D, $filter] = saved;\n        const { dilations, strides, pad, dataFormat } = attrs;\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of conv2D: dilation rates greater than 1 ' +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n        return {\n            x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad, dataFormat),\n            filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad, dataFormat)\n        };\n    }\n};\n//# sourceMappingURL=Conv2D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv2DBackpropInput } from '../kernel_names';\nimport { conv2d } from '../ops/conv2d';\nimport { conv2DBackpropFilter } from '../ops/conv2d_backprop_filter';\nexport const conv2DBackpropInputGradConfig = {\n    kernelName: Conv2DBackpropInput,\n    inputsToSave: ['dy', 'filter'],\n    gradFunc: (ddx, saved, attrs) => {\n        const [dy, filter] = saved;\n        const { strides, pad, dataFormat, dimRoundingMode } = attrs;\n        return {\n            dy: () => conv2d(ddx, filter, strides, pad, dataFormat, 1 /* dilations */, dimRoundingMode),\n            filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad, dataFormat, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=Conv2DBackpropInput_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3DBackpropFilterV2 } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the filter of a 3D convolution.\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     [batch, depth, height, width, inChannels]. If rank 4, batch of 1 is\n *     assumed.\n * @param dy The dy image, of rank 5 or rank 4, of shape\n *     [batch, depth, height, width, outDepth]. If rank 4, batch of 1 is\n *     assumed.\n * @param filterShape The shape of the filter, length 5,\n *     [filterDepth, filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideDepth, strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction conv3DBackpropFilter_(x, dy, filterShape, strides, pad) {\n    let x5D = x;\n    if (x.rank === 4) {\n        x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);\n    }\n    let dy5D = dy;\n    if (dy5D.rank === 4) {\n        dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ` +\n        `${x5D.shape}.`);\n    util.assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ` +\n        `${dy5D.shape}.`);\n    util.assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ` +\n        `${filterShape}.`);\n    util.assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must ` +\n        `match input depth in filter (${filterShape[3]}.`);\n    util.assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must ` +\n        `match output depth for filter (${filterShape[4]}).`);\n    const forward = backend => {\n        const dilations = 1;\n        const convInfo = conv_util.computeConv3DInfo(x5D.shape, filterShape, strides, dilations, pad);\n        return backend.conv3dDerFilter(x5D, dy5D, convInfo);\n    };\n    const inputs = { x: x5D, y: dy5D };\n    const attrs = { strides, pad };\n    return ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropFilterV2, attrs);\n}\nexport const conv3DBackpropFilter = op({ conv3DBackpropFilter_ });\n//# sourceMappingURL=conv3d_backprop_filter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv3D } from '../kernel_names';\nimport { conv3DBackpropFilter } from '../ops/conv3d_backprop_filter';\nimport { conv3DBackpropInput } from '../ops/conv3d_backprop_input';\nimport { tupleValuesAreOne } from '../ops/conv_util';\nimport * as util from '../util';\nexport const conv3DGradConfig = {\n    kernelName: Conv3D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const { dilations, strides, pad } = attrs;\n        util.assert(tupleValuesAreOne(dilations), () => 'Error in gradient of conv3D: dilation rates greater than 1 are ' +\n            `not yet supported in gradients. Got dilations '${dilations}'`);\n        const [x5D, $filter] = saved;\n        return {\n            x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad),\n            filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=Conv3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { sin } from '../ops/sin';\nexport const cosGradConfig = {\n    kernelName: Cos,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(neg(sin(cast(x, 'float32'))), dy) };\n    }\n};\n//# sourceMappingURL=Cos_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cosh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { sinh } from '../ops/sinh';\nexport const coshGradConfig = {\n    kernelName: Cosh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(sinh(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Cosh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cumsum } from '../kernel_names';\nimport { getAxesPermutation } from '../ops/axis_util';\nimport { cumsum } from '../ops/cumsum';\nimport { transpose } from '../ops/transpose';\nexport const cumsumGradConfig = {\n    kernelName: Cumsum,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { axis, exclusive, reverse } = attrs;\n        return {\n            x: () => {\n                const permutation = getAxesPermutation([axis], x.rank);\n                let out = cumsum(dy, axis, exclusive, !reverse);\n                if (permutation != null) {\n                    out = transpose(out, permutation);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Cumsum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { DepthwiseConv2dNative } from '../kernel_names';\nimport * as conv_util from '../ops/conv_util';\nimport { depthwiseConv2dNativeBackpropFilter } from '../ops/depthwise_conv2d_native_backprop_filter';\nimport { depthwiseConv2dNativeBackpropInput } from '../ops/depthwise_conv2d_native_backprop_input';\nimport * as util from '../util';\nexport const depthwiseConv2dNativeGradConfig = {\n    kernelName: DepthwiseConv2dNative,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const { dilations, strides, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1] : dilations;\n        util.assert(conv_util.tupleValuesAreOne($dilations), () => 'Error in gradient of depthwiseConv2dNative: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${$dilations}'`);\n        const [x, filter] = saved;\n        util.assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be ` +\n            `rank 4, but got rank ${x.rank}.`);\n        util.assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be ` +\n            `rank 4, but got rank ${filter.rank}.`);\n        util.assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input ` +\n            `channels (${x.shape[3]}) must match the inChannels dimension ` +\n            `in filter ${filter.shape[2]}.`);\n        util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, $dilations), () => 'Error in gradient of depthwiseConv2d: Either strides or ' +\n            `dilations must be  1. Got strides ${strides} and dilations ` +\n            `'${$dilations}'.`);\n        if (dimRoundingMode != null) {\n            util.assert(util.isInt(pad), () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n                `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n        }\n        const convInfo = conv_util.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad, dimRoundingMode, true /* depthwise */);\n        return {\n            x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, convInfo),\n            filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, convInfo),\n        };\n    }\n};\n//# sourceMappingURL=DepthwiseConv2dNative_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput } from '../kernel_names';\nexport const dilation2dGradConfig = {\n    kernelName: Dilation2D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, filter] = saved;\n        const inputInputs = { x, filter, dy };\n        const filterInputs = { x, filter, dy };\n        return {\n            x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),\n            filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)\n        };\n    }\n};\n//# sourceMappingURL=Dilation2D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const divGradConfig = {\n    kernelName: Div,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = div(dy, cast(b, 'float32'));\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            let res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = reshape(sum(res, reduceAxes), b.shape);\n            }\n            const tmp = square(b);\n            return neg(div(res, cast(tmp, 'float32')));\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Div_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Elu, EluGrad } from '../kernel_names';\nexport const eluGradConfig = {\n    kernelName: Elu,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        const backPropKernelFunc = (backend) => {\n            return backend.eluDer(dy, y);\n        };\n        const inputs = { dy, y };\n        return {\n            x: () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* grad */, EluGrad)\n        };\n    }\n};\n//# sourceMappingURL=Elu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Erf } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { square } from '../ops/square';\nexport const erfGradConfig = {\n    kernelName: Erf,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));\n        return { x: () => mul(dy, a) };\n    }\n};\n//# sourceMappingURL=Erf_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '../kernel_names';\nimport { mul } from '../ops/mul';\nexport const expGradConfig = {\n    kernelName: Exp,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(dy, y) };\n    }\n};\n//# sourceMappingURL=Exp_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nexport const expm1GradConfig = {\n    kernelName: Expm1,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, exp(x)) };\n    }\n};\n//# sourceMappingURL=Expm1_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const floorGradConfig = {\n    kernelName: Floor,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Floor_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FloorDiv } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const floorDivGradConfig = {\n    kernelName: FloorDiv,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = div(dy, cast(b, 'float32'));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            let res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = reshape(sum(res, reduceAxes), b.shape);\n            }\n            const tmp = square(b);\n            return neg(div(res, cast(tmp, 'float32')));\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=FloorDiv_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedBatchNorm } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { getReductionAxes } from '../ops/broadcast_util';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { rsqrt } from '../ops/rsqrt';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nimport { tile } from '../ops/tile';\nexport const fusedBatchNormGradConfig = {\n    kernelName: FusedBatchNorm,\n    inputsToSave: ['x', 'mean', 'variance', 'scale'],\n    gradFunc: (dy, saved, attrs) => {\n        const { varianceEpsilon } = attrs;\n        const [x, mean, variance, scale] = saved;\n        const scaleValue = scale == null ? scalar(1) : scale;\n        const reductionAxes = getReductionAxes(mean.shape, x.shape);\n        const tileShape = [];\n        if (mean.rank === 1) {\n            for (let i = 0; i < x.shape.length - 1; ++i) {\n                tileShape.push(x.shape[i]);\n            }\n            tileShape.push(1);\n        }\n        const xMinusMean = sub(x, mean);\n        const dyTimesScaleValue = mul(dy, scaleValue);\n        const oneOverSqrtVariance = rsqrt(add(variance, scalar(varianceEpsilon)));\n        const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));\n        const derX = () => {\n            if (mean.rank === 1) {\n                return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean.shape[0]]), tileShape)), scaleValue), x.shape);\n            }\n            else {\n                return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);\n            }\n        };\n        const derMean = () => {\n            let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);\n            if (mean.rank === 1) {\n                meanDer = sum(meanDer, reductionAxes);\n            }\n            return reshape(meanDer, mean.shape);\n        };\n        const derVariance = () => {\n            let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);\n            if (mean.rank === 1) {\n                varianceDer = sum(varianceDer, reductionAxes);\n            }\n            return reshape(varianceDer, mean.shape);\n        };\n        const derScale = () => {\n            const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);\n            let scaleDer = mul(dy, xMinusMean2TimesRsqrt);\n            if (mean.rank === 1) {\n                scaleDer = sum(scaleDer, reductionAxes);\n            }\n            return reshape(scaleDer, mean.shape);\n        };\n        const derOffset = () => {\n            let offsetDer = dy;\n            if (mean.rank === 1) {\n                offsetDer = sum(offsetDer, reductionAxes);\n            }\n            return reshape(offsetDer, mean.shape);\n        };\n        return {\n            x: derX,\n            mean: derMean,\n            variance: derVariance,\n            scale: derScale,\n            offset: derOffset\n        };\n    }\n};\n//# sourceMappingURL=FusedBatchNorm_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GatherV2 } from '../kernel_names';\nimport { getUndoAxesPermutation } from '../ops/axis_util';\nimport { reshape } from '../ops/reshape';\nimport { transpose } from '../ops/transpose';\nimport { unsortedSegmentSum } from '../ops/unsorted_segment_sum';\nimport { parseAxisParam } from '../util';\nexport const gatherGradConfig = {\n    kernelName: GatherV2,\n    inputsToSave: ['x', 'indices'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, indices] = saved;\n        const { axis } = attrs;\n        const parsedAxis = parseAxisParam(axis, x.shape)[0];\n        const derX = () => {\n            const paramsShape = x.shape;\n            const indicesSize = indices.size;\n            const outerShape = paramsShape.slice(0, parsedAxis);\n            const outerDims = outerShape.length;\n            const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);\n            const innerDims = innerShape.length;\n            const outerAxesIndices = arrayRange(0, outerDims);\n            const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);\n            const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);\n            const values = reshape(dy, valuesShape);\n            const reshapedIndices = reshape(indices, [indicesSize]);\n            const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);\n            const valuesTranspose = transpose(values, transposeDims);\n            let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);\n            const invertTransposeDims = getUndoAxesPermutation(transposeDims);\n            paramsGrad = transpose(paramsGrad, invertTransposeDims);\n            return paramsGrad;\n        };\n        return { x: derX, indices: () => indices };\n    }\n};\nfunction arrayRange(start, stop) {\n    const result = [];\n    for (let i = start; i < stop; ++i) {\n        result.push(i);\n    }\n    return result;\n}\nfunction arrayConcat(arrays) {\n    const result = [];\n    for (let i = 0; i < arrays.length; ++i) {\n        for (let j = 0; j < arrays[i].length; ++j) {\n            result.push(arrays[i][j]);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=GatherV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GreaterEqual } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const greaterEqualGradConfig = {\n    kernelName: GreaterEqual,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        return { a: () => zerosLike(a), b: () => zerosLike(b) };\n    }\n};\n//# sourceMappingURL=GreaterEqual_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '../kernel_names';\nimport { cast } from '../ops/cast';\nexport const identityGradConfig = {\n    kernelName: Identity,\n    gradFunc: (dy) => {\n        return { x: () => cast(dy, 'float32') };\n    }\n};\n//# sourceMappingURL=Identity_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsFinite } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isFiniteGradConfig = {\n    kernelName: IsFinite,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsFinite_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsInf } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isInfGradConfig = {\n    kernelName: IsInf,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsInf_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsNan } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isNanGradConfig = {\n    kernelName: IsNan,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsNan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log1p } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nexport const log1pGradConfig = {\n    kernelName: Log1p,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, add(x, 1)) };\n    }\n};\n//# sourceMappingURL=Log1p_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nexport const logGradConfig = {\n    kernelName: Log,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, cast(x, 'float32')) };\n    }\n};\n//# sourceMappingURL=Log_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LogSoftmax } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nexport const logSoftmaxGradConfig = {\n    kernelName: LogSoftmax,\n    inputsToSave: [],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [value] = saved;\n        const { axis } = attrs;\n        return {\n            logits: () => {\n                const keepDims = true;\n                const softmax = exp(value);\n                return sub(dy, mul(sum(dy, axis, keepDims), softmax));\n            }\n        };\n    }\n};\n//# sourceMappingURL=LogSoftmax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LRNBackprop } from '../kernel_names';\nimport { op } from './operation';\nfunction localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n    const forward = backend => backend.LRNGrad(dy, x, y, depthRadius, bias, alpha, beta);\n    const inputs = { x, y, dy };\n    const attrs = { depthRadius, bias, alpha, beta };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LRNBackprop, attrs);\n}\nexport const localResponseNormalizationBackprop = op({ localResponseNormalizationBackprop_ });\n//# sourceMappingURL=local_response_normalization_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LRN } from '../kernel_names';\nimport { localResponseNormalizationBackprop } from '../ops/local_response_normalization_backprop';\nexport const lrnGradConfig = {\n    kernelName: LRN,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { depthRadius, bias, alpha, beta } = attrs;\n        return {\n            x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)\n        };\n    }\n};\n//# sourceMappingURL=LRN_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as axis_util from '../ops/axis_util';\nimport { cast } from '../ops/cast';\nimport { equal } from '../ops/equal';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { transpose } from '../ops/transpose';\n/**\n * Gradient helper function for the min and max operations.\n */\nexport function gradForMinAndMax(dy, y, xOrig, origAxes, permutedAxes) {\n    if (y.rank < xOrig.rank) {\n        y = reshape(y, axis_util.expandShapeToKeepDim(y.shape, origAxes));\n    }\n    if (dy.rank < xOrig.rank) {\n        dy = reshape(dy, axis_util.expandShapeToKeepDim(dy.shape, origAxes));\n    }\n    return {\n        x: () => {\n            const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));\n            return permutedAxes == null ? dx : transpose(dx, permutedAxes);\n        }\n    };\n}\n//# sourceMappingURL=min_max_grad_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nimport * as util from '../util';\nimport { gradForMinAndMax } from './min_max_grad_util';\nexport const maxGradConfig = {\n    kernelName: Max,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const maxAttrs = attrs;\n        const { reductionIndices } = maxAttrs;\n        const [x, y] = saved;\n        const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(origAxes, x.rank);\n        const maxGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);\n        return {\n            x: () => {\n                let out = maxGrad['x']();\n                if (permutedAxes != null) {\n                    out = transpose(out);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Max_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Maximum } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { less } from '../ops/less';\nimport { mul } from '../ops/mul';\nexport const maximumGradConfig = {\n    kernelName: Maximum,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const derA = () => mul(dy, cast(greaterEqual(a, b), 'float32'));\n        const derB = () => mul(dy, cast(less(a, b), 'float32'));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Maximum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool3DBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of a 3d max pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank 4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param output The original output image, of rank 5 of shape\n *     [batchSize, outDepth, outHeight, outWidth, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPool3dBackprop_(dy, input, output, filterSize, strides, dilations = [1, 1, 1], pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'maxPool3dBackprop');\n    const $input = convertToTensor(input, 'input', 'maxPool3dBackprop');\n    const $output = convertToTensor(output, 'output', 'maxPool3dBackprop');\n    let dy5D = $dy;\n    let input5D = $input;\n    let output5D = $output;\n    let reshapedTo5D = false;\n    if ($input.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n        input5D = reshape($input, [\n            1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n        ]);\n        output5D = reshape($output, [\n            1, $output.shape[0], $output.shape[1], $output.shape[2], $output.shape[3]\n        ]);\n    }\n    util.assert(dy5D.rank === 5, () => `Error in maxPool3dBackprop: dy must be rank 5 but got rank ` +\n        `${dy5D.rank}.`);\n    util.assert(input5D.rank === 5, () => `Error in maxPool3dBackprop: input must be rank 5 but got rank ` +\n        `${input5D.rank}.`);\n    util.assert(output5D.rank === 5, () => `Error in maxPool3dBackprop: output must be rank 5 but got rank ` +\n        `${output5D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool3dBackprop: Either strides or dilations ' +\n        `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n        return backend.maxPool3dBackprop(dy5D, input5D, output5D, convInfo);\n    };\n    const inputs = { dy: dy5D, input: input5D, output: output5D };\n    const attrs = { filterSize, strides, dilations, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool3DBackprop, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const maxPool3dBackprop = op({ maxPool3dBackprop_ });\n//# sourceMappingURL=max_pool_3d_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPool3D } from '../kernel_names';\nimport { maxPool3dBackprop } from '../ops/max_pool_3d_backprop';\nexport const maxPool3DGradConfig = {\n    kernelName: MaxPool3D,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { filterSize, strides, dilations, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1, 1] : dilations;\n        return {\n            x: () => maxPool3dBackprop(dy, x, y, filterSize, strides, $dilations, pad, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=MaxPool3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPoolBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\n/**\n * Computes the backprop of a 2D max pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The original input image, of rank 4, of shape\n *     [batchSize, height, width, channels].\n * @param output The original output image, of rank 4, of shape\n *     [batchSize, outHeight, outWidth, channels].\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPoolBackprop_(dy, input, output, filterSize, strides, pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'maxPoolBackprop');\n    const $input = convertToTensor(input, 'input', 'maxPoolBackprop');\n    const $output = convertToTensor(output, 'output', 'maxPoolBackprop');\n    util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy ` +\n        `(${$dy.rank})`);\n    util.assert($dy.rank === 4, () => `Error in maxPoolBackprop: dy must be rank 4 but got rank ` +\n        `${$dy.rank}.`);\n    util.assert($input.rank === 4, () => `Error in maxPoolBackprop: input must be rank 4 but got rank ` +\n        `${$input.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPoolBackprop: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool2DInfo($input.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        return backend.maxPoolBackprop($dy, $input, $output, convInfo);\n    };\n    const inputs = { dy: $dy, input: $input, output: $output };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    return ENGINE.runKernelFunc(forward, inputs, null, MaxPoolBackprop, attrs);\n}\nexport const maxPoolBackprop = op({ maxPoolBackprop_ });\n//# sourceMappingURL=max_pool_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPool } from '../kernel_names';\nimport { maxPoolBackprop } from '../ops/max_pool_backprop';\nexport const maxPoolGradConfig = {\n    kernelName: MaxPool,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { filterSize, strides, pad } = attrs;\n        return {\n            x: () => maxPoolBackprop(dy, x, y, filterSize, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=MaxPool_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Min } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nimport * as util from '../util';\nimport { gradForMinAndMax } from './min_max_grad_util';\nexport const minGradConfig = {\n    kernelName: Min,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const minAttrs = attrs;\n        const { axis } = minAttrs;\n        const [x, y] = saved;\n        const origAxes = util.parseAxisParam(axis, x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(origAxes, x.rank);\n        const minGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);\n        return {\n            x: () => {\n                let out = minGrad['x']();\n                if (permutedAxes != null) {\n                    out = transpose(out);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Min_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Minimum } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { greater } from '../ops/greater';\nimport { lessEqual } from '../ops/less_equal';\nimport { mul } from '../ops/mul';\nexport const minimumGradConfig = {\n    kernelName: Minimum,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const derA = () => mul(dy, cast(lessEqual(a, b), 'float32'));\n        const derB = () => mul(dy, cast(greater(a, b), 'float32'));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Minimum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Mod } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { div } from '../ops/div';\nimport { floor } from '../ops/floor';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const modGradConfig = {\n    kernelName: Mod,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(dy, reduceAxes), a.shape);\n            }\n            return dy;\n        };\n        const derB = () => {\n            const res = mul(dy, neg(floor(div(a, b))));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), b.shape);\n            }\n            return res;\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Mod_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const multiplyGradConfig = {\n    kernelName: Multiply,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = mul(dy, cast(b, 'float32'));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            const res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), b.shape);\n            }\n            return res;\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Multiply_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Negate } from '../kernel_names';\nimport { neg } from '../ops/neg';\nexport const negateGradConfig = {\n    kernelName: Negate,\n    gradFunc: (dy) => {\n        return { x: () => neg(dy) };\n    }\n};\n//# sourceMappingURL=Negate_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { OneHot } from '../kernel_names';\nimport { zeros } from '../ops/zeros';\nexport const oneHotGradConfig = {\n    kernelName: OneHot,\n    inputsToSave: ['indices'],\n    gradFunc: (dy, saved) => {\n        const indices = saved[0];\n        return { indices: () => zeros(indices.shape, 'float32') };\n    }\n};\n//# sourceMappingURL=OneHot_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { OnesLike } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const onesLikeGradConfig = {\n    kernelName: OnesLike,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=OnesLike_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { PadV2 } from '../kernel_names';\nimport { slice } from '../ops/slice';\nexport const padV2GradConfig = {\n    kernelName: PadV2,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        // Pad introduces values around the original tensor, so the gradient\n        // slices the original shape out of the gradient.\n        const x = saved[0];\n        const { paddings } = attrs;\n        const begin = paddings.map(p => p[0]);\n        return { x: () => slice(dy, begin, x.shape) };\n    }\n};\n//# sourceMappingURL=PadV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Pow } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { greater } from '../ops/greater';\nimport { log } from '../ops/log';\nimport { mul } from '../ops/mul';\nimport { pow } from '../ops/pow';\nimport { reshape } from '../ops/reshape';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const powGradConfig = {\n    kernelName: Pow,\n    inputsToSave: ['a', 'b'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [a, b, y] = saved;\n        const base = a;\n        const exp = b;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(base.shape, exp.shape);\n        const derBase = () => {\n            const expFloat = cast(exp, 'float32');\n            let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));\n            const reduceAxes = broadcast_util.getReductionAxes(base.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, base.shape);\n        };\n        const derExp = () => {\n            const condition = greater(base, 0);\n            const logBase = where(condition, log(base), zerosLike(base));\n            let res = mul(dy, mul(y, logBase));\n            const reduceAxes = broadcast_util.getReductionAxes(exp.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, exp.shape);\n        };\n        return { a: derBase, b: derExp };\n    }\n};\n//# sourceMappingURL=Pow_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Prelu } from '../kernel_names';\nimport { getReductionAxes } from '../ops/broadcast_util';\nimport { greater } from '../ops/greater';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const preluGradConfig = {\n    kernelName: Prelu,\n    inputsToSave: ['x', 'alpha'],\n    gradFunc: (dy, saved) => {\n        const [x, alpha] = saved;\n        const mask = greater(x, 0);\n        return {\n            x: () => where(mask, dy, mul(dy, alpha)),\n            alpha: () => {\n                let res = where(mask, zerosLike(dy), mul(dy, x));\n                const reduceAxes = getReductionAxes(alpha.shape, dy.shape);\n                if (reduceAxes.length > 0) {\n                    res = sum(res, reduceAxes);\n                }\n                return reshape(res, alpha.shape);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Prelu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reciprocal } from '../kernel_names';\nimport { div } from '../ops/div';\nimport { neg } from '../ops/neg';\nimport { square } from '../ops/square';\nexport const reciprocalGradConfig = {\n    kernelName: Reciprocal,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, neg(square(x))) };\n    }\n};\n//# sourceMappingURL=Reciprocal_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu6 } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { lessEqual } from '../ops/less_equal';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const relu6GradConfig = {\n    kernelName: Relu6,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        const mask = mul(lessEqual(x, 6), step(x));\n        return { x: () => mul(dy, cast(mask, 'float32')) };\n    }\n};\n//# sourceMappingURL=Relu6_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const reluGradConfig = {\n    kernelName: Relu,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, cast(step(x), 'float32')) };\n    }\n};\n//# sourceMappingURL=Relu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape } from '../kernel_names';\nimport { reshape } from '../ops/reshape';\nexport const reshapeGradConfig = {\n    kernelName: Reshape,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => reshape(dy, x.shape) };\n    }\n};\n//# sourceMappingURL=Reshape_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ResizeBilinear, ResizeBilinearGrad } from '../kernel_names';\nexport const resizeBilinearGradConfig = {\n    kernelName: ResizeBilinear,\n    inputsToSave: ['images'],\n    gradFunc: (dy, saved, attrs) => {\n        const [images] = saved;\n        const backPropKernelFunc = (backend) => {\n            const { alignCorners } = attrs;\n            return backend.resizeBilinearBackprop(dy, images, alignCorners);\n        };\n        const inputs = { images };\n        const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* gradient */, ResizeBilinearGrad, attrs);\n        return { images: imagesDer };\n    }\n};\n//# sourceMappingURL=ResizeBilinear_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ResizeNearestNeighbor, ResizeNearestNeighborGrad } from '../kernel_names';\nexport const resizeNearestNeighborGradConfig = {\n    kernelName: ResizeNearestNeighbor,\n    inputsToSave: ['images'],\n    gradFunc: (dy, saved, attrs) => {\n        const [images] = saved;\n        const backPropKernelFunc = (backend) => {\n            const { alignCorners } = attrs;\n            return backend.resizeNearestNeighborBackprop(dy, images, alignCorners);\n        };\n        const inputs = { images };\n        const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* gradient */, ResizeNearestNeighborGrad, attrs);\n        return { images: imagesDer };\n    }\n};\n//# sourceMappingURL=ResizeNearestNeighbor_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reverse } from '../kernel_names';\nimport { reverse } from '../ops/reverse';\nimport { parseAxisParam } from '../util';\nexport const reverseGradConfig = {\n    kernelName: Reverse,\n    gradFunc: (dy, saved, attrs) => {\n        const { dims } = attrs;\n        const axes = parseAxisParam(dims, dy.shape);\n        return { x: () => reverse(dy, axes) };\n    }\n};\n//# sourceMappingURL=Reverse_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Round } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const roundGradConfig = {\n    kernelName: Round,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Round_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '../kernel_names';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { pow } from '../ops/pow';\nexport const rsqrtGradConfig = {\n    kernelName: Rsqrt,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => neg(div(dy, mul(pow(x, 1.5), 2))) };\n    }\n};\n//# sourceMappingURL=Rsqrt_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SelectV2 } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { logicalNot } from '../ops/logical_not';\nimport { mul } from '../ops/mul';\nimport { zerosLike } from '../ops/zeros_like';\nexport const selectV2PoolGradConfig = {\n    kernelName: SelectV2,\n    inputsToSave: ['condition'],\n    gradFunc: (dy, saved) => {\n        const [condition] = saved;\n        return {\n            // TODO(julianoks): Return null for condition gradient\n            // when backprop supports it.\n            condition: () => cast(zerosLike(condition), 'float32'),\n            t: () => mul(dy, cast(condition, dy.dtype)),\n            e: () => mul(dy, cast(logicalNot(condition), dy.dtype))\n        };\n    }\n};\n//# sourceMappingURL=SelectV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Selu } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { exp } from '../ops/exp';\nimport { greater } from '../ops/greater';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { SELU_SCALE, SELU_SCALEALPHA } from '../ops/selu_util';\nimport { where } from '../ops/where';\nexport const seluGradConfig = {\n    kernelName: Selu,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const mask = greater(x, scalar(0));\n                const scaleAlpha = scalar(SELU_SCALEALPHA);\n                const scale = scalar(SELU_SCALE);\n                const greaterThanZeroDer = mul(dy, scale);\n                const lessEqualZeroDer = mul(mul(dy, scaleAlpha), exp(cast(x, 'float32')));\n                return where(mask, greaterThanZeroDer, lessEqualZeroDer);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Selu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sigmoid } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nexport const sigmoidGradConfig = {\n    kernelName: Sigmoid,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(dy, mul(y, sub(scalar(1), y))) };\n    }\n};\n//# sourceMappingURL=Sigmoid_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sign } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const signGradConfig = {\n    kernelName: Sign,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Sign_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { cos } from '../ops/cos';\nimport { mul } from '../ops/mul';\nexport const sinGradConfig = {\n    kernelName: Sin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(cos(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Sin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sinh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { cosh } from '../ops/cosh';\nimport { mul } from '../ops/mul';\nexport const sinhGradConfig = {\n    kernelName: Sinh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(cosh(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Sinh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Slice } from '../kernel_names';\nimport { pad } from '../ops/pad';\nimport { parseSliceParams } from '../ops/slice_util';\nexport const sliceGradConfig = {\n    kernelName: Slice,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { begin, size } = attrs;\n        const inputShape = x.shape;\n        const [begin_, size_] = parseSliceParams(x, begin, size);\n        // Create an Nx2 padding where the first column represents how many\n        // zeros are prepended (at start) for each dimension, and the second\n        // column indicates how many zeros are appended (at end).\n        // The number of zeros to append is the shape of the input\n        // elementwise-subtracted by both the begin vector and sizes vector.\n        const paddings = [];\n        for (let i = 0; i < dy.rank; i++) {\n            paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);\n        }\n        return { x: () => pad(dy, paddings) };\n    }\n};\n//# sourceMappingURL=Slice_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softmax } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nexport const softmaxGradConfig = {\n    kernelName: Softmax,\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [y] = saved;\n        const { dim } = attrs;\n        const keepDims = true;\n        const dyTimesY = mul(dy, y);\n        return {\n            logits: () => sub(dyTimesY, mul(sum(dyTimesY, [dim], keepDims), y))\n        };\n    }\n};\n//# sourceMappingURL=Softmax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softplus } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { sigmoid } from '../ops/sigmoid';\nexport const softplusGradConfig = {\n    kernelName: Softplus,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, sigmoid(x)) };\n    }\n};\n//# sourceMappingURL=Softplus_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SpaceToBatchND } from '../kernel_names';\nimport { batchToSpaceND } from '../ops/batch_to_space_nd';\nexport const spaceToBatchNDGradConfig = {\n    kernelName: SpaceToBatchND,\n    gradFunc: (dy, saved, attrs) => {\n        const { blockShape, paddings } = attrs;\n        return { x: () => batchToSpaceND(dy, blockShape, paddings) };\n    }\n};\n//# sourceMappingURL=SpaceToBatchND_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SplitV } from '../kernel_names';\nimport { concat } from '../ops/concat';\nexport const splitVGradConfig = {\n    kernelName: SplitV,\n    gradFunc: (dy, saved, attrs) => {\n        const { axis } = attrs;\n        return { x: () => concat(dy, axis) };\n    }\n};\n//# sourceMappingURL=SplitV_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sqrt } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nexport const sqrtGradConfig = {\n    kernelName: Sqrt,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, mul(sqrt(cast(x, 'float32')), 2)) };\n    }\n};\n//# sourceMappingURL=Sqrt_grad.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nexport const squareGradConfig = {\n    kernelName: Square,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, mul(cast(x, 'float32'), 2)) };\n    }\n};\n//# sourceMappingURL=Square_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nexport const squaredDifferenceGradConfig = {\n    kernelName: SquaredDifference,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const two = scalar(2);\n        const derA = () => mul(dy, mul(two, sub(a, b)));\n        const derB = () => mul(dy, mul(two, sub(b, a)));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=SquaredDifference_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Step } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const stepGradConfig = {\n    kernelName: Step,\n    gradFunc: (dy) => {\n        // TODO(manrajgrover): Return null for gradients when backprop supports\n        // it.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Step_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const subGradConfig = {\n    kernelName: Sub,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(neg(res), b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Sub_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sum } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { ones } from '../ops/ones';\nimport { reshape } from '../ops/reshape';\nimport { parseAxisParam } from '../util';\nexport const sumGradConfig = {\n    kernelName: Sum,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const expandedDyShape = x.shape.slice();\n        const { axis } = attrs;\n        const axes = parseAxisParam(axis, x.shape);\n        axes.forEach(axis => {\n            expandedDyShape[axis] = 1;\n        });\n        const expandedDy = reshape(dy, expandedDyShape);\n        const derX = mul(expandedDy, ones(x.shape, 'float32'));\n        return { x: () => derX };\n    }\n};\n//# sourceMappingURL=Sum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '../kernel_names';\nimport { cos } from '../ops/cos';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nexport const tanGradConfig = {\n    kernelName: Tan,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, square(cos(x))) };\n    }\n};\n//# sourceMappingURL=Tan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tanh } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const tanhGradConfig = {\n    kernelName: Tanh,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(sub(scalar(1), square(y)), dy) };\n    }\n};\n//# sourceMappingURL=Tanh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tile } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { slice } from '../ops/slice';\nimport { zerosLike } from '../ops/zeros_like';\nexport const tileGradConfig = {\n    kernelName: Tile,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { reps } = attrs;\n        const derX = () => {\n            let xGrad = zerosLike(x);\n            // TODO(cais): Maybe reduce memory footprint by avoiding repeated\n            // slicing.\n            if (x.rank === 1) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    xGrad = add(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));\n                }\n            }\n            else if (x.rank === 2) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        xGrad = add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [\n                            x.shape[0], x.shape[1]\n                        ]));\n                    }\n                }\n            }\n            else if (x.rank === 3) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        for (let k = 0; k < reps[2]; ++k) {\n                            xGrad =\n                                add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));\n                        }\n                    }\n                }\n            }\n            else if (x.rank === 4) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        for (let k = 0; k < reps[2]; ++k) {\n                            for (let l = 0; l < reps[3]; ++l) {\n                                xGrad =\n                                    add(xGrad, slice(dy, [\n                                        i * x.shape[0], j * x.shape[1], k * x.shape[2],\n                                        l * x.shape[3]\n                                    ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));\n                            }\n                        }\n                    }\n                }\n            }\n            else {\n                throw new Error(`Gradient for tile operation is not implemented for rank-` +\n                    `${x.rank} tensors yet.`);\n            }\n            return xGrad;\n        };\n        return { x: derX };\n    },\n};\n//# sourceMappingURL=Tile_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nexport const transposeGradConfig = {\n    kernelName: Transpose,\n    gradFunc: (dy, saved, attrs) => {\n        const transposeAttrs = attrs;\n        const { perm } = transposeAttrs;\n        const undoPerm = axis_util.getUndoAxesPermutation(perm);\n        return { x: () => transpose(dy, undoPerm) };\n    }\n};\n//# sourceMappingURL=Transpose_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unpack } from '../kernel_names';\nimport { stack } from '../ops/stack';\nexport const unpackGradConfig = {\n    kernelName: Unpack,\n    gradFunc: (dy, saved, attrs) => {\n        const unpackAttrs = attrs;\n        const { axis } = unpackAttrs;\n        return { value: () => stack(dy, axis) };\n    }\n};\n//# sourceMappingURL=Unpack_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { UnsortedSegmentSum } from '../kernel_names';\nimport { expandDims } from '../ops/expand_dims';\nimport { gather } from '../ops/gather';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { logicalAnd } from '../ops/logical_and';\nimport { maximum } from '../ops/maximum';\nimport { ones } from '../ops/ones';\nimport { scalar } from '../ops/scalar';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const unsortedSegmentSumGradConfig = {\n    kernelName: UnsortedSegmentSum,\n    inputsToSave: ['segmentIds'],\n    gradFunc: (dy, saved) => {\n        const [segmentIds] = saved;\n        const derX = () => {\n            return gatherDropNegatives(dy, segmentIds);\n        };\n        return { x: derX };\n    }\n};\nfunction gatherDropNegatives(x, indices) {\n    // Helper function for unsorted segment ops. Gathers params for\n    // positive segment ids and gathers 0 for inputs with negative segment id.\n    // Mirrors _GatherDropNegatives from tensorflow/python/ops/math_grad.py\n    const zeroClippedIndices = maximum(indices, zerosLike(indices));\n    const gathered = gather(x, zeroClippedIndices);\n    let isPositive = greaterEqual(indices, scalar(0, 'int32'));\n    const numIters = gathered.rank - isPositive.rank;\n    for (let i = 0; i < numIters; ++i) {\n        isPositive = expandDims(isPositive, i + 1);\n    }\n    isPositive = logicalAnd(isPositive, ones(gathered.shape, 'bool'));\n    const zeroSlice = zerosLike(gathered);\n    return where(isPositive, gathered, zeroSlice);\n}\n//# sourceMappingURL=UnsortedSegmentSum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ZerosLike } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const zerosLikeGradConfig = {\n    kernelName: ZerosLike,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=ZerosLike_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { absGradConfig } from './gradients/Abs_grad';\nimport { acosGradConfig } from './gradients/Acos_grad';\nimport { acoshGradConfig } from './gradients/Acosh_grad';\nimport { addGradConfig } from './gradients/Add_grad';\nimport { addNGradConfig } from './gradients/AddN_grad';\nimport { argMaxGradConfig } from './gradients/ArgMax_grad';\nimport { argMinGradConfig } from './gradients/ArgMin_grad';\nimport { asinGradConfig } from './gradients/Asin_grad';\nimport { asinhGradConfig } from './gradients/Asinh_grad';\nimport { atan2GradConfig } from './gradients/Atan2_grad';\nimport { atanGradConfig } from './gradients/Atan_grad';\nimport { atanhGradConfig } from './gradients/Atanh_grad';\nimport { avgPool3DGradConfig } from './gradients/AvgPool3D_grad';\nimport { avgPoolGradConfig } from './gradients/AvgPool_grad';\nimport { batchMatMulGradConfig } from './gradients/BatchMatMul_grad';\nimport { batchToSpaceNDGradConfig } from './gradients/BatchToSpaceND_grad';\nimport { broadcastToGradConfig } from './gradients/BroadcastTo_grad';\nimport { castGradConfig } from './gradients/Cast_grad';\nimport { ceilGradConfig } from './gradients/Ceil_grad';\nimport { clipByValueGradConfig } from './gradients/ClipByValue_grad';\nimport { concatGradConfig } from './gradients/Concat_grad';\nimport { conv2DGradConfig } from './gradients/Conv2D_grad';\nimport { conv2DBackpropInputGradConfig } from './gradients/Conv2DBackpropInput_grad';\nimport { conv3DGradConfig } from './gradients/Conv3D_grad';\nimport { cosGradConfig } from './gradients/Cos_grad';\nimport { coshGradConfig } from './gradients/Cosh_grad';\nimport { cumsumGradConfig } from './gradients/Cumsum_grad';\nimport { depthwiseConv2dNativeGradConfig } from './gradients/DepthwiseConv2dNative_grad';\nimport { dilation2dGradConfig } from './gradients/Dilation2D_grad';\nimport { divGradConfig } from './gradients/Div_grad';\nimport { eluGradConfig } from './gradients/Elu_grad';\nimport { erfGradConfig } from './gradients/Erf_grad';\nimport { expGradConfig } from './gradients/Exp_grad';\nimport { expm1GradConfig } from './gradients/Expm1_grad';\nimport { floorGradConfig } from './gradients/Floor_grad';\nimport { floorDivGradConfig } from './gradients/FloorDiv_grad';\nimport { fusedBatchNormGradConfig } from './gradients/FusedBatchNorm_grad';\nimport { gatherGradConfig } from './gradients/GatherV2_grad';\nimport { greaterEqualGradConfig } from './gradients/GreaterEqual_grad';\nimport { identityGradConfig } from './gradients/Identity_grad';\nimport { isFiniteGradConfig } from './gradients/IsFinite_grad';\nimport { isInfGradConfig } from './gradients/IsInf_grad';\nimport { isNanGradConfig } from './gradients/IsNan_grad';\nimport { log1pGradConfig } from './gradients/Log1p_grad';\nimport { logGradConfig } from './gradients/Log_grad';\nimport { logSoftmaxGradConfig } from './gradients/LogSoftmax_grad';\nimport { lrnGradConfig } from './gradients/LRN_grad';\nimport { maxGradConfig } from './gradients/Max_grad';\nimport { maximumGradConfig } from './gradients/Maximum_grad';\nimport { maxPool3DGradConfig } from './gradients/MaxPool3D_grad';\nimport { maxPoolGradConfig } from './gradients/MaxPool_grad';\nimport { minGradConfig } from './gradients/Min_grad';\nimport { minimumGradConfig } from './gradients/Minimum_grad';\nimport { modGradConfig } from './gradients/Mod_grad';\nimport { multiplyGradConfig } from './gradients/Multiply_grad';\nimport { negateGradConfig } from './gradients/Negate_grad';\nimport { oneHotGradConfig } from './gradients/OneHot_grad';\nimport { onesLikeGradConfig } from './gradients/OnesLike_grad';\nimport { padV2GradConfig } from './gradients/PadV2_grad';\nimport { powGradConfig } from './gradients/Pow_grad';\nimport { preluGradConfig } from './gradients/Prelu_grad';\nimport { reciprocalGradConfig } from './gradients/Reciprocal_grad';\nimport { relu6GradConfig } from './gradients/Relu6_grad';\nimport { reluGradConfig } from './gradients/Relu_grad';\nimport { reshapeGradConfig } from './gradients/Reshape_grad';\nimport { resizeBilinearGradConfig } from './gradients/ResizeBilinear_grad';\nimport { resizeNearestNeighborGradConfig } from './gradients/ResizeNearestNeighbor_grad';\nimport { reverseGradConfig } from './gradients/Reverse_grad';\nimport { roundGradConfig } from './gradients/Round_grad';\nimport { rsqrtGradConfig } from './gradients/Rsqrt_grad';\nimport { selectV2PoolGradConfig } from './gradients/SelectV2_grad';\nimport { seluGradConfig } from './gradients/Selu_grad';\nimport { sigmoidGradConfig } from './gradients/Sigmoid_grad';\nimport { signGradConfig } from './gradients/Sign_grad';\nimport { sinGradConfig } from './gradients/Sin_grad';\nimport { sinhGradConfig } from './gradients/Sinh_grad';\nimport { sliceGradConfig } from './gradients/Slice_grad';\nimport { softmaxGradConfig } from './gradients/Softmax_grad';\nimport { softplusGradConfig } from './gradients/Softplus_grad';\nimport { spaceToBatchNDGradConfig } from './gradients/SpaceToBatchND_grad';\nimport { splitVGradConfig } from './gradients/SplitV_grad';\nimport { sqrtGradConfig } from './gradients/Sqrt_grad';\nimport { squareGradConfig } from './gradients/Square_grad';\nimport { squaredDifferenceGradConfig } from './gradients/SquaredDifference_grad';\nimport { stepGradConfig } from './gradients/Step_grad';\nimport { subGradConfig } from './gradients/Sub_grad';\nimport { sumGradConfig } from './gradients/Sum_grad';\nimport { tanGradConfig } from './gradients/Tan_grad';\nimport { tanhGradConfig } from './gradients/Tanh_grad';\nimport { tileGradConfig } from './gradients/Tile_grad';\nimport { transposeGradConfig } from './gradients/Transpose_grad';\nimport { unpackGradConfig } from './gradients/Unpack_grad';\nimport { unsortedSegmentSumGradConfig } from './gradients/UnsortedSegmentSum_grad';\nimport { zerosLikeGradConfig } from './gradients/ZerosLike_grad';\nimport { registerGradient } from './kernel_registry';\n// Export all kernel configs here so that the package can auto register them\nconst gradConfigs = [\n    absGradConfig,\n    acosGradConfig,\n    acoshGradConfig,\n    addGradConfig,\n    addNGradConfig,\n    argMaxGradConfig,\n    argMinGradConfig,\n    asinGradConfig,\n    asinhGradConfig,\n    atan2GradConfig,\n    atanGradConfig,\n    atanhGradConfig,\n    avgPool3DGradConfig,\n    avgPoolGradConfig,\n    batchMatMulGradConfig,\n    batchToSpaceNDGradConfig,\n    broadcastToGradConfig,\n    castGradConfig,\n    ceilGradConfig,\n    clipByValueGradConfig,\n    concatGradConfig,\n    conv2DBackpropInputGradConfig,\n    conv2DGradConfig,\n    conv3DGradConfig,\n    cosGradConfig,\n    coshGradConfig,\n    cumsumGradConfig,\n    depthwiseConv2dNativeGradConfig,\n    dilation2dGradConfig,\n    divGradConfig,\n    eluGradConfig,\n    erfGradConfig,\n    expGradConfig,\n    expm1GradConfig,\n    floorDivGradConfig,\n    floorGradConfig,\n    fusedBatchNormGradConfig,\n    gatherGradConfig,\n    greaterEqualGradConfig,\n    identityGradConfig,\n    isFiniteGradConfig,\n    isInfGradConfig,\n    isNanGradConfig,\n    log1pGradConfig,\n    logGradConfig,\n    logSoftmaxGradConfig,\n    lrnGradConfig,\n    maxGradConfig,\n    maxGradConfig,\n    maximumGradConfig,\n    maxPool3DGradConfig,\n    maxPoolGradConfig,\n    minGradConfig,\n    minimumGradConfig,\n    modGradConfig,\n    multiplyGradConfig,\n    negateGradConfig,\n    oneHotGradConfig,\n    onesLikeGradConfig,\n    padV2GradConfig,\n    padV2GradConfig,\n    powGradConfig,\n    preluGradConfig,\n    reciprocalGradConfig,\n    relu6GradConfig,\n    reluGradConfig,\n    reshapeGradConfig,\n    resizeBilinearGradConfig,\n    resizeNearestNeighborGradConfig,\n    reverseGradConfig,\n    roundGradConfig,\n    rsqrtGradConfig,\n    selectV2PoolGradConfig,\n    seluGradConfig,\n    sigmoidGradConfig,\n    signGradConfig,\n    sinGradConfig,\n    sinhGradConfig,\n    sliceGradConfig,\n    softmaxGradConfig,\n    softplusGradConfig,\n    spaceToBatchNDGradConfig,\n    spaceToBatchNDGradConfig,\n    splitVGradConfig,\n    splitVGradConfig,\n    sqrtGradConfig,\n    squaredDifferenceGradConfig,\n    squareGradConfig,\n    stepGradConfig,\n    subGradConfig,\n    sumGradConfig,\n    tanGradConfig,\n    tanhGradConfig,\n    tileGradConfig,\n    transposeGradConfig,\n    unpackGradConfig,\n    unsortedSegmentSumGradConfig,\n    zerosLikeGradConfig\n];\nfor (const gradientConfig of gradConfigs) {\n    registerGradient(gradientConfig);\n}\n//# sourceMappingURL=register_all_gradients.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { abs } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.abs = function () {\n    this.throwIfDisposed();\n    return abs(this);\n};\n//# sourceMappingURL=abs.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { acos } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.acos = function () {\n    this.throwIfDisposed();\n    return acos(this);\n};\n//# sourceMappingURL=acos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { acosh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.acosh = function () {\n    this.throwIfDisposed();\n    return acosh(this);\n};\n//# sourceMappingURL=acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { addStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.addStrict = function (x) {\n    this.throwIfDisposed();\n    return addStrict(this, x);\n};\n//# sourceMappingURL=add_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { add } from '../../ops/add';\nimport { Tensor } from '../../tensor';\nTensor.prototype.add = function (b) {\n    this.throwIfDisposed();\n    return add(this, b);\n};\n//# sourceMappingURL=add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { all } from '../../ops/all';\nimport { Tensor } from '../../tensor';\nTensor.prototype.all = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return all(this, axis, keepDims);\n};\n//# sourceMappingURL=all.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { any } from '../../ops/any';\nimport { Tensor } from '../../tensor';\nTensor.prototype.any = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return any(this, axis, keepDims);\n};\n//# sourceMappingURL=any.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { argMax } from '../../ops/arg_max';\nimport { Tensor } from '../../tensor';\nTensor.prototype.argMax = function (axis) {\n    this.throwIfDisposed();\n    return argMax(this, axis);\n};\n//# sourceMappingURL=arg_max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { argMin } from '../../ops/arg_min';\nimport { Tensor } from '../../tensor';\nTensor.prototype.argMin = function (axis) {\n    this.throwIfDisposed();\n    return argMin(this, axis);\n};\n//# sourceMappingURL=arg_min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\nimport { assert } from '../../util';\n/** Converts a size-1 `tf.Tensor` to a `tf.Scalar`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asScalar = function () {\n    this.throwIfDisposed();\n    assert(this.size === 1, () => 'The array must have only 1 element.');\n    return reshape(this, []);\n};\n//# sourceMappingURL=as_scalar.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * Casts a `tf.Tensor` to a specified dtype.\n *\n * @param dtype Data-type to cast the tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asType = function (dtype) {\n    this.throwIfDisposed();\n    return cast(this, dtype);\n};\n//# sourceMappingURL=as_type.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/** Converts a `tf.Tensor` to a `tf.Tensor1D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as1D = function () {\n    this.throwIfDisposed();\n    return reshape(this, [this.size]);\n};\n//# sourceMappingURL=as1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor2D`.\n *\n * @param rows Number of rows in `tf.Tensor2D`.\n * @param columns Number of columns in `tf.Tensor2D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as2D = function (rows, columns) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns]);\n};\n//# sourceMappingURL=as2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor3D`.\n *\n * @param rows Number of rows in `tf.Tensor3D`.\n * @param columns Number of columns in `tf.Tensor3D`.\n * @param depth Depth of `tf.Tensor3D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as3D = function (rows, columns, depth) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth]);\n};\n//# sourceMappingURL=as3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor4D`.\n *\n * @param rows Number of rows in `tf.Tensor4D`.\n * @param columns Number of columns in `tf.Tensor4D`.\n * @param depth Depth of `tf.Tensor4D`.\n * @param depth2 4th dimension of `tf.Tensor4D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as4D = function (rows, columns, depth, depth2) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth, depth2]);\n};\n//# sourceMappingURL=as4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor5D`.\n *\n * @param rows Number of rows in `tf.Tensor5D`.\n * @param columns Number of columns in `tf.Tensor5D`.\n * @param depth Depth of `tf.Tensor5D`.\n * @param depth2 4th dimension of `tf.Tensor5D`.\n * @param depth3 5th dimension of 'tf.Tensor5D'\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as5D = function (rows, columns, depth, depth2, depth3) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth, depth2, depth3]);\n};\n//# sourceMappingURL=as5d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { asin } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.asin = function () {\n    this.throwIfDisposed();\n    return asin(this);\n};\n//# sourceMappingURL=asin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { asinh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.asinh = function () {\n    this.throwIfDisposed();\n    return asinh(this);\n};\n//# sourceMappingURL=asinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { atan } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atan = function () {\n    this.throwIfDisposed();\n    return atan(this);\n};\n//# sourceMappingURL=atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { atan2 } from '../../ops/atan2';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atan2 = function (b) {\n    this.throwIfDisposed();\n    return atan2(this, b);\n};\n//# sourceMappingURL=atan2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { atanh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atanh = function () {\n    this.throwIfDisposed();\n    return atanh(this);\n};\n//# sourceMappingURL=atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { avgPool } from '../../ops/avg_pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.avgPool = function (filterSize, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return avgPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=avg_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { batchToSpaceND } from '../../ops/batch_to_space_nd';\nimport { Tensor } from '../../tensor';\nTensor.prototype.batchToSpaceND = function (blockShape, crops) {\n    this.throwIfDisposed();\n    return batchToSpaceND(this, blockShape, crops);\n};\n//# sourceMappingURL=batch_to_space_nd.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { batchNorm } from '../../ops/batchnorm';\nimport { Tensor } from '../../tensor';\nTensor.prototype.batchNorm = function (mean, variance, offset, scale, varianceEpsilon) {\n    this.throwIfDisposed();\n    return batchNorm(this, mean, variance, offset, scale, varianceEpsilon);\n};\n//# sourceMappingURL=batchnorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { broadcastTo } from '../../ops/broadcast_to';\nimport { Tensor } from '../../tensor';\nTensor.prototype.broadcastTo = function (shape) {\n    this.throwIfDisposed();\n    return broadcastTo(this, shape);\n};\n//# sourceMappingURL=broadcast_to.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cast = function (dtype) {\n    this.throwIfDisposed();\n    return cast(this, dtype);\n};\n//# sourceMappingURL=cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { ceil } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.ceil = function () {\n    this.throwIfDisposed();\n    return ceil(this);\n};\n//# sourceMappingURL=ceil.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { clipByValue } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.clipByValue = function (min, max) {\n    this.throwIfDisposed();\n    return clipByValue(this, min, max);\n};\n//# sourceMappingURL=clip_by_value.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../../ops/concat';\nimport { Tensor } from '../../tensor';\nTensor.prototype.concat = function (x, axis) {\n    this.throwIfDisposed();\n    if (x instanceof Tensor) {\n        x = [x];\n    }\n    return concat([this, ...x], axis);\n};\n//# sourceMappingURL=concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv1d } from '../../ops/conv1d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv1d = function (filter, stride, pad, dataFormat, dilation, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv1d(this, filter, stride, pad, dataFormat, dilation, dimRoundingMode);\n};\n//# sourceMappingURL=conv1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2dTranspose } from '../../ops/conv2d_transpose';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv2dTranspose = function (filter, outputShape, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv2dTranspose(this, filter, outputShape, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=conv2d_transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2d } from '../../ops/conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cos } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cos = function () {\n    this.throwIfDisposed();\n    return cos(this);\n};\n//# sourceMappingURL=cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cosh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cosh = function () {\n    this.throwIfDisposed();\n    return cosh(this);\n};\n//# sourceMappingURL=cosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { cumsum } from '../../ops/cumsum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cumsum = function (axis, exclusive, reverse) {\n    this.throwIfDisposed();\n    return cumsum(this, axis, exclusive, reverse);\n};\n//# sourceMappingURL=cumsum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { depthToSpace } from '../../ops/depth_to_space';\nimport { Tensor } from '../../tensor';\nTensor.prototype.depthToSpace = function (blockSize, dataFormat) {\n    this.throwIfDisposed();\n    return depthToSpace(this, blockSize, dataFormat);\n};\n//# sourceMappingURL=depth_to_space.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../../globals';\nimport { depthwiseConv2d } from '../../ops/depthwise_conv2d';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated Use `depthwiseConv2d` instead.\n */\nTensor.prototype.depthwiseConv2D = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    deprecationWarn('depthwiseConv2D is deprecated, use depthwiseConv2d instead');\n    this.throwIfDisposed();\n    return depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=depthwise_conv2D_deprecated.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { depthwiseConv2d } from '../../ops/depthwise_conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.depthwiseConv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    this.throwIfDisposed();\n    return depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dilation2d } from '../../ops/dilation2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.dilation2d = function (filter, strides, pad, dilations, dataFormat) {\n    this.throwIfDisposed();\n    return dilation2d(this, filter, strides, pad, dilations, dataFormat);\n};\n//# sourceMappingURL=dilation2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { divNoNan } from '../../ops/div_no_nan';\nimport { Tensor } from '../../tensor';\nTensor.prototype.divNoNan = function (b) {\n    this.throwIfDisposed();\n    return divNoNan(this, b);\n};\n//# sourceMappingURL=div_no_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { divStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.divStrict = function (x) {\n    this.throwIfDisposed();\n    return divStrict(this, x);\n};\n//# sourceMappingURL=div_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { div } from '../../ops/div';\nimport { Tensor } from '../../tensor';\nTensor.prototype.div = function (b) {\n    this.throwIfDisposed();\n    return div(this, b);\n};\n//# sourceMappingURL=div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dot } from '../../ops/dot';\nimport { Tensor } from '../../tensor';\nTensor.prototype.dot = function (b) {\n    this.throwIfDisposed();\n    return dot(this, b);\n};\n//# sourceMappingURL=dot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { elu } from '../../ops/elu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.elu = function () {\n    this.throwIfDisposed();\n    return elu(this);\n};\n//# sourceMappingURL=elu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { equalStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.equalStrict = function (x) {\n    this.throwIfDisposed();\n    return equalStrict(this, x);\n};\n//# sourceMappingURL=equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { equal } from '../../ops/equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.equal = function (b) {\n    this.throwIfDisposed();\n    return equal(this, b);\n};\n//# sourceMappingURL=equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { erf } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.erf = function () {\n    this.throwIfDisposed();\n    return erf(this);\n};\n//# sourceMappingURL=erf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { exp } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.exp = function () {\n    this.throwIfDisposed();\n    return exp(this);\n};\n//# sourceMappingURL=exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { expandDims } from '../../ops/expand_dims';\nimport { Tensor } from '../../tensor';\nTensor.prototype.expandDims = function (axis) {\n    this.throwIfDisposed();\n    return expandDims(this, axis);\n};\n//# sourceMappingURL=expand_dims.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { expm1 } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.expm1 = function () {\n    this.throwIfDisposed();\n    return expm1(this);\n};\n//# sourceMappingURL=expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { fft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.fft = function () {\n    this.throwIfDisposed();\n    return fft(this);\n};\n//# sourceMappingURL=fft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/** Flatten a Tensor to a 1D array.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.flatten = function () {\n    this.throwIfDisposed();\n    return reshape(this, [this.size]);\n};\n//# sourceMappingURL=flatten.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { floor } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.floor = function () {\n    this.throwIfDisposed();\n    return floor(this);\n};\n//# sourceMappingURL=floor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { floorDiv } from '../../ops/floorDiv';\nimport { Tensor } from '../../tensor';\nTensor.prototype.floorDiv = function (b) {\n    this.throwIfDisposed();\n    return floorDiv(this, b);\n};\n//# sourceMappingURL=floorDiv.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { gather } from '../../ops/gather';\nimport { Tensor } from '../../tensor';\nTensor.prototype.gather = function (indices, axis) {\n    this.throwIfDisposed();\n    return gather(this, indices, axis);\n};\n//# sourceMappingURL=gather.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { greaterEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return greaterEqualStrict(this, x);\n};\n//# sourceMappingURL=greater_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { greaterEqual } from '../../ops/greater_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.greaterEqual = function (b) {\n    this.throwIfDisposed();\n    return greaterEqual(this, b);\n};\n//# sourceMappingURL=greater_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { greaterStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterStrict = function (x) {\n    this.throwIfDisposed();\n    return greaterStrict(this, x);\n};\n//# sourceMappingURL=greater_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { greater } from '../../ops/greater';\nimport { Tensor } from '../../tensor';\nTensor.prototype.greater = function (b) {\n    this.throwIfDisposed();\n    return greater(this, b);\n};\n//# sourceMappingURL=greater.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { ifft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.ifft = function () {\n    this.throwIfDisposed();\n    return ifft(this);\n};\n//# sourceMappingURL=ifft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { irfft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.irfft = function () {\n    this.throwIfDisposed();\n    return irfft(this);\n};\n//# sourceMappingURL=irfft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isFinite } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isFinite = function () {\n    this.throwIfDisposed();\n    return isFinite(this);\n};\n//# sourceMappingURL=is_finite.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isInf } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isInf = function () {\n    this.throwIfDisposed();\n    return isInf(this);\n};\n//# sourceMappingURL=is_inf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isNaN } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isNaN = function () {\n    this.throwIfDisposed();\n    return isNaN(this);\n};\n//# sourceMappingURL=is_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { leakyRelu } from '../../ops/leaky_relu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.leakyRelu = function (alpha) {\n    this.throwIfDisposed();\n    return leakyRelu(this, alpha);\n};\n//# sourceMappingURL=leaky_relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { lessEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.lessEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return lessEqualStrict(this, x);\n};\n//# sourceMappingURL=less_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { lessEqual } from '../../ops/less_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.lessEqual = function (b) {\n    this.throwIfDisposed();\n    return lessEqual(this, b);\n};\n//# sourceMappingURL=less_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { lessStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.lessStrict = function (x) {\n    this.throwIfDisposed();\n    return lessStrict(this, x);\n};\n//# sourceMappingURL=less_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { less } from '../../ops/less';\nimport { Tensor } from '../../tensor';\nTensor.prototype.less = function (b) {\n    this.throwIfDisposed();\n    return less(this, b);\n};\n//# sourceMappingURL=less.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { localResponseNormalization } from '../../ops/local_response_normalization';\nimport { Tensor } from '../../tensor';\nTensor.prototype.localResponseNormalization = function (depthRadius, bias, alpha, beta) {\n    this.throwIfDisposed();\n    return localResponseNormalization(this, depthRadius, bias, alpha, beta);\n};\n//# sourceMappingURL=local_response_normalization.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { logSigmoid } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSigmoid = function () {\n    this.throwIfDisposed();\n    return logSigmoid(this);\n};\n//# sourceMappingURL=log_sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { logSoftmax } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSoftmax = function (axis) {\n    this.throwIfDisposed();\n    return logSoftmax(this, axis);\n};\n//# sourceMappingURL=log_softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logSumExp } from '../../ops/log_sum_exp';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSumExp = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return logSumExp(this, axis, keepDims);\n};\n//# sourceMappingURL=log_sum_exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { log } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.log = function () {\n    this.throwIfDisposed();\n    return log(this);\n};\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { log1p } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.log1p = function () {\n    this.throwIfDisposed();\n    return log1p(this);\n};\n//# sourceMappingURL=log1p.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalAnd } from '../../ops/logical_and';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalAnd = function (b) {\n    this.throwIfDisposed();\n    return logicalAnd(this, b);\n};\n//# sourceMappingURL=logical_and.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalNot } from '../../ops/logical_not';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalNot = function () {\n    this.throwIfDisposed();\n    return logicalNot(this);\n};\n//# sourceMappingURL=logical_not.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalOr } from '../../ops/logical_or';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalOr = function (b) {\n    this.throwIfDisposed();\n    return logicalOr(this, b);\n};\n//# sourceMappingURL=logical_or.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalXor } from '../../ops/logical_xor';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalXor = function (b) {\n    this.throwIfDisposed();\n    return logicalXor(this, b);\n};\n//# sourceMappingURL=logical_xor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { matMul } from '../../ops/mat_mul';\nimport { Tensor } from '../../tensor';\nTensor.prototype.matMul = function (b, transposeA, transposeB) {\n    this.throwIfDisposed();\n    return matMul(this, b, transposeA, transposeB);\n};\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { maxPool } from '../../ops/max_pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.maxPool = function (filterSize, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return maxPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=max_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { max } from '../../ops/max';\nimport { Tensor } from '../../tensor';\nTensor.prototype.max = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return max(this, axis, keepDims);\n};\n//# sourceMappingURL=max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { maximumStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.maximumStrict = function (x) {\n    this.throwIfDisposed();\n    return maximumStrict(this, x);\n};\n//# sourceMappingURL=maximum_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { maximum } from '../../ops/maximum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.maximum = function (b) {\n    this.throwIfDisposed();\n    return maximum(this, b);\n};\n//# sourceMappingURL=maximum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mean } from '../../ops/mean';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mean = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return mean(this, axis, keepDims);\n};\n//# sourceMappingURL=mean.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { min } from '../../ops/min';\nimport { Tensor } from '../../tensor';\nTensor.prototype.min = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return min(this, axis, keepDims);\n};\n//# sourceMappingURL=min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { minimumStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.minimumStrict = function (x) {\n    this.throwIfDisposed();\n    return minimumStrict(this, x);\n};\n//# sourceMappingURL=minimum_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { minimum } from '../../ops/minimum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.minimum = function (b) {\n    this.throwIfDisposed();\n    return minimum(this, b);\n};\n//# sourceMappingURL=minimum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { modStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.modStrict = function (x) {\n    this.throwIfDisposed();\n    return modStrict(this, x);\n};\n//# sourceMappingURL=mod_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mod } from '../../ops/mod';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mod = function (b) {\n    this.throwIfDisposed();\n    return mod(this, b);\n};\n//# sourceMappingURL=mod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { mulStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.mulStrict = function (x) {\n    this.throwIfDisposed();\n    return mulStrict(this, x);\n};\n//# sourceMappingURL=mul_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mul } from '../../ops/mul';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mul = function (b) {\n    this.throwIfDisposed();\n    return mul(this, b);\n};\n//# sourceMappingURL=mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { neg } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.neg = function () {\n    this.throwIfDisposed();\n    return neg(this);\n};\n//# sourceMappingURL=neg.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { norm } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.norm = function (ord, axis, keepDims) {\n    this.throwIfDisposed();\n    return norm(this, ord, axis, keepDims);\n};\n//# sourceMappingURL=norm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { notEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.notEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return notEqualStrict(this, x);\n};\n//# sourceMappingURL=not_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { notEqual } from '../../ops/not_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.notEqual = function (b) {\n    this.throwIfDisposed();\n    return notEqual(this, b);\n};\n//# sourceMappingURL=not_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { oneHot } from '../../ops/one_hot';\nimport { Tensor } from '../../tensor';\nTensor.prototype.oneHot = function (depth, onValue = 1, offValue = 0) {\n    this.throwIfDisposed();\n    return oneHot(this, depth, onValue, offValue);\n};\n//# sourceMappingURL=one_hot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { onesLike } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.onesLike = function () {\n    this.throwIfDisposed();\n    return onesLike(this);\n};\n//# sourceMappingURL=ones_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pad } from '../../ops/pad';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pad = function (paddings, constantValue) {\n    this.throwIfDisposed();\n    return pad(this, paddings, constantValue);\n};\n//# sourceMappingURL=pad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pool } from '../../ops/pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pool = function (windowShape, poolingType, padding, dilationRate, strides) {\n    this.throwIfDisposed();\n    return pool(this, windowShape, poolingType, padding, dilationRate, strides);\n};\n//# sourceMappingURL=pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { powStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.powStrict = function (exp) {\n    this.throwIfDisposed();\n    return powStrict(this, exp);\n};\n//# sourceMappingURL=pow_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pow } from '../../ops/pow';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pow = function (exp) {\n    this.throwIfDisposed();\n    return pow(this, exp);\n};\n//# sourceMappingURL=pow.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { prelu } from '../../ops/prelu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.prelu = function (alpha) {\n    this.throwIfDisposed();\n    return prelu(this, alpha);\n};\n//# sourceMappingURL=prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { prod } from '../../ops/prod';\nimport { Tensor } from '../../tensor';\nTensor.prototype.prod = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return prod(this, axis, keepDims);\n};\n//# sourceMappingURL=prod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { reciprocal } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reciprocal = function () {\n    this.throwIfDisposed();\n    return reciprocal(this);\n};\n//# sourceMappingURL=reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { relu } from '../../ops/relu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.relu = function () {\n    this.throwIfDisposed();\n    return relu(this);\n};\n//# sourceMappingURL=relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { relu6 } from '../../ops/relu6';\nimport { Tensor } from '../../tensor';\nTensor.prototype.relu6 = function () {\n    this.throwIfDisposed();\n    return relu6(this);\n};\n//# sourceMappingURL=relu6.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Reshapes the tensor into the shape of the provided tensor.\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.reshapeAs = function (x) {\n    this.throwIfDisposed();\n    return reshape(this, x.shape);\n};\n//# sourceMappingURL=reshape_as.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reshape = function (shape) {\n    this.throwIfDisposed();\n    return reshape(this, shape);\n};\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { resizeBilinear } from '../../ops/image/resize_bilinear';\nimport { Tensor } from '../../tensor';\nTensor.prototype.resizeBilinear = function (newShape2D, alignCorners) {\n    this.throwIfDisposed();\n    return resizeBilinear(this, newShape2D, alignCorners);\n};\n//# sourceMappingURL=resize_bilinear.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { resizeNearestNeighbor } from '../../ops/image/resize_nearest_neighbor';\nimport { Tensor } from '../../tensor';\nTensor.prototype.resizeNearestNeighbor = function (newShape2D, alignCorners) {\n    this.throwIfDisposed();\n    return resizeNearestNeighbor(this, newShape2D, alignCorners);\n};\n//# sourceMappingURL=resize_nearest_neighbor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reverse } from '../../ops/reverse';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reverse = function (axis) {\n    this.throwIfDisposed();\n    return reverse(this, axis);\n};\n//# sourceMappingURL=reverse.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { rfft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.rfft = function () {\n    this.throwIfDisposed();\n    return rfft(this);\n};\n//# sourceMappingURL=rfft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { round } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.round = function () {\n    this.throwIfDisposed();\n    return round(this);\n};\n//# sourceMappingURL=round.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { rsqrt } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.rsqrt = function () {\n    this.throwIfDisposed();\n    return rsqrt(this);\n};\n//# sourceMappingURL=rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { selu } from '../../ops/selu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.selu = function () {\n    this.throwIfDisposed();\n    return selu(this);\n};\n//# sourceMappingURL=selu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { separableConv2d } from '../../ops/separable_conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.separableConv2d = function (depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat) {\n    this.throwIfDisposed();\n    return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat);\n};\n//# sourceMappingURL=separable_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sigmoid } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sigmoid = function () {\n    this.throwIfDisposed();\n    return sigmoid(this);\n};\n//# sourceMappingURL=sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sign } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sign = function () {\n    this.throwIfDisposed();\n    return sign(this);\n};\n//# sourceMappingURL=sign.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sin } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sin = function () {\n    this.throwIfDisposed();\n    return sin(this);\n};\n//# sourceMappingURL=sin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sinh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sinh = function () {\n    this.throwIfDisposed();\n    return sinh(this);\n};\n//# sourceMappingURL=sinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { slice } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.slice = function (begin, size) {\n    this.throwIfDisposed();\n    return slice(this, begin, size);\n};\n//# sourceMappingURL=slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { softmax } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.softmax = function (dim) {\n    this.throwIfDisposed();\n    return softmax(this, dim);\n};\n//# sourceMappingURL=softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { softplus } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.softplus = function () {\n    this.throwIfDisposed();\n    return softplus(this);\n};\n//# sourceMappingURL=softplus.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { spaceToBatchND } from '../../ops/space_to_batch_nd';\nimport { Tensor } from '../../tensor';\nTensor.prototype.spaceToBatchND = function (blockShape, paddings) {\n    this.throwIfDisposed();\n    return spaceToBatchND(this, blockShape, paddings);\n};\n//# sourceMappingURL=space_to_batch_nd.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { split } from '../../ops/split';\nimport { Tensor } from '../../tensor';\nTensor.prototype.split = function (numOrSizeSplits, axis) {\n    this.throwIfDisposed();\n    return split(this, numOrSizeSplits, axis);\n};\n//# sourceMappingURL=split.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sqrt } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sqrt = function () {\n    this.throwIfDisposed();\n    return sqrt(this);\n};\n//# sourceMappingURL=sqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { square } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.square = function () {\n    this.throwIfDisposed();\n    return square(this);\n};\n//# sourceMappingURL=square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { squaredDifference } from '../../ops/squared_difference';\nimport { Tensor } from '../../tensor';\nTensor.prototype.squaredDifference = function (b) {\n    this.throwIfDisposed();\n    return squaredDifference(this, b);\n};\n//# sourceMappingURL=squared_difference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { squaredDifferenceStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.squaredDifferenceStrict = function (x) {\n    this.throwIfDisposed();\n    return squaredDifferenceStrict(this, x);\n};\n//# sourceMappingURL=squared_difference_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { squeeze } from '../../ops/squeeze';\nimport { Tensor } from '../../tensor';\nTensor.prototype.squeeze = function (axis) {\n    this.throwIfDisposed();\n    return squeeze(this, axis);\n};\n//# sourceMappingURL=squeeze.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { stack } from '../../ops/stack';\nimport { Tensor } from '../../tensor';\nTensor.prototype.stack = function (x, axis) {\n    this.throwIfDisposed();\n    const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];\n    return stack(tensorsToBeStacked, axis);\n};\n//# sourceMappingURL=stack.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { step } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.step = function (alpha) {\n    this.throwIfDisposed();\n    return step(this, alpha);\n};\n//# sourceMappingURL=step.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { stridedSlice } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.stridedSlice = function (begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n    this.throwIfDisposed();\n    return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n};\n//# sourceMappingURL=strided_slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { subStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.subStrict = function (x) {\n    this.throwIfDisposed();\n    return subStrict(this, x);\n};\n//# sourceMappingURL=sub_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { sub } from '../../ops/sub';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sub = function (b) {\n    this.throwIfDisposed();\n    return sub(this, b);\n};\n//# sourceMappingURL=sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { sum } from '../../ops/sum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sum = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return sum(this, axis, keepDims);\n};\n//# sourceMappingURL=sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { tan } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tan = function () {\n    this.throwIfDisposed();\n    return tan(this);\n};\n//# sourceMappingURL=tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { tanh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tanh = function () {\n    this.throwIfDisposed();\n    return tanh(this);\n};\n//# sourceMappingURL=tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tile } from '../../ops/tile';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tile = function (reps) {\n    this.throwIfDisposed();\n    return tile(this, reps);\n};\n//# sourceMappingURL=tile.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `bool`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toBool = function () {\n    this.throwIfDisposed();\n    return cast(this, 'bool');\n};\n//# sourceMappingURL=to_bool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `float32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toFloat = function () {\n    this.throwIfDisposed();\n    return cast(this, 'float32');\n};\n//# sourceMappingURL=to_float.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `int32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toInt = function () {\n    this.throwIfDisposed();\n    return cast(this, 'int32');\n};\n//# sourceMappingURL=to_int.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { topk } from '../../ops/topk';\nimport { Tensor } from '../../tensor';\nTensor.prototype.topk = function (k, sorted) {\n    this.throwIfDisposed();\n    return topk(this, k, sorted);\n};\n//# sourceMappingURL=topk.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { transpose } from '../../ops/transpose';\nimport { Tensor } from '../../tensor';\nTensor.prototype.transpose = function (perm) {\n    this.throwIfDisposed();\n    return transpose(this, perm);\n};\n//# sourceMappingURL=transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unique } from '../../ops/unique';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unique = function (axis) {\n    this.throwIfDisposed();\n    return unique(this, axis);\n};\n//# sourceMappingURL=unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unsortedSegmentSum } from '../../ops/unsorted_segment_sum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unsortedSegmentSum = function (segmentIds, numSegments) {\n    this.throwIfDisposed();\n    return unsortedSegmentSum(this, segmentIds, numSegments);\n};\n//# sourceMappingURL=unsorted_segment_sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unstack } from '../../ops/unstack';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unstack = function (axis) {\n    this.throwIfDisposed();\n    return unstack(this, axis);\n};\n//# sourceMappingURL=unstack.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { where } from '../../ops/where';\nimport { Tensor } from '../../tensor';\nTensor.prototype.where = function (condition, x) {\n    this.throwIfDisposed();\n    return where(condition, this, x);\n};\n//# sourceMappingURL=where.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { zerosLike } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.zerosLike = function () {\n    this.throwIfDisposed();\n    return zerosLike(this);\n};\n//# sourceMappingURL=zeros_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './abs';\nimport './acos';\nimport './acosh';\nimport './add_strict';\nimport './add';\nimport './all';\nimport './any';\nimport './arg_max';\nimport './arg_min';\nimport './as_scalar';\nimport './as_type';\nimport './as1d';\nimport './as2d';\nimport './as3d';\nimport './as4d';\nimport './as5d';\nimport './asin';\nimport './asinh';\nimport './atan';\nimport './atan2';\nimport './atanh';\nimport './avg_pool';\nimport './batch_to_space_nd';\nimport './batchnorm';\nimport './broadcast_to';\nimport './cast';\nimport './ceil';\nimport './clip_by_value';\nimport './concat';\nimport './conv1d';\nimport './conv2d_transpose';\nimport './conv2d';\nimport './cos';\nimport './cosh';\nimport './cumsum';\nimport './depth_to_space';\nimport './depthwise_conv2D_deprecated';\nimport './depthwise_conv2d';\nimport './dilation2d';\nimport './div_no_nan';\nimport './div_strict';\nimport './div';\nimport './dot';\nimport './elu';\nimport './equal_strict';\nimport './equal';\nimport './erf';\nimport './exp';\nimport './expand_dims';\nimport './expm1';\nimport './fft';\nimport './flatten';\nimport './floor';\nimport './floorDiv';\nimport './gather';\nimport './greater_equal_strict';\nimport './greater_equal';\nimport './greater_strict';\nimport './greater';\nimport './ifft';\nimport './irfft';\nimport './is_finite';\nimport './is_inf';\nimport './is_nan';\nimport './leaky_relu';\nimport './less_equal_strict';\nimport './less_equal';\nimport './less_strict';\nimport './less';\nimport './local_response_normalization';\nimport './log_sigmoid';\nimport './log_softmax';\nimport './log_sum_exp';\nimport './log';\nimport './log1p';\nimport './logical_and';\nimport './logical_not';\nimport './logical_or';\nimport './logical_xor';\nimport './mat_mul';\nimport './max_pool';\nimport './max';\nimport './maximum_strict';\nimport './maximum';\nimport './mean';\nimport './min';\nimport './minimum_strict';\nimport './minimum';\nimport './mod_strict';\nimport './mod';\nimport './mul_strict';\nimport './mul';\nimport './neg';\nimport './norm';\nimport './not_equal_strict';\nimport './not_equal';\nimport './one_hot';\nimport './ones_like';\nimport './pad';\nimport './pool';\nimport './pow_strict';\nimport './pow';\nimport './prelu';\nimport './prod';\nimport './reciprocal';\nimport './relu';\nimport './relu6';\nimport './reshape_as';\nimport './reshape';\nimport './resize_bilinear';\nimport './resize_nearest_neighbor';\nimport './reverse';\nimport './rfft';\nimport './round';\nimport './rsqrt';\nimport './selu';\nimport './separable_conv2d';\nimport './sigmoid';\nimport './sign';\nimport './sin';\nimport './sinh';\nimport './slice';\nimport './softmax';\nimport './softplus';\nimport './space_to_batch_nd';\nimport './split';\nimport './sqrt';\nimport './square';\nimport './squared_difference';\nimport './squared_difference_strict';\nimport './squeeze';\nimport './stack';\nimport './step';\nimport './strided_slice';\nimport './sub_strict';\nimport './sub';\nimport './sum';\nimport './tan';\nimport './tanh';\nimport './tile';\nimport './to_bool';\nimport './to_float';\nimport './to_int';\nimport './topk';\nimport './transpose';\nimport './unique';\nimport './unsorted_segment_sum';\nimport './unstack';\nimport './where';\nimport './zeros_like';\n//# sourceMappingURL=register_all_chained_ops.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Required side effectful code.\nimport './base_side_effects';\n// All exports from this package should be in base.\nexport * from './base';\n// Register all the gradients.\nimport './register_all_gradients';\n// Import all op chainers and add type info to Tensor.\nimport './public/chained_ops/register_all_chained_ops';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { backend } from '@tensorflow/tfjs-core';\nlet _epsilon;\n/**\n * Returns the value of the fuzz factor used in numeric expressions.\n */\nexport function epsilon() {\n    if (_epsilon == null) {\n        _epsilon = backend().epsilon();\n    }\n    return _epsilon;\n}\n/**\n * Sets the value of the fuzz factor used in numeric expressions.\n * @param e New value of epsilon.\n */\nexport function setEpsilon(e) {\n    _epsilon = e;\n}\n/**\n * Returns the default image data format convention.\n */\nexport function imageDataFormat() {\n    return 'channelsLast';\n}\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Explicit error types.\n *\n * See the following link for more information about why the code includes\n * calls to setPrototypeOf:\n *\n * https://github.com/Microsoft/TypeScript-wiki/blob/master/Breaking-Changes.md#extending-built-ins-like-error-array-and-map-may-no-longer-work\n */\n// tslint:enable\n/**\n * Equivalent of Python's AttributeError.\n */\nexport class AttributeError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, AttributeError.prototype);\n    }\n}\n/**\n * Equivalent of Python's RuntimeError.\n */\nexport class RuntimeError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, RuntimeError.prototype);\n    }\n}\n/**\n * Equivalent of Python's ValueError.\n */\nexport class ValueError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, ValueError.prototype);\n    }\n}\n/**\n * Equivalent of Python's NotImplementedError.\n */\nexport class NotImplementedError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, NotImplementedError.prototype);\n    }\n}\n/**\n * Equivalent of Python's AssertionError.\n */\nexport class AssertionError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, AssertionError.prototype);\n    }\n}\n/**\n * Equivalent of Python's IndexError.\n */\nexport class IndexError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, IndexError.prototype);\n    }\n}\n//# sourceMappingURL=errors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: utils/generic_utils.py */\nimport { util } from '@tensorflow/tfjs-core';\nimport { AssertionError, ValueError } from '../errors';\n// tslint:enable\n/**\n * If `value` is an Array, equivalent to Python's `value * numValues`.\n * If `value` is not an Array, equivalent to Python's `[value] * numValues`\n */\n// tslint:disable-next-line:no-any\nexport function pyListRepeat(value, numValues) {\n    if (Array.isArray(value)) {\n        // tslint:disable-next-line:no-any\n        let newArray = [];\n        for (let i = 0; i < numValues; i++) {\n            newArray = newArray.concat(value);\n        }\n        return newArray;\n    }\n    else {\n        const newArray = new Array(numValues);\n        newArray.fill(value);\n        return newArray;\n    }\n}\nexport function assert(val, message) {\n    if (!val) {\n        throw new AssertionError(message);\n    }\n}\n/**\n * Count the number of elements of the `array` that are equal to `reference`.\n */\nexport function count(array, refernce) {\n    let counter = 0;\n    for (const item of array) {\n        if (item === refernce) {\n            counter++;\n        }\n    }\n    return counter;\n}\n/**\n * If an array is of length 1, just return the first element. Otherwise, return\n * the full array.\n * @param tensors\n */\nexport function singletonOrArray(xs) {\n    if (xs.length === 1) {\n        return xs[0];\n    }\n    return xs;\n}\n/**\n * Normalizes a list/tensor into a list.\n *\n * If a tensor is passed, we return\n * a list of size 1 containing the tensor.\n *\n * @param x target object to be normalized.\n */\n// tslint:disable-next-line:no-any\nexport function toList(x) {\n    if (Array.isArray(x)) {\n        return x;\n    }\n    return [x];\n}\n/**\n * Generate a UID for a list\n */\n// tslint:disable-next-line:no-any\nexport function objectListUid(objs) {\n    const objectList = toList(objs);\n    let retVal = '';\n    for (const obj of objectList) {\n        if (obj.id == null) {\n            throw new ValueError(`Object ${obj} passed to objectListUid without an id`);\n        }\n        if (retVal !== '') {\n            retVal = retVal + ', ';\n        }\n        retVal = `${retVal}${Math.abs(obj.id)}`;\n    }\n    return retVal;\n}\n/**\n * Converts string to snake-case.\n * @param name\n */\nexport function toSnakeCase(name) {\n    const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, '$1_$2');\n    const insecure = intermediate.replace(/([a-z])([A-Z])/g, '$1_$2').toLowerCase();\n    /*\n     If the class is private the name starts with \"_\" which is not secure\n     for creating scopes. We prefix the name with \"private\" in this case.\n     */\n    if (insecure[0] !== '_') {\n        return insecure;\n    }\n    return 'private' + insecure;\n}\nexport function toCamelCase(identifier) {\n    // quick return for empty string or single character strings\n    if (identifier.length <= 1) {\n        return identifier;\n    }\n    // Check for the underscore indicating snake_case\n    if (identifier.indexOf('_') === -1) {\n        return identifier;\n    }\n    return identifier.replace(/[_]+(\\w|$)/g, (m, p1) => p1.toUpperCase());\n}\n// tslint:disable-next-line:no-any\nlet _GLOBAL_CUSTOM_OBJECTS = {};\nexport function serializeKerasObject(instance) {\n    if (instance === null || instance === undefined) {\n        return null;\n    }\n    const dict = {};\n    dict['className'] = instance.getClassName();\n    dict['config'] = instance.getConfig();\n    return dict;\n}\n/**\n * Replace ndarray-style scalar objects in serialization objects with numbers.\n *\n * Background: In some versions of tf.keras, certain scalar values in the HDF5\n * model save file can be serialized as: `{'type': 'ndarray', 'value': num}`,\n * where in `num` is a plain number. This method converts such serialization\n * to a `number`.\n *\n * @param config The keras-format serialization object to be processed\n *   (in place).\n */\nfunction convertNDArrayScalarsInConfig(config) {\n    if (config == null || typeof config !== 'object') {\n        return;\n    }\n    else if (Array.isArray(config)) {\n        config.forEach(configItem => convertNDArrayScalarsInConfig(configItem));\n    }\n    else {\n        const fields = Object.keys(config);\n        for (const field of fields) {\n            const value = config[field];\n            if (value != null && typeof value === 'object') {\n                if (!Array.isArray(value) && value['type'] === 'ndarray' &&\n                    typeof value['value'] === 'number') {\n                    config[field] = value['value'];\n                }\n                else {\n                    convertNDArrayScalarsInConfig(value);\n                }\n            }\n        }\n    }\n}\n/**\n * Deserialize a saved Keras Object\n * @param identifier either a string ID or a saved Keras dictionary\n * @param moduleObjects a list of Python class names to object constructors\n * @param customObjects a list of Python class names to object constructors\n * @param printableModuleName debug text for the object being reconstituted\n * @param fastWeightInit Optional flag to use fast weight initialization\n *   during deserialization. This is applicable to cases in which\n *   the initialization will be immediately overwritten by loaded weight\n *   values. Default: `false`.\n * @returns a TensorFlow.js Layers object\n */\n// tslint:disable:no-any\nexport function deserializeKerasObject(identifier, moduleObjects = {}, customObjects = {}, printableModuleName = 'object', fastWeightInit = false) {\n    // tslint:enable\n    if (typeof identifier === 'string') {\n        const functionName = identifier;\n        let fn;\n        if (functionName in customObjects) {\n            fn = customObjects[functionName];\n        }\n        else if (functionName in _GLOBAL_CUSTOM_OBJECTS) {\n            fn = _GLOBAL_CUSTOM_OBJECTS[functionName];\n        }\n        else {\n            fn = moduleObjects[functionName];\n            if (fn == null) {\n                throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. ` +\n                    `This may be due to one of the following reasons:\\n` +\n                    `1. The ${printableModuleName} is defined in Python, in which ` +\n                    `case it needs to be ported to TensorFlow.js or your JavaScript ` +\n                    `code.\\n` +\n                    `2. The custom ${printableModuleName} is defined in JavaScript, ` +\n                    `but is not registered properly with ` +\n                    `tf.serialization.registerClass().`);\n                // TODO(cais): Add link to tutorial page on custom layers.\n            }\n        }\n        return fn;\n    }\n    else {\n        // In this case we are dealing with a Keras config dictionary.\n        const config = identifier;\n        if (config['className'] == null || config['config'] == null) {\n            throw new ValueError(`${printableModuleName}: Improper config format: ` +\n                `${JSON.stringify(config)}.\\n` +\n                `'className' and 'config' must set.`);\n        }\n        const className = config['className'];\n        let cls, fromConfig;\n        if (className in customObjects) {\n            [cls, fromConfig] = customObjects[className];\n        }\n        else if (className in _GLOBAL_CUSTOM_OBJECTS) {\n            [cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS['className'];\n        }\n        else if (className in moduleObjects) {\n            [cls, fromConfig] = moduleObjects[className];\n        }\n        if (cls == null) {\n            throw new ValueError(`Unknown ${printableModuleName}: ${className}. ` +\n                `This may be due to one of the following reasons:\\n` +\n                `1. The ${printableModuleName} is defined in Python, in which ` +\n                `case it needs to be ported to TensorFlow.js or your JavaScript ` +\n                `code.\\n` +\n                `2. The custom ${printableModuleName} is defined in JavaScript, ` +\n                `but is not registered properly with ` +\n                `tf.serialization.registerClass().`);\n            // TODO(cais): Add link to tutorial page on custom layers.\n        }\n        if (fromConfig != null) {\n            // Porting notes: Instead of checking to see whether fromConfig accepts\n            // customObjects, we create a customObjects dictionary and tack it on to\n            // config['config'] as config['config'].customObjects. Objects can use it,\n            // if they want.\n            // tslint:disable-next-line:no-any\n            const customObjectsCombined = {};\n            for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) {\n                customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key];\n            }\n            for (const key of Object.keys(customObjects)) {\n                customObjectsCombined[key] = customObjects[key];\n            }\n            // Add the customObjects to config\n            const nestedConfig = config['config'];\n            nestedConfig['customObjects'] = customObjectsCombined;\n            const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n            for (const key of Object.keys(customObjects)) {\n                _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n            }\n            convertNDArrayScalarsInConfig(config['config']);\n            const returnObj = fromConfig(cls, config['config'], customObjects, fastWeightInit);\n            _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n            return returnObj;\n        }\n        else {\n            // Then `cls` may be a function returning a class.\n            // In this case by convention `config` holds\n            // the kwargs of the function.\n            const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n            for (const key of Object.keys(customObjects)) {\n                _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n            }\n            // In python this is **config['config'], for tfjs-layers we require\n            // classes that use this fall-through construction method to take\n            // a config interface that mimics the expansion of named parameters.\n            const returnObj = new cls(config['config']);\n            _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n            return returnObj;\n        }\n    }\n}\n/**\n * Compares two numbers for sorting.\n * @param a\n * @param b\n */\nexport function numberCompare(a, b) {\n    return (a < b) ? -1 : ((a > b) ? 1 : 0);\n}\n/**\n * Comparison of two numbers for reverse sorting.\n * @param a\n * @param b\n */\nexport function reverseNumberCompare(a, b) {\n    return -1 * numberCompare(a, b);\n}\n/**\n * Convert a string into the corresponding DType.\n * @param dtype\n * @returns An instance of DType.\n */\nexport function stringToDType(dtype) {\n    switch (dtype) {\n        case 'float32':\n            return 'float32';\n        default:\n            throw new ValueError(`Invalid dtype: ${dtype}`);\n    }\n}\n/**\n * Test the element-by-element equality of two Arrays of strings.\n * @param xs First array of strings.\n * @param ys Second array of strings.\n * @returns Wether the two arrays are all equal, element by element.\n */\nexport function stringsEqual(xs, ys) {\n    if (xs == null || ys == null) {\n        return xs === ys;\n    }\n    if (xs.length !== ys.length) {\n        return false;\n    }\n    for (let i = 0; i < xs.length; ++i) {\n        if (xs[i] !== ys[i]) {\n            return false;\n        }\n    }\n    return true;\n}\n/**\n * Get the unique elements of an array.\n * @param xs Array.\n * @returns An Array consisting of the unique elements in `xs`.\n */\nexport function unique(xs) {\n    if (xs == null) {\n        return xs;\n    }\n    const out = [];\n    // TODO(cais): Maybe improve performance by sorting.\n    for (const x of xs) {\n        if (out.indexOf(x) === -1) {\n            out.push(x);\n        }\n    }\n    return out;\n}\n/**\n * Determine if an Object is empty (i.e., does not have own properties).\n * @param obj Object\n * @returns Whether the Object is empty.\n * @throws ValueError: If object is `null` or `undefined`.\n */\nexport function isObjectEmpty(obj) {\n    if (obj == null) {\n        throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);\n    }\n    for (const key in obj) {\n        if (obj.hasOwnProperty(key)) {\n            return false;\n        }\n    }\n    return true;\n}\n/**\n * Helper function used to build type union/enum run-time checkers.\n * @param values The list of allowed values.\n * @param label A string name for the type\n * @param value The value to test.\n * @throws ValueError: If the value is not in values nor `undefined`/`null`.\n */\nexport function checkStringTypeUnionValue(values, label, value) {\n    if (value == null) {\n        return;\n    }\n    if (values.indexOf(value) < 0) {\n        throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`);\n    }\n}\n/**\n * Helper function for verifying the types of inputs.\n *\n * Ensures that the elements of `x` are all of type `expectedType`.\n * Also verifies that the length of `x` is within bounds.\n *\n * @param x Object to test.\n * @param expectedType The string expected type of all of the elements in the\n * Array.\n * @param minLength Return false if x.length is less than this.\n * @param maxLength Return false if x.length is greater than this.\n * @returns true if and only if `x` is an `Array<expectedType>` with\n * length >= `minLength` and <= `maxLength`.\n */\n// tslint:disable:no-any\nexport function checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) {\n    assert(minLength >= 0);\n    assert(maxLength >= minLength);\n    return (Array.isArray(x) && x.length >= minLength && x.length <= maxLength &&\n        x.every(e => typeof e === expectedType));\n}\n// tslint:enable:no-any\n/**\n * Assert that a value or an array of value are positive integer.\n *\n * @param value The value being asserted on. May be a single number or an array\n *   of numbers.\n * @param name Name of the value, used to make the error message.\n */\nexport function assertPositiveInteger(value, name) {\n    if (Array.isArray(value)) {\n        util.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`);\n        value.forEach((v, i) => assertPositiveInteger(v, `element ${i + 1} of ${name}`));\n    }\n    else {\n        util.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ` +\n            `${formatAsFriendlyString(value)}.`);\n    }\n}\n/**\n * Format a value into a display-friendly, human-readable fashion.\n *\n * - `null` is formatted as `'null'`\n * - Strings are formated with flanking pair of quotes.\n * - Arrays are formatted with flanking pair of square brackets.\n *\n * @param value The value to display.\n * @return Formatted string.\n */\n// tslint:disable-next-line:no-any\nexport function formatAsFriendlyString(value) {\n    if (value === null) {\n        return 'null';\n    }\n    else if (Array.isArray(value)) {\n        return '[' + value.map(v => formatAsFriendlyString(v)).join(',') + ']';\n    }\n    else if (typeof value === 'string') {\n        return `\"${value}\"`;\n    }\n    else {\n        return `${value}`;\n    }\n}\n/**\n * Returns a function `f2` (decorator) which wraps the original function\n * `f`. `f2` guarantees that `f` can be called at most once\n * every `waitMs` ms. If `f2` is called more often, it will return\n * the last returned result of `f`.\n *\n * @param f The original function `f` to wrap.\n * @param waitMs The time between two consecutive calls to `f` in ms.\n */\nexport function debounce(f, waitMs) {\n    let lastTime = util.now();\n    let lastResult;\n    const f2 = (...args) => {\n        const now = util.now();\n        if (now - lastTime < waitMs) {\n            return lastResult;\n        }\n        lastTime = now;\n        lastResult = f(...args);\n        return lastResult;\n    };\n    return f2;\n}\n/**\n * Returns the fusable activation given a layers identifier.\n *\n * @param activationName The layers identifier string.\n * @return The name of the fusable activation.\n */\nexport function mapActivationToFusedKernel(activationName) {\n    if (activationName === 'relu') {\n        return 'relu';\n    }\n    if (activationName === 'linear') {\n        return 'linear';\n    }\n    if (activationName === 'elu') {\n        return 'elu';\n    }\n    return null;\n}\n/**\n * Returns the cartesian product of sets of values.\n * This works the same as itertools.product in Python.\n *\n * Example:\n *\n * filters = [128, 256, 512]\n * paddings = ['same', 'valid']\n *\n * product = [ [128, 'same'], [128, 'valid'], [256, 'same'], [256, 'valid'],\n * [512, 'same'], [512, 'valid']]\n *\n * @param arrayOfValues List/array of values.\n * @return The cartesian product.\n */\nexport function getCartesianProductOfValues(...arrayOfValues) {\n    assert(arrayOfValues.length > 0, 'arrayOfValues is empty');\n    for (const values of arrayOfValues) {\n        assert(Array.isArray(values), 'one of the values is not an array');\n        assert(values.length > 0, 'one of the values is empty');\n    }\n    return arrayOfValues.reduce((products, values) => {\n        if (products.length === 0) {\n            return values.map(value => [value]);\n        }\n        return values\n            .map(value => {\n            return products.map((prevValue) => [...prevValue, value]);\n        })\n            .reduce((flattenedProduct, unflattenedProduct) => {\n            return flattenedProduct.concat(unflattenedProduct);\n        }, []);\n    }, []);\n}\n//# sourceMappingURL=generic_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/contraints.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport { deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\n/**\n * Helper function used by many of the Constraints to find the L2Norms.\n */\nfunction calcL2Norms(w, axis) {\n    return tidy(() => tfc.sqrt(tfc.sum(tfc.mul(w, w), axis, true)));\n}\n/**\n * Base class for functions that impose constraints on weight values\n *\n * @doc {\n *   heading: 'Constraints',\n *   subheading: 'Classes',\n *   namespace: 'constraints'\n * }\n */\nexport class Constraint extends serialization.Serializable {\n    getConfig() {\n        return {};\n    }\n}\nexport class MaxNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultMaxValue = 2;\n        this.defaultAxis = 0;\n        this.maxValue =\n            args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => {\n            const norms = calcL2Norms(w, this.axis);\n            const desired = tfc.clipByValue(norms, 0, this.maxValue);\n            return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));\n        });\n    }\n    getConfig() {\n        return { maxValue: this.maxValue, axis: this.axis };\n    }\n}\n/** @nocollapse */\nMaxNorm.className = 'MaxNorm';\nserialization.registerClass(MaxNorm);\nexport class UnitNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultAxis = 0;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => tfc.div(w, tfc.add(epsilon(), calcL2Norms(w, this.axis))));\n    }\n    getConfig() {\n        return { axis: this.axis };\n    }\n}\n/** @nocollapse */\nUnitNorm.className = 'UnitNorm';\nserialization.registerClass(UnitNorm);\nexport class NonNeg extends Constraint {\n    apply(w) {\n        return tfc.relu(w);\n    }\n}\n/** @nocollapse */\nNonNeg.className = 'NonNeg';\nserialization.registerClass(NonNeg);\nexport class MinMaxNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultMinValue = 0.0;\n        this.defaultMaxValue = 1.0;\n        this.defaultRate = 1.0;\n        this.defaultAxis = 0;\n        this.minValue =\n            args.minValue != null ? args.minValue : this.defaultMinValue;\n        this.maxValue =\n            args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n        this.rate = args.rate != null ? args.rate : this.defaultRate;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => {\n            const norms = calcL2Norms(w, this.axis);\n            const desired = tfc.add(tfc.mul(this.rate, tfc.clipByValue(norms, this.minValue, this.maxValue)), tfc.mul(1.0 - this.rate, norms));\n            return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));\n        });\n    }\n    getConfig() {\n        return {\n            minValue: this.minValue,\n            maxValue: this.maxValue,\n            rate: this.rate,\n            axis: this.axis\n        };\n    }\n}\n/** @nocollapse */\nMinMaxNorm.className = 'MinMaxNorm';\nserialization.registerClass(MinMaxNorm);\n// Maps the JavaScript-like identifier keys to the corresponding registry\n// symbols.\nexport const CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'maxNorm': 'MaxNorm',\n    'minMaxNorm': 'MinMaxNorm',\n    'nonNeg': 'NonNeg',\n    'unitNorm': 'UnitNorm'\n};\nexport function serializeConstraint(constraint) {\n    return serializeKerasObject(constraint);\n}\nexport function deserializeConstraint(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'constraint');\n}\nexport function getConstraint(identifier) {\n    if (identifier == null) {\n        return null;\n    }\n    if (typeof identifier === 'string') {\n        const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        const config = { className, config: {} };\n        return deserializeConstraint(config);\n    }\n    else if (identifier instanceof Constraint) {\n        return identifier;\n    }\n    else {\n        return deserializeConstraint(identifier);\n    }\n}\n//# sourceMappingURL=constraints.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// tslint:disable-next-line:max-line-length\nimport { MaxNorm, MinMaxNorm, NonNeg, UnitNorm } from './constraints';\n/**\n * MaxNorm weight constraint.\n *\n * Constrains the weights incident to each hidden unit\n * to have a norm less than or equal to a desired value.\n *\n * References\n *       - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting\n * Srivastava, Hinton, et al.\n * 2014](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)\n *\n * @doc {heading: 'Constraints',namespace: 'constraints'}\n */\nexport function maxNorm(args) {\n    return new MaxNorm(args);\n}\n/**\n * Constrains the weights incident to each hidden unit to have unit norm.\n *\n * @doc {heading: 'Constraints', namespace: 'constraints'}\n */\nexport function unitNorm(args) {\n    return new UnitNorm(args);\n}\n/**\n * Constains the weight to be non-negative.\n *\n * @doc {heading: 'Constraints', namespace: 'constraints'}\n */\nexport function nonNeg() {\n    return new NonNeg();\n}\n/** @doc {heading: 'Constraints', namespace: 'constraints'} */\nexport function minMaxNorm(config) {\n    return new MinMaxNorm(config);\n}\n//# sourceMappingURL=exports_constraints.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport const VALID_DATA_FORMAT_VALUES = ['channelsFirst', 'channelsLast'];\nexport const VALID_PADDING_MODE_VALUES = ['valid', 'same', 'causal'];\nexport const VALID_POOL_MODE_VALUES = ['max', 'avg'];\nexport const VALID_BIDIRECTIONAL_MERGE_MODES = ['sum', 'mul', 'concat', 'ave'];\nexport const VALID_SAMPLE_WEIGHT_MODES = ['temporal'];\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Common functions for TensorFlow.js Layers.\n */\nimport { VALID_DATA_FORMAT_VALUES, VALID_PADDING_MODE_VALUES, VALID_POOL_MODE_VALUES } from './keras_format/common';\nimport { checkStringTypeUnionValue } from './utils/generic_utils';\n// A map from the requested scoped name of a Tensor to the number of Tensors\n// wanting that name so far.  This allows enforcing name uniqueness by appending\n// an incrementing index, e.g. scope/name, scope/name_1, scope/name_2, etc.\nconst nameMap = new Map();\nexport function checkDataFormat(value) {\n    checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, 'DataFormat', value);\n}\nexport function checkPaddingMode(value) {\n    checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, 'PaddingMode', value);\n}\nexport function checkPoolMode(value) {\n    checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, 'PoolMode', value);\n}\nconst _nameScopeStack = [];\nconst _nameScopeDivider = '/';\n/**\n * Enter namescope, which can be nested.\n */\nexport function nameScope(name, fn) {\n    _nameScopeStack.push(name);\n    try {\n        const val = fn();\n        _nameScopeStack.pop();\n        return val;\n    }\n    catch (e) {\n        _nameScopeStack.pop();\n        throw e;\n    }\n}\n/**\n * Get the current namescope as a flat, concatenated string.\n */\nfunction currentNameScopePrefix() {\n    if (_nameScopeStack.length === 0) {\n        return '';\n    }\n    else {\n        return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider;\n    }\n}\n/**\n * Get the name a Tensor (or Variable) would have if not uniqueified.\n * @param tensorName\n * @return Scoped name string.\n */\nexport function getScopedTensorName(tensorName) {\n    if (!isValidTensorName(tensorName)) {\n        throw new Error('Not a valid tensor name: \\'' + tensorName + '\\'');\n    }\n    return currentNameScopePrefix() + tensorName;\n}\n/**\n * Get unique names for Tensors and Variables.\n * @param scopedName The fully-qualified name of the Tensor, i.e. as produced by\n *  `getScopedTensorName()`.\n * @return A unique version of the given fully scoped name.\n *   If this is the first time that the scoped name is seen in this session,\n *   then the given `scopedName` is returned unaltered.  If the same name is\n *   seen again (producing a collision), an incrementing suffix is added to the\n *   end of the name, so it takes the form 'scope/name_1', 'scope/name_2', etc.\n */\nexport function getUniqueTensorName(scopedName) {\n    if (!isValidTensorName(scopedName)) {\n        throw new Error('Not a valid tensor name: \\'' + scopedName + '\\'');\n    }\n    if (!nameMap.has(scopedName)) {\n        nameMap.set(scopedName, 0);\n    }\n    const index = nameMap.get(scopedName);\n    nameMap.set(scopedName, nameMap.get(scopedName) + 1);\n    if (index > 0) {\n        const result = `${scopedName}_${index}`;\n        // Mark the composed name as used in case someone wants\n        // to call getUniqueTensorName(\"name_1\").\n        nameMap.set(result, 1);\n        return result;\n    }\n    else {\n        return scopedName;\n    }\n}\nconst tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\\._\\/]*$/);\n/**\n * Determine whether a string is a valid tensor name.\n * @param name\n * @returns A Boolean indicating whether `name` is a valid tensor name.\n */\nexport function isValidTensorName(name) {\n    return !!name.match(tensorNameRegex);\n}\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Math utility functions.\n *\n * This file contains some frequently used math function that operates on\n * number[] or Float32Array and return a number. Many of these functions are\n * not-so-thick wrappers around TF.js Core functions. But they offer the\n * convenience of\n * 1) not having to convert the inputs into Tensors,\n * 2) not having to convert the returned Tensors to numbers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { scalar, tensor1d } from '@tensorflow/tfjs-core';\nimport { ValueError } from '../errors';\n/**\n * Determine if a number is an integer.\n */\nexport function isInteger(x) {\n    return x === parseInt(x.toString(), 10);\n}\n/**\n * Calculate the product of an array of numbers.\n * @param array The array to calculate the product over.\n * @param begin Beginning index, inclusive.\n * @param end Ending index, exclusive.\n * @return The product.\n */\nexport function arrayProd(array, begin, end) {\n    if (begin == null) {\n        begin = 0;\n    }\n    if (end == null) {\n        end = array.length;\n    }\n    let prod = 1;\n    for (let i = begin; i < end; ++i) {\n        prod *= array[i];\n    }\n    return prod;\n}\n/**\n * A helper function transforms the two input types to an instance of Tensor1D,\n * so the return value can be fed directly into various TF.js Core functions.\n * @param array\n */\nfunction toArray1D(array) {\n    array = Array.isArray(array) ? new Float32Array(array) : array;\n    return tensor1d(array);\n}\n/**\n * Compute minimum value.\n * @param array\n * @return minimum value.\n */\nexport function min(array) {\n    return tfc.min(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute maximum value.\n * @param array\n * @return maximum value\n */\nexport function max(array) {\n    return tfc.max(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute sum of array.\n * @param array\n * @return The sum.\n */\nexport function sum(array) {\n    return tfc.sum(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute mean of array.\n * @param array\n * @return The mean.\n */\nexport function mean(array) {\n    return sum(array) / array.length;\n}\n/**\n * Compute variance of array.\n * @param array\n * @return The variance.\n */\nexport function variance(array) {\n    const demeaned = tfc.sub(toArray1D(array), scalar(mean(array)));\n    const sumSquare = tfc.sum(tfc.mul(demeaned, demeaned)).dataSync()[0];\n    return sumSquare / array.length;\n}\n/**\n * Compute median of array.\n * @param array\n * @return The median value.\n */\nexport function median(array) {\n    const arraySorted = array.slice().sort((a, b) => a - b);\n    const lowIdx = Math.floor((arraySorted.length - 1) / 2);\n    const highIdx = Math.ceil((arraySorted.length - 1) / 2);\n    if (lowIdx === highIdx) {\n        return arraySorted[lowIdx];\n    }\n    return (arraySorted[lowIdx] + arraySorted[highIdx]) / 2;\n}\n/**\n * Generate an array of integers in [begin, end).\n * @param begin Beginning integer, inclusive.\n * @param end Ending integer, exclusive.\n * @returns Range array.\n * @throws ValueError, iff `end` < `begin`.\n */\nexport function range(begin, end) {\n    if (end < begin) {\n        throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);\n    }\n    const out = [];\n    for (let i = begin; i < end; ++i) {\n        out.push(i);\n    }\n    return out;\n}\n//# sourceMappingURL=math_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * deeplearn.js backend.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { onesLike as coreOnesLike, scalar, tensor1d, tidy, where, zerosLike as coreZerosLike } from '@tensorflow/tfjs-core';\nimport { checkDataFormat } from '../common';\nimport { NotImplementedError, ValueError } from '../errors';\nimport * as math_utils from '../utils/math_utils';\nimport { imageDataFormat } from './common';\n// tslint:enable\n/* Setting and getting backend from deeplearn.js. */\n// Default deeplearn.js backend is WebGL (GPU).\nlet backend = 'webgl';\nexport function setBackend(requestedBackend) {\n    tfc.setBackend(requestedBackend);\n    backend = requestedBackend;\n}\nexport function getBackend() {\n    return backend;\n}\n/**\n * Indicates whether the backend is operating symbolically.\n *\n * This function will be used to determine how to interpret user code. If\n * it returns true, calls to the backend construct a symbolic graph; if\n * it returns false, calls to the backend execute immediately.\n */\nexport function isBackendSymbolic() {\n    return false;\n}\n/**\n * Get the number of elements in a Tensor.\n * @param x The Tensor.\n * @return Number of elements in `x`.\n */\nexport function countParams(x) {\n    const shape = x.shape;\n    if (shape.length > 0) {\n        return shape.reduce((a, b) => a * b);\n    }\n    else {\n        // Scalar.\n        return 1;\n    }\n}\n/**\n * Casts a tensor to a different dtype and returns it.\n * @param x Input tensor.\n * @param dtype String: 'float32'|'int32'|'bool'.\n * @returns Tensor of the specified `dtype`.\n */\nexport function cast(x, dtype) {\n    return x.asType(dtype);\n}\n/**\n * Adds a 1-sized dimension at index \"axis\".\n * @param x Input tensor.\n * @param axis Position where to add the new axis.\n * @returns Result of the dimension expansion.\n */\nexport function expandDims(x, axis = -1) {\n    const outShape = x.shape.slice();\n    if (axis < 0) {\n        axis = outShape.length + axis + 1;\n    }\n    outShape.splice(axis, 0, 1);\n    return x.reshape(outShape);\n}\n/**\n * Repeats a 2D tensor.\n *\n * If `x` has shape `[samples, dim]` and `n` is 2, for example, the output\n * will have shape `[samples, 2, dim]`.\n *\n * @param x Input tensor.\n * @param n Integer, number of times to repeat.\n * @returns The result of the repeat operation.\n * @throws ValueError: If input tensor is not 2D.\n */\nexport function repeat(x, n) {\n    return tidy(() => {\n        if (x.shape.length !== 2) {\n            throw new ValueError(`repeat() expects a rank-2 tensor, but received a ` +\n                `rank-${x.shape.length} tensor.`);\n        }\n        const y = expandDims(x, 1);\n        return tile(y, [1, n, 1]);\n    });\n}\n/**\n * Flatten a Tensor into 1D.\n * @param x Input tensor.\n * @return The result of the flattening `x`.\n */\nexport function flatten(x) {\n    const newShape = [math_utils.arrayProd(x.shape)];\n    return x.reshape(newShape);\n}\n/**\n * Turn a nD tensor into a 2D tensor with same 0th dimension.\n * In other words, it flattens each data samples of a batch.\n *\n * @param x The tensor to flatten. The rank of this tensor is required to be 2\n *   or higher.\n * @return The result of the flattening.\n */\nexport function batchFlatten(x) {\n    if (x.rank <= 1) {\n        throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);\n    }\n    const newShape = [x.shape[0], math_utils.arrayProd(x.shape, 1)];\n    return x.reshape(newShape);\n}\n/**\n * Do slicing along the first axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size size of the slice along the first axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongFirstAxis(array, start, size) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                return tfc.slice2d(array, [start, 0], [size, array.shape[1]]);\n            case 3:\n                return tfc.slice3d(array, [start, 0, 0], [size, array.shape[1], array.shape[2]]);\n            case 4:\n                return tfc.slice4d(array, [start, 0, 0, 0], [size, array.shape[1], array.shape[2], array.shape[3]]);\n            case 5:\n                return tfc.slice(array, [start, 0, 0, 0, 0], [\n                    size, array.shape[1], array.shape[2], array.shape[3], array.shape[4]\n                ]);\n            case 6:\n                return tfc.slice(array, [start, 0, 0, 0, 0, 0], [\n                    size, array.shape[1], array.shape[2], array.shape[3], array.shape[4],\n                    array.shape[5]\n                ]);\n            default:\n                throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Do slicing along the last axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size size of the slice along the last axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongLastAxis(array, start, size) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                return tfc.slice2d(array, [0, start], [array.shape[0], size]);\n            case 3:\n                return tfc.slice3d(array, [0, 0, start], [array.shape[0], array.shape[1], size]);\n            case 4:\n                return tfc.slice4d(array, [0, 0, 0, start], [array.shape[0], array.shape[1], array.shape[2], size]);\n            default:\n                throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Do slicing along the sepcified axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size of the slice along the chosen axis.\n * @param choose an axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongAxis(array, start, size, axis) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            case 3:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return tfc.slice3d(array, [0, start, 0], [array.shape[0], size, array.shape[2]]);\n                    case 3:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            case 4:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return tfc.slice4d(array, [0, start, 0, 0], [array.shape[0], size, array.shape[2], array.shape[3]]);\n                    case 3:\n                        return tfc.slice4d(array, [0, 0, start, 0], [array.shape[0], array.shape[1], size, array.shape[3]]);\n                    case 4:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            default:\n                throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Concatenates a list of tensors alongside the specified axis.\n * @param tensors `Array` of tensors to concatenate.\n * @param axis Concatenation axis.\n * @returns The result of the concatenation.\n */\nexport function concatenate(tensors, axis = -1) {\n    let rank;\n    if (axis < 0) {\n        rank = tensors[0].rank;\n        if (rank !== 0) {\n            axis = rank;\n        }\n        else {\n            axis = 0;\n        }\n    }\n    if (axis === tensors[0].rank) {\n        // Porting Note: This is necessary because tfc.concat() requires axis to be\n        //   in the interval [-rank, rank).\n        axis = -1;\n    }\n    // Porting Note: Sparse concat is not supported yet.\n    return tfc.concat(tensors, axis);\n}\n/**\n * Concatenate two arrays along the first dimension.\n * @param a The 1st `tf.Tensor` to concatenate.\n * @param b The 2nd `tf.Tensor` to concatenate.\n * @returns Result of the concatenation.\n * @throws ValueError: If `a` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function concatAlongFirstAxis(a, b) {\n    switch (a.rank) {\n        case 1:\n            return tfc.concat1d([a, b]);\n        case 2:\n            return tfc.concat2d([a, b], 0);\n        case 3:\n            return tfc.concat3d([a, b], 0);\n        case 4:\n            return tfc.concat4d([a, b], 0);\n        default:\n            throw new ValueError(`concatAlongFirstAxis() received an unsupported ` +\n                `tensor rank: ${a.rank}`);\n    }\n}\n/**\n * Creates a tensor by tiling `x` by `n`.\n * @param x A tensor.\n * @param n An Array of integers or a single integer. If an Array, the length\n *   must be the same as the number of dimensions in `x`. If a single integer,\n *   it will be treated as an Array of length 1.\n */\nexport function tile(x, n) {\n    if (!Array.isArray(n)) {\n        n = [n];\n    }\n    if (x.rank !== n.length) {\n        throw new ValueError(`The length of input n (${n.length}) does not match ` +\n            `the number of dimensions in input x (${x.rank})`);\n    }\n    return tfc.tile(x, n);\n}\n/* Creation of random tensors. */\n/**\n * Get a tensor with normal distribution of values.\n *\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @return The normal tensor.\n */\nexport function randomNormal(shape, mean = 0.0, stddev = 1.0, dtype, seed) {\n    return tfc.randomNormal(shape, mean, stddev, dtype, seed);\n}\n/* Linear Algebra */\n/**\n * Multiply two tensors and returns the result as a tensor.\n *\n * For 2D tensors, this is equivalent to matrix multiplication (matMul).\n * For tensors of higher ranks, it follows the Theano behavior,\n * (e.g. `(2, 3) * (4, 3, 5) -> (2, 4, 5)`).  From the Theano documentation:\n *\n * For N dimensions it is a sum product over the last axis of x and the\n * second-to-last of y:\n *\n * @param a A tensor of at least rank 2.\n * @param b A tensor of at least rank 2.\n * @param activation (optional) A string identifying the activation\n *   function.\n * @return Result of the dot operation.\n */\nexport function dot(a, b, activation, bias) {\n    if ((a.rank < 2) || (b.rank < 2)) {\n        throw new NotImplementedError(`dot requires both inputs to be rank >= 2` +\n            ` but got x shape = ${a.shape} and y shape = ${b.shape}`);\n    }\n    if (b.rank >= 3) {\n        const xLastDim = a.shape.slice(-1)[0];\n        const ySecondLastDim = b.shape.slice(-2)[0];\n        if (xLastDim !== ySecondLastDim) {\n            throw new NotImplementedError(`If rank y >= 3, then the second last dim` +\n                ` of y must equal the last dim of x but got x shape = ${a.shape} and ` +\n                ` y shape = ${b.shape}`);\n        }\n    }\n    // Handle basic 2D x 2D case.\n    if ((a.rank === 2) && (b.rank === 2)) {\n        const transposeA = false;\n        const transposeB = false;\n        // tfc.fused.matMul only fuses certain activation functions. Unsupported\n        // activation functions are treated as 'linear' activations, which is\n        // equivalent to a no-op.\n        return tfc.fused.matMul({\n            a,\n            b: b,\n            transposeA,\n            transposeB,\n            bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n            activation\n        });\n    }\n    else {\n        // Reshape x into the analogous 2D Tensor.\n        const aFirstDims = a.shape.slice(); // Holds all but the last dim of x.\n        const aLastDim = aFirstDims.pop();\n        a = a.reshape([-1, aLastDim]);\n        // Reshape y into the analogous 2D Tensor, and keep track of the\n        // required dimensions to reproduce the output shape.\n        const bShape = b.shape.slice();\n        const bLastDim = bShape.pop();\n        const ySecondLastDim = bShape.pop();\n        const yOtherDims = [...bShape, bLastDim];\n        // permutation should be like [r-2, 0, 1, 2, ... r-4, r-3, r-1]\n        // where r is the rank of y.\n        const perm = Array.from({ length: b.rank }, (_, i) => {\n            if (i === 0) {\n                return b.rank - 2;\n            }\n            else if (i <= b.rank - 2) {\n                return i - 1;\n            }\n            return i;\n        });\n        b = b.transpose(perm).reshape([ySecondLastDim, -1]);\n        // Multiply x and y as 2D Tensors, and then reshape back to original.\n        const outputShape = [...aFirstDims, ...yOtherDims];\n        const transposeA = false;\n        const transposeB = false;\n        return tfc.fused\n            .matMul({\n            a,\n            b,\n            transposeA,\n            transposeB,\n            bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n            activation\n        })\n            .reshape(outputShape);\n    }\n}\n/**\n * Compute the sign Tensor of an input Tensor.\n *\n * Elements of the input `tf.Tensor` that are === 0 are mapped to 0.\n * Elements of the input `tf.Tensor` that are > 0 are mapped to 1.\n * Elements of the input `tf.Tensor` that are < 0 are mapped to -1.\n *\n * @param x Input `tf.Tensor`.\n * @return The sign `tf.Tensor`.\n */\nexport function sign(x) {\n    // TODO(cais): Move to the core.\n    return tidy(() => {\n        const zerosLikeX = coreZerosLike(x);\n        const onesLikeX = coreOnesLike(x);\n        return where(tfc.equal(x, zerosLikeX), zerosLikeX, where(tfc.greater(x, coreZerosLike(x)), onesLikeX, tfc.mul(-1, onesLikeX)));\n    });\n}\n/**\n * Computes the one-hot representation of an integer tensor.\n * @param indices nD integer tensor of shape\n *   `(batch_size, dim1, dim2, ... dim(n-1))`\n * @param numClasses Integer, number of classes to consider.\n * @returns (n + 1)D one hot representation of the input\n *   with shape `(batch_size, dim1, dim2, ... dim(n-1), num_classes)`\n */\nexport function oneHot(indices, numClasses) {\n    return tidy(() => {\n        if (indices.rank !== 1) {\n            throw new Error('Only 1D one-hot tensors are supported in the ' +\n                'deeplearn backend, at present.');\n        }\n        indices = indices.toInt();\n        return tfc.oneHot(indices, numClasses).toFloat();\n    });\n}\n/* Elementary math functions. */\n/**\n * Retrieves the elements of indices `indices` in the tensor `reference`.\n * @param reference A tensor.\n * @param indices An integer tensor of indices or an `Array` of integers.\n * @param axis Axis along which to perform the gather operation.\n * @returns The result of the gathering as a tensor.\n */\nexport function gather(reference, indices, axis) {\n    return tidy(() => {\n        if (Array.isArray(indices)) {\n            indices = tensor1d(indices, 'int32');\n        }\n        else {\n            indices = indices.toInt();\n        }\n        return tfc.gather(reference, indices, axis);\n    });\n}\n/**\n * Element-wise square.\n * @param x Input tensor.\n * @return element-wise x^2\n */\nexport function square(x) {\n    return tfc.mul(x, x);\n}\n/**\n * Element-wise exponentiation.\n *\n * Porting Note: In PyKeras, `a` (the exponent) is a Python integer, which\n *   takes advatnage of the backend's (e.g., TensorFlow's) automatic\n * conversion to tensor. Here we allow `a` to be either a number or a tensor.\n *\n * @param x The base tensor.\n * @param a The exponent, tensor or number. If a number, it is rounded to the\n *   nearest integer and converted to a tensor.\n * @returns A tensor of the same shape as `x`.\n */\nexport function pow(x, a) {\n    return tidy(() => {\n        if (typeof (a) === 'number') {\n            a = scalar(Math.round(a), 'int32');\n        }\n        if (a.dtype !== 'int32') {\n            throw new NotImplementedError(`Non-int32 dtype (${a.dtype}) is not supported by pow() yet`);\n        }\n        return tfc.pow(x, a);\n    });\n}\n/**\n * Reshapes bias tensor according to rank of x.\n */\nfunction reshapeBias(xRank, bias, dataFormat) {\n    const biasShape = bias.shape;\n    if (bias.rank !== 1 && bias.rank !== xRank) {\n        throw new ValueError(`Unexpected bias dimensions: ${bias.rank}` +\n            `; expected it to be 1 or ${xRank}`);\n    }\n    if (xRank === 5) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1, 1, 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, 1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank === 4) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1, 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[2], biasShape[0], biasShape[1]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank === 3) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[1], biasShape[0]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank < 3) {\n        return bias;\n    }\n    throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);\n}\n/* Neural-network operations. */\n/**\n * Add a bias to a tensor.\n *\n * @param x The tensor to add the bias to.\n * @param bias The bias to add to `x`. Must be 1D or the same rank as `x`.\n * @return Result of the bias adding.\n * @throws ValueError: If the rank of `bias` is incorrect.\n */\nexport function biasAdd(x, bias, dataFormat) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        return x.add(reshapeBias(x.rank, bias, dataFormat));\n    });\n}\n/**\n * Exponential linear unit (ELU).\n * @param x A tensor or variable to compute the activation function for.\n * @param alpha: A scalar, a scaling factor for the negative section.\n * @return Output of the ELU operation.\n */\nexport function elu(x, alpha = 1) {\n    // TODO(cais): Add support for alpha values other than 1.\n    if (alpha !== 1) {\n        throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented ` +\n            `yet.`);\n    }\n    return tfc.elu(x);\n}\n/**\n * Softsign of a tensor.\n *\n * Defined as x / (abs(x) + 1), element-wise.\n *\n * @param x: Input.\n * @returns Output.\n */\nexport function softsign(x) {\n    return tidy(() => tfc.div(x, tfc.abs(x).add(1)));\n}\n/**\n * Sets entries in `x` to zero at random, while scaling the entire tensor.\n *\n * @param x input tensor.\n * @param level fraction of the entries in the tensor that will be set to 0.\n * @param noiseShape shape of randomly generated keep/drop flags, must be\n *   broadcastable to the shape of `x`. Optional.\n * @param seed random seed to ensure determinism. Optional.\n * @returns Result of the dropout operation.\n */\nexport function dropout(x, level, noiseShape, seed) {\n    return tidy(() => tfc.dropout(x, level, noiseShape, seed));\n}\n/**\n * Element-wise, segment-wise linear approximation of sigmoid.\n *\n * Returns `0.` if `x < -2.5`, `1.` if `x > 2.5`.\n * In `-2.5 <= x <= 2.5`, returns `0.2 * x + 0.5`.\n *\n * @param x Input tensor.\n * @returns Output tensor.\n */\nexport function hardSigmoid(x) {\n    return tidy(() => {\n        const y = tfc.add(.5, tfc.mul(.2, x));\n        return tfc.clipByValue(y, 0, 1);\n    });\n}\n/**\n * Invoke `x` in the training phase, and `alt` otherwise.\n *\n * Porting Note: We do not create placeholder tensors for the `training`\n * boolean flag here, because there is no such thing in the TF.js imperative\n * backend.\n *\n * @param x The function to invoke iff `training` is `true`.\n * @param alt The function to invoke iff `training` is `false`.\n * @param training Boolean flag for whether training phase is active.\n * @returns The return value of `x()` if `training` is `true`, or the return\n *   value of `alt()` if `training` is `false`.\n */\nexport function inTrainPhase(x, alt, training = false) {\n    return training ? x() : alt();\n}\n//# sourceMappingURL=tfjs_backend.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport const VALID_FAN_MODE_VALUES = ['fanIn', 'fanOut', 'fanAvg'];\nexport const VALID_DISTRIBUTION_VALUES = ['normal', 'uniform', 'truncatedNormal'];\n// We can't easily extract a string[] from the string union type, but we can\n// recapitulate the list, enforcing at compile time that the values are valid\n// and that we have the right number of them.\n/**\n * A string array of valid Initializer class names.\n *\n * This is guaranteed to match the `InitializerClassName` union type.\n */\nexport const initializerClassNames = [\n    'Zeros', 'Ones', 'Constant', 'RandomNormal', 'RandomUniform',\n    'TruncatedNormal', 'VarianceScaling', 'Orthogonal', 'Identity'\n];\n//# sourceMappingURL=initializer_config.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { eye, linalg, mul, ones, randomUniform, scalar, serialization, tidy, truncatedNormal, zeros } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { checkDataFormat } from './common';\nimport { NotImplementedError, ValueError } from './errors';\nimport { VALID_DISTRIBUTION_VALUES, VALID_FAN_MODE_VALUES } from './keras_format/initializer_config';\nimport { checkStringTypeUnionValue, deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\nimport { arrayProd } from './utils/math_utils';\nexport function checkFanMode(value) {\n    checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, 'FanMode', value);\n}\nexport function checkDistribution(value) {\n    checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, 'Distribution', value);\n}\n/**\n * Initializer base class.\n *\n * @doc {\n *   heading: 'Initializers', subheading: 'Classes', namespace: 'initializers'}\n */\nexport class Initializer extends serialization.Serializable {\n    fromConfigUsesCustomObjects() {\n        return false;\n    }\n    getConfig() {\n        return {};\n    }\n}\nexport class Zeros extends Initializer {\n    apply(shape, dtype) {\n        return zeros(shape, dtype);\n    }\n}\n/** @nocollapse */\nZeros.className = 'Zeros';\nserialization.registerClass(Zeros);\nexport class Ones extends Initializer {\n    apply(shape, dtype) {\n        return ones(shape, dtype);\n    }\n}\n/** @nocollapse */\nOnes.className = 'Ones';\nserialization.registerClass(Ones);\nexport class Constant extends Initializer {\n    constructor(args) {\n        super();\n        if (typeof args !== 'object') {\n            throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);\n        }\n        if (args.value === undefined) {\n            throw new ValueError(`config must have value set but got ${args}`);\n        }\n        this.value = args.value;\n    }\n    apply(shape, dtype) {\n        return tidy(() => mul(scalar(this.value), ones(shape, dtype)));\n    }\n    getConfig() {\n        return {\n            value: this.value,\n        };\n    }\n}\n/** @nocollapse */\nConstant.className = 'Constant';\nserialization.registerClass(Constant);\nexport class RandomUniform extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MINVAL = -0.05;\n        this.DEFAULT_MAXVAL = 0.05;\n        this.minval = args.minval || this.DEFAULT_MINVAL;\n        this.maxval = args.maxval || this.DEFAULT_MAXVAL;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        return randomUniform(shape, this.minval, this.maxval, dtype);\n    }\n    getConfig() {\n        return { minval: this.minval, maxval: this.maxval, seed: this.seed };\n    }\n}\n/** @nocollapse */\nRandomUniform.className = 'RandomUniform';\nserialization.registerClass(RandomUniform);\nexport class RandomNormal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MEAN = 0.;\n        this.DEFAULT_STDDEV = 0.05;\n        this.mean = args.mean || this.DEFAULT_MEAN;\n        this.stddev = args.stddev || this.DEFAULT_STDDEV;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        dtype = dtype || 'float32';\n        if (dtype !== 'float32' && dtype !== 'int32') {\n            throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n        }\n        return K.randomNormal(shape, this.mean, this.stddev, dtype, this.seed);\n    }\n    getConfig() {\n        return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n    }\n}\n/** @nocollapse */\nRandomNormal.className = 'RandomNormal';\nserialization.registerClass(RandomNormal);\nexport class TruncatedNormal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MEAN = 0.;\n        this.DEFAULT_STDDEV = 0.05;\n        this.mean = args.mean || this.DEFAULT_MEAN;\n        this.stddev = args.stddev || this.DEFAULT_STDDEV;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        dtype = dtype || 'float32';\n        if (dtype !== 'float32' && dtype !== 'int32') {\n            throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);\n        }\n        return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed);\n    }\n    getConfig() {\n        return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n    }\n}\n/** @nocollapse */\nTruncatedNormal.className = 'TruncatedNormal';\nserialization.registerClass(TruncatedNormal);\nexport class Identity extends Initializer {\n    constructor(args) {\n        super();\n        this.gain = args.gain != null ? args.gain : 1.0;\n    }\n    apply(shape, dtype) {\n        return tidy(() => {\n            if (shape.length !== 2 || shape[0] !== shape[1]) {\n                throw new ValueError('Identity matrix initializer can only be used for' +\n                    ' 2D square matrices.');\n            }\n            else {\n                return mul(this.gain, eye(shape[0]));\n            }\n        });\n    }\n    getConfig() {\n        return { gain: this.gain };\n    }\n}\n/** @nocollapse */\nIdentity.className = 'Identity';\nserialization.registerClass(Identity);\n/**\n * Computes the number of input and output units for a weight shape.\n * @param shape Shape of weight.\n * @param dataFormat data format to use for convolution kernels.\n *   Note that all kernels in Keras are standardized on the\n *   CHANNEL_LAST ordering (even when inputs are set to CHANNEL_FIRST).\n * @return An length-2 array: fanIn, fanOut.\n */\nfunction computeFans(shape, dataFormat = 'channelsLast') {\n    let fanIn;\n    let fanOut;\n    checkDataFormat(dataFormat);\n    if (shape.length === 2) {\n        fanIn = shape[0];\n        fanOut = shape[1];\n    }\n    else if ([3, 4, 5].indexOf(shape.length) !== -1) {\n        if (dataFormat === 'channelsFirst') {\n            const receptiveFieldSize = arrayProd(shape, 2);\n            fanIn = shape[1] * receptiveFieldSize;\n            fanOut = shape[0] * receptiveFieldSize;\n        }\n        else if (dataFormat === 'channelsLast') {\n            const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2);\n            fanIn = shape[shape.length - 2] * receptiveFieldSize;\n            fanOut = shape[shape.length - 1] * receptiveFieldSize;\n        }\n    }\n    else {\n        const shapeProd = arrayProd(shape);\n        fanIn = Math.sqrt(shapeProd);\n        fanOut = Math.sqrt(shapeProd);\n    }\n    return [fanIn, fanOut];\n}\nexport class VarianceScaling extends Initializer {\n    /**\n     * Constructor of VarianceScaling.\n     * @throws ValueError for invalid value in scale.\n     */\n    constructor(args) {\n        super();\n        if (args.scale < 0.0) {\n            throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);\n        }\n        this.scale = args.scale == null ? 1.0 : args.scale;\n        this.mode = args.mode == null ? 'fanIn' : args.mode;\n        checkFanMode(this.mode);\n        this.distribution =\n            args.distribution == null ? 'normal' : args.distribution;\n        checkDistribution(this.distribution);\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        const fans = computeFans(shape);\n        const fanIn = fans[0];\n        const fanOut = fans[1];\n        let scale = this.scale;\n        if (this.mode === 'fanIn') {\n            scale /= Math.max(1, fanIn);\n        }\n        else if (this.mode === 'fanOut') {\n            scale /= Math.max(1, fanOut);\n        }\n        else {\n            scale /= Math.max(1, (fanIn + fanOut) / 2);\n        }\n        if (this.distribution === 'normal') {\n            const stddev = Math.sqrt(scale);\n            dtype = dtype || 'float32';\n            if (dtype !== 'float32' && dtype !== 'int32') {\n                throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);\n            }\n            return truncatedNormal(shape, 0, stddev, dtype, this.seed);\n        }\n        else {\n            const limit = Math.sqrt(3 * scale);\n            return randomUniform(shape, -limit, limit, dtype);\n        }\n    }\n    getConfig() {\n        return {\n            scale: this.scale,\n            mode: this.mode,\n            distribution: this.distribution,\n            seed: this.seed\n        };\n    }\n}\n/** @nocollapse */\nVarianceScaling.className = 'VarianceScaling';\nserialization.registerClass(VarianceScaling);\nexport class GlorotUniform extends VarianceScaling {\n    /**\n     * Constructor of GlorotUniform\n     * @param scale\n     * @param mode\n     * @param distribution\n     * @param seed\n     */\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanAvg',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, GlorotUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nGlorotUniform.className = 'GlorotUniform';\nserialization.registerClass(GlorotUniform);\nexport class GlorotNormal extends VarianceScaling {\n    /**\n     * Constructor of GlorotNormal.\n     * @param scale\n     * @param mode\n     * @param distribution\n     * @param seed\n     */\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanAvg',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, GlorotNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nGlorotNormal.className = 'GlorotNormal';\nserialization.registerClass(GlorotNormal);\nexport class HeNormal extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 2.0,\n            mode: 'fanIn',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, HeNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nHeNormal.className = 'HeNormal';\nserialization.registerClass(HeNormal);\nexport class HeUniform extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 2.0,\n            mode: 'fanIn',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, HeUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nHeUniform.className = 'HeUniform';\nserialization.registerClass(HeUniform);\nexport class LeCunNormal extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanIn',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, LeCunNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nLeCunNormal.className = 'LeCunNormal';\nserialization.registerClass(LeCunNormal);\nexport class LeCunUniform extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanIn',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, LeCunUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nLeCunUniform.className = 'LeCunNormal';\nserialization.registerClass(LeCunUniform);\nexport class Orthogonal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_GAIN = 1;\n        this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain;\n        this.seed = args.seed;\n        if (this.seed != null) {\n            throw new NotImplementedError('Random seed is not implemented for Orthogonal Initializer yet.');\n        }\n    }\n    apply(shape, dtype) {\n        return tidy(() => {\n            if (shape.length < 2) {\n                throw new NotImplementedError('Shape must be at least 2D.');\n            }\n            if (shape[0] * shape[1] > 2000) {\n                console.warn(`Orthogonal initializer is being called on a matrix with more ` +\n                    `than 2000 (${shape[0] * shape[1]}) elements: ` +\n                    `Slowness may result.`);\n            }\n            // TODO(cais): Add seed support.\n            const normalizedShape = shape[0] > shape[1] ? [shape[1], shape[0]] : shape;\n            const a = K.randomNormal(normalizedShape, 0, 1, 'float32');\n            let q = linalg.gramSchmidt(a);\n            if (shape[0] > shape[1]) {\n                q = q.transpose();\n            }\n            return mul(this.gain, q);\n        });\n    }\n    getConfig() {\n        return {\n            gain: this.gain,\n            seed: this.seed,\n        };\n    }\n}\n/** @nocollapse */\nOrthogonal.className = 'Orthogonal';\nserialization.registerClass(Orthogonal);\n// Maps the JavaScript-like identifier keys to the corresponding registry\n// symbols.\nexport const INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'constant': 'Constant',\n    'glorotNormal': 'GlorotNormal',\n    'glorotUniform': 'GlorotUniform',\n    'heNormal': 'HeNormal',\n    'heUniform': 'HeUniform',\n    'identity': 'Identity',\n    'leCunNormal': 'LeCunNormal',\n    'leCunUniform': 'LeCunUniform',\n    'ones': 'Ones',\n    'orthogonal': 'Orthogonal',\n    'randomNormal': 'RandomNormal',\n    'randomUniform': 'RandomUniform',\n    'truncatedNormal': 'TruncatedNormal',\n    'varianceScaling': 'VarianceScaling',\n    'zeros': 'Zeros'\n};\nfunction deserializeInitializer(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'initializer');\n}\nexport function serializeInitializer(initializer) {\n    return serializeKerasObject(initializer);\n}\nexport function getInitializer(identifier) {\n    if (typeof identifier === 'string') {\n        const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        /* We have four 'helper' classes for common initializers that\n        all get serialized as 'VarianceScaling' and shouldn't go through\n        the deserializeInitializer pathway. */\n        if (className === 'GlorotNormal') {\n            return new GlorotNormal();\n        }\n        else if (className === 'GlorotUniform') {\n            return new GlorotUniform();\n        }\n        else if (className === 'HeNormal') {\n            return new HeNormal();\n        }\n        else if (className === 'HeUniform') {\n            return new HeUniform();\n        }\n        else if (className === 'LeCunNormal') {\n            return new LeCunNormal();\n        }\n        else if (className === 'LeCunUniform') {\n            return new LeCunUniform();\n        }\n        else {\n            const config = {};\n            config['className'] = className;\n            config['config'] = {};\n            return deserializeInitializer(config);\n        }\n    }\n    else if (identifier instanceof Initializer) {\n        return identifier;\n    }\n    else {\n        return deserializeInitializer(identifier);\n    }\n}\n//# sourceMappingURL=initializers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// tslint:disable-next-line:max-line-length\nimport { Constant, GlorotNormal, GlorotUniform, HeNormal, HeUniform, Identity, LeCunNormal, LeCunUniform, Ones, Orthogonal, RandomNormal, RandomUniform, TruncatedNormal, VarianceScaling, Zeros } from './initializers';\n/**\n * Initializer that generates tensors initialized to 0.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function zeros() {\n    return new Zeros();\n}\n/**\n * Initializer that generates tensors initialized to 1.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function ones() {\n    return new Ones();\n}\n/**\n * Initializer that generates values initialized to some constant.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function constant(args) {\n    return new Constant(args);\n}\n/**\n * Initializer that generates random values initialized to a uniform\n * distribution.\n *\n * Values will be distributed uniformly between the configured minval and\n * maxval.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function randomUniform(args) {\n    return new RandomUniform(args);\n}\n/**\n * Initializer that generates random values initialized to a normal\n * distribution.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function randomNormal(args) {\n    return new RandomNormal(args);\n}\n/**\n * Initializer that generates random values initialized to a truncated normal.\n * distribution.\n *\n * These values are similar to values from a `RandomNormal` except that values\n * more than two standard deviations from the mean are discarded and re-drawn.\n * This is the recommended initializer for neural network weights and filters.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function truncatedNormal(args) {\n    return new TruncatedNormal(args);\n}\n/**\n * Initializer that generates the identity matrix.\n * Only use for square 2D matrices.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function identity(args) {\n    return new Identity(args);\n}\n/**\n * Initializer capable of adapting its scale to the shape of weights.\n * With distribution=NORMAL, samples are drawn from a truncated normal\n * distribution centered on zero, with `stddev = sqrt(scale / n)` where n is:\n *   - number of input units in the weight tensor, if mode = FAN_IN.\n *   - number of output units, if mode = FAN_OUT.\n *   - average of the numbers of input and output units, if mode = FAN_AVG.\n * With distribution=UNIFORM,\n * samples are drawn from a uniform distribution\n * within [-limit, limit], with `limit = sqrt(3 * scale / n)`.\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function varianceScaling(config) {\n    return new VarianceScaling(config);\n}\n/**\n * Glorot uniform initializer, also called Xavier uniform initializer.\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotUniform(args) {\n    return new GlorotUniform(args);\n}\n/**\n * Glorot normal initializer, also called Xavier normal initializer.\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor.\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotNormal(args) {\n    return new GlorotNormal(args);\n}\n/**\n * He normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function heNormal(args) {\n    return new HeNormal(args);\n}\n/**\n * He uniform initializer.\n *\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / fan_in)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function heUniform(args) {\n    return new HeUniform(args);\n}\n/**\n * LeCun normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(1 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * References:\n *   [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n *   [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunNormal(args) {\n    return new LeCunNormal(args);\n}\n/**\n * LeCun uniform initializer.\n *\n * It draws samples from a uniform distribution in the interval\n * `[-limit, limit]` with `limit = sqrt(3 / fanIn)`,\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunUniform(args) {\n    return new LeCunUniform(args);\n}\n/**\n * Initializer that generates a random orthogonal matrix.\n *\n * Reference:\n * [Saxe et al., http://arxiv.org/abs/1312.6120](http://arxiv.org/abs/1312.6120)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function orthogonal(args) {\n    return new Orthogonal(args);\n}\n//# sourceMappingURL=exports_initializers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Utilities related to persistent state in the backend.\n */\n/**\n * An ID to track `tf.SymbolicTensor`s and derived classes.\n * Required in different places in engine/topology.ts to identify unique\n * tensors.\n */\nlet _nextUniqueTensorId = 0;\nexport function getNextUniqueTensorId() {\n    return _nextUniqueTensorId++;\n}\nconst _uidPrefixes = {};\n/**\n * Provides a unique UID given a string prefix.\n *\n * @param prefix\n */\nexport function getUid(prefix = '') {\n    if (!(prefix in _uidPrefixes)) {\n        _uidPrefixes[prefix] = 0;\n    }\n    _uidPrefixes[prefix] += 1;\n    return prefix + _uidPrefixes[prefix].toString();\n}\n//# sourceMappingURL=state.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { ValueError } from '../errors';\n// tslint:enable\n/**\n * Determine whether the input is an Array of Shapes.\n */\nexport function isArrayOfShapes(x) {\n    return Array.isArray(x) && Array.isArray(x[0]);\n}\n/**\n * Special case of normalizing shapes to lists.\n *\n * @param x A shape or list of shapes to normalize into a list of Shapes.\n * @return A list of Shapes.\n */\nexport function normalizeShapeList(x) {\n    if (x.length === 0) {\n        return [];\n    }\n    if (!Array.isArray(x[0])) {\n        return [x];\n    }\n    return x;\n}\n/**\n * Helper function to obtain exactly one Tensor.\n * @param xs: A single `tf.Tensor` or an `Array` of `tf.Tensor`s.\n * @return A single `tf.Tensor`. If `xs` is an `Array`, return the first one.\n * @throws ValueError: If `xs` is an `Array` and its length is not 1.\n */\nexport function getExactlyOneTensor(xs) {\n    let x;\n    if (Array.isArray(xs)) {\n        if (xs.length !== 1) {\n            throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);\n        }\n        x = xs[0];\n    }\n    else {\n        x = xs;\n    }\n    return x;\n}\n/**\n * Helper function to obtain exactly on instance of Shape.\n *\n * @param shapes Input single `Shape` or Array of `Shape`s.\n * @returns If input is a single `Shape`, return it unchanged. If the input is\n *   an `Array` containing exactly one instance of `Shape`, return the instance.\n *   Otherwise, throw a `ValueError`.\n * @throws ValueError: If input is an `Array` of `Shape`s, and its length is not\n *   1.\n */\nexport function getExactlyOneShape(shapes) {\n    if (Array.isArray(shapes) && Array.isArray(shapes[0])) {\n        if (shapes.length === 1) {\n            shapes = shapes;\n            return shapes[0];\n        }\n        else {\n            throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`);\n        }\n    }\n    else {\n        return shapes;\n    }\n}\n//# sourceMappingURL=types_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Count the elements in an Array of LayerVariables.\n *\n * @param weights: The LayerVariables of which the constituent numbers are to\n *   be counted.\n * @returns A count of the elements in all the LayerVariables\n */\nexport function countParamsInWeights(weights) {\n    let count = 0;\n    for (const weight of weights) {\n        if (weight.shape.length === 0) {\n            count += 1;\n        }\n        else {\n            count += weight.shape.reduce((a, b) => a * b);\n        }\n    }\n    return count;\n}\n//# sourceMappingURL=variable_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { variableGrads } from '@tensorflow/tfjs-core';\nimport { getNextUniqueTensorId } from './backend/state';\nimport { getScopedTensorName, getUniqueTensorName } from './common';\nimport { NotImplementedError } from './errors';\nconst DEFAULT_VARIABLE_NAME_PREFIX = 'Variable';\n/**\n * A `tf.layers.LayerVariable` is similar to a `tf.Tensor` in that it has a\n * dtype and shape, but its value is mutable.  The value is itself represented\n * as a`tf.Tensor`, and can be read with the `read()` method and updated with\n * the `write()` method.\n */\nexport class LayerVariable {\n    /**\n     * Construct Variable from a `tf.Tensor`.\n     *\n     * If not explicitly named, the Variable will be given a name with the\n     * prefix 'Variable'. Variable names are unique. In the case of name\n     * collision, suffixies '_<num>' will be added to the name.\n     *\n     * @param val Initial value of the Variable.\n     * @param name Name of the variable. If `null` or `undefined` is provided, it\n     *   will default a name with the prefix 'Variable'.\n     * @param constraint Optional, projection function to be applied to the\n     * variable after optimize updates\n     * @throws ValueError if `name` is `null` or `undefined`.\n     */\n    constructor(val, dtype = 'float32', name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true, constraint = null) {\n        this.dtype = dtype == null ? 'float32' : dtype;\n        this.shape = val.shape;\n        this.id = getNextUniqueTensorId();\n        name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name;\n        this.originalName = getScopedTensorName(name);\n        this.name = getUniqueTensorName(this.originalName);\n        this.trainable_ = trainable;\n        this.constraint = constraint;\n        this.val = tfc.variable(val, this.trainable_, this.name, this.dtype);\n    }\n    /**\n     * Get a snapshot of the Variable's value.\n     *\n     * The returned value is a snapshot of the Variable's value at the time of\n     * the invocation. Future mutations in the value of the tensor will only\n     * be reflected by future calls to this method.\n     */\n    read() {\n        this.assertNotDisposed();\n        return this.val;\n    }\n    /**\n     * Update the value of the Variable.\n     *\n     * @param newVal: The new value to update to. Must be consistent with the\n     *   dtype and shape of the Variable.\n     * @return This Variable.\n     */\n    write(newVal) {\n        // TODO(cais): Once  TF.js Core supports Tensor.dtype, check dtype match.\n        this.assertNotDisposed();\n        checkShapesMatch(this.val, newVal);\n        // Skip updating if this is the exact same tensor.\n        if (this.val.id !== newVal.id) {\n            this.val.assign(newVal);\n            if (this.constraint != null) {\n                this.val.assign(this.constraint.apply(this.val));\n            }\n        }\n        return this;\n    }\n    /**\n     * Dispose this LayersVariable instance from memory.\n     */\n    dispose() {\n        this.assertNotDisposed();\n        this.val.dispose();\n    }\n    assertNotDisposed() {\n        if (this.val.isDisposed) {\n            throw new Error(`LayersVariable ${this.name} is already disposed.`);\n        }\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this.trainable_ = trainable;\n        this.val.trainable = trainable;\n    }\n}\nfunction checkShapesMatch(x, y) {\n    if (x.shape.toString() !== y.shape.toString()) {\n        throw new Error('Shape mismatch: ' + JSON.stringify(x.shape) + ' vs. ' +\n            JSON.stringify(y.shape));\n    }\n}\n/**\n * Create a Variable.\n * @param x The initial value of the `Variable`.\n * @param dtype optional, the type of the variable.\n * @param name optional, the name of the variable, default provided by\n * Variable.\n * @param constraint optional, a constraint to be applied after every update.\n * @return The newly instantiated `Variable`.\n */\nexport function variable(x, dtype, name, constraint) {\n    return new LayerVariable(x, dtype, name, true, constraint);\n}\n/**\n * Instantiates an all-zeros Variable and returns it.\n *\n * @param shape Shape of the tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return An all-zero Variable.\n */\nexport function zerosVariable(shape, dtype, name) {\n    // TODO(cais): Implement logic for dtype.\n    return new LayerVariable(tfc.zeros(shape), dtype, name);\n}\n/**\n * Instantiates an all-zeros tensor of the same shape as another tensor.\n *\n * @param x The other tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return A newly instantiated Variable.\n */\nexport function zerosLike(x, dtype, name) {\n    return new LayerVariable(tfc.zerosLike(x), dtype, name);\n}\n/**\n * Instantiates an all-ones tensor and returns it.\n *\n * @param shape Shape of the tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return An all-ones Variable.\n */\nexport function onesVariable(shape, dtype, name) {\n    // TODO(cais): Implement logic for dtype.\n    const allocated = tfc.ones(shape);\n    return new LayerVariable(allocated, dtype, name);\n}\n/**\n * Instantiates an all-ones tensor of the same shape as another tensor.\n *\n * @param x The other tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return A newly instantiated Variable.\n */\nexport function onesLike(x, dtype, name) {\n    const allocated = tfc.onesLike(x);\n    return new LayerVariable(allocated, dtype, name);\n}\n/**\n * Instantiate an identity matrix and returns it, as a Variable\n *\n * @param size Number of rows/columns.\n * @param dtype Data type of returned Variable.\n * @param name Name of returned Variable.\n * @return A Variable, an identity matrix.\n */\nexport function eyeVariable(size, dtype, name) {\n    return new LayerVariable(tfc.eye(size), dtype, name);\n}\n/**\n * Get a Variable with uniform distribution of values.\n * @param shape Shape of the tensor.\n * @param minval Lower bound of the uniform distribution.\n * @param maxval Upper bound of the uniform distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The uniform-random Variable.\n */\nexport function randomUniformVariable(shape, minval, maxval, dtype, seed, name = 'randomUniform') {\n    return new LayerVariable(tfc.randomUniform(shape, minval, maxval, dtype), dtype, name);\n}\n/**\n * Get a Variable with truncated-normal distribution of values.\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The truncated-normal-random Variable.\n */\nexport function truncatedNormalVariable(shape, mean = 0.0, stddev = 1.0, dtype, seed, name = 'truncatedNormal') {\n    // TODO(cais): Implement logic for dtype and seed once they are supported\n    // by deeplearn.js.\n    dtype = dtype || 'float32';\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n    }\n    return new LayerVariable(tfc.truncatedNormal(shape, mean, stddev, dtype, seed), dtype, name);\n}\n/**\n * Get a Variable with normal distribution of values.\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The truncated-normal-random Variable.\n */\nexport function randomNormalVariable(shape, mean = 0.0, stddev = 1.0, dtype, seed, name = 'randomNormal') {\n    dtype = dtype || 'float32';\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new NotImplementedError(`randomNormalVariable does not support dType ${dtype}.`);\n    }\n    return new LayerVariable(tfc.randomNormal(shape, mean, stddev, dtype, seed), dtype, name);\n}\n/**\n * Update the value of a Variable.\n * @param x The Variable to be updated.\n * @param xNew The new value to update to.\n * @return The Variable updated.\n */\nexport function update(x, xNew) {\n    return x.write(xNew);\n}\n/**\n * Update the value of a Variable by adding an increment.\n * @param x The Variable to be updated.\n * @param increment The incrment to add to `x`.\n * @return The Variable updated.\n */\nexport function updateAdd(x, increment) {\n    return x.write(tfc.add(x.read(), increment));\n}\n/**\n * Update the value of a Variable by subtracting a decrement.\n * @param x The Variable to be updated.\n * @param decrement The decrement to subtract from `x`.\n * @return The Variable updated.\n */\nexport function updateSub(x, decrement) {\n    return x.write(tfc.sub(x.read(), decrement));\n}\n/**\n * Get the values of an array of Variables.\n *\n * @param tensors An `Array` of `Variable`s to get the values of.\n * @return The values of the inputs, as an `Array` of`tf.Tensor`s.\n */\nexport function batchGetValue(xs) {\n    return xs.map(x => x.read());\n}\n/**\n * Update the value of multiple Variables at once.\n *\n * @param variablesAndValues An `Array`, each element is of type\n *   [Variable, Tensor]. The first item is the\n *   `Variable` of which the value is to be updated. The second item\n *   carries the new value.\n */\nexport function batchSetValue(variablesAndValues) {\n    variablesAndValues.forEach(variableAndValue => {\n        const variable = variableAndValue[0];\n        variable.write(variableAndValue[1]);\n    });\n}\n/**\n * Returns the gradients of `variables` w.r.t. the return value of `lossFn`.\n * @param lossFn A function which returns a Scalar to be used as the function\n *   value (i.e., numerator) for differentiation.\n * @param variables List of variables to be used as the independent variables\n *   (i.e., denominator) for differentiation.\n * @returns An Array of gradients tensors.\n */\nexport function gradients(lossFn, variables) {\n    // TODO(cais): The return type signature can be simplified if deeplearn makes\n    //   the corresponding type public.\n    const variableList = variables.map(variable => variable.read());\n    const valudAndGrads = variableGrads(lossFn, variableList);\n    return variables.map(variable => valudAndGrads.grads[variable.name]);\n}\n//# sourceMappingURL=variables.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/engine/topology.py */\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getNextUniqueTensorId, getUid } from '../backend/state';\nimport { getScopedTensorName, getUniqueTensorName, nameScope } from '../common';\nimport { AttributeError, NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { getInitializer } from '../initializers';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as types_utils from '../utils/types_utils';\nimport * as variable_utils from '../utils/variable_utils';\nimport { batchGetValue, batchSetValue, LayerVariable } from '../variables';\n/**\n * Specifies the ndim, dtype and shape of every input to a layer.\n *\n * Every layer should expose (if appropriate) an `inputSpec` attribute:\n * a list of instances of InputSpec (one per input tensor).\n *\n * A null entry in a shape is compatible with any dimension,\n * a null shape is compatible with any shape.\n */\nexport class InputSpec {\n    constructor(args) {\n        this.dtype = args.dtype;\n        this.shape = args.shape;\n        /*\n          TODO(michaelterry): Could throw error if ndim and shape are both defined\n            (then backport).\n        */\n        if (args.shape != null) {\n            this.ndim = args.shape.length;\n        }\n        else {\n            this.ndim = args.ndim;\n        }\n        this.maxNDim = args.maxNDim;\n        this.minNDim = args.minNDim;\n        this.axes = args.axes || {};\n    }\n}\n/**\n * `tf.SymbolicTensor` is a placeholder for a Tensor without any concrete value.\n *\n * They are most often encountered when building a graph of `Layer`s for a\n * a `tf.LayersModel` and the input data's shape, but not values are known.\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\nexport class SymbolicTensor {\n    /**\n     *\n     * @param dtype\n     * @param shape\n     * @param sourceLayer The Layer that produced this symbolic tensor.\n     * @param inputs The inputs passed to sourceLayer's __call__() method.\n     * @param nodeIndex\n     * @param tensorIndex\n     * @param callArgs The keyword arguments passed to the __call__() method.\n     * @param name\n     * @param outputTensorIndex The index of this tensor in the list of outputs\n     *   returned by apply().\n     */\n    constructor(dtype, shape, sourceLayer, inputs, callArgs, name, outputTensorIndex) {\n        this.dtype = dtype;\n        this.shape = shape;\n        this.sourceLayer = sourceLayer;\n        this.inputs = inputs;\n        this.callArgs = callArgs;\n        this.outputTensorIndex = outputTensorIndex;\n        this.id = getNextUniqueTensorId();\n        if (name != null) {\n            this.originalName = getScopedTensorName(name);\n            this.name = getUniqueTensorName(this.originalName);\n        }\n        this.rank = shape.length;\n    }\n}\nlet _nextNodeID = 0;\n/**\n * A `Node` describes the connectivity between two layers.\n *\n * Each time a layer is connected to some new input,\n * a node is added to `layer.inboundNodes`.\n *\n * Each time the output of a layer is used by another layer,\n * a node is added to `layer.outboundNodes`.\n *\n * `nodeIndices` and `tensorIndices` are basically fine-grained coordinates\n * describing the origin of the `inputTensors`, verifying the following:\n *\n * `inputTensors[i] ==\n * inboundLayers[i].inboundNodes[nodeIndices[i]].outputTensors[\n *   tensorIndices[i]]`\n *\n * A node from layer A to layer B is added to:\n *     A.outboundNodes\n *     B.inboundNodes\n */\nexport class Node {\n    constructor(args, \n    // TODO(michaelterry): Define actual type for this.\n    callArgs) {\n        this.callArgs = callArgs;\n        this.id = _nextNodeID++;\n        /*\n          Layer instance (NOT a list).\n          this is the layer that takes a list of input tensors\n          and turns them into a list of output tensors.\n          the current node will be added to\n          the inboundNodes of outboundLayer.\n        */\n        this.outboundLayer = args.outboundLayer;\n        /*\n            The following 3 properties describe where\n            the input tensors come from: which layers,\n            and for each layer, which node and which\n            tensor output of each node.\n        */\n        // List of layer instances.\n        this.inboundLayers = args.inboundLayers;\n        // List of integers, 1:1 mapping with inboundLayers.\n        this.nodeIndices = args.nodeIndices;\n        // List of integers, 1:1 mapping with inboundLayers.\n        this.tensorIndices = args.tensorIndices;\n        /*\n            Following 2 properties:\n            tensor inputs and outputs of outboundLayer.\n        */\n        // List of tensors. 1:1 mapping with inboundLayers.\n        this.inputTensors = args.inputTensors;\n        // List of tensors, created by outboundLayer.call().\n        this.outputTensors = args.outputTensors;\n        /*\n            Following 2 properties: input and output masks.\n            List of tensors, 1:1 mapping with inputTensor.\n        */\n        this.inputMasks = args.inputMasks;\n        // List of tensors, created by outboundLayer.computeMask().\n        this.outputMasks = args.outputMasks;\n        // Following 2 properties: input and output shapes.\n        // List of shape tuples, shapes of inputTensors.\n        this.inputShapes = args.inputShapes;\n        // List of shape tuples, shapes of outputTensors.\n        this.outputShapes = args.outputShapes;\n        // Add nodes to all layers involved.\n        for (const layer of args.inboundLayers) {\n            if (layer != null) {\n                layer.outboundNodes.push(this);\n            }\n        }\n        args.outboundLayer.inboundNodes.push(this);\n    }\n    getConfig() {\n        const inboundNames = [];\n        for (const layer of this.inboundLayers) {\n            if (layer != null) {\n                inboundNames.push(layer.name);\n            }\n            else {\n                inboundNames.push(null);\n            }\n        }\n        return {\n            outboundLayer: this.outboundLayer ? this.outboundLayer.name : null,\n            inboundLayers: inboundNames,\n            nodeIndices: this.nodeIndices,\n            tensorIndices: this.tensorIndices\n        };\n    }\n}\nlet _nextLayerID = 0;\n/**\n * A layer is a grouping of operations and weights that can be composed to\n * create a `tf.LayersModel`.\n *\n * Layers are constructed by using the functions under the\n * [tf.layers](#Layers-Basic) namespace.\n *\n * @doc {heading: 'Layers', subheading: 'Classes', namespace: 'layers'}\n */\nexport class Layer extends serialization.Serializable {\n    constructor(args = {}) {\n        super();\n        this._callHook = null;\n        this._addedWeightNames = [];\n        // Porting Notes: PyKeras does not have this property in this base Layer\n        //   class. Instead lets Layer subclass set it dynamically and checks the\n        //   value with `hasattr`. In tfjs-layers, we let this be a member of this\n        //   base class.\n        this._stateful = false;\n        this.id = _nextLayerID++;\n        this.activityRegularizer = null;\n        this.inputSpec = null;\n        this.supportsMasking = false;\n        // These properties will be set upon call of this.build()\n        this._trainableWeights = [];\n        this._nonTrainableWeights = [];\n        this._losses = [];\n        this._updates = [];\n        this._built = false;\n        /*\n          These lists will be filled via successive calls\n          to this.addInboundNode().\n         */\n        this.inboundNodes = [];\n        this.outboundNodes = [];\n        let name = args.name;\n        if (!name) {\n            const prefix = this.getClassName();\n            name = generic_utils.toSnakeCase(prefix) + '_' + getUid(prefix);\n        }\n        this.name = name;\n        this.trainable_ = args.trainable == null ? true : args.trainable;\n        if (args.inputShape != null || args.batchInputShape != null) {\n            /*\n              In this case we will later create an input layer\n              to insert before the current layer\n             */\n            let batchInputShape;\n            if (args.batchInputShape != null) {\n                batchInputShape = args.batchInputShape;\n            }\n            else if (args.inputShape != null) {\n                let batchSize = null;\n                if (args.batchSize != null) {\n                    batchSize = args.batchSize;\n                }\n                batchInputShape = [batchSize].concat(args.inputShape);\n            }\n            this.batchInputShape = batchInputShape;\n            // Set dtype.\n            let dtype = args.dtype;\n            if (dtype == null) {\n                dtype = args.inputDType;\n            }\n            if (dtype == null) {\n                dtype = 'float32';\n            }\n            this.dtype = dtype;\n        }\n        if (args.weights != null) {\n            this.initialWeights = args.weights;\n        }\n        else {\n            this.initialWeights = null;\n        }\n        // The value of `_refCount` is initialized to null. When the layer is used\n        // in a symbolic way for the first time, it will be set to 1.\n        this._refCount = null;\n        this.fastWeightInitDuringBuild = false;\n    }\n    /**\n     * Converts a layer and its index to a unique (immutable type) name.\n     * This function is used internally with `this.containerNodes`.\n     * @param layer The layer.\n     * @param nodeIndex The layer's position (e.g. via enumerate) in a list of\n     *   nodes.\n     *\n     * @returns The unique name.\n     */\n    static nodeKey(layer, nodeIndex) {\n        return layer.name + '_ib-' + nodeIndex.toString();\n    }\n    /**\n     * Returns this.inboundNode at index nodeIndex.\n     *\n     * Porting note: This is a replacement for _get_node_attribute_at_index()\n     * @param nodeIndex\n     * @param attrName The name of the attribute related to request for this node.\n     */\n    getNodeAtIndex(nodeIndex, attrName) {\n        if (this.inboundNodes.length === 0) {\n            throw new RuntimeError('The layer has never been called ' +\n                `and thus has no defined ${attrName}.`);\n        }\n        if (this.inboundNodes.length <= nodeIndex) {\n            throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, ` +\n                `but the layer has only ${this.inboundNodes.length} inbound nodes.`);\n        }\n        return this.inboundNodes[nodeIndex];\n    }\n    /**\n     * Retrieves the input tensor(s) of a layer at a given node.\n     *\n     * @param nodeIndex Integer, index of the node from which to retrieve the\n     *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n     *   was called.\n     *\n     * @return A tensor (or list of tensors if the layer has multiple inputs).\n     */\n    getInputAt(nodeIndex) {\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(nodeIndex, 'input').inputTensors);\n    }\n    /**\n     * Retrieves the output tensor(s) of a layer at a given node.\n     *\n     * @param nodeIndex Integer, index of the node from which to retrieve the\n     *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n     *   was called.\n     *\n     * @return A tensor (or list of tensors if the layer has multiple outputs).\n     */\n    getOutputAt(nodeIndex) {\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(nodeIndex, 'output').outputTensors);\n    }\n    // Properties\n    /**\n     * Retrieves the input tensor(s) of a layer.\n     *\n     * Only applicable if the layer has exactly one inbound node,\n     * i.e. if it is connected to one incoming layer.\n     *\n     * @return Input tensor or list of input tensors.\n     *\n     * @exception AttributeError if the layer is connected to more than one\n     *   incoming layers.\n     */\n    get input() {\n        if (this.inboundNodes.length > 1) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has multiple inbound nodes, ' +\n                'hence the notion of \"layer input\" ' +\n                'is ill-defined. ' +\n                'Use `getInputAt(nodeIndex)` instead.');\n        }\n        else if (this.inboundNodes.length === 0) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' is not connected, no input to return.');\n        }\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(0, 'input').inputTensors);\n    }\n    /**\n     * Retrieves the output tensor(s) of a layer.\n     *\n     * Only applicable if the layer has exactly one inbound node,\n     * i.e. if it is connected to one incoming layer.\n     *\n     * @return Output tensor or list of output tensors.\n     *\n     * @exception AttributeError if the layer is connected to more than one\n     *   incoming layers.\n     */\n    get output() {\n        if (this.inboundNodes.length === 0) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has no inbound nodes.');\n        }\n        if (this.inboundNodes.length > 1) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has multiple inbound nodes, ' +\n                'hence the notion of \"layer output\" ' +\n                'is ill-defined. ' +\n                'Use `getOutputAt(nodeIndex)` instead.');\n        }\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(0, 'output').outputTensors);\n    }\n    get losses() {\n        return this._losses;\n    }\n    /**\n     * Retrieves the Layer's current loss values.\n     *\n     * Used for regularizers during training.\n     */\n    calculateLosses() {\n        // Porting Node: This is an augmentation to Layer.loss in PyKeras.\n        //   In PyKeras, Layer.loss returns symbolic tensors. Here a concrete\n        //   Tensor (specifically Scalar) values are returned. This is due to the\n        //   imperative backend.\n        return this.losses.map(lossFn => lossFn());\n    }\n    get updates() {\n        return this._updates;\n    }\n    get built() {\n        return this._built;\n    }\n    set built(built) {\n        this._built = built;\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this._trainableWeights.forEach(w => w.trainable = trainable);\n        this.trainable_ = trainable;\n    }\n    get trainableWeights() {\n        if (this.trainable_) {\n            return this._trainableWeights.filter(w => w.trainable);\n        }\n        else {\n            return [];\n        }\n    }\n    set trainableWeights(weights) {\n        this._trainableWeights = weights;\n    }\n    get nonTrainableWeights() {\n        if (this.trainable) {\n            return this._trainableWeights.filter(w => !w.trainable)\n                .concat(this._nonTrainableWeights);\n        }\n        else {\n            return this._trainableWeights.concat(this._nonTrainableWeights);\n        }\n    }\n    set nonTrainableWeights(weights) {\n        this._nonTrainableWeights = weights;\n    }\n    /**\n     * The concatenation of the lists trainableWeights and nonTrainableWeights\n     * (in this order).\n     */\n    get weights() {\n        return this.trainableWeights.concat(this.nonTrainableWeights);\n    }\n    get stateful() {\n        return this._stateful;\n    }\n    /**\n     * Reset the states of the layer.\n     *\n     * This method of the base Layer class is essentially a no-op.\n     * Subclasses that are stateful (e.g., stateful RNNs) should override this\n     * method.\n     */\n    resetStates() {\n        if (!this.stateful) {\n            throw new Error('Cannot call the resetStates() method of a non-stateful Layer ' +\n                'object.');\n        }\n    }\n    /**\n     * Checks compatibility between the layer and provided inputs.\n     *\n     * This checks that the tensor(s) `input`\n     * verify the input assumptions of the layer\n     * (if any). If not, exceptions are raised.\n     *\n     * @param inputs Input tensor or list of input tensors.\n     *\n     * @exception ValueError in case of mismatch between\n     *   the provided inputs and the expectations of the layer.\n     */\n    assertInputCompatibility(inputs) {\n        inputs = generic_utils.toList(inputs);\n        if (this.inputSpec == null || this.inputSpec.length === 0) {\n            return;\n        }\n        const inputSpec = generic_utils.toList(this.inputSpec);\n        if (inputs.length !== inputSpec.length) {\n            throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, ` +\n                `but it received ${inputs.length} input tensors. ` +\n                `Input received: ${inputs}`);\n        }\n        for (let inputIndex = 0; inputIndex < inputs.length; inputIndex++) {\n            const x = inputs[inputIndex];\n            const spec = inputSpec[inputIndex];\n            if (spec == null) {\n                continue;\n            }\n            // Check ndim.\n            const ndim = x.rank;\n            if (spec.ndim != null) {\n                if (ndim !== spec.ndim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: ` +\n                        `expected ndim=${spec.ndim}, found ndim=${ndim}`);\n                }\n            }\n            if (spec.maxNDim != null) {\n                if (ndim > spec.maxNDim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}` +\n                        `: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);\n                }\n            }\n            if (spec.minNDim != null) {\n                if (ndim < spec.minNDim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}` +\n                        `: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);\n                }\n            }\n            // Check dtype.\n            if (spec.dtype != null) {\n                if (x.dtype !== spec.dtype) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} ` +\n                        `: expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);\n                }\n            }\n            // Check specific shape axes.\n            if (spec.axes) {\n                const xShape = x.shape;\n                for (const key in spec.axes) {\n                    const axis = Number(key);\n                    const value = spec.axes[key];\n                    // Perform Python-style slicing in case axis < 0;\n                    // TODO(cais): Use https://github.com/alvivi/typescript-underscore to\n                    // ensure type safety through Underscore calls.\n                    const xShapeAtAxis = axis >= 0 ? xShape[axis] : xShape[xShape.length + axis];\n                    if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) {\n                        throw new ValueError(`Input ${inputIndex} is incompatible with layer ` +\n                            `${this.name}: expected axis ${axis} of input shape to ` +\n                            `have value ${value} but got shape ${xShape}.`);\n                    }\n                }\n            }\n            // Check shape.\n            if (spec.shape != null) {\n                for (let i = 0; i < spec.shape.length; ++i) {\n                    const specDim = spec.shape[i];\n                    const dim = x.shape[i];\n                    if (specDim != null && dim != null) {\n                        if (specDim !== dim) {\n                            throw new ValueError(`Input ${inputIndex} is incompatible with layer ` +\n                                `${this.name}: expected shape=${spec.shape}, ` +\n                                `found shape=${x.shape}.`);\n                        }\n                    }\n                }\n            }\n        }\n    }\n    /**\n     * This is where the layer's logic lives.\n     *\n     * @param inputs Input tensor, or list/tuple of input tensors.\n     * @param kwargs Additional keyword arguments.\n     *\n     * @return A tensor or list/tuple of tensors.\n     */\n    call(inputs, kwargs) {\n        return inputs;\n    }\n    invokeCallHook(inputs, kwargs) {\n        if (this._callHook != null) {\n            this._callHook(inputs, kwargs);\n        }\n    }\n    /**\n     * Set call hook.\n     * This is currently used for testing only.\n     * @param callHook\n     */\n    setCallHook(callHook) {\n        this._callHook = callHook;\n    }\n    /**\n     * Clear call hook.\n     * This is currently used for testing only.\n     */\n    clearCallHook() {\n        this._callHook = null;\n    }\n    /**\n     * Builds or executes a `Layer's logic.\n     *\n     * When called with `tf.Tensor`(s), execute the `Layer`s computation and\n     * return Tensor(s). For example:\n     *\n     * ```js\n     * const denseLayer = tf.layers.dense({\n     *   units: 1,\n     *   kernelInitializer: 'zeros',\n     *   useBias: false\n     * });\n     *\n     * // Invoke the layer's apply() method with a `tf.Tensor` (with concrete\n     * // numeric values).\n     * const input = tf.ones([2, 2]);\n     * const output = denseLayer.apply(input);\n     *\n     * // The output's value is expected to be [[0], [0]], due to the fact that\n     * // the dense layer has a kernel initialized to all-zeros and does not have\n     * // a bias.\n     * output.print();\n     * ```\n     *\n     * When called with `tf.SymbolicTensor`(s), this will prepare the layer for\n     * future execution.  This entails internal book-keeping on shapes of\n     * expected Tensors, wiring layers together, and initializing weights.\n     *\n     * Calling `apply` with `tf.SymbolicTensor`s are typically used during the\n     * building of non-`tf.Sequential` models. For example:\n     *\n     * ```js\n     * const flattenLayer = tf.layers.flatten();\n     * const denseLayer = tf.layers.dense({units: 1});\n     *\n     * // Use tf.layers.input() to obtain a SymbolicTensor as input to apply().\n     * const input = tf.input({shape: [2, 2]});\n     * const output1 = flattenLayer.apply(input);\n     *\n     * // output1.shape is [null, 4]. The first dimension is the undetermined\n     * // batch size. The second dimension comes from flattening the [2, 2]\n     * // shape.\n     * console.log(JSON.stringify(output1.shape));\n     *\n     * // The output SymbolicTensor of the flatten layer can be used to call\n     * // the apply() of the dense layer:\n     * const output2 = denseLayer.apply(output1);\n     *\n     * // output2.shape is [null, 1]. The first dimension is the undetermined\n     * // batch size. The second dimension matches the number of units of the\n     * // dense layer.\n     * console.log(JSON.stringify(output2.shape));\n     *\n     * // The input and output and be used to construct a model that consists\n     * // of the flatten and dense layers.\n     * const model = tf.model({inputs: input, outputs: output2});\n     * ```\n     *\n     * @param inputs a `tf.Tensor` or `tf.SymbolicTensor` or an Array of them.\n     * @param kwargs Additional keyword arguments to be passed to `call()`.\n     *\n     * @return Output of the layer's `call` method.\n     *\n     * @exception ValueError error in case the layer is missing shape information\n     *   for its `build` call.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    // Porting Note: This is a replacement for __call__() in Python.\n    apply(inputs, kwargs) {\n        kwargs = kwargs || {};\n        this.assertNotDisposed();\n        // Ensure inputs are all the same type.\n        const inputsList = generic_utils.toList(inputs);\n        let allAreSymbolic = true;\n        for (const input of inputsList) {\n            if (!(input instanceof SymbolicTensor)) {\n                allAreSymbolic = false;\n                break;\n            }\n        }\n        let noneAreSymbolic = true;\n        for (const input of inputsList) {\n            if (input instanceof SymbolicTensor) {\n                noneAreSymbolic = false;\n                break;\n            }\n        }\n        if (allAreSymbolic === noneAreSymbolic) {\n            throw new ValueError('Arguments to apply() must be all ' +\n                'SymbolicTensors or all Tensors');\n        }\n        // TODO(michaelterry): nameScope() may not be necessary.\n        return nameScope(this.name, () => {\n            // Handle laying building (weight creating, input spec locking).\n            if (!this.built) {\n                /*\n                  Throw exceptions in case the input is not compatible\n                  with the inputSpec specified in the layer constructor.\n                 */\n                this.assertInputCompatibility(inputs);\n                // Collect input shapes to build layer.\n                const inputShapes = [];\n                for (const xElem of generic_utils.toList(inputs)) {\n                    inputShapes.push(xElem.shape);\n                }\n                this.build(generic_utils.singletonOrArray(inputShapes));\n                this.built = true;\n                // Load weights that were specified at layer instantiation.\n                if (this.initialWeights) {\n                    this.setWeights(this.initialWeights);\n                }\n                if (this._refCount === null && noneAreSymbolic) {\n                    // The first use of this layer is a non-symbolic call, set ref count\n                    // to 1 so the Layer can be properly disposed if its dispose() method\n                    // is called.\n                    this._refCount = 1;\n                }\n            }\n            /*\n              Throw exceptions in case the input is not compatible\n              with the inputSpec set at build time.\n            */\n            this.assertInputCompatibility(inputs);\n            // Handle mask propagation.\n            // TODO(michaelterry): Mask propagation not currently implemented.\n            // Actually call the layer, collecting output(s), mask(s), and shape(s).\n            if (noneAreSymbolic) {\n                let output = this.call(inputs, kwargs);\n                // TODO(michaelterry): Compute the outputMask\n                // If the layer returns tensors from its inputs, unmodified,\n                // we copy them to avoid loss of tensor metadata.\n                const outputList = generic_utils.toList(output);\n                const outputListCopy = [];\n                // TODO(michaelterry): This copying may not be necessary given our eager\n                // backend.\n                for (let x of outputList) {\n                    if (inputsList.indexOf(x) !== -1) {\n                        x = x.clone();\n                    }\n                    outputListCopy.push(x);\n                }\n                output = generic_utils.singletonOrArray(outputListCopy);\n                if (this.activityRegularizer != null) {\n                    throw new NotImplementedError('Layer invocation in the presence of activity ' +\n                        'regularizer(s) is not supported yet.');\n                }\n                // TODO(michaelterry): Call addInboundNode()?\n                return output;\n            }\n            else {\n                const inputShape = collectInputShape(inputs);\n                const outputShape = this.computeOutputShape(inputShape);\n                let output;\n                const outputDType = guessOutputDType(inputs);\n                this.warnOnIncompatibleInputShape(Array.isArray(inputs) ? inputShape[0] :\n                    inputShape);\n                if (outputShape != null && outputShape.length > 0 &&\n                    Array.isArray(outputShape[0])) {\n                    // We have multiple output shapes. Create multiple output tensors.\n                    output = outputShape\n                        .map((shape, index) => new SymbolicTensor(outputDType, shape, this, generic_utils.toList(inputs), kwargs, this.name, index));\n                }\n                else {\n                    output = new SymbolicTensor(outputDType, outputShape, this, generic_utils.toList(inputs), kwargs, this.name);\n                }\n                /*\n                  Add an inbound node to the layer, so that it keeps track\n                  of the call and of all new variables created during the call.\n                  This also updates the layer history of the output tensor(s).\n                  If the input tensor(s) had no previous history,\n                  this does nothing.\n                */\n                this.addInboundNode(inputs, output, null, null, inputShape, outputShape, kwargs);\n                this._refCount++;\n                if (this.activityRegularizer != null) {\n                    throw new NotImplementedError('Layer invocation in the presence of activity ' +\n                        'regularizer(s) is not supported yet.');\n                }\n                return output;\n            }\n        });\n    }\n    /**\n     * Check compatibility between input shape and this layer's batchInputShape.\n     *\n     * Print warning if any incompatibility is found.\n     *\n     * @param inputShape Input shape to be checked.\n     */\n    warnOnIncompatibleInputShape(inputShape) {\n        if (this.batchInputShape == null) {\n            return;\n        }\n        else if (inputShape.length !== this.batchInputShape.length) {\n            console.warn(`The rank of the input tensor provided (shape: ` +\n                `${JSON.stringify(inputShape)}) does not match that of the ` +\n                `batchInputShape (${JSON.stringify(this.batchInputShape)}) ` +\n                `of the layer ${this.name}`);\n        }\n        else {\n            let dimMismatch = false;\n            this.batchInputShape.forEach((dimension, i) => {\n                if (dimension != null && inputShape[i] != null &&\n                    inputShape[i] !== dimension) {\n                    dimMismatch = true;\n                }\n            });\n            if (dimMismatch) {\n                console.warn(`The shape of the input tensor ` +\n                    `(${JSON.stringify(inputShape)}) does not ` +\n                    `match the expectation of layer ${this.name}: ` +\n                    `${JSON.stringify(this.batchInputShape)}`);\n            }\n        }\n    }\n    /**\n     * Retrieves the output shape(s) of a layer.\n     *\n     * Only applicable if the layer has only one inbound node, or if all inbound\n     * nodes have the same output shape.\n     *\n     * @returns Output shape or shapes.\n     * @throws AttributeError: if the layer is connected to more than one incoming\n     *   nodes.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    get outputShape() {\n        if (this.inboundNodes == null || this.inboundNodes.length === 0) {\n            throw new AttributeError(`The layer ${this.name} has never been called and thus has no ` +\n                `defined output shape.`);\n        }\n        const allOutputShapes = [];\n        for (const node of this.inboundNodes) {\n            const shapeString = JSON.stringify(node.outputShapes);\n            if (allOutputShapes.indexOf(shapeString) === -1) {\n                allOutputShapes.push(shapeString);\n            }\n        }\n        if (allOutputShapes.length === 1) {\n            const outputShapes = this.inboundNodes[0].outputShapes;\n            if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) &&\n                outputShapes.length === 1) {\n                return outputShapes[0];\n            }\n            else {\n                return outputShapes;\n            }\n        }\n        else {\n            throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different ` +\n                `output shapes. Hence the notion of \"output shape\" is ill-defined ` +\n                `for the layer.`);\n            // TODO(cais): Implement getOutputShapeAt().\n        }\n    }\n    /**\n     * Counts the total number of numbers (e.g., float32, int32) in the\n     * weights.\n     *\n     * @returns An integer count.\n     * @throws RuntimeError: If the layer is not built yet (in which case its\n     *   weights are not defined yet.)\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    countParams() {\n        if (!this.built) {\n            throw new RuntimeError(`You tried to call countParams() on ${this.name}, ` +\n                `but the layer is not built yet. Build it first by calling ` +\n                `build(batchInputShape).`);\n        }\n        return variable_utils.countParamsInWeights(this.weights);\n    }\n    /**\n     * Creates the layer weights.\n     *\n     * Must be implemented on all layers that have weights.\n     *\n     * Called when apply() is called to construct the weights.\n     *\n     * @param inputShape A `Shape` or array of `Shape` (unused).\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    build(inputShape) {\n        this.built = true;\n    }\n    /**\n     * Returns the current values of the weights of the layer.\n     *\n     * @param trainableOnly Whether to get the values of only trainable weights.\n     * @returns Weight values as an `Array` of `tf.Tensor`s.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    getWeights(trainableOnly = false) {\n        return batchGetValue(trainableOnly ? this.trainableWeights : this.weights);\n    }\n    /**\n     * Sets the weights of the layer, from Tensors.\n     *\n     * @param weights a list of Tensors. The number of arrays and their shape\n     *   must match number of the dimensions of the weights of the layer (i.e.\n     *   it should match the output of `getWeights`).\n     *\n     * @exception ValueError If the provided weights list does not match the\n     *   layer's specifications.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    setWeights(weights) {\n        tidy(() => {\n            const params = this.weights;\n            if (params.length !== weights.length) {\n                // TODO(cais): Restore the following and use `providedWeights`, instead\n                // of `weights` in the error message, once the deeplearn.js bug is\n                // fixed: https://github.com/PAIR-code/deeplearnjs/issues/498 const\n                // providedWeights = JSON.stringify(weights).substr(0, 50);\n                throw new ValueError(`You called setWeights(weights) on layer \"${this.name}\" ` +\n                    `with a weight list of length ${weights.length}, ` +\n                    `but the layer was expecting ${params.length} weights. ` +\n                    `Provided weights: ${weights}...`);\n            }\n            if (params.length === 0) {\n                return;\n            }\n            const weightValueTuples = [];\n            const paramValues = batchGetValue(params);\n            for (let i = 0; i < paramValues.length; ++i) {\n                const pv = paramValues[i];\n                const p = params[i];\n                const w = weights[i];\n                if (!util.arraysEqual(pv.shape, w.shape)) {\n                    throw new ValueError(`Layer weight shape ${pv.shape} ` +\n                        `not compatible with provided weight shape ${w.shape}`);\n                }\n                weightValueTuples.push([p, w]);\n            }\n            batchSetValue(weightValueTuples);\n        });\n    }\n    /**\n     * Adds a weight variable to the layer.\n     *\n     * @param name Name of the new weight variable.\n     * @param shape The shape of the weight.\n     * @param dtype The dtype of the weight.\n     * @param initializer An initializer instance.\n     * @param regularizer A regularizer instance.\n     * @param trainable Whether the weight should be trained via backprop or not\n     *   (assuming that the layer itself is also trainable).\n     * @param constraint An optional trainable.\n     * @return The created weight variable.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint) {\n        // Reject duplicate weight names.\n        if (this._addedWeightNames.indexOf(name) !== -1) {\n            throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);\n        }\n        this._addedWeightNames.push(name);\n        if (dtype == null) {\n            dtype = 'float32';\n        }\n        if (this.fastWeightInitDuringBuild) {\n            initializer = getInitializer('zeros');\n        }\n        const initValue = initializer.apply(shape, dtype);\n        const weight = new LayerVariable(initValue, dtype, name, trainable, constraint);\n        initValue.dispose();\n        // Request backend not to dispose the weights of the model on scope() exit.\n        if (regularizer != null) {\n            this.addLoss(() => regularizer.apply(weight.read()));\n        }\n        if (trainable == null) {\n            trainable = true;\n        }\n        if (trainable) {\n            this._trainableWeights.push(weight);\n        }\n        else {\n            this._nonTrainableWeights.push(weight);\n        }\n        return weight;\n    }\n    /**\n     * Set the fast-weight-initialization flag.\n     *\n     * In cases where the initialized weight values will be immediately\n     * overwritten by loaded weight values during model loading, setting\n     * the flag to `true` saves unnecessary calls to potentially expensive\n     * initializers and speeds up the loading process.\n     *\n     * @param value Target value of the flag.\n     */\n    setFastWeightInitDuringBuild(value) {\n        this.fastWeightInitDuringBuild = value;\n    }\n    /**\n     * Add losses to the layer.\n     *\n     * The loss may potentionally be conditional on some inputs tensors,\n     * for instance activity losses are conditional on the layer's inputs.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    addLoss(losses) {\n        if (losses == null || Array.isArray(losses) && losses.length === 0) {\n            return;\n        }\n        // Update this.losses\n        losses = generic_utils.toList(losses);\n        if (this._losses !== undefined && this._losses !== null) {\n            this.losses.push(...losses);\n        }\n    }\n    /**\n     * Computes the output shape of the layer.\n     *\n     * Assumes that the layer will be built to match that input shape provided.\n     *\n     * @param inputShape A shape (tuple of integers) or a list of shape tuples\n     *   (one per output tensor of the layer). Shape tuples can include null for\n     *   free dimensions, instead of an integer.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    /**\n     * Computes an output mask tensor.\n     *\n     * @param inputs Tensor or list of tensors.\n     * @param mask Tensor or list of tensors.\n     *\n     * @return null or a tensor (or list of tensors, one per output tensor of the\n     * layer).\n     */\n    computeMask(inputs, mask) {\n        if (!this.supportsMasking) {\n            if (mask != null) {\n                if (Array.isArray(mask)) {\n                    mask.forEach(maskElement => {\n                        if (maskElement != null) {\n                            throw new TypeError(`Layer ${this.name} does not support masking, ` +\n                                'but was passed an inputMask.');\n                        }\n                    });\n                }\n                else {\n                    throw new TypeError(`Layer ${this.name} does not support masking, ` +\n                        'but was passed an inputMask.');\n                }\n            }\n            // masking not explicitly supported: return null as mask\n            return null;\n        }\n        // if masking is explictly supported, by default\n        // carry over the input mask\n        return mask;\n    }\n    /**\n     * Internal method to create an inbound node for the layer.\n     *\n     * @param inputTensors List of input tensors.\n     * @param outputTensors List of output tensors.\n     * @param inputMasks List of input masks (a mask can be a tensor, or null).\n     * @param outputMasks List of output masks (a mask can be a tensor, or null).\n     * @param inputShapes List of input shape tuples.\n     * @param outputShapes List of output shape tuples.\n     * @param kwargs Dictionary of keyword arguments that were passed to the\n     *   `call` method of the layer at the call that created the node.\n     */\n    addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) {\n        const inputTensorList = generic_utils.toList(inputTensors);\n        outputTensors = generic_utils.toList(outputTensors);\n        inputMasks = generic_utils.toList(inputMasks);\n        outputMasks = generic_utils.toList(outputMasks);\n        inputShapes = types_utils.normalizeShapeList(inputShapes);\n        outputShapes = types_utils.normalizeShapeList(outputShapes);\n        // Collect input tensor(s) coordinates.\n        const inboundLayers = [];\n        const nodeIndices = [];\n        const tensorIndices = [];\n        for (const x of inputTensorList) {\n            /*\n             * TODO(michaelterry): Keras adds this value to tensors; it's not\n             * clear whether we'll use this or not.\n             */\n            inboundLayers.push(x.sourceLayer);\n            nodeIndices.push(x.nodeIndex);\n            tensorIndices.push(x.tensorIndex);\n        }\n        // Create node, add it to inbound nodes.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers,\n            nodeIndices,\n            tensorIndices,\n            inputTensors: inputTensorList,\n            outputTensors,\n            inputMasks,\n            outputMasks,\n            inputShapes,\n            outputShapes\n        }, kwargs);\n        // Update tensor history\n        for (let i = 0; i < outputTensors.length; i++) {\n            // TODO(michaelterry: _uses_learning_phase not tracked.\n            outputTensors[i].sourceLayer = this;\n            outputTensors[i].nodeIndex = this.inboundNodes.length - 1;\n            outputTensors[i].tensorIndex = i;\n        }\n    }\n    /**\n     * Returns the config of the layer.\n     *\n     * A layer config is a TS dictionary (serializable)\n     * containing the configuration of a layer.\n     * The same layer can be reinstantiated later\n     * (without its trained weights) from this configuration.\n     *\n     * The config of a layer does not include connectivity\n     * information, nor the layer class name.  These are handled\n     * by 'Container' (one layer of abstraction above).\n     *\n     * Porting Note: The TS dictionary follows TS naming standrds for\n     * keys, and uses tfjs-layers type-safe Enums.  Serialization methods\n     * should use a helper function to convert to the pythonic storage\n     * standard. (see serialization_utils.convertTsToPythonic)\n     *\n     * @returns TS dictionary of configuration.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    getConfig() {\n        const config = { name: this.name, trainable: this.trainable };\n        if (this.batchInputShape != null) {\n            config['batchInputShape'] = this.batchInputShape;\n        }\n        if (this.dtype != null) {\n            config['dtype'] = this.dtype;\n        }\n        return config;\n    }\n    /**\n     * Dispose the weight variables that this Layer instance holds.\n     *\n     * @returns {number} Number of disposed variables.\n     */\n    disposeWeights() {\n        this.weights.forEach(weight => weight.dispose());\n        return this.weights.length;\n    }\n    assertNotDisposed() {\n        if (this._refCount === 0) {\n            throw new Error(`Layer '${this.name}' is already disposed.`);\n        }\n    }\n    /**\n     * Attempt to dispose layer's weights.\n     *\n     * This method decrease the reference count of the Layer object by 1.\n     *\n     * A Layer is reference-counted. Its reference count is incremented by 1\n     * the first item its `apply()` method is called and when it becomes a part\n     * of a new `Node` (through calling the `apply()`) method on a\n     * `tf.SymbolicTensor`).\n     *\n     * If the reference count of a Layer becomes 0, all the weights will be\n     * disposed and the underlying memory (e.g., the textures allocated in WebGL)\n     * will be freed.\n     *\n     * Note: If the reference count is greater than 0 after the decrement, the\n     * weights of the Layer will *not* be disposed.\n     *\n     * After a Layer is disposed, it cannot be used in calls such as `apply()`,\n     * `getWeights()` or `setWeights()` anymore.\n     *\n     * @returns A DisposeResult Object with the following fields:\n     *   - refCountAfterDispose: The reference count of the Container after this\n     *     `dispose()` call.\n     *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n     *     during this `dispose()` call.\n     * @throws {Error} If the layer is not built yet, or if the layer has already\n     *   been disposed.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    dispose() {\n        if (!this.built) {\n            throw new Error(`Cannot dispose Layer ${this.name} because it has not been ` +\n                `built yet.`);\n        }\n        if (this._refCount === null) {\n            throw new Error(`Cannot dispose Layer ${this.name} because it has not been used ` +\n                `yet.`);\n        }\n        this.assertNotDisposed();\n        let numDisposedVariables = 0;\n        if (--this._refCount === 0) {\n            numDisposedVariables = this.disposeWeights();\n        }\n        return { refCountAfterDispose: this._refCount, numDisposedVariables };\n    }\n}\n/**\n * Collects the input shape(s) of a list of `tf.Tensor`s or\n * `tf.SymbolicTensor`s.\n *\n * TODO(michaelterry): Update PyKeras docs (backport).\n *\n * @param inputTensors List of input tensors (or single input tensor).\n *\n * @return List of shape tuples (or single tuple), one tuple per input.\n */\nfunction collectInputShape(inputTensors) {\n    inputTensors =\n        generic_utils.toList(inputTensors);\n    const shapes = [];\n    for (const x of inputTensors) {\n        shapes.push(x.shape);\n    }\n    return generic_utils.singletonOrArray(shapes);\n}\n/**\n * Guesses output dtype based on inputs.\n *\n * At present, just returns 'float32' for any input.\n *\n * @param inputTensors List of input tensors (or single input tensor).\n *\n * @return The guessed DType. At present, always returns 'float32'.\n */\nfunction guessOutputDType(inputTensors) {\n    return 'float32';\n}\n/**\n * Returns the list of input tensors necessary to compute `tensor`.\n *\n * Output will always be a list of tensors (potentially with 1 element).\n *\n * @param tensor The tensor to start from.\n * @param layer Origin layer of the tensor.\n * @param nodeIndex Origin node index of the tensor.\n *\n * @return Array of input tensors.\n */\nexport function getSourceInputs(tensor, layer, nodeIndex) {\n    if (layer == null || (nodeIndex != null && nodeIndex > 0)) {\n        layer = tensor.sourceLayer;\n        nodeIndex = tensor.nodeIndex;\n    }\n    if (layer.inboundNodes.length === 0) {\n        return [tensor];\n    }\n    else {\n        const node = layer.inboundNodes[nodeIndex];\n        if (node.inboundLayers.length === 0) {\n            return node.inputTensors;\n        }\n        else {\n            const sourceTensors = [];\n            for (let i = 0; i < node.inboundLayers.length; i++) {\n                const x = node.inputTensors[i];\n                const layer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const previousSources = getSourceInputs(x, layer, nodeIndex);\n                // Avoid input redundancy.\n                for (const x of previousSources) {\n                    if (sourceTensors.indexOf(x) === -1) {\n                        sourceTensors.push(x);\n                    }\n                }\n            }\n            return sourceTensors;\n        }\n    }\n}\n//# sourceMappingURL=topology.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { serialization } from '@tensorflow/tfjs-core';\nimport { getUid } from '../backend/state';\nimport { ValueError } from '../errors';\nimport { Layer, Node, SymbolicTensor } from './topology';\nexport class InputLayer extends Layer {\n    constructor(args) {\n        super({\n            dtype: args.dtype,\n            name: args.name != null ? args.name : getUid('input').toString()\n        });\n        // Normalize config.batchSize and config.sparse\n        if (args.batchSize == null) {\n            args.batchSize = null;\n        }\n        if (args.sparse == null) {\n            args.sparse = false;\n        }\n        this.trainable = false;\n        this.built = true;\n        this.sparse = args.sparse;\n        if (args.inputShape != null && args.batchInputShape != null) {\n            throw new ValueError('Only provide the inputShape OR ' +\n                'batchInputShape argument to inputLayer, not both at the same time.');\n        }\n        let batchInputShape = args.batchInputShape;\n        if (batchInputShape == null) {\n            if (args.inputShape == null) {\n                throw new ValueError('An InputLayer should be passed either a ' +\n                    '`batchInputShape` or an `inputShape`.');\n            }\n            else {\n                batchInputShape = [args.batchSize].concat(args.inputShape);\n            }\n        }\n        else {\n            // TODO(michaelterry): Backport to PyKeras\n            if (args.batchSize != null) {\n                throw new ValueError('Cannot specify batchSize if batchInputShape is ' +\n                    'specified when creating an InputLayer.');\n            }\n        }\n        const dtype = args.dtype || 'float32';\n        this.batchInputShape = batchInputShape;\n        this.dtype = dtype;\n        // TODO(michaelterry): Backport this to PyKeras?\n        this.inputSpec = [{ shape: batchInputShape }];\n        const inputTensor = new SymbolicTensor(this.dtype, this.batchInputShape, this, [], {}, this.name);\n        inputTensor.nodeIndex = 0;\n        inputTensor.tensorIndex = 0;\n        // Create an input node to add to this.outboundNode.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers: [],\n            nodeIndices: [],\n            tensorIndices: [],\n            inputTensors: [inputTensor],\n            outputTensors: [inputTensor],\n            inputMasks: [null],\n            outputMasks: [null],\n            inputShapes: [batchInputShape],\n            outputShapes: [batchInputShape]\n        });\n    }\n    apply(inputs, kwargs) {\n        throw new ValueError('Cannot pass any input to an ' +\n            `InputLayer's apply() method. InputLayer name: ${this.name}`);\n    }\n    dispose() {\n        // dispose() for InputLayer is overridden as no-op.\n        return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 };\n    }\n    getConfig() {\n        return {\n            batchInputShape: this.batchInputShape,\n            dtype: this.dtype,\n            sparse: this.sparse,\n            name: this.name\n        };\n    }\n}\n/** @nocollapse */\nInputLayer.className = 'InputLayer';\nserialization.registerClass(InputLayer);\nexport function Input(config) {\n    if (config.batchShape == null && config.shape == null) {\n        throw new Error('Please provide to Input either a `shape`' +\n            ' or a `batchShape` argument. Note that ' +\n            '`shape` does not include the batch ' +\n            'dimension.');\n    }\n    if (config.batchShape != null && config.shape != null) {\n        // TODO(michaelterry): Backport to PyKeras.\n        throw new ValueError('Please provide either a `shape` or `batchShape` ' +\n            'argument to Input, but not both.');\n    }\n    let batchShape = config.batchShape;\n    if (config.shape != null && batchShape == null) {\n        batchShape = [null].concat(config.shape);\n    }\n    let dtype = config.dtype;\n    if (dtype == null) {\n        dtype = 'float32';\n    }\n    const inputLayer = new InputLayer({\n        batchInputShape: batchShape,\n        name: config.name,\n        dtype,\n        sparse: config.sparse\n    });\n    const outputs = inputLayer.inboundNodes[0].outputTensors;\n    return outputs[0];\n}\n//# sourceMappingURL=input_layer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { dispose } from '@tensorflow/tfjs-core';\n/**\n * Turn any Scalar values in a Logs object into actual number values.\n *\n * @param logs The `Logs` object to be resolved in place.\n */\nexport async function resolveScalarsInLogs(logs) {\n    if (logs == null) {\n        return;\n    }\n    const promises = [];\n    const keys = [];\n    const scalarsToDispose = [];\n    for (const key in logs) {\n        const value = logs[key];\n        if (typeof value !== 'number') {\n            const valueScalar = value;\n            promises.push(valueScalar.data());\n            keys.push(key);\n            scalarsToDispose.push(valueScalar);\n        }\n    }\n    if (promises.length > 0) {\n        const values = await Promise.all(promises);\n        for (let i = 0; i < values.length; ++i) {\n            logs[keys[i]] = values[i][0];\n        }\n        // Dispose the original scalar tensors.\n        dispose(scalarsToDispose);\n    }\n}\n/**\n * Dispose all Tensors in an UnresolvedLogs object.\n *\n * @param logs An `UnresolvedLogs` object potentially containing `tf.Tensor`s in\n *   places where the values can be `tf.Tensor` or `number`.\n */\nexport function disposeTensorsInLogs(logs) {\n    if (logs == null) {\n        return;\n    }\n    for (const key in logs) {\n        const value = logs[key];\n        if (typeof value !== 'number') {\n            value.dispose();\n        }\n    }\n}\n//# sourceMappingURL=logs.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/callbacks.py */\nimport { add, div, keep, mul, nextFrame, tidy, util } from '@tensorflow/tfjs-core';\nimport { ValueError } from './errors';\nimport { resolveScalarsInLogs } from './logs';\nimport * as generic_utils from './utils/generic_utils';\n/** Verbosity logging level when fitting a model. */\nexport var ModelLoggingVerbosity;\n(function (ModelLoggingVerbosity) {\n    ModelLoggingVerbosity[ModelLoggingVerbosity[\"SILENT\"] = 0] = \"SILENT\";\n    ModelLoggingVerbosity[ModelLoggingVerbosity[\"VERBOSE\"] = 1] = \"VERBOSE\";\n})(ModelLoggingVerbosity || (ModelLoggingVerbosity = {}));\n/** How often to yield to the main thread when training (in ms). */\nexport const DEFAULT_YIELD_EVERY_MS = 125;\n/**\n * Abstract base class used to build new callbacks.\n *\n * The `logs` dictionary that callback methods take as argument will contain\n * keys for quantities relevant to the current batch or epoch.\n *\n * Currently, the `.fit()` method of the `Sequential` model class\n * will include the following quantities in the `logs` that\n * it passes to its callbacks:\n *\n * onEpochEnd: Logs include `acc` and `loss`, and optionally include `valLoss`\n *   (if validation is enabled in `fit`), and `valAcc` (if validation and\n *   accuracy monitoring are enabled).\n * onBatchBegin: Logs include `size`, the number of samples in the current\n *   batch.\n * onBatchEnd: Logs include `loss`, and optionally `acc` (if accuracy monitoring\n *   is enabled).\n */\nexport class BaseCallback {\n    constructor() {\n        // TODO(michaelterry): This type is a best guess.\n        this.validationData = null;\n    }\n    setParams(params) {\n        this.params = params;\n    }\n    async onEpochBegin(epoch, logs) { }\n    async onEpochEnd(epoch, logs) { }\n    async onBatchBegin(batch, logs) { }\n    async onBatchEnd(batch, logs) { }\n    async onTrainBegin(logs) { }\n    async onTrainEnd(logs) { }\n    // LayersModel needs to call Callback.setModel(), but cannot actually depend\n    // on Callback because that creates a cyclic dependency.  Providing this no-op\n    // method on BaseCallback breaks the cycle: this way LayersModel can depend on\n    // BaseCallback but not on Callback.  The argument is typed as `Container`\n    // (the superclass of LayersModel) to avoid recapitulating the cycle. Callback\n    // overrides this method and enforces that the argument is really a\n    // LayersModel.\n    setModel(model) {\n        // Do nothing. Use Callback instead of BaseCallback to track the model.\n    }\n}\n/**\n * Container abstracting a list of callbacks.\n */\nexport class CallbackList {\n    // TODO(cais): When the need arises, uncomment the following lines and\n    // implement the queue for time values.\n    // private deltaTBatch: number;\n    // private deltaTsBatchBegin: Array<number>;\n    // private deltaTsBatchEnd: Array<number>;\n    /**\n     * Constructor of CallbackList.\n     * @param callbacks Array of `Callback` instances.\n     * @param queueLength Queue length for keeping running statistics over\n     *   callback execution time.\n     */\n    constructor(callbacks, queueLength = 10) {\n        // TODO(cais): Make use of queueLength when implementing the queue for time\n        // values.\n        if (callbacks == null) {\n            callbacks = [];\n        }\n        this.callbacks = callbacks;\n        this.queueLength = queueLength;\n    }\n    append(callback) {\n        this.callbacks.push(callback);\n    }\n    setParams(params) {\n        for (const callback of this.callbacks) {\n            callback.setParams(params);\n        }\n    }\n    setModel(model) {\n        for (const callback of this.callbacks) {\n            callback.setModel(model);\n        }\n    }\n    /**\n     * Called at the start of an epoch.\n     * @param epoch Index of epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onEpochBegin(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onEpochBegin(epoch, logs);\n        }\n    }\n    /**\n     * Called at the end of an epoch.\n     * @param epoch Index of epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onEpochEnd(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onEpochEnd(epoch, logs);\n        }\n    }\n    /**\n     * Called  right before processing a batch.\n     * @param batch Index of batch within the current epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onBatchBegin(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onBatchBegin(batch, logs);\n        }\n    }\n    /**\n     * Called at the end of a batch.\n     * @param batch Index of batch within the current epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onBatchEnd(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onBatchEnd(batch, logs);\n        }\n    }\n    /**\n     * Called at the beginning of training.\n     * @param logs Dictionary of logs.\n     */\n    async onTrainBegin(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onTrainBegin(logs);\n        }\n    }\n    /**\n     * Called at the end of training.\n     * @param logs Dictionary of logs.\n     */\n    async onTrainEnd(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onTrainEnd(logs);\n        }\n    }\n}\n/**\n * Callback that accumulates epoch averages of metrics.\n *\n * This callback is automatically applied to every LayersModel.\n */\nexport class BaseLogger extends BaseCallback {\n    constructor() {\n        super();\n    }\n    async onEpochBegin(epoch) {\n        this.seen = 0;\n        this.totals = {};\n    }\n    async onBatchEnd(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        const batchSize = logs['size'] == null ? 0 : logs['size'];\n        this.seen += batchSize;\n        for (const key in logs) {\n            const value = logs[key];\n            if (typeof value === 'number') {\n                if (!this.totals.hasOwnProperty(key)) {\n                    this.totals[key] = 0;\n                }\n                this.totals[key] = this.totals[key] + value * batchSize;\n            }\n            else {\n                let oldTotalsToDispose;\n                if (key in this.totals) {\n                    oldTotalsToDispose = this.totals[key];\n                }\n                else {\n                    this.totals[key] = 0;\n                }\n                const total = tidy(() => add((this.totals[key]), mul(value, batchSize)));\n                this.totals[key] = total;\n                if (oldTotalsToDispose != null) {\n                    oldTotalsToDispose.dispose();\n                }\n            }\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        if (logs != null) {\n            for (const key of this.params['metrics']) {\n                if (this.totals[key] == null) {\n                    continue;\n                }\n                if (typeof this.totals[key] === 'number') {\n                    logs[key] = this.totals[key] / this.seen;\n                }\n                else {\n                    tidy(() => {\n                        const log = mul(div(1, this.seen), this.totals[key]);\n                        logs[key] = log;\n                        this.totals[key].dispose();\n                        keep(logs[key]);\n                    });\n                }\n            }\n        }\n    }\n}\n/**\n * Callback that records events into a `History` object. This callback is\n * automatically applied to every TF.js Layers model. The `History` object\n * gets returned by the `fit` method of models.\n */\nexport class History extends BaseCallback {\n    async onTrainBegin(logs) {\n        this.epoch = [];\n        this.history = {};\n    }\n    async onEpochEnd(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        this.epoch.push(epoch);\n        for (const key in logs) {\n            if (this.history[key] == null) {\n                this.history[key] = [];\n            }\n            this.history[key].push(logs[key]);\n        }\n    }\n    /**\n     * Await the values of all losses and metrics.\n     */\n    async syncData() {\n        const promises = [];\n        const keys = [];\n        const indices = [];\n        for (const key in this.history) {\n            const valueArray = this.history[key];\n            for (let i = 0; i < valueArray.length; ++i) {\n                if (typeof valueArray[i] !== 'number') {\n                    const valueScalar = valueArray[i];\n                    promises.push(valueScalar.data());\n                    keys.push(key);\n                    indices.push(i);\n                }\n            }\n        }\n        const values = await Promise.all(promises);\n        for (let n = 0; n < values.length; ++n) {\n            const tensorToDispose = this.history[keys[n]][indices[n]];\n            tensorToDispose.dispose();\n            this.history[keys[n]][indices[n]] = values[n][0];\n        }\n    }\n}\n/**\n * Custom callback for training.\n */\nexport class CustomCallback extends BaseCallback {\n    constructor(args, yieldEvery) {\n        super();\n        this.currentEpoch = 0;\n        this.yieldEvery = yieldEvery || 'auto';\n        if (this.yieldEvery === 'auto') {\n            this.yieldEvery = DEFAULT_YIELD_EVERY_MS;\n        }\n        if (this.yieldEvery === 'never' && args.onYield != null) {\n            throw new Error('yieldEvery is `never` but you provided an `onYield` callback. ' +\n                'Either change `yieldEvery` or remove the callback');\n        }\n        if (util.isNumber(this.yieldEvery)) {\n            // Decorate `maybeWait` so it will be called at most once every\n            // `yieldEvery` ms.\n            this.maybeWait = generic_utils.debounce(this.maybeWait.bind(this), this.yieldEvery);\n        }\n        this.trainBegin = args.onTrainBegin;\n        this.trainEnd = args.onTrainEnd;\n        this.epochBegin = args.onEpochBegin;\n        this.epochEnd = args.onEpochEnd;\n        this.batchBegin = args.onBatchBegin;\n        this.batchEnd = args.onBatchEnd;\n        this.yield = args.onYield;\n    }\n    async maybeWait(epoch, batch, logs) {\n        const ps = [];\n        if (this.yield != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.yield(epoch, batch, logs));\n        }\n        ps.push(nextFrame());\n        await Promise.all(ps);\n    }\n    async onEpochBegin(epoch, logs) {\n        this.currentEpoch = epoch;\n        if (this.epochBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.epochBegin(epoch, logs);\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        const ps = [];\n        if (this.epochEnd != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.epochEnd(epoch, logs));\n        }\n        if (this.yieldEvery === 'epoch') {\n            ps.push(nextFrame());\n        }\n        await Promise.all(ps);\n    }\n    async onBatchBegin(batch, logs) {\n        if (this.batchBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.batchBegin(batch, logs);\n        }\n    }\n    async onBatchEnd(batch, logs) {\n        const ps = [];\n        if (this.batchEnd != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.batchEnd(batch, logs));\n        }\n        if (this.yieldEvery === 'batch') {\n            ps.push(nextFrame());\n        }\n        else if (util.isNumber(this.yieldEvery)) {\n            ps.push(this.maybeWait(this.currentEpoch, batch, logs));\n        }\n        await Promise.all(ps);\n    }\n    async onTrainBegin(logs) {\n        if (this.trainBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.trainBegin(logs);\n        }\n    }\n    async onTrainEnd(logs) {\n        if (this.trainEnd != null) {\n            await resolveScalarsInLogs(logs);\n            await this.trainEnd(logs);\n        }\n    }\n}\n/**\n * Standardize callbacks or configurations of them to an Array of callbacks.\n */\nexport function standardizeCallbacks(callbacks, yieldEvery) {\n    if (callbacks == null) {\n        callbacks = {};\n    }\n    if (callbacks instanceof BaseCallback) {\n        return [callbacks];\n    }\n    if (Array.isArray(callbacks) && callbacks[0] instanceof BaseCallback) {\n        return callbacks;\n    }\n    // Convert custom callback configs to custom callback objects.\n    const callbackConfigs = generic_utils.toList(callbacks);\n    return callbackConfigs.map(callbackConfig => new CustomCallback(callbackConfig, yieldEvery));\n}\n/**\n * A global registry for callback constructors to be used during\n * LayersModel.fit().\n */\nexport class CallbackConstructorRegistry {\n    /**\n     * Blocks public access to constructor.\n     */\n    constructor() { }\n    /**\n     * Register a tf.LayersModel.fit() callback constructor.\n     *\n     * The registered callback constructor will be used to instantiate\n     * callbacks for every tf.LayersModel.fit() call afterwards.\n     *\n     * @param verbosityLevel Level of verbosity at which the `callbackConstructor`\n     *   is to be reigstered.\n     * @param callbackConstructor A no-arg constructor for `tf.Callback`.\n     * @throws Error, if the same callbackConstructor has been registered before,\n     *   either at the same or a different `verbosityLevel`.\n     */\n    static registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n        util.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, ` +\n            `but got ${verbosityLevel}`);\n        CallbackConstructorRegistry.checkForDuplicate(callbackConstructor);\n        if (CallbackConstructorRegistry.constructors[verbosityLevel] == null) {\n            CallbackConstructorRegistry.constructors[verbosityLevel] = [];\n        }\n        CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor);\n    }\n    static checkForDuplicate(callbackConstructor) {\n        for (const levelName in CallbackConstructorRegistry.constructors) {\n            const constructors = CallbackConstructorRegistry.constructors[+levelName];\n            constructors.forEach(ctor => {\n                if (ctor === callbackConstructor) {\n                    throw new ValueError('Duplicate callback constructor.');\n                }\n            });\n        }\n    }\n    /**\n     * Clear all registered callback constructors.\n     */\n    static clear() {\n        CallbackConstructorRegistry.constructors = {};\n    }\n    /**\n     * Create callbacks using the registered callback constructors.\n     *\n     * Given `verbosityLevel`, all constructors registered at that level or above\n     * will be called and the instantiated callbacks will be used.\n     *\n     * @param verbosityLevel: Level of verbosity.\n     */\n    static createCallbacks(verbosityLevel) {\n        const constructors = [];\n        for (const levelName in CallbackConstructorRegistry.constructors) {\n            const level = +levelName;\n            if (verbosityLevel >= level) {\n                constructors.push(...CallbackConstructorRegistry.constructors[level]);\n            }\n        }\n        return constructors.map(ctor => new ctor());\n    }\n}\nCallbackConstructorRegistry.constructors = {};\nexport function configureCallbacks(callbacks, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) {\n    const history = new History();\n    const actualCallbacks = [\n        new BaseLogger(), ...CallbackConstructorRegistry.createCallbacks(verbose)\n    ];\n    if (callbacks != null) {\n        actualCallbacks.push(...callbacks);\n    }\n    actualCallbacks.push(history);\n    const callbackList = new CallbackList(actualCallbacks);\n    // TODO(cais): Figure out when this LayersModel instance can have a\n    // dynamically\n    //   set property called 'callback_model' as in PyKeras.\n    callbackList.setParams({\n        epochs,\n        initialEpoch,\n        samples: numTrainSamples,\n        steps: stepsPerEpoch,\n        batchSize,\n        verbose,\n        doValidation,\n        metrics: callbackMetrics,\n    });\n    return { callbackList, history };\n}\n//# sourceMappingURL=base_callbacks.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source layers/__init__.py */\nimport { serialization } from '@tensorflow/tfjs-core';\nimport { deserializeKerasObject } from '../utils/generic_utils';\n/**\n * Instantiate a layer from a config dictionary.\n * @param config dict of the form {class_name: str, config: dict}\n * @param customObjects dict mapping class names (or function names)\n *   of custom (non-Keras) objects to class/functions\n * @param fastWeightInit Optional flag to use fast weight initialization\n *   during deserialization. This is applicable to cases in which\n *   the initialization will be immediately overwritten by loaded weight\n *   values. Default: `false`.\n * @returns Layer instance (may be LayersModel, Sequential, Layer...)\n */\nexport function deserialize(config, customObjects = {}, fastWeightInit = false) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'layer', fastWeightInit);\n}\n//# sourceMappingURL=serialization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source: losses.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { tidy, util } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport * as K from './backend/tfjs_backend';\nimport { ValueError } from './errors';\n/**\n * Normalizes a tensor wrt the L2 norm alongside the specified axis.\n * @param x\n * @param axis Axis along which to perform normalization.\n */\nexport function l2Normalize(x, axis) {\n    return tidy(() => {\n        if (x.dtype !== 'float32') {\n            x = x.asType('float32');\n        }\n        const squareSum = tfc.sum(K.square(x), axis, true);\n        const epsilonTensor = tfc.fill(squareSum.shape, epsilon());\n        const norm = tfc.sqrt(tfc.maximum(squareSum, epsilonTensor));\n        return tfc.div(x, norm);\n    });\n}\nexport function meanSquaredError(yTrue, yPred) {\n    return tidy(() => tfc.mean(K.square(tfc.sub(yPred, yTrue)), -1));\n}\nexport function meanAbsoluteError(yTrue, yPred) {\n    return tidy(() => tfc.mean(tfc.abs(tfc.sub(yPred, yTrue)), -1));\n}\nexport function meanAbsolutePercentageError(yTrue, yPred) {\n    return tidy(() => {\n        const diff = tfc.sub(yTrue, yPred);\n        const clippedTrue = tfc.clipByValue(tfc.abs(yTrue), epsilon(), Number.MAX_VALUE);\n        const absResult = tfc.abs(tfc.div(diff, clippedTrue));\n        return tfc.mul(100, tfc.mean(absResult, -1));\n    });\n}\nexport function meanSquaredLogarithmicError(yTrue, yPred) {\n    return tidy(() => {\n        const clippedPred = tfc.clipByValue(yPred, epsilon(), Number.MAX_VALUE);\n        const firstLog = tfc.log(tfc.add(1, clippedPred));\n        const clippedTrue = tfc.clipByValue(yTrue, epsilon(), Number.MAX_VALUE);\n        const secondLog = tfc.log(tfc.add(1, clippedTrue));\n        return tfc.mean(K.square(tfc.sub(firstLog, secondLog)), -1);\n    });\n}\nexport function squaredHinge(yTrue, yPred) {\n    return tidy(() => {\n        const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n        return tfc.mean(K.square(maxResult), -1);\n    });\n}\nexport function hinge(yTrue, yPred) {\n    return tidy(() => {\n        const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n        return tfc.mean(maxResult, -1);\n    });\n}\nexport function categoricalHinge(yTrue, yPred) {\n    return tidy(() => {\n        const pos = tfc.sum(tfc.mul(yTrue, yPred), -1);\n        const neg = tfc.max(tfc.mul(tfc.sub(1, yTrue), yPred), -1);\n        return tfc.maximum(0, tfc.add(1, tfc.sub(neg, pos)));\n    });\n}\n/**\n * Logarithm of the hyperbolic cosine of the prediction error.\n *\n * `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and\n * to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly\n * like the mean squared error, but will not be so strongly affected by the\n * occasional wildly incorrect prediction.\n */\nexport function logcosh(yTrue, yPred) {\n    return tidy(() => {\n        const log2 = Math.log(2);\n        const predictionDiff = tfc.sub(yPred, yTrue);\n        const logcoshResult = tfc.sub(tfc.add(predictionDiff, tfc.softplus(tfc.mul(-2, predictionDiff))), log2);\n        return tfc.mean(logcoshResult, -1);\n    });\n}\nexport function categoricalCrossentropy(target, output, fromLogits = false) {\n    return tidy(() => {\n        if (fromLogits) {\n            output = tfc.softmax(output);\n        }\n        else {\n            // scale preds so that the class probabilities of each sample sum to 1.\n            const outputSum = tfc.sum(output, output.shape.length - 1, true);\n            output = tfc.div(output, outputSum);\n        }\n        output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n        return tfc.neg(tfc.sum(tfc.mul(target.toFloat(), tfc.log(output)), output.shape.length - 1));\n    });\n}\n/**\n * Categorical crossentropy with integer targets.\n *\n * @param target An integer tensor.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n */\nexport function sparseCategoricalCrossentropy(target, output, fromLogits = false) {\n    return tidy(() => {\n        const flatTarget = tfc.floor(K.flatten(target)).toInt();\n        output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n        const outputShape = output.shape;\n        const oneHotTarget = tfc.oneHot(flatTarget, outputShape[outputShape.length - 1])\n            .reshape(outputShape);\n        return categoricalCrossentropy(oneHotTarget, output, fromLogits);\n    });\n}\n/**\n * From TensorFlow's implementation in nn_impl.py:\n *\n * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n *      z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n *    = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n *    = (1 - z) * x + log(1 + exp(-x))\n *    = x - x * z + log(1 + exp(-x))\n * For x < 0, to avoid overflow in exp(-x), we reformulate the above\n *      x - x * z + log(1 + exp(-x))\n *    = log(exp(x)) - x * z + log(1 + exp(-x))\n *    = - x * z + log(1 + exp(x))\n * Hence, to ensure stability and avoid overflow, the implementation uses this\n * equivalent formulation\n *    max(x, 0) - x * z + log(1 + exp(-abs(x)))\n *\n * @param labels The labels.\n * @param logits The logits.\n */\nexport function sigmoidCrossEntropyWithLogits(labels, logits) {\n    if (!util.arraysEqual(labels.shape, logits.shape)) {\n        throw new ValueError(`logits and labels must have the same shape, but got shapes ` +\n            `${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);\n    }\n    return tidy(() => {\n        // The logistic loss formula from above is\n        //   x - x * z + log(1 + exp(-x))\n        // For x < 0, a more numerically stable formula is\n        //   -x * z + log(1 + exp(x))\n        // Note that these two expressions can be combined into the following:\n        //   max(x, 0) - x * z + log(1 + exp(-abs(x)))\n        const reluLogits = logits.relu();\n        const negAbsLogits = logits.abs().neg();\n        return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p());\n    });\n}\nexport function binaryCrossentropy(yTrue, yPred) {\n    return tidy(() => {\n        let y;\n        y = tfc.clipByValue(yPred, epsilon(), 1 - epsilon());\n        y = tfc.log(tfc.div(y, tfc.sub(1, y)));\n        return tfc.mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);\n    });\n}\nexport function kullbackLeiblerDivergence(yTrue, yPred) {\n    return tidy(() => {\n        const clippedTrue = tfc.clipByValue(yTrue, epsilon(), 1);\n        const clippedPred = tfc.clipByValue(yPred, epsilon(), 1);\n        return tfc.sum(tfc.mul(yTrue, tfc.log(tfc.div(clippedTrue, clippedPred))), -1);\n    });\n}\nexport function poisson(yTrue, yPred) {\n    return tidy(() => {\n        const logPred = tfc.log(tfc.add(epsilon(), yPred));\n        return tfc.mean(tfc.sub(yPred, tfc.mul(yTrue, logPred)), -1);\n    });\n}\nexport function cosineProximity(yTrue, yPred) {\n    return tidy(() => {\n        const trueNormalized = l2Normalize(yTrue, -1);\n        const predNormalized = l2Normalize(yPred, -1);\n        const trueXPred = tfc.mul(trueNormalized, predNormalized);\n        return tfc.neg(tfc.sum(trueXPred, -1));\n    });\n}\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const msle = meanSquaredLogarithmicError;\nexport const MSLE = meanSquaredLogarithmicError;\nexport const kld = kullbackLeiblerDivergence;\nexport const KLD = kullbackLeiblerDivergence;\nexport const cosine = cosineProximity;\n// TODO(michaelterry): Add deserialize() function.\nexport const lossesMap = {\n    meanSquaredError,\n    meanAbsoluteError,\n    meanAbsolutePercentageError,\n    meanSquaredLogarithmicError,\n    squaredHinge,\n    hinge,\n    categoricalHinge,\n    logcosh,\n    categoricalCrossentropy,\n    sparseCategoricalCrossentropy,\n    binaryCrossentropy,\n    kullbackLeiblerDivergence,\n    poisson,\n    cosineProximity\n};\n// Porting note: This diverges from the PyKeras implementation and may need to\n// change based on (de)serialization requirements.\nexport function get(identifierOrFn) {\n    if (typeof identifierOrFn === 'string') {\n        if (identifierOrFn in lossesMap) {\n            return lossesMap[identifierOrFn];\n        }\n        let errMsg = `Unknown loss ${identifierOrFn}`;\n        if (identifierOrFn.toLowerCase().includes('softmaxcrossentropy')) {\n            errMsg = `Unknown loss ${identifierOrFn}. ` +\n                'Use \"categoricalCrossentropy\" as the string name for ' +\n                'tf.losses.softmaxCrossEntropy';\n        }\n        throw new ValueError(errMsg);\n    }\n    else {\n        return identifierOrFn;\n    }\n}\n//# sourceMappingURL=losses.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Built-in metrics.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { tidy } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { NotImplementedError, ValueError } from './errors';\nimport { categoricalCrossentropy as categoricalCrossentropyLoss, cosineProximity, meanAbsoluteError, meanAbsolutePercentageError, meanSquaredError, sparseCategoricalCrossentropy as sparseCategoricalCrossentropyLoss } from './losses';\nimport { binaryCrossentropy as lossBinaryCrossentropy } from './losses';\nimport { lossesMap } from './losses';\nimport * as util from './utils/generic_utils';\nexport function binaryAccuracy(yTrue, yPred) {\n    return tidy(() => {\n        const threshold = tfc.mul(.5, tfc.onesLike(yPred));\n        const yPredThresholded = K.cast(tfc.greater(yPred, threshold), yTrue.dtype);\n        return tfc.mean(tfc.equal(yTrue, yPredThresholded), -1);\n    });\n}\nexport function categoricalAccuracy(yTrue, yPred) {\n    return tidy(() => K.cast(tfc.equal(tfc.argMax(yTrue, -1), tfc.argMax(yPred, -1)), 'float32'));\n}\nfunction truePositives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(1), yPred.equal(1)).sum().cast('float32');\n    });\n}\nfunction falseNegatives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(1), yPred.equal(0)).sum().cast('float32');\n    });\n}\nfunction falsePositives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(0), yPred.equal(1)).sum().cast('float32');\n    });\n}\nexport function precision(yTrue, yPred) {\n    return tidy(() => {\n        const tp = truePositives(yTrue, yPred);\n        const fp = falsePositives(yTrue, yPred);\n        const denominator = tp.add(fp);\n        return tfc.where(tfc.greater(denominator, 0), tp.div(denominator), 0)\n            .cast('float32');\n    });\n}\nexport function recall(yTrue, yPred) {\n    return tidy(() => {\n        const tp = truePositives(yTrue, yPred);\n        const fn = falseNegatives(yTrue, yPred);\n        const denominator = tp.add(fn);\n        return tfc.where(tfc.greater(denominator, 0), tp.div(denominator), 0)\n            .cast('float32');\n    });\n}\nexport function binaryCrossentropy(yTrue, yPred) {\n    return lossBinaryCrossentropy(yTrue, yPred);\n}\nexport function sparseCategoricalAccuracy(yTrue, yPred) {\n    if (yTrue.rank === yPred.rank) {\n        yTrue = yTrue.squeeze([yTrue.rank - 1]);\n    }\n    yPred = yPred.argMax(-1);\n    if (yPred.dtype !== yTrue.dtype) {\n        yPred = yPred.asType(yTrue.dtype);\n    }\n    return tfc.equal(yTrue, yPred).asType('float32');\n}\nexport function topKCategoricalAccuracy(yTrue, yPred) {\n    throw new NotImplementedError();\n}\nexport function sparseTopKCategoricalAccuracy(yTrue, yPred) {\n    throw new NotImplementedError();\n}\n// Aliases.\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const categoricalCrossentropy = categoricalCrossentropyLoss;\nexport const cosine = cosineProximity;\nexport const sparseCategoricalCrossentropy = sparseCategoricalCrossentropyLoss;\n// TODO(cais, nielsene): Add serialize().\nexport const metricsMap = {\n    binaryAccuracy,\n    categoricalAccuracy,\n    precision,\n    categoricalCrossentropy,\n    sparseCategoricalCrossentropy,\n    mse,\n    MSE,\n    mae,\n    MAE,\n    mape,\n    MAPE,\n    cosine\n};\nexport function get(identifier) {\n    if (typeof identifier === 'string' && identifier in metricsMap) {\n        return metricsMap[identifier];\n    }\n    else if (typeof identifier !== 'string' && identifier != null) {\n        return identifier;\n    }\n    else {\n        throw new ValueError(`Unknown metric ${identifier}`);\n    }\n}\n/**\n * Get the shortcut function name.\n *\n * If the fn name is a string,\n *   directly return the string name.\n * If the function is included in metricsMap or lossesMap,\n *   return key of the map.\n *   - If the function relative to multiple keys,\n *     return the first found key as the function name.\n *   - If the function exists in both lossesMap and metricsMap,\n *     search lossesMap first.\n * If the function is not included in metricsMap or lossesMap,\n *   return the function name.\n *\n * @param fn loss function, metric function, or short cut name.\n * @returns Loss or Metric name in string.\n */\nexport function getLossOrMetricName(fn) {\n    util.assert(fn !== null, `Unknown LossOrMetricFn ${fn}`);\n    if (typeof fn === 'string') {\n        return fn;\n    }\n    else {\n        let fnName;\n        for (const key of Object.keys(lossesMap)) {\n            if (lossesMap[key] === fn) {\n                fnName = key;\n                break;\n            }\n        }\n        if (fnName !== undefined) {\n            return fnName;\n        }\n        for (const key of Object.keys(metricsMap)) {\n            if (metricsMap[key] === fn) {\n                fnName = key;\n                break;\n            }\n        }\n        if (fnName !== undefined) {\n            return fnName;\n        }\n        return fn.name;\n    }\n}\n//# sourceMappingURL=metrics.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Optimizers.\n */\nimport { train } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport { ValueError } from './errors';\n// Add (de)serialize()\n// Porting note: This diverges from the PyKeras implementation and may need to\n// change based on (de)serialization requirements.\nexport function getOptimizer(identifier) {\n    const optimizerMap = {\n        'Adagrad': () => train.adagrad(0.01),\n        'Adadelta': () => train.adadelta(1, 0.95, epsilon()),\n        'Adam': () => train.adam(0.001, 0.9, 0.999, epsilon()),\n        'Adamax': () => train.adamax(0.002, 0.9, 0.999, epsilon(), 0),\n        'RMSProp': () => train.rmsprop(0.001, 0.9, 0, epsilon()),\n        'SGD': () => train.sgd(0.01)\n    };\n    optimizerMap['adagrad'] = optimizerMap['Adagrad'];\n    optimizerMap['adadelta'] = optimizerMap['Adadelta'];\n    optimizerMap['adam'] = optimizerMap['Adam'];\n    optimizerMap['adamax'] = optimizerMap['Adamax'];\n    optimizerMap['rmsprop'] = optimizerMap['RMSProp'];\n    optimizerMap['sgd'] = optimizerMap['SGD'];\n    if (identifier in optimizerMap) {\n        return optimizerMap[identifier]();\n    }\n    throw new ValueError(`Unknown Optimizer ${identifier}`);\n}\n//# sourceMappingURL=optimizers.js.map", "/**\n * @license\n * Copyright 2019 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/** Utility functions related to user-defined metadata. */\n// Maximum recommended serialized size for user-defined metadata.\n// Beyond this limit, a warning message will be printed during model loading and\n// saving.\nexport const MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024;\n/**\n * Check validity of user-defined metadata.\n *\n * @param userDefinedMetadata\n * @param modelName Name of the model that the user-defined metadata belongs to.\n *   Used during construction of error messages.\n * @param checkSize Whether to check the size of the metadata is under\n *   recommended limit. Default: `false`. If `true`, will try stringify the\n *   JSON object and print a console warning if the serialzied size is above the\n *   limit.\n * @throws Error if `userDefinedMetadata` is not a plain JSON object.\n */\nexport function checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) {\n    if (userDefinedMetadata == null ||\n        typeof userDefinedMetadata !== 'object' ||\n        Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype ||\n        !plainObjectCheck(userDefinedMetadata)) {\n        throw new Error('User-defined metadata is expected to be a JSON object, but is not.');\n    }\n    if (checkSize) {\n        const out = JSON.stringify(userDefinedMetadata);\n        if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) {\n            console.warn(`User-defined metadata of model \"${modelName}\" is too large in ` +\n                `size (length=${out.length} when serialized). It is not ` +\n                `recommended to store such large objects in user-defined metadata. ` +\n                `Please make sure its serialized length is <= ` +\n                `${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`);\n        }\n    }\n}\n/**\n * Check if an input is plain JSON object or any valid subfield of it.\n *\n * @param x The input to be checked.\n * @param assertObject Whether to assert `x` is a JSON object, i.e., reject\n *   cases of arrays and primitives.\n * @return Returns `true` if and only if `x` is a plain JSON object,\n *   a JSON-valid primitive including string, number, boolean and null,\n *   or an array of the said types.\n */\n// tslint:disable-next-line:no-any\nexport function plainObjectCheck(x) {\n    if (x === null) {\n        // Note: typeof `null` is 'object', and `null` is valid in JSON.\n        return true;\n    }\n    else if (typeof x === 'object') {\n        if (Object.getPrototypeOf(x) === Object.prototype) {\n            // `x` is a JavaScript object and its prototype is Object.\n            const keys = Object.keys(x);\n            for (const key of keys) {\n                if (typeof key !== 'string') {\n                    // JSON keys must be strings.\n                    return false;\n                }\n                if (!plainObjectCheck(x[key])) { // Recursive call.\n                    return false;\n                }\n            }\n            return true;\n        }\n        else {\n            // `x` is a JavaScript object but its prototype is not Object.\n            if (Array.isArray(x)) {\n                // `x` is a JavaScript array.\n                for (const item of x) {\n                    if (!plainObjectCheck(item)) { // Recursive call.\n                        return false;\n                    }\n                }\n                return true;\n            }\n            else {\n                // `x` is a JavaScript object and its prototype is not Object,\n                // and it's not an Array. I.e., it's a complex object such as\n                // `Error` and `Date`.\n                return false;\n            }\n        }\n    }\n    else {\n        // `x` is not a JavaScript object or `null`.\n        const xType = typeof x;\n        return xType === 'string' || xType === 'number' || xType === 'boolean';\n    }\n}\n//# sourceMappingURL=user_defined_metadata.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { countParamsInWeights } from './variable_utils';\n/**\n * Print the summary of a LayersModel object.\n *\n * @param model tf.LayersModel instance.\n * @param lineLength Total length of printed lines. Set this to adapt to the\n *   display to different terminal or console sizes.\n * @param positions Relative or absolute positions of log elements in each\n *   line. Each number corresponds to right-most (i.e., ending) position of a\n *   column.\n *   If not provided, defaults to `[0.45, 0.85, 1]` for sequential-like\n *   models and `[0.33, 0.55, 0.67, 1]` for non-sequential like models.\n * @param printFn Print function to use.\n *   It will be called on each line of the summary. You can provide a custom\n *   function in order to capture the string summary. Defaults to `console.log`.\n */\nexport function printSummary(model, lineLength, positions, \n// tslint:disable-next-line:no-any\nprintFn = console.log) {\n    const sequentialLike = isModelSequentialLike(model);\n    // Header names for different log elements.\n    const toDisplay = ['Layer (type)', 'Output shape', 'Param #'];\n    if (sequentialLike) {\n        lineLength = lineLength || 65;\n        positions = positions || [0.45, 0.85, 1];\n    }\n    else {\n        lineLength = lineLength || 98;\n        positions = positions || [0.33, 0.55, 0.67, 1];\n        // Header names for different log elements.\n    }\n    if (positions[positions.length - 1] <= 1) {\n        // `positions` is relative. Convert it to absolute positioning.\n        positions = positions.map(p => Math.floor(lineLength * p));\n    }\n    let relevantNodes;\n    if (!sequentialLike) {\n        toDisplay.push('Receives inputs');\n        relevantNodes = [];\n        for (const depth in model.nodesByDepth) {\n            relevantNodes.push(...model.nodesByDepth[depth]);\n        }\n    }\n    printFn('_'.repeat(lineLength));\n    printRow(toDisplay, positions, printFn);\n    printFn('='.repeat(lineLength));\n    const layers = model.layers;\n    for (let i = 0; i < layers.length; ++i) {\n        if (sequentialLike) {\n            printLayerSummary(layers[i], positions, printFn);\n        }\n        else {\n            printLayerSummaryWithConnections(layers[i], positions, relevantNodes, printFn);\n        }\n        printFn((i === layers.length - 1 ? '=' : '_').repeat(lineLength));\n    }\n    // tslint:disable-next-line:no-any\n    model.checkTrainableWeightsConsistency();\n    const trainableCount = countTrainableParams(model);\n    const nonTrainableCount = countParamsInWeights(model.nonTrainableWeights);\n    printFn(`Total params: ${trainableCount + nonTrainableCount}`);\n    printFn(`Trainable params: ${trainableCount}`);\n    printFn(`Non-trainable params: ${nonTrainableCount}`);\n    printFn('_'.repeat(lineLength));\n}\nfunction countTrainableParams(model) {\n    let trainableCount;\n    // tslint:disable:no-any\n    if (model.collectedTrainableWeights != null) {\n        trainableCount =\n            countParamsInWeights(model.collectedTrainableWeights);\n    }\n    else {\n        trainableCount = countParamsInWeights(model.trainableWeights);\n    }\n    // tslint:enable:no-any\n    return trainableCount;\n}\nfunction isModelSequentialLike(model) {\n    let sequentialLike = true;\n    const nodesByDepth = [];\n    const nodes = [];\n    for (const depth in model.nodesByDepth) {\n        nodesByDepth.push(model.nodesByDepth[depth]);\n    }\n    for (const depthNodes of nodesByDepth) {\n        if (depthNodes.length > 1 ||\n            depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) {\n            sequentialLike = false;\n            break;\n        }\n        nodes.push(...depthNodes);\n    }\n    if (sequentialLike) {\n        // Search for shared layers.\n        for (const layer of model.layers) {\n            let flag = false;\n            for (const node of layer.inboundNodes) {\n                if (nodes.indexOf(node) !== -1) {\n                    if (flag) {\n                        sequentialLike = false;\n                        break;\n                    }\n                    else {\n                        flag = true;\n                    }\n                }\n            }\n            if (!sequentialLike) {\n                break;\n            }\n        }\n    }\n    return sequentialLike;\n}\nfunction printRow(fields, positions, \n// tslint:disable-next-line:no-any\nprintFn = console.log) {\n    let line = '';\n    for (let i = 0; i < fields.length; ++i) {\n        if (i > 0) {\n            line = line.slice(0, line.length - 1) + ' ';\n        }\n        line += fields[i];\n        line = line.slice(0, positions[i]);\n        line += ' '.repeat(positions[i] - line.length);\n    }\n    printFn(line);\n}\n/**\n * Prints a summary for a single Layer, without connectivity information.\n *\n * @param layer: Layer instance to print.\n */\nfunction printLayerSummary(layer, positions, \n// tslint:disable-next-line:no-any\nprintFn) {\n    let outputShape;\n    try {\n        outputShape = JSON.stringify(layer.outputShape);\n    }\n    catch (err) {\n        outputShape = 'multiple';\n    }\n    const name = layer.name;\n    const className = layer.getClassName();\n    const fields = [`${name} (${className})`, outputShape, layer.countParams().toString()];\n    printRow(fields, positions, printFn);\n}\n/**\n * Prints a summary for a single Layer, with connectivity information.\n */\nfunction printLayerSummaryWithConnections(layer, positions, relevantNodes, \n// tslint:disable-next-line:no-any\nprintFn) {\n    let outputShape;\n    try {\n        outputShape = JSON.stringify(layer.outputShape);\n    }\n    catch (err) {\n        outputShape = 'multiple';\n    }\n    const connections = [];\n    for (const node of layer.inboundNodes) {\n        if (relevantNodes != null && relevantNodes.length > 0 &&\n            relevantNodes.indexOf(node) === -1) {\n            continue;\n        }\n        for (let i = 0; i < node.inboundLayers.length; ++i) {\n            const inboundLayer = node.inboundLayers[i].name;\n            const inboundLayerIndex = node.nodeIndices[i];\n            const inboundTensorIndex = node.tensorIndices[i];\n            connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);\n        }\n    }\n    const name = layer.name;\n    const className = layer.getClassName();\n    const firstConnection = connections.length === 0 ? '' : connections[0];\n    const fields = [\n        `${name} (${className})`, outputShape, layer.countParams().toString(),\n        firstConnection\n    ];\n    printRow(fields, positions, printFn);\n    for (let i = 1; i < connections.length; ++i) {\n        printRow(['', '', '', connections[i]], positions, printFn);\n    }\n}\n//# sourceMappingURL=layer_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as generic_utils from '../utils/generic_utils';\n// tslint:enable\n/**\n * Test whether a value in an array is the name of a LayersModel or Layer.\n * @param key The key name that the value is found under. Note that the key\n *   may not be at the level immediately above the value, if the value is in a\n *   nested array.\n * @param index Index of the value in the Array that it is found in.\n * @param value The value object.\n * @returns A boolean indicating whether value is a name.\n */\nfunction isArrayItemInputOrOutputName(key, index, value) {\n    return (key === 'inboundNodes' || key === 'outputLayers' ||\n        key === 'inputLayers') &&\n        index === 0 && typeof value === 'string';\n}\n/**\n * Convert a Pythonic config object to TypeScript config object.\n * @param pythonicConfig The config object to convert.\n * @param key Optional key name of the object being converted.\n * @returns Result of the conversion.\n */\nexport function convertPythonicToTs(pythonicConfig, key) {\n    if (pythonicConfig === null) {\n        return null;\n    }\n    else if (typeof pythonicConfig === 'string') {\n        return generic_utils.toCamelCase(pythonicConfig);\n    }\n    else if ((typeof pythonicConfig === 'number') ||\n        (typeof pythonicConfig === 'boolean')) {\n        return pythonicConfig;\n    }\n    else if (pythonicConfig instanceof Array) {\n        const tsArray = [];\n        const arrayLength = pythonicConfig.length;\n        for (let i = 0; i < arrayLength; ++i) {\n            const item = pythonicConfig[i];\n            if (isArrayItemInputOrOutputName(key, i, item)) {\n                tsArray.push(item);\n            }\n            else {\n                tsArray.push(convertPythonicToTs(item, key));\n            }\n        }\n        return tsArray;\n    }\n    else {\n        const tsDict = {};\n        for (const pythonicKey of Object.keys(pythonicConfig)) {\n            const pythonicValue = pythonicConfig[pythonicKey];\n            if (pythonicKey === 'name' && typeof pythonicValue === 'string') {\n                // Special case the 'name' key with a string value. Name values, such as\n                // the names of LayersModel and Layer instances, should not undergo the\n                // camel-case conversion.\n                tsDict[pythonicKey] = pythonicValue;\n            }\n            else {\n                const tsKey = generic_utils.toCamelCase(pythonicKey);\n                tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey);\n            }\n        }\n        return tsDict;\n    }\n}\n/**\n * Convert a TypeScript config object to Python config object.\n * @param tsConfig The config object to convert.\n * @param key Optional key name of the object being converted.\n * @returns Result of the conversion.\n */\nexport function convertTsToPythonic(tsConfig, key) {\n    if (tsConfig === null || tsConfig === undefined) {\n        return null;\n    }\n    else if (typeof tsConfig === 'string') {\n        return generic_utils.toSnakeCase(tsConfig);\n    }\n    else if ((typeof tsConfig === 'number') || (typeof tsConfig === 'boolean')) {\n        return tsConfig;\n    }\n    else if (tsConfig instanceof Array) {\n        const pyArray = [];\n        const arrayLength = tsConfig.length;\n        for (let i = 0; i < arrayLength; ++i) {\n            const item = tsConfig[i];\n            if (isArrayItemInputOrOutputName(key, i, item)) {\n                pyArray.push(item);\n            }\n            else {\n                pyArray.push(convertTsToPythonic(item, key));\n            }\n        }\n        return pyArray;\n    }\n    else {\n        const pyDict = {};\n        for (const tsKey of Object.keys(tsConfig)) {\n            const tsValue = tsConfig[tsKey];\n            const pyKey = generic_utils.toSnakeCase(tsKey);\n            if ((tsKey === 'name' || tsKey === 'className') &&\n                typeof tsValue === 'string') {\n                // Special case the 'name' key with a string value. Name values, such as\n                // the names of LayersModel and Layer instances, should not undergo the\n                // snake-case conversion.\n                pyDict[pyKey] = tsValue;\n            }\n            else {\n                pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey);\n            }\n        }\n        return pyDict;\n    }\n}\n//# sourceMappingURL=serialization_utils.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Executor: Evaluates SymbolicTensor based on feeds.\n */\nimport { cast, dispose, memory, util } from '@tensorflow/tfjs-core';\nimport { ValueError } from '../errors';\nimport { toList } from '../utils/generic_utils';\nimport { InputLayer } from './input_layer';\nimport { SymbolicTensor } from './topology';\n/**\n * Helper function to check the dtype and shape compatibility of a feed value.\n */\nfunction assertFeedCompatibility(key, val) {\n    // Check dtype compatibility.\n    if (key.dtype == null || key.dtype === val.dtype) {\n        //  a.  If types match, return val tensor as is.\n        return val;\n    }\n    try {\n        //  b. Attempt to convert to expected type.\n        return cast(val, key.dtype);\n    }\n    catch (err) {\n        //  c. If conversion fails, return helpful error.\n        throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype ` +\n            `of the key '${key.name}' (${key.dtype}).`);\n    }\n}\n/**\n * FeedDict: A mapping from unique SymbolicTensors to feed values for them.\n * A feed value is a concrete value represented as an `Tensor`.\n */\nexport class FeedDict {\n    /**\n     * Constructor, optionally does copy-construction.\n     * @param feeds An Array of `Feed`s, or another `FeedDict`, in which case\n     *   copy-construction will be performed.\n     */\n    constructor(feeds) {\n        this.id2Value = {};\n        this.id2Mask = {};\n        this.name2Id = {};\n        if (feeds instanceof FeedDict) {\n            for (const id in feeds.id2Value) {\n                this.id2Value[id] = feeds.id2Value[id];\n                if (id in feeds.id2Mask) {\n                    this.id2Mask[id] = feeds.id2Mask[id];\n                }\n            }\n        }\n        else {\n            if (feeds == null) {\n                return;\n            }\n            for (const feed of feeds) {\n                this.add(feed.key, feed.value);\n            }\n        }\n    }\n    /**\n     * Add a key-value pair to the FeedDict.\n     *\n     * @param key The key of the feed.\n     * @param value The value of the tensor feed.\n     * @param mask The value of the mask feed (optional).\n     * @returns This `FeedDict`.\n     * @throws ValueError: If the key `SymbolicTensor` already exists in the\n     *   `FeedDict`.\n     */\n    add(key, value, mask) {\n        if (this.id2Value[key.id] == null) {\n            this.id2Value[key.id] = assertFeedCompatibility(key, value);\n            this.name2Id[key.name] = key.id;\n            if (mask != null) {\n                this.id2Mask[key.id] = mask;\n            }\n        }\n        else {\n            throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);\n        }\n        return this;\n    }\n    /**\n     * Add a Feed to the FeedDict.\n     * @param feed The new `Feed` to add.\n     * @returns This `FeedDict`.\n     */\n    addFeed(feed) {\n        this.add(feed.key, feed.value);\n    }\n    /**\n     * Probe whether a key already exists in the FeedDict.\n     * @param key\n     */\n    hasKey(key) {\n        return this.id2Value[key.id] != null;\n    }\n    /**\n     * Get all the SymbolicTensor available in this FeedDict.\n     */\n    names() {\n        return Object.keys(this.name2Id);\n    }\n    /**\n     * Get the feed value for given key.\n     * @param key The SymbolicTensor, or its name (as a string), of which the\n     *     value is sought.\n     * @returns If `key` exists, the corresponding feed value.\n     * @throws ValueError: If `key` does not exist in this `FeedDict`.\n     */\n    getValue(key) {\n        if (key instanceof SymbolicTensor) {\n            if (this.id2Value[key.id] == null) {\n                throw new ValueError(`Nonexistent key: ${key.name}`);\n            }\n            else {\n                return this.id2Value[key.id];\n            }\n        }\n        else {\n            const id = this.name2Id[key];\n            if (id == null) {\n                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n            }\n            return this.id2Value[id];\n        }\n    }\n    /**\n     * Get the feed mask for given key.\n     * @param key The SymbolicTensor, or its name (as a string), of which the\n     *     value is sought.\n     * @returns If `key` exists, the corresponding feed mask.\n     * @throws ValueError: If `key` does not exist in this `FeedDict`.\n     */\n    getMask(key) {\n        if (key instanceof SymbolicTensor) {\n            if (this.id2Value[key.id] == null) {\n                throw new ValueError(`Nonexistent key: ${key.name}`);\n            }\n            else {\n                return this.id2Mask[key.id];\n            }\n        }\n        else {\n            const id = this.name2Id[key];\n            if (id == null) {\n                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n            }\n            return this.id2Mask[id];\n        }\n    }\n    /** Dispose all mask Tensors held by this object. */\n    disposeMasks() {\n        if (this.id2Mask != null) {\n            dispose(this.id2Mask);\n        }\n    }\n}\n// Cache for topologically sorted SymbolicTensors for given execution\n// targets (i.e., fetches).\nconst cachedSorted = {};\n// Cache for recipient count maps for given execution targets (i.e., fetches).\nconst cachedRecipientCounts = {};\n/**\n * Execute a SymbolicTensor by using concrete feed values.\n *\n * A `SymbolicTensor` object is a node in a computation graph of TF.js\n * Layers. The object is backed by a source layer and input\n * `SymbolicTensor`s to the source layer. This method evaluates\n * the `call()` method of the source layer, using concrete values of the\n * inputs obtained from either\n * * `feedDict`, if the input key exists in `feedDict`, or else,\n * * a recursive call to `execute()` itself.\n *\n * @param x: The `SymbolicTensor` to execute.\n * @param feedDict: The feed values, as base condition of the recursion.\n *   execution.\n * @param kwargs: Optional keyword arguments.\n * @param probe: A probe object (of interface `ExecutionProbe`) used for\n *   testing memory footprint of `execute` calls.\n * @returns Result of the execution.\n * @throws ValueError: If any `SymbolicTensor`s from `InputLayer`s\n *   encountered during the execution lacks a feed value in `feedDict`.\n */\nexport function execute(fetches, feedDict, kwargs, probe) {\n    const training = kwargs == null ? false : kwargs['training'];\n    const arrayFetches = Array.isArray(fetches);\n    const fetchArray = arrayFetches ? fetches : [fetches];\n    const outputNames = fetchArray.map(t => t.name);\n    const finalOutputs = [];\n    const feedNames = feedDict.names();\n    for (const outputName of outputNames) {\n        if (feedNames.indexOf(outputName) !== -1) {\n            finalOutputs.push(feedDict.getValue(outputName));\n        }\n        else {\n            finalOutputs.push(null);\n        }\n    }\n    if (probe != null) {\n        // For optional probing of memory footprint during execution.\n        probe.maxNumTensors = -Infinity;\n        probe.minNumTensors = Infinity;\n    }\n    // Check cache.\n    const fetchAndFeedKey = outputNames.join(',') + '|' + feedDict.names().join(',');\n    let sorted;\n    let recipientCounts;\n    if (cachedSorted[fetchAndFeedKey] == null) {\n        // Cache doesn't contain the desired combination of fetches. Compute\n        // topological sort for the combination for the first time.\n        const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);\n        sorted = out.sorted;\n        recipientCounts = out.recipientCounts;\n        // Store results in cache for future use.\n        cachedSorted[fetchAndFeedKey] = sorted;\n        cachedRecipientCounts[fetchAndFeedKey] = recipientCounts;\n    }\n    sorted = cachedSorted[fetchAndFeedKey];\n    recipientCounts = {};\n    if (!training) {\n        Object.assign(recipientCounts, cachedRecipientCounts[fetchAndFeedKey]);\n    }\n    const internalFeedDict = new FeedDict(feedDict);\n    // Start iterative execution on the topologically-sorted SymbolicTensors.\n    for (let i = 0; i < sorted.length; ++i) {\n        if (probe != null) {\n            // For optional probing of memory usage during execution.\n            const numTensors = memory().numTensors;\n            if (numTensors > probe.maxNumTensors) {\n                probe.maxNumTensors = numTensors;\n            }\n            if (numTensors < probe.minNumTensors) {\n                probe.minNumTensors = numTensors;\n            }\n        }\n        const symbolic = sorted[i];\n        const srcLayer = symbolic.sourceLayer;\n        if (srcLayer instanceof InputLayer) {\n            continue;\n        }\n        const inputValues = [];\n        const inputMasks = [];\n        const tensorsToDispose = [];\n        let maskExists = false;\n        for (const input of symbolic.inputs) {\n            const value = internalFeedDict.getValue(input);\n            const mask = internalFeedDict.getMask(input);\n            inputValues.push(value);\n            inputMasks.push(mask);\n            if (mask != null) {\n                maskExists = true;\n            }\n            if (!training) {\n                recipientCounts[input.name]--;\n                if (recipientCounts[input.name] === 0 && !feedDict.hasKey(input) &&\n                    outputNames.indexOf(input.name) === -1 && !value.isDisposed &&\n                    input.sourceLayer.stateful !== true) {\n                    tensorsToDispose.push(value);\n                }\n            }\n        }\n        if (maskExists) {\n            kwargs = kwargs || {};\n            kwargs['mask'] = inputMasks[0];\n        }\n        const outputTensors = toList(srcLayer.apply(inputValues, kwargs));\n        let outputMask = null;\n        if (srcLayer.supportsMasking) {\n            outputMask = srcLayer.computeMask(inputValues, inputMasks);\n        }\n        const layerOutputs = getNodeOutputs(symbolic);\n        const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];\n        for (let i = 0; i < outputSymbolicTensors.length; ++i) {\n            if (!internalFeedDict.hasKey(outputSymbolicTensors[i])) {\n                internalFeedDict.add(outputSymbolicTensors[i], outputTensors[i], Array.isArray(outputMask) ? outputMask[0] : outputMask);\n            }\n            const index = outputNames.indexOf(outputSymbolicTensors[i].name);\n            if (index !== -1) {\n                finalOutputs[index] = outputTensors[i];\n            }\n        }\n        if (!training) {\n            // Clean up Tensors that are no longer needed.\n            dispose(tensorsToDispose);\n        }\n    }\n    // NOTE(cais): Unlike intermediate tensors, we don't discard mask\n    // tensors as we go, because these tensors are sometimes passed over a\n    // series of mutliple layers, i.e., not obeying the immediate input\n    // relations in the graph. If this becomes a memory-usage concern,\n    // we can improve this in the future.\n    internalFeedDict.disposeMasks();\n    return arrayFetches ? finalOutputs : finalOutputs[0];\n}\n/**\n * Sort the `SymbolicTensor`s topologically, for an array of fetches.\n *\n * This function calls getTopologicalSortAndRecipientCountsForOneFetch and\n * merges their results.\n *\n * @param fetch The array of fetches requested. Must be a non-empty array.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientCounts: Recipient counts for all SymbolicTensors in `sorted`.\n */\nfunction getTopologicalSortAndRecipientCounts(fetches, feedDict) {\n    util.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`);\n    let finalSorted = [];\n    let finalRecipientMap = {};\n    if (fetches.length === 1) {\n        // Special-casing 1 fetch for efficiency.\n        const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);\n        finalSorted = out.sorted;\n        finalRecipientMap = out.recipientMap;\n    }\n    else {\n        const visited = new Set();\n        for (const fetch of fetches) {\n            const { sorted, recipientMap } = getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict);\n            // Merge sorted SymbolicTensor Arrays.\n            for (const symbolicTensor of sorted) {\n                if (!visited.has(symbolicTensor.name)) {\n                    finalSorted.push(symbolicTensor);\n                    visited.add(symbolicTensor.name);\n                }\n            }\n            // Merge recipient maps.\n            for (const name in recipientMap) {\n                if (finalRecipientMap[name] == null) {\n                    finalRecipientMap[name] = new Set();\n                }\n                recipientMap[name].forEach(recipient => finalRecipientMap[name].add(recipient));\n            }\n        }\n    }\n    return {\n        sorted: finalSorted,\n        recipientCounts: recipientMap2Counts(finalRecipientMap)\n    };\n}\nfunction recipientMap2Counts(recipientMap) {\n    const recipientCounts = {};\n    for (const name in recipientMap) {\n        recipientCounts[name] = recipientMap[name].size;\n    }\n    return recipientCounts;\n}\n/**\n * Sort the `SymbolicTensor`s topologically, for a single fetch.\n *\n * This helper function processes the upstream SymbolicTensors of a single\n * fetch.\n *\n * @param fetch The single fetch requested.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientMap: Recipient names for all SymbolicTensors in `sorted`.\n */\nexport function getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict) {\n    const visited = new Set();\n    const sorted = [];\n    const recipientMap = {};\n    // Put keys of the feedDict into visited first, so they don't have to be\n    // walked. This is needed in case where there are feeds for intermediate\n    // SymbolicTensors of the graph.\n    for (const key of feedDict.names()) {\n        visited.add(key);\n    }\n    const stack = [];\n    const marks = [];\n    // Initial population of stack and marks.\n    stack.push(fetch);\n    while (stack.length > 0) {\n        const top = stack[stack.length - 1];\n        if (visited.has(top.name)) {\n            stack.pop();\n            continue;\n        }\n        const topIsMarked = marks[marks.length - 1] === stack.length - 1;\n        if (top.inputs.length === 0 || topIsMarked) {\n            // Input SymbolicTensor or all children have been visited.\n            stack.pop();\n            sorted.push(top);\n            visited.add(top.name);\n            if (topIsMarked) {\n                marks.pop();\n            }\n        }\n        else {\n            // A non-input SymbolicTensor whose upstream SymbolicTensors haven't\n            // been visited yet. Push them onto the stack.\n            marks.push(stack.length - 1);\n            for (const input of top.inputs) {\n                // Increment the recipient count. Note that this needs to happen\n                // regardless of whether the SymbolicTensor has been visited before.\n                if (recipientMap[input.name] == null) {\n                    recipientMap[input.name] = new Set();\n                }\n                recipientMap[input.name].add(top.name);\n                if (visited.has(input.name)) {\n                    continue; // Avoid repeated visits to the same SymbolicTensor.\n                }\n                stack.push(input);\n            }\n        }\n    }\n    return { sorted, recipientMap };\n}\n/**\n * Get the symbolic output tensors of the node to which a given fetch belongs.\n * @param fetch The fetched symbolic tensor.\n * @returns The Array of symbolic tensors output by the node to which `fetch`\n *   belongs.\n */\nfunction getNodeOutputs(fetch) {\n    let layerOutputs;\n    if (fetch.sourceLayer.inboundNodes.length === 1) {\n        layerOutputs = fetch.sourceLayer.output;\n    }\n    else {\n        let nodeIndex = null;\n        for (let i = 0; i < fetch.sourceLayer.inboundNodes.length; ++i) {\n            for (const outputTensor of fetch.sourceLayer.inboundNodes[i]\n                .outputTensors) {\n                if (outputTensor.id === fetch.id) {\n                    nodeIndex = i;\n                    break;\n                }\n            }\n        }\n        layerOutputs = fetch.sourceLayer.getOutputAt(nodeIndex);\n    }\n    return layerOutputs;\n}\n//# sourceMappingURL=executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/engine/topology.py */\nimport { tidy } from '@tensorflow/tfjs-core';\nimport { getUid } from '../backend/state';\nimport { NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { deserialize as deserializeLayer } from '../layers/serialization';\nimport * as generic_utils from '../utils/generic_utils';\nimport { convertTsToPythonic } from '../utils/serialization_utils';\nimport * as types_utils from '../utils/types_utils';\nimport { batchSetValue } from '../variables';\nimport { version as layersVersion } from '../version';\nimport { execute, FeedDict } from './executor';\nimport { InputLayer } from './input_layer';\nimport { Layer, Node } from './topology';\n/**\n * A Container is a directed acyclic graph of layers.\n *\n * It is the topological form of a \"model\". A LayersModel\n * is simply a Container with added training routines.\n *\n */\nexport class Container extends Layer {\n    constructor(args) {\n        // No args passed to super's constructor.\n        super({});\n        this.containerNodes = new Set();\n        this.name = args.name;\n        if (this.name == null) {\n            const prefix = this.getClassName().toLowerCase();\n            this.name = getUid(prefix);\n        }\n        this.supportsMasking = false;\n        this.trainable_ = true;\n        // TODO(michaelterry): Initialize perInputLosses/Updates here.\n        // Container-specific properties.\n        if (Array.isArray(args.inputs)) {\n            this.inputs = args.inputs.slice();\n        }\n        else {\n            this.inputs = [args.inputs];\n        }\n        if (Array.isArray(args.outputs)) {\n            this.outputs = args.outputs.slice();\n        }\n        else {\n            this.outputs = [args.outputs];\n        }\n        // Check for redundancy in inputs.\n        if (generic_utils.unique(this.inputs).length !== this.inputs.length) {\n            throw new ValueError('The list of inputs passed to the model is ' +\n                'redundant. All inputs should only appear once. Found: ' +\n                `${this.inputs.map(x => x.name)}`);\n        }\n        // Check for redundancy in outputs.\n        if (generic_utils.unique(this.outputs).length !== this.outputs.length) {\n            console.warn('The list of outputs passed to the model is redundant. ' +\n                'All outputs should only appear once. Found: ' +\n                `${this.outputs.map(x => x.name)}`);\n        }\n        /*\n          List of initial layers (1 to 1 mapping with this.inputs, hence the same\n          layer might appear twice)\n        */\n        this.inputLayers = [];\n        this.inputLayersNodeIndices = [];\n        this.inputLayersTensorIndices = [];\n        /*\n          List of layers (1 to 1 mapping with this.outputs, hence the same layer\n          might appear twice)\n        */\n        this.outputLayers = [];\n        this.outputLayersNodeIndices = [];\n        this.outputLayersTensorIndices = [];\n        /*\n          All layers in order of horizontal graph traversal. Entries are unique.\n          Includes input and output layers.\n        */\n        this.layers = [];\n        /*\n          References to container layers that were constructed internally. We need\n          these to properly dispose of tensors from nested containers.\n        */\n        this.internalContainerRefs = [];\n        // TODO(michaelterry): Determine if caching still needed with eager\n        // backend.\n        /*\n          This is for performance optimization when calling the Container on new\n          inputs. Every time the Container is called on a set on input tensors,\n          we compute the output tensors, output masks and output shapes in one pass,\n          then cache them here. When one of these outputs is queried later,\n          we retrieve it from there instead of recomputing it.\n        */\n        // this.outputTensorCache = {};\n        // this.outputShapeCache = {};\n        // Build this.outputLayers:\n        for (const x of this.outputs) {\n            const layer = x.sourceLayer;\n            const nodeIndex = x.nodeIndex;\n            const tensorIndex = x.tensorIndex;\n            this.outputLayers.push(layer);\n            this.outputLayersNodeIndices.push(nodeIndex);\n            this.outputLayersTensorIndices.push(tensorIndex);\n        }\n        // TODO(michaelterry): Add output mask cache code.\n        // Build this.inputLayers:\n        for (const x of this.inputs) {\n            const layer = x.sourceLayer;\n            const nodeIndex = x.nodeIndex;\n            const tensorIndex = x.tensorIndex;\n            /*\n              It's supposed to be an input layer, so only one node\n              and one tensor output.\n            */\n            generic_utils.assert(nodeIndex === 0, 'input layer has >1 nodes');\n            generic_utils.assert(tensorIndex === 0, 'input layer has >1 tensors');\n            this.inputLayers.push(layer);\n            this.inputLayersNodeIndices.push(nodeIndex);\n            this.inputLayersTensorIndices.push(tensorIndex);\n        }\n        // Build this.inputNames and this.outputNames.\n        this.inputNames = [];\n        this.outputNames = [];\n        this.feedInputShapes = [];\n        this.feedInputNames = [];\n        this.feedOutputNames = [];\n        for (let i = 0; i < this.inputLayers.length; i++) {\n            const layer = this.inputLayers[i];\n            // Check that layer is an InputLayer.\n            if (!(layer instanceof InputLayer)) {\n                throw new TypeError('Input layers to a LayersModel must be InputLayer objects. ' +\n                    `Received inputs: ${args.inputs}. ` +\n                    `Input ${i} (0-based) originates ` +\n                    `from layer type ${layer.getClassName()}.`);\n            }\n            this.inputNames.push(layer.name);\n            this.feedInputShapes.push(layer.batchInputShape);\n            this.feedInputNames.push(layer.name);\n        }\n        for (const layer of this.outputLayers) {\n            this.outputNames.push(layer.name);\n        }\n        this.internalInputShapes = this.inputs.map(x => x.shape);\n        this.internalOutputShapes = this.outputs.map(x => x.shape);\n        /*\n          Container_nodes: set of nodes included in the graph (not all nodes\n          included in the layers are relevant to the current graph).\n        */\n        // ids of all nodes relevant to the Container:\n        const nodesDepths = {};\n        // To recover nodes from their ID.\n        const nodeIDToNode = {};\n        const layersDepths = {};\n        // To layers from their ID.\n        const layerIDToLayer = {};\n        const layerIndices = {};\n        const nodesInDecreasingDepth = [];\n        /**\n         * Builds a map of the graph of layers.\n         *\n         * This recursively updates the map `layerIndices`,\n         * the list `nodesInDecreasingDepth` and the set `containerNodes`.\n         *\n         * @param tensor Some tensor in a graph.\n         * @param finishedNodes Set of nodes whose subgraphs have been traversed\n         *         completely. Useful to prevent duplicated work.\n         * @param nodesInProgress Set of nodes that are currently active on the\n         *         recursion stack. Useful to detect cycles.\n         * @param layer Layer from which `tensor` comes from. If not provided,\n         *   will be obtained from tensor.sourceLayer.\n         * @param nodeIndex Node index from which `tensor` comes from.\n         * @param tensorIndex TensorIndex from which `tensor` comes from.\n         *\n         * @exception RuntimeError if a cycle is detected.\n         */\n        const buildMapOfGraph = (tensor, finishedNodes, nodesInProgress, layer, nodeIndex, tensorIndex) => {\n            if (layer == null || nodeIndex == null || tensorIndex == null) {\n                layer = tensor.sourceLayer;\n                nodeIndex = tensor.nodeIndex;\n                tensorIndex = tensor.tensorIndex;\n            }\n            const node = layer.inboundNodes[nodeIndex];\n            // Prevent cycles.\n            if (nodesInProgress.indexOf(node) !== -1) {\n                throw new RuntimeError(`The tensor ${tensor.name} at layer \"${layer.name}\" ` +\n                    'is part of a cycle.');\n            }\n            // Don't repeat work for shared subgraphs\n            if (finishedNodes.indexOf(node) !== -1) {\n                return;\n            }\n            // Update containerNodes.\n            this.containerNodes.add(Container.nodeKey(layer, nodeIndex));\n            // Store the traversal order for layer sorting.\n            if (!(layer.id in layerIndices)) {\n                layerIndices[layer.id] = Object.keys(layerIndices).length;\n            }\n            if (nodesInProgress.indexOf(node) === -1) {\n                nodesInProgress.push(node);\n            }\n            // Propagate to all previous tensors connected to this node.\n            const numInboundLayers = node.inboundLayers.length;\n            for (let i = 0; i < numInboundLayers; i++) {\n                const x = node.inputTensors[i];\n                const layer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const tensorIndex = node.tensorIndices[i];\n                buildMapOfGraph(x, finishedNodes, nodesInProgress, layer, nodeIndex, tensorIndex);\n            }\n            finishedNodes.push(node);\n            while (nodesInProgress.indexOf(node) >= 0) {\n                nodesInProgress.splice(nodesInProgress.indexOf(node), 1);\n            }\n            nodesInDecreasingDepth.push(node);\n        };\n        const finishedNodes = [];\n        const nodesInProgress = [];\n        for (const x of this.outputs) {\n            buildMapOfGraph(x, finishedNodes, nodesInProgress);\n        }\n        const reversedNodesInDecreasingDepth = nodesInDecreasingDepth.slice().reverse();\n        for (const node of reversedNodesInDecreasingDepth) {\n            nodeIDToNode[node.id] = node;\n            // If the depth is not set, the node has no outbound nodes (depth 0).\n            if (!(node.id in nodesDepths)) {\n                nodesDepths[node.id] = 0;\n            }\n            let depth = nodesDepths[node.id];\n            // Update the depth of the corresponding layer\n            const previousDepth = (layersDepths[node.outboundLayer.id] == null ?\n                0 :\n                layersDepths[node.outboundLayer.id]);\n            /*\n              If we've seen this layer before at a higher depth, we should use that\n              depth instead of the node depth.  This is necessary for shared layers\n              that have inputs at different depth levels in the graph.\n            */\n            depth = Math.max(depth, previousDepth);\n            layersDepths[node.outboundLayer.id] = depth;\n            layerIDToLayer[node.outboundLayer.id] = node.outboundLayer;\n            nodesDepths[node.id] = depth;\n            // Update the depth of inbound nodes.\n            for (let i = 0; i < node.inboundLayers.length; i++) {\n                const inboundLayer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const inboundNode = inboundLayer.inboundNodes[nodeIndex];\n                const previousDepth = (nodesDepths[inboundNode.id] == null ? 0 :\n                    nodesDepths[inboundNode.id]);\n                nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth);\n                nodeIDToNode[inboundNode.id] = inboundNode;\n            }\n        }\n        // Build a dict {depth: list of nodes with this depth}\n        const nodesByDepth = {};\n        for (const nodeID in nodesDepths) {\n            const depth = nodesDepths[nodeID];\n            if (!(depth in nodesByDepth)) {\n                nodesByDepth[depth] = [];\n            }\n            nodesByDepth[depth].push(nodeIDToNode[nodeID]);\n        }\n        // Build a dict {depth: list of layers with this depth}\n        const layersByDepth = {};\n        for (const layerID in layersDepths) {\n            const depth = layersDepths[layerID];\n            if (!(depth in layersByDepth)) {\n                layersByDepth[depth] = [];\n            }\n            layersByDepth[depth].push(layerIDToLayer[layerID]);\n        }\n        // Get sorted list of layer depths.\n        let depthKeys = Object.keys(layersByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Set this.layers and this.layersByDepth.\n        this.layers = [];\n        for (const depth of depthKeys) {\n            const layersForDepth = layersByDepth[depth];\n            // Container.layers needs to have a deterministic order:\n            // here we order them by traversal order.\n            layersForDepth.sort((a, b) => {\n                const aIndex = layerIndices[a.id];\n                const bIndex = layerIndices[b.id];\n                if (aIndex < bIndex) {\n                    return -1;\n                }\n                if (aIndex > bIndex) {\n                    return 1;\n                }\n                return 0;\n            });\n            for (const layer of layersForDepth) {\n                if (layer instanceof Container) {\n                    this.internalContainerRefs.push(layer);\n                }\n                this.layers.push(layer);\n            }\n        }\n        this.layersByDepth = layersByDepth;\n        // Get sorted list of node depths;\n        depthKeys = Object.keys(nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Check that all tensors required are computable.\n        // computable_tensors: all tensors in the graph\n        // that can be computed from the inputs provided.\n        const computableTensors = this.inputs.slice();\n        // To provide a better error msg.\n        const layersWithCompleteInput = [];\n        for (const depth of depthKeys) {\n            for (const node of nodesByDepth[depth]) {\n                const layer = node.outboundLayer;\n                if (layer != null) {\n                    for (const x of node.inputTensors) {\n                        if (computableTensors.indexOf(x) === -1) {\n                            throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x}` +\n                                ` at layer \"${layer.name}\". ` +\n                                'The following previous layers were accessed without ' +\n                                `issue: ${layersWithCompleteInput}`);\n                        }\n                    }\n                    for (const x of node.outputTensors) {\n                        computableTensors.push(x);\n                    }\n                    layersWithCompleteInput.push(layer.name);\n                }\n            }\n        }\n        // Set this.containerNodes and this.nodesByDepth.\n        this.nodesByDepth = nodesByDepth;\n        // Ensure name unicity, which will be crucial for serialization\n        // (since serialized nodes refer to layers by their name).\n        const allNames = this.layers.map(x => x.name);\n        for (const name of allNames) {\n            const numOccurrences = allNames.filter(x => x === name).length;\n            if (numOccurrences !== 1) {\n                throw new RuntimeError(`The name \"${name}\" is used ${numOccurrences} times ` +\n                    'in the model. All layer names should be unique. Layer names: ' +\n                    JSON.stringify(allNames));\n            }\n        }\n        // Layer parameters.\n        // The new container starts with a single inbound node\n        // for its inputs, and no outbound nodes.\n        // Will be appended to by future calls to apply().\n        this.outboundNodes = [];\n        // Will be appended to below, and by future calls to apply().\n        this.inboundNodes = [];\n        // Create the node linking internal inputs to internal outputs.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers: [],\n            nodeIndices: [],\n            tensorIndices: [],\n            inputTensors: this.inputs,\n            outputTensors: this.outputs,\n            inputMasks: this.inputs.map(x => null),\n            outputMasks: this.outputs.map(x => null),\n            inputShapes: this.inputs.map(x => x.shape),\n            outputShapes: this.outputs.map(x => x.shape)\n        });\n        this.built = true;\n        this._refCount = 1; // The ref count of a container always start at 1.\n    }\n    assertNotDisposed() {\n        if (this._refCount === 0) {\n            throw new Error(`Container '${this.name}' is already disposed.`);\n        }\n    }\n    /**\n     * Attempt to dispose a LayersModel's weights.\n     *\n     * This method decrease the reference count of the LayersModel object by 1.\n     *\n     * A LayersModel is reference-counted. Its reference count is incremented by 1\n     * when it is first constructed and when it is used as a Layer of another\n     * LayersModel.\n     *\n     * If the reference count of a LayersModel becomes 0, the `dispose` method of\n     * all its constituent `Layer`s will be called.\n     *\n     * Note: If the reference count is greater than 0 after the decrement, the\n     * `dispose` method of its constituent `Layer`s will *not* be called.\n     *\n     * After a LayersModel is disposed, it cannot be used in calls such as\n     * 'predict`, `evaluate` or `fit` anymore.\n     *\n     * @returns A DisposeResult Object with the following fields:\n     *   - refCountAfterDispose: The reference count of the LayersModel after this\n     *     `dispose()` call.\n     *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n     *     during this `dispose()` call.\n     * @throws {Error} If the layer is not built yet, or if the LayersModel has\n     *   already been disposed.\n     */\n    dispose() {\n        this.assertNotDisposed();\n        const result = { refCountAfterDispose: null, numDisposedVariables: 0 };\n        if (--this._refCount === 0) {\n            for (const layer of this.layers) {\n                result.numDisposedVariables += layer.dispose().numDisposedVariables;\n            }\n            // Call dispose on each internally created container layer again to ensure\n            // their refCounts hit zero and their tensors are subsequently deleted.\n            for (const container of this.internalContainerRefs) {\n                result.numDisposedVariables += container.dispose().numDisposedVariables;\n            }\n        }\n        result.refCountAfterDispose = this._refCount;\n        return result;\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this.layers.forEach(layer => {\n            // tslint:disable-next-line:no-any\n            layer._trainableWeights\n                .forEach(w => w.trainable = trainable);\n        });\n        this.trainable_ = trainable;\n    }\n    get trainableWeights() {\n        // Porting Note: This check below is to prevent errors where the\n        //   _trainableWeights inherited from the parent class (Layer) gets\n        //   inadvertently used.\n        if (this._trainableWeights.length > 0) {\n            throw new ValueError('Container instance unexpectedly contains _trainableWeights.' +\n                'The trainable weights of a Container are a union of the ' +\n                'trainable weights of its consituent Layers. Its own ' +\n                '_trainableWeights must remain an empty Array.');\n        }\n        if (!this.trainable) {\n            return [];\n        }\n        let weights = [];\n        for (const layer of this.layers) {\n            weights = weights.concat(layer.trainableWeights);\n        }\n        return weights;\n    }\n    get nonTrainableWeights() {\n        const weights = [];\n        for (const layer of this.layers) {\n            weights.push(...layer.nonTrainableWeights);\n        }\n        if (!this.trainable) {\n            const trainableWeights = [];\n            for (const layer of this.layers) {\n                trainableWeights.push(...layer.trainableWeights);\n            }\n            return trainableWeights.concat(weights);\n        }\n        return weights;\n    }\n    get weights() {\n        return this.trainableWeights.concat(this.nonTrainableWeights);\n    }\n    /**\n     * Loads all layer weights from a JSON object.\n     *\n     * Porting Note: HDF5 weight files cannot be directly loaded in JavaScript /\n     *   TypeScript. The utility script at `scripts/pykeras.py` offers means\n     *   to convert them into JSON strings compatible with this method.\n     * Porting Note: TensorFlow.js Layers supports only loading by name currently.\n     *\n     * @param weights A JSON mapping weight names to weight values as nested\n     *   arrays of numbers, or a `NamedTensorMap`, i.e., a JSON mapping weight\n     *   names to `tf.Tensor` objects.\n     * @param strict Require that the provided weights exactly match those\n     *   required by the container.  Default: `true`.  Passing `false` means that\n     *   extra weights and missing weights will be silently ignored.\n     */\n    loadWeights(weights, strict = true) {\n        const nameToWeight = {};\n        let totalWeightsCount = 0;\n        for (const layer of this.layers) {\n            for (const weight of layer.weights) {\n                if (nameToWeight[weight.originalName] != null) {\n                    throw new ValueError(`Duplicate weight name: ${weight.originalName}`);\n                }\n                nameToWeight[weight.originalName] = weight;\n                totalWeightsCount++;\n            }\n        }\n        const weightValueTuples = [];\n        for (const name in weights) {\n            // TF 2.2.0 added cell name to the weight name in the format of\n            // layer_name/cell_name/weight_name, we need to remove\n            // the inner cell name.\n            let validatedName = name;\n            if (nameToWeight[name] == null) {\n                const tokens = name.split('/');\n                const shortenNameArray = tokens.slice(0, -2).concat([tokens[tokens.length - 1]]);\n                validatedName = shortenNameArray.join('/');\n            }\n            if (nameToWeight[validatedName] != null) {\n                weightValueTuples.push([nameToWeight[validatedName], weights[name]]);\n            }\n            else if (strict) {\n                throw new ValueError(`Provided weight data has no target variable: ${name}`);\n            }\n            delete nameToWeight[validatedName];\n        }\n        if (strict) {\n            // Check that all weights are set.\n            const unsetNames = [];\n            for (const name in nameToWeight) {\n                unsetNames.push(name);\n            }\n            if (unsetNames.length > 0) {\n                throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ` +\n                    `${unsetNames}`);\n            }\n        }\n        batchSetValue(weightValueTuples);\n    }\n    /**\n     * Util shared between different serialization methods.\n     * @returns LayersModel config with Keras version information added.\n     */\n    updatedConfig() {\n        const theConfig = this.getConfig();\n        const modelConfig = {};\n        modelConfig['className'] = this.getClassName();\n        modelConfig['config'] = theConfig;\n        modelConfig['kerasVersion'] = `tfjs-layers ${layersVersion}`;\n        // TODO(nielsene): Replace something like K.backend() once\n        // possible.\n        modelConfig['backend'] = 'TensorFlow.js';\n        return modelConfig;\n    }\n    /**\n     * Returns a JSON string containing the network configuration.\n     *\n     * To load a network from a JSON save file, use\n     * models.modelFromJSON(jsonString);\n     * @param extraJsonArgs Unused in tfjs-layers, maintained for PyKeras\n     * @param returnString Whether the return value should be stringified\n     *    (default: `true`).\n     * @returns a JSON string if `returnString` (default), or a JSON object if\n     *   `!returnString`.\n     */\n    // tslint:disable-next-line:no-any\n    toJSON(unused, returnString = true) {\n        const modelConfig = convertTsToPythonic(this.updatedConfig());\n        return returnString ? JSON.stringify(modelConfig) : modelConfig;\n    }\n    /**\n     * Call the model on new inputs.\n     *\n     * In this case `call` just reapplies all ops in the graph to the new inputs\n     * (e.g. build a new computational graph from the provided inputs).\n     *\n     * @param inputs A tensor or list of tensors.\n     * @param mask A mask or list of masks. A mask can be either a tensor or null\n     *   (no mask).\n     *\n     * @return A tensor if there is a single output, or a list of tensors if there\n     *   are more than one outputs.\n     */\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = generic_utils.toList(inputs);\n            const feedDict = new FeedDict();\n            for (let i = 0; i < this.inputs.length; ++i) {\n                feedDict.add(this.inputs[i], inputs[i]);\n            }\n            return execute(this.outputs, feedDict, kwargs);\n        });\n    }\n    /**\n     * Computes an output mask tensor.\n     *\n     * @param inputs Tensor or list of tensors.\n     * @param mask Tensor or list of tensors.\n     *\n     * @return null or a tensor (or list of tensors, one per output tensor of the\n     * layer).\n     */\n    computeMask(inputs, mask) {\n        return tidy(() => {\n            inputs = generic_utils.toList(inputs);\n            let masks;\n            if (mask == null) {\n                masks = generic_utils.pyListRepeat(null, inputs.length);\n            }\n            else {\n                masks = generic_utils.toList(mask);\n            }\n            // TODO(michaelterry): Add support for mask caching.\n            return this.runInternalGraph(inputs, masks)[1];\n        });\n    }\n    /**\n     * Computes the output shape of the layer.\n     *\n     * Assumes that the layer will be built to match that input shape provided.\n     *\n     * @param inputShape A shape (tuple of integers) or a list of shape tuples\n     *   (one per output tensor of the layer). Shape tuples can include null for\n     *   free dimensions, instead of an integer.\n     */\n    computeOutputShape(inputShape) {\n        const inputShapes = types_utils.normalizeShapeList(inputShape);\n        if (inputShapes.length !== this.inputLayers.length) {\n            throw new ValueError(`Invalid inputShape argument ${inputShape}: ` +\n                `model has ${this.inputLayers.length} tensor inputs.`);\n        }\n        // TODO(michaelterry): Add caching\n        const layersToOutputShapes = {};\n        for (let i = 0; i < inputShapes.length; i++) {\n            const layer = this.inputLayers[i];\n            const inputShape = inputShapes[i];\n            // It's an input layer: computeOutputShape is identity,\n            // and there is only one node and one tensor output.\n            const shapeKey = layer.name + '_0_0';\n            layersToOutputShapes[shapeKey] = inputShape;\n        }\n        const depthKeys = Object.keys(this.nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Iterate over nodes, by depth level.\n        if (depthKeys.length > 1) {\n            for (const depth of depthKeys) {\n                const nodes = this.nodesByDepth[depth];\n                for (const node of nodes) {\n                    // This is always a single layer, never a list.\n                    const layer = node.outboundLayer;\n                    if (this.inputLayers.map(x => x.id).indexOf(layer.id) !== -1) {\n                        // We've already covered the input layers a few lines above.\n                        continue;\n                    }\n                    // Potentially redundant list, same size of node.inputTensors.\n                    const inputShapes = [];\n                    for (let j = 0; j < node.inboundLayers.length; j++) {\n                        const inboundLayer = node.inboundLayers[j];\n                        const nodeIndex = node.nodeIndices[j];\n                        const tensorIndex = node.tensorIndices[j];\n                        const shapeKey = `${inboundLayer.name}_${nodeIndex}_${tensorIndex}`;\n                        const inputShape = layersToOutputShapes[shapeKey];\n                        inputShapes.push(inputShape);\n                    }\n                    const outputShape = layer.computeOutputShape(generic_utils.singletonOrArray(inputShapes));\n                    const outputShapes = types_utils.normalizeShapeList(outputShape);\n                    const nodeIndex = layer.inboundNodes.indexOf(node);\n                    for (let j = 0; j < outputShapes.length; j++) {\n                        const shapeKey = `${layer.name}_${nodeIndex}_${j}`;\n                        layersToOutputShapes[shapeKey] = outputShapes[j];\n                    }\n                }\n            }\n        }\n        // Read final output shapes from layersToOutputShapes.\n        const outputShapes = [];\n        const outputShapeKeys = [];\n        for (let i = 0; i < this.outputLayers.length; i++) {\n            const layer = this.outputLayers[i];\n            const nodeIndex = this.outputLayersNodeIndices[i];\n            const tensorIndex = this.outputLayersTensorIndices[i];\n            const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;\n            outputShapeKeys.push(shapeKey);\n        }\n        for (let i = 0; i < outputShapeKeys.length; i++) {\n            const key = outputShapeKeys[i];\n            generic_utils.assert(key in layersToOutputShapes);\n            outputShapes.push(layersToOutputShapes[key]);\n        }\n        // TODO(michaelterry): Update cache\n        return generic_utils.singletonOrArray(outputShapes);\n    }\n    /**\n     * Computes output tensors for new inputs.\n     *\n     * Note:\n     *   - Expects `inputs` to be a list (potentially with 1 element).\n     *\n     * @param inputs List of tensors\n     * @param masks List of masks (tensors or null).\n     * @return Three lists: outputTensors, outputMasks, outputShapes\n     */\n    runInternalGraph(inputs, masks) {\n        if (masks == null) {\n            masks = generic_utils.pyListRepeat(null, inputs.length);\n        }\n        // Dictionary mapping reference tensors to tuples\n        // (computed tensor, compute mask)\n        // we assume a 1:1 mapping from tensor to mask\n        // TODO: raise exception when a `.computeMask()` call\n        // does not return a list the same size as `call`\n        const tensorMap = {};\n        for (let i = 0; i < this.inputs.length; ++i) {\n            const x = this.inputs[i];\n            const y = inputs[i];\n            const mask = masks[i];\n            tensorMap[x.id] = [y, mask];\n        }\n        const depthKeys = Object.keys(this.nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        for (const depth of depthKeys) {\n            const nodes = this.nodesByDepth[depth];\n            for (const node of nodes) {\n                // This is always a single layer, never a list.\n                const layer = node.outboundLayer;\n                const referenceInputTensors = node.inputTensors;\n                const referenceOutputTensors = node.outputTensors;\n                // If all previous input tensors are available in tensorMap,\n                // then call node.inboundLayer on them.\n                // List of tuples [input, mask]:\n                const computedData = new Array();\n                for (const x of referenceInputTensors) {\n                    if (x.id in tensorMap) {\n                        computedData.push(tensorMap[x.id]);\n                    }\n                }\n                if (computedData.length === referenceInputTensors.length) {\n                    // TODO(michaelterry): Add K.name_scope here, if we need it.\n                    let kwargs = {};\n                    let computedTensors;\n                    let computedMasks;\n                    let outputTensors;\n                    let outputMasks;\n                    // call layer\n                    if (node.callArgs != null) {\n                        kwargs = node.callArgs;\n                    }\n                    if (computedData.length === 1) {\n                        const [computedTensor, computedMask] = computedData[0];\n                        if (kwargs['mask'] == null) {\n                            kwargs['mask'] = computedMask;\n                        }\n                        outputTensors =\n                            generic_utils.toList(layer.call(computedTensor, kwargs));\n                        outputMasks = generic_utils.toList(layer.computeMask(computedTensor, computedMask));\n                        computedTensors = [computedTensor];\n                        computedMasks = [computedMask];\n                    }\n                    else {\n                        computedTensors = computedData.map(x => x[0]);\n                        computedMasks = computedData.map(x => x[1]);\n                        if (kwargs['mask'] == null) {\n                            kwargs['mask'] = computedMasks;\n                        }\n                        outputTensors =\n                            generic_utils.toList(layer.call(computedTensors, kwargs));\n                        outputMasks = generic_utils.toList(layer.computeMask(computedTensors, computedMasks));\n                    }\n                    if (layer.activityRegularizer) {\n                        throw new NotImplementedError('LayersModel invocation with concrete Tensor value(s) in the ' +\n                            'presence of activity regularizer(s) is not supported yet.');\n                    }\n                    // TODO(michaelterry): Add model updates and losses\n                    // Update tensor map.\n                    for (let i = 0; i < referenceOutputTensors.length; ++i) {\n                        const x = referenceOutputTensors[i];\n                        const y = outputTensors[i];\n                        const mask = outputMasks[i];\n                        tensorMap[x.id] = [y, mask];\n                    }\n                }\n            }\n        }\n        const outputTensors = [];\n        const outputMasks = [];\n        const outputShapes = [];\n        for (const x of this.outputs) {\n            generic_utils.assert(x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`);\n            const [tensor, mask] = tensorMap[x.id];\n            outputShapes.push(tensor.shape);\n            outputTensors.push(tensor);\n            outputMasks.push(mask);\n        }\n        // TODO(michaelterry): Add support for caches.\n        return [outputTensors, outputMasks, outputShapes];\n    }\n    /**\n     * Builds a map of internal node keys to node ordering.\n     * Used in serializaion a node orderings may change as unused nodes are\n     * dropped. Porting Note:  This helper method was pulled out of getConfig to\n     * improve readability.\n     * @param layers An array of Layers in the model.\n     * @returns Map of Node Keys to index order within the layer.\n     */\n    buildNodeConversionMap(layers) {\n        const nodeConversionMap = {};\n        let keptNodes;\n        for (const layer of this.layers) {\n            keptNodes = layer instanceof Container ? 1 : 0;\n            for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n                const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n                if (this.containerNodes.has(nodeKey)) {\n                    // i.e. we mark it to be saved\n                    nodeConversionMap[nodeKey] = keptNodes;\n                    keptNodes += 1;\n                }\n            }\n        }\n        return nodeConversionMap;\n    }\n    /**\n     * Retrieves a layer based on either its name (unique) or index.\n     *\n     * Indices are based on order of horizontal graph traversal (bottom-up).\n     *\n     * If both `name` and `index` are specified, `index` takes precedence.\n     *\n     * @param name Name of layer.\n     * @param index Index of layer.\n     * @returns A Layer instance.\n     * @throws ValueError: In case of invalid layer name or index.\n     *\n     * @doc {\n     *    heading: 'Layers',\n     *    subheading: 'Classes',\n     *    namespace: 'layers',\n     *    subclasses: ['LayersModel']\n     * }\n     */\n    getLayer(name, index) {\n        if (index != null) {\n            if (this.layers.length <= index) {\n                throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only ` +\n                    `has ${this.layers.length} layer(s).`);\n            }\n            else {\n                return this.layers[index];\n            }\n        }\n        else {\n            if (name == null) {\n                throw new ValueError('Provide either a layer name or layer index');\n            }\n        }\n        for (const layer of this.layers) {\n            if (layer.name === name) {\n                return layer;\n            }\n        }\n        throw new ValueError(`No such layer: ${name}`);\n    }\n    /**\n     * Retrieves the Container's current loss values.\n     *\n     * Used for regularizers during training.\n     */\n    calculateLosses() {\n        // Porting Node: This is an augmentation to Container.loss in PyKeras.\n        //   In PyKeras, Container.loss returns symbolic tensors. Here a concrete\n        //   Tensor (specifically Scalar) values are returned. This is due to the\n        //   imperative backend.\n        return tidy(() => {\n            const losses = [];\n            for (const layer of this.layers) {\n                for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length; ++nodeIndex) {\n                    const nodeKey = Container.nodeKey(layer, nodeIndex);\n                    if (this.containerNodes.has(nodeKey)) {\n                        losses.push(...layer.calculateLosses());\n                    }\n                }\n            }\n            // TODO(cais): Add any unconditional model-level losses?\n            return losses;\n        });\n    }\n    getConfig() {\n        const config = { name: this.name };\n        // Build a map from layer unique name (self._node_key)\n        // to the index of the nodes that are saved in the config.\n        // Only nodes in container_nodes are saved.\n        const nodeConversionMap = this.buildNodeConversionMap(this.layers);\n        // Serialize and save the layers in layerConfigs\n        const layerConfigs = [];\n        for (const layer of this.layers) {\n            const layerClassName = layer.getClassName();\n            const layerConfig = layer.getConfig();\n            const filteredInboundNodes = [];\n            for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n                const node = layer.inboundNodes[originalNodeIndex];\n                const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n                let kwargs = {};\n                if (this.containerNodes.has(nodeKey)) {\n                    // The node is relevant to the model:\n                    // add to filteredInboundNodes.\n                    if (node.callArgs) {\n                        try {\n                            JSON.stringify(node.callArgs);\n                            kwargs = node.callArgs;\n                        }\n                        catch (err) {\n                            console.warn(`Layer ${layer.name} was passed ` +\n                                `non-serializable keyword arguments: ` +\n                                `${node.callArgs}. They will not be included ` +\n                                `in the serialized model (and thus will be ` +\n                                `missing at deserialization time).`);\n                            kwargs = {};\n                        }\n                    }\n                    if (node.inboundLayers.length > 0) {\n                        const nodeData = [];\n                        for (let i = 0; i < node.inboundLayers.length; i++) {\n                            const inboundLayer = node.inboundLayers[i];\n                            const nodeIndex = node.nodeIndices[i];\n                            const tensorIndex = node.tensorIndices[i];\n                            const nodeKey = Container.nodeKey(inboundLayer, nodeIndex);\n                            let newNodeIndex = nodeConversionMap[nodeKey];\n                            if (newNodeIndex == null) {\n                                newNodeIndex = 0;\n                            }\n                            nodeData.push([inboundLayer.name, newNodeIndex, tensorIndex, kwargs]);\n                        }\n                        filteredInboundNodes.push(nodeData);\n                    }\n                }\n            }\n            const dict = {};\n            dict['name'] = layer.name;\n            dict['className'] = layerClassName;\n            dict['config'] = layerConfig;\n            dict['inboundNodes'] = filteredInboundNodes;\n            layerConfigs.push(dict);\n        }\n        config['layers'] = layerConfigs;\n        // Gather info about inputs and outputs\n        const modelInputs = [];\n        for (let i = 0; i < this.inputLayers.length; i++) {\n            const layer = this.inputLayers[i];\n            const nodeIndex = this.inputLayersNodeIndices[i];\n            const nodeKey = Container.nodeKey(layer, nodeIndex);\n            if (!this.containerNodes.has(nodeKey)) {\n                continue;\n            }\n            let newNodeIndex = nodeConversionMap[nodeKey];\n            if (newNodeIndex === null || newNodeIndex === undefined) {\n                newNodeIndex = 0;\n            }\n            const tensorIndex = this.inputLayersTensorIndices[i];\n            modelInputs.push([layer.name, newNodeIndex, tensorIndex]);\n        }\n        config['inputLayers'] = modelInputs;\n        const modelOutputs = [];\n        for (let i = 0; i < this.outputLayers.length; i++) {\n            const layer = this.outputLayers[i];\n            const nodeIndex = this.outputLayersNodeIndices[i];\n            const nodeKey = Container.nodeKey(layer, nodeIndex);\n            if (!this.containerNodes.has(nodeKey)) {\n                continue;\n            }\n            let newNodeIndex = nodeConversionMap[nodeKey];\n            if (newNodeIndex === null || newNodeIndex === undefined) {\n                newNodeIndex = 0;\n            }\n            const tensorIndex = this.outputLayersTensorIndices[i];\n            modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);\n        }\n        config['outputLayers'] = modelOutputs;\n        return config;\n    }\n    /**\n     * Instantiates a LayersModel from its config (output of `get_config()`).\n     * @param cls the class to create\n     * @param config LayersModel config dictionary.\n     * @param customObjects An optional dictionary of custom objects.\n     * @param fastWeightInit Optional flag to use fast weight initialization\n     *   during deserialization. This is applicable to cases in which\n     *   the initialization will be immediately overwritten by loaded weight\n     *   values. Default: `false`.\n     * @returns A LayersModel instance.\n     * @throws ValueError: In case of improperly formatted config dict.\n     */\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n        // Layer instances created during\n        // the graph reconstruction process\n        const createdLayers = {};\n        // Dictionary mapping layer instances to\n        // node data that specifies a layer call.\n        // It acts as a queue that maintains any unprocessed\n        // layer call until it becomes possible to process it\n        // (i.e. until the input tensors to the call all exist).\n        const unprocessedNodes = {};\n        function addUnprocessedNode(layer, nodeData) {\n            if (!(layer.name in unprocessedNodes)) {\n                unprocessedNodes[layer.name] = [nodeData];\n            }\n            else {\n                unprocessedNodes[layer.name].push(nodeData);\n            }\n        }\n        function processNode(layer, nodeData) {\n            const inputTensors = [];\n            let kwargs;\n            for (const inputData of nodeData) {\n                const inboundLayerName = inputData[0];\n                const inboundNodeIndex = inputData[1];\n                const inboundTensorIndex = inputData[2];\n                kwargs = inputData[3] == null ?\n                    {} :\n                    inputData[3];\n                if (!(inboundLayerName in createdLayers)) {\n                    addUnprocessedNode(layer, nodeData);\n                    return;\n                }\n                const inboundLayer = createdLayers[inboundLayerName];\n                if (inboundLayer.inboundNodes.length <= inboundNodeIndex) {\n                    addUnprocessedNode(layer, nodeData);\n                    return;\n                }\n                const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex];\n                inputTensors.push(inboundNode.outputTensors[inboundTensorIndex]);\n            }\n            // Call layer on its inputs, thus creating the node\n            // and building the layer if needed.\n            // Note: This has Eager vs Graph Implications.\n            if (inputTensors.length > 0) {\n                layer.apply(generic_utils.singletonOrArray(inputTensors), kwargs); // was ** kwargs\n            }\n        }\n        /**\n         * Deserialize a layer, then call it on appropriate inputs.\n         * @param layerData: layer config dict.\n         * @throws ValueError: In case of improperly formatted `layer_data`\n         * dict.\n         */\n        function processLayer(layerData) {\n            const layerName = layerData['name'];\n            // Instantiate layer.\n            const layer = deserializeLayer(layerData, config['customObjects'] != null ?\n                config['customObjects'] :\n                {});\n            layer.setFastWeightInitDuringBuild(fastWeightInit);\n            createdLayers[layerName] = layer;\n            // Gather layer inputs.\n            const inboundNodesData = layerData['inboundNodes'];\n            inboundNodesData.forEach(nodeData => {\n                if (!(nodeData instanceof Array)) {\n                    throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);\n                }\n                // We don't process nodes (i.e. make layer calls)\n                // on the fly because the inbound node may not yet exist,\n                // in case of layer shared at different topological depths\n                // (e.g.a model such as A(B(A(B(x)))))\n                addUnprocessedNode(layer, nodeData);\n            });\n        }\n        // First, we create all layers and enqueue nodes to be processed.\n        const name = config['name'];\n        const layersFromConfig = config['layers'];\n        for (const layerData of layersFromConfig) {\n            processLayer(layerData);\n        }\n        // Then we process nodes in order of layer depth.\n        // Nodes that cannot yet be processed(if the inbound node\n        // does not yet exist) are re - enqueued, and the process\n        // is repeated until all nodes are processed.\n        while (!generic_utils.isObjectEmpty(unprocessedNodes)) {\n            for (const layerData of layersFromConfig) {\n                const layer = createdLayers[layerData['name']];\n                if (layer.name in unprocessedNodes) {\n                    const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name];\n                    delete unprocessedNodes[layer.name];\n                    for (const nodeData of currentUnprocessedNodesForLayer) {\n                        processNode(layer, nodeData);\n                    }\n                }\n            }\n        }\n        const inputTensors = [];\n        const outputTensors = [];\n        const inputLayersFromConfig = config['inputLayers'];\n        for (const layerData of inputLayersFromConfig) {\n            const layerName = layerData[0];\n            const nodeIndex = layerData[1];\n            const tensorIndex = layerData[2];\n            generic_utils.assert(layerName in createdLayers);\n            const layer = createdLayers[layerName];\n            const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n            inputTensors.push(layerOutputTensors[tensorIndex]);\n        }\n        const outputLayersFromConfig = config['outputLayers'];\n        for (const layerData of outputLayersFromConfig) {\n            const layerName = layerData[0];\n            const nodeIndex = layerData[1];\n            const tensorIndex = layerData[2];\n            generic_utils.assert(layerName in createdLayers);\n            const layer = createdLayers[layerName];\n            const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n            outputTensors.push(layerOutputTensors[tensorIndex]);\n        }\n        return new cls({ inputs: inputTensors, outputs: outputTensors, name });\n    }\n    /**\n     * Determine whether the container is stateful.\n     *\n     * Porting Note: this is the equivalent of the stateful @property of\n     *   the Container class in PyKeras.\n     */\n    get stateful() {\n        // Porting Note: This check is to prevent inadvertent setting of the\n        //   _stateful property of the Container instance.\n        if (this._stateful) {\n            throw new ValueError('Container instance unexpectedly has _stateful = true. The ' +\n                'statefulness of a Container is determined by the Layers it ' +\n                'contains. Its _stateful property must remain the default false.');\n        }\n        for (const layer of this.layers) {\n            if (layer.stateful) {\n                return true;\n            }\n        }\n        return false;\n    }\n    /**\n     * Reset the state of all stateful constituent layers (if any).\n     *\n     * Examples of stateful layers include RNN layers whose `stateful` property\n     * is set as `true`.\n     */\n    resetStates() {\n        tidy(() => {\n            this.layers.forEach(layer => {\n                // tslint:disable:no-any\n                if (layer.stateful) {\n                    layer.resetStates();\n                }\n                // tslint:enable:no-any\n            });\n        });\n    }\n}\n//# sourceMappingURL=container.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { dispose, mul, tensor1d, tidy } from '@tensorflow/tfjs-core';\nfunction standardizeSampleOrClassWeights(xWeight, outputNames, weightType) {\n    const numOutputs = outputNames.length;\n    if (xWeight == null || (Array.isArray(xWeight) && xWeight.length === 0)) {\n        return outputNames.map(name => null);\n    }\n    if (numOutputs === 1) {\n        if (Array.isArray(xWeight) && xWeight.length === 1) {\n            return xWeight;\n        }\n        else if (typeof xWeight === 'object' && outputNames[0] in xWeight) {\n            return [xWeight[outputNames[0]]];\n        }\n        else {\n            return [xWeight];\n        }\n    }\n    if (Array.isArray(xWeight)) {\n        if (xWeight.length !== numOutputs) {\n            throw new Error(`Provided ${weightType} is an array of ${xWeight.length} ` +\n                `element(s), but the model has ${numOutputs} outputs. ` +\n                `Make sure a set of weights is provided for each model output.`);\n        }\n        return xWeight;\n    }\n    else if (typeof xWeight === 'object' && Object.keys(xWeight).length > 0 &&\n        typeof xWeight[Object.keys(xWeight)[0]] ===\n            'object') {\n        const output = [];\n        outputNames.forEach(outputName => {\n            if (outputName in xWeight) {\n                output.push(xWeight[outputName]);\n            }\n            else {\n                output.push(null);\n            }\n        });\n        return output;\n    }\n    else {\n        throw new Error(`The model has multiple (${numOutputs}) outputs, ` +\n            `so ${weightType} must be either an array with ` +\n            `${numOutputs} elements or an object with ${outputNames} keys. ` +\n            `Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);\n    }\n}\n/**\n * Standardize class weighting objects.\n *\n * This function takes a single class-weighting object, an array of them,\n * or a map from output name to class-weighting object. It compares it to the\n * output name(s) of the model, base on which it outputs an array of\n * class-weighting objects of which the length matches the number of outputs.\n *\n * @param classWeight Input class-weighting object(s).\n * @param outputNames All output name(s) of the model.\n * @return An array of class-weighting objects. The length of the array matches\n *   the model's number of outputs.\n */\nexport function standardizeClassWeights(classWeight, outputNames) {\n    return standardizeSampleOrClassWeights(classWeight, outputNames, 'classWeight');\n}\nexport function standardizeSampleWeights(classWeight, outputNames) {\n    return standardizeSampleOrClassWeights(classWeight, outputNames, 'sampleWeight');\n}\n/**\n * Standardize by-sample and/or by-class weights for training.\n *\n * Note that this function operates on one model output at a time. For a model\n * with multiple outputs, you must call this function multiple times.\n *\n * @param y The target tensor that the by-sample and/or by-class weight is for.\n *     The values of y are assumed to encode the classes, either directly\n *     as an integer index, or as one-hot encoding.\n * @param sampleWeight By-sample weights.\n * @param classWeight By-class weights: an object mapping class indices\n *     (integers) to a weight (float) to apply to the model's loss for the\n *     samples from this class during training. This can be useful to tell the\n *     model to \"pay more attention\" to samples from an under-represented class.\n * @param sampleWeightMode The mode for the sample weights.\n * @return A Promise of weight tensor, of which the size of the first dimension\n *     matches that of `y`.\n */\nexport async function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) {\n    if (sampleWeight != null || sampleWeightMode != null) {\n        // TODO(cais): Once 'temporal' mode is implemented, document it in the doc\n        // string.\n        throw new Error('Support sampleWeight is not implemented yet');\n    }\n    if (classWeight != null) {\n        // Apply class weights per sample.\n        const yClasses = tidy(() => {\n            if (y.shape.length === 1) {\n                // Assume class indices.\n                return y.clone();\n            }\n            else if (y.shape.length === 2) {\n                if (y.shape[1] > 1) {\n                    // Assume one-hot encoding of classes.\n                    const axis = 1;\n                    return y.argMax(axis);\n                }\n                else if (y.shape[1] === 1) {\n                    // Class index.\n                    return y.reshape([y.shape[0]]);\n                }\n                else {\n                    throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) ` +\n                        `during handling of class weights. The size is expected to be ` +\n                        `>= 1.`);\n                }\n            }\n            else {\n                throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during ` +\n                    `handling of class weights. The rank is expected to be 1 or 2.`);\n            }\n        });\n        const yClassIndices = Array.from(await yClasses.data());\n        dispose(yClasses);\n        const classSampleWeight = [];\n        yClassIndices.forEach(classIndex => {\n            if (classWeight[classIndex] == null) {\n                throw new Error(`classWeight must contain all classes in the training data. ` +\n                    `The class ${classIndex} exists in the data but not in ` +\n                    `classWeight`);\n            }\n            else {\n                classSampleWeight.push(classWeight[classIndex]);\n            }\n        });\n        return tensor1d(classSampleWeight, 'float32');\n    }\n    else {\n        return null;\n    }\n}\n/**\n * Apply per-sample weights on the loss values from a number of samples.\n *\n * @param losses Loss tensor of shape `[batchSize]`.\n * @param sampleWeights Per-sample weight tensor of shape `[batchSize]`.\n * @returns Tensor of the same shape as`losses`.\n */\nexport function computeWeightedLoss(losses, sampleWeights) {\n    return mul(losses, sampleWeights);\n}\n//# sourceMappingURL=training_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Interfaces and methods for training models using TensorFlow.js datasets.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { scalar } from '@tensorflow/tfjs-core';\nimport { configureCallbacks, standardizeCallbacks } from '../base_callbacks';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { disposeTensorsInLogs } from '../logs';\nimport { singletonOrArray, toList } from '../utils/generic_utils';\nimport { standardizeClassWeights, standardizeWeights } from './training_utils';\n// Default batch size used during tensor-based validation.\nconst DEFAULT_VALIDATION_BATCH_SIZE = 32;\n/**\n * Standardize the output of a dataset iterator for use by\n * LayersModel.fitDataset().\n *\n * @param model: A `tf.LayersModel` object.\n * @param iteratorOut The output of a dataset iterator. It is required to be\n *   an object of the form `{xs: TensorOrArrayOrMap, ys:\n * TensorOrArrayOrMap}`, where `TensorOrArrayOrMap` is a single `tf.Tensor`,\n * a `tf.Tensor[]`, or a flat map from string names to `tf.Tensor`s.\n * @returns A flat array of `tf.Tensor` objects: the input `tf.Tensor`s\n *   followed by the target `tf.Tensor`s.  When `tf.Tensor`s are provided\n *   as a map, the order in the resulting array is taken from the `inputNames`\n *   and `outputNames` of the model.\n */\nfunction standardizeDataIteratorOutput(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, iteratorOut) {\n    let xs;\n    let ys;\n    const iteratorOutObj = iteratorOut;\n    xs = iteratorOutObj['xs'];\n    ys = iteratorOutObj['ys'];\n    tfc.util.assert(xs != null && ys != null, () => 'A Dataset iterator for fitDataset() is expected to generate ' +\n        'objects of the form `{xs: xVal, ys: yVal}`, where the two ' +\n        'values may be `tf.Tensor`, an array of Tensors, or a map of ' +\n        'string to Tensor.  The provided Dataset instead generates ' +\n        `${iteratorOut}`);\n    const flattenedXs = flattenTensorOrArrayOrMap('input', model.inputNames, xs);\n    const flattenedYs = flattenTensorOrArrayOrMap('output', model.outputNames, ys);\n    const batchSize = flattenedXs[0].shape[0];\n    tfc.util.assert(flattenedXs.length === model.inputs.length, () => `LayersModel has ${model.inputs.length} inputs, but the dataset ` +\n        `provides ${flattenedXs.length} inputs.  (Expected input keys: ` +\n        `${JSON.stringify(model.inputNames)})`);\n    tfc.util.assert(flattenedYs.length === model.outputs.length, () => `LayersModel has ${model.outputs.length} outputs, but the dataset ` +\n        `provides ${flattenedYs.length} outputs.  (Expected output keys: ` +\n        `${JSON.stringify(model.outputNames)})`);\n    for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {\n        tfc.util.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ` +\n            `${model.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; ` +\n            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);\n    }\n    for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {\n        tfc.util.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ` +\n            `${model.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; ` +\n            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);\n    }\n    return { xs: flattenedXs, ys: flattenedYs };\n}\nfunction flattenTensorOrArrayOrMap(inputOrOutput, names, values) {\n    if (values instanceof tfc.Tensor) {\n        return [values];\n    }\n    else if (Array.isArray(values)) {\n        tfc.util.assert(values.length === names.length, () => `Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`);\n        return values;\n    }\n    else {\n        const result = [];\n        // Check that all the required keys are available.\n        for (const name of names) {\n            if (values[name] == null) {\n                throw new ValueError(`The feature data generated by the dataset lacks the required ` +\n                    `${inputOrOutput} key '${name}'.`);\n            }\n            result.push(values[name]);\n        }\n        return result;\n    }\n}\nfunction standardizeTensorValidationData(data) {\n    if (data.length === 3) {\n        throw new NotImplementedError('Validation with sample weights is not implemented yet.');\n    }\n    return { xs: data[0], ys: data[1] };\n}\nexport async function fitDataset(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, dataset, args) {\n    const hasBatchesPerEpoch = args.batchesPerEpoch != null;\n    tfc.util.assert(model.optimizer != null, () => 'You must compile a model before training/testing. Use ' +\n        'LayersModel.compile(modelCompileConfig).');\n    tfc.util.assert(args != null, () => `For fitDataset(), the 2nd argument (config) is required, ` +\n        `but it is not provided in this call.`);\n    tfc.util.assert(args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs), () => `For fitDataset(), config.epochs is expected to be a positive ` +\n        `integer, but got ${args.epochs}`);\n    tfc.util.assert(!hasBatchesPerEpoch ||\n        (args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch)), () => `For fitDataset(), config.batchesPerEpoch is expected to be a ` +\n        `positive integer if specified, but got ${args.batchesPerEpoch}`);\n    tfc.util.assert(\n    // tslint:disable-next-line:no-any\n    args['validationSplit'] == null, () => '`validationSplit` is not supported by `fitDataset()`. ' +\n        'Use validationData instead.');\n    if (model.isTraining) {\n        throw new Error('Cannot start training because another fit() call is ongoing.');\n    }\n    model.isTraining = true;\n    try {\n        const doValidation = args.validationData != null;\n        let valXs;\n        let valYs;\n        if (doValidation) {\n            if (isDatasetObject(args.validationData)) {\n                tfc.util.assert(args.validationBatches == null ||\n                    (args.validationBatches > 0 &&\n                        Number.isInteger(args.validationBatches)), () => `For fitDataset() with dataset-based validation, ` +\n                    `config.validationBatches is expected not to be provided, ` +\n                    `or to be a positive integer, ` +\n                    `but got ${args.validationBatches}`);\n            }\n            else {\n                const validationData = standardizeTensorValidationData(args.validationData);\n                valXs = validationData.xs;\n                valYs = validationData.ys;\n            }\n        }\n        const trainFunction = model.makeTrainFunction();\n        const outLabels = model.getDedupedMetricsNames();\n        let callbackMetrics;\n        if (doValidation) {\n            callbackMetrics =\n                outLabels.slice().concat(outLabels.map(n => 'val_' + n));\n        }\n        else {\n            callbackMetrics = outLabels.slice();\n        }\n        const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);\n        const verbose = args.verbose == null ? 1 : args.verbose;\n        const { callbackList, history } = configureCallbacks(callbacks, verbose, args.epochs, null, null, getStepsPerEpoch(dataset, args), null, // Batch size determined by the dataset itself.\n        doValidation, callbackMetrics);\n        callbackList.setModel(model);\n        model.history = history;\n        await callbackList.onTrainBegin();\n        model.stopTraining_ = false;\n        let epoch = args.initialEpoch == null ? 0 : args.initialEpoch;\n        let dataIterator = await dataset.iterator();\n        while (epoch < args.epochs) {\n            const epochLogs = {};\n            await callbackList.onEpochBegin(epoch);\n            let stepsDone = 0;\n            let batchIndex = 0;\n            if (!hasBatchesPerEpoch) {\n                dataIterator = await dataset.iterator();\n            }\n            while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) {\n                const iteratorOut = await dataIterator.next();\n                // If `batchesPerEpoch` is specified, the dataset should not be\n                // exhausted until all epoches are done.\n                if (hasBatchesPerEpoch && iteratorOut.done) {\n                    console.warn('You provided `batchesPerEpoch` as ' +\n                        `${args.batchesPerEpoch}, ` +\n                        'but your dataset iterator ran out of data after ' +\n                        `${stepsDone} batches; ` +\n                        'interrupting training. Make sure that your ' +\n                        'dataset can generate at least `batchesPerEpoch * epochs` ' +\n                        'batches (in this case, ' +\n                        `${args.batchesPerEpoch * args.epochs} batches). ` +\n                        'You may need to use the repeat() function when building ' +\n                        'your dataset.');\n                    break;\n                }\n                if (iteratorOut.value != null) {\n                    const { xs, ys } = standardizeDataIteratorOutput(model, iteratorOut.value);\n                    const batchLogs = {};\n                    batchLogs['batch'] = batchIndex;\n                    batchLogs['size'] = xs[0].shape[0];\n                    await callbackList.onBatchBegin(batchIndex, batchLogs);\n                    const sampleWeights = [];\n                    if (args.classWeight != null) {\n                        const standardClassWeights = standardizeClassWeights(args.classWeight, model.outputNames);\n                        for (let i = 0; i < standardClassWeights.length; ++i) {\n                            sampleWeights.push(await standardizeWeights(ys[i], null, standardClassWeights[i]));\n                        }\n                    }\n                    // Train on batch.\n                    const ins = xs.concat(ys).concat(sampleWeights);\n                    const outs = trainFunction(ins);\n                    tfc.dispose(ins);\n                    for (let i = 0; i < outLabels.length; ++i) {\n                        const label = outLabels[i];\n                        const out = outs[i];\n                        batchLogs[label] = out;\n                        tfc.keep(out);\n                    }\n                    await callbackList.onBatchEnd(batchIndex, batchLogs);\n                    disposeTensorsInLogs(batchLogs);\n                    batchIndex++;\n                    stepsDone++;\n                }\n                if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch :\n                    iteratorOut.done) {\n                    // Epoch finished. Perform validation.\n                    if (doValidation) {\n                        let valOuts;\n                        if (isDatasetObject(args.validationData)) {\n                            valOuts = toList(await model.evaluateDataset(args.validationData, { batches: args.validationBatches }));\n                        }\n                        else {\n                            valOuts = toList(model.evaluate(valXs, valYs, {\n                                batchSize: args.validationBatchSize == null ?\n                                    DEFAULT_VALIDATION_BATCH_SIZE :\n                                    args.validationBatchSize,\n                                verbose: 0\n                            }));\n                        }\n                        for (let i = 0; i < model.metricsNames.length; ++i) {\n                            epochLogs[`val_${model.metricsNames[i]}`] = valOuts[i];\n                        }\n                    }\n                    // Call `break` to exit one epoch lopp after validation is done. If\n                    // config.batchesPerEpoch is specified, an epoch while loop will\n                    // stop when `stepsDone >= config.batchesPerEpoch`. When\n                    // config.batchesPerEpoch is not provided, the following `break` is\n                    // required to exit the while lopp after dataset is exhausted.\n                    break;\n                }\n                if (model.stopTraining_) {\n                    break;\n                }\n            }\n            await callbackList.onEpochEnd(epoch, epochLogs);\n            epoch++;\n            if (model.stopTraining_) {\n                break;\n            }\n        }\n        await callbackList.onTrainEnd();\n        await model.history.syncData();\n        return model.history;\n    }\n    finally {\n        model.isTraining = false;\n    }\n}\n/** Helper function that determines number of steps (batches) per epoch. */\nfunction getStepsPerEpoch(dataset, args) {\n    // Attempt to determine # of batches in an epoch.\n    let stepsPerEpoch = null;\n    if (args.batchesPerEpoch != null) {\n        stepsPerEpoch = args.batchesPerEpoch;\n    }\n    else if (Number.isFinite(dataset.size)) {\n        stepsPerEpoch = dataset.size;\n    }\n    return stepsPerEpoch;\n}\n// Check if provided object is a Dataset object by checking its .iterator\n// element.\nfunction isDatasetObject(dataset) {\n    return (typeof dataset.iterator === 'function');\n}\n// Check if provided object is a LazyIterator object by checking it's .next\n// element.\nfunction isLazyIteratorObject(iterator) {\n    return (typeof iterator.next === 'function');\n}\nexport async function evaluateDataset(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, dataset, args) {\n    args = args || {};\n    const hasBatches = args.batches != null;\n    const f = model.testFunction;\n    let outs = [];\n    if (args.verbose > 0) {\n        throw new NotImplementedError('Verbose mode is not implemented yet.');\n    }\n    tfc.util.assert(!hasBatches || (args.batches > 0 && Number.isInteger(args.batches)), () => 'Test loop expects `batches` to be a positive integer, but ' +\n        `received ${JSON.stringify(args.batches)}`);\n    const dataIterator = isLazyIteratorObject(dataset) ?\n        dataset :\n        await dataset.iterator();\n    // Keeps track of number of examples used in this evaluation.\n    let numExamples = 0;\n    let batch = 0;\n    while (hasBatches ? batch < args.batches : true) {\n        const iteratorOut = await dataIterator.next();\n        outs = tfc.tidy(() => {\n            if (iteratorOut.value) {\n                // TODO(cais): Once real dataset is available, use\n                //   `map(x => standardizeDataIteratorOutput(model, x).map(f)`.\n                const { xs, ys } = standardizeDataIteratorOutput(model, iteratorOut.value);\n                const xsAndYs = xs.concat(ys);\n                const batchOuts = tfc.tidy(() => f(xsAndYs));\n                tfc.dispose(xsAndYs);\n                if (batch === 0) {\n                    for (let i = 0; i < batchOuts.length; ++i) {\n                        outs.push(scalar(0));\n                    }\n                }\n                const batchSize = xsAndYs[0].shape[0];\n                for (let i = 0; i < batchOuts.length; ++i) {\n                    const batchOut = batchOuts[i];\n                    const oldScalar = outs[i];\n                    outs[i] =\n                        tfc.tidy(() => tfc.add(outs[i], tfc.mul(batchSize, batchOut)));\n                    if (batch > 0) {\n                        tfc.dispose(oldScalar);\n                    }\n                }\n                tfc.dispose(batchOuts);\n                numExamples += batchSize;\n                ++batch;\n            }\n            return outs;\n        });\n        if (iteratorOut.done) {\n            if (hasBatches) {\n                console.warn('Your dataset iterator ran out of data during evaluateDataset(). ' +\n                    'Interrupting evalution. Make sure that your ' +\n                    'dataset can generate at least `batches` ' +\n                    `batches (in this case, ${args.batches} batches). ` +\n                    'You may need to use the repeat() function when building ' +\n                    'your dataset.');\n            }\n            break;\n        }\n    }\n    for (let i = 0; i < outs.length; ++i) {\n        const oldScalar = outs[i];\n        outs[i] = tfc.div(outs[i], numExamples);\n        tfc.dispose(oldScalar);\n    }\n    return singletonOrArray(outs);\n}\n//# sourceMappingURL=training_dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Interfaces and methods for training models using tf.Tensor objects.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { Tensor, tensor1d, util } from '@tensorflow/tfjs-core';\nimport { expandDims, gather, sliceAlongFirstAxis } from '../backend/tfjs_backend';\nimport { configureCallbacks, standardizeCallbacks } from '../base_callbacks';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { disposeTensorsInLogs } from '../logs';\nimport { range } from '../utils/math_utils';\nexport function checkBatchSize(batchSize) {\n    tfc.util.assert(batchSize > 0 && Number.isInteger(batchSize), () => `batchSize is required to be a positive integer, but got ${batchSize}`);\n}\n/**\n * Slice a Tensor or an Array of Tensors, by start and stop indices.\n *\n * Porting Note: The `_slice_arrays` function in PyKeras is covered by this\n *   function and `sliceArraysByIndices()` together.\n *\n * @param arrays: the input.\n * @param start: the starting index (inclusive).\n * @param stop: the stopping index (exclusive).\n * @returns The result of the slicing. If `arrays` is an `Array` of\n *   `tf.Tensor`s, the slicing will be applied to all elements of the `Array`\n *   in the same way.\n */\nexport function sliceArrays(arrays, start, stop) {\n    if (arrays == null) {\n        return [null];\n    }\n    else if (Array.isArray(arrays)) {\n        return arrays.map(array => sliceAlongFirstAxis(array, start, stop - start));\n    }\n    else { // Tensor.\n        return sliceAlongFirstAxis(arrays, start, stop - start);\n    }\n}\n/**\n * Slice a Tensor or an Array of Tensors, by random-order indices.\n *\n * Porting Note: The `_slice_arrays` function in PyKeras is covered by this\n *   function and `sliceArrays()` together.\n *\n * @param arrays The input `tf.Tensor` or `Array` of `tf.Tensor`s to slice.\n *   If an `Array` of `tf.Tensor`s, all `tf.Tensor`s will be sliced in the\n *   same fashion.\n * @param indices The indices to use for slicing along the first (batch)\n *   dimension.\n * @returns Result(s) of the slicing.\n */\nexport function sliceArraysByIndices(arrays, indices) {\n    return tfc.tidy(() => {\n        if (arrays == null) {\n            return null;\n        }\n        else if (Array.isArray(arrays)) {\n            return arrays.map(array => sliceArraysByIndices(array, indices));\n        }\n        else {\n            // TODO(cais): indices should be a pre-constructed Tensor1D to avoid\n            //   tensor1d() calls.\n            return gather(arrays, indices.dtype === 'int32' ? indices : indices.toInt());\n        }\n    });\n}\n/**\n * Returns a list of batch indices (tuples of indices).\n * @param size: Integer, total size of the data to slice into batches.\n * @param batchSize: Integer, batch size.\n * @returns An Array of [batchStart, batchEnd] tuples. batchStart is\n *   inclusive; batchEnd is exclusive. I.e., each batch consists of indices x\n *   that satisfy batchStart <= x < batchEnd.\n */\nexport function makeBatches(size, batchSize) {\n    const output = [];\n    let batchStart = 0;\n    let batchEnd = null;\n    while (batchStart < size) {\n        batchEnd = batchStart + batchSize;\n        if (batchEnd >= size) {\n            batchEnd = size;\n        }\n        output.push([batchStart, batchEnd]);\n        batchStart = batchEnd;\n    }\n    return output;\n}\n/**\n * Abstract fit function for `f(ins)`.\n * @param f A Function returning a list of tensors. For training, this\n *   function is expected to perform the updates to the variables.\n * @param ins List of tensors to be fed to `f`.\n * @param outLabels List of strings, display names of the outputs of `f`.\n * @param batchSize Integer batch size or `== null` if unknown. Default : 32.\n * @param epochs Number of times to iterate over the data. Default : 1.\n * @param verbose Verbosity mode: 0, 1, or 2. Default: 1.\n * @param callbacks List of callbacks to be called during training.\n * @param valF Function to call for validation.\n * @param valIns List of tensors to be fed to `valF`.\n * @param shuffle Whether to shuffle the data at the beginning of every\n * epoch. Default : true.\n * @param callbackMetrics List of strings, the display names of the metrics\n *   passed to the callbacks. They should be the concatenation of the\n *   display names of the outputs of `f` and the list of display names\n *   of the outputs of `valF`.\n * @param initialEpoch Epoch at which to start training (useful for\n *   resuming a previous training run). Default : 0.\n * @param stepsPerEpoch Total number of steps (batches on samples) before\n *   declaring one epoch finished and starting the next epoch. Ignored with\n *   the default value of `undefined` or `null`.\n * @param validationSteps Number of steps to run validation for (only if\n *   doing validation from data tensors). Not applicable for tfjs-layers.\n * @returns A `History` object.\n */\nasync function fitLoop(\n// Type `model` as `any` here to avoid circular dependency w/ training.ts.\n// tslint:disable-next-line:no-any\nmodel, f, ins, outLabels, batchSize, epochs, verbose, callbacks, valF, valIns, shuffle, callbackMetrics, initialEpoch, stepsPerEpoch, validationSteps) {\n    if (batchSize == null) {\n        batchSize = 32;\n    }\n    if (epochs == null) {\n        epochs = 1;\n    }\n    if (shuffle == null) {\n        shuffle = true;\n    }\n    if (initialEpoch == null) {\n        initialEpoch = 0;\n    }\n    // TODO(cais): Change const to let below when implementing validation.\n    let doValidation = false;\n    if (valF != null && valIns != null) {\n        doValidation = true;\n        // TODO(cais): verbose message.\n    }\n    if (validationSteps != null) {\n        doValidation = true;\n        if (stepsPerEpoch == null) {\n            throw new ValueError('Can only use `validationSteps` when doing step-wise training, ' +\n                'i.e., `stepsPerEpoch` must be set.');\n        }\n    }\n    const numTrainSamples = model.checkNumSamples(ins, batchSize, stepsPerEpoch, 'steps_per_epoch');\n    let indexArray;\n    if (numTrainSamples != null) {\n        indexArray = range(0, numTrainSamples);\n    }\n    if (verbose == null) {\n        verbose = 1;\n    }\n    const { callbackList, history } = configureCallbacks(callbacks, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics);\n    callbackList.setModel(model);\n    model.history = history;\n    await callbackList.onTrainBegin();\n    model.stopTraining_ = false;\n    // TODO(cais): Take care of callbacks.validation_data as in PyKeras.\n    // TODO(cais): Pre-convert feeds for performance as in PyKeras.\n    for (let epoch = initialEpoch; epoch < epochs; ++epoch) {\n        await callbackList.onEpochBegin(epoch);\n        const epochLogs = {};\n        if (stepsPerEpoch != null) {\n            throw new NotImplementedError('stepsPerEpoch mode is not implemented yet.');\n        }\n        else {\n            if (shuffle === 'batch') {\n                throw new NotImplementedError('batch shuffling is not implemneted yet');\n            }\n            else if (shuffle) {\n                util.shuffle(indexArray);\n            }\n            // Convert the potentially shuffled indices to Tensor1D, to avoid the\n            // cost of repeated creation of Array1Ds later on.\n            const epochIndexArray1D = tensor1d(indexArray);\n            const batches = makeBatches(numTrainSamples, batchSize);\n            for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                const batchLogs = {};\n                await callbackList.onBatchBegin(batchIndex, batchLogs);\n                tfc.tidy(() => {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    const batchIds = sliceAlongFirstAxis(epochIndexArray1D, batchStart, batchEnd - batchStart);\n                    batchLogs['batch'] = batchIndex;\n                    batchLogs['size'] = batchEnd - batchStart;\n                    // TODO(cais): In ins, train flag can be a number, instead of an\n                    //   Tensor? Do we need to handle this in tfjs-layers?\n                    const insBatch = sliceArraysByIndices(ins, batchIds);\n                    const outs = f(insBatch);\n                    for (let i = 0; i < outLabels.length; ++i) {\n                        const label = outLabels[i];\n                        const out = outs[i];\n                        batchLogs[label] = out;\n                        tfc.keep(out);\n                        // TODO(cais): Use scope() to avoid ownership.\n                    }\n                    if (batchIndex === batches.length - 1) { // Last batch.\n                        if (doValidation) {\n                            const valOuts = model.testLoop(valF, valIns, batchSize);\n                            // Porting Notes: In tfjs-layers, valOuts is always an Array.\n                            for (let i = 0; i < outLabels.length; ++i) {\n                                const label = outLabels[i];\n                                const out = valOuts[i];\n                                tfc.keep(out);\n                                // TODO(cais): Use scope() to avoid ownership.\n                                epochLogs['val_' + label] = out;\n                            }\n                        }\n                    }\n                });\n                await callbackList.onBatchEnd(batchIndex, batchLogs);\n                disposeTensorsInLogs(batchLogs);\n                if (model.stopTraining_) {\n                    break;\n                }\n                // TODO(cais): return outs as list of Tensor.\n            }\n            epochIndexArray1D.dispose();\n        }\n        // TODO(cais): Run validation at the end of the epoch.\n        await callbackList.onEpochEnd(epoch, epochLogs);\n        if (model.stopTraining_) {\n            break;\n        }\n    }\n    await callbackList.onTrainEnd();\n    await model.history.syncData();\n    return model.history;\n}\nexport async function fitTensors(\n// Type `model` as `any` here to avoid circular dependency w/ training.ts.\n// tslint:disable-next-line:no-any\nmodel, x, y, args = {}) {\n    if (model.isTraining) {\n        throw new Error('Cannot start training because another fit() call is ongoing.');\n    }\n    model.isTraining = true;\n    let inputs;\n    let targets;\n    let inputValX;\n    let inputValY;\n    let valX;\n    let valY;\n    let sampleWeights;\n    try {\n        const batchSize = args.batchSize == null ? 32 : args.batchSize;\n        checkBatchSize(batchSize);\n        // Validate user data.\n        // TODO(cais): Support sampleWeight.\n        const checkBatchAxis = false;\n        const standardizedOuts = await model.standardizeUserData(x, y, args.sampleWeight, args.classWeight, checkBatchAxis, batchSize);\n        inputs = standardizedOuts[0];\n        targets = standardizedOuts[1];\n        sampleWeights = standardizedOuts[2];\n        // Prepare validation data.\n        let doValidation = false;\n        let valIns;\n        if (args.validationData != null && args.validationData.length > 0) {\n            doValidation = true;\n            if (args.validationData.length === 2) {\n                // config.validationData consists of valX and valY.\n                inputValX = args.validationData[0];\n                inputValY = args.validationData[1];\n            }\n            else if (args.validationData.length === 3) {\n                throw new NotImplementedError('validationData including sample weights is not supported yet.');\n            }\n            else {\n                throw new ValueError(`When passing validation data, it must contain 2 (valX, valY) ` +\n                    `or 3 (valX, valY, valSampleWeight) items; ` +\n                    `${args.validationData} is invalid.`);\n            }\n            const checkBatchAxis = true;\n            const valStandardized = await model.standardizeUserData(inputValX, inputValY, null, /** Unused sample weights. */ null, /** Unused class weights. */ checkBatchAxis, batchSize);\n            valX = valStandardized[0];\n            valY = valStandardized[1];\n            valIns = valX.concat(valY);\n            // TODO(cais): Add useLearningPhase data properly.\n        }\n        else if (args.validationSplit != null && args.validationSplit > 0 &&\n            args.validationSplit < 1) {\n            doValidation = true;\n            // Porting Note: In tfjs-layers, inputs[0] is always a Tensor.\n            const splitAt = Math.floor(inputs[0].shape[0] * (1 - args.validationSplit));\n            const originalBatchSize = inputs[0].shape[0];\n            valX = sliceArrays(inputs, splitAt, originalBatchSize);\n            inputs = sliceArrays(inputs, 0, splitAt);\n            valY = sliceArrays(targets, splitAt, originalBatchSize);\n            targets = sliceArrays(targets, 0, splitAt);\n            // TODO(cais): Once sampleWeights becomes available, slice it to get\n            //   valSampleWeights.\n            valIns = valX.concat(valY);\n            // TODO(cais): Add useLearningPhase data properly.\n        }\n        else if (args.validationSteps != null) {\n            doValidation = true;\n            // TODO(cais): Add useLearningPhase.\n        }\n        const ins = inputs.concat(targets).concat(sampleWeights);\n        model.checkTrainableWeightsConsistency();\n        // TODO(cais): Handle use_learning_phase and learning_phase?\n        // Porting Note: Here we see a key deviation of tfjs-layers from\n        // Keras.\n        //  Due to the imperative nature of tfjs-layers' backend (tfjs-core),\n        //  we do not construct symbolic computation graphs to embody the\n        //  training process. Instead, we define a function that performs the\n        //  training action. In PyKeras, the data (inputs and targets) are fed\n        //  through graph placeholders. In tfjs-layers, the data are fed as\n        //  function arguments. Since the function are defined below in the\n        //  scope, we don't have equivalents of PyKeras's\n        //  `_make_train_funciton`.\n        const trainFunction = model.makeTrainFunction();\n        const outLabels = model.getDedupedMetricsNames();\n        let valFunction;\n        let callbackMetrics;\n        if (doValidation) {\n            model.makeTestFunction();\n            valFunction = model.testFunction;\n            callbackMetrics =\n                outLabels.slice().concat(outLabels.map(n => 'val_' + n));\n        }\n        else {\n            valFunction = null;\n            valIns = [];\n            callbackMetrics = outLabels.slice();\n        }\n        const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);\n        const out = await fitLoop(model, trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null);\n        return out;\n    }\n    finally {\n        model.isTraining = false;\n        // Memory clean up.\n        disposeNewTensors(inputs, x);\n        disposeNewTensors(targets, y);\n        disposeNewTensors(valX, inputValX);\n        disposeNewTensors(valY, inputValY);\n        if (sampleWeights != null) {\n            tfc.dispose(sampleWeights);\n        }\n    }\n    // TODO(cais): Add value to outLabels.\n}\n/**\n * Ensure tensors all have a rank of at least 2.\n *\n * If a tensor has a rank of 1, it is dimension-expanded to rank 2.\n * If any tensor has a rank of 0 (i.e., is a scalar), an error will be thrown.\n */\nexport function ensureTensorsRank2OrHigher(tensors) {\n    const outs = [];\n    if (tensors instanceof Tensor) {\n        tensors = [tensors];\n    }\n    // Make Tensors at least 2D.\n    for (let i = 0; i < tensors.length; ++i) {\n        const tensor = tensors[i];\n        if (tensor.rank === 1) {\n            outs.push(expandDims(tensor, 1));\n        }\n        else if (tensor.rank === 0) {\n            throw new Error('Expected tensor to be at least 1D, but received a 0D tensor ' +\n                '(scalar).');\n        }\n        else {\n            outs.push(tensor);\n        }\n    }\n    return outs;\n}\n/**\n * Compare a set of tensors with a reference (old) set, discard the ones\n * in the new set that are not present in the reference set.\n *\n * This method is used for memory clenaup during calls such as\n * LayersModel.fit().\n *\n * @param tensors New set which may contain Tensors not present in\n *   `refTensors`.\n * @param refTensors Reference Tensor set.\n */\n// TODO(cais, kangyizhang): Deduplicate with tfjs-data.\nexport function disposeNewTensors(tensors, refTensors) {\n    if (tensors == null) {\n        return;\n    }\n    const oldTensorIds = [];\n    if (refTensors instanceof Tensor) {\n        oldTensorIds.push(refTensors.id);\n    }\n    else if (Array.isArray(refTensors)) {\n        refTensors.forEach(t => oldTensorIds.push(t.id));\n    }\n    else if (refTensors != null) {\n        // `oldTensors` is a map from string name to Tensor.\n        for (const name in refTensors) {\n            const oldTensor = refTensors[name];\n            oldTensorIds.push(oldTensor.id);\n        }\n    }\n    const tensorsToDispose = [];\n    if (tensors instanceof Tensor) {\n        if (oldTensorIds.indexOf(tensors.id) === -1) {\n            tensorsToDispose.push(tensors);\n        }\n    }\n    else if (Array.isArray(tensors)) {\n        tensors.forEach(t => {\n            if (oldTensorIds.indexOf(t.id) === -1) {\n                tensorsToDispose.push(t);\n            }\n        });\n    }\n    else if (tensors != null) {\n        // `oldTensors` is a map from string name to Tensor.\n        for (const name in tensors) {\n            const tensor = tensors[name];\n            if (oldTensorIds.indexOf(tensor.id) === -1) {\n                tensorsToDispose.push(tensor);\n            }\n        }\n    }\n    tensorsToDispose.forEach(t => {\n        if (!t.isDisposed) {\n            t.dispose();\n        }\n    });\n}\n//# sourceMappingURL=training_tensors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source: engine/training.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { io, Optimizer, scalar, serialization, Tensor, tensor1d, util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { deserialize } from '../layers/serialization';\nimport * as losses from '../losses';\nimport * as Metrics from '../metrics';\nimport * as optimizers from '../optimizers';\nimport { checkUserDefinedMetadata } from '../user_defined_metadata';\nimport { count, pyListRepeat, singletonOrArray, toCamelCase, toSnakeCase, unique } from '../utils/generic_utils';\nimport { printSummary } from '../utils/layer_utils';\nimport { range } from '../utils/math_utils';\nimport { convertPythonicToTs } from '../utils/serialization_utils';\nimport { version } from '../version';\nimport { Container } from './container';\nimport { execute, FeedDict } from './executor';\nimport { evaluateDataset, fitDataset } from './training_dataset';\nimport { checkBatchSize, disposeNewTensors, ensureTensorsRank2OrHigher, fitTensors, makeBatches, sliceArrays, sliceArraysByIndices } from './training_tensors';\nimport { computeWeightedLoss, standardizeClassWeights, standardizeWeights } from './training_utils';\n/**\n * Helper function for polymorphic input data: 1. singleton Tensor.\n */\nexport function isDataTensor(x) {\n    return x instanceof Tensor;\n}\n/**\n * Helper function for polymorphic input data: 2. Array of Tensor.\n */\nexport function isDataArray(x) {\n    return Array.isArray(x);\n}\n/**\n * Helper function for polymorphic input data: 3. \"dict\" of Tensor.\n */\nexport function isDataDict(x) {\n    return !isDataTensor(x) && !isDataArray(x);\n}\n/**\n * Normalizes inputs and targets provided by users.\n * @param data User-provided input data (polymorphic).\n * @param names An Array of expected Tensor names.\n * @param shapes Optional Array of expected Tensor shapes.\n * @param checkBatchAxis Whether to check that the batch axis of the arrays\n *   match  the expected value found in `shapes`.\n * @param exceptionPrefix String prefix used for exception formatting.\n * @returns List of standardized input Tensors (one Tensor per model input).\n * @throws ValueError: in case of improperly formatted user data.\n */\nexport function standardizeInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = '') {\n    if (names == null || names.length === 0) {\n        // Check for the case where the model expected no data, but some data got\n        // sent.\n        if (data != null) {\n            let gotUnexpectedData = false;\n            if (isDataArray(data) && data.length > 0) {\n                gotUnexpectedData = true;\n            }\n            else if (isDataDict(data)) {\n                for (const key in data) {\n                    if (data.hasOwnProperty(key)) {\n                        gotUnexpectedData = true;\n                        break;\n                    }\n                }\n            }\n            else {\n                // `data` is a singleton Tensor in this case.\n                gotUnexpectedData = true;\n            }\n            if (gotUnexpectedData) {\n                throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, ` +\n                    `but got ${data}`);\n            }\n        }\n        return [];\n    }\n    if (data == null) {\n        return names.map(name => null);\n    }\n    let arrays;\n    if (isDataDict(data)) {\n        data = data;\n        arrays = [];\n        for (const name of names) {\n            if (data[name] == null) {\n                throw new ValueError(`No data provided for \"${name}\". Need data for each key in: ` +\n                    `${names}`);\n            }\n            arrays.push(data[name]);\n        }\n    }\n    else if (isDataArray(data)) {\n        data = data;\n        if (data.length !== names.length) {\n            throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of ` +\n                `Tensors that you are passing to your model is not the size the ` +\n                `model expected. Expected to see ${names.length} Tensor(s), but ` +\n                `instead got the following list of Tensor(s): ${data}`);\n        }\n        arrays = data;\n    }\n    else {\n        data = data;\n        if (names.length > 1) {\n            throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), ` +\n                `but only received one Tensor. Found: Tensor with shape ${data.shape}`);\n        }\n        arrays = [data];\n    }\n    arrays = ensureTensorsRank2OrHigher(arrays);\n    // Check shape compatibility.\n    if (shapes != null) {\n        for (let i = 0; i < names.length; ++i) {\n            if (shapes[i] == null) {\n                continue;\n            }\n            const array = arrays[i];\n            if (array.shape.length !== shapes[i].length) {\n                throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                    `to have ${shapes[i].length} dimension(s). but got array with ` +\n                    `shape ${array.shape}`);\n            }\n            for (let j = 0; j < shapes[i].length; ++j) {\n                if (j === 0 && !checkBatchAxis) {\n                    // Skip the first (batch) axis.\n                    continue;\n                }\n                const dim = array.shape[j];\n                const refDim = shapes[i][j];\n                if (refDim != null && refDim >= 0 && dim !== refDim) {\n                    throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                        `to have shape [${shapes[i]}], but got array with shape ` +\n                        `[${array.shape}].`);\n                }\n            }\n        }\n    }\n    return arrays;\n}\n/**\n * User input validation for Tensors.\n * @param inputs `Array` of `tf.Tensor`s for inputs.\n * @param targets `Array` of `tf.Tensor`s for targets.\n * @param weights Optional `Array` of `tf.Tensor`s for sample weights.\n * @throws ValueError: in case of incorrectly formatted data.\n */\nexport function checkArrayLengths(inputs, targets, weights) {\n    const setX = unique(inputs.map(input => input.shape[0]));\n    setX.sort();\n    const setY = unique(targets.map(target => target.shape[0]));\n    setY.sort();\n    // TODO(cais): Check `weights` as well.\n    if (setX.length > 1) {\n        throw new ValueError(`All input Tensors (x) should have the same number of samples. ` +\n            `Got array shapes: ` +\n            `${JSON.stringify(inputs.map(input => input.shape))}`);\n    }\n    if (setY.length > 1) {\n        throw new ValueError(`All target Tensors (y) should have the same number of samples. ` +\n            `Got array shapes: ` +\n            `${JSON.stringify(targets.map(target => target.shape))}`);\n    }\n    if (setX.length > 0 && setY.length > 0 && !util.arraysEqual(setX, setY)) {\n        throw new ValueError(`Input Tensors should have the same number of samples as target ` +\n            `Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target ` +\n            `sample(s).`);\n    }\n}\n/**\n * Validation on the compatibility of targes and loss functions.\n *\n * This helps prevent users from using loss functions incorrectly.\n *\n * @param targets `Array` of `tf.Tensor`s of targets.\n * @param lossFns `Array` of loss functions.\n * @param outputShapes `Array` of shapes of model outputs.\n */\nfunction checkLossAndTargetCompatibility(targets, lossFns, outputShapes) {\n    // TODO(cais): Dedicated test coverage?\n    const keyLosses = [\n        losses.meanSquaredError, losses.binaryCrossentropy,\n        losses.categoricalCrossentropy\n    ];\n    for (let i = 0; i < targets.length; ++i) {\n        const y = targets[i];\n        const loss = lossFns[i];\n        const shape = outputShapes[i];\n        if (loss == null) {\n            continue;\n        }\n        if (loss === losses.categoricalCrossentropy) {\n            if (y.shape[y.shape.length - 1] === 1) {\n                throw new ValueError(`You are passing a target array of shape ${y.shape} while using ` +\n                    `a loss 'categorical_crossentropy'. 'categorical_crossentropy'` +\n                    `expects targets to be binary matrices (1s and 0s) of shape ` +\n                    `[samples, classes].`);\n                // TODO(cais): Example code in error message.\n            }\n        }\n        if (keyLosses.indexOf(loss) !== -1) {\n            const slicedYShape = y.shape.slice(1);\n            const slicedShape = shape.slice(1);\n            for (let j = 0; j < slicedYShape.length; ++j) {\n                const targetDim = slicedYShape[j];\n                const outDim = slicedShape[j];\n                if (outDim != null && targetDim !== outDim) {\n                    throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an ` +\n                        `output of shape ${shape}, while using a loss function that ` +\n                        `expects targets to have the same shape as the output.`);\n                }\n            }\n        }\n    }\n}\n/**\n * Check inputs provided by the user.\n *\n * Porting Note: This corresponds to _standardize_input_data() in Python\n *   Keras. Because of the strong typing in TF.js, we do not need to convert\n *   the data. Specifically:\n *   1) in PyKeras, `data` can be `DataFrame` instances from pandas, for\n *      example. We don't need to worry about that here because there is no\n *      widely popular javascript/typesdcript equivalent of pandas (so far).\n *      If one becomes available in the future, we can add support.\n *   2) in PyKeras, inputs can be Python dict. But here we are stipulating\n * that the data is either a single `tf.Tensor` or an Array of `tf.Tensor`s. We\n * may add support for `Object` data inputs in the future when the need\n * arises.\n *\n * Instead, we perform basic checks for number of parameters and shapes.\n *\n * @param data: The input data.\n * @param names: Name for the inputs, from the model.\n * @param shapes: Expected shapes for the input data, from the model.\n * @param checkBatchAxis: Whether the size along the batch axis (i.e., the\n *   first dimension) will be checked for matching.\n * @param exceptionPrefix: Execption prefix message, used in generating error\n *   messages.\n * @throws ValueError: on incorrect number of inputs or mismatches in shapes.\n */\nfunction checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = '') {\n    let arrays;\n    if (Array.isArray(data)) {\n        if (data.length !== names.length) {\n            throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of ` +\n                `Tensors that you are passing to your model is not the size the ` +\n                `the model expected. Expected to see ${names.length} Tensor(s),` +\n                ` but instead got ${data.length} Tensors(s).`);\n        }\n        arrays = data;\n    }\n    else {\n        if (names.length > 1) {\n            throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, ` +\n                `but only received one Tensor. Found: array with shape ` +\n                `${JSON.stringify(data.shape)}.`);\n        }\n        arrays = [data];\n    }\n    if (shapes != null) {\n        for (let i = 0; i < names.length; ++i) {\n            if (shapes[i] == null) {\n                continue;\n            }\n            const array = arrays[i];\n            if (array.shape.length !== shapes[i].length) {\n                throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                    `to have ${shapes[i].length} dimension(s), but got array with ` +\n                    `shape ${JSON.stringify(array.shape)}`);\n            }\n            for (let j = 0; j < shapes[i].length; ++j) {\n                if (j === 0 && !checkBatchAxis) {\n                    continue;\n                }\n                const dim = array.shape[j];\n                const refDim = shapes[i][j];\n                if (refDim != null) {\n                    if (refDim !== dim) {\n                        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ` +\n                            `${names[i]} to have shape ${JSON.stringify(shapes[i])} but ` +\n                            `got array with shape ${JSON.stringify(array.shape)}.`);\n                    }\n                }\n            }\n        }\n    }\n}\n/**\n * Maps metric functions to model outputs.\n * @param metrics An shortcut strings name, metric function, `Array` or dict\n *   (`Object`) of metric functions.\n * @param outputNames An `Array` of the names of model outputs.\n * @returns An `Array` (one entry per model output) of `Array` of metric\n *   functions. For instance, if the model has 2 outputs, and for the first\n *   output we want to compute `binaryAccuracy` and `binaryCrossentropy`,\n *   and just `binaryAccuracy` for the second output, the `Array` would look\n *   like:\n *     `[[binaryAccuracy, binaryCrossentropy],  [binaryAccuracy]]`\n * @throws TypeError: incompatible metrics format.\n */\nexport function collectMetrics(metrics, outputNames) {\n    if (metrics == null || Array.isArray(metrics) && metrics.length === 0) {\n        return outputNames.map(name => []);\n    }\n    let wrappedMetrics;\n    if (typeof metrics === 'string' || typeof metrics === 'function') {\n        wrappedMetrics = [metrics];\n    }\n    else if (Array.isArray(metrics) || typeof metrics === 'object') {\n        wrappedMetrics = metrics;\n    }\n    else {\n        throw new TypeError('Type of metrics argument not understood. Expected an string,' +\n            `function, Array, or Object, found: ${metrics}`);\n    }\n    if (Array.isArray(wrappedMetrics)) {\n        // We then apply all metrics to all outputs.\n        return outputNames.map(name => wrappedMetrics);\n    }\n    else {\n        // In this case, metrics is a dict.\n        const nestedMetrics = [];\n        for (const name of outputNames) {\n            let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : [];\n            if (!Array.isArray(outputMetrics)) {\n                outputMetrics = [outputMetrics];\n            }\n            nestedMetrics.push(outputMetrics);\n        }\n        return nestedMetrics;\n    }\n}\nconst LAYERS_MODEL_FORMAT_NAME = 'layers-model';\n/**\n * A `tf.LayersModel` is a directed, acyclic graph of `tf.Layer`s plus methods\n * for training, evaluation, prediction and saving.\n *\n * `tf.LayersModel` is the basic unit of training, inference and evaluation in\n * TensorFlow.js. To create a `tf.LayersModel`, use `tf.LayersModel`.\n *\n * See also:\n *   `tf.Sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class LayersModel extends Container {\n    constructor(args) {\n        super(args);\n        this.isTraining = false;\n    }\n    /**\n     * Print a text summary of the model's layers.\n     *\n     * The summary includes\n     * - Name and type of all layers that comprise the model.\n     * - Output shape(s) of the layers\n     * - Number of weight parameters of each layer\n     * - If the model has non-sequential-like topology, the inputs each layer\n     *   receives\n     * - The total number of trainable and non-trainable parameters of the model.\n     *\n     * ```js\n     * const input1 = tf.input({shape: [10]});\n     * const input2 = tf.input({shape: [20]});\n     * const dense1 = tf.layers.dense({units: 4}).apply(input1);\n     * const dense2 = tf.layers.dense({units: 8}).apply(input2);\n     * const concat = tf.layers.concatenate().apply([dense1, dense2]);\n     * const output =\n     *     tf.layers.dense({units: 3, activation: 'softmax'}).apply(concat);\n     *\n     * const model = tf.model({inputs: [input1, input2], outputs: output});\n     * model.summary();\n     * ```\n     *\n     * @param lineLength Custom line length, in number of characters.\n     * @param positions Custom widths of each of the columns, as either\n     *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n     *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n     *   right-most (i.e., ending) position of a column.\n     * @param printFn Custom print function. Can be used to replace the default\n     *   `console.log`. For example, you can use `x => {}` to mute the printed\n     *   messages in the console.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    summary(lineLength, positions, printFn = console.log) {\n        if (!this.built) {\n            throw new ValueError(`This model has never been called, thus its weights have not been ` +\n                `created yet. So no summary can be displayed. Build the model ` +\n                `first (e.g., by calling it on some test data).`);\n        }\n        printSummary(this, lineLength, positions, printFn);\n    }\n    /**\n     * Configures and prepares the model for training and evaluation.  Compiling\n     * outfits the model with an optimizer, loss, and/or metrics.  Calling `fit`\n     * or `evaluate` on an un-compiled model will throw an error.\n     *\n     * @param args a `ModelCompileArgs` specifying the loss, optimizer, and\n     * metrics to be used for fitting and evaluating this model.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    compile(args) {\n        if (args.loss == null) {\n            args.loss = [];\n        }\n        this.loss = args.loss;\n        if (typeof args.optimizer === 'string') {\n            this.optimizer_ = optimizers.getOptimizer(args.optimizer);\n            this.isOptimizerOwned = true;\n        }\n        else {\n            if (!(args.optimizer instanceof Optimizer)) {\n                throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`);\n            }\n            this.optimizer_ = args.optimizer;\n            this.isOptimizerOwned = false;\n        }\n        // TODO(cais): Add lossWeights.\n        // TODO(cais): Add sampleWeightMode.\n        // Prepare loss functions.\n        let lossFunctions = [];\n        if (!Array.isArray(args.loss) && typeof args.loss !== 'string' &&\n            typeof args.loss !== 'function') {\n            args.loss = args.loss;\n            for (const name in args.loss) {\n                if (this.outputNames.indexOf(name) === -1) {\n                    throw new ValueError(`Unknown entry in loss dictionary: \"${name}\". ` +\n                        `Only expected the following keys: ${this.outputNames}`);\n                }\n            }\n            for (const name of this.outputNames) {\n                if (args.loss[name] == null) {\n                    console.warn(`Output \"${name}\" is missing from loss dictionary. We assume ` +\n                        `this was done on purpose, and we will not be expecting data ` +\n                        `to be passed to ${name} during training`);\n                }\n                lossFunctions.push(losses.get(args.loss[name]));\n            }\n        }\n        else if (Array.isArray(args.loss)) {\n            if (args.loss.length !== this.outputs.length) {\n                throw new ValueError(`When passing an Array as loss, it should have one entry per ` +\n                    `model output. The model has ${this.outputs.length} output(s), ` +\n                    `but you passed loss=${args.loss}.`);\n            }\n            const theLosses = args.loss;\n            lossFunctions = theLosses.map(l => losses.get(l));\n        }\n        else {\n            const lossFunction = losses.get(args.loss);\n            this.outputs.forEach(_ => {\n                lossFunctions.push(lossFunction);\n            });\n        }\n        this.lossFunctions = lossFunctions;\n        this.feedOutputNames = [];\n        this.feedOutputShapes = [];\n        this.feedLossFns = [];\n        for (let i = 0; i < this.outputs.length; ++i) {\n            // TODO(cais): Logic for skipping target(s).\n            const shape = this.internalOutputShapes[i];\n            const name = this.outputNames[i];\n            this.feedOutputNames.push(name);\n            this.feedOutputShapes.push(shape);\n            this.feedLossFns.push(this.lossFunctions[i]);\n        }\n        // TODO(cais): Add logic for output masks.\n        // TODO(cais): Add logic for sample weights.\n        const skipTargetIndices = [];\n        // Prepare metrics.\n        this.metrics = args.metrics;\n        // TODO(cais): Add weightedMetrics.\n        this.metricsNames = ['loss'];\n        this.metricsTensors = [];\n        // Compute total loss.\n        // Porting Note: In PyKeras, metrics_tensors are symbolic tensor objects.\n        //   Here, metricsTensors are TypeScript functions. This difference is due\n        //   to the difference in symbolic/imperative property of the backends.\n        nameScope('loss', () => {\n            for (let i = 0; i < this.outputs.length; ++i) {\n                if (skipTargetIndices.indexOf(i) !== -1) {\n                    continue;\n                }\n                // TODO(cais): Add weightedLoss, sampleWeight and mask.\n                //   The following line should be weightedLoss\n                const weightedLoss = this.lossFunctions[i];\n                if (this.outputs.length > 1) {\n                    this.metricsTensors.push([weightedLoss, i]);\n                    this.metricsNames.push(this.outputNames[i] + '_loss');\n                }\n            }\n            // Porting Note: Due to the imperative nature of the backend, we calculate\n            //   the regularizer penalties in the totalLossFunction, instead of here.\n        });\n        const nestedMetrics = collectMetrics(args.metrics, this.outputNames);\n        // TODO(cais): Add nestedWeightedMetrics.\n        /**\n         * Helper function used in loop below.\n         */\n        const appendMetric = (outputIndex, metricName, metricTensor) => {\n            if (this.outputNames.length > 1) {\n                metricName = this.outputNames[outputIndex] + '_' + metricName;\n            }\n            this.metricsNames.push(metricName);\n            this.metricsTensors.push([metricTensor, outputIndex]);\n        };\n        nameScope('metric', () => {\n            for (let i = 0; i < this.outputs.length; ++i) {\n                if (skipTargetIndices.indexOf(i) !== -1) {\n                    continue;\n                }\n                const outputMetrics = nestedMetrics[i];\n                // TODO(cais): Add weights and outputWeightedMetrics.\n                // TODO(cais): Add optional arg `weights` to the following function.\n                const handleMetrics = (metrics) => {\n                    const metricNamePrefix = '';\n                    let metricName;\n                    let accFn;\n                    let weightedMetricFn;\n                    //  TODO(cais): Use 'weights_' for weighted metrics.\n                    for (const metric of metrics) {\n                        if (typeof metric === 'string' &&\n                            ['accuracy', 'acc', 'crossentropy', 'ce'].indexOf(metric) !==\n                                -1) {\n                            const outputShape = this.internalOutputShapes[i];\n                            if (outputShape[outputShape.length - 1] === 1 ||\n                                this.lossFunctions[i] === losses.binaryCrossentropy) {\n                                // case: binary accuracy/crossentropy.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.binaryAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.binaryCrossentropy;\n                                }\n                            }\n                            else if (this.lossFunctions[i] ===\n                                losses.sparseCategoricalCrossentropy) {\n                                // case: categorical accuracy / crossentropy with sparse\n                                // targets.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.sparseCategoricalAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.sparseCategoricalCrossentropy;\n                                }\n                            }\n                            else {\n                                // case: categorical accuracy / crossentropy.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.categoricalAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.categoricalCrossentropy;\n                                }\n                            }\n                            let suffix;\n                            if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                suffix = 'acc';\n                            }\n                            else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                suffix = 'ce';\n                            }\n                            // TODO(cais): Add weighting actually.\n                            weightedMetricFn = accFn;\n                            metricName = metricNamePrefix + suffix;\n                        }\n                        else {\n                            const metricFn = Metrics.get(metric);\n                            // TODO(cais): Add weighting actually.\n                            weightedMetricFn = metricFn;\n                            metricName =\n                                metricNamePrefix + Metrics.getLossOrMetricName(metric);\n                        }\n                        // TODO(cais): Add weighting and masking to metricResult.\n                        let metricResult;\n                        nameScope(metricName, () => {\n                            metricResult = weightedMetricFn;\n                        });\n                        appendMetric(i, metricName, metricResult);\n                    }\n                };\n                handleMetrics(outputMetrics);\n                // TODO(cais): Call handleMetrics with weights.\n            }\n        });\n        // Porting Notes: Given the imperative backend of tfjs-core,\n        //   there is no need for constructing the symbolic graph and placeholders.\n        this.collectedTrainableWeights = this.trainableWeights;\n    }\n    /**\n     * Check trainable weights count consistency.\n     *\n     * This will raise a warning if `this.trainableWeights` and\n     * `this.collectedTrainableWeights` are inconsistent (i.e., have different\n     * numbers of parameters).\n     * Inconsistency will typically arise when one modifies `model.trainable`\n     * without calling `model.compile()` again.\n     */\n    checkTrainableWeightsConsistency() {\n        if (this.collectedTrainableWeights == null) {\n            return;\n        }\n        if (this.trainableWeights.length !==\n            this.collectedTrainableWeights.length) {\n            console.warn('Discrepancy between trainableweights and collected trainable ' +\n                'weights. Did you set `model.trainable` without calling ' +\n                '`model.compile()` afterwards?');\n        }\n    }\n    /**\n     * Returns the loss value & metrics values for the model in test mode.\n     *\n     * Loss and metrics are specified during `compile()`, which needs to happen\n     * before calls to `evaluate()`.\n     *\n     * Computation is done in batches.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const result = model.evaluate(\n     *     tf.ones([8, 10]), tf.ones([8, 1]), {batchSize: 4});\n     * result.print();\n     * ```\n     *\n     * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple inputs.\n     * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple outputs.\n     * @param args A `ModelEvaluateArgs`, containing optional fields.\n     *\n     * @return `Scalar` test loss (if the model has a single output and no\n     *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n     *   and/or metrics). The attribute `model.metricsNames`\n     *   will give you the display labels for the scalar outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    evaluate(x, y, args = {}) {\n        const batchSize = args.batchSize == null ? 32 : args.batchSize;\n        checkBatchSize(batchSize);\n        // TODO(cais): Standardize `config.sampleWeights` as well.\n        // Validate user data.\n        const checkBatchAxis = true;\n        const standardizedOuts = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n        try {\n            // TODO(cais): If uses `useLearningPhase`, set the corresponding element\n            // of the input to 0.\n            const ins = standardizedOuts[0].concat(standardizedOuts[1]);\n            this.makeTestFunction();\n            const f = this.testFunction;\n            const testOuts = this.testLoop(f, ins, batchSize, args.verbose, args.steps);\n            return singletonOrArray(testOuts);\n        }\n        finally {\n            disposeNewTensors(standardizedOuts[0], x);\n            disposeNewTensors(standardizedOuts[1], y);\n        }\n    }\n    // TODO(cais): Add code snippet below once real dataset objects are\n    //   available.\n    /**\n     * Evaluate model using a dataset object.\n     *\n     * Note: Unlike `evaluate()`, this method is asynchronous (`async`);\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for evaluation. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs. Of the two items in the array, the\n     *   first is the input feature(s) and the second is the output target(s).\n     * @param args A configuration object for the dataset-based evaluation.\n     * @returns Loss and metric values as an Array of `Scalar` objects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async evaluateDataset(dataset, args) {\n        this.makeTestFunction();\n        return evaluateDataset(this, dataset, args);\n    }\n    /**\n     * Get number of samples provided for training, evaluation or prediction.\n     *\n     * @param ins Input `tf.Tensor`.\n     * @param batchSize Integer batch size, optional.\n     * @param steps Total number of steps (batches of samples) before\n     * declaring loop finished. Optional.\n     * @param stepsName The public API's parameter name for `steps`.\n     * @returns Number of samples provided.\n     */\n    checkNumSamples(ins, batchSize, steps, stepsName = 'steps') {\n        let numSamples;\n        if (steps != null) {\n            numSamples = null;\n            if (batchSize != null) {\n                throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.` +\n                    `Got batchSize = ${batchSize}`);\n            }\n        }\n        else if (ins != null) {\n            if (Array.isArray(ins)) {\n                numSamples = ins[0].shape[0];\n            }\n            else {\n                numSamples = ins.shape[0];\n            }\n        }\n        else {\n            throw new ValueError(`Either the input data should have a defined shape, or ` +\n                `${stepsName} shoud be specified.`);\n        }\n        return numSamples;\n    }\n    /**\n     * Execute internal tensors of the model with input data feed.\n     * @param inputs Input data feed. Must match the inputs of the model.\n     * @param outputs Names of the output tensors to be fetched. Must match\n     *   names of the SymbolicTensors that belong to the graph.\n     * @returns Fetched values for `outputs`.\n     */\n    execute(inputs, outputs) {\n        if (Array.isArray(outputs) && outputs.length === 0) {\n            throw new ValueError('`outputs` is an empty Array, which is not allowed.');\n        }\n        const outputsIsArray = Array.isArray(outputs);\n        const outputNames = (outputsIsArray ? outputs : [outputs]);\n        const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames);\n        // Format the input into a FeedDict.\n        const feedDict = new FeedDict();\n        if (inputs instanceof Tensor) {\n            inputs = [inputs];\n        }\n        if (Array.isArray(inputs)) {\n            if (inputs.length !== this.inputs.length) {\n                throw new ValueError(`The number of inputs provided (${inputs.length}) ` +\n                    `does not match the number of inputs of this model ` +\n                    `(${this.inputs.length}).`);\n            }\n            for (let i = 0; i < this.inputs.length; ++i) {\n                feedDict.add(this.inputs[i], inputs[i]);\n            }\n        }\n        else {\n            for (const input of this.inputs) {\n                const tensorValue = inputs[input.name];\n                if (tensorValue == null) {\n                    throw new ValueError(`No value is provided for the model's input ${input.name}`);\n                }\n                feedDict.add(input, tensorValue);\n            }\n        }\n        // Run execution.\n        const executeOutputs = execute(outputSymbolicTensors, feedDict);\n        return outputsIsArray ? executeOutputs : executeOutputs[0];\n    }\n    /**\n     * Retrieve the model's internal symbolic tensors from symbolic-tensor names.\n     */\n    retrieveSymbolicTensors(symbolicTensorNames) {\n        const outputSymbolicTensors = pyListRepeat(null, symbolicTensorNames.length);\n        let outputsRemaining = symbolicTensorNames.length;\n        for (const layer of this.layers) {\n            const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output];\n            const layerOutputNames = layerOutputs.map(output => output.name);\n            for (let i = 0; i < symbolicTensorNames.length; ++i) {\n                const index = layerOutputNames.indexOf(symbolicTensorNames[i]);\n                if (index !== -1) {\n                    outputSymbolicTensors[i] = layerOutputs[index];\n                    outputsRemaining--;\n                }\n                if (outputsRemaining === 0) {\n                    break;\n                }\n            }\n            if (outputsRemaining === 0) {\n                break;\n            }\n        }\n        if (outputsRemaining > 0) {\n            const remainingNames = [];\n            outputSymbolicTensors.forEach((tensor, i) => {\n                if (tensor == null) {\n                    remainingNames.push(symbolicTensorNames[i]);\n                }\n            });\n            throw new ValueError(`Cannot find SymbolicTensors for output name(s): ` +\n                `${JSON.stringify(remainingNames)}`);\n        }\n        return outputSymbolicTensors;\n    }\n    /**\n     * Helper method to loop over some data in batches.\n     *\n     * Porting Note: Not using the functional approach in the Python equivalent\n     *   due to the imperative backend.\n     * Porting Note: Does not support step mode currently.\n     *\n     * @param ins: input data\n     * @param batchSize: integer batch size.\n     * @param verbose: verbosity model\n     * @returns: Predictions as `tf.Tensor` (if a single output) or an `Array` of\n     *   `tf.Tensor` (if multipe outputs).\n     */\n    predictLoop(ins, batchSize = 32, verbose = false) {\n        return tfc.tidy(() => {\n            const numSamples = this.checkNumSamples(ins);\n            if (verbose) {\n                throw new NotImplementedError('Verbose predictLoop() is not implemented yet.');\n            }\n            // Sample-based predictions.\n            // Porting Note: Tensor currently does not support sliced assignments as\n            //   in numpy, e.g., x[1:3] = y. Therefore we use concatenation while\n            //   iterating over the batches.\n            const batches = makeBatches(numSamples, batchSize);\n            const outsBatches = this.outputs.map(output => []);\n            // TODO(cais): Can the scope() be pushed down inside the for loop?\n            for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                const batchOuts = tfc.tidy(() => {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    // TODO(cais): Take care of the case of the last element is a flag for\n                    //   training/test.\n                    const insBatch = sliceArrays(ins, batchStart, batchEnd);\n                    // Construct the feeds for execute();\n                    const feeds = [];\n                    if (Array.isArray(insBatch)) {\n                        for (let i = 0; i < insBatch.length; ++i) {\n                            feeds.push({ key: this.inputs[i], value: insBatch[i] });\n                        }\n                    }\n                    else {\n                        feeds.push({ key: this.inputs[0], value: insBatch });\n                    }\n                    const feedDict = new FeedDict(feeds);\n                    return execute(this.outputs, feedDict);\n                });\n                batchOuts.forEach((batchOut, i) => outsBatches[i].push(batchOut));\n            }\n            return singletonOrArray(outsBatches.map(batches => tfc.concat(batches, 0)));\n        });\n    }\n    /**\n     * Generates output predictions for the input samples.\n     *\n     * Computation is done in batches.\n     *\n     * Note: the \"step\" mode of predict() is currently not supported.\n     *   This is because the TensorFlow.js core backend is imperative only.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predict(tf.ones([8, 10]), {batchSize: 4}).print();\n     * ```\n     *\n     * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n     *   the model has multiple inputs.\n     * @param args A `ModelPredictArgs` object containing optional fields.\n     *\n     * @return Prediction results as a `tf.Tensor`(s).\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and the model's expectations, or in case a stateful model receives a\n     *   number of samples that is not a multiple of the batch size.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(x, args = {}) {\n        const xsRank2OrHigher = ensureTensorsRank2OrHigher(x);\n        checkInputData(xsRank2OrHigher, this.inputNames, this.feedInputShapes, false);\n        try {\n            // TODO(cais): Take care of stateful models.\n            //   if (this.stateful) ...\n            // TODO(cais): Take care of the learning_phase boolean flag.\n            //   if (this.useLearningPhase) ...\n            const batchSize = args.batchSize == null ? 32 : args.batchSize;\n            checkBatchSize(batchSize);\n            return this.predictLoop(xsRank2OrHigher, batchSize);\n        }\n        finally {\n            disposeNewTensors(xsRank2OrHigher, x);\n        }\n    }\n    /**\n     * Returns predictions for a single batch of samples.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predictOnBatch(tf.ones([8, 10])).print();\n     * ```\n     * @param x: Input samples, as a Tensor (for models with exactly one\n     *   input) or an array of Tensors (for models with more than one input).\n     * @return Tensor(s) of predictions\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predictOnBatch(x) {\n        checkInputData(x, this.inputNames, this.feedInputShapes, true);\n        // TODO(cais): Take care of the learning_phase boolean flag.\n        //   if (this.useLearningPhase) ...\n        const batchSize = (Array.isArray(x) ? x[0] : x).shape[0];\n        return this.predictLoop(x, batchSize);\n    }\n    standardizeUserDataXY(x, y, checkBatchAxis = true, batchSize) {\n        // TODO(cais): Add sampleWeight, classWeight\n        if (this.optimizer_ == null) {\n            throw new RuntimeError('You must compile a model before training/testing. Use ' +\n                'LayersModel.compile(modelCompileArgs).');\n        }\n        const outputShapes = [];\n        for (let i = 0; i < this.feedOutputShapes.length; ++i) {\n            const outputShape = this.feedOutputShapes[i];\n            const lossFn = this.feedLossFns[i];\n            if (lossFn === losses.sparseCategoricalCrossentropy) {\n                outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1]));\n            }\n            else {\n                // Porting Note: Because of strong typing `lossFn` must be a function.\n                outputShapes.push(outputShape);\n            }\n        }\n        x = standardizeInputData(x, this.feedInputNames, this.feedInputShapes, false, 'input');\n        y = standardizeInputData(y, this.feedOutputNames, outputShapes, false, 'target');\n        // TODO(cais): Standardize sampleWeights & classWeights.\n        checkArrayLengths(x, y, null);\n        // TODO(cais): Check sampleWeights as well.\n        checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes);\n        if (this.stateful && batchSize != null && batchSize > 0) {\n            if (x[0].shape[0] % batchSize !== 0) {\n                throw new ValueError(`In a stateful network, you should only pass inputs with a ` +\n                    `number of samples that is divisible by the batch size ` +\n                    `${batchSize}. Found: ${x[0].shape[0]} sample(s).`);\n            }\n        }\n        return [x, y];\n    }\n    async standardizeUserData(x, y, sampleWeight, classWeight, checkBatchAxis = true, batchSize) {\n        const [standardXs, standardYs] = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n        // TODO(cais): Handle sampleWeights.\n        if (sampleWeight != null) {\n            throw new Error('sample weight is not supported yet.');\n        }\n        let standardSampleWeights = null;\n        if (classWeight != null) {\n            const classWeights = standardizeClassWeights(classWeight, this.outputNames);\n            standardSampleWeights = [];\n            for (let i = 0; i < classWeights.length; ++i) {\n                standardSampleWeights.push(await standardizeWeights(standardYs[i], null, classWeights[i]));\n            }\n        }\n        // TODO(cais): Deal with the case of model.stateful == true.\n        return [standardXs, standardYs, standardSampleWeights];\n    }\n    /**\n     * Loop over some test data in batches.\n     * @param f A Function returning a list of tensors.\n     * @param ins Array of tensors to be fed to `f`.\n     * @param batchSize Integer batch size or `null` / `undefined`.\n     * @param verbose verbosity mode.\n     * @param steps Total number of steps (batches of samples) before\n     * declaring test finished. Ignored with the default value of `null` /\n     * `undefined`.\n     * @returns Array of Scalars.\n     */\n    testLoop(f, ins, batchSize, verbose = 0, steps) {\n        return tfc.tidy(() => {\n            const numSamples = this.checkNumSamples(ins, batchSize, steps, 'steps');\n            const outs = [];\n            if (verbose > 0) {\n                throw new NotImplementedError('Verbose mode is not implemented yet.');\n            }\n            // TODO(cais): Use `indicesForConversionToDense' to prevent slow down.\n            if (steps != null) {\n                throw new NotImplementedError('steps mode in testLoop() is not implemented yet');\n            }\n            else {\n                const batches = makeBatches(numSamples, batchSize);\n                const indexArray = tensor1d(range(0, numSamples));\n                for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    const batchIds = K.sliceAlongFirstAxis(indexArray, batchStart, batchEnd - batchStart);\n                    // TODO(cais): In ins, train flag can be a number, instead of an\n                    //   Tensor? Do we need to handle this in tfjs-layers?\n                    const insBatch = sliceArraysByIndices(ins, batchIds);\n                    const batchOuts = f(insBatch);\n                    if (batchIndex === 0) {\n                        for (let i = 0; i < batchOuts.length; ++i) {\n                            outs.push(scalar(0));\n                        }\n                    }\n                    for (let i = 0; i < batchOuts.length; ++i) {\n                        const batchOut = batchOuts[i];\n                        outs[i] =\n                            tfc.add(outs[i], tfc.mul(batchEnd - batchStart, batchOut));\n                    }\n                }\n                for (let i = 0; i < outs.length; ++i) {\n                    outs[i] = tfc.div(outs[i], numSamples);\n                }\n            }\n            return outs;\n        });\n    }\n    getDedupedMetricsNames() {\n        const outLabels = this.metricsNames;\n        // Rename duplicated metrics names (can happen with an output layer\n        // shared among multiple dataflows).\n        const dedupedOutLabels = [];\n        for (let i = 0; i < outLabels.length; ++i) {\n            const label = outLabels[i];\n            let newLabel = label;\n            if (count(outLabels, label) > 1) {\n                const dupIndex = count(outLabels.slice(0, i), label);\n                newLabel += `_${dupIndex}`;\n            }\n            dedupedOutLabels.push(newLabel);\n        }\n        return dedupedOutLabels;\n    }\n    /**\n     * Creates a function that performs the following actions:\n     *\n     * 1. computes the losses\n     * 2. sums them to get the total loss\n     * 3. call the optimizer computes the gradients of the LayersModel's\n     *    trainable weights w.r.t. the total loss and update the variables\n     * 4. calculates the metrics\n     * 5. returns the values of the losses and metrics.\n     */\n    makeTrainFunction() {\n        return (data) => {\n            const lossValues = [];\n            const inputs = data.slice(0, this.inputs.length);\n            const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n            const sampleWeights = data.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2);\n            const metricsValues = [];\n            // Create a function that computes the total loss based on the\n            // inputs. This function is used for obtaining gradients through\n            // backprop.\n            const totalLossFunction = () => {\n                const feeds = [];\n                for (let i = 0; i < this.inputs.length; ++i) {\n                    feeds.push({ key: this.inputs[i], value: inputs[i] });\n                }\n                const feedDict = new FeedDict(feeds);\n                const outputs = execute(this.outputs, feedDict, { 'training': true });\n                // TODO(cais): Take care of the case of multiple outputs from a\n                //   single layer?\n                let totalLoss;\n                for (let i = 0; i < this.lossFunctions.length; ++i) {\n                    const lossFunction = this.lossFunctions[i];\n                    let loss = lossFunction(targets[i], outputs[i]);\n                    if (sampleWeights[i] != null) {\n                        loss = computeWeightedLoss(loss, sampleWeights[i]);\n                    }\n                    // TODO(cais): push Scalar instead.\n                    const meanLoss = tfc.mean(loss);\n                    // TODO(cais): Use a scope() instead, to avoid ownership.\n                    lossValues.push(meanLoss);\n                    if (i === 0) {\n                        totalLoss = loss;\n                    }\n                    else {\n                        totalLoss = tfc.add(totalLoss, loss);\n                    }\n                }\n                // Compute the metrics.\n                // TODO(cais): These should probably be calculated outside\n                //   totalLossFunction to benefit speed?\n                for (let i = 0; i < this.metricsTensors.length; ++i) {\n                    let weightedMetric;\n                    if (this.outputs.length > 1 && i < this.outputs.length) {\n                        weightedMetric = lossValues[i];\n                    }\n                    else {\n                        const metric = this.metricsTensors[i][0];\n                        const outputIndex = this.metricsTensors[i][1];\n                        weightedMetric =\n                            tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));\n                    }\n                    tfc.keep(weightedMetric);\n                    // TODO(cais): Use a scope() instead, to avoid ownership.\n                    metricsValues.push(weightedMetric);\n                }\n                totalLoss = tfc.mean(totalLoss);\n                // Add regularizer penalties.\n                this.calculateLosses().forEach(regularizerLoss => {\n                    totalLoss = tfc.add(totalLoss, regularizerLoss);\n                });\n                return totalLoss;\n            };\n            const variables = this.collectedTrainableWeights.map(param => param.read());\n            const returnCost = true;\n            const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables);\n            return [totalLossValue].concat(metricsValues);\n        };\n    }\n    /**\n     * Create a function which, when invoked with an array of `tf.Tensor`s as a\n     * batch of inputs, returns the prespecified loss and metrics of the model\n     * under the batch of input data.\n     */\n    makeTestFunction() {\n        this.testFunction = (data) => {\n            return tfc.tidy(() => {\n                const valOutputs = [];\n                let totalLoss;\n                const inputs = data.slice(0, this.inputs.length);\n                const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n                const feeds = [];\n                for (let i = 0; i < this.inputs.length; ++i) {\n                    feeds.push({ key: this.inputs[i], value: inputs[i] });\n                }\n                const feedDict = new FeedDict(feeds);\n                const outputs = execute(this.outputs, feedDict);\n                // Compute total loss.\n                for (let i = 0; i < this.lossFunctions.length; ++i) {\n                    const lossFunction = this.lossFunctions[i];\n                    // TODO(cais): Add sample weighting and replace the simple\n                    // averaging.\n                    const loss = tfc.mean(lossFunction(targets[i], outputs[i]));\n                    if (i === 0) {\n                        totalLoss = loss;\n                    }\n                    else {\n                        totalLoss = tfc.add(totalLoss, loss);\n                    }\n                    valOutputs.push(totalLoss);\n                }\n                // Compute the metrics.\n                for (let i = 0; i < this.metricsTensors.length; ++i) {\n                    const metric = this.metricsTensors[i][0];\n                    const outputIndex = this.metricsTensors[i][1];\n                    // TODO(cais): Replace K.mean() with a proper weighting function.\n                    const meanMetric = tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));\n                    valOutputs.push(meanMetric);\n                }\n                return valOutputs;\n            });\n        };\n    }\n    /**\n     * Trains the model for a fixed number of epochs (iterations on a\n     * dataset).\n     *\n     * ```js\n     * const model = tf.sequential({\n     *     layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * for (let i = 1; i < 5 ; ++i) {\n     *   const h = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *       batchSize: 4,\n     *       epochs: 3\n     *   });\n     *   console.log(\"Loss after Epoch \" + i + \" : \" + h.history.loss[0]);\n     * }\n     * ```\n     *\n     * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n     * model has multiple inputs. If all inputs in the model are named, you\n     * can also pass a dictionary mapping input names to `tf.Tensor`s.\n     * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n     * the model has multiple outputs. If all outputs in the model are named,\n     * you can also pass a dictionary mapping output names to `tf.Tensor`s.\n     * @param args A `ModelFitArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @exception ValueError In case of mismatch between the provided input\n     * data and what the model expects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fit(x, y, args = {}) {\n        return fitTensors(this, x, y, args);\n    }\n    // TODO(cais): Add code snippet below when it's possible to instantiate\n    //   actual dataset objects.\n    /**\n     * Trains the model using a dataset object.\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for training. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs.\n     *   Of the two items in the array, the first is the input feature(s) and\n     *   the second is the output target(s).\n     * @param args A `ModelFitDatasetArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fitDataset(dataset, args) {\n        return fitDataset(this, dataset, args);\n    }\n    /**\n     * Runs a single gradient update on a single batch of data.\n     *\n     * This method differs from `fit()` and `fitDataset()` in the following\n     * regards:\n     *   - It operates on exactly one batch of data.\n     *   - It returns only the loss and matric values, instead of\n     *     returning the batch-by-batch loss and metric values.\n     *   - It doesn't support fine-grained options such as verbosity and\n     *     callbacks.\n     *\n     * @param x Input data. It could be one of the following:\n     *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n     *     multiple inputs).\n     *   - An Object mapping input names to corresponding `tf.Tensor` (if the\n     *     model has named inputs).\n     * @param y Target darta. It could be either a `tf.Tensor` a multiple\n     *   `tf.Tensor`s. It should be consistent with `x`.\n     * @returns Training loss or losses (in case the model has\n     *   multiple outputs), along with metrics (if any), as numbers.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async trainOnBatch(x, y) {\n        // TODO(cais): Support sampleWeight and classWeight.\n        // TODO(cais): Support Dataset objects.\n        const standardizeOut = await this.standardizeUserData(x, y);\n        const inputs = standardizeOut[0];\n        const targets = standardizeOut[1];\n        const trainFunction = this.makeTrainFunction();\n        const losses = trainFunction(inputs.concat(targets));\n        const lossValues = [];\n        for (const loss of losses) {\n            const v = await loss.data();\n            lossValues.push(v[0]);\n        }\n        tfc.dispose(losses);\n        return singletonOrArray(lossValues);\n    }\n    /**\n     * Extract weight values of the model.\n     *\n     * @param config: An instance of `io.SaveConfig`, which specifies\n     * model-saving options such as whether only trainable weights are to be\n     * saved.\n     * @returns A `NamedTensorMap` mapping original weight names (i.e.,\n     *   non-uniqueified weight names) to their values.\n     */\n    getNamedWeights(config) {\n        const namedWeights = [];\n        const trainableOnly = config != null && config.trainableOnly;\n        const weights = trainableOnly ? this.trainableWeights : this.weights;\n        const weightValues = this.getWeights(trainableOnly);\n        for (let i = 0; i < weights.length; ++i) {\n            if (trainableOnly && !weights[i].trainable) {\n                // Optionally skip non-trainable weights.\n                continue;\n            }\n            namedWeights.push({ name: weights[i].originalName, tensor: weightValues[i] });\n        }\n        return namedWeights;\n    }\n    /**\n     * Setter used for force stopping of LayersModel.fit() (i.e., training).\n     *\n     * Example:\n     *\n     * ```js\n     * const input = tf.input({shape: [10]});\n     * const output = tf.layers.dense({units: 1}).apply(input);\n     * const model = tf.model({inputs: [input], outputs: [output]});\n     * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n     * const xs = tf.ones([8, 10]);\n     * const ys = tf.zeros([8, 1]);\n     *\n     * const history = await model.fit(xs, ys, {\n     *   epochs: 10,\n     *   callbacks: {\n     *     onEpochEnd: async (epoch, logs) => {\n     *       if (epoch === 2) {\n     *         model.stopTraining = true;\n     *       }\n     *     }\n     *   }\n     * });\n     *\n     * // There should be only 3 values in the loss array, instead of 10\n     * values,\n     * // due to the stopping after 3 epochs.\n     * console.log(history.history.loss);\n     * ```\n     */\n    set stopTraining(stop) {\n        this.stopTraining_ = stop;\n    }\n    get stopTraining() {\n        return this.stopTraining_;\n    }\n    get optimizer() {\n        return this.optimizer_;\n    }\n    set optimizer(optimizer) {\n        if (this.optimizer_ !== optimizer) {\n            this.optimizer_ = optimizer;\n            this.isOptimizerOwned = false;\n        }\n    }\n    dispose() {\n        const result = super.dispose();\n        if (result.refCountAfterDispose === 0 && this.optimizer != null &&\n            this.isOptimizerOwned) {\n            const numTensorsBeforeOptmizerDisposal = tfc.memory().numTensors;\n            this.optimizer_.dispose();\n            result.numDisposedVariables +=\n                numTensorsBeforeOptmizerDisposal - tfc.memory().numTensors;\n        }\n        return result;\n    }\n    getLossIdentifiers() {\n        let lossNames;\n        if (typeof this.loss === 'string') {\n            lossNames = toSnakeCase(this.loss);\n        }\n        else if (Array.isArray(this.loss)) {\n            for (const loss of this.loss) {\n                if (typeof loss !== 'string') {\n                    throw new Error('Serialization of non-string loss is not supported.');\n                }\n            }\n            lossNames = this.loss.map(name => toSnakeCase(name));\n        }\n        else {\n            const outputNames = Object.keys(this.loss);\n            lossNames = {};\n            const losses = this.loss;\n            for (const outputName of outputNames) {\n                if (typeof losses[outputName] === 'string') {\n                    lossNames[outputName] =\n                        toSnakeCase(losses[outputName]);\n                }\n                else {\n                    throw new Error('Serialization of non-string loss is not supported.');\n                }\n            }\n        }\n        return lossNames;\n    }\n    getMetricIdentifiers() {\n        if (typeof this.metrics === 'string' ||\n            typeof this.metrics === 'function') {\n            return [toSnakeCase(Metrics.getLossOrMetricName(this.metrics))];\n        }\n        else if (Array.isArray(this.metrics)) {\n            return this.metrics.map(metric => toSnakeCase(Metrics.getLossOrMetricName(metric)));\n        }\n        else {\n            const metricsIdentifiers = {};\n            for (const key in this.metrics) {\n                metricsIdentifiers[key] =\n                    toSnakeCase(Metrics.getLossOrMetricName(this.metrics[key]));\n            }\n            return metricsIdentifiers;\n        }\n    }\n    getTrainingConfig() {\n        return {\n            loss: this.getLossIdentifiers(),\n            metrics: this.getMetricIdentifiers(),\n            optimizer_config: {\n                class_name: this.optimizer.getClassName(),\n                config: this.optimizer.getConfig()\n            }\n        };\n        // TODO(cais): Add weight_metrics when they are supported.\n        // TODO(cais): Add sample_weight_mode when it's supported.\n        // TODO(cais): Add loss_weights when it's supported.\n    }\n    loadTrainingConfig(trainingConfig) {\n        if (trainingConfig.weighted_metrics != null) {\n            throw new Error('Loading weight_metrics is not supported yet.');\n        }\n        if (trainingConfig.loss_weights != null) {\n            throw new Error('Loading loss_weights is not supported yet.');\n        }\n        if (trainingConfig.sample_weight_mode != null) {\n            throw new Error('Loading sample_weight_mode is not supported yet.');\n        }\n        const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config);\n        const optimizer = deserialize(tsConfig);\n        let loss;\n        if (typeof trainingConfig.loss === 'string') {\n            loss = toCamelCase(trainingConfig.loss);\n        }\n        else if (Array.isArray(trainingConfig.loss)) {\n            loss = trainingConfig.loss.map(lossEntry => toCamelCase(lossEntry));\n        }\n        else if (trainingConfig.loss != null) {\n            loss = {};\n            for (const key in trainingConfig.loss) {\n                loss[key] = toCamelCase(trainingConfig.loss[key]);\n            }\n        }\n        let metrics;\n        if (Array.isArray(trainingConfig.metrics)) {\n            metrics = trainingConfig.metrics.map(metric => toCamelCase(metric));\n        }\n        else if (trainingConfig.metrics != null) {\n            metrics = {};\n            for (const key in trainingConfig.metrics) {\n                metrics[key] = toCamelCase(trainingConfig.metrics[key]);\n            }\n        }\n        this.compile({ loss, metrics, optimizer });\n    }\n    /**\n     * Save the configuration and/or weights of the LayersModel.\n     *\n     * An `IOHandler` is an object that has a `save` method of the proper\n     * signature defined. The `save` method manages the storing or\n     * transmission of serialized data (\"artifacts\") that represent the\n     * model's topology and weights onto or via a specific medium, such as\n     * file downloads, local storage, IndexedDB in the web browser and HTTP\n     * requests to a server. TensorFlow.js provides `IOHandler`\n     * implementations for a number of frequently used saving mediums, such as\n     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n     * for more details.\n     *\n     * This method also allows you to refer to certain types of `IOHandler`s\n     * as URL-like string shortcuts, such as 'localstorage://' and\n     * 'indexeddb://'.\n     *\n     * Example 1: Save `model`'s topology and weights to browser [local\n     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n     * then load it back.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * console.log('Prediction from original model:');\n     * model.predict(tf.ones([1, 3])).print();\n     *\n     * const saveResults = await model.save('localstorage://my-model-1');\n     *\n     * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * loadedModel.predict(tf.ones([1, 3])).print();\n     * ```\n     *\n     * Example 2. Saving `model`'s topology and weights to browser\n     * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n     * then load it back.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * console.log('Prediction from original model:');\n     * model.predict(tf.ones([1, 3])).print();\n     *\n     * const saveResults = await model.save('indexeddb://my-model-1');\n     *\n     * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * loadedModel.predict(tf.ones([1, 3])).print();\n     * ```\n     *\n     * Example 3. Saving `model`'s topology and weights as two files\n     * (`my-model-1.json` and `my-model-1.weights.bin`) downloaded from\n     * browser.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * const saveResults = await model.save('downloads://my-model-1');\n     * ```\n     *\n     * Example 4. Send  `model`'s topology and weights to an HTTP server.\n     * See the documentation of `tf.io.http` for more details\n     * including specifying request parameters and implementation of the\n     * server.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * const saveResults = await model.save('http://my-server/model/upload');\n     * ```\n     *\n     * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n     * scheme-based string shortcut for `IOHandler`.\n     * @param config Options for saving the model.\n     * @returns A `Promise` of `SaveResult`, which summarizes the result of\n     * the saving, such as byte sizes of the saved artifacts for the model's\n     *   topology and weight values.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async save(handlerOrURL, config) {\n        if (typeof handlerOrURL === 'string') {\n            const handlers = io.getSaveHandlers(handlerOrURL);\n            if (handlers.length === 0) {\n                throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n            }\n            else if (handlers.length > 1) {\n                throw new ValueError(`Found more than one (${handlers.length}) save handlers for ` +\n                    `URL '${handlerOrURL}'`);\n            }\n            handlerOrURL = handlers[0];\n        }\n        if (handlerOrURL.save == null) {\n            throw new ValueError('LayersModel.save() cannot proceed because the IOHandler ' +\n                'provided does not have the `save` attribute defined.');\n        }\n        const weightDataAndSpecs = await io.encodeWeights(this.getNamedWeights(config));\n        const returnString = false;\n        const unusedArg = null;\n        const modelConfig = this.toJSON(unusedArg, returnString);\n        const modelArtifacts = {\n            modelTopology: modelConfig,\n            format: LAYERS_MODEL_FORMAT_NAME,\n            generatedBy: `TensorFlow.js tfjs-layers v${version}`,\n            convertedBy: null,\n        };\n        const includeOptimizer = config == null ? false : config.includeOptimizer;\n        if (includeOptimizer && this.optimizer != null) {\n            modelArtifacts.trainingConfig = this.getTrainingConfig();\n            const weightType = 'optimizer';\n            const { data: optimizerWeightData, specs: optimizerWeightSpecs } = await io.encodeWeights(await this.optimizer.getWeights(), weightType);\n            weightDataAndSpecs.specs.push(...optimizerWeightSpecs);\n            weightDataAndSpecs.data = io.concatenateArrayBuffers([weightDataAndSpecs.data, optimizerWeightData]);\n        }\n        if (this.userDefinedMetadata != null) {\n            // Check serialized size of user-defined metadata.\n            const checkSize = true;\n            checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize);\n            modelArtifacts.userDefinedMetadata = this.userDefinedMetadata;\n        }\n        modelArtifacts.weightData = weightDataAndSpecs.data;\n        modelArtifacts.weightSpecs = weightDataAndSpecs.specs;\n        return handlerOrURL.save(modelArtifacts);\n    }\n    /**\n     * Set user-defined metadata.\n     *\n     * The set metadata will be serialized together with the topology\n     * and weights of the model during `save()` calls.\n     *\n     * @param setUserDefinedMetadata\n     */\n    setUserDefinedMetadata(userDefinedMetadata) {\n        checkUserDefinedMetadata(userDefinedMetadata, this.name);\n        this.userDefinedMetadata = userDefinedMetadata;\n    }\n    /**\n     * Get user-defined metadata.\n     *\n     * The metadata is supplied via one of the two routes:\n     *   1. By calling `setUserDefinedMetadata()`.\n     *   2. Loaded during model loading (if the model is constructed\n     *      via `tf.loadLayersModel()`.)\n     *\n     * If no user-defined metadata is available from either of the\n     * two routes, this function will return `undefined`.\n     */\n    getUserDefinedMetadata() {\n        return this.userDefinedMetadata;\n    }\n}\n// The class name is 'Model' rather than 'LayersModel' for backwards\n// compatibility since this class name shows up in the serialization format.\n/** @nocollapse */\nLayersModel.className = 'Model';\nserialization.registerClass(LayersModel);\n/**\n * A `tf.Functional` is an alias to `tf.LayersModel`.\n *\n * See also:\n *   `tf.LayersModel`, `tf.Sequential`, `tf.loadLayersModel`.\n */\n/** @doc {heading: 'Models', subheading: 'Classes'} */\nexport class Functional extends LayersModel {\n}\nFunctional.className = 'Functional';\nserialization.registerClass(Functional);\n//# sourceMappingURL=training.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source keras/models.py */\nimport { dispose, io, serialization, util } from '@tensorflow/tfjs-core';\nimport { getUid } from './backend/state';\nimport { Input } from './engine/input_layer';\nimport { getSourceInputs, Node } from './engine/topology';\nimport { LayersModel } from './engine/training';\nimport { NotImplementedError, RuntimeError, ValueError } from './errors';\nimport { deserialize } from './layers/serialization';\nimport * as generic_utils from './utils/generic_utils';\nimport { convertPythonicToTs } from './utils/serialization_utils';\nimport { getExactlyOneShape } from './utils/types_utils';\n/**\n * Parses a JSON model configuration file and returns a model instance.\n *\n * ```js\n * // This example shows how to serialize a model using `toJSON()` and\n * // deserialize it as another model using `tf.models.modelFromJSON()`.\n * // Note: this example serializes and deserializes only the topology\n * // of the model; the weights of the loaded model will be different\n * // from those of the the original model, due to random weight\n * // initialization.\n * // To load the topology and weights of a model, use `tf.loadLayersModel()`.\n * const model1 = tf.sequential();\n * model1.add(tf.layers.repeatVector({inputShape: [2], n: 4}));\n * // Serialize `model1` as a JSON object.\n * const model1JSON = model1.toJSON(null, false);\n * model1.summary();\n *\n * const model2 = await tf.models.modelFromJSON(model1JSON);\n * model2.summary();\n * ```\n *\n *  @param modelAndWeightsConfig JSON object or string encoding a model and\n *       weights configuration. It can also be only the topology JSON of the\n *       model, in which case the weights will not be loaded.\n *  @param custom_objects Optional dictionary mapping names\n *       (strings) to custom classes or functions to be\n *       considered during deserialization.\n * @returns A TensorFlow.js Layers `tf.LayersModel` instance (uncompiled).\n */\nexport async function modelFromJSON(modelAndWeightsConfig, customObjects) {\n    if (!('modelTopology' in modelAndWeightsConfig)) {\n        modelAndWeightsConfig = { modelTopology: modelAndWeightsConfig };\n    }\n    modelAndWeightsConfig = modelAndWeightsConfig;\n    let modelTopology = modelAndWeightsConfig.modelTopology;\n    if (modelTopology['model_config'] != null) {\n        // If the model-topology JSON contains a 'model_config' field, then it is\n        // a full model JSON (e.g., from `keras.Model.save()`), which contains\n        // not only the model's architecture in its 'model_config' field, but\n        // additional information such as the model's optimizer. We use only the\n        // 'model_config' field currently.\n        modelTopology = modelTopology['model_config'];\n    }\n    const tsConfig = convertPythonicToTs(modelTopology);\n    const model = deserialize(tsConfig, customObjects);\n    if (modelAndWeightsConfig.weightsManifest != null) {\n        // Load the weight values keyed by the original tensor names in the model\n        // file that was loaded.  These should match the keys of the weight\n        // manifest.\n        const weightValues = await io.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model.weights.map(weight => weight.originalName));\n        // Map the weights to the unique tensor names generated during model loading\n        const uniqueWeightValues = {};\n        for (const weight of model.weights) {\n            uniqueWeightValues[weight.originalName] =\n                weightValues[weight.originalName];\n        }\n        model.loadWeights(uniqueWeightValues);\n        // Dispose temporary weight values.\n        dispose(weightValues);\n    }\n    return model;\n}\n/**\n * Load a model, including its topology and optionally weights.  See the\n * Tutorial named \"How to import a Keras Model\" for usage examples.\n *\n * Example 1: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 2. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Load a model from user-selected files from HTML\n * [file input\n * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).\n *\n * ```js\n * // Note: this code snippet will not work without the HTML elements in the\n * //   page\n * const jsonUpload = document.getElementById('json-upload');\n * const weightsUpload = document.getElementById('weights-upload');\n *\n * const model = await tf.loadLayersModel(\n *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));\n * ```\n *\n * Example 4. Load a model from an HTTP server.\n *\n * ```js\n * const model = await\n *     tf.loadLayersModel('https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');\n * model.summary();\n * ```\n *\n * @param pathOrIOHandler Can be either of the two formats\n *   1. A string path to the `ModelAndWeightsConfig` JSON describing\n *      the model in the canonical TensorFlow.js format. This path will be\n *      interpreted as a relative HTTP path, to which `fetch` will be used to\n *      request the model topology and weight manifest JSON.\n *      The content of the JSON file is assumed to be a JSON object with the\n *      following fields and values:\n *      - 'modelTopology': A JSON object that can be either of:\n *        1. a model architecture JSON consistent with the format of the return\n *            value of `keras.Model.to_json()`\n *        2. a full model JSON in the format of `keras.models.save_model()`.\n *      - 'weightsManifest': A TensorFlow.js weights manifest.\n *      See the Python converter function `save_model()` for more details.\n *      It is also assumed that model weights can be accessed from relative\n *      paths described by the `paths` fields in weights manifest.\n *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`\n *      method.\n * @param options Optional configuration arguments for the model loading,\n *   including:\n *   - `strict`: Require that the provided weights exactly match those required\n *     by the layers.  Default true.  Passing false means that both extra\n *     weights and missing weights will be silently ignored.\n *   - `onProgress`: A progress callback of the form:\n *     `(fraction: number) => void`. This callback can be used to monitor the\n *     model-loading process.\n * @returns A `Promise` of `tf.LayersModel`, with the topology and weights\n *     loaded.\n */\nexport async function loadLayersModelInternal(pathOrIOHandler, options) {\n    if (options == null) {\n        options = {};\n    }\n    if (typeof pathOrIOHandler === 'string') {\n        const handlers = io.getLoadHandlers(pathOrIOHandler, options);\n        if (handlers.length === 0) {\n            // For backward compatibility: if no load handler can be found,\n            // assume it is a relative http path.\n            // TODO(cais): Reformat the args into a single `LoadOptions` once the core\n            // is refactored.\n            handlers.push(io.browserHTTPRequest(pathOrIOHandler, options));\n        }\n        else if (handlers.length > 1) {\n            throw new ValueError(`Found more than one (${handlers.length}) load handlers for ` +\n                `URL '${pathOrIOHandler}'`);\n        }\n        pathOrIOHandler = handlers[0];\n    }\n    return loadLayersModelFromIOHandler(pathOrIOHandler, undefined, options);\n}\n/**\n * Load a model and optionally its weights, using an IOHandler object.\n *\n * @param handler The instance of `IOHandler` to be used during the model\n *   loading.\n * @param customObjects Any optional custom objects to be used during model\n *   loading.\n * @param strict Whether the weight loading will be done in strict mode.\n *   Default: `true`.\n */\nexport async function loadLayersModelFromIOHandler(handler, customObjects, options) {\n    if (options == null) {\n        options = {};\n    }\n    if (handler.load == null) {\n        throw new ValueError('Cannot proceed with model loading because the IOHandler provided ' +\n            'does not have the `load` method implemented.');\n    }\n    const artifacts = await handler.load();\n    let modelTopology = artifacts.modelTopology;\n    if (modelTopology['model_config'] != null) {\n        modelTopology = modelTopology['model_config'];\n    }\n    const strict = options.strict == null ? true : options.strict;\n    // If weights are provided and the weight-loading mode is strict, use\n    // fast weight initialization. This skips costly initializers such as\n    // 'orthogonal' and saves unnecessary computation in cases where\n    // the initialized weight values will immediately be overwritten by\n    // loaded weight values.\n    const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict;\n    const model = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit);\n    const trainingConfig = artifacts.trainingConfig;\n    if (trainingConfig != null) {\n        model.loadTrainingConfig(trainingConfig);\n    }\n    if (artifacts.userDefinedMetadata != null) {\n        model.setUserDefinedMetadata(artifacts.userDefinedMetadata);\n    }\n    // If weightData is present, load the weights into the model.\n    if (artifacts.weightData != null) {\n        // Loading weights requires weightSpecs.\n        if (artifacts.weightSpecs == null) {\n            throw new ValueError('LayersModel artifacts contains weight data, but not weight specs. ' +\n                'Therefore loading of weights cannot proceed.');\n        }\n        const { modelWeights, optimizerWeights } = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs);\n        model.loadWeights(modelWeights, strict);\n        if (model.optimizer != null && optimizerWeights.length > 0) {\n            await model.optimizer.setWeights(optimizerWeights);\n        }\n        // Dispose temporary weight values.\n        dispose(modelWeights);\n        dispose(optimizerWeights.map(w => w.tensor));\n    }\n    return model;\n}\nfunction decodeModelAndOptimizerWeights(buffer, specs) {\n    const name2Tensor = io.decodeWeights(buffer, specs);\n    const modelWeights = {};\n    const optimizerWeights = [];\n    specs.forEach(spec => {\n        if (spec.group === 'optimizer') {\n            optimizerWeights.push({ name: spec.name, tensor: name2Tensor[spec.name] });\n        }\n        else {\n            modelWeights[spec.name] = name2Tensor[spec.name];\n        }\n    });\n    return { modelWeights, optimizerWeights };\n}\n/**\n * A model with a stack of layers, feeding linearly from one to the next.\n *\n * `tf.sequential` is a factory function that creates an instance of\n * `tf.Sequential`.\n *\n * ```js\n *  // Define a model for linear regression.\n *  const model = tf.sequential();\n *  model.add(tf.layers.dense({units: 1, inputShape: [1]}));\n *\n *  // Prepare the model for training: Specify the loss and the optimizer.\n *  model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n *\n *  // Generate some synthetic data for training.\n *  const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);\n *  const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);\n *\n *  // Train the model using the data then do inference on a data point the\n *  // model hasn't seen:\n *  await model.fit(xs, ys);\n *  model.predict(tf.tensor2d([5], [1, 1])).print();\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class Sequential extends LayersModel {\n    constructor(args) {\n        super({ inputs: [], outputs: [] });\n        args = args || {};\n        this.trainable = true;\n        this.built = false;\n        // Set model name.\n        this.name = (args.name != null) ? args.name : getUid('sequential_');\n        // Add to the model any layers passed to the constructor.\n        if (args.layers != null) {\n            for (const layer of args.layers) {\n                this.add(layer);\n            }\n        }\n    }\n    // Helper function to Sequential.add  Throws if the new output shape will be\n    // invalid.\n    checkShape(layer) {\n        const shape = layer.inboundNodes[0].outputTensors[0].shape;\n        if (shape.some(x => x < 0)) {\n            throw new ValueError('Negative dimension size caused by adding layer ' +\n                `${layer.name} with input shape [` +\n                `${layer.inboundNodes[0].inputTensors[0].shape}]`);\n        }\n    }\n    /**\n     * Adds a layer instance on top of the layer stack.\n     *\n     * ```js\n     *  const model = tf.sequential();\n     *  model.add(tf.layers.dense({units: 8, inputShape: [1]}));\n     *  model.add(tf.layers.dense({units: 4, activation: 'relu6'}));\n     *  model.add(tf.layers.dense({units: 1, activation: 'relu6'}));\n     *  // Note that the untrained model is random at this point.\n     *  model.predict(tf.randomNormal([10, 1])).print();\n     * ```\n     * @param layer Layer instance.\n     *\n     * @exception ValueError In case the `layer` argument does not know its\n     * input shape.\n     * @exception ValueError In case the `layer` argument has multiple output\n     *   tensors, or is already connected somewhere else (forbidden in\n     *   `Sequential` models).\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    add(layer) {\n        const isLayerModelInstance = layer instanceof Sequential || layer instanceof LayersModel;\n        let modelLayer;\n        if (isLayerModelInstance) {\n            modelLayer = layer;\n            if (modelLayer.outputs.length !== 1) {\n                throw new ValueError('All layers in a Sequential model ' +\n                    'should have a single output tensor. ' +\n                    'For multi-output layers, ' +\n                    'use the functional API.');\n            }\n            if (modelLayer.inputs.length !== 1) {\n                throw new ValueError('All layers in a Sequential model ' +\n                    'should have a single input tensor. ' +\n                    'For multi-input layers, ' +\n                    'use the functional API.');\n            }\n        }\n        if (this.outputs.length === 0) {\n            // first layer in model: check that it is an input layer\n            if (layer.inboundNodes.length === 0) {\n                // create an input layer\n                if (layer.batchInputShape == null) {\n                    throw new ValueError('The first layer in a Sequential model must ' +\n                        'get an `inputShape` or `batchInputShape` argument.');\n                }\n                // Instantiate the input layer.\n                const x = Input({\n                    batchShape: layer.batchInputShape,\n                    dtype: layer.dtype,\n                    name: layer.name + '_input'\n                });\n                // This will build the current layer and create the node connecting\n                // the current layer to the input layer we just created.\n                layer.apply(x);\n            }\n            if (isLayerModelInstance) {\n                this.outputs = modelLayer.outputs;\n                this.inputs = modelLayer.inputs;\n            }\n            else {\n                if (layer.inboundNodes.length !== 1) {\n                    throw new ValueError('A layer added to a Sequential model must not already be ' +\n                        `connected somewhere else. LayersModel received layer ${layer.name} ` +\n                        `which has ${layer.inboundNodes.length} pre-existing inbound ` +\n                        'connections.');\n                }\n                if (layer.inboundNodes[0].outputTensors.length !== 1) {\n                    throw new ValueError('All layers in a Sequential model ' +\n                        'should have a single output tensor. ' +\n                        'For multi-output layers, ' +\n                        'use the functional API.');\n                }\n                this.checkShape(layer);\n                this.outputs = [layer.inboundNodes[0].outputTensors[0]];\n                this.inputs = getSourceInputs(this.outputs[0]);\n            }\n            this.inboundNodes = [];\n            // We create an input node, which we will keep updated\n            // as we add more layers.\n            // (This call has side effects.)\n            // tslint:disable-next-line:no-unused-expression\n            new Node({\n                outboundLayer: this,\n                inboundLayers: [],\n                nodeIndices: [],\n                tensorIndices: [],\n                inputTensors: this.inputs,\n                outputTensors: this.outputs,\n                // no model-level masking for now\n                inputMasks: generic_utils.pyListRepeat(null, this.inputs.length),\n                outputMasks: [null],\n                inputShapes: this.inputs.map(x => x.shape),\n                outputShapes: this.outputs[0].shape\n            });\n        }\n        else {\n            const outputTensor = layer.apply(this.outputs[0]);\n            if (Array.isArray(outputTensor)) {\n                throw new TypeError('All layers in a Sequential model ' +\n                    'should have a single output tensor. ' +\n                    'For multi-output layers, ' +\n                    'use the functional API.');\n            }\n            this.checkShape(layer);\n            this.outputs = [outputTensor];\n            // update self.inbound_nodes\n            this.inboundNodes[0].outputTensors = this.outputs;\n            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n        }\n        this.layers.push(layer);\n        this.built = false;\n    }\n    /**\n     * Removes the last layer in the model.\n     *\n     * @exception TypeError if there are no layers in the model.\n     */\n    pop() {\n        if (this.layers.length === 0) {\n            throw new TypeError('There are no layers in the model.');\n        }\n        this.layers.pop();\n        if (this.layers.length === 0) {\n            this.outputs = [];\n            this.inboundNodes = [];\n            this.outboundNodes = [];\n        }\n        else {\n            const lastLayerIndex = this.layers.length - 1;\n            this.layers[lastLayerIndex].outboundNodes = [];\n            this.outputs = [this.layers[lastLayerIndex].output];\n            // update self.inbound_nodes\n            this.inboundNodes[0].outputTensors = this.outputs;\n            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n        }\n    }\n    call(inputs, kwargs) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.call(inputs, kwargs);\n    }\n    build(inputShape) {\n        // Call `getExactlyOneShape` without using its return value,\n        // to verify that exactly one input shape is provided.\n        getExactlyOneShape(inputShape);\n        if (this.inputs.length === 0 || this.outputs.length === 0) {\n            throw new TypeError('Sequential model cannot be built: model is empty.' +\n                ' Add some layers first.');\n        }\n        // actually create the model\n        this.model = new LayersModel({\n            inputs: this.inputs,\n            outputs: this.outputs[0],\n            name: this.name + '_model'\n        });\n        this.model.trainable = this.trainable;\n        // mirror model attributes\n        this.supportsMasking = this.model.supportsMasking;\n        // TODO(michaelterry): Add caches\n        this.inputLayers = this.model.inputLayers;\n        this.inputLayersNodeIndices = this.model.inputLayersNodeIndices;\n        this.inputLayersTensorIndices = this.model.inputLayersTensorIndices;\n        this.outputLayers = this.model.outputLayers;\n        this.outputLayersNodeIndices = this.model.outputLayersNodeIndices;\n        this.outputLayersTensorIndices = this.model.outputLayersTensorIndices;\n        this.nodesByDepth = this.model.nodesByDepth;\n        this.containerNodes = this.model.containerNodes;\n        this.outputNames = this.model.outputNames;\n        this.inputNames = this.model.inputNames;\n        // TODO(michaelterry): Add feedInputNames, feedInputs, if needed.\n        // TODO(michaelterry): Add callbackModel if needed.\n        this.built = true;\n    }\n    countParams() {\n        if (!this.built) {\n            this.build();\n        }\n        return super.countParams();\n    }\n    /**\n     * Print a text summary of the Sequential model's layers.\n     *\n     * The summary includes\n     * - Name and type of all layers that comprise the model.\n     * - Output shape(s) of the layers\n     * - Number of weight parameters of each layer\n     * - The total number of trainable and non-trainable parameters of the\n     * model.\n     *\n     * ```js\n     * const model = tf.sequential();\n     * model.add(\n     *     tf.layers.dense({units: 100, inputShape: [10], activation: 'relu'}));\n     * model.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));\n     *\n     * model.summary();\n     * ```\n     *\n     * @param lineLength Custom line length, in number of characters.\n     * @param positions Custom widths of each of the columns, as either\n     *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n     *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n     *   right-most (i.e., ending) position of a column.\n     * @param printFn Custom print function. Can be used to replace the default\n     *   `console.log`. For example, you can use `x => {}` to mute the printed\n     *   messages in the console.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    summary(lineLength, positions, printFn = console.log) {\n        if (!this.built) {\n            this.build();\n        }\n        super.summary(lineLength, positions, printFn);\n    }\n    /**\n     * Sets the weights of the model.\n     *\n     * @param weights Should be a list of Tensors with shapes and types matching\n     *   the output of `model.getWeights()`.\n     */\n    setWeights(weights) {\n        if (this.model == null) {\n            this.build();\n        }\n        this.model.setWeights(weights);\n    }\n    /**\n     * Returns the loss value & metrics values for the model in test mode.\n     *\n     * Loss and metrics are specified during `compile()`, which needs to happen\n     * before calls to `evaluate()`.\n     *\n     * Computation is done in batches.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const result = model.evaluate(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *   batchSize: 4,\n     * });\n     * result.print();\n     * ```\n     *\n     * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple inputs.\n     * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple outputs.\n     * @param args A `ModelEvaluateConfig`, containing optional fields.\n     *\n     * @return `Scalar` test loss (if the model has a single output and no\n     *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n     *   and/or metrics). The attribute `model.metricsNames`\n     *   will give you the display labels for the scalar outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    evaluate(x, y, args = {}) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before being used.');\n        }\n        return this.model.evaluate(x, y, args);\n    }\n    // TODO(cais): Add code snippet below once real dataset objects are\n    //   available.\n    /**\n     * Evaluate model using a dataset object.\n     *\n     * Note: Unlike `evaluate()`, this method is asynchronous (`async`);\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for evaluation. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs. Of the two items in the array, the\n     *   first is the input feature(s) and the second is the output target(s).\n     * @param args A configuration object for the dataset-based evaluation.\n     * @returns Loss and metric values as an Array of `Scalar` objects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async evaluateDataset(dataset, args) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before being used.');\n        }\n        return this.model.evaluateDataset(dataset, args);\n    }\n    /**\n     * Generates output predictions for the input samples.\n     *\n     * Computation is done in batches.\n     *\n     * Note: the \"step\" mode of predict() is currently not supported.\n     *   This is because the TensorFow.js core backend is imperative only.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predict(tf.ones([2, 10])).print();\n     * ```\n     *\n     * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n     *   the model has multiple inputs.\n     * @param conifg A `ModelPredictConfig` object containing optional fields.\n     *\n     * @return `tf.Tensor`(s) of predictions.\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and the model's expectations, or in case a stateful model receives a\n     *   number of samples that is not a multiple of the batch size.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(x, args = {}) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.predict(x, args);\n    }\n    /**\n     * Returns predictions for a single batch of samples.\n     *\n     * @param x: Input samples, as a Tensor, or list of Tensors (if the model\n     *   has multiple inputs).\n     * @return Tensor(s) of predictions\n     */\n    predictOnBatch(x) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.predictOnBatch(x);\n    }\n    /**\n     * See `LayersModel.compile`.\n     *\n     * @param args\n     */\n    compile(args) {\n        this.build();\n        this.model.compile(args);\n        this.optimizer_ = this.model.optimizer;\n        // tslint:disable-next-line:no-any\n        this.isOptimizerOwned = this.model.isOptimizerOwned;\n        this.loss = this.model.loss;\n        this.metrics = this.model.metrics;\n        // TODO(cais): Add this.lossWeights, this.sampleWeightMode,\n        //   this.weightedMetrics, this.targets.\n        this.metricsTensors = this.model.metricsTensors;\n        this.metricsNames = this.model.metricsNames;\n        // TODO(cais): Add sampleWeights.\n    }\n    get optimizer() {\n        return this.model == null ? undefined : this.model.optimizer;\n    }\n    set optimizer(optimizer) {\n        this.model.optimizer = optimizer;\n    }\n    /**\n     * Trains the model for a fixed number of epochs (iterations on a dataset).\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const history = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *   batchSize: 4,\n     *   epochs: 3\n     * });\n     * console.log(history.history.loss[0]);\n     * ```\n     *\n     * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n     * model has multiple inputs. If all inputs in the model are named, you can\n     * also pass a dictionary mapping input names to `tf.Tensor`s.\n     * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n     * the model has multiple outputs. If all outputs in the model are named, you\n     *  can also pass a dictionary mapping output names to `tf.Tensor`s.\n     * @param args  A `ModelFitConfig`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and what the model expects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fit(x, y, args = {}) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before ' +\n                'being used.');\n        }\n        return this.model.fit(x, y, args);\n    }\n    /**\n     * Trains the model using a dataset object.\n     *\n     * ```js\n     * const xArray = [\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     * ];\n     * const yArray = [1, 1, 1, 1];\n     * // Create a dataset from the JavaScript array.\n     * const xDataset = tf.data.array(xArray);\n     * const yDataset = tf.data.array(yArray);\n     * // Zip combines the `x` and `y` Datasets into a single Dataset, the\n     * // iterator of which will return an object containing of two tensors,\n     * // corresponding to `x` and `y`.  The call to `batch(4)` will bundle\n     * // four such samples into a single object, with the same keys now pointing\n     * // to tensors that hold 4 examples, organized along the batch dimension.\n     * // The call to `shuffle(4)` causes each iteration through the dataset to\n     * // happen in a different order.  The size of the shuffle window is 4.\n     * const xyDataset = tf.data.zip({xs: xDataset, ys: yDataset})\n     *     .batch(4)\n     *     .shuffle(4);\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [9]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const history = await model.fitDataset(xyDataset, {\n     *   epochs: 4,\n     *   callbacks: {onEpochEnd: (epoch, logs) => console.log(logs.loss)}\n     * });\n     * ```\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected to\n     *   generate a dataset iterator object, the `next()` method of which is\n     *   expected to produce data batches for evaluation. The return value of the\n     *   `next()` call ought to contain a boolean `done` field and a `value`\n     *   field.\n     *\n     *   The `value` field is expected to be an object of with fields\n     *   `xs` and `ys`, which point to the feature tensor and the target tensor,\n     *   respectively. This case is for models with exactly one input and one\n     *   output (e.g.. a sequential model). For example:\n     *   ```js\n     *   {value: {xs: xsTensor, ys: ysTensor}, done: false}\n     *   ```\n     *\n     *   If the model has multiple inputs, the `xs` field of `value` should\n     *   be an object mapping input names to their respective feature tensors.\n     *   For example:\n     *   ```js\n     *   {\n     *     value: {\n     *       xs: {\n     *         input_1: xsTensor1,\n     *         input_2: xsTensor2\n     *       },\n     *       ys: ysTensor\n     *     },\n     *     done: false\n     *   }\n     *   ```\n     *   If the model has multiple outputs, the `ys` field of `value` should\n     *   be an object mapping output names to their respective target tensors.\n     *   For example:\n     *   ```js\n     *   {\n     *     value: {\n     *       xs: xsTensor,\n     *       ys: {\n     *         output_1: ysTensor1,\n     *         output_2: ysTensor2\n     *       },\n     *     },\n     *     done: false\n     *   }\n     *   ```\n     * @param args A `ModelFitDatasetArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async fitDataset(dataset, args) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before ' +\n                'being used.');\n        }\n        return this.model.fitDataset(dataset, args);\n    }\n    /**\n     * Runs a single gradient update on a single batch of data.\n     *\n     * This method differs from `fit()` and `fitDataset()` in the following\n     * regards:\n     *   - It operates on exactly one batch of data.\n     *   - It returns only the loss and matric values, instead of\n     *     returning the batch-by-batch loss and metric values.\n     *   - It doesn't support fine-grained options such as verbosity and\n     *     callbacks.\n     *\n     * @param x Input data. It could be one of the following:\n     *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n     *     multiple inputs).\n     *   - An Object mapping input names to corresponding `tf.Tensor` (if the\n     *     model has named inputs).\n     * @param y Target darta. It could be either a `tf.Tensor` a multiple\n     *   `tf.Tensor`s. It should be consistent with `x`.\n     * @returns Training loss or losses (in case the model has\n     *   multiple outputs), along with metrics (if any), as numbers.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async trainOnBatch(x, y) {\n        return this.model.trainOnBatch(x, y);\n    }\n    /* See parent class for JsDoc */\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n        let configArray;\n        let extraModelConfig = {};\n        if (config instanceof Array) {\n            if (!(config[0].className != null) ||\n                config[0]['className'] === 'Merge') {\n                throw new ValueError('Legacy serialization format not supported yet.');\n            }\n            configArray = config;\n        }\n        else {\n            util.assert(config['layers'] != null, () => `When the config data for a Sequential model is not an Array, ` +\n                `it must be an Object that contains the 'layers' field.`);\n            configArray = config['layers'];\n            delete config['layers'];\n            extraModelConfig = config;\n        }\n        const model = new cls(extraModelConfig);\n        if (!(model instanceof Sequential)) {\n            throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model}`);\n        }\n        for (const conf of configArray) {\n            const customObjects = undefined;\n            const layer = deserialize(conf, customObjects, fastWeightInit);\n            if (fastWeightInit) {\n                layer.setFastWeightInitDuringBuild(true);\n            }\n            model.add(layer);\n        }\n        return model;\n    }\n    /**\n     * Setter used for force stopping of LayersModel.fit() (i.e., training).\n     *\n     * Example:\n     *\n     * ```js\n     * const model = tf.sequential();\n     * model.add(tf.layers.dense({units: 1, inputShape: [10]}));\n     * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n     * const xs = tf.ones([8, 10]);\n     * const ys = tf.zeros([8, 1]);\n     *\n     * const history = await model.fit(xs, ys, {\n     *   epochs: 10,\n     *   callbacks: {\n     *     onEpochEnd: async (epoch, logs) => {\n     *       if (epoch === 2) {\n     *         model.stopTraining = true;\n     *       }\n     *     }\n     *   }\n     * });\n     *\n     * // There should be only 3 values in the loss array, instead of 10 values,\n     * // due to the stopping after 3 epochs.\n     * console.log(history.history.loss);\n     * ```\n     */\n    set stopTraining(stop) {\n        // TODO(cais): When refactoring to remove the composition pattern happens,\n        // remove this method overriding.\n        if (this.model == null) {\n            throw new ValueError('Cannot set the stopTraining property of a sequential model before ' +\n                'it is compiled.');\n        }\n        this.model.stopTraining = stop;\n    }\n    get stopTraining() {\n        if (this.model == null) {\n            throw new ValueError('Cannot get the stopTraining property of a sequential model before ' +\n                'it is compiled.');\n        }\n        return this.model.stopTraining;\n    }\n    // TODO(cais): Override get trainableWeights() here\n    // tslint:disable-next-line:no-any\n    getConfig() {\n        // NOTE(cais): We override the return type of getConfig() to `any` here,\n        //   because the `Sequential` class is a special case among `Container`\n        //   subtypes in that its getConfig() method returns an Array (not a\n        //   dict).\n        const layers = [];\n        for (const layer of this.layers) {\n            const dict = {};\n            dict['className'] = layer.getClassName();\n            dict['config'] = layer.getConfig();\n            layers.push(dict);\n        }\n        return { name: this.name, layers };\n    }\n}\n/** @nocollapse */\nSequential.className = 'Sequential';\nserialization.registerClass(Sequential);\n//# sourceMappingURL=models.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { CallbackConstructorRegistry } from './base_callbacks';\nimport { Input, } from './engine/input_layer';\nimport { LayersModel } from './engine/training';\nimport { loadLayersModelInternal, Sequential } from './models';\n// TODO(cais): Add doc string to all the public static functions in this\n//   class; include exectuable JavaScript code snippets where applicable\n//   (b/74074458).\n// LayersModel and related factory methods.\n/**\n * A model is a data structure that consists of `Layers` and defines inputs\n * and outputs.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.model` is more generic, supporting an arbitrary graph (without\n * cycles) of layers. `tf.sequential` is less generic and supports only a linear\n * stack of layers.\n *\n * When creating a `tf.LayersModel`, specify its input(s) and output(s). Layers\n * are used to wire input(s) to output(s).\n *\n * For example, the following code snippet defines a model consisting of\n * two `dense` layers, with 10 and 4 units, respectively.\n *\n * ```js\n * // Define input, which has a size of 5 (not including batch dimension).\n * const input = tf.input({shape: [5]});\n *\n * // First dense layer uses relu activation.\n * const denseLayer1 = tf.layers.dense({units: 10, activation: 'relu'});\n * // Second dense layer uses softmax activation.\n * const denseLayer2 = tf.layers.dense({units: 4, activation: 'softmax'});\n *\n * // Obtain the output symbolic tensor by applying the layers on the input.\n * const output = denseLayer2.apply(denseLayer1.apply(input));\n *\n * // Create the model based on the inputs.\n * const model = tf.model({inputs: input, outputs: output});\n *\n * // The model can be used for training, evaluation and prediction.\n * // For example, the following line runs prediction with the model on\n * // some fake data.\n * model.predict(tf.ones([2, 5])).print();\n * ```\n * See also:\n *   `tf.sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function model(args) {\n    return new LayersModel(args);\n}\n/**\n * Creates a `tf.Sequential` model.  A sequential model is any model where the\n * outputs of one layer are the inputs to the next layer, i.e. the model\n * topology is a simple 'stack' of layers, with no branching or skipping.\n *\n * This means that the first layer passed to a `tf.Sequential` model should have\n * a defined input shape. What that means is that it should have received an\n * `inputShape` or `batchInputShape` argument, or for some type of layers\n * (recurrent, Dense...) an `inputDim` argument.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.sequential` is less generic, supporting only a linear stack of layers.\n * `tf.model` is more generic and supports an arbitrary graph (without\n * cycles) of layers.\n *\n * Examples:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have an input shape defined.\n * model.add(tf.layers.dense({units: 32, inputShape: [50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output, which equals\n * // `[null, 4]`. The 1st dimension is the undetermined batch dimension; the\n * // 2nd is the output size of the model's last layer.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * It is also possible to specify a batch size (with potentially undetermined\n * batch dimension, denoted by \"null\") for the first layer using the\n * `batchInputShape` key. The following example is equivalent to the above:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have a defined input shape\n * model.add(tf.layers.dense({units: 32, batchInputShape: [null, 50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * You can also use an `Array` of already-constructed `Layer`s to create\n * a `tf.Sequential` model:\n *\n * ```js\n * const model = tf.sequential({\n *   layers: [tf.layers.dense({units: 32, inputShape: [50]}),\n *            tf.layers.dense({units: 4})]\n * });\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function sequential(config) {\n    return new Sequential(config);\n}\n/**\n * Load a model composed of Layer objects, including its topology and optionally\n * weights. See the Tutorial named \"How to import a Keras Model\" for usage\n * examples.\n *\n * This method is applicable to:\n *\n * 1. Models created with the `tf.layers.*`, `tf.sequential`, and\n * `tf.model` APIs of TensorFlow.js and later saved with the\n * `tf.LayersModel.save` method.\n * 2. Models converted from Keras or TensorFlow tf.keras using the\n * [tensorflowjs_converter](https://github.com/tensorflow/tfjs/tree/master/tfjs-converter).\n *\n * This mode is *not* applicable to TensorFlow `SavedModel`s or their converted\n * forms. For those models, use `tf.loadGraphModel`.\n *\n * Example 1. Load a model from an HTTP server.\n *\n * ```js\n * const model = await tf.loadLayersModel(\n *     'https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');\n * model.summary();\n * ```\n *\n * Example 2: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 4. Load a model from user-selected files from HTML\n * [file input\n * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).\n *\n * ```js\n * // Note: this code snippet will not work without the HTML elements in the\n * //   page\n * const jsonUpload = document.getElementById('json-upload');\n * const weightsUpload = document.getElementById('weights-upload');\n *\n * const model = await tf.loadLayersModel(\n *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));\n * ```\n *\n * @param pathOrIOHandler Can be either of the two formats\n *   1. A string path to the `ModelAndWeightsConfig` JSON describing\n *      the model in the canonical TensorFlow.js format. For file://\n *      (tfjs-node-only), http:// and https:// schemas, the path can be\n *      either absolute or relative.\n *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`\n *      method.\n * @param options Optional configuration arguments for the model loading,\n *   including:\n *   - `strict`: Require that the provided weights exactly match those required\n *     by the layers.  Default true.  Passing false means that both extra\n *     weights and missing weights will be silently ignored.\n *   - `onProgress`: A function of the signature `(fraction: number) => void',\n *     that can be used as the progress callback for the model loading.\n * @returns A `Promise` of `tf.LayersModel`, with the topology and weights\n *     loaded.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport function loadLayersModel(pathOrIOHandler, options) {\n    if (options == null) {\n        options = {};\n    }\n    return loadLayersModelInternal(pathOrIOHandler, options);\n}\n/**\n * Used to instantiate an input to a model as a `tf.SymbolicTensor`.\n *\n * Users should call the `input` factory function for\n * consistency with other generator functions.\n *\n * Example:\n *\n * ```js\n * // Defines a simple logistic regression model with 32 dimensional input\n * // and 3 dimensional output.\n * const x = tf.input({shape: [32]});\n * const y = tf.layers.dense({units: 3, activation: 'softmax'}).apply(x);\n * const model = tf.model({inputs: x, outputs: y});\n * model.predict(tf.ones([2, 32])).print();\n * ```\n *\n * Note: `input` is only necessary when using `model`. When using\n * `sequential`, specify `inputShape` for the first layer or use `inputLayer`\n * as the first layer.\n *\n * @doc {heading: 'Models', subheading: 'Inputs'}\n */\nexport function input(config) {\n    return Input(config);\n}\nexport function registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n    CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);\n}\n//# sourceMappingURL=exports.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// Layer activation functions\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { deserializeKerasObject } from './utils/generic_utils';\n/**\n * Base class for Activations.\n *\n * Special note: due to cross-language compatibility reasons, the\n * static readonly className field in this family of classes must be set to\n * the initialLowerCamelCase name of the activation.\n */\nexport class Activation extends serialization.Serializable {\n    getConfig() {\n        return {};\n    }\n}\n/**\n * Exponential linear unit (ELU).\n * Reference: https://arxiv.org/abs/1511.07289\n */\nexport class Elu extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x: Input.\n     * @param alpha: Scaling factor the negative section.\n     * @return Output of the ELU activation.\n     */\n    apply(x, alpha = 1) {\n        return K.elu(x, alpha);\n    }\n}\n/** @nocollapse */\nElu.className = 'elu';\nserialization.registerClass(Elu);\n/**\n * Scaled Exponential Linear Unit. (Klambauer et al., 2017).\n * Reference: Self-Normalizing Neural Networks, https://arxiv.org/abs/1706.02515\n * Notes:\n *   - To be used together with the initialization \"lecunNormal\".\n *   - To be used together with the dropout variant \"AlphaDropout\".\n */\nexport class Selu extends Activation {\n    apply(x) {\n        return tfc.selu(x);\n    }\n}\n/** @nocollapse */\nSelu.className = 'selu';\nserialization.registerClass(Selu);\n/**\n *  Rectified linear unit\n */\nexport class Relu extends Activation {\n    apply(x) {\n        return tfc.relu(x);\n    }\n}\n/** @nocollapse */\nRelu.className = 'relu';\nserialization.registerClass(Relu);\n/**\n * Rectified linear unit activation maxing out at 6.0.\n */\nexport class Relu6 extends Activation {\n    apply(x) {\n        return tidy(() => tfc.minimum(6.0, tfc.relu(x)));\n    }\n}\n/** @nocollapse */\nRelu6.className = 'relu6';\nserialization.registerClass(Relu6);\n//* Linear activation (no-op) */\nexport class Linear extends Activation {\n    apply(x) {\n        return x;\n    }\n}\n/** @nocollapse */\nLinear.className = 'linear';\nserialization.registerClass(Linear);\n/**\n * Sigmoid activation function.\n */\nexport class Sigmoid extends Activation {\n    apply(x) {\n        return tfc.sigmoid(x);\n    }\n}\n/** @nocollapse */\nSigmoid.className = 'sigmoid';\nserialization.registerClass(Sigmoid);\n/**\n * Segment-wise linear approximation of sigmoid.\n */\nexport class HardSigmoid extends Activation {\n    apply(x) {\n        return K.hardSigmoid(x);\n    }\n}\n/** @nocollapse */\nHardSigmoid.className = 'hardSigmoid';\nserialization.registerClass(HardSigmoid);\n/**\n * Softplus activation function.\n */\nexport class Softplus extends Activation {\n    apply(x) {\n        return tfc.softplus(x);\n    }\n}\n/** @nocollapse */\nSoftplus.className = 'softplus';\nserialization.registerClass(Softplus);\n/**\n * Softsign activation function.\n */\nexport class Softsign extends Activation {\n    apply(x) {\n        return K.softsign(x);\n    }\n}\n/** @nocollapse */\nSoftsign.className = 'softsign';\nserialization.registerClass(Softsign);\n/**\n * Hyperbolic tangent function.\n */\nexport class Tanh extends Activation {\n    apply(x) {\n        return tfc.tanh(x);\n    }\n}\n/** @nocollapse */\nTanh.className = 'tanh';\nserialization.registerClass(Tanh);\n/**\n * Softmax activation function\n */\nexport class Softmax extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x Tensor.\n     * @param axis Integer, axis along which the softmax normalization is applied.\n     * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n     * an error.\n     *\n     * @returns a Tensor of the same shape as x\n     *\n     * @throws ValueError: In case `dim(x) < 2`.\n     */\n    apply(x, axis = (-1)) {\n        return tfc.softmax(x, axis);\n    }\n}\n/** @nocollapse */\nSoftmax.className = 'softmax';\nserialization.registerClass(Softmax);\n/**\n * Log softmax activation function\n */\nexport class LogSoftmax extends Activation {\n    /**\n     * Calculate the activation function of log softmax:\n     * log( exp(x_i) / sum(exp(x)) )\n     *\n     * @param x Tensor.\n     * @param axis Integer, axis along which the softmax normalization is applied.\n     * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n     * an error.\n     *\n     * @returns a Tensor of the same shape as x\n     *\n     * @throws ValueError: In case `dim(x) < 2`.\n     */\n    apply(x, axis = (-1)) {\n        return tfc.logSoftmax(x, axis);\n    }\n}\n/** @nocollapse */\nLogSoftmax.className = 'logSoftmax';\nserialization.registerClass(LogSoftmax);\n/**\n * Swish activation function\n */\nexport class Swish extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x Tensor.\n     * @param alpha Scaling factor for the sigmoid function.\n     * @returns a Tensor of the same shape as x\n     */\n    apply(x, alpha = 1) {\n        return tidy(() => tfc.sigmoid(x.mul(alpha)).mul(x));\n    }\n}\n/** @nocollapse */\nSwish.className = 'swish';\nserialization.registerClass(Swish);\nexport function serializeActivation(activation) {\n    return activation.getClassName();\n}\nexport function deserializeActivation(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'activation');\n}\nexport function getActivation(identifier) {\n    if (identifier == null) {\n        const config = {};\n        config['className'] = 'linear';\n        config['config'] = {};\n        return deserializeActivation(config);\n    }\n    if (typeof identifier === 'string') {\n        const config = {};\n        config['className'] = identifier;\n        config['config'] = {};\n        return deserializeActivation(config);\n    }\n    else if (identifier instanceof Activation) {\n        return identifier;\n    }\n    else {\n        return deserializeActivation(identifier);\n    }\n}\n//# sourceMappingURL=activations.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* original source: keras/regularizers.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { abs, add, serialization, sum, tidy, zeros } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\nfunction assertObjectArgs(args) {\n    if (args != null && typeof args !== 'object') {\n        throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an ` +\n            `object, but received: ${args}`);\n    }\n}\n/**\n * Regularizer base class.\n */\nexport class Regularizer extends serialization.Serializable {\n}\nexport class L1L2 extends Regularizer {\n    constructor(args) {\n        super();\n        assertObjectArgs(args);\n        this.l1 = args == null || args.l1 == null ? 0.01 : args.l1;\n        this.l2 = args == null || args.l2 == null ? 0.01 : args.l2;\n        this.hasL1 = this.l1 !== 0;\n        this.hasL2 = this.l2 !== 0;\n    }\n    /**\n     * Porting note: Renamed from __call__.\n     * @param x Variable of which to calculate the regularization score.\n     */\n    apply(x) {\n        return tidy(() => {\n            let regularization = zeros([1]);\n            if (this.hasL1) {\n                regularization = add(regularization, sum(tfc.mul(this.l1, abs(x))));\n            }\n            if (this.hasL2) {\n                regularization =\n                    add(regularization, sum(tfc.mul(this.l2, K.square(x))));\n            }\n            return regularization.asScalar();\n        });\n    }\n    getConfig() {\n        return { 'l1': this.l1, 'l2': this.l2 };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls({ l1: config['l1'], l2: config['l2'] });\n    }\n}\n/** @nocollapse */\nL1L2.className = 'L1L2';\nserialization.registerClass(L1L2);\nexport function l1(args) {\n    assertObjectArgs(args);\n    return new L1L2({ l1: args != null ? args.l1 : null, l2: 0 });\n}\nexport function l2(args) {\n    assertObjectArgs(args);\n    return new L1L2({ l2: args != null ? args.l2 : null, l1: 0 });\n}\n// Maps the JavaScript-like identifier keys to the corresponding keras symbols.\nexport const REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'l1l2': 'L1L2'\n};\nexport function serializeRegularizer(constraint) {\n    return serializeKerasObject(constraint);\n}\nexport function deserializeRegularizer(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'regularizer');\n}\nexport function getRegularizer(identifier) {\n    if (identifier == null) {\n        return null;\n    }\n    if (typeof identifier === 'string') {\n        const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        const config = { className, config: {} };\n        return deserializeRegularizer(config);\n    }\n    else if (identifier instanceof Regularizer) {\n        return identifier;\n    }\n    else {\n        return deserializeRegularizer(identifier);\n    }\n}\n//# sourceMappingURL=regularizers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n *  Advanced activation layers.\n */\nimport { clipByValue, elu, leakyRelu, prelu, relu, serialization } from '@tensorflow/tfjs-core';\nimport { Softmax as softmaxActivation } from '../activations';\nimport { cast } from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class ReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.supportsMasking = true;\n        if (args != null) {\n            this.maxValue = args.maxValue;\n        }\n    }\n    call(inputs, kwargs) {\n        inputs = getExactlyOneTensor(inputs);\n        let output = relu(inputs);\n        if (this.maxValue != null) {\n            output = clipByValue(output, 0, this.maxValue);\n        }\n        return output;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { maxValue: this.maxValue };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nReLU.className = 'ReLU';\nserialization.registerClass(ReLU);\nexport class LeakyReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA = 0.3;\n        if (args == null) {\n            args = {};\n        }\n        this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return leakyRelu(x, this.alpha);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { alpha: this.alpha };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nLeakyReLU.className = 'LeakyReLU';\nserialization.registerClass(LeakyReLU);\nexport class PReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA_INITIALIZER = 'zeros';\n        if (args == null) {\n            args = {};\n        }\n        this.supportsMasking = true;\n        this.alphaInitializer =\n            getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER);\n        this.alphaRegularizer = getRegularizer(args.alphaRegularizer);\n        this.alphaConstraint = getConstraint(args.alphaConstraint);\n        if (args.sharedAxes == null) {\n            this.sharedAxes = null;\n        }\n        else if (Array.isArray(args.sharedAxes)) {\n            this.sharedAxes = args.sharedAxes;\n        }\n        else if (typeof args.sharedAxes === 'number') {\n            this.sharedAxes = [args.sharedAxes];\n        }\n        else {\n            throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, ` +\n                `but got ${args.sharedAxes}`);\n        }\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const paramShape = inputShape.slice(1);\n        if (this.sharedAxes != null) {\n            for (const i of this.sharedAxes) {\n                paramShape[i - 1] = 1;\n            }\n        }\n        this.alpha = this.addWeight('alpha', paramShape, 'float32', this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);\n        // Set input spec.\n        const axes = {};\n        if (this.sharedAxes != null) {\n            for (let i = 1; i < inputShape.length; ++i) {\n                axes[i] = inputShape[i];\n            }\n        }\n        this.inputSpec = [new InputSpec({\n                ndim: inputShape.length,\n                axes,\n            })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        inputs = getExactlyOneTensor(inputs);\n        return prelu(inputs, this.alpha.read());\n    }\n    getConfig() {\n        const config = {\n            alphaInitializer: serializeInitializer(this.alphaInitializer),\n            alphaRegularizer: serializeRegularizer(this.alphaRegularizer),\n            alphaConstraint: serializeConstraint(this.alphaConstraint),\n            sharedAxes: this.sharedAxes\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nPReLU.className = 'PReLU';\nserialization.registerClass(PReLU);\nexport class ELU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) {\n            throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ` +\n                `ELU layer yet.`);\n        }\n        this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return elu(x);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { alpha: this.alpha };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nELU.className = 'ELU';\nserialization.registerClass(ELU);\nexport class ThresholdedReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_THETA = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return x.mul(cast(x.greater(this.theta), 'float32'));\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { theta: this.theta };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nThresholdedReLU.className = 'ThresholdedReLU';\nserialization.registerClass(ThresholdedReLU);\nexport class Softmax extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_AXIS = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        this.softmax = new softmaxActivation().apply;\n        this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return this.softmax(x, this.axis);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { axis: this.axis };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nSoftmax.className = 'Softmax';\nserialization.registerClass(Softmax);\n//# sourceMappingURL=advanced_activations.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { ValueError } from '../errors';\nimport { pyListRepeat } from './generic_utils';\nimport { isInteger, max } from './math_utils';\n/**\n * Transforms a single number of array of numbers into an array of numbers.\n * @param value\n * @param n: The size of the tuple to be returned.\n * @param name: Name of the parameter, used for generating error messages.\n * @returns An array of numbers.\n */\nexport function normalizeArray(value, n, name) {\n    if (typeof value === 'number') {\n        return pyListRepeat(value, n);\n    }\n    else {\n        if (value.length !== n) {\n            throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers.` +\n                ` Received: ${value.length} elements.`);\n        }\n        for (let i = 0; i < n; ++i) {\n            const singleValue = value[i];\n            if (!isInteger(singleValue)) {\n                throw new ValueError(`The ${name} argument must be an integer or tuple of ${n}` +\n                    ` integers. Received: ${JSON.stringify(value)} including a` +\n                    ` non-integer number ${singleValue}`);\n            }\n        }\n        return value;\n    }\n}\n/**\n * Determines output length of a convolution given input length.\n * @param inputLength\n * @param filterSize\n * @param padding\n * @param stride\n * @param dilation: dilation rate.\n */\nexport function convOutputLength(inputLength, filterSize, padding, stride, dilation = 1) {\n    if (inputLength == null) {\n        return inputLength;\n    }\n    const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1);\n    let outputLength;\n    if (padding === 'same') {\n        outputLength = inputLength;\n    }\n    else { // VALID\n        outputLength = inputLength - dilatedFilterSize + 1;\n    }\n    return Math.floor((outputLength + stride - 1) / stride);\n}\nexport function deconvLength(dimSize, strideSize, kernelSize, padding) {\n    if (dimSize == null) {\n        return null;\n    }\n    if (padding === 'valid') {\n        dimSize = dimSize * strideSize + max([kernelSize - strideSize, 0]);\n    }\n    else if (padding === 'same') {\n        dimSize = dimSize * strideSize;\n    }\n    else {\n        throw new ValueError(`Unsupport padding mode: ${padding}.`);\n    }\n    return dimSize;\n}\n//# sourceMappingURL=conv_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Convolutional Layers\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { convOutputLength, deconvLength, normalizeArray } from '../utils/conv_utils';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Transpose and cast the input before the conv2d.\n * @param x Input image tensor.\n * @param dataFormat\n */\nexport function preprocessConv2DInput(x, dataFormat) {\n    // TODO(cais): Cast type to float32 if not.\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        if (dataFormat === 'channelsFirst') {\n            return tfc.transpose(x, [0, 2, 3, 1]); // NCHW -> NHWC.\n        }\n        else {\n            return x;\n        }\n    });\n}\n/**\n * Transpose and cast the input before the conv3d.\n * @param x Input image tensor.\n * @param dataFormat\n */\nexport function preprocessConv3DInput(x, dataFormat) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        if (dataFormat === 'channelsFirst') {\n            return tfc.transpose(x, [0, 2, 3, 4, 1]); // NCDHW -> NDHWC.\n        }\n        else {\n            return x;\n        }\n    });\n}\n/**\n * 1D-convolution with bias added.\n *\n * Porting Note: This function does not exist in the Python Keras backend.\n *   It is exactly the same as `conv2d`, except the added `bias`.\n *\n * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.\n * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.\n * @param bias Bias, rank-3, of shape `[outDepth]`.\n * @param strides\n * @param padding Padding mode.\n * @param dataFormat Data format.\n * @param dilationRate\n * @returns The result of the 1D convolution.\n * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.\n */\nexport function conv1dWithBias(x, kernel, bias, strides = 1, padding = 'valid', dataFormat, dilationRate = 1) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        // Check the ranks of x, kernel and bias.\n        if (x.shape.length !== 3) {\n            throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ` +\n                `${x.shape.length} instead.`);\n        }\n        if (kernel.shape.length !== 3) {\n            throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ` +\n                `${kernel.shape.length} instead`);\n        }\n        if (bias != null && bias.shape.length !== 1) {\n            throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ` +\n                `${kernel.shape.length} instead`);\n        }\n        // TODO(cais): Support CAUSAL padding mode.\n        if (dataFormat === 'channelsFirst') {\n            x = tfc.transpose(x, [0, 2, 1]); // NCW -> NWC.\n        }\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv1dWithBias is not ' +\n                'implemented yet.');\n        }\n        let y = tfc.conv1d(x, kernel, strides, padding === 'same' ? 'same' : 'valid', 'NWC', dilationRate);\n        if (bias != null) {\n            y = K.biasAdd(y, bias);\n        }\n        return y;\n    });\n}\n/**\n * 1D-convolution.\n *\n * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.\n * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.s\n * @param strides\n * @param padding Padding mode.\n * @param dataFormat Data format.\n * @param dilationRate\n * @returns The result of the 1D convolution.\n * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.\n */\nexport function conv1d(x, kernel, strides = 1, padding = 'valid', dataFormat, dilationRate = 1) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv1dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 2D Convolution\n * @param x\n * @param kernel kernel of the convolution.\n * @param strides strides array.\n * @param padding padding mode. Default to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param dilationRate dilation rate array.\n * @returns Result of the 2D pooling.\n */\nexport function conv2d(x, kernel, strides = [1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv2dWithBiasActivation(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 2D Convolution with an added bias and optional activation.\n * Note: This function does not exist in the Python Keras Backend. This function\n * is exactly the same as `conv2d`, except the added `bias`.\n */\nexport function conv2dWithBiasActivation(x, kernel, bias, strides = [1, 1], padding = 'valid', dataFormat, dilationRate, activation = null) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        if (x.rank !== 3 && x.rank !== 4) {\n            throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, ` +\n                `but received ${x.rank}.`);\n        }\n        if (kernel.rank !== 3 && kernel.rank !== 4) {\n            throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, ` +\n                `but received ${x.rank}.`);\n        }\n        let y = preprocessConv2DInput(x, dataFormat);\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv1dWithBias is not ' +\n                'implemented yet.');\n        }\n        y = tfc.fused.conv2d({\n            x: y,\n            filter: kernel,\n            strides: strides,\n            pad: padding === 'same' ? 'same' : 'valid',\n            dilations: dilationRate,\n            dataFormat: 'NHWC',\n            bias,\n            activation\n        });\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]);\n        }\n        return y;\n    });\n}\n/**\n * 3D Convolution.\n * @param x\n * @param kernel kernel of the convolution.\n * @param strides strides array.\n * @param padding padding mode. Default to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param dilationRate dilation rate array.\n * @returns Result of the 3D convolution.\n */\nexport function conv3d(x, kernel, strides = [1, 1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv3dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 3D Convolution with an added bias.\n * Note: This function does not exist in the Python Keras Backend. This function\n * is exactly the same as `conv3d`, except the added `bias`.\n */\nexport function conv3dWithBias(x, kernel, bias, strides = [1, 1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        if (x.rank !== 4 && x.rank !== 5) {\n            throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ` +\n                `${x.rank}.`);\n        }\n        if (kernel.rank !== 4 && kernel.rank !== 5) {\n            throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ` +\n                `${x.rank}.`);\n        }\n        let y = preprocessConv3DInput(x, dataFormat);\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv3dWithBias is not ' +\n                'implemented yet.');\n        }\n        y = tfc.conv3d(y, kernel, strides, padding === 'same' ? 'same' : 'valid', 'NDHWC', dilationRate);\n        if (bias != null) {\n            y = K.biasAdd(y, bias);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 4, 1, 2, 3]);\n        }\n        return y;\n    });\n}\n/**\n * Abstract convolution layer.\n */\nexport class BaseConv extends Layer {\n    constructor(rank, args) {\n        super(args);\n        this.bias = null;\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        BaseConv.verifyArgs(args);\n        this.rank = rank;\n        generic_utils.assertPositiveInteger(this.rank, 'rank');\n        if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) {\n            throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is ` +\n                `not implemented yet.`);\n        }\n        this.kernelSize = normalizeArray(args.kernelSize, rank, 'kernelSize');\n        this.strides = normalizeArray(args.strides == null ? 1 : args.strides, rank, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        checkPaddingMode(this.padding);\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        this.activation = getActivation(args.activation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.dilationRate = normalizeArray(args.dilationRate == null ? 1 : args.dilationRate, rank, 'dilationRate');\n        if (this.rank === 1 &&\n            (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) {\n            throw new ValueError(`dilationRate must be a number or an array of a single number ` +\n                `for 1D convolution, but received ` +\n                `${JSON.stringify(this.dilationRate)}`);\n        }\n        else if (this.rank === 2) {\n            if (typeof this.dilationRate === 'number') {\n                this.dilationRate = [this.dilationRate, this.dilationRate];\n            }\n            else if (this.dilationRate.length !== 2) {\n                throw new ValueError(`dilationRate must be a number or array of two numbers for 2D ` +\n                    `convolution, but received ${JSON.stringify(this.dilationRate)}`);\n            }\n        }\n        else if (this.rank === 3) {\n            if (typeof this.dilationRate === 'number') {\n                this.dilationRate =\n                    [this.dilationRate, this.dilationRate, this.dilationRate];\n            }\n            else if (this.dilationRate.length !== 3) {\n                throw new ValueError(`dilationRate must be a number or array of three numbers for 3D ` +\n                    `convolution, but received ${JSON.stringify(this.dilationRate)}`);\n            }\n        }\n    }\n    static verifyArgs(args) {\n        // Check config.kernelSize type and shape.\n        generic_utils.assert('kernelSize' in args, `required key 'kernelSize' not in config`);\n        if (typeof args.kernelSize !== 'number' &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 3)) {\n            throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with ` +\n                `length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n    getConfig() {\n        const config = {\n            kernelSize: this.kernelSize,\n            strides: this.strides,\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n            dilationRate: this.dilationRate,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            biasConstraint: serializeConstraint(this.biasConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/**\n * Abstract nD convolution layer.  Ancestor of convolution layers which reduce\n * across channels, i.e., Conv1D and Conv2D, but not DepthwiseConv2D.\n */\nexport class Conv extends BaseConv {\n    constructor(rank, args) {\n        super(rank, args);\n        this.kernel = null;\n        Conv.verifyArgs(args);\n        this.filters = args.filters;\n        generic_utils.assertPositiveInteger(this.filters, 'filters');\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError(`The channel dimension of the input should be defined. ` +\n                `Found ${inputShape[channelAxis]}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const kernelShape = this.kernelSize.concat([inputDim, this.filters]);\n        this.kernel = this.addWeight('kernel', kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        this.inputSpec = [{ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } }];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let outputs;\n            const biasValue = this.bias == null ? null : this.bias.read();\n            const fusedActivationName = generic_utils.mapActivationToFusedKernel(this.activation.getClassName());\n            if (fusedActivationName != null && this.rank === 2) {\n                outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate, fusedActivationName);\n            }\n            else {\n                if (this.rank === 1) {\n                    outputs = conv1dWithBias(inputs, this.kernel.read(), biasValue, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);\n                }\n                else if (this.rank === 2) {\n                    // TODO(cais): Move up to constructor.\n                    outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n                }\n                else if (this.rank === 3) {\n                    outputs = conv3dWithBias(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n                }\n                else {\n                    throw new NotImplementedError('convolutions greater than 3D are not implemented yet.');\n                }\n                if (this.activation != null) {\n                    outputs = this.activation.apply(outputs);\n                }\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const newSpace = [];\n        const space = (this.dataFormat === 'channelsLast') ?\n            inputShape.slice(1, inputShape.length - 1) :\n            inputShape.slice(2);\n        for (let i = 0; i < space.length; ++i) {\n            const newDim = convOutputLength(space[i], this.kernelSize[i], this.padding, this.strides[i], typeof this.dilationRate === 'number' ? this.dilationRate :\n                this.dilationRate[i]);\n            newSpace.push(newDim);\n        }\n        let outputShape = [inputShape[0]];\n        if (this.dataFormat === 'channelsLast') {\n            outputShape = outputShape.concat(newSpace);\n            outputShape.push(this.filters);\n        }\n        else {\n            outputShape.push(this.filters);\n            outputShape = outputShape.concat(newSpace);\n        }\n        return outputShape;\n    }\n    getConfig() {\n        const config = {\n            filters: this.filters,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    static verifyArgs(args) {\n        // Check config.filters type, shape, and value.\n        if (!('filters' in args) || typeof args.filters !== 'number' ||\n            args.filters < 1) {\n            throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 ` +\n                `but got ${JSON.stringify(args.filters)}`);\n        }\n    }\n}\nexport class Conv2D extends Conv {\n    constructor(args) {\n        super(2, args);\n        Conv2D.verifyArgs(args);\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if ((typeof args.kernelSize !== 'number') &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 2)) {\n            throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with ` +\n                `length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n}\n/** @nocollapse */\nConv2D.className = 'Conv2D';\nserialization.registerClass(Conv2D);\nexport class Conv3D extends Conv {\n    constructor(args) {\n        super(3, args);\n        Conv3D.verifyArgs(args);\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if (typeof args.kernelSize !== 'number') {\n            if (!(Array.isArray(args.kernelSize) &&\n                (args.kernelSize.length === 1 || args.kernelSize.length === 3))) {\n                throw new ValueError(`Conv3D expects config.kernelSize to be number or` +\n                    ` [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`);\n            }\n        }\n    }\n}\n/** @nocollapse */\nConv3D.className = 'Conv3D';\nserialization.registerClass(Conv3D);\nexport class Conv2DTranspose extends Conv2D {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n        if (this.padding !== 'same' && this.padding !== 'valid') {\n            throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' ` +\n                `and 'valid', but received padding mode ${this.padding}`);\n        }\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length !== 4) {\n            throw new ValueError('Input should have rank 4; Received input shape: ' +\n                JSON.stringify(inputShape));\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError('The channel dimension of the inputs should be defined. ' +\n                'Found `None`.');\n        }\n        const inputDim = inputShape[channelAxis];\n        const kernelShape = this.kernelSize.concat([this.filters, inputDim]);\n        this.kernel = this.addWeight('kernel', kernelShape, 'float32', this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], 'float32', this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        // Set input spec.\n        this.inputSpec =\n            [new InputSpec({ ndim: 4, axes: { [channelAxis]: inputDim } })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            let input = getExactlyOneTensor(inputs);\n            if (input.shape.length !== 4) {\n                throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but ` +\n                    `received a tensor of rank-${input.shape.length}`);\n            }\n            const inputShape = input.shape;\n            const batchSize = inputShape[0];\n            let hAxis;\n            let wAxis;\n            if (this.dataFormat === 'channelsFirst') {\n                hAxis = 2;\n                wAxis = 3;\n            }\n            else {\n                hAxis = 1;\n                wAxis = 2;\n            }\n            const height = inputShape[hAxis];\n            const width = inputShape[wAxis];\n            const kernelH = this.kernelSize[0];\n            const kernelW = this.kernelSize[1];\n            const strideH = this.strides[0];\n            const strideW = this.strides[1];\n            // Infer the dynamic output shape.\n            const outHeight = deconvLength(height, strideH, kernelH, this.padding);\n            const outWidth = deconvLength(width, strideW, kernelW, this.padding);\n            // Porting Note: We don't branch based on `this.dataFormat` here,\n            // because\n            //   the tjfs-core function `conv2dTranspose` called below always\n            //   assumes channelsLast.\n            const outputShape = [batchSize, outHeight, outWidth, this.filters];\n            if (this.dataFormat !== 'channelsLast') {\n                input = tfc.transpose(input, [0, 2, 3, 1]);\n            }\n            let outputs = tfc.conv2dTranspose(input, this.kernel.read(), outputShape, this.strides, this.padding);\n            if (this.dataFormat !== 'channelsLast') {\n                outputs = tfc.transpose(outputs, [0, 3, 1, 2]);\n            }\n            if (this.bias != null) {\n                outputs =\n                    K.biasAdd(outputs, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                outputs = this.activation.apply(outputs);\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        let channelAxis;\n        let heightAxis;\n        let widthAxis;\n        if (this.dataFormat === 'channelsFirst') {\n            channelAxis = 1;\n            heightAxis = 2;\n            widthAxis = 3;\n        }\n        else {\n            channelAxis = 3;\n            heightAxis = 1;\n            widthAxis = 2;\n        }\n        const kernelH = this.kernelSize[0];\n        const kernelW = this.kernelSize[1];\n        const strideH = this.strides[0];\n        const strideW = this.strides[1];\n        outputShape[channelAxis] = this.filters;\n        outputShape[heightAxis] =\n            deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);\n        outputShape[widthAxis] =\n            deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);\n        return outputShape;\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['dilationRate'];\n        return config;\n    }\n}\n/** @nocollapse */\nConv2DTranspose.className = 'Conv2DTranspose';\nserialization.registerClass(Conv2DTranspose);\nexport class SeparableConv extends Conv {\n    constructor(rank, config) {\n        super(rank, config);\n        this.DEFAULT_DEPTHWISE_INITIALIZER = 'glorotUniform';\n        this.DEFAULT_POINTWISE_INITIALIZER = 'glorotUniform';\n        this.depthwiseKernel = null;\n        this.pointwiseKernel = null;\n        if (config.filters == null) {\n            throw new ValueError('The `filters` configuration field is required by SeparableConv, ' +\n                'but is unspecified.');\n        }\n        if (config.kernelInitializer != null || config.kernelRegularizer != null ||\n            config.kernelConstraint != null) {\n            throw new ValueError('Fields kernelInitializer, kernelRegularizer and kernelConstraint ' +\n                'are invalid for SeparableConv2D. Use depthwiseInitializer, ' +\n                'depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, ' +\n                'pointwiseRegularizer and pointwiseConstraint instead.');\n        }\n        if (config.padding != null && config.padding !== 'same' &&\n            config.padding !== 'valid') {\n            throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: ` +\n                `'same' and 'valid', but received ${JSON.stringify(config.padding)}`);\n        }\n        this.depthMultiplier =\n            config.depthMultiplier == null ? 1 : config.depthMultiplier;\n        this.depthwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER);\n        this.depthwiseRegularizer = getRegularizer(config.depthwiseRegularizer);\n        this.depthwiseConstraint = getConstraint(config.depthwiseConstraint);\n        this.pointwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER);\n        this.pointwiseRegularizer = getRegularizer(config.pointwiseRegularizer);\n        this.pointwiseConstraint = getConstraint(config.pointwiseConstraint);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < this.rank + 2) {\n            throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ` +\n                `${this.rank + 2}, but received input shape: ` +\n                `${JSON.stringify(inputShape)}`);\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n            throw new ValueError(`The channel dimension of the inputs should be defined, ` +\n                `but found ${JSON.stringify(inputShape[channelAxis])}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]);\n        const pointwiseKernelShape = [];\n        for (let i = 0; i < this.rank; ++i) {\n            pointwiseKernelShape.push(1);\n        }\n        pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);\n        const trainable = true;\n        this.depthwiseKernel = this.addWeight('depthwise_kernel', depthwiseKernelShape, 'float32', this.depthwiseInitializer, this.depthwiseRegularizer, trainable, this.depthwiseConstraint);\n        this.pointwiseKernel = this.addWeight('pointwise_kernel', pointwiseKernelShape, 'float32', this.pointwiseInitializer, this.pointwiseRegularizer, trainable, this.pointwiseConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], 'float32', this.biasInitializer, this.biasRegularizer, trainable, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.inputSpec =\n            [new InputSpec({ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let output;\n            if (this.rank === 1) {\n                throw new NotImplementedError('1D separable convolution is not implemented yet.');\n            }\n            else if (this.rank === 2) {\n                if (this.dataFormat === 'channelsFirst') {\n                    inputs = tfc.transpose(inputs, [0, 2, 3, 1]); // NCHW -> NHWC.\n                }\n                output = tfc.separableConv2d(inputs, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, 'NHWC');\n            }\n            if (this.useBias) {\n                output = K.biasAdd(output, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                output = this.activation.apply(output);\n            }\n            if (this.dataFormat === 'channelsFirst') {\n                output = tfc.transpose(output, [0, 3, 1, 2]); // NHWC -> NCHW.\n            }\n            return output;\n        });\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        delete config['kernelInitializer'];\n        delete config['kernelRegularizer'];\n        delete config['kernelConstraint'];\n        config['depthwiseInitializer'] =\n            serializeInitializer(this.depthwiseInitializer);\n        config['pointwiseInitializer'] =\n            serializeInitializer(this.pointwiseInitializer);\n        config['depthwiseRegularizer'] =\n            serializeRegularizer(this.depthwiseRegularizer);\n        config['pointwiseRegularizer'] =\n            serializeRegularizer(this.pointwiseRegularizer);\n        config['depthwiseConstraint'] =\n            serializeConstraint(this.depthwiseConstraint);\n        config['pointwiseConstraint'] =\n            serializeConstraint(this.pointwiseConstraint);\n        return config;\n    }\n}\n/** @nocollapse */\nSeparableConv.className = 'SeparableConv';\nexport class SeparableConv2D extends SeparableConv {\n    constructor(args) {\n        super(2, args);\n    }\n}\n/** @nocollapse */\nSeparableConv2D.className = 'SeparableConv2D';\nserialization.registerClass(SeparableConv2D);\nexport class Conv1D extends Conv {\n    constructor(args) {\n        super(1, args);\n        Conv1D.verifyArgs(args);\n        this.inputSpec = [{ ndim: 3 }];\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        delete config['dataFormat'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if (typeof args.kernelSize !== 'number' &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 1)) {\n            throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with ` +\n                `length 1, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n}\n/** @nocollapse */\nConv1D.className = 'Conv1D';\nserialization.registerClass(Conv1D);\nexport class Cropping2D extends Layer {\n    constructor(args) {\n        super(args);\n        if (typeof args.cropping === 'number') {\n            this.cropping =\n                [[args.cropping, args.cropping], [args.cropping, args.cropping]];\n        }\n        else if (typeof args.cropping[0] === 'number') {\n            this.cropping = [\n                [args.cropping[0], args.cropping[0]],\n                [args.cropping[1], args.cropping[1]]\n            ];\n        }\n        else {\n            this.cropping = args.cropping;\n        }\n        this.dataFormat =\n            args.dataFormat === undefined ? 'channelsLast' : args.dataFormat;\n        this.inputSpec = [{ ndim: 4 }];\n    }\n    computeOutputShape(inputShape) {\n        if (this.dataFormat === 'channelsFirst') {\n            return [\n                inputShape[0], inputShape[1],\n                inputShape[2] - this.cropping[0][0] - this.cropping[0][1],\n                inputShape[3] - this.cropping[1][0] - this.cropping[1][1]\n            ];\n        }\n        else {\n            return [\n                inputShape[0],\n                inputShape[1] - this.cropping[0][0] - this.cropping[0][1],\n                inputShape[2] - this.cropping[1][0] - this.cropping[1][1], inputShape[3]\n            ];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                const hSliced = K.sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);\n                return K.sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);\n            }\n            else {\n                const hSliced = K.sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);\n                return K.sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);\n            }\n        });\n    }\n    getConfig() {\n        const config = { cropping: this.cropping, dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nCropping2D.className = 'Cropping2D';\nserialization.registerClass(Cropping2D);\nexport class UpSampling2D extends Layer {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_SIZE = [2, 2];\n        this.inputSpec = [{ ndim: 4 }];\n        this.size = args.size == null ? this.DEFAULT_SIZE : args.size;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n    }\n    computeOutputShape(inputShape) {\n        if (this.dataFormat === 'channelsFirst') {\n            const height = inputShape[2] == null ? null : this.size[0] * inputShape[2];\n            const width = inputShape[3] == null ? null : this.size[1] * inputShape[3];\n            return [inputShape[0], inputShape[1], height, width];\n        }\n        else {\n            const height = inputShape[1] == null ? null : this.size[0] * inputShape[1];\n            const width = inputShape[2] == null ? null : this.size[1] * inputShape[2];\n            return [inputShape[0], height, width, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            let input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            if (this.dataFormat === 'channelsFirst') {\n                input = tfc.transpose(input, [0, 2, 3, 1]);\n                const height = this.size[0] * inputShape[2];\n                const width = this.size[1] * inputShape[3];\n                const resized = input.resizeNearestNeighbor([height, width]);\n                return tfc.transpose(resized, [0, 3, 1, 2]);\n            }\n            else {\n                const height = this.size[0] * inputShape[1];\n                const width = this.size[1] * inputShape[2];\n                return input.resizeNearestNeighbor([height, width]);\n            }\n        });\n    }\n    getConfig() {\n        const config = { size: this.size, dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nUpSampling2D.className = 'UpSampling2D';\nserialization.registerClass(UpSampling2D);\n//# sourceMappingURL=convolutional.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Depthwise Convolutional Layers\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { convOutputLength } from '../utils/conv_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { BaseConv, preprocessConv2DInput } from './convolutional';\n/**\n * 2D convolution with separable filters.\n * @param x Input tensor.\n * @param depthwiseKernel Convolution kernel for depthwise convolution.\n * @param strides Strides (Array of two integers).\n * @param padding Padding model.\n * @param dataFormat Data format.\n * @param dilationRate Array of two integers, dilation rates for the separable\n *   convolution.\n * @returns Output tensor.\n * @throws ValueError If depthwiseKernel is not a 4D array.\n */\nexport function depthwiseConv2d(x, depthwiseKernel, strides = [1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        let y = preprocessConv2DInput(x, dataFormat);\n        if (x.rank !== 4) {\n            throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ` +\n                `${x.rank}-D`);\n        }\n        if (depthwiseKernel.rank !== 4) {\n            throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ` +\n                `${depthwiseKernel.rank}-D`);\n        }\n        y = tfc.depthwiseConv2d(y, depthwiseKernel, strides, padding === 'same' ? 'same' : 'valid', 'NHWC', dilationRate);\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]);\n        }\n        return y;\n    });\n}\nexport class DepthwiseConv2D extends BaseConv {\n    constructor(args) {\n        super(2, args);\n        this.depthwiseKernel = null;\n        this.depthMultiplier =\n            args.depthMultiplier == null ? 1 : args.depthMultiplier;\n        this.depthwiseInitializer = getInitializer(args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.depthwiseConstraint = getConstraint(args.depthwiseConstraint);\n        this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < 4) {\n            throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. ` +\n                `Received input shape: ${JSON.stringify(inputShape)}.`);\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : 3;\n        if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n            throw new ValueError('The channel dimension of the inputs to DepthwiseConv2D should ' +\n                `be defined, but is not (${inputShape[channelAxis]}).`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const depthwiseKernelShape = [\n            this.kernelSize[0], this.kernelSize[1], inputDim, this.depthMultiplier\n        ];\n        this.depthwiseKernel = this.addWeight('depthwise_kernel', depthwiseKernelShape, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [inputDim * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let outputs = depthwiseConv2d(inputs, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);\n            // TODO(cais): Add support for dilation.\n            if (this.useBias) {\n                outputs = K.biasAdd(outputs, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                outputs = this.activation.apply(outputs);\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const rows = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        const cols = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        const outFilters = this.dataFormat === 'channelsFirst' ?\n            inputShape[1] * this.depthMultiplier :\n            inputShape[3] * this.depthMultiplier;\n        const outRows = convOutputLength(rows, this.kernelSize[0], this.padding, this.strides[0]);\n        const outCols = convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], outFilters, outRows, outCols];\n        }\n        else {\n            // In this case, assume 'channelsLast'.\n            return [inputShape[0], outRows, outCols, outFilters];\n        }\n    }\n    getConfig() {\n        const config = super.getConfig();\n        config['depthMultiplier'] = this.depthMultiplier;\n        config['depthwiseInitializer'] =\n            serializeInitializer(this.depthwiseInitializer);\n        config['depthwiseRegularizer'] =\n            serializeRegularizer(this.depthwiseRegularizer);\n        config['depthwiseConstraint'] =\n            serializeConstraint(this.depthwiseRegularizer);\n        return config;\n    }\n}\n/** @nocollapse */\nDepthwiseConv2D.className = 'DepthwiseConv2D';\nserialization.registerClass(DepthwiseConv2D);\n//# sourceMappingURL=convolutional_depthwise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Recurrent Neural Network Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, SymbolicTensor } from '../engine/topology';\nimport { Layer } from '../engine/topology';\nimport { AttributeError, NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, Initializer, Ones, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor, isArrayOfShapes } from '../utils/types_utils';\nimport { batchGetValue, batchSetValue } from '../variables';\nimport { deserialize } from './serialization';\n/**\n * Standardize `apply()` args to a single list of tensor inputs.\n *\n * When running a model loaded from file, the input tensors `initialState` and\n * `constants` are passed to `RNN.apply()` as part of `inputs` instead of the\n * dedicated kwargs fields. `inputs` consists of\n * `[inputs, initialState0, initialState1, ..., constant0, constant1]` in this\n * case.\n * This method makes sure that arguments are\n * separated and that `initialState` and `constants` are `Array`s of tensors\n * (or None).\n *\n * @param inputs Tensor or `Array` of  tensors.\n * @param initialState Tensor or `Array` of tensors or `null`/`undefined`.\n * @param constants Tensor or `Array` of tensors or `null`/`undefined`.\n * @returns An object consisting of\n *   inputs: A tensor.\n *   initialState: `Array` of tensors or `null`.\n *   constants: `Array` of tensors or `null`.\n * @throws ValueError, if `inputs` is an `Array` but either `initialState` or\n *   `constants` is provided.\n */\nexport function standardizeArgs(inputs, initialState, constants, numConstants) {\n    if (Array.isArray(inputs)) {\n        if (initialState != null || constants != null) {\n            throw new ValueError('When inputs is an array, neither initialState or constants ' +\n                'should be provided');\n        }\n        if (numConstants != null) {\n            constants = inputs.slice(inputs.length - numConstants, inputs.length);\n            inputs = inputs.slice(0, inputs.length - numConstants);\n        }\n        if (inputs.length > 1) {\n            initialState = inputs.slice(1, inputs.length);\n        }\n        inputs = inputs[0];\n    }\n    function toListOrNull(x) {\n        if (x == null || Array.isArray(x)) {\n            return x;\n        }\n        else {\n            return [x];\n        }\n    }\n    initialState = toListOrNull(initialState);\n    constants = toListOrNull(constants);\n    return { inputs, initialState, constants };\n}\n/**\n * Iterates over the time dimension of a tensor.\n *\n * @param stepFunction RNN step function.\n *   Parameters:\n *     inputs: tensor with shape `[samples, ...]` (no time dimension),\n *       representing input for the batch of samples at a certain time step.\n *     states: an Array of tensors.\n *   Returns:\n *     outputs: tensor with shape `[samples, outputDim]` (no time dimension).\n *     newStates: list of tensors, same length and shapes as `states`. The first\n *       state in the list must be the output tensor at the previous timestep.\n * @param inputs Tensor of temporal data of shape `[samples, time, ...]` (at\n *   least 3D).\n * @param initialStates Tensor with shape `[samples, outputDim]` (no time\n *   dimension), containing the initial values of the states used in the step\n *   function.\n * @param goBackwards If `true`, do the iteration over the time dimension in\n *   reverse order and return the reversed sequence.\n * @param mask Binary tensor with shape `[sample, time, 1]`, with a zero for\n *   every element that is masked.\n * @param constants An Array of constant values passed at each step.\n * @param unroll Whether to unroll the RNN or to use a symbolic loop. *Not*\n *   applicable to this imperative deeplearn.js backend. Its value is ignored.\n * @param needPerStepOutputs Whether the per-step outputs are to be\n *   concatenated into a single tensor and returned (as the second return\n *   value). Default: `false`. This arg is included so that the relatively\n *   expensive concatenation of the stepwise outputs can be omitted unless\n *   the stepwise outputs need to be kept (e.g., for an LSTM layer of which\n *   `returnSequence` is `true`.)\n * @returns An Array: `[lastOutput, outputs, newStates]`.\n *   lastOutput: the lastest output of the RNN, of shape `[samples, ...]`.\n *   outputs: tensor with shape `[samples, time, ...]` where each entry\n *     `output[s, t]` is the output of the step function at time `t` for sample\n *     `s`. This return value is provided if and only if the\n *     `needPerStepOutputs` is set as `true`. If it is set as `false`, this\n *     return value will be `undefined`.\n *   newStates: Array of tensors, latest states returned by the step function,\n *      of shape `(samples, ...)`.\n * @throws ValueError If input dimension is less than 3.\n *\n * TODO(nielsene): This needs to be tidy-ed.\n */\nexport function rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, constants, unroll = false, needPerStepOutputs = false) {\n    return tfc.tidy(() => {\n        const ndim = inputs.shape.length;\n        if (ndim < 3) {\n            throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);\n        }\n        // Transpose to time-major, i.e., from [batch, time, ...] to [time, batch,\n        // ...].\n        const axes = [1, 0].concat(math_utils.range(2, ndim));\n        inputs = tfc.transpose(inputs, axes);\n        if (constants != null) {\n            throw new NotImplementedError('The rnn() functoin of the deeplearn.js backend does not support ' +\n                'constants yet.');\n        }\n        // Porting Note: the unroll option is ignored by the imperative backend.\n        if (unroll) {\n            console.warn('Backend rnn(): the unroll = true option is not applicable to the ' +\n                'imperative deeplearn.js backend.');\n        }\n        if (mask != null) {\n            mask = mask.asType('bool').asType('float32');\n            if (mask.rank === ndim - 1) {\n                mask = tfc.expandDims(mask, -1);\n            }\n            mask = tfc.transpose(mask, axes);\n        }\n        if (goBackwards) {\n            inputs = tfc.reverse(inputs, 0);\n            if (mask != null) {\n                mask = tfc.reverse(mask, 0);\n            }\n        }\n        // Porting Note: PyKeras with TensorFlow backend uses a symbolic loop\n        //   (tf.while_loop). But for the imperative deeplearn.js backend, we just\n        //   use the usual TypeScript control flow to iterate over the time steps in\n        //   the inputs.\n        // Porting Note: PyKeras patches a \"_use_learning_phase\" attribute to\n        // outputs.\n        //   This is not idiomatic in TypeScript. The info regarding whether we are\n        //   in a learning (i.e., training) phase for RNN is passed in a different\n        //   way.\n        const perStepOutputs = [];\n        let lastOutput;\n        let states = initialStates;\n        const timeSteps = inputs.shape[0];\n        const perStepInputs = tfc.unstack(inputs);\n        let perStepMasks;\n        if (mask != null) {\n            perStepMasks = tfc.unstack(mask);\n        }\n        for (let t = 0; t < timeSteps; ++t) {\n            const currentInput = perStepInputs[t];\n            const stepOutputs = tfc.tidy(() => stepFunction(currentInput, states));\n            if (mask == null) {\n                lastOutput = stepOutputs[0];\n                states = stepOutputs[1];\n            }\n            else {\n                const maskedOutputs = tfc.tidy(() => {\n                    const stepMask = perStepMasks[t];\n                    const negStepMask = tfc.onesLike(stepMask).sub(stepMask);\n                    // TODO(cais): Would tfc.where() be better for performance?\n                    const output = stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask));\n                    const newStates = states.map((state, i) => {\n                        return stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask));\n                    });\n                    return { output, newStates };\n                });\n                lastOutput = maskedOutputs.output;\n                states = maskedOutputs.newStates;\n            }\n            if (needPerStepOutputs) {\n                perStepOutputs.push(lastOutput);\n            }\n        }\n        let outputs;\n        if (needPerStepOutputs) {\n            const axis = 1;\n            outputs = tfc.stack(perStepOutputs, axis);\n        }\n        return [lastOutput, outputs, states];\n    });\n}\nexport class RNN extends Layer {\n    constructor(args) {\n        super(args);\n        let cell;\n        if (args.cell == null) {\n            throw new ValueError('cell property is missing for the constructor of RNN.');\n        }\n        else if (Array.isArray(args.cell)) {\n            cell = new StackedRNNCells({ cells: args.cell });\n        }\n        else {\n            cell = args.cell;\n        }\n        if (cell.stateSize == null) {\n            throw new ValueError('The RNN cell should have an attribute `stateSize` (tuple of ' +\n                'integers, one integer per RNN state).');\n        }\n        this.cell = cell;\n        this.returnSequences =\n            args.returnSequences == null ? false : args.returnSequences;\n        this.returnState = args.returnState == null ? false : args.returnState;\n        this.goBackwards = args.goBackwards == null ? false : args.goBackwards;\n        this._stateful = args.stateful == null ? false : args.stateful;\n        this.unroll = args.unroll == null ? false : args.unroll;\n        this.supportsMasking = true;\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n        this.stateSpec = null;\n        this.states_ = null;\n        // TODO(cais): Add constantsSpec and numConstants.\n        this.numConstants = null;\n        // TODO(cais): Look into the use of initial_state in the kwargs of the\n        //   constructor.\n        this.keptStates = [];\n    }\n    // Porting Note: This is the equivalent of `RNN.states` property getter in\n    //   PyKeras.\n    getStates() {\n        if (this.states_ == null) {\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            return math_utils.range(0, numStates).map(x => null);\n        }\n        else {\n            return this.states_;\n        }\n    }\n    // Porting Note: This is the equivalent of the `RNN.states` property setter in\n    //   PyKeras.\n    setStates(states) {\n        this.states_ = states;\n    }\n    computeOutputShape(inputShape) {\n        if (isArrayOfShapes(inputShape)) {\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        // TODO(cais): Remove the casting once stacked RNN cells become supported.\n        let stateSize = this.cell.stateSize;\n        if (!Array.isArray(stateSize)) {\n            stateSize = [stateSize];\n        }\n        const outputDim = stateSize[0];\n        let outputShape;\n        if (this.returnSequences) {\n            outputShape = [inputShape[0], inputShape[1], outputDim];\n        }\n        else {\n            outputShape = [inputShape[0], outputDim];\n        }\n        if (this.returnState) {\n            const stateShape = [];\n            for (const dim of stateSize) {\n                stateShape.push([inputShape[0], dim]);\n            }\n            return [outputShape].concat(stateShape);\n        }\n        else {\n            return outputShape;\n        }\n    }\n    computeMask(inputs, mask) {\n        return tfc.tidy(() => {\n            if (Array.isArray(mask)) {\n                mask = mask[0];\n            }\n            const outputMask = this.returnSequences ? mask : null;\n            if (this.returnState) {\n                const stateMask = this.states.map(s => null);\n                return [outputMask].concat(stateMask);\n            }\n            else {\n                return outputMask;\n            }\n        });\n    }\n    /**\n     * Get the current state tensors of the RNN.\n     *\n     * If the state hasn't been set, return an array of `null`s of the correct\n     * length.\n     */\n    get states() {\n        if (this.states_ == null) {\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            const output = [];\n            for (let i = 0; i < numStates; ++i) {\n                output.push(null);\n            }\n            return output;\n        }\n        else {\n            return this.states_;\n        }\n    }\n    set states(s) {\n        this.states_ = s;\n    }\n    build(inputShape) {\n        // Note inputShape will be an Array of Shapes of initial states and\n        // constants if these are passed in apply().\n        const constantShape = null;\n        if (this.numConstants != null) {\n            throw new NotImplementedError('Constants support is not implemented in RNN yet.');\n        }\n        if (isArrayOfShapes(inputShape)) {\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        const batchSize = this.stateful ? inputShape[0] : null;\n        const inputDim = inputShape.slice(2);\n        this.inputSpec[0] = new InputSpec({ shape: [batchSize, null, ...inputDim] });\n        // Allow cell (if RNNCell Layer) to build before we set or validate\n        // stateSpec.\n        const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        if (constantShape != null) {\n            throw new NotImplementedError('Constants support is not implemented in RNN yet.');\n        }\n        else {\n            this.cell.build(stepInputShape);\n        }\n        // Set or validate stateSpec.\n        let stateSize;\n        if (Array.isArray(this.cell.stateSize)) {\n            stateSize = this.cell.stateSize;\n        }\n        else {\n            stateSize = [this.cell.stateSize];\n        }\n        if (this.stateSpec != null) {\n            if (!util.arraysEqual(this.stateSpec.map(spec => spec.shape[spec.shape.length - 1]), stateSize)) {\n                throw new ValueError(`An initialState was passed that is not compatible with ` +\n                    `cell.stateSize. Received stateSpec=${this.stateSpec}; ` +\n                    `However cell.stateSize is ${this.cell.stateSize}`);\n            }\n        }\n        else {\n            this.stateSpec =\n                stateSize.map(dim => new InputSpec({ shape: [null, dim] }));\n        }\n        if (this.stateful) {\n            this.resetStates();\n        }\n    }\n    /**\n     * Reset the state tensors of the RNN.\n     *\n     * If the `states` argument is `undefined` or `null`, will set the\n     * state tensor(s) of the RNN to all-zero tensors of the appropriate\n     * shape(s).\n     *\n     * If `states` is provided, will set the state tensors of the RNN to its\n     * value.\n     *\n     * @param states Optional externally-provided initial states.\n     * @param training Whether this call is done during training. For stateful\n     *   RNNs, this affects whether the old states are kept or discarded. In\n     *   particular, if `training` is `true`, the old states will be kept so\n     *   that subsequent backpropgataion through time (BPTT) may work properly.\n     *   Else, the old states will be discarded.\n     */\n    resetStates(states, training = false) {\n        tidy(() => {\n            if (!this.stateful) {\n                throw new AttributeError('Cannot call resetStates() on an RNN Layer that is not stateful.');\n            }\n            const batchSize = this.inputSpec[0].shape[0];\n            if (batchSize == null) {\n                throw new ValueError('If an RNN is stateful, it needs to know its batch size. Specify ' +\n                    'the batch size of your input tensors: \\n' +\n                    '- If using a Sequential model, specify the batch size by ' +\n                    'passing a `batchInputShape` option to your first layer.\\n' +\n                    '- If using the functional API, specify the batch size by ' +\n                    'passing a `batchShape` option to your Input layer.');\n            }\n            // Initialize state if null.\n            if (this.states_ == null) {\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ =\n                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n                }\n                else {\n                    this.states_ = [tfc.zeros([batchSize, this.cell.stateSize])];\n                }\n            }\n            else if (states == null) {\n                // Dispose old state tensors.\n                tfc.dispose(this.states_);\n                // For stateful RNNs, fully dispose kept old states.\n                if (this.keptStates != null) {\n                    tfc.dispose(this.keptStates);\n                    this.keptStates = [];\n                }\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ =\n                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n                }\n                else {\n                    this.states_[0] = tfc.zeros([batchSize, this.cell.stateSize]);\n                }\n            }\n            else {\n                if (!Array.isArray(states)) {\n                    states = [states];\n                }\n                if (states.length !== this.states_.length) {\n                    throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), ` +\n                        `but it received ${states.length} state value(s). Input ` +\n                        `received: ${states}`);\n                }\n                if (training === true) {\n                    // Store old state tensors for complete disposal later, i.e., during\n                    // the next no-arg call to this method. We do not dispose the old\n                    // states immediately because that BPTT (among other things) require\n                    // them.\n                    this.keptStates.push(this.states_.slice());\n                }\n                else {\n                    tfc.dispose(this.states_);\n                }\n                for (let index = 0; index < this.states_.length; ++index) {\n                    const value = states[index];\n                    const dim = Array.isArray(this.cell.stateSize) ?\n                        this.cell.stateSize[index] :\n                        this.cell.stateSize;\n                    const expectedShape = [batchSize, dim];\n                    if (!util.arraysEqual(value.shape, expectedShape)) {\n                        throw new ValueError(`State ${index} is incompatible with layer ${this.name}: ` +\n                            `expected shape=${expectedShape}, received shape=${value.shape}`);\n                    }\n                    this.states_[index] = value;\n                }\n            }\n            this.states_ = this.states_.map(state => tfc.keep(state.clone()));\n        });\n    }\n    apply(inputs, kwargs) {\n        // TODO(cais): Figure out whether initialState is in kwargs or inputs.\n        let initialState = kwargs == null ? null : kwargs['initialState'];\n        let constants = kwargs == null ? null : kwargs['constants'];\n        if (kwargs == null) {\n            kwargs = {};\n        }\n        const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n        inputs = standardized.inputs;\n        initialState = standardized.initialState;\n        constants = standardized.constants;\n        // If any of `initial_state` or `constants` are specified and are\n        // `tf.SymbolicTensor`s, then add them to the inputs and temporarily modify\n        // the input_spec to include them.\n        let additionalInputs = [];\n        let additionalSpecs = [];\n        if (initialState != null) {\n            kwargs['initialState'] = initialState;\n            additionalInputs = additionalInputs.concat(initialState);\n            this.stateSpec = [];\n            for (const state of initialState) {\n                this.stateSpec.push(new InputSpec({ shape: state.shape }));\n            }\n            // TODO(cais): Use the following instead.\n            // this.stateSpec = initialState.map(state => new InputSpec({shape:\n            // state.shape}));\n            additionalSpecs = additionalSpecs.concat(this.stateSpec);\n        }\n        if (constants != null) {\n            kwargs['constants'] = constants;\n            additionalInputs = additionalInputs.concat(constants);\n            // TODO(cais): Add this.constantsSpec.\n            this.numConstants = constants.length;\n        }\n        const isTensor = additionalInputs[0] instanceof SymbolicTensor;\n        if (isTensor) {\n            // Compute full input spec, including state and constants.\n            const fullInput = [inputs].concat(additionalInputs);\n            const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n            // Perform the call with temporarily replaced inputSpec.\n            const originalInputSpec = this.inputSpec;\n            this.inputSpec = fullInputSpec;\n            const output = super.apply(fullInput, kwargs);\n            this.inputSpec = originalInputSpec;\n            return output;\n        }\n        else {\n            return super.apply(inputs, kwargs);\n        }\n    }\n    // tslint:disable-next-line:no-any\n    call(inputs, kwargs) {\n        // Input shape: `[samples, time (padded with zeros), input_dim]`.\n        // Note that the .build() method of subclasses **must** define\n        // this.inputSpec and this.stateSpec owith complete input shapes.\n        return tidy(() => {\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            let initialState = kwargs == null ? null : kwargs['initialState'];\n            inputs = getExactlyOneTensor(inputs);\n            if (initialState == null) {\n                if (this.stateful) {\n                    initialState = this.states_;\n                }\n                else {\n                    initialState = this.getInitialState(inputs);\n                }\n            }\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            if (initialState.length !== numStates) {\n                throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ` +\n                    `${initialState.length} initial state(s).`);\n            }\n            if (this.unroll) {\n                console.warn('Ignoring unroll = true for RNN layer, due to imperative backend.');\n            }\n            const cellCallKwargs = { training };\n            // TODO(cais): Add support for constants.\n            const step = (inputs, states) => {\n                // `inputs` and `states` are concatenated to form a single `Array` of\n                // `tf.Tensor`s as the input to `cell.call()`.\n                const outputs = this.cell.call([inputs].concat(states), cellCallKwargs);\n                // Marshall the return value into output and new states.\n                return [outputs[0], outputs.slice(1)];\n            };\n            // TODO(cais): Add support for constants.\n            const rnnOutputs = rnn(step, inputs, initialState, this.goBackwards, mask, null, this.unroll, this.returnSequences);\n            const lastOutput = rnnOutputs[0];\n            const outputs = rnnOutputs[1];\n            const states = rnnOutputs[2];\n            if (this.stateful) {\n                this.resetStates(states, training);\n            }\n            const output = this.returnSequences ? outputs : lastOutput;\n            // TODO(cais): Porperty set learning phase flag.\n            if (this.returnState) {\n                return [output].concat(states);\n            }\n            else {\n                return output;\n            }\n        });\n    }\n    getInitialState(inputs) {\n        return tidy(() => {\n            // Build an all-zero tensor of shape [samples, outputDim].\n            // [Samples, timeSteps, inputDim].\n            let initialState = tfc.zeros(inputs.shape);\n            // [Samples].\n            initialState = tfc.sum(initialState, [1, 2]);\n            initialState = K.expandDims(initialState); // [Samples, 1].\n            if (Array.isArray(this.cell.stateSize)) {\n                return this.cell.stateSize.map(dim => dim > 1 ? K.tile(initialState, [1, dim]) : initialState);\n            }\n            else {\n                return this.cell.stateSize > 1 ?\n                    [K.tile(initialState, [1, this.cell.stateSize])] :\n                    [initialState];\n            }\n        });\n    }\n    get trainableWeights() {\n        if (!this.trainable) {\n            return [];\n        }\n        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n        return this.cell.trainableWeights;\n    }\n    get nonTrainableWeights() {\n        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n        if (!this.trainable) {\n            return this.cell.weights;\n        }\n        return this.cell.nonTrainableWeights;\n    }\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.cell != null) {\n            this.cell.setFastWeightInitDuringBuild(value);\n        }\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            returnSequences: this.returnSequences,\n            returnState: this.returnState,\n            goBackwards: this.goBackwards,\n            stateful: this.stateful,\n            unroll: this.unroll,\n        };\n        if (this.numConstants != null) {\n            config['numConstants'] = this.numConstants;\n        }\n        const cellConfig = this.cell.getConfig();\n        if (this.getClassName() === RNN.className) {\n            config['cell'] = {\n                'className': this.cell.getClassName(),\n                'config': cellConfig,\n            };\n        }\n        // this order is necessary, to prevent cell name from replacing layer name\n        return Object.assign({}, cellConfig, baseConfig, config);\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const cellConfig = config['cell'];\n        const cell = deserialize(cellConfig, customObjects);\n        return new cls(Object.assign(config, { cell }));\n    }\n}\n/** @nocollapse */\nRNN.className = 'RNN';\nserialization.registerClass(RNN);\n// Porting Note: This is a common parent class for RNN cells. There is no\n// equivalent of this in PyKeras. Having a common parent class forgoes the\n//  need for `has_attr(cell, ...)` checks or its TypeScript equivalent.\n/**\n * An RNNCell layer.\n *\n * @doc {heading: 'Layers', subheading: 'Classes'}\n */\nexport class RNNCell extends Layer {\n}\nexport class SimpleRNNCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        this.units = args.units;\n        assertPositiveInteger(this.units, `units`);\n        this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.stateSize = this.units;\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        // TODO(cais): Use regularizer.\n        this.kernel = this.addWeight('kernel', [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.built = true;\n    }\n    // Porting Note: PyKeras' equivalent of this method takes two tensor inputs:\n    //   `inputs` and `states`. Here, the two tensors are combined into an\n    //   `Tensor[]` Array as the first input argument.\n    //   Similarly, PyKeras' equivalent of this method returns two values:\n    //    `output` and `[output]`. Here the two are combined into one length-2\n    //    `Tensor[]`, consisting of `output` repeated.\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (inputs.length !== 2) {\n                throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);\n            }\n            let prevOutput = inputs[1];\n            inputs = inputs[0];\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(prevOutput),\n                    rate: this.recurrentDropout,\n                    training\n                });\n            }\n            let h;\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            if (dpMask != null) {\n                h = K.dot(tfc.mul(inputs, dpMask), this.kernel.read());\n            }\n            else {\n                h = K.dot(inputs, this.kernel.read());\n            }\n            if (this.bias != null) {\n                h = K.biasAdd(h, this.bias.read());\n            }\n            if (recDpMask != null) {\n                prevOutput = tfc.mul(prevOutput, recDpMask);\n            }\n            let output = tfc.add(h, K.dot(prevOutput, this.recurrentKernel.read()));\n            if (this.activation != null) {\n                output = this.activation.apply(output);\n            }\n            // TODO(cais): Properly set learning phase on output tensor?\n            return [output, output];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nSimpleRNNCell.className = 'SimpleRNNCell';\nserialization.registerClass(SimpleRNNCell);\nexport class SimpleRNN extends RNN {\n    constructor(args) {\n        args.cell = new SimpleRNNCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nSimpleRNN.className = 'SimpleRNN';\nserialization.registerClass(SimpleRNN);\nexport class GRUCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        if (args.resetAfter) {\n            throw new ValueError(`GRUCell does not support reset_after parameter set to true.`);\n        }\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :\n            args.activation);\n        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.implementation = args.implementation;\n        this.stateSize = this.units;\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const inputDim = inputShape[inputShape.length - 1];\n        this.kernel = this.addWeight('kernel', [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n        //   of the weights and bias in the call() method, at execution time.\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (inputs.length !== 2) {\n                throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            let hTMinus1 = inputs[1]; // Previous memory state.\n            inputs = inputs[0];\n            // Note: For superior performance, TensorFlow.js always uses\n            // implementation 2, regardless of the actual value of\n            // config.implementation.\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training,\n                    count: 3\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: 3\n                });\n            }\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            let z;\n            let r;\n            let hh;\n            if (0 < this.dropout && this.dropout < 1) {\n                inputs = tfc.mul(inputs, dpMask[0]);\n            }\n            let matrixX = K.dot(inputs, this.kernel.read());\n            if (this.useBias) {\n                matrixX = K.biasAdd(matrixX, this.bias.read());\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n            }\n            const recurrentKernelValue = this.recurrentKernel.read();\n            const [rk1, rk2] = tfc.split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1);\n            const matrixInner = K.dot(hTMinus1, rk1);\n            const [xZ, xR, xH] = tfc.split(matrixX, 3, matrixX.rank - 1);\n            const [recurrentZ, recurrentR] = tfc.split(matrixInner, 2, matrixInner.rank - 1);\n            z = this.recurrentActivation.apply(tfc.add(xZ, recurrentZ));\n            r = this.recurrentActivation.apply(tfc.add(xR, recurrentR));\n            const recurrentH = K.dot(tfc.mul(r, hTMinus1), rk2);\n            hh = this.activation.apply(tfc.add(xH, recurrentH));\n            const h = tfc.add(tfc.mul(z, hTMinus1), tfc.mul(tfc.add(1, tfc.neg(z)), hh));\n            // TODO(cais): Add use_learning_phase flag properly.\n            return [h, h];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            recurrentActivation: serializeActivation(this.recurrentActivation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n            implementation: this.implementation,\n            resetAfter: false\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nGRUCell.className = 'GRUCell';\nserialization.registerClass(GRUCell);\nexport class GRU extends RNN {\n    constructor(args) {\n        if (args.implementation === 0) {\n            console.warn('`implementation=0` has been deprecated, and now defaults to ' +\n                '`implementation=1`. Please update your layer call.');\n        }\n        args.cell = new GRUCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        if (config['implmentation'] === 0) {\n            config['implementation'] = 1;\n        }\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nGRU.className = 'GRU';\nserialization.registerClass(GRU);\nexport class LSTMCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :\n            args.activation);\n        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.unitForgetBias = args.unitForgetBias;\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.implementation = args.implementation;\n        this.stateSize = [this.units, this.units];\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        var _a;\n        inputShape = getExactlyOneShape(inputShape);\n        const inputDim = inputShape[inputShape.length - 1];\n        this.kernel = this.addWeight('kernel', [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        let biasInitializer;\n        if (this.useBias) {\n            if (this.unitForgetBias) {\n                const capturedBiasInit = this.biasInitializer;\n                const capturedUnits = this.units;\n                biasInitializer = new (_a = class CustomInit extends Initializer {\n                        apply(shape, dtype) {\n                            // TODO(cais): More informative variable names?\n                            const bI = capturedBiasInit.apply([capturedUnits]);\n                            const bF = (new Ones()).apply([capturedUnits]);\n                            const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);\n                            return K.concatAlongFirstAxis(K.concatAlongFirstAxis(bI, bF), bCAndH);\n                        }\n                    },\n                    /** @nocollapse */\n                    _a.className = 'CustomInit',\n                    _a)();\n            }\n            else {\n                biasInitializer = this.biasInitializer;\n            }\n            this.bias = this.addWeight('bias', [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n        //   of the weights and bias in the call() method, at execution time.\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            inputs = inputs;\n            if (inputs.length !== 3) {\n                throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            let hTMinus1 = inputs[1]; // Previous memory state.\n            const cTMinus1 = inputs[2]; // Previous carry state.\n            inputs = inputs[0];\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training,\n                    count: 4\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: 4\n                });\n            }\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            // Note: For superior performance, TensorFlow.js always uses\n            // implementation 2 regardless of the actual value of\n            // config.implementation.\n            let i;\n            let f;\n            let c;\n            let o;\n            if (0 < this.dropout && this.dropout < 1) {\n                inputs = tfc.mul(inputs, dpMask[0]);\n            }\n            let z = K.dot(inputs, this.kernel.read());\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n            }\n            z = tfc.add(z, K.dot(hTMinus1, this.recurrentKernel.read()));\n            if (this.useBias) {\n                z = K.biasAdd(z, this.bias.read());\n            }\n            const [z0, z1, z2, z3] = tfc.split(z, 4, z.rank - 1);\n            i = this.recurrentActivation.apply(z0);\n            f = this.recurrentActivation.apply(z1);\n            c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(z2)));\n            o = this.recurrentActivation.apply(z3);\n            const h = tfc.mul(o, this.activation.apply(c));\n            // TODO(cais): Add use_learning_phase flag properly.\n            return [h, h, c];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            recurrentActivation: serializeActivation(this.recurrentActivation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            unitForgetBias: this.unitForgetBias,\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n            implementation: this.implementation,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nLSTMCell.className = 'LSTMCell';\nserialization.registerClass(LSTMCell);\nexport class LSTM extends RNN {\n    constructor(args) {\n        if (args.implementation === 0) {\n            console.warn('`implementation=0` has been deprecated, and now defaults to ' +\n                '`implementation=1`. Please update your layer call.');\n        }\n        args.cell = new LSTMCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        if (config['implmentation'] === 0) {\n            config['implementation'] = 1;\n        }\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nLSTM.className = 'LSTM';\nserialization.registerClass(LSTM);\nexport class StackedRNNCells extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.cells = args.cells;\n    }\n    get stateSize() {\n        // States are a flat list in reverse order of the cell stack.\n        // This allows perserving the requirement `stack.statesize[0] ===\n        // outputDim`. E.g., states of a 2-layer LSTM would be `[h2, c2, h1, c1]`,\n        // assuming one LSTM has states `[h, c]`.\n        const stateSize = [];\n        for (const cell of this.cells.slice().reverse()) {\n            if (Array.isArray(cell.stateSize)) {\n                stateSize.push(...cell.stateSize);\n            }\n            else {\n                stateSize.push(cell.stateSize);\n            }\n        }\n        return stateSize;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            let states = inputs.slice(1);\n            // Recover per-cell states.\n            const nestedStates = [];\n            for (const cell of this.cells.slice().reverse()) {\n                if (Array.isArray(cell.stateSize)) {\n                    nestedStates.push(states.splice(0, cell.stateSize.length));\n                }\n                else {\n                    nestedStates.push(states.splice(0, 1));\n                }\n            }\n            nestedStates.reverse();\n            // Call the cells in order and store the returned states.\n            const newNestedStates = [];\n            let callInputs;\n            for (let i = 0; i < this.cells.length; ++i) {\n                const cell = this.cells[i];\n                states = nestedStates[i];\n                // TODO(cais): Take care of constants.\n                if (i === 0) {\n                    callInputs = [inputs[0]].concat(states);\n                }\n                else {\n                    callInputs = [callInputs[0]].concat(states);\n                }\n                callInputs = cell.call(callInputs, kwargs);\n                newNestedStates.push(callInputs.slice(1));\n            }\n            // Format the new states as a flat list in reverse cell order.\n            states = [];\n            for (const cellStates of newNestedStates.slice().reverse()) {\n                states.push(...cellStates);\n            }\n            return [callInputs[0]].concat(states);\n        });\n    }\n    build(inputShape) {\n        if (isArrayOfShapes(inputShape)) {\n            // TODO(cais): Take care of input constants.\n            // const constantShape = inputShape.slice(1);\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        let outputDim;\n        this.cells.forEach((cell, i) => {\n            nameScope(`RNNCell_${i}`, () => {\n                // TODO(cais): Take care of input constants.\n                cell.build(inputShape);\n                if (Array.isArray(cell.stateSize)) {\n                    outputDim = cell.stateSize[0];\n                }\n                else {\n                    outputDim = cell.stateSize;\n                }\n                inputShape = [inputShape[0], outputDim];\n            });\n        });\n        this.built = true;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const getCellConfig = (cell) => {\n            return {\n                'className': cell.getClassName(),\n                'config': cell.getConfig(),\n            };\n        };\n        const cellConfigs = this.cells.map(getCellConfig);\n        const config = { 'cells': cellConfigs };\n        return Object.assign({}, baseConfig, config);\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const cells = [];\n        for (const cellConfig of config['cells']) {\n            cells.push(deserialize(cellConfig, customObjects));\n        }\n        return new cls({ cells });\n    }\n    get trainableWeights() {\n        if (!this.trainable) {\n            return [];\n        }\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.trainableWeights);\n        }\n        return weights;\n    }\n    get nonTrainableWeights() {\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.nonTrainableWeights);\n        }\n        if (!this.trainable) {\n            const trainableWeights = [];\n            for (const cell of this.cells) {\n                trainableWeights.push(...cell.trainableWeights);\n            }\n            return trainableWeights.concat(weights);\n        }\n        return weights;\n    }\n    /**\n     * Retrieve the weights of a the model.\n     *\n     * @returns A flat `Array` of `tf.Tensor`s.\n     */\n    getWeights() {\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.weights);\n        }\n        return batchGetValue(weights);\n    }\n    /**\n     * Set the weights of the model.\n     *\n     * @param weights An `Array` of `tf.Tensor`s with shapes and types matching\n     *     the output of `getWeights()`.\n     */\n    setWeights(weights) {\n        const tuples = [];\n        for (const cell of this.cells) {\n            const numParams = cell.weights.length;\n            const inputWeights = weights.splice(numParams);\n            for (let i = 0; i < cell.weights.length; ++i) {\n                tuples.push([cell.weights[i], inputWeights[i]]);\n            }\n        }\n        batchSetValue(tuples);\n    }\n}\n/** @nocollapse */\nStackedRNNCells.className = 'StackedRNNCells';\nserialization.registerClass(StackedRNNCells);\nexport function generateDropoutMask(args) {\n    const { ones, rate, training = false, count = 1 } = args;\n    const droppedInputs = () => K.dropout(ones(), rate);\n    const createMask = () => K.inTrainPhase(droppedInputs, ones, training);\n    // just in case count is provided with null or undefined\n    if (!count || count <= 1) {\n        return tfc.keep(createMask().clone());\n    }\n    const masks = Array(count).fill(undefined).map(createMask);\n    return masks.map(m => tfc.keep(m.clone()));\n}\n//# sourceMappingURL=recurrent.js.map", "/**\n * @license\n * Copyright 2020 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nvar __rest = (this && this.__rest) || function (s, e) {\n    var t = {};\n    for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\n        t[p] = s[p];\n    if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\n        for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\n            if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\n                t[p[i]] = s[p[i]];\n        }\n    return t;\n};\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode } from '../common';\nimport { InputSpec } from '../engine/topology';\nimport { AttributeError, NotImplementedError, ValueError } from '../errors';\nimport { Initializer } from '../initializers';\nimport { convOutputLength, normalizeArray } from '../utils/conv_utils';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport { getExactlyOneShape } from '../utils/types_utils';\nimport { generateDropoutMask, LSTMCell, RNN, RNNCell } from './recurrent';\nclass ConvRNN2DCell extends RNNCell {\n}\n/**\n * Base class for convolutional-recurrent layers.\n */\nclass ConvRNN2D extends RNN {\n    constructor(args) {\n        if (args.unroll) {\n            throw new NotImplementedError('Unrolling is not possible with convolutional RNNs.');\n        }\n        if (Array.isArray(args.cell)) {\n            throw new NotImplementedError('It is not possible at the moment to stack convolutional cells.');\n        }\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 5 })];\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            if (kwargs && kwargs['constants']) {\n                throw new ValueError('ConvRNN2D cell does not support constants');\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    computeOutputShape(inputShape) {\n        let outShape = this.computeSingleOutputShape(inputShape);\n        if (!this.returnSequences) {\n            outShape = [outShape[0], ...outShape.slice(2)];\n        }\n        if (this.returnState) {\n            outShape =\n                [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])];\n        }\n        return outShape;\n    }\n    getInitialState(inputs) {\n        return tfc.tidy(() => {\n            const { stateSize } = this.cell;\n            const inputShape = inputs.shape;\n            const outputShape = this.computeSingleOutputShape(inputShape);\n            const stateShape = [outputShape[0], ...outputShape.slice(2)];\n            const initialState = tfc.zeros(stateShape);\n            if (Array.isArray(stateSize)) {\n                return Array(stateSize.length).fill(initialState);\n            }\n            return [initialState];\n        });\n    }\n    resetStates(states, training = false) {\n        tfc.tidy(() => {\n            if (!this.stateful) {\n                throw new AttributeError('Cannot call resetStates() on an RNN Layer that is not stateful.');\n            }\n            const inputShape = this.inputSpec[0].shape;\n            const outputShape = this.computeSingleOutputShape(inputShape);\n            const stateShape = [outputShape[0], ...outputShape.slice(2)];\n            const batchSize = inputShape[0];\n            if (batchSize == null) {\n                throw new ValueError('If an RNN is stateful, it needs to know its batch size. Specify ' +\n                    'the batch size of your input tensors: \\n' +\n                    '- If using a Sequential model, specify the batch size by ' +\n                    'passing a `batchInputShape` option to your first layer.\\n' +\n                    '- If using the functional API, specify the batch size by ' +\n                    'passing a `batchShape` option to your Input layer.');\n            }\n            // Initialize state if null.\n            if (this.getStates() == null) {\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));\n                }\n                else {\n                    this.states_ = [tfc.zeros(stateShape)];\n                }\n            }\n            else if (states == null) {\n                // Dispose old state tensors.\n                tfc.dispose(this.states_);\n                // For stateful RNNs, fully dispose kept old states.\n                if (this.keptStates != null) {\n                    tfc.dispose(this.keptStates);\n                    this.keptStates = [];\n                }\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));\n                }\n                else {\n                    this.states_[0] = tfc.zeros(stateShape);\n                }\n            }\n            else {\n                if (!Array.isArray(states)) {\n                    states = [states];\n                }\n                if (states.length !== this.states_.length) {\n                    throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), ` +\n                        `but it received ${states.length} state value(s). Input ` +\n                        `received: ${states}`);\n                }\n                if (training) {\n                    // Store old state tensors for complete disposal later, i.e., during\n                    // the next no-arg call to this method. We do not dispose the old\n                    // states immediately because that BPTT (among other things) require\n                    // them.\n                    this.keptStates.push(this.states_.slice());\n                }\n                else {\n                    tfc.dispose(this.states_);\n                }\n                for (let index = 0; index < this.states_.length; ++index) {\n                    const value = states[index];\n                    const expectedShape = stateShape;\n                    if (!util.arraysEqual(value.shape, expectedShape)) {\n                        throw new ValueError(`State ${index} is incompatible with layer ${this.name}: ` +\n                            `expected shape=${expectedShape}, received shape=${value.shape}`);\n                    }\n                    this.states_[index] = value;\n                }\n            }\n            this.states_ = this.states_.map(state => tfc.keep(state.clone()));\n        });\n    }\n    computeSingleOutputShape(inputShape) {\n        const { dataFormat, filters, kernelSize, padding, strides, dilationRate } = this.cell;\n        const isChannelsFirst = dataFormat === 'channelsFirst';\n        const h = inputShape[isChannelsFirst ? 3 : 2];\n        const w = inputShape[isChannelsFirst ? 4 : 3];\n        const hOut = convOutputLength(h, kernelSize[0], padding, strides[0], dilationRate[0]);\n        const wOut = convOutputLength(w, kernelSize[1], padding, strides[1], dilationRate[1]);\n        const outShape = [\n            ...inputShape.slice(0, 2),\n            ...(isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters])\n        ];\n        return outShape;\n    }\n}\n/** @nocollapse */\nConvRNN2D.className = 'ConvRNN2D';\nexport class ConvLSTM2DCell extends LSTMCell {\n    constructor(args) {\n        const { filters, kernelSize, strides, padding, dataFormat, dilationRate, } = args;\n        super(Object.assign({}, args, { units: filters }));\n        this.filters = filters;\n        assertPositiveInteger(this.filters, 'filters');\n        this.kernelSize = normalizeArray(kernelSize, 2, 'kernelSize');\n        this.kernelSize.forEach(size => assertPositiveInteger(size, 'kernelSize'));\n        this.strides = normalizeArray(strides || 1, 2, 'strides');\n        this.strides.forEach(stride => assertPositiveInteger(stride, 'strides'));\n        this.padding = padding || 'valid';\n        checkPaddingMode(this.padding);\n        this.dataFormat = dataFormat || 'channelsLast';\n        checkDataFormat(this.dataFormat);\n        this.dilationRate = normalizeArray(dilationRate || 1, 2, 'dilationRate');\n        this.dilationRate.forEach(rate => assertPositiveInteger(rate, 'dilationRate'));\n    }\n    build(inputShape) {\n        var _a;\n        inputShape = getExactlyOneShape(inputShape);\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError(`The channel dimension of the input should be defined. ` +\n                `Found ${inputShape[channelAxis]}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const numOfKernels = 4;\n        const kernelShape = this.kernelSize.concat([inputDim, this.filters * numOfKernels]);\n        this.kernel = this.addWeight('kernel', kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        const recurrentKernelShape = this.kernelSize.concat([this.filters, this.filters * numOfKernels]);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', recurrentKernelShape, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            let biasInitializer;\n            if (this.unitForgetBias) {\n                const init = this.biasInitializer;\n                const filters = this.filters;\n                biasInitializer = new (_a = class CustomInit extends Initializer {\n                        apply(shape, dtype) {\n                            const biasI = init.apply([filters]);\n                            const biasF = tfc.ones([filters]);\n                            const biasCAndO = init.apply([filters * 2]);\n                            return K.concatenate([biasI, biasF, biasCAndO]);\n                        }\n                    },\n                    /** @nocollapse */\n                    _a.className = 'CustomInit',\n                    _a)();\n            }\n            else {\n                biasInitializer = this.biasInitializer;\n            }\n            this.bias = this.addWeight('bias', [this.filters * numOfKernels], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            if (inputs.length !== 3) {\n                throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            const training = kwargs['training'] || false;\n            const x = inputs[0]; // Current input\n            const hTMinus1 = inputs[1]; // Previous memory state.\n            const cTMinus1 = inputs[2]; // Previous carry state.\n            const numOfKernels = 4;\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(x),\n                    rate: this.dropout,\n                    training,\n                    count: numOfKernels\n                });\n            }\n            const dropoutMask = this.dropoutMask;\n            const applyDropout = (x, mask, index) => {\n                if (!mask || !mask[index]) {\n                    return x;\n                }\n                return tfc.mul(mask[index], x);\n            };\n            let xI = applyDropout(x, dropoutMask, 0);\n            let xF = applyDropout(x, dropoutMask, 1);\n            let xC = applyDropout(x, dropoutMask, 2);\n            let xO = applyDropout(x, dropoutMask, 3);\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: numOfKernels\n                });\n            }\n            const recDropoutMask = this.recurrentDropoutMask;\n            let hI = applyDropout(hTMinus1, recDropoutMask, 0);\n            let hF = applyDropout(hTMinus1, recDropoutMask, 1);\n            let hC = applyDropout(hTMinus1, recDropoutMask, 2);\n            let hO = applyDropout(hTMinus1, recDropoutMask, 3);\n            const kernelChannelAxis = 3;\n            const [kernelI, kernelF, kernelC, kernelO] = tfc.split(this.kernel.read(), numOfKernels, kernelChannelAxis);\n            const [biasI, biasF, biasC, biasO] = this.useBias ?\n                tfc.split(this.bias.read(), numOfKernels) :\n                [null, null, null, null];\n            xI = this.inputConv(xI, kernelI, biasI, this.padding);\n            xF = this.inputConv(xF, kernelF, biasF, this.padding);\n            xC = this.inputConv(xC, kernelC, biasC, this.padding);\n            xO = this.inputConv(xO, kernelO, biasO, this.padding);\n            const [recKernelI, recKernelF, recKernelC, recKernelO] = tfc.split(this.recurrentKernel.read(), numOfKernels, kernelChannelAxis);\n            hI = this.recurrentConv(hI, recKernelI);\n            hF = this.recurrentConv(hF, recKernelF);\n            hC = this.recurrentConv(hC, recKernelC);\n            hO = this.recurrentConv(hO, recKernelO);\n            const i = this.recurrentActivation.apply(tfc.add(xI, hI));\n            const f = this.recurrentActivation.apply(tfc.add(xF, hF));\n            const c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(tfc.add(xC, hC))));\n            const h = tfc.mul(this.recurrentActivation.apply(tfc.add(xO, hO)), this.activation.apply(c));\n            return [h, h, c];\n        });\n    }\n    getConfig() {\n        const _a = super.getConfig(), { 'units': _ } = _a, baseConfig = __rest(_a, ['units']);\n        const config = {\n            filters: this.filters,\n            kernelSize: this.kernelSize,\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n            dilationRate: this.dilationRate,\n            strides: this.strides,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n    inputConv(x, w, b, padding) {\n        const out = tfc.conv2d(x, w, this.strides, (padding || 'valid'), this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC', this.dilationRate);\n        if (b) {\n            return K.biasAdd(out, b, this.dataFormat);\n        }\n        return out;\n    }\n    recurrentConv(x, w) {\n        const strides = 1;\n        return tfc.conv2d(x, w, strides, 'same', this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC');\n    }\n}\n/** @nocollapse */\nConvLSTM2DCell.className = 'ConvLSTM2DCell';\ntfc.serialization.registerClass(ConvLSTM2DCell);\nexport class ConvLSTM2D extends ConvRNN2D {\n    constructor(args) {\n        const cell = new ConvLSTM2DCell(args);\n        super(Object.assign({}, args, { cell }));\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nConvLSTM2D.className = 'ConvLSTM2D';\ntfc.serialization.registerClass(ConvLSTM2D);\n//# sourceMappingURL=convolutional_recurrent.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Basic Layers.\n */\nimport { any, notEqual, serialization, tidy, transpose, util } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport * as K from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { assertPositiveInteger, mapActivationToFusedKernel } from '../utils/generic_utils';\nimport { arrayProd, range } from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class Dropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.rate = Math.max(Math.min(args.rate, 1), 0);\n        // So that the scalar doesn't get tidied up between executions.\n        this.noiseShape = args.noiseShape;\n        this.seed = args.seed;\n        this.supportsMasking = true;\n    }\n    getNoiseShape(input) {\n        if (this.noiseShape == null) {\n            return this.noiseShape;\n        }\n        const inputShape = input.shape;\n        const noiseShape = [];\n        for (let i = 0; i < this.noiseShape.length; ++i) {\n            noiseShape.push(this.noiseShape[i] == null ? inputShape[i] : this.noiseShape[i]);\n        }\n        return noiseShape;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            if (0 < this.rate && this.rate < 1) {\n                const training = kwargs['training'] == null ? false : kwargs['training'];\n                const noiseShape = this.getNoiseShape(input);\n                const output = K.inTrainPhase(() => K.dropout(input, this.rate, noiseShape, this.seed), () => input, training);\n                return output;\n            }\n            return inputs;\n        });\n    }\n    getConfig() {\n        const config = {\n            rate: this.rate,\n            noiseShape: this.noiseShape,\n            seed: this.seed,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    dispose() {\n        return super.dispose();\n    }\n}\n/** @nocollapse */\nDropout.className = 'Dropout';\nserialization.registerClass(Dropout);\nexport class SpatialDropout1D extends Dropout {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [{ ndim: 3 }];\n    }\n    getNoiseShape(input) {\n        const inputShape = input.shape;\n        return [inputShape[0], 1, inputShape[2]];\n    }\n}\n/** @nocollapse */\nSpatialDropout1D.className = 'SpatialDropout1D';\nserialization.registerClass(SpatialDropout1D);\nexport class Dense extends Layer {\n    constructor(args) {\n        super(args);\n        // Default activation: Linear (none).\n        this.activation = null;\n        this.useBias = true;\n        this.kernel = null;\n        this.bias = null;\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        if (args.batchInputShape == null && args.inputShape == null &&\n            args.inputDim != null) {\n            // This logic is copied from Layer's constructor, since we can't\n            // do exactly what the Python constructor does for Dense().\n            let batchSize = null;\n            if (args.batchSize != null) {\n                batchSize = args.batchSize;\n            }\n            this.batchInputShape = [batchSize, args.inputDim];\n        }\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation);\n        if (args.useBias != null) {\n            this.useBias = args.useBias;\n        }\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.supportsMasking = true;\n        this.inputSpec = [{ minNDim: 2 }];\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const inputLastDim = inputShape[inputShape.length - 1];\n        if (this.kernel == null) {\n            this.kernel = this.addWeight('kernel', [inputLastDim, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n            if (this.useBias) {\n                this.bias = this.addWeight('bias', [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n            }\n        }\n        this.inputSpec = [{ minNDim: 2, axes: { [-1]: inputLastDim } }];\n        this.built = true;\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        outputShape[outputShape.length - 1] = this.units;\n        return outputShape;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Dense layer accepts only a single input.\n            const input = getExactlyOneTensor(inputs);\n            const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());\n            let output;\n            if (fusedActivationName != null) {\n                output = K.dot(input, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null);\n            }\n            else {\n                output = K.dot(input, this.kernel.read());\n                if (this.bias != null) {\n                    output = K.biasAdd(output, this.bias.read());\n                }\n                if (this.activation != null) {\n                    output = this.activation.apply(output);\n                }\n            }\n            return output;\n        });\n    }\n    getConfig() {\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nDense.className = 'Dense';\nserialization.registerClass(Dense);\nexport class Flatten extends Layer {\n    constructor(args) {\n        args = args || {};\n        super(args);\n        this.inputSpec = [{ minNDim: 3 }];\n        this.dataFormat = args.dataFormat;\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        for (const dim of inputShape.slice(1)) {\n            if (dim == null) {\n                throw new ValueError(`The shape of the input to \"Flatten\" is not fully defined ` +\n                    `(got ${inputShape.slice(1)}). Make sure to pass a complete ` +\n                    `\"input_shape\" or \"batch_input_shape\" argument to the first ` +\n                    `layer in your model.`);\n            }\n        }\n        return [inputShape[0], arrayProd(inputShape, 1)];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            let input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsFirst' && input.rank > 1) {\n                const permutation = [0];\n                for (let i = 2; i < input.rank; ++i) {\n                    permutation.push(i);\n                }\n                permutation.push(1);\n                input = input.transpose(permutation);\n            }\n            return K.batchFlatten(input);\n        });\n    }\n    getConfig() {\n        const config = {};\n        if (this.dataFormat != null) {\n            config['dataFormat'] = this.dataFormat;\n        }\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nFlatten.className = 'Flatten';\nserialization.registerClass(Flatten);\nexport class Activation extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.activation = getActivation(args.activation);\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            return this.activation.apply(input);\n        });\n    }\n    getConfig() {\n        const config = { activation: serializeActivation(this.activation) };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nActivation.className = 'Activation';\nserialization.registerClass(Activation);\nexport class RepeatVector extends Layer {\n    constructor(args) {\n        super(args);\n        this.n = args.n;\n        this.inputSpec = [{ ndim: 2 }];\n    }\n    computeOutputShape(inputShape) {\n        return [inputShape[0], this.n, inputShape[1]];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            return K.repeat(inputs, this.n);\n        });\n    }\n    getConfig() {\n        const config = {\n            n: this.n,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nRepeatVector.className = 'RepeatVector';\nserialization.registerClass(RepeatVector);\nexport class Reshape extends Layer {\n    constructor(args) {\n        super(args);\n        this.targetShape = args.targetShape;\n        // Make sure that all unknown dimensions are represented as `null`.\n        for (let i = 0; i < this.targetShape.length; ++i) {\n            if (this.isUnknown(this.targetShape[i])) {\n                this.targetShape[i] = null;\n            }\n        }\n    }\n    isUnknown(dim) {\n        return dim < 0 || dim == null;\n    }\n    /**\n     * Finds and replaces a missing dimension in output shape.\n     *\n     * This is a near direct port of the internal Numpy function\n     * `_fix_unknown_dimension` in `numpy/core/src/multiarray/shape.c`.\n     *\n     * @param inputShape: Original shape of array begin reshape.\n     * @param outputShape: Target shape of the array, with at most a single\n     * `null` or negative number, which indicates an underdetermined dimension\n     * that should be derived from `inputShape` and the known dimensions of\n     *   `outputShape`.\n     * @returns: The output shape with `null` replaced with its computed value.\n     * @throws: ValueError: If `inputShape` and `outputShape` do not match.\n     */\n    fixUnknownDimension(inputShape, outputShape) {\n        const errorMsg = 'Total size of new array must be unchanged.';\n        const finalShape = outputShape.slice();\n        let known = 1;\n        let unknown = null;\n        for (let i = 0; i < finalShape.length; ++i) {\n            const dim = finalShape[i];\n            if (this.isUnknown(dim)) {\n                if (unknown === null) {\n                    unknown = i;\n                }\n                else {\n                    throw new ValueError('Can only specifiy one unknown dimension.');\n                }\n            }\n            else {\n                known *= dim;\n            }\n        }\n        const originalSize = arrayProd(inputShape);\n        if (unknown !== null) {\n            if (known === 0 || originalSize % known !== 0) {\n                throw new ValueError(errorMsg);\n            }\n            finalShape[unknown] = originalSize / known;\n        }\n        else if (originalSize !== known) {\n            throw new ValueError(errorMsg);\n        }\n        return finalShape;\n    }\n    computeOutputShape(inputShape) {\n        let anyUnknownDims = false;\n        for (let i = 0; i < inputShape.length; ++i) {\n            if (this.isUnknown(inputShape[i])) {\n                anyUnknownDims = true;\n                break;\n            }\n        }\n        if (anyUnknownDims) {\n            return inputShape.slice(0, 1).concat(this.targetShape);\n        }\n        else {\n            return inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            const outputShape = inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n            return input.reshape(outputShape);\n        });\n    }\n    getConfig() {\n        const config = {\n            targetShape: this.targetShape,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nReshape.className = 'Reshape';\nserialization.registerClass(Reshape);\nexport class Permute extends Layer {\n    constructor(args) {\n        super(args);\n        if (args.dims == null) {\n            throw new Error('Required configuration field `dims` is missing during Permute ' +\n                'constructor call.');\n        }\n        if (!Array.isArray(args.dims)) {\n            throw new Error('Permute constructor requires `dims` to be an Array, but received ' +\n                `${args.dims} instead.`);\n        }\n        // Check the validity of the permutation indices.\n        const expectedSortedIndices = range(1, args.dims.length + 1);\n        if (!util.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) {\n            throw new Error('Invalid permutation `dims`: ' + JSON.stringify(args.dims) +\n                ' `dims` must contain consecutive integers starting from 1.');\n        }\n        this.dims = args.dims;\n        this.dimsIncludingBatch = [0].concat(this.dims);\n        this.inputSpec = [new InputSpec({ ndim: this.dims.length + 1 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        this.dims.forEach((dim, i) => {\n            outputShape[i + 1] = inputShape[dim];\n        });\n        return outputShape;\n    }\n    call(inputs, kwargs) {\n        return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch);\n    }\n    getConfig() {\n        const config = {\n            dims: this.dims,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nPermute.className = 'Permute';\nserialization.registerClass(Permute);\nexport class Masking extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.supportsMasking = true;\n        if (args != null) {\n            this.maskValue = args.maskValue == null ? 0 : args.maskValue;\n        }\n        else {\n            this.maskValue = 0;\n        }\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { maskValue: this.maskValue };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    computeMask(inputs, mask) {\n        const input = getExactlyOneTensor(inputs);\n        const axis = -1;\n        return any(notEqual(input, this.maskValue), axis);\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const axis = -1;\n            const keepDims = true;\n            const booleanMask = any(notEqual(input, this.maskValue), axis, keepDims);\n            const output = input.mul(booleanMask.asType(input.dtype));\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMasking.className = 'Masking';\nserialization.registerClass(Masking);\n//# sourceMappingURL=core.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Embedding Layer.\n *\n * Original source: keras/constraints.py\n */\nimport { notEqual, serialization, tidy, zerosLike } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class Embedding extends Layer {\n    constructor(args) {\n        super(args);\n        this.embeddings = null;\n        this.DEFAULT_EMBEDDINGS_INITIALIZER = 'randomUniform';\n        if (args.batchInputShape == null && args.inputShape == null) {\n            // Porting Note: This logic is copied from Layer's constructor, since we\n            // can't do exactly what the Python constructor does for Embedding().\n            // Specifically, the super constructor can not be called after the\n            // mutation of the `config` argument.\n            let batchSize = null;\n            if (args.batchSize != null) {\n                batchSize = args.batchSize;\n            }\n            if (args.inputLength == null) {\n                // Fix super-constructor to what it would have done if\n                // 'config.inputShape' were (None, )\n                this.batchInputShape = [batchSize, null];\n            }\n            else {\n                // Fix super-constructor to what it would have done if\n                // 'config.inputShape' were (config.inputLength, )\n                this.batchInputShape =\n                    [batchSize].concat(generic_utils.toList(args.inputLength));\n            }\n        }\n        this.inputDim = args.inputDim;\n        generic_utils.assertPositiveInteger(this.inputDim, 'inputDim');\n        this.outputDim = args.outputDim;\n        generic_utils.assertPositiveInteger(this.outputDim, 'outputDim');\n        this.embeddingsInitializer = getInitializer(args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER);\n        this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.embeddingsConstraint = getConstraint(args.embeddingsConstraint);\n        this.maskZero = args.maskZero;\n        this.supportsMasking = args.maskZero;\n        this.inputLength = args.inputLength;\n    }\n    build(inputShape) {\n        this.embeddings = this.addWeight('embeddings', [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint);\n        this.built = true;\n    }\n    // Override warnOnIncompatibleInputShape because an embedding layer allows\n    // the input to have varying ranks.\n    warnOnIncompatibleInputShape(inputShape) { }\n    computeMask(inputs, mask) {\n        return tidy(() => {\n            if (!this.maskZero) {\n                return null;\n            }\n            else {\n                inputs = getExactlyOneTensor(inputs);\n                return notEqual(inputs, zerosLike(inputs));\n            }\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (this.inputLength == null) {\n            return [...inputShape, this.outputDim];\n        }\n        // inputLength can be an array if input is 3D or higher.\n        const inLens = generic_utils.toList(this.inputLength);\n        if (inLens.length !== inputShape.length - 1) {\n            throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received ` +\n                `input shape has shape ${inputShape}`);\n        }\n        else {\n            let i = 0;\n            for (let k = 0; k < inLens.length; ++k) {\n                const s1 = inLens[k];\n                const s2 = inputShape[k + 1];\n                if ((s1 != null) && (s2 != null) && (s1 !== s2)) {\n                    throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received ` +\n                        `input shape has shape ${inputShape}`);\n                }\n                else if (s1 == null) {\n                    inLens[i] = s2;\n                }\n                i++;\n            }\n        }\n        return [inputShape[0], ...inLens, this.outputDim];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Embedding layer accepts only a single input.\n            let input = getExactlyOneTensor(inputs);\n            if (input.dtype !== 'int32') {\n                input = K.cast(input, 'int32');\n            }\n            const output = K.gather(this.embeddings.read(), input.as1D());\n            return output.reshape(getExactlyOneShape(this.computeOutputShape(input.shape)));\n        });\n    }\n    getConfig() {\n        const config = {\n            inputDim: this.inputDim,\n            outputDim: this.outputDim,\n            embeddingsInitializer: serializeInitializer(this.embeddingsInitializer),\n            embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            embeddingsConstraint: serializeConstraint(this.embeddingsConstraint),\n            maskZero: this.maskZero,\n            inputLength: this.inputLength\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nEmbedding.className = 'Embedding';\nserialization.registerClass(Embedding);\n//# sourceMappingURL=embeddings.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Merge Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { l2Normalize } from '../losses';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as mathUtils from '../utils/math_utils';\nimport { getExactlyOneShape } from '../utils/types_utils';\n/**\n * Generic Merge layer for element-wise merge functions.\n *\n * Used to implement `Sum`, `Average`, `Concatenate`, etc.\n */\nexport class Merge extends Layer {\n    constructor(args) {\n        super(args || {});\n        this.supportsMasking = true;\n    }\n    /**\n     * Logic for merging multiple tensors, to be overridden by subclasses.\n     * @param inputs\n     */\n    mergeFunction(inputs) {\n        throw new NotImplementedError();\n    }\n    /**\n     * Computes the shape of the result of an elementwise operation.\n     *\n     * @param shape1: Shape of the first tensor.\n     * @param shape2: Shape of the second tensor.\n     * @returns Expected output shape when an elementwise operation is carried\n     *   out on 2 tensors with shapes `shape1` and `shape2`.\n     * @throws ValueError: If `shape1` and `shape2` are not compatible for\n     *   element-wise operations.\n     */\n    computeElementwiseOpOutputShape(shape1, shape2) {\n        if (shape1 == null || shape2 == null) {\n            return null;\n        }\n        else if (shape1.length < shape2.length) {\n            return this.computeElementwiseOpOutputShape(shape2, shape1);\n        }\n        else if (shape2.length === 0) {\n            return shape1;\n        }\n        const outputShape = shape1.slice(0, shape1.length - shape2.length);\n        for (let k = 0; k < shape2.length; ++k) {\n            const i = shape1[shape1.length - shape2.length + k];\n            const j = shape2[k];\n            if (i == null || j == null || i < 0 || j < 0) {\n                outputShape.push(null);\n            }\n            else if (i === 1) {\n                outputShape.push(j);\n            }\n            else if (j === 1) {\n                outputShape.push(i);\n            }\n            else {\n                if (i !== j) {\n                    throw new ValueError('Operands could not be broadcast together with shapes ' +\n                        JSON.stringify(shape1) + ' ' + JSON.stringify(shape2));\n                }\n                outputShape.push(i);\n            }\n        }\n        return outputShape;\n    }\n    build(inputShape) {\n        // Used purely for shape validation.\n        if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) {\n            // Make sure that inputShape is an Array of shape.\n            inputShape = [getExactlyOneShape(inputShape)];\n        }\n        inputShape = inputShape;\n        if (inputShape.length < 2) {\n            throw new ValueError('A merge layer should be called on an Array of at least 2 inputs.' +\n                ` Got ${inputShape.length} input(s).`);\n        }\n        // Make sure that there is at most one unique batch size among the input\n        // shapes.\n        let batchSizes = [];\n        for (const shape of inputShape) {\n            if (shape != null && shape[0] !== null) {\n                batchSizes.push(shape[0]);\n            }\n        }\n        batchSizes = generic_utils.unique(batchSizes);\n        if (batchSizes.length > 1) {\n            throw new ValueError(`Can not merge tensors with different batch sizes. ` +\n                `Got tensors with shapes: ${JSON.stringify(inputShape)}.`);\n        }\n        let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1);\n        for (let i = 1; i < inputShape.length; ++i) {\n            const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n            outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n        }\n        // If the inputs have different ranks, we have to reshape them to make them\n        // broadcastable.\n        const allRanks = inputShape.map(shape => shape.length);\n        if (inputShape.indexOf(null) === -1 &&\n            generic_utils.unique(allRanks).length === 1) {\n            this.reshapeRequired = false;\n        }\n        else {\n            this.reshapeRequired = true;\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (this.reshapeRequired) {\n                const reshapedInputs = [];\n                const inputDims = inputs.map(input => input.rank);\n                if (inputDims.indexOf(null) === -1) {\n                    // If ranks of all inputs are available, we simply expand each of them\n                    // at axis=1 until all of them have the same rank.\n                    const maxNDim = mathUtils.max(inputDims);\n                    for (let x of inputs) {\n                        const xNDim = x.rank;\n                        for (let k = 0; k < maxNDim - xNDim; ++k) {\n                            x = K.expandDims(x, 1);\n                        }\n                        reshapedInputs.push(x);\n                    }\n                    return this.mergeFunction(reshapedInputs);\n                }\n                else {\n                    // Transpose all inputs so that batch size is the last dimension.\n                    // [batchSize, dim1, dim2, ...] -> [dim1, dim2, ..., batchSize]\n                    let transposed = false;\n                    for (const x of inputs) {\n                        const xNDim = x.rank;\n                        if (xNDim == null) {\n                            const xShape = x.shape;\n                            const batchSize = xShape[0];\n                            const newShape = xShape.slice(1).concat([batchSize]);\n                            let xTransposed = x.reshape([batchSize].concat(mathUtils.arrayProd(xShape.slice(1))));\n                            xTransposed = tfc.transpose(xTransposed, [1, 0]);\n                            xTransposed = xTransposed.reshape(newShape);\n                            reshapedInputs.push(xTransposed);\n                            transposed = true;\n                        }\n                        else if (xNDim > 1) {\n                            const dims = mathUtils.range(1, xNDim).concat([0]);\n                            reshapedInputs.push(tfc.transpose(x, dims));\n                            transposed = true;\n                        }\n                        else {\n                            // We don't transpose inputs if they are 1D vectors or scalars.\n                            reshapedInputs.push(x);\n                        }\n                    }\n                    let y = this.mergeFunction(reshapedInputs);\n                    const yNDim = y.rank;\n                    if (transposed) {\n                        // If inputs have been transposed, we have to transpose the output\n                        // too.\n                        if (yNDim == null) {\n                            const yShape = y.shape;\n                            const yNDim = yShape.length;\n                            const batchSize = yShape[yNDim - 1];\n                            const newShape = [batchSize].concat(yShape.slice(0, yShape.length - 1));\n                            y = tfc.transpose(y.reshape([-1, batchSize]), [1, 0])\n                                .reshape(newShape);\n                        }\n                        else if (yNDim > 1) {\n                            const dims = [yNDim - 1].concat(mathUtils.range(0, yNDim - 1));\n                            y = tfc.transpose(y, dims);\n                        }\n                    }\n                    return y;\n                }\n            }\n            else {\n                return this.mergeFunction(inputs);\n            }\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = inputShape;\n        let outputShape;\n        if (inputShape[0] == null) {\n            outputShape = null;\n        }\n        else {\n            outputShape = inputShape[0].slice(1);\n        }\n        for (let i = 1; i < inputShape.length; ++i) {\n            const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n            outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n        }\n        let batchSizes = [];\n        for (const shape of inputShape) {\n            if (shape != null && shape[0] !== null) {\n                batchSizes.push(shape[0]);\n            }\n        }\n        batchSizes = generic_utils.unique(batchSizes);\n        if (batchSizes.length === 1) {\n            outputShape = batchSizes.concat(outputShape);\n        }\n        else {\n            outputShape = [null].concat(outputShape);\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        return tfc.tidy(() => {\n            if (mask == null) {\n                return null;\n            }\n            if (!Array.isArray(mask)) {\n                throw new ValueError('`mask` should be an Array');\n            }\n            if (!Array.isArray(inputs)) {\n                throw new ValueError('`inputs` should be an Array');\n            }\n            if (mask.length !== inputs.length) {\n                throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same ` +\n                    `length, but have different lengths ` +\n                    `(${inputs.length} vs ${mask.length})`);\n            }\n            if (mask.every(m => m == null)) {\n                return null;\n            }\n            mask = mask.map(m => m == null ? m : tfc.expandDims(m, 0));\n            let output = mask[0];\n            for (let i = 1; i < mask.length - 1; ++i) {\n                output = tfc.logicalAnd(output, mask[i]);\n            }\n            return output;\n        });\n    }\n}\nexport class Add extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.add(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nAdd.className = 'Add';\nserialization.registerClass(Add);\n/**\n * Calculate the element-wise sum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Add` layer, by using no input argument\n *    or a single configuration argument. The resultant `Add` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const addLayer = tf.layers.add();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = addLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.add([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.add([input1, input2]).print();\n * // Gives [[11, 22], [33, 44]].\n *\n */\nexport function add(config) {\n    if (Array.isArray(config)) {\n        const layer = new Add({});\n        return layer.apply(config);\n    }\n    else {\n        return new Add(config);\n    }\n}\nexport class Multiply extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.mul(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMultiply.className = 'Multiply';\nserialization.registerClass(Multiply);\n/**\n * Calculate the element-wise product of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Multiply` layer, by using no input argument\n *    or a single configuration argument. The resultant `Multiply` layer can\n *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const multiplyLayer = tf.layers.multiply();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = multiplyLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.multiply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.multiply([input1, input2]).print();\n * // Gives [[10, 40], [90, 160]].\n *\n */\nexport function multiply(config) {\n    if (Array.isArray(config)) {\n        const layer = new Multiply({});\n        return layer.apply(config);\n    }\n    else {\n        return new Multiply(config);\n    }\n}\nexport class Average extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.add(output, inputs[i]);\n            }\n            return tfc.mul(1 / inputs.length, output);\n        });\n    }\n}\n/** @nocollapse */\nAverage.className = 'Average';\nserialization.registerClass(Average);\n/**\n * Calculate the element-wise arithmetic mean of inputs, which all have the same\n * shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Average` layer, by using no input argument\n *    or a single configuration argument. The resultant `Average` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const averageLayer = tf.layers.average();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = averageLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.average([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.average([input1, input2]).print();\n * // Gives [[5.5, 11], [16.5, 22]].\n *\n */\nexport function average(config) {\n    if (Array.isArray(config)) {\n        const layer = new Average({});\n        return layer.apply(config);\n    }\n    else {\n        return new Average(config);\n    }\n}\nexport class Maximum extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0];\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.maximum(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMaximum.className = 'Maximum';\nserialization.registerClass(Maximum);\n/**\n * Calculate the element-wise maximum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Maximum` layer, by using no input argument\n *    or a single configuration argument. The resultant `Maximum` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const maximumLayer = tf.layers.maximum();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = maximumLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.maximum([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);\n * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);\n * tf.layers.maximum([input1, input2]).print();\n * // Gives [[10, 20], [30, 40]].\n *\n */\nexport function maximum(config) {\n    if (Array.isArray(config)) {\n        const layer = new Maximum({});\n        return layer.apply(config);\n    }\n    else {\n        return new Maximum(config);\n    }\n}\nexport class Minimum extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0];\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.minimum(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMinimum.className = 'Minimum';\nserialization.registerClass(Minimum);\n/**\n * Calculate the element-wise minimum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Minimum` layer, by using no input argument\n *    or a single configuration argument. The resultant `Minimum` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const minimumLayer = tf.layers.minimum();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = minimumLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.minimum([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);\n * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);\n * tf.layers.minimum([input1, input2]).print();\n * // Gives [[1, 2], [3, 4]].\n *\n */\nexport function minimum(config) {\n    if (Array.isArray(config)) {\n        const layer = new Minimum({});\n        return layer.apply(config);\n    }\n    else {\n        return new Minimum(config);\n    }\n}\nexport class Concatenate extends Merge {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_AXIS = -1;\n        if (args == null) {\n            args = {};\n        }\n        this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n        this.supportsMasking = true;\n        this.reshapeRequired = false;\n    }\n    build(inputShape) {\n        // Used purely for shape validation.]\n        if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) ||\n            inputShape.length === 1) {\n            throw new ValueError('A `Concatenate` layer should be called on a list of at least 2 ' +\n                'inputs');\n        }\n        inputShape = inputShape;\n        let allNoneShape = true;\n        for (const shape of inputShape) {\n            if (shape != null) {\n                allNoneShape = false;\n                break;\n            }\n        }\n        if (allNoneShape) {\n            return;\n        }\n        const shapeSet = [];\n        for (let i = 0; i < inputShape.length; ++i) {\n            const shapeWithoutConcatAxis = inputShape[i].slice();\n            shapeWithoutConcatAxis.splice(this.axis, 1);\n            let exists = false;\n            for (const shape of shapeSet) {\n                if (util.arraysEqual(shape, shapeWithoutConcatAxis)) {\n                    exists = true;\n                    break;\n                }\n            }\n            if (!exists) {\n                shapeSet.push(shapeWithoutConcatAxis);\n            }\n        }\n        if (shapeSet.length > 1) {\n            throw new ValueError('A `Concatenate` layer requires inputs with matching shapes ' +\n                'except for the concat axis. Got input shapes: ' +\n                JSON.stringify(inputShape));\n        }\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            return K.concatenate(inputs, this.axis);\n        });\n    }\n    computeOutputShape(inputShape) {\n        if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) {\n            throw new ValueError('A `Concatenate` layer should be called on a list of inputs.');\n        }\n        const inputShapes = inputShape;\n        const outputShape = inputShapes[0].slice();\n        const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis;\n        // Porting Note: the line above is because TypeScript doesn't support\n        //   negative indices.\n        for (const shape of inputShapes.slice(1)) {\n            if (outputShape[axis] == null || shape[axis] == null) {\n                outputShape[axis] = null;\n                break;\n            }\n            outputShape[axis] += shape[axis];\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        if (mask == null) {\n            return null;\n        }\n        if (!Array.isArray(mask)) {\n            throw new ValueError('`mask` should be an array for Concatenate');\n        }\n        if (!Array.isArray(inputs)) {\n            throw new ValueError('`inputs` should be an array for Concatenate');\n        }\n        if (mask.length !== inputs.length) {\n            throw new ValueError(`Mismatch in the length of mask (${mask.length}) ` +\n                `and the legnth of inputs (${inputs.length})`);\n        }\n        return tfc.tidy(() => {\n            let allNullMasks = true;\n            mask.forEach(m => {\n                if (m != null) {\n                    allNullMasks = false;\n                    return;\n                }\n            });\n            if (allNullMasks) {\n                return null;\n            }\n            const outputMasks = [];\n            for (let i = 0; i < inputs.length; ++i) {\n                if (mask[i] == null) {\n                    // Input is unmasked. Append all 1's to masks.\n                    outputMasks.push(tfc.onesLike(inputs[i]).asType('bool'));\n                }\n                else if (mask[i].rank < inputs[i].rank) {\n                    // Mask is smaller than the input, expand it.\n                    outputMasks.push(tfc.expandDims(mask[i], -1));\n                }\n                else {\n                    outputMasks.push(mask[i]);\n                }\n            }\n            const concatenatedMasks = tfc.concat(outputMasks, this.axis);\n            return tfc.all(concatenatedMasks, -1, false);\n        });\n    }\n    getConfig() {\n        const config = {\n            'axis': this.axis,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nConcatenate.className = 'Concatenate';\nserialization.registerClass(Concatenate);\n/**\n * Concatenate an `Array` of inputs.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Concatenate` layer, by using no input argument\n *    or a single configuration argument. The resultant `Concatenate` layer can\n *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const concatLayer = tf.layers.concatenate();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 3]});\n * const input2 = tf.input({shape: [2, 4]});\n * const output = concatLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 7], with the first dimension as the undetermined batch\n * // dimension and the last dimension as the result of concatenating the\n * // last dimensions of the two inputs.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 3]});\n * const input2 = tf.input({shape: [2, 4]});\n * const output = tf.layers.concatenate([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension and the last dimension as the result of concatenating the\n * // last dimensions of the two inputs.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);\n * const input2 = tf.tensor2d([[10, 20], [30, 40]], [2, 2]);\n * tf.layers.concatenate([input1, input2]).print();\n * // Gives [[1, 2, 10, 20], [3, 4, 30, 40]].\n *\n */\nexport function concatenate(config) {\n    if (Array.isArray(config)) {\n        const layer = new Concatenate({});\n        return layer.apply(config);\n    }\n    else {\n        return new Concatenate(config);\n    }\n}\n/**\n * Interpretable potentially negative axis index.\n *\n * For example, given axis = -1, and dim = 3, this function will return 2.\n *\n * @param axis The axis index, may be a positive, zero or negative integer.\n * @param dim Total number of dimensions, a positive integer.\n * @returns A non-negative axis index equivalent to the input `axis`.\n */\nfunction interpretAxis(axis, dim) {\n    while (axis < 0) {\n        axis += dim;\n    }\n    return axis;\n}\nfunction batchDot(x, y, axes) {\n    if (x.shape.length > 3 || y.shape.length > 3) {\n        throw new NotImplementedError('batchDot is not implemented for tensors of 4D or higher rank yet');\n    }\n    tfc.util.assert(x.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, ` +\n        `but got ${x.shape.length}`);\n    tfc.util.assert(x.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, ` +\n        `but got ${y.shape.length}`);\n    if (typeof axes === 'number') {\n        axes = [axes, axes];\n    }\n    if (x.dtype === 'complex64' || y.dtype === 'complex64') {\n        throw new NotImplementedError('batchDot is not implemented for complex64-type Tensors yet.');\n    }\n    const xNDim = x.shape.length;\n    const yNDim = y.shape.length;\n    if (axes == null) {\n        // Behave like batchMatmul by default.\n        axes = [xNDim - 1, yNDim - 2];\n    }\n    const axesArray = axes;\n    return tfc.tidy(() => {\n        let diff;\n        if (xNDim > yNDim) {\n            diff = xNDim - yNDim;\n            const diffShape = [];\n            for (let i = 0; i < diff; ++i) {\n                diffShape.push(1);\n            }\n            y = y.reshape(y.shape.concat(diffShape));\n        }\n        else if (yNDim > xNDim) {\n            diff = yNDim - xNDim;\n            const diffShape = [];\n            for (let i = 0; i < diff; ++i) {\n                diffShape.push(1);\n            }\n            x = x.reshape(x.shape.concat(diffShape));\n        }\n        else {\n            diff = 0;\n        }\n        let out;\n        if (x.shape.length === 2 && y.shape.length === 2) {\n            if (axesArray[0] === axesArray[1]) {\n                out = x.mul(y).sum(axesArray[0]);\n            }\n            else {\n                out = x.transpose([1, 0]).mul(y).sum(axesArray[1]);\n            }\n        }\n        else {\n            const adjX = axesArray[0] !== x.shape.length - 1;\n            const adjY = axesArray[1] === y.shape.length - 1;\n            out = x.matMul(y, adjX, adjY);\n        }\n        if (diff > 0) {\n            let idx;\n            if (xNDim > yNDim) {\n                idx = xNDim + yNDim - 3;\n            }\n            else {\n                idx = xNDim - 1;\n            }\n            const squeezeAxes = [];\n            for (let i = idx; i < idx + diff; ++i) {\n                squeezeAxes.push(i);\n            }\n            out = out.squeeze(squeezeAxes);\n        }\n        if (out.shape.length === 1) {\n            out = out.expandDims(1);\n        }\n        return out;\n    });\n}\nexport class Dot extends Merge {\n    constructor(args) {\n        super(args);\n        this.axes = args.axes;\n        this.normalize = args.normalize == null ? false : args.normalize;\n        this.supportsMasking = true;\n        this.reshapeRequired = false;\n    }\n    build(inputShape) {\n        tfc.util.assert(Array.isArray(inputShape) && inputShape.length === 2 &&\n            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');\n        const shape1 = inputShape[0];\n        const shape2 = inputShape[1];\n        if (shape1.length > 3 || shape2.length > 3) {\n            throw new NotImplementedError('Dot layer does not support tensors of 4D or higher rank yet.');\n        }\n        const axes = this.interpretAxes(shape1, shape2);\n        if (shape1[axes[0]] !== shape2[axes[1]]) {\n            throw new ValueError(`Dimension incompatibility: ` +\n                `${shape1[axes[0]]} !== ${shape2[axes[1]]}`);\n        }\n    }\n    mergeFunction(inputs) {\n        if (inputs.length !== 2) {\n            throw new ValueError('A `Dot` layer must be called on exactly 2 inputs, ' +\n                `but received ${inputs.length} input(s).`);\n        }\n        let x1 = inputs[0];\n        let x2 = inputs[1];\n        let axes;\n        if (!Array.isArray(this.axes)) {\n            axes = [\n                interpretAxis(this.axes, x1.shape.length),\n                interpretAxis(this.axes, x2.shape.length)\n            ];\n        }\n        else {\n            axes = this.axes.map((axis, i) => interpretAxis(axis, inputs[i].shape.length));\n        }\n        if (this.normalize) {\n            x1 = l2Normalize(x1, axes[0]);\n            x2 = l2Normalize(x2, axes[1]);\n        }\n        return batchDot(x1, x2, axes);\n    }\n    interpretAxes(shape1, shape2) {\n        let axes;\n        if (!Array.isArray(this.axes)) {\n            // `this.axes` is a single integer.\n            axes = [\n                interpretAxis(this.axes, shape1.length),\n                interpretAxis(this.axes, shape2.length)\n            ];\n        }\n        else {\n            // `this.axes` is an Array of integers.\n            axes = this.axes;\n        }\n        return axes;\n    }\n    computeOutputShape(inputShape) {\n        tfc.util.assert(Array.isArray(inputShape) && inputShape.length === 2 &&\n            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');\n        const shape1 = inputShape[0].slice();\n        const shape2 = inputShape[1].slice();\n        if (shape1.length > 3 || shape2.length > 3) {\n            throw new NotImplementedError('Dot layer does not support tensors of 4D or higher rank yet.');\n        }\n        const axes = this.interpretAxes(shape1, shape2);\n        shape1.splice(axes[0], 1);\n        shape2.splice(axes[1], 1);\n        shape2.splice(0, 1);\n        const outputShape = shape1.concat(shape2);\n        if (outputShape.length === 1) {\n            outputShape.push(1);\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        return null;\n    }\n    getConfig() {\n        const config = {\n            'axes': this.axes,\n            'normalize': this.normalize\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nDot.className = 'Dot';\nserialization.registerClass(Dot);\n// TODO(cais): Add functional interfaces for the merge layers.\n//# sourceMappingURL=merge.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Noise Layers.\n */\nimport { greaterEqual, randomUniform, serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { Layer } from '../engine/topology';\nimport { getExactlyOneTensor } from '../utils/types_utils';\nexport class GaussianNoise extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.stddev = args.stddev;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { stddev: this.stddev };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const noised = () => K.randomNormal(input.shape, 0, this.stddev).add(input);\n            const output = K.inTrainPhase(noised, () => input, kwargs['training'] || false);\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nGaussianNoise.className = 'GaussianNoise';\nserialization.registerClass(GaussianNoise);\nexport class GaussianDropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.rate = args.rate;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { rate: this.rate };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            if (this.rate > 0 && this.rate < 1) {\n                const noised = () => {\n                    const stddev = Math.sqrt(this.rate / (1 - this.rate));\n                    return input.mul(K.randomNormal(input.shape, 1, stddev));\n                };\n                return K.inTrainPhase(noised, () => input, kwargs['training'] || false);\n            }\n            return input;\n        });\n    }\n}\n/** @nocollapse */\nGaussianDropout.className = 'GaussianDropout';\nserialization.registerClass(GaussianDropout);\n/**\n * Applies Alpha Dropout to the input.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs\n * to their original values, in order to ensure the self-normalizing property\n * even after this dropout.\n * Alpha Dropout fits well to Scaled Exponential Linear Units\n * by randomly setting activations to the negative saturation value.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the\n *     shape for randomly generated keep/drop flags.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n */\nexport class AlphaDropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.rate = args.rate;\n        this.noiseShape = args.noiseShape;\n    }\n    _getNoiseShape(inputs) {\n        return this.noiseShape || getExactlyOneTensor(inputs).shape;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { rate: this.rate };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.rate < 1 && this.rate > 0) {\n                const noiseShape = this._getNoiseShape(inputs);\n                const droppedInputs = () => {\n                    const input = getExactlyOneTensor(inputs);\n                    const alpha = 1.6732632423543772848170429916717;\n                    const scale = 1.0507009873554804934193349852946;\n                    const alphaP = -alpha * scale;\n                    let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate);\n                    keptIdx = K.cast(keptIdx, 'float32'); // get default dtype.\n                    // Get affine transformation params.\n                    const a = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5;\n                    const b = -a * alphaP * this.rate;\n                    // Apply mask.\n                    const x = input.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP));\n                    return x.mul(a).add(b);\n                };\n                return K.inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs['training'] || false);\n            }\n            return inputs;\n        });\n    }\n}\n/** @nocollapse */\nAlphaDropout.className = 'AlphaDropout';\nserialization.registerClass(AlphaDropout);\n//# sourceMappingURL=noise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Normalization layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { moments, serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Applies batch normalization on x given mean, var, beta and gamma.\n *\n * I.e. returns:\n *   `output = (x - mean) / (sqrt(var) + epsilon) * gamma + beta`\n *\n * @param x Input tensor.\n * @param mean Mean of batch.\n * @param variance Variance of batch.\n * @param beta Tensor with which to center the input.\n * @param gamma Tensor by which to scale the input.\n * @param epsilon Fuzz factor.\n * @returns The result of the batch normalization.\n */\nexport function batchNormalization(x, mean, variance, beta, gamma, epsilon = 1e-3) {\n    let out;\n    if (x.rank === 2) {\n        out = tfc.batchNorm2d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else if (x.rank === 3) {\n        // TODO(cais): Check rank; give proper error message.\n        out = tfc.batchNorm3d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else if (x.rank === 4) {\n        out = tfc.batchNorm4d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else {\n        throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} ` +\n            `yet`);\n    }\n    return out;\n}\n/**\n * Non-broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    return tidy(() => {\n        const meanAndVariance = tfc.moments(x, reductionAxes);\n        const mean = meanAndVariance.mean;\n        const variance = meanAndVariance.variance;\n        const normed = batchNormalization(x, mean, variance, beta, gamma, epsilon);\n        return [normed, mean, variance];\n    });\n}\n/**\n * Broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    return tidy(() => {\n        const meanAndVariance = tfc.moments(x, reductionAxes);\n        const mean = meanAndVariance.mean;\n        const variance = meanAndVariance.variance;\n        const targetShape = [];\n        for (const axis of math_utils.range(0, x.rank)) {\n            if (reductionAxes.indexOf(axis) !== -1) {\n                targetShape.push(1);\n            }\n            else {\n                targetShape.push(x.shape[axis]);\n            }\n        }\n        const broadcastMean = mean.reshape(targetShape);\n        const broadcastVariance = variance.reshape(targetShape);\n        const broadcastGamma = gamma == null ? null : gamma.reshape(targetShape);\n        const broadcastBeta = beta == null ? null : beta.reshape(targetShape);\n        const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon);\n        return [normed, mean, variance];\n    });\n}\n/**\n * Batch normalization for use in training (not inference).\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nexport function normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    if (util.arraysEqual(reductionAxes.slice().sort(), math_utils.range(0, x.rank - 1))) {\n        return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);\n    }\n    else {\n        return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);\n    }\n}\nexport class BatchNormalization extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.supportsMasking = true;\n        this.axis = args.axis == null ? -1 : args.axis;\n        this.momentum = args.momentum == null ? 0.99 : args.momentum;\n        this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n        this.center = args.center == null ? true : args.center;\n        this.scale = args.scale == null ? true : args.scale;\n        this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n        this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n        this.movingMeanInitializer =\n            getInitializer(args.movingMeanInitializer || 'zeros');\n        this.movingVarianceInitializer =\n            getInitializer(args.movingVarianceInitializer || 'ones');\n        this.betaConstraint = getConstraint(args.betaConstraint);\n        this.gammaConstraint = getConstraint(args.gammaConstraint);\n        this.betaRegularizer = getRegularizer(args.betaRegularizer);\n        this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const axis = this.axis >= 0 ? this.axis : (this.axis + inputShape.length);\n        const dim = inputShape[axis];\n        if (dim == null) {\n            throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but ` +\n                `the layer received an input with shape ` +\n                `${JSON.stringify(inputShape)}.`);\n        }\n        this.inputSpec =\n            [new InputSpec({ ndim: inputShape.length, axes: { [axis]: dim } })];\n        const shape = [dim];\n        if (this.scale) {\n            this.gamma = this.addWeight('gamma', shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint);\n        }\n        if (this.center) {\n            this.beta = this.addWeight('beta', shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint);\n        }\n        this.movingMean = this.addWeight('moving_mean', shape, null, this.movingMeanInitializer, null, false);\n        this.movingVariance = this.addWeight('moving_variance', shape, null, this.movingVarianceInitializer, null, false);\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            const input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            const ndim = inputShape.length;\n            const reductionAxes = math_utils.range(0, ndim);\n            const axis = this.axis >= 0 ? this.axis : (this.axis + ndim);\n            reductionAxes.splice(axis, 1);\n            const broadcastShape = generic_utils.pyListRepeat(1, ndim);\n            broadcastShape[axis] = inputShape[axis];\n            const sortedReductionAxes = reductionAxes.slice();\n            sortedReductionAxes.sort();\n            const needsBroadcasting = !util.arraysEqual(sortedReductionAxes, math_utils.range(0, ndim).slice(0, ndim - 1));\n            const normalizeInference = () => {\n                if (needsBroadcasting) {\n                    const broadcastMovingMean = this.movingMean.read().reshape(broadcastShape);\n                    const broadcastMovingVariance = this.movingVariance.read().reshape(broadcastShape);\n                    const broadcastBeta = this.center ? this.beta.read().reshape(broadcastShape) : null;\n                    const broadcastGamma = this.scale ? this.gamma.read().reshape(broadcastShape) : null;\n                    return batchNormalization(input, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon);\n                }\n                else {\n                    return batchNormalization(input, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);\n                }\n            };\n            if (!training) {\n                return normalizeInference();\n            }\n            const [normedTraining, mean, variance] = normalizeBatchInTraining(input, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon);\n            const doMovingAverage = (variable, value, momentum) => {\n                tfc.tidy(() => {\n                    const decay = 1 - momentum;\n                    const origValue = variable.read();\n                    const updateDelta = origValue.sub(value).mul(decay);\n                    variable.write(origValue.sub(updateDelta));\n                });\n            };\n            // Perform updates to moving mean and moving variance for training.\n            // Porting Note: In PyKeras, these updates to `movingMean` and\n            //   `movingAverage` are done as a deferred Graph, added to the `Layer`'s\n            //   `update`s using the `add_update()` method. Here we do it imperatively\n            //   and encapsulate the updates in a function that is invoked\n            //   immediately.\n            const updateMovingMeanAndVariance = () => {\n                doMovingAverage(this.movingMean, mean, this.momentum);\n                doMovingAverage(this.movingVariance, variance, this.momentum);\n            };\n            updateMovingMeanAndVariance();\n            return normedTraining;\n        });\n    }\n    getConfig() {\n        const config = {\n            axis: this.axis,\n            momentum: this.momentum,\n            epsilon: this.epsilon,\n            center: this.center,\n            scale: this.scale,\n            betaInitializer: serializeInitializer(this.betaInitializer),\n            gammaInitializer: serializeInitializer(this.gammaInitializer),\n            movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),\n            movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer),\n            betaRegularizer: serializeRegularizer(this.betaRegularizer),\n            gammaRegularizer: serializeRegularizer(this.gammaRegularizer),\n            betaConstraint: serializeConstraint(this.betaConstraint),\n            gammaConstraint: serializeConstraint(this.gammaConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nBatchNormalization.className = 'BatchNormalization';\nserialization.registerClass(BatchNormalization);\nexport class LayerNormalization extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.axis = args.axis == null ? -1 : args.axis;\n        if (typeof this.axis === 'number') {\n            if (!Number.isInteger(this.axis)) {\n                throw new Error(`Expected axis to be an integer, but received ${this.axis}`);\n            }\n        }\n        else if (Array.isArray(this.axis)) {\n            for (const axis of this.axis) {\n                if (!Number.isInteger(axis)) {\n                    throw new Error(`Expected axis to be an array of integers, ` +\n                        `but received ${JSON.stringify(this.axis)}`);\n                }\n            }\n        }\n        else {\n            throw new Error(`Expected axis to be an integer or an array of integers, ` +\n                `but received ${JSON.stringify(this.axis)}`);\n        }\n        this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n        this.center = args.center == null ? true : args.center;\n        this.scale = args.scale == null ? true : args.scale;\n        this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n        this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n        this.betaRegularizer = getRegularizer(args.betaRegularizer);\n        this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n        this.supportsMasking = true;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const nDims = inputShape.length;\n        // Convert axis to array and resolve negatives.\n        if (typeof this.axis === 'number') {\n            this.axis = [this.axis];\n        }\n        for (let i = 0; i < this.axis.length; ++i) {\n            if (this.axis[i] < 0) {\n                this.axis[i] += nDims;\n            }\n        }\n        // Further validate axes.\n        for (const axis of this.axis) {\n            if (axis < 0 || axis >= nDims) {\n                throw new Error(`Invalid axis: ${axis}`);\n            }\n        }\n        if (this.axis.length !== generic_utils.unique(this.axis).length) {\n            throw new Error(`Found duplicate axes in: ${this.axis}`);\n        }\n        const paramShape = this.axis.map(axis => inputShape[axis]);\n        const trainable = true;\n        if (this.scale) {\n            this.gamma = this.addWeight('gamma', paramShape, 'float32', this.gammaInitializer, this.gammaRegularizer, trainable);\n        }\n        else {\n            this.gamma = null;\n        }\n        if (this.center) {\n            this.beta = this.addWeight('beta', paramShape, 'float32', this.betaInitializer, this.betaRegularizer, trainable);\n        }\n        else {\n            this.beta = null;\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        const input = getExactlyOneTensor(inputs);\n        const inputShape = input.shape;\n        const nDims = inputShape.length;\n        return tidy(() => {\n            const keepDims = true;\n            let { mean, variance } = moments(input, this.axis, keepDims);\n            const broadcastShape = generic_utils.pyListRepeat(1, nDims);\n            for (const dim of this.axis) {\n                broadcastShape[dim] = inputShape[dim];\n            }\n            const broadcast = (v) => {\n                if (v != null && v.shape.length !== nDims &&\n                    this.axis !== [nDims - 1]) {\n                    return v.reshape(broadcastShape);\n                }\n                else {\n                    return v;\n                }\n            };\n            let scale = broadcast(this.gamma.read());\n            let offset = broadcast(this.beta.read());\n            // TODO(https://github.com/tensorflow/tfjs/issues/2120): The tiling below\n            // is a workaround for the limitation of core's batchNormalization?d don't\n            // support broadcasting in their gradients. In addition, the tiling is\n            // necessary to ensure correctness on the browser CPU backend regardless\n            // of forward or backward computation. Remove this workaround once the\n            // limitation is addressed. See .\n            const momentsTiling = [];\n            const scaleOffsetTiling = [];\n            for (let i = 0; i < nDims; ++i) {\n                if (this.axis.indexOf(i) !== -1) {\n                    momentsTiling.push(inputShape[i]);\n                    scaleOffsetTiling.push(1);\n                }\n                else {\n                    momentsTiling.push(1);\n                    scaleOffsetTiling.push(inputShape[i]);\n                }\n            }\n            mean = mean.tile(momentsTiling);\n            variance = variance.tile(momentsTiling);\n            scale = scale.tile(scaleOffsetTiling);\n            offset = offset.tile(scaleOffsetTiling);\n            return batchNormalization(input, mean, variance, offset, scale, this.epsilon);\n        });\n    }\n    getConfig() {\n        const config = {\n            axis: this.axis,\n            epsilon: this.epsilon,\n            center: this.center,\n            scale: this.scale,\n            betaInitializer: serializeInitializer(this.betaInitializer),\n            gammaInitializer: serializeInitializer(this.gammaInitializer),\n            betaRegularizer: serializeRegularizer(this.betaRegularizer),\n            gammaRegularizer: serializeRegularizer(this.gammaRegularizer)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nLayerNormalization.className = 'LayerNormalization';\nserialization.registerClass(LayerNormalization);\n//# sourceMappingURL=normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Padding Layers.\n */\n// Porting Note: In Python Keras, the padding layers are in convolutional.py,\n//   but we decided to put them in a separate file (padding.ts) for clarity.\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Pads the middle dimension of a 3D tensor.\n *\n * @param x Input `tf.Tensor` to be padded.\n * @param padding `Array` of 2 integers, how many zeros to add at the start and\n *   end of the middle dimension (i.e., dimension 1).\n * @return A padded 3D `tf.Tensor`.\n */\nexport function temporalPadding(x, padding) {\n    return tidy(() => {\n        if (x.rank !== 3) {\n            throw new ValueError(`temporalPadding expects input tensor to be 3-D, but received a ` +\n                `${x.rank}-D tensor.`);\n        }\n        if (padding == null) {\n            padding = [1, 1];\n        }\n        if (padding.length !== 2) {\n            throw new ValueError(`temporalPadding expects input padding pattern to be a length-2 ` +\n                `array, but received a length-${padding.length} array.`);\n        }\n        const pattern = [[0, 0], padding, [0, 0]];\n        return tfc.pad(x, pattern);\n    });\n}\n/**\n * Pads the 2nd and 3rd dimensions of a 4D tensor.\n *\n * @param x Input `tf.Tensor` to be padded.\n * @param padding `Array` of two `Array`s, each of which is an `Array` of two\n *   integers. The amount of padding at the beginning and end of the 2nd and 3rd\n *   dimensions, respectively.\n * @param dataFormat 'channelsLast' (default) or 'channelsFirst'.\n * @return Padded 4D `tf.Tensor`.\n */\nexport function spatial2dPadding(x, padding, dataFormat) {\n    return tidy(() => {\n        if (x.rank !== 4) {\n            throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ` +\n                `${x.rank}-D tensor.`);\n        }\n        if (padding == null) {\n            padding = [[1, 1], [1, 1]];\n        }\n        if (padding.length !== 2 || padding[0].length !== 2 ||\n            padding[1].length !== 2) {\n            throw new ValueError('spatial2dPadding expects `padding` to be an Array of two Arrays, ' +\n                'each of which is an Array of two integers.');\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (dataFormat !== 'channelsLast' && dataFormat !== 'channelsFirst') {\n            throw new ValueError(`Unknown data format: ${dataFormat}. ` +\n                `Supported data formats are 'channelsLast' and 'channelsFirst.`);\n        }\n        let pattern;\n        if (dataFormat === 'channelsFirst') {\n            pattern = [[0, 0], [0, 0], padding[0], padding[1]];\n        }\n        else {\n            pattern = [[0, 0], padding[0], padding[1], [0, 0]];\n        }\n        return tfc.pad(x, pattern);\n    });\n}\nexport class ZeroPadding2D extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.dataFormat =\n            args.dataFormat == null ? imageDataFormat() : args.dataFormat;\n        // TODO(cais): Maybe refactor the following logic surrounding `padding`\n        //   into a helper method.\n        if (args.padding == null) {\n            this.padding = [[1, 1], [1, 1]];\n        }\n        else if (typeof args.padding === 'number') {\n            this.padding =\n                [[args.padding, args.padding], [args.padding, args.padding]];\n        }\n        else {\n            args.padding = args.padding;\n            if (args.padding.length !== 2) {\n                throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but ` +\n                    `received a length-${args.padding.length} array.`);\n            }\n            let heightPadding;\n            let widthPadding;\n            if (typeof args.padding[0] === 'number') {\n                heightPadding = [args.padding[0], args.padding[0]];\n                widthPadding = [args.padding[1], args.padding[1]];\n            }\n            else {\n                args.padding = args.padding;\n                if (args.padding[0].length !== 2) {\n                    throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, ` +\n                        `but received a length-${args.padding[0].length} array.`);\n                }\n                heightPadding = args.padding[0];\n                if (args.padding[1].length !== 2) {\n                    throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, ` +\n                        `but received a length-${args.padding[1].length} array.`);\n                }\n                widthPadding = args.padding[1];\n            }\n            this.padding = [heightPadding, widthPadding];\n        }\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let rows;\n        let cols;\n        if (this.dataFormat === 'channelsFirst') {\n            if (inputShape[2] != null && inputShape[2] >= 0) {\n                rows = inputShape[2] + this.padding[0][0] + this.padding[0][1];\n            }\n            else {\n                rows = null;\n            }\n            if (inputShape[3] != null && inputShape[3] >= 0) {\n                cols = inputShape[3] + this.padding[1][0] + this.padding[1][1];\n            }\n            else {\n                cols = null;\n            }\n            return [inputShape[0], inputShape[1], rows, cols];\n        }\n        else {\n            if (inputShape[1] != null && inputShape[1] >= 0) {\n                rows = inputShape[1] + this.padding[0][0] + this.padding[0][1];\n            }\n            else {\n                rows = null;\n            }\n            if (inputShape[2] != null && inputShape[2] >= 0) {\n                cols = inputShape[2] + this.padding[1][0] + this.padding[1][1];\n            }\n            else {\n                cols = null;\n            }\n            return [inputShape[0], rows, cols, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => spatial2dPadding(getExactlyOneTensor(inputs), this.padding, this.dataFormat));\n    }\n    getConfig() {\n        const config = {\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nZeroPadding2D.className = 'ZeroPadding2D';\nserialization.registerClass(ZeroPadding2D);\n//# sourceMappingURL=padding.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Pooling Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode, checkPoolMode } from '../common';\nimport { InputSpec } from '../engine/topology';\nimport { Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { convOutputLength } from '../utils/conv_utils';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { preprocessConv2DInput, preprocessConv3DInput } from './convolutional';\n/**\n * 2D pooling.\n * @param x\n * @param poolSize\n * @param stridesdes strides. Defaults to [1, 1].\n * @param padding padding. Defaults to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param poolMode Mode of pooling. Defaults to 'max'.\n * @returns Result of the 2D pooling.\n */\nexport function pool2d(x, poolSize, strides, padding, dataFormat, poolMode) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        checkPoolMode(poolMode);\n        checkPaddingMode(padding);\n        if (strides == null) {\n            strides = [1, 1];\n        }\n        if (padding == null) {\n            padding = 'valid';\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (poolMode == null) {\n            poolMode = 'max';\n        }\n        // TODO(cais): Remove the preprocessing step once deeplearn.js supports\n        // dataFormat as an input argument.\n        x = preprocessConv2DInput(x, dataFormat); // x is NHWC after preprocessing.\n        let y;\n        const paddingString = (padding === 'same') ? 'same' : 'valid';\n        if (poolMode === 'max') {\n            // TODO(cais): Rank check?\n            y = tfc.maxPool(x, poolSize, strides, paddingString);\n        }\n        else { // 'avg'\n            // TODO(cais): Check the dtype and rank of x and give clear error message\n            //   if those are incorrect.\n            y = tfc.avgPool(\n            // TODO(cais): Rank check?\n            x, poolSize, strides, paddingString);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]); // NHWC -> NCHW.\n        }\n        return y;\n    });\n}\n/**\n * 3D pooling.\n * @param x\n * @param poolSize. Default to [1, 1, 1].\n * @param strides strides. Defaults to [1, 1, 1].\n * @param padding padding. Defaults to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param poolMode Mode of pooling. Defaults to 'max'.\n * @returns Result of the 3D pooling.\n */\nexport function pool3d(x, poolSize, strides, padding, dataFormat, poolMode) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        checkPoolMode(poolMode);\n        checkPaddingMode(padding);\n        if (strides == null) {\n            strides = [1, 1, 1];\n        }\n        if (padding == null) {\n            padding = 'valid';\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (poolMode == null) {\n            poolMode = 'max';\n        }\n        // x is NDHWC after preprocessing.\n        x = preprocessConv3DInput(x, dataFormat);\n        let y;\n        const paddingString = (padding === 'same') ? 'same' : 'valid';\n        if (poolMode === 'max') {\n            y = tfc.maxPool3d(x, poolSize, strides, paddingString);\n        }\n        else { // 'avg'\n            y = tfc.avgPool3d(x, poolSize, strides, paddingString);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 4, 1, 2, 3]); // NDHWC -> NCDHW.\n        }\n        return y;\n    });\n}\n/**\n * Abstract class for different pooling 1D layers.\n */\nexport class Pooling1D extends Layer {\n    /**\n     *\n     * @param args Parameters for the Pooling layer.\n     *\n     * config.poolSize defaults to 2.\n     */\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = 2;\n        }\n        super(args);\n        if (typeof args.poolSize === 'number') {\n            this.poolSize = [args.poolSize];\n        }\n        else if (Array.isArray(args.poolSize) &&\n            args.poolSize.length === 1 &&\n            typeof args.poolSize[0] === 'number') {\n            this.poolSize = args.poolSize;\n        }\n        else {\n            throw new ValueError(`poolSize for 1D convolutional layer must be a number or an ` +\n                `Array of a single number, but received ` +\n                `${JSON.stringify(args.poolSize)}`);\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else {\n            if (typeof args.strides === 'number') {\n                this.strides = [args.strides];\n            }\n            else if (Array.isArray(args.strides) &&\n                args.strides.length === 1 &&\n                typeof args.strides[0] === 'number') {\n                this.strides = args.strides;\n            }\n            else {\n                throw new ValueError(`strides for 1D convolutional layer must be a number or an ` +\n                    `Array of a single number, but received ` +\n                    `${JSON.stringify(args.strides)}`);\n            }\n        }\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const length = convOutputLength(inputShape[1], this.poolSize[0], this.padding, this.strides[0]);\n        return [inputShape[0], length, inputShape[2]];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Add dummy last dimension.\n            inputs = K.expandDims(getExactlyOneTensor(inputs), 2);\n            const output = this.poolingFunction(getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, 'channelsLast');\n            // Remove dummy last dimension.\n            return tfc.squeeze(output, [2]);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling1D extends Pooling1D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling1D.className = 'MaxPooling1D';\nserialization.registerClass(MaxPooling1D);\nexport class AveragePooling1D extends Pooling1D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling1D.className = 'AveragePooling1D';\nserialization.registerClass(AveragePooling1D);\n/**\n * Abstract class for different pooling 2D layers.\n */\nexport class Pooling2D extends Layer {\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = [2, 2];\n        }\n        super(args);\n        this.poolSize = Array.isArray(args.poolSize) ?\n            args.poolSize :\n            [args.poolSize, args.poolSize];\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else if (Array.isArray(args.strides)) {\n            if (args.strides.length !== 2) {\n                throw new ValueError(`If the strides property of a 2D pooling layer is an Array, ` +\n                    `it is expected to have a length of 2, but received length ` +\n                    `${args.strides.length}.`);\n            }\n            this.strides = args.strides;\n        }\n        else {\n            // `config.strides` is a number.\n            this.strides = [args.strides, args.strides];\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let rows = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        let cols = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        rows =\n            convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]);\n        cols =\n            convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], inputShape[1], rows, cols];\n        }\n        else {\n            return [inputShape[0], rows, cols, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n            dataFormat: this.dataFormat\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling2D extends Pooling2D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling2D.className = 'MaxPooling2D';\nserialization.registerClass(MaxPooling2D);\nexport class AveragePooling2D extends Pooling2D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling2D.className = 'AveragePooling2D';\nserialization.registerClass(AveragePooling2D);\n/**\n * Abstract class for different pooling 3D layers.\n */\nexport class Pooling3D extends Layer {\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = [2, 2, 2];\n        }\n        super(args);\n        this.poolSize = Array.isArray(args.poolSize) ?\n            args.poolSize :\n            [args.poolSize, args.poolSize, args.poolSize];\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else if (Array.isArray(args.strides)) {\n            if (args.strides.length !== 3) {\n                throw new ValueError(`If the strides property of a 3D pooling layer is an Array, ` +\n                    `it is expected to have a length of 3, but received length ` +\n                    `${args.strides.length}.`);\n            }\n            this.strides = args.strides;\n        }\n        else {\n            // `config.strides` is a number.\n            this.strides = [args.strides, args.strides, args.strides];\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 5 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let depths = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        let rows = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        let cols = this.dataFormat === 'channelsFirst' ? inputShape[4] : inputShape[3];\n        depths = convOutputLength(depths, this.poolSize[0], this.padding, this.strides[0]);\n        rows =\n            convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]);\n        cols =\n            convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], inputShape[1], depths, rows, cols];\n        }\n        else {\n            return [inputShape[0], depths, rows, cols, inputShape[4]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n            dataFormat: this.dataFormat\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling3D extends Pooling3D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool3d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling3D.className = 'MaxPooling3D';\nserialization.registerClass(MaxPooling3D);\nexport class AveragePooling3D extends Pooling3D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool3d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling3D.className = 'AveragePooling3D';\nserialization.registerClass(AveragePooling3D);\n/**\n * Abstract class for different global pooling 1D layers.\n */\nexport class GlobalPooling1D extends Layer {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n    }\n    computeOutputShape(inputShape) {\n        return [inputShape[0], inputShape[2]];\n    }\n    call(inputs, kwargs) {\n        throw new NotImplementedError();\n    }\n}\nexport class GlobalAveragePooling1D extends GlobalPooling1D {\n    constructor(args) {\n        super(args || {});\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            return tfc.mean(input, 1);\n        });\n    }\n}\n/** @nocollapse */\nGlobalAveragePooling1D.className = 'GlobalAveragePooling1D';\nserialization.registerClass(GlobalAveragePooling1D);\nexport class GlobalMaxPooling1D extends GlobalPooling1D {\n    constructor(args) {\n        super(args || {});\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            return tfc.max(input, 1);\n        });\n    }\n}\n/** @nocollapse */\nGlobalMaxPooling1D.className = 'GlobalMaxPooling1D';\nserialization.registerClass(GlobalMaxPooling1D);\n/**\n * Abstract class for different global pooling 2D layers.\n */\nexport class GlobalPooling2D extends Layer {\n    constructor(args) {\n        super(args);\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = inputShape;\n        if (this.dataFormat === 'channelsLast') {\n            return [inputShape[0], inputShape[3]];\n        }\n        else {\n            return [inputShape[0], inputShape[1]];\n        }\n    }\n    call(inputs, kwargs) {\n        throw new NotImplementedError();\n    }\n    getConfig() {\n        const config = { dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class GlobalAveragePooling2D extends GlobalPooling2D {\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                return tfc.mean(input, [1, 2]);\n            }\n            else {\n                return tfc.mean(input, [2, 3]);\n            }\n        });\n    }\n}\n/** @nocollapse */\nGlobalAveragePooling2D.className = 'GlobalAveragePooling2D';\nserialization.registerClass(GlobalAveragePooling2D);\nexport class GlobalMaxPooling2D extends GlobalPooling2D {\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                return tfc.max(input, [1, 2]);\n            }\n            else {\n                return tfc.max(input, [2, 3]);\n            }\n        });\n    }\n}\n/** @nocollapse */\nGlobalMaxPooling2D.className = 'GlobalMaxPooling2D';\nserialization.registerClass(GlobalMaxPooling2D);\n//# sourceMappingURL=pooling.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Layers that augment the functionality of a base layer.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { InputSpec, Layer, SymbolicTensor } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { VALID_BIDIRECTIONAL_MERGE_MODES } from '../keras_format/common';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { rnn, standardizeArgs } from './recurrent';\nimport { deserialize } from './serialization';\n/**\n * Abstract wrapper base class.\n *\n * Wrappers take another layer and augment it in various ways.\n * Do not use this class as a layer, it is only an abstract base class.\n * Two usable wrappers are the `TimeDistributed` and `Bidirectional` wrappers.\n */\nexport class Wrapper extends Layer {\n    constructor(args) {\n        // Porting Note: In PyKeras, `self.layer` is set prior to the calling\n        //   `super()`. But we can't do that here due to TypeScript's restriction.\n        //   See: https://github.com/Microsoft/TypeScript/issues/8277\n        //   As a result, we have to add checks in `get trainable()` and\n        //   `set trainable()` below in order to prevent using `this.layer` when\n        //   its value is `undefined`. The super constructor does use the getter\n        //   and the setter of `this.layer`.\n        super(args);\n        this.layer = args.layer;\n    }\n    build(inputShape) {\n        this.built = true;\n    }\n    // TODO(cais): Implement activityRegularizer getter.\n    get trainable() {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        if (this.layer != null) {\n            return this.layer.trainable;\n        }\n        else {\n            return false;\n        }\n    }\n    set trainable(value) {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        if (this.layer != null) {\n            this.layer.trainable = value;\n        }\n    }\n    get trainableWeights() {\n        return this.layer.trainableWeights;\n    }\n    // TODO(cais): Implement setter for trainableWeights.\n    get nonTrainableWeights() {\n        return this.layer.nonTrainableWeights;\n    }\n    // TODO(cais): Implement setter for nonTrainableWeights.\n    get updates() {\n        // tslint:disable-next-line:no-any\n        return this.layer._updates;\n    }\n    // TODO(cais): Implement getUpdatesFor().\n    get losses() {\n        return this.layer.losses;\n    }\n    // TODO(cais): Implement getLossesFor().\n    getWeights() {\n        return this.layer.getWeights();\n    }\n    setWeights(weights) {\n        this.layer.setWeights(weights);\n    }\n    getConfig() {\n        const config = {\n            'layer': {\n                'className': this.layer.getClassName(),\n                'config': this.layer.getConfig(),\n            }\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.layer != null) {\n            this.layer.setFastWeightInitDuringBuild(value);\n        }\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const layerConfig = config['layer'];\n        const layer = deserialize(layerConfig, customObjects);\n        delete config['layer'];\n        const newConfig = { layer };\n        Object.assign(newConfig, config);\n        return new cls(newConfig);\n    }\n}\nexport class TimeDistributed extends Wrapper {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < 3) {\n            throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received ` +\n                `input shape ${JSON.stringify(inputShape)}`);\n        }\n        this.inputSpec = [{ shape: inputShape }];\n        const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        if (!this.layer.built) {\n            this.layer.build(childInputShape);\n            this.layer.built = true;\n        }\n        super.build(inputShape);\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        const childOutputShape = this.layer.computeOutputShape(childInputShape);\n        const timesteps = inputShape[1];\n        return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1));\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            // TODO(cais): Add 'training' and 'useLearningPhase' to kwargs.\n            inputs = getExactlyOneTensor(inputs);\n            // Porting Note: In tfjs-layers, `inputs` are always concrete tensor\n            // values. Hence the inputs can't have an undetermined first (batch)\n            // dimension, which is why we always use the K.rnn approach here.\n            const step = (inputs, states) => {\n                // TODO(cais): Add useLearningPhase.\n                // NOTE(cais): `layer.call` may return a length-1 array of Tensor in\n                //   some cases (e.g., `layer` is a `Sequential` instance), which is\n                //   why `getExactlyOneTensor` is used below.\n                const output = getExactlyOneTensor(this.layer.call(inputs, kwargs));\n                return [output, []];\n            };\n            const rnnOutputs = rnn(step, inputs, [], false /* goBackwards */, null /* mask */, null /* constants */, false /* unroll */, true /* needPerStepOutputs */);\n            const y = rnnOutputs[1];\n            // TODO(cais): Add activity regularization.\n            // TODO(cais): Add useLearningPhase.\n            return y;\n        });\n    }\n}\n/** @nocollapse */\nTimeDistributed.className = 'TimeDistributed';\nserialization.registerClass(TimeDistributed);\nexport function checkBidirectionalMergeMode(value) {\n    generic_utils.checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, 'BidirectionalMergeMode', value);\n}\nconst DEFAULT_BIDIRECTIONAL_MERGE_MODE = 'concat';\nexport class Bidirectional extends Wrapper {\n    constructor(args) {\n        super(args);\n        // Note: When creating `this.forwardLayer`, the original Layer object\n        //   (`config.layer`) ought to be cloned. This is why we call\n        //   `getConfig()` followed by `deserialize()`. Without this cloning,\n        //   the layer names saved during serialization will incorrectly contain\n        //   the 'forward_' prefix. In Python Keras, this is done using\n        //   `copy.copy` (shallow copy), which does not have a simple equivalent\n        //   in JavaScript. JavaScript's `Object.assign()` does not copy\n        //   methods.\n        const layerConfig = args.layer.getConfig();\n        const forwDict = {};\n        forwDict['className'] = args.layer.getClassName();\n        forwDict['config'] = layerConfig;\n        this.forwardLayer = deserialize(forwDict);\n        layerConfig['goBackwards'] =\n            layerConfig['goBackwards'] === true ? false : true;\n        const backDict = {};\n        backDict['className'] = args.layer.getClassName();\n        backDict['config'] = layerConfig;\n        this.backwardLayer = deserialize(backDict);\n        this.forwardLayer.name = 'forward_' + this.forwardLayer.name;\n        this.backwardLayer.name = 'backward_' + this.backwardLayer.name;\n        this.mergeMode = args.mergeMode === undefined ?\n            DEFAULT_BIDIRECTIONAL_MERGE_MODE :\n            args.mergeMode;\n        checkBidirectionalMergeMode(this.mergeMode);\n        if (args.weights) {\n            throw new NotImplementedError('weights support is not implemented for Bidirectional layer yet.');\n        }\n        this._stateful = args.layer.stateful;\n        this.returnSequences = args.layer.returnSequences;\n        this.returnState = args.layer.returnState;\n        this.supportsMasking = true;\n        this._trainable = true;\n        this.inputSpec = args.layer.inputSpec;\n        this.numConstants = null;\n    }\n    get trainable() {\n        return this._trainable;\n    }\n    set trainable(value) {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        this._trainable = value;\n        if (this.forwardLayer != null) {\n            this.forwardLayer.trainable = value;\n        }\n        if (this.backwardLayer != null) {\n            this.backwardLayer.trainable = value;\n        }\n    }\n    getWeights() {\n        return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights());\n    }\n    setWeights(weights) {\n        const numWeights = weights.length;\n        const numeightsOver2 = Math.floor(numWeights / 2);\n        this.forwardLayer.setWeights(weights.slice(0, numeightsOver2));\n        this.backwardLayer.setWeights(weights.slice(numeightsOver2));\n    }\n    computeOutputShape(inputShape) {\n        let layerShapes = this.forwardLayer.computeOutputShape(inputShape);\n        if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) {\n            layerShapes = [layerShapes];\n        }\n        layerShapes = layerShapes;\n        let outputShape;\n        let outputShapes;\n        let stateShape;\n        if (this.returnState) {\n            stateShape = layerShapes.slice(1);\n            outputShape = layerShapes[0];\n        }\n        else {\n            outputShape = layerShapes[0];\n        }\n        outputShape = outputShape;\n        if (this.mergeMode === 'concat') {\n            outputShape[outputShape.length - 1] *= 2;\n            outputShapes = [outputShape];\n        }\n        else if (this.mergeMode == null) {\n            outputShapes = [outputShape, outputShape.slice()];\n        }\n        else {\n            outputShapes = [outputShape];\n        }\n        if (this.returnState) {\n            if (this.mergeMode == null) {\n                return outputShapes.concat(stateShape).concat(stateShape.slice());\n            }\n            return [outputShape].concat(stateShape).concat(stateShape.slice());\n        }\n        return generic_utils.singletonOrArray(outputShapes);\n    }\n    apply(inputs, kwargs) {\n        let initialState = kwargs == null ? null : kwargs['initialState'];\n        let constants = kwargs == null ? null : kwargs['constants'];\n        if (kwargs == null) {\n            kwargs = {};\n        }\n        const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n        inputs = standardized.inputs;\n        initialState = standardized.initialState;\n        constants = standardized.constants;\n        if (Array.isArray(inputs)) {\n            initialState = inputs.slice(1);\n            inputs = inputs[0];\n        }\n        if ((initialState == null || initialState.length === 0) &&\n            constants == null) {\n            return super.apply(inputs, kwargs);\n        }\n        const additionalInputs = [];\n        const additionalSpecs = [];\n        if (initialState != null) {\n            const numStates = initialState.length;\n            if (numStates % 2 > 0) {\n                throw new ValueError('When passing `initialState` to a Bidrectional RNN, ' +\n                    'the state should be an Array containing the states of ' +\n                    'the underlying RNNs.');\n            }\n            kwargs['initialState'] = initialState;\n            additionalInputs.push(...initialState);\n            const stateSpecs = initialState\n                .map(state => new InputSpec({ shape: state.shape }));\n            this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2);\n            this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2);\n            additionalSpecs.push(...stateSpecs);\n        }\n        if (constants != null) {\n            throw new NotImplementedError('Support for constants in Bidirectional layers is not ' +\n                'implemented yet.');\n        }\n        const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor;\n        for (const tensor of additionalInputs) {\n            if (tensor instanceof SymbolicTensor !== isSymbolicTensor) {\n                throw new ValueError('The initial state of a Bidirectional layer cannot be ' +\n                    'specified as a mix of symbolic and non-symbolic tensors');\n            }\n        }\n        if (isSymbolicTensor) {\n            // Compute the full input and specs, including the states.\n            const fullInput = [inputs].concat(additionalInputs);\n            const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n            // Perform the call temporarily and replace inputSpec.\n            // Note: with initial states symbolic calls and non-symbolic calls to\n            // this method differ in how the initial states are passed. For\n            // symbolic calls, the initial states are passed in the first arg, as\n            // an Array of SymbolicTensors; for non-symbolic calls, they are\n            // passed in the second arg as a part of the kwargs. Hence the need to\n            // temporarily modify inputSpec here.\n            // TODO(cais): Make refactoring so that this hacky code below is no\n            // longer needed.\n            const originalInputSpec = this.inputSpec;\n            this.inputSpec = fullInputSpec;\n            const output = super.apply(fullInput, kwargs);\n            this.inputSpec = originalInputSpec;\n            return output;\n        }\n        else {\n            return super.apply(inputs, kwargs);\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const initialState = kwargs['initialState'];\n            let y;\n            let yRev;\n            if (initialState == null) {\n                y = this.forwardLayer.call(inputs, kwargs);\n                yRev = this.backwardLayer.call(inputs, kwargs);\n            }\n            else {\n                const forwardState = initialState.slice(0, initialState.length / 2);\n                const backwardState = initialState.slice(initialState.length / 2);\n                y = this.forwardLayer.call(inputs, Object.assign(kwargs, { initialState: forwardState }));\n                yRev = this.backwardLayer.call(inputs, Object.assign(kwargs, { initialState: backwardState }));\n            }\n            let states;\n            if (this.returnState) {\n                if (Array.isArray(y)) {\n                    states = y.slice(1).concat(yRev.slice(1));\n                }\n                else {\n                }\n                y = y[0];\n                yRev = yRev[0];\n            }\n            if (this.returnSequences) {\n                yRev = tfc.reverse(yRev, 1);\n            }\n            let output;\n            if (this.mergeMode === 'concat') {\n                output = K.concatenate([y, yRev]);\n            }\n            else if (this.mergeMode === 'sum') {\n                output = tfc.add(y, yRev);\n            }\n            else if (this.mergeMode === 'ave') {\n                output = tfc.mul(.5, tfc.add(y, yRev));\n            }\n            else if (this.mergeMode === 'mul') {\n                output = tfc.mul(y, yRev);\n            }\n            else if (this.mergeMode == null) {\n                output = [y, yRev];\n            }\n            // TODO(cais): Properly set learning phase.\n            if (this.returnState) {\n                if (this.mergeMode == null) {\n                    return output.concat(states);\n                }\n                return [output].concat(states);\n            }\n            return output;\n        });\n    }\n    resetStates(states) {\n        this.forwardLayer.resetStates();\n        this.backwardLayer.resetStates();\n    }\n    build(inputShape) {\n        nameScope(this.forwardLayer.name, () => {\n            this.forwardLayer.build(inputShape);\n        });\n        nameScope(this.backwardLayer.name, () => {\n            this.backwardLayer.build(inputShape);\n        });\n        this.built = true;\n    }\n    computeMask(inputs, mask) {\n        if (Array.isArray(mask)) {\n            mask = mask[0];\n        }\n        let outputMask;\n        if (this.returnSequences) {\n            if (this.mergeMode == null) {\n                outputMask = [mask, mask];\n            }\n            else {\n                outputMask = mask;\n            }\n        }\n        else {\n            if (this.mergeMode == null) {\n                outputMask = [null, null];\n            }\n            else {\n                outputMask = null;\n            }\n        }\n        if (this.returnState) {\n            const states = this.forwardLayer.states;\n            const stateMask = states.map(state => null);\n            if (Array.isArray(outputMask)) {\n                return outputMask.concat(stateMask).concat(stateMask);\n            }\n            else {\n                return [outputMask].concat(stateMask).concat(stateMask);\n            }\n        }\n        else {\n            return outputMask;\n        }\n    }\n    get trainableWeights() {\n        return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights);\n    }\n    get nonTrainableWeights() {\n        return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights);\n    }\n    // TODO(cais): Implement constraints().\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.forwardLayer != null) {\n            this.forwardLayer.setFastWeightInitDuringBuild(value);\n        }\n        if (this.backwardLayer != null) {\n            this.backwardLayer.setFastWeightInitDuringBuild(value);\n        }\n    }\n    getConfig() {\n        const config = {\n            'mergeMode': this.mergeMode,\n        };\n        // TODO(cais): Add logic for `numConstants` once the property is added.\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        const rnnLayer = deserialize(config['layer']);\n        delete config['layer'];\n        // TODO(cais): Add logic for `numConstants` once the property is added.\n        if (config['numConstants'] != null) {\n            throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants ` +\n                `present is not supported yet.`);\n        }\n        // tslint:disable-next-line:no-any\n        const newConfig = config;\n        newConfig['layer'] = rnnLayer;\n        return new cls(newConfig);\n    }\n}\n/** @nocollapse */\nBidirectional.className = 'Bidirectional';\nserialization.registerClass(Bidirectional);\n//# sourceMappingURL=wrappers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { InputLayer } from './engine/input_layer';\nimport { Layer } from './engine/topology';\nimport { input } from './exports';\nimport { ELU, LeakyReLU, PReLU, ReLU, Softmax, ThresholdedReLU } from './layers/advanced_activations';\nimport { Conv1D, Conv2D, Conv2DTranspose, Conv3D, Cropping2D, SeparableConv2D, UpSampling2D } from './layers/convolutional';\nimport { DepthwiseConv2D } from './layers/convolutional_depthwise';\nimport { ConvLSTM2D, ConvLSTM2DCell } from './layers/convolutional_recurrent';\nimport { Activation, Dense, Dropout, Flatten, Masking, Permute, RepeatVector, Reshape, SpatialDropout1D } from './layers/core';\nimport { Embedding } from './layers/embeddings';\nimport { Add, Average, Concatenate, Dot, Maximum, Minimum, Multiply } from './layers/merge';\nimport { AlphaDropout, GaussianDropout, GaussianNoise } from './layers/noise';\nimport { BatchNormalization, LayerNormalization } from './layers/normalization';\nimport { ZeroPadding2D } from './layers/padding';\nimport { AveragePooling1D, AveragePooling2D, AveragePooling3D, GlobalAveragePooling1D, GlobalAveragePooling2D, GlobalMaxPooling1D, GlobalMaxPooling2D, MaxPooling1D, MaxPooling2D, MaxPooling3D } from './layers/pooling';\nimport { GRU, GRUCell, LSTM, LSTMCell, RNN, RNNCell, SimpleRNN, SimpleRNNCell, StackedRNNCells } from './layers/recurrent';\nimport { Bidirectional, TimeDistributed } from './layers/wrappers';\n// TODO(cais): Add doc string to all the public static functions in this\n//   class; include exectuable JavaScript code snippets where applicable\n//   (b/74074458).\n// Input Layer.\n/**\n * An input layer is an entry point into a `tf.LayersModel`.\n *\n * `InputLayer` is generated automatically for `tf.Sequential`` models by\n * specifying the `inputshape` or `batchInputShape` for the first layer.  It\n * should not be specified explicitly. However, it can be useful sometimes,\n * e.g., when constructing a sequential model from a subset of another\n * sequential model's layers. Like the code snippet below shows.\n *\n * ```js\n * // Define a model which simply adds two inputs.\n * const model1 = tf.sequential();\n * model1.add(tf.layers.dense({inputShape: [4], units: 3, activation: 'relu'}));\n * model1.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));\n * model1.summary();\n * model1.predict(tf.zeros([1, 4])).print();\n *\n * // Construct another model, reusing the second layer of `model1` while\n * // not using the first layer of `model1`. Note that you cannot add the second\n * // layer of `model` directly as the first layer of the new sequential model,\n * // because doing so will lead to an error related to the fact that the layer\n * // is not an input layer. Instead, you need to create an `inputLayer` and add\n * // it to the new sequential model before adding the reused layer.\n * const model2 = tf.sequential();\n * // Use an inputShape that matches the input shape of `model1`'s second\n * // layer.\n * model2.add(tf.layers.inputLayer({inputShape: [3]}));\n * model2.add(model1.layers[1]);\n * model2.summary();\n * model2.predict(tf.zeros([1, 3])).print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Inputs', namespace: 'layers'}\n */\nexport function inputLayer(args) {\n    return new InputLayer(args);\n}\n// Advanced Activation Layers.\n/**\n * Exponetial Linear Unit (ELU).\n *\n * It follows:\n * `f(x) =  alpha * (exp(x) - 1.) for x < 0`,\n * `f(x) = x for x >= 0`.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * References:\n *   - [Fast and Accurate Deep Network Learning by Exponential Linear Units\n * (ELUs)](https://arxiv.org/abs/1511.07289v1)\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function elu(args) {\n    return new ELU(args);\n}\n/**\n * Rectified Linear Unit activation function.\n *\n * Input shape:\n *   Arbitrary. Use the config field `inputShape` (Array of integers, does\n *   not include the sample axis) when using this layer as the first layer\n *   in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function reLU(args) {\n    return new ReLU(args);\n}\n/**\n * Leaky version of a rectified linear unit.\n *\n * It allows a small gradient when the unit is not active:\n * `f(x) = alpha * x for x < 0.`\n * `f(x) = x for x >= 0.`\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function leakyReLU(args) {\n    return new LeakyReLU(args);\n}\n/**\n * Parameterized version of a leaky rectified linear unit.\n *\n * It follows\n * `f(x) = alpha * x for x < 0.`\n * `f(x) = x for x >= 0.`\n * wherein `alpha` is a trainable weight.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function prelu(args) {\n    return new PReLU(args);\n}\n/**\n * Softmax activation layer.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function softmax(args) {\n    return new Softmax(args);\n}\n/**\n * Thresholded Rectified Linear Unit.\n *\n * It follows:\n * `f(x) = x for x > theta`,\n * `f(x) = 0 otherwise`.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * References:\n *   - [Zero-Bias Autoencoders and the Benefits of Co-Adapting\n * Features](http://arxiv.org/abs/1402.3337)\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function thresholdedReLU(args) {\n    return new ThresholdedReLU(args);\n}\n// Convolutional Layers.\n/**\n * 1D convolution layer (e.g., temporal convolution).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input over a single spatial (or temporal) dimension\n * to produce a tensor of outputs.\n *\n * If `use_bias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model, provide an\n * `inputShape` argument `Array` or `null`.\n *\n * For example, `inputShape` would be:\n * - `[10, 128]` for sequences of 10 vectors of 128-dimensional vectors\n * - `[null, 128]` for variable-length sequences of 128-dimensional vectors.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional',  namespace: 'layers'}\n */\nexport function conv1d(args) {\n    return new Conv1D(args);\n}\n/**\n * 2D convolution layer (e.g. spatial convolution over images).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input to produce a tensor of outputs.\n *\n * If `useBias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model,\n * provide the keyword argument `inputShape`\n * (Array of integers, does not include the sample axis),\n * e.g. `inputShape=[128, 128, 3]` for 128x128 RGB pictures\n * in `dataFormat='channelsLast'`.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv2d(args) {\n    return new Conv2D(args);\n}\n/**\n * Transposed convolutional layer (sometimes called Deconvolution).\n *\n * The need for transposed convolutions generally arises\n * from the desire to use a transformation going in the opposite direction of\n * a normal convolution, i.e., from something that has the shape of the output\n * of some convolution to something that has the shape of its input while\n * maintaining a connectivity pattern that is compatible with said\n * convolution.\n *\n * When using this layer as the first layer in a model, provide the\n * configuration `inputShape` (`Array` of integers, does not include the\n * sample axis), e.g., `inputShape: [128, 128, 3]` for 128x128 RGB pictures in\n * `dataFormat: 'channelsLast'`.\n *\n * Input shape:\n *   4D tensor with shape:\n *   `[batch, channels, rows, cols]` if `dataFormat` is `'channelsFirst'`.\n *   or 4D tensor with shape\n *   `[batch, rows, cols, channels]` if `dataFormat` is `'channelsLast`.\n *\n * Output shape:\n *   4D tensor with shape:\n *   `[batch, filters, newRows, newCols]` if `dataFormat` is\n * `'channelsFirst'`. or 4D tensor with shape:\n *   `[batch, newRows, newCols, filters]` if `dataFormat` is `'channelsLast'`.\n *\n * References:\n *   - [A guide to convolution arithmetic for deep\n * learning](https://arxiv.org/abs/1603.07285v1)\n *   - [Deconvolutional\n * Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv2dTranspose(args) {\n    return new Conv2DTranspose(args);\n}\n/**\n * 3D convolution layer (e.g. spatial convolution over volumes).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input to produce a tensor of outputs.\n *\n * If `useBias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model,\n * provide the keyword argument `inputShape`\n * (Array of integers, does not include the sample axis),\n * e.g. `inputShape=[128, 128, 128, 1]` for 128x128x128 grayscale volumes\n * in `dataFormat='channelsLast'`.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv3d(args) {\n    return new Conv3D(args);\n}\n/**\n * Depthwise separable 2D convolution.\n *\n * Separable convolution consists of first performing\n * a depthwise spatial convolution\n * (which acts on each input channel separately)\n * followed by a pointwise convolution which mixes together the resulting\n * output channels. The `depthMultiplier` argument controls how many\n * output channels are generated per input channel in the depthwise step.\n *\n * Intuitively, separable convolutions can be understood as\n * a way to factorize a convolution kernel into two smaller kernels,\n * or as an extreme version of an Inception block.\n *\n * Input shape:\n *   4D tensor with shape:\n *     `[batch, channels, rows, cols]` if data_format='channelsFirst'\n *   or 4D tensor with shape:\n *     `[batch, rows, cols, channels]` if data_format='channelsLast'.\n *\n * Output shape:\n *   4D tensor with shape:\n *     `[batch, filters, newRows, newCols]` if data_format='channelsFirst'\n *   or 4D tensor with shape:\n *     `[batch, newRows, newCols, filters]` if data_format='channelsLast'.\n *     `rows` and `cols` values might have changed due to padding.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function separableConv2d(args) {\n    return new SeparableConv2D(args);\n}\n/**\n * Cropping layer for 2D input (e.g., image).\n *\n * This layer can crop an input\n * at the top, bottom, left and right side of an image tensor.\n *\n * Input shape:\n *   4D tensor with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, rows, cols, channels]`\n *   - If `data_format` is `\"channels_first\"`:\n *     `[batch, channels, rows, cols]`.\n *\n * Output shape:\n *   4D with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, croppedRows, croppedCols, channels]`\n *    - If `dataFormat` is `\"channelsFirst\"`:\n *     `[batch, channels, croppedRows, croppedCols]`.\n *\n * Examples\n * ```js\n *\n * const model = tf.sequential();\n * model.add(tf.layers.cropping2D({cropping:[[2, 2], [2, 2]],\n *                                inputShape: [128, 128, 3]}));\n * //now output shape is [batch, 124, 124, 3]\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function cropping2D(args) {\n    return new Cropping2D(args);\n}\n/**\n * Upsampling layer for 2D inputs.\n *\n * Repeats the rows and columns of the data\n * by size[0] and size[1] respectively.\n *\n *\n * Input shape:\n *    4D tensor with shape:\n *     - If `dataFormat` is `\"channelsLast\"`:\n *         `[batch, rows, cols, channels]`\n *     - If `dataFormat` is `\"channelsFirst\"`:\n *        `[batch, channels, rows, cols]`\n *\n * Output shape:\n *     4D tensor with shape:\n *     - If `dataFormat` is `\"channelsLast\"`:\n *        `[batch, upsampledRows, upsampledCols, channels]`\n *     - If `dataFormat` is `\"channelsFirst\"`:\n *         `[batch, channels, upsampledRows, upsampledCols]`\n *\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function upSampling2d(args) {\n    return new UpSampling2D(args);\n}\n// Convolutional(depthwise) Layers.\n/**\n * Depthwise separable 2D convolution.\n *\n * Depthwise Separable convolutions consists in performing just the first step\n * in a depthwise spatial convolution (which acts on each input channel\n * separately). The `depthMultplier` argument controls how many output channels\n * are generated per input channel in the depthwise step.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function depthwiseConv2d(args) {\n    return new DepthwiseConv2D(args);\n}\n// Basic Layers.\n/**\n * Applies an activation function to an output.\n *\n * This layer applies element-wise activation function.  Other layers, notably\n * `dense` can also apply activation functions.  Use this isolated activation\n * function to extract the values before and after the\n * activation. For instance:\n *\n * ```js\n * const input = tf.input({shape: [5]});\n * const denseLayer = tf.layers.dense({units: 1});\n * const activationLayer = tf.layers.activation({activation: 'relu6'});\n *\n * // Obtain the output symbolic tensors by applying the layers in order.\n * const denseOutput = denseLayer.apply(input);\n * const activationOutput = activationLayer.apply(denseOutput);\n *\n * // Create the model based on the inputs.\n * const model = tf.model({\n *     inputs: input,\n *     outputs: [denseOutput, activationOutput]\n * });\n *\n * // Collect both outputs and print separately.\n * const [denseOut, activationOut] = model.predict(tf.randomNormal([6, 5]));\n * denseOut.print();\n * activationOut.print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function activation(args) {\n    return new Activation(args);\n}\n/**\n * Creates a dense (fully connected) layer.\n *\n * This layer implements the operation:\n *   `output = activation(dot(input, kernel) + bias)`\n *\n * `activation` is the element-wise activation function\n *   passed as the `activation` argument.\n *\n * `kernel` is a weights matrix created by the layer.\n *\n * `bias` is a bias vector created by the layer (only applicable if `useBias`\n * is `true`).\n *\n * **Input shape:**\n *\n *   nD `tf.Tensor` with shape: `(batchSize, ..., inputDim)`.\n *\n *   The most common situation would be\n *   a 2D input with shape `(batchSize, inputDim)`.\n *\n * **Output shape:**\n *\n *   nD tensor with shape: `(batchSize, ..., units)`.\n *\n *   For instance, for a 2D input with shape `(batchSize, inputDim)`,\n *   the output would have shape `(batchSize, units)`.\n *\n * Note: if the input to the layer has a rank greater than 2, then it is\n * flattened prior to the initial dot product with the kernel.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function dense(args) {\n    return new Dense(args);\n}\n/**\n * Applies\n * [dropout](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf) to\n * the input.\n *\n * Dropout consists in randomly setting a fraction `rate` of input units to 0 at\n * each update during training time, which helps prevent overfitting.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function dropout(args) {\n    return new Dropout(args);\n}\n/**\n * Spatial 1D version of Dropout.\n *\n * This Layer type performs the same function as the Dropout layer, but it drops\n * entire 1D feature maps instead of individual elements. For example, if an\n * input example consists of 3 timesteps and the feature map for each timestep\n * has a size of 4, a `spatialDropout1d` layer may zero out the feature maps\n * of the 1st timesteps and 2nd timesteps completely while sparing all feature\n * elements of the 3rd timestep.\n *\n * If adjacent frames (timesteps) are strongly correlated (as is normally the\n * case in early convolution layers), regular dropout will not regularize the\n * activation and will otherwise just result in merely an effective learning\n * rate decrease. In this case, `spatialDropout1d` will help promote\n * independence among feature maps and should be used instead.\n *\n * **Arguments:**\n *   rate: A floating-point number >=0 and <=1. Fraction of the input elements\n *     to drop.\n *\n * **Input shape:**\n *   3D tensor with shape `(samples, timesteps, channels)`.\n *\n * **Output shape:**\n *   Same as the input shape.\n *\n * References:\n *   - [Efficient Object Localization Using Convolutional\n *      Networks](https://arxiv.org/abs/1411.4280)\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function spatialDropout1d(args) {\n    return new SpatialDropout1D(args);\n}\n/**\n * Flattens the input. Does not affect the batch size.\n *\n * A `Flatten` layer flattens each batch in its inputs to 1D (making the output\n * 2D).\n *\n * For example:\n *\n * ```js\n * const input = tf.input({shape: [4, 3]});\n * const flattenLayer = tf.layers.flatten();\n * // Inspect the inferred output shape of the flatten layer, which\n * // equals `[null, 12]`. The 2nd dimension is 4 * 3, i.e., the result of the\n * // flattening. (The 1st dimension is the undermined batch size.)\n * console.log(JSON.stringify(flattenLayer.apply(input).shape));\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function flatten(args) {\n    return new Flatten(args);\n}\n/**\n * Repeats the input n times in a new dimension.\n *\n * ```js\n *  const model = tf.sequential();\n *  model.add(tf.layers.repeatVector({n: 4, inputShape: [2]}));\n *  const x = tf.tensor2d([[10, 20]]);\n *  // Use the model to do inference on a data point the model hasn't see\n *  model.predict(x).print();\n *  // output shape is now [batch, 2, 4]\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function repeatVector(args) {\n    return new RepeatVector(args);\n}\n/**\n * Reshapes an input to a certain shape.\n *\n * ```js\n * const input = tf.input({shape: [4, 3]});\n * const reshapeLayer = tf.layers.reshape({targetShape: [2, 6]});\n * // Inspect the inferred output shape of the Reshape layer, which\n * // equals `[null, 2, 6]`. (The 1st dimension is the undermined batch size.)\n * console.log(JSON.stringify(reshapeLayer.apply(input).shape));\n * ```\n *\n * Input shape:\n *   Arbitrary, although all dimensions in the input shape must be fixed.\n *   Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n *\n * Output shape:\n *   [batchSize, targetShape[0], targetShape[1], ...,\n *    targetShape[targetShape.length - 1]].\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function reshape(args) {\n    return new Reshape(args);\n}\n/**\n * Permutes the dimensions of the input according to a given pattern.\n *\n * Useful for, e.g., connecting RNNs and convnets together.\n *\n * Example:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.permute({\n *   dims: [2, 1],\n *   inputShape: [10, 64]\n * }));\n * console.log(model.outputShape);\n * // Now model's output shape is [null, 64, 10], where null is the\n * // unpermuted sample (batch) dimension.\n * ```\n *\n * Input shape:\n *   Arbitrary. Use the configuration field `inputShape` when using this\n *   layer as the first layer in a model.\n *\n * Output shape:\n *   Same rank as the input shape, but with the dimensions re-ordered (i.e.,\n *   permuted) according to the `dims` configuration of this layer.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function permute(args) {\n    return new Permute(args);\n}\n/**\n * Maps positive integers (indices) into dense vectors of fixed size.\n * eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]\n *\n * **Input shape:** 2D tensor with shape: `[batchSize, sequenceLength]`.\n *\n * **Output shape:** 3D tensor with shape: `[batchSize, sequenceLength,\n * outputDim]`.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function embedding(args) {\n    return new Embedding(args);\n}\n// Merge Layers.\n/**\n * Layer that performs element-wise addition on an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape, and returns a\n * single tensor (also of the same shape). The inputs are specified as an\n * `Array` when the `apply` method of the `Add` layer instance is called. For\n * example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const addLayer = tf.layers.add();\n * const sum = addLayer.apply([input1, input2]);\n * console.log(JSON.stringify(sum.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function add(args) {\n    return new Add(args);\n}\n/**\n * Layer that performs element-wise averaging on an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape, and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const averageLayer = tf.layers.average();\n * const average = averageLayer.apply([input1, input2]);\n * console.log(JSON.stringify(average.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function average(args) {\n    return new Average(args);\n}\n/**\n * Layer that concatenates an `Array` of inputs.\n *\n * It takes a list of tensors, all of the same shape except for the\n * concatenation axis, and returns a single tensor, the concatenation\n * of all inputs. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 3]});\n * const concatLayer = tf.layers.concatenate();\n * const output = concatLayer.apply([input1, input2]);\n * console.log(JSON.stringify(output.shape));\n * // You get [null, 2, 5], with the first dimension as the undetermined batch\n * // dimension. The last dimension (5) is the result of concatenating the\n * // last dimensions of the inputs (2 and 3).\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function concatenate(args) {\n    return new Concatenate(args);\n}\n/**\n * Layer that computes the element-wise maximum an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const maxLayer = tf.layers.maximum();\n * const max = maxLayer.apply([input1, input2]);\n * console.log(JSON.stringify(max.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function maximum(args) {\n    return new Maximum(args);\n}\n/**\n * Layer that computes the element-wise minimum of an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const minLayer = tf.layers.minimum();\n * const min = minLayer.apply([input1, input2]);\n * console.log(JSON.stringify(min.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function minimum(args) {\n    return new Minimum(args);\n}\n/**\n * Layer that multiplies (element-wise) an `Array` of inputs.\n *\n * It takes as input an Array of tensors, all of the same\n * shape, and returns a single tensor (also of the same shape).\n * For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const input3 = tf.input({shape: [2, 2]});\n * const multiplyLayer = tf.layers.multiply();\n * const product = multiplyLayer.apply([input1, input2, input3]);\n * console.log(product.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function multiply(args) {\n    return new Multiply(args);\n}\n/**\n * Layer that computes a dot product between samples in two tensors.\n *\n * E.g., if applied to a list of two tensors `a` and `b` both of shape\n * `[batchSize, n]`, the output will be a tensor of shape `[batchSize, 1]`,\n * where each entry at index `[i, 0]` will be the dot product between\n * `a[i, :]` and `b[i, :]`.\n *\n * Example:\n *\n * ```js\n * const dotLayer = tf.layers.dot({axes: -1});\n * const x1 = tf.tensor2d([[10, 20], [30, 40]]);\n * const x2 = tf.tensor2d([[-1, -2], [-3, -4]]);\n *\n * // Invoke the layer's apply() method in eager (imperative) mode.\n * const y = dotLayer.apply([x1, x2]);\n * y.print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function dot(args) {\n    return new Dot(args);\n}\n// Normalization Layers.\n/**\n * Batch normalization layer (Ioffe and Szegedy, 2014).\n *\n * Normalize the activations of the previous layer at each batch,\n * i.e. applies a transformation that maintains the mean activation\n * close to 0 and the activation standard deviation close to 1.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape` (Array of integers, does\n *   not include the sample axis) when calling the constructor of this class,\n *   if this layer is used as a first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Batch Normalization: Accelerating Deep Network Training by Reducing\n * Internal Covariate Shift](https://arxiv.org/abs/1502.03167)\n *\n * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}\n */\nexport function batchNormalization(args) {\n    return new BatchNormalization(args);\n}\n/**\n * Layer-normalization layer (Ba et al., 2016).\n *\n * Normalizes the activations of the previous layer for each given example in a\n * batch independently, instead of across a batch like in `batchNormalization`.\n * In other words, this layer applies a transformation that maintanis the mean\n * activation within each example close to0 and activation variance close to 1.\n *\n * Input shape:\n *   Arbitrary. Use the argument `inputShape` when using this layer as the first\n *   layer in a model.\n *\n * Output shape:\n *   Same as input.\n *\n * References:\n *   - [Layer Normalization](https://arxiv.org/abs/1607.06450)\n *\n * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}\n */\nexport function layerNormalization(args) {\n    return new LayerNormalization(args);\n}\n// Padding Layers.\n/**\n * Zero-padding layer for 2D input (e.g., image).\n *\n * This layer can add rows and columns of zeros\n * at the top, bottom, left and right side of an image tensor.\n *\n * Input shape:\n *   4D tensor with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, rows, cols, channels]`\n *   - If `data_format` is `\"channels_first\"`:\n *     `[batch, channels, rows, cols]`.\n *\n * Output shape:\n *   4D with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, paddedRows, paddedCols, channels]`\n *    - If `dataFormat` is `\"channelsFirst\"`:\n *     `[batch, channels, paddedRows, paddedCols]`.\n *\n * @doc {heading: 'Layers', subheading: 'Padding', namespace: 'layers'}\n */\nexport function zeroPadding2d(args) {\n    return new ZeroPadding2D(args);\n}\n// Pooling Layers.\n/**\n * Average pooling operation for spatial data.\n *\n * Input shape: `[batchSize, inLength, channels]`\n *\n * Output shape: `[batchSize, pooledLength, channels]`\n *\n * `tf.avgPool1d` is an alias.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling1d(args) {\n    return new AveragePooling1D(args);\n}\nexport function avgPool1d(args) {\n    return averagePooling1d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling1d(args) {\n    return averagePooling1d(args);\n}\n/**\n * Average pooling operation for spatial data.\n *\n * Input shape:\n *  - If `dataFormat === CHANNEL_LAST`:\n *      4D tensor with shape:\n *      `[batchSize, rows, cols, channels]`\n *  - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *      `[batchSize, channels, rows, cols]`\n *\n * Output shape\n *  - If `dataFormat === CHANNEL_LAST`:\n *      4D tensor with shape:\n *      `[batchSize, pooleRows, pooledCols, channels]`\n *  - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *      `[batchSize, channels, pooleRows, pooledCols]`\n *\n * `tf.avgPool2d` is an alias.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling2d(args) {\n    return new AveragePooling2D(args);\n}\nexport function avgPool2d(args) {\n    return averagePooling2d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling2d(args) {\n    return averagePooling2d(args);\n}\n/**\n * Average pooling operation for 3D data.\n *\n * Input shape\n *   - If `dataFormat === channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, depths, rows, cols, channels]`\n *   - If `dataFormat === channelsFirst`:\n *      4D tensor with shape:\n *       `[batchSize, channels, depths, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`\n *   - If `dataFormat=channelsFirst`:\n *       5D tensor with shape:\n *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling3d(args) {\n    return new AveragePooling3D(args);\n}\nexport function avgPool3d(args) {\n    return averagePooling3d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling3d(args) {\n    return averagePooling3d(args);\n}\n/**\n * Global average pooling operation for temporal data.\n *\n * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.\n *\n * Output Shape:2D tensor with shape: `[batchSize, features]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalAveragePooling1d(args) {\n    return new GlobalAveragePooling1D(args);\n}\n/**\n * Global average pooling operation for spatial data.\n *\n * Input shape:\n *   - If `dataFormat` is `CHANNEL_LAST`:\n *       4D tensor with shape: `[batchSize, rows, cols, channels]`.\n *   - If `dataFormat` is `CHANNEL_FIRST`:\n *       4D tensor with shape: `[batchSize, channels, rows, cols]`.\n *\n * Output shape:\n *   2D tensor with shape: `[batchSize, channels]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalAveragePooling2d(args) {\n    return new GlobalAveragePooling2D(args);\n}\n/**\n * Global max pooling operation for temporal data.\n *\n * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.\n *\n * Output Shape:2D tensor with shape: `[batchSize, features]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalMaxPooling1d(args) {\n    return new GlobalMaxPooling1D(args);\n}\n/**\n * Global max pooling operation for spatial data.\n *\n * Input shape:\n *   - If `dataFormat` is `CHANNEL_LAST`:\n *       4D tensor with shape: `[batchSize, rows, cols, channels]`.\n *   - If `dataFormat` is `CHANNEL_FIRST`:\n *       4D tensor with shape: `[batchSize, channels, rows, cols]`.\n *\n * Output shape:\n *   2D tensor with shape: `[batchSize, channels]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalMaxPooling2d(args) {\n    return new GlobalMaxPooling2D(args);\n}\n/**\n * Max pooling operation for temporal data.\n *\n * Input shape:  `[batchSize, inLength, channels]`\n *\n * Output shape: `[batchSize, pooledLength, channels]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling1d(args) {\n    return new MaxPooling1D(args);\n}\n/**\n * Max pooling operation for spatial data.\n *\n * Input shape\n *   - If `dataFormat === CHANNEL_LAST`:\n *       4D tensor with shape:\n *       `[batchSize, rows, cols, channels]`\n *   - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *       `[batchSize, channels, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=CHANNEL_LAST`:\n *       4D tensor with shape:\n *       `[batchSize, pooleRows, pooledCols, channels]`\n *   - If `dataFormat=CHANNEL_FIRST`:\n *       4D tensor with shape:\n *       `[batchSize, channels, pooleRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling2d(args) {\n    return new MaxPooling2D(args);\n}\n/**\n * Max pooling operation for 3D data.\n *\n * Input shape\n *   - If `dataFormat === channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, depths, rows, cols, channels]`\n *   - If `dataFormat === channelsFirst`:\n *      5D tensor with shape:\n *       `[batchSize, channels, depths, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`\n *   - If `dataFormat=channelsFirst`:\n *       5D tensor with shape:\n *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling3d(args) {\n    return new MaxPooling3D(args);\n}\n// Recurrent Layers.\n/**\n * Gated Recurrent Unit - Cho et al. 2014.\n *\n * This is an `RNN` layer consisting of one `GRUCell`. However, unlike\n * the underlying `GRUCell`, the `apply` method of `SimpleRNN` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const rnn = tf.layers.gru({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `GRUCell`'s number of units.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function gru(args) {\n    return new GRU(args);\n}\n/**\n * Cell class for `GRU`.\n *\n * `GRUCell` is distinct from the `RNN` subclass `GRU` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `GRU` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.gruCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `GRUCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.gruCell({units: 4}),\n *   tf.layers.gruCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `gruCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `GRUCell`, use the\n * `tf.layers.gru`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function gruCell(args) {\n    return new GRUCell(args);\n}\n/**\n * Long-Short Term Memory layer - Hochreiter 1997.\n *\n * This is an `RNN` layer consisting of one `LSTMCell`. However, unlike\n * the underlying `LSTMCell`, the `apply` method of `LSTM` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const lstm = tf.layers.lstm({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = lstm.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `LSTMCell`'s number of units.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function lstm(args) {\n    return new LSTM(args);\n}\n/**\n * Cell class for `LSTM`.\n *\n * `LSTMCell` is distinct from the `RNN` subclass `LSTM` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `LSTM` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.lstmCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `LSTMCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.lstmCell({units: 4}),\n *   tf.layers.lstmCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `lstmCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `LSTMCell`, use the\n * `tf.layers.lstm`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function lstmCell(args) {\n    return new LSTMCell(args);\n}\n/**\n * Fully-connected RNN where the output is to be fed back to input.\n *\n * This is an `RNN` layer consisting of one `SimpleRNNCell`. However, unlike\n * the underlying `SimpleRNNCell`, the `apply` method of `SimpleRNN` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const rnn = tf.layers.simpleRNN({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `SimpleRNNCell`'s number of units.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function simpleRNN(args) {\n    return new SimpleRNN(args);\n}\n/**\n * Cell class for `SimpleRNN`.\n *\n * `SimpleRNNCell` is distinct from the `RNN` subclass `SimpleRNN` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `SimpleRNN` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.simpleRNNCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `SimpleRNNCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.simpleRNNCell({units: 4}),\n *   tf.layers.simpleRNNCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `SimpleRNNCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `SimpleRNNCell`, use the\n * `tf.layers.simpleRNN`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function simpleRNNCell(args) {\n    return new SimpleRNNCell(args);\n}\n/**\n * Convolutional LSTM layer - Xingjian Shi 2015.\n *\n * This is an `ConvRNN2D` layer consisting of one `ConvLSTM2DCell`. However,\n * unlike the underlying `ConvLSTM2DCell`, the `apply` method of `ConvLSTM2D`\n * operates on a sequence of inputs. The shape of the input (not including the\n * first, batch dimension) needs to be 4-D, with the first dimension being time\n * steps. For example:\n *\n * ```js\n * const filters = 3;\n * const kernelSize = 3;\n *\n * const batchSize = 4;\n * const sequenceLength = 2;\n * const size = 5;\n * const channels = 3;\n *\n * const inputShape = [batchSize, sequenceLength, size, size, channels];\n * const input = tf.ones(inputShape);\n *\n * const layer = tf.layers.convLstm2d({filters, kernelSize});\n *\n * const output = layer.apply(input);\n * ```\n */\n/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */\nexport function convLstm2d(args) {\n    return new ConvLSTM2D(args);\n}\n/**\n * Cell class for `ConvLSTM2D`.\n *\n * `ConvLSTM2DCell` is distinct from the `ConvRNN2D` subclass `ConvLSTM2D` in\n * that its `call` method takes the input data of only a single time step and\n * returns the cell's output at the time step, while `ConvLSTM2D` takes the\n * input data over a number of time steps. For example:\n *\n * ```js\n * const filters = 3;\n * const kernelSize = 3;\n *\n * const sequenceLength = 1;\n * const size = 5;\n * const channels = 3;\n *\n * const inputShape = [sequenceLength, size, size, channels];\n * const input = tf.ones(inputShape);\n *\n * const cell = tf.layers.convLstm2dCell({filters, kernelSize});\n *\n * cell.build(input.shape);\n *\n * const outputSize = size - kernelSize + 1;\n * const outShape = [sequenceLength, outputSize, outputSize, filters];\n *\n * const initialH = tf.zeros(outShape);\n * const initialC = tf.zeros(outShape);\n *\n * const [o, h, c] = cell.call([input, initialH, initialC], {});\n * ```\n */\n/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */\nexport function convLstm2dCell(args) {\n    return new ConvLSTM2DCell(args);\n}\n/**\n * Base class for recurrent layers.\n *\n * Input shape:\n *   3D tensor with shape `[batchSize, timeSteps, inputDim]`.\n *\n * Output shape:\n *   - if `returnState`, an Array of tensors (i.e., `tf.Tensor`s). The first\n *     tensor is the output. The remaining tensors are the states at the\n *     last time step, each with shape `[batchSize, units]`.\n *   - if `returnSequences`, the output will have shape\n *     `[batchSize, timeSteps, units]`.\n *   - else, the output will have shape `[batchSize, units]`.\n *\n * Masking:\n *   This layer supports masking for input data with a variable number\n *   of timesteps. To introduce masks to your data,\n *   use an embedding layer with the `mask_zero` parameter\n *   set to `True`.\n *\n * Notes on using statefulness in RNNs:\n *   You can set RNN layers to be 'stateful', which means that the states\n *   computed for the samples in one batch will be reused as initial states\n *   for the samples in the next batch. This assumes a one-to-one mapping\n *   between samples in different successive batches.\n *\n *   To enable statefulness:\n *     - specify `stateful: true` in the layer constructor.\n *     - specify a fixed batch size for your model, by passing\n *       if sequential model:\n *         `batchInputShape=[...]` to the first layer in your model.\n *       else for functional model with 1 or more Input layers:\n *         `batchShape=[...]` to all the first layers in your model.\n *       This is the expected shape of your inputs *including the batch size*.\n *       It should be a tuple of integers, e.g. `(32, 10, 100)`.\n *     - specify `shuffle=False` when calling fit().\n *\n *   To reset the states of your model, call `.resetStates()` on either\n *   a specific layer, or on your entire model.\n *\n * Note on specifying the initial state of RNNs\n *   You can specify the initial state of RNN layers symbolically by\n *   calling them with the option `initialState`. The value of\n *   `initialState` should be a tensor or list of tensors representing\n *   the initial state of the RNN layer.\n *\n *   You can specify the initial state of RNN layers numerically by\n *   calling `resetStates` with the keyword argument `states`. The value of\n *   `states` should be a numpy array or list of numpy arrays representing\n *   the initial state of the RNN layer.\n *\n * Note on passing external constants to RNNs\n *   You can pass \"external\" constants to the cell using the `constants`\n *   keyword argument of `RNN.call` method. This requires that the `cell.call`\n *   method accepts the same keyword argument `constants`. Such constants\n *   can be used to conditon the cell transformation on additional static inputs\n *   (not changing over time), a.k.a an attention mechanism.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function rnn(args) {\n    return new RNN(args);\n}\n/**\n * Wrapper allowing a stack of RNN cells to behave as a single cell.\n *\n * Used to implement efficient stacked RNNs.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function stackedRNNCells(args) {\n    return new StackedRNNCells(args);\n}\n// Wrapper Layers.\n/** @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'} */\nexport function bidirectional(args) {\n    return new Bidirectional(args);\n}\n/**\n * This wrapper applies a layer to every temporal slice of an input.\n *\n * The input should be at least 3D,  and the dimension of the index `1` will be\n * considered to be the temporal dimension.\n *\n * Consider a batch of 32 samples, where each sample is a sequence of 10 vectors\n * of 16 dimensions. The batch input shape of the layer is then `[32,  10,\n * 16]`, and the `inputShape`, not including the sample dimension, is\n * `[10, 16]`.\n *\n * You can then use `TimeDistributed` to apply a `Dense` layer to each of the 10\n * timesteps, independently:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.timeDistributed({\n *   layer: tf.layers.dense({units: 8}),\n *   inputShape: [10, 16],\n * }));\n *\n * // Now model.outputShape = [null, 10, 8].\n * // The output will then have shape `[32, 10, 8]`.\n *\n * // In subsequent layers, there is no need for `inputShape`:\n * model.add(tf.layers.timeDistributed({layer: tf.layers.dense({units: 32})}));\n * console.log(JSON.stringify(model.outputs[0].shape));\n * // Now model.outputShape = [null, 10, 32].\n * ```\n *\n * The output will then have shape `[32, 10, 32]`.\n *\n * `TimeDistributed` can be used with arbitrary layers, not just `Dense`, for\n * instance a `Conv2D` layer.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.timeDistributed({\n *   layer: tf.layers.conv2d({filters: 64, kernelSize: [3, 3]}),\n *   inputShape: [10, 299, 299, 3],\n * }));\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'}\n */\nexport function timeDistributed(args) {\n    return new TimeDistributed(args);\n}\n// Aliases for pooling.\nexport const globalMaxPool1d = globalMaxPooling1d;\nexport const globalMaxPool2d = globalMaxPooling2d;\nexport const maxPool1d = maxPooling1d;\nexport const maxPool2d = maxPooling2d;\nexport { Layer, RNN, RNNCell, input /* alias for tf.input */ };\n/**\n * Apply additive zero-centered Gaussian noise.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * This is useful to mitigate overfitting\n * (you could see it as a form of random data augmentation).\n * Gaussian Noise (GS) is a natural choice as corruption process\n * for real valued inputs.\n *\n * # Arguments\n *     stddev: float, standard deviation of the noise distribution.\n *\n * # Input shape\n *         Arbitrary. Use the keyword argument `input_shape`\n *         (tuple of integers, does not include the samples axis)\n *         when using this layer as the first layer in a model.\n *\n * # Output shape\n *         Same shape as input.\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function gaussianNoise(args) {\n    return new GaussianNoise(args);\n}\n/**\n * Apply multiplicative 1-centered Gaussian noise.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](\n *      http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function gaussianDropout(args) {\n    return new GaussianDropout(args);\n}\n/**\n * Applies Alpha Dropout to the input.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs\n * to their original values, in order to ensure the self-normalizing property\n * even after this dropout.\n * Alpha Dropout fits well to Scaled Exponential Linear Units\n * by randomly setting activations to the negative saturation value.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the\n *     shape for randomly generated keep/drop flags.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function alphaDropout(args) {\n    return new AlphaDropout(args);\n}\n/**\n * Masks a sequence by using a mask value to skip timesteps.\n *\n * If all features for a given sample timestep are equal to `mask_value`,\n * then the sample timestep will be masked (skipped) in all downstream layers\n * (as long as they support masking).\n *\n * If any downstream layer does not support masking yet receives such\n * an input mask, an exception will be raised.\n *\n * Arguments:\n *   - `maskValue`: Either None or mask value to skip.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * @doc {heading: 'Layers', subheading: 'Mask', namespace: 'layers'}\n */\nexport function masking(args) {\n    return new Masking(args);\n}\n//# sourceMappingURL=exports_layers.js.map", "import * as losses from './losses';\nimport * as metrics from './metrics';\n/**\n * Binary accuracy metric function.\n *\n * `yTrue` and `yPred` can have 0-1 values. Example:\n * ```js\n * const x = tf.tensor2d([[1, 1, 1, 1], [0, 0, 0, 0]], [2, 4]);\n * const y = tf.tensor2d([[1, 0, 1, 0], [0, 0, 0, 1]], [2, 4]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * `yTrue` and `yPred` can also have floating-number values between 0 and 1, in\n * which case the values will be thresholded at 0.5 to yield 0-1 values (i.e.,\n * a value >= 0.5 and <= 1.0 is interpreted as 1.\n * )\n * Example:\n * ```js\n * const x = tf.tensor1d([1, 1, 1, 1, 0, 0, 0, 0]);\n * const y = tf.tensor1d([0.2, 0.4, 0.6, 0.8, 0.2, 0.3, 0.4, 0.7]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryAccuracy(yTrue, yPred) {\n    return metrics.binaryAccuracy(yTrue, yPred);\n}\n/**\n * Binary crossentropy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0], [1], [1], [1]]);\n * const y = tf.tensor2d([[0], [0], [0.5], [1]]);\n * const crossentropy = tf.metrics.binaryCrossentropy(x, y);\n * crossentropy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction, probabilities for the `1` case.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryCrossentropy(yTrue, yPred) {\n    return metrics.binaryCrossentropy(yTrue, yPred);\n}\n/**\n * Sparse categorical accuracy metric function.\n *\n * Example:\n * ```js\n *\n * const yTrue = tf.tensor1d([1, 1, 2, 2, 0]);\n * const yPred = tf.tensor2d(\n *      [[0, 1, 0], [1, 0, 0], [0, 0.4, 0.6], [0, 0.6, 0.4], [0.7, 0.3, 0]]);\n * const crossentropy = tf.metrics.sparseCategoricalAccuracy(yTrue, yPred);\n * crossentropy.print();\n * ```\n *\n * @param yTrue True labels: indices.\n * @param yPred Predicted probabilities or logits.\n * @returns Accuracy tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function sparseCategoricalAccuracy(yTrue, yPred) {\n    return metrics.sparseCategoricalAccuracy(yTrue, yPred);\n}\n/**\n * Categorical accuracy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0, 0, 0, 1], [0, 0, 0, 1]]);\n * const y = tf.tensor2d([[0.1, 0.8, 0.05, 0.05], [0.1, 0.05, 0.05, 0.8]]);\n * const accuracy = tf.metrics.categoricalAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth: one-hot encoding of categories.\n * @param yPred Binary Tensor of prediction: probabilities or logits for the\n *   same categories as in `yTrue`.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalAccuracy(yTrue, yPred) {\n    return metrics.categoricalAccuracy(yTrue, yPred);\n}\n/**\n * Categorical crossentropy between an output tensor and a target tensor.\n *\n * @param target A tensor of the same shape as `output`.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalCrossentropy(yTrue, yPred) {\n    return metrics.categoricalCrossentropy(yTrue, yPred);\n}\n/**\n * Computes the precision of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const precision = tf.metrics.precision(x, y);\n * precision.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to be contain only 0-1 values.\n * @param yPred The predicted values. Expected to be contain only 0-1 values.\n * @return Precision Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function precision(yTrue, yPred) {\n    return metrics.precision(yTrue, yPred);\n}\n/**\n * Computes the recall of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const recall = tf.metrics.recall(x, y);\n * recall.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to be contain only 0-1 values.\n * @param yPred The predicted values. Expected to be contain only 0-1 values.\n * @return Recall Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function recall(yTrue, yPred) {\n    return metrics.recall(yTrue, yPred);\n}\n/**\n * Loss or metric function: Cosine proximity.\n *\n * Mathematically, cosine proximity is defined as:\n *   `-sum(l2Normalize(yTrue) * l2Normalize(yPred))`,\n * wherein `l2Normalize()` normalizes the L2 norm of the input to 1 and `*`\n * represents element-wise multiplication.\n *\n * ```js\n * const yTrue = tf.tensor2d([[1, 0], [1, 0]]);\n * const yPred = tf.tensor2d([[1 / Math.sqrt(2), 1 / Math.sqrt(2)], [0, 1]]);\n * const proximity = tf.metrics.cosineProximity(yTrue, yPred);\n * proximity.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Cosine proximity Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function cosineProximity(yTrue, yPred) {\n    return losses.cosineProximity(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean absolute error.\n *\n * Mathematically, mean absolute error is defined as:\n *   `mean(abs(yPred - yTrue))`,\n * wherein the `mean` is applied over feature dimensions.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [0, 0], [2, 3]]);\n * const yPred = tf.tensor2d([[0, 1], [0, 1], [-2, -3]]);\n * const mse = tf.metrics.meanAbsoluteError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsoluteError(yTrue, yPred) {\n    return losses.meanAbsoluteError(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean absolute percentage error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [10, 20]]);\n * const yPred = tf.tensor2d([[0, 1], [11, 24]]);\n * const mse = tf.metrics.meanAbsolutePercentageError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MAPE`, `tf.metrics.mape`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute percentage error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsolutePercentageError(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\nexport function MAPE(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\nexport function mape(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean squared error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [3, 4]]);\n * const yPred = tf.tensor2d([[0, 1], [-3, -4]]);\n * const mse = tf.metrics.meanSquaredError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MSE`, `tf.metrics.mse`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean squared error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanSquaredError(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\nexport function MSE(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\nexport function mse(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\n//# sourceMappingURL=exports_metrics.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport { modelFromJSON } from './models';\n//# sourceMappingURL=exports_models.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as regularizers from './regularizers';\n// tslint:disable-next-line:max-line-length\nimport { L1L2 } from './regularizers';\n/**\n * Regularizer for L1 and L2 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l1 * abs(x)) + sum(l2 * x^2)\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l1l2(config) {\n    return new L1L2(config);\n}\n/**\n * Regularizer for L1 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l1 * abs(x))\n * @param args l1 config.\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l1(config) {\n    return regularizers.l1(config);\n}\n/**\n * Regularizer for L2 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l2 * x^2)\n * @param args l2 config.\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l2(config) {\n    return regularizers.l2(config);\n}\n//# sourceMappingURL=exports_regularizers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/callbacks.py */\nimport { BaseCallback } from './base_callbacks';\nimport { LayersModel } from './engine/training';\nimport { NotImplementedError } from './errors';\nimport { resolveScalarsInLogs } from './logs';\nexport class Callback extends BaseCallback {\n    constructor() {\n        super(...arguments);\n        /** Instance of `keras.models.Model`. Reference of the model being trained. */\n        this.model = null;\n    }\n    setModel(model) {\n        if (!(model instanceof LayersModel)) {\n            throw new Error('model must be a LayersModel, not some other Container');\n        }\n        this.model = model;\n    }\n}\nfunction less(currVal, prevVal) {\n    return currVal < prevVal;\n}\nfunction greater(currVal, prevVal) {\n    return currVal > prevVal;\n}\n/**\n * A Callback that stops training when a monitored quantity has stopped\n * improving.\n */\nexport class EarlyStopping extends Callback {\n    constructor(args) {\n        super();\n        if (args == null) {\n            args = {};\n        }\n        if (args.restoreBestWeights) {\n            throw new NotImplementedError('restoreBestWeights = True is not implemented in EarlyStopping yet.');\n        }\n        this.monitor = args.monitor || 'val_loss';\n        this.minDelta = Math.abs(args.minDelta || 0);\n        this.patience = args.patience || 0;\n        this.verbose = args.verbose || 0;\n        this.mode = args.mode || 'auto';\n        this.baseline = args.baseline;\n        if (['auto', 'min', 'max'].indexOf(this.mode) === -1) {\n            console.warn(`EarlyStopping mode '${this.mode}' is invalid. ` +\n                `Falling back to mode 'auto'.`);\n            this.mode = 'auto';\n        }\n        if (this.mode === 'min') {\n            this.monitorFunc = less;\n        }\n        else if (this.mode === 'max') {\n            this.monitorFunc = greater;\n        }\n        else {\n            // For mode === 'auto'.\n            if (this.monitor.indexOf('acc') !== -1) {\n                this.monitorFunc = greater;\n            }\n            else {\n                this.monitorFunc = less;\n            }\n        }\n        if (this.monitorFunc === less) {\n            this.minDelta *= -1;\n        }\n    }\n    async onTrainBegin(logs) {\n        this.wait = 0;\n        this.stoppedEpoch = 0;\n        if (this.baseline != null) {\n            this.best = this.baseline;\n        }\n        else {\n            this.best = this.monitorFunc === less ? Infinity : -Infinity;\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        await resolveScalarsInLogs(logs);\n        const current = this.getMonitorValue(logs);\n        if (current == null) {\n            return;\n        }\n        if (this.monitorFunc(current - this.minDelta, this.best)) {\n            this.best = current;\n            this.wait = 0;\n            // TODO(cais): Logic for restoreBestWeights.\n        }\n        else {\n            this.wait++;\n            if (this.wait >= this.patience) {\n                this.stoppedEpoch = epoch;\n                this.model.stopTraining = true;\n            }\n            // TODO(cais): Logic for restoreBestWeights.\n        }\n    }\n    async onTrainEnd(logs) {\n        if (this.stoppedEpoch > 0 && this.verbose) {\n            console.log(`Epoch ${this.stoppedEpoch}: early stopping.`);\n        }\n    }\n    getMonitorValue(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        const monitorValue = logs[this.monitor];\n        if (monitorValue == null) {\n            console.warn(`Metric for EarlyStopping ${this.monitor} is not available. ` +\n                `Available metrics are: ${Object.keys(logs)}`);\n        }\n        return monitorValue;\n    }\n}\n/**\n * Factory function for a Callback that stops training when a monitored\n * quantity has stopped improving.\n *\n * Early stopping is a type of regularization, and protects model against\n * overfitting.\n *\n * The following example based on fake data illustrates how this callback\n * can be used during `tf.LayersModel.fit()`:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense({\n *   units: 3,\n *   activation: 'softmax',\n *   kernelInitializer: 'ones',\n *   inputShape: [2]\n * }));\n * const xs = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const ys = tf.tensor2d([[1, 0, 0], [0, 1, 0]], [2, 3]);\n * const xsVal = tf.tensor2d([4, 3, 2, 1], [2, 2]);\n * const ysVal = tf.tensor2d([[0, 0, 1], [0, 1, 0]], [2, 3]);\n * model.compile(\n *     {loss: 'categoricalCrossentropy', optimizer: 'sgd', metrics: ['acc']});\n *\n * // Without the EarlyStopping callback, the val_acc value would be:\n * //   0.5, 0.5, 0.5, 0.5, ...\n * // With val_acc being monitored, training should stop after the 2nd epoch.\n * const history = await model.fit(xs, ys, {\n *   epochs: 10,\n *   validationData: [xsVal, ysVal],\n *   callbacks: tf.callbacks.earlyStopping({monitor: 'val_acc'})\n * });\n *\n * // Expect to see a length-2 array.\n * console.log(history.history.val_acc);\n * ```\n *\n * @doc {\n *   heading: 'Callbacks',\n *   namespace: 'callbacks'\n * }\n */\nexport function earlyStopping(args) {\n    return new EarlyStopping(args);\n}\nexport const callbacks = { earlyStopping };\n//# sourceMappingURL=callbacks.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// This file lists all exports of TensorFlow.js Layers\nimport * as constraints from './exports_constraints';\nimport * as initializers from './exports_initializers';\nimport * as layers from './exports_layers';\nimport * as metrics from './exports_metrics';\nimport * as models from './exports_models';\nimport * as regularizers from './exports_regularizers';\nexport { CallbackList, CustomCallback, History } from './base_callbacks';\nexport { Callback, callbacks, EarlyStopping } from './callbacks';\nexport { InputSpec, SymbolicTensor } from './engine/topology';\nexport { LayersModel } from './engine/training';\nexport { input, loadLayersModel, model, registerCallbackConstructor, sequential } from './exports';\nexport { RNN } from './layers/recurrent';\nexport { Sequential } from './models';\nexport { LayerVariable } from './variables';\nexport { version as version_layers } from './version';\nexport { constraints, initializers, layers, metrics, models, regularizers };\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/** DataType enum. */\nexport var DataType;\n(function (DataType) {\n    DataType[DataType[\"DT_INVALID\"] = 0] = \"DT_INVALID\";\n    DataType[DataType[\"DT_FLOAT\"] = 1] = \"DT_FLOAT\";\n    DataType[DataType[\"DT_DOUBLE\"] = 2] = \"DT_DOUBLE\";\n    DataType[DataType[\"DT_INT32\"] = 3] = \"DT_INT32\";\n    DataType[DataType[\"DT_UINT8\"] = 4] = \"DT_UINT8\";\n    DataType[DataType[\"DT_INT16\"] = 5] = \"DT_INT16\";\n    DataType[DataType[\"DT_INT8\"] = 6] = \"DT_INT8\";\n    DataType[DataType[\"DT_STRING\"] = 7] = \"DT_STRING\";\n    DataType[DataType[\"DT_COMPLEX64\"] = 8] = \"DT_COMPLEX64\";\n    DataType[DataType[\"DT_INT64\"] = 9] = \"DT_INT64\";\n    DataType[DataType[\"DT_BOOL\"] = 10] = \"DT_BOOL\";\n    DataType[DataType[\"DT_QINT8\"] = 11] = \"DT_QINT8\";\n    DataType[DataType[\"DT_QUINT8\"] = 12] = \"DT_QUINT8\";\n    DataType[DataType[\"DT_QINT32\"] = 13] = \"DT_QINT32\";\n    DataType[DataType[\"DT_BFLOAT16\"] = 14] = \"DT_BFLOAT16\";\n    DataType[DataType[\"DT_FLOAT_REF\"] = 101] = \"DT_FLOAT_REF\";\n    DataType[DataType[\"DT_DOUBLE_REF\"] = 102] = \"DT_DOUBLE_REF\";\n    DataType[DataType[\"DT_INT32_REF\"] = 103] = \"DT_INT32_REF\";\n    DataType[DataType[\"DT_UINT8_REF\"] = 104] = \"DT_UINT8_REF\";\n    DataType[DataType[\"DT_INT16_REF\"] = 105] = \"DT_INT16_REF\";\n    DataType[DataType[\"DT_INT8_REF\"] = 106] = \"DT_INT8_REF\";\n    DataType[DataType[\"DT_STRING_REF\"] = 107] = \"DT_STRING_REF\";\n    DataType[DataType[\"DT_COMPLEX64_REF\"] = 108] = \"DT_COMPLEX64_REF\";\n    DataType[DataType[\"DT_INT64_REF\"] = 109] = \"DT_INT64_REF\";\n    DataType[DataType[\"DT_BOOL_REF\"] = 110] = \"DT_BOOL_REF\";\n    DataType[DataType[\"DT_QINT8_REF\"] = 111] = \"DT_QINT8_REF\";\n    DataType[DataType[\"DT_QUINT8_REF\"] = 112] = \"DT_QUINT8_REF\";\n    DataType[DataType[\"DT_QINT32_REF\"] = 113] = \"DT_QINT32_REF\";\n    DataType[DataType[\"DT_BFLOAT16_REF\"] = 114] = \"DT_BFLOAT16_REF\";\n})(DataType || (DataType = {}));\nexport var SaverDef;\n(function (SaverDef) {\n    /** CheckpointFormatVersion enum. */\n    let CheckpointFormatVersion;\n    (function (CheckpointFormatVersion) {\n        CheckpointFormatVersion[CheckpointFormatVersion[\"LEGACY\"] = 0] = \"LEGACY\";\n        CheckpointFormatVersion[CheckpointFormatVersion[\"V1\"] = 1] = \"V1\";\n        CheckpointFormatVersion[CheckpointFormatVersion[\"V2\"] = 2] = \"V2\";\n    })(CheckpointFormatVersion = SaverDef.CheckpointFormatVersion || (SaverDef.CheckpointFormatVersion = {}));\n})(SaverDef || (SaverDef = {}));\n//# sourceMappingURL=compiled_api.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst CUSTOM_OPS = {};\n/**\n * Register an Op for graph model executor. This allow you to register\n * TensorFlow custom op or override existing op.\n *\n * Here is an example of registering a new MatMul Op.\n * ```js\n * const customMatmul = (node) =>\n *    tf.matMul(\n *        node.inputs[0], node.inputs[1],\n *        node.attrs['transpose_a'], node.attrs['transpose_b']);\n *\n * tf.registerOp('MatMul', customMatmul);\n * ```\n * The inputs and attrs of the node object is based on the TensorFlow op\n * registry.\n *\n * @param name The Tensorflow Op name.\n * @param opFunc An op function which is called with the current graph node\n * during execution and needs to return a tensor or a list of tensors. The node\n * has the following attributes:\n *    - attr: A map from attribute name to its value\n *    - inputs: A list of input tensors\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function registerOp(name, opFunc) {\n    const opMapper = {\n        tfOpName: name,\n        category: 'custom',\n        inputs: [],\n        attrs: [],\n        customExecutor: opFunc\n    };\n    CUSTOM_OPS[name] = opMapper;\n}\n/**\n * Retrieve the OpMapper object for the registered op.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function getRegisteredOp(name) {\n    return CUSTOM_OPS[name];\n}\n/**\n * Deregister the Op for graph model executor.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function deregisterOp(name) {\n    delete CUSTOM_OPS[name];\n}\n//# sourceMappingURL=register.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { clone, util } from '@tensorflow/tfjs-core';\nexport function getParamValue(paramName, node, tensorMap, context) {\n    const inputParam = node.inputParams[paramName];\n    if (inputParam && inputParam.inputIndexStart !== undefined) {\n        const start = inputParam.inputIndexStart;\n        const end = inputParam.inputIndexEnd === 0 ?\n            undefined :\n            (inputParam.inputIndexEnd === undefined ? start + 1 :\n                inputParam.inputIndexEnd);\n        if (inputParam.type === 'tensor') {\n            return getTensor(node.inputNames[inputParam.inputIndexStart], tensorMap, context);\n        }\n        if (inputParam.type === 'tensors') {\n            const inputs = node.inputNames.slice(start, end);\n            return inputs.map(name => getTensor(name, tensorMap, context));\n        }\n        const tensor = getTensor(node.inputNames.slice(start)[0], tensorMap, context);\n        const data = tensor.dataSync();\n        return inputParam.type === 'number' ?\n            data[0] :\n            util.toNestedArray(tensor.shape, data);\n    }\n    const attrParam = node.attrParams[paramName];\n    return attrParam && attrParam.value;\n}\n/**\n * Retrieve the tensor based on input name by extracting the node name and\n * output index information.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n */\nexport function getTensor(name, tensorsMap, context) {\n    const [nodeName, index] = parseNodeName(name);\n    const contextId = context.currentContextIds.find(contextId => {\n        return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId)];\n    });\n    return contextId !== undefined ?\n        tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] :\n        undefined;\n}\n/**\n * Retrieve the tensors based on input name for current context.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n */\nexport function getTensorsForCurrentContenxt(name, tensorsMap, context) {\n    return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];\n}\n/**\n * Returns the node name and index from the Node input name.\n * @param inputName The input name of the node, in format of\n * node_name:output_index, i.e. MatMul:0, if the output_index is not set, it is\n * default to 0.\n */\nexport function getNodeNameAndIndex(inputName, context) {\n    const [nodeName, index] = parseNodeName(inputName);\n    return [\n        getNodeNameWithContextId(nodeName, context && context.currentContextId),\n        index\n    ];\n}\nfunction getNodeNameWithContextId(name, contextId) {\n    return !!contextId ? `${name}-${contextId}` : name;\n}\nexport function parseNodeName(name) {\n    const parts = name.split(':');\n    if (parts.length === 1) {\n        return [name, 0];\n    }\n    const nodeName = parts[0];\n    return [nodeName, Number(parts[parts.length - 1])];\n}\nexport function split(arr, size) {\n    const res = [];\n    for (let i = 0; i < arr.length; i += size) {\n        res.push(arr.slice(i, i + size));\n    }\n    return res;\n}\nexport function getPadding(node, tensorMap, context) {\n    let pad = getParamValue('pad', node, tensorMap, context);\n    if (pad === 'explicit') {\n        // This is 1d array, we need to convert it to 2d array\n        pad = getParamValue('explicitPaddings', node, tensorMap, context);\n        const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];\n        for (let i = 0; i < 4; i++) {\n            explicitPadding[i][0] = pad[i * 2];\n            explicitPadding[i][1] = pad[i * 2 + 1];\n        }\n        return explicitPadding;\n    }\n    return pad;\n}\n/**\n *  Reuse the tensor if it is marked as keep, otherwise clone the tensor to\n *  avoid disposal. This is important for TensorArray and TensorList ops, since\n *  internally they use a tensor as the id for TensorArray and TensorList, and\n * to simplify lookup, they also use Tensor.id as the key to the internal map.\n * These id tensors have been marked as kept in the backend, we need avoid clone\n * them in order to create new Tensor.id.\n * @param tensor\n */\nexport function cloneTensor(tensor) {\n    return tensor.kept ? tensor : clone(tensor);\n}\n//# sourceMappingURL=utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Add',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AddV2',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AddN',\n        'category': 'arithmetic',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'BiasAdd',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sub',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'RealDiv',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Div',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'DivNoNan',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'FloorDiv',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Mul',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Maximum',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Minimum',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Pow',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'SquaredDifference',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Mod',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'FloorMod',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    }\n];\n//# sourceMappingURL=arithmetic.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Abs',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Acos',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Asin',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atan',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atan2',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'y', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Ceil',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ClipByValue',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'clip_value_min', 'name': 'clipValueMin', 'type': 'number' },\n            { 'tfName': 'clip_value_max', 'name': 'clipValueMax', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Complex',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'real', 'type': 'tensor' },\n            { 'start': 1, 'name': 'imag', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ComplexAbs',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Cos',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Cosh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Elu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Exp',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Floor',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Log',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Imag',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'outputType',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Neg',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Real',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'outputType',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Prelu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'alpha', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Relu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Relu6',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'clipValueMin',\n                'name': 'clipValueMin',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'clipValueMax',\n                'name': 'clipValueMax',\n                'type': 'number',\n                'defaultValue': 6\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Selu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sigmoid',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sin',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sinh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sqrt',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Rsqrt',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Square',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Tan',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Tanh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sign',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Round',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Expm1',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Log1p',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Reciprocal',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Softplus',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Asinh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Acosh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atanh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Erf',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Prod',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axes', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'keep_dims',\n                'name': 'keepDims',\n                'type': 'bool',\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LeakyRelu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'alpha',\n                'name': 'alpha',\n                'type': 'number',\n                'defaultValue': 0.2\n            },\n            {\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=basic_math.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'LoopCond',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'name': 'pred', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Switch',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'data', 'type': 'tensor' },\n            { 'start': 1, 'name': 'pred', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Merge',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Enter',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'frame_name', 'name': 'frameName', 'type': 'string' },\n            { 'tfName': 'is_constant', 'name': 'isConstant', 'type': 'bool' }\n        ]\n    },\n    {\n        'tfOpName': 'Exit',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'NextIteration',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'size', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },\n            { 'tfName': 'dynamic_size', 'name': 'dynamicSize', 'type': 'bool' },\n            { 'tfName': 'clear_after_read', 'name': 'clearAfterRead', 'type': 'bool' },\n            {\n                'tfName': 'identical_element_shapes',\n                'name': 'identicalElementShapes',\n                'type': 'bool'\n            },\n            { 'tfName': 'tensor_array_name', 'name': 'name', 'type': 'string' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayWriteV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayReadV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{\n                'tfName': 'dtype',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    },\n    {\n        'tfOpName': 'TensorArrayGatherV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayScatterV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorArrayConcatV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }, {\n                'tfName': 'element_shape_except0',\n                'name': 'elementShapeExcept0',\n                'type': 'shape',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArraySplitV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 2, 'name': 'lengths', 'type': 'number[]' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorArraySizeV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'flowIn', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayCloseV3',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'StatelessIf',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'cond', 'type': 'tensor' },\n            { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }\n        ],\n        'attrs': [\n            { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },\n            { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'If',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'cond', 'type': 'tensor' },\n            { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }\n        ],\n        'attrs': [\n            { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },\n            { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'StatelessWhile',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },\n            { 'tfName': 'body', 'name': 'body', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'While',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },\n            { 'tfName': 'body', 'name': 'body', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListScatter',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' }\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListScatterV2',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 3, 'name': 'numElements', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListGather',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListGetItem',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListSetItem',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListReserve',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 1, 'name': 'numElements', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListFromTensor',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' }\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListStack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' },\n            { 'tfName': 'num_elements', 'name': 'numElements', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListSplit',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 2, 'name': 'lengths', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListConcat',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListPopBack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListPushBack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n];\n//# sourceMappingURL=control.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'AvgPool',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPool',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPoolWithArgmax',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' }, {\n                'tfName': 'include_batch_in_index',\n                'name': 'includeBatchInIndex',\n                'type': 'bool'\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AvgPool3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPool3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Conv1D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'stride', 'name': 'stride', 'type': 'number' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NWC'\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'dilation',\n                'name': 'dilation',\n                'type': 'number',\n                'defaultValue': 1\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Conv2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            { 'tfName': 'useCudnnOnGpu', 'name': 'useCudnnOnGpu', 'type': 'bool' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': '_FusedConv2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'use_cudnn_on_gpu',\n                'name': 'useCudnnOnGpu',\n                'type': 'bool',\n                'defaultValue': true\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'dilations',\n                'name': 'dilations',\n                'type': 'number[]',\n                'defaultValue': [1, 1, 1, 1]\n            },\n            {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.0001\n            },\n        ]\n    },\n    {\n        'tfOpName': 'Conv2DBackpropInput',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 2, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 0, 'name': 'outputShape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n        ]\n    },\n    {\n        'tfOpName': 'DepthwiseConv2d',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'input', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'DepthwiseConv2dNative',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'input', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'FusedDepthwiseConv2dNative',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'dilations',\n                'name': 'dilations',\n                'type': 'number[]',\n                'defaultValue': [1, 1, 1, 1]\n            },\n            {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Conv3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ],\n    },\n    {\n        'tfOpName': 'Dilation2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'rates', 'name': 'dilations', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }\n        ]\n    }\n];\n//# sourceMappingURL=convolution.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Fill',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n            { 'start': 1, 'name': 'value', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'LinSpace',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'start', 'type': 'number' },\n            { 'start': 1, 'name': 'stop', 'type': 'number' },\n            { 'start': 2, 'name': 'num', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'OneHot',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'depth', 'type': 'number' },\n            { 'start': 2, 'name': 'onValue', 'type': 'number', 'defaultValue': 1 },\n            { 'start': 3, 'name': 'offValue', 'type': 'number', 'defaultValue': 0 },\n        ],\n        'attrs': [\n            {\n                'tfName': 'axis',\n                'name': 'axis',\n                'type': 'number',\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Ones',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'OnesLike',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'RandomUniform',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'minval',\n                'name': 'minval',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'maxval',\n                'name': 'maxval',\n                'type': 'number',\n                'defaultValue': 1\n            },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'seed2',\n                'name': 'seed2',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Range',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'start', 'type': 'number' },\n            { 'start': 1, 'name': 'stop', 'type': 'number' },\n            { 'start': 2, 'name': 'step', 'type': 'number', 'defaultValue': 0 },\n        ],\n        'attrs': [{ 'tfName': 'Tidx', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TruncatedNormal',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'means',\n                'name': 'mean',\n                'type': 'number',\n                'defaultValue': 0.0\n            },\n            {\n                'tfName': 'stddev',\n                'name': 'stdDev',\n                'type': 'number',\n                'defaultValue': 1.0\n            },\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number' }, {\n                'tfName': 'seed2',\n                'name': 'seed2',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Zeros',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'ZerosLike',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'Multinomial',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'logits', 'type': 'tensor' },\n            { 'start': 1, 'name': 'numSamples', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number' },\n            { 'tfName': 'seed2', 'name': 'seed2', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'output_dtype', 'name': 'output_dtype', 'type': 'dtype' }\n        ]\n    }\n];\n//# sourceMappingURL=creation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'NonMaxSuppressionV2',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV3',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV4',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'T_threshold',\n                'name': 'threshold',\n                'type': 'dtype',\n                'notSupported': true\n            },\n            {\n                'tfName': 'pad_to_max_output_size',\n                'name': 'padToMaxOutputSize',\n                'type': 'bool'\n            }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV5',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' },\n            { 'start': 5, 'name': 'softNmsSigma', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Where',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ListDiff',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'y', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=dynamic.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'TopKV2',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'k', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'sorted', 'name': 'sorted', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Unique',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n    },\n    {\n        'tfOpName': 'UniqueV2',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' },\n        ],\n    },\n];\n//# sourceMappingURL=evaluation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'PlaceholderWithDefault',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'default', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'Placeholder',\n        'category': 'graph',\n        'attrs': [\n            { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    { 'tfOpName': 'Const', 'category': 'graph' }, {\n        'tfOpName': 'Identity',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'IdentityN',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Snapshot',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Rank',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Size',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Shape',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'ShapeN',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Print',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'data', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'message', 'name': 'message', 'type': 'string' }, {\n                'tfName': 'first_n',\n                'name': 'firstN',\n                'type': 'number',\n                'notSupported': true\n            },\n            {\n                'tfName': 'summarize',\n                'name': 'summarize',\n                'type': 'number',\n                'defaultValue': 3\n            }\n        ]\n    },\n    { 'tfOpName': 'NoOp', 'category': 'graph', 'inputs': [] }, {\n        'tfOpName': 'StopGradient',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'FakeQuantWithMinMaxVars',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'min', 'name': 'min', 'type': 'number' },\n            { 'tfName': 'max', 'name': 'max', 'type': 'number' }\n        ]\n    }\n];\n//# sourceMappingURL=graph.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'ResizeBilinear',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'images', 'type': 'tensor' },\n            { 'start': 1, 'name': 'size', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ResizeNearestNeighbor',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'images', 'type': 'tensor' },\n            { 'start': 1, 'name': 'size', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'CropAndResize',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'image', 'type': 'tensor' },\n            { 'start': 1, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 2, 'name': 'boxInd', 'type': 'tensor' },\n            { 'start': 3, 'name': 'cropSize', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'method', 'name': 'method', 'type': 'string' }, {\n                'tfName': 'extrapolation_value',\n                'name': 'extrapolationValue',\n                'type': 'number'\n            }\n        ]\n    }\n];\n//# sourceMappingURL=image.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Equal',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'NotEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Greater',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'GreaterEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Less',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LessEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalAnd',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalNot',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalOr',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Select',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n            { 'start': 1, 'name': 'a', 'type': 'tensor' },\n            { 'start': 2, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'SelectV2',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n            { 'start': 1, 'name': 'a', 'type': 'tensor' },\n            { 'start': 2, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=logical.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': '_FusedMatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' }, {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.0001\n            },\n            {\n                'tfName': 'transpose_a',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'transpose_b',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'transpose_a',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'transpose_b',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'BatchMatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'adj_x',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'adj_y',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'BatchMatMulV2',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'adj_x',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'adj_y',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Transpose',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'perm', 'type': 'number[]' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=matrices.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'FusedBatchNorm',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'FusedBatchNormV2',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'FusedBatchNormV3',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'LRN',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'depth_radius',\n                'name': 'radius',\n                'type': 'number',\n                'defaultValue': 5\n            },\n            { 'tfName': 'bias', 'name': 'bias', 'type': 'number', 'defaultValue': 1.0 },\n            {\n                'tfName': 'alpha',\n                'name': 'alpha',\n                'type': 'number',\n                'defaultValue': 1.0\n            },\n            {\n                'tfName': 'beta',\n                'name': 'beta',\n                'type': 'number',\n                'defaultValue': 0.5\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Softmax',\n        'category': 'normalization',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'LogSoftmax',\n        'category': 'normalization',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'SparseToDense',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },\n            { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'defaultValue': true,\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Max',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Mean',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Min',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Sum',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'All',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Any',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'ArgMax',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'ArgMin',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Prod',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Cumsum',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'exclusive', 'name': 'exclusive', 'type': 'bool' },\n            { 'tfName': 'reverse', 'name': 'reverse', 'type': 'bool' }\n        ]\n    }\n];\n//# sourceMappingURL=reduction.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'ConcatV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'end': -1, 'name': 'tensors', 'type': 'tensors' },\n            { 'start': -1, 'name': 'axis', 'type': 'number' }\n        ],\n        'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]\n    },\n    {\n        'tfOpName': 'Concat',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 1, 'end': 0, 'name': 'tensors', 'type': 'tensors' },\n            { 'start': 0, 'name': 'axis', 'type': 'number' }\n        ],\n        'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]\n    },\n    {\n        'tfOpName': 'GatherV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'Gather',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Reverse',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'dims', 'type': 'bool', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ReverseV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Slice',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'begin', 'type': 'number[]' },\n            { 'start': 2, 'name': 'size', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'StridedSlice',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'begin', 'type': 'number[]' },\n            { 'start': 2, 'name': 'end', 'type': 'number[]' },\n            { 'start': 3, 'name': 'strides', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'begin_mask',\n                'name': 'beginMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'end_mask',\n                'name': 'endMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'new_axis_mask',\n                'name': 'newAxisMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'ellipsis_mask',\n                'name': 'ellipsisMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'shrink_axis_mask',\n                'name': 'shrinkAxisMask',\n                'type': 'number',\n                'defaultValue': 0\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Pack',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'Unpack',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'num',\n                'name': 'num',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Tile',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'reps', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Split',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'axis', 'type': 'number', 'defaultValue': 0 },\n            { 'start': 1, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'num_split',\n                'name': 'numOrSizeSplits',\n                'type': 'number',\n                'defaultValue': 1\n            }]\n    },\n    {\n        'tfOpName': 'SplitV',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'numOrSizeSplits', 'type': 'number[]' },\n            { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'ScatterNd',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'values', 'type': 'tensor' },\n            { 'start': 2, 'name': 'shape', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'GatherNd',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'SparseToDense',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },\n            { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'defaultValue': false,\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=slice_join.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'FFT',\n        'category': 'spectral',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'IFFT',\n        'category': 'spectral',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'RFFT',\n        'category': 'spectral',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' }, {\n                'start': 1,\n                'name': 'fft_length',\n                'type': 'number',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'IRFFT',\n        'category': 'spectral',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' }, {\n                'start': 1,\n                'name': 'fft_length',\n                'type': 'number',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=spectral.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Cast',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'SrcT',\n                'name': 'sdtype',\n                'type': 'dtype',\n                'notSupported': true\n            },\n            { 'tfName': 'DstT', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'ExpandDims',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Pad',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' },\n        ],\n        'attrs': [{\n                'tfName': 'constant_value',\n                'name': 'constantValue',\n                'type': 'number',\n                'defaultValue': 0\n            }]\n    },\n    {\n        'tfOpName': 'PadV2',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' }, {\n                'start': 2,\n                'name': 'constantValue',\n                'type': 'number',\n                'defaultValue': 0\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Reshape',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'shape', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Squeeze',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'axis',\n                'tfDeprecatedName': 'squeeze_dims',\n                'name': 'axis',\n                'type': 'number[]'\n            }]\n    },\n    {\n        'tfOpName': 'SpaceToBatchND',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'paddings', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'BatchToSpaceND',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'crops', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'DepthToSpace',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'block_size', 'name': 'blockSize', 'type': 'number' },\n            { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string' }\n        ]\n    },\n    {\n        'tfOpName': 'BroadcastTo',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': []\n    }\n];\n//# sourceMappingURL=transformation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport * as tensorflow from '../data/compiled_api';\nimport { getRegisteredOp } from './custom_op/register';\nimport { getNodeNameAndIndex } from './executors/utils';\nimport * as arithmetic from './op_list/arithmetic';\nimport * as basicMath from './op_list/basic_math';\nimport * as control from './op_list/control';\nimport * as convolution from './op_list/convolution';\nimport * as creation from './op_list/creation';\nimport * as dynamic from './op_list/dynamic';\nimport * as evaluation from './op_list/evaluation';\nimport * as graph from './op_list/graph';\nimport * as image from './op_list/image';\nimport * as logical from './op_list/logical';\nimport * as matrices from './op_list/matrices';\nimport * as normalization from './op_list/normalization';\nimport * as reduction from './op_list/reduction';\nimport * as sliceJoin from './op_list/slice_join';\nimport * as spectral from './op_list/spectral';\nimport * as transformation from './op_list/transformation';\nexport class OperationMapper {\n    // Singleton instance for the mapper\n    static get Instance() {\n        return this._instance || (this._instance = new this());\n    }\n    // Loads the op mapping from the JSON file.\n    constructor() {\n        const ops = [\n            arithmetic, basicMath, control, convolution, creation, dynamic,\n            evaluation, logical, image, graph, matrices, normalization, reduction,\n            sliceJoin, spectral, transformation\n        ];\n        const mappersJson = [].concat(...ops.map(op => op.json));\n        this.opMappers = mappersJson.reduce((map, mapper) => {\n            map[mapper.tfOpName] = mapper;\n            return map;\n        }, {});\n    }\n    // Converts the model inference graph from Tensorflow GraphDef to local\n    // representation for TensorFlow.js API\n    transformGraph(graph, signature = {}) {\n        const tfNodes = graph.node;\n        const placeholders = [];\n        const weights = [];\n        const initNodes = [];\n        const nodes = tfNodes.reduce((map, node) => {\n            map[node.name] = this.mapNode(node);\n            if (node.op.startsWith('Placeholder')) {\n                placeholders.push(map[node.name]);\n            }\n            else if (node.op === 'Const') {\n                weights.push(map[node.name]);\n            }\n            else if (node.input == null || node.input.length === 0) {\n                initNodes.push(map[node.name]);\n            }\n            return map;\n        }, {});\n        let inputs = [];\n        const outputs = [];\n        let inputNodeNameToKey = {};\n        let outputNodeNameToKey = {};\n        if (signature != null) {\n            inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);\n            outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);\n        }\n        const allNodes = Object.keys(nodes);\n        allNodes.forEach(key => {\n            const node = nodes[key];\n            node.inputNames.forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                node.inputs.push(nodes[nodeName]);\n                nodes[nodeName].children.push(node);\n            });\n        });\n        // if signature has not outputs set, add any node that does not have\n        // outputs.\n        if (Object.keys(outputNodeNameToKey).length === 0) {\n            allNodes.forEach(key => {\n                const node = nodes[key];\n                if (node.children.length === 0) {\n                    outputs.push(node);\n                }\n            });\n        }\n        else {\n            Object.keys(outputNodeNameToKey).forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                const node = nodes[nodeName];\n                if (node != null) {\n                    node.signatureKey = outputNodeNameToKey[name];\n                    outputs.push(node);\n                }\n            });\n        }\n        if (Object.keys(inputNodeNameToKey).length > 0) {\n            Object.keys(inputNodeNameToKey).forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                const node = nodes[nodeName];\n                if (node) {\n                    node.signatureKey = inputNodeNameToKey[name];\n                    inputs.push(node);\n                }\n            });\n        }\n        else {\n            inputs = placeholders;\n        }\n        let functions = {};\n        if (graph.library != null && graph.library.function != null) {\n            functions = graph.library.function.reduce((functions, func) => {\n                functions[func.signature.name] = this.mapFunction(func);\n                return functions;\n            }, {});\n        }\n        const result = { nodes, inputs, outputs, weights, placeholders, signature, functions };\n        if (initNodes.length > 0) {\n            result.initNodes = initNodes;\n        }\n        return result;\n    }\n    mapSignatureEntries(entries) {\n        return Object.keys(entries || {})\n            .reduce((prev, curr) => {\n            prev[entries[curr].name] = curr;\n            return prev;\n        }, {});\n    }\n    mapNode(node) {\n        // Unsupported ops will cause an error at run-time (not parse time), since\n        // they may not be used by the actual execution subgraph.\n        const mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {};\n        if (node.attr == null) {\n            node.attr = {};\n        }\n        const newNode = {\n            name: node.name,\n            op: node.op,\n            category: mapper.category,\n            inputNames: (node.input ||\n                []).map(input => input.startsWith('^') ? input.substr(1) : input),\n            inputs: [],\n            children: [],\n            inputParams: {},\n            attrParams: {},\n            rawAttrs: node.attr\n        };\n        if (mapper.inputs != null) {\n            newNode.inputParams =\n                mapper.inputs.reduce((map, param) => {\n                    map[param.name] = {\n                        type: param.type,\n                        inputIndexStart: param.start,\n                        inputIndexEnd: param.end\n                    };\n                    return map;\n                }, {});\n        }\n        if (mapper.attrs != null) {\n            newNode.attrParams =\n                mapper.attrs.reduce((map, param) => {\n                    const type = param.type;\n                    let value = undefined;\n                    switch (param.type) {\n                        case 'string':\n                            value = getStringParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'string[]':\n                            value = getStringArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'number':\n                            value = getNumberParam(node.attr, param.tfName, (param.defaultValue || 0));\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'number[]':\n                            value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'bool':\n                            value = getBoolParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'bool[]':\n                            value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'shape':\n                            value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'shape[]':\n                            value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'dtype':\n                            value = getDtypeParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'dtype[]':\n                            value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'func':\n                            value = getFuncParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'tensor':\n                        case 'tensors':\n                            break;\n                        default:\n                            throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`);\n                    }\n                    map[param.name] = { value, type };\n                    return map;\n                }, {});\n        }\n        return newNode;\n    }\n    // map the TFunctionDef to TFJS graph object\n    mapFunction(functionDef) {\n        const tfNodes = functionDef.nodeDef;\n        const placeholders = [];\n        const weights = [];\n        let nodes = {};\n        if (tfNodes != null) {\n            nodes = tfNodes.reduce((map, node) => {\n                map[node.name] = this.mapNode(node);\n                if (node.op === 'Const') {\n                    weights.push(map[node.name]);\n                }\n                return map;\n            }, {});\n        }\n        const inputs = [];\n        const outputs = [];\n        functionDef.signature.inputArg.forEach(arg => {\n            const [nodeName,] = getNodeNameAndIndex(arg.name);\n            const node = {\n                name: nodeName,\n                op: 'Placeholder',\n                inputs: [],\n                inputNames: [],\n                category: 'graph',\n                inputParams: {},\n                attrParams: { dtype: { value: parseDtypeParam(arg.type), type: 'dtype' } },\n                children: []\n            };\n            node.signatureKey = arg.name;\n            inputs.push(node);\n            nodes[nodeName] = node;\n        });\n        const allNodes = Object.keys(nodes);\n        allNodes.forEach(key => {\n            const node = nodes[key];\n            node.inputNames.forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                node.inputs.push(nodes[nodeName]);\n                nodes[nodeName].children.push(node);\n            });\n        });\n        const returnNodeMap = functionDef.ret;\n        functionDef.signature.outputArg.forEach(output => {\n            const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);\n            const node = nodes[nodeName];\n            if (node != null) {\n                node.defaultOutput = index;\n                outputs.push(node);\n            }\n        });\n        const signature = this.mapArgsToSignature(functionDef);\n        return { nodes, inputs, outputs, weights, placeholders, signature };\n    }\n    mapArgsToSignature(functionDef) {\n        return {\n            methodName: functionDef.signature.name,\n            inputs: functionDef.signature.inputArg.reduce((map, arg) => {\n                map[arg.name] = this.mapArgToTensorInfo(arg);\n                return map;\n            }, {}),\n            outputs: functionDef.signature.outputArg.reduce((map, arg) => {\n                map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);\n                return map;\n            }, {}),\n        };\n    }\n    mapArgToTensorInfo(arg, nameMap) {\n        let name = arg.name;\n        if (nameMap != null) {\n            name = nameMap[name];\n        }\n        return { name, dtype: arg.type };\n    }\n}\nexport function decodeBase64(text) {\n    const global = env().global;\n    if (typeof global.atob !== 'undefined') {\n        return global.atob(text);\n    }\n    else if (typeof Buffer !== 'undefined') {\n        return new Buffer(text, 'base64').toString();\n    }\n    else {\n        throw new Error('Unable to decode base64 in this environment. ' +\n            'Missing built-in atob() or Buffer()');\n    }\n}\nexport function parseStringParam(s, keepCase) {\n    const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);\n    return keepCase ? value : value.toLowerCase();\n}\nexport function getStringParam(attrs, name, def, keepCase = false) {\n    const param = attrs[name];\n    if (param != null) {\n        return parseStringParam(param.s, keepCase);\n    }\n    return def;\n}\nexport function getBoolParam(attrs, name, def) {\n    const param = attrs[name];\n    return param ? param.b : def;\n}\nexport function getNumberParam(attrs, name, def) {\n    const param = attrs[name] || {};\n    const value = param['i'] != null ? param['i'] : (param['f'] != null ? param['f'] : def);\n    return (typeof value === 'number') ? value : parseInt(value, 10);\n}\nexport function parseDtypeParam(value) {\n    if (typeof (value) === 'string') {\n        // tslint:disable-next-line:no-any\n        value = tensorflow.DataType[value];\n    }\n    switch (value) {\n        case tensorflow.DataType.DT_FLOAT:\n            return 'float32';\n        case tensorflow.DataType.DT_INT32:\n        case tensorflow.DataType.DT_INT64:\n        case tensorflow.DataType.DT_INT8:\n        case tensorflow.DataType.DT_UINT8:\n            return 'int32';\n        case tensorflow.DataType.DT_BOOL:\n            return 'bool';\n        case tensorflow.DataType.DT_DOUBLE:\n            return 'float32';\n        case tensorflow.DataType.DT_STRING:\n            return 'string';\n        default:\n            // Unknown dtype error will happen at runtime (instead of parse time),\n            // since these nodes might not be used by the actual subgraph execution.\n            return null;\n    }\n}\nexport function getFuncParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.func) {\n        return param.func.name;\n    }\n    return def;\n}\nexport function getDtypeParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.type) {\n        return parseDtypeParam(param.type);\n    }\n    return def;\n}\nexport function getDtypeArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.type) {\n        return param.list.type.map(v => parseDtypeParam(v));\n    }\n    return def;\n}\nexport function parseTensorShapeParam(shape) {\n    if (shape.unknownRank) {\n        return undefined;\n    }\n    if (shape.dim != null) {\n        return shape.dim.map(dim => (typeof dim.size === 'number') ? dim.size : parseInt(dim.size, 10));\n    }\n    return [];\n}\nexport function getTensorShapeParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.shape) {\n        return parseTensorShapeParam(param.shape);\n    }\n    return def;\n}\nexport function getNumericArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param) {\n        return ((param.list.f && param.list.f.length ? param.list.f :\n            param.list.i) ||\n            [])\n            .map(v => (typeof v === 'number') ? v : parseInt(v, 10));\n    }\n    return def;\n}\nexport function getStringArrayParam(attrs, name, def, keepCase = false) {\n    const param = attrs[name];\n    if (param && param.list && param.list.s) {\n        return param.list.s.map((v) => {\n            return parseStringParam(v, keepCase);\n        });\n    }\n    return def;\n}\nexport function getTensorShapeArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.shape) {\n        return param.list.shape.map((v) => {\n            return parseTensorShapeParam(v);\n        });\n    }\n    return def;\n}\nexport function getBoolArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.b) {\n        return param.list.b;\n    }\n    return def;\n}\n//# sourceMappingURL=operation_mapper.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getTensor } from '../executors/utils';\nimport { getBoolArrayParam, getBoolParam, getDtypeArrayParam, getDtypeParam, getNumberParam, getNumericArrayParam, getStringArrayParam, getStringParam, getTensorShapeArrayParam, getTensorShapeParam } from '../operation_mapper';\n/**\n * Helper class for lookup inputs and params for nodes in the model graph.\n */\nexport class NodeValueImpl {\n    constructor(node, tensorMap, context) {\n        this.node = node;\n        this.tensorMap = tensorMap;\n        this.context = context;\n        this.inputs = [];\n        this.attrs = {};\n        this.inputs = node.inputNames.map(name => this.getInput(name));\n        if (node.rawAttrs != null) {\n            this.attrs = Object.keys(node.rawAttrs)\n                .reduce((attrs, key) => {\n                attrs[key] = this.getAttr(key);\n                return attrs;\n            }, {});\n        }\n    }\n    /**\n     * Return the value of the attribute or input param.\n     * @param name String: name of attribute or input param.\n     */\n    getInput(name) {\n        return getTensor(name, this.tensorMap, this.context);\n    }\n    /**\n     * Return the value of the attribute or input param.\n     * @param name String: name of attribute or input param.\n     */\n    getAttr(name, defaultValue) {\n        const value = this.node.rawAttrs[name];\n        if (value.tensor != null) {\n            return getTensor(name, this.tensorMap, this.context);\n        }\n        if (value.i != null || value.f != null) {\n            return getNumberParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.s != null) {\n            return getStringParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.b != null) {\n            return getBoolParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.shape != null) {\n            return getTensorShapeParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.type != null) {\n            return getDtypeParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.list != null) {\n            if (value.list.i != null || value.list.f != null) {\n                return getNumericArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.s != null) {\n                return getStringArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.shape != null) {\n                return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.b != null) {\n                return getBoolArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.type != null) {\n                return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n        }\n        return defaultValue;\n    }\n}\n//# sourceMappingURL=node_value_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This file exports ops used by the converters executors. By default it\n * re-exports all ops. In a custom build this is aliased to a file that will\n * only exports ops for a given model.json.\n */\nexport * from './ops';\n//# sourceMappingURL=ops_for_converter.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'BiasAdd':\n        case 'AddV2':\n        case 'Add': {\n            return [tfOps.add(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'AddN': {\n            return [tfOps.addN(getParamValue('tensors', node, tensorMap, context))];\n        }\n        case 'FloorMod':\n        case 'Mod':\n            return [tfOps.mod(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        case 'Mul':\n            return [tfOps.mul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        case 'RealDiv':\n        case 'Div': {\n            return [tfOps.div(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'DivNoNan': {\n            return [tfOps.divNoNan(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'FloorDiv': {\n            return [tfOps.floorDiv(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Sub': {\n            return [tfOps.sub(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Minimum': {\n            return [tfOps.minimum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Maximum': {\n            return [tfOps.maximum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Pow': {\n            return [tfOps.pow(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'SquaredDifference': {\n            return [tfOps.squaredDifference(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'arithmetic';\n//# sourceMappingURL=arithmetic_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Abs':\n        case 'ComplexAbs':\n            return [tfOps.abs(getParamValue('x', node, tensorMap, context))];\n        case 'Acos':\n            return [tfOps.acos(getParamValue('x', node, tensorMap, context))];\n        case 'Acosh':\n            return [tfOps.acosh(getParamValue('x', node, tensorMap, context))];\n        case 'Asin':\n            return [tfOps.asin(getParamValue('x', node, tensorMap, context))];\n        case 'Asinh':\n            return [tfOps.asinh(getParamValue('x', node, tensorMap, context))];\n        case 'Atan':\n            return [tfOps.atan(getParamValue('x', node, tensorMap, context))];\n        case 'Atan2':\n            return [tfOps.atan2(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context))];\n        case 'Atanh':\n            return [tfOps.atanh(getParamValue('x', node, tensorMap, context))];\n        case 'Ceil':\n            return [tfOps.ceil(getParamValue('x', node, tensorMap, context))];\n        case 'Complex':\n            return [tfOps.complex(getParamValue('real', node, tensorMap, context), getParamValue('imag', node, tensorMap, context))];\n        case 'Cos':\n            return [tfOps.cos(getParamValue('x', node, tensorMap, context))];\n        case 'Cosh':\n            return [tfOps.cosh(getParamValue('x', node, tensorMap, context))];\n        case 'Elu':\n            return [tfOps.elu(getParamValue('x', node, tensorMap, context))];\n        case 'Erf':\n            return [tfOps.erf(getParamValue('x', node, tensorMap, context))];\n        case 'Exp':\n            return [tfOps.exp(getParamValue('x', node, tensorMap, context))];\n        case 'Expm1': {\n            return [tfOps.expm1(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Floor':\n            return [tfOps.floor(getParamValue('x', node, tensorMap, context))];\n        case 'Log':\n            return [tfOps.log(getParamValue('x', node, tensorMap, context))];\n        case 'Log1p': {\n            return [tfOps.log1p(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Imag':\n            return [tfOps.imag(getParamValue('x', node, tensorMap, context))];\n        case 'Neg':\n            return [tfOps.neg(getParamValue('x', node, tensorMap, context))];\n        case 'Reciprocal': {\n            return [tfOps.reciprocal(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Real':\n            return [tfOps.real(getParamValue('x', node, tensorMap, context))];\n        case 'Relu':\n            return [tfOps.relu(getParamValue('x', node, tensorMap, context))];\n        case 'Round': {\n            return [tfOps.round(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Selu':\n            return [tfOps.selu(getParamValue('x', node, tensorMap, context))];\n        case 'Sigmoid':\n            return [tfOps.sigmoid(getParamValue('x', node, tensorMap, context))];\n        case 'Sin':\n            return [tfOps.sin(getParamValue('x', node, tensorMap, context))];\n        case 'Sign': {\n            return [tfOps.sign(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Sinh': {\n            return [tfOps.sinh(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Softplus': {\n            return [tfOps.softplus(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Sqrt': {\n            return [tfOps.sqrt(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Square': {\n            return [tfOps.square(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Tanh': {\n            return [tfOps.tanh(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Tan':\n            return [tfOps.tan(getParamValue('x', node, tensorMap, context))];\n        case 'Relu6':\n        case 'ClipByValue':\n            return [tfOps.clipByValue(getParamValue('x', node, tensorMap, context), getParamValue('clipValueMin', node, tensorMap, context), getParamValue('clipValueMax', node, tensorMap, context))];\n        case 'Rsqrt':\n            return [tfOps.rsqrt(getTensor(node.inputNames[0], tensorMap, context))];\n        case 'Prod':\n            return [tfOps.prod(getParamValue('x', node, tensorMap, context), getParamValue('axes', node, tensorMap, context))];\n        case 'LeakyRelu':\n            return [tfOps.leakyRelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n        case 'Prelu':\n            return [tfOps.prelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'basic_math';\n//# sourceMappingURL=basic_math_executor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This differs from util.assertShapesMatch in that it allows values of\n * negative one, an undefined size of a dimensinon, in a shape to match\n * anything.\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = '') {\n    util.assert(shapesEqualAllowUndefinedSize(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nexport function shapesEqualAllowUndefinedSize(n1, n2) {\n    if (n1.length !== n2.length) {\n        return false;\n    }\n    for (let i = 0; i < n1.length; i++) {\n        if (n1[i] !== -1 && n2[i] !== -1 && n1[i] !== n2[i]) {\n            return false;\n        }\n    }\n    return true;\n}\n//# sourceMappingURL=tensor_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize } from './tensor_utils';\n/**\n * The TensorArray object keeps an array of Tensors.  It\n * allows reading from the array and writing to the array.\n */\nexport class TensorArray {\n    constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) {\n        this.name = name;\n        this.dtype = dtype;\n        this.maxSize = maxSize;\n        this.elementShape = elementShape;\n        this.identicalElementShapes = identicalElementShapes;\n        this.dynamicSize = dynamicSize;\n        this.clearAfterRead = clearAfterRead;\n        this.tensors = [];\n        this.closed_ = false;\n        this.idTensor = scalar(0);\n        keep(this.idTensor);\n    }\n    get id() {\n        return this.idTensor.id;\n    }\n    get closed() {\n        return this.closed_;\n    }\n    /**\n     * Dispose the tensors and idTensor and mark the TensoryArray as closed.\n     */\n    clearAndClose(keepIds) {\n        this.tensors.forEach(tensor => {\n            if (keepIds == null || !keepIds.has(tensor.tensor.id)) {\n                tensor.tensor.dispose();\n            }\n        });\n        this.tensors = [];\n        this.closed_ = true;\n        this.idTensor.dispose();\n    }\n    size() {\n        return this.tensors.length;\n    }\n    /**\n     * Read the value at location index in the TensorArray.\n     * @param index Number the index to read from.\n     */\n    read(index) {\n        if (this.closed_) {\n            throw new Error(`TensorArray ${this.name} has already been closed.`);\n        }\n        if (index < 0 || index >= this.size()) {\n            throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);\n        }\n        const tensorWithState = this.tensors[index];\n        if (tensorWithState.cleared) {\n            throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read ` +\n                `(perhaps try setting clear_after_read = false?).`);\n        }\n        if (this.clearAfterRead) {\n            tensorWithState.cleared = true;\n        }\n        tensorWithState.read = true;\n        return tensorWithState.tensor;\n    }\n    /**\n     * Helper method to read multiple tensors from the specified indices.\n     */\n    readMany(indices) {\n        return indices.map(index => this.read(index));\n    }\n    /**\n     * Write value into the index of the TensorArray.\n     * @param index number the index to write to.\n     * @param tensor\n     */\n    write(index, tensor) {\n        if (this.closed_) {\n            throw new Error(`TensorArray ${this.name} has already been closed.`);\n        }\n        if (index < 0 || !this.dynamicSize && index >= this.maxSize) {\n            throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);\n        }\n        const t = this.tensors[index] || {};\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},\n          because the value dtype is ${tensor.dtype}, but TensorArray dtype is ${this.dtype}.`);\n        }\n        // Set the shape for the first time write to unknow shape tensor array\n        if (this.size() === 0 &&\n            (this.elementShape == null || this.elementShape.length === 0)) {\n            this.elementShape = tensor.shape;\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`);\n        if (t.read) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);\n        }\n        if (t.written) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);\n        }\n        t.tensor = tensor;\n        keep(tensor);\n        t.written = true;\n        this.tensors[index] = t;\n    }\n    /**\n     * Helper method to write multiple tensors to the specified indices.\n     */\n    writeMany(indices, tensors) {\n        if (indices.length !== tensors.length) {\n            throw new Error(`TensorArray ${this.name}: could not write multiple tensors,` +\n                `because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);\n        }\n        indices.forEach((i, index) => this.write(i, tensors[index]));\n    }\n    /**\n     * Return selected values in the TensorArray as a packed Tensor. All of\n     * selected values must have been written and their shapes must all match.\n     * @param [indices] number[] Optional. Taking values in [0, max_value). If the\n     *    TensorArray is not dynamic, max_value=size(). If not specified returns\n     *    all tensors in the original order.\n     * @param [dtype]\n     */\n    gather(indices, dtype) {\n        if (!!dtype && dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);\n        }\n        if (!indices) {\n            indices = [];\n            for (let i = 0; i < this.size(); i++) {\n                indices.push(i);\n            }\n        }\n        else {\n            indices = indices.slice(0, this.size());\n        }\n        if (indices.length === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        // Read all the PersistentTensors into a vector to keep track of\n        // their memory.\n        const tensors = this.readMany(indices);\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, 'TensorArray shape mismatch: ');\n        return stack(tensors, 0);\n    }\n    /**\n     * Return the values in the TensorArray as a concatenated Tensor.\n     */\n    concat(dtype) {\n        if (!!dtype && dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);\n        }\n        if (this.size() === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        const indices = [];\n        for (let i = 0; i < this.size(); i++) {\n            indices.push(i);\n        }\n        // Collect all the tensors from the tensors array.\n        const tensors = this.readMany(indices);\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);\n        return concat(tensors, 0);\n    }\n    /**\n     * Scatter the values of a Tensor in specific indices of a TensorArray.\n     * @param indices nummber[] values in [0, max_value). If the\n     *    TensorArray is not dynamic, max_value=size().\n     * @param tensor Tensor input tensor.\n     */\n    scatter(indices, tensor) {\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n        }\n        if (indices.length !== tensor.shape[0]) {\n            throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n        }\n        const maxIndex = Math.max(...indices);\n        if (!this.dynamicSize && maxIndex >= this.maxSize) {\n            throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);\n        }\n        this.writeMany(indices, unstack(tensor, 0));\n    }\n    /**\n     * Split the values of a Tensor into the TensorArray.\n     * @param length number[] with the lengths to use when splitting value along\n     *    its first dimension.\n     * @param tensor Tensor, the tensor to split.\n     */\n    split(length, tensor) {\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n        }\n        let totalLength = 0;\n        const cumulativeLengths = length.map(len => {\n            totalLength += len;\n            return totalLength;\n        });\n        if (totalLength !== tensor.shape[0]) {\n            throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n        }\n        if (!this.dynamicSize && length.length !== this.maxSize) {\n            throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), ` +\n                'and the TensorArray is not marked as dynamically resizeable');\n        }\n        const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n        const tensors = [];\n        tidy(() => {\n            tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n            for (let i = 0; i < length.length; ++i) {\n                const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n                const indices = [0, previousLength, 0];\n                const sizes = [1, length[i], elementPerRow];\n                tensors[i] = reshape(slice(tensor, indices, sizes), this.elementShape);\n            }\n            return tensors;\n        });\n        const indices = [];\n        for (let i = 0; i < length.length; i++) {\n            indices[i] = i;\n        }\n        this.writeMany(indices, tensors);\n    }\n}\n//# sourceMappingURL=tensor_array.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize } from './tensor_utils';\n/**\n * TensorList stores a container of `tf.Tensor` objects, which are accessible\n * via tensors field.\n *\n * In order to get a copy of the underlying list, use the copy method:\n * ```\n *    TensorList b = a.copy();\n *    b.tensors().pushBack(t);  // This does not modify a.tensors().\n * ```\n *\n * Note that this is not a deep copy: the memory locations of the underlying\n * tensors will still point to the same locations of the corresponding tensors\n * in the original.\n */\nexport class TensorList {\n    /**\n     *\n     * @param tensors list of tensors\n     * @param elementShape shape of each tensor\n     * @param elementDtype data type of each tensor\n     * @param maxNumElements The maximum allowed size of `tensors`. Defaults to -1\n     *   meaning that the size of `tensors` is unbounded.\n     */\n    constructor(tensors, elementShape, elementDtype, maxNumElements = -1) {\n        this.tensors = tensors;\n        this.elementShape = elementShape;\n        this.elementDtype = elementDtype;\n        if (tensors != null) {\n            tensors.forEach(tensor => {\n                if (elementDtype !== tensor.dtype) {\n                    throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor.dtype}`);\n                }\n                assertShapesMatchAllowUndefinedSize(elementShape, tensor.shape, 'TensorList shape mismatch: ');\n                keep(tensor);\n            });\n        }\n        this.idTensor = scalar(0);\n        this.maxNumElements = maxNumElements;\n        keep(this.idTensor);\n    }\n    get id() {\n        return this.idTensor.id;\n    }\n    /**\n     * Get a new TensorList containing a copy of the underlying tensor container.\n     */\n    copy() {\n        return new TensorList([...this.tensors], this.elementShape, this.elementDtype);\n    }\n    /**\n     * Dispose the tensors and idTensor and clear the tensor list.\n     */\n    clearAndClose(keepIds) {\n        this.tensors.forEach(tensor => {\n            if (keepIds == null || !keepIds.has(tensor.id)) {\n                tensor.dispose();\n            }\n        });\n        this.tensors.length = 0;\n        this.idTensor.dispose();\n    }\n    /**\n     * The size of the tensors in the tensor list.\n     */\n    size() {\n        return this.tensors.length;\n    }\n    /**\n     * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1)\n     * tf.Tensor.\n     * @param elementShape shape of each tensor\n     * @param elementDtype data type of each tensor\n     * @param numElements the number of elements to stack\n     */\n    stack(elementShape, elementDtype, numElements = -1) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (numElements !== -1 && this.tensors.length !== numElements) {\n            throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);\n        }\n        assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, 'TensorList shape mismatch: ');\n        return tidy(() => {\n            const reshapedTensors = this.tensors.map(tensor => reshape(tensor, elementShape));\n            return stack(reshapedTensors, 0);\n        });\n    }\n    /**\n     * Pop a tensor from the end of the list.\n     * @param elementShape shape of the tensor\n     * @param elementDtype data type of the tensor\n     */\n    popBack(elementShape, elementDtype) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (this.size() === 0) {\n            throw new Error('Trying to pop from an empty list.');\n        }\n        const tensor = this.tensors.pop();\n        assertShapesMatchAllowUndefinedSize(tensor.shape, elementShape, 'TensorList shape mismatch: ');\n        return reshape(tensor, elementShape);\n    }\n    /**\n     * Push a tensor to the end of the list.\n     * @param tensor Tensor to be pushed.\n     */\n    pushBack(tensor) {\n        if (tensor.dtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(tensor.shape, this.elementShape, 'TensorList shape mismatch: ');\n        if (this.maxNumElements === this.size()) {\n            throw new Error(`Trying to push element into a full list.`);\n        }\n        keep(tensor);\n        this.tensors.push(tensor);\n    }\n    /**\n     * Update the size of the list.\n     * @param size the new size of the list.\n     */\n    resize(size) {\n        if (size < 0) {\n            throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);\n        }\n        if (this.maxNumElements !== -1 && size > this.maxNumElements) {\n            throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);\n        }\n        this.tensors.length = size;\n    }\n    /**\n     * Retrieve the element at the provided index\n     * @param elementShape shape of the tensor\n     * @param elementDtype dtype of the tensor\n     * @param elementIndex index of the tensor\n     */\n    getItem(elementIndex, elementShape, elementDtype) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (elementIndex < 0 || elementIndex > this.tensors.length) {\n            throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);\n        }\n        if (this.tensors[elementIndex] == null) {\n            throw new Error(`element at index ${elementIndex} is null.`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, 'TensorList shape mismatch: ');\n        return this.tensors[elementIndex];\n    }\n    /**\n     * Set the tensor at the index\n     * @param elementIndex index of the tensor\n     * @param tensor the tensor to be inserted into the list\n     */\n    setItem(elementIndex, tensor) {\n        if (tensor.dtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n        }\n        if (elementIndex < 0 ||\n            this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {\n            throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, 'TensorList shape mismatch: ');\n        keep(tensor);\n        this.tensors[elementIndex] = tensor;\n    }\n    /**\n     * Return selected values in the TensorList as a stacked Tensor. All of\n     * selected values must have been written and their shapes must all match.\n     * @param indices indices of tensors to gather\n     * @param elementDtype output tensor dtype\n     * @param elementShape output tensor element shape\n     */\n    gather(indices, elementDtype, elementShape) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n        // When indices is greater than the size of the list, indices beyond the\n        // size of the list are ignored.\n        indices = indices.slice(0, this.size());\n        if (indices.length === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        return tidy(() => {\n            const tensors = indices.map(i => reshape(this.tensors[i], elementShape));\n            return stack(tensors, 0);\n        });\n    }\n    /**\n     * Return the values in the TensorList as a concatenated Tensor.\n     * @param elementDtype output tensor dtype\n     * @param elementShape output tensor element shape\n     */\n    concat(elementDtype, elementShape) {\n        if (!!elementDtype && elementDtype !== this.elementDtype) {\n            throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n        if (this.size() === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        return tidy(() => {\n            const tensors = this.tensors.map(t => reshape(t, elementShape));\n            return concat(tensors, 0);\n        });\n    }\n}\n/**\n * Creates a TensorList which, when stacked, has the value of tensor.\n * @param tensor from tensor\n * @param elementShape output tensor element shape\n */\nexport function fromTensor(tensor, elementShape, elementDtype) {\n    const dtype = tensor.dtype;\n    if (tensor.shape.length < 1) {\n        throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor.shape}`);\n    }\n    if (tensor.dtype !== elementDtype) {\n        throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${elementDtype}`);\n    }\n    const outputShape = tensor.shape.slice(1);\n    assertShapesMatchAllowUndefinedSize(outputShape, elementShape, 'TensorList shape mismatch: ');\n    const tensorList = unstack(tensor);\n    return new TensorList(tensorList, elementShape, dtype);\n}\n/**\n * Return a TensorList of the given size with empty elements.\n * @param elementShape the shape of the future elements of the list\n * @param elementDtype the desired type of elements in the list\n * @param numElements the number of elements to reserve\n */\nexport function reserve(elementShape, elementDtype, numElements) {\n    return new TensorList([], elementShape, elementDtype, numElements);\n}\n/**\n * Put tensors at specific indices of a stacked tensor into a TensorList.\n * @param indices list of indices on how to scatter the tensor.\n * @param tensor input tensor.\n * @param elementShape the shape of the future elements of the list\n * @param numElements the number of elements to scatter\n */\nexport function scatter(tensor, indices, elementShape, numElements) {\n    if (indices.length !== tensor.shape[0]) {\n        throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n    }\n    const maxIndex = Math.max(...indices);\n    if (numElements != null && numElements !== -1 && maxIndex >= numElements) {\n        throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);\n    }\n    const list = new TensorList([], elementShape, tensor.dtype, numElements);\n    const tensors = unstack(tensor, 0);\n    indices.forEach((value, index) => {\n        list.setItem(value, tensors[index]);\n    });\n    return list;\n}\n/**\n * Split the values of a Tensor into a TensorList.\n * @param length the lengths to use when splitting value along\n *    its first dimension.\n * @param tensor the tensor to split.\n * @param elementShape the shape of the future elements of the list\n */\nexport function split(tensor, length, elementShape) {\n    let totalLength = 0;\n    const cumulativeLengths = length.map(len => {\n        totalLength += len;\n        return totalLength;\n    });\n    if (totalLength !== tensor.shape[0]) {\n        throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n    }\n    const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n    const tensors = tidy(() => {\n        const tensors = [];\n        tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n        for (let i = 0; i < length.length; ++i) {\n            const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n            const indices = [0, previousLength, 0];\n            const sizes = [1, length[i], elementPerRow];\n            tensors[i] = reshape(slice(tensor, indices, sizes), elementShape);\n        }\n        tensor.dispose();\n        return tensors;\n    });\n    const list = new TensorList([], elementShape, tensor.dtype, length.length);\n    for (let i = 0; i < tensors.length; i++) {\n        list.setItem(i, tensors[i]);\n    }\n    return list;\n}\n//# sourceMappingURL=tensor_list.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { scalar } from '@tensorflow/tfjs-core';\nimport { TensorArray } from '../../executor/tensor_array';\nimport { fromTensor, reserve, scatter, split } from '../../executor/tensor_list';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = async (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'If':\n        case 'StatelessIf': {\n            const thenFunc = getParamValue('thenBranch', node, tensorMap, context);\n            const elseFunc = getParamValue('elseBranch', node, tensorMap, context);\n            const cond = getParamValue('cond', node, tensorMap, context);\n            const args = getParamValue('args', node, tensorMap, context);\n            const condValue = await cond.data();\n            if (condValue[0]) {\n                return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n            }\n            else {\n                return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n            }\n        }\n        case 'While':\n        case 'StatelessWhile': {\n            const bodyFunc = getParamValue('body', node, tensorMap, context);\n            const condFunc = getParamValue('cond', node, tensorMap, context);\n            const args = getParamValue('args', node, tensorMap, context);\n            // Calculate the condition of the loop\n            const condResult = (await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap));\n            const argIds = args.map(tensor => tensor.id);\n            let condValue = await condResult[0].data();\n            // Dispose the intermediate tensors for condition function\n            condResult.forEach(tensor => {\n                if (!tensor.kept && argIds.indexOf(tensor.id) === -1) {\n                    tensor.dispose();\n                }\n            });\n            let result = args;\n            while (condValue[0]) {\n                // Record the previous result for intermediate tensor tracking\n                const origResult = result;\n                // Execution the body of the loop\n                result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);\n                const resultIds = result.map(tensor => tensor.id);\n                // Dispose the intermediate tensor for body function that is not global\n                // kept, not input/output of the body function\n                origResult.forEach(tensor => {\n                    if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n                        resultIds.indexOf(tensor.id) === -1) {\n                        tensor.dispose();\n                    }\n                });\n                // Recalcuate the condition of the loop using the latest results.\n                const condResult = (await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap));\n                condValue = await condResult[0].data();\n                // Dispose the intermediate tensors for condition function\n                condResult.forEach(tensor => {\n                    if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n                        resultIds.indexOf(tensor.id) === -1) {\n                        tensor.dispose();\n                    }\n                });\n            }\n            return result;\n        }\n        case 'LoopCond': {\n            const pred = getParamValue('pred', node, tensorMap, context);\n            return [cloneTensor(pred)];\n        }\n        case 'Switch': {\n            const pred = getParamValue('pred', node, tensorMap, context);\n            let data = getParamValue('data', node, tensorMap, context);\n            if (!data.kept) {\n                data = cloneTensor(data);\n            }\n            // Outputs nodes :0 => false, :1 => true\n            return (await pred.data())[0] ? [undefined, data] : [data, undefined];\n        }\n        case 'Merge': {\n            const inputName = node.inputNames.find(name => getTensor(name, tensorMap, context) !== undefined);\n            if (inputName) {\n                const data = getTensor(inputName, tensorMap, context);\n                return [cloneTensor(data)];\n            }\n            return undefined;\n        }\n        case 'Enter': {\n            const frameId = getParamValue('frameName', node, tensorMap, context);\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.enterFrame(frameId);\n            return [cloneTensor(data)];\n        }\n        case 'Exit': {\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.exitFrame();\n            return [cloneTensor(data)];\n        }\n        case 'NextIteration': {\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.nextIteration();\n            return [cloneTensor(data)];\n        }\n        case 'TensorArrayV3': {\n            const size = getParamValue('size', node, tensorMap, context);\n            const dtype = getParamValue('dtype', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const dynamicSize = getParamValue('dynamicSize', node, tensorMap, context);\n            const clearAfterRead = getParamValue('clearAfterRead', node, tensorMap, context);\n            const identicalElementShapes = getParamValue('identicalElementShapes', node, tensorMap, context);\n            const name = getParamValue('name', node, tensorMap, context);\n            const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);\n            context.addTensorArray(tensorArray);\n            return [tensorArray.idTensor, scalar(1.0)];\n        }\n        case 'TensorArrayWriteV3': {\n            const id = getParamValue('tensorArrayId', node, tensorMap, context);\n            const index = getParamValue('index', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const writeTensorArray = context.getTensorArray(id.id);\n            writeTensorArray.write(index, writeTensor);\n            return [writeTensorArray.idTensor];\n        }\n        case 'TensorArrayReadV3': {\n            const readId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const readIndex = getParamValue('index', node, tensorMap, context);\n            const readTensorArray = context.getTensorArray(readId.id);\n            return [readTensorArray.read(readIndex)];\n        }\n        case 'TensorArrayGatherV3': {\n            const gatherId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const gatherIndices = getParamValue('indices', node, tensorMap, context);\n            const gatherDtype = getParamValue('dtype', node, tensorMap, context);\n            const gatherTensorArray = context.getTensorArray(gatherId.id);\n            return [gatherTensorArray.gather(gatherIndices, gatherDtype)];\n        }\n        case 'TensorArrayScatterV3': {\n            const scatterId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const scatterIndices = getParamValue('indices', node, tensorMap, context);\n            const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n            const scatterTensorArray = context.getTensorArray(scatterId.id);\n            scatterTensorArray.scatter(scatterIndices, scatterTensor);\n            return [scatterTensorArray.idTensor];\n        }\n        case 'TensorArrayConcatV3': {\n            const concatId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const concatTensorArray = context.getTensorArray(concatId.id);\n            const concatDtype = getParamValue('dtype', node, tensorMap, context);\n            return [concatTensorArray.concat(concatDtype)];\n        }\n        case 'TensorArraySplitV3': {\n            const splitId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const splitTensor = getParamValue('tensor', node, tensorMap, context);\n            const lengths = getParamValue('lengths', node, tensorMap, context);\n            const splitTensorArray = context.getTensorArray(splitId.id);\n            splitTensorArray.split(lengths, splitTensor);\n            return [splitTensorArray.idTensor];\n        }\n        case 'TensorArraySizeV3': {\n            const sizeId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const sizeTensorArray = context.getTensorArray(sizeId.id);\n            return [scalar(sizeTensorArray.size(), 'int32')];\n        }\n        case 'TensorArrayCloseV3': {\n            const closeId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const closeTensorArray = context.getTensorArray(closeId.id);\n            closeTensorArray.clearAndClose();\n            return [closeTensorArray.idTensor];\n        }\n        case 'TensorListSetItem': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const index = getParamValue('index', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            tensorList.setItem(index, writeTensor);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListGetItem': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const readIndex = getParamValue('index', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDType = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.getItem(readIndex, elementShape, elementDType)];\n        }\n        case 'TensorListScatterV2':\n        case 'TensorListScatter': {\n            const scatterIndices = getParamValue('indices', node, tensorMap, context);\n            const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListReserve': {\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = reserve(elementShape, elementDtype, numElements);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListGather': {\n            const gatherId = getParamValue('tensorListId', node, tensorMap, context);\n            const gatherIndices = getParamValue('indices', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(gatherId.id);\n            return [tensorList.gather(gatherIndices, elementDtype, elementShape)];\n        }\n        case 'TensorListStack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.stack(elementShape, elementDtype, numElements)];\n        }\n        case 'TensorListFromTensor': {\n            const tensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = fromTensor(tensor, elementShape, elementDtype);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListConcat': {\n            const concatId = getParamValue('tensorListId', node, tensorMap, context);\n            const tensorList = context.getTensorList(concatId.id);\n            const concatDtype = getParamValue('dtype', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            return [tensorList.concat(concatDtype, elementShape)];\n        }\n        case 'TensorListPushBack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            tensorList.pushBack(writeTensor);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListPopBack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDType = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.popBack(elementShape, elementDType)];\n        }\n        case 'TensorListSplit': {\n            const splitTensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const lengths = getParamValue('lengths', node, tensorMap, context);\n            const tensorList = split(splitTensor, lengths, elementShape);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'control';\n//# sourceMappingURL=control_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getPadding, getParamValue } from './utils';\nfunction fusedConvAndDepthWiseParams(node, tensorMap, context) {\n    const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n    const isBiasAdd = extraOp === 'biasadd';\n    const isPrelu = activationFunc === 'prelu';\n    const isBatchNorm = extraOp === 'fusedbatchnorm';\n    const numArgs = getParamValue('numArgs', node, tensorMap, context);\n    if (isBiasAdd) {\n        if (isPrelu && numArgs !== 2) {\n            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu ' +\n                'must have two extra arguments: bias and alpha.');\n        }\n        if (!isPrelu && numArgs !== 1) {\n            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd must have ' +\n                'one extra argument: bias.');\n        }\n    }\n    if (isBatchNorm) {\n        throw new Error('FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.');\n    }\n    const stride = getParamValue('strides', node, tensorMap, context);\n    const pad = getPadding(node, tensorMap, context);\n    const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n        .toUpperCase();\n    const dilations = getParamValue('dilations', node, tensorMap, context);\n    const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n    return {\n        stride,\n        pad,\n        dataFormat,\n        dilations,\n        biasArg,\n        preluArg,\n        activationFunc\n    };\n}\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Conv1D': {\n            const stride = getParamValue('stride', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilation = getParamValue('dilation', node, tensorMap, context);\n            return [tfOps.conv1d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), stride, pad, dataFormat, dilation)];\n        }\n        case 'Conv2D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            return [tfOps.conv2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n        }\n        case '_FusedConv2D': {\n            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n            return [tfOps.fused.conv2d({\n                    x: getParamValue('x', node, tensorMap, context),\n                    filter: getParamValue('filter', node, tensorMap, context),\n                    strides: [stride[1], stride[2]],\n                    pad: pad,\n                    dataFormat: dataFormat,\n                    dilations: [dilations[1], dilations[2]],\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        }\n        case 'FusedDepthwiseConv2dNative': {\n            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n            return [tfOps.fused.depthwiseConv2d({\n                    x: getParamValue('x', node, tensorMap, context),\n                    filter: getParamValue('filter', node, tensorMap, context),\n                    strides: [stride[1], stride[2]],\n                    pad: pad,\n                    dataFormat: dataFormat,\n                    dilations: [dilations[1], dilations[2]],\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        }\n        case 'Conv2DBackpropInput':\n        case 'Conv2dTranspose': {\n            const shape = getParamValue('outputShape', node, tensorMap, context);\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            return [tfOps.conv2dTranspose(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), shape, [stride[1], stride[2]], pad)];\n        }\n        case 'DepthwiseConv2dNative':\n        case 'DepthwiseConv2d': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            return [tfOps.depthwiseConv2d(getParamValue('input', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n        }\n        case 'Conv3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            return [tfOps.conv3d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2], stride[3]], pad, dataFormat, [dilations[1], dilations[2], dilations[3]])];\n        }\n        case 'AvgPool': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.avgPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n        }\n        case 'MaxPool': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.maxPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n        }\n        case 'MaxPoolWithArgmax': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            const includeBatchInIndex = getParamValue('includeBatchInIndex', node, tensorMap, context);\n            const { result, indexes } = tfOps.maxPoolWithArgmax(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad, includeBatchInIndex);\n            return [result, indexes];\n        }\n        case 'AvgPool3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.avgPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n        }\n        case 'MaxPool3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.maxPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n        }\n        case 'Dilation2D': {\n            const strides = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            // strides: [1, stride_height, stride_width, 1].\n            const strideHeight = strides[1];\n            const strideWidth = strides[2];\n            // dilations: [1, dilation_height, dilation_width, 1].\n            const dilationHeight = dilations[1];\n            const dilationWidth = dilations[2];\n            return [tfOps.dilation2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [strideHeight, strideWidth], pad, [dilationHeight, dilationWidth], 'NHWC' /* dataFormat */)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'convolution';\n//# sourceMappingURL=convolution_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Fill': {\n            const shape = getParamValue('shape', node, tensorMap, context);\n            const dtype = getParamValue('dtype', node, tensorMap, context);\n            const value = getParamValue('value', node, tensorMap, context);\n            return [tfOps.fill(shape, value, dtype)];\n        }\n        case 'LinSpace': {\n            const start = getParamValue('start', node, tensorMap, context);\n            const stop = getParamValue('stop', node, tensorMap, context);\n            const num = getParamValue('num', node, tensorMap, context);\n            return [tfOps.linspace(start, stop, num)];\n        }\n        case 'Multinomial': {\n            const logits = getParamValue('logits', node, tensorMap, context);\n            const numSamples = getParamValue('numSamples', node, tensorMap, context);\n            const seed = getParamValue('seed', node, tensorMap, context);\n            return [tfOps.multinomial(logits, numSamples, seed)];\n        }\n        case 'OneHot': {\n            const indices = getParamValue('indices', node, tensorMap, context);\n            const depth = getParamValue('depth', node, tensorMap, context);\n            const onValue = getParamValue('onValue', node, tensorMap, context);\n            const offValue = getParamValue('offValue', node, tensorMap, context);\n            return [tfOps.oneHot(indices, depth, onValue, offValue)];\n        }\n        case 'Ones': {\n            return [tfOps.ones(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'OnesLike': {\n            return [tfOps.onesLike(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'RandomUniform': {\n            return [tfOps.randomUniform(\n                // tslint:disable-next-line:no-any\n                getParamValue('shape', node, tensorMap, context), getParamValue('minval', node, tensorMap, context), getParamValue('maxval', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'Range': {\n            const start = getParamValue('start', node, tensorMap, context);\n            const stop = getParamValue('stop', node, tensorMap, context);\n            const step = getParamValue('step', node, tensorMap, context);\n            return [tfOps.range(start, stop, step, getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'TruncatedNormal': {\n            const shape = getParamValue('shape', node, tensorMap, context);\n            const mean = getParamValue('mean', node, tensorMap, context);\n            const stdDev = getParamValue('stdDev', node, tensorMap, context);\n            const seed = getParamValue('seed', node, tensorMap, context);\n            return [tfOps.truncatedNormal(shape, mean, stdDev, getParamValue('dtype', node, tensorMap, context), seed)];\n        }\n        case 'Zeros': {\n            return [tfOps.zeros(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'ZerosLike': {\n            return [tfOps.zerosLike(getParamValue('x', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'creation';\n//# sourceMappingURL=creation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nfunction nmsParams(node, tensorMap, context) {\n    const boxes = getParamValue('boxes', node, tensorMap, context);\n    const scores = getParamValue('scores', node, tensorMap, context);\n    const maxOutputSize = getParamValue('maxOutputSize', node, tensorMap, context);\n    const iouThreshold = getParamValue('iouThreshold', node, tensorMap, context);\n    const scoreThreshold = getParamValue('scoreThreshold', node, tensorMap, context);\n    const softNmsSigma = getParamValue('softNmsSigma', node, tensorMap, context);\n    return {\n        boxes,\n        scores,\n        maxOutputSize,\n        iouThreshold,\n        scoreThreshold,\n        softNmsSigma\n    };\n}\nexport const executeOp = async (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'NonMaxSuppressionV5': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node, tensorMap, context);\n            const result = await tfOps.image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n            return [result.selectedIndices, result.selectedScores];\n        }\n        case 'NonMaxSuppressionV4': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n            const padToMaxOutputSize = getParamValue('padToMaxOutputSize', node, tensorMap, context);\n            const result = await tfOps.image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n            return [result.selectedIndices, result.validOutputs];\n        }\n        case 'NonMaxSuppressionV3':\n        case 'NonMaxSuppressionV2': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n            return [await tfOps.image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];\n        }\n        case 'Where': {\n            const condition = tfOps.cast(getParamValue('condition', node, tensorMap, context), 'bool');\n            const result = [await tfOps.whereAsync(condition)];\n            condition.dispose();\n            return result;\n        }\n        case 'ListDiff': {\n            return tfOps.setdiff1dAsync(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context));\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'dynamic';\n//# sourceMappingURL=dynamic_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'TopKV2': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const k = getParamValue('k', node, tensorMap, context);\n            const sorted = getParamValue('sorted', node, tensorMap, context);\n            const result = tfOps.topk(x, k, sorted);\n            return [result.values, result.indices];\n        }\n        case 'Unique': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const result = tfOps.unique(x);\n            return [result.values, result.indices];\n        }\n        case 'UniqueV2': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const result = tfOps.unique(x, axis);\n            return [result.values, result.indices];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'evaluation';\n//# sourceMappingURL=evaluation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Const': {\n            return tensorMap[node.name];\n        }\n        case 'PlaceholderWithDefault':\n            const def = getParamValue('default', node, tensorMap, context);\n            return [getTensor(node.name, tensorMap, context) || def];\n        case 'Placeholder':\n            return [getTensor(node.name, tensorMap, context)];\n        case 'Identity':\n        case 'StopGradient':\n        case 'FakeQuantWithMinMaxVars': { // This op is currently ignored.\n            const data = getParamValue('x', node, tensorMap, context);\n            return [cloneTensor(data)];\n        }\n        case 'IdentityN':\n            return getParamValue('x', node, tensorMap, context)\n                .map((t) => cloneTensor(t));\n        case 'Snapshot':\n            const snapshot = getParamValue('x', node, tensorMap, context);\n            return [cloneTensor(snapshot)];\n        case 'Shape':\n            return [tfOps.tensor1d(getParamValue('x', node, tensorMap, context).shape, 'int32')];\n        case 'ShapeN':\n            return getParamValue('x', node, tensorMap, context)\n                .map((t) => tfOps.tensor1d(t.shape));\n        case 'Size':\n            return [tfOps.scalar(getParamValue('x', node, tensorMap, context).size, 'int32')];\n        case 'Rank':\n            return [tfOps.scalar(getParamValue('x', node, tensorMap, context).rank, 'int32')];\n        case 'NoOp':\n            return [tfOps.scalar(1)];\n        case 'Print':\n            const input = getParamValue('x', node, tensorMap, context);\n            const data = getParamValue('data', node, tensorMap, context);\n            const message = getParamValue('message', node, tensorMap, context);\n            const summarize = getParamValue('summarize', node, tensorMap, context);\n            console.warn('The graph has a tf.print() operation,' +\n                'usually used for debugging, which slows down performance.');\n            console.log(message);\n            for (let i = 0; i < data.length; i++) {\n                console.log(Array.prototype.slice.call(data[i].dataSync())\n                    .slice(0, summarize));\n            }\n            return [input];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'graph';\n//# sourceMappingURL=graph_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'ResizeBilinear': {\n            const images = getParamValue('images', node, tensorMap, context);\n            const size = getParamValue('size', node, tensorMap, context);\n            const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n            return [tfOps.image.resizeBilinear(images, [size[0], size[1]], alignCorners)];\n        }\n        case 'ResizeNearestNeighbor': {\n            const images = getParamValue('images', node, tensorMap, context);\n            const size = getParamValue('size', node, tensorMap, context);\n            const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n            return [tfOps.image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners)];\n        }\n        case 'CropAndResize': {\n            const image = getParamValue('image', node, tensorMap, context);\n            const boxes = getParamValue('boxes', node, tensorMap, context);\n            const boxInd = getParamValue('boxInd', node, tensorMap, context);\n            const cropSize = getParamValue('cropSize', node, tensorMap, context);\n            const method = getParamValue('method', node, tensorMap, context);\n            const extrapolationValue = getParamValue('extrapolationValue', node, tensorMap, context);\n            return [tfOps.image.cropAndResize(image, boxes, boxInd, cropSize, method, extrapolationValue)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'image';\n//# sourceMappingURL=image_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Equal': {\n            return [tfOps.equal(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'NotEqual': {\n            return [tfOps.notEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Greater': {\n            return [tfOps.greater(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'GreaterEqual': {\n            return [tfOps.greaterEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Less': {\n            return [tfOps.less(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LessEqual': {\n            return [tfOps.lessEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LogicalAnd': {\n            return [tfOps.logicalAnd(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LogicalNot': {\n            return [tfOps.logicalNot(getParamValue('a', node, tensorMap, context))];\n        }\n        case 'LogicalOr': {\n            return [tfOps.logicalOr(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Select':\n        case 'SelectV2': {\n            return [tfOps.where(getParamValue('condition', node, tensorMap, context), getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'logical';\n//# sourceMappingURL=logical_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'BatchMatMul':\n        case 'BatchMatMulV2':\n        case 'MatMul':\n            return [tfOps.matMul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context), getParamValue('transposeA', node, tensorMap, context), getParamValue('transposeB', node, tensorMap, context))];\n        case 'Transpose':\n            return [tfOps.transpose(getParamValue('x', node, tensorMap, context), getParamValue('perm', node, tensorMap, context))];\n        case '_FusedMatMul':\n            const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n            const isBiasAdd = extraOp === 'biasadd';\n            const isPrelu = activationFunc === 'prelu';\n            const numArgs = getParamValue('numArgs', node, tensorMap, context);\n            if (isBiasAdd) {\n                if (isPrelu && numArgs !== 2) {\n                    throw new Error('Fused MatMul with BiasAdd and Prelu must have two ' +\n                        'extra arguments: bias and alpha.');\n                }\n                if (!isPrelu && numArgs !== 1) {\n                    throw new Error('Fused MatMul with BiasAdd must have one extra argument: bias.');\n                }\n            }\n            const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n            return [tfOps.fused.matMul({\n                    a: getParamValue('a', node, tensorMap, context),\n                    b: getParamValue('b', node, tensorMap, context),\n                    transposeA: getParamValue('transposeA', node, tensorMap, context),\n                    transposeB: getParamValue('transposeB', node, tensorMap, context),\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'matrices';\n//# sourceMappingURL=matrices_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'FusedBatchNorm':\n        case 'FusedBatchNormV2': {\n            return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n        }\n        case 'FusedBatchNormV3': {\n            return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n        }\n        case 'LRN': {\n            return [tfOps.localResponseNormalization(getParamValue('x', node, tensorMap, context), getParamValue('radius', node, tensorMap, context), getParamValue('bias', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context), getParamValue('beta', node, tensorMap, context))];\n        }\n        case 'Softmax': {\n            return [tfOps.softmax(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'LogSoftmax': {\n            return [tfOps.logSoftmax(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'SparseToDense': {\n            return [tfOps.sparseToDense(getParamValue('sparseIndices', node, tensorMap, context), getParamValue('outputShape', node, tensorMap, context), getParamValue('sparseValues', node, tensorMap, context), getParamValue('defaultValue', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'normalization';\n//# sourceMappingURL=normalization_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Max': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.max(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Mean': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.mean(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Min': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.min(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Sum': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.sum(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'All': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.all(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Any': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.any(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'ArgMax': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.argMax(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'ArgMin': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.argMin(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Prod': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.prod(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Cumsum': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const exclusive = getParamValue('exclusive', node, tensorMap, context);\n            const reverse = getParamValue('reverse', node, tensorMap, context);\n            return [tfOps.cumsum(getParamValue('x', node, tensorMap, context), axis, exclusive, reverse)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'reduction';\n//# sourceMappingURL=reduction_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tidy, util } from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'ConcatV2':\n        case 'Concat': {\n            const n = getParamValue('n', node, tensorMap, context);\n            const axis = getParamValue('axis', node, tensorMap, context);\n            let inputs = getParamValue('tensors', node, tensorMap, context);\n            inputs = inputs.slice(0, n);\n            return [tfOps.concat(inputs, axis)];\n        }\n        case 'GatherV2':\n        case 'Gather': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const input = getParamValue('x', node, tensorMap, context);\n            const indices = getParamValue('indices', node, tensorMap, context);\n            return [tfOps.gather(input, tfOps.cast(indices, 'int32'), axis)];\n        }\n        case 'ReverseV2':\n        case 'Reverse': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const input = getParamValue('x', node, tensorMap, context);\n            return [tfOps.reverse(input, axis)];\n        }\n        case 'Slice': {\n            // tslint:disable-next-line:no-any\n            const begin = getParamValue('begin', node, tensorMap, context);\n            // tslint:disable-next-line:no-any\n            const size = getParamValue('size', node, tensorMap, context);\n            return [tfOps.slice(getParamValue('x', node, tensorMap, context), begin, size)];\n        }\n        case 'StridedSlice': {\n            const begin = getParamValue('begin', node, tensorMap, context);\n            const end = getParamValue('end', node, tensorMap, context);\n            const strides = getParamValue('strides', node, tensorMap, context);\n            const beginMask = getParamValue('beginMask', node, tensorMap, context);\n            const endMask = getParamValue('endMask', node, tensorMap, context);\n            const ellipsisMask = getParamValue('ellipsisMask', node, tensorMap, context);\n            const newAxisMask = getParamValue('newAxisMask', node, tensorMap, context);\n            const shrinkAxisMask = getParamValue('shrinkAxisMask', node, tensorMap, context);\n            const tensor = getParamValue('x', node, tensorMap, context);\n            return [tfOps.stridedSlice(tensor, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)];\n        }\n        case 'Pack': {\n            return tidy(() => {\n                const axis = getParamValue('axis', node, tensorMap, context);\n                const tensors = getParamValue('tensors', node, tensorMap, context);\n                // Reshape the tensors to the first tensor's shape if they don't\n                // match.\n                const shape = tensors[0].shape;\n                const squeezedShape = tfOps.squeeze(tensors[0]).shape;\n                const mapped = tensors.map(tensor => {\n                    const sameShape = util.arraysEqual(tensor.shape, shape);\n                    if (!sameShape &&\n                        !util.arraysEqual(tfOps.squeeze(tensor).shape, squeezedShape)) {\n                        throw new Error('the input tensors shape does not match');\n                    }\n                    return sameShape ? tensor : tfOps.reshape(tensor, shape);\n                });\n                return [tfOps.stack(mapped, axis)];\n            });\n        }\n        case 'Unpack': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const tensor = getParamValue('tensor', node, tensorMap, context);\n            return tfOps.unstack(tensor, axis);\n        }\n        case 'Tile': {\n            const reps = getParamValue('reps', node, tensorMap, context);\n            return [tfOps.tile(getParamValue('x', node, tensorMap, context), reps)];\n        }\n        case 'Split':\n        case 'SplitV': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const numOrSizeSplits = getParamValue('numOrSizeSplits', node, tensorMap, context);\n            const tensor = getParamValue('x', node, tensorMap, context);\n            return tfOps.split(tensor, numOrSizeSplits, axis);\n        }\n        case 'ScatterNd': {\n            const indices = getParamValue('indices', node, tensorMap, context);\n            const values = getParamValue('values', node, tensorMap, context);\n            const shape = getParamValue('shape', node, tensorMap, context);\n            return [tfOps.scatterND(indices, values, shape)];\n        }\n        case 'GatherNd': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const indices = getParamValue('indices', node, tensorMap, context);\n            return [tfOps.gatherND(x, indices)];\n        }\n        case 'SparseToDense': {\n            const indices = getParamValue('sparseIndices', node, tensorMap, context);\n            const shape = getParamValue('outputShape', node, tensorMap, context);\n            const sparseValues = getParamValue('sparseValues', node, tensorMap, context);\n            const defaultValue = getParamValue('defaultValue', node, tensorMap, context);\n            return [tfOps.sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ?\n                    defaultValue :\n                    tfOps.cast(defaultValue, sparseValues.dtype))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'slice_join';\n//# sourceMappingURL=slice_join_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'FFT': {\n            return [tfOps.fft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'IFFT': {\n            return [tfOps.ifft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'RFFT': {\n            return [tfOps.rfft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'IRFFT': {\n            return [tfOps.irfft(getParamValue('x', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'spectral';\n//# sourceMappingURL=spectral_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Cast': {\n            return [tfOps.cast(getParamValue('x', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'ExpandDims': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.expandDims(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Squeeze': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.squeeze(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Reshape': {\n            return [tfOps.reshape(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n        }\n        case 'PadV2':\n        case 'Pad': {\n            return [tfOps.pad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('constantValue', node, tensorMap, context))];\n        }\n        case 'SpaceToBatchND': {\n            const blockShape = getParamValue('blockShape', node, tensorMap, context);\n            const paddings = getParamValue('paddings', node, tensorMap, context);\n            return [tfOps.spaceToBatchND(getParamValue('x', node, tensorMap, context), blockShape, paddings)];\n        }\n        case 'BatchToSpaceND': {\n            const blockShape = getParamValue('blockShape', node, tensorMap, context);\n            const crops = getParamValue('crops', node, tensorMap, context);\n            return [tfOps.batchToSpaceND(getParamValue('x', node, tensorMap, context), blockShape, crops)];\n        }\n        case 'DepthToSpace': {\n            const blockSize = getParamValue('blockSize', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context).toUpperCase();\n            return [tfOps.depthToSpace(getParamValue('x', node, tensorMap, context), blockSize, dataFormat)];\n        }\n        case 'BroadcastTo': {\n            return [tfOps.broadcastTo(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'transformation';\n//# sourceMappingURL=transformation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { NodeValueImpl } from './custom_op/node_value_impl';\nimport { getRegisteredOp } from './custom_op/register';\nimport * as arithmetic from './executors/arithmetic_executor';\nimport * as basicMath from './executors/basic_math_executor';\nimport * as control from './executors/control_executor';\nimport * as convolution from './executors/convolution_executor';\nimport * as creation from './executors/creation_executor';\nimport * as dynamic from './executors/dynamic_executor';\nimport * as evaluation from './executors/evaluation_executor';\nimport * as graph from './executors/graph_executor';\nimport * as image from './executors/image_executor';\nimport * as logical from './executors/logical_executor';\nimport * as matrices from './executors/matrices_executor';\nimport * as normalization from './executors/normalization_executor';\nimport * as reduction from './executors/reduction_executor';\nimport * as sliceJoin from './executors/slice_join_executor';\nimport * as spectral from './executors/spectral_executor';\nimport * as transformation from './executors/transformation_executor';\n/**\n * Executes the op defined by the node object.\n * @param node\n * @param tensorMap contains tensors for executed nodes and weights\n */\nexport function executeOp(node, tensorMap, context) {\n    const value = ((node, tensorMap, context) => {\n        switch (node.category) {\n            case 'arithmetic':\n                return tfc.tidy(() => arithmetic.executeOp(node, tensorMap, context));\n            case 'basic_math':\n                return tfc.tidy(() => basicMath.executeOp(node, tensorMap, context));\n            case 'control':\n                return control.executeOp(node, tensorMap, context);\n            case 'convolution':\n                return tfc.tidy(() => convolution.executeOp(node, tensorMap, context));\n            case 'creation':\n                return tfc.tidy(() => creation.executeOp(node, tensorMap, context));\n            case 'dynamic':\n                return dynamic.executeOp(node, tensorMap, context);\n            case 'evaluation':\n                return tfc.tidy(() => evaluation.executeOp(node, tensorMap, context));\n            case 'image':\n                return tfc.tidy(() => image.executeOp(node, tensorMap, context));\n            case 'graph':\n                return tfc.tidy(() => graph.executeOp(node, tensorMap, context));\n            case 'logical':\n                return tfc.tidy(() => logical.executeOp(node, tensorMap, context));\n            case 'matrices':\n                return tfc.tidy(() => matrices.executeOp(node, tensorMap, context));\n            case 'normalization':\n                return tfc.tidy(() => normalization.executeOp(node, tensorMap, context));\n            case 'reduction':\n                return tfc.tidy(() => reduction.executeOp(node, tensorMap, context));\n            case 'slice_join':\n                return tfc.tidy(() => sliceJoin.executeOp(node, tensorMap, context));\n            case 'spectral':\n                return tfc.tidy(() => spectral.executeOp(node, tensorMap, context));\n            case 'transformation':\n                return tfc.tidy(() => transformation.executeOp(node, tensorMap, context));\n            case 'custom':\n                const opMapper = getRegisteredOp(node.op);\n                if (opMapper && opMapper.customExecutor) {\n                    return opMapper.customExecutor(new NodeValueImpl(node, tensorMap, context));\n                }\n                else {\n                    throw TypeError(`Custom op ${node.op} is not registered.`);\n                }\n            default:\n                throw TypeError(`Unknown op '${node.op}'. File an issue at ` +\n                    `https://github.com/tensorflow/tfjs/issues so we can add it` +\n                    `, or register a custom execution with tf.registerOp()`);\n        }\n    })(node, tensorMap, context);\n    if (value instanceof Promise) {\n        return value.then((data) => [].concat(data));\n    }\n    return [].concat(value);\n}\n//# sourceMappingURL=operation_executor.js.map", "/**\n * ExecutionContext captures the runtime environment of the node. It keeps\n * track of the current frame and iteration for the control flow ops.\n *\n * For example, typical Dynamic RNN model may contain loops, for which\n * TensorFlow will generate graphs with Enter/Exit nodes to control the\n * current execution frame, and NextIteration Nodes for iteration id increment.\n * For model with branch logic, TensorFLow will generate Switch/Merge ops.\n */\nexport class ExecutionContext {\n    constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}) {\n        this.weightMap = weightMap;\n        this.tensorArrayMap = tensorArrayMap;\n        this.tensorListMap = tensorListMap;\n        this.functionMap = functionMap;\n        this.rootContext = { id: 0, frameName: '', iterationId: 0 };\n        this.contexts = [this.rootContext];\n        this.lastId = 0;\n        this.generateCurrentContextIds();\n    }\n    newFrame(id, frameName) {\n        return { id, frameName, iterationId: 0 };\n    }\n    /**\n     * Set the current context\n     * @param contexts: ExecutionContextInfo[] the current path of execution\n     * frames\n     */\n    set currentContext(contexts) {\n        if (this.contexts !== contexts) {\n            this.contexts = contexts;\n            this.generateCurrentContextIds();\n        }\n    }\n    get currentContext() {\n        return this.contexts;\n    }\n    /**\n     * Returns the current context in string format.\n     */\n    get currentContextId() {\n        return this._currentContextIds[0];\n    }\n    /**\n     * Returns the current context and all parent contexts in string format.\n     * This allow access to the nodes in the current and parent frames.\n     */\n    get currentContextIds() {\n        return this._currentContextIds;\n    }\n    generateCurrentContextIds() {\n        const names = [];\n        for (let i = 0; i < this.contexts.length - 1; i++) {\n            const contexts = this.contexts.slice(0, this.contexts.length - i);\n            names.push(this.contextIdforContexts(contexts));\n        }\n        names.push('');\n        this._currentContextIds = names;\n    }\n    contextIdforContexts(contexts) {\n        return contexts ?\n            contexts\n                .map(context => (context.id === 0 && context.iterationId === 0) ?\n                '' :\n                `${context.frameName}-${context.iterationId}`)\n                .join('/') :\n            '';\n    }\n    /**\n     * Enter a new frame, a new context is pushed on the current context list.\n     * @param frameId new frame id\n     */\n    enterFrame(frameId) {\n        if (this.contexts) {\n            this.lastId++;\n            this.contexts = this.contexts.slice();\n            this.contexts.push(this.newFrame(this.lastId, frameId));\n            this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));\n        }\n    }\n    /**\n     * Exit the current frame, the last context is removed from the current\n     * context list.\n     */\n    exitFrame() {\n        if (this.contexts && this.contexts.length > 1) {\n            this.contexts = this.contexts.slice();\n            this.contexts.splice(-1);\n            this.currentContextIds.shift();\n        }\n        else {\n            throw new Error('Cannot exit frame, the context is empty');\n        }\n    }\n    /**\n     * Enter the next iteration of a loop, the iteration id of last context is\n     * increased.\n     */\n    nextIteration() {\n        if (this.contexts && this.contexts.length > 0) {\n            this.contexts = this.contexts.slice();\n            this.lastId++;\n            const context = Object.assign({}, this.contexts[this.contexts.length - 1]);\n            context.iterationId += 1;\n            context.id = this.lastId;\n            this.contexts.splice(-1, 1, context);\n            this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));\n        }\n        else {\n            throw new Error('Cannot increase frame iteration, the context is empty');\n        }\n    }\n    getWeight(name) {\n        return this.weightMap[name];\n    }\n    addTensorArray(tensorArray) {\n        this.tensorArrayMap[tensorArray.id] = tensorArray;\n    }\n    getTensorArray(id) {\n        return this.tensorArrayMap[id];\n    }\n    addTensorList(tensorList) {\n        this.tensorListMap[tensorList.id] = tensorList;\n    }\n    getTensorList(id) {\n        return this.tensorListMap[id];\n    }\n    dispose(keepIds) {\n        for (const key in this.tensorArrayMap) {\n            this.tensorArrayMap[key].clearAndClose(keepIds);\n        }\n        for (const key in this.tensorListMap) {\n            this.tensorListMap[key].clearAndClose(keepIds);\n        }\n    }\n}\n//# sourceMappingURL=execution_context.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { parseNodeName } from '../operations/executors/utils';\n/**\n * Given graph inputs and desired outputs, find the minimal set of nodes\n * to execute in order to compute the outputs. In addition return other useful\n * info such:\n * - Missing inputs needed to compute the output.\n * - Whether the subgraph contains dynamic ops (control flow, dynamic shape).\n * - Alternative inputs in order to avoid async (dynamic op) execution.\n */\nexport function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {\n    const usedNodes = new Set();\n    const missingInputs = [];\n    let dynamicNode = null;\n    let syncInputs = null;\n    // Start with the outputs, going backwards and find all the nodes that are\n    // needed to compute those outputs.\n    const seen = new Set();\n    const inputNodeNames = Object.keys(inputs).map(name => parseNodeName(name)[0]);\n    let initNodeNames = [];\n    if (initNodes != null) {\n        initNodeNames = initNodes.map(node => parseNodeName(node.name)[0]);\n    }\n    const frontier = [...outputs];\n    while (frontier.length > 0) {\n        const node = frontier.pop();\n        if (isControlFlow(node) || isDynamicShape(node)) {\n            if (dynamicNode == null) {\n                dynamicNode = node;\n                syncInputs = dynamicNode.children.map(child => child.name)\n                    .filter(name => usedNodes.has(name));\n            }\n        }\n        usedNodes.add(node.name);\n        // Weights are dead end since we already have their values.\n        if (weightMap[node.name] != null) {\n            continue;\n        }\n        // This node is a dead end since it's one of the user-provided inputs.\n        if (inputNodeNames.indexOf(node.name) !== -1) {\n            continue;\n        }\n        // This node is a dead end since it doesn't have any inputs.\n        if (initNodeNames.indexOf(node.name) !== -1) {\n            continue;\n        }\n        if (node.inputs.length === 0) {\n            missingInputs.push(node.name);\n            continue;\n        }\n        node.inputs.forEach(input => {\n            // Don't add to the frontier if it is already there.\n            if (seen.has(input.name)) {\n                return;\n            }\n            seen.add(input.name);\n            frontier.push(input);\n        });\n    }\n    return { inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs };\n}\n/**\n * Given the execution info, return a list of nodes in topological order that\n * need to be executed to compute the output.\n */\nexport function getNodesInTopologicalOrder(graph, weightMap, executionInfo) {\n    const { usedNodes, inputs } = executionInfo;\n    const frontier = [];\n    const inputNodes = Object.keys(inputs)\n        .map(name => parseNodeName(name)[0])\n        .map(name => graph.nodes[name]);\n    const initNodes = graph.initNodes;\n    inputNodes.forEach(input => {\n        if (usedNodes.has(input.name)) {\n            frontier.push(input);\n        }\n    });\n    graph.weights.forEach(weight => {\n        if (usedNodes.has(weight.name)) {\n            frontier.push(weight);\n        }\n    });\n    if (initNodes != null) {\n        initNodes.forEach(node => {\n            if (usedNodes.has(node.name)) {\n                frontier.push(node);\n            }\n        });\n    }\n    const seen = new Set();\n    const orderedNodes = [];\n    while (frontier.length > 0) {\n        const node = frontier.pop();\n        seen.add(node.name);\n        if (!weightMap[node.name]) {\n            orderedNodes.push(node);\n        }\n        node.children.forEach(child => {\n            if (!seen.has(child.name) && usedNodes.has(child.name) &&\n                child.inputs.every(input => seen.has(input.name))) {\n                frontier.push(child);\n            }\n        });\n    }\n    return orderedNodes;\n}\nconst CONTROL_FLOW_OPS = [\n    'Switch', 'Merge', 'Enter', 'Exit', 'NextIteration', 'StatelessIf',\n    'StatelessWhile', 'if', 'While'\n];\nconst DYNAMIC_SHAPE_OPS = [\n    'NonMaxSuppressionV2', 'NonMaxSuppressionV3', 'NonMaxSuppressionV5', 'Where'\n];\nexport function isControlFlow(node) {\n    return CONTROL_FLOW_OPS.indexOf(node.op) >= 0;\n}\nexport function isDynamicShape(node) {\n    return DYNAMIC_SHAPE_OPS.indexOf(node.op) >= 0;\n}\n//# sourceMappingURL=model_analysis.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tidy, util } from '@tensorflow/tfjs-core';\nimport { getNodeNameAndIndex, getParamValue, getTensor, getTensorsForCurrentContenxt, parseNodeName } from '../operations/executors/utils';\nimport { executeOp } from '../operations/operation_executor';\nimport { ExecutionContext } from './execution_context';\nimport { getExecutionSubgraph, getNodesInTopologicalOrder, isControlFlow } from './model_analysis';\nexport class GraphExecutor {\n    /**\n     *\n     * @param graph Graph the model or function graph to be executed.\n     * @param parent When building function exector you need to set the parent\n     * executor. Since the weights and function executor maps are set at parant\n     * level, that function executor can access the function maps and weight maps\n     * through the parent.\n     */\n    constructor(graph, parent) {\n        this.graph = graph;\n        this.parent = parent;\n        this.compiledMap = new Map();\n        this._weightMap = {};\n        this.SEPERATOR = ',';\n        this._functions = {};\n        this._functionExecutorMap = {};\n        this._outputs = graph.outputs;\n        this._inputs = graph.inputs;\n        this._initNodes = graph.initNodes;\n        this._signature = graph.signature;\n        this._functions = graph.functions;\n        // create sub-graph executors\n        if (graph.functions != null) {\n            Object.keys(graph.functions).forEach(name => {\n                this._functionExecutorMap[name] =\n                    new GraphExecutor(graph.functions[name], this);\n            });\n        }\n    }\n    get weightIds() {\n        return this.parent ? this.parent.weightIds : this._weightIds;\n    }\n    get functionExecutorMap() {\n        return this.parent ? this.parent.functionExecutorMap :\n            this._functionExecutorMap;\n    }\n    get weightMap() {\n        return this.parent ? this.parent.weightMap : this._weightMap;\n    }\n    set weightMap(weightMap) {\n        const weightIds = Object.keys(weightMap).map(key => weightMap[key].map(tensor => tensor.id));\n        this._weightIds = [].concat(...weightIds);\n        this._weightMap = weightMap;\n    }\n    get inputs() {\n        return this._inputs.map(node => {\n            return {\n                name: node.name,\n                shape: node.attrParams['shape'] ?\n                    node.attrParams['shape'].value :\n                    undefined,\n                dtype: node.attrParams['dtype'] ?\n                    node.attrParams['dtype'].value :\n                    undefined\n            };\n        });\n    }\n    get outputs() {\n        return this._outputs.map(node => {\n            return {\n                name: node.name,\n                shape: node.attrParams['shape'] ?\n                    node.attrParams['shape'].value :\n                    undefined,\n                dtype: node.attrParams['dtype'] ?\n                    node.attrParams['dtype'].value :\n                    undefined\n            };\n        });\n    }\n    get inputNodes() {\n        return this._inputs.map(node => node.signatureKey || node.name);\n    }\n    get outputNodes() {\n        return this._outputs.map((node) => {\n            const name = node.signatureKey || node.name;\n            return node.defaultOutput ? (`${name}:${node.defaultOutput}`) : name;\n        });\n    }\n    get functions() {\n        return Object.keys(this._functions).reduce((map, key) => {\n            map[key] = this._functions[key].signature;\n            return map;\n        }, {});\n    }\n    getCompilationKey(inputs, outputs) {\n        const sortedInputs = inputs.map(node => node.name).sort();\n        const sortedOutputs = outputs.map(node => node.name).sort();\n        return sortedInputs.join(this.SEPERATOR) + '--' +\n            sortedOutputs.join(this.SEPERATOR);\n    }\n    /**\n     * Compiles the inference graph and returns the minimal set of nodes that are\n     * required for execution, in the correct execution order.\n     */\n    compile(inputs, outputs) {\n        const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);\n        const { missingInputs, dynamicNode, syncInputs } = executionInfo;\n        if (dynamicNode != null) {\n            throw new Error(`This execution contains the node '${dynamicNode.name}', which has ` +\n                `the dynamic op '${dynamicNode.op}'. Please use ` +\n                `model.executeAsync() instead. Alternatively, to avoid the ` +\n                `dynamic ops, specify the inputs [${syncInputs}]`);\n        }\n        if (missingInputs.length > 0) {\n            const outNames = outputs.map(n => n.name);\n            const inNames = Object.keys(inputs);\n            throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs ` +\n                `[${inNames}]. Missing the following inputs: [${missingInputs}]`);\n        }\n        return getNodesInTopologicalOrder(this.graph, this.weightMap, executionInfo);\n    }\n    /**\n     * Executes the inference for given input tensors.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     */\n    execute(inputs, outputs) {\n        inputs = this.mapInputs(inputs);\n        const names = Object.keys(inputs).sort();\n        this.checkInputs(inputs);\n        this.checkInputShapeAndType(inputs);\n        outputs = this.mapOutputs(outputs);\n        this.checkOutputs(outputs);\n        const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n        const outputNodeNames = outputs.map(name => parseNodeName(name)[0]);\n        let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n        // If no outputs are specified, then use the default outputs of the model.\n        if (outputNodes.length === 0) {\n            outputNodes = this._outputs;\n        }\n        const compilationKey = this.getCompilationKey(inputNodes, outputNodes);\n        // Do nothing if the compiled graph cache contains the input.\n        let orderedNodes = this.compiledMap.get(compilationKey);\n        if (orderedNodes == null) {\n            orderedNodes = this.compile(inputs, outputNodes);\n            this.compiledMap.set(compilationKey, orderedNodes);\n        }\n        const tensorArrayMap = {};\n        const tensorListMap = {};\n        return tidy(() => {\n            const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n            const tensorsMap = Object.assign({}, this.weightMap);\n            Object.keys(inputs).forEach(name => {\n                const [nodeName, index] = parseNodeName(name);\n                const tensors = [];\n                tensors[index] = inputs[name];\n                tensorsMap[nodeName] = tensors;\n            });\n            const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n            const intermediateTensorConsumerCount = {};\n            for (let i = 0; i < orderedNodes.length; i++) {\n                const node = orderedNodes[i];\n                if (!tensorsMap[node.name]) {\n                    const tensors = executeOp(node, tensorsMap, context);\n                    if (tensors instanceof Promise) {\n                        throw new Error(`The execution of the op '${node.op}' returned a promise. ` +\n                            `Please use model.executeAsync() instead.`);\n                    }\n                    tensorsMap[node.name] = tensors;\n                    this.checkTensorForDisposal(node.name, node, tensorsMap, context, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount);\n                }\n            }\n            // dispose the context for the root executor\n            if (this.parent == null) {\n                context.dispose(tensorsToKeep);\n            }\n            return outputs.map(name => getTensor(name, tensorsMap, context));\n        });\n    }\n    getFrozenTensorIds(tensorMap) {\n        const ids = [].concat.apply([], Object.keys(tensorMap)\n            .map(key => tensorMap[key])\n            .map(tensors => tensors.map(tensor => tensor.id)));\n        return new Set(ids);\n    }\n    checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount) {\n        // Skip output nodes and any control flow nodes, since its dependency is\n        // tricky to track correctly.\n        if (node.category === 'control' || outputNames.indexOf(nodeName) !== -1) {\n            return;\n        }\n        tensorMap[nodeName].forEach(tensor => {\n            if (tensor != null) {\n                intermediateTensorConsumerCount[tensor.id] =\n                    (intermediateTensorConsumerCount[tensor.id] || 0) +\n                        node.children.length;\n            }\n        });\n        node.inputs.forEach(input => {\n            // Skip any control flow nodes, since its dependency is tricky to track\n            // correctly.\n            if (input.category !== 'control') {\n                const tensors = getTensorsForCurrentContenxt(input.name, tensorMap, context);\n                if (tensors != null) {\n                    tensors.forEach(tensor => {\n                        if (tensor && !tensorsToKeep.has(tensor.id)) {\n                            const count = intermediateTensorConsumerCount[tensor.id];\n                            if (count === 1) {\n                                tensor.dispose();\n                                delete intermediateTensorConsumerCount[tensor.id];\n                            }\n                            else if (count != null) {\n                                // only intermediate nodes has count set, inputs and weights are\n                                // not.\n                                intermediateTensorConsumerCount[tensor.id]--;\n                            }\n                        }\n                    });\n                }\n            }\n        });\n    }\n    /**\n     * Executes the inference for given input tensors in Async fashion.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     */\n    async executeAsync(inputs, outputs) {\n        return this._executeAsync(inputs, outputs);\n    }\n    /**\n     * Executes the inference for given input tensors in Async fashion.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     * @param isFunctionExecution Flag for executing a function.\n     * @param tensorArrayMap Optional, global TensorArray map by id. Used for\n     * function execution.\n     * @param tensorArrayMap Optinal global TensorList map by id. Used for\n     * function execution.\n     */\n    async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {\n        if (!isFunctionExecution) {\n            inputs = this.mapInputs(inputs);\n            this.checkInputs(inputs);\n            this.checkInputShapeAndType(inputs);\n            outputs = this.mapOutputs(outputs);\n            this.checkOutputs(outputs);\n        }\n        const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n        // Graph with control flow op requires runtime evaluation of the execution\n        // order, while without control flow the execution order is pre-determined\n        // in the compile method.\n        const tensorMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);\n        const results = outputs.map(name => getTensor(name, tensorMap, context));\n        // dispose all the intermediate tensors\n        const outputIds = results.map(t => t.id);\n        const inputIds = Object.keys(inputs).map(name => inputs[name].id);\n        const keepIds = new Set([...outputIds, ...inputIds, ...this.weightIds]);\n        Object.keys(tensorMap).forEach(key => {\n            const tensorArray = tensorMap[key];\n            tensorArray.forEach(tensor => {\n                if (tensor && !tensor.isDisposed && !keepIds.has(tensor.id)) {\n                    tensor.dispose();\n                }\n            });\n        });\n        // dispose the context for the root executor\n        if (this.parent == null) {\n            context.dispose(keepIds);\n        }\n        return results;\n    }\n    async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {\n        const mappedInputs = inputs.reduce((map, tensor, index) => {\n            map[this.inputs[index].name] = tensor;\n            return map;\n        }, {});\n        return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);\n    }\n    /**\n     * When there are control flow nodes in the graph, the graph execution use\n     * ExecutionContext to keep track of the frames and loop iterators.\n     * @param inputs placeholder tensors for the graph.\n     * @param context the execution context object for current execution.\n     * @param isFunctionExecution Flag for executing a function.\n     */\n    async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {\n        const names = Object.keys(inputs);\n        const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n        const outputNodeNames = outputNames.map(name => parseNodeName(name)[0]);\n        const outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n        const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes, this.weightMap);\n        const stack = [...inputNodes, ...this.graph.weights].map(node => {\n            return { node, contexts: context.currentContext };\n        });\n        const tensorsMap = Object.assign({}, this.weightMap);\n        Object.keys(inputs).forEach(name => {\n            const [nodeName, index] = parseNodeName(name);\n            const tensors = [];\n            tensors[index] = inputs[name];\n            tensorsMap[nodeName] = tensors;\n        });\n        const intermediateTensorConsumerCount = {};\n        const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n        const added = {};\n        while (stack.length > 0) {\n            const promises = this.processStack(inputNodes, stack, context, tensorsMap, added, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount, usedNodes);\n            await Promise.all(promises);\n        }\n        if (dynamicNode == null && !isFunctionExecution) {\n            console.warn(`This model execution did not contain any nodes with control flow ` +\n                `or dynamic output shapes. You can use model.execute() instead.`);\n        }\n        const missingOutputs = outputNodes\n            .filter(node => !isControlFlow(node) &&\n            !getTensor(node.name, tensorsMap, context))\n            .map(node => node.name);\n        if (missingOutputs.length > 0) {\n            let alternativeMsg = '';\n            if (dynamicNode != null) {\n                alternativeMsg =\n                    `Alternatively, to avoid the dynamic ops, use model.execute() ` +\n                        `and specify the inputs [${syncInputs}]`;\n            }\n            throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided ` +\n                `inputs [${names}]. Consider providing the following inputs: ` +\n                `[${missingInputs}]. ${alternativeMsg}`);\n        }\n        return tensorsMap;\n    }\n    processStack(inputNodes, stack, context, tensorMap, added, tensorsToKeep, outputNames, intermediateTensorConsumerCount, usedNodes) {\n        const promises = [];\n        while (stack.length > 0) {\n            const item = stack.pop();\n            context.currentContext = item.contexts;\n            let nodeName = '';\n            // The tensor of the Enter op with isConstant set should be set\n            // in the parent scope, so it will be available as constant for the\n            // whole loop.\n            if (item.node.op === 'Enter' &&\n                getParamValue('isConstant', item.node, tensorMap, context)) {\n                [nodeName] = getNodeNameAndIndex(item.node.name, context);\n            }\n            // only process nodes that are not provided as input nodes.\n            if (inputNodes.indexOf(item.node) === -1) {\n                const tensors = executeOp(item.node, tensorMap, context);\n                if (!nodeName) {\n                    [nodeName] = getNodeNameAndIndex(item.node.name, context);\n                }\n                const currentContext = context.currentContext;\n                if (tensors instanceof Promise) {\n                    promises.push(tensors.then(t => {\n                        tensorMap[nodeName] = t;\n                        context.currentContext = currentContext;\n                        this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n                        this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n                        return t;\n                    }));\n                }\n                else {\n                    tensorMap[nodeName] = tensors;\n                    this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n                    this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n                }\n            }\n            else {\n                this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n            }\n        }\n        return promises;\n    }\n    processChildNodes(node, stack, context, tensorMap, added, usedNodes) {\n        node.children.forEach((childNode) => {\n            const [nodeName,] = getNodeNameAndIndex(childNode.name, context);\n            if (added[nodeName] || !usedNodes.has(childNode.name)) {\n                return;\n            }\n            // Merge op can be pushed if any of its inputs has value.\n            if (childNode.op === 'Merge') {\n                if (childNode.inputNames.some(name => {\n                    return !!getTensor(name, tensorMap, context);\n                })) {\n                    added[nodeName] = true;\n                    stack.push({ contexts: context.currentContext, node: childNode });\n                }\n            }\n            else // Otherwise all inputs must to have value.\n             if (childNode.inputNames.every(name => {\n                return !!getTensor(name, tensorMap, context);\n            })) {\n                added[nodeName] = true;\n                stack.push({ contexts: context.currentContext, node: childNode });\n            }\n        });\n    }\n    /**\n     * Releases the memory used by the weight tensors.\n     */\n    dispose() {\n        Object.keys(this.weightMap)\n            .forEach(key => this.weightMap[key].forEach(tensor => tensor.dispose()));\n    }\n    checkInputShapeAndType(inputs) {\n        Object.keys(inputs).forEach(name => {\n            const input = inputs[name];\n            const [nodeName,] = parseNodeName(name);\n            const node = this.graph.nodes[nodeName];\n            if (node.attrParams['shape'] && node.attrParams['shape'].value) {\n                const shape = node.attrParams['shape'].value;\n                const match = shape.length === input.shape.length &&\n                    input.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim);\n                util.assert(match, () => `The shape of dict['${node.name}'] provided in ` +\n                    `model.execute(dict) must be [${shape}], but was ` +\n                    `[${input.shape}]`);\n            }\n            if (node.attrParams['dtype'] && node.attrParams['dtype'].value) {\n                util.assert(input.dtype === node.attrParams['dtype'].value, () => `The dtype of dict['${node.name}'] provided in ` +\n                    `model.execute(dict) must be ` +\n                    `${node.attrParams['dtype'].value}, but was ${input.dtype}`);\n            }\n        });\n    }\n    mapInputs(inputs) {\n        const result = {};\n        for (const inputName in inputs) {\n            if (this._signature != null && this._signature.inputs != null &&\n                this._signature.inputs[inputName] != null) {\n                const tensor = this._signature.inputs[inputName];\n                result[tensor.name] = inputs[inputName];\n            }\n            else {\n                result[inputName] = inputs[inputName];\n            }\n        }\n        return result;\n    }\n    checkInputs(inputs) {\n        const notInGraph = Object.keys(inputs).filter(name => {\n            const [nodeName] = parseNodeName(name);\n            return this.graph.nodes[nodeName] == null;\n        });\n        if (notInGraph.length > 0) {\n            throw new Error(`The dict provided in model.execute(dict) has ` +\n                `keys: [${notInGraph}] that are not part of graph`);\n        }\n    }\n    mapOutputs(outputs) {\n        return outputs.map(name => {\n            if (this._signature != null && this._signature.outputs != null &&\n                this._signature.outputs[name] != null) {\n                const tensor = this._signature.outputs[name];\n                return tensor.name;\n            }\n            return name;\n        }, {});\n    }\n    checkOutputs(outputs) {\n        outputs.forEach(name => {\n            const [normalizedName] = parseNodeName(name);\n            if (!this.graph.nodes[normalizedName]) {\n                throw new Error(`The output '${name}' is not found in the graph`);\n            }\n        });\n    }\n}\n//# sourceMappingURL=graph_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { io, Tensor } from '@tensorflow/tfjs-core';\nimport { OperationMapper } from '../operations/operation_mapper';\nimport { GraphExecutor } from './graph_executor';\nexport const TFHUB_SEARCH_PARAM = '?tfjs-format=file';\nexport const DEFAULT_MODEL_NAME = 'model.json';\n/**\n * A `tf.GraphModel` is a directed, acyclic graph built from a\n * SavedModel GraphDef and allows inference execution.\n *\n * A `tf.GraphModel` can only be created by loading from a model converted from\n * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using\n * the command line converter tool and loaded via `tf.loadGraphModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class GraphModel {\n    /**\n     * @param modelUrl url for the model, or an `io.IOHandler`.\n     * @param weightManifestUrl url for the weight file generated by\n     * scripts/convert.py script.\n     * @param requestOption options for Request, which allows to send credentials\n     * and custom headers.\n     * @param onProgress Optional, progress callback function, fired periodically\n     * before the load is completed.\n     */\n    constructor(modelUrl, loadOptions = {}) {\n        this.modelUrl = modelUrl;\n        this.loadOptions = loadOptions;\n        this.version = 'n/a';\n        if (loadOptions == null) {\n            this.loadOptions = {};\n        }\n    }\n    // Returns the version information for the tensorflow model GraphDef.\n    get modelVersion() {\n        return this.version;\n    }\n    get inputNodes() {\n        return this.executor.inputNodes;\n    }\n    get outputNodes() {\n        return this.executor.outputNodes;\n    }\n    get inputs() {\n        return this.executor.inputs;\n    }\n    get outputs() {\n        return this.executor.outputs;\n    }\n    get weights() {\n        return this.executor.weightMap;\n    }\n    findIOHandler() {\n        const path = this.modelUrl;\n        if (path.load != null) {\n            // Path is an IO Handler.\n            this.handler = path;\n        }\n        else if (this.loadOptions.requestInit != null) {\n            this.handler = io.browserHTTPRequest(path, this.loadOptions);\n        }\n        else {\n            const handlers = io.getLoadHandlers(path, this.loadOptions);\n            if (handlers.length === 0) {\n                // For backward compatibility: if no load handler can be found,\n                // assume it is a relative http path.\n                handlers.push(io.browserHTTPRequest(path, this.loadOptions));\n            }\n            else if (handlers.length > 1) {\n                throw new Error(`Found more than one (${handlers.length}) load handlers for ` +\n                    `URL '${[path]}'`);\n            }\n            this.handler = handlers[0];\n        }\n    }\n    /**\n     * Loads the model and weight files, construct the in memory weight map and\n     * compile the inference graph.\n     */\n    async load() {\n        this.findIOHandler();\n        if (this.handler.load == null) {\n            throw new Error('Cannot proceed with model loading because the IOHandler provided ' +\n                'does not have the `load` method implemented.');\n        }\n        const artifacts = await this.handler.load();\n        return this.loadSync(artifacts);\n    }\n    /**\n     * Synchronously construct the in memory weight map and\n     * compile the inference graph. Also initialize hashtable if any.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    loadSync(artifacts) {\n        this.artifacts = artifacts;\n        const graph = this.artifacts.modelTopology;\n        let signature = {};\n        if (this.artifacts.userDefinedMetadata != null) {\n            signature = // tslint:disable-next-line:no-any\n                this.artifacts.userDefinedMetadata.signature;\n        }\n        this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;\n        const weightMap = io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);\n        this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, signature));\n        this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);\n        if (artifacts.modelInitializer != null) {\n            const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer);\n            this.initializer = new GraphExecutor(initializer);\n            this.initializer.weightMap = this.executor.weightMap;\n            this.initializer.execute({}, []);\n        }\n        return true;\n    }\n    /**\n     * Save the configuration and/or weights of the GraphModel.\n     *\n     * An `IOHandler` is an object that has a `save` method of the proper\n     * signature defined. The `save` method manages the storing or\n     * transmission of serialized data (\"artifacts\") that represent the\n     * model's topology and weights onto or via a specific medium, such as\n     * file downloads, local storage, IndexedDB in the web browser and HTTP\n     * requests to a server. TensorFlow.js provides `IOHandler`\n     * implementations for a number of frequently used saving mediums, such as\n     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n     * for more details.\n     *\n     * This method also allows you to refer to certain types of `IOHandler`s\n     * as URL-like string shortcuts, such as 'localstorage://' and\n     * 'indexeddb://'.\n     *\n     * Example 1: Save `model`'s topology and weights to browser [local\n     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n     * then load it back.\n     *\n     * ```js\n     * const modelUrl =\n     *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n     * const model = await tf.loadGraphModel(modelUrl);\n     * const zeros = tf.zeros([1, 224, 224, 3]);\n     * model.predict(zeros).print();\n     *\n     * const saveResults = await model.save('localstorage://my-model-1');\n     *\n     * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * model.predict(zeros).print();\n     * ```\n     *\n     * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n     * scheme-based string shortcut for `IOHandler`.\n     * @param config Options for saving the model.\n     * @returns A `Promise` of `SaveResult`, which summarizes the result of\n     * the saving, such as byte sizes of the saved artifacts for the model's\n     *   topology and weight values.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async save(handlerOrURL, config) {\n        if (typeof handlerOrURL === 'string') {\n            const handlers = io.getSaveHandlers(handlerOrURL);\n            if (handlers.length === 0) {\n                throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n            }\n            else if (handlers.length > 1) {\n                throw new Error(`Found more than one (${handlers.length}) save handlers for ` +\n                    `URL '${handlerOrURL}'`);\n            }\n            handlerOrURL = handlers[0];\n        }\n        if (handlerOrURL.save == null) {\n            throw new Error('GraphModel.save() cannot proceed because the IOHandler ' +\n                'provided does not have the `save` attribute defined.');\n        }\n        return handlerOrURL.save(this.artifacts);\n    }\n    /**\n     * Execute the inference for the input tensors.\n     *\n     * @param input The input tensors, when there is single input for the model,\n     * inputs param should be a `tf.Tensor`. For models with mutliple inputs,\n     * inputs params should be in either `tf.Tensor`[] if the input order is\n     * fixed, or otherwise NamedTensorMap format.\n     *\n     * For model with multiple inputs, we recommend you use NamedTensorMap as the\n     * input type, if you use `tf.Tensor`[], the order of the array needs to\n     * follow the\n     * order of inputNodes array. @see {@link GraphModel.inputNodes}\n     *\n     * You can also feed any intermediate nodes using the NamedTensorMap as the\n     * input type. For example, given the graph\n     *    InputNode => Intermediate => OutputNode,\n     * you can execute the subgraph Intermediate => OutputNode by calling\n     *    model.execute('IntermediateNode' : tf.tensor(...));\n     *\n     * This is useful for models that uses tf.dynamic_rnn, where the intermediate\n     * state needs to be fed manually.\n     *\n     * For batch inference execution, the tensors for each input need to be\n     * concatenated together. For example with mobilenet, the required input shape\n     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].\n     * If we are provide a batched data of 100 images, the input tensor should be\n     * in the shape of [100, 244, 244, 3].\n     *\n     * @param config Prediction configuration for specifying the batch size and\n     * output node names. Currently the batch size option is ignored for graph\n     * model.\n     *\n     * @returns Inference result tensors. The output would be single `tf.Tensor`\n     * if model has single output node, otherwise Tensor[] or NamedTensorMap[]\n     * will be returned for model with multiple outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(inputs, config) {\n        return this.execute(inputs, this.outputNodes);\n    }\n    normalizeInputs(inputs) {\n        if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {\n            // The input is already a NamedTensorMap.\n            return inputs;\n        }\n        inputs = Array.isArray(inputs) ? inputs : [inputs];\n        if (inputs.length !== this.inputNodes.length) {\n            throw new Error('Input tensor count mismatch,' +\n                `the graph model has ${this.inputNodes.length} placeholders, ` +\n                `while there are ${inputs.length} input tensors.`);\n        }\n        return this.inputNodes.reduce((map, inputName, i) => {\n            map[inputName] = inputs[i];\n            return map;\n        }, {});\n    }\n    normalizeOutputs(outputs) {\n        outputs = outputs || this.outputNodes;\n        return !Array.isArray(outputs) ? [outputs] : outputs;\n    }\n    /**\n     * Executes inference for the model for given input tensors.\n     * @param inputs tensor, tensor array or tensor map of the inputs for the\n     * model, keyed by the input node names.\n     * @param outputs output node name from the Tensorflow model, if no\n     * outputs are specified, the default outputs of the model would be used.\n     * You can inspect intermediate nodes of the model by adding them to the\n     * outputs array.\n     *\n     * @returns A single tensor if provided with a single output or no outputs\n     * are provided and there is only one default output, otherwise return a\n     * tensor array. The order of the tensor array is the same as the outputs\n     * if provided, otherwise the order of outputNodes attribute of the model.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    execute(inputs, outputs) {\n        inputs = this.normalizeInputs(inputs);\n        outputs = this.normalizeOutputs(outputs);\n        const result = this.executor.execute(inputs, outputs);\n        return result.length > 1 ? result : result[0];\n    }\n    /**\n     * Executes inference for the model for given input tensors in async\n     * fashion, use this method when your model contains control flow ops.\n     * @param inputs tensor, tensor array or tensor map of the inputs for the\n     * model, keyed by the input node names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     *\n     * @returns A Promise of single tensor if provided with a single output or\n     * no outputs are provided and there is only one default output, otherwise\n     * return a tensor map.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async executeAsync(inputs, outputs) {\n        inputs = this.normalizeInputs(inputs);\n        outputs = this.normalizeOutputs(outputs);\n        const result = await this.executor.executeAsync(inputs, outputs);\n        return result.length > 1 ? result : result[0];\n    }\n    convertTensorMapToTensorsMap(map) {\n        return Object.keys(map).reduce((newMap, key) => {\n            newMap[key] = [map[key]];\n            return newMap;\n        }, {});\n    }\n    /**\n     * Releases the memory used by the weight tensors.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    dispose() {\n        this.executor.dispose();\n        if (this.initializer) {\n            this.initializer.dispose();\n        }\n    }\n}\n/**\n * Load a graph model given a URL to the model definition.\n *\n * Example of loading MobileNetV2 from a URL and making a prediction with a\n * zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n * const model = await tf.loadGraphModel(modelUrl);\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n *\n * Example of loading MobileNetV2 from a TF Hub URL and making a prediction with\n * a zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';\n * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n * @param modelUrl The url or an `io.IOHandler` that loads the model.\n * @param options Options for the HTTP request, which allows to send credentials\n *    and custom headers.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport async function loadGraphModel(modelUrl, options = {}) {\n    if (modelUrl == null) {\n        throw new Error('modelUrl in loadGraphModel() cannot be null. Please provide a url ' +\n            'or an IOHandler that loads the model');\n    }\n    if (options == null) {\n        options = {};\n    }\n    if (options.fromTFHub) {\n        if (modelUrl.load == null) {\n            if (!modelUrl.endsWith('/')) {\n                modelUrl = modelUrl + '/';\n            }\n            modelUrl = `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;\n        }\n    }\n    const model = new GraphModel(modelUrl, options);\n    await model.load();\n    return model;\n}\n//# sourceMappingURL=graph_model.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { GraphModel, loadGraphModel } from './executor/graph_model';\nexport { deregisterOp, registerOp } from './operations/custom_op/register';\nexport { version as version_converter } from './version';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\n/**\n * Apply a mapping function to a nested structure in a recursive manner.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapResult`.  The `DeepMapResult` either provides a\n *   replacement value for that node (i.e., replacing the subtree), or indicates\n *   that the node should be processed recursively.\n */\nexport function deepMap(input, mapFn) {\n    return deepMapInternal(input, mapFn);\n}\n/**\n * @param seen: A Map of known object mappings (i.e., memoized results of\n *   `mapFn()`)\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepMapInternal(input, mapFn, seen = new Map(), containedIn = new Set()) {\n    if (input == null) {\n        return null;\n    }\n    if (containedIn.has(input)) {\n        throw new Error('Circular references are not supported.');\n    }\n    if (seen.has(input)) {\n        return seen.get(input);\n    }\n    const result = mapFn(input);\n    if (result.recurse && result.value !== null) {\n        throw new Error('A deep map function may not return both a value and recurse=true.');\n    }\n    if (!result.recurse) {\n        seen.set(input, result.value);\n        return result.value;\n    }\n    else if (isIterable(input)) {\n        // tslint:disable-next-line:no-any\n        const mappedIterable = Array.isArray(input) ? [] : {};\n        containedIn.add(input);\n        for (const k in input) {\n            const child = input[k];\n            const childResult = deepMapInternal(child, mapFn, seen, containedIn);\n            mappedIterable[k] = childResult;\n        }\n        containedIn.delete(input);\n        return mappedIterable;\n    }\n    else {\n        throw new Error(`Can't recurse into non-iterable type: ${input}`);\n    }\n}\n// TODO(soergel, kangyizhang) Reconsider naming of deepZip() to avoid confusion\n// with zip()\n/**\n * Zip nested structures together in a recursive manner.\n *\n * This has the effect of transposing or pivoting data, e.g. converting it from\n * a row-major representation to a column-major representation.\n *\n * For example, `deepZip([{a: 1, b: 2}, {a: 3, b: 4}])` returns\n * `{a: [1, 3], b: [2, 4]}`.\n *\n * The inputs should all have the same nested structure (i.e., of arrays and\n * dicts).  The result is a single object with the same nested structure, where\n * the leaves are arrays collecting the values of the inputs at that location\n * (or, optionally, the result of a custom function applied to those arrays).\n *\n * @param inputs: An array of the objects to zip together.\n * @param zipFn: (optional) A function that expects an array of elements at a\n *   single node of the object tree, and returns a `DeepMapResult`.  The\n *   `DeepMapResult` either provides a result value for that node (i.e.,\n *   representing the subtree), or indicates that the node should be processed\n *   recursively.  The default zipFn recurses as far as possible and places\n *   arrays at the leaves.\n */\nexport function deepZip(inputs, zipFn = zipToList) {\n    return deepZipInternal(inputs, zipFn);\n}\n/**\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepZipInternal(inputs, zipFn, containedIn = new Set()) {\n    // The recursion follows the structure of input 0; it's assumed that all the\n    // other inputs have the same structure.\n    const input = inputs[0];\n    if (containedIn.has(input)) {\n        throw new Error('Circular references are not supported.');\n    }\n    const result = zipFn(inputs);\n    if (result.recurse && result.value !== null) {\n        throw new Error('A deep zip function may not return both a value and recurse=true.');\n    }\n    if (!result.recurse) {\n        return result.value;\n    }\n    else if (isIterable(input)) {\n        // tslint:disable-next-line:no-any\n        const mappedIterable = Array.isArray(input) ? [] : {};\n        containedIn.add(input);\n        for (const k in input) {\n            const children = inputs.map(x => x[k]);\n            const childResult = deepZipInternal(children, zipFn, containedIn);\n            mappedIterable[k] = childResult;\n        }\n        containedIn.delete(input);\n        return mappedIterable;\n    }\n    else {\n        throw new Error(`Can't recurse into non-iterable type: ${input}`);\n    }\n}\n// tslint:disable-next-line:no-any\nexport function zipToList(x) {\n    if (x === null) {\n        return null;\n    }\n    // TODO(soergel): validate array type?\n    if (isIterable(x[0])) {\n        return { value: null, recurse: true };\n    }\n    else {\n        return { value: x, recurse: false };\n    }\n}\n/**\n * Apply an async mapping function to a nested structure in a recursive manner.\n *\n * This first creates a nested structure of Promises, and then awaits all of\n * those, resulting in a single Promise for a resolved nested structure.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapAsyncResult`.  The `DeepMapAsyncResult` either provides\n *   a `Promise` for a replacement value for that node (i.e., replacing the\n *   subtree), or indicates that the node should be processed recursively.  Note\n *   that the decision whether or not to recurse must be made immediately; only\n *   the mapped value may be promised.\n */\nexport async function deepMapAndAwaitAll(input, mapFn) {\n    const seen = new Map();\n    // First do a normal deepMap, collecting Promises in 'seen' as a side effect.\n    deepMapInternal(input, mapFn, seen);\n    // Replace the Promises in 'seen' in place.\n    // Note TypeScript provides no async map iteration, and regular map iteration\n    // is broken too, so sadly we have to do Array.from() to make it work.\n    // (There's no advantage to Promise.all(), and that would be tricky anyway.)\n    for (const key of Array.from(seen.keys())) {\n        const value = seen.get(key);\n        if (value instanceof Promise) {\n            const mappedValue = await value;\n            seen.set(key, mappedValue);\n        }\n    }\n    // Normal deepMap again, this time filling in the resolved values.\n    // It's unfortunate that we have to do two passes.\n    // TODO(soergel): test performance and think harder about a fast solution.\n    const result = deepMapInternal(input, mapFn, seen);\n    return result;\n}\n/**\n * Determine whether the argument is iterable.\n *\n * @returns true if the argument is an array or any non-Tensor object.\n */\n// tslint:disable-next-line:no-any\nexport function isIterable(obj) {\n    return obj != null && (!ArrayBuffer.isView(obj)) &&\n        (Array.isArray(obj) ||\n            (typeof obj === 'object' && !(obj instanceof tf.Tensor)));\n}\n/**\n * Determine whether the argument can be converted to Tensor.\n *\n * Tensors, primitives, arrays, and TypedArrays all qualify; anything else does\n * not.\n *\n * @returns true if the argument can be converted to Tensor.\n */\n// tslint:disable-next-line:no-any\nexport function canTensorify(obj) {\n    return obj == null || isPrimitive(obj) || Array.isArray(obj) ||\n        (typeof obj === 'object' && (obj instanceof tf.Tensor)) ||\n        tf.util.isTypedArray(obj);\n}\n/**\n * Returns true if the given `value` is a primitive type. Otherwise returns\n * false. This is equivalant to node util.isPrimitive\n */\nfunction isPrimitive(value) {\n    return (value === null ||\n        (typeof value !== 'object' && typeof value !== 'function'));\n}\n//# sourceMappingURL=deep_map.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { deepMap, isIterable } from './deep_map';\nexport function deepClone(container) {\n    return deepMap(container, cloneIfTensor);\n}\n// tslint:disable-next-line: no-any\nfunction cloneIfTensor(item) {\n    if (item instanceof tf.Tensor) {\n        return ({ value: item.clone(), recurse: false });\n    }\n    else if (isIterable(item)) {\n        return { value: null, recurse: true };\n    }\n    else {\n        return { value: item, recurse: false };\n    }\n}\n//# sourceMappingURL=deep_clone.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * A ring buffer, providing O(1) FIFO, LIFO, and related operations.\n */\nexport class RingBuffer {\n    /**\n     * Constructs a `RingBuffer`.\n     * @param capacity The number of items that the buffer can accomodate.\n     */\n    constructor(capacity) {\n        this.capacity = capacity;\n        // Note we store the indices in the range 0 <= index < 2*capacity.\n        // This allows us to distinguish the full from the empty case.\n        // See https://www.snellman.net/blog/archive/2016-12-13-ring-buffers/\n        this.begin = 0; // inclusive\n        this.end = 0; // exclusive\n        if (capacity == null) {\n            throw new RangeError('Can\\'t create a ring buffer of unknown capacity.');\n        }\n        if (capacity < 1) {\n            throw new RangeError('Can\\'t create ring buffer of capacity < 1.');\n        }\n        this.data = new Array(capacity);\n        this.doubledCapacity = 2 * capacity;\n    }\n    /**\n     * Map any index into the range 0 <= index < 2*capacity.\n     */\n    wrap(index) {\n        // don't trust % on negative numbers\n        while (index < 0) {\n            index += this.doubledCapacity;\n        }\n        return index % this.doubledCapacity;\n    }\n    get(index) {\n        if (index < 0) {\n            throw new RangeError('Can\\'t get item at a negative index.');\n        }\n        return this.data[index % this.capacity];\n    }\n    set(index, value) {\n        if (index < 0) {\n            throw new RangeError('Can\\'t set item at a negative index.');\n        }\n        this.data[index % this.capacity] = value;\n    }\n    /**\n     * Returns the current number of items in the buffer.\n     */\n    length() {\n        let length = this.end - this.begin;\n        if (length < 0) {\n            length = this.doubledCapacity + length;\n        }\n        return length;\n    }\n    /**\n     * Reports whether the buffer is full.\n     * @returns true if the number of items in the buffer equals its capacity, and\n     *   false otherwise.\n     */\n    isFull() {\n        return this.length() === this.capacity;\n    }\n    /**\n     * Reports whether the buffer is empty.\n     * @returns true if the number of items in the buffer equals zero, and\n     *   false otherwise.\n     */\n    isEmpty() {\n        return this.length() === 0;\n    }\n    /**\n     * Adds an item to the end of the buffer.\n     */\n    push(value) {\n        if (this.isFull()) {\n            throw new RangeError('Ring buffer is full.');\n        }\n        this.set(this.end, value);\n        this.end = this.wrap(this.end + 1);\n    }\n    /**\n     * Adds many items to the end of the buffer, in order.\n     */\n    pushAll(values) {\n        for (const value of values) {\n            this.push(value);\n        }\n    }\n    /**\n     * Removes and returns the last item in the buffer.\n     */\n    pop() {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        this.end = this.wrap(this.end - 1);\n        const result = this.get(this.end);\n        this.set(this.end, undefined);\n        return result;\n    }\n    /**\n     * Adds an item to the beginning of the buffer.\n     */\n    unshift(value) {\n        if (this.isFull()) {\n            throw new RangeError('Ring buffer is full.');\n        }\n        this.begin = this.wrap(this.begin - 1);\n        this.set(this.begin, value);\n    }\n    /**\n     * Removes and returns the first item in the buffer.\n     */\n    shift() {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        const result = this.get(this.begin);\n        this.set(this.begin, undefined);\n        this.begin = this.wrap(this.begin + 1);\n        return result;\n    }\n    /**\n     * Removes and returns a specific item in the buffer, and moves the last item\n     * to the vacated slot.  This is useful for implementing a shuffling stream.\n     * Note that this operation necessarily scrambles the original order.\n     *\n     * @param relativeIndex: the index of the item to remove, relative to the\n     *   first item in the buffer (e.g., hiding the ring nature of the underlying\n     *   storage).\n     */\n    shuffleExcise(relativeIndex) {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        const index = this.wrap(this.begin + relativeIndex);\n        const result = this.get(index);\n        this.set(index, this.pop());\n        return result;\n    }\n}\n//# sourceMappingURL=ring_buffer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { RingBuffer } from './ring_buffer';\nexport class GrowingRingBuffer extends RingBuffer {\n    /**\n     * Constructs a `GrowingRingBuffer`.\n     */\n    constructor() {\n        super(GrowingRingBuffer.INITIAL_CAPACITY);\n    }\n    isFull() {\n        return false;\n    }\n    push(value) {\n        if (super.isFull()) {\n            this.expand();\n        }\n        super.push(value);\n    }\n    unshift(value) {\n        if (super.isFull()) {\n            this.expand();\n        }\n        super.unshift(value);\n    }\n    /**\n     * Doubles the capacity of the buffer.\n     */\n    expand() {\n        const newCapacity = this.capacity * 2;\n        const newData = new Array(newCapacity);\n        const len = this.length();\n        // Rotate the buffer to start at index 0 again, since we can't just\n        // allocate more space at the end.\n        for (let i = 0; i < len; i++) {\n            newData[i] = this.get(this.wrap(this.begin + i));\n        }\n        this.data = newData;\n        this.capacity = newCapacity;\n        this.doubledCapacity = 2 * this.capacity;\n        this.begin = 0;\n        this.end = len;\n    }\n}\nGrowingRingBuffer.INITIAL_CAPACITY = 32;\n//# sourceMappingURL=growing_ring_buffer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport * as seedrandom from 'seedrandom';\nimport { deepClone } from '../util/deep_clone';\nimport { deepMapAndAwaitAll, deepZip, zipToList } from '../util/deep_map';\nimport { GrowingRingBuffer } from '../util/growing_ring_buffer';\nimport { RingBuffer } from '../util/ring_buffer';\n// Here we implement a simple asynchronous iterator.\n// This lets us avoid using either third-party stream libraries or\n// recent TypeScript language support requiring polyfills.\n/**\n * Create a `LazyIterator` from an array of items.\n */\nexport function iteratorFromItems(items) {\n    return new ArrayIterator(items);\n}\n/**\n * Create a `LazyIterator` of incrementing integers.\n */\nexport function iteratorFromIncrementing(start) {\n    let i = start;\n    return iteratorFromFunction(() => ({ value: i++, done: false }));\n}\n/**\n * Create a `LazyIterator` from a function.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const iter = tf.data.iteratorFromFunction(func);\n * await iter.forEachAsync(e => console.log(e));\n * ```\n *\n * @param func A function that produces data on each call.\n */\nexport function iteratorFromFunction(func) {\n    return new FunctionCallIterator(func);\n}\n/**\n * Create a `LazyIterator` by concatenating underlying streams, which are\n * themselves provided as a stream.\n *\n * This can also be thought of as a \"stream flatten\" operation.\n *\n * @param baseIterators A stream of streams to be concatenated.\n * @param baseErrorHandler An optional function that can intercept `Error`s\n *   raised during a `next()` call on the base stream.  This function can decide\n *   whether the error should be propagated, whether the error should be\n *   ignored, or whether the base stream should be terminated.\n */\nexport function iteratorFromConcatenated(baseIterators, baseErrorHandler) {\n    return new ChainedIterator(baseIterators, baseErrorHandler);\n}\n/**\n * Create a `LazyIterator` by concatenating streams produced by calling a\n * stream-generating function a given number of times.\n *\n * Since a `LazyIterator` is read-once, it cannot be repeated, but this\n * function can be used to achieve a similar effect:\n *\n *   LazyIterator.ofConcatenatedFunction(() => new MyIterator(), 6);\n *\n * @param iteratorFunc: A function that produces a new stream on each call.\n * @param count: The number of times to call the function.\n * @param baseErrorHandler An optional function that can intercept `Error`s\n *   raised during a `next()` call on the base stream.  This function can decide\n *   whether the error should be propagated, whether the error should be\n *   ignored, or whether the base stream should be terminated.\n */\nexport function iteratorFromConcatenatedFunction(iteratorFunc, count, baseErrorHandler) {\n    return iteratorFromConcatenated(iteratorFromFunction(iteratorFunc).take(count), baseErrorHandler);\n}\n/**\n * Create a `LazyIterator` by zipping together an array, dict, or nested\n * structure of `LazyIterator`s (and perhaps additional constants).\n *\n * The underlying streams must provide elements in a consistent order such\n * that they correspond.\n *\n * Typically, the underlying streams should have the same number of\n * elements. If they do not, the behavior is determined by the\n * `mismatchMode` argument.\n *\n * The nested structure of the `iterators` argument determines the\n * structure of elements in the resulting iterator.\n *\n * @param iterators: An array or object containing LazyIterators at the\n * leaves.\n * @param mismatchMode: Determines what to do when one underlying iterator\n * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)\n * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`\n * causes the zipped iterator to terminate with the furst underlying\n * streams, so elements remaining on the longer streams are ignored.\n * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling\n * in nulls for the exhausted streams, until all streams are exhausted.\n */\nexport function iteratorFromZipped(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n    return new ZipIterator(iterators, mismatchMode);\n}\n/**\n * An asynchronous iterator, providing lazy access to a potentially\n * unbounded stream of elements.\n *\n * Iterator can be obtained from a dataset:\n * `const iter = await dataset.iterator();`\n */\nexport class LazyIterator {\n    /**\n     * Collect all remaining elements of a bounded stream into an array.\n     * Obviously this will succeed only for small streams that fit in memory.\n     * Useful for testing.\n     *\n     * @returns A Promise for an array of stream elements, which will resolve\n     *   when the stream is exhausted.\n     */\n    async toArray() {\n        const result = [];\n        let x = await this.next();\n        while (!x.done) {\n            result.push(x.value);\n            x = await this.next();\n        }\n        return result;\n    }\n    /**\n     * Collect all elements of this dataset into an array with prefetching 100\n     * elements. This is useful for testing, because the prefetch changes the\n     * order in which the Promises are resolved along the processing pipeline.\n     * This may help expose bugs where results are dependent on the order of\n     * Promise resolution rather than on the logical order of the stream (i.e.,\n     * due to hidden mutable state).\n     *\n     * @returns A Promise for an array of stream elements, which will resolve\n     *   when the stream is exhausted.\n     */\n    async toArrayForTest() {\n        const stream = this.prefetch(100);\n        const result = [];\n        let x = await stream.next();\n        while (!x.done) {\n            result.push(x.value);\n            x = await stream.next();\n        }\n        return result;\n    }\n    /**\n     * Draw items from the stream until it is exhausted.\n     *\n     * This can be useful when the stream has side effects but no output.  In\n     * that case, calling this function guarantees that the stream will be\n     * fully processed.\n     */\n    async resolveFully() {\n        let x = await this.next();\n        while (!x.done) {\n            x = await this.next();\n        }\n    }\n    /**\n     * Draw items from the stream until it is exhausted, or a predicate fails.\n     *\n     * This can be useful when the stream has side effects but no output.  In\n     * that case, calling this function guarantees that the stream will be\n     * fully processed.\n     */\n    async resolveWhile(predicate) {\n        let x = await this.next();\n        let shouldContinue = predicate(x.value);\n        while ((!x.done) && shouldContinue) {\n            x = await this.next();\n            shouldContinue = predicate(x.value);\n        }\n    }\n    /**\n     * Handles errors thrown on this stream using a provided handler function.\n     *\n     * @param handler A function that handles any `Error` thrown during a `next()`\n     *   call and returns true if the stream should continue (dropping the failed\n     *   call) or false if the stream should quietly terminate.  If the handler\n     *   itself throws (or rethrows) an `Error`, that will be propagated.\n     *\n     * @returns A `LazyIterator` of elements passed through from upstream,\n     *   possibly filtering or terminating on upstream `next()` calls that\n     *   throw an `Error`.\n     */\n    handleErrors(handler) {\n        return new ErrorHandlingLazyIterator(this, handler);\n    }\n    // TODO(soergel): Implement reduce() etc.\n    /**\n     * Filters this stream according to `predicate`.\n     *\n     * @param predicate A function mapping a stream element to a boolean or a\n     * `Promise` for one.\n     *\n     * @returns A `LazyIterator` of elements for which the predicate was true.\n     */\n    filter(predicate) {\n        return new FilterIterator(this, predicate);\n    }\n    /**\n     * Maps this stream through a 1-to-1 transform.\n     *\n     * @param transform A function mapping a stream element to a transformed\n     *   element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    map(transform) {\n        return new MapIterator(this, transform);\n    }\n    /**\n     * Maps this stream through an async 1-to-1 transform.\n     *\n     * @param transform A function mapping a stream element to a `Promise` for a\n     *   transformed stream element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    mapAsync(transform) {\n        return new AsyncMapIterator(this, transform);\n    }\n    /**\n     * Maps this stream through a 1-to-1 transform, forcing serial execution.\n     *\n     * @param transform A function mapping a stream element to a transformed\n     *   element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    serialMapAsync(transform) {\n        return new AsyncMapIterator(this, transform).serial();\n    }\n    /**\n     * Maps this stream through a 1-to-many transform.\n     *\n     * @param transform A function mapping a stream element to an array of\n     *   transformed elements.\n     *\n     * @returns A `DataStream` of transformed elements.\n     */\n    flatmap(transform) {\n        return new FlatmapIterator(this, transform);\n    }\n    /**\n     * Apply a function to every element of the stream.\n     *\n     * @param f A function to apply to each stream element.\n     */\n    async forEachAsync(f) {\n        return this.map(f).resolveFully();\n    }\n    /**\n     * Apply a function to every element of the stream, forcing serial execution.\n     *\n     * @param f A function to apply to each stream element.  Should return 'true'\n     *   to indicate that the stream should continue, or 'false' to cause it to\n     *   terminate.\n     */\n    async serialForEach(f) {\n        return this.serialMapAsync(f).resolveWhile(x => (x === true));\n    }\n    /**\n     * Groups elements into batches, represented as arrays of elements.\n     *\n     * We can think of the elements of this iterator as 'rows' (even if they are\n     * nested structures).  By the same token, consecutive values for a given\n     * key within the elements form a 'column'.  This matches the usual sense of\n     * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n     *\n     * Thus, \"Row-major\" means that the resulting batch is simply a collection of\n     * rows: `[row1, row2, row3, ...]`.  This is contrast to the column-major\n     * form, which is needed for vectorized computation.\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @returns A `LazyIterator` of batches of elements, represented as arrays\n     *   of the original element type.\n     */\n    rowMajorBatch(batchSize, smallLastBatch = true) {\n        return new RowMajorBatchIterator(this, batchSize, smallLastBatch);\n    }\n    /**\n     * Groups elements into batches, represented in column-major form.\n     *\n     * We can think of the elements of this iterator as 'rows' (even if they are\n     * nested structures).  By the same token, consecutive values for a given\n     * key within the elements form a 'column'.  This matches the usual sense of\n     * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n     *\n     * Thus, \"column-major\" means that the resulting batch is a (potentially\n     * nested) structure representing the columns.  Each column entry, then,\n     * contains a collection of the values found in that column for a range of\n     * input elements.  This representation allows for vectorized computation, in\n     * contrast to the row-major form.\n     *\n     * The inputs should all have the same nested structure (i.e., of arrays and\n     * dicts).  The result is a single object with the same nested structure,\n     * where the leaves are arrays collecting the values of the inputs at that\n     * location (or, optionally, the result of a custom function applied to those\n     * arrays).\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @param zipFn: (optional) A function that expects an array of elements at a\n     *   single node of the object tree, and returns a `DeepMapResult`.  The\n     *   `DeepMapResult` either provides a result value for that node (i.e.,\n     *   representing the subtree), or indicates that the node should be processed\n     *   recursively.  The default zipFn recurses as far as possible and places\n     *   arrays at the leaves.\n     * @returns A `LazyIterator` of batches of elements, represented as an object\n     *   with collections at the leaves.\n     */\n    columnMajorBatch(batchSize, smallLastBatch = true, \n    // tslint:disable-next-line:no-any\n    zipFn = zipToList) {\n        // First collect the desired number of input elements as a row-major batch.\n        const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch);\n        // Now 'rotate' or 'pivot' the data, collecting all values from each column\n        // in the batch (i.e., for each key within the elements) into an array.\n        return rowBatches.map(x => deepZip(x, zipFn));\n    }\n    /**\n     * Concatenate this `LazyIterator` with another.\n     *\n     * @param iterator A `LazyIterator` to be concatenated onto this one.\n     * @param baseErrorHandler An optional function that can intercept `Error`s\n     *   raised during a `next()` call on the base stream.  This function can\n     *   decide whether the error should be propagated, whether the error should\n     *   be ignored, or whether the base stream should be terminated.\n     * @returns A `LazyIterator`.\n     */\n    concatenate(iterator, baseErrorHandler) {\n        return new ChainedIterator(iteratorFromItems([this, iterator]), baseErrorHandler);\n    }\n    /**\n     * Limits this stream to return at most `count` items.\n     *\n     * @param count The maximum number of items to provide from the stream. If\n     * a negative or undefined value is given, the entire stream is returned\n     *   unaltered.\n     */\n    take(count) {\n        if (count < 0 || count == null) {\n            return this;\n        }\n        return new TakeIterator(this, count);\n    }\n    /**\n     * Skips the first `count` items in this stream.\n     *\n     * @param count The number of items to skip.  If a negative or undefined\n     * value is given, the entire stream is returned unaltered.\n     */\n    skip(count) {\n        if (count < 0 || count == null) {\n            return this;\n        }\n        return new SkipIterator(this, count);\n    }\n    /**\n     * Prefetch the first `bufferSize` items in this stream.\n     *\n     * Note this prefetches Promises, but makes no guarantees about when those\n     * Promises resolve.\n     *\n     * @param bufferSize: An integer specifying the number of elements to be\n     *   prefetched.\n     */\n    prefetch(bufferSize) {\n        return new PrefetchIterator(this, bufferSize);\n    }\n    // TODO(soergel): deep sharded shuffle, where supported\n    /**\n     * Randomly shuffles the elements of this stream.\n     *\n     * @param bufferSize: An integer specifying the number of elements from\n     * this stream from which the new stream will sample.\n     * @param seed: (Optional.) An integer specifying the random seed that\n     * will be used to create the distribution.\n     */\n    shuffle(windowSize, seed) {\n        return new ShuffleIterator(this, windowSize, seed);\n    }\n    /**\n     * Force an iterator to execute serially: each next() call will await the\n     * prior one, so that they cannot execute concurrently.\n     */\n    serial() {\n        return new SerialIterator(this);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on LazyIterator.  Unfortunately they can't be placed in separate files,\n// due to resulting trouble with circular imports.\n// ============================================================================\n// Iterators that just extend LazyIterator directly\n// ============================================================================\nclass ArrayIterator extends LazyIterator {\n    constructor(items) {\n        super();\n        this.items = items;\n        this.trav = 0;\n    }\n    summary() {\n        return `Array of ${this.items.length} items`;\n    }\n    async next() {\n        if (this.trav >= this.items.length) {\n            return { value: null, done: true };\n        }\n        const item = this.items[this.trav];\n        this.trav++;\n        return { value: deepClone(item), done: false };\n    }\n}\nclass FunctionCallIterator extends LazyIterator {\n    constructor(nextFn) {\n        super();\n        this.nextFn = nextFn;\n    }\n    summary() {\n        return `Function call`;\n    }\n    async next() {\n        try {\n            return this.nextFn();\n        }\n        catch (e) {\n            // Modify the error message but leave the stack trace intact\n            e.message =\n                `Error thrown while iterating through a dataset: ${e.message}`;\n            throw e;\n        }\n    }\n}\nclass SerialIterator extends LazyIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Serial`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        return this.upstream.next();\n    }\n}\nclass SkipIterator extends LazyIterator {\n    constructor(upstream, maxCount) {\n        super();\n        this.upstream = upstream;\n        this.maxCount = maxCount;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.count = 0;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Skip`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        // TODO(soergel): consider tradeoffs of reading in parallel, eg.\n        // collecting next() promises in an Array and then waiting for\n        // Promise.all() of those. Benefit: pseudo-parallel execution.  Drawback:\n        // maybe delayed GC.\n        while (this.count++ < this.maxCount) {\n            const skipped = await this.upstream.next();\n            // short-circuit if upstream is already empty\n            if (skipped.done) {\n                return skipped;\n            }\n            tf.dispose(skipped.value);\n        }\n        return this.upstream.next();\n    }\n}\nclass TakeIterator extends LazyIterator {\n    constructor(upstream, maxCount) {\n        super();\n        this.upstream = upstream;\n        this.maxCount = maxCount;\n        this.count = 0;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Take`;\n    }\n    async next() {\n        if (this.count++ >= this.maxCount) {\n            return { value: null, done: true };\n        }\n        return this.upstream.next();\n    }\n}\n// Note this batch just groups items into row-wise element arrays.\n// Rotating these to a column-wise representation happens only at the dataset\n// level.\nclass RowMajorBatchIterator extends LazyIterator {\n    constructor(upstream, batchSize, enableSmallLastBatch = true) {\n        super();\n        this.upstream = upstream;\n        this.batchSize = batchSize;\n        this.enableSmallLastBatch = enableSmallLastBatch;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> RowMajorBatch`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        const batch = [];\n        while (batch.length < this.batchSize) {\n            const item = await this.upstream.next();\n            if (item.done) {\n                if (this.enableSmallLastBatch && batch.length > 0) {\n                    return { value: batch, done: false };\n                }\n                return { value: null, done: true };\n            }\n            batch.push(item.value);\n        }\n        return { value: batch, done: false };\n    }\n}\nclass FilterIterator extends LazyIterator {\n    constructor(upstream, predicate) {\n        super();\n        this.upstream = upstream;\n        this.predicate = predicate;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Filter`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        while (true) {\n            const item = await this.upstream.next();\n            if (item.done || this.predicate(item.value)) {\n                return item;\n            }\n            tf.dispose(item.value);\n        }\n    }\n}\nclass MapIterator extends LazyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Map`;\n    }\n    async next() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return { value: null, done: true };\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // That's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying\n        // any intermediate Tensors.  Here we are concerned only about the\n        // inputs.\n        const mapped = this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mapped);\n        // TODO(soergel) faster intersection\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return { value: mapped, done: false };\n    }\n}\nclass ErrorHandlingLazyIterator extends LazyIterator {\n    constructor(upstream, handler) {\n        super();\n        this.upstream = upstream;\n        this.handler = handler;\n        this.count = 0;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> handleErrors`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        while (true) {\n            try {\n                return await this.upstream.next();\n            }\n            catch (e) {\n                if (!this.handler(e)) {\n                    return { value: null, done: true };\n                }\n                // If the handler returns true, loop and fetch the next upstream item.\n                // If the upstream iterator throws an endless stream of errors, and if\n                // the handler says to ignore them, then we loop forever here.  That is\n                // the correct behavior-- it's up to the handler to decide when to stop.\n            }\n        }\n    }\n}\nclass AsyncMapIterator extends LazyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> AsyncMap`;\n    }\n    async next() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return { value: null, done: true };\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // That's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying\n        // any intermediate Tensors.  Here we are concerned only about the\n        // inputs.\n        const mapped = await this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mapped);\n        // TODO(soergel) faster intersection\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return { value: mapped, done: false };\n    }\n}\n// Iterators that maintain a queue of pending items\n// ============================================================================\n/**\n * A base class for transforming streams that operate by maintaining an\n * output queue of elements that are ready to return via next().  This is\n * commonly required when the transformation is 1-to-many:  A call to next()\n * may trigger a call to the underlying stream, which will produce many\n * mapped elements of this stream-- of which we need to return only one, so\n * we have to queue the rest.\n */\nexport class OneToManyIterator extends LazyIterator {\n    constructor() {\n        super();\n        this.outputQueue = new GrowingRingBuffer();\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        // Fetch so that the queue contains at least one item if possible.\n        // If the upstream source is exhausted, AND there are no items left in\n        // the output queue, then this stream is also exhausted.\n        while (this.outputQueue.length() === 0) {\n            // TODO(soergel): consider parallel reads.\n            if (!await this.pump()) {\n                return { value: null, done: true };\n            }\n        }\n        return { value: this.outputQueue.shift(), done: false };\n    }\n}\nclass FlatmapIterator extends OneToManyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Flatmap`;\n    }\n    async pump() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return false;\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // that's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying any\n        // intermediate Tensors.  Here we are concerned only about the inputs.\n        const mappedArray = this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mappedArray);\n        this.outputQueue.pushAll(mappedArray);\n        // TODO(soergel) faster intersection, and deduplicate outputTensors\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return true;\n    }\n}\n/**\n * Provides a `LazyIterator` that concatenates a stream of underlying\n * streams.\n *\n * Doing this in a concurrency-safe way requires some trickery.  In\n * particular, we want this stream to return the elements from the\n * underlying streams in the correct order according to when next() was\n * called, even if the resulting Promises resolve in a different order.\n */\nexport class ChainedIterator extends LazyIterator {\n    constructor(iterators, baseErrorHandler) {\n        super();\n        this.baseErrorHandler = baseErrorHandler;\n        // Strict Promise execution order:\n        // a next() call may not even begin until the previous one completes.\n        this.lastRead = null;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.iterator = null;\n        this.moreIterators = iterators;\n    }\n    summary() {\n        const upstreamSummaries = 'TODO: fill in upstream of chained summaries';\n        return `${upstreamSummaries} -> Chained`;\n    }\n    async next() {\n        this.lastRead = this.readFromChain(this.lastRead);\n        return this.lastRead;\n    }\n    async readFromChain(lastRead) {\n        // Must await on the previous read since the previous read may have advanced\n        // the stream of streams, from which we need to read.\n        // This is unfortunate since we can't parallelize reads. Which means\n        // prefetching of chained streams is a no-op.\n        // One solution is to prefetch immediately upstream of this.\n        await lastRead;\n        if (this.iterator == null) {\n            const iteratorResult = await this.moreIterators.next();\n            if (iteratorResult.done) {\n                // No more streams to stream from.\n                return { value: null, done: true };\n            }\n            this.iterator = iteratorResult.value;\n            if (this.baseErrorHandler != null) {\n                this.iterator = this.iterator.handleErrors(this.baseErrorHandler);\n            }\n        }\n        const itemResult = await this.iterator.next();\n        if (itemResult.done) {\n            this.iterator = null;\n            return this.readFromChain(lastRead);\n        }\n        return itemResult;\n    }\n}\nexport var ZipMismatchMode;\n(function (ZipMismatchMode) {\n    ZipMismatchMode[ZipMismatchMode[\"FAIL\"] = 0] = \"FAIL\";\n    ZipMismatchMode[ZipMismatchMode[\"SHORTEST\"] = 1] = \"SHORTEST\";\n    ZipMismatchMode[ZipMismatchMode[\"LONGEST\"] = 2] = \"LONGEST\"; // use nulls for exhausted streams; use up the longest stream.\n})(ZipMismatchMode || (ZipMismatchMode = {}));\n/**\n * Provides a `LazyIterator` that zips together an array, dict, or nested\n * structure of `LazyIterator`s (and perhaps additional constants).\n *\n * The underlying streams must provide elements in a consistent order such\n * that they correspond.\n *\n * Typically, the underlying streams should have the same number of\n * elements. If they do not, the behavior is determined by the\n * `mismatchMode` argument.\n *\n * The nested structure of the `iterators` argument determines the\n * structure of elements in the resulting iterator.\n *\n * Doing this in a concurrency-safe way requires some trickery.  In\n * particular, we want this stream to return the elements from the\n * underlying streams in the correct order according to when next() was\n * called, even if the resulting Promises resolve in a different order.\n *\n * @param iterators: An array or object containing LazyIterators at the\n * leaves.\n * @param mismatchMode: Determines what to do when one underlying iterator\n * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)\n * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`\n * causes the zipped iterator to terminate with the furst underlying\n * streams, so elements remaining on the longer streams are ignored.\n * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling\n * in nulls for the exhausted streams, until all streams are exhausted.\n */\nclass ZipIterator extends LazyIterator {\n    constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n        super();\n        this.iterators = iterators;\n        this.mismatchMode = mismatchMode;\n        this.count = 0;\n        this.currentPromise = null;\n    }\n    summary() {\n        const upstreamSummaries = 'TODO: fill in upstream of zip summaries';\n        return `{${upstreamSummaries}} -> Zip`;\n    }\n    async nextState(afterState) {\n        // This chaining ensures that the underlying next() are not even called\n        // before the previous ones have resolved.\n        await afterState;\n        // Collect underlying iterator \"done\" signals as a side effect in\n        // getNext()\n        let numIterators = 0;\n        let iteratorsDone = 0;\n        function getNext(container) {\n            if (container instanceof LazyIterator) {\n                const result = container.next();\n                return {\n                    value: result.then(x => {\n                        numIterators++;\n                        if (x.done) {\n                            iteratorsDone++;\n                        }\n                        return x.value;\n                    }),\n                    recurse: false\n                };\n            }\n            else {\n                return { value: null, recurse: true };\n            }\n        }\n        const mapped = await deepMapAndAwaitAll(this.iterators, getNext);\n        if (numIterators === iteratorsDone) {\n            // The streams have all ended.\n            return { value: null, done: true };\n        }\n        if (iteratorsDone > 0) {\n            switch (this.mismatchMode) {\n                case ZipMismatchMode.FAIL:\n                    throw new Error('Zipped streams should have the same length. ' +\n                        `Mismatched at element ${this.count}.`);\n                case ZipMismatchMode.SHORTEST:\n                    return { value: null, done: true };\n                case ZipMismatchMode.LONGEST:\n                default:\n                // Continue.  The exhausted streams already produced value: null.\n            }\n        }\n        this.count++;\n        return { value: mapped, done: false };\n    }\n    async next() {\n        this.currentPromise = this.nextState(this.currentPromise);\n        return this.currentPromise;\n    }\n}\n// Iterators that maintain a ring buffer of pending promises\n// ============================================================================\n/**\n * A stream that prefetches a given number of items from an upstream source,\n * returning them in FIFO order.\n *\n * Note this prefetches Promises, but makes no guarantees about when those\n * Promises resolve.\n */\nexport class PrefetchIterator extends LazyIterator {\n    constructor(upstream, bufferSize) {\n        super();\n        this.upstream = upstream;\n        this.bufferSize = bufferSize;\n        this.buffer = new RingBuffer(bufferSize);\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Prefetch`;\n    }\n    /**\n     * Refill the prefetch buffer.  Returns only after the buffer is full, or\n     * the upstream source is exhausted.\n     */\n    refill() {\n        while (!this.buffer.isFull()) {\n            const v = this.upstream.next();\n            this.buffer.push(v);\n        }\n    }\n    next() {\n        this.refill();\n        // This shift will never throw an error because the buffer is always\n        // full after a refill. If the stream is exhausted, the buffer will be\n        // full of Promises that will resolve to the end-of-stream signal.\n        return this.buffer.shift();\n    }\n}\n/**\n * A stream that performs a sliding-window random shuffle on an upstream\n * source. This is like a `PrefetchIterator` except that the items are\n * returned in randomized order.  Mixing naturally improves as the buffer\n * size increases.\n */\nexport class ShuffleIterator extends PrefetchIterator {\n    constructor(upstream, windowSize, seed) {\n        super(upstream, windowSize);\n        this.upstream = upstream;\n        this.windowSize = windowSize;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.upstreamExhausted = false;\n        this.random = seedrandom.alea(seed || tf.util.now().toString());\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    randomInt(max) {\n        return Math.floor(this.random() * max);\n    }\n    chooseIndex() {\n        return this.randomInt(this.buffer.length());\n    }\n    async serialNext() {\n        // TODO(soergel): consider performance\n        if (!this.upstreamExhausted) {\n            this.refill();\n        }\n        while (!this.buffer.isEmpty()) {\n            const chosenIndex = this.chooseIndex();\n            const result = await this.buffer.shuffleExcise(chosenIndex);\n            if (result.done) {\n                this.upstreamExhausted = true;\n            }\n            else {\n                this.refill();\n                return result;\n            }\n        }\n        return { value: null, done: true };\n    }\n}\n//# sourceMappingURL=lazy_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport * as seedrandom from 'seedrandom';\nimport { iteratorFromConcatenated, iteratorFromFunction, iteratorFromItems, iteratorFromZipped, ZipMismatchMode } from './iterators/lazy_iterator';\nimport { canTensorify, deepMapAndAwaitAll, isIterable } from './util/deep_map';\n// TODO(soergel): consider vectorized operations within the pipeline.\n/**\n * Represents a potentially large list of independent data elements (typically\n * 'samples' or 'examples').\n *\n * A 'data example' may be a primitive, an array, a map from string keys to\n * values, or any nested structure of these.\n *\n * A `Dataset` represents an ordered collection of elements, together with a\n * chain of transformations to be performed on those elements. Each\n * transformation is a method of `Dataset` that returns another `Dataset`, so\n * these may be chained, e.g.\n * `const processedDataset = rawDataset.filter(...).map(...).batch(...)`.\n *\n * Data loading and transformation is done in a lazy, streaming fashion.  The\n * dataset may be iterated over multiple times; each iteration starts the data\n * loading anew and recapitulates the transformations.\n *\n * A `Dataset` is typically processed as a stream of unbatched examples --i.e.,\n * its transformations are applied one example at a time. Batching produces a\n * new `Dataset` where each element is a batch. Batching should usually come\n * last in a pipeline, because data transformations are easier to express on a\n * per-example basis than on a per-batch basis.\n *\n * The following code examples are calling `await dataset.forEachAsync(...)` to\n * iterate once over the entire dataset in order to print out the data.\n *\n * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}\n */\nexport class Dataset {\n    constructor() {\n        this.size = null;\n    }\n    // TODO(soergel): Make Datasets report whether repeated iterator() calls\n    // produce the same result (e.g., reading from a file) or different results\n    // (e.g., from the webcam).  Currently we don't make this distinction but it\n    // could be important for the user to know.\n    // abstract isDeterministic(): boolean;\n    /**\n     * Groups elements into batches.\n     *\n     * It is assumed that each of the incoming dataset elements has the same\n     * structure-- i.e. the same set of keys at each location in an object\n     * hierarchy.  For each key, the resulting `Dataset` provides a batched\n     * element collecting all of the incoming values for that key.\n     *\n     *  * Incoming primitives are grouped into a 1-D Tensor.\n     *  * Incoming Tensors are grouped into a new Tensor where the 0'th axis is\n     *    the batch dimension.\n     *  * Incoming arrays are converted to Tensor and then batched.\n     *  * A nested array is interpreted as an n-D Tensor, so the batched result\n     *    has n+1 dimensions.\n     *  * An array that cannot be converted to Tensor produces an error.\n     *\n     * If an array should not be batched as a unit, it should first be converted\n     * to an object with integer keys.\n     *\n     * Here are a few examples:\n     *\n     * Batch a dataset of numbers:\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8]).batch(4);\n     * await a.forEachAsync(e => e.print());\n     * ```\n     *\n     * Batch a dataset of arrays:\n     * ```js\n     * const b = tf.data.array([[1], [2], [3], [4], [5], [6], [7], [8]]).batch(4);\n     * await b.forEachAsync(e => e.print());\n     * ```\n     *\n     * Batch a dataset of objects:\n     * ```js\n     * const c = tf.data.array([{a: 1, b: 11}, {a: 2, b: 12}, {a: 3, b: 13},\n     *   {a: 4, b: 14}, {a: 5, b: 15}, {a: 6, b: 16}, {a: 7, b: 17},\n     *   {a: 8, b: 18}]).batch(4);\n     * await c.forEachAsync(e => {\n     *   console.log('{');\n     *   for(var key in e) {\n     *     console.log(key+':');\n     *     e[key].print();\n     *   }\n     *   console.log('}');\n     * })\n     * ```\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @returns A `Dataset`, from which a stream of batches can be obtained.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    batch(batchSize, smallLastBatch = true) {\n        const base = this;\n        tf.util.assert(batchSize > 0, () => `batchSize needs to be positive, but it is\n      ${batchSize}`);\n        let size;\n        if (this.size === Infinity || this.size == null) {\n            // If the size of this dataset is infinity or null, the new size keeps the\n            // same.\n            size = this.size;\n        }\n        else if (smallLastBatch) {\n            // If the size of this dataset is known and include small last batch, the\n            // new size is full batch count plus last batch.\n            size = Math.ceil(this.size / batchSize);\n        }\n        else {\n            // If the size of this dataset is known and not include small last batch,\n            // the new size is full batch count.\n            size = Math.floor(this.size / batchSize);\n        }\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator())\n                .columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat);\n        }, size);\n    }\n    /**\n     * Concatenates this `Dataset` with another.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]);\n     * const b = tf.data.array([4, 5, 6]);\n     * const c = a.concatenate(b);\n     * await c.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param dataset A `Dataset` to be concatenated onto this one.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    concatenate(dataset) {\n        const base = this;\n        let size;\n        if (this.size === Infinity || dataset.size === Infinity) {\n            // If the size of any of these two dataset is infinity, new size is\n            // infinity.\n            size = Infinity;\n        }\n        else if (this.size != null && dataset.size != null) {\n            // If the size of both datasets are known and not infinity, new size is\n            // sum the size of these two datasets.\n            size = this.size + dataset.size;\n        }\n        else {\n            // If neither of these two datasets has infinite size and any of these two\n            // datasets' size is null, the new size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).concatenate(await dataset.iterator()), size);\n    }\n    /**\n     * Filters this dataset according to `predicate`.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n     *   .filter(x => x%2 === 0);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param predicate A function mapping a dataset element to a boolean or a\n     * `Promise` for one.\n     *\n     * @returns A `Dataset` of elements for which the predicate was true.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    filter(predicate) {\n        const base = this;\n        let size;\n        if (this.size === Infinity) {\n            // If the size of this dataset is infinity, new size is infinity\n            size = Infinity;\n        }\n        else {\n            // If this dataset has limited elements, new size is null because it might\n            // exhausted randomly.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).filter(x => tf.tidy(() => predicate(x)));\n        }, size);\n    }\n    /**\n     * Apply a function to every element of the dataset.\n     *\n     * After the function is applied to a dataset element, any Tensors contained\n     * within that element are disposed.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param f A function to apply to each dataset element.\n     * @returns A `Promise` that resolves after all elements have been processed.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async forEachAsync(f) {\n        return (await this.iterator()).forEachAsync(f);\n    }\n    /**\n     * Maps this dataset through a 1-to-1 transform.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]).map(x => x*x);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param transform A function mapping a dataset element to a transformed\n     *   dataset element.\n     *\n     * @returns A `Dataset` of transformed elements.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    map(transform) {\n        const base = this;\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).map(x => tf.tidy(() => transform(x)));\n        }, this.size);\n    }\n    /**\n     * Maps this dataset through an async 1-to-1 transform.\n     *\n     * ```js\n     * const a =\n     *  tf.data.array([1, 2, 3]).mapAsync(x => new Promise(function(resolve){\n     *    setTimeout(() => {\n     *      resolve(x * x);\n     *    }, Math.random()*1000 + 500);\n     *  }));\n     * console.log(await a.toArray());\n     * ```\n     *\n     * @param transform A function mapping a dataset element to a `Promise` for a\n     *   transformed dataset element.  This transform is responsible for disposing\n     *   any intermediate `Tensor`s, i.e. by wrapping its computation in\n     *   `tf.tidy()`; that cannot be automated here (as it is in the synchronous\n     *   `map()` case).\n     *\n     * @returns A `Dataset` of transformed elements.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    mapAsync(transform) {\n        const base = this;\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).mapAsync(transform);\n        }, this.size);\n    }\n    /**\n     *  Creates a `Dataset` that prefetches elements from this dataset.\n     *\n     * @param bufferSize: An integer specifying the number of elements to be\n     *   prefetched.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    prefetch(bufferSize) {\n        if (bufferSize == null) {\n            throw new RangeError('`Dataset.prefetch()` requires bufferSize to be specified.');\n        }\n        const base = this;\n        return datasetFromIteratorFn(async () => (await base.iterator()).prefetch(bufferSize), this.size);\n    }\n    /**\n     * Repeats this dataset `count` times.\n     *\n     * NOTE: If this dataset is a function of global state (e.g. a random number\n     * generator), then different repetitions may produce different elements.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]).repeat(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: (Optional) An integer, representing the number of times\n     *   the dataset should be repeated. The default behavior (if `count` is\n     *   `undefined` or negative) is for the dataset be repeated indefinitely.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    repeat(count) {\n        const base = this;\n        let size;\n        if (this.size != null && count > 0) {\n            // If this dataset has size and count is positive, new size is current\n            // size multiply count. This also covers the case that current size is\n            // infinity.\n            size = this.size * count;\n        }\n        else if (count === 0) {\n            // If count is 0, new size is 0.\n            size = 0;\n        }\n        else if (this.size != null && (count === undefined || count < 0)) {\n            // If this dataset has size and count is undefined or negative, the\n            // dataset will be repeated indefinitely and new size is infinity.\n            size = Infinity;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => {\n            const iteratorIterator = iteratorFromFunction(async () => ({ value: await base.iterator(), done: false }));\n            return iteratorFromConcatenated(iteratorIterator.take(count));\n        }, size);\n    }\n    /**\n     * Creates a `Dataset` that skips `count` initial elements from this dataset.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).skip(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: The number of elements of this dataset that should be skipped\n     *   to form the new dataset.  If `count` is greater than the size of this\n     *   dataset, the new dataset will contain no elements.  If `count`\n     *   is `undefined` or negative, skips the entire dataset.\n     *\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    skip(count) {\n        const base = this;\n        let size;\n        if (this.size != null && count >= 0 && this.size >= count) {\n            // If the size of this dataset is greater than count, the new dataset's\n            // size is current size minus skipped size.This also covers the case that\n            // current size is infinity.\n            size = this.size - count;\n        }\n        else if (this.size != null &&\n            (this.size < count || count === undefined || count < 0)) {\n            // If the size of this dataset is smaller than count, or count is\n            // undefined or negative, skips the entire dataset and the new size is 0.\n            size = 0;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).skip(count), size);\n    }\n    /**\n     * Pseudorandomly shuffles the elements of this dataset. This is done in a\n     * streaming manner, by sampling from a given number of prefetched elements.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).shuffle(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param bufferSize: An integer specifying the number of elements from this\n     *   dataset from which the new dataset will sample.\n     * @param seed: (Optional) An integer specifying the random seed that will\n     *   be used to create the distribution.\n     * @param reshuffleEachIteration: (Optional) A boolean, which if true\n     *   indicates that the dataset should be pseudorandomly reshuffled each time\n     *   it is iterated over. If false, elements will be returned in the same\n     *   shuffled order on each iteration. (Defaults to `true`.)\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    shuffle(bufferSize, seed, reshuffleEachIteration = true) {\n        if (bufferSize == null || bufferSize < 0) {\n            if (this.size == null) {\n                throw new RangeError('`Dataset.shuffle()` requires bufferSize to be specified.');\n            }\n            else {\n                throw new RangeError('`Dataset.shuffle()` requires bufferSize to be specified.  ' +\n                    'If your data fits in main memory (for regular JS objects), ' +\n                    'and/or GPU memory (for `tf.Tensor`s), consider setting ' +\n                    `bufferSize to the dataset size (${this.size} elements)`);\n            }\n        }\n        const base = this;\n        const random = seedrandom.alea(seed || tf.util.now().toString());\n        return datasetFromIteratorFn(async () => {\n            let seed2 = random.int32();\n            if (reshuffleEachIteration) {\n                seed2 += random.int32();\n            }\n            return (await base.iterator()).shuffle(bufferSize, seed2.toString());\n        }, this.size);\n    }\n    /**\n     * Creates a `Dataset` with at most `count` initial elements from this\n     * dataset.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).take(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: The number of elements of this dataset that should be taken\n     *   to form the new dataset.  If `count` is `undefined` or negative, or if\n     *   `count` is greater than the size of this dataset, the new dataset will\n     *   contain all elements of this dataset.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    take(count) {\n        const base = this;\n        let size;\n        if (this.size != null && this.size > count) {\n            // If the size of this dataset is greater than count, the new dataset's\n            // size is count.\n            size = count;\n        }\n        else if (this.size != null && this.size <= count) {\n            // If the size of this dataset is equal or smaller than count, the new\n            // dataset's size is the size of this dataset.\n            size = this.size;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).take(count), size);\n    }\n    /**\n     * Collect all elements of this dataset into an array.\n     *\n     * Obviously this will succeed only for small datasets that fit in memory.\n     * Useful for testing and generally should be avoided if possible.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]);\n     * console.log(await a.toArray());\n     * ```\n     *\n     * @returns A Promise for an array of elements, which will resolve\n     *   when a new stream has been obtained and fully consumed.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async toArray() {\n        if (this.size === Infinity) {\n            throw new Error('Can not convert infinite data stream to array.');\n        }\n        return (await this.iterator()).toArray();\n    }\n    /**\n     * Collect all elements of this dataset into an array with prefetching 100\n     * elements. This is useful for testing, because the prefetch changes the\n     * order in which the Promises are resolved along the processing pipeline.\n     * This may help expose bugs where results are dependent on the order of\n     * Promise resolution rather than on the logical order of the stream (i.e.,\n     * due to hidden mutable state).\n     *\n     * @returns A Promise for an array of elements, which will resolve\n     *   when a new stream has been obtained and fully consumed.\n     */\n    async toArrayForTest() {\n        if (this.size === Infinity) {\n            throw new Error('Can not convert infinite data stream to array.');\n        }\n        return (await this.iterator()).toArrayForTest();\n    }\n}\n// TODO(soergel): deep sharded shuffle, where supported\nDataset.MAX_BUFFER_SIZE = 10000;\n/**\n * Create a `Dataset` defined by a provided iterator() function.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const iter = tf.data.iteratorFromFunction(func);\n * const ds = tf.data.datasetFromIteratorFn(iter);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n */\nexport function datasetFromIteratorFn(iteratorFn, size = null) {\n    return new class extends Dataset {\n        constructor() {\n            super(...arguments);\n            this.size = size;\n        }\n        /*\n         * Provide a new stream of elements.  Note this will also start new streams\n         * from any underlying `Dataset`s.\n         */\n        async iterator() {\n            return iteratorFn();\n        }\n    }();\n}\n/**\n * Create a `Dataset` from an array of elements.\n *\n * Create a Dataset from an array of objects:\n * ```js\n * const a = tf.data.array([{'item': 1}, {'item': 2}, {'item': 3}]);\n * await a.forEachAsync(e => console.log(e));\n * ```\n *\n * Create a Dataset from an array of numbers:\n * ```js\n * const a = tf.data.array([4, 5, 6]);\n * await a.forEachAsync(e => console.log(e));\n * ```\n * @param items An array of elements that will be parsed as items in a dataset.\n *\n * @doc {heading: 'Data', subheading: 'Creation', namespace: 'data'}\n */\nexport function array(items) {\n    return datasetFromIteratorFn(async () => iteratorFromItems(items), items.length);\n}\n/**\n * Create a `Dataset` by zipping together an array, dict, or nested\n * structure of `Dataset`s (and perhaps additional constants).\n * The underlying datasets must provide elements in a consistent order such that\n * they correspond.\n *\n * The number of elements in the resulting dataset is the same as the size of\n * the smallest dataset in datasets.\n *\n * The nested structure of the `datasets` argument determines the\n * structure of elements in the resulting iterator.\n *\n * Note this means that, given an array of two datasets that produce dict\n * elements, the result is a dataset that produces elements that are arrays\n * of two dicts:\n *\n * Zip an array of datasets:\n * ```js\n * console.log('Zip two datasets of objects:');\n * const ds1 = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);\n * const ds2 = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);\n * const ds3 = tf.data.zip([ds1, ds2]);\n * await ds3.forEachAsync(e => console.log(JSON.stringify(e)));\n *\n * // If the goal is to merge the dicts in order to produce elements like\n * // {a: ..., b: ...}, this requires a second step such as:\n * console.log('Merge the objects:');\n * const ds4 = ds3.map(x => {return {a: x[0].a, b: x[1].b}});\n * await ds4.forEachAsync(e => console.log(e));\n * ```\n *\n * Zip a dict of datasets:\n * ```js\n * const a = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);\n * const b = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);\n * const c = tf.data.zip({c: a, d: b});\n * await c.forEachAsync(e => console.log(JSON.stringify(e)));\n * ```\n *\n * @doc {heading: 'Data', subheading: 'Operations', namespace: 'data'}\n */\nexport function zip(datasets) {\n    // manually type-check the argument for JS users\n    if (!isIterable(datasets)) {\n        throw new Error('The argument to zip() must be an object or array.');\n    }\n    let size;\n    if (Array.isArray(datasets)) {\n        for (let i = 0; i < datasets.length; i++) {\n            size = size == null ? datasets[i].size :\n                Math.min(size, datasets[i].size);\n        }\n    }\n    else if (datasets instanceof Object) {\n        for (const ds in datasets) {\n            size = size == null ? datasets[ds].size :\n                Math.min(size, datasets[ds].size);\n        }\n    }\n    return datasetFromIteratorFn(async () => {\n        const streams = await deepMapAndAwaitAll(datasets, d => {\n            if (d instanceof Dataset) {\n                return { value: d.iterator(), recurse: false };\n            }\n            else if (isIterable(d)) {\n                return { value: null, recurse: true };\n            }\n            else {\n                throw new Error('Leaves of the structure passed to zip() must be Datasets, ' +\n                    'not primitives.');\n            }\n        });\n        return iteratorFromZipped(streams, ZipMismatchMode.SHORTEST);\n    }, size);\n}\n/**\n * A zip function for use with deepZip, passed via the columnMajorBatch call.\n *\n * Accepts an array of identically-structured nested elements and either batches\n * them (if they are primitives, numeric arrays, or Tensors) or requests\n * recursion (if not).\n */\n// tslint:disable-next-line:no-any\nfunction deepBatchConcat(rows) {\n    if (rows === null) {\n        return null;\n    }\n    // use the first item to decide whether to recurse or batch here.\n    const exampleRow = rows[0];\n    if (canTensorify(exampleRow)) {\n        // rows is an array of primitives, Tensors, or arrays.  Batch them.\n        const value = batchConcat(rows);\n        return { value, recurse: false };\n    }\n    // the example row is an object, so recurse into it.\n    return { value: null, recurse: true };\n}\n/**\n * Assembles a list of same-shaped numbers, number arrays, or Tensors\n * into a single new Tensor where axis 0 is the batch dimension.\n */\nfunction batchConcat(arrays) {\n    if (arrays.length === 0) {\n        // We can't return an empty Tensor because we don't know the element shape.\n        throw new Error('Can\\'t make a batch of zero elements.');\n    }\n    if (arrays[0] instanceof tf.Tensor) {\n        // Input is an array of Tensors\n        return tf.stack(arrays);\n    }\n    else {\n        // Input is a possibly-nested array of numbers.\n        return tf.tensor(arrays);\n    }\n}\n//# sourceMappingURL=dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { Dataset } from '../dataset';\n/**\n * Represents a potentially large collection of text lines.\n *\n * The results are not batched.\n */\nexport class TextLineDataset extends Dataset {\n    /**\n     * Create a `TextLineDataset`.\n     *\n     * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n     */\n    constructor(input) {\n        super();\n        this.input = input;\n    }\n    async iterator() {\n        const inputIterator = await this.input.iterator();\n        const utf8Iterator = inputIterator.decodeUTF8();\n        const lineIterator = utf8Iterator.split('\\n').map(line => {\n            // Windows/DOS format text file has extra line breaker at the end of line.\n            if (line.endsWith('\\r')) {\n                line = line.slice(0, -1);\n            }\n            return line;\n        });\n        return lineIterator;\n    }\n}\n//# sourceMappingURL=text_line_dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { Dataset } from '../dataset';\nimport { TextLineDataset } from './text_line_dataset';\nconst CODE_QUOTE = '\"';\nconst STATE_OUT = Symbol('out');\nconst STATE_FIELD = Symbol('field');\nconst STATE_QUOTE = Symbol('quote');\nconst STATE_QUOTE_AFTER_QUOTE = Symbol('quoteafterquote');\nconst STATE_WITHIN_QUOTE_IN_QUOTE = Symbol('quoteinquote');\n/**\n * Represents a potentially large collection of delimited text records.\n *\n * The produced `TensorContainer`s each contain one key-value pair for\n * every column of the table.  When a field is empty in the incoming data, the\n * resulting value is `undefined`, or throw error if it is required.  Values\n * that can be parsed as numbers are emitted as type `number`, other values\n * are parsed as `string`.\n *\n * The results are not batched.\n *\n * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}\n */\nexport class CSVDataset extends Dataset {\n    /**\n     * Create a `CSVDataset`.\n     *\n     * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n     * @param csvConfig (Optional) A CSVConfig object that contains configurations\n     *     of reading and decoding from CSV file(s).\n     *\n     *     hasHeader: (Optional) A boolean value that indicates whether the first\n     *     row of provided CSV file is a header line with column names, and should\n     *     not be included in the data. Defaults to `true`.\n     *\n     *     columnNames: (Optional) A list of strings that corresponds to\n     *     the CSV column names, in order. If provided, it ignores the column\n     *     names inferred from the header row. If not provided, infers the column\n     *     names from the first row of the records. If hasHeader is false and\n     *     columnNames is not provided, this method throws an error.\n     *\n     *     columnConfigs: (Optional) A dictionary whose key is column names, value\n     *     is an object stating if this column is required, column's data type,\n     *     default value, and if this column is label. If provided, keys must\n     *     correspond to names provided in columnNames or inferred from the file\n     *     header lines. If isLabel is true any column, returns an array of two\n     *     items: the first item is a dict of features key/value pairs, the second\n     *     item is a dict of labels key/value pairs. If no feature is marked as\n     *     label, returns a dict of features only.\n     *\n     *     configuredColumnsOnly (Optional) If true, only columns provided in\n     *     columnConfigs will be parsed and provided during iteration.\n     *\n     *     delimiter (Optional) The string used to parse each line of the input\n     *     file. Defaults to `,`.\n     */\n    constructor(input, csvConfig) {\n        super();\n        this.input = input;\n        this.hasHeader = true;\n        this.fullColumnNames = null;\n        this.columnNamesValidated = false;\n        this.columnConfigs = null;\n        this.configuredColumnsOnly = false;\n        this.delimiter = ',';\n        this.delimWhitespace = false;\n        this.base = new TextLineDataset(input);\n        if (!csvConfig) {\n            csvConfig = {};\n        }\n        this.hasHeader = csvConfig.hasHeader === false ? false : true;\n        this.fullColumnNames = csvConfig.columnNames;\n        this.columnConfigs = csvConfig.columnConfigs;\n        this.configuredColumnsOnly = csvConfig.configuredColumnsOnly;\n        if (csvConfig.delimWhitespace) {\n            util.assert(csvConfig.delimiter == null, () => 'Delimiter should not be provided when delimWhitespace is true.');\n            this.delimWhitespace = true;\n            this.delimiter = ' ';\n        }\n        else {\n            this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : ',';\n        }\n    }\n    /**\n     * Returns column names of the csv dataset. If `configuredColumnsOnly` is\n     * true, return column names in `columnConfigs`. If `configuredColumnsOnly` is\n     * false and `columnNames` is provided, `columnNames`. If\n     * `configuredColumnsOnly` is false and `columnNames` is not provided, return\n     * all column names parsed from the csv file. For example usage please go to\n     * `tf.data.csv`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async columnNames() {\n        if (!this.columnNamesValidated) {\n            await this.setColumnNames();\n        }\n        return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) :\n            this.fullColumnNames;\n    }\n    /* 1) If `columnNames` is provided as string[], use this string[] as output\n     * keys in corresponding order. The length must match the number of inferred\n     * columns if `hasHeader` is true .\n     * 2) If `columnNames` is not provided, parse header line as `columnNames` if\n     * hasHeader is true. If `hasHeader` is false, throw an error.\n     * 3) If `columnConfigs` is provided, all the keys in `columnConfigs` must\n     * exist in parsed `columnNames`.\n     */\n    async setColumnNames() {\n        const columnNamesFromFile = await this.maybeReadHeaderLine();\n        if (!this.fullColumnNames && !columnNamesFromFile) {\n            // Throw an error if columnNames is not provided and no header line.\n            throw new Error('Column names must be provided if there is no header line.');\n        }\n        else if (this.fullColumnNames && columnNamesFromFile) {\n            // Check provided columnNames match header line.\n            util.assert(columnNamesFromFile.length === this.fullColumnNames.length, () => 'The length of provided columnNames (' +\n                this.fullColumnNames.length.toString() +\n                ') does not match the length of the header line read from ' +\n                'file (' + columnNamesFromFile.length.toString() + ').');\n        }\n        if (!this.fullColumnNames) {\n            this.fullColumnNames = columnNamesFromFile;\n        }\n        // Check if there are duplicate column names.\n        const counts = this.fullColumnNames.reduce((countAcc, name) => {\n            countAcc[name] = (countAcc[name] + 1) || 1;\n            return countAcc;\n        }, {});\n        const duplicateNames = Object.keys(counts).filter((name) => (counts[name] > 1));\n        util.assert(duplicateNames.length === 0, () => 'Duplicate column names found: ' + duplicateNames.toString());\n        // Check if keys in columnConfigs match columnNames.\n        if (this.columnConfigs) {\n            for (const key of Object.keys(this.columnConfigs)) {\n                const index = this.fullColumnNames.indexOf(key);\n                if (index === -1) {\n                    throw new Error('The key \"' + key +\n                        '\" provided in columnConfigs does not match any of the column ' +\n                        'names (' + this.fullColumnNames.toString() + ').');\n                }\n            }\n        }\n        this.columnNamesValidated = true;\n    }\n    async maybeReadHeaderLine() {\n        if (this.hasHeader) {\n            const iter = await this.base.iterator();\n            const firstElement = await iter.next();\n            if (firstElement.done) {\n                throw new Error('No data was found for CSV parsing.');\n            }\n            const firstLine = firstElement.value;\n            const headers = this.parseRow(firstLine, false);\n            return headers;\n        }\n        else {\n            return null;\n        }\n    }\n    async iterator() {\n        if (!this.columnNamesValidated) {\n            await this.setColumnNames();\n        }\n        let lines = await this.base.iterator();\n        if (this.hasHeader) {\n            // We previously read the first line to get the columnNames.\n            // Now that we're providing data, skip it.\n            lines = lines.skip(1);\n        }\n        return lines.map(x => this.makeDataElement(x));\n    }\n    makeDataElement(line) {\n        const values = this.parseRow(line);\n        const features = {};\n        const labels = {};\n        for (let i = 0; i < this.fullColumnNames.length; i++) {\n            const key = this.fullColumnNames[i];\n            const config = this.columnConfigs ? this.columnConfigs[key] : null;\n            if (this.configuredColumnsOnly && !config) {\n                // This column is not selected.\n                continue;\n            }\n            else {\n                const value = values[i];\n                let parsedValue = null;\n                if (value === '') {\n                    // If default value is provided, use it. If default value is not\n                    // provided, set as undefined.\n                    if (config && config.default !== undefined) {\n                        parsedValue = config.default;\n                    }\n                    else if (config && (config.required || config.isLabel)) {\n                        throw new Error(`Required column ${key} is empty in this line: ${line}`);\n                    }\n                    else {\n                        parsedValue = undefined;\n                    }\n                }\n                else {\n                    // A value is present, so parse it based on type\n                    const valueAsNum = Number(value);\n                    if (isNaN(valueAsNum)) {\n                        // The value is a string and this column is declared as boolean\n                        // in config, parse it as boolean.\n                        if (config && config.dtype === 'bool') {\n                            parsedValue = this.getBoolean(value);\n                        }\n                        else {\n                            // Set value as string\n                            parsedValue = value;\n                        }\n                    }\n                    else if (!config || !config.dtype) {\n                        // If this value is a number and no type config is provided, return\n                        // it as number.\n                        parsedValue = valueAsNum;\n                    }\n                    else {\n                        // If this value is a number and data type is provided, parse it\n                        // according to provided data type.\n                        switch (config.dtype) {\n                            case 'float32':\n                                parsedValue = valueAsNum;\n                                break;\n                            case 'int32':\n                                parsedValue = Math.floor(valueAsNum);\n                                break;\n                            case 'bool':\n                                parsedValue = this.getBoolean(value);\n                                break;\n                            default:\n                                parsedValue = valueAsNum;\n                        }\n                    }\n                }\n                // Check if this column is label.\n                (config && config.isLabel) ? labels[key] = parsedValue :\n                    features[key] = parsedValue;\n            }\n        }\n        // If label exists, return an object of features and labels as {xs:features,\n        // ys:labels}, otherwise return features only.\n        if (Object.keys(labels).length === 0) {\n            return features;\n        }\n        else {\n            return { xs: features, ys: labels };\n        }\n    }\n    getBoolean(value) {\n        if (value === '1' || value.toLowerCase() === 'true') {\n            return 1;\n        }\n        else {\n            return 0;\n        }\n    }\n    // adapted from https://beta.observablehq.com/@mbostock/streaming-csv\n    parseRow(line, validateElementCount = true) {\n        const result = [];\n        let readOffset = 0;\n        const readLength = line.length;\n        let currentState = STATE_OUT;\n        // Goes through the line to parse quote.\n        for (let i = 0; i < readLength; i++) {\n            switch (currentState) {\n                // Before enter a new field\n                case STATE_OUT:\n                    switch (line.charAt(i)) {\n                        // Enter a quoted field\n                        case CODE_QUOTE:\n                            readOffset = i + 1;\n                            currentState = STATE_QUOTE;\n                            break;\n                        // Read an empty field\n                        case this.delimiter:\n                            readOffset = i + 1;\n                            // If delimiter is white space and configured to collapse\n                            // multiple white spaces, ignore this white space.\n                            if (this.delimiter === ' ' && this.delimWhitespace) {\n                                break;\n                            }\n                            result.push('');\n                            currentState = STATE_OUT;\n                            break;\n                        // Enter an unquoted field\n                        default:\n                            currentState = STATE_FIELD;\n                            readOffset = i;\n                            break;\n                    }\n                    break;\n                // In an unquoted field\n                case STATE_FIELD:\n                    switch (line.charAt(i)) {\n                        // Exit an unquoted field, add it to result\n                        case this.delimiter:\n                            result.push(line.substring(readOffset, i));\n                            currentState = STATE_OUT;\n                            readOffset = i + 1;\n                            break;\n                        default:\n                    }\n                    break;\n                // In a quoted field\n                case STATE_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Read a quote after a quote\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE_AFTER_QUOTE;\n                            break;\n                        default:\n                    }\n                    break;\n                // This state means it's right after a second quote in a field\n                case STATE_QUOTE_AFTER_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Finished a quoted field\n                        case this.delimiter:\n                            result.push(line.substring(readOffset, i - 1));\n                            currentState = STATE_OUT;\n                            readOffset = i + 1;\n                            break;\n                        // Finished a quoted part in a quoted field\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE;\n                            break;\n                        // In a quoted part in a quoted field\n                        default:\n                            currentState = STATE_WITHIN_QUOTE_IN_QUOTE;\n                            break;\n                    }\n                    break;\n                case STATE_WITHIN_QUOTE_IN_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Exit a quoted part in a quoted field\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE;\n                            break;\n                        default:\n                    }\n                    break;\n                default:\n            }\n        }\n        // Adds last item based on if it is quoted.\n        if (currentState === STATE_QUOTE_AFTER_QUOTE) {\n            result.push(line.substring(readOffset, readLength - 1));\n        }\n        else {\n            result.push(line.substring(readOffset));\n        }\n        // Check if each row has the same number of elements as column names.\n        if (validateElementCount && result.length !== this.fullColumnNames.length) {\n            throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);\n        }\n        return result;\n    }\n}\n// TODO(soergel): add more basic datasets for parity with tf.data\n// tf.data.FixedLengthRecordDataset()\n// tf.data.TFRecordDataset()\n//# sourceMappingURL=csv_dataset.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env, tensor, util } from '@tensorflow/tfjs-core';\nimport { LazyIterator } from './lazy_iterator';\n/**\n * Provide a stream of tensors from microphone audio stream. The tensors are\n * representing audio data as frequency-domain spectrogram generated with\n * browser's native FFT. Tensors representing time-domain waveform is available\n * based on configuration. Only works in browser environment.\n */\nexport class MicrophoneIterator extends LazyIterator {\n    constructor(microphoneConfig) {\n        super();\n        this.microphoneConfig = microphoneConfig;\n        this.isClosed = false;\n        this.fftSize = microphoneConfig.fftSize || 1024;\n        const fftSizeLog2 = Math.log2(this.fftSize);\n        if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 ||\n            !Number.isInteger(fftSizeLog2)) {\n            throw new Error(`Invalid fftSize: it must be a power of 2 between ` +\n                `2 to 4 and 2 to 14, but got ${this.fftSize}`);\n        }\n        this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43;\n        this.sampleRateHz = microphoneConfig.sampleRateHz;\n        this.columnTruncateLength =\n            microphoneConfig.columnTruncateLength || this.fftSize;\n        this.audioTrackConstraints = microphoneConfig.audioTrackConstraints;\n        this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0;\n        this.includeSpectrogram =\n            microphoneConfig.includeSpectrogram === false ? false : true;\n        this.includeWaveform =\n            microphoneConfig.includeWaveform === true ? true : false;\n        if (!this.includeSpectrogram && !this.includeWaveform) {\n            throw new Error('Both includeSpectrogram and includeWaveform are false. ' +\n                'At least one type of data should be returned.');\n        }\n    }\n    summary() {\n        return `microphone`;\n    }\n    // Construct a MicrophoneIterator and start the audio stream.\n    static async create(microphoneConfig = {}) {\n        if (env().get('IS_NODE')) {\n            throw new Error('microphone API is only supported in browser environment.');\n        }\n        const microphoneIterator = new MicrophoneIterator(microphoneConfig);\n        // Call async function start() to initialize the audio stream.\n        await microphoneIterator.start();\n        return microphoneIterator;\n    }\n    // Start the audio stream and FFT.\n    async start() {\n        try {\n            this.stream = await navigator.mediaDevices.getUserMedia({\n                audio: this.audioTrackConstraints == null ? true :\n                    this.audioTrackConstraints,\n                video: false\n            });\n        }\n        catch (e) {\n            throw new Error(`Error thrown while initializing video stream: ${e.message}`);\n        }\n        if (!this.stream) {\n            throw new Error('Could not obtain audio from microphone.');\n        }\n        const ctxConstructor = \n        // tslint:disable-next-line:no-any\n        window.AudioContext || window.webkitAudioContext;\n        this.audioContext = new ctxConstructor();\n        if (!this.sampleRateHz) {\n            // If sample rate is not provided, use the available sample rate on\n            // device.\n            this.sampleRateHz = this.audioContext.sampleRate;\n        }\n        else if (this.audioContext.sampleRate !== this.sampleRateHz) {\n            throw new Error(`Mismatch in sampling rate: ` +\n                `Expected: ${this.sampleRateHz}; ` +\n                `Actual: ${this.audioContext.sampleRate}`);\n        }\n        const streamSource = this.audioContext.createMediaStreamSource(this.stream);\n        this.analyser = this.audioContext.createAnalyser();\n        this.analyser.fftSize = this.fftSize * 2;\n        this.analyser.smoothingTimeConstant = this.smoothingTimeConstant;\n        streamSource.connect(this.analyser);\n        this.freqData = new Float32Array(this.fftSize);\n        this.timeData = new Float32Array(this.fftSize);\n        return;\n    }\n    async next() {\n        if (this.isClosed) {\n            return { value: null, done: true };\n        }\n        let spectrogramTensor;\n        let waveformTensor;\n        const audioDataQueue = await this.getAudioData();\n        if (this.includeSpectrogram) {\n            const freqData = this.flattenQueue(audioDataQueue.freqDataQueue);\n            spectrogramTensor = this.getTensorFromAudioDataArray(freqData, [this.numFrames, this.columnTruncateLength, 1]);\n        }\n        if (this.includeWaveform) {\n            const timeData = this.flattenQueue(audioDataQueue.timeDataQueue);\n            waveformTensor = this.getTensorFromAudioDataArray(timeData, [this.numFrames * this.fftSize, 1]);\n        }\n        return {\n            value: { 'spectrogram': spectrogramTensor, 'waveform': waveformTensor },\n            done: false\n        };\n    }\n    // Capture one result from the audio stream, and extract the value from\n    // iterator.next() result.\n    async capture() {\n        return (await this.next()).value;\n    }\n    async getAudioData() {\n        const freqDataQueue = [];\n        const timeDataQueue = [];\n        let currentFrames = 0;\n        return new Promise(resolve => {\n            const intervalID = setInterval(() => {\n                if (this.includeSpectrogram) {\n                    this.analyser.getFloatFrequencyData(this.freqData);\n                    // If the audio stream is initializing, return empty queue.\n                    if (this.freqData[0] === -Infinity) {\n                        resolve({ freqDataQueue, timeDataQueue });\n                    }\n                    freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength));\n                }\n                if (this.includeWaveform) {\n                    this.analyser.getFloatTimeDomainData(this.timeData);\n                    timeDataQueue.push(this.timeData.slice());\n                }\n                // Clean interval and return when all frames have been collected\n                if (++currentFrames === this.numFrames) {\n                    clearInterval(intervalID);\n                    resolve({ freqDataQueue, timeDataQueue });\n                }\n            }, this.fftSize / this.sampleRateHz * 1e3);\n        });\n    }\n    // Stop the audio stream and pause the iterator.\n    stop() {\n        if (!this.isClosed) {\n            this.isClosed = true;\n            this.analyser.disconnect();\n            this.audioContext.close();\n            if (this.stream != null && this.stream.getTracks().length > 0) {\n                this.stream.getTracks()[0].stop();\n            }\n        }\n    }\n    // Override toArray() function to prevent collecting.\n    toArray() {\n        throw new Error('Can not convert infinite audio stream to array.');\n    }\n    // Return audio sampling rate in Hz\n    getSampleRate() {\n        return this.sampleRateHz;\n    }\n    flattenQueue(queue) {\n        const frameSize = queue[0].length;\n        const freqData = new Float32Array(queue.length * frameSize);\n        queue.forEach((data, i) => freqData.set(data, i * frameSize));\n        return freqData;\n    }\n    getTensorFromAudioDataArray(freqData, shape) {\n        const vals = new Float32Array(util.sizeFromShape(shape));\n        // If the data is less than the output shape, the rest is padded with zeros.\n        vals.set(freqData, vals.length - freqData.length);\n        return tensor(vals, shape);\n    }\n}\n//# sourceMappingURL=microphone_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { browser, env, image, tensor1d, tensor2d, tidy, util } from '@tensorflow/tfjs-core';\nimport { LazyIterator } from './lazy_iterator';\n/**\n * Provide a stream of image tensors from webcam video stream. Only works in\n * browser environment.\n */\nexport class WebcamIterator extends LazyIterator {\n    constructor(webcamVideoElement, webcamConfig) {\n        super();\n        this.webcamVideoElement = webcamVideoElement;\n        this.webcamConfig = webcamConfig;\n        this.isClosed = true;\n        this.resize = false;\n        if (this.needToResize()) {\n            this.resize = true;\n            this.cropSize =\n                [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth];\n            this.cropBoxInd = tensor1d([0], 'int32');\n            if (this.webcamConfig.centerCrop) {\n                // Calculate the box based on resizing shape.\n                const widthCroppingRatio = this.webcamConfig.resizeWidth * 1.0 / this.webcamVideoElement.width;\n                const heightCroppingRatio = this.webcamConfig.resizeHeight * 1.0 /\n                    this.webcamVideoElement.height;\n                const widthCropStart = (1 - widthCroppingRatio) / 2;\n                const heightCropStart = (1 - heightCroppingRatio) / 2;\n                const widthCropEnd = widthCropStart + widthCroppingRatio;\n                const heightCropEnd = heightCroppingRatio + heightCropStart;\n                this.cropBox = tensor2d([heightCropStart, widthCropStart, heightCropEnd, widthCropEnd], [1, 4]);\n            }\n            else {\n                this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]);\n            }\n        }\n    }\n    summary() {\n        return `webcam`;\n    }\n    // Construct a WebcamIterator and start it's video stream.\n    static async create(webcamVideoElement, webcamConfig = {}) {\n        if (env().get('IS_NODE')) {\n            throw new Error('tf.data.webcam is only supported in browser environment.');\n        }\n        if (!webcamVideoElement) {\n            // If webcam video element is not provided, create a hidden video element\n            // with provided width and height.\n            webcamVideoElement = document.createElement('video');\n            if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) {\n                throw new Error('Please provide webcam video element, or resizeWidth and ' +\n                    'resizeHeight to create a hidden video element.');\n            }\n            webcamVideoElement.width = webcamConfig.resizeWidth;\n            webcamVideoElement.height = webcamConfig.resizeHeight;\n        }\n        const webcamIterator = new WebcamIterator(webcamVideoElement, webcamConfig);\n        // Call async function to initialize the video stream.\n        await webcamIterator.start();\n        return webcamIterator;\n    }\n    // Async function to start video stream.\n    async start() {\n        if (this.webcamConfig.facingMode) {\n            util.assert((this.webcamConfig.facingMode === 'user') ||\n                (this.webcamConfig.facingMode === 'environment'), () => `Invalid webcam facing mode: ${this.webcamConfig.facingMode}. ` +\n                `Please provide 'user' or 'environment'`);\n        }\n        try {\n            this.stream = await navigator.mediaDevices.getUserMedia({\n                video: {\n                    deviceId: this.webcamConfig.deviceId,\n                    facingMode: this.webcamConfig.facingMode ?\n                        this.webcamConfig.facingMode :\n                        'user',\n                    width: this.webcamVideoElement.width,\n                    height: this.webcamVideoElement.height\n                }\n            });\n        }\n        catch (e) {\n            // Modify the error message but leave the stack trace intact\n            e.message = `Error thrown while initializing video stream: ${e.message}`;\n            throw e;\n        }\n        if (!this.stream) {\n            throw new Error('Could not obtain video from webcam.');\n        }\n        // Older browsers may not have srcObject\n        try {\n            this.webcamVideoElement.srcObject = this.stream;\n        }\n        catch (error) {\n            console.log(error);\n            this.webcamVideoElement.src = window.URL.createObjectURL(this.stream);\n        }\n        // Start the webcam video stream\n        this.webcamVideoElement.play();\n        this.isClosed = false;\n        return new Promise(resolve => {\n            // Add event listener to make sure the webcam has been fully initialized.\n            this.webcamVideoElement.onloadedmetadata = () => {\n                resolve();\n            };\n        });\n    }\n    async next() {\n        if (this.isClosed) {\n            return { value: null, done: true };\n        }\n        let img;\n        try {\n            img = browser.fromPixels(this.webcamVideoElement);\n        }\n        catch (e) {\n            throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`);\n        }\n        if (this.resize) {\n            try {\n                return { value: this.cropAndResizeFrame(img), done: false };\n            }\n            catch (e) {\n                throw new Error(`Error thrown cropping the video: ${e.message}`);\n            }\n            finally {\n                img.dispose();\n            }\n        }\n        else {\n            return { value: img, done: false };\n        }\n    }\n    needToResize() {\n        // If resizeWidth and resizeHeight are provided, and different from the\n        // width and height of original HTMLVideoElement, then resizing and cropping\n        // is required.\n        if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight &&\n            (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth ||\n                this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) {\n            return true;\n        }\n        return false;\n    }\n    // Cropping and resizing each frame based on config\n    cropAndResizeFrame(img) {\n        return tidy(() => {\n            const expandedImage = img.toFloat().expandDims(0);\n            let resizedImage;\n            resizedImage = image.cropAndResize(expandedImage, this.cropBox, this.cropBoxInd, this.cropSize, 'bilinear');\n            // Extract image from batch cropping.\n            const shape = resizedImage.shape;\n            return resizedImage.reshape(shape.slice(1));\n        });\n    }\n    // Capture one frame from the video stream, and extract the value from\n    // iterator.next() result.\n    async capture() {\n        return (await this.next()).value;\n    }\n    // Stop the video stream and pause webcam iterator.\n    stop() {\n        const tracks = this.stream.getTracks();\n        tracks.forEach(track => track.stop());\n        try {\n            this.webcamVideoElement.srcObject = null;\n        }\n        catch (error) {\n            console.log(error);\n            this.webcamVideoElement.src = null;\n        }\n        this.isClosed = true;\n    }\n    // Override toArray() function to prevent collecting.\n    toArray() {\n        throw new Error('Can not convert infinite video stream to array.');\n    }\n}\n//# sourceMappingURL=webcam_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * Represents a data source readable as a stream of binary data chunks.\n *\n * Because `Dataset`s can be read repeatedly (via `Dataset.iterator()`), this\n * provides a means to repeatedly create streams from the underlying data\n * sources.\n */\nexport class DataSource {\n}\n// TODO(soergel): consider convenience factory functions here\n// in combination with chainable source->dataset above, e.g.:\n// tf.data.url(...).asCsvDataset().shuffle().batch()\n//# sourceMappingURL=datasource.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nexport class StringIterator extends LazyIterator {\n    /**\n     * Splits a string stream on a given separator.\n     *\n     * It is assumed that the incoming chunk boundaries have no semantic meaning,\n     * so conceptually the incoming stream is treated simply as the concatenation\n     * of its elements.\n     *\n     * The outgoing stream provides chunks corresponding to the results of the\n     * standard string split() operation (even if such a chunk spanned incoming\n     * chunks).  The separators are not included.\n     *\n     * A typical usage is to split a text file (represented as a stream with\n     * arbitrary chunk boundaries) into lines.\n     *\n     * @param upstream A readable stream of strings that can be treated as\n     *   concatenated.\n     * @param separator A character to split on.\n     */\n    split(separator) {\n        return new SplitIterator(this, separator);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on StringIterator.  Unfortunately they can't be placed in separate files, due\n// to resulting trouble with circular imports.\n// ============================================================================\n// We wanted multiple inheritance, e.g.\n//   class SplitIterator extends QueueIterator<string>, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\nclass SplitIterator extends StringIterator {\n    constructor(upstream, separator) {\n        super();\n        this.upstream = upstream;\n        this.impl = new SplitIteratorImpl(upstream, separator);\n    }\n    summary() {\n        return this.impl.summary();\n    }\n    async next() {\n        return this.impl.next();\n    }\n}\nclass SplitIteratorImpl extends OneToManyIterator {\n    constructor(upstream, separator) {\n        super();\n        this.upstream = upstream;\n        this.separator = separator;\n        // A partial string at the end of an upstream chunk\n        this.carryover = '';\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Split('${this.separator}')`;\n    }\n    async pump() {\n        const chunkResult = await this.upstream.next();\n        if (chunkResult.done) {\n            if (this.carryover === '') {\n                return false;\n            }\n            // Pretend that the pump succeeded in order to emit the small last batch.\n            // The next pump() call will actually fail.\n            this.outputQueue.push(this.carryover);\n            this.carryover = '';\n            return true;\n        }\n        const lines = chunkResult.value.split(this.separator);\n        // Note the behavior: \" ab \".split(' ') === ['', 'ab', '']\n        // Thus the carryover may be '' if the separator falls on a chunk\n        // boundary; this produces the correct result.\n        lines[0] = this.carryover + lines[0];\n        for (const line of lines.slice(0, -1)) {\n            this.outputQueue.push(line);\n        }\n        this.carryover = lines[lines.length - 1];\n        return true;\n    }\n}\n//# sourceMappingURL=string_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nimport { StringIterator } from './string_iterator';\nexport class ByteChunkIterator extends LazyIterator {\n    /**\n     * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n     *\n     * The byte arrays producetd from the ByteChunkIterator on which this is\n     * called will be interpreted as concatenated.  No assumptions are made about\n     * the boundaries of the incoming chunks, so a multi-byte UTF8 encoding of a\n     * character may span the boundary between chunks.  This naturally happens,\n     * for instance, when reading fixed-size byte arrays from a file.\n     */\n    decodeUTF8() {\n        return new Utf8Iterator(this);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on ByteChunkIterator.  Unfortunately they can't be placed in separate files,\n// due to resulting trouble with circular imports.\n// ============================================================================\n// We wanted multiple inheritance, e.g.\n//   class Utf8Iterator extends QueueIterator<string>, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\nclass Utf8Iterator extends StringIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        this.impl = new Utf8IteratorImpl(upstream);\n    }\n    summary() {\n        return this.impl.summary();\n    }\n    async next() {\n        return this.impl.next();\n    }\n}\n/**\n * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n *\n * This is tricky because the incoming byte array boundaries may disrupt a\n * multi-byte UTF8 character. Thus any incomplete character data at the end of\n * a chunk must be carried over and prepended to the next chunk before\n * decoding. Luckily with native decoder, TextDecoder in browser and\n * string_decoder in node, byte array boundaries are handled automatically.\n *\n * In the context of an input pipeline for machine learning, UTF8 decoding is\n * needed to parse text files containing training examples or prediction\n * requests (e.g., formatted as CSV or JSON). We cannot use the built-in\n * decoding provided by FileReader.readAsText() because here we are in a\n * streaming context, which FileReader does not support.\n *\n * @param upstream A `LazyIterator` of `Uint8Arrays` containing UTF8-encoded\n *   text, which should be interpreted as concatenated.  No assumptions are\n *   made about the boundaries of the incoming chunks, so a multi-byte UTF8\n *   encoding of a character may span the boundary between chunks.  This\n *   naturally happens, for instance, when reading fixed-size byte arrays from a\n *   file.\n */\nclass Utf8IteratorImpl extends OneToManyIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        if (env().get('IS_BROWSER')) {\n            this.decoder = new TextDecoder('utf-8');\n        }\n        else {\n            // tslint:disable-next-line:no-require-imports\n            const { StringDecoder } = require('string_decoder');\n            this.decoder = new StringDecoder('utf8');\n        }\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Utf8`;\n    }\n    async pump() {\n        const chunkResult = await this.upstream.next();\n        let chunk;\n        if (chunkResult.done) {\n            return false;\n        }\n        else {\n            chunk = chunkResult.value;\n        }\n        let text;\n        if (env().get('IS_BROWSER')) {\n            text = this.decoder.decode(chunk, { stream: true });\n        }\n        else {\n            text = this.decoder.write(Buffer.from(chunk.buffer));\n        }\n        this.outputQueue.push(text);\n        return true;\n    }\n}\n//# sourceMappingURL=byte_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n// inspired by https://github.com/maxogden/filereader-stream\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { ByteChunkIterator } from './byte_chunk_iterator';\n/**\n * Provide a stream of chunks from a File, Blob, or Uint8Array.\n * @param file The source File, Blob or Uint8Array.\n * @param options Optional settings controlling file reading.\n * @returns a lazy Iterator of Uint8Arrays containing sequential chunks of the\n *   input File, Blob or Uint8Array.\n */\nexport class FileChunkIterator extends ByteChunkIterator {\n    constructor(file, options = {}) {\n        super();\n        this.file = file;\n        this.options = options;\n        util.assert((file instanceof Uint8Array) ||\n            (env().get('IS_BROWSER') ?\n                (file instanceof File || file instanceof Blob) :\n                false), () => 'FileChunkIterator only supports File, Blob and Uint8Array ' +\n            'right now.');\n        this.offset = options.offset || 0;\n        // default 1MB chunk has tolerable perf on large files\n        this.chunkSize = options.chunkSize || 1024 * 1024;\n    }\n    summary() {\n        return `FileChunks ${this.file}`;\n    }\n    async next() {\n        if (this.offset >= ((this.file instanceof Uint8Array) ?\n            this.file.byteLength :\n            this.file.size)) {\n            return { value: null, done: true };\n        }\n        const chunk = new Promise((resolve, reject) => {\n            const end = this.offset + this.chunkSize;\n            if (this.file instanceof Uint8Array) {\n                // Note if end > this.uint8Array.byteLength, we just get a small last\n                // chunk.\n                resolve(new Uint8Array(this.file.slice(this.offset, end)));\n            }\n            else {\n                // This branch assumes that this.file type is File or Blob, which\n                // means it is in the browser environment.\n                // TODO(soergel): is this a performance issue?\n                const fileReader = new FileReader();\n                fileReader.onload = (event) => {\n                    let data = fileReader.result;\n                    // Not sure we can trust the return type of\n                    // FileReader.readAsArrayBuffer See e.g.\n                    // https://github.com/node-file-api/FileReader/issues/2\n                    if (data instanceof ArrayBuffer) {\n                        data = new Uint8Array(data);\n                    }\n                    if (!(data instanceof Uint8Array)) {\n                        return reject(new TypeError('FileReader returned unknown type.'));\n                    }\n                    resolve(data);\n                };\n                fileReader.onabort = (event) => {\n                    return reject(new Error('Aborted'));\n                };\n                fileReader.onerror = (event) => {\n                    return reject(new Error(event.type));\n                };\n                // TODO(soergel): better handle onabort, onerror\n                // Note if end > this.file.size, we just get a small last chunk.\n                const slice = this.file.slice(this.offset, end);\n                // We can't use readAsText here (even if we know the file is text)\n                // because the slice boundary may fall within a multi-byte character.\n                fileReader.readAsArrayBuffer(slice);\n            }\n            this.offset = end;\n        });\n        return { value: (await chunk), done: false };\n    }\n}\n//# sourceMappingURL=file_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { FileChunkIterator } from './file_chunk_iterator';\n/**\n * Provide a stream of chunks from a URL.\n *\n * Note this class first downloads the entire file into memory before providing\n * the first element from the stream.  This is because the Fetch API does not\n * yet reliably provide a reader stream for the response body.\n */\nexport async function urlChunkIterator(url, options = {}) {\n    let urlString;\n    let requestInit;\n    if ((typeof url) === 'string') {\n        urlString = url;\n    }\n    else {\n        urlString = url.url;\n        requestInit = getRequestInitFromRequest(url);\n    }\n    const response = await util.fetch(urlString, requestInit);\n    if (response.ok) {\n        const uint8Array = new Uint8Array(await response.arrayBuffer());\n        return new FileChunkIterator(uint8Array, options);\n    }\n    else {\n        throw new Error(response.statusText);\n    }\n}\n// Generate RequestInit from Request to match tf.util.fetch signature.\nconst getRequestInitFromRequest = (request) => {\n    const init = {\n        method: request.method,\n        headers: request.headers,\n        body: request.body,\n        mode: request.mode,\n        credentials: request.credentials,\n        cache: request.cache,\n        redirect: request.redirect,\n        referrer: request.referrer,\n        integrity: request.integrity,\n    };\n    return init;\n};\n//# sourceMappingURL=url_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n// Skip tslint any type check cause this method is aiming to check type of\n// input.\n// tslint:disable-next-line:no-any\nexport function isLocalPath(source) {\n    return (typeof source === 'string') && source.substr(0, 7) === 'file://';\n}\n//# sourceMappingURL=source_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { DataSource } from '../datasource';\nimport { FileChunkIterator } from '../iterators/file_chunk_iterator';\nimport { isLocalPath } from '../util/source_util';\n/**\n * Represents a file, blob, or Uint8Array readable as a stream of binary data\n * chunks.\n */\nexport class FileDataSource extends DataSource {\n    /**\n     * Create a `FileDataSource`.\n     *\n     * @param input Local file path, or `File`/`Blob`/`Uint8Array` object to\n     *     read. Local file only works in node environment.\n     * @param options Options passed to the underlying `FileChunkIterator`s,\n     *   such as {chunksize: 1024}.\n     */\n    constructor(input, options = {}) {\n        super();\n        this.input = input;\n        this.options = options;\n    }\n    async iterator() {\n        if (isLocalPath(this.input) && env().get('IS_NODE')) {\n            // tslint:disable-next-line:no-require-imports\n            const fs = require('fs');\n            this.input = fs.readFileSync(this.input.substr(7));\n        }\n        // TODO(kangyizhang): Add LocalFileChunkIterator to split local streaming\n        // with file in browser.\n        return new FileChunkIterator(this.input, this.options);\n    }\n}\n//# sourceMappingURL=file_data_source.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { DataSource } from '../datasource';\nimport { urlChunkIterator } from '../iterators/url_chunk_iterator';\nimport { isLocalPath } from '../util/source_util';\nimport { FileDataSource } from './file_data_source';\n/*\n * Represents a URL readable as a stream of binary data chunks.\n */\nexport class URLDataSource extends DataSource {\n    /**\n     * Create a `URLDataSource`.\n     *\n     * @param url A source URL string, or a `Request` object.\n     * @param options Options passed to the underlying `FileChunkIterator`s,\n     *   such as {chunksize: 1024}.\n     */\n    constructor(url, fileOptions = {}) {\n        super();\n        this.url = url;\n        this.fileOptions = fileOptions;\n    }\n    // TODO(soergel): provide appropriate caching options.  Currently this\n    // will download the URL anew for each call to iterator().  Since we have\n    // to treat the downloaded file as a blob/buffer anyway, we may as well retain\n    // it-- but that raises GC issues.  Also we may want a persistent disk cache.\n    async iterator() {\n        if (isLocalPath(this.url)) {\n            return (new FileDataSource(this.url, this.fileOptions))\n                .iterator();\n        }\n        else {\n            return urlChunkIterator(this.url, this.fileOptions);\n        }\n    }\n}\n//# sourceMappingURL=url_data_source.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { datasetFromIteratorFn } from './dataset';\nimport { CSVDataset } from './datasets/csv_dataset';\nimport { iteratorFromFunction } from './iterators/lazy_iterator';\nimport { MicrophoneIterator } from './iterators/microphone_iterator';\nimport { WebcamIterator } from './iterators/webcam_iterator';\nimport { URLDataSource } from './sources/url_data_source';\n/**\n * Create a `CSVDataset` by reading and decoding CSV file(s) from provided URL\n * or local path if it's in Node environment.\n *\n * Note: If isLabel in columnConfigs is `true` for at least one column, the\n * element in returned `CSVDataset` will be an object of\n * `{xs:features, ys:labels}`: xs is a dict of features key/value pairs, ys\n * is a dict of labels key/value pairs. If no column is marked as label,\n * returns a dict of features only.\n *\n * ```js\n * const csvUrl =\n * 'https://storage.googleapis.com/tfjs-examples/multivariate-linear-regression/data/boston-housing-train.csv';\n *\n * async function run() {\n *   // We want to predict the column \"medv\", which represents a median value of\n *   // a home (in $1000s), so we mark it as a label.\n *   const csvDataset = tf.data.csv(\n *     csvUrl, {\n *       columnConfigs: {\n *         medv: {\n *           isLabel: true\n *         }\n *       }\n *     });\n *\n *   // Number of features is the number of column names minus one for the label\n *   // column.\n *   const numOfFeatures = (await csvDataset.columnNames()).length - 1;\n *\n *   // Prepare the Dataset for training.\n *   const flattenedDataset =\n *     csvDataset\n *     .map(({xs, ys}) =>\n *       {\n *         // Convert xs(features) and ys(labels) from object form (keyed by\n *         // column name) to array form.\n *         return {xs:Object.values(xs), ys:Object.values(ys)};\n *       })\n *     .batch(10);\n *\n *   // Define the model.\n *   const model = tf.sequential();\n *   model.add(tf.layers.dense({\n *     inputShape: [numOfFeatures],\n *     units: 1\n *   }));\n *   model.compile({\n *     optimizer: tf.train.sgd(0.000001),\n *     loss: 'meanSquaredError'\n *   });\n *\n *   // Fit the model using the prepared Dataset\n *   return model.fitDataset(flattenedDataset, {\n *     epochs: 10,\n *     callbacks: {\n *       onEpochEnd: async (epoch, logs) => {\n *         console.log(epoch + ':' + logs.loss);\n *       }\n *     }\n *   });\n * }\n *\n * await run();\n * ```\n *\n * @param source URL or local path to get CSV file. If it's a local path, it\n * must have prefix `file://` and it only works in node environment.\n * @param csvConfig (Optional) A CSVConfig object that contains configurations\n *     of reading and decoding from CSV file(s).\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   configParamIndices: [1]\n *  }\n */\nexport function csv(source, csvConfig = {}) {\n    return new CSVDataset(new URLDataSource(source), csvConfig);\n}\n/**\n * Create a `Dataset` that produces each element by calling a provided function.\n *\n * Note that repeated iterations over this `Dataset` may produce different\n * results, because the function will be called anew for each element of each\n * iteration.\n *\n * Also, beware that the sequence of calls to this function may be out of order\n * in time with respect to the logical order of the Dataset. This is due to the\n * asynchronous lazy nature of stream processing, and depends on downstream\n * transformations (e.g. .shuffle()). If the provided function is pure, this is\n * no problem, but if it is a closure over a mutable state (e.g., a traversal\n * pointer), then the order of the produced elements may be scrambled.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const ds = tf.data.func(func);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * @param f A function that produces one data element on each call.\n */\nexport function func(f) {\n    const iter = iteratorFromFunction(f);\n    return datasetFromIteratorFn(async () => iter);\n}\n/**\n * Create a `Dataset` that produces each element from provided JavaScript\n * generator, which is a function*\n * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions),\n * or a function that returns an\n * iterator\n * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions).\n *\n * The returned iterator should have `.next()` function that returns element in\n * format of `{value: TensorContainer, done:boolean}`.\n *\n * Example of creating a dataset from an iterator factory:\n * ```js\n * function makeIterator() {\n *   const numElements = 10;\n *   let index = 0;\n *\n *   const iterator = {\n *     next: () => {\n *       let result;\n *       if (index < numElements) {\n *         result = {value: index, done: false};\n *         index++;\n *         return result;\n *       }\n *       return {value: index, done: true};\n *     }\n *   };\n *   return iterator;\n * }\n * const ds = tf.data.generator(makeIterator);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * Example of creating a dataset from a generator:\n * ```js\n * function* dataGenerator() {\n *   const numElements = 10;\n *   let index = 0;\n *   while (index < numElements) {\n *     const x = index;\n *     index++;\n *     yield x;\n *   }\n * }\n *\n * const ds = tf.data.generator(dataGenerator);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * @param generator A Javascript generator function that returns a JavaScript\n *     iterator.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   configParamIndices: [1]\n *  }\n */\nexport function generator(generator) {\n    return datasetFromIteratorFn(async () => {\n        const gen = await generator();\n        return iteratorFromFunction(() => gen.next());\n    });\n}\n/**\n * Create an iterator that generate `Tensor`s from webcam video stream. This API\n * only works in Browser environment when the device has webcam.\n *\n * Note: this code snippet only works when the device has a webcam. It will\n * request permission to open the webcam when running.\n * ```js\n * const videoElement = document.createElement('video');\n * videoElement.width = 100;\n * videoElement.height = 100;\n * const cam = await tf.data.webcam(videoElement);\n * const img = await cam.capture();\n * img.print();\n * cam.stop();\n * ```\n *\n * @param webcamVideoElement A `HTMLVideoElement` used to play video from\n *     webcam. If this element is not provided, a hidden `HTMLVideoElement` will\n *     be created. In that case, `resizeWidth` and `resizeHeight` must be\n *     provided to set the generated tensor shape.\n * @param webcamConfig A `WebcamConfig` object that contains configurations of\n *     reading and manipulating data from webcam video stream.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   ignoreCI: true\n *  }\n */\nexport async function webcam(webcamVideoElement, webcamConfig) {\n    return WebcamIterator.create(webcamVideoElement, webcamConfig);\n}\n/**\n * Create an iterator that generate frequency-domain spectrogram `Tensor`s from\n * microphone audio stream with browser's native FFT. This API only works in\n * browser environment when the device has microphone.\n *\n * Note: this code snippet only works when the device has a microphone. It will\n * request permission to open the microphone when running.\n * ```js\n * const mic = await tf.data.microphone({\n *   fftSize: 1024,\n *   columnTruncateLength: 232,\n *   numFramesPerSpectrogram: 43,\n *   sampleRateHz:44100,\n *   includeSpectrogram: true,\n *   includeWaveform: true\n * });\n * const audioData = await mic.capture();\n * const spectrogramTensor = audioData.spectrogram;\n * spectrogramTensor.print();\n * const waveformTensor = audioData.waveform;\n * waveformTensor.print();\n * mic.stop();\n * ```\n *\n * @param microphoneConfig A `MicrophoneConfig` object that contains\n *     configurations of reading audio data from microphone.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   ignoreCI: true\n *  }\n */\nexport async function microphone(microphoneConfig) {\n    return MicrophoneIterator.create(microphoneConfig);\n}\n//# sourceMappingURL=readers.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { array, Dataset, zip } from './dataset';\nexport { CSVDataset } from './datasets/csv_dataset';\nexport { TextLineDataset } from './datasets/text_line_dataset';\nexport { csv, func, generator, microphone, webcam } from './readers';\nexport { FileDataSource } from './sources/file_data_source';\nexport { URLDataSource } from './sources/url_data_source';\nexport { version as version_data } from './version';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function assertNotComplex(tensor, opName) {\n    if (!Array.isArray(tensor)) {\n        tensor = [tensor];\n    }\n    tensor.forEach(t => {\n        if (t != null) {\n            util.assert(t.dtype !== 'complex64', () => `${opName} does not support complex64 tensors in the CPU backend.`);\n        }\n    });\n}\n//# sourceMappingURL=cpu_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { backend_util, DataStorage, engine, env, kernel_impls, KernelBackend, max, slice_util, TensorBuffer, upcastType, util } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV3Impl = kernel_impls.nonMaxSuppressionV3Impl;\nconst split = kernel_impls.split;\nconst tile = kernel_impls.tile;\nconst topkImpl = kernel_impls.topkImpl;\nconst whereImpl = kernel_impls.whereImpl;\nimport * as seedrandom from 'seedrandom';\nimport { assertNotComplex } from './cpu_util';\nfunction mapActivation(backend, x, activation, preluActivationWeights) {\n    if (activation === 'linear') {\n        return backend.linear(x);\n    }\n    else if (activation === 'relu') {\n        return backend.relu(x);\n    }\n    else if (activation === 'elu') {\n        return tf.elu(x);\n    }\n    else if (activation === 'relu6') {\n        return backend.relu6(x);\n    }\n    else if (activation === 'prelu') {\n        return backend.prelu(x, preluActivationWeights);\n    }\n    throw new Error(`Activation ${activation} has not been implemented for the CPU backend.`);\n}\nexport class MathBackendCPU extends KernelBackend {\n    constructor() {\n        super();\n        this.blockSize = 48;\n        this.firstUse = true;\n        this.data = new DataStorage(this, engine());\n    }\n    write(values, shape, dtype) {\n        if (this.firstUse) {\n            this.firstUse = false;\n            if (env().get('IS_NODE')) {\n                backend_util.warn('\\n============================\\n' +\n                    'Hi there \uD83D\uDC4B. Looks like you are running TensorFlow.js in ' +\n                    'Node.js. To speed things up dramatically, install our node ' +\n                    'backend, which binds to TensorFlow C++, by running ' +\n                    'npm i @tensorflow/tfjs-node, ' +\n                    'or npm i @tensorflow/tfjs-node-gpu if you have CUDA. ' +\n                    'Then call require(\\'@tensorflow/tfjs-node\\'); (-gpu ' +\n                    'suffix for CUDA) at the start of your program. ' +\n                    'Visit https://github.com/tensorflow/tfjs-node for more details.' +\n                    '\\n============================');\n            }\n        }\n        const dataId = {};\n        this.data.set(dataId, { values, dtype, refCount: 1 });\n        return dataId;\n    }\n    /**\n     * Create a data bucket in cpu backend.\n     * @param shape Shape of the `TensorInfo`.\n     * @param dtype DType of the `TensorInfo`.\n     * @param values The value of the `TensorInfo` stored as a flattened array.\n     */\n    makeTensorInfo(shape, dtype, values) {\n        const outId = this.write(values, shape, dtype);\n        return { dataId: outId, shape, dtype };\n    }\n    /** Increase refCount of a `TensorData`. */\n    incRef(dataId) {\n        const tensorData = this.data.get(dataId);\n        tensorData.refCount++;\n    }\n    /** Decrease refCount of a `TensorData`. */\n    decRef(dataId) {\n        if (this.data.has(dataId)) {\n            const tensorData = this.data.get(dataId);\n            tensorData.refCount--;\n        }\n    }\n    move(dataId, values, shape, dtype) {\n        this.data.set(dataId, { values, dtype, refCount: 1 });\n    }\n    numDataIds() {\n        return this.data.numDataIds();\n    }\n    async read(dataId) {\n        return this.readSync(dataId);\n    }\n    readSync(dataId) {\n        const { dtype, complexTensorInfos } = this.data.get(dataId);\n        if (dtype === 'complex64') {\n            const realValues = this.readSync(complexTensorInfos.real.dataId);\n            const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n            return backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        return this.data.get(dataId).values;\n    }\n    bufferSync(t) {\n        const data = this.readSync(t.dataId);\n        let decodedData = data;\n        if (t.dtype === 'string') {\n            try {\n                // Decode the bytes into string.\n                decodedData = data.map(d => util.decodeString(d));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode encoded string bytes into utf-8');\n            }\n        }\n        return tf.buffer(t.shape, t.dtype, decodedData);\n    }\n    makeOutput(values, shape, dtype) {\n        const dataId = this.write(values, shape, dtype);\n        return engine().makeTensorFromDataId(dataId, shape, dtype, this);\n    }\n    disposeData(dataId) {\n        if (this.data.has(dataId)) {\n            const { complexTensorInfos } = this.data.get(dataId);\n            if (complexTensorInfos != null) {\n                this.disposeData(complexTensorInfos.real.dataId);\n                this.disposeData(complexTensorInfos.imag.dataId);\n            }\n            this.data.delete(dataId);\n        }\n    }\n    disposeIntermediateTensorInfo(tensorInfo) {\n        const dataId = tensorInfo.dataId;\n        if (this.data.has(dataId)) {\n            const tensorData = this.data.get(dataId);\n            tensorData.refCount--;\n            if (tensorData.refCount < 1) {\n                this.disposeData(dataId);\n            }\n        }\n    }\n    async time(f) {\n        const start = util.now();\n        f();\n        const kernelMs = util.now() - start;\n        return { kernelMs };\n    }\n    memory() {\n        return {\n            // Unreliable due to automatic gc. The numbers above are cumulative.\n            unreliable: true,\n            reasons: ['The reported memory is an upper bound. Due to automatic garbage ' +\n                    'collection, the true allocated memory may be less.']\n        };\n    }\n    stridedSlice(x, begin, end, strides) {\n        assertNotComplex(x, 'stridedSlice');\n        const outShape = slice_util.computeOutShape(begin, end, strides);\n        if (outShape.some(axis => axis === 0)) {\n            return tf.tensor([], outShape);\n        }\n        const buffer = tf.buffer(outShape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < buffer.size; i++) {\n            const loc = buffer.indexToLoc(i);\n            const newLoc = new Array(loc.length);\n            for (let j = 0; j < newLoc.length; j++) {\n                newLoc[j] = loc[j] * strides[j] + begin[j];\n            }\n            buffer.set(xBuf.get(...newLoc), ...loc);\n        }\n        return buffer.toTensor();\n    }\n    diag(x) {\n        const xVals = this.readSync(x.dataId);\n        const buffer = tf.buffer([x.size, x.size], x.dtype);\n        const vals = buffer.values;\n        for (let i = 0; i < xVals.length; i++) {\n            vals[i * x.size + i] = xVals[i];\n        }\n        return buffer.toTensor();\n    }\n    unstack(x, axis) {\n        const num = x.shape[axis];\n        const outShape = new Array(x.rank - 1);\n        let outIndex = 0;\n        for (let i = 0; i < x.rank; i++) {\n            if (i !== axis) {\n                outShape[outIndex++] = x.shape[i];\n            }\n        }\n        const begin = new Array(x.rank).fill(0);\n        const size = x.shape.slice();\n        size[axis] = 1;\n        const res = new Array(num);\n        for (let i = 0; i < res.length; i++) {\n            begin[axis] = i;\n            res[i] = tf.slice(x, begin, size).reshape(outShape);\n        }\n        return res;\n    }\n    reverse(x, axis) {\n        assertNotComplex(x, 'reverse');\n        const buffer = tf.buffer(x.shape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < buffer.size; i++) {\n            const outLoc = buffer.indexToLoc(i);\n            const inLoc = outLoc.slice();\n            axis.forEach(ax => inLoc[ax] = x.shape[ax] - 1 - inLoc[ax]);\n            buffer.set(xBuf.get(...inLoc), ...outLoc);\n        }\n        return buffer.toTensor();\n    }\n    neg(x) {\n        assertNotComplex(x, 'neg');\n        // TODO(lina128): Use mul directly once neg is modularized.\n        return tf.mul(tf.scalar(-1), x);\n    }\n    addN(tensors) {\n        assertNotComplex(tensors, 'addN');\n        const vals = tensors.map(t => this.readSync(t.dataId));\n        const result = tf.buffer(tensors[0].shape, tensors[0].dtype);\n        const resultVals = result.values;\n        for (let i = 0; i < tensors.length; i++) {\n            const currVals = vals[i];\n            for (let j = 0; j < resultVals.length; j++) {\n                resultVals[j] += currVals[j];\n            }\n        }\n        return result.toTensor();\n    }\n    softmax(logits, dim) {\n        const axes = util.parseAxisParam([dim], logits.shape);\n        // TODO(annxingyuan): Call maxImpl rather than op as part of softmax kernel\n        // modularization.\n        const maxLogit = max(logits, axes);\n        const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n        // TODO(lina128): Use sub directly once softmax is modularized.\n        const a = tf.sub(logits, maxLogit.reshape(expandedShape));\n        const b = tf.exp(a);\n        const sumExp = this.sum(b, axes).reshape(expandedShape);\n        // TODO(annxingyuan): Call divImpl rather than op as part of softmax\n        // kernel modularization.\n        return tf.div(b, sumExp);\n    }\n    pow(a, b) {\n        assertNotComplex([a, b], 'pow');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aValue, bValue) => Math.pow(aValue, bValue));\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        assertNotComplex([a, b], 'matMul');\n        const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n        const leftDim = transposeA ? a.shape[2] : a.shape[1];\n        const rightDim = transposeB ? b.shape[1] : b.shape[2];\n        const batchDim = a.shape[0];\n        const aValues = this.readSync(a.dataId);\n        const bValues = this.readSync(b.dataId);\n        const [aBatch, aOuterStep, aInnerStep] = transposeA ?\n            [a.strides[0], 1, a.strides[1]] :\n            [a.strides[0], a.strides[1], 1];\n        const [bInnerStep, bOuterStep, bBatch] = transposeB ?\n            [1, b.strides[1], b.strides[0]] :\n            [b.strides[1], 1, b.strides[0]];\n        const size = leftDim * rightDim;\n        const result = tf.buffer([batchDim, leftDim, rightDim], a.dtype);\n        const resVals = result.values;\n        const blockSize = this.blockSize;\n        for (let b = 0; b < batchDim; b++) {\n            for (let i0 = 0; i0 < leftDim; i0 += blockSize) {\n                for (let j0 = 0; j0 < rightDim; j0 += blockSize) {\n                    for (let k0 = 0; k0 < sharedDim; k0 += blockSize) {\n                        // for when blockSize doesn't evenly divide the input\n                        const iBlock = Math.min(i0 + blockSize, leftDim);\n                        const jBlock = Math.min(j0 + blockSize, rightDim);\n                        const kBlock = Math.min(k0 + blockSize, sharedDim);\n                        for (let i = i0; i < iBlock; i++) {\n                            for (let j = j0; j < jBlock; j++) {\n                                let sum = 0.0;\n                                for (let k = k0; k < kBlock; k++) {\n                                    sum += aValues[b * aBatch + i * aOuterStep + k * aInnerStep] *\n                                        bValues[k * bInnerStep + j * bOuterStep + b * bBatch];\n                                }\n                                resVals[b * size + (i * rightDim + j)] += sum;\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return result.toTensor();\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        let result = this.batchMatMul(a, b, transposeA, transposeB);\n        if (bias) {\n            // TODO(lina128): Use add directly once fusedBatchMatMul is modularized.\n            result = tf.add(result, bias);\n        }\n        if (activation) {\n            result =\n                mapActivation(this, result, activation, preluActivationWeights);\n        }\n        return result;\n    }\n    floorDiv(a, b) {\n        assertNotComplex([a, b], 'floorDiv');\n        const op = (a, b) => Math.floor(a / b);\n        const outputDtype = 'int32';\n        return this.broadcastedBinaryOp(a, b, outputDtype, op);\n    }\n    sum(x, axes) {\n        assertNotComplex(x, 'sum');\n        backend_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(outShape, resultDtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let sum = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                sum += aVals[offset + j];\n            }\n            vals[i] = sum;\n        }\n        return result;\n    }\n    prod(x, axes) {\n        assertNotComplex(x, 'sum');\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(outShape, resultDtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let prod = 1;\n            for (let j = 0; j < reduceSize; ++j) {\n                prod *= aVals[offset + j];\n            }\n            vals[i] = prod;\n        }\n        return result;\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        assertNotComplex(x, 'unsortedSegmentSum');\n        const res = [];\n        // Reshape the segment id's so that they can be broadcast with\n        // x. The new shape should be [segmentIds.shape, 1, ..., 1]\n        const numIters = x.rank - segmentIds.rank;\n        for (let i = 0; i < numIters; ++i) {\n            segmentIds = segmentIds.expandDims(i + 1);\n        }\n        for (let i = 0; i < numSegments; ++i) {\n            const segmentId = tf.scalar(i, 'int32');\n            const mask = tf.equal(segmentId, segmentIds).asType('float32');\n            const sum = mask.mul(x).sum(0);\n            res.push(sum);\n        }\n        return tf.stack(res);\n    }\n    argMin(x, axis) {\n        assertNotComplex(x, 'argMin');\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('argMin', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, 'int32');\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let min = aVals[offset];\n            let minIndex = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value < min) {\n                    min = value;\n                    minIndex = j;\n                }\n            }\n            vals[i] = minIndex;\n        }\n        return result;\n    }\n    argMax(x, axis) {\n        assertNotComplex(x, 'argMax');\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('argMax', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, 'int32');\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let max = aVals[offset];\n            let maxIndex = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value > max) {\n                    max = value;\n                    maxIndex = j;\n                }\n            }\n            vals[i] = maxIndex;\n        }\n        return result;\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        assertNotComplex(x, 'cumsum');\n        if (axis !== x.rank - 1) {\n            throw new Error(`backend.cumsum in CPU expects an inner-most axis=${x.rank - 1} ` +\n                `but got axis=${axis}`);\n        }\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(x.shape, resultDtype);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        const finalDim = x.shape[x.rank - 1];\n        const indexAdjuster = reverse ?\n            (i, j) => i + finalDim - j - 1 :\n            (i, j) => i + j;\n        for (let i = 0; i < aVals.length; i += finalDim) {\n            for (let j = 0; j < finalDim; j++) {\n                const idx = indexAdjuster(i, j);\n                if (j === 0) {\n                    vals[idx] = exclusive ? 0 : aVals[idx];\n                }\n                else {\n                    const prevIdx = indexAdjuster(i, j - 1);\n                    vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :\n                        aVals[idx] + vals[prevIdx];\n                }\n            }\n        }\n        return result;\n    }\n    equal(a, b) {\n        assertNotComplex([a, b], 'equal');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal === bVal) ? 1 : 0;\n        });\n    }\n    notEqual(a, b) {\n        assertNotComplex([a, b], 'notEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal !== bVal) ? 1 : 0;\n        });\n    }\n    less(a, b) {\n        assertNotComplex([a, b], 'less');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal < bVal) ? 1 : 0;\n        });\n    }\n    lessEqual(a, b) {\n        assertNotComplex([a, b], 'lessEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal <= bVal) ? 1 : 0;\n        });\n    }\n    greater(a, b) {\n        assertNotComplex([a, b], 'greater');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal > bVal) ? 1 : 0;\n        });\n    }\n    greaterEqual(a, b) {\n        assertNotComplex([a, b], 'greaterEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal >= bVal) ? 1 : 0;\n        });\n    }\n    logicalAnd(a, b) {\n        assertNotComplex([a, b], 'logicalAnd');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return aVal && bVal;\n        });\n    }\n    logicalOr(a, b) {\n        assertNotComplex([a, b], 'logicalOr');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return aVal || bVal;\n        });\n    }\n    select(condition, a, b) {\n        assertNotComplex([condition, a, b], 'select');\n        const values = this.readSync(condition.dataId);\n        const aValues = this.readSync(a.dataId);\n        const bValues = this.readSync(b.dataId);\n        const result = tf.zeros(a.shape, upcastType(a.dtype, b.dtype));\n        const newValues = this.readSync(result.dataId);\n        let index = 0;\n        const offset = condition.rank === 0 || condition.rank > 1 || a.rank === 1 ?\n            1 :\n            util.sizeFromShape(a.shape.slice(1));\n        for (let i = 0; i < values.length; i++) {\n            for (let j = 0; j < offset; j++) {\n                if (values[i] === 1) {\n                    newValues[index++] = aValues[i];\n                }\n                else {\n                    newValues[index++] = bValues[i];\n                }\n            }\n        }\n        return result;\n    }\n    where(condition) {\n        assertNotComplex([condition], 'where');\n        const condVals = this.readSync(condition.dataId);\n        return whereImpl(condition.shape, condVals);\n    }\n    topk(x, k, sorted) {\n        assertNotComplex(x, 'topk');\n        const xVals = this.readSync(x.dataId);\n        return topkImpl(xVals, x.shape, x.dtype, k, sorted);\n    }\n    min(x, axes) {\n        assertNotComplex(x, 'min');\n        backend_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let min = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value < min) {\n                    min = value;\n                }\n            }\n            vals[i] = min;\n        }\n        return result;\n    }\n    minimum(a, b) {\n        assertNotComplex([a, b], 'minimum');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => Math.min(aVal, bVal));\n    }\n    mod(a, b) {\n        assertNotComplex([a, b], 'mod');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n            const rem = aVal % bVal;\n            if ((aVal < 0 && bVal < 0) || (aVal >= 0 && bVal >= 0)) {\n                return rem;\n            }\n            else {\n                return (rem + bVal) % bVal;\n            }\n        });\n    }\n    maximum(a, b) {\n        assertNotComplex([a, b], 'maximum');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => Math.max(aVal, bVal));\n    }\n    all(x, axes) {\n        assertNotComplex(x, 'all');\n        backend_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let all = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                all = all && value;\n            }\n            vals[i] = all;\n        }\n        return result;\n    }\n    any(x, axes) {\n        assertNotComplex(x, 'any');\n        backend_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let anyVal = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                anyVal = anyVal || value;\n            }\n            vals[i] = anyVal;\n        }\n        return result;\n    }\n    squaredDifference(a, b) {\n        assertNotComplex([a, b], 'squaredDifference');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n            const diff = aVal - bVal;\n            return diff * diff;\n        });\n    }\n    linear(x) {\n        return x;\n    }\n    relu(x) {\n        assertNotComplex(x, 'relu');\n        const res = tf.zeros(x.shape, x.dtype);\n        const resVals = this.readSync(res.dataId);\n        const inVals = this.readSync(x.dataId);\n        for (let i = 0; i < inVals.length; ++i) {\n            resVals[i] = Math.max(0, inVals[i]);\n        }\n        return res;\n    }\n    relu6(x) {\n        assertNotComplex(x, 'relu');\n        const res = tf.zeros(x.shape, x.dtype);\n        const resVals = this.readSync(res.dataId);\n        const inVals = this.readSync(x.dataId);\n        for (let i = 0; i < inVals.length; ++i) {\n            resVals[i] = Math.min(Math.max(0, inVals[i]), 6);\n        }\n        return res;\n    }\n    prelu(x, a) {\n        assertNotComplex([x, a], 'prelu');\n        return this.broadcastedBinaryOp(x, a, x.dtype, (xValue, aValue) => xValue < 0 ? aValue * xValue : xValue);\n    }\n    eluDer(dy, y) {\n        assertNotComplex([dy, y], 'eluDer');\n        const resultValues = new Float32Array(y.size);\n        const values = this.readSync(y.dataId);\n        const dyValues = this.readSync(dy.dataId);\n        for (let i = 0; i < values.length; ++i) {\n            const v = values[i];\n            if (v >= 1) {\n                resultValues[i] = dyValues[i];\n            }\n            else {\n                resultValues[i] = dyValues[i] * (v + 1);\n            }\n        }\n        return this.makeOutput(resultValues, y.shape, 'float32');\n    }\n    atan2(a, b) {\n        assertNotComplex([a, b], 'atan2');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aValue, bValue) => Math.atan2(aValue, bValue));\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        let result = this.conv2d(input, filter, convInfo);\n        if (bias) {\n            // TODO(lina128): Use add directly once fusedConv2d is modularized.\n            result = tf.add(result, bias);\n        }\n        if (activation) {\n            result =\n                mapActivation(this, result, activation, preluActivationWeights);\n        }\n        return result;\n    }\n    conv2d(x, filter, convInfo) {\n        assertNotComplex([x, filter], 'conv2d');\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const padLeft = convInfo.padInfo.left;\n        const padTop = convInfo.padInfo.top;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const y = tf.buffer(convInfo.outShape, x.dtype);\n        const xBatchStride = x.strides[0];\n        const xRowStride = isChannelsLast ? x.strides[1] : x.strides[2];\n        const xColStride = isChannelsLast ? x.strides[2] : 1;\n        const xChannelStride = isChannelsLast ? 1 : x.strides[1];\n        const yBatchStride = y.strides[0];\n        const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];\n        const yColStride = isChannelsLast ? y.strides[2] : 1;\n        const yChannelStride = isChannelsLast ? 1 : y.strides[1];\n        const xVals = this.readSync(x.dataId);\n        const wVals = this.readSync(filter.dataId);\n        const yVals = y.values;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n            const xOffset1 = b * xBatchStride;\n            const yOffset1 = b * yBatchStride;\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const yOffset2 = yOffset1 + yR * yRowStride;\n                const xRCorner = yR * convInfo.strideHeight - padTop;\n                for (let wR = 0; wR < filterHeight; wR++) {\n                    const xR = xRCorner + wR * dilationHeight;\n                    if (xR < 0 || xR >= convInfo.inHeight) {\n                        continue;\n                    }\n                    const wOffset1 = wR * filter.strides[0];\n                    const xOffset2 = xOffset1 + xR * xRowStride;\n                    for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                        const yOffset3 = yOffset2 + yC * yColStride;\n                        const xCCorner = yC * convInfo.strideWidth - padLeft;\n                        for (let wC = 0; wC < filterWidth; wC++) {\n                            const xC = xCCorner + wC * dilationWidth;\n                            if (xC < 0 || xC >= convInfo.inWidth) {\n                                continue;\n                            }\n                            const wOffset2 = wOffset1 + wC * filter.strides[1];\n                            const xOffset3 = xOffset2 + xC * xColStride;\n                            let wOffset3 = wOffset2;\n                            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                const xVal = xVals[xOffset3 + d1 * xChannelStride];\n                                for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                                    yVals[yOffset3 + d2 * yChannelStride] +=\n                                        xVal * wVals[wOffset3 + d2];\n                                }\n                                wOffset3 += convInfo.outChannels;\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return y.toTensor();\n    }\n    conv3d(x, filter, convInfo) {\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const padFront = convInfo.padInfo.front;\n        const padLeft = convInfo.padInfo.left;\n        const padTop = convInfo.padInfo.top;\n        const y = tf.buffer(convInfo.outShape, x.dtype);\n        const xVals = this.readSync(x.dataId);\n        const wVals = this.readSync(filter.dataId);\n        const yVals = y.values;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n            const xOffset1 = b * x.strides[0];\n            const yOffset1 = b * y.strides[0];\n            for (let yF = 0; yF < convInfo.outDepth; ++yF) {\n                const yOffset2 = yOffset1 + yF * y.strides[1];\n                const xFCorner = yF * convInfo.strideDepth - padFront;\n                for (let wF = 0; wF < filterDepth; wF++) {\n                    const xF = xFCorner + wF * dilationDepth;\n                    if (xF < 0 || xF >= convInfo.inDepth) {\n                        continue;\n                    }\n                    const wOffset1 = wF * filter.strides[0];\n                    const xOffset2 = xOffset1 + xF * x.strides[1];\n                    for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                        const yOffset3 = yOffset2 + yR * y.strides[2];\n                        const xRCorner = yR * convInfo.strideHeight - padTop;\n                        for (let wR = 0; wR < filterHeight; wR++) {\n                            const xR = xRCorner + wR * dilationHeight;\n                            if (xR < 0 || xR >= convInfo.inHeight) {\n                                continue;\n                            }\n                            const wOffset2 = wOffset1 + wR * filter.strides[1];\n                            const xOffset3 = xOffset2 + xR * x.strides[2];\n                            for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                                const yOffset4 = yOffset3 + yC * convInfo.outChannels;\n                                const xCCorner = yC * convInfo.strideWidth - padLeft;\n                                for (let wC = 0; wC < filterWidth; wC++) {\n                                    const xC = xCCorner + wC * dilationWidth;\n                                    if (xC < 0 || xC >= convInfo.inWidth) {\n                                        continue;\n                                    }\n                                    const wOffset3 = wOffset2 + wC * filter.strides[2];\n                                    const xOffset4 = xOffset3 + xC * convInfo.inChannels;\n                                    let wOffset4 = wOffset3;\n                                    for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                        const xVal = xVals[xOffset4 + d1];\n                                        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                                            yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];\n                                        }\n                                        wOffset4 += convInfo.outChannels;\n                                    }\n                                }\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return y.toTensor();\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        assertNotComplex([dy, filter], 'conv2dDerInput');\n        const dx = tf.buffer(convInfo.inShape, 'float32');\n        const dxValues = dx.values;\n        const dyValues = this.readSync(dy.dataId);\n        const fltValues = this.readSync(filter.dataId);\n        const [fltS0, fltS1, fltS2] = filter.strides;\n        const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth, dataFormat } = convInfo;\n        const topPad = filterHeight - 1 - convInfo.padInfo.top;\n        const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const xBatchStride = dx.strides[0];\n        const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];\n        const xColStride = isChannelsLast ? dx.strides[2] : 1;\n        const xChannelStride = isChannelsLast ? 1 : dx.strides[1];\n        const yBatchStride = dy.strides[0];\n        const yRowStride = isChannelsLast ? dy.strides[1] : dy.strides[2];\n        const yColStride = isChannelsLast ? dy.strides[2] : 1;\n        const yChannelStride = isChannelsLast ? 1 : dy.strides[1];\n        for (let b = 0; b < batchSize; ++b) {\n            for (let d1 = 0; d1 < inChannels; ++d1) {\n                for (let xR = 0; xR < inHeight; ++xR) {\n                    const xRCorner = xR - topPad;\n                    const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                    const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                    for (let xC = 0; xC < inWidth; ++xC) {\n                        const xCCorner = xC - leftPad;\n                        const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                        const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                        let dotProd = 0;\n                        for (let yR = xRMin; yR < yRMax; ++yR) {\n                            const wR = yR * strideHeight - xRCorner;\n                            for (let yC = xCMin; yC < yCMax; ++yC) {\n                                const wC = yC * strideWidth - xCCorner;\n                                const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;\n                                const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n                                for (let d2 = 0; d2 < outChannels; ++d2) {\n                                    const pixel = dyValues[dyOffset + yChannelStride * d2];\n                                    const weight = fltValues[fltOffset + d2];\n                                    dotProd += pixel * weight;\n                                }\n                            }\n                        }\n                        const dxOffset = xBatchStride * b + xRowStride * xR +\n                            xColStride * xC + xChannelStride * d1;\n                        dxValues[dxOffset] = dotProd;\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        const dx = tf.buffer(convInfo.inShape, 'float32');\n        const dxValues = dx.values;\n        const [dxS0, dxS1, dxS2, dxS3] = dx.strides;\n        const dyValues = this.readSync(dy.dataId);\n        const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n        const fltValues = this.readSync(filter.dataId);\n        const [fltS0, fltS1, fltS2, fltS3] = filter.strides;\n        const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo;\n        const frontPad = filterDepth - 1 - convInfo.padInfo.front;\n        const topPad = filterHeight - 1 - convInfo.padInfo.top;\n        const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let d1 = 0; d1 < inChannels; ++d1) {\n                // Frames of depth\n                for (let xF = 0; xF < inDepth; ++xF) {\n                    const xFCorner = xF - frontPad;\n                    const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));\n                    const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);\n                    // Rows as per standard 2d matrix notation\n                    for (let xR = 0; xR < inHeight; ++xR) {\n                        const xRCorner = xR - topPad;\n                        const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                        const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                        // Columns as per standard 2d matrix notation\n                        for (let xC = 0; xC < inWidth; ++xC) {\n                            const xCCorner = xC - leftPad;\n                            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                            const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                            let dotProd = 0;\n                            for (let yF = xFMin; yF < yFMax; ++yF) {\n                                const wF = yF * strideDepth - xFCorner;\n                                for (let yR = xRMin; yR < yRMax; ++yR) {\n                                    const wR = yR * strideHeight - xRCorner;\n                                    for (let yC = xCMin; yC < yCMax; ++yC) {\n                                        const wC = yC * strideWidth - xCCorner;\n                                        const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;\n                                        const fltOffset = fltS0 * (filterDepth - 1 - wF) +\n                                            fltS1 * (filterHeight - 1 - wR) +\n                                            fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;\n                                        for (let d2 = 0; d2 < outChannels; ++d2) {\n                                            const pixel = dyValues[dyOffset + d2];\n                                            const weight = fltValues[fltOffset + d2];\n                                            dotProd += pixel * weight;\n                                        }\n                                    }\n                                }\n                            }\n                            dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =\n                                dotProd;\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    conv2dDerFilter(x, dy, convInfo) {\n        assertNotComplex([x, dy], 'conv2dDerFilter');\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const dW = tf.buffer(convInfo.filterShape, 'float32');\n        const leftPad = convInfo.padInfo.left;\n        const topPad = convInfo.padInfo.top;\n        const xBuf = this.bufferSync(x);\n        const dyBuf = this.bufferSync(dy);\n        for (let wR = 0; wR < filterHeight; ++wR) {\n            const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n            const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n            for (let wC = 0; wC < filterWidth; ++wC) {\n                const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                    for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                        // Need to convolve.\n                        let dotProd = 0;\n                        for (let b = 0; b < convInfo.batchSize; ++b) {\n                            for (let yR = yRMin; yR < yRMax; ++yR) {\n                                const xR = wR + yR * strideHeight - topPad;\n                                for (let yC = yCMin; yC < yCMax; ++yC) {\n                                    const xC = wC + yC * strideWidth - leftPad;\n                                    if (isChannelsLast) {\n                                        dotProd +=\n                                            xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n                                    }\n                                    else {\n                                        dotProd +=\n                                            xBuf.get(b, d1, xR, xC) * dyBuf.get(b, d2, yR, yC);\n                                    }\n                                }\n                            }\n                        }\n                        dW.set(dotProd, wR, wC, d1, d2);\n                    }\n                }\n            }\n        }\n        return dW.toTensor();\n    }\n    conv3dDerFilter(x, dy, convInfo) {\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dw = tf.buffer(convInfo.filterShape, 'float32');\n        const dwValues = dw.values;\n        const [dwS0, dwS1, dwS2, dwS3] = dw.strides;\n        const dyValues = this.readSync(dy.dataId);\n        const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n        const xValues = this.readSync(x.dataId);\n        const [xS0, xS1, xS2, xS3] = x.strides;\n        const frontPad = convInfo.padInfo.front;\n        const leftPad = convInfo.padInfo.left;\n        const topPad = convInfo.padInfo.top;\n        for (let wF = 0; wF < filterDepth; ++wF) {\n            const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));\n            const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);\n            const wOffset1 = wF * dwS0;\n            for (let wR = 0; wR < filterHeight; ++wR) {\n                const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n                const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n                const wOffset2 = wR * dwS1 + wOffset1;\n                for (let wC = 0; wC < filterWidth; ++wC) {\n                    const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                    const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                    const wOffset3 = wC * dwS2 + wOffset2;\n                    for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                        const wOffset4 = d1 * dwS3 + wOffset3;\n                        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                            let dotProd = 0;\n                            for (let b = 0; b < convInfo.batchSize; ++b) {\n                                const xOffset1 = b * xS0;\n                                const yOffset1 = b * dyS0;\n                                for (let yF = yFMin; yF < yFMax; ++yF) {\n                                    const xF = wF + yF * strideDepth - frontPad;\n                                    const xOffset2 = xF * xS1 + xOffset1;\n                                    const yOffset2 = yF * dyS1 + yOffset1;\n                                    for (let yR = yRMin; yR < yRMax; ++yR) {\n                                        const xR = wR + yR * strideHeight - topPad;\n                                        const xOffset3 = xR * xS2 + xOffset2;\n                                        const yOffset3 = yR * dyS2 + yOffset2;\n                                        for (let yC = yCMin; yC < yCMax; ++yC) {\n                                            const xC = wC + yC * strideWidth - leftPad;\n                                            const xOffset4 = xC * xS3 + xOffset3;\n                                            const yOffset4 = yC * dyS3 + yOffset3;\n                                            dotProd +=\n                                                xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];\n                                        }\n                                    }\n                                }\n                            }\n                            dwValues[wOffset4 + d2] = dotProd;\n                        }\n                    }\n                }\n            }\n        }\n        return dw.toTensor();\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        let result = this.depthwiseConv2D(input, filter, convInfo);\n        if (bias) {\n            // TODO(lina128): Use add directly once fusedDepthwiseConv2D is\n            // modularized.\n            result = tf.add(result, bias);\n        }\n        if (activation) {\n            result =\n                mapActivation(this, result, activation, preluActivationWeights);\n        }\n        return result;\n    }\n    depthwiseConv2D(x, filter, convInfo) {\n        assertNotComplex([x, filter], 'depthwiseConv2D');\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const padLeft = convInfo.padInfo.left;\n        const padTop = convInfo.padInfo.top;\n        const chMul = convInfo.outChannels / convInfo.inChannels;\n        const y = tf.buffer(convInfo.outShape, x.dtype);\n        const xVals = this.readSync(x.dataId);\n        const wVals = this.readSync(filter.dataId);\n        const yVals = y.values;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n            const xOffset1 = b * x.strides[0];\n            const yOffset1 = b * y.strides[0];\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const yOffset2 = yOffset1 + yR * y.strides[1];\n                const xRCorner = yR * convInfo.strideHeight - padLeft;\n                for (let wR = 0; wR < filterHeight; ++wR) {\n                    const xR = xRCorner + wR * dilationHeight;\n                    if (xR < 0 || xR >= convInfo.inHeight) {\n                        continue;\n                    }\n                    const wOffset1 = wR * filter.strides[0];\n                    const xOffset2 = xOffset1 + xR * x.strides[1];\n                    for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                        const yOffset3 = yOffset2 + yC * y.strides[2];\n                        const xCCorner = yC * convInfo.strideWidth - padTop;\n                        for (let wC = 0; wC < filterWidth; ++wC) {\n                            const xC = xCCorner + wC * dilationWidth;\n                            if (xC < 0 || xC >= convInfo.inWidth) {\n                                continue;\n                            }\n                            const wOffset2 = wOffset1 + wC * filter.strides[1];\n                            const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n                            let yOffset4 = yOffset3;\n                            let wOffset3 = wOffset2;\n                            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                const xVal = xVals[xOffset3 + d1];\n                                for (let q = 0; q < chMul; ++q) {\n                                    yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];\n                                }\n                                yOffset4 += chMul;\n                                wOffset3 += chMul;\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return y.toTensor();\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        assertNotComplex([dy, filter], 'depthwiseConv2DDerInput');\n        const dx = tf.buffer(convInfo.inShape, 'float32');\n        const dxValues = dx.values;\n        const [dxS0, dxS1, dxS2] = dx.strides;\n        const dyValues = this.readSync(dy.dataId);\n        const [dyS0, dyS1, dyS2] = dy.strides;\n        const fltValues = this.readSync(filter.dataId);\n        const [fltS0, fltS1, fltS2] = filter.strides;\n        const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n        const topPad = filterHeight - 1 - convInfo.padInfo.top;\n        const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n        const chMul = outChannels / inChannels;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let d1 = 0; d1 < inChannels; ++d1) {\n                for (let xR = 0; xR < inHeight; ++xR) {\n                    const xRCorner = xR - topPad;\n                    const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                    const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                    for (let xC = 0; xC < inWidth; ++xC) {\n                        const xCCorner = xC - leftPad;\n                        const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                        const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                        let dotProd = 0;\n                        for (let yR = xRMin; yR < yRMax; ++yR) {\n                            const wR = yR * strideHeight - xRCorner;\n                            for (let yC = xCMin; yC < yCMax; ++yC) {\n                                const wC = yC * strideWidth - xCCorner;\n                                const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n                                const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n                                for (let dm = 0; dm < chMul; ++dm) {\n                                    const d2 = d1 * chMul + dm;\n                                    const pixel = dyValues[dyOffset + d2];\n                                    const weight = fltValues[fltOffset + dm];\n                                    dotProd += pixel * weight;\n                                }\n                            }\n                        }\n                        dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    depthwiseConv2DDerFilter(x, dy, convInfo) {\n        assertNotComplex([x, dy], 'depthwiseConv2DDerFilter');\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dW = tf.buffer(convInfo.filterShape, 'float32');\n        const leftPad = convInfo.padInfo.left;\n        const topPad = convInfo.padInfo.top;\n        const chMul = convInfo.outChannels / convInfo.inChannels;\n        const xBuf = this.bufferSync(x);\n        const dyBuf = this.bufferSync(dy);\n        for (let wR = 0; wR < filterHeight; ++wR) {\n            const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n            const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n            for (let wC = 0; wC < filterWidth; ++wC) {\n                const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                    const d1 = Math.trunc(d2 / chMul);\n                    const dm = d2 % chMul;\n                    let dotProd = 0;\n                    for (let b = 0; b < convInfo.batchSize; ++b) {\n                        for (let yR = yRMin; yR < yRMax; ++yR) {\n                            const xR = wR + yR * strideHeight - topPad;\n                            for (let yC = yCMin; yC < yCMax; ++yC) {\n                                const xC = wC + yC * strideWidth - leftPad;\n                                dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n                            }\n                        }\n                    }\n                    dW.set(dotProd, wR, wC, d1, dm);\n                }\n            }\n        }\n        return dW.toTensor();\n    }\n    tile(x, reps) {\n        assertNotComplex(x, 'tile');\n        return tile(this.bufferSync(x), reps);\n    }\n    gather(x, indices, axis) {\n        assertNotComplex([x, indices], 'gather');\n        const newShape = x.shape.slice();\n        const indicesValues = this.readSync(indices.dataId);\n        newShape[axis] = indicesValues.length;\n        const result = tf.buffer(newShape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < result.size; ++i) {\n            const newLoc = result.indexToLoc(i);\n            const originalLoc = newLoc.slice();\n            originalLoc[axis] = indicesValues[newLoc[axis]];\n            const originalIndex = xBuf.locToIndex(originalLoc);\n            result.values[i] = xBuf.values[originalIndex];\n        }\n        return result.toTensor();\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        assertNotComplex([x], 'batchToSpaceND');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n        const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n        const reshapedPermuted = backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n        const sliceBeginCoords = backend_util.getSliceBeginCoords(crops, blockShape.length);\n        const sliceSize = backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n        return tf.transpose(x.reshape(reshaped), permuted)\n            .reshape(reshapedPermuted)\n            .slice(sliceBeginCoords, sliceSize);\n    }\n    pool3d(x, convInfo, poolType) {\n        assertNotComplex(x, 'pool3d');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n            Number.POSITIVE_INFINITY);\n        const xValues = this.readSync(x.dataId);\n        const output = tf.buffer(convInfo.outShape, x.dtype);\n        const outputVals = output.values;\n        const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] *\n            convInfo.outShape[3] * convInfo.outShape[4];\n        const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];\n        const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];\n        const outputColStrides = convInfo.outShape[4];\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            const outputBatchOffset = batch * outputBatchStrides;\n            const inputBatchOffset = batch * x.strides[0];\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n                    const xDepthCorner = yDepth * strideDepth - padFront;\n                    let xDepthMin = xDepthCorner;\n                    while (xDepthMin < 0) {\n                        xDepthMin += dilationDepth;\n                    }\n                    const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n                    const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides;\n                    for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n                        const xRowCorner = yRow * strideHeight - padTop;\n                        let xRowMin = xRowCorner;\n                        while (xRowMin < 0) {\n                            xRowMin += dilationHeight;\n                        }\n                        const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n                        const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;\n                        for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n                            const xColCorner = yCol * strideWidth - padLeft;\n                            let xColMin = xColCorner;\n                            while (xColMin < 0) {\n                                xColMin += dilationWidth;\n                            }\n                            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n                            // Shader code begins\n                            const outputColOffset = outputRowOffset + yCol * outputColStrides;\n                            let minMaxValue = initialValue;\n                            let avgValue = 0;\n                            let count = 0;\n                            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n                                const xDepthOffset = inputBatchOffset + xDepth * x.strides[1];\n                                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                                    const xRowOffset = xDepthOffset + xRow * x.strides[2];\n                                    for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                                        const xColOffset = xRowOffset + xCol * x.strides[3];\n                                        const pixel = xValues[xColOffset + channel];\n                                        if ((poolType === 'max' && pixel > minMaxValue)) {\n                                            minMaxValue = pixel;\n                                        }\n                                        else if (poolType === 'avg') {\n                                            avgValue += pixel;\n                                            count++;\n                                        }\n                                        if (isNaN(minMaxValue)) {\n                                            break;\n                                        }\n                                    }\n                                    if (isNaN(minMaxValue)) {\n                                        break;\n                                    }\n                                }\n                                if (isNaN(minMaxValue)) {\n                                    break;\n                                }\n                            }\n                            const outputOffset = outputColOffset + channel;\n                            outputVals[outputOffset] =\n                                poolType === 'avg' ? avgValue / count : minMaxValue;\n                        }\n                    }\n                }\n            }\n        }\n        return output.toTensor();\n    }\n    avgPool3d(x, convInfo) {\n        assertNotComplex(x, 'avgPool3d');\n        return this.pool3d(x, convInfo, 'avg').toFloat();\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        assertNotComplex([dy, x], 'avgPool3dBackprop');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const dx = tf.buffer(x.shape, 'float32');\n        const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n        const dyBuf = this.bufferSync(dy);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n                    for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n                        for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n                            // Shader code begins.\n                            const dyDepthCorner = dxDepth - padFront;\n                            const dyRowCorner = dxRow - padTop;\n                            const dyColCorner = dxCol - padLeft;\n                            let dotProd = 0;\n                            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n                                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n                                    Math.floor(dyDepth) !== dyDepth) {\n                                    continue;\n                                }\n                                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                                    const dyRow = (dyRowCorner + wRow) / strideHeight;\n                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n                                        Math.floor(dyRow) !== dyRow) {\n                                        continue;\n                                    }\n                                    for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                                        const dyCol = (dyColCorner + wCol) / strideWidth;\n                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n                                            Math.floor(dyCol) !== dyCol) {\n                                            continue;\n                                        }\n                                        const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        dotProd += pixel;\n                                    }\n                                }\n                            }\n                            dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    maxPool3d(x, convInfo) {\n        assertNotComplex(x, 'maxPool3d');\n        return this.pool3d(x, convInfo, 'max').toFloat();\n    }\n    maxPool3dPositions(x, convInfo) {\n        const maxPositions = tf.buffer(convInfo.outShape, 'int32');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const xBuf = this.bufferSync(x);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n                    const xDepthCorner = yDepth * strideDepth - padFront;\n                    let xDepthMin = xDepthCorner;\n                    while (xDepthMin < 0) {\n                        xDepthMin += dilationDepth;\n                    }\n                    const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n                    for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n                        const xRowCorner = yRow * strideHeight - padTop;\n                        let xRowMin = xRowCorner;\n                        while (xRowMin < 0) {\n                            xRowMin += dilationHeight;\n                        }\n                        const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n                        for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n                            const xColCorner = yCol * strideWidth - padLeft;\n                            let xColMin = xColCorner;\n                            while (xColMin < 0) {\n                                xColMin += dilationWidth;\n                            }\n                            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n                            // Shader code begins\n                            let maxValue = Number.NEGATIVE_INFINITY;\n                            let maxPosition = -1;\n                            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n                                const wDepth = xDepth - xDepthCorner;\n                                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                                    const wRow = xRow - xRowCorner;\n                                    for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                                        const wCol = xCol - xColCorner;\n                                        const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);\n                                        if (pixel >= maxValue) {\n                                            maxValue = pixel;\n                                            maxPosition = wDepth * effectiveFilterHeight *\n                                                effectiveFilterWidth +\n                                                wRow * effectiveFilterHeight + wCol;\n                                        }\n                                    }\n                                }\n                            }\n                            maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return maxPositions.toTensor();\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        assertNotComplex([x, y], 'maxPool3dBackprop');\n        const maxPositions = this.maxPool3dPositions(x, convInfo);\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const dx = tf.buffer(x.shape, 'float32');\n        const maxPosBuf = this.bufferSync(maxPositions);\n        const dyBuf = this.bufferSync(dy);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n                    for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n                        for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n                            // Shader code begins\n                            const dyDepthCorner = dxDepth - padFront;\n                            const dyRowCorner = dxRow - padTop;\n                            const dyColCorner = dxCol - padLeft;\n                            let dotProd = 0;\n                            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n                                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n                                    Math.floor(dyDepth) !== dyDepth) {\n                                    continue;\n                                }\n                                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                                    const dyRow = (dyRowCorner + wRow) / strideHeight;\n                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n                                        Math.floor(dyRow) !== dyRow) {\n                                        continue;\n                                    }\n                                    for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                                        const dyCol = (dyColCorner + wCol) / strideWidth;\n                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n                                            Math.floor(dyCol) !== dyCol) {\n                                            continue;\n                                        }\n                                        const maxPos = effectiveFilterDepth *\n                                            effectiveFilterHeight * effectiveFilterWidth -\n                                            1 -\n                                            maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth +\n                                            wRow * effectiveFilterWidth + wCol;\n                                        const mask = maxPos === curPos ? 1 : 0;\n                                        if (mask === 0) {\n                                            continue;\n                                        }\n                                        const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        dotProd += pixel * mask;\n                                    }\n                                }\n                            }\n                            dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        assertNotComplex(x, 'resizeBilinear');\n        const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n        const xValues = this.readSync(x.dataId);\n        const result = new Float32Array(util.sizeFromShape([batch, newHeight, newWidth, numChannels]));\n        const effectiveInputSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutputSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        let outputIdx = 0;\n        const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n        const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n        for (let b = 0; b < batch; b++) {\n            for (let r = 0; r < newHeight; r++) {\n                const sourceFracRow = effectiveRowSizeRatio * r;\n                const sourceRowFloor = Math.floor(sourceFracRow);\n                const rowFrac = sourceFracRow - sourceRowFloor;\n                const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));\n                const topRowOffset = b * x.strides[0] + sourceRowFloor * x.strides[1];\n                const botRowOffset = b * x.strides[0] + sourceRowCeil * x.strides[1];\n                for (let c = 0; c < newWidth; c++) {\n                    const sourceFracCol = effectiveColSizeRatio * c;\n                    const sourceColFloor = Math.floor(sourceFracCol);\n                    const colFrac = sourceFracCol - sourceColFloor;\n                    const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));\n                    const topLeftOffest = topRowOffset + sourceColFloor * x.strides[2];\n                    const botLeftOffset = botRowOffset + sourceColFloor * x.strides[2];\n                    const topRightOffset = topRowOffset + sourceColCeil * x.strides[2];\n                    const botRightOffest = botRowOffset + sourceColCeil * x.strides[2];\n                    for (let d = 0; d < numChannels; d++) {\n                        // Begin shader.\n                        // Compute the fractional index of the source.\n                        const topLeft = xValues[topLeftOffest + d];\n                        const bottomLeft = xValues[botLeftOffset + d];\n                        const topRight = xValues[topRightOffset + d];\n                        const bottomRight = xValues[botRightOffest + d];\n                        const top = topLeft + (topRight - topLeft) * colFrac;\n                        const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;\n                        const newValue = top + (bottom - top) * rowFrac;\n                        result[outputIdx++] = newValue;\n                    }\n                }\n            }\n        }\n        return tf.tensor(result, [batch, newHeight, newWidth, numChannels]);\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        assertNotComplex([dy, x], 'resizeBilinearBackprop');\n        const [batch, xHeight, xWidth, depth] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        const output = new Float32Array(batch * xHeight * xWidth * depth);\n        // In the backwards pass, we want to find the pixels that were generated\n        // for each pixel in the input image the forward pass and add the\n        // corresponding coefficient from dy to the gradient (with some\n        // interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/3039375c86a5bbc9610c7725dcaa95d635f87ba2/tensorflow/core/kernels/resize_bilinear_op.cc#L275\n        const dyValues = this.readSync(dy.dataId);\n        let offset = 0;\n        for (let b = 0; b < batch; b++) {\n            const bOffset = b * x.strides[0];\n            for (let r = 0; r < yHeight; r++) {\n                const dxR = r * heightScale;\n                const topDxRIndex = Math.floor(dxR);\n                const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);\n                const topDxROffset = bOffset + topDxRIndex * x.strides[1];\n                const bottomDxROffset = bOffset + bottomDxRIndex * x.strides[1];\n                const dxRLerp = dxR - topDxRIndex;\n                const inverseDxRLerp = 1.0 - dxRLerp;\n                for (let c = 0; c < yWidth; c++) {\n                    const dxC = c * widthScale;\n                    const leftDxCIndex = Math.floor(dxC);\n                    const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);\n                    const dxCLerp = dxC - leftDxCIndex;\n                    const inverseDxCLerp = 1.0 - dxCLerp;\n                    const topLeftRCOffset = topDxROffset + leftDxCIndex * x.strides[2];\n                    const topRightRCOffset = topDxROffset + rightDxCIndex * x.strides[2];\n                    const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * x.strides[2];\n                    const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * x.strides[2];\n                    const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;\n                    const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;\n                    const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;\n                    const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;\n                    for (let d = 0; d < depth; d++) {\n                        const dyVal = dyValues[offset++];\n                        output[topLeftRCOffset + d] +=\n                            dyVal * inverseDxRLerpTimesInverseDxCLerp;\n                        output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;\n                        output[bottomLeftRCOffset + d] +=\n                            dyVal * dxRLerpTimesInverseDxCLerp;\n                        output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(output, [batch, xWidth, xHeight, depth], x.dtype);\n    }\n    resizeNearestNeighbor(x, newHeight, newWidth, alignCorners) {\n        assertNotComplex(x, 'resizeNearestNeighbor');\n        const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n        const xValues = this.readSync(x.dataId);\n        const output = new Float32Array(batch * newHeight * newWidth * numChannels);\n        const effectiveInputSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutputSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n        const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n        let outputOffset = 0;\n        for (let b = 0; b < batch; b++) {\n            const batchOffset = b * x.strides[0];\n            for (let r = 0; r < newHeight; r++) {\n                const sourceFracRow = effectiveRowSizeRatio * r;\n                const sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) :\n                    Math.floor(sourceFracRow));\n                const rowOffset = batchOffset + sourceNearestRow * x.strides[1];\n                for (let c = 0; c < newWidth; c++) {\n                    const sourceFracCol = effectiveColSizeRatio * c;\n                    const sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) :\n                        Math.floor(sourceFracCol));\n                    const colOffset = rowOffset + sourceNearestCol * x.strides[2];\n                    for (let d = 0; d < numChannels; d++) {\n                        // Begin shader.\n                        // Compute the fractional index of the source.\n                        const newVal = xValues[colOffset + d];\n                        output[outputOffset++] = newVal;\n                    }\n                }\n            }\n        }\n        return tf.tensor(output, [batch, newHeight, newWidth, numChannels], x.dtype);\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        assertNotComplex([dy, x], 'resizeNearestNeighborBackprop');\n        const [batch, xHeight, xWidth, depth] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        const output = new Float32Array(batch * xHeight * xWidth * depth);\n        const dyValues = this.readSync(dy.dataId);\n        // In the backwards pass, we want to find the pixels that were generated\n        // for each pixel in the input image the forward pass\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        // Loop over the output space.\n        for (let b = 0; b < batch; b++) {\n            const batchOffset = b * x.strides[0];\n            for (let r = 0; r < xHeight; r++) {\n                const rowOffset = batchOffset + r * x.strides[1];\n                // Compute bounds for where in dy we will look\n                const startRLerp = Math.floor(r * invHeightScale);\n                const startDyR = Math.floor(startRLerp - (winHeight / 2));\n                for (let c = 0; c < xWidth; c++) {\n                    const colOffset = rowOffset + c * x.strides[2];\n                    // Compute bounds for where in dy we will look\n                    const startCLerp = Math.floor(c * invWidthScale);\n                    const startDyC = Math.floor(startCLerp - (winWidth / 2));\n                    for (let d = 0; d < depth; d++) {\n                        let accum = 0;\n                        // loop over dy\n                        for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {\n                            const dyR = dyRIndex + startDyR;\n                            // Guard against the window exceeding the bounds of dy\n                            if (dyR < 0 || dyR >= yHeight) {\n                                continue;\n                            }\n                            const dyROffset = batchOffset + dyR * dy.strides[1];\n                            const sourceFracRow = dyR * heightScale;\n                            const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) :\n                                Math.floor(sourceFracRow));\n                            if (r !== sourceNearestRow) {\n                                continue;\n                            }\n                            for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {\n                                const dyC = dyCIndex + startDyC;\n                                // Guard against the window exceeding the bounds of dy\n                                if (dyC < 0 || dyC >= yWidth) {\n                                    continue;\n                                }\n                                const dyCOffset = dyROffset + dyC * dy.strides[2];\n                                const sourceFracCol = dyC * widthScale;\n                                const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) :\n                                    Math.floor(sourceFracCol));\n                                if (c === sourceNearestCol) {\n                                    accum += dyValues[dyCOffset + d];\n                                }\n                            }\n                        }\n                        output[colOffset + d] = accum;\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(output, x.shape, x.dtype);\n    }\n    localResponseNormalization4D(x, depthRadius, bias, alpha, beta) {\n        assertNotComplex(x, 'localResponseNormalization4D');\n        const channels = x.shape[3];\n        const maxD = channels - 1;\n        const xValues = this.readSync(x.dataId);\n        const size = x.size;\n        const result = new Float32Array(size);\n        function sumAcrossChannels(offset) {\n            const currentChannel = offset % channels;\n            let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n            const endSumOffset = offset - currentChannel +\n                Math.min(currentChannel + depthRadius, maxD);\n            let sum = 0.0;\n            for (; beginSumOffset <= endSumOffset; beginSumOffset++) {\n                const z = xValues[beginSumOffset];\n                sum += z * z;\n            }\n            return sum;\n        }\n        for (let offset = 0; offset < size; offset++) {\n            const sum = sumAcrossChannels(offset);\n            const val = xValues[offset] * Math.pow(bias + alpha * sum, -beta);\n            result[offset] = val;\n        }\n        return tf.tensor4d(result, x.shape);\n    }\n    LRNGrad(dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {\n        assertNotComplex(dy, 'LRNGrad');\n        const channels = dy.shape[3];\n        const dyValues = this.readSync(dy.dataId);\n        const inputImageValues = this.readSync(inputImage.dataId);\n        const outputImageValues = this.readSync(outputImage.dataId);\n        const result = new Float32Array(dy.size);\n        const size = dy.size;\n        for (let offset = 0; offset < size; offset++) {\n            const currentChannel = offset % channels;\n            const depthBegin = (offset - currentChannel) + Math.max(0, currentChannel - depthRadius);\n            const depthEnd = (offset - currentChannel) +\n                Math.min(channels, currentChannel + depthRadius + 1);\n            let norm = 0;\n            for (let k = depthBegin; k < depthEnd; k++) {\n                norm += Math.pow(inputImageValues[k], 2);\n            }\n            norm = alpha * norm + bias;\n            for (let k = depthBegin; k < depthEnd; k++) {\n                let dyi = -2 * alpha * beta * inputImageValues[k] *\n                    outputImageValues[offset] / norm;\n                if (offset === k) {\n                    dyi += Math.pow(norm, -beta);\n                }\n                dyi *= dyValues[offset];\n                result[k] += dyi;\n            }\n        }\n        return tf.tensor4d(result, dy.shape);\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        assertNotComplex(logits, 'multinomial');\n        const probabilities = normalized ? logits : tf.softmax(logits);\n        const batchSize = probabilities.shape[0];\n        const numEvents = probabilities.shape[1];\n        const res = tf.zeros([batchSize, numSamples], 'int32');\n        const resVals = this.readSync(res.dataId);\n        const probVals = this.readSync(probabilities.dataId);\n        for (let b = 0; b < batchSize; ++b) {\n            const offset = b * numEvents;\n            // The cdf won't include the last event. It will be implicit if no other\n            // event happened.\n            const cdf = new Float32Array(numEvents - 1);\n            cdf[0] = probVals[offset];\n            for (let event = 1; event < cdf.length; ++event) {\n                cdf[event] = cdf[event - 1] + probVals[offset + event];\n            }\n            const random = seedrandom.alea(seed.toString());\n            const outOffset = b * numSamples;\n            for (let sampleId = 0; sampleId < numSamples; ++sampleId) {\n                const r = random();\n                // Assume last event happened by default.\n                resVals[outOffset + sampleId] = cdf.length;\n                for (let event = 0; event < cdf.length; event++) {\n                    if (r < cdf[event]) {\n                        resVals[outOffset + sampleId] = event;\n                        break;\n                    }\n                }\n            }\n        }\n        return res;\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        assertNotComplex(indices, 'oneHot');\n        const res = new Float32Array(indices.size * depth);\n        res.fill(offValue);\n        const indicesVal = this.readSync(indices.dataId);\n        for (let event = 0; event < indices.size; ++event) {\n            if (indicesVal[event] >= 0 && indicesVal[event] < depth) {\n                res[event * depth + indicesVal[event]] = onValue;\n            }\n        }\n        return tf.tensor2d(res, [indices.size, depth], 'int32');\n    }\n    nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n        assertNotComplex(boxes, 'nonMaxSuppression');\n        const boxesVals = this.readSync(boxes.dataId);\n        const scoresVals = this.readSync(scores.dataId);\n        return nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        util.assert(dataFormat === 'NHWC', () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`);\n        util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n        const batchSize = x.shape[0];\n        const inputHeight = x.shape[1];\n        const inputWidth = x.shape[2];\n        const inputDepth = x.shape[3];\n        const outputHeight = inputHeight * blockSize;\n        const outputWidth = inputWidth * blockSize;\n        const outputDepth = inputDepth / (blockSize * blockSize);\n        const xValues = this.readSync(x.dataId);\n        const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);\n        let outputIdx = 0;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let h = 0; h < outputHeight; ++h) {\n                const inH = Math.floor(h / blockSize);\n                const offsetH = (h % blockSize);\n                for (let w = 0; w < outputWidth; ++w) {\n                    const inW = Math.floor(w / blockSize);\n                    const offsetW = (w % blockSize);\n                    const offsetD = (offsetH * blockSize + offsetW) * outputDepth;\n                    for (let d = 0; d < outputDepth; ++d) {\n                        const inD = d + offsetD;\n                        const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));\n                        result[outputIdx++] = xValues[inputIdx];\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(result, [batchSize, outputHeight, outputWidth, outputDepth]);\n    }\n    broadcastedBinaryOp(a, b, dtype, op) {\n        const newShape = backend_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const result = tf.buffer(newShape, dtype);\n        const aVals = this.readSync(a.dataId);\n        const bVals = this.readSync(b.dataId);\n        const aBroadcastDims = backend_util.getBroadcastDims(a.shape, newShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(b.shape, newShape);\n        const resVals = result.values;\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < resVals.length; ++i) {\n                resVals[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n            }\n        }\n        else {\n            const aBuf = this.bufferSync(a);\n            const bBuf = this.bufferSync(b);\n            for (let i = 0; i < resVals.length; ++i) {\n                const loc = result.indexToLoc(i);\n                const aLoc = loc.slice(-a.rank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = aBuf.locToIndex(aLoc);\n                const bLoc = loc.slice(-b.rank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = bBuf.locToIndex(bLoc);\n                resVals[i] = op(aVals[aIndex], bVals[bIndex]);\n            }\n        }\n        return result.toTensor();\n    }\n    split(x, sizeSplits, axis) {\n        return split(x, sizeSplits, axis);\n    }\n    dispose() { }\n    floatPrecision() {\n        return 32;\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return super.epsilon();\n    }\n    cropAndResize(images, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        const [batch, imageHeight, imageWidth, numChannels] = images.shape;\n        const numBoxes = boxes.shape[0];\n        const [cropHeight, cropWidth] = cropSize;\n        const output = tf.buffer([numBoxes, cropHeight, cropWidth, numChannels], 'float32');\n        const boxVals = this.readSync(boxes.dataId);\n        const boxIndVals = this.readSync(boxIndex.dataId);\n        const imageVals = this.readSync(images.dataId);\n        const inStride = images.strides; // to calculate flat indexes into image\n        const outStride = output.strides; // to calculate flat indexes into output\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc\n        for (let b = 0; b < numBoxes; b++) {\n            const startInd = b * 4;\n            const y1 = boxVals[startInd];\n            const x1 = boxVals[startInd + 1];\n            const y2 = boxVals[startInd + 2];\n            const x2 = boxVals[startInd + 3];\n            const bInd = boxIndVals[b];\n            if (bInd >= batch) {\n                continue;\n            }\n            const heightScale = (cropHeight > 1) ?\n                (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) :\n                0;\n            const widthScale = (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;\n            for (let y = 0; y < cropHeight; y++) {\n                const yInd = (cropHeight > 1) ?\n                    y1 * (imageHeight - 1) + y * (heightScale) :\n                    0.5 * (y1 + y2) * (imageHeight - 1);\n                if (yInd < 0 || yInd > imageHeight - 1) {\n                    for (let x = 0; x < cropWidth; x++) {\n                        for (let c = 0; c < numChannels; c++) {\n                            const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[ind] = extrapolationValue;\n                        }\n                    }\n                    continue;\n                }\n                if (method === 'bilinear') {\n                    const topInd = Math.floor(yInd);\n                    const bottomInd = Math.ceil(yInd);\n                    const yLerp = yInd - topInd;\n                    for (let x = 0; x < cropWidth; x++) {\n                        const xInd = (cropWidth > 1) ?\n                            x1 * (imageWidth - 1) + x * widthScale :\n                            0.5 * (x1 + x2) * (imageWidth - 1);\n                        if (xInd < 0 || xInd > imageWidth - 1) {\n                            for (let c = 0; c < numChannels; c++) {\n                                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                                output.values[ind] = extrapolationValue;\n                            }\n                            continue;\n                        }\n                        const leftInd = Math.floor(xInd);\n                        const rightInd = Math.ceil(xInd);\n                        const xLerp = xInd - leftInd;\n                        for (let c = 0; c < numChannels; c++) {\n                            let ind = c + leftInd * inStride[2] + topInd * inStride[1] +\n                                bInd * inStride[0];\n                            const topLeft = imageVals[ind];\n                            ind = c + rightInd * inStride[2] + topInd * inStride[1] +\n                                bInd * inStride[0];\n                            const topRight = imageVals[ind];\n                            ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +\n                                bInd * inStride[0];\n                            const bottomLeft = imageVals[ind];\n                            ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +\n                                bInd * inStride[0];\n                            const bottomRight = imageVals[ind];\n                            const top = topLeft + (topRight - topLeft) * xLerp;\n                            const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;\n                            ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[ind] = top + ((bottom - top) * yLerp);\n                        }\n                    }\n                }\n                else { // method == \"nearest\"\n                    for (let x = 0; x < cropWidth; ++x) {\n                        const xInd = (cropWidth > 1) ?\n                            x1 * (imageWidth - 1) + x * widthScale :\n                            0.5 * (x1 + x2) * (imageWidth - 1);\n                        if (xInd < 0 || xInd > imageWidth - 1) {\n                            for (let c = 0; c < numChannels; c++) {\n                                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                                output.values[ind] = extrapolationValue;\n                            }\n                            continue;\n                        }\n                        const closestX = Math.round(xInd);\n                        const closestY = Math.round(yInd);\n                        for (let c = 0; c < numChannels; c++) {\n                            const inInd = c + closestX * inStride[2] +\n                                closestY * inStride[1] + bInd * inStride[0];\n                            const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[outInd] = imageVals[inInd];\n                        }\n                    }\n                }\n            }\n        }\n        return output.toTensor();\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n        const sumDupeIndices = false;\n        return this.scatter(sparseIndices, sparseValues, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n    }\n    gatherND(x, indices) {\n        const indicesShape = indices.shape;\n        const sliceRank = indicesShape[indicesShape.length - 1];\n        const [resultShape, numSlices, sliceSize, strides] = backend_util.prepareAndValidate(x, indices);\n        if (numSlices === 0) {\n            return tf.tensor([], resultShape, x.dtype);\n        }\n        const buffer = new TensorBuffer([numSlices, sliceSize], x.dtype);\n        const indicesData = this.readSync(indices.dataId);\n        const xData = this.readSync(x.dataId);\n        for (let i = 0; i < numSlices; i++) {\n            const index = [];\n            let flattenIndex = 0;\n            for (let j = 0; j < sliceRank; j++) {\n                const dim = indicesData[i * sliceRank + j];\n                flattenIndex += dim * strides[j];\n                index.push(dim);\n            }\n            if (flattenIndex < 0 || flattenIndex >= x.size / sliceSize) {\n                throw new Error(`Invalid indices: ${index} does not index into ${x.shape}`);\n            }\n            for (let k = 0; k < sliceSize; k++) {\n                buffer.values[i * sliceSize + k] = xData[flattenIndex * sliceSize + k];\n            }\n        }\n        return buffer.toTensor().reshape(resultShape);\n    }\n    scatterND(indices, updates, shape) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(updates, indices, shape);\n        const defaultValue = tf.scalar(0);\n        const sumDupeIndices = true;\n        return this.scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n    }\n    fill(shape, value, dtype) {\n        dtype = dtype || util.inferDtype(value);\n        const values = util.getArrayFromDType(dtype, util.sizeFromShape(shape));\n        values.fill(value);\n        return engine().makeTensor(values, shape, dtype, this);\n    }\n    onesLike(x) {\n        if (x.dtype === 'string') {\n            throw new Error('onesLike is not supported for string tensors');\n        }\n        else {\n            return this.fill(x.shape, 1, x.dtype);\n        }\n    }\n    zerosLike(x) {\n        const values = util.getArrayFromDType(x.dtype, util.sizeFromShape(x.shape));\n        return this.makeOutput(values, x.shape, x.dtype);\n    }\n    linspace(start, stop, num) {\n        return backend_util.linspaceImpl(start, stop, num);\n    }\n    scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {\n        const flattenShape = [outputSize / sliceSize, sliceSize];\n        const indicesData = this.readSync(indices.dataId);\n        const updatesData = this.readSync(updates.dataId);\n        if (outputSize === 0) {\n            return tf.tensor([], shape, updates.dtype);\n        }\n        const buffer = new TensorBuffer(flattenShape, updates.dtype);\n        buffer.values.fill(this.readSync(defaultValue.dataId)[0]);\n        for (let i = 0; i < numUpdates; i++) {\n            const index = [];\n            let flattenIndex = 0;\n            for (let j = 0; j < sliceRank; j++) {\n                const dim = indicesData[i * sliceRank + j];\n                index.push(dim);\n                flattenIndex += dim * strides[j];\n            }\n            if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {\n                throw new Error(`Invalid indices: ${index} does not index into ${shape}`);\n            }\n            for (let k = 0; k < sliceSize; k++) {\n                if (sumDupeIndices) {\n                    buffer.values[flattenIndex * sliceSize + k] +=\n                        updatesData[i * sliceSize + k];\n                }\n                else {\n                    buffer.values[flattenIndex * sliceSize + k] = updates.rank === 0 ?\n                        updatesData[0] :\n                        updatesData[i * sliceSize + k];\n                }\n            }\n        }\n        return buffer.toTensor().reshape(shape);\n    }\n}\n//# sourceMappingURL=backend_cpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs, util } from '@tensorflow/tfjs-core';\nexport function simpleAbsImpl(vals) {\n    const resultValues = new Float32Array(vals.length);\n    for (let i = 0; i < vals.length; ++i) {\n        resultValues[i] = Math.abs(vals[i]);\n    }\n    return resultValues;\n}\nexport const absKernelFunc = (args) => {\n    const { x } = args.inputs;\n    const cpuBackend = args.backend;\n    let resultValues = new Float32Array(util.sizeFromShape(x.shape));\n    if (x.dtype !== 'complex64') {\n        const values = cpuBackend.data.get(x.dataId).values;\n        resultValues = simpleAbsImpl(values);\n    }\n    else {\n        const complexVals = cpuBackend.data.get(x.dataId);\n        const real = complexVals.complexTensorInfos.real;\n        const imag = complexVals.complexTensorInfos.imag;\n        const realVals = cpuBackend.data.get(real.dataId).values;\n        const imagVals = cpuBackend.data.get(imag.dataId).values;\n        for (let i = 0; i < realVals.length; i++) {\n            const real = realVals[i];\n            const imag = imagVals[i];\n            resultValues[i] = Math.hypot(real, imag);\n        }\n    }\n    return cpuBackend.makeOutput(resultValues, x.shape, 'float32');\n};\nexport const absConfig = {\n    kernelName: Abs,\n    backendName: 'cpu',\n    kernelFunc: absKernelFunc,\n};\n//# sourceMappingURL=Abs.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for binary ops. Supports broadcast.\n */\nexport function createSimpleBinaryKernelImpl(op) {\n    return (aShape, bShape, aVals, bVals, dtype) => {\n        const newShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const resultRank = newShape.length;\n        const resultStrides = util.computeStrides(newShape);\n        const resultSize = util.sizeFromShape(newShape);\n        const result = util.getTypedArrayFromDType(dtype, resultSize);\n        const aRank = aShape.length;\n        const bRank = bShape.length;\n        const aStrides = util.computeStrides(aShape);\n        const bStrides = util.computeStrides(bShape);\n        const aBroadcastDims = backend_util.getBroadcastDims(aShape, newShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(bShape, newShape);\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < result.length; ++i) {\n                result[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n            }\n        }\n        else {\n            for (let i = 0; i < result.length; ++i) {\n                const loc = util.indexToLoc(i, resultRank, resultStrides);\n                const aLoc = loc.slice(-aRank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n                const bLoc = loc.slice(-bRank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n                result[i] = op(aVals[aIndex], bVals[bIndex]);\n            }\n        }\n        return [result, newShape];\n    };\n}\n//# sourceMappingURL=binary_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Complex } from '@tensorflow/tfjs-core';\nexport function complex(args) {\n    const { inputs, backend } = args;\n    const { real, imag } = inputs;\n    const realVals = backend.data.get(real.dataId).values;\n    const imagVals = backend.data.get(imag.dataId).values;\n    const complexInfo = backend.makeTensorInfo(real.shape, 'complex64');\n    const complex = backend.data.get(complexInfo.dataId);\n    // The complex tensor owns the underlying real and imag tensorInfos, only the\n    // complex tensor tracks refCount, when complexData is disposed the\n    // underlying tensorData will be disposed.\n    complex.complexTensorInfos = {\n        real: backend.makeTensorInfo(real.shape, 'float32', realVals),\n        imag: backend.makeTensorInfo(imag.shape, 'float32', imagVals)\n    };\n    return complexInfo;\n}\nexport const complexConfig = {\n    kernelName: Complex,\n    backendName: 'cpu',\n    kernelFunc: complex\n};\n//# sourceMappingURL=Complex.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '@tensorflow/tfjs-core';\nexport function identity(args) {\n    const { inputs, backend } = args;\n    const { x } = inputs;\n    backend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nexport const identityConfig = {\n    kernelName: Identity,\n    backendName: 'cpu',\n    kernelFunc: identity\n};\n//# sourceMappingURL=Identity.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Real } from '@tensorflow/tfjs-core';\nexport function real(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const real = backend.data.get(input.dataId).complexTensorInfos.real;\n    const realVal = backend.data.get(real.dataId).values;\n    // When complex tensor is disposed, its underlying parts will be disposed too.\n    // Make new tensor out of the real value of the complex. This makes sure the\n    // value is still accessible even if complex tensor is disposed.\n    return backend.makeTensorInfo(real.shape, real.dtype, realVal);\n}\nexport const realConfig = {\n    kernelName: Real,\n    backendName: 'cpu',\n    kernelFunc: real\n};\n//# sourceMappingURL=Real.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { Cast, util } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { complex } from './Complex';\nimport { identity } from './Identity';\nimport { real } from './Real';\nexport function cast(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { dtype } = attrs;\n    // Casting to complex64.\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return identity({ inputs: { x }, backend });\n        }\n        // TODO(lina128): Import kernel function once zeros is modularized.\n        const zerosTensor = tf.zeros(x.shape);\n        const floatX = cast({ inputs: { x }, backend, attrs: { dtype: 'float32' } });\n        const result = complex({ inputs: { real: floatX, imag: zerosTensor }, backend });\n        zerosTensor.dispose();\n        backend.disposeIntermediateTensorInfo(floatX);\n        return result;\n    }\n    // Casting from complex64\n    if (x.dtype === 'complex64') {\n        const realPart = real({ inputs: { input: x }, backend });\n        const result = cast({ inputs: { x: realPart }, backend, attrs: { dtype } });\n        backend.disposeIntermediateTensorInfo(realPart);\n        return result;\n    }\n    if (!util.hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        const result = identity({ inputs: { x }, backend });\n        return { dataId: result.dataId, shape: result.shape, dtype };\n    }\n    if (dtype === 'int32') {\n        const values = backend.data.get(x.dataId).values;\n        const resultValues = Int32Array.from(values);\n        return backend.makeTensorInfo(x.shape, 'int32', resultValues);\n    }\n    if (dtype === 'bool') {\n        // This is essentially the result of notEqual(x, 0). We avoid using\n        // kernel notEqual to avoid circular dependency, i.e. binary_utils ->\n        // cast -> notEqual -> binary_utils.\n        const xVals = backend.data.get(x.dataId).values;\n        const zero = util.toTypedArray([0], x.dtype);\n        const [resultData, resultShape] = createSimpleBinaryKernelImpl((a, b) => (a !== b) ? 1 : 0)(x.shape, [], xVals, zero, 'bool');\n        return backend.makeTensorInfo(resultShape, 'bool', resultData);\n    }\n    throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nexport const castConfig = {\n    kernelName: Cast,\n    backendName: 'cpu',\n    kernelFunc: cast\n};\n//# sourceMappingURL=Cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { cast } from '../kernels/Cast';\nimport { complex } from '../kernels/Complex';\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param name Kernel name.\n * @param binaryKernelImpl A `SimpleBinaryKernelImpl` for the kernel.\n * @param binaryKernelComplexImpl Optional. If exists, represents a\n *     `ComplexBinaryKernelImpl` for the kernel, will be used when input dtype\n *     is `complex64`.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc(name, simpleImpl, complexImpl, dtype) {\n    if (complexImpl == null) {\n        return ({ inputs, backend }) => {\n            const { a, b } = inputs;\n            const cpuBackend = backend;\n            assertNotComplex([a, b], name);\n            const aVals = cpuBackend.data.get(a.dataId).values;\n            const bVals = cpuBackend.data.get(b.dataId).values;\n            const $dtype = dtype || a.dtype;\n            const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n            return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n        };\n    }\n    return ({ inputs, backend }) => {\n        const { a, b } = inputs;\n        const cpuBackend = backend;\n        if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n            const $aComplex = cast({ inputs: { x: a }, backend: cpuBackend, attrs: { dtype: 'complex64' } });\n            const $aComplexVals = cpuBackend.data.get($aComplex.dataId);\n            const aReal = $aComplexVals.complexTensorInfos.real;\n            const aImag = $aComplexVals.complexTensorInfos.imag;\n            const aRealVals = cpuBackend.data.get(aReal.dataId).values;\n            const aImagVals = cpuBackend.data.get(aImag.dataId).values;\n            const $bComplex = cast({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: 'complex64' } });\n            const $bComplexVals = cpuBackend.data.get($bComplex.dataId);\n            const bReal = $bComplexVals.complexTensorInfos.real;\n            const bImag = $bComplexVals.complexTensorInfos.imag;\n            const bRealVals = cpuBackend.data.get(bReal.dataId).values;\n            const bImagVals = cpuBackend.data.get(bImag.dataId).values;\n            const [resultRealData, resultImagData, resultShape] = complexImpl(a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);\n            const resultReal = cpuBackend.makeTensorInfo(resultShape, 'float32', resultRealData);\n            const resultImag = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImagData);\n            const result = complex({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend });\n            cpuBackend.disposeIntermediateTensorInfo($aComplex);\n            cpuBackend.disposeIntermediateTensorInfo($bComplex);\n            cpuBackend.disposeIntermediateTensorInfo(resultReal);\n            cpuBackend.disposeIntermediateTensorInfo(resultImag);\n            return result;\n        }\n        else {\n            const aVals = cpuBackend.data.get(a.dataId).values;\n            const bVals = cpuBackend.data.get(b.dataId).values;\n            const $dtype = dtype || a.dtype;\n            const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n            return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n        }\n    };\n}\n/**\n * Template that creates the complex type implementation for binary ops.\n * Supports broadcast.\n */\nexport function createComplexBinaryKernelImpl(op) {\n    return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) => {\n        const resultShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const resultSize = util.sizeFromShape(resultShape);\n        const resultRank = resultShape.length;\n        const resultStrides = util.computeStrides(resultShape);\n        const resultRealVals = util.getTypedArrayFromDType('float32', resultSize);\n        const resultImagVals = util.getTypedArrayFromDType('float32', resultSize);\n        const aBroadcastDims = backend_util.getBroadcastDims(aShape, resultShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(bShape, resultShape);\n        const aVals = backend_util.mergeRealAndImagArrays(aRealVals, aImagVals);\n        const bVals = backend_util.mergeRealAndImagArrays(bRealVals, bImagVals);\n        const aRank = aShape.length;\n        const aStrides = util.computeStrides(aShape);\n        const bRank = bShape.length;\n        const bStrides = util.computeStrides(bShape);\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < resultRealVals.length; i++) {\n                const aIdx = i % aVals.length;\n                const bIdx = i % bVals.length;\n                const result = op(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);\n                resultRealVals[i] = result.real;\n                resultImagVals[i] = result.imag;\n            }\n        }\n        else {\n            for (let i = 0; i < resultRealVals.length; i++) {\n                const loc = util.indexToLoc(i, resultRank, resultStrides);\n                const aLoc = loc.slice(-aRank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n                const bLoc = loc.slice(-bRank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n                const opResult = op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);\n                resultRealVals[i] = opResult.real;\n                resultImagVals[i] = opResult.imag;\n            }\n        }\n        return [resultRealVals, resultImagVals, resultShape];\n    };\n}\n//# sourceMappingURL=kernel_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const addImpl = createSimpleBinaryKernelImpl(((a, b) => a + b));\nexport const addComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal + bReal, imag: aImag + bImag };\n}));\nexport const add = binaryKernelFunc(Add, addImpl, addComplexImpl);\nexport const addConfig = {\n    kernelName: Add,\n    backendName: 'cpu',\n    kernelFunc: add\n};\n//# sourceMappingURL=Add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for unary op.\n */\nexport function createSimpleUnaryImpl(op) {\n    return (values, dtype, attrs) => {\n        const newValues = util.getTypedArrayFromDType(dtype, values.length);\n        for (let i = 0; i < values.length; ++i) {\n            newValues[i] = op(values[i], attrs);\n        }\n        return newValues;\n    };\n}\n//# sourceMappingURL=unary_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param name Kernel name.\n * @param op A `SimpleUnaryOperation` for the kernel.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the input. This is mainly used in certain\n *     kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFunc(name, op, dtype) {\n    return ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        assertNotComplex(x, name);\n        if (x.dtype === 'string' || dtype === 'string') {\n            throw new Error('unaryKernelFunc does not support string input/output');\n        }\n        const cpuBackend = backend;\n        const values = cpuBackend.data.get(x.dataId).values;\n        const xSize = util.sizeFromShape(x.shape);\n        const $dtype = dtype || x.dtype;\n        const newValues = util.getArrayFromDType($dtype, xSize);\n        for (let i = 0; i < xSize; ++i) {\n            newValues[i] = op(values[i], attrs);\n        }\n        return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n    };\n}\n/**\n * Template that creates a `KernelFunc` for unary ops from the given\n * `SimpleUnaryImpl`..\n * @param name Kernel name.\n * @param unaryImpl A `SimpleUnaryImpl` that implements the op.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the input. This is mainly used in certain\n *     kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFuncFromImpl(name, unaryImpl, dtype) {\n    return ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        assertNotComplex(x, name);\n        if (x.dtype === 'string' || dtype === 'string') {\n            throw new Error('unaryKernelFunc does not support string input/output');\n        }\n        const cpuBackend = backend;\n        const values = cpuBackend.data.get(x.dataId).values;\n        const $dtype = dtype || x.dtype;\n        const newValues = unaryImpl(values, $dtype, attrs);\n        return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n    };\n}\n//# sourceMappingURL=unary_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));\nexport const ceilKernelFunc = unaryKernelFuncFromImpl(Ceil, ceilImpl);\nexport const ceilConfig = {\n    kernelName: Ceil,\n    backendName: 'cpu',\n    kernelFunc: ceilKernelFunc,\n};\n//# sourceMappingURL=Ceil.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));\nexport const expKernelFunc = unaryKernelFuncFromImpl(Exp, expImpl);\nexport const expConfig = {\n    kernelName: Exp,\n    backendName: 'cpu',\n    kernelFunc: expKernelFunc,\n};\n//# sourceMappingURL=Exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));\nexport const expm1KernelFunc = unaryKernelFuncFromImpl(Expm1, expm1Impl);\nexport const expm1Config = {\n    kernelName: Expm1,\n    backendName: 'cpu',\n    kernelFunc: expm1KernelFunc,\n};\n//# sourceMappingURL=Expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));\nexport const floorKernelFunc = unaryKernelFuncFromImpl(Floor, floorImpl);\nexport const floorConfig = {\n    kernelName: Floor,\n    backendName: 'cpu',\n    kernelFunc: floorKernelFunc,\n};\n//# sourceMappingURL=Floor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));\nexport const logKernelFunc = unaryKernelFuncFromImpl(Log, logImpl);\nexport const logConfig = {\n    kernelName: Log,\n    backendName: 'cpu',\n    kernelFunc: logKernelFunc,\n};\n//# sourceMappingURL=Log.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function maxImpl(aVals, reduceSize, outShape, dtype) {\n    const vals = util.getTypedArrayFromDType(dtype, util.sizeFromShape(outShape));\n    for (let i = 0; i < vals.length; ++i) {\n        const offset = i * reduceSize;\n        let max = aVals[offset];\n        for (let j = 0; j < reduceSize; ++j) {\n            const value = aVals[offset + j];\n            if (value > max) {\n                max = value;\n            }\n        }\n        vals[i] = max;\n    }\n    return vals;\n}\n//# sourceMappingURL=Max_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const multiplyImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue * bValue));\nexport const multiplyComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return {\n        real: aReal * bReal - aImag * bImag,\n        imag: aReal * bImag + aImag * bReal\n    };\n}));\nexport const multiply = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);\nexport const multiplyConfig = {\n    kernelName: Multiply,\n    backendName: 'cpu',\n    kernelFunc: multiply\n};\n//# sourceMappingURL=Multiply.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));\nexport const rsqrtKernelFunc = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);\nexport const rsqrtConfig = {\n    kernelName: Rsqrt,\n    backendName: 'cpu',\n    kernelFunc: rsqrtKernelFunc,\n};\n//# sourceMappingURL=Rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Slice, slice_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function sliceImpl(vals, begin, size, shape, dtype) {\n    const isContinous = slice_util.isSliceContinous(shape, begin, size);\n    const length = util.sizeFromShape(size);\n    const xStrides = util.computeStrides(shape);\n    if (isContinous) {\n        const flatOffset = slice_util.computeFlatOffset(begin, xStrides);\n        return vals.subarray(flatOffset, flatOffset + length);\n    }\n    const outVals = util.getTypedArrayFromDType(dtype, length);\n    for (let i = 0; i < length; ++i) {\n        const rank = size.length;\n        const strides = util.computeStrides(size);\n        const loc = util.indexToLoc(i, rank, strides);\n        const xLoc = loc.map((idx, j) => idx + begin[j]);\n        const xIndex = util.locToIndex(xLoc, shape.length, xStrides);\n        outVals[i] = vals[xIndex];\n    }\n    return outVals;\n}\nexport function slice(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { begin, size } = attrs;\n    assertNotComplex(x, 'slice');\n    const [$begin, $size] = slice_util.parseSliceParams(x, begin, size);\n    slice_util.assertParamsValid(x, $begin, $size);\n    const vals = backend.data.get(x.dataId).values;\n    const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);\n    return backend.makeTensorInfo($size, x.dtype, outVals);\n}\nexport const sliceConfig = {\n    kernelName: Slice,\n    backendName: 'cpu',\n    kernelFunc: slice\n};\n//# sourceMappingURL=Slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const subImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue - bValue));\nexport const subComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal - bReal, imag: aImag - bImag };\n}));\nexport const sub = binaryKernelFunc(Sub, subImpl, subComplexImpl);\nexport const subConfig = {\n    kernelName: Sub,\n    backendName: 'cpu',\n    kernelFunc: sub\n};\n//# sourceMappingURL=Sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function transposeImpl(xVals, xShape, dtype, perm, newShape) {\n    const xRank = xShape.length;\n    const xSize = util.sizeFromShape(xShape);\n    const xStrides = util.computeStrides(xShape);\n    const newStrides = util.computeStrides(newShape);\n    const result = util.getTypedArrayFromDType(dtype, util.sizeFromShape(newShape));\n    for (let i = 0; i < xSize; ++i) {\n        const loc = util.indexToLoc(i, xRank, xStrides);\n        // Permute location.\n        const newLoc = new Array(loc.length);\n        for (let i = 0; i < newLoc.length; i++) {\n            newLoc[i] = loc[perm[i]];\n        }\n        const newIndex = util.locToIndex(newLoc, xRank, newStrides);\n        result[newIndex] = xVals[i];\n    }\n    return result;\n}\n//# sourceMappingURL=Transpose_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer, util } from '@tensorflow/tfjs-core';\nexport function uniqueImpl(values, axis, shape, dtype) {\n    // Normalize and validate axis.\n    const $axis = util.parseAxisParam(axis, shape)[0];\n    // Calculate the new shape that is suitable for extracting data along the\n    // given axis.\n    //\n    // The rank is 3.\n    // The size of the 1st dimension is the size of all the axes < the given axis.\n    // The size of the 2nd dimension is the same as the size of the given axis.\n    // The size of the 3rd dimension is the size of all the axes > the given axis.\n    //\n    // For example, for a 4D tensor with shape=[2, 3, 5, 4] and axis=2, the\n    // newShape would be: [2*3, 5, 4].\n    //\n    // Note that this is not the final output shape. This will be the shape for an\n    // intermediate TensorBuffer (see inputBuffer below) to allow us to extract\n    // values along the given axis. To demonstrate how it works, consider the\n    // following example:\n    //\n    // Input: a 3D tensor, with shape [1, 2, 3]\n    // [\n    //   [\n    //      [1,2,3],\n    //      [4,5,6]\n    //   ]\n    // ]\n    // Axis: 2 (the last axis).\n    // Along axis 2, we expect to extract 3 tensors: [1,4], [2,5], [3,6].\n    //\n    // For this example, newShape would be: [2, 3, 1], where 2 is calculated from\n    // 1*2. The re-shaped data would look like:\n    //\n    // [\n    //   [\n    //     [1], [2], [3]\n    //   ],\n    //   [\n    //     [4], [5], [6]\n    //   ]\n    // ]\n    //\n    // Then, we can construct a 3-level nested loop by the following dimension\n    // order to extract the values along the axis (dimension1):\n    // i: dimension1       // 0,1,2 (newShape[1])\n    //   m: dimension0     // 0,1   (newShape[0])\n    //     n: dimension2   // 0     (newShape[2])\n    //\n    //                       m, i, n\n    //                      ---------\n    // Iteration 0: data at [0, 0, 0] => \"1\"\n    // Iteration 1: data at [1, 0, 0] => \"4\"\n    // We got [1,4].\n    // Iteration 2: data at [0, 1, 0] => \"2\"\n    // Iteration 3: data at [1, 1, 0] => \"5\"\n    // We got [2,5].\n    // Iteration 4: data at [0, 2, 0] => \"3\"\n    // Iteration 5: data at [1, 2, 0] => \"6\"\n    // We got [3,6].\n    const newShape = [1, shape[0], 1];\n    for (let i = 0; i < $axis; i++) {\n        newShape[0] *= shape[i];\n    }\n    newShape[1] = shape[$axis];\n    for (let i = $axis + 1; i < shape.length; i++) {\n        newShape[2] *= shape[i];\n    }\n    // A map from unique elements (their string representations) to their values\n    // in \"indices\" (below).\n    const uniqueElements = {};\n    // The indices of each unique element in the original tensor along the given\n    // axis. It is 1D and has the same size as the given axis.\n    const indices = new Int32Array(shape[$axis]);\n    // Create a buffer so we can easily extract value at a given location.\n    const inputBuffer = new TensorBuffer(newShape, dtype, values);\n    // The indices along the given axis that have unique elements. This is a\n    // de-duped version of \"indices\" above.\n    const uniqueIndices = [];\n    const is1DTensor = newShape[0] === 1 && newShape[2] === 1;\n    for (let i = 0; i < shape[$axis]; i++) {\n        // Extract values along the axis.\n        let element;\n        if (is1DTensor) {\n            // Fast path for 1D tensor input.\n            element = values[i].toString();\n        }\n        else {\n            const axisValues = [];\n            for (let m = 0; m < newShape[0]; m++) {\n                for (let n = 0; n < newShape[2]; n++) {\n                    axisValues.push(inputBuffer.get(m, i, n));\n                }\n            }\n            element = axisValues.join(',');\n        }\n        // Dedup and update various indices.\n        if (uniqueElements[element] !== undefined) {\n            indices[i] = uniqueElements[element];\n        }\n        else {\n            const uniqueIndex = Object.keys(uniqueElements).length;\n            uniqueElements[element] = uniqueIndex;\n            indices[i] = uniqueIndex;\n            uniqueIndices.push(i);\n        }\n    }\n    // Now we know where each of the unique elements are located along the axis\n    // (uniqueIndices). Extract them from input buffer and store them in the\n    // output buffer.\n    const outputTmpShape = newShape.slice();\n    outputTmpShape[1] = Object.keys(uniqueElements).length;\n    const outputBuffer = new TensorBuffer(outputTmpShape, dtype);\n    uniqueIndices.forEach((uniqueElementIndex, i) => {\n        for (let m = 0; m < newShape[0]; m++) {\n            for (let n = 0; n < newShape[2]; n++) {\n                outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);\n            }\n        }\n    });\n    // The output shape can be calculated from the input shape with the size of\n    // the given axis replaced by the number of unique elements along that axis.\n    const outputShape = shape.slice();\n    outputShape[$axis] = outputTmpShape[1];\n    return {\n        outputValues: outputBuffer.values,\n        outputShape,\n        indices,\n    };\n}\n//# sourceMappingURL=Unique_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Shared kernel impls for use in other backends.\nexport { simpleAbsImpl } from './kernels/Abs';\nexport { addImpl } from './kernels/Add';\nexport { ceilImpl } from './kernels/Ceil';\nexport { expImpl } from './kernels/Exp';\nexport { expm1Impl } from './kernels/Expm1';\nexport { floorImpl } from './kernels/Floor';\nexport { logImpl } from './kernels/Log';\nexport { maxImpl } from './kernels/Max_impl';\nexport { multiplyImpl } from './kernels/Multiply';\nexport { rsqrtImpl } from './kernels/Rsqrt';\nexport { sliceImpl } from './kernels/Slice';\nexport { subImpl } from './kernels/Sub';\nexport { transposeImpl } from './kernels/Transpose_impl';\nexport { uniqueImpl } from './kernels/Unique_impl';\n//# sourceMappingURL=shared.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/*\n * base.ts contains all the exports from tfjs-backend-cpu\n * without auto-kernel registration\n */\nimport { registerBackend } from '@tensorflow/tfjs-core';\nimport { MathBackendCPU } from './backend_cpu';\nimport * as shared from './shared';\nexport { MathBackendCPU } from './backend_cpu';\nexport { version as version_cpu } from './version';\nexport { shared };\n// Side effects for default initialization of MathBackendCPU\nregisterBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acos } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const acosKernelFunc = unaryKernelFunc(Acos, (xi) => Math.acos(xi));\nexport const acosConfig = {\n    kernelName: Acos,\n    backendName: 'cpu',\n    kernelFunc: acosKernelFunc,\n};\n//# sourceMappingURL=Acos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acosh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const acoshKernelFunc = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi));\nexport const acoshConfig = {\n    kernelName: Acosh,\n    backendName: 'cpu',\n    kernelFunc: acoshKernelFunc,\n};\n//# sourceMappingURL=Acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asin } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const asinKernelFunc = unaryKernelFunc(Asin, (xi) => Math.asin(xi));\nexport const asinConfig = {\n    kernelName: Asin,\n    backendName: 'cpu',\n    kernelFunc: asinKernelFunc,\n};\n//# sourceMappingURL=Asin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asinh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const asinhKernelFunc = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi));\nexport const asinhConfig = {\n    kernelName: Asinh,\n    backendName: 'cpu',\n    kernelFunc: asinhKernelFunc,\n};\n//# sourceMappingURL=Asinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const atanKernelFunc = unaryKernelFunc(Atan, (xi) => Math.atan(xi));\nexport const atanConfig = {\n    kernelName: Atan,\n    backendName: 'cpu',\n    kernelFunc: atanKernelFunc,\n};\n//# sourceMappingURL=Atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atanh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const atanhKernelFunc = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi));\nexport const atanhConfig = {\n    kernelName: Atanh,\n    backendName: 'cpu',\n    kernelFunc: atanhKernelFunc,\n};\n//# sourceMappingURL=Atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from '@tensorflow/tfjs-core';\nexport function pool(xValues, xShape, dtype, strides, convInfo, poolType) {\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n        Number.POSITIVE_INFINITY);\n    const output = buffer(convInfo.outShape, dtype);\n    const outputVals = output.values;\n    const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];\n    const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];\n    const outputColStrides = convInfo.outShape[3];\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        const outputBatchOffset = b * outputBatchStrides;\n        const inputBatchOffset = b * strides[0];\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const xRCorner = yR * strideHeight - padTop;\n                const xRMin = Math.max(0, xRCorner);\n                const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n                const outputRowOffset = outputBatchOffset + yR * outputRowStrides;\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const xCCorner = yC * strideWidth - padLeft;\n                    const xCMin = Math.max(0, xCCorner);\n                    const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n                    let minMaxValue = initialValue;\n                    let avgValue = 0;\n                    let count = 0;\n                    for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n                        const xROffset = inputBatchOffset + xR * strides[1];\n                        for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                            const xCOffset = xROffset + xC * strides[2];\n                            const pixel = xValues[xCOffset + d];\n                            if ((poolType === 'max' && pixel > minMaxValue)) {\n                                minMaxValue = pixel;\n                            }\n                            else if (poolType === 'avg') {\n                                avgValue += pixel;\n                                count++;\n                            }\n                        }\n                        if (isNaN(minMaxValue)) {\n                            break;\n                        }\n                    }\n                    const outputOffset = outputRowOffset + yC * outputColStrides + d;\n                    outputVals[outputOffset] =\n                        poolType === 'avg' ? avgValue / count : minMaxValue;\n                }\n            }\n        }\n    }\n    return output;\n}\nexport function maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) {\n    const maxPositions = buffer(convInfo.outShape, 'int32');\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const xBuf = buffer(xShape, dtype, xValues);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const xRCorner = yR * strideHeight - padTop;\n                let xRMin = xRCorner;\n                while (xRMin < 0) {\n                    xRMin += dilationHeight;\n                }\n                // const xRMin = Math.max(0, xRCorner);\n                const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const xCCorner = yC * strideWidth - padLeft;\n                    let xCMin = xCCorner;\n                    while (xCMin < 0) {\n                        xCMin += dilationWidth;\n                    }\n                    const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n                    let maxValue = Number.NEGATIVE_INFINITY;\n                    let maxPosition = -1;\n                    for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n                        const wR = xR - xRCorner;\n                        for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                            const wC = xC - xCCorner;\n                            const pixel = xBuf.get(b, xR, xC, d);\n                            if (pixel > maxValue) {\n                                maxValue = pixel;\n                                if (flattenPositions) {\n                                    maxPosition = includeBatchInIndex ?\n                                        ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) *\n                                            convInfo.inChannels +\n                                            d :\n                                        (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;\n                                }\n                                else {\n                                    maxPosition = wR * effectiveFilterWidth + wC;\n                                }\n                            }\n                        }\n                    }\n                    maxPositions.set(maxPosition, b, yR, yC, d);\n                }\n            }\n        }\n    }\n    return maxPositions;\n}\n//# sourceMappingURL=pool_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool, backend_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { pool } from '../utils/pool_utils';\nimport { identity } from './Identity';\nexport function avgPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'avgPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    let res;\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        res = identity({ inputs: { x }, backend });\n    }\n    else {\n        const xValues = backend.data.get(x.dataId).values;\n        const strides = util.computeStrides(x.shape);\n        const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'avg');\n        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);\n    }\n    return res;\n}\nexport const avgPoolConfig = {\n    kernelName: AvgPool,\n    backendName: 'cpu',\n    kernelFunc: avgPool\n};\n//# sourceMappingURL=AvgPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPoolBackprop, backend_util, buffer } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function avgPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input } = inputs;\n    const x = input;\n    assertNotComplex([dy, input], 'avgPoolBackprop');\n    const { filterSize, strides, pad } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = buffer(x.shape, 'float32');\n    const avgMultiplier = 1 / (filterHeight * filterWidth);\n    const dyData = backend.data.get(dy.dataId).values;\n    const dyBuf = buffer(dy.shape, 'float32', dyData);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n                for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n                    // Shader code begins.\n                    const dyRCorner = dxR - padTop;\n                    const dyCCorner = dxC - padLeft;\n                    let dotProd = 0;\n                    for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n                        const dyR = (dyRCorner + wR) / strideHeight;\n                        if (dyR < 0 || dyR >= convInfo.outHeight ||\n                            Math.floor(dyR) !== dyR) {\n                            continue;\n                        }\n                        for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                            const dyC = (dyCCorner + wC) / strideWidth;\n                            if (dyC < 0 || dyC >= convInfo.outWidth ||\n                                Math.floor(dyC) !== dyC) {\n                                continue;\n                            }\n                            const pixel = dyBuf.get(b, dyR, dyC, d);\n                            dotProd += pixel;\n                        }\n                    }\n                    dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const avgPoolBackpropConfig = {\n    kernelName: AvgPoolBackprop,\n    backendName: 'cpu',\n    kernelFunc: avgPoolBackprop\n};\n//# sourceMappingURL=AvgPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedBatchNorm, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function batchNormKernelFunc(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, scale, offset, mean, variance } = inputs;\n    util.assert(mean.shape.length === variance.shape.length, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert(offset == null || mean.shape.length === offset.shape.length, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert(scale == null || mean.shape.length === scale.shape.length, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    assertNotComplex([x, mean, variance, scale, offset], 'batchNorm');\n    let { varianceEpsilon } = attrs;\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const xVals = backend.data.get(x.dataId).values;\n    const mVals = backend.data.get(mean.dataId).values;\n    const varVals = backend.data.get(variance.dataId).values;\n    const sVals = scale ? backend.data.get(scale.dataId).values :\n        new Float32Array([1]);\n    const offVals = offset ?\n        backend.data.get(offset.dataId).values :\n        new Float32Array([0]);\n    const outVals = new Float32Array(xVals.length);\n    const offValsLength = offVals.length;\n    const sValsLength = sVals.length;\n    const varValsLength = varVals.length;\n    const mValsLength = mVals.length;\n    let offi = 0;\n    let mi = 0;\n    let si = 0;\n    let vi = 0;\n    for (let i = 0; i < xVals.length; ++i) {\n        outVals[i] = offVals[offi++] +\n            (xVals[i] - mVals[mi++]) * sVals[si++] /\n                Math.sqrt(varVals[vi++] + varianceEpsilon);\n        if (offi >= offValsLength) {\n            offi = 0;\n        }\n        if (mi >= mValsLength) {\n            mi = 0;\n        }\n        if (si >= sValsLength) {\n            si = 0;\n        }\n        if (vi >= varValsLength) {\n            vi = 0;\n        }\n    }\n    return backend.makeTensorInfo(x.shape, x.dtype, outVals);\n}\nexport const batchNormConfig = {\n    kernelName: FusedBatchNorm,\n    backendName: 'cpu',\n    kernelFunc: batchNormKernelFunc,\n};\n//# sourceMappingURL=BatchNorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ClipByValue } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const clipKernelFunc = unaryKernelFunc(ClipByValue, (xi, attrs) => {\n    const clipAttrs = attrs;\n    if (xi > clipAttrs.clipValueMax) {\n        return clipAttrs.clipValueMax;\n    }\n    return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;\n});\nexport const clipConfig = {\n    kernelName: ClipByValue,\n    backendName: 'cpu',\n    kernelFunc: clipKernelFunc,\n};\n//# sourceMappingURL=Clip.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Imag } from '@tensorflow/tfjs-core';\nexport function imag(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const imag = backend.data.get(input.dataId).complexTensorInfos.imag;\n    const imagVal = backend.data.get(imag.dataId).values;\n    // When complex tensor is disposed, its underlying parts will be disposed too.\n    // Make new tensor out of the imag value of the complex. This makes sure the\n    // value is still accessible even if complex tensor is disposed.\n    return backend.makeTensorInfo(imag.shape, imag.dtype, imagVal);\n}\nexport const imagConfig = {\n    kernelName: Imag,\n    backendName: 'cpu',\n    kernelFunc: imag\n};\n//# sourceMappingURL=Imag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape, util } from '@tensorflow/tfjs-core';\nexport function reshape(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { shape } = attrs;\n    const xSize = util.sizeFromShape(x.shape);\n    const $shape = util.inferFromImplicitShape(shape, xSize);\n    const $xSize = util.sizeFromShape($shape);\n    util.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n        `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n        `shape must have the same number of elements.`);\n    backend.incRef(x.dataId);\n    const xData = backend.data.get(x.dataId);\n    if (xData.complexTensorInfos != null) {\n        const real = xData.complexTensorInfos.real;\n        const imag = xData.complexTensorInfos.imag;\n        real.shape = $shape;\n        imag.shape = $shape;\n    }\n    return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nexport const reshapeConfig = {\n    kernelName: Reshape,\n    backendName: 'cpu',\n    kernelFunc: reshape\n};\n//# sourceMappingURL=Reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Concat, util } from '@tensorflow/tfjs-core';\nimport { complex } from './Complex';\nimport { imag } from './Imag';\nimport { real } from './Real';\nimport { reshape } from './Reshape';\nexport function concat(args) {\n    const { inputs, backend, attrs } = args;\n    const { axis } = attrs;\n    const $axis = util.parseAxisParam(axis, inputs[0].shape)[0];\n    let outShape = backend_util.computeOutShape(inputs.map(t => t.shape), $axis);\n    if (util.sizeFromShape(outShape) === 0) {\n        return backend.makeTensorInfo(outShape, inputs[0].dtype, []);\n    }\n    // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n    const $inputs = inputs.filter(t => util.sizeFromShape(t.shape) > 0);\n    if ($inputs.length === 1) {\n        return $inputs[0];\n    }\n    const shapes = $inputs.map(t => t.shape);\n    backend_util.assertParamsConsistent(shapes, $axis);\n    if ($inputs[0].dtype === 'complex64') {\n        const reals = $inputs.map((t) => real({ inputs: { input: t }, backend }));\n        const imags = $inputs.map((t) => imag({ inputs: { input: t }, backend }));\n        const realConcated = concat({ inputs: reals, backend, attrs: { axis } });\n        const imagConcated = concat({ inputs: imags, backend, attrs: { axis } });\n        const result = complex({ inputs: { real: realConcated, imag: imagConcated }, backend });\n        reals.forEach(r => backend.disposeIntermediateTensorInfo(r));\n        imags.forEach(i => backend.disposeIntermediateTensorInfo(i));\n        backend.disposeIntermediateTensorInfo(realConcated);\n        backend.disposeIntermediateTensorInfo(imagConcated);\n        return result;\n    }\n    // Any concat of n-dimensional tensors across any axis can be reduced to\n    // a concatenation of two-dimensional tensors across the axis 1 by first\n    // partitioning the axes of the original tensors into those less than the\n    // axis to be concatenated and the rest. Then reshape the tensors\n    // into a two-dimensional tensor by collapsing these two sets of axes and\n    // concatenate the resulting matrices across the axis 1, finally reshaping\n    // the result to have the proper shape.\n    const inputs2D = $inputs.map(t => {\n        const innerSize = util.sizeFromShape(t.shape.slice($axis));\n        const shape = [-1, innerSize];\n        return reshape({ inputs: { x: t }, backend, attrs: { shape } });\n    });\n    // Concats 2d tensors along axis=1.\n    outShape =\n        backend_util.computeOutShape(inputs2D.map(t => t.shape), 1 /* axis */);\n    const outVals = util.getTypedArrayFromDType($inputs[0].dtype, util.sizeFromShape(outShape));\n    if (inputs2D[0].shape[0] === 1) {\n        // Use built-in TypedArray.set() method for speed.\n        let offset = 0;\n        inputs2D.forEach(t => {\n            const val = backend.data.get(t.dataId).values;\n            const size = util.sizeFromShape(t.shape);\n            outVals.set(val, offset);\n            offset += size;\n        });\n    }\n    else {\n        let colOffset = 0;\n        inputs2D.forEach(t => {\n            const tVals = backend.data.get(t.dataId).values;\n            let tIdx = 0;\n            for (let row = 0; row < t.shape[0]; ++row) {\n                const resIdx = row * outShape[1] + colOffset;\n                for (let col = 0; col < t.shape[1]; ++col) {\n                    outVals[resIdx + col] = tVals[tIdx++];\n                }\n            }\n            colOffset += t.shape[1];\n        });\n    }\n    const finalOutShape = backend_util.computeOutShape($inputs.map(t => t.shape), $axis);\n    const outInfo = backend.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);\n    inputs2D.forEach(t => backend.disposeIntermediateTensorInfo(t));\n    return outInfo;\n}\nexport const concatConfig = {\n    kernelName: Concat,\n    backendName: 'cpu',\n    kernelFunc: concat\n};\n//# sourceMappingURL=Concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const cosKernelFunc = unaryKernelFunc(Cos, (xi) => Math.cos(xi));\nexport const cosConfig = {\n    kernelName: Cos,\n    backendName: 'cpu',\n    kernelFunc: cosKernelFunc,\n};\n//# sourceMappingURL=Cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cosh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const coshKernelFunc = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi));\nexport const coshConfig = {\n    kernelName: Cosh,\n    backendName: 'cpu',\n    kernelFunc: coshKernelFunc,\n};\n//# sourceMappingURL=Cosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2D, util } from '@tensorflow/tfjs-core';\nexport const dilation2dConfig = {\n    kernelName: Dilation2D,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const xVals = cpuBackend.data.get(x.dataId).values;\n        const xRank = x.shape.length;\n        const filterVals = cpuBackend.data.get(filter.dataId).values;\n        const filterRank = filter.shape.length;\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        const outSize = util.sizeFromShape(outShape);\n        const outRank = outShape.length;\n        const outputVals = util.getArrayFromDType(x.dtype, outSize);\n        // Upsampling the input by fill in `dilation size - 1` values between each\n        // input value.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const xIndex = util.locToIndex([b, hIn, wIn, d], xRank, util.computeStrides(x.shape));\n                                        const filterIndex = util.locToIndex([h, w, d], filterRank, util.computeStrides(filter.shape));\n                                        const val = xVals[xIndex] + filterVals[filterIndex];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        const outputIndex = util.locToIndex([b, hOut, wOut, d], outRank, util.computeStrides(outShape));\n                        outputVals[outputIndex] = curVal;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(outputVals, x.dtype), outShape, x.dtype);\n        return { dataId, shape: outShape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2DBackpropFilter, util } from '@tensorflow/tfjs-core';\nexport const dilation2dBackpropFilterConfig = {\n    kernelName: Dilation2DBackpropFilter,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter, dy } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const $x = util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n        const $filter = util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        util.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy ` +\n            `must have the same rank as output ${outShape.length}, but got ` +\n            `${dy.rank}`);\n        const $dy = util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n        // The computed filter gradients has the same dimensions as the filter:\n        // [filterHeight, filterWidth, depth]\n        const gradients = util.makeZerosNestedTypedArray(filter.shape, filter.dtype);\n        // In the case of multiple argmax branches, we only back-propagate along the\n        // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n        // similarly to the max-pooling backward routines.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        let hMax = 0;\n                        let wMax = 0;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                            hMax = h;\n                                            wMax = w;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);\n        return { dataId, shape: filter.shape, dtype: filter.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2DBackpropFilter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2DBackpropInput, util } from '@tensorflow/tfjs-core';\nexport const dilation2dBackpropInputConfig = {\n    kernelName: Dilation2DBackpropInput,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter, dy } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const $x = util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n        const $filter = util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        util.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy ` +\n            `must have the same rank as output ${outShape.length}, but got ` +\n            `${dy.rank}`);\n        const $dy = util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n        // The computed gradients has the same dimensions as the input:\n        // [batch, inputHeight, inputCols, inChannel]\n        const gradients = util.makeZerosNestedTypedArray(x.shape, x.dtype);\n        // In the case of multiple argmax branches, we only back-propagate along the\n        // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n        // similarly to the max-pooling backward routines.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        let hInMax = (hBeg < 0) ? 0 : hBeg;\n                        let wInMax = (wBeg < 0) ? 0 : wBeg;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                            hInMax = hIn;\n                                            wInMax = wIn;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(gradients, x.dtype), x.shape, x.dtype);\n        return { dataId, shape: x.shape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2DBackpropInput.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const divImpl = createSimpleBinaryKernelImpl((a, b) => a / b);\nexport const div = binaryKernelFunc(Div, divImpl);\nexport const divConfig = {\n    kernelName: Div,\n    backendName: 'cpu',\n    kernelFunc: div\n};\n//# sourceMappingURL=Div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Elu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const eluKernelFunc = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : (Math.exp(xi) - 1));\nexport const eluConfig = {\n    kernelName: Elu,\n    backendName: 'cpu',\n    kernelFunc: eluKernelFunc,\n};\n//# sourceMappingURL=Elu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Erf } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nconst p = backend_util.ERF_P;\nconst a1 = backend_util.ERF_A1;\nconst a2 = backend_util.ERF_A2;\nconst a3 = backend_util.ERF_A3;\nconst a4 = backend_util.ERF_A4;\nconst a5 = backend_util.ERF_A5;\nexport const erfKernelFunc = unaryKernelFunc(Erf, (xi) => {\n    const sign = Math.sign(xi);\n    const v = Math.abs(xi);\n    const t = 1.0 / (1.0 + p * v);\n    return sign *\n        (1.0 -\n            (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t *\n                Math.exp(-v * v));\n});\nexport const erfConfig = {\n    kernelName: Erf,\n    backendName: 'cpu',\n    kernelFunc: erfKernelFunc,\n};\n//# sourceMappingURL=Erf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { add } from '../kernels/Add';\nimport { complex } from '../kernels/Complex';\nimport { concat } from '../kernels/Concat';\nimport { divConfig } from '../kernels/Div';\nimport { identity } from '../kernels/Identity';\nimport { imag } from '../kernels/Imag';\nimport { multiply } from '../kernels/Multiply';\nimport { real } from '../kernels/Real';\nimport { slice } from '../kernels/Slice';\nimport { sub } from '../kernels/Sub';\n/**\n * Calculate FFT of inner most elements of batch tensor.\n */\nexport function fftBatch(input, inverse, cpuBackend) {\n    const inputShape = input.shape;\n    const batch = inputShape[0];\n    const innerDim = inputShape[1];\n    const inputVals = cpuBackend.data.get(input.dataId);\n    const real2D = inputVals.complexTensorInfos.real;\n    const imag2D = inputVals.complexTensorInfos.imag;\n    // Collects real and imaginary values separately.\n    const resultShape = [batch, innerDim];\n    const resultSize = util.sizeFromShape(resultShape);\n    const resultReal = util.getTypedArrayFromDType('float32', resultSize);\n    const resultImag = util.getTypedArrayFromDType('float32', resultSize);\n    for (let b = 0; b < batch; b++) {\n        // TODO: Support slice ops for complex type.\n        const r = slice({\n            inputs: { x: real2D },\n            backend: cpuBackend,\n            attrs: { begin: [b, 0], size: [1, innerDim] }\n        });\n        const i = slice({\n            inputs: { x: imag2D },\n            backend: cpuBackend,\n            attrs: { begin: [b, 0], size: [1, innerDim] }\n        });\n        const input = complex({ inputs: { real: r, imag: i }, backend: cpuBackend });\n        // Run FFT by batch element.\n        const { real, imag } = fftImpl(input, inverse, cpuBackend);\n        const res = backend_util.mergeRealAndImagArrays(real, imag);\n        for (let d = 0; d < innerDim; d++) {\n            const c = backend_util.getComplexWithIndex(res, d);\n            resultReal[b * innerDim + d] = c.real;\n            resultImag[b * innerDim + d] = c.imag;\n        }\n        cpuBackend.disposeIntermediateTensorInfo(r);\n        cpuBackend.disposeIntermediateTensorInfo(i);\n        cpuBackend.disposeIntermediateTensorInfo(input);\n    }\n    const $realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultReal);\n    const $imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImag);\n    const result = complex({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend });\n    cpuBackend.disposeIntermediateTensorInfo($realInfo);\n    cpuBackend.disposeIntermediateTensorInfo($imagInfo);\n    return result;\n}\nexport function fftImpl(input, inverse, cpuBackend) {\n    const inputSize = util.sizeFromShape(input.shape);\n    const inputVals = cpuBackend.data.get(input.dataId);\n    const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values;\n    const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;\n    if (isExponentOf2(inputSize)) {\n        const result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);\n        const resultShape = [input.shape[0], input.shape[1]];\n        if (inverse) {\n            const realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.real);\n            const imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.imag);\n            const sizeInfo = cpuBackend.makeTensorInfo([], 'float32', util.createScalarValue(inputSize, 'float32'));\n            const sizeInfoCopy = identity({ inputs: { x: sizeInfo }, backend: cpuBackend });\n            const divRealInfo = divConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend });\n            const divImagInfo = divConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend });\n            const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values;\n            const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values;\n            cpuBackend.disposeIntermediateTensorInfo(realInfo);\n            cpuBackend.disposeIntermediateTensorInfo(imagInfo);\n            cpuBackend.disposeIntermediateTensorInfo(sizeInfo);\n            cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);\n            cpuBackend.disposeIntermediateTensorInfo(divRealInfo);\n            cpuBackend.disposeIntermediateTensorInfo(divImagInfo);\n            return { real: divRealVals, imag: divImagVals };\n        }\n        return result;\n    }\n    else {\n        const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n        const rawOutput = fourierTransformByMatmul(data, inputSize, inverse);\n        return backend_util.splitRealAndImagArrays(rawOutput);\n    }\n}\nfunction isExponentOf2(size) {\n    return (size & size - 1) === 0;\n}\n// FFT using Cooley-Tukey algorithm on radix 2 dimensional input.\nfunction fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {\n    if (size === 1) {\n        return { real: realVals, imag: imagVals };\n    }\n    const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n    const half = size / 2;\n    const evenComplex = backend_util.complexWithEvenIndex(data);\n    const evenRealVals = evenComplex.real;\n    const evenImagVals = evenComplex.imag;\n    const evenShape = [evenRealVals.length];\n    const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenRealVals);\n    const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenImagVals);\n    const evenTensorInfo = complex({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend });\n    const oddComplex = backend_util.complexWithOddIndex(data);\n    const oddRealVals = oddComplex.real;\n    const oddImagVals = oddComplex.imag;\n    const oddShape = [oddRealVals.length];\n    const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddRealVals);\n    const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddImagVals);\n    const oddTensorInfo = complex({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend });\n    // Recursive call for half part of original input.\n    const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);\n    const $evenRealVals = $evenComplex.real;\n    const $evenImagVals = $evenComplex.imag;\n    const $evenShape = [$evenRealVals.length];\n    const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenRealVals);\n    const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenImagVals);\n    const $evenTensorInfo = complex({\n        inputs: { real: $evenRealInfo, imag: $evenImagInfo },\n        backend: cpuBackend\n    });\n    const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);\n    const $oddRealVals = $oddComplex.real;\n    const $oddImagVals = $oddComplex.imag;\n    const $oddShape = [$oddRealVals.length];\n    const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddRealVals);\n    const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddImagVals);\n    const $oddTensorInfo = complex({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend });\n    const e = backend_util.exponents(size, inverse);\n    const eShape = [e.real.length];\n    const eRealInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.real);\n    const eImagInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.imag);\n    const complexInfo = complex({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend });\n    const exponentInfo = multiply({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend });\n    const addPart = add({\n        inputs: { a: $evenTensorInfo, b: exponentInfo },\n        backend: cpuBackend\n    });\n    const subPart = sub({\n        inputs: { a: $evenTensorInfo, b: exponentInfo },\n        backend: cpuBackend\n    });\n    const addPartReal = real({ inputs: { input: addPart }, backend: cpuBackend });\n    const subPartReal = real({ inputs: { input: subPart }, backend: cpuBackend });\n    const addPartImag = imag({ inputs: { input: addPart }, backend: cpuBackend });\n    const subPartImag = imag({ inputs: { input: subPart }, backend: cpuBackend });\n    const $real = concat({\n        inputs: [addPartReal, subPartReal],\n        backend: cpuBackend,\n        attrs: { axis: 0 }\n    });\n    const $imag = concat({\n        inputs: [addPartImag, subPartImag],\n        backend: cpuBackend,\n        attrs: { axis: 0 }\n    });\n    const $realVals = cpuBackend.data.get($real.dataId).values;\n    const $imagVals = cpuBackend.data.get($imag.dataId).values;\n    cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo(eRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(eImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(complexInfo);\n    cpuBackend.disposeIntermediateTensorInfo(exponentInfo);\n    cpuBackend.disposeIntermediateTensorInfo(addPart);\n    cpuBackend.disposeIntermediateTensorInfo(subPart);\n    cpuBackend.disposeIntermediateTensorInfo(addPartReal);\n    cpuBackend.disposeIntermediateTensorInfo(addPartImag);\n    cpuBackend.disposeIntermediateTensorInfo(subPartReal);\n    cpuBackend.disposeIntermediateTensorInfo(subPartImag);\n    cpuBackend.disposeIntermediateTensorInfo($real);\n    cpuBackend.disposeIntermediateTensorInfo($imag);\n    return { real: $realVals, imag: $imagVals };\n}\n// Calculate fourier transform by multplying sinusoid matrix.\nfunction fourierTransformByMatmul(data, size, inverse) {\n    const ret = new Float32Array(size * 2);\n    // TODO: Use matmul instead once it supports complex64 type.\n    for (let r = 0; r < size; r++) {\n        let real = 0.0;\n        let imag = 0.0;\n        for (let c = 0; c < size; c++) {\n            const e = backend_util.exponent(r * c, size, inverse);\n            const term = backend_util.getComplexWithIndex(data, c);\n            real += term.real * e.real - term.imag * e.imag;\n            imag += term.real * e.imag + term.imag * e.real;\n        }\n        if (inverse) {\n            real /= size;\n            imag /= size;\n        }\n        backend_util.assignToTypedArray(ret, real, imag, r);\n    }\n    return ret;\n}\n//# sourceMappingURL=fft_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FFT, util } from '@tensorflow/tfjs-core';\nimport { fftBatch } from '../utils/fft_utils';\nimport { reshape } from './Reshape';\nexport function fft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputSize = util.sizeFromShape(input.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({\n        inputs: { x: input },\n        backend,\n        attrs: { shape: [batch, innerDimensionSize] }\n    });\n    const result = fftBatch(input2D, false, backend);\n    const resultReshaped = reshape({ inputs: { x: result }, backend, attrs: { shape: input.shape } });\n    backend.disposeIntermediateTensorInfo(input2D);\n    backend.disposeIntermediateTensorInfo(result);\n    return resultReshaped;\n}\nexport const fftConfig = {\n    kernelName: FFT,\n    backendName: 'cpu',\n    kernelFunc: fft\n};\n//# sourceMappingURL=FFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FlipLeftRight, util } from '@tensorflow/tfjs-core';\nexport const flipLeftRightConfig = {\n    kernelName: FlipLeftRight,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const cpuBackend = backend;\n        const output = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(image.shape));\n        const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n        const imageVals = cpuBackend.data.get(image.dataId).values;\n        for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n            for (let row = 0; row < imageHeight; row++) {\n                const rowOffset = row * (imageWidth * numChannels);\n                for (let col = 0; col < imageWidth; col++) {\n                    const colOffset = col * numChannels;\n                    for (let channel = 0; channel < numChannels; channel++) {\n                        const coords = [batch, row, col, channel];\n                        const x = coords[2];\n                        const coordX = Math.round(imageWidth - x);\n                        const outIdx = batchOffset + rowOffset + colOffset + channel;\n                        let outputValue = imageVals[outIdx];\n                        // If the coordinate position falls within the image boundaries...\n                        if (coordX >= 0 && coordX < imageWidth) {\n                            // set the output to the image value at the coordinate position.\n                            const rotatedColOffset = coordX * numChannels;\n                            const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel;\n                            outputValue = imageVals[imageIdx];\n                        }\n                        output[outIdx] = outputValue;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(output, image.shape, image.dtype);\n        return { dataId, shape: image.shape, dtype: image.dtype };\n    }\n};\n//# sourceMappingURL=FlipLeftRight.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IFFT, util } from '@tensorflow/tfjs-core';\nimport { fftBatch } from '../utils/fft_utils';\nimport { reshape } from './Reshape';\nexport function ifft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputSize = util.sizeFromShape(input.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({\n        inputs: { x: input },\n        backend,\n        attrs: { shape: [batch, innerDimensionSize] }\n    });\n    const result = fftBatch(input2D, true, backend);\n    const resultReshaped = reshape({ inputs: { x: result }, backend, attrs: { shape: input.shape } });\n    backend.disposeIntermediateTensorInfo(input2D);\n    backend.disposeIntermediateTensorInfo(result);\n    return resultReshaped;\n}\nexport const ifftConfig = {\n    kernelName: IFFT,\n    backendName: 'cpu',\n    kernelFunc: ifft\n};\n//# sourceMappingURL=IFFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsFinite } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isFiniteKernelFunc = unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, 'bool');\nexport const isFiniteConfig = {\n    kernelName: IsFinite,\n    backendName: 'cpu',\n    kernelFunc: isFiniteKernelFunc,\n};\n//# sourceMappingURL=IsFinite.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsInf } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isInfKernelFunc = unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, 'bool');\nexport const isInfConfig = {\n    kernelName: IsInf,\n    backendName: 'cpu',\n    kernelFunc: isInfKernelFunc,\n};\n//# sourceMappingURL=IsInf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsNan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isNaNKernelFunc = unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, 'bool');\nexport const isNaNConfig = {\n    kernelName: IsNan,\n    backendName: 'cpu',\n    kernelFunc: isNaNKernelFunc,\n};\n//# sourceMappingURL=IsNaN.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log1p } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const log1pKernelFunc = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi));\nexport const log1pConfig = {\n    kernelName: Log1p,\n    backendName: 'cpu',\n    kernelFunc: log1pKernelFunc,\n};\n//# sourceMappingURL=Log1p.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LogicalNot } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const logicalNotKernelFunc = unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, 'bool');\nexport const logicalNotConfig = {\n    kernelName: LogicalNot,\n    backendName: 'cpu',\n    kernelFunc: logicalNotKernelFunc,\n};\n//# sourceMappingURL=LogicalNot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '@tensorflow/tfjs-core';\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxImpl } from './Max_impl';\nimport { transposeImpl } from './Transpose_impl';\nexport const maxConfig = {\n    kernelName: Max,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { reductionIndices, keepDims } = attrs;\n        const cpuBackend = backend;\n        let xShape = x.shape;\n        const xRank = xShape.length;\n        const origAxes = util.parseAxisParam(reductionIndices, xShape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        let xVals = cpuBackend.data.get(x.dataId).values;\n        if (permutedAxes != null) {\n            const newShape = new Array(xRank);\n            for (let i = 0; i < newShape.length; i++) {\n                newShape[i] = xShape[permutedAxes[i]];\n            }\n            xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n            xShape = newShape;\n        }\n        assertNotComplex(x, 'max');\n        backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n        const [maxOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(xShape, axes);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);\n        const dataId = cpuBackend.write(result, maxOutShape, x.dtype);\n        let outShape = maxOutShape;\n        if (keepDims) {\n            // reshape\n            const newShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n            outShape = newShape;\n        }\n        return { dataId, shape: outShape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPool, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { pool } from '../utils/pool_utils';\nimport { identity } from './Identity';\nexport function maxPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'maxPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    let res;\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        res = identity({ inputs: { x }, backend });\n    }\n    else {\n        const xValues = backend.data.get(x.dataId).values;\n        const strides = util.computeStrides(x.shape);\n        const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'max');\n        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);\n    }\n    return res;\n}\nexport const maxPoolConfig = {\n    kernelName: MaxPool,\n    backendName: 'cpu',\n    kernelFunc: maxPool\n};\n//# sourceMappingURL=MaxPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, buffer, MaxPoolBackprop } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxPoolPositions } from '../utils/pool_utils';\nexport function maxPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input, output } = inputs;\n    const x = input;\n    assertNotComplex([input, output], 'maxPoolBackprop');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n    const xValues = backend.data.get(x.dataId).values;\n    const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = buffer(x.shape, 'float32');\n    const dyData = backend.data.get(dy.dataId).values;\n    const dyBuf = buffer(dy.shape, 'float32', dyData);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n                for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n                    // Shader code begins.\n                    const dyRCorner = dxR - padTop;\n                    const dyCCorner = dxC - padLeft;\n                    let dotProd = 0;\n                    for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n                        const dyR = (dyRCorner + wR) / strideHeight;\n                        if (dyR < 0 || dyR >= convInfo.outHeight ||\n                            Math.floor(dyR) !== dyR) {\n                            continue;\n                        }\n                        for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                            const dyC = (dyCCorner + wC) / strideWidth;\n                            if (dyC < 0 || dyC >= convInfo.outWidth ||\n                                Math.floor(dyC) !== dyC) {\n                                continue;\n                            }\n                            const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 -\n                                maxPosBuf.get(b, dyR, dyC, d);\n                            const curPos = wR * effectiveFilterWidth + wC;\n                            const mask = maxPos === curPos ? 1 : 0;\n                            if (mask === 0) {\n                                continue;\n                            }\n                            const pixel = dyBuf.get(b, dyR, dyC, d);\n                            dotProd += pixel * mask;\n                        }\n                    }\n                    dx.set(dotProd, b, dxR, dxC, d);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const maxPoolBackpropConfig = {\n    kernelName: MaxPoolBackprop,\n    backendName: 'cpu',\n    kernelFunc: maxPoolBackprop\n};\n//# sourceMappingURL=MaxPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { maxPoolPositions, pool } from '../utils/pool_utils';\nexport function maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) {\n    const strides = util.computeStrides(xShape);\n    const maxPools = pool(xValues, xShape, dtype, strides, convInfo, 'max');\n    const maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex);\n    return [maxPools.values, maxPositions.values];\n}\n//# sourceMappingURL=MaxPoolWithArgmax_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPoolWithArgmax } from '@tensorflow/tfjs-core';\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxPoolWithArgmaxImpl } from './MaxPoolWithArgmax_impl';\nexport const maxPoolWithArgmaxConfig = {\n    kernelName: MaxPoolWithArgmax,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { filterSize, strides, pad, includeBatchInIndex } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(x, 'MaxPoolWithArgmax');\n        const values = cpuBackend.data.get(x.dataId).values;\n        const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad);\n        const [pooled, indexes] = maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo);\n        const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype);\n        const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype);\n        return [\n            { dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype },\n            { dataId: indexesDataId, shape: convInfo.outShape, dtype: 'int32' }\n        ];\n    }\n};\n//# sourceMappingURL=MaxPoolWithArgmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NonMaxSuppressionV4 } from '@tensorflow/tfjs-core';\nimport { kernel_impls } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\nimport { assertNotComplex } from '../cpu_util';\nexport const nonMaxSuppressionV4Config = {\n    kernelName: NonMaxSuppressionV4,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(boxes, 'NonMaxSuppressionPadded');\n        const boxesVals = cpuBackend.data.get(boxes.dataId).values;\n        const scoresVals = cpuBackend.data.get(scores.dataId).values;\n        const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n        return [selectedIndices, validOutputs];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV4.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NonMaxSuppressionV5 } from '@tensorflow/tfjs-core';\nimport { kernel_impls } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nimport { assertNotComplex } from '../cpu_util';\nexport const nonMaxSuppressionV5Config = {\n    kernelName: NonMaxSuppressionV5,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(boxes, 'NonMaxSuppressionWithScore');\n        const boxesVals = cpuBackend.data.get(boxes.dataId).values;\n        const scoresVals = cpuBackend.data.get(scores.dataId).values;\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        const softNmsSigmaVal = softNmsSigma;\n        const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n        return [selectedIndices, selectedScores];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV5.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NotEqual } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const notEqualImpl = createSimpleBinaryKernelImpl(((a, b) => (a !== b) ? 1 : 0));\nexport const notEqual = binaryKernelFunc(NotEqual, notEqualImpl, null /* complexOp */, 'bool');\nexport const notEqualConfig = {\n    kernelName: NotEqual,\n    backendName: 'cpu',\n    kernelFunc: notEqual\n};\n//# sourceMappingURL=NotEqual.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { PadV2, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function padV2(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { paddings, constantValue } = attrs;\n    assertNotComplex(x, 'pad');\n    const outShape = paddings.map((p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);\n    const start = paddings.map(p => p[0]);\n    const xVals = backend.data.get(x.dataId).values;\n    const xSize = util.sizeFromShape(x.shape);\n    const xRank = x.shape.length;\n    const xStrides = util.computeStrides(x.shape);\n    const resultSize = util.sizeFromShape(outShape);\n    const resultRank = outShape.length;\n    const resultStrides = util.computeStrides(outShape);\n    const resVals = util.getTypedArrayFromDType(x.dtype, resultSize);\n    if (constantValue !== 0) {\n        resVals.fill(constantValue);\n    }\n    for (let i = 0; i < xSize; i++) {\n        const coords = util.indexToLoc(i, xRank, xStrides);\n        const outCoords = coords.map((c, i) => c + start[i]);\n        const outIndex = util.locToIndex(outCoords, resultRank, resultStrides);\n        resVals[outIndex] = xVals[i];\n    }\n    const outId = backend.write(resVals, outShape, x.dtype);\n    return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nexport const padV2Config = {\n    kernelName: PadV2,\n    backendName: 'cpu',\n    kernelFunc: padV2\n};\n//# sourceMappingURL=PadV2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reciprocal } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const reciprocalKernelFunc = unaryKernelFunc(Reciprocal, (xi) => 1 / xi);\nexport const reciprocalConfig = {\n    kernelName: Reciprocal,\n    backendName: 'cpu',\n    kernelFunc: reciprocalKernelFunc,\n};\n//# sourceMappingURL=Reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, RotateWithOffset, util } from '@tensorflow/tfjs-core';\nexport const rotateWithOffsetConfig = {\n    kernelName: RotateWithOffset,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const { radians, fillValue, center } = attrs;\n        const cpuBackend = backend;\n        const output = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(image.shape));\n        const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n        const [centerX, centerY] = backend_util.getImageCenter(center, imageHeight, imageWidth);\n        const fullOpacityValue = 255;\n        const sinFactor = Math.sin(radians);\n        const cosFactor = Math.cos(radians);\n        const imageVals = cpuBackend.data.get(image.dataId).values;\n        for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n            for (let row = 0; row < imageHeight; row++) {\n                const rowOffset = row * (imageWidth * numChannels);\n                for (let col = 0; col < imageWidth; col++) {\n                    const colOffset = col * numChannels;\n                    for (let channel = 0; channel < numChannels; channel++) {\n                        const coords = [batch, row, col, channel];\n                        const x = coords[2];\n                        const y = coords[1];\n                        // coordX/coordY are the result of rotating and translating x/y.\n                        let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;\n                        let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;\n                        coordX = Math.round(coordX + centerX);\n                        coordY = Math.round(coordY + centerY);\n                        let outputValue = fillValue;\n                        if (typeof fillValue !== 'number') {\n                            if (channel === 3) {\n                                outputValue = fullOpacityValue;\n                            }\n                            else {\n                                outputValue = fillValue[channel];\n                            }\n                        }\n                        // If the coordinate position falls within the image boundaries...\n                        if (coordX >= 0 && coordX < imageWidth && coordY >= 0 &&\n                            coordY < imageHeight) {\n                            // set the output to the image value at the coordinate position.\n                            const rotatedRowOffset = coordY * (imageWidth * numChannels);\n                            const rotatedColOffset = coordX * numChannels;\n                            const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;\n                            outputValue = imageVals[imageIdx];\n                        }\n                        const outIdx = batchOffset + rowOffset + colOffset + channel;\n                        output[outIdx] = outputValue;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(output, image.shape, image.dtype);\n        return { dataId, shape: image.shape, dtype: image.dtype };\n    }\n};\n//# sourceMappingURL=RotateWithOffset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Round } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const roundKernelFunc = unaryKernelFunc(Round, (xi) => {\n    // The algorithm is based on banker's rounding.\n    const base = Math.floor(xi);\n    if (xi - base < 0.5) {\n        return Math.floor(xi);\n    }\n    else if (xi - base > 0.5) {\n        return Math.ceil(xi);\n    }\n    else {\n        if (base % 2.0 === 0.0) {\n            return base;\n        }\n        else {\n            return base + 1.0;\n        }\n    }\n});\nexport const roundConfig = {\n    kernelName: Round,\n    backendName: 'cpu',\n    kernelFunc: roundKernelFunc,\n};\n//# sourceMappingURL=Round.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Selu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nconst scaleAlpha = backend_util.SELU_SCALEALPHA;\nconst scale = backend_util.SELU_SCALE;\nexport const seluKernelFunc = unaryKernelFunc(Selu, (xi) => {\n    if (xi >= 0) {\n        return scale * xi;\n    }\n    else {\n        return scaleAlpha * (Math.exp(xi) - 1);\n    }\n});\nexport const seluConfig = {\n    kernelName: Selu,\n    backendName: 'cpu',\n    kernelFunc: seluKernelFunc,\n};\n//# sourceMappingURL=Selu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sigmoid } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sigmoidKernelFunc = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));\nexport const sigmoidConfig = {\n    kernelName: Sigmoid,\n    backendName: 'cpu',\n    kernelFunc: sigmoidKernelFunc,\n};\n//# sourceMappingURL=Sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sign } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const signKernelFunc = unaryKernelFunc(Sign, (xi) => {\n    if (xi < 0) {\n        return -1;\n    }\n    else if (xi > 0) {\n        return 1;\n    }\n    else {\n        return 0;\n    }\n});\nexport const signConfig = {\n    kernelName: Sign,\n    backendName: 'cpu',\n    kernelFunc: signKernelFunc,\n};\n//# sourceMappingURL=Sign.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sinKernelFunc = unaryKernelFunc(Sin, (xi) => Math.sin(xi));\nexport const sinConfig = {\n    kernelName: Sin,\n    backendName: 'cpu',\n    kernelFunc: sinKernelFunc,\n};\n//# sourceMappingURL=Sin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sinh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sinhKernelFunc = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi));\nexport const sinhConfig = {\n    kernelName: Sinh,\n    backendName: 'cpu',\n    kernelFunc: sinhKernelFunc,\n};\n//# sourceMappingURL=Sinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softplus } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\n// mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n// epsilon is the difference between 1.0 and the next representable float.\n// For a single precision 32 bit float this should be 2^-23, see:\n// https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\nconst epsilon = 1.1920928955078125e-7;\nconst threshold = Math.log(epsilon) + 2.0;\nexport const softplusKernelFunc = unaryKernelFunc(Softplus, (xi) => {\n    // Value above which exp(x) may overflow, but softplus(x) == x\n    // is within machine epsilon.\n    const tooLarge = xi > -threshold;\n    // Value below which exp(x) may underflow, but softplus(x) == exp(x)\n    // is within machine epsilon.\n    const tooSmall = xi < threshold;\n    const expX = Math.exp(xi);\n    let result;\n    if (tooSmall) {\n        result = expX;\n    }\n    else if (tooLarge) {\n        result = xi;\n    }\n    else {\n        result = Math.log(1.0 + expX);\n    }\n    return result;\n});\nexport const softplusConfig = {\n    kernelName: Softplus,\n    backendName: 'cpu',\n    kernelFunc: softplusKernelFunc,\n};\n//# sourceMappingURL=Softplus.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { transposeImpl } from './Transpose_impl';\nexport function transpose(args) {\n    const { inputs, attrs, backend } = args;\n    const { x } = inputs;\n    const { perm } = attrs;\n    assertNotComplex(x, 'transpose');\n    const xRank = x.shape.length;\n    const newShape = new Array(xRank);\n    for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = x.shape[perm[i]];\n    }\n    const values = backend.data.get(x.dataId).values;\n    const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);\n    const dataId = backend.write(result, newShape, x.dtype);\n    return { dataId, shape: newShape, dtype: x.dtype };\n}\nexport const transposeConfig = {\n    kernelName: Transpose,\n    backendName: 'cpu',\n    kernelFunc: transpose\n};\n//# sourceMappingURL=Transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, SpaceToBatchND, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { padV2Config } from './PadV2';\nimport { reshape } from './Reshape';\nimport { transpose } from './Transpose';\nexport function spaceToBatchND(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { blockShape, paddings } = attrs;\n    assertNotComplex([x], 'spaceToBatchND');\n    const prod = util.sizeFromShape(blockShape);\n    const completePaddings = [[0, 0]];\n    completePaddings.push(...paddings);\n    for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n        completePaddings.push([0, 0]);\n    }\n    const paddedX = padV2Config.kernelFunc({\n        inputs: { x },\n        backend,\n        attrs: { paddings: completePaddings, constantValue: 0 }\n    });\n    const reshapedPaddedShape = backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n    const permutedReshapedPaddedPermutation = backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n    const flattenShape = backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n    const reshapeInputs = { x: paddedX };\n    const reshapeAttrs = { shape: reshapedPaddedShape };\n    const paddedXReshaped = reshape({ inputs: reshapeInputs, backend, attrs: reshapeAttrs });\n    const transposeInputs = { x: paddedXReshaped };\n    const transposeAttrs = { perm: permutedReshapedPaddedPermutation };\n    const paddedXT = transpose({ inputs: transposeInputs, backend, attrs: transposeAttrs });\n    const resultReshapeInputs = { x: paddedXT };\n    const resultReshapeAttrs = { shape: flattenShape };\n    const result = reshape({ inputs: resultReshapeInputs, backend, attrs: resultReshapeAttrs });\n    backend.disposeIntermediateTensorInfo(paddedX);\n    backend.disposeIntermediateTensorInfo(paddedXReshaped);\n    backend.disposeIntermediateTensorInfo(paddedXT);\n    return result;\n}\nexport const spaceToBatchNDConfig = {\n    kernelName: SpaceToBatchND,\n    backendName: 'cpu',\n    kernelFunc: spaceToBatchND\n};\n//# sourceMappingURL=SpaceToBatchND.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sqrt } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sqrtKernelFunc = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));\nexport const sqrtConfig = {\n    kernelName: Sqrt,\n    backendName: 'cpu',\n    kernelFunc: sqrtKernelFunc,\n};\n//# sourceMappingURL=Sqrt.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport const squareConfig = {\n    kernelName: Square,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend }) => {\n        const { x } = inputs;\n        const cpuBackend = backend;\n        assertNotComplex(x, 'square');\n        const values = cpuBackend.data.get(x.dataId).values;\n        const newValues = new Float32Array(values.length);\n        for (let i = 0; i < values.length; ++i) {\n            const value = values[i];\n            newValues[i] = value * value;\n        }\n        const dataId = cpuBackend.write(newValues, x.shape, x.dtype);\n        return { dataId, shape: x.shape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const squaredDifferenceImpl = createSimpleBinaryKernelImpl(((a, b) => {\n    const diff = a - b;\n    return diff * diff;\n}));\nexport const squaredDifference = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);\nexport const squaredDifferenceConfig = {\n    kernelName: SquaredDifference,\n    backendName: 'cpu',\n    kernelFunc: squaredDifference\n};\n//# sourceMappingURL=SquaredDifference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Step } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const stepKernelFunc = unaryKernelFunc(Step, (xi, attrs) => {\n    const stepAttrs = attrs;\n    if (isNaN(xi)) {\n        return NaN;\n    }\n    else {\n        return xi > 0 ? 1 : stepAttrs.alpha;\n    }\n});\nexport const stepConfig = {\n    kernelName: Step,\n    backendName: 'cpu',\n    kernelFunc: stepKernelFunc,\n};\n//# sourceMappingURL=Step.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const tanKernelFunc = unaryKernelFunc(Tan, (xi) => Math.tan(xi));\nexport const tanConfig = {\n    kernelName: Tan,\n    backendName: 'cpu',\n    kernelFunc: tanKernelFunc,\n};\n//# sourceMappingURL=Tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tanh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const tanhKernelFunc = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi));\nexport const tanhConfig = {\n    kernelName: Tanh,\n    backendName: 'cpu',\n    kernelFunc: tanhKernelFunc,\n};\n//# sourceMappingURL=Tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unique } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { uniqueImpl } from './Unique_impl';\nexport function unique(args) {\n    const { inputs, attrs, backend } = args;\n    const { axis } = attrs;\n    const { x } = inputs;\n    assertNotComplex(x, 'unique');\n    const values = backend.data.get(x.dataId).values;\n    const { outputValues, outputShape, indices } = uniqueImpl(values, axis, x.shape, x.dtype);\n    return [\n        backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n        backend.makeTensorInfo([indices.length], 'int32', indices),\n    ];\n}\nexport const uniqueConfig = {\n    kernelName: Unique,\n    backendName: 'cpu',\n    kernelFunc: unique,\n};\n//# sourceMappingURL=Unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// We explicitly import the modular kernels so they get registered in the\n// global registry when we compile the library. A modular build would replace\n// the contents of this file and import only the kernels that are needed.\nimport { registerKernel } from '@tensorflow/tfjs-core';\nimport { absConfig } from './kernels/Abs';\nimport { acosConfig } from './kernels/Acos';\nimport { acoshConfig } from './kernels/Acosh';\nimport { addConfig } from './kernels/Add';\nimport { asinConfig } from './kernels/Asin';\nimport { asinhConfig } from './kernels/Asinh';\nimport { atanConfig } from './kernels/Atan';\nimport { atanhConfig } from './kernels/Atanh';\nimport { avgPoolConfig } from './kernels/AvgPool';\nimport { avgPoolBackpropConfig } from './kernels/AvgPoolBackprop';\nimport { batchNormConfig } from './kernels/BatchNorm';\nimport { castConfig } from './kernels/Cast';\nimport { ceilConfig } from './kernels/Ceil';\nimport { clipConfig } from './kernels/Clip';\nimport { complexConfig } from './kernels/Complex';\nimport { concatConfig } from './kernels/Concat';\nimport { cosConfig } from './kernels/Cos';\nimport { coshConfig } from './kernels/Cosh';\nimport { dilation2dConfig } from './kernels/Dilation2D';\nimport { dilation2dBackpropFilterConfig } from './kernels/Dilation2DBackpropFilter';\nimport { dilation2dBackpropInputConfig } from './kernels/Dilation2DBackpropInput';\nimport { divConfig } from './kernels/Div';\nimport { eluConfig } from './kernels/Elu';\nimport { erfConfig } from './kernels/Erf';\nimport { expConfig } from './kernels/Exp';\nimport { expm1Config } from './kernels/Expm1';\nimport { fftConfig } from './kernels/FFT';\nimport { flipLeftRightConfig } from './kernels/FlipLeftRight';\nimport { floorConfig } from './kernels/Floor';\nimport { identityConfig } from './kernels/Identity';\nimport { ifftConfig } from './kernels/IFFT';\nimport { imagConfig } from './kernels/Imag';\nimport { isFiniteConfig } from './kernels/IsFinite';\nimport { isInfConfig } from './kernels/IsInf';\nimport { isNaNConfig } from './kernels/IsNaN';\nimport { logConfig } from './kernels/Log';\nimport { log1pConfig } from './kernels/Log1p';\nimport { logicalNotConfig } from './kernels/LogicalNot';\nimport { maxConfig } from './kernels/Max';\nimport { maxPoolConfig } from './kernels/MaxPool';\nimport { maxPoolBackpropConfig } from './kernels/MaxPoolBackprop';\nimport { maxPoolWithArgmaxConfig } from './kernels/MaxPoolWithArgmax';\nimport { multiplyConfig } from './kernels/Multiply';\nimport { nonMaxSuppressionV4Config } from './kernels/NonMaxSuppressionV4';\nimport { nonMaxSuppressionV5Config } from './kernels/NonMaxSuppressionV5';\nimport { notEqualConfig } from './kernels/NotEqual';\nimport { padV2Config } from './kernels/PadV2';\nimport { realConfig } from './kernels/Real';\nimport { reciprocalConfig } from './kernels/Reciprocal';\nimport { reshapeConfig } from './kernels/Reshape';\nimport { rotateWithOffsetConfig } from './kernels/RotateWithOffset';\nimport { roundConfig } from './kernels/Round';\nimport { rsqrtConfig } from './kernels/Rsqrt';\nimport { seluConfig } from './kernels/Selu';\nimport { sigmoidConfig } from './kernels/Sigmoid';\nimport { signConfig } from './kernels/Sign';\nimport { sinConfig } from './kernels/Sin';\nimport { sinhConfig } from './kernels/Sinh';\nimport { sliceConfig } from './kernels/Slice';\nimport { softplusConfig } from './kernels/Softplus';\nimport { spaceToBatchNDConfig } from './kernels/SpaceToBatchND';\nimport { sqrtConfig } from './kernels/Sqrt';\nimport { squareConfig } from './kernels/Square';\nimport { squaredDifferenceConfig } from './kernels/SquaredDifference';\nimport { stepConfig } from './kernels/Step';\nimport { subConfig } from './kernels/Sub';\nimport { tanConfig } from './kernels/Tan';\nimport { tanhConfig } from './kernels/Tanh';\nimport { transposeConfig } from './kernels/Transpose';\nimport { uniqueConfig } from './kernels/Unique';\n// List all kernel configs here\nconst kernelConfigs = [\n    absConfig,\n    acosConfig,\n    acoshConfig,\n    addConfig,\n    asinConfig,\n    asinhConfig,\n    atanConfig,\n    atanhConfig,\n    avgPoolConfig,\n    avgPoolBackpropConfig,\n    batchNormConfig,\n    castConfig,\n    ceilConfig,\n    clipConfig,\n    complexConfig,\n    concatConfig,\n    cosConfig,\n    coshConfig,\n    dilation2dConfig,\n    dilation2dBackpropInputConfig,\n    dilation2dBackpropFilterConfig,\n    divConfig,\n    eluConfig,\n    erfConfig,\n    expConfig,\n    expm1Config,\n    fftConfig,\n    flipLeftRightConfig,\n    floorConfig,\n    identityConfig,\n    ifftConfig,\n    imagConfig,\n    isFiniteConfig,\n    isInfConfig,\n    isNaNConfig,\n    logConfig,\n    log1pConfig,\n    logicalNotConfig,\n    maxPoolConfig,\n    maxPoolBackpropConfig,\n    maxPoolWithArgmaxConfig,\n    maxConfig,\n    multiplyConfig,\n    nonMaxSuppressionV4Config,\n    nonMaxSuppressionV5Config,\n    notEqualConfig,\n    padV2Config,\n    realConfig,\n    reciprocalConfig,\n    reshapeConfig,\n    rotateWithOffsetConfig,\n    roundConfig,\n    rsqrtConfig,\n    seluConfig,\n    sigmoidConfig,\n    signConfig,\n    sinConfig,\n    sinhConfig,\n    sliceConfig,\n    softplusConfig,\n    spaceToBatchNDConfig,\n    sqrtConfig,\n    squareConfig,\n    squaredDifferenceConfig,\n    stepConfig,\n    subConfig,\n    tanConfig,\n    tanhConfig,\n    transposeConfig,\n    uniqueConfig,\n];\nfor (const kernelConfig of kernelConfigs) {\n    registerKernel(kernelConfig);\n}\n//# sourceMappingURL=register_all_kernels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// All exports from this package should be in base.\nexport * from './base';\nimport './register_all_kernels';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst contexts = {};\nconst WEBGL_ATTRIBUTES = {\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: true\n};\nexport function setWebGLContext(webGLVersion, gl) {\n    contexts[webGLVersion] = gl;\n}\nexport function getWebGLContext(webGLVersion) {\n    if (!(webGLVersion in contexts)) {\n        const newCtx = getWebGLRenderingContext(webGLVersion);\n        if (newCtx !== null) {\n            contexts[webGLVersion] = newCtx;\n        }\n        else {\n            console.log('Could not get context for WebGL version', webGLVersion);\n            return null;\n        }\n    }\n    const gl = contexts[webGLVersion];\n    if (gl.isContextLost()) {\n        delete contexts[webGLVersion];\n        return getWebGLContext(webGLVersion);\n    }\n    gl.disable(gl.DEPTH_TEST);\n    gl.disable(gl.STENCIL_TEST);\n    gl.disable(gl.BLEND);\n    gl.disable(gl.DITHER);\n    gl.disable(gl.POLYGON_OFFSET_FILL);\n    gl.disable(gl.SAMPLE_COVERAGE);\n    gl.enable(gl.SCISSOR_TEST);\n    gl.enable(gl.CULL_FACE);\n    gl.cullFace(gl.BACK);\n    return contexts[webGLVersion];\n}\nfunction createCanvas(webGLVersion) {\n    if (typeof OffscreenCanvas !== 'undefined' && webGLVersion === 2) {\n        return new OffscreenCanvas(300, 150);\n    }\n    else if (typeof document !== 'undefined') {\n        return document.createElement('canvas');\n    }\n    else {\n        throw new Error('Cannot create a canvas in this context');\n    }\n}\nfunction getWebGLRenderingContext(webGLVersion) {\n    if (webGLVersion !== 1 && webGLVersion !== 2) {\n        throw new Error('Cannot get WebGL rendering context, WebGL is disabled.');\n    }\n    const canvas = createCanvas(webGLVersion);\n    canvas.addEventListener('webglcontextlost', (ev) => {\n        ev.preventDefault();\n        delete contexts[webGLVersion];\n    }, false);\n    if (webGLVersion === 1) {\n        return (canvas.getContext('webgl', WEBGL_ATTRIBUTES) ||\n            canvas.getContext('experimental-webgl', WEBGL_ATTRIBUTES));\n    }\n    return canvas.getContext('webgl2', WEBGL_ATTRIBUTES);\n}\n//# sourceMappingURL=canvas_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nexport var PackingScheme;\n(function (PackingScheme) {\n    /**\n     * All values in a single texel are densely packed without any constraints.\n     *\n     * This is how the shader encodes a tensor with shape = [2, 3, 4]\n     * (indices are [batch, row, col]).\n     *\n     * 000|001   010|011   020|021\n     * -------   -------   -------\n     * 002|003   012|013   022|023\n     *\n     * 100|101   110|111   120|121\n     * -------   -------   -------\n     * 102|103   112|113   122|123\n     *\n     */\n    PackingScheme[PackingScheme[\"DENSE\"] = 0] = \"DENSE\";\n    /**\n     * Single texels contain only values from the same batch, and from adjacent\n     * rows and columns.\n     *\n     * This is how the shader encodes a tensor with shape = [2, 3, 5]\n     * (indices are [batch, row, col]).\n     *\n     * 000|001   002|003   004|xxx   020|021   022|023   024|xxx\n     * -------   -------   -------   -------   -------   -------\n     * 010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n     *\n     * 100|101   102|103   104|xxx   120|121   122|123   124|xxx\n     * -------   -------   -------   -------   -------   -------\n     * 110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n     *\n     */\n    PackingScheme[PackingScheme[\"SHARED_BATCH\"] = 1] = \"SHARED_BATCH\";\n})(PackingScheme || (PackingScheme = {}));\nexport var TextureUsage;\n(function (TextureUsage) {\n    TextureUsage[TextureUsage[\"RENDER\"] = 0] = \"RENDER\";\n    TextureUsage[TextureUsage[\"UPLOAD\"] = 1] = \"UPLOAD\";\n    TextureUsage[TextureUsage[\"PIXELS\"] = 2] = \"PIXELS\";\n    TextureUsage[TextureUsage[\"DOWNLOAD\"] = 3] = \"DOWNLOAD\";\n})(TextureUsage || (TextureUsage = {}));\nexport var PhysicalTextureType;\n(function (PhysicalTextureType) {\n    PhysicalTextureType[PhysicalTextureType[\"UNPACKED_FLOAT16\"] = 0] = \"UNPACKED_FLOAT16\";\n    PhysicalTextureType[PhysicalTextureType[\"UNPACKED_FLOAT32\"] = 1] = \"UNPACKED_FLOAT32\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_4X1_UNSIGNED_BYTE\"] = 2] = \"PACKED_4X1_UNSIGNED_BYTE\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_2X2_FLOAT32\"] = 3] = \"PACKED_2X2_FLOAT32\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_2X2_FLOAT16\"] = 4] = \"PACKED_2X2_FLOAT16\";\n})(PhysicalTextureType || (PhysicalTextureType = {}));\nexport function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {\n    return [columns, rows];\n}\nexport function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {\n    return matrixSize * channelsPerTexture;\n}\nexport function getColorMatrixTextureShapeWidthHeight(rows, columns) {\n    return [columns * 4, rows];\n}\n/**\n * Get shape for densely packed RGBA texture.\n */\nexport function getDenseTexShape(shape) {\n    const size = util.sizeFromShape(shape);\n    const texelsNeeded = Math.ceil(size / 4);\n    return util.sizeToSquarishShape(texelsNeeded);\n}\nexport function getMatrixSizeFromUnpackedArraySize(unpackedSize, channelsPerTexture) {\n    if (unpackedSize % channelsPerTexture !== 0) {\n        throw new Error(`unpackedSize (${unpackedSize}) must be a multiple of ` +\n            `${channelsPerTexture}`);\n    }\n    return unpackedSize / channelsPerTexture;\n}\nexport function decodeMatrixFromUnpackedColorRGBAArray(unpackedArray, matrix, channels) {\n    const requiredSize = unpackedArray.length * channels / 4;\n    if (matrix.length < requiredSize) {\n        throw new Error(`matrix length (${matrix.length}) must be >= ${requiredSize}`);\n    }\n    let dst = 0;\n    for (let src = 0; src < unpackedArray.length; src += 4) {\n        for (let c = 0; c < channels; c++) {\n            matrix[dst++] = unpackedArray[src + c];\n        }\n    }\n}\nexport function getPackedMatrixTextureShapeWidthHeight(rows, columns) {\n    return [\n        Math.max(1, Math.ceil(columns / 2)), Math.max(1, Math.ceil(rows / 2))\n    ];\n}\nexport function getPackedRGBAArraySizeFromMatrixShape(rows, columns) {\n    const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return w * h * 4;\n}\nexport function getTextureConfig(\n// tslint:disable-next-line:no-any\ngl, textureHalfFloatExtension) {\n    // tslint:disable-next-line:no-any\n    const glany = gl;\n    let internalFormatFloat;\n    let internalFormatHalfFloat;\n    let internalFormatPackedHalfFloat;\n    let internalFormatPackedFloat;\n    let textureFormatFloat;\n    let downloadTextureFormat;\n    let downloadUnpackNumChannels;\n    let defaultNumChannels;\n    let textureTypeHalfFloat;\n    let textureTypeFloat;\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        internalFormatFloat = glany.R32F;\n        internalFormatHalfFloat = glany.R16F;\n        internalFormatPackedHalfFloat = glany.RGBA16F;\n        internalFormatPackedFloat = glany.RGBA32F;\n        textureFormatFloat = glany.RED;\n        downloadUnpackNumChannels = 4;\n        defaultNumChannels = 1;\n        textureTypeHalfFloat = glany.HALF_FLOAT;\n        textureTypeFloat = glany.FLOAT;\n    }\n    else {\n        internalFormatFloat = gl.RGBA;\n        internalFormatHalfFloat = gl.RGBA;\n        internalFormatPackedHalfFloat = gl.RGBA;\n        internalFormatPackedFloat = glany.RGBA;\n        textureFormatFloat = gl.RGBA;\n        downloadUnpackNumChannels = 4;\n        defaultNumChannels = 4;\n        textureTypeHalfFloat = textureHalfFloatExtension != null ?\n            textureHalfFloatExtension.HALF_FLOAT_OES :\n            null;\n        textureTypeFloat = gl.FLOAT;\n    }\n    downloadTextureFormat = gl.RGBA;\n    return {\n        internalFormatFloat,\n        internalFormatHalfFloat,\n        internalFormatPackedHalfFloat,\n        internalFormatPackedFloat,\n        textureFormatFloat,\n        downloadTextureFormat,\n        downloadUnpackNumChannels,\n        defaultNumChannels,\n        textureTypeHalfFloat,\n        textureTypeFloat\n    };\n}\n//# sourceMappingURL=tex_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { getWebGLContext } from './canvas_util';\nimport { getTextureConfig } from './tex_util';\nexport function callAndCheck(gl, func) {\n    const returnValue = func();\n    if (env().getBool('DEBUG')) {\n        checkWebGLError(gl);\n    }\n    return returnValue;\n}\nfunction checkWebGLError(gl) {\n    const error = gl.getError();\n    if (error !== gl.NO_ERROR) {\n        throw new Error('WebGL Error: ' + getWebGLErrorMessage(gl, error));\n    }\n}\n// https://en.wikipedia.org/wiki/Half-precision_floating-point_format\nconst MIN_FLOAT16 = 5.96e-8;\nconst MAX_FLOAT16 = 65504;\nexport function canBeRepresented(num) {\n    if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED') || num === 0 ||\n        (MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16)) {\n        return true;\n    }\n    return false;\n}\nexport function getWebGLErrorMessage(gl, status) {\n    switch (status) {\n        case gl.NO_ERROR:\n            return 'NO_ERROR';\n        case gl.INVALID_ENUM:\n            return 'INVALID_ENUM';\n        case gl.INVALID_VALUE:\n            return 'INVALID_VALUE';\n        case gl.INVALID_OPERATION:\n            return 'INVALID_OPERATION';\n        case gl.INVALID_FRAMEBUFFER_OPERATION:\n            return 'INVALID_FRAMEBUFFER_OPERATION';\n        case gl.OUT_OF_MEMORY:\n            return 'OUT_OF_MEMORY';\n        case gl.CONTEXT_LOST_WEBGL:\n            return 'CONTEXT_LOST_WEBGL';\n        default:\n            return `Unknown error code ${status}`;\n    }\n}\nexport function getExtensionOrThrow(gl, extensionName) {\n    return throwIfNull(gl, () => gl.getExtension(extensionName), 'Extension \"' + extensionName + '\" not supported on this browser.');\n}\nexport function createVertexShader(gl, vertexShaderSource) {\n    const vertexShader = throwIfNull(gl, () => gl.createShader(gl.VERTEX_SHADER), 'Unable to create vertex WebGLShader.');\n    callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));\n    callAndCheck(gl, () => gl.compileShader(vertexShader));\n    if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {\n        console.log(gl.getShaderInfoLog(vertexShader));\n        throw new Error('Failed to compile vertex shader.');\n    }\n    return vertexShader;\n}\nexport function createFragmentShader(gl, fragmentShaderSource) {\n    const fragmentShader = throwIfNull(gl, () => gl.createShader(gl.FRAGMENT_SHADER), 'Unable to create fragment WebGLShader.');\n    callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));\n    callAndCheck(gl, () => gl.compileShader(fragmentShader));\n    if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {\n        logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));\n        throw new Error('Failed to compile fragment shader.');\n    }\n    return fragmentShader;\n}\nconst lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;\nfunction logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {\n    const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);\n    if (lineNumberRegexResult == null) {\n        console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);\n        console.log(shaderSource);\n        return;\n    }\n    const lineNumber = +lineNumberRegexResult[1];\n    const shaderLines = shaderSource.split('\\n');\n    const pad = shaderLines.length.toString().length + 2;\n    const linesWithLineNumbers = shaderLines.map((line, lineNumber) => util.rightPad((lineNumber + 1).toString(), pad) + line);\n    let maxLineLength = 0;\n    for (let i = 0; i < linesWithLineNumbers.length; i++) {\n        maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);\n    }\n    const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);\n    const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);\n    const afterErrorLines = linesWithLineNumbers.slice(lineNumber);\n    console.log(beforeErrorLines.join('\\n'));\n    console.log(shaderInfoLog.split('\\n')[0]);\n    console.log(`%c ${util.rightPad(errorLine[0], maxLineLength)}`, 'border:1px solid red; background-color:#e3d2d2; color:#a61717');\n    console.log(afterErrorLines.join('\\n'));\n}\nexport function createProgram(gl) {\n    return throwIfNull(gl, () => gl.createProgram(), 'Unable to create WebGLProgram.');\n}\nexport function linkProgram(gl, program) {\n    callAndCheck(gl, () => gl.linkProgram(program));\n    if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {\n        console.log(gl.getProgramInfoLog(program));\n        throw new Error('Failed to link vertex and fragment shaders.');\n    }\n}\nexport function validateProgram(gl, program) {\n    callAndCheck(gl, () => gl.validateProgram(program));\n    if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {\n        console.log(gl.getProgramInfoLog(program));\n        throw new Error('Shader program validation failed.');\n    }\n}\nexport function createStaticVertexBuffer(gl, data) {\n    const buffer = throwIfNull(gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));\n    return buffer;\n}\nexport function createStaticIndexBuffer(gl, data) {\n    const buffer = throwIfNull(gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n    callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));\n    return buffer;\n}\nexport function getNumChannels() {\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        return 1;\n    }\n    return 4;\n}\nexport function createTexture(gl) {\n    return throwIfNull(gl, () => gl.createTexture(), 'Unable to create WebGLTexture.');\n}\nexport function validateTextureSize(width, height) {\n    const maxTextureSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n    if ((width <= 0) || (height <= 0)) {\n        const requested = `[${width}x${height}]`;\n        throw new Error('Requested texture size ' + requested + ' is invalid.');\n    }\n    if ((width > maxTextureSize) || (height > maxTextureSize)) {\n        const requested = `[${width}x${height}]`;\n        const max = `[${maxTextureSize}x${maxTextureSize}]`;\n        throw new Error('Requested texture size ' + requested +\n            ' greater than WebGL maximum on this browser / GPU ' + max + '.');\n    }\n}\nexport function createFramebuffer(gl) {\n    return throwIfNull(gl, () => gl.createFramebuffer(), 'Unable to create WebGLFramebuffer.');\n}\nexport function bindVertexBufferToProgramAttribute(gl, program, attribute, buffer, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {\n    const loc = gl.getAttribLocation(program, attribute);\n    if (loc === -1) {\n        // The GPU compiler decided to strip out this attribute because it's unused,\n        // thus no need to bind.\n        return false;\n    }\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes));\n    callAndCheck(gl, () => gl.enableVertexAttribArray(loc));\n    return true;\n}\nexport function bindTextureUnit(gl, texture, textureUnit) {\n    validateTextureUnit(gl, textureUnit);\n    callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n}\nexport function unbindTextureUnit(gl, textureUnit) {\n    validateTextureUnit(gl, textureUnit);\n    callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function getProgramUniformLocationOrThrow(gl, program, uniformName) {\n    return throwIfNull(gl, () => gl.getUniformLocation(program, uniformName), 'uniform \"' + uniformName + '\" not present in program.');\n}\nexport function getProgramUniformLocation(gl, program, uniformName) {\n    return gl.getUniformLocation(program, uniformName);\n}\nexport function bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) {\n    callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));\n    callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));\n}\nexport function bindCanvasToFramebuffer(gl) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n    callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));\n    callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));\n}\nexport function bindColorTextureToFramebuffer(gl, texture, framebuffer) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));\n}\nexport function unbindColorTextureFromFramebuffer(gl, framebuffer) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));\n}\nexport function validateFramebuffer(gl) {\n    const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);\n    if (status !== gl.FRAMEBUFFER_COMPLETE) {\n        throw new Error('Error binding framebuffer: ' + getFramebufferErrorMessage(gl, status));\n    }\n}\nexport function getFramebufferErrorMessage(gl, status) {\n    switch (status) {\n        case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:\n            return 'FRAMEBUFFER_INCOMPLETE_ATTACHMENT';\n        case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:\n            return 'FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT';\n        case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:\n            return 'FRAMEBUFFER_INCOMPLETE_DIMENSIONS';\n        case gl.FRAMEBUFFER_UNSUPPORTED:\n            return 'FRAMEBUFFER_UNSUPPORTED';\n        default:\n            return `unknown error ${status}`;\n    }\n}\nfunction throwIfNull(gl, returnTOrNull, failureMessage) {\n    const tOrNull = callAndCheck(gl, () => returnTOrNull());\n    if (tOrNull == null) {\n        throw new Error(failureMessage);\n    }\n    return tOrNull;\n}\nfunction validateTextureUnit(gl, textureUnit) {\n    const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;\n    const glTextureUnit = textureUnit + gl.TEXTURE0;\n    if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {\n        const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;\n        throw new Error(`textureUnit must be in ${textureUnitRange}.`);\n    }\n}\nexport function getBatchDim(shape, dimsToSkip = 2) {\n    return util.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));\n}\nexport function getRowsCols(shape) {\n    if (shape.length === 0) {\n        throw Error('Cannot get rows and columns of an empty shape array.');\n    }\n    return [\n        shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]\n    ];\n}\nexport function getShapeAs3D(shape) {\n    let shapeAs3D = [1, 1, 1];\n    const isScalar = shape.length === 0 || (shape.length === 1 && shape[0] === 1);\n    if (!isScalar) {\n        shapeAs3D =\n            [getBatchDim(shape), ...getRowsCols(shape)];\n    }\n    return shapeAs3D;\n}\nexport function getTextureShapeFromLogicalShape(logShape, isPacked = false) {\n    let maxTexSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n    if (isPacked) {\n        maxTexSize = maxTexSize * 2;\n        // This logic ensures we accurately count the number of packed texels needed\n        // to accommodate the tensor. We can only pack values in the same texel if\n        // they are from adjacent pairs of rows/cols within the same batch. So if a\n        // tensor has 3 rows, we pretend it has 4 rows in order to account for the\n        // fact that the texels containing the third row are half empty.\n        logShape = logShape.map((d, i) => i >= logShape.length - 2 ?\n            util.nearestLargerEven(logShape[i]) :\n            logShape[i]);\n        // Packed texture height is at least 2 (the channel height of a single\n        // texel).\n        if (logShape.length === 1) {\n            logShape = [2, logShape[0]];\n        }\n    }\n    // If logical shape is 2, we don't squeeze, since we want to match physical.\n    if (logShape.length !== 2) {\n        const squeezeResult = util.squeezeShape(logShape);\n        logShape = squeezeResult.newShape;\n    }\n    let size = util.sizeFromShape(logShape);\n    if (logShape.length <= 1 && size <= maxTexSize) {\n        return [1, size];\n    }\n    else if (logShape.length === 2 && logShape[0] <= maxTexSize &&\n        logShape[1] <= maxTexSize) {\n        return logShape;\n    }\n    else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize &&\n        logShape[2] <= maxTexSize) {\n        return [logShape[0] * logShape[1], logShape[2]];\n    }\n    else if (logShape.length === 3 && logShape[0] <= maxTexSize &&\n        logShape[1] * logShape[2] <= maxTexSize) {\n        return [logShape[0], logShape[1] * logShape[2]];\n    }\n    else if (logShape.length === 4 &&\n        logShape[0] * logShape[1] * logShape[2] <= maxTexSize &&\n        logShape[3] <= maxTexSize) {\n        return [logShape[0] * logShape[1] * logShape[2], logShape[3]];\n    }\n    else if (logShape.length === 4 && logShape[0] <= maxTexSize &&\n        logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {\n        return [logShape[0], logShape[1] * logShape[2] * logShape[3]];\n    }\n    else {\n        if (isPacked) {\n            // For packed textures size equals the number of channels required to\n            // accommodate the texture data. However in order to squarify such that\n            // inner dimensions stay even, we rewrite size to equal the number of\n            // texels. Then in the return statement we rehydrate the squarified\n            // dimensions to channel units.\n            const batchDim = getBatchDim(logShape);\n            let rows = 2, cols = 2;\n            if (logShape.length) {\n                [rows, cols] = getRowsCols(logShape);\n            }\n            size = batchDim * (rows / 2) * (cols / 2);\n            return util.sizeToSquarishShape(size).map(d => d * 2);\n        }\n        return util.sizeToSquarishShape(size);\n    }\n}\nfunction isEven(n) {\n    return n % 2 === 0;\n}\n/**\n * This determines whether reshaping a packed texture requires rearranging\n * the data within the texture, assuming 2x2 packing.\n */\nexport function isReshapeFree(shape1, shape2) {\n    shape1 = shape1.slice(-2);\n    shape2 = shape2.slice(-2);\n    if (util.arraysEqual(shape1, shape2)) {\n        return true;\n    }\n    if (!shape1.length || !shape2.length) { // One of the shapes is a scalar.\n        return true;\n    }\n    if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 ||\n        shape2[1] === 0) {\n        return true;\n    }\n    if (shape1.length !== shape2.length) { // One of the shapes is a vector.\n        const shape1Cols = shape1.slice(-1)[0];\n        const shape2Cols = shape2.slice(-1)[0];\n        if (shape1Cols === shape2Cols) {\n            return true;\n        }\n        if (isEven(shape1Cols) && isEven(shape2Cols) &&\n            (shape1[0] === 1 || shape2[0] === 1)) {\n            return true;\n        }\n    }\n    return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);\n}\n// We cache webgl params because the environment gets reset between\n// unit tests and we don't want to constantly query the WebGLContext for\n// MAX_TEXTURE_SIZE.\nlet MAX_TEXTURE_SIZE;\nlet MAX_TEXTURES_IN_SHADER;\nexport function getWebGLMaxTextureSize(webGLVersion) {\n    if (MAX_TEXTURE_SIZE == null) {\n        const gl = getWebGLContext(webGLVersion);\n        MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n    }\n    return MAX_TEXTURE_SIZE;\n}\nexport function resetMaxTextureSize() {\n    MAX_TEXTURE_SIZE = null;\n}\nexport function resetMaxTexturesInShader() {\n    MAX_TEXTURES_IN_SHADER = null;\n}\nexport function getMaxTexturesInShader(webGLVersion) {\n    if (MAX_TEXTURES_IN_SHADER == null) {\n        const gl = getWebGLContext(webGLVersion);\n        MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);\n    }\n    // We cap at 16 to avoid spurious runtime \"memory exhausted\" error.\n    return Math.min(16, MAX_TEXTURES_IN_SHADER);\n}\nexport function getWebGLDisjointQueryTimerVersion(webGLVersion) {\n    if (webGLVersion === 0) {\n        return 0;\n    }\n    let queryTimerVersion;\n    const gl = getWebGLContext(webGLVersion);\n    if (hasExtension(gl, 'EXT_disjoint_timer_query_webgl2') &&\n        webGLVersion === 2) {\n        queryTimerVersion = 2;\n    }\n    else if (hasExtension(gl, 'EXT_disjoint_timer_query')) {\n        queryTimerVersion = 1;\n    }\n    else {\n        queryTimerVersion = 0;\n    }\n    return queryTimerVersion;\n}\nexport function hasExtension(gl, extensionName) {\n    const ext = gl.getExtension(extensionName);\n    return ext != null;\n}\nexport function isWebGLVersionEnabled(webGLVersion) {\n    try {\n        const gl = getWebGLContext(webGLVersion);\n        if (gl != null) {\n            return true;\n        }\n    }\n    catch (e) {\n        console.log('Error when getting WebGL context: ', e);\n        return false;\n    }\n    return false;\n}\nexport function isCapableOfRenderingToFloatTexture(webGLVersion) {\n    if (webGLVersion === 0) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    if (webGLVersion === 1) {\n        if (!hasExtension(gl, 'OES_texture_float')) {\n            return false;\n        }\n    }\n    else {\n        if (!hasExtension(gl, 'EXT_color_buffer_float')) {\n            return false;\n        }\n    }\n    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n    return isFrameBufferComplete;\n}\n/**\n * Check if we can download values from a float/half-float texture.\n *\n * Note that for performance reasons we use binding a texture to a framebuffer\n * as a proxy for ability to download float values later using readPixels. The\n * texture params of this texture will not match those in readPixels exactly\n * but if we are unable to bind some kind of float texture to the frameBuffer\n * then we definitely will not be able to read float values from it.\n */\nexport function isDownloadFloatTextureEnabled(webGLVersion) {\n    if (webGLVersion === 0) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    if (webGLVersion === 1) {\n        if (!hasExtension(gl, 'OES_texture_float')) {\n            return false;\n        }\n        if (!hasExtension(gl, 'WEBGL_color_buffer_float')) {\n            return false;\n        }\n    }\n    else {\n        if (hasExtension(gl, 'EXT_color_buffer_float')) {\n            return createFloatTextureAndBindToFramebuffer(gl);\n        }\n        const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n        if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {\n            const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n            return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);\n        }\n        return false;\n    }\n    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n    return isFrameBufferComplete;\n}\nfunction createFloatTextureAndBindToFramebuffer(gl) {\n    const texConfig = getTextureConfig(gl);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    const width = 1;\n    const height = 1;\n    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);\n    const frameBuffer = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    gl.deleteTexture(texture);\n    gl.deleteFramebuffer(frameBuffer);\n    return isFrameBufferComplete;\n}\nfunction createHalfFloatTextureAndBindToFramebuffer(\n// tslint:disable-next-line:no-any\ngl, textureHalfFloatExtension) {\n    const texConfig = getTextureConfig(gl, textureHalfFloatExtension);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    const width = 1;\n    const height = 1;\n    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);\n    const frameBuffer = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    gl.deleteTexture(texture);\n    gl.deleteFramebuffer(frameBuffer);\n    return isFrameBufferComplete;\n}\nexport function isWebGLFenceEnabled(webGLVersion) {\n    if (webGLVersion !== 2) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    // tslint:disable-next-line:no-any\n    const isEnabled = gl.fenceSync != null;\n    return isEnabled;\n}\nexport function assertNotComplex(tensor, opName) {\n    if (!Array.isArray(tensor)) {\n        tensor = [tensor];\n    }\n    tensor.forEach(t => {\n        if (t != null) {\n            util.assert(t.dtype !== 'complex64', () => `${opName} does not support complex64 tensors ` +\n                'in the WebGL backend.');\n        }\n    });\n}\n//# sourceMappingURL=webgl_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { device_util, env } from '@tensorflow/tfjs-core';\nimport { getMaxTexturesInShader, getWebGLDisjointQueryTimerVersion, getWebGLMaxTextureSize, isCapableOfRenderingToFloatTexture, isDownloadFloatTextureEnabled, isWebGLFenceEnabled, isWebGLVersionEnabled } from './webgl_util';\nconst ENV = env();\n/**\n * This file contains WebGL-specific flag registrations.\n */\n/**\n * True if WebGL is supported.\n */\nENV.registerFlag('HAS_WEBGL', () => ENV.getNumber('WEBGL_VERSION') > 0);\n/** 0: No WebGL, 1: WebGL 1.0, 2: WebGL 2.0. */\nENV.registerFlag('WEBGL_VERSION', () => {\n    if (isWebGLVersionEnabled(2)) {\n        return 2;\n    }\n    else if (isWebGLVersionEnabled(1)) {\n        return 1;\n    }\n    return 0;\n});\n/** Whether to check for numerical representation problems. */\nENV.registerFlag('WEBGL_CHECK_NUMERICAL_PROBLEMS', () => false);\nENV.registerFlag('WEBGL_BUFFER_SUPPORTED', () => ENV.get('WEBGL_VERSION') === 2);\n/** Whether the WebGL backend will sometimes forward ops to the CPU. */\nENV.registerFlag('WEBGL_CPU_FORWARD', () => true);\n/** Whether the WebGL backend will always use f16 textures for rendering. */\nENV.registerFlag('WEBGL_FORCE_F16_TEXTURES', () => false);\n/** Whether to turn all packing related flags on. */\nENV.registerFlag('WEBGL_PACK', () => ENV.getBool('HAS_WEBGL'));\n/** Whether we will pack the batchnormalization op. */\nENV.registerFlag('WEBGL_PACK_NORMALIZATION', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack the clip op. */\nENV.registerFlag('WEBGL_PACK_CLIP', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack the depthwise conv op. */\n// TODO: https://github.com/tensorflow/tfjs/issues/1679\nENV.registerFlag('WEBGL_PACK_DEPTHWISECONV', () => false);\n/** Whether we will pack binary ops. */\nENV.registerFlag('WEBGL_PACK_BINARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack unary ops. */\nENV.registerFlag('WEBGL_PACK_UNARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack array ops. */\nENV.registerFlag('WEBGL_PACK_ARRAY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack image ops. */\nENV.registerFlag('WEBGL_PACK_IMAGE_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack reduce ops. */\nENV.registerFlag('WEBGL_PACK_REDUCE', () => ENV.getBool('WEBGL_PACK'));\n/** Whether packed WebGL kernels lazily unpack their outputs. */\nENV.registerFlag('WEBGL_LAZILY_UNPACK', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will use the im2col algorithm to speed up convolutions. */\nENV.registerFlag('WEBGL_CONV_IM2COL', () => ENV.getBool('WEBGL_PACK'));\n/** The maximum texture dimension. */\nENV.registerFlag('WEBGL_MAX_TEXTURE_SIZE', () => getWebGLMaxTextureSize(ENV.getNumber('WEBGL_VERSION')));\n/** The maximum texture dimension. */\nENV.registerFlag('WEBGL_MAX_TEXTURES_IN_SHADER', () => getMaxTexturesInShader(ENV.getNumber('WEBGL_VERSION')));\n/**\n * The disjoint_query_timer extension version.\n * 0: disabled, 1: EXT_disjoint_timer_query, 2:\n * EXT_disjoint_timer_query_webgl2.\n * In Firefox with WebGL 2.0,\n * EXT_disjoint_timer_query_webgl2 is not available, so we must use the\n * WebGL 1.0 extension.\n */\nENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', () => {\n    const webGLVersion = ENV.getNumber('WEBGL_VERSION');\n    if (webGLVersion === 0) {\n        return 0;\n    }\n    return getWebGLDisjointQueryTimerVersion(webGLVersion);\n});\n/**\n * Whether the timer object from the disjoint_query_timer extension gives\n * timing information that is reliable.\n */\nENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', () => ENV.getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&\n    !device_util.isMobile());\n/**\n * Whether the device is physically capable of rendering to float32 textures.\n */\nENV.registerFlag('WEBGL_RENDER_FLOAT32_CAPABLE', () => isCapableOfRenderingToFloatTexture(ENV.getNumber('WEBGL_VERSION')));\n/**\n * Whether rendering to float32 textures is enabled. If disabled, renders to\n * float16 textures.\n */\nENV.registerFlag('WEBGL_RENDER_FLOAT32_ENABLED', () => {\n    return ENV.getBool('WEBGL_FORCE_F16_TEXTURES') ?\n        false :\n        ENV.getBool('WEBGL_RENDER_FLOAT32_CAPABLE');\n});\n/**\n * Whether downloading float textures is enabled (16 or 32 bit). If disabled,\n * uses IEEE 754 encoding of the float32 values to 4 uint8 when downloading.\n */\nENV.registerFlag('WEBGL_DOWNLOAD_FLOAT_ENABLED', () => isDownloadFloatTextureEnabled(ENV.getNumber('WEBGL_VERSION')));\n/** Whether the fence API is available. */\nENV.registerFlag('WEBGL_FENCE_API_ENABLED', () => isWebGLFenceEnabled(ENV.getNumber('WEBGL_VERSION')));\n/**\n * Tensors with size <= than this will be uploaded as uniforms, not textures.\n */\nENV.registerFlag('WEBGL_SIZE_UPLOAD_UNIFORM', () => {\n    // Use uniform uploads only when 32bit floats are supported. In\n    // 16bit\n    // environments there are problems with comparing a 16bit texture value\n    // with a 32bit uniform value.\n    const useUniforms = ENV.getBool('WEBGL_RENDER_FLOAT32_ENABLED');\n    return useUniforms ? 4 : 0;\n});\n/**\n * If the total number of bytes allocated on the GPU is greater than this\n * number, we will aggressively delete textures upon disposal with\n * gl.deleteMatrixTexture, rather than making them available for reuse.\n *\n * Default value -1 indicates that we will never aggressively delete textures.\n */\nENV.registerFlag('WEBGL_DELETE_TEXTURE_THRESHOLD', () => {\n    return -1;\n}, threshold => {\n    if (threshold < 0 && threshold !== -1) {\n        throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never ` +\n            `delete) or at least 0, but got ${threshold}.`);\n    }\n});\n//# sourceMappingURL=flags_webgl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Import shared functionality from tfjs-backend-cpu without triggering\n// side effects.\n// tslint:disable-next-line: no-imports-from-dist\nimport * as shared from '@tensorflow/tfjs-backend-cpu/dist/shared';\nconst { simpleAbsImpl: simpleAbsImplCPU, addImpl: addImplCPU, ceilImpl: ceilImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, multiplyImpl: multiplyImplCPU, rsqrtImpl: rsqrtImplCPU, sliceImpl: sliceImplCPU, subImpl: subImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU, } = shared;\nexport { simpleAbsImplCPU, addImplCPU, ceilImplCPU, expImplCPU, expm1ImplCPU, logImplCPU, multiplyImplCPU, sliceImplCPU, subImplCPU, floorImplCPU, maxImplCPU, rsqrtImplCPU, transposeImplCPU, uniqueImplCPU, };\n//# sourceMappingURL=shared.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AddNProgram {\n    constructor(outputShape, shapes) {\n        this.outputShape = [];\n        this.outputShape = outputShape;\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const snippets = [];\n        // Get target elements from every input tensor.\n        this.variableNames.forEach(variable => {\n            snippets.push(`float v${variable} = get${variable}AtOutCoords();`);\n        });\n        // Calculate the sum of all elements.\n        const operation = this.variableNames\n            .map(variable => {\n            return `v${variable}`;\n        })\n            .join(' + ');\n        this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        float result = ${operation};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=addn_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AddNPackedProgram {\n    constructor(outputShape, shapes) {\n        this.outputShape = [];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const snippets = [];\n        // Get target elements from every input tensor.\n        this.variableNames.forEach(variable => {\n            snippets.push(`vec4 v${variable} = get${variable}AtOutCoords();`);\n        });\n        // Calculate the sum of all elements.\n        const operation = this.variableNames\n            .map(variable => {\n            return `v${variable}`;\n        })\n            .join(' + ');\n        this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        vec4 result = ${operation};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=addn_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ArgMinMaxProgram {\n    constructor(reduceInfo, op, firstPass) {\n        this.variableNames = ['A'];\n        const { windowSize, batchSize, outSize } = reduceInfo;\n        if (!firstPass) {\n            this.variableNames.push('bestIndicesA');\n        }\n        this.outputShape = [batchSize, outSize];\n        const compOp = (op === 'max') ? '>' : '<';\n        const indexSnippet = firstPass ?\n            'inOffset + i;' :\n            'round(getBestIndicesA(batch, inOffset + i));';\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          int inIdx = ${indexSnippet};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${compOp} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=argminmax_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport function getVecChannels(name, rank) {\n    return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank).map(d => `${name}.${d}`);\n}\nexport function getChannels(name, rank) {\n    if (rank === 1) {\n        return [name];\n    }\n    return getVecChannels(name, rank);\n}\nexport function getSourceCoords(rank, dims) {\n    if (rank === 1) {\n        return 'rc';\n    }\n    let coords = '';\n    for (let i = 0; i < rank; i++) {\n        coords += dims[i];\n        if (i < rank - 1) {\n            coords += ',';\n        }\n    }\n    return coords;\n}\n//# sourceMappingURL=packing_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nexport function getGlslDifferences() {\n    let version;\n    let attribute;\n    let varyingVs;\n    let varyingFs;\n    let texture2D;\n    let output;\n    let defineOutput;\n    let defineSpecialNaN;\n    let defineSpecialInf;\n    let defineRound;\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        version = '#version 300 es';\n        attribute = 'in';\n        varyingVs = 'out';\n        varyingFs = 'in';\n        texture2D = 'texture';\n        output = 'outputColor';\n        defineOutput = 'out vec4 outputColor;';\n        // Use custom isnan definition to work across differences between\n        // implementations on various platforms. While this should happen in ANGLE\n        // we still see differences between android and windows (on chrome) when\n        // using isnan directly.\n        defineSpecialNaN = `\n      bool isnan_custom(float val) {\n        return (val > 0.0 || val < 0.0) ? false : val != 0.0;\n      }\n\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan_custom(val.x),\n          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n      }\n\n      #define isnan(value) isnan_custom(value)\n    `;\n        // In webgl 2 we do not need to specify a custom isinf so there is no\n        // need for a special INFINITY constant.\n        defineSpecialInf = ``;\n        defineRound = `\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n    }\n    else {\n        version = '';\n        attribute = 'attribute';\n        varyingVs = 'varying';\n        varyingFs = 'varying';\n        texture2D = 'texture2D';\n        output = 'gl_FragColor';\n        defineOutput = '';\n        // WebGL1 has no built in isnan so we define one here.\n        defineSpecialNaN = `\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    `;\n        defineSpecialInf = `\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    `;\n        defineRound = `\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n    }\n    return {\n        version,\n        attribute,\n        varyingVs,\n        varyingFs,\n        texture2D,\n        output,\n        defineOutput,\n        defineSpecialNaN,\n        defineSpecialInf,\n        defineRound\n    };\n}\n//# sourceMappingURL=glsl_version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Produces GLSL code that derives logical coordinates from a flat\n * index. The code performs integer division with each stride and decrements\n * the index until the index equals the final dimension coordinate.\n */\nexport function getLogicalCoordinatesFromFlatIndex(coords, shape, index = 'index') {\n    const strides = util.computeStrides(shape);\n    return strides\n        .map((stride, i) => {\n        const line1 = `int ${coords[i]} = ${index} / ${stride}`;\n        const line2 = i === strides.length - 1 ?\n            `int ${coords[i + 1]} = ${index} - ${coords[i]} * ${stride}` :\n            `index -= ${coords[i]} * ${stride}`;\n        return `${line1}; ${line2};`;\n    })\n        .join('');\n}\nfunction buildVec(x) {\n    if (x.length === 1) {\n        return `${x[0]}`;\n    }\n    return `vec${x.length}(${x.join(',')})`;\n}\n/**\n * Produces GLSL code that computes the dot product of the input x and y\n * vectors. Handles splitting inputs into increments of vec4s when necessary.\n */\nexport function dotify(x, y) {\n    if (x.length !== y.length) {\n        throw new Error(`Vectors to be dotted must be of the same length -` +\n            `got ${x.length} and ${y.length}`);\n    }\n    const slices = [];\n    const nearestVec4 = Math.floor(x.length / 4);\n    const nearestVec4Remainder = x.length % 4;\n    for (let i = 0; i < nearestVec4; i++) {\n        const xSlice = x.slice(i * 4, i * 4 + 4);\n        const ySlice = y.slice(i * 4, i * 4 + 4);\n        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n    }\n    if (nearestVec4Remainder !== 0) {\n        let xSlice = x.slice(nearestVec4 * 4);\n        let ySlice = y.slice(nearestVec4 * 4);\n        if (xSlice.length === 1) {\n            xSlice = xSlice.map(d => `float(${d})`);\n            ySlice = ySlice.map(d => `float(${d})`);\n        }\n        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n    }\n    return slices.map((d, i) => `dot(${d})`).join('+');\n}\n/**\n * Produces GLSL that computes the flat index from 3D coordinates.\n */\nexport function getFlatIndexFrom3D(shape) {\n    const strides = util.computeStrides(shape).map(d => d.toString());\n    return `\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;\n  }\n`;\n}\nexport const ENCODE_FLOAT_SNIPPET = `\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`;\n//# sourceMappingURL=shader_compiler_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nconst { getBroadcastDims } = backend_util;\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nexport function makeShader(inputsInfo, outputShape, userCode, usesPackedTextures) {\n    const prefixSnippets = [];\n    inputsInfo.forEach(x => {\n        const size = util.sizeFromShape(x.shapeInfo.logicalShape);\n        // Snippet when we decided to upload the values as uniform.\n        if (x.shapeInfo.isUniform) {\n            prefixSnippets.push(`uniform float ${x.name}${size > 1 ? `[${size}]` : ''};`);\n        }\n        else {\n            prefixSnippets.push(`uniform sampler2D ${x.name};`);\n            prefixSnippets.push(`uniform int offset${x.name};`);\n        }\n    });\n    const inputPrefixSnippet = prefixSnippets.join('\\n');\n    const inputSamplingSnippet = inputsInfo\n        .map(x => getInputSamplingSnippet(x, outputShape, usesPackedTextures))\n        .join('\\n');\n    const outTexShape = outputShape.texShape;\n    const glsl = getGlslDifferences();\n    const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);\n    let outputSamplingSnippet;\n    let floatTextureSetOutputSnippet;\n    let shaderPrefix = getShaderPrefix(glsl);\n    if (outputShape.isPacked) {\n        outputSamplingSnippet =\n            getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n        floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);\n    }\n    else {\n        outputSamplingSnippet =\n            getOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n        floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);\n    }\n    if (usesPackedTextures) {\n        shaderPrefix += SHADER_PACKED_PREFIX;\n    }\n    const source = [\n        shaderPrefix, floatTextureSampleSnippet, floatTextureSetOutputSnippet,\n        inputPrefixSnippet, outputSamplingSnippet, inputSamplingSnippet, userCode\n    ].join('\\n');\n    return source;\n}\nfunction getSamplerFromInInfo(inInfo) {\n    const shape = inInfo.shapeInfo.logicalShape;\n    switch (shape.length) {\n        case 0:\n            return getSamplerScalar(inInfo);\n        case 1:\n            return getSampler1D(inInfo);\n        case 2:\n            return getSampler2D(inInfo);\n        case 3:\n            return getSampler3D(inInfo);\n        case 4:\n            return getSampler4D(inInfo);\n        case 5:\n            return getSampler5D(inInfo);\n        case 6:\n            return getSampler6D(inInfo);\n        default:\n            throw new Error(`${shape.length}-D input sampling` +\n                ` is not yet supported`);\n    }\n}\nfunction getPackedSamplerFromInInfo(inInfo) {\n    const shape = inInfo.shapeInfo.logicalShape;\n    switch (shape.length) {\n        case 0:\n            return getPackedSamplerScalar(inInfo);\n        case 1:\n            return getPackedSampler1D(inInfo);\n        case 2:\n            return getPackedSampler2D(inInfo);\n        case 3:\n            return getPackedSampler3D(inInfo);\n        default:\n            return getPackedSamplerND(inInfo);\n    }\n}\nfunction getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false) {\n    let res = '';\n    if (usesPackedTextures) {\n        res += getPackedSamplerFromInInfo(inInfo);\n    }\n    else {\n        res += getSamplerFromInInfo(inInfo);\n    }\n    const inShape = inInfo.shapeInfo.logicalShape;\n    const outShape = outShapeInfo.logicalShape;\n    if (inShape.length <= outShape.length) {\n        if (usesPackedTextures) {\n            res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);\n        }\n        else {\n            res += getSamplerAtOutputCoords(inInfo, outShapeInfo);\n        }\n    }\n    return res;\n}\nfunction getPackedOutputSamplingSnippet(outShape, outTexShape) {\n    switch (outShape.length) {\n        case 0:\n            return getOutputScalarCoords();\n        case 1:\n            return getOutputPacked1DCoords(outShape, outTexShape);\n        case 2:\n            return getOutputPacked2DCoords(outShape, outTexShape);\n        case 3:\n            return getOutputPacked3DCoords(outShape, outTexShape);\n        default:\n            return getOutputPackedNDCoords(outShape, outTexShape);\n    }\n}\nfunction getOutputSamplingSnippet(outShape, outTexShape) {\n    switch (outShape.length) {\n        case 0:\n            return getOutputScalarCoords();\n        case 1:\n            return getOutput1DCoords(outShape, outTexShape);\n        case 2:\n            return getOutput2DCoords(outShape, outTexShape);\n        case 3:\n            return getOutput3DCoords(outShape, outTexShape);\n        case 4:\n            return getOutput4DCoords(outShape, outTexShape);\n        case 5:\n            return getOutput5DCoords(outShape, outTexShape);\n        case 6:\n            return getOutput6DCoords(outShape, outTexShape);\n        default:\n            throw new Error(`${outShape.length}-D output sampling is not yet supported`);\n    }\n}\nfunction getFloatTextureSampleSnippet(glsl) {\n    return `\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${glsl.texture2D}(textureSampler, uv).r;\n    }\n  `;\n}\nfunction getFloatTextureSetRSnippet(glsl) {\n    return `\n    void setOutput(float val) {\n      ${glsl.output} = vec4(val, 0, 0, 0);\n    }\n  `;\n}\nfunction getFloatTextureSetRGBASnippet(glsl) {\n    return `\n    void setOutput(vec4 val) {\n      ${glsl.output} = val;\n    }\n  `;\n}\nfunction getShaderPrefix(glsl) {\n    const SHADER_PREFIX = `${glsl.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${glsl.varyingFs} vec2 resultUV;\n    ${glsl.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${glsl.defineSpecialNaN}\n    ${glsl.defineSpecialInf}\n    ${glsl.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${SAMPLE_1D_SNIPPET}\n    ${SAMPLE_2D_SNIPPET}\n    ${SAMPLE_3D_SNIPPET}\n  `;\n    return SHADER_PREFIX;\n}\nconst SAMPLE_1D_SNIPPET = `\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SAMPLE_2D_SNIPPET = `\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SAMPLE_3D_SNIPPET = `\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SHADER_PACKED_PREFIX = `\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;\nfunction getOutputScalarCoords() {\n    return `\n    int getOutputCoords() {\n      return 0;\n    }\n  `;\n}\nfunction getOutputPacked1DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (packedTexShape[0] === 1) {\n        return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);\n      }\n    `;\n    }\n    if (packedTexShape[1] === 1) {\n        return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);\n      }\n    `;\n    }\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);\n    }\n  `;\n}\nfunction getOutput1DCoords(shape, texShape) {\n    if (texShape[0] === 1) {\n        return `\n      int getOutputCoords() {\n        return int(resultUV.x * ${texShape[1]}.0);\n      }\n    `;\n    }\n    if (texShape[1] === 1) {\n        return `\n      int getOutputCoords() {\n        return int(resultUV.y * ${texShape[0]}.0);\n      }\n    `;\n    }\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      return resTexRC.x * ${texShape[1]} + resTexRC.y;\n    }\n  `;\n}\nfunction getOutputPacked3DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texelsInLogicalRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;\n}\nfunction getOutput3DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\nfunction getOutputPackedNDCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);\n    let texelsInBatchN = texelsInBatch;\n    let batches = ``;\n    let coords = 'b, r, c';\n    for (let b = 2; b < shape.length - 1; b++) {\n        texelsInBatchN *= shape[shape.length - b - 1];\n        batches = `\n      int b${b} = index / ${texelsInBatchN};\n      index -= b${b} * ${texelsInBatchN};\n    ` + batches;\n        coords = `b${b}, ` + coords;\n    }\n    return `\n    ivec${shape.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      ${batches}\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec${shape.length}(${coords});\n    }\n  `;\n}\nfunction getOutput4DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2'], shape);\n    return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec4(r, c, d, d2);\n    }\n  `;\n}\nfunction getOutput5DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3'], shape);\n    return `\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},\n                             ${texShape[1]}));\n\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `;\n}\nfunction getOutput6DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3', 'd4'], shape);\n    return `\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `;\n}\nfunction getOutputPacked2DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (util.arraysEqual(shape, texShape)) {\n        return `\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      }\n    `;\n    }\n    // texels needed to accommodate a logical row\n    const texelsInLogicalRow = Math.ceil(shape[1] / 2);\n    /**\n     * getOutputCoords\n     *\n     * resTexRC: The rows and columns of the texels. If you move over one\n     * texel to the right in the packed texture, you are moving over one column\n     * (not two).\n     *\n     * index: The texel index\n     */\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getOutput2DCoords(shape, texShape) {\n    if (util.arraysEqual(shape, texShape)) {\n        return `\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));\n      }\n    `;\n    }\n    if (shape[1] === 1) {\n        return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;\n    }\n    if (shape[0] === 1) {\n        return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;\n    }\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      int r = index / ${shape[1]};\n      int c = index - r * ${shape[1]};\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getFlatOffsetUniformName(texName) {\n    return `offset${texName}`;\n}\nfunction getPackedSamplerScalar(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}() {\n      return ${glsl.texture2D}(${texName}, halfCR);\n    }\n  `;\n}\nfunction getSamplerScalar(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    if (inputInfo.shapeInfo.isUniform) {\n        return `float ${funcName}() {return ${texName};}`;\n    }\n    const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;\n    if (texNumR === 1 && texNumC === 1) {\n        return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n    }\n    const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}() {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler1D(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${packedTexShape[0]}, ${packedTexShape[1]}, index);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler1D(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int index) {\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texShape = inputInfo.shapeInfo.texShape;\n    const tNumR = texShape[0];\n    const tNumC = texShape[1];\n    if (tNumC === 1 && tNumR === 1) {\n        return `\n      float ${funcName}(int index) {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    if (tNumC === 1) {\n        return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (tNumR === 1) {\n        return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n    float ${funcName}(int index) {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler2D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const glsl = getGlslDifferences();\n    if (texShape != null && util.arraysEqual(shape, texShape)) {\n        return `\n      vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n\n        return ${glsl.texture2D}(${texName}, uv);\n      }\n    `;\n    }\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const valuesPerRow = Math.ceil(shape[1] / 2);\n    return `\n    vec4 ${funcName}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler2D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    if (texShape != null && util.arraysEqual(shape, texShape)) {\n        const texNumR = texShape[0];\n        const texNumC = texShape[1];\n        return `\n    float ${funcName}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['row', 'col'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const offset = getFlatOffsetUniformName(texName);\n    if (texNumC === 1) {\n        // index is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    if (texNumR === 1) {\n        // index is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    return `\n  float ${funcName}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${shape[1]} + col + ${offset};\n    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n    return sampleTexture(${texName}, uv);\n  }\n`;\n}\nfunction getPackedSampler3D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (shape[0] === 1) {\n        const squeezedShape = shape.slice(1);\n        const keptDims = [1, 2];\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['b', 'row', 'col'];\n        return `\n        ${getPackedSamplerFromInInfo(newInputInfo)}\n        vec4 ${funcName}(int b, int row, int col) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n    }\n    const texNumR = packedTexShape[0];\n    const texNumC = packedTexShape[1];\n    const valuesPerRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler3D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride0 = shape[1] * shape[2];\n    const stride1 = shape[2];\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['row', 'col', 'depth'];\n        return `\n        ${getSamplerFromInInfo(newInputInfo)}\n        float ${funcName}(int row, int col, int depth) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${stride0}, ${stride1}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n        float ${funcName}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${texNumC}.0, ${texNumR}.0);\n          return sampleTexture(${texName}, uv);\n        }\n      `;\n    }\n    if (texNumC === stride1 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n      float ${funcName}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};\n        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n        return sampleTexture(${texName}, uv);\n      }\n  `;\n}\nfunction getPackedSamplerND(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const rank = shape.length;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texNumR = packedTexShape[0];\n    const texNumC = packedTexShape[1];\n    const valuesPerRow = Math.ceil(shape[rank - 1] / 2);\n    let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);\n    let params = `int b, int row, int col`;\n    let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;\n    for (let b = 2; b < rank - 1; b++) {\n        params = `int b${b}, ` + params;\n        texelsInBatch *= shape[rank - b - 1];\n        index = `b${b} * ${texelsInBatch} + ` + index;\n    }\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(${params}) {\n      int index = ${index};\n      int texR = index / ${texNumC};\n      int texC = index - texR * ${texNumC};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler4D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride2 = shape[3];\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${stride1}, ${stride2}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride2 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} +\n          depth * ${stride2} + depth2;\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler5D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride3 = shape[4];\n    const stride2 = shape[3] * stride3;\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2', 'depth3'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          depth3;\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${stride1}, ${stride2}, ${stride3}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride3 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3]},\n               ${shape[2] * shape[3]}, ${shape[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler6D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    const stride4 = shape[5];\n    const stride3 = shape[4] * stride4;\n    const stride2 = shape[3] * stride3;\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${stride4}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride4 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},\n               ${shape[2] * shape[3] * shape[4]},\n               ${shape[3] * shape[4]},\n               ${shape[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getUniformSampler(inputInfo) {\n    const texName = inputInfo.name;\n    const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n    if (inSize < 2) {\n        return `return ${texName};`;\n    }\n    return `\n    for (int i = 0; i < ${inSize}; i++) {\n      if (i == index) {\n        return ${texName}[i];\n      }\n    }\n  `;\n}\nfunction getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n    const texName = inputInfo.name;\n    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n    const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n    const inRank = inputInfo.shapeInfo.logicalShape.length;\n    const outRank = outShapeInfo.logicalShape.length;\n    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n    const type = getCoordsDataType(outRank);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet;\n    const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n    if (inRank === 0) {\n        coordsSnippet = '';\n    }\n    else if (outRank < 2 && broadcastDims.length >= 1) {\n        coordsSnippet = 'coords = 0;';\n    }\n    else {\n        coordsSnippet =\n            broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n                .join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n        unpackedCoordsSnippet = 'coords';\n    }\n    else {\n        unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n            .map((s, i) => `coords.${fields[i + rankDiff]}`)\n            .join(', ');\n    }\n    let output = `return outputValue;`;\n    const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n    const isInputScalar = inSize === 1;\n    const outSize = util.sizeFromShape(outShapeInfo.logicalShape);\n    const isOutputScalar = outSize === 1;\n    if (inRank === 1 && !isInputScalar && !isOutputScalar) {\n        output = `\n      return vec4(outputValue.xy, outputValue.xy);\n    `;\n    }\n    else if (isInputScalar && !isOutputScalar) {\n        if (outRank === 1) {\n            output = `\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `;\n        }\n        else {\n            output = `\n        return vec4(outputValue.x);\n      `;\n        }\n    }\n    else if (broadcastDims.length) {\n        const rows = inRank - 2;\n        const cols = inRank - 1;\n        if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {\n            output = `return vec4(outputValue.x);`;\n        }\n        else if (broadcastDims.indexOf(rows) > -1) {\n            output = `return vec4(outputValue.x, outputValue.y, ` +\n                `outputValue.x, outputValue.y);`;\n        }\n        else if (broadcastDims.indexOf(cols) > -1) {\n            output = `return vec4(outputValue.xx, outputValue.zz);`;\n        }\n    }\n    return `\n    vec4 ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});\n      ${output}\n    }\n  `;\n}\nfunction getSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n    const texName = inputInfo.name;\n    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n    const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n    const outTexShape = outShapeInfo.texShape;\n    const inTexShape = inputInfo.shapeInfo.texShape;\n    const inRank = inputInfo.shapeInfo.logicalShape.length;\n    const outRank = outShapeInfo.logicalShape.length;\n    if (!inputInfo.shapeInfo.isUniform && inRank === outRank &&\n        inputInfo.shapeInfo.flatOffset == null &&\n        util.arraysEqual(inTexShape, outTexShape)) {\n        return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, resultUV);\n      }\n    `;\n    }\n    const type = getCoordsDataType(outRank);\n    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet;\n    const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n    if (inRank === 0) {\n        coordsSnippet = '';\n    }\n    else if (outRank < 2 && broadcastDims.length >= 1) {\n        coordsSnippet = 'coords = 0;';\n    }\n    else {\n        coordsSnippet =\n            broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n                .join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n        unpackedCoordsSnippet = 'coords';\n    }\n    else {\n        unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n            .map((s, i) => `coords.${fields[i + rankDiff]}`)\n            .join(', ');\n    }\n    return `\n    float ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      return get${texFuncSnippet}(${unpackedCoordsSnippet});\n    }\n  `;\n}\nexport function getCoordsDataType(rank) {\n    if (rank <= 1) {\n        return 'int';\n    }\n    else if (rank === 2) {\n        return 'ivec2';\n    }\n    else if (rank === 3) {\n        return 'ivec3';\n    }\n    else if (rank === 4) {\n        return 'ivec4';\n    }\n    else if (rank === 5) {\n        return 'ivec5';\n    }\n    else if (rank === 6) {\n        return 'ivec6';\n    }\n    else {\n        throw Error(`GPU for rank ${rank} is not yet supported`);\n    }\n}\n/** Returns a new input info (a copy) that has a squeezed logical shape. */\nfunction squeezeInputInfo(inInfo, squeezedShape) {\n    // Deep copy.\n    const newInputInfo = JSON.parse(JSON.stringify(inInfo));\n    newInputInfo.shapeInfo.logicalShape = squeezedShape;\n    return newInputInfo;\n}\nfunction getSqueezedParams(params, keptDims) {\n    return keptDims.map(d => params[d]).join(', ');\n}\n//# sourceMappingURL=shader_compiler.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ArgMinMaxPackedProgram {\n    constructor(shape, windowSize, op, firstPass) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        util.assert(shape.length > 2, () => `Packed arg${op.charAt(0).toUpperCase() +\n            op.slice(1)} supports only inputs with rank above 2.`);\n        const inSize = shape[shape.length - 1];\n        const outSize = Math.ceil(inSize / windowSize);\n        this.outputShape = shape.slice(0, -1);\n        if (outSize > 1) {\n            this.outputShape.push(outSize);\n        }\n        if (!firstPass) {\n            this.variableNames.push('bestIndicesA');\n        }\n        const outShape = this.outputShape;\n        const rank = outShape.length;\n        const dtype = getCoordsDataType(rank);\n        const coords = getChannels('coords', rank);\n        let sourceLocSetup;\n        let sourceRank;\n        if (outSize === 1) {\n            sourceRank = rank + 1;\n            const sourceLocDType = getCoordsDataType(sourceRank);\n            sourceLocSetup = `\n        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 1]};\n        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 2]};\n        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 1]};\n        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 2]};`;\n        }\n        else {\n            sourceRank = rank;\n            sourceLocSetup = `\n        ${dtype} sourceLocR = coords;\n        ++${coords[rank - 1]};\n        ${dtype} sourceLocG = coords;\n        ++${coords[rank - 2]};\n        ${dtype} sourceLocA = coords;\n        --${coords[rank - 1]};\n        ${dtype} sourceLocB = coords;\n        --${coords[rank - 2]};`;\n        }\n        const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, sourceRank);\n        const inChannel = '.' + channels[sourceRank - 1]; // e.g. \".b\" for rank 3.\n        const intChannels = channels.map(x => 'int ' + x);\n        const srcRCoords = getChannels('sourceLocR', sourceRank - 1).concat('inIdx.r');\n        const srcGCoords = getChannels('sourceLocG', sourceRank - 1).concat('inIdx.g');\n        const srcBCoords = getChannels('sourceLocB', sourceRank - 1).concat('inIdx.b');\n        const srcACoords = getChannels('sourceLocA', sourceRank - 1).concat('inIdx.a');\n        const compOp = (op === 'max') ? 'greaterThan' : 'lessThan';\n        const fetchCandidateIdx = firstPass ? '' : `\n          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),\n                             getBestIndicesAChannel(${srcGCoords.join()}),\n                             getBestIndicesAChannel(${srcBCoords.join()}),\n                             getBestIndicesAChannel(${srcACoords.join()})));`;\n        const fetchValue = `vec4(\n            getAChannel(${srcRCoords.join()}),\n            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,\n            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;\n        const getBestIndicesAChannelSnippet = firstPass ? '' : `\n      float getBestIndicesAChannel(${intChannels.join()}) {\n        return getChannel(getBestIndicesA(${channels.join()}),\n                                          vec2(${channels.slice(-2).join()}));\n      }`;\n        this.userCode = `\n      float getAChannel(${intChannels.join()}) {\n        return getChannel(getA(${channels.join()}),\n                               vec2(${channels.slice(-2).join()}));\n      }\n      ${getBestIndicesAChannelSnippet}\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        bool hasNextCol = ${coords[rank - 1]} < ${outShape[rank - 1] - 1};\n        bool hasNextRow = ${coords[rank - 2]} < ${outShape[rank - 2] - 1};\n        ${sourceLocSetup}\n        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},\n          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${fetchValue};\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          inIdx = srcIdx;\n          ${fetchCandidateIdx}\n          vec4 candidate = ${fetchValue};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=argminmax_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AvgPool2DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const avgMultiplier = 1 / (filterHeight * filterWidth);\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth};\n            wC+= ${dilationWidth}) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class AvgPool3DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy'];\n        this.outputShape = convInfo.inShape;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=avg_pool_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\n// (Ar + Ai)(Br + Bi) =\n// ArBr + ArBi + AiBr + AiBi = ArBr - AB + ArBi + AiBr\n// Yr = ArBr - AB\n// Yi = ArBi + AiBr\nexport const COMPLEX_MULTIPLY = {\n    REAL: 'return areal * breal - aimag * bimag;',\n    IMAG: 'return areal * bimag + aimag * breal;'\n};\nexport class BinaryOpComplexProgram {\n    constructor(op, aShape, bShape) {\n        this.variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        this.userCode = `\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${op}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_complex_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nconst CHECK_NAN_SNIPPET = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nexport const ADD = 'return a + b;';\nexport const SUB = 'return a - b;';\nexport const MUL = 'return a * b;';\n// We use native integer division to deal with floating point imprecision. Since\n// we implement floor division and glsl implements truncated division, we\n// correct for this by subtracting 1 from result when the result is negative and\n// there is a remainder.\nexport const INT_DIV = `\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`;\nexport const POW = `\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nif (b == 0.0) {\n  return 1.0;\n}\nreturn (round(mod(b, 2.0)) != 1) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`;\nexport const SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport const EQUAL = `return float(a == b);`;\nexport const NOT_EQUAL = `return float(a != b);`;\nexport const LESS = `return float(a < b);`;\nexport const LESS_EQUAL = `return float(a <= b);`;\nexport const GREATER = `return float(a > b);`;\nexport const GREATER_EQUAL = `return float(a >= b);`;\nexport const LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;\nexport const LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;\nexport const MAX = CHECK_NAN_SNIPPET + `\n  return max(a, b);\n`;\nexport const MIN = CHECK_NAN_SNIPPET + `\n  return min(a, b);\n`;\nexport const MOD = `if (b == 0.0) return NAN;\n  return mod(a, b);`;\nexport const ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;\nexport const PRELU = `return (a < 0.) ? b * a : a;`;\nexport class BinaryOpProgram {\n    constructor(op, aShape, bShape) {\n        this.variableNames = ['A', 'B'];\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        this.userCode = `\n      float binaryOperation(float a, float b) {\n        ${op}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nconst CHECK_NAN_SNIPPET = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\nexport const INT_DIV = `\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n`;\nexport const POW = `\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const PRELU = `\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;\nexport const ELU_DER = `\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`;\nexport const EQUAL = `\n  return vec4(equal(a, b));\n`;\nexport const NOT_EQUAL = `\n  return vec4(notEqual(a, b));\n`;\nexport const LESS = `\n  return vec4(lessThan(a, b));\n`;\nexport const LESS_EQUAL = `\n  return vec4(lessThanEqual(a, b));\n`;\nexport const GREATER = `\n  return vec4(greaterThan(a, b));\n`;\nexport const GREATER_EQUAL = `\n  return vec4(greaterThanEqual(a, b));\n`;\nexport const LOGICAL_AND = `\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`;\nexport const LOGICAL_OR = `\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`;\nexport const MAX = `\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const MIN = `\n  vec4 result = vec4(min(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const MOD = `\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport class BinaryOpPackedProgram {\n    constructor(op, aShape, bShape, checkOutOfBounds = false) {\n        this.variableNames = ['A', 'B'];\n        this.supportsBroadcasting = true;\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const rank = this.outputShape.length;\n        let checkOutOfBoundsString = '';\n        if (checkOutOfBounds) {\n            if (rank === 0 || util.sizeFromShape(this.outputShape) === 1) {\n                checkOutOfBoundsString = `\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;\n            }\n            else {\n                const dtype = getCoordsDataType(rank);\n                checkOutOfBoundsString = `\n          ${dtype} coords = getOutputCoords();\n        `;\n                if (rank === 1) {\n                    checkOutOfBoundsString += `\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;\n                }\n                else {\n                    const channels = getChannels('coords', rank);\n                    checkOutOfBoundsString += `\n            bool nextRowOutOfBounds =\n              (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};\n            bool nextColOutOfBounds =\n              (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `;\n                }\n            }\n        }\n        this.userCode = `\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${op}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${checkOutOfBoundsString}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ClipProgram {\n    constructor(aShape) {\n        this.variableNames = ['A'];\n        this.outputShape = aShape;\n        this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    `;\n    }\n    getCustomSetupFunc(min, max) {\n        return (gpgpu, webGLProgram) => {\n            if (this.minLoc == null) {\n                this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n                this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n            }\n            gpgpu.gl.uniform1f(this.minLoc, min);\n            gpgpu.gl.uniform1f(this.maxLoc, max);\n        };\n    }\n}\n//# sourceMappingURL=clip_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ClipPackedProgram {\n    constructor(aShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = aShape;\n        this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    `;\n    }\n    getCustomSetupFunc(min, max) {\n        return (gpgpu, webGLProgram) => {\n            if (this.minLoc == null) {\n                this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n                this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n            }\n            gpgpu.gl.uniform1f(this.minLoc, min);\n            gpgpu.gl.uniform1f(this.maxLoc, max);\n        };\n    }\n}\n//# sourceMappingURL=clip_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ComplexAbsProgram {\n    constructor(shape) {\n        this.variableNames = ['real', 'imag'];\n        this.outputShape = shape;\n        this.userCode = `\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `;\n    }\n}\n//# sourceMappingURL=complex_abs_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class ConcatProgram {\n    // Concats 2d tensors along axis=1. See comments in MathBackendWebGL.concat().\n    constructor(shapes) {\n        this.outputShape = [];\n        this.outputShape = backend_util.computeOutShape(shapes, 1 /* axis */);\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const offsets = new Array(shapes.length - 1);\n        offsets[0] = shapes[0][1];\n        for (let i = 1; i < offsets.length; i++) {\n            offsets[i] = offsets[i - 1] + shapes[i][1];\n        }\n        const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];\n        for (let i = 1; i < offsets.length; i++) {\n            const shift = offsets[i - 1];\n            snippets.push(`else if (yC < ${offsets[i]}) ` +\n                `setOutput(getT${i}(yR, yC-${shift}));`);\n        }\n        const lastIndex = offsets.length;\n        const lastShift = offsets[offsets.length - 1];\n        snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${snippets.join('\\n        ')}\n      }\n    `;\n    }\n}\n//# sourceMappingURL=concat_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ConcatPackedProgram {\n    constructor(shapes, axis) {\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = [];\n        this.outputShape = backend_util.computeOutShape(shapes, axis);\n        const shape = this.outputShape;\n        const rank = shape.length;\n        const dtype = getCoordsDataType(rank);\n        const coords = getChannels('coords', rank);\n        const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank);\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const offsets = new Array(shapes.length - 1);\n        offsets[0] = shapes[0][axis];\n        for (let i = 1; i < offsets.length; i++) {\n            offsets[i] = offsets[i - 1] + shapes[i][axis];\n        }\n        const channel = channels[axis];\n        const lastChannels = channels.slice(-2);\n        const allChannels = channels.join();\n        let getValueSnippet = `if (${channel} < ${offsets[0]}) {\n        return getChannel(\n            getT0(${allChannels}), vec2(${lastChannels.join()}));\n        }`;\n        for (let i = 1; i < offsets.length; i++) {\n            const shift = offsets[i - 1];\n            // Note: the >= comparison below may seem unnecessary given the check\n            // above but is needed to workaround branch execution issues on some\n            // devices. It makes all the conditions exclusive without relying on\n            // execution order.\n            getValueSnippet += `\n        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i - 1]}) {\n          return getChannel(\n            getT${i}(${shiftedChannels(channels, channel, shift)}),\n            vec2(${shiftedChannels(lastChannels, channel, shift)}));\n        }`;\n        }\n        const lastIndex = offsets.length;\n        const shift = offsets[offsets.length - 1];\n        getValueSnippet += `\n        return getChannel(\n          getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),\n          vec2(${shiftedChannels(lastChannels, channel, shift)}));`;\n        this.userCode = `\n      float getValue(${channels.map(x => 'int ' + x)}) {\n        ${getValueSnippet}\n      }\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${coords}), 0., 0., 0.);\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} + 1;\n        if (${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.g = getValue(${coords});\n        }\n\n        ${coords[rank - 2]} = ${coords[rank - 2]} + 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]}) {\n          result.a = getValue(${coords});\n        }\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} - 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]} &&\n            ${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.b = getValue(${coords});\n        }\n        setOutput(result);\n      }\n    `;\n    }\n}\n/**\n * Return an expression for coordinates into a vector where a given channel\n * will be offset by [shift].\n *\n * @param channels the channels to consider\n * @param channel the channel we want shifted\n * @param shift  the amount to subtract from the channel.\n *\n * @returns a string of the form 'x, y-[shift], z' where any one channel can\n * have the shift applied.\n */\nfunction shiftedChannels(channels, channel, shift) {\n    const channelIdx = channels.indexOf(channel);\n    const res = channels.map((c, idx) => {\n        if (idx === channelIdx) {\n            return `${c} - ${shift}`;\n        }\n        else {\n            return c;\n        }\n    });\n    return res.join();\n}\n//# sourceMappingURL=concat_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Conv2DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              if (${isChannelsLast}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv2DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        const rowDim = isChannelsLast ? 1 : 2;\n        const colDim = isChannelsLast ? 2 : 3;\n        const channelDim = isChannelsLast ? 3 : 1;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${channelDim}];\n\n        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n\n              if (${isChannelsLast}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv3DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.userCode = `\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {\n            int xF = wF + yF * ${strideDepth} - ${padFront};\n\n            if (xF < 0 || xF >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n              int xR = wR + yR * ${strideHeight} - ${padTop};\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n                int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv3DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padFront = filterDepth - 1 - convInfo.padInfo.front;\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;\n\n          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${filterDepth} - 1 - wF;\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${filterHeight} - 1 - wR;\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${filterWidth} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthwiseConv2DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${channelMul} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class DepthwiseConv2DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${channelMul}; dm++) {\n              int d2 = d1 * ${channelMul} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_backprop_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Conv2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivationWeights = false) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n        const inputDepthVec4Remainder = convInfo.inChannels % 4;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const rowDim = isChannelsLast ? 1 : 2;\n        const colDim = isChannelsLast ? 2 : 3;\n        const channelDim = isChannelsLast ? 3 : 1;\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivationWeights) {\n                activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivationWeights) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${channelDim}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${convInfo.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${inputDepthVec4Remainder === 1}) {\n\n              if (${isChannelsLast}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              }\n\n            } else if (${inputDepthVec4Remainder === 2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${inputDepthVec4Remainder === 3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\nexport class Conv3DProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n        const inputDepthVec4Remainder = convInfo.inChannels % 4;\n        this.userCode = `\n      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          int xF = xFCorner + wF * ${dilationDepth};\n\n          if (xF < 0 || xF >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            int xR = xRCorner + wR * ${dilationHeight};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${inputDepthVec4Remainder === 1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);\n              } else if (${inputDepthVec4Remainder === 2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${inputDepthVec4Remainder === 3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthwiseConv2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const xNumRows = convInfo.inHeight;\n        const xNumCols = convInfo.inWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${xNumRows}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${xNumCols}) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport class DepthwiseConvPacked2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['x', 'W'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = convInfo.outShape;\n        const xNumRows = convInfo.inHeight;\n        const xNumCols = convInfo.inWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const texelsAcross = filterWidth;\n        let mainLoop = `int xR; int xC; int xCOffset;`;\n        for (let r = 0; r < filterHeight; r++) {\n            for (let c = 0; c < filterWidth; c++) {\n                mainLoop += `\n          vec4 xTexelR${r}C${c * 2} = vec4(0.);\n          vec4 wR${r}C${c} = vec4(0.);\n          vec4 xR${r}C${c} = vec4(0.);`;\n            }\n        }\n        /**\n         * This vectorized implementation works by gathering the values needed for\n         * each output channel's dot product into vec4's and then multiplying them\n         * all together (this happens in the final double for-loop below). Most of\n         * the main loop consists of constructing these vec4's with the minimum\n         * number of texture2D calls, which means making use of all four returned\n         * values from a texture2D call at once.\n         */\n        for (let r = 0; r < filterHeight; r++) {\n            for (let texelC = 0; texelC < texelsAcross; texelC++) {\n                const c = texelC * 2;\n                mainLoop += `\n          xR = xRCorner + ${r * dilationHeight};\n          xC = xCCorner + ${c * dilationWidth};\n        `;\n                if (strideWidth === 1) {\n                    if (c < filterWidth) {\n                        // If padding is odd, the outer texels have to be composed.\n                        if (padLeft % 2 === 1) {\n                            // TODO: Ensure vec4 previous does not result in redundant sample,\n                            // and avoid setting xTexelRC's that exceed the boundary in the\n                            // first place rather than resetting them to vec4(0)).\n                            // To compute xCOffset:\n                            // - If padding is odd, we must add 1 to ensure we ask for an\n                            // even-numbered row.\n                            // - We subtract 2 to access the previous texel.\n                            mainLoop += `\n                xCOffset = xC + 1;\n                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= ${xNumCols}) {\n                    xTexelR${r}C${c}.zw = vec2(0.);\n                  }\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xCOffset = xC + 1 - 2;\n                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  vec4 previous = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= ${xNumCols}) {\n                    previous.zw = vec2(0.);\n                  }\n\n                  xR${r}C${c} = vec4(previous.zw, xTexelR${r}C${c}.xy);\n                } else {\n                  xR${r}C${c} = vec4(0, 0, xTexelR${r}C${c}.xy);\n                }\n              `;\n                        }\n                        else {\n                            // Padding is even, so xRC corresponds to a single texel.\n                            mainLoop += `\n                if(xR >= 0 && xR < ${xNumRows} && xC >= 0 && xC < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xR${r}C${c} = xTexelR${r}C${c};\n              `;\n                        }\n                        if (c + 1 < filterWidth) {\n                            // If dilation is even, the second entry should match the first\n                            // (either both are composed or both are single samples). But if\n                            // dilation is odd, then the second entry should be the opposite\n                            // of the first (if the first is composed, the second is a single\n                            // sample, and vice versa.)\n                            const nextTexelOffset = padLeft % 2 === 0 ?\n                                util.nearestLargerEven(dilationWidth) :\n                                dilationWidth;\n                            if ((dilationWidth % 2 === 0 && padLeft % 2 === 1) ||\n                                (dilationWidth % 2 !== 0 && padLeft % 2 !== 1)) {\n                                mainLoop += `\n                  xCOffset = xC + ${padLeft % 2} + ${nextTexelOffset};\n\n                  if(xR >= 0 && xR < ${xNumRows} &&\n                    xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                  }\n                `;\n                                // If dilation > 1 then the xRC's will not be able to share any\n                                // values, so each xRC will require two unique calls to getX.\n                                if (dilationWidth > 1) {\n                                    mainLoop += `\n                    xCOffset -= 2;\n                    if(xR >= 0 && xR < ${xNumRows} &&\n                      xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                      xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n                    } else {\n                      xTexelR${r}C${c} = vec4(0.);\n                    }\n                  `;\n                                }\n                                mainLoop += `\n                  xR${r}C${c + 1} = vec4(\n                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.xy);\n                `;\n                            }\n                            else {\n                                mainLoop += `\n                  xCOffset = xC + ${nextTexelOffset};\n\n                  if(xR >= 0 && xR < ${xNumRows} &&\n                    xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                  }\n\n                  xR${r}C${c + 1} = xTexelR${r}C${c + 2};\n                `;\n                            }\n                        }\n                    }\n                }\n                else { // stride > 1\n                    if (c < filterWidth) {\n                        mainLoop += `\n              if(xR >= 0 && xR < ${xNumRows}) {\n            `;\n                        // Depending on whether padLeft is even or odd, we want either the\n                        // xy or zw channels from X texels for xR${r}C${c}. If padLeft is\n                        // even, xR${r}C${c + 1} is simply the zw channels of texels we've\n                        // already sampled. But if padLeft is odd, xR${r}C{$c + 1}.zw will\n                        // need to come from the xy channels of a new texel, hence the `vec4\n                        // final` initialized below.\n                        if (padLeft % 2 === 1) {\n                            mainLoop += `\n                xCOffset = xC + 1 - ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < ${xNumCols}) {\n                  xTexelR${r}C${c + 2} = getX(batch, xR, xC + 1, d1);\n                } else {\n                  xTexelR${r}C${c + 2} = vec4(0.);\n                }\n\n                xR${r}C${c} = vec4(\n                  xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n              `;\n                            if (c + 1 < filterWidth) {\n                                mainLoop += `\n                  vec4 final = vec4(0.);\n                  xCOffset = xC + 1 + ${strideWidth};\n                  if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xR${r}C${c + 1} = vec4(xTexelR${r}C${c + 2}.xy, final.xy);\n                `;\n                            }\n                        }\n                        else {\n                            mainLoop += `\n                if(xC >= 0 && xC < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xCOffset = xC + ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${r}C${c + 2} = vec4(0.);\n                }\n\n                xR${r}C${c} = vec4(\n                  xTexelR${r}C${c}.xy, xTexelR${r}C${c + 2}.xy);\n              `;\n                            if (c + 1 < filterWidth) {\n                                mainLoop += `\n                  xR${r}C${c + 1} = vec4(\n                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n                `;\n                            }\n                        }\n                        mainLoop += `}`;\n                    }\n                }\n                if (c < filterWidth) {\n                    mainLoop += `\n            vec4 wTexelR${r}C${c} = getW(${r}, ${c}, d1, q);\n            wR${r}C${c} = vec4(wTexelR${r}C${c}.xz, wTexelR${r}C${c}.xz);\n          `;\n                    if (c + 1 < filterWidth) {\n                        mainLoop += `\n              vec4 wTexelR${r}C${c + 1} = getW(${r}, ${c + 1}, d1, q);\n              wR${r}C${c + 1} =\n                vec4(wTexelR${r}C${c + 1}.xz, wTexelR${r}C${c + 1}.xz);`;\n                    }\n                }\n            }\n        }\n        for (let r = 0; r < filterHeight; r++) {\n            for (let c = 0; c < filterWidth; c++) {\n                mainLoop += `dotProd += xR${r}C${c} * wR${r}C${c};`;\n            }\n        }\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 dotProd = vec4(0.);\n\n        ${mainLoop}\n\n        vec4 result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_packed_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class CropAndResizeProgram {\n    constructor(imageShape, boxShape, cropSize, method, extrapolationValue) {\n        this.variableNames = ['Image', 'Boxes', 'BoxInd'];\n        this.outputShape = [];\n        const [batch, imageHeight, imageWidth, depth] = imageShape;\n        const [numBoxes,] = boxShape;\n        const [cropHeight, cropWidth] = cropSize;\n        this.outputShape = [numBoxes, cropHeight, cropWidth, depth];\n        const methodId = method === 'bilinear' ? 1 : 0;\n        const [inputHeightFloat, inputWidthFloat] = [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];\n        const [heightRatio, heightScale, inY] = cropHeight > 1 ?\n            [\n                `${(imageHeight - 1) / (cropHeight - 1)}`,\n                '(y2-y1) * height_ratio',\n                `y1*${inputHeightFloat} + float(y)*(height_scale)`,\n            ] :\n            [\n                '0.0',\n                '0.0',\n                `0.5 * (y1+y2) * ${inputHeightFloat}`,\n            ];\n        const [widthRatio, widthScale, inX] = cropWidth > 1 ?\n            [\n                `${(imageWidth - 1) / (cropWidth - 1)}`,\n                '(x2-x1) * width_ratio',\n                `x1*${inputWidthFloat} + float(x)*(width_scale)`,\n            ] :\n            [\n                '0.0',\n                '0.0',\n                `0.5 * (x1+x2) * ${inputWidthFloat}`,\n            ];\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc\n        this.userCode = `\n      const float height_ratio = float(${heightRatio});\n      const float width_ratio = float(${widthRatio});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${batch}) {\n          return;\n        }\n\n        float height_scale = ${heightScale};\n        float width_scale = ${widthScale};\n\n        float in_y = ${inY};\n        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n        float in_x = ${inX};\n        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${methodId} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=crop_and_resize_gpu.js.map", "import { getCoordsDataType } from './shader_compiler';\nexport class CumSumProgram {\n    constructor(shape, exclusive, reverse) {\n        this.variableNames = ['x'];\n        this.outputShape = shape;\n        const rank = shape.length;\n        const val = exclusive ? '0.0' : `getX(${getCoords(rank, 'coords')})`;\n        const length = shape[shape.length - 1];\n        let condition = '';\n        let idxString = '';\n        // When exclusive is set, the cumsum op becomes roll op that copies the\n        // value from the previous index based on the direction specified by the\n        // reverse flag.\n        if (exclusive) {\n            condition = reverse ? `end != ${length - 1}` : 'end != 0';\n            idxString = reverse ? 'end + 1' : 'end - 1';\n        }\n        else {\n            condition = reverse ? `end + pow2 < ${length}` : 'end >= pow2';\n            idxString = (reverse ? 'end + pow2' : 'end - pow2');\n        }\n        this.userCode = `\n      uniform float index;\n      void main() {\n        ${getCoordsDataType(rank)} coords = getOutputCoords();\n        int end = ${getFinalCoord(rank, 'coords')};\n        float val = ${val};\n        int pow2 = int(pow(2.0, index));\n        if (${condition}) {\n          int idx = ${idxString};\n          ${getFinalCoord(rank, 'coords')} = idx;\n          val += getX(${getCoords(rank, 'coords')});\n        }\n        setOutput(val);\n      }\n    `;\n    }\n    getCustomSetupFunc(index) {\n        return (gpgpu, webGLProgram) => {\n            if (this.index == null) {\n                this.index = gpgpu.getUniformLocation(webGLProgram, 'index');\n            }\n            gpgpu.gl.uniform1f(this.index, index);\n        };\n    }\n}\nfunction getCoords(rank, name) {\n    if (rank === 1) {\n        return `${name}`;\n    }\n    else if (rank === 2) {\n        return `${name}.x, ${name}.y`;\n    }\n    else if (rank === 3) {\n        return `${name}.x, ${name}.y, ${name}.z`;\n    }\n    else if (rank === 4) {\n        return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n    }\n    else {\n        throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n    }\n}\nfunction getFinalCoord(rank, name) {\n    if (rank === 1) {\n        return `${name}`;\n    }\n    else if (rank === 2) {\n        return `${name}.y`;\n    }\n    else if (rank === 3) {\n        return `${name}.z`;\n    }\n    else if (rank === 4) {\n        return `${name}.w`;\n    }\n    else {\n        throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n    }\n}\n//# sourceMappingURL=cumsum_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nimport { getDenseTexShape, PackingScheme } from './tex_util';\nexport class DecodeMatrixProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        this.outPackingScheme = PackingScheme.DENSE;\n        const texShape = getDenseTexShape(outputShape);\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=decode_matrix_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nimport { getDenseTexShape, PackingScheme } from './tex_util';\nexport class DecodeMatrixPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outPackingScheme = PackingScheme.DENSE;\n        const texShape = getDenseTexShape(outputShape);\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=decode_matrix_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthToSpaceProgram {\n    constructor(outputShape, blockSize, dataFormat) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        this.outputShape = outputShape;\n        this.blockSize = blockSize;\n        this.dataFormat = dataFormat;\n        this.userCode = `\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${blockSize};\n      int offset_h = imod(h, ${blockSize});\n      int in_w = w / ${blockSize};\n      int offset_w = imod(w, ${blockSize});\n      int offset_d = (offset_h * ${blockSize} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `;\n    }\n    getHeightCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[1]`;\n        }\n        else {\n            return `coords[2]`;\n        }\n    }\n    getWidthCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[2]`;\n        }\n        else {\n            return `coords[3]`;\n        }\n    }\n    getDepthCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[3]`;\n        }\n        else {\n            return `coords[1]`;\n        }\n    }\n    getOutputDepthSize() {\n        if (this.dataFormat === 'NHWC') {\n            return this.outputShape[3];\n        }\n        else {\n            return this.outputShape[1];\n        }\n    }\n    getInputSamplingString() {\n        if (this.dataFormat === 'NHWC') {\n            return `getX(b, in_h, in_w, in_d)`;\n        }\n        else {\n            return `getX(b, in_d, in_h, in_w)`;\n        }\n    }\n}\n//# sourceMappingURL=depth_to_space_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DiagProgram {\n    constructor(size) {\n        this.variableNames = ['X'];\n        this.outputShape = [size, size];\n        this.userCode = `\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=diag_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport { ENCODE_FLOAT_SNIPPET } from './shader_compiler_util';\nimport { TextureUsage } from './tex_util';\nexport class EncodeFloatProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.outTexUsage = TextureUsage.DOWNLOAD;\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_float_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport { ENCODE_FLOAT_SNIPPET } from './shader_compiler_util';\nimport { TextureUsage } from './tex_util';\nexport class EncodeFloatPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = false;\n        this.outTexUsage = TextureUsage.DOWNLOAD;\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_float_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nexport class EncodeMatrixProgram {\n    constructor(outputShape, texShape, inputIsUnsignedByte = false) {\n        this.variableNames = ['A'];\n        const glsl = getGlslDifferences();\n        const [height, width] = texShape;\n        this.outputShape = outputShape;\n        let output = `result`;\n        if (inputIsUnsignedByte) {\n            output = `floor(result * 255. + 0.5)`;\n        }\n        this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / ${width};\n        int c = imod(flatIndex, ${width});\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n        vec4 values = ${glsl.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${glsl.output} = vec4(${output}, 0., 0., 0.);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_matrix_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\n/*\nThis is how the shader encodes a tensor with shape = [2, 3, 5]\n(indices are [batch, row, col]).\n\n000|001   002|003   004|xxx   020|021   022|023   024|xxx\n-------   -------   -------   -------   -------   -------\n010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\n100|101   102|103   104|xxx   120|121   122|123   124|xxx\n-------   -------   -------   -------   -------   -------\n110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\nSingle texels contain only values from the same batch, and from adjacent rows\nand columns.\n */\nexport class EncodeMatrixPackedProgram {\n    constructor(outputShape, texShape, inputIsUnsignedByte = false) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        const glsl = getGlslDifferences();\n        const [height, width] = texShape;\n        this.outputShape = outputShape;\n        let mainLoop = '';\n        let output = 'result';\n        if (inputIsUnsignedByte) {\n            output = 'floor(result * 255. + 0.5)';\n        }\n        for (let row = 0; row <= 1; row++) {\n            for (let col = 0; col <= 1; col++) {\n                const channel = row * 2 + col;\n                mainLoop += `\n          localCoords = coords;\n          if(localCoords[2] + ${col} < ${outputShape[2]}) {\n            localCoords[2] += ${col};\n            if(localCoords[1] + ${row} < ${outputShape[1]}) {\n              localCoords[1] += ${row};\n\n              flatIndex = getFlatIndex(localCoords);\n              offset = imod(flatIndex, 4);\n\n              flatIndex = idiv(flatIndex, 4, 1.);\n\n              r = flatIndex / ${width};\n              c = imod(flatIndex, ${width});\n              uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n              values = ${glsl.texture2D}(A, uv);\n\n              if(offset == 0) {\n                result[${channel}] = values[0];\n              } else if(offset == 1) {\n                result[${channel}] = values[1];\n              } else if(offset == 2) {\n                result[${channel}] = values[2];\n              } else {\n                result[${channel}] = values[3];\n              }\n            }\n          }\n        `;\n            }\n        }\n        this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        vec4 result = vec4(0.);\n        int flatIndex, r, c, offset;\n        ivec3 localCoords;\n        vec2 uv;\n        vec4 values;\n\n        ${mainLoop}\n\n        ${glsl.output} = ${output};\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_matrix_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const COMPLEX_FFT = {\n    REAL: 'return real * expR - imag * expI;',\n    IMAG: 'return real * expI + imag * expR;'\n};\nexport class FFTProgram {\n    constructor(op, inputShape, inverse) {\n        this.variableNames = ['real', 'imag'];\n        const innerDim = inputShape[1];\n        this.outputShape = inputShape;\n        const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;\n        const resultDenominator = inverse ? `${innerDim}.0` : '1.0';\n        this.userCode = `\n      const float exponentMultiplier = ${exponentMultiplierSnippet};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${op}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${innerDim});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${innerDim}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=fft_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FillProgram {\n    constructor(shape, value) {\n        this.outputShape = [];\n        this.variableNames = ['x'];\n        this.outputShape = shape;\n        this.userCode = `\n      uniform float value;\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    `;\n    }\n    getCustomSetupFunc(value) {\n        return (gpgpu, webGLProgram) => {\n            if (this.valueLoc == null) {\n                this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'value');\n            }\n            gpgpu.gl.uniform1f(this.valueLoc, value);\n        };\n    }\n}\n//# sourceMappingURL=fill_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class GatherProgram {\n    constructor(aShape, indicesLength, axis) {\n        this.variableNames = ['A', 'indices'];\n        const outputShape = aShape.slice();\n        outputShape[axis] = indicesLength;\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const sourceCoords = getSourceCoords(aShape, axis);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n    }\n}\nfunction getSourceCoords(aShape, axis) {\n    const rank = aShape.length;\n    if (rank > 4) {\n        throw Error(`Gather for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n        return `int(getIndices(resRC))`;\n    }\n    const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n    const sourceCoords = [];\n    for (let i = 0; i < aShape.length; i++) {\n        if (i === axis) {\n            sourceCoords.push(`int(getIndices(${currentCoords[i]}))`);\n        }\n        else {\n            sourceCoords.push(`${currentCoords[i]}`);\n        }\n    }\n    return sourceCoords.join();\n}\n//# sourceMappingURL=gather_gpu.js.map", "import { getCoordsDataType } from './shader_compiler';\nexport class GatherNDProgram {\n    constructor(sliceDim, strides, shape) {\n        this.sliceDim = sliceDim;\n        this.strides = strides;\n        this.variableNames = ['x', 'indices'];\n        this.outputShape = shape;\n        const stridesType = getCoordsDataType(strides.length);\n        const dtype = getCoordsDataType(shape.length);\n        const strideString = this.sliceDim > 1 ? 'strides[j]' : 'strides';\n        this.userCode = `\n        ${stridesType} strides = ${stridesType}(${this.strides});\n         void main() {\n          ${dtype} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${strideString};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `;\n    }\n}\n//# sourceMappingURL=gather_nd_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\nexport function createVertexShader(gl) {\n    const glsl = getGlslDifferences();\n    const vertexShaderSource = `${glsl.version}\n    precision highp float;\n    ${glsl.attribute} vec3 clipSpacePos;\n    ${glsl.attribute} vec2 uv;\n    ${glsl.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;\n    return webgl_util.createVertexShader(gl, vertexShaderSource);\n}\nexport function createVertexBuffer(gl) {\n    // [x y z u v] * [upper-left, lower-left, upper-right, lower-right]\n    const vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);\n    return webgl_util.createStaticVertexBuffer(gl, vertexArray);\n}\nexport function createIndexBuffer(gl) {\n    // OpenGL (and WebGL) have \"CCW == front\" winding\n    const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);\n    return webgl_util.createStaticIndexBuffer(gl, triangleVertexIndices);\n}\nfunction createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {\n    webgl_util.validateTextureSize(width, height);\n    const texture = webgl_util.createTexture(gl);\n    const tex2d = gl.TEXTURE_2D;\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(tex2d, texture));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));\n    webgl_util.callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null));\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n    return texture;\n}\nexport function getInternalFormatForFloat32MatrixTexture(textureConfig) {\n    return textureConfig.internalFormatFloat;\n}\nexport function createFloat32MatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT);\n}\nexport function getInternalFormatForFloat16MatrixTexture(textureConfig) {\n    return textureConfig.internalFormatHalfFloat;\n}\nexport function createFloat16MatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);\n}\nexport function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) {\n    return textureConfig.downloadTextureFormat;\n}\nexport function createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE);\n}\nexport function getInternalFormatForPackedMatrixTexture(textureConfig) {\n    return textureConfig.internalFormatPackedFloat;\n}\nexport function createPackedMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT);\n}\nexport function getInternalFormatForFloat16PackedMatrixTexture(textureConfig) {\n    return textureConfig.internalFormatPackedHalfFloat;\n}\nexport function createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat);\n}\nexport function bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {\n    const posOffset = 0; // x is the first buffer element\n    const uvOffset = 3 * 4; // uv comes after [x y z]\n    const stride = (3 * 4) + (2 * 4); // xyz + uv, each entry is 4-byte float.\n    webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));\n    const success = webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'clipSpacePos', vertexBuffer, 3, stride, posOffset);\n    return success &&\n        webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'uv', vertexBuffer, 2, stride, uvOffset);\n}\nexport function uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) {\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n    let dataForUpload, texelDataType, internalFormat;\n    if (data instanceof Uint8Array) {\n        dataForUpload = new Uint8Array(width * height * 4);\n        texelDataType = gl.UNSIGNED_BYTE;\n        internalFormat = gl.RGBA;\n    }\n    else {\n        dataForUpload = new Float32Array(width * height * 4);\n        texelDataType = gl.FLOAT;\n        internalFormat = textureConfig.internalFormatPackedFloat;\n    }\n    dataForUpload.set(data);\n    webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload));\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function uploadPixelDataToTexture(gl, texture, pixels) {\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n    if (pixels.data instanceof Uint8Array) {\n        webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));\n    }\n    else {\n        webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n    }\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function createBufferFromOutputTexture(gl2, rows, columns, textureConfig) {\n    // Create and bind the buffer.\n    const buffer = gl2.createBuffer();\n    webgl_util.callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer));\n    // Initialize the buffer to the size of the texture in bytes.\n    const bytesPerFloat = 4;\n    const valuesPerTexel = 4;\n    const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;\n    webgl_util.callAndCheck(gl2, () => gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));\n    // Enqueue a command on the GPU command queue to copy of texture into the\n    // buffer.\n    webgl_util.callAndCheck(gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));\n    webgl_util.callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));\n    return buffer;\n}\nexport function downloadFloat32MatrixFromBuffer(gl, buffer, size) {\n    const gl2 = gl;\n    const downloadTarget = new Float32Array(size);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n    return downloadTarget;\n}\nexport function downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {\n    const [w, h] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    const numChannels = 4;\n    const downloadTarget = new Uint8Array(tex_util.getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));\n    webgl_util.callAndCheck(gl, () => gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget));\n    // By wrapping the buffer in a Float32Array, we use native browser IEEE 754\n    // decoding of the 4 bytes that back each 32 bit float.\n    return new Float32Array(downloadTarget.buffer);\n}\nexport function downloadPackedMatrixFromBuffer(gl, buffer, batch, rows, cols, physicalRows, physicalCols, textureConfig) {\n    const gl2 = gl;\n    const downloadTarget = new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n    return downloadTarget;\n}\nexport function downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {\n    const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);\n    webgl_util.callAndCheck(gl, () => gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));\n    return packedRGBA;\n}\n//# sourceMappingURL=gpgpu_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { getWebGLContext, setWebGLContext } from './canvas_util';\nimport * as gpgpu_util from './gpgpu_util';\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\nexport class GPGPUContext {\n    constructor(gl) {\n        this.outputTexture = null;\n        this.program = null;\n        this.disposed = false;\n        this.vertexAttrsAreBound = false;\n        this.itemsToPoll = [];\n        const glVersion = env().getNumber('WEBGL_VERSION');\n        if (gl != null) {\n            this.gl = gl;\n            setWebGLContext(glVersion, gl);\n        }\n        else {\n            this.gl = getWebGLContext(glVersion);\n        }\n        // WebGL 2.0 enables texture floats without an extension.\n        let COLOR_BUFFER_FLOAT = 'WEBGL_color_buffer_float';\n        const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n        if (env().getNumber('WEBGL_VERSION') === 1) {\n            const TEXTURE_FLOAT = 'OES_texture_float';\n            const TEXTURE_HALF_FLOAT = 'OES_texture_half_float';\n            this.textureFloatExtension =\n                webgl_util.getExtensionOrThrow(this.gl, TEXTURE_FLOAT);\n            if (webgl_util.hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {\n                this.textureHalfFloatExtension =\n                    webgl_util.getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);\n            }\n            else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n                throw new Error('GL context does not support half float textures, yet the ' +\n                    'environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n            }\n            this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);\n            if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n                this.colorBufferHalfFloatExtension =\n                    webgl_util.getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);\n            }\n            else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n                throw new Error('GL context does not support color renderable half floats, yet ' +\n                    'the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n            }\n        }\n        else {\n            COLOR_BUFFER_FLOAT = 'EXT_color_buffer_float';\n            if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {\n                this.colorBufferFloatExtension =\n                    this.gl.getExtension(COLOR_BUFFER_FLOAT);\n            }\n            else if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n                this.colorBufferHalfFloatExtension =\n                    this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n            }\n            else {\n                throw new Error('GL context does not support color renderable floats');\n            }\n        }\n        this.vertexBuffer = gpgpu_util.createVertexBuffer(this.gl);\n        this.indexBuffer = gpgpu_util.createIndexBuffer(this.gl);\n        this.framebuffer = webgl_util.createFramebuffer(this.gl);\n        this.textureConfig =\n            tex_util.getTextureConfig(this.gl, this.textureHalfFloatExtension);\n    }\n    get debug() {\n        return env().getBool('DEBUG');\n    }\n    dispose() {\n        if (this.disposed) {\n            return;\n        }\n        if (this.program != null) {\n            console.warn('Disposing a GPGPUContext that still has a bound WebGLProgram.' +\n                ' This is probably a resource leak, delete the program with ' +\n                'GPGPUContext.deleteProgram before disposing.');\n        }\n        if (this.outputTexture != null) {\n            console.warn('Disposing a GPGPUContext that still has a bound output matrix ' +\n                'texture.  This is probably a resource leak, delete the output ' +\n                'matrix texture with GPGPUContext.deleteMatrixTexture before ' +\n                'disposing.');\n        }\n        const gl = this.gl;\n        webgl_util.callAndCheck(gl, () => gl.finish());\n        webgl_util.callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));\n        webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));\n        this.disposed = true;\n    }\n    createFloat32MatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createFloat16MatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createUnsignedBytesMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    uploadPixelDataToTexture(texture, pixels) {\n        this.throwIfDisposed();\n        gpgpu_util.uploadPixelDataToTexture(this.gl, texture, pixels);\n    }\n    uploadDenseMatrixToTexture(texture, width, height, data) {\n        this.throwIfDisposed();\n        gpgpu_util.uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig);\n    }\n    createFloat16PackedMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createPackedMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    deleteMatrixTexture(texture) {\n        this.throwIfDisposed();\n        if (this.outputTexture === texture) {\n            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n            this.outputTexture = null;\n        }\n        webgl_util.callAndCheck(this.gl, () => this.gl.deleteTexture(texture));\n    }\n    downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) {\n        return this.downloadMatrixDriver(texture, () => gpgpu_util.downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig));\n    }\n    downloadPackedMatrixFromBuffer(buffer, batch, rows, columns, physicalRows, physicalCols) {\n        return gpgpu_util.downloadPackedMatrixFromBuffer(this.gl, buffer, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);\n    }\n    downloadFloat32MatrixFromBuffer(buffer, size) {\n        return gpgpu_util.downloadFloat32MatrixFromBuffer(this.gl, buffer, size);\n    }\n    createBufferFromTexture(texture, rows, columns) {\n        this.bindTextureToFrameBuffer(texture);\n        const result = gpgpu_util.createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig);\n        this.unbindTextureToFrameBuffer();\n        return result;\n    }\n    createAndWaitForFence() {\n        const fenceContext = this.createFence(this.gl);\n        return this.pollFence(fenceContext);\n    }\n    createFence(gl) {\n        let query;\n        let isFencePassed;\n        if (env().getBool('WEBGL_FENCE_API_ENABLED')) {\n            const gl2 = gl;\n            const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);\n            gl.flush();\n            isFencePassed = () => {\n                const status = gl2.clientWaitSync(sync, 0, 0);\n                return status === gl2.ALREADY_SIGNALED ||\n                    status === gl2.CONDITION_SATISFIED;\n            };\n            query = sync;\n        }\n        else if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n            query = this.beginQuery();\n            this.endQuery();\n            isFencePassed = () => this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n        }\n        else {\n            // If we have no way to fence, return true immediately. This will fire in\n            // WebGL 1.0 when there is no disjoint query timer. In this case, because\n            // the fence passes immediately, we'll immediately ask for a download of\n            // the texture, which will cause the UI thread to hang.\n            isFencePassed = () => true;\n        }\n        return { query, isFencePassed };\n    }\n    downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {\n        return this.downloadMatrixDriver(texture, () => gpgpu_util.downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));\n    }\n    createProgram(fragmentShaderSource) {\n        this.throwIfDisposed();\n        const gl = this.gl;\n        const fragmentShader = webgl_util.createFragmentShader(gl, fragmentShaderSource);\n        const vertexShader = gpgpu_util.createVertexShader(gl);\n        const program = webgl_util.createProgram(gl);\n        webgl_util.callAndCheck(gl, () => gl.attachShader(program, vertexShader));\n        webgl_util.callAndCheck(gl, () => gl.attachShader(program, fragmentShader));\n        webgl_util.linkProgram(gl, program);\n        if (this.debug) {\n            webgl_util.validateProgram(gl, program);\n        }\n        if (!this.vertexAttrsAreBound) {\n            this.setProgram(program);\n            this.vertexAttrsAreBound = gpgpu_util.bindVertexProgramAttributeStreams(gl, this.program, this.vertexBuffer);\n        }\n        return program;\n    }\n    deleteProgram(program) {\n        this.throwIfDisposed();\n        if (program === this.program) {\n            this.program = null;\n        }\n        if (program != null) {\n            webgl_util.callAndCheck(this.gl, () => this.gl.deleteProgram(program));\n        }\n    }\n    setProgram(program) {\n        this.throwIfDisposed();\n        this.program = program;\n        if ((this.program != null) && this.debug) {\n            webgl_util.validateProgram(this.gl, this.program);\n        }\n        webgl_util.callAndCheck(this.gl, () => this.gl.useProgram(program));\n    }\n    getUniformLocation(program, uniformName, shouldThrow = true) {\n        this.throwIfDisposed();\n        if (shouldThrow) {\n            return webgl_util.getProgramUniformLocationOrThrow(this.gl, program, uniformName);\n        }\n        else {\n            return webgl_util.getProgramUniformLocation(this.gl, program, uniformName);\n        }\n    }\n    getAttributeLocation(program, attribute) {\n        this.throwIfDisposed();\n        return webgl_util.callAndCheck(this.gl, () => this.gl.getAttribLocation(program, attribute));\n    }\n    getUniformLocationNoThrow(program, uniformName) {\n        this.throwIfDisposed();\n        return this.gl.getUniformLocation(program, uniformName);\n    }\n    setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) {\n        this.throwIfDisposed();\n        this.throwIfNoProgram();\n        webgl_util.bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit);\n    }\n    setOutputMatrixTexture(outputMatrixTexture, rows, columns) {\n        this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);\n    }\n    setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) {\n        this.throwIfDisposed();\n        const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n        this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);\n    }\n    setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n        this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);\n    }\n    setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n        throw new Error('setOutputPackedMatrixWriteRegion not implemented.');\n    }\n    debugValidate() {\n        if (this.program != null) {\n            webgl_util.validateProgram(this.gl, this.program);\n        }\n        webgl_util.validateFramebuffer(this.gl);\n    }\n    executeProgram() {\n        this.throwIfDisposed();\n        this.throwIfNoProgram();\n        const gl = this.gl;\n        if (this.debug) {\n            this.debugValidate();\n        }\n        webgl_util.callAndCheck(gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));\n    }\n    blockUntilAllProgramsCompleted() {\n        this.throwIfDisposed();\n        webgl_util.callAndCheck(this.gl, () => this.gl.finish());\n    }\n    getQueryTimerExtension() {\n        if (this.disjointQueryTimerExtension == null) {\n            this.disjointQueryTimerExtension =\n                webgl_util.getExtensionOrThrow(this.gl, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2 ?\n                    'EXT_disjoint_timer_query_webgl2' :\n                    'EXT_disjoint_timer_query');\n        }\n        return this.disjointQueryTimerExtension;\n    }\n    getQueryTimerExtensionWebGL2() {\n        return this.getQueryTimerExtension();\n    }\n    getQueryTimerExtensionWebGL1() {\n        return this.getQueryTimerExtension();\n    }\n    beginQuery() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            const query = gl2.createQuery();\n            gl2.beginQuery(ext.TIME_ELAPSED_EXT, query);\n            return query;\n        }\n        const ext = this.getQueryTimerExtensionWebGL1();\n        const query = ext.createQueryEXT();\n        ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);\n        return query;\n    }\n    endQuery() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            gl2.endQuery(ext.TIME_ELAPSED_EXT);\n            return;\n        }\n        const ext = this.getQueryTimerExtensionWebGL1();\n        ext.endQueryEXT(ext.TIME_ELAPSED_EXT);\n    }\n    async waitForQueryAndGetTime(query) {\n        await util.repeatedTry(() => this.disposed || // while testing contexts are created / disposed\n            // in rapid succession, so without this check we\n            // may poll for the query timer indefinitely\n            this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')));\n        return this.getQueryTime(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n    }\n    getQueryTime(query, queryTimerVersion) {\n        if (queryTimerVersion === 0) {\n            return null;\n        }\n        if (queryTimerVersion === 2) {\n            const gl2 = this.gl;\n            const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);\n            // Return milliseconds.\n            return timeElapsedNanos / 1000000;\n        }\n        else {\n            const ext = this.getQueryTimerExtensionWebGL1();\n            const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);\n            // Return milliseconds.\n            return timeElapsedNanos / 1000000;\n        }\n    }\n    isQueryAvailable(query, queryTimerVersion) {\n        if (queryTimerVersion === 0) {\n            return true;\n        }\n        if (queryTimerVersion === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);\n            if (this.disjoint == null) {\n                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n            }\n            return available && !this.disjoint;\n        }\n        else {\n            const ext = this.getQueryTimerExtensionWebGL1();\n            const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);\n            if (this.disjoint == null) {\n                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n            }\n            return available && !this.disjoint;\n        }\n    }\n    pollFence(fenceContext) {\n        return new Promise(resolve => {\n            this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());\n        });\n    }\n    pollItems() {\n        // Find the last query that has finished.\n        const index = linearSearchLastTrue(this.itemsToPoll.map(x => x.isDoneFn));\n        for (let i = 0; i <= index; ++i) {\n            const { resolveFn } = this.itemsToPoll[i];\n            resolveFn();\n        }\n        this.itemsToPoll = this.itemsToPoll.slice(index + 1);\n    }\n    addItemToPoll(isDoneFn, resolveFn) {\n        this.itemsToPoll.push({ isDoneFn, resolveFn });\n        if (this.itemsToPoll.length > 1) {\n            // We already have a running loop that polls.\n            return;\n        }\n        // Start a new loop that polls.\n        util.repeatedTry(() => {\n            this.pollItems();\n            // End the loop if no more items to poll.\n            return this.itemsToPoll.length === 0;\n        });\n    }\n    bindTextureToFrameBuffer(texture) {\n        this.throwIfDisposed();\n        webgl_util.bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);\n        if (this.debug) {\n            webgl_util.validateFramebuffer(this.gl);\n        }\n    }\n    unbindTextureToFrameBuffer() {\n        if (this.outputTexture != null) {\n            webgl_util.bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);\n            if (this.debug) {\n                webgl_util.validateFramebuffer(this.gl);\n            }\n        }\n        else {\n            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n        }\n    }\n    downloadMatrixDriver(texture, downloadAndDecode) {\n        this.bindTextureToFrameBuffer(texture);\n        const result = downloadAndDecode();\n        this.unbindTextureToFrameBuffer();\n        return result;\n    }\n    setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) {\n        this.throwIfDisposed();\n        const gl = this.gl;\n        webgl_util.bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);\n        if (this.debug) {\n            webgl_util.validateFramebuffer(gl);\n        }\n        this.outputTexture = outputMatrixTextureMaybePacked;\n        webgl_util.callAndCheck(gl, () => gl.viewport(0, 0, width, height));\n        webgl_util.callAndCheck(gl, () => gl.scissor(0, 0, width, height));\n    }\n    setOutputMatrixWriteRegionDriver(x, y, width, height) {\n        this.throwIfDisposed();\n        webgl_util.callAndCheck(this.gl, () => this.gl.scissor(x, y, width, height));\n    }\n    throwIfDisposed() {\n        if (this.disposed) {\n            throw new Error('Attempted to use disposed GPGPUContext.');\n        }\n    }\n    throwIfNoProgram() {\n        if (this.program == null) {\n            throw new Error('No GPU program is currently set.');\n        }\n    }\n}\n/**\n * Finds the index of the last true element using linear search.\n * Note: We can't do binary search because Chrome expects us to explicitly\n * test all fences before download:\n * https://github.com/tensorflow/tfjs/issues/1145\n */\nexport function linearSearchLastTrue(arr) {\n    let i = 0;\n    for (; i < arr.length; ++i) {\n        const isDone = arr[i]();\n        if (!isDone) {\n            break;\n        }\n    }\n    return i - 1;\n}\n//# sourceMappingURL=gpgpu_context.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport * as shader_compiler from './shader_compiler';\nexport function compileProgram(gpgpu, program, inputs, output) {\n    const userCode = program.userCode;\n    const inputInfos = inputs.map((input, i) => {\n        const shapeInfo = {\n            logicalShape: input.shape,\n            texShape: input.isUniform ? null : input.texData.texShape,\n            isUniform: input.isUniform,\n            isPacked: input.isUniform ? false : input.texData.isPacked,\n            flatOffset: null\n        };\n        if (input.texData != null && input.texData.slice != null &&\n            input.texData.slice.flatOffset > 0) {\n            shapeInfo.flatOffset = input.texData.slice.flatOffset;\n        }\n        return { name: program.variableNames[i], shapeInfo };\n    });\n    const inShapeInfos = inputInfos.map(x => x.shapeInfo);\n    const outShapeInfo = {\n        logicalShape: output.shape,\n        texShape: output.texData.texShape,\n        isUniform: false,\n        isPacked: output.texData.isPacked,\n        flatOffset: null\n    };\n    const source = shader_compiler.makeShader(inputInfos, outShapeInfo, userCode, program.packedInputs);\n    const webGLProgram = gpgpu.createProgram(source);\n    // Add special uniforms (NAN, INFINITY)\n    let infLoc = null;\n    const nanLoc = gpgpu.getUniformLocation(webGLProgram, 'NAN', false);\n    if (env().getNumber('WEBGL_VERSION') === 1) {\n        infLoc = gpgpu.getUniformLocation(webGLProgram, 'INFINITY', false);\n    }\n    // Add user-defined uniforms\n    const uniformLocations = {};\n    for (let i = 0; i < program.variableNames.length; i++) {\n        const varName = program.variableNames[i];\n        const shouldThrow = false;\n        uniformLocations[varName] =\n            gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);\n        uniformLocations[`offset${varName}`] =\n            gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);\n    }\n    return {\n        program,\n        source,\n        webGLProgram,\n        uniformLocations,\n        inShapeInfos,\n        outShapeInfo,\n        infLoc,\n        nanLoc,\n    };\n}\nfunction validateBinaryAndProgram(shapeInfos, inputs) {\n    if (shapeInfos.length !== inputs.length) {\n        throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but ` +\n            `was executed with ${inputs.length} inputs`);\n    }\n    shapeInfos.forEach((s, i) => {\n        const shapeA = s.logicalShape;\n        const input = inputs[i];\n        const shapeB = input.shape;\n        if (!util.arraysEqual(shapeA, shapeB)) {\n            throw Error(`Binary was compiled with different shapes than ` +\n                `the current args. Shapes ${shapeA} and ${shapeB} must match`);\n        }\n        // The input is uploaded as uniform.\n        if (s.isUniform && input.isUniform) {\n            return;\n        }\n        const texShapeA = s.texShape;\n        const texShapeB = input.isUniform ? null : input.texData.texShape;\n        if (!util.arraysEqual(texShapeA, texShapeB)) {\n            throw Error(`Binary was compiled with different texture shapes than the` +\n                ` current args. Shape ${texShapeA} and ${texShapeB} must match`);\n        }\n    });\n}\nexport function runProgram(gpgpu, binary, inputs, output, customSetup) {\n    validateBinaryAndProgram(binary.inShapeInfos, inputs);\n    validateBinaryAndProgram([binary.outShapeInfo], [output]);\n    const outTex = output.texData.texture;\n    const outTexShape = output.texData.texShape;\n    if (output.texData.isPacked) {\n        gpgpu.setOutputPackedMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n    }\n    else {\n        gpgpu.setOutputMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n    }\n    gpgpu.setProgram(binary.webGLProgram);\n    // Set special uniforms (NAN, INFINITY)\n    if (env().getNumber('WEBGL_VERSION') === 1) {\n        if (binary.infLoc !== null) {\n            gpgpu.gl.uniform1f(binary.infLoc, Infinity);\n        }\n    }\n    if (binary.nanLoc !== null) {\n        gpgpu.gl.uniform1f(binary.nanLoc, NaN);\n    }\n    // Set user-defined inputs\n    inputs.forEach((input, i) => {\n        const varName = binary.program.variableNames[i];\n        const varLoc = binary.uniformLocations[varName];\n        const varOffsetLoc = binary.uniformLocations[`offset${varName}`];\n        if (varLoc == null) {\n            // The compiler inferred that this variable is not used in this shader.\n            return;\n        }\n        if (input.isUniform) {\n            // Upload the values of the tensor as uniform.\n            if (util.sizeFromShape(input.shape) < 2) {\n                gpgpu.gl.uniform1f(varLoc, input.uniformValues[0]);\n            }\n            else {\n                let vals = input.uniformValues;\n                if (!(vals instanceof Float32Array)) {\n                    vals = new Float32Array(vals);\n                }\n                gpgpu.gl.uniform1fv(varLoc, vals);\n            }\n            return;\n        }\n        // If the input was sliced, upload the flat offset index.\n        if (input.texData.slice != null && varOffsetLoc != null) {\n            gpgpu.gl.uniform1i(varOffsetLoc, input.texData.slice.flatOffset);\n        }\n        gpgpu.setInputMatrixTexture(input.texData.texture, varLoc, i);\n    });\n    if (customSetup != null) {\n        customSetup(gpgpu, binary.webGLProgram);\n    }\n    gpgpu.executeProgram();\n}\nexport function makeShaderKey(program, inputs, output) {\n    let keyInputs = '';\n    inputs.concat(output).forEach(x => {\n        const hasOffset = x.texData != null && x.texData.slice != null &&\n            x.texData.slice.flatOffset > 0;\n        const texShape = x.isUniform ? 'uniform' : x.texData.texShape;\n        keyInputs += `${x.shape}_${texShape}_${hasOffset}`;\n    });\n    const keyUserCode = program.userCode;\n    let key = program.constructor.name;\n    // Fast string concat. See https://jsperf.com/string-concatenation/14.\n    key += '_' + keyInputs + '_' + keyUserCode;\n    return key;\n}\n//# sourceMappingURL=gpgpu_math.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nexport class Im2ColPackedProgram {\n    constructor(outputShape, inputShape, convInfo) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        const { filterWidth, inChannels, strideWidth, strideHeight, padInfo, outWidth, dilationWidth, dilationHeight, dataFormat } = convInfo;\n        const { left, top } = padInfo;\n        const itemsPerBlockRow = inChannels * filterWidth;\n        const glsl = getGlslDifferences();\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const rowDim = isChannelsLast ? 0 : 1;\n        const colDim = isChannelsLast ? 1 : 2;\n        let unrolled = ``;\n        for (let row = 0; row <= 1; row++) {\n            for (let col = 0; col <= 1; col++) {\n                unrolled += `\n          blockIndex = rc.y + ${col};\n          pos = rc.x + ${row};\n\n          if(blockIndex < ${outputShape[1]} && pos < ${outputShape[0]}) {\n            offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${top};\n            d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});\n\n            if(d0 < ${inputShape[rowDim]} && d0 >= 0) {\n\n              offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${strideWidth}. - ${left}.);\n              d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${itemsPerBlockRow}.) / ${inChannels}.));\n\n              if(d1 < ${inputShape[colDim]} && d1 >= 0) {\n\n                ch = int(mod(float(pos), ${inChannels}.));\n\n                if (${isChannelsLast}) {\n                  innerDims = vec2(d1, ch);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;\n            }\n        }\n        this.userCode = `\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${unrolled}\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=im2col_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNProgram {\n    constructor(xShape, radius, bias, alpha, beta) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        const rad = radius;\n        const maxD = xShape[3] - 1;\n        this.outputShape = xShape;\n        // optimize pow(bias + alpha * sum, -beta)\n        // src: https://github.com/tensorflow/tensorflow/..\n        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n        // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n        let powOperator;\n        const basis = `float(${bias}) + float(${alpha}) * sum`;\n        if (beta === 0.5) {\n            powOperator = `inversesqrt(${basis})`;\n        }\n        else if (beta === 1.0) {\n            powOperator = `1.0/(${basis})`;\n        }\n        else {\n            powOperator = `exp(log(${basis}) * float(-${beta}));`;\n        }\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${rad}; j <= ${rad}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${maxD}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${powOperator};\n        setOutput(val);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNGradProgram {\n    constructor(inputShape, depthRadius, bias, alpha, beta) {\n        this.variableNames = ['inputImage', 'outputImage', 'dy'];\n        this.outputShape = [];\n        this.outputShape = inputShape;\n        this.depth = inputShape[3];\n        this.depthRadius = depthRadius;\n        this.bias = bias;\n        this.alpha = alpha;\n        this.beta = beta;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${depthRadius})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${depthRadius} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${alpha}) * norm + float(${bias});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${alpha})\n                * float(${beta})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${beta});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_grad_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNPackedProgram {\n    constructor(xShape, radius, bias, alpha, beta) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const rad = radius;\n        const maxD = xShape[3] - 1;\n        this.outputShape = xShape;\n        // optimize pow(bias + alpha * sum, -beta)\n        // src: https://github.com/tensorflow/tensorflow/..\n        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n        // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n        let powOperator;\n        const basis = `float(${bias}) + float(${alpha}) * sum`;\n        if (beta === 0.5) {\n            powOperator = `inversesqrt(${basis})`;\n        }\n        else if (beta === 1.0) {\n            powOperator = `1.0/(${basis})`;\n        }\n        else {\n            powOperator = `exp(log(${basis}) * float(-${beta}));`;\n        }\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${rad};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${rad}; j <= ${rad}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${powOperator};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MaxPool2DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'maxPos'];\n        this.outputShape = convInfo.inShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n          wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${effectiveFilterWidth} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class MaxPool3DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'maxPos'];\n        this.outputShape = convInfo.inShape;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n           wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${lastIndex} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                  wR * ${effectiveFilterWidth} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=max_pool_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MatMulPackedProgram {\n    constructor(aShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['matrixA', 'matrixB'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        const sharedDim = transposeA ? aShape[1] : aShape[2];\n        const sharedDimensionPacked = Math.ceil(sharedDim / 2);\n        const aSample = transposeA ? 'i * 2, rc.y' : 'rc.y, i * 2';\n        const bSample = transposeB ? 'rc.z, i * 2' : 'i * 2, rc.z';\n        const aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];\n        const bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const float sharedDimension = ${sharedDimensionPacked}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${sharedDimensionPacked}; i++) {\n          vec4 a = getMatrixA(rc.x, ${aSample});\n          vec4 b = getMatrixB(rc.x, ${bSample});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${aSwizzle[0]} * ${bSwizzle[0]});\n          result += (${aSwizzle[1]} * ${bSwizzle[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${addBiasSnippet}\n\n        ${applyActivationSnippet}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=mulmat_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MultinomialProgram {\n    constructor(batchSize, numOutcomes, numSamples) {\n        this.variableNames = ['probs'];\n        this.outputShape = [batchSize, numSamples];\n        this.userCode = `\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < ${numOutcomes - 1}; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(${numOutcomes - 1}));\n      }\n    `;\n    }\n    getCustomSetupFunc(seed) {\n        return (gpgpu, webGLProgram) => {\n            if (this.seedLoc == null) {\n                this.seedLoc = gpgpu.getUniformLocation(webGLProgram, 'seed');\n            }\n            gpgpu.gl.uniform1f(this.seedLoc, seed);\n        };\n    }\n}\n//# sourceMappingURL=multinomial_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class OneHotProgram {\n    constructor(numIndices, depth, onValue, offValue) {\n        this.variableNames = ['indices'];\n        this.outputShape = [numIndices, depth];\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${offValue}), float(${onValue}),\n                      float(index == coords.y)));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=onehot_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class PackProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        // Only input / output 3D tensors.\n        this.outputShape = outputShape;\n        const rank = outputShape.length;\n        if (rank === 0) {\n            this.userCode = `\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;\n        }\n        else {\n            const channels = getChannels('rc', rank);\n            const dtype = getCoordsDataType(rank);\n            const outOfBoundsCondition = getOutOfBoundsCondition(rank, outputShape, channels);\n            const setup = getSetup(rank, outputShape[outputShape.length - 1], outputShape[outputShape.length - 2], channels);\n            const output = getOutput(outputShape, channels);\n            this.userCode = `\n        void main() {\n          ${dtype} rc = getOutputCoords();\n\n          if(${outOfBoundsCondition}) {\n            setOutput(vec4(0));\n          } else {\n            ${setup}\n\n            setOutput(vec4(${output}));\n          }\n        }\n      `;\n        }\n    }\n}\nfunction getSourceCoordsArr(rank, dims) {\n    const coords = [];\n    for (let row = 0; row <= 1; row++) {\n        for (let col = 0; col <= 1; col++) {\n            let coord = `${row === 0 ? 'r' : 'rp1'}, ${col === 0 ? 'c' : 'cp1'}`;\n            for (let d = 2; d < rank; d++) {\n                coord = `${dims[dims.length - 1 - d]},` + coord;\n            }\n            coords.push(coord);\n        }\n    }\n    return coords;\n}\nfunction getOutOfBoundsCondition(rank, shape, dims) {\n    if (rank === 1) {\n        return `rc > ${shape[0]}`;\n    }\n    let cond = '';\n    for (let i = rank - 2; i < rank; i++) {\n        cond += `${dims[i]} >= ${shape[i]}`;\n        if (i < rank - 1) {\n            cond += '||';\n        }\n    }\n    return cond;\n}\nfunction getSetup(rank, cols, rows, dims) {\n    if (rank === 1) {\n        return '';\n    }\n    const innerDims = dims.slice(-2);\n    return `\n    int r = ${innerDims[0]};\n    int c = ${innerDims[1]};\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= ${cols};\n    bool rEdge = rp1 >= ${rows};\n  `;\n}\nfunction getOutput(shape, dims) {\n    const rank = shape.length;\n    const sourceCoords = getSourceCoordsArr(rank, dims);\n    if (rank === 1) {\n        return `getA(rc),\n            rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),\n            0, 0`;\n    }\n    return `getA(${sourceCoords[0]}),\n          cEdge ? 0. : getA(${sourceCoords[1]}),\n          rEdge ? 0. : getA(${sourceCoords[2]}),\n          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;\n}\n//# sourceMappingURL=pack_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class PadProgram {\n    constructor(xShape, paddings, constantValue) {\n        this.variableNames = ['x'];\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const type = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const unpackedCoords = ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);\n        if (rank === 1) {\n            this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float(${constantValue}));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `;\n            return;\n        }\n        this.userCode = `\n      ${type} start = ${type}(${start});\n      ${type} end = ${type}(${end});\n\n      void main() {\n        ${type} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float(${constantValue}));\n        } else {\n          ${type} coords = outC - start;\n          setOutput(getX(${unpackedCoords}));\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pad_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class PadPackedProgram {\n    constructor(xShape, paddings, constantValue) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const dtype = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const coords = getChannels('rc', rank);\n        const source = getChannels('source', rank);\n        const cLimit = `${coords[rank - 1]} < ${this.outputShape[rank - 1]}`;\n        const innerDims = rank === 1 ? 'source' : `vec2(${source.slice(-2).join()})`;\n        const componentSetup = [\n            `${dtype} rc = outputLoc;`, `${coords[rank - 1]} += 1;\n       if(${cLimit}) {\n      `,\n            rank === 1 ? '' : `}\n       rc = outputLoc;\n       ${coords[rank - 2]} += 1;\n       if(${coords[rank - 2]} < ${this.outputShape[rank - 2]}) {`,\n            rank === 1 ? '' : `  ${coords[rank - 1]} += 1;\n         if(${cLimit}) {`\n        ];\n        const paddingArea = rank === 1 ?\n            'rc < start || rc >= end' :\n            'any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))';\n        let mainLoop = '';\n        for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {\n            mainLoop += `\n        ${componentSetup[i]}\n        if (${paddingArea}) {\n          result[${i}] = float(${constantValue});\n        } else {\n          ${dtype} source = rc - start;\n          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n        }\n        mainLoop += (rank === 1 ? `} ` : `}}`);\n        this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pad_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Pool2DProgram {\n    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n        this.variableNames = ['x'];\n        if (poolType === 'avg' && computePositions) {\n            throw new Error('Cannot compute positions for average pool.');\n        }\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.outputShape = convInfo.outShape;\n        const isAvgPool = poolType === 'avg';\n        const batchFlattenPositionStr = `((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n        const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n        let initializationValue = '0.0';\n        if (!isAvgPool) {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n        }\n        if (computePositions) {\n            const compareOp = '>=';\n            this.userCode = `\n        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n        const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${compareOp} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${flattenPositions ? (includeBatchInIndex ? batchFlattenPositionStr :\n                flattenPositionStr) :\n                `wR * ${effectiveFilterWidth} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n            return;\n        }\n        const compareOp = 'max';\n        let returnValue = `${poolType}(${poolType}(${poolType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (poolType === 'avg') {\n            returnValue = `avgValue / count`;\n        }\n        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n        const filterWidthVec4Remainder = filterWidth % 4;\n        const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)\n            );\n\n            ${updateSnippet}\n          }\n\n          int xC = xCCorner + ${filterWidthNearestVec4};\n          if (${filterWidthVec4Remainder === 1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              initializationValue\n            );\n\n            ${updateSnippet}\n          }\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\nexport class Pool3DProgram {\n    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n        this.variableNames = ['x'];\n        if (poolType === 'avg' && computePositions) {\n            throw new Error('Cannot compute positions for average pool.');\n        }\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.outputShape = convInfo.outShape;\n        const isAvgPool = poolType === 'avg';\n        let initializationValue = '0.0';\n        if (!isAvgPool) {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n        }\n        if (computePositions) {\n            const compareOp = '>=';\n            this.userCode = `\n        const ivec3 strides =\n            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${effectiveFilterDepth};\n              wD += ${dilationDepth}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${effectiveFilterHeight};\n                wR += ${dilationHeight}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${effectiveFilterWidth};\n                  wC += ${dilationWidth}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${compareOp} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${flattenPositions ?\n                (includeBatchInIndex ?\n                    `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` :\n                    `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch`) :\n                `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                      wR * ${effectiveFilterWidth} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n            return;\n        }\n        const compareOp = 'max';\n        let returnValue = `${poolType}(${poolType}(${poolType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (poolType === 'avg') {\n            returnValue = `avgValue / count`;\n        }\n        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n        const filterWidthVec4Remainder = filterWidth % 4;\n        const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        this.userCode = `\n      const ivec3 strides =\n        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)\n              );\n\n              ${updateSnippet}\n            }\n\n            int xC = xCCorner + ${filterWidthNearestVec4};\n            if (${filterWidthVec4Remainder === 1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                initializationValue\n              );\n\n              ${updateSnippet}\n            }\n          }\n          setOutput(${returnValue});\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pool_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ReduceProgram {\n    constructor(reduceInfo, reduceType) {\n        this.variableNames = ['x'];\n        const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n        this.outputShape = [batchSize, outSize];\n        let initializationValue = '0.0';\n        let compareOp = ``;\n        if (reduceType === 'prod') {\n            initializationValue = '1.0';\n        }\n        else if (reduceType === 'min') {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '1.0 / 1e-20';\n            compareOp = `min`;\n        }\n        else if (reduceType === 'max') {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n            compareOp = `max`;\n        }\n        let returnValue = `${reduceType}(${reduceType}(${reduceType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (reduceType === 'sum') {\n            returnValue = `sumValue`;\n        }\n        else if (reduceType === 'prod') {\n            returnValue = `prodValue`;\n        }\n        else if (reduceType === 'all') {\n            returnValue = `allValue`;\n        }\n        else if (reduceType === 'any') {\n            returnValue = `anyValue`;\n        }\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        let updateSnippet = `\n      if (${reduceType === 'sum'}) {\n        sumValue += dot(values, ones);\n      } else if (${reduceType === 'prod'}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        let vecType = `vec4`;\n        if (reduceType === 'all') {\n            initializationValue = '1.0';\n            updateSnippet = `\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `;\n            vecType = `bvec4`;\n        }\n        else if (reduceType === 'any') {\n            initializationValue = '0.0';\n            updateSnippet = `\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `;\n            vecType = `bvec4`;\n        }\n        let checkOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n        }\n        this.userCode = `\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        vec4 minMaxValue = vec4(${initializationValue});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\n//# sourceMappingURL=reduce_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as shader_util from './shader_compiler_util';\nexport class ReshapePackedProgram {\n    constructor(outputShape, inputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        let mainLoop = ``;\n        for (let i = 0; i < 4; i++) {\n            let thisRC = `thisRC = rc;`;\n            if (i % 2 === 1) {\n                thisRC += `thisRC.z += 1;`;\n            }\n            if (i > 1) {\n                thisRC += `thisRC.y += 1;`;\n            }\n            mainLoop += `\n        ${thisRC}\n        ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ''}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${i}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${i > 0 ? '}' : ''}\n      `;\n        }\n        this.userCode = `\n      ${getReshapedInputCoords(inputShape)}\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${outputShape[1]};\n        int cols = ${outputShape[2]};\n\n        ${mainLoop}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\nfunction getReshapedInputCoords(shape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n    return `\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\n//# sourceMappingURL=reshape_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearBackpropProgram {\n    constructor(dy, x, alignCorners) {\n        this.variableNames = ['dy'];\n        this.outputShape = [];\n        this.outputShape = x.shape;\n        const [, xHeight, xWidth,] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        // In the backwards pass, we want to find the pixels that were generated for\n        // each pixel in the input image the forward pass and add the corresponding\n        // coefficient from dy to the gradient (with some interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearPackedProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        this.userCode = `\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,\n                                     ${oldWidth}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${depth - 1};\n        bool hasNextRow = coords.z < ${newWidth - 1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeNearestNeigborBackpropProgram {\n    constructor(dy, x, alignCorners) {\n        this.variableNames = ['dy'];\n        this.outputShape = [];\n        this.outputShape = x.shape;\n        const [, xHeight, xWidth,] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        // In the backwards pass, we want to find the pixels that were generated for\n        // each pixel in the input image the forward pass and add the corresponding\n        // coefficient from dy to the gradient (with some interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${effectiveXSize[0]}) *\n                (float(dyR) / float(${effectiveYSize[0]}));\n\n            float sourceFracCol =\n                float(${effectiveXSize[1]}) *\n                  (float(dyC) / float(${effectiveYSize[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${xHeight}) - 1),\n                ${alignCorners} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${xWidth}) - 1),\n                ${alignCorners} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_nearest_neighbor_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeNearestNeighborProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        // When align corners is false, we rounds the value with floor.\n        const roundBase = alignCorners ? '0.5' : '0.0';\n        this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_nearest_neighbor_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class ReverseProgram {\n    constructor(xShape, axis) {\n        this.variableNames = ['x'];\n        const rank = xShape.length;\n        if (rank > 4) {\n            throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n        }\n        this.outputShape = xShape;\n        if (rank === 1) {\n            this.userCode = `\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${xShape[0]} - coord - 1));\n        }\n      `;\n            return;\n        }\n        const getInCoord = (i) => {\n            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n                return `${xShape[i]} - coords[${i}] - 1`;\n            }\n            return `coords[${i}]`;\n        };\n        const inCoords = xShape.map((_, i) => getInCoord(i)).join(',');\n        const type = getCoordsDataType(rank);\n        this.userCode = `\n      void main() {\n        ${type} coords = getOutputCoords();\n        setOutput(getX(${inCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=reverse_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ReversePackedProgram {\n    constructor(xShape, axis) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const rank = xShape.length;\n        if (rank > 4) {\n            throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n        }\n        this.outputShape = xShape;\n        const channels = getChannels('rc', rank);\n        const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;\n        const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;\n        const type = getCoordsDataType(rank);\n        if (rank === 1) {\n            this.userCode = `\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${xShape[0]} - rc - 1),\n            ${xShape[0]} - rc - 1);\n          if(${nextColumn}){\n              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),\n                ${xShape[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `;\n        }\n        else {\n            this.userCode = `\n        void main() {\n          ${type} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${getR(channels.slice())};\n          if(${nextColumn}){\n            result.g = ${getG(channels.slice())};\n          }\n          if(${nextRow}) {\n            result.b = ${getB(channels.slice())};\n            if(${nextColumn}) {\n              result.a = ${getA(channels.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `;\n        }\n        function getR(channels) {\n            return getChannel(channels);\n        }\n        function getG(channels) {\n            channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getB(channels) {\n            channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getA(channels) {\n            channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n            channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getChannel(channels) {\n            const inCoordsArray = xShape.map((_, i) => getInCoord(i, channels));\n            const inCoords = inCoordsArray.join(',');\n            const innerDims = inCoordsArray.slice(-2).join(',');\n            return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;\n        }\n        function getInCoord(i, channels1) {\n            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n                return `${xShape[i]} - ${channels1[i]} - 1`;\n            }\n            else {\n                return `${channels1[i]}`;\n            }\n        }\n    }\n}\n//# sourceMappingURL=reverse_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class ScatterProgram {\n    constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true) {\n        this.variableNames = ['updates', 'indices', 'defaultValue'];\n        this.outputShape = shape;\n        const stridesType = getCoordsDataType(strides.length);\n        const dtype = getCoordsDataType(shape.length);\n        let indicesString = '';\n        if (indicesRank === 1) {\n            indicesString = 'i';\n        }\n        else if (indicesRank === 2) {\n            indicesString = 'i, j';\n        }\n        const indicesSnippet = `getIndices(${indicesString})`;\n        let updatesString = '';\n        if (updatesRank === 1) {\n            updatesString = 'i';\n        }\n        else if (updatesRank === 2) {\n            updatesString = 'i, coords[1]';\n        }\n        const updatesSnippet = `getUpdates(${updatesString})`;\n        const strideString = sliceDim > 1 ? 'strides[j]' : 'strides';\n        this.userCode = `\n        ${stridesType} strides = ${stridesType}(${strides});\n\n        void main() {\n          ${dtype} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${updateSize}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${sliceDim}; j++) {\n              int index = round(${indicesSnippet});\n              flattenedIndex += index * ${strideString};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${updatesSnippet};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `;\n    }\n}\n//# sourceMappingURL=scatter_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class SegmentOpProgram {\n    constructor(segOpInfo, segOpType) {\n        this.variableNames = ['x', 'segmentIds'];\n        const windowSize = segOpInfo.windowSize;\n        const batchSize = segOpInfo.batchSize;\n        const inSize = segOpInfo.inSize;\n        const numSegments = segOpInfo.numSegments;\n        const outSize = numSegments * Math.ceil(inSize / windowSize);\n        this.outputShape = [batchSize, outSize];\n        const initializationValue = '0.0';\n        const returnValue = `sumValue`;\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        const updateSnippet = `\n        sumValue += dot(values, segFilter);\n    `;\n        let checkValueOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkValueOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n        }\n        let checkSegmentIdOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkSegmentIdOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return -1.0;\n        }\n      `;\n        }\n        this.userCode = `\n      const float initializationValue = ${initializationValue};\n\n      float getValue(int batch, int inIdx) {\n        ${checkValueOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${checkSegmentIdOutOfBounds}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${numSegments})) * float(${windowSize}));\n        int currentSeg = int(mod(float(outIdx), float(${numSegments})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\n//# sourceMappingURL=segment_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class SelectProgram {\n    constructor(cRank, shape, rank) {\n        this.variableNames = ['c', 'a', 'b'];\n        this.outputShape = shape;\n        let cCoords;\n        let abCoords;\n        if (rank > 4) {\n            throw Error(`Where for rank ${rank} is not yet supported`);\n        }\n        if (rank === 1) {\n            abCoords = `resRC`;\n            cCoords = `resRC`;\n        }\n        else {\n            const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n            const cCoordVars = [];\n            const abCoordVars = [];\n            for (let i = 0; i < shape.length; i++) {\n                abCoordVars.push(`${currentCoords[i]}`);\n                if (i < cRank) {\n                    cCoordVars.push(`${currentCoords[i]}`);\n                }\n            }\n            cCoords = cCoordVars.join();\n            abCoords = abCoordVars.join();\n        }\n        const dtype = getCoordsDataType(rank);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        float cVal = getC(${cCoords});\n        if (cVal >= 1.0) {\n          setOutput(getA(${abCoords}));\n        } else {\n          setOutput(getB(${abCoords}));\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=select_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class SliceProgram {\n    constructor(destSize) {\n        this.variableNames = ['source'];\n        this.outputShape = destSize;\n        this.rank = destSize.length;\n        const dtype = getCoordsDataType(this.rank);\n        const uniformPart = `uniform int start[${this.rank}];`;\n        const sourceCoords = getCoords(this.rank);\n        let body;\n        const coordSum = destSize.map((_, i) => {\n            return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`;\n        });\n        body = `\n        ${dtype} sourceLoc;\n        ${dtype} coords = getOutputCoords();\n        ${coordSum.join('\\n')}\n      `;\n        this.userCode = `\n      ${uniformPart}\n      void main() {\n        ${body}\n        setOutput(getSource(${sourceCoords}));\n      }\n    `;\n    }\n    getCustomSetupFunc(start) {\n        if (start.length !== this.rank) {\n            throw Error(`The rank (${this.rank}) of the program must match the ` +\n                `length of start (${start.length})`);\n        }\n        return (gpgpu, webGLProgram) => {\n            if (this.startLoc == null) {\n                this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n                if (this.startLoc == null) {\n                    // This means the compiler has optimized and realized it doesn't need\n                    // the uniform.\n                    return;\n                }\n            }\n            gpgpu.gl.uniform1iv(this.startLoc, start);\n        };\n    }\n}\nconst coords = ['x', 'y', 'z', 'w', 'u', 'v'];\nfunction getCoords(rank) {\n    if (rank === 1) {\n        return 'sourceLoc';\n    }\n    else if (rank <= 6) {\n        return coords.slice(0, rank).map(x => 'sourceLoc.' + x).join(',');\n    }\n    else {\n        throw Error(`Slicing for rank ${rank} is not yet supported`);\n    }\n}\n//# sourceMappingURL=slice_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class SlicePackedProgram {\n    constructor(destSize) {\n        this.variableNames = ['source'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = destSize;\n        this.rank = destSize.length;\n        const dtype = getCoordsDataType(this.rank);\n        const coords = getChannels('coords', this.rank);\n        const sourceLoc = getChannels('sourceLoc', this.rank);\n        const innerDims = this.rank === 1 ? 'sourceLoc' : `vec2(${sourceLoc.slice(-2).join()})`;\n        const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;\n        const upperRow = `\n      result.x = ${getChannel};\n      if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n        ++${sourceLoc[this.rank - 1]};\n        result.y = ${getChannel};\n        --${sourceLoc[this.rank - 1]};\n      }\n    `;\n        const lowerRow = this.rank === 1 ? '' : `\n      --${coords[this.rank - 1]};\n      if (++${coords[this.rank - 2]} < ${destSize[this.rank - 2]}) {\n        ++${sourceLoc[this.rank - 2]};\n        result.z = ${getChannel};\n        if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n          ++${sourceLoc[this.rank - 1]};\n          result.w = ${getChannel};\n        }\n      }\n    `;\n        const sourceLocSetup = this.rank <= 4 ?\n            `sourceLoc = coords +\n            ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` :\n            destSize.map((_, i) => `${sourceLoc[i]} = ${coords[i]} + start[${i}];`)\n                .join('\\n');\n        this.userCode = `\n      uniform int start[${this.rank}];\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        ${dtype} sourceLoc;\n        ${sourceLocSetup}\n        vec4 result = vec4(0.);\n        ${upperRow}\n        ${lowerRow}\n        setOutput(result);\n      }\n    `;\n    }\n    getCustomSetupFunc(start) {\n        if (start.length !== this.rank) {\n            throw Error(`The rank (${this.rank}) of the program must match the ` +\n                `length of start (${start.length})`);\n        }\n        return (gpgpu, webGLProgram) => {\n            if (this.startLoc == null) {\n                this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n                if (this.startLoc == null) {\n                    // This means the compiler has optimized and realized it doesn't need\n                    // the uniform.\n                    return;\n                }\n            }\n            gpgpu.gl.uniform1iv(this.startLoc, start);\n        };\n    }\n}\n//# sourceMappingURL=slice_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class StridedSliceProgram {\n    constructor(begin, strides, size) {\n        this.variableNames = ['x'];\n        this.outputShape = size;\n        const rank = size.length;\n        const inputDtype = getCoordsDataType(size.length);\n        const dtype = getCoordsDataType(size.length);\n        let newCoords = '';\n        if (rank === 1) {\n            newCoords = 'coords * strides + begin';\n        }\n        else {\n            let outputAxis = 0;\n            newCoords =\n                size.map((_, i) => {\n                    outputAxis++;\n                    return size.length === 1 ?\n                        `coords * strides[${i}] + begin[${i}]` :\n                        `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;\n                })\n                    .join(',');\n        }\n        this.userCode = `\n      ${inputDtype} begin = ${inputDtype}(${begin});\n      ${inputDtype} strides = ${inputDtype}(${strides});\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        setOutput(getX(${newCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=strided_slice_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { getInternalFormatForFloat16MatrixTexture, getInternalFormatForFloat16PackedMatrixTexture, getInternalFormatForFloat32MatrixTexture, getInternalFormatForPackedMatrixTexture, getInternalFormatForUnsignedBytesMatrixTexture } from './gpgpu_util';\nimport { getPackedMatrixTextureShapeWidthHeight, getUnpackedMatrixTextureShapeWidthHeight, PhysicalTextureType, TextureUsage } from './tex_util';\nexport class TextureManager {\n    constructor(gpgpu) {\n        this.gpgpu = gpgpu;\n        this.numUsedTextures = 0;\n        this.numFreeTextures = 0;\n        this._numBytesAllocated = 0;\n        this._numBytesFree = 0; // How many bytes that have been allocated\n        // are available for reuse.\n        this.freeTextures = {};\n        this.logEnabled = false;\n        this.usedTextures = {};\n    }\n    acquireTexture(shapeRC, usage, isPacked) {\n        const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);\n        const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);\n        if (!(shapeKey in this.freeTextures)) {\n            this.freeTextures[shapeKey] = [];\n        }\n        if (!(shapeKey in this.usedTextures)) {\n            this.usedTextures[shapeKey] = [];\n        }\n        const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n        if (this.freeTextures[shapeKey].length > 0) {\n            this.numFreeTextures--;\n            this.numUsedTextures++;\n            this._numBytesFree -= texBytes;\n            this.log();\n            const newTexture = this.freeTextures[shapeKey].shift();\n            this.usedTextures[shapeKey].push(newTexture);\n            return newTexture;\n        }\n        let newTexture;\n        if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {\n            newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {\n            newTexture =\n                this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {\n            newTexture =\n                this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {\n            newTexture =\n                this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {\n            newTexture =\n                this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        this.usedTextures[shapeKey].push(newTexture);\n        this.numUsedTextures++;\n        this._numBytesAllocated += texBytes;\n        this.log();\n        return newTexture;\n    }\n    releaseTexture(texture, shape, logicalTexType, isPacked) {\n        if (this.freeTextures == null) {\n            // Already disposed.\n            return;\n        }\n        const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);\n        const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);\n        if (!(shapeKey in this.freeTextures)) {\n            this.freeTextures[shapeKey] = [];\n        }\n        const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n        const deleteTexThreshold = env().get('WEBGL_DELETE_TEXTURE_THRESHOLD');\n        if (deleteTexThreshold !== -1 &&\n            this._numBytesAllocated > deleteTexThreshold) {\n            this.gpgpu.deleteMatrixTexture(texture);\n            this._numBytesAllocated -= texBytes;\n        }\n        else {\n            this.freeTextures[shapeKey].push(texture);\n            this.numFreeTextures++;\n            this._numBytesFree += texBytes;\n        }\n        this.numUsedTextures--;\n        const texList = this.usedTextures[shapeKey];\n        const texIndex = texList.indexOf(texture);\n        if (texIndex < 0) {\n            throw new Error('Cannot release a texture that was never provided by this ' +\n                'texture manager');\n        }\n        texList.splice(texIndex, 1);\n        this.log();\n    }\n    log() {\n        if (!this.logEnabled) {\n            return;\n        }\n        const total = this.numFreeTextures + this.numUsedTextures;\n        console.log('Free/Used', `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`);\n        const freeRatio = this._numBytesFree / this._numBytesAllocated;\n        console.log(`Bytes allocated: ${this._numBytesAllocated}`);\n        console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`);\n    }\n    get numBytesAllocated() {\n        return this._numBytesAllocated;\n    }\n    get numBytesFree() {\n        return this._numBytesFree;\n    }\n    getNumUsedTextures() {\n        return this.numUsedTextures;\n    }\n    getNumFreeTextures() {\n        return this.numFreeTextures;\n    }\n    dispose() {\n        if (this.freeTextures == null) {\n            // Already disposed.\n            return;\n        }\n        for (const texShape in this.freeTextures) {\n            this.freeTextures[texShape].forEach(tex => {\n                this.gpgpu.deleteMatrixTexture(tex);\n            });\n        }\n        for (const texShape in this.usedTextures) {\n            this.usedTextures[texShape].forEach(tex => {\n                this.gpgpu.deleteMatrixTexture(tex);\n            });\n        }\n        this.freeTextures = null;\n        this.usedTextures = null;\n        this.numUsedTextures = 0;\n        this.numFreeTextures = 0;\n        this._numBytesAllocated = 0;\n        this._numBytesFree = 0;\n    }\n}\nfunction numBytesForInternalFormat(gl, internalFormat) {\n    // tslint:disable-next-line:no-any\n    const glany = gl;\n    if (internalFormat === glany.R32F) {\n        return 4;\n    }\n    else if (internalFormat === glany.R16F) {\n        return 2;\n    }\n    else if (internalFormat === glany.RGBA32F) {\n        return 16;\n    }\n    else if (internalFormat === gl.RGBA) {\n        return 16;\n    }\n    else if (internalFormat === glany.RGBA16F) {\n        return 8;\n    }\n    throw new Error(`Unknown internal format ${internalFormat}`);\n}\nexport function computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) {\n    // It is not possible to infer packed status from the texture type because\n    // depending on the textureConfig, different  texture types may resolve to the\n    // same internal format (e.g. in WebGL1, the internal format for\n    // UNPACKED_FLOAT16 textures is gl.RGBA). Therefore we pass in `isPacked`\n    // explicitly.\n    const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig);\n    let numElements;\n    if (isPacked) {\n        const [packedWidth, packedHeight] = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n        numElements = packedWidth * packedHeight;\n    }\n    else {\n        const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n        numElements = width * height;\n    }\n    const bytesPerElement = numBytesForInternalFormat(gl, internalFormat);\n    return numElements * bytesPerElement;\n}\nfunction internalFormatForPhysicalTexType(physicalTexType, textureConfig) {\n    switch (physicalTexType) {\n        case PhysicalTextureType.PACKED_2X2_FLOAT32:\n            return getInternalFormatForPackedMatrixTexture(textureConfig);\n        case PhysicalTextureType.PACKED_2X2_FLOAT16:\n            return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);\n        case PhysicalTextureType.UNPACKED_FLOAT32:\n            return getInternalFormatForFloat32MatrixTexture(textureConfig);\n        case PhysicalTextureType.UNPACKED_FLOAT16:\n            return getInternalFormatForFloat16MatrixTexture(textureConfig);\n        case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:\n            return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);\n        default:\n            throw new Error(`Unknown physical texture type ${physicalTexType}`);\n    }\n}\nfunction getPhysicalTextureForRendering(isPacked) {\n    if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED')) {\n        if (isPacked) {\n            return PhysicalTextureType.PACKED_2X2_FLOAT32;\n        }\n        return PhysicalTextureType.UNPACKED_FLOAT32;\n    }\n    if (isPacked) {\n        return PhysicalTextureType.PACKED_2X2_FLOAT16;\n    }\n    return PhysicalTextureType.UNPACKED_FLOAT16;\n}\nfunction getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {\n    if (logicalTexType === TextureUsage.UPLOAD) {\n        return PhysicalTextureType.PACKED_2X2_FLOAT32;\n    }\n    else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {\n        return getPhysicalTextureForRendering(isPacked);\n    }\n    else if (logicalTexType === TextureUsage.DOWNLOAD ||\n        logicalTexType === TextureUsage.PIXELS) {\n        return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;\n    }\n    throw new Error(`Unknown logical texture type ${logicalTexType}`);\n}\nfunction getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {\n    return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;\n}\n//# sourceMappingURL=texture_manager.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class TileProgram {\n    constructor(aShape, reps) {\n        this.variableNames = ['A'];\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[i] * reps[i];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const sourceCoords = getSourceCoords(aShape);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n    }\n}\nfunction getSourceCoords(aShape) {\n    const rank = aShape.length;\n    if (rank > 5) {\n        throw Error(`Tile for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n        return `imod(resRC, ${aShape[0]})`;\n    }\n    const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u'];\n    const sourceCoords = [];\n    for (let i = 0; i < aShape.length; i++) {\n        sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);\n    }\n    return sourceCoords.join();\n}\n//# sourceMappingURL=tile_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class UnaryOpProgram {\n    constructor(aShape, opSnippet) {\n        this.variableNames = ['A'];\n        this.outputShape = aShape;\n        this.userCode = `\n      float unaryOperation(float x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n    }\n}\nconst CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;\nexport const LINEAR = `return x;`;\nexport const ABS = `return abs(x);`;\nexport const RELU = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\nexport const RELU6 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`;\nexport const ELU = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\nexport const SELU = `\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${backend_util.SELU_SCALEALPHA};\n  float scale = ${backend_util.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`;\nexport function STEP(alpha = 0.0) {\n    return CHECK_NAN_SNIPPET + `\n    return x > 0.0 ? 1.0 : float(${alpha});\n  `;\n}\nexport const NEG = `return -x;`;\nexport const CEIL = `return ceil(x);`;\nexport const FLOOR = `return floor(x);`;\nexport const SIGN = `\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n`;\nexport const IS_NAN = `return float(isnan(x));`;\nexport const IS_INF = `return float(isinf(x));`;\nexport const IS_FINITE = `return float(!isnan(x) && !isinf(x));`;\nexport const ROUND = `\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`;\nexport const EXP = `return exp(x);`;\nexport const EXPM1 = `return exp(x) - 1.0;`;\nexport const LOG = `if (x < 0.0) return NAN;\n  return log(x);`;\nexport const LOG1P = `return log(1.0 + x);`;\nexport const SQRT = `return sqrt(x);`;\nexport const RSQRT = `return inversesqrt(x);`;\nexport const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n/**\n * mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n *\n * epsilon is the difference between 1.0 and the next representable\n * float. For a single precision 32 bit float this should be 2^-23, see:\n * https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\n *\n * too_large = (x > -threshold) is value above which exp(x) may overflow\n * but softplus(x) == x is within machine epsilon\n *\n * too_small = (x < threshold) is value below which exp(x) may underflow,\n * but softplus(x) == exp(x) is within machine epsilon.\n */\nexport const SOFTPLUS = `\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`;\nexport const ASIN = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`;\nexport const ACOS = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`;\nexport const ATAN = CHECK_NAN_SNIPPET + `\n  return atan(x);\n`;\nexport const SINH = `\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`;\nexport const COSH = `\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`;\nexport const TANH = `\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`;\nexport const ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`;\nexport const ACOSH = CHECK_NAN_SNIPPET + `\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));`;\nexport const ATANH = CHECK_NAN_SNIPPET + `\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;\nexport const ERF = `\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = ${backend_util.ERF_P};\n  float a1 = ${backend_util.ERF_A1};\n  float a2 = ${backend_util.ERF_A2};\n  float a3 = ${backend_util.ERF_A3};\n  float a4 = ${backend_util.ERF_A4};\n  float a5 = ${backend_util.ERF_A5};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`;\nexport const RECIPROCAL = `return 1.0 / x;`;\nexport const LOGICAL_NOT = `return float(!(x >= 1.0));`;\nexport const TO_INT = `return float(int(x));`;\nexport const CLONE = 'return x;';\n//# sourceMappingURL=unaryop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const LINEAR = `return x;`;\nexport const LOG = `\n  vec4 result = log(x);\n  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\n  result.r = isNaN.r == 1.0 ? NAN : result.r;\n  result.g = isNaN.g == 1.0 ? NAN : result.g;\n  result.b = isNaN.b == 1.0 ? NAN : result.b;\n  result.a = isNaN.a == 1.0 ? NAN : result.a;\n\n  return result;\n`;\nexport const RELU = `\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nexport const RELU6 = `\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nexport const ELU = `\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`;\nexport class UnaryOpPackedProgram {\n    constructor(aShape, opSnippet) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = aShape;\n        this.userCode = `\n      vec4 unaryOperation(vec4 x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=unaryop_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels, getSourceCoords } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class UnpackProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = false;\n        this.outputShape = outputShape;\n        const rank = outputShape.length;\n        const channels = getChannels('rc', rank);\n        const dtype = getCoordsDataType(rank);\n        const sourceCoords = getSourceCoords(rank, channels);\n        const innerDims = channels.slice(-2);\n        const coords = rank <= 1 ? 'rc' : `vec2(${innerDims.join(',')})`;\n        this.userCode = `\n      void main() {\n        ${dtype} rc = getOutputCoords();\n        vec4 packedInput = getA(${sourceCoords});\n\n        setOutput(getChannel(packedInput, ${coords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=unpack_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Import webgl flags.\nimport './flags_webgl';\nimport * as tf from '@tensorflow/tfjs-core';\nimport { complex, div, engine, env, imag, max, range, real, reshape, scalar, softmax, tensor, tidy, transpose } from '@tensorflow/tfjs-core';\nimport { backend_util, buffer, kernel_impls, slice_util, util } from '@tensorflow/tfjs-core';\nimport { DataStorage, KernelBackend, upcastType } from '@tensorflow/tfjs-core';\nimport { addImplCPU, ceilImplCPU, expImplCPU, expm1ImplCPU, floorImplCPU, logImplCPU, multiplyImplCPU, rsqrtImplCPU, simpleAbsImplCPU, sliceImplCPU, subImplCPU } from './kernel_utils/shared';\nconst { segment_util } = backend_util;\nconst split = kernel_impls.split;\nconst tile = kernel_impls.tile;\nconst topkImpl = kernel_impls.topkImpl;\nconst whereImpl = kernel_impls.whereImpl;\nimport { AddNProgram } from './addn_gpu';\nimport { AddNPackedProgram } from './addn_packed_gpu';\nimport { ArgMinMaxProgram } from './argminmax_gpu';\nimport { ArgMinMaxPackedProgram } from './argminmax_packed_gpu';\nimport { AvgPool3DBackpropProgram } from './avg_pool_backprop_gpu';\nimport * as binaryop_complex_gpu from './binaryop_complex_gpu';\nimport { BinaryOpComplexProgram } from './binaryop_complex_gpu';\nimport * as binaryop_gpu from './binaryop_gpu';\nimport { BinaryOpProgram } from './binaryop_gpu';\nimport * as binaryop_packed_gpu from './binaryop_packed_gpu';\nimport { BinaryOpPackedProgram } from './binaryop_packed_gpu';\nimport { getWebGLContext } from './canvas_util';\nimport { ClipProgram } from './clip_gpu';\nimport { ClipPackedProgram } from './clip_packed_gpu';\nimport { ComplexAbsProgram } from './complex_abs_gpu';\nimport { ConcatProgram } from './concat_gpu';\nimport { ConcatPackedProgram } from './concat_packed_gpu';\nimport { Conv2DDerFilterProgram, Conv2DDerInputProgram, Conv3DDerFilterProgram, Conv3DDerInputProgram } from './conv_backprop_gpu';\nimport { DepthwiseConv2DDerFilterProgram, DepthwiseConv2DDerInputProgram } from './conv_backprop_gpu_depthwise';\nimport { Conv2DProgram, Conv3DProgram } from './conv_gpu';\nimport { DepthwiseConv2DProgram } from './conv_gpu_depthwise';\nimport { DepthwiseConvPacked2DProgram } from './conv_packed_gpu_depthwise';\nimport { CropAndResizeProgram } from './crop_and_resize_gpu';\nimport { CumSumProgram } from './cumsum_gpu';\nimport { DecodeMatrixProgram } from './decode_matrix_gpu';\nimport { DecodeMatrixPackedProgram } from './decode_matrix_packed_gpu';\nimport { DepthToSpaceProgram } from './depth_to_space_gpu';\nimport { DiagProgram } from './diag_gpu';\nimport { EncodeFloatProgram } from './encode_float_gpu';\nimport { EncodeFloatPackedProgram } from './encode_float_packed_gpu';\nimport { EncodeMatrixProgram } from './encode_matrix_gpu';\nimport { EncodeMatrixPackedProgram } from './encode_matrix_packed_gpu';\nimport * as fft_gpu from './fft_gpu';\nimport { FFTProgram } from './fft_gpu';\nimport { FillProgram } from './fill_gpu';\nimport { GatherProgram } from './gather_gpu';\nimport { GatherNDProgram } from './gather_nd_gpu';\nimport { GPGPUContext } from './gpgpu_context';\nimport * as gpgpu_math from './gpgpu_math';\nimport { Im2ColPackedProgram } from './im2col_packed_gpu';\nimport { LRNProgram } from './lrn_gpu';\nimport { LRNGradProgram } from './lrn_grad_gpu';\nimport { LRNPackedProgram } from './lrn_packed_gpu';\nimport { MaxPool3DBackpropProgram } from './max_pool_backprop_gpu';\nimport { MatMulPackedProgram } from './mulmat_packed_gpu';\nimport { MultinomialProgram } from './multinomial_gpu';\nimport { OneHotProgram } from './onehot_gpu';\nimport { PackProgram } from './pack_gpu';\nimport { PadProgram } from './pad_gpu';\nimport { PadPackedProgram } from './pad_packed_gpu';\nimport { Pool3DProgram } from './pool_gpu';\nimport { ReduceProgram } from './reduce_gpu';\nimport { ReshapePackedProgram } from './reshape_packed_gpu';\nimport { ResizeBilinearBackpropProgram } from './resize_bilinear_backprop_gpu';\nimport { ResizeBilinearProgram } from './resize_bilinear_gpu';\nimport { ResizeBilinearPackedProgram } from './resize_bilinear_packed_gpu';\nimport { ResizeNearestNeigborBackpropProgram } from './resize_nearest_neighbor_backprop_gpu';\nimport { ResizeNearestNeighborProgram } from './resize_nearest_neighbor_gpu';\nimport { ReverseProgram } from './reverse_gpu';\nimport { ReversePackedProgram } from './reverse_packed_gpu';\nimport { ScatterProgram } from './scatter_gpu';\nimport { SegmentOpProgram } from './segment_gpu';\nimport { SelectProgram } from './select_gpu';\nimport { SliceProgram } from './slice_gpu';\nimport { SlicePackedProgram } from './slice_packed_gpu';\nimport { StridedSliceProgram } from './strided_slice_gpu';\nimport * as tex_util from './tex_util';\nimport { TextureUsage } from './tex_util';\nimport { TextureManager } from './texture_manager';\nimport { TileProgram } from './tile_gpu';\nimport * as unary_op from './unaryop_gpu';\nimport { UnaryOpProgram } from './unaryop_gpu';\nimport * as unary_packed_op from './unaryop_packed_gpu';\nimport { UnaryOpPackedProgram } from './unaryop_packed_gpu';\nimport { UnpackProgram } from './unpack_gpu';\nimport * as webgl_util from './webgl_util';\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\nconst binaryCaches = {};\nexport function getBinaryCache(webGLVersion) {\n    if (webGLVersion in binaryCaches) {\n        return binaryCaches[webGLVersion];\n    }\n    binaryCaches[webGLVersion] = {};\n    return binaryCaches[webGLVersion];\n}\nfunction mapActivationToShaderProgram(activation, packed = false) {\n    if (activation === 'linear') {\n        if (packed) {\n            return unary_packed_op.LINEAR;\n        }\n        return unary_op.LINEAR;\n    }\n    else if (activation === 'relu') {\n        if (packed) {\n            return unary_packed_op.RELU;\n        }\n        return unary_op.RELU;\n    }\n    else if (activation === 'elu') {\n        if (packed) {\n            return unary_packed_op.ELU;\n        }\n        return unary_op.ELU;\n    }\n    else if (activation === 'relu6') {\n        if (packed) {\n            return unary_packed_op.RELU6;\n        }\n        return unary_op.RELU6;\n    }\n    else if (activation === 'prelu') {\n        if (packed) {\n            return binaryop_packed_gpu.PRELU;\n        }\n        return binaryop_gpu.PRELU;\n    }\n    throw new Error(`Activation ${activation} has not been implemented for the WebGL backend.`);\n}\n// Empirically determined constant used to determine size threshold for handing\n// off execution to the CPU.\nconst CPU_HANDOFF_SIZE_THRESHOLD = 128;\n// Empirically determined constant used to decide the number of MB on GPU\n// before we warn about high memory use. The MB are this constant * screen area\n// * dpi / 1024 / 1024.\nconst BEFORE_PAGING_CONSTANT = 600;\nfunction numMBBeforeWarning() {\n    if (env().global.screen == null) {\n        return 1024; // 1 GB.\n    }\n    return (env().global.screen.height * env().global.screen.width *\n        window.devicePixelRatio) *\n        BEFORE_PAGING_CONSTANT / 1024 / 1024;\n}\n// Empirically determined minimal shared dimension in matmul before we forward\n// to a.mul(b).sum() in order to take advantage of GPU parallelism. See\n// https://github.com/tensorflow/tfjs-core/pull/1379 for benchmarks.\nexport const MATMUL_SHARED_DIM_THRESHOLD = 1000;\nexport class MathBackendWebGL extends KernelBackend {\n    constructor(gpgpu) {\n        super();\n        // Maps data ids that have a pending read operation, to list of subscribers.\n        this.pendingRead = new WeakMap();\n        // List of data ids that are scheduled for disposal, but are waiting on a\n        // pending read operation.\n        this.pendingDisposal = new WeakSet();\n        // Used to count the number of 'shallow' sliced tensors that point to the\n        // same data id.\n        this.dataRefCount = new WeakMap();\n        this.numBytesInGPU = 0;\n        // Accumulated time spent (including blocking) in uploading data to webgl.\n        this.uploadWaitMs = 0;\n        // Accumulated time spent (including blocking in downloading data from webgl.\n        this.downloadWaitMs = 0;\n        this.warnedAboutMemory = false;\n        this.warnedAboutCPUBackend = false;\n        this.pendingDeletes = 0;\n        this.disposed = false;\n        if (!env().getBool('HAS_WEBGL')) {\n            throw new Error('WebGL is not supported on this device');\n        }\n        if (gpgpu == null) {\n            const gl = getWebGLContext(env().getNumber('WEBGL_VERSION'));\n            this.binaryCache = getBinaryCache(env().getNumber('WEBGL_VERSION'));\n            this.gpgpu = new GPGPUContext(gl);\n            this.canvas = gl.canvas;\n            this.gpgpuCreatedLocally = true;\n        }\n        else {\n            this.gpgpu = gpgpu;\n            this.binaryCache = {};\n            this.gpgpuCreatedLocally = false;\n            this.canvas = gpgpu.gl.canvas;\n        }\n        this.textureManager = new TextureManager(this.gpgpu);\n        this.numMBBeforeWarning = numMBBeforeWarning();\n        this.texData = new DataStorage(this, engine());\n    }\n    numDataIds() {\n        return this.texData.numDataIds() +\n            (this.cpuBackend ? this.cpuBackend.numDataIds() : 0) -\n            this.pendingDeletes;\n    }\n    write(values, shape, dtype) {\n        if (env().getBool('WEBGL_CHECK_NUMERICAL_PROBLEMS') ||\n            env().getBool('DEBUG')) {\n            this.checkNumericalProblems(values);\n        }\n        if (dtype === 'complex64' && values != null) {\n            throw new Error(`Cannot write to a complex64 dtype. ` +\n                `Please use tf.complex(real, imag).`);\n        }\n        const dataId = {};\n        this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });\n        return dataId;\n    }\n    /** Increase refCount of a `TextureData`. */\n    incRef(dataId) {\n        const texData = this.texData.get(dataId);\n        texData.refCount++;\n    }\n    /** Decrease refCount of a `TextureData`. */\n    decRef(dataId) {\n        if (this.texData.has(dataId)) {\n            const texData = this.texData.get(dataId);\n            texData.refCount--;\n        }\n    }\n    move(dataId, values, shape, dtype) {\n        if (env().getBool('DEBUG')) {\n            this.checkNumericalProblems(values);\n        }\n        if (dtype === 'complex64') {\n            throw new Error(`Cannot write to a complex64 dtype. ` +\n                `Please use tf.complex(real, imag).`);\n        }\n        this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });\n    }\n    disposeIntermediateTensorInfo(tensorInfo) {\n        const dataId = tensorInfo.dataId;\n        if (this.texData.has(dataId)) {\n            const textureData = this.texData.get(dataId);\n            textureData.refCount--;\n            if (textureData.refCount < 1) {\n                this.disposeData(dataId);\n            }\n        }\n    }\n    readSync(dataId) {\n        const texData = this.texData.get(dataId);\n        const { values, dtype, complexTensors, slice, shape, isPacked } = texData;\n        // The presence of `slice` indicates this tensor is a shallow slice of a\n        // different tensor, and is using that original tensor's texture. Run\n        // `clone` in order to copy that texture and read from it.\n        if (slice != null) {\n            let program;\n            if (isPacked) {\n                program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n            }\n            else {\n                program = new UnaryOpProgram(shape, unary_op.CLONE);\n            }\n            const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n            const data = this.readSync(res.dataId);\n            this.disposeIntermediateTensorInfo(res);\n            return data;\n        }\n        if (values != null) {\n            return this.convertAndCacheOnCPU(dataId);\n        }\n        if (dtype === 'string') {\n            return values;\n        }\n        const shouldTimeProgram = this.activeTimers != null;\n        let start;\n        if (shouldTimeProgram) {\n            start = util.now();\n        }\n        let result;\n        if (dtype === 'complex64') {\n            const realValues = complexTensors.real.dataSync();\n            const imagValues = complexTensors.imag.dataSync();\n            result = backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        else {\n            result = this.getValuesFromTexture(dataId);\n        }\n        if (shouldTimeProgram) {\n            this.downloadWaitMs += util.now() - start;\n        }\n        return this.convertAndCacheOnCPU(dataId, result);\n    }\n    async read(dataId) {\n        if (this.pendingRead.has(dataId)) {\n            const subscribers = this.pendingRead.get(dataId);\n            return new Promise(resolve => subscribers.push(resolve));\n        }\n        const texData = this.texData.get(dataId);\n        const { values, shape, slice, dtype, complexTensors, isPacked } = texData;\n        // The presence of `slice` indicates this tensor is a shallow slice of a\n        // different tensor, and is using that original tensor's texture. Run\n        // `clone` in order to copy that texture and read from it.\n        if (slice != null) {\n            let program;\n            if (isPacked) {\n                program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n            }\n            else {\n                program = new UnaryOpProgram(shape, unary_op.CLONE);\n            }\n            const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n            const data = this.read(res.dataId);\n            this.disposeIntermediateTensorInfo(res);\n            return data;\n        }\n        if (values != null) {\n            return this.convertAndCacheOnCPU(dataId);\n        }\n        if (!env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED') &&\n            env().getNumber('WEBGL_VERSION') === 2) {\n            throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and ` +\n                `WEBGL_VERSION=2 not yet supported.`);\n        }\n        let buffer = null;\n        let tmpDownloadTarget;\n        if (dtype !== 'complex64' && env().get('WEBGL_BUFFER_SUPPORTED')) {\n            // Possibly copy the texture into a buffer before inserting a fence.\n            tmpDownloadTarget = this.decode(dataId);\n            const tmpData = this.texData.get(tmpDownloadTarget.dataId);\n            buffer = this.gpgpu.createBufferFromTexture(tmpData.texture, ...tex_util.getDenseTexShape(shape));\n        }\n        this.pendingRead.set(dataId, []);\n        if (dtype !== 'complex64') {\n            // Create a fence and wait for it to resolve.\n            await this.gpgpu.createAndWaitForFence();\n        }\n        // Download the values from the GPU.\n        let vals;\n        if (dtype === 'complex64') {\n            const ps = await Promise.all([complexTensors.real.data(), complexTensors.imag.data()]);\n            const realValues = ps[0];\n            const imagValues = ps[1];\n            vals = backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        else if (buffer == null) {\n            vals = this.getValuesFromTexture(dataId);\n        }\n        else {\n            const size = util.sizeFromShape(shape);\n            vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer, size);\n        }\n        if (tmpDownloadTarget != null) {\n            this.disposeIntermediateTensorInfo(tmpDownloadTarget);\n        }\n        const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);\n        const subscribers = this.pendingRead.get(dataId);\n        this.pendingRead.delete(dataId);\n        // Notify all pending reads.\n        subscribers.forEach(resolve => resolve(dTypeVals));\n        if (this.pendingDisposal.has(dataId)) {\n            this.pendingDisposal.delete(dataId);\n            this.disposeData(dataId);\n            this.pendingDeletes--;\n        }\n        return dTypeVals;\n    }\n    checkNumericalProblems(values) {\n        if (values == null) {\n            return;\n        }\n        for (let i = 0; i < values.length; i++) {\n            const num = values[i];\n            if (!webgl_util.canBeRepresented(num)) {\n                if (env().getBool('WEBGL_RENDER_FLOAT32_CAPABLE')) {\n                    throw Error(`The value ${num} cannot be represented with your ` +\n                        `current settings. Consider enabling float32 rendering: ` +\n                        `'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);\n                }\n                throw Error(`The value ${num} cannot be represented on this device.`);\n            }\n        }\n    }\n    getValuesFromTexture(dataId) {\n        const { shape, dtype, isPacked } = this.texData.get(dataId);\n        const size = util.sizeFromShape(shape);\n        if (env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {\n            const tmpTarget = this.decode(dataId);\n            const tmpData = this.texData.get(tmpTarget.dataId);\n            const vals = this.gpgpu\n                .downloadMatrixFromPackedTexture(tmpData.texture, ...tex_util.getDenseTexShape(shape))\n                .subarray(0, size);\n            this.disposeIntermediateTensorInfo(tmpTarget);\n            return vals;\n        }\n        const shouldUsePackedProgram = env().getBool('WEBGL_PACK') && isPacked === true;\n        const outputShape = shouldUsePackedProgram ? webgl_util.getShapeAs3D(shape) : shape;\n        const program = shouldUsePackedProgram ?\n            new EncodeFloatPackedProgram(outputShape) :\n            new EncodeFloatProgram(outputShape);\n        const output = this.runWebGLProgram(program, [{ shape: outputShape, dtype, dataId }], 'float32');\n        const tmpData = this.texData.get(output.dataId);\n        const vals = this.gpgpu\n            .downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture, tmpData.texShape[0], tmpData.texShape[1])\n            .subarray(0, size);\n        this.disposeIntermediateTensorInfo(output);\n        return vals;\n    }\n    async time(f) {\n        const oldActiveTimers = this.activeTimers;\n        const newActiveTimers = [];\n        let outerMostTime = false;\n        if (this.programTimersStack == null) {\n            this.programTimersStack = newActiveTimers;\n            outerMostTime = true;\n        }\n        else {\n            this.activeTimers.push(newActiveTimers);\n        }\n        this.activeTimers = newActiveTimers;\n        f();\n        // needing to split these up because util.flatten only accepts certain types\n        const flattenedActiveTimerQueries = util.flatten(this.activeTimers.map((d) => d.query))\n            .filter(d => d != null);\n        const flattenedActiveTimerNames = util.flatten(this.activeTimers.map((d) => d.name))\n            .filter(d => d != null);\n        this.activeTimers = oldActiveTimers;\n        if (outerMostTime) {\n            this.programTimersStack = null;\n        }\n        const res = {\n            uploadWaitMs: this.uploadWaitMs,\n            downloadWaitMs: this.downloadWaitMs,\n            kernelMs: null,\n            wallMs: null // will be filled by the engine\n        };\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n            res['kernelMs'] = util.sum(kernelMs);\n            res['getExtraProfileInfo'] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d }))\n                .map(d => `${d.name}: ${d.ms}`)\n                .join(', ');\n        }\n        else {\n            res['kernelMs'] = {\n                error: 'WebGL query timers are not supported in this environment.'\n            };\n        }\n        this.uploadWaitMs = 0;\n        this.downloadWaitMs = 0;\n        return res;\n    }\n    memory() {\n        return {\n            unreliable: false,\n            numBytesInGPU: this.numBytesInGPU,\n            numBytesInGPUAllocated: this.textureManager.numBytesAllocated,\n            numBytesInGPUFree: this.textureManager.numBytesFree\n        };\n    }\n    startTimer() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            return this.gpgpu.beginQuery();\n        }\n        return { startMs: util.now(), endMs: null };\n    }\n    endTimer(query) {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            this.gpgpu.endQuery();\n            return query;\n        }\n        query.endMs = util.now();\n        return query;\n    }\n    async getQueryTime(query) {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            return this.gpgpu.waitForQueryAndGetTime(query);\n        }\n        const timerQuery = query;\n        return timerQuery.endMs - timerQuery.startMs;\n    }\n    disposeData(dataId) {\n        if (this.pendingDisposal.has(dataId)) {\n            return;\n        }\n        if (this.pendingRead.has(dataId)) {\n            this.pendingDisposal.add(dataId);\n            this.pendingDeletes++;\n            return;\n        }\n        // No-op if already disposed.\n        if (!this.texData.has(dataId)) {\n            return;\n        }\n        this.releaseGPUData(dataId);\n        const { complexTensors } = this.texData.get(dataId);\n        if (complexTensors != null) {\n            complexTensors.real.dispose();\n            complexTensors.imag.dispose();\n        }\n        this.texData.delete(dataId);\n    }\n    releaseGPUData(dataId) {\n        const { texture, dtype, texShape, usage, isPacked, slice } = this.texData.get(dataId);\n        const key = slice && slice.origDataId || dataId;\n        const refCount = this.dataRefCount.get(key);\n        if (refCount > 1) {\n            this.dataRefCount.set(key, refCount - 1);\n        }\n        else {\n            this.dataRefCount.delete(key);\n            if (texture != null) {\n                this.numBytesInGPU -= this.computeBytes(texShape, dtype);\n                this.textureManager.releaseTexture(texture, texShape, usage, isPacked);\n            }\n        }\n        const texData = this.texData.get(dataId);\n        texData.texture = null;\n        texData.texShape = null;\n        texData.isPacked = false;\n        texData.slice = null;\n    }\n    getTexture(dataId) {\n        this.uploadToGPU(dataId);\n        return this.texData.get(dataId).texture;\n    }\n    /**\n     * Returns internal information for the specific data bucket. Used in unit\n     * tests.\n     */\n    getDataInfo(dataId) {\n        return this.texData.get(dataId);\n    }\n    getCPUBackend() {\n        if (!env().getBool('WEBGL_CPU_FORWARD')) {\n            return null;\n        }\n        if (this.cpuBackend == null) {\n            this.cpuBackend = engine().findBackend('cpu');\n        }\n        return this.cpuBackend;\n    }\n    /*\n    Tests whether all the inputs to an op are small and on the CPU. This heuristic\n    determines when it would be faster to execute a kernel on the CPU. WebGL\n    kernels opt into running this check and forwarding when appropriate.\n    TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more\n    sustainable strategy for optimizing backend execution of ops.\n     */\n    shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) {\n        const cpuBackend = this.getCPUBackend();\n        if (!this.warnedAboutCPUBackend && cpuBackend == null) {\n            console.warn('Your application contains ops that are small enough to be ' +\n                'executed on the CPU backend, however the CPU backend cannot ' +\n                'be found. Consider importing the CPU backend ' +\n                '(@tensorflow/tfjs-backend-cpu) for better performance.');\n            this.warnedAboutCPUBackend = true;\n        }\n        return cpuBackend != null &&\n            inputs.every(input => this.texData.get(input.dataId).texture == null &&\n                util.sizeFromShape(input.shape) < sizeThreshold);\n    }\n    getGPGPUContext() {\n        return this.gpgpu;\n    }\n    complex(real, imag) {\n        const result = this.makeOutput(real.shape, 'complex64');\n        const resultData = this.texData.get(result.dataId);\n        // The backend owns the reference to the underlying real and imaginary\n        // clones. These will explicitly get disposed when the complex tensor is\n        // disposed.\n        resultData.complexTensors = {\n            real: engine().keep(real.clone()),\n            imag: engine().keep(imag.clone())\n        };\n        return result;\n    }\n    real(input) {\n        const resultData = this.texData.get(input.dataId);\n        return resultData.complexTensors.real.clone();\n    }\n    imag(input) {\n        const resultData = this.texData.get(input.dataId);\n        return resultData.complexTensors.imag.clone();\n    }\n    slice(x, begin, size) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = sliceImplCPU(this.texData.get(x.dataId).values, begin, size, x.shape, x.dtype);\n            return this.makeOutput(size, x.dtype, outValues);\n        }\n        // Short-circuit computation if the slice is zero-sized.\n        if (util.sizeFromShape(size) === 0) {\n            return tensor([], size, x.dtype);\n        }\n        const { isPacked } = this.texData.get(x.dataId);\n        const isContinous = slice_util.isSliceContinous(x.shape, begin, size);\n        if (isPacked || !isContinous) {\n            const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n                new SlicePackedProgram(size) :\n                new SliceProgram(size);\n            const customSetup = program.getCustomSetupFunc(begin);\n            return this.compileAndRun(program, [x], null, customSetup);\n        }\n        this.uploadToGPU(x.dataId);\n        return this.shallowSlice(x, begin, size);\n    }\n    shallowSlice(x, begin, size) {\n        const xTexData = this.texData.get(x.dataId);\n        const t = this.makeOutput(size, x.dtype);\n        const newTexData = this.texData.get(t.dataId);\n        // Copy texture data from the original tensor.\n        Object.assign(newTexData, xTexData);\n        newTexData.shape = size;\n        newTexData.dtype = x.dtype;\n        let flatOffset = slice_util.computeFlatOffset(begin, x.strides);\n        if (xTexData.slice) {\n            // We are slicing an already sliced tensor, so we have to accumulate\n            // the offset.\n            flatOffset += xTexData.slice.flatOffset;\n        }\n        newTexData.slice = {\n            flatOffset,\n            // Point to the original dataId, which is used to do ref counting.\n            origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId\n        };\n        // Increase the ref count for that data bucket.\n        const refCount = this.dataRefCount.get(newTexData.slice.origDataId) || 1;\n        this.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);\n        return t;\n    }\n    stridedSlice(x, begin, end, strides) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.stridedSlice(x, begin, end, strides));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const outShape = slice_util.computeOutShape(begin, end, strides);\n        if (outShape.some(axis => axis === 0)) {\n            return tensor([], outShape);\n        }\n        const program = new StridedSliceProgram(begin, strides, outShape);\n        return this.compileAndRun(program, [x]);\n    }\n    reverse(x, axis) {\n        const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n            new ReversePackedProgram(x.shape, axis) :\n            new ReverseProgram(x.shape, axis);\n        return this.compileAndRun(program, [x]);\n    }\n    concat(tensors, axis) {\n        if (tensors[0].dtype === 'complex64') {\n            const reals = tensors.map((t) => real(t));\n            const imags = tensors.map((t) => imag(t));\n            return complex(this.concat(reals, axis), this.concat(imags, axis));\n        }\n        if (tensors.length === 1) {\n            return tensors[0];\n        }\n        if (tensors.length > env().getNumber('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n            const midIndex = Math.floor(tensors.length / 2);\n            const leftSide = this.concat(tensors.slice(0, midIndex), axis);\n            const rightSide = this.concat(tensors.slice(midIndex), axis);\n            return this.concat([leftSide, rightSide], axis);\n        }\n        if (env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') && tensors[0].rank > 1) {\n            const program = new ConcatPackedProgram(tensors.map(t => t.shape), axis);\n            return this.compileAndRun(program, tensors);\n        }\n        // Any concat of n-dimensional tensors across any axis can be reduced to\n        // a concatenation of two-dimensional tensors across the axis 1 by first\n        // partitioning the axes of the original tensors into those less than the\n        // axis to be concatenated and the rest. Then reshape the tensors\n        // into a two-dimensional tensor by collapsing these two sets of axes and\n        // concatenate the resulting matrices across the axis 1, finally reshaping\n        // the result to have the proper shape.\n        const outShape = backend_util.computeOutShape(tensors.map(t => t.shape), axis);\n        const tensors2D = tensors.map(t => t.as2D(-1, util.sizeFromShape(t.shape.slice(axis))));\n        const program = new ConcatProgram(tensors2D.map(t => t.shape));\n        const res = this.compileAndRun(program, tensors2D);\n        return res.reshape(outShape);\n    }\n    neg(x) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.neg(x));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.NEG, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.NEG);\n        return this.compileAndRun(program, [x]);\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n        const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n        const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n        const [batch, ,] = a.shape;\n        // Since the matrices are vectors, it is faster to call mul().sum()\n        // because sum() is O(sqrt(N)) due to divide-and-conquer.\n        if ((outerShapeA === 1 || outerShapeB === 1) &&\n            sharedDim > MATMUL_SHARED_DIM_THRESHOLD) {\n            if (transposeA) {\n                a = transpose(a, [0, 2, 1]);\n            }\n            if (transposeB) {\n                b = transpose(b, [0, 2, 1]);\n            }\n            const a3D = outerShapeB === 1 ? a : a.as3D(batch, sharedDim, 1);\n            const axis = outerShapeB === 1 ? 2 : 1;\n            const b3D = outerShapeB === 1 ? b.as3D(batch, 1, sharedDim) : b;\n            return this.multiply(a3D, b3D).sum(axis, true /* keepDims */);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        const program = new MatMulPackedProgram(a.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n        const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n        const [batch, ,] = a.shape;\n        const dtype = upcastType(a.dtype, b.dtype);\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;\n        const program = new MatMulPackedProgram(a.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [a, b];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (preluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        return this.compileAndRun(program, inputs, dtype);\n    }\n    multiply(a, b) {\n        if (a.dtype === 'complex64') {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const realProgram = new BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.REAL, a.shape, b.shape);\n            const imagProgram = new BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);\n            const inputs = [\n                this.makeComplexComponentTensorInfo(a, aData.complexTensors.real),\n                this.makeComplexComponentTensorInfo(a, aData.complexTensors.imag),\n                this.makeComplexComponentTensorInfo(b, bData.complexTensors.real),\n                this.makeComplexComponentTensorInfo(b, bData.complexTensors.imag)\n            ];\n            const real = this.compileAndRun(realProgram, inputs);\n            const imag = this.compileAndRun(imagProgram, inputs);\n            const complex = this.complex(real, imag);\n            real.dispose();\n            imag.dispose();\n            return complex;\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        if (this.shouldExecuteOnCPU([a, b])) {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const [outValues, outShape] = multiplyImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n            return this.makeOutput(outShape, dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_gpu.MUL, a.dtype);\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.MUL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], a.dtype);\n    }\n    localResponseNormalization4D(x, radius, bias, alpha, beta) {\n        const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n            new LRNPackedProgram(x.shape, radius, bias, alpha, beta) :\n            new LRNProgram(x.shape, radius, bias, alpha, beta);\n        return this.compileAndRun(program, [x]);\n    }\n    LRNGrad(dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {\n        const program = new LRNGradProgram(inputImage.shape, depthRadius, bias, alpha, beta);\n        return this.compileAndRun(program, [inputImage, outputImage, dy]);\n    }\n    tile(x, reps) {\n        if (x.dtype === 'string') {\n            const data = this.readSync(x.dataId);\n            const decodedData = data.map(d => util.decodeString(d));\n            const buf = buffer(x.shape, x.dtype, decodedData);\n            return tile(buf, reps);\n        }\n        const program = new TileProgram(x.shape, reps);\n        return this.compileAndRun(program, [x]);\n    }\n    pad(x, paddings, constantValue) {\n        const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n            new PadPackedProgram(x.shape, paddings, constantValue) :\n            new PadProgram(x.shape, paddings, constantValue);\n        return this.compileAndRun(program, [x]);\n    }\n    gather(x, indices, axis) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x, indices], () => this.cpuBackend.gather(x, indices, axis));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = new GatherProgram(x.shape, indices.size, axis);\n        return this.compileAndRun(program, [x, indices]);\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        util.assert(x.rank <= 4, () => 'batchToSpaceND for rank > 4 with a WebGL backend not ' +\n            'implemented yet');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n        const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n        const reshapedPermuted = backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n        const sliceBeginCoords = backend_util.getSliceBeginCoords(crops, blockShape.length);\n        const sliceSize = backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n        return transpose(x.reshape(reshaped), permuted)\n            .reshape(reshapedPermuted)\n            .slice(sliceBeginCoords, sliceSize);\n    }\n    spaceToBatchND(x, blockShape, paddings) {\n        util.assert(x.rank <= 4, () => 'spaceToBatchND for rank > 4 with a WebGL backend not ' +\n            'implemented yet');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const completePaddings = [[0, 0]];\n        completePaddings.push(...paddings);\n        for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n            completePaddings.push([0, 0]);\n        }\n        const paddedX = x.pad(completePaddings);\n        const reshapedPaddedShape = backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n        const permutedReshapedPaddedPermutation = backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n        const flattenShape = backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n        const paddedXT = transpose(paddedX.reshape(reshapedPaddedShape), permutedReshapedPaddedPermutation);\n        return reshape(paddedXT, flattenShape);\n    }\n    reduce(x, reduceType, dtype) {\n        const batchSize = x.shape[0];\n        const inSize = x.shape[1];\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const outSize = Math.ceil(inSize / windowSize);\n        const reduceInfo = { windowSize, inSize, batchSize, outSize };\n        const program = new ReduceProgram(reduceInfo, reduceType);\n        const output = this.compileAndRun(program, [x], dtype);\n        // No need to run another GPGPU program.\n        if (output.shape[1] === 1) {\n            return output;\n        }\n        return this.reduce(output, reduceType, dtype);\n    }\n    argReduce(x, reduceType, bestIndicesA = null) {\n        let batchSize = x.shape[0];\n        let inSize = x.shape[1];\n        if (bestIndicesA != null) {\n            batchSize = bestIndicesA.shape[0];\n            inSize = bestIndicesA.shape[1];\n        }\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const reduceInfo = {\n            windowSize,\n            inSize,\n            batchSize,\n            outSize: Math.ceil(inSize / windowSize)\n        };\n        const program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);\n        const inputs = [x];\n        if (bestIndicesA != null) {\n            inputs.push(bestIndicesA);\n        }\n        const output = this.compileAndRun(program, inputs, 'int32');\n        // No need to run another GPGPU program.\n        if (output.shape[1] === 1) {\n            return output;\n        }\n        return this.argReduce(x, reduceType, output);\n    }\n    argReducePacked(x, reduceType, bestIndicesA = null) {\n        const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;\n        const inSize = inShape[inShape.length - 1];\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null);\n        const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];\n        const output = this.compileAndRun(program, inputs, 'int32');\n        if (output.rank === x.rank) {\n            return this.argReducePacked(x, reduceType, output);\n        }\n        return output;\n    }\n    sum(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        return this.reduce(a2D, 'sum', outputDType).reshape(outShape);\n    }\n    prod(x, axes) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.prod(x, axes));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        return this.reduce(a2D, 'prod', outputDType).reshape(outShape);\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        let axis = 0;\n        const permutation = backend_util.getAxesPermutation([axis], x.rank);\n        let permutedX = x;\n        if (permutation != null) {\n            permutedX = transpose(x, permutation);\n            axis = backend_util.getInnerMostAxes(1, x.rank)[0];\n        }\n        const outShape = segment_util.computeOutShape(permutedX.shape, axis, numSegments);\n        const inSize = util.sizeFromShape([permutedX.shape[axis]]);\n        const a2D = permutedX.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        let result = this.segOpCompute(a2D, 'unsortedSegmentSum', segmentIds, outputDType, numSegments)\n            .reshape(outShape);\n        if (permutation != null) {\n            result =\n                transpose(result, backend_util.getUndoAxesPermutation(permutation));\n        }\n        return result;\n    }\n    segOpCompute(x, segOpType, segmentIds, dtype, numSegments) {\n        const batchSize = x.shape[0];\n        const inSize = x.shape[1];\n        const windowSize = segment_util.segOpComputeOptimalWindowSize(inSize, numSegments);\n        const segOpInfo = { windowSize, inSize, batchSize, numSegments };\n        const program = new SegmentOpProgram(segOpInfo, segOpType);\n        const output = this.compileAndRun(program, [x, segmentIds], dtype);\n        // No need to run another GPGPU program.\n        if (output.shape[1] === numSegments) {\n            return output;\n        }\n        segmentIds = range(0, numSegments).tile([inSize / windowSize]);\n        return this.segOpCompute(output, segOpType, segmentIds, dtype, numSegments);\n    }\n    argMinMaxReduce(x, axis, reduceType) {\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('arg' + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.rank);\n        if (!env().getBool('WEBGL_PACK_REDUCE') || x.rank <= 2) {\n            const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n            const inSize = util.sizeFromShape(reduceShape);\n            const a2D = x.as2D(-1, inSize);\n            return this.argReduce(a2D, reduceType).reshape(outShape);\n        }\n        return this.argReducePacked(x, reduceType);\n    }\n    argMin(x, axis) {\n        return this.argMinMaxReduce(x, axis, 'min');\n    }\n    argMax(x, axis) {\n        return this.argMinMaxReduce(x, axis, 'max');\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        if (axis !== x.rank - 1) {\n            throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.rank - 1} ` +\n                `but got axis=${axis}`);\n        }\n        const size = x.shape[axis];\n        let result = x;\n        // Use cumsum parallel algorithm, ref:\n        // https://developer.nvidia.com/gpugems/gpugems3/part-vi-gpu-computing/chapter-39-parallel-prefix-sum-scan-cuda\n        for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {\n            const program = new CumSumProgram(x.shape, false, reverse);\n            const customSetup = program.getCustomSetupFunc(i);\n            const prevResult = result;\n            result = this.compileAndRun(program, [result], result.dtype, customSetup);\n            prevResult.dispose();\n        }\n        // For exclusive cumsum, shift the end result in the direction of sum and\n        // add 0 to the front index.\n        if (exclusive) {\n            const program = new CumSumProgram(x.shape, exclusive, reverse);\n            const prevResult = result;\n            result = this.compileAndRun(program, [result]);\n            prevResult.dispose();\n        }\n        return result;\n    }\n    equal(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    notEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.NOT_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.NOT_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    less(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.less(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LESS, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LESS, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    lessEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LESS_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LESS_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    greater(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.greater(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.GREATER, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.GREATER, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    greaterEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.GREATER_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.GREATER_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    logicalNot(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.LOGICAL_NOT);\n        return this.compileAndRun(program, [x]);\n    }\n    logicalAnd(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LOGICAL_AND, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LOGICAL_AND, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    logicalOr(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LOGICAL_OR, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LOGICAL_OR, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    select(condition, a, b) {\n        const program = new SelectProgram(condition.rank, a.shape, a.rank);\n        return this.compileAndRun(program, [condition, a, b], upcastType(a.dtype, b.dtype));\n    }\n    where(condition) {\n        backend_util.warn('tf.where() in webgl locks the UI thread. ' +\n            'Call tf.whereAsync() instead');\n        const condVals = condition.dataSync();\n        return whereImpl(condition.shape, condVals);\n    }\n    topk(x, k, sorted) {\n        const xVals = x.dataSync();\n        return topkImpl(xVals, x.shape, x.dtype, k, sorted);\n    }\n    min(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'min', a2D.dtype).reshape(outShape);\n    }\n    minimum(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.minimum(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MIN, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MIN, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    mod(a, b) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MOD, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MOD, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    maximum(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.maximum(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MAX, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MAX, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    all(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'all', a2D.dtype).reshape(outShape);\n    }\n    any(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'any', a2D.dtype).reshape(outShape);\n    }\n    floorDiv(a, b) {\n        const op = binaryop_gpu.INT_DIV;\n        const outputDtype = 'int32';\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.INT_DIV, outputDtype);\n        }\n        const program = new BinaryOpProgram(op, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], outputDtype);\n    }\n    add(a, b) {\n        if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n            return this.complexSeparableBinaryOp(a, b, binaryop_gpu.ADD);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        if (this.shouldExecuteOnCPU([a, b])) {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const [outValues, outShape] = addImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n            return this.makeOutput(outShape, dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_gpu.ADD, dtype);\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.ADD, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    packedUnaryOp(x, op, dtype) {\n        const program = new UnaryOpPackedProgram(x.shape, op);\n        return this.compileAndRun(program, [x], dtype);\n    }\n    packedBinaryOp(a, b, op, dtype, checkOutOfBounds = false) {\n        const program = new BinaryOpPackedProgram(op, a.shape, b.shape, checkOutOfBounds);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    /**\n     * Computes a complex binary operation that can be decomposed into a simple\n     * binary operation on both the real and imagary parts.\n     */\n    complexSeparableBinaryOp(a, b, op) {\n        const aData = this.texData.get(a.dataId);\n        const bData = this.texData.get(b.dataId);\n        const [real, imag] = [\n            [aData.complexTensors.real, bData.complexTensors.real],\n            [aData.complexTensors.imag, bData.complexTensors.imag]\n        ].map(complexParts => {\n            const [aPart, bPart] = complexParts;\n            const aHandle = this.makeComplexComponentTensorInfo(a, aPart);\n            const bHandle = this.makeComplexComponentTensorInfo(b, bPart);\n            const program = new BinaryOpProgram(op, a.shape, b.shape);\n            return this.compileAndRun(program, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));\n        });\n        const complex = this.complex(real, imag);\n        real.dispose();\n        imag.dispose();\n        return complex;\n    }\n    // Returns a TensorInfo with the complex shape and the dataId of the\n    // underlying part. We need to do this because a reshaped complex tensor is\n    // not reflected in its parts.\n    makeComplexComponentTensorInfo(complexTensor, complexPart) {\n        return {\n            dataId: complexPart.dataId,\n            dtype: complexPart.dtype,\n            shape: complexTensor.shape\n        };\n    }\n    addN(tensors) {\n        if (tensors.length === 1) {\n            return tensors[0];\n        }\n        // Limit the number of uploaded textures for optimization.\n        if (tensors.length > env().get('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n            const midIndex = Math.floor(tensors.length / 2);\n            const leftSide = this.addN(tensors.slice(0, midIndex));\n            const rightSide = this.addN(tensors.slice(midIndex));\n            return this.addN([leftSide, rightSide]);\n        }\n        const dtype = tensors.map(t => t.dtype).reduce((d1, d2) => upcastType(d1, d2));\n        const shapes = tensors.map(t => t.shape);\n        // We can make sure shapes are identical in op level.\n        const usePackedOp = env().getBool('WEBGL_PACK');\n        const program = usePackedOp ?\n            new AddNPackedProgram(tensors[0].shape, shapes) :\n            new AddNProgram(tensors[0].shape, shapes);\n        return this.compileAndRun(program, tensors, dtype);\n    }\n    subtract(a, b) {\n        if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n            return this.complexSeparableBinaryOp(a, b, binaryop_gpu.SUB);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        if (this.shouldExecuteOnCPU([a, b])) {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const [outValues, outShape] = subImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n            return this.makeOutput(outShape, dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_gpu.SUB, a.dtype);\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.SUB, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    pow(a, b) {\n        const usePackedOp = env().getBool('WEBGL_PACK_BINARY_OPERATIONS');\n        const program = usePackedOp ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.POW, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.POW, a.shape, b.shape);\n        const dtype = upcastType(a.dtype, b.dtype);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    ceil(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = ceilImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.CEIL, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.CEIL);\n        return this.compileAndRun(program, [x]);\n    }\n    floor(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = floorImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.FLOOR, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.FLOOR);\n        return this.compileAndRun(program, [x]);\n    }\n    sign(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SIGN);\n        return this.compileAndRun(program, [x]);\n    }\n    isNaN(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_NAN);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    isInf(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_INF);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    isFinite(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_FINITE);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    round(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ROUND);\n        return this.compileAndRun(program, [x]);\n    }\n    exp(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = expImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.EXP, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.EXP);\n        return this.compileAndRun(program, [x]);\n    }\n    expm1(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = expm1ImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.EXPM1, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.EXPM1);\n        return this.compileAndRun(program, [x]);\n    }\n    softmax(logits, dim) {\n        const axes = util.parseAxisParam([dim], logits.shape);\n        // TODO(annxingyuan): Call maxImpl rather than op as part of softmax kernel\n        // modularization.\n        const maxLogit = max(logits, axes);\n        const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n        const a = this.subtract(logits, maxLogit.reshape(expandedShape));\n        const b = this.exp(a);\n        const sumExp = this.sum(b, axes).reshape(expandedShape);\n        // TODO(annxingyuan): Call divImpl rather than op as part of softmax kernel\n        // modularization.\n        return div(b, sumExp);\n    }\n    log(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = logImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_packed_op.LOG, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.LOG);\n        return this.compileAndRun(program, [x]);\n    }\n    log1p(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.LOG1P);\n        return this.compileAndRun(program, [x]);\n    }\n    sqrt(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SQRT);\n        return this.compileAndRun(program, [x]);\n    }\n    rsqrt(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = rsqrtImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.RSQRT);\n        return this.compileAndRun(program, [x]);\n    }\n    reciprocal(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.RECIPROCAL);\n        return this.compileAndRun(program, [x]);\n    }\n    relu(x) {\n        let program;\n        if (env().getBool('WEBGL_PACK')) {\n            program = new UnaryOpPackedProgram(x.shape, unary_packed_op.RELU);\n        }\n        else {\n            program = new UnaryOpProgram(x.shape, unary_op.RELU);\n        }\n        return this.compileAndRun(program, [x]);\n    }\n    relu6(x) {\n        let program;\n        if (env().getBool('WEBGL_PACK')) {\n            program = new UnaryOpPackedProgram(x.shape, unary_packed_op.RELU6);\n        }\n        else {\n            program = new UnaryOpProgram(x.shape, unary_op.RELU6);\n        }\n        return this.compileAndRun(program, [x]);\n    }\n    prelu(x, alpha) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.PRELU, x.shape, alpha.shape) :\n            new BinaryOpProgram(binaryop_gpu.PRELU, x.shape, alpha.shape);\n        return this.compileAndRun(program, [x, alpha]);\n    }\n    elu(x) {\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_packed_op.ELU, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.ELU);\n        return this.compileAndRun(program, [x]);\n    }\n    eluDer(dy, y) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.ELU_DER, dy.shape, y.shape) :\n            new BinaryOpProgram(binaryop_gpu.ELU_DER, dy.shape, y.shape);\n        return this.compileAndRun(program, [dy, y]);\n    }\n    selu(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SELU);\n        return this.compileAndRun(program, [x]);\n    }\n    int(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.TO_INT);\n        return this.compileAndRun(program, [x], 'int32');\n    }\n    clip(x, min, max) {\n        let program;\n        if (env().getBool('WEBGL_PACK_CLIP')) {\n            program = new ClipPackedProgram(x.shape);\n        }\n        else {\n            program = new ClipProgram(x.shape);\n        }\n        const customSetup = program.getCustomSetupFunc(min, max);\n        return this.compileAndRun(program, [x], null, customSetup);\n    }\n    abs(x) {\n        // TODO: handle cases when x is complex.\n        if (this.shouldExecuteOnCPU([x]) && x.dtype !== 'complex64') {\n            const outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.ABS, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.ABS);\n        return this.compileAndRun(program, [x]);\n    }\n    complexAbs(x) {\n        const xData = this.texData.get(x.dataId);\n        const program = new ComplexAbsProgram(x.shape);\n        const inputs = [\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.real),\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.imag),\n        ];\n        return this.compileAndRun(program, inputs);\n    }\n    sigmoid(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SIGMOID);\n        return this.compileAndRun(program, [x]);\n    }\n    softplus(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SOFTPLUS);\n        return this.compileAndRun(program, [x]);\n    }\n    asin(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ASIN);\n        return this.compileAndRun(program, [x]);\n    }\n    acos(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ACOS);\n        return this.compileAndRun(program, [x]);\n    }\n    atan(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ATAN);\n        return this.compileAndRun(program, [x]);\n    }\n    sinh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SINH);\n        return this.compileAndRun(program, [x]);\n    }\n    cosh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.COSH);\n        return this.compileAndRun(program, [x]);\n    }\n    tanh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.TANH);\n        return this.compileAndRun(program, [x]);\n    }\n    asinh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ASINH);\n        return this.compileAndRun(program, [x]);\n    }\n    acosh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ACOSH);\n        return this.compileAndRun(program, [x]);\n    }\n    atanh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ATANH);\n        return this.compileAndRun(program, [x]);\n    }\n    erf(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ERF);\n        return this.compileAndRun(program, [x]);\n    }\n    step(x, alpha) {\n        const program = new UnaryOpProgram(x.shape, unary_op.STEP(alpha));\n        return this.compileAndRun(program, [x]);\n    }\n    conv2dByMatMul(x, filter, convInfo, bias, activation, preluActivationWeights) {\n        // Reshapes conv2D input to 2D tensors, uses matMul and then reshape the\n        // result from 2D to 4D.\n        const xShape = x.shape;\n        const xTexData = this.texData.get(x.dataId);\n        const sharedMatMulDim = convInfo.inChannels;\n        const outerShapeX = xShape[0] * xShape[1] * xShape[2];\n        const outerShapeFilter = convInfo.outChannels;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const transposeA = false;\n        const transposeB = false;\n        // TODO: Once reduction ops are packed, batchMatMul will always be packed\n        // and we can remove this condition.\n        const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) &&\n            sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;\n        const reshapeWillBeExpensive = xShape[2] % 2 !== 0 && !!xTexData.isPacked;\n        if (batchMatMulWillBeUnpacked || !env().getBool('WEBGL_LAZILY_UNPACK') ||\n            !env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ||\n            !reshapeWillBeExpensive) {\n            const targetShape = isChannelsLast ? xShape[0] * xShape[1] * xShape[2] :\n                xShape[0] * xShape[2] * xShape[3];\n            const xReshaped = reshape(x, [1, targetShape, convInfo.inChannels]);\n            const filterReshaped = reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);\n            const result = this.fusedBatchMatMul({\n                a: xReshaped,\n                b: filterReshaped,\n                transposeA,\n                transposeB,\n                bias,\n                activation,\n                preluActivationWeights\n            });\n            return reshape(result, convInfo.outShape);\n        }\n        // Following optimization is specific to packed |x| with odd row count\n        // (For example, in channelLast mode, 'row count' refers to x.shape[2]):\n        // we avoid expensive packed 2x2 reshape by padding row count to next,\n        // even number. When x.shape[2] is odd, the result of packed batchMatMul is\n        // the same (has the same texture layout and and values in the texture) as\n        // it is for even x.shape[2] + 1. We make the odd-rows tensor to look like\n        // even-rows tensor before the operation and, after the batchMatMul,\n        // fix the even-rows result to have odd number of rows.\n        const targetShape = isChannelsLast ?\n            xShape[0] * xShape[1] * (xShape[2] + 1) :\n            xShape[0] * xShape[2] * (xShape[3] + 1);\n        const xReshaped = {\n            dataId: x.dataId,\n            shape: [1, targetShape, convInfo.inChannels],\n            dtype: x.dtype\n        };\n        // xTexData.shape gets referenced from GPGPUBinary.inShapeInfos.\n        // Decrementing row count, after batchMatMul->...->compileProgram leads to\n        // invalid row count within the reference in GPGPUBinary.inShapeInfos.\n        // Alternative fix would be to provide a copy to GPGPUBinary.inShapeInfos\n        // in compileProgram method, but that would affect compilation of all\n        // programs - instead, provide a copy here, with even row count, before\n        // calling batchMatMul->...->compileProgram and after that, the original\n        // xTexData.shape is restored.\n        const originalXTexDataShape = xTexData.shape;\n        xTexData.shape = xTexData.shape.slice();\n        xTexData.shape[xTexData.shape.length - 2]++;\n        util.assert(webgl_util.isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);\n        const filterReshaped = reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);\n        const pointwiseConv = this.fusedBatchMatMul({\n            a: xReshaped,\n            b: filterReshaped,\n            transposeA,\n            transposeB,\n            bias,\n            activation,\n            preluActivationWeights\n        });\n        const pointwiseConvTexData = this.texData.get(pointwiseConv.dataId);\n        util.assert(pointwiseConvTexData.isPacked, () => 'batchMatMul result is expected to be packed');\n        // Restore the input shape to original.\n        xTexData.shape = originalXTexDataShape;\n        // Set the output shape - there is no need for expensive reshape as data\n        // layout is already correct.\n        pointwiseConvTexData.shape = convInfo.outShape;\n        return engine().makeTensorFromDataId(pointwiseConv.dataId, convInfo.outShape, pointwiseConv.dtype);\n    }\n    conv2dWithIm2Row(x, filter, convInfo, bias, activation, preluActivationWeights) {\n        // Rearranges conv2d input so each block to be convolved over forms the\n        // column of a new matrix with shape [filterWidth * filterHeight *\n        // inChannels, outHeight * outWidth]. The filter is also rearranged so each\n        // output channel forms a row of a new matrix with shape [outChannels,\n        // filterWidth * filterHeight * inChannels]. The convolution is then\n        // computed by multiplying these matrices and reshaping the result.\n        const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo;\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const sharedDim = filterWidth * filterHeight * inChannels;\n        const numCols = outHeight * outWidth;\n        const x2ColShape = [sharedDim, numCols];\n        const transposeA = true;\n        const transposeB = false;\n        const xSqueezed = x.squeeze([0]);\n        const w2Row = filter.reshape([1, sharedDim, -1]);\n        const im2ColProgram = new Im2ColPackedProgram(x2ColShape, xSqueezed.shape, convInfo);\n        const im2Col = this.compileAndRun(im2ColProgram, [xSqueezed]).reshape([\n            1, x2ColShape[0], x2ColShape[1]\n        ]);\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;\n        const matmulProgram = new MatMulPackedProgram(im2Col.shape, [1, numCols, convInfo.outChannels], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [im2Col, w2Row];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (hasPreluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        const product = this.compileAndRun(matmulProgram, inputs);\n        if (isChannelsLast) {\n            return product.reshape([1, outHeight, outWidth, convInfo.outChannels]);\n        }\n        else {\n            return product.reshape([1, convInfo.outChannels, outHeight, outWidth]);\n        }\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n            (convInfo.padInfo.type === 'SAME' ||\n                convInfo.padInfo.type === 'VALID')) {\n            return this.conv2dByMatMul(input, filter, convInfo, bias, activation, preluActivationWeights);\n        }\n        if (env().getBool('WEBGL_CONV_IM2COL') && input.shape[0] === 1) {\n            return this.conv2dWithIm2Row(input, filter, convInfo, bias, activation, preluActivationWeights);\n        }\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, false) : null;\n        const program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [input, filter];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (preluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        return this.compileAndRun(program, inputs);\n    }\n    conv2d(x, filter, convInfo) {\n        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n            (convInfo.padInfo.type === 'SAME' ||\n                convInfo.padInfo.type === 'VALID')) {\n            return this.conv2dByMatMul(x, filter, convInfo);\n        }\n        if (env().getBool('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {\n            return this.conv2dWithIm2Row(x, filter, convInfo);\n        }\n        const program = new Conv2DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        const program = new Conv2DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    conv2dDerFilter(x, dy, convInfo) {\n        const program = new Conv2DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        const shouldPackDepthwiseConv = env().getBool('WEBGL_PACK_DEPTHWISECONV') &&\n            convInfo.strideWidth <= 2 &&\n            convInfo.outChannels / convInfo.inChannels === 1;\n        const fusedActivation = activation ?\n            mapActivationToShaderProgram(activation, shouldPackDepthwiseConv) :\n            null;\n        const inputs = [input, filter];\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        if (hasBias) {\n            inputs.push(bias);\n        }\n        if (hasPreluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        let program;\n        if (shouldPackDepthwiseConv) {\n            program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n            return this.compileAndRun(program, inputs);\n        }\n        program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n        return this.compileAndRun(program, inputs);\n    }\n    depthwiseConv2D(x, filter, convInfo) {\n        let program;\n        if (env().getBool('WEBGL_PACK_DEPTHWISECONV') &&\n            convInfo.strideWidth <= 2 &&\n            convInfo.outChannels / convInfo.inChannels === 1) {\n            program = new DepthwiseConvPacked2DProgram(convInfo);\n            return this.compileAndRun(program, [x, filter]);\n        }\n        program = new DepthwiseConv2DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        const program = new DepthwiseConv2DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    depthwiseConv2DDerFilter(x, dy, convInfo) {\n        const program = new DepthwiseConv2DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    conv3d(x, filter, convInfo) {\n        const program = new Conv3DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        const program = new Conv3DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    conv3dDerFilter(x, dy, convInfo) {\n        const program = new Conv3DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    cast(x, dtype) {\n        return backend_util.castTensor(x, dtype, this);\n    }\n    unstack(x, axis) {\n        const num = x.shape[axis];\n        const outShape = new Array(x.rank - 1);\n        let outIndex = 0;\n        for (let i = 0; i < x.rank; i++) {\n            if (i !== axis) {\n                outShape[outIndex++] = x.shape[i];\n            }\n        }\n        const begin = new Array(x.rank).fill(0);\n        const size = x.shape.slice();\n        size[axis] = 1;\n        const res = new Array(num);\n        for (let i = 0; i < res.length; i++) {\n            begin[axis] = i;\n            res[i] = this.slice(x, begin, size).reshape(outShape);\n        }\n        return res;\n    }\n    avgPool3d(x, convInfo) {\n        const program = new Pool3DProgram(convInfo, 'avg', false);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        const avgPool3dBackpropProgram = new AvgPool3DBackpropProgram(convInfo);\n        return this.compileAndRun(avgPool3dBackpropProgram, [dy], x.dtype);\n    }\n    maxPool3d(x, convInfo) {\n        const program = new Pool3DProgram(convInfo, 'max', false);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        const getPositions = true;\n        const maxPool3dPositionsProgram = new Pool3DProgram(convInfo, 'max', getPositions);\n        const maxPool3dPositions = this.compileAndRun(maxPool3dPositionsProgram, [x]);\n        const maxPool3dBackPropProgram = new MaxPool3DBackpropProgram(convInfo);\n        const result = this.compileAndRun(maxPool3dBackPropProgram, [dy, maxPool3dPositions], x.dtype);\n        maxPool3dPositions.dispose();\n        return result;\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        const program = env().getBool('WEBGL_PACK_IMAGE_OPERATIONS') ?\n            new ResizeBilinearPackedProgram(x.shape, newHeight, newWidth, alignCorners) :\n            new ResizeBilinearProgram(x.shape, newHeight, newWidth, alignCorners);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        const program = new ResizeBilinearBackpropProgram(dy, x, alignCorners);\n        return this.compileAndRun(program, [dy]);\n    }\n    resizeNearestNeighbor(x, newHeight, newWidth, alignCorners) {\n        const program = new ResizeNearestNeighborProgram(x.shape, newHeight, newWidth, alignCorners);\n        return this.compileAndRun(program, [x]);\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        const program = new ResizeNearestNeigborBackpropProgram(dy, x, alignCorners);\n        return this.compileAndRun(program, [dy]);\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        const probs = normalized ? logits : softmax(logits);\n        const batchSize = probs.shape[0];\n        const numOutcomes = probs.shape[1];\n        const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);\n        const customSetup = program.getCustomSetupFunc(seed);\n        return this.compileAndRun(program, [probs], 'int32', customSetup);\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        const program = new OneHotProgram(indices.size, depth, onValue, offValue);\n        return this.compileAndRun(program, [indices]);\n    }\n    diag(x) {\n        const program = new DiagProgram(x.size);\n        return this.compileAndRun(program, [x]);\n    }\n    cropAndResize(image, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        const program = new CropAndResizeProgram(image.shape, boxes.shape, cropSize, method, extrapolationValue);\n        return this.compileAndRun(program, [image, boxes, boxIndex], 'float32');\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n        const batchSize = x.shape[0];\n        const inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];\n        const inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];\n        const inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];\n        const outputHeight = inputHeight * blockSize;\n        const outputWidth = inputWidth * blockSize;\n        const outputDepth = inputDepth / (blockSize * blockSize);\n        const outputShape = (dataFormat === 'NHWC') ?\n            [batchSize, outputHeight, outputWidth, outputDepth] :\n            [batchSize, outputDepth, outputHeight, outputWidth];\n        const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);\n        return this.compileAndRun(program, [x]);\n    }\n    split(x, sizeSplits, axis) {\n        return split(x, sizeSplits, axis);\n    }\n    scatterND(indices, updates, shape) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(updates, indices, shape);\n        const flattenShape = [outputSize / sliceSize, sliceSize];\n        const flattenIndices = indices.reshape([numUpdates, sliceRank]);\n        const flattenX = updates.reshape([numUpdates, sliceSize]);\n        if (outputSize === 0) {\n            return backend_util.reshapeTensor(tensor([]), shape);\n        }\n        const defaultValue = scalar(0);\n        const program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.rank, flattenX.rank, strides, flattenShape);\n        const res = this.compileAndRun(program, [flattenX, flattenIndices, defaultValue]);\n        return res.reshape(shape);\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        const { sliceRank, numUpdates, strides, outputSize } = backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n        const sumDupeIndices = false;\n        const program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.rank, sparseValues.rank, strides, [outputSize, 1], sumDupeIndices);\n        const res = this.compileAndRun(program, [sparseValues, sparseIndices, defaultValue]);\n        return res.reshape(outputShape);\n    }\n    fft(x) {\n        const inverse = false;\n        return this.fftImpl(x, inverse);\n    }\n    ifft(x) {\n        const inverse = true;\n        return this.fftImpl(x, inverse);\n    }\n    fftImpl(x, inverse) {\n        const xData = this.texData.get(x.dataId);\n        const realProgram = new FFTProgram(fft_gpu.COMPLEX_FFT.REAL, x.shape, inverse);\n        const imagProgram = new FFTProgram(fft_gpu.COMPLEX_FFT.IMAG, x.shape, inverse);\n        const inputs = [\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.real),\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.imag),\n        ];\n        const real = this.compileAndRun(realProgram, inputs);\n        const imag = this.compileAndRun(imagProgram, inputs);\n        const complex = this.complex(real, imag).as2D(x.shape[0], x.shape[1]);\n        real.dispose();\n        imag.dispose();\n        return complex;\n    }\n    gatherND(x, indices) {\n        const indicesShape = indices.shape;\n        const sliceRank = indicesShape[indicesShape.length - 1];\n        const [resultShape, numSlices, sliceSize, strides] = backend_util.prepareAndValidate(x, indices);\n        const flattenIndices = indices.reshape([numSlices, sliceRank]);\n        const flattenX = x.reshape([x.size / sliceSize, sliceSize]);\n        const program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);\n        const res = this.compileAndRun(program, [flattenX, flattenIndices]);\n        return res.reshape(resultShape);\n    }\n    fill(shape, value, dtype) {\n        dtype = dtype || util.inferDtype(value);\n        if (dtype === 'string') {\n            // String type should be handled in CPU memory.\n            const values = util.getArrayFromDType(dtype, util.sizeFromShape(shape));\n            values.fill(value);\n            return engine().makeTensor(values, shape, dtype, this);\n        }\n        else {\n            const program = new FillProgram(shape, value);\n            const customSetup = program.getCustomSetupFunc(value);\n            return this.compileAndRun(program, [], dtype, customSetup);\n        }\n    }\n    onesLike(x) {\n        if (x.dtype === 'string') {\n            throw new Error('onesLike is not supported under string dtype');\n        }\n        else {\n            // TODO(cais, smilkov): Add WebGL shader for onesLike:\n            //   https://github.com/tensorflow/tfjs/issues/1293\n            return this.fill(x.shape, 1, x.dtype);\n        }\n    }\n    zerosLike(x) {\n        return this.fill(x.shape, x.dtype === 'string' ? '' : 0, x.dtype);\n    }\n    linspace(start, stop, num) {\n        // TODO: Use CPU implementation due to the precision problem in Safari.\n        return backend_util.linspaceImpl(start, stop, num);\n    }\n    makeTensorInfo(shape, dtype, values) {\n        const dataId = this.write(values, shape, dtype);\n        this.texData.get(dataId).usage = null;\n        return { dataId, shape, dtype };\n    }\n    makeOutput(shape, dtype, values) {\n        const { dataId } = this.makeTensorInfo(shape, dtype, values);\n        return engine().makeTensorFromDataId(dataId, shape, dtype, this);\n    }\n    unpackTensor(input) {\n        const program = new UnpackProgram(input.shape);\n        return this.runWebGLProgram(program, [input], input.dtype);\n    }\n    packTensor(input) {\n        const program = new PackProgram(input.shape);\n        const preventEagerUnpackingOutput = true;\n        return this.runWebGLProgram(program, [input], input.dtype, null /* customSetup */, preventEagerUnpackingOutput);\n    }\n    packedReshape(input, afterShape) {\n        const input3DShape = [\n            webgl_util.getBatchDim(input.shape),\n            ...webgl_util.getRowsCols(input.shape)\n        ];\n        const input3D = {\n            dtype: input.dtype,\n            shape: input3DShape,\n            dataId: input.dataId\n        };\n        const afterShapeAs3D = [\n            webgl_util.getBatchDim(afterShape), ...webgl_util.getRowsCols(afterShape)\n        ];\n        const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n        const preventEagerUnpackingOfOutput = true;\n        const output = this.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n        return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n    }\n    decode(dataId) {\n        const texData = this.texData.get(dataId);\n        const { isPacked, shape, dtype } = texData;\n        const shapeAs3D = webgl_util.getShapeAs3D(shape);\n        let program;\n        if (isPacked) {\n            program = new DecodeMatrixPackedProgram(shapeAs3D);\n        }\n        else {\n            program = new DecodeMatrixProgram(shapeAs3D);\n        }\n        const preventEagerUnpackingOfOutput = true;\n        const out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype, dataId }], dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n        return { dtype, shape, dataId: out.dataId };\n    }\n    runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {\n        const output = this.makeTensorInfo(program.outputShape, outputDtype);\n        const outData = this.texData.get(output.dataId);\n        if (program.packedOutput) {\n            outData.isPacked = true;\n        }\n        if (program.outPackingScheme === tex_util.PackingScheme.DENSE) {\n            const texelShape = tex_util.getDenseTexShape(program.outputShape);\n            // For a densely packed output, we explicitly set texShape\n            // so it doesn't get assigned later according to our typical packing\n            // scheme wherein a single texel can only contain values from adjacent\n            // rows/cols.\n            outData.texShape = texelShape.map(d => d * 2);\n        }\n        if (program.outTexUsage != null) {\n            outData.usage = program.outTexUsage;\n        }\n        if (util.sizeFromShape(output.shape) === 0) {\n            // Short-circuit the computation since the result is empty (has 0 in its\n            // shape).\n            outData.values =\n                util.getTypedArrayFromDType(output.dtype, 0);\n            return output;\n        }\n        const dataToDispose = [];\n        const inputsData = inputs.map(input => {\n            if (input.dtype === 'complex64') {\n                throw new Error(`GPGPUProgram does not support complex64 input. For complex64 ` +\n                    `dtypes, please separate the program into real and imaginary ` +\n                    `parts.`);\n            }\n            let texData = this.texData.get(input.dataId);\n            if (texData.texture == null) {\n                if (!program.packedInputs &&\n                    util.sizeFromShape(input.shape) <=\n                        env().getNumber('WEBGL_SIZE_UPLOAD_UNIFORM')) {\n                    // Upload small tensors that live on the CPU as uniforms, not as\n                    // textures. Do this only when the environment supports 32bit floats\n                    // due to problems when comparing 16bit floats with 32bit floats.\n                    // TODO(https://github.com/tensorflow/tfjs/issues/821): Make it\n                    // possible for packed shaders to sample from uniforms.\n                    return {\n                        shape: input.shape,\n                        texData: null,\n                        isUniform: true,\n                        uniformValues: texData.values\n                    };\n                }\n                // This ensures that if a packed program's inputs have not yet been\n                // uploaded to the GPU, they get uploaded as packed right off the bat.\n                if (program.packedInputs) {\n                    texData.isPacked = true;\n                    texData.shape = input.shape;\n                }\n            }\n            else if (!!texData.isPacked !== !!program.packedInputs) {\n                input = texData.isPacked ? this.unpackTensor(input) :\n                    this.packTensor(input);\n                dataToDispose.push(input);\n                texData = this.texData.get(input.dataId);\n            }\n            else if (texData.isPacked &&\n                !webgl_util.isReshapeFree(texData.shape, input.shape)) {\n                // This is a special case where a texture exists for a tensor\n                // but the shapes are incompatible (due to packing constraints) because\n                // the tensor did not have a chance to go through the packed reshape\n                // shader. This only happens when we reshape the *same* tensor to form\n                // *distinct* inputs to an op, e.g. dotting a vector with itself. This\n                // case will disappear once packed uploading is the default.\n                const savedInput = input;\n                const targetShape = input.shape;\n                input.shape = texData.shape;\n                input = this.packedReshape(input, targetShape);\n                dataToDispose.push(input);\n                texData = this.texData.get(input.dataId);\n                savedInput.shape = targetShape;\n            }\n            this.uploadToGPU(input.dataId);\n            return { shape: input.shape, texData, isUniform: false };\n        });\n        this.uploadToGPU(output.dataId);\n        const outputData = { shape: output.shape, texData: outData, isUniform: false };\n        const key = gpgpu_math.makeShaderKey(program, inputsData, outputData);\n        const binary = this.getAndSaveBinary(key, () => {\n            return gpgpu_math.compileProgram(this.gpgpu, program, inputsData, outputData);\n        });\n        const shouldTimeProgram = this.activeTimers != null;\n        let query;\n        if (shouldTimeProgram) {\n            query = this.startTimer();\n        }\n        gpgpu_math.runProgram(this.gpgpu, binary, inputsData, outputData, customSetup);\n        dataToDispose.forEach(info => this.disposeIntermediateTensorInfo(info));\n        if (shouldTimeProgram) {\n            query = this.endTimer(query);\n            this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });\n        }\n        if (!env().getBool('WEBGL_LAZILY_UNPACK') && outData.isPacked &&\n            preventEagerUnpackingOfOutput === false) {\n            const unpacked = this.unpackTensor(output);\n            this.disposeIntermediateTensorInfo(output);\n            return unpacked;\n        }\n        return output;\n    }\n    compileAndRun(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {\n        outputDtype = outputDtype || inputs[0].dtype;\n        const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput);\n        return engine().makeTensorFromDataId(outInfo.dataId, outInfo.shape, outInfo.dtype);\n    }\n    getAndSaveBinary(key, getBinary) {\n        if (!(key in this.binaryCache)) {\n            this.binaryCache[key] = getBinary();\n        }\n        return this.binaryCache[key];\n    }\n    getTextureManager() {\n        return this.textureManager;\n    }\n    dispose() {\n        if (this.disposed) {\n            return;\n        }\n        // Avoid disposing the compiled webgl programs during unit testing because\n        // it slows down test execution.\n        if (!env().getBool('IS_TEST')) {\n            const allKeys = Object.keys(this.binaryCache);\n            allKeys.forEach(key => {\n                this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);\n                delete this.binaryCache[key];\n            });\n        }\n        this.textureManager.dispose();\n        if (this.canvas != null &&\n            (typeof (HTMLCanvasElement) !== 'undefined' &&\n                this.canvas instanceof HTMLCanvasElement)) {\n            this.canvas.remove();\n        }\n        else {\n            this.canvas = null;\n        }\n        if (this.gpgpuCreatedLocally) {\n            this.gpgpu.program = null;\n            this.gpgpu.dispose();\n        }\n        this.disposed = true;\n    }\n    floatPrecision() {\n        if (this.floatPrecisionValue == null) {\n            this.floatPrecisionValue = tidy(() => {\n                if (!env().get('WEBGL_RENDER_FLOAT32_ENABLED')) {\n                    // Momentarily switching DEBUG flag to false so we don't throw an\n                    // error trying to upload a small value.\n                    const debugFlag = env().getBool('DEBUG');\n                    env().set('DEBUG', false);\n                    const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];\n                    env().set('DEBUG', debugFlag);\n                    if (underflowCheckValue > 0) {\n                        return 32;\n                    }\n                }\n                return 16;\n            });\n        }\n        return this.floatPrecisionValue;\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n    }\n    uploadToGPU(dataId) {\n        const texData = this.texData.get(dataId);\n        const { shape, dtype, values, texture, usage, isPacked } = texData;\n        if (texture != null) {\n            // Array is already on GPU. No-op.\n            return;\n        }\n        const shouldTimeProgram = this.activeTimers != null;\n        let start;\n        if (shouldTimeProgram) {\n            start = util.now();\n        }\n        let texShape = texData.texShape;\n        if (texShape == null) {\n            texShape = webgl_util.getTextureShapeFromLogicalShape(shape, isPacked);\n            texData.texShape = texShape;\n        }\n        if (values != null) {\n            const shapeAs3D = webgl_util.getShapeAs3D(shape);\n            let program;\n            let width = texShape[1], height = texShape[0];\n            const isByteArray = values instanceof Uint8Array;\n            if (isPacked) {\n                [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]);\n                program = new EncodeMatrixPackedProgram(shapeAs3D, [height, width], isByteArray);\n            }\n            else {\n                program =\n                    new EncodeMatrixProgram(shapeAs3D, [height, width], isByteArray);\n            }\n            const tempDenseInputHandle = this.makeTensorInfo([height, width], dtype);\n            if (isByteArray) {\n                this.texData.get(tempDenseInputHandle.dataId).usage =\n                    TextureUsage.PIXELS;\n            }\n            else {\n                this.texData.get(tempDenseInputHandle.dataId).usage =\n                    TextureUsage.UPLOAD;\n            }\n            this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);\n            // We want the output to remain packed regardless of the value of\n            // WEBGL_PACK.\n            const preventEagerUnpacking = true;\n            const encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, null, preventEagerUnpacking);\n            // Have the original texture assume the identity of the encoded output.\n            const outputTexData = this.texData.get(encodedOutputTarget.dataId);\n            texData.texture = outputTexData.texture;\n            texData.texShape = outputTexData.texShape;\n            texData.isPacked = outputTexData.isPacked;\n            texData.usage = outputTexData.usage;\n            this.disposeIntermediateTensorInfo(tempDenseInputHandle);\n            this.texData.delete(encodedOutputTarget.dataId);\n            // Once uploaded, don't store the values on cpu.\n            texData.values = null;\n            if (shouldTimeProgram) {\n                this.uploadWaitMs += util.now() - start;\n            }\n        }\n        else {\n            const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);\n            texData.texture = newTexture;\n        }\n    }\n    convertAndCacheOnCPU(dataId, float32Values) {\n        const texData = this.texData.get(dataId);\n        const { dtype } = texData;\n        this.releaseGPUData(dataId);\n        if (float32Values != null) {\n            texData.values = float32ToTypedArray(float32Values, dtype);\n        }\n        return texData.values;\n    }\n    acquireTexture(texShape, texType, dtype, isPacked) {\n        this.numBytesInGPU += this.computeBytes(texShape, dtype);\n        if (!this.warnedAboutMemory &&\n            this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {\n            const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);\n            this.warnedAboutMemory = true;\n            console.warn(`High memory usage in GPU: ${mb} MB, ` +\n                `most likely due to a memory leak`);\n        }\n        return this.textureManager.acquireTexture(texShape, texType, isPacked);\n    }\n    computeBytes(shape, dtype) {\n        return shape[0] * shape[1] * util.bytesPerElement(dtype);\n    }\n    tryRunOnCpuOrThrow(inputs, fn) {\n        if (this.shouldExecuteOnCPU(inputs)) {\n            try {\n                return fn();\n            }\n            catch (e) {\n                if (env().getBool('IS_TEST')) {\n                    throw new Error('CPU forwarding failed');\n                }\n            }\n        }\n        return null;\n    }\n}\nfunction float32ToTypedArray(a, dtype) {\n    if (dtype === 'float32' || dtype === 'complex64') {\n        return a;\n    }\n    else if (dtype === 'int32' || dtype === 'bool') {\n        const result = (dtype === 'int32') ? new Int32Array(a.length) :\n            new Uint8Array(a.length);\n        for (let i = 0; i < result.length; ++i) {\n            result[i] = Math.round(a[i]);\n        }\n        return result;\n    }\n    else {\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n}\n//# sourceMappingURL=backend_webgl.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport * as gpgpu_util from './gpgpu_util';\nimport * as webgl_util from './webgl_util';\nexport { MathBackendWebGL } from './backend_webgl';\nexport { setWebGLContext } from './canvas_util';\nexport { GPGPUContext } from './gpgpu_context';\n// WebGL specific utils.\nexport { gpgpu_util, webgl_util };\n/**\n * Enforce use of half precision textures if available on the platform.\n *\n * @doc {heading: 'Environment', namespace: 'webgl'}\n */\nexport function forceHalfFloat() {\n    env().set('WEBGL_FORCE_F16_TEXTURES', true);\n}\n//# sourceMappingURL=webgl.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// base.ts is the webgl backend without auto kernel registration.\nimport { device_util, registerBackend } from '@tensorflow/tfjs-core';\nimport { MathBackendWebGL } from './backend_webgl';\nexport { version as version_webgl } from './version';\nif (device_util.isBrowser()) {\n    registerBackend('webgl', () => new MathBackendWebGL(), 2 /* priority */);\n}\n// Export webgl utilities\nexport * from './webgl';\n// Export forceHalfFlost under webgl namespace for the union bundle.\nimport { forceHalfFloat } from './webgl';\nexport const webgl = { forceHalfFloat };\n//# sourceMappingURL=base.js.map", "import { env } from '@tensorflow/tfjs-core';\nimport { BinaryOpProgram } from '../binaryop_gpu';\nimport { BinaryOpPackedProgram } from '../binaryop_packed_gpu';\nimport { UnaryOpProgram } from '../unaryop_gpu';\nexport const CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;\nexport const CHECK_NAN_SNIPPET_BINARY = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nexport const CHECK_NAN_SNIPPET_BINARY_PACKED = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param opSnippets Op snippet to create `UnaryOpProgram`.\n */\nexport function unaryKernelFunc(opSnippet) {\n    return ({ inputs, backend }) => {\n        const { x } = inputs;\n        const webglBackend = backend;\n        const program = new UnaryOpProgram(x.shape, opSnippet);\n        return webglBackend.runWebGLProgram(program, [x], x.dtype);\n    };\n}\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param opSnippet Op snippet to create `BinaryOpProgram`.\n * @param packedOpSnippet Op snippet to create `BinaryOpPackedProgram`.\n * @param checkOutOfBoundsForPackedProgram Whether to set checkOutOfBounds=true\n *     when creating BinaryOpPackedProgram.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc(opSnippet, packedOpSnippet, checkOutOfBoundsForPackedProgram, dtype) {\n    // TODO(jingjin): handle complex64.\n    return ({ inputs, backend }) => {\n        const { a, b } = inputs;\n        const webglBackend = backend;\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(packedOpSnippet, a.shape, b.shape, !!checkOutOfBoundsForPackedProgram) :\n            new BinaryOpProgram(opSnippet, a.shape, b.shape);\n        const $dtype = dtype || a.dtype;\n        const output = webglBackend.runWebGLProgram(program, [a, b], $dtype);\n        return output;\n    };\n}\n//# sourceMappingURL=kernel_funcs_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan2 } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc, CHECK_NAN_SNIPPET_BINARY, CHECK_NAN_SNIPPET_BINARY_PACKED } from '../kernel_utils/kernel_funcs_utils';\nconst ATAN2 = CHECK_NAN_SNIPPET_BINARY + `\n  return atan(a, b);\n`;\nconst ATAN2_PACKED = `\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET_BINARY_PACKED + `\n  return result;\n`;\nexport const atan2KernelFunc = binaryKernelFunc(ATAN2, ATAN2_PACKED);\nexport const atan2Config = {\n    kernelName: Atan2,\n    backendName: 'webgl',\n    kernelFunc: atan2KernelFunc,\n};\n//# sourceMappingURL=Atan2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '@tensorflow/tfjs-core';\nexport function identity(args) {\n    const { inputs, backend } = args;\n    const { x } = inputs;\n    backend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nexport const identityConfig = {\n    kernelName: Identity,\n    backendName: 'webgl',\n    kernelFunc: identity\n};\n//# sourceMappingURL=Identity.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool, backend_util, util } from '@tensorflow/tfjs-core';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nimport { identity } from './Identity';\nexport function avgPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'avgPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        return identity({ inputs: { x }, backend });\n    }\n    const avgPoolProgram = new Pool2DProgram(convInfo, 'avg', false);\n    return backend.runWebGLProgram(avgPoolProgram, [x], 'float32');\n}\nexport const avgPoolConfig = {\n    kernelName: AvgPool,\n    backendName: 'webgl',\n    kernelFunc: avgPool\n};\n//# sourceMappingURL=AvgPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPoolBackprop, backend_util } from '@tensorflow/tfjs-core';\nimport { AvgPool2DBackpropProgram } from '../avg_pool_backprop_gpu';\nimport { assertNotComplex } from '../webgl_util';\nexport function avgPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input } = inputs;\n    const x = input;\n    assertNotComplex([dy, input], 'avgPoolBackprop');\n    const { filterSize, strides, pad } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);\n    const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);\n    return backend.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);\n}\nexport const avgPoolBackpropConfig = {\n    kernelName: AvgPoolBackprop,\n    backendName: 'webgl',\n    kernelFunc: avgPoolBackprop\n};\n//# sourceMappingURL=AvgPoolBackprop.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class BatchNormProgram {\n    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n        this.outputShape = [];\n        this.variableNames = ['x', 'mean', 'variance'];\n        backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n        backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n        let offsetSnippet = '0.0';\n        if (offsetShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n            this.variableNames.push('offset');\n            offsetSnippet = 'getOffsetAtOutCoords()';\n        }\n        let scaleSnippet = '1.0';\n        if (scaleShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n            this.variableNames.push('scale');\n            scaleSnippet = 'getScaleAtOutCoords()';\n        }\n        this.outputShape = xShape;\n        this.userCode = `\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${offsetSnippet};\n        float scale = ${scaleSnippet};\n        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=batchnorm_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class BatchNormPackedProgram {\n    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.variableNames = ['x', 'mean', 'variance'];\n        backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n        backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n        let offsetSnippet = 'vec4(0.0)';\n        if (offsetShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n            this.variableNames.push('offset');\n            offsetSnippet = 'getOffsetAtOutCoords()';\n        }\n        let scaleSnippet = 'vec4(1.0)';\n        if (scaleShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n            this.variableNames.push('scale');\n            scaleSnippet = 'getScaleAtOutCoords()';\n        }\n        this.outputShape = xShape;\n        this.userCode = `\n      void main() {\n        vec4 offset = ${offsetSnippet};\n        vec4 scale = ${scaleSnippet};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=batchnorm_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, FusedBatchNorm, util } from '@tensorflow/tfjs-core';\nimport { BatchNormProgram } from '../batchnorm_gpu';\nimport { BatchNormPackedProgram } from '../batchnorm_packed_gpu';\nexport const batchNormKernelFunc = ({ inputs, backend, attrs }) => {\n    const { x, mean, variance, offset, scale } = inputs;\n    util.assert(mean.shape.length === variance.shape.length, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert(offset == null || mean.shape.length === offset.shape.length, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert(scale == null || mean.shape.length === scale.shape.length, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    let { varianceEpsilon } = attrs;\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const finalInputs = [x, mean, variance];\n    let offsetShape = null;\n    if (offset != null) {\n        offsetShape = offset.shape;\n        finalInputs.push(offset);\n    }\n    let scaleShape = null;\n    if (scale != null) {\n        scaleShape = scale.shape;\n        finalInputs.push(scale);\n    }\n    const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n        new BatchNormPackedProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) :\n        new BatchNormProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);\n    const output = backend.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);\n    return output;\n};\nexport const batchNormConfig = {\n    kernelName: FusedBatchNorm,\n    backendName: 'webgl',\n    kernelFunc: batchNormKernelFunc,\n};\n//# sourceMappingURL=BatchNorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '@tensorflow/tfjs-core';\nimport { CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst COS = CHECK_NAN_SNIPPET_UNARY + `\n  return cos(x);\n`;\nexport const cosKernelFunc = unaryKernelFunc(COS);\nexport const cosConfig = {\n    kernelName: Cos,\n    backendName: 'webgl',\n    kernelFunc: cosKernelFunc,\n};\n//# sourceMappingURL=Cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\n// Without the equality check div produces 0.9999 for a = b, which when\n// floored can cause errors.\nconst DIV = `\nif (a == b) {\n  return 1.0;\n};\nreturn a / b;`;\n// We do the same as in ./binaryop_gpu, with vec4 and ivec4.\n// On Linux, the vectorized implementation produces NaNs when a and b are 0.\nconst DIV_PACKED = `\n  // vec4 one = vec4(equal(a, b));\n  // return one + (vec4(1.0) - one) * a / b;\n  vec4 result = a / b;\n  if(a.x == b.x) {\n    result.x = 1.;\n  }\n  if(a.y == b.y) {\n    result.y = 1.;\n  }\n  if(a.z == b.z) {\n    result.z = 1.;\n  }\n  if(a.w == b.w) {\n    result.w = 1.;\n  }\n\n  return result;\n`;\nexport const divKernelFunc = binaryKernelFunc(DIV, DIV_PACKED, true /* checkOutOfBoundsForPackedProgram */);\nexport const divConfig = {\n    kernelName: Div,\n    backendName: 'webgl',\n    kernelFunc: divKernelFunc,\n};\n//# sourceMappingURL=Div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FlipLeftRightProgram {\n    constructor(imageShape) {\n        this.variableNames = ['Image'];\n        this.outputShape = [];\n        const imageWidth = imageShape[2];\n        this.outputShape = imageShape;\n        this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${imageWidth} - x;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${imageWidth}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n    }\n}\n//# sourceMappingURL=flip_left_right_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FlipLeftRight } from '@tensorflow/tfjs-core';\nimport { FlipLeftRightProgram } from '../flip_left_right_gpu';\nexport const flipLeftRightConfig = {\n    kernelName: FlipLeftRight,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend }) => {\n        const { image } = inputs;\n        const webglBackend = backend;\n        const program = new FlipLeftRightProgram(image.shape);\n        const output = webglBackend.runWebGLProgram(program, [image], image.dtype);\n        return output;\n    }\n};\n//# sourceMappingURL=FlipLeftRight.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from '../../glsl_version';\nexport class FromPixelsProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        const glsl = getGlslDifferences();\n        const [height, width,] = outputShape;\n        this.outputShape = outputShape;\n        this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);\n\n        vec4 values = ${glsl.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=from_pixels_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from '../../glsl_version';\nexport class FromPixelsPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        const glsl = getGlslDifferences();\n        const [height, width,] = outputShape;\n        this.outputShape = outputShape;\n        this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(${width}.0, ${height}.0);\n            vec4 values = ${glsl.texture2D}(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=from_pixels_packed_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { FromPixels } from '@tensorflow/tfjs-core';\nimport { TextureUsage } from '../tex_util';\nimport { FromPixelsProgram } from './FromPixels_utils/from_pixels_gpu';\nimport { FromPixelsPackedProgram } from './FromPixels_utils/from_pixels_packed_gpu';\nexport const fromPixelsConfig = {\n    kernelName: FromPixels,\n    backendName: 'webgl',\n    kernelFunc: fromPixels,\n};\nlet fromPixels2DContext;\nfunction fromPixels(args) {\n    const { inputs, backend, attrs } = args;\n    let { pixels } = inputs;\n    const { numChannels } = attrs;\n    const isVideo = typeof (HTMLVideoElement) !== 'undefined' &&\n        pixels instanceof HTMLVideoElement;\n    const isImage = typeof (HTMLImageElement) !== 'undefined' &&\n        pixels instanceof HTMLImageElement;\n    const [width, height] = isVideo ?\n        [\n            pixels.videoWidth,\n            pixels.videoHeight\n        ] :\n        [pixels.width, pixels.height];\n    const texShape = [height, width];\n    const outShape = [height, width, numChannels];\n    if (isImage || isVideo) {\n        if (fromPixels2DContext == null) {\n            fromPixels2DContext = document.createElement('canvas').getContext('2d');\n        }\n        fromPixels2DContext.canvas.width = width;\n        fromPixels2DContext.canvas.height = height;\n        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n        pixels = fromPixels2DContext.canvas;\n    }\n    const tempPixelHandle = backend.makeTensorInfo(texShape, 'int32');\n    // This is a byte texture with pixels.\n    backend.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;\n    backend.gpgpu.uploadPixelDataToTexture(backend.getTexture(tempPixelHandle.dataId), pixels);\n    const program = env().getBool('WEBGL_PACK') ?\n        new FromPixelsPackedProgram(outShape) :\n        new FromPixelsProgram(outShape);\n    const res = backend.runWebGLProgram(program, [tempPixelHandle], 'int32');\n    backend.disposeData(tempPixelHandle.dataId);\n    return res;\n}\n//# sourceMappingURL=FromPixels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { ReduceProgram } from '../reduce_gpu';\n// Returns an array of configuration objects that describe each stage of the\n// reduction.\nfunction getReductionStages(inShape) {\n    const stages = [];\n    while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {\n        const outSize = stages.length ? stages[stages.length - 1].outSize : inShape[1];\n        const windowSize = backend_util.computeOptimalWindowSize(outSize);\n        stages.push({\n            inSize: outSize,\n            windowSize,\n            outSize: Math.ceil(outSize / windowSize)\n        });\n    }\n    return stages;\n}\nexport function reduce(x, dtype, reductionType, backend) {\n    const reductionStages = getReductionStages(x.shape);\n    let result = x;\n    for (let i = 0; i < reductionStages.length; i++) {\n        const { inSize, windowSize, outSize } = reductionStages[i];\n        const program = new ReduceProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, reductionType);\n        const previousResult = result;\n        result = backend.runWebGLProgram(program, [result], dtype);\n        if (previousResult.dataId !== x.dataId) {\n            backend.disposeData(previousResult.dataId);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=reduce.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ReshapePackedProgram } from '../reshape_packed_gpu';\nimport { getBatchDim, getRowsCols } from '../webgl_util';\nexport function packedReshape(input, afterShape, backend) {\n    const input3DShape = [getBatchDim(input.shape),\n        ...getRowsCols(input.shape)];\n    const input3D = {\n        dtype: input.dtype,\n        shape: input3DShape,\n        dataId: input.dataId\n    };\n    const afterShapeAs3D = [getBatchDim(afterShape),\n        ...getRowsCols(afterShape)];\n    const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n    const preventEagerUnpackingOfOutput = true;\n    const output = backend.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n    return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n}\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape, util } from '@tensorflow/tfjs-core';\nimport { packedReshape } from '../kernel_utils/reshape';\nimport { isReshapeFree } from '../webgl_util';\nexport function reshape(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { shape } = attrs;\n    const webglBackend = backend;\n    const xSize = util.sizeFromShape(x.shape);\n    const $shape = util.inferFromImplicitShape(shape, xSize);\n    const $xSize = util.sizeFromShape($shape);\n    util.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n        `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n        `shape must have the same number of elements.`);\n    const xTexData = webglBackend.texData.get(x.dataId);\n    if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) &&\n        !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {\n        return packedReshape(x, $shape, webglBackend);\n    }\n    webglBackend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nexport const reshapeConfig = {\n    kernelName: Reshape,\n    backendName: 'webgl',\n    kernelFunc: reshape\n};\n//# sourceMappingURL=Reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { reduce } from '../kernel_utils/reduce';\nimport { reshape } from '../kernels/Reshape';\nexport function maxImpl(x, reduceShape, outShape, backend) {\n    const inSize = util.sizeFromShape(reduceShape);\n    const xSize = util.sizeFromShape(x.shape);\n    const batchSize = xSize / inSize;\n    const reshapedInput = reshape({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend });\n    const reduced = reduce(reshapedInput, x.dtype, 'max', backend);\n    const reshapedOutput = reshape({ inputs: { x: reduced }, attrs: { shape: outShape }, backend });\n    backend.disposeIntermediateTensorInfo(reshapedInput);\n    backend.disposeIntermediateTensorInfo(reduced);\n    return reshapedOutput;\n}\n//# sourceMappingURL=Max_impl.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class TransposeProgram {\n    constructor(aShape, newDim) {\n        this.variableNames = ['A'];\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[newDim[i]];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const switched = getSwitchedCoords(newDim);\n        this.userCode = `\n    void main() {\n      ${dtype} resRC = getOutputCoords();\n      setOutput(getA(${switched}));\n    }\n    `;\n    }\n}\nfunction getSwitchedCoords(newDim) {\n    const rank = newDim.length;\n    if (rank > 6) {\n        throw Error(`Transpose for rank ${rank} is not yet supported`);\n    }\n    const originalOrder = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u', 'resRC.v'];\n    const switchedCoords = new Array(rank);\n    for (let i = 0; i < newDim.length; i++) {\n        switchedCoords[newDim[i]] = originalOrder[i];\n    }\n    return switchedCoords.join();\n}\n//# sourceMappingURL=transpose_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getVecChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class TransposePackedProgram {\n    constructor(aShape, newDim) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[newDim[i]];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        if (this.rank > 6) {\n            throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);\n        }\n        const dtype = getCoordsDataType(this.rank);\n        const outputOrder = getVecChannels('rc', this.rank);\n        const switchedOrder = new Array(this.rank);\n        for (let i = 0; i < newDim.length; i++) {\n            switchedOrder[newDim[i]] = outputOrder[i];\n        }\n        const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;\n        const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;\n        const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;\n        this.userCode = `\n    void main() {\n      ${dtype} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${getc};\n      if(${nextColumn}) {\n        result[1] = ${getc};\n      }\n      --${outputOrder[this.rank - 1]};\n      if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {\n        result[2] = ${getc};\n        if(${nextColumn}) {\n          result[3] = ${getc};\n        }\n      }\n      setOutput(result);\n    }\n    `;\n    }\n}\n//# sourceMappingURL=transpose_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { transposeImplCPU } from '../kernel_utils/shared';\nimport { TransposeProgram } from '../transpose_gpu';\nimport { TransposePackedProgram } from '../transpose_packed_gpu';\nexport function transposeImpl(x, perm, backend) {\n    const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n        new TransposePackedProgram(x.shape, perm) :\n        new TransposeProgram(x.shape, perm);\n    return backend.runWebGLProgram(program, [x], x.dtype);\n}\nexport { transposeImplCPU };\n//# sourceMappingURL=Transpose_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '@tensorflow/tfjs-core';\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { maxImplCPU } from '../kernel_utils/shared';\nimport { maxImpl } from './Max_impl';\nimport { transposeImpl, transposeImplCPU } from './Transpose_impl';\nexport const maxConfig = {\n    kernelName: Max,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { reductionIndices, keepDims } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        const maxInputIsTransposed = permutedAxes != null;\n        const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);\n        let maxInput = x;\n        if (maxInputIsTransposed) {\n            if (shouldExecuteOnCPU) {\n                const xTexData = webglBackend.texData.get(maxInput.dataId);\n                const values = xTexData.values;\n                const newShape = new Array(xRank);\n                for (let i = 0; i < newShape.length; i++) {\n                    newShape[i] = x.shape[permutedAxes[i]];\n                }\n                const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n                maxInput = webglBackend.makeTensorInfo(newShape, x.dtype);\n                const maxInputData = webglBackend.texData.get(maxInput.dataId);\n                maxInputData.values = maxInputValues;\n            }\n            else {\n                maxInput = transposeImpl(x, permutedAxes, webglBackend);\n            }\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n        }\n        backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n        const [maxOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(maxInput.shape, axes);\n        let outShape = maxOutShape;\n        if (keepDims) {\n            // rather than reshape at the end, set the target shape here.\n            outShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n        }\n        let out;\n        if (shouldExecuteOnCPU) {\n            const xTexData = webglBackend.texData.get(maxInput.dataId);\n            const values = xTexData.values;\n            const outValues = maxImplCPU(values, util.sizeFromShape(reduceShape), outShape, x.dtype);\n            out = webglBackend.makeTensorInfo(outShape, x.dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n        }\n        else {\n            out = maxImpl(maxInput, reduceShape, outShape, webglBackend);\n        }\n        if (maxInputIsTransposed) {\n            webglBackend.disposeIntermediateTensorInfo(maxInput);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPool, util } from '@tensorflow/tfjs-core';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nimport { identity } from './Identity';\nexport function maxPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'maxPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        return identity({ inputs: { x }, backend });\n    }\n    const maxPoolProgram = new Pool2DProgram(convInfo, 'max', false);\n    return backend.runWebGLProgram(maxPoolProgram, [x], x.dtype);\n}\nexport const maxPoolConfig = {\n    kernelName: MaxPool,\n    backendName: 'webgl',\n    kernelFunc: maxPool\n};\n//# sourceMappingURL=MaxPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPoolBackprop } from '@tensorflow/tfjs-core';\nimport { MaxPool2DBackpropProgram } from '../max_pool_backprop_gpu';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nexport function maxPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input, output } = inputs;\n    const x = input;\n    assertNotComplex([input, output], 'maxPoolBackprop');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n    const getPositions = true;\n    const maxPoolPositionsProgram = new Pool2DProgram(convInfo, 'max', getPositions);\n    const maxPoolPositions = backend.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);\n    const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);\n    const result = backend.runWebGLProgram(maxPoolBackPropProgram, [dy, maxPoolPositions], x.dtype);\n    backend.disposeIntermediateTensorInfo(maxPoolPositions);\n    return result;\n}\nexport const maxPoolBackpropConfig = {\n    kernelName: MaxPoolBackprop,\n    backendName: 'webgl',\n    kernelFunc: maxPoolBackprop\n};\n//# sourceMappingURL=MaxPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Pool2DProgram } from '../pool_gpu';\nexport function maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, backend) {\n    let program = new Pool2DProgram(convInfo, 'max', false);\n    const poolOutput = backend.runWebGLProgram(program, [x], 'float32');\n    program = new Pool2DProgram(convInfo, 'max', true, true, includeBatchInIndex);\n    const indexOutput = backend.runWebGLProgram(program, [x], 'float32');\n    return [poolOutput, indexOutput];\n}\n//# sourceMappingURL=MaxPoolWithArgmax_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPoolWithArgmax } from '@tensorflow/tfjs-core';\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { maxPoolWithArgmaxImpl } from './MaxPoolWithArgmax_impl';\nexport const maxPoolWithArgmaxConfig = {\n    kernelName: MaxPoolWithArgmax,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { filterSize, strides, pad, includeBatchInIndex } = attrs;\n        const webglBackend = backend;\n        util.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);\n        const dilations = [1, 1];\n        util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n            `Got strides ${strides} and dilations '${dilations}'`);\n        const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad);\n        const [result, indexes] = maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, webglBackend);\n        return [result, indexes];\n    }\n};\n//# sourceMappingURL=MaxPoolWithArgmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls, NonMaxSuppressionV3 } from '@tensorflow/tfjs-core';\nexport const nonMaxSuppressionV3Config = {\n    kernelName: NonMaxSuppressionV3,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        return kernel_impls.nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal);\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV3.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls, NonMaxSuppressionV4 } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\nexport const nonMaxSuppressionV4Config = {\n    kernelName: NonMaxSuppressionV4,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n        return [selectedIndices, validOutputs];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV4.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls } from '@tensorflow/tfjs-core';\nimport { NonMaxSuppressionV5 } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nexport const nonMaxSuppressionV5Config = {\n    kernelName: NonMaxSuppressionV5,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        const softNmsSigmaVal = softNmsSigma;\n        const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n        return [selectedIndices, selectedScores];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV5.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class RotateProgram {\n    constructor(imageShape, radians, fillValue, center) {\n        this.variableNames = ['Image'];\n        this.outputShape = [];\n        const imageHeight = imageShape[1];\n        const imageWidth = imageShape[2];\n        const sinFactor = Math.sin(radians).toFixed(3);\n        const cosFactor = Math.cos(radians).toFixed(3);\n        this.outputShape = imageShape;\n        const [centerX, centerY] = backend_util.getImageCenter(center, imageHeight, imageWidth);\n        const centerXString = centerX.toFixed(3);\n        const centerYString = centerY.toFixed(3);\n        let fillSnippet = '';\n        if (typeof fillValue === 'number') {\n            fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;\n        }\n        else {\n            fillSnippet = `\n        vec3 fill = vec3(${fillValue.join(',')});\n        float outputValue = fill[coords[3]];`;\n        }\n        this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - ${centerXString}) * ${cosFactor} - (float(y) - ${centerYString}) * ${sinFactor};\n          float coordYFloat = (float(x) - ${centerXString}) * ${sinFactor} + (float(y) - ${centerYString}) * ${cosFactor};\n          int coordX = int(round(coordXFloat + ${centerXString}));\n          int coordY = int(round(coordYFloat + ${centerYString}));\n          ${fillSnippet}\n          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n    }\n}\n//# sourceMappingURL=rotate_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { RotateWithOffset } from '@tensorflow/tfjs-core';\nimport { RotateProgram } from '../rotate_gpu';\nexport const rotateWithOffsetConfig = {\n    kernelName: RotateWithOffset,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const { radians, fillValue, center } = attrs;\n        const webglBackend = backend;\n        const program = new RotateProgram(image.shape, radians, fillValue, center);\n        const output = webglBackend.runWebGLProgram(program, [image], image.dtype);\n        return output;\n    }\n};\n//# sourceMappingURL=RotateWithOffset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '@tensorflow/tfjs-core';\nimport { CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SIN = CHECK_NAN_SNIPPET_UNARY + `\n  return sin(x);\n`;\nexport const sinKernelFunc = unaryKernelFunc(SIN);\nexport const sinConfig = {\n    kernelName: Sin,\n    backendName: 'webgl',\n    kernelFunc: sinKernelFunc,\n};\n//# sourceMappingURL=Sin.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SQUARE = `return x * x;`;\nexport const squareKernelFunc = unaryKernelFunc(SQUARE);\nexport const squareConfig = {\n    kernelName: Square,\n    backendName: 'webgl',\n    kernelFunc: squareKernelFunc,\n};\n//# sourceMappingURL=Square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport const squaredDifferenceKernelFunc = binaryKernelFunc(SQUARED_DIFFERENCE, SQUARED_DIFFERENCE);\nexport const squaredDifferenceConfig = {\n    kernelName: SquaredDifference,\n    backendName: 'webgl',\n    kernelFunc: squaredDifferenceKernelFunc,\n};\n//# sourceMappingURL=SquaredDifference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst TAN = `return tan(x);`;\nexport const tanKernelFunc = unaryKernelFunc(TAN);\nexport const tanConfig = {\n    kernelName: Tan,\n    backendName: 'webgl',\n    kernelFunc: tanKernelFunc,\n};\n//# sourceMappingURL=Tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '@tensorflow/tfjs-core';\nimport { transposeImpl } from './Transpose_impl';\nimport { transposeImplCPU as cpuTranspose } from './Transpose_impl';\nexport const transposeConfig = {\n    kernelName: Transpose,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { perm } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const newShape = new Array(xRank);\n        for (let i = 0; i < newShape.length; i++) {\n            newShape[i] = x.shape[perm[i]];\n        }\n        let out;\n        if (webglBackend.shouldExecuteOnCPU([x])) {\n            const xTexData = webglBackend.texData.get(x.dataId);\n            const values = xTexData.values;\n            const outValues = cpuTranspose(values, x.shape, x.dtype, perm, newShape);\n            out = webglBackend.makeTensorInfo(newShape, x.dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n        }\n        else {\n            out = transposeImpl(x, perm, webglBackend);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unique } from '@tensorflow/tfjs-core';\nimport { uniqueImplCPU } from '../kernel_utils/shared';\nimport { assertNotComplex } from '../webgl_util';\nexport function unique(args) {\n    const { inputs, attrs, backend } = args;\n    const { axis } = attrs;\n    const { x } = inputs;\n    assertNotComplex(x, 'unique');\n    // For now, always forward calculation to the CPU backend.\n    console.warn('WARNING: ', 'UI might be locked temporarily as data is being downloaded');\n    const values = backend.readSync(x.dataId);\n    const { outputValues, outputShape, indices } = uniqueImplCPU(values, axis, x.shape, x.dtype);\n    return [\n        backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n        backend.makeTensorInfo([indices.length], 'int32', indices),\n    ];\n}\nexport const uniqueConfig = {\n    kernelName: Unique,\n    backendName: 'webgl',\n    kernelFunc: unique,\n};\n//# sourceMappingURL=Unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { registerKernel } from '@tensorflow/tfjs-core';\nimport { atan2Config } from './kernels/Atan2';\nimport { avgPoolConfig } from './kernels/AvgPool';\nimport { avgPoolBackpropConfig } from './kernels/AvgPoolBackprop';\nimport { batchNormConfig } from './kernels/BatchNorm';\nimport { cosConfig } from './kernels/Cos';\nimport { divConfig } from './kernels/Div';\nimport { flipLeftRightConfig } from './kernels/FlipLeftRight';\nimport { fromPixelsConfig } from './kernels/FromPixels';\nimport { identityConfig } from './kernels/Identity';\nimport { maxConfig } from './kernels/Max';\nimport { maxPoolConfig } from './kernels/MaxPool';\nimport { maxPoolBackpropConfig } from './kernels/MaxPoolBackprop';\nimport { maxPoolWithArgmaxConfig } from './kernels/MaxPoolWithArgmax';\nimport { nonMaxSuppressionV3Config } from './kernels/NonMaxSuppressionV3';\nimport { nonMaxSuppressionV4Config } from './kernels/NonMaxSuppressionV4';\nimport { nonMaxSuppressionV5Config } from './kernels/NonMaxSuppressionV5';\nimport { reshapeConfig } from './kernels/Reshape';\nimport { rotateWithOffsetConfig } from './kernels/RotateWithOffset';\nimport { sinConfig } from './kernels/Sin';\nimport { squareConfig } from './kernels/Square';\nimport { squaredDifferenceConfig } from './kernels/SquaredDifference';\nimport { tanConfig } from './kernels/Tan';\nimport { transposeConfig } from './kernels/Transpose';\nimport { uniqueConfig } from './kernels/Unique';\n// List all kernel configs here\nconst kernelConfigs = [\n    atan2Config,\n    avgPoolConfig,\n    avgPoolBackpropConfig,\n    batchNormConfig,\n    cosConfig,\n    divConfig,\n    flipLeftRightConfig,\n    fromPixelsConfig,\n    identityConfig,\n    maxConfig,\n    maxPoolConfig,\n    maxPoolBackpropConfig,\n    maxPoolWithArgmaxConfig,\n    nonMaxSuppressionV3Config,\n    nonMaxSuppressionV4Config,\n    nonMaxSuppressionV5Config,\n    reshapeConfig,\n    rotateWithOffsetConfig,\n    sinConfig,\n    squareConfig,\n    squaredDifferenceConfig,\n    tanConfig,\n    transposeConfig,\n    uniqueConfig,\n];\nfor (const kernelConfig of kernelConfigs) {\n    registerKernel(kernelConfig);\n}\n//# sourceMappingURL=register_all_kernels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// All exports from this package should be in base.\nexport * from './base';\nimport './register_all_kernels';\n//# sourceMappingURL=index.js.map", "/** @license See the LICENSE file. */\n\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport {version};\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport * from '@tensorflow/tfjs-core';\nexport * from '@tensorflow/tfjs-layers';\nexport * from '@tensorflow/tfjs-converter';\n\n// Export data api as tf.data\nimport * as data from '@tensorflow/tfjs-data';\nexport {data};\n\n// Import and register backends.\nimport '@tensorflow/tfjs-backend-cpu';\nimport '@tensorflow/tfjs-backend-webgl';\n\n// Import versions of all sub-packages.\nimport {version_core} from '@tensorflow/tfjs-core';\nimport {version_cpu} from '@tensorflow/tfjs-backend-cpu';\nimport {version_webgl} from '@tensorflow/tfjs-backend-webgl';\nimport {version_data} from '@tensorflow/tfjs-data';\nimport {version_layers} from '@tensorflow/tfjs-layers';\nimport {version_converter} from '@tensorflow/tfjs-converter';\nimport {version as version_union} from './version';\n\nexport const version = {\n  'tfjs-core': version_core,\n  'tfjs-backend-cpu': version_cpu,\n  'tfjs-backend-webgl': version_webgl,\n  'tfjs-data': version_data,\n  'tfjs-layers': version_layers,\n  'tfjs-converter': version_converter,\n  'tfjs': version_union\n};\n", "export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32')\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32')\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32')\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0, 'float32')\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n"],
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diff --git a/dist/face-api.js b/dist/face-api.js
index 798bc9a..6db2ddc 100644
--- a/dist/face-api.js
+++ b/dist/face-api.js
@@ -1,39 +1,39 @@
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To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();const e=Ii().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>zl(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();const e=await Ii().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){if(this.isDisposed)return;Ii().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Aa.print(this,e)}clone(){return this.throwIfDisposed(),Aa.clone(this)}toString(e=!1){const t=this.dataSync();return ck(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Aa.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Ii().makeVariable(this,e,t,n)}}Object.defineProperty(Q,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});class Hl extends Q{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!ot(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Ii().disposeTensor(this),this.dataId=e.dataId,Ii().incRef(this,null)}dispose(){Ii().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(Hl,Symbol.hasInstance,{value:e=>e instanceof Q&&e.assign!=null&&e.assign instanceof Function});(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(r.Rank||(r.Rank={}));var By;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(By||(By={}));var My;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(My||(My={}));var Py;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(Py||(Py={}));var zy;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(zy||(zy={}));const pk={float32:Py,int32:By,bool:My,complex64:zy};function Nn(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return pk[e][t]}function bd(e){return Nn(e,"int32")}function Mt(e,t){if(e.dtype===t.dtype)return[e,t];const n=Nn(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function jx(e,t){k(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function wd(e,t){return t.some(n=>n.id===e.id)}function ji(e){const t=[],n=new Set;return Kx(e,t,n),t}function Kx(e,t,n){if(e==null)return;if(e instanceof Q){t.push(e);return}if(!mk(e))return;const s=e;for(const i in s){const o=s[i];n.has(o)||(n.add(o),Kx(o,t,n))}}function mk(e){return Array.isArray(e)||typeof e=="object"}var fk=Object.freeze({__proto__:null,makeTypesMatch:Mt,assertTypesMatch:jx,isTensorInList:wd,getTensorsInContainer:ji});class Xx{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class ql{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Xx}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],s=await this.initializeBackend(n).success;if(s){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){const{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;const{success:t,asyncInit:n}=this.initializeBackend(e),s=n?await t:t;if(!s)return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new sk(this.backendInstance),!0}setupRegisteredKernels(){const e=ud(this.backendName);e.forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){const t=ud(e);t.forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){const t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{const n=t.factory();if(n&&!(n instanceof g)&&typeof n.then=="function"){const s=++this.pendingBackendInitId,i=n.then(o=>s<this.pendingBackendInitId?!1:(this.registry[e]=o,this.pendingBackendInit=null,!0)).catch(o=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(o.stack||o.message)),!1));return this.pendingBackendInit=i,{success:i,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],{success:s,asyncInit:i}=this.initializeBackend(n);if(i||s)return{name:n,asyncInit:i}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),s=n.backend,i=this.readSync(t);s.disposeData(t),n.backend=e,e.move(t,i,n.shape,n.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{const s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return ql.nextTensorId++}nextVariableId(){return ql.nextVariableId++}clone(e){const t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),n={x:e},s=o=>({x:()=>{const a="float32",c={x:o},h={dtype:a};return V.runKernelFunc(d=>d.cast(o,a),c,null,al,h)}}),i=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,i,{}),t}runKernel(e,t,n,s,i){const o=null,a=null;return this.runKernelFunc(o,t,a,e,n,s,i)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const s=this.backend.numDataIds();let i=0;n.forEach(c=>{i+=c.dtype==="complex64"?3:1});const o=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=s-t-i-o;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${e}'`)}runKernelFunc(e,t,n,s,i,o,a){let c,h=[];const d=this.isTapeOn();s==null&&(s=this.state.activeScope!=null?this.state.activeScope.name:"");const m=this.state.numBytes,y=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let b;const w=vy(s,this.backendName);let L;if(w!=null)b=()=>{const A=this.backend.numDataIds();L=w.kernelFunc({inputs:t,attrs:i,backend:this.backend});const N=Array.isArray(L)?L:[L];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,A,N);const E=N.map(({dataId:D,shape:F,dtype:_})=>this.makeTensorFromDataId(D,F,_));if(d){let D=this.getTensorsForGradient(s,t,E);if(D==null){a==null&&(a=[]);const F=E.filter((_,B)=>a[B]);D=(o||[]).slice().concat(F)}h=this.saveTensorsForBackwardMode(D)}return E};else{const A=N=>{if(!d)return;h=N.map(E=>this.keep(this.clone(E)))};b=()=>{const N=this.backend.numDataIds();L=this.tidy(()=>e(this.backend,A));const E=Array.isArray(L)?L:[L];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,N,E),E}}let T;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?c=b():(T=this.profiler.profileKernel(s,t,()=>b()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(T),c=T.outputs)}),d&&this.addTapeNode(s,t,c,n,h,i),this.state.profiling&&this.state.activeProfile.kernels.push({name:s,bytesAdded:this.state.numBytes-m,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-y,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(A=>t[A]!=null?t[A].shape:null),outputShapes:c.map(A=>A.shape),kernelTimeMs:T.timeMs,extraInfo:T.extraInfo}),Array.isArray(L)?c:c[0]}saveTensorsForBackwardMode(e){const t=e.map(n=>this.keep(this.clone(n)));return t}getTensorsForGradient(e,t,n){const s=Ny(e);if(s!=null){const i=s.inputsToSave||[],o=s.outputsToSave||[];let a;s.saveAllInputs?(k(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(t).map(h=>t[h])):a=i.map(h=>t[h]);const c=n.filter((h,d)=>o[d]);return a.concat(c)}return null}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let i=e;n==="string"&&xr(e[0])&&(i=e.map(c=>$y(c)));const o=s.write(i,t,n),a=new Q(t,n,o,this.nextTensorId());if(this.incRef(a,s),n==="string"){const c=this.state.tensorInfo.get(o),h=Mx(i);this.state.numBytes+=h-c.bytes,c.bytes=h}return a}makeTensorFromDataId(e,t,n,s){n=n||"float32";const i=new Q(t,n,e,this.nextTensorId());return this.incRef(i,s),i}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));const i=new Hl(e,t,n,this.nextTensorId());if(this.state.registeredVariables[i.name]!=null)throw new Error(`Variable with name ${i.name} was already registered`);return this.state.registeredVariables[i.name]=i,this.incRef(i,this.backend),i}incRef(e,t){const n=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,n===0){this.state.numDataBuffers++;let s=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(s=e.size*ky(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:s,refCount:0}),this.state.numBytes+=s}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof Hl||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;const t=this.state.tensorInfo.get(e.dataId),n=t.refCount;n<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):this.state.tensorInfo.get(e.dataId).refCount--}disposeVariables(){for(const e in this.state.registeredVariables){const t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){const e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;const t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(const s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,i,o){const a={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:i},c=Ny(e);c!=null&&(s=c.gradFunc),s!=null&&(a.gradient=h=>(h=h.map((d,m)=>{if(d==null){const y=n[m],b=xa(y.size,y.dtype);return this.makeTensor(b,y.shape,y.dtype)}return d}),s(h.length>1?h:h[0],i,o))),this.state.activeTape.push(a)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){const t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){const t=ji(e),n=new Set(t.map(i=>i.id));for(let i=0;i<this.state.activeScope.track.length;i++){const o=this.state.activeScope.track[i];!o.kept&&!n.has(o.id)&&o.dispose()}const s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(i=>{!i.kept&&i.scopeId===s.id&&this.track(i)})}gradients(e,t,n,s=!1){if(k(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);const i=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));k(i instanceof Q,()=>"The result y returned by f() must be a tensor.");const o=ok(this.state.activeTape,t,i);if(!s&&o.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!wn(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=s!=="string"?Ar(e,s):qi(e,[],!0),V.makeTensor(e,t,s)}function en(e,t,n){const s=xi(e,n);return Nr(e,t,s,n)}const Vy={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8};const Ld=4;async function Yy(e,t){const n=[],s=[],i=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);for(let a=0;a<i.length;++a){const c=i[a],h=Array.isArray(e)?e[a].tensor:e[c];if(h.dtype!=="float32"&&h.dtype!=="int32"&&h.dtype!=="bool"&&h.dtype!=="string"&&h.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${c}': ${h.dtype}`);const d={name:c,shape:h.shape,dtype:h.dtype};if(h.dtype==="string"){const m=new Promise(async y=>{const b=await h.bytes(),w=b.reduce((A,N)=>A+N.length,0)+Ld*b.length,L=new Uint8Array(w);let T=0;for(let A=0;A<b.length;A++){const N=b[A],E=new Uint8Array(new Uint32Array([N.length]).buffer);L.set(E,T),T+=Ld,L.set(N,T),T+=N.length}y(L)});s.push(m)}else s.push(h.data());t!=null&&(d.group=t),n.push(d)}const o=await Promise.all(s);return{data:Sk(o),specs:n}}function Sd(e,t){const n={};let s,i=0;for(const o of t){const a=o.name,c=o.dtype,h=o.shape,d=we(h);let m;if("quantization"in o){const y=o.quantization;if(y.dtype==="uint8"||y.dtype==="uint16"){if(!("min"in y&&"scale"in y))throw new Error(`Weight ${o.name} with quantization ${y.dtype} doesn't have corresponding metadata min and scale.`)}else if(y.dtype==="float16"){if(c!=="float32")throw new Error(`Weight ${o.name} is quantized with ${y.dtype} which only supports weights of type float32 not ${c}.`)}else throw new Error(`Weight ${o.name} has unknown quantization dtype ${y.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);const b=Vy[y.dtype],w=e.slice(i,i+d*b),L=y.dtype==="uint8"?new Uint8Array(w):new Uint16Array(w);if(c==="float32")if(y.dtype==="uint8"||y.dtype==="uint16"){m=new Float32Array(L.length);for(let T=0;T<L.length;T++){const A=L[T];m[T]=A*y.scale+y.min}}else if(y.dtype==="float16")s===void 0&&(s=Nk()),m=s(L);else throw new Error(`Unsupported quantization type ${y.dtype} for weight type float32.`);else if(c==="int32"){if(y.dtype!=="uint8"&&y.dtype!=="uint16")throw new Error(`Unsupported quantization type ${y.dtype} for weight type int32.`);m=new Int32Array(L.length);for(let T=0;T<L.length;T++){const A=L[T];m[T]=Math.round(A*y.scale+y.min)}}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=d*b}else if(c==="string"){const y=we(o.shape);m=[];for(let b=0;b<y;b++){const w=new Uint32Array(e.slice(i,i+Ld))[0];i+=Ld;const L=new Uint8Array(e.slice(i,i+w));m.push(L),i+=w}}else{const y=Vy[c],b=e.slice(i,i+d*y);if(c==="float32")m=new Float32Array(b);else if(c==="int32")m=new Int32Array(b);else if(c==="bool")m=new Uint8Array(b);else if(c==="complex64"){m=new Float32Array(b);const w=new Float32Array(m.length/2),L=new Float32Array(m.length/2);for(let N=0;N<w.length;N++)w[N]=m[N*2],L[N]=m[N*2+1];const T=en(w,h,"float32"),A=en(L,h,"float32");n[a]=Ti(T,A)}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=d*y}c!=="complex64"&&(n[a]=en(m,h,c))}return n}function Sk(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0;const n=[];e.forEach(o=>{if(t+=o.byteLength,n.push(o.byteLength===o.buffer.byteLength?o:new o.constructor(o)),!(o instanceof Float32Array||o instanceof Int32Array||o instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${o.constructor.name}`)});const s=new Uint8Array(t);let i=0;return n.forEach(o=>{s.set(new Uint8Array(o.buffer),i),i+=o.byteLength}),s.buffer}const Hy=typeof 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c=s[a],h=e[n[c>>10]+(c&1023)]+t[c>>10];o[a]=h}return new Float32Array(i)}}class Xt{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Xt.instance==null&&(Xt.instance=new Xt),Xt.instance}static registerSaveRouter(e){Xt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Xt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Xt.getHandlers(e,"save")}static getLoadHandlers(e,t){return Xt.getHandlers(e,"load",t)}static getHandlers(e,t,n){const s=[],i=t==="load"?Xt.getInstance().loadRouters:Xt.getInstance().saveRouters;return i.forEach(o=>{const a=o(e,n);a!==null&&s.push(a)}),s}}const Ck=e=>Xt.registerSaveRouter(e),Rk=e=>Xt.registerLoadRouter(e),qy=e=>Xt.getSaveHandlers(e),jy=(e,t)=>Xt.getLoadHandlers(e,t);const xd="tensorflowjs",Ky=1,bo="models_store",Cr="model_info_store";async function _Z(){const e=Xy();return new Promise((t,n)=>{const s=e.deleteDatabase(xd);s.onsuccess=()=>t(),s.onerror=i=>n(i)})}function Xy(){if(!C().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");const e=typeof window=="undefined"?self:window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(t==null)throw new Error("The current browser does not appear to support IndexedDB.");return t}function Jy(e){const t=e.result;t.createObjectStore(bo,{keyPath:"modelPath"}),t.createObjectStore(Cr,{keyPath:"modelPath"})}class wo{constructor(e){if(this.indexedDB=Xy(),e==null||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,e)}async load(){return this.databaseAction(this.modelPath)}databaseAction(e,t){return new Promise((n,s)=>{const i=this.indexedDB.open(xd,Ky);i.onupgradeneeded=()=>Jy(i),i.onsuccess=()=>{const o=i.result;if(t==null){const a=o.transaction(bo,"readonly"),c=a.objectStore(bo),h=c.get(this.modelPath);h.onsuccess=()=>{if(h.result==null)return o.close(),s(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(h.result.modelArtifacts)},h.onerror=d=>(o.close(),s(h.error)),a.oncomplete=()=>o.close()}else{const a=Kl(t),c=o.transaction(Cr,"readwrite");let h=c.objectStore(Cr);const d=h.put({modelPath:this.modelPath,modelArtifactsInfo:a});let m;d.onsuccess=()=>{m=o.transaction(bo,"readwrite");const y=m.objectStore(bo),b=y.put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:a});b.onsuccess=()=>n({modelArtifactsInfo:a}),b.onerror=w=>{h=c.objectStore(Cr);const L=h.delete(this.modelPath);L.onsuccess=()=>(o.close(),s(b.error)),L.onerror=T=>(o.close(),s(b.error))}},d.onerror=y=>(o.close(),s(d.error)),c.oncomplete=()=>{m==null?o.close():m.oncomplete=()=>o.close()}}},i.onerror=o=>s(i.error)})}}wo.URL_SCHEME="indexeddb://";const iT=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(wo.URL_SCHEME))?Ok(e.slice(wo.URL_SCHEME.length)):null;Xt.registerSaveRouter(iT),Xt.registerLoadRouter(iT);function Ok(e){return new wo(e)}function Ek(e){return e.startsWith(wo.URL_SCHEME)?e.slice(wo.URL_SCHEME.length):e}class Dk{constructor(){this.indexedDB=Xy()}async listModels(){return new Promise((e,t)=>{const n=this.indexedDB.open(xd,Ky);n.onupgradeneeded=()=>Jy(n),n.onsuccess=()=>{const s=n.result,i=s.transaction(Cr,"readonly"),o=i.objectStore(Cr),a=o.getAll();a.onsuccess=()=>{const c={};for(const h of a.result)c[h.modelPath]=h.modelArtifactsInfo;e(c)},a.onerror=c=>(s.close(),t(a.error)),i.oncomplete=()=>s.close()},n.onerror=s=>t(n.error)})}async 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a=JSON.parse(i);t.format=a.format,t.generatedBy=a.generatedBy,t.convertedBy=a.convertedBy,t.userDefinedMetadata=a.userDefinedMetadata}const o=this.LS.getItem(this.keys.weightData);if(o==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=xk(o),t}}So.URL_SCHEME="localstorage://";const cT=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(So.URL_SCHEME))?Uk(e.slice(So.URL_SCHEME.length)):null;Xt.registerSaveRouter(cT),Xt.registerLoadRouter(cT);function Uk(e){return new So(e)}class Bk{constructor(){k(C().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),k(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){const e={},t=Lo+Ai,n=Ai+rT;for(let s=0;s<this.LS.length;++s){const i=this.LS.key(s);if(i.startsWith(t)&&i.endsWith(n)){const o=aT(i);e[o]=JSON.parse(this.LS.getItem(i))}}return e}async removeModel(e){e=$k(e);const t=oT(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);const n=JSON.parse(this.LS.getItem(t.info));return this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),n}}const va="://";class bs{constructor(){this.managers={}}static getInstance(){return bs.instance==null&&(bs.instance=new bs),bs.instance}static registerManager(e,t){k(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(va)&&(e=e.slice(0,e.indexOf(va))),k(e.length>0,()=>"scheme must not be an empty string.");const n=bs.getInstance();k(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),n.managers[e]=t}static getManager(e){const t=this.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}}function Td(e){if(e.indexOf(va)===-1)throw new Error(`The url string provided does not contain a scheme. 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s){const o=n+va+i;t[o]=s[i]}}return t}async function Pk(e){const t=Td(e),n=bs.getManager(t.scheme);return n.removeModel(t.path)}async function zk(e,t){const n=!1;return lT(e,t,n)}async function Gk(e,t){const n=!0;return lT(e,t,n)}class Vk{fetch(e,t){return fetch(e,t)}now(){return performance.now()}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${t}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(e)}decode(e,t){return new TextDecoder(t).decode(e)}}if(C().get("IS_BROWSER")){C().setPlatform("browser",new Vk);try{bs.registerManager(So.URL_SCHEME,new Bk)}catch(e){}try{bs.registerManager(wo.URL_SCHEME,new Dk)}catch(e){}}const Yk={importFetch:()=>RC()};let Na;function $Z(){Na=null}function UZ(e){Na=e}function BZ(){return Na}class Hk{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return C().global.fetch!=null?C().global.fetch(e,t):(Na==null&&(Na=Yk.importFetch()),Na(e,t))}now(){const e=process.hrtime();return e[0]*1e3+e[1]/1e6}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${t}`);return this.textEncoder.encode(e)}decode(e,t){return e.length===0?"":new this.util.TextDecoder(t).decode(e)}}C().get("IS_NODE")&&C().setPlatform("node",new Hk);function Ze(e,t="float32",n){return t=t||"float32",Wy(e),new vr(e,t,n)}function qk(e,t){const n=W(e,"x","cast");if(!Bx(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");const s={x:n},i={dtype:t};return V.runKernelFunc(o=>o.cast(n,t),s,null,al,i)}const ve=P({cast_:qk});function jk(e){const t=W(e,"x","clone",null),n=()=>V.makeTensorFromDataId(t.dataId,t.shape,t.dtype),s={x:t};return V.runKernelFunc(n,s,null,yl)}const Rr=P({clone_:jk});function hT(e,t=!1){console.log(e.toString(t))}Jx();const Kk={buffer:Ze,cast:ve,clone:Rr,print:hT};uk(Kk);const Xk="model",Jk=".json",Zk=".weights.bin";function uT(e){return new Promise(t=>setTimeout(t)).then(e)}class Ca{constructor(e){if(!C().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(Ca.URL_SCHEME)&&(e=e.slice(Ca.URL_SCHEME.length)),(e==null||e.length===0)&&(e=Xk),this.modelTopologyFileName=e+Jk,this.weightDataFileName=e+Zk}async save(e){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");const t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{const n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],s={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n},i=window.URL.createObjectURL(new Blob([JSON.stringify(s)],{type:"application/json"})),o=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(o.download=this.modelTopologyFileName,o.href=i,await uT(()=>o.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){const a=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;a.download=this.weightDataFileName,a.href=t,await uT(()=>a.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Kl(e)}}}}Ca.URL_SCHEME="downloads://";class Qk{constructor(e){if(e==null||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.files=e}async load(){const e=this.files[0],t=this.files.slice(1);return new Promise((n,s)=>{const i=new FileReader;i.onload=o=>{const a=JSON.parse(o.target.result),c=a.modelTopology;if(c==null){s(new Error(`modelTopology field is missing from file ${e.name}`));return}t.length===0&&n({modelTopology:c});const h=a.weightsManifest;if(h==null){s(new Error(`weightManifest field is missing from file ${e.name}`));return}let d;try{d=this.checkManifestAndWeightFiles(h,t)}catch(w){s(w);return}const m=[],y=[],b=[];h.forEach(w=>{w.paths.forEach(L=>{y.push(L),b.push(null)}),m.push(...w.weights)}),h.forEach(w=>{w.paths.forEach(L=>{const T=new FileReader;T.onload=A=>{const N=A.target.result,E=y.indexOf(L);b[E]=N,b.indexOf(null)===-1&&n({modelTopology:c,weightSpecs:m,weightData:Id(b),format:a.format,generatedBy:a.generatedBy,convertedBy:a.convertedBy,userDefinedMetadata:a.userDefinedMetadata})},T.onerror=A=>s(`Failed to weights data from file of path '${L}'.`),T.readAsArrayBuffer(d[L])})})},i.onerror=o=>s(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),i.readAsText(e)})}checkManifestAndWeightFiles(e,t){const n=[],s=t.map(o=>sT(o.name)),i={};for(const o of e)o.paths.forEach(a=>{const c=sT(a);if(n.indexOf(c)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${c}'`);if(n.push(c),s.indexOf(c)===-1)throw new Error(`Weight file with basename '${c}' is not provided.`);i[a]=t[s.indexOf(c)]});if(n.length!==t.length)throw new Error(`Mismatch in the number of files in weights manifest (${n.length}) and the number of weight files provided (${t.length}).`);return i}}const eF=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(Ca.URL_SCHEME))?tF(e.slice(Ca.URL_SCHEME.length)):null;Xt.registerSaveRouter(eF);function tF(e="model"){return new Ca(e)}function nF(e){return new Qk(e)}function dT(e,t,n,s){a(e),n=n==null?0:n,s=s==null?1:s,c(n,s);let i=0;const o=h=>(h.then(d=>{const m=n+ ++i/e.length*(s-n);return t(m),d}),h);function a(h){k(h!=null&&Array.isArray(h)&&h.length>0,()=>"promises must be a none empty array")}function c(h,d){k(h>=0&&h<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${h}`),k(d>=0&&d<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${d}`),k(d>=h,()=>`startFraction must be no more than endFraction, but got startFraction ${h} and endFraction ${d}`)}return Promise.all(e.map(o))}async function pT(e,t){t==null&&(t={});const n=t.fetchFunc==null?C().platform.fetch:t.fetchFunc,s=e.map(y=>n(y,t.requestInit,{isBinary:!0})),i=0,o=.5,a=t.onProgress==null?await Promise.all(s):await dT(s,t.onProgress,i,o),c=a.map(y=>y.arrayBuffer()),h=.5,d=1,m=t.onProgress==null?await Promise.all(c):await dT(c,t.onProgress,h,d);return m}async function mT(e,t="",n,s){const i=a=>pT(a,{requestInit:s}),o=fT(i);return o(e,t,n)}function fT(e){return async(t,n="",s)=>{const i=t.map(()=>!1),o={},a=s!=null?s.map(()=>!1):[],c=[];if(t.forEach((w,L)=>{let T=0;w.weights.forEach(A=>{const N="quantization"in A?A.quantization.dtype:A.dtype,E=Vy[N]*we(A.shape),D=()=>{i[L]=!0,o[L]==null&&(o[L]=[]),o[L].push({manifestEntry:A,groupOffset:T,sizeBytes:E})};s!=null?s.forEach((F,_)=>{F===A.name&&(D(),a[_]=!0)}):D(),c.push(A.name),T+=E})}),!a.every(w=>w)){const w=s.filter((L,T)=>!a[T]);throw new Error(`Could not find weights in manifest with names: ${w.join(", ")}. 
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Buffer shape=${this.shape}`;throw new Error(i)}t++}let n=e[e.length-1];for(let s=0;s<e.length-1;++s)n+=this.strides[s]*e[s];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];const t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return Ii().makeTensor(this.values,this.shape,this.dtype)}}let Ii=null,Na=null,qx=null;function hk(e){Ii=e}function uk(e){Na=e}function dk(e){qx=e}class Q{constructor(e,t,n,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=we(e),this.strides=Ot(e),this.dataId=n,this.id=s,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const e=await this.data();return Na.buffer(this.shape,this.dtype,e)}bufferSync(){return Na.buffer(this.shape,this.dtype,this.dataSync())}async array(){const e=await this.data();return ys(this.shape,e)}arraySync(){return ys(this.shape,this.dataSync())}async data(){this.throwIfDisposed();const e=Ii().read(this.dataId);if(this.dtype==="string"){const t=await e;try{return t.map(n=>zl(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();const e=Ii().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>zl(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();const e=await Ii().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){if(this.isDisposed)return;Ii().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Na.print(this,e)}clone(){return this.throwIfDisposed(),Na.clone(this)}toString(e=!1){const t=this.dataSync();return ck(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Na.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Ii().makeVariable(this,e,t,n)}}Object.defineProperty(Q,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});class Hl extends Q{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!ot(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Ii().disposeTensor(this),this.dataId=e.dataId,Ii().incRef(this,null)}dispose(){Ii().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(Hl,Symbol.hasInstance,{value:e=>e instanceof Q&&e.assign!=null&&e.assign instanceof Function});(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(r.Rank||(r.Rank={}));var By;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(By||(By={}));var My;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(My||(My={}));var Py;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(Py||(Py={}));var zy;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(zy||(zy={}));const pk={float32:Py,int32:By,bool:My,complex64:zy};function Nn(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return pk[e][t]}function bd(e){return Nn(e,"int32")}function Mt(e,t){if(e.dtype===t.dtype)return[e,t];const n=Nn(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function jx(e,t){k(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function wd(e,t){return t.some(n=>n.id===e.id)}function ji(e){const t=[],n=new Set;return Kx(e,t,n),t}function Kx(e,t,n){if(e==null)return;if(e instanceof Q){t.push(e);return}if(!mk(e))return;const s=e;for(const i in s){const o=s[i];n.has(o)||(n.add(o),Kx(o,t,n))}}function mk(e){return Array.isArray(e)||typeof e=="object"}var fk=Object.freeze({__proto__:null,makeTypesMatch:Mt,assertTypesMatch:jx,isTensorInList:wd,getTensorsInContainer:ji});class Xx{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class ql{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Xx}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],s=await this.initializeBackend(n).success;if(s){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){const{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;const{success:t,asyncInit:n}=this.initializeBackend(e),s=n?await t:t;if(!s)return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new sk(this.backendInstance),!0}setupRegisteredKernels(){const e=ud(this.backendName);e.forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){const t=ud(e);t.forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){const t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{const n=t.factory();if(n&&!(n instanceof g)&&typeof n.then=="function"){const s=++this.pendingBackendInitId,i=n.then(o=>s<this.pendingBackendInitId?!1:(this.registry[e]=o,this.pendingBackendInit=null,!0)).catch(o=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(o.stack||o.message)),!1));return this.pendingBackendInit=i,{success:i,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],{success:s,asyncInit:i}=this.initializeBackend(n);if(i||s)return{name:n,asyncInit:i}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),s=n.backend,i=this.readSync(t);s.disposeData(t),n.backend=e,e.move(t,i,n.shape,n.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{const s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return ql.nextTensorId++}nextVariableId(){return ql.nextVariableId++}clone(e){const t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),n={x:e},s=o=>({x:()=>{const a="float32",c={x:o},h={dtype:a};return V.runKernelFunc(d=>d.cast(o,a),c,null,al,h)}}),i=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,i,{}),t}runKernel(e,t,n,s,i){const o=null,a=null;return this.runKernelFunc(o,t,a,e,n,s,i)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const s=this.backend.numDataIds();let i=0;n.forEach(c=>{i+=c.dtype==="complex64"?3:1});const o=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=s-t-i-o;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${e}'`)}runKernelFunc(e,t,n,s,i,o,a){let c,h=[];const d=this.isTapeOn();s==null&&(s=this.state.activeScope!=null?this.state.activeScope.name:"");const m=this.state.numBytes,y=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let b;const w=vy(s,this.backendName);let L;if(w!=null)b=()=>{const A=this.backend.numDataIds();L=w.kernelFunc({inputs:t,attrs:i,backend:this.backend});const N=Array.isArray(L)?L:[L];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,A,N);const E=N.map(({dataId:D,shape:F,dtype:_})=>this.makeTensorFromDataId(D,F,_));if(d){let D=this.getTensorsForGradient(s,t,E);if(D==null){a==null&&(a=[]);const F=E.filter((_,B)=>a[B]);D=(o||[]).slice().concat(F)}h=this.saveTensorsForBackwardMode(D)}return E};else{const A=N=>{if(!d)return;h=N.map(E=>this.keep(this.clone(E)))};b=()=>{const N=this.backend.numDataIds();L=this.tidy(()=>e(this.backend,A));const E=Array.isArray(L)?L:[L];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,N,E),E}}let T;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?c=b():(T=this.profiler.profileKernel(s,t,()=>b()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(T),c=T.outputs)}),d&&this.addTapeNode(s,t,c,n,h,i),this.state.profiling&&this.state.activeProfile.kernels.push({name:s,bytesAdded:this.state.numBytes-m,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-y,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(A=>t[A]!=null?t[A].shape:null),outputShapes:c.map(A=>A.shape),kernelTimeMs:T.timeMs,extraInfo:T.extraInfo}),Array.isArray(L)?c:c[0]}saveTensorsForBackwardMode(e){const t=e.map(n=>this.keep(this.clone(n)));return t}getTensorsForGradient(e,t,n){const s=Ny(e);if(s!=null){const i=s.inputsToSave||[],o=s.outputsToSave||[];let a;s.saveAllInputs?(k(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(t).map(h=>t[h])):a=i.map(h=>t[h]);const c=n.filter((h,d)=>o[d]);return a.concat(c)}return null}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let i=e;n==="string"&&xr(e[0])&&(i=e.map(c=>$y(c)));const o=s.write(i,t,n),a=new Q(t,n,o,this.nextTensorId());if(this.incRef(a,s),n==="string"){const c=this.state.tensorInfo.get(o),h=Mx(i);this.state.numBytes+=h-c.bytes,c.bytes=h}return a}makeTensorFromDataId(e,t,n,s){n=n||"float32";const i=new Q(t,n,e,this.nextTensorId());return this.incRef(i,s),i}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));const i=new Hl(e,t,n,this.nextTensorId());if(this.state.registeredVariables[i.name]!=null)throw new Error(`Variable with name ${i.name} was already registered`);return this.state.registeredVariables[i.name]=i,this.incRef(i,this.backend),i}incRef(e,t){const n=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,n===0){this.state.numDataBuffers++;let s=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(s=e.size*ky(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:s,refCount:0}),this.state.numBytes+=s}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof Hl||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;const t=this.state.tensorInfo.get(e.dataId),n=t.refCount;n<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):this.state.tensorInfo.get(e.dataId).refCount--}disposeVariables(){for(const e in this.state.registeredVariables){const t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){const e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;const t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(const s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,i,o){const a={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:i},c=Ny(e);c!=null&&(s=c.gradFunc),s!=null&&(a.gradient=h=>(h=h.map((d,m)=>{if(d==null){const y=n[m],b=Aa(y.size,y.dtype);return this.makeTensor(b,y.shape,y.dtype)}return d}),s(h.length>1?h:h[0],i,o))),this.state.activeTape.push(a)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){const t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){const t=ji(e),n=new Set(t.map(i=>i.id));for(let i=0;i<this.state.activeScope.track.length;i++){const o=this.state.activeScope.track[i];!o.kept&&!n.has(o.id)&&o.dispose()}const s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(i=>{!i.kept&&i.scopeId===s.id&&this.track(i)})}gradients(e,t,n,s=!1){if(k(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);const i=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));k(i instanceof Q,()=>"The result y returned by f() must be a tensor.");const o=ok(this.state.activeTape,t,i);if(!s&&o.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!wn(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=s!=="string"?Ar(e,s):qi(e,[],!0),V.makeTensor(e,t,s)}function en(e,t,n){const s=xi(e,n);return Nr(e,t,s,n)}const Vy={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8};const Ld=4;async function Yy(e,t){const n=[],s=[],i=Array.isArray(e)?e.map(a=>a.name):Object.keys(e);for(let a=0;a<i.length;++a){const c=i[a],h=Array.isArray(e)?e[a].tensor:e[c];if(h.dtype!=="float32"&&h.dtype!=="int32"&&h.dtype!=="bool"&&h.dtype!=="string"&&h.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${c}': ${h.dtype}`);const d={name:c,shape:h.shape,dtype:h.dtype};if(h.dtype==="string"){const m=new Promise(async y=>{const b=await h.bytes(),w=b.reduce((A,N)=>A+N.length,0)+Ld*b.length,L=new Uint8Array(w);let T=0;for(let A=0;A<b.length;A++){const N=b[A],E=new Uint8Array(new Uint32Array([N.length]).buffer);L.set(E,T),T+=Ld,L.set(N,T),T+=N.length}y(L)});s.push(m)}else s.push(h.data());t!=null&&(d.group=t),n.push(d)}const o=await Promise.all(s);return{data:Sk(o),specs:n}}function Sd(e,t){const n={};let s,i=0;for(const o of t){const a=o.name,c=o.dtype,h=o.shape,d=we(h);let m;if("quantization"in o){const y=o.quantization;if(y.dtype==="uint8"||y.dtype==="uint16"){if(!("min"in y&&"scale"in y))throw new Error(`Weight ${o.name} with quantization ${y.dtype} doesn't have corresponding metadata min and scale.`)}else if(y.dtype==="float16"){if(c!=="float32")throw new Error(`Weight ${o.name} is quantized with ${y.dtype} which only supports weights of type float32 not ${c}.`)}else throw new Error(`Weight ${o.name} has unknown quantization dtype ${y.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);const b=Vy[y.dtype],w=e.slice(i,i+d*b),L=y.dtype==="uint8"?new Uint8Array(w):new Uint16Array(w);if(c==="float32")if(y.dtype==="uint8"||y.dtype==="uint16"){m=new Float32Array(L.length);for(let T=0;T<L.length;T++){const A=L[T];m[T]=A*y.scale+y.min}}else if(y.dtype==="float16")s===void 0&&(s=Nk()),m=s(L);else throw new Error(`Unsupported quantization type ${y.dtype} for weight type float32.`);else if(c==="int32"){if(y.dtype!=="uint8"&&y.dtype!=="uint16")throw new Error(`Unsupported quantization type ${y.dtype} for weight type int32.`);m=new Int32Array(L.length);for(let T=0;T<L.length;T++){const A=L[T];m[T]=Math.round(A*y.scale+y.min)}}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=d*b}else if(c==="string"){const y=we(o.shape);m=[];for(let b=0;b<y;b++){const w=new Uint32Array(e.slice(i,i+Ld))[0];i+=Ld;const L=new Uint8Array(e.slice(i,i+w));m.push(L),i+=w}}else{const y=Vy[c],b=e.slice(i,i+d*y);if(c==="float32")m=new Float32Array(b);else if(c==="int32")m=new Int32Array(b);else if(c==="bool")m=new Uint8Array(b);else if(c==="complex64"){m=new Float32Array(b);const w=new Float32Array(m.length/2),L=new Float32Array(m.length/2);for(let N=0;N<w.length;N++)w[N]=m[N*2],L[N]=m[N*2+1];const T=en(w,h,"float32"),A=en(L,h,"float32");n[a]=Ti(T,A)}else throw new Error(`Unsupported dtype in weight '${a}': ${c}`);i+=d*y}c!=="complex64"&&(n[a]=en(m,h,c))}return n}function Sk(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0;const n=[];e.forEach(o=>{if(t+=o.byteLength,n.push(o.byteLength===o.buffer.byteLength?o:new o.constructor(o)),!(o instanceof Float32Array||o instanceof Int32Array||o instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${o.constructor.name}`)});const s=new Uint8Array(t);let i=0;return n.forEach(o=>{s.set(new Uint8Array(o.buffer),i),i+=o.byteLength}),s.buffer}const Hy=typeof 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c=s[a],h=e[n[c>>10]+(c&1023)]+t[c>>10];o[a]=h}return new Float32Array(i)}}class Xt{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Xt.instance==null&&(Xt.instance=new Xt),Xt.instance}static registerSaveRouter(e){Xt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Xt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Xt.getHandlers(e,"save")}static getLoadHandlers(e,t){return Xt.getHandlers(e,"load",t)}static getHandlers(e,t,n){const s=[],i=t==="load"?Xt.getInstance().loadRouters:Xt.getInstance().saveRouters;return i.forEach(o=>{const a=o(e,n);a!==null&&s.push(a)}),s}}const Ck=e=>Xt.registerSaveRouter(e),Rk=e=>Xt.registerLoadRouter(e),qy=e=>Xt.getSaveHandlers(e),jy=(e,t)=>Xt.getLoadHandlers(e,t);const xd="tensorflowjs",Ky=1,Lo="models_store",Cr="model_info_store";async function _Z(){const e=Xy();return new Promise((t,n)=>{const s=e.deleteDatabase(xd);s.onsuccess=()=>t(),s.onerror=i=>n(i)})}function Xy(){if(!C().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");const e=typeof window=="undefined"?self:window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(t==null)throw new Error("The current browser does not appear to support IndexedDB.");return t}function Jy(e){const t=e.result;t.createObjectStore(Lo,{keyPath:"modelPath"}),t.createObjectStore(Cr,{keyPath:"modelPath"})}class So{constructor(e){if(this.indexedDB=Xy(),e==null||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,e)}async load(){return this.databaseAction(this.modelPath)}databaseAction(e,t){return new Promise((n,s)=>{const i=this.indexedDB.open(xd,Ky);i.onupgradeneeded=()=>Jy(i),i.onsuccess=()=>{const o=i.result;if(t==null){const a=o.transaction(Lo,"readonly"),c=a.objectStore(Lo),h=c.get(this.modelPath);h.onsuccess=()=>{if(h.result==null)return o.close(),s(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(h.result.modelArtifacts)},h.onerror=d=>(o.close(),s(h.error)),a.oncomplete=()=>o.close()}else{const a=Kl(t),c=o.transaction(Cr,"readwrite");let h=c.objectStore(Cr);const d=h.put({modelPath:this.modelPath,modelArtifactsInfo:a});let m;d.onsuccess=()=>{m=o.transaction(Lo,"readwrite");const y=m.objectStore(Lo),b=y.put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:a});b.onsuccess=()=>n({modelArtifactsInfo:a}),b.onerror=w=>{h=c.objectStore(Cr);const L=h.delete(this.modelPath);L.onsuccess=()=>(o.close(),s(b.error)),L.onerror=T=>(o.close(),s(b.error))}},d.onerror=y=>(o.close(),s(d.error)),c.oncomplete=()=>{m==null?o.close():m.oncomplete=()=>o.close()}}},i.onerror=o=>s(i.error)})}}So.URL_SCHEME="indexeddb://";const iT=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(So.URL_SCHEME))?Ok(e.slice(So.URL_SCHEME.length)):null;Xt.registerSaveRouter(iT),Xt.registerLoadRouter(iT);function Ok(e){return new So(e)}function Ek(e){return e.startsWith(So.URL_SCHEME)?e.slice(So.URL_SCHEME.length):e}class Dk{constructor(){this.indexedDB=Xy()}async listModels(){return new Promise((e,t)=>{const n=this.indexedDB.open(xd,Ky);n.onupgradeneeded=()=>Jy(n),n.onsuccess=()=>{const s=n.result,i=s.transaction(Cr,"readonly"),o=i.objectStore(Cr),a=o.getAll();a.onsuccess=()=>{const c={};for(const h of a.result)c[h.modelPath]=h.modelArtifactsInfo;e(c)},a.onerror=c=>(s.close(),t(a.error)),i.oncomplete=()=>s.close()},n.onerror=s=>t(n.error)})}async 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storage is unavailable in the current environment.");const e=window.localStorage,t=[];for(let n=0;n<e.length;++n){const s=e.key(n),i=Io+Ai;if(s.startsWith(i)&&s.length>i.length){e.removeItem(s);const o=aT(s);t.indexOf(o)===-1&&t.push(o)}}return t}function oT(e){return{info:[Io,e,rT].join(Ai),topology:[Io,e,kk].join(Ai),weightSpecs:[Io,e,Fk].join(Ai),weightData:[Io,e,_k].join(Ai),modelMetadata:[Io,e,Wk].join(Ai)}}function aT(e){const t=e.split(Ai);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(Ai)}function $k(e){return e.startsWith(xo.URL_SCHEME)?e.slice(xo.URL_SCHEME.length):e}class xo{constructor(e){if(!C().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or 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is a possible cause of this failure: modelTopologyBytes=${s.modelTopologyBytes}, weightSpecsBytes=${s.weightSpecsBytes}, weightDataBytes=${s.weightDataBytes}.`)}}}async load(){const e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(e.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");const t={},n=JSON.parse(this.LS.getItem(this.keys.topology));if(n==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);t.modelTopology=n;const s=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(s==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=s;const i=this.LS.getItem(this.keys.modelMetadata);if(i!=null){const a=JSON.parse(i);t.format=a.format,t.generatedBy=a.generatedBy,t.convertedBy=a.convertedBy,t.userDefinedMetadata=a.userDefinedMetadata}const o=this.LS.getItem(this.keys.weightData);if(o==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=xk(o),t}}xo.URL_SCHEME="localstorage://";const cT=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(xo.URL_SCHEME))?Uk(e.slice(xo.URL_SCHEME.length)):null;Xt.registerSaveRouter(cT),Xt.registerLoadRouter(cT);function Uk(e){return new xo(e)}class Bk{constructor(){k(C().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),k(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){const e={},t=Io+Ai,n=Ai+rT;for(let s=0;s<this.LS.length;++s){const i=this.LS.key(s);if(i.startsWith(t)&&i.endsWith(n)){const o=aT(i);e[o]=JSON.parse(this.LS.getItem(i))}}return e}async removeModel(e){e=$k(e);const t=oT(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);const n=JSON.parse(this.LS.getItem(t.info));return this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),n}}const Ca="://";class bs{constructor(){this.managers={}}static getInstance(){return bs.instance==null&&(bs.instance=new bs),bs.instance}static registerManager(e,t){k(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(Ca)&&(e=e.slice(0,e.indexOf(Ca))),k(e.length>0,()=>"scheme must not be an empty string.");const n=bs.getInstance();k(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),n.managers[e]=t}static getManager(e){const t=this.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}}function Td(e){if(e.indexOf(Ca)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${bs.getSchemes().join(",")}`);return{scheme:e.split(Ca)[0],path:e.split(Ca)[1]}}async function lT(e,t,n=!1){k(e!==t,()=>`Old path and new path are the same: '${e}'`);const s=Xt.getLoadHandlers(e);k(s.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),k(s.length<2,()=>`Copying failed because more than one (${s.length}) load handlers for source URL ${e}.`);const i=s[0],o=Xt.getSaveHandlers(t);k(o.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),k(o.length<2,()=>`Copying failed because more than one (${s.length}) save handlers for destination URL ${t}.`);const a=o[0],c=Td(e).scheme,h=Td(e).path,d=c===Td(e).scheme,m=await i.load();n&&d&&await bs.getManager(c).removeModel(h);const y=await a.save(m);return n&&!d&&await bs.getManager(c).removeModel(h),y.modelArtifactsInfo}async function Mk(){const e=bs.getSchemes(),t={};for(const n of e){const s=await bs.getManager(n).listModels();for(const i in s){const o=n+Ca+i;t[o]=s[i]}}return t}async function Pk(e){const t=Td(e),n=bs.getManager(t.scheme);return n.removeModel(t.path)}async function zk(e,t){const n=!1;return lT(e,t,n)}async function Gk(e,t){const n=!0;return lT(e,t,n)}class Vk{fetch(e,t){return fetch(e,t)}now(){return performance.now()}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${t}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(e)}decode(e,t){return new TextDecoder(t).decode(e)}}if(C().get("IS_BROWSER")){C().setPlatform("browser",new Vk);try{bs.registerManager(xo.URL_SCHEME,new Bk)}catch(e){}try{bs.registerManager(So.URL_SCHEME,new Dk)}catch(e){}}const Yk={importFetch:()=>RC()};let Ra;function $Z(){Ra=null}function UZ(e){Ra=e}function BZ(){return Ra}class Hk{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return C().global.fetch!=null?C().global.fetch(e,t):(Ra==null&&(Ra=Yk.importFetch()),Ra(e,t))}now(){const e=process.hrtime();return e[0]*1e3+e[1]/1e6}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${t}`);return this.textEncoder.encode(e)}decode(e,t){return e.length===0?"":new this.util.TextDecoder(t).decode(e)}}C().get("IS_NODE")&&C().setPlatform("node",new Hk);function Ze(e,t="float32",n){return t=t||"float32",Wy(e),new vr(e,t,n)}function qk(e,t){const n=W(e,"x","cast");if(!Bx(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");const s={x:n},i={dtype:t};return V.runKernelFunc(o=>o.cast(n,t),s,null,al,i)}const ve=P({cast_:qk});function jk(e){const t=W(e,"x","clone",null),n=()=>V.makeTensorFromDataId(t.dataId,t.shape,t.dtype),s={x:t};return V.runKernelFunc(n,s,null,yl)}const Rr=P({clone_:jk});function hT(e,t=!1){console.log(e.toString(t))}Jx();const Kk={buffer:Ze,cast:ve,clone:Rr,print:hT};uk(Kk);const Xk="model",Jk=".json",Zk=".weights.bin";function uT(e){return new Promise(t=>setTimeout(t)).then(e)}class Oa{constructor(e){if(!C().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(Oa.URL_SCHEME)&&(e=e.slice(Oa.URL_SCHEME.length)),(e==null||e.length===0)&&(e=Xk),this.modelTopologyFileName=e+Jk,this.weightDataFileName=e+Zk}async save(e){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");const t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{const n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],s={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n},i=window.URL.createObjectURL(new Blob([JSON.stringify(s)],{type:"application/json"})),o=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(o.download=this.modelTopologyFileName,o.href=i,await uT(()=>o.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){const a=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;a.download=this.weightDataFileName,a.href=t,await uT(()=>a.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Kl(e)}}}}Oa.URL_SCHEME="downloads://";class Qk{constructor(e){if(e==null||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.files=e}async load(){const e=this.files[0],t=this.files.slice(1);return new Promise((n,s)=>{const i=new FileReader;i.onload=o=>{const a=JSON.parse(o.target.result),c=a.modelTopology;if(c==null){s(new Error(`modelTopology field is missing from file ${e.name}`));return}t.length===0&&n({modelTopology:c});const h=a.weightsManifest;if(h==null){s(new Error(`weightManifest field is missing from file ${e.name}`));return}let d;try{d=this.checkManifestAndWeightFiles(h,t)}catch(w){s(w);return}const m=[],y=[],b=[];h.forEach(w=>{w.paths.forEach(L=>{y.push(L),b.push(null)}),m.push(...w.weights)}),h.forEach(w=>{w.paths.forEach(L=>{const T=new FileReader;T.onload=A=>{const N=A.target.result,E=y.indexOf(L);b[E]=N,b.indexOf(null)===-1&&n({modelTopology:c,weightSpecs:m,weightData:Id(b),format:a.format,generatedBy:a.generatedBy,convertedBy:a.convertedBy,userDefinedMetadata:a.userDefinedMetadata})},T.onerror=A=>s(`Failed to weights data from file of path '${L}'.`),T.readAsArrayBuffer(d[L])})})},i.onerror=o=>s(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),i.readAsText(e)})}checkManifestAndWeightFiles(e,t){const n=[],s=t.map(o=>sT(o.name)),i={};for(const o of e)o.paths.forEach(a=>{const c=sT(a);if(n.indexOf(c)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${c}'`);if(n.push(c),s.indexOf(c)===-1)throw new Error(`Weight file with basename '${c}' is not provided.`);i[a]=t[s.indexOf(c)]});if(n.length!==t.length)throw new Error(`Mismatch in the number of files in weights manifest (${n.length}) and the number of weight files provided (${t.length}).`);return i}}const eF=e=>C().getBool("IS_BROWSER")&&(!Array.isArray(e)&&e.startsWith(Oa.URL_SCHEME))?tF(e.slice(Oa.URL_SCHEME.length)):null;Xt.registerSaveRouter(eF);function tF(e="model"){return new Oa(e)}function nF(e){return new Qk(e)}function dT(e,t,n,s){a(e),n=n==null?0:n,s=s==null?1:s,c(n,s);let i=0;const o=h=>(h.then(d=>{const m=n+ ++i/e.length*(s-n);return t(m),d}),h);function a(h){k(h!=null&&Array.isArray(h)&&h.length>0,()=>"promises must be a none empty array")}function c(h,d){k(h>=0&&h<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${h}`),k(d>=0&&d<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${d}`),k(d>=h,()=>`startFraction must be no more than endFraction, but got startFraction ${h} and endFraction ${d}`)}return Promise.all(e.map(o))}async function pT(e,t){t==null&&(t={});const n=t.fetchFunc==null?C().platform.fetch:t.fetchFunc,s=e.map(y=>n(y,t.requestInit,{isBinary:!0})),i=0,o=.5,a=t.onProgress==null?await Promise.all(s):await dT(s,t.onProgress,i,o),c=a.map(y=>y.arrayBuffer()),h=.5,d=1,m=t.onProgress==null?await Promise.all(c):await dT(c,t.onProgress,h,d);return m}async function mT(e,t="",n,s){const i=a=>pT(a,{requestInit:s}),o=fT(i);return o(e,t,n)}function fT(e){return async(t,n="",s)=>{const i=t.map(()=>!1),o={},a=s!=null?s.map(()=>!1):[],c=[];if(t.forEach((w,L)=>{let T=0;w.weights.forEach(A=>{const N="quantization"in A?A.quantization.dtype:A.dtype,E=Vy[N]*we(A.shape),D=()=>{i[L]=!0,o[L]==null&&(o[L]=[]),o[L].push({manifestEntry:A,groupOffset:T,sizeBytes:E})};s!=null?s.forEach((F,_)=>{F===A.name&&(D(),a[_]=!0)}):D(),c.push(A.name),T+=E})}),!a.every(w=>w)){const w=s.filter((L,T)=>!a[T]);throw new Error(`Could not find weights in manifest with names: ${w.join(", ")}. 
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sh=P({batchToSpaceND_:y_});function b_(e){let t;return e.rank===0||e.rank===1?t=K(e,[1,1,1,e.size]):e.rank===2?t=K(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=K(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function w_(e,t,n,s,i,o){o==null&&(o=.001);const a=W(e,"x","batchNorm"),c=W(t,"mean","batchNorm"),h=W(n,"variance","batchNorm");let d;i!=null&&(d=W(i,"scale","batchNorm"));let m;s!=null&&(m=W(s,"offset","batchNorm")),k(c.rank===h.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k(m==null||c.rank===m.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k(d==null||c.rank===d.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");const y=b_(a),b=(A,N)=>(N([y,c,h,d]),A.batchNorm(y,Wd(c),Wd(h),Wd(m),Wd(d),o)),w={x:y,scale:d,offset:m,mean:c,variance:h},L={varianceEpsilon:o},T=V.runKernelFunc(b,w,null,gl,L);return K(T,a.shape)}function Wd(e){return 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a=W(e,"x","batchNorm"),c=W(t,"mean","batchNorm"),h=W(n,"variance","batchNorm");let d;i!=null&&(d=W(i,"scale","batchNorm"));let m;return s!=null&&(m=W(s,"offset","batchNorm")),k(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),k(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${c.rank}.`),k(h.rank===3||h.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${h.rank}.`),d!=null&&k(d.rank===3||d.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${d.rank}.`),m!=null&&k(m.rank===3||m.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${m.rank}.`),No(a,c,h,m,d,o)}const WT=P({batchNorm3d_:S_});function I_(e,t,n,s,i,o){const a=W(e,"x","batchNorm"),c=W(t,"mean","batchNorm"),h=W(n,"variance","batchNorm");let d;i!=null&&(d=W(i,"scale","batchNorm"));let m;return s!=null&&(m=W(s,"offset","batchNorm")),k(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),k(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${c.rank}.`),k(h.rank===4||h.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${h.rank}.`),d!=null&&k(d.rank===4||d.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${d.rank}.`),m!=null&&k(m.rank===4||m.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${m.rank}.`),No(a,c,h,m,d,o)}const $T=P({batchNorm4d_:I_});function x_(e,t){let n=W(e,"broadcastTo","x");const s=n.shape;if(t.some(m=>!(m>0)||m%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){const m=n.shape.slice();for(;m.length<t.length;)m.unshift(1);n=K(n,m)}const i=n.shape,o=Array.from(t);for(let m=t.length-1;m>=0;m--)if(i[m]===t[m])o[m]=1;else 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c=W(e,"x","conv2d"),h=W(t,"filter","conv2d");let d=c,m=!1;c.rank===3&&(m=!0,d=K(c,[1,c.shape[0],c.shape[1],c.shape[2]])),k(d.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${d.rank}.`),k(h.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${h.rank}.`),a!=null&&k(Bt(s),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${s}.`);const y=i==="NHWC"?d.shape[3]:d.shape[1];k(y===h.shape[2],()=>`Error in conv2d: depth of input (${y}) must match input depth for filter ${h.shape[2]}.`),k(rn(n,o),()=>`Error in conv2D: Either strides or dilations must be 1. 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BB(e,t,n){k(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),k(n%1===0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);const s=W(e,"a","bandPart");k(s.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${s.rank}.`);const i=s.shape,[o,a]=s.shape.slice(-2);if(!(t<=o))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${o}).`);if(!(n<=a))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${a}).`);t<0&&(t=o),n<0&&(n=a);const c=K(ph(0,o,1,"int32"),[-1,1]),h=ph(0,a,1,"int32"),d=Ce(c,h),m=$s(kr(d,Ne(+t,"int32")),Ji(d,Ne(-n,"int32"))),y=ct([o,a],s.dtype);return K(rs(Ei(K(s,[-1,o,a])).map(b=>_n(m,b,y))),i)}const MB=P({bandPart_:BB});function PB(e){let t;if(Array.isArray(e)){t=!1,k(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");const i=e[0].shape[0];for(let o=1;o<e.length;++o)k(e[o].shape[0]===i,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[o].shape[0]} vs. ${i})`)}else t=!0,e=is(e,e.shape[0],0).map(i=>_r(i,[0]));k(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);const n=[],s=e;for(let i=0;i<e.length;++i)n.push(V.tidy(()=>{let o=s[i];if(i>0)for(let a=0;a<i;++a){const c=X(Ue(X(n[a],o)),n[a]);o=Ce(o,c)}return _e(o,rp(o,"euclidean"))}));return t?rs(n,0):n}const zB=P({gramSchmidt_:PB});function GB(e,t=!1){if(k(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return EA(e,t);{const n=e.shape.slice(0,e.shape.length-2).reduce((h,d)=>h*d),s=Ei(K(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),i=[],o=[];s.forEach(h=>{const[d,m]=EA(h,t);i.push(d),o.push(m)});const a=K(rs(i,0),e.shape),c=K(rs(o,0),e.shape);return[a,c]}}function EA(e,t=!1){return V.tidy(()=>{k(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);const n=e.shape[0],s=e.shape[1];let i=Pd(n),o=Rr(e);const a=Wr([[1]],[1,1]);let c=Rr(a);const h=n>=s?s:n;for(let d=0;d<h;++d){const m=o,y=c,b=i;[c,o,i]=V.tidy(()=>{const w=nt(o,[d,d],[n-d,1]),L=rp(w),T=nt(o,[d,d],[1,1]),A=_n(Ss(T,0),Wr([[-1]]),Wr([[1]])),N=Ce(T,X(A,L)),E=_e(w,N);E.shape[0]===1?c=Rr(a):c=Pt([a,nt(E,[1,0],[E.shape[0]-1,E.shape[1]])],0);const D=zt(_e(at(A,N),L)),F=nt(o,[d,0],[n-d,s]),_=X(D,c),B=Me(c);if(d===0)o=Ce(F,at(_,at(B,F)));else{const q=Ce(F,at(_,at(B,F)));o=Pt([nt(o,[0,0],[d,s]),q],0)}const $=Me(_),H=nt(i,[0,d],[n,i.shape[1]-d]);if(d===0)i=Ce(H,at(at(H,c),$));else{const q=Ce(H,at(at(H,c),$));i=Pt([nt(i,[0,0],[n,d]),q],1)}return[c,o,i]}),He([m,y,b])}return!t&&n>s&&(i=nt(i,[0,0],[n,s]),o=nt(o,[0,0],[s,s])),[i,o]})}const VB=P({qr_:GB});(function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(r.Reduction||(r.Reduction={}));function YB(e,t,n=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const s=W(e,"losses","computeWeightedLoss");let i=null;t!=null&&(i=W(t,"weights","computeWeightedLoss"));const o=i==null?s:X(s,i);if(n===r.Reduction.NONE)return o;if(n===r.Reduction.SUM)return Ue(o);if(n===r.Reduction.MEAN){if(i==null)return Gt(o);{const a=s.size/i.size,c=_e(Ue(o),Ue(i));return a>1?_e(c,Ne(a)):c}}if(n===r.Reduction.SUM_BY_NONZERO_WEIGHTS){if(i==null)return _e(Ue(o),Ne(s.size));{const a=X(i,ni(s.shape)),c=ve(Ue(Fr(a,Ne(0))),"float32");return _e(Ue(o),c)}}throw Error(`Unknown reduction: ${n}`)}const Zi=P({computeWeightedLoss_:YB});function HB(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=W(e,"labels","absoluteDifference"),o=W(t,"predictions","absoluteDifference");let a=null;n!=null&&(a=W(n,"weights","absoluteDifference")),pt(i.shape,o.shape,"Error in absoluteDifference: ");const c=nn(Ce(i,o));return Zi(c,a,s)}const qB=P({absoluteDifference_:HB});function jB(e,t,n,s,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","cosineDistance"),a=W(t,"predictions","cosineDistance");let c=null;s!=null&&(c=W(s,"weights","cosineDistance")),pt(o.shape,a.shape,"Error in cosineDistance: ");const h=Ne(1),d=Ce(h,Ue(X(o,a),n,!0));return Zi(d,c,i)}const KB=P({cosineDistance_:jB});function XB(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let i=W(e,"labels","hingeLoss");const o=W(t,"predictions","hingeLoss");let a=null;n!=null&&(a=W(n,"weights","hingeLoss")),pt(i.shape,o.shape,"Error in hingeLoss: ");const c=Ne(1);i=Ce(X(Ne(2),i),c);const h=Oi(Ce(c,X(i,o)));return Zi(h,a,s)}const JB=P({hingeLoss_:XB});function ZB(e,t,n,s=1,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","huberLoss"),a=W(t,"predictions","huberLoss");let c=null;n!=null&&(c=W(n,"weights","huberLoss")),pt(o.shape,a.shape,"Error in huberLoss: ");const h=Ne(s),d=nn(Ce(a,o)),m=Ro(d,h),y=Ce(d,m),b=be(X(Ne(.5),wt(m)),X(h,y));return Zi(b,c,i)}const QB=P({huberLoss_:ZB});function eM(e,t,n,s=1e-7,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","logLoss"),a=W(t,"predictions","logLoss");let c=null;n!=null&&(c=W(n,"weights","logLoss")),pt(o.shape,a.shape,"Error in logLoss: ");const h=Ne(1),d=Ne(s),m=zt(X(o,ns(be(a,d)))),y=X(Ce(h,o),ns(be(Ce(h,a),d))),b=Ce(m,y);return Zi(b,c,i)}const tM=P({logLoss_:eM});function nM(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=W(e,"labels","meanSquaredError"),o=W(t,"predictions","meanSquaredError");let a=null;n!=null&&(a=W(n,"weights","meanSquaredError")),pt(i.shape,o.shape,"Error in meanSquaredError: ");const c=yh(i,o);return Zi(c,a,s)}const sM=P({meanSquaredError_:nM});function iM(e,t){const n=W(e,"labels","sigmoidCrossEntropyWithLogits"),s=W(t,"logits","sigmoidCrossEntropyWithLogits");pt(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");const i=Oi(s),o=X(s,n),a=Gd(Ls(zt(nn(s))));return be(Ce(i,o),a)}function rM(e,t,n,s=0,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=W(e,"multiClassLabels","sigmoidCrossEntropy");const a=W(t,"logits","sigmoidCrossEntropy");let c=null;if(n!=null&&(c=W(n,"weights","sigmoidCrossEntropy")),pt(o.shape,a.shape,"Error in sigmoidCrossEntropy: "),s>0){const d=Ne(s),m=Ne(1),y=Ne(.5);o=be(X(o,Ce(m,d)),X(y,d))}const h=iM(o,a);return Zi(h,c,i)}const oM=P({sigmoidCrossEntropy_:rM});function aM(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);const s=Ci((i,o,a)=>{const c=!0,h=_b(o,[n],c),d=Ce(ve(o,"float32"),h);a([i,d]);const m=zt(X(d,i)),y=Ue(m,[n]),b=(w,L)=>{const[T,A]=L,N=On(w.shape,[n]);return[X(K(w,N),Ce(ve(T,"float32"),Ls(A))),X(K(w,N),Ce(Ls(A),ve(T,"float32")))]};return{value:y,gradFunc:b}});return s(e,t)}function cM(e,t,n,s=0,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=W(e,"onehotLabels","softmaxCrossEntropy");const a=W(t,"logits","softmaxCrossEntropy");let c=null;if(n!=null&&(c=W(n,"weights","softmaxCrossEntropy")),pt(o.shape,a.shape,"Error in softmaxCrossEntropy: "),s>0){const d=Ne(s),m=Ne(1),y=Ne(o.shape[1]);o=be(X(o,Ce(m,d)),_e(d,y))}const h=aM(o,a);return Zi(h,c,i)}const lM=P({softmaxCrossEntropy_:cM});const hM={fft:fh,ifft:Ma,rfft:gh,irfft:np},uM={hammingWindow:cB,hannWindow:vA,frame:NA,stft:dB},$r={flipLeftRight:gB,resizeNearestNeighbor:OA,resizeBilinear:RA,rotateWithOffset:bB,cropAndResize:mB,nonMaxSuppression:LB,nonMaxSuppressionAsync:CB,nonMaxSuppressionWithScore:OB,nonMaxSuppressionWithScoreAsync:DB,nonMaxSuppressionPadded:FB,nonMaxSuppressionPaddedAsync:WB},DA={bandPart:MB,gramSchmidt:zB,qr:VB},dM={absoluteDifference:qB,computeWeightedLoss:Zi,cosineDistance:KB,hingeLoss:JB,huberLoss:QB,logLoss:tM,meanSquaredError:sM,sigmoidCrossEntropy:oM,softmaxCrossEntropy:lM};class Qi extends xo{minimize(e,t=!1,n){const{value:s,grads:i}=this.computeGradients(e,n);if(n!=null){const o=n.map(a=>({name:a.name,tensor:i[a.name]}));this.applyGradients(o)}else this.applyGradients(i);return He(i),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Fb(e,t)}dispose(){this.iterations_!=null&&He(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ne(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(Qi,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});class wh extends Qi{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n],o=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:ee(()=>Qe(i).variable(o))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:ee(()=>Qe(i).variable(o))});const a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;const c=this.accumulatedGrads[s].variable,h=this.accumulatedUpdates[s].variable;ee(()=>{const d=be(X(c,this.rho),X(wt(a),1-this.rho)),m=X(_e(Ln(be(h,this.epsilon)),Ln(be(c,this.epsilon))),a),y=be(X(h,this.rho),X(wt(m),1-this.rho));c.assign(d),h.assign(y);const b=be(X(m,-this.learningRate),i);i.assign(b)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(He(this.accumulatedGrads.map(e=>e.variable)),He(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}wh.className="Adadelta",me(wh);class Lh extends Qi{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n];if(this.accumulatedGrads[s]==null){const c=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:ee(()=>oh(i.shape,this.initialAccumulatorValue).variable(c))}}const o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;const a=this.accumulatedGrads[s].variable;ee(()=>{const c=be(a,wt(o));a.assign(c);const h=be(X(_e(o,Ln(be(c,V.backend.epsilon()))),-this.learningRate),i);i.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&He(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}Lh.className="Adagrad",me(Lh);class Sh extends Qi{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],ee(()=>{this.accBeta1=Ne(t).variable(),this.accBeta2=Ne(n).variable()}),s==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);ee(()=>{const n=Ce(1,this.accBeta1),s=Ce(1,this.accBeta2);t.forEach((i,o)=>{const a=V.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:ee(()=>Qe(a).variable(c))}),this.accumulatedSecondMoment[o]==null&&(this.accumulatedSecondMoment[o]={originalName:`${i}/v`,variable:ee(()=>Qe(a).variable(c))});const h=Array.isArray(e)?e[o].tensor:e[i];if(h==null)return;const d=this.accumulatedFirstMoment[o].variable,m=this.accumulatedSecondMoment[o].variable,y=be(X(d,this.beta1),X(h,1-this.beta1)),b=be(X(m,this.beta2),X(wt(h),1-this.beta2)),w=_e(y,n),L=_e(b,s);d.assign(y),m.assign(b);const T=be(X(_e(w,be(Ln(L),this.epsilon)),-this.learningRate),a);a.assign(T)}),this.accBeta1.assign(X(this.accBeta1,this.beta1)),this.accBeta2.assign(X(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&He(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&He(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),ee(()=>{this.accBeta1.assign(si(this.beta1,this.iterations_+1)),this.accBeta2.assign(si(this.beta2,this.iterations_+1))});const t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}}Sh.className="Adam",me(Sh);class Ih extends Qi{constructor(e,t,n,s=null,i=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=i,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],ee(()=>{this.iteration=Ne(0).variable(),this.accBeta1=Ne(t).variable()}),s==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);ee(()=>{const n=Ce(1,this.accBeta1),s=_e(-this.learningRate,be(X(this.iteration,this.decay),1));t.forEach((i,o)=>{const a=V.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:Qe(a).variable(c)}),this.accumulatedWeightedInfNorm[o]==null&&(this.accumulatedWeightedInfNorm[o]={originalName:`${i}/v`,variable:Qe(a).variable(c)});const h=Array.isArray(e)?e[o].tensor:e[i];if(h==null)return;const d=this.accumulatedFirstMoment[o].variable,m=this.accumulatedWeightedInfNorm[o].variable,y=be(X(d,this.beta1),X(h,1-this.beta1)),b=X(m,this.beta2),w=nn(h),L=Ws(b,w);d.assign(y),m.assign(L);const T=be(X(_e(s,n),_e(y,be(L,this.epsilon))),a);a.assign(T)}),this.iteration.assign(be(this.iteration,1)),this.accBeta1.assign(X(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&He(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&He(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}}Ih.className="Adamax",me(Ih);class Va extends Qi{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;const o=V.registeredVariables[n];ee(()=>{const a=be(X(this.c,i),o);o.assign(a)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Cn(Ne(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}Va.className="SGD",me(Va);class xh extends Va{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Ne(this.momentum)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n];if(this.accumulations[s]==null){const c=!1;this.accumulations[s]={originalName:`${n}/momentum`,variable:ee(()=>Qe(i).variable(c))}}const o=this.accumulations[s].variable,a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;ee(()=>{let c;const h=be(X(this.m,o),a);this.useNesterov?c=be(X(this.c,be(a,X(h,this.m))),i):c=be(X(this.c,h),i),o.assign(h),i.assign(c)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&He(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}xh.className="Momentum",me(xh);class Th extends Qi{constructor(e,t=.9,n=0,s=null,i=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=i,s==null&&(this.epsilon=V.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n],o=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:ee(()=>Qe(i).variable(o))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:ee(()=>Qe(i).variable(o))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:ee(()=>Qe(i).variable(o))});const a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;const 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this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}}Th.className="RMSProp",me(Th);class _o{static sgd(e){return new Va(e)}static momentum(e,t,n=!1){return new xh(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,i=!1){return new Th(e,t,n,s,i)}static 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i=s,o=!1;s.rank===3&&(o=!0,i=K(s,[1,s.shape[0],s.shape[1],s.shape[2]]));const[a,c]=t,h=(b,w)=>(w([i]),b.resizeBilinear(i,a,c,n)),d={images:i},m={alignCorners:n,size:t},y=V.runKernelFunc(h,d,null,dy,m);return o?K(y,[y.shape[1],y.shape[2],y.shape[3]]):y}const RA=P({resizeBilinear_:$B});function UB(e,t,n=!1){const s=W(e,"images","resizeNearestNeighbor");k(s.rank===3||s.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${s.rank}.`),k(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),k(s.dtype==="float32"||s.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype");let i=s,o=!1;s.rank===3&&(o=!0,i=K(s,[1,s.shape[0],s.shape[1],s.shape[2]]));const[a,c]=t,h={images:i},d={alignCorners:n,size:t},m=(b,w)=>(w([i]),b.resizeNearestNeighbor(i,a,c,n)),y=V.runKernelFunc(m,h,null,uy,d);return o?K(y,[y.shape[1],y.shape[2],y.shape[3]]):y}const OA=P({resizeNearestNeighbor_:UB});function BB(e,t,n){k(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),k(n%1===0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);const s=W(e,"a","bandPart");k(s.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${s.rank}.`);const i=s.shape,[o,a]=s.shape.slice(-2);if(!(t<=o))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${o}).`);if(!(n<=a))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${a}).`);t<0&&(t=o),n<0&&(n=a);const c=K(ph(0,o,1,"int32"),[-1,1]),h=ph(0,a,1,"int32"),d=Ce(c,h),m=$s(kr(d,Ne(+t,"int32")),Ji(d,Ne(-n,"int32"))),y=ct([o,a],s.dtype);return K(rs(Ei(K(s,[-1,o,a])).map(b=>_n(m,b,y))),i)}const MB=P({bandPart_:BB});function PB(e){let t;if(Array.isArray(e)){t=!1,k(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");const i=e[0].shape[0];for(let o=1;o<e.length;++o)k(e[o].shape[0]===i,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[o].shape[0]} vs. ${i})`)}else t=!0,e=is(e,e.shape[0],0).map(i=>_r(i,[0]));k(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);const n=[],s=e;for(let i=0;i<e.length;++i)n.push(V.tidy(()=>{let o=s[i];if(i>0)for(let a=0;a<i;++a){const c=X(Ue(X(n[a],o)),n[a]);o=Ce(o,c)}return _e(o,rp(o,"euclidean"))}));return t?rs(n,0):n}const zB=P({gramSchmidt_:PB});function GB(e,t=!1){if(k(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return EA(e,t);{const n=e.shape.slice(0,e.shape.length-2).reduce((h,d)=>h*d),s=Ei(K(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),i=[],o=[];s.forEach(h=>{const[d,m]=EA(h,t);i.push(d),o.push(m)});const a=K(rs(i,0),e.shape),c=K(rs(o,0),e.shape);return[a,c]}}function EA(e,t=!1){return V.tidy(()=>{k(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);const n=e.shape[0],s=e.shape[1];let i=Pd(n),o=Rr(e);const a=Wr([[1]],[1,1]);let c=Rr(a);const h=n>=s?s:n;for(let d=0;d<h;++d){const m=o,y=c,b=i;[c,o,i]=V.tidy(()=>{const w=nt(o,[d,d],[n-d,1]),L=rp(w),T=nt(o,[d,d],[1,1]),A=_n(Ss(T,0),Wr([[-1]]),Wr([[1]])),N=Ce(T,X(A,L)),E=_e(w,N);E.shape[0]===1?c=Rr(a):c=Pt([a,nt(E,[1,0],[E.shape[0]-1,E.shape[1]])],0);const D=zt(_e(at(A,N),L)),F=nt(o,[d,0],[n-d,s]),_=X(D,c),B=Me(c);if(d===0)o=Ce(F,at(_,at(B,F)));else{const q=Ce(F,at(_,at(B,F)));o=Pt([nt(o,[0,0],[d,s]),q],0)}const $=Me(_),H=nt(i,[0,d],[n,i.shape[1]-d]);if(d===0)i=Ce(H,at(at(H,c),$));else{const q=Ce(H,at(at(H,c),$));i=Pt([nt(i,[0,0],[n,d]),q],1)}return[c,o,i]}),He([m,y,b])}return!t&&n>s&&(i=nt(i,[0,0],[n,s]),o=nt(o,[0,0],[s,s])),[i,o]})}const VB=P({qr_:GB});(function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(r.Reduction||(r.Reduction={}));function YB(e,t,n=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const s=W(e,"losses","computeWeightedLoss");let i=null;t!=null&&(i=W(t,"weights","computeWeightedLoss"));const o=i==null?s:X(s,i);if(n===r.Reduction.NONE)return o;if(n===r.Reduction.SUM)return Ue(o);if(n===r.Reduction.MEAN){if(i==null)return Gt(o);{const a=s.size/i.size,c=_e(Ue(o),Ue(i));return a>1?_e(c,Ne(a)):c}}if(n===r.Reduction.SUM_BY_NONZERO_WEIGHTS){if(i==null)return _e(Ue(o),Ne(s.size));{const a=X(i,ni(s.shape)),c=ve(Ue(Fr(a,Ne(0))),"float32");return _e(Ue(o),c)}}throw Error(`Unknown reduction: ${n}`)}const Zi=P({computeWeightedLoss_:YB});function HB(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=W(e,"labels","absoluteDifference"),o=W(t,"predictions","absoluteDifference");let a=null;n!=null&&(a=W(n,"weights","absoluteDifference")),pt(i.shape,o.shape,"Error in absoluteDifference: ");const c=sn(Ce(i,o));return Zi(c,a,s)}const qB=P({absoluteDifference_:HB});function jB(e,t,n,s,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","cosineDistance"),a=W(t,"predictions","cosineDistance");let c=null;s!=null&&(c=W(s,"weights","cosineDistance")),pt(o.shape,a.shape,"Error in cosineDistance: ");const h=Ne(1),d=Ce(h,Ue(X(o,a),n,!0));return Zi(d,c,i)}const KB=P({cosineDistance_:jB});function XB(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let i=W(e,"labels","hingeLoss");const o=W(t,"predictions","hingeLoss");let a=null;n!=null&&(a=W(n,"weights","hingeLoss")),pt(i.shape,o.shape,"Error in hingeLoss: ");const c=Ne(1);i=Ce(X(Ne(2),i),c);const h=Oi(Ce(c,X(i,o)));return Zi(h,a,s)}const JB=P({hingeLoss_:XB});function ZB(e,t,n,s=1,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","huberLoss"),a=W(t,"predictions","huberLoss");let c=null;n!=null&&(c=W(n,"weights","huberLoss")),pt(o.shape,a.shape,"Error in huberLoss: ");const h=Ne(s),d=sn(Ce(a,o)),m=Eo(d,h),y=Ce(d,m),b=be(X(Ne(.5),wt(m)),X(h,y));return Zi(b,c,i)}const QB=P({huberLoss_:ZB});function eM(e,t,n,s=1e-7,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const o=W(e,"labels","logLoss"),a=W(t,"predictions","logLoss");let c=null;n!=null&&(c=W(n,"weights","logLoss")),pt(o.shape,a.shape,"Error in logLoss: ");const h=Ne(1),d=Ne(s),m=zt(X(o,ns(be(a,d)))),y=X(Ce(h,o),ns(be(Ce(h,a),d))),b=Ce(m,y);return Zi(b,c,i)}const tM=P({logLoss_:eM});function nM(e,t,n,s=r.Reduction.SUM_BY_NONZERO_WEIGHTS){const i=W(e,"labels","meanSquaredError"),o=W(t,"predictions","meanSquaredError");let a=null;n!=null&&(a=W(n,"weights","meanSquaredError")),pt(i.shape,o.shape,"Error in meanSquaredError: ");const c=yh(i,o);return Zi(c,a,s)}const sM=P({meanSquaredError_:nM});function iM(e,t){const n=W(e,"labels","sigmoidCrossEntropyWithLogits"),s=W(t,"logits","sigmoidCrossEntropyWithLogits");pt(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");const i=Oi(s),o=X(s,n),a=Gd(Ls(zt(sn(s))));return be(Ce(i,o),a)}function rM(e,t,n,s=0,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=W(e,"multiClassLabels","sigmoidCrossEntropy");const a=W(t,"logits","sigmoidCrossEntropy");let c=null;if(n!=null&&(c=W(n,"weights","sigmoidCrossEntropy")),pt(o.shape,a.shape,"Error in sigmoidCrossEntropy: "),s>0){const d=Ne(s),m=Ne(1),y=Ne(.5);o=be(X(o,Ce(m,d)),X(y,d))}const h=iM(o,a);return Zi(h,c,i)}const oM=P({sigmoidCrossEntropy_:rM});function aM(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);const s=Ci((i,o,a)=>{const c=!0,h=_b(o,[n],c),d=Ce(ve(o,"float32"),h);a([i,d]);const m=zt(X(d,i)),y=Ue(m,[n]),b=(w,L)=>{const[T,A]=L,N=On(w.shape,[n]);return[X(K(w,N),Ce(ve(T,"float32"),Ls(A))),X(K(w,N),Ce(Ls(A),ve(T,"float32")))]};return{value:y,gradFunc:b}});return s(e,t)}function cM(e,t,n,s=0,i=r.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=W(e,"onehotLabels","softmaxCrossEntropy");const a=W(t,"logits","softmaxCrossEntropy");let c=null;if(n!=null&&(c=W(n,"weights","softmaxCrossEntropy")),pt(o.shape,a.shape,"Error in softmaxCrossEntropy: "),s>0){const d=Ne(s),m=Ne(1),y=Ne(o.shape[1]);o=be(X(o,Ce(m,d)),_e(d,y))}const h=aM(o,a);return Zi(h,c,i)}const lM=P({softmaxCrossEntropy_:cM});const hM={fft:fh,ifft:za,rfft:gh,irfft:np},uM={hammingWindow:cB,hannWindow:vA,frame:NA,stft:dB},$r={flipLeftRight:gB,resizeNearestNeighbor:OA,resizeBilinear:RA,rotateWithOffset:bB,cropAndResize:mB,nonMaxSuppression:LB,nonMaxSuppressionAsync:CB,nonMaxSuppressionWithScore:OB,nonMaxSuppressionWithScoreAsync:DB,nonMaxSuppressionPadded:FB,nonMaxSuppressionPaddedAsync:WB},DA={bandPart:MB,gramSchmidt:zB,qr:VB},dM={absoluteDifference:qB,computeWeightedLoss:Zi,cosineDistance:KB,hingeLoss:JB,huberLoss:QB,logLoss:tM,meanSquaredError:sM,sigmoidCrossEntropy:oM,softmaxCrossEntropy:lM};class Qi extends Ao{minimize(e,t=!1,n){const{value:s,grads:i}=this.computeGradients(e,n);if(n!=null){const o=n.map(a=>({name:a.name,tensor:i[a.name]}));this.applyGradients(o)}else this.applyGradients(i);return He(i),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Fb(e,t)}dispose(){this.iterations_!=null&&He(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ne(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(Qi,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});class wh extends Qi{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n],o=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:ee(()=>Qe(i).variable(o))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:ee(()=>Qe(i).variable(o))});const a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;const c=this.accumulatedGrads[s].variable,h=this.accumulatedUpdates[s].variable;ee(()=>{const d=be(X(c,this.rho),X(wt(a),1-this.rho)),m=X(_e(Ln(be(h,this.epsilon)),Ln(be(c,this.epsilon))),a),y=be(X(h,this.rho),X(wt(m),1-this.rho));c.assign(d),h.assign(y);const b=be(X(m,-this.learningRate),i);i.assign(b)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(He(this.accumulatedGrads.map(e=>e.variable)),He(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}wh.className="Adadelta",me(wh);class Lh extends Qi{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n];if(this.accumulatedGrads[s]==null){const c=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:ee(()=>oh(i.shape,this.initialAccumulatorValue).variable(c))}}const o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;const a=this.accumulatedGrads[s].variable;ee(()=>{const c=be(a,wt(o));a.assign(c);const h=be(X(_e(o,Ln(be(c,V.backend.epsilon()))),-this.learningRate),i);i.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&He(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}Lh.className="Adagrad",me(Lh);class Sh extends Qi{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],ee(()=>{this.accBeta1=Ne(t).variable(),this.accBeta2=Ne(n).variable()}),s==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);ee(()=>{const n=Ce(1,this.accBeta1),s=Ce(1,this.accBeta2);t.forEach((i,o)=>{const a=V.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:ee(()=>Qe(a).variable(c))}),this.accumulatedSecondMoment[o]==null&&(this.accumulatedSecondMoment[o]={originalName:`${i}/v`,variable:ee(()=>Qe(a).variable(c))});const h=Array.isArray(e)?e[o].tensor:e[i];if(h==null)return;const d=this.accumulatedFirstMoment[o].variable,m=this.accumulatedSecondMoment[o].variable,y=be(X(d,this.beta1),X(h,1-this.beta1)),b=be(X(m,this.beta2),X(wt(h),1-this.beta2)),w=_e(y,n),L=_e(b,s);d.assign(y),m.assign(b);const T=be(X(_e(w,be(Ln(L),this.epsilon)),-this.learningRate),a);a.assign(T)}),this.accBeta1.assign(X(this.accBeta1,this.beta1)),this.accBeta2.assign(X(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&He(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&He(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),ee(()=>{this.accBeta1.assign(si(this.beta1,this.iterations_+1)),this.accBeta2.assign(si(this.beta2,this.iterations_+1))});const t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}}Sh.className="Adam",me(Sh);class Ih extends Qi{constructor(e,t,n,s=null,i=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=i,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],ee(()=>{this.iteration=Ne(0).variable(),this.accBeta1=Ne(t).variable()}),s==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);ee(()=>{const n=Ce(1,this.accBeta1),s=_e(-this.learningRate,be(X(this.iteration,this.decay),1));t.forEach((i,o)=>{const a=V.registeredVariables[i],c=!1;this.accumulatedFirstMoment[o]==null&&(this.accumulatedFirstMoment[o]={originalName:`${i}/m`,variable:Qe(a).variable(c)}),this.accumulatedWeightedInfNorm[o]==null&&(this.accumulatedWeightedInfNorm[o]={originalName:`${i}/v`,variable:Qe(a).variable(c)});const h=Array.isArray(e)?e[o].tensor:e[i];if(h==null)return;const d=this.accumulatedFirstMoment[o].variable,m=this.accumulatedWeightedInfNorm[o].variable,y=be(X(d,this.beta1),X(h,1-this.beta1)),b=X(m,this.beta2),w=sn(h),L=Ws(b,w);d.assign(y),m.assign(L);const T=be(X(_e(s,n),_e(y,be(L,this.epsilon))),a);a.assign(T)}),this.iteration.assign(be(this.iteration,1)),this.accBeta1.assign(X(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&He(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&He(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}}Ih.className="Adamax",me(Ih);class Ha extends Qi{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;const o=V.registeredVariables[n];ee(()=>{const a=be(X(this.c,i),o);o.assign(a)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Cn(Ne(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}Ha.className="SGD",me(Ha);class xh extends Ha{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Ne(this.momentum)}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n];if(this.accumulations[s]==null){const c=!1;this.accumulations[s]={originalName:`${n}/momentum`,variable:ee(()=>Qe(i).variable(c))}}const o=this.accumulations[s].variable,a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;ee(()=>{let c;const h=be(X(this.m,o),a);this.useNesterov?c=be(X(this.c,be(a,X(h,this.m))),i):c=be(X(this.c,h),i),o.assign(h),i.assign(c)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&He(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}xh.className="Momentum",me(xh);class Th extends Qi{constructor(e,t=.9,n=0,s=null,i=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=i,s==null&&(this.epsilon=V.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){const t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);t.forEach((n,s)=>{const i=V.registeredVariables[n],o=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:ee(()=>Qe(i).variable(o))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:ee(()=>Qe(i).variable(o))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:ee(()=>Qe(i).variable(o))});const a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;const c=this.accumulatedMeanSquares[s].variable,h=this.accumulatedMoments[s].variable;ee(()=>{const d=be(X(c,this.decay),X(wt(a),1-this.decay));if(this.centered){const m=this.accumulatedMeanGrads[s].variable,y=be(X(m,this.decay),X(a,1-this.decay)),b=_e(X(a,this.learningRate),Ln(Ce(d,be(wt(y),this.epsilon)))),w=be(X(h,this.momentum),b);c.assign(d),m.assign(y),h.assign(w);const L=Ce(i,w);i.assign(L)}else{const m=be(X(c,this.decay),X(wt(a),1-this.decay)),y=be(X(h,this.momentum),_e(X(a,this.learningRate),Ln(be(m,this.epsilon))));c.assign(m),h.assign(y);const b=Ce(i,y);i.assign(b)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&He(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&He(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&He(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}}Th.className="RMSProp",me(Th);class $o{static sgd(e){return new Ha(e)}static momentum(e,t,n=!1){return new xh(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,i=!1){return new Th(e,t,n,s,i)}static 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NM={kernelName:Zs,inputsToSave:["x"],gradFunc:(e,t)=>{const[n]=t;return{x:()=>_e(e,Ce(Ne(1),wt(ve(n,"float32"))))}}};function CM(e,t,n,s,i=[1,1,1],o,a){const c=W(e,"dy","avgPool3dBackprop"),h=W(t,"input","avgPool3dBackprop");let d=c,m=h,y=!1;h.rank===4&&(y=!0,d=K(c,[1,c.shape[0],c.shape[1],c.shape[2],c.shape[3]]),m=K(h,[1,h.shape[0],h.shape[1],h.shape[2],h.shape[3]])),k(d.rank===5,()=>`Error in avgPool3dBackprop: dy must be rank 5 but got rank ${d.rank}.`),k(m.rank===5,()=>`Error in avgPool3dBackprop: input must be rank 5 but got rank ${m.rank}.`),k(rn(s,i),()=>`Error in avgPool3dBackprop: Either strides or dilations must be 1. 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Xn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Xn.className}}Tp.className="GlorotNormal",me(Tp);class Ap extends Xn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Xn.className}}Ap.className="HeNormal",me(Ap);class vp extends Xn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Xn.className}}vp.className="HeUniform",me(vp);class Np extends Xn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Xn.className}}Np.className="LeCunNormal",me(Np);class Cp extends Xn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Xn.className}}Cp.className="LeCunNormal",me(Cp);class _w extends Bs{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new ze("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return ee(()=>{if(e.length<2)throw new ze("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);const n=e[0]>e[1]?[e[1],e[0]]:e,s=Sp(n,0,1,"float32");let i=DA.gramSchmidt(s);return e[0]>e[1]&&(i=i.transpose()),X(this.gain,i)})}getConfig(){return{gain:this.gain,seed:this.seed}}}_w.className="Orthogonal",me(_w);const lv={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function hv(e,t={}){return Ch(e,Fs.getMap().classNameMap,t,"initializer")}function Yt(e){return yw(e)}function Ft(e){if(typeof e=="string"){const t=e in lv?lv[e]:e;if(t==="GlorotNormal")return new Tp;if(t==="GlorotUniform")return new xp;if(t==="HeNormal")return new Ap;if(t==="HeUniform")return new vp;if(t==="LeCunNormal")return new Np;if(t==="LeCunUniform")return new Cp;{const n={};return n.className=t,n.config={},hv(n)}}else return e instanceof Bs?e:hv(e)}function Pz(){return new Rw}function zz(){return new Ip}function Gz(e){return new Ow(e)}function Vz(e){return new Ew(e)}function Yz(e){return new Dw(e)}function Hz(e){return new kw(e)}function qz(e){return new Fw(e)}function jz(e){return new Xn(e)}function Kz(e){return new xp(e)}function Xz(e){return new Tp(e)}function Jz(e){return new Ap(e)}function Zz(e){return new vp(e)}function Qz(e){return new Np(e)}function e3(e){return new Cp(e)}function t3(e){return new _w(e)}var n3=Object.freeze({__proto__:null,zeros:Pz,ones:zz,constant:Gz,randomUniform:Vz,randomNormal:Yz,truncatedNormal:Hz,identity:qz,varianceScaling:jz,glorotUniform:Kz,glorotNormal:Xz,heNormal:Jz,heUniform:Zz,leCunNormal:Qz,leCunUniform:e3,orthogonal:t3});let s3=0;function uv(){return s3++}const Rp={};function Op(e=""){return e in Rp||(Rp[e]=0),Rp[e]+=1,e+Rp[e].toString()}function Ww(e){return Array.isArray(e)&&Array.isArray(e[0])}function Ep(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function Ke(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new j(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function St(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new j(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function Dp(e){let t=0;for(const n of e)n.shape.length===0?t+=1:t+=n.shape.reduce((s,i)=>s*i);return t}const dv="Variable";class ai{constructor(e,t="float32",n=dv,s=!0,i=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=uv(),n=n==null?dv:n,this.originalName=nv(n),this.name=sv(this.originalName),this.trainable_=s,this.constraint=i,this.val=nA(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),i3(this.val,e),this.val.id!==e.id&&(this.val.assign(e),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}}function i3(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function aQ(e,t,n,s){return new ai(e,t,n,!0,s)}function cQ(e,t,n){return new ai(ct(e),t,n)}function lQ(e,t,n){return new ai(Qe(e),t,n)}function hQ(e,t,n){const s=ni(e);return new ai(s,t,n)}function uQ(e,t,n){const s=En(e);return new ai(s,t,n)}function dQ(e,t,n){return new ai(Pd(e),t,n)}function pQ(e,t,n,s,i,o="randomUniform"){return new ai(ko(e,t,n,s),s,o)}function mQ(e,t=0,n=1,s,i,o="truncatedNormal"){if(s=s||"float32",s!=="float32"&&s!=="int32")throw new ze(`randomNormal does not support dType ${s}.`);return new ai(bh(e,t,n,s,i),s,o)}function fQ(e,t=0,n=1,s,i,o="randomNormal"){if(s=s||"float32",s!=="float32"&&s!=="int32")throw new ze(`randomNormalVariable does not support dType ${s}.`);return new ai(Bb(e,t,n,s,i),s,o)}function gQ(e,t){return e.write(t)}function yQ(e,t){return e.write(be(e.read(),t))}function bQ(e,t){return e.write(Ce(e.read(),t))}function $w(e){return e.map(t=>t.read())}function Uw(e){e.forEach(t=>{const n=t[0];n.write(t[1])})}function wQ(e,t){const n=t.map(i=>i.read()),s=Fb(e,n);return t.map(i=>s.grads[i.name])}class mn{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}}class ci{constructor(e,t,n,s,i,o,a){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=i,this.outputTensorIndex=a,this.id=uv(),o!=null&&(this.originalName=nv(o),this.name=sv(this.originalName)),this.rank=t.length}}let r3=0;class kp{constructor(e,t){this.callArgs=t,this.id=r3++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(const n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){const e=[];for(const t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}}let o3=0;class lt extends xo{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=o3++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){const n=this.getClassName();t=nr(n)+"_"+Op(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let i=null;e.batchSize!=null&&(i=e.batchSize),n=[i].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new ri(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new j(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Kn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Kn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new tr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new tr(`Layer ${this.name} is not connected, no input to return.`);return Kn(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new tr(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new tr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Kn(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=Rt(e),this.inputSpec==null||this.inputSpec.length===0)return;const t=Rt(this.inputSpec);if(e.length!==t.length)throw new j(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){const s=e[n],i=t[n];if(i==null)continue;const o=s.rank;if(i.ndim!=null&&o!==i.ndim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${i.ndim}, found ndim=${o}`);if(i.maxNDim!=null&&o>i.maxNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${i.maxNDim}, found ndim=${o}`);if(i.minNDim!=null&&o<i.minNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${i.minNDim}, found ndim=${o}.`);if(i.dtype!=null&&s.dtype!==i.dtype)throw new j(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${i.dtype}, found dtype=${s.dtype}.`);if(i.axes){const a=s.shape;for(const c in i.axes){const h=Number(c),d=i.axes[c],m=h>=0?a[h]:a[a.length+h];if(d!=null&&[d,null].indexOf(m)===-1)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected axis ${h} of input shape to have value ${d} but got shape ${a}.`)}}if(i.shape!=null)for(let a=0;a<i.shape.length;++a){const c=i.shape[a],h=s.shape[a];if(c!=null&&h!=null&&c!==h)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected shape=${i.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();const n=Rt(e);let s=!0;for(const o of n)if(!(o instanceof ci)){s=!1;break}let i=!0;for(const o of n)if(o instanceof ci){i=!1;break}if(s===i)throw new j("Arguments to apply() must be all SymbolicTensors or all Tensors");return Bo(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);const o=[];for(const a of Rt(e))o.push(a.shape);this.build(Kn(o)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&i&&(this._refCount=1)}if(this.assertInputCompatibility(e),i){let o=this.call(e,t);const a=Rt(o),c=[];for(let h of a)n.indexOf(h)!==-1&&(h=h.clone()),c.push(h);if(o=Kn(c),this.activityRegularizer!=null)throw new ze("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}else{const o=a3(e),a=this.computeOutputShape(o);let c;const h=c3(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?o[0]:o),a!=null&&a.length>0&&Array.isArray(a[0])?c=a.map((d,m)=>new ci(h,d,this,Rt(e),t,this.name,m)):c=new ci(h,a,this,Rt(e),t,this.name),this.addInboundNode(e,c,null,null,o,a,t),this._refCount++,this.activityRegularizer!=null)throw new ze("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return c}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape==null)return;if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new tr(`The layer ${this.name} has never been called and thus has no defined output shape.`);const e=[];for(const t of this.inboundNodes){const n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){const t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new tr(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new ri(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Dp(this.weights)}build(e){this.built=!0}getWeights(e=!1){return $w(e?this.trainableWeights:this.weights)}setWeights(e){ee(()=>{const t=this.weights;if(t.length!==e.length)throw new j(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;const n=[],s=$w(t);for(let i=0;i<s.length;++i){const o=s[i],a=t[i],c=e[i];if(!ot(o.shape,c.shape))throw new j(`Layer weight shape ${o.shape} not compatible with provided weight shape ${c.shape}`);n.push([a,c])}Uw(n)})}addWeight(e,t,n,s,i,o,a){if(this._addedWeightNames.indexOf(e)!==-1)throw new j(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=Ft("zeros"));const c=s.apply(t,n),h=new ai(c,n,e,o,a);return c.dispose(),i!=null&&this.addLoss(()=>i.apply(h.read())),o==null&&(o=!0),o?this._trainableWeights.push(h):this._nonTrainableWeights.push(h),h}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){if(e==null||Array.isArray(e)&&e.length===0)return;e=Rt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e)}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,i,o,a=null){const c=Rt(e);t=Rt(t),n=Rt(n),s=Rt(s),i=Ep(i),o=Ep(o);const h=[],d=[],m=[];for(const y of c)h.push(y.sourceLayer),d.push(y.nodeIndex),m.push(y.tensorIndex);new kp({outboundLayer:this,inboundLayers:h,nodeIndices:d,tensorIndices:m,inputTensors:c,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:i,outputShapes:o},a);for(let y=0;y<t.length;y++)t[y].sourceLayer=this,t[y].nodeIndex=this.inboundNodes.length-1,t[y].tensorIndex=y}getConfig(){const e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount===0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}}function a3(e){e=Rt(e);const t=[];for(const n of e)t.push(n.shape);return Kn(t)}function c3(e){return"float32"}function pv(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{const s=t.inboundNodes[n];if(s.inboundLayers.length===0)return s.inputTensors;{const i=[];for(let o=0;o<s.inboundLayers.length;o++){const a=s.inputTensors[o],c=s.inboundLayers[o],h=s.nodeIndices[o],d=pv(a,c,h);for(const m of d)i.indexOf(m)===-1&&i.push(m)}return i}}}class Ka extends lt{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Op("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new j("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new j("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new j("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");const n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];const s=new ci(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new kp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new j(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}}Ka.className="InputLayer",me(Ka);function mv(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. 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params: ${h}`),s(`Non-trainable params: ${d}`),s("_".repeat(t))}function O3(e){let t;return e.collectedTrainableWeights!=null?t=Dp(e.collectedTrainableWeights):t=Dp(e.trainableWeights),t}function E3(e){let t=!0;const n=[],s=[];for(const i in e.nodesByDepth)n.push(e.nodesByDepth[i]);for(const i of n){if(i.length>1||i.length===1&&i[0].inboundLayers.length>1){t=!1;break}s.push(...i)}if(t)for(const i of e.layers){let o=!1;for(const a of i.inboundNodes)if(s.indexOf(a)!==-1)if(o){t=!1;break}else o=!0;if(!t)break}return t}function Pp(e,t,n=console.log){let s="";for(let i=0;i<e.length;++i)i>0&&(s=s.slice(0,s.length-1)+" "),s+=e[i],s=s.slice(0,t[i]),s+=" ".repeat(t[i]-s.length);n(s)}function D3(e,t,n){let s;try{s=JSON.stringify(e.outputShape)}catch(c){s="multiple"}const i=e.name,o=e.getClassName(),a=[`${i} (${o})`,s,e.countParams().toString()];Pp(a,t,n)}function k3(e,t,n,s){let i;try{i=JSON.stringify(e.outputShape)}catch(m){i="multiple"}const o=[];for(const m of e.inboundNodes){if(n!=null&&n.length>0&&n.indexOf(m)===-1)continue;for(let y=0;y<m.inboundLayers.length;++y){const b=m.inboundLayers[y].name,w=m.nodeIndices[y],L=m.tensorIndices[y];o.push(`${b}[${w}][${L}]`)}}const a=e.name,c=e.getClassName(),h=o.length===0?"":o[0],d=[`${a} (${c})`,i,e.countParams().toString(),h];Pp(d,t,s);for(let m=1;m<o.length;++m)Pp(["","","",o[m]],t,s)}function Rv(e,t,n){return(e==="inboundNodes"||e==="outputLayers"||e==="inputLayers")&&t===0&&typeof n=="string"}function Wh(e,t){if(e===null)return null;if(typeof e=="string")return Uo(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){const n=[],s=e.length;for(let i=0;i<s;++i){const o=e[i];Rv(t,i,o)?n.push(o):n.push(Wh(o,t))}return n}else{const n={};for(const s of Object.keys(e)){const i=e[s];if(s==="name"&&typeof i=="string")n[s]=i;else{const o=Uo(s);n[o]=Wh(i,o)}}return n}}function Hw(e,t){if(e==null)return null;if(typeof e=="string")return nr(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){const n=[],s=e.length;for(let i=0;i<s;++i){const o=e[i];Rv(t,i,o)?n.push(o):n.push(Hw(o,t))}return n}else{const n={};for(const s of Object.keys(e)){const i=e[s],o=nr(s);(s==="name"||s==="className")&&typeof i=="string"?n[o]=i:n[o]=Hw(i,s)}return n}}const zp="2.6.0";function F3(e,t){if(e.dtype==null||e.dtype===t.dtype)return t;try{return ve(t,e.dtype)}catch(n){throw new j(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}class Po{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof Po)for(const t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(e==null)return;for(const t of e)this.add(t.key,t.value)}}add(e,t,n){if(this.id2Value[e.id]==null)this.id2Value[e.id]=F3(e,t),this.name2Id[e.name]=e.id,n!=null&&(this.id2Mask[e.id]=n);else throw new j(`Duplicate key: name=${e.name}, id=${e.id}`);return this}addFeed(e){this.add(e.key,e.value)}hasKey(e){return this.id2Value[e.id]!=null}names(){return Object.keys(this.name2Id)}getValue(e){if(e instanceof ci){if(this.id2Value[e.id]==null)throw new j(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}else{const t=this.name2Id[e];if(t==null)throw new j(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof ci){if(this.id2Value[e.id]==null)throw new j(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}else{const t=this.name2Id[e];if(t==null)throw new j(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&He(this.id2Mask)}}const qw={},Ov={};function $h(e,t,n,s){const i=n==null?!1:n.training,o=Array.isArray(e),a=o?e:[e],c=a.map(L=>L.name),h=[],d=t.names();for(const L of c)d.indexOf(L)!==-1?h.push(t.getValue(L)):h.push(null);s!=null&&(s.maxNumTensors=-Infinity,s.minNumTensors=Infinity);const m=c.join(",")+"|"+t.names().join(",");let y,b;if(qw[m]==null){const L=_3(a,t);y=L.sorted,b=L.recipientCounts,qw[m]=y,Ov[m]=b}y=qw[m],b={},i||Object.assign(b,Ov[m]);const w=new Po(t);for(let L=0;L<y.length;++L){if(s!=null){const q=Ed().numTensors;q>s.maxNumTensors&&(s.maxNumTensors=q),q<s.minNumTensors&&(s.minNumTensors=q)}const T=y[L],A=T.sourceLayer;if(A instanceof Ka)continue;const N=[],E=[],D=[];let F=!1;for(const q of T.inputs){const J=w.getValue(q),re=w.getMask(q);N.push(J),E.push(re),re!=null&&(F=!0),i||(b[q.name]--,b[q.name]===0&&!t.hasKey(q)&&c.indexOf(q.name)===-1&&!J.isDisposed&&q.sourceLayer.stateful!==!0&&D.push(J))}F&&(n=n||{},n.mask=E[0]);const _=Rt(A.apply(N,n));let B=null;A.supportsMasking&&(B=A.computeMask(N,E));const $=$3(T),H=Array.isArray($)?$:[$];for(let q=0;q<H.length;++q){w.hasKey(H[q])||w.add(H[q],_[q],Array.isArray(B)?B[0]:B);const J=c.indexOf(H[q].name);J!==-1&&(h[J]=_[q])}i||He(D)}return w.disposeMasks(),o?h:h[0]}function _3(e,t){k(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],s={};if(e.length===1){const i=Ev(e[0],t);n=i.sorted,s=i.recipientMap}else{const i=new Set;for(const o of e){const{sorted:a,recipientMap:c}=Ev(o,t);for(const h of a)i.has(h.name)||(n.push(h),i.add(h.name));for(const h in c)s[h]==null&&(s[h]=new Set),c[h].forEach(d=>s[h].add(d))}}return{sorted:n,recipientCounts:W3(s)}}function W3(e){const t={};for(const n in e)t[n]=e[n].size;return t}function Ev(e,t){const n=new Set,s=[],i={};for(const c of t.names())n.add(c);const o=[],a=[];for(o.push(e);o.length>0;){const c=o[o.length-1];if(n.has(c.name)){o.pop();continue}const h=a[a.length-1]===o.length-1;if(c.inputs.length===0||h)o.pop(),s.push(c),n.add(c.name),h&&a.pop();else{a.push(o.length-1);for(const d of c.inputs){if(i[d.name]==null&&(i[d.name]=new Set),i[d.name].add(c.name),n.has(d.name))continue;o.push(d)}}}return{sorted:s,recipientMap:i}}function $3(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(const i of e.sourceLayer.inboundNodes[s].outputTensors)if(i.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}class Fi extends lt{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){const N=this.getClassName().toLowerCase();this.name=Op(N)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],Ur(this.inputs).length!==this.inputs.length)throw new j(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(N=>N.name)}`);Ur(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(N=>N.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(const N of this.outputs){const E=N.sourceLayer,D=N.nodeIndex,F=N.tensorIndex;this.outputLayers.push(E),this.outputLayersNodeIndices.push(D),this.outputLayersTensorIndices.push(F)}for(const N of this.inputs){const E=N.sourceLayer,D=N.nodeIndex,F=N.tensorIndex;xs(D===0,"input layer has >1 nodes"),xs(F===0,"input layer has >1 tensors"),this.inputLayers.push(E),this.inputLayersNodeIndices.push(D),this.inputLayersTensorIndices.push(F)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let N=0;N<this.inputLayers.length;N++){const E=this.inputLayers[N];if(!(E instanceof Ka))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${N} (0-based) originates from layer type ${E.getClassName()}.`);this.inputNames.push(E.name),this.feedInputShapes.push(E.batchInputShape),this.feedInputNames.push(E.name)}for(const N of this.outputLayers)this.outputNames.push(N.name);this.internalInputShapes=this.inputs.map(N=>N.shape),this.internalOutputShapes=this.outputs.map(N=>N.shape);const t={},n={},s={},i={},o={},a=[],c=(N,E,D,F,_,B)=>{(F==null||_==null||B==null)&&(F=N.sourceLayer,_=N.nodeIndex,B=N.tensorIndex);const $=F.inboundNodes[_];if(D.indexOf($)!==-1)throw new ri(`The tensor ${N.name} at layer "${F.name}" is part of a cycle.`);if(E.indexOf($)!==-1)return;this.containerNodes.add(Fi.nodeKey(F,_)),F.id in o||(o[F.id]=Object.keys(o).length),D.indexOf($)===-1&&D.push($);const H=$.inboundLayers.length;for(let q=0;q<H;q++){const J=$.inputTensors[q],re=$.inboundLayers[q],ce=$.nodeIndices[q],ue=$.tensorIndices[q];c(J,E,D,re,ce,ue)}for(E.push($);D.indexOf($)>=0;)D.splice(D.indexOf($),1);a.push($)},h=[],d=[];for(const N of this.outputs)c(N,h,d);const m=a.slice().reverse();for(const N of m){n[N.id]=N,N.id in t||(t[N.id]=0);let E=t[N.id];const D=s[N.outboundLayer.id]==null?0:s[N.outboundLayer.id];E=Math.max(E,D),s[N.outboundLayer.id]=E,i[N.outboundLayer.id]=N.outboundLayer,t[N.id]=E;for(let F=0;F<N.inboundLayers.length;F++){const _=N.inboundLayers[F],B=N.nodeIndices[F],$=_.inboundNodes[B],H=t[$.id]==null?0:t[$.id];t[$.id]=Math.max(E+1,H),n[$.id]=$}}const y={};for(const N in t){const E=t[N];E in y||(y[E]=[]),y[E].push(n[N])}const b={};for(const N in s){const E=s[N];E in b||(b[E]=[]),b[E].push(i[N])}let w=Object.keys(b).map(N=>parseInt(N,10)).sort(bp);this.layers=[];for(const N of w){const E=b[N];E.sort((D,F)=>{const _=o[D.id],B=o[F.id];return _<B?-1:_>B?1:0});for(const D of E)D instanceof Fi&&this.internalContainerRefs.push(D),this.layers.push(D)}this.layersByDepth=b,w=Object.keys(y).map(N=>parseInt(N,10)).sort(bp);const L=this.inputs.slice(),T=[];for(const N of w)for(const E of y[N]){const D=E.outboundLayer;if(D!=null){for(const F of E.inputTensors)if(L.indexOf(F)===-1)throw new ri(`Graph disconnected: cannot obtain value for tensor ${F} at layer "${D.name}". The following previous layers were accessed without issue: ${T}`);for(const F of E.outputTensors)L.push(F);T.push(D.name)}}this.nodesByDepth=y;const A=this.layers.map(N=>N.name);for(const N of A){const E=A.filter(D=>D===N).length;if(E!==1)throw new ri(`The name "${N}" is used ${E} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(A))}this.outboundNodes=[],this.inboundNodes=[],new kp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(N=>null),outputMasks:this.outputs.map(N=>null),inputShapes:this.inputs.map(N=>N.shape),outputShapes:this.outputs.map(N=>N.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();const e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(const t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(const t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new j("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(const t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){const n={};let s=0;for(const o of this.layers)for(const a of o.weights){if(n[a.originalName]!=null)throw new j(`Duplicate weight name: ${a.originalName}`);n[a.originalName]=a,s++}const i=[];for(const o in e){let a=o;if(n[o]==null){const c=o.split("/"),h=c.slice(0,-2).concat([c[c.length-1]]);a=h.join("/")}if(n[a]!=null)i.push([n[a],e[o]]);else if(t)throw new j(`Provided weight data has no target variable: ${o}`);delete n[a]}if(t){const o=[];for(const a in n)o.push(a);if(o.length>0)throw new j(`${o.length} of ${s} weights are not set: ${o}`)}Uw(i)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${zp}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){const n=Hw(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return ee(()=>{e=Rt(e);const n=new Po;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return $h(this.outputs,n,t)})}computeMask(e,t){return ee(()=>{e=Rt(e);let n;return t==null?n=$o(null,e.length):n=Rt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){const t=Ep(e);if(t.length!==this.inputLayers.length)throw new j(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);const n={};for(let a=0;a<t.length;a++){const c=this.inputLayers[a],h=t[a],d=c.name+"_0_0";n[d]=h}const s=Object.keys(this.nodesByDepth).map(a=>parseInt(a,10)).sort(bp);if(s.length>1)for(const a of s){const c=this.nodesByDepth[a];for(const h of c){const d=h.outboundLayer;if(this.inputLayers.map(L=>L.id).indexOf(d.id)!==-1)continue;const m=[];for(let L=0;L<h.inboundLayers.length;L++){const T=h.inboundLayers[L],A=h.nodeIndices[L],N=h.tensorIndices[L],E=`${T.name}_${A}_${N}`,D=n[E];m.push(D)}const y=d.computeOutputShape(Kn(m)),b=Ep(y),w=d.inboundNodes.indexOf(h);for(let L=0;L<b.length;L++){const T=`${d.name}_${w}_${L}`;n[T]=b[L]}}}const i=[],o=[];for(let a=0;a<this.outputLayers.length;a++){const c=this.outputLayers[a],h=this.outputLayersNodeIndices[a],d=this.outputLayersTensorIndices[a],m=`${c.name}_${h}_${d}`;o.push(m)}for(let a=0;a<o.length;a++){const c=o[a];xs(c in n),i.push(n[c])}return Kn(i)}runInternalGraph(e,t){t==null&&(t=$o(null,e.length));const n={};for(let c=0;c<this.inputs.length;++c){const h=this.inputs[c],d=e[c],m=t[c];n[h.id]=[d,m]}const s=Object.keys(this.nodesByDepth).map(c=>parseInt(c,10)).sort(bp);for(const c of s){const h=this.nodesByDepth[c];for(const d of h){const m=d.outboundLayer,y=d.inputTensors,b=d.outputTensors,w=new Array;for(const L of y)L.id in n&&w.push(n[L.id]);if(w.length===y.length){let L={},T,A,N,E;if(d.callArgs!=null&&(L=d.callArgs),w.length===1){const[D,F]=w[0];L.mask==null&&(L.mask=F),N=Rt(m.call(D,L)),E=Rt(m.computeMask(D,F)),T=[D],A=[F]}else T=w.map(D=>D[0]),A=w.map(D=>D[1]),L.mask==null&&(L.mask=A),N=Rt(m.call(T,L)),E=Rt(m.computeMask(T,A));if(m.activityRegularizer)throw new ze("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let D=0;D<b.length;++D){const F=b[D],_=N[D],B=E[D];n[F.id]=[_,B]}}}}const i=[],o=[],a=[];for(const c of this.outputs){xs(c.id in n,`Could not compute output ${c.name} : ${c.id}`);const[h,d]=n[c.id];a.push(h.shape),i.push(h),o.push(d)}return[i,o,a]}buildNodeConversionMap(e){const t={};let n;for(const s of this.layers){n=s instanceof Fi?1:0;for(let i=0;i<s.inboundNodes.length;i++){const o=Fi.nodeKey(s,i);this.containerNodes.has(o)&&(t[o]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new j(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new j("Provide either a layer name or layer index");for(const n of this.layers)if(n.name===e)return n;throw new j(`No such layer: ${e}`)}calculateLosses(){return ee(()=>{const e=[];for(const t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){const s=Fi.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){const e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(const o of this.layers){const a=o.getClassName(),c=o.getConfig(),h=[];for(let m=0;m<o.inboundNodes.length;m++){const y=o.inboundNodes[m],b=Fi.nodeKey(o,m);let w={};if(this.containerNodes.has(b)){if(y.callArgs)try{JSON.stringify(y.callArgs),w=y.callArgs}catch(L){console.warn(`Layer ${o.name} was passed non-serializable keyword arguments: ${y.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),w={}}if(y.inboundLayers.length>0){const L=[];for(let T=0;T<y.inboundLayers.length;T++){const A=y.inboundLayers[T],N=y.nodeIndices[T],E=y.tensorIndices[T],D=Fi.nodeKey(A,N);let F=t[D];F==null&&(F=0),L.push([A.name,F,E,w])}h.push(L)}}}const d={};d.name=o.name,d.className=a,d.config=c,d.inboundNodes=h,n.push(d)}e.layers=n;const s=[];for(let o=0;o<this.inputLayers.length;o++){const a=this.inputLayers[o],c=this.inputLayersNodeIndices[o],h=Fi.nodeKey(a,c);if(!this.containerNodes.has(h))continue;let d=t[h];d==null&&(d=0);const m=this.inputLayersTensorIndices[o];s.push([a.name,d,m])}e.inputLayers=s;const i=[];for(let o=0;o<this.outputLayers.length;o++){const a=this.outputLayers[o],c=this.outputLayersNodeIndices[o],h=Fi.nodeKey(a,c);if(!this.containerNodes.has(h))continue;let d=t[h];d==null&&(d=0);const m=this.outputLayersTensorIndices[o];i.push([a.name,d,m])}return e.outputLayers=i,e}static fromConfig(e,t,n={},s=!1){const i={},o={};function a(T,A){T.name in o?o[T.name].push(A):o[T.name]=[A]}function c(T,A){const N=[];let E;for(const D of A){const F=D[0],_=D[1],B=D[2];if(E=D[3]==null?{}:D[3],!(F in i)){a(T,A);return}const $=i[F];if($.inboundNodes.length<=_){a(T,A);return}const H=$.inboundNodes[_];N.push(H.outputTensors[B])}N.length>0&&T.apply(Kn(N),E)}function h(T){const A=T.name,N=li(T,t.customObjects!=null?t.customObjects:{});N.setFastWeightInitDuringBuild(s),i[A]=N;const E=T.inboundNodes;E.forEach(D=>{if(!(D instanceof Array))throw new j(`Corrupted configuration, expected array for nodeData: ${D}`);a(N,D)})}const d=t.name,m=t.layers;for(const T of m)h(T);for(;!mz(o);)for(const T of m){const A=i[T.name];if(A.name in o){const N=o[A.name];delete o[A.name];for(const E of N)c(A,E)}}const y=[],b=[],w=t.inputLayers;for(const T of w){const A=T[0],N=T[1],E=T[2];xs(A in i);const D=i[A],F=D.inboundNodes[N].outputTensors;y.push(F[E])}const L=t.outputLayers;for(const T of L){const A=T[0],N=T[1],E=T[2];xs(A in i);const D=i[A],F=D.inboundNodes[N].outputTensors;b.push(F[E])}return new e({inputs:y,outputs:b,name:d})}get stateful(){if(this._stateful)throw new j("Container instance unexpectedly has _stateful = true. 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Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). 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Need data for each key in: ${t}`);o.push(e[a])}}else if(Jw(e)){if(e=e,e.length!==t.length)throw new j(`Error when checking model ${i}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${t.length} Tensor(s), but instead got the following list of Tensor(s): ${e}`);o=e}else{if(e=e,t.length>1)throw new j(`The model ${i} expects ${t.length} Tensor(s), but only received one Tensor. 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Build the model first (e.g., by calling it on some test data).");R3(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=C3(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof Qi))throw new j("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(const o in e.loss)if(this.outputNames.indexOf(o)===-1)throw new j(`Unknown entry in loss dictionary: "${o}". Only expected the following keys: ${this.outputNames}`);for(const o of this.outputNames)e.loss[o]==null&&console.warn(`Output "${o}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${o} during training`),t.push(Pw(e.loss[o]))}else if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new j(`When passing an Array as loss, it should have one entry per model output. 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Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(e,t,n={}){const s=n.batchSize==null?32:n.batchSize;jw(s);const i=!0,o=this.standardizeUserDataXY(e,t,i,s);try{const a=o[0].concat(o[1]);this.makeTestFunction();const c=this.testFunction,h=this.testLoop(c,a,s,n.verbose,n.steps);return Kn(h)}finally{zo(o[0],e),zo(o[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),V3(this,e,t)}checkNumSamples(e,t,n,s="steps"){let i;if(n!=null){if(i=null,t!=null)throw new j(`If ${s} is set, batchSize must be null or undefined.Got batchSize = ${t}`)}else if(e!=null)Array.isArray(e)?i=e[0].shape[0]:i=e.shape[0];else throw new j(`Either the input data should have a defined shape, or ${s} shoud be specified.`);return i}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new j("`outputs` is an empty Array, which is not allowed.");const n=Array.isArray(t),s=n?t:[t],i=this.retrieveSymbolicTensors(s),o=new Po;if(e instanceof Q&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new j(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let c=0;c<this.inputs.length;++c)o.add(this.inputs[c],e[c])}else for(const c of this.inputs){const h=e[c.name];if(h==null)throw new j(`No value is provided for the model's input ${c.name}`);o.add(c,h)}const a=$h(i,o);return n?a:a[0]}retrieveSymbolicTensors(e){const t=$o(null,e.length);let n=e.length;for(const s of this.layers){const i=Array.isArray(s.output)?s.output:[s.output],o=i.map(a=>a.name);for(let a=0;a<e.length;++a){const c=o.indexOf(e[a]);if(c!==-1&&(t[a]=i[c],n--),n===0)break}if(n===0)break}if(n>0){const s=[];throw t.forEach((i,o)=>{i==null&&s.push(e[o])}),new j(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(s)}`)}return t}predictLoop(e,t=32,n=!1){return ee(()=>{const s=this.checkNumSamples(e);if(n)throw new ze("Verbose predictLoop() is not implemented yet.");const i=Xw(s,t),o=this.outputs.map(a=>[]);for(let a=0;a<i.length;++a){const c=ee(()=>{const h=i[a][0],d=i[a][1],m=Uh(e,h,d),y=[];if(Array.isArray(m))for(let w=0;w<m.length;++w)y.push({key:this.inputs[w],value:m[w]});else y.push({key:this.inputs[0],value:m});const b=new Po(y);return $h(this.outputs,b)});c.forEach((h,d)=>o[d].push(h))}return Kn(o.map(a=>Pt(a,0)))})}predict(e,t={}){const n=Uv(e);Pv(n,this.inputNames,this.feedInputShapes,!1);try{const s=t.batchSize==null?32:t.batchSize;return jw(s),this.predictLoop(n,s)}finally{zo(n,e)}}predictOnBatch(e){Pv(e,this.inputNames,this.feedInputShapes,!0);const t=(Array.isArray(e)?e[0]:e).shape[0];return this.predictLoop(e,t)}standardizeUserDataXY(e,t,n=!0,s){if(this.optimizer_==null)throw new ri("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");const i=[];for(let o=0;o<this.feedOutputShapes.length;++o){const a=this.feedOutputShapes[o],c=this.feedLossFns[o];c===_p?i.push(a.slice(0,a.length-1).concat([1])):i.push(a)}if(e=Mv(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=Mv(t,this.feedOutputNames,i,!1,"target"),j3(e,t,null),K3(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new j(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,i=!0,o){const[a,c]=this.standardizeUserDataXY(e,t,i,o);if(n!=null)throw new Error("sample weight is not supported yet.");let h=null;if(s!=null){const d=kv(s,this.outputNames);h=[];for(let m=0;m<d.length;++m)h.push(await Fv(c[m],null,d[m]))}return[a,c,h]}testLoop(e,t,n,s=0,i){return ee(()=>{const o=this.checkNumSamples(t,n,i,"steps"),a=[];if(s>0)throw new ze("Verbose mode is not implemented yet.");if(i!=null)throw new ze("steps mode in testLoop() is not implemented yet");{const c=Xw(o,n),h=ss(oi(0,o));for(let d=0;d<c.length;++d){const m=c[d][0],y=c[d][1],b=Mo(h,m,y-m),w=Kw(t,b),L=e(w);if(d===0)for(let T=0;T<L.length;++T)a.push(Ne(0));for(let T=0;T<L.length;++T){const A=L[T];a[T]=be(a[T],X(y-m,A))}}for(let d=0;d<a.length;++d)a[d]=_e(a[d],o)}return a})}getDedupedMetricsNames(){const e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){const s=e[n];let i=s;if(KA(e,s)>1){const o=KA(e.slice(0,n),s);i+=`_${o}`}t.push(i)}return t}makeTrainFunction(){return e=>{const t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),i=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),o=[],a=()=>{const m=[];for(let L=0;L<this.inputs.length;++L)m.push({key:this.inputs[L],value:n[L]});const y=new Po(m),b=$h(this.outputs,y,{training:!0});let w;for(let L=0;L<this.lossFunctions.length;++L){const T=this.lossFunctions[L];let A=T(s[L],b[L]);i[L]!=null&&(A=U3(A,i[L]));const N=Gt(A);t.push(N),L===0?w=A:w=be(w,A)}for(let L=0;L<this.metricsTensors.length;++L){let T;if(this.outputs.length>1&&L<this.outputs.length)T=t[L];else{const A=this.metricsTensors[L][0],N=this.metricsTensors[L][1];T=Gt(A(s[N],b[N]))}Cn(T),o.push(T)}return w=Gt(w),this.calculateLosses().forEach(L=>{w=be(w,L)}),w},c=this.collectedTrainableWeights.map(m=>m.read()),h=!0,d=this.optimizer_.minimize(a,h,c);return[d].concat(o)}}makeTestFunction(){this.testFunction=e=>ee(()=>{const t=[];let n;const s=e.slice(0,this.inputs.length),i=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),o=[];for(let h=0;h<this.inputs.length;++h)o.push({key:this.inputs[h],value:s[h]});const a=new Po(o),c=$h(this.outputs,a);for(let h=0;h<this.lossFunctions.length;++h){const d=this.lossFunctions[h],m=Gt(d(i[h],c[h]));h===0?n=m:n=be(n,m),t.push(n)}for(let h=0;h<this.metricsTensors.length;++h){const d=this.metricsTensors[h][0],m=this.metricsTensors[h][1],y=Gt(d(i[m],c[m]));t.push(y)}return t})}async fit(e,t,n={}){return H3(this,e,t,n)}async fitDataset(e,t){return P3(this,e,t)}async trainOnBatch(e,t){const n=await this.standardizeUserData(e,t),s=n[0],i=n[1],o=this.makeTrainFunction(),a=o(s.concat(i)),c=[];for(const h of a){const d=await h.data();c.push(d[0])}return He(a),Kn(c)}getNamedWeights(e){const t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,i=this.getWeights(n);for(let o=0;o<s.length;++o){if(n&&!s[o].trainable)continue;t.push({name:s[o].originalName,tensor:i[o]})}return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){const e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){const t=Ed().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Ed().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=nr(this.loss);else if(Array.isArray(this.loss)){for(const t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>nr(t))}else{const t=Object.keys(this.loss);e={};const n=this.loss;for(const s of t)if(typeof n[s]=="string")e[s]=nr(n[s]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[nr(Mp(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>nr(Mp(e)));{const e={};for(const t in this.metrics)e[t]=nr(Mp(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");const t=Wh(e.optimizer_config),n=li(t);let s;if(typeof e.loss=="string")s=Uo(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(o=>Uo(o));else if(e.loss!=null){s={};for(const o in e.loss)s[o]=Uo(e.loss[o])}let i;if(Array.isArray(e.metrics))i=e.metrics.map(o=>Uo(o));else if(e.metrics!=null){i={};for(const o in e.metrics)i[o]=Uo(e.metrics[o])}this.compile({loss:s,metrics:i,optimizer:n})}async save(e,t){if(typeof e=="string"){const h=qy(e);if(h.length===0)throw new j(`Cannot find any save handlers for URL '${e}'`);if(h.length>1)throw new j(`Found more than one (${h.length}) save handlers for URL '${e}'`);e=h[0]}if(e.save==null)throw new j("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const n=await Yy(this.getNamedWeights(t)),s=!1,i=null,o=this.toJSON(i,s),a={modelTopology:o,format:J3,generatedBy:`TensorFlow.js tfjs-layers v${zp}`,convertedBy:null},c=t==null?!1:t.includeOptimizer;if(c&&this.optimizer!=null){a.trainingConfig=this.getTrainingConfig();const h="optimizer",{data:d,specs:m}=await Yy(await this.optimizer.getWeights(),h);n.specs.push(...m),n.data=Id([n.data,d])}if(this.userDefinedMetadata!=null){const h=!0;Cv(this.userDefinedMetadata,this.name,h),a.userDefinedMetadata=this.userDefinedMetadata}return a.weightData=n.data,a.weightSpecs=n.specs,e.save(a)}setUserDefinedMetadata(e){Cv(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}}ir.className="Model",me(ir);class zv extends ir{}zv.className="Functional",me(zv);async function Z3(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);const s=Wh(n),i=li(s,t);if(e.weightsManifest!=null){const o=await mT(e.weightsManifest,e.pathPrefix,i.weights.map(c=>c.originalName)),a={};for(const c of i.weights)a[c.originalName]=o[c.originalName];i.loadWeights(a),He(o)}return i}async function Q3(e,t){if(t==null&&(t={}),typeof e=="string"){const n=jy(e,t);if(n.length===0)n.push(Ad(e,t));else if(n.length>1)throw new j(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return eG(e,void 0,t)}async function eG(e,t,n){if(n==null&&(n={}),e.load==null)throw new j("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const s=await e.load();let i=s.modelTopology;i.model_config!=null&&(i=i.model_config);const o=n.strict==null?!0:n.strict,a=s.weightData!=null&&s.weightSpecs!=null&&o,c=li(Wh(i),t,a),h=s.trainingConfig;if(h!=null&&c.loadTrainingConfig(h),s.userDefinedMetadata!=null&&c.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new j("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");const{modelWeights:d,optimizerWeights:m}=tG(s.weightData,s.weightSpecs);c.loadWeights(d,o),c.optimizer!=null&&m.length>0&&await c.optimizer.setWeights(m),He(d),He(m.map(y=>y.tensor))}return c}function tG(e,t){const n=Sd(e,t),s={},i=[];return t.forEach(o=>{o.group==="optimizer"?i.push({name:o.name,tensor:n[o.name]}):s[o.name]=n[o.name]}),{modelWeights:s,optimizerWeights:i}}class Za extends ir{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Op("sequential_"),e.layers!=null)for(const t of e.layers)this.add(t)}checkShape(e){const t=e.inboundNodes[0].outputTensors[0].shape;if(t.some(n=>n<0))throw new j(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){const t=e instanceof Za||e instanceof ir;let n;if(t){if(n=e,n.outputs.length!==1)throw new j("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new j("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new j("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");const s=mv({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new j(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new j("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=pv(this.outputs[0])}this.inboundNodes=[],new kp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:$o(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{const s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{const e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(St(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new ir({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new ri("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new ri("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new ri("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new ri("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let i,o={};if(t instanceof Array){if(!(t[0].className!=null)||t[0].className==="Merge")throw new j("Legacy serialization format not supported yet.");i=t}else k(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),i=t.layers,delete t.layers,o=t;const a=new e(o);if(!(a instanceof Za))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(const c of i){const h=void 0,d=li(c,h,s);s&&d.setFastWeightInitDuringBuild(!0),a.add(d)}return a}set stopTraining(e){if(this.model==null)throw new j("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new j("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){const e=[];for(const t of this.layers){const n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}}Za.className="Sequential",me(Za);function nG(e){return new ir(e)}function sG(e){return new Za(e)}function iG(e,t){return t==null&&(t={}),Q3(e,t)}function Gv(e){return mv(e)}function rG(e,t){Ms.registerCallbackConstructor(e,t)}class os extends xo{getConfig(){return{}}}class Vv extends os{apply(e,t=1){return kz(e,t)}}Vv.className="elu",me(Vv);class Yv extends os{apply(e){return Jd(e)}}Yv.className="selu",me(Yv);class Hv extends os{apply(e){return Oi(e)}}Hv.className="relu",me(Hv);class qv extends os{apply(e){return ee(()=>Ro(6,Oi(e)))}}qv.className="relu6",me(qv);class jv extends os{apply(e){return e}}jv.className="linear",me(jv);class Kv extends os{apply(e){return Ni(e)}}Kv.className="sigmoid",me(Kv);class Xv extends os{apply(e){return _z(e)}}Xv.className="hardSigmoid",me(Xv);class Jv extends os{apply(e){return Wa(e)}}Jv.className="softplus",me(Jv);class Zv extends os{apply(e){return Fz(e)}}Zv.className="softsign",me(Zv);class Qv extends os{apply(e){return Da(e)}}Qv.className="tanh",me(Qv);class Zw extends os{apply(e,t=-1){return Fo(e,t)}}Zw.className="softmax",me(Zw);class eN extends os{apply(e,t=-1){return Yd(e,t)}}eN.className="logSoftmax",me(eN);class tN extends os{apply(e,t=1){return ee(()=>Ni(e.mul(t)).mul(e))}}tN.className="swish",me(tN);function Gr(e){return e.getClassName()}function Qw(e,t={}){return Ch(e,Fs.getMap().classNameMap,t,"activation")}function Vr(e){if(e==null){const t={};return t.className="linear",t.config={},Qw(t)}if(typeof e=="string"){const t={};return t.className=e,t.config={},Qw(t)}else return e instanceof os?e:Qw(e)}function eL(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}class nN extends xo{}class Bh extends nN{constructor(e){super();eL(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return ee(()=>{let t=ct([1]);return this.hasL1&&(t=be(t,Ue(X(this.l1,nn(e))))),this.hasL2&&(t=be(t,Ue(X(this.l2,kh(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}Bh.className="L1L2",me(Bh);function oG(e){return eL(e),new Bh({l1:e!=null?e.l1:null,l2:0})}function aG(e){return eL(e),new Bh({l2:e!=null?e.l2:null,l1:0})}const sN={l1l2:"L1L2"};function It(e){return yw(e)}function iN(e,t={}){return Ch(e,Fs.getMap().classNameMap,t,"regularizer")}function _t(e){if(e==null)return null;if(typeof e=="string"){const t=e in sN?sN[e]:e,n={className:t,config:{}};return iN(n)}else return e instanceof nN?e:iN(e)}class tL extends lt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ke(e);let n=Oi(e);return this.maxValue!=null&&(n=Hn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}tL.className="ReLU",me(tL);class nL extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=Ke(e);return zd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}nL.className="LeakyReLU",me(nL);class sL extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Ft(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=_t(e.alphaRegularizer),this.alphaConstraint=cn(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new j(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=St(e);const t=e.slice(1);if(this.sharedAxes!=null)for(const s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new mn({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Ke(e),dh(e,this.alpha.read())}getConfig(){const e={alphaInitializer:Yt(this.alphaInitializer),alphaRegularizer:It(this.alphaRegularizer),alphaConstraint:an(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}}sL.className="PReLU",me(sL);class iL extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new ze(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=Ke(e);return Co(n)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}iL.className="ELU",me(iL);class rL extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){const n=Ke(e);return n.mul(Eh(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){const e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}}rL.className="ThresholdedReLU",me(rL);class oL extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Zw().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){const n=Ke(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}oL.className="Softmax",me(oL);function Qa(e,t,n){if(typeof e=="number")return $o(e,t);if(e.length!==t)throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. 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instead`);if(o==="channelsFirst"&&(e=Me(e,[0,2,1])),i==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let c=$d(e,t,s,i==="same"?"same":"valid","NWC",a);return n!=null&&(c=ki(c,n)),c})}function FQ(e,t,n=1,s="valid",i,o=1){return ee(()=>(Vt(i),oN(e,t,null,n,s,i,o)))}function _Q(e,t,n=[1,1],s="valid",i,o){return ee(()=>(Vt(i),cL(e,t,null,n,s,i,o)))}function cL(e,t,n,s=[1,1],i="valid",o,a,c=null){return ee(()=>{if(o==null&&(o=ii()),Vt(o),e.rank!==3&&e.rank!==4)throw new j(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new j(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let h=aL(e,o);if(i==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return h=ew({x:h,filter:t,strides:s,pad:i==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:n,activation:c}),o==="channelsFirst"&&(h=Me(h,[0,3,1,2])),h})}function WQ(e,t,n=[1,1,1],s="valid",i,o){return ee(()=>(Vt(i),aN(e,t,null,n,s,i,o)))}function aN(e,t,n,s=[1,1,1],i="valid",o,a){return ee(()=>{if(o==null&&(o=ii()),Vt(o),e.rank!==4&&e.rank!==5)throw new j(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new j(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let c=rN(e,o);if(i==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return c=vb(c,t,s,i==="same"?"same":"valid","NDHWC",a),n!=null&&(c=ki(c,n)),o==="channelsFirst"&&(c=Me(c,[0,4,1,2,3])),c})}class lL extends lt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",lL.verifyArgs(t),this.rank=e,pn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Qa(t.kernelSize,e,"kernelSize"),this.strides=Qa(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Ts(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Vt(this.dataFormat),this.activation=Vr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Ft(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=cn(t.biasConstraint),this.biasRegularizer=_t(t.biasRegularizer),this.activityRegularizer=_t(t.activityRegularizer),this.dilationRate=Qa(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new j(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new j(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new j(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(xs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!ww(e.kernelSize,"number",1,3))throw new j(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){const e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Gr(this.activation),useBias:this.useBias,biasInitializer:Yt(this.biasInitializer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),biasConstraint:an(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class Mh extends lL{constructor(e,t){super(e,t);this.kernel=null,Mh.verifyArgs(t),this.filters=t.filters,pn(this.filters,"filters"),this.kernelInitializer=Ft(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=cn(t.kernelConstraint),this.kernelRegularizer=_t(t.kernelRegularizer)}build(e){e=St(e);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[t]}`);const n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return ee(()=>{e=Ke(e);let n;const s=this.bias==null?null:this.bias.read(),i=JA(this.activation.getClassName());if(i!=null&&this.rank===2)n=cL(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,i);else{if(this.rank===1)n=oN(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=cL(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=aN(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=St(e);const t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let i=0;i<n.length;++i){const o=hi(n[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);t.push(o)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){const e={filters:this.filters,kernelInitializer:Yt(this.kernelInitializer),kernelRegularizer:It(this.kernelRegularizer),kernelConstraint:an(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new j(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}}class Ph extends Mh{constructor(e){super(2,e);Ph.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!ww(e.kernelSize,"number",1,2))throw new j(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}Ph.className="Conv2D",me(Ph);class Vp extends Mh{constructor(e){super(3,e);Vp.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new j(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}Vp.className="Conv3D",me(Vp);class hL extends Ph{constructor(e){super(e);if(this.inputSpec=[new mn({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new j(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=St(e),e.length!==4)throw new j("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new mn({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return ee(()=>{let n=Ke(e);if(n.shape.length!==4)throw new j(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);const s=n.shape,i=s[0];let o,a;this.dataFormat==="channelsFirst"?(o=2,a=3):(o=1,a=2);const c=s[o],h=s[a],d=this.kernelSize[0],m=this.kernelSize[1],y=this.strides[0],b=this.strides[1],w=Gp(c,y,d,this.padding),L=Gp(h,b,m,this.padding),T=[i,w,L,this.filters];this.dataFormat!=="channelsLast"&&(n=Me(n,[0,2,3,1]));let A=Ud(n,this.kernel.read(),T,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=Me(A,[0,3,1,2])),this.bias!=null&&(A=ki(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=St(e);const t=e.slice();let n,s,i;this.dataFormat==="channelsFirst"?(n=1,s=2,i=3):(n=3,s=1,i=2);const o=this.kernelSize[0],a=this.kernelSize[1],c=this.strides[0],h=this.strides[1];return t[n]=this.filters,t[s]=Gp(t[s],c,o,this.padding),t[i]=Gp(t[i],h,a,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}hL.className="Conv2DTranspose",me(hL);class cN extends Mh{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new j("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new j("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new j(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Ft(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=_t(t.depthwiseRegularizer),this.depthwiseConstraint=cn(t.depthwiseConstraint),this.pointwiseInitializer=Ft(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=_t(t.pointwiseRegularizer),this.pointwiseConstraint=cn(t.pointwiseConstraint)}build(e){if(e=St(e),e.length<this.rank+2)throw new j(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);const n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),i=[];for(let a=0;a<this.rank;++a)i.push(1);i.push(n*this.depthMultiplier,this.filters);const o=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,o,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",i,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,o,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,o,this.biasConstraint):this.bias=null,this.inputSpec=[new mn({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return ee(()=>{e=Ke(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Me(e,[0,2,3,1])),n=Gb(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=ki(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Me(n,[0,3,1,2])),n})}getConfig(){const e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Yt(this.depthwiseInitializer),e.pointwiseInitializer=Yt(this.pointwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.pointwiseRegularizer=It(this.pointwiseRegularizer),e.depthwiseConstraint=an(this.depthwiseConstraint),e.pointwiseConstraint=an(this.pointwiseConstraint),e}}cN.className="SeparableConv";class uL extends cN{constructor(e){super(2,e)}}uL.className="SeparableConv2D",me(uL);class Yp extends Mh{constructor(e){super(1,e);Yp.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){const e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!ww(e.kernelSize,"number",1,1))throw new j(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}Yp.className="Conv1D",me(Yp);class dL extends lt{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return ee(()=>{if(e=Ke(e),this.dataFormat==="channelsLast"){const n=Lp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Lp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{const n=Lp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Lp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){const e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}dL.className="Cropping2D",me(dL);class pL extends lt{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){const t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{const t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return ee(()=>{let n=Ke(e);const s=n.shape;if(this.dataFormat==="channelsFirst"){n=Me(n,[0,2,3,1]);const i=this.size[0]*s[2],o=this.size[1]*s[3],a=n.resizeNearestNeighbor([i,o]);return Me(a,[0,3,1,2])}else{const i=this.size[0]*s[1],o=this.size[1]*s[2];return n.resizeNearestNeighbor([i,o])}})}getConfig(){const e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}pL.className="UpSampling2D",me(pL);function cG(e,t,n=[1,1],s="valid",i,o){return ee(()=>{i==null&&(i=ii()),Vt(i);let a=aL(e,i);if(e.rank!==4)throw new j(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new j(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return a=vo(a,t,n,s==="same"?"same":"valid","NHWC",o),i==="channelsFirst"&&(a=Me(a,[0,3,1,2])),a})}class mL extends lL{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Ft(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=cn(e.depthwiseConstraint),this.depthwiseRegularizer=_t(e.depthwiseRegularizer)}build(e){if(e=St(e),e.length<4)throw new j(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);const n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{e=Ke(e);let n=cG(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ki(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=St(e);const t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,i=hi(t,this.kernelSize[0],this.padding,this.strides[0]),o=hi(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,i,o]:[e[0],i,o,s]}getConfig(){const e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Yt(this.depthwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.depthwiseConstraint=an(this.depthwiseRegularizer),e}}mL.className="DepthwiseConv2D",me(mL);function lN(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new j("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function i(o){return o==null||Array.isArray(o)?o:[o]}return t=i(t),n=i(n),{inputs:e,initialState:t,constants:n}}function hN(e,t,n,s=!1,i,o,a=!1,c=!1){return ee(()=>{const h=t.shape.length;if(h<3)throw new j(`Input should be at least 3D, but is ${h}D.`);const d=[1,0].concat(oi(2,h));if(t=Me(t,d),o!=null)throw new ze("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),i!=null&&(i=i.asType("bool").asType("float32"),i.rank===h-1&&(i=qn(i,-1)),i=Me(i,d)),s&&(t=Is(t,0),i!=null&&(i=Is(i,0)));const m=[];let y,b=n;const w=t.shape[0],L=Ei(t);let T;i!=null&&(T=Ei(i));for(let N=0;N<w;++N){const E=L[N],D=ee(()=>e(E,b));if(i==null)y=D[0],b=D[1];else{const F=ee(()=>{const _=T[N],B=En(_).sub(_),$=D[0].mul(_).add(b[0].mul(B)),H=b.map((q,J)=>D[1][J].mul(_).add(q.mul(B)));return{output:$,newStates:H}});y=F.output,b=F.newStates}c&&m.push(y)}let A;if(c){const N=1;A=rs(m,N)}return[y,A,b]})}class _i extends lt{constructor(e){super(e);let t;if(e.cell==null)throw new j("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new jp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new j("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new mn({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return oi(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Ww(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);const n=t[0];let s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){const i=[];for(const o of t)i.push([e[0],o]);return[s].concat(i)}else return s}computeMask(e,t){return ee(()=>{Array.isArray(t)&&(t=t[0]);const n=this.returnSequences?t:null;if(this.returnState){const s=this.states.map(i=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){const t=null;if(this.numConstants!=null)throw new ze("Constants support is not implemented in RNN yet.");Ww(e)&&(e=e[0]),e=e;const n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new mn({shape:[n,null,...s]});const i=[e[0]].concat(e.slice(2));if(t!=null)throw new ze("Constants support is not implemented in RNN yet.");this.cell.build(i);let o;if(Array.isArray(this.cell.stateSize)?o=this.cell.stateSize:o=[this.cell.stateSize],this.stateSpec!=null){if(!ot(this.stateSpec.map(a=>a.shape[a.shape.length-1]),o))throw new j(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=o.map(a=>new mn({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){ee(()=>{if(!this.stateful)throw new tr("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape[0];if(n==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>ct([n,s])):this.states_=[ct([n,this.cell.stateSize])];else if(e==null)He(this.states_),this.keptStates!=null&&(He(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>ct([n,s])):this.states_[0]=ct([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):He(this.states_);for(let s=0;s<this.states_.length;++s){const i=e[s],o=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,a=[n,o];if(!ot(i.shape,a))throw new j(`State ${s} is incompatible with layer ${this.name}: expected shape=${a}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Cn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=lN(e,n,s,this.numConstants);e=i.inputs,n=i.initialState,s=i.constants;let o=[],a=[];if(n!=null){t.initialState=n,o=o.concat(n),this.stateSpec=[];for(const h of n)this.stateSpec.push(new mn({shape:h.shape}));a=a.concat(this.stateSpec)}s!=null&&(t.constants=s,o=o.concat(s),this.numConstants=s.length);const c=o[0]instanceof ci;if(c){const h=[e].concat(o),d=this.inputSpec.concat(a),m=this.inputSpec;this.inputSpec=d;const y=super.apply(h,t);return this.inputSpec=m,y}else return super.apply(e,t)}call(e,t){return ee(()=>{const n=t==null?null:t.mask,s=t==null?null:t.training;let i=t==null?null:t.initialState;e=Ke(e),i==null&&(this.stateful?i=this.states_:i=this.getInitialState(e));const o=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(i.length!==o)throw new j(`RNN Layer has ${o} state(s) but was passed ${i.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const a={training:s},c=(w,L)=>{const T=this.cell.call([w].concat(L),a);return[T[0],T.slice(1)]},h=hN(c,e,i,this.goBackwards,n,null,this.unroll,this.returnSequences),d=h[0],m=h[1],y=h[2];this.stateful&&this.resetStates(y,s);const b=this.returnSequences?m:d;return this.returnState?[b].concat(y):b})}getInitialState(e){return ee(()=>{let t=ct(e.shape);return t=Ue(t,[1,2]),t=Dh(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Nw(t,[1,n]):t):this.cell.stateSize>1?[Nw(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){const e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);const n=this.cell.getConfig();return this.getClassName()===_i.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){const s=t.cell,i=li(s,n);return new e(Object.assign(t,{cell:i}))}}_i.className="RNN",me(_i);class ec extends lt{}class Hp extends ec{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,pn(this.units,"units"),this.activation=Vr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=cn(e.kernelConstraint),this.recurrentConstraint=cn(e.recurrentConstraint),this.biasConstraint=cn(e.biasConstraint),this.dropout=ja([1,Mr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ja([1,Mr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=St(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{if(e=e,e.length!==2)throw new j(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];const s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Yr({ones:()=>En(e),rate:this.dropout,training:s})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Yr({ones:()=>En(n),rate:this.recurrentDropout,training:s}));let i;const o=this.dropoutMask,a=this.recurrentDropoutMask;o!=null?i=Di(X(e,o),this.kernel.read()):i=Di(e,this.kernel.read()),this.bias!=null&&(i=ki(i,this.bias.read())),a!=null&&(n=X(n,a));let c=be(i,Di(n,this.recurrentKernel.read()));return this.activation!=null&&(c=this.activation.apply(c)),[c,c]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Gr(this.activation),useBias:this.useBias,kernelInitializer:Yt(this.kernelInitializer),recurrentInitializer:Yt(this.recurrentInitializer),biasInitializer:Yt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}}Hp.className="SimpleRNNCell",me(Hp);class fL extends _i{constructor(e){e.cell=new Hp(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return new e(t)}}fL.className="SimpleRNN",me(fL);class qp extends ec{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new j("GRUCell does not support reset_after parameter set to true.");this.units=e.units,pn(this.units,"units"),this.activation=Vr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Vr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=cn(e.kernelConstraint),this.recurrentConstraint=cn(e.recurrentConstraint),this.biasConstraint=cn(e.biasConstraint),this.dropout=ja([1,Mr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ja([1,Mr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=St(e);const t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{if(e=e,e.length!==2)throw new j(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training==null?!1:t.training;let s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Yr({ones:()=>En(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Yr({ones:()=>En(s),rate:this.recurrentDropout,training:n,count:3}));const i=this.dropoutMask,o=this.recurrentDropoutMask;let a,c,h;0<this.dropout&&this.dropout<1&&(e=X(e,i[0]));let d=Di(e,this.kernel.read());this.useBias&&(d=ki(d,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,o[0]));const m=this.recurrentKernel.read(),[y,b]=is(m,[2*this.units,this.units],m.rank-1),w=Di(s,y),[L,T,A]=is(d,3,d.rank-1),[N,E]=is(w,2,w.rank-1);a=this.recurrentActivation.apply(be(L,N)),c=this.recurrentActivation.apply(be(T,E));const D=Di(X(c,s),b);h=this.activation.apply(be(A,D));const F=be(X(a,s),X(be(1,zt(a)),h));return[F,F]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Gr(this.activation),recurrentActivation:Gr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Yt(this.kernelInitializer),recurrentInitializer:Yt(this.recurrentInitializer),biasInitializer:Yt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}}qp.className="GRUCell",me(qp);class gL extends _i{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new qp(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}gL.className="GRU",me(gL);class zh extends ec{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,pn(this.units,"units"),this.activation=Vr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Vr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=cn(e.kernelConstraint),this.recurrentConstraint=cn(e.recurrentConstraint),this.biasConstraint=cn(e.biasConstraint),this.dropout=ja([1,Mr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ja([1,Mr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=St(e);const n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){const i=this.biasInitializer,o=this.units;s=new(t=class extends Bs{apply(c,h){const d=i.apply([o]),m=new Ip().apply([o]),y=i.apply([o*2]);return ov(ov(d,m),y)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return ee(()=>{const n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new j(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1];const i=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Yr({ones:()=>En(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Yr({ones:()=>En(s),rate:this.recurrentDropout,training:n,count:4}));const o=this.dropoutMask,a=this.recurrentDropoutMask;let c,h,d,m;0<this.dropout&&this.dropout<1&&(e=X(e,o[0]));let y=Di(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,a[0])),y=be(y,Di(s,this.recurrentKernel.read())),this.useBias&&(y=ki(y,this.bias.read()));const[b,w,L,T]=is(y,4,y.rank-1);c=this.recurrentActivation.apply(b),h=this.recurrentActivation.apply(w),d=be(X(h,i),X(c,this.activation.apply(L))),m=this.recurrentActivation.apply(T);const A=X(m,this.activation.apply(d));return[A,A,d]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Gr(this.activation),recurrentActivation:Gr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Yt(this.kernelInitializer),recurrentInitializer:Yt(this.recurrentInitializer),biasInitializer:Yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}}zh.className="LSTMCell",me(zh);class yL extends _i{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new zh(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}yL.className="LSTM",me(yL);class jp extends ec{constructor(e){super(e);this.cells=e.cells}get stateSize(){const e=[];for(const t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return ee(()=>{e=e;let n=e.slice(1);const s=[];for(const a of this.cells.slice().reverse())Array.isArray(a.stateSize)?s.push(n.splice(0,a.stateSize.length)):s.push(n.splice(0,1));s.reverse();const i=[];let o;for(let a=0;a<this.cells.length;++a){const c=this.cells[a];n=s[a],a===0?o=[e[0]].concat(n):o=[o[0]].concat(n),o=c.call(o,t),i.push(o.slice(1))}n=[];for(const a of i.slice().reverse())n.push(...a);return[o[0]].concat(n)})}build(e){Ww(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Bo(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){const e=super.getConfig(),t=i=>({className:i.getClassName(),config:i.getConfig()}),n=this.cells.map(t),s={cells:n};return Object.assign({},e,s)}static fromConfig(e,t,n={}){const s=[];for(const i of t.cells)s.push(li(i,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];const e=[];for(const t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){const e=[];for(const t of this.cells)e.push(...t.weights);return $w(e)}setWeights(e){const t=[];for(const n of this.cells){const s=n.weights.length,i=e.splice(s);for(let o=0;o<n.weights.length;++o)t.push([n.weights[o],i[o]])}Uw(t)}}jp.className="StackedRNNCells",me(jp);function Yr(e){const{ones:t,rate:n,training:s=!1,count:i=1}=e,o=()=>cv(t(),n),a=()=>Fh(o,t,s);if(!i||i<=1)return Cn(a().clone());const c=Array(i).fill(void 0).map(a);return c.map(h=>Cn(h.clone()))}var lG=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,s=Object.getOwnPropertySymbols(e);i<s.length;i++)t.indexOf(s[i])<0&&Object.prototype.propertyIsEnumerable.call(e,s[i])&&(n[s[i]]=e[s[i]]);return n};class $Q extends ec{}class uN extends _i{constructor(e){if(e.unroll)throw new ze("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new ze("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new mn({ndim:5})]}call(e,t){return ee(()=>{if(this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new j("ConvRNN2D cell does not support constants");const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return ee(()=>{const{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),i=[s[0],...s.slice(2)],o=ct(i);return Array.isArray(t)?Array(t.length).fill(o):[o]})}resetStates(e,t=!1){ee(()=>{if(!this.stateful)throw new tr("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),i=[s[0],...s.slice(2)],o=n[0];if(o==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ct(i)):this.states_=[ct(i)];else if(e==null)He(this.states_),this.keptStates!=null&&(He(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ct(i)):this.states_[0]=ct(i);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):He(this.states_);for(let a=0;a<this.states_.length;++a){const c=e[a],h=i;if(!ot(c.shape,h))throw new j(`State ${a} is incompatible with layer ${this.name}: expected shape=${h}, received shape=${c.shape}`);this.states_[a]=c}}this.states_=this.states_.map(a=>Cn(a.clone()))})}computeSingleOutputShape(e){const{dataFormat:t,filters:n,kernelSize:s,padding:i,strides:o,dilationRate:a}=this.cell,c=t==="channelsFirst",h=e[c?3:2],d=e[c?4:3],m=hi(h,s[0],i,o[0],a[0]),y=hi(d,s[1],i,o[1],a[1]),b=[...e.slice(0,2),...c?[n,m,y]:[m,y,n]];return b}}uN.className="ConvRNN2D";class Kp extends zh{constructor(e){const{filters:t,kernelSize:n,strides:s,padding:i,dataFormat:o,dilationRate:a}=e;super(Object.assign({},e,{units:t}));this.filters=t,pn(this.filters,"filters"),this.kernelSize=Qa(n,2,"kernelSize"),this.kernelSize.forEach(c=>pn(c,"kernelSize")),this.strides=Qa(s||1,2,"strides"),this.strides.forEach(c=>pn(c,"strides")),this.padding=i||"valid",Ts(this.padding),this.dataFormat=o||"channelsLast",Vt(this.dataFormat),this.dilationRate=Qa(a||1,2,"dilationRate"),this.dilationRate.forEach(c=>pn(c,"dilationRate"))}build(e){var t;e=St(e);const n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[n]}`);const s=e[n],i=4,o=this.kernelSize.concat([s,this.filters*i]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const a=this.kernelSize.concat([this.filters,this.filters*i]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let c;if(this.unitForgetBias){const h=this.biasInitializer,d=this.filters;c=new(t=class extends Bs{apply(y,b){const w=h.apply([d]),L=ni([d]),T=h.apply([d*2]);return vw([w,L,T])}},t.className="CustomInit",t)}else c=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*i],null,c,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return ee(()=>{if(e.length!==3)throw new j(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training||!1,s=e[0],i=e[1],o=e[2],a=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Yr({ones:()=>En(s),rate:this.dropout,training:n,count:a}));const c=this.dropoutMask,h=(Ie,Se,Ee)=>!Se||!Se[Ee]?Ie:X(Se[Ee],Ie);let d=h(s,c,0),m=h(s,c,1),y=h(s,c,2),b=h(s,c,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Yr({ones:()=>En(i),rate:this.recurrentDropout,training:n,count:a}));const w=this.recurrentDropoutMask;let L=h(i,w,0),T=h(i,w,1),A=h(i,w,2),N=h(i,w,3);const E=3,[D,F,_,B]=is(this.kernel.read(),a,E),[$,H,q,J]=this.useBias?is(this.bias.read(),a):[null,null,null,null];d=this.inputConv(d,D,$,this.padding),m=this.inputConv(m,F,H,this.padding),y=this.inputConv(y,_,q,this.padding),b=this.inputConv(b,B,J,this.padding);const[re,ce,ue,he]=is(this.recurrentKernel.read(),a,E);L=this.recurrentConv(L,re),T=this.recurrentConv(T,ce),A=this.recurrentConv(A,ue),N=this.recurrentConv(N,he);const de=this.recurrentActivation.apply(be(d,L)),le=this.recurrentActivation.apply(be(m,T)),ye=be(X(le,o),X(de,this.activation.apply(be(y,A)))),pe=X(this.recurrentActivation.apply(be(b,N)),this.activation.apply(ye));return[pe,pe,ye]})}getConfig(){const e=super.getConfig(),{units:t}=e,n=lG(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){const i=Xi(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?ki(i,n,this.dataFormat):i}recurrentConv(e,t){const n=1;return Xi(e,t,n,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}Kp.className="ConvLSTM2DCell",me(Kp);class bL extends uN{constructor(e){const t=new Kp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}}bL.className="ConvLSTM2D",me(bL);class Xp extends lt{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;const t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Ke(e);if(0<this.rate&&this.rate<1){const s=t.training==null?!1:t.training,i=this.getNoiseShape(n),o=Fh(()=>cv(n,this.rate,i,this.seed),()=>n,s);return o}return e})}getConfig(){const e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}}Xp.className="Dropout",me(Xp);class wL extends Xp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}wL.className="SpatialDropout1D",me(wL);class LL extends lt{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,pn(this.units,"units"),this.activation=Vr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=cn(e.kernelConstraint),this.biasConstraint=cn(e.biasConstraint),this.kernelRegularizer=_t(e.kernelRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=St(e);const t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=St(e);const t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Ke(e),s=JA(this.activation.getClassName());let i;return s!=null?i=Di(n,this.kernel.read(),s,this.bias?this.bias.read():null):(i=Di(n,this.kernel.read()),this.bias!=null&&(i=ki(i,this.bias.read())),this.activation!=null&&(i=this.activation.apply(i))),i})}getConfig(){const e={units:this.units,activation:Gr(this.activation),useBias:this.useBias,kernelInitializer:Yt(this.kernelInitializer),biasInitializer:Yt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),biasConstraint:an(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}LL.className="Dense",me(LL);class SL extends lt{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=St(e);for(const t of e.slice(1))if(t==null)throw new j(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Br(e,1)]}call(e,t){return ee(()=>{this.invokeCallHook(e,t);let n=Ke(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){const s=[0];for(let i=2;i<n.rank;++i)s.push(i);s.push(1),n=n.transpose(s)}return Dz(n)})}getConfig(){const e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}SL.className="Flatten",me(SL);class IL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.activation=Vr(e.activation)}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Ke(e);return this.activation.apply(n)})}getConfig(){const e={activation:Gr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}IL.className="Activation",me(IL);class xL extends lt{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return ee(()=>(e=Ke(e),Oz(e,this.n)))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}xL.className="RepeatVector",me(xL);class TL extends lt{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){const n="Total size of new array must be unchanged.",s=t.slice();let i=1,o=null;for(let c=0;c<s.length;++c){const h=s[c];if(this.isUnknown(h))if(o===null)o=c;else throw new j("Can only specifiy one unknown dimension.");else i*=h}const a=Br(e);if(o!==null){if(i===0||a%i!==0)throw new j(n);s[o]=a/i}else if(a!==i)throw new j(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Ke(e),s=n.shape,i=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return n.reshape(i)})}getConfig(){const e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}}TL.className="Reshape",me(TL);class AL extends lt{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);const t=oi(1,e.dims.length+1);if(!ot(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new mn({ndim:this.dims.length+1})]}computeOutputShape(e){e=St(e);const t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Me(Ke(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}AL.className="Permute",me(AL);class vL extends lt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){const n=Ke(e),s=-1;return Jl(Fr(n,this.maskValue),s)}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Ke(e),s=-1,i=!0,o=Jl(Fr(n,this.maskValue),s,i),a=n.mul(o.asType(n.dtype));return a})}}vL.className="Masking",me(vL);class NL extends lt{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(Rt(e.inputLength))}this.inputDim=e.inputDim,pn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,pn(this.outputDim,"outputDim"),this.embeddingsInitializer=Ft(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=_t(e.embeddingsRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.embeddingsConstraint=cn(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return ee(()=>this.maskZero?(e=Ke(e),Fr(e,Qe(e))):null)}computeOutputShape(e){if(e=St(e),this.inputLength==null)return[...e,this.outputDim];const t=Rt(this.inputLength);if(t.length!==e.length-1)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){const i=t[s],o=e[s+1];if(i!=null&&o!=null&&i!==o)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);i==null&&(t[n]=o),n++}}return[e[0],...t,this.outputDim]}call(e,t){return ee(()=>{this.invokeCallHook(e,t);let n=Ke(e);n.dtype!=="int32"&&(n=Eh(n,"int32"));const s=av(this.embeddings.read(),n.as1D());return s.reshape(St(this.computeOutputShape(n.shape)))})}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Yt(this.embeddingsInitializer),embeddingsRegularizer:It(this.embeddingsRegularizer),activityRegularizer:It(this.activityRegularizer),embeddingsConstraint:an(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}NL.className="Embedding",me(NL);class Go extends lt{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new ze}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;const n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){const i=e[e.length-t.length+s],o=t[s];if(i==null||o==null||i<0||o<0)n.push(null);else if(i===1)n.push(o);else if(o===1)n.push(i);else{if(i!==o)throw new j("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(i)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[St(e)]),e=e,e.length<2)throw new j(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(const i of e)i!=null&&i[0]!==null&&t.push(i[0]);if(t=Ur(t),t.length>1)throw new j(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let i=1;i<e.length;++i){const o=e[i]==null?null:e[i].slice(1);n=this.computeElementwiseOpOutputShape(n,o)}const s=e.map(i=>i.length);e.indexOf(null)===-1&&Ur(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return ee(()=>{if(e=e,this.reshapeRequired){const n=[],s=e.map(i=>i.rank);if(s.indexOf(null)===-1){const i=Mr(s);for(let o of e){const a=o.rank;for(let c=0;c<i-a;++c)o=Dh(o,1);n.push(o)}return this.mergeFunction(n)}else{let i=!1;for(const c of e){const h=c.rank;if(h==null){const d=c.shape,m=d[0],y=d.slice(1).concat([m]);let b=c.reshape([m].concat(Br(d.slice(1))));b=Me(b,[1,0]),b=b.reshape(y),n.push(b),i=!0}else if(h>1){const d=oi(1,h).concat([0]);n.push(Me(c,d)),i=!0}else n.push(c)}let o=this.mergeFunction(n);const a=o.rank;if(i){if(a==null){const c=o.shape,h=c.length,d=c[h-1],m=[d].concat(c.slice(0,c.length-1));o=Me(o.reshape([-1,d]),[1,0]).reshape(m)}else if(a>1){const c=[a-1].concat(oi(0,a-1));o=Me(o,c)}}return o}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){const i=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,i)}let n=[];for(const s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=Ur(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return ee(()=>{if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an Array");if(!Array.isArray(e))throw new j("`inputs` should be an Array");if(t.length!==e.length)throw new j(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:qn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=$s(n,t[s]);return n})}}class Gh extends Go{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return t})}}Gh.className="Add",me(Gh);function UQ(e){if(Array.isArray(e)){const t=new Gh({});return t.apply(e)}else return new Gh(e)}class Vh extends Go{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=X(t,e[n]);return t})}}Vh.className="Multiply",me(Vh);function BQ(e){if(Array.isArray(e)){const t=new Vh({});return t.apply(e)}else return new Vh(e)}class Yh extends Go{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return X(1/e.length,t)})}}Yh.className="Average",me(Yh);function MQ(e){if(Array.isArray(e)){const t=new Yh({});return t.apply(e)}else return new Yh(e)}class Hh extends Go{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ws(t,e[n]);return t})}}Hh.className="Maximum",me(Hh);function PQ(e){if(Array.isArray(e)){const t=new Hh({});return t.apply(e)}else return new Hh(e)}class qh extends Go{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ro(t,e[n]);return t})}}qh.className="Minimum",me(qh);function zQ(e){if(Array.isArray(e)){const t=new qh({});return t.apply(e)}else return new qh(e)}class jh extends Go{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new j("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(const s of e)if(s!=null){t=!1;break}if(t)return;const n=[];for(let s=0;s<e.length;++s){const i=e[s].slice();i.splice(this.axis,1);let o=!1;for(const a of n)if(ot(a,i)){o=!0;break}o||n.push(i)}if(n.length>1)throw new j("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return ee(()=>vw(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new j("A `Concatenate` layer should be called on a list of inputs.");const t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(const i of t.slice(1)){if(n[s]==null||i[s]==null){n[s]=null;break}n[s]+=i[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new j("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new j(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return ee(()=>{let n=!0;if(t.forEach(o=>{if(o!=null){n=!1;return}}),n)return null;const s=[];for(let o=0;o<e.length;++o)t[o]==null?s.push(En(e[o]).asType("bool")):t[o].rank<e[o].rank?s.push(qn(t[o],-1)):s.push(t[o]);const i=Pt(s,this.axis);return kd(i,-1,!1)})}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}jh.className="Concatenate",me(jh);function GQ(e){if(Array.isArray(e)){const t=new jh({});return t.apply(e)}else return new jh(e)}function Kh(e,t){for(;e<0;)e+=t;return e}function hG(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new ze("batchDot is not implemented for tensors of 4D or higher rank yet");if(k(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new ze("batchDot is not implemented for complex64-type Tensors yet.");const s=e.shape.length,i=t.shape.length;n==null&&(n=[s-1,i-2]);const o=n;return ee(()=>{let a;if(s>i){a=s-i;const h=[];for(let d=0;d<a;++d)h.push(1);t=t.reshape(t.shape.concat(h))}else if(i>s){a=i-s;const h=[];for(let d=0;d<a;++d)h.push(1);e=e.reshape(e.shape.concat(h))}else a=0;let c;if(e.shape.length===2&&t.shape.length===2)o[0]===o[1]?c=e.mul(t).sum(o[0]):c=e.transpose([1,0]).mul(t).sum(o[1]);else{const h=o[0]!==e.shape.length-1,d=o[1]===t.shape.length-1;c=e.matMul(t,h,d)}if(a>0){let h;s>i?h=s+i-3:h=s-1;const d=[];for(let m=h;m<h+a;++m)d.push(m);c=c.squeeze(d)}return c.shape.length===1&&(c=c.expandDims(1)),c})}class CL extends Go{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const t=e[0],n=e[1];if(t.length>3||n.length>3)throw new ze("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new j(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new j(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((i,o)=>Kh(i,e[o].shape.length)):s=[Kh(this.axes,t.shape.length),Kh(this.axes,n.shape.length)],this.normalize&&(t=Fp(t,s[0]),n=Fp(n,s[1])),hG(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Kh(this.axes,e.length),Kh(this.axes,t.length)],n}computeOutputShape(e){k(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new ze("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);const i=t.concat(n);return i.length===1&&i.push(1),i}computeMask(e,t){return null}getConfig(){const e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}}CL.className="Dot",me(CL);class RL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Ke(e),s=()=>Sp(n.shape,0,this.stddev).add(n),i=Fh(s,()=>n,t.training||!1);return i})}}RL.className="GaussianNoise",me(RL);class OL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Ke(e);if(this.rate>0&&this.rate<1){const s=()=>{const i=Math.sqrt(this.rate/(1-this.rate));return n.mul(Sp(n.shape,1,i))};return Fh(s,()=>n,t.training||!1)}return n})}}OL.className="GaussianDropout",me(OL);class EL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ke(e).shape}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return ee(()=>{if(this.rate<1&&this.rate>0){const n=this._getNoiseShape(e),s=()=>{const i=Ke(e),o=1.6732632423543772,a=1.0507009873554805,c=-o*a;let h=Ji(ko(n),this.rate);h=Eh(h,"float32");const d=((1-this.rate)*(1+this.rate*c**2))**-.5,m=-d*c*this.rate,y=i.mul(h).add(h.add(-1).mul(c));return y.mul(d).add(m)};return Fh(s,()=>Ke(e),t.training||!1)}return e})}}EL.className="AlphaDropout",me(EL);function Xh(e,t,n,s,i,o=.001){let a;if(e.rank===2)a=_T(e,t,n,s,i,o);else if(e.rank===3)a=WT(e,t,n,s,i,o);else if(e.rank===4)a=$T(e,t,n,s,i,o);else throw new ze(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return a}function uG(e,t,n,s,i=.001){return ee(()=>{const o=jd(e,s),a=o.mean,c=o.variance,h=Xh(e,a,c,n,t,i);return[h,a,c]})}function dG(e,t,n,s,i=.001){return ee(()=>{const o=jd(e,s),a=o.mean,c=o.variance,h=[];for(const L of oi(0,e.rank))s.indexOf(L)!==-1?h.push(1):h.push(e.shape[L]);const d=a.reshape(h),m=c.reshape(h),y=t==null?null:t.reshape(h),b=n==null?null:n.reshape(h),w=Xh(e,d,m,b,y,i);return[w,a,c]})}function pG(e,t,n,s,i=.001){return ot(s.slice().sort(),oi(0,e.rank-1))?uG(e,t,n,s,i):dG(e,t,n,s,i)}class DL extends lt{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ft(e.betaInitializer||"zeros"),this.gammaInitializer=Ft(e.gammaInitializer||"ones"),this.movingMeanInitializer=Ft(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Ft(e.movingVarianceInitializer||"ones"),this.betaConstraint=cn(e.betaConstraint),this.gammaConstraint=cn(e.gammaConstraint),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer)}build(e){e=St(e);const t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new j(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new mn({ndim:e.length,axes:{[t]:n}})];const s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return ee(()=>{const n=t.training==null?!1:t.training,s=Ke(e),i=s.shape,o=i.length,a=oi(0,o),c=this.axis>=0?this.axis:this.axis+o;a.splice(c,1);const h=$o(1,o);h[c]=i[c];const d=a.slice();d.sort();const m=!ot(d,oi(0,o).slice(0,o-1)),y=()=>{if(m){const N=this.movingMean.read().reshape(h),E=this.movingVariance.read().reshape(h),D=this.center?this.beta.read().reshape(h):null,F=this.scale?this.gamma.read().reshape(h):null;return Xh(s,N,E,D,F,this.epsilon)}else return Xh(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return y();const[b,w,L]=pG(s,this.gamma.read(),this.beta.read(),a,this.epsilon),T=(N,E,D)=>{ee(()=>{const F=1-D,_=N.read(),B=_.sub(E).mul(F);N.write(_.sub(B))})},A=()=>{T(this.movingMean,w,this.momentum),T(this.movingVariance,L,this.momentum)};return A(),b})}getConfig(){const e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Yt(this.betaInitializer),gammaInitializer:Yt(this.gammaInitializer),movingMeanInitializer:Yt(this.movingMeanInitializer),movingVarianceInitializer:Yt(this.movingVarianceInitializer),betaRegularizer:It(this.betaRegularizer),gammaRegularizer:It(this.gammaRegularizer),betaConstraint:an(this.betaConstraint),gammaConstraint:an(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}DL.className="BatchNormalization",me(DL);class kL extends lt{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(const t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ft(e.betaInitializer||"zeros"),this.gammaInitializer=Ft(e.gammaInitializer||"ones"),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=St(e);const t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i<this.axis.length;++i)this.axis[i]<0&&(this.axis[i]+=t);for(const i of this.axis)if(i<0||i>=t)throw new Error(`Invalid axis: ${i}`);if(this.axis.length!==Ur(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);const n=this.axis.map(i=>e[i]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){const n=Ke(e),s=n.shape,i=s.length;return ee(()=>{const o=!0;let{mean:a,variance:c}=jd(n,this.axis,o);const h=$o(1,i);for(const L of this.axis)h[L]=s[L];const d=L=>L!=null&&L.shape.length!==i&&this.axis!==[i-1]?L.reshape(h):L;let m=d(this.gamma.read()),y=d(this.beta.read());const b=[],w=[];for(let L=0;L<i;++L)this.axis.indexOf(L)!==-1?(b.push(s[L]),w.push(1)):(b.push(1),w.push(s[L]));return a=a.tile(b),c=c.tile(b),m=m.tile(w),y=y.tile(w),Xh(n,a,c,y,m,this.epsilon)})}getConfig(){const e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Yt(this.betaInitializer),gammaInitializer:Yt(this.gammaInitializer),betaRegularizer:It(this.betaRegularizer),gammaRegularizer:It(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}}kL.className="LayerNormalization",me(kL);function VQ(e,t){return ee(()=>{if(e.rank!==3)throw new j(`temporalPadding expects input tensor to be 3-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[1,1]),t.length!==2)throw new j(`temporalPadding expects input padding pattern to be a length-2 array, but received a length-${t.length} array.`);const n=[[0,0],t,[0,0]];return Ri(e,n)})}function mG(e,t,n){return ee(()=>{if(e.rank!==4)throw new j(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new j("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ii()),n!=="channelsLast"&&n!=="channelsFirst")throw new j(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],Ri(e,s)})}class FL extends lt{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ii():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new j(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new j(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new j(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new mn({ndim:4})]}computeOutputShape(e){e=St(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return ee(()=>mG(Ke(e),this.padding,this.dataFormat))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}FL.className="ZeroPadding2D",me(FL);function Jp(e,t,n,s,i,o){return ee(()=>{Vt(i),ev(o),Ts(s),n==null&&(n=[1,1]),s==null&&(s="valid"),i==null&&(i=ii()),o==null&&(o="max"),e=aL(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=hh(e,t,n,c):a=nh(e,t,n,c),i==="channelsFirst"&&(a=Me(a,[0,3,1,2])),a})}function dN(e,t,n,s,i,o){return ee(()=>{Vt(i),ev(o),Ts(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),i==null&&(i=ii()),o==null&&(o="max"),e=rN(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=Wb(e,t,n,c):a=Ib(e,t,n,c),i==="channelsFirst"&&(a=Me(a,[0,4,1,2,3])),a})}class pN extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new j(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(pn(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new j(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);pn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Ts(this.padding),this.inputSpec=[new mn({ndim:3})]}computeOutputShape(e){e=St(e);const t=hi(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return ee(()=>{this.invokeCallHook(e,t),e=Dh(Ke(e),2);const n=this.poolingFunction(Ke(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return _r(n,[2])})}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class _L extends pN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Vt(i),Ts(s),Jp(e,t,n,s,i,"max")}}_L.className="MaxPooling1D",me(_L);class WL extends pN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Vt(i),Ts(s),Jp(e,t,n,s,i,"avg")}}WL.className="AveragePooling1D",me(WL);class mN extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new j(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];pn(this.poolSize,"poolSize"),pn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vt(this.dataFormat),Ts(this.padding),this.inputSpec=[new mn({ndim:4})]}computeOutputShape(e){e=St(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=hi(t,this.poolSize[0],this.padding,this.strides[0]),n=hi(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return ee(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ke(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class $L extends mN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Vt(i),Ts(s),Jp(e,t,n,s,i,"max")}}$L.className="MaxPooling2D",me($L);class UL extends mN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Vt(i),Ts(s),Jp(e,t,n,s,i,"avg")}}UL.className="AveragePooling2D",me(UL);class fN extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new j(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];pn(this.poolSize,"poolSize"),pn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vt(this.dataFormat),Ts(this.padding),this.inputSpec=[new mn({ndim:5})]}computeOutputShape(e){e=St(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=hi(t,this.poolSize[0],this.padding,this.strides[0]),n=hi(n,this.poolSize[1],this.padding,this.strides[1]),s=hi(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return ee(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ke(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class BL extends fN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Vt(i),Ts(s),dN(e,t,n,s,i,"max")}}BL.className="MaxPooling3D",me(BL);class ML extends fN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Vt(i),Ts(s),dN(e,t,n,s,i,"avg")}}ML.className="AveragePooling3D",me(ML);class gN extends lt{constructor(e){super(e);this.inputSpec=[new mn({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new ze}}class PL extends gN{constructor(e){super(e||{})}call(e,t){return ee(()=>{const n=Ke(e);return Gt(n,1)})}}PL.className="GlobalAveragePooling1D",me(PL);class zL extends gN{constructor(e){super(e||{})}call(e,t){return ee(()=>{const n=Ke(e);return jn(n,1)})}}zL.className="GlobalMaxPooling1D",me(zL);class yN extends lt{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vt(this.dataFormat),this.inputSpec=[new mn({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new ze}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class GL extends yN{call(e,t){return ee(()=>{const n=Ke(e);return this.dataFormat==="channelsLast"?Gt(n,[1,2]):Gt(n,[2,3])})}}GL.className="GlobalAveragePooling2D",me(GL);class VL extends yN{call(e,t){return ee(()=>{const n=Ke(e);return this.dataFormat==="channelsLast"?jn(n,[1,2]):jn(n,[2,3])})}}VL.className="GlobalMaxPooling2D",me(VL);class bN extends lt{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){const e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){const s=t.layer,i=li(s,n);delete t.layer;const o={layer:i};return Object.assign(o,t),new e(o)}}class YL extends bN{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=St(e),e.length<3)throw new j(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];const t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=St(e);const t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return ee(()=>{e=Ke(e);const n=(o,a)=>{const c=Ke(this.layer.call(o,t));return[c,[]]},s=hN(n,e,[],!1,null,null,!1,!0),i=s[1];return i})}}YL.className="TimeDistributed",me(YL);function fG(e){Ha(Tz,"BidirectionalMergeMode",e)}const gG="concat";class HL extends bN{constructor(e){super(e);const t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=li(n),t.goBackwards=!(t.goBackwards===!0);const s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=li(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?gG:e.mergeMode,fG(this.mergeMode),e.weights)throw new ze("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){const t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,i;return this.returnState&&(i=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(i).concat(i.slice()):[n].concat(i).concat(i.slice()):Kn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=lN(e,n,s,this.numConstants);if(e=i.inputs,n=i.initialState,s=i.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);const o=[],a=[];if(n!=null){const h=n.length;if(h%2>0)throw new j("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,o.push(...n);const d=n.map(m=>new mn({shape:m.shape}));this.forwardLayer.stateSpec=d.slice(0,h/2),this.backwardLayer.stateSpec=d.slice(h/2),a.push(...d)}if(s!=null)throw new ze("Support for constants in Bidirectional layers is not implemented yet.");const c=o[0]instanceof ci;for(const h of o)if(h instanceof ci!==c)throw new j("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(c){const h=[e].concat(o),d=this.inputSpec.concat(a),m=this.inputSpec;this.inputSpec=d;const y=super.apply(h,t);return this.inputSpec=m,y}else return super.apply(e,t)}call(e,t){return ee(()=>{const n=t.initialState;let s,i;if(n==null)s=this.forwardLayer.call(e,t),i=this.backwardLayer.call(e,t);else{const c=n.slice(0,n.length/2),h=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:c})),i=this.backwardLayer.call(e,Object.assign(t,{initialState:h}))}let o;this.returnState&&(Array.isArray(s)&&(o=s.slice(1).concat(i.slice(1))),s=s[0],i=i[0]),this.returnSequences&&(i=Is(i,1));let a;return this.mergeMode==="concat"?a=vw([s,i]):this.mergeMode==="sum"?a=be(s,i):this.mergeMode==="ave"?a=X(.5,be(s,i)):this.mergeMode==="mul"?a=X(s,i):this.mergeMode==null&&(a=[s,i]),this.returnState?this.mergeMode==null?a.concat(o):[a].concat(o):a})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Bo(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Bo(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){const s=this.forwardLayer.states,i=s.map(o=>null);return Array.isArray(n)?n.concat(i).concat(i):[n].concat(i).concat(i)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){const e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){const n=li(t.layer);if(delete t.layer,t.numConstants!=null)throw new ze("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const s=t;return s.layer=n,new e(s)}}HL.className="Bidirectional",me(HL);function yG(e){return new Ka(e)}function bG(e){return new iL(e)}function wG(e){return new tL(e)}function LG(e){return new nL(e)}function SG(e){return new sL(e)}function IG(e){return new oL(e)}function xG(e){return new rL(e)}function TG(e){return new Yp(e)}function AG(e){return new Ph(e)}function vG(e){return new hL(e)}function NG(e){return new Vp(e)}function CG(e){return new uL(e)}function RG(e){return new dL(e)}function OG(e){return new pL(e)}function EG(e){return new mL(e)}function DG(e){return new IL(e)}function kG(e){return new LL(e)}function FG(e){return new Xp(e)}function _G(e){return new wL(e)}function WG(e){return new SL(e)}function $G(e){return new xL(e)}function UG(e){return new TL(e)}function BG(e){return new AL(e)}function MG(e){return new NL(e)}function PG(e){return new Gh(e)}function zG(e){return new Yh(e)}function GG(e){return new jh(e)}function VG(e){return new Hh(e)}function YG(e){return new qh(e)}function HG(e){return new Vh(e)}function qG(e){return new CL(e)}function jG(e){return new DL(e)}function KG(e){return new kL(e)}function XG(e){return new FL(e)}function qL(e){return new WL(e)}function JG(e){return qL(e)}function ZG(e){return qL(e)}function jL(e){return new UL(e)}function QG(e){return jL(e)}function eV(e){return jL(e)}function KL(e){return new ML(e)}function tV(e){return KL(e)}function nV(e){return KL(e)}function sV(e){return new PL(e)}function iV(e){return new GL(e)}function wN(e){return new zL(e)}function LN(e){return new VL(e)}function SN(e){return new _L(e)}function IN(e){return new $L(e)}function rV(e){return new BL(e)}function oV(e){return new gL(e)}function aV(e){return new qp(e)}function cV(e){return new yL(e)}function lV(e){return new zh(e)}function hV(e){return new fL(e)}function uV(e){return new Hp(e)}function dV(e){return new bL(e)}function pV(e){return new Kp(e)}function mV(e){return new _i(e)}function fV(e){return new jp(e)}function gV(e){return new HL(e)}function yV(e){return new YL(e)}const bV=wN,wV=LN,LV=SN,SV=IN;function IV(e){return new RL(e)}function xV(e){return new OL(e)}function TV(e){return new EL(e)}function AV(e){return new vL(e)}var vV=Object.freeze({__proto__:null,inputLayer:yG,elu:bG,reLU:wG,leakyReLU:LG,prelu:SG,softmax:IG,thresholdedReLU:xG,conv1d:TG,conv2d:AG,conv2dTranspose:vG,conv3d:NG,separableConv2d:CG,cropping2D:RG,upSampling2d:OG,depthwiseConv2d:EG,activation:DG,dense:kG,dropout:FG,spatialDropout1d:_G,flatten:WG,repeatVector:$G,reshape:UG,permute:BG,embedding:MG,add:PG,average:zG,concatenate:GG,maximum:VG,minimum:YG,multiply:HG,dot:qG,batchNormalization:jG,layerNormalization:KG,zeroPadding2d:XG,averagePooling1d:qL,avgPool1d:JG,avgPooling1d:ZG,averagePooling2d:jL,avgPool2d:QG,avgPooling2d:eV,averagePooling3d:KL,avgPool3d:tV,avgPooling3d:nV,globalAveragePooling1d:sV,globalAveragePooling2d:iV,globalMaxPooling1d:wN,globalMaxPooling2d:LN,maxPooling1d:SN,maxPooling2d:IN,maxPooling3d:rV,gru:oV,gruCell:aV,lstm:cV,lstmCell:lV,simpleRNN:hV,simpleRNNCell:uV,convLstm2d:dV,convLstm2dCell:pV,rnn:mV,stackedRNNCells:fV,bidirectional:gV,timeDistributed:yV,globalMaxPool1d:bV,globalMaxPool2d:wV,maxPool1d:LV,maxPool2d:SV,Layer:lt,RNN:_i,RNNCell:ec,input:Gv,gaussianNoise:IV,gaussianDropout:xV,alphaDropout:TV,masking:AV});function NV(e,t){return zw(e,t)}function CV(e,t){return Tv(e,t)}function RV(e,t){return Av(e,t)}function OV(e,t){return Gw(e,t)}function EV(e,t){return Vw(e,t)}function DV(e,t){return xv(e,t)}function kV(e,t){return w3(e,t)}function FV(e,t){return $p(e,t)}function _V(e,t){return Ja(e,t)}function WV(e,t){return zr(e,t)}function $V(e,t){return zr(e,t)}function UV(e,t){return zr(e,t)}function BV(e,t){return sr(e,t)}function MV(e,t){return sr(e,t)}function PV(e,t){return sr(e,t)}var zV=Object.freeze({__proto__:null,binaryAccuracy:NV,binaryCrossentropy:CV,sparseCategoricalAccuracy:RV,categoricalAccuracy:OV,categoricalCrossentropy:EV,precision:DV,recall:kV,cosineProximity:FV,meanAbsoluteError:_V,meanAbsolutePercentageError:WV,MAPE:$V,mape:UV,meanSquaredError:BV,MSE:MV,mse:PV});var GV=Object.freeze({__proto__:null,modelFromJSON:Z3});function VV(e){return new Bh(e)}function YV(e){return oG(e)}function HV(e){return aG(e)}var qV=Object.freeze({__proto__:null,l1l2:VV,l1:YV,l2:HV});class xN extends Xa{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof ir))throw new Error("model must be a LayersModel, not some other Container");this.model=e}}function Zp(e,t){return e<t}function TN(e,t){return e>t}class AN extends xN{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new ze("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. 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TY=Object.freeze({__proto__:null,json:xY});const 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kY=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[be(R("a",e,t,n),R("b",e,t,n))];case"AddN":return[kT(R("tensors",e,t,n))];case"FloorMod":case"Mod":return[qd(R("a",e,t,n),R("b",e,t,n))];case"Mul":return[X(R("a",e,t,n),R("b",e,t,n))];case"RealDiv":case"Div":return[_e(R("a",e,t,n),R("b",e,t,n))];case"DivNoNan":return[Rb(R("a",e,t,n),R("b",e,t,n))];case"FloorDiv":return[Dd(R("a",e,t,n),R("b",e,t,n))];case"Sub":return[Ce(R("a",e,t,n),R("b",e,t,n))];case"Minimum":return[Ro(R("a",e,t,n),R("b",e,t,n))];case"Maximum":return[Ws(R("a",e,t,n),R("b",e,t,n))];case"Pow":return[si(R("a",e,t,n),R("b",e,t,n))];case"SquaredDifference":return[yh(R("a",e,t,n),R("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},HQ="arithmetic";const 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St(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new j(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function Dp(e){let t=0;for(const n of e)n.shape.length===0?t+=1:t+=n.shape.reduce((s,i)=>s*i);return t}const dv="Variable";class ai{constructor(e,t="float32",n=dv,s=!0,i=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=uv(),n=n==null?dv:n,this.originalName=nv(n),this.name=sv(this.originalName),this.trainable_=s,this.constraint=i,this.val=nA(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),i3(this.val,e),this.val.id!==e.id&&(this.val.assign(e),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}}function i3(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function aQ(e,t,n,s){return new ai(e,t,n,!0,s)}function cQ(e,t,n){return new ai(ct(e),t,n)}function lQ(e,t,n){return new ai(Qe(e),t,n)}function hQ(e,t,n){const s=ni(e);return new ai(s,t,n)}function uQ(e,t,n){const s=En(e);return new ai(s,t,n)}function dQ(e,t,n){return new ai(Pd(e),t,n)}function pQ(e,t,n,s,i,o="randomUniform"){return new ai(_o(e,t,n,s),s,o)}function mQ(e,t=0,n=1,s,i,o="truncatedNormal"){if(s=s||"float32",s!=="float32"&&s!=="int32")throw new ze(`randomNormal does not support dType ${s}.`);return new ai(bh(e,t,n,s,i),s,o)}function fQ(e,t=0,n=1,s,i,o="randomNormal"){if(s=s||"float32",s!=="float32"&&s!=="int32")throw new ze(`randomNormalVariable does not support dType ${s}.`);return new ai(Bb(e,t,n,s,i),s,o)}function gQ(e,t){return e.write(t)}function yQ(e,t){return e.write(be(e.read(),t))}function bQ(e,t){return e.write(Ce(e.read(),t))}function $w(e){return e.map(t=>t.read())}function Uw(e){e.forEach(t=>{const n=t[0];n.write(t[1])})}function wQ(e,t){const n=t.map(i=>i.read()),s=Fb(e,n);return t.map(i=>s.grads[i.name])}class mn{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}}class ci{constructor(e,t,n,s,i,o,a){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=i,this.outputTensorIndex=a,this.id=uv(),o!=null&&(this.originalName=nv(o),this.name=sv(this.originalName)),this.rank=t.length}}let r3=0;class kp{constructor(e,t){this.callArgs=t,this.id=r3++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(const n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){const e=[];for(const t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}}let o3=0;class lt extends Ao{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=o3++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){const n=this.getClassName();t=nr(n)+"_"+Op(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let i=null;e.batchSize!=null&&(i=e.batchSize),n=[i].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new ri(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new j(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Kn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Kn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new tr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new tr(`Layer ${this.name} is not connected, no input to return.`);return Kn(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new tr(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new tr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Kn(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=Rt(e),this.inputSpec==null||this.inputSpec.length===0)return;const t=Rt(this.inputSpec);if(e.length!==t.length)throw new j(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){const s=e[n],i=t[n];if(i==null)continue;const o=s.rank;if(i.ndim!=null&&o!==i.ndim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${i.ndim}, found ndim=${o}`);if(i.maxNDim!=null&&o>i.maxNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${i.maxNDim}, found ndim=${o}`);if(i.minNDim!=null&&o<i.minNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${i.minNDim}, found ndim=${o}.`);if(i.dtype!=null&&s.dtype!==i.dtype)throw new j(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${i.dtype}, found dtype=${s.dtype}.`);if(i.axes){const a=s.shape;for(const c in i.axes){const h=Number(c),d=i.axes[c],m=h>=0?a[h]:a[a.length+h];if(d!=null&&[d,null].indexOf(m)===-1)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected axis ${h} of input shape to have value ${d} but got shape ${a}.`)}}if(i.shape!=null)for(let a=0;a<i.shape.length;++a){const c=i.shape[a],h=s.shape[a];if(c!=null&&h!=null&&c!==h)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected shape=${i.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();const n=Rt(e);let s=!0;for(const o of n)if(!(o instanceof ci)){s=!1;break}let i=!0;for(const o of n)if(o instanceof ci){i=!1;break}if(s===i)throw new j("Arguments to apply() must be all SymbolicTensors or all Tensors");return Po(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);const o=[];for(const a of Rt(e))o.push(a.shape);this.build(Kn(o)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&i&&(this._refCount=1)}if(this.assertInputCompatibility(e),i){let o=this.call(e,t);const a=Rt(o),c=[];for(let h of a)n.indexOf(h)!==-1&&(h=h.clone()),c.push(h);if(o=Kn(c),this.activityRegularizer!=null)throw new ze("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}else{const o=a3(e),a=this.computeOutputShape(o);let c;const h=c3(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?o[0]:o),a!=null&&a.length>0&&Array.isArray(a[0])?c=a.map((d,m)=>new ci(h,d,this,Rt(e),t,this.name,m)):c=new ci(h,a,this,Rt(e),t,this.name),this.addInboundNode(e,c,null,null,o,a,t),this._refCount++,this.activityRegularizer!=null)throw new ze("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return c}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape==null)return;if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new tr(`The layer ${this.name} has never been called and thus has no defined output shape.`);const e=[];for(const t of this.inboundNodes){const n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){const t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new tr(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new ri(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Dp(this.weights)}build(e){this.built=!0}getWeights(e=!1){return $w(e?this.trainableWeights:this.weights)}setWeights(e){ee(()=>{const t=this.weights;if(t.length!==e.length)throw new j(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;const n=[],s=$w(t);for(let i=0;i<s.length;++i){const o=s[i],a=t[i],c=e[i];if(!ot(o.shape,c.shape))throw new j(`Layer weight shape ${o.shape} not compatible with provided weight shape ${c.shape}`);n.push([a,c])}Uw(n)})}addWeight(e,t,n,s,i,o,a){if(this._addedWeightNames.indexOf(e)!==-1)throw new j(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=Ft("zeros"));const c=s.apply(t,n),h=new ai(c,n,e,o,a);return c.dispose(),i!=null&&this.addLoss(()=>i.apply(h.read())),o==null&&(o=!0),o?this._trainableWeights.push(h):this._nonTrainableWeights.push(h),h}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){if(e==null||Array.isArray(e)&&e.length===0)return;e=Rt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e)}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,i,o,a=null){const c=Rt(e);t=Rt(t),n=Rt(n),s=Rt(s),i=Ep(i),o=Ep(o);const h=[],d=[],m=[];for(const y of c)h.push(y.sourceLayer),d.push(y.nodeIndex),m.push(y.tensorIndex);new kp({outboundLayer:this,inboundLayers:h,nodeIndices:d,tensorIndices:m,inputTensors:c,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:i,outputShapes:o},a);for(let y=0;y<t.length;y++)t[y].sourceLayer=this,t[y].nodeIndex=this.inboundNodes.length-1,t[y].tensorIndex=y}getConfig(){const e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount===0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}}function a3(e){e=Rt(e);const t=[];for(const n of e)t.push(n.shape);return Kn(t)}function c3(e){return"float32"}function pv(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{const s=t.inboundNodes[n];if(s.inboundLayers.length===0)return s.inputTensors;{const i=[];for(let o=0;o<s.inboundLayers.length;o++){const a=s.inputTensors[o],c=s.inboundLayers[o],h=s.nodeIndices[o],d=pv(a,c,h);for(const m of d)i.indexOf(m)===-1&&i.push(m)}return i}}}class Ja extends lt{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Op("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new j("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new j("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new j("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");const n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];const s=new ci(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new kp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new j(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}}Ja.className="InputLayer",me(Ja);function mv(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. 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y,b;if(qw[m]==null){const L=_3(a,t);y=L.sorted,b=L.recipientCounts,qw[m]=y,Ov[m]=b}y=qw[m],b={},i||Object.assign(b,Ov[m]);const w=new Go(t);for(let L=0;L<y.length;++L){if(s!=null){const q=Ed().numTensors;q>s.maxNumTensors&&(s.maxNumTensors=q),q<s.minNumTensors&&(s.minNumTensors=q)}const T=y[L],A=T.sourceLayer;if(A instanceof Ja)continue;const N=[],E=[],D=[];let F=!1;for(const q of T.inputs){const J=w.getValue(q),re=w.getMask(q);N.push(J),E.push(re),re!=null&&(F=!0),i||(b[q.name]--,b[q.name]===0&&!t.hasKey(q)&&c.indexOf(q.name)===-1&&!J.isDisposed&&q.sourceLayer.stateful!==!0&&D.push(J))}F&&(n=n||{},n.mask=E[0]);const _=Rt(A.apply(N,n));let B=null;A.supportsMasking&&(B=A.computeMask(N,E));const $=$3(T),H=Array.isArray($)?$:[$];for(let q=0;q<H.length;++q){w.hasKey(H[q])||w.add(H[q],_[q],Array.isArray(B)?B[0]:B);const J=c.indexOf(H[q].name);J!==-1&&(h[J]=_[q])}i||He(D)}return w.disposeMasks(),o?h:h[0]}function _3(e,t){k(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],s={};if(e.length===1){const i=Ev(e[0],t);n=i.sorted,s=i.recipientMap}else{const i=new Set;for(const o of e){const{sorted:a,recipientMap:c}=Ev(o,t);for(const h of a)i.has(h.name)||(n.push(h),i.add(h.name));for(const h in c)s[h]==null&&(s[h]=new Set),c[h].forEach(d=>s[h].add(d))}}return{sorted:n,recipientCounts:W3(s)}}function W3(e){const t={};for(const n in e)t[n]=e[n].size;return t}function Ev(e,t){const n=new Set,s=[],i={};for(const c of t.names())n.add(c);const o=[],a=[];for(o.push(e);o.length>0;){const c=o[o.length-1];if(n.has(c.name)){o.pop();continue}const h=a[a.length-1]===o.length-1;if(c.inputs.length===0||h)o.pop(),s.push(c),n.add(c.name),h&&a.pop();else{a.push(o.length-1);for(const d of c.inputs){if(i[d.name]==null&&(i[d.name]=new Set),i[d.name].add(c.name),n.has(d.name))continue;o.push(d)}}}return{sorted:s,recipientMap:i}}function $3(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(const i of e.sourceLayer.inboundNodes[s].outputTensors)if(i.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}class Fi extends lt{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){const N=this.getClassName().toLowerCase();this.name=Op(N)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],Ur(this.inputs).length!==this.inputs.length)throw new j(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(N=>N.name)}`);Ur(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(N=>N.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(const N of this.outputs){const E=N.sourceLayer,D=N.nodeIndex,F=N.tensorIndex;this.outputLayers.push(E),this.outputLayersNodeIndices.push(D),this.outputLayersTensorIndices.push(F)}for(const N of this.inputs){const E=N.sourceLayer,D=N.nodeIndex,F=N.tensorIndex;xs(D===0,"input layer has >1 nodes"),xs(F===0,"input layer has >1 tensors"),this.inputLayers.push(E),this.inputLayersNodeIndices.push(D),this.inputLayersTensorIndices.push(F)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let N=0;N<this.inputLayers.length;N++){const E=this.inputLayers[N];if(!(E instanceof Ja))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${N} (0-based) originates from layer type ${E.getClassName()}.`);this.inputNames.push(E.name),this.feedInputShapes.push(E.batchInputShape),this.feedInputNames.push(E.name)}for(const N of this.outputLayers)this.outputNames.push(N.name);this.internalInputShapes=this.inputs.map(N=>N.shape),this.internalOutputShapes=this.outputs.map(N=>N.shape);const t={},n={},s={},i={},o={},a=[],c=(N,E,D,F,_,B)=>{(F==null||_==null||B==null)&&(F=N.sourceLayer,_=N.nodeIndex,B=N.tensorIndex);const $=F.inboundNodes[_];if(D.indexOf($)!==-1)throw new ri(`The tensor ${N.name} at layer "${F.name}" is part of a cycle.`);if(E.indexOf($)!==-1)return;this.containerNodes.add(Fi.nodeKey(F,_)),F.id in o||(o[F.id]=Object.keys(o).length),D.indexOf($)===-1&&D.push($);const H=$.inboundLayers.length;for(let q=0;q<H;q++){const J=$.inputTensors[q],re=$.inboundLayers[q],ce=$.nodeIndices[q],ue=$.tensorIndices[q];c(J,E,D,re,ce,ue)}for(E.push($);D.indexOf($)>=0;)D.splice(D.indexOf($),1);a.push($)},h=[],d=[];for(const N of this.outputs)c(N,h,d);const m=a.slice().reverse();for(const N of m){n[N.id]=N,N.id in t||(t[N.id]=0);let E=t[N.id];const D=s[N.outboundLayer.id]==null?0:s[N.outboundLayer.id];E=Math.max(E,D),s[N.outboundLayer.id]=E,i[N.outboundLayer.id]=N.outboundLayer,t[N.id]=E;for(let F=0;F<N.inboundLayers.length;F++){const _=N.inboundLayers[F],B=N.nodeIndices[F],$=_.inboundNodes[B],H=t[$.id]==null?0:t[$.id];t[$.id]=Math.max(E+1,H),n[$.id]=$}}const y={};for(const N in t){const E=t[N];E in y||(y[E]=[]),y[E].push(n[N])}const b={};for(const N in s){const E=s[N];E in b||(b[E]=[]),b[E].push(i[N])}let w=Object.keys(b).map(N=>parseInt(N,10)).sort(bp);this.layers=[];for(const N of w){const E=b[N];E.sort((D,F)=>{const _=o[D.id],B=o[F.id];return _<B?-1:_>B?1:0});for(const D of E)D instanceof Fi&&this.internalContainerRefs.push(D),this.layers.push(D)}this.layersByDepth=b,w=Object.keys(y).map(N=>parseInt(N,10)).sort(bp);const L=this.inputs.slice(),T=[];for(const N of w)for(const E of y[N]){const D=E.outboundLayer;if(D!=null){for(const F of E.inputTensors)if(L.indexOf(F)===-1)throw new ri(`Graph disconnected: cannot obtain value for tensor ${F} at layer "${D.name}". The following previous layers were accessed without issue: ${T}`);for(const F of E.outputTensors)L.push(F);T.push(D.name)}}this.nodesByDepth=y;const A=this.layers.map(N=>N.name);for(const N of A){const E=A.filter(D=>D===N).length;if(E!==1)throw new ri(`The name "${N}" is used ${E} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(A))}this.outboundNodes=[],this.inboundNodes=[],new kp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(N=>null),outputMasks:this.outputs.map(N=>null),inputShapes:this.inputs.map(N=>N.shape),outputShapes:this.outputs.map(N=>N.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();const e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(const t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(const t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new j("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(const t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){const n={};let s=0;for(const o of this.layers)for(const a of o.weights){if(n[a.originalName]!=null)throw new j(`Duplicate weight name: ${a.originalName}`);n[a.originalName]=a,s++}const i=[];for(const o in e){let a=o;if(n[o]==null){const c=o.split("/"),h=c.slice(0,-2).concat([c[c.length-1]]);a=h.join("/")}if(n[a]!=null)i.push([n[a],e[o]]);else if(t)throw new j(`Provided weight data has no target variable: ${o}`);delete n[a]}if(t){const o=[];for(const a in n)o.push(a);if(o.length>0)throw new j(`${o.length} of ${s} weights are not set: ${o}`)}Uw(i)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${zp}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){const n=Hw(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return ee(()=>{e=Rt(e);const n=new Go;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return $h(this.outputs,n,t)})}computeMask(e,t){return ee(()=>{e=Rt(e);let n;return t==null?n=Bo(null,e.length):n=Rt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){const t=Ep(e);if(t.length!==this.inputLayers.length)throw new j(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);const n={};for(let a=0;a<t.length;a++){const c=this.inputLayers[a],h=t[a],d=c.name+"_0_0";n[d]=h}const s=Object.keys(this.nodesByDepth).map(a=>parseInt(a,10)).sort(bp);if(s.length>1)for(const a of s){const c=this.nodesByDepth[a];for(const h of c){const d=h.outboundLayer;if(this.inputLayers.map(L=>L.id).indexOf(d.id)!==-1)continue;const m=[];for(let L=0;L<h.inboundLayers.length;L++){const T=h.inboundLayers[L],A=h.nodeIndices[L],N=h.tensorIndices[L],E=`${T.name}_${A}_${N}`,D=n[E];m.push(D)}const y=d.computeOutputShape(Kn(m)),b=Ep(y),w=d.inboundNodes.indexOf(h);for(let L=0;L<b.length;L++){const T=`${d.name}_${w}_${L}`;n[T]=b[L]}}}const i=[],o=[];for(let a=0;a<this.outputLayers.length;a++){const c=this.outputLayers[a],h=this.outputLayersNodeIndices[a],d=this.outputLayersTensorIndices[a],m=`${c.name}_${h}_${d}`;o.push(m)}for(let a=0;a<o.length;a++){const c=o[a];xs(c in n),i.push(n[c])}return Kn(i)}runInternalGraph(e,t){t==null&&(t=Bo(null,e.length));const n={};for(let c=0;c<this.inputs.length;++c){const h=this.inputs[c],d=e[c],m=t[c];n[h.id]=[d,m]}const s=Object.keys(this.nodesByDepth).map(c=>parseInt(c,10)).sort(bp);for(const c of s){const h=this.nodesByDepth[c];for(const d of h){const m=d.outboundLayer,y=d.inputTensors,b=d.outputTensors,w=new Array;for(const L of y)L.id in n&&w.push(n[L.id]);if(w.length===y.length){let L={},T,A,N,E;if(d.callArgs!=null&&(L=d.callArgs),w.length===1){const[D,F]=w[0];L.mask==null&&(L.mask=F),N=Rt(m.call(D,L)),E=Rt(m.computeMask(D,F)),T=[D],A=[F]}else T=w.map(D=>D[0]),A=w.map(D=>D[1]),L.mask==null&&(L.mask=A),N=Rt(m.call(T,L)),E=Rt(m.computeMask(T,A));if(m.activityRegularizer)throw new ze("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let D=0;D<b.length;++D){const F=b[D],_=N[D],B=E[D];n[F.id]=[_,B]}}}}const i=[],o=[],a=[];for(const c of this.outputs){xs(c.id in n,`Could not compute output ${c.name} : ${c.id}`);const[h,d]=n[c.id];a.push(h.shape),i.push(h),o.push(d)}return[i,o,a]}buildNodeConversionMap(e){const t={};let n;for(const s of this.layers){n=s instanceof Fi?1:0;for(let i=0;i<s.inboundNodes.length;i++){const o=Fi.nodeKey(s,i);this.containerNodes.has(o)&&(t[o]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new j(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new j("Provide either a layer name or layer index");for(const n of this.layers)if(n.name===e)return n;throw new j(`No such layer: ${e}`)}calculateLosses(){return ee(()=>{const e=[];for(const t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){const s=Fi.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){const e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(const o of this.layers){const a=o.getClassName(),c=o.getConfig(),h=[];for(let m=0;m<o.inboundNodes.length;m++){const y=o.inboundNodes[m],b=Fi.nodeKey(o,m);let w={};if(this.containerNodes.has(b)){if(y.callArgs)try{JSON.stringify(y.callArgs),w=y.callArgs}catch(L){console.warn(`Layer ${o.name} was passed non-serializable keyword arguments: ${y.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),w={}}if(y.inboundLayers.length>0){const L=[];for(let T=0;T<y.inboundLayers.length;T++){const A=y.inboundLayers[T],N=y.nodeIndices[T],E=y.tensorIndices[T],D=Fi.nodeKey(A,N);let F=t[D];F==null&&(F=0),L.push([A.name,F,E,w])}h.push(L)}}}const d={};d.name=o.name,d.className=a,d.config=c,d.inboundNodes=h,n.push(d)}e.layers=n;const s=[];for(let o=0;o<this.inputLayers.length;o++){const a=this.inputLayers[o],c=this.inputLayersNodeIndices[o],h=Fi.nodeKey(a,c);if(!this.containerNodes.has(h))continue;let d=t[h];d==null&&(d=0);const m=this.inputLayersTensorIndices[o];s.push([a.name,d,m])}e.inputLayers=s;const i=[];for(let o=0;o<this.outputLayers.length;o++){const a=this.outputLayers[o],c=this.outputLayersNodeIndices[o],h=Fi.nodeKey(a,c);if(!this.containerNodes.has(h))continue;let d=t[h];d==null&&(d=0);const m=this.outputLayersTensorIndices[o];i.push([a.name,d,m])}return e.outputLayers=i,e}static fromConfig(e,t,n={},s=!1){const i={},o={};function a(T,A){T.name in o?o[T.name].push(A):o[T.name]=[A]}function c(T,A){const N=[];let E;for(const D of A){const F=D[0],_=D[1],B=D[2];if(E=D[3]==null?{}:D[3],!(F in i)){a(T,A);return}const $=i[F];if($.inboundNodes.length<=_){a(T,A);return}const H=$.inboundNodes[_];N.push(H.outputTensors[B])}N.length>0&&T.apply(Kn(N),E)}function h(T){const A=T.name,N=li(T,t.customObjects!=null?t.customObjects:{});N.setFastWeightInitDuringBuild(s),i[A]=N;const E=T.inboundNodes;E.forEach(D=>{if(!(D instanceof Array))throw new j(`Corrupted configuration, expected array for nodeData: ${D}`);a(N,D)})}const d=t.name,m=t.layers;for(const T of m)h(T);for(;!mz(o);)for(const T of m){const A=i[T.name];if(A.name in o){const N=o[A.name];delete o[A.name];for(const E of N)c(A,E)}}const y=[],b=[],w=t.inputLayers;for(const T of w){const A=T[0],N=T[1],E=T[2];xs(A in i);const D=i[A],F=D.inboundNodes[N].outputTensors;y.push(F[E])}const L=t.outputLayers;for(const T of L){const A=T[0],N=T[1],E=T[2];xs(A in i);const D=i[A],F=D.inboundNodes[N].outputTensors;b.push(F[E])}return new e({inputs:y,outputs:b,name:d})}get stateful(){if(this._stateful)throw new j("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(const e of this.layers)if(e.stateful)return!0;return!1}resetStates(){ee(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}}function Dv(e,t,n){const s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(i=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){const i=[];return t.forEach(o=>{o in e?i.push(e[o]):i.push(null)}),i}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function kv(e,t){return Dv(e,t,"classWeight")}function kQ(e,t){return Dv(e,t,"sampleWeight")}async function Fv(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){const i=ee(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){const c=1;return e.argMax(c)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),o=Array.from(await i.data());He(i);const a=[];return o.forEach(c=>{if(n[c]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${c} exists in the data but not in classWeight`);a.push(n[c])}),ss(a,"float32")}else return null}function U3(e,t){return X(e,t)}const B3=32;function _v(e,t){let n,s;const i=t;n=i.xs,s=i.ys,k(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);const o=Wv("input",e.inputNames,n),a=Wv("output",e.outputNames,s),c=o[0].shape[0];k(o.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${o.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),k(a.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${a.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let h=0;h<o.length;h++)k(o[h].shape[0]===c,()=>`Batch size mismatch: input ${e.inputNames[h]} has ${o[h].shape[0]}; expected  ${c} based on input ${e.inputNames[0]}.`);for(let h=0;h<a.length;h++)k(a[h].shape[0]===c,()=>`Batch size mismatch: output ${e.outputNames[h]} has ${a[h].shape[0]}; expected  ${c} based on input ${e.inputNames[0]}.`);return{xs:o,ys:a}}function Wv(e,t,n){if(n instanceof Q)return[n];if(Array.isArray(n))return k(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{const s=[];for(const i of t){if(n[i]==null)throw new j(`The feature data generated by the dataset lacks the required ${e} key '${i}'.`);s.push(n[i])}return s}}function M3(e){if(e.length===3)throw new ze("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function P3(e,t,n){const s=n.batchesPerEpoch!=null;if(k(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),k(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),k(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{const i=n.validationData!=null;let o,a;if(i)if($v(n.validationData))k(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{const A=M3(n.validationData);o=A.xs,a=A.ys}const c=e.makeTrainFunction(),h=e.getDedupedMetricsNames();let d;i?d=h.slice().concat(h.map(A=>"val_"+A)):d=h.slice();const m=Lv(n.callbacks,n.yieldEvery),y=n.verbose==null?1:n.verbose,{callbackList:b,history:w}=Sv(m,y,n.epochs,null,null,z3(t,n),null,i,d);b.setModel(e),e.history=w,await b.onTrainBegin(),e.stopTraining_=!1;let L=n.initialEpoch==null?0:n.initialEpoch,T=await t.iterator();for(;L<n.epochs;){const A={};await b.onEpochBegin(L);let N=0,E=0;for(s||(T=await t.iterator());s?N<n.batchesPerEpoch:!0;){const D=await T.next();if(s&&D.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${N} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(D.value!=null){const{xs:F,ys:_}=_v(e,D.value),B={};B.batch=E,B.size=F[0].shape[0],await b.onBatchBegin(E,B);const $=[];if(n.classWeight!=null){const J=kv(n.classWeight,e.outputNames);for(let re=0;re<J.length;++re)$.push(await Fv(_[re],null,J[re]))}const H=F.concat(_).concat($),q=c(H);He(H);for(let J=0;J<h.length;++J){const re=h[J],ce=q[J];B[re]=ce,Cn(ce)}await b.onBatchEnd(E,B),fv(B),E++,N++}if(s?N>=n.batchesPerEpoch:D.done){if(i){let F;$v(n.validationData)?F=Rt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):F=Rt(e.evaluate(o,a,{batchSize:n.validationBatchSize==null?B3:n.validationBatchSize,verbose:0}));for(let _=0;_<e.metricsNames.length;++_)A[`val_${e.metricsNames[_]}`]=F[_]}break}if(e.stopTraining_)break}if(await b.onEpochEnd(L,A),L++,e.stopTraining_)break}return await b.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function z3(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function $v(e){return typeof e.iterator=="function"}function G3(e){return typeof e.next=="function"}async function V3(e,t,n){n=n||{};const s=n.batches!=null,i=e.testFunction;let o=[];if(n.verbose>0)throw new ze("Verbose mode is not implemented yet.");k(!s||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);const a=G3(t)?t:await t.iterator();let c=0,h=0;for(;s?h<n.batches:!0;){const d=await a.next();if(o=ee(()=>{if(d.value){const{xs:m,ys:y}=_v(e,d.value),b=m.concat(y),w=ee(()=>i(b));if(He(b),h===0)for(let T=0;T<w.length;++T)o.push(Ne(0));const L=b[0].shape[0];for(let T=0;T<w.length;++T){const A=w[T],N=o[T];o[T]=ee(()=>be(o[T],X(L,A))),h>0&&He(N)}He(w),c+=L,++h}return o}),d.done){s&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). 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You may need to use the repeat() function when building your dataset.`);break}}for(let d=0;d<o.length;++d){const m=o[d];o[d]=_e(o[d],c),He(m)}return Kn(o)}function jw(e){k(e>0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function Uh(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(s=>zo(s,t,n-t)):zo(e,t,n-t)}function Kw(e,t){return ee(()=>e==null?null:Array.isArray(e)?e.map(n=>Kw(n,t)):av(e,t.dtype==="int32"?t:t.toInt()))}function Xw(e,t){const n=[];let s=0,i=null;for(;s<e;)i=s+t,i>=e&&(i=e),n.push([s,i]),s=i;return n}async function Y3(e,t,n,s,i,o,a,c,h,d,m,y,b,w,L){i==null&&(i=32),o==null&&(o=1),m==null&&(m=!0),b==null&&(b=0);let T=!1;if(h!=null&&d!=null&&(T=!0),L!=null&&(T=!0,w==null))throw new j("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");const A=e.checkNumSamples(n,i,w,"steps_per_epoch");let N;A!=null&&(N=oi(0,A)),a==null&&(a=1);const{callbackList:E,history:D}=Sv(c,a,o,b,A,w,i,T,y);E.setModel(e),e.history=D,await E.onTrainBegin(),e.stopTraining_=!1;for(let F=b;F<o;++F){await E.onEpochBegin(F);const _={};if(w!=null)throw new ze("stepsPerEpoch mode is not implemented yet.");{if(m==="batch")throw new ze("batch shuffling is not implemneted yet");m&&Ry(N);const B=ss(N),$=Xw(A,i);for(let H=0;H<$.length;++H){const q={};if(await E.onBatchBegin(H,q),ee(()=>{const J=$[H][0],re=$[H][1],ce=zo(B,J,re-J);q.batch=H,q.size=re-J;const ue=Kw(n,ce),he=t(ue);for(let de=0;de<s.length;++de){const le=s[de],ye=he[de];q[le]=ye,Cn(ye)}if(H===$.length-1&&T){const de=e.testLoop(h,d,i);for(let le=0;le<s.length;++le){const ye=s[le],pe=de[le];Cn(pe),_["val_"+ye]=pe}}}),await E.onBatchEnd(H,q),fv(q),e.stopTraining_)break}B.dispose()}if(await E.onEpochEnd(F,_),e.stopTraining_)break}return await E.onTrainEnd(),await e.history.syncData(),e.history}async function H3(e,t,n,s={}){if(e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;let i,o,a,c,h,d,m;try{const y=s.batchSize==null?32:s.batchSize;jw(y);const b=!1,w=await e.standardizeUserData(t,n,s.sampleWeight,s.classWeight,b,y);i=w[0],o=w[1],m=w[2];let L=!1,T;if(s.validationData!=null&&s.validationData.length>0){if(L=!0,s.validationData.length===2)a=s.validationData[0],c=s.validationData[1];else throw s.validationData.length===3?new ze("validationData including sample weights is not supported yet."):new j(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${s.validationData} is invalid.`);const $=!0,H=await e.standardizeUserData(a,c,null,null,$,y);h=H[0],d=H[1],T=h.concat(d)}else if(s.validationSplit!=null&&s.validationSplit>0&&s.validationSplit<1){L=!0;const $=Math.floor(i[0].shape[0]*(1-s.validationSplit)),H=i[0].shape[0];h=Uh(i,$,H),i=Uh(i,0,$),d=Uh(o,$,H),o=Uh(o,0,$),T=h.concat(d)}else s.validationSteps!=null&&(L=!0);const A=i.concat(o).concat(m);e.checkTrainableWeightsConsistency();const N=e.makeTrainFunction(),E=e.getDedupedMetricsNames();let D,F;L?(e.makeTestFunction(),D=e.testFunction,F=E.slice().concat(E.map($=>"val_"+$))):(D=null,T=[],F=E.slice());const _=Lv(s.callbacks,s.yieldEvery),B=await Y3(e,N,A,E,y,s.epochs,s.verbose,_,D,T,s.shuffle,F,s.initialEpoch,null,null);return B}finally{e.isTraining=!1,Vo(i,t),Vo(o,n),Vo(h,a),Vo(d,c),m!=null&&He(m)}}function Uv(e){const t=[];e instanceof Q&&(e=[e]);for(let n=0;n<e.length;++n){const s=e[n];if(s.rank===1)t.push(Dh(s,1));else{if(s.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");t.push(s)}}return t}function Vo(e,t){if(e==null)return;const n=[];if(t instanceof Q)n.push(t.id);else if(Array.isArray(t))t.forEach(i=>n.push(i.id));else if(t!=null)for(const i in t){const o=t[i];n.push(o.id)}const s=[];if(e instanceof Q)n.indexOf(e.id)===-1&&s.push(e);else if(Array.isArray(e))e.forEach(i=>{n.indexOf(i.id)===-1&&s.push(i)});else if(e!=null)for(const i in e){const o=e[i];n.indexOf(o.id)===-1&&s.push(o)}s.forEach(i=>{i.isDisposed||i.dispose()})}function q3(e){return e instanceof Q}function Jw(e){return Array.isArray(e)}function Bv(e){return!q3(e)&&!Jw(e)}function Mv(e,t,n,s=!0,i=""){if(t==null||t.length===0){if(e!=null){let a=!1;if(Jw(e)&&e.length>0)a=!0;else if(Bv(e)){for(const c in e)if(e.hasOwnProperty(c)){a=!0;break}}else a=!0;if(a)throw new j(`Error when checking model ${i} expected no data, but got ${e}`)}return[]}if(e==null)return t.map(a=>null);let o;if(Bv(e)){e=e,o=[];for(const a of t){if(e[a]==null)throw new j(`No data provided for "${a}". 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Use LayersModel.compile(modelCompileArgs).");const i=[];for(let o=0;o<this.feedOutputShapes.length;++o){const a=this.feedOutputShapes[o],c=this.feedLossFns[o];c===_p?i.push(a.slice(0,a.length-1).concat([1])):i.push(a)}if(e=Mv(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=Mv(t,this.feedOutputNames,i,!1,"target"),j3(e,t,null),K3(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new j(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,i=!0,o){const[a,c]=this.standardizeUserDataXY(e,t,i,o);if(n!=null)throw new Error("sample weight is not supported yet.");let h=null;if(s!=null){const d=kv(s,this.outputNames);h=[];for(let m=0;m<d.length;++m)h.push(await Fv(c[m],null,d[m]))}return[a,c,h]}testLoop(e,t,n,s=0,i){return ee(()=>{const o=this.checkNumSamples(t,n,i,"steps"),a=[];if(s>0)throw new ze("Verbose mode is not implemented yet.");if(i!=null)throw new ze("steps mode in testLoop() is not implemented yet");{const c=Xw(o,n),h=ss(oi(0,o));for(let d=0;d<c.length;++d){const m=c[d][0],y=c[d][1],b=zo(h,m,y-m),w=Kw(t,b),L=e(w);if(d===0)for(let T=0;T<L.length;++T)a.push(Ne(0));for(let T=0;T<L.length;++T){const A=L[T];a[T]=be(a[T],X(y-m,A))}}for(let d=0;d<a.length;++d)a[d]=_e(a[d],o)}return a})}getDedupedMetricsNames(){const e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){const s=e[n];let i=s;if(KA(e,s)>1){const o=KA(e.slice(0,n),s);i+=`_${o}`}t.push(i)}return t}makeTrainFunction(){return e=>{const t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),i=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),o=[],a=()=>{const m=[];for(let L=0;L<this.inputs.length;++L)m.push({key:this.inputs[L],value:n[L]});const y=new Go(m),b=$h(this.outputs,y,{training:!0});let w;for(let L=0;L<this.lossFunctions.length;++L){const T=this.lossFunctions[L];let A=T(s[L],b[L]);i[L]!=null&&(A=U3(A,i[L]));const N=Gt(A);t.push(N),L===0?w=A:w=be(w,A)}for(let L=0;L<this.metricsTensors.length;++L){let T;if(this.outputs.length>1&&L<this.outputs.length)T=t[L];else{const A=this.metricsTensors[L][0],N=this.metricsTensors[L][1];T=Gt(A(s[N],b[N]))}Cn(T),o.push(T)}return w=Gt(w),this.calculateLosses().forEach(L=>{w=be(w,L)}),w},c=this.collectedTrainableWeights.map(m=>m.read()),h=!0,d=this.optimizer_.minimize(a,h,c);return[d].concat(o)}}makeTestFunction(){this.testFunction=e=>ee(()=>{const t=[];let n;const s=e.slice(0,this.inputs.length),i=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),o=[];for(let h=0;h<this.inputs.length;++h)o.push({key:this.inputs[h],value:s[h]});const a=new Go(o),c=$h(this.outputs,a);for(let h=0;h<this.lossFunctions.length;++h){const d=this.lossFunctions[h],m=Gt(d(i[h],c[h]));h===0?n=m:n=be(n,m),t.push(n)}for(let h=0;h<this.metricsTensors.length;++h){const d=this.metricsTensors[h][0],m=this.metricsTensors[h][1],y=Gt(d(i[m],c[m]));t.push(y)}return t})}async fit(e,t,n={}){return H3(this,e,t,n)}async fitDataset(e,t){return P3(this,e,t)}async trainOnBatch(e,t){const n=await this.standardizeUserData(e,t),s=n[0],i=n[1],o=this.makeTrainFunction(),a=o(s.concat(i)),c=[];for(const h of a){const d=await h.data();c.push(d[0])}return He(a),Kn(c)}getNamedWeights(e){const t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,i=this.getWeights(n);for(let o=0;o<s.length;++o){if(n&&!s[o].trainable)continue;t.push({name:s[o].originalName,tensor:i[o]})}return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){const e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){const t=Ed().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Ed().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=nr(this.loss);else if(Array.isArray(this.loss)){for(const t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>nr(t))}else{const t=Object.keys(this.loss);e={};const n=this.loss;for(const s of t)if(typeof n[s]=="string")e[s]=nr(n[s]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[nr(Mp(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>nr(Mp(e)));{const e={};for(const t in this.metrics)e[t]=nr(Mp(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");const t=Wh(e.optimizer_config),n=li(t);let s;if(typeof e.loss=="string")s=Mo(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(o=>Mo(o));else if(e.loss!=null){s={};for(const o in e.loss)s[o]=Mo(e.loss[o])}let i;if(Array.isArray(e.metrics))i=e.metrics.map(o=>Mo(o));else if(e.metrics!=null){i={};for(const o in e.metrics)i[o]=Mo(e.metrics[o])}this.compile({loss:s,metrics:i,optimizer:n})}async save(e,t){if(typeof e=="string"){const h=qy(e);if(h.length===0)throw new j(`Cannot find any save handlers for URL '${e}'`);if(h.length>1)throw new j(`Found more than one (${h.length}) save handlers for URL '${e}'`);e=h[0]}if(e.save==null)throw new j("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const n=await Yy(this.getNamedWeights(t)),s=!1,i=null,o=this.toJSON(i,s),a={modelTopology:o,format:J3,generatedBy:`TensorFlow.js tfjs-layers v${zp}`,convertedBy:null},c=t==null?!1:t.includeOptimizer;if(c&&this.optimizer!=null){a.trainingConfig=this.getTrainingConfig();const h="optimizer",{data:d,specs:m}=await Yy(await this.optimizer.getWeights(),h);n.specs.push(...m),n.data=Id([n.data,d])}if(this.userDefinedMetadata!=null){const h=!0;Cv(this.userDefinedMetadata,this.name,h),a.userDefinedMetadata=this.userDefinedMetadata}return a.weightData=n.data,a.weightSpecs=n.specs,e.save(a)}setUserDefinedMetadata(e){Cv(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}}ir.className="Model",me(ir);class zv extends ir{}zv.className="Functional",me(zv);async function Z3(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);const s=Wh(n),i=li(s,t);if(e.weightsManifest!=null){const o=await mT(e.weightsManifest,e.pathPrefix,i.weights.map(c=>c.originalName)),a={};for(const c of i.weights)a[c.originalName]=o[c.originalName];i.loadWeights(a),He(o)}return i}async function Q3(e,t){if(t==null&&(t={}),typeof e=="string"){const n=jy(e,t);if(n.length===0)n.push(Ad(e,t));else if(n.length>1)throw new j(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return eG(e,void 0,t)}async function eG(e,t,n){if(n==null&&(n={}),e.load==null)throw new j("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const s=await e.load();let i=s.modelTopology;i.model_config!=null&&(i=i.model_config);const o=n.strict==null?!0:n.strict,a=s.weightData!=null&&s.weightSpecs!=null&&o,c=li(Wh(i),t,a),h=s.trainingConfig;if(h!=null&&c.loadTrainingConfig(h),s.userDefinedMetadata!=null&&c.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new j("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");const{modelWeights:d,optimizerWeights:m}=tG(s.weightData,s.weightSpecs);c.loadWeights(d,o),c.optimizer!=null&&m.length>0&&await c.optimizer.setWeights(m),He(d),He(m.map(y=>y.tensor))}return c}function tG(e,t){const n=Sd(e,t),s={},i=[];return t.forEach(o=>{o.group==="optimizer"?i.push({name:o.name,tensor:n[o.name]}):s[o.name]=n[o.name]}),{modelWeights:s,optimizerWeights:i}}class ec extends ir{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Op("sequential_"),e.layers!=null)for(const t of e.layers)this.add(t)}checkShape(e){const t=e.inboundNodes[0].outputTensors[0].shape;if(t.some(n=>n<0))throw new j(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){const t=e instanceof ec||e instanceof ir;let n;if(t){if(n=e,n.outputs.length!==1)throw new j("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new j("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new j("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");const s=mv({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new j(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new j("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=pv(this.outputs[0])}this.inboundNodes=[],new kp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Bo(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{const s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{const e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(St(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new ir({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new ri("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new ri("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new ri("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new ri("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let i,o={};if(t instanceof Array){if(!(t[0].className!=null)||t[0].className==="Merge")throw new j("Legacy serialization format not supported yet.");i=t}else k(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),i=t.layers,delete t.layers,o=t;const a=new e(o);if(!(a instanceof ec))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(const c of i){const h=void 0,d=li(c,h,s);s&&d.setFastWeightInitDuringBuild(!0),a.add(d)}return a}set stopTraining(e){if(this.model==null)throw new j("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new j("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){const e=[];for(const t of this.layers){const n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}}ec.className="Sequential",me(ec);function nG(e){return new ir(e)}function sG(e){return new ec(e)}function iG(e,t){return t==null&&(t={}),Q3(e,t)}function Gv(e){return mv(e)}function rG(e,t){Ms.registerCallbackConstructor(e,t)}class os extends Ao{getConfig(){return{}}}class Vv extends os{apply(e,t=1){return kz(e,t)}}Vv.className="elu",me(Vv);class Yv extends os{apply(e){return Jd(e)}}Yv.className="selu",me(Yv);class Hv extends os{apply(e){return Oi(e)}}Hv.className="relu",me(Hv);class qv extends os{apply(e){return ee(()=>Eo(6,Oi(e)))}}qv.className="relu6",me(qv);class jv extends os{apply(e){return e}}jv.className="linear",me(jv);class Kv extends os{apply(e){return Ni(e)}}Kv.className="sigmoid",me(Kv);class Xv extends os{apply(e){return _z(e)}}Xv.className="hardSigmoid",me(Xv);class Jv extends os{apply(e){return Ua(e)}}Jv.className="softplus",me(Jv);class Zv extends os{apply(e){return Fz(e)}}Zv.className="softsign",me(Zv);class Qv extends os{apply(e){return Fa(e)}}Qv.className="tanh",me(Qv);class Zw extends os{apply(e,t=-1){return Wo(e,t)}}Zw.className="softmax",me(Zw);class eN extends os{apply(e,t=-1){return Yd(e,t)}}eN.className="logSoftmax",me(eN);class tN extends os{apply(e,t=1){return ee(()=>Ni(e.mul(t)).mul(e))}}tN.className="swish",me(tN);function Gr(e){return e.getClassName()}function Qw(e,t={}){return Ch(e,Fs.getMap().classNameMap,t,"activation")}function Vr(e){if(e==null){const t={};return t.className="linear",t.config={},Qw(t)}if(typeof e=="string"){const t={};return t.className=e,t.config={},Qw(t)}else return e instanceof os?e:Qw(e)}function eL(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}class nN extends Ao{}class Bh extends nN{constructor(e){super();eL(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return ee(()=>{let t=ct([1]);return this.hasL1&&(t=be(t,Ue(X(this.l1,sn(e))))),this.hasL2&&(t=be(t,Ue(X(this.l2,kh(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}Bh.className="L1L2",me(Bh);function oG(e){return eL(e),new Bh({l1:e!=null?e.l1:null,l2:0})}function aG(e){return eL(e),new Bh({l2:e!=null?e.l2:null,l1:0})}const sN={l1l2:"L1L2"};function It(e){return yw(e)}function iN(e,t={}){return Ch(e,Fs.getMap().classNameMap,t,"regularizer")}function _t(e){if(e==null)return null;if(typeof e=="string"){const t=e in sN?sN[e]:e,n={className:t,config:{}};return iN(n)}else return e instanceof nN?e:iN(e)}class tL extends lt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ke(e);let n=Oi(e);return this.maxValue!=null&&(n=Hn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}tL.className="ReLU",me(tL);class nL extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=Ke(e);return zd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}nL.className="LeakyReLU",me(nL);class sL extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Ft(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=_t(e.alphaRegularizer),this.alphaConstraint=ln(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new j(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=St(e);const t=e.slice(1);if(this.sharedAxes!=null)for(const s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new mn({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Ke(e),dh(e,this.alpha.read())}getConfig(){const e={alphaInitializer:Yt(this.alphaInitializer),alphaRegularizer:It(this.alphaRegularizer),alphaConstraint:cn(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}}sL.className="PReLU",me(sL);class iL extends lt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new ze(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=Ke(e);return Oo(n)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}iL.className="ELU",me(iL);class rL extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){const n=Ke(e);return n.mul(Eh(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){const e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}}rL.className="ThresholdedReLU",me(rL);class oL extends lt{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Zw().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){const n=Ke(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}oL.className="Softmax",me(oL);function tc(e,t,n){if(typeof e=="number")return Bo(e,t);if(e.length!==t)throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){const i=e[s];if(!Nz(i))throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${i}`)}return e}function hi(e,t,n,s,i=1){if(e==null)return e;const o=t+(t-1)*(i-1);let a;return n==="same"?a=e:a=e-o+1,Math.floor((a+s-1)/s)}function Gp(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+Mr([n-t,0]);else if(s==="same")e=e*t;else throw new j(`Unsupport padding mode: ${s}.`);return e}function aL(e,t){return ee(()=>(Vt(t),t==="channelsFirst"?Me(e,[0,2,3,1]):e))}function rN(e,t){return ee(()=>(Vt(t),t==="channelsFirst"?Me(e,[0,2,3,4,1]):e))}function oN(e,t,n,s=1,i="valid",o,a=1){return ee(()=>{if(o==null&&(o=ii()),Vt(o),e.shape.length!==3)throw new j(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new j(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new j(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(o==="channelsFirst"&&(e=Me(e,[0,2,1])),i==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let c=$d(e,t,s,i==="same"?"same":"valid","NWC",a);return n!=null&&(c=ki(c,n)),c})}function FQ(e,t,n=1,s="valid",i,o=1){return ee(()=>(Vt(i),oN(e,t,null,n,s,i,o)))}function _Q(e,t,n=[1,1],s="valid",i,o){return ee(()=>(Vt(i),cL(e,t,null,n,s,i,o)))}function cL(e,t,n,s=[1,1],i="valid",o,a,c=null){return ee(()=>{if(o==null&&(o=ii()),Vt(o),e.rank!==3&&e.rank!==4)throw new j(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new j(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let h=aL(e,o);if(i==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return h=ew({x:h,filter:t,strides:s,pad:i==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:n,activation:c}),o==="channelsFirst"&&(h=Me(h,[0,3,1,2])),h})}function WQ(e,t,n=[1,1,1],s="valid",i,o){return ee(()=>(Vt(i),aN(e,t,null,n,s,i,o)))}function aN(e,t,n,s=[1,1,1],i="valid",o,a){return ee(()=>{if(o==null&&(o=ii()),Vt(o),e.rank!==4&&e.rank!==5)throw new j(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new j(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let c=rN(e,o);if(i==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return c=vb(c,t,s,i==="same"?"same":"valid","NDHWC",a),n!=null&&(c=ki(c,n)),o==="channelsFirst"&&(c=Me(c,[0,4,1,2,3])),c})}class lL extends lt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",lL.verifyArgs(t),this.rank=e,pn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=tc(t.kernelSize,e,"kernelSize"),this.strides=tc(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Ts(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Vt(this.dataFormat),this.activation=Vr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Ft(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=ln(t.biasConstraint),this.biasRegularizer=_t(t.biasRegularizer),this.activityRegularizer=_t(t.activityRegularizer),this.dilationRate=tc(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new j(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new j(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new j(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(xs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!ww(e.kernelSize,"number",1,3))throw new j(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){const e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Gr(this.activation),useBias:this.useBias,biasInitializer:Yt(this.biasInitializer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),biasConstraint:cn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class Mh extends lL{constructor(e,t){super(e,t);this.kernel=null,Mh.verifyArgs(t),this.filters=t.filters,pn(this.filters,"filters"),this.kernelInitializer=Ft(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=ln(t.kernelConstraint),this.kernelRegularizer=_t(t.kernelRegularizer)}build(e){e=St(e);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[t]}`);const n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return ee(()=>{e=Ke(e);let n;const s=this.bias==null?null:this.bias.read(),i=JA(this.activation.getClassName());if(i!=null&&this.rank===2)n=cL(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,i);else{if(this.rank===1)n=oN(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=cL(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=aN(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=St(e);const t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let i=0;i<n.length;++i){const o=hi(n[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);t.push(o)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){const e={filters:this.filters,kernelInitializer:Yt(this.kernelInitializer),kernelRegularizer:It(this.kernelRegularizer),kernelConstraint:cn(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new j(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}}class Ph extends Mh{constructor(e){super(2,e);Ph.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!ww(e.kernelSize,"number",1,2))throw new j(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}Ph.className="Conv2D",me(Ph);class Vp extends Mh{constructor(e){super(3,e);Vp.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new j(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}Vp.className="Conv3D",me(Vp);class hL extends Ph{constructor(e){super(e);if(this.inputSpec=[new mn({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new j(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=St(e),e.length!==4)throw new j("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new mn({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return ee(()=>{let n=Ke(e);if(n.shape.length!==4)throw new j(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);const s=n.shape,i=s[0];let o,a;this.dataFormat==="channelsFirst"?(o=2,a=3):(o=1,a=2);const c=s[o],h=s[a],d=this.kernelSize[0],m=this.kernelSize[1],y=this.strides[0],b=this.strides[1],w=Gp(c,y,d,this.padding),L=Gp(h,b,m,this.padding),T=[i,w,L,this.filters];this.dataFormat!=="channelsLast"&&(n=Me(n,[0,2,3,1]));let A=Ud(n,this.kernel.read(),T,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=Me(A,[0,3,1,2])),this.bias!=null&&(A=ki(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=St(e);const t=e.slice();let n,s,i;this.dataFormat==="channelsFirst"?(n=1,s=2,i=3):(n=3,s=1,i=2);const o=this.kernelSize[0],a=this.kernelSize[1],c=this.strides[0],h=this.strides[1];return t[n]=this.filters,t[s]=Gp(t[s],c,o,this.padding),t[i]=Gp(t[i],h,a,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}hL.className="Conv2DTranspose",me(hL);class cN extends Mh{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new j("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new j("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new j(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Ft(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=_t(t.depthwiseRegularizer),this.depthwiseConstraint=ln(t.depthwiseConstraint),this.pointwiseInitializer=Ft(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=_t(t.pointwiseRegularizer),this.pointwiseConstraint=ln(t.pointwiseConstraint)}build(e){if(e=St(e),e.length<this.rank+2)throw new j(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);const t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);const n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),i=[];for(let a=0;a<this.rank;++a)i.push(1);i.push(n*this.depthMultiplier,this.filters);const o=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,o,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",i,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,o,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,o,this.biasConstraint):this.bias=null,this.inputSpec=[new mn({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return ee(()=>{e=Ke(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Me(e,[0,2,3,1])),n=Gb(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=ki(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Me(n,[0,3,1,2])),n})}getConfig(){const e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Yt(this.depthwiseInitializer),e.pointwiseInitializer=Yt(this.pointwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.pointwiseRegularizer=It(this.pointwiseRegularizer),e.depthwiseConstraint=cn(this.depthwiseConstraint),e.pointwiseConstraint=cn(this.pointwiseConstraint),e}}cN.className="SeparableConv";class uL extends cN{constructor(e){super(2,e)}}uL.className="SeparableConv2D",me(uL);class Yp extends Mh{constructor(e){super(1,e);Yp.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){const e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!ww(e.kernelSize,"number",1,1))throw new j(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}Yp.className="Conv1D",me(Yp);class dL extends lt{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return ee(()=>{if(e=Ke(e),this.dataFormat==="channelsLast"){const n=Lp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Lp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{const n=Lp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Lp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){const e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}dL.className="Cropping2D",me(dL);class pL extends lt{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){const t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{const t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return ee(()=>{let n=Ke(e);const s=n.shape;if(this.dataFormat==="channelsFirst"){n=Me(n,[0,2,3,1]);const i=this.size[0]*s[2],o=this.size[1]*s[3],a=n.resizeNearestNeighbor([i,o]);return Me(a,[0,3,1,2])}else{const i=this.size[0]*s[1],o=this.size[1]*s[2];return n.resizeNearestNeighbor([i,o])}})}getConfig(){const e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}pL.className="UpSampling2D",me(pL);function cG(e,t,n=[1,1],s="valid",i,o){return ee(()=>{i==null&&(i=ii()),Vt(i);let a=aL(e,i);if(e.rank!==4)throw new j(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new j(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return a=Co(a,t,n,s==="same"?"same":"valid","NHWC",o),i==="channelsFirst"&&(a=Me(a,[0,3,1,2])),a})}class mL extends lL{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Ft(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=ln(e.depthwiseConstraint),this.depthwiseRegularizer=_t(e.depthwiseRegularizer)}build(e){if(e=St(e),e.length<4)throw new j(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);const n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{e=Ke(e);let n=cG(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ki(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=St(e);const t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,i=hi(t,this.kernelSize[0],this.padding,this.strides[0]),o=hi(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,i,o]:[e[0],i,o,s]}getConfig(){const e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Yt(this.depthwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.depthwiseConstraint=cn(this.depthwiseRegularizer),e}}mL.className="DepthwiseConv2D",me(mL);function lN(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new j("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function i(o){return o==null||Array.isArray(o)?o:[o]}return t=i(t),n=i(n),{inputs:e,initialState:t,constants:n}}function hN(e,t,n,s=!1,i,o,a=!1,c=!1){return ee(()=>{const h=t.shape.length;if(h<3)throw new j(`Input should be at least 3D, but is ${h}D.`);const d=[1,0].concat(oi(2,h));if(t=Me(t,d),o!=null)throw new ze("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),i!=null&&(i=i.asType("bool").asType("float32"),i.rank===h-1&&(i=qn(i,-1)),i=Me(i,d)),s&&(t=Is(t,0),i!=null&&(i=Is(i,0)));const m=[];let y,b=n;const w=t.shape[0],L=Ei(t);let T;i!=null&&(T=Ei(i));for(let N=0;N<w;++N){const E=L[N],D=ee(()=>e(E,b));if(i==null)y=D[0],b=D[1];else{const F=ee(()=>{const _=T[N],B=En(_).sub(_),$=D[0].mul(_).add(b[0].mul(B)),H=b.map((q,J)=>D[1][J].mul(_).add(q.mul(B)));return{output:$,newStates:H}});y=F.output,b=F.newStates}c&&m.push(y)}let A;if(c){const N=1;A=rs(m,N)}return[y,A,b]})}class _i extends lt{constructor(e){super(e);let t;if(e.cell==null)throw new j("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new jp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new j("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new mn({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return oi(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Ww(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);const n=t[0];let s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){const i=[];for(const o of t)i.push([e[0],o]);return[s].concat(i)}else return s}computeMask(e,t){return ee(()=>{Array.isArray(t)&&(t=t[0]);const n=this.returnSequences?t:null;if(this.returnState){const s=this.states.map(i=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){const t=null;if(this.numConstants!=null)throw new ze("Constants support is not implemented in RNN yet.");Ww(e)&&(e=e[0]),e=e;const n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new mn({shape:[n,null,...s]});const i=[e[0]].concat(e.slice(2));if(t!=null)throw new ze("Constants support is not implemented in RNN yet.");this.cell.build(i);let o;if(Array.isArray(this.cell.stateSize)?o=this.cell.stateSize:o=[this.cell.stateSize],this.stateSpec!=null){if(!ot(this.stateSpec.map(a=>a.shape[a.shape.length-1]),o))throw new j(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=o.map(a=>new mn({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){ee(()=>{if(!this.stateful)throw new tr("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape[0];if(n==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>ct([n,s])):this.states_=[ct([n,this.cell.stateSize])];else if(e==null)He(this.states_),this.keptStates!=null&&(He(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>ct([n,s])):this.states_[0]=ct([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):He(this.states_);for(let s=0;s<this.states_.length;++s){const i=e[s],o=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,a=[n,o];if(!ot(i.shape,a))throw new j(`State ${s} is incompatible with layer ${this.name}: expected shape=${a}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Cn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=lN(e,n,s,this.numConstants);e=i.inputs,n=i.initialState,s=i.constants;let o=[],a=[];if(n!=null){t.initialState=n,o=o.concat(n),this.stateSpec=[];for(const h of n)this.stateSpec.push(new mn({shape:h.shape}));a=a.concat(this.stateSpec)}s!=null&&(t.constants=s,o=o.concat(s),this.numConstants=s.length);const c=o[0]instanceof ci;if(c){const h=[e].concat(o),d=this.inputSpec.concat(a),m=this.inputSpec;this.inputSpec=d;const y=super.apply(h,t);return this.inputSpec=m,y}else return super.apply(e,t)}call(e,t){return ee(()=>{const n=t==null?null:t.mask,s=t==null?null:t.training;let i=t==null?null:t.initialState;e=Ke(e),i==null&&(this.stateful?i=this.states_:i=this.getInitialState(e));const o=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(i.length!==o)throw new j(`RNN Layer has ${o} state(s) but was passed ${i.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const a={training:s},c=(w,L)=>{const T=this.cell.call([w].concat(L),a);return[T[0],T.slice(1)]},h=hN(c,e,i,this.goBackwards,n,null,this.unroll,this.returnSequences),d=h[0],m=h[1],y=h[2];this.stateful&&this.resetStates(y,s);const b=this.returnSequences?m:d;return this.returnState?[b].concat(y):b})}getInitialState(e){return ee(()=>{let t=ct(e.shape);return t=Ue(t,[1,2]),t=Dh(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Nw(t,[1,n]):t):this.cell.stateSize>1?[Nw(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){const e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);const n=this.cell.getConfig();return this.getClassName()===_i.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){const s=t.cell,i=li(s,n);return new e(Object.assign(t,{cell:i}))}}_i.className="RNN",me(_i);class nc extends lt{}class Hp extends nc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,pn(this.units,"units"),this.activation=Vr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=ln(e.kernelConstraint),this.recurrentConstraint=ln(e.recurrentConstraint),this.biasConstraint=ln(e.biasConstraint),this.dropout=Xa([1,Mr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Xa([1,Mr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=St(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{if(e=e,e.length!==2)throw new j(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];const s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Yr({ones:()=>En(e),rate:this.dropout,training:s})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Yr({ones:()=>En(n),rate:this.recurrentDropout,training:s}));let i;const o=this.dropoutMask,a=this.recurrentDropoutMask;o!=null?i=Di(X(e,o),this.kernel.read()):i=Di(e,this.kernel.read()),this.bias!=null&&(i=ki(i,this.bias.read())),a!=null&&(n=X(n,a));let c=be(i,Di(n,this.recurrentKernel.read()));return this.activation!=null&&(c=this.activation.apply(c)),[c,c]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Gr(this.activation),useBias:this.useBias,kernelInitializer:Yt(this.kernelInitializer),recurrentInitializer:Yt(this.recurrentInitializer),biasInitializer:Yt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:cn(this.kernelConstraint),recurrentConstraint:cn(this.recurrentConstraint),biasConstraint:cn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}}Hp.className="SimpleRNNCell",me(Hp);class fL extends _i{constructor(e){e.cell=new Hp(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return new e(t)}}fL.className="SimpleRNN",me(fL);class qp extends nc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new j("GRUCell does not support reset_after parameter set to true.");this.units=e.units,pn(this.units,"units"),this.activation=Vr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Vr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=ln(e.kernelConstraint),this.recurrentConstraint=ln(e.recurrentConstraint),this.biasConstraint=ln(e.biasConstraint),this.dropout=Xa([1,Mr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Xa([1,Mr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=St(e);const t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return ee(()=>{if(e=e,e.length!==2)throw new j(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training==null?!1:t.training;let s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Yr({ones:()=>En(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Yr({ones:()=>En(s),rate:this.recurrentDropout,training:n,count:3}));const i=this.dropoutMask,o=this.recurrentDropoutMask;let a,c,h;0<this.dropout&&this.dropout<1&&(e=X(e,i[0]));let d=Di(e,this.kernel.read());this.useBias&&(d=ki(d,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,o[0]));const m=this.recurrentKernel.read(),[y,b]=is(m,[2*this.units,this.units],m.rank-1),w=Di(s,y),[L,T,A]=is(d,3,d.rank-1),[N,E]=is(w,2,w.rank-1);a=this.recurrentActivation.apply(be(L,N)),c=this.recurrentActivation.apply(be(T,E));const D=Di(X(c,s),b);h=this.activation.apply(be(A,D));const F=be(X(a,s),X(be(1,zt(a)),h));return[F,F]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Gr(this.activation),recurrentActivation:Gr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Yt(this.kernelInitializer),recurrentInitializer:Yt(this.recurrentInitializer),biasInitializer:Yt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:cn(this.kernelConstraint),recurrentConstraint:cn(this.recurrentConstraint),biasConstraint:cn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}}qp.className="GRUCell",me(qp);class gL extends _i{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new qp(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}gL.className="GRU",me(gL);class zh extends nc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,pn(this.units,"units"),this.activation=Vr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Vr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=_t(e.kernelRegularizer),this.recurrentRegularizer=_t(e.recurrentRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.kernelConstraint=ln(e.kernelConstraint),this.recurrentConstraint=ln(e.recurrentConstraint),this.biasConstraint=ln(e.biasConstraint),this.dropout=Xa([1,Mr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Xa([1,Mr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=St(e);const n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){const i=this.biasInitializer,o=this.units;s=new(t=class extends Bs{apply(c,h){const d=i.apply([o]),m=new Ip().apply([o]),y=i.apply([o*2]);return ov(ov(d,m),y)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return ee(()=>{const n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new j(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1];const i=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Yr({ones:()=>En(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Yr({ones:()=>En(s),rate:this.recurrentDropout,training:n,count:4}));const o=this.dropoutMask,a=this.recurrentDropoutMask;let c,h,d,m;0<this.dropout&&this.dropout<1&&(e=X(e,o[0]));let y=Di(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=X(s,a[0])),y=be(y,Di(s,this.recurrentKernel.read())),this.useBias&&(y=ki(y,this.bias.read()));const[b,w,L,T]=is(y,4,y.rank-1);c=this.recurrentActivation.apply(b),h=this.recurrentActivation.apply(w),d=be(X(h,i),X(c,this.activation.apply(L))),m=this.recurrentActivation.apply(T);const A=X(m,this.activation.apply(d));return[A,A,d]})}getConfig(){const e=super.getConfig(),t={units:this.units,activation:Gr(this.activation),recurrentActivation:Gr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Yt(this.kernelInitializer),recurrentInitializer:Yt(this.recurrentInitializer),biasInitializer:Yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:cn(this.kernelConstraint),recurrentConstraint:cn(this.recurrentConstraint),biasConstraint:cn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}}zh.className="LSTMCell",me(zh);class yL extends _i{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new zh(e),super(e)}call(e,t){return ee(()=>{this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}}yL.className="LSTM",me(yL);class jp extends nc{constructor(e){super(e);this.cells=e.cells}get stateSize(){const e=[];for(const t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return ee(()=>{e=e;let n=e.slice(1);const s=[];for(const a of this.cells.slice().reverse())Array.isArray(a.stateSize)?s.push(n.splice(0,a.stateSize.length)):s.push(n.splice(0,1));s.reverse();const i=[];let o;for(let a=0;a<this.cells.length;++a){const c=this.cells[a];n=s[a],a===0?o=[e[0]].concat(n):o=[o[0]].concat(n),o=c.call(o,t),i.push(o.slice(1))}n=[];for(const a of i.slice().reverse())n.push(...a);return[o[0]].concat(n)})}build(e){Ww(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Po(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){const e=super.getConfig(),t=i=>({className:i.getClassName(),config:i.getConfig()}),n=this.cells.map(t),s={cells:n};return Object.assign({},e,s)}static fromConfig(e,t,n={}){const s=[];for(const i of t.cells)s.push(li(i,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];const e=[];for(const t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){const e=[];for(const t of this.cells)e.push(...t.weights);return $w(e)}setWeights(e){const t=[];for(const n of this.cells){const s=n.weights.length,i=e.splice(s);for(let o=0;o<n.weights.length;++o)t.push([n.weights[o],i[o]])}Uw(t)}}jp.className="StackedRNNCells",me(jp);function Yr(e){const{ones:t,rate:n,training:s=!1,count:i=1}=e,o=()=>cv(t(),n),a=()=>Fh(o,t,s);if(!i||i<=1)return Cn(a().clone());const c=Array(i).fill(void 0).map(a);return c.map(h=>Cn(h.clone()))}var lG=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,s=Object.getOwnPropertySymbols(e);i<s.length;i++)t.indexOf(s[i])<0&&Object.prototype.propertyIsEnumerable.call(e,s[i])&&(n[s[i]]=e[s[i]]);return n};class $Q extends nc{}class uN extends _i{constructor(e){if(e.unroll)throw new ze("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new ze("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new mn({ndim:5})]}call(e,t){return ee(()=>{if(this.cell.dropoutMask!=null&&(He(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(He(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new j("ConvRNN2D cell does not support constants");const n=t==null?null:t.mask,s=t==null?null:t.training,i=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:i})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return ee(()=>{const{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),i=[s[0],...s.slice(2)],o=ct(i);return Array.isArray(t)?Array(t.length).fill(o):[o]})}resetStates(e,t=!1){ee(()=>{if(!this.stateful)throw new tr("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),i=[s[0],...s.slice(2)],o=n[0];if(o==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ct(i)):this.states_=[ct(i)];else if(e==null)He(this.states_),this.keptStates!=null&&(He(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ct(i)):this.states_[0]=ct(i);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):He(this.states_);for(let a=0;a<this.states_.length;++a){const c=e[a],h=i;if(!ot(c.shape,h))throw new j(`State ${a} is incompatible with layer ${this.name}: expected shape=${h}, received shape=${c.shape}`);this.states_[a]=c}}this.states_=this.states_.map(a=>Cn(a.clone()))})}computeSingleOutputShape(e){const{dataFormat:t,filters:n,kernelSize:s,padding:i,strides:o,dilationRate:a}=this.cell,c=t==="channelsFirst",h=e[c?3:2],d=e[c?4:3],m=hi(h,s[0],i,o[0],a[0]),y=hi(d,s[1],i,o[1],a[1]),b=[...e.slice(0,2),...c?[n,m,y]:[m,y,n]];return b}}uN.className="ConvRNN2D";class Kp extends zh{constructor(e){const{filters:t,kernelSize:n,strides:s,padding:i,dataFormat:o,dilationRate:a}=e;super(Object.assign({},e,{units:t}));this.filters=t,pn(this.filters,"filters"),this.kernelSize=tc(n,2,"kernelSize"),this.kernelSize.forEach(c=>pn(c,"kernelSize")),this.strides=tc(s||1,2,"strides"),this.strides.forEach(c=>pn(c,"strides")),this.padding=i||"valid",Ts(this.padding),this.dataFormat=o||"channelsLast",Vt(this.dataFormat),this.dilationRate=tc(a||1,2,"dilationRate"),this.dilationRate.forEach(c=>pn(c,"dilationRate"))}build(e){var t;e=St(e);const n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[n]}`);const s=e[n],i=4,o=this.kernelSize.concat([s,this.filters*i]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const a=this.kernelSize.concat([this.filters,this.filters*i]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let c;if(this.unitForgetBias){const h=this.biasInitializer,d=this.filters;c=new(t=class extends Bs{apply(y,b){const w=h.apply([d]),L=ni([d]),T=h.apply([d*2]);return vw([w,L,T])}},t.className="CustomInit",t)}else c=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*i],null,c,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return ee(()=>{if(e.length!==3)throw new j(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training||!1,s=e[0],i=e[1],o=e[2],a=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Yr({ones:()=>En(s),rate:this.dropout,training:n,count:a}));const c=this.dropoutMask,h=(Ie,Se,Ee)=>!Se||!Se[Ee]?Ie:X(Se[Ee],Ie);let d=h(s,c,0),m=h(s,c,1),y=h(s,c,2),b=h(s,c,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Yr({ones:()=>En(i),rate:this.recurrentDropout,training:n,count:a}));const w=this.recurrentDropoutMask;let L=h(i,w,0),T=h(i,w,1),A=h(i,w,2),N=h(i,w,3);const E=3,[D,F,_,B]=is(this.kernel.read(),a,E),[$,H,q,J]=this.useBias?is(this.bias.read(),a):[null,null,null,null];d=this.inputConv(d,D,$,this.padding),m=this.inputConv(m,F,H,this.padding),y=this.inputConv(y,_,q,this.padding),b=this.inputConv(b,B,J,this.padding);const[re,ce,ue,he]=is(this.recurrentKernel.read(),a,E);L=this.recurrentConv(L,re),T=this.recurrentConv(T,ce),A=this.recurrentConv(A,ue),N=this.recurrentConv(N,he);const de=this.recurrentActivation.apply(be(d,L)),le=this.recurrentActivation.apply(be(m,T)),ye=be(X(le,o),X(de,this.activation.apply(be(y,A)))),pe=X(this.recurrentActivation.apply(be(b,N)),this.activation.apply(ye));return[pe,pe,ye]})}getConfig(){const e=super.getConfig(),{units:t}=e,n=lG(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){const i=Xi(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?ki(i,n,this.dataFormat):i}recurrentConv(e,t){const n=1;return Xi(e,t,n,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}Kp.className="ConvLSTM2DCell",me(Kp);class bL extends uN{constructor(e){const t=new Kp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}}bL.className="ConvLSTM2D",me(bL);class Xp extends lt{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;const t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Ke(e);if(0<this.rate&&this.rate<1){const s=t.training==null?!1:t.training,i=this.getNoiseShape(n),o=Fh(()=>cv(n,this.rate,i,this.seed),()=>n,s);return o}return e})}getConfig(){const e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}}Xp.className="Dropout",me(Xp);class wL extends Xp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}wL.className="SpatialDropout1D",me(wL);class LL extends lt{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,pn(this.units,"units"),this.activation=Vr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=ln(e.kernelConstraint),this.biasConstraint=ln(e.biasConstraint),this.kernelRegularizer=_t(e.kernelRegularizer),this.biasRegularizer=_t(e.biasRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=St(e);const t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=St(e);const t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Ke(e),s=JA(this.activation.getClassName());let i;return s!=null?i=Di(n,this.kernel.read(),s,this.bias?this.bias.read():null):(i=Di(n,this.kernel.read()),this.bias!=null&&(i=ki(i,this.bias.read())),this.activation!=null&&(i=this.activation.apply(i))),i})}getConfig(){const e={units:this.units,activation:Gr(this.activation),useBias:this.useBias,kernelInitializer:Yt(this.kernelInitializer),biasInitializer:Yt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:cn(this.kernelConstraint),biasConstraint:cn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}LL.className="Dense",me(LL);class SL extends lt{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=St(e);for(const t of e.slice(1))if(t==null)throw new j(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Br(e,1)]}call(e,t){return ee(()=>{this.invokeCallHook(e,t);let n=Ke(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){const s=[0];for(let i=2;i<n.rank;++i)s.push(i);s.push(1),n=n.transpose(s)}return Dz(n)})}getConfig(){const e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}SL.className="Flatten",me(SL);class IL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.activation=Vr(e.activation)}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Ke(e);return this.activation.apply(n)})}getConfig(){const e={activation:Gr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}IL.className="Activation",me(IL);class xL extends lt{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return ee(()=>(e=Ke(e),Oz(e,this.n)))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}xL.className="RepeatVector",me(xL);class TL extends lt{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){const n="Total size of new array must be unchanged.",s=t.slice();let i=1,o=null;for(let c=0;c<s.length;++c){const h=s[c];if(this.isUnknown(h))if(o===null)o=c;else throw new j("Can only specifiy one unknown dimension.");else i*=h}const a=Br(e);if(o!==null){if(i===0||a%i!==0)throw new j(n);s[o]=a/i}else if(a!==i)throw new j(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Ke(e),s=n.shape,i=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return n.reshape(i)})}getConfig(){const e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}}TL.className="Reshape",me(TL);class AL extends lt{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);const t=oi(1,e.dims.length+1);if(!ot(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new mn({ndim:this.dims.length+1})]}computeOutputShape(e){e=St(e);const t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Me(Ke(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}AL.className="Permute",me(AL);class vL extends lt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){const n=Ke(e),s=-1;return Jl(Fr(n,this.maskValue),s)}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Ke(e),s=-1,i=!0,o=Jl(Fr(n,this.maskValue),s,i),a=n.mul(o.asType(n.dtype));return a})}}vL.className="Masking",me(vL);class NL extends lt{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(Rt(e.inputLength))}this.inputDim=e.inputDim,pn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,pn(this.outputDim,"outputDim"),this.embeddingsInitializer=Ft(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=_t(e.embeddingsRegularizer),this.activityRegularizer=_t(e.activityRegularizer),this.embeddingsConstraint=ln(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return ee(()=>this.maskZero?(e=Ke(e),Fr(e,Qe(e))):null)}computeOutputShape(e){if(e=St(e),this.inputLength==null)return[...e,this.outputDim];const t=Rt(this.inputLength);if(t.length!==e.length-1)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){const i=t[s],o=e[s+1];if(i!=null&&o!=null&&i!==o)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);i==null&&(t[n]=o),n++}}return[e[0],...t,this.outputDim]}call(e,t){return ee(()=>{this.invokeCallHook(e,t);let n=Ke(e);n.dtype!=="int32"&&(n=Eh(n,"int32"));const s=av(this.embeddings.read(),n.as1D());return s.reshape(St(this.computeOutputShape(n.shape)))})}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Yt(this.embeddingsInitializer),embeddingsRegularizer:It(this.embeddingsRegularizer),activityRegularizer:It(this.activityRegularizer),embeddingsConstraint:cn(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}NL.className="Embedding",me(NL);class Yo extends lt{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new ze}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;const n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){const i=e[e.length-t.length+s],o=t[s];if(i==null||o==null||i<0||o<0)n.push(null);else if(i===1)n.push(o);else if(o===1)n.push(i);else{if(i!==o)throw new j("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(i)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[St(e)]),e=e,e.length<2)throw new j(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(const i of e)i!=null&&i[0]!==null&&t.push(i[0]);if(t=Ur(t),t.length>1)throw new j(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let i=1;i<e.length;++i){const o=e[i]==null?null:e[i].slice(1);n=this.computeElementwiseOpOutputShape(n,o)}const s=e.map(i=>i.length);e.indexOf(null)===-1&&Ur(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return ee(()=>{if(e=e,this.reshapeRequired){const n=[],s=e.map(i=>i.rank);if(s.indexOf(null)===-1){const i=Mr(s);for(let o of e){const a=o.rank;for(let c=0;c<i-a;++c)o=Dh(o,1);n.push(o)}return this.mergeFunction(n)}else{let i=!1;for(const c of e){const h=c.rank;if(h==null){const d=c.shape,m=d[0],y=d.slice(1).concat([m]);let b=c.reshape([m].concat(Br(d.slice(1))));b=Me(b,[1,0]),b=b.reshape(y),n.push(b),i=!0}else if(h>1){const d=oi(1,h).concat([0]);n.push(Me(c,d)),i=!0}else n.push(c)}let o=this.mergeFunction(n);const a=o.rank;if(i){if(a==null){const c=o.shape,h=c.length,d=c[h-1],m=[d].concat(c.slice(0,c.length-1));o=Me(o.reshape([-1,d]),[1,0]).reshape(m)}else if(a>1){const c=[a-1].concat(oi(0,a-1));o=Me(o,c)}}return o}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){const i=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,i)}let n=[];for(const s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=Ur(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return ee(()=>{if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an Array");if(!Array.isArray(e))throw new j("`inputs` should be an Array");if(t.length!==e.length)throw new j(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:qn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=$s(n,t[s]);return n})}}class Gh extends Yo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return t})}}Gh.className="Add",me(Gh);function UQ(e){if(Array.isArray(e)){const t=new Gh({});return t.apply(e)}else return new Gh(e)}class Vh extends Yo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=X(t,e[n]);return t})}}Vh.className="Multiply",me(Vh);function BQ(e){if(Array.isArray(e)){const t=new Vh({});return t.apply(e)}else return new Vh(e)}class Yh extends Yo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=be(t,e[n]);return X(1/e.length,t)})}}Yh.className="Average",me(Yh);function MQ(e){if(Array.isArray(e)){const t=new Yh({});return t.apply(e)}else return new Yh(e)}class Hh extends Yo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ws(t,e[n]);return t})}}Hh.className="Maximum",me(Hh);function PQ(e){if(Array.isArray(e)){const t=new Hh({});return t.apply(e)}else return new Hh(e)}class qh extends Yo{constructor(e){super(e)}mergeFunction(e){return ee(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Eo(t,e[n]);return t})}}qh.className="Minimum",me(qh);function zQ(e){if(Array.isArray(e)){const t=new qh({});return t.apply(e)}else return new qh(e)}class jh extends Yo{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new j("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(const s of e)if(s!=null){t=!1;break}if(t)return;const n=[];for(let s=0;s<e.length;++s){const i=e[s].slice();i.splice(this.axis,1);let o=!1;for(const a of n)if(ot(a,i)){o=!0;break}o||n.push(i)}if(n.length>1)throw new j("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return ee(()=>vw(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new j("A `Concatenate` layer should be called on a list of inputs.");const t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(const i of t.slice(1)){if(n[s]==null||i[s]==null){n[s]=null;break}n[s]+=i[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new j("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new j(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return ee(()=>{let n=!0;if(t.forEach(o=>{if(o!=null){n=!1;return}}),n)return null;const s=[];for(let o=0;o<e.length;++o)t[o]==null?s.push(En(e[o]).asType("bool")):t[o].rank<e[o].rank?s.push(qn(t[o],-1)):s.push(t[o]);const i=Pt(s,this.axis);return kd(i,-1,!1)})}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}jh.className="Concatenate",me(jh);function GQ(e){if(Array.isArray(e)){const t=new jh({});return t.apply(e)}else return new jh(e)}function Kh(e,t){for(;e<0;)e+=t;return e}function hG(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new ze("batchDot is not implemented for tensors of 4D or higher rank yet");if(k(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new ze("batchDot is not implemented for complex64-type Tensors yet.");const s=e.shape.length,i=t.shape.length;n==null&&(n=[s-1,i-2]);const o=n;return ee(()=>{let a;if(s>i){a=s-i;const h=[];for(let d=0;d<a;++d)h.push(1);t=t.reshape(t.shape.concat(h))}else if(i>s){a=i-s;const h=[];for(let d=0;d<a;++d)h.push(1);e=e.reshape(e.shape.concat(h))}else a=0;let c;if(e.shape.length===2&&t.shape.length===2)o[0]===o[1]?c=e.mul(t).sum(o[0]):c=e.transpose([1,0]).mul(t).sum(o[1]);else{const h=o[0]!==e.shape.length-1,d=o[1]===t.shape.length-1;c=e.matMul(t,h,d)}if(a>0){let h;s>i?h=s+i-3:h=s-1;const d=[];for(let m=h;m<h+a;++m)d.push(m);c=c.squeeze(d)}return c.shape.length===1&&(c=c.expandDims(1)),c})}class CL extends Yo{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const t=e[0],n=e[1];if(t.length>3||n.length>3)throw new ze("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new j(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new j(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((i,o)=>Kh(i,e[o].shape.length)):s=[Kh(this.axes,t.shape.length),Kh(this.axes,n.shape.length)],this.normalize&&(t=Fp(t,s[0]),n=Fp(n,s[1])),hG(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Kh(this.axes,e.length),Kh(this.axes,t.length)],n}computeOutputShape(e){k(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new ze("Dot layer does not support tensors of 4D or higher rank yet.");const s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);const i=t.concat(n);return i.length===1&&i.push(1),i}computeMask(e,t){return null}getConfig(){const e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}}CL.className="Dot",me(CL);class RL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Ke(e),s=()=>Sp(n.shape,0,this.stddev).add(n),i=Fh(s,()=>n,t.training||!1);return i})}}RL.className="GaussianNoise",me(RL);class OL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return ee(()=>{this.invokeCallHook(e,t);const n=Ke(e);if(this.rate>0&&this.rate<1){const s=()=>{const i=Math.sqrt(this.rate/(1-this.rate));return n.mul(Sp(n.shape,1,i))};return Fh(s,()=>n,t.training||!1)}return n})}}OL.className="GaussianDropout",me(OL);class EL extends lt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ke(e).shape}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return ee(()=>{if(this.rate<1&&this.rate>0){const n=this._getNoiseShape(e),s=()=>{const i=Ke(e),o=1.6732632423543772,a=1.0507009873554805,c=-o*a;let h=Ji(_o(n),this.rate);h=Eh(h,"float32");const d=((1-this.rate)*(1+this.rate*c**2))**-.5,m=-d*c*this.rate,y=i.mul(h).add(h.add(-1).mul(c));return y.mul(d).add(m)};return Fh(s,()=>Ke(e),t.training||!1)}return e})}}EL.className="AlphaDropout",me(EL);function Xh(e,t,n,s,i,o=.001){let a;if(e.rank===2)a=_T(e,t,n,s,i,o);else if(e.rank===3)a=WT(e,t,n,s,i,o);else if(e.rank===4)a=$T(e,t,n,s,i,o);else throw new ze(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return a}function uG(e,t,n,s,i=.001){return ee(()=>{const o=jd(e,s),a=o.mean,c=o.variance,h=Xh(e,a,c,n,t,i);return[h,a,c]})}function dG(e,t,n,s,i=.001){return ee(()=>{const o=jd(e,s),a=o.mean,c=o.variance,h=[];for(const L of oi(0,e.rank))s.indexOf(L)!==-1?h.push(1):h.push(e.shape[L]);const d=a.reshape(h),m=c.reshape(h),y=t==null?null:t.reshape(h),b=n==null?null:n.reshape(h),w=Xh(e,d,m,b,y,i);return[w,a,c]})}function pG(e,t,n,s,i=.001){return ot(s.slice().sort(),oi(0,e.rank-1))?uG(e,t,n,s,i):dG(e,t,n,s,i)}class DL extends lt{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ft(e.betaInitializer||"zeros"),this.gammaInitializer=Ft(e.gammaInitializer||"ones"),this.movingMeanInitializer=Ft(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Ft(e.movingVarianceInitializer||"ones"),this.betaConstraint=ln(e.betaConstraint),this.gammaConstraint=ln(e.gammaConstraint),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer)}build(e){e=St(e);const t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new j(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new mn({ndim:e.length,axes:{[t]:n}})];const s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return ee(()=>{const n=t.training==null?!1:t.training,s=Ke(e),i=s.shape,o=i.length,a=oi(0,o),c=this.axis>=0?this.axis:this.axis+o;a.splice(c,1);const h=Bo(1,o);h[c]=i[c];const d=a.slice();d.sort();const m=!ot(d,oi(0,o).slice(0,o-1)),y=()=>{if(m){const N=this.movingMean.read().reshape(h),E=this.movingVariance.read().reshape(h),D=this.center?this.beta.read().reshape(h):null,F=this.scale?this.gamma.read().reshape(h):null;return Xh(s,N,E,D,F,this.epsilon)}else return Xh(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return y();const[b,w,L]=pG(s,this.gamma.read(),this.beta.read(),a,this.epsilon),T=(N,E,D)=>{ee(()=>{const F=1-D,_=N.read(),B=_.sub(E).mul(F);N.write(_.sub(B))})},A=()=>{T(this.movingMean,w,this.momentum),T(this.movingVariance,L,this.momentum)};return A(),b})}getConfig(){const e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Yt(this.betaInitializer),gammaInitializer:Yt(this.gammaInitializer),movingMeanInitializer:Yt(this.movingMeanInitializer),movingVarianceInitializer:Yt(this.movingVarianceInitializer),betaRegularizer:It(this.betaRegularizer),gammaRegularizer:It(this.gammaRegularizer),betaConstraint:cn(this.betaConstraint),gammaConstraint:cn(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}DL.className="BatchNormalization",me(DL);class kL extends lt{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(const t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ft(e.betaInitializer||"zeros"),this.gammaInitializer=Ft(e.gammaInitializer||"ones"),this.betaRegularizer=_t(e.betaRegularizer),this.gammaRegularizer=_t(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=St(e);const t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i<this.axis.length;++i)this.axis[i]<0&&(this.axis[i]+=t);for(const i of this.axis)if(i<0||i>=t)throw new Error(`Invalid axis: ${i}`);if(this.axis.length!==Ur(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);const n=this.axis.map(i=>e[i]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){const n=Ke(e),s=n.shape,i=s.length;return ee(()=>{const o=!0;let{mean:a,variance:c}=jd(n,this.axis,o);const h=Bo(1,i);for(const L of this.axis)h[L]=s[L];const d=L=>L!=null&&L.shape.length!==i&&this.axis!==[i-1]?L.reshape(h):L;let m=d(this.gamma.read()),y=d(this.beta.read());const b=[],w=[];for(let L=0;L<i;++L)this.axis.indexOf(L)!==-1?(b.push(s[L]),w.push(1)):(b.push(1),w.push(s[L]));return a=a.tile(b),c=c.tile(b),m=m.tile(w),y=y.tile(w),Xh(n,a,c,y,m,this.epsilon)})}getConfig(){const e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Yt(this.betaInitializer),gammaInitializer:Yt(this.gammaInitializer),betaRegularizer:It(this.betaRegularizer),gammaRegularizer:It(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}}kL.className="LayerNormalization",me(kL);function VQ(e,t){return ee(()=>{if(e.rank!==3)throw new j(`temporalPadding expects input tensor to be 3-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[1,1]),t.length!==2)throw new j(`temporalPadding expects input padding pattern to be a length-2 array, but received a length-${t.length} array.`);const n=[[0,0],t,[0,0]];return Ri(e,n)})}function mG(e,t,n){return ee(()=>{if(e.rank!==4)throw new j(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new j("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ii()),n!=="channelsLast"&&n!=="channelsFirst")throw new j(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],Ri(e,s)})}class FL extends lt{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ii():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new j(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new j(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new j(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new mn({ndim:4})]}computeOutputShape(e){e=St(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return ee(()=>mG(Ke(e),this.padding,this.dataFormat))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}FL.className="ZeroPadding2D",me(FL);function Jp(e,t,n,s,i,o){return ee(()=>{Vt(i),ev(o),Ts(s),n==null&&(n=[1,1]),s==null&&(s="valid"),i==null&&(i=ii()),o==null&&(o="max"),e=aL(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=hh(e,t,n,c):a=nh(e,t,n,c),i==="channelsFirst"&&(a=Me(a,[0,3,1,2])),a})}function dN(e,t,n,s,i,o){return ee(()=>{Vt(i),ev(o),Ts(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),i==null&&(i=ii()),o==null&&(o="max"),e=rN(e,i);let a;const c=s==="same"?"same":"valid";return o==="max"?a=Wb(e,t,n,c):a=Ib(e,t,n,c),i==="channelsFirst"&&(a=Me(a,[0,4,1,2,3])),a})}class pN extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new j(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(pn(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new j(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);pn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Ts(this.padding),this.inputSpec=[new mn({ndim:3})]}computeOutputShape(e){e=St(e);const t=hi(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return ee(()=>{this.invokeCallHook(e,t),e=Dh(Ke(e),2);const n=this.poolingFunction(Ke(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return _r(n,[2])})}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class _L extends pN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Vt(i),Ts(s),Jp(e,t,n,s,i,"max")}}_L.className="MaxPooling1D",me(_L);class WL extends pN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Vt(i),Ts(s),Jp(e,t,n,s,i,"avg")}}WL.className="AveragePooling1D",me(WL);class mN extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new j(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];pn(this.poolSize,"poolSize"),pn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vt(this.dataFormat),Ts(this.padding),this.inputSpec=[new mn({ndim:4})]}computeOutputShape(e){e=St(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=hi(t,this.poolSize[0],this.padding,this.strides[0]),n=hi(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return ee(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ke(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class $L extends mN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Vt(i),Ts(s),Jp(e,t,n,s,i,"max")}}$L.className="MaxPooling2D",me($L);class UL extends mN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Vt(i),Ts(s),Jp(e,t,n,s,i,"avg")}}UL.className="AveragePooling2D",me(UL);class fN extends lt{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new j(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];pn(this.poolSize,"poolSize"),pn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vt(this.dataFormat),Ts(this.padding),this.inputSpec=[new mn({ndim:5})]}computeOutputShape(e){e=St(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=hi(t,this.poolSize[0],this.padding,this.strides[0]),n=hi(n,this.poolSize[1],this.padding,this.strides[1]),s=hi(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return ee(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ke(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class BL extends fN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Vt(i),Ts(s),dN(e,t,n,s,i,"max")}}BL.className="MaxPooling3D",me(BL);class ML extends fN{constructor(e){super(e)}poolingFunction(e,t,n,s,i){return Vt(i),Ts(s),dN(e,t,n,s,i,"avg")}}ML.className="AveragePooling3D",me(ML);class gN extends lt{constructor(e){super(e);this.inputSpec=[new mn({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new ze}}class PL extends gN{constructor(e){super(e||{})}call(e,t){return ee(()=>{const n=Ke(e);return Gt(n,1)})}}PL.className="GlobalAveragePooling1D",me(PL);class zL extends gN{constructor(e){super(e||{})}call(e,t){return ee(()=>{const n=Ke(e);return jn(n,1)})}}zL.className="GlobalMaxPooling1D",me(zL);class yN extends lt{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vt(this.dataFormat),this.inputSpec=[new mn({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new ze}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class GL extends yN{call(e,t){return ee(()=>{const n=Ke(e);return this.dataFormat==="channelsLast"?Gt(n,[1,2]):Gt(n,[2,3])})}}GL.className="GlobalAveragePooling2D",me(GL);class VL extends yN{call(e,t){return ee(()=>{const n=Ke(e);return this.dataFormat==="channelsLast"?jn(n,[1,2]):jn(n,[2,3])})}}VL.className="GlobalMaxPooling2D",me(VL);class bN extends lt{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){const e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){const s=t.layer,i=li(s,n);delete t.layer;const o={layer:i};return Object.assign(o,t),new e(o)}}class YL extends bN{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=St(e),e.length<3)throw new j(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];const t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=St(e);const t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return ee(()=>{e=Ke(e);const n=(o,a)=>{const c=Ke(this.layer.call(o,t));return[c,[]]},s=hN(n,e,[],!1,null,null,!1,!0),i=s[1];return i})}}YL.className="TimeDistributed",me(YL);function fG(e){ja(Tz,"BidirectionalMergeMode",e)}const gG="concat";class HL extends bN{constructor(e){super(e);const t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=li(n),t.goBackwards=!(t.goBackwards===!0);const s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=li(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?gG:e.mergeMode,fG(this.mergeMode),e.weights)throw new ze("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){const t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,i;return this.returnState&&(i=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(i).concat(i.slice()):[n].concat(i).concat(i.slice()):Kn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});const i=lN(e,n,s,this.numConstants);if(e=i.inputs,n=i.initialState,s=i.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);const o=[],a=[];if(n!=null){const h=n.length;if(h%2>0)throw new j("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,o.push(...n);const d=n.map(m=>new mn({shape:m.shape}));this.forwardLayer.stateSpec=d.slice(0,h/2),this.backwardLayer.stateSpec=d.slice(h/2),a.push(...d)}if(s!=null)throw new ze("Support for constants in Bidirectional layers is not implemented yet.");const c=o[0]instanceof ci;for(const h of o)if(h instanceof ci!==c)throw new j("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(c){const h=[e].concat(o),d=this.inputSpec.concat(a),m=this.inputSpec;this.inputSpec=d;const y=super.apply(h,t);return this.inputSpec=m,y}else return super.apply(e,t)}call(e,t){return ee(()=>{const n=t.initialState;let s,i;if(n==null)s=this.forwardLayer.call(e,t),i=this.backwardLayer.call(e,t);else{const c=n.slice(0,n.length/2),h=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:c})),i=this.backwardLayer.call(e,Object.assign(t,{initialState:h}))}let o;this.returnState&&(Array.isArray(s)&&(o=s.slice(1).concat(i.slice(1))),s=s[0],i=i[0]),this.returnSequences&&(i=Is(i,1));let a;return this.mergeMode==="concat"?a=vw([s,i]):this.mergeMode==="sum"?a=be(s,i):this.mergeMode==="ave"?a=X(.5,be(s,i)):this.mergeMode==="mul"?a=X(s,i):this.mergeMode==null&&(a=[s,i]),this.returnState?this.mergeMode==null?a.concat(o):[a].concat(o):a})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Po(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Po(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){const s=this.forwardLayer.states,i=s.map(o=>null);return Array.isArray(n)?n.concat(i).concat(i):[n].concat(i).concat(i)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){const e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){const n=li(t.layer);if(delete t.layer,t.numConstants!=null)throw new ze("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const s=t;return s.layer=n,new e(s)}}HL.className="Bidirectional",me(HL);function yG(e){return new Ja(e)}function bG(e){return new iL(e)}function wG(e){return new tL(e)}function LG(e){return new nL(e)}function SG(e){return new sL(e)}function IG(e){return new oL(e)}function xG(e){return new rL(e)}function TG(e){return new Yp(e)}function AG(e){return new Ph(e)}function vG(e){return new hL(e)}function 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JL(this.node.rawAttrs,e,t);if(n.b!=null)return ZL(this.node.rawAttrs,e,t);if(n.shape!=null)return sS(this.node.rawAttrs,e,t);if(n.type!=null)return tS(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return iS(this.node.rawAttrs,e,t);if(n.list.s!=null)return rS(this.node.rawAttrs,e,t);if(n.list.shape!=null)return oS(this.node.rawAttrs,e,t);if(n.list.b!=null)return aS(this.node.rawAttrs,e,t);if(n.list.type!=null)return nS(this.node.rawAttrs,e,t)}return t}}const kY=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[be(R("a",e,t,n),R("b",e,t,n))];case"AddN":return[kT(R("tensors",e,t,n))];case"FloorMod":case"Mod":return[qd(R("a",e,t,n),R("b",e,t,n))];case"Mul":return[X(R("a",e,t,n),R("b",e,t,n))];case"RealDiv":case"Div":return[_e(R("a",e,t,n),R("b",e,t,n))];case"DivNoNan":return[Rb(R("a",e,t,n),R("b",e,t,n))];case"FloorDiv":return[Dd(R("a",e,t,n),R("b",e,t,n))];case"Sub":return[Ce(R("a",e,t,n),R("b",e,t,n))];case"Minimum":return[Eo(R("a",e,t,n),R("b",e,t,n))];case"Maximum":return[Ws(R("a",e,t,n),R("b",e,t,n))];case"Pow":return[si(R("a",e,t,n),R("b",e,t,n))];case"SquaredDifference":return[yh(R("a",e,t,n),R("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},HQ="arithmetic";const FY=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[sn(R("x",e,t,n))];case"Acos":return[ub(R("x",e,t,n))];case"Acosh":return[db(R("x",e,t,n))];case"Asin":return[fb(R("x",e,t,n))];case"Asinh":return[gb(R("x",e,t,n))];case"Atan":return[yb(R("x",e,t,n))];case"Atan2":return[bb(R("x",e,t,n),R("y",e,t,n))];case"Atanh":return[wb(R("x",e,t,n))];case"Ceil":return[Tb(R("x",e,t,n))];case"Complex":return[Ti(R("real",e,t,n),R("imag",e,t,n))];case"Cos":return[rh(R("x",e,t,n))];case"Cosh":return[Bd(R("x",e,t,n))];case"Elu":return[Oo(R("x",e,t,n))];case"Erf":return[Ob(R("x",e,t,n))];case"Exp":return[Ls(R("x",e,t,n))];case"Expm1":return[Eb(R("x",e,t,n))];case"Floor":return[_a(R("x",e,t,n))];case"Log":return[ns(R("x",e,t,n))];case"Log1p":return[Gd(R("x",e,t,n))];case"Imag":return[$a(R("x",e,t,n))];case"Neg":return[zt(R("x",e,t,n))];case"Reciprocal":return[Mb(R("x",e,t,n))];case"Real":return[Do(R("x",e,t,n))];case"Relu":return[Oi(R("x",e,t,n))];case"Round":return[zb(R("x",e,t,n))];case"Selu":return[Jd(R("x",e,t,n))];case"Sigmoid":return[Ni(R("x",e,t,n))];case"Sin":return[Zd(R("x",e,t,n))];case"Sign":return[Vb(R("x",e,t,n))];case"Sinh":return[Qd(R("x",e,t,n))];case"Softplus":return[Ua(R("x",e,t,n))];case"Sqrt":return[Ln(R("x",e,t,n))];case"Square":return[wt(R("x",e,t,n))];case"Tanh":return[Fa(R("x",e,t,n))];case"Tan":return[qb(R("x",e,t,n))];case"Relu6":case"ClipByValue":return[Hn(R("x",e,t,n),R("clipValueMin",e,t,n),R("clipValueMax",e,t,n))];case"Rsqrt":return[Xd(Jn(e.inputNames[0],t,n))];case"Prod":return[Kd(R("x",e,t,n),R("axes",e,t,n))];case"LeakyRelu":return[zd(R("x",e,t,n),R("alpha",e,t,n))];case"Prelu":return[dh(R("x",e,t,n),R("alpha",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},qQ="basic_math";function Ps(e,t,n=""){k(_Y(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function _Y(e,t){if(e.length!==t.length)return!1;for(let n=0;n<e.length;n++)if(e[n]!==-1&&t[n]!==-1&&e[n]!==t[n])return!1;return!0}class WY{constructor(e,t,n,s,i,o,a){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=s,this.identicalElementShapes=i,this.dynamicSize=o,this.clearAfterRead=a,this.tensors=[],this.closed_=!1,this.idTensor=Ne(0),Cn(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);const t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);const n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
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         ${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);const i=n===0?0:t.size/n,o=[];ee(()=>{t=K(t,[1,n,i]);for(let c=0;c<e.length;++c){const h=c===0?0:s[c-1],d=[0,h,0],m=[1,e[c],i];o[c]=K(nt(t,d,m),this.elementShape)}return o});const a=[];for(let c=0;c<e.length;c++)a[c]=c;this.writeMany(a,o)}}class Jh{constructor(e,t,n,s=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(i=>{if(n!==i.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${i.dtype}`);Ps(t,i.shape,"TensorList shape mismatch: "),Cn(i)}),this.idTensor=Ne(0),this.maxNumElements=s,Cn(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Jh([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);return Ps(e,this.elementShape,"TensorList shape mismatch: "),ee(()=>{const s=this.tensors.map(i=>K(i,e));return rs(s,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");const n=this.tensors.pop();return Ps(n.shape,e,"TensorList shape mismatch: "),K(n,e)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Ps(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Cn(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);return Ps(this.tensors[e].shape,t,"TensorList shape mismatch: "),this.tensors[e]}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);Ps(this.elementShape,t.shape,"TensorList shape mismatch: "),Cn(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);return Ps(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size()),e.length===0?en([],[0].concat(this.elementShape)):ee(()=>{const s=e.map(i=>K(this.tensors[i],n));return rs(s,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);return Ps(this.elementShape,t,"TensorList shape mismatch: "),this.size()===0?en([],[0].concat(this.elementShape)):ee(()=>{const n=this.tensors.map(s=>K(s,t));return Pt(n,0)})}}function $Y(e,t,n){const s=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);const i=e.shape.slice(1);Ps(i,t,"TensorList shape mismatch: ");const o=Ei(e);return new Jh(o,t,s)}function UY(e,t,n){return new Jh([],e,t,n)}function BY(e,t,n,s){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);const i=Math.max(...t);if(s!=null&&s!==-1&&i>=s)throw new Error(`Max index must be < array size (${i}  vs. ${s})`);const o=new Jh([],n,e.dtype,s),a=Ei(e,0);return t.forEach((c,h)=>{o.setItem(c,a[h])}),o}function MY(e,t,n){let s=0;const i=t.map(h=>(s+=h,s));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
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n.functionMap[s].executeFunctionAsync(d,n.tensorArrayMap,n.tensorListMap);const y=d.map(w=>w.id);m.forEach(w=>{!w.kept&&c.indexOf(w.id)===-1&&y.indexOf(w.id)===-1&&w.dispose()});const b=await n.functionMap[i].executeFunctionAsync(d,n.tensorArrayMap,n.tensorListMap);h=await b[0].data(),b.forEach(w=>{!w.kept&&c.indexOf(w.id)===-1&&y.indexOf(w.id)===-1&&w.dispose()})}return d}case"LoopCond":{const s=R("pred",e,t,n);return[or(s)]}case"Switch":{const s=R("pred",e,t,n);let i=R("data",e,t,n);return i.kept||(i=or(i)),(await s.data())[0]?[void 0,i]:[i,void 0]}case"Merge":{const s=e.inputNames.find(i=>Jn(i,t,n)!==void 0);if(s){const i=Jn(s,t,n);return[or(i)]}return}case"Enter":{const s=R("frameName",e,t,n),i=R("tensor",e,t,n);return n.enterFrame(s),[or(i)]}case"Exit":{const s=R("tensor",e,t,n);return n.exitFrame(),[or(s)]}case"NextIteration":{const s=R("tensor",e,t,n);return n.nextIteration(),[or(s)]}case"TensorArrayV3":{const s=R("size",e,t,n),i=R("dtype",e,t,n),o=R("elementShape",e,t,n),a=R("dynamicSize",e,t,n),c=R("clearAfterRead",e,t,n),h=R("identicalElementShapes",e,t,n),d=R("name",e,t,n),m=new WY(d,i,s,o,h,a,c);return n.addTensorArray(m),[m.idTensor,Ne(1)]}case"TensorArrayWriteV3":{const s=R("tensorArrayId",e,t,n),i=R("index",e,t,n),o=R("tensor",e,t,n),a=n.getTensorArray(s.id);return a.write(i,o),[a.idTensor]}case"TensorArrayReadV3":{const s=R("tensorArrayId",e,t,n),i=R("index",e,t,n),o=n.getTensorArray(s.id);return[o.read(i)]}case"TensorArrayGatherV3":{const s=R("tensorArrayId",e,t,n),i=R("indices",e,t,n),o=R("dtype",e,t,n),a=n.getTensorArray(s.id);return[a.gather(i,o)]}case"TensorArrayScatterV3":{const s=R("tensorArrayId",e,t,n),i=R("indices",e,t,n),o=R("tensor",e,t,n),a=n.getTensorArray(s.id);return a.scatter(i,o),[a.idTensor]}case"TensorArrayConcatV3":{const s=R("tensorArrayId",e,t,n),i=n.getTensorArray(s.id),o=R("dtype",e,t,n);return[i.concat(o)]}case"TensorArraySplitV3":{const 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s=R("axis",e,t,n);return[_r(R("x",e,t,n),s)]}case"Reshape":return[K(R("x",e,t,n),R("shape",e,t,n))];case"PadV2":case"Pad":return[Ri(R("x",e,t,n),R("padding",e,t,n),R("constantValue",e,t,n))];case"SpaceToBatchND":{const s=R("blockShape",e,t,n),i=R("paddings",e,t,n);return[uh(R("x",e,t,n),s,i)]}case"BatchToSpaceND":{const s=R("blockShape",e,t,n),i=R("crops",e,t,n);return[sh(R("x",e,t,n),s,i)]}case"DepthToSpace":{const s=R("blockSize",e,t,n),i=R("dataFormat",e,t,n).toUpperCase();return[Nb(R("x",e,t,n),s,i)]}case"BroadcastTo":return[ih(R("x",e,t,n),R("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},aee="transformation";function kN(e,t,n){const s=((i,o,a)=>{switch(i.category){case"arithmetic":return ee(()=>kY(i,o,a));case"basic_math":return ee(()=>FY(i,o,a));case"control":return PY(i,o,a);case"convolution":return ee(()=>zY(i,o,a));case"creation":return ee(()=>GY(i,o,a));case"dynamic":return VY(i,o,a);case"evaluation":return ee(()=>YY(i,o,a));case"image":return ee(()=>qY(i,o,a));case"graph":return ee(()=>HY(i,o,a));case"logical":return ee(()=>jY(i,o,a));case"matrices":return ee(()=>KY(i,o,a));case"normalization":return ee(()=>XY(i,o,a));case"reduction":return ee(()=>JY(i,o,a));case"slice_join":return ee(()=>ZY(i,o,a));case"spectral":return ee(()=>QY(i,o,a));case"transformation":return ee(()=>eH(i,o,a));case"custom":const c=NN(i.op);if(c&&c.customExecutor)return c.customExecutor(new DY(i,o,a));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return s instanceof Promise?s.then(i=>[].concat(i)):[].concat(s)}class FN{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){const e=[];for(let t=0;t<this.contexts.length-1;t++){const n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;const e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(const t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(const t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}}function _N(e,t,n,s){const i=new Set,o=[];let a=null,c=null;const h=new Set,d=Object.keys(e).map(b=>as(b)[0]);let m=[];s!=null&&(m=s.map(b=>as(b.name)[0]));const y=[...t];for(;y.length>0;){const b=y.pop();if((WN(b)||iH(b))&&(a==null&&(a=b,c=a.children.map(w=>w.name).filter(w=>i.has(w)))),i.add(b.name),n[b.name]!=null)continue;if(d.indexOf(b.name)!==-1)continue;if(m.indexOf(b.name)!==-1)continue;if(b.inputs.length===0){o.push(b.name);continue}b.inputs.forEach(w=>{if(h.has(w.name))return;h.add(w.name),y.push(w)})}return{inputs:e,outputs:t,usedNodes:i,missingInputs:o,dynamicNode:a,syncInputs:c}}function tH(e,t,n){const{usedNodes:s,inputs:i}=n,o=[],a=Object.keys(i).map(m=>as(m)[0]).map(m=>e.nodes[m]),c=e.initNodes;a.forEach(m=>{s.has(m.name)&&o.push(m)}),e.weights.forEach(m=>{s.has(m.name)&&o.push(m)}),c!=null&&c.forEach(m=>{s.has(m.name)&&o.push(m)});const h=new Set,d=[];for(;o.length>0;){const m=o.pop();h.add(m.name),t[m.name]||d.push(m),m.children.forEach(y=>{!h.has(y.name)&&s.has(y.name)&&y.inputs.every(b=>h.has(b.name))&&o.push(y)})}return d}const nH=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],sH=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"];function WN(e){return nH.indexOf(e.op)>=0}function iH(e){return sH.indexOf(e.op)>=0}class lS{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new lS(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){const t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{const t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){const n=e.map(i=>i.name).sort(),s=t.map(i=>i.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){const n=_N(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:i,syncInputs:o}=n;if(i!=null)throw new Error(`This execution contains the node '${i.name}', which has the dynamic op '${i.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${o}]`);if(s.length>0){const a=t.map(h=>h.name),c=Object.keys(e);throw new Error(`Cannot compute the outputs [${a}] from the provided inputs [${c}]. Missing the following inputs: [${s}]`)}return tH(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);const n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);const s=n.map(m=>this.graph.nodes[as(m)[0]]),i=t.map(m=>as(m)[0]);let o=i.map(m=>this.graph.nodes[m]);o.length===0&&(o=this._outputs);const a=this.getCompilationKey(s,o);let c=this.compiledMap.get(a);c==null&&(c=this.compile(e,o),this.compiledMap.set(a,c));const h={},d={};return ee(()=>{const m=new FN(this.weightMap,h,d,this.functionExecutorMap),y=Object.assign({},this.weightMap);Object.keys(e).forEach(L=>{const[T,A]=as(L),N=[];N[A]=e[L],y[T]=N});const b=this.getFrozenTensorIds(y),w={};for(let L=0;L<c.length;L++){const T=c[L];if(!y[T.name]){const A=kN(T,y,m);if(A instanceof Promise)throw new Error(`The execution of the op '${T.op}' returned a promise. Please use model.executeAsync() instead.`);y[T.name]=A,this.checkTensorForDisposal(T.name,T,y,m,b,i,w)}}return this.parent==null&&m.dispose(b),t.map(L=>Jn(L,y,m))})}getFrozenTensorIds(e){const t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,i,o,a){if(t.category==="control"||o.indexOf(e)!==-1)return;n[e].forEach(c=>{c!=null&&(a[c.id]=(a[c.id]||0)+t.children.length)}),t.inputs.forEach(c=>{if(c.category!=="control"){const h=ZV(c.name,n,s);h!=null&&h.forEach(d=>{if(d&&!i.has(d.id)){const m=a[d.id];m===1?(d.dispose(),delete a[d.id]):m!=null&&a[d.id]--}})}})}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,s={},i={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));const o=new FN(this.weightMap,s,i,this.functionExecutorMap),a=await this.executeWithControlFlow(e,o,t,n),c=t.map(y=>Jn(y,a,o)),h=c.map(y=>y.id),d=Object.keys(e).map(y=>e[y].id),m=new Set([...h,...d,...this.weightIds]);return Object.keys(a).forEach(y=>{const b=a[y];b.forEach(w=>{w&&!w.isDisposed&&!m.has(w.id)&&w.dispose()})}),this.parent==null&&o.dispose(m),c}async executeFunctionAsync(e,t,n){const s=e.reduce((i,o,a)=>(i[this.inputs[a].name]=o,i),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){const i=Object.keys(e),o=i.map(E=>this.graph.nodes[as(E)[0]]),a=n.map(E=>as(E)[0]),c=a.map(E=>this.graph.nodes[E]),{usedNodes:h,missingInputs:d,dynamicNode:m,syncInputs:y}=_N(e,c,this.weightMap),b=[...o,...this.graph.weights].map(E=>({node:E,contexts:t.currentContext})),w=Object.assign({},this.weightMap);Object.keys(e).forEach(E=>{const[D,F]=as(E),_=[];_[F]=e[E],w[D]=_});const L={},T=this.getFrozenTensorIds(w),A={};for(;b.length>0;){const E=this.processStack(o,b,t,w,A,T,a,L,h);await Promise.all(E)}m==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");const N=c.filter(E=>!WN(E)&&!Jn(E.name,w,t)).map(E=>E.name);if(N.length>0){let E="";throw m!=null&&(E=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${y}]`),new Error(`Cannot compute the outputs [${N}] from the provided inputs [${i}]. Consider providing the following inputs: [${d}]. ${E}`)}return w}processStack(e,t,n,s,i,o,a,c,h){const d=[];for(;t.length>0;){const m=t.pop();n.currentContext=m.contexts;let y="";if(m.node.op==="Enter"&&R("isConstant",m.node,s,n)&&([y]=rr(m.node.name,n)),e.indexOf(m.node)===-1){const b=kN(m.node,s,n);y||([y]=rr(m.node.name,n));const w=n.currentContext;b instanceof Promise?d.push(b.then(L=>(s[y]=L,n.currentContext=w,this.checkTensorForDisposal(y,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,h),L))):(s[y]=b,this.checkTensorForDisposal(y,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,h))}else this.processChildNodes(m.node,t,n,s,i,h)}return d}processChildNodes(e,t,n,s,i,o){e.children.forEach(a=>{const[c]=rr(a.name,n);if(i[c]||!o.has(a.name))return;a.op==="Merge"?a.inputNames.some(h=>!!Jn(h,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a})):a.inputNames.every(h=>!!Jn(h,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a}))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{const n=e[t],[s]=as(t),i=this.graph.nodes[s];if(i.attrParams.shape&&i.attrParams.shape.value){const o=i.attrParams.shape.value,a=o.length===n.shape.length&&n.shape.every((c,h)=>o[h]===-1||o[h]===c);k(a,()=>`The shape of dict['${i.name}'] provided in model.execute(dict) must be [${o}], but was [${n.shape}]`)}i.attrParams.dtype&&i.attrParams.dtype.value&&k(n.dtype===i.attrParams.dtype.value,()=>`The dtype of dict['${i.name}'] provided in model.execute(dict) must be ${i.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){const t={};for(const n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){const s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){const t=Object.keys(e).filter(n=>{const[s]=as(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{if(this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null){const n=this._signature.outputs[t];return n.name}return t},{})}checkOutputs(e){e.forEach(t=>{const[n]=as(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}}const rH="?tfjs-format=file",oH="model.json";class $N{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={})}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){const e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Ad(e,this.loadOptions);else{const t=jy(e,this.loadOptions);if(t.length===0)t.push(Ad(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;const t=this.artifacts.modelTopology;let n={};this.artifacts.userDefinedMetadata!=null&&(n=this.artifacts.userDefinedMetadata.signature),this.version=`${t.versions.producer}.${t.versions.minConsumer}`;const s=Sd(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new lS(CN.Instance.transformGraph(t,n)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),e.modelInitializer!=null){const i=CN.Instance.transformGraph(e.modelInitializer);this.initializer=new lS(i),this.initializer.weightMap=this.executor.weightMap,this.initializer.execute({},[])}return!0}async save(e,t){if(typeof e=="string"){const n=qy(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Q)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose()}}async function aH(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&(e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${oH}${rH}`));const n=new $N(e,t);return await n.load(),n}const UN="2.6.0";function cH(e,t){return tm(e,t)}function tm(e,t,n=new Map,s=new Set){if(e==null)return null;if(s.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(i.recurse)if(tc(e)){const o=Array.isArray(e)?[]:{};s.add(e);for(const a in e){const c=e[a],h=tm(c,t,n,s);o[a]=h}return s.delete(e),o}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,i.value),i.value}function lH(e,t=MN){return BN(e,t)}function BN(e,t,n=new Set){const s=e[0];if(n.has(s))throw new Error("Circular references are not supported.");const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(i.recurse)if(tc(s)){const o=Array.isArray(s)?[]:{};n.add(s);for(const a in s){const c=e.map(d=>d[a]),h=BN(c,t,n);o[a]=h}return n.delete(s),o}else throw new Error(`Can't recurse into non-iterable type: ${s}`);else return i.value}function MN(e){return e===null?null:tc(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function PN(e,t){const n=new Map;tm(e,t,n);for(const i of Array.from(n.keys())){const o=n.get(i);if(o instanceof Promise){const a=await o;n.set(i,a)}}const s=tm(e,t,n);return s}function tc(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Q))}function hH(e){return e==null||uH(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Q||wn(e)}function uH(e){return e===null||typeof e!="object"&&typeof e!="function"}function dH(e){return cH(e,pH)}function pH(e){return e instanceof Q?{value:e.clone(),recurse:!1}:tc(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class zN{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(const t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);const e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}}class hS extends zN{constructor(){super(hS.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){const e=this.capacity*2,t=new Array(e),n=this.length();for(let s=0;s<n;s++)t[s]=this.get(this.wrap(this.begin+s));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}}hS.INITIAL_CAPACITY=32;function GN(e){return new fH(e)}function cee(e){let t=e;return Zh(()=>({value:t++,done:!1}))}function Zh(e){return new gH(e)}function VN(e,t){return new HN(e,t)}function lee(e,t,n){return VN(Zh(e).take(t),n)}function mH(e,t=Hr.FAIL){return new AH(e,t)}class fn{async toArray(){const e=[];let t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){const e=this.prefetch(100),t=[];let n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new xH(this,e)}filter(e){return new SH(this,e)}map(e){return new IH(this,e)}mapAsync(e){return new YN(this,e)}serialMapAsync(e){return new YN(this,e).serial()}flatmap(e){return new TH(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new LH(this,e,t)}columnMajorBatch(e,t=!0,n=MN){const s=this.rowMajorBatch(e,t);return s.map(i=>lH(i,n))}concatenate(e,t){return new HN(GN([this,e]),t)}take(e){return e<0||e==null?this:new wH(this,e)}skip(e){return e<0||e==null?this:new bH(this,e)}prefetch(e){return new qN(this,e)}shuffle(e,t){return new vH(this,e,t)}serial(){return new yH(this)}}class fH extends fn{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};const e=this.items[this.trav];return this.trav++,{value:dH(e),done:!1}}}class gH extends fn{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}}class yH extends fn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class bH extends fn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){const e=await this.upstream.next();if(e.done)return e;He(e.value)}return this.upstream.next()}}class wH extends fn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class LH extends fn{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){const e=[];for(;e.length<this.batchSize;){const t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class SH extends fn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){const e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;He(e.value)}}}class IH extends fn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=ji(e.value),n=this.transform(e.value),s=ji(n);for(const i of t)wd(i,s)||i.dispose();return{value:n,done:!1}}}class xH extends fn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}}class YN extends fn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=ji(e.value),n=await this.transform(e.value),s=ji(n);for(const i of t)wd(i,s)||i.dispose();return{value:n,done:!1}}}class uS extends fn{constructor(){super();this.outputQueue=new hS,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class TH extends uS{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=ji(e.value),n=this.transform(e.value),s=ji(n);this.outputQueue.pushAll(n);for(const i of t)wd(i,s)||i.dispose();return!0}}class HN extends fn{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){const e="TODO: fill in upstream of chained summaries";return`${e} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){const n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}var Hr;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Hr||(Hr={}));class AH extends fn{constructor(e,t=Hr.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){const e="TODO: fill in upstream of zip summaries";return`{${e}} -> Zip`}async nextState(e){await e;let t=0,n=0;function s(o){if(o instanceof fn){const a=o.next();return{value:a.then(c=>(t++,c.done&&n++,c.value)),recurse:!1}}else return{value:null,recurse:!0}}const i=await PN(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Hr.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Hr.SHORTEST:return{value:null,done:!0};case Hr.LONGEST:default:}return this.count++,{value:i,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class qN extends fn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new zN(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){const e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}}class vH extends qN{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Ba(n||Yn().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){const e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}}class nc{constructor(){this.size=null}batch(e,t=!0){const n=this;k(e>0,()=>`batchSize needs to be positive, but it is
-      ${e}`);let s;return this.size===Infinity||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),cs(async()=>(await n.iterator()).columnMajorBatch(e,t,RH),s)}concatenate(e){const t=this;let n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,cs(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){const t=this;let n;return this.size===Infinity?n=Infinity:n=null,cs(async()=>(await t.iterator()).filter(s=>ee(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return cs(async()=>(await t.iterator()).map(n=>ee(()=>e(n))),this.size)}mapAsync(e){const t=this;return cs(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const t=this;return cs(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){const t=this;let n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,cs(async()=>{const s=Zh(async()=>({value:await t.iterator(),done:!1}));return VN(s.take(e))},n)}skip(e){const t=this;let n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,cs(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const s=this,i=Ba(t||Yn().toString());return cs(async()=>{let o=i.int32();return n&&(o+=i.int32()),(await s.iterator()).shuffle(e,o.toString())},this.size)}take(e){const t=this;let n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,cs(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}}nc.MAX_BUFFER_SIZE=1e4;function cs(e,t=null){return new class extends nc{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function NH(e){return cs(async()=>GN(e),e.length)}function CH(e){if(!tc(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(const n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return cs(async()=>{const n=await PN(e,s=>{if(s instanceof nc)return{value:s.iterator(),recurse:!1};if(tc(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return mH(n,Hr.SHORTEST)},t)}function RH(e){if(e===null)return null;const t=e[0];if(hH(t)){const n=OH(e);return{value:n,recurse:!1}}return{value:null,recurse:!0}}function OH(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Q?rs(e):en(e)}class jN extends nc{constructor(e){super();this.input=e}async iterator(){const e=await this.input.iterator(),t=e.decodeUTF8(),n=t.split(`
-`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s));return n}}const nm='"',Qh=Symbol("out"),KN=Symbol("field"),sm=Symbol("quote"),dS=Symbol("quoteafterquote"),XN=Symbol("quoteinquote");class JN extends nc{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new jN(e),t||(t={}),this.hasHeader=!(t.hasHeader===!1),this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){const e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&k(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);const t=this.fullColumnNames.reduce((s,i)=>(s[i]=s[i]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(k(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs)for(const s of Object.keys(this.columnConfigs)){const i=this.fullColumnNames.indexOf(s);if(i===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){const e=await this.base.iterator(),t=await e.next();if(t.done)throw new Error("No data was found for CSV parsing.");const n=t.value,s=this.parseRow(n,!1);return s}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){const t=this.parseRow(e),n={},s={};for(let i=0;i<this.fullColumnNames.length;i++){const o=this.fullColumnNames[i],a=this.columnConfigs?this.columnConfigs[o]:null;if(this.configuredColumnsOnly&&!a)continue;{const c=t[i];let h=null;if(c==="")if(a&&a.default!==void 0)h=a.default;else{if(a&&(a.required||a.isLabel))throw new Error(`Required column ${o} is empty in this line: ${e}`);h=void 0}else{const d=Number(c);if(isNaN(d))a&&a.dtype==="bool"?h=this.getBoolean(c):h=c;else if(!a||!a.dtype)h=d;else switch(a.dtype){case"float32":h=d;break;case"int32":h=Math.floor(d);break;case"bool":h=this.getBoolean(c);break;default:h=d}}a&&a.isLabel?s[o]=h:n[o]=h}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){const n=[];let s=0;const i=e.length;let o=Qh;for(let a=0;a<i;a++)switch(o){case Qh:switch(e.charAt(a)){case nm:s=a+1,o=sm;break;case this.delimiter:if(s=a+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),o=Qh;break;default:o=KN,s=a;break}break;case KN:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a)),o=Qh,s=a+1;break;default:}break;case sm:switch(e.charAt(a)){case nm:o=dS;break;default:}break;case dS:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a-1)),o=Qh,s=a+1;break;case nm:o=sm;break;default:o=XN;break}break;case XN:switch(e.charAt(a)){case nm:o=sm;break;default:}break;default:}if(o===dS?n.push(e.substring(s,i-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}}class ZN extends fn{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;const t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=!(e.includeSpectrogram===!1),this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(C().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");const t=new ZN(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");const e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);const t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize);return}async next(){if(this.isClosed)return{value:null,done:!0};let e,t;const n=await this.getAudioData();if(this.includeSpectrogram){const s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){const s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){const e=[],t=[];let n=0;return new Promise(s=>{const i=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(i),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){const t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,i)=>n.set(s,i*t)),n}getTensorFromAudioDataArray(e,t){const n=new Float32Array(we(t));return n.set(e,n.length-e.length),en(n,t)}}class QN extends fn{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=ss([0],"int32"),this.webcamConfig.centerCrop){const n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,i=(1-n)/2,o=(1-s)/2,a=i+n,c=s+o;this.cropBox=Wr([o,i,c,a],[1,4])}else this.cropBox=Wr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(C().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}const n=new QN(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=bT(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return ee(()=>{const t=e.toFloat().expandDims(0);let n;n=$r.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");const s=n.shape;return n.reshape(s.slice(1))})}async capture(){return(await this.next()).value}stop(){const e=this.stream.getTracks();e.forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class e0{}class t0 extends fn{split(e){return new EH(this,e)}}class EH extends t0{constructor(e,t){super();this.upstream=e,this.impl=new DH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class DH extends uS{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){const e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);const t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(const n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}}class kH extends fn{decodeUTF8(){return new FH(this)}}class FH extends t0{constructor(e){super();this.upstream=e,this.impl=new _H(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class _H extends uS{constructor(e){super();if(this.upstream=e,C().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{const{StringDecoder:t}=require("string_decoder");this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){const e=await this.upstream.next();let t;if(e.done)return!1;t=e.value;let n;return C().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}}class n0 extends kH{constructor(e,t={}){super();this.file=e,this.options=t,k(e instanceof Uint8Array||(C().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};const e=new Promise((t,n)=>{const s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{const i=new FileReader;i.onload=a=>{let c=i.result;if(c instanceof ArrayBuffer&&(c=new Uint8Array(c)),!(c instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(c)},i.onabort=a=>n(new Error("Aborted")),i.onerror=a=>n(new Error(a.type));const o=this.file.slice(this.offset,s);i.readAsArrayBuffer(o)}this.offset=s});return{value:await e,done:!1}}}async function WH(e,t={}){let n,s;typeof e=="string"?n=e:(n=e.url,s=$H(e));const i=await Vx(n,s);if(i.ok){const o=new Uint8Array(await i.arrayBuffer());return new n0(o,t)}else throw new Error(i.statusText)}const $H=e=>{const t={method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity};return t};function s0(e){return typeof e=="string"&&e.substr(0,7)==="file://"}class i0 extends e0{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(s0(this.input)&&C().get("IS_NODE")){const e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new n0(this.input,this.options)}}class r0 extends e0{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return s0(this.url)?new i0(this.url,this.fileOptions).iterator():WH(this.url,this.fileOptions)}}function UH(e,t={}){return new JN(new r0(e),t)}function BH(e){const t=Zh(e);return cs(async()=>t)}function MH(e){return cs(async()=>{const t=await e();return Zh(()=>t.next())})}async function PH(e,t){return QN.create(e,t)}async function zH(e){return ZN.create(e)}const o0="2.6.0";var GH=Object.freeze({__proto__:null,array:NH,Dataset:nc,zip:CH,CSVDataset:JN,TextLineDataset:jN,csv:UH,func:BH,generator:MH,microphone:zH,webcam:PH,FileDataSource:i0,URLDataSource:r0,version_data:o0});function xe(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}const VH=up,YH=mw,HH=fw,qH=gw,jH=ip;function pS(e,t,n,s){if(n==="linear")return e.linear(t);if(n==="relu")return e.relu(t);if(n==="elu")return Co(t);if(n==="relu6")return e.relu6(t);if(n==="prelu")return e.prelu(t,s);throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}class KH extends g{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new p(this,_s())}write(e,t,n){this.firstUse&&(this.firstUse=!1,C().get("IS_NODE")&&Ya(`
+        ${s}, and tensor's shape is: ${e.shape}`);const o=s===0?0:e.size/s,a=ee(()=>{const h=[];e=K(e,[1,s,o]);for(let d=0;d<t.length;++d){const m=d===0?0:i[d-1],y=[0,m,0],b=[1,t[d],o];h[d]=K(nt(e,y,b),n)}return e.dispose(),h}),c=new Jh([],n,e.dtype,t.length);for(let h=0;h<a.length;h++)c.setItem(h,a[h]);return c}const PY=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{const s=R("thenBranch",e,t,n),i=R("elseBranch",e,t,n),o=R("cond",e,t,n),a=R("args",e,t,n),c=await o.data();return c[0]?n.functionMap[s].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap):n.functionMap[i].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{const s=R("body",e,t,n),i=R("cond",e,t,n),o=R("args",e,t,n),a=await n.functionMap[i].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap),c=o.map(m=>m.id);let h=await a[0].data();a.forEach(m=>{!m.kept&&c.indexOf(m.id)===-1&&m.dispose()});let d=o;for(;h[0];){const m=d;d=await n.functionMap[s].executeFunctionAsync(d,n.tensorArrayMap,n.tensorListMap);const y=d.map(w=>w.id);m.forEach(w=>{!w.kept&&c.indexOf(w.id)===-1&&y.indexOf(w.id)===-1&&w.dispose()});const b=await n.functionMap[i].executeFunctionAsync(d,n.tensorArrayMap,n.tensorListMap);h=await b[0].data(),b.forEach(w=>{!w.kept&&c.indexOf(w.id)===-1&&y.indexOf(w.id)===-1&&w.dispose()})}return d}case"LoopCond":{const s=R("pred",e,t,n);return[or(s)]}case"Switch":{const s=R("pred",e,t,n);let i=R("data",e,t,n);return i.kept||(i=or(i)),(await s.data())[0]?[void 0,i]:[i,void 0]}case"Merge":{const s=e.inputNames.find(i=>Jn(i,t,n)!==void 0);if(s){const i=Jn(s,t,n);return[or(i)]}return}case"Enter":{const s=R("frameName",e,t,n),i=R("tensor",e,t,n);return n.enterFrame(s),[or(i)]}case"Exit":{const s=R("tensor",e,t,n);return n.exitFrame(),[or(s)]}case"NextIteration":{const s=R("tensor",e,t,n);return n.nextIteration(),[or(s)]}case"TensorArrayV3":{const s=R("size",e,t,n),i=R("dtype",e,t,n),o=R("elementShape",e,t,n),a=R("dynamicSize",e,t,n),c=R("clearAfterRead",e,t,n),h=R("identicalElementShapes",e,t,n),d=R("name",e,t,n),m=new WY(d,i,s,o,h,a,c);return n.addTensorArray(m),[m.idTensor,Ne(1)]}case"TensorArrayWriteV3":{const s=R("tensorArrayId",e,t,n),i=R("index",e,t,n),o=R("tensor",e,t,n),a=n.getTensorArray(s.id);return a.write(i,o),[a.idTensor]}case"TensorArrayReadV3":{const s=R("tensorArrayId",e,t,n),i=R("index",e,t,n),o=n.getTensorArray(s.id);return[o.read(i)]}case"TensorArrayGatherV3":{const s=R("tensorArrayId",e,t,n),i=R("indices",e,t,n),o=R("dtype",e,t,n),a=n.getTensorArray(s.id);return[a.gather(i,o)]}case"TensorArrayScatterV3":{const s=R("tensorArrayId",e,t,n),i=R("indices",e,t,n),o=R("tensor",e,t,n),a=n.getTensorArray(s.id);return a.scatter(i,o),[a.idTensor]}case"TensorArrayConcatV3":{const s=R("tensorArrayId",e,t,n),i=n.getTensorArray(s.id),o=R("dtype",e,t,n);return[i.concat(o)]}case"TensorArraySplitV3":{const s=R("tensorArrayId",e,t,n),i=R("tensor",e,t,n),o=R("lengths",e,t,n),a=n.getTensorArray(s.id);return a.split(o,i),[a.idTensor]}case"TensorArraySizeV3":{const s=R("tensorArrayId",e,t,n),i=n.getTensorArray(s.id);return[Ne(i.size(),"int32")]}case"TensorArrayCloseV3":{const s=R("tensorArrayId",e,t,n),i=n.getTensorArray(s.id);return i.clearAndClose(),[i.idTensor]}case"TensorListSetItem":{const s=R("tensorListId",e,t,n),i=R("index",e,t,n),o=R("tensor",e,t,n),a=n.getTensorList(s.id);return a.setItem(i,o),[a.idTensor]}case"TensorListGetItem":{const s=R("tensorListId",e,t,n),i=R("index",e,t,n),o=R("elementShape",e,t,n),a=R("elementDType",e,t,n),c=n.getTensorList(s.id);return[c.getItem(i,o,a)]}case"TensorListScatterV2":case"TensorListScatter":{const s=R("indices",e,t,n),i=R("tensor",e,t,n),o=R("elementShape",e,t,n),a=R("numElements",e,t,n),c=BY(i,s,o,a);return n.addTensorList(c),[c.idTensor]}case"TensorListReserve":{const s=R("elementShape",e,t,n),i=R("elementDType",e,t,n),o=R("numElements",e,t,n),a=UY(s,i,o);return n.addTensorList(a),[a.idTensor]}case"TensorListGather":{const s=R("tensorListId",e,t,n),i=R("indices",e,t,n),o=R("elementShape",e,t,n),a=R("elementDType",e,t,n),c=n.getTensorList(s.id);return[c.gather(i,a,o)]}case"TensorListStack":{const s=R("tensorListId",e,t,n),i=R("elementShape",e,t,n),o=R("elementDType",e,t,n),a=R("numElements",e,t,n),c=n.getTensorList(s.id);return[c.stack(i,o,a)]}case"TensorListFromTensor":{const s=R("tensor",e,t,n),i=R("elementShape",e,t,n),o=R("elementDType",e,t,n),a=$Y(s,i,o);return n.addTensorList(a),[a.idTensor]}case"TensorListConcat":{const s=R("tensorListId",e,t,n),i=n.getTensorList(s.id),o=R("dtype",e,t,n),a=R("elementShape",e,t,n);return[i.concat(o,a)]}case"TensorListPushBack":{const s=R("tensorListId",e,t,n),i=R("tensor",e,t,n),o=n.getTensorList(s.id);return o.pushBack(i),[o.idTensor]}case"TensorListPopBack":{const s=R("tensorListId",e,t,n),i=R("elementShape",e,t,n),o=R("elementDType",e,t,n),a=n.getTensorList(s.id);return[a.popBack(i,o)]}case"TensorListSplit":{const s=R("tensor",e,t,n),i=R("elementShape",e,t,n),o=R("lengths",e,t,n),a=MY(s,o,i);return n.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},jQ="control";function DN(e,t,n){const[s,i]=R("fusedOps",e,t,n),o=s==="biasadd",a=i==="prelu",c=s==="fusedbatchnorm",h=R("numArgs",e,t,n);if(o){if(a&&h!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&h!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(c)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");const d=R("strides",e,t,n),m=em(e,t,n),y=R("dataFormat",e,t,n).toUpperCase(),b=R("dilations",e,t,n),[w,L]=R("args",e,t,n);return{stride:d,pad:m,dataFormat:y,dilations:b,biasArg:w,preluArg:L,activationFunc:i}}const zY=(e,t,n)=>{switch(e.op){case"Conv1D":{const s=R("stride",e,t,n),i=R("pad",e,t,n),o=R("dataFormat",e,t,n).toUpperCase(),a=R("dilation",e,t,n);return[$d(R("x",e,t,n),R("filter",e,t,n),s,i,o,a)]}case"Conv2D":{const s=R("strides",e,t,n),i=em(e,t,n),o=R("dataFormat",e,t,n).toUpperCase(),a=R("dilations",e,t,n);return[Xi(R("x",e,t,n),R("filter",e,t,n),[s[1],s[2]],i,o,[a[1],a[2]])]}case"_FusedConv2D":{const{stride:s,pad:i,dataFormat:o,dilations:a,biasArg:c,preluArg:h,activationFunc:d}=DN(e,t,n);return[ew({x:R("x",e,t,n),filter:R("filter",e,t,n),strides:[s[1],s[2]],pad:i,dataFormat:o,dilations:[a[1],a[2]],bias:c,activation:d,preluActivationWeights:h})]}case"FusedDepthwiseConv2dNative":{const{stride:s,pad:i,dataFormat:o,dilations:a,biasArg:c,preluArg:h,activationFunc:d}=DN(e,t,n);return[AA({x:R("x",e,t,n),filter:R("filter",e,t,n),strides:[s[1],s[2]],pad:i,dataFormat:o,dilations:[a[1],a[2]],bias:c,activation:d,preluActivationWeights:h})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{const s=R("outputShape",e,t,n),i=R("strides",e,t,n),o=em(e,t,n);return[Ud(R("x",e,t,n),R("filter",e,t,n),s,[i[1],i[2]],o)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{const s=R("strides",e,t,n),i=em(e,t,n),o=R("dilations",e,t,n),a=R("dataFormat",e,t,n).toUpperCase();return[Co(R("input",e,t,n),R("filter",e,t,n),[s[1],s[2]],i,a,[o[1],o[2]])]}case"Conv3D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("dataFormat",e,t,n).toUpperCase(),a=R("dilations",e,t,n);return[vb(R("x",e,t,n),R("filter",e,t,n),[s[1],s[2],s[3]],i,o,[a[1],a[2],a[3]])]}case"AvgPool":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[nh(R("x",e,t,n),[o[1],o[2]],[s[1],s[2]],i)]}case"MaxPool":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[hh(R("x",e,t,n),[o[1],o[2]],[s[1],s[2]],i)]}case"MaxPoolWithArgmax":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n),a=R("includeBatchInIndex",e,t,n),{result:c,indexes:h}=JT(R("x",e,t,n),[o[1],o[2]],[s[1],s[2]],i,a);return[c,h]}case"AvgPool3D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[Ib(R("x",e,t,n),[o[1],o[2],o[3]],[s[1],s[2],s[3]],i)]}case"MaxPool3D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("kernelSize",e,t,n);return[Wb(R("x",e,t,n),[o[1],o[2],o[3]],[s[1],s[2],s[3]],i)]}case"Dilation2D":{const s=R("strides",e,t,n),i=R("pad",e,t,n),o=R("dilations",e,t,n),a=s[1],c=s[2],h=o[1],d=o[2];return[Cb(R("x",e,t,n),R("filter",e,t,n),[a,c],i,[h,d],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},KQ="convolution";const GY=(e,t,n)=>{switch(e.op){case"Fill":{const s=R("shape",e,t,n),i=R("dtype",e,t,n),o=R("value",e,t,n);return[oh(s,o,i)]}case"LinSpace":{const s=R("start",e,t,n),i=R("stop",e,t,n),o=R("num",e,t,n);return[jT(s,i,o)]}case"Multinomial":{const s=R("logits",e,t,n),i=R("numSamples",e,t,n),o=R("seed",e,t,n);return[ZT(s,i,o)]}case"OneHot":{const 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jY=(e,t,n)=>{switch(e.op){case"Equal":return[ti(R("a",e,t,n),R("b",e,t,n))];case"NotEqual":return[Fr(R("a",e,t,n),R("b",e,t,n))];case"Greater":return[Ss(R("a",e,t,n),R("b",e,t,n))];case"GreaterEqual":return[Ji(R("a",e,t,n),R("b",e,t,n))];case"Less":return[ch(R("a",e,t,n),R("b",e,t,n))];case"LessEqual":return[kr(R("a",e,t,n),R("b",e,t,n))];case"LogicalAnd":return[$s(R("a",e,t,n),R("b",e,t,n))];case"LogicalNot":return[lh(R("a",e,t,n))];case"LogicalOr":return[Hd(R("a",e,t,n),R("b",e,t,n))];case"Select":case"SelectV2":return[_n(R("condition",e,t,n),R("a",e,t,n),R("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},tee="logical";const KY=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[at(R("a",e,t,n),R("b",e,t,n),R("transposeA",e,t,n),R("transposeB",e,t,n))];case"Transpose":return[Me(R("x",e,t,n),R("perm",e,t,n))];case"_FusedMatMul":const[s,i]=R("fusedOps",e,t,n),o=s==="biasadd",a=i==="prelu",c=R("numArgs",e,t,n);if(o){if(a&&c!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&c!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}const[h,d]=R("args",e,t,n);return[hp({a:R("a",e,t,n),b:R("b",e,t,n),transposeA:R("transposeA",e,t,n),transposeB:R("transposeB",e,t,n),bias:h,activation:i,preluActivationWeights:d})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},nee="matrices";const 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ee(()=>YY(i,o,a));case"image":return ee(()=>qY(i,o,a));case"graph":return ee(()=>HY(i,o,a));case"logical":return ee(()=>jY(i,o,a));case"matrices":return ee(()=>KY(i,o,a));case"normalization":return ee(()=>XY(i,o,a));case"reduction":return ee(()=>JY(i,o,a));case"slice_join":return ee(()=>ZY(i,o,a));case"spectral":return ee(()=>QY(i,o,a));case"transformation":return ee(()=>eH(i,o,a));case"custom":const c=NN(i.op);if(c&&c.customExecutor)return c.customExecutor(new DY(i,o,a));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. 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this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(const t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(const t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}}function _N(e,t,n,s){const i=new Set,o=[];let a=null,c=null;const h=new Set,d=Object.keys(e).map(b=>as(b)[0]);let m=[];s!=null&&(m=s.map(b=>as(b.name)[0]));const y=[...t];for(;y.length>0;){const b=y.pop();if((WN(b)||iH(b))&&(a==null&&(a=b,c=a.children.map(w=>w.name).filter(w=>i.has(w)))),i.add(b.name),n[b.name]!=null)continue;if(d.indexOf(b.name)!==-1)continue;if(m.indexOf(b.name)!==-1)continue;if(b.inputs.length===0){o.push(b.name);continue}b.inputs.forEach(w=>{if(h.has(w.name))return;h.add(w.name),y.push(w)})}return{inputs:e,outputs:t,usedNodes:i,missingInputs:o,dynamicNode:a,syncInputs:c}}function tH(e,t,n){const{usedNodes:s,inputs:i}=n,o=[],a=Object.keys(i).map(m=>as(m)[0]).map(m=>e.nodes[m]),c=e.initNodes;a.forEach(m=>{s.has(m.name)&&o.push(m)}),e.weights.forEach(m=>{s.has(m.name)&&o.push(m)}),c!=null&&c.forEach(m=>{s.has(m.name)&&o.push(m)});const h=new Set,d=[];for(;o.length>0;){const m=o.pop();h.add(m.name),t[m.name]||d.push(m),m.children.forEach(y=>{!h.has(y.name)&&s.has(y.name)&&y.inputs.every(b=>h.has(b.name))&&o.push(y)})}return d}const nH=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],sH=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"];function WN(e){return nH.indexOf(e.op)>=0}function iH(e){return sH.indexOf(e.op)>=0}class lS{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new lS(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){const t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{const t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){const n=e.map(i=>i.name).sort(),s=t.map(i=>i.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){const n=_N(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:i,syncInputs:o}=n;if(i!=null)throw new Error(`This execution contains the node '${i.name}', which has the dynamic op '${i.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${o}]`);if(s.length>0){const a=t.map(h=>h.name),c=Object.keys(e);throw new Error(`Cannot compute the outputs [${a}] from the provided inputs [${c}]. Missing the following inputs: [${s}]`)}return tH(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);const n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);const s=n.map(m=>this.graph.nodes[as(m)[0]]),i=t.map(m=>as(m)[0]);let o=i.map(m=>this.graph.nodes[m]);o.length===0&&(o=this._outputs);const a=this.getCompilationKey(s,o);let c=this.compiledMap.get(a);c==null&&(c=this.compile(e,o),this.compiledMap.set(a,c));const h={},d={};return ee(()=>{const m=new FN(this.weightMap,h,d,this.functionExecutorMap),y=Object.assign({},this.weightMap);Object.keys(e).forEach(L=>{const[T,A]=as(L),N=[];N[A]=e[L],y[T]=N});const b=this.getFrozenTensorIds(y),w={};for(let L=0;L<c.length;L++){const T=c[L];if(!y[T.name]){const A=kN(T,y,m);if(A instanceof Promise)throw new Error(`The execution of the op '${T.op}' returned a promise. Please use model.executeAsync() instead.`);y[T.name]=A,this.checkTensorForDisposal(T.name,T,y,m,b,i,w)}}return this.parent==null&&m.dispose(b),t.map(L=>Jn(L,y,m))})}getFrozenTensorIds(e){const t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,i,o,a){if(t.category==="control"||o.indexOf(e)!==-1)return;n[e].forEach(c=>{c!=null&&(a[c.id]=(a[c.id]||0)+t.children.length)}),t.inputs.forEach(c=>{if(c.category!=="control"){const h=ZV(c.name,n,s);h!=null&&h.forEach(d=>{if(d&&!i.has(d.id)){const m=a[d.id];m===1?(d.dispose(),delete a[d.id]):m!=null&&a[d.id]--}})}})}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,s={},i={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));const o=new FN(this.weightMap,s,i,this.functionExecutorMap),a=await this.executeWithControlFlow(e,o,t,n),c=t.map(y=>Jn(y,a,o)),h=c.map(y=>y.id),d=Object.keys(e).map(y=>e[y].id),m=new Set([...h,...d,...this.weightIds]);return Object.keys(a).forEach(y=>{const b=a[y];b.forEach(w=>{w&&!w.isDisposed&&!m.has(w.id)&&w.dispose()})}),this.parent==null&&o.dispose(m),c}async executeFunctionAsync(e,t,n){const s=e.reduce((i,o,a)=>(i[this.inputs[a].name]=o,i),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){const i=Object.keys(e),o=i.map(E=>this.graph.nodes[as(E)[0]]),a=n.map(E=>as(E)[0]),c=a.map(E=>this.graph.nodes[E]),{usedNodes:h,missingInputs:d,dynamicNode:m,syncInputs:y}=_N(e,c,this.weightMap),b=[...o,...this.graph.weights].map(E=>({node:E,contexts:t.currentContext})),w=Object.assign({},this.weightMap);Object.keys(e).forEach(E=>{const[D,F]=as(E),_=[];_[F]=e[E],w[D]=_});const L={},T=this.getFrozenTensorIds(w),A={};for(;b.length>0;){const E=this.processStack(o,b,t,w,A,T,a,L,h);await Promise.all(E)}m==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");const N=c.filter(E=>!WN(E)&&!Jn(E.name,w,t)).map(E=>E.name);if(N.length>0){let E="";throw m!=null&&(E=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${y}]`),new Error(`Cannot compute the outputs [${N}] from the provided inputs [${i}]. Consider providing the following inputs: [${d}]. ${E}`)}return w}processStack(e,t,n,s,i,o,a,c,h){const d=[];for(;t.length>0;){const m=t.pop();n.currentContext=m.contexts;let y="";if(m.node.op==="Enter"&&R("isConstant",m.node,s,n)&&([y]=rr(m.node.name,n)),e.indexOf(m.node)===-1){const b=kN(m.node,s,n);y||([y]=rr(m.node.name,n));const w=n.currentContext;b instanceof Promise?d.push(b.then(L=>(s[y]=L,n.currentContext=w,this.checkTensorForDisposal(y,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,h),L))):(s[y]=b,this.checkTensorForDisposal(y,m.node,s,n,o,a,c),this.processChildNodes(m.node,t,n,s,i,h))}else this.processChildNodes(m.node,t,n,s,i,h)}return d}processChildNodes(e,t,n,s,i,o){e.children.forEach(a=>{const[c]=rr(a.name,n);if(i[c]||!o.has(a.name))return;a.op==="Merge"?a.inputNames.some(h=>!!Jn(h,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a})):a.inputNames.every(h=>!!Jn(h,s,n))&&(i[c]=!0,t.push({contexts:n.currentContext,node:a}))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{const n=e[t],[s]=as(t),i=this.graph.nodes[s];if(i.attrParams.shape&&i.attrParams.shape.value){const o=i.attrParams.shape.value,a=o.length===n.shape.length&&n.shape.every((c,h)=>o[h]===-1||o[h]===c);k(a,()=>`The shape of dict['${i.name}'] provided in model.execute(dict) must be [${o}], but was [${n.shape}]`)}i.attrParams.dtype&&i.attrParams.dtype.value&&k(n.dtype===i.attrParams.dtype.value,()=>`The dtype of dict['${i.name}'] provided in model.execute(dict) must be ${i.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){const t={};for(const n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){const s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){const t=Object.keys(e).filter(n=>{const[s]=as(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{if(this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null){const n=this._signature.outputs[t];return n.name}return t},{})}checkOutputs(e){e.forEach(t=>{const[n]=as(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}}const rH="?tfjs-format=file",oH="model.json";class $N{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={})}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){const e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Ad(e,this.loadOptions);else{const t=jy(e,this.loadOptions);if(t.length===0)t.push(Ad(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;const t=this.artifacts.modelTopology;let n={};this.artifacts.userDefinedMetadata!=null&&(n=this.artifacts.userDefinedMetadata.signature),this.version=`${t.versions.producer}.${t.versions.minConsumer}`;const s=Sd(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new lS(CN.Instance.transformGraph(t,n)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),e.modelInitializer!=null){const i=CN.Instance.transformGraph(e.modelInitializer);this.initializer=new lS(i),this.initializer.weightMap=this.executor.weightMap,this.initializer.execute({},[])}return!0}async save(e,t){if(typeof e=="string"){const n=qy(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Q)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose()}}async function aH(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&(e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${oH}${rH}`));const n=new $N(e,t);return await n.load(),n}const UN="2.6.0";function cH(e,t){return tm(e,t)}function tm(e,t,n=new Map,s=new Set){if(e==null)return null;if(s.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(i.recurse)if(sc(e)){const o=Array.isArray(e)?[]:{};s.add(e);for(const a in e){const c=e[a],h=tm(c,t,n,s);o[a]=h}return s.delete(e),o}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,i.value),i.value}function lH(e,t=MN){return BN(e,t)}function BN(e,t,n=new Set){const s=e[0];if(n.has(s))throw new Error("Circular references are not supported.");const i=t(e);if(i.recurse&&i.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(i.recurse)if(sc(s)){const o=Array.isArray(s)?[]:{};n.add(s);for(const a in s){const c=e.map(d=>d[a]),h=BN(c,t,n);o[a]=h}return n.delete(s),o}else throw new Error(`Can't recurse into non-iterable type: ${s}`);else return i.value}function MN(e){return e===null?null:sc(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function PN(e,t){const n=new Map;tm(e,t,n);for(const i of Array.from(n.keys())){const o=n.get(i);if(o instanceof Promise){const a=await o;n.set(i,a)}}const s=tm(e,t,n);return s}function sc(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Q))}function hH(e){return e==null||uH(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Q||wn(e)}function uH(e){return e===null||typeof e!="object"&&typeof e!="function"}function dH(e){return cH(e,pH)}function pH(e){return e instanceof Q?{value:e.clone(),recurse:!1}:sc(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class zN{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(const t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);const e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}}class hS extends zN{constructor(){super(hS.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){const e=this.capacity*2,t=new Array(e),n=this.length();for(let s=0;s<n;s++)t[s]=this.get(this.wrap(this.begin+s));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}}hS.INITIAL_CAPACITY=32;function GN(e){return new fH(e)}function cee(e){let t=e;return Zh(()=>({value:t++,done:!1}))}function Zh(e){return new gH(e)}function VN(e,t){return new HN(e,t)}function lee(e,t,n){return VN(Zh(e).take(t),n)}function mH(e,t=Hr.FAIL){return new AH(e,t)}class fn{async toArray(){const e=[];let t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){const e=this.prefetch(100),t=[];let n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new xH(this,e)}filter(e){return new SH(this,e)}map(e){return new IH(this,e)}mapAsync(e){return new YN(this,e)}serialMapAsync(e){return new YN(this,e).serial()}flatmap(e){return new TH(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new LH(this,e,t)}columnMajorBatch(e,t=!0,n=MN){const s=this.rowMajorBatch(e,t);return s.map(i=>lH(i,n))}concatenate(e,t){return new HN(GN([this,e]),t)}take(e){return e<0||e==null?this:new wH(this,e)}skip(e){return e<0||e==null?this:new bH(this,e)}prefetch(e){return new qN(this,e)}shuffle(e,t){return new vH(this,e,t)}serial(){return new yH(this)}}class fH extends fn{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};const e=this.items[this.trav];return this.trav++,{value:dH(e),done:!1}}}class gH extends fn{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}}class yH extends fn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class bH extends fn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){const e=await this.upstream.next();if(e.done)return e;He(e.value)}return this.upstream.next()}}class wH extends fn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class LH extends fn{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){const e=[];for(;e.length<this.batchSize;){const t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class SH extends fn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){const e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;He(e.value)}}}class IH extends fn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=ji(e.value),n=this.transform(e.value),s=ji(n);for(const i of t)wd(i,s)||i.dispose();return{value:n,done:!1}}}class xH extends fn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}}class YN extends fn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=ji(e.value),n=await this.transform(e.value),s=ji(n);for(const i of t)wd(i,s)||i.dispose();return{value:n,done:!1}}}class uS extends fn{constructor(){super();this.outputQueue=new hS,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class TH extends uS{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=ji(e.value),n=this.transform(e.value),s=ji(n);this.outputQueue.pushAll(n);for(const i of t)wd(i,s)||i.dispose();return!0}}class HN extends fn{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){const e="TODO: fill in upstream of chained summaries";return`${e} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){const n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}var Hr;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Hr||(Hr={}));class AH extends fn{constructor(e,t=Hr.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){const e="TODO: fill in upstream of zip summaries";return`{${e}} -> Zip`}async nextState(e){await e;let t=0,n=0;function s(o){if(o instanceof fn){const a=o.next();return{value:a.then(c=>(t++,c.done&&n++,c.value)),recurse:!1}}else return{value:null,recurse:!0}}const i=await PN(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Hr.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Hr.SHORTEST:return{value:null,done:!0};case Hr.LONGEST:default:}return this.count++,{value:i,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class qN extends fn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new zN(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){const e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}}class vH extends qN{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Pa(n||Yn().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){const e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}}class ic{constructor(){this.size=null}batch(e,t=!0){const n=this;k(e>0,()=>`batchSize needs to be positive, but it is
+      ${e}`);let s;return this.size===Infinity||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),cs(async()=>(await n.iterator()).columnMajorBatch(e,t,RH),s)}concatenate(e){const t=this;let n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,cs(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){const t=this;let n;return this.size===Infinity?n=Infinity:n=null,cs(async()=>(await t.iterator()).filter(s=>ee(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return cs(async()=>(await t.iterator()).map(n=>ee(()=>e(n))),this.size)}mapAsync(e){const t=this;return cs(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const t=this;return cs(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){const t=this;let n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,cs(async()=>{const s=Zh(async()=>({value:await t.iterator(),done:!1}));return VN(s.take(e))},n)}skip(e){const t=this;let n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,cs(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const s=this,i=Pa(t||Yn().toString());return cs(async()=>{let o=i.int32();return n&&(o+=i.int32()),(await s.iterator()).shuffle(e,o.toString())},this.size)}take(e){const t=this;let n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,cs(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}}ic.MAX_BUFFER_SIZE=1e4;function cs(e,t=null){return new class extends ic{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function NH(e){return cs(async()=>GN(e),e.length)}function CH(e){if(!sc(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(const n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return cs(async()=>{const n=await PN(e,s=>{if(s instanceof ic)return{value:s.iterator(),recurse:!1};if(sc(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return mH(n,Hr.SHORTEST)},t)}function RH(e){if(e===null)return null;const t=e[0];if(hH(t)){const n=OH(e);return{value:n,recurse:!1}}return{value:null,recurse:!0}}function OH(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Q?rs(e):en(e)}class jN extends ic{constructor(e){super();this.input=e}async iterator(){const e=await this.input.iterator(),t=e.decodeUTF8(),n=t.split(`
+`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s));return n}}const nm='"',Qh=Symbol("out"),KN=Symbol("field"),sm=Symbol("quote"),dS=Symbol("quoteafterquote"),XN=Symbol("quoteinquote");class JN extends ic{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new jN(e),t||(t={}),this.hasHeader=!(t.hasHeader===!1),this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){const e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&k(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);const t=this.fullColumnNames.reduce((s,i)=>(s[i]=s[i]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(k(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs)for(const s of Object.keys(this.columnConfigs)){const i=this.fullColumnNames.indexOf(s);if(i===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){const e=await this.base.iterator(),t=await e.next();if(t.done)throw new Error("No data was found for CSV parsing.");const n=t.value,s=this.parseRow(n,!1);return s}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){const t=this.parseRow(e),n={},s={};for(let i=0;i<this.fullColumnNames.length;i++){const o=this.fullColumnNames[i],a=this.columnConfigs?this.columnConfigs[o]:null;if(this.configuredColumnsOnly&&!a)continue;{const c=t[i];let h=null;if(c==="")if(a&&a.default!==void 0)h=a.default;else{if(a&&(a.required||a.isLabel))throw new Error(`Required column ${o} is empty in this line: ${e}`);h=void 0}else{const d=Number(c);if(isNaN(d))a&&a.dtype==="bool"?h=this.getBoolean(c):h=c;else if(!a||!a.dtype)h=d;else switch(a.dtype){case"float32":h=d;break;case"int32":h=Math.floor(d);break;case"bool":h=this.getBoolean(c);break;default:h=d}}a&&a.isLabel?s[o]=h:n[o]=h}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){const n=[];let s=0;const i=e.length;let o=Qh;for(let a=0;a<i;a++)switch(o){case Qh:switch(e.charAt(a)){case nm:s=a+1,o=sm;break;case this.delimiter:if(s=a+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),o=Qh;break;default:o=KN,s=a;break}break;case KN:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a)),o=Qh,s=a+1;break;default:}break;case sm:switch(e.charAt(a)){case nm:o=dS;break;default:}break;case dS:switch(e.charAt(a)){case this.delimiter:n.push(e.substring(s,a-1)),o=Qh,s=a+1;break;case nm:o=sm;break;default:o=XN;break}break;case XN:switch(e.charAt(a)){case nm:o=sm;break;default:}break;default:}if(o===dS?n.push(e.substring(s,i-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}}class ZN extends fn{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;const t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=!(e.includeSpectrogram===!1),this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(C().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");const t=new ZN(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");const e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);const t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize);return}async next(){if(this.isClosed)return{value:null,done:!0};let e,t;const n=await this.getAudioData();if(this.includeSpectrogram){const s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){const s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){const e=[],t=[];let n=0;return new Promise(s=>{const i=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(i),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){const t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,i)=>n.set(s,i*t)),n}getTensorFromAudioDataArray(e,t){const n=new Float32Array(we(t));return n.set(e,n.length-e.length),en(n,t)}}class QN extends fn{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=ss([0],"int32"),this.webcamConfig.centerCrop){const n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,i=(1-n)/2,o=(1-s)/2,a=i+n,c=s+o;this.cropBox=Wr([o,i,c,a],[1,4])}else this.cropBox=Wr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(C().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}const n=new QN(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=bT(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return ee(()=>{const t=e.toFloat().expandDims(0);let n;n=$r.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");const s=n.shape;return n.reshape(s.slice(1))})}async capture(){return(await this.next()).value}stop(){const e=this.stream.getTracks();e.forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class e0{}class t0 extends fn{split(e){return new EH(this,e)}}class EH extends t0{constructor(e,t){super();this.upstream=e,this.impl=new DH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class DH extends uS{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){const e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);const t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(const n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}}class kH extends fn{decodeUTF8(){return new FH(this)}}class FH extends t0{constructor(e){super();this.upstream=e,this.impl=new _H(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class _H extends uS{constructor(e){super();if(this.upstream=e,C().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{const{StringDecoder:t}=require("string_decoder");this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){const e=await this.upstream.next();let t;if(e.done)return!1;t=e.value;let n;return C().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}}class n0 extends kH{constructor(e,t={}){super();this.file=e,this.options=t,k(e instanceof Uint8Array||(C().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};const e=new Promise((t,n)=>{const s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{const i=new FileReader;i.onload=a=>{let c=i.result;if(c instanceof ArrayBuffer&&(c=new Uint8Array(c)),!(c instanceof Uint8Array))return n(new 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 ============================
 Hi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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s=t.strideDepth,i=t.strideHeight,o=t.strideWidth,a=t.dilationDepth,c=t.dilationHeight,h=t.dilationWidth,d=t.effectiveFilterDepth,m=t.effectiveFilterHeight,y=t.effectiveFilterWidth,b=t.padInfo.front,w=t.padInfo.top,L=t.padInfo.left,T=n==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,A=this.readSync(e.dataId),N=Ze(t.outShape,e.dtype),E=N.values,D=t.outShape[1]*t.outShape[2]*t.outShape[3]*t.outShape[4],F=t.outShape[2]*t.outShape[3]*t.outShape[4],_=t.outShape[3]*t.outShape[4],B=t.outShape[4];for(let $=0;$<t.batchSize;++$){const H=$*D,q=$*e.strides[0];for(let J=0;J<t.inChannels;++J)for(let re=0;re<t.outDepth;++re){const ce=re*s-b;let ue=ce;for(;ue<0;)ue+=a;const he=Math.min(t.inDepth,d+ce),de=H+re*F;for(let le=0;le<t.outHeight;++le){const ye=le*i-w;let pe=ye;for(;pe<0;)pe+=c;const Ie=Math.min(t.inHeight,m+ye),Se=de+le*_;for(let Ee=0;Ee<t.outWidth;++Ee){const We=Ee*o-L;let Oe=We;for(;Oe<0;)Oe+=h;const $e=Math.min(t.inWidth,y+We),Ye=Se+Ee*B;let et=T,bt=0,Jt=0;for(let hs=ue;hs<he;hs+=a){const Ht=q+hs*e.strides[1];for(let $n=pe;$n<Ie;$n+=c){const pi=Ht+$n*e.strides[2];for(let vs=Oe;vs<$e;vs+=h){const us=pi+vs*e.strides[3],Gs=A[us+J];if(n==="max"&&Gs>et?et=Gs:n==="avg"&&(bt+=Gs,Jt++),isNaN(et))break}if(isNaN(et))break}if(isNaN(et))break}const hn=Ye+J;E[hn]=n==="avg"?bt/Jt:et}}}}return N.toTensor()}avgPool3d(e,t){return xe(e,"avgPool3d"),this.pool3d(e,t,"avg").toFloat()}avgPool3dBackprop(e,t,n){xe([e,t],"avgPool3dBackprop");const s=n.strideDepth,i=n.strideHeight,o=n.strideWidth,a=n.filterDepth,c=n.filterHeight,h=n.filterWidth,d=n.dilationDepth,m=n.dilationHeight,y=n.dilationWidth,b=n.effectiveFilterDepth,w=n.effectiveFilterHeight,L=n.effectiveFilterWidth,T=b-1-n.padInfo.front,A=L-1-n.padInfo.left,N=w-1-n.padInfo.top,E=Ze(t.shape,"float32"),D=1/(a*c*h),F=this.bufferSync(e);for(let _=0;_<n.batchSize;++_)for(let B=0;B<n.inChannels;++B)for(let $=0;$<n.inDepth;++$)for(let H=0;H<n.inHeight;++H)for(let q=0;q<n.inWidth;++q){const J=$-T,re=H-N,ce=q-A;let ue=0;for(let he=0;he<b;he+=d){const de=(J+he)/s;if(de<0||de>=n.outDepth||Math.floor(de)!==de)continue;for(let le=0;le<w;le+=m){const ye=(re+le)/i;if(ye<0||ye>=n.outHeight||Math.floor(ye)!==ye)continue;for(let pe=0;pe<L;pe+=y){const Ie=(ce+pe)/o;if(Ie<0||Ie>=n.outWidth||Math.floor(Ie)!==Ie)continue;const Se=F.get(_,de,ye,Ie,B);ue+=Se}}}E.set(ue*D,_,$,H,q,B)}return E.toTensor()}maxPool3d(e,t){return xe(e,"maxPool3d"),this.pool3d(e,t,"max").toFloat()}maxPool3dPositions(e,t){const n=Ze(t.outShape,"int32"),s=t.strideDepth,i=t.strideHeight,o=t.strideWidth,a=t.dilationDepth,c=t.dilationHeight,h=t.dilationWidth,d=t.effectiveFilterDepth,m=t.effectiveFilterHeight,y=t.effectiveFilterWidth,b=t.padInfo.front,w=t.padInfo.top,L=t.padInfo.left,T=this.bufferSync(e);for(let A=0;A<t.batchSize;++A)for(let N=0;N<t.inChannels;++N)for(let E=0;E<t.outDepth;++E){const D=E*s-b;let F=D;for(;F<0;)F+=a;const _=Math.min(t.inDepth,d+D);for(let B=0;B<t.outHeight;++B){const $=B*i-w;let H=$;for(;H<0;)H+=c;const q=Math.min(t.inHeight,m+$);for(let J=0;J<t.outWidth;++J){const re=J*o-L;let ce=re;for(;ce<0;)ce+=h;const ue=Math.min(t.inWidth,y+re);let he=Number.NEGATIVE_INFINITY,de=-1;for(let le=F;le<_;le+=a){const ye=le-D;for(let pe=H;pe<q;pe+=c){const Ie=pe-$;for(let Se=ce;Se<ue;Se+=h){const Ee=Se-re,We=T.get(A,le,pe,Se,N);We>=he&&(he=We,de=ye*m*y+Ie*m+Ee)}}}n.set(de,A,E,B,J,N)}}}return n.toTensor()}maxPool3dBackprop(e,t,n,s){xe([t,n],"maxPool3dBackprop");const i=this.maxPool3dPositions(t,s),o=s.strideDepth,a=s.strideHeight,c=s.strideWidth,h=s.dilationDepth,d=s.dilationHeight,m=s.dilationWidth,y=s.effectiveFilterDepth,b=s.effectiveFilterHeight,w=s.effectiveFilterWidth,L=y-1-s.padInfo.front,T=w-1-s.padInfo.left,A=b-1-s.padInfo.top,N=Ze(t.shape,"float32"),E=this.bufferSync(i),D=this.bufferSync(e);for(let F=0;F<s.batchSize;++F)for(let _=0;_<s.inChannels;++_)for(let B=0;B<s.inDepth;++B)for(let $=0;$<s.inHeight;++$)for(let H=0;H<s.inWidth;++H){const q=B-L,J=$-A,re=H-T;let ce=0;for(let 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Xq={kernelName:ju,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s,filter:i}=e,{strides:o,pad:a,dilations:c}=n,h=t,d=h.data.get(s.dataId).values,m=s.shape.length,y=h.data.get(i.dataId).values,b=i.shape.length,{batchSize:w,inHeight:L,inWidth:T,inChannels:A,outHeight:N,outWidth:E,padInfo:D,strideHeight:F,strideWidth:_,filterHeight:B,filterWidth:$,dilationHeight:H,dilationWidth:q,outShape:J}=Fd(s.shape,i.shape,o,a,"NHWC",c),re=we(J),ce=J.length,ue=yo(s.dtype,re);for(let de=0;de<w;++de)for(let le=0;le<N;++le){const ye=le*F-D.top;for(let pe=0;pe<E;++pe){const Ie=pe*_-D.left;for(let Se=0;Se<A;++Se){let Ee=Number.MIN_SAFE_INTEGER;for(let Oe=0;Oe<B;++Oe){const $e=ye+Oe*H;if($e>=0&&$e<L)for(let Ye=0;Ye<$;++Ye){const et=Ie+Ye*q;if(et>=0&&et<T){const bt=ei([de,$e,et,Se],m,Ot(s.shape)),Jt=ei([Oe,Ye,Se],b,Ot(i.shape)),hn=d[bt]+y[Jt];hn>Ee&&(Ee=hn)}}}const We=ei([de,le,pe,Se],ce,Ot(J));ue[We]=Ee}}}const 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s=t.strideDepth,i=t.strideHeight,o=t.strideWidth,a=t.dilationDepth,c=t.dilationHeight,h=t.dilationWidth,d=t.effectiveFilterDepth,m=t.effectiveFilterHeight,y=t.effectiveFilterWidth,b=t.padInfo.front,w=t.padInfo.top,L=t.padInfo.left,T=n==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,A=this.readSync(e.dataId),N=Ze(t.outShape,e.dtype),E=N.values,D=t.outShape[1]*t.outShape[2]*t.outShape[3]*t.outShape[4],F=t.outShape[2]*t.outShape[3]*t.outShape[4],_=t.outShape[3]*t.outShape[4],B=t.outShape[4];for(let $=0;$<t.batchSize;++$){const H=$*D,q=$*e.strides[0];for(let J=0;J<t.inChannels;++J)for(let re=0;re<t.outDepth;++re){const ce=re*s-b;let ue=ce;for(;ue<0;)ue+=a;const he=Math.min(t.inDepth,d+ce),de=H+re*F;for(let le=0;le<t.outHeight;++le){const ye=le*i-w;let pe=ye;for(;pe<0;)pe+=c;const Ie=Math.min(t.inHeight,m+ye),Se=de+le*_;for(let Ee=0;Ee<t.outWidth;++Ee){const We=Ee*o-L;let Oe=We;for(;Oe<0;)Oe+=h;const $e=Math.min(t.inWidth,y+We),Ye=Se+Ee*B;let et=T,bt=0,Jt=0;for(let hs=ue;hs<he;hs+=a){const Ht=q+hs*e.strides[1];for(let $n=pe;$n<Ie;$n+=c){const pi=Ht+$n*e.strides[2];for(let vs=Oe;vs<$e;vs+=h){const us=pi+vs*e.strides[3],Gs=A[us+J];if(n==="max"&&Gs>et?et=Gs:n==="avg"&&(bt+=Gs,Jt++),isNaN(et))break}if(isNaN(et))break}if(isNaN(et))break}const un=Ye+J;E[un]=n==="avg"?bt/Jt:et}}}}return N.toTensor()}avgPool3d(e,t){return xe(e,"avgPool3d"),this.pool3d(e,t,"avg").toFloat()}avgPool3dBackprop(e,t,n){xe([e,t],"avgPool3dBackprop");const s=n.strideDepth,i=n.strideHeight,o=n.strideWidth,a=n.filterDepth,c=n.filterHeight,h=n.filterWidth,d=n.dilationDepth,m=n.dilationHeight,y=n.dilationWidth,b=n.effectiveFilterDepth,w=n.effectiveFilterHeight,L=n.effectiveFilterWidth,T=b-1-n.padInfo.front,A=L-1-n.padInfo.left,N=w-1-n.padInfo.top,E=Ze(t.shape,"float32"),D=1/(a*c*h),F=this.bufferSync(e);for(let _=0;_<n.batchSize;++_)for(let B=0;B<n.inChannels;++B)for(let $=0;$<n.inDepth;++$)for(let H=0;H<n.inHeight;++H)for(let q=0;q<n.inWidth;++q){const J=$-T,re=H-N,ce=q-A;let ue=0;for(let he=0;he<b;he+=d){const de=(J+he)/s;if(de<0||de>=n.outDepth||Math.floor(de)!==de)continue;for(let le=0;le<w;le+=m){const ye=(re+le)/i;if(ye<0||ye>=n.outHeight||Math.floor(ye)!==ye)continue;for(let pe=0;pe<L;pe+=y){const Ie=(ce+pe)/o;if(Ie<0||Ie>=n.outWidth||Math.floor(Ie)!==Ie)continue;const Se=F.get(_,de,ye,Ie,B);ue+=Se}}}E.set(ue*D,_,$,H,q,B)}return E.toTensor()}maxPool3d(e,t){return xe(e,"maxPool3d"),this.pool3d(e,t,"max").toFloat()}maxPool3dPositions(e,t){const n=Ze(t.outShape,"int32"),s=t.strideDepth,i=t.strideHeight,o=t.strideWidth,a=t.dilationDepth,c=t.dilationHeight,h=t.dilationWidth,d=t.effectiveFilterDepth,m=t.effectiveFilterHeight,y=t.effectiveFilterWidth,b=t.padInfo.front,w=t.padInfo.top,L=t.padInfo.left,T=this.bufferSync(e);for(let A=0;A<t.batchSize;++A)for(let N=0;N<t.inChannels;++N)for(let E=0;E<t.outDepth;++E){const D=E*s-b;let F=D;for(;F<0;)F+=a;const _=Math.min(t.inDepth,d+D);for(let B=0;B<t.outHeight;++B){const $=B*i-w;let H=$;for(;H<0;)H+=c;const q=Math.min(t.inHeight,m+$);for(let J=0;J<t.outWidth;++J){const re=J*o-L;let ce=re;for(;ce<0;)ce+=h;const ue=Math.min(t.inWidth,y+re);let he=Number.NEGATIVE_INFINITY,de=-1;for(let le=F;le<_;le+=a){const ye=le-D;for(let pe=H;pe<q;pe+=c){const Ie=pe-$;for(let Se=ce;Se<ue;Se+=h){const Ee=Se-re,We=T.get(A,le,pe,Se,N);We>=he&&(he=We,de=ye*m*y+Ie*m+Ee)}}}n.set(de,A,E,B,J,N)}}}return n.toTensor()}maxPool3dBackprop(e,t,n,s){xe([t,n],"maxPool3dBackprop");const i=this.maxPool3dPositions(t,s),o=s.strideDepth,a=s.strideHeight,c=s.strideWidth,h=s.dilationDepth,d=s.dilationHeight,m=s.dilationWidth,y=s.effectiveFilterDepth,b=s.effectiveFilterHeight,w=s.effectiveFilterWidth,L=y-1-s.padInfo.front,T=w-1-s.padInfo.left,A=b-1-s.padInfo.top,N=Ze(t.shape,"float32"),E=this.bufferSync(i),D=this.bufferSync(e);for(let F=0;F<s.batchSize;++F)for(let _=0;_<s.inChannels;++_)for(let B=0;B<s.inDepth;++B)for(let $=0;$<s.inHeight;++$)for(let H=0;H<s.inWidth;++H){const q=B-L,J=$-A,re=H-T;let ce=0;for(let 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T=d[y*s+L];w+=T*c[L],b.push(T)}if(w<0||w>=e.size/a)throw new Error(`Invalid indices: ${b} does not index into ${e.shape}`);for(let L=0;L<a;L++)h.values[y*a+L]=m[w*a+L]}return h.toTensor().reshape(i)}scatterND(e,t,n){const{sliceRank:s,numUpdates:i,sliceSize:o,strides:a,outputSize:c}=Da(t,e,n),h=Ne(0),d=!0;return this.scatter(e,t,n,c,o,i,s,a,h,d)}fill(e,t,n){n=n||Ta(t);const s=wo(n,we(e));return s.fill(t),_s().makeTensor(s,e,n,this)}onesLike(e){if(e.dtype==="string")throw new Error("onesLike is not supported for string tensors");return this.fill(e.shape,1,e.dtype)}zerosLike(e){const t=wo(e.dtype,we(e.shape));return this.makeOutput(t,e.shape,e.dtype)}linspace(e,t,n){return dw(e,t,n)}scatter(e,t,n,s,i,o,a,c,h,d){const m=[s/i,i],y=this.readSync(e.dataId),b=this.readSync(t.dataId);if(s===0)return en([],n,t.dtype);const w=new vr(m,t.dtype);w.values.fill(this.readSync(h.dataId)[0]);for(let L=0;L<o;L++){const T=[];let A=0;for(let N=0;N<a;N++){const 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eq={kernelName:ly,backendName:"cpu",kernelFunc:eu};function tu(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{dtype:o}=s;if(o==="complex64"){if(i.dtype==="complex64")return rc({inputs:{x:i},backend:n});const a=ct(i.shape),c=tu({inputs:{x:i},backend:n,attrs:{dtype:"float32"}}),h=di({inputs:{real:c,imag:a},backend:n});return a.dispose(),n.disposeIntermediateTensorInfo(c),h}if(i.dtype==="complex64"){const a=eu({inputs:{input:i},backend:n}),c=tu({inputs:{x:a},backend:n,attrs:{dtype:o}});return n.disposeIntermediateTensorInfo(a),c}if(!Dy(i.dtype,o)){const a=rc({inputs:{x:i},backend:n});return{dataId:a.dataId,shape:a.shape,dtype:o}}if(o==="int32"){const a=n.data.get(i.dataId).values,c=Int32Array.from(a);return n.makeTensorInfo(i.shape,"int32",c)}if(o==="bool"){const a=n.data.get(i.dataId).values,c=Ar([0],i.dtype),[h,d]=Ho((m,y)=>m!==y?1:0)(i.shape,[],a,c,"bool");return n.makeTensorInfo(d,"bool",h)}throw new Error(`Error in Cast: failed to cast ${i.dtype} to ${o}`)}const tq={kernelName:al,backendName:"cpu",kernelFunc:tu};function oc(e,t,n,s){return n==null?({inputs:i,backend:o})=>{const{a,b:c}=i,h=o;xe([a,c],e);const d=h.data.get(a.dataId).values,m=h.data.get(c.dataId).values,y=s||a.dtype,[b,w]=t(a.shape,c.shape,d,m,y);return h.makeTensorInfo(w,y,b)}:({inputs:i,backend:o})=>{const{a,b:c}=i,h=o;if(a.dtype==="complex64"||c.dtype==="complex64"){const d=tu({inputs:{x:a},backend:h,attrs:{dtype:"complex64"}}),m=h.data.get(d.dataId),y=m.complexTensorInfos.real,b=m.complexTensorInfos.imag,w=h.data.get(y.dataId).values,L=h.data.get(b.dataId).values,T=tu({inputs:{x:c},backend:h,attrs:{dtype:"complex64"}}),A=h.data.get(T.dataId),N=A.complexTensorInfos.real,E=A.complexTensorInfos.imag,D=h.data.get(N.dataId).values,F=h.data.get(E.dataId).values,[_,B,$]=n(a.shape,c.shape,w,L,D,F),H=h.makeTensorInfo($,"float32",_),q=h.makeTensorInfo($,"float32",B),J=di({inputs:{real:H,imag:q},backend:h});return 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c0=Ho((e,t)=>e+t),nq=mS((e,t,n,s)=>({real:e+n,imag:t+s})),l0=oc(Te,c0,nq),sq={kernelName:Te,backendName:"cpu",kernelFunc:l0};function ac(e){return(t,n,s)=>{const i=bn(n,t.length);for(let o=0;o<t.length;++o)i[o]=e(t[o],s);return i}}function xt(e,t,n){return({inputs:s,attrs:i,backend:o})=>{const{x:a}=s;if(xe(a,e),a.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");const c=o,h=c.data.get(a.dataId).values,d=we(a.shape),m=n||a.dtype,y=wo(m,d);for(let b=0;b<d;++b)y[b]=t(h[b],i);return c.makeTensorInfo(a.shape,m,y)}}function cc(e,t,n){return({inputs:s,attrs:i,backend:o})=>{const{x:a}=s;if(xe(a,e),a.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");const c=o,h=c.data.get(a.dataId).values,d=n||a.dtype,m=t(h,d,i);return c.makeTensorInfo(a.shape,d,m)}}const h0=ac(e=>Math.ceil(e)),iq=cc(cl,h0),rq={kernelName:cl,backendName:"cpu",kernelFunc:iq};const 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Xq={kernelName:ju,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:s,filter:i}=e,{strides:o,pad:a,dilations:c}=n,h=t,d=h.data.get(s.dataId).values,m=s.shape.length,y=h.data.get(i.dataId).values,b=i.shape.length,{batchSize:w,inHeight:L,inWidth:T,inChannels:A,outHeight:N,outWidth:E,padInfo:D,strideHeight:F,strideWidth:_,filterHeight:B,filterWidth:$,dilationHeight:H,dilationWidth:q,outShape:J}=Fd(s.shape,i.shape,o,a,"NHWC",c),re=we(J),ce=J.length,ue=wo(s.dtype,re);for(let de=0;de<w;++de)for(let le=0;le<N;++le){const ye=le*F-D.top;for(let pe=0;pe<E;++pe){const Ie=pe*_-D.left;for(let Se=0;Se<A;++Se){let Ee=Number.MIN_SAFE_INTEGER;for(let Oe=0;Oe<B;++Oe){const $e=ye+Oe*H;if($e>=0&&$e<L)for(let Ye=0;Ye<$;++Ye){const et=Ie+Ye*q;if(et>=0&&et<T){const bt=ei([de,$e,et,Se],m,Ot(s.shape)),Jt=ei([Oe,Ye,Se],b,Ot(i.shape)),un=d[bt]+y[Jt];un>Ee&&(Ee=un)}}}const We=ei([de,le,pe,Se],ce,Ot(J));ue[We]=Ee}}}const 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s=we(e);if(e.length<=1&&s<=n)return[1,s];if(e.length===2&&e[0]<=n&&e[1]<=n)return e;if(e.length===3&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){const i=hc(e);let o=2,a=2;return e.length&&([o,a]=uc(e)),s=i*(o/2)*(a/2),pd(s).map(c=>c*2)}return pd(s)}function am(e){return e%2===0}function cm(e,t){if(e=e.slice(-2),t=t.slice(-2),ot(e,t))return!0;if(!e.length||!t.length)return!0;if(e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){const n=e.slice(-1)[0],s=t.slice(-1)[0];if(n===s)return!0;if(am(n)&&am(s)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&am(e[0])&&am(t[0])}let lm,hm;function tK(e){if(lm==null){const t=Wi(e);lm=t.getParameter(t.MAX_TEXTURE_SIZE)}return lm}function gee(){lm=null}function yee(){hm=null}function nK(e){if(hm==null){const t=Wi(e);hm=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,hm)}function sK(e){if(e===0)return 0;let t;const n=Wi(e);return zs(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:zs(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function zs(e,t){const n=e.getExtension(t);return n!=null}function E0(e){try{const t=Wi(e);if(t!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function iK(e){if(e===0)return!1;const t=Wi(e);if(e===1){if(!zs(t,"OES_texture_float"))return!1}else if(!zs(t,"EXT_color_buffer_float"))return!1;const n=AS(t);return n}function rK(e){if(e===0)return!1;const t=Wi(e);if(e===1){if(!zs(t,"OES_texture_float"))return!1;if(!zs(t,"WEBGL_color_buffer_float"))return!1}else{if(zs(t,"EXT_color_buffer_float"))return AS(t);const s="EXT_color_buffer_half_float";if(zs(t,s)){const i=t.getExtension(s);return oK(t,i)}return!1}const n=AS(t);return n}function AS(e){const t=SS(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);const s=1,i=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,s,i,0,t.textureFormatFloat,t.textureTypeFloat,null);const o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);const a=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(o),a}function oK(e,t){const n=SS(e,t),s=e.createTexture();e.bindTexture(e.TEXTURE_2D,s);const i=1,o=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,i,o,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);const a=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,a),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);const c=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(a),c}function aK(e){if(e!==2)return!1;const t=Wi(e),n=t.fenceSync!=null;return n}function ou(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}const Ge=C();Ge.registerFlag("HAS_WEBGL",()=>Ge.getNumber("WEBGL_VERSION")>0),Ge.registerFlag("WEBGL_VERSION",()=>E0(2)?2:E0(1)?1:0),Ge.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1),Ge.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ge.get("WEBGL_VERSION")===2),Ge.registerFlag("WEBGL_CPU_FORWARD",()=>!0),Ge.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1),Ge.registerFlag("WEBGL_PACK",()=>Ge.getBool("HAS_WEBGL")),Ge.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ge.getBool("WEBGL_PACK")),Ge.registerFlag("WEBGL_PACK_CLIP",()=>Ge.getBool("WEBGL_PACK")),Ge.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1),Ge.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ge.getBool("WEBGL_PACK")),Ge.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ge.getBool("WEBGL_PACK")),Ge.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ge.getBool("WEBGL_PACK")),Ge.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ge.getBool("WEBGL_PACK")),Ge.registerFlag("WEBGL_PACK_REDUCE",()=>Ge.getBool("WEBGL_PACK")),Ge.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ge.getBool("WEBGL_PACK")),Ge.registerFlag("WEBGL_CONV_IM2COL",()=>Ge.getBool("WEBGL_PACK")),Ge.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>tK(Ge.getNumber("WEBGL_VERSION"))),Ge.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>nK(Ge.getNumber("WEBGL_VERSION"))),Ge.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{const e=Ge.getNumber("WEBGL_VERSION");return e===0?0:sK(e)}),Ge.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ge.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Zx()),Ge.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>iK(Ge.getNumber("WEBGL_VERSION"))),Ge.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ge.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ge.getBool("WEBGL_RENDER_FLOAT32_CAPABLE")),Ge.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>rK(Ge.getNumber("WEBGL_VERSION"))),Ge.registerFlag("WEBGL_FENCE_API_ENABLED",()=>aK(Ge.getNumber("WEBGL_VERSION"))),Ge.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>{const e=Ge.getBool("WEBGL_RENDER_FLOAT32_ENABLED");return e?4:0}),Ge.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});const{simpleAbsImpl:cK,addImpl:lK,ceilImpl:hK,expImpl:uK,expm1Impl:dK,floorImpl:pK,logImpl:mK,maxImpl:fK,multiplyImpl:gK,rsqrtImpl:yK,sliceImpl:bK,subImpl:wK,transposeImpl:D0,uniqueImpl:LK}=Sq;class SK{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((i,o)=>`T${o}`);const n=[];this.variableNames.forEach(i=>{n.push(`float v${i} = get${i}AtOutCoords();`)});const s=this.variableNames.map(i=>`v${i}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -114,7 +114,7 @@ Hi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed thi
       ivec4 round(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:i,output:o,defineOutput:a,defineSpecialNaN:c,defineSpecialInf:h,defineRound:d}}function Ho(e,t,n="index"){const s=Ot(t);return s.map((i,o)=>{const a=`int ${e[o]} = ${n} / ${i}`,c=o===s.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${i}`:`index -= ${e[o]} * ${i}`;return`${a}; ${c};`}).join("")}function um(e){return e.length===1?`${e[0]}`:`vec${e.length}(${e.join(",")})`}function bee(e,t){if(e.length!==t.length)throw new Error(`Vectors to be dotted must be of the same length -got ${e.length} and ${t.length}`);const n=[],s=Math.floor(e.length/4),i=e.length%4;for(let o=0;o<s;o++){const a=e.slice(o*4,o*4+4),c=t.slice(o*4,o*4+4);n.push(`${um(a)}, ${um(c)}`)}if(i!==0){let o=e.slice(s*4),a=t.slice(s*4);o.length===1&&(o=o.map(c=>`float(${c})`),a=a.map(c=>`float(${c})`)),n.push(`${um(o)}, ${um(a)}`)}return n.map((o,a)=>`dot(${o})`).join("+")}function vS(e){const t=Ot(e).map(n=>n.toString());return`
+    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:i,output:o,defineOutput:a,defineSpecialNaN:c,defineSpecialInf:h,defineRound:d}}function jo(e,t,n="index"){const s=Ot(t);return s.map((i,o)=>{const a=`int ${e[o]} = ${n} / ${i}`,c=o===s.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${i}`:`index -= ${e[o]} * ${i}`;return`${a}; ${c};`}).join("")}function um(e){return e.length===1?`${e[0]}`:`vec${e.length}(${e.join(",")})`}function bee(e,t){if(e.length!==t.length)throw new Error(`Vectors to be dotted must be of the same length -got ${e.length} and ${t.length}`);const n=[],s=Math.floor(e.length/4),i=e.length%4;for(let o=0;o<s;o++){const a=e.slice(o*4,o*4+4),c=t.slice(o*4,o*4+4);n.push(`${um(a)}, ${um(c)}`)}if(i!==0){let o=e.slice(s*4),a=t.slice(s*4);o.length===1&&(o=o.map(c=>`float(${c})`),a=a.map(c=>`float(${c})`)),n.push(`${um(o)}, ${um(a)}`)}return n.map((o,a)=>`dot(${o})`).join("+")}function vS(e){const t=Ot(e).map(n=>n.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
   }
@@ -160,7 +160,7 @@ Hi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed thi
 `;const{getBroadcastDims:_0}=pw;function AK(e,t,n,s){const i=[];e.forEach(L=>{const T=we(L.shapeInfo.logicalShape);L.shapeInfo.isUniform?i.push(`uniform float ${L.name}${T>1?`[${T}]`:""};`):(i.push(`uniform sampler2D ${L.name};`),i.push(`uniform int offset${L.name};`))});const o=i.join(`
 `),a=e.map(L=>vK(L,t,s)).join(`
 `),c=t.texShape,h=Wn(),d=RK(h);let m,y,b=DK(h);t.isPacked?(m=NK(t.logicalShape,c),y=EK(h)):(m=CK(t.logicalShape,c),y=OK(h)),s&&(b+=WK);const w=[b,d,y,o,m,a,n].join(`
-`);return w}function hc(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return jK(e);case 1:return XK(e);case 2:return ZK(e);case 3:return e5(e);case 4:return n5(e);case 5:return s5(e);case 6:return i5(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function W0(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return qK(e);case 1:return KK(e);case 2:return JK(e);case 3:return QK(e);default:return t5(e)}}function vK(e,t,n=!1){let s="";n?s+=W0(e):s+=hc(e);const i=e.shapeInfo.logicalShape,o=t.logicalShape;return i.length<=o.length&&(n?s+=r5(e,t):s+=o5(e,t)),s}function NK(e,t){switch(e.length){case 0:return $0();case 1:return $K(e,t);case 2:return YK(e,t);case 3:return BK(e,t);default:return PK(e,t)}}function CK(e,t){switch(e.length){case 0:return $0();case 1:return UK(e,t);case 2:return HK(e,t);case 3:return MK(e,t);case 4:return zK(e,t);case 5:return GK(e,t);case 6:return VK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function RK(e){return`
+`);return w}function dc(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return jK(e);case 1:return XK(e);case 2:return ZK(e);case 3:return e5(e);case 4:return n5(e);case 5:return s5(e);case 6:return i5(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function W0(e){const t=e.shapeInfo.logicalShape;switch(t.length){case 0:return qK(e);case 1:return KK(e);case 2:return JK(e);case 3:return QK(e);default:return t5(e)}}function vK(e,t,n=!1){let s="";n?s+=W0(e):s+=dc(e);const i=e.shapeInfo.logicalShape,o=t.logicalShape;return i.length<=o.length&&(n?s+=r5(e,t):s+=o5(e,t)),s}function NK(e,t){switch(e.length){case 0:return $0();case 1:return $K(e,t);case 2:return YK(e,t);case 3:return BK(e,t);default:return PK(e,t)}}function CK(e,t){switch(e.length){case 0:return $0();case 1:return UK(e,t);case 2:return HK(e,t);case 3:return MK(e,t);case 4:return zK(e,t);case 5:return GK(e,t);case 6:return VK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function RK(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
@@ -316,7 +316,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec3(b, r, c);
     }
-  `}function MK(e,t){const n=Ho(["r","c","d"],e);return`
+  `}function MK(e,t){const n=jo(["r","c","d"],e);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${t[0]}, ${t[1]}));
@@ -343,7 +343,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec${e.length}(${c});
     }
-  `}function zK(e,t){const n=Ho(["r","c","d","d2"],e);return`
+  `}function zK(e,t){const n=jo(["r","c","d","d2"],e);return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -351,7 +351,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec4(r, c, d, d2);
     }
-  `}function GK(e,t){const n=Ho(["r","c","d","d2","d3"],e);return`
+  `}function GK(e,t){const n=jo(["r","c","d","d2","d3"],e);return`
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                              ${t[1]}));
@@ -363,7 +363,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function VK(e,t){const n=Ho(["r","c","d","d2","d3","d4"],e);return`
+  `}function VK(e,t){const n=jo(["r","c","d","d2","d3","d4"],e);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -416,7 +416,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       int c = index - r * ${e[1]};
       return ivec2(r, c);
     }
-  `}function qo(e){return`offset${e}`}function qK(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Wn();return`
+  `}function Ko(e){return`offset${e}`}function qK(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Wn();return`
     vec4 ${n}() {
       return ${s.texture2D}(${t}, halfCR);
     }
@@ -424,7 +424,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       float ${n}() {
         return sampleTexture(${t}, halfCR);
       }
-    `;const[o,a]=e.shapeInfo.texShape,c=qo(t);return`
+    `;const[o,a]=e.shapeInfo.texShape,c=Ko(t);return`
     float ${n}() {
       vec2 uv = uvFromFlat(${o}, ${a}, ${c});
       return sampleTexture(${t}, uv);
@@ -437,13 +437,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     }
   `}function XK(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
       float ${n}(int index) {
-        ${uc(e)}
+        ${pc(e)}
       }
     `;const s=e.shapeInfo.texShape,i=s[0],o=s[1];if(o===1&&i===1)return`
       float ${n}(int index) {
         return sampleTexture(${t}, halfCR);
       }
-    `;const a=qo(t);return o===1?`
+    `;const a=Ko(t);return o===1?`
       float ${n}(int index) {
         vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${i}.0);
         return sampleTexture(${t}, uv);
@@ -474,17 +474,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${b}.0, ${y}.0);
       return sampleTexture(${n}, uv);
     }
-  `}const{newShape:o,keptDims:a}=Ir(t),c=o;if(c.length<t.length){const y=dc(e,c),b=["row","col"];return`
-      ${hc(y)}
+  `}const{newShape:o,keptDims:a}=Ir(t),c=o;if(c.length<t.length){const y=mc(e,c),b=["row","col"];return`
+      ${dc(y)}
       float ${s}(int row, int col) {
-        return ${s}(${pc(b,a)});
+        return ${s}(${fc(b,a)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col) {
         int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
-        ${uc(e)}
+        ${pc(e)}
       }
-    `;const h=i[0],d=i[1],m=qo(n);return d===1?`
+    `;const h=i[0],d=i[1],m=Ko(n);return d===1?`
     float ${s}(int row, int col) {
       float index = dot(vec3(row, col, ${m}), vec3(${t[1]}, 1, 1));
       vec2 uv = vec2(0.5, (index + 0.5) / ${h}.0);
@@ -503,10 +503,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     vec2 uv = uvFromFlat(${h}, ${d}, index);
     return sampleTexture(${n}, uv);
   }
-`}function QK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=e.shapeInfo.texShape,o=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)];if(t[0]===1){const y=t.slice(1),b=[1,2],w=dc(e,y),L=["b","row","col"];return`
+`}function QK(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=e.shapeInfo.texShape,o=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)];if(t[0]===1){const y=t.slice(1),b=[1,2],w=mc(e,y),L=["b","row","col"];return`
         ${W0(w)}
         vec4 ${s}(int b, int row, int col) {
-          return ${s}(${pc(L,b)});
+          return ${s}(${fc(L,b)});
         }
       `}const a=o[0],c=o[1],h=Math.ceil(t[2]/2),d=h*Math.ceil(t[1]/2),m=Wn();return`
     vec4 ${s}(int b, int row, int col) {
@@ -514,16 +514,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${a}, ${c}, ${d}, ${h}, b, row, col);
       return ${m.texture2D}(${n}, uv);
     }
-  `}function e5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[1]*t[2],o=t[2],{newShape:a,keptDims:c}=Ir(t),h=a;if(h.length<t.length){const L=dc(e,h),T=["row","col","depth"];return`
-        ${hc(L)}
+  `}function e5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[1]*t[2],o=t[2],{newShape:a,keptDims:c}=Ir(t),h=a;if(h.length<t.length){const L=mc(e,h),T=["row","col","depth"];return`
+        ${dc(L)}
         float ${s}(int row, int col, int depth) {
-          return ${s}(${pc(T,c)});
+          return ${s}(${fc(T,c)});
         }
       `}if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
                           vec3(${i}, ${o}, 1)));
-        ${uc(e)}
+        ${pc(e)}
       }
     `;const d=e.shapeInfo.texShape,m=d[0],y=d[1],b=e.shapeInfo.flatOffset;if(y===i&&b==null)return`
         float ${s}(int row, int col, int depth) {
@@ -540,7 +540,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${y}.0, ${m}.0);
       return sampleTexture(${n}, uv);
     }
-  `;const w=qo(n);return`
+  `;const w=Ko(n);return`
       float ${s}(int row, int col, int depth) {
         // Explicitly use integer operations as dot() only works on floats.
         int index = row * ${i} + col * ${o} + depth + ${w};
@@ -555,16 +555,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}, ${c});
       return ${w.texture2D}(${s}, uv);
     }
-  `}function n5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[3],o=t[2]*i,a=t[1]*o,{newShape:c,keptDims:h}=Ir(t);if(c.length<t.length){const L=dc(e,c),T=["row","col","depth","depth2"];return`
-      ${hc(L)}
+  `}function n5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[3],o=t[2]*i,a=t[1]*o,{newShape:c,keptDims:h}=Ir(t);if(c.length<t.length){const L=mc(e,c),T=["row","col","depth","depth2"];return`
+      ${dc(L)}
       float ${s}(int row, int col, int depth, int depth2) {
-        return ${s}(${pc(T,h)});
+        return ${s}(${fc(T,h)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
                           vec4(${a}, ${o}, ${i}, 1)));
-        ${uc(e)}
+        ${pc(e)}
       }
     `;const d=e.shapeInfo.flatOffset,m=e.shapeInfo.texShape,y=m[0],b=m[1];if(b===a&&d==null)return`
       float ${s}(int row, int col, int depth, int depth2) {
@@ -585,7 +585,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${b}.0, ${y}.0);
         return sampleTexture(${n}, uv);
       }
-    `;const w=qo(n);return`
+    `;const w=Ko(n);return`
     float ${s}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${a} + col * ${o} +
@@ -593,10 +593,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${y}, ${b}, index + ${w});
       return sampleTexture(${n}, uv);
     }
-  `}function s5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[4],o=t[3]*i,a=t[2]*o,c=t[1]*a,{newShape:h,keptDims:d}=Ir(t);if(h.length<t.length){const T=dc(e,h),A=["row","col","depth","depth2","depth3"];return`
-      ${hc(T)}
+  `}function s5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),i=t[4],o=t[3]*i,a=t[2]*o,c=t[1]*a,{newShape:h,keptDims:d}=Ir(t);if(h.length<t.length){const T=mc(e,h),A=["row","col","depth","depth2","depth3"];return`
+      ${dc(T)}
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
-        return ${s}(${pc(A,d)});
+        return ${s}(${fc(A,d)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
@@ -604,7 +604,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           vec4(row, col, depth, depth2),
           vec4(${c}, ${a}, ${o}, ${i})) +
           depth3;
-        ${uc(e)}
+        ${pc(e)}
       }
     `;const m=e.shapeInfo.flatOffset,y=e.shapeInfo.texShape,b=y[0],w=y[1];if(w===c&&m==null)return`
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
@@ -626,7 +626,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${w}.0, ${b}.0);
         return sampleTexture(${n}, uv);
       }
-    `;const L=qo(n);return`
+    `;const L=Ko(n);return`
     float ${s}(int row, int col, int depth, int depth2, int depth3) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${c} + col * ${a} + depth * ${o} +
@@ -634,11 +634,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${b}, ${w}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function i5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:i,keptDims:o}=Ir(t);if(i.length<t.length){const A=dc(e,i),N=["row","col","depth","depth2","depth3","depth4"];return`
-      ${hc(A)}
+  `}function i5(e){const t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:i,keptDims:o}=Ir(t);if(i.length<t.length){const A=mc(e,i),N=["row","col","depth","depth2","depth3","depth4"];return`
+      ${dc(A)}
       float ${s}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${s}(${pc(N,o)});
+        return ${s}(${fc(N,o)});
       }
     `}const a=t[5],c=t[4]*a,h=t[3]*c,d=t[2]*h,m=t[1]*d;if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth,
@@ -649,7 +649,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           dot(
             vec2(depth3, depth4),
             vec2(${a}, 1)));
-        ${uc(e)}
+        ${pc(e)}
       }
     `;const y=e.shapeInfo.flatOffset,b=e.shapeInfo.texShape,w=b[0],L=b[1];if(L===m&&y==null)return`
       float ${s}(int row, int col, int depth,
@@ -675,7 +675,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${L}.0, ${w}.0);
         return sampleTexture(${n}, uv);
       }
-    `;const T=qo(n);return`
+    `;const T=Ko(n);return`
     float ${s}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
       // Explicitly use integer operations as dot() only works on floats.
@@ -684,7 +684,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${w}, ${L}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function uc(e){const t=e.name,n=we(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
+  `}function pc(e){const t=e.name,n=we(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
     for (int i = 0; i < ${n}; i++) {
       if (i == index) {
         return ${t}[i];
@@ -715,7 +715,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${b}
       return get${s}(${L});
     }
-  `}function Et(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function dc(e,t){const n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function pc(e,t){return t.map(n=>e[n]).join(", ")}class a5{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);const i=e[e.length-1],o=Math.ceil(i/t);this.outputShape=e.slice(0,-1),o>1&&this.outputShape.push(o),s||this.variableNames.push("bestIndicesA");const a=this.outputShape,c=a.length,h=Et(c),d=ls("coords",c);let m,y;if(o===1){y=c+1;const $=Et(y);m=`
+  `}function Et(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function mc(e,t){const n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function fc(e,t){return t.map(n=>e[n]).join(", ")}class a5{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);const i=e[e.length-1],o=Math.ceil(i/t);this.outputShape=e.slice(0,-1),o>1&&this.outputShape.push(o),s||this.variableNames.push("bestIndicesA");const a=this.outputShape,c=a.length,h=Et(c),d=ls("coords",c);let m,y;if(o===1){y=c+1;const $=Et(y);m=`
         ${$} sourceLocR = ${$}(${d.join()}, 0);
         ++${d[c-1]};
         ${$} sourceLocG = ${$}(${d.join()}, 0);
@@ -915,7 +915,7 @@ return (round(mod(b, 2.0)) != 1) ?
 `,S5=M0+`
   return min(a, b);
 `,I5=`if (b == 0.0) return NAN;
-  return mod(a, b);`,x5="return (b >= 1.0) ? a : a * (b + 1.0);",z0="return (a < 0.) ? b * a : a;";class ln{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=tt(t,n),this.userCode=`
+  return mod(a, b);`,x5="return (b >= 1.0) ? a : a * (b + 1.0);",z0="return (a < 0.) ? b * a : a;";class hn{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=tt(t,n),this.userCode=`
       float binaryOperation(float a, float b) {
         ${e}
       }
@@ -1900,7 +1900,7 @@ return (round(mod(b, 2.0)) != 1) ?
       }
     `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}}function j0(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function K0(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}class J5{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=su.DENSE;const t=ru(e),n=Wn();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${Ho(["r","c","d"],e)}
+        ${jo(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -1921,7 +1921,7 @@ return (round(mod(b, 2.0)) != 1) ?
       }
     `}}class Z5{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=su.DENSE;const t=ru(e),n=Wn();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${Ho(["r","c","d"],e)}
+        ${jo(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -2118,7 +2118,7 @@ return (round(mod(b, 2.0)) != 1) ?
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return Uj(e,n)}function h8(e){const t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return Vj(e,t)}function u8(e){const t=new Uint16Array([0,1,2,2,1,3]);return Yj(e,t)}function au(e,t,n,s,i,o){qj(t,n);const a=Hj(e),c=e.TEXTURE_2D;return Re(e,()=>e.bindTexture(c,a)),Re(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Re(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Re(e,()=>e.texParameteri(c,e.TEXTURE_MIN_FILTER,e.NEAREST)),Re(e,()=>e.texParameteri(c,e.TEXTURE_MAG_FILTER,e.NEAREST)),Re(e,()=>e.texImage2D(c,0,s,t,n,0,i,o,null)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null)),a}function Z0(e){return e.internalFormatFloat}function d8(e,t,n,s){const[i,o]=iu(t,n);return au(e,i,o,Z0(s),s.textureFormatFloat,e.FLOAT)}function Q0(e){return e.internalFormatHalfFloat}function p8(e,t,n,s){const[i,o]=iu(t,n);return au(e,i,o,Q0(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function eC(e){return e.downloadTextureFormat}function m8(e,t,n,s){const[i,o]=iu(t,n);return au(e,i,o,eC(s),e.RGBA,e.UNSIGNED_BYTE)}function tC(e){return e.internalFormatPackedFloat}function f8(e,t,n,s){const[i,o]=ac(t,n);return au(e,i,o,tC(s),e.RGBA,e.FLOAT)}function nC(e){return e.internalFormatPackedHalfFloat}function g8(e,t,n,s){const[i,o]=ac(t,n);return au(e,i,o,nC(s),e.RGBA,s.textureTypeHalfFloat)}function y8(e,t,n){const s=0,i=3*4,o=3*4+2*4;Re(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n));const a=C0(e,t,"clipSpacePos",n,3,o,s);return a&&C0(e,t,"uv",n,2,o,i)}function b8(e,t,n,s,i,o){Re(e,()=>e.bindTexture(e.TEXTURE_2D,t));let a,c,h;i instanceof Uint8Array?(a=new Uint8Array(n*s*4),c=e.UNSIGNED_BYTE,h=e.RGBA):(a=new Float32Array(n*s*4),c=e.FLOAT,h=o.internalFormatPackedFloat),a.set(i),Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,h,n,s,0,e.RGBA,c,a)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function w8(e,t,n){Re(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function L8(e,t,n,s){const i=e.createBuffer();Re(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,i));const o=4,a=4,c=o*a*t*n;return Re(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,c,e.STREAM_READ)),Re(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Re(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),i}function S8(e,t,n){const s=e,i=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,i),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),i}function I8(e,t,n,s){const[i,o]=iu(t,n),a=4,c=new Uint8Array(Ej(t*n,a));return Re(e,()=>e.readPixels(0,0,i,o,s.downloadTextureFormat,e.UNSIGNED_BYTE,c)),new Float32Array(c.buffer)}function x8(e,t,n,s,i,o,a,c){const h=e,d=new Float32Array(Dj(o,a));return h.bindBuffer(h.PIXEL_PACK_BUFFER,t),h.getBufferSubData(h.PIXEL_PACK_BUFFER,0,d),h.bindBuffer(h.PIXEL_PACK_BUFFER,null),d}function T8(e,t,n){const s=new Float32Array(t*n*4);return Re(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}class A8{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];const t=C().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Cj(t,e)):this.gl=Wi(t);let n="WEBGL_color_buffer_float";const s="EXT_color_buffer_half_float";if(C().getNumber("WEBGL_VERSION")===1){const i="OES_texture_float",o="OES_texture_half_float";if(this.textureFloatExtension=rm(this.gl,i),zs(this.gl,o))this.textureHalfFloatExtension=rm(this.gl,o);else if(C().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),zs(this.gl,s))this.colorBufferHalfFloatExtension=rm(this.gl,s);else if(C().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",zs(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(zs(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=h8(this.gl),this.indexBuffer=u8(this.gl),this.framebuffer=jj(this.gl),this.textureConfig=SS(this.gl,this.textureHalfFloatExtension)}get debug(){return C().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");const e=this.gl;Re(e,()=>e.finish()),Re(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Re(e,()=>e.deleteFramebuffer(this.framebuffer)),Re(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Re(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Re(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),d8(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),p8(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),m8(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),w8(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),b8(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),g8(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),f8(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(R0(this.gl,this.framebuffer),this.outputTexture=null),Re(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>I8(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,i,o){return x8(this.gl,e,t,n,s,i,o,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return S8(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);const s=L8(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){const e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(C().getBool("WEBGL_FENCE_API_ENABLED")){const s=e,i=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{const o=s.clientWaitSync(i,0,0);return o===s.ALREADY_SIGNALED||o===s.CONDITION_SATISFIED},t=i}else C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>T8(this.gl,t,n))}createProgram(e){this.throwIfDisposed();const t=this.gl,n=Bj(t,e),s=l8(t),i=zj(t);return Re(t,()=>t.attachShader(i,s)),Re(t,()=>t.attachShader(i,n)),Gj(t,i),this.debug&&IS(t,i),this.vertexAttrsAreBound||(this.setProgram(i),this.vertexAttrsAreBound=y8(t,this.program,this.vertexBuffer)),i}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Re(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&IS(this.gl,this.program),Re(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?Xj(this.gl,e,t):Jj(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Re(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),Zj(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();const[s,i]=ac(t,n);this.setOutputMatrixTextureDriver(e,s,i)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&IS(this.gl,this.program),om(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;this.debug&&this.debugValidate(),Re(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Re(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=rm(this.gl,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const n=this.gl,s=this.getQueryTimerExtensionWebGL2(),i=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,i),i}const e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}const e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await Ey(()=>this.disposed||this.isQueryAvailable(e,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){const n=this.gl,s=n.getQueryParameter(e,n.QUERY_RESULT);return s/1e6}else{const n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT);return s/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){const n=this.gl,s=this.getQueryTimerExtensionWebGL2(),i=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),i&&!this.disjoint}else{const n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){const e=v8(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){const{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;Ey(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),xS(this.gl,e,this.framebuffer),this.debug&&om(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(xS(this.gl,this.outputTexture,this.framebuffer),this.debug&&om(this.gl)):R0(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);const n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();const s=this.gl;xS(s,e,this.framebuffer),this.debug&&om(s),this.outputTexture=e,Re(s,()=>s.viewport(0,0,t,n)),Re(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Re(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}}function v8(e){let t=0;for(;t<e.length;++t){const n=e[t]();if(!n)break}return t-1}function N8(e,t,n,s){const i=t.userCode,o=n.map((w,L)=>{const T={logicalShape:w.shape,texShape:w.isUniform?null:w.texData.texShape,isUniform:w.isUniform,isPacked:w.isUniform?!1:w.texData.isPacked,flatOffset:null};return w.texData!=null&&w.texData.slice!=null&&w.texData.slice.flatOffset>0&&(T.flatOffset=w.texData.slice.flatOffset),{name:t.variableNames[L],shapeInfo:T}}),a=o.map(w=>w.shapeInfo),c={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},h=AK(o,c,i,t.packedInputs),d=e.createProgram(h);let m=null;const y=e.getUniformLocation(d,"NAN",!1);C().getNumber("WEBGL_VERSION")===1&&(m=e.getUniformLocation(d,"INFINITY",!1));const b={};for(let w=0;w<t.variableNames.length;w++){const L=t.variableNames[w],T=!1;b[L]=e.getUniformLocation(d,L,T),b[`offset${L}`]=e.getUniformLocation(d,`offset${L}`,T)}return{program:t,source:h,webGLProgram:d,uniformLocations:b,inShapeInfos:a,outShapeInfo:c,infLoc:m,nanLoc:y}}function sC(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{const i=n.logicalShape,o=t[s],a=o.shape;if(!ot(i,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${i} and ${a} must match`);if(n.isUniform&&o.isUniform)return;const c=n.texShape,h=o.isUniform?null:o.texData.texShape;if(!ot(c,h))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${c} and ${h} must match`)})}function C8(e,t,n,s,i){sC(t.inShapeInfos,n),sC([t.outShapeInfo],[s]);const o=s.texData.texture,a=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(o,a[0],a[1]):e.setOutputMatrixTexture(o,a[0],a[1]),e.setProgram(t.webGLProgram),C().getNumber("WEBGL_VERSION")===1&&(t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity)),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((c,h)=>{const d=t.program.variableNames[h],m=t.uniformLocations[d],y=t.uniformLocations[`offset${d}`];if(m==null)return;if(c.isUniform){if(we(c.shape)<2)e.gl.uniform1f(m,c.uniformValues[0]);else{let b=c.uniformValues;b instanceof Float32Array||(b=new Float32Array(b)),e.gl.uniform1fv(m,b)}return}c.texData.slice!=null&&y!=null&&e.gl.uniform1i(y,c.texData.slice.flatOffset),e.setInputMatrixTexture(c.texData.texture,m,h)}),i!=null&&i(e,t.webGLProgram),e.executeProgram()}function R8(e,t,n){let s="";t.concat(n).forEach(a=>{const c=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0,h=a.isUniform?"uniform":a.texData.texShape;s+=`${a.shape}_${h}_${c}`});const i=e.userCode;let o=e.constructor.name;return o+="_"+s+"_"+i,o}class O8{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;const{filterWidth:s,inChannels:i,strideWidth:o,strideHeight:a,padInfo:c,outWidth:h,dilationWidth:d,dilationHeight:m,dataFormat:y}=n,{left:b,top:w}=c,L=i*s,T=Wn(),A=y==="channelsLast",N=A?0:1,E=A?1:2;let D="";for(let F=0;F<=1;F++)for(let _=0;_<=1;_++)D+=`
+    }`;return Uj(e,n)}function h8(e){const t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return Vj(e,t)}function u8(e){const t=new Uint16Array([0,1,2,2,1,3]);return Yj(e,t)}function au(e,t,n,s,i,o){qj(t,n);const a=Hj(e),c=e.TEXTURE_2D;return Re(e,()=>e.bindTexture(c,a)),Re(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Re(e,()=>e.texParameteri(c,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Re(e,()=>e.texParameteri(c,e.TEXTURE_MIN_FILTER,e.NEAREST)),Re(e,()=>e.texParameteri(c,e.TEXTURE_MAG_FILTER,e.NEAREST)),Re(e,()=>e.texImage2D(c,0,s,t,n,0,i,o,null)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null)),a}function Z0(e){return e.internalFormatFloat}function d8(e,t,n,s){const[i,o]=iu(t,n);return au(e,i,o,Z0(s),s.textureFormatFloat,e.FLOAT)}function Q0(e){return e.internalFormatHalfFloat}function p8(e,t,n,s){const[i,o]=iu(t,n);return au(e,i,o,Q0(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function eC(e){return e.downloadTextureFormat}function m8(e,t,n,s){const[i,o]=iu(t,n);return au(e,i,o,eC(s),e.RGBA,e.UNSIGNED_BYTE)}function tC(e){return e.internalFormatPackedFloat}function f8(e,t,n,s){const[i,o]=lc(t,n);return au(e,i,o,tC(s),e.RGBA,e.FLOAT)}function nC(e){return e.internalFormatPackedHalfFloat}function g8(e,t,n,s){const[i,o]=lc(t,n);return au(e,i,o,nC(s),e.RGBA,s.textureTypeHalfFloat)}function y8(e,t,n){const s=0,i=3*4,o=3*4+2*4;Re(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n));const a=C0(e,t,"clipSpacePos",n,3,o,s);return a&&C0(e,t,"uv",n,2,o,i)}function b8(e,t,n,s,i,o){Re(e,()=>e.bindTexture(e.TEXTURE_2D,t));let a,c,h;i instanceof Uint8Array?(a=new Uint8Array(n*s*4),c=e.UNSIGNED_BYTE,h=e.RGBA):(a=new Float32Array(n*s*4),c=e.FLOAT,h=o.internalFormatPackedFloat),a.set(i),Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,h,n,s,0,e.RGBA,c,a)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function w8(e,t,n){Re(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Re(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Re(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function L8(e,t,n,s){const i=e.createBuffer();Re(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,i));const o=4,a=4,c=o*a*t*n;return Re(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,c,e.STREAM_READ)),Re(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Re(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),i}function S8(e,t,n){const s=e,i=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,i),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),i}function I8(e,t,n,s){const[i,o]=iu(t,n),a=4,c=new Uint8Array(Ej(t*n,a));return Re(e,()=>e.readPixels(0,0,i,o,s.downloadTextureFormat,e.UNSIGNED_BYTE,c)),new Float32Array(c.buffer)}function x8(e,t,n,s,i,o,a,c){const h=e,d=new Float32Array(Dj(o,a));return h.bindBuffer(h.PIXEL_PACK_BUFFER,t),h.getBufferSubData(h.PIXEL_PACK_BUFFER,0,d),h.bindBuffer(h.PIXEL_PACK_BUFFER,null),d}function T8(e,t,n){const s=new Float32Array(t*n*4);return Re(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}class A8{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];const t=C().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Cj(t,e)):this.gl=Wi(t);let n="WEBGL_color_buffer_float";const s="EXT_color_buffer_half_float";if(C().getNumber("WEBGL_VERSION")===1){const i="OES_texture_float",o="OES_texture_half_float";if(this.textureFloatExtension=rm(this.gl,i),zs(this.gl,o))this.textureHalfFloatExtension=rm(this.gl,o);else if(C().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),zs(this.gl,s))this.colorBufferHalfFloatExtension=rm(this.gl,s);else if(C().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",zs(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(zs(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=h8(this.gl),this.indexBuffer=u8(this.gl),this.framebuffer=jj(this.gl),this.textureConfig=SS(this.gl,this.textureHalfFloatExtension)}get debug(){return C().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");const e=this.gl;Re(e,()=>e.finish()),Re(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Re(e,()=>e.deleteFramebuffer(this.framebuffer)),Re(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Re(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Re(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),d8(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),p8(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),m8(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),w8(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),b8(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),g8(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),f8(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(R0(this.gl,this.framebuffer),this.outputTexture=null),Re(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>I8(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,i,o){return x8(this.gl,e,t,n,s,i,o,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return S8(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);const s=L8(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){const e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(C().getBool("WEBGL_FENCE_API_ENABLED")){const s=e,i=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{const o=s.clientWaitSync(i,0,0);return o===s.ALREADY_SIGNALED||o===s.CONDITION_SATISFIED},t=i}else C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>T8(this.gl,t,n))}createProgram(e){this.throwIfDisposed();const t=this.gl,n=Bj(t,e),s=l8(t),i=zj(t);return Re(t,()=>t.attachShader(i,s)),Re(t,()=>t.attachShader(i,n)),Gj(t,i),this.debug&&IS(t,i),this.vertexAttrsAreBound||(this.setProgram(i),this.vertexAttrsAreBound=y8(t,this.program,this.vertexBuffer)),i}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Re(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&IS(this.gl,this.program),Re(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?Xj(this.gl,e,t):Jj(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Re(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),Zj(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();const[s,i]=lc(t,n);this.setOutputMatrixTextureDriver(e,s,i)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&IS(this.gl,this.program),om(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;this.debug&&this.debugValidate(),Re(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Re(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=rm(this.gl,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const n=this.gl,s=this.getQueryTimerExtensionWebGL2(),i=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,i),i}const e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}const e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await Ey(()=>this.disposed||this.isQueryAvailable(e,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){const n=this.gl,s=n.getQueryParameter(e,n.QUERY_RESULT);return s/1e6}else{const n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT);return s/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){const n=this.gl,s=this.getQueryTimerExtensionWebGL2(),i=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),i&&!this.disjoint}else{const n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){const e=v8(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){const{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;Ey(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),xS(this.gl,e,this.framebuffer),this.debug&&om(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(xS(this.gl,this.outputTexture,this.framebuffer),this.debug&&om(this.gl)):R0(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);const n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();const s=this.gl;xS(s,e,this.framebuffer),this.debug&&om(s),this.outputTexture=e,Re(s,()=>s.viewport(0,0,t,n)),Re(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Re(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}}function v8(e){let t=0;for(;t<e.length;++t){const n=e[t]();if(!n)break}return t-1}function N8(e,t,n,s){const i=t.userCode,o=n.map((w,L)=>{const T={logicalShape:w.shape,texShape:w.isUniform?null:w.texData.texShape,isUniform:w.isUniform,isPacked:w.isUniform?!1:w.texData.isPacked,flatOffset:null};return w.texData!=null&&w.texData.slice!=null&&w.texData.slice.flatOffset>0&&(T.flatOffset=w.texData.slice.flatOffset),{name:t.variableNames[L],shapeInfo:T}}),a=o.map(w=>w.shapeInfo),c={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},h=AK(o,c,i,t.packedInputs),d=e.createProgram(h);let m=null;const y=e.getUniformLocation(d,"NAN",!1);C().getNumber("WEBGL_VERSION")===1&&(m=e.getUniformLocation(d,"INFINITY",!1));const b={};for(let w=0;w<t.variableNames.length;w++){const L=t.variableNames[w],T=!1;b[L]=e.getUniformLocation(d,L,T),b[`offset${L}`]=e.getUniformLocation(d,`offset${L}`,T)}return{program:t,source:h,webGLProgram:d,uniformLocations:b,inShapeInfos:a,outShapeInfo:c,infLoc:m,nanLoc:y}}function sC(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{const i=n.logicalShape,o=t[s],a=o.shape;if(!ot(i,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${i} and ${a} must match`);if(n.isUniform&&o.isUniform)return;const c=n.texShape,h=o.isUniform?null:o.texData.texShape;if(!ot(c,h))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${c} and ${h} must match`)})}function C8(e,t,n,s,i){sC(t.inShapeInfos,n),sC([t.outShapeInfo],[s]);const o=s.texData.texture,a=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(o,a[0],a[1]):e.setOutputMatrixTexture(o,a[0],a[1]),e.setProgram(t.webGLProgram),C().getNumber("WEBGL_VERSION")===1&&(t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity)),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((c,h)=>{const d=t.program.variableNames[h],m=t.uniformLocations[d],y=t.uniformLocations[`offset${d}`];if(m==null)return;if(c.isUniform){if(we(c.shape)<2)e.gl.uniform1f(m,c.uniformValues[0]);else{let b=c.uniformValues;b instanceof Float32Array||(b=new Float32Array(b)),e.gl.uniform1fv(m,b)}return}c.texData.slice!=null&&y!=null&&e.gl.uniform1i(y,c.texData.slice.flatOffset),e.setInputMatrixTexture(c.texData.texture,m,h)}),i!=null&&i(e,t.webGLProgram),e.executeProgram()}function R8(e,t,n){let s="";t.concat(n).forEach(a=>{const c=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0,h=a.isUniform?"uniform":a.texData.texShape;s+=`${a.shape}_${h}_${c}`});const i=e.userCode;let o=e.constructor.name;return o+="_"+s+"_"+i,o}class O8{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;const{filterWidth:s,inChannels:i,strideWidth:o,strideHeight:a,padInfo:c,outWidth:h,dilationWidth:d,dilationHeight:m,dataFormat:y}=n,{left:b,top:w}=c,L=i*s,T=Wn(),A=y==="channelsLast",N=A?0:1,E=A?1:2;let D="";for(let F=0;F<=1;F++)for(let _=0;_<=1;_++)D+=`
           blockIndex = rc.y + ${_};
           pos = rc.x + ${F};
 
@@ -2966,7 +2966,7 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(result);
       }
-    `}}function Y8(e){const t=Ho(["r","c","d"],e);return`
+    `}}function Y8(e){const t=jo(["r","c","d"],e);return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
       ${t}
       return ivec3(r, c, d);
@@ -3478,7 +3478,7 @@ return (round(mod(b, 2.0)) != 1) ?
         ${o} coords = getOutputCoords();
         setOutput(getX(${a}));
       }
-    `}}class r6{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){const s=cC(t,n),i=lC(e,s,n);i in this.freeTextures||(this.freeTextures[i]=[]),i in this.usedTextures||(this.usedTextures[i]=[]);const o=aC(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[i].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=o,this.log();const c=this.freeTextures[i].shift();return this.usedTextures[i].push(c),c}let a;return s===Sn.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===Sn.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===Sn.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===Sn.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===Sn.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[i].push(a),this.numUsedTextures++,this._numBytesAllocated+=o,this.log(),a}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;const i=cC(n,s),o=lC(t,i,s);o in this.freeTextures||(this.freeTextures[o]=[]);const a=aC(t,i,this.gpgpu.gl,this.gpgpu.textureConfig,s),c=C().get("WEBGL_DELETE_TEXTURE_THRESHOLD");c!==-1&&this._numBytesAllocated>c?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=a):(this.freeTextures[o].push(e),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;const h=this.usedTextures[o],d=h.indexOf(e);if(d<0)throw new Error("Cannot release a texture that was never provided by this texture manager");h.splice(d,1),this.log()}log(){if(!this.logEnabled)return;const e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);const t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures==null)return;for(const e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(const e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}function o6(e,t){const n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function aC(e,t,n,s,i){const o=a6(t,s);let a;if(i){const[h,d]=ac(e[0],e[1]);a=h*d}else{const[h,d]=iu(e[0],e[1]);a=h*d}const c=o6(n,o);return a*c}function a6(e,t){switch(e){case Sn.PACKED_2X2_FLOAT32:return tC(t);case Sn.PACKED_2X2_FLOAT16:return nC(t);case Sn.UNPACKED_FLOAT32:return Z0(t);case Sn.UNPACKED_FLOAT16:return Q0(t);case Sn.PACKED_4X1_UNSIGNED_BYTE:return eC(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function c6(e){return C().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Sn.PACKED_2X2_FLOAT32:Sn.UNPACKED_FLOAT32:e?Sn.PACKED_2X2_FLOAT16:Sn.UNPACKED_FLOAT16}function cC(e,t){if(e===As.UPLOAD)return Sn.PACKED_2X2_FLOAT32;if(e===As.RENDER||e==null)return c6(t);if(e===As.DOWNLOAD||e===As.PIXELS)return Sn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function lC(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}class l6{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let o=0;o<n.length;o++)n[o]=e[o]*t[o];this.outputShape=n,this.rank=n.length;const s=Et(this.rank),i=h6(e);this.userCode=`
+    `}}class r6{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){const s=cC(t,n),i=lC(e,s,n);i in this.freeTextures||(this.freeTextures[i]=[]),i in this.usedTextures||(this.usedTextures[i]=[]);const o=aC(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[i].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=o,this.log();const c=this.freeTextures[i].shift();return this.usedTextures[i].push(c),c}let a;return s===Sn.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===Sn.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===Sn.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===Sn.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===Sn.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[i].push(a),this.numUsedTextures++,this._numBytesAllocated+=o,this.log(),a}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;const i=cC(n,s),o=lC(t,i,s);o in this.freeTextures||(this.freeTextures[o]=[]);const a=aC(t,i,this.gpgpu.gl,this.gpgpu.textureConfig,s),c=C().get("WEBGL_DELETE_TEXTURE_THRESHOLD");c!==-1&&this._numBytesAllocated>c?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=a):(this.freeTextures[o].push(e),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;const h=this.usedTextures[o],d=h.indexOf(e);if(d<0)throw new Error("Cannot release a texture that was never provided by this texture manager");h.splice(d,1),this.log()}log(){if(!this.logEnabled)return;const e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);const t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures==null)return;for(const e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(const e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}function o6(e,t){const n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function aC(e,t,n,s,i){const o=a6(t,s);let a;if(i){const[h,d]=lc(e[0],e[1]);a=h*d}else{const[h,d]=iu(e[0],e[1]);a=h*d}const c=o6(n,o);return a*c}function a6(e,t){switch(e){case Sn.PACKED_2X2_FLOAT32:return tC(t);case Sn.PACKED_2X2_FLOAT16:return nC(t);case Sn.UNPACKED_FLOAT32:return Z0(t);case Sn.UNPACKED_FLOAT16:return Q0(t);case Sn.PACKED_4X1_UNSIGNED_BYTE:return eC(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function c6(e){return C().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Sn.PACKED_2X2_FLOAT32:Sn.UNPACKED_FLOAT32:e?Sn.PACKED_2X2_FLOAT16:Sn.UNPACKED_FLOAT16}function cC(e,t){if(e===As.UPLOAD)return Sn.PACKED_2X2_FLOAT32;if(e===As.RENDER||e==null)return c6(t);if(e===As.DOWNLOAD||e===As.PIXELS)return Sn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function lC(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}class l6{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let o=0;o<n.length;o++)n[o]=e[o]*t[o];this.outputShape=n,this.rank=n.length;const s=Et(this.rank),i=h6(e);this.userCode=`
       void main() {
         ${s} resRC = getOutputCoords();
         setOutput(getA(${i}));
@@ -3641,7 +3641,7 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(getChannel(packedInput, ${a}));
       }
-    `}}const{segment_util:IC}=pw,P6=mw,z6=fw,G6=gw,V6=ip,Y6=1e-7,H6=1e-4,fm={};function q6(e){return e in fm||(fm[e]={}),fm[e]}function gm(e,t=!1){if(e==="linear")return t?U6:u6;if(e==="relu")return t?wC:uC;if(e==="elu")return t?SC:pC;if(e==="relu6")return t?LC:dC;if(e==="prelu")return t?G0:z0;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}const j6=128,K6=600;function X6(){return C().global.screen==null?1024:C().global.screen.height*C().global.screen.width*window.devicePixelRatio*K6/1024/1024}const xC=1e3;class J6 extends g{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!C().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){const t=Wi(C().getNumber("WEBGL_VERSION"));this.binaryCache=q6(C().getNumber("WEBGL_VERSION")),this.gpgpu=new A8(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new r6(this.gpgpu),this.numMBBeforeWarning=X6(),this.texData=new p(this,_s())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((C().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||C().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");const s={};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:As.UPLOAD,refCount:1}),s}incRef(e){const t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){const t=this.texData.get(e);t.refCount--}}move(e,t,n,s){if(C().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:As.UPLOAD,refCount:1})}disposeIntermediateTensorInfo(e){const t=e.dataId;if(this.texData.has(t)){const n=this.texData.get(t);n.refCount--,n.refCount<1&&this.disposeData(t)}}readSync(e){const t=this.texData.get(e),{values:n,dtype:s,complexTensors:i,slice:o,shape:a,isPacked:c}=t;if(o!=null){let y;c?y=new lu(a,mm):y=new st(a,mm);const b=this.runWebGLProgram(y,[{dataId:e,shape:a,dtype:s}],s),w=this.readSync(b.dataId);return this.disposeIntermediateTensorInfo(b),w}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;const h=this.activeTimers!=null;let d;h&&(d=Yn());let m;if(s==="complex64"){const y=i.real.dataSync(),b=i.imag.dataSync();m=er(y,b)}else m=this.getValuesFromTexture(e);return h&&(this.downloadWaitMs+=Yn()-d),this.convertAndCacheOnCPU(e,m)}async read(e){if(this.pendingRead.has(e)){const w=this.pendingRead.get(e);return new Promise(L=>w.push(L))}const t=this.texData.get(e),{values:n,shape:s,slice:i,dtype:o,complexTensors:a,isPacked:c}=t;if(i!=null){let w;c?w=new lu(s,mm):w=new st(s,mm);const L=this.runWebGLProgram(w,[{dataId:e,shape:s,dtype:o}],o),T=this.read(L.dataId);return this.disposeIntermediateTensorInfo(L),T}if(n!=null)return this.convertAndCacheOnCPU(e);if(!C().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&C().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let h=null,d;if(o!=="complex64"&&C().get("WEBGL_BUFFER_SUPPORTED")){d=this.decode(e);const w=this.texData.get(d.dataId);h=this.gpgpu.createBufferFromTexture(w.texture,...ru(s))}this.pendingRead.set(e,[]),o!=="complex64"&&await this.gpgpu.createAndWaitForFence();let m;if(o==="complex64"){const w=await Promise.all([a.real.data(),a.imag.data()]),L=w[0],T=w[1];m=er(L,T)}else if(h==null)m=this.getValuesFromTexture(e);else{const w=we(s);m=this.gpgpu.downloadFloat32MatrixFromBuffer(h,w)}d!=null&&this.disposeIntermediateTensorInfo(d);const y=this.convertAndCacheOnCPU(e,m),b=this.pendingRead.get(e);return this.pendingRead.delete(e),b.forEach(w=>w(y)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),y}checkNumericalProblems(e){if(e==null)return;for(let t=0;t<e.length;t++){const n=e[t];if(!Wj(n))throw C().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){const{shape:t,dtype:n,isPacked:s}=this.texData.get(e),i=we(t);if(C().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const y=this.decode(e),b=this.texData.get(y.dataId),w=this.gpgpu.downloadMatrixFromPackedTexture(b.texture,...ru(t)).subarray(0,i);return this.disposeIntermediateTensorInfo(y),w}const o=C().getBool("WEBGL_PACK")&&s===!0,a=o?TS(t):t,c=o?new n8(a):new t8(a),h=this.runWebGLProgram(c,[{shape:a,dtype:n,dataId:e}],"float32"),d=this.texData.get(h.dataId),m=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(d.texture,d.texShape[0],d.texShape[1]).subarray(0,i);return this.disposeIntermediateTensorInfo(h),m}async time(e){const t=this.activeTimers,n=[];let s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();const i=qi(this.activeTimers.map(c=>c.query)).filter(c=>c!=null),o=qi(this.activeTimers.map(c=>c.name)).filter(c=>c!=null);this.activeTimers=t,s&&(this.programTimersStack=null);const a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const c=await Promise.all(i);a.kernelMs=$x(c),a.getExtraProfileInfo=()=>c.map((h,d)=>({name:o[d],ms:h})).map(h=>`${h.name}: ${h.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:Yn(),endMs:null}}endTimer(e){return C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=Yn(),e)}async getQueryTime(e){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);const t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;this.releaseGPUData(e);const{complexTensors:t}=this.texData.get(e);t!=null&&(t.real.dispose(),t.imag.dispose()),this.texData.delete(e)}releaseGPUData(e){const{texture:t,dtype:n,texShape:s,usage:i,isPacked:o,slice:a}=this.texData.get(e),c=a&&a.origDataId||e,h=this.dataRefCount.get(c);h>1?this.dataRefCount.set(c,h-1):(this.dataRefCount.delete(c),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,i,o)));const d=this.texData.get(e);d.texture=null,d.texShape=null,d.isPacked=!1,d.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return C().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=_s().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=j6){const n=this.getCPUBackend();return!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(s=>this.texData.get(s.dataId).texture==null&&we(s.shape)<t)}getGPGPUContext(){return this.gpgpu}complex(e,t){const n=this.makeOutput(e.shape,"complex64"),s=this.texData.get(n.dataId);return s.complexTensors={real:_s().keep(e.clone()),imag:_s().keep(t.clone())},n}real(e){const t=this.texData.get(e.dataId);return t.complexTensors.real.clone()}imag(e){const t=this.texData.get(e.dataId);return t.complexTensors.imag.clone()}slice(e,t,n){if(this.shouldExecuteOnCPU([e])){const o=bK(this.texData.get(e.dataId).values,t,n,e.shape,e.dtype);return this.makeOutput(n,e.dtype,o)}if(we(n)===0)return en([],n,e.dtype);const{isPacked:s}=this.texData.get(e.dataId),i=rb(e.shape,t,n);if(s||!i){const o=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new s6(n):new t6(n),a=o.getCustomSetupFunc(t);return this.compileAndRun(o,[e],null,a)}return this.uploadToGPU(e.dataId),this.shallowSlice(e,t,n)}shallowSlice(e,t,n){const s=this.texData.get(e.dataId),i=this.makeOutput(n,e.dtype),o=this.texData.get(i.dataId);Object.assign(o,s),o.shape=n,o.dtype=e.dtype;let a=ob(t,e.strides);s.slice&&(a+=s.slice.flatOffset),o.slice={flatOffset:a,origDataId:s.slice&&s.slice.origDataId||e.dataId};const c=this.dataRefCount.get(o.slice.origDataId)||1;return this.dataRefCount.set(o.slice.origDataId,c+1),i}stridedSlice(e,t,n,s){const i=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.stridedSlice(e,t,n,s));if(i)return i;const o=Cd(t,n,s);if(o.some(c=>c===0))return en([],o);const a=new i6(t,s,o);return this.compileAndRun(a,[e])}reverse(e,t){const n=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Z8(e.shape,t):new J8(e.shape,t);return this.compileAndRun(n,[e])}concat(e,t){if(e[0].dtype==="complex64"){const a=e.map(h=>Oo(h)),c=e.map(h=>_a(h));return Ti(this.concat(a,t),this.concat(c,t))}if(e.length===1)return e[0];if(e.length>C().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){const a=Math.floor(e.length/2),c=this.concat(e.slice(0,a),t),h=this.concat(e.slice(a),t);return this.concat([c,h],t)}if(C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].rank>1){const a=new z5(e.map(c=>c.shape),t);return this.compileAndRun(a,e)}const n=Er(e.map(a=>a.shape),t),s=e.map(a=>a.as2D(-1,we(a.shape.slice(t)))),i=new P5(s.map(a=>a.shape)),o=this.compileAndRun(i,s);return o.reshape(n)}neg(e){const t=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.neg(e));if(t)return t;if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,mC,e.dtype);const n=new st(e.shape,mC);return this.compileAndRun(n,[e])}batchMatMul(e,t,n,s){const i=n?e.shape[2]:e.shape[1],o=s?t.shape[1]:t.shape[2],a=n?e.shape[1]:e.shape[2],[c,,]=e.shape;if((i===1||o===1)&&a>xC){n&&(e=Me(e,[0,2,1])),s&&(t=Me(t,[0,2,1]));const m=o===1?e:e.as3D(c,a,1),y=o===1?2:1,b=o===1?t.as3D(c,1,a):t;return this.multiply(m,b).sum(y,!0)}const h=Nn(e.dtype,t.dtype),d=new RS(e.shape,[c,i,o],n,s);return this.compileAndRun(d,[e,t],h)}fusedBatchMatMul({a:e,b:t,transposeA:n,transposeB:s,bias:i,activation:o,preluActivationWeights:a}){const c=n?e.shape[2]:e.shape[1],h=s?t.shape[1]:t.shape[2],[d,,]=e.shape,m=Nn(e.dtype,t.dtype),y=i!=null,b=a!=null,w=o?gm(o,!0):null,L=new RS(e.shape,[d,c,h],n,s,y,w,b),T=[e,t];return i&&T.push(i),a&&T.push(a),this.compileAndRun(L,T,m)}multiply(e,t){if(e.dtype==="complex64"){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),a=new B0(U0.REAL,e.shape,t.shape),c=new B0(U0.IMAG,e.shape,t.shape),h=[this.makeComplexComponentTensorInfo(e,i.complexTensors.real),this.makeComplexComponentTensorInfo(e,i.complexTensors.imag),this.makeComplexComponentTensorInfo(t,o.complexTensors.real),this.makeComplexComponentTensorInfo(t,o.complexTensors.imag)],d=this.compileAndRun(a,h),m=this.compileAndRun(c,h),y=this.complex(d,m);return d.dispose(),m.dispose(),y}const n=Nn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=gK(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,P0,e.dtype);const s=new ln(P0,e.shape,t.shape);return this.compileAndRun(s,[e,t],e.dtype)}localResponseNormalization4D(e,t,n,s,i){const o=C().getBool("WEBGL_PACK_NORMALIZATION")?new k8(e.shape,t,n,s,i):new E8(e.shape,t,n,s,i);return this.compileAndRun(o,[e])}LRNGrad(e,t,n,s,i,o,a){const c=new D8(t.shape,s,i,o,a);return this.compileAndRun(c,[t,n,e])}tile(e,t){if(e.dtype==="string"){const s=this.readSync(e.dataId),i=s.map(a=>zl(a)),o=Ze(e.shape,e.dtype,i);return z6(o,t)}const n=new l6(e.shape,t);return this.compileAndRun(n,[e])}pad(e,t,n){const s=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new V8(e.shape,t,n):new G8(e.shape,t,n);return this.compileAndRun(s,[e])}gather(e,t,n){const s=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.gather(e,t,n));if(s)return s;const i=new o8(e.shape,t.size,n);return this.compileAndRun(i,[e,t])}batchToSpaceND(e,t,n){k(e.rank<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");const s=t.reduce((d,m)=>d*m),i=Ah(e.shape,t,s),o=vh(i.length,t.length),a=Nh(e.shape,t,s),c=sw(n,t.length),h=iw(a,n,t.length);return Me(e.reshape(i),o).reshape(a).slice(c,h)}spaceToBatchND(e,t,n){k(e.rank<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");const s=t.reduce((m,y)=>m*y),i=[[0,0]];i.push(...n);for(let m=1+t.length;m<e.shape.length;++m)i.push([0,0]);const o=e.pad(i),a=Ah(o.shape,t,s,!1),c=vh(a.length,t.length,!1),h=Nh(o.shape,t,s,!1),d=Me(o.reshape(a),c);return K(d,h)}reduce(e,t,n){const s=e.shape[0],i=e.shape[1],o=ah(i),a=Math.ceil(i/o),c={windowSize:o,inSize:i,batchSize:s,outSize:a},h=new iC(c,t),d=this.compileAndRun(h,[e],n);return d.shape[1]===1?d:this.reduce(d,t,n)}argReduce(e,t,n=null){let s=e.shape[0],i=e.shape[1];n!=null&&(s=n.shape[0],i=n.shape[1]);const o=ah(i),a={windowSize:o,inSize:i,batchSize:s,outSize:Math.ceil(i/o)},c=new xK(a,t,n==null),h=[e];n!=null&&h.push(n);const d=this.compileAndRun(c,h,"int32");return d.shape[1]===1?d:this.argReduce(e,t,d)}argReducePacked(e,t,n=null){const s=n!=null?n.shape:e.shape,i=s[s.length-1],o=ah(i),a=new a5(s,o,t,n==null),c=n==null?[e]:[e,n],h=this.compileAndRun(a,c,"int32");return h.rank===e.rank?this.argReducePacked(e,t,h):h}sum(e,t){ts("sum",t,e.rank);const[n,s]=Rn(e.shape,t),i=we(s),o=e.as2D(-1,i),a=bd(e.dtype);return this.reduce(o,"sum",a).reshape(n)}prod(e,t){const n=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.prod(e,t));if(n)return n;const[s,i]=Rn(e.shape,t),o=we(i),a=e.as2D(-1,o),c=bd(e.dtype);return this.reduce(a,"prod",c).reshape(s)}unsortedSegmentSum(e,t,n){let s=0;const i=kn([s],e.rank);let o=e;i!=null&&(o=Me(e,i),s=ws(1,e.rank)[0]);const a=IC.computeOutShape(o.shape,s,n),c=we([o.shape[s]]),h=o.as2D(-1,c),d=bd(e.dtype);let m=this.segOpCompute(h,"unsortedSegmentSum",t,d,n).reshape(a);return i!=null&&(m=Me(m,Xl(i))),m}segOpCompute(e,t,n,s,i){const o=e.shape[0],a=e.shape[1],c=IC.segOpComputeOptimalWindowSize(a,i),h={windowSize:c,inSize:a,batchSize:o,numSegments:i},d=new Q8(h,t),m=this.compileAndRun(d,[e,n],s);return m.shape[1]===i?m:(n=ph(0,i).tile([a/c]),this.segOpCompute(m,t,n,s,i))}argMinMaxReduce(e,t,n){const s=[t];if(ts("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.rank),!C().getBool("WEBGL_PACK_REDUCE")||e.rank<=2){const[i,o]=Rn(e.shape,s),a=we(o),c=e.as2D(-1,a);return this.argReduce(c,n).reshape(i)}return this.argReducePacked(e,n)}argMin(e,t){return this.argMinMaxReduce(e,t,"min")}argMax(e,t){return this.argMinMaxReduce(e,t,"max")}cumsum(e,t,n,s){if(t!==e.rank-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${e.rank-1} but got axis=${t}`);const i=e.shape[t];let o=e;for(let a=0;a<=Math.ceil(Math.log2(i))-1;a++){const c=new q0(e.shape,!1,s),h=c.getCustomSetupFunc(a),d=o;o=this.compileAndRun(c,[o],o.dtype,h),d.dispose()}if(n){const a=new q0(e.shape,n,s),c=o;o=this.compileAndRun(a,[o]),c.dispose()}return o}equal(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,N5,"bool");const n=new ln(d5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}notEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,C5,"bool");const n=new ln(p5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}less(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.less(e,t));if(n)return n;if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,R5,"bool");const s=new ln(m5,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}lessEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,O5,"bool");const n=new ln(f5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}greater(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.greater(e,t));if(n)return n;if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,E5,"bool");const s=new ln(g5,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}greaterEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,D5,"bool");const n=new ln(y5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}logicalNot(e){const t=new st(e.shape,W6);return this.compileAndRun(t,[e])}logicalAnd(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,k5,"bool");const n=new ln(b5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}logicalOr(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,F5,"bool");const n=new ln(w5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}select(e,t,n){const s=new e6(e.rank,t.shape,t.rank);return this.compileAndRun(s,[e,t,n],Nn(t.dtype,n.dtype))}where(e){Ya("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");const t=e.dataSync();return V6(e.shape,t)}topk(e,t,n){const s=e.dataSync();return G6(s,e.shape,e.dtype,t,n)}min(e,t){ts("min",t,e.rank);const[n,s]=Rn(e.shape,t),i=we(s),o=e.as2D(-1,i);return this.reduce(o,"min",o.dtype).reshape(n)}minimum(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.minimum(e,t));if(n)return n;const s=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(W5,e.shape,t.shape):new ln(S5,e.shape,t.shape);return this.compileAndRun(s,[e,t])}mod(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr($5,e.shape,t.shape):new ln(I5,e.shape,t.shape);return this.compileAndRun(n,[e,t])}maximum(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.maximum(e,t));if(n)return n;const s=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(_5,e.shape,t.shape):new ln(L5,e.shape,t.shape);return this.compileAndRun(s,[e,t])}all(e,t){ts("all",t,e.rank);const[n,s]=Rn(e.shape,t),i=we(s),o=e.as2D(-1,i);return this.reduce(o,"all",o.dtype).reshape(n)}any(e,t){ts("any",t,e.rank);const[n,s]=Rn(e.shape,t),i=we(s),o=e.as2D(-1,i);return this.reduce(o,"any",o.dtype).reshape(n)}floorDiv(e,t){const n=h5,s="int32";if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,T5,s);const i=new ln(n,e.shape,t.shape);return this.compileAndRun(i,[e,t],s)}add(e,t){if(e.dtype==="complex64"&&t.dtype==="complex64")return this.complexSeparableBinaryOp(e,t,NS);const n=Nn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=lK(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,NS,n);const s=new ln(NS,e.shape,t.shape);return this.compileAndRun(s,[e,t],n)}packedUnaryOp(e,t,n){const s=new lu(e.shape,t);return this.compileAndRun(s,[e],n)}packedBinaryOp(e,t,n,s,i=!1){const o=new jr(n,e.shape,t.shape,i);return this.compileAndRun(o,[e,t],s)}complexSeparableBinaryOp(e,t,n){const s=this.texData.get(e.dataId),i=this.texData.get(t.dataId),[o,a]=[[s.complexTensors.real,i.complexTensors.real],[s.complexTensors.imag,i.complexTensors.imag]].map(h=>{const[d,m]=h,y=this.makeComplexComponentTensorInfo(e,d),b=this.makeComplexComponentTensorInfo(t,m),w=new ln(n,e.shape,t.shape);return this.compileAndRun(w,[y,b],Nn(d.dtype,m.dtype))}),c=this.complex(o,a);return o.dispose(),a.dispose(),c}makeComplexComponentTensorInfo(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}addN(e){if(e.length===1)return e[0];if(e.length>C().get("WEBGL_MAX_TEXTURES_IN_SHADER")){const o=Math.floor(e.length/2),a=this.addN(e.slice(0,o)),c=this.addN(e.slice(o));return this.addN([a,c])}const t=e.map(o=>o.dtype).reduce((o,a)=>Nn(o,a)),n=e.map(o=>o.shape),s=C().getBool("WEBGL_PACK"),i=s?new IK(e[0].shape,n):new SK(e[0].shape,n);return this.compileAndRun(i,e,t)}subtract(e,t){if(e.dtype==="complex64"&&t.dtype==="complex64")return this.complexSeparableBinaryOp(e,t,CS);const n=Nn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=wK(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,CS,e.dtype);const s=new ln(CS,e.shape,t.shape);return this.compileAndRun(s,[e,t],n)}pow(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS"),s=n?new jr(A5,e.shape,t.shape):new ln(u5,e.shape,t.shape),i=Nn(e.dtype,t.dtype);return this.compileAndRun(s,[e,t],i)}ceil(e){if(this.shouldExecuteOnCPU([e])){const n=hK(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,fC,e.dtype);const t=new st(e.shape,fC);return this.compileAndRun(t,[e])}floor(e){if(this.shouldExecuteOnCPU([e])){const n=pK(this.texData.get(e.dataId).values,e.dtype);return 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this.compileAndRun(t,[e])}relu(e){let t;return C().getBool("WEBGL_PACK")?t=new lu(e.shape,wC):t=new st(e.shape,uC),this.compileAndRun(t,[e])}relu6(e){let t;return C().getBool("WEBGL_PACK")?t=new lu(e.shape,LC):t=new st(e.shape,dC),this.compileAndRun(t,[e])}prelu(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(G0,e.shape,t.shape):new ln(z0,e.shape,t.shape);return this.compileAndRun(n,[e,t])}elu(e){if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,SC,e.dtype);const t=new st(e.shape,pC);return this.compileAndRun(t,[e])}eluDer(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(v5,e.shape,t.shape):new ln(x5,e.shape,t.shape);return this.compileAndRun(n,[e,t])}selu(e){const t=new st(e.shape,d6);return this.compileAndRun(t,[e])}int(e){const t=new st(e.shape,$6);return this.compileAndRun(t,[e],"int32")}clip(e,t,n){let s;C().getBool("WEBGL_PACK_CLIP")?s=new B5(e.shape):s=new U5(e.shape);const i=s.getCustomSetupFunc(t,n);return 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st(e.shape,R6);return this.compileAndRun(t,[e])}tanh(e){const t=new st(e.shape,O6);return this.compileAndRun(t,[e])}asinh(e){const t=new st(e.shape,E6);return this.compileAndRun(t,[e])}acosh(e){const t=new st(e.shape,D6);return this.compileAndRun(t,[e])}atanh(e){const t=new st(e.shape,k6);return this.compileAndRun(t,[e])}erf(e){const t=new st(e.shape,F6);return this.compileAndRun(t,[e])}step(e,t){const n=new st(e.shape,p6(t));return this.compileAndRun(n,[e])}conv2dByMatMul(e,t,n,s,i,o){const a=e.shape,c=this.texData.get(e.dataId),h=n.inChannels,d=a[0]*a[1]*a[2],m=n.outChannels,y=n.dataFormat==="channelsLast",b=!1,w=!1,L=(d===1||m===1)&&h>xC,T=a[2]%2!==0&&!!c.isPacked;if(L||!C().getBool("WEBGL_LAZILY_UNPACK")||!C().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!T){const B=y?a[0]*a[1]*a[2]:a[0]*a[2]*a[3],$=K(e,[1,B,n.inChannels]),H=K(t,[1,n.inChannels,n.outChannels]),q=this.fusedBatchMatMul({a:$,b:H,transposeA:b,transposeB:w,bias:s,activation:i,preluActivationWeights:o});return K(q,n.outShape)}const A=y?a[0]*a[1]*(a[2]+1):a[0]*a[2]*(a[3]+1),N={dataId:e.dataId,shape:[1,A,n.inChannels],dtype:e.dtype},E=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k(cm(c.shape,N.shape),()=>`packed reshape ${c.shape} to ${N.shape} isn't free`);const D=K(t,[1,n.inChannels,n.outChannels]),F=this.fusedBatchMatMul({a:N,b:D,transposeA:b,transposeB:w,bias:s,activation:i,preluActivationWeights:o}),_=this.texData.get(F.dataId);return k(_.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=E,_.shape=n.outShape,_s().makeTensorFromDataId(F.dataId,n.outShape,F.dtype)}conv2dWithIm2Row(e,t,n,s,i,o){const{filterWidth:a,filterHeight:c,inChannels:h,outWidth:d,outHeight:m,dataFormat:y}=n,b=y==="channelsLast",w=a*c*h,L=m*d,T=[w,L],A=!0,N=!1,E=e.squeeze([0]),D=t.reshape([1,w,-1]),F=new O8(T,E.shape,n),_=this.compileAndRun(F,[E]).reshape([1,T[0],T[1]]),B=s!=null,$=o!=null,H=i?gm(i,!0):null,q=new RS(_.shape,[1,L,n.outChannels],A,N,B,H,$),J=[_,D];s&&J.push(s),$&&J.push(o);const re=this.compileAndRun(q,J);return b?re.reshape([1,m,d,n.outChannels]):re.reshape([1,n.outChannels,m,d])}fusedConv2d({input:e,filter:t,convInfo:n,bias:s,activation:i,preluActivationWeights:o}){if(n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return this.conv2dByMatMul(e,t,n,s,i,o);if(C().getBool("WEBGL_CONV_IM2COL")&&e.shape[0]===1)return this.conv2dWithIm2Row(e,t,n,s,i,o);const a=s!=null,c=o!=null,h=i?gm(i,!1):null,d=new V0(n,a,h,c),m=[e,t];return s&&m.push(s),o&&m.push(o),this.compileAndRun(d,m)}conv2d(e,t,n){if(n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return this.conv2dByMatMul(e,t,n);if(C().getBool("WEBGL_CONV_IM2COL")&&e.shape[0]===1)return this.conv2dWithIm2Row(e,t,n);const s=new V0(n);return this.compileAndRun(s,[e,t])}conv2dDerInput(e,t,n){const s=new V5(n);return this.compileAndRun(s,[e,t])}conv2dDerFilter(e,t,n){const s=new G5(n);return this.compileAndRun(s,[e,t])}fusedDepthwiseConv2D({input:e,filter:t,convInfo:n,bias:s,activation:i,preluActivationWeights:o}){const a=C().getBool("WEBGL_PACK_DEPTHWISECONV")&&n.strideWidth<=2&&n.outChannels/n.inChannels===1,c=i?gm(i,a):null,h=[e,t],d=s!=null,m=o!=null;d&&h.push(s),m&&h.push(o);let y;return a?(y=new H0(n,d,c,m),this.compileAndRun(y,h)):(y=new Y0(n,d,c,m),this.compileAndRun(y,h))}depthwiseConv2D(e,t,n){let s;return C().getBool("WEBGL_PACK_DEPTHWISECONV")&&n.strideWidth<=2&&n.outChannels/n.inChannels===1?(s=new H0(n),this.compileAndRun(s,[e,t])):(s=new Y0(n),this.compileAndRun(s,[e,t]))}depthwiseConv2DDerInput(e,t,n){const s=new j5(n);return this.compileAndRun(s,[e,t])}depthwiseConv2DDerFilter(e,t,n){const s=new q5(n);return this.compileAndRun(s,[e,t])}conv3d(e,t,n){const s=new K5(n);return this.compileAndRun(s,[e,t])}conv3dDerInput(e,t,n){const s=new H5(n);return this.compileAndRun(s,[e,t])}conv3dDerFilter(e,t,n){const s=new Y5(n);return this.compileAndRun(s,[e,t])}cast(e,t){return BA(e,t,this)}unstack(e,t){const n=e.shape[t],s=new Array(e.rank-1);let i=0;for(let h=0;h<e.rank;h++)h!==t&&(s[i++]=e.shape[h]);const o=new Array(e.rank).fill(0),a=e.shape.slice();a[t]=1;const c=new Array(n);for(let h=0;h<c.length;h++)o[t]=h,c[h]=this.slice(e,o,a).reshape(s);return c}avgPool3d(e,t){const n=new OS(t,"avg",!1);return this.compileAndRun(n,[e],"float32")}avgPool3dBackprop(e,t,n){const s=new l5(n);return this.compileAndRun(s,[e],t.dtype)}maxPool3d(e,t){const n=new OS(t,"max",!1);return this.compileAndRun(n,[e],"float32")}maxPool3dBackprop(e,t,n,s){const i=!0,o=new OS(s,"max",i),a=this.compileAndRun(o,[t]),c=new _8(s),h=this.compileAndRun(c,[e,a],t.dtype);return a.dispose(),h}resizeBilinear(e,t,n,s){const i=C().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new 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this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){const{dataId:s}=this.makeTensorInfo(e,t,n);return _s().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){const t=new M6(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new U8(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){const n=[cc(e.shape),...lc(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},i=[cc(t),...lc(t)],o=new rC(i,n),a=!0,c=this.runWebGLProgram(o,[s],e.dtype,null,a);return{dataId:c.dataId,shape:t,dtype:c.dtype}}decode(e){const t=this.texData.get(e),{isPacked:n,shape:s,dtype:i}=t,o=TS(s);let a;n?a=new Z5(o):a=new J5(o);const c=!0,h=this.runWebGLProgram(a,[{shape:o,dtype:i,dataId:e}],i,null,c);return{dtype:i,shape:s,dataId:h.dataId}}runWebGLProgram(e,t,n,s,i=!1){const o=this.makeTensorInfo(e.outputShape,n),a=this.texData.get(o.dataId);if(e.packedOutput&&(a.isPacked=!0),e.outPackingScheme===su.DENSE){const L=ru(e.outputShape);a.texShape=L.map(T=>T*2)}if(e.outTexUsage!=null&&(a.usage=e.outTexUsage),we(o.shape)===0)return a.values=bn(o.dtype,0),o;const c=[],h=t.map(L=>{if(L.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let T=this.texData.get(L.dataId);if(T.texture==null){if(!e.packedInputs&&we(L.shape)<=C().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:L.shape,texData:null,isUniform:!0,uniformValues:T.values};e.packedInputs&&(T.isPacked=!0,T.shape=L.shape)}else if(!!T.isPacked!==!!e.packedInputs)L=T.isPacked?this.unpackTensor(L):this.packTensor(L),c.push(L),T=this.texData.get(L.dataId);else if(T.isPacked&&!cm(T.shape,L.shape)){const A=L,N=L.shape;L.shape=T.shape,L=this.packedReshape(L,N),c.push(L),T=this.texData.get(L.dataId),A.shape=N}return this.uploadToGPU(L.dataId),{shape:L.shape,texData:T,isUniform:!1}});this.uploadToGPU(o.dataId);const d={shape:o.shape,texData:a,isUniform:!1},m=R8(e,h,d),y=this.getAndSaveBinary(m,()=>N8(this.gpgpu,e,h,d)),b=this.activeTimers!=null;let w;if(b&&(w=this.startTimer()),C8(this.gpgpu,y,h,d,s),c.forEach(L=>this.disposeIntermediateTensorInfo(L)),b&&(w=this.endTimer(w),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(w)})),!C().getBool("WEBGL_LAZILY_UNPACK")&&a.isPacked&&i===!1){const L=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),L}return o}compileAndRun(e,t,n,s,i=!1){n=n||t[0].dtype;const o=this.runWebGLProgram(e,t,n,s,i);return _s().makeTensorFromDataId(o.dataId,o.shape,o.dtype)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){if(this.disposed)return;if(!C().getBool("IS_TEST")){const e=Object.keys(this.binaryCache);e.forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]})}this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=ee(()=>{if(!C().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=C().getBool("DEBUG");C().set("DEBUG",!1);const t=this.abs(Ne(1e-8)).dataSync()[0];if(C().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Y6:H6}uploadToGPU(e){const t=this.texData.get(e),{shape:n,dtype:s,values:i,texture:o,usage:a,isPacked:c}=t;if(o!=null)return;const h=this.activeTimers!=null;let d;h&&(d=Yn());let m=t.texShape;if(m==null&&(m=eK(n,c),t.texShape=m),i!=null){const y=TS(n);let b,w=m[1],L=m[0];const T=i instanceof Uint8Array;c?([w,L]=ac(m[0],m[1]),b=new i8(y,[L,w],T)):b=new s8(y,[L,w],T);const A=this.makeTensorInfo([L,w],s);T?this.texData.get(A.dataId).usage=As.PIXELS:this.texData.get(A.dataId).usage=As.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),w,L,i);const N=!0,E=this.runWebGLProgram(b,[A],s,null,N),D=this.texData.get(E.dataId);t.texture=D.texture,t.texShape=D.texShape,t.isPacked=D.isPacked,t.usage=D.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(E.dataId),t.values=null,h&&(this.uploadWaitMs+=Yn()-d)}else{const y=this.acquireTexture(m,a,s,c);t.texture=y}}convertAndCacheOnCPU(e,t){const n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=Z6(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){const i=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${i} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*ky(t)}tryRunOnCpuOrThrow(e,t){if(this.shouldExecuteOnCPU(e))try{return t()}catch(n){if(C().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}}function Z6(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){const n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}const Q6="2.6.0";function eX(){C().set("WEBGL_FORCE_F16_TEXTURES",!0)}Gy()&&hb("webgl",()=>new J6,2);const Lee={forceHalfFloat:eX},TC="if (isnan(x)) return x;",tX=`
+    `}}const{segment_util:IC}=pw,P6=mw,z6=fw,G6=gw,V6=ip,Y6=1e-7,H6=1e-4,fm={};function q6(e){return e in fm||(fm[e]={}),fm[e]}function gm(e,t=!1){if(e==="linear")return t?U6:u6;if(e==="relu")return t?wC:uC;if(e==="elu")return t?SC:pC;if(e==="relu6")return t?LC:dC;if(e==="prelu")return t?G0:z0;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}const j6=128,K6=600;function X6(){return C().global.screen==null?1024:C().global.screen.height*C().global.screen.width*window.devicePixelRatio*K6/1024/1024}const xC=1e3;class J6 extends g{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!C().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){const t=Wi(C().getNumber("WEBGL_VERSION"));this.binaryCache=q6(C().getNumber("WEBGL_VERSION")),this.gpgpu=new A8(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new r6(this.gpgpu),this.numMBBeforeWarning=X6(),this.texData=new p(this,_s())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((C().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||C().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");const s={};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:As.UPLOAD,refCount:1}),s}incRef(e){const t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){const t=this.texData.get(e);t.refCount--}}move(e,t,n,s){if(C().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:As.UPLOAD,refCount:1})}disposeIntermediateTensorInfo(e){const t=e.dataId;if(this.texData.has(t)){const n=this.texData.get(t);n.refCount--,n.refCount<1&&this.disposeData(t)}}readSync(e){const t=this.texData.get(e),{values:n,dtype:s,complexTensors:i,slice:o,shape:a,isPacked:c}=t;if(o!=null){let y;c?y=new lu(a,mm):y=new st(a,mm);const b=this.runWebGLProgram(y,[{dataId:e,shape:a,dtype:s}],s),w=this.readSync(b.dataId);return this.disposeIntermediateTensorInfo(b),w}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;const h=this.activeTimers!=null;let d;h&&(d=Yn());let m;if(s==="complex64"){const y=i.real.dataSync(),b=i.imag.dataSync();m=er(y,b)}else m=this.getValuesFromTexture(e);return h&&(this.downloadWaitMs+=Yn()-d),this.convertAndCacheOnCPU(e,m)}async read(e){if(this.pendingRead.has(e)){const w=this.pendingRead.get(e);return new Promise(L=>w.push(L))}const t=this.texData.get(e),{values:n,shape:s,slice:i,dtype:o,complexTensors:a,isPacked:c}=t;if(i!=null){let w;c?w=new lu(s,mm):w=new st(s,mm);const L=this.runWebGLProgram(w,[{dataId:e,shape:s,dtype:o}],o),T=this.read(L.dataId);return this.disposeIntermediateTensorInfo(L),T}if(n!=null)return this.convertAndCacheOnCPU(e);if(!C().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&C().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let h=null,d;if(o!=="complex64"&&C().get("WEBGL_BUFFER_SUPPORTED")){d=this.decode(e);const w=this.texData.get(d.dataId);h=this.gpgpu.createBufferFromTexture(w.texture,...ru(s))}this.pendingRead.set(e,[]),o!=="complex64"&&await this.gpgpu.createAndWaitForFence();let m;if(o==="complex64"){const w=await Promise.all([a.real.data(),a.imag.data()]),L=w[0],T=w[1];m=er(L,T)}else if(h==null)m=this.getValuesFromTexture(e);else{const w=we(s);m=this.gpgpu.downloadFloat32MatrixFromBuffer(h,w)}d!=null&&this.disposeIntermediateTensorInfo(d);const y=this.convertAndCacheOnCPU(e,m),b=this.pendingRead.get(e);return this.pendingRead.delete(e),b.forEach(w=>w(y)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),y}checkNumericalProblems(e){if(e==null)return;for(let t=0;t<e.length;t++){const n=e[t];if(!Wj(n))throw C().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){const{shape:t,dtype:n,isPacked:s}=this.texData.get(e),i=we(t);if(C().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const y=this.decode(e),b=this.texData.get(y.dataId),w=this.gpgpu.downloadMatrixFromPackedTexture(b.texture,...ru(t)).subarray(0,i);return this.disposeIntermediateTensorInfo(y),w}const o=C().getBool("WEBGL_PACK")&&s===!0,a=o?TS(t):t,c=o?new n8(a):new t8(a),h=this.runWebGLProgram(c,[{shape:a,dtype:n,dataId:e}],"float32"),d=this.texData.get(h.dataId),m=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(d.texture,d.texShape[0],d.texShape[1]).subarray(0,i);return this.disposeIntermediateTensorInfo(h),m}async time(e){const t=this.activeTimers,n=[];let s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();const i=qi(this.activeTimers.map(c=>c.query)).filter(c=>c!=null),o=qi(this.activeTimers.map(c=>c.name)).filter(c=>c!=null);this.activeTimers=t,s&&(this.programTimersStack=null);const a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const c=await Promise.all(i);a.kernelMs=$x(c),a.getExtraProfileInfo=()=>c.map((h,d)=>({name:o[d],ms:h})).map(h=>`${h.name}: ${h.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:Yn(),endMs:null}}endTimer(e){return C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=Yn(),e)}async getQueryTime(e){if(C().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);const t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;this.releaseGPUData(e);const{complexTensors:t}=this.texData.get(e);t!=null&&(t.real.dispose(),t.imag.dispose()),this.texData.delete(e)}releaseGPUData(e){const{texture:t,dtype:n,texShape:s,usage:i,isPacked:o,slice:a}=this.texData.get(e),c=a&&a.origDataId||e,h=this.dataRefCount.get(c);h>1?this.dataRefCount.set(c,h-1):(this.dataRefCount.delete(c),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,i,o)));const d=this.texData.get(e);d.texture=null,d.texShape=null,d.isPacked=!1,d.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return C().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=_s().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=j6){const n=this.getCPUBackend();return!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(s=>this.texData.get(s.dataId).texture==null&&we(s.shape)<t)}getGPGPUContext(){return this.gpgpu}complex(e,t){const n=this.makeOutput(e.shape,"complex64"),s=this.texData.get(n.dataId);return s.complexTensors={real:_s().keep(e.clone()),imag:_s().keep(t.clone())},n}real(e){const t=this.texData.get(e.dataId);return t.complexTensors.real.clone()}imag(e){const t=this.texData.get(e.dataId);return t.complexTensors.imag.clone()}slice(e,t,n){if(this.shouldExecuteOnCPU([e])){const o=bK(this.texData.get(e.dataId).values,t,n,e.shape,e.dtype);return this.makeOutput(n,e.dtype,o)}if(we(n)===0)return en([],n,e.dtype);const{isPacked:s}=this.texData.get(e.dataId),i=rb(e.shape,t,n);if(s||!i){const o=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new s6(n):new t6(n),a=o.getCustomSetupFunc(t);return this.compileAndRun(o,[e],null,a)}return this.uploadToGPU(e.dataId),this.shallowSlice(e,t,n)}shallowSlice(e,t,n){const s=this.texData.get(e.dataId),i=this.makeOutput(n,e.dtype),o=this.texData.get(i.dataId);Object.assign(o,s),o.shape=n,o.dtype=e.dtype;let a=ob(t,e.strides);s.slice&&(a+=s.slice.flatOffset),o.slice={flatOffset:a,origDataId:s.slice&&s.slice.origDataId||e.dataId};const c=this.dataRefCount.get(o.slice.origDataId)||1;return this.dataRefCount.set(o.slice.origDataId,c+1),i}stridedSlice(e,t,n,s){const i=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.stridedSlice(e,t,n,s));if(i)return i;const o=Cd(t,n,s);if(o.some(c=>c===0))return en([],o);const a=new i6(t,s,o);return this.compileAndRun(a,[e])}reverse(e,t){const n=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Z8(e.shape,t):new J8(e.shape,t);return this.compileAndRun(n,[e])}concat(e,t){if(e[0].dtype==="complex64"){const a=e.map(h=>Do(h)),c=e.map(h=>$a(h));return Ti(this.concat(a,t),this.concat(c,t))}if(e.length===1)return e[0];if(e.length>C().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){const a=Math.floor(e.length/2),c=this.concat(e.slice(0,a),t),h=this.concat(e.slice(a),t);return this.concat([c,h],t)}if(C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].rank>1){const a=new z5(e.map(c=>c.shape),t);return this.compileAndRun(a,e)}const n=Er(e.map(a=>a.shape),t),s=e.map(a=>a.as2D(-1,we(a.shape.slice(t)))),i=new P5(s.map(a=>a.shape)),o=this.compileAndRun(i,s);return o.reshape(n)}neg(e){const t=this.tryRunOnCpuOrThrow([e],()=>this.cpuBackend.neg(e));if(t)return t;if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,mC,e.dtype);const n=new st(e.shape,mC);return this.compileAndRun(n,[e])}batchMatMul(e,t,n,s){const i=n?e.shape[2]:e.shape[1],o=s?t.shape[1]:t.shape[2],a=n?e.shape[1]:e.shape[2],[c,,]=e.shape;if((i===1||o===1)&&a>xC){n&&(e=Me(e,[0,2,1])),s&&(t=Me(t,[0,2,1]));const m=o===1?e:e.as3D(c,a,1),y=o===1?2:1,b=o===1?t.as3D(c,1,a):t;return this.multiply(m,b).sum(y,!0)}const h=Nn(e.dtype,t.dtype),d=new RS(e.shape,[c,i,o],n,s);return this.compileAndRun(d,[e,t],h)}fusedBatchMatMul({a:e,b:t,transposeA:n,transposeB:s,bias:i,activation:o,preluActivationWeights:a}){const c=n?e.shape[2]:e.shape[1],h=s?t.shape[1]:t.shape[2],[d,,]=e.shape,m=Nn(e.dtype,t.dtype),y=i!=null,b=a!=null,w=o?gm(o,!0):null,L=new RS(e.shape,[d,c,h],n,s,y,w,b),T=[e,t];return i&&T.push(i),a&&T.push(a),this.compileAndRun(L,T,m)}multiply(e,t){if(e.dtype==="complex64"){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),a=new B0(U0.REAL,e.shape,t.shape),c=new B0(U0.IMAG,e.shape,t.shape),h=[this.makeComplexComponentTensorInfo(e,i.complexTensors.real),this.makeComplexComponentTensorInfo(e,i.complexTensors.imag),this.makeComplexComponentTensorInfo(t,o.complexTensors.real),this.makeComplexComponentTensorInfo(t,o.complexTensors.imag)],d=this.compileAndRun(a,h),m=this.compileAndRun(c,h),y=this.complex(d,m);return d.dispose(),m.dispose(),y}const n=Nn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=gK(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,P0,e.dtype);const s=new hn(P0,e.shape,t.shape);return this.compileAndRun(s,[e,t],e.dtype)}localResponseNormalization4D(e,t,n,s,i){const o=C().getBool("WEBGL_PACK_NORMALIZATION")?new k8(e.shape,t,n,s,i):new E8(e.shape,t,n,s,i);return this.compileAndRun(o,[e])}LRNGrad(e,t,n,s,i,o,a){const c=new D8(t.shape,s,i,o,a);return this.compileAndRun(c,[t,n,e])}tile(e,t){if(e.dtype==="string"){const s=this.readSync(e.dataId),i=s.map(a=>zl(a)),o=Ze(e.shape,e.dtype,i);return z6(o,t)}const n=new l6(e.shape,t);return this.compileAndRun(n,[e])}pad(e,t,n){const s=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new V8(e.shape,t,n):new G8(e.shape,t,n);return this.compileAndRun(s,[e])}gather(e,t,n){const s=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.gather(e,t,n));if(s)return s;const i=new o8(e.shape,t.size,n);return this.compileAndRun(i,[e,t])}batchToSpaceND(e,t,n){k(e.rank<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");const s=t.reduce((d,m)=>d*m),i=Ah(e.shape,t,s),o=vh(i.length,t.length),a=Nh(e.shape,t,s),c=sw(n,t.length),h=iw(a,n,t.length);return Me(e.reshape(i),o).reshape(a).slice(c,h)}spaceToBatchND(e,t,n){k(e.rank<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");const s=t.reduce((m,y)=>m*y),i=[[0,0]];i.push(...n);for(let m=1+t.length;m<e.shape.length;++m)i.push([0,0]);const o=e.pad(i),a=Ah(o.shape,t,s,!1),c=vh(a.length,t.length,!1),h=Nh(o.shape,t,s,!1),d=Me(o.reshape(a),c);return K(d,h)}reduce(e,t,n){const s=e.shape[0],i=e.shape[1],o=ah(i),a=Math.ceil(i/o),c={windowSize:o,inSize:i,batchSize:s,outSize:a},h=new iC(c,t),d=this.compileAndRun(h,[e],n);return 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a=0;a<=Math.ceil(Math.log2(i))-1;a++){const c=new q0(e.shape,!1,s),h=c.getCustomSetupFunc(a),d=o;o=this.compileAndRun(c,[o],o.dtype,h),d.dispose()}if(n){const a=new q0(e.shape,n,s),c=o;o=this.compileAndRun(a,[o]),c.dispose()}return o}equal(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,N5,"bool");const n=new hn(d5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}notEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,C5,"bool");const n=new hn(p5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}less(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.less(e,t));if(n)return n;if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,R5,"bool");const s=new hn(m5,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}lessEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,O5,"bool");const n=new hn(f5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}greater(e,t){const n=this.tryRunOnCpuOrThrow([e,t],()=>this.cpuBackend.greater(e,t));if(n)return n;if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,E5,"bool");const s=new hn(g5,e.shape,t.shape);return this.compileAndRun(s,[e,t],"bool")}greaterEqual(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,D5,"bool");const n=new hn(y5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}logicalNot(e){const t=new st(e.shape,W6);return this.compileAndRun(t,[e])}logicalAnd(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,k5,"bool");const n=new hn(b5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}logicalOr(e,t){if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,F5,"bool");const n=new hn(w5,e.shape,t.shape);return this.compileAndRun(n,[e,t],"bool")}select(e,t,n){const s=new e6(e.rank,t.shape,t.rank);return this.compileAndRun(s,[e,t,n],Nn(t.dtype,n.dtype))}where(e){qa("tf.where() in webgl locks the UI thread. 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this.reduce(o,"all",o.dtype).reshape(n)}any(e,t){ts("any",t,e.rank);const[n,s]=Rn(e.shape,t),i=we(s),o=e.as2D(-1,i);return this.reduce(o,"any",o.dtype).reshape(n)}floorDiv(e,t){const n=h5,s="int32";if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,T5,s);const i=new hn(n,e.shape,t.shape);return this.compileAndRun(i,[e,t],s)}add(e,t){if(e.dtype==="complex64"&&t.dtype==="complex64")return this.complexSeparableBinaryOp(e,t,NS);const n=Nn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=lK(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,NS,n);const s=new hn(NS,e.shape,t.shape);return this.compileAndRun(s,[e,t],n)}packedUnaryOp(e,t,n){const s=new lu(e.shape,t);return this.compileAndRun(s,[e],n)}packedBinaryOp(e,t,n,s,i=!1){const o=new jr(n,e.shape,t.shape,i);return this.compileAndRun(o,[e,t],s)}complexSeparableBinaryOp(e,t,n){const s=this.texData.get(e.dataId),i=this.texData.get(t.dataId),[o,a]=[[s.complexTensors.real,i.complexTensors.real],[s.complexTensors.imag,i.complexTensors.imag]].map(h=>{const[d,m]=h,y=this.makeComplexComponentTensorInfo(e,d),b=this.makeComplexComponentTensorInfo(t,m),w=new hn(n,e.shape,t.shape);return this.compileAndRun(w,[y,b],Nn(d.dtype,m.dtype))}),c=this.complex(o,a);return o.dispose(),a.dispose(),c}makeComplexComponentTensorInfo(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}addN(e){if(e.length===1)return e[0];if(e.length>C().get("WEBGL_MAX_TEXTURES_IN_SHADER")){const o=Math.floor(e.length/2),a=this.addN(e.slice(0,o)),c=this.addN(e.slice(o));return this.addN([a,c])}const t=e.map(o=>o.dtype).reduce((o,a)=>Nn(o,a)),n=e.map(o=>o.shape),s=C().getBool("WEBGL_PACK"),i=s?new IK(e[0].shape,n):new SK(e[0].shape,n);return this.compileAndRun(i,e,t)}subtract(e,t){if(e.dtype==="complex64"&&t.dtype==="complex64")return this.complexSeparableBinaryOp(e,t,CS);const n=Nn(e.dtype,t.dtype);if(this.shouldExecuteOnCPU([e,t])){const i=this.texData.get(e.dataId),o=this.texData.get(t.dataId),[a,c]=wK(e.shape,t.shape,i.values,o.values,n);return this.makeOutput(c,n,a)}if(C().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(e,t,CS,e.dtype);const s=new hn(CS,e.shape,t.shape);return this.compileAndRun(s,[e,t],n)}pow(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS"),s=n?new jr(A5,e.shape,t.shape):new hn(u5,e.shape,t.shape),i=Nn(e.dtype,t.dtype);return this.compileAndRun(s,[e,t],i)}ceil(e){if(this.shouldExecuteOnCPU([e])){const n=hK(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,fC,e.dtype);const t=new st(e.shape,fC);return this.compileAndRun(t,[e])}floor(e){if(this.shouldExecuteOnCPU([e])){const n=pK(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,gC,e.dtype);const t=new st(e.shape,gC);return this.compileAndRun(t,[e])}sign(e){const t=new st(e.shape,m6);return this.compileAndRun(t,[e])}isNaN(e){const t=new st(e.shape,f6);return this.compileAndRun(t,[e],"bool")}isInf(e){const t=new st(e.shape,g6);return this.compileAndRun(t,[e],"bool")}isFinite(e){const t=new st(e.shape,y6);return this.compileAndRun(t,[e],"bool")}round(e){const t=new st(e.shape,b6);return this.compileAndRun(t,[e])}exp(e){if(this.shouldExecuteOnCPU([e])){const n=uK(this.texData.get(e.dataId).values,e.dtype);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,yC,e.dtype);const t=new st(e.shape,yC);return this.compileAndRun(t,[e])}expm1(e){if(this.shouldExecuteOnCPU([e])){const n=dK(this.texData.get(e.dataId).values,e.dtype);return 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this.compileAndRun(t,[e])}relu(e){let t;return C().getBool("WEBGL_PACK")?t=new lu(e.shape,wC):t=new st(e.shape,uC),this.compileAndRun(t,[e])}relu6(e){let t;return C().getBool("WEBGL_PACK")?t=new lu(e.shape,LC):t=new st(e.shape,dC),this.compileAndRun(t,[e])}prelu(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(G0,e.shape,t.shape):new hn(z0,e.shape,t.shape);return this.compileAndRun(n,[e,t])}elu(e){if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,SC,e.dtype);const t=new st(e.shape,pC);return this.compileAndRun(t,[e])}eluDer(e,t){const n=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(v5,e.shape,t.shape):new hn(x5,e.shape,t.shape);return this.compileAndRun(n,[e,t])}selu(e){const t=new st(e.shape,d6);return this.compileAndRun(t,[e])}int(e){const t=new st(e.shape,$6);return this.compileAndRun(t,[e],"int32")}clip(e,t,n){let s;C().getBool("WEBGL_PACK_CLIP")?s=new B5(e.shape):s=new U5(e.shape);const i=s.getCustomSetupFunc(t,n);return this.compileAndRun(s,[e],null,i)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){const n=cK(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(C().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,hC,e.dtype);const t=new st(e.shape,hC);return this.compileAndRun(t,[e])}complexAbs(e){const t=this.texData.get(e.dataId),n=new M5(e.shape),s=[this.makeComplexComponentTensorInfo(e,t.complexTensors.real),this.makeComplexComponentTensorInfo(e,t.complexTensors.imag)];return this.compileAndRun(n,s)}sigmoid(e){const t=new st(e.shape,x6);return this.compileAndRun(t,[e])}softplus(e){const t=new st(e.shape,T6);return this.compileAndRun(t,[e])}asin(e){const t=new st(e.shape,A6);return this.compileAndRun(t,[e])}acos(e){const t=new st(e.shape,v6);return this.compileAndRun(t,[e])}atan(e){const t=new st(e.shape,N6);return this.compileAndRun(t,[e])}sinh(e){const t=new st(e.shape,C6);return this.compileAndRun(t,[e])}cosh(e){const t=new st(e.shape,R6);return this.compileAndRun(t,[e])}tanh(e){const t=new st(e.shape,O6);return this.compileAndRun(t,[e])}asinh(e){const t=new st(e.shape,E6);return this.compileAndRun(t,[e])}acosh(e){const t=new st(e.shape,D6);return this.compileAndRun(t,[e])}atanh(e){const t=new st(e.shape,k6);return this.compileAndRun(t,[e])}erf(e){const t=new st(e.shape,F6);return this.compileAndRun(t,[e])}step(e,t){const n=new st(e.shape,p6(t));return this.compileAndRun(n,[e])}conv2dByMatMul(e,t,n,s,i,o){const a=e.shape,c=this.texData.get(e.dataId),h=n.inChannels,d=a[0]*a[1]*a[2],m=n.outChannels,y=n.dataFormat==="channelsLast",b=!1,w=!1,L=(d===1||m===1)&&h>xC,T=a[2]%2!==0&&!!c.isPacked;if(L||!C().getBool("WEBGL_LAZILY_UNPACK")||!C().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!T){const B=y?a[0]*a[1]*a[2]:a[0]*a[2]*a[3],$=K(e,[1,B,n.inChannels]),H=K(t,[1,n.inChannels,n.outChannels]),q=this.fusedBatchMatMul({a:$,b:H,transposeA:b,transposeB:w,bias:s,activation:i,preluActivationWeights:o});return K(q,n.outShape)}const A=y?a[0]*a[1]*(a[2]+1):a[0]*a[2]*(a[3]+1),N={dataId:e.dataId,shape:[1,A,n.inChannels],dtype:e.dtype},E=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k(cm(c.shape,N.shape),()=>`packed reshape ${c.shape} to ${N.shape} isn't free`);const D=K(t,[1,n.inChannels,n.outChannels]),F=this.fusedBatchMatMul({a:N,b:D,transposeA:b,transposeB:w,bias:s,activation:i,preluActivationWeights:o}),_=this.texData.get(F.dataId);return k(_.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=E,_.shape=n.outShape,_s().makeTensorFromDataId(F.dataId,n.outShape,F.dtype)}conv2dWithIm2Row(e,t,n,s,i,o){const{filterWidth:a,filterHeight:c,inChannels:h,outWidth:d,outHeight:m,dataFormat:y}=n,b=y==="channelsLast",w=a*c*h,L=m*d,T=[w,L],A=!0,N=!1,E=e.squeeze([0]),D=t.reshape([1,w,-1]),F=new O8(T,E.shape,n),_=this.compileAndRun(F,[E]).reshape([1,T[0],T[1]]),B=s!=null,$=o!=null,H=i?gm(i,!0):null,q=new RS(_.shape,[1,L,n.outChannels],A,N,B,H,$),J=[_,D];s&&J.push(s),$&&J.push(o);const re=this.compileAndRun(q,J);return b?re.reshape([1,m,d,n.outChannels]):re.reshape([1,n.outChannels,m,d])}fusedConv2d({input:e,filter:t,convInfo:n,bias:s,activation:i,preluActivationWeights:o}){if(n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return this.conv2dByMatMul(e,t,n,s,i,o);if(C().getBool("WEBGL_CONV_IM2COL")&&e.shape[0]===1)return this.conv2dWithIm2Row(e,t,n,s,i,o);const a=s!=null,c=o!=null,h=i?gm(i,!1):null,d=new V0(n,a,h,c),m=[e,t];return s&&m.push(s),o&&m.push(o),this.compileAndRun(d,m)}conv2d(e,t,n){if(n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return this.conv2dByMatMul(e,t,n);if(C().getBool("WEBGL_CONV_IM2COL")&&e.shape[0]===1)return this.conv2dWithIm2Row(e,t,n);const s=new V0(n);return this.compileAndRun(s,[e,t])}conv2dDerInput(e,t,n){const s=new V5(n);return this.compileAndRun(s,[e,t])}conv2dDerFilter(e,t,n){const s=new G5(n);return this.compileAndRun(s,[e,t])}fusedDepthwiseConv2D({input:e,filter:t,convInfo:n,bias:s,activation:i,preluActivationWeights:o}){const a=C().getBool("WEBGL_PACK_DEPTHWISECONV")&&n.strideWidth<=2&&n.outChannels/n.inChannels===1,c=i?gm(i,a):null,h=[e,t],d=s!=null,m=o!=null;d&&h.push(s),m&&h.push(o);let y;return a?(y=new H0(n,d,c,m),this.compileAndRun(y,h)):(y=new Y0(n,d,c,m),this.compileAndRun(y,h))}depthwiseConv2D(e,t,n){let s;return C().getBool("WEBGL_PACK_DEPTHWISECONV")&&n.strideWidth<=2&&n.outChannels/n.inChannels===1?(s=new H0(n),this.compileAndRun(s,[e,t])):(s=new Y0(n),this.compileAndRun(s,[e,t]))}depthwiseConv2DDerInput(e,t,n){const s=new j5(n);return this.compileAndRun(s,[e,t])}depthwiseConv2DDerFilter(e,t,n){const s=new q5(n);return this.compileAndRun(s,[e,t])}conv3d(e,t,n){const s=new K5(n);return this.compileAndRun(s,[e,t])}conv3dDerInput(e,t,n){const s=new H5(n);return this.compileAndRun(s,[e,t])}conv3dDerFilter(e,t,n){const s=new Y5(n);return this.compileAndRun(s,[e,t])}cast(e,t){return BA(e,t,this)}unstack(e,t){const n=e.shape[t],s=new Array(e.rank-1);let i=0;for(let h=0;h<e.rank;h++)h!==t&&(s[i++]=e.shape[h]);const o=new Array(e.rank).fill(0),a=e.shape.slice();a[t]=1;const c=new Array(n);for(let h=0;h<c.length;h++)o[t]=h,c[h]=this.slice(e,o,a).reshape(s);return c}avgPool3d(e,t){const n=new OS(t,"avg",!1);return this.compileAndRun(n,[e],"float32")}avgPool3dBackprop(e,t,n){const s=new l5(n);return this.compileAndRun(s,[e],t.dtype)}maxPool3d(e,t){const n=new OS(t,"max",!1);return this.compileAndRun(n,[e],"float32")}maxPool3dBackprop(e,t,n,s){const i=!0,o=new OS(s,"max",i),a=this.compileAndRun(o,[t]),c=new _8(s),h=this.compileAndRun(c,[e,a],t.dtype);return a.dispose(),h}resizeBilinear(e,t,n,s){const i=C().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new j8(e.shape,t,n,s):new q8(e.shape,t,n,s);return this.compileAndRun(i,[e],"float32")}resizeBilinearBackprop(e,t,n){const s=new H8(e,t,n);return this.compileAndRun(s,[e])}resizeNearestNeighbor(e,t,n,s){const i=new X8(e.shape,t,n,s);return this.compileAndRun(i,[e])}resizeNearestNeighborBackprop(e,t,n){const s=new K8(e,t,n);return this.compileAndRun(s,[e])}multinomial(e,t,n,s){const i=t?e:Wo(e),o=i.shape[0],a=i.shape[1],c=new W8(o,a,n),h=c.getCustomSetupFunc(s);return this.compileAndRun(c,[i],"int32",h)}oneHot(e,t,n,s){const i=new $8(e.size,t,n,s);return this.compileAndRun(i,[e])}diag(e){const t=new e8(e.size);return this.compileAndRun(t,[e])}cropAndResize(e,t,n,s,i,o){const a=new X5(e.shape,t.shape,s,i,o);return this.compileAndRun(a,[e,t,n],"float32")}depthToSpace(e,t,n){k(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`);const s=e.shape[0],i=n==="NHWC"?e.shape[1]:e.shape[2],o=n==="NHWC"?e.shape[2]:e.shape[3],a=n==="NHWC"?e.shape[3]:e.shape[1],c=i*t,h=o*t,d=a/(t*t),m=n==="NHWC"?[s,c,h,d]:[s,d,c,h],y=new Q5(m,t,n);return this.compileAndRun(y,[e])}split(e,t,n){return P6(e,t,n)}scatterND(e,t,n){const{sliceRank:s,numUpdates:i,sliceSize:o,strides:a,outputSize:c}=Da(t,e,n),h=[c/o,o],d=e.reshape([i,s]),m=t.reshape([i,o]);if(c===0)return MA(en([]),n);const y=Ne(0),b=new oC(i,s,d.rank,m.rank,a,h),w=this.compileAndRun(b,[m,d,y]);return w.reshape(n)}sparseToDense(e,t,n,s){const{sliceRank:i,numUpdates:o,strides:a,outputSize:c}=Da(t,e,n),h=!1,d=new oC(o,i,e.rank,t.rank,a,[c,1],h),m=this.compileAndRun(d,[t,e,s]);return m.reshape(n)}fft(e){const t=!1;return this.fftImpl(e,t)}ifft(e){const t=!0;return this.fftImpl(e,t)}fftImpl(e,t){const n=this.texData.get(e.dataId),s=new J0(X0.REAL,e.shape,t),i=new J0(X0.IMAG,e.shape,t),o=[this.makeComplexComponentTensorInfo(e,n.complexTensors.real),this.makeComplexComponentTensorInfo(e,n.complexTensors.imag)],a=this.compileAndRun(s,o),c=this.compileAndRun(i,o),h=this.complex(a,c).as2D(e.shape[0],e.shape[1]);return a.dispose(),c.dispose(),h}gatherND(e,t){const n=t.shape,s=n[n.length-1],[i,o,a,c]=vd(e,t),h=t.reshape([o,s]),d=e.reshape([e.size/a,a]),m=new c8(s,c,[o,a]),y=this.compileAndRun(m,[d,h]);return y.reshape(i)}fill(e,t,n){if(n=n||Ta(t),n==="string"){const s=wo(n,we(e));return s.fill(t),_s().makeTensor(s,e,n,this)}else{const s=new r8(e,t),i=s.getCustomSetupFunc(t);return this.compileAndRun(s,[],n,i)}}onesLike(e){if(e.dtype==="string")throw new Error("onesLike is not supported under string dtype");return this.fill(e.shape,1,e.dtype)}zerosLike(e){return this.fill(e.shape,e.dtype==="string"?"":0,e.dtype)}linspace(e,t,n){return dw(e,t,n)}makeTensorInfo(e,t,n){const s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){const{dataId:s}=this.makeTensorInfo(e,t,n);return _s().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){const t=new M6(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new U8(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){const n=[hc(e.shape),...uc(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},i=[hc(t),...uc(t)],o=new rC(i,n),a=!0,c=this.runWebGLProgram(o,[s],e.dtype,null,a);return{dataId:c.dataId,shape:t,dtype:c.dtype}}decode(e){const t=this.texData.get(e),{isPacked:n,shape:s,dtype:i}=t,o=TS(s);let a;n?a=new Z5(o):a=new J5(o);const c=!0,h=this.runWebGLProgram(a,[{shape:o,dtype:i,dataId:e}],i,null,c);return{dtype:i,shape:s,dataId:h.dataId}}runWebGLProgram(e,t,n,s,i=!1){const o=this.makeTensorInfo(e.outputShape,n),a=this.texData.get(o.dataId);if(e.packedOutput&&(a.isPacked=!0),e.outPackingScheme===su.DENSE){const L=ru(e.outputShape);a.texShape=L.map(T=>T*2)}if(e.outTexUsage!=null&&(a.usage=e.outTexUsage),we(o.shape)===0)return a.values=bn(o.dtype,0),o;const c=[],h=t.map(L=>{if(L.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let T=this.texData.get(L.dataId);if(T.texture==null){if(!e.packedInputs&&we(L.shape)<=C().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:L.shape,texData:null,isUniform:!0,uniformValues:T.values};e.packedInputs&&(T.isPacked=!0,T.shape=L.shape)}else if(!!T.isPacked!==!!e.packedInputs)L=T.isPacked?this.unpackTensor(L):this.packTensor(L),c.push(L),T=this.texData.get(L.dataId);else if(T.isPacked&&!cm(T.shape,L.shape)){const A=L,N=L.shape;L.shape=T.shape,L=this.packedReshape(L,N),c.push(L),T=this.texData.get(L.dataId),A.shape=N}return this.uploadToGPU(L.dataId),{shape:L.shape,texData:T,isUniform:!1}});this.uploadToGPU(o.dataId);const d={shape:o.shape,texData:a,isUniform:!1},m=R8(e,h,d),y=this.getAndSaveBinary(m,()=>N8(this.gpgpu,e,h,d)),b=this.activeTimers!=null;let w;if(b&&(w=this.startTimer()),C8(this.gpgpu,y,h,d,s),c.forEach(L=>this.disposeIntermediateTensorInfo(L)),b&&(w=this.endTimer(w),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(w)})),!C().getBool("WEBGL_LAZILY_UNPACK")&&a.isPacked&&i===!1){const L=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),L}return o}compileAndRun(e,t,n,s,i=!1){n=n||t[0].dtype;const o=this.runWebGLProgram(e,t,n,s,i);return _s().makeTensorFromDataId(o.dataId,o.shape,o.dtype)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){if(this.disposed)return;if(!C().getBool("IS_TEST")){const e=Object.keys(this.binaryCache);e.forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]})}this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=ee(()=>{if(!C().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=C().getBool("DEBUG");C().set("DEBUG",!1);const t=this.abs(Ne(1e-8)).dataSync()[0];if(C().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Y6:H6}uploadToGPU(e){const t=this.texData.get(e),{shape:n,dtype:s,values:i,texture:o,usage:a,isPacked:c}=t;if(o!=null)return;const h=this.activeTimers!=null;let d;h&&(d=Yn());let m=t.texShape;if(m==null&&(m=eK(n,c),t.texShape=m),i!=null){const y=TS(n);let b,w=m[1],L=m[0];const T=i instanceof Uint8Array;c?([w,L]=lc(m[0],m[1]),b=new i8(y,[L,w],T)):b=new s8(y,[L,w],T);const A=this.makeTensorInfo([L,w],s);T?this.texData.get(A.dataId).usage=As.PIXELS:this.texData.get(A.dataId).usage=As.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),w,L,i);const N=!0,E=this.runWebGLProgram(b,[A],s,null,N),D=this.texData.get(E.dataId);t.texture=D.texture,t.texShape=D.texShape,t.isPacked=D.isPacked,t.usage=D.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(E.dataId),t.values=null,h&&(this.uploadWaitMs+=Yn()-d)}else{const y=this.acquireTexture(m,a,s,c);t.texture=y}}convertAndCacheOnCPU(e,t){const n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=Z6(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){const i=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${i} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*ky(t)}tryRunOnCpuOrThrow(e,t){if(this.shouldExecuteOnCPU(e))try{return t()}catch(n){if(C().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}}function Z6(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){const n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}const Q6="2.6.0";function eX(){C().set("WEBGL_FORCE_F16_TEXTURES",!0)}Gy()&&hb("webgl",()=>new J6,2);const Lee={forceHalfFloat:eX},TC="if (isnan(x)) return x;",tX=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `,nX=`
@@ -3649,14 +3649,14 @@ return (round(mod(b, 2.0)) != 1) ?
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;function ym(e){return({inputs:t,backend:n})=>{const{x:s}=t,i=n,o=new st(s.shape,e);return i.runWebGLProgram(o,[s],s.dtype)}}function DS(e,t,n,s){return({inputs:i,backend:o})=>{const{a,b:c}=i,h=o,d=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(t,a.shape,c.shape,!!n):new ln(e,a.shape,c.shape),m=s||a.dtype,y=h.runWebGLProgram(d,[a,c],m);return y}}const sX=tX+`
+`;function ym(e){return({inputs:t,backend:n})=>{const{x:s}=t,i=n,o=new st(s.shape,e);return i.runWebGLProgram(o,[s],s.dtype)}}function DS(e,t,n,s){return({inputs:i,backend:o})=>{const{a,b:c}=i,h=o,d=C().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jr(t,a.shape,c.shape,!!n):new hn(e,a.shape,c.shape),m=s||a.dtype,y=h.runWebGLProgram(d,[a,c],m);return y}}const sX=tX+`
   return atan(a, b);
 `,iX=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+nX+`
   return result;
-`,rX=DS(sX,iX),oX={kernelName:Si,backendName:"webgl",kernelFunc:rX};function kS(e){const{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}const aX={kernelName:yl,backendName:"webgl",kernelFunc:kS};function cX(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;ou(i,"avgPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:h}=s,d=1;k(sn(a,d),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${d}'`);const m=Fn(i.shape,o,a,d,c,h);if(m.filterWidth===1&&m.filterHeight===1&&ot(m.inShape,m.outShape))return kS({inputs:{x:i},backend:n});const y=new cu(m,"avg",!1);return n.runWebGLProgram(y,[i],"float32")}const lX={kernelName:Qs,backendName:"webgl",kernelFunc:cX};function hX(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o}=t,a=o;ou([i,o],"avgPoolBackprop");const{filterSize:c,strides:h,pad:d}=s,m=Fn(a.shape,c,h,1,d),y=new c5(m);return n.runWebGLProgram(y,[i],a.dtype)}const uX={kernelName:fa,backendName:"webgl",kernelFunc:hX};class dX{constructor(e,t,n,s,i,o){this.outputShape=[],this.variableNames=["x","mean","variance"],tt(e,t),tt(e,n);let a="0.0";s!=null&&(tt(e,s),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let c="1.0";i!=null&&(tt(e,i),this.variableNames.push("scale"),c="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+`,rX=DS(sX,iX),oX={kernelName:Si,backendName:"webgl",kernelFunc:rX};function kS(e){const{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}const aX={kernelName:yl,backendName:"webgl",kernelFunc:kS};function cX(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;ou(i,"avgPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:h}=s,d=1;k(rn(a,d),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${d}'`);const m=Fn(i.shape,o,a,d,c,h);if(m.filterWidth===1&&m.filterHeight===1&&ot(m.inShape,m.outShape))return kS({inputs:{x:i},backend:n});const y=new cu(m,"avg",!1);return n.runWebGLProgram(y,[i],"float32")}const lX={kernelName:Qs,backendName:"webgl",kernelFunc:cX};function hX(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o}=t,a=o;ou([i,o],"avgPoolBackprop");const{filterSize:c,strides:h,pad:d}=s,m=Fn(a.shape,c,h,1,d),y=new c5(m);return n.runWebGLProgram(y,[i],a.dtype)}const uX={kernelName:ya,backendName:"webgl",kernelFunc:hX};class dX{constructor(e,t,n,s,i,o){this.outputShape=[],this.variableNames=["x","mean","variance"],tt(e,t),tt(e,n);let a="0.0";s!=null&&(tt(e,s),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let c="1.0";i!=null&&(tt(e,i),this.variableNames.push("scale"),c="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -3681,7 +3681,7 @@ return (round(mod(b, 2.0)) != 1) ?
       }
     `}}const mX=({inputs:e,backend:t,attrs:n})=>{const{x:s,mean:i,variance:o,offset:a,scale:c}=e;k(i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k(c==null||i.shape.length===c.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:h}=n;h==null&&(h=.001);const d=[s,i,o];let m=null;a!=null&&(m=a.shape,d.push(a));let y=null;c!=null&&(y=c.shape,d.push(c));const b=C().getBool("WEBGL_PACK_NORMALIZATION")?new pX(s.shape,i.shape,o.shape,m,y,h):new dX(s.shape,i.shape,o.shape,m,y,h),w=t.runWebGLProgram(b,d,d[0].dtype);return w},fX={kernelName:gl,backendName:"webgl",kernelFunc:mX};const gX=TC+`
   return cos(x);
-`,yX=ym(gX),bX={kernelName:ga,backendName:"webgl",kernelFunc:yX};const wX=`
+`,yX=ym(gX),bX={kernelName:ba,backendName:"webgl",kernelFunc:yX};const wX=`
 if (a == b) {
   return 1.0;
 };
@@ -3703,7 +3703,7 @@ return a / b;`,LX=`
   }
 
   return result;
-`,SX=DS(wX,LX,!0),IX={kernelName:ya,backendName:"webgl",kernelFunc:SX};class xX{constructor(e){this.variableNames=["Image"],this.outputShape=[];const t=e[2];this.outputShape=e,this.userCode=`
+`,SX=DS(wX,LX,!0),IX={kernelName:wa,backendName:"webgl",kernelFunc:SX};class xX{constructor(e){this.variableNames=["Image"],this.outputShape=[];const t=e[2];this.outputShape=e,this.userCode=`
         void main() {
           ivec4 coords = getOutputCoords();
           int x = coords[2];
@@ -3773,7 +3773,7 @@ return a / b;`,LX=`
 
         ${t.output} = result;
       }
-    `}}const NX={kernelName:ld,backendName:"webgl",kernelFunc:CX};let mc;function CX(e){const{inputs:t,backend:n,attrs:s}=e;let{pixels:i}=t;const{numChannels:o}=s,a=typeof HTMLVideoElement!="undefined"&&i instanceof HTMLVideoElement,c=typeof HTMLImageElement!="undefined"&&i instanceof HTMLImageElement,[h,d]=a?[i.videoWidth,i.videoHeight]:[i.width,i.height],m=[d,h],y=[d,h,o];(c||a)&&(mc==null&&(mc=document.createElement("canvas").getContext("2d")),mc.canvas.width=h,mc.canvas.height=d,mc.drawImage(i,0,0,h,d),i=mc.canvas);const b=n.makeTensorInfo(m,"int32");n.texData.get(b.dataId).usage=As.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(b.dataId),i);const w=C().getBool("WEBGL_PACK")?new vX(y):new AX(y),L=n.runWebGLProgram(w,[b],"int32");return n.disposeData(b.dataId),L}function RX(e){const t=[];for(;t.length===0||t[t.length-1].outSize!==1;){const n=t.length?t[t.length-1].outSize:e[1],s=ah(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function OX(e,t,n,s){const i=RX(e.shape);let o=e;for(let a=0;a<i.length;a++){const{inSize:c,windowSize:h,outSize:d}=i[a],m=new iC({windowSize:h,inSize:c,batchSize:e.shape[0],outSize:d},n),y=o;o=s.runWebGLProgram(m,[o],t),y.dataId!==e.dataId&&s.disposeData(y.dataId)}return o}function EX(e,t,n){const s=[cc(e.shape),...lc(e.shape)],i={dtype:e.dtype,shape:s,dataId:e.dataId},o=[cc(t),...lc(t)],a=new rC(o,s),c=!0,h=n.runWebGLProgram(a,[i],e.dtype,null,c);return{dataId:h.dataId,shape:t,dtype:h.dtype}}function FS(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{shape:o}=s,a=n,c=we(i.shape),h=md(o,c),d=we(h);k(c===d,()=>`The new shape (${h}) has ${d} elements and the old shape (${i.shape}) has ${c} elements. The new shape and old shape must have the same number of elements.`);const m=a.texData.get(i.dataId);return m.isPacked&&!cm(i.shape,h)&&!(m.texture!==null&&cm(m.shape,h))?EX(i,h,a):(a.incRef(i.dataId),{dataId:i.dataId,shape:h,dtype:i.dtype})}const DX={kernelName:Nl,backendName:"webgl",kernelFunc:FS};function kX(e,t,n,s){const i=we(t),o=we(e.shape),a=o/i,c=FS({inputs:{x:e},attrs:{shape:[a,i]},backend:s}),h=OX(c,e.dtype,"max",s),d=FS({inputs:{x:h},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(c),s.disposeIntermediateTensorInfo(h),d}class FX{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let o=0;o<n.length;o++)n[o]=e[t[o]];this.outputShape=n,this.rank=n.length;const s=Et(this.rank),i=_X(t);this.userCode=`
+    `}}const NX={kernelName:ld,backendName:"webgl",kernelFunc:CX};let gc;function CX(e){const{inputs:t,backend:n,attrs:s}=e;let{pixels:i}=t;const{numChannels:o}=s,a=typeof HTMLVideoElement!="undefined"&&i instanceof HTMLVideoElement,c=typeof HTMLImageElement!="undefined"&&i instanceof HTMLImageElement,[h,d]=a?[i.videoWidth,i.videoHeight]:[i.width,i.height],m=[d,h],y=[d,h,o];(c||a)&&(gc==null&&(gc=document.createElement("canvas").getContext("2d")),gc.canvas.width=h,gc.canvas.height=d,gc.drawImage(i,0,0,h,d),i=gc.canvas);const b=n.makeTensorInfo(m,"int32");n.texData.get(b.dataId).usage=As.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(b.dataId),i);const w=C().getBool("WEBGL_PACK")?new vX(y):new AX(y),L=n.runWebGLProgram(w,[b],"int32");return n.disposeData(b.dataId),L}function RX(e){const t=[];for(;t.length===0||t[t.length-1].outSize!==1;){const n=t.length?t[t.length-1].outSize:e[1],s=ah(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function OX(e,t,n,s){const i=RX(e.shape);let o=e;for(let a=0;a<i.length;a++){const{inSize:c,windowSize:h,outSize:d}=i[a],m=new iC({windowSize:h,inSize:c,batchSize:e.shape[0],outSize:d},n),y=o;o=s.runWebGLProgram(m,[o],t),y.dataId!==e.dataId&&s.disposeData(y.dataId)}return o}function EX(e,t,n){const s=[hc(e.shape),...uc(e.shape)],i={dtype:e.dtype,shape:s,dataId:e.dataId},o=[hc(t),...uc(t)],a=new rC(o,s),c=!0,h=n.runWebGLProgram(a,[i],e.dtype,null,c);return{dataId:h.dataId,shape:t,dtype:h.dtype}}function FS(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t,{shape:o}=s,a=n,c=we(i.shape),h=md(o,c),d=we(h);k(c===d,()=>`The new shape (${h}) has ${d} elements and the old shape (${i.shape}) has ${c} elements. The new shape and old shape must have the same number of elements.`);const m=a.texData.get(i.dataId);return m.isPacked&&!cm(i.shape,h)&&!(m.texture!==null&&cm(m.shape,h))?EX(i,h,a):(a.incRef(i.dataId),{dataId:i.dataId,shape:h,dtype:i.dtype})}const DX={kernelName:Nl,backendName:"webgl",kernelFunc:FS};function kX(e,t,n,s){const i=we(t),o=we(e.shape),a=o/i,c=FS({inputs:{x:e},attrs:{shape:[a,i]},backend:s}),h=OX(c,e.dtype,"max",s),d=FS({inputs:{x:h},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(c),s.disposeIntermediateTensorInfo(h),d}class FX{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let o=0;o<n.length;o++)n[o]=e[t[o]];this.outputShape=n,this.rank=n.length;const s=Et(this.rank),i=_X(t);this.userCode=`
     void main() {
       ${s} resRC = getOutputCoords();
       setOutput(getA(${i}));
@@ -3795,7 +3795,7 @@ return a / b;`,LX=`
       }
       setOutput(result);
     }
-    `}}function AC(e,t,n){const s=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new WX(e.shape,t):new FX(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}const $X={kernelName:xl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{reductionIndices:i,keepDims:o}=t,a=n,c=s.shape.length,h=gt(i,s.shape);let d=h;const m=kn(d,c),y=m!=null,b=a.shouldExecuteOnCPU([s]);let w=s;if(y){if(b){const E=a.texData.get(w.dataId),D=E.values,F=new Array(c);for(let $=0;$<F.length;$++)F[$]=s.shape[m[$]];const _=D0(D,s.shape,s.dtype,m,F);w=a.makeTensorInfo(F,s.dtype);const B=a.texData.get(w.dataId);B.values=_}else w=AC(s,m,a);d=ws(d.length,c)}ts("max",d,c);const[L,T]=Rn(w.shape,d);let A=L;o&&(A=On(L,h));let N;if(b){const E=a.texData.get(w.dataId),D=E.values,F=fK(D,we(T),A,s.dtype);N=a.makeTensorInfo(A,s.dtype);const _=a.texData.get(N.dataId);_.values=F}else N=kX(w,T,A,a);return y&&a.disposeIntermediateTensorInfo(w),N}};function UX(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;ou(i,"maxPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:h}=s,d=1;k(sn(a,d),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${d}'`);const m=Fn(i.shape,o,a,d,c,h);if(m.filterWidth===1&&m.filterHeight===1&&ot(m.inShape,m.outShape))return kS({inputs:{x:i},backend:n});const y=new cu(m,"max",!1);return n.runWebGLProgram(y,[i],i.dtype)}const BX={kernelName:Tl,backendName:"webgl",kernelFunc:UX};function MX(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o,output:a}=t,c=o;ou([o,a],"maxPoolBackprop");const{filterSize:h,strides:d,pad:m,dimRoundingMode:y}=s,b=Fn(c.shape,h,d,1,m,y),w=!0,L=new cu(b,"max",w),T=n.runWebGLProgram(L,[c],c.dtype),A=new F8(b),N=n.runWebGLProgram(A,[i,T],c.dtype);return n.disposeIntermediateTensorInfo(T),N}const PX={kernelName:Qu,backendName:"webgl",kernelFunc:MX};function zX(e,t,n,s){let i=new cu(n,"max",!1);const o=s.runWebGLProgram(i,[e],"float32");i=new cu(n,"max",!0,!0,t);const a=s.runWebGLProgram(i,[e],"float32");return[o,a]}const GX={kernelName:ed,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{filterSize:i,strides:o,pad:a,includeBatchInIndex:c}=t,h=n;k(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);const d=[1,1];k(sn(o,d),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${d}'`);const m=Fn(s.shape,i,o,d,a),[y,b]=zX(s,c,m,h);return[y,b]}};const VX={kernelName:iy,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Ya("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c}=n,h=t,d=h.readSync(s.dataId),m=h.readSync(i.dataId),y=o,b=a,w=c;return up(d,m,y,b,w)}};const YX=dp,HX={kernelName:nd,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Ya("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,padToMaxOutputSize:h}=n,d=t,m=d.readSync(s.dataId),y=d.readSync(i.dataId),{selectedIndices:b,validOutputs:w}=YX(m,y,o,a,c,h);return[b,w]}};const qX=pp,jX={kernelName:sd,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{Ya("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,softNmsSigma:h}=n,d=t,m=d.readSync(s.dataId),y=d.readSync(i.dataId),b=o,w=a,L=c,T=h,{selectedIndices:A,selectedScores:N}=qX(m,y,b,w,L,T);return[A,N]}};class KX{constructor(e,t,n,s){this.variableNames=["Image"],this.outputShape=[];const i=e[1],o=e[2],a=Math.sin(t).toFixed(3),c=Math.cos(t).toFixed(3);this.outputShape=e;const[h,d]=nw(s,i,o),m=h.toFixed(3),y=d.toFixed(3);let b="";typeof n=="number"?b=`float outputValue = ${n.toFixed(2)};`:b=`
+    `}}function AC(e,t,n){const s=C().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new WX(e.shape,t):new FX(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}const $X={kernelName:xl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{reductionIndices:i,keepDims:o}=t,a=n,c=s.shape.length,h=gt(i,s.shape);let d=h;const m=kn(d,c),y=m!=null,b=a.shouldExecuteOnCPU([s]);let w=s;if(y){if(b){const E=a.texData.get(w.dataId),D=E.values,F=new Array(c);for(let $=0;$<F.length;$++)F[$]=s.shape[m[$]];const _=D0(D,s.shape,s.dtype,m,F);w=a.makeTensorInfo(F,s.dtype);const B=a.texData.get(w.dataId);B.values=_}else w=AC(s,m,a);d=ws(d.length,c)}ts("max",d,c);const[L,T]=Rn(w.shape,d);let A=L;o&&(A=On(L,h));let N;if(b){const E=a.texData.get(w.dataId),D=E.values,F=fK(D,we(T),A,s.dtype);N=a.makeTensorInfo(A,s.dtype);const _=a.texData.get(N.dataId);_.values=F}else N=kX(w,T,A,a);return y&&a.disposeIntermediateTensorInfo(w),N}};function UX(e){const{inputs:t,backend:n,attrs:s}=e,{x:i}=t;ou(i,"maxPool");const{filterSize:o,strides:a,pad:c,dimRoundingMode:h}=s,d=1;k(rn(a,d),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${d}'`);const m=Fn(i.shape,o,a,d,c,h);if(m.filterWidth===1&&m.filterHeight===1&&ot(m.inShape,m.outShape))return kS({inputs:{x:i},backend:n});const y=new cu(m,"max",!1);return n.runWebGLProgram(y,[i],i.dtype)}const BX={kernelName:Tl,backendName:"webgl",kernelFunc:UX};function MX(e){const{inputs:t,backend:n,attrs:s}=e,{dy:i,input:o,output:a}=t,c=o;ou([o,a],"maxPoolBackprop");const{filterSize:h,strides:d,pad:m,dimRoundingMode:y}=s,b=Fn(c.shape,h,d,1,m,y),w=!0,L=new cu(b,"max",w),T=n.runWebGLProgram(L,[c],c.dtype),A=new F8(b),N=n.runWebGLProgram(A,[i,T],c.dtype);return n.disposeIntermediateTensorInfo(T),N}const PX={kernelName:Qu,backendName:"webgl",kernelFunc:MX};function zX(e,t,n,s){let i=new cu(n,"max",!1);const o=s.runWebGLProgram(i,[e],"float32");i=new cu(n,"max",!0,!0,t);const a=s.runWebGLProgram(i,[e],"float32");return[o,a]}const GX={kernelName:ed,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{filterSize:i,strides:o,pad:a,includeBatchInIndex:c}=t,h=n;k(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);const d=[1,1];k(rn(o,d),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${d}'`);const m=Fn(s.shape,i,o,d,a),[y,b]=zX(s,c,m,h);return[y,b]}};const VX={kernelName:iy,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{qa("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c}=n,h=t,d=h.readSync(s.dataId),m=h.readSync(i.dataId),y=o,b=a,w=c;return up(d,m,y,b,w)}};const YX=dp,HX={kernelName:nd,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{qa("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,padToMaxOutputSize:h}=n,d=t,m=d.readSync(s.dataId),y=d.readSync(i.dataId),{selectedIndices:b,validOutputs:w}=YX(m,y,o,a,c,h);return[b,w]}};const qX=pp,jX={kernelName:sd,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{qa("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes:s,scores:i}=e,{maxOutputSize:o,iouThreshold:a,scoreThreshold:c,softNmsSigma:h}=n,d=t,m=d.readSync(s.dataId),y=d.readSync(i.dataId),b=o,w=a,L=c,T=h,{selectedIndices:A,selectedScores:N}=qX(m,y,b,w,L,T);return[A,N]}};class KX{constructor(e,t,n,s){this.variableNames=["Image"],this.outputShape=[];const i=e[1],o=e[2],a=Math.sin(t).toFixed(3),c=Math.cos(t).toFixed(3);this.outputShape=e;const[h,d]=nw(s,i,o),m=h.toFixed(3),y=d.toFixed(3);let b="";typeof n=="number"?b=`float outputValue = ${n.toFixed(2)};`:b=`
         vec3 fill = vec3(${n.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         void main() {
@@ -3814,12 +3814,12 @@ return a / b;`,LX=`
         }
     `}}const XX={kernelName:hd,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{image:s}=e,{radians:i,fillValue:o,center:a}=t,c=n,h=new KX(s.shape,i,o,a),d=c.runWebGLProgram(h,[s],s.dtype);return d}};const JX=TC+`
   return sin(x);
-`,ZX=ym(JX),QX={kernelName:ba,backendName:"webgl",kernelFunc:ZX};const e7="return x * x;",t7=ym(e7),n7={kernelName:ad,backendName:"webgl",kernelFunc:t7};const vC="return (a - b) * (a - b);",s7=DS(vC,vC),i7={kernelName:wa,backendName:"webgl",kernelFunc:s7};const r7="return tan(x);",o7=ym(r7),a7={kernelName:La,backendName:"webgl",kernelFunc:o7};const c7={kernelName:Ul,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:s}=e,{perm:i}=t,o=n,a=s.shape.length,c=new Array(a);for(let d=0;d<c.length;d++)c[d]=s.shape[i[d]];let h;if(o.shouldExecuteOnCPU([s])){const d=o.texData.get(s.dataId),m=d.values,y=D0(m,s.shape,s.dtype,i,c);h=o.makeTensorInfo(c,s.dtype);const b=o.texData.get(h.dataId);b.values=y}else h=AC(s,i,o);return h}};function l7(e){const{inputs:t,attrs:n,backend:s}=e,{axis:i}=n,{x:o}=t;ou(o,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");const a=s.readSync(o.dataId),{outputValues:c,outputShape:h,indices:d}=LK(a,i,o.shape,o.dtype);return[s.makeTensorInfo(h,o.dtype,c),s.makeTensorInfo([d.length],"int32",d)]}const h7={kernelName:cd,backendName:"webgl",kernelFunc:l7};const u7=[oX,lX,uX,fX,bX,IX,TX,NX,aX,$X,BX,PX,GX,VX,HX,jX,DX,XX,QX,n7,i7,a7,c7,h7];for(const e of u7)dd(e);const d7="2.6.0";const 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At({x:r,y:l,width:u,height:p})}toSquare(){let{x:r,y:l,width:u,height:p}=this;const g=Math.abs(u-p);return u<p&&(r-=g/2,u+=g),p<u&&(l-=g/2,p+=g),new At({x:r,y:l,width:u,height:p})}rescale(r){const l=Xm(r)?r.width:r,u=Xm(r)?r.height:r;return new At({x:this.x*l,y:this.y*u,width:this.width*l,height:this.height*u})}pad(r,l){let[u,p,g,f]=[this.x-r/2,this.y-l/2,this.width+r,this.height+l];return new At({x:u,y:p,width:g,height:f})}clipAtImageBorders(r,l){const{x:u,y:p,right:g,bottom:f}=this,I=Math.max(u,0),S=Math.max(p,0),x=g-I,v=f-S,O=Math.min(x,r-I),C=Math.min(v,l-S);return new At({x:I,y:S,width:O,height:C}).floor()}shift(r,l){const{width:u,height:p}=this,g=this.x+r,f=this.y+l;return new At({x:g,y:f,width:u,height:p})}padAtBorders(r,l){const u=this.width+1,p=this.height+1;let g=1,f=1,I=u,S=p,x=this.left,v=this.top,O=this.right,C=this.bottom;return O>l&&(I=-O+l+u,O=l),C>r&&(S=-C+r+p,C=r),x<1&&(S=2-x,x=1),v<1&&(S=2-v,v=1),{dy:f,edy:S,dx:g,edx:I,y:v,ey:C,x,ex:O,w:u,h:p}}calibrate(r){return new At({left:this.left+r.left*this.width,top:this.top+r.top*this.height,right:this.right+r.right*this.width,bottom:this.bottom+r.bottom*this.height}).toSquare().round()}}class Cc extends At{constructor(r,l,u,p,g=!1){super({left:r,top:l,right:u,bottom:p},g)}}class Qo{constructor(r,l,u,p,g){this._imageDims=new Zn(g.width,g.height),this._score=r,this._classScore=l,this._className=u,this._box=new At(p).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new At(this._box).rescale(this.imageDims.reverse())}forSize(r,l){return new Qo(this.score,this.classScore,this.className,this.relativeBox,{width:r,height:l})}}class Wt extends Qo{constructor(r,l,u){super(r,r,"",l,u)}forSize(r,l){const{score:u,relativeBox:p,imageDims:g}=super.forSize(r,l);return new Wt(u,p,g)}}function Om(r,l,u=!0){const p=Math.max(0,Math.min(r.right,l.right)-Math.max(r.left,l.left)),g=Math.max(0,Math.min(r.bottom,l.bottom)-Math.max(r.top,l.top)),f=p*g;return u?f/(r.area+l.area-f):f/Math.min(r.area,l.area)}function Rm(r){const l=r.map(S=>S.x),u=r.map(S=>S.y),p=l.reduce((S,x)=>x<S?x:S,Infinity),g=u.reduce((S,x)=>x<S?x:S,Infinity),f=l.reduce((S,x)=>S<x?x:S,0),I=u.reduce((S,x)=>S<x?x:S,0);return new Cc(p,g,f,I)}function Cm(r,l,u,p=!0){let g=l.map((I,S)=>({score:I,boxIndex:S})).sort((I,S)=>I.score-S.score).map(I=>I.boxIndex);const f=[];for(;g.length>0;){const I=g.pop();f.push(I);const S=g,x=[];for(let v=0;v<S.length;v++){const O=S[v],C=r[I],U=r[O];x.push(Om(C,U,p))}g=g.filter((v,O)=>x[O]<=u)}return f}const Mi=Xe(Je());function Ys(r,l){return Mi.tidy(()=>{const[u,p,g]=l,f=Mi.fill([...r.shape.slice(0,3),1],u),I=Mi.fill([...r.shape.slice(0,3),1],p),S=Mi.fill([...r.shape.slice(0,3),1],g),x=Mi.concat([f,I,S],3);return Mi.sub(r,x)})}const io=Xe(Je());function Nm(r,l=!1){return io.tidy(()=>{const[u,p]=r.shape.slice(1);if(u===p)return r;const g=Math.abs(u-p),f=Math.round(g*(l?.5:1)),I=u>p?2:1,S=U=>{const G=r.shape.slice();return G[I]=U,io.fill(G,0)},x=S(f),v=g-x.shape[I],O=l&&v?S(v):null,C=[O,r,x].filter(U=>!!U).map(U=>io.cast(U,"float32"));return io.concat(C,I)})}function MC(r){const l=r.slice();for(let u=l.length-1;u>0;u--){const p=Math.floor(Math.random()*(u+1)),g=l[u];l[u]=l[p],l[p]=g}return l}function Sc(r){return 1/(1+Math.exp(-r))}function PC(r){return Math.log(r/(1-r))}class vc extends At{constructor(r,l,u,p,g=!1){super({x:r,y:l,width:u,height:p},g)}}const v7=.5,N7=.43,C7=.45;class Ns{constructor(r,l,u=new qe(0,0)){const{width:p,height:g}=l;this._imgDims=new Zn(p,g),this._shift=u,this._positions=r.map(f=>f.mul(new qe(p,g)).add(u))}get shift(){return new qe(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(r=>r.sub(this._shift).div(new qe(this.imageWidth,this.imageHeight)))}forSize(r,l){return new this.constructor(this.relativePositions,{width:r,height:l})}shiftBy(r,l){return new this.constructor(this.relativePositions,this._imgDims,new qe(r,l))}shiftByPoint(r){return this.shiftBy(r.x,r.y)}align(r,l={}){if(r){const g=r instanceof Wt?r.box.floor():new At(r);return this.shiftBy(g.x,g.y).align(null,l)}const{useDlibAlignment:u,minBoxPadding:p}=Object.assign({},{useDlibAlignment:!1,minBoxPadding:.2},l);return u?this.alignDlib():this.alignMinBbox(p)}alignDlib(){const r=this.getRefPointsForAlignment(),[l,u,p]=r,g=O=>p.sub(O).magnitude(),f=(g(l)+g(u))/2,I=Math.floor(f/C7),S=na(r),x=Math.floor(Math.max(0,S.x-v7*I)),v=Math.floor(Math.max(0,S.y-N7*I));return new vc(x,v,Math.min(I,this.imageWidth+x),Math.min(I,this.imageHeight+v))}alignMinBbox(r){const l=Rm(this.positions);return l.pad(l.width*r,l.height*r)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}}class f2 extends Ns{getRefPointsForAlignment(){const r=this.positions;return[r[0],r[1],na([r[3],r[4]])]}}class Nc extends Ns{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(na)}}class Lu{constructor(r,l){this._label=r,this._distance=l}get label(){return this._label}get distance(){return this._distance}toString(r=!0){return`${this.label}${r?` (${ta(this.distance)})`:""}`}}class wu extends At{static assertIsValidLabeledBox(r,l){if(At.assertIsValidBox(r,l),!fi(r.label))throw new Error(`${l} - expected property label (${r.label}) to be a number`)}constructor(r,l){super(r);this._label=l}get label(){return this._label}}class so{constructor(r,l){if(!(typeof r=="string"))throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(l)||l.some(u=>!(u instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=r,this._descriptors=l}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(r=>Array.from(r))}}static fromJSON(r){const l=r.descriptors.map(u=>new Float32Array(u));return new so(r.label,l)}}class m2 extends wu{static assertIsValidPredictedBox(r,l){if(wu.assertIsValidLabeledBox(r,l),!Rc(r.score)||!Rc(r.classScore))throw new Error(`${l} - expected properties score (${r.score}) and (${r.classScore}) to be a number between [0, 1]`)}constructor(r,l,u,p){super(r,l);this._score=u,this._classScore=p}get score(){return this._score}get classScore(){return this._classScore}}function mi(r){return r.detection instanceof Wt}function Zr(r,l){const u={detection:l};return Object.assign({},r,u)}function qS(){const r=window.fetch||function(){throw new Error("fetch - missing fetch implementation for browser environment")},l=function(){throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:r,readFile:l}}function Jm(r){let l="";if(!r)try{r=require("fs")}catch(p){l=p.toString()}const u=r?function(p){return new Promise((g,f)=>{r.readFile(p,function(I,S){return I?f(I):g(S)})})}:function(){throw new Error(`readFile - failed to require fs in nodejs environment with error: ${l}`)};return{readFile:u}}function jS(){const r=global.Canvas||global.HTMLCanvasElement,l=global.Image||global.HTMLImageElement,u=function(){if(r)return new r;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},p=function(){if(l)return new l;throw new Error("createImageElement - missing Image implementation for nodejs environment")},g=global.fetch||function(){throw new Error("fetch - missing fetch implementation for nodejs 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isBrowser()");const{Canvas:l=gn.Canvas,Image:u=gn.Image}=r;gn.Canvas=l,gn.Image=u,gn.createCanvasElement=r.createCanvasElement||(()=>new l),gn.createImageElement=r.createImageElement||(()=>new u),gn.ImageData=r.ImageData||gn.ImageData,gn.Video=r.Video||gn.Video,gn.fetch=r.fetch||gn.fetch,gn.readFile=r.readFile||gn.readFile}const mt={getEnv:R7,setEnv:JS,initialize:ZS,createBrowserEnv:qS,createFileSystem:Jm,createNodejsEnv:jS,monkeyPatch:O7,isBrowser:KS,isNodejs:XS.isNodejs};ZS();function eo(r){return!mt.isNodejs()&&typeof r=="string"?document.getElementById(r):r}function Un(r){const{Canvas:l,CanvasRenderingContext2D:u}=mt.getEnv();if(r instanceof u)return r;const p=eo(r);if(!(p instanceof l))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");const g=p.getContext("2d");if(!g)throw new Error("resolveContext2d - canvas 2d context is null");return g}var Pi;(function(r){r.TOP_LEFT="TOP_LEFT",r.TOP_RIGHT="TOP_RIGHT",r.BOTTOM_LEFT="BOTTOM_LEFT",r.BOTTOM_RIGHT="BOTTOM_RIGHT"})(Pi||(Pi={}));class Zm{constructor(r={}){const{anchorPosition:l,backgroundColor:u,fontColor:p,fontSize:g,fontStyle:f,padding:I}=r;this.anchorPosition=l||Pi.TOP_LEFT,this.backgroundColor=u||"rgba(0, 0, 0, 0.5)",this.fontColor=p||"rgba(255, 255, 255, 1)",this.fontSize=g||14,this.fontStyle=f||"Georgia",this.padding=I||4}}class Oc{constructor(r,l,u={}){this.text=typeof r=="string"?[r]:r instanceof Oc?r.text:r,this.anchor=l,this.options=new Zm(u)}measureWidth(r){const{padding:l}=this.options;return this.text.map(u=>r.measureText(u).width).reduce((u,p)=>u<p?p:u,0)+2*l}measureHeight(){const{fontSize:r,padding:l}=this.options;return this.text.length*r+2*l}getUpperLeft(r,l){const{anchorPosition:u}=this.options,p=u===Pi.BOTTOM_RIGHT||u===Pi.TOP_RIGHT,g=u===Pi.BOTTOM_LEFT||u===Pi.BOTTOM_RIGHT,f=this.measureWidth(r),I=this.measureHeight(),S=p?this.anchor.x-f:this.anchor.x,x=g?this.anchor.y-I:this.anchor.y;if(l){const{width:v,height:O}=l,C=Math.max(Math.min(S,v-f),0),U=Math.max(Math.min(x,O-I),0);return{x:C,y:U}}return{x:S,y:x}}draw(r){const l=eo(r),u=Un(l),{backgroundColor:p,fontColor:g,fontSize:f,fontStyle:I,padding:S}=this.options;u.font=`${f}px ${I}`;const x=this.measureWidth(u),v=this.measureHeight();u.fillStyle=p;const O=this.getUpperLeft(u,l);u.fillRect(O.x,O.y,x,v),u.fillStyle=g,this.text.forEach((C,U)=>{const G=S+O.x,ne=S+O.y+(U+1)*f;u.fillText(C,G,ne)})}}class b2{constructor(r={}){const{boxColor:l,lineWidth:u,label:p,drawLabelOptions:g}=r;this.boxColor=l||"rgba(0, 0, 255, 1)",this.lineWidth=u||2,this.label=p;const f={anchorPosition:Pi.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Zm(Object.assign({},f,g))}}class QS{constructor(r,l={}){this.box=new At(r),this.options=new b2(l)}draw(r){const l=Un(r),{boxColor:u,lineWidth:p}=this.options,{x:g,y:f,width:I,height:S}=this.box;l.strokeStyle=u,l.lineWidth=p,l.strokeRect(g,f,I,S);const{label:x}=this.options;x&&new Oc([x],{x:g-p/2,y:f},this.options.drawLabelOptions).draw(r)}}function E7(r,l){const u=Array.isArray(l)?l:[l];u.forEach(p=>{const g=p instanceof Wt?p.score:mi(p)?p.detection.score:void 0,f=p instanceof Wt?p.box:mi(p)?p.detection.box:new At(p),I=g?`${ta(g)}`:void 0;new QS(f,{label:I}).draw(r)})}function Tc(r){const{Image:l,Video:u}=mt.getEnv();return r instanceof l&&r.complete||r instanceof u&&r.readyState>=3}function qm(r){return new Promise((l,u)=>{if(r instanceof mt.getEnv().Canvas||Tc(r))return l(null);function p(f){if(!f.currentTarget)return;f.currentTarget.removeEventListener("load",p),f.currentTarget.removeEventListener("error",g),l(f)}function 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se("logicalAnd")}logicalOr(r,l){return se("logicalOr")}where(r){return se("where")}select(r,l,u){return se("select")}topk(r,l,u){return se("topk")}min(r,l){return se("min")}minimum(r,l){return se("minimum")}mod(r,l){return se("mod")}max(r,l){return se("max")}maximum(r,l){return se("maximum")}all(r,l){return se("all")}any(r,l){return se("any")}squaredDifference(r,l){return se("squaredDifference")}ceil(r){return se("ceil")}floor(r){return se("floor")}round(r){return se("round")}sign(r){return se("sign")}isNaN(r){return se("isNaN")}isInf(r){return se("isInf")}isFinite(r){return se("isFinite")}pow(r,l){return se("pow")}exp(r){return se("exp")}expm1(r){return se("expm1")}softmax(r,l){return se("softmax")}log(r){return se("log")}log1p(r){return se("log1p")}sqrt(r){return se("sqrt")}rsqrt(r){return se("rsqrt")}square(r){return se("square")}reciprocal(r){return se("reciprocal")}relu(r){return se("relu")}relu6(r){return se("relu6")}prelu(r,l){return se("prelu")}elu(r){return se("elu")}eluDer(r,l){return se("eluDer")}selu(r){return se("selu")}int(r){return se("int")}clip(r,l,u){return se("clip")}abs(r){return se("abs")}complexAbs(r){return se("complexAbs")}sigmoid(r){return se("sigmoid")}softplus(r){return se("softplus")}sin(r){return se("sin")}cos(r){return se("cos")}tan(r){return se("tan")}asin(r){return se("asin")}acos(r){return se("acos")}atan(r){return se("atan")}atan2(r,l){return se("atan2")}sinh(r){return se("sinh")}cosh(r){return se("cosh")}tanh(r){return se("tanh")}asinh(r){return se("asinh")}acosh(r){return se("acosh")}atanh(r){return se("atanh")}erf(r){return se("erf")}step(r,l){return se("step")}fusedConv2d({input:r,filter:l,convInfo:u,bias:p,activation:g,preluActivationWeights:f}){return se("fusedConv2d")}conv2d(r,l,u){return se("conv2d")}conv2dDerInput(r,l,u){return se("conv2dDerInput")}conv2dDerFilter(r,l,u){return se("conv2dDerFilter")}fusedDepthwiseConv2D({input:r,filter:l,convInfo:u,bias:p,activation:g,preluActivationWeights:f}){return se("fusedDepthwiseConv2D")}depthwiseConv2D(r,l,u){return se("depthwiseConv2D")}depthwiseConv2DDerInput(r,l,u){return se("depthwiseConv2DDerInput")}depthwiseConv2DDerFilter(r,l,u){return se("depthwiseConv2DDerFilter")}conv3d(r,l,u){return se("conv3d")}conv3dDerInput(r,l,u){return se("conv3dDerInput")}conv3dDerFilter(r,l,u){return se("conv3dDerFilter")}maxPool(r,l){return se("maxPool")}maxPoolBackprop(r,l,u,p){return se("maxPoolBackprop")}avgPool(r,l){return se("avgPool")}avgPoolBackprop(r,l,u){return se("avgPoolBackprop")}avgPool3d(r,l){return se("avgPool3d")}avgPool3dBackprop(r,l,u){return se("avgPool3dBackprop")}maxPool3d(r,l){return se("maxPool3d")}maxPool3dBackprop(r,l,u,p){return se("maxPool3dBackprop")}reshape(r,l){return se("reshape")}cast(r,l){return se("cast")}tile(r,l){return se("tile")}pad(r,l,u){return se("pad")}transpose(r,l){return se("transpose")}gather(r,l,u){return se("gather")}gatherND(r,l){return se("gatherND")}scatterND(r,l,u){return 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At({x:r,y:l,width:u,height:p})}toSquare(){let{x:r,y:l,width:u,height:p}=this;const g=Math.abs(u-p);return u<p&&(r-=g/2,u+=g),p<u&&(l-=g/2,p+=g),new At({x:r,y:l,width:u,height:p})}rescale(r){const l=Xm(r)?r.width:r,u=Xm(r)?r.height:r;return new At({x:this.x*l,y:this.y*u,width:this.width*l,height:this.height*u})}pad(r,l){let[u,p,g,f]=[this.x-r/2,this.y-l/2,this.width+r,this.height+l];return new At({x:u,y:p,width:g,height:f})}clipAtImageBorders(r,l){const{x:u,y:p,right:g,bottom:f}=this,I=Math.max(u,0),S=Math.max(p,0),x=g-I,v=f-S,O=Math.min(x,r-I),C=Math.min(v,l-S);return new At({x:I,y:S,width:O,height:C}).floor()}shift(r,l){const{width:u,height:p}=this,g=this.x+r,f=this.y+l;return new At({x:g,y:f,width:u,height:p})}padAtBorders(r,l){const u=this.width+1,p=this.height+1;let g=1,f=1,I=u,S=p,x=this.left,v=this.top,O=this.right,C=this.bottom;return O>l&&(I=-O+l+u,O=l),C>r&&(S=-C+r+p,C=r),x<1&&(S=2-x,x=1),v<1&&(S=2-v,v=1),{dy:f,edy:S,dx:g,edx:I,y:v,ey:C,x,ex:O,w:u,h:p}}calibrate(r){return new At({left:this.left+r.left*this.width,top:this.top+r.top*this.height,right:this.right+r.right*this.width,bottom:this.bottom+r.bottom*this.height}).toSquare().round()}}class Oc extends At{constructor(r,l,u,p,g=!1){super({left:r,top:l,right:u,bottom:p},g)}}class ta{constructor(r,l,u,p,g){this._imageDims=new Zn(g.width,g.height),this._score=r,this._classScore=l,this._className=u,this._box=new At(p).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new At(this._box).rescale(this.imageDims.reverse())}forSize(r,l){return new ta(this.score,this.classScore,this.className,this.relativeBox,{width:r,height:l})}}class Wt extends ta{constructor(r,l,u){super(r,r,"",l,u)}forSize(r,l){const{score:u,relativeBox:p,imageDims:g}=super.forSize(r,l);return new Wt(u,p,g)}}function Om(r,l,u=!0){const p=Math.max(0,Math.min(r.right,l.right)-Math.max(r.left,l.left)),g=Math.max(0,Math.min(r.bottom,l.bottom)-Math.max(r.top,l.top)),f=p*g;return u?f/(r.area+l.area-f):f/Math.min(r.area,l.area)}function Rm(r){const l=r.map(S=>S.x),u=r.map(S=>S.y),p=l.reduce((S,x)=>x<S?x:S,Infinity),g=u.reduce((S,x)=>x<S?x:S,Infinity),f=l.reduce((S,x)=>S<x?x:S,0),I=u.reduce((S,x)=>S<x?x:S,0);return new Oc(p,g,f,I)}function Cm(r,l,u,p=!0){let g=l.map((I,S)=>({score:I,boxIndex:S})).sort((I,S)=>I.score-S.score).map(I=>I.boxIndex);const f=[];for(;g.length>0;){const I=g.pop();f.push(I);const S=g,x=[];for(let v=0;v<S.length;v++){const O=S[v],C=r[I],U=r[O];x.push(Om(C,U,p))}g=g.filter((v,O)=>x[O]<=u)}return f}const Mi=Xe(Je());function Ys(r,l){return Mi.tidy(()=>{const[u,p,g]=l,f=Mi.fill([...r.shape.slice(0,3),1],u,"float32"),I=Mi.fill([...r.shape.slice(0,3),1],p,"float32"),S=Mi.fill([...r.shape.slice(0,3),1],g,"float32"),x=Mi.concat([f,I,S],3);return Mi.sub(r,x)})}const io=Xe(Je());function Nm(r,l=!1){return io.tidy(()=>{const[u,p]=r.shape.slice(1);if(u===p)return r;const g=Math.abs(u-p),f=Math.round(g*(l?.5:1)),I=u>p?2:1,S=U=>{const G=r.shape.slice();return G[I]=U,io.fill(G,0,"float32")},x=S(f),v=g-x.shape[I],O=l&&v?S(v):null,C=[O,r,x].filter(U=>!!U).map(U=>io.cast(U,"float32"));return io.concat(C,I)})}function MC(r){const l=r.slice();for(let u=l.length-1;u>0;u--){const p=Math.floor(Math.random()*(u+1)),g=l[u];l[u]=l[p],l[p]=g}return l}function xc(r){return 1/(1+Math.exp(-r))}function PC(r){return Math.log(r/(1-r))}class Cc extends At{constructor(r,l,u,p,g=!1){super({x:r,y:l,width:u,height:p},g)}}const v7=.5,N7=.43,C7=.45;class Ns{constructor(r,l,u=new qe(0,0)){const{width:p,height:g}=l;this._imgDims=new Zn(p,g),this._shift=u,this._positions=r.map(f=>f.mul(new qe(p,g)).add(u))}get shift(){return new qe(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(r=>r.sub(this._shift).div(new qe(this.imageWidth,this.imageHeight)))}forSize(r,l){return new this.constructor(this.relativePositions,{width:r,height:l})}shiftBy(r,l){return new this.constructor(this.relativePositions,this._imgDims,new qe(r,l))}shiftByPoint(r){return this.shiftBy(r.x,r.y)}align(r,l={}){if(r){const g=r instanceof Wt?r.box.floor():new At(r);return this.shiftBy(g.x,g.y).align(null,l)}const{useDlibAlignment:u,minBoxPadding:p}=Object.assign({},{useDlibAlignment:!1,minBoxPadding:.2},l);return u?this.alignDlib():this.alignMinBbox(p)}alignDlib(){const r=this.getRefPointsForAlignment(),[l,u,p]=r,g=O=>p.sub(O).magnitude(),f=(g(l)+g(u))/2,I=Math.floor(f/C7),S=ia(r),x=Math.floor(Math.max(0,S.x-v7*I)),v=Math.floor(Math.max(0,S.y-N7*I));return new Cc(x,v,Math.min(I,this.imageWidth+x),Math.min(I,this.imageHeight+v))}alignMinBbox(r){const l=Rm(this.positions);return l.pad(l.width*r,l.height*r)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}}class f2 extends Ns{getRefPointsForAlignment(){const r=this.positions;return[r[0],r[1],ia([r[3],r[4]])]}}class Rc extends Ns{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(ia)}}class Lu{constructor(r,l){this._label=r,this._distance=l}get label(){return this._label}get distance(){return this._distance}toString(r=!0){return`${this.label}${r?` (${sa(this.distance)})`:""}`}}class wu extends At{static assertIsValidLabeledBox(r,l){if(At.assertIsValidBox(r,l),!fi(r.label))throw new Error(`${l} - expected property label (${r.label}) to be a number`)}constructor(r,l){super(r);this._label=l}get label(){return this._label}}class so{constructor(r,l){if(!(typeof r=="string"))throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(l)||l.some(u=>!(u instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=r,this._descriptors=l}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(r=>Array.from(r))}}static fromJSON(r){const l=r.descriptors.map(u=>new Float32Array(u));return new so(r.label,l)}}class m2 extends wu{static assertIsValidPredictedBox(r,l){if(wu.assertIsValidLabeledBox(r,l),!Ec(r.score)||!Ec(r.classScore))throw new Error(`${l} - expected properties score (${r.score}) and (${r.classScore}) to be a number between [0, 1]`)}constructor(r,l,u,p){super(r,l);this._score=u,this._classScore=p}get score(){return this._score}get classScore(){return this._classScore}}function mi(r){return r.detection instanceof Wt}function Zr(r,l){const u={detection:l};return Object.assign({},r,u)}function qS(){const r=window.fetch||function(){throw new Error("fetch - missing fetch implementation for browser environment")},l=function(){throw new Error("readFile - filesystem not available for browser environment")};return{Canvas:HTMLCanvasElement,CanvasRenderingContext2D,Image:HTMLImageElement,ImageData,Video:HTMLVideoElement,createCanvasElement:()=>document.createElement("canvas"),createImageElement:()=>document.createElement("img"),fetch:r,readFile:l}}function Jm(r){let l="";if(!r)try{r=require("fs")}catch(p){l=p.toString()}const u=r?function(p){return new Promise((g,f)=>{r.readFile(p,function(I,S){return I?f(I):g(S)})})}:function(){throw new Error(`readFile - failed to require fs in nodejs environment with error: ${l}`)};return{readFile:u}}function jS(){const r=global.Canvas||global.HTMLCanvasElement,l=global.Image||global.HTMLImageElement,u=function(){if(r)return new r;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},p=function(){if(l)return new l;throw new Error("createImageElement - missing Image implementation for nodejs environment")},g=global.fetch||function(){throw new Error("fetch - missing fetch implementation for nodejs environment")},f=Jm();return{Canvas:r||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:l||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement:u,createImageElement:p,fetch:g,...f}}function KS(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}const XS=Xe(DC());let gn;function R7(){if(!gn)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return gn}function JS(r){gn=r}function ZS(){if(KS())return JS(qS());if(XS.isNodejs())return JS(jS())}function O7(r){if(gn||ZS(),!gn)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");const{Canvas:l=gn.Canvas,Image:u=gn.Image}=r;gn.Canvas=l,gn.Image=u,gn.createCanvasElement=r.createCanvasElement||(()=>new l),gn.createImageElement=r.createImageElement||(()=>new u),gn.ImageData=r.ImageData||gn.ImageData,gn.Video=r.Video||gn.Video,gn.fetch=r.fetch||gn.fetch,gn.readFile=r.readFile||gn.readFile}const mt={getEnv:R7,setEnv:JS,initialize:ZS,createBrowserEnv:qS,createFileSystem:Jm,createNodejsEnv:jS,monkeyPatch:O7,isBrowser:KS,isNodejs:XS.isNodejs};ZS();function eo(r){return!mt.isNodejs()&&typeof r=="string"?document.getElementById(r):r}function Un(r){const{Canvas:l,CanvasRenderingContext2D:u}=mt.getEnv();if(r instanceof u)return r;const p=eo(r);if(!(p instanceof l))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");const g=p.getContext("2d");if(!g)throw new Error("resolveContext2d - canvas 2d context is null");return g}var Pi;(function(r){r.TOP_LEFT="TOP_LEFT",r.TOP_RIGHT="TOP_RIGHT",r.BOTTOM_LEFT="BOTTOM_LEFT",r.BOTTOM_RIGHT="BOTTOM_RIGHT"})(Pi||(Pi={}));class Zm{constructor(r={}){const{anchorPosition:l,backgroundColor:u,fontColor:p,fontSize:g,fontStyle:f,padding:I}=r;this.anchorPosition=l||Pi.TOP_LEFT,this.backgroundColor=u||"rgba(0, 0, 0, 0.5)",this.fontColor=p||"rgba(255, 255, 255, 1)",this.fontSize=g||14,this.fontStyle=f||"Georgia",this.padding=I||4}}class Dc{constructor(r,l,u={}){this.text=typeof r=="string"?[r]:r instanceof Dc?r.text:r,this.anchor=l,this.options=new Zm(u)}measureWidth(r){const{padding:l}=this.options;return this.text.map(u=>r.measureText(u).width).reduce((u,p)=>u<p?p:u,0)+2*l}measureHeight(){const{fontSize:r,padding:l}=this.options;return this.text.length*r+2*l}getUpperLeft(r,l){const{anchorPosition:u}=this.options,p=u===Pi.BOTTOM_RIGHT||u===Pi.TOP_RIGHT,g=u===Pi.BOTTOM_LEFT||u===Pi.BOTTOM_RIGHT,f=this.measureWidth(r),I=this.measureHeight(),S=p?this.anchor.x-f:this.anchor.x,x=g?this.anchor.y-I:this.anchor.y;if(l){const{width:v,height:O}=l,C=Math.max(Math.min(S,v-f),0),U=Math.max(Math.min(x,O-I),0);return{x:C,y:U}}return{x:S,y:x}}draw(r){const l=eo(r),u=Un(l),{backgroundColor:p,fontColor:g,fontSize:f,fontStyle:I,padding:S}=this.options;u.font=`${f}px ${I}`;const x=this.measureWidth(u),v=this.measureHeight();u.fillStyle=p;const O=this.getUpperLeft(u,l);u.fillRect(O.x,O.y,x,v),u.fillStyle=g,this.text.forEach((C,U)=>{const G=S+O.x,ne=S+O.y+(U+1)*f;u.fillText(C,G,ne)})}}class b2{constructor(r={}){const{boxColor:l,lineWidth:u,label:p,drawLabelOptions:g}=r;this.boxColor=l||"rgba(0, 0, 255, 1)",this.lineWidth=u||2,this.label=p;const f={anchorPosition:Pi.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Zm(Object.assign({},f,g))}}class QS{constructor(r,l={}){this.box=new At(r),this.options=new b2(l)}draw(r){const l=Un(r),{boxColor:u,lineWidth:p}=this.options,{x:g,y:f,width:I,height:S}=this.box;l.strokeStyle=u,l.lineWidth=p,l.strokeRect(g,f,I,S);const{label:x}=this.options;x&&new Dc([x],{x:g-p/2,y:f},this.options.drawLabelOptions).draw(r)}}function E7(r,l){const u=Array.isArray(l)?l:[l];u.forEach(p=>{const g=p instanceof Wt?p.score:mi(p)?p.detection.score:void 0,f=p instanceof Wt?p.box:mi(p)?p.detection.box:new At(p),I=g?`${sa(g)}`:void 0;new QS(f,{label:I}).draw(r)})}function vc(r){const{Image:l,Video:u}=mt.getEnv();return r instanceof l&&r.complete||r instanceof u&&r.readyState>=3}function qm(r){return new Promise((l,u)=>{if(r instanceof mt.getEnv().Canvas||vc(r))return l(null);function p(f){if(!f.currentTarget)return;f.currentTarget.removeEventListener("load",p),f.currentTarget.removeEventListener("error",g),l(f)}function g(f){if(!f.currentTarget)return;f.currentTarget.removeEventListener("load",p),f.currentTarget.removeEventListener("error",g),u(f)}r.addEventListener("load",p),r.addEventListener("error",g)})}function Hm(r){return new Promise((l,u)=>{if(!(r instanceof Blob))return u("bufferToImage - expected buf to be of type: Blob");const p=new FileReader;p.onload=()=>{if(typeof p.result!="string")return u("bufferToImage - expected reader.result to be a string, in onload");const g=mt.getEnv().createImageElement();g.onload=()=>l(g),g.onerror=u,g.src=p.result},p.onerror=u,p.readAsDataURL(r)})}function to(r){const{Image:l,Video:u}=mt.getEnv();return r instanceof l?new Zn(r.naturalWidth,r.naturalHeight):r instanceof u?new Zn(r.videoWidth,r.videoHeight):new Zn(r.width,r.height)}function ea({width:r,height:l}){const{createCanvasElement:u}=mt.getEnv(),p=u();return p.width=r,p.height=l,p}function Nc(r,l){const{ImageData:u}=mt.getEnv();if(!(r instanceof u)&&!vc(r))throw new Error("createCanvasFromMedia - media 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se("logicalAnd")}logicalOr(r,l){return se("logicalOr")}where(r){return se("where")}select(r,l,u){return se("select")}topk(r,l,u){return se("topk")}min(r,l){return se("min")}minimum(r,l){return se("minimum")}mod(r,l){return se("mod")}max(r,l){return se("max")}maximum(r,l){return se("maximum")}all(r,l){return se("all")}any(r,l){return se("any")}squaredDifference(r,l){return se("squaredDifference")}ceil(r){return se("ceil")}floor(r){return se("floor")}round(r){return se("round")}sign(r){return se("sign")}isNaN(r){return se("isNaN")}isInf(r){return se("isInf")}isFinite(r){return se("isFinite")}pow(r,l){return se("pow")}exp(r){return se("exp")}expm1(r){return se("expm1")}softmax(r,l){return se("softmax")}log(r){return se("log")}log1p(r){return se("log1p")}sqrt(r){return se("sqrt")}rsqrt(r){return se("rsqrt")}square(r){return se("square")}reciprocal(r){return se("reciprocal")}relu(r){return se("relu")}relu6(r){return se("relu6")}prelu(r,l){return se("prelu")}elu(r){return se("elu")}eluDer(r,l){return se("eluDer")}selu(r){return se("selu")}int(r){return se("int")}clip(r,l,u){return se("clip")}abs(r){return se("abs")}complexAbs(r){return se("complexAbs")}sigmoid(r){return se("sigmoid")}softplus(r){return se("softplus")}sin(r){return se("sin")}cos(r){return se("cos")}tan(r){return se("tan")}asin(r){return se("asin")}acos(r){return se("acos")}atan(r){return se("atan")}atan2(r,l){return se("atan2")}sinh(r){return se("sinh")}cosh(r){return se("cosh")}tanh(r){return se("tanh")}asinh(r){return se("asinh")}acosh(r){return se("acosh")}atanh(r){return se("atanh")}erf(r){return se("erf")}step(r,l){return se("step")}fusedConv2d({input:r,filter:l,convInfo:u,bias:p,activation:g,preluActivationWeights:f}){return se("fusedConv2d")}conv2d(r,l,u){return se("conv2d")}conv2dDerInput(r,l,u){return se("conv2dDerInput")}conv2dDerFilter(r,l,u){return se("conv2dDerFilter")}fusedDepthwiseConv2D({input:r,filter:l,convInfo:u,bias:p,activation:g,preluActivationWeights:f}){return se("fusedDepthwiseConv2D")}depthwiseConv2D(r,l,u){return se("depthwiseConv2D")}depthwiseConv2DDerInput(r,l,u){return se("depthwiseConv2DDerInput")}depthwiseConv2DDerFilter(r,l,u){return se("depthwiseConv2DDerFilter")}conv3d(r,l,u){return se("conv3d")}conv3dDerInput(r,l,u){return se("conv3dDerInput")}conv3dDerFilter(r,l,u){return se("conv3dDerFilter")}maxPool(r,l){return se("maxPool")}maxPoolBackprop(r,l,u,p){return se("maxPoolBackprop")}avgPool(r,l){return se("avgPool")}avgPoolBackprop(r,l,u){return se("avgPoolBackprop")}avgPool3d(r,l){return se("avgPool3d")}avgPool3dBackprop(r,l,u){return se("avgPool3dBackprop")}maxPool3d(r,l){return se("maxPool3d")}maxPool3dBackprop(r,l,u,p){return se("maxPool3dBackprop")}reshape(r,l){return se("reshape")}cast(r,l){return se("cast")}tile(r,l){return se("tile")}pad(r,l,u){return se("pad")}transpose(r,l){return se("transpose")}gather(r,l,u){return se("gather")}gatherND(r,l){return se("gatherND")}scatterND(r,l,u){return 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Buffer shape=${this.shape}`;throw new Error(g)}l++}let u=r[r.length-1];for(let p=0;p<r.length-1;++p)u+=this.strides[p]*r[p];return this.values[u]}locToIndex(r){if(this.rank===0)return 0;if(this.rank===1)return r[0];let l=r[r.length-1];for(let u=0;u<r.length-1;++u)l+=this.strides[u]*r[u];return l}indexToLoc(r){if(this.rank===0)return[];if(this.rank===1)return[r];const l=new Array(this.shape.length);for(let u=0;u<l.length-1;++u)l[u]=Math.floor(r/this.strides[u]),r-=l[u]*this.strides[u];return l[l.length-1]=r,l}get rank(){return this.shape.length}toTensor(){return zi().makeTensor(this.values,this.shape,this.dtype)}}let zi=null,Wc=null,H7=null;function hO(r){zi=r}function uO(r){Wc=r}function dO(r){H7=r}class xn{constructor(r,l,u,p){this.kept=!1,this.isDisposedInternal=!1,this.shape=r.slice(),this.dtype=l||"float32",this.size=qt(r),this.strides=xu(r),this.dataId=u,this.id=p,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const r=await this.data();return Wc.buffer(this.shape,this.dtype,r)}bufferSync(){return Wc.buffer(this.shape,this.dtype,this.dataSync())}async array(){const r=await this.data();return cI(this.shape,r)}arraySync(){return cI(this.shape,this.dataSync())}async data(){this.throwIfDisposed();const r=zi().read(this.dataId);if(this.dtype==="string"){const l=await r;try{return l.map(u=>hI(u))}catch(u){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return r}dataSync(){this.throwIfDisposed();const r=zi().readSync(this.dataId);if(this.dtype==="string")try{return r.map(l=>hI(l))}catch(l){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return r}async bytes(){this.throwIfDisposed();const r=await zi().read(this.dataId);return this.dtype==="string"?r:new Uint8Array(r.buffer)}dispose(){if(this.isDisposed)return;zi().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(r=!1){return Wc.print(this,r)}clone(){return this.throwIfDisposed(),Wc.clone(this)}toString(r=!1){const l=this.dataSync();return aO(l,this.shape,this.dtype,r)}cast(r){return this.throwIfDisposed(),Wc.cast(this,r)}variable(r=!0,l,u){return this.throwIfDisposed(),zi().makeVariable(this,r,l,u)}}Object.defineProperty(xn,Symbol.hasInstance,{value:r=>!!r&&r.data!=null&&r.dataSync!=null&&r.throwIfDisposed!=null});class ng extends xn{constructor(r,l,u,p){super(r.shape,r.dtype,r.dataId,p);this.trainable=l,this.name=u}assign(r){if(r.dtype!==this.dtype)throw new Error(`dtype of the new value (${r.dtype}) and previous value (${this.dtype}) must match`);if(!sa(r.shape,this.shape))throw new Error(`shape of the new value (${r.shape}) and previous value (${this.shape}) must match`);zi().disposeTensor(this),this.dataId=r.dataId,zi().incRef(this,null)}dispose(){zi().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(ng,Symbol.hasInstance,{value:r=>r instanceof xn&&r.assign!=null&&r.assign instanceof Function});var pO;(function(r){r.R0="R0",r.R1="R1",r.R2="R2",r.R3="R3",r.R4="R4",r.R5="R5",r.R6="R6"})(pO||(pO={}));var dI;(function(r){r.float32="float32",r.int32="int32",r.bool="int32",r.complex64="complex64"})(dI||(dI={}));var pI;(function(r){r.float32="float32",r.int32="int32",r.bool="bool",r.complex64="complex64"})(pI||(pI={}));var mI;(function(r){r.float32="float32",r.int32="float32",r.bool="float32",r.complex64="complex64"})(mI||(mI={}));var fI;(function(r){r.float32="complex64",r.int32="complex64",r.bool="complex64",r.complex64="complex64"})(fI||(fI={}));const q7={float32:mI,int32:dI,bool:pI,complex64:fI};function mO(r,l){if(r==="string"||l==="string"){if(r==="string"&&l==="string")return"string";throw new Error(`Can not upcast ${r} with ${l}`)}return q7[r][l]}function ft(r,l){if(r.dtype===l.dtype)return[r,l];const u=mO(r.dtype,l.dtype);return[r.cast(u),l.cast(u)]}function sg(r){const l=[],u=new Set;return fO(r,l,u),l}function fO(r,l,u){if(r==null)return;if(r instanceof xn){l.push(r);return}if(!j7(r))return;const p=r;for(const g in p){const f=p[g];u.has(f)||(u.add(f),fO(f,l,u))}}function j7(r){return Array.isArray(r)||typeof r=="object"}class gO{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const r in this.registeredVariables)this.registeredVariables[r].dispose()}}class Nu{constructor(r){this.ENV=r,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new gO}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const r=this.getSortedBackends();for(let l=0;l<r.length;l++){const u=r[l],p=await this.initializeBackend(u).success;if(p){await this.setBackend(u);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){const{name:r,asyncInit:l}=this.initializeBackendsAndReturnBest();if(l)throw new Error(`The highest priority backend '${r}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(r)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(r){if(!(r in this.registry))if(r in this.registryFactory){const{asyncInit:l}=this.initializeBackend(r);if(l)return null}else return null;return this.registry[r]}findBackendFactory(r){return r in this.registryFactory?this.registryFactory[r].factory:null}registerBackend(r,l,u=1){return r in this.registryFactory?(console.warn(`${r} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[r]={factory:l,priority:u},!0)}async setBackend(r){if(this.registryFactory[r]==null)throw new Error(`Backend name '${r}' not found in registry`);if(this.backendName=r,this.registry[r]==null){this.backendInstance=null;const{success:l,asyncInit:u}=this.initializeBackend(r),p=u?await l:l;if(!p)return!1}return this.backendInstance=this.registry[r],this.setupRegisteredKernels(),this.profiler=new sO(this.backendInstance),!0}setupRegisteredKernels(){const r=oI(this.backendName);r.forEach(l=>{l.setupFunc!=null&&l.setupFunc(this.backendInstance)})}disposeRegisteredKernels(r){const l=oI(r);l.forEach(u=>{u.disposeFunc!=null&&u.disposeFunc(this.registry[r])})}initializeBackend(r){const l=this.registryFactory[r];if(l==null)throw new Error(`Cannot initialize backend ${r}, no registration found.`);try{const u=l.factory();if(u&&!(u instanceof w2)&&typeof u.then=="function"){const p=++this.pendingBackendInitId,g=u.then(f=>p<this.pendingBackendInitId?!1:(this.registry[r]=f,this.pendingBackendInit=null,!0)).catch(f=>(p<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${r} failed`),console.warn(f.stack||f.message)),!1));return this.pendingBackendInit=g,{success:g,asyncInit:!0}}else return this.registry[r]=u,{success:!0,asyncInit:!1}}catch(u){return console.warn(`Initialization of backend ${r} failed`),console.warn(u.stack||u.message),{success:!1,asyncInit:!1}}}removeBackend(r){if(!(r in this.registryFactory))throw new Error(`${r} backend not found in registry`);this.backendName===r&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,r in this.registry&&(this.disposeRegisteredKernels(r),this.registry[r].dispose(),delete this.registry[r]),delete this.registryFactory[r],this.backendName===r&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((r,l)=>this.registryFactory[l].priority-this.registryFactory[r].priority)}initializeBackendsAndReturnBest(){const r=this.getSortedBackends();for(let l=0;l<r.length;l++){const u=r[l],{success:p,asyncInit:g}=this.initializeBackend(u);if(g||p)return{name:u,asyncInit:g}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(r,l){const u=this.state.tensorInfo.get(l),p=u.backend,g=this.readSync(l);p.disposeData(l),u.backend=r,r.move(l,g,u.shape,u.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(r,l){let u=null;if(l==null){if(typeof r!="function")throw new Error("Please provide a function to tidy()");l=r}else{if(typeof r!="string"&&!(r instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof l!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");u=r}let p;return this.scopedRun(()=>this.startScope(u),()=>this.endScope(p),()=>(p=l(),p instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),p))}scopedRun(r,l,u){r();try{const p=u();return l(),p}catch(p){throw l(),p}}nextTensorId(){return Nu.nextTensorId++}nextVariableId(){return Nu.nextVariableId++}clone(r){const l=this.makeTensorFromDataId(r.dataId,r.shape,r.dtype),u={x:r},p=f=>({x:()=>{const I="float32",S={x:f},x={dtype:I};return Y.runKernelFunc(v=>v.cast(f,I),S,null,Dc,x)}}),g=[];return this.addTapeNode(this.state.activeScope.name,u,[l],p,g,{}),l}runKernel(r,l,u,p,g){const f=null,I=null;return this.runKernelFunc(f,l,I,r,u,p,g)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(r,l,u){const p=this.backend.numDataIds();let g=0;u.forEach(S=>{g+=S.dtype==="complex64"?3:1});const f=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],I=p-l-g-f;if(I>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${I} data ids) after running '${r}'`)}runKernelFunc(r,l,u,p,g,f,I){let S,x=[];const v=this.isTapeOn();p==null&&(p=this.state.activeScope!=null?this.state.activeScope.name:"");const O=this.state.numBytes,C=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let U;const G=Jf(p,this.backendName);let ne;if(G!=null)U=()=>{const oe=this.backend.numDataIds();ne=G.kernelFunc({inputs:l,attrs:g,backend:this.backend});const ge=Array.isArray(ne)?ne:[ne];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,oe,ge);const fe=ge.map(({dataId:Ae,shape:Te,dtype:Ve})=>this.makeTensorFromDataId(Ae,Te,Ve));if(v){let Ae=this.getTensorsForGradient(p,l,fe);if(Ae==null){I==null&&(I=[]);const Te=fe.filter((Ve,rt)=>I[rt]);Ae=(f||[]).slice().concat(Te)}x=this.saveTensorsForBackwardMode(Ae)}return fe};else{const oe=ge=>{if(!v)return;x=ge.map(fe=>this.keep(this.clone(fe)))};U=()=>{const ge=this.backend.numDataIds();ne=this.tidy(()=>r(this.backend,oe));const fe=Array.isArray(ne)?ne:[ne];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,ge,fe),fe}}let te;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?S=U():(te=this.profiler.profileKernel(p,l,()=>U()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(te),S=te.outputs)}),v&&this.addTapeNode(p,l,S,u,x,g),this.state.profiling&&this.state.activeProfile.kernels.push({name:p,bytesAdded:this.state.numBytes-O,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-C,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(oe=>l[oe]!=null?l[oe].shape:null),outputShapes:S.map(oe=>oe.shape),kernelTimeMs:te.timeMs,extraInfo:te.extraInfo}),Array.isArray(ne)?S:S[0]}saveTensorsForBackwardMode(r){const l=r.map(u=>this.keep(this.clone(u)));return l}getTensorsForGradient(r,l,u){const p=rI(r);if(p!=null){const g=p.inputsToSave||[],f=p.outputsToSave||[];let I;p.saveAllInputs?(Z(Array.isArray(l),()=>"saveAllInputs is true, expected inputs to be an array."),I=Object.keys(l).map(x=>l[x])):I=g.map(x=>l[x]);const S=u.filter((x,v)=>f[v]);return I.concat(S)}return null}makeTensor(r,l,u,p){if(r==null)throw new Error("Values passed to engine.makeTensor() are null");u=u||"float32",p=p||this.backend;let g=r;u==="string"&&Su(r[0])&&(g=r.map(S=>nO(S)));const f=p.write(g,l,u),I=new xn(l,u,f,this.nextTensorId());if(this.incRef(I,p),u==="string"){const S=this.state.tensorInfo.get(f),x=eO(g);this.state.numBytes+=x-S.bytes,S.bytes=x}return I}makeTensorFromDataId(r,l,u,p){u=u||"float32";const g=new xn(l,u,r,this.nextTensorId());return this.incRef(g,p),g}makeVariable(r,l=!0,u,p){u=u||this.nextVariableId().toString(),p!=null&&p!==r.dtype&&(r=r.cast(p));const g=new ng(r,l,u,this.nextTensorId());if(this.state.registeredVariables[g.name]!=null)throw new Error(`Variable with name ${g.name} was already registered`);return this.state.registeredVariables[g.name]=g,this.incRef(g,this.backend),g}incRef(r,l){const u=this.state.tensorInfo.has(r.dataId)?this.state.tensorInfo.get(r.dataId).refCount:0;if(this.state.numTensors++,r.dtype==="string"&&this.state.numStringTensors++,u===0){this.state.numDataBuffers++;let p=0;r.dtype!=="complex64"&&r.dtype!=="string"&&(p=r.size*QR(r.dtype)),this.state.tensorInfo.set(r.dataId,{backend:l||this.backend,dtype:r.dtype,shape:r.shape,bytes:p,refCount:0}),this.state.numBytes+=p}this.state.tensorInfo.get(r.dataId).refCount++,r instanceof ng||this.track(r)}disposeTensor(r){if(!this.state.tensorInfo.has(r.dataId))return;this.state.numTensors--,r.dtype==="string"&&this.state.numStringTensors--;const l=this.state.tensorInfo.get(r.dataId),u=l.refCount;u<=1?(r.dtype!=="complex64"&&(this.state.numBytes-=l.bytes),this.state.numDataBuffers--,l.backend.disposeData(r.dataId),this.state.tensorInfo.delete(r.dataId)):this.state.tensorInfo.get(r.dataId).refCount--}disposeVariables(){for(const r in this.state.registeredVariables){const l=this.state.registeredVariables[r];this.disposeVariable(l)}}disposeVariable(r){this.disposeTensor(r),this.state.registeredVariables[r.name]!=null&&delete this.state.registeredVariables[r.name]}memory(){const r=this.backend.memory();return r.numTensors=this.state.numTensors,r.numDataBuffers=this.state.numDataBuffers,r.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(r.unreliable=!0,r.reasons==null&&(r.reasons=[]),r.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),r}async profile(r){this.state.profiling=!0;const l=this.state.numBytes,u=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await r(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(p=>p.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-l,this.state.activeProfile.newTensors=this.state.numTensors-u;for(const p of this.state.activeProfile.kernels)p.kernelTimeMs=await p.kernelTimeMs,p.extraInfo=await p.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(r,l,u,p,g,f){const I={id:this.state.nextTapeNodeId++,kernelName:r,inputs:l,outputs:u,saved:g},S=rI(r);S!=null&&(p=S.gradFunc),p!=null&&(I.gradient=x=>(x=x.map((v,O)=>{if(v==null){const C=u[O],U=ia(C.size,C.dtype);return this.makeTensor(U,C.shape,C.dtype)}return v}),p(x.length>1?x:x[0],g,f))),this.state.activeTape.push(I)}keep(r){return r.kept=!0,r}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(r){const l={track:[],name:"unnamed scope",id:this.state.nextScopeId++};r&&(l.name=r),this.state.scopeStack.push(l),this.state.activeScope=l}endScope(r){const l=sg(r),u=new Set(l.map(g=>g.id));for(let g=0;g<this.state.activeScope.track.length;g++){const f=this.state.activeScope.track[g];!f.kept&&!u.has(f.id)&&f.dispose()}const p=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],l.forEach(g=>{!g.kept&&g.scopeId===p.id&&this.track(g)})}gradients(r,l,u,p=!1){if(Z(l.length>0,()=>"gradients() received an empty list of xs."),u!=null&&u.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${u.dtype}'`);const g=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",r));Z(g instanceof xn,()=>"The result y returned by f() must be a tensor.");const f=iO(this.state.activeTape,l,g);if(!p&&f.length===0&&l.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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In{constructor(r){this._name=r;this._params=void 0;this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(r){const{obj:l,objProp:u}=this.traversePropertyPath(r);return l[u]}reassignParamFromPath(r,l){const{obj:u,objProp:p}=this.traversePropertyPath(r);u[p].dispose(),u[p]=l}getParamList(){return this._paramMappings.map(({paramPath:r})=>({path:r,tensor:this.getParamFromPath(r)}))}getTrainableParams(){return this.getParamList().filter(r=>r.tensor instanceof br.Variable)}getFrozenParams(){return this.getParamList().filter(r=>!(r.tensor instanceof br.Variable))}variable(){this.getFrozenParams().forEach(({path:r,tensor:l})=>{this.reassignParamFromPath(r,l.variable())})}freeze(){this.getTrainableParams().forEach(({path:r,tensor:l})=>{const u=br.tensor(l.dataSync());l.dispose(),this.reassignParamFromPath(r,u)})}dispose(r=!0){this.getParamList().forEach(l=>{if(r&&l.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${l.path}`);l.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:r})=>Array.from(r.dataSync())).reduce((r,l)=>r.concat(l)))}async load(r){if(r instanceof Float32Array){this.extractWeights(r);return}await this.loadFromUri(r)}async loadFromUri(r){if(r&&typeof r!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);const l=await zm(r,this.getDefaultModelName());this.loadFromWeightMap(l)}async loadFromDisk(r){if(r&&typeof r!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);const{readFile:l}=mt.getEnv(),{manifestUri:u,modelBaseUri:p}=Lg(r,this.getDefaultModelName()),g=x=>Promise.all(x.map(v=>l(v).then(O=>O.buffer))),f=br.io.weightsLoaderFactory(g),I=JSON.parse((await l(u)).toString()),S=await f(I,p);this.loadFromWeightMap(S)}loadFromWeightMap(r){const{paramMappings:l,params:u}=this.extractParamsFromWeigthMap(r);this._paramMappings=l,this._params=u}extractWeights(r){const{paramMappings:l,params:u}=this.extractParams(r);this._paramMappings=l,this._params=u}traversePropertyPath(r){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");const l=r.split("/").reduce((g,f)=>{if(!g.nextObj.hasOwnProperty(f))throw new Error(`traversePropertyPath - object does not have property ${f}, for path ${r}`);return{obj:g.nextObj,objProp:f,nextObj:g.nextObj[f]}},{nextObj:this.params}),{obj:u,objProp:p}=l;if(!u||!p||!(u[p]instanceof br.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${r}`);return{obj:u,objProp:p}}}const Vc=Xe(Je());function es(r,l,u){return Vc.tidy(()=>{let p=Vc.separableConv2d(r,l.depthwise_filter,l.pointwise_filter,u,"same");return p=Vc.add(p,l.bias),p})}const Dt=Xe(Je());function Sg(r,l,u=!1){return Dt.tidy(()=>{const p=Dt.relu(u?Dt.add(Dt.conv2d(r,l.conv0.filters,[2,2],"same"),l.conv0.bias):es(r,l.conv0,[2,2])),g=es(p,l.conv1,[1,1]),f=Dt.relu(Dt.add(p,g)),I=es(f,l.conv2,[1,1]);return Dt.relu(Dt.add(p,Dt.add(g,I)))})}function Wu(r,l,u=!1,p=!0){return Dt.tidy(()=>{const g=Dt.relu(u?Dt.add(Dt.conv2d(r,l.conv0.filters,p?[2,2]:[1,1],"same"),l.conv0.bias):es(r,l.conv0,p?[2,2]:[1,1])),f=es(g,l.conv1,[1,1]),I=Dt.relu(Dt.add(g,f)),S=es(I,l.conv2,[1,1]),x=Dt.relu(Dt.add(g,Dt.add(f,S))),v=es(x,l.conv3,[1,1]);return Dt.relu(Dt.add(g,Dt.add(f,Dt.add(S,v))))})}const uo=Xe(Je());function da(r,l,u="same",p=!1){return uo.tidy(()=>{const g=uo.add(uo.conv2d(r,l.filters,[1,1],u),l.bias);return p?uo.relu(g):g})}function zn(r,l){Object.keys(r).forEach(u=>{l.some(p=>p.originalPath===u)||r[u].dispose()})}const Ig=Xe(Je());function Yc(r,l){return function(u,p,g,f){const I=Ig.tensor4d(r(u*p*g*g),[g,g,u,p]),S=Ig.tensor1d(r(p));return l.push({paramPath:`${f}/filters`},{paramPath:`${f}/bias`}),{filters:I,bias:S}}}const xg=Xe(Je());function Tg(r,l){return function(u,p,g){const f=xg.tensor2d(r(u*p),[u,p]),I=xg.tensor1d(r(p));return l.push({paramPath:`${g}/weights`},{paramPath:`${g}/bias`}),{weights:f,bias:I}}}class tx{constructor(r,l,u){this.depthwise_filter=r;this.pointwise_filter=l;this.bias=u}}const $u=Xe(Je());function Hc(r,l){return function(u,p,g){const f=$u.tensor4d(r(3*3*u),[3,3,u,1]),I=$u.tensor4d(r(u*p),[1,1,u,p]),S=$u.tensor1d(r(p));return l.push({paramPath:`${g}/depthwise_filter`},{paramPath:`${g}/pointwise_filter`},{paramPath:`${g}/bias`}),new tx(f,I,S)}}function qc(r){return function(l){const u=r(`${l}/depthwise_filter`,4),p=r(`${l}/pointwise_filter`,4),g=r(`${l}/bias`,1);return new tx(u,p,g)}}function ms(r,l){return function(u,p,g){const f=r[u];if(!ea(f,p))throw new Error(`expected weightMap[${u}] to be a Tensor${p}D, instead have ${f}`);return l.push({originalPath:u,paramPath:g||u}),f}}function Gn(r){let l=r;function u(g){const f=l.slice(0,g);return l=l.slice(g),f}function p(){return l}return{extractWeights:u,getRemainingWeights:p}}function Ag(r,l){const u=Yc(r,l),p=Hc(r,l);function g(I,S,x,v=!1){const O=v?u(I,S,3,`${x}/conv0`):p(I,S,`${x}/conv0`),C=p(S,S,`${x}/conv1`),U=p(S,S,`${x}/conv2`);return{conv0:O,conv1:C,conv2:U}}function f(I,S,x,v=!1){const{conv0:O,conv1:C,conv2:U}=g(I,S,x,v),G=p(S,S,`${x}/conv3`);return{conv0:O,conv1:C,conv2:U,conv3:G}}return{extractDenseBlock3Params:g,extractDenseBlock4Params:f}}function oD(r){const l=[],{extractWeights:u,getRemainingWeights:p}=Gn(r),{extractDenseBlock4Params:g}=Ag(u,l),f=g(3,32,"dense0",!0),I=g(32,64,"dense1"),S=g(64,128,"dense2"),x=g(128,256,"dense3");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{dense0:f,dense1:I,dense2:S,dense3:x}}}function vg(r){return function(l){const u=r(`${l}/filters`,4),p=r(`${l}/bias`,1);return{filters:u,bias:p}}}function Ng(r,l){const u=ms(r,l),p=vg(u),g=qc(u);function f(S,x=!1){const v=x?p(`${S}/conv0`):g(`${S}/conv0`),O=g(`${S}/conv1`),C=g(`${S}/conv2`);return{conv0:v,conv1:O,conv2:C}}function I(S,x=!1){const v=x?p(`${S}/conv0`):g(`${S}/conv0`),O=g(`${S}/conv1`),C=g(`${S}/conv2`),U=g(`${S}/conv3`);return{conv0:v,conv1:O,conv2:C,conv3:U}}return{extractDenseBlock3Params:f,extractDenseBlock4Params:I}}function aD(r){const l=[],{extractDenseBlock4Params:u}=Ng(r,l),p={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2"),dense3:u("dense3")};return zn(r,l),{params:p,paramMappings:l}}const jc=Xe(Je());class Cg extends In{constructor(){super("FaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceFeatureExtractor - load model before inference");return jc.tidy(()=>{const u=r.toBatchTensor(112,!0),p=[122.782,117.001,104.298],g=Ys(u,p).div(jc.scalar(255));let f=Wu(g,l.dense0,!0);return f=Wu(f,l.dense1),f=Wu(f,l.dense2),f=Wu(f,l.dense3),f=jc.avgPool(f,[7,7],[2,2],"valid"),f})}async forward(r){return this.forwardInput(await Tt(r))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(r){return aD(r)}extractParams(r){return oD(r)}}const Kc=Xe(Je());function Uu(r,l){return Kc.tidy(()=>Kc.add(Kc.matMul(r,l.weights),l.bias))}function cD(r,l,u){const p=[],{extractWeights:g,getRemainingWeights:f}=Gn(r),I=Tg(g,p),S=I(l,u,"fc");if(f().length!==0)throw new Error(`weights remaing after extract: ${f().length}`);return{paramMappings:p,params:{fc:S}}}function lD(r){const l=[],u=ms(r,l);function p(f){const I=u(`${f}/weights`,2),S=u(`${f}/bias`,1);return{weights:I,bias:S}}const g={fc:p("fc")};return zn(r,l),{params:g,paramMappings:l}}function Rg(r){const l={},u={};return Object.keys(r).forEach(p=>{const g=p.startsWith("fc")?u:l;g[p]=r[p]}),{featureExtractorMap:l,classifierMap:u}}const hD=Xe(Je());class Og extends In{constructor(r,l){super(r);this._faceFeatureExtractor=l}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return hD.tidy(()=>{const u=r instanceof hr?this.faceFeatureExtractor.forwardInput(r):r;return Uu(u.as2D(u.shape[0],-1),l.fc)})}dispose(r=!0){this.faceFeatureExtractor.dispose(r),super.dispose(r)}loadClassifierParams(r){const{params:l,paramMappings:u}=this.extractClassifierParams(r);this._params=l,this._paramMappings=u}extractClassifierParams(r){return cD(r,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:u}=Rg(r);return this.faceFeatureExtractor.loadFromWeightMap(l),lD(u)}extractParams(r){const l=this.getClassifierChannelsIn(),u=this.getClassifierChannelsOut(),p=u*l+u,g=r.slice(0,r.length-p),f=r.slice(r.length-p);return this.faceFeatureExtractor.extractWeights(g),this.extractClassifierParams(f)}}const Bm=["neutral","happy","sad","angry","fearful","disgusted","surprised"];class Qr{constructor(r){if(r.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${r.length}`);Bm.forEach((l,u)=>{this[l]=r[u]})}asSortedArray(){return Bm.map(r=>({expression:r,probability:this[r]})).sort((r,l)=>l.probability-r.probability)}}const Xc=Xe(Je());class Mm extends Og{constructor(r=new Cg){super("FaceExpressionNet",r)}forwardInput(r){return Xc.tidy(()=>Xc.softmax(this.runNet(r)))}async forward(r){return this.forwardInput(await Tt(r))}async predictExpressions(r){const l=await Tt(r),u=await this.forwardInput(l),p=await Promise.all(Xc.unstack(u).map(async f=>{const I=await f.data();return f.dispose(),I}));u.dispose();const g=p.map(f=>new Qr(f));return l.isBatchInput?g:g[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}}function $m(r){return r.expressions instanceof Qr}function fu(r,l){const u={expressions:l};return Object.assign({},r,u)}function bZ(r,l,u=.1,p){const g=Array.isArray(l)?l:[l];g.forEach(f=>{const I=f instanceof Qr?f:$m(f)?f.expressions:void 0;if(!I)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");const S=I.asSortedArray(),x=S.filter(C=>C.probability>u),v=mi(f)?f.detection.box.bottomLeft:p||new qe(0,0),O=new Oc(x.map(C=>`${C.expression} (${ta(C.probability)})`),v);O.draw(r)})}function Jr(r){return mi(r)&&r.landmarks instanceof Ns&&r.unshiftedLandmarks instanceof Ns&&r.alignedRect instanceof Wt}function Ko(r,l){const{box:u}=r.detection,p=l.shiftBy(u.x,u.y),g=p.align(),{imageDims:f}=r.detection,I=new Wt(r.detection.score,g.rescale(f.reverse()),f),S={landmarks:p,unshiftedLandmarks:l,alignedRect:I};return Object.assign({},r,S)}class uD{constructor(r={}){const{drawLines:l=!0,drawPoints:u=!0,lineWidth:p,lineColor:g,pointSize:f,pointColor:I}=r;this.drawLines=l,this.drawPoints=u,this.lineWidth=p||1,this.pointSize=f||2,this.lineColor=g||"rgba(0, 255, 255, 1)",this.pointColor=I||"rgba(255, 0, 255, 1)"}}class dD{constructor(r,l={}){this.faceLandmarks=r,this.options=new uD(l)}draw(r){const l=Un(r),{drawLines:u,drawPoints:p,lineWidth:g,lineColor:f,pointSize:I,pointColor:S}=this.options;if(u&&this.faceLandmarks instanceof Nc&&(l.strokeStyle=f,l.lineWidth=g,ur(l,this.faceLandmarks.getJawOutline()),ur(l,this.faceLandmarks.getLeftEyeBrow()),ur(l,this.faceLandmarks.getRightEyeBrow()),ur(l,this.faceLandmarks.getNose()),ur(l,this.faceLandmarks.getLeftEye(),!0),ur(l,this.faceLandmarks.getRightEye(),!0),ur(l,this.faceLandmarks.getMouth(),!0)),p){l.strokeStyle=S,l.fillStyle=S;const x=v=>{l.beginPath(),l.arc(v.x,v.y,I,0,2*Math.PI),l.fill()};this.faceLandmarks.positions.forEach(x)}}}function wZ(r,l){const u=Array.isArray(l)?l:[l];u.forEach(p=>{const g=p instanceof Ns?p:Jr(p)?p.landmarks:void 0;if(!g)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new dD(g).draw(r)})}const Im={};uu(Im,{AnchorPosition:()=>Pi,DrawBox:()=>QS,DrawBoxOptions:()=>b2,DrawFaceLandmarks:()=>dD,DrawFaceLandmarksOptions:()=>uD,DrawTextField:()=>Oc,DrawTextFieldOptions:()=>Zm,drawContour:()=>ur,drawDetections:()=>E7,drawFaceExpressions:()=>bZ,drawFaceLandmarks:()=>wZ});function LZ(r,l){const u=Yc(r,l),p=Hc(r,l);function g(I,S,x){const v=p(I,S,`${x}/separable_conv0`),O=p(S,S,`${x}/separable_conv1`),C=u(I,S,1,`${x}/expansion_conv`);return{separable_conv0:v,separable_conv1:O,expansion_conv:C}}function f(I,S){const x=p(I,I,`${S}/separable_conv0`),v=p(I,I,`${S}/separable_conv1`),O=p(I,I,`${S}/separable_conv2`);return{separable_conv0:x,separable_conv1:v,separable_conv2:O}}return{extractConvParams:u,extractSeparableConvParams:p,extractReductionBlockParams:g,extractMainBlockParams:f}}function pD(r,l){const u=[],{extractWeights:p,getRemainingWeights:g}=Gn(r),{extractConvParams:f,extractSeparableConvParams:I,extractReductionBlockParams:S,extractMainBlockParams:x}=LZ(p,u),v=f(3,32,3,"entry_flow/conv_in"),O=S(32,64,"entry_flow/reduction_block_0"),C=S(64,128,"entry_flow/reduction_block_1"),U={conv_in:v,reduction_block_0:O,reduction_block_1:C},G={};Bi(l,0,1).forEach(ge=>{G[`main_block_${ge}`]=x(128,`middle_flow/main_block_${ge}`)});const ne=S(128,256,"exit_flow/reduction_block"),te=I(256,512,"exit_flow/separable_conv"),oe={reduction_block:ne,separable_conv:te};if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{paramMappings:u,params:{entry_flow:U,middle_flow:G,exit_flow:oe}}}function SZ(r,l){const u=ms(r,l),p=vg(u),g=qc(u);function f(S){const x=g(`${S}/separable_conv0`),v=g(`${S}/separable_conv1`),O=p(`${S}/expansion_conv`);return{separable_conv0:x,separable_conv1:v,expansion_conv:O}}function I(S){const x=g(`${S}/separable_conv0`),v=g(`${S}/separable_conv1`),O=g(`${S}/separable_conv2`);return{separable_conv0:x,separable_conv1:v,separable_conv2:O}}return{extractConvParams:p,extractSeparableConvParams:g,extractReductionBlockParams:f,extractMainBlockParams:I}}function mD(r,l){const u=[],{extractConvParams:p,extractSeparableConvParams:g,extractReductionBlockParams:f,extractMainBlockParams:I}=SZ(r,u),S=p("entry_flow/conv_in"),x=f("entry_flow/reduction_block_0"),v=f("entry_flow/reduction_block_1"),O={conv_in:S,reduction_block_0:x,reduction_block_1:v},C={};Bi(l,0,1).forEach(te=>{C[`main_block_${te}`]=I(`middle_flow/main_block_${te}`)});const U=f("exit_flow/reduction_block"),G=g("exit_flow/separable_conv"),ne={reduction_block:U,separable_conv:G};return zn(r,u),{params:{entry_flow:O,middle_flow:C,exit_flow:ne},paramMappings:u}}const dn=Xe(Je());function fD(r,l,u){return dn.add(dn.conv2d(r,l.filters,u,"same"),l.bias)}function nx(r,l,u=!0){let p=u?dn.relu(r):r;return p=es(p,l.separable_conv0,[1,1]),p=es(dn.relu(p),l.separable_conv1,[1,1]),p=dn.maxPool(p,[3,3],[2,2],"same"),p=dn.add(p,fD(r,l.expansion_conv,[2,2])),p}function IZ(r,l){let u=es(dn.relu(r),l.separable_conv0,[1,1]);return u=es(dn.relu(u),l.separable_conv1,[1,1]),u=es(dn.relu(u),l.separable_conv2,[1,1]),u=dn.add(u,r),u}class gD extends In{constructor(r){super("TinyXception");this._numMainBlocks=r}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyXception - load model before inference");return dn.tidy(()=>{const u=r.toBatchTensor(112,!0),p=[122.782,117.001,104.298],g=Ys(u,p).div(dn.scalar(256));let f=dn.relu(fD(g,l.entry_flow.conv_in,[2,2]));return f=nx(f,l.entry_flow.reduction_block_0,!1),f=nx(f,l.entry_flow.reduction_block_1),Bi(this._numMainBlocks,0,1).forEach(I=>{f=IZ(f,l.middle_flow[`main_block_${I}`])}),f=nx(f,l.exit_flow.reduction_block),f=dn.relu(es(f,l.exit_flow.separable_conv,[1,1])),f})}async forward(r){return this.forwardInput(await Tt(r))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(r){return mD(r,this._numMainBlocks)}extractParams(r){return pD(r,this._numMainBlocks)}}function yD(r){const l=[],{extractWeights:u,getRemainingWeights:p}=Gn(r),g=Tg(u,l),f=g(512,1,"fc/age"),I=g(512,2,"fc/gender");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{fc:{age:f,gender:I}}}}function bD(r){const l=[],u=ms(r,l);function p(f){const I=u(`${f}/weights`,2),S=u(`${f}/bias`,1);return{weights:I,bias:S}}const g={fc:{age:p("fc/age"),gender:p("fc/gender")}};return zn(r,l),{params:g,paramMappings:l}}var Ui;(function(r){r.FEMALE="female",r.MALE="male"})(Ui||(Ui={}));const Hi=Xe(Je());class jm extends In{constructor(r=new gD(2)){super("AgeGenderNet");this._faceFeatureExtractor=r}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return Hi.tidy(()=>{const u=r instanceof hr?this.faceFeatureExtractor.forwardInput(r):r,p=Hi.avgPool(u,[7,7],[2,2],"valid").as2D(u.shape[0],-1),g=Uu(p,l.fc.age).as1D(),f=Uu(p,l.fc.gender);return{age:g,gender:f}})}forwardInput(r){return Hi.tidy(()=>{const{age:l,gender:u}=this.runNet(r);return{age:l,gender:Hi.softmax(u)}})}async forward(r){return this.forwardInput(await Tt(r))}async predictAgeAndGender(r){const l=await Tt(r),u=await this.forwardInput(l),p=Hi.unstack(u.age),g=Hi.unstack(u.gender),f=p.map((S,x)=>({ageTensor:S,genderTensor:g[x]})),I=await Promise.all(f.map(async({ageTensor:S,genderTensor:x})=>{const v=(await S.data())[0],O=(await x.data())[0],C=O>.5,U=C?Ui.MALE:Ui.FEMALE,G=C?O:1-O;return S.dispose(),x.dispose(),{age:v,gender:U,genderProbability:G}}));return u.age.dispose(),u.gender.dispose(),l.isBatchInput?I:I[0]}getDefaultModelName(){return"age_gender_model"}dispose(r=!0){this.faceFeatureExtractor.dispose(r),super.dispose(r)}loadClassifierParams(r){const{params:l,paramMappings:u}=this.extractClassifierParams(r);this._params=l,this._paramMappings=u}extractClassifierParams(r){return yD(r)}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:u}=Rg(r);return this.faceFeatureExtractor.loadFromWeightMap(l),bD(u)}extractParams(r){const l=512*1+1+(512*2+2),u=r.slice(0,r.length-l),p=r.slice(r.length-l);return this.faceFeatureExtractor.extractWeights(u),this.extractClassifierParams(p)}}const fs=Xe(Je());class Eg extends Og{postProcess(r,l,u){const p=u.map(({width:f,height:I})=>{const S=l/Math.max(I,f);return{width:f*S,height:I*S}}),g=p.length;return fs.tidy(()=>{const f=(O,C)=>fs.stack([fs.fill([68],O),fs.fill([68],C)],1).as2D(1,136).as1D(),I=(O,C)=>{const{width:U,height:G}=p[O];return C(U,G)?Math.abs(U-G)/2:0},S=O=>I(O,(C,U)=>C<U),x=O=>I(O,(C,U)=>U<C),v=r.mul(fs.fill([g,136],l)).sub(fs.stack(Array.from(Array(g),(O,C)=>f(S(C),x(C))))).div(fs.stack(Array.from(Array(g),(O,C)=>f(p[C].width,p[C].height))));return v})}forwardInput(r){return fs.tidy(()=>{const l=this.runNet(r);return this.postProcess(l,r.inputSize,r.inputDimensions.map(([u,p])=>({height:u,width:p})))})}async forward(r){return this.forwardInput(await Tt(r))}async detectLandmarks(r){const l=await Tt(r),u=fs.tidy(()=>fs.unstack(this.forwardInput(l))),p=await Promise.all(u.map(async(g,f)=>{const I=Array.from(await g.data()),S=I.filter((v,O)=>Km(O)),x=I.filter((v,O)=>!Km(O));return new Nc(Array(68).fill(0).map((v,O)=>new qe(S[O],x[O])),{height:l.getInputHeight(f),width:l.getInputWidth(f)})}));return u.forEach(g=>g.dispose()),l.isBatchInput?p:p[0]}getClassifierChannelsOut(){return 136}}class xc extends Eg{constructor(r=new Cg){super("FaceLandmark68Net",r)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}}function wD(r){const l=[],{extractDenseBlock3Params:u}=Ng(r,l),p={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2")};return zn(r,l),{params:p,paramMappings:l}}function LD(r){const l=[],{extractWeights:u,getRemainingWeights:p}=Gn(r),{extractDenseBlock3Params:g}=Ag(u,l),f=g(3,32,"dense0",!0),I=g(32,64,"dense1"),S=g(64,128,"dense2");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{dense0:f,dense1:I,dense2:S}}}const Jc=Xe(Je());class SD extends In{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyFaceFeatureExtractor - load model before inference");return Jc.tidy(()=>{const u=r.toBatchTensor(112,!0),p=[122.782,117.001,104.298],g=Ys(u,p).div(Jc.scalar(255));let f=Sg(g,l.dense0,!0);return f=Sg(f,l.dense1),f=Sg(f,l.dense2),f=Jc.avgPool(f,[14,14],[2,2],"valid"),f})}async forward(r){return this.forwardInput(await Tt(r))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(r){return wD(r)}extractParams(r){return LD(r)}}class Um extends Eg{constructor(r=new SD){super("FaceLandmark68TinyNet",r)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}}class h2 extends xc{}const Dg=Xe(Je());function ID(r,l){return Dg.add(Dg.mul(r,l.weights),l.biases)}const Zc=Xe(Je());function sx(r,l,u,p,g="same"){const{filters:f,bias:I}=l.conv;let S=Zc.conv2d(r,f,u,g);return S=Zc.add(S,I),S=ID(S,l.scale),p?Zc.relu(S):S}function xD(r,l){return sx(r,l,[1,1],!0)}function ix(r,l){return sx(r,l,[1,1],!1)}function kg(r,l){return sx(r,l,[2,2],!0,"valid")}const gs=Xe(Je());function xZ(r,l){function u(S,x,v){const O=r(S),C=O.length/(x*v*v);if(YS(C))throw new Error(`depth has to be an integer: ${C}, weights.length: ${O.length}, numFilters: ${x}, filterSize: ${v}`);return gs.tidy(()=>gs.transpose(gs.tensor4d(O,[x,C,v,v]),[2,3,1,0]))}function p(S,x,v,O){const C=u(S,x,v),U=gs.tensor1d(r(x));return l.push({paramPath:`${O}/filters`},{paramPath:`${O}/bias`}),{filters:C,bias:U}}function g(S,x){const v=gs.tensor1d(r(S)),O=gs.tensor1d(r(S));return l.push({paramPath:`${x}/weights`},{paramPath:`${x}/biases`}),{weights:v,biases:O}}function f(S,x,v,O){const C=p(S,x,v,`${O}/conv`),U=g(x,`${O}/scale`);return{conv:C,scale:U}}function I(S,x,v,O,C=!1){const U=f((C?.5:1)*S,x,v,`${O}/conv1`),G=f(S,x,v,`${O}/conv2`);return{conv1:U,conv2:G}}return{extractConvLayerParams:f,extractResidualLayerParams:I}}function TD(r){const{extractWeights:l,getRemainingWeights:u}=Gn(r),p=[],{extractConvLayerParams:g,extractResidualLayerParams:f}=xZ(l,p),I=g(4704,32,7,"conv32_down"),S=f(9216,32,3,"conv32_1"),x=f(9216,32,3,"conv32_2"),v=f(9216,32,3,"conv32_3"),O=f(36864,64,3,"conv64_down",!0),C=f(36864,64,3,"conv64_1"),U=f(36864,64,3,"conv64_2"),G=f(36864,64,3,"conv64_3"),ne=f(147456,128,3,"conv128_down",!0),te=f(147456,128,3,"conv128_1"),oe=f(147456,128,3,"conv128_2"),ge=f(589824,256,3,"conv256_down",!0),fe=f(589824,256,3,"conv256_1"),Ae=f(589824,256,3,"conv256_2"),Te=f(589824,256,3,"conv256_down_out"),Ve=gs.tidy(()=>gs.transpose(gs.tensor2d(l(256*128),[128,256]),[1,0]));if(p.push({paramPath:"fc"}),u().length!==0)throw new Error(`weights remaing after extract: ${u().length}`);const rt={conv32_down:I,conv32_1:S,conv32_2:x,conv32_3:v,conv64_down:O,conv64_1:C,conv64_2:U,conv64_3:G,conv128_down:ne,conv128_1:te,conv128_2:oe,conv256_down:ge,conv256_1:fe,conv256_2:Ae,conv256_down_out:Te,fc:Ve};return{params:rt,paramMappings:p}}function TZ(r,l){const u=ms(r,l);function p(I){const S=u(`${I}/scale/weights`,1),x=u(`${I}/scale/biases`,1);return{weights:S,biases:x}}function g(I){const S=u(`${I}/conv/filters`,4),x=u(`${I}/conv/bias`,1),v=p(I);return{conv:{filters:S,bias:x},scale:v}}function f(I){return{conv1:g(`${I}/conv1`),conv2:g(`${I}/conv2`)}}return{extractConvLayerParams:g,extractResidualLayerParams:f}}function AD(r){const l=[],{extractConvLayerParams:u,extractResidualLayerParams:p}=TZ(r,l),g=u("conv32_down"),f=p("conv32_1"),I=p("conv32_2"),S=p("conv32_3"),x=p("conv64_down"),v=p("conv64_1"),O=p("conv64_2"),C=p("conv64_3"),U=p("conv128_down"),G=p("conv128_1"),ne=p("conv128_2"),te=p("conv256_down"),oe=p("conv256_1"),ge=p("conv256_2"),fe=p("conv256_down_out"),Ae=r.fc;if(l.push({originalPath:"fc",paramPath:"fc"}),!VS(Ae))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${Ae}`);const Te={conv32_down:g,conv32_1:f,conv32_2:I,conv32_3:S,conv64_down:x,conv64_1:v,conv64_2:O,conv64_3:C,conv128_down:U,conv128_1:G,conv128_2:ne,conv256_down:te,conv256_1:oe,conv256_2:ge,conv256_down_out:fe,fc:Ae};return zn(r,l),{params:Te,paramMappings:l}}const Vn=Xe(Je());function bi(r,l){let u=xD(r,l.conv1);return u=ix(u,l.conv2),u=Vn.add(u,r),u=Vn.relu(u),u}function Bu(r,l){let u=kg(r,l.conv1);u=ix(u,l.conv2);let p=Vn.avgPool(r,2,2,"valid");const g=Vn.zeros(p.shape),f=p.shape[3]!==u.shape[3],I=p.shape[1]!==u.shape[1]||p.shape[2]!==u.shape[2];if(I){const S=[...u.shape];S[1]=1;const x=Vn.zeros(S);u=Vn.concat([u,x],1);const v=[...u.shape];v[2]=1;const O=Vn.zeros(v);u=Vn.concat([u,O],2)}return p=f?Vn.concat([p,g],3):p,u=Vn.add(p,u),u=Vn.relu(u),u}const ks=Xe(Je());class Ic extends In{constructor(){super("FaceRecognitionNet")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceRecognitionNet - load model before inference");return ks.tidy(()=>{const u=ks.cast(r.toBatchTensor(150,!0),"float32"),p=[122.782,117.001,104.298],g=Ys(u,p).div(ks.scalar(256));let f=kg(g,l.conv32_down);f=ks.maxPool(f,3,2,"valid"),f=bi(f,l.conv32_1),f=bi(f,l.conv32_2),f=bi(f,l.conv32_3),f=Bu(f,l.conv64_down),f=bi(f,l.conv64_1),f=bi(f,l.conv64_2),f=bi(f,l.conv64_3),f=Bu(f,l.conv128_down),f=bi(f,l.conv128_1),f=bi(f,l.conv128_2),f=Bu(f,l.conv256_down),f=bi(f,l.conv256_1),f=bi(f,l.conv256_2),f=Bu(f,l.conv256_down_out);const I=f.mean([1,2]),S=ks.matMul(I,l.fc);return S})}async forward(r){return this.forwardInput(await Tt(r))}async computeFaceDescriptor(r){const l=await Tt(r),u=ks.tidy(()=>ks.unstack(this.forwardInput(l))),p=await Promise.all(u.map(g=>g.data()));return u.forEach(g=>g.dispose()),l.isBatchInput?p:p[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(r){return AD(r)}extractParams(r){return TD(r)}}function l2(r){const l=new Ic;return l.extractWeights(r),l}function gu(r,l){const u={descriptor:l};return Object.assign({},r,u)}function c2(r){return typeof r.age=="number"}function yu(r,l){const u={age:l};return Object.assign({},r,u)}function a2(r){return(r.gender===Ui.MALE||r.gender===Ui.FEMALE)&&Rc(r.genderProbability)}function mu(r,l,u){const p={gender:l,genderProbability:u};return Object.assign({},r,p)}const wi=Xe(Je());function AZ(r,l){function u(x,v){const O=wi.tensor4d(r(3*3*x),[3,3,x,1]),C=wi.tensor1d(r(x)),U=wi.tensor1d(r(x)),G=wi.tensor1d(r(x)),ne=wi.tensor1d(r(x));return l.push({paramPath:`${v}/filters`},{paramPath:`${v}/batch_norm_scale`},{paramPath:`${v}/batch_norm_offset`},{paramPath:`${v}/batch_norm_mean`},{paramPath:`${v}/batch_norm_variance`}),{filters:O,batch_norm_scale:C,batch_norm_offset:U,batch_norm_mean:G,batch_norm_variance:ne}}function p(x,v,O,C,U){const G=wi.tensor4d(r(x*v*O*O),[O,O,x,v]),ne=wi.tensor1d(r(v));return l.push({paramPath:`${C}/filters`},{paramPath:`${C}/${U?"batch_norm_offset":"bias"}`}),{filters:G,bias:ne}}function g(x,v,O,C){const{filters:U,bias:G}=p(x,v,O,C,!0);return{filters:U,batch_norm_offset:G}}function f(x,v,O){const C=u(x,`${O}/depthwise_conv`),U=g(x,v,1,`${O}/pointwise_conv`);return{depthwise_conv:C,pointwise_conv:U}}function I(){const x=g(3,32,3,"mobilenetv1/conv_0"),v=f(32,64,"mobilenetv1/conv_1"),O=f(64,128,"mobilenetv1/conv_2"),C=f(128,128,"mobilenetv1/conv_3"),U=f(128,256,"mobilenetv1/conv_4"),G=f(256,256,"mobilenetv1/conv_5"),ne=f(256,512,"mobilenetv1/conv_6"),te=f(512,512,"mobilenetv1/conv_7"),oe=f(512,512,"mobilenetv1/conv_8"),ge=f(512,512,"mobilenetv1/conv_9"),fe=f(512,512,"mobilenetv1/conv_10"),Ae=f(512,512,"mobilenetv1/conv_11"),Te=f(512,1024,"mobilenetv1/conv_12"),Ve=f(1024,1024,"mobilenetv1/conv_13");return{conv_0:x,conv_1:v,conv_2:O,conv_3:C,conv_4:U,conv_5:G,conv_6:ne,conv_7:te,conv_8:oe,conv_9:ge,conv_10:fe,conv_11:Ae,conv_12:Te,conv_13:Ve}}function S(){const x=g(1024,256,1,"prediction_layer/conv_0"),v=g(256,512,3,"prediction_layer/conv_1"),O=g(512,128,1,"prediction_layer/conv_2"),C=g(128,256,3,"prediction_layer/conv_3"),U=g(256,128,1,"prediction_layer/conv_4"),G=g(128,256,3,"prediction_layer/conv_5"),ne=g(256,64,1,"prediction_layer/conv_6"),te=g(64,128,3,"prediction_layer/conv_7"),oe=p(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),ge=p(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),fe=p(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),Ae=p(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),Te=p(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),Ve=p(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),rt=p(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),Ct=p(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),Ut=p(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),Kt=p(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),Dn=p(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),An=p(128,18,1,"prediction_layer/box_predictor_5/class_predictor"),vn={box_encoding_predictor:oe,class_predictor:ge},Zs={box_encoding_predictor:fe,class_predictor:Ae},Si={box_encoding_predictor:Te,class_predictor:Ve},Qs={box_encoding_predictor:rt,class_predictor:Ct},fa={box_encoding_predictor:Ut,class_predictor:Kt},ol={box_encoding_predictor:Dn,class_predictor:An};return{conv_0:x,conv_1:v,conv_2:O,conv_3:C,conv_4:U,conv_5:G,conv_6:ne,conv_7:te,box_predictor_0:vn,box_predictor_1:Zs,box_predictor_2:Si,box_predictor_3:Qs,box_predictor_4:fa,box_predictor_5:ol}}return{extractMobilenetV1Params:I,extractPredictionLayerParams:S}}function 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O=`mobilenetv1/conv_${v}`,C=`MobilenetV1/Conv2d_${v}_depthwise`,U=`${O}/depthwise_conv`,G=`${O}/pointwise_conv`,ne=u(`${C}/depthwise_weights`,4,`${U}/filters`),te=u(`${C}/BatchNorm/gamma`,1,`${U}/batch_norm_scale`),oe=u(`${C}/BatchNorm/beta`,1,`${U}/batch_norm_offset`),ge=u(`${C}/BatchNorm/moving_mean`,1,`${U}/batch_norm_mean`),fe=u(`${C}/BatchNorm/moving_variance`,1,`${U}/batch_norm_variance`);return{depthwise_conv:{filters:ne,batch_norm_scale:te,batch_norm_offset:oe,batch_norm_mean:ge,batch_norm_variance:fe},pointwise_conv:p("MobilenetV1",v,G)}}function f(){return{conv_0:p("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:g(1),conv_2:g(2),conv_3:g(3),conv_4:g(4),conv_5:g(5),conv_6:g(6),conv_7:g(7),conv_8:g(8),conv_9:g(9),conv_10:g(10),conv_11:g(11),conv_12:g(12),conv_13:g(13)}}function I(v,O){const C=u(`${v}/weights`,4,`${O}/filters`),U=u(`${v}/biases`,1,`${O}/bias`);return{filters:C,bias:U}}function S(v){const O=I(`Prediction/BoxPredictor_${v}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${v}/box_encoding_predictor`),C=I(`Prediction/BoxPredictor_${v}/ClassPredictor`,`prediction_layer/box_predictor_${v}/class_predictor`);return{box_encoding_predictor:O,class_predictor:C}}function x(){return{conv_0:p("Prediction",0,"prediction_layer/conv_0"),conv_1:p("Prediction",1,"prediction_layer/conv_1"),conv_2:p("Prediction",2,"prediction_layer/conv_2"),conv_3:p("Prediction",3,"prediction_layer/conv_3"),conv_4:p("Prediction",4,"prediction_layer/conv_4"),conv_5:p("Prediction",5,"prediction_layer/conv_5"),conv_6:p("Prediction",6,"prediction_layer/conv_6"),conv_7:p("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:S(0),box_predictor_1:S(1),box_predictor_2:S(2),box_predictor_3:S(3),box_predictor_4:S(4),box_predictor_5:S(5)}}return{extractMobilenetV1Params:f,extractPredictionLayerParams:x}}function ND(r){const l=[],{extractMobilenetV1Params:u,extractPredictionLayerParams:p}=vZ(r,l),g=r["Output/extra_dim"];if(l.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!dr(g))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${g}`);const f={mobilenetv1:u(),prediction_layer:p(),output_layer:{extra_dim:g}};return zn(r,l),{params:f,paramMappings:l}}const po=Xe(Je());function Ks(r,l,u){return po.tidy(()=>{let p=po.conv2d(r,l.filters,u,"same");return p=po.add(p,l.batch_norm_offset),po.clipByValue(p,0,6)})}const wr=Xe(Je()),NZ=.0010000000474974513;function CZ(r,l,u){return wr.tidy(()=>{let p=wr.depthwiseConv2d(r,l.filters,u,"same");return p=wr.batchNorm(p,l.batch_norm_mean,l.batch_norm_variance,l.batch_norm_offset,l.batch_norm_scale,NZ),wr.clipByValue(p,0,6)})}function RZ(r){return[2,4,6,12].some(l=>l===r)?[2,2]:[1,1]}function CD(r,l){return wr.tidy(()=>{let u,p=Ks(r,l.conv_0,[2,2]);const g=[l.conv_1,l.conv_2,l.conv_3,l.conv_4,l.conv_5,l.conv_6,l.conv_7,l.conv_8,l.conv_9,l.conv_10,l.conv_11,l.conv_12,l.conv_13];if(g.forEach((f,I)=>{const S=I+1,x=RZ(S);p=CZ(p,f.depthwise_conv,x),p=Ks(p,f.pointwise_conv,[1,1]),S===11&&(u=p)}),u===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:p,conv11:u}})}function RD(r,l,u,p,g){const f=r.shape[0],I=Math.min(u,f),S=l.map((O,C)=>({score:O,boxIndex:C})).filter(O=>O.score>g).sort((O,C)=>C.score-O.score),x=O=>O<=p?1:0,v=[];return S.forEach(O=>{if(v.length>=I)return;const C=O.score;for(let U=v.length-1;U>=0;--U){const G=OZ(r,O.boxIndex,v[U]);if(G===0)continue;if(O.score*=x(G),O.score<=g)break}C===O.score&&v.push(O.boxIndex)}),v}function OZ(r,l,u){const p=r.arraySync(),g=Math.min(p[l][0],p[l][2]),f=Math.min(p[l][1],p[l][3]),I=Math.max(p[l][0],p[l][2]),S=Math.max(p[l][1],p[l][3]),x=Math.min(p[u][0],p[u][2]),v=Math.min(p[u][1],p[u][3]),O=Math.max(p[u][0],p[u][2]),C=Math.max(p[u][1],p[u][3]),U=(I-g)*(S-f),G=(O-x)*(C-v);if(U<=0||G<=0)return 0;const ne=Math.max(g,x),te=Math.max(f,v),oe=Math.min(I,O),ge=Math.min(S,C),fe=Math.max(oe-ne,0)*Math.max(ge-te,0);return fe/(U+G-fe)}const De=Xe(Je());function EZ(r){const l=De.unstack(De.transpose(r,[1,0])),u=[De.sub(l[2],l[0]),De.sub(l[3],l[1])],p=[De.add(l[0],De.div(u[0],De.scalar(2))),De.add(l[1],De.div(u[1],De.scalar(2)))];return{sizes:u,centers:p}}function DZ(r,l){const{sizes:u,centers:p}=EZ(r),g=De.unstack(De.transpose(l,[1,0])),f=De.div(De.mul(De.exp(De.div(g[2],De.scalar(5))),u[0]),De.scalar(2)),I=De.add(De.mul(De.div(g[0],De.scalar(10)),u[0]),p[0]),S=De.div(De.mul(De.exp(De.div(g[3],De.scalar(5))),u[1]),De.scalar(2)),x=De.add(De.mul(De.div(g[1],De.scalar(10)),u[1]),p[1]);return De.transpose(De.stack([De.sub(I,f),De.sub(x,S),De.add(I,f),De.add(x,S)]),[1,0])}function OD(r,l,u){return De.tidy(()=>{const p=r.shape[0];let g=DZ(De.reshape(De.tile(u.extra_dim,[p,1,1]),[-1,4]),De.reshape(r,[-1,4]));g=De.reshape(g,[p,g.shape[0]/p,4]);const f=De.sigmoid(De.slice(l,[0,0,1],[-1,-1,-1]));let I=De.slice(f,[0,0,0],[-1,-1,1]);I=De.reshape(I,[p,I.shape[1]]);const S=De.unstack(g),x=De.unstack(I);return{boxes:S,scores:x}})}const Mu=Xe(Je());function pa(r,l){return Mu.tidy(()=>{const u=r.shape[0],p=Mu.reshape(da(r,l.box_encoding_predictor),[u,-1,1,4]),g=Mu.reshape(da(r,l.class_predictor),[u,-1,3]);return{boxPredictionEncoding:p,classPrediction:g}})}const Pu=Xe(Je());function ED(r,l,u){return Pu.tidy(()=>{const p=Ks(r,u.conv_0,[1,1]),g=Ks(p,u.conv_1,[2,2]),f=Ks(g,u.conv_2,[1,1]),I=Ks(f,u.conv_3,[2,2]),S=Ks(I,u.conv_4,[1,1]),x=Ks(S,u.conv_5,[2,2]),v=Ks(x,u.conv_6,[1,1]),O=Ks(v,u.conv_7,[2,2]),C=pa(l,u.box_predictor_0),U=pa(r,u.box_predictor_1),G=pa(g,u.box_predictor_2),ne=pa(I,u.box_predictor_3),te=pa(x,u.box_predictor_4),oe=pa(O,u.box_predictor_5),ge=Pu.concat([C.boxPredictionEncoding,U.boxPredictionEncoding,G.boxPredictionEncoding,ne.boxPredictionEncoding,te.boxPredictionEncoding,oe.boxPredictionEncoding],1),fe=Pu.concat([C.classPrediction,U.classPrediction,G.classPrediction,ne.classPrediction,te.classPrediction,oe.classPrediction],1);return{boxPredictions:ge,classPredictions:fe}})}class Vs{constructor({minConfidence:r,maxResults:l}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=r||.5,this._maxResults=l||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}}const Li=Xe(Je());class jo extends In{constructor(){super("SsdMobilenetv1")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("SsdMobilenetv1 - load model before inference");return Li.tidy(()=>{const u=Li.cast(r.toBatchTensor(512,!1),"float32"),p=Li.sub(Li.mul(u,Li.scalar(.007843137718737125)),Li.scalar(1)),g=CD(p,l.mobilenetv1),{boxPredictions:f,classPredictions:I}=ED(g.out,g.conv11,l.prediction_layer);return OD(f,I,l.output_layer)})}async forward(r){return this.forwardInput(await Tt(r))}async locateFaces(r,l={}){const{maxResults:u,minConfidence:p}=new Vs(l),g=await Tt(r),{boxes:f,scores:I}=this.forwardInput(g),S=f[0],x=I[0];for(let fe=1;fe<f.length;fe++)f[fe].dispose(),I[fe].dispose();const v=Array.from(await x.data()),O=.5,C=RD(S,v,u,O,p),U=g.getReshapedInputDimensions(0),G=g.inputSize,ne=G/U.width,te=G/U.height,oe=S.arraySync(),ge=C.map(fe=>{const[Ae,Te]=[Math.max(0,oe[fe][0]),Math.min(1,oe[fe][2])].map(Ct=>Ct*te),[Ve,rt]=[Math.max(0,oe[fe][1]),Math.min(1,oe[fe][3])].map(Ct=>Ct*ne);return new Wt(v[fe],new vc(Ve,Ae,rt-Ve,Te-Ae),{height:g.getInputHeight(0),width:g.getInputWidth(0)})});return S.dispose(),x.dispose(),ge}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(r){return ND(r)}extractParams(r){return vD(r)}}function $S(r){const l=new jo;return l.extractWeights(r),l}function UC(r){return $S(r)}class BC extends jo{}const DD=.4,kD=[new qe(.738768,.874946),new qe(2.42204,2.65704),new qe(4.30971,7.04493),new qe(10.246,4.59428),new qe(12.6868,11.8741)],FD=[new qe(1.603231,2.094468),new qe(6.041143,7.080126),new qe(2.882459,3.518061),new qe(4.266906,5.178857),new qe(9.041765,10.66308)],_D=[117.001,114.697,97.404],WD="tiny_yolov2_model",$D="tiny_yolov2_separable_conv_model";const Fg=r=>typeof r=="number";function Tm(r){if(!r)throw new Error(`invalid config: ${r}`);if(typeof r.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${r.withSeparableConvs}`);if(!Fg(r.iouThreshold)||r.iouThreshold<0||r.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${r.iouThreshold}`);if(!Array.isArray(r.classes)||!r.classes.length||!r.classes.every(l=>typeof l=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(r.classes)}`);if(!Array.isArray(r.anchors)||!r.anchors.length||!r.anchors.map(l=>l||{}).every(l=>Fg(l.x)&&Fg(l.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(r.anchors)}`);if(r.meanRgb&&(!Array.isArray(r.meanRgb)||r.meanRgb.length!==3||!r.meanRgb.every(Fg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(r.meanRgb)}`)}const Xs=Xe(Je());function Qc(r){return Xs.tidy(()=>{const l=Xs.mul(r,Xs.scalar(.10000000149011612));return Xs.add(Xs.relu(Xs.sub(r,l)),l)})}const Js=Xe(Je());function Lr(r,l){return Js.tidy(()=>{let u=Js.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return u=Js.conv2d(u,l.conv.filters,[1,1],"valid"),u=Js.sub(u,l.bn.sub),u=Js.mul(u,l.bn.truediv),u=Js.add(u,l.conv.bias),Qc(u)})}const mo=Xe(Je());function Sr(r,l){return mo.tidy(()=>{let u=mo.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return u=mo.separableConv2d(u,l.depthwise_filter,l.pointwise_filter,[1,1],"valid"),u=mo.add(u,l.bias),Qc(u)})}const rx=Xe(Je());function kZ(r,l){const u=Yc(r,l);function p(I,S){const x=rx.tensor1d(r(I)),v=rx.tensor1d(r(I));return l.push({paramPath:`${S}/sub`},{paramPath:`${S}/truediv`}),{sub:x,truediv:v}}function g(I,S,x){const v=u(I,S,3,`${x}/conv`),O=p(S,`${x}/bn`);return{conv:v,bn:O}}const f=Hc(r,l);return{extractConvParams:u,extractConvWithBatchNormParams:g,extractSeparableConvParams:f}}function UD(r,l,u,p){const{extractWeights:g,getRemainingWeights:f}=Gn(r),I=[],{extractConvParams:S,extractConvWithBatchNormParams:x,extractSeparableConvParams:v}=kZ(g,I);let O;if(l.withSeparableConvs){const[C,U,G,ne,te,oe,ge,fe,Ae]=p,Te=l.isFirstLayerConv2d?S(C,U,3,"conv0"):v(C,U,"conv0"),Ve=v(U,G,"conv1"),rt=v(G,ne,"conv2"),Ct=v(ne,te,"conv3"),Ut=v(te,oe,"conv4"),Kt=v(oe,ge,"conv5"),Dn=fe?v(ge,fe,"conv6"):void 0,An=Ae?v(fe,Ae,"conv7"):void 0,vn=S(Ae||fe||ge,5*u,1,"conv8");O={conv0:Te,conv1:Ve,conv2:rt,conv3:Ct,conv4:Ut,conv5:Kt,conv6:Dn,conv7:An,conv8:vn}}else{const[C,U,G,ne,te,oe,ge,fe,Ae]=p,Te=x(C,U,"conv0"),Ve=x(U,G,"conv1"),rt=x(G,ne,"conv2"),Ct=x(ne,te,"conv3"),Ut=x(te,oe,"conv4"),Kt=x(oe,ge,"conv5"),Dn=x(ge,fe,"conv6"),An=x(fe,Ae,"conv7"),vn=S(Ae,5*u,1,"conv8");O={conv0:Te,conv1:Ve,conv2:rt,conv3:Ct,conv4:Ut,conv5:Kt,conv6:Dn,conv7:An,conv8:vn}}if(f().length!==0)throw new Error(`weights remaing after extract: ${f().length}`);return{params:O,paramMappings:I}}function FZ(r,l){const u=ms(r,l);function p(S){const x=u(`${S}/sub`,1),v=u(`${S}/truediv`,1);return{sub:x,truediv:v}}function g(S){const x=u(`${S}/filters`,4),v=u(`${S}/bias`,1);return{filters:x,bias:v}}function f(S){const x=g(`${S}/conv`),v=p(`${S}/bn`);return{conv:x,bn:v}}const I=qc(u);return{extractConvParams:g,extractConvWithBatchNormParams:f,extractSeparableConvParams:I}}function BD(r,l){const u=[],{extractConvParams:p,extractConvWithBatchNormParams:g,extractSeparableConvParams:f}=FZ(r,u);let I;if(l.withSeparableConvs){const S=l.filterSizes&&l.filterSizes.length||9;I={conv0:l.isFirstLayerConv2d?p("conv0"):f("conv0"),conv1:f("conv1"),conv2:f("conv2"),conv3:f("conv3"),conv4:f("conv4"),conv5:f("conv5"),conv6:S>7?f("conv6"):void 0,conv7:S>8?f("conv7"):void 0,conv8:p("conv8")}}else I={conv0:g("conv0"),conv1:g("conv1"),conv2:g("conv2"),conv3:g("conv3"),conv4:g("conv4"),conv5:g("conv5"),conv6:g("conv6"),conv7:g("conv7"),conv8:p("conv8")};return zn(r,u),{params:I,paramMappings:u}}var Am;(function(r){r[r.XS=224]="XS",r[r.SM=320]="SM",r[r.MD=416]="MD",r[r.LG=608]="LG"})(Am||(Am={}));class $i{constructor({inputSize:r,scoreThreshold:l}={}){this._name="TinyYolov2Options";if(this._inputSize=r||416,this._scoreThreshold=l||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}}const kt=Xe(Je());class el extends In{constructor(r){super("TinyYolov2");Tm(r),this._config=r}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(r,l){let u=Lr(r,l.conv0);return u=kt.maxPool(u,[2,2],[2,2],"same"),u=Lr(u,l.conv1),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Lr(u,l.conv2),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Lr(u,l.conv3),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Lr(u,l.conv4),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Lr(u,l.conv5),u=kt.maxPool(u,[2,2],[1,1],"same"),u=Lr(u,l.conv6),u=Lr(u,l.conv7),da(u,l.conv8,"valid",!1)}runMobilenet(r,l){let u=this.config.isFirstLayerConv2d?Qc(da(r,l.conv0,"valid",!1)):Sr(r,l.conv0);return u=kt.maxPool(u,[2,2],[2,2],"same"),u=Sr(u,l.conv1),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Sr(u,l.conv2),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Sr(u,l.conv3),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Sr(u,l.conv4),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Sr(u,l.conv5),u=kt.maxPool(u,[2,2],[1,1],"same"),u=l.conv6?Sr(u,l.conv6):u,u=l.conv7?Sr(u,l.conv7):u,da(u,l.conv8,"valid",!1)}forwardInput(r,l){const{params:u}=this;if(!u)throw new Error("TinyYolov2 - load model before inference");return kt.tidy(()=>{let p=kt.cast(r.toBatchTensor(l,!1),"float32");return p=this.config.meanRgb?Ys(p,this.config.meanRgb):p,p=p.div(kt.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(p,u):this.runTinyYolov2(p,u)})}async forward(r,l){return await this.forwardInput(await Tt(r),l)}async detect(r,l={}){const{inputSize:u,scoreThreshold:p}=new $i(l),g=await Tt(r),f=await this.forwardInput(g,u),I=kt.tidy(()=>kt.unstack(f)[0].expandDims()),S={width:g.getInputWidth(0),height:g.getInputHeight(0)},x=await this.extractBoxes(I,g.getReshapedInputDimensions(0),p);f.dispose(),I.dispose();const v=x.map(te=>te.box),O=x.map(te=>te.score),C=x.map(te=>te.classScore),U=x.map(te=>this.config.classes[te.label]),G=Cm(v.map(te=>te.rescale(u)),O,this.config.iouThreshold,!0),ne=G.map(te=>new Qo(O[te],C[te],U[te],v[te],S));return ne}getDefaultModelName(){return""}extractParamsFromWeigthMap(r){return BD(r,this.config)}extractParams(r){const l=this.config.filterSizes||el.DEFAULT_FILTER_SIZES,u=l?l.length:void 0;if(u!==7&&u!==8&&u!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${u} filterSizes in config`);return UD(r,this.config,this.boxEncodingSize,l)}async extractBoxes(r,l,u){const{width:p,height:g}=l,f=Math.max(p,g),I=f/p,S=f/g,x=r.shape[1],v=this.config.anchors.length,[O,C,U]=kt.tidy(()=>{const oe=r.reshape([x,x,v,this.boxEncodingSize]),ge=oe.slice([0,0,0,0],[x,x,v,4]),fe=oe.slice([0,0,0,4],[x,x,v,1]),Ae=this.withClassScores?kt.softmax(oe.slice([0,0,0,5],[x,x,v,this.config.classes.length]),3):kt.scalar(0);return[ge,fe,Ae]}),G=[],ne=await C.array(),te=await O.array();for(let oe=0;oe<x;oe++)for(let ge=0;ge<x;ge++)for(let fe=0;fe<v;fe++){const Ae=Sc(ne[oe][ge][fe][0]);if(!u||Ae>u){const Te=(ge+Sc(te[oe][ge][fe][0]))/x*I,Ve=(oe+Sc(te[oe][ge][fe][1]))/x*S,rt=Math.exp(te[oe][ge][fe][2])*this.config.anchors[fe].x/x*I,Ct=Math.exp(te[oe][ge][fe][3])*this.config.anchors[fe].y/x*S,Ut=Te-rt/2,Kt=Ve-Ct/2,Dn={row:oe,col:ge,anchor:fe},{classScore:An,label:vn}=this.withClassScores?await this.extractPredictedClass(U,Dn):{classScore:1,label:0};G.push({box:new Cc(Ut,Kt,Ut+rt,Kt+Ct),score:Ae,classScore:Ae*An,label:vn,...Dn})}}return O.dispose(),C.dispose(),U.dispose(),G}async extractPredictedClass(r,l){const{row:u,col:p,anchor:g}=l,f=await r.array();return Array(this.config.classes.length).fill(0).map((I,S)=>f[u][p][g][S]).map((I,S)=>({classScore:I,label:S})).reduce((I,S)=>I.classScore>S.classScore?I:S)}}el.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];class wc extends el{constructor(r=!0){const l=Object.assign({},{withSeparableConvs:r,iouThreshold:DD,classes:["face"]},r?{anchors:FD,meanRgb:_D}:{anchors:kD,withClassScores:!0});super(l)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(r,l){const u=await this.detect(r,l);return u.map(p=>new Wt(p.score,p.relativeBox,{width:p.imageWidth,height:p.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?$D:WD}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}function WC(r,l=!0){const u=new wc(l);return u.extractWeights(r),u}class vm extends $i{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}}class Hs{async then(r){return r(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}}const ox=Xe(Je());async function ma(r,l,u,p,g=({alignedRect:f})=>f){const f=r.map(x=>Jr(x)?g(x):x.detection),I=p||(l instanceof ox.Tensor?await Jo(l,f):await Xo(l,f)),S=await u(I);return I.forEach(x=>x instanceof ox.Tensor&&x.dispose()),S}async function tl(r,l,u,p,g){return ma([r],l,async f=>u(f[0]),p,g)}const MD=.4,PD=[new qe(1.603231,2.094468),new qe(6.041143,7.080126),new qe(2.882459,3.518061),new qe(4.266906,5.178857),new qe(9.041765,10.66308)],zD=[117.001,114.697,97.404];class Lc extends el{constructor(){const r={withSeparableConvs:!0,iouThreshold:MD,classes:["face"],anchors:PD,meanRgb:zD,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(r)}get anchors(){return this.config.anchors}async locateFaces(r,l){const u=await this.detect(r,l);return u.map(p=>new Wt(p.score,p.relativeBox,{width:p.imageWidth,height:p.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}const ht={ssdMobilenetv1:new jo,tinyFaceDetector:new Lc,tinyYolov2:new wc,faceLandmark68Net:new xc,faceLandmark68TinyNet:new Um,faceRecognitionNet:new Ic,faceExpressionNet:new Mm,ageGenderNet:new jm},BS=(r,l)=>ht.ssdMobilenetv1.locateFaces(r,l),zC=(r,l)=>ht.tinyFaceDetector.locateFaces(r,l),GC=(r,l)=>ht.tinyYolov2.locateFaces(r,l),MS=r=>ht.faceLandmark68Net.detectLandmarks(r),VC=r=>ht.faceLandmark68TinyNet.detectLandmarks(r),YC=r=>ht.faceRecognitionNet.computeFaceDescriptor(r),HC=r=>ht.faceExpressionNet.predictExpressions(r),qC=r=>ht.ageGenderNet.predictAgeAndGender(r),PS=r=>ht.ssdMobilenetv1.load(r),jC=r=>ht.tinyFaceDetector.load(r),KC=r=>ht.tinyYolov2.load(r),XC=r=>ht.faceLandmark68Net.load(r),JC=r=>ht.faceLandmark68TinyNet.load(r),ZC=r=>ht.faceRecognitionNet.load(r),QC=r=>ht.faceExpressionNet.load(r),e2=r=>ht.ageGenderNet.load(r),t2=PS,n2=BS,s2=MS;class GD extends Hs{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.extractedFaces=u}}class Vu extends GD{async run(){const r=await this.parentTask,l=await ma(r,this.input,async u=>await Promise.all(u.map(p=>ht.faceExpressionNet.predictExpressions(p))),this.extractedFaces);return r.map((u,p)=>fu(u,l[p]))}withAgeAndGender(){return new zu(this,this.input)}}class Yu extends GD{async run(){const r=await this.parentTask;if(!r)return;const l=await tl(r,this.input,u=>ht.faceExpressionNet.predictExpressions(u),this.extractedFaces);return fu(r,l)}withAgeAndGender(){return new Gu(this,this.input)}}class il extends Vu{withAgeAndGender(){return new nl(this,this.input)}withFaceDescriptors(){return new Kr(this,this.input)}}class rl extends Yu{withAgeAndGender(){return new sl(this,this.input)}withFaceDescriptor(){return new Xr(this,this.input)}}class VD extends Hs{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.extractedFaces=u}}class zu extends VD{async run(){const r=await this.parentTask,l=await ma(r,this.input,async u=>await Promise.all(u.map(p=>ht.ageGenderNet.predictAgeAndGender(p))),this.extractedFaces);return r.map((u,p)=>{const{age:g,gender:f,genderProbability:I}=l[p];return yu(mu(u,f,I),g)})}withFaceExpressions(){return new Vu(this,this.input)}}class Gu extends VD{async run(){const r=await this.parentTask;if(!r)return;const{age:l,gender:u,genderProbability:p}=await tl(r,this.input,g=>ht.ageGenderNet.predictAgeAndGender(g),this.extractedFaces);return yu(mu(r,u,p),l)}withFaceExpressions(){return new Yu(this,this.input)}}class nl extends zu{withFaceExpressions(){return new il(this,this.input)}withFaceDescriptors(){return new Kr(this,this.input)}}class sl extends Gu{withFaceExpressions(){return new rl(this,this.input)}withFaceDescriptor(){return new Xr(this,this.input)}}class Wm extends Hs{constructor(r,l){super();this.parentTask=r;this.input=l}}class Kr extends Wm{async run(){const r=await this.parentTask,l=await ma(r,this.input,u=>Promise.all(u.map(p=>ht.faceRecognitionNet.computeFaceDescriptor(p))),null,u=>u.landmarks.align(null,{useDlibAlignment:!0}));return l.map((u,p)=>gu(r[p],u))}withFaceExpressions(){return new il(this,this.input)}withAgeAndGender(){return new nl(this,this.input)}}class Xr extends Wm{async run(){const r=await this.parentTask;if(!r)return;const l=await tl(r,this.input,u=>ht.faceRecognitionNet.computeFaceDescriptor(u),null,u=>u.landmarks.align(null,{useDlibAlignment:!0}));return gu(r,l)}withFaceExpressions(){return new rl(this,this.input)}withAgeAndGender(){return new sl(this,this.input)}}const Hu=Xe(Je());class km extends Hs{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.useTinyLandmarkNet=u}get landmarkNet(){return this.useTinyLandmarkNet?ht.faceLandmark68TinyNet:ht.faceLandmark68Net}}class Fm extends km{async run(){const r=await this.parentTask,l=r.map(g=>g.detection),u=this.input instanceof Hu.Tensor?await Jo(this.input,l):await Xo(this.input,l),p=await Promise.all(u.map(g=>this.landmarkNet.detectLandmarks(g)));return u.forEach(g=>g instanceof Hu.Tensor&&g.dispose()),r.map((g,f)=>Ko(g,p[f]))}withFaceExpressions(){return new il(this,this.input)}withAgeAndGender(){return new nl(this,this.input)}withFaceDescriptors(){return new Kr(this,this.input)}}class _m extends km{async run(){const r=await this.parentTask;if(!r)return;const{detection:l}=r,u=this.input instanceof Hu.Tensor?await Jo(this.input,[l]):await Xo(this.input,[l]),p=await this.landmarkNet.detectLandmarks(u[0]);return u.forEach(g=>g instanceof Hu.Tensor&&g.dispose()),Ko(r,p)}withFaceExpressions(){return new rl(this,this.input)}withAgeAndGender(){return new sl(this,this.input)}withFaceDescriptor(){return new Xr(this,this.input)}}class Em extends Hs{constructor(r,l=new Vs){super();this.input=r;this.options=l}}class pu extends Em{async run(){const{input:r,options:l}=this,u=l instanceof vm?p=>ht.tinyFaceDetector.locateFaces(p,l):l instanceof Vs?p=>ht.ssdMobilenetv1.locateFaces(p,l):l instanceof $i?p=>ht.tinyYolov2.locateFaces(p,l):null;if(!u)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return u(r)}runAndExtendWithFaceDetections(){return new Promise(async r=>{const l=await this.run();return r(l.map(u=>Zr({},u)))})}withFaceLandmarks(r=!1){return new Fm(this.runAndExtendWithFaceDetections(),this.input,r)}withFaceExpressions(){return new Vu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new zu(this.runAndExtendWithFaceDetections(),this.input)}}class Dm extends Em{async run(){const r=await new pu(this.input,this.options);let l=r[0];return r.forEach(u=>{u.score>l.score&&(l=u)}),l}runAndExtendWithFaceDetection(){return new Promise(async r=>{const l=await this.run();return r(l?Zr({},l):void 0)})}withFaceLandmarks(r=!1){return new _m(this.runAndExtendWithFaceDetection(),this.input,r)}withFaceExpressions(){return new Yu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Gu(this.runAndExtendWithFaceDetection(),this.input)}}function i2(r,l=new Vs){return new Dm(r,l)}function du(r,l=new Vs){return new pu(r,l)}async function zS(r,l){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await du(r,new Vs(l?{minConfidence:l}:{})).withFaceLandmarks().withFaceDescriptors()}async function r2(r,l={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await du(r,new $i(l)).withFaceLandmarks().withFaceDescriptors()}const o2=zS;function Pm(r,l){if(r.length!==l.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");const u=Array.from(r),p=Array.from(l);return Math.sqrt(u.map((g,f)=>g-p[f]).reduce((g,f)=>g+Math.pow(f,2),0))}class GS{constructor(r,l=.6){this._distanceThreshold=l;const u=Array.isArray(r)?r:[r];if(!u.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let p=1;const g=()=>`person ${p++}`;this._labeledDescriptors=u.map(f=>{if(f instanceof so)return f;if(f instanceof Float32Array)return new so(g(),[f]);if(f.descriptor&&f.descriptor instanceof Float32Array)return new so(g(),[f.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(r,l){return l.map(u=>Pm(u,r)).reduce((u,p)=>u+p,0)/(l.length||1)}matchDescriptor(r){return this.labeledDescriptors.map(({descriptors:l,label:u})=>new Lu(u,this.computeMeanDistance(r,l))).reduce((l,u)=>l.distance<u.distance?l:u)}findBestMatch(r){const l=this.matchDescriptor(r);return l.distance<this.distanceThreshold?l:new Lu("unknown",l.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(r=>r.toJSON())}}static fromJSON(r){const l=r.labeledDescriptors.map(u=>so.fromJSON(u));return new GS(l,r.distanceThreshold)}}function $C(r){const l=new Lc;return l.extractWeights(r),l}function US(r,l){const{width:u,height:p}=new Zn(l.width,l.height);if(u<=0||p<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:u,height:p})}`);if(Array.isArray(r))return r.map(g=>US(g,{width:u,height:p}));if(Jr(r)){const g=r.detection.forSize(u,p),f=r.unshiftedLandmarks.forSize(g.box.width,g.box.height);return Ko(Zr(r,g),f)}return mi(r)?Zr(r,r.detection.forSize(u,p)):r instanceof Ns||r instanceof Wt?r.forSize(u,p):r}var FC="0.7.4";return g2();})();
+`;return U[U.length-1]=" "+U[U.length-1]+"]"+(f?"":ne),U}function vu(r){const l=[];for(let u=0;u<r.length;u+=2)l.push([r[u],r[u+1]]);return l}class lO{constructor(r,l,u){if(this.dtype=l,this.shape=r.slice(),this.size=qt(r),u!=null){const p=u.length;Z(p===this.size,()=>`Length of values '${p}' does not match the size inferred by the shape '${this.size}'.`)}if(l==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=u||JR(l,this.size),this.strides=xu(r)}set(r,...l){l.length===0&&(l=[0]),Z(l.length===this.rank,()=>`The number of provided coordinates (${l.length}) must match the rank (${this.rank})`);const u=this.locToIndex(l);this.values[u]=r}get(...r){r.length===0&&(r=[0]);let l=0;for(const p of r){if(p<0||p>=this.shape[l]){const g=`Requested out of range element at ${r}.   Buffer shape=${this.shape}`;throw new Error(g)}l++}let u=r[r.length-1];for(let p=0;p<r.length-1;++p)u+=this.strides[p]*r[p];return this.values[u]}locToIndex(r){if(this.rank===0)return 0;if(this.rank===1)return r[0];let l=r[r.length-1];for(let u=0;u<r.length-1;++u)l+=this.strides[u]*r[u];return l}indexToLoc(r){if(this.rank===0)return[];if(this.rank===1)return[r];const l=new Array(this.shape.length);for(let u=0;u<l.length-1;++u)l[u]=Math.floor(r/this.strides[u]),r-=l[u]*this.strides[u];return l[l.length-1]=r,l}get rank(){return this.shape.length}toTensor(){return zi().makeTensor(this.values,this.shape,this.dtype)}}let zi=null,Uc=null,H7=null;function hO(r){zi=r}function uO(r){Uc=r}function dO(r){H7=r}class xn{constructor(r,l,u,p){this.kept=!1,this.isDisposedInternal=!1,this.shape=r.slice(),this.dtype=l||"float32",this.size=qt(r),this.strides=xu(r),this.dataId=u,this.id=p,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const r=await this.data();return Uc.buffer(this.shape,this.dtype,r)}bufferSync(){return Uc.buffer(this.shape,this.dtype,this.dataSync())}async array(){const r=await this.data();return cI(this.shape,r)}arraySync(){return cI(this.shape,this.dataSync())}async data(){this.throwIfDisposed();const r=zi().read(this.dataId);if(this.dtype==="string"){const l=await r;try{return l.map(u=>hI(u))}catch(u){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return r}dataSync(){this.throwIfDisposed();const r=zi().readSync(this.dataId);if(this.dtype==="string")try{return r.map(l=>hI(l))}catch(l){throw new Error("Failed to decode the string bytes into utf-8. 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p=++this.pendingBackendInitId,g=u.then(f=>p<this.pendingBackendInitId?!1:(this.registry[r]=f,this.pendingBackendInit=null,!0)).catch(f=>(p<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${r} failed`),console.warn(f.stack||f.message)),!1));return this.pendingBackendInit=g,{success:g,asyncInit:!0}}else return this.registry[r]=u,{success:!0,asyncInit:!1}}catch(u){return console.warn(`Initialization of backend ${r} failed`),console.warn(u.stack||u.message),{success:!1,asyncInit:!1}}}removeBackend(r){if(!(r in this.registryFactory))throw new Error(`${r} backend not found in registry`);this.backendName===r&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,r in this.registry&&(this.disposeRegisteredKernels(r),this.registry[r].dispose(),delete this.registry[r]),delete 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yZ=[f1,g1,y1,b1,w1,L1,S1,I1,x1,T1,A1,v1,C1,O1,E1,D1,k1,F1,_1,W1,$1,B1,U1,P1,z1,G1,V1,Y1,H1,q1,j1,K1,X1,J1,Q1,Z1,eE,sE,iE,rE,oE,aE,cE,lE,hE,uE,pE,JI,JI,mE,gE,bE,wE,LE,SE,IE,xE,TE,AE,ZI,ZI,vE,NE,CE,RE,OE,EE,DE,kE,FE,_E,WE,$E,UE,BE,ME,PE,zE,GE,VE,YE,QI,QI,ex,ex,HE,jE,qE,KE,XE,JE,ZE,QE,eD,tD,nD,sD,iD];for(const r of yZ)jR(r);function Gm(r,l,u=!1){const{Image:p,Canvas:g}=mt.getEnv();if(!(r instanceof p||r instanceof g))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");const f=to(r),I=l/Math.max(f.height,f.width),S=I*f.width,x=I*f.height,v=ea({width:l,height:l}),O=r instanceof g?r:Nc(r),C=Math.abs(S-x)/2,U=u&&S<x?C:0,G=u&&x<S?C:0;return Un(v).drawImage(O,U,G,S,x),v}class hr{constructor(r,l=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];if(!Array.isArray(r))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have 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this._inputDimensions[r]}getInputHeight(r){return this._inputDimensions[r][0]}getInputWidth(r){return this._inputDimensions[r][1]}getReshapedInputDimensions(r){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");const l=this.getInputWidth(r),u=this.getInputHeight(r);return HS({width:l,height:u},this.inputSize)}toBatchTensor(r,l=!0){return this._inputSize=r,IO(()=>{const u=Bi(this.batchSize,0,1).map(g=>{const f=this.getInput(g);if(f instanceof xn){let I=Cs(f)?f:f.expandDims();return I=Nm(I,l),(I.shape[1]!==r||I.shape[2]!==r)&&(I=d1.resizeBilinear(I,[r,r])),I.as3D(r,r,3)}if(f instanceof mt.getEnv().Canvas)return LI.fromPixels(Gm(f,r,l));throw new Error(`toBatchTensor - at batchIdx ${g}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${f}`)}),p=qs(u.map(g=>Le(g,"float32"))).as4D(this.batchSize,r,r,3);return p})}}async function Tt(r){if(r instanceof hr)return r;let l=Array.isArray(r)?r:[r];if(!l.length)throw new Error("toNetInput - empty array passed as input");const u=g=>Array.isArray(r)?` at input index ${g}:`:"",p=l.map(eo);return p.forEach((g,f)=>{if(!bu(g)&&!dr(g)&&!Cs(g))throw typeof l[f]=="string"?new Error(`toNetInput -${u(f)} string passed, but could not resolve HTMLElement for element id ${l[f]}`):new Error(`toNetInput -${u(f)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(Cs(g)){const I=g.shape[0];if(I!==1)throw new Error(`toNetInput -${u(f)} tf.Tensor4D with batchSize ${I} passed, but not supported in input array`)}}),await Promise.all(p.map(g=>bu(g)&&qm(g))),new hr(p,Array.isArray(r))}async function Zo(r,l){const{Canvas:u}=mt.getEnv();let p=r;if(!(r instanceof u)){const I=await Tt(r);if(I.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");const S=I.getInput(0);p=S instanceof u?S:await Vm(S)}const g=Un(p),f=l.map(I=>I instanceof Wt?I.forSize(p.width,p.height).box.floor():I).map(I=>I.clipAtImageBorders(p.width,p.height));return f.map(({x:I,y:S,width:x,height:v})=>{const O=ea({width:x,height:v});return Un(O).putImageData(g.getImageData(I,S,x,v),0,0),O})}const wg=Xe(Je());async function Qo(r,l){if(!dr(r)&&!Cs(r))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(Cs(r)&&r.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return wg.tidy(()=>{const[u,p,g]=r.shape.slice(Cs(r)?1:0),f=l.map(S=>S instanceof Wt?S.forSize(p,u).box:S).map(S=>S.clipAtImageBorders(p,u)),I=f.map(({x:S,y:x,width:v,height:O})=>wg.slice3d(r.as3D(u,p,g),[x,S,0],[O,v,g]));return I})}async function no(r,l){const u=mt.getEnv().fetch,p=await u(r,l);if(!(p.status<400))throw new Error(`failed to fetch: (${p.status}) ${p.statusText}, from url: ${p.url}`);return p}async function p2(r){const l=await no(r),u=await l.blob();if(!u.type.startsWith("image/"))throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${u.type}, for url: ${l.url}`);return Hm(u)}async function Ym(r){return(await no(r)).json()}async function d2(r){return new Float32Array(await(await no(r)).arrayBuffer())}function Lg(r,l){const u=`${l}-weights_manifest.json`;if(!r)return{modelBaseUri:"",manifestUri:u};if(r==="/")return{modelBaseUri:"/",manifestUri:`/${u}`};const p=r.startsWith("http://")?"http://":r.startsWith("https://")?"https://":"";r=r.replace(p,"");const g=r.split("/").filter(S=>S),f=r.endsWith(".json")?g[g.length-1]:u;let I=p+(r.endsWith(".json")?g.slice(0,g.length-1):g).join("/");return I=r.startsWith("/")?`/${I}`:I,{modelBaseUri:I,manifestUri:I==="/"?`/${f}`:`${I}/${f}`}}const rD=Xe(Je());async function zm(r,l){const{manifestUri:u,modelBaseUri:p}=Lg(r,l);let g=await Ym(u);return rD.io.loadWeights(g,p)}function u2(r,l,u=!1){const{width:p,height:g}=u?to(l):l;return r.width=p,r.height=g,{width:p,height:g}}const br=Xe(Je());class In{constructor(r){this._name=r;this._params=void 0;this._paramMappings=[]}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(r){const{obj:l,objProp:u}=this.traversePropertyPath(r);return l[u]}reassignParamFromPath(r,l){const{obj:u,objProp:p}=this.traversePropertyPath(r);u[p].dispose(),u[p]=l}getParamList(){return this._paramMappings.map(({paramPath:r})=>({path:r,tensor:this.getParamFromPath(r)}))}getTrainableParams(){return this.getParamList().filter(r=>r.tensor instanceof br.Variable)}getFrozenParams(){return this.getParamList().filter(r=>!(r.tensor instanceof br.Variable))}variable(){this.getFrozenParams().forEach(({path:r,tensor:l})=>{this.reassignParamFromPath(r,l.variable())})}freeze(){this.getTrainableParams().forEach(({path:r,tensor:l})=>{const u=br.tensor(l.dataSync());l.dispose(),this.reassignParamFromPath(r,u)})}dispose(r=!0){this.getParamList().forEach(l=>{if(r&&l.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${l.path}`);l.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:r})=>Array.from(r.dataSync())).reduce((r,l)=>r.concat(l)))}async load(r){if(r instanceof Float32Array){this.extractWeights(r);return}await this.loadFromUri(r)}async loadFromUri(r){if(r&&typeof r!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);const l=await zm(r,this.getDefaultModelName());this.loadFromWeightMap(l)}async loadFromDisk(r){if(r&&typeof r!="string")throw new Error(`${this._name}.loadFromDisk - expected model file path`);const{readFile:l}=mt.getEnv(),{manifestUri:u,modelBaseUri:p}=Lg(r,this.getDefaultModelName()),g=x=>Promise.all(x.map(v=>l(v).then(O=>O.buffer))),f=br.io.weightsLoaderFactory(g),I=JSON.parse((await l(u)).toString()),S=await f(I,p);this.loadFromWeightMap(S)}loadFromWeightMap(r){const{paramMappings:l,params:u}=this.extractParamsFromWeigthMap(r);this._paramMappings=l,this._params=u}extractWeights(r){const{paramMappings:l,params:u}=this.extractParams(r);this._paramMappings=l,this._params=u}traversePropertyPath(r){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");const l=r.split("/").reduce((g,f)=>{if(!g.nextObj.hasOwnProperty(f))throw new Error(`traversePropertyPath - object does not have property ${f}, for path ${r}`);return{obj:g.nextObj,objProp:f,nextObj:g.nextObj[f]}},{nextObj:this.params}),{obj:u,objProp:p}=l;if(!u||!p||!(u[p]instanceof br.Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${r}`);return{obj:u,objProp:p}}}const Hc=Xe(Je());function es(r,l,u){return Hc.tidy(()=>{let p=Hc.separableConv2d(r,l.depthwise_filter,l.pointwise_filter,u,"same");return p=Hc.add(p,l.bias),p})}const Dt=Xe(Je());function Sg(r,l,u=!1){return Dt.tidy(()=>{const p=Dt.relu(u?Dt.add(Dt.conv2d(r,l.conv0.filters,[2,2],"same"),l.conv0.bias):es(r,l.conv0,[2,2])),g=es(p,l.conv1,[1,1]),f=Dt.relu(Dt.add(p,g)),I=es(f,l.conv2,[1,1]);return Dt.relu(Dt.add(p,Dt.add(g,I)))})}function Wu(r,l,u=!1,p=!0){return Dt.tidy(()=>{const g=Dt.relu(u?Dt.add(Dt.conv2d(r,l.conv0.filters,p?[2,2]:[1,1],"same"),l.conv0.bias):es(r,l.conv0,p?[2,2]:[1,1])),f=es(g,l.conv1,[1,1]),I=Dt.relu(Dt.add(g,f)),S=es(I,l.conv2,[1,1]),x=Dt.relu(Dt.add(g,Dt.add(f,S))),v=es(x,l.conv3,[1,1]);return Dt.relu(Dt.add(g,Dt.add(f,Dt.add(S,v))))})}const uo=Xe(Je());function ma(r,l,u="same",p=!1){return uo.tidy(()=>{const g=uo.add(uo.conv2d(r,l.filters,[1,1],u),l.bias);return p?uo.relu(g):g})}function zn(r,l){Object.keys(r).forEach(u=>{l.some(p=>p.originalPath===u)||r[u].dispose()})}const Ig=Xe(Je());function qc(r,l){return function(u,p,g,f){const I=Ig.tensor4d(r(u*p*g*g),[g,g,u,p]),S=Ig.tensor1d(r(p));return l.push({paramPath:`${f}/filters`},{paramPath:`${f}/bias`}),{filters:I,bias:S}}}const xg=Xe(Je());function Tg(r,l){return function(u,p,g){const f=xg.tensor2d(r(u*p),[u,p]),I=xg.tensor1d(r(p));return l.push({paramPath:`${g}/weights`},{paramPath:`${g}/bias`}),{weights:f,bias:I}}}class tx{constructor(r,l,u){this.depthwise_filter=r;this.pointwise_filter=l;this.bias=u}}const $u=Xe(Je());function jc(r,l){return function(u,p,g){const f=$u.tensor4d(r(3*3*u),[3,3,u,1]),I=$u.tensor4d(r(u*p),[1,1,u,p]),S=$u.tensor1d(r(p));return l.push({paramPath:`${g}/depthwise_filter`},{paramPath:`${g}/pointwise_filter`},{paramPath:`${g}/bias`}),new tx(f,I,S)}}function Kc(r){return function(l){const u=r(`${l}/depthwise_filter`,4),p=r(`${l}/pointwise_filter`,4),g=r(`${l}/bias`,1);return new tx(u,p,g)}}function ms(r,l){return function(u,p,g){const f=r[u];if(!na(f,p))throw new Error(`expected weightMap[${u}] to be a Tensor${p}D, instead have ${f}`);return l.push({originalPath:u,paramPath:g||u}),f}}function Gn(r){let l=r;function u(g){const f=l.slice(0,g);return l=l.slice(g),f}function p(){return l}return{extractWeights:u,getRemainingWeights:p}}function Ag(r,l){const u=qc(r,l),p=jc(r,l);function g(I,S,x,v=!1){const O=v?u(I,S,3,`${x}/conv0`):p(I,S,`${x}/conv0`),C=p(S,S,`${x}/conv1`),U=p(S,S,`${x}/conv2`);return{conv0:O,conv1:C,conv2:U}}function f(I,S,x,v=!1){const{conv0:O,conv1:C,conv2:U}=g(I,S,x,v),G=p(S,S,`${x}/conv3`);return{conv0:O,conv1:C,conv2:U,conv3:G}}return{extractDenseBlock3Params:g,extractDenseBlock4Params:f}}function oD(r){const l=[],{extractWeights:u,getRemainingWeights:p}=Gn(r),{extractDenseBlock4Params:g}=Ag(u,l),f=g(3,32,"dense0",!0),I=g(32,64,"dense1"),S=g(64,128,"dense2"),x=g(128,256,"dense3");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{dense0:f,dense1:I,dense2:S,dense3:x}}}function vg(r){return function(l){const u=r(`${l}/filters`,4),p=r(`${l}/bias`,1);return{filters:u,bias:p}}}function Ng(r,l){const u=ms(r,l),p=vg(u),g=Kc(u);function f(S,x=!1){const v=x?p(`${S}/conv0`):g(`${S}/conv0`),O=g(`${S}/conv1`),C=g(`${S}/conv2`);return{conv0:v,conv1:O,conv2:C}}function I(S,x=!1){const v=x?p(`${S}/conv0`):g(`${S}/conv0`),O=g(`${S}/conv1`),C=g(`${S}/conv2`),U=g(`${S}/conv3`);return{conv0:v,conv1:O,conv2:C,conv3:U}}return{extractDenseBlock3Params:f,extractDenseBlock4Params:I}}function aD(r){const l=[],{extractDenseBlock4Params:u}=Ng(r,l),p={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2"),dense3:u("dense3")};return zn(r,l),{params:p,paramMappings:l}}const po=Xe(Je());class Cg extends In{constructor(){super("FaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceFeatureExtractor - load model before inference");return po.tidy(()=>{const u=po.cast(r.toBatchTensor(112,!0),"float32"),p=[122.782,117.001,104.298],g=Ys(u,p).div(po.scalar(255));let f=Wu(g,l.dense0,!0);return f=Wu(f,l.dense1),f=Wu(f,l.dense2),f=Wu(f,l.dense3),f=po.avgPool(f,[7,7],[2,2],"valid"),f})}async forward(r){return this.forwardInput(await Tt(r))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(r){return aD(r)}extractParams(r){return oD(r)}}const Xc=Xe(Je());function Uu(r,l){return Xc.tidy(()=>Xc.add(Xc.matMul(r,l.weights),l.bias))}function cD(r,l,u){const p=[],{extractWeights:g,getRemainingWeights:f}=Gn(r),I=Tg(g,p),S=I(l,u,"fc");if(f().length!==0)throw new Error(`weights remaing after extract: ${f().length}`);return{paramMappings:p,params:{fc:S}}}function lD(r){const l=[],u=ms(r,l);function p(f){const I=u(`${f}/weights`,2),S=u(`${f}/bias`,1);return{weights:I,bias:S}}const g={fc:p("fc")};return zn(r,l),{params:g,paramMappings:l}}function Rg(r){const l={},u={};return Object.keys(r).forEach(p=>{const g=p.startsWith("fc")?u:l;g[p]=r[p]}),{featureExtractorMap:l,classifierMap:u}}const hD=Xe(Je());class Og extends In{constructor(r,l){super(r);this._faceFeatureExtractor=l}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return hD.tidy(()=>{const u=r instanceof hr?this.faceFeatureExtractor.forwardInput(r):r;return Uu(u.as2D(u.shape[0],-1),l.fc)})}dispose(r=!0){this.faceFeatureExtractor.dispose(r),super.dispose(r)}loadClassifierParams(r){const{params:l,paramMappings:u}=this.extractClassifierParams(r);this._params=l,this._paramMappings=u}extractClassifierParams(r){return cD(r,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:u}=Rg(r);return this.faceFeatureExtractor.loadFromWeightMap(l),lD(u)}extractParams(r){const l=this.getClassifierChannelsIn(),u=this.getClassifierChannelsOut(),p=u*l+u,g=r.slice(0,r.length-p),f=r.slice(r.length-p);return this.faceFeatureExtractor.extractWeights(g),this.extractClassifierParams(f)}}const Bm=["neutral","happy","sad","angry","fearful","disgusted","surprised"];class Qr{constructor(r){if(r.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${r.length}`);Bm.forEach((l,u)=>{this[l]=r[u]})}asSortedArray(){return Bm.map(r=>({expression:r,probability:this[r]})).sort((r,l)=>l.probability-r.probability)}}const Jc=Xe(Je());class Mm extends Og{constructor(r=new Cg){super("FaceExpressionNet",r)}forwardInput(r){return Jc.tidy(()=>Jc.softmax(this.runNet(r)))}async forward(r){return this.forwardInput(await Tt(r))}async predictExpressions(r){const l=await Tt(r),u=await this.forwardInput(l),p=await Promise.all(Jc.unstack(u).map(async f=>{const I=await f.data();return f.dispose(),I}));u.dispose();const g=p.map(f=>new Qr(f));return l.isBatchInput?g:g[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}}function $m(r){return r.expressions instanceof Qr}function fu(r,l){const u={expressions:l};return Object.assign({},r,u)}function bZ(r,l,u=.1,p){const g=Array.isArray(l)?l:[l];g.forEach(f=>{const I=f instanceof Qr?f:$m(f)?f.expressions:void 0;if(!I)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");const S=I.asSortedArray(),x=S.filter(C=>C.probability>u),v=mi(f)?f.detection.box.bottomLeft:p||new qe(0,0),O=new Dc(x.map(C=>`${C.expression} (${sa(C.probability)})`),v);O.draw(r)})}function Jr(r){return mi(r)&&r.landmarks instanceof Ns&&r.unshiftedLandmarks instanceof Ns&&r.alignedRect instanceof Wt}function Jo(r,l){const{box:u}=r.detection,p=l.shiftBy(u.x,u.y),g=p.align(),{imageDims:f}=r.detection,I=new Wt(r.detection.score,g.rescale(f.reverse()),f),S={landmarks:p,unshiftedLandmarks:l,alignedRect:I};return Object.assign({},r,S)}class uD{constructor(r={}){const{drawLines:l=!0,drawPoints:u=!0,lineWidth:p,lineColor:g,pointSize:f,pointColor:I}=r;this.drawLines=l,this.drawPoints=u,this.lineWidth=p||1,this.pointSize=f||2,this.lineColor=g||"rgba(0, 255, 255, 1)",this.pointColor=I||"rgba(255, 0, 255, 1)"}}class dD{constructor(r,l={}){this.faceLandmarks=r,this.options=new uD(l)}draw(r){const l=Un(r),{drawLines:u,drawPoints:p,lineWidth:g,lineColor:f,pointSize:I,pointColor:S}=this.options;if(u&&this.faceLandmarks instanceof Rc&&(l.strokeStyle=f,l.lineWidth=g,ur(l,this.faceLandmarks.getJawOutline()),ur(l,this.faceLandmarks.getLeftEyeBrow()),ur(l,this.faceLandmarks.getRightEyeBrow()),ur(l,this.faceLandmarks.getNose()),ur(l,this.faceLandmarks.getLeftEye(),!0),ur(l,this.faceLandmarks.getRightEye(),!0),ur(l,this.faceLandmarks.getMouth(),!0)),p){l.strokeStyle=S,l.fillStyle=S;const x=v=>{l.beginPath(),l.arc(v.x,v.y,I,0,2*Math.PI),l.fill()};this.faceLandmarks.positions.forEach(x)}}}function wZ(r,l){const u=Array.isArray(l)?l:[l];u.forEach(p=>{const g=p instanceof Ns?p:Jr(p)?p.landmarks:void 0;if(!g)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new dD(g).draw(r)})}const Im={};uu(Im,{AnchorPosition:()=>Pi,DrawBox:()=>QS,DrawBoxOptions:()=>b2,DrawFaceLandmarks:()=>dD,DrawFaceLandmarksOptions:()=>uD,DrawTextField:()=>Dc,DrawTextFieldOptions:()=>Zm,drawContour:()=>ur,drawDetections:()=>E7,drawFaceExpressions:()=>bZ,drawFaceLandmarks:()=>wZ});function LZ(r,l){const u=qc(r,l),p=jc(r,l);function g(I,S,x){const v=p(I,S,`${x}/separable_conv0`),O=p(S,S,`${x}/separable_conv1`),C=u(I,S,1,`${x}/expansion_conv`);return{separable_conv0:v,separable_conv1:O,expansion_conv:C}}function f(I,S){const x=p(I,I,`${S}/separable_conv0`),v=p(I,I,`${S}/separable_conv1`),O=p(I,I,`${S}/separable_conv2`);return{separable_conv0:x,separable_conv1:v,separable_conv2:O}}return{extractConvParams:u,extractSeparableConvParams:p,extractReductionBlockParams:g,extractMainBlockParams:f}}function pD(r,l){const u=[],{extractWeights:p,getRemainingWeights:g}=Gn(r),{extractConvParams:f,extractSeparableConvParams:I,extractReductionBlockParams:S,extractMainBlockParams:x}=LZ(p,u),v=f(3,32,3,"entry_flow/conv_in"),O=S(32,64,"entry_flow/reduction_block_0"),C=S(64,128,"entry_flow/reduction_block_1"),U={conv_in:v,reduction_block_0:O,reduction_block_1:C},G={};Bi(l,0,1).forEach(ge=>{G[`main_block_${ge}`]=x(128,`middle_flow/main_block_${ge}`)});const ne=S(128,256,"exit_flow/reduction_block"),te=I(256,512,"exit_flow/separable_conv"),oe={reduction_block:ne,separable_conv:te};if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{paramMappings:u,params:{entry_flow:U,middle_flow:G,exit_flow:oe}}}function SZ(r,l){const u=ms(r,l),p=vg(u),g=Kc(u);function f(S){const x=g(`${S}/separable_conv0`),v=g(`${S}/separable_conv1`),O=p(`${S}/expansion_conv`);return{separable_conv0:x,separable_conv1:v,expansion_conv:O}}function I(S){const x=g(`${S}/separable_conv0`),v=g(`${S}/separable_conv1`),O=g(`${S}/separable_conv2`);return{separable_conv0:x,separable_conv1:v,separable_conv2:O}}return{extractConvParams:p,extractSeparableConvParams:g,extractReductionBlockParams:f,extractMainBlockParams:I}}function mD(r,l){const u=[],{extractConvParams:p,extractSeparableConvParams:g,extractReductionBlockParams:f,extractMainBlockParams:I}=SZ(r,u),S=p("entry_flow/conv_in"),x=f("entry_flow/reduction_block_0"),v=f("entry_flow/reduction_block_1"),O={conv_in:S,reduction_block_0:x,reduction_block_1:v},C={};Bi(l,0,1).forEach(te=>{C[`main_block_${te}`]=I(`middle_flow/main_block_${te}`)});const U=f("exit_flow/reduction_block"),G=g("exit_flow/separable_conv"),ne={reduction_block:U,separable_conv:G};return zn(r,u),{params:{entry_flow:O,middle_flow:C,exit_flow:ne},paramMappings:u}}const tn=Xe(Je());function fD(r,l,u){return tn.add(tn.conv2d(r,l.filters,u,"same"),l.bias)}function nx(r,l,u=!0){let p=u?tn.relu(r):r;return p=es(p,l.separable_conv0,[1,1]),p=es(tn.relu(p),l.separable_conv1,[1,1]),p=tn.maxPool(p,[3,3],[2,2],"same"),p=tn.add(p,fD(r,l.expansion_conv,[2,2])),p}function IZ(r,l){let u=es(tn.relu(r),l.separable_conv0,[1,1]);return u=es(tn.relu(u),l.separable_conv1,[1,1]),u=es(tn.relu(u),l.separable_conv2,[1,1]),u=tn.add(u,r),u}class gD extends In{constructor(r){super("TinyXception");this._numMainBlocks=r}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyXception - load model before inference");return tn.tidy(()=>{const u=tn.cast(r.toBatchTensor(112,!0),"float32"),p=[122.782,117.001,104.298],g=Ys(u,p).div(tn.scalar(256));let f=tn.relu(fD(g,l.entry_flow.conv_in,[2,2]));return f=nx(f,l.entry_flow.reduction_block_0,!1),f=nx(f,l.entry_flow.reduction_block_1),Bi(this._numMainBlocks,0,1).forEach(I=>{f=IZ(f,l.middle_flow[`main_block_${I}`])}),f=nx(f,l.exit_flow.reduction_block),f=tn.relu(es(f,l.exit_flow.separable_conv,[1,1])),f})}async forward(r){return this.forwardInput(await Tt(r))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(r){return mD(r,this._numMainBlocks)}extractParams(r){return pD(r,this._numMainBlocks)}}function yD(r){const l=[],{extractWeights:u,getRemainingWeights:p}=Gn(r),g=Tg(u,l),f=g(512,1,"fc/age"),I=g(512,2,"fc/gender");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{fc:{age:f,gender:I}}}}function bD(r){const l=[],u=ms(r,l);function p(f){const I=u(`${f}/weights`,2),S=u(`${f}/bias`,1);return{weights:I,bias:S}}const g={fc:{age:p("fc/age"),gender:p("fc/gender")}};return zn(r,l),{params:g,paramMappings:l}}var Ui;(function(r){r.FEMALE="female",r.MALE="male"})(Ui||(Ui={}));const Hi=Xe(Je());class jm extends In{constructor(r=new gD(2)){super("AgeGenderNet");this._faceFeatureExtractor=r}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(r){const{params:l}=this;if(!l)throw new Error(`${this._name} - load model before inference`);return Hi.tidy(()=>{const u=r instanceof hr?this.faceFeatureExtractor.forwardInput(r):r,p=Hi.avgPool(u,[7,7],[2,2],"valid").as2D(u.shape[0],-1),g=Uu(p,l.fc.age).as1D(),f=Uu(p,l.fc.gender);return{age:g,gender:f}})}forwardInput(r){return Hi.tidy(()=>{const{age:l,gender:u}=this.runNet(r);return{age:l,gender:Hi.softmax(u)}})}async forward(r){return this.forwardInput(await Tt(r))}async predictAgeAndGender(r){const l=await Tt(r),u=await this.forwardInput(l),p=Hi.unstack(u.age),g=Hi.unstack(u.gender),f=p.map((S,x)=>({ageTensor:S,genderTensor:g[x]})),I=await Promise.all(f.map(async({ageTensor:S,genderTensor:x})=>{const v=(await S.data())[0],O=(await x.data())[0],C=O>.5,U=C?Ui.MALE:Ui.FEMALE,G=C?O:1-O;return S.dispose(),x.dispose(),{age:v,gender:U,genderProbability:G}}));return u.age.dispose(),u.gender.dispose(),l.isBatchInput?I:I[0]}getDefaultModelName(){return"age_gender_model"}dispose(r=!0){this.faceFeatureExtractor.dispose(r),super.dispose(r)}loadClassifierParams(r){const{params:l,paramMappings:u}=this.extractClassifierParams(r);this._params=l,this._paramMappings=u}extractClassifierParams(r){return yD(r)}extractParamsFromWeigthMap(r){const{featureExtractorMap:l,classifierMap:u}=Rg(r);return this.faceFeatureExtractor.loadFromWeightMap(l),bD(u)}extractParams(r){const l=512*1+1+(512*2+2),u=r.slice(0,r.length-l),p=r.slice(r.length-l);return this.faceFeatureExtractor.extractWeights(u),this.extractClassifierParams(p)}}const fs=Xe(Je());class Eg extends Og{postProcess(r,l,u){const p=u.map(({width:f,height:I})=>{const S=l/Math.max(I,f);return{width:f*S,height:I*S}}),g=p.length;return fs.tidy(()=>{const f=(O,C)=>fs.stack([fs.fill([68],O,"float32"),fs.fill([68],C,"float32")],1).as2D(1,136).as1D(),I=(O,C)=>{const{width:U,height:G}=p[O];return C(U,G)?Math.abs(U-G)/2:0},S=O=>I(O,(C,U)=>C<U),x=O=>I(O,(C,U)=>U<C),v=r.mul(fs.fill([g,136],l,"float32")).sub(fs.stack(Array.from(Array(g),(O,C)=>f(S(C),x(C))))).div(fs.stack(Array.from(Array(g),(O,C)=>f(p[C].width,p[C].height))));return v})}forwardInput(r){return fs.tidy(()=>{const l=this.runNet(r);return this.postProcess(l,r.inputSize,r.inputDimensions.map(([u,p])=>({height:u,width:p})))})}async forward(r){return this.forwardInput(await Tt(r))}async detectLandmarks(r){const l=await Tt(r),u=fs.tidy(()=>fs.unstack(this.forwardInput(l))),p=await Promise.all(u.map(async(g,f)=>{const I=Array.from(await g.data()),S=I.filter((v,O)=>Km(O)),x=I.filter((v,O)=>!Km(O));return new Rc(Array(68).fill(0).map((v,O)=>new qe(S[O],x[O])),{height:l.getInputHeight(f),width:l.getInputWidth(f)})}));return u.forEach(g=>g.dispose()),l.isBatchInput?p:p[0]}getClassifierChannelsOut(){return 136}}class Ac extends Eg{constructor(r=new Cg){super("FaceLandmark68Net",r)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}}function wD(r){const l=[],{extractDenseBlock3Params:u}=Ng(r,l),p={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2")};return zn(r,l),{params:p,paramMappings:l}}function LD(r){const l=[],{extractWeights:u,getRemainingWeights:p}=Gn(r),{extractDenseBlock3Params:g}=Ag(u,l),f=g(3,32,"dense0",!0),I=g(32,64,"dense1"),S=g(64,128,"dense2");if(p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{paramMappings:l,params:{dense0:f,dense1:I,dense2:S}}}const mo=Xe(Je());class SD extends In{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("TinyFaceFeatureExtractor - load model before inference");return mo.tidy(()=>{const u=mo.cast(r.toBatchTensor(112,!0),"float32"),p=[122.782,117.001,104.298],g=Ys(u,p).div(mo.scalar(255));let f=Sg(g,l.dense0,!0);return f=Sg(f,l.dense1),f=Sg(f,l.dense2),f=mo.avgPool(f,[14,14],[2,2],"valid"),f})}async forward(r){return this.forwardInput(await Tt(r))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(r){return wD(r)}extractParams(r){return LD(r)}}class Um extends Eg{constructor(r=new SD){super("FaceLandmark68TinyNet",r)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}}class h2 extends Ac{}const Dg=Xe(Je());function ID(r,l){return Dg.add(Dg.mul(r,l.weights),l.biases)}const Zc=Xe(Je());function sx(r,l,u,p,g="same"){const{filters:f,bias:I}=l.conv;let S=Zc.conv2d(r,f,u,g);return S=Zc.add(S,I),S=ID(S,l.scale),p?Zc.relu(S):S}function xD(r,l){return sx(r,l,[1,1],!0)}function ix(r,l){return sx(r,l,[1,1],!1)}function kg(r,l){return sx(r,l,[2,2],!0,"valid")}const gs=Xe(Je());function xZ(r,l){function u(S,x,v){const O=r(S),C=O.length/(x*v*v);if(YS(C))throw new Error(`depth has to be an integer: ${C}, weights.length: ${O.length}, numFilters: ${x}, filterSize: ${v}`);return gs.tidy(()=>gs.transpose(gs.tensor4d(O,[x,C,v,v]),[2,3,1,0]))}function p(S,x,v,O){const C=u(S,x,v),U=gs.tensor1d(r(x));return l.push({paramPath:`${O}/filters`},{paramPath:`${O}/bias`}),{filters:C,bias:U}}function g(S,x){const v=gs.tensor1d(r(S)),O=gs.tensor1d(r(S));return l.push({paramPath:`${x}/weights`},{paramPath:`${x}/biases`}),{weights:v,biases:O}}function f(S,x,v,O){const C=p(S,x,v,`${O}/conv`),U=g(x,`${O}/scale`);return{conv:C,scale:U}}function I(S,x,v,O,C=!1){const U=f((C?.5:1)*S,x,v,`${O}/conv1`),G=f(S,x,v,`${O}/conv2`);return{conv1:U,conv2:G}}return{extractConvLayerParams:f,extractResidualLayerParams:I}}function TD(r){const{extractWeights:l,getRemainingWeights:u}=Gn(r),p=[],{extractConvLayerParams:g,extractResidualLayerParams:f}=xZ(l,p),I=g(4704,32,7,"conv32_down"),S=f(9216,32,3,"conv32_1"),x=f(9216,32,3,"conv32_2"),v=f(9216,32,3,"conv32_3"),O=f(36864,64,3,"conv64_down",!0),C=f(36864,64,3,"conv64_1"),U=f(36864,64,3,"conv64_2"),G=f(36864,64,3,"conv64_3"),ne=f(147456,128,3,"conv128_down",!0),te=f(147456,128,3,"conv128_1"),oe=f(147456,128,3,"conv128_2"),ge=f(589824,256,3,"conv256_down",!0),fe=f(589824,256,3,"conv256_1"),Ae=f(589824,256,3,"conv256_2"),Te=f(589824,256,3,"conv256_down_out"),Ve=gs.tidy(()=>gs.transpose(gs.tensor2d(l(256*128),[128,256]),[1,0]));if(p.push({paramPath:"fc"}),u().length!==0)throw new Error(`weights remaing after extract: ${u().length}`);const rt={conv32_down:I,conv32_1:S,conv32_2:x,conv32_3:v,conv64_down:O,conv64_1:C,conv64_2:U,conv64_3:G,conv128_down:ne,conv128_1:te,conv128_2:oe,conv256_down:ge,conv256_1:fe,conv256_2:Ae,conv256_down_out:Te,fc:Ve};return{params:rt,paramMappings:p}}function TZ(r,l){const u=ms(r,l);function p(I){const S=u(`${I}/scale/weights`,1),x=u(`${I}/scale/biases`,1);return{weights:S,biases:x}}function g(I){const S=u(`${I}/conv/filters`,4),x=u(`${I}/conv/bias`,1),v=p(I);return{conv:{filters:S,bias:x},scale:v}}function f(I){return{conv1:g(`${I}/conv1`),conv2:g(`${I}/conv2`)}}return{extractConvLayerParams:g,extractResidualLayerParams:f}}function AD(r){const l=[],{extractConvLayerParams:u,extractResidualLayerParams:p}=TZ(r,l),g=u("conv32_down"),f=p("conv32_1"),I=p("conv32_2"),S=p("conv32_3"),x=p("conv64_down"),v=p("conv64_1"),O=p("conv64_2"),C=p("conv64_3"),U=p("conv128_down"),G=p("conv128_1"),ne=p("conv128_2"),te=p("conv256_down"),oe=p("conv256_1"),ge=p("conv256_2"),fe=p("conv256_down_out"),Ae=r.fc;if(l.push({originalPath:"fc",paramPath:"fc"}),!VS(Ae))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${Ae}`);const Te={conv32_down:g,conv32_1:f,conv32_2:I,conv32_3:S,conv64_down:x,conv64_1:v,conv64_2:O,conv64_3:C,conv128_down:U,conv128_1:G,conv128_2:ne,conv256_down:te,conv256_1:oe,conv256_2:ge,conv256_down_out:fe,fc:Ae};return zn(r,l),{params:Te,paramMappings:l}}const Vn=Xe(Je());function bi(r,l){let u=xD(r,l.conv1);return u=ix(u,l.conv2),u=Vn.add(u,r),u=Vn.relu(u),u}function Bu(r,l){let u=kg(r,l.conv1);u=ix(u,l.conv2);let p=Vn.avgPool(r,2,2,"valid");const g=Vn.zeros(p.shape),f=p.shape[3]!==u.shape[3],I=p.shape[1]!==u.shape[1]||p.shape[2]!==u.shape[2];if(I){const S=[...u.shape];S[1]=1;const x=Vn.zeros(S);u=Vn.concat([u,x],1);const v=[...u.shape];v[2]=1;const O=Vn.zeros(v);u=Vn.concat([u,O],2)}return p=f?Vn.concat([p,g],3):p,u=Vn.add(p,u),u=Vn.relu(u),u}const ks=Xe(Je());class Tc extends In{constructor(){super("FaceRecognitionNet")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("FaceRecognitionNet - load model before inference");return ks.tidy(()=>{const u=ks.cast(r.toBatchTensor(150,!0),"float32"),p=[122.782,117.001,104.298],g=Ys(u,p).div(ks.scalar(256));let f=kg(g,l.conv32_down);f=ks.maxPool(f,3,2,"valid"),f=bi(f,l.conv32_1),f=bi(f,l.conv32_2),f=bi(f,l.conv32_3),f=Bu(f,l.conv64_down),f=bi(f,l.conv64_1),f=bi(f,l.conv64_2),f=bi(f,l.conv64_3),f=Bu(f,l.conv128_down),f=bi(f,l.conv128_1),f=bi(f,l.conv128_2),f=Bu(f,l.conv256_down),f=bi(f,l.conv256_1),f=bi(f,l.conv256_2),f=Bu(f,l.conv256_down_out);const I=f.mean([1,2]),S=ks.matMul(I,l.fc);return S})}async forward(r){return this.forwardInput(await Tt(r))}async computeFaceDescriptor(r){const l=await Tt(r),u=ks.tidy(()=>ks.unstack(this.forwardInput(l))),p=await Promise.all(u.map(g=>g.data()));return u.forEach(g=>g.dispose()),l.isBatchInput?p:p[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(r){return AD(r)}extractParams(r){return TD(r)}}function l2(r){const l=new Tc;return l.extractWeights(r),l}function gu(r,l){const u={descriptor:l};return Object.assign({},r,u)}function c2(r){return typeof r.age=="number"}function yu(r,l){const u={age:l};return Object.assign({},r,u)}function a2(r){return(r.gender===Ui.MALE||r.gender===Ui.FEMALE)&&Ec(r.genderProbability)}function mu(r,l,u){const p={gender:l,genderProbability:u};return Object.assign({},r,p)}const wi=Xe(Je());function AZ(r,l){function u(x,v){const O=wi.tensor4d(r(3*3*x),[3,3,x,1]),C=wi.tensor1d(r(x)),U=wi.tensor1d(r(x)),G=wi.tensor1d(r(x)),ne=wi.tensor1d(r(x));return l.push({paramPath:`${v}/filters`},{paramPath:`${v}/batch_norm_scale`},{paramPath:`${v}/batch_norm_offset`},{paramPath:`${v}/batch_norm_mean`},{paramPath:`${v}/batch_norm_variance`}),{filters:O,batch_norm_scale:C,batch_norm_offset:U,batch_norm_mean:G,batch_norm_variance:ne}}function p(x,v,O,C,U){const G=wi.tensor4d(r(x*v*O*O),[O,O,x,v]),ne=wi.tensor1d(r(v));return l.push({paramPath:`${C}/filters`},{paramPath:`${C}/${U?"batch_norm_offset":"bias"}`}),{filters:G,bias:ne}}function g(x,v,O,C){const{filters:U,bias:G}=p(x,v,O,C,!0);return{filters:U,batch_norm_offset:G}}function f(x,v,O){const C=u(x,`${O}/depthwise_conv`),U=g(x,v,1,`${O}/pointwise_conv`);return{depthwise_conv:C,pointwise_conv:U}}function I(){const x=g(3,32,3,"mobilenetv1/conv_0"),v=f(32,64,"mobilenetv1/conv_1"),O=f(64,128,"mobilenetv1/conv_2"),C=f(128,128,"mobilenetv1/conv_3"),U=f(128,256,"mobilenetv1/conv_4"),G=f(256,256,"mobilenetv1/conv_5"),ne=f(256,512,"mobilenetv1/conv_6"),te=f(512,512,"mobilenetv1/conv_7"),oe=f(512,512,"mobilenetv1/conv_8"),ge=f(512,512,"mobilenetv1/conv_9"),fe=f(512,512,"mobilenetv1/conv_10"),Ae=f(512,512,"mobilenetv1/conv_11"),Te=f(512,1024,"mobilenetv1/conv_12"),Ve=f(1024,1024,"mobilenetv1/conv_13");return{conv_0:x,conv_1:v,conv_2:O,conv_3:C,conv_4:U,conv_5:G,conv_6:ne,conv_7:te,conv_8:oe,conv_9:ge,conv_10:fe,conv_11:Ae,conv_12:Te,conv_13:Ve}}function S(){const x=g(1024,256,1,"prediction_layer/conv_0"),v=g(256,512,3,"prediction_layer/conv_1"),O=g(512,128,1,"prediction_layer/conv_2"),C=g(128,256,3,"prediction_layer/conv_3"),U=g(256,128,1,"prediction_layer/conv_4"),G=g(128,256,3,"prediction_layer/conv_5"),ne=g(256,64,1,"prediction_layer/conv_6"),te=g(64,128,3,"prediction_layer/conv_7"),oe=p(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),ge=p(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),fe=p(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),Ae=p(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),Te=p(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),Ve=p(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),rt=p(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),Ct=p(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),Ut=p(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),Kt=p(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),Dn=p(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),An=p(128,18,1,"prediction_layer/box_predictor_5/class_predictor"),vn={box_encoding_predictor:oe,class_predictor:ge},Zs={box_encoding_predictor:fe,class_predictor:Ae},Si={box_encoding_predictor:Te,class_predictor:Ve},Qs={box_encoding_predictor:rt,class_predictor:Ct},ya={box_encoding_predictor:Ut,class_predictor:Kt},ol={box_encoding_predictor:Dn,class_predictor:An};return{conv_0:x,conv_1:v,conv_2:O,conv_3:C,conv_4:U,conv_5:G,conv_6:ne,conv_7:te,box_predictor_0:vn,box_predictor_1:Zs,box_predictor_2:Si,box_predictor_3:Qs,box_predictor_4:ya,box_predictor_5:ol}}return{extractMobilenetV1Params:I,extractPredictionLayerParams:S}}function vD(r){const l=[],{extractWeights:u,getRemainingWeights:p}=Gn(r),{extractMobilenetV1Params:g,extractPredictionLayerParams:f}=AZ(u,l),I=g(),S=f(),x=wi.tensor3d(u(5118*4),[1,5118,4]),v={extra_dim:x};if(l.push({paramPath:"output_layer/extra_dim"}),p().length!==0)throw new Error(`weights remaing after extract: ${p().length}`);return{params:{mobilenetv1:I,prediction_layer:S,output_layer:v},paramMappings:l}}function vZ(r,l){const u=ms(r,l);function p(v,O,C){const U=u(`${v}/Conv2d_${O}_pointwise/weights`,4,`${C}/filters`),G=u(`${v}/Conv2d_${O}_pointwise/convolution_bn_offset`,1,`${C}/batch_norm_offset`);return{filters:U,batch_norm_offset:G}}function g(v){const O=`mobilenetv1/conv_${v}`,C=`MobilenetV1/Conv2d_${v}_depthwise`,U=`${O}/depthwise_conv`,G=`${O}/pointwise_conv`,ne=u(`${C}/depthwise_weights`,4,`${U}/filters`),te=u(`${C}/BatchNorm/gamma`,1,`${U}/batch_norm_scale`),oe=u(`${C}/BatchNorm/beta`,1,`${U}/batch_norm_offset`),ge=u(`${C}/BatchNorm/moving_mean`,1,`${U}/batch_norm_mean`),fe=u(`${C}/BatchNorm/moving_variance`,1,`${U}/batch_norm_variance`);return{depthwise_conv:{filters:ne,batch_norm_scale:te,batch_norm_offset:oe,batch_norm_mean:ge,batch_norm_variance:fe},pointwise_conv:p("MobilenetV1",v,G)}}function f(){return{conv_0:p("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:g(1),conv_2:g(2),conv_3:g(3),conv_4:g(4),conv_5:g(5),conv_6:g(6),conv_7:g(7),conv_8:g(8),conv_9:g(9),conv_10:g(10),conv_11:g(11),conv_12:g(12),conv_13:g(13)}}function I(v,O){const C=u(`${v}/weights`,4,`${O}/filters`),U=u(`${v}/biases`,1,`${O}/bias`);return{filters:C,bias:U}}function S(v){const O=I(`Prediction/BoxPredictor_${v}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${v}/box_encoding_predictor`),C=I(`Prediction/BoxPredictor_${v}/ClassPredictor`,`prediction_layer/box_predictor_${v}/class_predictor`);return{box_encoding_predictor:O,class_predictor:C}}function x(){return{conv_0:p("Prediction",0,"prediction_layer/conv_0"),conv_1:p("Prediction",1,"prediction_layer/conv_1"),conv_2:p("Prediction",2,"prediction_layer/conv_2"),conv_3:p("Prediction",3,"prediction_layer/conv_3"),conv_4:p("Prediction",4,"prediction_layer/conv_4"),conv_5:p("Prediction",5,"prediction_layer/conv_5"),conv_6:p("Prediction",6,"prediction_layer/conv_6"),conv_7:p("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:S(0),box_predictor_1:S(1),box_predictor_2:S(2),box_predictor_3:S(3),box_predictor_4:S(4),box_predictor_5:S(5)}}return{extractMobilenetV1Params:f,extractPredictionLayerParams:x}}function ND(r){const l=[],{extractMobilenetV1Params:u,extractPredictionLayerParams:p}=vZ(r,l),g=r["Output/extra_dim"];if(l.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!dr(g))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${g}`);const f={mobilenetv1:u(),prediction_layer:p(),output_layer:{extra_dim:g}};return zn(r,l),{params:f,paramMappings:l}}const fo=Xe(Je());function Ks(r,l,u){return fo.tidy(()=>{let p=fo.conv2d(r,l.filters,u,"same");return p=fo.add(p,l.batch_norm_offset),fo.clipByValue(p,0,6)})}const wr=Xe(Je()),NZ=.0010000000474974513;function CZ(r,l,u){return wr.tidy(()=>{let p=wr.depthwiseConv2d(r,l.filters,u,"same");return p=wr.batchNorm(p,l.batch_norm_mean,l.batch_norm_variance,l.batch_norm_offset,l.batch_norm_scale,NZ),wr.clipByValue(p,0,6)})}function RZ(r){return[2,4,6,12].some(l=>l===r)?[2,2]:[1,1]}function CD(r,l){return wr.tidy(()=>{let u,p=Ks(r,l.conv_0,[2,2]);const g=[l.conv_1,l.conv_2,l.conv_3,l.conv_4,l.conv_5,l.conv_6,l.conv_7,l.conv_8,l.conv_9,l.conv_10,l.conv_11,l.conv_12,l.conv_13];if(g.forEach((f,I)=>{const S=I+1,x=RZ(S);p=CZ(p,f.depthwise_conv,x),p=Ks(p,f.pointwise_conv,[1,1]),S===11&&(u=p)}),u===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:p,conv11:u}})}function RD(r,l,u,p,g){const f=r.shape[0],I=Math.min(u,f),S=l.map((O,C)=>({score:O,boxIndex:C})).filter(O=>O.score>g).sort((O,C)=>C.score-O.score),x=O=>O<=p?1:0,v=[];return S.forEach(O=>{if(v.length>=I)return;const C=O.score;for(let U=v.length-1;U>=0;--U){const G=OZ(r,O.boxIndex,v[U]);if(G===0)continue;if(O.score*=x(G),O.score<=g)break}C===O.score&&v.push(O.boxIndex)}),v}function OZ(r,l,u){const p=r.arraySync(),g=Math.min(p[l][0],p[l][2]),f=Math.min(p[l][1],p[l][3]),I=Math.max(p[l][0],p[l][2]),S=Math.max(p[l][1],p[l][3]),x=Math.min(p[u][0],p[u][2]),v=Math.min(p[u][1],p[u][3]),O=Math.max(p[u][0],p[u][2]),C=Math.max(p[u][1],p[u][3]),U=(I-g)*(S-f),G=(O-x)*(C-v);if(U<=0||G<=0)return 0;const ne=Math.max(g,x),te=Math.max(f,v),oe=Math.min(I,O),ge=Math.min(S,C),fe=Math.max(oe-ne,0)*Math.max(ge-te,0);return fe/(U+G-fe)}const De=Xe(Je());function EZ(r){const l=De.unstack(De.transpose(r,[1,0])),u=[De.sub(l[2],l[0]),De.sub(l[3],l[1])],p=[De.add(l[0],De.div(u[0],De.scalar(2))),De.add(l[1],De.div(u[1],De.scalar(2)))];return{sizes:u,centers:p}}function DZ(r,l){const{sizes:u,centers:p}=EZ(r),g=De.unstack(De.transpose(l,[1,0])),f=De.div(De.mul(De.exp(De.div(g[2],De.scalar(5))),u[0]),De.scalar(2)),I=De.add(De.mul(De.div(g[0],De.scalar(10)),u[0]),p[0]),S=De.div(De.mul(De.exp(De.div(g[3],De.scalar(5))),u[1]),De.scalar(2)),x=De.add(De.mul(De.div(g[1],De.scalar(10)),u[1]),p[1]);return De.transpose(De.stack([De.sub(I,f),De.sub(x,S),De.add(I,f),De.add(x,S)]),[1,0])}function OD(r,l,u){return De.tidy(()=>{const p=r.shape[0];let g=DZ(De.reshape(De.tile(u.extra_dim,[p,1,1]),[-1,4]),De.reshape(r,[-1,4]));g=De.reshape(g,[p,g.shape[0]/p,4]);const f=De.sigmoid(De.slice(l,[0,0,1],[-1,-1,-1]));let I=De.slice(f,[0,0,0],[-1,-1,1]);I=De.reshape(I,[p,I.shape[1]]);const S=De.unstack(g),x=De.unstack(I);return{boxes:S,scores:x}})}const Mu=Xe(Je());function fa(r,l){return Mu.tidy(()=>{const u=r.shape[0],p=Mu.reshape(ma(r,l.box_encoding_predictor),[u,-1,1,4]),g=Mu.reshape(ma(r,l.class_predictor),[u,-1,3]);return{boxPredictionEncoding:p,classPrediction:g}})}const Pu=Xe(Je());function ED(r,l,u){return Pu.tidy(()=>{const p=Ks(r,u.conv_0,[1,1]),g=Ks(p,u.conv_1,[2,2]),f=Ks(g,u.conv_2,[1,1]),I=Ks(f,u.conv_3,[2,2]),S=Ks(I,u.conv_4,[1,1]),x=Ks(S,u.conv_5,[2,2]),v=Ks(x,u.conv_6,[1,1]),O=Ks(v,u.conv_7,[2,2]),C=fa(l,u.box_predictor_0),U=fa(r,u.box_predictor_1),G=fa(g,u.box_predictor_2),ne=fa(I,u.box_predictor_3),te=fa(x,u.box_predictor_4),oe=fa(O,u.box_predictor_5),ge=Pu.concat([C.boxPredictionEncoding,U.boxPredictionEncoding,G.boxPredictionEncoding,ne.boxPredictionEncoding,te.boxPredictionEncoding,oe.boxPredictionEncoding],1),fe=Pu.concat([C.classPrediction,U.classPrediction,G.classPrediction,ne.classPrediction,te.classPrediction,oe.classPrediction],1);return{boxPredictions:ge,classPredictions:fe}})}class Vs{constructor({minConfidence:r,maxResults:l}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=r||.5,this._maxResults=l||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}}const Li=Xe(Je());class Xo extends In{constructor(){super("SsdMobilenetv1")}forwardInput(r){const{params:l}=this;if(!l)throw new Error("SsdMobilenetv1 - load model before inference");return Li.tidy(()=>{const u=Li.cast(r.toBatchTensor(512,!1),"float32"),p=Li.sub(Li.mul(u,Li.scalar(.007843137718737125)),Li.scalar(1)),g=CD(p,l.mobilenetv1),{boxPredictions:f,classPredictions:I}=ED(g.out,g.conv11,l.prediction_layer);return OD(f,I,l.output_layer)})}async forward(r){return this.forwardInput(await Tt(r))}async locateFaces(r,l={}){const{maxResults:u,minConfidence:p}=new Vs(l),g=await Tt(r),{boxes:f,scores:I}=this.forwardInput(g),S=f[0],x=I[0];for(let fe=1;fe<f.length;fe++)f[fe].dispose(),I[fe].dispose();const v=Array.from(await x.data()),O=.5,C=RD(S,v,u,O,p),U=g.getReshapedInputDimensions(0),G=g.inputSize,ne=G/U.width,te=G/U.height,oe=S.arraySync(),ge=C.map(fe=>{const[Ae,Te]=[Math.max(0,oe[fe][0]),Math.min(1,oe[fe][2])].map(Ct=>Ct*te),[Ve,rt]=[Math.max(0,oe[fe][1]),Math.min(1,oe[fe][3])].map(Ct=>Ct*ne);return new Wt(v[fe],new Cc(Ve,Ae,rt-Ve,Te-Ae),{height:g.getInputHeight(0),width:g.getInputWidth(0)})});return S.dispose(),x.dispose(),ge}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(r){return ND(r)}extractParams(r){return vD(r)}}function $S(r){const l=new Xo;return l.extractWeights(r),l}function UC(r){return $S(r)}class BC extends Xo{}const DD=.4,kD=[new qe(.738768,.874946),new qe(2.42204,2.65704),new qe(4.30971,7.04493),new qe(10.246,4.59428),new qe(12.6868,11.8741)],FD=[new qe(1.603231,2.094468),new qe(6.041143,7.080126),new qe(2.882459,3.518061),new qe(4.266906,5.178857),new qe(9.041765,10.66308)],_D=[117.001,114.697,97.404],WD="tiny_yolov2_model",$D="tiny_yolov2_separable_conv_model";const Fg=r=>typeof r=="number";function Tm(r){if(!r)throw new Error(`invalid config: ${r}`);if(typeof r.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${r.withSeparableConvs}`);if(!Fg(r.iouThreshold)||r.iouThreshold<0||r.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${r.iouThreshold}`);if(!Array.isArray(r.classes)||!r.classes.length||!r.classes.every(l=>typeof l=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(r.classes)}`);if(!Array.isArray(r.anchors)||!r.anchors.length||!r.anchors.map(l=>l||{}).every(l=>Fg(l.x)&&Fg(l.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(r.anchors)}`);if(r.meanRgb&&(!Array.isArray(r.meanRgb)||r.meanRgb.length!==3||!r.meanRgb.every(Fg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(r.meanRgb)}`)}const Xs=Xe(Je());function Qc(r){return Xs.tidy(()=>{const l=Xs.mul(r,Xs.scalar(.10000000149011612));return Xs.add(Xs.relu(Xs.sub(r,l)),l)})}const Js=Xe(Je());function Lr(r,l){return Js.tidy(()=>{let u=Js.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return u=Js.conv2d(u,l.conv.filters,[1,1],"valid"),u=Js.sub(u,l.bn.sub),u=Js.mul(u,l.bn.truediv),u=Js.add(u,l.conv.bias),Qc(u)})}const go=Xe(Je());function Sr(r,l){return go.tidy(()=>{let u=go.pad(r,[[0,0],[1,1],[1,1],[0,0]]);return u=go.separableConv2d(u,l.depthwise_filter,l.pointwise_filter,[1,1],"valid"),u=go.add(u,l.bias),Qc(u)})}const rx=Xe(Je());function kZ(r,l){const u=qc(r,l);function p(I,S){const x=rx.tensor1d(r(I)),v=rx.tensor1d(r(I));return l.push({paramPath:`${S}/sub`},{paramPath:`${S}/truediv`}),{sub:x,truediv:v}}function g(I,S,x){const v=u(I,S,3,`${x}/conv`),O=p(S,`${x}/bn`);return{conv:v,bn:O}}const f=jc(r,l);return{extractConvParams:u,extractConvWithBatchNormParams:g,extractSeparableConvParams:f}}function UD(r,l,u,p){const{extractWeights:g,getRemainingWeights:f}=Gn(r),I=[],{extractConvParams:S,extractConvWithBatchNormParams:x,extractSeparableConvParams:v}=kZ(g,I);let O;if(l.withSeparableConvs){const[C,U,G,ne,te,oe,ge,fe,Ae]=p,Te=l.isFirstLayerConv2d?S(C,U,3,"conv0"):v(C,U,"conv0"),Ve=v(U,G,"conv1"),rt=v(G,ne,"conv2"),Ct=v(ne,te,"conv3"),Ut=v(te,oe,"conv4"),Kt=v(oe,ge,"conv5"),Dn=fe?v(ge,fe,"conv6"):void 0,An=Ae?v(fe,Ae,"conv7"):void 0,vn=S(Ae||fe||ge,5*u,1,"conv8");O={conv0:Te,conv1:Ve,conv2:rt,conv3:Ct,conv4:Ut,conv5:Kt,conv6:Dn,conv7:An,conv8:vn}}else{const[C,U,G,ne,te,oe,ge,fe,Ae]=p,Te=x(C,U,"conv0"),Ve=x(U,G,"conv1"),rt=x(G,ne,"conv2"),Ct=x(ne,te,"conv3"),Ut=x(te,oe,"conv4"),Kt=x(oe,ge,"conv5"),Dn=x(ge,fe,"conv6"),An=x(fe,Ae,"conv7"),vn=S(Ae,5*u,1,"conv8");O={conv0:Te,conv1:Ve,conv2:rt,conv3:Ct,conv4:Ut,conv5:Kt,conv6:Dn,conv7:An,conv8:vn}}if(f().length!==0)throw new Error(`weights remaing after extract: ${f().length}`);return{params:O,paramMappings:I}}function FZ(r,l){const u=ms(r,l);function p(S){const x=u(`${S}/sub`,1),v=u(`${S}/truediv`,1);return{sub:x,truediv:v}}function g(S){const x=u(`${S}/filters`,4),v=u(`${S}/bias`,1);return{filters:x,bias:v}}function f(S){const x=g(`${S}/conv`),v=p(`${S}/bn`);return{conv:x,bn:v}}const I=Kc(u);return{extractConvParams:g,extractConvWithBatchNormParams:f,extractSeparableConvParams:I}}function BD(r,l){const u=[],{extractConvParams:p,extractConvWithBatchNormParams:g,extractSeparableConvParams:f}=FZ(r,u);let I;if(l.withSeparableConvs){const S=l.filterSizes&&l.filterSizes.length||9;I={conv0:l.isFirstLayerConv2d?p("conv0"):f("conv0"),conv1:f("conv1"),conv2:f("conv2"),conv3:f("conv3"),conv4:f("conv4"),conv5:f("conv5"),conv6:S>7?f("conv6"):void 0,conv7:S>8?f("conv7"):void 0,conv8:p("conv8")}}else I={conv0:g("conv0"),conv1:g("conv1"),conv2:g("conv2"),conv3:g("conv3"),conv4:g("conv4"),conv5:g("conv5"),conv6:g("conv6"),conv7:g("conv7"),conv8:p("conv8")};return zn(r,u),{params:I,paramMappings:u}}var Am;(function(r){r[r.XS=224]="XS",r[r.SM=320]="SM",r[r.MD=416]="MD",r[r.LG=608]="LG"})(Am||(Am={}));class $i{constructor({inputSize:r,scoreThreshold:l}={}){this._name="TinyYolov2Options";if(this._inputSize=r||416,this._scoreThreshold=l||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}}const kt=Xe(Je());class el extends In{constructor(r){super("TinyYolov2");Tm(r),this._config=r}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(r,l){let u=Lr(r,l.conv0);return u=kt.maxPool(u,[2,2],[2,2],"same"),u=Lr(u,l.conv1),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Lr(u,l.conv2),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Lr(u,l.conv3),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Lr(u,l.conv4),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Lr(u,l.conv5),u=kt.maxPool(u,[2,2],[1,1],"same"),u=Lr(u,l.conv6),u=Lr(u,l.conv7),ma(u,l.conv8,"valid",!1)}runMobilenet(r,l){let u=this.config.isFirstLayerConv2d?Qc(ma(r,l.conv0,"valid",!1)):Sr(r,l.conv0);return u=kt.maxPool(u,[2,2],[2,2],"same"),u=Sr(u,l.conv1),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Sr(u,l.conv2),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Sr(u,l.conv3),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Sr(u,l.conv4),u=kt.maxPool(u,[2,2],[2,2],"same"),u=Sr(u,l.conv5),u=kt.maxPool(u,[2,2],[1,1],"same"),u=l.conv6?Sr(u,l.conv6):u,u=l.conv7?Sr(u,l.conv7):u,ma(u,l.conv8,"valid",!1)}forwardInput(r,l){const{params:u}=this;if(!u)throw new Error("TinyYolov2 - load model before inference");return kt.tidy(()=>{let p=kt.cast(r.toBatchTensor(l,!1),"float32");return p=this.config.meanRgb?Ys(p,this.config.meanRgb):p,p=p.div(kt.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(p,u):this.runTinyYolov2(p,u)})}async forward(r,l){return await this.forwardInput(await Tt(r),l)}async detect(r,l={}){const{inputSize:u,scoreThreshold:p}=new $i(l),g=await Tt(r),f=await this.forwardInput(g,u),I=kt.tidy(()=>kt.unstack(f)[0].expandDims()),S={width:g.getInputWidth(0),height:g.getInputHeight(0)},x=await this.extractBoxes(I,g.getReshapedInputDimensions(0),p);f.dispose(),I.dispose();const v=x.map(te=>te.box),O=x.map(te=>te.score),C=x.map(te=>te.classScore),U=x.map(te=>this.config.classes[te.label]),G=Cm(v.map(te=>te.rescale(u)),O,this.config.iouThreshold,!0),ne=G.map(te=>new ta(O[te],C[te],U[te],v[te],S));return ne}getDefaultModelName(){return""}extractParamsFromWeigthMap(r){return BD(r,this.config)}extractParams(r){const l=this.config.filterSizes||el.DEFAULT_FILTER_SIZES,u=l?l.length:void 0;if(u!==7&&u!==8&&u!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${u} filterSizes in config`);return UD(r,this.config,this.boxEncodingSize,l)}async extractBoxes(r,l,u){const{width:p,height:g}=l,f=Math.max(p,g),I=f/p,S=f/g,x=r.shape[1],v=this.config.anchors.length,[O,C,U]=kt.tidy(()=>{const oe=r.reshape([x,x,v,this.boxEncodingSize]),ge=oe.slice([0,0,0,0],[x,x,v,4]),fe=oe.slice([0,0,0,4],[x,x,v,1]),Ae=this.withClassScores?kt.softmax(oe.slice([0,0,0,5],[x,x,v,this.config.classes.length]),3):kt.scalar(0);return[ge,fe,Ae]}),G=[],ne=await C.array(),te=await O.array();for(let oe=0;oe<x;oe++)for(let ge=0;ge<x;ge++)for(let fe=0;fe<v;fe++){const Ae=xc(ne[oe][ge][fe][0]);if(!u||Ae>u){const Te=(ge+xc(te[oe][ge][fe][0]))/x*I,Ve=(oe+xc(te[oe][ge][fe][1]))/x*S,rt=Math.exp(te[oe][ge][fe][2])*this.config.anchors[fe].x/x*I,Ct=Math.exp(te[oe][ge][fe][3])*this.config.anchors[fe].y/x*S,Ut=Te-rt/2,Kt=Ve-Ct/2,Dn={row:oe,col:ge,anchor:fe},{classScore:An,label:vn}=this.withClassScores?await this.extractPredictedClass(U,Dn):{classScore:1,label:0};G.push({box:new Oc(Ut,Kt,Ut+rt,Kt+Ct),score:Ae,classScore:Ae*An,label:vn,...Dn})}}return O.dispose(),C.dispose(),U.dispose(),G}async extractPredictedClass(r,l){const{row:u,col:p,anchor:g}=l,f=await r.array();return Array(this.config.classes.length).fill(0).map((I,S)=>f[u][p][g][S]).map((I,S)=>({classScore:I,label:S})).reduce((I,S)=>I.classScore>S.classScore?I:S)}}el.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];class Sc extends el{constructor(r=!0){const l=Object.assign({},{withSeparableConvs:r,iouThreshold:DD,classes:["face"]},r?{anchors:FD,meanRgb:_D}:{anchors:kD,withClassScores:!0});super(l)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(r,l){const u=await this.detect(r,l);return u.map(p=>new Wt(p.score,p.relativeBox,{width:p.imageWidth,height:p.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?$D:WD}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}function WC(r,l=!0){const u=new Sc(l);return u.extractWeights(r),u}class vm extends $i{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}}class Hs{async then(r){return r(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}}const ox=Xe(Je());async function ga(r,l,u,p,g=({alignedRect:f})=>f){const f=r.map(x=>Jr(x)?g(x):x.detection),I=p||(l instanceof ox.Tensor?await Qo(l,f):await Zo(l,f)),S=await u(I);return I.forEach(x=>x instanceof ox.Tensor&&x.dispose()),S}async function tl(r,l,u,p,g){return ga([r],l,async f=>u(f[0]),p,g)}const MD=.4,PD=[new qe(1.603231,2.094468),new qe(6.041143,7.080126),new qe(2.882459,3.518061),new qe(4.266906,5.178857),new qe(9.041765,10.66308)],zD=[117.001,114.697,97.404];class Ic extends el{constructor(){const r={withSeparableConvs:!0,iouThreshold:MD,classes:["face"],anchors:PD,meanRgb:zD,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(r)}get anchors(){return this.config.anchors}async locateFaces(r,l){const u=await this.detect(r,l);return u.map(p=>new Wt(p.score,p.relativeBox,{width:p.imageWidth,height:p.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(r){return super.extractParamsFromWeigthMap(r)}}const ht={ssdMobilenetv1:new Xo,tinyFaceDetector:new Ic,tinyYolov2:new Sc,faceLandmark68Net:new Ac,faceLandmark68TinyNet:new Um,faceRecognitionNet:new Tc,faceExpressionNet:new Mm,ageGenderNet:new jm},BS=(r,l)=>ht.ssdMobilenetv1.locateFaces(r,l),zC=(r,l)=>ht.tinyFaceDetector.locateFaces(r,l),GC=(r,l)=>ht.tinyYolov2.locateFaces(r,l),MS=r=>ht.faceLandmark68Net.detectLandmarks(r),VC=r=>ht.faceLandmark68TinyNet.detectLandmarks(r),YC=r=>ht.faceRecognitionNet.computeFaceDescriptor(r),HC=r=>ht.faceExpressionNet.predictExpressions(r),qC=r=>ht.ageGenderNet.predictAgeAndGender(r),PS=r=>ht.ssdMobilenetv1.load(r),jC=r=>ht.tinyFaceDetector.load(r),KC=r=>ht.tinyYolov2.load(r),XC=r=>ht.faceLandmark68Net.load(r),JC=r=>ht.faceLandmark68TinyNet.load(r),ZC=r=>ht.faceRecognitionNet.load(r),QC=r=>ht.faceExpressionNet.load(r),e2=r=>ht.ageGenderNet.load(r),t2=PS,n2=BS,s2=MS;class GD extends Hs{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.extractedFaces=u}}class Vu extends GD{async run(){const r=await this.parentTask,l=await ga(r,this.input,async u=>await Promise.all(u.map(p=>ht.faceExpressionNet.predictExpressions(p))),this.extractedFaces);return r.map((u,p)=>fu(u,l[p]))}withAgeAndGender(){return new zu(this,this.input)}}class Yu extends GD{async run(){const r=await this.parentTask;if(!r)return;const l=await tl(r,this.input,u=>ht.faceExpressionNet.predictExpressions(u),this.extractedFaces);return fu(r,l)}withAgeAndGender(){return new Gu(this,this.input)}}class il extends Vu{withAgeAndGender(){return new nl(this,this.input)}withFaceDescriptors(){return new Kr(this,this.input)}}class rl extends Yu{withAgeAndGender(){return new sl(this,this.input)}withFaceDescriptor(){return new Xr(this,this.input)}}class VD extends Hs{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.extractedFaces=u}}class zu extends VD{async run(){const r=await this.parentTask,l=await ga(r,this.input,async u=>await Promise.all(u.map(p=>ht.ageGenderNet.predictAgeAndGender(p))),this.extractedFaces);return r.map((u,p)=>{const{age:g,gender:f,genderProbability:I}=l[p];return yu(mu(u,f,I),g)})}withFaceExpressions(){return new Vu(this,this.input)}}class Gu extends VD{async run(){const r=await this.parentTask;if(!r)return;const{age:l,gender:u,genderProbability:p}=await tl(r,this.input,g=>ht.ageGenderNet.predictAgeAndGender(g),this.extractedFaces);return yu(mu(r,u,p),l)}withFaceExpressions(){return new Yu(this,this.input)}}class nl extends zu{withFaceExpressions(){return new il(this,this.input)}withFaceDescriptors(){return new Kr(this,this.input)}}class sl extends Gu{withFaceExpressions(){return new rl(this,this.input)}withFaceDescriptor(){return new Xr(this,this.input)}}class Wm extends Hs{constructor(r,l){super();this.parentTask=r;this.input=l}}class Kr extends Wm{async run(){const r=await this.parentTask,l=await ga(r,this.input,u=>Promise.all(u.map(p=>ht.faceRecognitionNet.computeFaceDescriptor(p))),null,u=>u.landmarks.align(null,{useDlibAlignment:!0}));return l.map((u,p)=>gu(r[p],u))}withFaceExpressions(){return new il(this,this.input)}withAgeAndGender(){return new nl(this,this.input)}}class Xr extends Wm{async run(){const r=await this.parentTask;if(!r)return;const l=await tl(r,this.input,u=>ht.faceRecognitionNet.computeFaceDescriptor(u),null,u=>u.landmarks.align(null,{useDlibAlignment:!0}));return gu(r,l)}withFaceExpressions(){return new rl(this,this.input)}withAgeAndGender(){return new sl(this,this.input)}}const Hu=Xe(Je());class km extends Hs{constructor(r,l,u){super();this.parentTask=r;this.input=l;this.useTinyLandmarkNet=u}get landmarkNet(){return this.useTinyLandmarkNet?ht.faceLandmark68TinyNet:ht.faceLandmark68Net}}class Fm extends km{async run(){const r=await this.parentTask,l=r.map(g=>g.detection),u=this.input instanceof Hu.Tensor?await Qo(this.input,l):await Zo(this.input,l),p=await Promise.all(u.map(g=>this.landmarkNet.detectLandmarks(g)));return u.forEach(g=>g instanceof Hu.Tensor&&g.dispose()),r.map((g,f)=>Jo(g,p[f]))}withFaceExpressions(){return new il(this,this.input)}withAgeAndGender(){return new nl(this,this.input)}withFaceDescriptors(){return new Kr(this,this.input)}}class _m extends km{async run(){const r=await this.parentTask;if(!r)return;const{detection:l}=r,u=this.input instanceof Hu.Tensor?await Qo(this.input,[l]):await Zo(this.input,[l]),p=await this.landmarkNet.detectLandmarks(u[0]);return u.forEach(g=>g instanceof Hu.Tensor&&g.dispose()),Jo(r,p)}withFaceExpressions(){return new rl(this,this.input)}withAgeAndGender(){return new sl(this,this.input)}withFaceDescriptor(){return new Xr(this,this.input)}}class Em extends Hs{constructor(r,l=new Vs){super();this.input=r;this.options=l}}class pu extends Em{async run(){const{input:r,options:l}=this,u=l instanceof vm?p=>ht.tinyFaceDetector.locateFaces(p,l):l instanceof Vs?p=>ht.ssdMobilenetv1.locateFaces(p,l):l instanceof $i?p=>ht.tinyYolov2.locateFaces(p,l):null;if(!u)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return u(r)}runAndExtendWithFaceDetections(){return new Promise(async r=>{const l=await this.run();return r(l.map(u=>Zr({},u)))})}withFaceLandmarks(r=!1){return new Fm(this.runAndExtendWithFaceDetections(),this.input,r)}withFaceExpressions(){return new Vu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new zu(this.runAndExtendWithFaceDetections(),this.input)}}class Dm extends Em{async run(){const r=await new pu(this.input,this.options);let l=r[0];return r.forEach(u=>{u.score>l.score&&(l=u)}),l}runAndExtendWithFaceDetection(){return new Promise(async r=>{const l=await this.run();return r(l?Zr({},l):void 0)})}withFaceLandmarks(r=!1){return new _m(this.runAndExtendWithFaceDetection(),this.input,r)}withFaceExpressions(){return new Yu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Gu(this.runAndExtendWithFaceDetection(),this.input)}}function i2(r,l=new Vs){return new Dm(r,l)}function du(r,l=new Vs){return new pu(r,l)}async function zS(r,l){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await du(r,new Vs(l?{minConfidence:l}:{})).withFaceLandmarks().withFaceDescriptors()}async function r2(r,l={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await du(r,new $i(l)).withFaceLandmarks().withFaceDescriptors()}const o2=zS;function Pm(r,l){if(r.length!==l.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");const u=Array.from(r),p=Array.from(l);return Math.sqrt(u.map((g,f)=>g-p[f]).reduce((g,f)=>g+Math.pow(f,2),0))}class GS{constructor(r,l=.6){this._distanceThreshold=l;const u=Array.isArray(r)?r:[r];if(!u.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let p=1;const g=()=>`person ${p++}`;this._labeledDescriptors=u.map(f=>{if(f instanceof so)return f;if(f instanceof Float32Array)return new so(g(),[f]);if(f.descriptor&&f.descriptor instanceof Float32Array)return new so(g(),[f.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(r,l){return l.map(u=>Pm(u,r)).reduce((u,p)=>u+p,0)/(l.length||1)}matchDescriptor(r){return this.labeledDescriptors.map(({descriptors:l,label:u})=>new Lu(u,this.computeMeanDistance(r,l))).reduce((l,u)=>l.distance<u.distance?l:u)}findBestMatch(r){const l=this.matchDescriptor(r);return l.distance<this.distanceThreshold?l:new Lu("unknown",l.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(r=>r.toJSON())}}static fromJSON(r){const l=r.labeledDescriptors.map(u=>so.fromJSON(u));return new GS(l,r.distanceThreshold)}}function $C(r){const l=new Ic;return l.extractWeights(r),l}function US(r,l){const{width:u,height:p}=new Zn(l.width,l.height);if(u<=0||p<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:u,height:p})}`);if(Array.isArray(r))return r.map(g=>US(g,{width:u,height:p}));if(Jr(r)){const g=r.detection.forSize(u,p),f=r.unshiftedLandmarks.forSize(g.box.width,g.box.height);return Jo(Zr(r,g),f)}return mi(r)?Zr(r,r.detection.forSize(u,p)):r instanceof Ns||r instanceof Wt?r.forSize(u,p):r}var FC="0.8.1";return g2();})();
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/face-api.js.map b/dist/face-api.js.map
index 51ca778..ca5ad54 100644
--- a/dist/face-api.js.map
+++ b/dist/face-api.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["node_modules/node-fetch/browser.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/backends/backend.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/environment.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/global_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/kernel_names.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/profiler.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/tape.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/tensor_format.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/tensor.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/types.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/tensor_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/engine.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/device_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/flags.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/operation.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/complex.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/types.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/model_management.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/platforms/platform_browser.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/platforms/platform_node.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/cast.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/clone.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/ops/print.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/base_side_effects.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/progress.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/http.js", "node_modules/@tensorflow/tfjs/dist/../node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js", 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"src/ssdMobilenetv1/mobileNetV1.ts", "src/ssdMobilenetv1/nonMaxSuppression.ts", "src/ssdMobilenetv1/outputLayer.ts", "src/ssdMobilenetv1/boxPredictionLayer.ts", "src/ssdMobilenetv1/predictionLayer.ts", "src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "src/ssdMobilenetv1/SsdMobilenetv1.ts", "src/ssdMobilenetv1/index.ts", "src/tinyYolov2/const.ts", "src/tinyYolov2/config.ts", "src/tinyYolov2/leaky.ts", "src/tinyYolov2/convWithBatchNorm.ts", "src/tinyYolov2/depthwiseSeparableConv.ts", "src/tinyYolov2/extractParams.ts", "src/tinyYolov2/extractParamsFromWeigthMap.ts", "src/tinyYolov2/TinyYolov2Options.ts", "src/tinyYolov2/TinyYolov2Base.ts", "src/tinyYolov2/TinyYolov2.ts", "src/tinyYolov2/index.ts", "src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "src/globalApi/ComposableTask.ts", "src/globalApi/extractFacesAndComputeResults.ts", "src/tinyFaceDetector/const.ts", "src/tinyFaceDetector/TinyFaceDetector.ts", "src/globalApi/nets.ts", "src/globalApi/PredictFaceExpressionsTask.ts", "src/globalApi/PredictAgeAndGenderTask.ts", "src/globalApi/ComputeFaceDescriptorsTasks.ts", "src/globalApi/DetectFaceLandmarksTasks.ts", "src/globalApi/DetectFacesTasks.ts", "src/globalApi/detectFaces.ts", "src/globalApi/allFaces.ts", "src/euclideanDistance.ts", "src/globalApi/FaceMatcher.ts", "src/tinyFaceDetector/index.ts", "src/resizeResults.ts"],
-  "sourcesContent": ["\"use strict\";\n\n// ref: https://github.com/tc39/proposal-global\nvar getGlobal = function () {\n\t// the only reliable means to get the global object is\n\t// `Function('return this')()`\n\t// However, this causes CSP violations in Chrome apps.\n\tif (typeof self !== 'undefined') { return self; }\n\tif (typeof window !== 'undefined') { return window; }\n\tif (typeof global !== 'undefined') { return global; }\n\tthrow new Error('unable to locate global object');\n}\n\nvar global = getGlobal();\n\nmodule.exports = exports = global.fetch;\n\n// Needed for TypeScript and Webpack.\nif (global.fetch) {\n\texports.default = global.fetch.bind(global);\n}\n\nexports.Headers = global.Headers;\nexports.Request = global.Request;\nexports.Response = global.Response;", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage {\n    constructor(backend, dataMover) {\n        this.backend = backend;\n        this.dataMover = dataMover;\n        this.data = new WeakMap();\n        this.dataIdsCount = 0;\n    }\n    get(dataId) {\n        if (!this.data.has(dataId)) {\n            this.dataMover.moveData(this.backend, dataId);\n        }\n        return this.data.get(dataId);\n    }\n    set(dataId, value) {\n        this.dataIdsCount++;\n        this.data.set(dataId, value);\n    }\n    has(dataId) {\n        return this.data.has(dataId);\n    }\n    delete(dataId) {\n        this.dataIdsCount--;\n        return this.data.delete(dataId);\n    }\n    numDataIds() {\n        return this.dataIdsCount;\n    }\n}\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend {\n    time(f) {\n        return notYetImplemented('time');\n    }\n    read(dataId) {\n        return notYetImplemented('read');\n    }\n    readSync(dataId) {\n        return notYetImplemented('readSync');\n    }\n    numDataIds() {\n        return notYetImplemented('numDataIds');\n    }\n    disposeData(dataId) {\n        return notYetImplemented('disposeData');\n    }\n    write(values, shape, dtype) {\n        return notYetImplemented('write');\n    }\n    move(dataId, values, shape, dtype) {\n        return notYetImplemented('move');\n    }\n    memory() {\n        return notYetImplemented('memory');\n    }\n    /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n    floatPrecision() {\n        return notYetImplemented('floatPrecision');\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        return notYetImplemented('batchMatMul');\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedBatchMatMul');\n    }\n    slice(x, begin, size) {\n        return notYetImplemented('slice');\n    }\n    stridedSlice(x, begin, end, strides) {\n        return notYetImplemented('stridedSlice');\n    }\n    unstack(x, axis) {\n        return notYetImplemented('unstack');\n    }\n    reverse(a, axis) {\n        return notYetImplemented('reverse');\n    }\n    concat(tensors, axis) {\n        return notYetImplemented('concat');\n    }\n    neg(a) {\n        return notYetImplemented('neg');\n    }\n    add(a, b) {\n        return notYetImplemented('add');\n    }\n    addN(tensors) {\n        return notYetImplemented('addN');\n    }\n    subtract(a, b) {\n        return notYetImplemented('subtract');\n    }\n    multiply(a, b) {\n        return notYetImplemented('multiply');\n    }\n    realDivide(a, b) {\n        return notYetImplemented('realDivide');\n    }\n    floorDiv(a, b) {\n        return notYetImplemented('floorDiv');\n    }\n    sum(x, axes) {\n        return notYetImplemented('sum');\n    }\n    prod(x, axes) {\n        return notYetImplemented('prod');\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        return notYetImplemented('unsortedSegmentSum');\n    }\n    argMin(x, axis) {\n        return notYetImplemented('argMin');\n    }\n    argMax(x, axis) {\n        return notYetImplemented('argMax');\n    }\n    equal(a, b) {\n        return notYetImplemented('equal');\n    }\n    notEqual(a, b) {\n        return notYetImplemented('notEqual');\n    }\n    less(a, b) {\n        return notYetImplemented('less');\n    }\n    lessEqual(a, b) {\n        return notYetImplemented('lessEqual');\n    }\n    greater(a, b) {\n        return notYetImplemented('greater');\n    }\n    greaterEqual(a, b) {\n        return notYetImplemented('greaterEqual');\n    }\n    logicalNot(a) {\n        return notYetImplemented('logicalNot');\n    }\n    logicalAnd(a, b) {\n        return notYetImplemented('logicalAnd');\n    }\n    logicalOr(a, b) {\n        return notYetImplemented('logicalOr');\n    }\n    where(condition) {\n        return notYetImplemented('where');\n    }\n    select(condition, a, b) {\n        return notYetImplemented('select');\n    }\n    topk(x, k, sorted) {\n        return notYetImplemented('topk');\n    }\n    min(x, axes) {\n        return notYetImplemented('min');\n    }\n    minimum(a, b) {\n        return notYetImplemented('minimum');\n    }\n    mod(a, b) {\n        return notYetImplemented('mod');\n    }\n    max(x, axes) {\n        return notYetImplemented('max');\n    }\n    maximum(a, b) {\n        return notYetImplemented('maximum');\n    }\n    all(x, axes) {\n        return notYetImplemented('all');\n    }\n    any(x, axes) {\n        return notYetImplemented('any');\n    }\n    squaredDifference(a, b) {\n        return notYetImplemented('squaredDifference');\n    }\n    ceil(x) {\n        return notYetImplemented('ceil');\n    }\n    floor(x) {\n        return notYetImplemented('floor');\n    }\n    round(x) {\n        return notYetImplemented('round');\n    }\n    sign(x) {\n        return notYetImplemented('sign');\n    }\n    isNaN(x) {\n        return notYetImplemented('isNaN');\n    }\n    isInf(x) {\n        return notYetImplemented('isInf');\n    }\n    isFinite(x) {\n        return notYetImplemented('isFinite');\n    }\n    pow(a, b) {\n        return notYetImplemented('pow');\n    }\n    exp(x) {\n        return notYetImplemented('exp');\n    }\n    expm1(x) {\n        return notYetImplemented('expm1');\n    }\n    softmax(x, dim) {\n        return notYetImplemented('softmax');\n    }\n    log(x) {\n        return notYetImplemented('log');\n    }\n    log1p(x) {\n        return notYetImplemented('log1p');\n    }\n    sqrt(x) {\n        return notYetImplemented('sqrt');\n    }\n    rsqrt(x) {\n        return notYetImplemented('rsqrt');\n    }\n    square(x) {\n        return notYetImplemented('square');\n    }\n    reciprocal(x) {\n        return notYetImplemented('reciprocal');\n    }\n    relu(x) {\n        return notYetImplemented('relu');\n    }\n    relu6(x) {\n        return notYetImplemented('relu6');\n    }\n    prelu(x, a) {\n        return notYetImplemented('prelu');\n    }\n    elu(x) {\n        return notYetImplemented('elu');\n    }\n    eluDer(dy, y) {\n        return notYetImplemented('eluDer');\n    }\n    selu(x) {\n        return notYetImplemented('selu');\n    }\n    int(x) {\n        return notYetImplemented('int');\n    }\n    clip(x, min, max) {\n        return notYetImplemented('clip');\n    }\n    abs(x) {\n        return notYetImplemented('abs');\n    }\n    complexAbs(x) {\n        return notYetImplemented('complexAbs');\n    }\n    sigmoid(x) {\n        return notYetImplemented('sigmoid');\n    }\n    softplus(x) {\n        return notYetImplemented('softplus');\n    }\n    sin(x) {\n        return notYetImplemented('sin');\n    }\n    cos(x) {\n        return notYetImplemented('cos');\n    }\n    tan(x) {\n        return notYetImplemented('tan');\n    }\n    asin(x) {\n        return notYetImplemented('asin');\n    }\n    acos(x) {\n        return notYetImplemented('acos');\n    }\n    atan(x) {\n        return notYetImplemented('atan');\n    }\n    atan2(a, b) {\n        return notYetImplemented('atan2');\n    }\n    sinh(x) {\n        return notYetImplemented('sinh');\n    }\n    cosh(x) {\n        return notYetImplemented('cosh');\n    }\n    tanh(x) {\n        return notYetImplemented('tanh');\n    }\n    asinh(x) {\n        return notYetImplemented('asinh');\n    }\n    acosh(x) {\n        return notYetImplemented('acosh');\n    }\n    atanh(x) {\n        return notYetImplemented('atanh');\n    }\n    erf(x) {\n        return notYetImplemented('erf');\n    }\n    step(x, alpha) {\n        return notYetImplemented('step');\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedConv2d');\n    }\n    conv2d(x, filter, convInfo) {\n        return notYetImplemented('conv2d');\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        return notYetImplemented('conv2dDerInput');\n    }\n    conv2dDerFilter(x, dY, convInfo) {\n        return notYetImplemented('conv2dDerFilter');\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedDepthwiseConv2D');\n    }\n    depthwiseConv2D(input, filter, convInfo) {\n        return notYetImplemented('depthwiseConv2D');\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        return notYetImplemented('depthwiseConv2DDerInput');\n    }\n    depthwiseConv2DDerFilter(x, dY, convInfo) {\n        return notYetImplemented('depthwiseConv2DDerFilter');\n    }\n    conv3d(x, filter, convInfo) {\n        return notYetImplemented('conv3d');\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        return notYetImplemented('conv3dDerInput');\n    }\n    conv3dDerFilter(x, dY, convInfo) {\n        return notYetImplemented('conv3dDerFilter');\n    }\n    maxPool(x, convInfo) {\n        return notYetImplemented('maxPool');\n    }\n    maxPoolBackprop(dy, x, y, convInfo) {\n        return notYetImplemented('maxPoolBackprop');\n    }\n    avgPool(x, convInfo) {\n        return notYetImplemented('avgPool');\n    }\n    avgPoolBackprop(dy, x, convInfo) {\n        return notYetImplemented('avgPoolBackprop');\n    }\n    avgPool3d(x, convInfo) {\n        return notYetImplemented('avgPool3d');\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        return notYetImplemented('avgPool3dBackprop');\n    }\n    maxPool3d(x, convInfo) {\n        return notYetImplemented('maxPool3d');\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        return notYetImplemented('maxPool3dBackprop');\n    }\n    reshape(x, shape) {\n        return notYetImplemented('reshape');\n    }\n    cast(x, dtype) {\n        return notYetImplemented('cast');\n    }\n    tile(x, reps) {\n        return notYetImplemented('tile');\n    }\n    pad(x, paddings, constantValue) {\n        return notYetImplemented('pad');\n    }\n    transpose(x, perm) {\n        return notYetImplemented('transpose');\n    }\n    gather(x, indices, axis) {\n        return notYetImplemented('gather');\n    }\n    gatherND(x, indices) {\n        return notYetImplemented('gatherND');\n    }\n    scatterND(indices, updates, shape) {\n        return notYetImplemented('scatterND');\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        return notYetImplemented('batchToSpaceND');\n    }\n    spaceToBatchND(x, blockShape, paddings) {\n        return notYetImplemented('spaceToBatchND');\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        return notYetImplemented('resizeBilinear');\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        return notYetImplemented('resizeBilinearBackprop');\n    }\n    resizeNearestNeighbor(x, newHEight, newWidth, alignCorners) {\n        return notYetImplemented('resizeNearestNeighbor');\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        return notYetImplemented('resizeNearestNeighborBackprop');\n    }\n    batchNorm(x, mean, variance, offset, scale, varianceEpsilon) {\n        return notYetImplemented('batchNorm');\n    }\n    localResponseNormalization4D(x, radius, bias, alpha, beta) {\n        return notYetImplemented('localResponseNormalization4D');\n    }\n    LRNGrad(dy, inputImage, outputImage, radius, bias, alpha, beta) {\n        return notYetImplemented('LRNGrad');\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        return notYetImplemented('multinomial');\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        return notYetImplemented('oneHot');\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        return notYetImplemented('cumsum');\n    }\n    nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n        return notYetImplemented('nonMaxSuppression');\n    }\n    fft(x) {\n        return notYetImplemented('fft');\n    }\n    ifft(x) {\n        return notYetImplemented('ifft');\n    }\n    complex(real, imag) {\n        return notYetImplemented('complex');\n    }\n    real(input) {\n        return notYetImplemented('real');\n    }\n    imag(input) {\n        return notYetImplemented('imag');\n    }\n    cropAndResize(image, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        return notYetImplemented('cropAndResize');\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        return notYetImplemented('depthToSpace');\n    }\n    // Aligns with the \"SplitV\" kernel in TensorFlow.\n    split(value, sizeSplits, axis) {\n        return notYetImplemented('split');\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        return notYetImplemented('sparseToDense');\n    }\n    diag(x) {\n        return notYetImplemented('diag');\n    }\n    fill(shape, value, dtype) {\n        return notYetImplemented('fill');\n    }\n    onesLike(x) {\n        return notYetImplemented('onesLike');\n    }\n    zerosLike(x) {\n        return notYetImplemented('zerosLike');\n    }\n    linspace(start, stop, num) {\n        return notYetImplemented('linspace');\n    }\n    dispose() {\n        return notYetImplemented('dispose');\n    }\n}\nfunction notYetImplemented(kernelName) {\n    throw new Error(`'${kernelName}' not yet implemented or not found in the registry. ` +\n        `This kernel may not be supported by the tfjs backend you have chosen`);\n}\n//# sourceMappingURL=backend.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.\nconst TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';\n/**\n * The environment contains evaluated flags as well as the registered platform.\n * This is always used as a global singleton and can be retrieved with\n * `tf.env()`.\n *\n * @doc {heading: 'Environment'}\n */\nexport class Environment {\n    // tslint:disable-next-line: no-any\n    constructor(global) {\n        this.global = global;\n        this.flags = {};\n        this.flagRegistry = {};\n        this.urlFlags = {};\n        this.populateURLFlags();\n    }\n    setPlatform(platformName, platform) {\n        if (this.platform != null) {\n            console.warn(`Platform ${this.platformName} has already been set. ` +\n                `Overwriting the platform with ${platform}.`);\n        }\n        this.platformName = platformName;\n        this.platform = platform;\n    }\n    registerFlag(flagName, evaluationFn, setHook) {\n        this.flagRegistry[flagName] = { evaluationFn, setHook };\n        // Override the flag value from the URL. This has to happen here because the\n        // environment is initialized before flags get registered.\n        if (this.urlFlags[flagName] != null) {\n            const flagValue = this.urlFlags[flagName];\n            console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);\n            this.set(flagName, flagValue);\n        }\n    }\n    async getAsync(flagName) {\n        if (flagName in this.flags) {\n            return this.flags[flagName];\n        }\n        this.flags[flagName] = await this.evaluateFlag(flagName);\n        return this.flags[flagName];\n    }\n    get(flagName) {\n        if (flagName in this.flags) {\n            return this.flags[flagName];\n        }\n        const flagValue = this.evaluateFlag(flagName);\n        if (flagValue instanceof Promise) {\n            throw new Error(`Flag ${flagName} cannot be synchronously evaluated. ` +\n                `Please use getAsync() instead.`);\n        }\n        this.flags[flagName] = flagValue;\n        return this.flags[flagName];\n    }\n    getNumber(flagName) {\n        return this.get(flagName);\n    }\n    getBool(flagName) {\n        return this.get(flagName);\n    }\n    getFlags() {\n        return this.flags;\n    }\n    // For backwards compatibility.\n    get features() {\n        return this.flags;\n    }\n    set(flagName, value) {\n        if (this.flagRegistry[flagName] == null) {\n            throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);\n        }\n        this.flags[flagName] = value;\n        if (this.flagRegistry[flagName].setHook != null) {\n            this.flagRegistry[flagName].setHook(value);\n        }\n    }\n    evaluateFlag(flagName) {\n        if (this.flagRegistry[flagName] == null) {\n            throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);\n        }\n        return this.flagRegistry[flagName].evaluationFn();\n    }\n    setFlags(flags) {\n        this.flags = Object.assign({}, flags);\n    }\n    reset() {\n        this.flags = {};\n        this.urlFlags = {};\n        this.populateURLFlags();\n    }\n    populateURLFlags() {\n        if (typeof this.global === 'undefined' ||\n            typeof this.global.location === 'undefined' ||\n            typeof this.global.location.search === 'undefined') {\n            return;\n        }\n        const urlParams = getQueryParams(this.global.location.search);\n        if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n            const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');\n            keyValues.forEach(keyValue => {\n                const [key, value] = keyValue.split(':');\n                this.urlFlags[key] = parseValue(key, value);\n            });\n        }\n    }\n}\nexport function getQueryParams(queryString) {\n    const params = {};\n    queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n        decodeParam(params, t[0], t[1]);\n        return t.join('=');\n    });\n    return params;\n}\nfunction decodeParam(params, name, value) {\n    params[decodeURIComponent(name)] = decodeURIComponent(value || '');\n}\nfunction parseValue(flagName, value) {\n    value = value.toLowerCase();\n    if (value === 'true' || value === 'false') {\n        return value === 'true';\n    }\n    else if (`${+value}` === value) {\n        return +value;\n    }\n    throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);\n}\n/**\n * Returns the current environment (a global singleton).\n *\n * The environment object contains the evaluated feature values as well as the\n * active platform.\n *\n * @doc {heading: 'Environment'}\n */\nexport function env() {\n    return ENV;\n}\nexport let ENV = null;\nexport function setEnvironmentGlobal(environment) {\n    ENV = environment;\n}\n//# sourceMappingURL=environment.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Note that the identifier globalNameSpace is scoped to this module, but will\n// always resolve to the same global object regardless of how the module is\n// resolved.\n// tslint:disable-next-line:no-any\nlet globalNameSpace;\n// tslint:disable-next-line:no-any\nexport function getGlobalNamespace() {\n    if (globalNameSpace == null) {\n        // tslint:disable-next-line:no-any\n        let ns;\n        if (typeof (window) !== 'undefined') {\n            ns = window;\n        }\n        else if (typeof (global) !== 'undefined') {\n            ns = global;\n        }\n        else if (typeof (process) !== 'undefined') {\n            ns = process;\n        }\n        else if (typeof (self) !== 'undefined') {\n            ns = self;\n        }\n        else {\n            throw new Error('Could not find a global object');\n        }\n        globalNameSpace = ns;\n    }\n    return globalNameSpace;\n}\n// tslint:disable-next-line:no-any\nfunction getGlobalMap() {\n    const ns = getGlobalNamespace();\n    if (ns._tfGlobals == null) {\n        ns._tfGlobals = new Map();\n    }\n    return ns._tfGlobals;\n}\n/**\n * Returns a globally accessible 'singleton' object.\n *\n * @param key the name of the object\n * @param init a function to initialize to initialize this object\n *             the first time it is fetched.\n */\nexport function getGlobal(key, init) {\n    const globalMap = getGlobalMap();\n    if (globalMap.has(key)) {\n        return globalMap.get(key);\n    }\n    else {\n        const singleton = init();\n        globalMap.set(key, singleton);\n        return globalMap.get(key);\n    }\n}\n//# sourceMappingURL=global_util.js.map", "export const Abs = 'Abs';\nexport const Acos = 'Acos';\nexport const Acosh = 'Acosh';\nexport const Add = 'Add';\nexport const AddN = 'AddN';\nexport const All = 'All';\nexport const Any = 'Any';\nexport const ArgMax = 'ArgMax';\nexport const ArgMin = 'ArgMin';\nexport const Asin = 'Asin';\nexport const Asinh = 'Asinh';\nexport const Atan = 'Atan';\nexport const Atanh = 'Atanh';\nexport const Atan2 = 'Atan2';\nexport const AvgPool = 'AvgPool';\nexport const AvgPoolBackprop = 'AvgPoolBackprop';\nexport const AvgPool3D = 'AvgPool3D';\nexport const AvgPool3DBackprop = 'AvgPool3DBackprop';\nexport const BatchMatMul = 'BatchMatMul';\nexport const BatchToSpaceND = 'BatchToSpaceND';\nexport const BroadcastTo = 'BroadcastTo';\nexport const Cast = 'Cast';\nexport const Ceil = 'Ceil';\nexport const ClipByValue = 'ClipByValue';\nexport const Complex = 'Complex';\nexport const Concat = 'Concat';\nexport const Conv2D = 'Conv2D';\nexport const Conv2DBackpropFilter = 'Conv2DBackpropFilter';\nexport const Conv2DBackpropInput = 'Conv2DBackpropInput';\nexport const Conv3D = 'Conv3D';\nexport const Conv3DBackpropFilterV2 = 'Conv3DBackpropFilterV2';\nexport const Conv3DBackpropInputV2 = 'Conv3DBackpropInputV2';\nexport const Cos = 'Cos';\nexport const Cosh = 'Cosh';\nexport const Cumsum = 'Cumsum';\nexport const CropAndResize = 'CropAndResize';\nexport const DepthToSpace = 'DepthToSpace';\nexport const DepthwiseConv2dNative = 'DepthwiseConv2dNative';\nexport const DepthwiseConv2dNativeBackpropFilter = 'DepthwiseConv2dNativeBackpropFilter';\nexport const DepthwiseConv2dNativeBackpropInput = 'DepthwiseConv2dNativeBackpropInput';\nexport const Diag = 'Diag';\nexport const Dilation2D = 'Dilation2D';\nexport const Dilation2DBackpropInput = 'Dilation2DBackpropInput';\nexport const Dilation2DBackpropFilter = 'Dilation2DBackpropFilter';\nexport const Div = 'Div';\nexport const Elu = 'Elu';\nexport const EluGrad = 'EluGrad';\nexport const Erf = 'Erf';\nexport const Equal = 'Equal';\nexport const Exp = 'Exp';\nexport const Expm1 = 'Expm1';\nexport const FFT = 'FFT';\nexport const Fill = 'Fill';\nexport const FlipLeftRight = 'FlipLeftRight';\nexport const Floor = 'Floor';\nexport const FloorDiv = 'FloorDiv';\nexport const FusedBatchNorm = 'FusedBatchNorm';\nexport const GatherV2 = 'GatherV2';\nexport const GatherNd = 'GatherNd';\nexport const Greater = 'Greater';\nexport const GreaterEqual = 'GreaterEqual';\nexport const Identity = 'Identity';\nexport const IFFT = 'IFFT';\nexport const Imag = 'Imag';\nexport const IsFinite = 'IsFinite';\nexport const IsInf = 'IsInf';\nexport const IsNan = 'IsNan';\nexport const Less = 'Less';\nexport const LessEqual = 'LessEqual';\nexport const LinSpace = 'LinSpace';\nexport const Log = 'Log';\nexport const Log1p = 'Log1p';\nexport const LogicalAnd = 'LogicalAnd';\nexport const LogicalNot = 'LogicalNot';\nexport const LogicalOr = 'LogicalOr';\nexport const LogSoftmax = 'LogSoftmax';\nexport const LRN = 'LRN';\nexport const LRNBackprop = 'LRNBackprop';\nexport const Max = 'Max';\nexport const Maximum = 'Maximum';\nexport const MaxPool = 'MaxPool';\nexport const MaxPoolBackprop = 'MaxPoolBackprop';\nexport const MaxPool3D = 'MaxPool3D';\nexport const MaxPool3DBackprop = 'MaxPool3DBackprop';\nexport const MaxPoolWithArgmax = 'MaxPoolWithArgmax';\nexport const Mean = 'Mean';\nexport const Min = 'Min';\nexport const Minimum = 'Minimum';\nexport const Mod = 'Mod';\nexport const Multiply = 'Multiply';\nexport const Negate = 'Negate';\nexport const NotEqual = 'NotEqual';\nexport const NonMaxSuppressionV3 = 'NonMaxSuppressionV3';\nexport const NonMaxSuppressionV4 = 'NonMaxSuppressionV4';\nexport const NonMaxSuppressionV5 = 'NonMaxSuppressionV5';\nexport const OnesLike = 'OnesLike';\nexport const OneHot = 'OneHot';\nexport const PadV2 = 'PadV2';\nexport const Pool = 'Pool';\nexport const Pow = 'Pow';\nexport const Prelu = 'Prelu';\nexport const Prod = 'Prod';\nexport const Range = 'Range';\nexport const Real = 'Real';\nexport const Reciprocal = 'Reciprocal';\nexport const Relu = 'Relu';\nexport const Reshape = 'Reshape';\nexport const ResizeNearestNeighbor = 'ResizeNearestNeighbor';\nexport const ResizeNearestNeighborGrad = 'ResizeNearestNeighborGrad';\nexport const ResizeBilinear = 'ResizeBilinear';\nexport const ResizeBilinearGrad = 'ResizeBilinearGrad';\nexport const Relu6 = 'Relu6';\nexport const Reverse = 'Reverse';\nexport const Round = 'Round';\nexport const Rsqrt = 'Rsqrt';\nexport const ScatterNd = 'ScatterNd';\nexport const SelectV2 = 'SelectV2';\nexport const Selu = 'Selu';\nexport const Slice = 'Slice';\nexport const Sin = 'Sin';\nexport const Sinh = 'Sinh';\nexport const Sign = 'Sign';\nexport const Sigmoid = 'Sigmoid';\nexport const Softplus = 'Softplus';\nexport const Sqrt = 'Sqrt';\nexport const Sum = 'Sum';\nexport const SpaceToBatchND = 'SpaceToBatchND';\nexport const SplitV = 'SplitV';\nexport const Softmax = 'Softmax';\nexport const SquaredDifference = 'SquaredDifference';\nexport const Square = 'Square';\nexport const Sub = 'Sub';\nexport const SparseToDense = 'SparseToDense';\nexport const StridedSlice = 'StridedSlice';\nexport const Tan = 'Tan';\nexport const Tanh = 'Tanh';\nexport const Tile = 'Tile';\nexport const TopK = 'TopK';\nexport const Transpose = 'Transpose';\nexport const Unique = 'Unique';\nexport const Unpack = 'Unpack';\nexport const UnsortedSegmentSum = 'UnsortedSegmentSum';\nexport const ZerosLike = 'ZerosLike';\n/**\n * TensorFlow.js-only kernels\n */\nexport const Step = 'Step';\nexport const FromPixels = 'FromPixels';\nexport const RotateWithOffset = 'RotateWithOffset';\nexport const _FusedMatMul = '_FusedMatMul';\nexport const FusedConv2D = 'FusedConv2D';\nexport const FusedDepthwiseConv2D = 'FusedDepthwiseConv2D';\n//# sourceMappingURL=kernel_names.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nimport { getGlobal } from './global_util';\nconst kernelRegistry = getGlobal('kernelRegistry', () => new Map());\nconst gradRegistry = getGlobal('gradRegistry', () => new Map());\n/**\n * Returns the kernel function (code) associated with the provided names.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n */\nexport function getKernel(kernelName, backendName) {\n    const key = makeKey(kernelName, backendName);\n    return kernelRegistry.get(key);\n}\n/**\n * Returns the registered gradient info associated with the provided kernel.\n * @param kernelName The official TF kernel name.\n */\nexport function getGradient(kernelName) {\n    return gradRegistry.get(kernelName);\n}\nexport function getKernelsForBackend(backendName) {\n    const it = kernelRegistry.entries();\n    const result = [];\n    while (true) {\n        const { done, value } = it.next();\n        if (done) {\n            break;\n        }\n        const [key, config] = value;\n        const [backend,] = key.split('_');\n        if (backend === backendName) {\n            result.push(config);\n        }\n    }\n    return result;\n}\n/**\n * Registers the function (forward pass) for the kernel in a global registry.\n *\n * @param config A config object with the following properties:\n * - `kernelName` The official name of the kernel.\n * - `backendName` The official name of the backend.\n * - `kernelFunc` The function to run during the forward pass of the kernel.\n * - `setupFunc` Optional. Gets called once, after the backend initializes.\n * - `disposeFunc` Optional. Gets called once, right before the backend is\n * disposed.\n */\nexport function registerKernel(config) {\n    const { kernelName, backendName } = config;\n    const key = makeKey(kernelName, backendName);\n    if (kernelRegistry.has(key)) {\n        console.warn(`The kernel '${kernelName}' for backend ` +\n            `'${backendName}' is already registered`);\n    }\n    kernelRegistry.set(key, config);\n}\n/**\n * Registers a gradient function for a given kernel in the global registry,\n * to be used during the back-propagation of that kernel.\n *\n * @param config An object with the following properties:\n * - `kernelName` The name of the kernel that the gradient function is for.\n * - `gradFunc` The function to run during back-propagation.\n */\nexport function registerGradient(config) {\n    const { kernelName } = config;\n    if (gradRegistry.has(kernelName)) {\n        // TODO (yassogba) after 3.0 assess whether we need to keep this gated\n        // to debug mode.\n        if (env().getBool('DEBUG')) {\n            console.warn(`Overriding the gradient for '${kernelName}'`);\n        }\n    }\n    gradRegistry.set(kernelName, config);\n}\n/**\n * Removes the kernel function from the registry.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n *\n */\nexport function unregisterKernel(kernelName, backendName) {\n    const key = makeKey(kernelName, backendName);\n    if (!kernelRegistry.has(key)) {\n        throw new Error(`The kernel '${kernelName}' for backend ` +\n            `'${backendName}' is not registered`);\n    }\n    kernelRegistry.delete(key);\n}\n/** Removes the registered gradient from the global registry. */\nexport function unregisterGradient(kernelName) {\n    if (!gradRegistry.has(kernelName)) {\n        throw new Error(`The gradient '${kernelName}' for backend is not registered`);\n    }\n    gradRegistry.delete(kernelName);\n}\n/**\n * Finds kernels that have already been registered to a backend and re-registers\n * them for a new backend. Useful for registering custom backends.\n * @param registeredBackendName Already registered backend.\n * @param newBackendName New backend.\n */\nexport function copyRegisteredKernels(registeredBackendName, newBackendName) {\n    const kernels = getKernelsForBackend(registeredBackendName);\n    kernels.forEach(kernelConfig => {\n        const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName });\n        registerKernel(newKernelConfig);\n    });\n}\nfunction makeKey(kernelName, backendName) {\n    return `${backendName}_${kernelName}`;\n}\n//# sourceMappingURL=kernel_registry.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\n/**\n * Shuffles the array in-place using Fisher-Yates algorithm.\n *\n * ```js\n * const a = [1, 2, 3, 4, 5];\n * tf.util.shuffle(a);\n * console.log(a);\n * ```\n *\n * @param array The array to shuffle in-place.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\n// tslint:disable-next-line:no-any\nexport function shuffle(array) {\n    let counter = array.length;\n    let temp = 0;\n    let index = 0;\n    // While there are elements in the array\n    while (counter > 0) {\n        // Pick a random index\n        index = (Math.random() * counter) | 0;\n        // Decrease counter by 1\n        counter--;\n        // And swap the last element with it\n        temp = array[counter];\n        array[counter] = array[index];\n        array[index] = temp;\n    }\n}\n/** Clamps a value to a specified range. */\nexport function clamp(min, x, max) {\n    return Math.max(min, Math.min(x, max));\n}\nexport function nearestLargerEven(val) {\n    return val % 2 === 0 ? val : val + 1;\n}\nexport function sum(arr) {\n    let sum = 0;\n    for (let i = 0; i < arr.length; i++) {\n        sum += arr[i];\n    }\n    return sum;\n}\n/**\n * Returns a sample from a uniform [a, b) distribution.\n *\n * @param a The minimum support (inclusive).\n * @param b The maximum support (exclusive).\n * @return A pseudorandom number on the half-open interval [a,b).\n */\nexport function randUniform(a, b) {\n    const r = Math.random();\n    return (b * r) + (1 - r) * a;\n}\n/** Returns the squared Euclidean distance between two vectors. */\nexport function distSquared(a, b) {\n    let result = 0;\n    for (let i = 0; i < a.length; i++) {\n        const diff = Number(a[i]) - Number(b[i]);\n        result += diff * diff;\n    }\n    return result;\n}\n/**\n * Asserts that the expression is true. Otherwise throws an error with the\n * provided message.\n *\n * ```js\n * const x = 2;\n * tf.util.assert(x === 2, 'x is not 2');\n * ```\n *\n * @param expr The expression to assert (as a boolean).\n * @param msg A function that returns the message to report when throwing an\n *     error. We use a function for performance reasons.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function assert(expr, msg) {\n    if (!expr) {\n        throw new Error(typeof msg === 'string' ? msg : msg());\n    }\n}\nexport function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = '') {\n    assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nexport function assertNonNull(a) {\n    assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);\n}\n// NOTE: We explicitly type out what T extends instead of any so that\n// util.flatten on a nested array of number doesn't try to infer T as a\n// number[][], causing us to explicitly type util.flatten<number>().\n/**\n *  Flattens an arbitrarily nested array.\n *\n * ```js\n * const a = [[1, 2], [3, 4], [5, [6, [7]]]];\n * const flat = tf.util.flatten(a);\n * console.log(flat);\n * ```\n *\n *  @param arr The nested array to flatten.\n *  @param result The destination array which holds the elements.\n *  @param skipTypedArray If true, avoids flattening the typed arrays. Defaults\n *      to false.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function flatten(arr, result = [], skipTypedArray = false) {\n    if (result == null) {\n        result = [];\n    }\n    if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {\n        for (let i = 0; i < arr.length; ++i) {\n            flatten(arr[i], result, skipTypedArray);\n        }\n    }\n    else {\n        result.push(arr);\n    }\n    return result;\n}\n/**\n * Returns the size (number of elements) of the tensor given its shape.\n *\n * ```js\n * const shape = [3, 4, 2];\n * const size = tf.util.sizeFromShape(shape);\n * console.log(size);\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function sizeFromShape(shape) {\n    if (shape.length === 0) {\n        // Scalar.\n        return 1;\n    }\n    let size = shape[0];\n    for (let i = 1; i < shape.length; i++) {\n        size *= shape[i];\n    }\n    return size;\n}\nexport function isScalarShape(shape) {\n    return shape.length === 0;\n}\nexport function arraysEqual(n1, n2) {\n    if (n1 === n2) {\n        return true;\n    }\n    if (n1 == null || n2 == null) {\n        return false;\n    }\n    if (n1.length !== n2.length) {\n        return false;\n    }\n    for (let i = 0; i < n1.length; i++) {\n        if (n1[i] !== n2[i]) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function isInt(a) {\n    return a % 1 === 0;\n}\nexport function tanh(x) {\n    // tslint:disable-next-line:no-any\n    if (Math.tanh != null) {\n        // tslint:disable-next-line:no-any\n        return Math.tanh(x);\n    }\n    if (x === Infinity) {\n        return 1;\n    }\n    else if (x === -Infinity) {\n        return -1;\n    }\n    else {\n        const e2x = Math.exp(2 * x);\n        return (e2x - 1) / (e2x + 1);\n    }\n}\nexport function sizeToSquarishShape(size) {\n    const width = Math.ceil(Math.sqrt(size));\n    return [width, Math.ceil(size / width)];\n}\n/**\n * Creates a new array with randomized indicies to a given quantity.\n *\n * ```js\n * const randomTen = tf.util.createShuffledIndices(10);\n * console.log(randomTen);\n * ```\n *\n * @param number Quantity of how many shuffled indicies to create.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function createShuffledIndices(n) {\n    const shuffledIndices = new Uint32Array(n);\n    for (let i = 0; i < n; ++i) {\n        shuffledIndices[i] = i;\n    }\n    shuffle(shuffledIndices);\n    return shuffledIndices;\n}\nexport function rightPad(a, size) {\n    if (size <= a.length) {\n        return a;\n    }\n    return a + ' '.repeat(size - a.length);\n}\nexport function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {\n    return new Promise((resolve, reject) => {\n        let tryCount = 0;\n        const tryFn = () => {\n            if (checkFn()) {\n                resolve();\n                return;\n            }\n            tryCount++;\n            const nextBackoff = delayFn(tryCount);\n            if (maxCounter != null && tryCount >= maxCounter) {\n                reject();\n                return;\n            }\n            setTimeout(tryFn, nextBackoff);\n        };\n        tryFn();\n    });\n}\n/**\n * Given the full size of the array and a shape that may contain -1 as the\n * implicit dimension, returns the inferred shape where -1 is replaced.\n * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].\n *\n * @param shape The shape, which may contain -1 in some dimension.\n * @param size The full size (number of elements) of the array.\n * @return The inferred shape where -1 is replaced with the inferred size.\n */\nexport function inferFromImplicitShape(shape, size) {\n    let shapeProd = 1;\n    let implicitIdx = -1;\n    for (let i = 0; i < shape.length; ++i) {\n        if (shape[i] >= 0) {\n            shapeProd *= shape[i];\n        }\n        else if (shape[i] === -1) {\n            if (implicitIdx !== -1) {\n                throw Error(`Shapes can only have 1 implicit size. ` +\n                    `Found -1 at dim ${implicitIdx} and dim ${i}`);\n            }\n            implicitIdx = i;\n        }\n        else if (shape[i] < 0) {\n            throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n        }\n    }\n    if (implicitIdx === -1) {\n        if (size > 0 && size !== shapeProd) {\n            throw Error(`Size(${size}) must match the product of shape ${shape}`);\n        }\n        return shape;\n    }\n    if (shapeProd === 0) {\n        throw Error(`Cannot infer the missing size in [${shape}] when ` +\n            `there are 0 elements`);\n    }\n    if (size % shapeProd !== 0) {\n        throw Error(`The implicit shape can't be a fractional number. ` +\n            `Got ${size} / ${shapeProd}`);\n    }\n    const newShape = shape.slice();\n    newShape[implicitIdx] = size / shapeProd;\n    return newShape;\n}\nexport function parseAxisParam(axis, shape) {\n    const rank = shape.length;\n    // Normalize input\n    axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n    // Check for valid range\n    assert(axis.every(ax => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but ` +\n        `got axis ${axis}`);\n    // Check for only integers\n    assert(axis.every(ax => isInt(ax)), () => `All values in axis param must be integers but ` +\n        `got axis ${axis}`);\n    // Handle negative axis.\n    return axis.map(a => a < 0 ? rank + a : a);\n}\n/** Reduces the shape by removing all dimensions of shape 1. */\nexport function squeezeShape(shape, axis) {\n    const newShape = [];\n    const keptDims = [];\n    const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;\n    const axes = (axis == null || isEmptyArray) ?\n        null :\n        parseAxisParam(axis, shape).sort();\n    let j = 0;\n    for (let i = 0; i < shape.length; ++i) {\n        if (axes != null) {\n            if (axes[j] === i && shape[i] !== 1) {\n                throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n            }\n            if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {\n                newShape.push(shape[i]);\n                keptDims.push(i);\n            }\n            if (axes[j] <= i) {\n                j++;\n            }\n        }\n        if (shape[i] !== 1) {\n            newShape.push(shape[i]);\n            keptDims.push(i);\n        }\n    }\n    return { newShape, keptDims };\n}\nexport function getTypedArrayFromDType(dtype, size) {\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    return values;\n}\nexport function getArrayFromDType(dtype, size) {\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else if (dtype === 'string') {\n        values = new Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    return values;\n}\nexport function checkConversionForErrors(vals, dtype) {\n    for (let i = 0; i < vals.length; i++) {\n        const num = vals[i];\n        if (isNaN(num) || !isFinite(num)) {\n            throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n        }\n    }\n}\n/** Returns true if the dtype is valid. */\nexport function isValidDtype(dtype) {\n    return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' ||\n        dtype === 'int32' || dtype === 'string';\n}\n/**\n * Returns true if the new type can't encode the old type without loss of\n * precision.\n */\nexport function hasEncodingLoss(oldType, newType) {\n    if (newType === 'complex64') {\n        return false;\n    }\n    if (newType === 'float32' && oldType !== 'complex64') {\n        return false;\n    }\n    if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {\n        return false;\n    }\n    if (newType === 'bool' && oldType === 'bool') {\n        return false;\n    }\n    return true;\n}\nexport function isTypedArray(a) {\n    return a instanceof Float32Array || a instanceof Int32Array ||\n        a instanceof Uint8Array;\n}\nexport function bytesPerElement(dtype) {\n    if (dtype === 'float32' || dtype === 'int32') {\n        return 4;\n    }\n    else if (dtype === 'complex64') {\n        return 8;\n    }\n    else if (dtype === 'bool') {\n        return 1;\n    }\n    else {\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n}\n/**\n * Returns the approximate number of bytes allocated in the string array - 2\n * bytes per character. Computing the exact bytes for a native string in JS is\n * not possible since it depends on the encoding of the html page that serves\n * the website.\n */\nexport function bytesFromStringArray(arr) {\n    if (arr == null) {\n        return 0;\n    }\n    let bytes = 0;\n    arr.forEach(x => bytes += x.length);\n    return bytes;\n}\n/** Returns true if the value is a string. */\nexport function isString(value) {\n    return typeof value === 'string' || value instanceof String;\n}\nexport function isBoolean(value) {\n    return typeof value === 'boolean';\n}\nexport function isNumber(value) {\n    return typeof value === 'number';\n}\nexport function inferDtype(values) {\n    if (Array.isArray(values)) {\n        return inferDtype(values[0]);\n    }\n    if (values instanceof Float32Array) {\n        return 'float32';\n    }\n    else if (values instanceof Int32Array || values instanceof Uint8Array) {\n        return 'int32';\n    }\n    else if (isNumber(values)) {\n        return 'float32';\n    }\n    else if (isString(values)) {\n        return 'string';\n    }\n    else if (isBoolean(values)) {\n        return 'bool';\n    }\n    return 'float32';\n}\nexport function isFunction(f) {\n    return !!(f && f.constructor && f.call && f.apply);\n}\nexport function nearestDivisor(size, start) {\n    for (let i = start; i < size; ++i) {\n        if (size % i === 0) {\n            return i;\n        }\n    }\n    return size;\n}\nexport function computeStrides(shape) {\n    const rank = shape.length;\n    if (rank < 2) {\n        return [];\n    }\n    // Last dimension has implicit stride of 1, thus having D-1 (instead of D)\n    // strides.\n    const strides = new Array(rank - 1);\n    strides[rank - 2] = shape[rank - 1];\n    for (let i = rank - 3; i >= 0; --i) {\n        strides[i] = strides[i + 1] * shape[i + 1];\n    }\n    return strides;\n}\n/**\n * Create typed array for scalar value. Used for storing in `DataStorage`.\n */\nexport function createScalarValue(value, dtype) {\n    if (dtype === 'string') {\n        return encodeString(value);\n    }\n    return toTypedArray([value], dtype);\n}\nexport function toTypedArray(a, dtype) {\n    if (dtype === 'string') {\n        throw new Error('Cannot convert a string[] to a TypedArray');\n    }\n    if (Array.isArray(a)) {\n        a = flatten(a);\n    }\n    if (env().getBool('DEBUG')) {\n        checkConversionForErrors(a, dtype);\n    }\n    if (noConversionNeeded(a, dtype)) {\n        return a;\n    }\n    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n        return new Float32Array(a);\n    }\n    else if (dtype === 'int32') {\n        return new Int32Array(a);\n    }\n    else if (dtype === 'bool') {\n        const bool = new Uint8Array(a.length);\n        for (let i = 0; i < bool.length; ++i) {\n            if (Math.round(a[i]) !== 0) {\n                bool[i] = 1;\n            }\n        }\n        return bool;\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\nfunction createNestedArray(offset, shape, a) {\n    const ret = new Array();\n    if (shape.length === 1) {\n        const d = shape[0];\n        for (let i = 0; i < d; i++) {\n            ret[i] = a[offset + i];\n        }\n    }\n    else {\n        const d = shape[0];\n        const rest = shape.slice(1);\n        const len = rest.reduce((acc, c) => acc * c);\n        for (let i = 0; i < d; i++) {\n            ret[i] = createNestedArray(offset + i * len, rest, a);\n        }\n    }\n    return ret;\n}\n// Provide a nested array of TypedArray in given shape.\nexport function toNestedArray(shape, a) {\n    if (shape.length === 0) {\n        // Scalar type should return a single number.\n        return a[0];\n    }\n    const size = shape.reduce((acc, c) => acc * c);\n    if (size === 0) {\n        // A tensor with shape zero should be turned into empty list.\n        return [];\n    }\n    if (size !== a.length) {\n        throw new Error(`[${shape}] does not match the input size ${a.length}.`);\n    }\n    return createNestedArray(0, shape, a);\n}\nfunction noConversionNeeded(a, dtype) {\n    return (a instanceof Float32Array && dtype === 'float32') ||\n        (a instanceof Int32Array && dtype === 'int32') ||\n        (a instanceof Uint8Array && dtype === 'bool');\n}\nexport function makeOnesTypedArray(size, dtype) {\n    const array = makeZerosTypedArray(size, dtype);\n    for (let i = 0; i < array.length; i++) {\n        array[i] = 1;\n    }\n    return array;\n}\nexport function makeZerosTypedArray(size, dtype) {\n    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n        return new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        return new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        return new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\n/**\n * Make nested `TypedArray` filled with zeros.\n * @param shape The shape information for the nested array.\n * @param dtype dtype of the array element.\n */\nexport function makeZerosNestedTypedArray(shape, dtype) {\n    const size = shape.reduce((prev, curr) => prev * curr, 1);\n    if (dtype == null || dtype === 'float32') {\n        return toNestedArray(shape, new Float32Array(size));\n    }\n    else if (dtype === 'int32') {\n        return toNestedArray(shape, new Int32Array(size));\n    }\n    else if (dtype === 'bool') {\n        return toNestedArray(shape, new Uint8Array(size));\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\n/**\n * Returns the current high-resolution time in milliseconds relative to an\n * arbitrary time in the past. It works across different platforms (node.js,\n * browsers).\n *\n * ```js\n * console.log(tf.util.now());\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function now() {\n    return env().platform.now();\n}\nexport function assertNonNegativeIntegerDimensions(shape) {\n    shape.forEach(dimSize => {\n        assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got ` +\n            `shape [${shape}].`);\n    });\n}\n/**\n * Returns a platform-specific implementation of\n * [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n *\n * If `fetch` is defined on the global object (`window`, `process`, etc.),\n * `tf.util.fetch` returns that function.\n *\n * If not, `tf.util.fetch` returns a platform-specific solution.\n *\n * ```js\n * const resource = await tf.util.fetch('https://unpkg.com/@tensorflow/tfjs');\n * // handle response\n * ```\n *\n * @doc {heading: 'Util'}\n */\nexport function fetch(path, requestInits) {\n    return env().platform.fetch(path, requestInits);\n}\n/**\n * Encodes the provided string into bytes using the provided encoding scheme.\n *\n * @param s The string to encode.\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function encodeString(s, encoding = 'utf-8') {\n    encoding = encoding || 'utf-8';\n    return env().platform.encode(s, encoding);\n}\n/**\n * Decodes the provided bytes into a string using the provided encoding scheme.\n * @param bytes The bytes to decode.\n *\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function decodeString(bytes, encoding = 'utf-8') {\n    encoding = encoding || 'utf-8';\n    return env().platform.decode(bytes, encoding);\n}\n/**\n * Computes flat index for a given location (multidimentionsal index) in a\n * Tensor/multidimensional array.\n *\n * @param locs Location in the tensor.\n * @param rank Rank of the tensor.\n * @param strides Tensor strides.\n */\nexport function locToIndex(locs, rank, strides) {\n    if (rank === 0) {\n        return 0;\n    }\n    else if (rank === 1) {\n        return locs[0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n        index += strides[i] * locs[i];\n    }\n    return index;\n}\n/**\n * Computes the location (multidimensional index) in a tensor/multidimentional\n * array for a given flat index.\n *\n * @param index Index in flat array.\n * @param rank Rank of tensor.\n * @param strides Strides of tensor.\n */\nexport function indexToLoc(index, rank, strides) {\n    if (rank === 0) {\n        return [];\n    }\n    else if (rank === 1) {\n        return [index];\n    }\n    const locs = new Array(rank);\n    for (let i = 0; i < locs.length - 1; ++i) {\n        locs[i] = Math.floor(index / strides[i]);\n        index -= locs[i] * strides[i];\n    }\n    locs[locs.length - 1] = index;\n    return locs;\n}\n//# sourceMappingURL=util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from './util';\nexport class Profiler {\n    constructor(backendTimer, logger) {\n        this.backendTimer = backendTimer;\n        this.logger = logger;\n        if (logger == null) {\n            this.logger = new Logger();\n        }\n    }\n    profileKernel(kernelName, inputs, f) {\n        let outputs;\n        const holdResultWrapperFn = () => {\n            outputs = f();\n        };\n        const timer = this.backendTimer.time(holdResultWrapperFn);\n        for (let i = 0; i < outputs.length; i++) {\n            const output = outputs[i];\n            // Dangling promise here because we don't want to propagate up\n            // asynchronicity.\n            output.data().then(tensorVals => {\n                checkComputationForErrors(tensorVals, output.dtype, kernelName);\n            });\n        }\n        const kernelProfile = {\n            kernelName,\n            outputs,\n            inputs,\n            timeMs: timer.then(timing => timing.kernelMs),\n            extraInfo: timer.then(timing => timing.getExtraProfileInfo != null ?\n                timing.getExtraProfileInfo() :\n                '')\n        };\n        return kernelProfile;\n    }\n    logKernelProfile(kernelProfile) {\n        const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile;\n        outputs.forEach(result => {\n            Promise.all([result.data(), timeMs, extraInfo]).then(valueContainer => {\n                this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);\n            });\n        });\n    }\n}\nexport function checkComputationForErrors(vals, dtype, kernelName) {\n    if (dtype !== 'float32') {\n        // Only floating point computations will generate NaN values\n        return false;\n    }\n    for (let i = 0; i < vals.length; i++) {\n        const num = vals[i];\n        if (isNaN(num) || !isFinite(num)) {\n            // Throwing custom exception so behavior is testable.\n            console.warn(`Found ${num} in the result of '${kernelName}'`);\n            return true;\n        }\n    }\n    return false;\n}\nexport class Logger {\n    logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {\n        const time = typeof timeMs === 'number' ? util.rightPad(`${timeMs}ms`, 9) :\n            timeMs['error'];\n        const paddedName = util.rightPad(name, 25);\n        const rank = result.rank;\n        const size = result.size;\n        const shape = util.rightPad(result.shape.toString(), 14);\n        let inputShapesDescription = '';\n        for (const name in inputs) {\n            const input = inputs[name];\n            if (input != null) {\n                // The input might be a non-tensor (e.g HTMLImageElement), in which case\n                // we claim the output shape as input shape.\n                const inputShape = input.shape || result.shape;\n                const inputRank = inputShape.length;\n                inputShapesDescription +=\n                    `${name}: ${inputRank}D ${inputRank > 0 ? inputShape : ''} `;\n            }\n        }\n        console.log(`%c${paddedName}\\t%c${time}\\t%c${rank}D ${shape}\\t%c${size}\\t%c${inputShapesDescription}\\t%c${extraInfo}`, 'font-weight:bold', 'color:red', 'color:blue', 'color: orange', 'color: green', 'color: steelblue');\n    }\n}\n//# sourceMappingURL=profiler.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from './util';\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nexport function getFilteredNodesXToY(tape, xs, y) {\n    // Forward pass to compute all the nodes and Tensors that are transitively a\n    // function of x.\n    const tensorsFromX = {};\n    const nodesFromX = {};\n    for (let i = 0; i < xs.length; i++) {\n        tensorsFromX[xs[i].id] = true;\n    }\n    for (let i = 0; i < tape.length; i++) {\n        const node = tape[i];\n        const nodeInputs = node.inputs;\n        for (const inputName in nodeInputs) {\n            const input = nodeInputs[inputName];\n            let anyInputFromX = false;\n            for (let j = 0; j < xs.length; j++) {\n                if (tensorsFromX[input.id]) {\n                    node.outputs.forEach(output => tensorsFromX[output.id] = true);\n                    anyInputFromX = true;\n                    nodesFromX[node.id] = true;\n                    break;\n                }\n            }\n            if (anyInputFromX) {\n                break;\n            }\n        }\n    }\n    // Backward pass to find all of the nodes and Tensors that lead to y.\n    const tensorsLeadToY = {};\n    tensorsLeadToY[y.id] = true;\n    const nodesToY = {};\n    for (let i = tape.length - 1; i >= 0; i--) {\n        const node = tape[i];\n        const nodeInputs = node.inputs;\n        // If any of the outputs lead to y, mark all of the inputs as leading to y.\n        for (let j = 0; j < node.outputs.length; j++) {\n            if (tensorsLeadToY[node.outputs[j].id]) {\n                for (const inputName in nodeInputs) {\n                    tensorsLeadToY[nodeInputs[inputName].id] = true;\n                    nodesToY[node.id] = true;\n                }\n                break;\n            }\n        }\n    }\n    // Return the paths that come from x and lead to y.\n    const filteredTape = [];\n    for (let i = 0; i < tape.length; i++) {\n        const node = tape[i];\n        if (nodesFromX[node.id] && nodesToY[node.id]) {\n            // Prune the inputs from the node that aren't a function of x.\n            const prunedInputs = {};\n            for (const inputName in node.inputs) {\n                const nodeInput = node.inputs[inputName];\n                if (tensorsFromX[nodeInput.id]) {\n                    prunedInputs[inputName] = nodeInput;\n                }\n            }\n            // Copy the node and overwrite inputsAndArgs to the pruned version.\n            const prunedNode = Object.assign({}, node);\n            prunedNode.inputs = prunedInputs;\n            prunedNode.outputs = node.outputs;\n            filteredTape.push(prunedNode);\n        }\n    }\n    return filteredTape;\n}\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nexport function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy, add) {\n    // Walk the tape backward and keep a map of Tensor to its gradient.\n    for (let i = filteredTape.length - 1; i >= 0; i--) {\n        const node = filteredTape[i];\n        const dys = [];\n        node.outputs.forEach(o => {\n            const gradTensor = tensorAccumulatedGradientMap[o.id];\n            if (gradTensor != null) {\n                dys.push(gradTensor);\n            }\n            else {\n                // This particular output is not in the back-propagation subgraph, so it\n                // does not affect the final output, thus we put null for its dy.\n                dys.push(null);\n            }\n        });\n        if (node.gradient == null) {\n            throw new Error(`Cannot compute gradient: gradient function not found ` +\n                `for ${node.kernelName}.`);\n        }\n        // Backprop dy through this node and accumulate gradients over the inputs.\n        const inputGradients = node.gradient(dys);\n        for (const inputName in node.inputs) {\n            if (!(inputName in inputGradients)) {\n                throw new Error(`Cannot backprop through input ${inputName}. ` +\n                    `Available gradients found: ${Object.keys(inputGradients)}.`);\n            }\n            // Call the gradient function.\n            const dx = tidy(() => inputGradients[inputName]());\n            if (dx.dtype !== 'float32') {\n                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n                    `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n            }\n            const x = node.inputs[inputName];\n            if (!util.arraysEqual(dx.shape, x.shape)) {\n                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n                    `'${inputName}' has shape '${dx.shape}', which does not match ` +\n                    `the shape of the input '${x.shape}'`);\n            }\n            if (tensorAccumulatedGradientMap[x.id] == null) {\n                tensorAccumulatedGradientMap[x.id] = dx;\n            }\n            else {\n                const curGradient = tensorAccumulatedGradientMap[x.id];\n                tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);\n                curGradient.dispose();\n            }\n        }\n    }\n}\n//# sourceMappingURL=tape.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { computeStrides, isString, rightPad, sizeFromShape } from './util';\n// Maximum number of values before we decide to show ellipsis.\nconst FORMAT_LIMIT_NUM_VALS = 20;\n// Number of first and last values to show when displaying a, b,...,y, z.\nconst FORMAT_NUM_FIRST_LAST_VALS = 3;\n// Number of significant digits to show.\nconst FORMAT_NUM_SIG_DIGITS = 7;\nexport function tensorToString(vals, shape, dtype, verbose) {\n    const strides = computeStrides(shape);\n    const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n    const rank = shape.length;\n    const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n    const lines = ['Tensor'];\n    if (verbose) {\n        lines.push(`  dtype: ${dtype}`);\n        lines.push(`  rank: ${rank}`);\n        lines.push(`  shape: [${shape}]`);\n        lines.push(`  values:`);\n    }\n    lines.push(valsLines.map(l => '    ' + l).join('\\n'));\n    return lines.join('\\n');\n}\nfunction computeMaxSizePerColumn(vals, shape, dtype, strides) {\n    const n = sizeFromShape(shape);\n    const numCols = strides[strides.length - 1];\n    const padPerCol = new Array(numCols).fill(0);\n    const rank = shape.length;\n    const valuesOrTuples = dtype === 'complex64' ? createComplexTuples(vals) : vals;\n    if (rank > 1) {\n        for (let row = 0; row < n / numCols; row++) {\n            const offset = row * numCols;\n            for (let j = 0; j < numCols; j++) {\n                padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);\n            }\n        }\n    }\n    return padPerCol;\n}\nfunction valToString(val, pad, dtype) {\n    let valStr;\n    if (Array.isArray(val)) {\n        valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +\n            `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n    }\n    else if (isString(val)) {\n        valStr = `'${val}'`;\n    }\n    else if (dtype === 'bool') {\n        valStr = boolNumToString(val);\n    }\n    else {\n        valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n    }\n    return rightPad(valStr, pad);\n}\nfunction boolNumToString(v) {\n    return v === 0 ? 'false' : 'true';\n}\nfunction subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {\n    const storagePerElement = dtype === 'complex64' ? 2 : 1;\n    const size = shape[0];\n    const rank = shape.length;\n    if (rank === 0) {\n        if (dtype === 'complex64') {\n            const complexTuple = createComplexTuples(vals);\n            return [valToString(complexTuple[0], 0, dtype)];\n        }\n        if (dtype === 'bool') {\n            return [boolNumToString(vals[0])];\n        }\n        return [vals[0].toString()];\n    }\n    if (rank === 1) {\n        if (size > FORMAT_LIMIT_NUM_VALS) {\n            const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n            let firstVals = Array.from(vals.slice(0, firstValsSize));\n            let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));\n            if (dtype === 'complex64') {\n                firstVals = createComplexTuples(firstVals);\n                lastVals = createComplexTuples(lastVals);\n            }\n            return [\n                '[' +\n                    firstVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n                        .join(', ') +\n                    ', ..., ' +\n                    lastVals\n                        .map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype))\n                        .join(', ') +\n                    ']'\n            ];\n        }\n        const displayVals = dtype === 'complex64' ? createComplexTuples(vals) :\n            Array.from(vals);\n        return [\n            '[' +\n                displayVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n                    .join(', ') +\n                ']'\n        ];\n    }\n    // The array is rank 2 or more.\n    const subshape = shape.slice(1);\n    const substrides = strides.slice(1);\n    const stride = strides[0] * storagePerElement;\n    const lines = [];\n    if (size > FORMAT_LIMIT_NUM_VALS) {\n        for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false /* isLast */));\n        }\n        lines.push('...');\n        for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n        }\n    }\n    else {\n        for (let i = 0; i < size; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n        }\n    }\n    const sep = rank === 2 ? ',' : '';\n    lines[0] = '[' + lines[0] + sep;\n    for (let i = 1; i < lines.length - 1; i++) {\n        lines[i] = ' ' + lines[i] + sep;\n    }\n    let newLineSep = ',\\n';\n    for (let i = 2; i < rank; i++) {\n        newLineSep += '\\n';\n    }\n    lines[lines.length - 1] =\n        ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);\n    return lines;\n}\nfunction createComplexTuples(vals) {\n    const complexTuples = [];\n    for (let i = 0; i < vals.length; i += 2) {\n        complexTuples.push([vals[i], vals[i + 1]]);\n    }\n    return complexTuples;\n}\n//# sourceMappingURL=tensor_format.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensorToString } from './tensor_format';\nimport * as util from './util';\nimport { computeStrides, toNestedArray } from './util';\n/**\n * A mutable object, similar to `tf.Tensor`, that allows users to set values\n * at locations before converting to an immutable `tf.Tensor`.\n *\n * See `tf.buffer` for creating a tensor buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class TensorBuffer {\n    constructor(shape, dtype, values) {\n        this.dtype = dtype;\n        this.shape = shape.slice();\n        this.size = util.sizeFromShape(shape);\n        if (values != null) {\n            const n = values.length;\n            util.assert(n === this.size, () => `Length of values '${n}' does not match the size ` +\n                `inferred by the shape '${this.size}'.`);\n        }\n        if (dtype === 'complex64') {\n            throw new Error(`complex64 dtype TensorBuffers are not supported. Please create ` +\n                `a TensorBuffer for the real and imaginary parts separately and ` +\n                `call tf.complex(real, imag).`);\n        }\n        this.values = values || util.getArrayFromDType(dtype, this.size);\n        this.strides = computeStrides(shape);\n    }\n    /**\n     * Sets a value in the buffer at a given location.\n     *\n     * @param value The value to set.\n     * @param locs  The location indices.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    set(value, ...locs) {\n        if (locs.length === 0) {\n            locs = [0];\n        }\n        util.assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must ` +\n            `match the rank (${this.rank})`);\n        const index = this.locToIndex(locs);\n        this.values[index] = value;\n    }\n    /**\n     * Returns the value in the buffer at the provided location.\n     *\n     * @param locs The location indices.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    get(...locs) {\n        if (locs.length === 0) {\n            locs = [0];\n        }\n        let i = 0;\n        for (const loc of locs) {\n            if (loc < 0 || loc >= this.shape[i]) {\n                const msg = `Requested out of range element at ${locs}. ` +\n                    `  Buffer shape=${this.shape}`;\n                throw new Error(msg);\n            }\n            i++;\n        }\n        let index = locs[locs.length - 1];\n        for (let i = 0; i < locs.length - 1; ++i) {\n            index += this.strides[i] * locs[i];\n        }\n        return this.values[index];\n    }\n    locToIndex(locs) {\n        if (this.rank === 0) {\n            return 0;\n        }\n        else if (this.rank === 1) {\n            return locs[0];\n        }\n        let index = locs[locs.length - 1];\n        for (let i = 0; i < locs.length - 1; ++i) {\n            index += this.strides[i] * locs[i];\n        }\n        return index;\n    }\n    indexToLoc(index) {\n        if (this.rank === 0) {\n            return [];\n        }\n        else if (this.rank === 1) {\n            return [index];\n        }\n        const locs = new Array(this.shape.length);\n        for (let i = 0; i < locs.length - 1; ++i) {\n            locs[i] = Math.floor(index / this.strides[i]);\n            index -= locs[i] * this.strides[i];\n        }\n        locs[locs.length - 1] = index;\n        return locs;\n    }\n    get rank() {\n        return this.shape.length;\n    }\n    /**\n     * Creates an immutable `tf.Tensor` object from the buffer.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    toTensor() {\n        return trackerFn().makeTensor(this.values, this.shape, this.dtype);\n    }\n}\n// For tracking tensor creation and disposal.\nlet trackerFn = null;\n// Used by chaining methods to call into ops.\nlet opHandler = null;\n// Used to warn about deprecated methods.\nlet deprecationWarningFn = null;\n// This here so that we can use this method on dev branches and keep the\n// functionality at master.\n// tslint:disable-next-line:no-unused-expression\n[deprecationWarningFn];\n/**\n * An external consumer can register itself as the tensor tracker. This way\n * the Tensor class can notify the tracker for every tensor created and\n * disposed.\n */\nexport function setTensorTracker(fn) {\n    trackerFn = fn;\n}\n/**\n * An external consumer can register itself as the op handler. This way the\n * Tensor class can have chaining methods that call into ops via the op\n * handler.\n */\nexport function setOpHandler(handler) {\n    opHandler = handler;\n}\n/**\n * Sets the deprecation warning function to be used by this file. This way the\n * Tensor class can be a leaf but still use the environment.\n */\nexport function setDeprecationWarningFn(fn) {\n    deprecationWarningFn = fn;\n}\n/**\n * A `tf.Tensor` object represents an immutable, multidimensional array of\n * numbers that has a shape and a data type.\n *\n * See `tf.tensor` for details on how to create a `tf.Tensor`.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Tensor {\n    constructor(shape, dtype, dataId, id) {\n        /** Whether this tensor has been globally kept. */\n        this.kept = false;\n        this.isDisposedInternal = false;\n        this.shape = shape.slice();\n        this.dtype = dtype || 'float32';\n        this.size = util.sizeFromShape(shape);\n        this.strides = computeStrides(shape);\n        this.dataId = dataId;\n        this.id = id;\n        this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher');\n    }\n    get rank() {\n        return this.shape.length;\n    }\n    /**\n     * Returns a promise of `tf.TensorBuffer` that holds the underlying data.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async buffer() {\n        const vals = await this.data();\n        return opHandler.buffer(this.shape, this.dtype, vals);\n    }\n    /**\n     * Returns a `tf.TensorBuffer` that holds the underlying data.\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    bufferSync() {\n        return opHandler.buffer(this.shape, this.dtype, this.dataSync());\n    }\n    /**\n     * Returns the tensor data as a nested array. The transfer of data is done\n     * asynchronously.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async array() {\n        const vals = await this.data();\n        return toNestedArray(this.shape, vals);\n    }\n    /**\n     * Returns the tensor data as a nested array. The transfer of data is done\n     * synchronously.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    arraySync() {\n        return toNestedArray(this.shape, this.dataSync());\n    }\n    /**\n     * Asynchronously downloads the values from the `tf.Tensor`. Returns a\n     * promise of `TypedArray` that resolves when the computation has finished.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async data() {\n        this.throwIfDisposed();\n        const data = trackerFn().read(this.dataId);\n        if (this.dtype === 'string') {\n            const bytes = await data;\n            try {\n                return bytes.map(b => util.decodeString(b));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode the string bytes into utf-8. ' +\n                    'To get the original bytes, call tensor.bytes().');\n            }\n        }\n        return data;\n    }\n    /**\n     * Synchronously downloads the values from the `tf.Tensor`. This blocks the\n     * UI thread until the values are ready, which can cause performance issues.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    dataSync() {\n        this.throwIfDisposed();\n        const data = trackerFn().readSync(this.dataId);\n        if (this.dtype === 'string') {\n            try {\n                return data.map(b => util.decodeString(b));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode the string bytes into utf-8. ' +\n                    'To get the original bytes, call tensor.bytes().');\n            }\n        }\n        return data;\n    }\n    /** Returns the underlying bytes of the tensor's data. */\n    async bytes() {\n        this.throwIfDisposed();\n        const data = await trackerFn().read(this.dataId);\n        if (this.dtype === 'string') {\n            return data;\n        }\n        else {\n            return new Uint8Array(data.buffer);\n        }\n    }\n    /**\n     * Disposes `tf.Tensor` from memory.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    dispose() {\n        if (this.isDisposed) {\n            return;\n        }\n        trackerFn().disposeTensor(this);\n        this.isDisposedInternal = true;\n    }\n    get isDisposed() {\n        return this.isDisposedInternal;\n    }\n    throwIfDisposed() {\n        if (this.isDisposed) {\n            throw new Error(`Tensor is disposed.`);\n        }\n    }\n    /**\n     * Prints the `tf.Tensor`. See `tf.print` for details.\n     *\n     * @param verbose Whether to print verbose information about the tensor,\n     *    including dtype and size.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    print(verbose = false) {\n        return opHandler.print(this, verbose);\n    }\n    /**\n     * Returns a copy of the tensor. See `tf.clone` for details.\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    clone() {\n        this.throwIfDisposed();\n        return opHandler.clone(this);\n    }\n    /**\n     * Returns a human-readable description of the tensor. Useful for logging.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    toString(verbose = false) {\n        const vals = this.dataSync();\n        return tensorToString(vals, this.shape, this.dtype, verbose);\n    }\n    cast(dtype) {\n        this.throwIfDisposed();\n        return opHandler.cast(this, dtype);\n    }\n    variable(trainable = true, name, dtype) {\n        this.throwIfDisposed();\n        return trackerFn().makeVariable(this, trainable, name, dtype);\n    }\n}\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n    value: (instance) => {\n        // Implementation note: we should use properties of the object that will be\n        // defined before the constructor body has finished executing (methods).\n        // This is because when this code is transpiled by babel, babel will call\n        // classCallCheck before the constructor body is run.\n        // See https://github.com/tensorflow/tfjs/issues/3384 for backstory.\n        return !!instance && instance.data != null && instance.dataSync != null &&\n            instance.throwIfDisposed != null;\n    }\n});\n/**\n * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Variable extends Tensor {\n    constructor(initialValue, trainable, name, tensorId) {\n        super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);\n        this.trainable = trainable;\n        this.name = name;\n    }\n    /**\n     * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n     * the same shape and dtype as the old `tf.Tensor`.\n     *\n     * @param newValue New tensor to be assigned to this variable.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    assign(newValue) {\n        if (newValue.dtype !== this.dtype) {\n            throw new Error(`dtype of the new value (${newValue.dtype}) and ` +\n                `previous value (${this.dtype}) must match`);\n        }\n        if (!util.arraysEqual(newValue.shape, this.shape)) {\n            throw new Error(`shape of the new value (${newValue.shape}) and ` +\n                `previous value (${this.shape}) must match`);\n        }\n        trackerFn().disposeTensor(this);\n        this.dataId = newValue.dataId;\n        trackerFn().incRef(this, null /* backend */);\n    }\n    dispose() {\n        trackerFn().disposeVariable(this);\n        this.isDisposedInternal = true;\n    }\n}\nObject.defineProperty(Variable, Symbol.hasInstance, {\n    value: (instance) => {\n        return instance instanceof Tensor && instance.assign != null &&\n            instance.assign instanceof Function;\n    }\n});\n//# sourceMappingURL=tensor.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Rank;\n(function (Rank) {\n    Rank[\"R0\"] = \"R0\";\n    Rank[\"R1\"] = \"R1\";\n    Rank[\"R2\"] = \"R2\";\n    Rank[\"R3\"] = \"R3\";\n    Rank[\"R4\"] = \"R4\";\n    Rank[\"R5\"] = \"R5\";\n    Rank[\"R6\"] = \"R6\";\n})(Rank || (Rank = {}));\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nvar UpcastInt32AndMap;\n(function (UpcastInt32AndMap) {\n    UpcastInt32AndMap[\"float32\"] = \"float32\";\n    UpcastInt32AndMap[\"int32\"] = \"int32\";\n    UpcastInt32AndMap[\"bool\"] = \"int32\";\n    UpcastInt32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));\nvar UpcastBoolAndMap;\n(function (UpcastBoolAndMap) {\n    UpcastBoolAndMap[\"float32\"] = \"float32\";\n    UpcastBoolAndMap[\"int32\"] = \"int32\";\n    UpcastBoolAndMap[\"bool\"] = \"bool\";\n    UpcastBoolAndMap[\"complex64\"] = \"complex64\";\n})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));\nvar UpcastFloat32AndMap;\n(function (UpcastFloat32AndMap) {\n    UpcastFloat32AndMap[\"float32\"] = \"float32\";\n    UpcastFloat32AndMap[\"int32\"] = \"float32\";\n    UpcastFloat32AndMap[\"bool\"] = \"float32\";\n    UpcastFloat32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));\nvar UpcastComplex64AndMap;\n(function (UpcastComplex64AndMap) {\n    UpcastComplex64AndMap[\"float32\"] = \"complex64\";\n    UpcastComplex64AndMap[\"int32\"] = \"complex64\";\n    UpcastComplex64AndMap[\"bool\"] = \"complex64\";\n    UpcastComplex64AndMap[\"complex64\"] = \"complex64\";\n})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));\nconst upcastTypeMap = {\n    'float32': UpcastFloat32AndMap,\n    'int32': UpcastInt32AndMap,\n    'bool': UpcastBoolAndMap,\n    'complex64': UpcastComplex64AndMap\n};\nexport function upcastType(typeA, typeB) {\n    if (typeA === 'string' || typeB === 'string') {\n        if (typeA === 'string' && typeB === 'string') {\n            return 'string';\n        }\n        throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n    }\n    return upcastTypeMap[typeA][typeB];\n}\n/** Returns the output type after summation. */\nexport function sumOutType(type) {\n    return upcastType(type, 'int32');\n}\n//# sourceMappingURL=types.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from './tensor';\nimport { upcastType } from './types';\nimport { assert } from './util';\nexport function makeTypesMatch(a, b) {\n    if (a.dtype === b.dtype) {\n        return [a, b];\n    }\n    const dtype = upcastType(a.dtype, b.dtype);\n    return [a.cast(dtype), b.cast(dtype)];\n}\nexport function assertTypesMatch(a, b) {\n    assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and` +\n        ` second(${b.dtype}) input must match`);\n}\nexport function isTensorInList(tensor, tensorList) {\n    return tensorList.some(x => x.id === tensor.id);\n}\n/**\n * Extracts any `Tensor`s found within the provided object.\n *\n * @param container an object that may be a `Tensor` or may directly contain\n *   `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it\n *   is safe to pass any object here, except that `Promise`s are not\n *   supported.\n * @returns An array of `Tensors` found within the passed object. If the\n *   argument is simply a `Tensor', a list containing that `Tensor` is\n *   returned. If the object is not a `Tensor` or does not\n *   contain `Tensors`, an empty list is returned.\n */\nexport function getTensorsInContainer(result) {\n    const list = [];\n    const seen = new Set();\n    walkTensorContainer(result, list, seen);\n    return list;\n}\nfunction walkTensorContainer(container, list, seen) {\n    if (container == null) {\n        return;\n    }\n    if (container instanceof Tensor) {\n        list.push(container);\n        return;\n    }\n    if (!isIterable(container)) {\n        return;\n    }\n    // Iteration over keys works also for arrays.\n    const iterable = container;\n    for (const k in iterable) {\n        const val = iterable[k];\n        if (!seen.has(val)) {\n            seen.add(val);\n            walkTensorContainer(val, list, seen);\n        }\n    }\n}\n// tslint:disable-next-line:no-any\nfunction isIterable(obj) {\n    return Array.isArray(obj) || typeof obj === 'object';\n}\n//# sourceMappingURL=tensor_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { KernelBackend } from './backends/backend';\nimport { Environment, setEnvironmentGlobal } from './environment';\nimport { getGlobalNamespace } from './global_util';\nimport { Add, Cast } from './kernel_names';\nimport { getGradient, getKernel, getKernelsForBackend } from './kernel_registry';\nimport { Profiler } from './profiler';\nimport { backpropagateGradients, getFilteredNodesXToY } from './tape';\nimport { setTensorTracker, Tensor, Variable } from './tensor';\nimport { getTensorsInContainer } from './tensor_util';\nimport * as util from './util';\nimport { bytesFromStringArray, makeOnesTypedArray, now, sizeFromShape } from './util';\nclass EngineState {\n    constructor() {\n        // Public since optimizers will use it.\n        this.registeredVariables = {};\n        this.nextTapeNodeId = 0;\n        this.numBytes = 0;\n        this.numTensors = 0;\n        this.numStringTensors = 0;\n        this.numDataBuffers = 0;\n        // Number of nested tf.grad() statements when computing higher-order\n        // gradients. E.g. `1` for first-order gradients and `2` for second-order\n        // gradients. Used to track if the tape should be removed after a backprop.\n        this.gradientDepth = 0;\n        // Number of nested kernel calls. When kernel depth is greater than 1, we turn\n        // off the tape.\n        this.kernelDepth = 0;\n        this.scopeStack = [];\n        /**\n         * Keeps track of the number of data moves during a kernel execution. We\n         * maintain a stack since kernels can call other kernels, recursively.\n         */\n        this.numDataMovesStack = [];\n        this.nextScopeId = 0;\n        this.tensorInfo = new WeakMap();\n        this.profiling = false;\n        this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null };\n    }\n    dispose() {\n        for (const variableName in this.registeredVariables) {\n            this.registeredVariables[variableName].dispose();\n        }\n    }\n}\nexport class Engine {\n    constructor(ENV) {\n        this.ENV = ENV;\n        this.registry = {};\n        this.registryFactory = {};\n        this.pendingBackendInitId = 0;\n        this.state = new EngineState();\n    }\n    async ready() {\n        if (this.pendingBackendInit != null) {\n            return this.pendingBackendInit.then(() => { });\n        }\n        if (this.backendInstance != null) {\n            return;\n        }\n        const sortedBackends = this.getSortedBackends();\n        for (let i = 0; i < sortedBackends.length; i++) {\n            const backendName = sortedBackends[i];\n            const success = await this.initializeBackend(backendName).success;\n            if (success) {\n                await this.setBackend(backendName);\n                return;\n            }\n        }\n        throw new Error(`Could not initialize any backends, all backend initializations ` +\n            `failed.`);\n    }\n    get backend() {\n        if (this.pendingBackendInit != null) {\n            throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make ` +\n                `sure to await tf.ready() or await tf.setBackend() before calling ` +\n                `other methods`);\n        }\n        if (this.backendInstance == null) {\n            const { name, asyncInit } = this.initializeBackendsAndReturnBest();\n            if (asyncInit) {\n                throw new Error(`The highest priority backend '${name}' has not yet been ` +\n                    `initialized. Make sure to await tf.ready() or ` +\n                    `await tf.setBackend() before calling other methods`);\n            }\n            this.setBackend(name);\n        }\n        return this.backendInstance;\n    }\n    backendNames() {\n        return Object.keys(this.registryFactory);\n    }\n    findBackend(backendName) {\n        if (!(backendName in this.registry)) {\n            // If the backend hasn't been initialized but we have a registry entry for\n            // it, initialize it and return it.\n            if (backendName in this.registryFactory) {\n                const { asyncInit } = this.initializeBackend(backendName);\n                if (asyncInit) {\n                    // Backend is not ready yet.\n                    return null;\n                }\n            }\n            else {\n                return null;\n            }\n        }\n        return this.registry[backendName];\n    }\n    findBackendFactory(backendName) {\n        if (!(backendName in this.registryFactory)) {\n            return null;\n        }\n        return this.registryFactory[backendName].factory;\n    }\n    registerBackend(backendName, factory, priority = 1) {\n        if (backendName in this.registryFactory) {\n            console.warn(`${backendName} backend was already registered. ` +\n                `Reusing existing backend factory.`);\n            return false;\n        }\n        this.registryFactory[backendName] = { factory, priority };\n        return true;\n    }\n    async setBackend(backendName) {\n        if (this.registryFactory[backendName] == null) {\n            throw new Error(`Backend name '${backendName}' not found in registry`);\n        }\n        this.backendName = backendName;\n        if (this.registry[backendName] == null) {\n            this.backendInstance = null;\n            const { success, asyncInit } = this.initializeBackend(backendName);\n            const result = asyncInit ? await success : success;\n            if (!result) {\n                return false;\n            }\n        }\n        this.backendInstance = this.registry[backendName];\n        this.setupRegisteredKernels();\n        // Reset the profiler.\n        this.profiler = new Profiler(this.backendInstance);\n        return true;\n    }\n    setupRegisteredKernels() {\n        const kernels = getKernelsForBackend(this.backendName);\n        kernels.forEach(kernel => {\n            if (kernel.setupFunc != null) {\n                kernel.setupFunc(this.backendInstance);\n            }\n        });\n    }\n    disposeRegisteredKernels(backendName) {\n        const kernels = getKernelsForBackend(backendName);\n        kernels.forEach(kernel => {\n            if (kernel.disposeFunc != null) {\n                kernel.disposeFunc(this.registry[backendName]);\n            }\n        });\n    }\n    /**\n     * Initializes a backend by looking up the backend name in the factory\n     * registry and calling the factory method. Returns a boolean representing\n     * whether the initialization of the backend suceeded. Throws an error if\n     * there is no backend in the factory registry.\n     */\n    initializeBackend(backendName) {\n        const registryFactoryEntry = this.registryFactory[backendName];\n        if (registryFactoryEntry == null) {\n            throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);\n        }\n        try {\n            const backend = registryFactoryEntry.factory();\n            /* Test if the factory returns a promise.\n            Done in a more liberal way than\n            previous 'Promise.resolve(backend)===backend'\n            as we needed to account for custom Promise\n            implementations (e.g. Angular) */\n            if (backend && !(backend instanceof KernelBackend)\n                && typeof backend.then === 'function') {\n                const promiseId = ++this.pendingBackendInitId;\n                const success = backend\n                    .then(backendInstance => {\n                    // Outdated promise. Another backend was set in the meantime.\n                    if (promiseId < this.pendingBackendInitId) {\n                        return false;\n                    }\n                    this.registry[backendName] = backendInstance;\n                    this.pendingBackendInit = null;\n                    return true;\n                })\n                    .catch(err => {\n                    // Outdated promise. Another backend was set in the meantime.\n                    if (promiseId < this.pendingBackendInitId) {\n                        return false;\n                    }\n                    this.pendingBackendInit = null;\n                    console.warn(`Initialization of backend ${backendName} failed`);\n                    console.warn(err.stack || err.message);\n                    return false;\n                });\n                this.pendingBackendInit = success;\n                return { success, asyncInit: true };\n            }\n            else {\n                this.registry[backendName] = backend;\n                return { success: true, asyncInit: false };\n            }\n        }\n        catch (err) {\n            console.warn(`Initialization of backend ${backendName} failed`);\n            console.warn(err.stack || err.message);\n            return { success: false, asyncInit: false };\n        }\n    }\n    removeBackend(backendName) {\n        if (!(backendName in this.registryFactory)) {\n            throw new Error(`${backendName} backend not found in registry`);\n        }\n        if (this.backendName === backendName && this.pendingBackendInit != null) {\n            // There is a pending promise of the backend we want to remove. Make it\n            // obsolete.\n            this.pendingBackendInitId++;\n        }\n        if (backendName in this.registry) {\n            this.disposeRegisteredKernels(backendName);\n            this.registry[backendName].dispose();\n            delete this.registry[backendName];\n        }\n        delete this.registryFactory[backendName];\n        // Unset the backend if it is active.\n        if (this.backendName === backendName) {\n            this.pendingBackendInit = null;\n            this.backendName = null;\n            this.backendInstance = null;\n        }\n    }\n    getSortedBackends() {\n        if (Object.keys(this.registryFactory).length === 0) {\n            throw new Error('No backend found in registry.');\n        }\n        return Object.keys(this.registryFactory).sort((a, b) => {\n            // Highest priority comes first.\n            return this.registryFactory[b].priority -\n                this.registryFactory[a].priority;\n        });\n    }\n    initializeBackendsAndReturnBest() {\n        const sortedBackends = this.getSortedBackends();\n        for (let i = 0; i < sortedBackends.length; i++) {\n            const backendName = sortedBackends[i];\n            const { success, asyncInit } = this.initializeBackend(backendName);\n            if (asyncInit || success) {\n                return { name: backendName, asyncInit };\n            }\n        }\n        throw new Error(`Could not initialize any backends, all backend initializations ` +\n            `failed.`);\n    }\n    moveData(backend, dataId) {\n        const info = this.state.tensorInfo.get(dataId);\n        const srcBackend = info.backend;\n        const values = this.readSync(dataId);\n        // Delete the tensor from the old backend and move it to the new\n        // backend.\n        srcBackend.disposeData(dataId);\n        info.backend = backend;\n        backend.move(dataId, values, info.shape, info.dtype);\n        if (this.shouldCheckForMemLeaks()) {\n            // Track the number of moves during a kernel execution to correctly\n            // detect memory leaks.\n            this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;\n        }\n    }\n    tidy(nameOrFn, fn) {\n        let name = null;\n        if (fn == null) {\n            // Called with only 1 argument.\n            if (typeof nameOrFn !== 'function') {\n                throw new Error('Please provide a function to tidy()');\n            }\n            fn = nameOrFn;\n        }\n        else {\n            // Called with 2 arguments.\n            if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {\n                throw new Error('When calling with two arguments, the first argument ' +\n                    'to tidy() must be a string');\n            }\n            if (typeof fn !== 'function') {\n                throw new Error('When calling with two arguments, the 2nd argument ' +\n                    'to tidy() must be a function');\n            }\n            name = nameOrFn;\n            // TODO(nsthorat,smilkov): Do operation logging and performance\n            // profiling.\n        }\n        let result;\n        return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {\n            result = fn();\n            if (result instanceof Promise) {\n                console.error('Cannot return a Promise inside of tidy.');\n            }\n            return result;\n        });\n    }\n    scopedRun(start, end, f) {\n        start();\n        try {\n            const res = f();\n            end();\n            return res;\n        }\n        catch (ex) {\n            end();\n            throw ex;\n        }\n    }\n    nextTensorId() {\n        return Engine.nextTensorId++;\n    }\n    nextVariableId() {\n        return Engine.nextVariableId++;\n    }\n    /**\n     * This method is called instead of the public-facing tensor.clone() when\n     * saving a tensor for backwards pass. It makes sure to add the clone\n     * operation to the tape regardless of being called inside a kernel\n     * execution.\n     *\n     * This method will go away once all kernels are modularized since we won't\n     * need to turn off the tape inside runKernel().\n     */\n    clone(x) {\n        const y = this.makeTensorFromDataId(x.dataId, x.shape, x.dtype);\n        const inputs = { x };\n        const grad = (dy) => ({\n            x: () => {\n                const dtype = 'float32';\n                const gradInputs = { x: dy };\n                const attrs = { dtype };\n                return ENGINE.runKernelFunc(backend => backend.cast(dy, dtype), gradInputs, null /* grad */, Cast, attrs);\n            }\n        });\n        const saved = [];\n        this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {});\n        return y;\n    }\n    /**\n     * Execute a kernel with the given name and return the output tensor.\n     *\n     * @param kernelName The name of the kernel to execute.\n     * @param inputs A map of input names to tensors.\n     * @param attrs A map of attribute names to their values. An attribute is a\n     *     primitive (non-tensor) input to the kernel.\n     * @param inputsToSave A list of tensors, inputs to save for the backprop\n     *     computation.\n     * @param outputsToSave A list of booleans, specifying which output to save\n     *     for the backprop computation. These are booleans since the output\n     * tensors are not visible to the user.\n     */\n    runKernel(kernelName, inputs, attrs, inputsToSave, outputsToSave) {\n        const forwardFunc = null;\n        const backwardsFunc = null;\n        // Call runKernel as a stop-gap until we modularize all kernels.\n        // Once we modularize all kernels, we will remove the existing\n        // `runKernelFunc`.\n        return this.runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave);\n    }\n    shouldCheckForMemLeaks() {\n        return this.ENV.getBool('IS_TEST');\n    }\n    checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {\n        const numDataIdsAfter = this.backend.numDataIds();\n        // Count the number of data ids associated with the result of the kernel.\n        let numOutputDataIds = 0;\n        outInfos.forEach(info => {\n            // Complex numbers allocate 3 data ids, one for 'real', one for\n            // 'imaginary', and one for the container that holds the former two.\n            numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1);\n        });\n        // Account for the number of moves during kernel execution. A \"data move\"\n        // can happen in the middle of a kernel execution, placing a new (key,value)\n        // pair in the data storage. Since data moves have net zero effect (we\n        // always remove the data from the old backend), we have to cancel them out\n        // when detecting memory leaks.\n        const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];\n        const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;\n        if (dataIdsLeaked > 0) {\n            throw new Error(`Backend '${this.backendName}' has an internal memory leak ` +\n                `(${dataIdsLeaked} data ids) after running '${kernelName}'`);\n        }\n    }\n    /**\n     * @deprecated Use `runKernel` for newly added kernels. Keep using this method\n     *     only for kernels that are not yet fully modularized.\n     */\n    runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave) {\n        let outputs;\n        let saved = [];\n        const isTapeOn = this.isTapeOn();\n        if (kernelName == null) {\n            kernelName =\n                this.state.activeScope != null ? this.state.activeScope.name : '';\n        }\n        const startingBytecount = this.state.numBytes;\n        const startingNumTensors = this.state.numTensors;\n        if (this.shouldCheckForMemLeaks()) {\n            this.state.numDataMovesStack.push(0);\n        }\n        let kernelFunc;\n        const kernel = getKernel(kernelName, this.backendName);\n        let out;\n        if (kernel != null) {\n            kernelFunc = () => {\n                const numDataIdsBefore = this.backend.numDataIds();\n                out = kernel.kernelFunc({ inputs, attrs, backend: this.backend });\n                const outInfos = Array.isArray(out) ? out : [out];\n                if (this.shouldCheckForMemLeaks()) {\n                    this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);\n                }\n                const outTensors = outInfos.map(({ dataId, shape, dtype }) => this.makeTensorFromDataId(dataId, shape, dtype));\n                // Save the inputs and outputs.\n                // Do not save unless we are recording to the tape. Otherwise it would\n                // cause a mem leak since we would never run backprop, which disposes\n                // the kept tensors.\n                if (isTapeOn) {\n                    let tensorsToSave = this.getTensorsForGradient(kernelName, inputs, outTensors);\n                    if (tensorsToSave == null) {\n                        // Fallback for ops that call runKernelFunc and pass in\n                        // inputsToSave and outputsToSave. Currently this is the set of ops\n                        // with kernel support in the WASM backend. Once those ops and\n                        // respective gradients are modularised we can remove this path.\n                        if (outputsToSave == null) {\n                            outputsToSave = [];\n                        }\n                        const outsToSave = outTensors.filter((_, i) => outputsToSave[i]);\n                        tensorsToSave = (inputsToSave || []).slice().concat(outsToSave);\n                    }\n                    saved = this.saveTensorsForBackwardMode(tensorsToSave);\n                }\n                return outTensors;\n            };\n        }\n        else {\n            const saveFunc = (tensors) => {\n                // Do not save unless we are recording to the tape. Otherwise it would\n                // cause a mem leak since we would never run backprop, which disposes\n                // the kept tensors.\n                if (!isTapeOn) {\n                    return;\n                }\n                saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n            };\n            kernelFunc = () => {\n                const numDataIdsBefore = this.backend.numDataIds();\n                out = this.tidy(() => forwardFunc(this.backend, saveFunc));\n                const outs = (Array.isArray(out) ? out : [out]);\n                if (this.shouldCheckForMemLeaks()) {\n                    this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outs);\n                }\n                return outs;\n            };\n        }\n        // Stop recording to a tape when running a kernel.\n        let kernelProfile;\n        this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {\n            if (!this.ENV.getBool('DEBUG') && !this.state.profiling) {\n                outputs = kernelFunc();\n            }\n            else {\n                kernelProfile = this.profiler.profileKernel(kernelName, inputs, () => kernelFunc());\n                if (this.ENV.getBool('DEBUG')) {\n                    this.profiler.logKernelProfile(kernelProfile);\n                }\n                outputs = kernelProfile.outputs;\n            }\n        });\n        if (isTapeOn) {\n            this.addTapeNode(kernelName, inputs, outputs, backwardsFunc, saved, attrs);\n        }\n        if (this.state.profiling) {\n            this.state.activeProfile.kernels.push({\n                name: kernelName,\n                bytesAdded: this.state.numBytes - startingBytecount,\n                totalBytesSnapshot: this.state.numBytes,\n                tensorsAdded: this.state.numTensors - startingNumTensors,\n                totalTensorsSnapshot: this.state.numTensors,\n                inputShapes: Object.keys(inputs).map(key => inputs[key] != null ? inputs[key].shape : null),\n                outputShapes: outputs.map(item => item.shape),\n                kernelTimeMs: kernelProfile.timeMs,\n                extraInfo: kernelProfile.extraInfo\n            });\n        }\n        return (Array.isArray(out) ? outputs : outputs[0]);\n    }\n    /**\n     * Saves tensors used in forward mode for use in backward mode.\n     *\n     * @param tensors the list of tensors to save.\n     */\n    saveTensorsForBackwardMode(tensors) {\n        const saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n        return saved;\n    }\n    /**\n     * Returns a list of tensors to save for a given gradient calculation.\n     *\n     * Returns undefined if their is no registered gradient for this kernel in the\n     * gradient registry.\n     *\n     * @param kernelName name of kernel to look up gradient for.\n     * @param inputs a map of input tensors.\n     * @param outputs an array of output tensors from forward mode of kernel.\n     */\n    getTensorsForGradient(kernelName, inputs, outputs) {\n        const gradConfig = getGradient(kernelName);\n        if (gradConfig != null) {\n            const inputsToSave = gradConfig.inputsToSave || [];\n            const outputsToSave = gradConfig.outputsToSave || [];\n            // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs\n            // specified in inputsToSave will be saved.\n            let inputTensorsToSave;\n            if (gradConfig.saveAllInputs) {\n                util.assert(Array.isArray(inputs), () => 'saveAllInputs is true, expected inputs to be an array.');\n                inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);\n            }\n            else {\n                inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);\n            }\n            const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);\n            return inputTensorsToSave.concat(outputTensorsToSave);\n        }\n        // TODO(yassogba) throw exception here once all runkernelFunc calls with\n        // inputsToSave/outputsToSave are removed\n        return null;\n    }\n    /**\n     * Internal method used by public APIs for tensor creation. Makes a new\n     * tensor with the provided shape, dtype and values. It always\n     * creates a new data id and writes the values to the underlying backend.\n     */\n    makeTensor(values, shape, dtype, backend) {\n        if (values == null) {\n            throw new Error('Values passed to engine.makeTensor() are null');\n        }\n        dtype = dtype || 'float32';\n        backend = backend || this.backend;\n        let backendVals = values;\n        if (dtype === 'string' && util.isString(values[0])) {\n            backendVals = values.map(d => util.encodeString(d));\n        }\n        const dataId = backend.write(backendVals, shape, dtype);\n        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n        this.incRef(t, backend);\n        // Count bytes for string tensors.\n        if (dtype === 'string') {\n            const info = this.state.tensorInfo.get(dataId);\n            const newBytes = bytesFromStringArray(backendVals);\n            this.state.numBytes += newBytes - info.bytes;\n            info.bytes = newBytes;\n        }\n        return t;\n    }\n    /**\n     * Internal method used by backends. Makes a new tensor\n     * that is a wrapper around an existing data id. It doesn't create\n     * a new data id, only increments the ref count used in memory tracking.\n     */\n    makeTensorFromDataId(dataId, shape, dtype, backend) {\n        dtype = dtype || 'float32';\n        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n        this.incRef(t, backend);\n        return t;\n    }\n    makeVariable(initialValue, trainable = true, name, dtype) {\n        name = name || this.nextVariableId().toString();\n        if (dtype != null && dtype !== initialValue.dtype) {\n            initialValue = initialValue.cast(dtype);\n        }\n        const v = new Variable(initialValue, trainable, name, this.nextTensorId());\n        if (this.state.registeredVariables[v.name] != null) {\n            throw new Error(`Variable with name ${v.name} was already registered`);\n        }\n        this.state.registeredVariables[v.name] = v;\n        this.incRef(v, this.backend);\n        return v;\n    }\n    incRef(a, backend) {\n        const refCount = this.state.tensorInfo.has(a.dataId) ?\n            this.state.tensorInfo.get(a.dataId).refCount :\n            0;\n        this.state.numTensors++;\n        if (a.dtype === 'string') {\n            this.state.numStringTensors++;\n        }\n        if (refCount === 0) {\n            this.state.numDataBuffers++;\n            // Bytes for complex numbers are counted by their components. Bytes for\n            // string tensors are counted when writing values.\n            let bytes = 0;\n            if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n                bytes = a.size * util.bytesPerElement(a.dtype);\n            }\n            this.state.tensorInfo.set(a.dataId, {\n                backend: backend || this.backend,\n                dtype: a.dtype,\n                shape: a.shape,\n                bytes,\n                refCount: 0\n            });\n            this.state.numBytes += bytes;\n        }\n        this.state.tensorInfo.get(a.dataId).refCount++;\n        if (!(a instanceof Variable)) {\n            this.track(a);\n        }\n    }\n    disposeTensor(a) {\n        if (!this.state.tensorInfo.has(a.dataId)) {\n            return;\n        }\n        this.state.numTensors--;\n        if (a.dtype === 'string') {\n            this.state.numStringTensors--;\n        }\n        const info = this.state.tensorInfo.get(a.dataId);\n        const refCount = info.refCount;\n        if (refCount <= 1) {\n            // Don't count bytes for complex numbers as they are counted by their\n            // components.\n            if (a.dtype !== 'complex64') {\n                this.state.numBytes -= info.bytes;\n            }\n            this.state.numDataBuffers--;\n            info.backend.disposeData(a.dataId);\n            this.state.tensorInfo.delete(a.dataId);\n        }\n        else {\n            this.state.tensorInfo.get(a.dataId).refCount--;\n        }\n        // TODO(nsthorat): Construct an error and save the stack trace for\n        // debugging when in debug mode. Creating a stack trace is too expensive\n        // to do unconditionally.\n    }\n    disposeVariables() {\n        for (const varName in this.state.registeredVariables) {\n            const v = this.state.registeredVariables[varName];\n            this.disposeVariable(v);\n        }\n    }\n    disposeVariable(v) {\n        this.disposeTensor(v);\n        if (this.state.registeredVariables[v.name] != null) {\n            delete this.state.registeredVariables[v.name];\n        }\n    }\n    memory() {\n        const info = this.backend.memory();\n        info.numTensors = this.state.numTensors;\n        info.numDataBuffers = this.state.numDataBuffers;\n        info.numBytes = this.state.numBytes;\n        if (this.state.numStringTensors > 0) {\n            info.unreliable = true;\n            if (info.reasons == null) {\n                info.reasons = [];\n            }\n            info.reasons.push('Memory usage by string tensors is approximate ' +\n                '(2 bytes per character)');\n        }\n        return info;\n    }\n    async profile(query) {\n        this.state.profiling = true;\n        const startBytes = this.state.numBytes;\n        const startNumTensors = this.state.numTensors;\n        this.state.activeProfile.kernels = [];\n        this.state.activeProfile.result = await query();\n        this.state.profiling = false;\n        this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map(d => d.totalBytesSnapshot));\n        this.state.activeProfile.newBytes = this.state.numBytes - startBytes;\n        this.state.activeProfile.newTensors =\n            this.state.numTensors - startNumTensors;\n        for (const kernel of this.state.activeProfile.kernels) {\n            kernel.kernelTimeMs = await kernel.kernelTimeMs;\n            kernel.extraInfo = await kernel.extraInfo;\n        }\n        return this.state.activeProfile;\n    }\n    isTapeOn() {\n        return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;\n    }\n    addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {\n        const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved };\n        const gradConfig = getGradient(kernelName);\n        if (gradConfig != null) {\n            gradientsFunc = gradConfig.gradFunc;\n        }\n        if (gradientsFunc != null) {\n            tapeNode.gradient = (dys) => {\n                // TODO(smilkov): To optimize back-prop, pass dys that are not used in\n                // the backprop graph to the user as null instead of zeros\n                dys = dys.map((dy, i) => {\n                    if (dy == null) {\n                        const output = outputs[i];\n                        const vals = util.makeZerosTypedArray(output.size, output.dtype);\n                        return this.makeTensor(vals, output.shape, output.dtype);\n                    }\n                    return dy;\n                });\n                // Grad functions of ops with single outputs expect a dy, while ops\n                // with multiple outputs expect dys (array of dy).\n                return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);\n            };\n        }\n        this.state.activeTape.push(tapeNode);\n    }\n    keep(result) {\n        result.kept = true;\n        return result;\n    }\n    startTape() {\n        if (this.state.gradientDepth === 0) {\n            this.state.activeTape = [];\n        }\n        this.state.gradientDepth++;\n    }\n    endTape() {\n        this.state.gradientDepth--;\n    }\n    /**\n     * Start a scope. Use this with endScope() to achieve the same functionality\n     * as scope() without the need for a function closure.\n     */\n    startScope(name) {\n        const scopeInfo = {\n            track: [],\n            name: 'unnamed scope',\n            id: this.state.nextScopeId++\n        };\n        if (name) {\n            scopeInfo.name = name;\n        }\n        this.state.scopeStack.push(scopeInfo);\n        this.state.activeScope = scopeInfo;\n    }\n    /**\n     * End a scope. Use this with startScope() to achieve the same functionality\n     * as scope() without the need for a function closure.\n     */\n    endScope(result) {\n        const tensorsToTrackInParent = getTensorsInContainer(result);\n        const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map(t => t.id));\n        // Dispose the arrays tracked in this scope.\n        for (let i = 0; i < this.state.activeScope.track.length; i++) {\n            const tensor = this.state.activeScope.track[i];\n            if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) {\n                tensor.dispose();\n            }\n        }\n        const oldScope = this.state.scopeStack.pop();\n        this.state.activeScope = this.state.scopeStack.length === 0 ?\n            null :\n            this.state.scopeStack[this.state.scopeStack.length - 1];\n        // Track the current result in the parent scope.\n        tensorsToTrackInParent.forEach(tensor => {\n            // Only track the tensor if was allocated in the inner scope and is not\n            // globally kept.\n            if (!tensor.kept && tensor.scopeId === oldScope.id) {\n                this.track(tensor);\n            }\n        });\n    }\n    /**\n     * Returns gradients of `f` with respect to each of the `xs`. The gradients\n     * returned are of the same length as `xs`, but some might be null if `f`\n     * was not a function of that `x`. It also takes optional dy to multiply the\n     * gradient, which defaults to `1`.\n     */\n    gradients(f, xs, dy, allowNoGradients = false) {\n        util.assert(xs.length > 0, () => 'gradients() received an empty list of xs.');\n        if (dy != null && dy.dtype !== 'float32') {\n            throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n        }\n        const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy('forward', f));\n        util.assert(y instanceof Tensor, () => 'The result y returned by f() must be a tensor.');\n        // Filter out the nodes that don't connect x => y.\n        const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);\n        if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n            throw new Error('Cannot compute gradient of y=f(x) with respect to x. Make sure ' +\n                'that the f you passed encloses all operations that lead from x ' +\n                'to y.');\n        }\n        return this.tidy('backward', () => {\n            const accumulatedGradientMap = {};\n            accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;\n            // Backprop gradients through the filtered nodes.\n            backpropagateGradients(accumulatedGradientMap, filteredTape, \n            // Pass the tidy function to avoid circular dep with `tape.ts`.\n            f => this.tidy(f), \n            // Pass an add function to avoide a circular dep with `tape.ts`.\n            add);\n            const grads = xs.map(x => accumulatedGradientMap[x.id]);\n            if (this.state.gradientDepth === 0) {\n                // This means that we are not computing higher-order gradients\n                // and can clean up the tape.\n                this.state.activeTape.forEach(node => {\n                    for (const tensor of node.saved) {\n                        tensor.dispose();\n                    }\n                });\n                this.state.activeTape = null;\n            }\n            return { value: y, grads };\n        });\n    }\n    customGrad(f) {\n        util.assert(util.isFunction(f), () => 'The f passed in customGrad(f) must be a function.');\n        return (...inputs) => {\n            util.assert(inputs.every(t => t instanceof Tensor), () => 'The args passed in customGrad(f)(x1, x2,...) must all be ' +\n                'tensors');\n            let res;\n            const inputMap = {};\n            inputs.forEach((input, i) => {\n                inputMap[i] = input;\n            });\n            return this.runKernelFunc((_, save) => {\n                res = f(...[...inputs, save]);\n                util.assert(res.value instanceof Tensor, () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.value` is a tensor');\n                util.assert(util.isFunction(res.gradFunc), () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function.');\n                return res.value;\n            }, inputMap, (dy, saved) => {\n                const gradRes = res.gradFunc(dy, saved);\n                const grads = Array.isArray(gradRes) ? gradRes : [gradRes];\n                util.assert(grads.length === inputs.length, () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'the same number of tensors as inputs passed to f(...).');\n                util.assert(grads.every(t => t instanceof Tensor), () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'a list of only tensors.');\n                const gradMap = {};\n                grads.forEach((grad, i) => {\n                    gradMap[i] = () => grad;\n                });\n                return gradMap;\n            });\n        };\n    }\n    readSync(dataId) {\n        // Route the read to the correct backend.\n        const info = this.state.tensorInfo.get(dataId);\n        return info.backend.readSync(dataId);\n    }\n    read(dataId) {\n        // Route the read to the correct backend.\n        const info = this.state.tensorInfo.get(dataId);\n        return info.backend.read(dataId);\n    }\n    async time(query) {\n        const start = now();\n        const timingInfo = await this.backend.time(query);\n        timingInfo.wallMs = now() - start;\n        return timingInfo;\n    }\n    /**\n     * Tracks a Tensor in the current scope to be automatically cleaned up\n     * when the current scope ends, and returns the value.\n     *\n     * @param result The Tensor to track in the current scope.\n     */\n    track(result) {\n        if (this.state.activeScope != null) {\n            result.scopeId = this.state.activeScope.id;\n            this.state.activeScope.track.push(result);\n        }\n        return result;\n    }\n    get registeredVariables() {\n        return this.state.registeredVariables;\n    }\n    /**\n     * Resets the engine state. Removes all backends but does not remove\n     * registered backend factories.\n     */\n    reset() {\n        // Make any pending promise obsolete.\n        this.pendingBackendInitId++;\n        this.state.dispose();\n        this.ENV.reset();\n        this.state = new EngineState();\n        for (const backendName in this.registry) {\n            this.disposeRegisteredKernels(backendName);\n            this.registry[backendName].dispose();\n            delete this.registry[backendName];\n        }\n        this.backendName = null;\n        this.backendInstance = null;\n        this.pendingBackendInit = null;\n    }\n}\nEngine.nextTensorId = 0;\nEngine.nextVariableId = 0;\nfunction ones(shape) {\n    const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');\n    return ENGINE.makeTensor(values, shape, 'float32');\n}\nexport function getOrMakeEngine() {\n    const ns = getGlobalNamespace();\n    if (ns._tfengine == null) {\n        const environment = new Environment(ns);\n        ns._tfengine = new Engine(environment);\n    }\n    setEnvironmentGlobal(ns._tfengine.ENV);\n    // Tell the current tensor interface that the global engine is responsible\n    // for tracking.\n    setTensorTracker(() => ns._tfengine);\n    return ns._tfengine;\n}\nexport const ENGINE = getOrMakeEngine();\n/**\n * A implementation of the add op for use within engine and tape.\n *\n * This allows us to avoid a circular dependency between add.ts and engine.\n * It is exported to be available in tape tests.\n */\nexport function add(a, b) {\n    // We duplicate Add here to avoid a circular dependency with add.ts.\n    const inputs = { a, b };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.add(a, b);\n        save([a, b]);\n        return res;\n    }, inputs, null /* gradient */, Add);\n}\n//# sourceMappingURL=engine.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line:no-any\nfunction _isNavigatorDefined() {\n    return typeof navigator !== 'undefined' && navigator != null;\n}\nexport function isMobile() {\n    if (_isNavigatorDefined()) {\n        // tslint:disable-next-line:no-any\n        const a = navigator.userAgent || navigator.vendor || window.opera;\n        // tslint:disable-next-line:max-line-length\n        return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n            .test(a) ||\n            // tslint:disable-next-line:max-line-length\n            /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i\n                .test(a.substr(0, 4));\n    }\n    return false;\n}\nexport function isBrowser() {\n    return (typeof window !== 'undefined' && window.document != null) ||\n        //@ts-ignore\n        (typeof WorkerGlobalScope !== 'undefined');\n}\n//# sourceMappingURL=device_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './engine';\nimport * as device_util from './device_util';\nimport { env } from './environment';\nconst ENV = env();\n/**\n * This file contains environment-related flag registrations.\n */\n/** Whether to enable debug mode. */\nENV.registerFlag('DEBUG', () => false, debugValue => {\n    if (debugValue) {\n        console.warn('Debugging mode is ON. The output of every math call will ' +\n            'be downloaded to CPU and checked for NaNs. ' +\n            'This significantly impacts performance.');\n    }\n});\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_BROWSER', () => device_util.isBrowser());\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_NODE', () => (typeof process !== 'undefined') &&\n    (typeof process.versions !== 'undefined') &&\n    (typeof process.versions.node !== 'undefined'));\n/** Whether this browser is Chrome. */\nENV.registerFlag('IS_CHROME', () => typeof navigator !== 'undefined' && navigator != null &&\n    navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&\n    /Google Inc/.test(navigator.vendor));\n/**\n * True when the environment is \"production\" where we disable safety checks\n * to gain performance.\n */\nENV.registerFlag('PROD', () => false);\n/**\n * Whether to do sanity checks when inferring a shape from user-provided\n * values, used when creating a new tensor.\n */\nENV.registerFlag('TENSORLIKE_CHECK_SHAPE_CONSISTENCY', () => ENV.getBool('DEBUG'));\n/** Whether deprecation warnings are enabled. */\nENV.registerFlag('DEPRECATION_WARNINGS_ENABLED', () => true);\n/** True if running unit tests. */\nENV.registerFlag('IS_TEST', () => false);\n//# sourceMappingURL=flags.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { env } from './environment';\nimport { Tensor } from './tensor';\nimport { assert, flatten, inferDtype, isTypedArray, toTypedArray } from './util';\nexport function inferShape(val, dtype) {\n    let firstElem = val;\n    if (isTypedArray(val)) {\n        return dtype === 'string' ? [] : [val.length];\n    }\n    if (!Array.isArray(val)) {\n        return []; // Scalar.\n    }\n    const shape = [];\n    while (Array.isArray(firstElem) ||\n        isTypedArray(firstElem) && dtype !== 'string') {\n        shape.push(firstElem.length);\n        firstElem = firstElem[0];\n    }\n    if (Array.isArray(val) &&\n        env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {\n        deepAssertShapeConsistency(val, shape, []);\n    }\n    return shape;\n}\nfunction deepAssertShapeConsistency(val, shape, indices) {\n    indices = indices || [];\n    if (!(Array.isArray(val)) && !isTypedArray(val)) {\n        assert(shape.length === 0, () => `Element arr[${indices.join('][')}] is a primitive, ` +\n            `but should be an array/TypedArray of ${shape[0]} elements`);\n        return;\n    }\n    assert(shape.length > 0, () => `Element arr[${indices.join('][')}] should be a primitive, ` +\n        `but is an array of ${val.length} elements`);\n    assert(val.length === shape[0], () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +\n        `elements, but has ${val.length} elements`);\n    const subShape = shape.slice(1);\n    for (let i = 0; i < val.length; ++i) {\n        deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n    }\n}\nfunction assertDtype(expectedDtype, actualDType, argName, functionName) {\n    if (expectedDtype == null) {\n        return;\n    }\n    if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||\n        expectedDtype === 'numeric' && actualDType === 'string') {\n        throw new Error(`Argument '${argName}' passed to '${functionName}' must ` +\n            `be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n    }\n}\nexport function convertToTensor(x, argName, functionName, parseAsDtype = 'numeric') {\n    if (x instanceof Tensor) {\n        assertDtype(parseAsDtype, x.dtype, argName, functionName);\n        return x;\n    }\n    let inferredDtype = inferDtype(x);\n    // If the user expects a bool/int/float, use that info to update the\n    // inferredDtype when it is not a string.\n    if (inferredDtype !== 'string' &&\n        ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {\n        inferredDtype = parseAsDtype;\n    }\n    assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n    if ((x == null) ||\n        (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&\n            typeof x !== 'boolean' && typeof x !== 'string')) {\n        const type = x == null ? 'null' : x.constructor.name;\n        throw new Error(`Argument '${argName}' passed to '${functionName}' must be a ` +\n            `Tensor or TensorLike, but got '${type}'`);\n    }\n    const inferredShape = inferShape(x, inferredDtype);\n    if (!isTypedArray(x) && !Array.isArray(x)) {\n        x = [x];\n    }\n    const skipTypedArray = true;\n    const values = inferredDtype !== 'string' ?\n        toTypedArray(x, inferredDtype) :\n        flatten(x, [], skipTypedArray);\n    return ENGINE.makeTensor(values, inferredShape, inferredDtype);\n}\nexport function convertToTensorArray(arg, argName, functionName, parseAsDtype = 'numeric') {\n    if (!Array.isArray(arg)) {\n        throw new Error(`Argument ${argName} passed to ${functionName} must be a ` +\n            '`Tensor[]` or `TensorLike[]`');\n    }\n    const tensors = arg;\n    return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName), parseAsDtype);\n}\n//# sourceMappingURL=tensor_util_env.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nexport const OP_SCOPE_SUFFIX = '__op';\n/**\n * Used for wrapping functions that perform math operations on\n * Tensors. The function will be wrapped in a named scope that cleans all\n * memory usage after the function is done.\n */\nexport function op(f) {\n    const keys = Object.keys(f);\n    if (keys.length !== 1) {\n        throw new Error(`Please provide an object with a single key ` +\n            `(operation name) mapping to a function. Got an object with ` +\n            `${keys.length} keys.`);\n    }\n    let opName = keys[0];\n    const fn = f[opName];\n    // Strip the underscore from the end of the function name.\n    if (opName.endsWith('_')) {\n        opName = opName.substring(0, opName.length - 1);\n    }\n    // add an __op suffix to distinguish ops from kernels in tf.profile\n    opName = opName + OP_SCOPE_SUFFIX;\n    // tslint:disable-next-line:no-any\n    const f2 = (...args) => {\n        ENGINE.startScope(opName);\n        try {\n            const result = fn(...args);\n            if (result instanceof Promise) {\n                console.error('Cannot return a Promise inside of tidy.');\n            }\n            ENGINE.endScope(result);\n            return result;\n        }\n        catch (ex) {\n            ENGINE.endScope(null);\n            throw ex;\n        }\n    };\n    Object.defineProperty(f2, 'name', { value: opName, configurable: true });\n    // tslint:disable-next-line:no-any\n    return f2;\n}\n//# sourceMappingURL=operation.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Complex } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction complex_(real, imag) {\n    const $real = convertToTensor(real, 'real', 'complex');\n    const $imag = convertToTensor(imag, 'imag', 'complex');\n    util.assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n        `must match in call to tf.complex().`);\n    const forward = (backend) => {\n        return backend.complex($real, $imag);\n    };\n    const inputs = { real: $real, imag: $imag };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Complex);\n}\nexport const complex = op({ complex_ });\n//# sourceMappingURL=complex.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { assert, assertNonNegativeIntegerDimensions, flatten, inferDtype, isTypedArray, sizeFromShape, toTypedArray } from '../util';\n/** This is shared code across all tensor creation methods. */\nexport function makeTensor(values, shape, inferredShape, dtype) {\n    if (dtype == null) {\n        dtype = inferDtype(values);\n    }\n    if (dtype === 'complex64') {\n        throw new Error(`Cannot construct a complex64 tensor directly. ` +\n            `Please use tf.complex(real, imag).`);\n    }\n    if (!isTypedArray(values) && !Array.isArray(values) &&\n        typeof values !== 'number' && typeof values !== 'boolean' &&\n        typeof values !== 'string') {\n        throw new Error('values passed to tensor(values) must be a number/boolean/string or ' +\n            'an array of numbers/booleans/strings, or a TypedArray');\n    }\n    if (shape != null) {\n        assertNonNegativeIntegerDimensions(shape);\n        const providedSize = sizeFromShape(shape);\n        const inferredSize = sizeFromShape(inferredShape);\n        assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ` +\n            `${providedSize} values but has ${inferredSize}`);\n        for (let i = 0; i < inferredShape.length; ++i) {\n            const inferred = inferredShape[i];\n            const flatDimsDontMatch = i === inferredShape.length - 1 ?\n                inferred !== sizeFromShape(shape.slice(i)) :\n                true;\n            assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape ` +\n                `(${inferredShape}) does not match the provided ` +\n                `shape (${shape}). `);\n        }\n    }\n    if (!isTypedArray(values) && !Array.isArray(values)) {\n        values = [values];\n    }\n    shape = shape || inferredShape;\n    values = dtype !== 'string' ?\n        toTypedArray(values, dtype) :\n        flatten(values, [], true);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=tensor_ops_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`. If the values are strings,\n *     they will be encoded as utf-8 and kept as `Uint8Array[]`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor(values, shape, dtype) {\n    const inferredShape = inferShape(values, dtype);\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/* Type definitions for exporting and importing of models. */\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP = {\n    'float32': 4,\n    'float16': 2,\n    'int32': 4,\n    'uint16': 2,\n    'uint8': 1,\n    'bool': 1,\n    'complex64': 8\n};\n//# sourceMappingURL=types.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../ops/complex';\nimport { tensor } from '../ops/tensor';\nimport { sizeFromShape } from '../util';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/** Number of bytes reserved for the length of the string. (32bit integer). */\nconst NUM_BYTES_STRING_LENGTH = 4;\n/**\n * Encode a map from names to weight values as an ArrayBuffer, along with an\n * `Array` of `WeightsManifestEntry` as specification of the encoded weights.\n *\n * This function does not perform sharding.\n *\n * This function is the reverse of `decodeWeights`.\n *\n * @param tensors A map (\"dict\") from names to tensors.\n * @param group Group to which the weights belong (optional).\n * @returns A `Promise` of\n *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s\n *     concatenated.\n *   - An `Array` of `WeightManifestEntry`s, carrying information including\n *     tensor names, `dtype`s and shapes.\n * @throws Error: on unsupported tensor `dtype`.\n */\nexport async function encodeWeights(tensors, group) {\n    // TODO(adarob, cais): Support quantization.\n    const specs = [];\n    const dataPromises = [];\n    const names = Array.isArray(tensors) ?\n        tensors.map(tensor => tensor.name) :\n        Object.keys(tensors);\n    for (let i = 0; i < names.length; ++i) {\n        const name = names[i];\n        const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];\n        if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool' &&\n            t.dtype !== 'string' && t.dtype !== 'complex64') {\n            throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n        }\n        const spec = { name, shape: t.shape, dtype: t.dtype };\n        if (t.dtype === 'string') {\n            const utf8bytes = new Promise(async (resolve) => {\n                const vals = await t.bytes();\n                const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) +\n                    NUM_BYTES_STRING_LENGTH * vals.length;\n                const bytes = new Uint8Array(totalNumBytes);\n                let offset = 0;\n                for (let i = 0; i < vals.length; i++) {\n                    const val = vals[i];\n                    const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);\n                    bytes.set(bytesOfLength, offset);\n                    offset += NUM_BYTES_STRING_LENGTH;\n                    bytes.set(val, offset);\n                    offset += val.length;\n                }\n                resolve(bytes);\n            });\n            dataPromises.push(utf8bytes);\n        }\n        else {\n            dataPromises.push(t.data());\n        }\n        if (group != null) {\n            spec.group = group;\n        }\n        specs.push(spec);\n    }\n    const tensorValues = await Promise.all(dataPromises);\n    return { data: concatenateTypedArrays(tensorValues), specs };\n}\n/**\n * Decode flat ArrayBuffer as weights.\n *\n * This function does not handle sharding.\n *\n * This function is the reverse of `encodeWeights`.\n *\n * @param buffer A flat ArrayBuffer carrying the binary values of the tensors\n *   concatenated in the order specified in `specs`.\n * @param specs Specifications of the names, dtypes and shapes of the tensors\n *   whose value are encoded by `buffer`.\n * @return A map from tensor name to tensor value, with the names corresponding\n *   to names in `specs`.\n * @throws Error, if any of the tensors has unsupported dtype.\n */\nexport function decodeWeights(buffer, specs) {\n    // TODO(adarob, cais): Support quantization.\n    const out = {};\n    let float16Decode;\n    let offset = 0;\n    for (const spec of specs) {\n        const name = spec.name;\n        const dtype = spec.dtype;\n        const shape = spec.shape;\n        const size = sizeFromShape(shape);\n        let values;\n        if ('quantization' in spec) {\n            const quantization = spec.quantization;\n            if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n                if (!('min' in quantization && 'scale' in quantization)) {\n                    throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} ` +\n                        `doesn't have corresponding metadata min and scale.`);\n                }\n            }\n            else if (quantization.dtype === 'float16') {\n                if (dtype !== 'float32') {\n                    throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} ` +\n                        `which only supports weights of type float32 not ${dtype}.`);\n                }\n            }\n            else {\n                throw new Error(`Weight ${spec.name} has unknown ` +\n                    `quantization dtype ${quantization.dtype}. ` +\n                    `Supported quantization dtypes are: ` +\n                    `'uint8', 'uint16', and 'float16'.`);\n            }\n            const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n            const byteBuffer = buffer.slice(offset, offset + size * quantizationSizeFactor);\n            const quantizedArray = (quantization.dtype === 'uint8') ?\n                new Uint8Array(byteBuffer) :\n                new Uint16Array(byteBuffer);\n            if (dtype === 'float32') {\n                if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n                    values = new Float32Array(quantizedArray.length);\n                    for (let i = 0; i < quantizedArray.length; i++) {\n                        const v = quantizedArray[i];\n                        values[i] = v * quantization.scale + quantization.min;\n                    }\n                }\n                else if (quantization.dtype === 'float16') {\n                    if (float16Decode === undefined) {\n                        float16Decode = getFloat16Decoder();\n                    }\n                    values = float16Decode(quantizedArray);\n                }\n                else {\n                    throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n                        `for weight type float32.`);\n                }\n            }\n            else if (dtype === 'int32') {\n                if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {\n                    throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n                        `for weight type int32.`);\n                }\n                values = new Int32Array(quantizedArray.length);\n                for (let i = 0; i < quantizedArray.length; i++) {\n                    const v = quantizedArray[i];\n                    values[i] = Math.round(v * quantization.scale + quantization.min);\n                }\n            }\n            else {\n                throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n            }\n            offset += size * quantizationSizeFactor;\n        }\n        else if (dtype === 'string') {\n            const size = sizeFromShape(spec.shape);\n            values = [];\n            for (let i = 0; i < size; i++) {\n                const byteLength = new Uint32Array(buffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];\n                offset += NUM_BYTES_STRING_LENGTH;\n                const bytes = new Uint8Array(buffer.slice(offset, offset + byteLength));\n                values.push(bytes);\n                offset += byteLength;\n            }\n        }\n        else {\n            const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n            const byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);\n            if (dtype === 'float32') {\n                values = new Float32Array(byteBuffer);\n            }\n            else if (dtype === 'int32') {\n                values = new Int32Array(byteBuffer);\n            }\n            else if (dtype === 'bool') {\n                values = new Uint8Array(byteBuffer);\n            }\n            else if (dtype === 'complex64') {\n                values = new Float32Array(byteBuffer);\n                const real = new Float32Array(values.length / 2);\n                const image = new Float32Array(values.length / 2);\n                for (let i = 0; i < real.length; i++) {\n                    real[i] = values[i * 2];\n                    image[i] = values[i * 2 + 1];\n                }\n                const realTensor = tensor(real, shape, 'float32');\n                const imageTensor = tensor(image, shape, 'float32');\n                out[name] = complex(realTensor, imageTensor);\n            }\n            else {\n                throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n            }\n            offset += size * dtypeFactor;\n        }\n        if (dtype !== 'complex64') {\n            out[name] = tensor(values, shape, dtype);\n        }\n    }\n    return out;\n}\n/**\n * Concatenate TypedArrays into an ArrayBuffer.\n */\nexport function concatenateTypedArrays(xs) {\n    // TODO(adarob, cais): Support quantization.\n    if (xs === null) {\n        throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n    }\n    let totalByteLength = 0;\n    // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'\n    // can have a different byte length from that of the `TypedArray` itself,\n    // for example, when the `TypedArray` is created from an offset in an\n    // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match\n    // the `TypedArray` in byte length. If an element of `xs` does not show\n    // this property, a new `TypedArray` that satisfy this property will be\n    // constructed and pushed into `normalizedXs`.\n    const normalizedXs = [];\n    xs.forEach((x) => {\n        totalByteLength += x.byteLength;\n        // tslint:disable:no-any\n        normalizedXs.push(x.byteLength === x.buffer.byteLength ? x :\n            new x.constructor(x));\n        if (!(x instanceof Float32Array || x instanceof Int32Array ||\n            x instanceof Uint8Array)) {\n            throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n        }\n        // tslint:enable:no-any\n    });\n    const y = new Uint8Array(totalByteLength);\n    let offset = 0;\n    normalizedXs.forEach((x) => {\n        y.set(new Uint8Array(x.buffer), offset);\n        offset += x.byteLength;\n    });\n    return y.buffer;\n}\n// Use Buffer on Node.js instead of Blob/atob/btoa\nconst useNodeBuffer = typeof Buffer !== 'undefined' &&\n    (typeof Blob === 'undefined' || typeof atob === 'undefined' ||\n        typeof btoa === 'undefined');\n/**\n * Calculate the byte length of a JavaScript string.\n *\n * Note that a JavaScript string can contain wide characters, therefore the\n * length of the string is not necessarily equal to the byte length.\n *\n * @param str Input string.\n * @returns Byte length.\n */\nexport function stringByteLength(str) {\n    if (useNodeBuffer) {\n        return Buffer.byteLength(str);\n    }\n    return new Blob([str]).size;\n}\n/**\n * Encode an ArrayBuffer as a base64 encoded string.\n *\n * @param buffer `ArrayBuffer` to be converted.\n * @returns A string that base64-encodes `buffer`.\n */\nexport function arrayBufferToBase64String(buffer) {\n    if (useNodeBuffer) {\n        return Buffer.from(buffer).toString('base64');\n    }\n    const buf = new Uint8Array(buffer);\n    let s = '';\n    for (let i = 0, l = buf.length; i < l; i++) {\n        s += String.fromCharCode(buf[i]);\n    }\n    return btoa(s);\n}\n/**\n * Decode a base64 string as an ArrayBuffer.\n *\n * @param str Base64 string.\n * @returns Decoded `ArrayBuffer`.\n */\nexport function base64StringToArrayBuffer(str) {\n    if (useNodeBuffer) {\n        const buf = Buffer.from(str, 'base64');\n        return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n    }\n    const s = atob(str);\n    const buffer = new Uint8Array(s.length);\n    for (let i = 0; i < s.length; ++i) {\n        buffer.set([s.charCodeAt(i)], i);\n    }\n    return buffer.buffer;\n}\n/**\n * Concatenate a number of ArrayBuffers into one.\n *\n * @param buffers A number of array buffers to concatenate.\n * @returns Result of concatenating `buffers` in order.\n */\nexport function concatenateArrayBuffers(buffers) {\n    if (buffers.length === 1) {\n        return buffers[0];\n    }\n    let totalByteLength = 0;\n    buffers.forEach((buffer) => {\n        totalByteLength += buffer.byteLength;\n    });\n    const temp = new Uint8Array(totalByteLength);\n    let offset = 0;\n    buffers.forEach((buffer) => {\n        temp.set(new Uint8Array(buffer), offset);\n        offset += buffer.byteLength;\n    });\n    return temp.buffer;\n}\n/**\n * Get the basename of a path.\n *\n * Behaves in a way analogous to Linux's basename command.\n *\n * @param path\n */\nexport function basename(path) {\n    const SEPARATOR = '/';\n    path = path.trim();\n    while (path.endsWith(SEPARATOR)) {\n        path = path.slice(0, path.length - 1);\n    }\n    const items = path.split(SEPARATOR);\n    return items[items.length - 1];\n}\n/**\n * Populate ModelArtifactsInfo fields for a model with JSON topology.\n * @param modelArtifacts\n * @returns A ModelArtifactsInfo object.\n */\nexport function getModelArtifactsInfoForJSON(modelArtifacts) {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n        throw new Error('Expected JSON model topology, received ArrayBuffer.');\n    }\n    return {\n        dateSaved: new Date(),\n        modelTopologyType: 'JSON',\n        modelTopologyBytes: modelArtifacts.modelTopology == null ?\n            0 :\n            stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n        weightSpecsBytes: modelArtifacts.weightSpecs == null ?\n            0 :\n            stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n        weightDataBytes: modelArtifacts.weightData == null ?\n            0 :\n            modelArtifacts.weightData.byteLength,\n    };\n}\n/**\n * Computes mantisa table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 2048 mantissa lookup values.\n */\nfunction computeFloat16MantisaTable() {\n    const convertMantissa = (i) => {\n        let m = i << 13;\n        let e = 0;\n        while ((m & 0x00800000) === 0) {\n            e -= 0x00800000;\n            m <<= 1;\n        }\n        m &= ~0x00800000;\n        e += 0x38800000;\n        return m | e;\n    };\n    const mantisaTable = new Uint32Array(2048);\n    mantisaTable[0] = 0;\n    for (let i = 1; i < 1024; i++) {\n        mantisaTable[i] = convertMantissa(i);\n    }\n    for (let i = 1024; i < 2048; i++) {\n        mantisaTable[i] = 0x38000000 + ((i - 1024) << 13);\n    }\n    return mantisaTable;\n}\n/**\n * Computes exponent table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 64 exponent lookup values.\n */\nfunction computeFloat16ExponentTable() {\n    const exponentTable = new Uint32Array(64);\n    exponentTable[0] = 0;\n    exponentTable[31] = 0x47800000;\n    exponentTable[32] = 0x80000000;\n    exponentTable[63] = 0xc7800000;\n    for (let i = 1; i < 31; i++) {\n        exponentTable[i] = i << 23;\n    }\n    for (let i = 33; i < 63; i++) {\n        exponentTable[i] = 0x80000000 + ((i - 32) << 23);\n    }\n    return exponentTable;\n}\n/**\n * Computes offset table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 6d offset values.\n */\nfunction computeFloat16OffsetTable() {\n    const offsetTable = new Uint32Array(64);\n    for (let i = 0; i < 64; i++) {\n        offsetTable[i] = 1024;\n    }\n    offsetTable[0] = offsetTable[32] = 0;\n    return offsetTable;\n}\n/**\n * Retrieve a Float16 decoder which will decode a ByteArray of Float16 values\n * to a Float32Array.\n *\n * @returns Function (buffer: Uint16Array) => Float32Array which decodes\n *          the Uint16Array of Float16 bytes to a Float32Array.\n */\nexport function getFloat16Decoder() {\n    // Algorithm is based off of\n    // http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n    // Cache lookup tables\n    const mantisaTable = computeFloat16MantisaTable();\n    const exponentTable = computeFloat16ExponentTable();\n    const offsetTable = computeFloat16OffsetTable();\n    return (quantizedArray) => {\n        const buffer = new ArrayBuffer(4 * quantizedArray.length);\n        const bufferUint32View = new Uint32Array(buffer);\n        for (let index = 0; index < quantizedArray.length; index++) {\n            const float16Bits = quantizedArray[index];\n            const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 0x3ff)] +\n                exponentTable[float16Bits >> 10];\n            bufferUint32View[index] = float32Bits;\n        }\n        return new Float32Array(buffer);\n    };\n}\n//# sourceMappingURL=io_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class IORouterRegistry {\n    constructor() {\n        this.saveRouters = [];\n        this.loadRouters = [];\n    }\n    static getInstance() {\n        if (IORouterRegistry.instance == null) {\n            IORouterRegistry.instance = new IORouterRegistry();\n        }\n        return IORouterRegistry.instance;\n    }\n    /**\n     * Register a save-handler router.\n     *\n     * @param saveRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `save` method defined or `null`.\n     */\n    static registerSaveRouter(saveRouter) {\n        IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n    }\n    /**\n     * Register a load-handler router.\n     *\n     * @param loadRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `load` method defined or `null`.\n     */\n    static registerLoadRouter(loadRouter) {\n        IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n    }\n    /**\n     * Look up IOHandler for saving, given a URL-like string.\n     *\n     * @param url\n     * @returns If only one match is found, an instance of IOHandler with the\n     * `save` method defined. If no match is found, `null`.\n     * @throws Error, if more than one match is found.\n     */\n    static getSaveHandlers(url) {\n        return IORouterRegistry.getHandlers(url, 'save');\n    }\n    /**\n     * Look up IOHandler for loading, given a URL-like string.\n     *\n     * @param url\n     * @param loadOptions Optional, custom load options.\n     * @returns All valid handlers for `url`, given the currently registered\n     *   handler routers.\n     */\n    static getLoadHandlers(url, loadOptions) {\n        return IORouterRegistry.getHandlers(url, 'load', loadOptions);\n    }\n    static getHandlers(url, handlerType, loadOptions) {\n        const validHandlers = [];\n        const routers = handlerType === 'load' ?\n            IORouterRegistry.getInstance().loadRouters :\n            IORouterRegistry.getInstance().saveRouters;\n        routers.forEach(router => {\n            const handler = router(url, loadOptions);\n            if (handler !== null) {\n                validHandlers.push(handler);\n            }\n        });\n        return validHandlers;\n    }\n}\nexport const registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);\nexport const registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);\nexport const getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);\nexport const getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);\n//# sourceMappingURL=router_registry.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DATABASE_NAME = 'tensorflowjs';\nconst DATABASE_VERSION = 1;\n// Model data and ModelArtifactsInfo (metadata) are stored in two separate\n// stores for efficient access of the list of stored models and their metadata.\n// 1. The object store for model data: topology, weights and weight manifests.\nconst MODEL_STORE_NAME = 'models_store';\n// 2. The object store for ModelArtifactsInfo, including meta-information such\n//    as the type of topology (JSON vs binary), byte size of the topology, byte\n//    size of the weights, etc.\nconst INFO_STORE_NAME = 'model_info_store';\n/**\n * Delete the entire database for tensorflow.js, including the models store.\n */\nexport async function deleteDatabase() {\n    const idbFactory = getIndexedDBFactory();\n    return new Promise((resolve, reject) => {\n        const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);\n        deleteRequest.onsuccess = () => resolve();\n        deleteRequest.onerror = error => reject(error);\n    });\n}\nfunction getIndexedDBFactory() {\n    if (!env().getBool('IS_BROWSER')) {\n        // TODO(cais): Add more info about what IOHandler subtypes are available.\n        //   Maybe point to a doc page on the web and/or automatically determine\n        //   the available IOHandlers and print them in the error message.\n        throw new Error('Failed to obtain IndexedDB factory because the current environment' +\n            'is not a web browser.');\n    }\n    // tslint:disable-next-line:no-any\n    const theWindow = typeof window === 'undefined' ? self : window;\n    const factory = theWindow.indexedDB || theWindow.mozIndexedDB ||\n        theWindow.webkitIndexedDB || theWindow.msIndexedDB ||\n        theWindow.shimIndexedDB;\n    if (factory == null) {\n        throw new Error('The current browser does not appear to support IndexedDB.');\n    }\n    return factory;\n}\nfunction setUpDatabase(openRequest) {\n    const db = openRequest.result;\n    db.createObjectStore(MODEL_STORE_NAME, { keyPath: 'modelPath' });\n    db.createObjectStore(INFO_STORE_NAME, { keyPath: 'modelPath' });\n}\n/**\n * IOHandler subclass: Browser IndexedDB.\n *\n * See the doc string of `browserIndexedDB` for more details.\n */\nexport class BrowserIndexedDB {\n    constructor(modelPath) {\n        this.indexedDB = getIndexedDBFactory();\n        if (modelPath == null || !modelPath) {\n            throw new Error('For IndexedDB, modelPath must not be null, undefined or empty.');\n        }\n        this.modelPath = modelPath;\n    }\n    async save(modelArtifacts) {\n        // TODO(cais): Support saving GraphDef models.\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        return this.databaseAction(this.modelPath, modelArtifacts);\n    }\n    async load() {\n        return this.databaseAction(this.modelPath);\n    }\n    /**\n     * Perform database action to put model artifacts into or read model artifacts\n     * from IndexedDB object store.\n     *\n     * Whether the action is put or get depends on whether `modelArtifacts` is\n     * specified. If it is specified, the action will be put; otherwise the action\n     * will be get.\n     *\n     * @param modelPath A unique string path for the model.\n     * @param modelArtifacts If specified, it will be the model artifacts to be\n     *   stored in IndexedDB.\n     * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`\n     *   of `ModelArtifacts`, if the action is get.\n     */\n    databaseAction(modelPath, modelArtifacts) {\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                if (modelArtifacts == null) {\n                    // Read model out from object store.\n                    const modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');\n                    const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                    const getRequest = modelStore.get(this.modelPath);\n                    getRequest.onsuccess = () => {\n                        if (getRequest.result == null) {\n                            db.close();\n                            return reject(new Error(`Cannot find model with path '${this.modelPath}' ` +\n                                `in IndexedDB.`));\n                        }\n                        else {\n                            resolve(getRequest.result.modelArtifacts);\n                        }\n                    };\n                    getRequest.onerror = error => {\n                        db.close();\n                        return reject(getRequest.error);\n                    };\n                    modelTx.oncomplete = () => db.close();\n                }\n                else {\n                    // Put model into object store.\n                    const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n                    // First, put ModelArtifactsInfo into info store.\n                    const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n                    let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                    const putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo });\n                    let modelTx;\n                    putInfoRequest.onsuccess = () => {\n                        // Second, put model data into model store.\n                        modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n                        const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                        const putModelRequest = modelStore.put({\n                            modelPath: this.modelPath,\n                            modelArtifacts,\n                            modelArtifactsInfo\n                        });\n                        putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo });\n                        putModelRequest.onerror = error => {\n                            // If the put-model request fails, roll back the info entry as\n                            // well.\n                            infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                            const deleteInfoRequest = infoStore.delete(this.modelPath);\n                            deleteInfoRequest.onsuccess = () => {\n                                db.close();\n                                return reject(putModelRequest.error);\n                            };\n                            deleteInfoRequest.onerror = error => {\n                                db.close();\n                                return reject(putModelRequest.error);\n                            };\n                        };\n                    };\n                    putInfoRequest.onerror = error => {\n                        db.close();\n                        return reject(putInfoRequest.error);\n                    };\n                    infoTx.oncomplete = () => {\n                        if (modelTx == null) {\n                            db.close();\n                        }\n                        else {\n                            modelTx.oncomplete = () => db.close();\n                        }\n                    };\n                }\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n}\nBrowserIndexedDB.URL_SCHEME = 'indexeddb://';\nexport const indexedDBRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n            return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\n/**\n * Creates a browser IndexedDB IOHandler for saving and loading models.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save('indexeddb://MyModel'));\n * console.log(saveResult);\n * ```\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),\n *   which can be used with, e.g., `tf.Model.save`.\n */\nexport function browserIndexedDB(modelPath) {\n    return new BrowserIndexedDB(modelPath);\n}\nfunction maybeStripScheme(key) {\n    return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?\n        key.slice(BrowserIndexedDB.URL_SCHEME.length) :\n        key;\n}\nexport class BrowserIndexedDBManager {\n    constructor() {\n        this.indexedDB = getIndexedDBFactory();\n    }\n    async listModels() {\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                const tx = db.transaction(INFO_STORE_NAME, 'readonly');\n                const store = tx.objectStore(INFO_STORE_NAME);\n                // tslint:disable:max-line-length\n                // Need to cast `store` as `any` here because TypeScript's DOM\n                // library does not have the `getAll()` method even though the\n                // method is supported in the latest version of most mainstream\n                // browsers:\n                // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll\n                // tslint:enable:max-line-length\n                // tslint:disable-next-line:no-any\n                const getAllInfoRequest = store.getAll();\n                getAllInfoRequest.onsuccess = () => {\n                    const out = {};\n                    for (const item of getAllInfoRequest.result) {\n                        out[item.modelPath] = item.modelArtifactsInfo;\n                    }\n                    resolve(out);\n                };\n                getAllInfoRequest.onerror = error => {\n                    db.close();\n                    return reject(getAllInfoRequest.error);\n                };\n                tx.oncomplete = () => db.close();\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n    async removeModel(path) {\n        path = maybeStripScheme(path);\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n                const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                const getInfoRequest = infoStore.get(path);\n                let modelTx;\n                getInfoRequest.onsuccess = () => {\n                    if (getInfoRequest.result == null) {\n                        db.close();\n                        return reject(new Error(`Cannot find model with path '${path}' ` +\n                            `in IndexedDB.`));\n                    }\n                    else {\n                        // First, delete the entry in the info store.\n                        const deleteInfoRequest = infoStore.delete(path);\n                        const deleteModelData = () => {\n                            // Second, delete the entry in the model store.\n                            modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n                            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                            const deleteModelRequest = modelStore.delete(path);\n                            deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo);\n                            deleteModelRequest.onerror = error => reject(getInfoRequest.error);\n                        };\n                        // Proceed with deleting model data regardless of whether deletion\n                        // of info data succeeds or not.\n                        deleteInfoRequest.onsuccess = deleteModelData;\n                        deleteInfoRequest.onerror = error => {\n                            deleteModelData();\n                            db.close();\n                            return reject(getInfoRequest.error);\n                        };\n                    }\n                };\n                getInfoRequest.onerror = error => {\n                    db.close();\n                    return reject(getInfoRequest.error);\n                };\n                infoTx.oncomplete = () => {\n                    if (modelTx == null) {\n                        db.close();\n                    }\n                    else {\n                        modelTx.oncomplete = () => db.close();\n                    }\n                };\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n}\n//# sourceMappingURL=indexed_db.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { arrayBufferToBase64String, base64StringToArrayBuffer, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst PATH_SEPARATOR = '/';\nconst PATH_PREFIX = 'tensorflowjs_models';\nconst INFO_SUFFIX = 'info';\nconst MODEL_TOPOLOGY_SUFFIX = 'model_topology';\nconst WEIGHT_SPECS_SUFFIX = 'weight_specs';\nconst WEIGHT_DATA_SUFFIX = 'weight_data';\nconst MODEL_METADATA_SUFFIX = 'model_metadata';\n/**\n * Purge all tensorflow.js-saved model artifacts from local storage.\n *\n * @returns Paths of the models purged.\n */\nexport function purgeLocalStorageArtifacts() {\n    if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n        typeof window.localStorage === 'undefined') {\n        throw new Error('purgeLocalStorageModels() cannot proceed because local storage is ' +\n            'unavailable in the current environment.');\n    }\n    const LS = window.localStorage;\n    const purgedModelPaths = [];\n    for (let i = 0; i < LS.length; ++i) {\n        const key = LS.key(i);\n        const prefix = PATH_PREFIX + PATH_SEPARATOR;\n        if (key.startsWith(prefix) && key.length > prefix.length) {\n            LS.removeItem(key);\n            const modelName = getModelPathFromKey(key);\n            if (purgedModelPaths.indexOf(modelName) === -1) {\n                purgedModelPaths.push(modelName);\n            }\n        }\n    }\n    return purgedModelPaths;\n}\nfunction getModelKeys(path) {\n    return {\n        info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n        topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n        weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n        weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),\n        modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)\n    };\n}\n/**\n * Get model path from a local-storage key.\n *\n * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'\n *\n * @param key\n */\nfunction getModelPathFromKey(key) {\n    const items = key.split(PATH_SEPARATOR);\n    if (items.length < 3) {\n        throw new Error(`Invalid key format: ${key}`);\n    }\n    return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\nfunction maybeStripScheme(key) {\n    return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?\n        key.slice(BrowserLocalStorage.URL_SCHEME.length) :\n        key;\n}\n/**\n * IOHandler subclass: Browser Local Storage.\n *\n * See the doc string to `browserLocalStorage` for more details.\n */\nexport class BrowserLocalStorage {\n    constructor(modelPath) {\n        if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n            typeof window.localStorage === 'undefined') {\n            // TODO(cais): Add more info about what IOHandler subtypes are\n            // available.\n            //   Maybe point to a doc page on the web and/or automatically determine\n            //   the available IOHandlers and print them in the error message.\n            throw new Error('The current environment does not support local storage.');\n        }\n        this.LS = window.localStorage;\n        if (modelPath == null || !modelPath) {\n            throw new Error('For local storage, modelPath must not be null, undefined or empty.');\n        }\n        this.modelPath = modelPath;\n        this.keys = getModelKeys(this.modelPath);\n    }\n    /**\n     * Save model artifacts to browser local storage.\n     *\n     * See the documentation to `browserLocalStorage` for details on the saved\n     * artifacts.\n     *\n     * @param modelArtifacts The model artifacts to be stored.\n     * @returns An instance of SaveResult.\n     */\n    async save(modelArtifacts) {\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        else {\n            const topology = JSON.stringify(modelArtifacts.modelTopology);\n            const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n            const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n            try {\n                this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n                this.LS.setItem(this.keys.topology, topology);\n                this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n                this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData));\n                this.LS.setItem(this.keys.modelMetadata, JSON.stringify({\n                    format: modelArtifacts.format,\n                    generatedBy: modelArtifacts.generatedBy,\n                    convertedBy: modelArtifacts.convertedBy,\n                    userDefinedMetadata: modelArtifacts.userDefinedMetadata\n                }));\n                return { modelArtifactsInfo };\n            }\n            catch (err) {\n                // If saving failed, clean up all items saved so far.\n                this.LS.removeItem(this.keys.info);\n                this.LS.removeItem(this.keys.topology);\n                this.LS.removeItem(this.keys.weightSpecs);\n                this.LS.removeItem(this.keys.weightData);\n                this.LS.removeItem(this.keys.modelMetadata);\n                throw new Error(`Failed to save model '${this.modelPath}' to local storage: ` +\n                    `size quota being exceeded is a possible cause of this failure: ` +\n                    `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` +\n                    `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` +\n                    `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n            }\n        }\n    }\n    /**\n     * Load a model from local storage.\n     *\n     * See the documentation to `browserLocalStorage` for details on the saved\n     * artifacts.\n     *\n     * @returns The loaded model (if loading succeeds).\n     */\n    async load() {\n        const info = JSON.parse(this.LS.getItem(this.keys.info));\n        if (info == null) {\n            throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);\n        }\n        if (info.modelTopologyType !== 'JSON') {\n            throw new Error('BrowserLocalStorage does not support loading non-JSON model ' +\n                'topology yet.');\n        }\n        const out = {};\n        // Load topology.\n        const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n        if (topology == null) {\n            throw new Error(`In local storage, the topology of model '${this.modelPath}' ` +\n                `is missing.`);\n        }\n        out.modelTopology = topology;\n        // Load weight specs.\n        const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n        if (weightSpecs == null) {\n            throw new Error(`In local storage, the weight specs of model '${this.modelPath}' ` +\n                `are missing.`);\n        }\n        out.weightSpecs = weightSpecs;\n        // Load meta-data fields.\n        const metadataString = this.LS.getItem(this.keys.modelMetadata);\n        if (metadataString != null) {\n            const metadata = JSON.parse(metadataString);\n            out.format = metadata['format'];\n            out.generatedBy = metadata['generatedBy'];\n            out.convertedBy = metadata['convertedBy'];\n            out.userDefinedMetadata = metadata['userDefinedMetadata'];\n        }\n        // Load weight data.\n        const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n        if (weightDataBase64 == null) {\n            throw new Error(`In local storage, the binary weight values of model ` +\n                `'${this.modelPath}' are missing.`);\n        }\n        out.weightData = base64StringToArrayBuffer(weightDataBase64);\n        return out;\n    }\n}\nBrowserLocalStorage.URL_SCHEME = 'localstorage://';\nexport const localStorageRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n            return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\n/**\n * Factory function for local storage IOHandler.\n *\n * This `IOHandler` supports both `save` and `load`.\n *\n * For each model's saved artifacts, four items are saved to local storage.\n *   - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the\n *     model, such as date saved, type of the topology, size in bytes, etc.\n *   - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-\n *     style models, this is a stringized JSON.\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the\n *     model, can be used to decode the saved binary weight values (see\n *     item below).\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary\n *     weight values, stored as a base64-encoded string.\n *\n * Saving may throw an `Error` if the total size of the artifacts exceed the\n * browser-specific quota.\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `IOHandler`, which can be used with, e.g.,\n *   `tf.Model.save`.\n */\nexport function browserLocalStorage(modelPath) {\n    return new BrowserLocalStorage(modelPath);\n}\nexport class BrowserLocalStorageManager {\n    constructor() {\n        assert(env().getBool('IS_BROWSER'), () => 'Current environment is not a web browser');\n        assert(typeof window === 'undefined' ||\n            typeof window.localStorage !== 'undefined', () => 'Current browser does not appear to support localStorage');\n        this.LS = window.localStorage;\n    }\n    async listModels() {\n        const out = {};\n        const prefix = PATH_PREFIX + PATH_SEPARATOR;\n        const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n        for (let i = 0; i < this.LS.length; ++i) {\n            const key = this.LS.key(i);\n            if (key.startsWith(prefix) && key.endsWith(suffix)) {\n                const modelPath = getModelPathFromKey(key);\n                out[modelPath] = JSON.parse(this.LS.getItem(key));\n            }\n        }\n        return out;\n    }\n    async removeModel(path) {\n        path = maybeStripScheme(path);\n        const keys = getModelKeys(path);\n        if (this.LS.getItem(keys.info) == null) {\n            throw new Error(`Cannot find model at path '${path}'`);\n        }\n        const info = JSON.parse(this.LS.getItem(keys.info));\n        this.LS.removeItem(keys.info);\n        this.LS.removeItem(keys.topology);\n        this.LS.removeItem(keys.weightSpecs);\n        this.LS.removeItem(keys.weightData);\n        return info;\n    }\n}\n//# sourceMappingURL=local_storage.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Classes and functions for model management across multiple storage mediums.\n *\n * Supported client actions:\n * - Listing models on all registered storage mediums.\n * - Remove model by URL from any registered storage mediums, by using URL\n *   string.\n * - Moving or copying model from one path to another in the same medium or from\n *   one medium to another, by using URL strings.\n */\nimport { assert } from '../util';\nimport { IORouterRegistry } from './router_registry';\nconst URL_SCHEME_SUFFIX = '://';\nexport class ModelStoreManagerRegistry {\n    constructor() {\n        this.managers = {};\n    }\n    static getInstance() {\n        if (ModelStoreManagerRegistry.instance == null) {\n            ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n        }\n        return ModelStoreManagerRegistry.instance;\n    }\n    /**\n     * Register a save-handler router.\n     *\n     * @param saveRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `save` method defined or `null`.\n     */\n    static registerManager(scheme, manager) {\n        assert(scheme != null, () => 'scheme must not be undefined or null.');\n        if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n            scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n        }\n        assert(scheme.length > 0, () => 'scheme must not be an empty string.');\n        const registry = ModelStoreManagerRegistry.getInstance();\n        assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);\n        registry.managers[scheme] = manager;\n    }\n    static getManager(scheme) {\n        const manager = this.getInstance().managers[scheme];\n        if (manager == null) {\n            throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n        }\n        return manager;\n    }\n    static getSchemes() {\n        return Object.keys(this.getInstance().managers);\n    }\n}\n/**\n * Helper method for parsing a URL string into a scheme and a path.\n *\n * @param url E.g., 'localstorage://my-model'\n * @returns A dictionary with two fields: scheme and path.\n *   Scheme: e.g., 'localstorage' in the example above.\n *   Path: e.g., 'my-model' in the example above.\n */\nfunction parseURL(url) {\n    if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n        throw new Error(`The url string provided does not contain a scheme. ` +\n            `Supported schemes are: ` +\n            `${ModelStoreManagerRegistry.getSchemes().join(',')}`);\n    }\n    return {\n        scheme: url.split(URL_SCHEME_SUFFIX)[0],\n        path: url.split(URL_SCHEME_SUFFIX)[1],\n    };\n}\nasync function cloneModelInternal(sourceURL, destURL, deleteSource = false) {\n    assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);\n    const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n    assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);\n    assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n        `load handlers for source URL ${sourceURL}.`);\n    const loadHandler = loadHandlers[0];\n    const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n    assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination ` +\n        `URL ${destURL}.`);\n    assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n        `save handlers for destination URL ${destURL}.`);\n    const saveHandler = saveHandlers[0];\n    const sourceScheme = parseURL(sourceURL).scheme;\n    const sourcePath = parseURL(sourceURL).path;\n    const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n    const modelArtifacts = await loadHandler.load();\n    // If moving within the same storage medium, remove the old model as soon as\n    // the loading is done. Without doing this, it is possible that the combined\n    // size of the two models will cause the cloning to fail.\n    if (deleteSource && sameMedium) {\n        await ModelStoreManagerRegistry.getManager(sourceScheme)\n            .removeModel(sourcePath);\n    }\n    const saveResult = await saveHandler.save(modelArtifacts);\n    // If moving between mediums, the deletion is done after the save succeeds.\n    // This guards against the case in which saving to the destination medium\n    // fails.\n    if (deleteSource && !sameMedium) {\n        await ModelStoreManagerRegistry.getManager(sourceScheme)\n            .removeModel(sourcePath);\n    }\n    return saveResult.modelArtifactsInfo;\n}\n/**\n * List all models stored in registered storage mediums.\n *\n * For a web browser environment, the registered mediums are Local Storage and\n * IndexedDB.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @returns A `Promise` of a dictionary mapping URLs of existing models to\n * their model artifacts info. URLs include medium-specific schemes, e.g.,\n *   'indexeddb://my/model/1'. Model artifacts info include type of the\n * model's topology, byte sizes of the topology, weights, etc.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function listModels() {\n    const schemes = ModelStoreManagerRegistry.getSchemes();\n    const out = {};\n    for (const scheme of schemes) {\n        const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();\n        for (const path in schemeOut) {\n            const url = scheme + URL_SCHEME_SUFFIX + path;\n            out[url] = schemeOut[path];\n        }\n    }\n    return out;\n}\n/**\n * Remove a model specified by URL from a reigstered storage medium.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @param url A URL to a stored model, with a scheme prefix, e.g.,\n *   'localstorage://my-model-1', 'indexeddb://my/model/2'.\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n *   is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function removeModel(url) {\n    const schemeAndPath = parseURL(url);\n    const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n    return manager.removeModel(schemeAndPath.path);\n}\n/**\n * Copy a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Copying within a storage medium, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Copying between two storage mediums, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Copy the model, from Local Storage to IndexedDB.\n * await tf.io.copyModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove both models.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of copying.\n * @param destURL Destination URL of copying.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function copyModel(sourceURL, destURL) {\n    const deleteSource = false;\n    return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n/**\n * Move a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Moving within a storage medium, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Moving between two storage mediums, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Move the model, from Local Storage to IndexedDB.\n * await tf.io.moveModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove the moved model.\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of moving.\n * @param destURL Destination URL of moving.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function moveModel(sourceURL, destURL) {\n    const deleteSource = true;\n    return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\nexport { moveModel, copyModel, removeModel, listModels };\n//# sourceMappingURL=model_management.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { BrowserIndexedDB, BrowserIndexedDBManager } from '../io/indexed_db';\nimport { BrowserLocalStorage, BrowserLocalStorageManager } from '../io/local_storage';\nimport { ModelStoreManagerRegistry } from '../io/model_management';\nexport class PlatformBrowser {\n    fetch(path, init) {\n        return fetch(path, init);\n    }\n    now() {\n        return performance.now();\n    }\n    encode(text, encoding) {\n        if (encoding !== 'utf-8' && encoding !== 'utf8') {\n            throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);\n        }\n        if (this.textEncoder == null) {\n            this.textEncoder = new TextEncoder();\n        }\n        return this.textEncoder.encode(text);\n    }\n    decode(bytes, encoding) {\n        return new TextDecoder(encoding).decode(bytes);\n    }\n}\nif (env().get('IS_BROWSER')) {\n    env().setPlatform('browser', new PlatformBrowser());\n    // Register LocalStorage IOHandler\n    try {\n        ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n    }\n    catch (err) {\n    }\n    // Register IndexedDB IOHandler\n    try {\n        ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n    }\n    catch (err) {\n    }\n}\n//# sourceMappingURL=platform_browser.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\n// We are wrapping this within an object so it can be stubbed by Jasmine.\nexport const getNodeFetch = {\n    // tslint:disable-next-line:no-require-imports\n    importFetch: () => require('node-fetch')\n};\nlet systemFetch;\n// These getters and setters are for testing so we don't export a mutable\n// variable.\nexport function resetSystemFetch() {\n    systemFetch = null;\n}\nexport function setSystemFetch(fetchFn) {\n    systemFetch = fetchFn;\n}\nexport function getSystemFetch() {\n    return systemFetch;\n}\nexport class PlatformNode {\n    constructor() {\n        // tslint:disable-next-line:no-require-imports\n        this.util = require('util');\n        // According to the spec, the built-in encoder can do only UTF-8 encoding.\n        // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder\n        this.textEncoder = new this.util.TextEncoder();\n    }\n    fetch(path, requestInits) {\n        if (env().global.fetch != null) {\n            return env().global.fetch(path, requestInits);\n        }\n        if (systemFetch == null) {\n            systemFetch = getNodeFetch.importFetch();\n        }\n        return systemFetch(path, requestInits);\n    }\n    now() {\n        const time = process.hrtime();\n        return time[0] * 1000 + time[1] / 1000000;\n    }\n    encode(text, encoding) {\n        if (encoding !== 'utf-8' && encoding !== 'utf8') {\n            throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);\n        }\n        return this.textEncoder.encode(text);\n    }\n    decode(bytes, encoding) {\n        if (bytes.length === 0) {\n            return '';\n        }\n        return new this.util.TextDecoder(encoding).decode(bytes);\n    }\n}\nif (env().get('IS_NODE')) {\n    env().setPlatform('node', new PlatformNode());\n}\n//# sourceMappingURL=platform_node.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport * as util from '../util';\n/**\n * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.\n *\n * The values are stored in CPU as `TypedArray`. Fill the buffer using\n * `buffer.set()`, or by modifying directly `buffer.values`.\n *\n * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with\n * those values.\n *\n * ```js\n * // Create a buffer and set values at particular indices.\n * const buffer = tf.buffer([2, 2]);\n * buffer.set(3, 0, 0);\n * buffer.set(5, 1, 0);\n *\n * // Convert the buffer back to a tensor.\n * buffer.toTensor().print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The dtype of the buffer. Defaults to 'float32'.\n * @param values The values of the buffer as `TypedArray`. Defaults to\n * zeros.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function buffer(shape, dtype = 'float32', values) {\n    dtype = dtype || 'float32';\n    util.assertNonNegativeIntegerDimensions(shape);\n    return new TensorBuffer(shape, dtype, values);\n}\n//# sourceMappingURL=buffer.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cast } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Casts a `tf.Tensor` to a new dtype.\n *\n * ```js\n * const x = tf.tensor1d([1.5, 2.5, 3]);\n * tf.cast(x, 'int32').print();\n * ```\n * @param x The input tensor to be casted.\n * @param dtype The dtype to cast the input tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction cast_(x, dtype) {\n    const $x = convertToTensor(x, 'x', 'cast');\n    // Sanity checks.\n    if (!util.isValidDtype(dtype)) {\n        throw new Error(`Failed to cast to unknown dtype ${dtype}`);\n    }\n    if (dtype === 'string' && $x.dtype !== 'string' ||\n        dtype !== 'string' && $x.dtype === 'string') {\n        throw new Error('Only strings can be casted to strings');\n    }\n    const inputs = { x: $x };\n    const attrs = { dtype };\n    return ENGINE.runKernelFunc(backend => backend.cast($x, dtype), inputs, null /* grad */, Cast, attrs);\n}\nexport const cast = op({ cast_ });\n//# sourceMappingURL=cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Identity } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction clone_(x) {\n    const $x = convertToTensor(x, 'x', 'clone', null);\n    const forward = () => ENGINE.makeTensorFromDataId($x.dataId, $x.shape, $x.dtype);\n    const inputs = { x: $x };\n    // Note this op is called tf.identity in python. Hence the kernel name used\n    // here.\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Identity);\n}\nexport const clone = op({ clone_ });\n//# sourceMappingURL=clone.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function print(x, verbose = false) {\n    console.log(x.toString(verbose));\n}\n//# sourceMappingURL=print.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Required side effectful code for tfjs-core\n// Set up Engine and ENV\nimport { getOrMakeEngine } from './engine';\ngetOrMakeEngine();\n// Register backend-agnostic flags.\nimport './flags';\n// Register platforms\nimport './platforms/platform_browser';\nimport './platforms/platform_node';\n// Set up OpHandler\nimport { buffer } from './ops/buffer';\nimport { cast } from './ops/cast';\nimport { clone } from './ops/clone';\nimport { print } from './ops/print';\nimport { setOpHandler } from './tensor';\nconst opHandler = {\n    buffer,\n    cast,\n    clone,\n    print\n};\nsetOpHandler(opHandler);\n//# sourceMappingURL=base_side_effects.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandlers related to files, such as browser-triggered file downloads,\n * user-selected files in browser.\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { basename, concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DEFAULT_FILE_NAME_PREFIX = 'model';\nconst DEFAULT_JSON_EXTENSION_NAME = '.json';\nconst DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';\nfunction defer(f) {\n    return new Promise(resolve => setTimeout(resolve)).then(f);\n}\nexport class BrowserDownloads {\n    constructor(fileNamePrefix) {\n        if (!env().getBool('IS_BROWSER')) {\n            // TODO(cais): Provide info on what IOHandlers are available under the\n            //   current environment.\n            throw new Error('browserDownloads() cannot proceed because the current environment ' +\n                'is not a browser.');\n        }\n        if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n            fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n        }\n        if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n            fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n        }\n        this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n        this.weightDataFileName =\n            fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n    }\n    async save(modelArtifacts) {\n        if (typeof (document) === 'undefined') {\n            throw new Error('Browser downloads are not supported in ' +\n                'this environment since `document` is not present');\n        }\n        const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: 'application/octet-stream' }));\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserDownloads.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        else {\n            const weightsManifest = [{\n                    paths: ['./' + this.weightDataFileName],\n                    weights: modelArtifacts.weightSpecs\n                }];\n            const modelTopologyAndWeightManifest = {\n                modelTopology: modelArtifacts.modelTopology,\n                format: modelArtifacts.format,\n                generatedBy: modelArtifacts.generatedBy,\n                convertedBy: modelArtifacts.convertedBy,\n                weightsManifest\n            };\n            const modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: 'application/json' }));\n            // If anchor elements are not provided, create them without attaching them\n            // to parents, so that the downloaded file names can be controlled.\n            const jsonAnchor = this.jsonAnchor == null ? document.createElement('a') :\n                this.jsonAnchor;\n            jsonAnchor.download = this.modelTopologyFileName;\n            jsonAnchor.href = modelTopologyAndWeightManifestURL;\n            // Trigger downloads by evoking a click event on the download anchors.\n            // When multiple downloads are started synchronously, Firefox will only\n            // save the last one.\n            await defer(() => jsonAnchor.dispatchEvent(new MouseEvent('click')));\n            if (modelArtifacts.weightData != null) {\n                const weightDataAnchor = this.weightDataAnchor == null ?\n                    document.createElement('a') :\n                    this.weightDataAnchor;\n                weightDataAnchor.download = this.weightDataFileName;\n                weightDataAnchor.href = weightsURL;\n                await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent('click')));\n            }\n            return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) };\n        }\n    }\n}\nBrowserDownloads.URL_SCHEME = 'downloads://';\nclass BrowserFiles {\n    constructor(files) {\n        if (files == null || files.length < 1) {\n            throw new Error(`When calling browserFiles, at least 1 file is required, ` +\n                `but received ${files}`);\n        }\n        this.files = files;\n    }\n    async load() {\n        const jsonFile = this.files[0];\n        const weightFiles = this.files.slice(1);\n        return new Promise((resolve, reject) => {\n            const jsonReader = new FileReader();\n            jsonReader.onload = (event) => {\n                // tslint:disable-next-line:no-any\n                const modelJSON = JSON.parse(event.target.result);\n                const modelTopology = modelJSON.modelTopology;\n                if (modelTopology == null) {\n                    reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));\n                    return;\n                }\n                if (weightFiles.length === 0) {\n                    resolve({ modelTopology });\n                }\n                const weightsManifest = modelJSON.weightsManifest;\n                if (weightsManifest == null) {\n                    reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));\n                    return;\n                }\n                let pathToFile;\n                try {\n                    pathToFile =\n                        this.checkManifestAndWeightFiles(weightsManifest, weightFiles);\n                }\n                catch (err) {\n                    reject(err);\n                    return;\n                }\n                const weightSpecs = [];\n                const paths = [];\n                const perFileBuffers = [];\n                weightsManifest.forEach(weightsGroup => {\n                    weightsGroup.paths.forEach(path => {\n                        paths.push(path);\n                        perFileBuffers.push(null);\n                    });\n                    weightSpecs.push(...weightsGroup.weights);\n                });\n                weightsManifest.forEach(weightsGroup => {\n                    weightsGroup.paths.forEach(path => {\n                        const weightFileReader = new FileReader();\n                        weightFileReader.onload = (event) => {\n                            // tslint:disable-next-line:no-any\n                            const weightData = event.target.result;\n                            const index = paths.indexOf(path);\n                            perFileBuffers[index] = weightData;\n                            if (perFileBuffers.indexOf(null) === -1) {\n                                resolve({\n                                    modelTopology,\n                                    weightSpecs,\n                                    weightData: concatenateArrayBuffers(perFileBuffers),\n                                    format: modelJSON.format,\n                                    generatedBy: modelJSON.generatedBy,\n                                    convertedBy: modelJSON.convertedBy,\n                                    userDefinedMetadata: modelJSON.userDefinedMetadata\n                                });\n                            }\n                        };\n                        weightFileReader.onerror = error => reject(`Failed to weights data from file of path '${path}'.`);\n                        weightFileReader.readAsArrayBuffer(pathToFile[path]);\n                    });\n                });\n            };\n            jsonReader.onerror = error => reject(`Failed to read model topology and weights manifest JSON ` +\n                `from file '${jsonFile.name}'. BrowserFiles supports loading ` +\n                `Keras-style tf.Model artifacts only.`);\n            jsonReader.readAsText(jsonFile);\n        });\n    }\n    /**\n     * Check the compatibility between weights manifest and weight files.\n     */\n    checkManifestAndWeightFiles(manifest, files) {\n        const basenames = [];\n        const fileNames = files.map(file => basename(file.name));\n        const pathToFile = {};\n        for (const group of manifest) {\n            group.paths.forEach(path => {\n                const pathBasename = basename(path);\n                if (basenames.indexOf(pathBasename) !== -1) {\n                    throw new Error(`Duplicate file basename found in weights manifest: ` +\n                        `'${pathBasename}'`);\n                }\n                basenames.push(pathBasename);\n                if (fileNames.indexOf(pathBasename) === -1) {\n                    throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);\n                }\n                else {\n                    pathToFile[path] = files[fileNames.indexOf(pathBasename)];\n                }\n            });\n        }\n        if (basenames.length !== files.length) {\n            throw new Error(`Mismatch in the number of files in weights manifest ` +\n                `(${basenames.length}) and the number of weight files provided ` +\n                `(${files.length}).`);\n        }\n        return pathToFile;\n    }\n}\nexport const browserDownloadsRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n            return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\n/**\n * Creates an IOHandler that triggers file downloads from the browser.\n *\n * The returned `IOHandler` instance can be used as model exporting methods such\n * as `tf.Model.save` and supports only saving.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * const saveResult = await model.save('downloads://mymodel');\n * // This will trigger downloading of two files:\n * //   'mymodel.json' and 'mymodel.weights.bin'.\n * console.log(saveResult);\n * ```\n *\n * @param fileNamePrefix Prefix name of the files to be downloaded. For use with\n *   `tf.Model`, `fileNamePrefix` should follow either of the following two\n *   formats:\n *   1. `null` or `undefined`, in which case the default file\n *      names will be used:\n *      - 'model.json' for the JSON file containing the model topology and\n *        weights manifest.\n *      - 'model.weights.bin' for the binary file containing the binary weight\n *        values.\n *   2. A single string or an Array of a single string, as the file name prefix.\n *      For example, if `'foo'` is provided, the downloaded JSON\n *      file and binary weights file will be named 'foo.json' and\n *      'foo.weights.bin', respectively.\n * @param config Additional configuration for triggering downloads.\n * @returns An instance of `BrowserDownloads` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserDownloads(fileNamePrefix = 'model') {\n    return new BrowserDownloads(fileNamePrefix);\n}\n/**\n * Creates an IOHandler that loads model artifacts from user-selected files.\n *\n * This method can be used for loading from files such as user-selected files\n * in the browser.\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * // Note: This code snippet won't run properly without the actual file input\n * //   elements in the HTML DOM.\n *\n * // Suppose there are two HTML file input (`<input type=\"file\" ...>`)\n * // elements.\n * const uploadJSONInput = document.getElementById('upload-json');\n * const uploadWeightsInput = document.getElementById('upload-weights');\n * const model = await tf.loadLayersModel(tf.io.browserFiles(\n *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));\n * ```\n *\n * @param files `File`s to load from. Currently, this function supports only\n *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for\n *   which an `Array` of `File`s is expected (in that order):\n *   - A JSON file containing the model topology and weight manifest.\n *   - Optionally, One or more binary files containing the binary weights.\n *     These files must have names that match the paths in the `weightsManifest`\n *     contained by the aforementioned JSON file, or errors will be thrown\n *     during loading. These weights files have the same format as the ones\n *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`\n *     Python PIP package. If no weights files are provided, only the model\n *     topology will be loaded from the JSON file above.\n * @returns An instance of `Files` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserFiles(files) {\n    return new BrowserFiles(files);\n}\n//# sourceMappingURL=browser_files.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../util';\n/**\n * Monitor Promise.all progress, fire onProgress callback function.\n *\n * @param promises Promise list going to be monitored\n * @param onProgress Callback function. Fired when a promise resolved.\n * @param startFraction Optional fraction start. Default to 0.\n * @param endFraction Optional fraction end. Default to 1.\n */\nexport function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {\n    checkPromises(promises);\n    startFraction = startFraction == null ? 0 : startFraction;\n    endFraction = endFraction == null ? 1 : endFraction;\n    checkFraction(startFraction, endFraction);\n    let resolvedPromise = 0;\n    const registerMonitor = (promise) => {\n        promise.then(value => {\n            const fraction = startFraction +\n                ++resolvedPromise / promises.length * (endFraction - startFraction);\n            // pass fraction as parameter to callback function.\n            onProgress(fraction);\n            return value;\n        });\n        return promise;\n    };\n    function checkPromises(promises) {\n        assert(promises != null && Array.isArray(promises) && promises.length > 0, () => 'promises must be a none empty array');\n    }\n    function checkFraction(startFraction, endFraction) {\n        assert(startFraction >= 0 && startFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n            `got startFraction ${startFraction}`);\n        assert(endFraction >= 0 && endFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n            `got endFraction ${endFraction}`);\n        assert(endFraction >= startFraction, () => `startFraction must be no more than endFraction, but ` +\n            `got startFraction ${startFraction} and endFraction ` +\n            `${endFraction}`);\n    }\n    return Promise.all(promises.map(registerMonitor));\n}\n//# sourceMappingURL=progress.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\nimport * as util from '../util';\nimport { decodeWeights } from './io_utils';\nimport { monitorPromisesProgress } from './progress';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/**\n * Reads binary weights data from a number of URLs.\n *\n * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.\n * @param requestOptions RequestInit (options) for the HTTP requests.\n * @param fetchFunc Optional overriding value for the `window.fetch` function.\n * @param onProgress Optional, progress callback function, fired periodically\n *   before the load is completed.\n * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same\n *   length as `fetchURLs`.\n */\nexport async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {\n    if (loadOptions == null) {\n        loadOptions = {};\n    }\n    const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch :\n        loadOptions.fetchFunc;\n    // Create the requests for all of the weights in parallel.\n    const requests = fetchURLs.map(fetchURL => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true }));\n    const fetchStartFraction = 0;\n    const fetchEndFraction = 0.5;\n    const responses = loadOptions.onProgress == null ?\n        await Promise.all(requests) :\n        await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);\n    const bufferPromises = responses.map(response => response.arrayBuffer());\n    const bufferStartFraction = 0.5;\n    const bufferEndFraction = 1;\n    const buffers = loadOptions.onProgress == null ?\n        await Promise.all(bufferPromises) :\n        await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);\n    return buffers;\n}\n/**\n * Reads a weights manifest JSON configuration, fetches the weights and\n * returns them as `Tensor`s.\n *\n * @param manifest The weights manifest JSON.\n * @param filePathPrefix The path prefix for filenames given in the manifest.\n *     Defaults to the empty string.\n * @param weightNames The names of the weights to be fetched.\n */\nexport async function loadWeights(manifest, filePathPrefix = '', weightNames, requestInit) {\n    // TODO(nsthorat): Groups are currently fetched atomically. If you need a\n    // single weight from a group, the whole group will be fetched. At a future\n    // date, we should support fetching only the individual shards within a\n    // group that are needed to reconstruct the requested weight.\n    // TODO(cais): Use `decodeWeights` for implementation.\n    const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit });\n    const loadWeights = weightsLoaderFactory(fetchWeights);\n    return loadWeights(manifest, filePathPrefix, weightNames);\n}\n/**\n * Creates a function, which reads a weights manifest JSON configuration,\n * fetches the weight files using the specified function and returns them as\n * `Tensor`s.\n *\n * ```js\n * // example for creating a nodejs weight loader, which reads the weight files\n * // from disk using fs.readFileSync\n *\n * import * as fs from 'fs'\n *\n * const fetchWeightsFromDisk = (filePaths: string[]) =>\n *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)\n *\n * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n *\n * const manifest = JSON.parse(\n *   fs.readFileSync('./my_model-weights_manifest').toString()\n * )\n * const weightMap = await loadWeights(manifest, './')\n * ```\n * @param fetchWeightsFunction The function used for fetching the weight files.\n * @returns Weight loading function.\n */\nexport function weightsLoaderFactory(fetchWeightsFunction) {\n    return async (manifest, filePathPrefix = '', weightNames) => {\n        // Collect all the groups, weights, and their relative offsets to be\n        // fetched.\n        const groupIndicesToFetchMap = manifest.map(() => false);\n        const groupWeightsToFetch = {};\n        const weightsFound = weightNames != null ? weightNames.map(() => false) : [];\n        const allManifestWeightNames = [];\n        manifest.forEach((manifestGroupConfig, groupIndex) => {\n            let groupOffset = 0;\n            manifestGroupConfig.weights.forEach(weightsEntry => {\n                const rawDtype = ('quantization' in weightsEntry) ?\n                    weightsEntry.quantization.dtype :\n                    weightsEntry.dtype;\n                const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *\n                    util.sizeFromShape(weightsEntry.shape);\n                const enqueueWeightsForFetchingFn = () => {\n                    groupIndicesToFetchMap[groupIndex] = true;\n                    if (groupWeightsToFetch[groupIndex] == null) {\n                        groupWeightsToFetch[groupIndex] = [];\n                    }\n                    groupWeightsToFetch[groupIndex].push({\n                        manifestEntry: weightsEntry,\n                        groupOffset,\n                        sizeBytes: weightsBytes\n                    });\n                };\n                if (weightNames != null) {\n                    weightNames.forEach((weightName, weightIndex) => {\n                        if (weightName === weightsEntry.name) {\n                            enqueueWeightsForFetchingFn();\n                            weightsFound[weightIndex] = true;\n                        }\n                    });\n                }\n                else {\n                    enqueueWeightsForFetchingFn();\n                }\n                allManifestWeightNames.push(weightsEntry.name);\n                groupOffset += weightsBytes;\n            });\n        });\n        if (!weightsFound.every(found => found)) {\n            const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n            throw new Error(`Could not find weights in manifest with names: ` +\n                `${weightsNotFound.join(', ')}. \\n` +\n                `Manifest JSON has weights with names: ` +\n                `${allManifestWeightNames.join(', ')}.`);\n        }\n        // Convert the one-hot boolean groupId => shouldFetch map to a list of group\n        // IDs.\n        const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n            if (shouldFetch) {\n                accumulator.push(i);\n            }\n            return accumulator;\n        }, []);\n        const fetchUrls = [];\n        groupIndicesToFetch.forEach(i => {\n            manifest[i].paths.forEach(filepath => {\n                const fetchUrl = filePathPrefix +\n                    (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;\n                fetchUrls.push(fetchUrl);\n            });\n        });\n        const buffers = await fetchWeightsFunction(fetchUrls);\n        const weightsTensorMap = {};\n        let bufferIndexOffset = 0;\n        groupIndicesToFetch.forEach(i => {\n            const numBuffers = manifest[i].paths.length;\n            let groupBytes = 0;\n            for (let i = 0; i < numBuffers; i++) {\n                groupBytes += buffers[bufferIndexOffset + i].byteLength;\n            }\n            // Create a buffer for the whole group.\n            const groupBuffer = new ArrayBuffer(groupBytes);\n            const groupByteBuffer = new Uint8Array(groupBuffer);\n            let groupBufferOffset = 0;\n            for (let i = 0; i < numBuffers; i++) {\n                const buffer = new Uint8Array(buffers[bufferIndexOffset + i]);\n                groupByteBuffer.set(buffer, groupBufferOffset);\n                groupBufferOffset += buffer.byteLength;\n            }\n            const weightsEntries = groupWeightsToFetch[i];\n            weightsEntries.forEach(weightsEntry => {\n                const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);\n                const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n                for (const name in nameToTensorMap) {\n                    weightsTensorMap[name] = nameToTensorMap[name];\n                }\n            });\n            bufferIndexOffset += numBuffers;\n        });\n        return weightsTensorMap;\n    };\n}\n//# sourceMappingURL=weights_loader.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandler implementations based on HTTP requests in the web browser.\n *\n * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n */\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nimport { loadWeightsAsArrayBuffer } from './weights_loader';\nconst OCTET_STREAM_MIME_TYPE = 'application/octet-stream';\nconst JSON_TYPE = 'application/json';\nexport class HTTPRequest {\n    constructor(path, loadOptions) {\n        this.DEFAULT_METHOD = 'POST';\n        if (loadOptions == null) {\n            loadOptions = {};\n        }\n        this.weightPathPrefix = loadOptions.weightPathPrefix;\n        this.onProgress = loadOptions.onProgress;\n        this.weightUrlConverter = loadOptions.weightUrlConverter;\n        if (loadOptions.fetchFunc != null) {\n            assert(typeof loadOptions.fetchFunc === 'function', () => 'Must pass a function that matches the signature of ' +\n                '`fetch` (see ' +\n                'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');\n            this.fetch = loadOptions.fetchFunc;\n        }\n        else {\n            this.fetch = env().platform.fetch;\n        }\n        assert(path != null && path.length > 0, () => 'URL path for http must not be null, undefined or ' +\n            'empty.');\n        if (Array.isArray(path)) {\n            assert(path.length === 2, () => 'URL paths for http must have a length of 2, ' +\n                `(actual length is ${path.length}).`);\n        }\n        this.path = path;\n        if (loadOptions.requestInit != null &&\n            loadOptions.requestInit.body != null) {\n            throw new Error('requestInit is expected to have no pre-existing body, but has one.');\n        }\n        this.requestInit = loadOptions.requestInit || {};\n    }\n    async save(modelArtifacts) {\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserHTTPRequest.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        const init = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);\n        init.body = new FormData();\n        const weightsManifest = [{\n                paths: ['./model.weights.bin'],\n                weights: modelArtifacts.weightSpecs,\n            }];\n        const modelTopologyAndWeightManifest = {\n            modelTopology: modelArtifacts.modelTopology,\n            format: modelArtifacts.format,\n            generatedBy: modelArtifacts.generatedBy,\n            convertedBy: modelArtifacts.convertedBy,\n            userDefinedMetadata: modelArtifacts.userDefinedMetadata,\n            weightsManifest\n        };\n        init.body.append('model.json', new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), 'model.json');\n        if (modelArtifacts.weightData != null) {\n            init.body.append('model.weights.bin', new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), 'model.weights.bin');\n        }\n        const response = await this.fetch(this.path, init);\n        if (response.ok) {\n            return {\n                modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n                responses: [response],\n            };\n        }\n        else {\n            throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ` +\n                `${response.status}.`);\n        }\n    }\n    /**\n     * Load model artifacts via HTTP request(s).\n     *\n     * See the documentation to `tf.io.http` for details on the saved\n     * artifacts.\n     *\n     * @returns The loaded model artifacts (if loading succeeds).\n     */\n    async load() {\n        const modelConfigRequest = await this.fetch(this.path, this.requestInit);\n        if (!modelConfigRequest.ok) {\n            throw new Error(`Request to ${this.path} failed with status code ` +\n                `${modelConfigRequest.status}. Please verify this URL points to ` +\n                `the model JSON of the model to load.`);\n        }\n        let modelConfig;\n        try {\n            modelConfig = await modelConfigRequest.json();\n        }\n        catch (e) {\n            let message = `Failed to parse model JSON of response from ${this.path}.`;\n            // TODO(nsthorat): Remove this after some time when we're comfortable that\n            // .pb files are mostly gone.\n            if (this.path.endsWith('.pb')) {\n                message += ' Your path contains a .pb file extension. ' +\n                    'Support for .pb models have been removed in TensorFlow.js 1.0 ' +\n                    'in favor of .json models. You can re-convert your Python ' +\n                    'TensorFlow model using the TensorFlow.js 1.0 conversion scripts ' +\n                    'or you can convert your.pb models with the \\'pb2json\\'' +\n                    'NPM script in the tensorflow/tfjs-converter repository.';\n            }\n            else {\n                message += ' Please make sure the server is serving valid ' +\n                    'JSON for this request.';\n            }\n            throw new Error(message);\n        }\n        const modelTopology = modelConfig.modelTopology;\n        const weightsManifest = modelConfig.weightsManifest;\n        const generatedBy = modelConfig.generatedBy;\n        const convertedBy = modelConfig.convertedBy;\n        const format = modelConfig.format;\n        const userDefinedMetadata = modelConfig.userDefinedMetadata;\n        // We do not allow both modelTopology and weightsManifest to be missing.\n        if (modelTopology == null && weightsManifest == null) {\n            throw new Error(`The JSON from HTTP path ${this.path} contains neither model ` +\n                `topology or manifest for weights.`);\n        }\n        let weightSpecs;\n        let weightData;\n        if (weightsManifest != null) {\n            const results = await this.loadWeights(weightsManifest);\n            [weightSpecs, weightData] = results;\n        }\n        const artifacts = {\n            modelTopology,\n            weightSpecs,\n            weightData,\n            userDefinedMetadata,\n            generatedBy,\n            convertedBy,\n            format\n        };\n        const initializer = modelConfig.modelInitializer;\n        if (initializer) {\n            artifacts.modelInitializer = initializer;\n        }\n        return artifacts;\n    }\n    async loadWeights(weightsManifest) {\n        const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n        const [prefix, suffix] = parseUrl(weightPath);\n        const pathPrefix = this.weightPathPrefix || prefix;\n        const weightSpecs = [];\n        for (const entry of weightsManifest) {\n            weightSpecs.push(...entry.weights);\n        }\n        const fetchURLs = [];\n        const urlPromises = [];\n        for (const weightsGroup of weightsManifest) {\n            for (const path of weightsGroup.paths) {\n                if (this.weightUrlConverter != null) {\n                    urlPromises.push(this.weightUrlConverter(path));\n                }\n                else {\n                    fetchURLs.push(pathPrefix + path + suffix);\n                }\n            }\n        }\n        if (this.weightUrlConverter) {\n            fetchURLs.push(...await Promise.all(urlPromises));\n        }\n        const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {\n            requestInit: this.requestInit,\n            fetchFunc: this.fetch,\n            onProgress: this.onProgress\n        });\n        return [weightSpecs, concatenateArrayBuffers(buffers)];\n    }\n}\nHTTPRequest.URL_SCHEME_REGEX = /^https?:\\/\\//;\n/**\n * Extract the prefix and suffix of the url, where the prefix is the path before\n * the last file, and suffix is the search params after the last file.\n * ```\n * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'\n * [prefix, suffix] = parseUrl(url)\n * // prefix = 'http://tfhub.dev/model/1/'\n * // suffix = '?tfjs-format=file'\n * ```\n * @param url the model url to be parsed.\n */\nexport function parseUrl(url) {\n    const lastSlash = url.lastIndexOf('/');\n    const lastSearchParam = url.lastIndexOf('?');\n    const prefix = url.substring(0, lastSlash);\n    const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';\n    return [prefix + '/', suffix];\n}\nexport function isHTTPScheme(url) {\n    return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;\n}\nexport const httpRouter = (url, loadOptions) => {\n    if (typeof fetch === 'undefined' &&\n        (loadOptions == null || loadOptions.fetchFunc == null)) {\n        // `http` uses `fetch` or `node-fetch`, if one wants to use it in\n        // an environment that is not the browser or node they have to setup a\n        // global fetch polyfill.\n        return null;\n    }\n    else {\n        let isHTTP = true;\n        if (Array.isArray(url)) {\n            isHTTP = url.every(urlItem => isHTTPScheme(urlItem));\n        }\n        else {\n            isHTTP = isHTTPScheme(url);\n        }\n        if (isHTTP) {\n            return http(url, loadOptions);\n        }\n    }\n    return null;\n};\nIORouterRegistry.registerSaveRouter(httpRouter);\nIORouterRegistry.registerLoadRouter(httpRouter);\n/**\n * Creates an IOHandler subtype that sends model artifacts to HTTP server.\n *\n * An HTTP request of the `multipart/form-data` mime type will be sent to the\n * `path` URL. The form data includes artifacts that represent the topology\n * and/or weights of the model. In the case of Keras-style `tf.Model`, two\n * blobs (files) exist in form-data:\n *   - A JSON file consisting of `modelTopology` and `weightsManifest`.\n *   - A binary weights file consisting of the concatenated weight values.\n * These files are in the same format as the one generated by\n * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).\n *\n * The following code snippet exemplifies the client-side code that uses this\n * function:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save(tf.io.http(\n *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));\n * console.log(saveResult);\n * ```\n *\n * If the default `POST` method is to be used, without any custom parameters\n * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:\n *\n * ```js\n * const saveResult = await model.save('http://model-server:5000/upload');\n * ```\n *\n * The following GitHub Gist\n * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864\n * implements a server based on [flask](https://github.com/pallets/flask) that\n * can receive the request. Upon receiving the model artifacts via the requst,\n * this particular server reconsistutes instances of [Keras\n * Models](https://keras.io/models/model/) in memory.\n *\n *\n * @param path A URL path to the model.\n *   Can be an absolute HTTP path (e.g.,\n *   'http://localhost:8000/model-upload)') or a relative path (e.g.,\n *   './model-upload').\n * @param requestInit Request configurations to be used when sending\n *    HTTP request to server using `fetch`. It can contain fields such as\n *    `method`, `credentials`, `headers`, `mode`, etc. See\n *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request\n *    for more information. `requestInit` must not have a body, because the\n * body will be set by TensorFlow.js. File blobs representing the model\n * topology (filename: 'model.json') and the weights of the model (filename:\n * 'model.weights.bin') will be appended to the body. If `requestInit` has a\n * `body`, an Error will be thrown.\n * @param loadOptions Optional configuration for the loading. It includes the\n *   following fields:\n *   - weightPathPrefix Optional, this specifies the path prefix for weight\n *     files, by default this is calculated from the path param.\n *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,\n *     the `fetch` from node-fetch can be used here.\n *   - onProgress Optional, progress callback function, fired periodically\n *     before the load is completed.\n * @returns An instance of `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function http(path, loadOptions) {\n    return new HTTPRequest(path, loadOptions);\n}\n/**\n * Deprecated. Use `tf.io.http`.\n * @param path\n * @param loadOptions\n */\nexport function browserHTTPRequest(path, loadOptions) {\n    return http(path, loadOptions);\n}\n//# sourceMappingURL=http.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nclass PassthroughLoader {\n    constructor(modelArtifacts) {\n        this.modelArtifacts = modelArtifacts;\n    }\n    async load() {\n        return this.modelArtifacts;\n    }\n}\nclass PassthroughSaver {\n    constructor(saveHandler) {\n        this.saveHandler = saveHandler;\n    }\n    async save(modelArtifacts) {\n        return this.saveHandler(modelArtifacts);\n    }\n}\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadLayersModel(tf.io.fromMemory(\n *     modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelArtifacts a object containing model topology (i.e., parsed from\n *   the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n *   names, shapes, types, and quantization of the weight data.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n *   concatenated in the order described by the weightSpecs.\n * @param trainingConfig Model training configuration. Optional.\n *\n * @returns A passthrough `IOHandler` that simply loads the provided data.\n */\nexport function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {\n    if (arguments.length === 1) {\n        const isModelArtifacts = modelArtifacts.modelTopology != null ||\n            modelArtifacts.weightSpecs != null;\n        if (isModelArtifacts) {\n            return new PassthroughLoader(modelArtifacts);\n        }\n        else {\n            // Legacy support: with only modelTopology.\n            // TODO(cais): Remove this deprecated API.\n            console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n                'The argument should be of type ModelArtifacts. ' +\n                'The multi-argument signature of tf.io.fromMemory() has been ' +\n                'deprecated and will be removed in a future release.');\n            return new PassthroughLoader({ modelTopology: modelArtifacts });\n        }\n    }\n    else {\n        // Legacy support.\n        // TODO(cais): Remove this deprecated API.\n        console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n            'The argument should be of type ModelArtifacts. ' +\n            'The multi-argument signature of tf.io.fromMemory() has been ' +\n            'deprecated and will be removed in a future release.');\n        return new PassthroughLoader({\n            modelTopology: modelArtifacts,\n            weightSpecs,\n            weightData,\n            trainingConfig\n        });\n    }\n}\n/**\n * Creates an IOHandler that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n *   // ... do something with the artifacts ...\n *   return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n *     `SaveResult`.\n */\nexport function withSaveHandler(saveHandler) {\n    return new PassthroughSaver(saveHandler);\n}\n//# sourceMappingURL=passthrough.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Importing local_storage and indexed_db is necessary for the routers to be\n// registered.\nimport './indexed_db';\nimport './local_storage';\nimport { browserFiles } from './browser_files';\nimport { browserHTTPRequest, http, isHTTPScheme } from './http';\nimport { concatenateArrayBuffers, decodeWeights, encodeWeights, getModelArtifactsInfoForJSON } from './io_utils';\nimport { fromMemory, withSaveHandler } from './passthrough';\nimport { getLoadHandlers, getSaveHandlers, registerLoadRouter, registerSaveRouter } from './router_registry';\nimport { loadWeights, weightsLoaderFactory } from './weights_loader';\nexport { copyModel, listModels, moveModel, removeModel } from './model_management';\nexport { browserFiles, browserHTTPRequest, concatenateArrayBuffers, decodeWeights, encodeWeights, fromMemory, getLoadHandlers, getModelArtifactsInfoForJSON, getSaveHandlers, http, isHTTPScheme, loadWeights, registerLoadRouter, registerSaveRouter, weightsLoaderFactory, withSaveHandler };\n//# sourceMappingURL=io.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reshape } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Reshapes a `tf.Tensor` to a given shape.\n *\n * Given an input tensor, returns a new tensor with the same values as the\n * input tensor with shape `shape`.\n *\n * If one component of shape is the special value -1, the size of that\n * dimension is computed so that the total size remains constant. In\n * particular, a shape of [-1] flattens into 1-D. At most one component of\n * shape can be -1.\n *\n * If shape is 1-D or higher, then the operation returns a tensor with shape\n * shape filled with the values of tensor. In this case, the number of\n * elements implied by shape must be the same as the number of elements in\n * tensor.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.reshape([2, 2]).print();\n * ```\n *\n * @param x The input tensor to be reshaped.\n * @param shape An array of integers defining the output tensor shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction reshape_(x, shape) {\n    const $x = convertToTensor(x, 'x', 'reshape', null);\n    const inputs = { x: $x };\n    const attrs = { shape };\n    const forward = (backend, save) => {\n        shape = util.inferFromImplicitShape(shape, $x.size);\n        util.assert($x.size === util.sizeFromShape(shape), () => 'new shape and old shape must have the same number of elements.');\n        save([$x]);\n        return backend.reshape($x, shape);\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Reshape, attrs);\n}\nexport const reshape = op({ reshape_ });\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchMatMul } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the dot product of two matrices, A * B. These must be matrices.\n *\n * ```js\n * const a = tf.tensor2d([1, 2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.matMul(b).print();  // or tf.matMul(a, b)\n * ```\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction matMul_(a, b, transposeA = false, transposeB = false) {\n    let $a = convertToTensor(a, 'a', 'matMul');\n    let $b = convertToTensor(b, 'b', 'matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    util.assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in matMul: inputs must have the same rank of at least 2, ` +\n        `got ranks ${$a.rank} and ${$b.rank}.`);\n    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n    const outerDimsA = $a.shape.slice(0, -2);\n    const outerDimsB = $b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n    util.assert(util.arraysEqual(outerDimsA, outerDimsB), () => `Error in matMul: outer dimensions (${outerDimsA}) and (` +\n        `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} must match.`);\n    util.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (` +\n        `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} and transposeA=${transposeA}` +\n        ` and transposeB=${transposeB} must match.`);\n    const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n    const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n        reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n    const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n        reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n    const forward = (backend, save) => {\n        save([a3D, b3D]);\n        return backend.batchMatMul(a3D, b3D, transposeA, transposeB);\n    };\n    const inputs = { a: a3D, b: b3D };\n    const attrs = { transposeA, transposeB };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, BatchMatMul, attrs);\n    return reshape(res, outShape);\n}\nexport const matMul = op({ matMul_ });\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OneHot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank\n * `R+1` with the last axis of size `depth`.\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor` of indices with dtype `int32`.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n *     not match the location.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction oneHot_(indices, depth, onValue = 1, offValue = 0) {\n    if (depth < 2) {\n        throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n    }\n    const $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');\n    const outShape = [...$indices.shape, depth];\n    const forward = (backend, save) => {\n        save([$indices]);\n        return reshape(backend.oneHot(reshape($indices, [$indices.size]), depth, onValue, offValue), outShape);\n    };\n    const inputs = { indices: $indices };\n    const attrs = { depth, onValue, offValue };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, OneHot, attrs);\n}\nexport const oneHot = op({ oneHot_ });\n//# sourceMappingURL=one_hot.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Transpose } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print();  // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction transpose_(x, perm) {\n    const $x = convertToTensor(x, 'x', 'transpose');\n    if (perm == null) {\n        perm = $x.shape.map((s, i) => i).reverse();\n    }\n    util.assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n        `must match length of perm ${perm}.`);\n    perm.forEach(axis => {\n        util.assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n            ` but got ${perm}`);\n    });\n    if ($x.rank <= 1) {\n        return $x.clone();\n    }\n    const inputs = { x: $x };\n    const attrs = { perm };\n    return ENGINE.runKernelFunc(backend => backend.transpose($x, perm), inputs, null /* gradient */, Transpose, attrs);\n}\nexport const transpose = op({ transpose_ });\n//# sourceMappingURL=transpose.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { matMul } from './mat_mul';\nimport { oneHot } from './one_hot';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Computes the confusion matrix from true labels and predicted labels.\n *\n * ```js\n * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n * const numClasses = 3;\n * const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n * out.print();\n * // Expected output matrix:\n * // [[2, 0, 0],\n * //  [0, 1, 1],\n * //  [0, 0, 1]]\n * ```\n *\n * @param labels The target labels, assumed to be 0-based integers\n *   for the classes. The shape is `[numExamples]`, where\n *   `numExamples` is the number of examples included.\n * @param predictions The predicted classes, assumed to be\n *   0-based integers for the classes. Must have the same shape as `labels`.\n * @param numClasses Number of all classes, as an integer.\n *   Its value must be larger than the largest element in `labels` and\n *   `predictions`.\n * @returns The confusion matrix as a int32-type 2D tensor. The value at\n *   row `r` and column `c` is the number of times examples of actual class\n *   `r` were predicted as class `c`.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nexport function confusionMatrix_(labels, predictions, numClasses) {\n    const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');\n    const $predictions = convertToTensor(predictions, 'predictions', 'confusionMatrix');\n    util.assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, ` +\n        `but got ${numClasses}`);\n    util.assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n    util.assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, ` +\n        `but got ${$predictions.rank}`);\n    util.assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ` +\n        `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +\n        `Labels and predictions should have the same number of elements.`);\n    util.assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ` +\n        `${numClasses}`);\n    // TODO(cais): In the future, if oneHot supports tensors inputs for\n    //   `numClasses`, `confusionMatrix` can make `numClasses` optional.\n    const oneHotLabels = oneHot(cast($labels, 'int32'), numClasses);\n    const oneHotPredictions = oneHot(cast($predictions, 'int32'), numClasses);\n    const oneHotLabelsT = transpose(oneHotLabels);\n    return cast(matMul(oneHotLabelsT, oneHotPredictions), 'int32');\n}\nexport const confusionMatrix = op({ confusionMatrix_ });\n//# sourceMappingURL=confusion_matrix.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Exports under the tf.math.* namespace.\n */\nimport { confusionMatrix } from './ops/confusion_matrix';\nexport { confusionMatrix };\n//# sourceMappingURL=math.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor3d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided,  it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor3d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 3) {\n        throw new Error('tensor3d() requires shape to have three numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n        throw new Error('tensor3d() requires values to be number[][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor3d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor3d.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FromPixels } from '../kernel_names';\nimport { getKernel } from '../kernel_registry';\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { tensor3d } from './tensor3d';\nlet fromPixels2DContext;\n/**\n * Creates a `tf.Tensor` from an image.\n *\n * ```js\n * const image = new ImageData(1, 1);\n * image.data[0] = 100;\n * image.data[1] = 150;\n * image.data[2] = 200;\n * image.data[3] = 255;\n *\n * tf.browser.fromPixels(image).print();\n * ```\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel. You can also pass in an image\n * object with following attributes:\n * `{data: Uint8Array; width: number; height: number}`\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n *\n * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}\n */\nfunction fromPixels_(pixels, numChannels = 3) {\n    // Sanity checks.\n    if (numChannels > 4) {\n        throw new Error('Cannot construct Tensor with more than 4 channels from pixels.');\n    }\n    if (pixels == null) {\n        throw new Error('pixels passed to tf.browser.fromPixels() can not be null');\n    }\n    let isPixelData = false;\n    let isImageData = false;\n    let isVideo = false;\n    let isImage = false;\n    let isCanvasLike = false;\n    if (pixels.data instanceof Uint8Array) {\n        isPixelData = true;\n    }\n    else if (typeof (ImageData) !== 'undefined' && pixels instanceof ImageData) {\n        isImageData = true;\n    }\n    else if (typeof (HTMLVideoElement) !== 'undefined' &&\n        pixels instanceof HTMLVideoElement) {\n        isVideo = true;\n    }\n    else if (typeof (HTMLImageElement) !== 'undefined' &&\n        pixels instanceof HTMLImageElement) {\n        isImage = true;\n        // tslint:disable-next-line: no-any\n    }\n    else if (pixels.getContext != null) {\n        isCanvasLike = true;\n    }\n    else {\n        throw new Error('pixels passed to tf.browser.fromPixels() must be either an ' +\n            `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData ` +\n            `in browser, or OffscreenCanvas, ImageData in webworker` +\n            ` or {data: Uint32Array, width: number, height: number}, ` +\n            `but was ${pixels.constructor.name}`);\n    }\n    if (isVideo) {\n        const HAVE_CURRENT_DATA_READY_STATE = 2;\n        if (isVideo &&\n            pixels.readyState <\n                HAVE_CURRENT_DATA_READY_STATE) {\n            throw new Error('The video element has not loaded data yet. Please wait for ' +\n                '`loadeddata` event on the <video> element.');\n        }\n    }\n    // If the current backend has 'FromPixels' registered, it has a more\n    // efficient way of handling pixel uploads, so we call that.\n    const kernel = getKernel(FromPixels, ENGINE.backendName);\n    if (kernel != null) {\n        const inputs = { pixels };\n        const attrs = { numChannels };\n        return ENGINE.runKernel(FromPixels, inputs, attrs);\n    }\n    const [width, height] = isVideo ?\n        [\n            pixels.videoWidth,\n            pixels.videoHeight\n        ] :\n        [pixels.width, pixels.height];\n    let vals;\n    if (isCanvasLike) {\n        vals =\n            // tslint:disable-next-line:no-any\n            pixels.getContext('2d').getImageData(0, 0, width, height).data;\n    }\n    else if (isImageData || isPixelData) {\n        vals = pixels.data;\n    }\n    else if (isImage || isVideo) {\n        if (fromPixels2DContext == null) {\n            fromPixels2DContext = document.createElement('canvas').getContext('2d');\n        }\n        fromPixels2DContext.canvas.width = width;\n        fromPixels2DContext.canvas.height = height;\n        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n        vals = fromPixels2DContext.getImageData(0, 0, width, height).data;\n    }\n    let values;\n    if (numChannels === 4) {\n        values = new Int32Array(vals);\n    }\n    else {\n        const numPixels = width * height;\n        values = new Int32Array(numPixels * numChannels);\n        for (let i = 0; i < numPixels; i++) {\n            for (let channel = 0; channel < numChannels; ++channel) {\n                values[i * numChannels + channel] = vals[i * 4 + channel];\n            }\n        }\n    }\n    const outShape = [height, width, numChannels];\n    return tensor3d(values, outShape, 'int32');\n}\n/**\n * Draws a `tf.Tensor` of pixel values to a byte array or optionally a\n * canvas.\n *\n * When the dtype of the input is 'float32', we assume values in the range\n * [0-1]. Otherwise, when input is 'int32', we assume values in the range\n * [0-255].\n *\n * Returns a promise that resolves when the canvas has been drawn to.\n *\n * @param img A rank-2 or rank-3 tensor. If rank-2, draws grayscale. If\n *     rank-3, must have depth of 1, 3 or 4. When depth of 1, draws\n * grayscale. When depth of 3, we draw with the first three components of\n * the depth dimension corresponding to r, g, b and alpha = 1. When depth of\n * 4, all four components of the depth dimension correspond to r, g, b, a.\n * @param canvas The canvas to draw to.\n *\n * @doc {heading: 'Browser', namespace: 'browser'}\n */\nexport async function toPixels(img, canvas) {\n    let $img = convertToTensor(img, 'img', 'toPixels');\n    if (!(img instanceof Tensor)) {\n        // Assume int32 if user passed a native array.\n        const originalImgTensor = $img;\n        $img = cast(originalImgTensor, 'int32');\n        originalImgTensor.dispose();\n    }\n    if ($img.rank !== 2 && $img.rank !== 3) {\n        throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);\n    }\n    const [height, width] = $img.shape.slice(0, 2);\n    const depth = $img.rank === 2 ? 1 : $img.shape[2];\n    if (depth > 4 || depth === 2) {\n        throw new Error(`toPixels only supports depth of size ` +\n            `1, 3 or 4 but got ${depth}`);\n    }\n    if ($img.dtype !== 'float32' && $img.dtype !== 'int32') {\n        throw new Error(`Unsupported type for toPixels: ${$img.dtype}.` +\n            ` Please use float32 or int32 tensors.`);\n    }\n    const data = await $img.data();\n    const multiplier = $img.dtype === 'float32' ? 255 : 1;\n    const bytes = new Uint8ClampedArray(width * height * 4);\n    for (let i = 0; i < height * width; ++i) {\n        const rgba = [0, 0, 0, 255];\n        for (let d = 0; d < depth; d++) {\n            const value = data[i * depth + d];\n            if ($img.dtype === 'float32') {\n                if (value < 0 || value > 1) {\n                    throw new Error(`Tensor values for a float32 Tensor must be in the ` +\n                        `range [0 - 1] but encountered ${value}.`);\n                }\n            }\n            else if ($img.dtype === 'int32') {\n                if (value < 0 || value > 255) {\n                    throw new Error(`Tensor values for a int32 Tensor must be in the ` +\n                        `range [0 - 255] but encountered ${value}.`);\n                }\n            }\n            if (depth === 1) {\n                rgba[0] = value * multiplier;\n                rgba[1] = value * multiplier;\n                rgba[2] = value * multiplier;\n            }\n            else {\n                rgba[d] = value * multiplier;\n            }\n        }\n        const j = i * 4;\n        bytes[j + 0] = Math.round(rgba[0]);\n        bytes[j + 1] = Math.round(rgba[1]);\n        bytes[j + 2] = Math.round(rgba[2]);\n        bytes[j + 3] = Math.round(rgba[3]);\n    }\n    if (canvas != null) {\n        canvas.width = width;\n        canvas.height = height;\n        const ctx = canvas.getContext('2d');\n        const imageData = new ImageData(bytes, width, height);\n        ctx.putImageData(imageData, 0, 0);\n    }\n    if ($img !== img) {\n        $img.dispose();\n    }\n    return bytes;\n}\nexport const fromPixels = op({ fromPixels_ });\n//# sourceMappingURL=browser.js.map", "import { computeStrides } from '../util';\n/**\n * Validate gather nd inputs.\n *\n * @param tensor The tensor contains the source values.\n * @param indices The tensor contains the indices to slice the source.\n *\n * @returns [resultShape, numUpdates, sliceSize, strides]\n */\nexport function prepareAndValidate(tensor, indices) {\n    if (tensor.rank < 1) {\n        throw new Error('tf.gatherND() expects the input to be rank 1 or higher,' +\n            ` but the rank was ${tensor.rank}.`);\n    }\n    if (indices.rank < 1) {\n        throw new Error('tf.gatherND() expects the indices to be rank 1 or higher,' +\n            ` but the rank was ${indices.rank}.`);\n    }\n    if (indices.dtype !== 'int32') {\n        throw new Error('tf.gatherND() expects the indices to be int32 type,' +\n            ` but the dtype was ${indices.dtype}.`);\n    }\n    if (indices.shape[indices.rank - 1] > tensor.rank) {\n        throw new Error('index innermost dimension length must be <= tensor rank; saw: ' +\n            `${indices.shape[indices.rank - 1]} vs. ${tensor.rank}`);\n    }\n    if (tensor.size === 0) {\n        throw new Error('Requested more than 0 entries, but input is empty.' +\n            ` Input shape: ${tensor.shape}.`);\n    }\n    const indicesShape = indices.shape;\n    const sliceRank = indicesShape[indicesShape.length - 1];\n    // The result shape is\n    //   indices.shape[:-1] + params.shape[indices.shape[-1]:]\n    let nResult = 1;\n    for (let i = 0; i < indicesShape.length - 1; ++i) {\n        nResult *= indicesShape[i];\n    }\n    const inputShape = tensor.shape;\n    const resultShape = indicesShape.slice();\n    resultShape.pop();\n    let sliceSize = 1;\n    for (let i = sliceRank; i < tensor.rank; ++i) {\n        sliceSize *= inputShape[i];\n        resultShape.push(inputShape[i]);\n    }\n    const strides = [...computeStrides(tensor.shape).map(stride => stride / sliceSize),\n        1].slice(0, sliceRank);\n    return [resultShape, nResult, sliceSize, strides];\n}\n//# sourceMappingURL=gather_nd_util.js.map", "import { computeStrides, sizeFromShape } from '../util';\n/**\n * Check whether updates.shape = indices.shape[:batchDim] +\n * shape[sliceDim:]\n *\n * @param x The input tensor.\n */\nexport function validateUpdateShape(shape, indices, updates) {\n    const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n    const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;\n    const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +\n        `shape[sliceDim:], got updates.shape: ${updates.shape}` +\n        `, indices.shape: ${indices.shape}, shape: ${shape}` +\n        `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n    if (updates.rank < batchDim) {\n        throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n    }\n    if (shape.length < sliceDim + (updates.rank - batchDim)) {\n        throw new Error(shapeError +\n            ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n    }\n    if (updates.rank !== batchDim + shape.length - sliceDim) {\n        throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n    }\n    for (let d = 0; d < batchDim; ++d) {\n        if (updates.shape[d] !== indices.shape[d]) {\n            throw new Error(shapeError +\n                ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);\n        }\n    }\n    for (let d = 0; d < updates.rank - batchDim; ++d) {\n        if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n            throw new Error(shapeError +\n                ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);\n        }\n    }\n}\n/**\n * Validate scatter nd inputs.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n */\nexport function validateInput(updates, indices, shape) {\n    if (indices.rank < 1) {\n        throw new Error('tf.scatterND() expects the indices to be rank 1 or higher,' +\n            ` but the rank was ${indices.rank}.`);\n    }\n    if (updates.rank < 1) {\n        throw new Error('tf.scatterND() expects the updates to be rank 1 or higher,' +\n            ` but the rank was ${updates.rank}.`);\n    }\n    if (indices.dtype !== 'int32') {\n        throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);\n    }\n    if (shape.length < 1) {\n        throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);\n    }\n    if (shape.length === 0) {\n        if (indices.size === 0) {\n            throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);\n        }\n        if (updates.size === 0) {\n            throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);\n        }\n    }\n    validateUpdateShape(shape, indices, updates);\n}\n/**\n * Calculate the shape information for the output.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n *\n * @returns ScatterShapeInfo\n */\nexport function calculateShapes(updates, indices, shape) {\n    // Calculate the number of dimensions in indices\n    const indicesRank = indices.shape.length;\n    const sliceRank = (indicesRank > 1) ? indices.shape[indicesRank - 1] : 1;\n    // Calculate the number of elements that make up each slice of our updated\n    // tensor. This allows us to work with flattened tensors and copy over whole\n    // slices at a time.\n    const totalNd = shape.length;\n    let sliceSize = 1;\n    for (let i = sliceRank; i < totalNd; ++i) {\n        sliceSize *= shape[i];\n    }\n    const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;\n    const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;\n    const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];\n    const outputSize = sizeFromShape(shape);\n    return { sliceRank, numUpdates, sliceSize, strides, outputSize };\n}\n//# sourceMappingURL=scatter_nd_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nexport function assertParamsValid(input, begin, size) {\n    const inputRank = input.shape.length;\n    util.assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must ` +\n        `match the rank of the array (${inputRank}).`);\n    util.assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must ` +\n        `match the rank of the array (${inputRank}).`);\n    for (let i = 0; i < inputRank; ++i) {\n        util.assert(begin[i] + size[i] <= input.shape[i], () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] ` +\n            `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${input.shape[i]})`);\n    }\n}\n/** Converts a binary mask to an array of axes. Used in stridedSlice(). */\nexport function maskToAxes(mask) {\n    const axes = [];\n    let axis = 0;\n    while (mask > 0) {\n        if (mask & 1) {\n            axes.push(axis);\n        }\n        mask /= 2;\n        axis++;\n    }\n    return axes;\n}\n/** Computes the output shape given the strided slice params. */\nexport function computeOutShape(begin, end, strides) {\n    const size = [];\n    for (let axis = 0; axis < begin.length; axis++) {\n        size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);\n    }\n    return size;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stride value. Otherwise, insert.\nexport function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {\n    const newStrides = [...strides];\n    for (let i = newStrides.length; i < inputShape.length; i++) {\n        newStrides.push(1);\n    }\n    for (let i = 0; i < numElidedAxes; i++) {\n        if (i === 0) {\n            newStrides[ellipsisInsertionIndex] = 1;\n        }\n        else {\n            newStrides.splice(ellipsisInsertionIndex, 0 /* num elements to delete */, 1 /* element to add */);\n            newStrides.pop();\n        }\n    }\n    return newStrides;\n}\nfunction unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {\n    if (normalizedAxis <= ellipsisInsertionIndex) {\n        return normalizedAxis;\n    }\n    return normalizedAxis - (numElidedAxes - 1);\n}\nfunction getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {\n    const elidedAxes = [];\n    for (let i = 0; i < numElidedAxes; i++) {\n        elidedAxes.push(ellipsisInsertionIndex + i);\n    }\n    return elidedAxes;\n}\n// Normalize the start, end and strides.\nexport function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {\n    const inputRank = inputShape.length;\n    let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);\n    if (ellipsisAxes.length && numInterpolatedAxes > 0) {\n        const fullIndex = ellipsisAxes[0];\n        // The ellipsis applies to the masked index as well as any dimensions\n        // that are interpolated.\n        const numElidedAxes = numInterpolatedAxes + 1;\n        normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);\n        normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);\n        normalizedStrides =\n            stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);\n    }\n    else {\n        for (let axis = 0; axis < inputRank; axis++) {\n            normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);\n            normalizedEnd[axis] =\n                stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);\n            normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);\n        }\n    }\n    return {\n        begin: normalizedBegin,\n        end: normalizedEnd,\n        strides: normalizedStrides\n    };\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current start value. Otherwise, insert.\nexport function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {\n    const newIndices = [...inputShape];\n    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n    for (let axis = 0; axis < newIndices.length; axis++) {\n        if (elidedAxes.indexOf(axis) > -1) {\n            newIndices[axis] = 0;\n        }\n        else {\n            const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n            let originalValue = originalBegin[originalAxis];\n            if (beginMask & 1 << originalAxis) {\n                originalValue = 0;\n            }\n            newIndices[axis] = originalValue;\n        }\n    }\n    return newIndices;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stop value. Otherwise, insert.\nexport function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {\n    const newIndices = [...inputShape];\n    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n    for (let axis = 0; axis < newIndices.length; axis++) {\n        if (elidedAxes.indexOf(axis) > -1) {\n            newIndices[axis] = Number.MAX_SAFE_INTEGER;\n        }\n        else {\n            const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n            let originalValue = originalEnd[originalAxis];\n            if (endMask & 1 << originalAxis) {\n                originalValue = Number.MAX_SAFE_INTEGER;\n            }\n            newIndices[axis] = originalValue;\n        }\n    }\n    for (let i = 0; i < newIndices.length; i++) {\n        // Handle negative indices\n        const axisSize = inputShape[i];\n        if (newIndices[i] < 0) {\n            newIndices[i] += axisSize;\n        }\n        newIndices[i] = util.clamp(0, newIndices[i], inputShape[i]);\n    }\n    return newIndices;\n}\nexport function stridesForAxis(strides, axis, ellipsisMask) {\n    let stride = strides[axis];\n    if (ellipsisMask & (1 << axis) || stride == null) {\n        stride = 1;\n    }\n    return stride;\n}\nexport function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {\n    // Begin with the specified index\n    let start = startIndices[axis];\n    const stride = strides[axis] || 1;\n    // Check the axis bit from right of masked axes, or the begin index is not set\n    // for the axis.\n    if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {\n        if (stride > 0) {\n            // Forward iteration - use the first element. These values will get\n            // clamped below (Note: We could have set them to 0 and axis_size-1, but\n            // use lowest() and max() to maintain symmetry with StopForAxis())\n            start = Number.MIN_SAFE_INTEGER;\n        }\n        else {\n            // Backward iteration - use the last element.\n            start = Number.MAX_SAFE_INTEGER;\n        }\n    }\n    // Handle negative indices\n    const axisSize = inputShape[axis];\n    if (start < 0) {\n        start += axisSize;\n    }\n    // Clamping\n    start = util.clamp(0, start, axisSize - 1);\n    return start;\n}\nexport function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {\n    // Begin with the specified index\n    let stop = stopIndices[axis];\n    const stride = strides[axis] || 1;\n    // Check the axis bit from right of masked axes, or if the stop index is not\n    // set for this axis.\n    if (endMask & (1 << axis) || ellipsisMask & (1 << axis) || stop == null) {\n        if (stride > 0) {\n            // Forward iteration - use the last element. These values will get\n            // clamped below\n            stop = Number.MAX_SAFE_INTEGER;\n        }\n        else {\n            // Backward iteration - use the first element.\n            stop = Number.MIN_SAFE_INTEGER;\n        }\n    }\n    // Handle negative indices\n    const axisSize = inputShape[axis];\n    if (stop < 0) {\n        stop += axisSize;\n    }\n    // Clamping\n    // Because the end index points one past the last element, we need slightly\n    // different clamping ranges depending on the direction.\n    if (stride > 0) {\n        // Forward iteration\n        stop = util.clamp(0, stop, axisSize);\n    }\n    else {\n        // Backward iteration\n        stop = util.clamp(-1, stop, axisSize - 1);\n    }\n    return stop;\n}\n/**\n * Returns true if the slice occupies a continous set of elements in the\n * 'flat' space.\n */\nexport function isSliceContinous(shape, begin, size) {\n    // Index of the first axis that has size > 1.\n    let firstNonOneAxis = size.length;\n    for (let i = 0; i < size.length; i++) {\n        if (size[i] > 1) {\n            firstNonOneAxis = i;\n            break;\n        }\n    }\n    for (let i = firstNonOneAxis + 1; i < size.length; i++) {\n        if (begin[i] > 0 || size[i] !== shape[i]) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function computeFlatOffset(begin, strides) {\n    let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;\n    for (let i = 0; i < begin.length - 1; i++) {\n        flatOffset += begin[i] * strides[i];\n    }\n    return flatOffset;\n}\nexport function parseSliceParams(x, begin, size) {\n    // The following logic allows for more ergonomic calls.\n    let begin_;\n    const xRank = x.shape.length;\n    if (typeof begin === 'number') {\n        begin_ = [begin, ...new Array(xRank - 1).fill(0)];\n    }\n    else if (begin.length < xRank) {\n        begin_ = begin.concat(new Array(xRank - begin.length).fill(0));\n    }\n    else {\n        begin_ = begin.slice();\n    }\n    begin_.forEach(d => {\n        util.assert(d !== -1, () => 'slice() does not support negative begin indexing.');\n    });\n    let size_;\n    if (size == null) {\n        size_ = new Array(xRank).fill(-1);\n    }\n    else if (typeof size === 'number') {\n        size_ = [size, ...new Array(xRank - 1).fill(-1)];\n    }\n    else if (size.length < xRank) {\n        size_ = size.concat(new Array(xRank - size.length).fill(-1));\n    }\n    else {\n        size_ = size;\n    }\n    size_ = size_.map((d, i) => {\n        if (d >= 0) {\n            return d;\n        }\n        else {\n            util.assert(d === -1, () => `Negative size values should be exactly -1 but got ` +\n                `${d} for the slice() size at index ${i}.`);\n            return x.shape[i] - begin_[i];\n        }\n    });\n    return [begin_, size_];\n}\n//# sourceMappingURL=slice_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from './util';\n/**\n * Serializable defines the serialization contract.\n *\n * TFJS requires serializable classes to return their className when asked\n * to avoid issues with minification.\n */\nexport class Serializable {\n    /**\n     * Return the class name for this class to use in serialization contexts.\n     *\n     * Generally speaking this will be the same thing that constructor.name\n     * would have returned.  However, the class name needs to be robust\n     * against minification for serialization/deserialization to work properly.\n     *\n     * There's also places such as initializers.VarianceScaling, where\n     * implementation details between different languages led to different\n     * class hierarchies and a non-leaf node is used for serialization purposes.\n     */\n    getClassName() {\n        return this.constructor\n            .className;\n    }\n    /**\n     * Creates an instance of T from a ConfigDict.\n     *\n     * This works for most descendants of serializable.  A few need to\n     * provide special handling.\n     * @param cls A Constructor for the class to instantiate.\n     * @param config The Configuration for the object.\n     */\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/**\n * Maps string keys to class constructors.\n *\n * Used during (de)serialization from the cross-language JSON format, which\n * requires the class name in the serialization format matches the class\n * names as used in Python, should it exist.\n */\nexport class SerializationMap {\n    constructor() {\n        this.classNameMap = {};\n    }\n    /**\n     * Returns the singleton instance of the map.\n     */\n    static getMap() {\n        if (SerializationMap.instance == null) {\n            SerializationMap.instance = new SerializationMap();\n        }\n        return SerializationMap.instance;\n    }\n    /**\n     * Registers the class as serializable.\n     */\n    static register(cls) {\n        SerializationMap.getMap().classNameMap[cls.className] =\n            [cls, cls.fromConfig];\n    }\n}\n/**\n * Register a class with the serialization map of TensorFlow.js.\n *\n * This is often used for registering custom Layers, so they can be\n * serialized and deserialized.\n *\n * Example:\n *\n * ```js\n * class MyCustomLayer extends tf.layers.Layer {\n *   static className = 'MyCustomLayer';\n *\n *   constructor(config) {\n *     super(config);\n *   }\n * }\n * tf.serialization.registerClass(MyCustomLayer);\n * ```\n *\n * @param cls The class to be registered. It must have a public static member\n *   called `className` defined and the value must be a non-empty string.\n *\n * @doc {heading: 'Models', subheading: 'Serialization', ignoreCI: true}\n */\nexport function registerClass(cls) {\n    assert(cls.className != null, () => `Class being registered does not have the static className ` +\n        `property defined.`);\n    assert(typeof cls.className === 'string', () => `className is required to be a string, but got type ` +\n        typeof cls.className);\n    assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, ` +\n        `which is disallowed.`);\n    SerializationMap.register(cls);\n}\n//# sourceMappingURL=serialization.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { inferShape } from './tensor_util_env';\nimport { arraysEqual, flatten, isString, isTypedArray } from './util';\nconst TEST_EPSILON_FLOAT32 = 1e-3;\nexport const TEST_EPSILON_FLOAT16 = 1e-1;\nexport function expectArraysClose(actual, expected, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon));\n}\nexport function testEpsilon() {\n    return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :\n        TEST_EPSILON_FLOAT16;\n}\nfunction expectArraysPredicate(actual, expected, predicate) {\n    let checkClassType = true;\n    if (isTypedArray(actual) || isTypedArray(expected)) {\n        checkClassType = false;\n    }\n    if (isTypedArray(actual) && isTypedArray(expected)) {\n        checkClassType = true;\n    }\n    if (checkClassType) {\n        const aType = actual.constructor.name;\n        const bType = expected.constructor.name;\n        if (aType !== bType) {\n            throw new Error(`Arrays are of different type. Actual: ${aType}. ` +\n                `Expected: ${bType}`);\n        }\n    }\n    if (Array.isArray(actual) && Array.isArray(expected)) {\n        const actualShape = inferShape(actual);\n        const expectedShape = inferShape(expected);\n        if (!arraysEqual(actualShape, expectedShape)) {\n            throw new Error(`Arrays have different shapes. ` +\n                `Actual: [${actualShape}]. Expected: [${expectedShape}]`);\n        }\n    }\n    const actualFlat = isTypedArray(actual) ? actual : flatten(actual);\n    const expectedFlat = isTypedArray(expected) ?\n        expected :\n        flatten(expected);\n    if (actualFlat.length !== expectedFlat.length) {\n        throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs ` +\n            `expected: ${expectedFlat.length}.\\n` +\n            `Actual:   ${actualFlat}.\\n` +\n            `Expected: ${expectedFlat}.`);\n    }\n    for (let i = 0; i < expectedFlat.length; ++i) {\n        const a = actualFlat[i];\n        const e = expectedFlat[i];\n        if (!predicate(a, e)) {\n            throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\\n` +\n                `Actual:   ${actualFlat}.\\n` +\n                `Expected: ${expectedFlat}.`);\n        }\n    }\n}\nexport function expectPromiseToFail(fn, done) {\n    fn().then(() => done.fail(), () => done());\n}\nexport function expectArraysEqual(actual, expected) {\n    const exp = typeof expected === 'string' || typeof expected === 'number' ||\n        typeof expected === 'boolean' ?\n        [expected] :\n        expected;\n    if (isString(actual) || isString(actual[0]) ||\n        isString(expected) || isString(expected[0])) {\n        // tslint:disable-next-line: triple-equals\n        return expectArraysPredicate(actual, exp, (a, b) => a == b);\n    }\n    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));\n}\nexport function expectNumbersClose(a, e, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    if (!areClose(a, e, epsilon)) {\n        throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n    }\n}\nfunction areClose(a, e, epsilon) {\n    if (!isFinite(a) && !isFinite(e)) {\n        return true;\n    }\n    if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {\n        return false;\n    }\n    return true;\n}\nexport function expectValuesInRange(actual, low, high) {\n    for (let i = 0; i < actual.length; i++) {\n        if (actual[i] < low || actual[i] > high) {\n            throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);\n        }\n    }\n}\nexport function expectArrayBuffersEqual(actual, expected) {\n    // Safari & Jasmine don't like comparing ArrayBuffers directly. Wrapping in\n    // a Float32Array solves this issue.\n    expect(new Float32Array(actual)).toEqual(new Float32Array(expected));\n}\n//# sourceMappingURL=test_util.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { env } from './environment';\nimport { setDeprecationWarningFn } from './tensor';\nimport { getTensorsInContainer } from './tensor_util';\n/**\n * Enables production mode which disables correctness checks in favor of\n * performance.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableProdMode() {\n    env().set('PROD', true);\n}\n/**\n * Enables debug mode which will log information about all executed kernels:\n * the elapsed time of the kernel execution, as well as the rank, shape, and\n * size of the output tensor.\n *\n * Debug mode will significantly slow down your application as it will\n * download the result of every operation to the CPU. This should not be used in\n * production. Debug mode does not affect the timing information of the kernel\n * execution as we do not measure download time in the kernel execution time.\n *\n * See also: `tf.profile`, `tf.memory`.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableDebugMode() {\n    env().set('DEBUG', true);\n}\n/** Globally disables deprecation warnings */\nexport function disableDeprecationWarnings() {\n    env().set('DEPRECATION_WARNINGS_ENABLED', false);\n    console.warn(`TensorFlow.js deprecation warnings have been disabled.`);\n}\n/** Warn users about deprecated functionality. */\nexport function deprecationWarn(msg) {\n    if (env().getBool('DEPRECATION_WARNINGS_ENABLED')) {\n        console.warn(msg + ' You can disable deprecation warnings with ' +\n            'tf.disableDeprecationWarnings().');\n    }\n}\nsetDeprecationWarningFn(deprecationWarn);\n/**\n * Dispose all variables kept in backend engine.\n *\n * @doc {heading: 'Environment'}\n */\nexport function disposeVariables() {\n    ENGINE.disposeVariables();\n}\n/**\n * It returns the global engine that keeps track of all tensors and backends.\n *\n * @doc {heading: 'Environment'}\n */\nexport function engine() {\n    return ENGINE;\n}\n/**\n * Returns memory info at the current time in the program. The result is an\n * object with the following properties:\n *\n * - `numBytes`: Number of bytes allocated (undisposed) at this time.\n * - `numTensors`: Number of unique tensors allocated.\n * - `numDataBuffers`: Number of unique data buffers allocated\n *   (undisposed) at this time, which is \u2264 the number of tensors\n *   (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same\n *   data buffer with `a`).\n * - `unreliable`: True if the memory usage is unreliable. See `reasons` when\n *    `unreliable` is true.\n * - `reasons`: `string[]`, reasons why the memory is unreliable, present if\n *    `unreliable` is true.\n *\n * WebGL Properties:\n * - `numBytesInGPU`: Number of bytes allocated (undisposed) in the GPU only at\n *     this time.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function memory() {\n    return ENGINE.memory();\n}\n/**\n * Executes the provided function `f()` and returns a promise that resolves\n * with information about the function's memory use:\n * - `newBytes`: the number of new bytes allocated\n * - `newTensors`: the number of new tensors created\n * - `peakBytes`: the peak number of bytes allocated\n * - `kernels`: an array of objects for each kernel involved that reports\n * their input and output shapes, number of bytes used, and number of new\n * tensors created.\n *\n * ```js\n * const profile = await tf.profile(() => {\n *   const x = tf.tensor1d([1, 2, 3]);\n *   let x2 = x.square();\n *   x2.dispose();\n *   x2 = x.square();\n *   x2.dispose();\n *   return x;\n * });\n *\n * console.log(`newBytes: ${profile.newBytes}`);\n * console.log(`newTensors: ${profile.newTensors}`);\n * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>\n * k.totalBytesSnapshot)}`);\n * ```\n *\n *\n * @doc {heading: 'Performance', subheading: 'Profile'}\n */\nexport function profile(f) {\n    return ENGINE.profile(f);\n}\n/**\n * Executes the provided function `fn` and after it is executed, cleans up all\n * intermediate tensors allocated by `fn` except those returned by `fn`.\n * `fn` must not return a Promise (async functions not allowed). The returned\n * result can be a complex object.\n *\n * Using this method helps avoid memory leaks. In general, wrap calls to\n * operations in `tf.tidy` for automatic memory cleanup.\n *\n * NOTE: Variables do *not* get cleaned up when inside a tidy(). If you want to\n * dispose variables, please use `tf.disposeVariables` or call dispose()\n * directly on variables.\n *\n * ```js\n * // y = 2 ^ 2 + 1\n * const y = tf.tidy(() => {\n *   // a, b, and one will be cleaned up when the tidy ends.\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *   const b = a.square();\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * y.print();\n * ```\n *\n * @param nameOrFn The name of the closure, or the function to execute.\n *     If a name is provided, the 2nd argument should be the function.\n *     If debug mode is on, the timing and the memory usage of the function\n *     will be tracked and displayed on the console using the provided name.\n * @param fn The function to execute.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function tidy(nameOrFn, fn) {\n    return ENGINE.tidy(nameOrFn, fn);\n}\n/**\n * Disposes any `tf.Tensor`s found within the provided object.\n *\n * @param container an object that may be a `tf.Tensor` or may directly\n *     contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If\n *     the object is not a `tf.Tensor` or does not contain `Tensors`, nothing\n *     happens. In general it is safe to pass any object here, except that\n *     `Promise`s are not supported.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function dispose(container) {\n    const tensors = getTensorsInContainer(container);\n    tensors.forEach(tensor => tensor.dispose());\n}\n/**\n * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed\n * automatically.\n *\n * ```js\n * let b;\n * const y = tf.tidy(() => {\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *\n *   // b will not be cleaned up by the tidy. a and one will be cleaned up\n *   // when the tidy ends.\n *   b = tf.keep(a.square());\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * console.log('y:');\n * y.print();\n * console.log('b:');\n * b.print();\n * ```\n *\n * @param result The tensor to keep from being disposed.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function keep(result) {\n    return ENGINE.keep(result);\n}\n/**\n * Executes `f()` and returns a promise that resolves with timing\n * information.\n *\n * The result is an object with the following properties:\n *\n * - `wallMs`: Wall execution time.\n * - `kernelMs`: Kernel execution time, ignoring data transfer. If using the\n * WebGL backend and the query timer extension is not available, this will\n * return an error object.\n * - On `WebGL` The following additional properties exist:\n *   - `uploadWaitMs`: CPU blocking time on texture uploads.\n *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).\n *\n * ```js\n * const x = tf.randomNormal([20, 20]);\n * const time = await tf.time(() => x.matMul(x));\n *\n * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);\n * ```\n *\n * @param f The function to execute and time.\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nexport function time(f) {\n    return ENGINE.time(f);\n}\n/**\n * Sets the backend (cpu, webgl, wasm, etc) responsible for creating tensors and\n * executing operations on those tensors. Returns a promise that resolves\n * to a boolean if the backend initialization was successful.\n *\n * Note this disposes the current backend, if any, as well as any tensors\n * associated with it. A new backend is initialized, even if it is of the\n * same type as the previous one.\n *\n * @param backendName The name of the backend. Currently supports\n *     `'webgl'|'cpu'` in the browser, `'tensorflow'` under node.js\n *     (requires tfjs-node), and `'wasm'` (requires tfjs-backend-wasm).\n *\n * @doc {heading: 'Backends'}\n */\nexport function setBackend(backendName) {\n    return ENGINE.setBackend(backendName);\n}\n/**\n * Returns a promise that resolves when the currently selected backend (or the\n * highest priority one) has initialized. Await this promise when you are using\n * a backend that has async initialization.\n *\n * @doc {heading: 'Backends'}\n */\nexport function ready() {\n    return ENGINE.ready();\n}\n/**\n * Returns the current backend name (cpu, webgl, etc). The backend is\n * responsible for creating tensors and executing operations on those tensors.\n *\n * @doc {heading: 'Backends'}\n */\nexport function getBackend() {\n    return ENGINE.backendName;\n}\n/**\n * Removes a backend and the registered factory.\n *\n * @doc {heading: 'Backends'}\n */\nexport function removeBackend(name) {\n    ENGINE.removeBackend(name);\n}\n/**\n * Finds the backend registered under the provided name. Returns null if the\n * name is not in the registry, or the registration hasn't finished yet.\n */\nexport function findBackend(name) {\n    return ENGINE.findBackend(name);\n}\n/**\n * Finds the backend factory registered under the provided name. Returns a\n * function that produces a new backend when called. Returns null if the name\n * is not in the registry.\n */\nexport function findBackendFactory(name) {\n    return ENGINE.findBackendFactory(name);\n}\n/**\n * Registers a global backend. The registration should happen when importing\n * a module file (e.g. when importing `backend_webgl.ts`), and is used for\n * modular builds (e.g. custom tfjs bundle with only webgl support).\n *\n * @param factory The backend factory function. When called, it should\n * return a backend instance, or a promise of an instance.\n * @param priority The priority of the backend (higher = more important).\n *     In case multiple backends are registered, the priority is used to find\n *     the best backend. Defaults to 1.\n * @return False if there is already a registered backend under this name, true\n *     if not.\n *\n * @doc {heading: 'Backends'}\n */\nexport function registerBackend(name, factory, priority = 1) {\n    return ENGINE.registerBackend(name, factory, priority);\n}\n/**\n * Gets the current backend. If no backends have been initialized, this will\n * attempt to initialize the best backend. Will throw an error if the highest\n * priority backend has async initialization, in which case, you should call\n * 'await tf.ready()' before running other code.\n *\n * @doc {heading: 'Backends'}\n */\nexport function backend() {\n    return ENGINE.backend;\n}\n/**\n * Sets the global platform.\n *\n * @param platformName The name of this platform.\n * @param platform A platform implementation.\n */\nexport function setPlatform(platformName, platform) {\n    env().setPlatform(platformName, platform);\n}\n//# sourceMappingURL=globals.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Add } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n *\n * ```js\n * // Broadcast add a with b.\n * const a = tf.scalar(5);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n * @param a The first `tf.Tensor` to add.\n * @param b The second `tf.Tensor` to add. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction add_(a, b) {\n    let $a = convertToTensor(a, 'a', 'add');\n    let $b = convertToTensor(b, 'b', 'add');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.add($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Add);\n}\nexport const add = op({ add_ });\n//# sourceMappingURL=add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FloorDiv } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n * The result is rounded with floor function.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.floorDiv(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.floorDiv(b).print();  // or tf.floorDiv(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction floorDiv_(a, b) {\n    let $a = convertToTensor(a, 'a', 'floorDiv');\n    let $b = convertToTensor(b, 'b', 'floorDiv');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.floorDiv($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, FloorDiv);\n}\nexport const floorDiv = op({ floorDiv_ });\n//# sourceMappingURL=floorDiv.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Div } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { floorDiv } from './floorDiv';\nimport { op } from './operation';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction div_(a, b) {\n    let $a = convertToTensor(a, 'a', 'div');\n    let $b = convertToTensor(b, 'b', 'div');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'int32' && $b.dtype === 'int32') {\n        return floorDiv($a, $b);\n    }\n    const forward = (backend, save) => {\n        const res = backend.realDivide($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = {};\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Div, attrs);\n}\nexport const div = op({ div_ });\n//# sourceMappingURL=div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Multiply } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.\n *\n * We also expose `tf.mulStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([2, 3, 4, 5]);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n *\n * ```js\n * // Broadcast mul a with b.\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.scalar(5);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n * @param a The first tensor to multiply.\n * @param b The second tensor to multiply. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mul_(a, b) {\n    let $a = convertToTensor(a, 'a', 'mul');\n    let $b = convertToTensor(b, 'b', 'mul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.multiply($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Multiply);\n}\nexport const mul = op({ mul_ });\n//# sourceMappingURL=mul.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Abs } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes absolute value element-wise: `abs(x)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.abs().print();  // or tf.abs(x)\n * ```\n * @param x The input `tf.Tensor`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction abs_(x) {\n    const $x = convertToTensor(x, 'x', 'abs');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'complex64') {\n            return backend.complexAbs($x);\n        }\n        return backend.abs($x);\n    }, inputs, null /* grad */, Abs);\n}\nexport const abs = op({ abs_ });\n//# sourceMappingURL=abs.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.acos().print();  // or tf.acos(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acos_(x) {\n    const $x = convertToTensor(x, 'x', 'acos');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.acos($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Acos);\n}\nexport const acos = op({ acos_ });\n//# sourceMappingURL=acos.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:\n * `acosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([10, 1, 3, 5.7]);\n *\n * x.acosh().print();  // or tf.acosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acosh_(x) {\n    const $x = convertToTensor(x, 'x', 'acosh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.acosh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Acosh);\n}\nexport const acosh = op({ acosh_ });\n//# sourceMappingURL=acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AddN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n *\n * tf.addN([a, b, c]).print();\n * ```\n * @param tensors A list of tensors with the same shape and dtype.\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction addN_(tensors) {\n    util.assert(Array.isArray(tensors), () => 'The argument passed to tf.addN() must be a list of tensors');\n    util.assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ` +\n        `${tensors.length}`);\n    const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));\n    const firstTensor = $tensors[0];\n    $tensors.forEach(t => {\n        if (t.dtype !== firstTensor.dtype) {\n            throw new Error('All tensors passed to tf.addN() must have the same dtype');\n        }\n    });\n    $tensors.forEach(t => {\n        if (!util.arraysEqual(t.shape, firstTensor.shape)) {\n            throw new Error('All tensors passed to tf.addN() must have the same shape');\n        }\n    });\n    const forward = (backend, save) => {\n        const res = backend.addN($tensors);\n        save($tensors);\n        return res;\n    };\n    const inputs = $tensors;\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, AddN);\n}\nexport const addN = op({ addN_ });\n//# sourceMappingURL=add_n.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n * Returns true if the axis specifies the inner most dimensions of the\n * array.\n */\nexport function axesAreInnerMostDims(axes, rank) {\n    for (let i = 0; i < axes.length; ++i) {\n        if (axes[axes.length - i - 1] !== rank - 1 - i) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function combineLocations(outputLoc, reduceLoc, axes) {\n    const rank = outputLoc.length + reduceLoc.length;\n    const loc = [];\n    let outIdx = 0;\n    let reduceIdx = 0;\n    for (let dim = 0; dim < rank; dim++) {\n        if (axes.indexOf(dim) === -1) {\n            loc.push(outputLoc[outIdx++]);\n        }\n        else {\n            loc.push(reduceLoc[reduceIdx++]);\n        }\n    }\n    return loc;\n}\nexport function computeOutAndReduceShapes(aShape, axes) {\n    const outShape = [];\n    const rank = aShape.length;\n    for (let dim = 0; dim < rank; dim++) {\n        if (axes.indexOf(dim) === -1) {\n            outShape.push(aShape[dim]);\n        }\n    }\n    const reduceShape = axes.map(dim => aShape[dim]);\n    return [outShape, reduceShape];\n}\nexport function expandShapeToKeepDim(shape, axes) {\n    const reduceSubShape = axes.map(x => 1);\n    return combineLocations(shape, reduceSubShape, axes);\n}\nexport function assertAxesAreInnerMostDims(msg, axes, rank) {\n    util.assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. ` +\n        `Got axes ${axes} and rank-${rank} input.`);\n}\n/**\n * Returns the axes permutation to be used with `tf.transpose`, if such\n * permutation is necessary. Otherwise it returns null. This method is used by\n * operations that operate only on inner-most axes.\n */\nexport function getAxesPermutation(axes, rank) {\n    if (axesAreInnerMostDims(axes, rank)) {\n        return null;\n    }\n    const result = [];\n    for (let i = 0; i < rank; ++i) {\n        if (axes.indexOf(i) === -1) {\n            result.push(i);\n        }\n    }\n    axes.forEach(axis => result.push(axis));\n    return result;\n}\n/** Returns the axes permutation that undoes the original permutation. */\nexport function getUndoAxesPermutation(axes) {\n    return axes.map((axis, i) => [i, axis])\n        .sort((a, b) => a[1] - b[1])\n        .map(x => x[0]);\n}\nexport function getInnerMostAxes(numAxes, rank) {\n    const res = [];\n    for (let i = rank - numAxes; i < rank; ++i) {\n        res.push(i);\n    }\n    return res;\n}\n//# sourceMappingURL=axis_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { All } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the logical and of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.all().print();  // or tf.all(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.all(axis).print();  // or tf.all(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction all_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'all', 'bool');\n    const forward = (backend) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = getInnerMostAxes(axes.length, $x.rank);\n        }\n        const res = backend.all($x, axes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(res.shape, origAxes);\n            return reshape(res, newShape);\n        }\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, All, attrs);\n}\nexport const all = op({ all_ });\n//# sourceMappingURL=all.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Any } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the logical or of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.any().print();  // or tf.any(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.any(axis).print();  // or tf.any(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction any_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'any', 'bool');\n    const forward = (backend) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = getInnerMostAxes(axes.length, $x.rank);\n        }\n        const res = backend.any($x, axes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(res.shape, origAxes);\n            return reshape(res, newShape);\n        }\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Any, attrs);\n}\n// tslint:disable-next-line:variable-name\nexport const any = op({ any_ });\n//# sourceMappingURL=any.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Returns the indices of the maximum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMax().print();  // or tf.argMax(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMax(axis).print();  // or tf.argMax(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMax_(x, axis = 0) {\n    let $x = convertToTensor(x, 'x', 'argMax');\n    const forward = (backend, save) => {\n        save([$x]);\n        let axes = util.parseAxisParam(axis, $x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        return backend.argMax($x, axes[0]);\n    };\n    const inputs = { x: $x };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ArgMax, attrs);\n}\nexport const argMax = op({ argMax_ });\n//# sourceMappingURL=arg_max.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Returns the indices of the minimum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMin().print();  // or tf.argMin(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMin(axis).print();  // or tf.argMin(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMin_(x, axis = 0) {\n    let $x = convertToTensor(x, 'x', 'argMin');\n    const forward = (backend, save) => {\n        save([$x]);\n        if (axis == null) {\n            axis = 0;\n        }\n        let axes = util.parseAxisParam(axis, $x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        return backend.argMin($x, axes[0]);\n    };\n    const inputs = { x: $x };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ArgMin, attrs);\n}\nexport const argMin = op({ argMin_ });\n//# sourceMappingURL=arg_min.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asin().print();  // or tf.asin(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asin_(x) {\n    const $x = convertToTensor(x, 'x', 'asin');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.asin($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Asin);\n}\nexport const asin = op({ asin_ });\n//# sourceMappingURL=asin.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:\n * `asinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asinh().print();  // or tf.asinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asinh_(x) {\n    const $x = convertToTensor(x, 'x', 'asinh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.asinh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Asinh);\n}\nexport const asinh = op({ asinh_ });\n//# sourceMappingURL=asinh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.atan().print();  // or tf.atan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan_(x) {\n    const $x = convertToTensor(x, 'x', 'atan');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.atan($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Atan);\n}\nexport const atan = op({ atan_ });\n//# sourceMappingURL=atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan2 } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);\n * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);\n *\n * tf.atan2(a, b).print()\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan2_(a, b) {\n    let $a = convertToTensor(a, 'a', 'atan2');\n    let $b = convertToTensor(b, 'b', 'atan2');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.atan2($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Atan2);\n}\nexport const atan2 = op({ atan2_ });\n//# sourceMappingURL=atan2.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:\n * `atanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.atanh().print();  // or tf.atanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atanh_(x) {\n    const $x = convertToTensor(x, 'x', 'atanh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.atanh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Atanh);\n}\nexport const atanh = op({ atanh_ });\n//# sourceMappingURL=atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n *\n * @param inputShape Input tensor shape is of the following dimensions:\n *     `[batch, height, width, inChannels]`.\n * @param filterShape The filter shape is of the following dimensions:\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat The data format of the input and output data.\n *     Defaults to 'NHWC'.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`.\n *     Defaults to `[1, 1]`. If `dilations` is a single number, then\n *     `dilationHeight == dilationWidth`.\n */\nexport function computeDilation2DInfo(inputShape, filterShape, strides, pad, dataFormat = 'NHWC', dilations) {\n    // `computerConv2DInfo` require filterShape to be in the dimension of:\n    // `[filterHeight, filterWidth, depth, outDepth]`, dilation2d doesn't have\n    // outDepth, it should have the same depth as the input.\n    // Input shape: [batch, height, width, inChannels]\n    const inputChannels = inputShape[3];\n    const $filterShape = [...filterShape, inputChannels];\n    const $dataFormat = convertConv2DDataFormat(dataFormat);\n    return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad, null /* roundingMode */, null /* depthWise */, $dataFormat);\n}\nexport function computePool2DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'channelsLast') {\n    const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n    let filterShape;\n    if (dataFormat === 'channelsLast') {\n        filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n    }\n    else if (dataFormat === 'channelsFirst') {\n        filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    return computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, false, dataFormat);\n}\n/**\n * Computes the information for a forward pass of a pooling3D operation.\n */\nexport function computePool3DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'NDHWC') {\n    const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);\n    let filterShape;\n    let $dataFormat;\n    if (dataFormat === 'NDHWC') {\n        $dataFormat = 'channelsLast';\n        filterShape =\n            [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];\n    }\n    else if (dataFormat === 'NCDHW') {\n        $dataFormat = 'channelsFirst';\n        filterShape =\n            [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    return computeConv3DInfo(inShape, filterShape, strides, dilations, pad, false, $dataFormat, roundingMode);\n}\n/**\n * Computes the information for a forward pass of a convolution/pooling\n * operation.\n */\nexport function computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, depthwise = false, dataFormat = 'channelsLast') {\n    let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n    if (dataFormat === 'channelsLast') {\n        [batchSize, inHeight, inWidth, inChannels] = inShape;\n    }\n    else if (dataFormat === 'channelsFirst') {\n        [batchSize, inChannels, inHeight, inWidth] = inShape;\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    const [filterHeight, filterWidth, , filterChannels] = filterShape;\n    const [strideHeight, strideWidth] = parseTupleParam(strides);\n    const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n    const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);\n    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n    let outShape;\n    if (dataFormat === 'channelsFirst') {\n        outShape = [batchSize, outChannels, outHeight, outWidth];\n    }\n    else if (dataFormat === 'channelsLast') {\n        outShape = [batchSize, outHeight, outWidth, outChannels];\n    }\n    return {\n        batchSize,\n        dataFormat,\n        inHeight,\n        inWidth,\n        inChannels,\n        outHeight,\n        outWidth,\n        outChannels,\n        padInfo,\n        strideHeight,\n        strideWidth,\n        filterHeight,\n        filterWidth,\n        effectiveFilterHeight,\n        effectiveFilterWidth,\n        dilationHeight,\n        dilationWidth,\n        inShape,\n        outShape,\n        filterShape\n    };\n}\n/**\n * Computes the information for a forward pass of a 3D convolution/pooling\n * operation.\n */\nexport function computeConv3DInfo(inShape, filterShape, strides, dilations, pad, depthwise = false, dataFormat = 'channelsLast', roundingMode) {\n    let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];\n    if (dataFormat === 'channelsLast') {\n        [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n    }\n    else if (dataFormat === 'channelsFirst') {\n        [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;\n    const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n    const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);\n    const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);\n    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n    const { padInfo, outDepth, outHeight, outWidth } = get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);\n    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n    let outShape;\n    if (dataFormat === 'channelsFirst') {\n        outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n    }\n    else if (dataFormat === 'channelsLast') {\n        outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n    }\n    return {\n        batchSize,\n        dataFormat,\n        inDepth,\n        inHeight,\n        inWidth,\n        inChannels,\n        outDepth,\n        outHeight,\n        outWidth,\n        outChannels,\n        padInfo,\n        strideDepth,\n        strideHeight,\n        strideWidth,\n        filterDepth,\n        filterHeight,\n        filterWidth,\n        effectiveFilterDepth,\n        effectiveFilterHeight,\n        effectiveFilterWidth,\n        dilationDepth,\n        dilationHeight,\n        dilationWidth,\n        inShape,\n        outShape,\n        filterShape\n    };\n}\nfunction computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {\n    if (zeroPad == null) {\n        zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n    }\n    const inputRows = inShape[0];\n    const inputCols = inShape[1];\n    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    return [outputRows, outputCols];\n}\nfunction computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {\n    if (zeroPad == null) {\n        zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n    }\n    const inputDepth = inShape[0];\n    const inputRows = inShape[1];\n    const inputCols = inShape[2];\n    const outputDepths = conditionalRound((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputDepths), () => `The output # of depths (${outputDepths}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    return [outputDepths, outputRows, outputCols, outChannels];\n}\nexport function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {\n    const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n    return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\nfunction parseTupleParam(param) {\n    if (typeof param === 'number') {\n        return [param, param, param];\n    }\n    if (param.length === 2) {\n        return [param[0], param[1], 1];\n    }\n    return param;\n}\nfunction parse3TupleParam(param) {\n    return typeof param === 'number' ? [param, param, param] : param;\n}\n/* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d\n * Atrous convolution is equivalent to standard convolution with upsampled\n * filters with effective_filter_height =\n * filter_height + (filter_height - 1) * (dilation - 1)\n * and effective_filter_width =\n * filter_width + (filter_width - 1) * (dilation - 1),\n * produced by inserting dilation - 1 zeros along consecutive elements across\n * the filters' spatial dimensions.\n * When there is a dilation, this converts a filter dimension to the\n * effective filter dimension, so it can be used in a standard convolution.\n */\nfunction getEffectiveFilterSize(filterSize, dilation) {\n    if (dilation <= 1) {\n        return filterSize;\n    }\n    return filterSize + (filterSize - 1) * (dilation - 1);\n}\nfunction getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {\n    let padInfo;\n    let outHeight;\n    let outWidth;\n    if (typeof pad === 'number') {\n        const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n        padInfo = { top: pad, bottom: pad, left: pad, right: pad, type: padType };\n        const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad, roundingMode);\n        outHeight = outShape[0];\n        outWidth = outShape[1];\n    }\n    else if (pad === 'same') {\n        outHeight = Math.ceil(inHeight / strideHeight);\n        outWidth = Math.ceil(inWidth / strideWidth);\n        const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);\n        const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);\n        const top = Math.floor(padAlongHeight / 2);\n        const bottom = padAlongHeight - top;\n        const left = Math.floor(padAlongWidth / 2);\n        const right = padAlongWidth - left;\n        padInfo = { top, bottom, left, right, type: 'SAME' };\n    }\n    else if (pad === 'valid') {\n        padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: 'VALID' };\n        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n    }\n    else if (typeof pad === 'object') {\n        const top = dataFormat === 'channelsLast' ? pad[1][0] : pad[2][0];\n        const bottom = dataFormat === 'channelsLast' ? pad[1][1] : pad[2][1];\n        const left = dataFormat === 'channelsLast' ? pad[2][0] : pad[3][0];\n        const right = dataFormat === 'channelsLast' ? pad[2][1] : pad[3][1];\n        const padType = (top === 0 && bottom === 0 && left === 0 && right === 0) ?\n            'VALID' :\n            'EXPLICIT';\n        padInfo = { top, bottom, left, right, type: padType };\n        outHeight = conditionalRound((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);\n        outWidth = conditionalRound((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);\n    }\n    else {\n        throw Error(`Unknown padding parameter: ${pad}`);\n    }\n    return { padInfo, outHeight, outWidth };\n}\nfunction get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {\n    let padInfo;\n    let outDepth;\n    let outHeight;\n    let outWidth;\n    if (typeof pad === 'number') {\n        const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n        padInfo = {\n            top: pad,\n            bottom: pad,\n            left: pad,\n            right: pad,\n            front: pad,\n            back: pad,\n            type: padType\n        };\n        const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad, roundingMode);\n        outDepth = outShape[0];\n        outHeight = outShape[1];\n        outWidth = outShape[2];\n    }\n    else if (pad === 'same') {\n        outDepth = Math.ceil(inDepth / strideDepth);\n        outHeight = Math.ceil(inHeight / strideHeight);\n        outWidth = Math.ceil(inWidth / strideWidth);\n        const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n        const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;\n        const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n        const front = Math.floor(padAlongDepth / 2);\n        const back = padAlongDepth - front;\n        const top = Math.floor(padAlongHeight / 2);\n        const bottom = padAlongHeight - top;\n        const left = Math.floor(padAlongWidth / 2);\n        const right = padAlongWidth - left;\n        padInfo = { top, bottom, left, right, front, back, type: 'SAME' };\n    }\n    else if (pad === 'valid') {\n        padInfo = {\n            top: 0,\n            bottom: 0,\n            left: 0,\n            right: 0,\n            front: 0,\n            back: 0,\n            type: 'VALID'\n        };\n        outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);\n        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n    }\n    else {\n        throw Error(`Unknown padding parameter: ${pad}`);\n    }\n    return { padInfo, outDepth, outHeight, outWidth };\n}\n/**\n * Rounds a value depending on the rounding mode\n * @param value\n * @param roundingMode\n */\nfunction conditionalRound(value, roundingMode) {\n    if (!roundingMode) {\n        return value;\n    }\n    switch (roundingMode) {\n        case 'round':\n            // used for Caffe Conv\n            return Math.round(value);\n        case 'ceil':\n            // used for Caffe Pool\n            return Math.ceil(value);\n        case 'floor':\n            return Math.floor(value);\n        default:\n            throw new Error(`Unknown roundingMode ${roundingMode}`);\n    }\n}\nexport function tupleValuesAreOne(param) {\n    const [dimA, dimB, dimC] = parseTupleParam(param);\n    return dimA === 1 && dimB === 1 && dimC === 1;\n}\nexport function eitherStridesOrDilationsAreOne(strides, dilations) {\n    return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n/**\n * Convert Conv2D dataFormat from 'NHWC'|'NCHW' to\n *    'channelsLast'|'channelsFirst'\n * @param dataFormat in 'NHWC'|'NCHW' mode\n * @return dataFormat in 'channelsLast'|'channelsFirst' mode\n * @throws unknown dataFormat\n */\nexport function convertConv2DDataFormat(dataFormat) {\n    if (dataFormat === 'NHWC') {\n        return 'channelsLast';\n    }\n    else if (dataFormat === 'NCHW') {\n        return 'channelsFirst';\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n}\n//# sourceMappingURL=conv_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction avgPool_(x, filterSize, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'avgPool', 'float32');\n    const dilations = 1;\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in avgPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        save([x4D]);\n        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n            util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n            return x4D.clone();\n        }\n        return backend.avgPool(x4D, convInfo);\n    };\n    const inputs = { x: x4D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool, attrs);\n    res = cast(res, $x.dtype);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const avgPool = op({ avgPool_ });\n//# sourceMappingURL=avg_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { deprecationWarn } from '../globals';\nimport { AvgPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D average pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.avgPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates:\n *     `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction avgPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat = 'NDHWC', dilations) {\n    if (dilations == null) {\n        dilations = [1, 1, 1];\n    }\n    else {\n        deprecationWarn('dilations is deprecated, this field will be gone in ' +\n            'v3.0.0.');\n    }\n    const $x = convertToTensor(x, 'x', 'avgPool3d', 'float32');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in avgPool3d: Only NDHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool3d: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in avgPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1, 1];\n        }\n        const convInfo = conv_util.computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);\n        save([x5D]);\n        return backend.avgPool3d(x5D, convInfo);\n    };\n    const inputs = { x: x5D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat, dilations };\n    let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool3D, attrs);\n    res = cast(res, x5D.dtype);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const avgPool3d = op({ avgPool3d_ });\n//# sourceMappingURL=avg_pool_3d.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nexport function assertParamsConsistent(shapes, axis) {\n    const rank = shapes[0].length;\n    shapes.forEach((shape, i) => {\n        util.assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same ` +\n            `as the rank of the rest (${rank})`);\n    });\n    util.assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);\n    const firstShape = shapes[0];\n    shapes.forEach((shape, i) => {\n        for (let r = 0; r < rank; r++) {\n            util.assert((r === axis) || (shape[r] === firstShape[r]), () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) ` +\n                `does not match the shape of the rest (${firstShape}) ` +\n                `along the non-concatenated axis ${i}.`);\n        }\n    });\n}\nexport function computeOutShape(shapes, axis) {\n    const outputShape = shapes[0].slice();\n    for (let i = 1; i < shapes.length; i++) {\n        outputShape[axis] += shapes[i][axis];\n    }\n    return outputShape;\n}\n//# sourceMappingURL=concat_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Concat } from '../kernel_names';\nimport { convertToTensorArray } from '../tensor_util_env';\nimport { assert, parseAxisParam, sizeFromShape } from '../util';\nimport { assertParamsConsistent, computeOutShape } from './concat_util';\nimport { op } from './operation';\nimport { tensor } from './tensor';\n/**\n * Concatenates a list of `tf.Tensor`s along a given axis.\n *\n * The tensors ranks and types must match, and their sizes must match in all\n * dimensions except `axis`.\n *\n * Also available are stricter rank-specific methods that assert that\n * `tensors` are of the given rank:\n *   - `tf.concat1d`\n *   - `tf.concat2d`\n *   - `tf.concat3d`\n *   - `tf.concat4d`\n *\n * Except `tf.concat1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * a.concat(b).print();  // or a.concat(b)\n * ```\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.concat([a, b, c]).print();\n * ```\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [10, 20]]);\n * const b = tf.tensor2d([[3, 4], [30, 40]]);\n * const axis = 1;\n * tf.concat([a, b], axis).print();\n * ```\n * @param tensors A list of tensors to concatenate.\n * @param axis The axis to concate along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction concat_(tensors, axis = 0) {\n    assert(tensors.length >= 1, () => 'Pass at least one tensor to concat');\n    let $tensors = convertToTensorArray(tensors, 'tensors', 'concat');\n    if ($tensors[0].dtype === 'complex64') {\n        $tensors.forEach(tensor => {\n            if (tensor.dtype !== 'complex64') {\n                throw new Error(`Cannot concatenate complex64 tensors with a tensor\n          with dtype ${tensor.dtype}. `);\n            }\n        });\n    }\n    const forward = (backend, save) => {\n        const $axis = parseAxisParam(axis, $tensors[0].shape)[0];\n        const outShape = computeOutShape($tensors.map(t => t.shape), $axis);\n        if (sizeFromShape(outShape) === 0) {\n            return tensor([], outShape);\n        }\n        // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n        $tensors = $tensors.filter(t => t.size > 0);\n        if ($tensors.length === 1) {\n            return $tensors[0];\n        }\n        const shapes = $tensors.map(t => t.shape);\n        assertParamsConsistent(shapes, $axis);\n        const res = backend.concat($tensors, $axis);\n        save($tensors);\n        return res;\n    };\n    const inputs = $tensors;\n    const attr = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Concat, attr);\n}\nexport const concat = op({ concat_ });\n//# sourceMappingURL=concat.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sigmoid } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sigmoid element-wise, `1 / (1 + exp(-x))`\n *\n * ```js\n * const x = tf.tensor1d([0, -1, 2, -3]);\n *\n * x.sigmoid().print();  // or tf.sigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sigmoid_(x) {\n    const $x = convertToTensor(x, 'x', 'sigmoid');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sigmoid($x);\n        save([res]);\n        return res;\n    }, inputs, null /* grad */, Sigmoid);\n}\nexport const sigmoid = op({ sigmoid_ });\n//# sourceMappingURL=sigmoid.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Slice } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport * as slice_util from './slice_util';\n/**\n * Extracts a slice from a `tf.Tensor` starting at coordinates `begin`\n * and is of size `size`.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `x` is of the given rank:\n *   - `tf.slice1d`\n *   - `tf.slice2d`\n *   - `tf.slice3d`\n *   - `tf.slice4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.slice([1], [2]).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * x.slice([1, 0], [1, 2]).print();\n * ```\n * @param x The input `tf.Tensor` to slice from.\n * @param begin The coordinates to start the slice from. The length can be\n *     less than the rank of x - the rest of the axes will have implicit 0 as\n *     start. Can also be a single number, in which case it specifies the\n *     first axis.\n * @param size The size of the slice. The length can be less than the rank of\n *     x - the rest of the axes will have implicit -1. A value of -1 requests\n *     the rest of the dimensions in the axis. Can also be a single number,\n *     in which case it specifies the size of the first axis.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction slice_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice');\n    if ($x.rank === 0) {\n        throw new Error('Slicing scalar is not possible');\n    }\n    const forward = (backend, save) => {\n        const [begin_, size_] = slice_util.parseSliceParams($x, begin, size);\n        slice_util.assertParamsValid($x, begin_, size_);\n        save([$x]);\n        return backend.slice($x, begin_, size_);\n    };\n    const inputs = { x: $x };\n    const attrs = { begin, size };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Slice, attrs);\n}\nexport const slice = op({ slice_ });\n//# sourceMappingURL=slice.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, 70]);\n *\n * x.tanh().print();  // or tf.tanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tanh_(x) {\n    const $x = convertToTensor(x, 'x', 'tanh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const y = backend.tanh($x);\n        save([y]);\n        return y;\n    }, inputs, null /* grad */, Tanh);\n}\nexport const tanh = op({ tanh_ });\n//# sourceMappingURL=tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { add } from './add';\nimport { concat } from './concat';\nimport { matMul } from './mat_mul';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { slice } from './slice';\nimport { tanh } from './tanh';\n/**\n * Computes the next state and output of a BasicLSTMCell.\n *\n * Returns `[newC, newH]`.\n *\n * Derived from tf.contrib.rnn.BasicLSTMCell.\n *\n * @param forgetBias Forget bias for the cell.\n * @param lstmKernel The weights for the cell.\n * @param lstmBias The bias for the cell.\n * @param data The input to the cell.\n * @param c Previous cell state.\n * @param h Previous cell output.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {\n    const $forgetBias = convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');\n    const $lstmKernel = convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');\n    const $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');\n    const $data = convertToTensor(data, 'data', 'basicLSTMCell');\n    const $c = convertToTensor(c, 'c', 'basicLSTMCell');\n    const $h = convertToTensor(h, 'h', 'basicLSTMCell');\n    const combined = concat([$data, $h], 1);\n    const weighted = matMul(combined, $lstmKernel);\n    const res = add(weighted, $lstmBias);\n    // i = input_gate, j = new_input, f = forget_gate, o = output_gate\n    const batchSize = res.shape[0];\n    const sliceCols = res.shape[1] / 4;\n    const sliceSize = [batchSize, sliceCols];\n    const i = slice(res, [0, 0], sliceSize);\n    const j = slice(res, [0, sliceCols], sliceSize);\n    const f = slice(res, [0, sliceCols * 2], sliceSize);\n    const o = slice(res, [0, sliceCols * 3], sliceSize);\n    const newC = add(mul(sigmoid(i), tanh(j)), mul($c, sigmoid(add($forgetBias, f))));\n    const newH = mul(tanh(newC), sigmoid(o));\n    return [newC, newH];\n}\nexport const basicLSTMCell = op({ basicLSTMCell_ });\n//# sourceMappingURL=basic_lstm_cell.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchToSpaceND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation reshapes the \"batch\" dimension 0 into `M + 1` dimensions of\n * shape `blockShape + [batch]`, interleaves these blocks back into the grid\n * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with\n * the same rank as the input. The spatial dimensions of this intermediate\n * result are then optionally cropped according to `crops` to produce the\n * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise\n * description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n * const blockShape = [2, 2];\n * const crops = [[0, 0], [0, 0]];\n *\n * x.batchToSpaceND(blockShape, crops).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param crops A 2-D array.  Must have shape `[M, 2]`, all values must be >= 0.\n * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input\n * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required\n * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,\n * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,\n * x.shape[N-1]]`\n *\n * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /\n * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`\n * according to `crops` to produce the output of shape: `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],\n * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -\n * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction batchToSpaceND_(x, blockShape, crops) {\n    const $x = convertToTensor(x, 'x', 'batchToSpaceND');\n    const prod = blockShape.reduce((a, b) => a * b);\n    util.assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);\n    util.assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);\n    util.assert($x.shape[0] % prod === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of ` +\n        `the elements of blockShape ${blockShape.join(' * ')} === ${prod}`);\n    const forward = backend => {\n        return backend.batchToSpaceND($x, blockShape, crops);\n    };\n    const inputs = { x: $x };\n    const attrs = { blockShape, crops };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, BatchToSpaceND, attrs);\n}\nexport const batchToSpaceND = op({ batchToSpaceND_ });\n//# sourceMappingURL=batch_to_space_nd.js.map", "import { reshape } from './reshape';\nexport function xAs4D(x) {\n    let x4D;\n    if (x.rank === 0 || x.rank === 1) {\n        x4D = reshape(x, [1, 1, 1, x.size]);\n    }\n    else if (x.rank === 2) {\n        x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n    }\n    else if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    else {\n        x4D = x;\n    }\n    return x4D;\n}\n//# sourceMappingURL=batchnorm_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FusedBatchNorm } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { xAs4D } from './batchnorm_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Batch normalization.\n *\n * As described in\n * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).\n *\n * Mean, variance, scale, and offset can be of two shapes:\n *   - The same shape as the input.\n *   - In the common case, the depth dimension is the last dimension of x, so\n *     the values would be an `tf.Tensor1D` of shape [depth].\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that parameters passed are of given rank\n *   - `tf.batchNorm2d`\n *   - `tf.batchNorm3d`\n *   - `tf.batchNorm4d`\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction batchNorm_(x, mean, variance, offset, scale, varianceEpsilon) {\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($mean.rank === $variance.rank, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert($offset == null || $mean.rank === $offset.rank, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert($scale == null || $mean.rank === $scale.rank, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    const x4D = xAs4D($x);\n    const forward = (backend, save) => {\n        save([x4D, $mean, $variance, $scale]);\n        return backend.batchNorm(x4D, as1DOr4D($mean), as1DOr4D($variance), as1DOr4D($offset), as1DOr4D($scale), varianceEpsilon);\n    };\n    const inputs = {\n        x: x4D,\n        scale: $scale,\n        offset: $offset,\n        mean: $mean,\n        variance: $variance\n    };\n    const attrs = { varianceEpsilon };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, FusedBatchNorm, attrs);\n    return reshape(res, $x.shape);\n}\nfunction as1DOr4D(x) {\n    if (x == null) {\n        return null;\n    }\n    if (x.rank === 0) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [x.size]);\n    }\n    else if (x.rank === 1) {\n        return x;\n    }\n    else if (x.rank === 2) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n    }\n    else if (x.rank === 3) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    return x;\n}\nexport const batchNorm = op({ batchNorm_ });\n//# sourceMappingURL=batchnorm.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 2D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm2d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm2d = op({ batchNorm2d_ });\n//# sourceMappingURL=batchnorm2d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 3D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm3d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm3d = op({ batchNorm3d_ });\n//# sourceMappingURL=batchnorm3d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 4D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm4d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm4d = op({ batchNorm4d_ });\n//# sourceMappingURL=batchnorm4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BroadcastTo } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { clone } from './clone';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Broadcast an array to a compatible shape NumPy-style.\n *\n * The tensor's shape is compared to the broadcast shape from end to beginning.\n * Ones are prepended to the tensor's shape until is has the same length as\n * the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is\n * already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then\n * the input tensor is tiled N times along that axis (using tf.tile).\n *\n * @param input The tensor that is to be broadcasted.\n * @param shape The input is to be broadcast to this shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction broadcastTo_(x, shape) {\n    let input = convertToTensor(x, 'broadcastTo', 'x');\n    const xShape = input.shape;\n    if (shape.some(d => !(d > 0) || d % 1 !== 0)) {\n        throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);\n    }\n    if (shape.length < input.rank) {\n        throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input.rank}.`);\n    }\n    if (shape.length > input.rank) {\n        const newShape = input.shape.slice();\n        while (newShape.length < shape.length) {\n            newShape.unshift(1);\n        }\n        input = reshape(input, newShape);\n    }\n    const inputShape = input.shape;\n    const reps = Array.from(shape);\n    for (let i = shape.length - 1; i >= 0; i--) {\n        if (inputShape[i] === shape[i]) {\n            reps[i] = 1;\n        }\n        else if (input.shape[i] !== 1) {\n            throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);\n        }\n    }\n    const axes = reps.map((n, i) => n > 1 ? i : -1).filter(i => i >= 0);\n    if (axes.length === 0) {\n        return clone(input);\n    }\n    const forward = (backend) => backend.tile(input, reps);\n    const inputs = { x: input };\n    const attrs = { shape, inputShape };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, BroadcastTo, attrs);\n}\nexport const broadcastTo = op({ broadcastTo_ });\n//# sourceMappingURL=broadcast_to.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Ceil } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.ceil().print();  // or tf.ceil(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction ceil_(x) {\n    const $x = convertToTensor(x, 'x', 'ceil');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.ceil($x), inputs, null /* grad */, Ceil);\n}\nexport const ceil = op({ ceil_ });\n//# sourceMappingURL=ceil.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ClipByValue } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)\n * ```\n * @param x The input tensor.\n * @param clipValueMin Lower-bound of range to be clipped to.\n * @param clipValueMax Upper-bound of range to be clipped to.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction clipByValue_(x, clipValueMin, clipValueMax) {\n    const $x = convertToTensor(x, 'x', 'clipByValue');\n    util.assert((clipValueMin <= clipValueMax), () => `Error in clip: min (${clipValueMin}) must be ` +\n        `less than or equal to max (${clipValueMax}).`);\n    const inputs = { x: $x };\n    const attrs = { clipValueMin, clipValueMax };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.clip($x, clipValueMin, clipValueMax);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, ClipByValue, attrs);\n}\nexport const clipByValue = op({ clipByValue_ });\n//# sourceMappingURL=clip_by_value.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(3) = |r1, g1, b1|\n * B: shape(2) = |r2, g2|\n * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @return The concatenated array.\n */\nfunction concat1d_(tensors) {\n    return concat(tensors, 0 /* axis */);\n}\nexport const concat1d = op({ concat1d_ });\n//# sourceMappingURL=concat_1d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *\n * B: shape(2, 3) = | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * C = tf.concat2d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *                  | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * if axis = 1:\n * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |\n *                   | r2, g2, b2, r4, g4, b4 |\n *\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concatenate along.\n * @return The concatenated array.\n */\nfunction concat2d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat2d = op({ concat2d_ });\n//# sourceMappingURL=concat_2d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor3D`s along an axis.\n * See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *\n * B: shape(2, 1, 3) = | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * C = tf.concat3d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *                     | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * if axis = 1:\n * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |\n *                     | r2, g2, b2, r4, g4, b4 |\n *\n * if axis = 2:\n * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |\n *                      | r2, g2, b2, r4, g4, b4 |\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat3d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat3d = op({ concat3d_ });\n//# sourceMappingURL=concat_3d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor4D`s along an axis.\n * See `concat` for details.\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat4d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat4d = op({ concat4d_ });\n//# sourceMappingURL=concat_4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 2D convolution over the input x.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv2d');\n    const $filter = convertToTensor(filter, 'filter', 'conv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n    util.assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match ` +\n        `input depth for filter ${$filter.shape[2]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const forward = (backend, save) => {\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        const res = backend.conv2d(x4D, $filter, convInfo);\n        save([x4D, $filter]);\n        return res;\n    };\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv2D, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const conv2d = op({ conv2d_ });\n//# sourceMappingURL=conv2d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 1D convolution over the input x.\n *\n * @param x The input tensor, of rank 3 or rank 2, of shape\n *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.\n * @param filter The filter, rank 3, of shape\n *     `[filterWidth, inDepth, outDepth]`.\n * @param stride The number of entries by which the filter is moved right at\n *     each step.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from \"NWC\", \"NCW\". Defaults to \"NWC\",\n *     the data is stored in the order of [batch, in_width, in_channels]. Only\n *     \"NWC\" is currently supported.\n * @param dilation The dilation rate in which we sample input values in\n *     atrous convolution. Defaults to `1`. If it is greater than 1, then\n *     stride must be `1`.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv1d_(x, filter, stride, pad, dataFormat = 'NWC', dilation = 1, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv1d');\n    const $filter = convertToTensor(filter, 'filter', 'conv1d');\n    let x3D = $x;\n    let reshapedTo3D = false;\n    if ($x.rank === 2) {\n        reshapedTo3D = true;\n        x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);\n    }\n    util.assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n    util.assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv1d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    util.assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +\n        `input depth for filter ${$filter.shape[1]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(stride, dilation), () => 'Error in conv1D: Either stride or dilation must be 1. ' +\n        `Got stride ${stride} and dilation '${dilation}'`);\n    util.assert(dataFormat === 'NWC', () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);\n    const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);\n    const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);\n    const strides = [1, stride];\n    const dilations = [1, dilation];\n    const conv2dDataFormat = 'NHWC';\n    const res = conv2d(input4D, filter4D, strides, pad, conv2dDataFormat, dilations, dimRoundingMode);\n    if (reshapedTo3D) {\n        return reshape(res, [res.shape[2], res.shape[3]]);\n    }\n    return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);\n}\nexport const conv1d = op({ conv1d_ });\n//# sourceMappingURL=conv1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropInput } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 2D convolution.\n *\n * @param xShape The shape of the input: [batch, height, width, inDepth].\n * If length of 3, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 4 or rank 3 of shape\n *   `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction conv2DBackpropInput_(xShape, dy, filter, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n    util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n        `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n    let xShape4D = xShape;\n    let dy4D = dy;\n    let reshapedTo4D = false;\n    if (dy.rank === 3) {\n        reshapedTo4D = true;\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n        xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n    }\n    util.assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ` +\n        `${xShape4D.length}.`);\n    util.assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got ` +\n        `rank ${dy4D.rank}`);\n    util.assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got ` +\n        `rank ${filter.rank}`);\n    const inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1];\n    const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n    util.assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must ` +\n        `match input depth for filter ${filter.shape[2]}.`);\n    util.assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must ` +\n        `match output depth for filter ${filter.shape[3]}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2dDerInput: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const dilations = 1;\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(xShape4D, filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        const res = backend.conv2dDerInput(dy4D, filter, convInfo);\n        save([dy4D, filter]);\n        return res;\n    };\n    const inputs = { dy: dy4D, filter };\n    const attrs = { strides, pad, dataFormat, dimRoundingMode, inputShape: xShape4D };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv2DBackpropInput, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const conv2DBackpropInput = op({ conv2DBackpropInput_ });\n//# sourceMappingURL=conv2d_backprop_input.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport { conv2DBackpropInput } from './conv2d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 2D convolution of an image, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 4 or rank 3, of shape\n *   `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 4 or rank 3:\n *     `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.\n * @param strides The strides of the original convolution:\n *     `[strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n * @param dimRoundingMode The rounding mode used when computing output\n *    dimensions if pad is a number. If none is provided, it will not round\n *    and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2dTranspose_(x, filter, outputShape, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv2dTranspose');\n    const $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');\n    return conv2DBackpropInput(outputShape, $x, $filter, strides, pad, 'NHWC', dimRoundingMode);\n}\nexport const conv2dTranspose = op({ conv2dTranspose_ });\n//# sourceMappingURL=conv2d_transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { eitherStridesOrDilationsAreOne } from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 3D convolution over the input x.\n *\n * @param x The input tensor, of rank 5 or rank 4, of shape\n *     `[batch, depth, height, width, channels]`. If rank 4,\n * batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.\n *      inChannels must match between input and filter.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationDepth, dilationHeight,\n *     dilationWidth]` in which we sample input values across the height\n *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.\n *     If `dilations` is a single number, then\n *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater\n *     than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3d_(x, filter, strides, pad, dataFormat = 'NDHWC', dilations = [1, 1, 1]) {\n    const $x = convertToTensor(x, 'x', 'conv3d');\n    const $filter = convertToTensor(filter, 'filter', 'conv3d');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n    util.assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +\n        `input depth for filter ${$filter.shape[3]}.`);\n    util.assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv3D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computeConv3DInfo(x5D.shape, $filter.shape, strides, dilations, pad);\n        const res = backend.conv3d(x5D, $filter, convInfo);\n        save([x5D, $filter]);\n        return res;\n    };\n    const inputs = { x: x5D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv3D, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const conv3d = op({ conv3d_ });\n//# sourceMappingURL=conv3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3DBackpropInputV2 } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 3D convolution.\n *\n * @param xShape The shape of the input: [batch, depth, height, width,\n * in_channels]. If length of 4, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 5 or rank 4 of shape\n *   `[batch, outDepth, outHeight, outWidth, in_channels]`.\n * If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n */\nfunction conv3DBackpropInput_(xShape, dy, filter, strides, pad) {\n    util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n        `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n    let xShape5D = xShape;\n    let dy5D = dy;\n    let reshapedTo5D = false;\n    if (dy.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n        xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n    }\n    const inDepth = xShape5D[4];\n    const outDepth = dy5D.shape[4];\n    util.assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ` +\n        `${xShape5D.length}.`);\n    util.assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got ` +\n        `rank ${dy5D.rank}`);\n    util.assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got ` +\n        `rank ${filter.rank}`);\n    util.assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must ` +\n        `match input depth for filter ${filter.shape[3]}.`);\n    util.assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must ` +\n        `match output depth for filter ${filter.shape[4]}.`);\n    const forward = backend => {\n        const dilations = 1;\n        const convInfo = conv_util.computeConv3DInfo(xShape5D, filter.shape, strides, dilations, pad);\n        return backend.conv3dDerInput(dy5D, filter, convInfo);\n    };\n    const inputs = { dy: dy5D };\n    const attrs = { pad };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropInputV2, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const conv3DBackpropInput = op({ conv3DBackpropInput_ });\n//# sourceMappingURL=conv3d_backprop_input.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport { conv3DBackpropInput } from './conv3d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 3D convolution of a volume, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 5 or rank 4, of shape\n *   `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[depth, filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 5 or rank 4:\n *     `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is\n *    assumed.\n * @param strides The strides of the original convolution:\n *     `[strideDepth, strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3dTranspose_(x, filter, outputShape, strides, pad) {\n    const $x = convertToTensor(x, 'x', 'conv3dTranspose');\n    const $filter = convertToTensor(filter, 'filter', 'conv3dTranspose');\n    return conv3DBackpropInput(outputShape, $x, $filter, strides, pad);\n}\nexport const conv3dTranspose = op({ conv3dTranspose_ });\n//# sourceMappingURL=conv3d_transpose.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.cos().print();  // or tf.cos(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cos_(x) {\n    const $x = convertToTensor(x, 'x', 'cos');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.cos($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Cos);\n}\nexport const cos = op({ cos_ });\n//# sourceMappingURL=cos.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.cosh().print();  // or tf.cosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cosh_(x) {\n    const $x = convertToTensor(x, 'x', 'cosh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.cosh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Cosh);\n}\nexport const cosh = op({ cosh_ });\n//# sourceMappingURL=cosh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cumsum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { getAxesPermutation, getInnerMostAxes, getUndoAxesPermutation } from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Computes the cumulative sum of a `tf.Tensor` along `axis`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4]);\n * x.cumsum().print();\n * ```\n * ```js\n * const x = tf.tensor([[1, 2], [3, 4]]);\n * x.cumsum().print();\n * ```\n *\n * @param x The input tensor to be summed.\n * @param axis The axis along which to sum. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative sum. Optional.\n *     Defaults to false. If set to true then the sum of each tensor entry\n *     does not include its own value, but only the values previous to it\n *     along the specified axis.\n * @param reverse Whether to sum in the opposite direction. Optional.\n *     Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Scan'}\n */\nfunction cumsum_(x, axis = 0, exclusive = false, reverse = false) {\n    const $x = convertToTensor(x, 'x', 'cumsum');\n    const forward = (backend, save) => {\n        const permutation = getAxesPermutation([axis], $x.rank);\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n        }\n        const permutedAxis = getInnerMostAxes(1, $x.rank)[0];\n        let value = backend.cumsum(permutedX, permutedAxis, exclusive, reverse);\n        save([$x]);\n        if (permutation != null) {\n            const reversePermutation = getUndoAxesPermutation(permutation);\n            value = transpose(value, reversePermutation);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, exclusive, reverse };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Cumsum, attrs);\n}\nexport const cumsum = op({ cumsum_ });\n//# sourceMappingURL=cumsum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthToSpace } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Rearranges data from depth into blocks of spatial data. More specifically,\n * this op outputs a copy of the input tensor where values from the `depth`\n * dimension are moved in spatial blocks to the `height` and `width` dimensions.\n * The attr `blockSize` indicates the input block size and how the data is\n * moved.\n *\n *  - Chunks of data of size `blockSize * blockSize` from depth are rearranged\n * into non-overlapping blocks of size `blockSize x blockSize`\n *\n *  - The width the output tensor is `inputWidth * blockSize`, whereas the\n * height is `inputHeight * blockSize`\n *\n *  - The Y, X coordinates within each block of the output image are determined\n * by the high order component of the input channel index\n *\n *  - The depth of the input tensor must be divisible by `blockSize *\n * blockSize`\n *\n * The `dataFormat` attr specifies the layout of the input and output tensors\n * with the following options: \"NHWC\": [ `batch, height, width, channels` ]\n * \"NCHW\": [ `batch, channels, height, width` ]\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);\n * const blockSize = 2;\n * const dataFormat = \"NHWC\";\n *\n * tf.depthToSpace(x, blockSize, dataFormat).print();\n * ```\n *\n * @param x The input tensor of rank 4\n * @param blockSIze  An `int` that is `>= 2`. The size of the spatial block\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to \"NHWC\"\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction depthToSpace_(x, blockSize, dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'depthToSpace');\n    const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];\n    const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];\n    const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];\n    util.assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputHeight} and ${blockSize}  for depthToSpace with input shape\n    ${$x.shape}`);\n    util.assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputWidth} and ${blockSize} for depthToSpace with input shape\n        ${$x.shape}`);\n    util.assert((inputDepth % (blockSize * blockSize) === 0), () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);\n    const forward = backend => backend.depthToSpace($x, blockSize, dataFormat);\n    const inputs = { x: $x };\n    const attrs = { blockSize, dataFormat };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, DepthToSpace, attrs);\n}\nexport const depthToSpace = op({ depthToSpace_ });\n//# sourceMappingURL=depth_to_space.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNative } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Depthwise 2D convolution.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction depthwiseConv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n    const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels ` +\n        `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n        `filter ${$filter.shape[2]}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1];\n        }\n        util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +\n            `1. Got strides ${strides} and dilations '${dilations}'`);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n        const res = backend.depthwiseConv2D(x4D, $filter, convInfo);\n        save([x4D, $filter]);\n        return res;\n    };\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, DepthwiseConv2dNative, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const depthwiseConv2d = op({ depthwiseConv2d_ });\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Diag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Returns a diagonal tensor with a given diagonal values.\n *\n * Given a diagonal, this operation returns a tensor with the diagonal and\n * everything else padded with zeros.\n *\n * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor\n * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * tf.diag(x).print()\n * ```\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4, 5, 6, 6, 8], [4, 2])\n *\n * tf.diag(x).print()\n * ```\n * @param x The input tensor.\n */\nfunction diag_(x) {\n    const $x = convertToTensor(x, 'x', 'diag');\n    const forward = backend => {\n        const flat = reshape($x, [$x.size]);\n        const result = backend.diag(flat);\n        const outShape = [...x.shape, ...x.shape];\n        return reshape(result, outShape);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Diag);\n}\nexport const diag = op({ diag_ });\n//# sourceMappingURL=diag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Dilation2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the grayscale dilation over the input `x`.\n *\n * @param x The input tensor, rank 3 or rank 4 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter tensor, rank 3, of shape\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat Specify the data format of the input and output data.\n *      Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the\n *      default format \"NHWC\", the data is stored in the order of: [batch,\n *      height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations`\n *     is a single number, then `dilationHeight == dilationWidth`. If it is\n *     greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction dilation2d_(x, filter, strides, pad, dilations = [1, 1], dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'dilation2d');\n    const $filter = convertToTensor(filter, 'filter', 'dilation2d');\n    util.assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ` +\n        `${$x.rank}.`);\n    util.assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(dataFormat === 'NHWC', () => `Error in dilation2d: Only NHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n        reshapedTo4D = true;\n    }\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dilations };\n    const res = ENGINE.runKernel(Dilation2D, inputs, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const dilation2d = op({ dilation2d_ });\n//# sourceMappingURL=dilation2d.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the dimensions in the input shape that are broadcasted to\n * produce the provided output shape.\n *\n * The returned dimensions are 0-indexed and sorted. An example:\n * inShape = [4, 1, 3]\n * outShape = [5, 4, 3, 3]\n * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.\n */\nexport function getBroadcastDims(inShape, outShape) {\n    const inRank = inShape.length;\n    const dims = [];\n    for (let i = 0; i < inRank; i++) {\n        const dim = inRank - 1 - i;\n        const a = inShape[dim] || 1;\n        const b = outShape[outShape.length - 1 - i] || 1;\n        if (b > 1 && a === 1) {\n            dims.unshift(dim);\n        }\n    }\n    return dims;\n}\n/**\n * Returns the axes in the output space that should be reduced to produce\n * the input space.\n */\nexport function getReductionAxes(inShape, outShape) {\n    const result = [];\n    for (let i = 0; i < outShape.length; i++) {\n        const inDim = inShape[inShape.length - i - 1];\n        const outAxis = outShape.length - i - 1;\n        const outDim = outShape[outAxis];\n        if (inDim == null || (inDim === 1 && outDim > 1)) {\n            result.unshift(outAxis);\n        }\n    }\n    return result;\n}\nexport function assertAndGetBroadcastShape(shapeA, shapeB) {\n    const result = [];\n    const l = Math.max(shapeA.length, shapeB.length);\n    for (let i = 0; i < l; i++) {\n        let a = shapeA[shapeA.length - i - 1];\n        if (a == null) {\n            a = 1;\n        }\n        let b = shapeB[shapeB.length - i - 1];\n        if (b == null) {\n            b = 1;\n        }\n        if (a === 1) {\n            result.unshift(b);\n        }\n        else if (b === 1) {\n            result.unshift(a);\n        }\n        else if (a !== b) {\n            const errMsg = `Operands could not be broadcast together with shapes ` +\n                `${shapeA} and ${shapeB}.`;\n            throw Error(errMsg);\n        }\n        else {\n            result.unshift(a);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=broadcast_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Equal } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a == b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.equal(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction equal_(a, b) {\n    let $a = convertToTensor(a, 'a', 'equal');\n    let $b = convertToTensor(b, 'b', 'equal');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.equal($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null, Equal);\n}\nexport const equal = op({ equal_ });\n//# sourceMappingURL=equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SelectV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, assertShapesMatch } from '../util';\nimport { broadcastTo } from './broadcast_to';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the elements, either `a` or `b` depending on the `condition`.\n *\n * If the condition is true, select from `a`, otherwise select from `b`.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const a = tf.tensor1d([1 , 2, 3]);\n * const b = tf.tensor1d([-1, -2, -3]);\n *\n * a.where(cond, b).print();\n * ```\n *\n * @param condition The input condition. Must be of dtype bool.\n * @param a If `condition` is rank 1, `a` may have a higher rank but\n *     its first dimension must match the size of `condition`.\n * @param b A tensor with the same dtype as `a` and with shape that is\n *     compatible with `a`.\n * @return A tensor with same dtype as `a` and `b`, and shape that is\n *     broadcastable from `a` and `b`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction where_(condition, a, b) {\n    const $a = convertToTensor(a, 'a', 'where');\n    const $b = convertToTensor(b, 'b', 'where');\n    const $condition = convertToTensor(condition, 'condition', 'where', 'bool');\n    // TODO: move this logic to forward function when the broadcastTo op is\n    // implemented in WASM.\n    // Find the broadcastable shape for $a and $b.\n    const broadcastShape = assertAndGetBroadcastShape($a.shape, $b.shape);\n    const $broadcastedA = broadcastTo($a, broadcastShape);\n    const $broadcastedB = broadcastTo($b, broadcastShape);\n    if ($condition.rank === 1) {\n        // If condition rank is 1, then the first dimension must match the size of\n        // condition.\n        assert($condition.shape[0] === $a.shape[0], () => 'The first dimension of `a` must match the size of `condition`.');\n    }\n    if ($condition.rank !== 1) {\n        // A must have the same shape as condition.\n        assertShapesMatch($condition.shape, $broadcastedB.shape, 'Error in where: ');\n    }\n    const forward = (backend, save) => {\n        const res = backend.select($condition, $broadcastedA, $broadcastedB);\n        save([$condition]);\n        return res;\n    };\n    const inputs = {\n        condition: $condition,\n        t: $broadcastedA,\n        e: $broadcastedB\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, SelectV2);\n}\nexport const where = op({ where_ });\n//# sourceMappingURL=where.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ZerosLike } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.zerosLike(x).print();\n * ```\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction zerosLike_(x) {\n    const $x = convertToTensor(x, 'x', 'zerosLike');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.zerosLike($x), inputs, null /* grad */, ZerosLike);\n}\nexport const zerosLike = op({ zerosLike_ });\n//# sourceMappingURL=zeros_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { div } from './div';\nimport { equal } from './equal';\nimport { op } from './operation';\nimport { where } from './where';\nimport { zerosLike } from './zeros_like';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0\n * if denominator is 0.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n * const c = tf.tensor1d([0, 0, 0, 0]);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n * const c = tf.scalar(0);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction divNoNan_(a, b) {\n    // TODO: Make this into its own kernel.\n    let $a = convertToTensor(a, 'a', 'div');\n    let $b = convertToTensor(b, 'b', 'div');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const divResult = div($a, $b);\n    const zeros = zerosLike(divResult);\n    const bEqualsZero = equal($b, zeros);\n    return where(bEqualsZero, zeros, divResult);\n}\nexport const divNoNan = op({ divNoNan_ });\n//# sourceMappingURL=div_no_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor2d([[1, 2], [3, 4]]);\n * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n *\n * a.dot(b).print();  // or tf.dot(a, b)\n * b.dot(a).print();\n * b.dot(c).print();\n * ```\n * @param t1 The first tensor in the dot operation.\n * @param t2 The second tensor in the dot operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction dot_(t1, t2) {\n    const $t1 = convertToTensor(t1, 't1', 'dot');\n    const $t2 = convertToTensor(t2, 't2', 'dot');\n    util.assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ` +\n        `${$t1.rank} and ${$t2.rank}.`);\n    const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);\n    const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);\n    util.assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ` +\n        `${t1Inner} and ${t2Inner}.`);\n    if ($t1.rank === 1 && $t2.rank === 1) {\n        const t12D = reshape($t1, [1, -1]);\n        const t22D = reshape($t2, [-1, 1]);\n        const t1t2 = matMul(t12D, t22D);\n        return reshape(t1t2, []);\n    }\n    else if ($t1.rank === 1 && $t2.rank === 2) {\n        const t12D = reshape($t1, [1, -1]);\n        const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n        const t1t2 = matMul(t12D, t22D);\n        return reshape(t1t2, [t1t2.size]);\n    }\n    else if ($t1.rank === 2 && $t2.rank === 1) {\n        const t22D = reshape($t2, [-1, 1]);\n        const t1t2 = matMul($t1, t22D);\n        return reshape(t1t2, [t1t2.size]);\n    }\n    else {\n        const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n        const t1t2 = matMul($t1, t22D);\n        return t1t2;\n    }\n}\nexport const dot = op({ dot_ });\n//# sourceMappingURL=dot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Elu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential linear element-wise: `x > 0 ? e ^ x - 1 : 0`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 1, -3, 2]);\n *\n * x.elu().print();  // or tf.elu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction elu_(x) {\n    const $x = convertToTensor(x, 'x', 'elu');\n    const forward = (backend, save) => {\n        const y = backend.elu($x);\n        save([y]);\n        return y;\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Elu);\n}\nexport const elu = op({ elu_ });\n//# sourceMappingURL=elu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Erf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes gause error function of the input `tf.Tensor` element-wise:\n * `erf(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.erf().print(); // or tf.erf(x);\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction erf_(x) {\n    let $x = convertToTensor(x, 'x', 'erf');\n    util.assert($x.dtype === 'int32' || $x.dtype === 'float32', () => 'Input dtype must be `int32` or `float32`.');\n    if ($x.dtype === 'int32') {\n        $x = cast($x, 'float32');\n    }\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.erf($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Erf);\n}\nexport const erf = op({ erf_ });\n//# sourceMappingURL=erf.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Exp } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.exp().print();  // or tf.exp(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction exp_(x) {\n    const $x = convertToTensor(x, 'x', 'exp');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.exp($x);\n        save([res]);\n        return res;\n    }, inputs, null /* grad */, Exp);\n}\nexport const exp = op({ exp_ });\n//# sourceMappingURL=exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n * into the tensor's shape.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const axis = 1;\n * x.expandDims(axis).print();\n * ```\n *\n * @param x The input tensor whose dimensions to be expanded.\n * @param axis The dimension index at which to insert shape of `1`. Defaults\n *     to 0 (the first dimension).\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction expandDims_(x, axis = 0) {\n    const parseAs = null;\n    const $x = convertToTensor(x, 'x', 'expandDims', parseAs);\n    util.assert(axis <= $x.rank, () => 'Axis must be <= rank of the tensor');\n    const newShape = $x.shape.slice();\n    if (axis < 0) {\n        // Negative value is counted from the tail of rank.\n        util.assert(-($x.rank + 1) <= axis, () => `Axis must be in the interval [${-($x.rank + 1)}, ${$x.rank}]`);\n        axis = $x.rank + axis + 1;\n    }\n    newShape.splice(axis, 0, 1);\n    return reshape($x, newShape);\n}\nexport const expandDims = op({ expandDims_ });\n//# sourceMappingURL=expand_dims.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Expm1 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential of the input `tf.Tensor` minus one element-wise.\n * `e ^ x - 1`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.expm1().print();  // or tf.expm1(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction expm1_(x) {\n    const $x = convertToTensor(x, 'x', 'expm1');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.expm1($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Expm1);\n}\nexport const expm1 = op({ expm1_ });\n//# sourceMappingURL=expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tile } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Construct a tensor by repeating it the number of times given by reps.\n *\n * This operation creates a new tensor by replicating `input` `reps`\n * times. The output tensor's i'th dimension has `input.shape[i] *\n * reps[i]` elements, and the values of `input` are replicated\n * `reps[i]` times along the i'th dimension. For example, tiling\n * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n *\n * a.tile([2]).print();    // or a.tile([2])\n * ```\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.tile([1, 2]).print();  // or a.tile([1, 2])\n * ```\n * @param x The tensor to tile.\n * @param reps Determines the number of replications per dimension.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction tile_(x, reps) {\n    const parseAs = null;\n    const $x = convertToTensor(x, 'x', 'tile', parseAs);\n    util.assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n        `must match length of reps ${reps}.`);\n    const forward = (backend, save) => {\n        const res = backend.tile($x, reps);\n        save([$x]);\n        return res;\n    };\n    const inputsToSave = [$x];\n    const inputs = { x: $x };\n    const attrs = { reps };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Tile, attrs, inputsToSave);\n}\nexport const tile = op({ tile_ });\n//# sourceMappingURL=tile.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { expandDims } from './expand_dims';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { tile } from './tile';\n/**\n * Create an identity matrix.\n *\n * @param numRows Number of rows.\n * @param numColumns Number of columns. Defaults to `numRows`.\n * @param batchShape If provided, will add the batch shape to the beginning\n *   of the shape of the returned `tf.Tensor` by repeating the identity\n *   matrix.\n * @param dtype Data type.\n * @returns Identity matrix of the specified size and data type, possibly\n *   with batch repetition if `batchShape` is specified.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction eye_(numRows, numColumns, batchShape, dtype = 'float32') {\n    if (numColumns == null) {\n        numColumns = numRows;\n    }\n    const buff = buffer([numRows, numColumns], dtype);\n    const n = numRows <= numColumns ? numRows : numColumns;\n    for (let i = 0; i < n; ++i) {\n        buff.set(1, i, i);\n    }\n    const out = reshape(buff.toTensor(), [numRows, numColumns]);\n    if (batchShape == null) {\n        return out;\n    }\n    else {\n        if (batchShape.length === 1) {\n            return tile(expandDims(out, 0), [batchShape[0], 1, 1]);\n        }\n        else if (batchShape.length === 2) {\n            // tslint:disable-next-line:no-unnecessary-type-assertion\n            return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);\n        }\n        else if (batchShape.length === 3) {\n            // tslint:disable-next-line:no-unnecessary-type-assertion\n            return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [\n                batchShape[0], batchShape[1], batchShape[2], 1, 1\n            ]);\n        }\n        else {\n            throw new Error(`eye() currently supports only 1D and 2D ` +\n                // tslint:disable-next-line:no-any\n                `batchShapes, but received ${batchShape.length}D.`);\n        }\n    }\n}\nexport const eye = op({ eye_ });\n//# sourceMappingURL=eye.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Fill } from '../kernel_names';\n/**\n * Creates a `tf.Tensor` filled with a scalar value.\n *\n * ```js\n * tf.fill([2, 2], 4).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param value The scalar value to fill the tensor with.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction fill(shape, value, dtype) {\n    const attrs = { shape, value, dtype };\n    return ENGINE.runKernelFunc(backend => backend.fill(shape, value, dtype), {}, null, Fill, attrs);\n}\nexport { fill };\n//# sourceMappingURL=fill.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Floor } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.floor().print();  // or tf.floor(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction floor_(x) {\n    const $x = convertToTensor(x, 'x', 'floor');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.floor($x), inputs, null /* grad */, Floor);\n}\nexport const floor = op({ floor_ });\n//# sourceMappingURL=floor.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inputs of size above this threshold will be parallelized by calling multiple\n * shader programs.\n */\nimport { nearestDivisor } from '../util';\nexport const PARALLELIZE_THRESHOLD = 30;\nexport function computeOptimalWindowSize(inSize) {\n    if (inSize <= PARALLELIZE_THRESHOLD) {\n        return inSize;\n    }\n    return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n}\n//# sourceMappingURL=reduce_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nearestDivisor } from '../util';\nimport { PARALLELIZE_THRESHOLD } from './reduce_util';\nexport function segOpComputeOptimalWindowSize(inSize, numSegments) {\n    let done = false;\n    let res;\n    if (inSize <= PARALLELIZE_THRESHOLD) {\n        res = inSize;\n        done = true;\n    }\n    else {\n        res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n    }\n    while (!done) {\n        if (res > numSegments || res === inSize) {\n            done = true;\n        }\n        else {\n            res = nearestDivisor(inSize, res + 1);\n        }\n    }\n    return res;\n}\nexport function computeOutShape(aShape, axis, numSegments) {\n    const outShape = [];\n    const rank = aShape.length;\n    for (let dim = 0; dim < rank; dim++) {\n        if (dim !== axis) {\n            outShape.push(aShape[dim]);\n        }\n        else {\n            outShape.push(numSegments);\n        }\n    }\n    return outShape;\n}\nexport function collectGatherOpShapeInfo(x, indices, axis) {\n    const dimSize = x.shape[axis];\n    const outputShape = [];\n    let batchSize = 1;\n    let sliceSize = 1;\n    for (let i = 0; i < axis; i++) {\n        outputShape.push(x.shape[i]);\n        batchSize *= x.shape[i];\n    }\n    for (let i = 0; i < indices.rank; i++) {\n        outputShape.push(indices.shape[i]);\n    }\n    for (let i = axis + 1; i < x.rank; i++) {\n        outputShape.push(x.shape[i]);\n        sliceSize *= x.shape[i];\n    }\n    return { batchSize, sliceSize, dimSize, outputShape };\n}\n//# sourceMappingURL=segment_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { collectGatherOpShapeInfo } from './segment_util';\n/**\n * Gather slices from tensor `x`'s axis `axis` according to `indices`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const indices = tf.tensor1d([1, 3, 3], 'int32');\n *\n * x.gather(indices).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const indices = tf.tensor1d([1, 1, 0], 'int32');\n *\n * x.gather(indices).print();\n * ```\n * @param x The input tensor whose slices to be gathered.\n * @param indices The indices of the values to extract.\n * @param axis The axis over which to select values. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction gather_(x, indices, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'gather');\n    const $indices = convertToTensor(indices, 'indices', 'gather', 'int32');\n    const inputs = { x: $x, indices: $indices };\n    const attrs = { axis };\n    const forward = (backend, save) => {\n        const parsedAxis = parseAxisParam(axis, $x.shape)[0];\n        const shapeInfo = collectGatherOpShapeInfo($x, $indices, parsedAxis);\n        const res = backend.gather($x, reshape($indices, [$indices.size]), parsedAxis);\n        save([$x, $indices]);\n        return reshape(res, shapeInfo.outputShape);\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, GatherV2, attrs);\n}\nexport const gather = op({ gather_ });\n//# sourceMappingURL=gather.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Greater } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a > b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greater(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greater_(a, b) {\n    let $a = convertToTensor(a, 'a', 'greater');\n    let $b = convertToTensor(b, 'b', 'greater');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.greater($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Greater);\n}\nexport const greater = op({ greater_ });\n//# sourceMappingURL=greater.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GreaterEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a >= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greaterEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greaterEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'greaterEqual');\n    let $b = convertToTensor(b, 'b', 'greaterEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.greaterEqual($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, GreaterEqual);\n}\nexport const greaterEqual = op({ greaterEqual_ });\n//# sourceMappingURL=greater_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Imag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the imaginary part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the imaginary part of each element in input considered as a complex number.\n * If input is real, a tensor of all zeros is returned.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.imag(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction imag_(input) {\n    const $input = convertToTensor(input, 'input', 'imag');\n    const forward = (backend) => {\n        return backend.imag($input);\n    };\n    const inputs = { input: $input };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Imag);\n}\nexport const imag = op({ imag_ });\n//# sourceMappingURL=imag.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsFinite } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are finite.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isFinite().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isFinite_(x) {\n    const $x = convertToTensor(x, 'x', 'isFinite');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.isFinite($x), inputs, null /* grad */, IsFinite);\n}\nexport const isFinite = op({ isFinite_ });\n//# sourceMappingURL=is_finite.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsInf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are Infinity or -Infinity.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isInf().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isInf_(x) {\n    const $x = convertToTensor(x, 'x', 'isInf');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.isInf($x), inputs, null /* grad */, IsInf);\n}\nexport const isInf = op({ isInf_ });\n//# sourceMappingURL=is_inf.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsNan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * RReturns which elements of x are NaN.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isNaN().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isNaN_(x) {\n    const $x = convertToTensor(x, 'x', 'isNaN');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.isNaN($x), inputs, null /* grad */, IsNan);\n}\nexport const isNaN = op({ isNaN_ });\n//# sourceMappingURL=is_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Maximum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.maximumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * ```js\n * // Broadcast maximum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction maximum_(a, b) {\n    let $a = convertToTensor(a, 'a', 'maximum');\n    let $b = convertToTensor(b, 'b', 'maximum');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'bool') {\n        $a = cast($a, 'int32');\n        $b = cast($b, 'int32');\n    }\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.maximum($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Maximum);\n}\nexport const maximum = op({ maximum_ });\n//# sourceMappingURL=maximum.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { isTypedArray } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.scalar` as it makes the code more readable.\n *\n * ```js\n * tf.scalar(3.14).print();\n * ```\n *\n * @param value The value of the scalar.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function scalar(value, dtype) {\n    if (((isTypedArray(value) && dtype !== 'string') || Array.isArray(value)) &&\n        dtype !== 'complex64') {\n        throw new Error('Error creating a new Scalar: value must be a primitive ' +\n            '(number|boolean|string)');\n    }\n    if (dtype === 'string' && isTypedArray(value) &&\n        !(value instanceof Uint8Array)) {\n        throw new Error('When making a scalar from encoded string, ' +\n            'the value must be `Uint8Array`.');\n    }\n    const shape = [];\n    const inferredShape = [];\n    return makeTensor(value, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=scalar.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { maximum } from './maximum';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { scalar } from './scalar';\n/**\n * Computes leaky rectified linear element-wise.\n *\n * See\n * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](\n *     http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.leakyRelu(0.1).print();  // or tf.leakyRelu(x, 0.1)\n * ```\n * @param x The input tensor.\n * @param alpha The scaling factor for negative values, defaults to 0.2.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction leakyRelu_(x, alpha = 0.2) {\n    const $x = convertToTensor(x, 'x', 'leakyRelu');\n    return maximum(mul(scalar(alpha), $x), $x);\n}\nexport const leakyRelu = op({ leakyRelu_ });\n//# sourceMappingURL=leaky_relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Less } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a < b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.less(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction less_(a, b) {\n    let $a = convertToTensor(a, 'a', 'less');\n    let $b = convertToTensor(b, 'b', 'less');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.less($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Less);\n}\nexport const less = op({ less_ });\n//# sourceMappingURL=less.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LessEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a <= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.lessEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction lessEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'lessEqual');\n    let $b = convertToTensor(b, 'b', 'lessEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.lessEqual($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LessEqual);\n}\nexport const lessEqual = op({ lessEqual_ });\n//# sourceMappingURL=less_equal.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LinSpace } from '../kernel_names';\n/**\n * Return an evenly spaced sequence of numbers over the given interval.\n *\n * ```js\n * tf.linspace(0, 9, 10).print();\n * ```\n * @param start The start value of the sequence.\n * @param stop The end value of the sequence.\n * @param num The number of values to generate.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function linspace(start, stop, num) {\n    if (num <= 0) {\n        throw new Error('The number of values should be positive.');\n    }\n    const attrs = { start, stop, num };\n    return ENGINE.runKernelFunc(backend => backend.linspace(start, stop, num), {} /* inputs */, null /* grad */, LinSpace, attrs);\n}\n//# sourceMappingURL=linspace.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LRN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Normalizes the activation of a local neighborhood across or within\n * channels.\n *\n * @param x The input tensor. The 4-D input tensor is treated as a 3-D array\n *     of 1D vectors (along the last dimension), and each vector is\n *     normalized independently.\n * @param depthRadius The number of adjacent channels in the 1D normalization\n *     window.\n * @param bias A constant bias term for the basis.\n * @param alpha A scale factor, usually positive.\n * @param beta An exponent.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n    const $x = convertToTensor(x, 'x', 'localResponseNormalization');\n    util.assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${$x.rank}.`);\n    util.assert(util.isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an ` +\n        `integer but got depthRadius ${depthRadius}.`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    const forward = (backend, save) => {\n        const y = backend.localResponseNormalization4D(x4D, depthRadius, bias, alpha, beta);\n        save([x4D, y]);\n        return y;\n    };\n    const inputs = { x: x4D };\n    const attrs = { depthRadius, bias, alpha, beta };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, LRN, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    else {\n        return res;\n    }\n}\nexport const localResponseNormalization = op({ localResponseNormalization_ });\n//# sourceMappingURL=local_response_normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E]);\n *\n * x.log().print();  // or tf.log(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log_(x) {\n    const $x = convertToTensor(x, 'x', 'log');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.log($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Log);\n}\nexport const log = op({ log_ });\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log1p } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` plus one\n * element-wise: `ln(1 + x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E - 1]);\n *\n * x.log1p().print();  // or tf.log1p(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log1p_(x) {\n    const $x = convertToTensor(x, 'x', 'log1p');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.log1p($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Log1p);\n}\nexport const log1p = op({ log1p_ });\n//# sourceMappingURL=log1p.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { Tensor, Variable } from './tensor';\nimport { convertToTensor, convertToTensorArray } from './tensor_util_env';\nimport * as util from './util';\n/**\n * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the\n * gradient of `f(x)` with respect to `x`.\n *\n * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to\n * `x` is computed instead. `f(x)` must take a single tensor `x` and return a\n * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.grad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * g(x).print();\n * ```\n *\n * ```js\n * // f(x) = x ^ 3\n * const f = x => x.pow(tf.scalar(3, 'int32'));\n * // f'(x) = 3x ^ 2\n * const g = tf.grad(f);\n * // f''(x) = 6x\n * const gg = tf.grad(g);\n *\n * const x = tf.tensor1d([2, 3]);\n * gg(x).print();\n * ```\n *\n * @param f The function f(x), to compute gradient for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grad(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in grad(f) must be a function');\n    return (x, dy) => {\n        // x can be of any dtype, thus null as the last argument.\n        const $x = convertToTensor(x, 'x', 'tf.grad', null);\n        const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grad') : null;\n        return ENGINE.tidy(() => {\n            const { value, grads } = ENGINE.gradients(() => f($x), [$x], $dy);\n            if ($dy != null) {\n                util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grad(f)(x, dy) must match the shape ' +\n                    'returned by f(x)');\n            }\n            checkGrads(grads);\n            return grads[0];\n        });\n    };\n}\n/**\n * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,\n * which gives an array of gradients of `f()` with respect to each input\n * [`x1`,`x2`,...].\n *\n * If `dy` is passed when calling `g()`, the gradient of\n * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.\n * The provided `f` must take one or more tensors and return a single tensor\n * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df / da = b, df / db = a\n * const g = tf.grads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const [da, db] = g([a, b]);\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @param f The function `f(x1, x2,...)` to compute gradients for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grads(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in grads(f) must be a function');\n    return (args, dy) => {\n        util.assert(Array.isArray(args), () => 'The args passed in grads(f)(args) must be an array ' +\n            'of `Tensor`s or `TensorLike`s');\n        // args can be of any dtype, thus null as the last argument.\n        const $args = convertToTensorArray(args, 'args', 'tf.grads', null);\n        const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grads') : null;\n        return ENGINE.tidy(() => {\n            const { value, grads } = ENGINE.gradients(() => f(...$args), $args, $dy);\n            if ($dy != null) {\n                util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grads(f)([x1,...], dy) must ' +\n                    'match the shape returned by f([x1,...])');\n            }\n            checkGrads(grads);\n            return grads;\n        });\n    };\n}\n/**\n * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.valueAndGrad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * const {value, grad} = g(x);\n *\n * console.log('value');\n * value.print();\n * console.log('grad');\n * grad.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrad(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in valueAndGrad(f) must be a function');\n    return (x, dy) => {\n        util.assert(x instanceof Tensor, () => 'The x passed in valueAndGrad(f)(x) must be a tensor');\n        util.assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrad(f)(x, dy) must be a tensor');\n        const { grads, value } = ENGINE.gradients(() => f(x), [x], dy);\n        checkGrads(grads);\n        return { grad: grads[0], value };\n    };\n}\n/**\n * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df/da = b, df/db = a\n * const g = tf.valueAndGrads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const {value, grads} = g([a, b]);\n *\n * const [da, db] = grads;\n *\n * console.log('value');\n * value.print();\n *\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrads(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in valueAndGrads(f) must be a function');\n    return (args, dy) => {\n        util.assert(Array.isArray(args) && args.every(arg => arg instanceof Tensor), () => 'The args passed in valueAndGrads(f)(args) must be array of ' +\n            'tensors');\n        util.assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrads(f)(args, dy) must be a tensor');\n        const res = ENGINE.gradients(() => f(...args), args, dy);\n        if (dy != null) {\n            util.assertShapesMatch(res.value.shape, dy.shape, 'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +\n                'match the shape returned by f([x1,...])');\n        }\n        checkGrads(res.grads);\n        return res;\n    };\n}\n/**\n * Computes and returns the gradient of f(x) with respect to the list of\n * trainable variables provided by `varList`. If no list is provided, it\n * defaults to all trainable variables.\n *\n * ```js\n * const a = tf.variable(tf.tensor1d([3, 4]));\n * const b = tf.variable(tf.tensor1d([5, 6]));\n * const x = tf.tensor1d([1, 2]);\n *\n * // f(a, b) = a * x ^ 2 + b * x\n * const f = () => a.mul(x.square()).add(b.mul(x)).sum();\n * // df/da = x ^ 2, df/db = x\n * const {value, grads} = tf.variableGrads(f);\n *\n * Object.keys(grads).forEach(varName => grads[varName].print());\n * ```\n *\n * @param f The function to execute. f() should return a scalar.\n * @param varList The list of variables to compute the gradients with respect\n *     to. Defaults to all trainable variables.\n * @returns An object with the following keys and values:\n *   - `value`: The value of the function `f`.\n *   - `grads`: A map from the names of the variables to the gradients.\n *     If the `varList` argument is provided explicitly and contains a subset of\n *     non-trainable variables, this map in the return value will contain keys\n *     that map the names of the non-trainable variables to `null`.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction variableGrads(f, varList) {\n    util.assert(util.isFunction(f), () => 'The f passed in variableGrads(f) must be a function');\n    util.assert(varList == null ||\n        Array.isArray(varList) && varList.every(v => v instanceof Variable), () => 'The varList passed in variableGrads(f, varList) must be an array ' +\n        'of variables');\n    const specifiedVarList = varList != null;\n    if (!specifiedVarList) {\n        // Get all of the trainable variables.\n        varList = [];\n        for (const varName in ENGINE.registeredVariables) {\n            varList.push(ENGINE.registeredVariables[varName]);\n        }\n    }\n    const specifiedNonTrainable = specifiedVarList ? varList.filter(variable => !variable.trainable) : null;\n    // Prune non-trainable variables.\n    const originalVarCount = varList.length;\n    varList = varList.filter(variable => variable.trainable);\n    util.assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to ` +\n        `be trainable, but none of the ${originalVarCount} variables is ` +\n        `trainable.`);\n    const allowNoGradients = true;\n    const { value, grads } = ENGINE.gradients(f, varList, null, allowNoGradients);\n    util.assert(grads.some(g => g != null), () => 'Cannot find a connection between any variable and the result of ' +\n        'the loss function y=f(x). Please make sure the operations that ' +\n        'use variables are inside the function f passed to minimize().');\n    util.assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it ` +\n        `returned a rank-${value.rank} tensor`);\n    const namedGrads = {};\n    varList.forEach((v, i) => {\n        if (grads[i] != null) {\n            namedGrads[v.name] = grads[i];\n        }\n    });\n    if (specifiedNonTrainable != null) {\n        // If varList is explicitly provided and contains non-trainable values,\n        // add them to the returned gradients with `null` values.\n        specifiedNonTrainable.forEach(v => namedGrads[v.name] = null);\n    }\n    return { value, grads: namedGrads };\n}\n/**\n * Overrides the gradient computation of a function `f`.\n *\n * Takes a function\n * `f(...inputs, save) => {value: Tensor, gradFunc: (dy, saved) => Tensor[]}`\n * and returns another function `g(...inputs)` which takes the same inputs as\n * `f`. When called, `g` returns `f().value`. In backward mode, custom gradients\n * with respect to each input of `f` are computed using `f().gradFunc`.\n *\n * The `save` function passsed to `f` should be used for saving tensors needed\n * in the gradient. And the `saved` passed to the `gradFunc` is a\n * `NamedTensorMap`, which contains those saved tensor.\n *\n * ```js\n * const customOp = tf.customGrad((x, save) => {\n *   // Save x to make sure it's available later for the gradient.\n *   save([x]);\n *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);\n *   return {\n *     value: x.square(),\n *     // Note `saved.x` which points to the `x` we saved earlier.\n *     gradFunc: (dy, saved) => [dy.mul(saved[0].abs())]\n *   };\n * });\n *\n * const x = tf.tensor1d([-1, -2, 3]);\n * const dx = tf.grad(x => customOp(x));\n *\n * console.log(`f(x):`);\n * customOp(x).print();\n * console.log(`f'(x):`);\n * dx(x).print();\n * ```\n *\n * @param f The function to evaluate in forward mode, which should return\n *     `{value: Tensor, gradFunc: (dy, saved) => Tensor[]}`, where `gradFunc`\n *     returns the custom gradients of `f` with respect to its inputs.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction customGrad(f) {\n    return ENGINE.customGrad(f);\n}\nfunction checkGrads(grads) {\n    const numNullGradients = grads.filter(g => g == null).length;\n    if (numNullGradients > 0) {\n        throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.`);\n    }\n}\nexport { customGrad, variableGrads, valueAndGrad, valueAndGrads, grad, grads, };\n//# sourceMappingURL=gradients.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Negate } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes `-1 * x` element-wise.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);\n *\n * x.neg().print();  // or tf.neg(x)\n * ```\n *\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction neg_(x) {\n    const $x = convertToTensor(x, 'x', 'neg');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.neg($x), inputs, null /* grad */, Negate);\n}\nexport const neg = op({ neg_ });\n//# sourceMappingURL=neg.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softplus } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.softplus().print();  // or tf.softplus(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction softplus_(x) {\n    const $x = convertToTensor(x, 'x', 'softplus');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.softplus($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Softplus);\n}\nexport const softplus = op({ softplus_ });\n//# sourceMappingURL=softplus.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../gradients';\nimport { convertToTensor } from '../tensor_util_env';\nimport { mul } from './mul';\nimport { neg } from './neg';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { softplus } from './softplus';\n/**\n * Computes log sigmoid of the input `tf.Tensor` element-wise:\n * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.logSigmoid().print();  // or tf.logSigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction logSigmoid_(x) {\n    const $x = convertToTensor(x, 'x', 'logSigmoid');\n    // Use a custom gradient to maintain previous implementation.\n    // There is no LogSigmoid kernel in TF so we can't use engine.runKernel\n    // directly\n    const customOp = customGrad((x) => {\n        // TODO(yassogba) we can remove the chained softplus call here only\n        // after backends have modualrized softplus at which point we can call\n        // engine runKernel(..., Sotfplus, ...) directly.\n        const value = neg(softplus(neg(x)));\n        const gradFunc = (dy) => {\n            const derX = mul(dy, sigmoid(neg(x)));\n            return derX;\n        };\n        return { value, gradFunc };\n    });\n    return customOp($x);\n}\nexport const logSigmoid = op({ logSigmoid_ });\n//# sourceMappingURL=log_sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Max } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the maximum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.max().print();  // or tf.max(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.max(axis).print();  // or tf.max(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction max_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'max');\n    const forward = (backend, save) => {\n        const origAxes = util.parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let maxInput = $x;\n        if (permutedAxes != null) {\n            maxInput = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, maxInput.rank);\n        }\n        const y = backend.max(maxInput, axes);\n        if (permutedAxes != null) {\n            maxInput.dispose();\n        }\n        let res = y;\n        if (keepDims) {\n            const expandedShape = axis_util.expandShapeToKeepDim(res.shape, util.parseAxisParam(axis, $x.shape));\n            res = reshape(res, expandedShape);\n            y.dispose();\n        }\n        save([$x, res]);\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { reductionIndices: axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Max, attrs);\n}\nexport const max = op({ max_ });\n//# sourceMappingURL=max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sub } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n *\n * ```js\n * // Broadcast subtract a with b.\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.scalar(5);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n * @param a The first `tf.Tensor` to subtract from.\n * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction sub_(a, b) {\n    let $a = convertToTensor(a, 'a', 'sub');\n    let $b = convertToTensor(b, 'b', 'sub');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.subtract($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Sub);\n}\nexport const sub = op({ sub_ });\n//# sourceMappingURL=sub.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the sum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If axes has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.sum().print();  // or tf.sum(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.sum(axis).print();  // or tf.sum(x, axis)\n * ```\n *\n * @param x The input tensor to compute the sum over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction sum_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'sum');\n    if ($x.dtype === 'bool') {\n        $x = cast($x, 'int32');\n    }\n    const forward = (backend, save) => {\n        save([$x]);\n        const axes = parseAxisParam(axis, $x.shape);\n        const permutation = getAxesPermutation(axes, $x.rank);\n        let reductionAxes = axes;\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n        }\n        let value = backend.sum(permutedX, reductionAxes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(value.shape, axes);\n            value = reshape(value, newShape);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Sum, attrs);\n}\nexport const sum = op({ sum_ });\n//# sourceMappingURL=sum.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogSoftmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { op } from './operation';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log softmax.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param axis The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction logSoftmax_(logits, axis = -1) {\n    const $logits = convertToTensor(logits, 'logits', 'logSoftmax');\n    if (axis === -1) {\n        axis = $logits.rank - 1;\n    }\n    if (axis !== $logits.rank - 1) {\n        throw Error('Log Softmax along a non-last dimension is not yet supported. ' +\n            `Logits was rank ${$logits.rank} and axis was ${axis}`);\n    }\n    const forward = (backend, save) => {\n        const keepDims = true;\n        const xMax = max(logits, axis, true);\n        const shifted = sub(logits, xMax);\n        const value = sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis, keepDims)));\n        save([value]);\n        return value;\n    };\n    const inputs = { logits: $logits };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LogSoftmax, attrs);\n}\nexport const logSoftmax = op({ logSoftmax_ });\n//# sourceMappingURL=log_softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { add } from './add';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log(sum(exp(elements across the reduction dimensions)).\n *\n * Reduces the input along the dimensions given in `axis`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.logSumExp().print();  // or tf.logSumExp(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.logSumExp(axis).print();  // or tf.logSumExp(a, axis)\n * ```\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. If null (the default),\n *     reduces all dimensions.\n * @param keepDims If true, retains reduced dimensions with length\n *     of 1. Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction logSumExp_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'logSumExp');\n    const axes = parseAxisParam(axis, $x.shape);\n    const xMax = max($x, axes, true /* keepDims */);\n    const a = sub($x, xMax);\n    const b = exp(a);\n    const c = sum(b, axes);\n    const d = log(c);\n    const res = add(reshape(xMax, d.shape), d);\n    if (keepDims) {\n        const newShape = expandShapeToKeepDim(res.shape, axes);\n        return reshape(res, newShape);\n    }\n    return res;\n}\nexport const logSumExp = op({ logSumExp_ });\n//# sourceMappingURL=log_sum_exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalAnd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a AND b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalAnd(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalAnd_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(backend => backend.logicalAnd($a, $b), inputs, null /* grad */, LogicalAnd);\n}\nexport const logicalAnd = op({ logicalAnd_ });\n//# sourceMappingURL=logical_and.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalNot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the truth value of `NOT x` element-wise.\n *\n * ```js\n * const a = tf.tensor1d([false, true], 'bool');\n *\n * a.logicalNot().print();\n * ```\n *\n * @param x The input tensor. Must be of dtype 'bool'.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalNot_(x) {\n    const $x = convertToTensor(x, 'x', 'logicalNot', 'bool');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.logicalNot($x), inputs, null /* grad */, LogicalNot);\n}\nexport const logicalNot = op({ logicalNot_ });\n//# sourceMappingURL=logical_not.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalOr } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a OR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalOr(b).print();\n * ```\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalOr_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalOr', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalOr', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(backend => backend.logicalOr($a, $b), inputs, null /* grad */, LogicalOr);\n}\nexport const logicalOr = op({ logicalOr_ });\n//# sourceMappingURL=logical_or.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { logicalAnd } from './logical_and';\nimport { logicalNot } from './logical_not';\nimport { logicalOr } from './logical_or';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalXor(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalXor_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalXor', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalXor', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    // x ^ y = (x | y) & ~(x & y)\n    return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));\n}\nexport const logicalXor = op({ logicalXor_ });\n//# sourceMappingURL=logical_xor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction maxPool_(x, filterSize, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'maxPool');\n    const dilations = 1;\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        let y;\n        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n            util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n            y = x4D.clone();\n        }\n        else {\n            y = backend.maxPool(x4D, convInfo);\n        }\n        save([x4D, y]);\n        return y;\n    };\n    const inputs = { x: x4D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const maxPool = op({ maxPool_ });\n//# sourceMappingURL=max_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { deprecationWarn } from '../globals';\nimport { MaxPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D max pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.maxPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPool3d_(x, filterSize = [1, 1, 1], strides, pad, dimRoundingMode, dataFormat = 'NDHWC', dilations) {\n    if (dilations == null) {\n        dilations = [1, 1, 1];\n    }\n    else {\n        deprecationWarn('dilations is deprecated, this field will be gone in ' +\n            'v3.0.0.');\n    }\n    const $x = convertToTensor(x, 'x', 'maxPool3d');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in maxPool3d: Only NDHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool3d: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1, 1];\n        }\n        const convInfo = conv_util.computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);\n        const y = backend.maxPool3d(x5D, convInfo);\n        save([x5D, y]);\n        return y;\n    };\n    const inputs = { x: x5D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat, dilations };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool3D, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const maxPool3d = op({ maxPool3d_ });\n//# sourceMappingURL=max_pool_3d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPoolWithArgmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the 2D max pooling of an image with Argmax index.\n * The indices in argmax are flattened, so that a maximum value at position `[b,\n * y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if\n * include_batch_in_index is False; `((b * height + y) * width + x) * channels\n * +c` if include_batch_in_index is True.\n *\n * The indices returned are always in `[0, height) x [0, width)` before\n * flattening.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param includeBatchIndex Defaults to False. Whether to include batch\n *    dimension in flattened index of argmax.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPoolWithArgmax_(x, filterSize, strides, pad, includeBatchInIndex = false) {\n    const $x = convertToTensor(x, 'x', 'maxPoolWithArgmax');\n    const inputs = { x: $x };\n    const attrs = { filterSize, strides, pad, includeBatchInIndex };\n    const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);\n    return { result: result[0], indexes: result[1] };\n}\nexport const maxPoolWithArgmax = op({ maxPoolWithArgmax_ });\n//# sourceMappingURL=max_pool_with_argmax.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeZerosTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\n/**\n * Creates a `tf.Tensor` with all elements set to 0.\n *\n * ```js\n * tf.zeros([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Can\n *     be 'float32', 'int32' or 'bool'. Defaults to 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function zeros(shape, dtype = 'float32') {\n    if (dtype === 'complex64') {\n        const real = zeros(shape, 'float32');\n        const imag = zeros(shape, 'float32');\n        return complex(real, imag);\n    }\n    const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=zeros.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeOnesTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\nimport { zeros } from './zeros';\n/**\n * Creates a `tf.Tensor` with all elements set to 1.\n *\n * ```js\n * tf.ones([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n *     'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function ones(shape, dtype = 'float32') {\n    if (dtype === 'complex64') {\n        const real = ones(shape, 'float32');\n        const imag = zeros(shape, 'float32');\n        return complex(real, imag);\n    }\n    const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=ones.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../gradients';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam, sizeFromShape } from '../util';\nimport { computeOutAndReduceShapes } from './axis_util';\nimport { cast } from './cast';\nimport { div } from './div';\nimport { mul } from './mul';\nimport { ones } from './ones';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { scalar } from './scalar';\nimport { sum } from './sum';\n/**\n * Computes the mean of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is\n * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with\n * a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.mean().print();  // or tf.mean(a)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.mean(axis).print();  // or tf.mean(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction mean_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'mean');\n    const axes = parseAxisParam(axis, $x.shape);\n    const shapes = computeOutAndReduceShapes($x.shape, axes);\n    const reduceShape = shapes[1];\n    const reduceSize = sizeFromShape(reduceShape);\n    // Use a custom gradient to bypass 2 gradient backprops since mean is used\n    // extremely often.\n    const customOp = customGrad((x) => {\n        const reduceSizeScalar = scalar(reduceSize);\n        // Cast if needed.\n        const xReduce = reduceSizeScalar.dtype === x.dtype ?\n            x :\n            cast(x, reduceSizeScalar.dtype);\n        const res = div(xReduce, reduceSizeScalar);\n        const value = sum(res, axis, keepDims);\n        const gradFunc = (dy) => {\n            const expandedDyShape = x.shape.slice();\n            axes.forEach(axis => {\n                expandedDyShape[axis] = 1;\n            });\n            const expandedDy = reshape(dy, expandedDyShape);\n            const derX = div(mul(expandedDy, ones(x.shape, 'float32')), reduceSize);\n            return derX;\n        };\n        return { value, gradFunc };\n    });\n    return customOp($x);\n}\nexport const mean = op({ mean_ });\n//# sourceMappingURL=mean.js.map", "import { ENGINE } from '../engine';\nimport { Min } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the minimum value from the input.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axes`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axes` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.min().print();  // or tf.min(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.min(axis).print();  // or tf.min(x, axis)\n * ```\n *\n * @param x The input Tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction min_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'min');\n    const forward = (backend, save) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let minInput = $x;\n        if (permutedAxes != null) {\n            minInput = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        const y = backend.min(minInput, axes);\n        if (permutedAxes != null) {\n            minInput.dispose();\n        }\n        let res = y;\n        if (keepDims) {\n            const expandedShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n            res = reshape(y, expandedShape);\n            y.dispose();\n        }\n        save([$x, res]);\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Min, attrs);\n}\nexport const min = op({ min_ });\n//# sourceMappingURL=min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Minimum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `minimumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * ```js\n * // Broadcast minimum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction minimum_(a, b) {\n    let $a = convertToTensor(a, 'a', 'minimum');\n    let $b = convertToTensor(b, 'b', 'minimum');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'bool') {\n        $a = cast($a, 'int32');\n        $b = cast($b, 'int32');\n    }\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.minimum($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Minimum);\n}\nexport const minimum = op({ minimum_ });\n//# sourceMappingURL=minimum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Mod } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the mod of a and b element-wise.\n * `floor(x / y) * y + mod(x, y) = x`\n * Supports broadcasting.\n *\n * We also expose `tf.modStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * ```js\n * // Broadcast a mod b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mod_(a, b) {\n    let $a = convertToTensor(a, 'a', 'mod');\n    let $b = convertToTensor(b, 'b', 'mod');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.mod($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Mod);\n}\nexport const mod = op({ mod_ });\n//# sourceMappingURL=mod.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes square of `x` element-wise: `x ^ 2`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);\n *\n * x.square().print();  // or tf.square(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction square_(x) {\n    const $x = convertToTensor(x, 'x', 'square');\n    const attrs = {};\n    const inputsToSave = [$x];\n    const outputsToSave = [];\n    return ENGINE.runKernelFunc((backend, save) => {\n        save([$x]);\n        return backend.square($x);\n    }, { x: $x }, null /* grad */, 'Square', attrs, inputsToSave, outputsToSave);\n}\nexport const square = op({ square_ });\n//# sourceMappingURL=square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { cast } from './cast';\nimport { mean } from './mean';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { square } from './square';\nimport { sub } from './sub';\n/**\n * Calculates the mean and variance of `x`. The mean and variance are\n * calculated by aggregating the contents of `x` across `axes`. If `x` is\n * 1-D and `axes = [0]` this is just the mean and variance of a vector.\n *\n * @param x The input tensor.\n * @param axis The dimension(s) along with to compute mean and\n *     variance. By default it reduces all dimensions.\n * @param keepDims If true, the moments have the same dimensionality as the\n *     input.\n * @return An object with two keys: `mean` and `variance`.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction moments_(x, axis = null, keepDims = false) {\n    x = convertToTensor(x, 'x', 'moments');\n    const axes = parseAxisParam(axis, x.shape);\n    const xMean = mean(x, axes, keepDims);\n    let keepDimsShape = xMean.shape;\n    if (!keepDims) {\n        keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);\n    }\n    const devSquared = square(sub(cast(x, 'float32'), reshape(xMean, keepDimsShape)));\n    const variance = mean(devSquared, axes, keepDims);\n    return { mean: xMean, variance };\n}\nexport const moments = op({ moments_ });\n//# sourceMappingURL=moments.js.map", "import { convertToTensor, convertToTensorArray } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the next states and outputs of a stack of LSTMCells.\n *\n * Each cell output is used as input to the next cell.\n *\n * Returns `[cellState, cellOutput]`.\n *\n * Derived from tf.contrib.rn.MultiRNNCell.\n *\n * @param lstmCells Array of LSTMCell functions.\n * @param data The input to the cell.\n * @param c Array of previous cell states.\n * @param h Array of previous cell outputs.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction multiRNNCell_(lstmCells, data, c, h) {\n    const $data = convertToTensor(data, 'data', 'multiRNNCell');\n    const $c = convertToTensorArray(c, 'c', 'multiRNNCell');\n    const $h = convertToTensorArray(h, 'h', 'multiRNNCell');\n    let input = $data;\n    const newStates = [];\n    for (let i = 0; i < lstmCells.length; i++) {\n        const output = lstmCells[i](input, $c[i], $h[i]);\n        newStates.push(output[0]);\n        newStates.push(output[1]);\n        input = output[1];\n    }\n    const newC = [];\n    const newH = [];\n    for (let i = 0; i < newStates.length; i += 2) {\n        newC.push(newStates[i]);\n        newH.push(newStates[i + 1]);\n    }\n    return [newC, newH];\n}\nexport const multiRNNCell = op({ multiRNNCell_ });\n//# sourceMappingURL=multi_rnn_cell.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Creates a `tf.Tensor` with values drawn from a multinomial distribution.\n *\n * ```js\n * const probs = tf.tensor([.75, .25]);\n * tf.multinomial(probs, 3).print();\n * ```\n *\n * @param logits 1D array with unnormalized log-probabilities, or\n *     2D array of shape `[batchSize, numOutcomes]`. See the `normalized`\n *     parameter.\n * @param numSamples Number of samples to draw for each row slice.\n * @param seed The seed number.\n * @param normalized Whether the provided `logits` are normalized true\n *     probabilities (sum to 1). Defaults to false.\n * @return 1D array of shape `[numSamples]`, or 2D array of shape\n *     `[batchSize, numSamples]`, depending on the rank of the input.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction multinomial_(logits, numSamples, seed, normalized = false) {\n    const $logits = convertToTensor(logits, 'logits', 'multinomial');\n    const numOutcomes = $logits.size;\n    const origRank = $logits.rank;\n    if (numOutcomes < 2) {\n        throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ` +\n            `${numOutcomes}.`);\n    }\n    if (origRank > 2) {\n        throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n    }\n    seed = seed || Math.random();\n    const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;\n    const res = ENGINE.runKernelFunc(backend => backend.multinomial(logits2D, normalized, numSamples, seed), { logits2D });\n    // tslint:disable-next-line:no-unnecessary-type-assertion\n    return origRank === 1 ? reshape(res, [res.size]) : res;\n}\nexport const multinomial = op({ multinomial_ });\n//# sourceMappingURL=multinomial.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { NotEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a != b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([0, 2, 3]);\n *\n * a.notEqual(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction notEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'notEqual');\n    let $b = convertToTensor(b, 'b', 'notEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend) => backend.notEqual($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, NotEqual);\n}\nexport const notEqual = op({ notEqual_ });\n//# sourceMappingURL=not_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Real } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the real part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the real part of each element in input considered as a complex number.\n *\n * If the input is real, it simply makes a clone.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.real(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction real_(input) {\n    const $input = convertToTensor(input, 'input', 'real');\n    const forward = (backend) => {\n        return backend.real($input);\n    };\n    const inputs = { input: $input };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Real);\n}\nexport const real = op({ real_ });\n//# sourceMappingURL=real.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OnesLike } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { complex } from './complex';\nimport { imag } from './imag';\nimport { op } from './operation';\nimport { real } from './real';\nimport { zerosLike } from './zeros_like';\n/**\n * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.onesLike(x).print();\n * ```\n * @param x A tensor.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction onesLike_(x) {\n    const $x = convertToTensor(x, 'x', 'onesLike');\n    const forward = (backend, save) => {\n        if ($x.dtype === 'complex64') {\n            const r = onesLike(real($x));\n            const i = zerosLike(imag($x));\n            return complex(r, i);\n        }\n        return backend.onesLike($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, OnesLike);\n}\nexport const onesLike = op({ onesLike_ });\n//# sourceMappingURL=ones_like.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the outer product of two vectors, `v1` and `v2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([3, 4, 5]);\n *\n * tf.outerProduct(a, b).print();\n * ```\n * @param v1 The first vector in the outer product operation.\n * @param v2 The second vector in the outer product operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction outerProduct_(v1, v2) {\n    const $v1 = convertToTensor(v1, 'v1', 'outerProduct');\n    const $v2 = convertToTensor(v2, 'v2', 'outerProduct');\n    util.assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ` +\n        `${$v1.rank} and ${$v2.rank}.`);\n    const v12D = reshape($v1, [-1, 1]);\n    const v22D = reshape($v2, [1, -1]);\n    return matMul(v12D, v22D);\n}\nexport const outerProduct = op({ outerProduct_ });\n//# sourceMappingURL=outer_product.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { PadV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Pads a `tf.Tensor` with a given value and paddings.\n *\n * This operation currently only implements the `CONSTANT` mode.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `paddings` is of given length.\n *   - `tf.pad1d`\n *   - `tf.pad2d`\n *   - `tf.pad3d`\n *   - `tf.pad4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.pad([[1, 2]]).print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * @param constantValue The pad value to use. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction pad_(x, paddings, constantValue = 0) {\n    const $x = convertToTensor(x, 'x', 'pad');\n    if ($x.rank === 0) {\n        throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');\n    }\n    const forward = (backend, save) => {\n        save([$x]);\n        return backend.pad($x, paddings, constantValue);\n    };\n    const attrs = { paddings, constantValue };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, PadV2, attrs);\n}\nexport const pad = op({ pad_ });\n//# sourceMappingURL=pad.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.\n */\nfunction pad1d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 2, () => 'Invalid number of paddings. Must be length of 2.');\n    return pad(x, [paddings], constantValue);\n}\nexport const pad1d = op({ pad1d_ });\n//# sourceMappingURL=pad1d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.\n */\nfunction pad2d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 2 && paddings[0].length === 2 &&\n        paddings[1].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad2d = op({ pad2d_ });\n//# sourceMappingURL=pad2d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.\n */\nfunction pad3d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 3 && paddings[0].length === 2 &&\n        paddings[1].length === 2 && paddings[2].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad3d = op({ pad3d_ });\n//# sourceMappingURL=pad3d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.\n */\nfunction pad4d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 4 && paddings[0].length === 2 &&\n        paddings[1].length === 2 && paddings[2].length === 2 &&\n        paddings[3].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad4d = op({ pad4d_ });\n//# sourceMappingURL=pad4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SpaceToBatchND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation divides \"spatial\" dimensions `[1, ..., M]` of the input into\n * a grid of blocks of shape `blockShape`, and interleaves these blocks with\n * the \"batch\" dimension (0) such that in the output, the spatial\n * dimensions `[1, ..., M]` correspond to the position within the grid,\n * and the batch dimension combines both the position within a spatial block\n * and the original batch position. Prior to division into blocks,\n * the spatial dimensions of the input are optionally zero padded\n * according to `paddings`. See below for a precise description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n * const blockShape = [2, 2];\n * const paddings = [[0, 0], [0, 0]];\n *\n * x.spaceToBatchND(blockShape, paddings).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=\n *     0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad\n * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It\n * is required that\n * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input\n * according to `paddings` to produce `padded` of shape paddedShape.\n *\n * 2. Reshape `padded` to `reshapedPadded` of shape:\n * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`\n *\n * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`\n * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the\n * batch dimension, producing an output tensor of shape:\n * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction spaceToBatchND_(x, blockShape, paddings) {\n    const $x = convertToTensor(x, 'x', 'spaceToBatchND');\n    util.assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);\n    util.assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);\n    util.assert($x.shape.reduce((a, b, i) => {\n        if (i > 0 && i <= blockShape.length) {\n            return a &&\n                ((b + paddings[i - 1][0] + paddings[i - 1][1]) %\n                    blockShape[i - 1] ===\n                    0);\n        }\n        return a;\n    }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);\n    const forward = backend => backend.spaceToBatchND($x, blockShape, paddings);\n    const inputs = { x: $x };\n    const attrs = { blockShape, paddings };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, SpaceToBatchND, attrs);\n}\nexport const spaceToBatchND = op({ spaceToBatchND_ });\n//# sourceMappingURL=space_to_batch_nd.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { avgPool } from './avg_pool';\nimport { batchToSpaceND } from './batch_to_space_nd';\nimport * as conv_util from './conv_util';\nimport { maxPool } from './max_pool';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { spaceToBatchND } from './space_to_batch_nd';\n/**\n * Performs an N-D pooling operation\n *\n * @param input The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param windowShape The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param poolingType The type of pooling, either 'max' or 'avg'.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction pool_(input, windowShape, poolingType, pad, dilations, strides) {\n    if (dilations == null) {\n        dilations = [1, 1];\n    }\n    if (strides == null) {\n        strides = 1;\n    }\n    if (pad === 0) {\n        pad = 'valid';\n    }\n    const $x = convertToTensor(input, 'x', 'maxPool');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in pool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = conv_util.computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad);\n    const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];\n    // The following implementation does batchToSpace(pool(spaceToBatch(x)))\n    // whenever dilation > 1 since the TF kernels do not support dilation > 1.\n    // tslint:disable-next-line:max-line-length\n    // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037\n    let basePadding;\n    if (pad === 'same') {\n        basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);\n    }\n    else {\n        basePadding = [[0, 0], [0, 0]];\n    }\n    const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n    const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n    const convertedPad = isDilationOne ? pad : 'valid';\n    const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n    const forwardOp = poolingType === 'avg' ?\n        () => avgPool(convertedX, windowShape, strides, convertedPad) :\n        () => maxPool(convertedX, windowShape, strides, convertedPad);\n    const y = forwardOp();\n    const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\n// Helper function to compute crops and paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184\nfunction requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {\n    const padStart = basePadding.map(b => b[0]);\n    const origPadEnd = basePadding.map(b => b[1]);\n    const fullInputShape = inputShape.concat(padStart, origPadEnd);\n    const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n    const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n    const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n    const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n    return [paddings, crops];\n}\n// Helper function to compute base paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524\nfunction withSpaceToBatchBasePaddings(filterShape, dilation) {\n    // Spatial dimensions of the filters and the upsampled filters in which we\n    // introduce (rate - 1) zeros between consecutive filter values.\n    const dilatedFilterShape = filterShape.map((s, i) => {\n        return s + (s - 1) * (dilation[i] - 1);\n    });\n    const padExtraShape = dilatedFilterShape.map(s => s - 1);\n    // When padding is odd, we pad more at end, following the same\n    // convention as conv2d.\n    const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));\n    const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n    return padExtraShape.map((_, i) => {\n        return [padExtraStart[i], padExtraEnd[i]];\n    });\n}\nexport const pool = op({ pool_ });\n//# sourceMappingURL=pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Pow } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the power of one `tf.Tensor` to another. Supports broadcasting.\n *\n * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for\n * corresponding elements in x and y. The result's dtype will be the upcasted\n * type of the `base` and `exp` dtypes.\n *\n * ```js\n * const a = tf.tensor([[2, 3], [4, 5]])\n * const b = tf.tensor([[1, 2], [3, 0]]).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n *\n * ```js\n * const a = tf.tensor([[1, 2], [3, 4]])\n * const b = tf.tensor(2).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n * We also expose `powStrict` which has the same signature as this op and\n * asserts that `base` and `exp` are the same shape (does not broadcast).\n *\n * @param base The base `tf.Tensor` to pow element-wise.\n * @param exp The exponent `tf.Tensor` to pow element-wise.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction pow_(base, exp) {\n    let $base = convertToTensor(base, 'base', 'pow');\n    let $exp = convertToTensor(exp, 'exp', 'pow');\n    [$base, $exp] = makeTypesMatch($base, $exp);\n    const inputs = { a: $base, b: $exp };\n    const forward = (backend, save) => {\n        const y = backend.pow($base, $exp);\n        save([$base, $exp, y]);\n        return y;\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Pow);\n}\nexport const pow = op({ pow_ });\n//# sourceMappingURL=pow.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prelu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes leaky rectified linear element-wise with parametric alphas.\n *\n * `x < 0 ? alpha * x : f(x) = x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n * const alpha = tf.scalar(0.1);\n *\n * x.prelu(alpha).print();  // or tf.prelu(x, alpha)\n * ```\n * @param x The input tensor.\n * @param alpha Scaling factor for negative values.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction prelu_(x, alpha) {\n    const $x = convertToTensor(x, 'x', 'prelu');\n    const $alpha = convertToTensor(alpha, 'alpha', 'prelu');\n    const forward = (backend, save) => {\n        const res = backend.prelu($x, $alpha);\n        save([$x, $alpha]);\n        return res;\n    };\n    const inputs = { x: $x, alpha: $alpha };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Prelu);\n}\nexport const prelu = op({ prelu_ });\n//# sourceMappingURL=prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prod } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the product of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.prod().print();  // or tf.prod(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.prod(axis).print();  // or tf.prod(x, axis)\n * ```\n *\n * @param x The input tensor to compute the product over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction prod_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'prod');\n    const forward = (backend) => {\n        if ($x.dtype === 'bool') {\n            $x = cast($x, 'int32');\n        }\n        const axes = parseAxisParam(axis, $x.shape);\n        const permutation = getAxesPermutation(axes, $x.rank);\n        let reductionAxes = axes;\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n        }\n        let value = backend.prod(permutedX, reductionAxes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(value.shape, axes);\n            value = reshape(value, newShape);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Prod, attrs);\n}\nexport const prod = op({ prod_ });\n//# sourceMappingURL=prod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { sizeFromShape } from '../util';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with values sampled from a random number generator\n * function defined by the user.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param randFunction A random number generator function which is called\n * for each element in the output tensor.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\nfunction rand_(shape, randFunction, dtype) {\n    const size = sizeFromShape(shape);\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    for (let i = 0; i < size; i++) {\n        values[i] = randFunction();\n    }\n    return ENGINE.makeTensor(values, shape, dtype);\n}\nexport const rand = op({ rand_ });\n//# sourceMappingURL=rand.js.map", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as seedrandom from 'seedrandom';\nimport { expectNumbersClose, testEpsilon } from '../test_util';\n// https://en.wikipedia.org/wiki/Marsaglia_polar_method\nexport class MPRandGauss {\n    constructor(mean, stdDeviation, dtype, truncated, seed) {\n        this.mean = mean;\n        this.stdDev = stdDeviation;\n        this.dtype = dtype;\n        this.nextVal = NaN;\n        this.truncated = truncated;\n        if (this.truncated) {\n            this.upper = this.mean + this.stdDev * 2;\n            this.lower = this.mean - this.stdDev * 2;\n        }\n        const seedValue = seed ? seed : Math.random();\n        this.random = seedrandom.alea(seedValue.toString());\n    }\n    /** Returns next sample from a Gaussian distribution. */\n    nextValue() {\n        if (!isNaN(this.nextVal)) {\n            const value = this.nextVal;\n            this.nextVal = NaN;\n            return value;\n        }\n        let resultX, resultY;\n        let isValid = false;\n        while (!isValid) {\n            let v1, v2, s;\n            do {\n                v1 = 2 * this.random() - 1;\n                v2 = 2 * this.random() - 1;\n                s = v1 * v1 + v2 * v2;\n            } while (s >= 1 || s === 0);\n            const mul = Math.sqrt(-2.0 * Math.log(s) / s);\n            resultX = this.mean + this.stdDev * v1 * mul;\n            resultY = this.mean + this.stdDev * v2 * mul;\n            if (!this.truncated || this.isValidTruncated(resultX)) {\n                isValid = true;\n            }\n        }\n        if (!this.truncated || this.isValidTruncated(resultY)) {\n            this.nextVal = this.convertValue(resultY);\n        }\n        return this.convertValue(resultX);\n    }\n    /** Handles proper rounding for non-floating-point numbers. */\n    convertValue(value) {\n        if (this.dtype == null || this.dtype === 'float32') {\n            return value;\n        }\n        return Math.round(value);\n    }\n    /** Returns true if less than 2-standard-deviations from the mean. */\n    isValidTruncated(value) {\n        return value <= this.upper && value >= this.lower;\n    }\n}\n// Marsaglia, George, and Wai Wan Tsang. 2000. \"A Simple Method for Generating\n// Gamma Variables.\"\nexport class RandGamma {\n    constructor(alpha, beta, dtype, seed) {\n        this.alpha = alpha;\n        this.beta = 1 / beta; // convert rate to scale parameter\n        this.dtype = dtype;\n        const seedValue = seed ? seed : Math.random();\n        this.randu = seedrandom.alea(seedValue.toString());\n        this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());\n        if (alpha < 1) {\n            this.d = alpha + (2 / 3);\n        }\n        else {\n            this.d = alpha - (1 / 3);\n        }\n        this.c = 1 / Math.sqrt(9 * this.d);\n    }\n    /** Returns next sample from a gamma distribution. */\n    nextValue() {\n        let x2, v0, v1, x, u, v;\n        while (true) {\n            do {\n                x = this.randn.nextValue();\n                v = 1 + (this.c * x);\n            } while (v <= 0);\n            v *= v * v;\n            x2 = x * x;\n            v0 = 1 - (0.331 * x2 * x2);\n            v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v)));\n            u = this.randu();\n            if (u < v0 || Math.log(u) < v1) {\n                break;\n            }\n        }\n        v = (1 / this.beta) * this.d * v;\n        if (this.alpha < 1) {\n            v *= Math.pow(this.randu(), 1 / this.alpha);\n        }\n        return this.convertValue(v);\n    }\n    /** Handles proper rounding for non-floating-point numbers. */\n    convertValue(value) {\n        if (this.dtype === 'float32') {\n            return value;\n        }\n        return Math.round(value);\n    }\n}\nexport class UniformRandom {\n    constructor(min = 0, max = 1, dtype, seed) {\n        /** Handles proper rounding for non floating point numbers. */\n        this.canReturnFloat = () => (this.dtype == null || this.dtype === 'float32');\n        this.min = min;\n        this.range = max - min;\n        this.dtype = dtype;\n        if (seed == null) {\n            seed = Math.random();\n        }\n        if (typeof seed === 'number') {\n            seed = seed.toString();\n        }\n        if (!this.canReturnFloat() && this.range <= 1) {\n            throw new Error(`The difference between ${min} - ${max} <= 1 and dtype is not float`);\n        }\n        this.random = seedrandom.alea(seed);\n    }\n    convertValue(value) {\n        if (this.canReturnFloat()) {\n            return value;\n        }\n        return Math.round(value);\n    }\n    nextValue() {\n        return this.convertValue(this.min + this.range * this.random());\n    }\n}\nexport function jarqueBeraNormalityTest(values) {\n    // https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test\n    const n = values.length;\n    const s = skewness(values);\n    const k = kurtosis(values);\n    const jb = n / 6 * (Math.pow(s, 2) + 0.25 * Math.pow(k - 3, 2));\n    // JB test requires 2-degress of freedom from Chi-Square @ 0.95:\n    // http://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm\n    const CHI_SQUARE_2DEG = 5.991;\n    if (jb > CHI_SQUARE_2DEG) {\n        throw new Error(`Invalid p-value for JB: ${jb}`);\n    }\n}\nexport function expectArrayInMeanStdRange(actual, expectedMean, expectedStdDev, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    const actualMean = mean(actual);\n    expectNumbersClose(actualMean, expectedMean, epsilon);\n    expectNumbersClose(standardDeviation(actual, actualMean), expectedStdDev, epsilon);\n}\nfunction mean(values) {\n    let sum = 0;\n    for (let i = 0; i < values.length; i++) {\n        sum += values[i];\n    }\n    return sum / values.length;\n}\nfunction standardDeviation(values, mean) {\n    let squareDiffSum = 0;\n    for (let i = 0; i < values.length; i++) {\n        const diff = values[i] - mean;\n        squareDiffSum += diff * diff;\n    }\n    return Math.sqrt(squareDiffSum / values.length);\n}\nfunction kurtosis(values) {\n    // https://en.wikipedia.org/wiki/Kurtosis\n    const valuesMean = mean(values);\n    const n = values.length;\n    let sum2 = 0;\n    let sum4 = 0;\n    for (let i = 0; i < n; i++) {\n        const v = values[i] - valuesMean;\n        sum2 += Math.pow(v, 2);\n        sum4 += Math.pow(v, 4);\n    }\n    return (1 / n) * sum4 / Math.pow((1 / n) * sum2, 2);\n}\nfunction skewness(values) {\n    // https://en.wikipedia.org/wiki/Skewness\n    const valuesMean = mean(values);\n    const n = values.length;\n    let sum2 = 0;\n    let sum3 = 0;\n    for (let i = 0; i < n; i++) {\n        const v = values[i] - valuesMean;\n        sum2 += Math.pow(v, 2);\n        sum3 += Math.pow(v, 3);\n    }\n    return (1 / n) * sum3 / Math.pow((1 / (n - 1)) * sum2, 3 / 2);\n}\n//# sourceMappingURL=rand_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { RandGamma } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a gamma distribution.\n *\n * ```js\n * tf.randomGamma([2, 2], 1).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param alpha The shape parameter of the gamma distribution.\n * @param beta The inverse scale parameter of the gamma distribution. Defaults\n *     to 1.\n * @param dtype The data type of the output. Defaults to float32.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomGamma_(shape, alpha, beta = 1, dtype = 'float32', seed) {\n    if (beta == null) {\n        beta = 1;\n    }\n    if (dtype == null) {\n        dtype = 'float32';\n    }\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new Error(`Unsupported data type ${dtype}`);\n    }\n    const rgamma = new RandGamma(alpha, beta, dtype, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = rgamma.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomGamma = op({ randomGamma_ });\n//# sourceMappingURL=random_gamma.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a normal distribution.\n *\n * ```js\n * tf.randomNormal([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n    if (dtype != null && dtype === 'bool') {\n        throw new Error(`Unsupported data type ${dtype}`);\n    }\n    const randGauss = new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = randGauss.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomNormal = op({ randomNormal_ });\n//# sourceMappingURL=random_normal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { UniformRandom } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a uniform distribution.\n *\n * The generated values follow a uniform distribution in the range [minval,\n * maxval). The lower bound minval is included in the range, while the upper\n * bound maxval is excluded.\n *\n * ```js\n * tf.randomUniform([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param minval The lower bound on the range of random values to generate.\n *   Defaults to 0.\n * @param maxval The upper bound on the range of random values to generate.\n *   Defaults to 1.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomUniform_(shape, minval = 0, maxval = 1, dtype = 'float32', seed) {\n    const res = buffer(shape, dtype);\n    const random = new UniformRandom(minval, maxval, null, seed);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = random.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomUniform = op({ randomUniform_ });\n//# sourceMappingURL=random_uniform.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor1d` as it makes the code more readable.\n *\n * ```js\n * tf.tensor1d([1, 2, 3]).print();\n * ```\n *\n * @param values The values of the tensor. Can be array of numbers,\n *     or a `TypedArray`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor1d(values, dtype) {\n    assertNonNull(values);\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 1) {\n        throw new Error('tensor1d() requires values to be a flat/TypedArray');\n    }\n    const shape = null;\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor1d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Range } from '../kernel_names';\nimport { makeZerosTypedArray } from '../util';\nimport { tensor1d } from './tensor1d';\nimport { zeros } from './zeros';\n/**\n * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.\n *\n * The tensor is a is half-open interval meaning it includes start, but\n * excludes stop. Decrementing ranges and negative step values are also\n * supported.sv\n *\n *\n * ```js\n * tf.range(0, 9, 2).print();\n * ```\n *\n * @param start An integer start value\n * @param stop An integer stop value\n * @param step An integer increment (will default to 1 or -1)\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function range(start, stop, step = 1, dtype = 'float32') {\n    if (step === 0) {\n        throw new Error('Cannot have a step of zero');\n    }\n    const forward = () => {\n        const sameStartStop = start === stop;\n        const increasingRangeNegativeStep = start < stop && step < 0;\n        const decreasingRangePositiveStep = stop < start && step > 1;\n        if (sameStartStop || increasingRangeNegativeStep ||\n            decreasingRangePositiveStep) {\n            return zeros([0], dtype);\n        }\n        const numElements = Math.abs(Math.ceil((stop - start) / step));\n        const values = makeZerosTypedArray(numElements, dtype);\n        if (stop < start && step === 1) {\n            // Auto adjust the step's sign if it hasn't been set\n            // (or was set to 1)\n            step = -1;\n        }\n        values[0] = start;\n        for (let i = 1; i < values.length; i++) {\n            values[i] = values[i - 1] + step;\n        }\n        return tensor1d(values, dtype);\n    };\n    const attrs = { start, stop, step, dtype };\n    return ENGINE.runKernelFunc(forward, {} /* inputs */, null /* grad */, Range, attrs);\n}\n//# sourceMappingURL=range.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reciprocal } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of x element-wise: `1 / x`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, 2]);\n *\n * x.reciprocal().print();  // or tf.reciprocal(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction reciprocal_(x) {\n    const $x = convertToTensor(x, 'x', 'reciprocal');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.reciprocal($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Reciprocal);\n}\nexport const reciprocal = op({ reciprocal_ });\n//# sourceMappingURL=reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { cast } from './cast';\n/**\n * Computes rectified linear element-wise: `max(x, 0)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.relu().print();  // or tf.relu(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu_(x) {\n    const $x = convertToTensor(x, 'x', 'relu');\n    const forward = (backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'bool') {\n            return cast($x, 'int32');\n        }\n        return backend.relu($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Relu);\n}\nexport const relu = op({ relu_ });\n//# sourceMappingURL=relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu6 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes rectified linear 6 element-wise: `min(max(x, 0), 6)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 8]);\n *\n * x.relu6().print();  // or tf.relu6(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu6_(x) {\n    const $x = convertToTensor(x, 'x', 'relu6');\n    const forward = (backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'bool') {\n            return cast($x, 'int32');\n        }\n        return backend.relu6($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Relu6);\n}\nexport const relu6 = op({ relu6_ });\n//# sourceMappingURL=relu6.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reverse } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { clone } from './clone';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Reverses a `tf.Tensor` along a specified axis.\n *\n * Also available are stricter rank-specific methods that assert that `x` is\n * of the given rank:\n *   - `tf.reverse1d`\n *   - `tf.reverse2d`\n *   - `tf.reverse3d`\n *   - `tf.reverse4d`\n *\n * Except `tf.reverse1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.reverse().print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.reverse(axis).print();\n * ```\n * @param x The input tensor to be reversed.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction reverse_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    const forward = (backend) => {\n        const axes = parseAxisParam(axis, $x.shape);\n        if ($x.rank === 0) {\n            return clone($x);\n        }\n        const res = backend.reverse($x, axes);\n        return reshape(res, $x.shape);\n    };\n    const inputs = { x: $x };\n    const attrs = { dims: axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Reverse, attrs);\n}\nexport const reverse = op({ reverse_ });\n//# sourceMappingURL=reverse.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor1D`.\n *\n * @param x The input tensor.\n */\nfunction reverse1d_(x) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);\n    return reverse($x, 0);\n}\nexport const reverse1d = op({ reverse1d_ });\n//# sourceMappingURL=reverse_1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor2D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse2d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse2d = op({ reverse2d_ });\n//# sourceMappingURL=reverse_2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor3D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse3d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse3d = op({ reverse3d_ });\n//# sourceMappingURL=reverse_3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor4D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse4d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse4d = op({ reverse4d_ });\n//# sourceMappingURL=reverse_4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Round } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes round of input `tf.Tensor` element-wise: `round(x)`.\n * It implements banker's rounding.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.round().print();  // or tf.round(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction round_(x) {\n    const $x = convertToTensor(x, 'x', 'round');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.round($x), inputs, null /* grad */, Round);\n}\nexport const round = op({ round_ });\n//# sourceMappingURL=round.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Rsqrt } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of square root of the input `tf.Tensor` element-wise:\n * `y = 1 / sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.rsqrt().print();  // or tf.rsqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction rsqrt_(x) {\n    const $x = convertToTensor(x, 'x', 'rsqrt');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.rsqrt($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Rsqrt);\n}\nexport const rsqrt = op({ rsqrt_ });\n//# sourceMappingURL=rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Selu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes scaled exponential linear element-wise.\n *\n * `x < 0 ? scale * alpha * (exp(x) - 1) : x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.selu().print();  // or tf.selu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction selu_(x) {\n    const $x = convertToTensor(x, 'x', 'selu');\n    const forward = (backend, save) => {\n        const res = backend.selu($x);\n        save([$x]);\n        return res;\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Selu);\n}\nexport const selu = op({ selu_ });\n//# sourceMappingURL=selu.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport { depthwiseConv2d } from './depthwise_conv2d';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * 2-D convolution with separable filters.\n *\n * Performs a depthwise convolution that acts separately on channels followed\n * by a pointwise convolution that mixes channels. Note that this is\n * separability between dimensions [1, 2] and 3, not spatial separability\n * between dimensions 1 and 2.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is\n *     the filter used in the first step.\n * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape\n *     `[1, 1, inChannels * channelMultiplier, outChannels]`. This is\n *     the filter used in the second step.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation = [1, 1], dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'separableConv2d');\n    const $depthwiseFilter = convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');\n    const $pointwiseFilter = convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    if (dataFormat === 'NCHW') {\n        throw new Error('separableConv2d currently does not support dataFormat NCHW; only ' +\n            'NHWC is supported');\n    }\n    util.assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but ` +\n        `got rank ${$depthwiseFilter.rank}.`);\n    util.assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but ` +\n        `got rank ${$depthwiseFilter.rank}.`);\n    util.assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter ` +\n        ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n    util.assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise ` +\n        `filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n    const inChannels = $depthwiseFilter.shape[2];\n    const channelMultiplier = $depthwiseFilter.shape[3];\n    util.assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter ` +\n        `must be ${inChannels * channelMultiplier}, ` +\n        `but got ${$pointwiseFilter.shape[2]}.`);\n    const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);\n    const pointwiseStride = 1;\n    const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const separableConv2d = op({ separableConv2d_ });\n//# sourceMappingURL=separable_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\n/**\n * Computes the difference between two lists of numbers.\n *\n * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`\n * that represents all values that are in `x` but not in `y`. The returned\n * Tensor `out` is sorted in the same order that the numbers appear in `x`\n * (duplicates are preserved). This operation also returns a Tensor indices that\n * represents the position of each out element in `x`. In other words:\n *\n * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`\n *\n * ```js\n * const x = [1, 2, 3, 4, 5, 6];\n * const y = [1, 3, 5];\n *\n * const [out, indices] = await tf.setdiff1dAsync(x, y);\n * out.print(); // [2, 4, 6]\n * indices.print(); // [1, 3, 5]\n * ```\n *\n * @param x 1-D Tensor. Values to keep.\n * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the\n *     output.\n * @returns Promise of Tensor tuple [out, indices].\n *  out: Tensor with the same type as x.\n *  indices: A Tensor of type int32.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nasync function setdiff1dAsync_(x, y) {\n    const $x = convertToTensor(x, 'x', 'setdiff1d');\n    const $y = convertToTensor(y, 'y', 'setdiff1d');\n    util.assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);\n    util.assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);\n    util.assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);\n    const xVals = await $x.data();\n    const yVals = await $y.data();\n    const ySet = new Set(yVals);\n    let outputSize = 0;\n    for (let i = 0; i < xVals.length; i++) {\n        if (!ySet.has(xVals[i])) {\n            outputSize++;\n        }\n    }\n    const buffer = new TensorBuffer([outputSize], $x.dtype);\n    const indices = new TensorBuffer([outputSize], 'int32');\n    for (let i = 0, p = 0; i < xVals.length; i++) {\n        if (!ySet.has(xVals[i])) {\n            buffer.values[p] = xVals[i];\n            indices.values[p] = i;\n            p++;\n        }\n    }\n    return [buffer.toTensor(), indices.toTensor()];\n}\nexport const setdiff1dAsync = setdiff1dAsync_;\n//# sourceMappingURL=setdiff1d_async.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sign } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns an element-wise indication of the sign of a number.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);\n *\n * x.sign().print();  // or tf.sign(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sign_(x) {\n    const $x = convertToTensor(x, 'x', 'sign');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.sign($x), inputs, null /* grad */, Sign);\n}\nexport const sign = op({ sign_ });\n//# sourceMappingURL=sign.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sin of the input Tensor element-wise: `sin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.sin().print();  // or tf.sin(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sin_(x) {\n    const $x = convertToTensor(x, 'x', 'sin');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sin($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sin);\n}\nexport const sin = op({ sin_ });\n//# sourceMappingURL=sin.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.sinh().print();  // or tf.sinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sinh_(x) {\n    const $x = convertToTensor(x, 'x', 'sinh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sinh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sinh);\n}\nexport const sinh = op({ sinh_ });\n//# sourceMappingURL=sinh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 1D slice from 1D array starting at coordinates `begin` and is\n * of length `size`. See `slice` for details.\n */\nfunction slice1d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice1d');\n    util.assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, [begin], [size]);\n}\nexport const slice1d = op({ slice1d_ });\n//# sourceMappingURL=slice1d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 2D slice from a 2D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice2d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice2d');\n    util.assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice2d = op({ slice2d_ });\n//# sourceMappingURL=slice2d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 3D slice from a 3D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice3d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice3d');\n    util.assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice3d = op({ slice3d_ });\n//# sourceMappingURL=slice3d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 4D slice from a 4D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice4d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice4d');\n    util.assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice4d = op({ slice4d_ });\n//# sourceMappingURL=slice4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the softmax normalized vector given the logits.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction softmax_(logits, dim = -1) {\n    const $logits = convertToTensor(logits, 'logits', 'softmax', 'float32');\n    if (dim === -1) {\n        dim = $logits.rank - 1;\n    }\n    if (dim !== $logits.rank - 1) {\n        throw Error('Softmax along a non-last dimension is not yet supported. ' +\n            `Logits was rank ${$logits.rank} and dim was ${dim}`);\n    }\n    const inputs = { logits: $logits };\n    const attrs = { dim };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const y = backend.softmax($logits, dim);\n        save([y]);\n        return y;\n    }, inputs, null /* grad */, Softmax, attrs);\n}\nexport const softmax = op({ softmax_ });\n//# sourceMappingURL=softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\n/**\n * Fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the inner-most\n * dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.fft().print();  // tf.spectral.fft(x).print();\n * ```\n * @param input The complex input to compute an fft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction fft_(input) {\n    assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.fft() must be complex64 ` +\n        `but got ${input.dtype}.`);\n    const inputs = { input };\n    return ENGINE.runKernelFunc(backend => {\n        // Collapse all outer dimensions to a single batch dimension.\n        const innerDimensionSize = input.shape[input.shape.length - 1];\n        const batch = input.size / innerDimensionSize;\n        const input2D = input.as2D(batch, innerDimensionSize);\n        const result = backend.fft(input2D);\n        return result.reshape(input.shape);\n    }, inputs, null /* gradient */, FFT);\n}\nexport const fft = op({ fft_ });\n//# sourceMappingURL=fft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { IFFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Inverse fast Fourier transform.\n *\n * Computes the inverse 1-dimensional discrete Fourier transform over the\n * inner-most dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.ifft().print();  // tf.spectral.ifft(x).print();\n * ```\n * @param input The complex input to compute an ifft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction ifft_(input) {\n    assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.ifft() must be complex64 ` +\n        `but got ${input.dtype}.`);\n    const inputs = { input };\n    return ENGINE.runKernelFunc(backend => {\n        // Collapse all outer dimensions to a single batch dimension.\n        const innerDimensionSize = input.shape[input.shape.length - 1];\n        const batch = input.size / innerDimensionSize;\n        const input2D = reshape(input, [batch, innerDimensionSize]);\n        const result = backend.ifft(input2D);\n        return reshape(result, input.shape);\n    }, inputs, null /* gradient */, IFFT);\n}\nexport const ifft = op({ ifft_ });\n//# sourceMappingURL=ifft.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { reverse } from '../reverse';\nimport { scalar } from '../scalar';\nimport { slice } from '../slice';\nimport { ifft } from './ifft';\n/**\n * Inversed real value input fast Fourier transform.\n *\n * Computes the 1-dimensional inversed discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([0, 0, 0]);\n * const x = tf.complex(real, imag);\n *\n * x.irfft().print();\n * ```\n * @param input The real value input to compute an irfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction irfft_(input) {\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n    let ret;\n    if (innerDimensionSize <= 2) {\n        const complexInput = reshape(input, [batch, innerDimensionSize]);\n        ret = ifft(complexInput);\n    }\n    else {\n        // The length of unique components of the DFT of a real-valued signal\n        // is 2 * (input_len - 1)\n        const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n        const realInput = reshape(real(input), [batch, innerDimensionSize]);\n        const imagInput = reshape(imag(input), [batch, innerDimensionSize]);\n        const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);\n        const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));\n        const r = concat([realInput, realConjugate], 1);\n        const i = concat([imagInput, imagConjugate], 1);\n        const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);\n        ret = ifft(complexInput);\n    }\n    ret = real(ret);\n    // reshape the result if the input is 3D tensor.\n    if (input.rank === 3 && input.shape[0] !== 0) {\n        const temp = ret;\n        const batch = input.shape[0];\n        ret = reshape(ret, [batch, ret.shape[0] / batch, ret.shape[1]]);\n        temp.dispose();\n    }\n    return ret;\n}\nexport const irfft = op({ irfft_ });\n//# sourceMappingURL=irfft.js.map", "import { assert } from '../util';\n/**\n * Prepare the split size array. When the input is a number, the axis is evenly\n * divided among the split size. When the input contains the negative value, the\n * rest of the axis is allocated toward that.\n */\nexport function prepareSplitSize(x, numOrSizeSplits, axis = 0) {\n    let splitSizes = [];\n    if (typeof (numOrSizeSplits) === 'number') {\n        assert(x.shape[axis] % numOrSizeSplits === 0, () => 'Number of splits must evenly divide the axis.');\n        splitSizes =\n            new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);\n    }\n    else {\n        const numOfNegs = numOrSizeSplits.reduce((count, value) => {\n            if (value === -1) {\n                count += 1;\n            }\n            return count;\n        }, 0);\n        assert(numOfNegs <= 1, () => 'There should be only one negative value in split array.');\n        const negIndex = numOrSizeSplits.indexOf(-1);\n        // Allow the number of split array to be -1, which indicates the rest\n        // of dimension is allocated to that split.\n        if (negIndex !== -1) {\n            const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);\n            numOrSizeSplits[negIndex] = x.shape[axis] - total;\n        }\n        assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => 'The sum of sizes must match the size of the axis dimension.');\n        splitSizes = numOrSizeSplits;\n    }\n    return splitSizes;\n}\n//# sourceMappingURL=split_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SplitV } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { op } from './operation';\nimport { prepareSplitSize } from './split_util';\n/**\n * Splits a `tf.Tensor` into sub tensors.\n *\n * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`\n * into `numOrSizeSplits` smaller tensors.\n * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.\n *\n * If `numOrSizeSplits` is a number array, splits `x` into\n * `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the\n * same size as `x` except along dimension `axis` where the size is\n * `numOrSizeSplits[i]`.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);\n * const [a, b] = tf.split(x, 2, 1);\n * a.print();\n * b.print();\n *\n * const [c, d, e] = tf.split(x, [1, 2, 1], 1);\n * c.print();\n * d.print();\n * e.print();\n * ```\n *\n * @param x The input tensor to split.\n * @param numOrSizeSplits Either an integer indicating the number of\n * splits along the axis or an array of integers containing the sizes of\n * each output tensor along the axis. If a number then it must evenly divide\n * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.\n * Can contain one -1 indicating that dimension is to be inferred.\n * @param axis The dimension along which to split. Defaults to 0 (the first\n * dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction split_(x, numOrSizeSplits, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'split');\n    const forward = (backend, _) => {\n        const $axis = parseAxisParam(axis, $x.shape)[0];\n        const splitSizes = prepareSplitSize($x, numOrSizeSplits, $axis);\n        return backend.split($x, splitSizes, $axis);\n    };\n    const inputs = { x: $x };\n    const attr = { numOrSizeSplits, axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, SplitV, attr);\n}\nexport const split = op({ split_ });\n//# sourceMappingURL=split.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../../util';\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { split } from '../split';\nimport { zeros } from '../zeros';\nimport { zerosLike } from '../zeros_like';\nimport { fft } from './fft';\n/**\n * Real value input fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * real.rfft().print();\n * ```\n * @param input The real value input to compute an rfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction rfft_(input, fftLength) {\n    assert(input.dtype === 'float32', () => `The dtype for rfft() must be real value but got ${input.dtype}`);\n    let innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n    let adjustedInput;\n    if (fftLength != null && fftLength < innerDimensionSize) {\n        // Need to crop\n        const begin = input.shape.map(v => 0);\n        const size = input.shape.map(v => v);\n        size[input.shape.length - 1] = fftLength;\n        adjustedInput = slice(input, begin, size);\n        innerDimensionSize = fftLength;\n    }\n    else if (fftLength != null && fftLength > innerDimensionSize) {\n        // Need to pad with zeros\n        const zerosShape = input.shape.map(v => v);\n        zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;\n        adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);\n        innerDimensionSize = fftLength;\n    }\n    else {\n        adjustedInput = input;\n    }\n    // Complement the input with zero imaginary numbers.\n    const zerosInput = zerosLike(adjustedInput);\n    const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);\n    const ret = fft(complexInput);\n    // Exclude complex conjugations. These conjugations are put symmetrically.\n    const half = Math.floor(innerDimensionSize / 2) + 1;\n    const realValues = real(ret);\n    const imagValues = imag(ret);\n    const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);\n    const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);\n    const outputShape = adjustedInput.shape.slice();\n    outputShape[adjustedInput.shape.length - 1] = half;\n    return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);\n}\nexport const rfft = op({ rfft_ });\n//# sourceMappingURL=rfft.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sqrt } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.sqrt().print();  // or tf.sqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sqrt_(x) {\n    const $x = convertToTensor(x, 'x', 'sqrt');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sqrt($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sqrt);\n}\nexport const sqrt = op({ sqrt_ });\n//# sourceMappingURL=sqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SquaredDifference } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns (a - b) * (a - b) element-wise.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * ```js\n * // Broadcast squared difference  a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction squaredDifference_(a, b) {\n    let $a = convertToTensor(a, 'a', 'squaredDifference');\n    let $b = convertToTensor(b, 'b', 'squaredDifference');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.squaredDifference($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = {};\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, SquaredDifference, attrs);\n}\nexport const squaredDifference = op({ squaredDifference_ });\n//# sourceMappingURL=squared_difference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { squeezeShape } from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Removes dimensions of size 1 from the shape of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);\n * x.squeeze().print();\n * ```\n *\n * @param x The input tensor to be squeezed.\n * @param axis An optional list of numbers. If specified, only\n *     squeezes the dimensions listed. The dimension index starts at 0. It\n * is an error to squeeze a dimension that is not 1.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction squeeze_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'squeeze');\n    return reshape($x, squeezeShape($x.shape, axis).newShape);\n}\nexport const squeeze = op({ squeeze_ });\n//# sourceMappingURL=squeeze.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensorArray } from '../tensor_util_env';\nimport * as util from '../util';\nimport { concat } from './concat';\nimport { expandDims } from './expand_dims';\nimport { op } from './operation';\n/**\n * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.stack([a, b, c]).print();\n * ```\n *\n * @param tensors A list of tensor objects with the same shape and dtype.\n * @param axis The axis to stack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction stack_(tensors, axis = 0) {\n    const $tensors = convertToTensorArray(tensors, 'tensors', 'stack');\n    util.assert($tensors.length >= 1, () => 'Pass at least one tensor to tf.stack');\n    if ($tensors.length === 1) {\n        return expandDims($tensors[0], axis);\n    }\n    const rank = $tensors[0].rank;\n    const shape = $tensors[0].shape;\n    const dtype = $tensors[0].dtype;\n    util.assert(axis <= rank, () => 'Axis must be <= rank of the tensor');\n    $tensors.forEach(t => {\n        util.assertShapesMatch(shape, t.shape, 'All tensors passed to stack must have matching shapes');\n        util.assert(dtype === t.dtype, () => 'All tensors passed to stack must have matching dtypes');\n    });\n    const expandedTensors = $tensors.map(t => expandDims(t, axis));\n    // Stack exists in the TensorFlow C++ API\n    // (https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/stack) but not\n    // in\n    // https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/core/ops/ops.pbtxt.\n    // Therefore we are treating it like a high-level op rather than\n    // creating a dedicated stack kernel.\n    return concat(expandedTensors, axis);\n}\nexport const stack = op({ stack_ });\n//# sourceMappingURL=stack.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Step } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha * x`\n *\n * ```js\n * const x = tf.tensor1d([0, 2, -1, -3]);\n *\n * x.step(.5).print();  // or tf.step(x, .5)\n * ```\n * @param x The input tensor.\n * @param alpha The gradient when input is negative.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction step_(x, alpha = 0.0) {\n    const $x = convertToTensor(x, 'x', 'step');\n    const inputs = { x: $x };\n    const attrs = { alpha };\n    return ENGINE.runKernelFunc(backend => backend.step($x, alpha), inputs, null /* grad */, Step, attrs);\n}\nexport const step = op({ step_ });\n//# sourceMappingURL=step.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { StridedSlice } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { slice } from './slice';\nimport { computeOutShape, getNormalizedAxes, maskToAxes } from './slice_util';\n/**\n * Extracts a strided slice of a tensor.\n *\n * Roughly speaking, this op extracts a slice of size (end-begin)/stride from\n * the given input tensor (x). Starting at the location specified by begin the\n * slice continues by adding stride to the index until all dimensions are not\n * less than end. Note that a stride can be negative, which causes a reverse\n * slice.\n *\n * ```js\n * const t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],\n *    [3, 2, 3]);\n * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print()  // [[[3, 3, 3]]]\n * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print()  // [[[3, 3, 3],\n *                                                     // [4, 4, 4]]]\n * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],\n *                                                     // [3, 3, 3]]]\n * ```\n *\n * @param x The tensor to stride slice.\n * @param begin The coordinates to start the slice from.\n * @param end: The coordinates to end the slice at.\n * @param strides: The size of the slice.\n * @param beginMask: If the ith bit of beginMask is set, begin[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param endMask: If the ith bit of endMask is set, end[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param shrinkAxisMask: a bitmask where bit i implies that\n * the ith specification should shrink the dimensionality. begin and end must\n * imply a slice of size 1 in the dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {\n    let $x = convertToTensor(x, 'x', 'stridedSlice');\n    const forward = (backend) => {\n        if (strides == null) {\n            strides = new Array(begin.length);\n        }\n        const ellipsisAxes = maskToAxes(ellipsisMask);\n        if (ellipsisAxes.length > 1) {\n            throw new Error('Multiple ellipses in slice is not allowed.');\n        }\n        if (ellipsisMask !== 0 && newAxisMask !== 0) {\n            throw new Error('Using both ellipsisMask and newAxisMask is not yet supported.');\n        }\n        if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {\n            throw new Error('Using both ellipsisMask and shrinkAxisMask is not yet supported.');\n        }\n        const numInterpolatedAxes = $x.rank - begin.length;\n        // Expand the dims of x based on the newAxisMask.\n        const expandAxes = maskToAxes(newAxisMask);\n        const newShape = $x.shape.slice();\n        expandAxes.forEach(axis => {\n            begin[axis] = 0;\n            end[axis] = 1;\n            newShape.splice(axis, 0, 1);\n        });\n        $x = reshape($x, newShape);\n        const { begin: normalizedBegin, end: normalizedEnd, strides: normalizedStrides } = getNormalizedAxes($x.shape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask);\n        begin = normalizedBegin;\n        end = normalizedEnd;\n        strides = normalizedStrides;\n        const shrinkAxes = maskToAxes(shrinkAxisMask);\n        // Adjust the ends based on the shrink mask.\n        shrinkAxes.forEach(axis => {\n            end[axis] = begin[axis] + 1;\n            strides[axis] = 1;\n        });\n        // Figure out the output shape.\n        const size = computeOutShape(begin, end, strides);\n        // Remove the axes based on shrinkMask.\n        const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);\n        const nonStrided = strides.every(v => v === 1);\n        if (nonStrided) {\n            return reshape(slice($x, begin, size), outShape);\n        }\n        const res = backend.stridedSlice($x, begin, end, strides);\n        return reshape(res, outShape);\n    };\n    const inputs = { x: $x };\n    const attrs = {\n        begin,\n        end,\n        strides,\n        beginMask,\n        endMask,\n        ellipsisMask,\n        newAxisMask,\n        shrinkAxisMask\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, StridedSlice, attrs);\n}\nexport const stridedSlice = op({ stridedSlice_ });\n//# sourceMappingURL=strided_slice.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.tan().print();  // or tf.tan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tan_(x) {\n    const $x = convertToTensor(x, 'x', 'tan');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.tan($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Tan);\n}\nexport const tan = op({ tan_ });\n//# sourceMappingURL=tan.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor2d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor2d([[1, 2], [3, 4]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor2d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 2) {\n        throw new Error('tensor2d() requires shape to have two numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n        throw new Error('tensor2d() requires values to be number[][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor2d() requires shape to be provided when `values` ' +\n            'are a flat/TypedArray');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor2d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor4d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor4d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 4) {\n        throw new Error('tensor4d() requires shape to have four numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n        throw new Error('tensor4d() requires values to be number[][][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor4d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor5d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor5d([[[[[1], [2]], [[3], [4]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor5d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 5) {\n        throw new Error('tensor5d() requires shape to have five numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n        throw new Error('tensor5d() requires values to be ' +\n            'number[][][][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor5d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor5d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor6d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor6d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 6) {\n        throw new Error('tensor6d() requires shape to have six numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n        throw new Error('tensor6d() requires values to be number[][][][][][] or ' +\n            'flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor6d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    shape = shape ||\n        inferredShape;\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor6d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { TopK } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Finds the values and indices of the `k` largest entries along the last\n * dimension.\n *\n * If the input is a vector (rank=1), finds the k largest entries in the vector\n * and outputs their values and indices as vectors. Thus values[j] is the j-th\n * largest entry in input, and its index is indices[j].\n * For higher rank inputs, computes the top k entries along the last dimension.\n *\n * If two elements are equal, the lower-index element appears first.\n *\n * ```js\n * const a = tf.tensor2d([[1, 5], [4, 3]]);\n * const {values, indices} = tf.topk(a);\n * values.print();\n * indices.print();\n * ```\n * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.\n * @param k Number of top elements to look for along the last dimension.\n * @param sorted If true, the resulting `k` elements will be sorted by the\n *     values in descending order.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction topk_(x, k = 1, sorted = true) {\n    const $x = convertToTensor(x, 'x', 'topk');\n    if ($x.rank === 0) {\n        throw new Error('topk() expects the input to be of rank 1 or higher');\n    }\n    const lastDim = $x.shape[$x.shape.length - 1];\n    if (k > lastDim) {\n        throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) ` +\n            `but got ${k}`);\n    }\n    const inputs = { x: $x };\n    const attrs = { k, sorted };\n    const [values, indices] = ENGINE.runKernelFunc(b => b.topk($x, k, sorted), inputs, null /* grad */, TopK, attrs);\n    return { values, indices };\n}\nexport const topk = op({ topk_ });\n//# sourceMappingURL=topk.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a truncated normal\n * distribution.\n *\n * ```js\n * tf.truncatedNormal([2, 2]).print();\n * ```\n *\n * The generated values follow a normal distribution with specified mean and\n * standard deviation, except that values whose magnitude is more than 2\n * standard deviations from the mean are dropped and re-picked.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output tensor.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction truncatedNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n    if (dtype != null && dtype === 'bool') {\n        throw new Error(`Unsupported data type $ { dtype }`);\n    }\n    const randGauss = new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = randGauss.nextValue();\n    }\n    return res.toTensor();\n}\nexport const truncatedNormal = op({ truncatedNormal_ });\n//# sourceMappingURL=truncated_normal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unique } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert } from '../util';\nimport { op } from './operation';\n/**\n * Finds unique elements along an axis of a tensor.\n *\n * It returns a tensor `values` containing all of the unique elements along the\n * `axis` of the given tensor `x` in the same order that they occur along the\n * `axis` in `x`; `x` does not need to be sorted. It also returns a tensor\n * `indices` the same size as the number of the elements in `x` along the `axis`\n * dimension. It contains the index in the unique output `values`.\n *\n * ```js\n * // A 1-D tensor\n * const a = tf.tensor1d([1, 1, 2, 4, 4, 4, 7, 8, 8]);\n * const {values, indices} = tf.unique(a);\n * values.print();   // [1, 2, 4, 7, 8,]\n * indices.print();  // [0, 0, 1, 2, 2, 2, 3, 4, 4]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=0\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 0)\n * values.print();   // [[1, 0, 0],\n *                   //  [2, 0, 0]]\n * indices.print();  // [0, 0, 1]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=1\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 1)\n * values.print();   // [[1, 0],\n *                   //  [1, 0],\n *                   //  [2, 0]]\n * indices.print();  // [0, 1, 1]\n * ```\n * @param x A tensor (int32, string, bool).\n * @param axis The axis of the tensor to find the unique elements.\n * @returns [uniqueElements, indices] (see above for details)\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction unique_(x, axis = 0) {\n    // x can be of any dtype, thus null as the last argument.\n    const $x = convertToTensor(x, 'x', 'unique', null);\n    assert($x.rank > 0, () => 'The input tensor must be at least 1D');\n    const inputs = { x: $x };\n    const attrs = { axis };\n    const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs);\n    return { values, indices };\n}\nexport const unique = op({ unique_ });\n//# sourceMappingURL=unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { UnsortedSegmentSum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, isInt } from '../util';\nimport { op } from './operation';\n/**\n * Computes the sum along segments of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');\n * const numSegments = 3;\n *\n * x.unsortedSegmentSum(segmentIds, numSegments).print()\n * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)\n * ```\n * @param x The `tf.Tensor` that will be summed along its segments.\n * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s\n * dimension along the `axis`.  Maps each element of `x` to a segment.\n * @param numSegments The number of distinct `segmentIds`.\n *\n * @doc {heading: 'Operations', subheading: 'Segment'}\n */\nfunction unsortedSegmentSum_(x, segmentIds, numSegments) {\n    const $x = convertToTensor(x, 'x', 'unsortedSegmentSum');\n    const $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');\n    assert(isInt(numSegments), () => 'numSegments must be of dtype int');\n    const inputs = { x: $x, segmentIds: $segmentIds };\n    const attrs = { numSegments };\n    const forward = (backend, save) => {\n        const res = backend.unsortedSegmentSum($x, $segmentIds, numSegments);\n        save([$segmentIds]);\n        return res;\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, UnsortedSegmentSum, attrs);\n}\nexport const unsortedSegmentSum = op({ unsortedSegmentSum_ });\n//# sourceMappingURL=unsorted_segment_sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unpack } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * tf.unstack(a).forEach(tensor => tensor.print());\n * ```\n *\n * @param x A tensor object.\n * @param axis The axis to unstack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction unstack_(x, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'unstack');\n    util.assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);\n    if (axis < 0) {\n        axis += $x.shape.length;\n    }\n    const inputs = { value: $x };\n    const attrs = { axis };\n    const forward = (backend) => backend.unstack($x, axis);\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Unpack, attrs);\n}\nexport const unstack = op({ unstack_ });\n//# sourceMappingURL=unstack.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\n/**\n * Creates a new variable with the provided initial value.\n * ```js\n * const x = tf.variable(tf.tensor([1, 2, 3]));\n * x.assign(tf.tensor([4, 5, 6]));\n *\n * x.print();\n * ```\n *\n * @param initialValue Initial value for the tensor.\n * @param trainable If true, optimizers are allowed to update it.\n * @param name Name of the variable. Defaults to a unique id.\n * @param dtype If set, initialValue will be converted to the given type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function variable(initialValue, trainable = true, name, dtype) {\n    return ENGINE.makeVariable(initialValue, trainable, name, dtype);\n}\n//# sourceMappingURL=variable.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the Where kernel shared between cpu and webgl */\nimport { buffer } from '../ops/buffer';\nexport function whereImpl(condShape, condVals) {\n    const indices = [];\n    for (let i = 0; i < condVals.length; i++) {\n        if (condVals[i]) {\n            indices.push(i);\n        }\n    }\n    const inBuffer = buffer(condShape, 'int32');\n    const out = buffer([indices.length, condShape.length], 'int32');\n    for (let i = 0; i < indices.length; i++) {\n        const loc = inBuffer.indexToLoc(indices[i]);\n        const offset = i * condShape.length;\n        out.values.set(loc, offset);\n    }\n    return out.toTensor();\n}\n//# sourceMappingURL=where_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { whereImpl } from '../backends/where_impl';\nimport { convertToTensor } from '../tensor_util_env';\n/**\n * Returns the coordinates of true elements of condition.\n *\n * The coordinates are returned in a 2-D tensor where the first dimension (rows)\n * represents the number of true elements, and the second dimension (columns)\n * represents the coordinates of the true elements. Keep in mind, the shape of\n * the output tensor can vary depending on how many true values there are in\n * input. Indices are output in row-major order. The resulting tensor has the\n * shape `[numTrueElems, condition.rank]`.\n *\n * This is analogous to calling the python `tf.where(cond)` without an x or y.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const result = await tf.whereAsync(cond);\n * result.print();\n * ```\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nasync function whereAsync_(condition) {\n    const $condition = convertToTensor(condition, 'condition', 'whereAsync', 'bool');\n    const vals = await $condition.data();\n    const res = whereImpl($condition.shape, vals);\n    if (condition !== $condition) {\n        $condition.dispose();\n    }\n    return res;\n}\nexport const whereAsync = whereAsync_;\n//# sourceMappingURL=where_async.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { gather } from './gather';\nimport { reshape } from './reshape';\nimport { squeeze } from './squeeze';\nimport { whereAsync } from './where_async';\n/**\n * Apply boolean mask to tensor.\n *\n * ```js\n * const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);\n * const mask = tf.tensor1d([1, 0, 1], 'bool');\n * const result = await tf.booleanMaskAsync(tensor, mask);\n * result.print();\n * ```\n *\n * @param tensor N-D tensor.\n * @param mask K-D boolean tensor, K <= N and K must be known statically.\n * @param axis A 0-D int Tensor representing the axis in tensor to mask from.\n *     By default, axis is 0 which will mask from the first dimension.\n *     Otherwise K + axis <= N.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nasync function booleanMaskAsync_(tensor, mask, axis) {\n    const $tensor = convertToTensor(tensor, 'tensor', 'boolMask');\n    const $mask = convertToTensor(mask, 'mask', 'boolMask', 'bool');\n    const axisFrom = axis == null ? 0 : axis;\n    const maskDim = $mask.rank;\n    const tensorShape = $tensor.shape;\n    util.assert(maskDim > 0, () => 'mask cannot be scalar');\n    util.assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);\n    let leadingSize = 1;\n    for (let i = axisFrom; i < axisFrom + maskDim; i++) {\n        leadingSize *= tensorShape[i];\n    }\n    const targetTensorShape = tensorShape.slice(0, axisFrom)\n        .concat([leadingSize], tensorShape.slice(axisFrom + maskDim));\n    const reshapedTensor = reshape($tensor, targetTensorShape);\n    const reshapedMask = reshape($mask, [-1]);\n    const positivePositions = await whereAsync(reshapedMask);\n    const indices = squeeze(positivePositions, [1]);\n    const res = gather(reshapedTensor, indices, axisFrom);\n    // Ensure no memory leak.\n    if (tensor !== $tensor) {\n        $tensor.dispose();\n    }\n    if (mask !== $mask) {\n        $mask.dispose();\n    }\n    indices.dispose();\n    reshapedTensor.dispose();\n    reshapedMask.dispose();\n    positivePositions.dispose();\n    return res;\n}\nexport const booleanMaskAsync = booleanMaskAsync_;\n//# sourceMappingURL=boolean_mask.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../globals';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertShapesMatch } from '../util';\nimport { equal } from './equal';\nimport { greater } from './greater';\nimport { greaterEqual } from './greater_equal';\nimport { less } from './less';\nimport { lessEqual } from './less_equal';\nimport { notEqual } from './not_equal';\nimport { op } from './operation';\n/**\n * @deprecated\n * Strict version of `tf.notEqual` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction notEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'notEqualStrict');\n    const $b = convertToTensor(b, 'b', 'notEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in notEqualStrict: ');\n    return notEqual($a, $b);\n}\n/**\n * @deprecated\n * Strict version of `tf.less` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction lessStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'lessStrict');\n    const $b = convertToTensor(b, 'b', 'lessStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in lessStrict: ');\n    return less($a, $b);\n}\nfunction equalStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'equalStrict');\n    const $b = convertToTensor(b, 'b', 'equalStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in equalStrict: ');\n    return equal($a, $b);\n}\nfunction lessEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'lessEqualStrict');\n    const $b = convertToTensor(b, 'b', 'lessEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in lessEqualStrict: ');\n    return lessEqual($a, $b);\n}\nfunction greaterStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'greaterStrict');\n    const $b = convertToTensor(b, 'b', 'greaterStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in greaterStrict: ');\n    return greater($a, $b);\n}\nfunction greaterEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'greaterEqualStrict');\n    const $b = convertToTensor(b, 'b', 'greaterEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in greaterEqualStrict: ');\n    return greaterEqual($a, $b);\n}\nexport const equalStrict = op({ equalStrict_ });\nexport const greaterEqualStrict = op({ greaterEqualStrict_ });\nexport const greaterStrict = op({ greaterStrict_ });\nexport const lessEqualStrict = op({ lessEqualStrict_ });\nexport const lessStrict = op({ lessStrict_ });\nexport const notEqualStrict = op({ notEqualStrict_ });\n//# sourceMappingURL=compare.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../globals';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { maximum } from './maximum';\nimport { minimum } from './minimum';\nimport { mod } from './mod';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { squaredDifference } from './squared_difference';\nimport { sub } from './sub';\n/**\n * @deprecated\n * Adds two `tf.Tensor`s element-wise, A + B.\n *\n * Inputs must be the same shape. For broadcasting support, use add() instead.\n *\n * @param a The first Tensor to add element-wise.\n * @param b The second Tensor to add element-wise.\n */\nfunction addStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'addStrict');\n    const $b = convertToTensor(b, 'b', 'addStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in addStrict: ');\n    return add($a, $b);\n}\n/**\n * @deprecated\n * Subtracts two `tf.Tensor`s element-wise, A - B. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.sub` instead.\n *\n * @param a The first Tensor to subtract element-wise.\n * @param b The second Tensor to subtract element-wise.\n */\nfunction subStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'subStrict');\n    const $b = convertToTensor(b, 'b', 'subStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in subStrict: ');\n    return sub($a, $b);\n}\n/**\n * @deprecated\n * Computes the power of one `tf.Tensor` to another. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.pow` instead.\n *\n * @param base The base tensor to pow element-wise.\n * @param exp The exponent tensor to pow element-wise.\n */\nfunction powStrict_(base, exp) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    util.assertShapesMatch(base.shape, exp.shape, 'Error in powStrict: ');\n    return pow(base, exp);\n}\n/**\n * @deprecated\n * Multiplies two `tf.Tensor`s element-wise, A * B.\n *\n * Inputs must be the same shape. For broadcasting support, use `tf.mul`.\n *\n * @param a The first tensor to multiply.\n * @param b The first tensor to multiply. Must have the same\n *    dtype as `a`.\n */\nfunction mulStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'mul');\n    const $b = convertToTensor(b, 'b', 'mul');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in multiplyStrict: ');\n    return mul($a, $b);\n}\n/**\n * @deprecated\n * Divides two `tf.Tensor`s element-wise, A / B. Inputs must\n * be the same shape.\n *\n * @param a The first tensor as the numerator for element-wise division.\n * @param b The second tensor as the denominator for element-wise division.\n */\nfunction divStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'div');\n    const $b = convertToTensor(b, 'b', 'div');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in divideStrict: ');\n    return div($a, $b);\n}\n/**\n * @deprecated\n * Returns the mod of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use mod().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction modStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'modStrict');\n    const $b = convertToTensor(b, 'b', 'modStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in modStrict: ');\n    return mod($a, $b);\n}\n/**\n * @deprecated\n * Returns the min of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use minimum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction minimumStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'minimumStrict');\n    const $b = convertToTensor(b, 'b', 'minimumStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in minimumStrict: ');\n    return minimum($a, $b);\n}\n/**\n * @deprecated\n * Returns the max of a and b (`a > b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use maximum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction maximumStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'maximumStrict');\n    const $b = convertToTensor(b, 'b', 'maximumStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in maximumStrict: ');\n    return maximum($a, $b);\n}\n/**\n * @deprecated\n * Returns (a - b) * (a - b) element-wise.\n *\n * Inputs must be the same shape. For broadcasting support, use\n * `tf.squaredDifference` instead.\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\nfunction squaredDifferenceStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'squaredDifferenceStrict');\n    const $b = convertToTensor(b, 'b', 'squaredDifferenceStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in squaredDifferenceStrict: ');\n    return squaredDifference($a, $b);\n}\nexport const addStrict = op({ addStrict_ });\nexport const divStrict = op({ divStrict_ });\nexport const maximumStrict = op({ maximumStrict_ });\nexport const minimumStrict = op({ minimumStrict_ });\nexport const modStrict = op({ modStrict_ });\nexport const mulStrict = op({ mulStrict_ });\nexport const powStrict = op({ powStrict_ });\nexport const squaredDifferenceStrict = op({ squaredDifferenceStrict_ });\nexport const subStrict = op({ subStrict_ });\n//# sourceMappingURL=binary_ops.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { abs } from './abs';\nimport * as axis_util from './axis_util';\nimport { max } from './max';\nimport { min } from './min';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { reshape } from './reshape';\nimport { scalar } from './scalar';\nimport { sqrt } from './sqrt';\nimport { square } from './square';\nimport { sum } from './sum';\n/**\n * Computes the norm of scalar, vectors, and matrices.\n * This function can compute several different vector norms (the 1-norm, the\n * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)\n * and matrix norms (Frobenius, 1-norm, and inf-norm).\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.norm().print();  // or tf.norm(x)\n * ```\n *\n * @param x The input array.\n * @param ord Optional. Order of the norm. Supported norm types are\n * following:\n *\n *  | ord        | norm for matrices         | norm for vectors\n *  |------------|---------------------------|---------------------\n *  |'euclidean' |Frobenius norm             |2-norm\n *  |'fro'       |Frobenius norm\t           |\n *  |Infinity    |max(sum(abs(x), axis=1))   |max(abs(x))\n *  |-Infinity   |min(sum(abs(x), axis=1))   |min(abs(x))\n *  |1           |max(sum(abs(x), axis=0))   |sum(abs(x))\n *  |2           |                           |sum(abs(x)^2)^1/2*\n *\n * @param axis Optional. If axis is null (the default), the input is\n * considered a vector and a single vector norm is computed over the entire\n * set of values in the Tensor, i.e. norm(x, ord) is equivalent\n * to norm(x.reshape([-1]), ord). If axis is a integer, the input\n * is considered a batch of vectors, and axis determines the axis in x\n * over which to compute vector norms. If axis is a 2-tuple of integer it is\n * considered a batch of matrices and axis determines the axes in NDArray\n * over which to compute a matrix norm.\n * @param keepDims Optional. If true, the norm have the same dimensionality\n * as the input.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction norm_(x, ord = 'euclidean', axis = null, keepDims = false) {\n    x = convertToTensor(x, 'x', 'norm');\n    const norm = normImpl(x, ord, axis);\n    let keepDimsShape = norm.shape;\n    if (keepDims) {\n        const axes = parseAxisParam(axis, x.shape);\n        keepDimsShape = axis_util.expandShapeToKeepDim(norm.shape, axes);\n    }\n    return reshape(norm, keepDimsShape);\n}\nfunction normImpl(x, p, axis = null) {\n    if (x.rank === 0) {\n        return abs(x);\n    }\n    // consider vector when no axis is specified\n    if (x.rank !== 1 && axis === null) {\n        return normImpl(reshape(x, [-1]), p, axis);\n    }\n    // vector\n    if (x.rank === 1 || typeof axis === 'number' ||\n        Array.isArray(axis) && axis.length === 1) {\n        if (p === 1) {\n            return sum(abs(x), axis);\n        }\n        if (p === Infinity) {\n            return max(abs(x), axis);\n        }\n        if (p === -Infinity) {\n            return min(abs(x), axis);\n        }\n        if (p === 'euclidean' || p === 2) {\n            // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2\n            return sqrt(sum(pow(abs(x), scalar(2, 'int32')), axis));\n        }\n        throw new Error(`Error in norm: invalid ord value: ${p}`);\n    }\n    // matrix (assumption axis[0] < axis[1])\n    if (Array.isArray(axis) && axis.length === 2) {\n        if (p === 1) {\n            return max(sum(abs(x), axis[0]), axis[1] - 1);\n        }\n        if (p === Infinity) {\n            return max(sum(abs(x), axis[1]), axis[0]);\n        }\n        if (p === -Infinity) {\n            return min(sum(abs(x), axis[1]), axis[0]);\n        }\n        if (p === 'fro' || p === 'euclidean') {\n            // norm(x) = sqrt(sum(pow(x, 2)))\n            return sqrt(sum(square(x), axis));\n        }\n        throw new Error(`Error in norm: invalid ord value: ${p}`);\n    }\n    throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\nexport const norm = op({ norm_ });\n//# sourceMappingURL=norm.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assertTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { scalar } from './scalar';\nimport { sub } from './sub';\n/**\n * Compute the moving average of a variable.\n *\n * Without zeroDebias, the moving average operation is defined by:\n *   `v += delta`\n * where\n *   `delta = (1 - decay) * (x - v)`\n *\n * With zeroDebias (default), the `delta` term is scaled to debias the\n * effect of the (assumed) zero-initialization of `v`.\n *   `delta /= (1 - decay ^ step)`\n *\n * For more details on the zero-debiasing algorithm, see:\n *   https://arxiv.org/abs/1412.6980\n *\n * Note that this function is completely stateless and does not keep track of\n * step count. The step count needs to be maintained by the caller and passed\n * in as `step`.\n *\n * @param v The current moving average value.\n * @param x New input value, must have the same shape and dtype as `v`.\n * @param decay The decay factor. Typical values are 0.95 and 0.99.\n * @param step Step count.\n * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).\n * @returns The new moving average value.\n *\n * @doc {heading: 'Operations', subheading: 'Moving Average'}\n */\nfunction movingAverage_(v, x, decay, step, zeroDebias = true) {\n    const $v = convertToTensor(v, 'v', 'movingAverage');\n    const $x = convertToTensor(x, 'x', 'movingAverage');\n    const $decay = convertToTensor(decay, 'decay', 'movingAverage');\n    assertTypesMatch($v, $x);\n    util.assert(util.arraysEqual($v.shape, $x.shape), () => 'Shape mismatch in v and x');\n    const one = scalar(1);\n    const oneMinusDecay = sub(one, $decay);\n    let update = mul(sub($x, $v), oneMinusDecay);\n    if (zeroDebias) {\n        util.assert(step != null, () => 'When using zeroDebias: true, step is required.');\n        const $step = convertToTensor(step, 'step', 'movingAverage');\n        update = div(update, sub(one, pow($decay, $step)));\n    }\n    return add($v, update);\n}\nexport const movingAverage = op({ movingAverage_ });\n//# sourceMappingURL=moving_average.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ScatterNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport * as scatter_nd_util from './scatter_nd_util';\n/**\n * Creates a new tensor by applying sparse updates to individual\n * values or slices within a zero tensor of the given shape tensor according to\n * indices. This operator is the inverse of the `tf.gatherND` operator which\n * extracts values or slices from a given tensor.\n *\n * ```js\n * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32');\n * const updates = tf.tensor1d([9, 10, 11, 12]);\n * const shape = [8];\n * tf.scatterND(indices, updates, shape).print() //[0, 11, 0, 10, 9, 0, 0, 12]\n * ```\n *\n * @param indices The tensor contains the indices into the output tensor.\n * @param updates The tensor contains the value for the indices.\n * @param shape: The shape of the output tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction scatterND_(indices, updates, shape) {\n    const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');\n    const $updates = convertToTensor(updates, 'updates', 'scatterND');\n    scatter_nd_util.validateInput($updates, $indices, shape);\n    const forward = (backend) => {\n        return backend.scatterND($indices, $updates, shape);\n    };\n    const inputs = { indices: $indices, updates: $updates };\n    const attrs = { shape };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ScatterNd, attrs);\n}\nexport const scatterND = op({ scatterND_ });\n//# sourceMappingURL=scatter_nd.js.map", "/**\n * Validate sparseToDense inputs.\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape number[]. Shape of the dense output tensor.\n * @param validateIndices boolean. indice validation is not supported, error\n * will be thrown if it is set.\n */\nexport function validateInput(sparseIndices, sparseValues, outputShape, defaultValues) {\n    if (sparseIndices.dtype !== 'int32') {\n        throw new Error('tf.sparseToDense() expects the indices to be int32 type,' +\n            ` but the dtype was ${sparseIndices.dtype}.`);\n    }\n    if (sparseIndices.rank > 2) {\n        throw new Error('sparseIndices should be a scalar, vector, or matrix,' +\n            ` but got shape ${sparseIndices.shape}.`);\n    }\n    const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n    const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n    if (outputShape.length !== numDims) {\n        throw new Error('outputShape has incorrect number of elements:,' +\n            ` ${outputShape.length}, should be: ${numDims}.`);\n    }\n    const numValues = sparseValues.size;\n    if (!(sparseValues.rank === 0 ||\n        sparseValues.rank === 1 && numValues === numElems)) {\n        throw new Error('sparseValues has incorrect shape ' +\n            `${sparseValues.shape}, should be [] or [${numElems}]`);\n    }\n    if (sparseValues.dtype !== defaultValues.dtype) {\n        throw new Error('sparseValues.dtype must match defaultValues.dtype');\n    }\n}\n//# sourceMappingURL=sparse_to_dense_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SparseToDense } from '../kernel_names';\nimport * as sparse_to_dense from '../ops/sparse_to_dense_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Converts a sparse representation into a dense tensor.\n *\n * Builds an array dense with shape outputShape such that:\n *\n * // If sparseIndices is scalar\n * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)\n *\n * // If sparseIndices is a vector, then for each i\n * dense[sparseIndices[i]] = sparseValues[i]\n *\n * // If sparseIndices is an n by d matrix, then for each i in [0, n)\n * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]\n * All other values in dense are set to defaultValue. If sparseValues is a\n * scalar, all sparse indices are set to this single value.\n *\n * If indices are repeated the final value is summed over all values for those\n * indices.\n *\n * ```js\n * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');\n * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');\n * const shape = [8];\n * tf.sparseToDense(indices, values, shape).print();\n * ```\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape Shape of the dense output tensor. the type is inferred.\n * @param defaultValue Scalar. Value to set for indices not specified in\n * sparseIndices. Defaults to zero.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {\n    const $sparseIndices = convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');\n    const $sparseValues = convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');\n    const $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);\n    sparse_to_dense.validateInput($sparseIndices, $sparseValues, outputShape, $defaultValue);\n    const inputs = {\n        sparseIndices: $sparseIndices,\n        sparseValues: $sparseValues,\n        defaultValue: $defaultValue\n    };\n    const attrs = { outputShape };\n    return ENGINE.runKernelFunc(backend => backend.sparseToDense($sparseIndices, $sparseValues, outputShape, $defaultValue), inputs, null /* grad */, SparseToDense, attrs);\n}\nexport const sparseToDense = op({ sparseToDense_ });\n//# sourceMappingURL=sparse_to_dense.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Gather slices from input tensor into a Tensor with shape specified by\n * `indices`.\n *\n * `indices` is an K-dimensional integer tensor, best thought of as a\n * (K-1)-dimensional tensor of indices into input, where each element defines a\n * slice of input:\n * output[\\\\(i_0, ..., i_{K-2}\\\\)] = input[indices[\\\\(i_0, ..., i_{K-2}\\\\)]]\n *\n * Whereas in `tf.gather`, `indices` defines slices into the first dimension of\n * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions\n * of input, where N = indices.shape[-1].\n *\n * The last dimension of indices can be at most the rank of input:\n * indices.shape[-1] <= input.rank\n *\n * The last dimension of `indices` corresponds to elements\n * (if indices.shape[-1] == input.rank) or slices\n * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of\n * input.\n * The output tensor has shape\n * indices.shape[:-1] + input.shape[indices.shape[-1]:]\n *\n * Note that on CPU, if an out of bound index is found, an error is returned. On\n * GPU, if an out of bound index is found, a 0 is stored in the corresponding\n * output value.\n *\n * ```js\n * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');\n * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);\n * tf.gatherND(input, indices).print() // [10, 11]\n * ```\n *\n * @param x The tensor from which to gather values.\n * @param indices Index tensor, must be of type int32.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction gatherND_(x, indices) {\n    const $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');\n    const $x = convertToTensor(x, 'x', 'gatherND');\n    const forward = (backend) => {\n        return backend.gatherND($x, $indices);\n    };\n    const inputs = { params: $x, indices: $indices };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, GatherNd);\n}\nexport const gatherND = op({ gatherND_ });\n//# sourceMappingURL=gather_nd.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n * Normalize noise shape based on provided tensor and noise shape.\n *\n * @param x Tensor.\n * @param noiseShape The shape for the randomly generated keep/drop flags, as\n *   an array of numbers. Optional.\n * @returns Normalized noise shape.\n */\nexport function getNoiseShape(x, noiseShape) {\n    if (noiseShape == null) {\n        return x.shape.slice();\n    }\n    if (util.arraysEqual(x.shape, noiseShape)) {\n        return noiseShape;\n    }\n    if (x.shape.length === noiseShape.length) {\n        const newDimension = [];\n        for (let i = 0; i < x.shape.length; i++) {\n            if (noiseShape[i] == null && x.shape[i] != null) {\n                newDimension.push(x.shape[i]);\n            }\n            else {\n                newDimension.push(noiseShape[i]);\n            }\n        }\n        return newDimension;\n    }\n    return noiseShape;\n}\n//# sourceMappingURL=dropout_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { getNoiseShape } from './dropout_util';\nimport { floor } from './floor';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { randomUniform } from './random_uniform';\n/**\n * Computes dropout.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 2, 1]);\n * const rate = 0.75;\n * const output = tf.dropout(x, rate);\n * output.print();\n * ```\n *\n * @param x A floating point Tensor or TensorLike.\n * @param rate A float in the range [0, 1). The probability that each element\n *   of x is discarded.\n * @param noiseShape An array of numbers of type int32, representing the\n * shape for randomly generated keep/drop flags. If the noiseShape has null\n * value, it will be automatically replaced with the x's relative dimension\n * size. Optional.\n * @param seed Used to create random seeds. Optional.\n * @returns A Tensor of the same shape of x.\n *\n * @doc {heading: 'Operations', subheading: 'Dropout'}\n */\nfunction dropout_(x, rate, noiseShape, seed) {\n    const $x = convertToTensor(x, 'x', 'dropout');\n    util.assert($x.dtype === 'float32', () => `x has to be a floating point tensor since it's going to be ` +\n        `scaled, but got a ${$x.dtype} tensor instead.`);\n    util.assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);\n    if (rate === 0) {\n        return x instanceof Tensor ? $x.clone() : $x;\n    }\n    const $noiseShape = getNoiseShape($x, noiseShape);\n    const keepProb = 1 - rate;\n    const multiplier = div(floor(add(randomUniform($noiseShape, 0, 1, 'float32', seed), keepProb)), keepProb);\n    return mul($x, multiplier);\n}\nexport const dropout = op({ dropout_ });\n//# sourceMappingURL=dropout.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensor1d } from './tensor1d';\nexport function enclosingPowerOfTwo(value) {\n    // Return 2**N for integer N such that 2**N >= value.\n    return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2.0))));\n}\nexport function cosineWindow(windowLength, a, b) {\n    const even = 1 - windowLength % 2;\n    const newValues = new Float32Array(windowLength);\n    for (let i = 0; i < windowLength; ++i) {\n        const cosArg = (2.0 * Math.PI * i) / (windowLength + even - 1);\n        newValues[i] = a - b * Math.cos(cosArg);\n    }\n    return tensor1d(newValues, 'float32');\n}\n//# sourceMappingURL=signal_ops_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, assertShapesMatch, getTypedArrayFromDType } from '../util';\nimport { tensor } from './tensor';\n/**\n * Returns whether the targets are in the top K predictions.\n *\n * ```js\n * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);\n * const targets = tf.tensor1d([2, 0]);\n * const precision = await tf.inTopKAsync(predictions, targets);\n * precision.print();\n * ```\n * @param predictions 2-D or higher `tf.Tensor` with last dimension being\n *     at least `k`.\n * @param targets 1-D or higher `tf.Tensor`.\n * @param k Optional Number of top elements to look at for computing precision,\n *     default to 1.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nasync function inTopKAsync_(predictions, targets, k = 1) {\n    const $predictions = convertToTensor(predictions, 'predictions', 'inTopK');\n    const $targets = convertToTensor(targets, 'targets', 'inTopK');\n    assert($predictions.rank > 1, () => 'inTopK() expects the predictions to be of rank 2 or higher, ' +\n        `but got ${$predictions.rank}`);\n    assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than ` +\n        `targets rank, but got predictions rank ` +\n        `${$predictions.rank} and targets rank ${$targets.rank}`);\n    assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, ` +\n        'except the last dimension.');\n    const lastDim = $predictions.shape[$predictions.shape.length - 1];\n    assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last ` +\n        `dimension (${lastDim}), but got ${k}`);\n    const predictionsVals = await $predictions.data();\n    const targetsVals = await $targets.data();\n    // Reshape predictionsVals into a 2d tensor [batch, lastDim]\n    // and look up topK along lastDim.\n    const [batch, size] = [predictionsVals.length / lastDim, lastDim];\n    const precision = getTypedArrayFromDType('bool', batch);\n    for (let b = 0; b < batch; b++) {\n        const offset = b * size;\n        const vals = predictionsVals.subarray(offset, offset + size);\n        const valAndInd = [];\n        for (let i = 0; i < vals.length; i++) {\n            valAndInd.push({ value: vals[i], index: i });\n        }\n        valAndInd.sort((a, b) => b.value - a.value);\n        precision[b] = 0;\n        for (let i = 0; i < k; i++) {\n            if (valAndInd[i].index === targetsVals[b]) {\n                precision[b] = 1;\n                break;\n            }\n        }\n    }\n    if (predictions !== $predictions) {\n        $predictions.dispose();\n    }\n    if (targets !== $targets) {\n        $targets.dispose();\n    }\n    // Output precision has the same shape as targets.\n    return tensor(precision, $targets.shape, 'bool');\n}\nexport const inTopKAsync = inTopKAsync_;\n//# sourceMappingURL=in_top_k.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropFilter } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the filter of a 2D convolution.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.\n * @param dy The dy image, of rank 4 or rank 3, of shape\n *     [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.\n * @param filterShape The shape of the filter, length 4,\n *     [filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction conv2DBackpropFilter_(x, dy, filterShape, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n    let x4D = x;\n    if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    let dy4D = dy;\n    if (dy4D.rank === 3) {\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ` +\n        `${x4D.shape}.`);\n    util.assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ` +\n        `${dy4D.shape}.`);\n    util.assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ` +\n        `${filterShape}.`);\n    const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n    const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n    util.assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must ` +\n        `match input depth in filter (${filterShape[2]}.`);\n    util.assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must ` +\n        `match output depth for filter (${filterShape[3]}).`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2dDerFilter: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const dilations = 1;\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, filterShape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        return backend.conv2dDerFilter(x4D, dy4D, convInfo);\n    };\n    const inputs = { x: x4D, dy: dy4D };\n    const attrs = { strides, pad, dataFormat, dimRoundingMode };\n    return ENGINE.runKernelFunc(forward, inputs, null, Conv2DBackpropFilter, attrs);\n}\nexport const conv2DBackpropFilter = op({ conv2DBackpropFilter_ });\n//# sourceMappingURL=conv2d_backprop_filter.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as broadcast_util from './broadcast_util';\nimport { elu } from './elu';\nimport { mul } from './mul';\nimport { prelu } from './prelu';\nimport { relu } from './relu';\nimport { relu6 } from './relu6';\nimport { reshape } from './reshape';\nimport { step } from './step';\nimport { sum } from './sum';\n// Returns gradient for fused activation.\nexport function getFusedDyActivation(dy, y, activation) {\n    if (activation == null || activation === 'linear') {\n        return dy;\n    }\n    if (activation === 'relu') {\n        return mul(dy, step(y));\n    }\n    throw new Error(`Cannot compute gradient for fused activation ${activation}.`);\n}\n// Returns gradient for fused bias.\nexport function getFusedBiasGradient(bias, dyActivation) {\n    let res = dyActivation;\n    const reduceAxes = broadcast_util.getReductionAxes(bias.shape, dyActivation.shape);\n    if (reduceAxes.length > 0) {\n        res = sum(res, reduceAxes);\n    }\n    return reshape(res, bias.shape);\n}\nexport function applyActivation(x, activation, preluActivationWeights) {\n    if (activation === 'linear') {\n        return x;\n    }\n    else if (activation === 'relu') {\n        return relu(x);\n    }\n    else if (activation === 'elu') {\n        return elu(x);\n    }\n    else if (activation === 'relu6') {\n        return relu6(x);\n    }\n    else if (activation === 'prelu') {\n        return prelu(x, preluActivationWeights);\n    }\n    throw new Error(`Unknown fused activation ${activation}.`);\n}\n// Whether we should call fused ops.\nexport const shouldFuse = (gradientDepth, activation) => {\n    const gradientMode = gradientDepth > 0;\n    return !gradientMode || activation === 'linear';\n};\n//# sourceMappingURL=fused_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { conv2d as unfusedConv2d } from '../conv2d';\nimport { conv2DBackpropFilter } from '../conv2d_backprop_filter';\nimport { conv2DBackpropInput } from '../conv2d_backprop_input';\nimport * as conv_util from '../conv_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes a 2D convolution over the input x, optionally fused with adding a\n * bias and applying an activation.\n *\n * ```js\n * const inputDepth = 2;\n * const inShape = [2, 2, 2, inputDepth];\n * const outputDepth = 2;\n * const fSize = 1;\n * const pad = 0;\n * const strides = 1;\n *\n * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n * 16], inShape);\n * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,\n * outputDepth]);\n *\n * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',\n * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();\n * ```\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid` output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`) to be\n *     applied\n *      after biasAdd.\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights }) {\n    activation = activation || 'linear';\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    const $x = convertToTensor(x, 'x', 'conv2d');\n    const $filter = convertToTensor(filter, 'filter', 'conv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ` +\n        `${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in fused conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match ` +\n        `input depth for filter ${$filter.shape[2]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    util.assert(dataFormat === 'NHWC', () => `Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);\n    const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n        [$bias] = makeTypesMatch($bias, $x);\n        broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused conv2d');\n    }\n    const grad = (dy, saved) => {\n        const [$filter, x4D, y, $bias] = saved;\n        const dyActivation = getFusedDyActivation(dy, y, activation);\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused conv2D: ' +\n            `dilation rates greater than 1 ` +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n        const xDer = conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad);\n        const filterDer = conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad);\n        const der = [xDer, filterDer];\n        if ($bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            der.push(biasDer);\n        }\n        return der;\n    };\n    const forward = (backend) => {\n        const res = backend.fusedConv2d({\n            input: x4D,\n            filter: $filter,\n            convInfo,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return res;\n    };\n    const inputs = {\n        x: x4D,\n        filter: $filter,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((x4D, filter, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedConv2D, attrs);\n            save([filter, x4D, res]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOp(x4D, $filter);\n    }\n    else {\n        const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedConv2D, attrs);\n            save([filter, x4D, res, bias]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOpWithBias(x4D, $filter, $bias);\n    }\n}\nexport const conv2d = op({ fusedConv2d_ });\n//# sourceMappingURL=conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropFilter } from '../kernel_names';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, convInfo) {\n    let x4D = x;\n    if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    let dy4D = dy;\n    if (dy4D.rank === 3) {\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    const forward = backend => backend.depthwiseConv2DDerFilter(x4D, dy4D, convInfo);\n    const inputs = { x: x4D, dy: dy4D };\n    return ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropFilter);\n}\nexport const depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ });\n//# sourceMappingURL=depthwise_conv2d_native_backprop_filter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropInput } from '../kernel_names';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, convInfo) {\n    let dy4D = dy;\n    let reshapedTo4D = false;\n    if (dy.rank === 3) {\n        reshapedTo4D = true;\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    const forward = backend => backend.depthwiseConv2DDerInput(dy4D, filter, convInfo);\n    const inputs = { dy: dy4D };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropInput);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ });\n//# sourceMappingURL=depthwise_conv2d_native_backprop_input.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedDepthwiseConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport * as conv_util from '../conv_util';\nimport { depthwiseConv2d as unfusedDepthwiseConv2d } from '../depthwise_conv2d';\nimport { depthwiseConv2dNativeBackpropFilter } from '../depthwise_conv2d_native_backprop_filter';\nimport { depthwiseConv2dNativeBackpropInput } from '../depthwise_conv2d_native_backprop_input';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes depthwise 2D convolution, optionally fused with adding a\n * bias and applying an activation.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`).\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedDepthwiseConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights }) {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedDepthwiseConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n    const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, ` +\n        `but got rank ${$filter.rank}.`);\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels ` +\n        `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n        `filter ${$filter.shape[2]}.`);\n    if (dilations == null) {\n        dilations = [1, 1];\n    }\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in fused depthwiseConv2d: Either strides or dilations must ' +\n        `be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in fused depthwiseConv2d: pad must be an integer when ` +\n            `using dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n        [$bias] = makeTypesMatch($bias, $x);\n        broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');\n    }\n    const grad = (dy, saved) => {\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused depthwiseConv2d: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${dilations}'`);\n        const [$filter, x4D, y, bias] = saved;\n        const dyActivation = getFusedDyActivation(dy, y, activation);\n        const xDer = depthwiseConv2dNativeBackpropInput(x4D.shape, dyActivation, $filter, convInfo);\n        const filterDer = depthwiseConv2dNativeBackpropFilter(x4D, dyActivation, $filter.shape, convInfo);\n        if (bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            return [xDer, filterDer, biasDer];\n        }\n        return [xDer, filterDer];\n    };\n    const forward = (backend) => {\n        const res = backend.fusedDepthwiseConv2D({\n            input: x4D,\n            filter: $filter,\n            convInfo,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return res;\n    };\n    const inputs = {\n        x: x4D,\n        filter: $filter,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((x4D, filter, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedDepthwiseConv2D, attrs);\n            save([filter, x4D, res]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOp(x4D, $filter);\n    }\n    else {\n        const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedDepthwiseConv2D, attrs);\n            save([filter, x4D, res, bias]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOpWithBias(x4D, $filter, $bias);\n    }\n}\nexport const depthwiseConv2d = op({ fusedDepthwiseConv2d_ });\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { _FusedMatMul } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { matMul as unfusedMatMul } from '../mat_mul';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes the dot product of two matrices with optional activation and bias.\n *\n * ```js\n * const a = tf.tensor2d([-1, -2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const bias = tf.tensor2d([1, 2], [1, 2]);\n *\n * tf.fused.matMul({a, b, bias, activation: 'relu'}).print();\n * ```\n *\n * @param obj An object with the following properties:\n * - `a` First matrix in dot product operation.\n * - `b` Second matrix in dot product operation.\n * - `transposeA` If true, `a` is transposed before multiplication.\n * - `transposeB` If true, `b` is transposed before multiplication.\n * - `bias` Matrix to be added to the result.\n * - `activation` Name of activation kernel (defaults to `linear`).\n * - `preluActivationWeights` Tensor of prelu weights.\n */\nfunction fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation = 'linear', preluActivationWeights }) {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedMatMul(a, b, transposeA, transposeB);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    let $a = convertToTensor(a, 'a', 'fused matMul');\n    let $b = convertToTensor(b, 'b', 'fused matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n    const outerDimsA = $a.shape.slice(0, -2);\n    const outerDimsB = $b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n    util.assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in fused matMul: inputs must have the same rank of at least ` +\n        `2, got ranks ${$a.rank} and ${$b.rank}.`);\n    util.assert(util.arraysEqual(outerDimsA, outerDimsB), () => `Error in fused matMul: outer dimensions (${outerDimsA}) and (` +\n        `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} must match.`);\n    util.assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (` +\n        `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} and transposeA=${transposeA}` +\n        ` and transposeB=${transposeB} must match.`);\n    const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n    const a3D = transposeA ?\n        reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n        reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n    const b3D = transposeB ?\n        reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n        reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused matMul');\n        [$bias] = makeTypesMatch($bias, $a);\n        broadcast_util.assertAndGetBroadcastShape(outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused matMul');\n    }\n    const grad = (dy, saved) => {\n        const [a3D, b3D, y, $bias] = saved;\n        // we reshape dy because the result of the forward is not\n        // necessarily going to be a 3d tensor due to a reshape done at the end of\n        // the customOp.\n        const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation);\n        let aDer;\n        let bDer;\n        if (!transposeA && !transposeB) {\n            aDer = unfusedMatMul(dyActivation, b3D, false, true);\n            bDer = unfusedMatMul(a3D, dyActivation, true, false);\n        }\n        else if (!transposeA && transposeB) {\n            aDer = unfusedMatMul(dyActivation, b3D, false, false);\n            bDer = unfusedMatMul(dyActivation, a3D, true, false);\n        }\n        else if (transposeA && !transposeB) {\n            aDer = unfusedMatMul(b3D, dyActivation, false, true);\n            bDer = unfusedMatMul(a3D, dyActivation, false, false);\n        }\n        else {\n            aDer = unfusedMatMul(b3D, dyActivation, true, true);\n            bDer = unfusedMatMul(dyActivation, a3D, true, true);\n        }\n        if (bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            return [aDer, bDer, biasDer];\n        }\n        else {\n            return [aDer, bDer];\n        }\n    };\n    const forward = (backend) => {\n        const y = backend.fusedBatchMatMul({\n            a: a3D,\n            b: b3D,\n            transposeA,\n            transposeB,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return y;\n    };\n    const inputs = {\n        a: a3D,\n        b: b3D,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { transposeA, transposeB, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((a3D, b3D, save) => {\n            const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, _FusedMatMul, attrs);\n            save([a3D, b3D, res]);\n            return { value: reshape(res, outShape), gradFunc: grad };\n        });\n        return customOp(a3D, b3D);\n    }\n    else {\n        const customOpWithBias = customGrad((a3D, b3D, $bias, save) => {\n            const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, _FusedMatMul, attrs);\n            save([a3D, b3D, res, $bias]);\n            return { value: reshape(res, outShape), gradFunc: grad };\n        });\n        return customOpWithBias(a3D, b3D, $bias);\n    }\n}\nexport const matMul = op({ fusedMatMul_ });\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2d } from './fused/conv2d';\nimport { depthwiseConv2d } from './fused/depthwise_conv2d';\nimport { matMul } from './fused/mat_mul';\nexport { conv2d, depthwiseConv2d, matMul };\n//# sourceMappingURL=fused_ops.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a hamming window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hammingWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hammingWindow_(windowLength) {\n    return cosineWindow(windowLength, 0.54, 0.46);\n}\nexport const hammingWindow = op({ hammingWindow_ });\n//# sourceMappingURL=hamming_window.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a Hann window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hannWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hannWindow_(windowLength) {\n    return cosineWindow(windowLength, 0.5, 0.5);\n}\nexport const hannWindow = op({ hannWindow_ });\n//# sourceMappingURL=hann_window.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../concat';\nimport { fill } from '../fill';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { tensor2d } from '../tensor2d';\n/**\n * Expands input into frames of frameLength.\n * Slides a window size with frameStep.\n *\n * ```js\n * tf.signal.frame([1, 2, 3], 2, 1).print();\n * ```\n * @param signal The input tensor to be expanded\n * @param frameLength Length of each frame\n * @param frameStep The frame hop size in samples.\n * @param padEnd Whether to pad the end of signal with padValue.\n * @param padValue An number to use where the input signal does\n *     not exist when padEnd is True.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction frame_(signal, frameLength, frameStep, padEnd = false, padValue = 0) {\n    let start = 0;\n    const output = [];\n    while (start + frameLength <= signal.size) {\n        output.push(slice(signal, start, frameLength));\n        start += frameStep;\n    }\n    if (padEnd) {\n        while (start < signal.size) {\n            const padLen = (start + frameLength) - signal.size;\n            const pad = concat([\n                slice(signal, start, frameLength - padLen), fill([padLen], padValue)\n            ]);\n            output.push(pad);\n            start += frameStep;\n        }\n    }\n    if (output.length === 0) {\n        return tensor2d([], [0, frameLength]);\n    }\n    return reshape(concat(output), [output.length, frameLength]);\n}\nexport const frame = op({ frame_ });\n//# sourceMappingURL=frame.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../concat';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { enclosingPowerOfTwo } from '../signal_ops_util';\nimport { slice } from '../slice';\nimport { rfft } from '../spectral/rfft';\nimport { frame } from './frame';\nimport { hannWindow } from './hann_window';\n/**\n * Computes the Short-time Fourier Transform of signals\n * See: https://en.wikipedia.org/wiki/Short-time_Fourier_transform\n *\n * ```js\n * const input = tf.tensor1d([1, 1, 1, 1, 1])\n * tf.signal.stft(input, 3, 1).print();\n * ```\n * @param signal 1-dimensional real value tensor.\n * @param frameLength The window length of samples.\n * @param frameStep The number of samples to step.\n * @param fftLength The size of the FFT to apply.\n * @param windowFn A callable that takes a window length and returns 1-d tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction stft_(signal, frameLength, frameStep, fftLength, windowFn = hannWindow) {\n    if (fftLength == null) {\n        fftLength = enclosingPowerOfTwo(frameLength);\n    }\n    const framedSignal = frame(signal, frameLength, frameStep);\n    const windowedSignal = mul(framedSignal, windowFn(frameLength));\n    const output = [];\n    for (let i = 0; i < framedSignal.shape[0]; i++) {\n        output.push(rfft(slice(windowedSignal, [i, 0], [1, frameLength]), fftLength));\n    }\n    return concat(output);\n}\nexport const stft = op({ stft_ });\n//# sourceMappingURL=stft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { CropAndResize } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Extracts crops from the input image tensor and resizes them using bilinear\n * sampling or nearest neighbor sampling (possibly with aspect ratio change)\n * to a common output size specified by crop_size.\n *\n * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,\n *     where imageHeight and imageWidth must be positive, specifying the\n *     batch of images from which to take crops\n * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized\n *     coordinates of the box in the boxInd[i]'th image in the batch\n * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range\n *     `[0, batch)` that specifies the image that the `i`-th box refers to.\n * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`\n *     specifying the size to which all crops are resized to.\n * @param method Optional string from `'bilinear' | 'nearest'`,\n *     defaults to bilinear, which specifies the sampling method for resizing\n * @param extrapolationValue A threshold for deciding when to remove boxes based\n *     on score. Defaults to 0.\n * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction cropAndResize_(image, boxes, boxInd, cropSize, method, extrapolationValue) {\n    const $image = convertToTensor(image, 'image', 'cropAndResize');\n    const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');\n    const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');\n    method = method || 'bilinear';\n    extrapolationValue = extrapolationValue || 0;\n    const numBoxes = $boxes.shape[0];\n    util.assert($image.rank === 4, () => 'Error in cropAndResize: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    util.assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +\n        `but had shape ${$boxes.shape}.`);\n    util.assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +\n        `but had shape ${$boxes.shape}.`);\n    util.assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got ` +\n        `length ${cropSize.length}.`);\n    util.assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);\n    util.assert(method === 'bilinear' || method === 'nearest', () => `method must be bilinear or nearest, but was ${method}`);\n    const forward = (backend) => backend.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);\n    const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };\n    const attrs = { method, extrapolationValue, cropSize };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, CropAndResize, attrs);\n    return res;\n}\nexport const cropAndResize = op({ cropAndResize_ });\n//# sourceMappingURL=crop_and_resize.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FlipLeftRight } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Flips the image left to right. Currently available in the CPU, WebGL, and\n * WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction flipLeftRight_(image) {\n    const $image = convertToTensor(image, 'image', 'flipLeftRight', 'float32');\n    util.assert($image.rank === 4, () => 'Error in flipLeftRight: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    const inputs = { image: $image };\n    const res = ENGINE.runKernel(FlipLeftRight, inputs, {});\n    return res;\n}\nexport const flipLeftRight = op({ flipLeftRight_ });\n//# sourceMappingURL=flip_left_right.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { RotateWithOffset } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Rotates the input image tensor counter-clockwise with an optional offset\n * center of rotation. Currently available in the CPU, WebGL, and WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n * @param radians The amount of rotation.\n * @param fillValue The value to fill in the empty space leftover\n *     after rotation. Can be either a single grayscale value (0-255), or an\n *     array of three numbers `[red, green, blue]` specifying the red, green,\n *     and blue channels. Defaults to `0` (black).\n * @param center The center of rotation. Can be either a single value (0-1), or\n *     an array of two numbers `[centerX, centerY]`. Defaults to `0.5` (rotates\n *     the image around its center).\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction rotateWithOffset_(image, radians, fillValue = 0, center = 0.5) {\n    const $image = convertToTensor(image, 'image', 'rotateWithOffset', 'float32');\n    util.assert($image.rank === 4, () => 'Error in rotateWithOffset: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    const inputs = { image: $image };\n    const attrs = { radians, fillValue, center };\n    const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);\n    return res;\n}\nexport const rotateWithOffset = op({ rotateWithOffset_ });\n//# sourceMappingURL=rotate_with_offset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nfunction nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n    if (iouThreshold == null) {\n        iouThreshold = 0.5;\n    }\n    if (scoreThreshold == null) {\n        scoreThreshold = Number.NEGATIVE_INFINITY;\n    }\n    if (softNmsSigma == null) {\n        softNmsSigma = 0.0;\n    }\n    const numBoxes = boxes.shape[0];\n    maxOutputSize = Math.min(maxOutputSize, numBoxes);\n    util.assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n    util.assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n    util.assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n    util.assert(scores.rank === 1, () => 'scores must be a 1D tensor');\n    util.assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, ` +\n        `but was ${scores.shape[0]}`);\n    util.assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);\n    return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n}\nexport { nonMaxSuppSanityCheck };\n//# sourceMappingURL=nonmax_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV3 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\nfunction nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n    maxOutputSize = inputs.maxOutputSize;\n    iouThreshold = inputs.iouThreshold;\n    scoreThreshold = inputs.scoreThreshold;\n    const attrs = { maxOutputSize, iouThreshold, scoreThreshold };\n    return ENGINE.runKernelFunc(b => b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold), { boxes: $boxes, scores: $scores }, null /* grad */, NonMaxSuppressionV3, attrs);\n}\nexport const nonMaxSuppression = op({ nonMaxSuppression_ });\n//# sourceMappingURL=non_max_suppression.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inserts a value into a sorted array. This method allows duplicate, meaning it\n * allows inserting duplicate value, in which case, the element will be inserted\n * at the lowest index of the value.\n * @param arr The array to modify.\n * @param element The element to insert.\n * @param comparator Optional. If no comparator is specified, elements are\n * compared using array_util.defaultComparator, which is suitable for Strings\n * and Numbers in ascending arrays. If the array contains multiple instances of\n * the target value, the left-most instance will be returned. To provide a\n * comparator, it should take 2 arguments to compare and return a negative,\n * zero, or a positive number.\n */\nexport function binaryInsert(arr, element, comparator) {\n    const index = binarySearch(arr, element, comparator);\n    const insertionPoint = index < 0 ? -(index + 1) : index;\n    arr.splice(insertionPoint, 0, element);\n}\n/**\n * Searches the array for the target using binary search, returns the index\n * of the found element, or position to insert if element not found. If no\n * comparator is specified, elements are compared using array_\n * util.defaultComparator, which is suitable for Strings and Numbers in\n * ascending arrays. If the array contains multiple instances of the target\n * value, the left-most instance will be returned.\n * @param arr The array to be searched in.\n * @param target The target to be searched for.\n * @param comparator Should take 2 arguments to compare and return a negative,\n *    zero, or a positive number.\n * @return Lowest index of the target value if found, otherwise the insertion\n *    point where the target should be inserted, in the form of\n *    (-insertionPoint - 1).\n */\nexport function binarySearch(arr, target, comparator) {\n    return binarySearch_(arr, target, comparator || defaultComparator);\n}\n/**\n * Compares its two arguments for order.\n * @param a The first element to be compared.\n * @param b The second element to be compared.\n * @return A negative number, zero, or a positive number as the first\n *     argument is less than, equal to, or greater than the second.\n */\nfunction defaultComparator(a, b) {\n    return a > b ? 1 : a < b ? -1 : 0;\n}\nfunction binarySearch_(arr, target, comparator) {\n    let left = 0;\n    let right = arr.length;\n    let middle = 0;\n    let found = false;\n    while (left < right) {\n        middle = left + ((right - left) >>> 1);\n        const compareResult = comparator(target, arr[middle]);\n        if (compareResult > 0) {\n            left = middle + 1;\n        }\n        else {\n            right = middle;\n            // If compareResult is 0, the value is found. We record it is found,\n            // and then keep looking because there may be duplicate.\n            found = !compareResult;\n        }\n    }\n    return found ? left : -left - 1;\n}\n//# sourceMappingURL=array_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Implementation of the NonMaxSuppression kernel shared between webgl and cpu.\n */\nimport { scalar } from '../ops/scalar';\nimport { tensor1d } from '../ops/tensor1d';\nimport { binaryInsert } from './array_util';\nexport function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */)\n        .selectedIndices;\n}\nexport function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */, false /* returnScoresTensor */, padToMaxOutputSize /* padToMaxOutputSize */, true\n    /* returnValidOutputs */ );\n}\nexport function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true /* returnScoresTensor */);\n}\nfunction nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {\n    // The list is sorted in ascending order, so that we can always pop the\n    // candidate with the largest score in O(1) time.\n    const candidates = [];\n    for (let i = 0; i < scores.length; i++) {\n        if (scores[i] > scoreThreshold) {\n            candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 });\n        }\n    }\n    candidates.sort(ascendingComparator);\n    // If softNmsSigma is 0, the outcome of this algorithm is exactly same as\n    // before.\n    const scale = softNmsSigma > 0 ? (-0.5 / softNmsSigma) : 0.0;\n    const selectedIndices = [];\n    const selectedScores = [];\n    while (selectedIndices.length < maxOutputSize && candidates.length > 0) {\n        const candidate = candidates.pop();\n        const { score: originalScore, boxIndex, suppressBeginIndex } = candidate;\n        if (originalScore < scoreThreshold) {\n            break;\n        }\n        // Overlapping boxes are likely to have similar scores, therefore we\n        // iterate through the previously selected boxes backwards in order to\n        // see if candidate's score should be suppressed. We use\n        // suppressBeginIndex to track and ensure a candidate can be suppressed\n        // by a selected box no more than once. Also, if the overlap exceeds\n        // iouThreshold, we simply ignore the candidate.\n        let ignoreCandidate = false;\n        for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {\n            const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);\n            if (iou >= iouThreshold) {\n                ignoreCandidate = true;\n                break;\n            }\n            candidate.score =\n                candidate.score * suppressWeight(iouThreshold, scale, iou);\n            if (candidate.score <= scoreThreshold) {\n                break;\n            }\n        }\n        // At this point, if `candidate.score` has not dropped below\n        // `scoreThreshold`, then we know that we went through all of the\n        // previous selections and can safely update `suppressBeginIndex` to the\n        // end of the selected array. Then we can re-insert the candidate with\n        // the updated score and suppressBeginIndex back in the candidate list.\n        // If on the other hand, `candidate.score` has dropped below the score\n        // threshold, we will not add it back to the candidates list.\n        candidate.suppressBeginIndex = selectedIndices.length;\n        if (!ignoreCandidate) {\n            // Candidate has passed all the tests, and is not suppressed, so\n            // select the candidate.\n            if (candidate.score === originalScore) {\n                selectedIndices.push(boxIndex);\n                selectedScores.push(candidate.score);\n            }\n            else if (candidate.score > scoreThreshold) {\n                // Candidate's score is suppressed but is still high enough to be\n                // considered, so add back to the candidates list.\n                binaryInsert(candidates, candidate, ascendingComparator);\n            }\n        }\n    }\n    // NonMaxSuppressionV4 feature: padding output to maxOutputSize.\n    const validOutputs = selectedIndices.length;\n    const elemsToPad = maxOutputSize - validOutputs;\n    if (padToMaxOutputSize && elemsToPad > 0) {\n        selectedIndices.push(...new Array(elemsToPad).fill(0));\n        selectedScores.push(...new Array(elemsToPad).fill(0.0));\n    }\n    const result = { selectedIndices: tensor1d(selectedIndices, 'int32') };\n    if (returnScoresTensor) {\n        result['selectedScores'] = tensor1d(selectedScores, 'float32');\n    }\n    if (returnValidOutputs) {\n        result['validOutputs'] = scalar(validOutputs, 'int32');\n    }\n    return result;\n}\nfunction intersectionOverUnion(boxes, i, j) {\n    const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n    const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n    const yminI = Math.min(iCoord[0], iCoord[2]);\n    const xminI = Math.min(iCoord[1], iCoord[3]);\n    const ymaxI = Math.max(iCoord[0], iCoord[2]);\n    const xmaxI = Math.max(iCoord[1], iCoord[3]);\n    const yminJ = Math.min(jCoord[0], jCoord[2]);\n    const xminJ = Math.min(jCoord[1], jCoord[3]);\n    const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n    const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n    const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n    const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n    if (areaI <= 0 || areaJ <= 0) {\n        return 0.0;\n    }\n    const intersectionYmin = Math.max(yminI, yminJ);\n    const intersectionXmin = Math.max(xminI, xminJ);\n    const intersectionYmax = Math.min(ymaxI, ymaxJ);\n    const intersectionXmax = Math.min(xmaxI, xmaxJ);\n    const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *\n        Math.max(intersectionXmax - intersectionXmin, 0.0);\n    return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n// A Gaussian penalty function, this method always returns values in [0, 1].\n// The weight is a function of similarity, the more overlap two boxes are, the\n// smaller the weight is, meaning highly overlapping boxe will be significantly\n// penalized. On the other hand, a non-overlapping box will not be penalized.\nfunction suppressWeight(iouThreshold, scale, iou) {\n    const weight = Math.exp(scale * iou * iou);\n    return iou <= iouThreshold ? weight : 0.0;\n}\nfunction ascendingComparator(c1, c2) {\n    // For objects with same scores, we make the object with the larger index go\n    // first. In an array that pops from the end, this means that the object with\n    // the smaller index will be popped first. This ensures the same output as\n    // the TensorFlow python version.\n    return (c1.score - c2.score) ||\n        ((c1.score === c2.score) && (c2.boxIndex - c1.boxIndex));\n}\n//# sourceMappingURL=non_max_suppression_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV3Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This is the async version of `nonMaxSuppression`\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n    maxOutputSize = inputs.maxOutputSize;\n    iouThreshold = inputs.iouThreshold;\n    scoreThreshold = inputs.scoreThreshold;\n    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n    const boxesVals = boxesAndScores[0];\n    const scoresVals = boxesAndScores[1];\n    // We call a cpu based impl directly with the typedarray data  here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\n//# sourceMappingURL=non_max_suppression_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV5 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    maxOutputSize = params.maxOutputSize;\n    iouThreshold = params.iouThreshold;\n    scoreThreshold = params.scoreThreshold;\n    softNmsSigma = params.softNmsSigma;\n    const inputs = { boxes: $boxes, scores: $scores };\n    const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n    const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);\n    return { selectedIndices: result[0], selectedScores: result[1] };\n}\nexport const nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ });\n//# sourceMappingURL=non_max_suppression_with_score.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV5Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    maxOutputSize = params.maxOutputSize;\n    iouThreshold = params.iouThreshold;\n    scoreThreshold = params.scoreThreshold;\n    softNmsSigma = params.softNmsSigma;\n    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n    const boxesVals = boxesAndScores[0];\n    const scoresVals = boxesAndScores[1];\n    // We call a cpu based impl directly with the typedarray data  here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;\n//# sourceMappingURL=non_max_suppression_with_score_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV4 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n    const $maxOutputSize = params.maxOutputSize;\n    const $iouThreshold = params.iouThreshold;\n    const $scoreThreshold = params.scoreThreshold;\n    const inputs = { boxes: $boxes, scores: $scores };\n    const attrs = {\n        maxOutputSize: $maxOutputSize,\n        iouThreshold: $iouThreshold,\n        scoreThreshold: $scoreThreshold,\n        padToMaxOutputSize\n    };\n    const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);\n    return { selectedIndices: result[0], validOutputs: result[1] };\n}\nexport const nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ });\n//# sourceMappingURL=non_max_suppression_padded.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV4Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n    const $maxOutputSize = params.maxOutputSize;\n    const $iouThreshold = params.iouThreshold;\n    const $scoreThreshold = params.scoreThreshold;\n    const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);\n    // We call a cpu based impl directly with the typedarray data here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;\n//# sourceMappingURL=non_max_suppression_padded_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeBilinear } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Bilinear resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeBilinear_(images, size, alignCorners = false) {\n    const $images = convertToTensor(images, 'images', 'resizeBilinear');\n    util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got ` +\n        `rank ${$images.rank}.`);\n    util.assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ` +\n        `${size}.`);\n    let batchImages = $images;\n    let reshapedTo4D = false;\n    if ($images.rank === 3) {\n        reshapedTo4D = true;\n        batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n    }\n    const [newHeight, newWidth] = size;\n    const forward = (backend, save) => {\n        save([batchImages]);\n        return backend.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);\n    };\n    const inputs = { images: batchImages };\n    const attrs = { alignCorners, size };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, ResizeBilinear, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const resizeBilinear = op({ resizeBilinear_ });\n//# sourceMappingURL=resize_bilinear.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeNearestNeighbor } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * NearestNeighbor resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeNearestNeighbor_(images, size, alignCorners = false) {\n    const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');\n    util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` +\n        `rank ${$images.rank}.`);\n    util.assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ` +\n        `${size}.`);\n    util.assert($images.dtype === 'float32' || $images.dtype === 'int32', () => '`images` must have `int32` or `float32` as dtype');\n    let batchImages = $images;\n    let reshapedTo4D = false;\n    if ($images.rank === 3) {\n        reshapedTo4D = true;\n        batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n    }\n    const [newHeight, newWidth] = size;\n    const inputs = { images: batchImages };\n    const attrs = { alignCorners, size };\n    const forward = (backend, save) => {\n        save([batchImages]);\n        return backend.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);\n    };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, ResizeNearestNeighbor, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const resizeNearestNeighbor = op({ resizeNearestNeighbor_ });\n//# sourceMappingURL=resize_nearest_neighbor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assert } from '../../util';\nimport { greaterEqual } from '../greater_equal';\nimport { lessEqual } from '../less_equal';\nimport { logicalAnd } from '../logical_and';\nimport { op } from '../operation';\nimport { range } from '../range';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\nimport { zeros } from '../zeros';\n/**\n * Copy a tensor setting everything outside a central band in each innermost\n * matrix to zero.\n *\n * The band part is computed as follows: Assume input has `k` dimensions\n * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where\n * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.\n * The indicator function\n * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower))`\n * `&& (num_upper < 0 || (n-m) <= num_upper)`\n *\n * ```js\n * const x = tf.tensor2d([[ 0,  1,  2, 3],\n *                        [-1,  0,  1, 2],\n *                        [-2, -1,  0, 1],\n *                        [-3, -2, -1, 0]]);\n * let y = tf.linalg.bandPart(x, 1, -1);\n * y.print(); // [[ 0,  1,  2, 3],\n *            //  [-1,  0,  1, 2],\n *            //  [ 0, -1,  0, 1],\n *            //  [ 0, 0 , -1, 0]]\n * let z = tf.linalg.bandPart(x, 2, 1);\n * z.print(); // [[ 0,  1,  0, 0],\n *            //  [-1,  0,  1, 0],\n *            //  [-2, -1,  0, 1],\n *            //  [ 0, -2, -1, 0]]\n * ```\n *\n * @param x Rank `k` tensor\n * @param numLower Number of subdiagonals to keep.\n *   If negative, keep entire lower triangle.\n * @param numUpper Number of subdiagonals to keep.\n *   If negative, keep entire upper triangle.\n * @returns Rank `k` tensor of the same shape as input.\n *   The extracted banded tensor.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction bandPart_(a, numLower, numUpper) {\n    assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);\n    assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);\n    const $a = convertToTensor(a, 'a', 'bandPart');\n    assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);\n    const shape = $a.shape;\n    const [M, N] = $a.shape.slice(-2);\n    if (!(numLower <= M)) {\n        throw new Error(`bandPart(): numLower (${numLower})` +\n            ` must not be greater than the number of rows (${M}).`);\n    }\n    if (!(numUpper <= N)) {\n        throw new Error(`bandPart(): numUpper (${numUpper})` +\n            ` must not be greater than the number of columns (${N}).`);\n    }\n    if (numLower < 0) {\n        numLower = M;\n    }\n    if (numUpper < 0) {\n        numUpper = N;\n    }\n    const i = reshape(range(0, M, 1, 'int32'), [-1, 1]);\n    const j = range(0, N, 1, 'int32');\n    const ij = sub(i, j);\n    const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, 'int32')), greaterEqual(ij, scalar(-numUpper, 'int32')));\n    const zero = zeros([M, N], $a.dtype);\n    return reshape(stack(unstack(reshape($a, [-1, M, N]))\n        .map(mat => where(inBand, mat, zero))), shape);\n}\nexport const bandPart = op({ bandPart_ });\n//# sourceMappingURL=band_part.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { assert } from '../../util';\nimport { div } from '../div';\nimport { mul } from '../mul';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { split } from '../split';\nimport { squeeze } from '../squeeze';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\n/**\n * Gram-Schmidt orthogonalization.\n *\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * let y = tf.linalg.gramSchmidt(x);\n * y.print();\n * console.log('Othogonalized:');\n * y.dot(y.transpose()).print();  // should be nearly the identity matrix.\n * console.log('First row direction maintained:');\n * const data = await y.array();\n * console.log(data[0][1] / data[0][0]);  // should be nearly 2.\n * ```\n *\n * @param xs The vectors to be orthogonalized, in one of the two following\n *   formats:\n *   - An Array of `tf.Tensor1D`.\n *   - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows\n *     of `xs`.\n *   In each case, all the vectors must have the same length and the length\n *   must be greater than or equal to the number of vectors.\n * @returns The orthogonalized and normalized vectors or matrix.\n *   Orthogonalization means that the vectors or the rows of the matrix\n *   are orthogonal (zero inner products). Normalization means that each\n *   vector or each row of the matrix has an L2 norm that equals `1`.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction gramSchmidt_(xs) {\n    let inputIsTensor2D;\n    if (Array.isArray(xs)) {\n        inputIsTensor2D = false;\n        assert(xs != null && xs.length > 0, () => 'Gram-Schmidt process: input must not be null, undefined, or ' +\n            'empty');\n        const dim = xs[0].shape[0];\n        for (let i = 1; i < xs.length; ++i) {\n            assert(xs[i].shape[0] === dim, () => 'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +\n                `(${xs[i].shape[0]} vs. ${dim})`);\n        }\n    }\n    else {\n        inputIsTensor2D = true;\n        xs = split(xs, xs.shape[0], 0).map(x => squeeze(x, [0]));\n    }\n    assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds ` +\n        `number of dimensions (${xs[0].shape[0]}).`);\n    const ys = [];\n    const xs1d = xs;\n    for (let i = 0; i < xs.length; ++i) {\n        ys.push(ENGINE.tidy(() => {\n            let x = xs1d[i];\n            if (i > 0) {\n                for (let j = 0; j < i; ++j) {\n                    const proj = mul(sum(mul(ys[j], x)), ys[j]);\n                    x = sub(x, proj);\n                }\n            }\n            return div(x, norm(x, 'euclidean'));\n        }));\n    }\n    if (inputIsTensor2D) {\n        return stack(ys, 0);\n    }\n    else {\n        return ys;\n    }\n}\nexport const gramSchmidt = op({ gramSchmidt_ });\n//# sourceMappingURL=gram_schmidt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { dispose } from '../../globals';\nimport { assert } from '../../util';\nimport { clone } from '../clone';\nimport { concat } from '../concat';\nimport { div } from '../div';\nimport { eye } from '../eye';\nimport { greater } from '../greater';\nimport { matMul } from '../mat_mul';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { tensor2d } from '../tensor2d';\nimport { transpose } from '../transpose';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\n/**\n * Compute QR decomposition of m-by-n matrix using Householder transformation.\n *\n * Implementation based on\n *   [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]\n * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [3, 4]]);\n * let [q, r] = tf.linalg.qr(a);\n * console.log('Q');\n * q.print();\n * console.log('R');\n * r.print();\n * console.log('Orthogonalized');\n * q.dot(q.transpose()).print()  // should be nearly the identity matrix.\n * console.log('Reconstructed');\n * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];\n * ```\n *\n * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose\n *   it has the shape `[..., M, N]`.\n * @param fullMatrices An optional boolean parameter. Defaults to `false`.\n *   If `true`, compute full-sized `Q`. If `false` (the default),\n *   compute only the leading N columns of `Q` and `R`.\n * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,\n *   i.e., its columns all have unit norm and are mutually orthogonal.\n *   If `M >= N`,\n *     If `fullMatrices` is `false` (default),\n *       - `Q` has a shape of `[..., M, N]`,\n *       - `R` has a shape of `[..., N, N]`.\n *     If `fullMatrices` is `true` (default),\n *       - `Q` has a shape of `[..., M, M]`,\n *       - `R` has a shape of `[..., M, N]`.\n *   If `M < N`,\n *     - `Q` has a shape of `[..., M, M]`,\n *     - `R` has a shape of `[..., M, N]`.\n * @throws If the rank of `x` is less than 2.\n *\n * @doc {heading:'Operations',\n *       subheading:'Linear Algebra',\n *       namespace:'linalg'}\n */\nfunction qr_(x, fullMatrices = false) {\n    assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);\n    if (x.rank === 2) {\n        return qr2d(x, fullMatrices);\n    }\n    else {\n        // Rank > 2.\n        // TODO(cais): Below we split the input into individual 2D tensors,\n        //   perform QR decomposition on them and then stack the results back\n        //   together. We should explore whether this can be parallelized.\n        const outerDimsProd = x.shape.slice(0, x.shape.length - 2)\n            .reduce((value, prev) => value * prev);\n        const x2ds = unstack(reshape(x, [\n            outerDimsProd, x.shape[x.shape.length - 2],\n            x.shape[x.shape.length - 1]\n        ]), 0);\n        const q2ds = [];\n        const r2ds = [];\n        x2ds.forEach(x2d => {\n            const [q2d, r2d] = qr2d(x2d, fullMatrices);\n            q2ds.push(q2d);\n            r2ds.push(r2d);\n        });\n        const q = reshape(stack(q2ds, 0), x.shape);\n        const r = reshape(stack(r2ds, 0), x.shape);\n        return [q, r];\n    }\n}\nfunction qr2d(x, fullMatrices = false) {\n    return ENGINE.tidy(() => {\n        assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);\n        const m = x.shape[0];\n        const n = x.shape[1];\n        let q = eye(m); // Orthogonal transform so far.\n        let r = clone(x); // Transformed matrix so far.\n        const one2D = tensor2d([[1]], [1, 1]);\n        let w = clone(one2D);\n        const iters = m >= n ? n : m;\n        for (let j = 0; j < iters; ++j) {\n            // This tidy within the for-loop ensures we clean up temporary\n            // tensors as soon as they are no longer needed.\n            const rTemp = r;\n            const wTemp = w;\n            const qTemp = q;\n            [w, r, q] = ENGINE.tidy(() => {\n                // Find H = I - tau * w * w', to put zeros below R(j, j).\n                const rjEnd1 = slice(r, [j, j], [m - j, 1]);\n                const normX = norm(rjEnd1);\n                const rjj = slice(r, [j, j], [1, 1]);\n                // The sign() function returns 0 on 0, which causes division by zero.\n                const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));\n                const u1 = sub(rjj, mul(s, normX));\n                const wPre = div(rjEnd1, u1);\n                if (wPre.shape[0] === 1) {\n                    w = clone(one2D);\n                }\n                else {\n                    w = concat([\n                        one2D,\n                        slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])\n                    ], 0);\n                }\n                const tau = neg(div(matMul(s, u1), normX));\n                // -- R := HR, Q := QH.\n                const rjEndAll = slice(r, [j, 0], [m - j, n]);\n                const tauTimesW = mul(tau, w);\n                const wT = transpose(w);\n                if (j === 0) {\n                    r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n                }\n                else {\n                    const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n                    r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);\n                }\n                const tawTimesWT = transpose(tauTimesW);\n                const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);\n                if (j === 0) {\n                    q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n                }\n                else {\n                    const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n                    q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);\n                }\n                return [w, r, q];\n            });\n            dispose([rTemp, wTemp, qTemp]);\n        }\n        if (!fullMatrices && m > n) {\n            q = slice(q, [0, 0], [m, n]);\n            r = slice(r, [0, 0], [n, n]);\n        }\n        return [q, r];\n    });\n}\nexport const qr = op({ qr_ });\n//# sourceMappingURL=qr.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Reduction;\n(function (Reduction) {\n    Reduction[Reduction[\"NONE\"] = 0] = \"NONE\";\n    Reduction[Reduction[\"MEAN\"] = 1] = \"MEAN\";\n    Reduction[Reduction[\"SUM\"] = 2] = \"SUM\";\n    Reduction[Reduction[\"SUM_BY_NONZERO_WEIGHTS\"] = 3] = \"SUM_BY_NONZERO_WEIGHTS\";\n})(Reduction || (Reduction = {}));\n//# sourceMappingURL=loss_ops_utils.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { Reduction } from '../loss_ops_utils';\nimport { mean } from '../mean';\nimport { mul } from '../mul';\nimport { notEqual } from '../not_equal';\nimport { ones } from '../ones';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sum } from '../sum';\n/**\n * Computes the weighted loss between two tensors.\n *\n * @param losses Tensor of shape `[batch_size, d1, ... dN]`.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `losses`, and must be broadcastable to `losses` (i.e., all\n *    dimensions must be either `1`, or the same as the corresponding\n *    `losses` dimension).\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction computeWeightedLoss_(losses, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');\n    }\n    const weightedLoss = ($weights == null) ? $losses : mul($losses, $weights);\n    if (reduction === Reduction.NONE) {\n        return weightedLoss;\n    }\n    if (reduction === Reduction.SUM) {\n        return sum(weightedLoss);\n    }\n    if (reduction === Reduction.MEAN) {\n        if ($weights == null) {\n            return mean(weightedLoss);\n        }\n        else {\n            const broadcastFactor = $losses.size / $weights.size;\n            const result = div(sum(weightedLoss), sum($weights));\n            return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) :\n                result;\n        }\n    }\n    if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n        if ($weights == null) {\n            return div(sum(weightedLoss), scalar($losses.size));\n        }\n        else {\n            const broadcastedWeights = mul($weights, ones($losses.shape));\n            const numNonZeros = cast(sum(notEqual(broadcastedWeights, scalar(0))), 'float32');\n            return div(sum(weightedLoss), numNonZeros);\n        }\n    }\n    throw Error(`Unknown reduction: ${reduction}`);\n}\nexport const computeWeightedLoss = op({ computeWeightedLoss_ });\n//# sourceMappingURL=compute_weighted_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the absolute difference loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'absoluteDifference');\n    const $predictions = convertToTensor(predictions, 'predictions', 'absoluteDifference');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'absoluteDifference');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in absoluteDifference: ');\n    const losses = abs(sub($labels, $predictions));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const absoluteDifference = op({ absoluteDifference_ });\n//# sourceMappingURL=absolute_difference.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the cosine distance loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param axis The dimension along which the cosine distance is computed.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'cosineDistance');\n    const $predictions = convertToTensor(predictions, 'predictions', 'cosineDistance');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'cosineDistance');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in cosineDistance: ');\n    const one = scalar(1);\n    const losses = sub(one, sum(mul($labels, $predictions), axis, true));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const cosineDistance = op({ cosineDistance_ });\n//# sourceMappingURL=cosine_distance.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the Hinge loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $labels = convertToTensor(labels, 'labels', 'hingeLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'hingeLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');\n    const one = scalar(1);\n    // Convert binary labels to (-1, 1)\n    $labels = sub(mul(scalar(2), $labels), one);\n    const losses = relu(sub(one, mul($labels, $predictions)));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const hingeLoss = op({ hingeLoss_ });\n//# sourceMappingURL=hinge_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { Reduction } from '../loss_ops_utils';\nimport { minimum } from '../minimum';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { square } from '../square';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the huber loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param delta Point where huber loss changes from quadratic to linear.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`.\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction huberLoss_(labels, predictions, weights, delta = 1.0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'huberLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'huberLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');\n    const deltaScalar = scalar(delta);\n    const error = abs(sub($predictions, $labels));\n    const quadratic = minimum(error, deltaScalar);\n    const linear = sub(error, quadratic);\n    const losses = add(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const huberLoss = op({ huberLoss_ });\n//# sourceMappingURL=huber_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { log } from '../log';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the log loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param epsilon A small increment to avoid taking log of zero\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction logLoss_(labels, predictions, weights, epsilon = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'logLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'logLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'logLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');\n    const one = scalar(1);\n    const epsilonScalar = scalar(epsilon);\n    const l1 = neg(mul($labels, log(add($predictions, epsilonScalar))));\n    const l2 = mul(sub(one, $labels), log(add(sub(one, $predictions), epsilonScalar)));\n    const losses = sub(l1, l2);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const logLoss = op({ logLoss_ });\n//# sourceMappingURL=log_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { squaredDifference } from '../squared_difference';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the mean squared error between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'meanSquaredError');\n    const $predictions = convertToTensor(predictions, 'predictions', 'meanSquaredError');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'meanSquaredError');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in meanSquaredError: ');\n    const losses = squaredDifference($labels, $predictions);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const meanSquaredError = op({ meanSquaredError_ });\n//# sourceMappingURL=mean_squared_error.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { exp } from '../exp';\nimport { log1p } from '../log1p';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\nfunction sigmoidCrossEntropyWithLogits_(labels, logits) {\n    const $labels = convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');\n    const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');\n    assertShapesMatch($labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');\n    /**\n     * Implementation Details:\n     *\n     * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n     *     z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n     *   = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n     *   = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n     *   = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n     *   = (1 - z) * x + log(1 + exp(-x))\n     *   = x - x * z + log(1 + exp(-x))\n     *\n     *   For x < 0, to avoid overflow in exp(-x), we reformulate the above\n     *     x - x * z + log(1 + exp(-x))\n     *   = log(exp(x)) - x * z + log(1 + exp(-x))\n     *   = - x * z + log(1 + exp(x))\n     *\n     * Hence, to ensure stability and avoid overflow, the implementation uses\n     * this equivalent formulation:\n     *     max(x, 0) - x * z + log(1 + exp(-abs(x)))\n     */\n    const maxOutput = relu($logits);\n    const outputXTarget = mul($logits, $labels);\n    const sigmoidOutput = log1p(exp(neg(abs($logits))));\n    return add(sub(maxOutput, outputXTarget), sigmoidOutput);\n}\n/**\n * Computes the sigmoid cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)\n *                         + 0.5 * labelSmoothing\n *\n * @param multiClassLabels The ground truth output tensor of shape\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $multiClassLabels = convertToTensor(multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');\n    const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');\n    }\n    assertShapesMatch($multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');\n    if (labelSmoothing > 0) {\n        const labelSmoothingScalar = scalar(labelSmoothing);\n        const one = scalar(1);\n        const half = scalar(0.5);\n        $multiClassLabels =\n            add(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));\n    }\n    const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ });\n//# sourceMappingURL=sigmoid_cross_entropy.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../../gradients';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { expandShapeToKeepDim } from '../axis_util';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { exp } from '../exp';\nimport { logSumExp } from '../log_sum_exp';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes softmax cross entropy between logits and labels.\n *\n * Measures the probability error in discrete classification tasks in which\n * the classes are mutually exclusive (each entry is in exactly one class).\n * For example, each CIFAR-10 image is labeled with one and only one label: an\n * image can be a dog or a truck, but not both.\n *\n * `NOTE`: While the classes are mutually exclusive, their probabilities need\n * not be. All that is required is that each row of labels is a valid\n * probability distribution. If they are not, the computation of the gradient\n * will be incorrect.\n *\n * `WARNING`: This op expects unscaled logits, since it performs a softmax on\n * logits internally for efficiency. Do not call this op with the output of\n * softmax, as it will produce incorrect results.\n *\n * logits and labels must have the same shape, e.g. [batch_size, num_classes]\n * and the same dtype.\n * @param labels The labels array.\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\nfunction softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {\n    if (dim === -1) {\n        dim = logits.rank - 1;\n    }\n    if (dim !== logits.rank - 1) {\n        throw Error(`Softmax cross entropy along a non-last dimension is not yet ` +\n            `supported. Labels / logits was rank ${logits.rank} ` +\n            `and dim was ${dim}`);\n    }\n    // Use a custom gradient for numerical stability.\n    const customOp = customGrad((labels, logits, save) => {\n        // Reference:\n        //   1. http://cs231n.github.io/linear-classify/#softmax\n        //   2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/\n        const keepDims = true;\n        const lse = logSumExp(logits, [dim], keepDims);\n        const logResult = sub(cast(logits, 'float32'), lse);\n        save([labels, logResult]);\n        const costVector = neg(mul(logResult, labels));\n        const value = sum(costVector, [dim]);\n        const gradFunc = (dy, saved) => {\n            const [labels, logResult] = saved;\n            const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n            return [\n                mul(reshape(dy, dyShape), sub(cast(labels, 'float32'), exp(logResult))),\n                mul(reshape(dy, dyShape), sub(exp(logResult), cast(labels, 'float32'))),\n            ];\n        };\n        return { value, gradFunc };\n    });\n    return customOp(labels, logits);\n}\n/**\n * Computes the softmax cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newOnehotLabels = onehotLabels * (1 - labelSmoothing)\n *                         + labelSmoothing / numClasses\n *\n * @param onehotLabels One hot encoded labels\n *    [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or 1, and must be\n *    broadcastable to `loss`  of shape [batch_size]\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $onehotLabels = convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');\n    const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');\n    }\n    assertShapesMatch($onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');\n    if (labelSmoothing > 0) {\n        const labelSmoothingScalar = scalar(labelSmoothing);\n        const one = scalar(1);\n        const numClasses = scalar($onehotLabels.shape[1]);\n        $onehotLabels =\n            add(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));\n    }\n    const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const softmaxCrossEntropy = op({ softmaxCrossEntropy_ });\n//# sourceMappingURL=softmax_cross_entropy.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Modularized ops.\nexport { abs } from './abs';\nexport { acos } from './acos';\nexport { acosh } from './acosh';\nexport { add } from './add';\nexport { addN } from './add_n';\nexport { all } from './all';\nexport { any } from './any';\nexport { argMax } from './arg_max';\nexport { argMin } from './arg_min';\nexport { asin } from './asin';\nexport { asinh } from './asinh';\nexport { atan } from './atan';\nexport { atan2 } from './atan2';\nexport { atanh } from './atanh';\nexport { avgPool } from './avg_pool';\nexport { avgPool3d } from './avg_pool_3d';\nexport { basicLSTMCell } from './basic_lstm_cell';\nexport { batchToSpaceND } from './batch_to_space_nd';\nexport { batchNorm } from './batchnorm';\nexport { batchNorm2d } from './batchnorm2d';\nexport { batchNorm3d } from './batchnorm3d';\nexport { batchNorm4d } from './batchnorm4d';\nexport { broadcastTo } from './broadcast_to';\nexport { buffer } from './buffer';\nexport { cast } from './cast';\nexport { ceil } from './ceil';\nexport { clipByValue } from './clip_by_value';\nexport { clone } from './clone';\nexport { complex } from './complex';\nexport { concat } from './concat';\nexport { concat1d } from './concat_1d';\nexport { concat2d } from './concat_2d';\nexport { concat3d } from './concat_3d';\nexport { concat4d } from './concat_4d';\nexport { conv1d } from './conv1d';\nexport { conv2d } from './conv2d';\nexport { conv2dTranspose } from './conv2d_transpose';\nexport { conv3d } from './conv3d';\nexport { conv3dTranspose } from './conv3d_transpose';\nexport { cos } from './cos';\nexport { cosh } from './cosh';\nexport { cumsum } from './cumsum';\nexport { depthToSpace } from './depth_to_space';\nexport { depthwiseConv2d } from './depthwise_conv2d';\nexport { diag } from './diag';\nexport { dilation2d } from './dilation2d';\nexport { div } from './div';\nexport { divNoNan } from './div_no_nan';\nexport { dot } from './dot';\nexport { elu } from './elu';\nexport { equal } from './equal';\nexport { erf } from './erf';\nexport { exp } from './exp';\nexport { expandDims } from './expand_dims';\nexport { expm1 } from './expm1';\nexport { eye } from './eye';\nexport { fill } from './fill';\nexport { floor } from './floor';\nexport { floorDiv } from './floorDiv';\nexport { gather } from './gather';\nexport { greater } from './greater';\nexport { greaterEqual } from './greater_equal';\nexport { imag } from './imag';\nexport { isFinite } from './is_finite';\nexport { isInf } from './is_inf';\nexport { isNaN } from './is_nan';\nexport { leakyRelu } from './leaky_relu';\nexport { less } from './less';\nexport { lessEqual } from './less_equal';\nexport { linspace } from './linspace';\nexport { localResponseNormalization } from './local_response_normalization';\nexport { log } from './log';\nexport { log1p } from './log1p';\nexport { logSigmoid } from './log_sigmoid';\nexport { logSoftmax } from './log_softmax';\nexport { logSumExp } from './log_sum_exp';\nexport { logicalAnd } from './logical_and';\nexport { logicalNot } from './logical_not';\nexport { logicalOr } from './logical_or';\nexport { logicalXor } from './logical_xor';\nexport { matMul } from './mat_mul';\nexport { max } from './max';\nexport { maxPool } from './max_pool';\nexport { maxPool3d } from './max_pool_3d';\nexport { maxPoolWithArgmax } from './max_pool_with_argmax';\nexport { maximum } from './maximum';\nexport { mean } from './mean';\nexport { min } from './min';\nexport { minimum } from './minimum';\nexport { mod } from './mod';\nexport { moments } from './moments';\nexport { mul } from './mul';\nexport { multiRNNCell } from './multi_rnn_cell';\nexport { multinomial } from './multinomial';\nexport { neg } from './neg';\nexport { notEqual } from './not_equal';\nexport { oneHot } from './one_hot';\nexport { ones } from './ones';\nexport { onesLike } from './ones_like';\nexport { outerProduct } from './outer_product';\nexport { pad } from './pad';\nexport { pad1d } from './pad1d';\nexport { pad2d } from './pad2d';\nexport { pad3d } from './pad3d';\nexport { pad4d } from './pad4d';\nexport { pool } from './pool';\nexport { pow } from './pow';\nexport { prelu } from './prelu';\nexport { print } from './print';\nexport { prod } from './prod';\nexport { rand } from './rand';\nexport { randomGamma } from './random_gamma';\nexport { randomNormal } from './random_normal';\nexport { randomUniform } from './random_uniform';\nexport { range } from './range';\nexport { real } from './real';\nexport { reciprocal } from './reciprocal';\nexport { relu } from './relu';\nexport { relu6 } from './relu6';\nexport { reshape } from './reshape';\nexport { reverse } from './reverse';\nexport { reverse1d } from './reverse_1d';\nexport { reverse2d } from './reverse_2d';\nexport { reverse3d } from './reverse_3d';\nexport { reverse4d } from './reverse_4d';\nexport { round } from './round';\nexport { rsqrt } from './rsqrt';\nexport { scalar } from './scalar';\nexport { selu } from './selu';\nexport { separableConv2d } from './separable_conv2d';\nexport { setdiff1dAsync } from './setdiff1d_async';\nexport { sigmoid } from './sigmoid';\nexport { sign } from './sign';\nexport { sin } from './sin';\nexport { sinh } from './sinh';\nexport { slice } from './slice';\nexport { slice1d } from './slice1d';\nexport { slice2d } from './slice2d';\nexport { slice3d } from './slice3d';\nexport { slice4d } from './slice4d';\nexport { softmax } from './softmax';\nexport { softplus } from './softplus';\nexport { spaceToBatchND } from './space_to_batch_nd';\nexport { fft } from './spectral/fft';\nexport { ifft } from './spectral/ifft';\nexport { irfft } from './spectral/irfft';\nexport { rfft } from './spectral/rfft';\nexport { split } from './split';\nexport { sqrt } from './sqrt';\nexport { square } from './square';\nexport { squaredDifference } from './squared_difference';\nexport { squeeze } from './squeeze';\nexport { stack } from './stack';\nexport { step } from './step';\nexport { stridedSlice } from './strided_slice';\nexport { sub } from './sub';\nexport { sum } from './sum';\nexport { tan } from './tan';\nexport { tanh } from './tanh';\nexport { tensor } from './tensor';\nexport { tensor1d } from './tensor1d';\nexport { tensor2d } from './tensor2d';\nexport { tensor3d } from './tensor3d';\nexport { tensor4d } from './tensor4d';\nexport { tensor5d } from './tensor5d';\nexport { tensor6d } from './tensor6d';\nexport { tile } from './tile';\nexport { topk } from './topk';\nexport { truncatedNormal } from './truncated_normal';\nexport { unique } from './unique';\nexport { unsortedSegmentSum } from './unsorted_segment_sum';\nexport { unstack } from './unstack';\nexport { variable } from './variable';\nexport { where } from './where';\nexport { whereAsync } from './where_async';\nexport { zeros } from './zeros';\nexport { zerosLike } from './zeros_like';\nexport * from './boolean_mask';\nexport * from './compare';\nexport * from './binary_ops';\nexport * from './transpose';\nexport * from './norm';\nexport * from './moving_average';\nexport * from './scatter_nd';\nexport * from './sparse_to_dense';\nexport * from './gather_nd';\nexport * from './dropout';\nexport * from './signal_ops_util';\nexport * from './in_top_k';\nexport { op, OP_SCOPE_SUFFIX } from './operation';\nimport { rfft } from './spectral/rfft';\nimport { fft } from './spectral/fft';\nimport { ifft } from './spectral/ifft';\nimport { irfft } from './spectral/irfft';\nconst spectral = {\n    fft,\n    ifft,\n    rfft,\n    irfft\n};\nimport * as fused from './fused_ops';\nimport { hammingWindow } from './signal/hamming_window';\nimport { hannWindow } from './signal/hann_window';\nimport { frame } from './signal/frame';\nimport { stft } from './signal/stft';\nconst signal = {\n    hammingWindow,\n    hannWindow,\n    frame,\n    stft,\n};\n// Image Ops namespace\nimport { cropAndResize } from './image/crop_and_resize';\nimport { flipLeftRight } from './image/flip_left_right';\nimport { rotateWithOffset } from './image/rotate_with_offset';\nimport { nonMaxSuppression } from './image/non_max_suppression';\nimport { nonMaxSuppressionAsync } from './image/non_max_suppression_async';\nimport { nonMaxSuppressionWithScore } from './image/non_max_suppression_with_score';\nimport { nonMaxSuppressionWithScoreAsync } from './image/non_max_suppression_with_score_async';\nimport { nonMaxSuppressionPadded } from './image/non_max_suppression_padded';\nimport { nonMaxSuppressionPaddedAsync } from './image/non_max_suppression_padded_async';\nimport { resizeBilinear } from './image/resize_bilinear';\nimport { resizeNearestNeighbor } from './image/resize_nearest_neighbor';\nconst image = {\n    flipLeftRight,\n    resizeNearestNeighbor,\n    resizeBilinear,\n    rotateWithOffset,\n    cropAndResize,\n    nonMaxSuppression,\n    nonMaxSuppressionAsync,\n    nonMaxSuppressionWithScore,\n    nonMaxSuppressionWithScoreAsync,\n    nonMaxSuppressionPadded,\n    nonMaxSuppressionPaddedAsync\n};\n// linalg namespace\nimport { bandPart } from './linalg/band_part';\nimport { gramSchmidt } from './linalg/gram_schmidt';\nimport { qr } from './linalg/qr';\nconst linalg = {\n    bandPart,\n    gramSchmidt,\n    qr\n};\n// losses namespace;\nimport { absoluteDifference } from './losses/absolute_difference';\nimport { computeWeightedLoss } from './losses/compute_weighted_loss';\nimport { cosineDistance } from './losses/cosine_distance';\nimport { hingeLoss } from './losses/hinge_loss';\nimport { huberLoss } from './losses/huber_loss';\nimport { logLoss } from './losses/log_loss';\nimport { meanSquaredError } from './losses/mean_squared_error';\nimport { sigmoidCrossEntropy } from './losses/sigmoid_cross_entropy';\nimport { softmaxCrossEntropy } from './losses/softmax_cross_entropy';\nconst losses = {\n    absoluteDifference,\n    computeWeightedLoss,\n    cosineDistance,\n    hingeLoss,\n    huberLoss,\n    logLoss,\n    meanSquaredError,\n    sigmoidCrossEntropy,\n    softmaxCrossEntropy\n};\n// Second level exports.\nexport { image, linalg, losses, spectral, fused, signal };\n//# sourceMappingURL=ops.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dispose } from '../globals';\nimport { variableGrads } from '../gradients';\nimport { scalar } from '../ops/ops';\nimport { Serializable } from '../serialization';\n/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */\nexport class Optimizer extends Serializable {\n    /**\n     * Executes `f()` and minimizes the scalar output of `f()` by computing\n     * gradients of y with respect to the list of trainable variables provided by\n     * `varList`. If no list is provided, it defaults to all trainable variables.\n     *\n     * @param f The function to execute and whose output to minimize.\n     * @param returnCost Whether to return the scalar cost value produced by\n     * executing `f()`.\n     * @param varList An optional list of variables to update. If specified, only\n     * the trainable variables in varList will be updated by minimize. Defaults to\n     * all trainable variables.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers'}\n     */\n    minimize(f, returnCost = false, varList) {\n        const { value, grads } = this.computeGradients(f, varList);\n        if (varList != null) {\n            const gradArray = varList.map(v => ({ name: v.name, tensor: grads[v.name] }));\n            this.applyGradients(gradArray);\n        }\n        else {\n            this.applyGradients(grads);\n        }\n        // Dispose gradients.\n        dispose(grads);\n        if (returnCost) {\n            return value;\n        }\n        else {\n            value.dispose();\n            return null;\n        }\n    }\n    /**\n     * The number of iterations that this optimizer instance has been invoked for.\n     */\n    get iterations() {\n        if (this.iterations_ == null) {\n            this.iterations_ = 0;\n        }\n        return this.iterations_;\n    }\n    incrementIterations() {\n        this.iterations_ = this.iterations + 1;\n    }\n    /**\n     * Executes f() and computes the gradient of the scalar output of f() with\n     * respect to the list of trainable variables provided by `varList`. If no\n     * list is provided, it defaults to all trainable variables.\n     *\n     * @param f The function to execute and whose output to use for computing\n     * gradients with respect to variables.\n     * @param varList An optional list of variables to compute gradients with\n     * respect to. If specified, only the trainable variables in varList will have\n     * gradients computed with respect to. Defaults to all trainable variables.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers'}\n     */\n    computeGradients(f, varList) {\n        return variableGrads(f, varList);\n    }\n    /**\n     * Dispose the variables (if any) owned by this optimizer instance.\n     */\n    dispose() {\n        if (this.iterations_ != null) {\n            dispose(this.iterations_);\n        }\n    }\n    async saveIterations() {\n        if (this.iterations_ == null) {\n            this.iterations_ = 0;\n        }\n        return {\n            name: 'iter',\n            // TODO(cais): Use 'int64' type when available.\n            tensor: scalar(this.iterations_, 'int32')\n        };\n    }\n    async getWeights() {\n        throw new Error('getWeights() is not implemented for this optimizer yet.');\n    }\n    async setWeights(weightValues) {\n        throw new Error(`setWeights() is not implemented for this optimizer class ` +\n            `${this.getClassName()}`);\n    }\n    /**\n     * Extract the first element of the weight values and set it\n     * as the iterations counter variable of this instance of optimizer.\n     *\n     * @param weightValues\n     * @returns Weight values with the first element consumed and excluded.\n     */\n    async extractIterations(weightValues) {\n        this.iterations_ = (await weightValues[0].tensor.data())[0];\n        return weightValues.slice(1);\n    }\n}\nObject.defineProperty(Optimizer, Symbol.hasInstance, {\n    value: (instance) => {\n        return instance.minimize != null && instance.computeGradients != null &&\n            instance.applyGradients != null;\n    }\n});\n//# sourceMappingURL=optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/ops';\nimport { square } from '../ops/square';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class AdadeltaOptimizer extends Optimizer {\n    constructor(learningRate, rho, epsilon = null) {\n        super();\n        this.learningRate = learningRate;\n        this.rho = rho;\n        this.epsilon = epsilon;\n        this.accumulatedGrads = [];\n        this.accumulatedUpdates = [];\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            const trainable = false;\n            if (this.accumulatedGrads[i] == null) {\n                this.accumulatedGrads[i] = {\n                    originalName: `${name}/accum_grad`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedUpdates[i] == null) {\n                this.accumulatedUpdates[i] = {\n                    originalName: `${name}/accum_var`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedGrad = this.accumulatedGrads[i].variable;\n            const accumulatedUpdate = this.accumulatedUpdates[i].variable;\n            tidy(() => {\n                const newAccumulatedGrad = add(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));\n                const updates = mul(div(sqrt(add(accumulatedUpdate, this.epsilon)), sqrt(add(accumulatedGrad, this.epsilon))), gradient);\n                const newAccumulatedUpdate = add(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));\n                accumulatedGrad.assign(newAccumulatedGrad);\n                accumulatedUpdate.assign(newAccumulatedUpdate);\n                const newValue = add(mul(updates, -this.learningRate), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedUpdates != null) {\n            dispose(this.accumulatedGrads.map(v => v.variable));\n            dispose(this.accumulatedUpdates.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const variableCount = weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedGrads =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedUpdates =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'rho': this.rho,\n            'epsilon': this.epsilon\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['rho'], config['epsilon']);\n    }\n}\n/** @nocollapse */\nAdadeltaOptimizer.className = 'Adadelta'; // Name matters for Python compatibility.\nregisterClass(AdadeltaOptimizer);\n//# sourceMappingURL=adadelta_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { fill } from '../ops/fill';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class AdagradOptimizer extends Optimizer {\n    constructor(learningRate, initialAccumulatorValue = 0.1) {\n        super();\n        this.learningRate = learningRate;\n        this.initialAccumulatorValue = initialAccumulatorValue;\n        this.accumulatedGrads = [];\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            if (this.accumulatedGrads[i] == null) {\n                const trainable = false;\n                this.accumulatedGrads[i] = {\n                    originalName: `${name}/accumulator`,\n                    variable: tidy(() => fill(value.shape, this.initialAccumulatorValue)\n                        .variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedGrad = this.accumulatedGrads[i].variable;\n            tidy(() => {\n                const newAccumulatedGrad = add(accumulatedGrad, square(gradient));\n                accumulatedGrad.assign(newAccumulatedGrad);\n                const newValue = add(mul(div(gradient, sqrt(add(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedGrads != null) {\n            dispose(this.accumulatedGrads.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        return [await this.saveIterations()].concat(this.accumulatedGrads.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const trainable = false;\n        this.accumulatedGrads = weightValues.map(v => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'initialAccumulatorValue': this.initialAccumulatorValue,\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['initialAccumulatorValue']);\n    }\n}\n/** @nocollapse */\nAdagradOptimizer.className = 'Adagrad'; // Note: Name matters for Python compatibility.\nregisterClass(AdagradOptimizer);\n//# sourceMappingURL=adagrad_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { pow } from '../ops/pow';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\nexport class AdamOptimizer extends Optimizer {\n    constructor(learningRate, beta1, beta2, epsilon = null) {\n        super();\n        this.learningRate = learningRate;\n        this.beta1 = beta1;\n        this.beta2 = beta2;\n        this.epsilon = epsilon;\n        this.accumulatedFirstMoment = [];\n        this.accumulatedSecondMoment = [];\n        tidy(() => {\n            // accB* will be updated by batch.\n            this.accBeta1 = scalar(beta1).variable();\n            this.accBeta2 = scalar(beta2).variable();\n        });\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const varNames = Array.isArray(variableGradients) ?\n            variableGradients.map(v => v.name) :\n            Object.keys(variableGradients);\n        tidy(() => {\n            const oneMinusAccBeta1 = sub(1, this.accBeta1);\n            const oneMinusAccBeta2 = sub(1, this.accBeta2);\n            varNames.forEach((name, i) => {\n                const value = ENGINE.registeredVariables[name];\n                const trainable = false;\n                if (this.accumulatedFirstMoment[i] == null) {\n                    this.accumulatedFirstMoment[i] = {\n                        originalName: `${name}/m`,\n                        variable: tidy(() => zerosLike(value).variable(trainable))\n                    };\n                }\n                if (this.accumulatedSecondMoment[i] == null) {\n                    this.accumulatedSecondMoment[i] = {\n                        originalName: `${name}/v`,\n                        variable: tidy(() => zerosLike(value).variable(trainable))\n                    };\n                }\n                const gradient = Array.isArray(variableGradients) ?\n                    variableGradients[i].tensor :\n                    variableGradients[name];\n                if (gradient == null) {\n                    return;\n                }\n                const firstMoment = this.accumulatedFirstMoment[i].variable;\n                const secondMoment = this.accumulatedSecondMoment[i].variable;\n                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n                const newSecondMoment = add(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));\n                const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);\n                const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);\n                firstMoment.assign(newFirstMoment);\n                secondMoment.assign(newSecondMoment);\n                const newValue = add(mul(div(biasCorrectedFirstMoment, add(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);\n                value.assign(newValue);\n            });\n            this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n            this.accBeta2.assign(mul(this.accBeta2, this.beta2));\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.accBeta1.dispose();\n        this.accBeta2.dispose();\n        if (this.accumulatedFirstMoment != null) {\n            dispose(this.accumulatedFirstMoment.map(v => v.variable));\n        }\n        if (this.accumulatedSecondMoment != null) {\n            dispose(this.accumulatedSecondMoment.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        tidy(() => {\n            this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));\n            this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));\n        });\n        const variableCount = weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedFirstMoment =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedSecondMoment =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'beta1': this.beta1,\n            'beta2': this.beta2,\n            'epsilon': this.epsilon,\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon']);\n    }\n}\n/** @nocollapse */\nAdamOptimizer.className = 'Adam'; // Note: Name matters for Python compatibility.\nregisterClass(AdamOptimizer);\n//# sourceMappingURL=adam_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { abs } from '../ops/abs';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { maximum } from '../ops/maximum';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\nexport class AdamaxOptimizer extends Optimizer {\n    constructor(learningRate, beta1, beta2, epsilon = null, decay = 0.0) {\n        super();\n        this.learningRate = learningRate;\n        this.beta1 = beta1;\n        this.beta2 = beta2;\n        this.epsilon = epsilon;\n        this.decay = decay;\n        this.accumulatedFirstMoment = [];\n        this.accumulatedWeightedInfNorm = [];\n        tidy(() => {\n            this.iteration = scalar(0).variable();\n            this.accBeta1 = scalar(beta1).variable();\n        });\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        tidy(() => {\n            const oneMinusAccBeta1 = sub(1, this.accBeta1);\n            const lr = div(-this.learningRate, add(mul(this.iteration, this.decay), 1));\n            variableNames.forEach((name, i) => {\n                const value = ENGINE.registeredVariables[name];\n                const trainable = false;\n                if (this.accumulatedFirstMoment[i] == null) {\n                    this.accumulatedFirstMoment[i] = {\n                        originalName: `${name}/m`,\n                        variable: zerosLike(value).variable(trainable)\n                    };\n                }\n                if (this.accumulatedWeightedInfNorm[i] == null) {\n                    this.accumulatedWeightedInfNorm[i] = {\n                        originalName: `${name}/v`,\n                        variable: zerosLike(value).variable(trainable)\n                    };\n                }\n                const gradient = Array.isArray(variableGradients) ?\n                    variableGradients[i].tensor :\n                    variableGradients[name];\n                if (gradient == null) {\n                    return;\n                }\n                const firstMoment = this.accumulatedFirstMoment[i].variable;\n                const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;\n                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n                const ut0 = mul(weightedInfNorm, this.beta2);\n                const ut1 = abs(gradient);\n                const newWeightedInfNorm = maximum(ut0, ut1);\n                firstMoment.assign(newFirstMoment);\n                weightedInfNorm.assign(newWeightedInfNorm);\n                const newValue = add(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add(newWeightedInfNorm, this.epsilon))), value);\n                value.assign(newValue);\n            });\n            this.iteration.assign(add(this.iteration, 1));\n            this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.accBeta1.dispose();\n        this.iteration.dispose();\n        if (this.accumulatedFirstMoment != null) {\n            dispose(this.accumulatedFirstMoment.map(v => v.variable));\n        }\n        if (this.accumulatedWeightedInfNorm != null) {\n            dispose(this.accumulatedWeightedInfNorm.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        throw new Error('getWeights() is not implemented for Adamax yet.');\n    }\n    async setWeights(weightValues) {\n        throw new Error('setWeights() is not implemented for Adamax yet.');\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'beta1': this.beta1,\n            'beta2': this.beta2,\n            'epsilon': this.epsilon,\n            'decay': this.decay\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon'], config['decay']);\n    }\n}\n/** @nocollapse */\nAdamaxOptimizer.className = 'Adamax'; // Note: Name matters for Python compatbility.\nregisterClass(AdamaxOptimizer);\n//# sourceMappingURL=adamax_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { keep, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class SGDOptimizer extends Optimizer {\n    constructor(learningRate) {\n        super();\n        this.learningRate = learningRate;\n        this.setLearningRate(learningRate);\n    }\n    applyGradients(variableGradients) {\n        const varNames = Array.isArray(variableGradients) ?\n            variableGradients.map(v => v.name) :\n            Object.keys(variableGradients);\n        varNames.forEach((name, i) => {\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const value = ENGINE.registeredVariables[name];\n            tidy(() => {\n                const newValue = add(mul(this.c, gradient), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    /**\n     * Sets the learning rate of the optimizer.\n     */\n    setLearningRate(learningRate) {\n        this.learningRate = learningRate;\n        if (this.c != null) {\n            this.c.dispose();\n        }\n        this.c = keep(scalar(-learningRate));\n    }\n    dispose() {\n        this.c.dispose();\n    }\n    async getWeights() {\n        return [await this.saveIterations()];\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        if (weightValues.length !== 0) {\n            throw new Error('SGD optimizer does not have settable weights.');\n        }\n    }\n    getConfig() {\n        return { 'learningRate': this.learningRate };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate']);\n    }\n}\n/** @nocollapse */\nSGDOptimizer.className = 'SGD'; // Note: Name matters for Python compatibility.\nregisterClass(SGDOptimizer);\n//# sourceMappingURL=sgd_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { SGDOptimizer } from './sgd_optimizer';\n/** @doclink Optimizer */\nexport class MomentumOptimizer extends SGDOptimizer {\n    constructor(learningRate, momentum, useNesterov = false) {\n        super(learningRate);\n        this.learningRate = learningRate;\n        this.momentum = momentum;\n        this.useNesterov = useNesterov;\n        this.accumulations = [];\n        this.m = scalar(this.momentum);\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            if (this.accumulations[i] == null) {\n                const trainable = false;\n                this.accumulations[i] = {\n                    originalName: `${name}/momentum`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const accumulation = this.accumulations[i].variable;\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            tidy(() => {\n                let newValue;\n                const newAccumulation = add(mul(this.m, accumulation), gradient);\n                if (this.useNesterov) {\n                    newValue = add(mul(this.c, add(gradient, mul(newAccumulation, this.m))), value);\n                }\n                else {\n                    newValue = add(mul(this.c, newAccumulation), value);\n                }\n                accumulation.assign(newAccumulation);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.m.dispose();\n        if (this.accumulations != null) {\n            dispose(this.accumulations.map(v => v.variable));\n        }\n    }\n    /**\n     * Sets the momentum of the optimizer.\n     *\n     * @param momentum\n     */\n    setMomentum(momentum) {\n        this.momentum = momentum;\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        return [await this.saveIterations()].concat(this.accumulations.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const trainable = false;\n        this.accumulations = weightValues.map(v => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'momentum': this.momentum,\n            'useNesterov': this.useNesterov\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['momentum'], config['useNesterov']);\n    }\n}\n/** @nocollapse */\nMomentumOptimizer.className = 'Momentum'; // Name matters for Python compatibility.\nregisterClass(MomentumOptimizer);\n//# sourceMappingURL=momentum_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class RMSPropOptimizer extends Optimizer {\n    constructor(learningRate, decay = 0.9, momentum = 0.0, epsilon = null, centered = false) {\n        super();\n        this.learningRate = learningRate;\n        this.decay = decay;\n        this.momentum = momentum;\n        this.epsilon = epsilon;\n        this.accumulatedMeanSquares = [];\n        this.accumulatedMoments = [];\n        this.accumulatedMeanGrads = [];\n        this.centered = centered;\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n        if (learningRate == null) {\n            throw new Error(`learningRate for RMSPropOptimizer must be defined.`);\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            const trainable = false;\n            if (this.accumulatedMeanSquares[i] == null) {\n                this.accumulatedMeanSquares[i] = {\n                    originalName: `${name}/rms`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedMoments[i] == null) {\n                this.accumulatedMoments[i] = {\n                    originalName: `${name}/momentum`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedMeanGrads[i] == null && this.centered) {\n                this.accumulatedMeanGrads[i] = {\n                    originalName: `${name}/mg`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;\n            const accumulatedMoments = this.accumulatedMoments[i].variable;\n            tidy(() => {\n                const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n                if (this.centered) {\n                    const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;\n                    // Centered gradient\n                    const newAccumulatedMeanGrad = add(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));\n                    const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add(square(newAccumulatedMeanGrad), this.epsilon))));\n                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), gradContribution);\n                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n                    accumulatedMeanGrad.assign(newAccumulatedMeanGrad);\n                    accumulatedMoments.assign(newAccumulatedMoments);\n                    const newValue = sub(value, newAccumulatedMoments);\n                    value.assign(newValue);\n                }\n                else {\n                    // Plain gradient\n                    const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add(newAccumulatedMeanSquare, this.epsilon))));\n                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n                    accumulatedMoments.assign(newAccumulatedMoments);\n                    const newValue = sub(value, newAccumulatedMoments);\n                    value.assign(newValue);\n                }\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedMeanSquares != null) {\n            dispose(this.accumulatedMeanSquares.map(v => v.variable));\n        }\n        if (this.accumulatedMeanGrads != null && this.centered) {\n            dispose(this.accumulatedMeanGrads.map(v => v.variable));\n        }\n        if (this.accumulatedMoments != null) {\n            dispose(this.accumulatedMoments.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];\n        if (this.centered) {\n            variables.push(...this.accumulatedMeanGrads);\n        }\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedMeanSquares =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedMoments =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        if (this.centered) {\n            this.accumulatedMeanGrads =\n                weightValues.slice(variableCount * 2, variableCount * 3)\n                    .map(v => ({\n                    originalName: v.name,\n                    variable: v.tensor.variable(trainable)\n                }));\n        }\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'decay': this.decay,\n            'momentum': this.momentum,\n            'epsilon': this.epsilon,\n            'centered': this.centered\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['decay'], config['momentum'], config['epsilon'], config['centered']);\n    }\n}\n/** @nocollapse */\nRMSPropOptimizer.className = 'RMSProp'; // Note: Name matters for Python compatibility.\nregisterClass(RMSPropOptimizer);\n//# sourceMappingURL=rmsprop_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AdadeltaOptimizer } from './adadelta_optimizer';\nimport { AdagradOptimizer } from './adagrad_optimizer';\nimport { AdamOptimizer } from './adam_optimizer';\nimport { AdamaxOptimizer } from './adamax_optimizer';\nimport { MomentumOptimizer } from './momentum_optimizer';\nimport { RMSPropOptimizer } from './rmsprop_optimizer';\nimport { SGDOptimizer } from './sgd_optimizer';\nexport class OptimizerConstructors {\n    /**\n     * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.\n     *\n     * ```js\n     * // Fit a quadratic function by learning the coefficients a, b, c.\n     * const xs = tf.tensor1d([0, 1, 2, 3]);\n     * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);\n     *\n     * const a = tf.scalar(Math.random()).variable();\n     * const b = tf.scalar(Math.random()).variable();\n     * const c = tf.scalar(Math.random()).variable();\n     *\n     * // y = a * x^2 + b * x + c.\n     * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);\n     * const loss = (pred, label) => pred.sub(label).square().mean();\n     *\n     * const learningRate = 0.01;\n     * const optimizer = tf.train.sgd(learningRate);\n     *\n     * // Train the model.\n     * for (let i = 0; i < 10; i++) {\n     *   optimizer.minimize(() => loss(f(xs), ys));\n     * }\n     *\n     * // Make predictions.\n     * console.log(\n     *     `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);\n     * const preds = f(xs).dataSync();\n     * preds.forEach((pred, i) => {\n     *   console.log(`x: ${i}, pred: ${pred}`);\n     * });\n     * ```\n     *\n     * @param learningRate The learning rate to use for the SGD algorithm.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static sgd(learningRate) {\n        return new SGDOptimizer(learningRate);\n    }\n    /**\n     * Constructs a `tf.MomentumOptimizer` that uses momentum gradient\n     * descent.\n     *\n     * See\n     * [http://proceedings.mlr.press/v28/sutskever13.pdf](\n     * http://proceedings.mlr.press/v28/sutskever13.pdf)\n     *\n     * @param learningRate The learning rate to use for the Momentum gradient\n     * descent algorithm.\n     * @param momentum The momentum to use for the momentum gradient descent\n     * algorithm.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static momentum(learningRate, momentum, useNesterov = false) {\n        return new MomentumOptimizer(learningRate, momentum, useNesterov);\n    }\n    /**\n     * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient\n     * descent. This implementation uses plain momentum and is not centered\n     * version of RMSProp.\n     *\n     * See\n     * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](\n     * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\n     *\n     * @param learningRate The learning rate to use for the RMSProp gradient\n     * descent algorithm.\n     * @param decay The discounting factor for the history/coming gradient.\n     * @param momentum The momentum to use for the RMSProp gradient descent\n     * algorithm.\n     * @param epsilon Small value to avoid zero denominator.\n     * @param centered If true, gradients are normalized by the estimated\n     * variance of the gradient.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static rmsprop(learningRate, decay = .9, momentum = 0.0, epsilon = null, centered = false) {\n        return new RMSPropOptimizer(learningRate, decay, momentum, epsilon, centered);\n    }\n    /**\n     * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.\n     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n     *\n     * @param learningRate The learning rate to use for the Adam gradient\n     * descent algorithm.\n     * @param beta1 The exponential decay rate for the 1st moment estimates.\n     * @param beta2 The exponential decay rate for the 2nd moment estimates.\n     * @param epsilon A small constant for numerical stability.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adam(learningRate = 0.001, beta1 = 0.9, beta2 = 0.999, epsilon = null) {\n        return new AdamOptimizer(learningRate, beta1, beta2, epsilon);\n    }\n    /**\n     * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.\n     * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)\n     *\n     * @param learningRate The learning rate to use for the Adadelta gradient\n     * descent algorithm.\n     * @param rho The learning rate decay over each update.\n     * @param epsilon A constant epsilon used to better condition the grad\n     * update.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adadelta(learningRate = .001, rho = .95, epsilon = null) {\n        return new AdadeltaOptimizer(learningRate, rho, epsilon);\n    }\n    /**\n     * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.\n     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n     *\n     * @param learningRate The learning rate to use for the Adamax gradient\n     * descent algorithm.\n     * @param beta1 The exponential decay rate for the 1st moment estimates.\n     * @param beta2 The exponential decay rate for the 2nd moment estimates.\n     * @param epsilon A small constant for numerical stability.\n     * @param decay The learning rate decay over each update.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adamax(learningRate = 0.002, beta1 = 0.9, beta2 = 0.999, epsilon = null, decay = 0.0) {\n        return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);\n    }\n    /**\n     * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.\n     * See\n     * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](\n     * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\n     * or\n     * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](\n     * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)\n     *\n     * @param learningRate The learning rate to use for the Adagrad gradient\n     * descent algorithm.\n     * @param initialAccumulatorValue Starting value for the accumulators, must be\n     * positive.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adagrad(learningRate, initialAccumulatorValue = 0.1) {\n        return new AdagradOptimizer(learningRate, initialAccumulatorValue);\n    }\n}\n//# sourceMappingURL=optimizer_constructors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// So typings can propagate.\nimport { AdadeltaOptimizer } from './optimizers/adadelta_optimizer';\nimport { AdagradOptimizer } from './optimizers/adagrad_optimizer';\nimport { AdamOptimizer } from './optimizers/adam_optimizer';\nimport { AdamaxOptimizer } from './optimizers/adamax_optimizer';\nimport { MomentumOptimizer } from './optimizers/momentum_optimizer';\nimport { OptimizerConstructors } from './optimizers/optimizer_constructors';\nimport { RMSPropOptimizer } from './optimizers/rmsprop_optimizer';\nimport { SGDOptimizer } from './optimizers/sgd_optimizer';\n// tslint:disable-next-line:no-unused-expression\n[MomentumOptimizer, SGDOptimizer, AdadeltaOptimizer, AdagradOptimizer,\n    RMSPropOptimizer, AdamaxOptimizer, AdamOptimizer];\nexport const train = {\n    sgd: OptimizerConstructors.sgd,\n    momentum: OptimizerConstructors.momentum,\n    adadelta: OptimizerConstructors.adadelta,\n    adagrad: OptimizerConstructors.adagrad,\n    rmsprop: OptimizerConstructors.rmsprop,\n    adamax: OptimizerConstructors.adamax,\n    adam: OptimizerConstructors.adam\n};\n//# sourceMappingURL=train.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst delayCallback = (() => {\n    if (typeof requestAnimationFrame !== 'undefined') {\n        return requestAnimationFrame;\n    }\n    else if (typeof setImmediate !== 'undefined') {\n        return setImmediate;\n    }\n    return (f) => f(); // no delays\n})();\n/**\n * Returns a promise that resolve when a requestAnimationFrame has completed.\n *\n * On Node.js this uses setImmediate instead of requestAnimationFrame.\n *\n * This is simply a sugar method so that users can do the following:\n * `await tf.nextFrame();`\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nfunction nextFrame() {\n    return new Promise(resolve => delayCallback(() => resolve()));\n}\nexport { nextFrame };\n//# sourceMappingURL=browser_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Returns the image center in pixels.\nexport function getImageCenter(center, imageHeight, imageWidth) {\n    const centerX = imageWidth * (typeof center === 'number' ? center : center[0]);\n    const centerY = imageHeight * (typeof center === 'number' ? center : center[1]);\n    return [centerX, centerY];\n}\n//# sourceMappingURL=rotate_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Gets the new shape of the input Tensor after it's been reshaped\n * to:\n * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),\n * inputShape[1], ..., inputShape[N-1]]\n *\n * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshaped(inputShape, blockShape, prod, batchToSpace = true) {\n    let reshaped = [];\n    if (batchToSpace) {\n        reshaped = reshaped.concat(blockShape.slice(0));\n        reshaped.push(inputShape[0] / prod);\n        reshaped = reshaped.concat(inputShape.slice(1));\n    }\n    else {\n        reshaped = reshaped.concat(inputShape[0]);\n        const spatialLength = blockShape.length;\n        for (let i = 0; i < spatialLength; ++i) {\n            reshaped =\n                reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);\n        }\n        reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n    }\n    return reshaped;\n}\n/**\n * Gets the permutation that will transpose the dimensions of the\n * reshaped tensor to shape:\n *\n * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,\n * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {\n    const permuted = [];\n    if (batchToSpace) {\n        permuted.push(blockShapeRank);\n        for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {\n            if (i <= 2 * blockShapeRank) {\n                permuted.push(i);\n                permuted.push(i - (blockShapeRank + 1));\n            }\n            else {\n                permuted.push(i);\n            }\n        }\n    }\n    else {\n        const permutedBeforeBatch = [];\n        const permutedAfterBatch = [];\n        for (let i = 1; i < reshapedRank; ++i) {\n            if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {\n                permutedAfterBatch.push(i);\n            }\n            else {\n                permutedBeforeBatch.push(i);\n            }\n        }\n        permuted.push(...permutedBeforeBatch);\n        permuted.push(0);\n        permuted.push(...permutedAfterBatch);\n    }\n    return permuted;\n}\n/**\n * Gets the shape of the reshaped and permuted input Tensor before any cropping\n * is applied.  The new shape will be:\n *\n * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,\n * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshapedPermuted(inputShape, blockShape, prod, batchToSpace = true) {\n    const reshapedPermuted = [];\n    if (batchToSpace) {\n        reshapedPermuted.push(inputShape[0] / prod);\n    }\n    else {\n        reshapedPermuted.push(inputShape[0] * prod);\n    }\n    for (let i = 1; i < inputShape.length; ++i) {\n        if (i <= blockShape.length) {\n            if (batchToSpace) {\n                reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);\n            }\n            else {\n                reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);\n            }\n        }\n        else {\n            reshapedPermuted.push(inputShape[i]);\n        }\n    }\n    return reshapedPermuted;\n}\n/**\n * Converts the crops argument into the beginning coordinates of a slice\n * operation.\n */\nexport function getSliceBeginCoords(crops, blockShape) {\n    const sliceBeginCoords = [0];\n    for (let i = 0; i < blockShape; ++i) {\n        sliceBeginCoords.push(crops[i][0]);\n    }\n    return sliceBeginCoords;\n}\n/**\n * Converts the crops argument into the size of a slice operation.  When\n * combined with getSliceBeginCoords this function allows the reshaped and\n * permuted Tensor to be cropped to its final output shape of:\n *\n * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,\n * inputShape[M] * blockShape[M-1] -crops[M-1,0] -\n * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getSliceSize(uncroppedShape, crops, blockShape) {\n    const sliceSize = uncroppedShape.slice(0, 1);\n    for (let i = 0; i < blockShape; ++i) {\n        sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);\n    }\n    return sliceSize;\n}\n//# sourceMappingURL=array_ops_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const SELU_SCALEALPHA = 1.7580993408473768599402175208123;\nexport const SELU_SCALE = 1.0507009873554804934193349852946;\n//# sourceMappingURL=selu_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const ERF_P = 0.3275911;\nexport const ERF_A1 = 0.254829592;\nexport const ERF_A2 = -0.284496736;\nexport const ERF_A3 = 1.421413741;\nexport const ERF_A4 = -1.453152027;\nexport const ERF_A5 = 1.061405429;\n//# sourceMappingURL=erf_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nexport function warn(...msg) {\n    if (!env().getBool('IS_TEST')) {\n        console.warn(...msg);\n    }\n}\nexport function log(...msg) {\n    if (!env().getBool('IS_TEST')) {\n        console.log(...msg);\n    }\n}\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Merges real and imaginary Float32Arrays into a single complex Float32Array.\n *\n * The memory layout is interleaved as follows:\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n * complex: [r0, i0, r1, i1, r2, i2]\n *\n * This is the inverse of splitRealAndImagArrays.\n *\n * @param real The real values of the complex tensor values.\n * @param imag The imag values of the complex tensor values.\n * @returns A complex tensor as a Float32Array with merged values.\n */\nexport function mergeRealAndImagArrays(real, imag) {\n    if (real.length !== imag.length) {\n        throw new Error(`Cannot merge real and imag arrays of different lengths. real:` +\n            `${real.length}, imag: ${imag.length}.`);\n    }\n    const result = new Float32Array(real.length * 2);\n    for (let i = 0; i < result.length; i += 2) {\n        result[i] = real[i / 2];\n        result[i + 1] = imag[i / 2];\n    }\n    return result;\n}\n/**\n * Splits a complex Float32Array into real and imag parts.\n *\n * The memory layout is interleaved as follows:\n * complex: [r0, i0, r1, i1, r2, i2]\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n *\n * This is the inverse of mergeRealAndImagArrays.\n *\n * @param complex The complex tensor values.\n * @returns An object with real and imag Float32Array components of the complex\n *     tensor.\n */\nexport function splitRealAndImagArrays(complex) {\n    const real = new Float32Array(complex.length / 2);\n    const imag = new Float32Array(complex.length / 2);\n    for (let i = 0; i < complex.length; i += 2) {\n        real[i / 2] = complex[i];\n        imag[i / 2] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Extracts even indexed complex values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithEvenIndex(complex) {\n    const len = Math.ceil(complex.length / 4);\n    const real = new Float32Array(len);\n    const imag = new Float32Array(len);\n    for (let i = 0; i < complex.length; i += 4) {\n        real[Math.floor(i / 4)] = complex[i];\n        imag[Math.floor(i / 4)] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Extracts odd indexed comple values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithOddIndex(complex) {\n    const len = Math.floor(complex.length / 4);\n    const real = new Float32Array(len);\n    const imag = new Float32Array(len);\n    for (let i = 2; i < complex.length; i += 4) {\n        real[Math.floor(i / 4)] = complex[i];\n        imag[Math.floor(i / 4)] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Get the map representing a complex value in the given array.\n * @param complex The complex tensor values.\n * @param index An index of the target complex value.\n */\nexport function getComplexWithIndex(complex, index) {\n    const real = complex[index * 2];\n    const imag = complex[index * 2 + 1];\n    return { real, imag };\n}\n/**\n * Insert a given complex value into the TypedArray.\n * @param data The array in which the complex value is inserted.\n * @param c The complex value to be inserted.\n * @param index An index of the target complex value.\n */\nexport function assignToTypedArray(data, real, imag, index) {\n    data[index * 2] = real;\n    data[index * 2 + 1] = imag;\n}\n/**\n * Make the list of exponent terms used by FFT.\n */\nexport function exponents(n, inverse) {\n    const real = new Float32Array(n / 2);\n    const imag = new Float32Array(n / 2);\n    for (let i = 0; i < Math.ceil(n / 2); i++) {\n        const x = (inverse ? 2 : -2) * Math.PI * (i / n);\n        real[i] = Math.cos(x);\n        imag[i] = Math.sin(x);\n    }\n    return { real, imag };\n}\n/**\n * Make the exponent term used by FFT.\n */\nexport function exponent(k, n, inverse) {\n    const x = (inverse ? 2 : -2) * Math.PI * (k / n);\n    const real = Math.cos(x);\n    const imag = Math.sin(x);\n    return { real, imag };\n}\n//# sourceMappingURL=complex_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { cast } from '../ops/cast';\nimport { scalar } from '../ops/scalar';\nimport { tensor1d } from '../ops/tensor1d';\nimport { zeros } from '../ops/zeros';\nimport { hasEncodingLoss, makeZerosTypedArray } from '../util';\n// Utilities needed by backend consumers of tf-core.\nexport * from '../ops/axis_util';\nexport * from '../ops/broadcast_util';\nexport * from '../ops/concat_util';\nexport * from '../ops/conv_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/reduce_util';\nimport * as slice_util from '../ops/slice_util';\nexport { slice_util };\nexport { upcastType } from '../types';\nexport * from '../ops/rotate_util';\nexport * from '../ops/array_ops_util';\nexport * from '../ops/gather_nd_util';\nexport * from '../ops/scatter_nd_util';\nexport * from '../ops/selu_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/erf_util';\nexport * from '../log';\nexport * from '../backends/complex_util';\nexport * from '../ops/split_util';\nimport * as segment_util from '../ops/segment_util';\nexport { segment_util };\nexport function castTensor(x, dtype, backend) {\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return x.clone();\n        }\n        const zerosTensor = zeros(x.shape);\n        const floatX = cast(x, 'float32');\n        const result = backend.complex(floatX, zerosTensor);\n        zerosTensor.dispose();\n        floatX.dispose();\n        return result;\n    }\n    if (!hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        return ENGINE.makeTensorFromDataId(x.dataId, x.shape, dtype);\n    }\n    if (x.dtype === 'complex64') {\n        const real = backend.real(x);\n        const result = cast(real, dtype);\n        real.dispose();\n        return result;\n    }\n    if (dtype === 'int32') {\n        return backend.int(x);\n    }\n    else if (dtype === 'bool') {\n        const zero = scalar(0, x.dtype);\n        const result = backend.notEqual(x, zero);\n        zero.dispose();\n        return result;\n    }\n    else {\n        throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n    }\n}\nexport function reshapeTensor(x, shape) {\n    return ENGINE.makeTensorFromDataId(x.dataId, shape, x.dtype);\n}\nexport function linspaceImpl(start, stop, num) {\n    const step = (stop - start) / (num - 1);\n    const values = makeZerosTypedArray(num, 'float32');\n    values[0] = start;\n    for (let i = 1; i < values.length; i++) {\n        values[i] = values[i - 1] + step;\n    }\n    return tensor1d(values, 'float32');\n}\n//# sourceMappingURL=backend_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { slice } from '../ops/slice';\n// TODO(annxingyuan): Use this helper in WASM Split kernel once intermediate\n// kernels have been modularized in WebGL and CPU\n// https://github.com/tensorflow/tfjs/issues/2822.\n/** Shared implementation of the split kernel across WebGL and CPU. */\nexport function split(x, sizeSplits, axis) {\n    const begin = new Array(x.rank).fill(0);\n    const size = x.shape.slice();\n    return sizeSplits.map(s => {\n        const sliceSize = [...size];\n        sliceSize[axis] = s;\n        const sliceT = slice(x, begin, sliceSize);\n        begin[axis] += s;\n        return sliceT;\n    });\n}\n//# sourceMappingURL=split_shared.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * An implementation of the tile kernel shared between webgl and cpu for string\n * tensors only.\n */\nimport { buffer } from '../ops/buffer';\nexport function tile(xBuf, reps) {\n    const newShape = new Array(xBuf.rank);\n    for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = xBuf.shape[i] * reps[i];\n    }\n    const result = buffer(newShape, xBuf.dtype);\n    for (let i = 0; i < result.values.length; ++i) {\n        const newLoc = result.indexToLoc(i);\n        const originalLoc = new Array(xBuf.rank);\n        for (let j = 0; j < originalLoc.length; j++) {\n            originalLoc[j] = newLoc[j] % xBuf.shape[j];\n        }\n        const originalIndex = xBuf.locToIndex(originalLoc);\n        result.values[i] = xBuf.values[originalIndex];\n    }\n    return result.toTensor();\n}\n//# sourceMappingURL=tile_impl.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the TopK kernel shared between webgl and cpu. */\nimport { tensor } from '../ops/tensor';\nimport { getTypedArrayFromDType } from '../util';\nexport function topkImpl(x, xShape, xDtype, k, sorted) {\n    // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.\n    const lastDim = xShape[xShape.length - 1];\n    const [batch, size] = [x.length / lastDim, lastDim];\n    const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);\n    const allTopKIndices = getTypedArrayFromDType('int32', batch * k);\n    for (let b = 0; b < batch; b++) {\n        const offset = b * size;\n        const vals = x.subarray(offset, offset + size);\n        const valAndInd = [];\n        for (let i = 0; i < vals.length; i++) {\n            valAndInd.push({ value: vals[i], index: i });\n        }\n        valAndInd.sort((a, b) => b.value - a.value);\n        const outOffset = b * k;\n        const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n        const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n        for (let i = 0; i < k; i++) {\n            topKVals[i] = valAndInd[i].value;\n            topKIndices[i] = valAndInd[i].index;\n        }\n    }\n    // Reshape back to the original input shape, except that the last\n    // dimension is k.\n    const outputShape = xShape.slice();\n    outputShape[outputShape.length - 1] = k;\n    return [\n        tensor(allTopKVals, outputShape, xDtype),\n        tensor(allTopKIndices, outputShape, 'int32')\n    ];\n}\n//# sourceMappingURL=topk_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { nonMaxSuppressionV3Impl, nonMaxSuppressionV4Impl, nonMaxSuppressionV5Impl } from './non_max_suppression_impl';\nexport { split } from './split_shared';\nexport { tile } from './tile_impl';\nexport { topkImpl } from './topk_impl';\nexport { whereImpl } from './where_impl';\n//# sourceMappingURL=kernel_impls.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// base.ts is tfjs-core without auto registration of things like flags,\n// gradients, chained ops or the opHandler. See base_side_effects.ts for parts\n// tfjs core that are required side effects.\n/**\n * @fileoverview\n * @suppress {partialAlias} Optimization disabled due to passing the module\n * object into a function below:\n *\n *   import * as ops from './ops/ops';\n *   setOpHandler(ops);\n */\n// Serialization.\nimport * as io from './io/io';\nimport * as math from './math';\nimport * as browser from './ops/browser';\nimport * as gather_util from './ops/gather_nd_util';\nimport * as scatter_util from './ops/scatter_nd_util';\nimport * as slice_util from './ops/slice_util';\nimport * as serialization from './serialization';\nimport * as tensor_util from './tensor_util';\nimport * as test_util from './test_util';\nimport * as util from './util';\nimport { version } from './version';\n// Optimizers.\nexport { AdadeltaOptimizer } from './optimizers/adadelta_optimizer';\nexport { AdagradOptimizer } from './optimizers/adagrad_optimizer';\nexport { AdamOptimizer } from './optimizers/adam_optimizer';\nexport { AdamaxOptimizer } from './optimizers/adamax_optimizer';\nexport { MomentumOptimizer } from './optimizers/momentum_optimizer';\nexport { Optimizer } from './optimizers/optimizer';\nexport { RMSPropOptimizer } from './optimizers/rmsprop_optimizer';\nexport { SGDOptimizer } from './optimizers/sgd_optimizer';\nexport { Tensor, TensorBuffer, Variable } from './tensor';\nexport { Rank, sumOutType, upcastType } from './types';\nexport * from './ops/ops';\nexport { Reduction } from './ops/loss_ops_utils';\nexport * from './train';\nexport * from './globals';\nexport * from './kernel_registry';\nexport { customGrad, grad, grads, valueAndGrad, valueAndGrads, variableGrads } from './gradients';\nexport { Environment, env, ENV } from './environment';\nexport { version as version_core };\n// Top-level method exports.\nexport { nextFrame } from './browser_util';\n// Second level exports.\nimport * as backend_util from './backends/backend_util';\nimport * as device_util from './device_util';\nexport { browser, io, math, serialization, test_util, util, backend_util, tensor_util, slice_util, gather_util, scatter_util, device_util };\nimport * as kernel_impls from './backends/kernel_impls';\nexport { kernel_impls };\n// Backend specific.\nexport { KernelBackend, DataStorage } from './backends/backend';\n// Export all kernel names / info.\nexport * from './kernel_names';\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const absGradConfig = {\n    kernelName: Abs,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, step(cast(x, 'float32'), -1)) };\n    }\n};\n//# sourceMappingURL=Abs_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acos } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { neg } from '../ops/neg';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const acosGradConfig = {\n    kernelName: Acos,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = square(cast(x, 'float32'));\n                const b = sqrt(sub(scalar(1), a));\n                return neg(div(dy, b));\n            }\n        };\n    }\n};\n//# sourceMappingURL=Acos_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acosh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const acoshGradConfig = {\n    kernelName: Acosh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = sqrt(sub(square(cast(x, 'float32')), 1));\n                return div(dy, a);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Acosh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const addGradConfig = {\n    kernelName: Add,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Add_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AddN } from '../kernel_names';\nexport const addNGradConfig = {\n    kernelName: AddN,\n    saveAllInputs: true,\n    gradFunc: (dy, saved) => {\n        const ders = {};\n        saved.forEach((_, i) => {\n            ders[i] = () => dy.clone();\n        });\n        return ders;\n    }\n};\n//# sourceMappingURL=AddN_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ArgMax } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const argMaxGradConfig = {\n    kernelName: ArgMax,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => zerosLike(x) };\n    }\n};\n//# sourceMappingURL=ArgMax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ArgMin } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const argMinGradConfig = {\n    kernelName: ArgMin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => zerosLike(x) };\n    }\n};\n//# sourceMappingURL=ArgMin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asin } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const asinGradConfig = {\n    kernelName: Asin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, 'float32'))))) };\n    }\n};\n//# sourceMappingURL=Asin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asinh } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nexport const asinhGradConfig = {\n    kernelName: Asinh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = sqrt(add(scalar(1), square(cast(x, 'float32'))));\n                return div(dy, a);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Asinh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan2 } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const atan2GradConfig = {\n    kernelName: Atan2,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const d = add(square(a), square(b));\n            let res = mul(dy, div(b, d));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            const d = add(square(a), square(b));\n            let res = neg(mul(dy, div(a, d)));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Atan2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nexport const atanGradConfig = {\n    kernelName: Atan,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, add(square(cast(x, 'float32')), 1)) };\n    }\n};\n//# sourceMappingURL=Atan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atanh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { scalar } from '../ops/scalar';\nexport const atanhGradConfig = {\n    kernelName: Atanh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, sub(scalar(1), square(cast(x, 'float32')))) };\n    }\n};\n//# sourceMappingURL=Atanh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool3DBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of a 3d avg pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0. The dilation\n *     rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction avgPool3dBackprop_(dy, input, filterSize, strides, dilations = [1, 1, 1], pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'avgPool3dBackprop');\n    const $input = convertToTensor(input, 'input', 'avgPool3dBackprop');\n    let dy5D = $dy;\n    let input5D = $input;\n    let reshapedTo5D = false;\n    if ($input.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n        input5D = reshape($input, [\n            1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n        ]);\n    }\n    util.assert(dy5D.rank === 5, () => `Error in avgPool3dBackprop: dy must be rank 5 but got rank ` +\n        `${dy5D.rank}.`);\n    util.assert(input5D.rank === 5, () => `Error in avgPool3dBackprop: input must be rank 5 but got rank ` +\n        `${input5D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool3dBackprop: Either strides or dilations ' +\n        `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n        return backend.avgPool3dBackprop(dy5D, input5D, convInfo);\n    };\n    const inputs = { dy: dy5D, input: input5D };\n    const attrs = { filterSize, strides, dilations, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool3DBackprop, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const avgPool3dBackprop = op({ avgPool3dBackprop_ });\n//# sourceMappingURL=avg_pool_3d_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool3D } from '../kernel_names';\nimport { avgPool3dBackprop } from '../ops/avg_pool_3d_backprop';\nexport const avgPool3DGradConfig = {\n    kernelName: AvgPool3D,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { filterSize, strides, dilations, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1, 1] : dilations;\n        return {\n            x: () => avgPool3dBackprop(dy, x, filterSize, strides, $dilations, pad, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=AvgPool3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPoolBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of an 2D avg pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The input image, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction avgPoolBackprop_(dy, input, filterSize, strides, pad) {\n    const $dy = convertToTensor(dy, 'dy', 'avgPoolBackprop');\n    const $input = convertToTensor(input, 'input', 'avgPoolBackprop');\n    util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n    let input4D = $input;\n    let dy4D = $dy;\n    let reshapedTo4D = false;\n    if ($input.rank === 3) {\n        reshapedTo4D = true;\n        input4D =\n            reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);\n        dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);\n    }\n    util.assert(dy4D.rank === 4, () => `Error in avgPoolBackprop: dy must be rank 4 but got rank ` +\n        `${dy4D.rank}.`);\n    util.assert(input4D.rank === 4, () => `Error in avgPoolBackprop: input must be rank 4 but got rank ` +\n        `${input4D.rank}.`);\n    const forward = backend => {\n        const convInfo = conv_util.computePool2DInfo(input4D.shape, filterSize, strides, 1 /* dilations */, pad);\n        return backend.avgPoolBackprop(dy4D, input4D, convInfo);\n    };\n    const inputs = { dy: dy4D, input: input4D };\n    const attrs = { filterSize, strides, pad };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, AvgPoolBackprop, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const avgPoolBackprop = op({ avgPoolBackprop_ });\n//# sourceMappingURL=avg_pool_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool } from '../kernel_names';\nimport { avgPoolBackprop } from '../ops/avg_pool_backprop';\nexport const avgPoolGradConfig = {\n    kernelName: AvgPool,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { filterSize, strides, pad } = attrs;\n        return {\n            x: () => avgPoolBackprop(dy, x, filterSize, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=AvgPool_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchMatMul } from '../kernel_names';\nimport { matMul } from '../ops/mat_mul';\nexport const batchMatMulGradConfig = {\n    kernelName: BatchMatMul,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved, attrs) => {\n        const [a, b] = saved;\n        const { transposeA, transposeB } = attrs;\n        if (!transposeA && !transposeB) {\n            return {\n                a: () => matMul(dy, b, false, true),\n                b: () => matMul(a, dy, true, false)\n            };\n        }\n        else if (!transposeA && transposeB) {\n            return {\n                a: () => matMul(dy, b, false, false),\n                b: () => matMul(dy, a, true, false)\n            };\n        }\n        else if (transposeA && !transposeB) {\n            return {\n                a: () => matMul(b, dy, false, true),\n                b: () => matMul(a, dy, false, false)\n            };\n        }\n        else {\n            return {\n                a: () => matMul(b, dy, true, true),\n                b: () => matMul(dy, a, true, true)\n            };\n        }\n    }\n};\n//# sourceMappingURL=BatchMatMul_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchToSpaceND } from '../kernel_names';\nimport { spaceToBatchND } from '../ops/space_to_batch_nd';\nexport const batchToSpaceNDGradConfig = {\n    kernelName: BatchToSpaceND,\n    gradFunc: (dy, saved, attrs) => {\n        const { blockShape, crops } = attrs;\n        return { x: () => spaceToBatchND(dy, blockShape, crops) };\n    }\n};\n//# sourceMappingURL=BatchToSpaceND_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BroadcastTo } from '../kernel_names';\nimport { sum } from '../ops/sum';\nexport const broadcastToGradConfig = {\n    kernelName: BroadcastTo,\n    gradFunc: (dy, saved, attrs) => {\n        const broadCastToAttrs = attrs;\n        const inputShape = broadCastToAttrs.inputShape;\n        const outputShape = broadCastToAttrs.shape;\n        const reps = Array.from(outputShape);\n        for (let i = inputShape.length - 1; i >= 0; i--) {\n            if (inputShape[i] === outputShape[i]) {\n                reps[i] = 1;\n            }\n            else if (inputShape[i] !== 1) {\n                throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);\n            }\n        }\n        const axes = [];\n        for (let i = 0; i < reps.length; i++) {\n            if (reps[i] > 1) {\n                axes.push(i);\n            }\n        }\n        return { x: () => sum(dy, axes, true /* keepDims */) };\n    }\n};\n//# sourceMappingURL=BroadcastTo_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cast } from '../kernel_names';\nexport const castGradConfig = {\n    kernelName: Cast,\n    gradFunc: (dy) => {\n        return { x: () => dy.clone() };\n    }\n};\n//# sourceMappingURL=Cast_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const ceilGradConfig = {\n    kernelName: Ceil,\n    gradFunc: (dy) => {\n        // TODO(manrajgrover): Return null for gradients when backprop supports it.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Ceil_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ClipByValue } from '../kernel_names';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { lessEqual } from '../ops/less_equal';\nimport { logicalAnd } from '../ops/logical_and';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const clipByValueGradConfig = {\n    kernelName: ClipByValue,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { clipValueMin, clipValueMax } = attrs;\n        return {\n            x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy)),\n        };\n    }\n};\n//# sourceMappingURL=ClipByValue_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Concat } from '../kernel_names';\nimport { split } from '../ops/split';\nimport { parseAxisParam } from '../util';\nexport const concatGradConfig = {\n    kernelName: Concat,\n    saveAllInputs: true,\n    gradFunc: (dy, saved, attrs) => {\n        const shapes = saved.map(t => t.shape);\n        const { axis } = attrs;\n        const $axis = parseAxisParam(axis, saved[0].shape)[0];\n        const sizeSplits = shapes.map(s => s[$axis]);\n        const derTensors = split(dy, sizeSplits, $axis);\n        return derTensors.map(t => () => t);\n    }\n};\n//# sourceMappingURL=Concat_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv2D } from '../kernel_names';\nimport { conv2DBackpropFilter } from '../ops/conv2d_backprop_filter';\nimport { conv2DBackpropInput } from '../ops/conv2d_backprop_input';\nimport * as conv_util from '../ops/conv_util';\nimport * as util from '../util';\nexport const conv2DGradConfig = {\n    kernelName: Conv2D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x4D, $filter] = saved;\n        const { dilations, strides, pad, dataFormat } = attrs;\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of conv2D: dilation rates greater than 1 ' +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n        return {\n            x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad, dataFormat),\n            filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad, dataFormat)\n        };\n    }\n};\n//# sourceMappingURL=Conv2D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv2DBackpropInput } from '../kernel_names';\nimport { conv2d } from '../ops/conv2d';\nimport { conv2DBackpropFilter } from '../ops/conv2d_backprop_filter';\nexport const conv2DBackpropInputGradConfig = {\n    kernelName: Conv2DBackpropInput,\n    inputsToSave: ['dy', 'filter'],\n    gradFunc: (ddx, saved, attrs) => {\n        const [dy, filter] = saved;\n        const { strides, pad, dataFormat, dimRoundingMode } = attrs;\n        return {\n            dy: () => conv2d(ddx, filter, strides, pad, dataFormat, 1 /* dilations */, dimRoundingMode),\n            filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad, dataFormat, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=Conv2DBackpropInput_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3DBackpropFilterV2 } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the filter of a 3D convolution.\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     [batch, depth, height, width, inChannels]. If rank 4, batch of 1 is\n *     assumed.\n * @param dy The dy image, of rank 5 or rank 4, of shape\n *     [batch, depth, height, width, outDepth]. If rank 4, batch of 1 is\n *     assumed.\n * @param filterShape The shape of the filter, length 5,\n *     [filterDepth, filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideDepth, strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction conv3DBackpropFilter_(x, dy, filterShape, strides, pad) {\n    let x5D = x;\n    if (x.rank === 4) {\n        x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);\n    }\n    let dy5D = dy;\n    if (dy5D.rank === 4) {\n        dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ` +\n        `${x5D.shape}.`);\n    util.assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ` +\n        `${dy5D.shape}.`);\n    util.assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ` +\n        `${filterShape}.`);\n    util.assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must ` +\n        `match input depth in filter (${filterShape[3]}.`);\n    util.assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must ` +\n        `match output depth for filter (${filterShape[4]}).`);\n    const forward = backend => {\n        const dilations = 1;\n        const convInfo = conv_util.computeConv3DInfo(x5D.shape, filterShape, strides, dilations, pad);\n        return backend.conv3dDerFilter(x5D, dy5D, convInfo);\n    };\n    const inputs = { x: x5D, y: dy5D };\n    const attrs = { strides, pad };\n    return ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropFilterV2, attrs);\n}\nexport const conv3DBackpropFilter = op({ conv3DBackpropFilter_ });\n//# sourceMappingURL=conv3d_backprop_filter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv3D } from '../kernel_names';\nimport { conv3DBackpropFilter } from '../ops/conv3d_backprop_filter';\nimport { conv3DBackpropInput } from '../ops/conv3d_backprop_input';\nimport { tupleValuesAreOne } from '../ops/conv_util';\nimport * as util from '../util';\nexport const conv3DGradConfig = {\n    kernelName: Conv3D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const { dilations, strides, pad } = attrs;\n        util.assert(tupleValuesAreOne(dilations), () => 'Error in gradient of conv3D: dilation rates greater than 1 are ' +\n            `not yet supported in gradients. Got dilations '${dilations}'`);\n        const [x5D, $filter] = saved;\n        return {\n            x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad),\n            filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=Conv3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { sin } from '../ops/sin';\nexport const cosGradConfig = {\n    kernelName: Cos,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(neg(sin(cast(x, 'float32'))), dy) };\n    }\n};\n//# sourceMappingURL=Cos_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cosh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { sinh } from '../ops/sinh';\nexport const coshGradConfig = {\n    kernelName: Cosh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(sinh(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Cosh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cumsum } from '../kernel_names';\nimport { getAxesPermutation } from '../ops/axis_util';\nimport { cumsum } from '../ops/cumsum';\nimport { transpose } from '../ops/transpose';\nexport const cumsumGradConfig = {\n    kernelName: Cumsum,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { axis, exclusive, reverse } = attrs;\n        return {\n            x: () => {\n                const permutation = getAxesPermutation([axis], x.rank);\n                let out = cumsum(dy, axis, exclusive, !reverse);\n                if (permutation != null) {\n                    out = transpose(out, permutation);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Cumsum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { DepthwiseConv2dNative } from '../kernel_names';\nimport * as conv_util from '../ops/conv_util';\nimport { depthwiseConv2dNativeBackpropFilter } from '../ops/depthwise_conv2d_native_backprop_filter';\nimport { depthwiseConv2dNativeBackpropInput } from '../ops/depthwise_conv2d_native_backprop_input';\nimport * as util from '../util';\nexport const depthwiseConv2dNativeGradConfig = {\n    kernelName: DepthwiseConv2dNative,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const { dilations, strides, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1] : dilations;\n        util.assert(conv_util.tupleValuesAreOne($dilations), () => 'Error in gradient of depthwiseConv2dNative: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${$dilations}'`);\n        const [x, filter] = saved;\n        util.assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be ` +\n            `rank 4, but got rank ${x.rank}.`);\n        util.assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be ` +\n            `rank 4, but got rank ${filter.rank}.`);\n        util.assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input ` +\n            `channels (${x.shape[3]}) must match the inChannels dimension ` +\n            `in filter ${filter.shape[2]}.`);\n        util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, $dilations), () => 'Error in gradient of depthwiseConv2d: Either strides or ' +\n            `dilations must be  1. Got strides ${strides} and dilations ` +\n            `'${$dilations}'.`);\n        if (dimRoundingMode != null) {\n            util.assert(util.isInt(pad), () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n                `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n        }\n        const convInfo = conv_util.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad, dimRoundingMode, true /* depthwise */);\n        return {\n            x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, convInfo),\n            filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, convInfo),\n        };\n    }\n};\n//# sourceMappingURL=DepthwiseConv2dNative_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput } from '../kernel_names';\nexport const dilation2dGradConfig = {\n    kernelName: Dilation2D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, filter] = saved;\n        const inputInputs = { x, filter, dy };\n        const filterInputs = { x, filter, dy };\n        return {\n            x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),\n            filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)\n        };\n    }\n};\n//# sourceMappingURL=Dilation2D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const divGradConfig = {\n    kernelName: Div,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = div(dy, cast(b, 'float32'));\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            let res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = reshape(sum(res, reduceAxes), b.shape);\n            }\n            const tmp = square(b);\n            return neg(div(res, cast(tmp, 'float32')));\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Div_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Elu, EluGrad } from '../kernel_names';\nexport const eluGradConfig = {\n    kernelName: Elu,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        const backPropKernelFunc = (backend) => {\n            return backend.eluDer(dy, y);\n        };\n        const inputs = { dy, y };\n        return {\n            x: () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* grad */, EluGrad)\n        };\n    }\n};\n//# sourceMappingURL=Elu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Erf } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { square } from '../ops/square';\nexport const erfGradConfig = {\n    kernelName: Erf,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));\n        return { x: () => mul(dy, a) };\n    }\n};\n//# sourceMappingURL=Erf_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '../kernel_names';\nimport { mul } from '../ops/mul';\nexport const expGradConfig = {\n    kernelName: Exp,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(dy, y) };\n    }\n};\n//# sourceMappingURL=Exp_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nexport const expm1GradConfig = {\n    kernelName: Expm1,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, exp(x)) };\n    }\n};\n//# sourceMappingURL=Expm1_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const floorGradConfig = {\n    kernelName: Floor,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Floor_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FloorDiv } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const floorDivGradConfig = {\n    kernelName: FloorDiv,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = div(dy, cast(b, 'float32'));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            let res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = reshape(sum(res, reduceAxes), b.shape);\n            }\n            const tmp = square(b);\n            return neg(div(res, cast(tmp, 'float32')));\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=FloorDiv_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedBatchNorm } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { getReductionAxes } from '../ops/broadcast_util';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { rsqrt } from '../ops/rsqrt';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nimport { tile } from '../ops/tile';\nexport const fusedBatchNormGradConfig = {\n    kernelName: FusedBatchNorm,\n    inputsToSave: ['x', 'mean', 'variance', 'scale'],\n    gradFunc: (dy, saved, attrs) => {\n        const { varianceEpsilon } = attrs;\n        const [x, mean, variance, scale] = saved;\n        const scaleValue = scale == null ? scalar(1) : scale;\n        const reductionAxes = getReductionAxes(mean.shape, x.shape);\n        const tileShape = [];\n        if (mean.rank === 1) {\n            for (let i = 0; i < x.shape.length - 1; ++i) {\n                tileShape.push(x.shape[i]);\n            }\n            tileShape.push(1);\n        }\n        const xMinusMean = sub(x, mean);\n        const dyTimesScaleValue = mul(dy, scaleValue);\n        const oneOverSqrtVariance = rsqrt(add(variance, scalar(varianceEpsilon)));\n        const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));\n        const derX = () => {\n            if (mean.rank === 1) {\n                return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean.shape[0]]), tileShape)), scaleValue), x.shape);\n            }\n            else {\n                return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);\n            }\n        };\n        const derMean = () => {\n            let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);\n            if (mean.rank === 1) {\n                meanDer = sum(meanDer, reductionAxes);\n            }\n            return reshape(meanDer, mean.shape);\n        };\n        const derVariance = () => {\n            let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);\n            if (mean.rank === 1) {\n                varianceDer = sum(varianceDer, reductionAxes);\n            }\n            return reshape(varianceDer, mean.shape);\n        };\n        const derScale = () => {\n            const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);\n            let scaleDer = mul(dy, xMinusMean2TimesRsqrt);\n            if (mean.rank === 1) {\n                scaleDer = sum(scaleDer, reductionAxes);\n            }\n            return reshape(scaleDer, mean.shape);\n        };\n        const derOffset = () => {\n            let offsetDer = dy;\n            if (mean.rank === 1) {\n                offsetDer = sum(offsetDer, reductionAxes);\n            }\n            return reshape(offsetDer, mean.shape);\n        };\n        return {\n            x: derX,\n            mean: derMean,\n            variance: derVariance,\n            scale: derScale,\n            offset: derOffset\n        };\n    }\n};\n//# sourceMappingURL=FusedBatchNorm_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GatherV2 } from '../kernel_names';\nimport { getUndoAxesPermutation } from '../ops/axis_util';\nimport { reshape } from '../ops/reshape';\nimport { transpose } from '../ops/transpose';\nimport { unsortedSegmentSum } from '../ops/unsorted_segment_sum';\nimport { parseAxisParam } from '../util';\nexport const gatherGradConfig = {\n    kernelName: GatherV2,\n    inputsToSave: ['x', 'indices'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, indices] = saved;\n        const { axis } = attrs;\n        const parsedAxis = parseAxisParam(axis, x.shape)[0];\n        const derX = () => {\n            const paramsShape = x.shape;\n            const indicesSize = indices.size;\n            const outerShape = paramsShape.slice(0, parsedAxis);\n            const outerDims = outerShape.length;\n            const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);\n            const innerDims = innerShape.length;\n            const outerAxesIndices = arrayRange(0, outerDims);\n            const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);\n            const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);\n            const values = reshape(dy, valuesShape);\n            const reshapedIndices = reshape(indices, [indicesSize]);\n            const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);\n            const valuesTranspose = transpose(values, transposeDims);\n            let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);\n            const invertTransposeDims = getUndoAxesPermutation(transposeDims);\n            paramsGrad = transpose(paramsGrad, invertTransposeDims);\n            return paramsGrad;\n        };\n        return { x: derX, indices: () => indices };\n    }\n};\nfunction arrayRange(start, stop) {\n    const result = [];\n    for (let i = start; i < stop; ++i) {\n        result.push(i);\n    }\n    return result;\n}\nfunction arrayConcat(arrays) {\n    const result = [];\n    for (let i = 0; i < arrays.length; ++i) {\n        for (let j = 0; j < arrays[i].length; ++j) {\n            result.push(arrays[i][j]);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=GatherV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GreaterEqual } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const greaterEqualGradConfig = {\n    kernelName: GreaterEqual,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        return { a: () => zerosLike(a), b: () => zerosLike(b) };\n    }\n};\n//# sourceMappingURL=GreaterEqual_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '../kernel_names';\nimport { cast } from '../ops/cast';\nexport const identityGradConfig = {\n    kernelName: Identity,\n    gradFunc: (dy) => {\n        return { x: () => cast(dy, 'float32') };\n    }\n};\n//# sourceMappingURL=Identity_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsFinite } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isFiniteGradConfig = {\n    kernelName: IsFinite,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsFinite_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsInf } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isInfGradConfig = {\n    kernelName: IsInf,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsInf_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsNan } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isNanGradConfig = {\n    kernelName: IsNan,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsNan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log1p } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nexport const log1pGradConfig = {\n    kernelName: Log1p,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, add(x, 1)) };\n    }\n};\n//# sourceMappingURL=Log1p_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nexport const logGradConfig = {\n    kernelName: Log,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, cast(x, 'float32')) };\n    }\n};\n//# sourceMappingURL=Log_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LogSoftmax } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nexport const logSoftmaxGradConfig = {\n    kernelName: LogSoftmax,\n    inputsToSave: [],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [value] = saved;\n        const { axis } = attrs;\n        return {\n            logits: () => {\n                const keepDims = true;\n                const softmax = exp(value);\n                return sub(dy, mul(sum(dy, axis, keepDims), softmax));\n            }\n        };\n    }\n};\n//# sourceMappingURL=LogSoftmax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LRNBackprop } from '../kernel_names';\nimport { op } from './operation';\nfunction localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n    const forward = backend => backend.LRNGrad(dy, x, y, depthRadius, bias, alpha, beta);\n    const inputs = { x, y, dy };\n    const attrs = { depthRadius, bias, alpha, beta };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LRNBackprop, attrs);\n}\nexport const localResponseNormalizationBackprop = op({ localResponseNormalizationBackprop_ });\n//# sourceMappingURL=local_response_normalization_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LRN } from '../kernel_names';\nimport { localResponseNormalizationBackprop } from '../ops/local_response_normalization_backprop';\nexport const lrnGradConfig = {\n    kernelName: LRN,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { depthRadius, bias, alpha, beta } = attrs;\n        return {\n            x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)\n        };\n    }\n};\n//# sourceMappingURL=LRN_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as axis_util from '../ops/axis_util';\nimport { cast } from '../ops/cast';\nimport { equal } from '../ops/equal';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { transpose } from '../ops/transpose';\n/**\n * Gradient helper function for the min and max operations.\n */\nexport function gradForMinAndMax(dy, y, xOrig, origAxes, permutedAxes) {\n    if (y.rank < xOrig.rank) {\n        y = reshape(y, axis_util.expandShapeToKeepDim(y.shape, origAxes));\n    }\n    if (dy.rank < xOrig.rank) {\n        dy = reshape(dy, axis_util.expandShapeToKeepDim(dy.shape, origAxes));\n    }\n    return {\n        x: () => {\n            const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));\n            return permutedAxes == null ? dx : transpose(dx, permutedAxes);\n        }\n    };\n}\n//# sourceMappingURL=min_max_grad_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nimport * as util from '../util';\nimport { gradForMinAndMax } from './min_max_grad_util';\nexport const maxGradConfig = {\n    kernelName: Max,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const maxAttrs = attrs;\n        const { reductionIndices } = maxAttrs;\n        const [x, y] = saved;\n        const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(origAxes, x.rank);\n        const maxGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);\n        return {\n            x: () => {\n                let out = maxGrad['x']();\n                if (permutedAxes != null) {\n                    out = transpose(out);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Max_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Maximum } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { less } from '../ops/less';\nimport { mul } from '../ops/mul';\nexport const maximumGradConfig = {\n    kernelName: Maximum,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const derA = () => mul(dy, cast(greaterEqual(a, b), 'float32'));\n        const derB = () => mul(dy, cast(less(a, b), 'float32'));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Maximum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool3DBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of a 3d max pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank 4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param output The original output image, of rank 5 of shape\n *     [batchSize, outDepth, outHeight, outWidth, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPool3dBackprop_(dy, input, output, filterSize, strides, dilations = [1, 1, 1], pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'maxPool3dBackprop');\n    const $input = convertToTensor(input, 'input', 'maxPool3dBackprop');\n    const $output = convertToTensor(output, 'output', 'maxPool3dBackprop');\n    let dy5D = $dy;\n    let input5D = $input;\n    let output5D = $output;\n    let reshapedTo5D = false;\n    if ($input.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n        input5D = reshape($input, [\n            1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n        ]);\n        output5D = reshape($output, [\n            1, $output.shape[0], $output.shape[1], $output.shape[2], $output.shape[3]\n        ]);\n    }\n    util.assert(dy5D.rank === 5, () => `Error in maxPool3dBackprop: dy must be rank 5 but got rank ` +\n        `${dy5D.rank}.`);\n    util.assert(input5D.rank === 5, () => `Error in maxPool3dBackprop: input must be rank 5 but got rank ` +\n        `${input5D.rank}.`);\n    util.assert(output5D.rank === 5, () => `Error in maxPool3dBackprop: output must be rank 5 but got rank ` +\n        `${output5D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool3dBackprop: Either strides or dilations ' +\n        `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n        return backend.maxPool3dBackprop(dy5D, input5D, output5D, convInfo);\n    };\n    const inputs = { dy: dy5D, input: input5D, output: output5D };\n    const attrs = { filterSize, strides, dilations, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool3DBackprop, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const maxPool3dBackprop = op({ maxPool3dBackprop_ });\n//# sourceMappingURL=max_pool_3d_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPool3D } from '../kernel_names';\nimport { maxPool3dBackprop } from '../ops/max_pool_3d_backprop';\nexport const maxPool3DGradConfig = {\n    kernelName: MaxPool3D,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { filterSize, strides, dilations, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1, 1] : dilations;\n        return {\n            x: () => maxPool3dBackprop(dy, x, y, filterSize, strides, $dilations, pad, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=MaxPool3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPoolBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\n/**\n * Computes the backprop of a 2D max pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The original input image, of rank 4, of shape\n *     [batchSize, height, width, channels].\n * @param output The original output image, of rank 4, of shape\n *     [batchSize, outHeight, outWidth, channels].\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPoolBackprop_(dy, input, output, filterSize, strides, pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'maxPoolBackprop');\n    const $input = convertToTensor(input, 'input', 'maxPoolBackprop');\n    const $output = convertToTensor(output, 'output', 'maxPoolBackprop');\n    util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy ` +\n        `(${$dy.rank})`);\n    util.assert($dy.rank === 4, () => `Error in maxPoolBackprop: dy must be rank 4 but got rank ` +\n        `${$dy.rank}.`);\n    util.assert($input.rank === 4, () => `Error in maxPoolBackprop: input must be rank 4 but got rank ` +\n        `${$input.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPoolBackprop: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool2DInfo($input.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        return backend.maxPoolBackprop($dy, $input, $output, convInfo);\n    };\n    const inputs = { dy: $dy, input: $input, output: $output };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    return ENGINE.runKernelFunc(forward, inputs, null, MaxPoolBackprop, attrs);\n}\nexport const maxPoolBackprop = op({ maxPoolBackprop_ });\n//# sourceMappingURL=max_pool_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPool } from '../kernel_names';\nimport { maxPoolBackprop } from '../ops/max_pool_backprop';\nexport const maxPoolGradConfig = {\n    kernelName: MaxPool,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { filterSize, strides, pad } = attrs;\n        return {\n            x: () => maxPoolBackprop(dy, x, y, filterSize, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=MaxPool_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Min } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nimport * as util from '../util';\nimport { gradForMinAndMax } from './min_max_grad_util';\nexport const minGradConfig = {\n    kernelName: Min,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const minAttrs = attrs;\n        const { axis } = minAttrs;\n        const [x, y] = saved;\n        const origAxes = util.parseAxisParam(axis, x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(origAxes, x.rank);\n        const minGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);\n        return {\n            x: () => {\n                let out = minGrad['x']();\n                if (permutedAxes != null) {\n                    out = transpose(out);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Min_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Minimum } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { greater } from '../ops/greater';\nimport { lessEqual } from '../ops/less_equal';\nimport { mul } from '../ops/mul';\nexport const minimumGradConfig = {\n    kernelName: Minimum,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const derA = () => mul(dy, cast(lessEqual(a, b), 'float32'));\n        const derB = () => mul(dy, cast(greater(a, b), 'float32'));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Minimum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Mod } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { div } from '../ops/div';\nimport { floor } from '../ops/floor';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const modGradConfig = {\n    kernelName: Mod,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(dy, reduceAxes), a.shape);\n            }\n            return dy;\n        };\n        const derB = () => {\n            const res = mul(dy, neg(floor(div(a, b))));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), b.shape);\n            }\n            return res;\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Mod_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const multiplyGradConfig = {\n    kernelName: Multiply,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = mul(dy, cast(b, 'float32'));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            const res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), b.shape);\n            }\n            return res;\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Multiply_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Negate } from '../kernel_names';\nimport { neg } from '../ops/neg';\nexport const negateGradConfig = {\n    kernelName: Negate,\n    gradFunc: (dy) => {\n        return { x: () => neg(dy) };\n    }\n};\n//# sourceMappingURL=Negate_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { OneHot } from '../kernel_names';\nimport { zeros } from '../ops/zeros';\nexport const oneHotGradConfig = {\n    kernelName: OneHot,\n    inputsToSave: ['indices'],\n    gradFunc: (dy, saved) => {\n        const indices = saved[0];\n        return { indices: () => zeros(indices.shape, 'float32') };\n    }\n};\n//# sourceMappingURL=OneHot_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { OnesLike } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const onesLikeGradConfig = {\n    kernelName: OnesLike,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=OnesLike_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { PadV2 } from '../kernel_names';\nimport { slice } from '../ops/slice';\nexport const padV2GradConfig = {\n    kernelName: PadV2,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        // Pad introduces values around the original tensor, so the gradient\n        // slices the original shape out of the gradient.\n        const x = saved[0];\n        const { paddings } = attrs;\n        const begin = paddings.map(p => p[0]);\n        return { x: () => slice(dy, begin, x.shape) };\n    }\n};\n//# sourceMappingURL=PadV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Pow } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { greater } from '../ops/greater';\nimport { log } from '../ops/log';\nimport { mul } from '../ops/mul';\nimport { pow } from '../ops/pow';\nimport { reshape } from '../ops/reshape';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const powGradConfig = {\n    kernelName: Pow,\n    inputsToSave: ['a', 'b'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [a, b, y] = saved;\n        const base = a;\n        const exp = b;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(base.shape, exp.shape);\n        const derBase = () => {\n            const expFloat = cast(exp, 'float32');\n            let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));\n            const reduceAxes = broadcast_util.getReductionAxes(base.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, base.shape);\n        };\n        const derExp = () => {\n            const condition = greater(base, 0);\n            const logBase = where(condition, log(base), zerosLike(base));\n            let res = mul(dy, mul(y, logBase));\n            const reduceAxes = broadcast_util.getReductionAxes(exp.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, exp.shape);\n        };\n        return { a: derBase, b: derExp };\n    }\n};\n//# sourceMappingURL=Pow_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Prelu } from '../kernel_names';\nimport { getReductionAxes } from '../ops/broadcast_util';\nimport { greater } from '../ops/greater';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const preluGradConfig = {\n    kernelName: Prelu,\n    inputsToSave: ['x', 'alpha'],\n    gradFunc: (dy, saved) => {\n        const [x, alpha] = saved;\n        const mask = greater(x, 0);\n        return {\n            x: () => where(mask, dy, mul(dy, alpha)),\n            alpha: () => {\n                let res = where(mask, zerosLike(dy), mul(dy, x));\n                const reduceAxes = getReductionAxes(alpha.shape, dy.shape);\n                if (reduceAxes.length > 0) {\n                    res = sum(res, reduceAxes);\n                }\n                return reshape(res, alpha.shape);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Prelu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reciprocal } from '../kernel_names';\nimport { div } from '../ops/div';\nimport { neg } from '../ops/neg';\nimport { square } from '../ops/square';\nexport const reciprocalGradConfig = {\n    kernelName: Reciprocal,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, neg(square(x))) };\n    }\n};\n//# sourceMappingURL=Reciprocal_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu6 } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { lessEqual } from '../ops/less_equal';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const relu6GradConfig = {\n    kernelName: Relu6,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        const mask = mul(lessEqual(x, 6), step(x));\n        return { x: () => mul(dy, cast(mask, 'float32')) };\n    }\n};\n//# sourceMappingURL=Relu6_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const reluGradConfig = {\n    kernelName: Relu,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, cast(step(x), 'float32')) };\n    }\n};\n//# sourceMappingURL=Relu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape } from '../kernel_names';\nimport { reshape } from '../ops/reshape';\nexport const reshapeGradConfig = {\n    kernelName: Reshape,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => reshape(dy, x.shape) };\n    }\n};\n//# sourceMappingURL=Reshape_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ResizeBilinear, ResizeBilinearGrad } from '../kernel_names';\nexport const resizeBilinearGradConfig = {\n    kernelName: ResizeBilinear,\n    inputsToSave: ['images'],\n    gradFunc: (dy, saved, attrs) => {\n        const [images] = saved;\n        const backPropKernelFunc = (backend) => {\n            const { alignCorners } = attrs;\n            return backend.resizeBilinearBackprop(dy, images, alignCorners);\n        };\n        const inputs = { images };\n        const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* gradient */, ResizeBilinearGrad, attrs);\n        return { images: imagesDer };\n    }\n};\n//# sourceMappingURL=ResizeBilinear_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ResizeNearestNeighbor, ResizeNearestNeighborGrad } from '../kernel_names';\nexport const resizeNearestNeighborGradConfig = {\n    kernelName: ResizeNearestNeighbor,\n    inputsToSave: ['images'],\n    gradFunc: (dy, saved, attrs) => {\n        const [images] = saved;\n        const backPropKernelFunc = (backend) => {\n            const { alignCorners } = attrs;\n            return backend.resizeNearestNeighborBackprop(dy, images, alignCorners);\n        };\n        const inputs = { images };\n        const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* gradient */, ResizeNearestNeighborGrad, attrs);\n        return { images: imagesDer };\n    }\n};\n//# sourceMappingURL=ResizeNearestNeighbor_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reverse } from '../kernel_names';\nimport { reverse } from '../ops/reverse';\nimport { parseAxisParam } from '../util';\nexport const reverseGradConfig = {\n    kernelName: Reverse,\n    gradFunc: (dy, saved, attrs) => {\n        const { dims } = attrs;\n        const axes = parseAxisParam(dims, dy.shape);\n        return { x: () => reverse(dy, axes) };\n    }\n};\n//# sourceMappingURL=Reverse_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Round } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const roundGradConfig = {\n    kernelName: Round,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Round_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '../kernel_names';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { pow } from '../ops/pow';\nexport const rsqrtGradConfig = {\n    kernelName: Rsqrt,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => neg(div(dy, mul(pow(x, 1.5), 2))) };\n    }\n};\n//# sourceMappingURL=Rsqrt_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SelectV2 } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { logicalNot } from '../ops/logical_not';\nimport { mul } from '../ops/mul';\nimport { zerosLike } from '../ops/zeros_like';\nexport const selectV2PoolGradConfig = {\n    kernelName: SelectV2,\n    inputsToSave: ['condition'],\n    gradFunc: (dy, saved) => {\n        const [condition] = saved;\n        return {\n            // TODO(julianoks): Return null for condition gradient\n            // when backprop supports it.\n            condition: () => cast(zerosLike(condition), 'float32'),\n            t: () => mul(dy, cast(condition, dy.dtype)),\n            e: () => mul(dy, cast(logicalNot(condition), dy.dtype))\n        };\n    }\n};\n//# sourceMappingURL=SelectV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Selu } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { exp } from '../ops/exp';\nimport { greater } from '../ops/greater';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { SELU_SCALE, SELU_SCALEALPHA } from '../ops/selu_util';\nimport { where } from '../ops/where';\nexport const seluGradConfig = {\n    kernelName: Selu,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const mask = greater(x, scalar(0));\n                const scaleAlpha = scalar(SELU_SCALEALPHA);\n                const scale = scalar(SELU_SCALE);\n                const greaterThanZeroDer = mul(dy, scale);\n                const lessEqualZeroDer = mul(mul(dy, scaleAlpha), exp(cast(x, 'float32')));\n                return where(mask, greaterThanZeroDer, lessEqualZeroDer);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Selu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sigmoid } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nexport const sigmoidGradConfig = {\n    kernelName: Sigmoid,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(dy, mul(y, sub(scalar(1), y))) };\n    }\n};\n//# sourceMappingURL=Sigmoid_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sign } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const signGradConfig = {\n    kernelName: Sign,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Sign_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { cos } from '../ops/cos';\nimport { mul } from '../ops/mul';\nexport const sinGradConfig = {\n    kernelName: Sin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(cos(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Sin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sinh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { cosh } from '../ops/cosh';\nimport { mul } from '../ops/mul';\nexport const sinhGradConfig = {\n    kernelName: Sinh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(cosh(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Sinh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Slice } from '../kernel_names';\nimport { pad } from '../ops/pad';\nimport { parseSliceParams } from '../ops/slice_util';\nexport const sliceGradConfig = {\n    kernelName: Slice,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { begin, size } = attrs;\n        const inputShape = x.shape;\n        const [begin_, size_] = parseSliceParams(x, begin, size);\n        // Create an Nx2 padding where the first column represents how many\n        // zeros are prepended (at start) for each dimension, and the second\n        // column indicates how many zeros are appended (at end).\n        // The number of zeros to append is the shape of the input\n        // elementwise-subtracted by both the begin vector and sizes vector.\n        const paddings = [];\n        for (let i = 0; i < dy.rank; i++) {\n            paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);\n        }\n        return { x: () => pad(dy, paddings) };\n    }\n};\n//# sourceMappingURL=Slice_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softmax } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nexport const softmaxGradConfig = {\n    kernelName: Softmax,\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [y] = saved;\n        const { dim } = attrs;\n        const keepDims = true;\n        const dyTimesY = mul(dy, y);\n        return {\n            logits: () => sub(dyTimesY, mul(sum(dyTimesY, [dim], keepDims), y))\n        };\n    }\n};\n//# sourceMappingURL=Softmax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softplus } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { sigmoid } from '../ops/sigmoid';\nexport const softplusGradConfig = {\n    kernelName: Softplus,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, sigmoid(x)) };\n    }\n};\n//# sourceMappingURL=Softplus_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SpaceToBatchND } from '../kernel_names';\nimport { batchToSpaceND } from '../ops/batch_to_space_nd';\nexport const spaceToBatchNDGradConfig = {\n    kernelName: SpaceToBatchND,\n    gradFunc: (dy, saved, attrs) => {\n        const { blockShape, paddings } = attrs;\n        return { x: () => batchToSpaceND(dy, blockShape, paddings) };\n    }\n};\n//# sourceMappingURL=SpaceToBatchND_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SplitV } from '../kernel_names';\nimport { concat } from '../ops/concat';\nexport const splitVGradConfig = {\n    kernelName: SplitV,\n    gradFunc: (dy, saved, attrs) => {\n        const { axis } = attrs;\n        return { x: () => concat(dy, axis) };\n    }\n};\n//# sourceMappingURL=SplitV_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sqrt } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nexport const sqrtGradConfig = {\n    kernelName: Sqrt,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, mul(sqrt(cast(x, 'float32')), 2)) };\n    }\n};\n//# sourceMappingURL=Sqrt_grad.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nexport const squareGradConfig = {\n    kernelName: Square,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, mul(cast(x, 'float32'), 2)) };\n    }\n};\n//# sourceMappingURL=Square_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nexport const squaredDifferenceGradConfig = {\n    kernelName: SquaredDifference,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const two = scalar(2);\n        const derA = () => mul(dy, mul(two, sub(a, b)));\n        const derB = () => mul(dy, mul(two, sub(b, a)));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=SquaredDifference_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Step } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const stepGradConfig = {\n    kernelName: Step,\n    gradFunc: (dy) => {\n        // TODO(manrajgrover): Return null for gradients when backprop supports\n        // it.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Step_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const subGradConfig = {\n    kernelName: Sub,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(neg(res), b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Sub_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sum } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { ones } from '../ops/ones';\nimport { reshape } from '../ops/reshape';\nimport { parseAxisParam } from '../util';\nexport const sumGradConfig = {\n    kernelName: Sum,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const expandedDyShape = x.shape.slice();\n        const { axis } = attrs;\n        const axes = parseAxisParam(axis, x.shape);\n        axes.forEach(axis => {\n            expandedDyShape[axis] = 1;\n        });\n        const expandedDy = reshape(dy, expandedDyShape);\n        const derX = mul(expandedDy, ones(x.shape, 'float32'));\n        return { x: () => derX };\n    }\n};\n//# sourceMappingURL=Sum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '../kernel_names';\nimport { cos } from '../ops/cos';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nexport const tanGradConfig = {\n    kernelName: Tan,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, square(cos(x))) };\n    }\n};\n//# sourceMappingURL=Tan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tanh } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const tanhGradConfig = {\n    kernelName: Tanh,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(sub(scalar(1), square(y)), dy) };\n    }\n};\n//# sourceMappingURL=Tanh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tile } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { slice } from '../ops/slice';\nimport { zerosLike } from '../ops/zeros_like';\nexport const tileGradConfig = {\n    kernelName: Tile,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { reps } = attrs;\n        const derX = () => {\n            let xGrad = zerosLike(x);\n            // TODO(cais): Maybe reduce memory footprint by avoiding repeated\n            // slicing.\n            if (x.rank === 1) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    xGrad = add(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));\n                }\n            }\n            else if (x.rank === 2) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        xGrad = add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [\n                            x.shape[0], x.shape[1]\n                        ]));\n                    }\n                }\n            }\n            else if (x.rank === 3) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        for (let k = 0; k < reps[2]; ++k) {\n                            xGrad =\n                                add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));\n                        }\n                    }\n                }\n            }\n            else if (x.rank === 4) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        for (let k = 0; k < reps[2]; ++k) {\n                            for (let l = 0; l < reps[3]; ++l) {\n                                xGrad =\n                                    add(xGrad, slice(dy, [\n                                        i * x.shape[0], j * x.shape[1], k * x.shape[2],\n                                        l * x.shape[3]\n                                    ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));\n                            }\n                        }\n                    }\n                }\n            }\n            else {\n                throw new Error(`Gradient for tile operation is not implemented for rank-` +\n                    `${x.rank} tensors yet.`);\n            }\n            return xGrad;\n        };\n        return { x: derX };\n    },\n};\n//# sourceMappingURL=Tile_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nexport const transposeGradConfig = {\n    kernelName: Transpose,\n    gradFunc: (dy, saved, attrs) => {\n        const transposeAttrs = attrs;\n        const { perm } = transposeAttrs;\n        const undoPerm = axis_util.getUndoAxesPermutation(perm);\n        return { x: () => transpose(dy, undoPerm) };\n    }\n};\n//# sourceMappingURL=Transpose_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unpack } from '../kernel_names';\nimport { stack } from '../ops/stack';\nexport const unpackGradConfig = {\n    kernelName: Unpack,\n    gradFunc: (dy, saved, attrs) => {\n        const unpackAttrs = attrs;\n        const { axis } = unpackAttrs;\n        return { value: () => stack(dy, axis) };\n    }\n};\n//# sourceMappingURL=Unpack_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { UnsortedSegmentSum } from '../kernel_names';\nimport { expandDims } from '../ops/expand_dims';\nimport { gather } from '../ops/gather';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { logicalAnd } from '../ops/logical_and';\nimport { maximum } from '../ops/maximum';\nimport { ones } from '../ops/ones';\nimport { scalar } from '../ops/scalar';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const unsortedSegmentSumGradConfig = {\n    kernelName: UnsortedSegmentSum,\n    inputsToSave: ['segmentIds'],\n    gradFunc: (dy, saved) => {\n        const [segmentIds] = saved;\n        const derX = () => {\n            return gatherDropNegatives(dy, segmentIds);\n        };\n        return { x: derX };\n    }\n};\nfunction gatherDropNegatives(x, indices) {\n    // Helper function for unsorted segment ops. Gathers params for\n    // positive segment ids and gathers 0 for inputs with negative segment id.\n    // Mirrors _GatherDropNegatives from tensorflow/python/ops/math_grad.py\n    const zeroClippedIndices = maximum(indices, zerosLike(indices));\n    const gathered = gather(x, zeroClippedIndices);\n    let isPositive = greaterEqual(indices, scalar(0, 'int32'));\n    const numIters = gathered.rank - isPositive.rank;\n    for (let i = 0; i < numIters; ++i) {\n        isPositive = expandDims(isPositive, i + 1);\n    }\n    isPositive = logicalAnd(isPositive, ones(gathered.shape, 'bool'));\n    const zeroSlice = zerosLike(gathered);\n    return where(isPositive, gathered, zeroSlice);\n}\n//# sourceMappingURL=UnsortedSegmentSum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ZerosLike } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const zerosLikeGradConfig = {\n    kernelName: ZerosLike,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=ZerosLike_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { absGradConfig } from './gradients/Abs_grad';\nimport { acosGradConfig } from './gradients/Acos_grad';\nimport { acoshGradConfig } from './gradients/Acosh_grad';\nimport { addGradConfig } from './gradients/Add_grad';\nimport { addNGradConfig } from './gradients/AddN_grad';\nimport { argMaxGradConfig } from './gradients/ArgMax_grad';\nimport { argMinGradConfig } from './gradients/ArgMin_grad';\nimport { asinGradConfig } from './gradients/Asin_grad';\nimport { asinhGradConfig } from './gradients/Asinh_grad';\nimport { atan2GradConfig } from './gradients/Atan2_grad';\nimport { atanGradConfig } from './gradients/Atan_grad';\nimport { atanhGradConfig } from './gradients/Atanh_grad';\nimport { avgPool3DGradConfig } from './gradients/AvgPool3D_grad';\nimport { avgPoolGradConfig } from './gradients/AvgPool_grad';\nimport { batchMatMulGradConfig } from './gradients/BatchMatMul_grad';\nimport { batchToSpaceNDGradConfig } from './gradients/BatchToSpaceND_grad';\nimport { broadcastToGradConfig } from './gradients/BroadcastTo_grad';\nimport { castGradConfig } from './gradients/Cast_grad';\nimport { ceilGradConfig } from './gradients/Ceil_grad';\nimport { clipByValueGradConfig } from './gradients/ClipByValue_grad';\nimport { concatGradConfig } from './gradients/Concat_grad';\nimport { conv2DGradConfig } from './gradients/Conv2D_grad';\nimport { conv2DBackpropInputGradConfig } from './gradients/Conv2DBackpropInput_grad';\nimport { conv3DGradConfig } from './gradients/Conv3D_grad';\nimport { cosGradConfig } from './gradients/Cos_grad';\nimport { coshGradConfig } from './gradients/Cosh_grad';\nimport { cumsumGradConfig } from './gradients/Cumsum_grad';\nimport { depthwiseConv2dNativeGradConfig } from './gradients/DepthwiseConv2dNative_grad';\nimport { dilation2dGradConfig } from './gradients/Dilation2D_grad';\nimport { divGradConfig } from './gradients/Div_grad';\nimport { eluGradConfig } from './gradients/Elu_grad';\nimport { erfGradConfig } from './gradients/Erf_grad';\nimport { expGradConfig } from './gradients/Exp_grad';\nimport { expm1GradConfig } from './gradients/Expm1_grad';\nimport { floorGradConfig } from './gradients/Floor_grad';\nimport { floorDivGradConfig } from './gradients/FloorDiv_grad';\nimport { fusedBatchNormGradConfig } from './gradients/FusedBatchNorm_grad';\nimport { gatherGradConfig } from './gradients/GatherV2_grad';\nimport { greaterEqualGradConfig } from './gradients/GreaterEqual_grad';\nimport { identityGradConfig } from './gradients/Identity_grad';\nimport { isFiniteGradConfig } from './gradients/IsFinite_grad';\nimport { isInfGradConfig } from './gradients/IsInf_grad';\nimport { isNanGradConfig } from './gradients/IsNan_grad';\nimport { log1pGradConfig } from './gradients/Log1p_grad';\nimport { logGradConfig } from './gradients/Log_grad';\nimport { logSoftmaxGradConfig } from './gradients/LogSoftmax_grad';\nimport { lrnGradConfig } from './gradients/LRN_grad';\nimport { maxGradConfig } from './gradients/Max_grad';\nimport { maximumGradConfig } from './gradients/Maximum_grad';\nimport { maxPool3DGradConfig } from './gradients/MaxPool3D_grad';\nimport { maxPoolGradConfig } from './gradients/MaxPool_grad';\nimport { minGradConfig } from './gradients/Min_grad';\nimport { minimumGradConfig } from './gradients/Minimum_grad';\nimport { modGradConfig } from './gradients/Mod_grad';\nimport { multiplyGradConfig } from './gradients/Multiply_grad';\nimport { negateGradConfig } from './gradients/Negate_grad';\nimport { oneHotGradConfig } from './gradients/OneHot_grad';\nimport { onesLikeGradConfig } from './gradients/OnesLike_grad';\nimport { padV2GradConfig } from './gradients/PadV2_grad';\nimport { powGradConfig } from './gradients/Pow_grad';\nimport { preluGradConfig } from './gradients/Prelu_grad';\nimport { reciprocalGradConfig } from './gradients/Reciprocal_grad';\nimport { relu6GradConfig } from './gradients/Relu6_grad';\nimport { reluGradConfig } from './gradients/Relu_grad';\nimport { reshapeGradConfig } from './gradients/Reshape_grad';\nimport { resizeBilinearGradConfig } from './gradients/ResizeBilinear_grad';\nimport { resizeNearestNeighborGradConfig } from './gradients/ResizeNearestNeighbor_grad';\nimport { reverseGradConfig } from './gradients/Reverse_grad';\nimport { roundGradConfig } from './gradients/Round_grad';\nimport { rsqrtGradConfig } from './gradients/Rsqrt_grad';\nimport { selectV2PoolGradConfig } from './gradients/SelectV2_grad';\nimport { seluGradConfig } from './gradients/Selu_grad';\nimport { sigmoidGradConfig } from './gradients/Sigmoid_grad';\nimport { signGradConfig } from './gradients/Sign_grad';\nimport { sinGradConfig } from './gradients/Sin_grad';\nimport { sinhGradConfig } from './gradients/Sinh_grad';\nimport { sliceGradConfig } from './gradients/Slice_grad';\nimport { softmaxGradConfig } from './gradients/Softmax_grad';\nimport { softplusGradConfig } from './gradients/Softplus_grad';\nimport { spaceToBatchNDGradConfig } from './gradients/SpaceToBatchND_grad';\nimport { splitVGradConfig } from './gradients/SplitV_grad';\nimport { sqrtGradConfig } from './gradients/Sqrt_grad';\nimport { squareGradConfig } from './gradients/Square_grad';\nimport { squaredDifferenceGradConfig } from './gradients/SquaredDifference_grad';\nimport { stepGradConfig } from './gradients/Step_grad';\nimport { subGradConfig } from './gradients/Sub_grad';\nimport { sumGradConfig } from './gradients/Sum_grad';\nimport { tanGradConfig } from './gradients/Tan_grad';\nimport { tanhGradConfig } from './gradients/Tanh_grad';\nimport { tileGradConfig } from './gradients/Tile_grad';\nimport { transposeGradConfig } from './gradients/Transpose_grad';\nimport { unpackGradConfig } from './gradients/Unpack_grad';\nimport { unsortedSegmentSumGradConfig } from './gradients/UnsortedSegmentSum_grad';\nimport { zerosLikeGradConfig } from './gradients/ZerosLike_grad';\nimport { registerGradient } from './kernel_registry';\n// Export all kernel configs here so that the package can auto register them\nconst gradConfigs = [\n    absGradConfig,\n    acosGradConfig,\n    acoshGradConfig,\n    addGradConfig,\n    addNGradConfig,\n    argMaxGradConfig,\n    argMinGradConfig,\n    asinGradConfig,\n    asinhGradConfig,\n    atan2GradConfig,\n    atanGradConfig,\n    atanhGradConfig,\n    avgPool3DGradConfig,\n    avgPoolGradConfig,\n    batchMatMulGradConfig,\n    batchToSpaceNDGradConfig,\n    broadcastToGradConfig,\n    castGradConfig,\n    ceilGradConfig,\n    clipByValueGradConfig,\n    concatGradConfig,\n    conv2DBackpropInputGradConfig,\n    conv2DGradConfig,\n    conv3DGradConfig,\n    cosGradConfig,\n    coshGradConfig,\n    cumsumGradConfig,\n    depthwiseConv2dNativeGradConfig,\n    dilation2dGradConfig,\n    divGradConfig,\n    eluGradConfig,\n    erfGradConfig,\n    expGradConfig,\n    expm1GradConfig,\n    floorDivGradConfig,\n    floorGradConfig,\n    fusedBatchNormGradConfig,\n    gatherGradConfig,\n    greaterEqualGradConfig,\n    identityGradConfig,\n    isFiniteGradConfig,\n    isInfGradConfig,\n    isNanGradConfig,\n    log1pGradConfig,\n    logGradConfig,\n    logSoftmaxGradConfig,\n    lrnGradConfig,\n    maxGradConfig,\n    maxGradConfig,\n    maximumGradConfig,\n    maxPool3DGradConfig,\n    maxPoolGradConfig,\n    minGradConfig,\n    minimumGradConfig,\n    modGradConfig,\n    multiplyGradConfig,\n    negateGradConfig,\n    oneHotGradConfig,\n    onesLikeGradConfig,\n    padV2GradConfig,\n    padV2GradConfig,\n    powGradConfig,\n    preluGradConfig,\n    reciprocalGradConfig,\n    relu6GradConfig,\n    reluGradConfig,\n    reshapeGradConfig,\n    resizeBilinearGradConfig,\n    resizeNearestNeighborGradConfig,\n    reverseGradConfig,\n    roundGradConfig,\n    rsqrtGradConfig,\n    selectV2PoolGradConfig,\n    seluGradConfig,\n    sigmoidGradConfig,\n    signGradConfig,\n    sinGradConfig,\n    sinhGradConfig,\n    sliceGradConfig,\n    softmaxGradConfig,\n    softplusGradConfig,\n    spaceToBatchNDGradConfig,\n    spaceToBatchNDGradConfig,\n    splitVGradConfig,\n    splitVGradConfig,\n    sqrtGradConfig,\n    squaredDifferenceGradConfig,\n    squareGradConfig,\n    stepGradConfig,\n    subGradConfig,\n    sumGradConfig,\n    tanGradConfig,\n    tanhGradConfig,\n    tileGradConfig,\n    transposeGradConfig,\n    unpackGradConfig,\n    unsortedSegmentSumGradConfig,\n    zerosLikeGradConfig\n];\nfor (const gradientConfig of gradConfigs) {\n    registerGradient(gradientConfig);\n}\n//# sourceMappingURL=register_all_gradients.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { abs } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.abs = function () {\n    this.throwIfDisposed();\n    return abs(this);\n};\n//# sourceMappingURL=abs.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { acos } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.acos = function () {\n    this.throwIfDisposed();\n    return acos(this);\n};\n//# sourceMappingURL=acos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { acosh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.acosh = function () {\n    this.throwIfDisposed();\n    return acosh(this);\n};\n//# sourceMappingURL=acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { addStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.addStrict = function (x) {\n    this.throwIfDisposed();\n    return addStrict(this, x);\n};\n//# sourceMappingURL=add_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { add } from '../../ops/add';\nimport { Tensor } from '../../tensor';\nTensor.prototype.add = function (b) {\n    this.throwIfDisposed();\n    return add(this, b);\n};\n//# sourceMappingURL=add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { all } from '../../ops/all';\nimport { Tensor } from '../../tensor';\nTensor.prototype.all = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return all(this, axis, keepDims);\n};\n//# sourceMappingURL=all.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { any } from '../../ops/any';\nimport { Tensor } from '../../tensor';\nTensor.prototype.any = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return any(this, axis, keepDims);\n};\n//# sourceMappingURL=any.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { argMax } from '../../ops/arg_max';\nimport { Tensor } from '../../tensor';\nTensor.prototype.argMax = function (axis) {\n    this.throwIfDisposed();\n    return argMax(this, axis);\n};\n//# sourceMappingURL=arg_max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { argMin } from '../../ops/arg_min';\nimport { Tensor } from '../../tensor';\nTensor.prototype.argMin = function (axis) {\n    this.throwIfDisposed();\n    return argMin(this, axis);\n};\n//# sourceMappingURL=arg_min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\nimport { assert } from '../../util';\n/** Converts a size-1 `tf.Tensor` to a `tf.Scalar`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asScalar = function () {\n    this.throwIfDisposed();\n    assert(this.size === 1, () => 'The array must have only 1 element.');\n    return reshape(this, []);\n};\n//# sourceMappingURL=as_scalar.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * Casts a `tf.Tensor` to a specified dtype.\n *\n * @param dtype Data-type to cast the tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asType = function (dtype) {\n    this.throwIfDisposed();\n    return cast(this, dtype);\n};\n//# sourceMappingURL=as_type.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/** Converts a `tf.Tensor` to a `tf.Tensor1D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as1D = function () {\n    this.throwIfDisposed();\n    return reshape(this, [this.size]);\n};\n//# sourceMappingURL=as1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor2D`.\n *\n * @param rows Number of rows in `tf.Tensor2D`.\n * @param columns Number of columns in `tf.Tensor2D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as2D = function (rows, columns) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns]);\n};\n//# sourceMappingURL=as2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor3D`.\n *\n * @param rows Number of rows in `tf.Tensor3D`.\n * @param columns Number of columns in `tf.Tensor3D`.\n * @param depth Depth of `tf.Tensor3D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as3D = function (rows, columns, depth) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth]);\n};\n//# sourceMappingURL=as3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor4D`.\n *\n * @param rows Number of rows in `tf.Tensor4D`.\n * @param columns Number of columns in `tf.Tensor4D`.\n * @param depth Depth of `tf.Tensor4D`.\n * @param depth2 4th dimension of `tf.Tensor4D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as4D = function (rows, columns, depth, depth2) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth, depth2]);\n};\n//# sourceMappingURL=as4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor5D`.\n *\n * @param rows Number of rows in `tf.Tensor5D`.\n * @param columns Number of columns in `tf.Tensor5D`.\n * @param depth Depth of `tf.Tensor5D`.\n * @param depth2 4th dimension of `tf.Tensor5D`.\n * @param depth3 5th dimension of 'tf.Tensor5D'\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as5D = function (rows, columns, depth, depth2, depth3) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth, depth2, depth3]);\n};\n//# sourceMappingURL=as5d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { asin } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.asin = function () {\n    this.throwIfDisposed();\n    return asin(this);\n};\n//# sourceMappingURL=asin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { asinh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.asinh = function () {\n    this.throwIfDisposed();\n    return asinh(this);\n};\n//# sourceMappingURL=asinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { atan } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atan = function () {\n    this.throwIfDisposed();\n    return atan(this);\n};\n//# sourceMappingURL=atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { atan2 } from '../../ops/atan2';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atan2 = function (b) {\n    this.throwIfDisposed();\n    return atan2(this, b);\n};\n//# sourceMappingURL=atan2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { atanh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atanh = function () {\n    this.throwIfDisposed();\n    return atanh(this);\n};\n//# sourceMappingURL=atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { avgPool } from '../../ops/avg_pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.avgPool = function (filterSize, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return avgPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=avg_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { batchToSpaceND } from '../../ops/batch_to_space_nd';\nimport { Tensor } from '../../tensor';\nTensor.prototype.batchToSpaceND = function (blockShape, crops) {\n    this.throwIfDisposed();\n    return batchToSpaceND(this, blockShape, crops);\n};\n//# sourceMappingURL=batch_to_space_nd.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { batchNorm } from '../../ops/batchnorm';\nimport { Tensor } from '../../tensor';\nTensor.prototype.batchNorm = function (mean, variance, offset, scale, varianceEpsilon) {\n    this.throwIfDisposed();\n    return batchNorm(this, mean, variance, offset, scale, varianceEpsilon);\n};\n//# sourceMappingURL=batchnorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { broadcastTo } from '../../ops/broadcast_to';\nimport { Tensor } from '../../tensor';\nTensor.prototype.broadcastTo = function (shape) {\n    this.throwIfDisposed();\n    return broadcastTo(this, shape);\n};\n//# sourceMappingURL=broadcast_to.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cast = function (dtype) {\n    this.throwIfDisposed();\n    return cast(this, dtype);\n};\n//# sourceMappingURL=cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { ceil } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.ceil = function () {\n    this.throwIfDisposed();\n    return ceil(this);\n};\n//# sourceMappingURL=ceil.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { clipByValue } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.clipByValue = function (min, max) {\n    this.throwIfDisposed();\n    return clipByValue(this, min, max);\n};\n//# sourceMappingURL=clip_by_value.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../../ops/concat';\nimport { Tensor } from '../../tensor';\nTensor.prototype.concat = function (x, axis) {\n    this.throwIfDisposed();\n    if (x instanceof Tensor) {\n        x = [x];\n    }\n    return concat([this, ...x], axis);\n};\n//# sourceMappingURL=concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv1d } from '../../ops/conv1d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv1d = function (filter, stride, pad, dataFormat, dilation, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv1d(this, filter, stride, pad, dataFormat, dilation, dimRoundingMode);\n};\n//# sourceMappingURL=conv1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2dTranspose } from '../../ops/conv2d_transpose';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv2dTranspose = function (filter, outputShape, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv2dTranspose(this, filter, outputShape, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=conv2d_transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2d } from '../../ops/conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cos } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cos = function () {\n    this.throwIfDisposed();\n    return cos(this);\n};\n//# sourceMappingURL=cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cosh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cosh = function () {\n    this.throwIfDisposed();\n    return cosh(this);\n};\n//# sourceMappingURL=cosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { cumsum } from '../../ops/cumsum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cumsum = function (axis, exclusive, reverse) {\n    this.throwIfDisposed();\n    return cumsum(this, axis, exclusive, reverse);\n};\n//# sourceMappingURL=cumsum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { depthToSpace } from '../../ops/depth_to_space';\nimport { Tensor } from '../../tensor';\nTensor.prototype.depthToSpace = function (blockSize, dataFormat) {\n    this.throwIfDisposed();\n    return depthToSpace(this, blockSize, dataFormat);\n};\n//# sourceMappingURL=depth_to_space.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../../globals';\nimport { depthwiseConv2d } from '../../ops/depthwise_conv2d';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated Use `depthwiseConv2d` instead.\n */\nTensor.prototype.depthwiseConv2D = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    deprecationWarn('depthwiseConv2D is deprecated, use depthwiseConv2d instead');\n    this.throwIfDisposed();\n    return depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=depthwise_conv2D_deprecated.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { depthwiseConv2d } from '../../ops/depthwise_conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.depthwiseConv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    this.throwIfDisposed();\n    return depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dilation2d } from '../../ops/dilation2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.dilation2d = function (filter, strides, pad, dilations, dataFormat) {\n    this.throwIfDisposed();\n    return dilation2d(this, filter, strides, pad, dilations, dataFormat);\n};\n//# sourceMappingURL=dilation2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { divNoNan } from '../../ops/div_no_nan';\nimport { Tensor } from '../../tensor';\nTensor.prototype.divNoNan = function (b) {\n    this.throwIfDisposed();\n    return divNoNan(this, b);\n};\n//# sourceMappingURL=div_no_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { divStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.divStrict = function (x) {\n    this.throwIfDisposed();\n    return divStrict(this, x);\n};\n//# sourceMappingURL=div_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { div } from '../../ops/div';\nimport { Tensor } from '../../tensor';\nTensor.prototype.div = function (b) {\n    this.throwIfDisposed();\n    return div(this, b);\n};\n//# sourceMappingURL=div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dot } from '../../ops/dot';\nimport { Tensor } from '../../tensor';\nTensor.prototype.dot = function (b) {\n    this.throwIfDisposed();\n    return dot(this, b);\n};\n//# sourceMappingURL=dot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { elu } from '../../ops/elu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.elu = function () {\n    this.throwIfDisposed();\n    return elu(this);\n};\n//# sourceMappingURL=elu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { equalStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.equalStrict = function (x) {\n    this.throwIfDisposed();\n    return equalStrict(this, x);\n};\n//# sourceMappingURL=equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { equal } from '../../ops/equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.equal = function (b) {\n    this.throwIfDisposed();\n    return equal(this, b);\n};\n//# sourceMappingURL=equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { erf } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.erf = function () {\n    this.throwIfDisposed();\n    return erf(this);\n};\n//# sourceMappingURL=erf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { exp } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.exp = function () {\n    this.throwIfDisposed();\n    return exp(this);\n};\n//# sourceMappingURL=exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { expandDims } from '../../ops/expand_dims';\nimport { Tensor } from '../../tensor';\nTensor.prototype.expandDims = function (axis) {\n    this.throwIfDisposed();\n    return expandDims(this, axis);\n};\n//# sourceMappingURL=expand_dims.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { expm1 } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.expm1 = function () {\n    this.throwIfDisposed();\n    return expm1(this);\n};\n//# sourceMappingURL=expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { fft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.fft = function () {\n    this.throwIfDisposed();\n    return fft(this);\n};\n//# sourceMappingURL=fft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/** Flatten a Tensor to a 1D array.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.flatten = function () {\n    this.throwIfDisposed();\n    return reshape(this, [this.size]);\n};\n//# sourceMappingURL=flatten.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { floor } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.floor = function () {\n    this.throwIfDisposed();\n    return floor(this);\n};\n//# sourceMappingURL=floor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { floorDiv } from '../../ops/floorDiv';\nimport { Tensor } from '../../tensor';\nTensor.prototype.floorDiv = function (b) {\n    this.throwIfDisposed();\n    return floorDiv(this, b);\n};\n//# sourceMappingURL=floorDiv.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { gather } from '../../ops/gather';\nimport { Tensor } from '../../tensor';\nTensor.prototype.gather = function (indices, axis) {\n    this.throwIfDisposed();\n    return gather(this, indices, axis);\n};\n//# sourceMappingURL=gather.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { greaterEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return greaterEqualStrict(this, x);\n};\n//# sourceMappingURL=greater_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { greaterEqual } from '../../ops/greater_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.greaterEqual = function (b) {\n    this.throwIfDisposed();\n    return greaterEqual(this, b);\n};\n//# sourceMappingURL=greater_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { greaterStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterStrict = function (x) {\n    this.throwIfDisposed();\n    return greaterStrict(this, x);\n};\n//# sourceMappingURL=greater_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { greater } from '../../ops/greater';\nimport { Tensor } from '../../tensor';\nTensor.prototype.greater = function (b) {\n    this.throwIfDisposed();\n    return greater(this, b);\n};\n//# sourceMappingURL=greater.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { ifft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.ifft = function () {\n    this.throwIfDisposed();\n    return ifft(this);\n};\n//# sourceMappingURL=ifft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { irfft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.irfft = function () {\n    this.throwIfDisposed();\n    return irfft(this);\n};\n//# sourceMappingURL=irfft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isFinite } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isFinite = function () {\n    this.throwIfDisposed();\n    return isFinite(this);\n};\n//# sourceMappingURL=is_finite.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isInf } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isInf = function () {\n    this.throwIfDisposed();\n    return isInf(this);\n};\n//# sourceMappingURL=is_inf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isNaN } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isNaN = function () {\n    this.throwIfDisposed();\n    return isNaN(this);\n};\n//# sourceMappingURL=is_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { leakyRelu } from '../../ops/leaky_relu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.leakyRelu = function (alpha) {\n    this.throwIfDisposed();\n    return leakyRelu(this, alpha);\n};\n//# sourceMappingURL=leaky_relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { lessEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.lessEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return lessEqualStrict(this, x);\n};\n//# sourceMappingURL=less_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { lessEqual } from '../../ops/less_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.lessEqual = function (b) {\n    this.throwIfDisposed();\n    return lessEqual(this, b);\n};\n//# sourceMappingURL=less_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { lessStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.lessStrict = function (x) {\n    this.throwIfDisposed();\n    return lessStrict(this, x);\n};\n//# sourceMappingURL=less_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { less } from '../../ops/less';\nimport { Tensor } from '../../tensor';\nTensor.prototype.less = function (b) {\n    this.throwIfDisposed();\n    return less(this, b);\n};\n//# sourceMappingURL=less.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { localResponseNormalization } from '../../ops/local_response_normalization';\nimport { Tensor } from '../../tensor';\nTensor.prototype.localResponseNormalization = function (depthRadius, bias, alpha, beta) {\n    this.throwIfDisposed();\n    return localResponseNormalization(this, depthRadius, bias, alpha, beta);\n};\n//# sourceMappingURL=local_response_normalization.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { logSigmoid } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSigmoid = function () {\n    this.throwIfDisposed();\n    return logSigmoid(this);\n};\n//# sourceMappingURL=log_sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { logSoftmax } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSoftmax = function (axis) {\n    this.throwIfDisposed();\n    return logSoftmax(this, axis);\n};\n//# sourceMappingURL=log_softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logSumExp } from '../../ops/log_sum_exp';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSumExp = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return logSumExp(this, axis, keepDims);\n};\n//# sourceMappingURL=log_sum_exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { log } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.log = function () {\n    this.throwIfDisposed();\n    return log(this);\n};\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { log1p } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.log1p = function () {\n    this.throwIfDisposed();\n    return log1p(this);\n};\n//# sourceMappingURL=log1p.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalAnd } from '../../ops/logical_and';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalAnd = function (b) {\n    this.throwIfDisposed();\n    return logicalAnd(this, b);\n};\n//# sourceMappingURL=logical_and.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalNot } from '../../ops/logical_not';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalNot = function () {\n    this.throwIfDisposed();\n    return logicalNot(this);\n};\n//# sourceMappingURL=logical_not.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalOr } from '../../ops/logical_or';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalOr = function (b) {\n    this.throwIfDisposed();\n    return logicalOr(this, b);\n};\n//# sourceMappingURL=logical_or.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalXor } from '../../ops/logical_xor';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalXor = function (b) {\n    this.throwIfDisposed();\n    return logicalXor(this, b);\n};\n//# sourceMappingURL=logical_xor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { matMul } from '../../ops/mat_mul';\nimport { Tensor } from '../../tensor';\nTensor.prototype.matMul = function (b, transposeA, transposeB) {\n    this.throwIfDisposed();\n    return matMul(this, b, transposeA, transposeB);\n};\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { maxPool } from '../../ops/max_pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.maxPool = function (filterSize, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return maxPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=max_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { max } from '../../ops/max';\nimport { Tensor } from '../../tensor';\nTensor.prototype.max = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return max(this, axis, keepDims);\n};\n//# sourceMappingURL=max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { maximumStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.maximumStrict = function (x) {\n    this.throwIfDisposed();\n    return maximumStrict(this, x);\n};\n//# sourceMappingURL=maximum_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { maximum } from '../../ops/maximum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.maximum = function (b) {\n    this.throwIfDisposed();\n    return maximum(this, b);\n};\n//# sourceMappingURL=maximum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mean } from '../../ops/mean';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mean = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return mean(this, axis, keepDims);\n};\n//# sourceMappingURL=mean.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { min } from '../../ops/min';\nimport { Tensor } from '../../tensor';\nTensor.prototype.min = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return min(this, axis, keepDims);\n};\n//# sourceMappingURL=min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { minimumStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.minimumStrict = function (x) {\n    this.throwIfDisposed();\n    return minimumStrict(this, x);\n};\n//# sourceMappingURL=minimum_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { minimum } from '../../ops/minimum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.minimum = function (b) {\n    this.throwIfDisposed();\n    return minimum(this, b);\n};\n//# sourceMappingURL=minimum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { modStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.modStrict = function (x) {\n    this.throwIfDisposed();\n    return modStrict(this, x);\n};\n//# sourceMappingURL=mod_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mod } from '../../ops/mod';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mod = function (b) {\n    this.throwIfDisposed();\n    return mod(this, b);\n};\n//# sourceMappingURL=mod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { mulStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.mulStrict = function (x) {\n    this.throwIfDisposed();\n    return mulStrict(this, x);\n};\n//# sourceMappingURL=mul_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mul } from '../../ops/mul';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mul = function (b) {\n    this.throwIfDisposed();\n    return mul(this, b);\n};\n//# sourceMappingURL=mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { neg } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.neg = function () {\n    this.throwIfDisposed();\n    return neg(this);\n};\n//# sourceMappingURL=neg.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { norm } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.norm = function (ord, axis, keepDims) {\n    this.throwIfDisposed();\n    return norm(this, ord, axis, keepDims);\n};\n//# sourceMappingURL=norm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { notEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.notEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return notEqualStrict(this, x);\n};\n//# sourceMappingURL=not_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { notEqual } from '../../ops/not_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.notEqual = function (b) {\n    this.throwIfDisposed();\n    return notEqual(this, b);\n};\n//# sourceMappingURL=not_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { oneHot } from '../../ops/one_hot';\nimport { Tensor } from '../../tensor';\nTensor.prototype.oneHot = function (depth, onValue = 1, offValue = 0) {\n    this.throwIfDisposed();\n    return oneHot(this, depth, onValue, offValue);\n};\n//# sourceMappingURL=one_hot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { onesLike } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.onesLike = function () {\n    this.throwIfDisposed();\n    return onesLike(this);\n};\n//# sourceMappingURL=ones_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pad } from '../../ops/pad';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pad = function (paddings, constantValue) {\n    this.throwIfDisposed();\n    return pad(this, paddings, constantValue);\n};\n//# sourceMappingURL=pad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pool } from '../../ops/pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pool = function (windowShape, poolingType, padding, dilationRate, strides) {\n    this.throwIfDisposed();\n    return pool(this, windowShape, poolingType, padding, dilationRate, strides);\n};\n//# sourceMappingURL=pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { powStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.powStrict = function (exp) {\n    this.throwIfDisposed();\n    return powStrict(this, exp);\n};\n//# sourceMappingURL=pow_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pow } from '../../ops/pow';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pow = function (exp) {\n    this.throwIfDisposed();\n    return pow(this, exp);\n};\n//# sourceMappingURL=pow.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { prelu } from '../../ops/prelu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.prelu = function (alpha) {\n    this.throwIfDisposed();\n    return prelu(this, alpha);\n};\n//# sourceMappingURL=prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { prod } from '../../ops/prod';\nimport { Tensor } from '../../tensor';\nTensor.prototype.prod = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return prod(this, axis, keepDims);\n};\n//# sourceMappingURL=prod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { reciprocal } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reciprocal = function () {\n    this.throwIfDisposed();\n    return reciprocal(this);\n};\n//# sourceMappingURL=reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { relu } from '../../ops/relu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.relu = function () {\n    this.throwIfDisposed();\n    return relu(this);\n};\n//# sourceMappingURL=relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { relu6 } from '../../ops/relu6';\nimport { Tensor } from '../../tensor';\nTensor.prototype.relu6 = function () {\n    this.throwIfDisposed();\n    return relu6(this);\n};\n//# sourceMappingURL=relu6.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Reshapes the tensor into the shape of the provided tensor.\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.reshapeAs = function (x) {\n    this.throwIfDisposed();\n    return reshape(this, x.shape);\n};\n//# sourceMappingURL=reshape_as.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reshape = function (shape) {\n    this.throwIfDisposed();\n    return reshape(this, shape);\n};\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { resizeBilinear } from '../../ops/image/resize_bilinear';\nimport { Tensor } from '../../tensor';\nTensor.prototype.resizeBilinear = function (newShape2D, alignCorners) {\n    this.throwIfDisposed();\n    return resizeBilinear(this, newShape2D, alignCorners);\n};\n//# sourceMappingURL=resize_bilinear.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { resizeNearestNeighbor } from '../../ops/image/resize_nearest_neighbor';\nimport { Tensor } from '../../tensor';\nTensor.prototype.resizeNearestNeighbor = function (newShape2D, alignCorners) {\n    this.throwIfDisposed();\n    return resizeNearestNeighbor(this, newShape2D, alignCorners);\n};\n//# sourceMappingURL=resize_nearest_neighbor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reverse } from '../../ops/reverse';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reverse = function (axis) {\n    this.throwIfDisposed();\n    return reverse(this, axis);\n};\n//# sourceMappingURL=reverse.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { rfft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.rfft = function () {\n    this.throwIfDisposed();\n    return rfft(this);\n};\n//# sourceMappingURL=rfft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { round } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.round = function () {\n    this.throwIfDisposed();\n    return round(this);\n};\n//# sourceMappingURL=round.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { rsqrt } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.rsqrt = function () {\n    this.throwIfDisposed();\n    return rsqrt(this);\n};\n//# sourceMappingURL=rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { selu } from '../../ops/selu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.selu = function () {\n    this.throwIfDisposed();\n    return selu(this);\n};\n//# sourceMappingURL=selu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { separableConv2d } from '../../ops/separable_conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.separableConv2d = function (depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat) {\n    this.throwIfDisposed();\n    return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat);\n};\n//# sourceMappingURL=separable_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sigmoid } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sigmoid = function () {\n    this.throwIfDisposed();\n    return sigmoid(this);\n};\n//# sourceMappingURL=sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sign } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sign = function () {\n    this.throwIfDisposed();\n    return sign(this);\n};\n//# sourceMappingURL=sign.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sin } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sin = function () {\n    this.throwIfDisposed();\n    return sin(this);\n};\n//# sourceMappingURL=sin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sinh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sinh = function () {\n    this.throwIfDisposed();\n    return sinh(this);\n};\n//# sourceMappingURL=sinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { slice } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.slice = function (begin, size) {\n    this.throwIfDisposed();\n    return slice(this, begin, size);\n};\n//# sourceMappingURL=slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { softmax } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.softmax = function (dim) {\n    this.throwIfDisposed();\n    return softmax(this, dim);\n};\n//# sourceMappingURL=softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { softplus } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.softplus = function () {\n    this.throwIfDisposed();\n    return softplus(this);\n};\n//# sourceMappingURL=softplus.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { spaceToBatchND } from '../../ops/space_to_batch_nd';\nimport { Tensor } from '../../tensor';\nTensor.prototype.spaceToBatchND = function (blockShape, paddings) {\n    this.throwIfDisposed();\n    return spaceToBatchND(this, blockShape, paddings);\n};\n//# sourceMappingURL=space_to_batch_nd.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { split } from '../../ops/split';\nimport { Tensor } from '../../tensor';\nTensor.prototype.split = function (numOrSizeSplits, axis) {\n    this.throwIfDisposed();\n    return split(this, numOrSizeSplits, axis);\n};\n//# sourceMappingURL=split.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sqrt } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sqrt = function () {\n    this.throwIfDisposed();\n    return sqrt(this);\n};\n//# sourceMappingURL=sqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { square } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.square = function () {\n    this.throwIfDisposed();\n    return square(this);\n};\n//# sourceMappingURL=square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { squaredDifference } from '../../ops/squared_difference';\nimport { Tensor } from '../../tensor';\nTensor.prototype.squaredDifference = function (b) {\n    this.throwIfDisposed();\n    return squaredDifference(this, b);\n};\n//# sourceMappingURL=squared_difference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { squaredDifferenceStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.squaredDifferenceStrict = function (x) {\n    this.throwIfDisposed();\n    return squaredDifferenceStrict(this, x);\n};\n//# sourceMappingURL=squared_difference_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { squeeze } from '../../ops/squeeze';\nimport { Tensor } from '../../tensor';\nTensor.prototype.squeeze = function (axis) {\n    this.throwIfDisposed();\n    return squeeze(this, axis);\n};\n//# sourceMappingURL=squeeze.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { stack } from '../../ops/stack';\nimport { Tensor } from '../../tensor';\nTensor.prototype.stack = function (x, axis) {\n    this.throwIfDisposed();\n    const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];\n    return stack(tensorsToBeStacked, axis);\n};\n//# sourceMappingURL=stack.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { step } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.step = function (alpha) {\n    this.throwIfDisposed();\n    return step(this, alpha);\n};\n//# sourceMappingURL=step.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { stridedSlice } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.stridedSlice = function (begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n    this.throwIfDisposed();\n    return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n};\n//# sourceMappingURL=strided_slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { subStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.subStrict = function (x) {\n    this.throwIfDisposed();\n    return subStrict(this, x);\n};\n//# sourceMappingURL=sub_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { sub } from '../../ops/sub';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sub = function (b) {\n    this.throwIfDisposed();\n    return sub(this, b);\n};\n//# sourceMappingURL=sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { sum } from '../../ops/sum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sum = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return sum(this, axis, keepDims);\n};\n//# sourceMappingURL=sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { tan } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tan = function () {\n    this.throwIfDisposed();\n    return tan(this);\n};\n//# sourceMappingURL=tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { tanh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tanh = function () {\n    this.throwIfDisposed();\n    return tanh(this);\n};\n//# sourceMappingURL=tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tile } from '../../ops/tile';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tile = function (reps) {\n    this.throwIfDisposed();\n    return tile(this, reps);\n};\n//# sourceMappingURL=tile.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `bool`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toBool = function () {\n    this.throwIfDisposed();\n    return cast(this, 'bool');\n};\n//# sourceMappingURL=to_bool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `float32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toFloat = function () {\n    this.throwIfDisposed();\n    return cast(this, 'float32');\n};\n//# sourceMappingURL=to_float.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `int32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toInt = function () {\n    this.throwIfDisposed();\n    return cast(this, 'int32');\n};\n//# sourceMappingURL=to_int.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { topk } from '../../ops/topk';\nimport { Tensor } from '../../tensor';\nTensor.prototype.topk = function (k, sorted) {\n    this.throwIfDisposed();\n    return topk(this, k, sorted);\n};\n//# sourceMappingURL=topk.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { transpose } from '../../ops/transpose';\nimport { Tensor } from '../../tensor';\nTensor.prototype.transpose = function (perm) {\n    this.throwIfDisposed();\n    return transpose(this, perm);\n};\n//# sourceMappingURL=transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unique } from '../../ops/unique';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unique = function (axis) {\n    this.throwIfDisposed();\n    return unique(this, axis);\n};\n//# sourceMappingURL=unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unsortedSegmentSum } from '../../ops/unsorted_segment_sum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unsortedSegmentSum = function (segmentIds, numSegments) {\n    this.throwIfDisposed();\n    return unsortedSegmentSum(this, segmentIds, numSegments);\n};\n//# sourceMappingURL=unsorted_segment_sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unstack } from '../../ops/unstack';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unstack = function (axis) {\n    this.throwIfDisposed();\n    return unstack(this, axis);\n};\n//# sourceMappingURL=unstack.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { where } from '../../ops/where';\nimport { Tensor } from '../../tensor';\nTensor.prototype.where = function (condition, x) {\n    this.throwIfDisposed();\n    return where(condition, this, x);\n};\n//# sourceMappingURL=where.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { zerosLike } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.zerosLike = function () {\n    this.throwIfDisposed();\n    return zerosLike(this);\n};\n//# sourceMappingURL=zeros_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './abs';\nimport './acos';\nimport './acosh';\nimport './add_strict';\nimport './add';\nimport './all';\nimport './any';\nimport './arg_max';\nimport './arg_min';\nimport './as_scalar';\nimport './as_type';\nimport './as1d';\nimport './as2d';\nimport './as3d';\nimport './as4d';\nimport './as5d';\nimport './asin';\nimport './asinh';\nimport './atan';\nimport './atan2';\nimport './atanh';\nimport './avg_pool';\nimport './batch_to_space_nd';\nimport './batchnorm';\nimport './broadcast_to';\nimport './cast';\nimport './ceil';\nimport './clip_by_value';\nimport './concat';\nimport './conv1d';\nimport './conv2d_transpose';\nimport './conv2d';\nimport './cos';\nimport './cosh';\nimport './cumsum';\nimport './depth_to_space';\nimport './depthwise_conv2D_deprecated';\nimport './depthwise_conv2d';\nimport './dilation2d';\nimport './div_no_nan';\nimport './div_strict';\nimport './div';\nimport './dot';\nimport './elu';\nimport './equal_strict';\nimport './equal';\nimport './erf';\nimport './exp';\nimport './expand_dims';\nimport './expm1';\nimport './fft';\nimport './flatten';\nimport './floor';\nimport './floorDiv';\nimport './gather';\nimport './greater_equal_strict';\nimport './greater_equal';\nimport './greater_strict';\nimport './greater';\nimport './ifft';\nimport './irfft';\nimport './is_finite';\nimport './is_inf';\nimport './is_nan';\nimport './leaky_relu';\nimport './less_equal_strict';\nimport './less_equal';\nimport './less_strict';\nimport './less';\nimport './local_response_normalization';\nimport './log_sigmoid';\nimport './log_softmax';\nimport './log_sum_exp';\nimport './log';\nimport './log1p';\nimport './logical_and';\nimport './logical_not';\nimport './logical_or';\nimport './logical_xor';\nimport './mat_mul';\nimport './max_pool';\nimport './max';\nimport './maximum_strict';\nimport './maximum';\nimport './mean';\nimport './min';\nimport './minimum_strict';\nimport './minimum';\nimport './mod_strict';\nimport './mod';\nimport './mul_strict';\nimport './mul';\nimport './neg';\nimport './norm';\nimport './not_equal_strict';\nimport './not_equal';\nimport './one_hot';\nimport './ones_like';\nimport './pad';\nimport './pool';\nimport './pow_strict';\nimport './pow';\nimport './prelu';\nimport './prod';\nimport './reciprocal';\nimport './relu';\nimport './relu6';\nimport './reshape_as';\nimport './reshape';\nimport './resize_bilinear';\nimport './resize_nearest_neighbor';\nimport './reverse';\nimport './rfft';\nimport './round';\nimport './rsqrt';\nimport './selu';\nimport './separable_conv2d';\nimport './sigmoid';\nimport './sign';\nimport './sin';\nimport './sinh';\nimport './slice';\nimport './softmax';\nimport './softplus';\nimport './space_to_batch_nd';\nimport './split';\nimport './sqrt';\nimport './square';\nimport './squared_difference';\nimport './squared_difference_strict';\nimport './squeeze';\nimport './stack';\nimport './step';\nimport './strided_slice';\nimport './sub_strict';\nimport './sub';\nimport './sum';\nimport './tan';\nimport './tanh';\nimport './tile';\nimport './to_bool';\nimport './to_float';\nimport './to_int';\nimport './topk';\nimport './transpose';\nimport './unique';\nimport './unsorted_segment_sum';\nimport './unstack';\nimport './where';\nimport './zeros_like';\n//# sourceMappingURL=register_all_chained_ops.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Required side effectful code.\nimport './base_side_effects';\n// All exports from this package should be in base.\nexport * from './base';\n// Register all the gradients.\nimport './register_all_gradients';\n// Import all op chainers and add type info to Tensor.\nimport './public/chained_ops/register_all_chained_ops';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { backend } from '@tensorflow/tfjs-core';\nlet _epsilon;\n/**\n * Returns the value of the fuzz factor used in numeric expressions.\n */\nexport function epsilon() {\n    if (_epsilon == null) {\n        _epsilon = backend().epsilon();\n    }\n    return _epsilon;\n}\n/**\n * Sets the value of the fuzz factor used in numeric expressions.\n * @param e New value of epsilon.\n */\nexport function setEpsilon(e) {\n    _epsilon = e;\n}\n/**\n * Returns the default image data format convention.\n */\nexport function imageDataFormat() {\n    return 'channelsLast';\n}\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Explicit error types.\n *\n * See the following link for more information about why the code includes\n * calls to setPrototypeOf:\n *\n * https://github.com/Microsoft/TypeScript-wiki/blob/master/Breaking-Changes.md#extending-built-ins-like-error-array-and-map-may-no-longer-work\n */\n// tslint:enable\n/**\n * Equivalent of Python's AttributeError.\n */\nexport class AttributeError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, AttributeError.prototype);\n    }\n}\n/**\n * Equivalent of Python's RuntimeError.\n */\nexport class RuntimeError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, RuntimeError.prototype);\n    }\n}\n/**\n * Equivalent of Python's ValueError.\n */\nexport class ValueError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, ValueError.prototype);\n    }\n}\n/**\n * Equivalent of Python's NotImplementedError.\n */\nexport class NotImplementedError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, NotImplementedError.prototype);\n    }\n}\n/**\n * Equivalent of Python's AssertionError.\n */\nexport class AssertionError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, AssertionError.prototype);\n    }\n}\n/**\n * Equivalent of Python's IndexError.\n */\nexport class IndexError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, IndexError.prototype);\n    }\n}\n//# sourceMappingURL=errors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: utils/generic_utils.py */\nimport { util } from '@tensorflow/tfjs-core';\nimport { AssertionError, ValueError } from '../errors';\n// tslint:enable\n/**\n * If `value` is an Array, equivalent to Python's `value * numValues`.\n * If `value` is not an Array, equivalent to Python's `[value] * numValues`\n */\n// tslint:disable-next-line:no-any\nexport function pyListRepeat(value, numValues) {\n    if (Array.isArray(value)) {\n        // tslint:disable-next-line:no-any\n        let newArray = [];\n        for (let i = 0; i < numValues; i++) {\n            newArray = newArray.concat(value);\n        }\n        return newArray;\n    }\n    else {\n        const newArray = new Array(numValues);\n        newArray.fill(value);\n        return newArray;\n    }\n}\nexport function assert(val, message) {\n    if (!val) {\n        throw new AssertionError(message);\n    }\n}\n/**\n * Count the number of elements of the `array` that are equal to `reference`.\n */\nexport function count(array, refernce) {\n    let counter = 0;\n    for (const item of array) {\n        if (item === refernce) {\n            counter++;\n        }\n    }\n    return counter;\n}\n/**\n * If an array is of length 1, just return the first element. Otherwise, return\n * the full array.\n * @param tensors\n */\nexport function singletonOrArray(xs) {\n    if (xs.length === 1) {\n        return xs[0];\n    }\n    return xs;\n}\n/**\n * Normalizes a list/tensor into a list.\n *\n * If a tensor is passed, we return\n * a list of size 1 containing the tensor.\n *\n * @param x target object to be normalized.\n */\n// tslint:disable-next-line:no-any\nexport function toList(x) {\n    if (Array.isArray(x)) {\n        return x;\n    }\n    return [x];\n}\n/**\n * Generate a UID for a list\n */\n// tslint:disable-next-line:no-any\nexport function objectListUid(objs) {\n    const objectList = toList(objs);\n    let retVal = '';\n    for (const obj of objectList) {\n        if (obj.id == null) {\n            throw new ValueError(`Object ${obj} passed to objectListUid without an id`);\n        }\n        if (retVal !== '') {\n            retVal = retVal + ', ';\n        }\n        retVal = `${retVal}${Math.abs(obj.id)}`;\n    }\n    return retVal;\n}\n/**\n * Converts string to snake-case.\n * @param name\n */\nexport function toSnakeCase(name) {\n    const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, '$1_$2');\n    const insecure = intermediate.replace(/([a-z])([A-Z])/g, '$1_$2').toLowerCase();\n    /*\n     If the class is private the name starts with \"_\" which is not secure\n     for creating scopes. We prefix the name with \"private\" in this case.\n     */\n    if (insecure[0] !== '_') {\n        return insecure;\n    }\n    return 'private' + insecure;\n}\nexport function toCamelCase(identifier) {\n    // quick return for empty string or single character strings\n    if (identifier.length <= 1) {\n        return identifier;\n    }\n    // Check for the underscore indicating snake_case\n    if (identifier.indexOf('_') === -1) {\n        return identifier;\n    }\n    return identifier.replace(/[_]+(\\w|$)/g, (m, p1) => p1.toUpperCase());\n}\n// tslint:disable-next-line:no-any\nlet _GLOBAL_CUSTOM_OBJECTS = {};\nexport function serializeKerasObject(instance) {\n    if (instance === null || instance === undefined) {\n        return null;\n    }\n    const dict = {};\n    dict['className'] = instance.getClassName();\n    dict['config'] = instance.getConfig();\n    return dict;\n}\n/**\n * Replace ndarray-style scalar objects in serialization objects with numbers.\n *\n * Background: In some versions of tf.keras, certain scalar values in the HDF5\n * model save file can be serialized as: `{'type': 'ndarray', 'value': num}`,\n * where in `num` is a plain number. This method converts such serialization\n * to a `number`.\n *\n * @param config The keras-format serialization object to be processed\n *   (in place).\n */\nfunction convertNDArrayScalarsInConfig(config) {\n    if (config == null || typeof config !== 'object') {\n        return;\n    }\n    else if (Array.isArray(config)) {\n        config.forEach(configItem => convertNDArrayScalarsInConfig(configItem));\n    }\n    else {\n        const fields = Object.keys(config);\n        for (const field of fields) {\n            const value = config[field];\n            if (value != null && typeof value === 'object') {\n                if (!Array.isArray(value) && value['type'] === 'ndarray' &&\n                    typeof value['value'] === 'number') {\n                    config[field] = value['value'];\n                }\n                else {\n                    convertNDArrayScalarsInConfig(value);\n                }\n            }\n        }\n    }\n}\n/**\n * Deserialize a saved Keras Object\n * @param identifier either a string ID or a saved Keras dictionary\n * @param moduleObjects a list of Python class names to object constructors\n * @param customObjects a list of Python class names to object constructors\n * @param printableModuleName debug text for the object being reconstituted\n * @param fastWeightInit Optional flag to use fast weight initialization\n *   during deserialization. This is applicable to cases in which\n *   the initialization will be immediately overwritten by loaded weight\n *   values. Default: `false`.\n * @returns a TensorFlow.js Layers object\n */\n// tslint:disable:no-any\nexport function deserializeKerasObject(identifier, moduleObjects = {}, customObjects = {}, printableModuleName = 'object', fastWeightInit = false) {\n    // tslint:enable\n    if (typeof identifier === 'string') {\n        const functionName = identifier;\n        let fn;\n        if (functionName in customObjects) {\n            fn = customObjects[functionName];\n        }\n        else if (functionName in _GLOBAL_CUSTOM_OBJECTS) {\n            fn = _GLOBAL_CUSTOM_OBJECTS[functionName];\n        }\n        else {\n            fn = moduleObjects[functionName];\n            if (fn == null) {\n                throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. ` +\n                    `This may be due to one of the following reasons:\\n` +\n                    `1. The ${printableModuleName} is defined in Python, in which ` +\n                    `case it needs to be ported to TensorFlow.js or your JavaScript ` +\n                    `code.\\n` +\n                    `2. The custom ${printableModuleName} is defined in JavaScript, ` +\n                    `but is not registered properly with ` +\n                    `tf.serialization.registerClass().`);\n                // TODO(cais): Add link to tutorial page on custom layers.\n            }\n        }\n        return fn;\n    }\n    else {\n        // In this case we are dealing with a Keras config dictionary.\n        const config = identifier;\n        if (config['className'] == null || config['config'] == null) {\n            throw new ValueError(`${printableModuleName}: Improper config format: ` +\n                `${JSON.stringify(config)}.\\n` +\n                `'className' and 'config' must set.`);\n        }\n        const className = config['className'];\n        let cls, fromConfig;\n        if (className in customObjects) {\n            [cls, fromConfig] = customObjects[className];\n        }\n        else if (className in _GLOBAL_CUSTOM_OBJECTS) {\n            [cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS['className'];\n        }\n        else if (className in moduleObjects) {\n            [cls, fromConfig] = moduleObjects[className];\n        }\n        if (cls == null) {\n            throw new ValueError(`Unknown ${printableModuleName}: ${className}. ` +\n                `This may be due to one of the following reasons:\\n` +\n                `1. The ${printableModuleName} is defined in Python, in which ` +\n                `case it needs to be ported to TensorFlow.js or your JavaScript ` +\n                `code.\\n` +\n                `2. The custom ${printableModuleName} is defined in JavaScript, ` +\n                `but is not registered properly with ` +\n                `tf.serialization.registerClass().`);\n            // TODO(cais): Add link to tutorial page on custom layers.\n        }\n        if (fromConfig != null) {\n            // Porting notes: Instead of checking to see whether fromConfig accepts\n            // customObjects, we create a customObjects dictionary and tack it on to\n            // config['config'] as config['config'].customObjects. Objects can use it,\n            // if they want.\n            // tslint:disable-next-line:no-any\n            const customObjectsCombined = {};\n            for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) {\n                customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key];\n            }\n            for (const key of Object.keys(customObjects)) {\n                customObjectsCombined[key] = customObjects[key];\n            }\n            // Add the customObjects to config\n            const nestedConfig = config['config'];\n            nestedConfig['customObjects'] = customObjectsCombined;\n            const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n            for (const key of Object.keys(customObjects)) {\n                _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n            }\n            convertNDArrayScalarsInConfig(config['config']);\n            const returnObj = fromConfig(cls, config['config'], customObjects, fastWeightInit);\n            _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n            return returnObj;\n        }\n        else {\n            // Then `cls` may be a function returning a class.\n            // In this case by convention `config` holds\n            // the kwargs of the function.\n            const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n            for (const key of Object.keys(customObjects)) {\n                _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n            }\n            // In python this is **config['config'], for tfjs-layers we require\n            // classes that use this fall-through construction method to take\n            // a config interface that mimics the expansion of named parameters.\n            const returnObj = new cls(config['config']);\n            _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n            return returnObj;\n        }\n    }\n}\n/**\n * Compares two numbers for sorting.\n * @param a\n * @param b\n */\nexport function numberCompare(a, b) {\n    return (a < b) ? -1 : ((a > b) ? 1 : 0);\n}\n/**\n * Comparison of two numbers for reverse sorting.\n * @param a\n * @param b\n */\nexport function reverseNumberCompare(a, b) {\n    return -1 * numberCompare(a, b);\n}\n/**\n * Convert a string into the corresponding DType.\n * @param dtype\n * @returns An instance of DType.\n */\nexport function stringToDType(dtype) {\n    switch (dtype) {\n        case 'float32':\n            return 'float32';\n        default:\n            throw new ValueError(`Invalid dtype: ${dtype}`);\n    }\n}\n/**\n * Test the element-by-element equality of two Arrays of strings.\n * @param xs First array of strings.\n * @param ys Second array of strings.\n * @returns Wether the two arrays are all equal, element by element.\n */\nexport function stringsEqual(xs, ys) {\n    if (xs == null || ys == null) {\n        return xs === ys;\n    }\n    if (xs.length !== ys.length) {\n        return false;\n    }\n    for (let i = 0; i < xs.length; ++i) {\n        if (xs[i] !== ys[i]) {\n            return false;\n        }\n    }\n    return true;\n}\n/**\n * Get the unique elements of an array.\n * @param xs Array.\n * @returns An Array consisting of the unique elements in `xs`.\n */\nexport function unique(xs) {\n    if (xs == null) {\n        return xs;\n    }\n    const out = [];\n    // TODO(cais): Maybe improve performance by sorting.\n    for (const x of xs) {\n        if (out.indexOf(x) === -1) {\n            out.push(x);\n        }\n    }\n    return out;\n}\n/**\n * Determine if an Object is empty (i.e., does not have own properties).\n * @param obj Object\n * @returns Whether the Object is empty.\n * @throws ValueError: If object is `null` or `undefined`.\n */\nexport function isObjectEmpty(obj) {\n    if (obj == null) {\n        throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);\n    }\n    for (const key in obj) {\n        if (obj.hasOwnProperty(key)) {\n            return false;\n        }\n    }\n    return true;\n}\n/**\n * Helper function used to build type union/enum run-time checkers.\n * @param values The list of allowed values.\n * @param label A string name for the type\n * @param value The value to test.\n * @throws ValueError: If the value is not in values nor `undefined`/`null`.\n */\nexport function checkStringTypeUnionValue(values, label, value) {\n    if (value == null) {\n        return;\n    }\n    if (values.indexOf(value) < 0) {\n        throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`);\n    }\n}\n/**\n * Helper function for verifying the types of inputs.\n *\n * Ensures that the elements of `x` are all of type `expectedType`.\n * Also verifies that the length of `x` is within bounds.\n *\n * @param x Object to test.\n * @param expectedType The string expected type of all of the elements in the\n * Array.\n * @param minLength Return false if x.length is less than this.\n * @param maxLength Return false if x.length is greater than this.\n * @returns true if and only if `x` is an `Array<expectedType>` with\n * length >= `minLength` and <= `maxLength`.\n */\n// tslint:disable:no-any\nexport function checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) {\n    assert(minLength >= 0);\n    assert(maxLength >= minLength);\n    return (Array.isArray(x) && x.length >= minLength && x.length <= maxLength &&\n        x.every(e => typeof e === expectedType));\n}\n// tslint:enable:no-any\n/**\n * Assert that a value or an array of value are positive integer.\n *\n * @param value The value being asserted on. May be a single number or an array\n *   of numbers.\n * @param name Name of the value, used to make the error message.\n */\nexport function assertPositiveInteger(value, name) {\n    if (Array.isArray(value)) {\n        util.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`);\n        value.forEach((v, i) => assertPositiveInteger(v, `element ${i + 1} of ${name}`));\n    }\n    else {\n        util.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ` +\n            `${formatAsFriendlyString(value)}.`);\n    }\n}\n/**\n * Format a value into a display-friendly, human-readable fashion.\n *\n * - `null` is formatted as `'null'`\n * - Strings are formated with flanking pair of quotes.\n * - Arrays are formatted with flanking pair of square brackets.\n *\n * @param value The value to display.\n * @return Formatted string.\n */\n// tslint:disable-next-line:no-any\nexport function formatAsFriendlyString(value) {\n    if (value === null) {\n        return 'null';\n    }\n    else if (Array.isArray(value)) {\n        return '[' + value.map(v => formatAsFriendlyString(v)).join(',') + ']';\n    }\n    else if (typeof value === 'string') {\n        return `\"${value}\"`;\n    }\n    else {\n        return `${value}`;\n    }\n}\n/**\n * Returns a function `f2` (decorator) which wraps the original function\n * `f`. `f2` guarantees that `f` can be called at most once\n * every `waitMs` ms. If `f2` is called more often, it will return\n * the last returned result of `f`.\n *\n * @param f The original function `f` to wrap.\n * @param waitMs The time between two consecutive calls to `f` in ms.\n */\nexport function debounce(f, waitMs) {\n    let lastTime = util.now();\n    let lastResult;\n    const f2 = (...args) => {\n        const now = util.now();\n        if (now - lastTime < waitMs) {\n            return lastResult;\n        }\n        lastTime = now;\n        lastResult = f(...args);\n        return lastResult;\n    };\n    return f2;\n}\n/**\n * Returns the fusable activation given a layers identifier.\n *\n * @param activationName The layers identifier string.\n * @return The name of the fusable activation.\n */\nexport function mapActivationToFusedKernel(activationName) {\n    if (activationName === 'relu') {\n        return 'relu';\n    }\n    if (activationName === 'linear') {\n        return 'linear';\n    }\n    if (activationName === 'elu') {\n        return 'elu';\n    }\n    return null;\n}\n/**\n * Returns the cartesian product of sets of values.\n * This works the same as itertools.product in Python.\n *\n * Example:\n *\n * filters = [128, 256, 512]\n * paddings = ['same', 'valid']\n *\n * product = [ [128, 'same'], [128, 'valid'], [256, 'same'], [256, 'valid'],\n * [512, 'same'], [512, 'valid']]\n *\n * @param arrayOfValues List/array of values.\n * @return The cartesian product.\n */\nexport function getCartesianProductOfValues(...arrayOfValues) {\n    assert(arrayOfValues.length > 0, 'arrayOfValues is empty');\n    for (const values of arrayOfValues) {\n        assert(Array.isArray(values), 'one of the values is not an array');\n        assert(values.length > 0, 'one of the values is empty');\n    }\n    return arrayOfValues.reduce((products, values) => {\n        if (products.length === 0) {\n            return values.map(value => [value]);\n        }\n        return values\n            .map(value => {\n            return products.map((prevValue) => [...prevValue, value]);\n        })\n            .reduce((flattenedProduct, unflattenedProduct) => {\n            return flattenedProduct.concat(unflattenedProduct);\n        }, []);\n    }, []);\n}\n//# sourceMappingURL=generic_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/contraints.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport { deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\n/**\n * Helper function used by many of the Constraints to find the L2Norms.\n */\nfunction calcL2Norms(w, axis) {\n    return tidy(() => tfc.sqrt(tfc.sum(tfc.mul(w, w), axis, true)));\n}\n/**\n * Base class for functions that impose constraints on weight values\n *\n * @doc {\n *   heading: 'Constraints',\n *   subheading: 'Classes',\n *   namespace: 'constraints'\n * }\n */\nexport class Constraint extends serialization.Serializable {\n    getConfig() {\n        return {};\n    }\n}\nexport class MaxNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultMaxValue = 2;\n        this.defaultAxis = 0;\n        this.maxValue =\n            args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => {\n            const norms = calcL2Norms(w, this.axis);\n            const desired = tfc.clipByValue(norms, 0, this.maxValue);\n            return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));\n        });\n    }\n    getConfig() {\n        return { maxValue: this.maxValue, axis: this.axis };\n    }\n}\n/** @nocollapse */\nMaxNorm.className = 'MaxNorm';\nserialization.registerClass(MaxNorm);\nexport class UnitNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultAxis = 0;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => tfc.div(w, tfc.add(epsilon(), calcL2Norms(w, this.axis))));\n    }\n    getConfig() {\n        return { axis: this.axis };\n    }\n}\n/** @nocollapse */\nUnitNorm.className = 'UnitNorm';\nserialization.registerClass(UnitNorm);\nexport class NonNeg extends Constraint {\n    apply(w) {\n        return tfc.relu(w);\n    }\n}\n/** @nocollapse */\nNonNeg.className = 'NonNeg';\nserialization.registerClass(NonNeg);\nexport class MinMaxNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultMinValue = 0.0;\n        this.defaultMaxValue = 1.0;\n        this.defaultRate = 1.0;\n        this.defaultAxis = 0;\n        this.minValue =\n            args.minValue != null ? args.minValue : this.defaultMinValue;\n        this.maxValue =\n            args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n        this.rate = args.rate != null ? args.rate : this.defaultRate;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => {\n            const norms = calcL2Norms(w, this.axis);\n            const desired = tfc.add(tfc.mul(this.rate, tfc.clipByValue(norms, this.minValue, this.maxValue)), tfc.mul(1.0 - this.rate, norms));\n            return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));\n        });\n    }\n    getConfig() {\n        return {\n            minValue: this.minValue,\n            maxValue: this.maxValue,\n            rate: this.rate,\n            axis: this.axis\n        };\n    }\n}\n/** @nocollapse */\nMinMaxNorm.className = 'MinMaxNorm';\nserialization.registerClass(MinMaxNorm);\n// Maps the JavaScript-like identifier keys to the corresponding registry\n// symbols.\nexport const CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'maxNorm': 'MaxNorm',\n    'minMaxNorm': 'MinMaxNorm',\n    'nonNeg': 'NonNeg',\n    'unitNorm': 'UnitNorm'\n};\nexport function serializeConstraint(constraint) {\n    return serializeKerasObject(constraint);\n}\nexport function deserializeConstraint(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'constraint');\n}\nexport function getConstraint(identifier) {\n    if (identifier == null) {\n        return null;\n    }\n    if (typeof identifier === 'string') {\n        const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        const config = { className, config: {} };\n        return deserializeConstraint(config);\n    }\n    else if (identifier instanceof Constraint) {\n        return identifier;\n    }\n    else {\n        return deserializeConstraint(identifier);\n    }\n}\n//# sourceMappingURL=constraints.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// tslint:disable-next-line:max-line-length\nimport { MaxNorm, MinMaxNorm, NonNeg, UnitNorm } from './constraints';\n/**\n * MaxNorm weight constraint.\n *\n * Constrains the weights incident to each hidden unit\n * to have a norm less than or equal to a desired value.\n *\n * References\n *       - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting\n * Srivastava, Hinton, et al.\n * 2014](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)\n *\n * @doc {heading: 'Constraints',namespace: 'constraints'}\n */\nexport function maxNorm(args) {\n    return new MaxNorm(args);\n}\n/**\n * Constrains the weights incident to each hidden unit to have unit norm.\n *\n * @doc {heading: 'Constraints', namespace: 'constraints'}\n */\nexport function unitNorm(args) {\n    return new UnitNorm(args);\n}\n/**\n * Constains the weight to be non-negative.\n *\n * @doc {heading: 'Constraints', namespace: 'constraints'}\n */\nexport function nonNeg() {\n    return new NonNeg();\n}\n/** @doc {heading: 'Constraints', namespace: 'constraints'} */\nexport function minMaxNorm(config) {\n    return new MinMaxNorm(config);\n}\n//# sourceMappingURL=exports_constraints.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport const VALID_DATA_FORMAT_VALUES = ['channelsFirst', 'channelsLast'];\nexport const VALID_PADDING_MODE_VALUES = ['valid', 'same', 'causal'];\nexport const VALID_POOL_MODE_VALUES = ['max', 'avg'];\nexport const VALID_BIDIRECTIONAL_MERGE_MODES = ['sum', 'mul', 'concat', 'ave'];\nexport const VALID_SAMPLE_WEIGHT_MODES = ['temporal'];\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Common functions for TensorFlow.js Layers.\n */\nimport { VALID_DATA_FORMAT_VALUES, VALID_PADDING_MODE_VALUES, VALID_POOL_MODE_VALUES } from './keras_format/common';\nimport { checkStringTypeUnionValue } from './utils/generic_utils';\n// A map from the requested scoped name of a Tensor to the number of Tensors\n// wanting that name so far.  This allows enforcing name uniqueness by appending\n// an incrementing index, e.g. scope/name, scope/name_1, scope/name_2, etc.\nconst nameMap = new Map();\nexport function checkDataFormat(value) {\n    checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, 'DataFormat', value);\n}\nexport function checkPaddingMode(value) {\n    checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, 'PaddingMode', value);\n}\nexport function checkPoolMode(value) {\n    checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, 'PoolMode', value);\n}\nconst _nameScopeStack = [];\nconst _nameScopeDivider = '/';\n/**\n * Enter namescope, which can be nested.\n */\nexport function nameScope(name, fn) {\n    _nameScopeStack.push(name);\n    try {\n        const val = fn();\n        _nameScopeStack.pop();\n        return val;\n    }\n    catch (e) {\n        _nameScopeStack.pop();\n        throw e;\n    }\n}\n/**\n * Get the current namescope as a flat, concatenated string.\n */\nfunction currentNameScopePrefix() {\n    if (_nameScopeStack.length === 0) {\n        return '';\n    }\n    else {\n        return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider;\n    }\n}\n/**\n * Get the name a Tensor (or Variable) would have if not uniqueified.\n * @param tensorName\n * @return Scoped name string.\n */\nexport function getScopedTensorName(tensorName) {\n    if (!isValidTensorName(tensorName)) {\n        throw new Error('Not a valid tensor name: \\'' + tensorName + '\\'');\n    }\n    return currentNameScopePrefix() + tensorName;\n}\n/**\n * Get unique names for Tensors and Variables.\n * @param scopedName The fully-qualified name of the Tensor, i.e. as produced by\n *  `getScopedTensorName()`.\n * @return A unique version of the given fully scoped name.\n *   If this is the first time that the scoped name is seen in this session,\n *   then the given `scopedName` is returned unaltered.  If the same name is\n *   seen again (producing a collision), an incrementing suffix is added to the\n *   end of the name, so it takes the form 'scope/name_1', 'scope/name_2', etc.\n */\nexport function getUniqueTensorName(scopedName) {\n    if (!isValidTensorName(scopedName)) {\n        throw new Error('Not a valid tensor name: \\'' + scopedName + '\\'');\n    }\n    if (!nameMap.has(scopedName)) {\n        nameMap.set(scopedName, 0);\n    }\n    const index = nameMap.get(scopedName);\n    nameMap.set(scopedName, nameMap.get(scopedName) + 1);\n    if (index > 0) {\n        const result = `${scopedName}_${index}`;\n        // Mark the composed name as used in case someone wants\n        // to call getUniqueTensorName(\"name_1\").\n        nameMap.set(result, 1);\n        return result;\n    }\n    else {\n        return scopedName;\n    }\n}\nconst tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\\._\\/]*$/);\n/**\n * Determine whether a string is a valid tensor name.\n * @param name\n * @returns A Boolean indicating whether `name` is a valid tensor name.\n */\nexport function isValidTensorName(name) {\n    return !!name.match(tensorNameRegex);\n}\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Math utility functions.\n *\n * This file contains some frequently used math function that operates on\n * number[] or Float32Array and return a number. Many of these functions are\n * not-so-thick wrappers around TF.js Core functions. But they offer the\n * convenience of\n * 1) not having to convert the inputs into Tensors,\n * 2) not having to convert the returned Tensors to numbers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { scalar, tensor1d } from '@tensorflow/tfjs-core';\nimport { ValueError } from '../errors';\n/**\n * Determine if a number is an integer.\n */\nexport function isInteger(x) {\n    return x === parseInt(x.toString(), 10);\n}\n/**\n * Calculate the product of an array of numbers.\n * @param array The array to calculate the product over.\n * @param begin Beginning index, inclusive.\n * @param end Ending index, exclusive.\n * @return The product.\n */\nexport function arrayProd(array, begin, end) {\n    if (begin == null) {\n        begin = 0;\n    }\n    if (end == null) {\n        end = array.length;\n    }\n    let prod = 1;\n    for (let i = begin; i < end; ++i) {\n        prod *= array[i];\n    }\n    return prod;\n}\n/**\n * A helper function transforms the two input types to an instance of Tensor1D,\n * so the return value can be fed directly into various TF.js Core functions.\n * @param array\n */\nfunction toArray1D(array) {\n    array = Array.isArray(array) ? new Float32Array(array) : array;\n    return tensor1d(array);\n}\n/**\n * Compute minimum value.\n * @param array\n * @return minimum value.\n */\nexport function min(array) {\n    return tfc.min(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute maximum value.\n * @param array\n * @return maximum value\n */\nexport function max(array) {\n    return tfc.max(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute sum of array.\n * @param array\n * @return The sum.\n */\nexport function sum(array) {\n    return tfc.sum(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute mean of array.\n * @param array\n * @return The mean.\n */\nexport function mean(array) {\n    return sum(array) / array.length;\n}\n/**\n * Compute variance of array.\n * @param array\n * @return The variance.\n */\nexport function variance(array) {\n    const demeaned = tfc.sub(toArray1D(array), scalar(mean(array)));\n    const sumSquare = tfc.sum(tfc.mul(demeaned, demeaned)).dataSync()[0];\n    return sumSquare / array.length;\n}\n/**\n * Compute median of array.\n * @param array\n * @return The median value.\n */\nexport function median(array) {\n    const arraySorted = array.slice().sort((a, b) => a - b);\n    const lowIdx = Math.floor((arraySorted.length - 1) / 2);\n    const highIdx = Math.ceil((arraySorted.length - 1) / 2);\n    if (lowIdx === highIdx) {\n        return arraySorted[lowIdx];\n    }\n    return (arraySorted[lowIdx] + arraySorted[highIdx]) / 2;\n}\n/**\n * Generate an array of integers in [begin, end).\n * @param begin Beginning integer, inclusive.\n * @param end Ending integer, exclusive.\n * @returns Range array.\n * @throws ValueError, iff `end` < `begin`.\n */\nexport function range(begin, end) {\n    if (end < begin) {\n        throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);\n    }\n    const out = [];\n    for (let i = begin; i < end; ++i) {\n        out.push(i);\n    }\n    return out;\n}\n//# sourceMappingURL=math_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * deeplearn.js backend.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { onesLike as coreOnesLike, scalar, tensor1d, tidy, where, zerosLike as coreZerosLike } from '@tensorflow/tfjs-core';\nimport { checkDataFormat } from '../common';\nimport { NotImplementedError, ValueError } from '../errors';\nimport * as math_utils from '../utils/math_utils';\nimport { imageDataFormat } from './common';\n// tslint:enable\n/* Setting and getting backend from deeplearn.js. */\n// Default deeplearn.js backend is WebGL (GPU).\nlet backend = 'webgl';\nexport function setBackend(requestedBackend) {\n    tfc.setBackend(requestedBackend);\n    backend = requestedBackend;\n}\nexport function getBackend() {\n    return backend;\n}\n/**\n * Indicates whether the backend is operating symbolically.\n *\n * This function will be used to determine how to interpret user code. If\n * it returns true, calls to the backend construct a symbolic graph; if\n * it returns false, calls to the backend execute immediately.\n */\nexport function isBackendSymbolic() {\n    return false;\n}\n/**\n * Get the number of elements in a Tensor.\n * @param x The Tensor.\n * @return Number of elements in `x`.\n */\nexport function countParams(x) {\n    const shape = x.shape;\n    if (shape.length > 0) {\n        return shape.reduce((a, b) => a * b);\n    }\n    else {\n        // Scalar.\n        return 1;\n    }\n}\n/**\n * Casts a tensor to a different dtype and returns it.\n * @param x Input tensor.\n * @param dtype String: 'float32'|'int32'|'bool'.\n * @returns Tensor of the specified `dtype`.\n */\nexport function cast(x, dtype) {\n    return x.asType(dtype);\n}\n/**\n * Adds a 1-sized dimension at index \"axis\".\n * @param x Input tensor.\n * @param axis Position where to add the new axis.\n * @returns Result of the dimension expansion.\n */\nexport function expandDims(x, axis = -1) {\n    const outShape = x.shape.slice();\n    if (axis < 0) {\n        axis = outShape.length + axis + 1;\n    }\n    outShape.splice(axis, 0, 1);\n    return x.reshape(outShape);\n}\n/**\n * Repeats a 2D tensor.\n *\n * If `x` has shape `[samples, dim]` and `n` is 2, for example, the output\n * will have shape `[samples, 2, dim]`.\n *\n * @param x Input tensor.\n * @param n Integer, number of times to repeat.\n * @returns The result of the repeat operation.\n * @throws ValueError: If input tensor is not 2D.\n */\nexport function repeat(x, n) {\n    return tidy(() => {\n        if (x.shape.length !== 2) {\n            throw new ValueError(`repeat() expects a rank-2 tensor, but received a ` +\n                `rank-${x.shape.length} tensor.`);\n        }\n        const y = expandDims(x, 1);\n        return tile(y, [1, n, 1]);\n    });\n}\n/**\n * Flatten a Tensor into 1D.\n * @param x Input tensor.\n * @return The result of the flattening `x`.\n */\nexport function flatten(x) {\n    const newShape = [math_utils.arrayProd(x.shape)];\n    return x.reshape(newShape);\n}\n/**\n * Turn a nD tensor into a 2D tensor with same 0th dimension.\n * In other words, it flattens each data samples of a batch.\n *\n * @param x The tensor to flatten. The rank of this tensor is required to be 2\n *   or higher.\n * @return The result of the flattening.\n */\nexport function batchFlatten(x) {\n    if (x.rank <= 1) {\n        throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);\n    }\n    const newShape = [x.shape[0], math_utils.arrayProd(x.shape, 1)];\n    return x.reshape(newShape);\n}\n/**\n * Do slicing along the first axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size size of the slice along the first axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongFirstAxis(array, start, size) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                return tfc.slice2d(array, [start, 0], [size, array.shape[1]]);\n            case 3:\n                return tfc.slice3d(array, [start, 0, 0], [size, array.shape[1], array.shape[2]]);\n            case 4:\n                return tfc.slice4d(array, [start, 0, 0, 0], [size, array.shape[1], array.shape[2], array.shape[3]]);\n            case 5:\n                return tfc.slice(array, [start, 0, 0, 0, 0], [\n                    size, array.shape[1], array.shape[2], array.shape[3], array.shape[4]\n                ]);\n            case 6:\n                return tfc.slice(array, [start, 0, 0, 0, 0, 0], [\n                    size, array.shape[1], array.shape[2], array.shape[3], array.shape[4],\n                    array.shape[5]\n                ]);\n            default:\n                throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Do slicing along the last axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size size of the slice along the last axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongLastAxis(array, start, size) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                return tfc.slice2d(array, [0, start], [array.shape[0], size]);\n            case 3:\n                return tfc.slice3d(array, [0, 0, start], [array.shape[0], array.shape[1], size]);\n            case 4:\n                return tfc.slice4d(array, [0, 0, 0, start], [array.shape[0], array.shape[1], array.shape[2], size]);\n            default:\n                throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Do slicing along the sepcified axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size of the slice along the chosen axis.\n * @param choose an axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongAxis(array, start, size, axis) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            case 3:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return tfc.slice3d(array, [0, start, 0], [array.shape[0], size, array.shape[2]]);\n                    case 3:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            case 4:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return tfc.slice4d(array, [0, start, 0, 0], [array.shape[0], size, array.shape[2], array.shape[3]]);\n                    case 3:\n                        return tfc.slice4d(array, [0, 0, start, 0], [array.shape[0], array.shape[1], size, array.shape[3]]);\n                    case 4:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            default:\n                throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Concatenates a list of tensors alongside the specified axis.\n * @param tensors `Array` of tensors to concatenate.\n * @param axis Concatenation axis.\n * @returns The result of the concatenation.\n */\nexport function concatenate(tensors, axis = -1) {\n    let rank;\n    if (axis < 0) {\n        rank = tensors[0].rank;\n        if (rank !== 0) {\n            axis = rank;\n        }\n        else {\n            axis = 0;\n        }\n    }\n    if (axis === tensors[0].rank) {\n        // Porting Note: This is necessary because tfc.concat() requires axis to be\n        //   in the interval [-rank, rank).\n        axis = -1;\n    }\n    // Porting Note: Sparse concat is not supported yet.\n    return tfc.concat(tensors, axis);\n}\n/**\n * Concatenate two arrays along the first dimension.\n * @param a The 1st `tf.Tensor` to concatenate.\n * @param b The 2nd `tf.Tensor` to concatenate.\n * @returns Result of the concatenation.\n * @throws ValueError: If `a` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function concatAlongFirstAxis(a, b) {\n    switch (a.rank) {\n        case 1:\n            return tfc.concat1d([a, b]);\n        case 2:\n            return tfc.concat2d([a, b], 0);\n        case 3:\n            return tfc.concat3d([a, b], 0);\n        case 4:\n            return tfc.concat4d([a, b], 0);\n        default:\n            throw new ValueError(`concatAlongFirstAxis() received an unsupported ` +\n                `tensor rank: ${a.rank}`);\n    }\n}\n/**\n * Creates a tensor by tiling `x` by `n`.\n * @param x A tensor.\n * @param n An Array of integers or a single integer. If an Array, the length\n *   must be the same as the number of dimensions in `x`. If a single integer,\n *   it will be treated as an Array of length 1.\n */\nexport function tile(x, n) {\n    if (!Array.isArray(n)) {\n        n = [n];\n    }\n    if (x.rank !== n.length) {\n        throw new ValueError(`The length of input n (${n.length}) does not match ` +\n            `the number of dimensions in input x (${x.rank})`);\n    }\n    return tfc.tile(x, n);\n}\n/* Creation of random tensors. */\n/**\n * Get a tensor with normal distribution of values.\n *\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @return The normal tensor.\n */\nexport function randomNormal(shape, mean = 0.0, stddev = 1.0, dtype, seed) {\n    return tfc.randomNormal(shape, mean, stddev, dtype, seed);\n}\n/* Linear Algebra */\n/**\n * Multiply two tensors and returns the result as a tensor.\n *\n * For 2D tensors, this is equivalent to matrix multiplication (matMul).\n * For tensors of higher ranks, it follows the Theano behavior,\n * (e.g. `(2, 3) * (4, 3, 5) -> (2, 4, 5)`).  From the Theano documentation:\n *\n * For N dimensions it is a sum product over the last axis of x and the\n * second-to-last of y:\n *\n * @param a A tensor of at least rank 2.\n * @param b A tensor of at least rank 2.\n * @param activation (optional) A string identifying the activation\n *   function.\n * @return Result of the dot operation.\n */\nexport function dot(a, b, activation, bias) {\n    if ((a.rank < 2) || (b.rank < 2)) {\n        throw new NotImplementedError(`dot requires both inputs to be rank >= 2` +\n            ` but got x shape = ${a.shape} and y shape = ${b.shape}`);\n    }\n    if (b.rank >= 3) {\n        const xLastDim = a.shape.slice(-1)[0];\n        const ySecondLastDim = b.shape.slice(-2)[0];\n        if (xLastDim !== ySecondLastDim) {\n            throw new NotImplementedError(`If rank y >= 3, then the second last dim` +\n                ` of y must equal the last dim of x but got x shape = ${a.shape} and ` +\n                ` y shape = ${b.shape}`);\n        }\n    }\n    // Handle basic 2D x 2D case.\n    if ((a.rank === 2) && (b.rank === 2)) {\n        const transposeA = false;\n        const transposeB = false;\n        // tfc.fused.matMul only fuses certain activation functions. Unsupported\n        // activation functions are treated as 'linear' activations, which is\n        // equivalent to a no-op.\n        return tfc.fused.matMul({\n            a,\n            b: b,\n            transposeA,\n            transposeB,\n            bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n            activation\n        });\n    }\n    else {\n        // Reshape x into the analogous 2D Tensor.\n        const aFirstDims = a.shape.slice(); // Holds all but the last dim of x.\n        const aLastDim = aFirstDims.pop();\n        a = a.reshape([-1, aLastDim]);\n        // Reshape y into the analogous 2D Tensor, and keep track of the\n        // required dimensions to reproduce the output shape.\n        const bShape = b.shape.slice();\n        const bLastDim = bShape.pop();\n        const ySecondLastDim = bShape.pop();\n        const yOtherDims = [...bShape, bLastDim];\n        // permutation should be like [r-2, 0, 1, 2, ... r-4, r-3, r-1]\n        // where r is the rank of y.\n        const perm = Array.from({ length: b.rank }, (_, i) => {\n            if (i === 0) {\n                return b.rank - 2;\n            }\n            else if (i <= b.rank - 2) {\n                return i - 1;\n            }\n            return i;\n        });\n        b = b.transpose(perm).reshape([ySecondLastDim, -1]);\n        // Multiply x and y as 2D Tensors, and then reshape back to original.\n        const outputShape = [...aFirstDims, ...yOtherDims];\n        const transposeA = false;\n        const transposeB = false;\n        return tfc.fused\n            .matMul({\n            a,\n            b,\n            transposeA,\n            transposeB,\n            bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n            activation\n        })\n            .reshape(outputShape);\n    }\n}\n/**\n * Compute the sign Tensor of an input Tensor.\n *\n * Elements of the input `tf.Tensor` that are === 0 are mapped to 0.\n * Elements of the input `tf.Tensor` that are > 0 are mapped to 1.\n * Elements of the input `tf.Tensor` that are < 0 are mapped to -1.\n *\n * @param x Input `tf.Tensor`.\n * @return The sign `tf.Tensor`.\n */\nexport function sign(x) {\n    // TODO(cais): Move to the core.\n    return tidy(() => {\n        const zerosLikeX = coreZerosLike(x);\n        const onesLikeX = coreOnesLike(x);\n        return where(tfc.equal(x, zerosLikeX), zerosLikeX, where(tfc.greater(x, coreZerosLike(x)), onesLikeX, tfc.mul(-1, onesLikeX)));\n    });\n}\n/**\n * Computes the one-hot representation of an integer tensor.\n * @param indices nD integer tensor of shape\n *   `(batch_size, dim1, dim2, ... dim(n-1))`\n * @param numClasses Integer, number of classes to consider.\n * @returns (n + 1)D one hot representation of the input\n *   with shape `(batch_size, dim1, dim2, ... dim(n-1), num_classes)`\n */\nexport function oneHot(indices, numClasses) {\n    return tidy(() => {\n        if (indices.rank !== 1) {\n            throw new Error('Only 1D one-hot tensors are supported in the ' +\n                'deeplearn backend, at present.');\n        }\n        indices = indices.toInt();\n        return tfc.oneHot(indices, numClasses).toFloat();\n    });\n}\n/* Elementary math functions. */\n/**\n * Retrieves the elements of indices `indices` in the tensor `reference`.\n * @param reference A tensor.\n * @param indices An integer tensor of indices or an `Array` of integers.\n * @param axis Axis along which to perform the gather operation.\n * @returns The result of the gathering as a tensor.\n */\nexport function gather(reference, indices, axis) {\n    return tidy(() => {\n        if (Array.isArray(indices)) {\n            indices = tensor1d(indices, 'int32');\n        }\n        else {\n            indices = indices.toInt();\n        }\n        return tfc.gather(reference, indices, axis);\n    });\n}\n/**\n * Element-wise square.\n * @param x Input tensor.\n * @return element-wise x^2\n */\nexport function square(x) {\n    return tfc.mul(x, x);\n}\n/**\n * Element-wise exponentiation.\n *\n * Porting Note: In PyKeras, `a` (the exponent) is a Python integer, which\n *   takes advatnage of the backend's (e.g., TensorFlow's) automatic\n * conversion to tensor. Here we allow `a` to be either a number or a tensor.\n *\n * @param x The base tensor.\n * @param a The exponent, tensor or number. If a number, it is rounded to the\n *   nearest integer and converted to a tensor.\n * @returns A tensor of the same shape as `x`.\n */\nexport function pow(x, a) {\n    return tidy(() => {\n        if (typeof (a) === 'number') {\n            a = scalar(Math.round(a), 'int32');\n        }\n        if (a.dtype !== 'int32') {\n            throw new NotImplementedError(`Non-int32 dtype (${a.dtype}) is not supported by pow() yet`);\n        }\n        return tfc.pow(x, a);\n    });\n}\n/**\n * Reshapes bias tensor according to rank of x.\n */\nfunction reshapeBias(xRank, bias, dataFormat) {\n    const biasShape = bias.shape;\n    if (bias.rank !== 1 && bias.rank !== xRank) {\n        throw new ValueError(`Unexpected bias dimensions: ${bias.rank}` +\n            `; expected it to be 1 or ${xRank}`);\n    }\n    if (xRank === 5) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1, 1, 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, 1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank === 4) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1, 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[2], biasShape[0], biasShape[1]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank === 3) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[1], biasShape[0]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank < 3) {\n        return bias;\n    }\n    throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);\n}\n/* Neural-network operations. */\n/**\n * Add a bias to a tensor.\n *\n * @param x The tensor to add the bias to.\n * @param bias The bias to add to `x`. Must be 1D or the same rank as `x`.\n * @return Result of the bias adding.\n * @throws ValueError: If the rank of `bias` is incorrect.\n */\nexport function biasAdd(x, bias, dataFormat) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        return x.add(reshapeBias(x.rank, bias, dataFormat));\n    });\n}\n/**\n * Exponential linear unit (ELU).\n * @param x A tensor or variable to compute the activation function for.\n * @param alpha: A scalar, a scaling factor for the negative section.\n * @return Output of the ELU operation.\n */\nexport function elu(x, alpha = 1) {\n    // TODO(cais): Add support for alpha values other than 1.\n    if (alpha !== 1) {\n        throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented ` +\n            `yet.`);\n    }\n    return tfc.elu(x);\n}\n/**\n * Softsign of a tensor.\n *\n * Defined as x / (abs(x) + 1), element-wise.\n *\n * @param x: Input.\n * @returns Output.\n */\nexport function softsign(x) {\n    return tidy(() => tfc.div(x, tfc.abs(x).add(1)));\n}\n/**\n * Sets entries in `x` to zero at random, while scaling the entire tensor.\n *\n * @param x input tensor.\n * @param level fraction of the entries in the tensor that will be set to 0.\n * @param noiseShape shape of randomly generated keep/drop flags, must be\n *   broadcastable to the shape of `x`. Optional.\n * @param seed random seed to ensure determinism. Optional.\n * @returns Result of the dropout operation.\n */\nexport function dropout(x, level, noiseShape, seed) {\n    return tidy(() => tfc.dropout(x, level, noiseShape, seed));\n}\n/**\n * Element-wise, segment-wise linear approximation of sigmoid.\n *\n * Returns `0.` if `x < -2.5`, `1.` if `x > 2.5`.\n * In `-2.5 <= x <= 2.5`, returns `0.2 * x + 0.5`.\n *\n * @param x Input tensor.\n * @returns Output tensor.\n */\nexport function hardSigmoid(x) {\n    return tidy(() => {\n        const y = tfc.add(.5, tfc.mul(.2, x));\n        return tfc.clipByValue(y, 0, 1);\n    });\n}\n/**\n * Invoke `x` in the training phase, and `alt` otherwise.\n *\n * Porting Note: We do not create placeholder tensors for the `training`\n * boolean flag here, because there is no such thing in the TF.js imperative\n * backend.\n *\n * @param x The function to invoke iff `training` is `true`.\n * @param alt The function to invoke iff `training` is `false`.\n * @param training Boolean flag for whether training phase is active.\n * @returns The return value of `x()` if `training` is `true`, or the return\n *   value of `alt()` if `training` is `false`.\n */\nexport function inTrainPhase(x, alt, training = false) {\n    return training ? x() : alt();\n}\n//# sourceMappingURL=tfjs_backend.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport const VALID_FAN_MODE_VALUES = ['fanIn', 'fanOut', 'fanAvg'];\nexport const VALID_DISTRIBUTION_VALUES = ['normal', 'uniform', 'truncatedNormal'];\n// We can't easily extract a string[] from the string union type, but we can\n// recapitulate the list, enforcing at compile time that the values are valid\n// and that we have the right number of them.\n/**\n * A string array of valid Initializer class names.\n *\n * This is guaranteed to match the `InitializerClassName` union type.\n */\nexport const initializerClassNames = [\n    'Zeros', 'Ones', 'Constant', 'RandomNormal', 'RandomUniform',\n    'TruncatedNormal', 'VarianceScaling', 'Orthogonal', 'Identity'\n];\n//# sourceMappingURL=initializer_config.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { eye, linalg, mul, ones, randomUniform, scalar, serialization, tidy, truncatedNormal, zeros } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { checkDataFormat } from './common';\nimport { NotImplementedError, ValueError } from './errors';\nimport { VALID_DISTRIBUTION_VALUES, VALID_FAN_MODE_VALUES } from './keras_format/initializer_config';\nimport { checkStringTypeUnionValue, deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\nimport { arrayProd } from './utils/math_utils';\nexport function checkFanMode(value) {\n    checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, 'FanMode', value);\n}\nexport function checkDistribution(value) {\n    checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, 'Distribution', value);\n}\n/**\n * Initializer base class.\n *\n * @doc {\n *   heading: 'Initializers', subheading: 'Classes', namespace: 'initializers'}\n */\nexport class Initializer extends serialization.Serializable {\n    fromConfigUsesCustomObjects() {\n        return false;\n    }\n    getConfig() {\n        return {};\n    }\n}\nexport class Zeros extends Initializer {\n    apply(shape, dtype) {\n        return zeros(shape, dtype);\n    }\n}\n/** @nocollapse */\nZeros.className = 'Zeros';\nserialization.registerClass(Zeros);\nexport class Ones extends Initializer {\n    apply(shape, dtype) {\n        return ones(shape, dtype);\n    }\n}\n/** @nocollapse */\nOnes.className = 'Ones';\nserialization.registerClass(Ones);\nexport class Constant extends Initializer {\n    constructor(args) {\n        super();\n        if (typeof args !== 'object') {\n            throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);\n        }\n        if (args.value === undefined) {\n            throw new ValueError(`config must have value set but got ${args}`);\n        }\n        this.value = args.value;\n    }\n    apply(shape, dtype) {\n        return tidy(() => mul(scalar(this.value), ones(shape, dtype)));\n    }\n    getConfig() {\n        return {\n            value: this.value,\n        };\n    }\n}\n/** @nocollapse */\nConstant.className = 'Constant';\nserialization.registerClass(Constant);\nexport class RandomUniform extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MINVAL = -0.05;\n        this.DEFAULT_MAXVAL = 0.05;\n        this.minval = args.minval || this.DEFAULT_MINVAL;\n        this.maxval = args.maxval || this.DEFAULT_MAXVAL;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        return randomUniform(shape, this.minval, this.maxval, dtype);\n    }\n    getConfig() {\n        return { minval: this.minval, maxval: this.maxval, seed: this.seed };\n    }\n}\n/** @nocollapse */\nRandomUniform.className = 'RandomUniform';\nserialization.registerClass(RandomUniform);\nexport class RandomNormal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MEAN = 0.;\n        this.DEFAULT_STDDEV = 0.05;\n        this.mean = args.mean || this.DEFAULT_MEAN;\n        this.stddev = args.stddev || this.DEFAULT_STDDEV;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        dtype = dtype || 'float32';\n        if (dtype !== 'float32' && dtype !== 'int32') {\n            throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n        }\n        return K.randomNormal(shape, this.mean, this.stddev, dtype, this.seed);\n    }\n    getConfig() {\n        return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n    }\n}\n/** @nocollapse */\nRandomNormal.className = 'RandomNormal';\nserialization.registerClass(RandomNormal);\nexport class TruncatedNormal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MEAN = 0.;\n        this.DEFAULT_STDDEV = 0.05;\n        this.mean = args.mean || this.DEFAULT_MEAN;\n        this.stddev = args.stddev || this.DEFAULT_STDDEV;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        dtype = dtype || 'float32';\n        if (dtype !== 'float32' && dtype !== 'int32') {\n            throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);\n        }\n        return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed);\n    }\n    getConfig() {\n        return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n    }\n}\n/** @nocollapse */\nTruncatedNormal.className = 'TruncatedNormal';\nserialization.registerClass(TruncatedNormal);\nexport class Identity extends Initializer {\n    constructor(args) {\n        super();\n        this.gain = args.gain != null ? args.gain : 1.0;\n    }\n    apply(shape, dtype) {\n        return tidy(() => {\n            if (shape.length !== 2 || shape[0] !== shape[1]) {\n                throw new ValueError('Identity matrix initializer can only be used for' +\n                    ' 2D square matrices.');\n            }\n            else {\n                return mul(this.gain, eye(shape[0]));\n            }\n        });\n    }\n    getConfig() {\n        return { gain: this.gain };\n    }\n}\n/** @nocollapse */\nIdentity.className = 'Identity';\nserialization.registerClass(Identity);\n/**\n * Computes the number of input and output units for a weight shape.\n * @param shape Shape of weight.\n * @param dataFormat data format to use for convolution kernels.\n *   Note that all kernels in Keras are standardized on the\n *   CHANNEL_LAST ordering (even when inputs are set to CHANNEL_FIRST).\n * @return An length-2 array: fanIn, fanOut.\n */\nfunction computeFans(shape, dataFormat = 'channelsLast') {\n    let fanIn;\n    let fanOut;\n    checkDataFormat(dataFormat);\n    if (shape.length === 2) {\n        fanIn = shape[0];\n        fanOut = shape[1];\n    }\n    else if ([3, 4, 5].indexOf(shape.length) !== -1) {\n        if (dataFormat === 'channelsFirst') {\n            const receptiveFieldSize = arrayProd(shape, 2);\n            fanIn = shape[1] * receptiveFieldSize;\n            fanOut = shape[0] * receptiveFieldSize;\n        }\n        else if (dataFormat === 'channelsLast') {\n            const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2);\n            fanIn = shape[shape.length - 2] * receptiveFieldSize;\n            fanOut = shape[shape.length - 1] * receptiveFieldSize;\n        }\n    }\n    else {\n        const shapeProd = arrayProd(shape);\n        fanIn = Math.sqrt(shapeProd);\n        fanOut = Math.sqrt(shapeProd);\n    }\n    return [fanIn, fanOut];\n}\nexport class VarianceScaling extends Initializer {\n    /**\n     * Constructor of VarianceScaling.\n     * @throws ValueError for invalid value in scale.\n     */\n    constructor(args) {\n        super();\n        if (args.scale < 0.0) {\n            throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);\n        }\n        this.scale = args.scale == null ? 1.0 : args.scale;\n        this.mode = args.mode == null ? 'fanIn' : args.mode;\n        checkFanMode(this.mode);\n        this.distribution =\n            args.distribution == null ? 'normal' : args.distribution;\n        checkDistribution(this.distribution);\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        const fans = computeFans(shape);\n        const fanIn = fans[0];\n        const fanOut = fans[1];\n        let scale = this.scale;\n        if (this.mode === 'fanIn') {\n            scale /= Math.max(1, fanIn);\n        }\n        else if (this.mode === 'fanOut') {\n            scale /= Math.max(1, fanOut);\n        }\n        else {\n            scale /= Math.max(1, (fanIn + fanOut) / 2);\n        }\n        if (this.distribution === 'normal') {\n            const stddev = Math.sqrt(scale);\n            dtype = dtype || 'float32';\n            if (dtype !== 'float32' && dtype !== 'int32') {\n                throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);\n            }\n            return truncatedNormal(shape, 0, stddev, dtype, this.seed);\n        }\n        else {\n            const limit = Math.sqrt(3 * scale);\n            return randomUniform(shape, -limit, limit, dtype);\n        }\n    }\n    getConfig() {\n        return {\n            scale: this.scale,\n            mode: this.mode,\n            distribution: this.distribution,\n            seed: this.seed\n        };\n    }\n}\n/** @nocollapse */\nVarianceScaling.className = 'VarianceScaling';\nserialization.registerClass(VarianceScaling);\nexport class GlorotUniform extends VarianceScaling {\n    /**\n     * Constructor of GlorotUniform\n     * @param scale\n     * @param mode\n     * @param distribution\n     * @param seed\n     */\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanAvg',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, GlorotUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nGlorotUniform.className = 'GlorotUniform';\nserialization.registerClass(GlorotUniform);\nexport class GlorotNormal extends VarianceScaling {\n    /**\n     * Constructor of GlorotNormal.\n     * @param scale\n     * @param mode\n     * @param distribution\n     * @param seed\n     */\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanAvg',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, GlorotNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nGlorotNormal.className = 'GlorotNormal';\nserialization.registerClass(GlorotNormal);\nexport class HeNormal extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 2.0,\n            mode: 'fanIn',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, HeNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nHeNormal.className = 'HeNormal';\nserialization.registerClass(HeNormal);\nexport class HeUniform extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 2.0,\n            mode: 'fanIn',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, HeUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nHeUniform.className = 'HeUniform';\nserialization.registerClass(HeUniform);\nexport class LeCunNormal extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanIn',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, LeCunNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nLeCunNormal.className = 'LeCunNormal';\nserialization.registerClass(LeCunNormal);\nexport class LeCunUniform extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanIn',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, LeCunUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nLeCunUniform.className = 'LeCunNormal';\nserialization.registerClass(LeCunUniform);\nexport class Orthogonal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_GAIN = 1;\n        this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain;\n        this.seed = args.seed;\n        if (this.seed != null) {\n            throw new NotImplementedError('Random seed is not implemented for Orthogonal Initializer yet.');\n        }\n    }\n    apply(shape, dtype) {\n        return tidy(() => {\n            if (shape.length < 2) {\n                throw new NotImplementedError('Shape must be at least 2D.');\n            }\n            if (shape[0] * shape[1] > 2000) {\n                console.warn(`Orthogonal initializer is being called on a matrix with more ` +\n                    `than 2000 (${shape[0] * shape[1]}) elements: ` +\n                    `Slowness may result.`);\n            }\n            // TODO(cais): Add seed support.\n            const normalizedShape = shape[0] > shape[1] ? [shape[1], shape[0]] : shape;\n            const a = K.randomNormal(normalizedShape, 0, 1, 'float32');\n            let q = linalg.gramSchmidt(a);\n            if (shape[0] > shape[1]) {\n                q = q.transpose();\n            }\n            return mul(this.gain, q);\n        });\n    }\n    getConfig() {\n        return {\n            gain: this.gain,\n            seed: this.seed,\n        };\n    }\n}\n/** @nocollapse */\nOrthogonal.className = 'Orthogonal';\nserialization.registerClass(Orthogonal);\n// Maps the JavaScript-like identifier keys to the corresponding registry\n// symbols.\nexport const INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'constant': 'Constant',\n    'glorotNormal': 'GlorotNormal',\n    'glorotUniform': 'GlorotUniform',\n    'heNormal': 'HeNormal',\n    'heUniform': 'HeUniform',\n    'identity': 'Identity',\n    'leCunNormal': 'LeCunNormal',\n    'leCunUniform': 'LeCunUniform',\n    'ones': 'Ones',\n    'orthogonal': 'Orthogonal',\n    'randomNormal': 'RandomNormal',\n    'randomUniform': 'RandomUniform',\n    'truncatedNormal': 'TruncatedNormal',\n    'varianceScaling': 'VarianceScaling',\n    'zeros': 'Zeros'\n};\nfunction deserializeInitializer(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'initializer');\n}\nexport function serializeInitializer(initializer) {\n    return serializeKerasObject(initializer);\n}\nexport function getInitializer(identifier) {\n    if (typeof identifier === 'string') {\n        const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        /* We have four 'helper' classes for common initializers that\n        all get serialized as 'VarianceScaling' and shouldn't go through\n        the deserializeInitializer pathway. */\n        if (className === 'GlorotNormal') {\n            return new GlorotNormal();\n        }\n        else if (className === 'GlorotUniform') {\n            return new GlorotUniform();\n        }\n        else if (className === 'HeNormal') {\n            return new HeNormal();\n        }\n        else if (className === 'HeUniform') {\n            return new HeUniform();\n        }\n        else if (className === 'LeCunNormal') {\n            return new LeCunNormal();\n        }\n        else if (className === 'LeCunUniform') {\n            return new LeCunUniform();\n        }\n        else {\n            const config = {};\n            config['className'] = className;\n            config['config'] = {};\n            return deserializeInitializer(config);\n        }\n    }\n    else if (identifier instanceof Initializer) {\n        return identifier;\n    }\n    else {\n        return deserializeInitializer(identifier);\n    }\n}\n//# sourceMappingURL=initializers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// tslint:disable-next-line:max-line-length\nimport { Constant, GlorotNormal, GlorotUniform, HeNormal, HeUniform, Identity, LeCunNormal, LeCunUniform, Ones, Orthogonal, RandomNormal, RandomUniform, TruncatedNormal, VarianceScaling, Zeros } from './initializers';\n/**\n * Initializer that generates tensors initialized to 0.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function zeros() {\n    return new Zeros();\n}\n/**\n * Initializer that generates tensors initialized to 1.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function ones() {\n    return new Ones();\n}\n/**\n * Initializer that generates values initialized to some constant.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function constant(args) {\n    return new Constant(args);\n}\n/**\n * Initializer that generates random values initialized to a uniform\n * distribution.\n *\n * Values will be distributed uniformly between the configured minval and\n * maxval.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function randomUniform(args) {\n    return new RandomUniform(args);\n}\n/**\n * Initializer that generates random values initialized to a normal\n * distribution.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function randomNormal(args) {\n    return new RandomNormal(args);\n}\n/**\n * Initializer that generates random values initialized to a truncated normal.\n * distribution.\n *\n * These values are similar to values from a `RandomNormal` except that values\n * more than two standard deviations from the mean are discarded and re-drawn.\n * This is the recommended initializer for neural network weights and filters.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function truncatedNormal(args) {\n    return new TruncatedNormal(args);\n}\n/**\n * Initializer that generates the identity matrix.\n * Only use for square 2D matrices.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function identity(args) {\n    return new Identity(args);\n}\n/**\n * Initializer capable of adapting its scale to the shape of weights.\n * With distribution=NORMAL, samples are drawn from a truncated normal\n * distribution centered on zero, with `stddev = sqrt(scale / n)` where n is:\n *   - number of input units in the weight tensor, if mode = FAN_IN.\n *   - number of output units, if mode = FAN_OUT.\n *   - average of the numbers of input and output units, if mode = FAN_AVG.\n * With distribution=UNIFORM,\n * samples are drawn from a uniform distribution\n * within [-limit, limit], with `limit = sqrt(3 * scale / n)`.\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function varianceScaling(config) {\n    return new VarianceScaling(config);\n}\n/**\n * Glorot uniform initializer, also called Xavier uniform initializer.\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotUniform(args) {\n    return new GlorotUniform(args);\n}\n/**\n * Glorot normal initializer, also called Xavier normal initializer.\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor.\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotNormal(args) {\n    return new GlorotNormal(args);\n}\n/**\n * He normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function heNormal(args) {\n    return new HeNormal(args);\n}\n/**\n * He uniform initializer.\n *\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / fan_in)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function heUniform(args) {\n    return new HeUniform(args);\n}\n/**\n * LeCun normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(1 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * References:\n *   [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n *   [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunNormal(args) {\n    return new LeCunNormal(args);\n}\n/**\n * LeCun uniform initializer.\n *\n * It draws samples from a uniform distribution in the interval\n * `[-limit, limit]` with `limit = sqrt(3 / fanIn)`,\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunUniform(args) {\n    return new LeCunUniform(args);\n}\n/**\n * Initializer that generates a random orthogonal matrix.\n *\n * Reference:\n * [Saxe et al., http://arxiv.org/abs/1312.6120](http://arxiv.org/abs/1312.6120)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function orthogonal(args) {\n    return new Orthogonal(args);\n}\n//# sourceMappingURL=exports_initializers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Utilities related to persistent state in the backend.\n */\n/**\n * An ID to track `tf.SymbolicTensor`s and derived classes.\n * Required in different places in engine/topology.ts to identify unique\n * tensors.\n */\nlet _nextUniqueTensorId = 0;\nexport function getNextUniqueTensorId() {\n    return _nextUniqueTensorId++;\n}\nconst _uidPrefixes = {};\n/**\n * Provides a unique UID given a string prefix.\n *\n * @param prefix\n */\nexport function getUid(prefix = '') {\n    if (!(prefix in _uidPrefixes)) {\n        _uidPrefixes[prefix] = 0;\n    }\n    _uidPrefixes[prefix] += 1;\n    return prefix + _uidPrefixes[prefix].toString();\n}\n//# sourceMappingURL=state.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { ValueError } from '../errors';\n// tslint:enable\n/**\n * Determine whether the input is an Array of Shapes.\n */\nexport function isArrayOfShapes(x) {\n    return Array.isArray(x) && Array.isArray(x[0]);\n}\n/**\n * Special case of normalizing shapes to lists.\n *\n * @param x A shape or list of shapes to normalize into a list of Shapes.\n * @return A list of Shapes.\n */\nexport function normalizeShapeList(x) {\n    if (x.length === 0) {\n        return [];\n    }\n    if (!Array.isArray(x[0])) {\n        return [x];\n    }\n    return x;\n}\n/**\n * Helper function to obtain exactly one Tensor.\n * @param xs: A single `tf.Tensor` or an `Array` of `tf.Tensor`s.\n * @return A single `tf.Tensor`. If `xs` is an `Array`, return the first one.\n * @throws ValueError: If `xs` is an `Array` and its length is not 1.\n */\nexport function getExactlyOneTensor(xs) {\n    let x;\n    if (Array.isArray(xs)) {\n        if (xs.length !== 1) {\n            throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);\n        }\n        x = xs[0];\n    }\n    else {\n        x = xs;\n    }\n    return x;\n}\n/**\n * Helper function to obtain exactly on instance of Shape.\n *\n * @param shapes Input single `Shape` or Array of `Shape`s.\n * @returns If input is a single `Shape`, return it unchanged. If the input is\n *   an `Array` containing exactly one instance of `Shape`, return the instance.\n *   Otherwise, throw a `ValueError`.\n * @throws ValueError: If input is an `Array` of `Shape`s, and its length is not\n *   1.\n */\nexport function getExactlyOneShape(shapes) {\n    if (Array.isArray(shapes) && Array.isArray(shapes[0])) {\n        if (shapes.length === 1) {\n            shapes = shapes;\n            return shapes[0];\n        }\n        else {\n            throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`);\n        }\n    }\n    else {\n        return shapes;\n    }\n}\n//# sourceMappingURL=types_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Count the elements in an Array of LayerVariables.\n *\n * @param weights: The LayerVariables of which the constituent numbers are to\n *   be counted.\n * @returns A count of the elements in all the LayerVariables\n */\nexport function countParamsInWeights(weights) {\n    let count = 0;\n    for (const weight of weights) {\n        if (weight.shape.length === 0) {\n            count += 1;\n        }\n        else {\n            count += weight.shape.reduce((a, b) => a * b);\n        }\n    }\n    return count;\n}\n//# sourceMappingURL=variable_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { variableGrads } from '@tensorflow/tfjs-core';\nimport { getNextUniqueTensorId } from './backend/state';\nimport { getScopedTensorName, getUniqueTensorName } from './common';\nimport { NotImplementedError } from './errors';\nconst DEFAULT_VARIABLE_NAME_PREFIX = 'Variable';\n/**\n * A `tf.layers.LayerVariable` is similar to a `tf.Tensor` in that it has a\n * dtype and shape, but its value is mutable.  The value is itself represented\n * as a`tf.Tensor`, and can be read with the `read()` method and updated with\n * the `write()` method.\n */\nexport class LayerVariable {\n    /**\n     * Construct Variable from a `tf.Tensor`.\n     *\n     * If not explicitly named, the Variable will be given a name with the\n     * prefix 'Variable'. Variable names are unique. In the case of name\n     * collision, suffixies '_<num>' will be added to the name.\n     *\n     * @param val Initial value of the Variable.\n     * @param name Name of the variable. If `null` or `undefined` is provided, it\n     *   will default a name with the prefix 'Variable'.\n     * @param constraint Optional, projection function to be applied to the\n     * variable after optimize updates\n     * @throws ValueError if `name` is `null` or `undefined`.\n     */\n    constructor(val, dtype = 'float32', name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true, constraint = null) {\n        this.dtype = dtype == null ? 'float32' : dtype;\n        this.shape = val.shape;\n        this.id = getNextUniqueTensorId();\n        name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name;\n        this.originalName = getScopedTensorName(name);\n        this.name = getUniqueTensorName(this.originalName);\n        this.trainable_ = trainable;\n        this.constraint = constraint;\n        this.val = tfc.variable(val, this.trainable_, this.name, this.dtype);\n    }\n    /**\n     * Get a snapshot of the Variable's value.\n     *\n     * The returned value is a snapshot of the Variable's value at the time of\n     * the invocation. Future mutations in the value of the tensor will only\n     * be reflected by future calls to this method.\n     */\n    read() {\n        this.assertNotDisposed();\n        return this.val;\n    }\n    /**\n     * Update the value of the Variable.\n     *\n     * @param newVal: The new value to update to. Must be consistent with the\n     *   dtype and shape of the Variable.\n     * @return This Variable.\n     */\n    write(newVal) {\n        // TODO(cais): Once  TF.js Core supports Tensor.dtype, check dtype match.\n        this.assertNotDisposed();\n        checkShapesMatch(this.val, newVal);\n        // Skip updating if this is the exact same tensor.\n        if (this.val.id !== newVal.id) {\n            this.val.assign(newVal);\n            if (this.constraint != null) {\n                this.val.assign(this.constraint.apply(this.val));\n            }\n        }\n        return this;\n    }\n    /**\n     * Dispose this LayersVariable instance from memory.\n     */\n    dispose() {\n        this.assertNotDisposed();\n        this.val.dispose();\n    }\n    assertNotDisposed() {\n        if (this.val.isDisposed) {\n            throw new Error(`LayersVariable ${this.name} is already disposed.`);\n        }\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this.trainable_ = trainable;\n        this.val.trainable = trainable;\n    }\n}\nfunction checkShapesMatch(x, y) {\n    if (x.shape.toString() !== y.shape.toString()) {\n        throw new Error('Shape mismatch: ' + JSON.stringify(x.shape) + ' vs. ' +\n            JSON.stringify(y.shape));\n    }\n}\n/**\n * Create a Variable.\n * @param x The initial value of the `Variable`.\n * @param dtype optional, the type of the variable.\n * @param name optional, the name of the variable, default provided by\n * Variable.\n * @param constraint optional, a constraint to be applied after every update.\n * @return The newly instantiated `Variable`.\n */\nexport function variable(x, dtype, name, constraint) {\n    return new LayerVariable(x, dtype, name, true, constraint);\n}\n/**\n * Instantiates an all-zeros Variable and returns it.\n *\n * @param shape Shape of the tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return An all-zero Variable.\n */\nexport function zerosVariable(shape, dtype, name) {\n    // TODO(cais): Implement logic for dtype.\n    return new LayerVariable(tfc.zeros(shape), dtype, name);\n}\n/**\n * Instantiates an all-zeros tensor of the same shape as another tensor.\n *\n * @param x The other tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return A newly instantiated Variable.\n */\nexport function zerosLike(x, dtype, name) {\n    return new LayerVariable(tfc.zerosLike(x), dtype, name);\n}\n/**\n * Instantiates an all-ones tensor and returns it.\n *\n * @param shape Shape of the tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return An all-ones Variable.\n */\nexport function onesVariable(shape, dtype, name) {\n    // TODO(cais): Implement logic for dtype.\n    const allocated = tfc.ones(shape);\n    return new LayerVariable(allocated, dtype, name);\n}\n/**\n * Instantiates an all-ones tensor of the same shape as another tensor.\n *\n * @param x The other tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return A newly instantiated Variable.\n */\nexport function onesLike(x, dtype, name) {\n    const allocated = tfc.onesLike(x);\n    return new LayerVariable(allocated, dtype, name);\n}\n/**\n * Instantiate an identity matrix and returns it, as a Variable\n *\n * @param size Number of rows/columns.\n * @param dtype Data type of returned Variable.\n * @param name Name of returned Variable.\n * @return A Variable, an identity matrix.\n */\nexport function eyeVariable(size, dtype, name) {\n    return new LayerVariable(tfc.eye(size), dtype, name);\n}\n/**\n * Get a Variable with uniform distribution of values.\n * @param shape Shape of the tensor.\n * @param minval Lower bound of the uniform distribution.\n * @param maxval Upper bound of the uniform distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The uniform-random Variable.\n */\nexport function randomUniformVariable(shape, minval, maxval, dtype, seed, name = 'randomUniform') {\n    return new LayerVariable(tfc.randomUniform(shape, minval, maxval, dtype), dtype, name);\n}\n/**\n * Get a Variable with truncated-normal distribution of values.\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The truncated-normal-random Variable.\n */\nexport function truncatedNormalVariable(shape, mean = 0.0, stddev = 1.0, dtype, seed, name = 'truncatedNormal') {\n    // TODO(cais): Implement logic for dtype and seed once they are supported\n    // by deeplearn.js.\n    dtype = dtype || 'float32';\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n    }\n    return new LayerVariable(tfc.truncatedNormal(shape, mean, stddev, dtype, seed), dtype, name);\n}\n/**\n * Get a Variable with normal distribution of values.\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The truncated-normal-random Variable.\n */\nexport function randomNormalVariable(shape, mean = 0.0, stddev = 1.0, dtype, seed, name = 'randomNormal') {\n    dtype = dtype || 'float32';\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new NotImplementedError(`randomNormalVariable does not support dType ${dtype}.`);\n    }\n    return new LayerVariable(tfc.randomNormal(shape, mean, stddev, dtype, seed), dtype, name);\n}\n/**\n * Update the value of a Variable.\n * @param x The Variable to be updated.\n * @param xNew The new value to update to.\n * @return The Variable updated.\n */\nexport function update(x, xNew) {\n    return x.write(xNew);\n}\n/**\n * Update the value of a Variable by adding an increment.\n * @param x The Variable to be updated.\n * @param increment The incrment to add to `x`.\n * @return The Variable updated.\n */\nexport function updateAdd(x, increment) {\n    return x.write(tfc.add(x.read(), increment));\n}\n/**\n * Update the value of a Variable by subtracting a decrement.\n * @param x The Variable to be updated.\n * @param decrement The decrement to subtract from `x`.\n * @return The Variable updated.\n */\nexport function updateSub(x, decrement) {\n    return x.write(tfc.sub(x.read(), decrement));\n}\n/**\n * Get the values of an array of Variables.\n *\n * @param tensors An `Array` of `Variable`s to get the values of.\n * @return The values of the inputs, as an `Array` of`tf.Tensor`s.\n */\nexport function batchGetValue(xs) {\n    return xs.map(x => x.read());\n}\n/**\n * Update the value of multiple Variables at once.\n *\n * @param variablesAndValues An `Array`, each element is of type\n *   [Variable, Tensor]. The first item is the\n *   `Variable` of which the value is to be updated. The second item\n *   carries the new value.\n */\nexport function batchSetValue(variablesAndValues) {\n    variablesAndValues.forEach(variableAndValue => {\n        const variable = variableAndValue[0];\n        variable.write(variableAndValue[1]);\n    });\n}\n/**\n * Returns the gradients of `variables` w.r.t. the return value of `lossFn`.\n * @param lossFn A function which returns a Scalar to be used as the function\n *   value (i.e., numerator) for differentiation.\n * @param variables List of variables to be used as the independent variables\n *   (i.e., denominator) for differentiation.\n * @returns An Array of gradients tensors.\n */\nexport function gradients(lossFn, variables) {\n    // TODO(cais): The return type signature can be simplified if deeplearn makes\n    //   the corresponding type public.\n    const variableList = variables.map(variable => variable.read());\n    const valudAndGrads = variableGrads(lossFn, variableList);\n    return variables.map(variable => valudAndGrads.grads[variable.name]);\n}\n//# sourceMappingURL=variables.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/engine/topology.py */\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getNextUniqueTensorId, getUid } from '../backend/state';\nimport { getScopedTensorName, getUniqueTensorName, nameScope } from '../common';\nimport { AttributeError, NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { getInitializer } from '../initializers';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as types_utils from '../utils/types_utils';\nimport * as variable_utils from '../utils/variable_utils';\nimport { batchGetValue, batchSetValue, LayerVariable } from '../variables';\n/**\n * Specifies the ndim, dtype and shape of every input to a layer.\n *\n * Every layer should expose (if appropriate) an `inputSpec` attribute:\n * a list of instances of InputSpec (one per input tensor).\n *\n * A null entry in a shape is compatible with any dimension,\n * a null shape is compatible with any shape.\n */\nexport class InputSpec {\n    constructor(args) {\n        this.dtype = args.dtype;\n        this.shape = args.shape;\n        /*\n          TODO(michaelterry): Could throw error if ndim and shape are both defined\n            (then backport).\n        */\n        if (args.shape != null) {\n            this.ndim = args.shape.length;\n        }\n        else {\n            this.ndim = args.ndim;\n        }\n        this.maxNDim = args.maxNDim;\n        this.minNDim = args.minNDim;\n        this.axes = args.axes || {};\n    }\n}\n/**\n * `tf.SymbolicTensor` is a placeholder for a Tensor without any concrete value.\n *\n * They are most often encountered when building a graph of `Layer`s for a\n * a `tf.LayersModel` and the input data's shape, but not values are known.\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\nexport class SymbolicTensor {\n    /**\n     *\n     * @param dtype\n     * @param shape\n     * @param sourceLayer The Layer that produced this symbolic tensor.\n     * @param inputs The inputs passed to sourceLayer's __call__() method.\n     * @param nodeIndex\n     * @param tensorIndex\n     * @param callArgs The keyword arguments passed to the __call__() method.\n     * @param name\n     * @param outputTensorIndex The index of this tensor in the list of outputs\n     *   returned by apply().\n     */\n    constructor(dtype, shape, sourceLayer, inputs, callArgs, name, outputTensorIndex) {\n        this.dtype = dtype;\n        this.shape = shape;\n        this.sourceLayer = sourceLayer;\n        this.inputs = inputs;\n        this.callArgs = callArgs;\n        this.outputTensorIndex = outputTensorIndex;\n        this.id = getNextUniqueTensorId();\n        if (name != null) {\n            this.originalName = getScopedTensorName(name);\n            this.name = getUniqueTensorName(this.originalName);\n        }\n        this.rank = shape.length;\n    }\n}\nlet _nextNodeID = 0;\n/**\n * A `Node` describes the connectivity between two layers.\n *\n * Each time a layer is connected to some new input,\n * a node is added to `layer.inboundNodes`.\n *\n * Each time the output of a layer is used by another layer,\n * a node is added to `layer.outboundNodes`.\n *\n * `nodeIndices` and `tensorIndices` are basically fine-grained coordinates\n * describing the origin of the `inputTensors`, verifying the following:\n *\n * `inputTensors[i] ==\n * inboundLayers[i].inboundNodes[nodeIndices[i]].outputTensors[\n *   tensorIndices[i]]`\n *\n * A node from layer A to layer B is added to:\n *     A.outboundNodes\n *     B.inboundNodes\n */\nexport class Node {\n    constructor(args, \n    // TODO(michaelterry): Define actual type for this.\n    callArgs) {\n        this.callArgs = callArgs;\n        this.id = _nextNodeID++;\n        /*\n          Layer instance (NOT a list).\n          this is the layer that takes a list of input tensors\n          and turns them into a list of output tensors.\n          the current node will be added to\n          the inboundNodes of outboundLayer.\n        */\n        this.outboundLayer = args.outboundLayer;\n        /*\n            The following 3 properties describe where\n            the input tensors come from: which layers,\n            and for each layer, which node and which\n            tensor output of each node.\n        */\n        // List of layer instances.\n        this.inboundLayers = args.inboundLayers;\n        // List of integers, 1:1 mapping with inboundLayers.\n        this.nodeIndices = args.nodeIndices;\n        // List of integers, 1:1 mapping with inboundLayers.\n        this.tensorIndices = args.tensorIndices;\n        /*\n            Following 2 properties:\n            tensor inputs and outputs of outboundLayer.\n        */\n        // List of tensors. 1:1 mapping with inboundLayers.\n        this.inputTensors = args.inputTensors;\n        // List of tensors, created by outboundLayer.call().\n        this.outputTensors = args.outputTensors;\n        /*\n            Following 2 properties: input and output masks.\n            List of tensors, 1:1 mapping with inputTensor.\n        */\n        this.inputMasks = args.inputMasks;\n        // List of tensors, created by outboundLayer.computeMask().\n        this.outputMasks = args.outputMasks;\n        // Following 2 properties: input and output shapes.\n        // List of shape tuples, shapes of inputTensors.\n        this.inputShapes = args.inputShapes;\n        // List of shape tuples, shapes of outputTensors.\n        this.outputShapes = args.outputShapes;\n        // Add nodes to all layers involved.\n        for (const layer of args.inboundLayers) {\n            if (layer != null) {\n                layer.outboundNodes.push(this);\n            }\n        }\n        args.outboundLayer.inboundNodes.push(this);\n    }\n    getConfig() {\n        const inboundNames = [];\n        for (const layer of this.inboundLayers) {\n            if (layer != null) {\n                inboundNames.push(layer.name);\n            }\n            else {\n                inboundNames.push(null);\n            }\n        }\n        return {\n            outboundLayer: this.outboundLayer ? this.outboundLayer.name : null,\n            inboundLayers: inboundNames,\n            nodeIndices: this.nodeIndices,\n            tensorIndices: this.tensorIndices\n        };\n    }\n}\nlet _nextLayerID = 0;\n/**\n * A layer is a grouping of operations and weights that can be composed to\n * create a `tf.LayersModel`.\n *\n * Layers are constructed by using the functions under the\n * [tf.layers](#Layers-Basic) namespace.\n *\n * @doc {heading: 'Layers', subheading: 'Classes', namespace: 'layers'}\n */\nexport class Layer extends serialization.Serializable {\n    constructor(args = {}) {\n        super();\n        this._callHook = null;\n        this._addedWeightNames = [];\n        // Porting Notes: PyKeras does not have this property in this base Layer\n        //   class. Instead lets Layer subclass set it dynamically and checks the\n        //   value with `hasattr`. In tfjs-layers, we let this be a member of this\n        //   base class.\n        this._stateful = false;\n        this.id = _nextLayerID++;\n        this.activityRegularizer = null;\n        this.inputSpec = null;\n        this.supportsMasking = false;\n        // These properties will be set upon call of this.build()\n        this._trainableWeights = [];\n        this._nonTrainableWeights = [];\n        this._losses = [];\n        this._updates = [];\n        this._built = false;\n        /*\n          These lists will be filled via successive calls\n          to this.addInboundNode().\n         */\n        this.inboundNodes = [];\n        this.outboundNodes = [];\n        let name = args.name;\n        if (!name) {\n            const prefix = this.getClassName();\n            name = generic_utils.toSnakeCase(prefix) + '_' + getUid(prefix);\n        }\n        this.name = name;\n        this.trainable_ = args.trainable == null ? true : args.trainable;\n        if (args.inputShape != null || args.batchInputShape != null) {\n            /*\n              In this case we will later create an input layer\n              to insert before the current layer\n             */\n            let batchInputShape;\n            if (args.batchInputShape != null) {\n                batchInputShape = args.batchInputShape;\n            }\n            else if (args.inputShape != null) {\n                let batchSize = null;\n                if (args.batchSize != null) {\n                    batchSize = args.batchSize;\n                }\n                batchInputShape = [batchSize].concat(args.inputShape);\n            }\n            this.batchInputShape = batchInputShape;\n            // Set dtype.\n            let dtype = args.dtype;\n            if (dtype == null) {\n                dtype = args.inputDType;\n            }\n            if (dtype == null) {\n                dtype = 'float32';\n            }\n            this.dtype = dtype;\n        }\n        if (args.weights != null) {\n            this.initialWeights = args.weights;\n        }\n        else {\n            this.initialWeights = null;\n        }\n        // The value of `_refCount` is initialized to null. When the layer is used\n        // in a symbolic way for the first time, it will be set to 1.\n        this._refCount = null;\n        this.fastWeightInitDuringBuild = false;\n    }\n    /**\n     * Converts a layer and its index to a unique (immutable type) name.\n     * This function is used internally with `this.containerNodes`.\n     * @param layer The layer.\n     * @param nodeIndex The layer's position (e.g. via enumerate) in a list of\n     *   nodes.\n     *\n     * @returns The unique name.\n     */\n    static nodeKey(layer, nodeIndex) {\n        return layer.name + '_ib-' + nodeIndex.toString();\n    }\n    /**\n     * Returns this.inboundNode at index nodeIndex.\n     *\n     * Porting note: This is a replacement for _get_node_attribute_at_index()\n     * @param nodeIndex\n     * @param attrName The name of the attribute related to request for this node.\n     */\n    getNodeAtIndex(nodeIndex, attrName) {\n        if (this.inboundNodes.length === 0) {\n            throw new RuntimeError('The layer has never been called ' +\n                `and thus has no defined ${attrName}.`);\n        }\n        if (this.inboundNodes.length <= nodeIndex) {\n            throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, ` +\n                `but the layer has only ${this.inboundNodes.length} inbound nodes.`);\n        }\n        return this.inboundNodes[nodeIndex];\n    }\n    /**\n     * Retrieves the input tensor(s) of a layer at a given node.\n     *\n     * @param nodeIndex Integer, index of the node from which to retrieve the\n     *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n     *   was called.\n     *\n     * @return A tensor (or list of tensors if the layer has multiple inputs).\n     */\n    getInputAt(nodeIndex) {\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(nodeIndex, 'input').inputTensors);\n    }\n    /**\n     * Retrieves the output tensor(s) of a layer at a given node.\n     *\n     * @param nodeIndex Integer, index of the node from which to retrieve the\n     *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n     *   was called.\n     *\n     * @return A tensor (or list of tensors if the layer has multiple outputs).\n     */\n    getOutputAt(nodeIndex) {\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(nodeIndex, 'output').outputTensors);\n    }\n    // Properties\n    /**\n     * Retrieves the input tensor(s) of a layer.\n     *\n     * Only applicable if the layer has exactly one inbound node,\n     * i.e. if it is connected to one incoming layer.\n     *\n     * @return Input tensor or list of input tensors.\n     *\n     * @exception AttributeError if the layer is connected to more than one\n     *   incoming layers.\n     */\n    get input() {\n        if (this.inboundNodes.length > 1) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has multiple inbound nodes, ' +\n                'hence the notion of \"layer input\" ' +\n                'is ill-defined. ' +\n                'Use `getInputAt(nodeIndex)` instead.');\n        }\n        else if (this.inboundNodes.length === 0) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' is not connected, no input to return.');\n        }\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(0, 'input').inputTensors);\n    }\n    /**\n     * Retrieves the output tensor(s) of a layer.\n     *\n     * Only applicable if the layer has exactly one inbound node,\n     * i.e. if it is connected to one incoming layer.\n     *\n     * @return Output tensor or list of output tensors.\n     *\n     * @exception AttributeError if the layer is connected to more than one\n     *   incoming layers.\n     */\n    get output() {\n        if (this.inboundNodes.length === 0) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has no inbound nodes.');\n        }\n        if (this.inboundNodes.length > 1) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has multiple inbound nodes, ' +\n                'hence the notion of \"layer output\" ' +\n                'is ill-defined. ' +\n                'Use `getOutputAt(nodeIndex)` instead.');\n        }\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(0, 'output').outputTensors);\n    }\n    get losses() {\n        return this._losses;\n    }\n    /**\n     * Retrieves the Layer's current loss values.\n     *\n     * Used for regularizers during training.\n     */\n    calculateLosses() {\n        // Porting Node: This is an augmentation to Layer.loss in PyKeras.\n        //   In PyKeras, Layer.loss returns symbolic tensors. Here a concrete\n        //   Tensor (specifically Scalar) values are returned. This is due to the\n        //   imperative backend.\n        return this.losses.map(lossFn => lossFn());\n    }\n    get updates() {\n        return this._updates;\n    }\n    get built() {\n        return this._built;\n    }\n    set built(built) {\n        this._built = built;\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this._trainableWeights.forEach(w => w.trainable = trainable);\n        this.trainable_ = trainable;\n    }\n    get trainableWeights() {\n        if (this.trainable_) {\n            return this._trainableWeights.filter(w => w.trainable);\n        }\n        else {\n            return [];\n        }\n    }\n    set trainableWeights(weights) {\n        this._trainableWeights = weights;\n    }\n    get nonTrainableWeights() {\n        if (this.trainable) {\n            return this._trainableWeights.filter(w => !w.trainable)\n                .concat(this._nonTrainableWeights);\n        }\n        else {\n            return this._trainableWeights.concat(this._nonTrainableWeights);\n        }\n    }\n    set nonTrainableWeights(weights) {\n        this._nonTrainableWeights = weights;\n    }\n    /**\n     * The concatenation of the lists trainableWeights and nonTrainableWeights\n     * (in this order).\n     */\n    get weights() {\n        return this.trainableWeights.concat(this.nonTrainableWeights);\n    }\n    get stateful() {\n        return this._stateful;\n    }\n    /**\n     * Reset the states of the layer.\n     *\n     * This method of the base Layer class is essentially a no-op.\n     * Subclasses that are stateful (e.g., stateful RNNs) should override this\n     * method.\n     */\n    resetStates() {\n        if (!this.stateful) {\n            throw new Error('Cannot call the resetStates() method of a non-stateful Layer ' +\n                'object.');\n        }\n    }\n    /**\n     * Checks compatibility between the layer and provided inputs.\n     *\n     * This checks that the tensor(s) `input`\n     * verify the input assumptions of the layer\n     * (if any). If not, exceptions are raised.\n     *\n     * @param inputs Input tensor or list of input tensors.\n     *\n     * @exception ValueError in case of mismatch between\n     *   the provided inputs and the expectations of the layer.\n     */\n    assertInputCompatibility(inputs) {\n        inputs = generic_utils.toList(inputs);\n        if (this.inputSpec == null || this.inputSpec.length === 0) {\n            return;\n        }\n        const inputSpec = generic_utils.toList(this.inputSpec);\n        if (inputs.length !== inputSpec.length) {\n            throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, ` +\n                `but it received ${inputs.length} input tensors. ` +\n                `Input received: ${inputs}`);\n        }\n        for (let inputIndex = 0; inputIndex < inputs.length; inputIndex++) {\n            const x = inputs[inputIndex];\n            const spec = inputSpec[inputIndex];\n            if (spec == null) {\n                continue;\n            }\n            // Check ndim.\n            const ndim = x.rank;\n            if (spec.ndim != null) {\n                if (ndim !== spec.ndim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: ` +\n                        `expected ndim=${spec.ndim}, found ndim=${ndim}`);\n                }\n            }\n            if (spec.maxNDim != null) {\n                if (ndim > spec.maxNDim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}` +\n                        `: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);\n                }\n            }\n            if (spec.minNDim != null) {\n                if (ndim < spec.minNDim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}` +\n                        `: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);\n                }\n            }\n            // Check dtype.\n            if (spec.dtype != null) {\n                if (x.dtype !== spec.dtype) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} ` +\n                        `: expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);\n                }\n            }\n            // Check specific shape axes.\n            if (spec.axes) {\n                const xShape = x.shape;\n                for (const key in spec.axes) {\n                    const axis = Number(key);\n                    const value = spec.axes[key];\n                    // Perform Python-style slicing in case axis < 0;\n                    // TODO(cais): Use https://github.com/alvivi/typescript-underscore to\n                    // ensure type safety through Underscore calls.\n                    const xShapeAtAxis = axis >= 0 ? xShape[axis] : xShape[xShape.length + axis];\n                    if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) {\n                        throw new ValueError(`Input ${inputIndex} is incompatible with layer ` +\n                            `${this.name}: expected axis ${axis} of input shape to ` +\n                            `have value ${value} but got shape ${xShape}.`);\n                    }\n                }\n            }\n            // Check shape.\n            if (spec.shape != null) {\n                for (let i = 0; i < spec.shape.length; ++i) {\n                    const specDim = spec.shape[i];\n                    const dim = x.shape[i];\n                    if (specDim != null && dim != null) {\n                        if (specDim !== dim) {\n                            throw new ValueError(`Input ${inputIndex} is incompatible with layer ` +\n                                `${this.name}: expected shape=${spec.shape}, ` +\n                                `found shape=${x.shape}.`);\n                        }\n                    }\n                }\n            }\n        }\n    }\n    /**\n     * This is where the layer's logic lives.\n     *\n     * @param inputs Input tensor, or list/tuple of input tensors.\n     * @param kwargs Additional keyword arguments.\n     *\n     * @return A tensor or list/tuple of tensors.\n     */\n    call(inputs, kwargs) {\n        return inputs;\n    }\n    invokeCallHook(inputs, kwargs) {\n        if (this._callHook != null) {\n            this._callHook(inputs, kwargs);\n        }\n    }\n    /**\n     * Set call hook.\n     * This is currently used for testing only.\n     * @param callHook\n     */\n    setCallHook(callHook) {\n        this._callHook = callHook;\n    }\n    /**\n     * Clear call hook.\n     * This is currently used for testing only.\n     */\n    clearCallHook() {\n        this._callHook = null;\n    }\n    /**\n     * Builds or executes a `Layer's logic.\n     *\n     * When called with `tf.Tensor`(s), execute the `Layer`s computation and\n     * return Tensor(s). For example:\n     *\n     * ```js\n     * const denseLayer = tf.layers.dense({\n     *   units: 1,\n     *   kernelInitializer: 'zeros',\n     *   useBias: false\n     * });\n     *\n     * // Invoke the layer's apply() method with a `tf.Tensor` (with concrete\n     * // numeric values).\n     * const input = tf.ones([2, 2]);\n     * const output = denseLayer.apply(input);\n     *\n     * // The output's value is expected to be [[0], [0]], due to the fact that\n     * // the dense layer has a kernel initialized to all-zeros and does not have\n     * // a bias.\n     * output.print();\n     * ```\n     *\n     * When called with `tf.SymbolicTensor`(s), this will prepare the layer for\n     * future execution.  This entails internal book-keeping on shapes of\n     * expected Tensors, wiring layers together, and initializing weights.\n     *\n     * Calling `apply` with `tf.SymbolicTensor`s are typically used during the\n     * building of non-`tf.Sequential` models. For example:\n     *\n     * ```js\n     * const flattenLayer = tf.layers.flatten();\n     * const denseLayer = tf.layers.dense({units: 1});\n     *\n     * // Use tf.layers.input() to obtain a SymbolicTensor as input to apply().\n     * const input = tf.input({shape: [2, 2]});\n     * const output1 = flattenLayer.apply(input);\n     *\n     * // output1.shape is [null, 4]. The first dimension is the undetermined\n     * // batch size. The second dimension comes from flattening the [2, 2]\n     * // shape.\n     * console.log(JSON.stringify(output1.shape));\n     *\n     * // The output SymbolicTensor of the flatten layer can be used to call\n     * // the apply() of the dense layer:\n     * const output2 = denseLayer.apply(output1);\n     *\n     * // output2.shape is [null, 1]. The first dimension is the undetermined\n     * // batch size. The second dimension matches the number of units of the\n     * // dense layer.\n     * console.log(JSON.stringify(output2.shape));\n     *\n     * // The input and output and be used to construct a model that consists\n     * // of the flatten and dense layers.\n     * const model = tf.model({inputs: input, outputs: output2});\n     * ```\n     *\n     * @param inputs a `tf.Tensor` or `tf.SymbolicTensor` or an Array of them.\n     * @param kwargs Additional keyword arguments to be passed to `call()`.\n     *\n     * @return Output of the layer's `call` method.\n     *\n     * @exception ValueError error in case the layer is missing shape information\n     *   for its `build` call.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    // Porting Note: This is a replacement for __call__() in Python.\n    apply(inputs, kwargs) {\n        kwargs = kwargs || {};\n        this.assertNotDisposed();\n        // Ensure inputs are all the same type.\n        const inputsList = generic_utils.toList(inputs);\n        let allAreSymbolic = true;\n        for (const input of inputsList) {\n            if (!(input instanceof SymbolicTensor)) {\n                allAreSymbolic = false;\n                break;\n            }\n        }\n        let noneAreSymbolic = true;\n        for (const input of inputsList) {\n            if (input instanceof SymbolicTensor) {\n                noneAreSymbolic = false;\n                break;\n            }\n        }\n        if (allAreSymbolic === noneAreSymbolic) {\n            throw new ValueError('Arguments to apply() must be all ' +\n                'SymbolicTensors or all Tensors');\n        }\n        // TODO(michaelterry): nameScope() may not be necessary.\n        return nameScope(this.name, () => {\n            // Handle laying building (weight creating, input spec locking).\n            if (!this.built) {\n                /*\n                  Throw exceptions in case the input is not compatible\n                  with the inputSpec specified in the layer constructor.\n                 */\n                this.assertInputCompatibility(inputs);\n                // Collect input shapes to build layer.\n                const inputShapes = [];\n                for (const xElem of generic_utils.toList(inputs)) {\n                    inputShapes.push(xElem.shape);\n                }\n                this.build(generic_utils.singletonOrArray(inputShapes));\n                this.built = true;\n                // Load weights that were specified at layer instantiation.\n                if (this.initialWeights) {\n                    this.setWeights(this.initialWeights);\n                }\n                if (this._refCount === null && noneAreSymbolic) {\n                    // The first use of this layer is a non-symbolic call, set ref count\n                    // to 1 so the Layer can be properly disposed if its dispose() method\n                    // is called.\n                    this._refCount = 1;\n                }\n            }\n            /*\n              Throw exceptions in case the input is not compatible\n              with the inputSpec set at build time.\n            */\n            this.assertInputCompatibility(inputs);\n            // Handle mask propagation.\n            // TODO(michaelterry): Mask propagation not currently implemented.\n            // Actually call the layer, collecting output(s), mask(s), and shape(s).\n            if (noneAreSymbolic) {\n                let output = this.call(inputs, kwargs);\n                // TODO(michaelterry): Compute the outputMask\n                // If the layer returns tensors from its inputs, unmodified,\n                // we copy them to avoid loss of tensor metadata.\n                const outputList = generic_utils.toList(output);\n                const outputListCopy = [];\n                // TODO(michaelterry): This copying may not be necessary given our eager\n                // backend.\n                for (let x of outputList) {\n                    if (inputsList.indexOf(x) !== -1) {\n                        x = x.clone();\n                    }\n                    outputListCopy.push(x);\n                }\n                output = generic_utils.singletonOrArray(outputListCopy);\n                if (this.activityRegularizer != null) {\n                    throw new NotImplementedError('Layer invocation in the presence of activity ' +\n                        'regularizer(s) is not supported yet.');\n                }\n                // TODO(michaelterry): Call addInboundNode()?\n                return output;\n            }\n            else {\n                const inputShape = collectInputShape(inputs);\n                const outputShape = this.computeOutputShape(inputShape);\n                let output;\n                const outputDType = guessOutputDType(inputs);\n                this.warnOnIncompatibleInputShape(Array.isArray(inputs) ? inputShape[0] :\n                    inputShape);\n                if (outputShape != null && outputShape.length > 0 &&\n                    Array.isArray(outputShape[0])) {\n                    // We have multiple output shapes. Create multiple output tensors.\n                    output = outputShape\n                        .map((shape, index) => new SymbolicTensor(outputDType, shape, this, generic_utils.toList(inputs), kwargs, this.name, index));\n                }\n                else {\n                    output = new SymbolicTensor(outputDType, outputShape, this, generic_utils.toList(inputs), kwargs, this.name);\n                }\n                /*\n                  Add an inbound node to the layer, so that it keeps track\n                  of the call and of all new variables created during the call.\n                  This also updates the layer history of the output tensor(s).\n                  If the input tensor(s) had no previous history,\n                  this does nothing.\n                */\n                this.addInboundNode(inputs, output, null, null, inputShape, outputShape, kwargs);\n                this._refCount++;\n                if (this.activityRegularizer != null) {\n                    throw new NotImplementedError('Layer invocation in the presence of activity ' +\n                        'regularizer(s) is not supported yet.');\n                }\n                return output;\n            }\n        });\n    }\n    /**\n     * Check compatibility between input shape and this layer's batchInputShape.\n     *\n     * Print warning if any incompatibility is found.\n     *\n     * @param inputShape Input shape to be checked.\n     */\n    warnOnIncompatibleInputShape(inputShape) {\n        if (this.batchInputShape == null) {\n            return;\n        }\n        else if (inputShape.length !== this.batchInputShape.length) {\n            console.warn(`The rank of the input tensor provided (shape: ` +\n                `${JSON.stringify(inputShape)}) does not match that of the ` +\n                `batchInputShape (${JSON.stringify(this.batchInputShape)}) ` +\n                `of the layer ${this.name}`);\n        }\n        else {\n            let dimMismatch = false;\n            this.batchInputShape.forEach((dimension, i) => {\n                if (dimension != null && inputShape[i] != null &&\n                    inputShape[i] !== dimension) {\n                    dimMismatch = true;\n                }\n            });\n            if (dimMismatch) {\n                console.warn(`The shape of the input tensor ` +\n                    `(${JSON.stringify(inputShape)}) does not ` +\n                    `match the expectation of layer ${this.name}: ` +\n                    `${JSON.stringify(this.batchInputShape)}`);\n            }\n        }\n    }\n    /**\n     * Retrieves the output shape(s) of a layer.\n     *\n     * Only applicable if the layer has only one inbound node, or if all inbound\n     * nodes have the same output shape.\n     *\n     * @returns Output shape or shapes.\n     * @throws AttributeError: if the layer is connected to more than one incoming\n     *   nodes.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    get outputShape() {\n        if (this.inboundNodes == null || this.inboundNodes.length === 0) {\n            throw new AttributeError(`The layer ${this.name} has never been called and thus has no ` +\n                `defined output shape.`);\n        }\n        const allOutputShapes = [];\n        for (const node of this.inboundNodes) {\n            const shapeString = JSON.stringify(node.outputShapes);\n            if (allOutputShapes.indexOf(shapeString) === -1) {\n                allOutputShapes.push(shapeString);\n            }\n        }\n        if (allOutputShapes.length === 1) {\n            const outputShapes = this.inboundNodes[0].outputShapes;\n            if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) &&\n                outputShapes.length === 1) {\n                return outputShapes[0];\n            }\n            else {\n                return outputShapes;\n            }\n        }\n        else {\n            throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different ` +\n                `output shapes. Hence the notion of \"output shape\" is ill-defined ` +\n                `for the layer.`);\n            // TODO(cais): Implement getOutputShapeAt().\n        }\n    }\n    /**\n     * Counts the total number of numbers (e.g., float32, int32) in the\n     * weights.\n     *\n     * @returns An integer count.\n     * @throws RuntimeError: If the layer is not built yet (in which case its\n     *   weights are not defined yet.)\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    countParams() {\n        if (!this.built) {\n            throw new RuntimeError(`You tried to call countParams() on ${this.name}, ` +\n                `but the layer is not built yet. Build it first by calling ` +\n                `build(batchInputShape).`);\n        }\n        return variable_utils.countParamsInWeights(this.weights);\n    }\n    /**\n     * Creates the layer weights.\n     *\n     * Must be implemented on all layers that have weights.\n     *\n     * Called when apply() is called to construct the weights.\n     *\n     * @param inputShape A `Shape` or array of `Shape` (unused).\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    build(inputShape) {\n        this.built = true;\n    }\n    /**\n     * Returns the current values of the weights of the layer.\n     *\n     * @param trainableOnly Whether to get the values of only trainable weights.\n     * @returns Weight values as an `Array` of `tf.Tensor`s.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    getWeights(trainableOnly = false) {\n        return batchGetValue(trainableOnly ? this.trainableWeights : this.weights);\n    }\n    /**\n     * Sets the weights of the layer, from Tensors.\n     *\n     * @param weights a list of Tensors. The number of arrays and their shape\n     *   must match number of the dimensions of the weights of the layer (i.e.\n     *   it should match the output of `getWeights`).\n     *\n     * @exception ValueError If the provided weights list does not match the\n     *   layer's specifications.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    setWeights(weights) {\n        tidy(() => {\n            const params = this.weights;\n            if (params.length !== weights.length) {\n                // TODO(cais): Restore the following and use `providedWeights`, instead\n                // of `weights` in the error message, once the deeplearn.js bug is\n                // fixed: https://github.com/PAIR-code/deeplearnjs/issues/498 const\n                // providedWeights = JSON.stringify(weights).substr(0, 50);\n                throw new ValueError(`You called setWeights(weights) on layer \"${this.name}\" ` +\n                    `with a weight list of length ${weights.length}, ` +\n                    `but the layer was expecting ${params.length} weights. ` +\n                    `Provided weights: ${weights}...`);\n            }\n            if (params.length === 0) {\n                return;\n            }\n            const weightValueTuples = [];\n            const paramValues = batchGetValue(params);\n            for (let i = 0; i < paramValues.length; ++i) {\n                const pv = paramValues[i];\n                const p = params[i];\n                const w = weights[i];\n                if (!util.arraysEqual(pv.shape, w.shape)) {\n                    throw new ValueError(`Layer weight shape ${pv.shape} ` +\n                        `not compatible with provided weight shape ${w.shape}`);\n                }\n                weightValueTuples.push([p, w]);\n            }\n            batchSetValue(weightValueTuples);\n        });\n    }\n    /**\n     * Adds a weight variable to the layer.\n     *\n     * @param name Name of the new weight variable.\n     * @param shape The shape of the weight.\n     * @param dtype The dtype of the weight.\n     * @param initializer An initializer instance.\n     * @param regularizer A regularizer instance.\n     * @param trainable Whether the weight should be trained via backprop or not\n     *   (assuming that the layer itself is also trainable).\n     * @param constraint An optional trainable.\n     * @return The created weight variable.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint) {\n        // Reject duplicate weight names.\n        if (this._addedWeightNames.indexOf(name) !== -1) {\n            throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);\n        }\n        this._addedWeightNames.push(name);\n        if (dtype == null) {\n            dtype = 'float32';\n        }\n        if (this.fastWeightInitDuringBuild) {\n            initializer = getInitializer('zeros');\n        }\n        const initValue = initializer.apply(shape, dtype);\n        const weight = new LayerVariable(initValue, dtype, name, trainable, constraint);\n        initValue.dispose();\n        // Request backend not to dispose the weights of the model on scope() exit.\n        if (regularizer != null) {\n            this.addLoss(() => regularizer.apply(weight.read()));\n        }\n        if (trainable == null) {\n            trainable = true;\n        }\n        if (trainable) {\n            this._trainableWeights.push(weight);\n        }\n        else {\n            this._nonTrainableWeights.push(weight);\n        }\n        return weight;\n    }\n    /**\n     * Set the fast-weight-initialization flag.\n     *\n     * In cases where the initialized weight values will be immediately\n     * overwritten by loaded weight values during model loading, setting\n     * the flag to `true` saves unnecessary calls to potentially expensive\n     * initializers and speeds up the loading process.\n     *\n     * @param value Target value of the flag.\n     */\n    setFastWeightInitDuringBuild(value) {\n        this.fastWeightInitDuringBuild = value;\n    }\n    /**\n     * Add losses to the layer.\n     *\n     * The loss may potentionally be conditional on some inputs tensors,\n     * for instance activity losses are conditional on the layer's inputs.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    addLoss(losses) {\n        if (losses == null || Array.isArray(losses) && losses.length === 0) {\n            return;\n        }\n        // Update this.losses\n        losses = generic_utils.toList(losses);\n        if (this._losses !== undefined && this._losses !== null) {\n            this.losses.push(...losses);\n        }\n    }\n    /**\n     * Computes the output shape of the layer.\n     *\n     * Assumes that the layer will be built to match that input shape provided.\n     *\n     * @param inputShape A shape (tuple of integers) or a list of shape tuples\n     *   (one per output tensor of the layer). Shape tuples can include null for\n     *   free dimensions, instead of an integer.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    /**\n     * Computes an output mask tensor.\n     *\n     * @param inputs Tensor or list of tensors.\n     * @param mask Tensor or list of tensors.\n     *\n     * @return null or a tensor (or list of tensors, one per output tensor of the\n     * layer).\n     */\n    computeMask(inputs, mask) {\n        if (!this.supportsMasking) {\n            if (mask != null) {\n                if (Array.isArray(mask)) {\n                    mask.forEach(maskElement => {\n                        if (maskElement != null) {\n                            throw new TypeError(`Layer ${this.name} does not support masking, ` +\n                                'but was passed an inputMask.');\n                        }\n                    });\n                }\n                else {\n                    throw new TypeError(`Layer ${this.name} does not support masking, ` +\n                        'but was passed an inputMask.');\n                }\n            }\n            // masking not explicitly supported: return null as mask\n            return null;\n        }\n        // if masking is explictly supported, by default\n        // carry over the input mask\n        return mask;\n    }\n    /**\n     * Internal method to create an inbound node for the layer.\n     *\n     * @param inputTensors List of input tensors.\n     * @param outputTensors List of output tensors.\n     * @param inputMasks List of input masks (a mask can be a tensor, or null).\n     * @param outputMasks List of output masks (a mask can be a tensor, or null).\n     * @param inputShapes List of input shape tuples.\n     * @param outputShapes List of output shape tuples.\n     * @param kwargs Dictionary of keyword arguments that were passed to the\n     *   `call` method of the layer at the call that created the node.\n     */\n    addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) {\n        const inputTensorList = generic_utils.toList(inputTensors);\n        outputTensors = generic_utils.toList(outputTensors);\n        inputMasks = generic_utils.toList(inputMasks);\n        outputMasks = generic_utils.toList(outputMasks);\n        inputShapes = types_utils.normalizeShapeList(inputShapes);\n        outputShapes = types_utils.normalizeShapeList(outputShapes);\n        // Collect input tensor(s) coordinates.\n        const inboundLayers = [];\n        const nodeIndices = [];\n        const tensorIndices = [];\n        for (const x of inputTensorList) {\n            /*\n             * TODO(michaelterry): Keras adds this value to tensors; it's not\n             * clear whether we'll use this or not.\n             */\n            inboundLayers.push(x.sourceLayer);\n            nodeIndices.push(x.nodeIndex);\n            tensorIndices.push(x.tensorIndex);\n        }\n        // Create node, add it to inbound nodes.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers,\n            nodeIndices,\n            tensorIndices,\n            inputTensors: inputTensorList,\n            outputTensors,\n            inputMasks,\n            outputMasks,\n            inputShapes,\n            outputShapes\n        }, kwargs);\n        // Update tensor history\n        for (let i = 0; i < outputTensors.length; i++) {\n            // TODO(michaelterry: _uses_learning_phase not tracked.\n            outputTensors[i].sourceLayer = this;\n            outputTensors[i].nodeIndex = this.inboundNodes.length - 1;\n            outputTensors[i].tensorIndex = i;\n        }\n    }\n    /**\n     * Returns the config of the layer.\n     *\n     * A layer config is a TS dictionary (serializable)\n     * containing the configuration of a layer.\n     * The same layer can be reinstantiated later\n     * (without its trained weights) from this configuration.\n     *\n     * The config of a layer does not include connectivity\n     * information, nor the layer class name.  These are handled\n     * by 'Container' (one layer of abstraction above).\n     *\n     * Porting Note: The TS dictionary follows TS naming standrds for\n     * keys, and uses tfjs-layers type-safe Enums.  Serialization methods\n     * should use a helper function to convert to the pythonic storage\n     * standard. (see serialization_utils.convertTsToPythonic)\n     *\n     * @returns TS dictionary of configuration.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    getConfig() {\n        const config = { name: this.name, trainable: this.trainable };\n        if (this.batchInputShape != null) {\n            config['batchInputShape'] = this.batchInputShape;\n        }\n        if (this.dtype != null) {\n            config['dtype'] = this.dtype;\n        }\n        return config;\n    }\n    /**\n     * Dispose the weight variables that this Layer instance holds.\n     *\n     * @returns {number} Number of disposed variables.\n     */\n    disposeWeights() {\n        this.weights.forEach(weight => weight.dispose());\n        return this.weights.length;\n    }\n    assertNotDisposed() {\n        if (this._refCount === 0) {\n            throw new Error(`Layer '${this.name}' is already disposed.`);\n        }\n    }\n    /**\n     * Attempt to dispose layer's weights.\n     *\n     * This method decrease the reference count of the Layer object by 1.\n     *\n     * A Layer is reference-counted. Its reference count is incremented by 1\n     * the first item its `apply()` method is called and when it becomes a part\n     * of a new `Node` (through calling the `apply()`) method on a\n     * `tf.SymbolicTensor`).\n     *\n     * If the reference count of a Layer becomes 0, all the weights will be\n     * disposed and the underlying memory (e.g., the textures allocated in WebGL)\n     * will be freed.\n     *\n     * Note: If the reference count is greater than 0 after the decrement, the\n     * weights of the Layer will *not* be disposed.\n     *\n     * After a Layer is disposed, it cannot be used in calls such as `apply()`,\n     * `getWeights()` or `setWeights()` anymore.\n     *\n     * @returns A DisposeResult Object with the following fields:\n     *   - refCountAfterDispose: The reference count of the Container after this\n     *     `dispose()` call.\n     *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n     *     during this `dispose()` call.\n     * @throws {Error} If the layer is not built yet, or if the layer has already\n     *   been disposed.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    dispose() {\n        if (!this.built) {\n            throw new Error(`Cannot dispose Layer ${this.name} because it has not been ` +\n                `built yet.`);\n        }\n        if (this._refCount === null) {\n            throw new Error(`Cannot dispose Layer ${this.name} because it has not been used ` +\n                `yet.`);\n        }\n        this.assertNotDisposed();\n        let numDisposedVariables = 0;\n        if (--this._refCount === 0) {\n            numDisposedVariables = this.disposeWeights();\n        }\n        return { refCountAfterDispose: this._refCount, numDisposedVariables };\n    }\n}\n/**\n * Collects the input shape(s) of a list of `tf.Tensor`s or\n * `tf.SymbolicTensor`s.\n *\n * TODO(michaelterry): Update PyKeras docs (backport).\n *\n * @param inputTensors List of input tensors (or single input tensor).\n *\n * @return List of shape tuples (or single tuple), one tuple per input.\n */\nfunction collectInputShape(inputTensors) {\n    inputTensors =\n        generic_utils.toList(inputTensors);\n    const shapes = [];\n    for (const x of inputTensors) {\n        shapes.push(x.shape);\n    }\n    return generic_utils.singletonOrArray(shapes);\n}\n/**\n * Guesses output dtype based on inputs.\n *\n * At present, just returns 'float32' for any input.\n *\n * @param inputTensors List of input tensors (or single input tensor).\n *\n * @return The guessed DType. At present, always returns 'float32'.\n */\nfunction guessOutputDType(inputTensors) {\n    return 'float32';\n}\n/**\n * Returns the list of input tensors necessary to compute `tensor`.\n *\n * Output will always be a list of tensors (potentially with 1 element).\n *\n * @param tensor The tensor to start from.\n * @param layer Origin layer of the tensor.\n * @param nodeIndex Origin node index of the tensor.\n *\n * @return Array of input tensors.\n */\nexport function getSourceInputs(tensor, layer, nodeIndex) {\n    if (layer == null || (nodeIndex != null && nodeIndex > 0)) {\n        layer = tensor.sourceLayer;\n        nodeIndex = tensor.nodeIndex;\n    }\n    if (layer.inboundNodes.length === 0) {\n        return [tensor];\n    }\n    else {\n        const node = layer.inboundNodes[nodeIndex];\n        if (node.inboundLayers.length === 0) {\n            return node.inputTensors;\n        }\n        else {\n            const sourceTensors = [];\n            for (let i = 0; i < node.inboundLayers.length; i++) {\n                const x = node.inputTensors[i];\n                const layer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const previousSources = getSourceInputs(x, layer, nodeIndex);\n                // Avoid input redundancy.\n                for (const x of previousSources) {\n                    if (sourceTensors.indexOf(x) === -1) {\n                        sourceTensors.push(x);\n                    }\n                }\n            }\n            return sourceTensors;\n        }\n    }\n}\n//# sourceMappingURL=topology.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { serialization } from '@tensorflow/tfjs-core';\nimport { getUid } from '../backend/state';\nimport { ValueError } from '../errors';\nimport { Layer, Node, SymbolicTensor } from './topology';\nexport class InputLayer extends Layer {\n    constructor(args) {\n        super({\n            dtype: args.dtype,\n            name: args.name != null ? args.name : getUid('input').toString()\n        });\n        // Normalize config.batchSize and config.sparse\n        if (args.batchSize == null) {\n            args.batchSize = null;\n        }\n        if (args.sparse == null) {\n            args.sparse = false;\n        }\n        this.trainable = false;\n        this.built = true;\n        this.sparse = args.sparse;\n        if (args.inputShape != null && args.batchInputShape != null) {\n            throw new ValueError('Only provide the inputShape OR ' +\n                'batchInputShape argument to inputLayer, not both at the same time.');\n        }\n        let batchInputShape = args.batchInputShape;\n        if (batchInputShape == null) {\n            if (args.inputShape == null) {\n                throw new ValueError('An InputLayer should be passed either a ' +\n                    '`batchInputShape` or an `inputShape`.');\n            }\n            else {\n                batchInputShape = [args.batchSize].concat(args.inputShape);\n            }\n        }\n        else {\n            // TODO(michaelterry): Backport to PyKeras\n            if (args.batchSize != null) {\n                throw new ValueError('Cannot specify batchSize if batchInputShape is ' +\n                    'specified when creating an InputLayer.');\n            }\n        }\n        const dtype = args.dtype || 'float32';\n        this.batchInputShape = batchInputShape;\n        this.dtype = dtype;\n        // TODO(michaelterry): Backport this to PyKeras?\n        this.inputSpec = [{ shape: batchInputShape }];\n        const inputTensor = new SymbolicTensor(this.dtype, this.batchInputShape, this, [], {}, this.name);\n        inputTensor.nodeIndex = 0;\n        inputTensor.tensorIndex = 0;\n        // Create an input node to add to this.outboundNode.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers: [],\n            nodeIndices: [],\n            tensorIndices: [],\n            inputTensors: [inputTensor],\n            outputTensors: [inputTensor],\n            inputMasks: [null],\n            outputMasks: [null],\n            inputShapes: [batchInputShape],\n            outputShapes: [batchInputShape]\n        });\n    }\n    apply(inputs, kwargs) {\n        throw new ValueError('Cannot pass any input to an ' +\n            `InputLayer's apply() method. InputLayer name: ${this.name}`);\n    }\n    dispose() {\n        // dispose() for InputLayer is overridden as no-op.\n        return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 };\n    }\n    getConfig() {\n        return {\n            batchInputShape: this.batchInputShape,\n            dtype: this.dtype,\n            sparse: this.sparse,\n            name: this.name\n        };\n    }\n}\n/** @nocollapse */\nInputLayer.className = 'InputLayer';\nserialization.registerClass(InputLayer);\nexport function Input(config) {\n    if (config.batchShape == null && config.shape == null) {\n        throw new Error('Please provide to Input either a `shape`' +\n            ' or a `batchShape` argument. Note that ' +\n            '`shape` does not include the batch ' +\n            'dimension.');\n    }\n    if (config.batchShape != null && config.shape != null) {\n        // TODO(michaelterry): Backport to PyKeras.\n        throw new ValueError('Please provide either a `shape` or `batchShape` ' +\n            'argument to Input, but not both.');\n    }\n    let batchShape = config.batchShape;\n    if (config.shape != null && batchShape == null) {\n        batchShape = [null].concat(config.shape);\n    }\n    let dtype = config.dtype;\n    if (dtype == null) {\n        dtype = 'float32';\n    }\n    const inputLayer = new InputLayer({\n        batchInputShape: batchShape,\n        name: config.name,\n        dtype,\n        sparse: config.sparse\n    });\n    const outputs = inputLayer.inboundNodes[0].outputTensors;\n    return outputs[0];\n}\n//# sourceMappingURL=input_layer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { dispose } from '@tensorflow/tfjs-core';\n/**\n * Turn any Scalar values in a Logs object into actual number values.\n *\n * @param logs The `Logs` object to be resolved in place.\n */\nexport async function resolveScalarsInLogs(logs) {\n    if (logs == null) {\n        return;\n    }\n    const promises = [];\n    const keys = [];\n    const scalarsToDispose = [];\n    for (const key in logs) {\n        const value = logs[key];\n        if (typeof value !== 'number') {\n            const valueScalar = value;\n            promises.push(valueScalar.data());\n            keys.push(key);\n            scalarsToDispose.push(valueScalar);\n        }\n    }\n    if (promises.length > 0) {\n        const values = await Promise.all(promises);\n        for (let i = 0; i < values.length; ++i) {\n            logs[keys[i]] = values[i][0];\n        }\n        // Dispose the original scalar tensors.\n        dispose(scalarsToDispose);\n    }\n}\n/**\n * Dispose all Tensors in an UnresolvedLogs object.\n *\n * @param logs An `UnresolvedLogs` object potentially containing `tf.Tensor`s in\n *   places where the values can be `tf.Tensor` or `number`.\n */\nexport function disposeTensorsInLogs(logs) {\n    if (logs == null) {\n        return;\n    }\n    for (const key in logs) {\n        const value = logs[key];\n        if (typeof value !== 'number') {\n            value.dispose();\n        }\n    }\n}\n//# sourceMappingURL=logs.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/callbacks.py */\nimport { add, div, keep, mul, nextFrame, tidy, util } from '@tensorflow/tfjs-core';\nimport { ValueError } from './errors';\nimport { resolveScalarsInLogs } from './logs';\nimport * as generic_utils from './utils/generic_utils';\n/** Verbosity logging level when fitting a model. */\nexport var ModelLoggingVerbosity;\n(function (ModelLoggingVerbosity) {\n    ModelLoggingVerbosity[ModelLoggingVerbosity[\"SILENT\"] = 0] = \"SILENT\";\n    ModelLoggingVerbosity[ModelLoggingVerbosity[\"VERBOSE\"] = 1] = \"VERBOSE\";\n})(ModelLoggingVerbosity || (ModelLoggingVerbosity = {}));\n/** How often to yield to the main thread when training (in ms). */\nexport const DEFAULT_YIELD_EVERY_MS = 125;\n/**\n * Abstract base class used to build new callbacks.\n *\n * The `logs` dictionary that callback methods take as argument will contain\n * keys for quantities relevant to the current batch or epoch.\n *\n * Currently, the `.fit()` method of the `Sequential` model class\n * will include the following quantities in the `logs` that\n * it passes to its callbacks:\n *\n * onEpochEnd: Logs include `acc` and `loss`, and optionally include `valLoss`\n *   (if validation is enabled in `fit`), and `valAcc` (if validation and\n *   accuracy monitoring are enabled).\n * onBatchBegin: Logs include `size`, the number of samples in the current\n *   batch.\n * onBatchEnd: Logs include `loss`, and optionally `acc` (if accuracy monitoring\n *   is enabled).\n */\nexport class BaseCallback {\n    constructor() {\n        // TODO(michaelterry): This type is a best guess.\n        this.validationData = null;\n    }\n    setParams(params) {\n        this.params = params;\n    }\n    async onEpochBegin(epoch, logs) { }\n    async onEpochEnd(epoch, logs) { }\n    async onBatchBegin(batch, logs) { }\n    async onBatchEnd(batch, logs) { }\n    async onTrainBegin(logs) { }\n    async onTrainEnd(logs) { }\n    // LayersModel needs to call Callback.setModel(), but cannot actually depend\n    // on Callback because that creates a cyclic dependency.  Providing this no-op\n    // method on BaseCallback breaks the cycle: this way LayersModel can depend on\n    // BaseCallback but not on Callback.  The argument is typed as `Container`\n    // (the superclass of LayersModel) to avoid recapitulating the cycle. Callback\n    // overrides this method and enforces that the argument is really a\n    // LayersModel.\n    setModel(model) {\n        // Do nothing. Use Callback instead of BaseCallback to track the model.\n    }\n}\n/**\n * Container abstracting a list of callbacks.\n */\nexport class CallbackList {\n    // TODO(cais): When the need arises, uncomment the following lines and\n    // implement the queue for time values.\n    // private deltaTBatch: number;\n    // private deltaTsBatchBegin: Array<number>;\n    // private deltaTsBatchEnd: Array<number>;\n    /**\n     * Constructor of CallbackList.\n     * @param callbacks Array of `Callback` instances.\n     * @param queueLength Queue length for keeping running statistics over\n     *   callback execution time.\n     */\n    constructor(callbacks, queueLength = 10) {\n        // TODO(cais): Make use of queueLength when implementing the queue for time\n        // values.\n        if (callbacks == null) {\n            callbacks = [];\n        }\n        this.callbacks = callbacks;\n        this.queueLength = queueLength;\n    }\n    append(callback) {\n        this.callbacks.push(callback);\n    }\n    setParams(params) {\n        for (const callback of this.callbacks) {\n            callback.setParams(params);\n        }\n    }\n    setModel(model) {\n        for (const callback of this.callbacks) {\n            callback.setModel(model);\n        }\n    }\n    /**\n     * Called at the start of an epoch.\n     * @param epoch Index of epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onEpochBegin(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onEpochBegin(epoch, logs);\n        }\n    }\n    /**\n     * Called at the end of an epoch.\n     * @param epoch Index of epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onEpochEnd(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onEpochEnd(epoch, logs);\n        }\n    }\n    /**\n     * Called  right before processing a batch.\n     * @param batch Index of batch within the current epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onBatchBegin(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onBatchBegin(batch, logs);\n        }\n    }\n    /**\n     * Called at the end of a batch.\n     * @param batch Index of batch within the current epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onBatchEnd(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onBatchEnd(batch, logs);\n        }\n    }\n    /**\n     * Called at the beginning of training.\n     * @param logs Dictionary of logs.\n     */\n    async onTrainBegin(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onTrainBegin(logs);\n        }\n    }\n    /**\n     * Called at the end of training.\n     * @param logs Dictionary of logs.\n     */\n    async onTrainEnd(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onTrainEnd(logs);\n        }\n    }\n}\n/**\n * Callback that accumulates epoch averages of metrics.\n *\n * This callback is automatically applied to every LayersModel.\n */\nexport class BaseLogger extends BaseCallback {\n    constructor() {\n        super();\n    }\n    async onEpochBegin(epoch) {\n        this.seen = 0;\n        this.totals = {};\n    }\n    async onBatchEnd(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        const batchSize = logs['size'] == null ? 0 : logs['size'];\n        this.seen += batchSize;\n        for (const key in logs) {\n            const value = logs[key];\n            if (typeof value === 'number') {\n                if (!this.totals.hasOwnProperty(key)) {\n                    this.totals[key] = 0;\n                }\n                this.totals[key] = this.totals[key] + value * batchSize;\n            }\n            else {\n                let oldTotalsToDispose;\n                if (key in this.totals) {\n                    oldTotalsToDispose = this.totals[key];\n                }\n                else {\n                    this.totals[key] = 0;\n                }\n                const total = tidy(() => add((this.totals[key]), mul(value, batchSize)));\n                this.totals[key] = total;\n                if (oldTotalsToDispose != null) {\n                    oldTotalsToDispose.dispose();\n                }\n            }\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        if (logs != null) {\n            for (const key of this.params['metrics']) {\n                if (this.totals[key] == null) {\n                    continue;\n                }\n                if (typeof this.totals[key] === 'number') {\n                    logs[key] = this.totals[key] / this.seen;\n                }\n                else {\n                    tidy(() => {\n                        const log = mul(div(1, this.seen), this.totals[key]);\n                        logs[key] = log;\n                        this.totals[key].dispose();\n                        keep(logs[key]);\n                    });\n                }\n            }\n        }\n    }\n}\n/**\n * Callback that records events into a `History` object. This callback is\n * automatically applied to every TF.js Layers model. The `History` object\n * gets returned by the `fit` method of models.\n */\nexport class History extends BaseCallback {\n    async onTrainBegin(logs) {\n        this.epoch = [];\n        this.history = {};\n    }\n    async onEpochEnd(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        this.epoch.push(epoch);\n        for (const key in logs) {\n            if (this.history[key] == null) {\n                this.history[key] = [];\n            }\n            this.history[key].push(logs[key]);\n        }\n    }\n    /**\n     * Await the values of all losses and metrics.\n     */\n    async syncData() {\n        const promises = [];\n        const keys = [];\n        const indices = [];\n        for (const key in this.history) {\n            const valueArray = this.history[key];\n            for (let i = 0; i < valueArray.length; ++i) {\n                if (typeof valueArray[i] !== 'number') {\n                    const valueScalar = valueArray[i];\n                    promises.push(valueScalar.data());\n                    keys.push(key);\n                    indices.push(i);\n                }\n            }\n        }\n        const values = await Promise.all(promises);\n        for (let n = 0; n < values.length; ++n) {\n            const tensorToDispose = this.history[keys[n]][indices[n]];\n            tensorToDispose.dispose();\n            this.history[keys[n]][indices[n]] = values[n][0];\n        }\n    }\n}\n/**\n * Custom callback for training.\n */\nexport class CustomCallback extends BaseCallback {\n    constructor(args, yieldEvery) {\n        super();\n        this.currentEpoch = 0;\n        this.yieldEvery = yieldEvery || 'auto';\n        if (this.yieldEvery === 'auto') {\n            this.yieldEvery = DEFAULT_YIELD_EVERY_MS;\n        }\n        if (this.yieldEvery === 'never' && args.onYield != null) {\n            throw new Error('yieldEvery is `never` but you provided an `onYield` callback. ' +\n                'Either change `yieldEvery` or remove the callback');\n        }\n        if (util.isNumber(this.yieldEvery)) {\n            // Decorate `maybeWait` so it will be called at most once every\n            // `yieldEvery` ms.\n            this.maybeWait = generic_utils.debounce(this.maybeWait.bind(this), this.yieldEvery);\n        }\n        this.trainBegin = args.onTrainBegin;\n        this.trainEnd = args.onTrainEnd;\n        this.epochBegin = args.onEpochBegin;\n        this.epochEnd = args.onEpochEnd;\n        this.batchBegin = args.onBatchBegin;\n        this.batchEnd = args.onBatchEnd;\n        this.yield = args.onYield;\n    }\n    async maybeWait(epoch, batch, logs) {\n        const ps = [];\n        if (this.yield != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.yield(epoch, batch, logs));\n        }\n        ps.push(nextFrame());\n        await Promise.all(ps);\n    }\n    async onEpochBegin(epoch, logs) {\n        this.currentEpoch = epoch;\n        if (this.epochBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.epochBegin(epoch, logs);\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        const ps = [];\n        if (this.epochEnd != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.epochEnd(epoch, logs));\n        }\n        if (this.yieldEvery === 'epoch') {\n            ps.push(nextFrame());\n        }\n        await Promise.all(ps);\n    }\n    async onBatchBegin(batch, logs) {\n        if (this.batchBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.batchBegin(batch, logs);\n        }\n    }\n    async onBatchEnd(batch, logs) {\n        const ps = [];\n        if (this.batchEnd != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.batchEnd(batch, logs));\n        }\n        if (this.yieldEvery === 'batch') {\n            ps.push(nextFrame());\n        }\n        else if (util.isNumber(this.yieldEvery)) {\n            ps.push(this.maybeWait(this.currentEpoch, batch, logs));\n        }\n        await Promise.all(ps);\n    }\n    async onTrainBegin(logs) {\n        if (this.trainBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.trainBegin(logs);\n        }\n    }\n    async onTrainEnd(logs) {\n        if (this.trainEnd != null) {\n            await resolveScalarsInLogs(logs);\n            await this.trainEnd(logs);\n        }\n    }\n}\n/**\n * Standardize callbacks or configurations of them to an Array of callbacks.\n */\nexport function standardizeCallbacks(callbacks, yieldEvery) {\n    if (callbacks == null) {\n        callbacks = {};\n    }\n    if (callbacks instanceof BaseCallback) {\n        return [callbacks];\n    }\n    if (Array.isArray(callbacks) && callbacks[0] instanceof BaseCallback) {\n        return callbacks;\n    }\n    // Convert custom callback configs to custom callback objects.\n    const callbackConfigs = generic_utils.toList(callbacks);\n    return callbackConfigs.map(callbackConfig => new CustomCallback(callbackConfig, yieldEvery));\n}\n/**\n * A global registry for callback constructors to be used during\n * LayersModel.fit().\n */\nexport class CallbackConstructorRegistry {\n    /**\n     * Blocks public access to constructor.\n     */\n    constructor() { }\n    /**\n     * Register a tf.LayersModel.fit() callback constructor.\n     *\n     * The registered callback constructor will be used to instantiate\n     * callbacks for every tf.LayersModel.fit() call afterwards.\n     *\n     * @param verbosityLevel Level of verbosity at which the `callbackConstructor`\n     *   is to be reigstered.\n     * @param callbackConstructor A no-arg constructor for `tf.Callback`.\n     * @throws Error, if the same callbackConstructor has been registered before,\n     *   either at the same or a different `verbosityLevel`.\n     */\n    static registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n        util.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, ` +\n            `but got ${verbosityLevel}`);\n        CallbackConstructorRegistry.checkForDuplicate(callbackConstructor);\n        if (CallbackConstructorRegistry.constructors[verbosityLevel] == null) {\n            CallbackConstructorRegistry.constructors[verbosityLevel] = [];\n        }\n        CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor);\n    }\n    static checkForDuplicate(callbackConstructor) {\n        for (const levelName in CallbackConstructorRegistry.constructors) {\n            const constructors = CallbackConstructorRegistry.constructors[+levelName];\n            constructors.forEach(ctor => {\n                if (ctor === callbackConstructor) {\n                    throw new ValueError('Duplicate callback constructor.');\n                }\n            });\n        }\n    }\n    /**\n     * Clear all registered callback constructors.\n     */\n    static clear() {\n        CallbackConstructorRegistry.constructors = {};\n    }\n    /**\n     * Create callbacks using the registered callback constructors.\n     *\n     * Given `verbosityLevel`, all constructors registered at that level or above\n     * will be called and the instantiated callbacks will be used.\n     *\n     * @param verbosityLevel: Level of verbosity.\n     */\n    static createCallbacks(verbosityLevel) {\n        const constructors = [];\n        for (const levelName in CallbackConstructorRegistry.constructors) {\n            const level = +levelName;\n            if (verbosityLevel >= level) {\n                constructors.push(...CallbackConstructorRegistry.constructors[level]);\n            }\n        }\n        return constructors.map(ctor => new ctor());\n    }\n}\nCallbackConstructorRegistry.constructors = {};\nexport function configureCallbacks(callbacks, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) {\n    const history = new History();\n    const actualCallbacks = [\n        new BaseLogger(), ...CallbackConstructorRegistry.createCallbacks(verbose)\n    ];\n    if (callbacks != null) {\n        actualCallbacks.push(...callbacks);\n    }\n    actualCallbacks.push(history);\n    const callbackList = new CallbackList(actualCallbacks);\n    // TODO(cais): Figure out when this LayersModel instance can have a\n    // dynamically\n    //   set property called 'callback_model' as in PyKeras.\n    callbackList.setParams({\n        epochs,\n        initialEpoch,\n        samples: numTrainSamples,\n        steps: stepsPerEpoch,\n        batchSize,\n        verbose,\n        doValidation,\n        metrics: callbackMetrics,\n    });\n    return { callbackList, history };\n}\n//# sourceMappingURL=base_callbacks.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source layers/__init__.py */\nimport { serialization } from '@tensorflow/tfjs-core';\nimport { deserializeKerasObject } from '../utils/generic_utils';\n/**\n * Instantiate a layer from a config dictionary.\n * @param config dict of the form {class_name: str, config: dict}\n * @param customObjects dict mapping class names (or function names)\n *   of custom (non-Keras) objects to class/functions\n * @param fastWeightInit Optional flag to use fast weight initialization\n *   during deserialization. This is applicable to cases in which\n *   the initialization will be immediately overwritten by loaded weight\n *   values. Default: `false`.\n * @returns Layer instance (may be LayersModel, Sequential, Layer...)\n */\nexport function deserialize(config, customObjects = {}, fastWeightInit = false) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'layer', fastWeightInit);\n}\n//# sourceMappingURL=serialization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source: losses.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { tidy, util } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport * as K from './backend/tfjs_backend';\nimport { ValueError } from './errors';\n/**\n * Normalizes a tensor wrt the L2 norm alongside the specified axis.\n * @param x\n * @param axis Axis along which to perform normalization.\n */\nexport function l2Normalize(x, axis) {\n    return tidy(() => {\n        if (x.dtype !== 'float32') {\n            x = x.asType('float32');\n        }\n        const squareSum = tfc.sum(K.square(x), axis, true);\n        const epsilonTensor = tfc.fill(squareSum.shape, epsilon());\n        const norm = tfc.sqrt(tfc.maximum(squareSum, epsilonTensor));\n        return tfc.div(x, norm);\n    });\n}\nexport function meanSquaredError(yTrue, yPred) {\n    return tidy(() => tfc.mean(K.square(tfc.sub(yPred, yTrue)), -1));\n}\nexport function meanAbsoluteError(yTrue, yPred) {\n    return tidy(() => tfc.mean(tfc.abs(tfc.sub(yPred, yTrue)), -1));\n}\nexport function meanAbsolutePercentageError(yTrue, yPred) {\n    return tidy(() => {\n        const diff = tfc.sub(yTrue, yPred);\n        const clippedTrue = tfc.clipByValue(tfc.abs(yTrue), epsilon(), Number.MAX_VALUE);\n        const absResult = tfc.abs(tfc.div(diff, clippedTrue));\n        return tfc.mul(100, tfc.mean(absResult, -1));\n    });\n}\nexport function meanSquaredLogarithmicError(yTrue, yPred) {\n    return tidy(() => {\n        const clippedPred = tfc.clipByValue(yPred, epsilon(), Number.MAX_VALUE);\n        const firstLog = tfc.log(tfc.add(1, clippedPred));\n        const clippedTrue = tfc.clipByValue(yTrue, epsilon(), Number.MAX_VALUE);\n        const secondLog = tfc.log(tfc.add(1, clippedTrue));\n        return tfc.mean(K.square(tfc.sub(firstLog, secondLog)), -1);\n    });\n}\nexport function squaredHinge(yTrue, yPred) {\n    return tidy(() => {\n        const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n        return tfc.mean(K.square(maxResult), -1);\n    });\n}\nexport function hinge(yTrue, yPred) {\n    return tidy(() => {\n        const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n        return tfc.mean(maxResult, -1);\n    });\n}\nexport function categoricalHinge(yTrue, yPred) {\n    return tidy(() => {\n        const pos = tfc.sum(tfc.mul(yTrue, yPred), -1);\n        const neg = tfc.max(tfc.mul(tfc.sub(1, yTrue), yPred), -1);\n        return tfc.maximum(0, tfc.add(1, tfc.sub(neg, pos)));\n    });\n}\n/**\n * Logarithm of the hyperbolic cosine of the prediction error.\n *\n * `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and\n * to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly\n * like the mean squared error, but will not be so strongly affected by the\n * occasional wildly incorrect prediction.\n */\nexport function logcosh(yTrue, yPred) {\n    return tidy(() => {\n        const log2 = Math.log(2);\n        const predictionDiff = tfc.sub(yPred, yTrue);\n        const logcoshResult = tfc.sub(tfc.add(predictionDiff, tfc.softplus(tfc.mul(-2, predictionDiff))), log2);\n        return tfc.mean(logcoshResult, -1);\n    });\n}\nexport function categoricalCrossentropy(target, output, fromLogits = false) {\n    return tidy(() => {\n        if (fromLogits) {\n            output = tfc.softmax(output);\n        }\n        else {\n            // scale preds so that the class probabilities of each sample sum to 1.\n            const outputSum = tfc.sum(output, output.shape.length - 1, true);\n            output = tfc.div(output, outputSum);\n        }\n        output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n        return tfc.neg(tfc.sum(tfc.mul(target.toFloat(), tfc.log(output)), output.shape.length - 1));\n    });\n}\n/**\n * Categorical crossentropy with integer targets.\n *\n * @param target An integer tensor.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n */\nexport function sparseCategoricalCrossentropy(target, output, fromLogits = false) {\n    return tidy(() => {\n        const flatTarget = tfc.floor(K.flatten(target)).toInt();\n        output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n        const outputShape = output.shape;\n        const oneHotTarget = tfc.oneHot(flatTarget, outputShape[outputShape.length - 1])\n            .reshape(outputShape);\n        return categoricalCrossentropy(oneHotTarget, output, fromLogits);\n    });\n}\n/**\n * From TensorFlow's implementation in nn_impl.py:\n *\n * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n *      z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n *    = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n *    = (1 - z) * x + log(1 + exp(-x))\n *    = x - x * z + log(1 + exp(-x))\n * For x < 0, to avoid overflow in exp(-x), we reformulate the above\n *      x - x * z + log(1 + exp(-x))\n *    = log(exp(x)) - x * z + log(1 + exp(-x))\n *    = - x * z + log(1 + exp(x))\n * Hence, to ensure stability and avoid overflow, the implementation uses this\n * equivalent formulation\n *    max(x, 0) - x * z + log(1 + exp(-abs(x)))\n *\n * @param labels The labels.\n * @param logits The logits.\n */\nexport function sigmoidCrossEntropyWithLogits(labels, logits) {\n    if (!util.arraysEqual(labels.shape, logits.shape)) {\n        throw new ValueError(`logits and labels must have the same shape, but got shapes ` +\n            `${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);\n    }\n    return tidy(() => {\n        // The logistic loss formula from above is\n        //   x - x * z + log(1 + exp(-x))\n        // For x < 0, a more numerically stable formula is\n        //   -x * z + log(1 + exp(x))\n        // Note that these two expressions can be combined into the following:\n        //   max(x, 0) - x * z + log(1 + exp(-abs(x)))\n        const reluLogits = logits.relu();\n        const negAbsLogits = logits.abs().neg();\n        return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p());\n    });\n}\nexport function binaryCrossentropy(yTrue, yPred) {\n    return tidy(() => {\n        let y;\n        y = tfc.clipByValue(yPred, epsilon(), 1 - epsilon());\n        y = tfc.log(tfc.div(y, tfc.sub(1, y)));\n        return tfc.mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);\n    });\n}\nexport function kullbackLeiblerDivergence(yTrue, yPred) {\n    return tidy(() => {\n        const clippedTrue = tfc.clipByValue(yTrue, epsilon(), 1);\n        const clippedPred = tfc.clipByValue(yPred, epsilon(), 1);\n        return tfc.sum(tfc.mul(yTrue, tfc.log(tfc.div(clippedTrue, clippedPred))), -1);\n    });\n}\nexport function poisson(yTrue, yPred) {\n    return tidy(() => {\n        const logPred = tfc.log(tfc.add(epsilon(), yPred));\n        return tfc.mean(tfc.sub(yPred, tfc.mul(yTrue, logPred)), -1);\n    });\n}\nexport function cosineProximity(yTrue, yPred) {\n    return tidy(() => {\n        const trueNormalized = l2Normalize(yTrue, -1);\n        const predNormalized = l2Normalize(yPred, -1);\n        const trueXPred = tfc.mul(trueNormalized, predNormalized);\n        return tfc.neg(tfc.sum(trueXPred, -1));\n    });\n}\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const msle = meanSquaredLogarithmicError;\nexport const MSLE = meanSquaredLogarithmicError;\nexport const kld = kullbackLeiblerDivergence;\nexport const KLD = kullbackLeiblerDivergence;\nexport const cosine = cosineProximity;\n// TODO(michaelterry): Add deserialize() function.\nexport const lossesMap = {\n    meanSquaredError,\n    meanAbsoluteError,\n    meanAbsolutePercentageError,\n    meanSquaredLogarithmicError,\n    squaredHinge,\n    hinge,\n    categoricalHinge,\n    logcosh,\n    categoricalCrossentropy,\n    sparseCategoricalCrossentropy,\n    binaryCrossentropy,\n    kullbackLeiblerDivergence,\n    poisson,\n    cosineProximity\n};\n// Porting note: This diverges from the PyKeras implementation and may need to\n// change based on (de)serialization requirements.\nexport function get(identifierOrFn) {\n    if (typeof identifierOrFn === 'string') {\n        if (identifierOrFn in lossesMap) {\n            return lossesMap[identifierOrFn];\n        }\n        let errMsg = `Unknown loss ${identifierOrFn}`;\n        if (identifierOrFn.toLowerCase().includes('softmaxcrossentropy')) {\n            errMsg = `Unknown loss ${identifierOrFn}. ` +\n                'Use \"categoricalCrossentropy\" as the string name for ' +\n                'tf.losses.softmaxCrossEntropy';\n        }\n        throw new ValueError(errMsg);\n    }\n    else {\n        return identifierOrFn;\n    }\n}\n//# sourceMappingURL=losses.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Built-in metrics.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { tidy } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { NotImplementedError, ValueError } from './errors';\nimport { categoricalCrossentropy as categoricalCrossentropyLoss, cosineProximity, meanAbsoluteError, meanAbsolutePercentageError, meanSquaredError, sparseCategoricalCrossentropy as sparseCategoricalCrossentropyLoss } from './losses';\nimport { binaryCrossentropy as lossBinaryCrossentropy } from './losses';\nimport { lossesMap } from './losses';\nimport * as util from './utils/generic_utils';\nexport function binaryAccuracy(yTrue, yPred) {\n    return tidy(() => {\n        const threshold = tfc.mul(.5, tfc.onesLike(yPred));\n        const yPredThresholded = K.cast(tfc.greater(yPred, threshold), yTrue.dtype);\n        return tfc.mean(tfc.equal(yTrue, yPredThresholded), -1);\n    });\n}\nexport function categoricalAccuracy(yTrue, yPred) {\n    return tidy(() => K.cast(tfc.equal(tfc.argMax(yTrue, -1), tfc.argMax(yPred, -1)), 'float32'));\n}\nfunction truePositives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(1), yPred.equal(1)).sum().cast('float32');\n    });\n}\nfunction falseNegatives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(1), yPred.equal(0)).sum().cast('float32');\n    });\n}\nfunction falsePositives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(0), yPred.equal(1)).sum().cast('float32');\n    });\n}\nexport function precision(yTrue, yPred) {\n    return tidy(() => {\n        const tp = truePositives(yTrue, yPred);\n        const fp = falsePositives(yTrue, yPred);\n        const denominator = tp.add(fp);\n        return tfc.where(tfc.greater(denominator, 0), tp.div(denominator), 0)\n            .cast('float32');\n    });\n}\nexport function recall(yTrue, yPred) {\n    return tidy(() => {\n        const tp = truePositives(yTrue, yPred);\n        const fn = falseNegatives(yTrue, yPred);\n        const denominator = tp.add(fn);\n        return tfc.where(tfc.greater(denominator, 0), tp.div(denominator), 0)\n            .cast('float32');\n    });\n}\nexport function binaryCrossentropy(yTrue, yPred) {\n    return lossBinaryCrossentropy(yTrue, yPred);\n}\nexport function sparseCategoricalAccuracy(yTrue, yPred) {\n    if (yTrue.rank === yPred.rank) {\n        yTrue = yTrue.squeeze([yTrue.rank - 1]);\n    }\n    yPred = yPred.argMax(-1);\n    if (yPred.dtype !== yTrue.dtype) {\n        yPred = yPred.asType(yTrue.dtype);\n    }\n    return tfc.equal(yTrue, yPred).asType('float32');\n}\nexport function topKCategoricalAccuracy(yTrue, yPred) {\n    throw new NotImplementedError();\n}\nexport function sparseTopKCategoricalAccuracy(yTrue, yPred) {\n    throw new NotImplementedError();\n}\n// Aliases.\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const categoricalCrossentropy = categoricalCrossentropyLoss;\nexport const cosine = cosineProximity;\nexport const sparseCategoricalCrossentropy = sparseCategoricalCrossentropyLoss;\n// TODO(cais, nielsene): Add serialize().\nexport const metricsMap = {\n    binaryAccuracy,\n    categoricalAccuracy,\n    precision,\n    categoricalCrossentropy,\n    sparseCategoricalCrossentropy,\n    mse,\n    MSE,\n    mae,\n    MAE,\n    mape,\n    MAPE,\n    cosine\n};\nexport function get(identifier) {\n    if (typeof identifier === 'string' && identifier in metricsMap) {\n        return metricsMap[identifier];\n    }\n    else if (typeof identifier !== 'string' && identifier != null) {\n        return identifier;\n    }\n    else {\n        throw new ValueError(`Unknown metric ${identifier}`);\n    }\n}\n/**\n * Get the shortcut function name.\n *\n * If the fn name is a string,\n *   directly return the string name.\n * If the function is included in metricsMap or lossesMap,\n *   return key of the map.\n *   - If the function relative to multiple keys,\n *     return the first found key as the function name.\n *   - If the function exists in both lossesMap and metricsMap,\n *     search lossesMap first.\n * If the function is not included in metricsMap or lossesMap,\n *   return the function name.\n *\n * @param fn loss function, metric function, or short cut name.\n * @returns Loss or Metric name in string.\n */\nexport function getLossOrMetricName(fn) {\n    util.assert(fn !== null, `Unknown LossOrMetricFn ${fn}`);\n    if (typeof fn === 'string') {\n        return fn;\n    }\n    else {\n        let fnName;\n        for (const key of Object.keys(lossesMap)) {\n            if (lossesMap[key] === fn) {\n                fnName = key;\n                break;\n            }\n        }\n        if (fnName !== undefined) {\n            return fnName;\n        }\n        for (const key of Object.keys(metricsMap)) {\n            if (metricsMap[key] === fn) {\n                fnName = key;\n                break;\n            }\n        }\n        if (fnName !== undefined) {\n            return fnName;\n        }\n        return fn.name;\n    }\n}\n//# sourceMappingURL=metrics.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Optimizers.\n */\nimport { train } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport { ValueError } from './errors';\n// Add (de)serialize()\n// Porting note: This diverges from the PyKeras implementation and may need to\n// change based on (de)serialization requirements.\nexport function getOptimizer(identifier) {\n    const optimizerMap = {\n        'Adagrad': () => train.adagrad(0.01),\n        'Adadelta': () => train.adadelta(1, 0.95, epsilon()),\n        'Adam': () => train.adam(0.001, 0.9, 0.999, epsilon()),\n        'Adamax': () => train.adamax(0.002, 0.9, 0.999, epsilon(), 0),\n        'RMSProp': () => train.rmsprop(0.001, 0.9, 0, epsilon()),\n        'SGD': () => train.sgd(0.01)\n    };\n    optimizerMap['adagrad'] = optimizerMap['Adagrad'];\n    optimizerMap['adadelta'] = optimizerMap['Adadelta'];\n    optimizerMap['adam'] = optimizerMap['Adam'];\n    optimizerMap['adamax'] = optimizerMap['Adamax'];\n    optimizerMap['rmsprop'] = optimizerMap['RMSProp'];\n    optimizerMap['sgd'] = optimizerMap['SGD'];\n    if (identifier in optimizerMap) {\n        return optimizerMap[identifier]();\n    }\n    throw new ValueError(`Unknown Optimizer ${identifier}`);\n}\n//# sourceMappingURL=optimizers.js.map", "/**\n * @license\n * Copyright 2019 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/** Utility functions related to user-defined metadata. */\n// Maximum recommended serialized size for user-defined metadata.\n// Beyond this limit, a warning message will be printed during model loading and\n// saving.\nexport const MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024;\n/**\n * Check validity of user-defined metadata.\n *\n * @param userDefinedMetadata\n * @param modelName Name of the model that the user-defined metadata belongs to.\n *   Used during construction of error messages.\n * @param checkSize Whether to check the size of the metadata is under\n *   recommended limit. Default: `false`. If `true`, will try stringify the\n *   JSON object and print a console warning if the serialzied size is above the\n *   limit.\n * @throws Error if `userDefinedMetadata` is not a plain JSON object.\n */\nexport function checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) {\n    if (userDefinedMetadata == null ||\n        typeof userDefinedMetadata !== 'object' ||\n        Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype ||\n        !plainObjectCheck(userDefinedMetadata)) {\n        throw new Error('User-defined metadata is expected to be a JSON object, but is not.');\n    }\n    if (checkSize) {\n        const out = JSON.stringify(userDefinedMetadata);\n        if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) {\n            console.warn(`User-defined metadata of model \"${modelName}\" is too large in ` +\n                `size (length=${out.length} when serialized). It is not ` +\n                `recommended to store such large objects in user-defined metadata. ` +\n                `Please make sure its serialized length is <= ` +\n                `${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`);\n        }\n    }\n}\n/**\n * Check if an input is plain JSON object or any valid subfield of it.\n *\n * @param x The input to be checked.\n * @param assertObject Whether to assert `x` is a JSON object, i.e., reject\n *   cases of arrays and primitives.\n * @return Returns `true` if and only if `x` is a plain JSON object,\n *   a JSON-valid primitive including string, number, boolean and null,\n *   or an array of the said types.\n */\n// tslint:disable-next-line:no-any\nexport function plainObjectCheck(x) {\n    if (x === null) {\n        // Note: typeof `null` is 'object', and `null` is valid in JSON.\n        return true;\n    }\n    else if (typeof x === 'object') {\n        if (Object.getPrototypeOf(x) === Object.prototype) {\n            // `x` is a JavaScript object and its prototype is Object.\n            const keys = Object.keys(x);\n            for (const key of keys) {\n                if (typeof key !== 'string') {\n                    // JSON keys must be strings.\n                    return false;\n                }\n                if (!plainObjectCheck(x[key])) { // Recursive call.\n                    return false;\n                }\n            }\n            return true;\n        }\n        else {\n            // `x` is a JavaScript object but its prototype is not Object.\n            if (Array.isArray(x)) {\n                // `x` is a JavaScript array.\n                for (const item of x) {\n                    if (!plainObjectCheck(item)) { // Recursive call.\n                        return false;\n                    }\n                }\n                return true;\n            }\n            else {\n                // `x` is a JavaScript object and its prototype is not Object,\n                // and it's not an Array. I.e., it's a complex object such as\n                // `Error` and `Date`.\n                return false;\n            }\n        }\n    }\n    else {\n        // `x` is not a JavaScript object or `null`.\n        const xType = typeof x;\n        return xType === 'string' || xType === 'number' || xType === 'boolean';\n    }\n}\n//# sourceMappingURL=user_defined_metadata.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { countParamsInWeights } from './variable_utils';\n/**\n * Print the summary of a LayersModel object.\n *\n * @param model tf.LayersModel instance.\n * @param lineLength Total length of printed lines. Set this to adapt to the\n *   display to different terminal or console sizes.\n * @param positions Relative or absolute positions of log elements in each\n *   line. Each number corresponds to right-most (i.e., ending) position of a\n *   column.\n *   If not provided, defaults to `[0.45, 0.85, 1]` for sequential-like\n *   models and `[0.33, 0.55, 0.67, 1]` for non-sequential like models.\n * @param printFn Print function to use.\n *   It will be called on each line of the summary. You can provide a custom\n *   function in order to capture the string summary. Defaults to `console.log`.\n */\nexport function printSummary(model, lineLength, positions, \n// tslint:disable-next-line:no-any\nprintFn = console.log) {\n    const sequentialLike = isModelSequentialLike(model);\n    // Header names for different log elements.\n    const toDisplay = ['Layer (type)', 'Output shape', 'Param #'];\n    if (sequentialLike) {\n        lineLength = lineLength || 65;\n        positions = positions || [0.45, 0.85, 1];\n    }\n    else {\n        lineLength = lineLength || 98;\n        positions = positions || [0.33, 0.55, 0.67, 1];\n        // Header names for different log elements.\n    }\n    if (positions[positions.length - 1] <= 1) {\n        // `positions` is relative. Convert it to absolute positioning.\n        positions = positions.map(p => Math.floor(lineLength * p));\n    }\n    let relevantNodes;\n    if (!sequentialLike) {\n        toDisplay.push('Receives inputs');\n        relevantNodes = [];\n        for (const depth in model.nodesByDepth) {\n            relevantNodes.push(...model.nodesByDepth[depth]);\n        }\n    }\n    printFn('_'.repeat(lineLength));\n    printRow(toDisplay, positions, printFn);\n    printFn('='.repeat(lineLength));\n    const layers = model.layers;\n    for (let i = 0; i < layers.length; ++i) {\n        if (sequentialLike) {\n            printLayerSummary(layers[i], positions, printFn);\n        }\n        else {\n            printLayerSummaryWithConnections(layers[i], positions, relevantNodes, printFn);\n        }\n        printFn((i === layers.length - 1 ? '=' : '_').repeat(lineLength));\n    }\n    // tslint:disable-next-line:no-any\n    model.checkTrainableWeightsConsistency();\n    const trainableCount = countTrainableParams(model);\n    const nonTrainableCount = countParamsInWeights(model.nonTrainableWeights);\n    printFn(`Total params: ${trainableCount + nonTrainableCount}`);\n    printFn(`Trainable params: ${trainableCount}`);\n    printFn(`Non-trainable params: ${nonTrainableCount}`);\n    printFn('_'.repeat(lineLength));\n}\nfunction countTrainableParams(model) {\n    let trainableCount;\n    // tslint:disable:no-any\n    if (model.collectedTrainableWeights != null) {\n        trainableCount =\n            countParamsInWeights(model.collectedTrainableWeights);\n    }\n    else {\n        trainableCount = countParamsInWeights(model.trainableWeights);\n    }\n    // tslint:enable:no-any\n    return trainableCount;\n}\nfunction isModelSequentialLike(model) {\n    let sequentialLike = true;\n    const nodesByDepth = [];\n    const nodes = [];\n    for (const depth in model.nodesByDepth) {\n        nodesByDepth.push(model.nodesByDepth[depth]);\n    }\n    for (const depthNodes of nodesByDepth) {\n        if (depthNodes.length > 1 ||\n            depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) {\n            sequentialLike = false;\n            break;\n        }\n        nodes.push(...depthNodes);\n    }\n    if (sequentialLike) {\n        // Search for shared layers.\n        for (const layer of model.layers) {\n            let flag = false;\n            for (const node of layer.inboundNodes) {\n                if (nodes.indexOf(node) !== -1) {\n                    if (flag) {\n                        sequentialLike = false;\n                        break;\n                    }\n                    else {\n                        flag = true;\n                    }\n                }\n            }\n            if (!sequentialLike) {\n                break;\n            }\n        }\n    }\n    return sequentialLike;\n}\nfunction printRow(fields, positions, \n// tslint:disable-next-line:no-any\nprintFn = console.log) {\n    let line = '';\n    for (let i = 0; i < fields.length; ++i) {\n        if (i > 0) {\n            line = line.slice(0, line.length - 1) + ' ';\n        }\n        line += fields[i];\n        line = line.slice(0, positions[i]);\n        line += ' '.repeat(positions[i] - line.length);\n    }\n    printFn(line);\n}\n/**\n * Prints a summary for a single Layer, without connectivity information.\n *\n * @param layer: Layer instance to print.\n */\nfunction printLayerSummary(layer, positions, \n// tslint:disable-next-line:no-any\nprintFn) {\n    let outputShape;\n    try {\n        outputShape = JSON.stringify(layer.outputShape);\n    }\n    catch (err) {\n        outputShape = 'multiple';\n    }\n    const name = layer.name;\n    const className = layer.getClassName();\n    const fields = [`${name} (${className})`, outputShape, layer.countParams().toString()];\n    printRow(fields, positions, printFn);\n}\n/**\n * Prints a summary for a single Layer, with connectivity information.\n */\nfunction printLayerSummaryWithConnections(layer, positions, relevantNodes, \n// tslint:disable-next-line:no-any\nprintFn) {\n    let outputShape;\n    try {\n        outputShape = JSON.stringify(layer.outputShape);\n    }\n    catch (err) {\n        outputShape = 'multiple';\n    }\n    const connections = [];\n    for (const node of layer.inboundNodes) {\n        if (relevantNodes != null && relevantNodes.length > 0 &&\n            relevantNodes.indexOf(node) === -1) {\n            continue;\n        }\n        for (let i = 0; i < node.inboundLayers.length; ++i) {\n            const inboundLayer = node.inboundLayers[i].name;\n            const inboundLayerIndex = node.nodeIndices[i];\n            const inboundTensorIndex = node.tensorIndices[i];\n            connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);\n        }\n    }\n    const name = layer.name;\n    const className = layer.getClassName();\n    const firstConnection = connections.length === 0 ? '' : connections[0];\n    const fields = [\n        `${name} (${className})`, outputShape, layer.countParams().toString(),\n        firstConnection\n    ];\n    printRow(fields, positions, printFn);\n    for (let i = 1; i < connections.length; ++i) {\n        printRow(['', '', '', connections[i]], positions, printFn);\n    }\n}\n//# sourceMappingURL=layer_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as generic_utils from '../utils/generic_utils';\n// tslint:enable\n/**\n * Test whether a value in an array is the name of a LayersModel or Layer.\n * @param key The key name that the value is found under. Note that the key\n *   may not be at the level immediately above the value, if the value is in a\n *   nested array.\n * @param index Index of the value in the Array that it is found in.\n * @param value The value object.\n * @returns A boolean indicating whether value is a name.\n */\nfunction isArrayItemInputOrOutputName(key, index, value) {\n    return (key === 'inboundNodes' || key === 'outputLayers' ||\n        key === 'inputLayers') &&\n        index === 0 && typeof value === 'string';\n}\n/**\n * Convert a Pythonic config object to TypeScript config object.\n * @param pythonicConfig The config object to convert.\n * @param key Optional key name of the object being converted.\n * @returns Result of the conversion.\n */\nexport function convertPythonicToTs(pythonicConfig, key) {\n    if (pythonicConfig === null) {\n        return null;\n    }\n    else if (typeof pythonicConfig === 'string') {\n        return generic_utils.toCamelCase(pythonicConfig);\n    }\n    else if ((typeof pythonicConfig === 'number') ||\n        (typeof pythonicConfig === 'boolean')) {\n        return pythonicConfig;\n    }\n    else if (pythonicConfig instanceof Array) {\n        const tsArray = [];\n        const arrayLength = pythonicConfig.length;\n        for (let i = 0; i < arrayLength; ++i) {\n            const item = pythonicConfig[i];\n            if (isArrayItemInputOrOutputName(key, i, item)) {\n                tsArray.push(item);\n            }\n            else {\n                tsArray.push(convertPythonicToTs(item, key));\n            }\n        }\n        return tsArray;\n    }\n    else {\n        const tsDict = {};\n        for (const pythonicKey of Object.keys(pythonicConfig)) {\n            const pythonicValue = pythonicConfig[pythonicKey];\n            if (pythonicKey === 'name' && typeof pythonicValue === 'string') {\n                // Special case the 'name' key with a string value. Name values, such as\n                // the names of LayersModel and Layer instances, should not undergo the\n                // camel-case conversion.\n                tsDict[pythonicKey] = pythonicValue;\n            }\n            else {\n                const tsKey = generic_utils.toCamelCase(pythonicKey);\n                tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey);\n            }\n        }\n        return tsDict;\n    }\n}\n/**\n * Convert a TypeScript config object to Python config object.\n * @param tsConfig The config object to convert.\n * @param key Optional key name of the object being converted.\n * @returns Result of the conversion.\n */\nexport function convertTsToPythonic(tsConfig, key) {\n    if (tsConfig === null || tsConfig === undefined) {\n        return null;\n    }\n    else if (typeof tsConfig === 'string') {\n        return generic_utils.toSnakeCase(tsConfig);\n    }\n    else if ((typeof tsConfig === 'number') || (typeof tsConfig === 'boolean')) {\n        return tsConfig;\n    }\n    else if (tsConfig instanceof Array) {\n        const pyArray = [];\n        const arrayLength = tsConfig.length;\n        for (let i = 0; i < arrayLength; ++i) {\n            const item = tsConfig[i];\n            if (isArrayItemInputOrOutputName(key, i, item)) {\n                pyArray.push(item);\n            }\n            else {\n                pyArray.push(convertTsToPythonic(item, key));\n            }\n        }\n        return pyArray;\n    }\n    else {\n        const pyDict = {};\n        for (const tsKey of Object.keys(tsConfig)) {\n            const tsValue = tsConfig[tsKey];\n            const pyKey = generic_utils.toSnakeCase(tsKey);\n            if ((tsKey === 'name' || tsKey === 'className') &&\n                typeof tsValue === 'string') {\n                // Special case the 'name' key with a string value. Name values, such as\n                // the names of LayersModel and Layer instances, should not undergo the\n                // snake-case conversion.\n                pyDict[pyKey] = tsValue;\n            }\n            else {\n                pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey);\n            }\n        }\n        return pyDict;\n    }\n}\n//# sourceMappingURL=serialization_utils.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Executor: Evaluates SymbolicTensor based on feeds.\n */\nimport { cast, dispose, memory, util } from '@tensorflow/tfjs-core';\nimport { ValueError } from '../errors';\nimport { toList } from '../utils/generic_utils';\nimport { InputLayer } from './input_layer';\nimport { SymbolicTensor } from './topology';\n/**\n * Helper function to check the dtype and shape compatibility of a feed value.\n */\nfunction assertFeedCompatibility(key, val) {\n    // Check dtype compatibility.\n    if (key.dtype == null || key.dtype === val.dtype) {\n        //  a.  If types match, return val tensor as is.\n        return val;\n    }\n    try {\n        //  b. Attempt to convert to expected type.\n        return cast(val, key.dtype);\n    }\n    catch (err) {\n        //  c. If conversion fails, return helpful error.\n        throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype ` +\n            `of the key '${key.name}' (${key.dtype}).`);\n    }\n}\n/**\n * FeedDict: A mapping from unique SymbolicTensors to feed values for them.\n * A feed value is a concrete value represented as an `Tensor`.\n */\nexport class FeedDict {\n    /**\n     * Constructor, optionally does copy-construction.\n     * @param feeds An Array of `Feed`s, or another `FeedDict`, in which case\n     *   copy-construction will be performed.\n     */\n    constructor(feeds) {\n        this.id2Value = {};\n        this.id2Mask = {};\n        this.name2Id = {};\n        if (feeds instanceof FeedDict) {\n            for (const id in feeds.id2Value) {\n                this.id2Value[id] = feeds.id2Value[id];\n                if (id in feeds.id2Mask) {\n                    this.id2Mask[id] = feeds.id2Mask[id];\n                }\n            }\n        }\n        else {\n            if (feeds == null) {\n                return;\n            }\n            for (const feed of feeds) {\n                this.add(feed.key, feed.value);\n            }\n        }\n    }\n    /**\n     * Add a key-value pair to the FeedDict.\n     *\n     * @param key The key of the feed.\n     * @param value The value of the tensor feed.\n     * @param mask The value of the mask feed (optional).\n     * @returns This `FeedDict`.\n     * @throws ValueError: If the key `SymbolicTensor` already exists in the\n     *   `FeedDict`.\n     */\n    add(key, value, mask) {\n        if (this.id2Value[key.id] == null) {\n            this.id2Value[key.id] = assertFeedCompatibility(key, value);\n            this.name2Id[key.name] = key.id;\n            if (mask != null) {\n                this.id2Mask[key.id] = mask;\n            }\n        }\n        else {\n            throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);\n        }\n        return this;\n    }\n    /**\n     * Add a Feed to the FeedDict.\n     * @param feed The new `Feed` to add.\n     * @returns This `FeedDict`.\n     */\n    addFeed(feed) {\n        this.add(feed.key, feed.value);\n    }\n    /**\n     * Probe whether a key already exists in the FeedDict.\n     * @param key\n     */\n    hasKey(key) {\n        return this.id2Value[key.id] != null;\n    }\n    /**\n     * Get all the SymbolicTensor available in this FeedDict.\n     */\n    names() {\n        return Object.keys(this.name2Id);\n    }\n    /**\n     * Get the feed value for given key.\n     * @param key The SymbolicTensor, or its name (as a string), of which the\n     *     value is sought.\n     * @returns If `key` exists, the corresponding feed value.\n     * @throws ValueError: If `key` does not exist in this `FeedDict`.\n     */\n    getValue(key) {\n        if (key instanceof SymbolicTensor) {\n            if (this.id2Value[key.id] == null) {\n                throw new ValueError(`Nonexistent key: ${key.name}`);\n            }\n            else {\n                return this.id2Value[key.id];\n            }\n        }\n        else {\n            const id = this.name2Id[key];\n            if (id == null) {\n                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n            }\n            return this.id2Value[id];\n        }\n    }\n    /**\n     * Get the feed mask for given key.\n     * @param key The SymbolicTensor, or its name (as a string), of which the\n     *     value is sought.\n     * @returns If `key` exists, the corresponding feed mask.\n     * @throws ValueError: If `key` does not exist in this `FeedDict`.\n     */\n    getMask(key) {\n        if (key instanceof SymbolicTensor) {\n            if (this.id2Value[key.id] == null) {\n                throw new ValueError(`Nonexistent key: ${key.name}`);\n            }\n            else {\n                return this.id2Mask[key.id];\n            }\n        }\n        else {\n            const id = this.name2Id[key];\n            if (id == null) {\n                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n            }\n            return this.id2Mask[id];\n        }\n    }\n    /** Dispose all mask Tensors held by this object. */\n    disposeMasks() {\n        if (this.id2Mask != null) {\n            dispose(this.id2Mask);\n        }\n    }\n}\n// Cache for topologically sorted SymbolicTensors for given execution\n// targets (i.e., fetches).\nconst cachedSorted = {};\n// Cache for recipient count maps for given execution targets (i.e., fetches).\nconst cachedRecipientCounts = {};\n/**\n * Execute a SymbolicTensor by using concrete feed values.\n *\n * A `SymbolicTensor` object is a node in a computation graph of TF.js\n * Layers. The object is backed by a source layer and input\n * `SymbolicTensor`s to the source layer. This method evaluates\n * the `call()` method of the source layer, using concrete values of the\n * inputs obtained from either\n * * `feedDict`, if the input key exists in `feedDict`, or else,\n * * a recursive call to `execute()` itself.\n *\n * @param x: The `SymbolicTensor` to execute.\n * @param feedDict: The feed values, as base condition of the recursion.\n *   execution.\n * @param kwargs: Optional keyword arguments.\n * @param probe: A probe object (of interface `ExecutionProbe`) used for\n *   testing memory footprint of `execute` calls.\n * @returns Result of the execution.\n * @throws ValueError: If any `SymbolicTensor`s from `InputLayer`s\n *   encountered during the execution lacks a feed value in `feedDict`.\n */\nexport function execute(fetches, feedDict, kwargs, probe) {\n    const training = kwargs == null ? false : kwargs['training'];\n    const arrayFetches = Array.isArray(fetches);\n    const fetchArray = arrayFetches ? fetches : [fetches];\n    const outputNames = fetchArray.map(t => t.name);\n    const finalOutputs = [];\n    const feedNames = feedDict.names();\n    for (const outputName of outputNames) {\n        if (feedNames.indexOf(outputName) !== -1) {\n            finalOutputs.push(feedDict.getValue(outputName));\n        }\n        else {\n            finalOutputs.push(null);\n        }\n    }\n    if (probe != null) {\n        // For optional probing of memory footprint during execution.\n        probe.maxNumTensors = -Infinity;\n        probe.minNumTensors = Infinity;\n    }\n    // Check cache.\n    const fetchAndFeedKey = outputNames.join(',') + '|' + feedDict.names().join(',');\n    let sorted;\n    let recipientCounts;\n    if (cachedSorted[fetchAndFeedKey] == null) {\n        // Cache doesn't contain the desired combination of fetches. Compute\n        // topological sort for the combination for the first time.\n        const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);\n        sorted = out.sorted;\n        recipientCounts = out.recipientCounts;\n        // Store results in cache for future use.\n        cachedSorted[fetchAndFeedKey] = sorted;\n        cachedRecipientCounts[fetchAndFeedKey] = recipientCounts;\n    }\n    sorted = cachedSorted[fetchAndFeedKey];\n    recipientCounts = {};\n    if (!training) {\n        Object.assign(recipientCounts, cachedRecipientCounts[fetchAndFeedKey]);\n    }\n    const internalFeedDict = new FeedDict(feedDict);\n    // Start iterative execution on the topologically-sorted SymbolicTensors.\n    for (let i = 0; i < sorted.length; ++i) {\n        if (probe != null) {\n            // For optional probing of memory usage during execution.\n            const numTensors = memory().numTensors;\n            if (numTensors > probe.maxNumTensors) {\n                probe.maxNumTensors = numTensors;\n            }\n            if (numTensors < probe.minNumTensors) {\n                probe.minNumTensors = numTensors;\n            }\n        }\n        const symbolic = sorted[i];\n        const srcLayer = symbolic.sourceLayer;\n        if (srcLayer instanceof InputLayer) {\n            continue;\n        }\n        const inputValues = [];\n        const inputMasks = [];\n        const tensorsToDispose = [];\n        let maskExists = false;\n        for (const input of symbolic.inputs) {\n            const value = internalFeedDict.getValue(input);\n            const mask = internalFeedDict.getMask(input);\n            inputValues.push(value);\n            inputMasks.push(mask);\n            if (mask != null) {\n                maskExists = true;\n            }\n            if (!training) {\n                recipientCounts[input.name]--;\n                if (recipientCounts[input.name] === 0 && !feedDict.hasKey(input) &&\n                    outputNames.indexOf(input.name) === -1 && !value.isDisposed &&\n                    input.sourceLayer.stateful !== true) {\n                    tensorsToDispose.push(value);\n                }\n            }\n        }\n        if (maskExists) {\n            kwargs = kwargs || {};\n            kwargs['mask'] = inputMasks[0];\n        }\n        const outputTensors = toList(srcLayer.apply(inputValues, kwargs));\n        let outputMask = null;\n        if (srcLayer.supportsMasking) {\n            outputMask = srcLayer.computeMask(inputValues, inputMasks);\n        }\n        const layerOutputs = getNodeOutputs(symbolic);\n        const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];\n        for (let i = 0; i < outputSymbolicTensors.length; ++i) {\n            if (!internalFeedDict.hasKey(outputSymbolicTensors[i])) {\n                internalFeedDict.add(outputSymbolicTensors[i], outputTensors[i], Array.isArray(outputMask) ? outputMask[0] : outputMask);\n            }\n            const index = outputNames.indexOf(outputSymbolicTensors[i].name);\n            if (index !== -1) {\n                finalOutputs[index] = outputTensors[i];\n            }\n        }\n        if (!training) {\n            // Clean up Tensors that are no longer needed.\n            dispose(tensorsToDispose);\n        }\n    }\n    // NOTE(cais): Unlike intermediate tensors, we don't discard mask\n    // tensors as we go, because these tensors are sometimes passed over a\n    // series of mutliple layers, i.e., not obeying the immediate input\n    // relations in the graph. If this becomes a memory-usage concern,\n    // we can improve this in the future.\n    internalFeedDict.disposeMasks();\n    return arrayFetches ? finalOutputs : finalOutputs[0];\n}\n/**\n * Sort the `SymbolicTensor`s topologically, for an array of fetches.\n *\n * This function calls getTopologicalSortAndRecipientCountsForOneFetch and\n * merges their results.\n *\n * @param fetch The array of fetches requested. Must be a non-empty array.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientCounts: Recipient counts for all SymbolicTensors in `sorted`.\n */\nfunction getTopologicalSortAndRecipientCounts(fetches, feedDict) {\n    util.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`);\n    let finalSorted = [];\n    let finalRecipientMap = {};\n    if (fetches.length === 1) {\n        // Special-casing 1 fetch for efficiency.\n        const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);\n        finalSorted = out.sorted;\n        finalRecipientMap = out.recipientMap;\n    }\n    else {\n        const visited = new Set();\n        for (const fetch of fetches) {\n            const { sorted, recipientMap } = getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict);\n            // Merge sorted SymbolicTensor Arrays.\n            for (const symbolicTensor of sorted) {\n                if (!visited.has(symbolicTensor.name)) {\n                    finalSorted.push(symbolicTensor);\n                    visited.add(symbolicTensor.name);\n                }\n            }\n            // Merge recipient maps.\n            for (const name in recipientMap) {\n                if (finalRecipientMap[name] == null) {\n                    finalRecipientMap[name] = new Set();\n                }\n                recipientMap[name].forEach(recipient => finalRecipientMap[name].add(recipient));\n            }\n        }\n    }\n    return {\n        sorted: finalSorted,\n        recipientCounts: recipientMap2Counts(finalRecipientMap)\n    };\n}\nfunction recipientMap2Counts(recipientMap) {\n    const recipientCounts = {};\n    for (const name in recipientMap) {\n        recipientCounts[name] = recipientMap[name].size;\n    }\n    return recipientCounts;\n}\n/**\n * Sort the `SymbolicTensor`s topologically, for a single fetch.\n *\n * This helper function processes the upstream SymbolicTensors of a single\n * fetch.\n *\n * @param fetch The single fetch requested.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientMap: Recipient names for all SymbolicTensors in `sorted`.\n */\nexport function getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict) {\n    const visited = new Set();\n    const sorted = [];\n    const recipientMap = {};\n    // Put keys of the feedDict into visited first, so they don't have to be\n    // walked. This is needed in case where there are feeds for intermediate\n    // SymbolicTensors of the graph.\n    for (const key of feedDict.names()) {\n        visited.add(key);\n    }\n    const stack = [];\n    const marks = [];\n    // Initial population of stack and marks.\n    stack.push(fetch);\n    while (stack.length > 0) {\n        const top = stack[stack.length - 1];\n        if (visited.has(top.name)) {\n            stack.pop();\n            continue;\n        }\n        const topIsMarked = marks[marks.length - 1] === stack.length - 1;\n        if (top.inputs.length === 0 || topIsMarked) {\n            // Input SymbolicTensor or all children have been visited.\n            stack.pop();\n            sorted.push(top);\n            visited.add(top.name);\n            if (topIsMarked) {\n                marks.pop();\n            }\n        }\n        else {\n            // A non-input SymbolicTensor whose upstream SymbolicTensors haven't\n            // been visited yet. Push them onto the stack.\n            marks.push(stack.length - 1);\n            for (const input of top.inputs) {\n                // Increment the recipient count. Note that this needs to happen\n                // regardless of whether the SymbolicTensor has been visited before.\n                if (recipientMap[input.name] == null) {\n                    recipientMap[input.name] = new Set();\n                }\n                recipientMap[input.name].add(top.name);\n                if (visited.has(input.name)) {\n                    continue; // Avoid repeated visits to the same SymbolicTensor.\n                }\n                stack.push(input);\n            }\n        }\n    }\n    return { sorted, recipientMap };\n}\n/**\n * Get the symbolic output tensors of the node to which a given fetch belongs.\n * @param fetch The fetched symbolic tensor.\n * @returns The Array of symbolic tensors output by the node to which `fetch`\n *   belongs.\n */\nfunction getNodeOutputs(fetch) {\n    let layerOutputs;\n    if (fetch.sourceLayer.inboundNodes.length === 1) {\n        layerOutputs = fetch.sourceLayer.output;\n    }\n    else {\n        let nodeIndex = null;\n        for (let i = 0; i < fetch.sourceLayer.inboundNodes.length; ++i) {\n            for (const outputTensor of fetch.sourceLayer.inboundNodes[i]\n                .outputTensors) {\n                if (outputTensor.id === fetch.id) {\n                    nodeIndex = i;\n                    break;\n                }\n            }\n        }\n        layerOutputs = fetch.sourceLayer.getOutputAt(nodeIndex);\n    }\n    return layerOutputs;\n}\n//# sourceMappingURL=executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/engine/topology.py */\nimport { tidy } from '@tensorflow/tfjs-core';\nimport { getUid } from '../backend/state';\nimport { NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { deserialize as deserializeLayer } from '../layers/serialization';\nimport * as generic_utils from '../utils/generic_utils';\nimport { convertTsToPythonic } from '../utils/serialization_utils';\nimport * as types_utils from '../utils/types_utils';\nimport { batchSetValue } from '../variables';\nimport { version as layersVersion } from '../version';\nimport { execute, FeedDict } from './executor';\nimport { InputLayer } from './input_layer';\nimport { Layer, Node } from './topology';\n/**\n * A Container is a directed acyclic graph of layers.\n *\n * It is the topological form of a \"model\". A LayersModel\n * is simply a Container with added training routines.\n *\n */\nexport class Container extends Layer {\n    constructor(args) {\n        // No args passed to super's constructor.\n        super({});\n        this.containerNodes = new Set();\n        this.name = args.name;\n        if (this.name == null) {\n            const prefix = this.getClassName().toLowerCase();\n            this.name = getUid(prefix);\n        }\n        this.supportsMasking = false;\n        this.trainable_ = true;\n        // TODO(michaelterry): Initialize perInputLosses/Updates here.\n        // Container-specific properties.\n        if (Array.isArray(args.inputs)) {\n            this.inputs = args.inputs.slice();\n        }\n        else {\n            this.inputs = [args.inputs];\n        }\n        if (Array.isArray(args.outputs)) {\n            this.outputs = args.outputs.slice();\n        }\n        else {\n            this.outputs = [args.outputs];\n        }\n        // Check for redundancy in inputs.\n        if (generic_utils.unique(this.inputs).length !== this.inputs.length) {\n            throw new ValueError('The list of inputs passed to the model is ' +\n                'redundant. All inputs should only appear once. Found: ' +\n                `${this.inputs.map(x => x.name)}`);\n        }\n        // Check for redundancy in outputs.\n        if (generic_utils.unique(this.outputs).length !== this.outputs.length) {\n            console.warn('The list of outputs passed to the model is redundant. ' +\n                'All outputs should only appear once. Found: ' +\n                `${this.outputs.map(x => x.name)}`);\n        }\n        /*\n          List of initial layers (1 to 1 mapping with this.inputs, hence the same\n          layer might appear twice)\n        */\n        this.inputLayers = [];\n        this.inputLayersNodeIndices = [];\n        this.inputLayersTensorIndices = [];\n        /*\n          List of layers (1 to 1 mapping with this.outputs, hence the same layer\n          might appear twice)\n        */\n        this.outputLayers = [];\n        this.outputLayersNodeIndices = [];\n        this.outputLayersTensorIndices = [];\n        /*\n          All layers in order of horizontal graph traversal. Entries are unique.\n          Includes input and output layers.\n        */\n        this.layers = [];\n        /*\n          References to container layers that were constructed internally. We need\n          these to properly dispose of tensors from nested containers.\n        */\n        this.internalContainerRefs = [];\n        // TODO(michaelterry): Determine if caching still needed with eager\n        // backend.\n        /*\n          This is for performance optimization when calling the Container on new\n          inputs. Every time the Container is called on a set on input tensors,\n          we compute the output tensors, output masks and output shapes in one pass,\n          then cache them here. When one of these outputs is queried later,\n          we retrieve it from there instead of recomputing it.\n        */\n        // this.outputTensorCache = {};\n        // this.outputShapeCache = {};\n        // Build this.outputLayers:\n        for (const x of this.outputs) {\n            const layer = x.sourceLayer;\n            const nodeIndex = x.nodeIndex;\n            const tensorIndex = x.tensorIndex;\n            this.outputLayers.push(layer);\n            this.outputLayersNodeIndices.push(nodeIndex);\n            this.outputLayersTensorIndices.push(tensorIndex);\n        }\n        // TODO(michaelterry): Add output mask cache code.\n        // Build this.inputLayers:\n        for (const x of this.inputs) {\n            const layer = x.sourceLayer;\n            const nodeIndex = x.nodeIndex;\n            const tensorIndex = x.tensorIndex;\n            /*\n              It's supposed to be an input layer, so only one node\n              and one tensor output.\n            */\n            generic_utils.assert(nodeIndex === 0, 'input layer has >1 nodes');\n            generic_utils.assert(tensorIndex === 0, 'input layer has >1 tensors');\n            this.inputLayers.push(layer);\n            this.inputLayersNodeIndices.push(nodeIndex);\n            this.inputLayersTensorIndices.push(tensorIndex);\n        }\n        // Build this.inputNames and this.outputNames.\n        this.inputNames = [];\n        this.outputNames = [];\n        this.feedInputShapes = [];\n        this.feedInputNames = [];\n        this.feedOutputNames = [];\n        for (let i = 0; i < this.inputLayers.length; i++) {\n            const layer = this.inputLayers[i];\n            // Check that layer is an InputLayer.\n            if (!(layer instanceof InputLayer)) {\n                throw new TypeError('Input layers to a LayersModel must be InputLayer objects. ' +\n                    `Received inputs: ${args.inputs}. ` +\n                    `Input ${i} (0-based) originates ` +\n                    `from layer type ${layer.getClassName()}.`);\n            }\n            this.inputNames.push(layer.name);\n            this.feedInputShapes.push(layer.batchInputShape);\n            this.feedInputNames.push(layer.name);\n        }\n        for (const layer of this.outputLayers) {\n            this.outputNames.push(layer.name);\n        }\n        this.internalInputShapes = this.inputs.map(x => x.shape);\n        this.internalOutputShapes = this.outputs.map(x => x.shape);\n        /*\n          Container_nodes: set of nodes included in the graph (not all nodes\n          included in the layers are relevant to the current graph).\n        */\n        // ids of all nodes relevant to the Container:\n        const nodesDepths = {};\n        // To recover nodes from their ID.\n        const nodeIDToNode = {};\n        const layersDepths = {};\n        // To layers from their ID.\n        const layerIDToLayer = {};\n        const layerIndices = {};\n        const nodesInDecreasingDepth = [];\n        /**\n         * Builds a map of the graph of layers.\n         *\n         * This recursively updates the map `layerIndices`,\n         * the list `nodesInDecreasingDepth` and the set `containerNodes`.\n         *\n         * @param tensor Some tensor in a graph.\n         * @param finishedNodes Set of nodes whose subgraphs have been traversed\n         *         completely. Useful to prevent duplicated work.\n         * @param nodesInProgress Set of nodes that are currently active on the\n         *         recursion stack. Useful to detect cycles.\n         * @param layer Layer from which `tensor` comes from. If not provided,\n         *   will be obtained from tensor.sourceLayer.\n         * @param nodeIndex Node index from which `tensor` comes from.\n         * @param tensorIndex TensorIndex from which `tensor` comes from.\n         *\n         * @exception RuntimeError if a cycle is detected.\n         */\n        const buildMapOfGraph = (tensor, finishedNodes, nodesInProgress, layer, nodeIndex, tensorIndex) => {\n            if (layer == null || nodeIndex == null || tensorIndex == null) {\n                layer = tensor.sourceLayer;\n                nodeIndex = tensor.nodeIndex;\n                tensorIndex = tensor.tensorIndex;\n            }\n            const node = layer.inboundNodes[nodeIndex];\n            // Prevent cycles.\n            if (nodesInProgress.indexOf(node) !== -1) {\n                throw new RuntimeError(`The tensor ${tensor.name} at layer \"${layer.name}\" ` +\n                    'is part of a cycle.');\n            }\n            // Don't repeat work for shared subgraphs\n            if (finishedNodes.indexOf(node) !== -1) {\n                return;\n            }\n            // Update containerNodes.\n            this.containerNodes.add(Container.nodeKey(layer, nodeIndex));\n            // Store the traversal order for layer sorting.\n            if (!(layer.id in layerIndices)) {\n                layerIndices[layer.id] = Object.keys(layerIndices).length;\n            }\n            if (nodesInProgress.indexOf(node) === -1) {\n                nodesInProgress.push(node);\n            }\n            // Propagate to all previous tensors connected to this node.\n            const numInboundLayers = node.inboundLayers.length;\n            for (let i = 0; i < numInboundLayers; i++) {\n                const x = node.inputTensors[i];\n                const layer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const tensorIndex = node.tensorIndices[i];\n                buildMapOfGraph(x, finishedNodes, nodesInProgress, layer, nodeIndex, tensorIndex);\n            }\n            finishedNodes.push(node);\n            while (nodesInProgress.indexOf(node) >= 0) {\n                nodesInProgress.splice(nodesInProgress.indexOf(node), 1);\n            }\n            nodesInDecreasingDepth.push(node);\n        };\n        const finishedNodes = [];\n        const nodesInProgress = [];\n        for (const x of this.outputs) {\n            buildMapOfGraph(x, finishedNodes, nodesInProgress);\n        }\n        const reversedNodesInDecreasingDepth = nodesInDecreasingDepth.slice().reverse();\n        for (const node of reversedNodesInDecreasingDepth) {\n            nodeIDToNode[node.id] = node;\n            // If the depth is not set, the node has no outbound nodes (depth 0).\n            if (!(node.id in nodesDepths)) {\n                nodesDepths[node.id] = 0;\n            }\n            let depth = nodesDepths[node.id];\n            // Update the depth of the corresponding layer\n            const previousDepth = (layersDepths[node.outboundLayer.id] == null ?\n                0 :\n                layersDepths[node.outboundLayer.id]);\n            /*\n              If we've seen this layer before at a higher depth, we should use that\n              depth instead of the node depth.  This is necessary for shared layers\n              that have inputs at different depth levels in the graph.\n            */\n            depth = Math.max(depth, previousDepth);\n            layersDepths[node.outboundLayer.id] = depth;\n            layerIDToLayer[node.outboundLayer.id] = node.outboundLayer;\n            nodesDepths[node.id] = depth;\n            // Update the depth of inbound nodes.\n            for (let i = 0; i < node.inboundLayers.length; i++) {\n                const inboundLayer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const inboundNode = inboundLayer.inboundNodes[nodeIndex];\n                const previousDepth = (nodesDepths[inboundNode.id] == null ? 0 :\n                    nodesDepths[inboundNode.id]);\n                nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth);\n                nodeIDToNode[inboundNode.id] = inboundNode;\n            }\n        }\n        // Build a dict {depth: list of nodes with this depth}\n        const nodesByDepth = {};\n        for (const nodeID in nodesDepths) {\n            const depth = nodesDepths[nodeID];\n            if (!(depth in nodesByDepth)) {\n                nodesByDepth[depth] = [];\n            }\n            nodesByDepth[depth].push(nodeIDToNode[nodeID]);\n        }\n        // Build a dict {depth: list of layers with this depth}\n        const layersByDepth = {};\n        for (const layerID in layersDepths) {\n            const depth = layersDepths[layerID];\n            if (!(depth in layersByDepth)) {\n                layersByDepth[depth] = [];\n            }\n            layersByDepth[depth].push(layerIDToLayer[layerID]);\n        }\n        // Get sorted list of layer depths.\n        let depthKeys = Object.keys(layersByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Set this.layers and this.layersByDepth.\n        this.layers = [];\n        for (const depth of depthKeys) {\n            const layersForDepth = layersByDepth[depth];\n            // Container.layers needs to have a deterministic order:\n            // here we order them by traversal order.\n            layersForDepth.sort((a, b) => {\n                const aIndex = layerIndices[a.id];\n                const bIndex = layerIndices[b.id];\n                if (aIndex < bIndex) {\n                    return -1;\n                }\n                if (aIndex > bIndex) {\n                    return 1;\n                }\n                return 0;\n            });\n            for (const layer of layersForDepth) {\n                if (layer instanceof Container) {\n                    this.internalContainerRefs.push(layer);\n                }\n                this.layers.push(layer);\n            }\n        }\n        this.layersByDepth = layersByDepth;\n        // Get sorted list of node depths;\n        depthKeys = Object.keys(nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Check that all tensors required are computable.\n        // computable_tensors: all tensors in the graph\n        // that can be computed from the inputs provided.\n        const computableTensors = this.inputs.slice();\n        // To provide a better error msg.\n        const layersWithCompleteInput = [];\n        for (const depth of depthKeys) {\n            for (const node of nodesByDepth[depth]) {\n                const layer = node.outboundLayer;\n                if (layer != null) {\n                    for (const x of node.inputTensors) {\n                        if (computableTensors.indexOf(x) === -1) {\n                            throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x}` +\n                                ` at layer \"${layer.name}\". ` +\n                                'The following previous layers were accessed without ' +\n                                `issue: ${layersWithCompleteInput}`);\n                        }\n                    }\n                    for (const x of node.outputTensors) {\n                        computableTensors.push(x);\n                    }\n                    layersWithCompleteInput.push(layer.name);\n                }\n            }\n        }\n        // Set this.containerNodes and this.nodesByDepth.\n        this.nodesByDepth = nodesByDepth;\n        // Ensure name unicity, which will be crucial for serialization\n        // (since serialized nodes refer to layers by their name).\n        const allNames = this.layers.map(x => x.name);\n        for (const name of allNames) {\n            const numOccurrences = allNames.filter(x => x === name).length;\n            if (numOccurrences !== 1) {\n                throw new RuntimeError(`The name \"${name}\" is used ${numOccurrences} times ` +\n                    'in the model. All layer names should be unique. Layer names: ' +\n                    JSON.stringify(allNames));\n            }\n        }\n        // Layer parameters.\n        // The new container starts with a single inbound node\n        // for its inputs, and no outbound nodes.\n        // Will be appended to by future calls to apply().\n        this.outboundNodes = [];\n        // Will be appended to below, and by future calls to apply().\n        this.inboundNodes = [];\n        // Create the node linking internal inputs to internal outputs.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers: [],\n            nodeIndices: [],\n            tensorIndices: [],\n            inputTensors: this.inputs,\n            outputTensors: this.outputs,\n            inputMasks: this.inputs.map(x => null),\n            outputMasks: this.outputs.map(x => null),\n            inputShapes: this.inputs.map(x => x.shape),\n            outputShapes: this.outputs.map(x => x.shape)\n        });\n        this.built = true;\n        this._refCount = 1; // The ref count of a container always start at 1.\n    }\n    assertNotDisposed() {\n        if (this._refCount === 0) {\n            throw new Error(`Container '${this.name}' is already disposed.`);\n        }\n    }\n    /**\n     * Attempt to dispose a LayersModel's weights.\n     *\n     * This method decrease the reference count of the LayersModel object by 1.\n     *\n     * A LayersModel is reference-counted. Its reference count is incremented by 1\n     * when it is first constructed and when it is used as a Layer of another\n     * LayersModel.\n     *\n     * If the reference count of a LayersModel becomes 0, the `dispose` method of\n     * all its constituent `Layer`s will be called.\n     *\n     * Note: If the reference count is greater than 0 after the decrement, the\n     * `dispose` method of its constituent `Layer`s will *not* be called.\n     *\n     * After a LayersModel is disposed, it cannot be used in calls such as\n     * 'predict`, `evaluate` or `fit` anymore.\n     *\n     * @returns A DisposeResult Object with the following fields:\n     *   - refCountAfterDispose: The reference count of the LayersModel after this\n     *     `dispose()` call.\n     *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n     *     during this `dispose()` call.\n     * @throws {Error} If the layer is not built yet, or if the LayersModel has\n     *   already been disposed.\n     */\n    dispose() {\n        this.assertNotDisposed();\n        const result = { refCountAfterDispose: null, numDisposedVariables: 0 };\n        if (--this._refCount === 0) {\n            for (const layer of this.layers) {\n                result.numDisposedVariables += layer.dispose().numDisposedVariables;\n            }\n            // Call dispose on each internally created container layer again to ensure\n            // their refCounts hit zero and their tensors are subsequently deleted.\n            for (const container of this.internalContainerRefs) {\n                result.numDisposedVariables += container.dispose().numDisposedVariables;\n            }\n        }\n        result.refCountAfterDispose = this._refCount;\n        return result;\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this.layers.forEach(layer => {\n            // tslint:disable-next-line:no-any\n            layer._trainableWeights\n                .forEach(w => w.trainable = trainable);\n        });\n        this.trainable_ = trainable;\n    }\n    get trainableWeights() {\n        // Porting Note: This check below is to prevent errors where the\n        //   _trainableWeights inherited from the parent class (Layer) gets\n        //   inadvertently used.\n        if (this._trainableWeights.length > 0) {\n            throw new ValueError('Container instance unexpectedly contains _trainableWeights.' +\n                'The trainable weights of a Container are a union of the ' +\n                'trainable weights of its consituent Layers. Its own ' +\n                '_trainableWeights must remain an empty Array.');\n        }\n        if (!this.trainable) {\n            return [];\n        }\n        let weights = [];\n        for (const layer of this.layers) {\n            weights = weights.concat(layer.trainableWeights);\n        }\n        return weights;\n    }\n    get nonTrainableWeights() {\n        const weights = [];\n        for (const layer of this.layers) {\n            weights.push(...layer.nonTrainableWeights);\n        }\n        if (!this.trainable) {\n            const trainableWeights = [];\n            for (const layer of this.layers) {\n                trainableWeights.push(...layer.trainableWeights);\n            }\n            return trainableWeights.concat(weights);\n        }\n        return weights;\n    }\n    get weights() {\n        return this.trainableWeights.concat(this.nonTrainableWeights);\n    }\n    /**\n     * Loads all layer weights from a JSON object.\n     *\n     * Porting Note: HDF5 weight files cannot be directly loaded in JavaScript /\n     *   TypeScript. The utility script at `scripts/pykeras.py` offers means\n     *   to convert them into JSON strings compatible with this method.\n     * Porting Note: TensorFlow.js Layers supports only loading by name currently.\n     *\n     * @param weights A JSON mapping weight names to weight values as nested\n     *   arrays of numbers, or a `NamedTensorMap`, i.e., a JSON mapping weight\n     *   names to `tf.Tensor` objects.\n     * @param strict Require that the provided weights exactly match those\n     *   required by the container.  Default: `true`.  Passing `false` means that\n     *   extra weights and missing weights will be silently ignored.\n     */\n    loadWeights(weights, strict = true) {\n        const nameToWeight = {};\n        let totalWeightsCount = 0;\n        for (const layer of this.layers) {\n            for (const weight of layer.weights) {\n                if (nameToWeight[weight.originalName] != null) {\n                    throw new ValueError(`Duplicate weight name: ${weight.originalName}`);\n                }\n                nameToWeight[weight.originalName] = weight;\n                totalWeightsCount++;\n            }\n        }\n        const weightValueTuples = [];\n        for (const name in weights) {\n            // TF 2.2.0 added cell name to the weight name in the format of\n            // layer_name/cell_name/weight_name, we need to remove\n            // the inner cell name.\n            let validatedName = name;\n            if (nameToWeight[name] == null) {\n                const tokens = name.split('/');\n                const shortenNameArray = tokens.slice(0, -2).concat([tokens[tokens.length - 1]]);\n                validatedName = shortenNameArray.join('/');\n            }\n            if (nameToWeight[validatedName] != null) {\n                weightValueTuples.push([nameToWeight[validatedName], weights[name]]);\n            }\n            else if (strict) {\n                throw new ValueError(`Provided weight data has no target variable: ${name}`);\n            }\n            delete nameToWeight[validatedName];\n        }\n        if (strict) {\n            // Check that all weights are set.\n            const unsetNames = [];\n            for (const name in nameToWeight) {\n                unsetNames.push(name);\n            }\n            if (unsetNames.length > 0) {\n                throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ` +\n                    `${unsetNames}`);\n            }\n        }\n        batchSetValue(weightValueTuples);\n    }\n    /**\n     * Util shared between different serialization methods.\n     * @returns LayersModel config with Keras version information added.\n     */\n    updatedConfig() {\n        const theConfig = this.getConfig();\n        const modelConfig = {};\n        modelConfig['className'] = this.getClassName();\n        modelConfig['config'] = theConfig;\n        modelConfig['kerasVersion'] = `tfjs-layers ${layersVersion}`;\n        // TODO(nielsene): Replace something like K.backend() once\n        // possible.\n        modelConfig['backend'] = 'TensorFlow.js';\n        return modelConfig;\n    }\n    /**\n     * Returns a JSON string containing the network configuration.\n     *\n     * To load a network from a JSON save file, use\n     * models.modelFromJSON(jsonString);\n     * @param extraJsonArgs Unused in tfjs-layers, maintained for PyKeras\n     * @param returnString Whether the return value should be stringified\n     *    (default: `true`).\n     * @returns a JSON string if `returnString` (default), or a JSON object if\n     *   `!returnString`.\n     */\n    // tslint:disable-next-line:no-any\n    toJSON(unused, returnString = true) {\n        const modelConfig = convertTsToPythonic(this.updatedConfig());\n        return returnString ? JSON.stringify(modelConfig) : modelConfig;\n    }\n    /**\n     * Call the model on new inputs.\n     *\n     * In this case `call` just reapplies all ops in the graph to the new inputs\n     * (e.g. build a new computational graph from the provided inputs).\n     *\n     * @param inputs A tensor or list of tensors.\n     * @param mask A mask or list of masks. A mask can be either a tensor or null\n     *   (no mask).\n     *\n     * @return A tensor if there is a single output, or a list of tensors if there\n     *   are more than one outputs.\n     */\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = generic_utils.toList(inputs);\n            const feedDict = new FeedDict();\n            for (let i = 0; i < this.inputs.length; ++i) {\n                feedDict.add(this.inputs[i], inputs[i]);\n            }\n            return execute(this.outputs, feedDict, kwargs);\n        });\n    }\n    /**\n     * Computes an output mask tensor.\n     *\n     * @param inputs Tensor or list of tensors.\n     * @param mask Tensor or list of tensors.\n     *\n     * @return null or a tensor (or list of tensors, one per output tensor of the\n     * layer).\n     */\n    computeMask(inputs, mask) {\n        return tidy(() => {\n            inputs = generic_utils.toList(inputs);\n            let masks;\n            if (mask == null) {\n                masks = generic_utils.pyListRepeat(null, inputs.length);\n            }\n            else {\n                masks = generic_utils.toList(mask);\n            }\n            // TODO(michaelterry): Add support for mask caching.\n            return this.runInternalGraph(inputs, masks)[1];\n        });\n    }\n    /**\n     * Computes the output shape of the layer.\n     *\n     * Assumes that the layer will be built to match that input shape provided.\n     *\n     * @param inputShape A shape (tuple of integers) or a list of shape tuples\n     *   (one per output tensor of the layer). Shape tuples can include null for\n     *   free dimensions, instead of an integer.\n     */\n    computeOutputShape(inputShape) {\n        const inputShapes = types_utils.normalizeShapeList(inputShape);\n        if (inputShapes.length !== this.inputLayers.length) {\n            throw new ValueError(`Invalid inputShape argument ${inputShape}: ` +\n                `model has ${this.inputLayers.length} tensor inputs.`);\n        }\n        // TODO(michaelterry): Add caching\n        const layersToOutputShapes = {};\n        for (let i = 0; i < inputShapes.length; i++) {\n            const layer = this.inputLayers[i];\n            const inputShape = inputShapes[i];\n            // It's an input layer: computeOutputShape is identity,\n            // and there is only one node and one tensor output.\n            const shapeKey = layer.name + '_0_0';\n            layersToOutputShapes[shapeKey] = inputShape;\n        }\n        const depthKeys = Object.keys(this.nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Iterate over nodes, by depth level.\n        if (depthKeys.length > 1) {\n            for (const depth of depthKeys) {\n                const nodes = this.nodesByDepth[depth];\n                for (const node of nodes) {\n                    // This is always a single layer, never a list.\n                    const layer = node.outboundLayer;\n                    if (this.inputLayers.map(x => x.id).indexOf(layer.id) !== -1) {\n                        // We've already covered the input layers a few lines above.\n                        continue;\n                    }\n                    // Potentially redundant list, same size of node.inputTensors.\n                    const inputShapes = [];\n                    for (let j = 0; j < node.inboundLayers.length; j++) {\n                        const inboundLayer = node.inboundLayers[j];\n                        const nodeIndex = node.nodeIndices[j];\n                        const tensorIndex = node.tensorIndices[j];\n                        const shapeKey = `${inboundLayer.name}_${nodeIndex}_${tensorIndex}`;\n                        const inputShape = layersToOutputShapes[shapeKey];\n                        inputShapes.push(inputShape);\n                    }\n                    const outputShape = layer.computeOutputShape(generic_utils.singletonOrArray(inputShapes));\n                    const outputShapes = types_utils.normalizeShapeList(outputShape);\n                    const nodeIndex = layer.inboundNodes.indexOf(node);\n                    for (let j = 0; j < outputShapes.length; j++) {\n                        const shapeKey = `${layer.name}_${nodeIndex}_${j}`;\n                        layersToOutputShapes[shapeKey] = outputShapes[j];\n                    }\n                }\n            }\n        }\n        // Read final output shapes from layersToOutputShapes.\n        const outputShapes = [];\n        const outputShapeKeys = [];\n        for (let i = 0; i < this.outputLayers.length; i++) {\n            const layer = this.outputLayers[i];\n            const nodeIndex = this.outputLayersNodeIndices[i];\n            const tensorIndex = this.outputLayersTensorIndices[i];\n            const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;\n            outputShapeKeys.push(shapeKey);\n        }\n        for (let i = 0; i < outputShapeKeys.length; i++) {\n            const key = outputShapeKeys[i];\n            generic_utils.assert(key in layersToOutputShapes);\n            outputShapes.push(layersToOutputShapes[key]);\n        }\n        // TODO(michaelterry): Update cache\n        return generic_utils.singletonOrArray(outputShapes);\n    }\n    /**\n     * Computes output tensors for new inputs.\n     *\n     * Note:\n     *   - Expects `inputs` to be a list (potentially with 1 element).\n     *\n     * @param inputs List of tensors\n     * @param masks List of masks (tensors or null).\n     * @return Three lists: outputTensors, outputMasks, outputShapes\n     */\n    runInternalGraph(inputs, masks) {\n        if (masks == null) {\n            masks = generic_utils.pyListRepeat(null, inputs.length);\n        }\n        // Dictionary mapping reference tensors to tuples\n        // (computed tensor, compute mask)\n        // we assume a 1:1 mapping from tensor to mask\n        // TODO: raise exception when a `.computeMask()` call\n        // does not return a list the same size as `call`\n        const tensorMap = {};\n        for (let i = 0; i < this.inputs.length; ++i) {\n            const x = this.inputs[i];\n            const y = inputs[i];\n            const mask = masks[i];\n            tensorMap[x.id] = [y, mask];\n        }\n        const depthKeys = Object.keys(this.nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        for (const depth of depthKeys) {\n            const nodes = this.nodesByDepth[depth];\n            for (const node of nodes) {\n                // This is always a single layer, never a list.\n                const layer = node.outboundLayer;\n                const referenceInputTensors = node.inputTensors;\n                const referenceOutputTensors = node.outputTensors;\n                // If all previous input tensors are available in tensorMap,\n                // then call node.inboundLayer on them.\n                // List of tuples [input, mask]:\n                const computedData = new Array();\n                for (const x of referenceInputTensors) {\n                    if (x.id in tensorMap) {\n                        computedData.push(tensorMap[x.id]);\n                    }\n                }\n                if (computedData.length === referenceInputTensors.length) {\n                    // TODO(michaelterry): Add K.name_scope here, if we need it.\n                    let kwargs = {};\n                    let computedTensors;\n                    let computedMasks;\n                    let outputTensors;\n                    let outputMasks;\n                    // call layer\n                    if (node.callArgs != null) {\n                        kwargs = node.callArgs;\n                    }\n                    if (computedData.length === 1) {\n                        const [computedTensor, computedMask] = computedData[0];\n                        if (kwargs['mask'] == null) {\n                            kwargs['mask'] = computedMask;\n                        }\n                        outputTensors =\n                            generic_utils.toList(layer.call(computedTensor, kwargs));\n                        outputMasks = generic_utils.toList(layer.computeMask(computedTensor, computedMask));\n                        computedTensors = [computedTensor];\n                        computedMasks = [computedMask];\n                    }\n                    else {\n                        computedTensors = computedData.map(x => x[0]);\n                        computedMasks = computedData.map(x => x[1]);\n                        if (kwargs['mask'] == null) {\n                            kwargs['mask'] = computedMasks;\n                        }\n                        outputTensors =\n                            generic_utils.toList(layer.call(computedTensors, kwargs));\n                        outputMasks = generic_utils.toList(layer.computeMask(computedTensors, computedMasks));\n                    }\n                    if (layer.activityRegularizer) {\n                        throw new NotImplementedError('LayersModel invocation with concrete Tensor value(s) in the ' +\n                            'presence of activity regularizer(s) is not supported yet.');\n                    }\n                    // TODO(michaelterry): Add model updates and losses\n                    // Update tensor map.\n                    for (let i = 0; i < referenceOutputTensors.length; ++i) {\n                        const x = referenceOutputTensors[i];\n                        const y = outputTensors[i];\n                        const mask = outputMasks[i];\n                        tensorMap[x.id] = [y, mask];\n                    }\n                }\n            }\n        }\n        const outputTensors = [];\n        const outputMasks = [];\n        const outputShapes = [];\n        for (const x of this.outputs) {\n            generic_utils.assert(x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`);\n            const [tensor, mask] = tensorMap[x.id];\n            outputShapes.push(tensor.shape);\n            outputTensors.push(tensor);\n            outputMasks.push(mask);\n        }\n        // TODO(michaelterry): Add support for caches.\n        return [outputTensors, outputMasks, outputShapes];\n    }\n    /**\n     * Builds a map of internal node keys to node ordering.\n     * Used in serializaion a node orderings may change as unused nodes are\n     * dropped. Porting Note:  This helper method was pulled out of getConfig to\n     * improve readability.\n     * @param layers An array of Layers in the model.\n     * @returns Map of Node Keys to index order within the layer.\n     */\n    buildNodeConversionMap(layers) {\n        const nodeConversionMap = {};\n        let keptNodes;\n        for (const layer of this.layers) {\n            keptNodes = layer instanceof Container ? 1 : 0;\n            for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n                const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n                if (this.containerNodes.has(nodeKey)) {\n                    // i.e. we mark it to be saved\n                    nodeConversionMap[nodeKey] = keptNodes;\n                    keptNodes += 1;\n                }\n            }\n        }\n        return nodeConversionMap;\n    }\n    /**\n     * Retrieves a layer based on either its name (unique) or index.\n     *\n     * Indices are based on order of horizontal graph traversal (bottom-up).\n     *\n     * If both `name` and `index` are specified, `index` takes precedence.\n     *\n     * @param name Name of layer.\n     * @param index Index of layer.\n     * @returns A Layer instance.\n     * @throws ValueError: In case of invalid layer name or index.\n     *\n     * @doc {\n     *    heading: 'Layers',\n     *    subheading: 'Classes',\n     *    namespace: 'layers',\n     *    subclasses: ['LayersModel']\n     * }\n     */\n    getLayer(name, index) {\n        if (index != null) {\n            if (this.layers.length <= index) {\n                throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only ` +\n                    `has ${this.layers.length} layer(s).`);\n            }\n            else {\n                return this.layers[index];\n            }\n        }\n        else {\n            if (name == null) {\n                throw new ValueError('Provide either a layer name or layer index');\n            }\n        }\n        for (const layer of this.layers) {\n            if (layer.name === name) {\n                return layer;\n            }\n        }\n        throw new ValueError(`No such layer: ${name}`);\n    }\n    /**\n     * Retrieves the Container's current loss values.\n     *\n     * Used for regularizers during training.\n     */\n    calculateLosses() {\n        // Porting Node: This is an augmentation to Container.loss in PyKeras.\n        //   In PyKeras, Container.loss returns symbolic tensors. Here a concrete\n        //   Tensor (specifically Scalar) values are returned. This is due to the\n        //   imperative backend.\n        return tidy(() => {\n            const losses = [];\n            for (const layer of this.layers) {\n                for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length; ++nodeIndex) {\n                    const nodeKey = Container.nodeKey(layer, nodeIndex);\n                    if (this.containerNodes.has(nodeKey)) {\n                        losses.push(...layer.calculateLosses());\n                    }\n                }\n            }\n            // TODO(cais): Add any unconditional model-level losses?\n            return losses;\n        });\n    }\n    getConfig() {\n        const config = { name: this.name };\n        // Build a map from layer unique name (self._node_key)\n        // to the index of the nodes that are saved in the config.\n        // Only nodes in container_nodes are saved.\n        const nodeConversionMap = this.buildNodeConversionMap(this.layers);\n        // Serialize and save the layers in layerConfigs\n        const layerConfigs = [];\n        for (const layer of this.layers) {\n            const layerClassName = layer.getClassName();\n            const layerConfig = layer.getConfig();\n            const filteredInboundNodes = [];\n            for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n                const node = layer.inboundNodes[originalNodeIndex];\n                const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n                let kwargs = {};\n                if (this.containerNodes.has(nodeKey)) {\n                    // The node is relevant to the model:\n                    // add to filteredInboundNodes.\n                    if (node.callArgs) {\n                        try {\n                            JSON.stringify(node.callArgs);\n                            kwargs = node.callArgs;\n                        }\n                        catch (err) {\n                            console.warn(`Layer ${layer.name} was passed ` +\n                                `non-serializable keyword arguments: ` +\n                                `${node.callArgs}. They will not be included ` +\n                                `in the serialized model (and thus will be ` +\n                                `missing at deserialization time).`);\n                            kwargs = {};\n                        }\n                    }\n                    if (node.inboundLayers.length > 0) {\n                        const nodeData = [];\n                        for (let i = 0; i < node.inboundLayers.length; i++) {\n                            const inboundLayer = node.inboundLayers[i];\n                            const nodeIndex = node.nodeIndices[i];\n                            const tensorIndex = node.tensorIndices[i];\n                            const nodeKey = Container.nodeKey(inboundLayer, nodeIndex);\n                            let newNodeIndex = nodeConversionMap[nodeKey];\n                            if (newNodeIndex == null) {\n                                newNodeIndex = 0;\n                            }\n                            nodeData.push([inboundLayer.name, newNodeIndex, tensorIndex, kwargs]);\n                        }\n                        filteredInboundNodes.push(nodeData);\n                    }\n                }\n            }\n            const dict = {};\n            dict['name'] = layer.name;\n            dict['className'] = layerClassName;\n            dict['config'] = layerConfig;\n            dict['inboundNodes'] = filteredInboundNodes;\n            layerConfigs.push(dict);\n        }\n        config['layers'] = layerConfigs;\n        // Gather info about inputs and outputs\n        const modelInputs = [];\n        for (let i = 0; i < this.inputLayers.length; i++) {\n            const layer = this.inputLayers[i];\n            const nodeIndex = this.inputLayersNodeIndices[i];\n            const nodeKey = Container.nodeKey(layer, nodeIndex);\n            if (!this.containerNodes.has(nodeKey)) {\n                continue;\n            }\n            let newNodeIndex = nodeConversionMap[nodeKey];\n            if (newNodeIndex === null || newNodeIndex === undefined) {\n                newNodeIndex = 0;\n            }\n            const tensorIndex = this.inputLayersTensorIndices[i];\n            modelInputs.push([layer.name, newNodeIndex, tensorIndex]);\n        }\n        config['inputLayers'] = modelInputs;\n        const modelOutputs = [];\n        for (let i = 0; i < this.outputLayers.length; i++) {\n            const layer = this.outputLayers[i];\n            const nodeIndex = this.outputLayersNodeIndices[i];\n            const nodeKey = Container.nodeKey(layer, nodeIndex);\n            if (!this.containerNodes.has(nodeKey)) {\n                continue;\n            }\n            let newNodeIndex = nodeConversionMap[nodeKey];\n            if (newNodeIndex === null || newNodeIndex === undefined) {\n                newNodeIndex = 0;\n            }\n            const tensorIndex = this.outputLayersTensorIndices[i];\n            modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);\n        }\n        config['outputLayers'] = modelOutputs;\n        return config;\n    }\n    /**\n     * Instantiates a LayersModel from its config (output of `get_config()`).\n     * @param cls the class to create\n     * @param config LayersModel config dictionary.\n     * @param customObjects An optional dictionary of custom objects.\n     * @param fastWeightInit Optional flag to use fast weight initialization\n     *   during deserialization. This is applicable to cases in which\n     *   the initialization will be immediately overwritten by loaded weight\n     *   values. Default: `false`.\n     * @returns A LayersModel instance.\n     * @throws ValueError: In case of improperly formatted config dict.\n     */\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n        // Layer instances created during\n        // the graph reconstruction process\n        const createdLayers = {};\n        // Dictionary mapping layer instances to\n        // node data that specifies a layer call.\n        // It acts as a queue that maintains any unprocessed\n        // layer call until it becomes possible to process it\n        // (i.e. until the input tensors to the call all exist).\n        const unprocessedNodes = {};\n        function addUnprocessedNode(layer, nodeData) {\n            if (!(layer.name in unprocessedNodes)) {\n                unprocessedNodes[layer.name] = [nodeData];\n            }\n            else {\n                unprocessedNodes[layer.name].push(nodeData);\n            }\n        }\n        function processNode(layer, nodeData) {\n            const inputTensors = [];\n            let kwargs;\n            for (const inputData of nodeData) {\n                const inboundLayerName = inputData[0];\n                const inboundNodeIndex = inputData[1];\n                const inboundTensorIndex = inputData[2];\n                kwargs = inputData[3] == null ?\n                    {} :\n                    inputData[3];\n                if (!(inboundLayerName in createdLayers)) {\n                    addUnprocessedNode(layer, nodeData);\n                    return;\n                }\n                const inboundLayer = createdLayers[inboundLayerName];\n                if (inboundLayer.inboundNodes.length <= inboundNodeIndex) {\n                    addUnprocessedNode(layer, nodeData);\n                    return;\n                }\n                const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex];\n                inputTensors.push(inboundNode.outputTensors[inboundTensorIndex]);\n            }\n            // Call layer on its inputs, thus creating the node\n            // and building the layer if needed.\n            // Note: This has Eager vs Graph Implications.\n            if (inputTensors.length > 0) {\n                layer.apply(generic_utils.singletonOrArray(inputTensors), kwargs); // was ** kwargs\n            }\n        }\n        /**\n         * Deserialize a layer, then call it on appropriate inputs.\n         * @param layerData: layer config dict.\n         * @throws ValueError: In case of improperly formatted `layer_data`\n         * dict.\n         */\n        function processLayer(layerData) {\n            const layerName = layerData['name'];\n            // Instantiate layer.\n            const layer = deserializeLayer(layerData, config['customObjects'] != null ?\n                config['customObjects'] :\n                {});\n            layer.setFastWeightInitDuringBuild(fastWeightInit);\n            createdLayers[layerName] = layer;\n            // Gather layer inputs.\n            const inboundNodesData = layerData['inboundNodes'];\n            inboundNodesData.forEach(nodeData => {\n                if (!(nodeData instanceof Array)) {\n                    throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);\n                }\n                // We don't process nodes (i.e. make layer calls)\n                // on the fly because the inbound node may not yet exist,\n                // in case of layer shared at different topological depths\n                // (e.g.a model such as A(B(A(B(x)))))\n                addUnprocessedNode(layer, nodeData);\n            });\n        }\n        // First, we create all layers and enqueue nodes to be processed.\n        const name = config['name'];\n        const layersFromConfig = config['layers'];\n        for (const layerData of layersFromConfig) {\n            processLayer(layerData);\n        }\n        // Then we process nodes in order of layer depth.\n        // Nodes that cannot yet be processed(if the inbound node\n        // does not yet exist) are re - enqueued, and the process\n        // is repeated until all nodes are processed.\n        while (!generic_utils.isObjectEmpty(unprocessedNodes)) {\n            for (const layerData of layersFromConfig) {\n                const layer = createdLayers[layerData['name']];\n                if (layer.name in unprocessedNodes) {\n                    const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name];\n                    delete unprocessedNodes[layer.name];\n                    for (const nodeData of currentUnprocessedNodesForLayer) {\n                        processNode(layer, nodeData);\n                    }\n                }\n            }\n        }\n        const inputTensors = [];\n        const outputTensors = [];\n        const inputLayersFromConfig = config['inputLayers'];\n        for (const layerData of inputLayersFromConfig) {\n            const layerName = layerData[0];\n            const nodeIndex = layerData[1];\n            const tensorIndex = layerData[2];\n            generic_utils.assert(layerName in createdLayers);\n            const layer = createdLayers[layerName];\n            const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n            inputTensors.push(layerOutputTensors[tensorIndex]);\n        }\n        const outputLayersFromConfig = config['outputLayers'];\n        for (const layerData of outputLayersFromConfig) {\n            const layerName = layerData[0];\n            const nodeIndex = layerData[1];\n            const tensorIndex = layerData[2];\n            generic_utils.assert(layerName in createdLayers);\n            const layer = createdLayers[layerName];\n            const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n            outputTensors.push(layerOutputTensors[tensorIndex]);\n        }\n        return new cls({ inputs: inputTensors, outputs: outputTensors, name });\n    }\n    /**\n     * Determine whether the container is stateful.\n     *\n     * Porting Note: this is the equivalent of the stateful @property of\n     *   the Container class in PyKeras.\n     */\n    get stateful() {\n        // Porting Note: This check is to prevent inadvertent setting of the\n        //   _stateful property of the Container instance.\n        if (this._stateful) {\n            throw new ValueError('Container instance unexpectedly has _stateful = true. The ' +\n                'statefulness of a Container is determined by the Layers it ' +\n                'contains. Its _stateful property must remain the default false.');\n        }\n        for (const layer of this.layers) {\n            if (layer.stateful) {\n                return true;\n            }\n        }\n        return false;\n    }\n    /**\n     * Reset the state of all stateful constituent layers (if any).\n     *\n     * Examples of stateful layers include RNN layers whose `stateful` property\n     * is set as `true`.\n     */\n    resetStates() {\n        tidy(() => {\n            this.layers.forEach(layer => {\n                // tslint:disable:no-any\n                if (layer.stateful) {\n                    layer.resetStates();\n                }\n                // tslint:enable:no-any\n            });\n        });\n    }\n}\n//# sourceMappingURL=container.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { dispose, mul, tensor1d, tidy } from '@tensorflow/tfjs-core';\nfunction standardizeSampleOrClassWeights(xWeight, outputNames, weightType) {\n    const numOutputs = outputNames.length;\n    if (xWeight == null || (Array.isArray(xWeight) && xWeight.length === 0)) {\n        return outputNames.map(name => null);\n    }\n    if (numOutputs === 1) {\n        if (Array.isArray(xWeight) && xWeight.length === 1) {\n            return xWeight;\n        }\n        else if (typeof xWeight === 'object' && outputNames[0] in xWeight) {\n            return [xWeight[outputNames[0]]];\n        }\n        else {\n            return [xWeight];\n        }\n    }\n    if (Array.isArray(xWeight)) {\n        if (xWeight.length !== numOutputs) {\n            throw new Error(`Provided ${weightType} is an array of ${xWeight.length} ` +\n                `element(s), but the model has ${numOutputs} outputs. ` +\n                `Make sure a set of weights is provided for each model output.`);\n        }\n        return xWeight;\n    }\n    else if (typeof xWeight === 'object' && Object.keys(xWeight).length > 0 &&\n        typeof xWeight[Object.keys(xWeight)[0]] ===\n            'object') {\n        const output = [];\n        outputNames.forEach(outputName => {\n            if (outputName in xWeight) {\n                output.push(xWeight[outputName]);\n            }\n            else {\n                output.push(null);\n            }\n        });\n        return output;\n    }\n    else {\n        throw new Error(`The model has multiple (${numOutputs}) outputs, ` +\n            `so ${weightType} must be either an array with ` +\n            `${numOutputs} elements or an object with ${outputNames} keys. ` +\n            `Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);\n    }\n}\n/**\n * Standardize class weighting objects.\n *\n * This function takes a single class-weighting object, an array of them,\n * or a map from output name to class-weighting object. It compares it to the\n * output name(s) of the model, base on which it outputs an array of\n * class-weighting objects of which the length matches the number of outputs.\n *\n * @param classWeight Input class-weighting object(s).\n * @param outputNames All output name(s) of the model.\n * @return An array of class-weighting objects. The length of the array matches\n *   the model's number of outputs.\n */\nexport function standardizeClassWeights(classWeight, outputNames) {\n    return standardizeSampleOrClassWeights(classWeight, outputNames, 'classWeight');\n}\nexport function standardizeSampleWeights(classWeight, outputNames) {\n    return standardizeSampleOrClassWeights(classWeight, outputNames, 'sampleWeight');\n}\n/**\n * Standardize by-sample and/or by-class weights for training.\n *\n * Note that this function operates on one model output at a time. For a model\n * with multiple outputs, you must call this function multiple times.\n *\n * @param y The target tensor that the by-sample and/or by-class weight is for.\n *     The values of y are assumed to encode the classes, either directly\n *     as an integer index, or as one-hot encoding.\n * @param sampleWeight By-sample weights.\n * @param classWeight By-class weights: an object mapping class indices\n *     (integers) to a weight (float) to apply to the model's loss for the\n *     samples from this class during training. This can be useful to tell the\n *     model to \"pay more attention\" to samples from an under-represented class.\n * @param sampleWeightMode The mode for the sample weights.\n * @return A Promise of weight tensor, of which the size of the first dimension\n *     matches that of `y`.\n */\nexport async function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) {\n    if (sampleWeight != null || sampleWeightMode != null) {\n        // TODO(cais): Once 'temporal' mode is implemented, document it in the doc\n        // string.\n        throw new Error('Support sampleWeight is not implemented yet');\n    }\n    if (classWeight != null) {\n        // Apply class weights per sample.\n        const yClasses = tidy(() => {\n            if (y.shape.length === 1) {\n                // Assume class indices.\n                return y.clone();\n            }\n            else if (y.shape.length === 2) {\n                if (y.shape[1] > 1) {\n                    // Assume one-hot encoding of classes.\n                    const axis = 1;\n                    return y.argMax(axis);\n                }\n                else if (y.shape[1] === 1) {\n                    // Class index.\n                    return y.reshape([y.shape[0]]);\n                }\n                else {\n                    throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) ` +\n                        `during handling of class weights. The size is expected to be ` +\n                        `>= 1.`);\n                }\n            }\n            else {\n                throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during ` +\n                    `handling of class weights. The rank is expected to be 1 or 2.`);\n            }\n        });\n        const yClassIndices = Array.from(await yClasses.data());\n        dispose(yClasses);\n        const classSampleWeight = [];\n        yClassIndices.forEach(classIndex => {\n            if (classWeight[classIndex] == null) {\n                throw new Error(`classWeight must contain all classes in the training data. ` +\n                    `The class ${classIndex} exists in the data but not in ` +\n                    `classWeight`);\n            }\n            else {\n                classSampleWeight.push(classWeight[classIndex]);\n            }\n        });\n        return tensor1d(classSampleWeight, 'float32');\n    }\n    else {\n        return null;\n    }\n}\n/**\n * Apply per-sample weights on the loss values from a number of samples.\n *\n * @param losses Loss tensor of shape `[batchSize]`.\n * @param sampleWeights Per-sample weight tensor of shape `[batchSize]`.\n * @returns Tensor of the same shape as`losses`.\n */\nexport function computeWeightedLoss(losses, sampleWeights) {\n    return mul(losses, sampleWeights);\n}\n//# sourceMappingURL=training_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Interfaces and methods for training models using TensorFlow.js datasets.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { scalar } from '@tensorflow/tfjs-core';\nimport { configureCallbacks, standardizeCallbacks } from '../base_callbacks';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { disposeTensorsInLogs } from '../logs';\nimport { singletonOrArray, toList } from '../utils/generic_utils';\nimport { standardizeClassWeights, standardizeWeights } from './training_utils';\n// Default batch size used during tensor-based validation.\nconst DEFAULT_VALIDATION_BATCH_SIZE = 32;\n/**\n * Standardize the output of a dataset iterator for use by\n * LayersModel.fitDataset().\n *\n * @param model: A `tf.LayersModel` object.\n * @param iteratorOut The output of a dataset iterator. It is required to be\n *   an object of the form `{xs: TensorOrArrayOrMap, ys:\n * TensorOrArrayOrMap}`, where `TensorOrArrayOrMap` is a single `tf.Tensor`,\n * a `tf.Tensor[]`, or a flat map from string names to `tf.Tensor`s.\n * @returns A flat array of `tf.Tensor` objects: the input `tf.Tensor`s\n *   followed by the target `tf.Tensor`s.  When `tf.Tensor`s are provided\n *   as a map, the order in the resulting array is taken from the `inputNames`\n *   and `outputNames` of the model.\n */\nfunction standardizeDataIteratorOutput(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, iteratorOut) {\n    let xs;\n    let ys;\n    const iteratorOutObj = iteratorOut;\n    xs = iteratorOutObj['xs'];\n    ys = iteratorOutObj['ys'];\n    tfc.util.assert(xs != null && ys != null, () => 'A Dataset iterator for fitDataset() is expected to generate ' +\n        'objects of the form `{xs: xVal, ys: yVal}`, where the two ' +\n        'values may be `tf.Tensor`, an array of Tensors, or a map of ' +\n        'string to Tensor.  The provided Dataset instead generates ' +\n        `${iteratorOut}`);\n    const flattenedXs = flattenTensorOrArrayOrMap('input', model.inputNames, xs);\n    const flattenedYs = flattenTensorOrArrayOrMap('output', model.outputNames, ys);\n    const batchSize = flattenedXs[0].shape[0];\n    tfc.util.assert(flattenedXs.length === model.inputs.length, () => `LayersModel has ${model.inputs.length} inputs, but the dataset ` +\n        `provides ${flattenedXs.length} inputs.  (Expected input keys: ` +\n        `${JSON.stringify(model.inputNames)})`);\n    tfc.util.assert(flattenedYs.length === model.outputs.length, () => `LayersModel has ${model.outputs.length} outputs, but the dataset ` +\n        `provides ${flattenedYs.length} outputs.  (Expected output keys: ` +\n        `${JSON.stringify(model.outputNames)})`);\n    for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {\n        tfc.util.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ` +\n            `${model.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; ` +\n            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);\n    }\n    for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {\n        tfc.util.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ` +\n            `${model.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; ` +\n            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);\n    }\n    return { xs: flattenedXs, ys: flattenedYs };\n}\nfunction flattenTensorOrArrayOrMap(inputOrOutput, names, values) {\n    if (values instanceof tfc.Tensor) {\n        return [values];\n    }\n    else if (Array.isArray(values)) {\n        tfc.util.assert(values.length === names.length, () => `Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`);\n        return values;\n    }\n    else {\n        const result = [];\n        // Check that all the required keys are available.\n        for (const name of names) {\n            if (values[name] == null) {\n                throw new ValueError(`The feature data generated by the dataset lacks the required ` +\n                    `${inputOrOutput} key '${name}'.`);\n            }\n            result.push(values[name]);\n        }\n        return result;\n    }\n}\nfunction standardizeTensorValidationData(data) {\n    if (data.length === 3) {\n        throw new NotImplementedError('Validation with sample weights is not implemented yet.');\n    }\n    return { xs: data[0], ys: data[1] };\n}\nexport async function fitDataset(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, dataset, args) {\n    const hasBatchesPerEpoch = args.batchesPerEpoch != null;\n    tfc.util.assert(model.optimizer != null, () => 'You must compile a model before training/testing. Use ' +\n        'LayersModel.compile(modelCompileConfig).');\n    tfc.util.assert(args != null, () => `For fitDataset(), the 2nd argument (config) is required, ` +\n        `but it is not provided in this call.`);\n    tfc.util.assert(args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs), () => `For fitDataset(), config.epochs is expected to be a positive ` +\n        `integer, but got ${args.epochs}`);\n    tfc.util.assert(!hasBatchesPerEpoch ||\n        (args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch)), () => `For fitDataset(), config.batchesPerEpoch is expected to be a ` +\n        `positive integer if specified, but got ${args.batchesPerEpoch}`);\n    tfc.util.assert(\n    // tslint:disable-next-line:no-any\n    args['validationSplit'] == null, () => '`validationSplit` is not supported by `fitDataset()`. ' +\n        'Use validationData instead.');\n    if (model.isTraining) {\n        throw new Error('Cannot start training because another fit() call is ongoing.');\n    }\n    model.isTraining = true;\n    try {\n        const doValidation = args.validationData != null;\n        let valXs;\n        let valYs;\n        if (doValidation) {\n            if (isDatasetObject(args.validationData)) {\n                tfc.util.assert(args.validationBatches == null ||\n                    (args.validationBatches > 0 &&\n                        Number.isInteger(args.validationBatches)), () => `For fitDataset() with dataset-based validation, ` +\n                    `config.validationBatches is expected not to be provided, ` +\n                    `or to be a positive integer, ` +\n                    `but got ${args.validationBatches}`);\n            }\n            else {\n                const validationData = standardizeTensorValidationData(args.validationData);\n                valXs = validationData.xs;\n                valYs = validationData.ys;\n            }\n        }\n        const trainFunction = model.makeTrainFunction();\n        const outLabels = model.getDedupedMetricsNames();\n        let callbackMetrics;\n        if (doValidation) {\n            callbackMetrics =\n                outLabels.slice().concat(outLabels.map(n => 'val_' + n));\n        }\n        else {\n            callbackMetrics = outLabels.slice();\n        }\n        const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);\n        const verbose = args.verbose == null ? 1 : args.verbose;\n        const { callbackList, history } = configureCallbacks(callbacks, verbose, args.epochs, null, null, getStepsPerEpoch(dataset, args), null, // Batch size determined by the dataset itself.\n        doValidation, callbackMetrics);\n        callbackList.setModel(model);\n        model.history = history;\n        await callbackList.onTrainBegin();\n        model.stopTraining_ = false;\n        let epoch = args.initialEpoch == null ? 0 : args.initialEpoch;\n        let dataIterator = await dataset.iterator();\n        while (epoch < args.epochs) {\n            const epochLogs = {};\n            await callbackList.onEpochBegin(epoch);\n            let stepsDone = 0;\n            let batchIndex = 0;\n            if (!hasBatchesPerEpoch) {\n                dataIterator = await dataset.iterator();\n            }\n            while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) {\n                const iteratorOut = await dataIterator.next();\n                // If `batchesPerEpoch` is specified, the dataset should not be\n                // exhausted until all epoches are done.\n                if (hasBatchesPerEpoch && iteratorOut.done) {\n                    console.warn('You provided `batchesPerEpoch` as ' +\n                        `${args.batchesPerEpoch}, ` +\n                        'but your dataset iterator ran out of data after ' +\n                        `${stepsDone} batches; ` +\n                        'interrupting training. Make sure that your ' +\n                        'dataset can generate at least `batchesPerEpoch * epochs` ' +\n                        'batches (in this case, ' +\n                        `${args.batchesPerEpoch * args.epochs} batches). ` +\n                        'You may need to use the repeat() function when building ' +\n                        'your dataset.');\n                    break;\n                }\n                if (iteratorOut.value != null) {\n                    const { xs, ys } = standardizeDataIteratorOutput(model, iteratorOut.value);\n                    const batchLogs = {};\n                    batchLogs['batch'] = batchIndex;\n                    batchLogs['size'] = xs[0].shape[0];\n                    await callbackList.onBatchBegin(batchIndex, batchLogs);\n                    const sampleWeights = [];\n                    if (args.classWeight != null) {\n                        const standardClassWeights = standardizeClassWeights(args.classWeight, model.outputNames);\n                        for (let i = 0; i < standardClassWeights.length; ++i) {\n                            sampleWeights.push(await standardizeWeights(ys[i], null, standardClassWeights[i]));\n                        }\n                    }\n                    // Train on batch.\n                    const ins = xs.concat(ys).concat(sampleWeights);\n                    const outs = trainFunction(ins);\n                    tfc.dispose(ins);\n                    for (let i = 0; i < outLabels.length; ++i) {\n                        const label = outLabels[i];\n                        const out = outs[i];\n                        batchLogs[label] = out;\n                        tfc.keep(out);\n                    }\n                    await callbackList.onBatchEnd(batchIndex, batchLogs);\n                    disposeTensorsInLogs(batchLogs);\n                    batchIndex++;\n                    stepsDone++;\n                }\n                if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch :\n                    iteratorOut.done) {\n                    // Epoch finished. Perform validation.\n                    if (doValidation) {\n                        let valOuts;\n                        if (isDatasetObject(args.validationData)) {\n                            valOuts = toList(await model.evaluateDataset(args.validationData, { batches: args.validationBatches }));\n                        }\n                        else {\n                            valOuts = toList(model.evaluate(valXs, valYs, {\n                                batchSize: args.validationBatchSize == null ?\n                                    DEFAULT_VALIDATION_BATCH_SIZE :\n                                    args.validationBatchSize,\n                                verbose: 0\n                            }));\n                        }\n                        for (let i = 0; i < model.metricsNames.length; ++i) {\n                            epochLogs[`val_${model.metricsNames[i]}`] = valOuts[i];\n                        }\n                    }\n                    // Call `break` to exit one epoch lopp after validation is done. If\n                    // config.batchesPerEpoch is specified, an epoch while loop will\n                    // stop when `stepsDone >= config.batchesPerEpoch`. When\n                    // config.batchesPerEpoch is not provided, the following `break` is\n                    // required to exit the while lopp after dataset is exhausted.\n                    break;\n                }\n                if (model.stopTraining_) {\n                    break;\n                }\n            }\n            await callbackList.onEpochEnd(epoch, epochLogs);\n            epoch++;\n            if (model.stopTraining_) {\n                break;\n            }\n        }\n        await callbackList.onTrainEnd();\n        await model.history.syncData();\n        return model.history;\n    }\n    finally {\n        model.isTraining = false;\n    }\n}\n/** Helper function that determines number of steps (batches) per epoch. */\nfunction getStepsPerEpoch(dataset, args) {\n    // Attempt to determine # of batches in an epoch.\n    let stepsPerEpoch = null;\n    if (args.batchesPerEpoch != null) {\n        stepsPerEpoch = args.batchesPerEpoch;\n    }\n    else if (Number.isFinite(dataset.size)) {\n        stepsPerEpoch = dataset.size;\n    }\n    return stepsPerEpoch;\n}\n// Check if provided object is a Dataset object by checking its .iterator\n// element.\nfunction isDatasetObject(dataset) {\n    return (typeof dataset.iterator === 'function');\n}\n// Check if provided object is a LazyIterator object by checking it's .next\n// element.\nfunction isLazyIteratorObject(iterator) {\n    return (typeof iterator.next === 'function');\n}\nexport async function evaluateDataset(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, dataset, args) {\n    args = args || {};\n    const hasBatches = args.batches != null;\n    const f = model.testFunction;\n    let outs = [];\n    if (args.verbose > 0) {\n        throw new NotImplementedError('Verbose mode is not implemented yet.');\n    }\n    tfc.util.assert(!hasBatches || (args.batches > 0 && Number.isInteger(args.batches)), () => 'Test loop expects `batches` to be a positive integer, but ' +\n        `received ${JSON.stringify(args.batches)}`);\n    const dataIterator = isLazyIteratorObject(dataset) ?\n        dataset :\n        await dataset.iterator();\n    // Keeps track of number of examples used in this evaluation.\n    let numExamples = 0;\n    let batch = 0;\n    while (hasBatches ? batch < args.batches : true) {\n        const iteratorOut = await dataIterator.next();\n        outs = tfc.tidy(() => {\n            if (iteratorOut.value) {\n                // TODO(cais): Once real dataset is available, use\n                //   `map(x => standardizeDataIteratorOutput(model, x).map(f)`.\n                const { xs, ys } = standardizeDataIteratorOutput(model, iteratorOut.value);\n                const xsAndYs = xs.concat(ys);\n                const batchOuts = tfc.tidy(() => f(xsAndYs));\n                tfc.dispose(xsAndYs);\n                if (batch === 0) {\n                    for (let i = 0; i < batchOuts.length; ++i) {\n                        outs.push(scalar(0));\n                    }\n                }\n                const batchSize = xsAndYs[0].shape[0];\n                for (let i = 0; i < batchOuts.length; ++i) {\n                    const batchOut = batchOuts[i];\n                    const oldScalar = outs[i];\n                    outs[i] =\n                        tfc.tidy(() => tfc.add(outs[i], tfc.mul(batchSize, batchOut)));\n                    if (batch > 0) {\n                        tfc.dispose(oldScalar);\n                    }\n                }\n                tfc.dispose(batchOuts);\n                numExamples += batchSize;\n                ++batch;\n            }\n            return outs;\n        });\n        if (iteratorOut.done) {\n            if (hasBatches) {\n                console.warn('Your dataset iterator ran out of data during evaluateDataset(). ' +\n                    'Interrupting evalution. Make sure that your ' +\n                    'dataset can generate at least `batches` ' +\n                    `batches (in this case, ${args.batches} batches). ` +\n                    'You may need to use the repeat() function when building ' +\n                    'your dataset.');\n            }\n            break;\n        }\n    }\n    for (let i = 0; i < outs.length; ++i) {\n        const oldScalar = outs[i];\n        outs[i] = tfc.div(outs[i], numExamples);\n        tfc.dispose(oldScalar);\n    }\n    return singletonOrArray(outs);\n}\n//# sourceMappingURL=training_dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Interfaces and methods for training models using tf.Tensor objects.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { Tensor, tensor1d, util } from '@tensorflow/tfjs-core';\nimport { expandDims, gather, sliceAlongFirstAxis } from '../backend/tfjs_backend';\nimport { configureCallbacks, standardizeCallbacks } from '../base_callbacks';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { disposeTensorsInLogs } from '../logs';\nimport { range } from '../utils/math_utils';\nexport function checkBatchSize(batchSize) {\n    tfc.util.assert(batchSize > 0 && Number.isInteger(batchSize), () => `batchSize is required to be a positive integer, but got ${batchSize}`);\n}\n/**\n * Slice a Tensor or an Array of Tensors, by start and stop indices.\n *\n * Porting Note: The `_slice_arrays` function in PyKeras is covered by this\n *   function and `sliceArraysByIndices()` together.\n *\n * @param arrays: the input.\n * @param start: the starting index (inclusive).\n * @param stop: the stopping index (exclusive).\n * @returns The result of the slicing. If `arrays` is an `Array` of\n *   `tf.Tensor`s, the slicing will be applied to all elements of the `Array`\n *   in the same way.\n */\nexport function sliceArrays(arrays, start, stop) {\n    if (arrays == null) {\n        return [null];\n    }\n    else if (Array.isArray(arrays)) {\n        return arrays.map(array => sliceAlongFirstAxis(array, start, stop - start));\n    }\n    else { // Tensor.\n        return sliceAlongFirstAxis(arrays, start, stop - start);\n    }\n}\n/**\n * Slice a Tensor or an Array of Tensors, by random-order indices.\n *\n * Porting Note: The `_slice_arrays` function in PyKeras is covered by this\n *   function and `sliceArrays()` together.\n *\n * @param arrays The input `tf.Tensor` or `Array` of `tf.Tensor`s to slice.\n *   If an `Array` of `tf.Tensor`s, all `tf.Tensor`s will be sliced in the\n *   same fashion.\n * @param indices The indices to use for slicing along the first (batch)\n *   dimension.\n * @returns Result(s) of the slicing.\n */\nexport function sliceArraysByIndices(arrays, indices) {\n    return tfc.tidy(() => {\n        if (arrays == null) {\n            return null;\n        }\n        else if (Array.isArray(arrays)) {\n            return arrays.map(array => sliceArraysByIndices(array, indices));\n        }\n        else {\n            // TODO(cais): indices should be a pre-constructed Tensor1D to avoid\n            //   tensor1d() calls.\n            return gather(arrays, indices.dtype === 'int32' ? indices : indices.toInt());\n        }\n    });\n}\n/**\n * Returns a list of batch indices (tuples of indices).\n * @param size: Integer, total size of the data to slice into batches.\n * @param batchSize: Integer, batch size.\n * @returns An Array of [batchStart, batchEnd] tuples. batchStart is\n *   inclusive; batchEnd is exclusive. I.e., each batch consists of indices x\n *   that satisfy batchStart <= x < batchEnd.\n */\nexport function makeBatches(size, batchSize) {\n    const output = [];\n    let batchStart = 0;\n    let batchEnd = null;\n    while (batchStart < size) {\n        batchEnd = batchStart + batchSize;\n        if (batchEnd >= size) {\n            batchEnd = size;\n        }\n        output.push([batchStart, batchEnd]);\n        batchStart = batchEnd;\n    }\n    return output;\n}\n/**\n * Abstract fit function for `f(ins)`.\n * @param f A Function returning a list of tensors. For training, this\n *   function is expected to perform the updates to the variables.\n * @param ins List of tensors to be fed to `f`.\n * @param outLabels List of strings, display names of the outputs of `f`.\n * @param batchSize Integer batch size or `== null` if unknown. Default : 32.\n * @param epochs Number of times to iterate over the data. Default : 1.\n * @param verbose Verbosity mode: 0, 1, or 2. Default: 1.\n * @param callbacks List of callbacks to be called during training.\n * @param valF Function to call for validation.\n * @param valIns List of tensors to be fed to `valF`.\n * @param shuffle Whether to shuffle the data at the beginning of every\n * epoch. Default : true.\n * @param callbackMetrics List of strings, the display names of the metrics\n *   passed to the callbacks. They should be the concatenation of the\n *   display names of the outputs of `f` and the list of display names\n *   of the outputs of `valF`.\n * @param initialEpoch Epoch at which to start training (useful for\n *   resuming a previous training run). Default : 0.\n * @param stepsPerEpoch Total number of steps (batches on samples) before\n *   declaring one epoch finished and starting the next epoch. Ignored with\n *   the default value of `undefined` or `null`.\n * @param validationSteps Number of steps to run validation for (only if\n *   doing validation from data tensors). Not applicable for tfjs-layers.\n * @returns A `History` object.\n */\nasync function fitLoop(\n// Type `model` as `any` here to avoid circular dependency w/ training.ts.\n// tslint:disable-next-line:no-any\nmodel, f, ins, outLabels, batchSize, epochs, verbose, callbacks, valF, valIns, shuffle, callbackMetrics, initialEpoch, stepsPerEpoch, validationSteps) {\n    if (batchSize == null) {\n        batchSize = 32;\n    }\n    if (epochs == null) {\n        epochs = 1;\n    }\n    if (shuffle == null) {\n        shuffle = true;\n    }\n    if (initialEpoch == null) {\n        initialEpoch = 0;\n    }\n    // TODO(cais): Change const to let below when implementing validation.\n    let doValidation = false;\n    if (valF != null && valIns != null) {\n        doValidation = true;\n        // TODO(cais): verbose message.\n    }\n    if (validationSteps != null) {\n        doValidation = true;\n        if (stepsPerEpoch == null) {\n            throw new ValueError('Can only use `validationSteps` when doing step-wise training, ' +\n                'i.e., `stepsPerEpoch` must be set.');\n        }\n    }\n    const numTrainSamples = model.checkNumSamples(ins, batchSize, stepsPerEpoch, 'steps_per_epoch');\n    let indexArray;\n    if (numTrainSamples != null) {\n        indexArray = range(0, numTrainSamples);\n    }\n    if (verbose == null) {\n        verbose = 1;\n    }\n    const { callbackList, history } = configureCallbacks(callbacks, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics);\n    callbackList.setModel(model);\n    model.history = history;\n    await callbackList.onTrainBegin();\n    model.stopTraining_ = false;\n    // TODO(cais): Take care of callbacks.validation_data as in PyKeras.\n    // TODO(cais): Pre-convert feeds for performance as in PyKeras.\n    for (let epoch = initialEpoch; epoch < epochs; ++epoch) {\n        await callbackList.onEpochBegin(epoch);\n        const epochLogs = {};\n        if (stepsPerEpoch != null) {\n            throw new NotImplementedError('stepsPerEpoch mode is not implemented yet.');\n        }\n        else {\n            if (shuffle === 'batch') {\n                throw new NotImplementedError('batch shuffling is not implemneted yet');\n            }\n            else if (shuffle) {\n                util.shuffle(indexArray);\n            }\n            // Convert the potentially shuffled indices to Tensor1D, to avoid the\n            // cost of repeated creation of Array1Ds later on.\n            const epochIndexArray1D = tensor1d(indexArray);\n            const batches = makeBatches(numTrainSamples, batchSize);\n            for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                const batchLogs = {};\n                await callbackList.onBatchBegin(batchIndex, batchLogs);\n                tfc.tidy(() => {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    const batchIds = sliceAlongFirstAxis(epochIndexArray1D, batchStart, batchEnd - batchStart);\n                    batchLogs['batch'] = batchIndex;\n                    batchLogs['size'] = batchEnd - batchStart;\n                    // TODO(cais): In ins, train flag can be a number, instead of an\n                    //   Tensor? Do we need to handle this in tfjs-layers?\n                    const insBatch = sliceArraysByIndices(ins, batchIds);\n                    const outs = f(insBatch);\n                    for (let i = 0; i < outLabels.length; ++i) {\n                        const label = outLabels[i];\n                        const out = outs[i];\n                        batchLogs[label] = out;\n                        tfc.keep(out);\n                        // TODO(cais): Use scope() to avoid ownership.\n                    }\n                    if (batchIndex === batches.length - 1) { // Last batch.\n                        if (doValidation) {\n                            const valOuts = model.testLoop(valF, valIns, batchSize);\n                            // Porting Notes: In tfjs-layers, valOuts is always an Array.\n                            for (let i = 0; i < outLabels.length; ++i) {\n                                const label = outLabels[i];\n                                const out = valOuts[i];\n                                tfc.keep(out);\n                                // TODO(cais): Use scope() to avoid ownership.\n                                epochLogs['val_' + label] = out;\n                            }\n                        }\n                    }\n                });\n                await callbackList.onBatchEnd(batchIndex, batchLogs);\n                disposeTensorsInLogs(batchLogs);\n                if (model.stopTraining_) {\n                    break;\n                }\n                // TODO(cais): return outs as list of Tensor.\n            }\n            epochIndexArray1D.dispose();\n        }\n        // TODO(cais): Run validation at the end of the epoch.\n        await callbackList.onEpochEnd(epoch, epochLogs);\n        if (model.stopTraining_) {\n            break;\n        }\n    }\n    await callbackList.onTrainEnd();\n    await model.history.syncData();\n    return model.history;\n}\nexport async function fitTensors(\n// Type `model` as `any` here to avoid circular dependency w/ training.ts.\n// tslint:disable-next-line:no-any\nmodel, x, y, args = {}) {\n    if (model.isTraining) {\n        throw new Error('Cannot start training because another fit() call is ongoing.');\n    }\n    model.isTraining = true;\n    let inputs;\n    let targets;\n    let inputValX;\n    let inputValY;\n    let valX;\n    let valY;\n    let sampleWeights;\n    try {\n        const batchSize = args.batchSize == null ? 32 : args.batchSize;\n        checkBatchSize(batchSize);\n        // Validate user data.\n        // TODO(cais): Support sampleWeight.\n        const checkBatchAxis = false;\n        const standardizedOuts = await model.standardizeUserData(x, y, args.sampleWeight, args.classWeight, checkBatchAxis, batchSize);\n        inputs = standardizedOuts[0];\n        targets = standardizedOuts[1];\n        sampleWeights = standardizedOuts[2];\n        // Prepare validation data.\n        let doValidation = false;\n        let valIns;\n        if (args.validationData != null && args.validationData.length > 0) {\n            doValidation = true;\n            if (args.validationData.length === 2) {\n                // config.validationData consists of valX and valY.\n                inputValX = args.validationData[0];\n                inputValY = args.validationData[1];\n            }\n            else if (args.validationData.length === 3) {\n                throw new NotImplementedError('validationData including sample weights is not supported yet.');\n            }\n            else {\n                throw new ValueError(`When passing validation data, it must contain 2 (valX, valY) ` +\n                    `or 3 (valX, valY, valSampleWeight) items; ` +\n                    `${args.validationData} is invalid.`);\n            }\n            const checkBatchAxis = true;\n            const valStandardized = await model.standardizeUserData(inputValX, inputValY, null, /** Unused sample weights. */ null, /** Unused class weights. */ checkBatchAxis, batchSize);\n            valX = valStandardized[0];\n            valY = valStandardized[1];\n            valIns = valX.concat(valY);\n            // TODO(cais): Add useLearningPhase data properly.\n        }\n        else if (args.validationSplit != null && args.validationSplit > 0 &&\n            args.validationSplit < 1) {\n            doValidation = true;\n            // Porting Note: In tfjs-layers, inputs[0] is always a Tensor.\n            const splitAt = Math.floor(inputs[0].shape[0] * (1 - args.validationSplit));\n            const originalBatchSize = inputs[0].shape[0];\n            valX = sliceArrays(inputs, splitAt, originalBatchSize);\n            inputs = sliceArrays(inputs, 0, splitAt);\n            valY = sliceArrays(targets, splitAt, originalBatchSize);\n            targets = sliceArrays(targets, 0, splitAt);\n            // TODO(cais): Once sampleWeights becomes available, slice it to get\n            //   valSampleWeights.\n            valIns = valX.concat(valY);\n            // TODO(cais): Add useLearningPhase data properly.\n        }\n        else if (args.validationSteps != null) {\n            doValidation = true;\n            // TODO(cais): Add useLearningPhase.\n        }\n        const ins = inputs.concat(targets).concat(sampleWeights);\n        model.checkTrainableWeightsConsistency();\n        // TODO(cais): Handle use_learning_phase and learning_phase?\n        // Porting Note: Here we see a key deviation of tfjs-layers from\n        // Keras.\n        //  Due to the imperative nature of tfjs-layers' backend (tfjs-core),\n        //  we do not construct symbolic computation graphs to embody the\n        //  training process. Instead, we define a function that performs the\n        //  training action. In PyKeras, the data (inputs and targets) are fed\n        //  through graph placeholders. In tfjs-layers, the data are fed as\n        //  function arguments. Since the function are defined below in the\n        //  scope, we don't have equivalents of PyKeras's\n        //  `_make_train_funciton`.\n        const trainFunction = model.makeTrainFunction();\n        const outLabels = model.getDedupedMetricsNames();\n        let valFunction;\n        let callbackMetrics;\n        if (doValidation) {\n            model.makeTestFunction();\n            valFunction = model.testFunction;\n            callbackMetrics =\n                outLabels.slice().concat(outLabels.map(n => 'val_' + n));\n        }\n        else {\n            valFunction = null;\n            valIns = [];\n            callbackMetrics = outLabels.slice();\n        }\n        const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);\n        const out = await fitLoop(model, trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null);\n        return out;\n    }\n    finally {\n        model.isTraining = false;\n        // Memory clean up.\n        disposeNewTensors(inputs, x);\n        disposeNewTensors(targets, y);\n        disposeNewTensors(valX, inputValX);\n        disposeNewTensors(valY, inputValY);\n        if (sampleWeights != null) {\n            tfc.dispose(sampleWeights);\n        }\n    }\n    // TODO(cais): Add value to outLabels.\n}\n/**\n * Ensure tensors all have a rank of at least 2.\n *\n * If a tensor has a rank of 1, it is dimension-expanded to rank 2.\n * If any tensor has a rank of 0 (i.e., is a scalar), an error will be thrown.\n */\nexport function ensureTensorsRank2OrHigher(tensors) {\n    const outs = [];\n    if (tensors instanceof Tensor) {\n        tensors = [tensors];\n    }\n    // Make Tensors at least 2D.\n    for (let i = 0; i < tensors.length; ++i) {\n        const tensor = tensors[i];\n        if (tensor.rank === 1) {\n            outs.push(expandDims(tensor, 1));\n        }\n        else if (tensor.rank === 0) {\n            throw new Error('Expected tensor to be at least 1D, but received a 0D tensor ' +\n                '(scalar).');\n        }\n        else {\n            outs.push(tensor);\n        }\n    }\n    return outs;\n}\n/**\n * Compare a set of tensors with a reference (old) set, discard the ones\n * in the new set that are not present in the reference set.\n *\n * This method is used for memory clenaup during calls such as\n * LayersModel.fit().\n *\n * @param tensors New set which may contain Tensors not present in\n *   `refTensors`.\n * @param refTensors Reference Tensor set.\n */\n// TODO(cais, kangyizhang): Deduplicate with tfjs-data.\nexport function disposeNewTensors(tensors, refTensors) {\n    if (tensors == null) {\n        return;\n    }\n    const oldTensorIds = [];\n    if (refTensors instanceof Tensor) {\n        oldTensorIds.push(refTensors.id);\n    }\n    else if (Array.isArray(refTensors)) {\n        refTensors.forEach(t => oldTensorIds.push(t.id));\n    }\n    else if (refTensors != null) {\n        // `oldTensors` is a map from string name to Tensor.\n        for (const name in refTensors) {\n            const oldTensor = refTensors[name];\n            oldTensorIds.push(oldTensor.id);\n        }\n    }\n    const tensorsToDispose = [];\n    if (tensors instanceof Tensor) {\n        if (oldTensorIds.indexOf(tensors.id) === -1) {\n            tensorsToDispose.push(tensors);\n        }\n    }\n    else if (Array.isArray(tensors)) {\n        tensors.forEach(t => {\n            if (oldTensorIds.indexOf(t.id) === -1) {\n                tensorsToDispose.push(t);\n            }\n        });\n    }\n    else if (tensors != null) {\n        // `oldTensors` is a map from string name to Tensor.\n        for (const name in tensors) {\n            const tensor = tensors[name];\n            if (oldTensorIds.indexOf(tensor.id) === -1) {\n                tensorsToDispose.push(tensor);\n            }\n        }\n    }\n    tensorsToDispose.forEach(t => {\n        if (!t.isDisposed) {\n            t.dispose();\n        }\n    });\n}\n//# sourceMappingURL=training_tensors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source: engine/training.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { io, Optimizer, scalar, serialization, Tensor, tensor1d, util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { deserialize } from '../layers/serialization';\nimport * as losses from '../losses';\nimport * as Metrics from '../metrics';\nimport * as optimizers from '../optimizers';\nimport { checkUserDefinedMetadata } from '../user_defined_metadata';\nimport { count, pyListRepeat, singletonOrArray, toCamelCase, toSnakeCase, unique } from '../utils/generic_utils';\nimport { printSummary } from '../utils/layer_utils';\nimport { range } from '../utils/math_utils';\nimport { convertPythonicToTs } from '../utils/serialization_utils';\nimport { version } from '../version';\nimport { Container } from './container';\nimport { execute, FeedDict } from './executor';\nimport { evaluateDataset, fitDataset } from './training_dataset';\nimport { checkBatchSize, disposeNewTensors, ensureTensorsRank2OrHigher, fitTensors, makeBatches, sliceArrays, sliceArraysByIndices } from './training_tensors';\nimport { computeWeightedLoss, standardizeClassWeights, standardizeWeights } from './training_utils';\n/**\n * Helper function for polymorphic input data: 1. singleton Tensor.\n */\nexport function isDataTensor(x) {\n    return x instanceof Tensor;\n}\n/**\n * Helper function for polymorphic input data: 2. Array of Tensor.\n */\nexport function isDataArray(x) {\n    return Array.isArray(x);\n}\n/**\n * Helper function for polymorphic input data: 3. \"dict\" of Tensor.\n */\nexport function isDataDict(x) {\n    return !isDataTensor(x) && !isDataArray(x);\n}\n/**\n * Normalizes inputs and targets provided by users.\n * @param data User-provided input data (polymorphic).\n * @param names An Array of expected Tensor names.\n * @param shapes Optional Array of expected Tensor shapes.\n * @param checkBatchAxis Whether to check that the batch axis of the arrays\n *   match  the expected value found in `shapes`.\n * @param exceptionPrefix String prefix used for exception formatting.\n * @returns List of standardized input Tensors (one Tensor per model input).\n * @throws ValueError: in case of improperly formatted user data.\n */\nexport function standardizeInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = '') {\n    if (names == null || names.length === 0) {\n        // Check for the case where the model expected no data, but some data got\n        // sent.\n        if (data != null) {\n            let gotUnexpectedData = false;\n            if (isDataArray(data) && data.length > 0) {\n                gotUnexpectedData = true;\n            }\n            else if (isDataDict(data)) {\n                for (const key in data) {\n                    if (data.hasOwnProperty(key)) {\n                        gotUnexpectedData = true;\n                        break;\n                    }\n                }\n            }\n            else {\n                // `data` is a singleton Tensor in this case.\n                gotUnexpectedData = true;\n            }\n            if (gotUnexpectedData) {\n                throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, ` +\n                    `but got ${data}`);\n            }\n        }\n        return [];\n    }\n    if (data == null) {\n        return names.map(name => null);\n    }\n    let arrays;\n    if (isDataDict(data)) {\n        data = data;\n        arrays = [];\n        for (const name of names) {\n            if (data[name] == null) {\n                throw new ValueError(`No data provided for \"${name}\". Need data for each key in: ` +\n                    `${names}`);\n            }\n            arrays.push(data[name]);\n        }\n    }\n    else if (isDataArray(data)) {\n        data = data;\n        if (data.length !== names.length) {\n            throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of ` +\n                `Tensors that you are passing to your model is not the size the ` +\n                `model expected. Expected to see ${names.length} Tensor(s), but ` +\n                `instead got the following list of Tensor(s): ${data}`);\n        }\n        arrays = data;\n    }\n    else {\n        data = data;\n        if (names.length > 1) {\n            throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), ` +\n                `but only received one Tensor. Found: Tensor with shape ${data.shape}`);\n        }\n        arrays = [data];\n    }\n    arrays = ensureTensorsRank2OrHigher(arrays);\n    // Check shape compatibility.\n    if (shapes != null) {\n        for (let i = 0; i < names.length; ++i) {\n            if (shapes[i] == null) {\n                continue;\n            }\n            const array = arrays[i];\n            if (array.shape.length !== shapes[i].length) {\n                throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                    `to have ${shapes[i].length} dimension(s). but got array with ` +\n                    `shape ${array.shape}`);\n            }\n            for (let j = 0; j < shapes[i].length; ++j) {\n                if (j === 0 && !checkBatchAxis) {\n                    // Skip the first (batch) axis.\n                    continue;\n                }\n                const dim = array.shape[j];\n                const refDim = shapes[i][j];\n                if (refDim != null && refDim >= 0 && dim !== refDim) {\n                    throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                        `to have shape [${shapes[i]}], but got array with shape ` +\n                        `[${array.shape}].`);\n                }\n            }\n        }\n    }\n    return arrays;\n}\n/**\n * User input validation for Tensors.\n * @param inputs `Array` of `tf.Tensor`s for inputs.\n * @param targets `Array` of `tf.Tensor`s for targets.\n * @param weights Optional `Array` of `tf.Tensor`s for sample weights.\n * @throws ValueError: in case of incorrectly formatted data.\n */\nexport function checkArrayLengths(inputs, targets, weights) {\n    const setX = unique(inputs.map(input => input.shape[0]));\n    setX.sort();\n    const setY = unique(targets.map(target => target.shape[0]));\n    setY.sort();\n    // TODO(cais): Check `weights` as well.\n    if (setX.length > 1) {\n        throw new ValueError(`All input Tensors (x) should have the same number of samples. ` +\n            `Got array shapes: ` +\n            `${JSON.stringify(inputs.map(input => input.shape))}`);\n    }\n    if (setY.length > 1) {\n        throw new ValueError(`All target Tensors (y) should have the same number of samples. ` +\n            `Got array shapes: ` +\n            `${JSON.stringify(targets.map(target => target.shape))}`);\n    }\n    if (setX.length > 0 && setY.length > 0 && !util.arraysEqual(setX, setY)) {\n        throw new ValueError(`Input Tensors should have the same number of samples as target ` +\n            `Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target ` +\n            `sample(s).`);\n    }\n}\n/**\n * Validation on the compatibility of targes and loss functions.\n *\n * This helps prevent users from using loss functions incorrectly.\n *\n * @param targets `Array` of `tf.Tensor`s of targets.\n * @param lossFns `Array` of loss functions.\n * @param outputShapes `Array` of shapes of model outputs.\n */\nfunction checkLossAndTargetCompatibility(targets, lossFns, outputShapes) {\n    // TODO(cais): Dedicated test coverage?\n    const keyLosses = [\n        losses.meanSquaredError, losses.binaryCrossentropy,\n        losses.categoricalCrossentropy\n    ];\n    for (let i = 0; i < targets.length; ++i) {\n        const y = targets[i];\n        const loss = lossFns[i];\n        const shape = outputShapes[i];\n        if (loss == null) {\n            continue;\n        }\n        if (loss === losses.categoricalCrossentropy) {\n            if (y.shape[y.shape.length - 1] === 1) {\n                throw new ValueError(`You are passing a target array of shape ${y.shape} while using ` +\n                    `a loss 'categorical_crossentropy'. 'categorical_crossentropy'` +\n                    `expects targets to be binary matrices (1s and 0s) of shape ` +\n                    `[samples, classes].`);\n                // TODO(cais): Example code in error message.\n            }\n        }\n        if (keyLosses.indexOf(loss) !== -1) {\n            const slicedYShape = y.shape.slice(1);\n            const slicedShape = shape.slice(1);\n            for (let j = 0; j < slicedYShape.length; ++j) {\n                const targetDim = slicedYShape[j];\n                const outDim = slicedShape[j];\n                if (outDim != null && targetDim !== outDim) {\n                    throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an ` +\n                        `output of shape ${shape}, while using a loss function that ` +\n                        `expects targets to have the same shape as the output.`);\n                }\n            }\n        }\n    }\n}\n/**\n * Check inputs provided by the user.\n *\n * Porting Note: This corresponds to _standardize_input_data() in Python\n *   Keras. Because of the strong typing in TF.js, we do not need to convert\n *   the data. Specifically:\n *   1) in PyKeras, `data` can be `DataFrame` instances from pandas, for\n *      example. We don't need to worry about that here because there is no\n *      widely popular javascript/typesdcript equivalent of pandas (so far).\n *      If one becomes available in the future, we can add support.\n *   2) in PyKeras, inputs can be Python dict. But here we are stipulating\n * that the data is either a single `tf.Tensor` or an Array of `tf.Tensor`s. We\n * may add support for `Object` data inputs in the future when the need\n * arises.\n *\n * Instead, we perform basic checks for number of parameters and shapes.\n *\n * @param data: The input data.\n * @param names: Name for the inputs, from the model.\n * @param shapes: Expected shapes for the input data, from the model.\n * @param checkBatchAxis: Whether the size along the batch axis (i.e., the\n *   first dimension) will be checked for matching.\n * @param exceptionPrefix: Execption prefix message, used in generating error\n *   messages.\n * @throws ValueError: on incorrect number of inputs or mismatches in shapes.\n */\nfunction checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = '') {\n    let arrays;\n    if (Array.isArray(data)) {\n        if (data.length !== names.length) {\n            throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of ` +\n                `Tensors that you are passing to your model is not the size the ` +\n                `the model expected. Expected to see ${names.length} Tensor(s),` +\n                ` but instead got ${data.length} Tensors(s).`);\n        }\n        arrays = data;\n    }\n    else {\n        if (names.length > 1) {\n            throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, ` +\n                `but only received one Tensor. Found: array with shape ` +\n                `${JSON.stringify(data.shape)}.`);\n        }\n        arrays = [data];\n    }\n    if (shapes != null) {\n        for (let i = 0; i < names.length; ++i) {\n            if (shapes[i] == null) {\n                continue;\n            }\n            const array = arrays[i];\n            if (array.shape.length !== shapes[i].length) {\n                throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                    `to have ${shapes[i].length} dimension(s), but got array with ` +\n                    `shape ${JSON.stringify(array.shape)}`);\n            }\n            for (let j = 0; j < shapes[i].length; ++j) {\n                if (j === 0 && !checkBatchAxis) {\n                    continue;\n                }\n                const dim = array.shape[j];\n                const refDim = shapes[i][j];\n                if (refDim != null) {\n                    if (refDim !== dim) {\n                        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ` +\n                            `${names[i]} to have shape ${JSON.stringify(shapes[i])} but ` +\n                            `got array with shape ${JSON.stringify(array.shape)}.`);\n                    }\n                }\n            }\n        }\n    }\n}\n/**\n * Maps metric functions to model outputs.\n * @param metrics An shortcut strings name, metric function, `Array` or dict\n *   (`Object`) of metric functions.\n * @param outputNames An `Array` of the names of model outputs.\n * @returns An `Array` (one entry per model output) of `Array` of metric\n *   functions. For instance, if the model has 2 outputs, and for the first\n *   output we want to compute `binaryAccuracy` and `binaryCrossentropy`,\n *   and just `binaryAccuracy` for the second output, the `Array` would look\n *   like:\n *     `[[binaryAccuracy, binaryCrossentropy],  [binaryAccuracy]]`\n * @throws TypeError: incompatible metrics format.\n */\nexport function collectMetrics(metrics, outputNames) {\n    if (metrics == null || Array.isArray(metrics) && metrics.length === 0) {\n        return outputNames.map(name => []);\n    }\n    let wrappedMetrics;\n    if (typeof metrics === 'string' || typeof metrics === 'function') {\n        wrappedMetrics = [metrics];\n    }\n    else if (Array.isArray(metrics) || typeof metrics === 'object') {\n        wrappedMetrics = metrics;\n    }\n    else {\n        throw new TypeError('Type of metrics argument not understood. Expected an string,' +\n            `function, Array, or Object, found: ${metrics}`);\n    }\n    if (Array.isArray(wrappedMetrics)) {\n        // We then apply all metrics to all outputs.\n        return outputNames.map(name => wrappedMetrics);\n    }\n    else {\n        // In this case, metrics is a dict.\n        const nestedMetrics = [];\n        for (const name of outputNames) {\n            let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : [];\n            if (!Array.isArray(outputMetrics)) {\n                outputMetrics = [outputMetrics];\n            }\n            nestedMetrics.push(outputMetrics);\n        }\n        return nestedMetrics;\n    }\n}\nconst LAYERS_MODEL_FORMAT_NAME = 'layers-model';\n/**\n * A `tf.LayersModel` is a directed, acyclic graph of `tf.Layer`s plus methods\n * for training, evaluation, prediction and saving.\n *\n * `tf.LayersModel` is the basic unit of training, inference and evaluation in\n * TensorFlow.js. To create a `tf.LayersModel`, use `tf.LayersModel`.\n *\n * See also:\n *   `tf.Sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class LayersModel extends Container {\n    constructor(args) {\n        super(args);\n        this.isTraining = false;\n    }\n    /**\n     * Print a text summary of the model's layers.\n     *\n     * The summary includes\n     * - Name and type of all layers that comprise the model.\n     * - Output shape(s) of the layers\n     * - Number of weight parameters of each layer\n     * - If the model has non-sequential-like topology, the inputs each layer\n     *   receives\n     * - The total number of trainable and non-trainable parameters of the model.\n     *\n     * ```js\n     * const input1 = tf.input({shape: [10]});\n     * const input2 = tf.input({shape: [20]});\n     * const dense1 = tf.layers.dense({units: 4}).apply(input1);\n     * const dense2 = tf.layers.dense({units: 8}).apply(input2);\n     * const concat = tf.layers.concatenate().apply([dense1, dense2]);\n     * const output =\n     *     tf.layers.dense({units: 3, activation: 'softmax'}).apply(concat);\n     *\n     * const model = tf.model({inputs: [input1, input2], outputs: output});\n     * model.summary();\n     * ```\n     *\n     * @param lineLength Custom line length, in number of characters.\n     * @param positions Custom widths of each of the columns, as either\n     *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n     *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n     *   right-most (i.e., ending) position of a column.\n     * @param printFn Custom print function. Can be used to replace the default\n     *   `console.log`. For example, you can use `x => {}` to mute the printed\n     *   messages in the console.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    summary(lineLength, positions, printFn = console.log) {\n        if (!this.built) {\n            throw new ValueError(`This model has never been called, thus its weights have not been ` +\n                `created yet. So no summary can be displayed. Build the model ` +\n                `first (e.g., by calling it on some test data).`);\n        }\n        printSummary(this, lineLength, positions, printFn);\n    }\n    /**\n     * Configures and prepares the model for training and evaluation.  Compiling\n     * outfits the model with an optimizer, loss, and/or metrics.  Calling `fit`\n     * or `evaluate` on an un-compiled model will throw an error.\n     *\n     * @param args a `ModelCompileArgs` specifying the loss, optimizer, and\n     * metrics to be used for fitting and evaluating this model.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    compile(args) {\n        if (args.loss == null) {\n            args.loss = [];\n        }\n        this.loss = args.loss;\n        if (typeof args.optimizer === 'string') {\n            this.optimizer_ = optimizers.getOptimizer(args.optimizer);\n            this.isOptimizerOwned = true;\n        }\n        else {\n            if (!(args.optimizer instanceof Optimizer)) {\n                throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`);\n            }\n            this.optimizer_ = args.optimizer;\n            this.isOptimizerOwned = false;\n        }\n        // TODO(cais): Add lossWeights.\n        // TODO(cais): Add sampleWeightMode.\n        // Prepare loss functions.\n        let lossFunctions = [];\n        if (!Array.isArray(args.loss) && typeof args.loss !== 'string' &&\n            typeof args.loss !== 'function') {\n            args.loss = args.loss;\n            for (const name in args.loss) {\n                if (this.outputNames.indexOf(name) === -1) {\n                    throw new ValueError(`Unknown entry in loss dictionary: \"${name}\". ` +\n                        `Only expected the following keys: ${this.outputNames}`);\n                }\n            }\n            for (const name of this.outputNames) {\n                if (args.loss[name] == null) {\n                    console.warn(`Output \"${name}\" is missing from loss dictionary. We assume ` +\n                        `this was done on purpose, and we will not be expecting data ` +\n                        `to be passed to ${name} during training`);\n                }\n                lossFunctions.push(losses.get(args.loss[name]));\n            }\n        }\n        else if (Array.isArray(args.loss)) {\n            if (args.loss.length !== this.outputs.length) {\n                throw new ValueError(`When passing an Array as loss, it should have one entry per ` +\n                    `model output. The model has ${this.outputs.length} output(s), ` +\n                    `but you passed loss=${args.loss}.`);\n            }\n            const theLosses = args.loss;\n            lossFunctions = theLosses.map(l => losses.get(l));\n        }\n        else {\n            const lossFunction = losses.get(args.loss);\n            this.outputs.forEach(_ => {\n                lossFunctions.push(lossFunction);\n            });\n        }\n        this.lossFunctions = lossFunctions;\n        this.feedOutputNames = [];\n        this.feedOutputShapes = [];\n        this.feedLossFns = [];\n        for (let i = 0; i < this.outputs.length; ++i) {\n            // TODO(cais): Logic for skipping target(s).\n            const shape = this.internalOutputShapes[i];\n            const name = this.outputNames[i];\n            this.feedOutputNames.push(name);\n            this.feedOutputShapes.push(shape);\n            this.feedLossFns.push(this.lossFunctions[i]);\n        }\n        // TODO(cais): Add logic for output masks.\n        // TODO(cais): Add logic for sample weights.\n        const skipTargetIndices = [];\n        // Prepare metrics.\n        this.metrics = args.metrics;\n        // TODO(cais): Add weightedMetrics.\n        this.metricsNames = ['loss'];\n        this.metricsTensors = [];\n        // Compute total loss.\n        // Porting Note: In PyKeras, metrics_tensors are symbolic tensor objects.\n        //   Here, metricsTensors are TypeScript functions. This difference is due\n        //   to the difference in symbolic/imperative property of the backends.\n        nameScope('loss', () => {\n            for (let i = 0; i < this.outputs.length; ++i) {\n                if (skipTargetIndices.indexOf(i) !== -1) {\n                    continue;\n                }\n                // TODO(cais): Add weightedLoss, sampleWeight and mask.\n                //   The following line should be weightedLoss\n                const weightedLoss = this.lossFunctions[i];\n                if (this.outputs.length > 1) {\n                    this.metricsTensors.push([weightedLoss, i]);\n                    this.metricsNames.push(this.outputNames[i] + '_loss');\n                }\n            }\n            // Porting Note: Due to the imperative nature of the backend, we calculate\n            //   the regularizer penalties in the totalLossFunction, instead of here.\n        });\n        const nestedMetrics = collectMetrics(args.metrics, this.outputNames);\n        // TODO(cais): Add nestedWeightedMetrics.\n        /**\n         * Helper function used in loop below.\n         */\n        const appendMetric = (outputIndex, metricName, metricTensor) => {\n            if (this.outputNames.length > 1) {\n                metricName = this.outputNames[outputIndex] + '_' + metricName;\n            }\n            this.metricsNames.push(metricName);\n            this.metricsTensors.push([metricTensor, outputIndex]);\n        };\n        nameScope('metric', () => {\n            for (let i = 0; i < this.outputs.length; ++i) {\n                if (skipTargetIndices.indexOf(i) !== -1) {\n                    continue;\n                }\n                const outputMetrics = nestedMetrics[i];\n                // TODO(cais): Add weights and outputWeightedMetrics.\n                // TODO(cais): Add optional arg `weights` to the following function.\n                const handleMetrics = (metrics) => {\n                    const metricNamePrefix = '';\n                    let metricName;\n                    let accFn;\n                    let weightedMetricFn;\n                    //  TODO(cais): Use 'weights_' for weighted metrics.\n                    for (const metric of metrics) {\n                        if (typeof metric === 'string' &&\n                            ['accuracy', 'acc', 'crossentropy', 'ce'].indexOf(metric) !==\n                                -1) {\n                            const outputShape = this.internalOutputShapes[i];\n                            if (outputShape[outputShape.length - 1] === 1 ||\n                                this.lossFunctions[i] === losses.binaryCrossentropy) {\n                                // case: binary accuracy/crossentropy.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.binaryAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.binaryCrossentropy;\n                                }\n                            }\n                            else if (this.lossFunctions[i] ===\n                                losses.sparseCategoricalCrossentropy) {\n                                // case: categorical accuracy / crossentropy with sparse\n                                // targets.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.sparseCategoricalAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.sparseCategoricalCrossentropy;\n                                }\n                            }\n                            else {\n                                // case: categorical accuracy / crossentropy.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.categoricalAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.categoricalCrossentropy;\n                                }\n                            }\n                            let suffix;\n                            if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                suffix = 'acc';\n                            }\n                            else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                suffix = 'ce';\n                            }\n                            // TODO(cais): Add weighting actually.\n                            weightedMetricFn = accFn;\n                            metricName = metricNamePrefix + suffix;\n                        }\n                        else {\n                            const metricFn = Metrics.get(metric);\n                            // TODO(cais): Add weighting actually.\n                            weightedMetricFn = metricFn;\n                            metricName =\n                                metricNamePrefix + Metrics.getLossOrMetricName(metric);\n                        }\n                        // TODO(cais): Add weighting and masking to metricResult.\n                        let metricResult;\n                        nameScope(metricName, () => {\n                            metricResult = weightedMetricFn;\n                        });\n                        appendMetric(i, metricName, metricResult);\n                    }\n                };\n                handleMetrics(outputMetrics);\n                // TODO(cais): Call handleMetrics with weights.\n            }\n        });\n        // Porting Notes: Given the imperative backend of tfjs-core,\n        //   there is no need for constructing the symbolic graph and placeholders.\n        this.collectedTrainableWeights = this.trainableWeights;\n    }\n    /**\n     * Check trainable weights count consistency.\n     *\n     * This will raise a warning if `this.trainableWeights` and\n     * `this.collectedTrainableWeights` are inconsistent (i.e., have different\n     * numbers of parameters).\n     * Inconsistency will typically arise when one modifies `model.trainable`\n     * without calling `model.compile()` again.\n     */\n    checkTrainableWeightsConsistency() {\n        if (this.collectedTrainableWeights == null) {\n            return;\n        }\n        if (this.trainableWeights.length !==\n            this.collectedTrainableWeights.length) {\n            console.warn('Discrepancy between trainableweights and collected trainable ' +\n                'weights. Did you set `model.trainable` without calling ' +\n                '`model.compile()` afterwards?');\n        }\n    }\n    /**\n     * Returns the loss value & metrics values for the model in test mode.\n     *\n     * Loss and metrics are specified during `compile()`, which needs to happen\n     * before calls to `evaluate()`.\n     *\n     * Computation is done in batches.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const result = model.evaluate(\n     *     tf.ones([8, 10]), tf.ones([8, 1]), {batchSize: 4});\n     * result.print();\n     * ```\n     *\n     * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple inputs.\n     * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple outputs.\n     * @param args A `ModelEvaluateArgs`, containing optional fields.\n     *\n     * @return `Scalar` test loss (if the model has a single output and no\n     *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n     *   and/or metrics). The attribute `model.metricsNames`\n     *   will give you the display labels for the scalar outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    evaluate(x, y, args = {}) {\n        const batchSize = args.batchSize == null ? 32 : args.batchSize;\n        checkBatchSize(batchSize);\n        // TODO(cais): Standardize `config.sampleWeights` as well.\n        // Validate user data.\n        const checkBatchAxis = true;\n        const standardizedOuts = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n        try {\n            // TODO(cais): If uses `useLearningPhase`, set the corresponding element\n            // of the input to 0.\n            const ins = standardizedOuts[0].concat(standardizedOuts[1]);\n            this.makeTestFunction();\n            const f = this.testFunction;\n            const testOuts = this.testLoop(f, ins, batchSize, args.verbose, args.steps);\n            return singletonOrArray(testOuts);\n        }\n        finally {\n            disposeNewTensors(standardizedOuts[0], x);\n            disposeNewTensors(standardizedOuts[1], y);\n        }\n    }\n    // TODO(cais): Add code snippet below once real dataset objects are\n    //   available.\n    /**\n     * Evaluate model using a dataset object.\n     *\n     * Note: Unlike `evaluate()`, this method is asynchronous (`async`);\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for evaluation. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs. Of the two items in the array, the\n     *   first is the input feature(s) and the second is the output target(s).\n     * @param args A configuration object for the dataset-based evaluation.\n     * @returns Loss and metric values as an Array of `Scalar` objects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async evaluateDataset(dataset, args) {\n        this.makeTestFunction();\n        return evaluateDataset(this, dataset, args);\n    }\n    /**\n     * Get number of samples provided for training, evaluation or prediction.\n     *\n     * @param ins Input `tf.Tensor`.\n     * @param batchSize Integer batch size, optional.\n     * @param steps Total number of steps (batches of samples) before\n     * declaring loop finished. Optional.\n     * @param stepsName The public API's parameter name for `steps`.\n     * @returns Number of samples provided.\n     */\n    checkNumSamples(ins, batchSize, steps, stepsName = 'steps') {\n        let numSamples;\n        if (steps != null) {\n            numSamples = null;\n            if (batchSize != null) {\n                throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.` +\n                    `Got batchSize = ${batchSize}`);\n            }\n        }\n        else if (ins != null) {\n            if (Array.isArray(ins)) {\n                numSamples = ins[0].shape[0];\n            }\n            else {\n                numSamples = ins.shape[0];\n            }\n        }\n        else {\n            throw new ValueError(`Either the input data should have a defined shape, or ` +\n                `${stepsName} shoud be specified.`);\n        }\n        return numSamples;\n    }\n    /**\n     * Execute internal tensors of the model with input data feed.\n     * @param inputs Input data feed. Must match the inputs of the model.\n     * @param outputs Names of the output tensors to be fetched. Must match\n     *   names of the SymbolicTensors that belong to the graph.\n     * @returns Fetched values for `outputs`.\n     */\n    execute(inputs, outputs) {\n        if (Array.isArray(outputs) && outputs.length === 0) {\n            throw new ValueError('`outputs` is an empty Array, which is not allowed.');\n        }\n        const outputsIsArray = Array.isArray(outputs);\n        const outputNames = (outputsIsArray ? outputs : [outputs]);\n        const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames);\n        // Format the input into a FeedDict.\n        const feedDict = new FeedDict();\n        if (inputs instanceof Tensor) {\n            inputs = [inputs];\n        }\n        if (Array.isArray(inputs)) {\n            if (inputs.length !== this.inputs.length) {\n                throw new ValueError(`The number of inputs provided (${inputs.length}) ` +\n                    `does not match the number of inputs of this model ` +\n                    `(${this.inputs.length}).`);\n            }\n            for (let i = 0; i < this.inputs.length; ++i) {\n                feedDict.add(this.inputs[i], inputs[i]);\n            }\n        }\n        else {\n            for (const input of this.inputs) {\n                const tensorValue = inputs[input.name];\n                if (tensorValue == null) {\n                    throw new ValueError(`No value is provided for the model's input ${input.name}`);\n                }\n                feedDict.add(input, tensorValue);\n            }\n        }\n        // Run execution.\n        const executeOutputs = execute(outputSymbolicTensors, feedDict);\n        return outputsIsArray ? executeOutputs : executeOutputs[0];\n    }\n    /**\n     * Retrieve the model's internal symbolic tensors from symbolic-tensor names.\n     */\n    retrieveSymbolicTensors(symbolicTensorNames) {\n        const outputSymbolicTensors = pyListRepeat(null, symbolicTensorNames.length);\n        let outputsRemaining = symbolicTensorNames.length;\n        for (const layer of this.layers) {\n            const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output];\n            const layerOutputNames = layerOutputs.map(output => output.name);\n            for (let i = 0; i < symbolicTensorNames.length; ++i) {\n                const index = layerOutputNames.indexOf(symbolicTensorNames[i]);\n                if (index !== -1) {\n                    outputSymbolicTensors[i] = layerOutputs[index];\n                    outputsRemaining--;\n                }\n                if (outputsRemaining === 0) {\n                    break;\n                }\n            }\n            if (outputsRemaining === 0) {\n                break;\n            }\n        }\n        if (outputsRemaining > 0) {\n            const remainingNames = [];\n            outputSymbolicTensors.forEach((tensor, i) => {\n                if (tensor == null) {\n                    remainingNames.push(symbolicTensorNames[i]);\n                }\n            });\n            throw new ValueError(`Cannot find SymbolicTensors for output name(s): ` +\n                `${JSON.stringify(remainingNames)}`);\n        }\n        return outputSymbolicTensors;\n    }\n    /**\n     * Helper method to loop over some data in batches.\n     *\n     * Porting Note: Not using the functional approach in the Python equivalent\n     *   due to the imperative backend.\n     * Porting Note: Does not support step mode currently.\n     *\n     * @param ins: input data\n     * @param batchSize: integer batch size.\n     * @param verbose: verbosity model\n     * @returns: Predictions as `tf.Tensor` (if a single output) or an `Array` of\n     *   `tf.Tensor` (if multipe outputs).\n     */\n    predictLoop(ins, batchSize = 32, verbose = false) {\n        return tfc.tidy(() => {\n            const numSamples = this.checkNumSamples(ins);\n            if (verbose) {\n                throw new NotImplementedError('Verbose predictLoop() is not implemented yet.');\n            }\n            // Sample-based predictions.\n            // Porting Note: Tensor currently does not support sliced assignments as\n            //   in numpy, e.g., x[1:3] = y. Therefore we use concatenation while\n            //   iterating over the batches.\n            const batches = makeBatches(numSamples, batchSize);\n            const outsBatches = this.outputs.map(output => []);\n            // TODO(cais): Can the scope() be pushed down inside the for loop?\n            for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                const batchOuts = tfc.tidy(() => {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    // TODO(cais): Take care of the case of the last element is a flag for\n                    //   training/test.\n                    const insBatch = sliceArrays(ins, batchStart, batchEnd);\n                    // Construct the feeds for execute();\n                    const feeds = [];\n                    if (Array.isArray(insBatch)) {\n                        for (let i = 0; i < insBatch.length; ++i) {\n                            feeds.push({ key: this.inputs[i], value: insBatch[i] });\n                        }\n                    }\n                    else {\n                        feeds.push({ key: this.inputs[0], value: insBatch });\n                    }\n                    const feedDict = new FeedDict(feeds);\n                    return execute(this.outputs, feedDict);\n                });\n                batchOuts.forEach((batchOut, i) => outsBatches[i].push(batchOut));\n            }\n            return singletonOrArray(outsBatches.map(batches => tfc.concat(batches, 0)));\n        });\n    }\n    /**\n     * Generates output predictions for the input samples.\n     *\n     * Computation is done in batches.\n     *\n     * Note: the \"step\" mode of predict() is currently not supported.\n     *   This is because the TensorFlow.js core backend is imperative only.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predict(tf.ones([8, 10]), {batchSize: 4}).print();\n     * ```\n     *\n     * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n     *   the model has multiple inputs.\n     * @param args A `ModelPredictArgs` object containing optional fields.\n     *\n     * @return Prediction results as a `tf.Tensor`(s).\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and the model's expectations, or in case a stateful model receives a\n     *   number of samples that is not a multiple of the batch size.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(x, args = {}) {\n        const xsRank2OrHigher = ensureTensorsRank2OrHigher(x);\n        checkInputData(xsRank2OrHigher, this.inputNames, this.feedInputShapes, false);\n        try {\n            // TODO(cais): Take care of stateful models.\n            //   if (this.stateful) ...\n            // TODO(cais): Take care of the learning_phase boolean flag.\n            //   if (this.useLearningPhase) ...\n            const batchSize = args.batchSize == null ? 32 : args.batchSize;\n            checkBatchSize(batchSize);\n            return this.predictLoop(xsRank2OrHigher, batchSize);\n        }\n        finally {\n            disposeNewTensors(xsRank2OrHigher, x);\n        }\n    }\n    /**\n     * Returns predictions for a single batch of samples.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predictOnBatch(tf.ones([8, 10])).print();\n     * ```\n     * @param x: Input samples, as a Tensor (for models with exactly one\n     *   input) or an array of Tensors (for models with more than one input).\n     * @return Tensor(s) of predictions\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predictOnBatch(x) {\n        checkInputData(x, this.inputNames, this.feedInputShapes, true);\n        // TODO(cais): Take care of the learning_phase boolean flag.\n        //   if (this.useLearningPhase) ...\n        const batchSize = (Array.isArray(x) ? x[0] : x).shape[0];\n        return this.predictLoop(x, batchSize);\n    }\n    standardizeUserDataXY(x, y, checkBatchAxis = true, batchSize) {\n        // TODO(cais): Add sampleWeight, classWeight\n        if (this.optimizer_ == null) {\n            throw new RuntimeError('You must compile a model before training/testing. Use ' +\n                'LayersModel.compile(modelCompileArgs).');\n        }\n        const outputShapes = [];\n        for (let i = 0; i < this.feedOutputShapes.length; ++i) {\n            const outputShape = this.feedOutputShapes[i];\n            const lossFn = this.feedLossFns[i];\n            if (lossFn === losses.sparseCategoricalCrossentropy) {\n                outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1]));\n            }\n            else {\n                // Porting Note: Because of strong typing `lossFn` must be a function.\n                outputShapes.push(outputShape);\n            }\n        }\n        x = standardizeInputData(x, this.feedInputNames, this.feedInputShapes, false, 'input');\n        y = standardizeInputData(y, this.feedOutputNames, outputShapes, false, 'target');\n        // TODO(cais): Standardize sampleWeights & classWeights.\n        checkArrayLengths(x, y, null);\n        // TODO(cais): Check sampleWeights as well.\n        checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes);\n        if (this.stateful && batchSize != null && batchSize > 0) {\n            if (x[0].shape[0] % batchSize !== 0) {\n                throw new ValueError(`In a stateful network, you should only pass inputs with a ` +\n                    `number of samples that is divisible by the batch size ` +\n                    `${batchSize}. Found: ${x[0].shape[0]} sample(s).`);\n            }\n        }\n        return [x, y];\n    }\n    async standardizeUserData(x, y, sampleWeight, classWeight, checkBatchAxis = true, batchSize) {\n        const [standardXs, standardYs] = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n        // TODO(cais): Handle sampleWeights.\n        if (sampleWeight != null) {\n            throw new Error('sample weight is not supported yet.');\n        }\n        let standardSampleWeights = null;\n        if (classWeight != null) {\n            const classWeights = standardizeClassWeights(classWeight, this.outputNames);\n            standardSampleWeights = [];\n            for (let i = 0; i < classWeights.length; ++i) {\n                standardSampleWeights.push(await standardizeWeights(standardYs[i], null, classWeights[i]));\n            }\n        }\n        // TODO(cais): Deal with the case of model.stateful == true.\n        return [standardXs, standardYs, standardSampleWeights];\n    }\n    /**\n     * Loop over some test data in batches.\n     * @param f A Function returning a list of tensors.\n     * @param ins Array of tensors to be fed to `f`.\n     * @param batchSize Integer batch size or `null` / `undefined`.\n     * @param verbose verbosity mode.\n     * @param steps Total number of steps (batches of samples) before\n     * declaring test finished. Ignored with the default value of `null` /\n     * `undefined`.\n     * @returns Array of Scalars.\n     */\n    testLoop(f, ins, batchSize, verbose = 0, steps) {\n        return tfc.tidy(() => {\n            const numSamples = this.checkNumSamples(ins, batchSize, steps, 'steps');\n            const outs = [];\n            if (verbose > 0) {\n                throw new NotImplementedError('Verbose mode is not implemented yet.');\n            }\n            // TODO(cais): Use `indicesForConversionToDense' to prevent slow down.\n            if (steps != null) {\n                throw new NotImplementedError('steps mode in testLoop() is not implemented yet');\n            }\n            else {\n                const batches = makeBatches(numSamples, batchSize);\n                const indexArray = tensor1d(range(0, numSamples));\n                for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    const batchIds = K.sliceAlongFirstAxis(indexArray, batchStart, batchEnd - batchStart);\n                    // TODO(cais): In ins, train flag can be a number, instead of an\n                    //   Tensor? Do we need to handle this in tfjs-layers?\n                    const insBatch = sliceArraysByIndices(ins, batchIds);\n                    const batchOuts = f(insBatch);\n                    if (batchIndex === 0) {\n                        for (let i = 0; i < batchOuts.length; ++i) {\n                            outs.push(scalar(0));\n                        }\n                    }\n                    for (let i = 0; i < batchOuts.length; ++i) {\n                        const batchOut = batchOuts[i];\n                        outs[i] =\n                            tfc.add(outs[i], tfc.mul(batchEnd - batchStart, batchOut));\n                    }\n                }\n                for (let i = 0; i < outs.length; ++i) {\n                    outs[i] = tfc.div(outs[i], numSamples);\n                }\n            }\n            return outs;\n        });\n    }\n    getDedupedMetricsNames() {\n        const outLabels = this.metricsNames;\n        // Rename duplicated metrics names (can happen with an output layer\n        // shared among multiple dataflows).\n        const dedupedOutLabels = [];\n        for (let i = 0; i < outLabels.length; ++i) {\n            const label = outLabels[i];\n            let newLabel = label;\n            if (count(outLabels, label) > 1) {\n                const dupIndex = count(outLabels.slice(0, i), label);\n                newLabel += `_${dupIndex}`;\n            }\n            dedupedOutLabels.push(newLabel);\n        }\n        return dedupedOutLabels;\n    }\n    /**\n     * Creates a function that performs the following actions:\n     *\n     * 1. computes the losses\n     * 2. sums them to get the total loss\n     * 3. call the optimizer computes the gradients of the LayersModel's\n     *    trainable weights w.r.t. the total loss and update the variables\n     * 4. calculates the metrics\n     * 5. returns the values of the losses and metrics.\n     */\n    makeTrainFunction() {\n        return (data) => {\n            const lossValues = [];\n            const inputs = data.slice(0, this.inputs.length);\n            const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n            const sampleWeights = data.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2);\n            const metricsValues = [];\n            // Create a function that computes the total loss based on the\n            // inputs. This function is used for obtaining gradients through\n            // backprop.\n            const totalLossFunction = () => {\n                const feeds = [];\n                for (let i = 0; i < this.inputs.length; ++i) {\n                    feeds.push({ key: this.inputs[i], value: inputs[i] });\n                }\n                const feedDict = new FeedDict(feeds);\n                const outputs = execute(this.outputs, feedDict, { 'training': true });\n                // TODO(cais): Take care of the case of multiple outputs from a\n                //   single layer?\n                let totalLoss;\n                for (let i = 0; i < this.lossFunctions.length; ++i) {\n                    const lossFunction = this.lossFunctions[i];\n                    let loss = lossFunction(targets[i], outputs[i]);\n                    if (sampleWeights[i] != null) {\n                        loss = computeWeightedLoss(loss, sampleWeights[i]);\n                    }\n                    // TODO(cais): push Scalar instead.\n                    const meanLoss = tfc.mean(loss);\n                    // TODO(cais): Use a scope() instead, to avoid ownership.\n                    lossValues.push(meanLoss);\n                    if (i === 0) {\n                        totalLoss = loss;\n                    }\n                    else {\n                        totalLoss = tfc.add(totalLoss, loss);\n                    }\n                }\n                // Compute the metrics.\n                // TODO(cais): These should probably be calculated outside\n                //   totalLossFunction to benefit speed?\n                for (let i = 0; i < this.metricsTensors.length; ++i) {\n                    let weightedMetric;\n                    if (this.outputs.length > 1 && i < this.outputs.length) {\n                        weightedMetric = lossValues[i];\n                    }\n                    else {\n                        const metric = this.metricsTensors[i][0];\n                        const outputIndex = this.metricsTensors[i][1];\n                        weightedMetric =\n                            tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));\n                    }\n                    tfc.keep(weightedMetric);\n                    // TODO(cais): Use a scope() instead, to avoid ownership.\n                    metricsValues.push(weightedMetric);\n                }\n                totalLoss = tfc.mean(totalLoss);\n                // Add regularizer penalties.\n                this.calculateLosses().forEach(regularizerLoss => {\n                    totalLoss = tfc.add(totalLoss, regularizerLoss);\n                });\n                return totalLoss;\n            };\n            const variables = this.collectedTrainableWeights.map(param => param.read());\n            const returnCost = true;\n            const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables);\n            return [totalLossValue].concat(metricsValues);\n        };\n    }\n    /**\n     * Create a function which, when invoked with an array of `tf.Tensor`s as a\n     * batch of inputs, returns the prespecified loss and metrics of the model\n     * under the batch of input data.\n     */\n    makeTestFunction() {\n        this.testFunction = (data) => {\n            return tfc.tidy(() => {\n                const valOutputs = [];\n                let totalLoss;\n                const inputs = data.slice(0, this.inputs.length);\n                const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n                const feeds = [];\n                for (let i = 0; i < this.inputs.length; ++i) {\n                    feeds.push({ key: this.inputs[i], value: inputs[i] });\n                }\n                const feedDict = new FeedDict(feeds);\n                const outputs = execute(this.outputs, feedDict);\n                // Compute total loss.\n                for (let i = 0; i < this.lossFunctions.length; ++i) {\n                    const lossFunction = this.lossFunctions[i];\n                    // TODO(cais): Add sample weighting and replace the simple\n                    // averaging.\n                    const loss = tfc.mean(lossFunction(targets[i], outputs[i]));\n                    if (i === 0) {\n                        totalLoss = loss;\n                    }\n                    else {\n                        totalLoss = tfc.add(totalLoss, loss);\n                    }\n                    valOutputs.push(totalLoss);\n                }\n                // Compute the metrics.\n                for (let i = 0; i < this.metricsTensors.length; ++i) {\n                    const metric = this.metricsTensors[i][0];\n                    const outputIndex = this.metricsTensors[i][1];\n                    // TODO(cais): Replace K.mean() with a proper weighting function.\n                    const meanMetric = tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));\n                    valOutputs.push(meanMetric);\n                }\n                return valOutputs;\n            });\n        };\n    }\n    /**\n     * Trains the model for a fixed number of epochs (iterations on a\n     * dataset).\n     *\n     * ```js\n     * const model = tf.sequential({\n     *     layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * for (let i = 1; i < 5 ; ++i) {\n     *   const h = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *       batchSize: 4,\n     *       epochs: 3\n     *   });\n     *   console.log(\"Loss after Epoch \" + i + \" : \" + h.history.loss[0]);\n     * }\n     * ```\n     *\n     * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n     * model has multiple inputs. If all inputs in the model are named, you\n     * can also pass a dictionary mapping input names to `tf.Tensor`s.\n     * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n     * the model has multiple outputs. If all outputs in the model are named,\n     * you can also pass a dictionary mapping output names to `tf.Tensor`s.\n     * @param args A `ModelFitArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @exception ValueError In case of mismatch between the provided input\n     * data and what the model expects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fit(x, y, args = {}) {\n        return fitTensors(this, x, y, args);\n    }\n    // TODO(cais): Add code snippet below when it's possible to instantiate\n    //   actual dataset objects.\n    /**\n     * Trains the model using a dataset object.\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for training. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs.\n     *   Of the two items in the array, the first is the input feature(s) and\n     *   the second is the output target(s).\n     * @param args A `ModelFitDatasetArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fitDataset(dataset, args) {\n        return fitDataset(this, dataset, args);\n    }\n    /**\n     * Runs a single gradient update on a single batch of data.\n     *\n     * This method differs from `fit()` and `fitDataset()` in the following\n     * regards:\n     *   - It operates on exactly one batch of data.\n     *   - It returns only the loss and matric values, instead of\n     *     returning the batch-by-batch loss and metric values.\n     *   - It doesn't support fine-grained options such as verbosity and\n     *     callbacks.\n     *\n     * @param x Input data. It could be one of the following:\n     *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n     *     multiple inputs).\n     *   - An Object mapping input names to corresponding `tf.Tensor` (if the\n     *     model has named inputs).\n     * @param y Target darta. It could be either a `tf.Tensor` a multiple\n     *   `tf.Tensor`s. It should be consistent with `x`.\n     * @returns Training loss or losses (in case the model has\n     *   multiple outputs), along with metrics (if any), as numbers.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async trainOnBatch(x, y) {\n        // TODO(cais): Support sampleWeight and classWeight.\n        // TODO(cais): Support Dataset objects.\n        const standardizeOut = await this.standardizeUserData(x, y);\n        const inputs = standardizeOut[0];\n        const targets = standardizeOut[1];\n        const trainFunction = this.makeTrainFunction();\n        const losses = trainFunction(inputs.concat(targets));\n        const lossValues = [];\n        for (const loss of losses) {\n            const v = await loss.data();\n            lossValues.push(v[0]);\n        }\n        tfc.dispose(losses);\n        return singletonOrArray(lossValues);\n    }\n    /**\n     * Extract weight values of the model.\n     *\n     * @param config: An instance of `io.SaveConfig`, which specifies\n     * model-saving options such as whether only trainable weights are to be\n     * saved.\n     * @returns A `NamedTensorMap` mapping original weight names (i.e.,\n     *   non-uniqueified weight names) to their values.\n     */\n    getNamedWeights(config) {\n        const namedWeights = [];\n        const trainableOnly = config != null && config.trainableOnly;\n        const weights = trainableOnly ? this.trainableWeights : this.weights;\n        const weightValues = this.getWeights(trainableOnly);\n        for (let i = 0; i < weights.length; ++i) {\n            if (trainableOnly && !weights[i].trainable) {\n                // Optionally skip non-trainable weights.\n                continue;\n            }\n            namedWeights.push({ name: weights[i].originalName, tensor: weightValues[i] });\n        }\n        return namedWeights;\n    }\n    /**\n     * Setter used for force stopping of LayersModel.fit() (i.e., training).\n     *\n     * Example:\n     *\n     * ```js\n     * const input = tf.input({shape: [10]});\n     * const output = tf.layers.dense({units: 1}).apply(input);\n     * const model = tf.model({inputs: [input], outputs: [output]});\n     * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n     * const xs = tf.ones([8, 10]);\n     * const ys = tf.zeros([8, 1]);\n     *\n     * const history = await model.fit(xs, ys, {\n     *   epochs: 10,\n     *   callbacks: {\n     *     onEpochEnd: async (epoch, logs) => {\n     *       if (epoch === 2) {\n     *         model.stopTraining = true;\n     *       }\n     *     }\n     *   }\n     * });\n     *\n     * // There should be only 3 values in the loss array, instead of 10\n     * values,\n     * // due to the stopping after 3 epochs.\n     * console.log(history.history.loss);\n     * ```\n     */\n    set stopTraining(stop) {\n        this.stopTraining_ = stop;\n    }\n    get stopTraining() {\n        return this.stopTraining_;\n    }\n    get optimizer() {\n        return this.optimizer_;\n    }\n    set optimizer(optimizer) {\n        if (this.optimizer_ !== optimizer) {\n            this.optimizer_ = optimizer;\n            this.isOptimizerOwned = false;\n        }\n    }\n    dispose() {\n        const result = super.dispose();\n        if (result.refCountAfterDispose === 0 && this.optimizer != null &&\n            this.isOptimizerOwned) {\n            const numTensorsBeforeOptmizerDisposal = tfc.memory().numTensors;\n            this.optimizer_.dispose();\n            result.numDisposedVariables +=\n                numTensorsBeforeOptmizerDisposal - tfc.memory().numTensors;\n        }\n        return result;\n    }\n    getLossIdentifiers() {\n        let lossNames;\n        if (typeof this.loss === 'string') {\n            lossNames = toSnakeCase(this.loss);\n        }\n        else if (Array.isArray(this.loss)) {\n            for (const loss of this.loss) {\n                if (typeof loss !== 'string') {\n                    throw new Error('Serialization of non-string loss is not supported.');\n                }\n            }\n            lossNames = this.loss.map(name => toSnakeCase(name));\n        }\n        else {\n            const outputNames = Object.keys(this.loss);\n            lossNames = {};\n            const losses = this.loss;\n            for (const outputName of outputNames) {\n                if (typeof losses[outputName] === 'string') {\n                    lossNames[outputName] =\n                        toSnakeCase(losses[outputName]);\n                }\n                else {\n                    throw new Error('Serialization of non-string loss is not supported.');\n                }\n            }\n        }\n        return lossNames;\n    }\n    getMetricIdentifiers() {\n        if (typeof this.metrics === 'string' ||\n            typeof this.metrics === 'function') {\n            return [toSnakeCase(Metrics.getLossOrMetricName(this.metrics))];\n        }\n        else if (Array.isArray(this.metrics)) {\n            return this.metrics.map(metric => toSnakeCase(Metrics.getLossOrMetricName(metric)));\n        }\n        else {\n            const metricsIdentifiers = {};\n            for (const key in this.metrics) {\n                metricsIdentifiers[key] =\n                    toSnakeCase(Metrics.getLossOrMetricName(this.metrics[key]));\n            }\n            return metricsIdentifiers;\n        }\n    }\n    getTrainingConfig() {\n        return {\n            loss: this.getLossIdentifiers(),\n            metrics: this.getMetricIdentifiers(),\n            optimizer_config: {\n                class_name: this.optimizer.getClassName(),\n                config: this.optimizer.getConfig()\n            }\n        };\n        // TODO(cais): Add weight_metrics when they are supported.\n        // TODO(cais): Add sample_weight_mode when it's supported.\n        // TODO(cais): Add loss_weights when it's supported.\n    }\n    loadTrainingConfig(trainingConfig) {\n        if (trainingConfig.weighted_metrics != null) {\n            throw new Error('Loading weight_metrics is not supported yet.');\n        }\n        if (trainingConfig.loss_weights != null) {\n            throw new Error('Loading loss_weights is not supported yet.');\n        }\n        if (trainingConfig.sample_weight_mode != null) {\n            throw new Error('Loading sample_weight_mode is not supported yet.');\n        }\n        const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config);\n        const optimizer = deserialize(tsConfig);\n        let loss;\n        if (typeof trainingConfig.loss === 'string') {\n            loss = toCamelCase(trainingConfig.loss);\n        }\n        else if (Array.isArray(trainingConfig.loss)) {\n            loss = trainingConfig.loss.map(lossEntry => toCamelCase(lossEntry));\n        }\n        else if (trainingConfig.loss != null) {\n            loss = {};\n            for (const key in trainingConfig.loss) {\n                loss[key] = toCamelCase(trainingConfig.loss[key]);\n            }\n        }\n        let metrics;\n        if (Array.isArray(trainingConfig.metrics)) {\n            metrics = trainingConfig.metrics.map(metric => toCamelCase(metric));\n        }\n        else if (trainingConfig.metrics != null) {\n            metrics = {};\n            for (const key in trainingConfig.metrics) {\n                metrics[key] = toCamelCase(trainingConfig.metrics[key]);\n            }\n        }\n        this.compile({ loss, metrics, optimizer });\n    }\n    /**\n     * Save the configuration and/or weights of the LayersModel.\n     *\n     * An `IOHandler` is an object that has a `save` method of the proper\n     * signature defined. The `save` method manages the storing or\n     * transmission of serialized data (\"artifacts\") that represent the\n     * model's topology and weights onto or via a specific medium, such as\n     * file downloads, local storage, IndexedDB in the web browser and HTTP\n     * requests to a server. TensorFlow.js provides `IOHandler`\n     * implementations for a number of frequently used saving mediums, such as\n     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n     * for more details.\n     *\n     * This method also allows you to refer to certain types of `IOHandler`s\n     * as URL-like string shortcuts, such as 'localstorage://' and\n     * 'indexeddb://'.\n     *\n     * Example 1: Save `model`'s topology and weights to browser [local\n     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n     * then load it back.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * console.log('Prediction from original model:');\n     * model.predict(tf.ones([1, 3])).print();\n     *\n     * const saveResults = await model.save('localstorage://my-model-1');\n     *\n     * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * loadedModel.predict(tf.ones([1, 3])).print();\n     * ```\n     *\n     * Example 2. Saving `model`'s topology and weights to browser\n     * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n     * then load it back.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * console.log('Prediction from original model:');\n     * model.predict(tf.ones([1, 3])).print();\n     *\n     * const saveResults = await model.save('indexeddb://my-model-1');\n     *\n     * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * loadedModel.predict(tf.ones([1, 3])).print();\n     * ```\n     *\n     * Example 3. Saving `model`'s topology and weights as two files\n     * (`my-model-1.json` and `my-model-1.weights.bin`) downloaded from\n     * browser.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * const saveResults = await model.save('downloads://my-model-1');\n     * ```\n     *\n     * Example 4. Send  `model`'s topology and weights to an HTTP server.\n     * See the documentation of `tf.io.http` for more details\n     * including specifying request parameters and implementation of the\n     * server.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * const saveResults = await model.save('http://my-server/model/upload');\n     * ```\n     *\n     * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n     * scheme-based string shortcut for `IOHandler`.\n     * @param config Options for saving the model.\n     * @returns A `Promise` of `SaveResult`, which summarizes the result of\n     * the saving, such as byte sizes of the saved artifacts for the model's\n     *   topology and weight values.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async save(handlerOrURL, config) {\n        if (typeof handlerOrURL === 'string') {\n            const handlers = io.getSaveHandlers(handlerOrURL);\n            if (handlers.length === 0) {\n                throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n            }\n            else if (handlers.length > 1) {\n                throw new ValueError(`Found more than one (${handlers.length}) save handlers for ` +\n                    `URL '${handlerOrURL}'`);\n            }\n            handlerOrURL = handlers[0];\n        }\n        if (handlerOrURL.save == null) {\n            throw new ValueError('LayersModel.save() cannot proceed because the IOHandler ' +\n                'provided does not have the `save` attribute defined.');\n        }\n        const weightDataAndSpecs = await io.encodeWeights(this.getNamedWeights(config));\n        const returnString = false;\n        const unusedArg = null;\n        const modelConfig = this.toJSON(unusedArg, returnString);\n        const modelArtifacts = {\n            modelTopology: modelConfig,\n            format: LAYERS_MODEL_FORMAT_NAME,\n            generatedBy: `TensorFlow.js tfjs-layers v${version}`,\n            convertedBy: null,\n        };\n        const includeOptimizer = config == null ? false : config.includeOptimizer;\n        if (includeOptimizer && this.optimizer != null) {\n            modelArtifacts.trainingConfig = this.getTrainingConfig();\n            const weightType = 'optimizer';\n            const { data: optimizerWeightData, specs: optimizerWeightSpecs } = await io.encodeWeights(await this.optimizer.getWeights(), weightType);\n            weightDataAndSpecs.specs.push(...optimizerWeightSpecs);\n            weightDataAndSpecs.data = io.concatenateArrayBuffers([weightDataAndSpecs.data, optimizerWeightData]);\n        }\n        if (this.userDefinedMetadata != null) {\n            // Check serialized size of user-defined metadata.\n            const checkSize = true;\n            checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize);\n            modelArtifacts.userDefinedMetadata = this.userDefinedMetadata;\n        }\n        modelArtifacts.weightData = weightDataAndSpecs.data;\n        modelArtifacts.weightSpecs = weightDataAndSpecs.specs;\n        return handlerOrURL.save(modelArtifacts);\n    }\n    /**\n     * Set user-defined metadata.\n     *\n     * The set metadata will be serialized together with the topology\n     * and weights of the model during `save()` calls.\n     *\n     * @param setUserDefinedMetadata\n     */\n    setUserDefinedMetadata(userDefinedMetadata) {\n        checkUserDefinedMetadata(userDefinedMetadata, this.name);\n        this.userDefinedMetadata = userDefinedMetadata;\n    }\n    /**\n     * Get user-defined metadata.\n     *\n     * The metadata is supplied via one of the two routes:\n     *   1. By calling `setUserDefinedMetadata()`.\n     *   2. Loaded during model loading (if the model is constructed\n     *      via `tf.loadLayersModel()`.)\n     *\n     * If no user-defined metadata is available from either of the\n     * two routes, this function will return `undefined`.\n     */\n    getUserDefinedMetadata() {\n        return this.userDefinedMetadata;\n    }\n}\n// The class name is 'Model' rather than 'LayersModel' for backwards\n// compatibility since this class name shows up in the serialization format.\n/** @nocollapse */\nLayersModel.className = 'Model';\nserialization.registerClass(LayersModel);\n/**\n * A `tf.Functional` is an alias to `tf.LayersModel`.\n *\n * See also:\n *   `tf.LayersModel`, `tf.Sequential`, `tf.loadLayersModel`.\n */\n/** @doc {heading: 'Models', subheading: 'Classes'} */\nexport class Functional extends LayersModel {\n}\nFunctional.className = 'Functional';\nserialization.registerClass(Functional);\n//# sourceMappingURL=training.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source keras/models.py */\nimport { dispose, io, serialization, util } from '@tensorflow/tfjs-core';\nimport { getUid } from './backend/state';\nimport { Input } from './engine/input_layer';\nimport { getSourceInputs, Node } from './engine/topology';\nimport { LayersModel } from './engine/training';\nimport { NotImplementedError, RuntimeError, ValueError } from './errors';\nimport { deserialize } from './layers/serialization';\nimport * as generic_utils from './utils/generic_utils';\nimport { convertPythonicToTs } from './utils/serialization_utils';\nimport { getExactlyOneShape } from './utils/types_utils';\n/**\n * Parses a JSON model configuration file and returns a model instance.\n *\n * ```js\n * // This example shows how to serialize a model using `toJSON()` and\n * // deserialize it as another model using `tf.models.modelFromJSON()`.\n * // Note: this example serializes and deserializes only the topology\n * // of the model; the weights of the loaded model will be different\n * // from those of the the original model, due to random weight\n * // initialization.\n * // To load the topology and weights of a model, use `tf.loadLayersModel()`.\n * const model1 = tf.sequential();\n * model1.add(tf.layers.repeatVector({inputShape: [2], n: 4}));\n * // Serialize `model1` as a JSON object.\n * const model1JSON = model1.toJSON(null, false);\n * model1.summary();\n *\n * const model2 = await tf.models.modelFromJSON(model1JSON);\n * model2.summary();\n * ```\n *\n *  @param modelAndWeightsConfig JSON object or string encoding a model and\n *       weights configuration. It can also be only the topology JSON of the\n *       model, in which case the weights will not be loaded.\n *  @param custom_objects Optional dictionary mapping names\n *       (strings) to custom classes or functions to be\n *       considered during deserialization.\n * @returns A TensorFlow.js Layers `tf.LayersModel` instance (uncompiled).\n */\nexport async function modelFromJSON(modelAndWeightsConfig, customObjects) {\n    if (!('modelTopology' in modelAndWeightsConfig)) {\n        modelAndWeightsConfig = { modelTopology: modelAndWeightsConfig };\n    }\n    modelAndWeightsConfig = modelAndWeightsConfig;\n    let modelTopology = modelAndWeightsConfig.modelTopology;\n    if (modelTopology['model_config'] != null) {\n        // If the model-topology JSON contains a 'model_config' field, then it is\n        // a full model JSON (e.g., from `keras.Model.save()`), which contains\n        // not only the model's architecture in its 'model_config' field, but\n        // additional information such as the model's optimizer. We use only the\n        // 'model_config' field currently.\n        modelTopology = modelTopology['model_config'];\n    }\n    const tsConfig = convertPythonicToTs(modelTopology);\n    const model = deserialize(tsConfig, customObjects);\n    if (modelAndWeightsConfig.weightsManifest != null) {\n        // Load the weight values keyed by the original tensor names in the model\n        // file that was loaded.  These should match the keys of the weight\n        // manifest.\n        const weightValues = await io.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model.weights.map(weight => weight.originalName));\n        // Map the weights to the unique tensor names generated during model loading\n        const uniqueWeightValues = {};\n        for (const weight of model.weights) {\n            uniqueWeightValues[weight.originalName] =\n                weightValues[weight.originalName];\n        }\n        model.loadWeights(uniqueWeightValues);\n        // Dispose temporary weight values.\n        dispose(weightValues);\n    }\n    return model;\n}\n/**\n * Load a model, including its topology and optionally weights.  See the\n * Tutorial named \"How to import a Keras Model\" for usage examples.\n *\n * Example 1: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 2. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Load a model from user-selected files from HTML\n * [file input\n * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).\n *\n * ```js\n * // Note: this code snippet will not work without the HTML elements in the\n * //   page\n * const jsonUpload = document.getElementById('json-upload');\n * const weightsUpload = document.getElementById('weights-upload');\n *\n * const model = await tf.loadLayersModel(\n *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));\n * ```\n *\n * Example 4. Load a model from an HTTP server.\n *\n * ```js\n * const model = await\n *     tf.loadLayersModel('https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');\n * model.summary();\n * ```\n *\n * @param pathOrIOHandler Can be either of the two formats\n *   1. A string path to the `ModelAndWeightsConfig` JSON describing\n *      the model in the canonical TensorFlow.js format. This path will be\n *      interpreted as a relative HTTP path, to which `fetch` will be used to\n *      request the model topology and weight manifest JSON.\n *      The content of the JSON file is assumed to be a JSON object with the\n *      following fields and values:\n *      - 'modelTopology': A JSON object that can be either of:\n *        1. a model architecture JSON consistent with the format of the return\n *            value of `keras.Model.to_json()`\n *        2. a full model JSON in the format of `keras.models.save_model()`.\n *      - 'weightsManifest': A TensorFlow.js weights manifest.\n *      See the Python converter function `save_model()` for more details.\n *      It is also assumed that model weights can be accessed from relative\n *      paths described by the `paths` fields in weights manifest.\n *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`\n *      method.\n * @param options Optional configuration arguments for the model loading,\n *   including:\n *   - `strict`: Require that the provided weights exactly match those required\n *     by the layers.  Default true.  Passing false means that both extra\n *     weights and missing weights will be silently ignored.\n *   - `onProgress`: A progress callback of the form:\n *     `(fraction: number) => void`. This callback can be used to monitor the\n *     model-loading process.\n * @returns A `Promise` of `tf.LayersModel`, with the topology and weights\n *     loaded.\n */\nexport async function loadLayersModelInternal(pathOrIOHandler, options) {\n    if (options == null) {\n        options = {};\n    }\n    if (typeof pathOrIOHandler === 'string') {\n        const handlers = io.getLoadHandlers(pathOrIOHandler, options);\n        if (handlers.length === 0) {\n            // For backward compatibility: if no load handler can be found,\n            // assume it is a relative http path.\n            // TODO(cais): Reformat the args into a single `LoadOptions` once the core\n            // is refactored.\n            handlers.push(io.browserHTTPRequest(pathOrIOHandler, options));\n        }\n        else if (handlers.length > 1) {\n            throw new ValueError(`Found more than one (${handlers.length}) load handlers for ` +\n                `URL '${pathOrIOHandler}'`);\n        }\n        pathOrIOHandler = handlers[0];\n    }\n    return loadLayersModelFromIOHandler(pathOrIOHandler, undefined, options);\n}\n/**\n * Load a model and optionally its weights, using an IOHandler object.\n *\n * @param handler The instance of `IOHandler` to be used during the model\n *   loading.\n * @param customObjects Any optional custom objects to be used during model\n *   loading.\n * @param strict Whether the weight loading will be done in strict mode.\n *   Default: `true`.\n */\nexport async function loadLayersModelFromIOHandler(handler, customObjects, options) {\n    if (options == null) {\n        options = {};\n    }\n    if (handler.load == null) {\n        throw new ValueError('Cannot proceed with model loading because the IOHandler provided ' +\n            'does not have the `load` method implemented.');\n    }\n    const artifacts = await handler.load();\n    let modelTopology = artifacts.modelTopology;\n    if (modelTopology['model_config'] != null) {\n        modelTopology = modelTopology['model_config'];\n    }\n    const strict = options.strict == null ? true : options.strict;\n    // If weights are provided and the weight-loading mode is strict, use\n    // fast weight initialization. This skips costly initializers such as\n    // 'orthogonal' and saves unnecessary computation in cases where\n    // the initialized weight values will immediately be overwritten by\n    // loaded weight values.\n    const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict;\n    const model = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit);\n    const trainingConfig = artifacts.trainingConfig;\n    if (trainingConfig != null) {\n        model.loadTrainingConfig(trainingConfig);\n    }\n    if (artifacts.userDefinedMetadata != null) {\n        model.setUserDefinedMetadata(artifacts.userDefinedMetadata);\n    }\n    // If weightData is present, load the weights into the model.\n    if (artifacts.weightData != null) {\n        // Loading weights requires weightSpecs.\n        if (artifacts.weightSpecs == null) {\n            throw new ValueError('LayersModel artifacts contains weight data, but not weight specs. ' +\n                'Therefore loading of weights cannot proceed.');\n        }\n        const { modelWeights, optimizerWeights } = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs);\n        model.loadWeights(modelWeights, strict);\n        if (model.optimizer != null && optimizerWeights.length > 0) {\n            await model.optimizer.setWeights(optimizerWeights);\n        }\n        // Dispose temporary weight values.\n        dispose(modelWeights);\n        dispose(optimizerWeights.map(w => w.tensor));\n    }\n    return model;\n}\nfunction decodeModelAndOptimizerWeights(buffer, specs) {\n    const name2Tensor = io.decodeWeights(buffer, specs);\n    const modelWeights = {};\n    const optimizerWeights = [];\n    specs.forEach(spec => {\n        if (spec.group === 'optimizer') {\n            optimizerWeights.push({ name: spec.name, tensor: name2Tensor[spec.name] });\n        }\n        else {\n            modelWeights[spec.name] = name2Tensor[spec.name];\n        }\n    });\n    return { modelWeights, optimizerWeights };\n}\n/**\n * A model with a stack of layers, feeding linearly from one to the next.\n *\n * `tf.sequential` is a factory function that creates an instance of\n * `tf.Sequential`.\n *\n * ```js\n *  // Define a model for linear regression.\n *  const model = tf.sequential();\n *  model.add(tf.layers.dense({units: 1, inputShape: [1]}));\n *\n *  // Prepare the model for training: Specify the loss and the optimizer.\n *  model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n *\n *  // Generate some synthetic data for training.\n *  const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);\n *  const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);\n *\n *  // Train the model using the data then do inference on a data point the\n *  // model hasn't seen:\n *  await model.fit(xs, ys);\n *  model.predict(tf.tensor2d([5], [1, 1])).print();\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class Sequential extends LayersModel {\n    constructor(args) {\n        super({ inputs: [], outputs: [] });\n        args = args || {};\n        this.trainable = true;\n        this.built = false;\n        // Set model name.\n        this.name = (args.name != null) ? args.name : getUid('sequential_');\n        // Add to the model any layers passed to the constructor.\n        if (args.layers != null) {\n            for (const layer of args.layers) {\n                this.add(layer);\n            }\n        }\n    }\n    // Helper function to Sequential.add  Throws if the new output shape will be\n    // invalid.\n    checkShape(layer) {\n        const shape = layer.inboundNodes[0].outputTensors[0].shape;\n        if (shape.some(x => x < 0)) {\n            throw new ValueError('Negative dimension size caused by adding layer ' +\n                `${layer.name} with input shape [` +\n                `${layer.inboundNodes[0].inputTensors[0].shape}]`);\n        }\n    }\n    /**\n     * Adds a layer instance on top of the layer stack.\n     *\n     * ```js\n     *  const model = tf.sequential();\n     *  model.add(tf.layers.dense({units: 8, inputShape: [1]}));\n     *  model.add(tf.layers.dense({units: 4, activation: 'relu6'}));\n     *  model.add(tf.layers.dense({units: 1, activation: 'relu6'}));\n     *  // Note that the untrained model is random at this point.\n     *  model.predict(tf.randomNormal([10, 1])).print();\n     * ```\n     * @param layer Layer instance.\n     *\n     * @exception ValueError In case the `layer` argument does not know its\n     * input shape.\n     * @exception ValueError In case the `layer` argument has multiple output\n     *   tensors, or is already connected somewhere else (forbidden in\n     *   `Sequential` models).\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    add(layer) {\n        const isLayerModelInstance = layer instanceof Sequential || layer instanceof LayersModel;\n        let modelLayer;\n        if (isLayerModelInstance) {\n            modelLayer = layer;\n            if (modelLayer.outputs.length !== 1) {\n                throw new ValueError('All layers in a Sequential model ' +\n                    'should have a single output tensor. ' +\n                    'For multi-output layers, ' +\n                    'use the functional API.');\n            }\n            if (modelLayer.inputs.length !== 1) {\n                throw new ValueError('All layers in a Sequential model ' +\n                    'should have a single input tensor. ' +\n                    'For multi-input layers, ' +\n                    'use the functional API.');\n            }\n        }\n        if (this.outputs.length === 0) {\n            // first layer in model: check that it is an input layer\n            if (layer.inboundNodes.length === 0) {\n                // create an input layer\n                if (layer.batchInputShape == null) {\n                    throw new ValueError('The first layer in a Sequential model must ' +\n                        'get an `inputShape` or `batchInputShape` argument.');\n                }\n                // Instantiate the input layer.\n                const x = Input({\n                    batchShape: layer.batchInputShape,\n                    dtype: layer.dtype,\n                    name: layer.name + '_input'\n                });\n                // This will build the current layer and create the node connecting\n                // the current layer to the input layer we just created.\n                layer.apply(x);\n            }\n            if (isLayerModelInstance) {\n                this.outputs = modelLayer.outputs;\n                this.inputs = modelLayer.inputs;\n            }\n            else {\n                if (layer.inboundNodes.length !== 1) {\n                    throw new ValueError('A layer added to a Sequential model must not already be ' +\n                        `connected somewhere else. LayersModel received layer ${layer.name} ` +\n                        `which has ${layer.inboundNodes.length} pre-existing inbound ` +\n                        'connections.');\n                }\n                if (layer.inboundNodes[0].outputTensors.length !== 1) {\n                    throw new ValueError('All layers in a Sequential model ' +\n                        'should have a single output tensor. ' +\n                        'For multi-output layers, ' +\n                        'use the functional API.');\n                }\n                this.checkShape(layer);\n                this.outputs = [layer.inboundNodes[0].outputTensors[0]];\n                this.inputs = getSourceInputs(this.outputs[0]);\n            }\n            this.inboundNodes = [];\n            // We create an input node, which we will keep updated\n            // as we add more layers.\n            // (This call has side effects.)\n            // tslint:disable-next-line:no-unused-expression\n            new Node({\n                outboundLayer: this,\n                inboundLayers: [],\n                nodeIndices: [],\n                tensorIndices: [],\n                inputTensors: this.inputs,\n                outputTensors: this.outputs,\n                // no model-level masking for now\n                inputMasks: generic_utils.pyListRepeat(null, this.inputs.length),\n                outputMasks: [null],\n                inputShapes: this.inputs.map(x => x.shape),\n                outputShapes: this.outputs[0].shape\n            });\n        }\n        else {\n            const outputTensor = layer.apply(this.outputs[0]);\n            if (Array.isArray(outputTensor)) {\n                throw new TypeError('All layers in a Sequential model ' +\n                    'should have a single output tensor. ' +\n                    'For multi-output layers, ' +\n                    'use the functional API.');\n            }\n            this.checkShape(layer);\n            this.outputs = [outputTensor];\n            // update self.inbound_nodes\n            this.inboundNodes[0].outputTensors = this.outputs;\n            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n        }\n        this.layers.push(layer);\n        this.built = false;\n    }\n    /**\n     * Removes the last layer in the model.\n     *\n     * @exception TypeError if there are no layers in the model.\n     */\n    pop() {\n        if (this.layers.length === 0) {\n            throw new TypeError('There are no layers in the model.');\n        }\n        this.layers.pop();\n        if (this.layers.length === 0) {\n            this.outputs = [];\n            this.inboundNodes = [];\n            this.outboundNodes = [];\n        }\n        else {\n            const lastLayerIndex = this.layers.length - 1;\n            this.layers[lastLayerIndex].outboundNodes = [];\n            this.outputs = [this.layers[lastLayerIndex].output];\n            // update self.inbound_nodes\n            this.inboundNodes[0].outputTensors = this.outputs;\n            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n        }\n    }\n    call(inputs, kwargs) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.call(inputs, kwargs);\n    }\n    build(inputShape) {\n        // Call `getExactlyOneShape` without using its return value,\n        // to verify that exactly one input shape is provided.\n        getExactlyOneShape(inputShape);\n        if (this.inputs.length === 0 || this.outputs.length === 0) {\n            throw new TypeError('Sequential model cannot be built: model is empty.' +\n                ' Add some layers first.');\n        }\n        // actually create the model\n        this.model = new LayersModel({\n            inputs: this.inputs,\n            outputs: this.outputs[0],\n            name: this.name + '_model'\n        });\n        this.model.trainable = this.trainable;\n        // mirror model attributes\n        this.supportsMasking = this.model.supportsMasking;\n        // TODO(michaelterry): Add caches\n        this.inputLayers = this.model.inputLayers;\n        this.inputLayersNodeIndices = this.model.inputLayersNodeIndices;\n        this.inputLayersTensorIndices = this.model.inputLayersTensorIndices;\n        this.outputLayers = this.model.outputLayers;\n        this.outputLayersNodeIndices = this.model.outputLayersNodeIndices;\n        this.outputLayersTensorIndices = this.model.outputLayersTensorIndices;\n        this.nodesByDepth = this.model.nodesByDepth;\n        this.containerNodes = this.model.containerNodes;\n        this.outputNames = this.model.outputNames;\n        this.inputNames = this.model.inputNames;\n        // TODO(michaelterry): Add feedInputNames, feedInputs, if needed.\n        // TODO(michaelterry): Add callbackModel if needed.\n        this.built = true;\n    }\n    countParams() {\n        if (!this.built) {\n            this.build();\n        }\n        return super.countParams();\n    }\n    /**\n     * Print a text summary of the Sequential model's layers.\n     *\n     * The summary includes\n     * - Name and type of all layers that comprise the model.\n     * - Output shape(s) of the layers\n     * - Number of weight parameters of each layer\n     * - The total number of trainable and non-trainable parameters of the\n     * model.\n     *\n     * ```js\n     * const model = tf.sequential();\n     * model.add(\n     *     tf.layers.dense({units: 100, inputShape: [10], activation: 'relu'}));\n     * model.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));\n     *\n     * model.summary();\n     * ```\n     *\n     * @param lineLength Custom line length, in number of characters.\n     * @param positions Custom widths of each of the columns, as either\n     *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n     *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n     *   right-most (i.e., ending) position of a column.\n     * @param printFn Custom print function. Can be used to replace the default\n     *   `console.log`. For example, you can use `x => {}` to mute the printed\n     *   messages in the console.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    summary(lineLength, positions, printFn = console.log) {\n        if (!this.built) {\n            this.build();\n        }\n        super.summary(lineLength, positions, printFn);\n    }\n    /**\n     * Sets the weights of the model.\n     *\n     * @param weights Should be a list of Tensors with shapes and types matching\n     *   the output of `model.getWeights()`.\n     */\n    setWeights(weights) {\n        if (this.model == null) {\n            this.build();\n        }\n        this.model.setWeights(weights);\n    }\n    /**\n     * Returns the loss value & metrics values for the model in test mode.\n     *\n     * Loss and metrics are specified during `compile()`, which needs to happen\n     * before calls to `evaluate()`.\n     *\n     * Computation is done in batches.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const result = model.evaluate(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *   batchSize: 4,\n     * });\n     * result.print();\n     * ```\n     *\n     * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple inputs.\n     * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple outputs.\n     * @param args A `ModelEvaluateConfig`, containing optional fields.\n     *\n     * @return `Scalar` test loss (if the model has a single output and no\n     *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n     *   and/or metrics). The attribute `model.metricsNames`\n     *   will give you the display labels for the scalar outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    evaluate(x, y, args = {}) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before being used.');\n        }\n        return this.model.evaluate(x, y, args);\n    }\n    // TODO(cais): Add code snippet below once real dataset objects are\n    //   available.\n    /**\n     * Evaluate model using a dataset object.\n     *\n     * Note: Unlike `evaluate()`, this method is asynchronous (`async`);\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for evaluation. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs. Of the two items in the array, the\n     *   first is the input feature(s) and the second is the output target(s).\n     * @param args A configuration object for the dataset-based evaluation.\n     * @returns Loss and metric values as an Array of `Scalar` objects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async evaluateDataset(dataset, args) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before being used.');\n        }\n        return this.model.evaluateDataset(dataset, args);\n    }\n    /**\n     * Generates output predictions for the input samples.\n     *\n     * Computation is done in batches.\n     *\n     * Note: the \"step\" mode of predict() is currently not supported.\n     *   This is because the TensorFow.js core backend is imperative only.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predict(tf.ones([2, 10])).print();\n     * ```\n     *\n     * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n     *   the model has multiple inputs.\n     * @param conifg A `ModelPredictConfig` object containing optional fields.\n     *\n     * @return `tf.Tensor`(s) of predictions.\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and the model's expectations, or in case a stateful model receives a\n     *   number of samples that is not a multiple of the batch size.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(x, args = {}) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.predict(x, args);\n    }\n    /**\n     * Returns predictions for a single batch of samples.\n     *\n     * @param x: Input samples, as a Tensor, or list of Tensors (if the model\n     *   has multiple inputs).\n     * @return Tensor(s) of predictions\n     */\n    predictOnBatch(x) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.predictOnBatch(x);\n    }\n    /**\n     * See `LayersModel.compile`.\n     *\n     * @param args\n     */\n    compile(args) {\n        this.build();\n        this.model.compile(args);\n        this.optimizer_ = this.model.optimizer;\n        // tslint:disable-next-line:no-any\n        this.isOptimizerOwned = this.model.isOptimizerOwned;\n        this.loss = this.model.loss;\n        this.metrics = this.model.metrics;\n        // TODO(cais): Add this.lossWeights, this.sampleWeightMode,\n        //   this.weightedMetrics, this.targets.\n        this.metricsTensors = this.model.metricsTensors;\n        this.metricsNames = this.model.metricsNames;\n        // TODO(cais): Add sampleWeights.\n    }\n    get optimizer() {\n        return this.model == null ? undefined : this.model.optimizer;\n    }\n    set optimizer(optimizer) {\n        this.model.optimizer = optimizer;\n    }\n    /**\n     * Trains the model for a fixed number of epochs (iterations on a dataset).\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const history = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *   batchSize: 4,\n     *   epochs: 3\n     * });\n     * console.log(history.history.loss[0]);\n     * ```\n     *\n     * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n     * model has multiple inputs. If all inputs in the model are named, you can\n     * also pass a dictionary mapping input names to `tf.Tensor`s.\n     * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n     * the model has multiple outputs. If all outputs in the model are named, you\n     *  can also pass a dictionary mapping output names to `tf.Tensor`s.\n     * @param args  A `ModelFitConfig`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and what the model expects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fit(x, y, args = {}) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before ' +\n                'being used.');\n        }\n        return this.model.fit(x, y, args);\n    }\n    /**\n     * Trains the model using a dataset object.\n     *\n     * ```js\n     * const xArray = [\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     * ];\n     * const yArray = [1, 1, 1, 1];\n     * // Create a dataset from the JavaScript array.\n     * const xDataset = tf.data.array(xArray);\n     * const yDataset = tf.data.array(yArray);\n     * // Zip combines the `x` and `y` Datasets into a single Dataset, the\n     * // iterator of which will return an object containing of two tensors,\n     * // corresponding to `x` and `y`.  The call to `batch(4)` will bundle\n     * // four such samples into a single object, with the same keys now pointing\n     * // to tensors that hold 4 examples, organized along the batch dimension.\n     * // The call to `shuffle(4)` causes each iteration through the dataset to\n     * // happen in a different order.  The size of the shuffle window is 4.\n     * const xyDataset = tf.data.zip({xs: xDataset, ys: yDataset})\n     *     .batch(4)\n     *     .shuffle(4);\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [9]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const history = await model.fitDataset(xyDataset, {\n     *   epochs: 4,\n     *   callbacks: {onEpochEnd: (epoch, logs) => console.log(logs.loss)}\n     * });\n     * ```\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected to\n     *   generate a dataset iterator object, the `next()` method of which is\n     *   expected to produce data batches for evaluation. The return value of the\n     *   `next()` call ought to contain a boolean `done` field and a `value`\n     *   field.\n     *\n     *   The `value` field is expected to be an object of with fields\n     *   `xs` and `ys`, which point to the feature tensor and the target tensor,\n     *   respectively. This case is for models with exactly one input and one\n     *   output (e.g.. a sequential model). For example:\n     *   ```js\n     *   {value: {xs: xsTensor, ys: ysTensor}, done: false}\n     *   ```\n     *\n     *   If the model has multiple inputs, the `xs` field of `value` should\n     *   be an object mapping input names to their respective feature tensors.\n     *   For example:\n     *   ```js\n     *   {\n     *     value: {\n     *       xs: {\n     *         input_1: xsTensor1,\n     *         input_2: xsTensor2\n     *       },\n     *       ys: ysTensor\n     *     },\n     *     done: false\n     *   }\n     *   ```\n     *   If the model has multiple outputs, the `ys` field of `value` should\n     *   be an object mapping output names to their respective target tensors.\n     *   For example:\n     *   ```js\n     *   {\n     *     value: {\n     *       xs: xsTensor,\n     *       ys: {\n     *         output_1: ysTensor1,\n     *         output_2: ysTensor2\n     *       },\n     *     },\n     *     done: false\n     *   }\n     *   ```\n     * @param args A `ModelFitDatasetArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async fitDataset(dataset, args) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before ' +\n                'being used.');\n        }\n        return this.model.fitDataset(dataset, args);\n    }\n    /**\n     * Runs a single gradient update on a single batch of data.\n     *\n     * This method differs from `fit()` and `fitDataset()` in the following\n     * regards:\n     *   - It operates on exactly one batch of data.\n     *   - It returns only the loss and matric values, instead of\n     *     returning the batch-by-batch loss and metric values.\n     *   - It doesn't support fine-grained options such as verbosity and\n     *     callbacks.\n     *\n     * @param x Input data. It could be one of the following:\n     *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n     *     multiple inputs).\n     *   - An Object mapping input names to corresponding `tf.Tensor` (if the\n     *     model has named inputs).\n     * @param y Target darta. It could be either a `tf.Tensor` a multiple\n     *   `tf.Tensor`s. It should be consistent with `x`.\n     * @returns Training loss or losses (in case the model has\n     *   multiple outputs), along with metrics (if any), as numbers.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async trainOnBatch(x, y) {\n        return this.model.trainOnBatch(x, y);\n    }\n    /* See parent class for JsDoc */\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n        let configArray;\n        let extraModelConfig = {};\n        if (config instanceof Array) {\n            if (!(config[0].className != null) ||\n                config[0]['className'] === 'Merge') {\n                throw new ValueError('Legacy serialization format not supported yet.');\n            }\n            configArray = config;\n        }\n        else {\n            util.assert(config['layers'] != null, () => `When the config data for a Sequential model is not an Array, ` +\n                `it must be an Object that contains the 'layers' field.`);\n            configArray = config['layers'];\n            delete config['layers'];\n            extraModelConfig = config;\n        }\n        const model = new cls(extraModelConfig);\n        if (!(model instanceof Sequential)) {\n            throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model}`);\n        }\n        for (const conf of configArray) {\n            const customObjects = undefined;\n            const layer = deserialize(conf, customObjects, fastWeightInit);\n            if (fastWeightInit) {\n                layer.setFastWeightInitDuringBuild(true);\n            }\n            model.add(layer);\n        }\n        return model;\n    }\n    /**\n     * Setter used for force stopping of LayersModel.fit() (i.e., training).\n     *\n     * Example:\n     *\n     * ```js\n     * const model = tf.sequential();\n     * model.add(tf.layers.dense({units: 1, inputShape: [10]}));\n     * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n     * const xs = tf.ones([8, 10]);\n     * const ys = tf.zeros([8, 1]);\n     *\n     * const history = await model.fit(xs, ys, {\n     *   epochs: 10,\n     *   callbacks: {\n     *     onEpochEnd: async (epoch, logs) => {\n     *       if (epoch === 2) {\n     *         model.stopTraining = true;\n     *       }\n     *     }\n     *   }\n     * });\n     *\n     * // There should be only 3 values in the loss array, instead of 10 values,\n     * // due to the stopping after 3 epochs.\n     * console.log(history.history.loss);\n     * ```\n     */\n    set stopTraining(stop) {\n        // TODO(cais): When refactoring to remove the composition pattern happens,\n        // remove this method overriding.\n        if (this.model == null) {\n            throw new ValueError('Cannot set the stopTraining property of a sequential model before ' +\n                'it is compiled.');\n        }\n        this.model.stopTraining = stop;\n    }\n    get stopTraining() {\n        if (this.model == null) {\n            throw new ValueError('Cannot get the stopTraining property of a sequential model before ' +\n                'it is compiled.');\n        }\n        return this.model.stopTraining;\n    }\n    // TODO(cais): Override get trainableWeights() here\n    // tslint:disable-next-line:no-any\n    getConfig() {\n        // NOTE(cais): We override the return type of getConfig() to `any` here,\n        //   because the `Sequential` class is a special case among `Container`\n        //   subtypes in that its getConfig() method returns an Array (not a\n        //   dict).\n        const layers = [];\n        for (const layer of this.layers) {\n            const dict = {};\n            dict['className'] = layer.getClassName();\n            dict['config'] = layer.getConfig();\n            layers.push(dict);\n        }\n        return { name: this.name, layers };\n    }\n}\n/** @nocollapse */\nSequential.className = 'Sequential';\nserialization.registerClass(Sequential);\n//# sourceMappingURL=models.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { CallbackConstructorRegistry } from './base_callbacks';\nimport { Input, } from './engine/input_layer';\nimport { LayersModel } from './engine/training';\nimport { loadLayersModelInternal, Sequential } from './models';\n// TODO(cais): Add doc string to all the public static functions in this\n//   class; include exectuable JavaScript code snippets where applicable\n//   (b/74074458).\n// LayersModel and related factory methods.\n/**\n * A model is a data structure that consists of `Layers` and defines inputs\n * and outputs.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.model` is more generic, supporting an arbitrary graph (without\n * cycles) of layers. `tf.sequential` is less generic and supports only a linear\n * stack of layers.\n *\n * When creating a `tf.LayersModel`, specify its input(s) and output(s). Layers\n * are used to wire input(s) to output(s).\n *\n * For example, the following code snippet defines a model consisting of\n * two `dense` layers, with 10 and 4 units, respectively.\n *\n * ```js\n * // Define input, which has a size of 5 (not including batch dimension).\n * const input = tf.input({shape: [5]});\n *\n * // First dense layer uses relu activation.\n * const denseLayer1 = tf.layers.dense({units: 10, activation: 'relu'});\n * // Second dense layer uses softmax activation.\n * const denseLayer2 = tf.layers.dense({units: 4, activation: 'softmax'});\n *\n * // Obtain the output symbolic tensor by applying the layers on the input.\n * const output = denseLayer2.apply(denseLayer1.apply(input));\n *\n * // Create the model based on the inputs.\n * const model = tf.model({inputs: input, outputs: output});\n *\n * // The model can be used for training, evaluation and prediction.\n * // For example, the following line runs prediction with the model on\n * // some fake data.\n * model.predict(tf.ones([2, 5])).print();\n * ```\n * See also:\n *   `tf.sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function model(args) {\n    return new LayersModel(args);\n}\n/**\n * Creates a `tf.Sequential` model.  A sequential model is any model where the\n * outputs of one layer are the inputs to the next layer, i.e. the model\n * topology is a simple 'stack' of layers, with no branching or skipping.\n *\n * This means that the first layer passed to a `tf.Sequential` model should have\n * a defined input shape. What that means is that it should have received an\n * `inputShape` or `batchInputShape` argument, or for some type of layers\n * (recurrent, Dense...) an `inputDim` argument.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.sequential` is less generic, supporting only a linear stack of layers.\n * `tf.model` is more generic and supports an arbitrary graph (without\n * cycles) of layers.\n *\n * Examples:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have an input shape defined.\n * model.add(tf.layers.dense({units: 32, inputShape: [50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output, which equals\n * // `[null, 4]`. The 1st dimension is the undetermined batch dimension; the\n * // 2nd is the output size of the model's last layer.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * It is also possible to specify a batch size (with potentially undetermined\n * batch dimension, denoted by \"null\") for the first layer using the\n * `batchInputShape` key. The following example is equivalent to the above:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have a defined input shape\n * model.add(tf.layers.dense({units: 32, batchInputShape: [null, 50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * You can also use an `Array` of already-constructed `Layer`s to create\n * a `tf.Sequential` model:\n *\n * ```js\n * const model = tf.sequential({\n *   layers: [tf.layers.dense({units: 32, inputShape: [50]}),\n *            tf.layers.dense({units: 4})]\n * });\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function sequential(config) {\n    return new Sequential(config);\n}\n/**\n * Load a model composed of Layer objects, including its topology and optionally\n * weights. See the Tutorial named \"How to import a Keras Model\" for usage\n * examples.\n *\n * This method is applicable to:\n *\n * 1. Models created with the `tf.layers.*`, `tf.sequential`, and\n * `tf.model` APIs of TensorFlow.js and later saved with the\n * `tf.LayersModel.save` method.\n * 2. Models converted from Keras or TensorFlow tf.keras using the\n * [tensorflowjs_converter](https://github.com/tensorflow/tfjs/tree/master/tfjs-converter).\n *\n * This mode is *not* applicable to TensorFlow `SavedModel`s or their converted\n * forms. For those models, use `tf.loadGraphModel`.\n *\n * Example 1. Load a model from an HTTP server.\n *\n * ```js\n * const model = await tf.loadLayersModel(\n *     'https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');\n * model.summary();\n * ```\n *\n * Example 2: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 4. Load a model from user-selected files from HTML\n * [file input\n * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).\n *\n * ```js\n * // Note: this code snippet will not work without the HTML elements in the\n * //   page\n * const jsonUpload = document.getElementById('json-upload');\n * const weightsUpload = document.getElementById('weights-upload');\n *\n * const model = await tf.loadLayersModel(\n *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));\n * ```\n *\n * @param pathOrIOHandler Can be either of the two formats\n *   1. A string path to the `ModelAndWeightsConfig` JSON describing\n *      the model in the canonical TensorFlow.js format. For file://\n *      (tfjs-node-only), http:// and https:// schemas, the path can be\n *      either absolute or relative.\n *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`\n *      method.\n * @param options Optional configuration arguments for the model loading,\n *   including:\n *   - `strict`: Require that the provided weights exactly match those required\n *     by the layers.  Default true.  Passing false means that both extra\n *     weights and missing weights will be silently ignored.\n *   - `onProgress`: A function of the signature `(fraction: number) => void',\n *     that can be used as the progress callback for the model loading.\n * @returns A `Promise` of `tf.LayersModel`, with the topology and weights\n *     loaded.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport function loadLayersModel(pathOrIOHandler, options) {\n    if (options == null) {\n        options = {};\n    }\n    return loadLayersModelInternal(pathOrIOHandler, options);\n}\n/**\n * Used to instantiate an input to a model as a `tf.SymbolicTensor`.\n *\n * Users should call the `input` factory function for\n * consistency with other generator functions.\n *\n * Example:\n *\n * ```js\n * // Defines a simple logistic regression model with 32 dimensional input\n * // and 3 dimensional output.\n * const x = tf.input({shape: [32]});\n * const y = tf.layers.dense({units: 3, activation: 'softmax'}).apply(x);\n * const model = tf.model({inputs: x, outputs: y});\n * model.predict(tf.ones([2, 32])).print();\n * ```\n *\n * Note: `input` is only necessary when using `model`. When using\n * `sequential`, specify `inputShape` for the first layer or use `inputLayer`\n * as the first layer.\n *\n * @doc {heading: 'Models', subheading: 'Inputs'}\n */\nexport function input(config) {\n    return Input(config);\n}\nexport function registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n    CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);\n}\n//# sourceMappingURL=exports.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// Layer activation functions\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { deserializeKerasObject } from './utils/generic_utils';\n/**\n * Base class for Activations.\n *\n * Special note: due to cross-language compatibility reasons, the\n * static readonly className field in this family of classes must be set to\n * the initialLowerCamelCase name of the activation.\n */\nexport class Activation extends serialization.Serializable {\n    getConfig() {\n        return {};\n    }\n}\n/**\n * Exponential linear unit (ELU).\n * Reference: https://arxiv.org/abs/1511.07289\n */\nexport class Elu extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x: Input.\n     * @param alpha: Scaling factor the negative section.\n     * @return Output of the ELU activation.\n     */\n    apply(x, alpha = 1) {\n        return K.elu(x, alpha);\n    }\n}\n/** @nocollapse */\nElu.className = 'elu';\nserialization.registerClass(Elu);\n/**\n * Scaled Exponential Linear Unit. (Klambauer et al., 2017).\n * Reference: Self-Normalizing Neural Networks, https://arxiv.org/abs/1706.02515\n * Notes:\n *   - To be used together with the initialization \"lecunNormal\".\n *   - To be used together with the dropout variant \"AlphaDropout\".\n */\nexport class Selu extends Activation {\n    apply(x) {\n        return tfc.selu(x);\n    }\n}\n/** @nocollapse */\nSelu.className = 'selu';\nserialization.registerClass(Selu);\n/**\n *  Rectified linear unit\n */\nexport class Relu extends Activation {\n    apply(x) {\n        return tfc.relu(x);\n    }\n}\n/** @nocollapse */\nRelu.className = 'relu';\nserialization.registerClass(Relu);\n/**\n * Rectified linear unit activation maxing out at 6.0.\n */\nexport class Relu6 extends Activation {\n    apply(x) {\n        return tidy(() => tfc.minimum(6.0, tfc.relu(x)));\n    }\n}\n/** @nocollapse */\nRelu6.className = 'relu6';\nserialization.registerClass(Relu6);\n//* Linear activation (no-op) */\nexport class Linear extends Activation {\n    apply(x) {\n        return x;\n    }\n}\n/** @nocollapse */\nLinear.className = 'linear';\nserialization.registerClass(Linear);\n/**\n * Sigmoid activation function.\n */\nexport class Sigmoid extends Activation {\n    apply(x) {\n        return tfc.sigmoid(x);\n    }\n}\n/** @nocollapse */\nSigmoid.className = 'sigmoid';\nserialization.registerClass(Sigmoid);\n/**\n * Segment-wise linear approximation of sigmoid.\n */\nexport class HardSigmoid extends Activation {\n    apply(x) {\n        return K.hardSigmoid(x);\n    }\n}\n/** @nocollapse */\nHardSigmoid.className = 'hardSigmoid';\nserialization.registerClass(HardSigmoid);\n/**\n * Softplus activation function.\n */\nexport class Softplus extends Activation {\n    apply(x) {\n        return tfc.softplus(x);\n    }\n}\n/** @nocollapse */\nSoftplus.className = 'softplus';\nserialization.registerClass(Softplus);\n/**\n * Softsign activation function.\n */\nexport class Softsign extends Activation {\n    apply(x) {\n        return K.softsign(x);\n    }\n}\n/** @nocollapse */\nSoftsign.className = 'softsign';\nserialization.registerClass(Softsign);\n/**\n * Hyperbolic tangent function.\n */\nexport class Tanh extends Activation {\n    apply(x) {\n        return tfc.tanh(x);\n    }\n}\n/** @nocollapse */\nTanh.className = 'tanh';\nserialization.registerClass(Tanh);\n/**\n * Softmax activation function\n */\nexport class Softmax extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x Tensor.\n     * @param axis Integer, axis along which the softmax normalization is applied.\n     * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n     * an error.\n     *\n     * @returns a Tensor of the same shape as x\n     *\n     * @throws ValueError: In case `dim(x) < 2`.\n     */\n    apply(x, axis = (-1)) {\n        return tfc.softmax(x, axis);\n    }\n}\n/** @nocollapse */\nSoftmax.className = 'softmax';\nserialization.registerClass(Softmax);\n/**\n * Log softmax activation function\n */\nexport class LogSoftmax extends Activation {\n    /**\n     * Calculate the activation function of log softmax:\n     * log( exp(x_i) / sum(exp(x)) )\n     *\n     * @param x Tensor.\n     * @param axis Integer, axis along which the softmax normalization is applied.\n     * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n     * an error.\n     *\n     * @returns a Tensor of the same shape as x\n     *\n     * @throws ValueError: In case `dim(x) < 2`.\n     */\n    apply(x, axis = (-1)) {\n        return tfc.logSoftmax(x, axis);\n    }\n}\n/** @nocollapse */\nLogSoftmax.className = 'logSoftmax';\nserialization.registerClass(LogSoftmax);\n/**\n * Swish activation function\n */\nexport class Swish extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x Tensor.\n     * @param alpha Scaling factor for the sigmoid function.\n     * @returns a Tensor of the same shape as x\n     */\n    apply(x, alpha = 1) {\n        return tidy(() => tfc.sigmoid(x.mul(alpha)).mul(x));\n    }\n}\n/** @nocollapse */\nSwish.className = 'swish';\nserialization.registerClass(Swish);\nexport function serializeActivation(activation) {\n    return activation.getClassName();\n}\nexport function deserializeActivation(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'activation');\n}\nexport function getActivation(identifier) {\n    if (identifier == null) {\n        const config = {};\n        config['className'] = 'linear';\n        config['config'] = {};\n        return deserializeActivation(config);\n    }\n    if (typeof identifier === 'string') {\n        const config = {};\n        config['className'] = identifier;\n        config['config'] = {};\n        return deserializeActivation(config);\n    }\n    else if (identifier instanceof Activation) {\n        return identifier;\n    }\n    else {\n        return deserializeActivation(identifier);\n    }\n}\n//# sourceMappingURL=activations.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* original source: keras/regularizers.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { abs, add, serialization, sum, tidy, zeros } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\nfunction assertObjectArgs(args) {\n    if (args != null && typeof args !== 'object') {\n        throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an ` +\n            `object, but received: ${args}`);\n    }\n}\n/**\n * Regularizer base class.\n */\nexport class Regularizer extends serialization.Serializable {\n}\nexport class L1L2 extends Regularizer {\n    constructor(args) {\n        super();\n        assertObjectArgs(args);\n        this.l1 = args == null || args.l1 == null ? 0.01 : args.l1;\n        this.l2 = args == null || args.l2 == null ? 0.01 : args.l2;\n        this.hasL1 = this.l1 !== 0;\n        this.hasL2 = this.l2 !== 0;\n    }\n    /**\n     * Porting note: Renamed from __call__.\n     * @param x Variable of which to calculate the regularization score.\n     */\n    apply(x) {\n        return tidy(() => {\n            let regularization = zeros([1]);\n            if (this.hasL1) {\n                regularization = add(regularization, sum(tfc.mul(this.l1, abs(x))));\n            }\n            if (this.hasL2) {\n                regularization =\n                    add(regularization, sum(tfc.mul(this.l2, K.square(x))));\n            }\n            return regularization.asScalar();\n        });\n    }\n    getConfig() {\n        return { 'l1': this.l1, 'l2': this.l2 };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls({ l1: config['l1'], l2: config['l2'] });\n    }\n}\n/** @nocollapse */\nL1L2.className = 'L1L2';\nserialization.registerClass(L1L2);\nexport function l1(args) {\n    assertObjectArgs(args);\n    return new L1L2({ l1: args != null ? args.l1 : null, l2: 0 });\n}\nexport function l2(args) {\n    assertObjectArgs(args);\n    return new L1L2({ l2: args != null ? args.l2 : null, l1: 0 });\n}\n// Maps the JavaScript-like identifier keys to the corresponding keras symbols.\nexport const REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'l1l2': 'L1L2'\n};\nexport function serializeRegularizer(constraint) {\n    return serializeKerasObject(constraint);\n}\nexport function deserializeRegularizer(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'regularizer');\n}\nexport function getRegularizer(identifier) {\n    if (identifier == null) {\n        return null;\n    }\n    if (typeof identifier === 'string') {\n        const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        const config = { className, config: {} };\n        return deserializeRegularizer(config);\n    }\n    else if (identifier instanceof Regularizer) {\n        return identifier;\n    }\n    else {\n        return deserializeRegularizer(identifier);\n    }\n}\n//# sourceMappingURL=regularizers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n *  Advanced activation layers.\n */\nimport { clipByValue, elu, leakyRelu, prelu, relu, serialization } from '@tensorflow/tfjs-core';\nimport { Softmax as softmaxActivation } from '../activations';\nimport { cast } from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class ReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.supportsMasking = true;\n        if (args != null) {\n            this.maxValue = args.maxValue;\n        }\n    }\n    call(inputs, kwargs) {\n        inputs = getExactlyOneTensor(inputs);\n        let output = relu(inputs);\n        if (this.maxValue != null) {\n            output = clipByValue(output, 0, this.maxValue);\n        }\n        return output;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { maxValue: this.maxValue };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nReLU.className = 'ReLU';\nserialization.registerClass(ReLU);\nexport class LeakyReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA = 0.3;\n        if (args == null) {\n            args = {};\n        }\n        this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return leakyRelu(x, this.alpha);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { alpha: this.alpha };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nLeakyReLU.className = 'LeakyReLU';\nserialization.registerClass(LeakyReLU);\nexport class PReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA_INITIALIZER = 'zeros';\n        if (args == null) {\n            args = {};\n        }\n        this.supportsMasking = true;\n        this.alphaInitializer =\n            getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER);\n        this.alphaRegularizer = getRegularizer(args.alphaRegularizer);\n        this.alphaConstraint = getConstraint(args.alphaConstraint);\n        if (args.sharedAxes == null) {\n            this.sharedAxes = null;\n        }\n        else if (Array.isArray(args.sharedAxes)) {\n            this.sharedAxes = args.sharedAxes;\n        }\n        else if (typeof args.sharedAxes === 'number') {\n            this.sharedAxes = [args.sharedAxes];\n        }\n        else {\n            throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, ` +\n                `but got ${args.sharedAxes}`);\n        }\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const paramShape = inputShape.slice(1);\n        if (this.sharedAxes != null) {\n            for (const i of this.sharedAxes) {\n                paramShape[i - 1] = 1;\n            }\n        }\n        this.alpha = this.addWeight('alpha', paramShape, 'float32', this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);\n        // Set input spec.\n        const axes = {};\n        if (this.sharedAxes != null) {\n            for (let i = 1; i < inputShape.length; ++i) {\n                axes[i] = inputShape[i];\n            }\n        }\n        this.inputSpec = [new InputSpec({\n                ndim: inputShape.length,\n                axes,\n            })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        inputs = getExactlyOneTensor(inputs);\n        return prelu(inputs, this.alpha.read());\n    }\n    getConfig() {\n        const config = {\n            alphaInitializer: serializeInitializer(this.alphaInitializer),\n            alphaRegularizer: serializeRegularizer(this.alphaRegularizer),\n            alphaConstraint: serializeConstraint(this.alphaConstraint),\n            sharedAxes: this.sharedAxes\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nPReLU.className = 'PReLU';\nserialization.registerClass(PReLU);\nexport class ELU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) {\n            throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ` +\n                `ELU layer yet.`);\n        }\n        this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return elu(x);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { alpha: this.alpha };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nELU.className = 'ELU';\nserialization.registerClass(ELU);\nexport class ThresholdedReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_THETA = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return x.mul(cast(x.greater(this.theta), 'float32'));\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { theta: this.theta };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nThresholdedReLU.className = 'ThresholdedReLU';\nserialization.registerClass(ThresholdedReLU);\nexport class Softmax extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_AXIS = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        this.softmax = new softmaxActivation().apply;\n        this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return this.softmax(x, this.axis);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { axis: this.axis };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nSoftmax.className = 'Softmax';\nserialization.registerClass(Softmax);\n//# sourceMappingURL=advanced_activations.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { ValueError } from '../errors';\nimport { pyListRepeat } from './generic_utils';\nimport { isInteger, max } from './math_utils';\n/**\n * Transforms a single number of array of numbers into an array of numbers.\n * @param value\n * @param n: The size of the tuple to be returned.\n * @param name: Name of the parameter, used for generating error messages.\n * @returns An array of numbers.\n */\nexport function normalizeArray(value, n, name) {\n    if (typeof value === 'number') {\n        return pyListRepeat(value, n);\n    }\n    else {\n        if (value.length !== n) {\n            throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers.` +\n                ` Received: ${value.length} elements.`);\n        }\n        for (let i = 0; i < n; ++i) {\n            const singleValue = value[i];\n            if (!isInteger(singleValue)) {\n                throw new ValueError(`The ${name} argument must be an integer or tuple of ${n}` +\n                    ` integers. Received: ${JSON.stringify(value)} including a` +\n                    ` non-integer number ${singleValue}`);\n            }\n        }\n        return value;\n    }\n}\n/**\n * Determines output length of a convolution given input length.\n * @param inputLength\n * @param filterSize\n * @param padding\n * @param stride\n * @param dilation: dilation rate.\n */\nexport function convOutputLength(inputLength, filterSize, padding, stride, dilation = 1) {\n    if (inputLength == null) {\n        return inputLength;\n    }\n    const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1);\n    let outputLength;\n    if (padding === 'same') {\n        outputLength = inputLength;\n    }\n    else { // VALID\n        outputLength = inputLength - dilatedFilterSize + 1;\n    }\n    return Math.floor((outputLength + stride - 1) / stride);\n}\nexport function deconvLength(dimSize, strideSize, kernelSize, padding) {\n    if (dimSize == null) {\n        return null;\n    }\n    if (padding === 'valid') {\n        dimSize = dimSize * strideSize + max([kernelSize - strideSize, 0]);\n    }\n    else if (padding === 'same') {\n        dimSize = dimSize * strideSize;\n    }\n    else {\n        throw new ValueError(`Unsupport padding mode: ${padding}.`);\n    }\n    return dimSize;\n}\n//# sourceMappingURL=conv_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Convolutional Layers\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { convOutputLength, deconvLength, normalizeArray } from '../utils/conv_utils';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Transpose and cast the input before the conv2d.\n * @param x Input image tensor.\n * @param dataFormat\n */\nexport function preprocessConv2DInput(x, dataFormat) {\n    // TODO(cais): Cast type to float32 if not.\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        if (dataFormat === 'channelsFirst') {\n            return tfc.transpose(x, [0, 2, 3, 1]); // NCHW -> NHWC.\n        }\n        else {\n            return x;\n        }\n    });\n}\n/**\n * Transpose and cast the input before the conv3d.\n * @param x Input image tensor.\n * @param dataFormat\n */\nexport function preprocessConv3DInput(x, dataFormat) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        if (dataFormat === 'channelsFirst') {\n            return tfc.transpose(x, [0, 2, 3, 4, 1]); // NCDHW -> NDHWC.\n        }\n        else {\n            return x;\n        }\n    });\n}\n/**\n * 1D-convolution with bias added.\n *\n * Porting Note: This function does not exist in the Python Keras backend.\n *   It is exactly the same as `conv2d`, except the added `bias`.\n *\n * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.\n * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.\n * @param bias Bias, rank-3, of shape `[outDepth]`.\n * @param strides\n * @param padding Padding mode.\n * @param dataFormat Data format.\n * @param dilationRate\n * @returns The result of the 1D convolution.\n * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.\n */\nexport function conv1dWithBias(x, kernel, bias, strides = 1, padding = 'valid', dataFormat, dilationRate = 1) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        // Check the ranks of x, kernel and bias.\n        if (x.shape.length !== 3) {\n            throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ` +\n                `${x.shape.length} instead.`);\n        }\n        if (kernel.shape.length !== 3) {\n            throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ` +\n                `${kernel.shape.length} instead`);\n        }\n        if (bias != null && bias.shape.length !== 1) {\n            throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ` +\n                `${kernel.shape.length} instead`);\n        }\n        // TODO(cais): Support CAUSAL padding mode.\n        if (dataFormat === 'channelsFirst') {\n            x = tfc.transpose(x, [0, 2, 1]); // NCW -> NWC.\n        }\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv1dWithBias is not ' +\n                'implemented yet.');\n        }\n        let y = tfc.conv1d(x, kernel, strides, padding === 'same' ? 'same' : 'valid', 'NWC', dilationRate);\n        if (bias != null) {\n            y = K.biasAdd(y, bias);\n        }\n        return y;\n    });\n}\n/**\n * 1D-convolution.\n *\n * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.\n * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.s\n * @param strides\n * @param padding Padding mode.\n * @param dataFormat Data format.\n * @param dilationRate\n * @returns The result of the 1D convolution.\n * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.\n */\nexport function conv1d(x, kernel, strides = 1, padding = 'valid', dataFormat, dilationRate = 1) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv1dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 2D Convolution\n * @param x\n * @param kernel kernel of the convolution.\n * @param strides strides array.\n * @param padding padding mode. Default to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param dilationRate dilation rate array.\n * @returns Result of the 2D pooling.\n */\nexport function conv2d(x, kernel, strides = [1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv2dWithBiasActivation(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 2D Convolution with an added bias and optional activation.\n * Note: This function does not exist in the Python Keras Backend. This function\n * is exactly the same as `conv2d`, except the added `bias`.\n */\nexport function conv2dWithBiasActivation(x, kernel, bias, strides = [1, 1], padding = 'valid', dataFormat, dilationRate, activation = null) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        if (x.rank !== 3 && x.rank !== 4) {\n            throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, ` +\n                `but received ${x.rank}.`);\n        }\n        if (kernel.rank !== 3 && kernel.rank !== 4) {\n            throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, ` +\n                `but received ${x.rank}.`);\n        }\n        let y = preprocessConv2DInput(x, dataFormat);\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv1dWithBias is not ' +\n                'implemented yet.');\n        }\n        y = tfc.fused.conv2d({\n            x: y,\n            filter: kernel,\n            strides: strides,\n            pad: padding === 'same' ? 'same' : 'valid',\n            dilations: dilationRate,\n            dataFormat: 'NHWC',\n            bias,\n            activation\n        });\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]);\n        }\n        return y;\n    });\n}\n/**\n * 3D Convolution.\n * @param x\n * @param kernel kernel of the convolution.\n * @param strides strides array.\n * @param padding padding mode. Default to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param dilationRate dilation rate array.\n * @returns Result of the 3D convolution.\n */\nexport function conv3d(x, kernel, strides = [1, 1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv3dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 3D Convolution with an added bias.\n * Note: This function does not exist in the Python Keras Backend. This function\n * is exactly the same as `conv3d`, except the added `bias`.\n */\nexport function conv3dWithBias(x, kernel, bias, strides = [1, 1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        if (x.rank !== 4 && x.rank !== 5) {\n            throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ` +\n                `${x.rank}.`);\n        }\n        if (kernel.rank !== 4 && kernel.rank !== 5) {\n            throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ` +\n                `${x.rank}.`);\n        }\n        let y = preprocessConv3DInput(x, dataFormat);\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv3dWithBias is not ' +\n                'implemented yet.');\n        }\n        y = tfc.conv3d(y, kernel, strides, padding === 'same' ? 'same' : 'valid', 'NDHWC', dilationRate);\n        if (bias != null) {\n            y = K.biasAdd(y, bias);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 4, 1, 2, 3]);\n        }\n        return y;\n    });\n}\n/**\n * Abstract convolution layer.\n */\nexport class BaseConv extends Layer {\n    constructor(rank, args) {\n        super(args);\n        this.bias = null;\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        BaseConv.verifyArgs(args);\n        this.rank = rank;\n        generic_utils.assertPositiveInteger(this.rank, 'rank');\n        if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) {\n            throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is ` +\n                `not implemented yet.`);\n        }\n        this.kernelSize = normalizeArray(args.kernelSize, rank, 'kernelSize');\n        this.strides = normalizeArray(args.strides == null ? 1 : args.strides, rank, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        checkPaddingMode(this.padding);\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        this.activation = getActivation(args.activation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.dilationRate = normalizeArray(args.dilationRate == null ? 1 : args.dilationRate, rank, 'dilationRate');\n        if (this.rank === 1 &&\n            (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) {\n            throw new ValueError(`dilationRate must be a number or an array of a single number ` +\n                `for 1D convolution, but received ` +\n                `${JSON.stringify(this.dilationRate)}`);\n        }\n        else if (this.rank === 2) {\n            if (typeof this.dilationRate === 'number') {\n                this.dilationRate = [this.dilationRate, this.dilationRate];\n            }\n            else if (this.dilationRate.length !== 2) {\n                throw new ValueError(`dilationRate must be a number or array of two numbers for 2D ` +\n                    `convolution, but received ${JSON.stringify(this.dilationRate)}`);\n            }\n        }\n        else if (this.rank === 3) {\n            if (typeof this.dilationRate === 'number') {\n                this.dilationRate =\n                    [this.dilationRate, this.dilationRate, this.dilationRate];\n            }\n            else if (this.dilationRate.length !== 3) {\n                throw new ValueError(`dilationRate must be a number or array of three numbers for 3D ` +\n                    `convolution, but received ${JSON.stringify(this.dilationRate)}`);\n            }\n        }\n    }\n    static verifyArgs(args) {\n        // Check config.kernelSize type and shape.\n        generic_utils.assert('kernelSize' in args, `required key 'kernelSize' not in config`);\n        if (typeof args.kernelSize !== 'number' &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 3)) {\n            throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with ` +\n                `length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n    getConfig() {\n        const config = {\n            kernelSize: this.kernelSize,\n            strides: this.strides,\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n            dilationRate: this.dilationRate,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            biasConstraint: serializeConstraint(this.biasConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/**\n * Abstract nD convolution layer.  Ancestor of convolution layers which reduce\n * across channels, i.e., Conv1D and Conv2D, but not DepthwiseConv2D.\n */\nexport class Conv extends BaseConv {\n    constructor(rank, args) {\n        super(rank, args);\n        this.kernel = null;\n        Conv.verifyArgs(args);\n        this.filters = args.filters;\n        generic_utils.assertPositiveInteger(this.filters, 'filters');\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError(`The channel dimension of the input should be defined. ` +\n                `Found ${inputShape[channelAxis]}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const kernelShape = this.kernelSize.concat([inputDim, this.filters]);\n        this.kernel = this.addWeight('kernel', kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        this.inputSpec = [{ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } }];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let outputs;\n            const biasValue = this.bias == null ? null : this.bias.read();\n            const fusedActivationName = generic_utils.mapActivationToFusedKernel(this.activation.getClassName());\n            if (fusedActivationName != null && this.rank === 2) {\n                outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate, fusedActivationName);\n            }\n            else {\n                if (this.rank === 1) {\n                    outputs = conv1dWithBias(inputs, this.kernel.read(), biasValue, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);\n                }\n                else if (this.rank === 2) {\n                    // TODO(cais): Move up to constructor.\n                    outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n                }\n                else if (this.rank === 3) {\n                    outputs = conv3dWithBias(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n                }\n                else {\n                    throw new NotImplementedError('convolutions greater than 3D are not implemented yet.');\n                }\n                if (this.activation != null) {\n                    outputs = this.activation.apply(outputs);\n                }\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const newSpace = [];\n        const space = (this.dataFormat === 'channelsLast') ?\n            inputShape.slice(1, inputShape.length - 1) :\n            inputShape.slice(2);\n        for (let i = 0; i < space.length; ++i) {\n            const newDim = convOutputLength(space[i], this.kernelSize[i], this.padding, this.strides[i], typeof this.dilationRate === 'number' ? this.dilationRate :\n                this.dilationRate[i]);\n            newSpace.push(newDim);\n        }\n        let outputShape = [inputShape[0]];\n        if (this.dataFormat === 'channelsLast') {\n            outputShape = outputShape.concat(newSpace);\n            outputShape.push(this.filters);\n        }\n        else {\n            outputShape.push(this.filters);\n            outputShape = outputShape.concat(newSpace);\n        }\n        return outputShape;\n    }\n    getConfig() {\n        const config = {\n            filters: this.filters,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    static verifyArgs(args) {\n        // Check config.filters type, shape, and value.\n        if (!('filters' in args) || typeof args.filters !== 'number' ||\n            args.filters < 1) {\n            throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 ` +\n                `but got ${JSON.stringify(args.filters)}`);\n        }\n    }\n}\nexport class Conv2D extends Conv {\n    constructor(args) {\n        super(2, args);\n        Conv2D.verifyArgs(args);\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if ((typeof args.kernelSize !== 'number') &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 2)) {\n            throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with ` +\n                `length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n}\n/** @nocollapse */\nConv2D.className = 'Conv2D';\nserialization.registerClass(Conv2D);\nexport class Conv3D extends Conv {\n    constructor(args) {\n        super(3, args);\n        Conv3D.verifyArgs(args);\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if (typeof args.kernelSize !== 'number') {\n            if (!(Array.isArray(args.kernelSize) &&\n                (args.kernelSize.length === 1 || args.kernelSize.length === 3))) {\n                throw new ValueError(`Conv3D expects config.kernelSize to be number or` +\n                    ` [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`);\n            }\n        }\n    }\n}\n/** @nocollapse */\nConv3D.className = 'Conv3D';\nserialization.registerClass(Conv3D);\nexport class Conv2DTranspose extends Conv2D {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n        if (this.padding !== 'same' && this.padding !== 'valid') {\n            throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' ` +\n                `and 'valid', but received padding mode ${this.padding}`);\n        }\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length !== 4) {\n            throw new ValueError('Input should have rank 4; Received input shape: ' +\n                JSON.stringify(inputShape));\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError('The channel dimension of the inputs should be defined. ' +\n                'Found `None`.');\n        }\n        const inputDim = inputShape[channelAxis];\n        const kernelShape = this.kernelSize.concat([this.filters, inputDim]);\n        this.kernel = this.addWeight('kernel', kernelShape, 'float32', this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], 'float32', this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        // Set input spec.\n        this.inputSpec =\n            [new InputSpec({ ndim: 4, axes: { [channelAxis]: inputDim } })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            let input = getExactlyOneTensor(inputs);\n            if (input.shape.length !== 4) {\n                throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but ` +\n                    `received a tensor of rank-${input.shape.length}`);\n            }\n            const inputShape = input.shape;\n            const batchSize = inputShape[0];\n            let hAxis;\n            let wAxis;\n            if (this.dataFormat === 'channelsFirst') {\n                hAxis = 2;\n                wAxis = 3;\n            }\n            else {\n                hAxis = 1;\n                wAxis = 2;\n            }\n            const height = inputShape[hAxis];\n            const width = inputShape[wAxis];\n            const kernelH = this.kernelSize[0];\n            const kernelW = this.kernelSize[1];\n            const strideH = this.strides[0];\n            const strideW = this.strides[1];\n            // Infer the dynamic output shape.\n            const outHeight = deconvLength(height, strideH, kernelH, this.padding);\n            const outWidth = deconvLength(width, strideW, kernelW, this.padding);\n            // Porting Note: We don't branch based on `this.dataFormat` here,\n            // because\n            //   the tjfs-core function `conv2dTranspose` called below always\n            //   assumes channelsLast.\n            const outputShape = [batchSize, outHeight, outWidth, this.filters];\n            if (this.dataFormat !== 'channelsLast') {\n                input = tfc.transpose(input, [0, 2, 3, 1]);\n            }\n            let outputs = tfc.conv2dTranspose(input, this.kernel.read(), outputShape, this.strides, this.padding);\n            if (this.dataFormat !== 'channelsLast') {\n                outputs = tfc.transpose(outputs, [0, 3, 1, 2]);\n            }\n            if (this.bias != null) {\n                outputs =\n                    K.biasAdd(outputs, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                outputs = this.activation.apply(outputs);\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        let channelAxis;\n        let heightAxis;\n        let widthAxis;\n        if (this.dataFormat === 'channelsFirst') {\n            channelAxis = 1;\n            heightAxis = 2;\n            widthAxis = 3;\n        }\n        else {\n            channelAxis = 3;\n            heightAxis = 1;\n            widthAxis = 2;\n        }\n        const kernelH = this.kernelSize[0];\n        const kernelW = this.kernelSize[1];\n        const strideH = this.strides[0];\n        const strideW = this.strides[1];\n        outputShape[channelAxis] = this.filters;\n        outputShape[heightAxis] =\n            deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);\n        outputShape[widthAxis] =\n            deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);\n        return outputShape;\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['dilationRate'];\n        return config;\n    }\n}\n/** @nocollapse */\nConv2DTranspose.className = 'Conv2DTranspose';\nserialization.registerClass(Conv2DTranspose);\nexport class SeparableConv extends Conv {\n    constructor(rank, config) {\n        super(rank, config);\n        this.DEFAULT_DEPTHWISE_INITIALIZER = 'glorotUniform';\n        this.DEFAULT_POINTWISE_INITIALIZER = 'glorotUniform';\n        this.depthwiseKernel = null;\n        this.pointwiseKernel = null;\n        if (config.filters == null) {\n            throw new ValueError('The `filters` configuration field is required by SeparableConv, ' +\n                'but is unspecified.');\n        }\n        if (config.kernelInitializer != null || config.kernelRegularizer != null ||\n            config.kernelConstraint != null) {\n            throw new ValueError('Fields kernelInitializer, kernelRegularizer and kernelConstraint ' +\n                'are invalid for SeparableConv2D. Use depthwiseInitializer, ' +\n                'depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, ' +\n                'pointwiseRegularizer and pointwiseConstraint instead.');\n        }\n        if (config.padding != null && config.padding !== 'same' &&\n            config.padding !== 'valid') {\n            throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: ` +\n                `'same' and 'valid', but received ${JSON.stringify(config.padding)}`);\n        }\n        this.depthMultiplier =\n            config.depthMultiplier == null ? 1 : config.depthMultiplier;\n        this.depthwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER);\n        this.depthwiseRegularizer = getRegularizer(config.depthwiseRegularizer);\n        this.depthwiseConstraint = getConstraint(config.depthwiseConstraint);\n        this.pointwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER);\n        this.pointwiseRegularizer = getRegularizer(config.pointwiseRegularizer);\n        this.pointwiseConstraint = getConstraint(config.pointwiseConstraint);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < this.rank + 2) {\n            throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ` +\n                `${this.rank + 2}, but received input shape: ` +\n                `${JSON.stringify(inputShape)}`);\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n            throw new ValueError(`The channel dimension of the inputs should be defined, ` +\n                `but found ${JSON.stringify(inputShape[channelAxis])}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]);\n        const pointwiseKernelShape = [];\n        for (let i = 0; i < this.rank; ++i) {\n            pointwiseKernelShape.push(1);\n        }\n        pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);\n        const trainable = true;\n        this.depthwiseKernel = this.addWeight('depthwise_kernel', depthwiseKernelShape, 'float32', this.depthwiseInitializer, this.depthwiseRegularizer, trainable, this.depthwiseConstraint);\n        this.pointwiseKernel = this.addWeight('pointwise_kernel', pointwiseKernelShape, 'float32', this.pointwiseInitializer, this.pointwiseRegularizer, trainable, this.pointwiseConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], 'float32', this.biasInitializer, this.biasRegularizer, trainable, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.inputSpec =\n            [new InputSpec({ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let output;\n            if (this.rank === 1) {\n                throw new NotImplementedError('1D separable convolution is not implemented yet.');\n            }\n            else if (this.rank === 2) {\n                if (this.dataFormat === 'channelsFirst') {\n                    inputs = tfc.transpose(inputs, [0, 2, 3, 1]); // NCHW -> NHWC.\n                }\n                output = tfc.separableConv2d(inputs, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, 'NHWC');\n            }\n            if (this.useBias) {\n                output = K.biasAdd(output, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                output = this.activation.apply(output);\n            }\n            if (this.dataFormat === 'channelsFirst') {\n                output = tfc.transpose(output, [0, 3, 1, 2]); // NHWC -> NCHW.\n            }\n            return output;\n        });\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        delete config['kernelInitializer'];\n        delete config['kernelRegularizer'];\n        delete config['kernelConstraint'];\n        config['depthwiseInitializer'] =\n            serializeInitializer(this.depthwiseInitializer);\n        config['pointwiseInitializer'] =\n            serializeInitializer(this.pointwiseInitializer);\n        config['depthwiseRegularizer'] =\n            serializeRegularizer(this.depthwiseRegularizer);\n        config['pointwiseRegularizer'] =\n            serializeRegularizer(this.pointwiseRegularizer);\n        config['depthwiseConstraint'] =\n            serializeConstraint(this.depthwiseConstraint);\n        config['pointwiseConstraint'] =\n            serializeConstraint(this.pointwiseConstraint);\n        return config;\n    }\n}\n/** @nocollapse */\nSeparableConv.className = 'SeparableConv';\nexport class SeparableConv2D extends SeparableConv {\n    constructor(args) {\n        super(2, args);\n    }\n}\n/** @nocollapse */\nSeparableConv2D.className = 'SeparableConv2D';\nserialization.registerClass(SeparableConv2D);\nexport class Conv1D extends Conv {\n    constructor(args) {\n        super(1, args);\n        Conv1D.verifyArgs(args);\n        this.inputSpec = [{ ndim: 3 }];\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        delete config['dataFormat'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if (typeof args.kernelSize !== 'number' &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 1)) {\n            throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with ` +\n                `length 1, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n}\n/** @nocollapse */\nConv1D.className = 'Conv1D';\nserialization.registerClass(Conv1D);\nexport class Cropping2D extends Layer {\n    constructor(args) {\n        super(args);\n        if (typeof args.cropping === 'number') {\n            this.cropping =\n                [[args.cropping, args.cropping], [args.cropping, args.cropping]];\n        }\n        else if (typeof args.cropping[0] === 'number') {\n            this.cropping = [\n                [args.cropping[0], args.cropping[0]],\n                [args.cropping[1], args.cropping[1]]\n            ];\n        }\n        else {\n            this.cropping = args.cropping;\n        }\n        this.dataFormat =\n            args.dataFormat === undefined ? 'channelsLast' : args.dataFormat;\n        this.inputSpec = [{ ndim: 4 }];\n    }\n    computeOutputShape(inputShape) {\n        if (this.dataFormat === 'channelsFirst') {\n            return [\n                inputShape[0], inputShape[1],\n                inputShape[2] - this.cropping[0][0] - this.cropping[0][1],\n                inputShape[3] - this.cropping[1][0] - this.cropping[1][1]\n            ];\n        }\n        else {\n            return [\n                inputShape[0],\n                inputShape[1] - this.cropping[0][0] - this.cropping[0][1],\n                inputShape[2] - this.cropping[1][0] - this.cropping[1][1], inputShape[3]\n            ];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                const hSliced = K.sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);\n                return K.sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);\n            }\n            else {\n                const hSliced = K.sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);\n                return K.sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);\n            }\n        });\n    }\n    getConfig() {\n        const config = { cropping: this.cropping, dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nCropping2D.className = 'Cropping2D';\nserialization.registerClass(Cropping2D);\nexport class UpSampling2D extends Layer {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_SIZE = [2, 2];\n        this.inputSpec = [{ ndim: 4 }];\n        this.size = args.size == null ? this.DEFAULT_SIZE : args.size;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n    }\n    computeOutputShape(inputShape) {\n        if (this.dataFormat === 'channelsFirst') {\n            const height = inputShape[2] == null ? null : this.size[0] * inputShape[2];\n            const width = inputShape[3] == null ? null : this.size[1] * inputShape[3];\n            return [inputShape[0], inputShape[1], height, width];\n        }\n        else {\n            const height = inputShape[1] == null ? null : this.size[0] * inputShape[1];\n            const width = inputShape[2] == null ? null : this.size[1] * inputShape[2];\n            return [inputShape[0], height, width, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            let input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            if (this.dataFormat === 'channelsFirst') {\n                input = tfc.transpose(input, [0, 2, 3, 1]);\n                const height = this.size[0] * inputShape[2];\n                const width = this.size[1] * inputShape[3];\n                const resized = input.resizeNearestNeighbor([height, width]);\n                return tfc.transpose(resized, [0, 3, 1, 2]);\n            }\n            else {\n                const height = this.size[0] * inputShape[1];\n                const width = this.size[1] * inputShape[2];\n                return input.resizeNearestNeighbor([height, width]);\n            }\n        });\n    }\n    getConfig() {\n        const config = { size: this.size, dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nUpSampling2D.className = 'UpSampling2D';\nserialization.registerClass(UpSampling2D);\n//# sourceMappingURL=convolutional.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Depthwise Convolutional Layers\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { convOutputLength } from '../utils/conv_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { BaseConv, preprocessConv2DInput } from './convolutional';\n/**\n * 2D convolution with separable filters.\n * @param x Input tensor.\n * @param depthwiseKernel Convolution kernel for depthwise convolution.\n * @param strides Strides (Array of two integers).\n * @param padding Padding model.\n * @param dataFormat Data format.\n * @param dilationRate Array of two integers, dilation rates for the separable\n *   convolution.\n * @returns Output tensor.\n * @throws ValueError If depthwiseKernel is not a 4D array.\n */\nexport function depthwiseConv2d(x, depthwiseKernel, strides = [1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        let y = preprocessConv2DInput(x, dataFormat);\n        if (x.rank !== 4) {\n            throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ` +\n                `${x.rank}-D`);\n        }\n        if (depthwiseKernel.rank !== 4) {\n            throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ` +\n                `${depthwiseKernel.rank}-D`);\n        }\n        y = tfc.depthwiseConv2d(y, depthwiseKernel, strides, padding === 'same' ? 'same' : 'valid', 'NHWC', dilationRate);\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]);\n        }\n        return y;\n    });\n}\nexport class DepthwiseConv2D extends BaseConv {\n    constructor(args) {\n        super(2, args);\n        this.depthwiseKernel = null;\n        this.depthMultiplier =\n            args.depthMultiplier == null ? 1 : args.depthMultiplier;\n        this.depthwiseInitializer = getInitializer(args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.depthwiseConstraint = getConstraint(args.depthwiseConstraint);\n        this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < 4) {\n            throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. ` +\n                `Received input shape: ${JSON.stringify(inputShape)}.`);\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : 3;\n        if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n            throw new ValueError('The channel dimension of the inputs to DepthwiseConv2D should ' +\n                `be defined, but is not (${inputShape[channelAxis]}).`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const depthwiseKernelShape = [\n            this.kernelSize[0], this.kernelSize[1], inputDim, this.depthMultiplier\n        ];\n        this.depthwiseKernel = this.addWeight('depthwise_kernel', depthwiseKernelShape, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [inputDim * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let outputs = depthwiseConv2d(inputs, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);\n            // TODO(cais): Add support for dilation.\n            if (this.useBias) {\n                outputs = K.biasAdd(outputs, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                outputs = this.activation.apply(outputs);\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const rows = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        const cols = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        const outFilters = this.dataFormat === 'channelsFirst' ?\n            inputShape[1] * this.depthMultiplier :\n            inputShape[3] * this.depthMultiplier;\n        const outRows = convOutputLength(rows, this.kernelSize[0], this.padding, this.strides[0]);\n        const outCols = convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], outFilters, outRows, outCols];\n        }\n        else {\n            // In this case, assume 'channelsLast'.\n            return [inputShape[0], outRows, outCols, outFilters];\n        }\n    }\n    getConfig() {\n        const config = super.getConfig();\n        config['depthMultiplier'] = this.depthMultiplier;\n        config['depthwiseInitializer'] =\n            serializeInitializer(this.depthwiseInitializer);\n        config['depthwiseRegularizer'] =\n            serializeRegularizer(this.depthwiseRegularizer);\n        config['depthwiseConstraint'] =\n            serializeConstraint(this.depthwiseRegularizer);\n        return config;\n    }\n}\n/** @nocollapse */\nDepthwiseConv2D.className = 'DepthwiseConv2D';\nserialization.registerClass(DepthwiseConv2D);\n//# sourceMappingURL=convolutional_depthwise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Recurrent Neural Network Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, SymbolicTensor } from '../engine/topology';\nimport { Layer } from '../engine/topology';\nimport { AttributeError, NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, Initializer, Ones, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor, isArrayOfShapes } from '../utils/types_utils';\nimport { batchGetValue, batchSetValue } from '../variables';\nimport { deserialize } from './serialization';\n/**\n * Standardize `apply()` args to a single list of tensor inputs.\n *\n * When running a model loaded from file, the input tensors `initialState` and\n * `constants` are passed to `RNN.apply()` as part of `inputs` instead of the\n * dedicated kwargs fields. `inputs` consists of\n * `[inputs, initialState0, initialState1, ..., constant0, constant1]` in this\n * case.\n * This method makes sure that arguments are\n * separated and that `initialState` and `constants` are `Array`s of tensors\n * (or None).\n *\n * @param inputs Tensor or `Array` of  tensors.\n * @param initialState Tensor or `Array` of tensors or `null`/`undefined`.\n * @param constants Tensor or `Array` of tensors or `null`/`undefined`.\n * @returns An object consisting of\n *   inputs: A tensor.\n *   initialState: `Array` of tensors or `null`.\n *   constants: `Array` of tensors or `null`.\n * @throws ValueError, if `inputs` is an `Array` but either `initialState` or\n *   `constants` is provided.\n */\nexport function standardizeArgs(inputs, initialState, constants, numConstants) {\n    if (Array.isArray(inputs)) {\n        if (initialState != null || constants != null) {\n            throw new ValueError('When inputs is an array, neither initialState or constants ' +\n                'should be provided');\n        }\n        if (numConstants != null) {\n            constants = inputs.slice(inputs.length - numConstants, inputs.length);\n            inputs = inputs.slice(0, inputs.length - numConstants);\n        }\n        if (inputs.length > 1) {\n            initialState = inputs.slice(1, inputs.length);\n        }\n        inputs = inputs[0];\n    }\n    function toListOrNull(x) {\n        if (x == null || Array.isArray(x)) {\n            return x;\n        }\n        else {\n            return [x];\n        }\n    }\n    initialState = toListOrNull(initialState);\n    constants = toListOrNull(constants);\n    return { inputs, initialState, constants };\n}\n/**\n * Iterates over the time dimension of a tensor.\n *\n * @param stepFunction RNN step function.\n *   Parameters:\n *     inputs: tensor with shape `[samples, ...]` (no time dimension),\n *       representing input for the batch of samples at a certain time step.\n *     states: an Array of tensors.\n *   Returns:\n *     outputs: tensor with shape `[samples, outputDim]` (no time dimension).\n *     newStates: list of tensors, same length and shapes as `states`. The first\n *       state in the list must be the output tensor at the previous timestep.\n * @param inputs Tensor of temporal data of shape `[samples, time, ...]` (at\n *   least 3D).\n * @param initialStates Tensor with shape `[samples, outputDim]` (no time\n *   dimension), containing the initial values of the states used in the step\n *   function.\n * @param goBackwards If `true`, do the iteration over the time dimension in\n *   reverse order and return the reversed sequence.\n * @param mask Binary tensor with shape `[sample, time, 1]`, with a zero for\n *   every element that is masked.\n * @param constants An Array of constant values passed at each step.\n * @param unroll Whether to unroll the RNN or to use a symbolic loop. *Not*\n *   applicable to this imperative deeplearn.js backend. Its value is ignored.\n * @param needPerStepOutputs Whether the per-step outputs are to be\n *   concatenated into a single tensor and returned (as the second return\n *   value). Default: `false`. This arg is included so that the relatively\n *   expensive concatenation of the stepwise outputs can be omitted unless\n *   the stepwise outputs need to be kept (e.g., for an LSTM layer of which\n *   `returnSequence` is `true`.)\n * @returns An Array: `[lastOutput, outputs, newStates]`.\n *   lastOutput: the lastest output of the RNN, of shape `[samples, ...]`.\n *   outputs: tensor with shape `[samples, time, ...]` where each entry\n *     `output[s, t]` is the output of the step function at time `t` for sample\n *     `s`. This return value is provided if and only if the\n *     `needPerStepOutputs` is set as `true`. If it is set as `false`, this\n *     return value will be `undefined`.\n *   newStates: Array of tensors, latest states returned by the step function,\n *      of shape `(samples, ...)`.\n * @throws ValueError If input dimension is less than 3.\n *\n * TODO(nielsene): This needs to be tidy-ed.\n */\nexport function rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, constants, unroll = false, needPerStepOutputs = false) {\n    return tfc.tidy(() => {\n        const ndim = inputs.shape.length;\n        if (ndim < 3) {\n            throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);\n        }\n        // Transpose to time-major, i.e., from [batch, time, ...] to [time, batch,\n        // ...].\n        const axes = [1, 0].concat(math_utils.range(2, ndim));\n        inputs = tfc.transpose(inputs, axes);\n        if (constants != null) {\n            throw new NotImplementedError('The rnn() functoin of the deeplearn.js backend does not support ' +\n                'constants yet.');\n        }\n        // Porting Note: the unroll option is ignored by the imperative backend.\n        if (unroll) {\n            console.warn('Backend rnn(): the unroll = true option is not applicable to the ' +\n                'imperative deeplearn.js backend.');\n        }\n        if (mask != null) {\n            mask = mask.asType('bool').asType('float32');\n            if (mask.rank === ndim - 1) {\n                mask = tfc.expandDims(mask, -1);\n            }\n            mask = tfc.transpose(mask, axes);\n        }\n        if (goBackwards) {\n            inputs = tfc.reverse(inputs, 0);\n            if (mask != null) {\n                mask = tfc.reverse(mask, 0);\n            }\n        }\n        // Porting Note: PyKeras with TensorFlow backend uses a symbolic loop\n        //   (tf.while_loop). But for the imperative deeplearn.js backend, we just\n        //   use the usual TypeScript control flow to iterate over the time steps in\n        //   the inputs.\n        // Porting Note: PyKeras patches a \"_use_learning_phase\" attribute to\n        // outputs.\n        //   This is not idiomatic in TypeScript. The info regarding whether we are\n        //   in a learning (i.e., training) phase for RNN is passed in a different\n        //   way.\n        const perStepOutputs = [];\n        let lastOutput;\n        let states = initialStates;\n        const timeSteps = inputs.shape[0];\n        const perStepInputs = tfc.unstack(inputs);\n        let perStepMasks;\n        if (mask != null) {\n            perStepMasks = tfc.unstack(mask);\n        }\n        for (let t = 0; t < timeSteps; ++t) {\n            const currentInput = perStepInputs[t];\n            const stepOutputs = tfc.tidy(() => stepFunction(currentInput, states));\n            if (mask == null) {\n                lastOutput = stepOutputs[0];\n                states = stepOutputs[1];\n            }\n            else {\n                const maskedOutputs = tfc.tidy(() => {\n                    const stepMask = perStepMasks[t];\n                    const negStepMask = tfc.onesLike(stepMask).sub(stepMask);\n                    // TODO(cais): Would tfc.where() be better for performance?\n                    const output = stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask));\n                    const newStates = states.map((state, i) => {\n                        return stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask));\n                    });\n                    return { output, newStates };\n                });\n                lastOutput = maskedOutputs.output;\n                states = maskedOutputs.newStates;\n            }\n            if (needPerStepOutputs) {\n                perStepOutputs.push(lastOutput);\n            }\n        }\n        let outputs;\n        if (needPerStepOutputs) {\n            const axis = 1;\n            outputs = tfc.stack(perStepOutputs, axis);\n        }\n        return [lastOutput, outputs, states];\n    });\n}\nexport class RNN extends Layer {\n    constructor(args) {\n        super(args);\n        let cell;\n        if (args.cell == null) {\n            throw new ValueError('cell property is missing for the constructor of RNN.');\n        }\n        else if (Array.isArray(args.cell)) {\n            cell = new StackedRNNCells({ cells: args.cell });\n        }\n        else {\n            cell = args.cell;\n        }\n        if (cell.stateSize == null) {\n            throw new ValueError('The RNN cell should have an attribute `stateSize` (tuple of ' +\n                'integers, one integer per RNN state).');\n        }\n        this.cell = cell;\n        this.returnSequences =\n            args.returnSequences == null ? false : args.returnSequences;\n        this.returnState = args.returnState == null ? false : args.returnState;\n        this.goBackwards = args.goBackwards == null ? false : args.goBackwards;\n        this._stateful = args.stateful == null ? false : args.stateful;\n        this.unroll = args.unroll == null ? false : args.unroll;\n        this.supportsMasking = true;\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n        this.stateSpec = null;\n        this.states_ = null;\n        // TODO(cais): Add constantsSpec and numConstants.\n        this.numConstants = null;\n        // TODO(cais): Look into the use of initial_state in the kwargs of the\n        //   constructor.\n        this.keptStates = [];\n    }\n    // Porting Note: This is the equivalent of `RNN.states` property getter in\n    //   PyKeras.\n    getStates() {\n        if (this.states_ == null) {\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            return math_utils.range(0, numStates).map(x => null);\n        }\n        else {\n            return this.states_;\n        }\n    }\n    // Porting Note: This is the equivalent of the `RNN.states` property setter in\n    //   PyKeras.\n    setStates(states) {\n        this.states_ = states;\n    }\n    computeOutputShape(inputShape) {\n        if (isArrayOfShapes(inputShape)) {\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        // TODO(cais): Remove the casting once stacked RNN cells become supported.\n        let stateSize = this.cell.stateSize;\n        if (!Array.isArray(stateSize)) {\n            stateSize = [stateSize];\n        }\n        const outputDim = stateSize[0];\n        let outputShape;\n        if (this.returnSequences) {\n            outputShape = [inputShape[0], inputShape[1], outputDim];\n        }\n        else {\n            outputShape = [inputShape[0], outputDim];\n        }\n        if (this.returnState) {\n            const stateShape = [];\n            for (const dim of stateSize) {\n                stateShape.push([inputShape[0], dim]);\n            }\n            return [outputShape].concat(stateShape);\n        }\n        else {\n            return outputShape;\n        }\n    }\n    computeMask(inputs, mask) {\n        return tfc.tidy(() => {\n            if (Array.isArray(mask)) {\n                mask = mask[0];\n            }\n            const outputMask = this.returnSequences ? mask : null;\n            if (this.returnState) {\n                const stateMask = this.states.map(s => null);\n                return [outputMask].concat(stateMask);\n            }\n            else {\n                return outputMask;\n            }\n        });\n    }\n    /**\n     * Get the current state tensors of the RNN.\n     *\n     * If the state hasn't been set, return an array of `null`s of the correct\n     * length.\n     */\n    get states() {\n        if (this.states_ == null) {\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            const output = [];\n            for (let i = 0; i < numStates; ++i) {\n                output.push(null);\n            }\n            return output;\n        }\n        else {\n            return this.states_;\n        }\n    }\n    set states(s) {\n        this.states_ = s;\n    }\n    build(inputShape) {\n        // Note inputShape will be an Array of Shapes of initial states and\n        // constants if these are passed in apply().\n        const constantShape = null;\n        if (this.numConstants != null) {\n            throw new NotImplementedError('Constants support is not implemented in RNN yet.');\n        }\n        if (isArrayOfShapes(inputShape)) {\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        const batchSize = this.stateful ? inputShape[0] : null;\n        const inputDim = inputShape.slice(2);\n        this.inputSpec[0] = new InputSpec({ shape: [batchSize, null, ...inputDim] });\n        // Allow cell (if RNNCell Layer) to build before we set or validate\n        // stateSpec.\n        const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        if (constantShape != null) {\n            throw new NotImplementedError('Constants support is not implemented in RNN yet.');\n        }\n        else {\n            this.cell.build(stepInputShape);\n        }\n        // Set or validate stateSpec.\n        let stateSize;\n        if (Array.isArray(this.cell.stateSize)) {\n            stateSize = this.cell.stateSize;\n        }\n        else {\n            stateSize = [this.cell.stateSize];\n        }\n        if (this.stateSpec != null) {\n            if (!util.arraysEqual(this.stateSpec.map(spec => spec.shape[spec.shape.length - 1]), stateSize)) {\n                throw new ValueError(`An initialState was passed that is not compatible with ` +\n                    `cell.stateSize. Received stateSpec=${this.stateSpec}; ` +\n                    `However cell.stateSize is ${this.cell.stateSize}`);\n            }\n        }\n        else {\n            this.stateSpec =\n                stateSize.map(dim => new InputSpec({ shape: [null, dim] }));\n        }\n        if (this.stateful) {\n            this.resetStates();\n        }\n    }\n    /**\n     * Reset the state tensors of the RNN.\n     *\n     * If the `states` argument is `undefined` or `null`, will set the\n     * state tensor(s) of the RNN to all-zero tensors of the appropriate\n     * shape(s).\n     *\n     * If `states` is provided, will set the state tensors of the RNN to its\n     * value.\n     *\n     * @param states Optional externally-provided initial states.\n     * @param training Whether this call is done during training. For stateful\n     *   RNNs, this affects whether the old states are kept or discarded. In\n     *   particular, if `training` is `true`, the old states will be kept so\n     *   that subsequent backpropgataion through time (BPTT) may work properly.\n     *   Else, the old states will be discarded.\n     */\n    resetStates(states, training = false) {\n        tidy(() => {\n            if (!this.stateful) {\n                throw new AttributeError('Cannot call resetStates() on an RNN Layer that is not stateful.');\n            }\n            const batchSize = this.inputSpec[0].shape[0];\n            if (batchSize == null) {\n                throw new ValueError('If an RNN is stateful, it needs to know its batch size. Specify ' +\n                    'the batch size of your input tensors: \\n' +\n                    '- If using a Sequential model, specify the batch size by ' +\n                    'passing a `batchInputShape` option to your first layer.\\n' +\n                    '- If using the functional API, specify the batch size by ' +\n                    'passing a `batchShape` option to your Input layer.');\n            }\n            // Initialize state if null.\n            if (this.states_ == null) {\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ =\n                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n                }\n                else {\n                    this.states_ = [tfc.zeros([batchSize, this.cell.stateSize])];\n                }\n            }\n            else if (states == null) {\n                // Dispose old state tensors.\n                tfc.dispose(this.states_);\n                // For stateful RNNs, fully dispose kept old states.\n                if (this.keptStates != null) {\n                    tfc.dispose(this.keptStates);\n                    this.keptStates = [];\n                }\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ =\n                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n                }\n                else {\n                    this.states_[0] = tfc.zeros([batchSize, this.cell.stateSize]);\n                }\n            }\n            else {\n                if (!Array.isArray(states)) {\n                    states = [states];\n                }\n                if (states.length !== this.states_.length) {\n                    throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), ` +\n                        `but it received ${states.length} state value(s). Input ` +\n                        `received: ${states}`);\n                }\n                if (training === true) {\n                    // Store old state tensors for complete disposal later, i.e., during\n                    // the next no-arg call to this method. We do not dispose the old\n                    // states immediately because that BPTT (among other things) require\n                    // them.\n                    this.keptStates.push(this.states_.slice());\n                }\n                else {\n                    tfc.dispose(this.states_);\n                }\n                for (let index = 0; index < this.states_.length; ++index) {\n                    const value = states[index];\n                    const dim = Array.isArray(this.cell.stateSize) ?\n                        this.cell.stateSize[index] :\n                        this.cell.stateSize;\n                    const expectedShape = [batchSize, dim];\n                    if (!util.arraysEqual(value.shape, expectedShape)) {\n                        throw new ValueError(`State ${index} is incompatible with layer ${this.name}: ` +\n                            `expected shape=${expectedShape}, received shape=${value.shape}`);\n                    }\n                    this.states_[index] = value;\n                }\n            }\n            this.states_ = this.states_.map(state => tfc.keep(state.clone()));\n        });\n    }\n    apply(inputs, kwargs) {\n        // TODO(cais): Figure out whether initialState is in kwargs or inputs.\n        let initialState = kwargs == null ? null : kwargs['initialState'];\n        let constants = kwargs == null ? null : kwargs['constants'];\n        if (kwargs == null) {\n            kwargs = {};\n        }\n        const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n        inputs = standardized.inputs;\n        initialState = standardized.initialState;\n        constants = standardized.constants;\n        // If any of `initial_state` or `constants` are specified and are\n        // `tf.SymbolicTensor`s, then add them to the inputs and temporarily modify\n        // the input_spec to include them.\n        let additionalInputs = [];\n        let additionalSpecs = [];\n        if (initialState != null) {\n            kwargs['initialState'] = initialState;\n            additionalInputs = additionalInputs.concat(initialState);\n            this.stateSpec = [];\n            for (const state of initialState) {\n                this.stateSpec.push(new InputSpec({ shape: state.shape }));\n            }\n            // TODO(cais): Use the following instead.\n            // this.stateSpec = initialState.map(state => new InputSpec({shape:\n            // state.shape}));\n            additionalSpecs = additionalSpecs.concat(this.stateSpec);\n        }\n        if (constants != null) {\n            kwargs['constants'] = constants;\n            additionalInputs = additionalInputs.concat(constants);\n            // TODO(cais): Add this.constantsSpec.\n            this.numConstants = constants.length;\n        }\n        const isTensor = additionalInputs[0] instanceof SymbolicTensor;\n        if (isTensor) {\n            // Compute full input spec, including state and constants.\n            const fullInput = [inputs].concat(additionalInputs);\n            const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n            // Perform the call with temporarily replaced inputSpec.\n            const originalInputSpec = this.inputSpec;\n            this.inputSpec = fullInputSpec;\n            const output = super.apply(fullInput, kwargs);\n            this.inputSpec = originalInputSpec;\n            return output;\n        }\n        else {\n            return super.apply(inputs, kwargs);\n        }\n    }\n    // tslint:disable-next-line:no-any\n    call(inputs, kwargs) {\n        // Input shape: `[samples, time (padded with zeros), input_dim]`.\n        // Note that the .build() method of subclasses **must** define\n        // this.inputSpec and this.stateSpec owith complete input shapes.\n        return tidy(() => {\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            let initialState = kwargs == null ? null : kwargs['initialState'];\n            inputs = getExactlyOneTensor(inputs);\n            if (initialState == null) {\n                if (this.stateful) {\n                    initialState = this.states_;\n                }\n                else {\n                    initialState = this.getInitialState(inputs);\n                }\n            }\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            if (initialState.length !== numStates) {\n                throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ` +\n                    `${initialState.length} initial state(s).`);\n            }\n            if (this.unroll) {\n                console.warn('Ignoring unroll = true for RNN layer, due to imperative backend.');\n            }\n            const cellCallKwargs = { training };\n            // TODO(cais): Add support for constants.\n            const step = (inputs, states) => {\n                // `inputs` and `states` are concatenated to form a single `Array` of\n                // `tf.Tensor`s as the input to `cell.call()`.\n                const outputs = this.cell.call([inputs].concat(states), cellCallKwargs);\n                // Marshall the return value into output and new states.\n                return [outputs[0], outputs.slice(1)];\n            };\n            // TODO(cais): Add support for constants.\n            const rnnOutputs = rnn(step, inputs, initialState, this.goBackwards, mask, null, this.unroll, this.returnSequences);\n            const lastOutput = rnnOutputs[0];\n            const outputs = rnnOutputs[1];\n            const states = rnnOutputs[2];\n            if (this.stateful) {\n                this.resetStates(states, training);\n            }\n            const output = this.returnSequences ? outputs : lastOutput;\n            // TODO(cais): Porperty set learning phase flag.\n            if (this.returnState) {\n                return [output].concat(states);\n            }\n            else {\n                return output;\n            }\n        });\n    }\n    getInitialState(inputs) {\n        return tidy(() => {\n            // Build an all-zero tensor of shape [samples, outputDim].\n            // [Samples, timeSteps, inputDim].\n            let initialState = tfc.zeros(inputs.shape);\n            // [Samples].\n            initialState = tfc.sum(initialState, [1, 2]);\n            initialState = K.expandDims(initialState); // [Samples, 1].\n            if (Array.isArray(this.cell.stateSize)) {\n                return this.cell.stateSize.map(dim => dim > 1 ? K.tile(initialState, [1, dim]) : initialState);\n            }\n            else {\n                return this.cell.stateSize > 1 ?\n                    [K.tile(initialState, [1, this.cell.stateSize])] :\n                    [initialState];\n            }\n        });\n    }\n    get trainableWeights() {\n        if (!this.trainable) {\n            return [];\n        }\n        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n        return this.cell.trainableWeights;\n    }\n    get nonTrainableWeights() {\n        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n        if (!this.trainable) {\n            return this.cell.weights;\n        }\n        return this.cell.nonTrainableWeights;\n    }\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.cell != null) {\n            this.cell.setFastWeightInitDuringBuild(value);\n        }\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            returnSequences: this.returnSequences,\n            returnState: this.returnState,\n            goBackwards: this.goBackwards,\n            stateful: this.stateful,\n            unroll: this.unroll,\n        };\n        if (this.numConstants != null) {\n            config['numConstants'] = this.numConstants;\n        }\n        const cellConfig = this.cell.getConfig();\n        if (this.getClassName() === RNN.className) {\n            config['cell'] = {\n                'className': this.cell.getClassName(),\n                'config': cellConfig,\n            };\n        }\n        // this order is necessary, to prevent cell name from replacing layer name\n        return Object.assign({}, cellConfig, baseConfig, config);\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const cellConfig = config['cell'];\n        const cell = deserialize(cellConfig, customObjects);\n        return new cls(Object.assign(config, { cell }));\n    }\n}\n/** @nocollapse */\nRNN.className = 'RNN';\nserialization.registerClass(RNN);\n// Porting Note: This is a common parent class for RNN cells. There is no\n// equivalent of this in PyKeras. Having a common parent class forgoes the\n//  need for `has_attr(cell, ...)` checks or its TypeScript equivalent.\n/**\n * An RNNCell layer.\n *\n * @doc {heading: 'Layers', subheading: 'Classes'}\n */\nexport class RNNCell extends Layer {\n}\nexport class SimpleRNNCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        this.units = args.units;\n        assertPositiveInteger(this.units, `units`);\n        this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.stateSize = this.units;\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        // TODO(cais): Use regularizer.\n        this.kernel = this.addWeight('kernel', [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.built = true;\n    }\n    // Porting Note: PyKeras' equivalent of this method takes two tensor inputs:\n    //   `inputs` and `states`. Here, the two tensors are combined into an\n    //   `Tensor[]` Array as the first input argument.\n    //   Similarly, PyKeras' equivalent of this method returns two values:\n    //    `output` and `[output]`. Here the two are combined into one length-2\n    //    `Tensor[]`, consisting of `output` repeated.\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (inputs.length !== 2) {\n                throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);\n            }\n            let prevOutput = inputs[1];\n            inputs = inputs[0];\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(prevOutput),\n                    rate: this.recurrentDropout,\n                    training\n                });\n            }\n            let h;\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            if (dpMask != null) {\n                h = K.dot(tfc.mul(inputs, dpMask), this.kernel.read());\n            }\n            else {\n                h = K.dot(inputs, this.kernel.read());\n            }\n            if (this.bias != null) {\n                h = K.biasAdd(h, this.bias.read());\n            }\n            if (recDpMask != null) {\n                prevOutput = tfc.mul(prevOutput, recDpMask);\n            }\n            let output = tfc.add(h, K.dot(prevOutput, this.recurrentKernel.read()));\n            if (this.activation != null) {\n                output = this.activation.apply(output);\n            }\n            // TODO(cais): Properly set learning phase on output tensor?\n            return [output, output];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nSimpleRNNCell.className = 'SimpleRNNCell';\nserialization.registerClass(SimpleRNNCell);\nexport class SimpleRNN extends RNN {\n    constructor(args) {\n        args.cell = new SimpleRNNCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nSimpleRNN.className = 'SimpleRNN';\nserialization.registerClass(SimpleRNN);\nexport class GRUCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        if (args.resetAfter) {\n            throw new ValueError(`GRUCell does not support reset_after parameter set to true.`);\n        }\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :\n            args.activation);\n        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.implementation = args.implementation;\n        this.stateSize = this.units;\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const inputDim = inputShape[inputShape.length - 1];\n        this.kernel = this.addWeight('kernel', [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n        //   of the weights and bias in the call() method, at execution time.\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (inputs.length !== 2) {\n                throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            let hTMinus1 = inputs[1]; // Previous memory state.\n            inputs = inputs[0];\n            // Note: For superior performance, TensorFlow.js always uses\n            // implementation 2, regardless of the actual value of\n            // config.implementation.\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training,\n                    count: 3\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: 3\n                });\n            }\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            let z;\n            let r;\n            let hh;\n            if (0 < this.dropout && this.dropout < 1) {\n                inputs = tfc.mul(inputs, dpMask[0]);\n            }\n            let matrixX = K.dot(inputs, this.kernel.read());\n            if (this.useBias) {\n                matrixX = K.biasAdd(matrixX, this.bias.read());\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n            }\n            const recurrentKernelValue = this.recurrentKernel.read();\n            const [rk1, rk2] = tfc.split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1);\n            const matrixInner = K.dot(hTMinus1, rk1);\n            const [xZ, xR, xH] = tfc.split(matrixX, 3, matrixX.rank - 1);\n            const [recurrentZ, recurrentR] = tfc.split(matrixInner, 2, matrixInner.rank - 1);\n            z = this.recurrentActivation.apply(tfc.add(xZ, recurrentZ));\n            r = this.recurrentActivation.apply(tfc.add(xR, recurrentR));\n            const recurrentH = K.dot(tfc.mul(r, hTMinus1), rk2);\n            hh = this.activation.apply(tfc.add(xH, recurrentH));\n            const h = tfc.add(tfc.mul(z, hTMinus1), tfc.mul(tfc.add(1, tfc.neg(z)), hh));\n            // TODO(cais): Add use_learning_phase flag properly.\n            return [h, h];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            recurrentActivation: serializeActivation(this.recurrentActivation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n            implementation: this.implementation,\n            resetAfter: false\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nGRUCell.className = 'GRUCell';\nserialization.registerClass(GRUCell);\nexport class GRU extends RNN {\n    constructor(args) {\n        if (args.implementation === 0) {\n            console.warn('`implementation=0` has been deprecated, and now defaults to ' +\n                '`implementation=1`. Please update your layer call.');\n        }\n        args.cell = new GRUCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        if (config['implmentation'] === 0) {\n            config['implementation'] = 1;\n        }\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nGRU.className = 'GRU';\nserialization.registerClass(GRU);\nexport class LSTMCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :\n            args.activation);\n        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.unitForgetBias = args.unitForgetBias;\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.implementation = args.implementation;\n        this.stateSize = [this.units, this.units];\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        var _a;\n        inputShape = getExactlyOneShape(inputShape);\n        const inputDim = inputShape[inputShape.length - 1];\n        this.kernel = this.addWeight('kernel', [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        let biasInitializer;\n        if (this.useBias) {\n            if (this.unitForgetBias) {\n                const capturedBiasInit = this.biasInitializer;\n                const capturedUnits = this.units;\n                biasInitializer = new (_a = class CustomInit extends Initializer {\n                        apply(shape, dtype) {\n                            // TODO(cais): More informative variable names?\n                            const bI = capturedBiasInit.apply([capturedUnits]);\n                            const bF = (new Ones()).apply([capturedUnits]);\n                            const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);\n                            return K.concatAlongFirstAxis(K.concatAlongFirstAxis(bI, bF), bCAndH);\n                        }\n                    },\n                    /** @nocollapse */\n                    _a.className = 'CustomInit',\n                    _a)();\n            }\n            else {\n                biasInitializer = this.biasInitializer;\n            }\n            this.bias = this.addWeight('bias', [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n        //   of the weights and bias in the call() method, at execution time.\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            inputs = inputs;\n            if (inputs.length !== 3) {\n                throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            let hTMinus1 = inputs[1]; // Previous memory state.\n            const cTMinus1 = inputs[2]; // Previous carry state.\n            inputs = inputs[0];\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training,\n                    count: 4\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: 4\n                });\n            }\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            // Note: For superior performance, TensorFlow.js always uses\n            // implementation 2 regardless of the actual value of\n            // config.implementation.\n            let i;\n            let f;\n            let c;\n            let o;\n            if (0 < this.dropout && this.dropout < 1) {\n                inputs = tfc.mul(inputs, dpMask[0]);\n            }\n            let z = K.dot(inputs, this.kernel.read());\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n            }\n            z = tfc.add(z, K.dot(hTMinus1, this.recurrentKernel.read()));\n            if (this.useBias) {\n                z = K.biasAdd(z, this.bias.read());\n            }\n            const [z0, z1, z2, z3] = tfc.split(z, 4, z.rank - 1);\n            i = this.recurrentActivation.apply(z0);\n            f = this.recurrentActivation.apply(z1);\n            c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(z2)));\n            o = this.recurrentActivation.apply(z3);\n            const h = tfc.mul(o, this.activation.apply(c));\n            // TODO(cais): Add use_learning_phase flag properly.\n            return [h, h, c];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            recurrentActivation: serializeActivation(this.recurrentActivation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            unitForgetBias: this.unitForgetBias,\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n            implementation: this.implementation,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nLSTMCell.className = 'LSTMCell';\nserialization.registerClass(LSTMCell);\nexport class LSTM extends RNN {\n    constructor(args) {\n        if (args.implementation === 0) {\n            console.warn('`implementation=0` has been deprecated, and now defaults to ' +\n                '`implementation=1`. Please update your layer call.');\n        }\n        args.cell = new LSTMCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        if (config['implmentation'] === 0) {\n            config['implementation'] = 1;\n        }\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nLSTM.className = 'LSTM';\nserialization.registerClass(LSTM);\nexport class StackedRNNCells extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.cells = args.cells;\n    }\n    get stateSize() {\n        // States are a flat list in reverse order of the cell stack.\n        // This allows perserving the requirement `stack.statesize[0] ===\n        // outputDim`. E.g., states of a 2-layer LSTM would be `[h2, c2, h1, c1]`,\n        // assuming one LSTM has states `[h, c]`.\n        const stateSize = [];\n        for (const cell of this.cells.slice().reverse()) {\n            if (Array.isArray(cell.stateSize)) {\n                stateSize.push(...cell.stateSize);\n            }\n            else {\n                stateSize.push(cell.stateSize);\n            }\n        }\n        return stateSize;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            let states = inputs.slice(1);\n            // Recover per-cell states.\n            const nestedStates = [];\n            for (const cell of this.cells.slice().reverse()) {\n                if (Array.isArray(cell.stateSize)) {\n                    nestedStates.push(states.splice(0, cell.stateSize.length));\n                }\n                else {\n                    nestedStates.push(states.splice(0, 1));\n                }\n            }\n            nestedStates.reverse();\n            // Call the cells in order and store the returned states.\n            const newNestedStates = [];\n            let callInputs;\n            for (let i = 0; i < this.cells.length; ++i) {\n                const cell = this.cells[i];\n                states = nestedStates[i];\n                // TODO(cais): Take care of constants.\n                if (i === 0) {\n                    callInputs = [inputs[0]].concat(states);\n                }\n                else {\n                    callInputs = [callInputs[0]].concat(states);\n                }\n                callInputs = cell.call(callInputs, kwargs);\n                newNestedStates.push(callInputs.slice(1));\n            }\n            // Format the new states as a flat list in reverse cell order.\n            states = [];\n            for (const cellStates of newNestedStates.slice().reverse()) {\n                states.push(...cellStates);\n            }\n            return [callInputs[0]].concat(states);\n        });\n    }\n    build(inputShape) {\n        if (isArrayOfShapes(inputShape)) {\n            // TODO(cais): Take care of input constants.\n            // const constantShape = inputShape.slice(1);\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        let outputDim;\n        this.cells.forEach((cell, i) => {\n            nameScope(`RNNCell_${i}`, () => {\n                // TODO(cais): Take care of input constants.\n                cell.build(inputShape);\n                if (Array.isArray(cell.stateSize)) {\n                    outputDim = cell.stateSize[0];\n                }\n                else {\n                    outputDim = cell.stateSize;\n                }\n                inputShape = [inputShape[0], outputDim];\n            });\n        });\n        this.built = true;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const getCellConfig = (cell) => {\n            return {\n                'className': cell.getClassName(),\n                'config': cell.getConfig(),\n            };\n        };\n        const cellConfigs = this.cells.map(getCellConfig);\n        const config = { 'cells': cellConfigs };\n        return Object.assign({}, baseConfig, config);\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const cells = [];\n        for (const cellConfig of config['cells']) {\n            cells.push(deserialize(cellConfig, customObjects));\n        }\n        return new cls({ cells });\n    }\n    get trainableWeights() {\n        if (!this.trainable) {\n            return [];\n        }\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.trainableWeights);\n        }\n        return weights;\n    }\n    get nonTrainableWeights() {\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.nonTrainableWeights);\n        }\n        if (!this.trainable) {\n            const trainableWeights = [];\n            for (const cell of this.cells) {\n                trainableWeights.push(...cell.trainableWeights);\n            }\n            return trainableWeights.concat(weights);\n        }\n        return weights;\n    }\n    /**\n     * Retrieve the weights of a the model.\n     *\n     * @returns A flat `Array` of `tf.Tensor`s.\n     */\n    getWeights() {\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.weights);\n        }\n        return batchGetValue(weights);\n    }\n    /**\n     * Set the weights of the model.\n     *\n     * @param weights An `Array` of `tf.Tensor`s with shapes and types matching\n     *     the output of `getWeights()`.\n     */\n    setWeights(weights) {\n        const tuples = [];\n        for (const cell of this.cells) {\n            const numParams = cell.weights.length;\n            const inputWeights = weights.splice(numParams);\n            for (let i = 0; i < cell.weights.length; ++i) {\n                tuples.push([cell.weights[i], inputWeights[i]]);\n            }\n        }\n        batchSetValue(tuples);\n    }\n}\n/** @nocollapse */\nStackedRNNCells.className = 'StackedRNNCells';\nserialization.registerClass(StackedRNNCells);\nexport function generateDropoutMask(args) {\n    const { ones, rate, training = false, count = 1 } = args;\n    const droppedInputs = () => K.dropout(ones(), rate);\n    const createMask = () => K.inTrainPhase(droppedInputs, ones, training);\n    // just in case count is provided with null or undefined\n    if (!count || count <= 1) {\n        return tfc.keep(createMask().clone());\n    }\n    const masks = Array(count).fill(undefined).map(createMask);\n    return masks.map(m => tfc.keep(m.clone()));\n}\n//# sourceMappingURL=recurrent.js.map", "/**\n * @license\n * Copyright 2020 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nvar __rest = (this && this.__rest) || function (s, e) {\n    var t = {};\n    for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\n        t[p] = s[p];\n    if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\n        for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\n            if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\n                t[p[i]] = s[p[i]];\n        }\n    return t;\n};\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode } from '../common';\nimport { InputSpec } from '../engine/topology';\nimport { AttributeError, NotImplementedError, ValueError } from '../errors';\nimport { Initializer } from '../initializers';\nimport { convOutputLength, normalizeArray } from '../utils/conv_utils';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport { getExactlyOneShape } from '../utils/types_utils';\nimport { generateDropoutMask, LSTMCell, RNN, RNNCell } from './recurrent';\nclass ConvRNN2DCell extends RNNCell {\n}\n/**\n * Base class for convolutional-recurrent layers.\n */\nclass ConvRNN2D extends RNN {\n    constructor(args) {\n        if (args.unroll) {\n            throw new NotImplementedError('Unrolling is not possible with convolutional RNNs.');\n        }\n        if (Array.isArray(args.cell)) {\n            throw new NotImplementedError('It is not possible at the moment to stack convolutional cells.');\n        }\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 5 })];\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            if (kwargs && kwargs['constants']) {\n                throw new ValueError('ConvRNN2D cell does not support constants');\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    computeOutputShape(inputShape) {\n        let outShape = this.computeSingleOutputShape(inputShape);\n        if (!this.returnSequences) {\n            outShape = [outShape[0], ...outShape.slice(2)];\n        }\n        if (this.returnState) {\n            outShape =\n                [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])];\n        }\n        return outShape;\n    }\n    getInitialState(inputs) {\n        return tfc.tidy(() => {\n            const { stateSize } = this.cell;\n            const inputShape = inputs.shape;\n            const outputShape = this.computeSingleOutputShape(inputShape);\n            const stateShape = [outputShape[0], ...outputShape.slice(2)];\n            const initialState = tfc.zeros(stateShape);\n            if (Array.isArray(stateSize)) {\n                return Array(stateSize.length).fill(initialState);\n            }\n            return [initialState];\n        });\n    }\n    resetStates(states, training = false) {\n        tfc.tidy(() => {\n            if (!this.stateful) {\n                throw new AttributeError('Cannot call resetStates() on an RNN Layer that is not stateful.');\n            }\n            const inputShape = this.inputSpec[0].shape;\n            const outputShape = this.computeSingleOutputShape(inputShape);\n            const stateShape = [outputShape[0], ...outputShape.slice(2)];\n            const batchSize = inputShape[0];\n            if (batchSize == null) {\n                throw new ValueError('If an RNN is stateful, it needs to know its batch size. Specify ' +\n                    'the batch size of your input tensors: \\n' +\n                    '- If using a Sequential model, specify the batch size by ' +\n                    'passing a `batchInputShape` option to your first layer.\\n' +\n                    '- If using the functional API, specify the batch size by ' +\n                    'passing a `batchShape` option to your Input layer.');\n            }\n            // Initialize state if null.\n            if (this.getStates() == null) {\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));\n                }\n                else {\n                    this.states_ = [tfc.zeros(stateShape)];\n                }\n            }\n            else if (states == null) {\n                // Dispose old state tensors.\n                tfc.dispose(this.states_);\n                // For stateful RNNs, fully dispose kept old states.\n                if (this.keptStates != null) {\n                    tfc.dispose(this.keptStates);\n                    this.keptStates = [];\n                }\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));\n                }\n                else {\n                    this.states_[0] = tfc.zeros(stateShape);\n                }\n            }\n            else {\n                if (!Array.isArray(states)) {\n                    states = [states];\n                }\n                if (states.length !== this.states_.length) {\n                    throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), ` +\n                        `but it received ${states.length} state value(s). Input ` +\n                        `received: ${states}`);\n                }\n                if (training) {\n                    // Store old state tensors for complete disposal later, i.e., during\n                    // the next no-arg call to this method. We do not dispose the old\n                    // states immediately because that BPTT (among other things) require\n                    // them.\n                    this.keptStates.push(this.states_.slice());\n                }\n                else {\n                    tfc.dispose(this.states_);\n                }\n                for (let index = 0; index < this.states_.length; ++index) {\n                    const value = states[index];\n                    const expectedShape = stateShape;\n                    if (!util.arraysEqual(value.shape, expectedShape)) {\n                        throw new ValueError(`State ${index} is incompatible with layer ${this.name}: ` +\n                            `expected shape=${expectedShape}, received shape=${value.shape}`);\n                    }\n                    this.states_[index] = value;\n                }\n            }\n            this.states_ = this.states_.map(state => tfc.keep(state.clone()));\n        });\n    }\n    computeSingleOutputShape(inputShape) {\n        const { dataFormat, filters, kernelSize, padding, strides, dilationRate } = this.cell;\n        const isChannelsFirst = dataFormat === 'channelsFirst';\n        const h = inputShape[isChannelsFirst ? 3 : 2];\n        const w = inputShape[isChannelsFirst ? 4 : 3];\n        const hOut = convOutputLength(h, kernelSize[0], padding, strides[0], dilationRate[0]);\n        const wOut = convOutputLength(w, kernelSize[1], padding, strides[1], dilationRate[1]);\n        const outShape = [\n            ...inputShape.slice(0, 2),\n            ...(isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters])\n        ];\n        return outShape;\n    }\n}\n/** @nocollapse */\nConvRNN2D.className = 'ConvRNN2D';\nexport class ConvLSTM2DCell extends LSTMCell {\n    constructor(args) {\n        const { filters, kernelSize, strides, padding, dataFormat, dilationRate, } = args;\n        super(Object.assign({}, args, { units: filters }));\n        this.filters = filters;\n        assertPositiveInteger(this.filters, 'filters');\n        this.kernelSize = normalizeArray(kernelSize, 2, 'kernelSize');\n        this.kernelSize.forEach(size => assertPositiveInteger(size, 'kernelSize'));\n        this.strides = normalizeArray(strides || 1, 2, 'strides');\n        this.strides.forEach(stride => assertPositiveInteger(stride, 'strides'));\n        this.padding = padding || 'valid';\n        checkPaddingMode(this.padding);\n        this.dataFormat = dataFormat || 'channelsLast';\n        checkDataFormat(this.dataFormat);\n        this.dilationRate = normalizeArray(dilationRate || 1, 2, 'dilationRate');\n        this.dilationRate.forEach(rate => assertPositiveInteger(rate, 'dilationRate'));\n    }\n    build(inputShape) {\n        var _a;\n        inputShape = getExactlyOneShape(inputShape);\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError(`The channel dimension of the input should be defined. ` +\n                `Found ${inputShape[channelAxis]}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const numOfKernels = 4;\n        const kernelShape = this.kernelSize.concat([inputDim, this.filters * numOfKernels]);\n        this.kernel = this.addWeight('kernel', kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        const recurrentKernelShape = this.kernelSize.concat([this.filters, this.filters * numOfKernels]);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', recurrentKernelShape, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            let biasInitializer;\n            if (this.unitForgetBias) {\n                const init = this.biasInitializer;\n                const filters = this.filters;\n                biasInitializer = new (_a = class CustomInit extends Initializer {\n                        apply(shape, dtype) {\n                            const biasI = init.apply([filters]);\n                            const biasF = tfc.ones([filters]);\n                            const biasCAndO = init.apply([filters * 2]);\n                            return K.concatenate([biasI, biasF, biasCAndO]);\n                        }\n                    },\n                    /** @nocollapse */\n                    _a.className = 'CustomInit',\n                    _a)();\n            }\n            else {\n                biasInitializer = this.biasInitializer;\n            }\n            this.bias = this.addWeight('bias', [this.filters * numOfKernels], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            if (inputs.length !== 3) {\n                throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            const training = kwargs['training'] || false;\n            const x = inputs[0]; // Current input\n            const hTMinus1 = inputs[1]; // Previous memory state.\n            const cTMinus1 = inputs[2]; // Previous carry state.\n            const numOfKernels = 4;\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(x),\n                    rate: this.dropout,\n                    training,\n                    count: numOfKernels\n                });\n            }\n            const dropoutMask = this.dropoutMask;\n            const applyDropout = (x, mask, index) => {\n                if (!mask || !mask[index]) {\n                    return x;\n                }\n                return tfc.mul(mask[index], x);\n            };\n            let xI = applyDropout(x, dropoutMask, 0);\n            let xF = applyDropout(x, dropoutMask, 1);\n            let xC = applyDropout(x, dropoutMask, 2);\n            let xO = applyDropout(x, dropoutMask, 3);\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: numOfKernels\n                });\n            }\n            const recDropoutMask = this.recurrentDropoutMask;\n            let hI = applyDropout(hTMinus1, recDropoutMask, 0);\n            let hF = applyDropout(hTMinus1, recDropoutMask, 1);\n            let hC = applyDropout(hTMinus1, recDropoutMask, 2);\n            let hO = applyDropout(hTMinus1, recDropoutMask, 3);\n            const kernelChannelAxis = 3;\n            const [kernelI, kernelF, kernelC, kernelO] = tfc.split(this.kernel.read(), numOfKernels, kernelChannelAxis);\n            const [biasI, biasF, biasC, biasO] = this.useBias ?\n                tfc.split(this.bias.read(), numOfKernels) :\n                [null, null, null, null];\n            xI = this.inputConv(xI, kernelI, biasI, this.padding);\n            xF = this.inputConv(xF, kernelF, biasF, this.padding);\n            xC = this.inputConv(xC, kernelC, biasC, this.padding);\n            xO = this.inputConv(xO, kernelO, biasO, this.padding);\n            const [recKernelI, recKernelF, recKernelC, recKernelO] = tfc.split(this.recurrentKernel.read(), numOfKernels, kernelChannelAxis);\n            hI = this.recurrentConv(hI, recKernelI);\n            hF = this.recurrentConv(hF, recKernelF);\n            hC = this.recurrentConv(hC, recKernelC);\n            hO = this.recurrentConv(hO, recKernelO);\n            const i = this.recurrentActivation.apply(tfc.add(xI, hI));\n            const f = this.recurrentActivation.apply(tfc.add(xF, hF));\n            const c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(tfc.add(xC, hC))));\n            const h = tfc.mul(this.recurrentActivation.apply(tfc.add(xO, hO)), this.activation.apply(c));\n            return [h, h, c];\n        });\n    }\n    getConfig() {\n        const _a = super.getConfig(), { 'units': _ } = _a, baseConfig = __rest(_a, ['units']);\n        const config = {\n            filters: this.filters,\n            kernelSize: this.kernelSize,\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n            dilationRate: this.dilationRate,\n            strides: this.strides,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n    inputConv(x, w, b, padding) {\n        const out = tfc.conv2d(x, w, this.strides, (padding || 'valid'), this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC', this.dilationRate);\n        if (b) {\n            return K.biasAdd(out, b, this.dataFormat);\n        }\n        return out;\n    }\n    recurrentConv(x, w) {\n        const strides = 1;\n        return tfc.conv2d(x, w, strides, 'same', this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC');\n    }\n}\n/** @nocollapse */\nConvLSTM2DCell.className = 'ConvLSTM2DCell';\ntfc.serialization.registerClass(ConvLSTM2DCell);\nexport class ConvLSTM2D extends ConvRNN2D {\n    constructor(args) {\n        const cell = new ConvLSTM2DCell(args);\n        super(Object.assign({}, args, { cell }));\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nConvLSTM2D.className = 'ConvLSTM2D';\ntfc.serialization.registerClass(ConvLSTM2D);\n//# sourceMappingURL=convolutional_recurrent.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Basic Layers.\n */\nimport { any, notEqual, serialization, tidy, transpose, util } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport * as K from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { assertPositiveInteger, mapActivationToFusedKernel } from '../utils/generic_utils';\nimport { arrayProd, range } from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class Dropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.rate = Math.max(Math.min(args.rate, 1), 0);\n        // So that the scalar doesn't get tidied up between executions.\n        this.noiseShape = args.noiseShape;\n        this.seed = args.seed;\n        this.supportsMasking = true;\n    }\n    getNoiseShape(input) {\n        if (this.noiseShape == null) {\n            return this.noiseShape;\n        }\n        const inputShape = input.shape;\n        const noiseShape = [];\n        for (let i = 0; i < this.noiseShape.length; ++i) {\n            noiseShape.push(this.noiseShape[i] == null ? inputShape[i] : this.noiseShape[i]);\n        }\n        return noiseShape;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            if (0 < this.rate && this.rate < 1) {\n                const training = kwargs['training'] == null ? false : kwargs['training'];\n                const noiseShape = this.getNoiseShape(input);\n                const output = K.inTrainPhase(() => K.dropout(input, this.rate, noiseShape, this.seed), () => input, training);\n                return output;\n            }\n            return inputs;\n        });\n    }\n    getConfig() {\n        const config = {\n            rate: this.rate,\n            noiseShape: this.noiseShape,\n            seed: this.seed,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    dispose() {\n        return super.dispose();\n    }\n}\n/** @nocollapse */\nDropout.className = 'Dropout';\nserialization.registerClass(Dropout);\nexport class SpatialDropout1D extends Dropout {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [{ ndim: 3 }];\n    }\n    getNoiseShape(input) {\n        const inputShape = input.shape;\n        return [inputShape[0], 1, inputShape[2]];\n    }\n}\n/** @nocollapse */\nSpatialDropout1D.className = 'SpatialDropout1D';\nserialization.registerClass(SpatialDropout1D);\nexport class Dense extends Layer {\n    constructor(args) {\n        super(args);\n        // Default activation: Linear (none).\n        this.activation = null;\n        this.useBias = true;\n        this.kernel = null;\n        this.bias = null;\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        if (args.batchInputShape == null && args.inputShape == null &&\n            args.inputDim != null) {\n            // This logic is copied from Layer's constructor, since we can't\n            // do exactly what the Python constructor does for Dense().\n            let batchSize = null;\n            if (args.batchSize != null) {\n                batchSize = args.batchSize;\n            }\n            this.batchInputShape = [batchSize, args.inputDim];\n        }\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation);\n        if (args.useBias != null) {\n            this.useBias = args.useBias;\n        }\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.supportsMasking = true;\n        this.inputSpec = [{ minNDim: 2 }];\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const inputLastDim = inputShape[inputShape.length - 1];\n        if (this.kernel == null) {\n            this.kernel = this.addWeight('kernel', [inputLastDim, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n            if (this.useBias) {\n                this.bias = this.addWeight('bias', [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n            }\n        }\n        this.inputSpec = [{ minNDim: 2, axes: { [-1]: inputLastDim } }];\n        this.built = true;\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        outputShape[outputShape.length - 1] = this.units;\n        return outputShape;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Dense layer accepts only a single input.\n            const input = getExactlyOneTensor(inputs);\n            const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());\n            let output;\n            if (fusedActivationName != null) {\n                output = K.dot(input, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null);\n            }\n            else {\n                output = K.dot(input, this.kernel.read());\n                if (this.bias != null) {\n                    output = K.biasAdd(output, this.bias.read());\n                }\n                if (this.activation != null) {\n                    output = this.activation.apply(output);\n                }\n            }\n            return output;\n        });\n    }\n    getConfig() {\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nDense.className = 'Dense';\nserialization.registerClass(Dense);\nexport class Flatten extends Layer {\n    constructor(args) {\n        args = args || {};\n        super(args);\n        this.inputSpec = [{ minNDim: 3 }];\n        this.dataFormat = args.dataFormat;\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        for (const dim of inputShape.slice(1)) {\n            if (dim == null) {\n                throw new ValueError(`The shape of the input to \"Flatten\" is not fully defined ` +\n                    `(got ${inputShape.slice(1)}). Make sure to pass a complete ` +\n                    `\"input_shape\" or \"batch_input_shape\" argument to the first ` +\n                    `layer in your model.`);\n            }\n        }\n        return [inputShape[0], arrayProd(inputShape, 1)];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            let input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsFirst' && input.rank > 1) {\n                const permutation = [0];\n                for (let i = 2; i < input.rank; ++i) {\n                    permutation.push(i);\n                }\n                permutation.push(1);\n                input = input.transpose(permutation);\n            }\n            return K.batchFlatten(input);\n        });\n    }\n    getConfig() {\n        const config = {};\n        if (this.dataFormat != null) {\n            config['dataFormat'] = this.dataFormat;\n        }\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nFlatten.className = 'Flatten';\nserialization.registerClass(Flatten);\nexport class Activation extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.activation = getActivation(args.activation);\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            return this.activation.apply(input);\n        });\n    }\n    getConfig() {\n        const config = { activation: serializeActivation(this.activation) };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nActivation.className = 'Activation';\nserialization.registerClass(Activation);\nexport class RepeatVector extends Layer {\n    constructor(args) {\n        super(args);\n        this.n = args.n;\n        this.inputSpec = [{ ndim: 2 }];\n    }\n    computeOutputShape(inputShape) {\n        return [inputShape[0], this.n, inputShape[1]];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            return K.repeat(inputs, this.n);\n        });\n    }\n    getConfig() {\n        const config = {\n            n: this.n,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nRepeatVector.className = 'RepeatVector';\nserialization.registerClass(RepeatVector);\nexport class Reshape extends Layer {\n    constructor(args) {\n        super(args);\n        this.targetShape = args.targetShape;\n        // Make sure that all unknown dimensions are represented as `null`.\n        for (let i = 0; i < this.targetShape.length; ++i) {\n            if (this.isUnknown(this.targetShape[i])) {\n                this.targetShape[i] = null;\n            }\n        }\n    }\n    isUnknown(dim) {\n        return dim < 0 || dim == null;\n    }\n    /**\n     * Finds and replaces a missing dimension in output shape.\n     *\n     * This is a near direct port of the internal Numpy function\n     * `_fix_unknown_dimension` in `numpy/core/src/multiarray/shape.c`.\n     *\n     * @param inputShape: Original shape of array begin reshape.\n     * @param outputShape: Target shape of the array, with at most a single\n     * `null` or negative number, which indicates an underdetermined dimension\n     * that should be derived from `inputShape` and the known dimensions of\n     *   `outputShape`.\n     * @returns: The output shape with `null` replaced with its computed value.\n     * @throws: ValueError: If `inputShape` and `outputShape` do not match.\n     */\n    fixUnknownDimension(inputShape, outputShape) {\n        const errorMsg = 'Total size of new array must be unchanged.';\n        const finalShape = outputShape.slice();\n        let known = 1;\n        let unknown = null;\n        for (let i = 0; i < finalShape.length; ++i) {\n            const dim = finalShape[i];\n            if (this.isUnknown(dim)) {\n                if (unknown === null) {\n                    unknown = i;\n                }\n                else {\n                    throw new ValueError('Can only specifiy one unknown dimension.');\n                }\n            }\n            else {\n                known *= dim;\n            }\n        }\n        const originalSize = arrayProd(inputShape);\n        if (unknown !== null) {\n            if (known === 0 || originalSize % known !== 0) {\n                throw new ValueError(errorMsg);\n            }\n            finalShape[unknown] = originalSize / known;\n        }\n        else if (originalSize !== known) {\n            throw new ValueError(errorMsg);\n        }\n        return finalShape;\n    }\n    computeOutputShape(inputShape) {\n        let anyUnknownDims = false;\n        for (let i = 0; i < inputShape.length; ++i) {\n            if (this.isUnknown(inputShape[i])) {\n                anyUnknownDims = true;\n                break;\n            }\n        }\n        if (anyUnknownDims) {\n            return inputShape.slice(0, 1).concat(this.targetShape);\n        }\n        else {\n            return inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            const outputShape = inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n            return input.reshape(outputShape);\n        });\n    }\n    getConfig() {\n        const config = {\n            targetShape: this.targetShape,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nReshape.className = 'Reshape';\nserialization.registerClass(Reshape);\nexport class Permute extends Layer {\n    constructor(args) {\n        super(args);\n        if (args.dims == null) {\n            throw new Error('Required configuration field `dims` is missing during Permute ' +\n                'constructor call.');\n        }\n        if (!Array.isArray(args.dims)) {\n            throw new Error('Permute constructor requires `dims` to be an Array, but received ' +\n                `${args.dims} instead.`);\n        }\n        // Check the validity of the permutation indices.\n        const expectedSortedIndices = range(1, args.dims.length + 1);\n        if (!util.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) {\n            throw new Error('Invalid permutation `dims`: ' + JSON.stringify(args.dims) +\n                ' `dims` must contain consecutive integers starting from 1.');\n        }\n        this.dims = args.dims;\n        this.dimsIncludingBatch = [0].concat(this.dims);\n        this.inputSpec = [new InputSpec({ ndim: this.dims.length + 1 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        this.dims.forEach((dim, i) => {\n            outputShape[i + 1] = inputShape[dim];\n        });\n        return outputShape;\n    }\n    call(inputs, kwargs) {\n        return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch);\n    }\n    getConfig() {\n        const config = {\n            dims: this.dims,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nPermute.className = 'Permute';\nserialization.registerClass(Permute);\nexport class Masking extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.supportsMasking = true;\n        if (args != null) {\n            this.maskValue = args.maskValue == null ? 0 : args.maskValue;\n        }\n        else {\n            this.maskValue = 0;\n        }\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { maskValue: this.maskValue };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    computeMask(inputs, mask) {\n        const input = getExactlyOneTensor(inputs);\n        const axis = -1;\n        return any(notEqual(input, this.maskValue), axis);\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const axis = -1;\n            const keepDims = true;\n            const booleanMask = any(notEqual(input, this.maskValue), axis, keepDims);\n            const output = input.mul(booleanMask.asType(input.dtype));\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMasking.className = 'Masking';\nserialization.registerClass(Masking);\n//# sourceMappingURL=core.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Embedding Layer.\n *\n * Original source: keras/constraints.py\n */\nimport { notEqual, serialization, tidy, zerosLike } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class Embedding extends Layer {\n    constructor(args) {\n        super(args);\n        this.embeddings = null;\n        this.DEFAULT_EMBEDDINGS_INITIALIZER = 'randomUniform';\n        if (args.batchInputShape == null && args.inputShape == null) {\n            // Porting Note: This logic is copied from Layer's constructor, since we\n            // can't do exactly what the Python constructor does for Embedding().\n            // Specifically, the super constructor can not be called after the\n            // mutation of the `config` argument.\n            let batchSize = null;\n            if (args.batchSize != null) {\n                batchSize = args.batchSize;\n            }\n            if (args.inputLength == null) {\n                // Fix super-constructor to what it would have done if\n                // 'config.inputShape' were (None, )\n                this.batchInputShape = [batchSize, null];\n            }\n            else {\n                // Fix super-constructor to what it would have done if\n                // 'config.inputShape' were (config.inputLength, )\n                this.batchInputShape =\n                    [batchSize].concat(generic_utils.toList(args.inputLength));\n            }\n        }\n        this.inputDim = args.inputDim;\n        generic_utils.assertPositiveInteger(this.inputDim, 'inputDim');\n        this.outputDim = args.outputDim;\n        generic_utils.assertPositiveInteger(this.outputDim, 'outputDim');\n        this.embeddingsInitializer = getInitializer(args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER);\n        this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.embeddingsConstraint = getConstraint(args.embeddingsConstraint);\n        this.maskZero = args.maskZero;\n        this.supportsMasking = args.maskZero;\n        this.inputLength = args.inputLength;\n    }\n    build(inputShape) {\n        this.embeddings = this.addWeight('embeddings', [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint);\n        this.built = true;\n    }\n    // Override warnOnIncompatibleInputShape because an embedding layer allows\n    // the input to have varying ranks.\n    warnOnIncompatibleInputShape(inputShape) { }\n    computeMask(inputs, mask) {\n        return tidy(() => {\n            if (!this.maskZero) {\n                return null;\n            }\n            else {\n                inputs = getExactlyOneTensor(inputs);\n                return notEqual(inputs, zerosLike(inputs));\n            }\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (this.inputLength == null) {\n            return [...inputShape, this.outputDim];\n        }\n        // inputLength can be an array if input is 3D or higher.\n        const inLens = generic_utils.toList(this.inputLength);\n        if (inLens.length !== inputShape.length - 1) {\n            throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received ` +\n                `input shape has shape ${inputShape}`);\n        }\n        else {\n            let i = 0;\n            for (let k = 0; k < inLens.length; ++k) {\n                const s1 = inLens[k];\n                const s2 = inputShape[k + 1];\n                if ((s1 != null) && (s2 != null) && (s1 !== s2)) {\n                    throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received ` +\n                        `input shape has shape ${inputShape}`);\n                }\n                else if (s1 == null) {\n                    inLens[i] = s2;\n                }\n                i++;\n            }\n        }\n        return [inputShape[0], ...inLens, this.outputDim];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Embedding layer accepts only a single input.\n            let input = getExactlyOneTensor(inputs);\n            if (input.dtype !== 'int32') {\n                input = K.cast(input, 'int32');\n            }\n            const output = K.gather(this.embeddings.read(), input.as1D());\n            return output.reshape(getExactlyOneShape(this.computeOutputShape(input.shape)));\n        });\n    }\n    getConfig() {\n        const config = {\n            inputDim: this.inputDim,\n            outputDim: this.outputDim,\n            embeddingsInitializer: serializeInitializer(this.embeddingsInitializer),\n            embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            embeddingsConstraint: serializeConstraint(this.embeddingsConstraint),\n            maskZero: this.maskZero,\n            inputLength: this.inputLength\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nEmbedding.className = 'Embedding';\nserialization.registerClass(Embedding);\n//# sourceMappingURL=embeddings.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Merge Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { l2Normalize } from '../losses';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as mathUtils from '../utils/math_utils';\nimport { getExactlyOneShape } from '../utils/types_utils';\n/**\n * Generic Merge layer for element-wise merge functions.\n *\n * Used to implement `Sum`, `Average`, `Concatenate`, etc.\n */\nexport class Merge extends Layer {\n    constructor(args) {\n        super(args || {});\n        this.supportsMasking = true;\n    }\n    /**\n     * Logic for merging multiple tensors, to be overridden by subclasses.\n     * @param inputs\n     */\n    mergeFunction(inputs) {\n        throw new NotImplementedError();\n    }\n    /**\n     * Computes the shape of the result of an elementwise operation.\n     *\n     * @param shape1: Shape of the first tensor.\n     * @param shape2: Shape of the second tensor.\n     * @returns Expected output shape when an elementwise operation is carried\n     *   out on 2 tensors with shapes `shape1` and `shape2`.\n     * @throws ValueError: If `shape1` and `shape2` are not compatible for\n     *   element-wise operations.\n     */\n    computeElementwiseOpOutputShape(shape1, shape2) {\n        if (shape1 == null || shape2 == null) {\n            return null;\n        }\n        else if (shape1.length < shape2.length) {\n            return this.computeElementwiseOpOutputShape(shape2, shape1);\n        }\n        else if (shape2.length === 0) {\n            return shape1;\n        }\n        const outputShape = shape1.slice(0, shape1.length - shape2.length);\n        for (let k = 0; k < shape2.length; ++k) {\n            const i = shape1[shape1.length - shape2.length + k];\n            const j = shape2[k];\n            if (i == null || j == null || i < 0 || j < 0) {\n                outputShape.push(null);\n            }\n            else if (i === 1) {\n                outputShape.push(j);\n            }\n            else if (j === 1) {\n                outputShape.push(i);\n            }\n            else {\n                if (i !== j) {\n                    throw new ValueError('Operands could not be broadcast together with shapes ' +\n                        JSON.stringify(shape1) + ' ' + JSON.stringify(shape2));\n                }\n                outputShape.push(i);\n            }\n        }\n        return outputShape;\n    }\n    build(inputShape) {\n        // Used purely for shape validation.\n        if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) {\n            // Make sure that inputShape is an Array of shape.\n            inputShape = [getExactlyOneShape(inputShape)];\n        }\n        inputShape = inputShape;\n        if (inputShape.length < 2) {\n            throw new ValueError('A merge layer should be called on an Array of at least 2 inputs.' +\n                ` Got ${inputShape.length} input(s).`);\n        }\n        // Make sure that there is at most one unique batch size among the input\n        // shapes.\n        let batchSizes = [];\n        for (const shape of inputShape) {\n            if (shape != null && shape[0] !== null) {\n                batchSizes.push(shape[0]);\n            }\n        }\n        batchSizes = generic_utils.unique(batchSizes);\n        if (batchSizes.length > 1) {\n            throw new ValueError(`Can not merge tensors with different batch sizes. ` +\n                `Got tensors with shapes: ${JSON.stringify(inputShape)}.`);\n        }\n        let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1);\n        for (let i = 1; i < inputShape.length; ++i) {\n            const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n            outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n        }\n        // If the inputs have different ranks, we have to reshape them to make them\n        // broadcastable.\n        const allRanks = inputShape.map(shape => shape.length);\n        if (inputShape.indexOf(null) === -1 &&\n            generic_utils.unique(allRanks).length === 1) {\n            this.reshapeRequired = false;\n        }\n        else {\n            this.reshapeRequired = true;\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (this.reshapeRequired) {\n                const reshapedInputs = [];\n                const inputDims = inputs.map(input => input.rank);\n                if (inputDims.indexOf(null) === -1) {\n                    // If ranks of all inputs are available, we simply expand each of them\n                    // at axis=1 until all of them have the same rank.\n                    const maxNDim = mathUtils.max(inputDims);\n                    for (let x of inputs) {\n                        const xNDim = x.rank;\n                        for (let k = 0; k < maxNDim - xNDim; ++k) {\n                            x = K.expandDims(x, 1);\n                        }\n                        reshapedInputs.push(x);\n                    }\n                    return this.mergeFunction(reshapedInputs);\n                }\n                else {\n                    // Transpose all inputs so that batch size is the last dimension.\n                    // [batchSize, dim1, dim2, ...] -> [dim1, dim2, ..., batchSize]\n                    let transposed = false;\n                    for (const x of inputs) {\n                        const xNDim = x.rank;\n                        if (xNDim == null) {\n                            const xShape = x.shape;\n                            const batchSize = xShape[0];\n                            const newShape = xShape.slice(1).concat([batchSize]);\n                            let xTransposed = x.reshape([batchSize].concat(mathUtils.arrayProd(xShape.slice(1))));\n                            xTransposed = tfc.transpose(xTransposed, [1, 0]);\n                            xTransposed = xTransposed.reshape(newShape);\n                            reshapedInputs.push(xTransposed);\n                            transposed = true;\n                        }\n                        else if (xNDim > 1) {\n                            const dims = mathUtils.range(1, xNDim).concat([0]);\n                            reshapedInputs.push(tfc.transpose(x, dims));\n                            transposed = true;\n                        }\n                        else {\n                            // We don't transpose inputs if they are 1D vectors or scalars.\n                            reshapedInputs.push(x);\n                        }\n                    }\n                    let y = this.mergeFunction(reshapedInputs);\n                    const yNDim = y.rank;\n                    if (transposed) {\n                        // If inputs have been transposed, we have to transpose the output\n                        // too.\n                        if (yNDim == null) {\n                            const yShape = y.shape;\n                            const yNDim = yShape.length;\n                            const batchSize = yShape[yNDim - 1];\n                            const newShape = [batchSize].concat(yShape.slice(0, yShape.length - 1));\n                            y = tfc.transpose(y.reshape([-1, batchSize]), [1, 0])\n                                .reshape(newShape);\n                        }\n                        else if (yNDim > 1) {\n                            const dims = [yNDim - 1].concat(mathUtils.range(0, yNDim - 1));\n                            y = tfc.transpose(y, dims);\n                        }\n                    }\n                    return y;\n                }\n            }\n            else {\n                return this.mergeFunction(inputs);\n            }\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = inputShape;\n        let outputShape;\n        if (inputShape[0] == null) {\n            outputShape = null;\n        }\n        else {\n            outputShape = inputShape[0].slice(1);\n        }\n        for (let i = 1; i < inputShape.length; ++i) {\n            const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n            outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n        }\n        let batchSizes = [];\n        for (const shape of inputShape) {\n            if (shape != null && shape[0] !== null) {\n                batchSizes.push(shape[0]);\n            }\n        }\n        batchSizes = generic_utils.unique(batchSizes);\n        if (batchSizes.length === 1) {\n            outputShape = batchSizes.concat(outputShape);\n        }\n        else {\n            outputShape = [null].concat(outputShape);\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        return tfc.tidy(() => {\n            if (mask == null) {\n                return null;\n            }\n            if (!Array.isArray(mask)) {\n                throw new ValueError('`mask` should be an Array');\n            }\n            if (!Array.isArray(inputs)) {\n                throw new ValueError('`inputs` should be an Array');\n            }\n            if (mask.length !== inputs.length) {\n                throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same ` +\n                    `length, but have different lengths ` +\n                    `(${inputs.length} vs ${mask.length})`);\n            }\n            if (mask.every(m => m == null)) {\n                return null;\n            }\n            mask = mask.map(m => m == null ? m : tfc.expandDims(m, 0));\n            let output = mask[0];\n            for (let i = 1; i < mask.length - 1; ++i) {\n                output = tfc.logicalAnd(output, mask[i]);\n            }\n            return output;\n        });\n    }\n}\nexport class Add extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.add(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nAdd.className = 'Add';\nserialization.registerClass(Add);\n/**\n * Calculate the element-wise sum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Add` layer, by using no input argument\n *    or a single configuration argument. The resultant `Add` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const addLayer = tf.layers.add();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = addLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.add([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.add([input1, input2]).print();\n * // Gives [[11, 22], [33, 44]].\n *\n */\nexport function add(config) {\n    if (Array.isArray(config)) {\n        const layer = new Add({});\n        return layer.apply(config);\n    }\n    else {\n        return new Add(config);\n    }\n}\nexport class Multiply extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.mul(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMultiply.className = 'Multiply';\nserialization.registerClass(Multiply);\n/**\n * Calculate the element-wise product of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Multiply` layer, by using no input argument\n *    or a single configuration argument. The resultant `Multiply` layer can\n *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const multiplyLayer = tf.layers.multiply();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = multiplyLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.multiply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.multiply([input1, input2]).print();\n * // Gives [[10, 40], [90, 160]].\n *\n */\nexport function multiply(config) {\n    if (Array.isArray(config)) {\n        const layer = new Multiply({});\n        return layer.apply(config);\n    }\n    else {\n        return new Multiply(config);\n    }\n}\nexport class Average extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.add(output, inputs[i]);\n            }\n            return tfc.mul(1 / inputs.length, output);\n        });\n    }\n}\n/** @nocollapse */\nAverage.className = 'Average';\nserialization.registerClass(Average);\n/**\n * Calculate the element-wise arithmetic mean of inputs, which all have the same\n * shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Average` layer, by using no input argument\n *    or a single configuration argument. The resultant `Average` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const averageLayer = tf.layers.average();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = averageLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.average([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.average([input1, input2]).print();\n * // Gives [[5.5, 11], [16.5, 22]].\n *\n */\nexport function average(config) {\n    if (Array.isArray(config)) {\n        const layer = new Average({});\n        return layer.apply(config);\n    }\n    else {\n        return new Average(config);\n    }\n}\nexport class Maximum extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0];\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.maximum(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMaximum.className = 'Maximum';\nserialization.registerClass(Maximum);\n/**\n * Calculate the element-wise maximum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Maximum` layer, by using no input argument\n *    or a single configuration argument. The resultant `Maximum` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const maximumLayer = tf.layers.maximum();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = maximumLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.maximum([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);\n * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);\n * tf.layers.maximum([input1, input2]).print();\n * // Gives [[10, 20], [30, 40]].\n *\n */\nexport function maximum(config) {\n    if (Array.isArray(config)) {\n        const layer = new Maximum({});\n        return layer.apply(config);\n    }\n    else {\n        return new Maximum(config);\n    }\n}\nexport class Minimum extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0];\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.minimum(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMinimum.className = 'Minimum';\nserialization.registerClass(Minimum);\n/**\n * Calculate the element-wise minimum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Minimum` layer, by using no input argument\n *    or a single configuration argument. The resultant `Minimum` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const minimumLayer = tf.layers.minimum();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = minimumLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.minimum([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);\n * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);\n * tf.layers.minimum([input1, input2]).print();\n * // Gives [[1, 2], [3, 4]].\n *\n */\nexport function minimum(config) {\n    if (Array.isArray(config)) {\n        const layer = new Minimum({});\n        return layer.apply(config);\n    }\n    else {\n        return new Minimum(config);\n    }\n}\nexport class Concatenate extends Merge {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_AXIS = -1;\n        if (args == null) {\n            args = {};\n        }\n        this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n        this.supportsMasking = true;\n        this.reshapeRequired = false;\n    }\n    build(inputShape) {\n        // Used purely for shape validation.]\n        if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) ||\n            inputShape.length === 1) {\n            throw new ValueError('A `Concatenate` layer should be called on a list of at least 2 ' +\n                'inputs');\n        }\n        inputShape = inputShape;\n        let allNoneShape = true;\n        for (const shape of inputShape) {\n            if (shape != null) {\n                allNoneShape = false;\n                break;\n            }\n        }\n        if (allNoneShape) {\n            return;\n        }\n        const shapeSet = [];\n        for (let i = 0; i < inputShape.length; ++i) {\n            const shapeWithoutConcatAxis = inputShape[i].slice();\n            shapeWithoutConcatAxis.splice(this.axis, 1);\n            let exists = false;\n            for (const shape of shapeSet) {\n                if (util.arraysEqual(shape, shapeWithoutConcatAxis)) {\n                    exists = true;\n                    break;\n                }\n            }\n            if (!exists) {\n                shapeSet.push(shapeWithoutConcatAxis);\n            }\n        }\n        if (shapeSet.length > 1) {\n            throw new ValueError('A `Concatenate` layer requires inputs with matching shapes ' +\n                'except for the concat axis. Got input shapes: ' +\n                JSON.stringify(inputShape));\n        }\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            return K.concatenate(inputs, this.axis);\n        });\n    }\n    computeOutputShape(inputShape) {\n        if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) {\n            throw new ValueError('A `Concatenate` layer should be called on a list of inputs.');\n        }\n        const inputShapes = inputShape;\n        const outputShape = inputShapes[0].slice();\n        const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis;\n        // Porting Note: the line above is because TypeScript doesn't support\n        //   negative indices.\n        for (const shape of inputShapes.slice(1)) {\n            if (outputShape[axis] == null || shape[axis] == null) {\n                outputShape[axis] = null;\n                break;\n            }\n            outputShape[axis] += shape[axis];\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        if (mask == null) {\n            return null;\n        }\n        if (!Array.isArray(mask)) {\n            throw new ValueError('`mask` should be an array for Concatenate');\n        }\n        if (!Array.isArray(inputs)) {\n            throw new ValueError('`inputs` should be an array for Concatenate');\n        }\n        if (mask.length !== inputs.length) {\n            throw new ValueError(`Mismatch in the length of mask (${mask.length}) ` +\n                `and the legnth of inputs (${inputs.length})`);\n        }\n        return tfc.tidy(() => {\n            let allNullMasks = true;\n            mask.forEach(m => {\n                if (m != null) {\n                    allNullMasks = false;\n                    return;\n                }\n            });\n            if (allNullMasks) {\n                return null;\n            }\n            const outputMasks = [];\n            for (let i = 0; i < inputs.length; ++i) {\n                if (mask[i] == null) {\n                    // Input is unmasked. Append all 1's to masks.\n                    outputMasks.push(tfc.onesLike(inputs[i]).asType('bool'));\n                }\n                else if (mask[i].rank < inputs[i].rank) {\n                    // Mask is smaller than the input, expand it.\n                    outputMasks.push(tfc.expandDims(mask[i], -1));\n                }\n                else {\n                    outputMasks.push(mask[i]);\n                }\n            }\n            const concatenatedMasks = tfc.concat(outputMasks, this.axis);\n            return tfc.all(concatenatedMasks, -1, false);\n        });\n    }\n    getConfig() {\n        const config = {\n            'axis': this.axis,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nConcatenate.className = 'Concatenate';\nserialization.registerClass(Concatenate);\n/**\n * Concatenate an `Array` of inputs.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Concatenate` layer, by using no input argument\n *    or a single configuration argument. The resultant `Concatenate` layer can\n *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const concatLayer = tf.layers.concatenate();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 3]});\n * const input2 = tf.input({shape: [2, 4]});\n * const output = concatLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 7], with the first dimension as the undetermined batch\n * // dimension and the last dimension as the result of concatenating the\n * // last dimensions of the two inputs.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 3]});\n * const input2 = tf.input({shape: [2, 4]});\n * const output = tf.layers.concatenate([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension and the last dimension as the result of concatenating the\n * // last dimensions of the two inputs.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);\n * const input2 = tf.tensor2d([[10, 20], [30, 40]], [2, 2]);\n * tf.layers.concatenate([input1, input2]).print();\n * // Gives [[1, 2, 10, 20], [3, 4, 30, 40]].\n *\n */\nexport function concatenate(config) {\n    if (Array.isArray(config)) {\n        const layer = new Concatenate({});\n        return layer.apply(config);\n    }\n    else {\n        return new Concatenate(config);\n    }\n}\n/**\n * Interpretable potentially negative axis index.\n *\n * For example, given axis = -1, and dim = 3, this function will return 2.\n *\n * @param axis The axis index, may be a positive, zero or negative integer.\n * @param dim Total number of dimensions, a positive integer.\n * @returns A non-negative axis index equivalent to the input `axis`.\n */\nfunction interpretAxis(axis, dim) {\n    while (axis < 0) {\n        axis += dim;\n    }\n    return axis;\n}\nfunction batchDot(x, y, axes) {\n    if (x.shape.length > 3 || y.shape.length > 3) {\n        throw new NotImplementedError('batchDot is not implemented for tensors of 4D or higher rank yet');\n    }\n    tfc.util.assert(x.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, ` +\n        `but got ${x.shape.length}`);\n    tfc.util.assert(x.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, ` +\n        `but got ${y.shape.length}`);\n    if (typeof axes === 'number') {\n        axes = [axes, axes];\n    }\n    if (x.dtype === 'complex64' || y.dtype === 'complex64') {\n        throw new NotImplementedError('batchDot is not implemented for complex64-type Tensors yet.');\n    }\n    const xNDim = x.shape.length;\n    const yNDim = y.shape.length;\n    if (axes == null) {\n        // Behave like batchMatmul by default.\n        axes = [xNDim - 1, yNDim - 2];\n    }\n    const axesArray = axes;\n    return tfc.tidy(() => {\n        let diff;\n        if (xNDim > yNDim) {\n            diff = xNDim - yNDim;\n            const diffShape = [];\n            for (let i = 0; i < diff; ++i) {\n                diffShape.push(1);\n            }\n            y = y.reshape(y.shape.concat(diffShape));\n        }\n        else if (yNDim > xNDim) {\n            diff = yNDim - xNDim;\n            const diffShape = [];\n            for (let i = 0; i < diff; ++i) {\n                diffShape.push(1);\n            }\n            x = x.reshape(x.shape.concat(diffShape));\n        }\n        else {\n            diff = 0;\n        }\n        let out;\n        if (x.shape.length === 2 && y.shape.length === 2) {\n            if (axesArray[0] === axesArray[1]) {\n                out = x.mul(y).sum(axesArray[0]);\n            }\n            else {\n                out = x.transpose([1, 0]).mul(y).sum(axesArray[1]);\n            }\n        }\n        else {\n            const adjX = axesArray[0] !== x.shape.length - 1;\n            const adjY = axesArray[1] === y.shape.length - 1;\n            out = x.matMul(y, adjX, adjY);\n        }\n        if (diff > 0) {\n            let idx;\n            if (xNDim > yNDim) {\n                idx = xNDim + yNDim - 3;\n            }\n            else {\n                idx = xNDim - 1;\n            }\n            const squeezeAxes = [];\n            for (let i = idx; i < idx + diff; ++i) {\n                squeezeAxes.push(i);\n            }\n            out = out.squeeze(squeezeAxes);\n        }\n        if (out.shape.length === 1) {\n            out = out.expandDims(1);\n        }\n        return out;\n    });\n}\nexport class Dot extends Merge {\n    constructor(args) {\n        super(args);\n        this.axes = args.axes;\n        this.normalize = args.normalize == null ? false : args.normalize;\n        this.supportsMasking = true;\n        this.reshapeRequired = false;\n    }\n    build(inputShape) {\n        tfc.util.assert(Array.isArray(inputShape) && inputShape.length === 2 &&\n            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');\n        const shape1 = inputShape[0];\n        const shape2 = inputShape[1];\n        if (shape1.length > 3 || shape2.length > 3) {\n            throw new NotImplementedError('Dot layer does not support tensors of 4D or higher rank yet.');\n        }\n        const axes = this.interpretAxes(shape1, shape2);\n        if (shape1[axes[0]] !== shape2[axes[1]]) {\n            throw new ValueError(`Dimension incompatibility: ` +\n                `${shape1[axes[0]]} !== ${shape2[axes[1]]}`);\n        }\n    }\n    mergeFunction(inputs) {\n        if (inputs.length !== 2) {\n            throw new ValueError('A `Dot` layer must be called on exactly 2 inputs, ' +\n                `but received ${inputs.length} input(s).`);\n        }\n        let x1 = inputs[0];\n        let x2 = inputs[1];\n        let axes;\n        if (!Array.isArray(this.axes)) {\n            axes = [\n                interpretAxis(this.axes, x1.shape.length),\n                interpretAxis(this.axes, x2.shape.length)\n            ];\n        }\n        else {\n            axes = this.axes.map((axis, i) => interpretAxis(axis, inputs[i].shape.length));\n        }\n        if (this.normalize) {\n            x1 = l2Normalize(x1, axes[0]);\n            x2 = l2Normalize(x2, axes[1]);\n        }\n        return batchDot(x1, x2, axes);\n    }\n    interpretAxes(shape1, shape2) {\n        let axes;\n        if (!Array.isArray(this.axes)) {\n            // `this.axes` is a single integer.\n            axes = [\n                interpretAxis(this.axes, shape1.length),\n                interpretAxis(this.axes, shape2.length)\n            ];\n        }\n        else {\n            // `this.axes` is an Array of integers.\n            axes = this.axes;\n        }\n        return axes;\n    }\n    computeOutputShape(inputShape) {\n        tfc.util.assert(Array.isArray(inputShape) && inputShape.length === 2 &&\n            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');\n        const shape1 = inputShape[0].slice();\n        const shape2 = inputShape[1].slice();\n        if (shape1.length > 3 || shape2.length > 3) {\n            throw new NotImplementedError('Dot layer does not support tensors of 4D or higher rank yet.');\n        }\n        const axes = this.interpretAxes(shape1, shape2);\n        shape1.splice(axes[0], 1);\n        shape2.splice(axes[1], 1);\n        shape2.splice(0, 1);\n        const outputShape = shape1.concat(shape2);\n        if (outputShape.length === 1) {\n            outputShape.push(1);\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        return null;\n    }\n    getConfig() {\n        const config = {\n            'axes': this.axes,\n            'normalize': this.normalize\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nDot.className = 'Dot';\nserialization.registerClass(Dot);\n// TODO(cais): Add functional interfaces for the merge layers.\n//# sourceMappingURL=merge.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Noise Layers.\n */\nimport { greaterEqual, randomUniform, serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { Layer } from '../engine/topology';\nimport { getExactlyOneTensor } from '../utils/types_utils';\nexport class GaussianNoise extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.stddev = args.stddev;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { stddev: this.stddev };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const noised = () => K.randomNormal(input.shape, 0, this.stddev).add(input);\n            const output = K.inTrainPhase(noised, () => input, kwargs['training'] || false);\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nGaussianNoise.className = 'GaussianNoise';\nserialization.registerClass(GaussianNoise);\nexport class GaussianDropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.rate = args.rate;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { rate: this.rate };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            if (this.rate > 0 && this.rate < 1) {\n                const noised = () => {\n                    const stddev = Math.sqrt(this.rate / (1 - this.rate));\n                    return input.mul(K.randomNormal(input.shape, 1, stddev));\n                };\n                return K.inTrainPhase(noised, () => input, kwargs['training'] || false);\n            }\n            return input;\n        });\n    }\n}\n/** @nocollapse */\nGaussianDropout.className = 'GaussianDropout';\nserialization.registerClass(GaussianDropout);\n/**\n * Applies Alpha Dropout to the input.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs\n * to their original values, in order to ensure the self-normalizing property\n * even after this dropout.\n * Alpha Dropout fits well to Scaled Exponential Linear Units\n * by randomly setting activations to the negative saturation value.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the\n *     shape for randomly generated keep/drop flags.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n */\nexport class AlphaDropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.rate = args.rate;\n        this.noiseShape = args.noiseShape;\n    }\n    _getNoiseShape(inputs) {\n        return this.noiseShape || getExactlyOneTensor(inputs).shape;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { rate: this.rate };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.rate < 1 && this.rate > 0) {\n                const noiseShape = this._getNoiseShape(inputs);\n                const droppedInputs = () => {\n                    const input = getExactlyOneTensor(inputs);\n                    const alpha = 1.6732632423543772848170429916717;\n                    const scale = 1.0507009873554804934193349852946;\n                    const alphaP = -alpha * scale;\n                    let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate);\n                    keptIdx = K.cast(keptIdx, 'float32'); // get default dtype.\n                    // Get affine transformation params.\n                    const a = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5;\n                    const b = -a * alphaP * this.rate;\n                    // Apply mask.\n                    const x = input.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP));\n                    return x.mul(a).add(b);\n                };\n                return K.inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs['training'] || false);\n            }\n            return inputs;\n        });\n    }\n}\n/** @nocollapse */\nAlphaDropout.className = 'AlphaDropout';\nserialization.registerClass(AlphaDropout);\n//# sourceMappingURL=noise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Normalization layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { moments, serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Applies batch normalization on x given mean, var, beta and gamma.\n *\n * I.e. returns:\n *   `output = (x - mean) / (sqrt(var) + epsilon) * gamma + beta`\n *\n * @param x Input tensor.\n * @param mean Mean of batch.\n * @param variance Variance of batch.\n * @param beta Tensor with which to center the input.\n * @param gamma Tensor by which to scale the input.\n * @param epsilon Fuzz factor.\n * @returns The result of the batch normalization.\n */\nexport function batchNormalization(x, mean, variance, beta, gamma, epsilon = 1e-3) {\n    let out;\n    if (x.rank === 2) {\n        out = tfc.batchNorm2d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else if (x.rank === 3) {\n        // TODO(cais): Check rank; give proper error message.\n        out = tfc.batchNorm3d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else if (x.rank === 4) {\n        out = tfc.batchNorm4d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else {\n        throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} ` +\n            `yet`);\n    }\n    return out;\n}\n/**\n * Non-broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    return tidy(() => {\n        const meanAndVariance = tfc.moments(x, reductionAxes);\n        const mean = meanAndVariance.mean;\n        const variance = meanAndVariance.variance;\n        const normed = batchNormalization(x, mean, variance, beta, gamma, epsilon);\n        return [normed, mean, variance];\n    });\n}\n/**\n * Broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    return tidy(() => {\n        const meanAndVariance = tfc.moments(x, reductionAxes);\n        const mean = meanAndVariance.mean;\n        const variance = meanAndVariance.variance;\n        const targetShape = [];\n        for (const axis of math_utils.range(0, x.rank)) {\n            if (reductionAxes.indexOf(axis) !== -1) {\n                targetShape.push(1);\n            }\n            else {\n                targetShape.push(x.shape[axis]);\n            }\n        }\n        const broadcastMean = mean.reshape(targetShape);\n        const broadcastVariance = variance.reshape(targetShape);\n        const broadcastGamma = gamma == null ? null : gamma.reshape(targetShape);\n        const broadcastBeta = beta == null ? null : beta.reshape(targetShape);\n        const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon);\n        return [normed, mean, variance];\n    });\n}\n/**\n * Batch normalization for use in training (not inference).\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nexport function normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    if (util.arraysEqual(reductionAxes.slice().sort(), math_utils.range(0, x.rank - 1))) {\n        return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);\n    }\n    else {\n        return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);\n    }\n}\nexport class BatchNormalization extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.supportsMasking = true;\n        this.axis = args.axis == null ? -1 : args.axis;\n        this.momentum = args.momentum == null ? 0.99 : args.momentum;\n        this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n        this.center = args.center == null ? true : args.center;\n        this.scale = args.scale == null ? true : args.scale;\n        this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n        this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n        this.movingMeanInitializer =\n            getInitializer(args.movingMeanInitializer || 'zeros');\n        this.movingVarianceInitializer =\n            getInitializer(args.movingVarianceInitializer || 'ones');\n        this.betaConstraint = getConstraint(args.betaConstraint);\n        this.gammaConstraint = getConstraint(args.gammaConstraint);\n        this.betaRegularizer = getRegularizer(args.betaRegularizer);\n        this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const axis = this.axis >= 0 ? this.axis : (this.axis + inputShape.length);\n        const dim = inputShape[axis];\n        if (dim == null) {\n            throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but ` +\n                `the layer received an input with shape ` +\n                `${JSON.stringify(inputShape)}.`);\n        }\n        this.inputSpec =\n            [new InputSpec({ ndim: inputShape.length, axes: { [axis]: dim } })];\n        const shape = [dim];\n        if (this.scale) {\n            this.gamma = this.addWeight('gamma', shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint);\n        }\n        if (this.center) {\n            this.beta = this.addWeight('beta', shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint);\n        }\n        this.movingMean = this.addWeight('moving_mean', shape, null, this.movingMeanInitializer, null, false);\n        this.movingVariance = this.addWeight('moving_variance', shape, null, this.movingVarianceInitializer, null, false);\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            const input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            const ndim = inputShape.length;\n            const reductionAxes = math_utils.range(0, ndim);\n            const axis = this.axis >= 0 ? this.axis : (this.axis + ndim);\n            reductionAxes.splice(axis, 1);\n            const broadcastShape = generic_utils.pyListRepeat(1, ndim);\n            broadcastShape[axis] = inputShape[axis];\n            const sortedReductionAxes = reductionAxes.slice();\n            sortedReductionAxes.sort();\n            const needsBroadcasting = !util.arraysEqual(sortedReductionAxes, math_utils.range(0, ndim).slice(0, ndim - 1));\n            const normalizeInference = () => {\n                if (needsBroadcasting) {\n                    const broadcastMovingMean = this.movingMean.read().reshape(broadcastShape);\n                    const broadcastMovingVariance = this.movingVariance.read().reshape(broadcastShape);\n                    const broadcastBeta = this.center ? this.beta.read().reshape(broadcastShape) : null;\n                    const broadcastGamma = this.scale ? this.gamma.read().reshape(broadcastShape) : null;\n                    return batchNormalization(input, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon);\n                }\n                else {\n                    return batchNormalization(input, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);\n                }\n            };\n            if (!training) {\n                return normalizeInference();\n            }\n            const [normedTraining, mean, variance] = normalizeBatchInTraining(input, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon);\n            const doMovingAverage = (variable, value, momentum) => {\n                tfc.tidy(() => {\n                    const decay = 1 - momentum;\n                    const origValue = variable.read();\n                    const updateDelta = origValue.sub(value).mul(decay);\n                    variable.write(origValue.sub(updateDelta));\n                });\n            };\n            // Perform updates to moving mean and moving variance for training.\n            // Porting Note: In PyKeras, these updates to `movingMean` and\n            //   `movingAverage` are done as a deferred Graph, added to the `Layer`'s\n            //   `update`s using the `add_update()` method. Here we do it imperatively\n            //   and encapsulate the updates in a function that is invoked\n            //   immediately.\n            const updateMovingMeanAndVariance = () => {\n                doMovingAverage(this.movingMean, mean, this.momentum);\n                doMovingAverage(this.movingVariance, variance, this.momentum);\n            };\n            updateMovingMeanAndVariance();\n            return normedTraining;\n        });\n    }\n    getConfig() {\n        const config = {\n            axis: this.axis,\n            momentum: this.momentum,\n            epsilon: this.epsilon,\n            center: this.center,\n            scale: this.scale,\n            betaInitializer: serializeInitializer(this.betaInitializer),\n            gammaInitializer: serializeInitializer(this.gammaInitializer),\n            movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),\n            movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer),\n            betaRegularizer: serializeRegularizer(this.betaRegularizer),\n            gammaRegularizer: serializeRegularizer(this.gammaRegularizer),\n            betaConstraint: serializeConstraint(this.betaConstraint),\n            gammaConstraint: serializeConstraint(this.gammaConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nBatchNormalization.className = 'BatchNormalization';\nserialization.registerClass(BatchNormalization);\nexport class LayerNormalization extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.axis = args.axis == null ? -1 : args.axis;\n        if (typeof this.axis === 'number') {\n            if (!Number.isInteger(this.axis)) {\n                throw new Error(`Expected axis to be an integer, but received ${this.axis}`);\n            }\n        }\n        else if (Array.isArray(this.axis)) {\n            for (const axis of this.axis) {\n                if (!Number.isInteger(axis)) {\n                    throw new Error(`Expected axis to be an array of integers, ` +\n                        `but received ${JSON.stringify(this.axis)}`);\n                }\n            }\n        }\n        else {\n            throw new Error(`Expected axis to be an integer or an array of integers, ` +\n                `but received ${JSON.stringify(this.axis)}`);\n        }\n        this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n        this.center = args.center == null ? true : args.center;\n        this.scale = args.scale == null ? true : args.scale;\n        this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n        this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n        this.betaRegularizer = getRegularizer(args.betaRegularizer);\n        this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n        this.supportsMasking = true;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const nDims = inputShape.length;\n        // Convert axis to array and resolve negatives.\n        if (typeof this.axis === 'number') {\n            this.axis = [this.axis];\n        }\n        for (let i = 0; i < this.axis.length; ++i) {\n            if (this.axis[i] < 0) {\n                this.axis[i] += nDims;\n            }\n        }\n        // Further validate axes.\n        for (const axis of this.axis) {\n            if (axis < 0 || axis >= nDims) {\n                throw new Error(`Invalid axis: ${axis}`);\n            }\n        }\n        if (this.axis.length !== generic_utils.unique(this.axis).length) {\n            throw new Error(`Found duplicate axes in: ${this.axis}`);\n        }\n        const paramShape = this.axis.map(axis => inputShape[axis]);\n        const trainable = true;\n        if (this.scale) {\n            this.gamma = this.addWeight('gamma', paramShape, 'float32', this.gammaInitializer, this.gammaRegularizer, trainable);\n        }\n        else {\n            this.gamma = null;\n        }\n        if (this.center) {\n            this.beta = this.addWeight('beta', paramShape, 'float32', this.betaInitializer, this.betaRegularizer, trainable);\n        }\n        else {\n            this.beta = null;\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        const input = getExactlyOneTensor(inputs);\n        const inputShape = input.shape;\n        const nDims = inputShape.length;\n        return tidy(() => {\n            const keepDims = true;\n            let { mean, variance } = moments(input, this.axis, keepDims);\n            const broadcastShape = generic_utils.pyListRepeat(1, nDims);\n            for (const dim of this.axis) {\n                broadcastShape[dim] = inputShape[dim];\n            }\n            const broadcast = (v) => {\n                if (v != null && v.shape.length !== nDims &&\n                    this.axis !== [nDims - 1]) {\n                    return v.reshape(broadcastShape);\n                }\n                else {\n                    return v;\n                }\n            };\n            let scale = broadcast(this.gamma.read());\n            let offset = broadcast(this.beta.read());\n            // TODO(https://github.com/tensorflow/tfjs/issues/2120): The tiling below\n            // is a workaround for the limitation of core's batchNormalization?d don't\n            // support broadcasting in their gradients. In addition, the tiling is\n            // necessary to ensure correctness on the browser CPU backend regardless\n            // of forward or backward computation. Remove this workaround once the\n            // limitation is addressed. See .\n            const momentsTiling = [];\n            const scaleOffsetTiling = [];\n            for (let i = 0; i < nDims; ++i) {\n                if (this.axis.indexOf(i) !== -1) {\n                    momentsTiling.push(inputShape[i]);\n                    scaleOffsetTiling.push(1);\n                }\n                else {\n                    momentsTiling.push(1);\n                    scaleOffsetTiling.push(inputShape[i]);\n                }\n            }\n            mean = mean.tile(momentsTiling);\n            variance = variance.tile(momentsTiling);\n            scale = scale.tile(scaleOffsetTiling);\n            offset = offset.tile(scaleOffsetTiling);\n            return batchNormalization(input, mean, variance, offset, scale, this.epsilon);\n        });\n    }\n    getConfig() {\n        const config = {\n            axis: this.axis,\n            epsilon: this.epsilon,\n            center: this.center,\n            scale: this.scale,\n            betaInitializer: serializeInitializer(this.betaInitializer),\n            gammaInitializer: serializeInitializer(this.gammaInitializer),\n            betaRegularizer: serializeRegularizer(this.betaRegularizer),\n            gammaRegularizer: serializeRegularizer(this.gammaRegularizer)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nLayerNormalization.className = 'LayerNormalization';\nserialization.registerClass(LayerNormalization);\n//# sourceMappingURL=normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Padding Layers.\n */\n// Porting Note: In Python Keras, the padding layers are in convolutional.py,\n//   but we decided to put them in a separate file (padding.ts) for clarity.\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Pads the middle dimension of a 3D tensor.\n *\n * @param x Input `tf.Tensor` to be padded.\n * @param padding `Array` of 2 integers, how many zeros to add at the start and\n *   end of the middle dimension (i.e., dimension 1).\n * @return A padded 3D `tf.Tensor`.\n */\nexport function temporalPadding(x, padding) {\n    return tidy(() => {\n        if (x.rank !== 3) {\n            throw new ValueError(`temporalPadding expects input tensor to be 3-D, but received a ` +\n                `${x.rank}-D tensor.`);\n        }\n        if (padding == null) {\n            padding = [1, 1];\n        }\n        if (padding.length !== 2) {\n            throw new ValueError(`temporalPadding expects input padding pattern to be a length-2 ` +\n                `array, but received a length-${padding.length} array.`);\n        }\n        const pattern = [[0, 0], padding, [0, 0]];\n        return tfc.pad(x, pattern);\n    });\n}\n/**\n * Pads the 2nd and 3rd dimensions of a 4D tensor.\n *\n * @param x Input `tf.Tensor` to be padded.\n * @param padding `Array` of two `Array`s, each of which is an `Array` of two\n *   integers. The amount of padding at the beginning and end of the 2nd and 3rd\n *   dimensions, respectively.\n * @param dataFormat 'channelsLast' (default) or 'channelsFirst'.\n * @return Padded 4D `tf.Tensor`.\n */\nexport function spatial2dPadding(x, padding, dataFormat) {\n    return tidy(() => {\n        if (x.rank !== 4) {\n            throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ` +\n                `${x.rank}-D tensor.`);\n        }\n        if (padding == null) {\n            padding = [[1, 1], [1, 1]];\n        }\n        if (padding.length !== 2 || padding[0].length !== 2 ||\n            padding[1].length !== 2) {\n            throw new ValueError('spatial2dPadding expects `padding` to be an Array of two Arrays, ' +\n                'each of which is an Array of two integers.');\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (dataFormat !== 'channelsLast' && dataFormat !== 'channelsFirst') {\n            throw new ValueError(`Unknown data format: ${dataFormat}. ` +\n                `Supported data formats are 'channelsLast' and 'channelsFirst.`);\n        }\n        let pattern;\n        if (dataFormat === 'channelsFirst') {\n            pattern = [[0, 0], [0, 0], padding[0], padding[1]];\n        }\n        else {\n            pattern = [[0, 0], padding[0], padding[1], [0, 0]];\n        }\n        return tfc.pad(x, pattern);\n    });\n}\nexport class ZeroPadding2D extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.dataFormat =\n            args.dataFormat == null ? imageDataFormat() : args.dataFormat;\n        // TODO(cais): Maybe refactor the following logic surrounding `padding`\n        //   into a helper method.\n        if (args.padding == null) {\n            this.padding = [[1, 1], [1, 1]];\n        }\n        else if (typeof args.padding === 'number') {\n            this.padding =\n                [[args.padding, args.padding], [args.padding, args.padding]];\n        }\n        else {\n            args.padding = args.padding;\n            if (args.padding.length !== 2) {\n                throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but ` +\n                    `received a length-${args.padding.length} array.`);\n            }\n            let heightPadding;\n            let widthPadding;\n            if (typeof args.padding[0] === 'number') {\n                heightPadding = [args.padding[0], args.padding[0]];\n                widthPadding = [args.padding[1], args.padding[1]];\n            }\n            else {\n                args.padding = args.padding;\n                if (args.padding[0].length !== 2) {\n                    throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, ` +\n                        `but received a length-${args.padding[0].length} array.`);\n                }\n                heightPadding = args.padding[0];\n                if (args.padding[1].length !== 2) {\n                    throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, ` +\n                        `but received a length-${args.padding[1].length} array.`);\n                }\n                widthPadding = args.padding[1];\n            }\n            this.padding = [heightPadding, widthPadding];\n        }\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let rows;\n        let cols;\n        if (this.dataFormat === 'channelsFirst') {\n            if (inputShape[2] != null && inputShape[2] >= 0) {\n                rows = inputShape[2] + this.padding[0][0] + this.padding[0][1];\n            }\n            else {\n                rows = null;\n            }\n            if (inputShape[3] != null && inputShape[3] >= 0) {\n                cols = inputShape[3] + this.padding[1][0] + this.padding[1][1];\n            }\n            else {\n                cols = null;\n            }\n            return [inputShape[0], inputShape[1], rows, cols];\n        }\n        else {\n            if (inputShape[1] != null && inputShape[1] >= 0) {\n                rows = inputShape[1] + this.padding[0][0] + this.padding[0][1];\n            }\n            else {\n                rows = null;\n            }\n            if (inputShape[2] != null && inputShape[2] >= 0) {\n                cols = inputShape[2] + this.padding[1][0] + this.padding[1][1];\n            }\n            else {\n                cols = null;\n            }\n            return [inputShape[0], rows, cols, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => spatial2dPadding(getExactlyOneTensor(inputs), this.padding, this.dataFormat));\n    }\n    getConfig() {\n        const config = {\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nZeroPadding2D.className = 'ZeroPadding2D';\nserialization.registerClass(ZeroPadding2D);\n//# sourceMappingURL=padding.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Pooling Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode, checkPoolMode } from '../common';\nimport { InputSpec } from '../engine/topology';\nimport { Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { convOutputLength } from '../utils/conv_utils';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { preprocessConv2DInput, preprocessConv3DInput } from './convolutional';\n/**\n * 2D pooling.\n * @param x\n * @param poolSize\n * @param stridesdes strides. Defaults to [1, 1].\n * @param padding padding. Defaults to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param poolMode Mode of pooling. Defaults to 'max'.\n * @returns Result of the 2D pooling.\n */\nexport function pool2d(x, poolSize, strides, padding, dataFormat, poolMode) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        checkPoolMode(poolMode);\n        checkPaddingMode(padding);\n        if (strides == null) {\n            strides = [1, 1];\n        }\n        if (padding == null) {\n            padding = 'valid';\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (poolMode == null) {\n            poolMode = 'max';\n        }\n        // TODO(cais): Remove the preprocessing step once deeplearn.js supports\n        // dataFormat as an input argument.\n        x = preprocessConv2DInput(x, dataFormat); // x is NHWC after preprocessing.\n        let y;\n        const paddingString = (padding === 'same') ? 'same' : 'valid';\n        if (poolMode === 'max') {\n            // TODO(cais): Rank check?\n            y = tfc.maxPool(x, poolSize, strides, paddingString);\n        }\n        else { // 'avg'\n            // TODO(cais): Check the dtype and rank of x and give clear error message\n            //   if those are incorrect.\n            y = tfc.avgPool(\n            // TODO(cais): Rank check?\n            x, poolSize, strides, paddingString);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]); // NHWC -> NCHW.\n        }\n        return y;\n    });\n}\n/**\n * 3D pooling.\n * @param x\n * @param poolSize. Default to [1, 1, 1].\n * @param strides strides. Defaults to [1, 1, 1].\n * @param padding padding. Defaults to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param poolMode Mode of pooling. Defaults to 'max'.\n * @returns Result of the 3D pooling.\n */\nexport function pool3d(x, poolSize, strides, padding, dataFormat, poolMode) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        checkPoolMode(poolMode);\n        checkPaddingMode(padding);\n        if (strides == null) {\n            strides = [1, 1, 1];\n        }\n        if (padding == null) {\n            padding = 'valid';\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (poolMode == null) {\n            poolMode = 'max';\n        }\n        // x is NDHWC after preprocessing.\n        x = preprocessConv3DInput(x, dataFormat);\n        let y;\n        const paddingString = (padding === 'same') ? 'same' : 'valid';\n        if (poolMode === 'max') {\n            y = tfc.maxPool3d(x, poolSize, strides, paddingString);\n        }\n        else { // 'avg'\n            y = tfc.avgPool3d(x, poolSize, strides, paddingString);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 4, 1, 2, 3]); // NDHWC -> NCDHW.\n        }\n        return y;\n    });\n}\n/**\n * Abstract class for different pooling 1D layers.\n */\nexport class Pooling1D extends Layer {\n    /**\n     *\n     * @param args Parameters for the Pooling layer.\n     *\n     * config.poolSize defaults to 2.\n     */\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = 2;\n        }\n        super(args);\n        if (typeof args.poolSize === 'number') {\n            this.poolSize = [args.poolSize];\n        }\n        else if (Array.isArray(args.poolSize) &&\n            args.poolSize.length === 1 &&\n            typeof args.poolSize[0] === 'number') {\n            this.poolSize = args.poolSize;\n        }\n        else {\n            throw new ValueError(`poolSize for 1D convolutional layer must be a number or an ` +\n                `Array of a single number, but received ` +\n                `${JSON.stringify(args.poolSize)}`);\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else {\n            if (typeof args.strides === 'number') {\n                this.strides = [args.strides];\n            }\n            else if (Array.isArray(args.strides) &&\n                args.strides.length === 1 &&\n                typeof args.strides[0] === 'number') {\n                this.strides = args.strides;\n            }\n            else {\n                throw new ValueError(`strides for 1D convolutional layer must be a number or an ` +\n                    `Array of a single number, but received ` +\n                    `${JSON.stringify(args.strides)}`);\n            }\n        }\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const length = convOutputLength(inputShape[1], this.poolSize[0], this.padding, this.strides[0]);\n        return [inputShape[0], length, inputShape[2]];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Add dummy last dimension.\n            inputs = K.expandDims(getExactlyOneTensor(inputs), 2);\n            const output = this.poolingFunction(getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, 'channelsLast');\n            // Remove dummy last dimension.\n            return tfc.squeeze(output, [2]);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling1D extends Pooling1D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling1D.className = 'MaxPooling1D';\nserialization.registerClass(MaxPooling1D);\nexport class AveragePooling1D extends Pooling1D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling1D.className = 'AveragePooling1D';\nserialization.registerClass(AveragePooling1D);\n/**\n * Abstract class for different pooling 2D layers.\n */\nexport class Pooling2D extends Layer {\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = [2, 2];\n        }\n        super(args);\n        this.poolSize = Array.isArray(args.poolSize) ?\n            args.poolSize :\n            [args.poolSize, args.poolSize];\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else if (Array.isArray(args.strides)) {\n            if (args.strides.length !== 2) {\n                throw new ValueError(`If the strides property of a 2D pooling layer is an Array, ` +\n                    `it is expected to have a length of 2, but received length ` +\n                    `${args.strides.length}.`);\n            }\n            this.strides = args.strides;\n        }\n        else {\n            // `config.strides` is a number.\n            this.strides = [args.strides, args.strides];\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let rows = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        let cols = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        rows =\n            convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]);\n        cols =\n            convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], inputShape[1], rows, cols];\n        }\n        else {\n            return [inputShape[0], rows, cols, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n            dataFormat: this.dataFormat\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling2D extends Pooling2D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling2D.className = 'MaxPooling2D';\nserialization.registerClass(MaxPooling2D);\nexport class AveragePooling2D extends Pooling2D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling2D.className = 'AveragePooling2D';\nserialization.registerClass(AveragePooling2D);\n/**\n * Abstract class for different pooling 3D layers.\n */\nexport class Pooling3D extends Layer {\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = [2, 2, 2];\n        }\n        super(args);\n        this.poolSize = Array.isArray(args.poolSize) ?\n            args.poolSize :\n            [args.poolSize, args.poolSize, args.poolSize];\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else if (Array.isArray(args.strides)) {\n            if (args.strides.length !== 3) {\n                throw new ValueError(`If the strides property of a 3D pooling layer is an Array, ` +\n                    `it is expected to have a length of 3, but received length ` +\n                    `${args.strides.length}.`);\n            }\n            this.strides = args.strides;\n        }\n        else {\n            // `config.strides` is a number.\n            this.strides = [args.strides, args.strides, args.strides];\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 5 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let depths = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        let rows = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        let cols = this.dataFormat === 'channelsFirst' ? inputShape[4] : inputShape[3];\n        depths = convOutputLength(depths, this.poolSize[0], this.padding, this.strides[0]);\n        rows =\n            convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]);\n        cols =\n            convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], inputShape[1], depths, rows, cols];\n        }\n        else {\n            return [inputShape[0], depths, rows, cols, inputShape[4]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n            dataFormat: this.dataFormat\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling3D extends Pooling3D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool3d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling3D.className = 'MaxPooling3D';\nserialization.registerClass(MaxPooling3D);\nexport class AveragePooling3D extends Pooling3D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool3d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling3D.className = 'AveragePooling3D';\nserialization.registerClass(AveragePooling3D);\n/**\n * Abstract class for different global pooling 1D layers.\n */\nexport class GlobalPooling1D extends Layer {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n    }\n    computeOutputShape(inputShape) {\n        return [inputShape[0], inputShape[2]];\n    }\n    call(inputs, kwargs) {\n        throw new NotImplementedError();\n    }\n}\nexport class GlobalAveragePooling1D extends GlobalPooling1D {\n    constructor(args) {\n        super(args || {});\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            return tfc.mean(input, 1);\n        });\n    }\n}\n/** @nocollapse */\nGlobalAveragePooling1D.className = 'GlobalAveragePooling1D';\nserialization.registerClass(GlobalAveragePooling1D);\nexport class GlobalMaxPooling1D extends GlobalPooling1D {\n    constructor(args) {\n        super(args || {});\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            return tfc.max(input, 1);\n        });\n    }\n}\n/** @nocollapse */\nGlobalMaxPooling1D.className = 'GlobalMaxPooling1D';\nserialization.registerClass(GlobalMaxPooling1D);\n/**\n * Abstract class for different global pooling 2D layers.\n */\nexport class GlobalPooling2D extends Layer {\n    constructor(args) {\n        super(args);\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = inputShape;\n        if (this.dataFormat === 'channelsLast') {\n            return [inputShape[0], inputShape[3]];\n        }\n        else {\n            return [inputShape[0], inputShape[1]];\n        }\n    }\n    call(inputs, kwargs) {\n        throw new NotImplementedError();\n    }\n    getConfig() {\n        const config = { dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class GlobalAveragePooling2D extends GlobalPooling2D {\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                return tfc.mean(input, [1, 2]);\n            }\n            else {\n                return tfc.mean(input, [2, 3]);\n            }\n        });\n    }\n}\n/** @nocollapse */\nGlobalAveragePooling2D.className = 'GlobalAveragePooling2D';\nserialization.registerClass(GlobalAveragePooling2D);\nexport class GlobalMaxPooling2D extends GlobalPooling2D {\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                return tfc.max(input, [1, 2]);\n            }\n            else {\n                return tfc.max(input, [2, 3]);\n            }\n        });\n    }\n}\n/** @nocollapse */\nGlobalMaxPooling2D.className = 'GlobalMaxPooling2D';\nserialization.registerClass(GlobalMaxPooling2D);\n//# sourceMappingURL=pooling.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Layers that augment the functionality of a base layer.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { InputSpec, Layer, SymbolicTensor } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { VALID_BIDIRECTIONAL_MERGE_MODES } from '../keras_format/common';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { rnn, standardizeArgs } from './recurrent';\nimport { deserialize } from './serialization';\n/**\n * Abstract wrapper base class.\n *\n * Wrappers take another layer and augment it in various ways.\n * Do not use this class as a layer, it is only an abstract base class.\n * Two usable wrappers are the `TimeDistributed` and `Bidirectional` wrappers.\n */\nexport class Wrapper extends Layer {\n    constructor(args) {\n        // Porting Note: In PyKeras, `self.layer` is set prior to the calling\n        //   `super()`. But we can't do that here due to TypeScript's restriction.\n        //   See: https://github.com/Microsoft/TypeScript/issues/8277\n        //   As a result, we have to add checks in `get trainable()` and\n        //   `set trainable()` below in order to prevent using `this.layer` when\n        //   its value is `undefined`. The super constructor does use the getter\n        //   and the setter of `this.layer`.\n        super(args);\n        this.layer = args.layer;\n    }\n    build(inputShape) {\n        this.built = true;\n    }\n    // TODO(cais): Implement activityRegularizer getter.\n    get trainable() {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        if (this.layer != null) {\n            return this.layer.trainable;\n        }\n        else {\n            return false;\n        }\n    }\n    set trainable(value) {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        if (this.layer != null) {\n            this.layer.trainable = value;\n        }\n    }\n    get trainableWeights() {\n        return this.layer.trainableWeights;\n    }\n    // TODO(cais): Implement setter for trainableWeights.\n    get nonTrainableWeights() {\n        return this.layer.nonTrainableWeights;\n    }\n    // TODO(cais): Implement setter for nonTrainableWeights.\n    get updates() {\n        // tslint:disable-next-line:no-any\n        return this.layer._updates;\n    }\n    // TODO(cais): Implement getUpdatesFor().\n    get losses() {\n        return this.layer.losses;\n    }\n    // TODO(cais): Implement getLossesFor().\n    getWeights() {\n        return this.layer.getWeights();\n    }\n    setWeights(weights) {\n        this.layer.setWeights(weights);\n    }\n    getConfig() {\n        const config = {\n            'layer': {\n                'className': this.layer.getClassName(),\n                'config': this.layer.getConfig(),\n            }\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.layer != null) {\n            this.layer.setFastWeightInitDuringBuild(value);\n        }\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const layerConfig = config['layer'];\n        const layer = deserialize(layerConfig, customObjects);\n        delete config['layer'];\n        const newConfig = { layer };\n        Object.assign(newConfig, config);\n        return new cls(newConfig);\n    }\n}\nexport class TimeDistributed extends Wrapper {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < 3) {\n            throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received ` +\n                `input shape ${JSON.stringify(inputShape)}`);\n        }\n        this.inputSpec = [{ shape: inputShape }];\n        const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        if (!this.layer.built) {\n            this.layer.build(childInputShape);\n            this.layer.built = true;\n        }\n        super.build(inputShape);\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        const childOutputShape = this.layer.computeOutputShape(childInputShape);\n        const timesteps = inputShape[1];\n        return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1));\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            // TODO(cais): Add 'training' and 'useLearningPhase' to kwargs.\n            inputs = getExactlyOneTensor(inputs);\n            // Porting Note: In tfjs-layers, `inputs` are always concrete tensor\n            // values. Hence the inputs can't have an undetermined first (batch)\n            // dimension, which is why we always use the K.rnn approach here.\n            const step = (inputs, states) => {\n                // TODO(cais): Add useLearningPhase.\n                // NOTE(cais): `layer.call` may return a length-1 array of Tensor in\n                //   some cases (e.g., `layer` is a `Sequential` instance), which is\n                //   why `getExactlyOneTensor` is used below.\n                const output = getExactlyOneTensor(this.layer.call(inputs, kwargs));\n                return [output, []];\n            };\n            const rnnOutputs = rnn(step, inputs, [], false /* goBackwards */, null /* mask */, null /* constants */, false /* unroll */, true /* needPerStepOutputs */);\n            const y = rnnOutputs[1];\n            // TODO(cais): Add activity regularization.\n            // TODO(cais): Add useLearningPhase.\n            return y;\n        });\n    }\n}\n/** @nocollapse */\nTimeDistributed.className = 'TimeDistributed';\nserialization.registerClass(TimeDistributed);\nexport function checkBidirectionalMergeMode(value) {\n    generic_utils.checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, 'BidirectionalMergeMode', value);\n}\nconst DEFAULT_BIDIRECTIONAL_MERGE_MODE = 'concat';\nexport class Bidirectional extends Wrapper {\n    constructor(args) {\n        super(args);\n        // Note: When creating `this.forwardLayer`, the original Layer object\n        //   (`config.layer`) ought to be cloned. This is why we call\n        //   `getConfig()` followed by `deserialize()`. Without this cloning,\n        //   the layer names saved during serialization will incorrectly contain\n        //   the 'forward_' prefix. In Python Keras, this is done using\n        //   `copy.copy` (shallow copy), which does not have a simple equivalent\n        //   in JavaScript. JavaScript's `Object.assign()` does not copy\n        //   methods.\n        const layerConfig = args.layer.getConfig();\n        const forwDict = {};\n        forwDict['className'] = args.layer.getClassName();\n        forwDict['config'] = layerConfig;\n        this.forwardLayer = deserialize(forwDict);\n        layerConfig['goBackwards'] =\n            layerConfig['goBackwards'] === true ? false : true;\n        const backDict = {};\n        backDict['className'] = args.layer.getClassName();\n        backDict['config'] = layerConfig;\n        this.backwardLayer = deserialize(backDict);\n        this.forwardLayer.name = 'forward_' + this.forwardLayer.name;\n        this.backwardLayer.name = 'backward_' + this.backwardLayer.name;\n        this.mergeMode = args.mergeMode === undefined ?\n            DEFAULT_BIDIRECTIONAL_MERGE_MODE :\n            args.mergeMode;\n        checkBidirectionalMergeMode(this.mergeMode);\n        if (args.weights) {\n            throw new NotImplementedError('weights support is not implemented for Bidirectional layer yet.');\n        }\n        this._stateful = args.layer.stateful;\n        this.returnSequences = args.layer.returnSequences;\n        this.returnState = args.layer.returnState;\n        this.supportsMasking = true;\n        this._trainable = true;\n        this.inputSpec = args.layer.inputSpec;\n        this.numConstants = null;\n    }\n    get trainable() {\n        return this._trainable;\n    }\n    set trainable(value) {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        this._trainable = value;\n        if (this.forwardLayer != null) {\n            this.forwardLayer.trainable = value;\n        }\n        if (this.backwardLayer != null) {\n            this.backwardLayer.trainable = value;\n        }\n    }\n    getWeights() {\n        return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights());\n    }\n    setWeights(weights) {\n        const numWeights = weights.length;\n        const numeightsOver2 = Math.floor(numWeights / 2);\n        this.forwardLayer.setWeights(weights.slice(0, numeightsOver2));\n        this.backwardLayer.setWeights(weights.slice(numeightsOver2));\n    }\n    computeOutputShape(inputShape) {\n        let layerShapes = this.forwardLayer.computeOutputShape(inputShape);\n        if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) {\n            layerShapes = [layerShapes];\n        }\n        layerShapes = layerShapes;\n        let outputShape;\n        let outputShapes;\n        let stateShape;\n        if (this.returnState) {\n            stateShape = layerShapes.slice(1);\n            outputShape = layerShapes[0];\n        }\n        else {\n            outputShape = layerShapes[0];\n        }\n        outputShape = outputShape;\n        if (this.mergeMode === 'concat') {\n            outputShape[outputShape.length - 1] *= 2;\n            outputShapes = [outputShape];\n        }\n        else if (this.mergeMode == null) {\n            outputShapes = [outputShape, outputShape.slice()];\n        }\n        else {\n            outputShapes = [outputShape];\n        }\n        if (this.returnState) {\n            if (this.mergeMode == null) {\n                return outputShapes.concat(stateShape).concat(stateShape.slice());\n            }\n            return [outputShape].concat(stateShape).concat(stateShape.slice());\n        }\n        return generic_utils.singletonOrArray(outputShapes);\n    }\n    apply(inputs, kwargs) {\n        let initialState = kwargs == null ? null : kwargs['initialState'];\n        let constants = kwargs == null ? null : kwargs['constants'];\n        if (kwargs == null) {\n            kwargs = {};\n        }\n        const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n        inputs = standardized.inputs;\n        initialState = standardized.initialState;\n        constants = standardized.constants;\n        if (Array.isArray(inputs)) {\n            initialState = inputs.slice(1);\n            inputs = inputs[0];\n        }\n        if ((initialState == null || initialState.length === 0) &&\n            constants == null) {\n            return super.apply(inputs, kwargs);\n        }\n        const additionalInputs = [];\n        const additionalSpecs = [];\n        if (initialState != null) {\n            const numStates = initialState.length;\n            if (numStates % 2 > 0) {\n                throw new ValueError('When passing `initialState` to a Bidrectional RNN, ' +\n                    'the state should be an Array containing the states of ' +\n                    'the underlying RNNs.');\n            }\n            kwargs['initialState'] = initialState;\n            additionalInputs.push(...initialState);\n            const stateSpecs = initialState\n                .map(state => new InputSpec({ shape: state.shape }));\n            this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2);\n            this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2);\n            additionalSpecs.push(...stateSpecs);\n        }\n        if (constants != null) {\n            throw new NotImplementedError('Support for constants in Bidirectional layers is not ' +\n                'implemented yet.');\n        }\n        const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor;\n        for (const tensor of additionalInputs) {\n            if (tensor instanceof SymbolicTensor !== isSymbolicTensor) {\n                throw new ValueError('The initial state of a Bidirectional layer cannot be ' +\n                    'specified as a mix of symbolic and non-symbolic tensors');\n            }\n        }\n        if (isSymbolicTensor) {\n            // Compute the full input and specs, including the states.\n            const fullInput = [inputs].concat(additionalInputs);\n            const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n            // Perform the call temporarily and replace inputSpec.\n            // Note: with initial states symbolic calls and non-symbolic calls to\n            // this method differ in how the initial states are passed. For\n            // symbolic calls, the initial states are passed in the first arg, as\n            // an Array of SymbolicTensors; for non-symbolic calls, they are\n            // passed in the second arg as a part of the kwargs. Hence the need to\n            // temporarily modify inputSpec here.\n            // TODO(cais): Make refactoring so that this hacky code below is no\n            // longer needed.\n            const originalInputSpec = this.inputSpec;\n            this.inputSpec = fullInputSpec;\n            const output = super.apply(fullInput, kwargs);\n            this.inputSpec = originalInputSpec;\n            return output;\n        }\n        else {\n            return super.apply(inputs, kwargs);\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const initialState = kwargs['initialState'];\n            let y;\n            let yRev;\n            if (initialState == null) {\n                y = this.forwardLayer.call(inputs, kwargs);\n                yRev = this.backwardLayer.call(inputs, kwargs);\n            }\n            else {\n                const forwardState = initialState.slice(0, initialState.length / 2);\n                const backwardState = initialState.slice(initialState.length / 2);\n                y = this.forwardLayer.call(inputs, Object.assign(kwargs, { initialState: forwardState }));\n                yRev = this.backwardLayer.call(inputs, Object.assign(kwargs, { initialState: backwardState }));\n            }\n            let states;\n            if (this.returnState) {\n                if (Array.isArray(y)) {\n                    states = y.slice(1).concat(yRev.slice(1));\n                }\n                else {\n                }\n                y = y[0];\n                yRev = yRev[0];\n            }\n            if (this.returnSequences) {\n                yRev = tfc.reverse(yRev, 1);\n            }\n            let output;\n            if (this.mergeMode === 'concat') {\n                output = K.concatenate([y, yRev]);\n            }\n            else if (this.mergeMode === 'sum') {\n                output = tfc.add(y, yRev);\n            }\n            else if (this.mergeMode === 'ave') {\n                output = tfc.mul(.5, tfc.add(y, yRev));\n            }\n            else if (this.mergeMode === 'mul') {\n                output = tfc.mul(y, yRev);\n            }\n            else if (this.mergeMode == null) {\n                output = [y, yRev];\n            }\n            // TODO(cais): Properly set learning phase.\n            if (this.returnState) {\n                if (this.mergeMode == null) {\n                    return output.concat(states);\n                }\n                return [output].concat(states);\n            }\n            return output;\n        });\n    }\n    resetStates(states) {\n        this.forwardLayer.resetStates();\n        this.backwardLayer.resetStates();\n    }\n    build(inputShape) {\n        nameScope(this.forwardLayer.name, () => {\n            this.forwardLayer.build(inputShape);\n        });\n        nameScope(this.backwardLayer.name, () => {\n            this.backwardLayer.build(inputShape);\n        });\n        this.built = true;\n    }\n    computeMask(inputs, mask) {\n        if (Array.isArray(mask)) {\n            mask = mask[0];\n        }\n        let outputMask;\n        if (this.returnSequences) {\n            if (this.mergeMode == null) {\n                outputMask = [mask, mask];\n            }\n            else {\n                outputMask = mask;\n            }\n        }\n        else {\n            if (this.mergeMode == null) {\n                outputMask = [null, null];\n            }\n            else {\n                outputMask = null;\n            }\n        }\n        if (this.returnState) {\n            const states = this.forwardLayer.states;\n            const stateMask = states.map(state => null);\n            if (Array.isArray(outputMask)) {\n                return outputMask.concat(stateMask).concat(stateMask);\n            }\n            else {\n                return [outputMask].concat(stateMask).concat(stateMask);\n            }\n        }\n        else {\n            return outputMask;\n        }\n    }\n    get trainableWeights() {\n        return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights);\n    }\n    get nonTrainableWeights() {\n        return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights);\n    }\n    // TODO(cais): Implement constraints().\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.forwardLayer != null) {\n            this.forwardLayer.setFastWeightInitDuringBuild(value);\n        }\n        if (this.backwardLayer != null) {\n            this.backwardLayer.setFastWeightInitDuringBuild(value);\n        }\n    }\n    getConfig() {\n        const config = {\n            'mergeMode': this.mergeMode,\n        };\n        // TODO(cais): Add logic for `numConstants` once the property is added.\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        const rnnLayer = deserialize(config['layer']);\n        delete config['layer'];\n        // TODO(cais): Add logic for `numConstants` once the property is added.\n        if (config['numConstants'] != null) {\n            throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants ` +\n                `present is not supported yet.`);\n        }\n        // tslint:disable-next-line:no-any\n        const newConfig = config;\n        newConfig['layer'] = rnnLayer;\n        return new cls(newConfig);\n    }\n}\n/** @nocollapse */\nBidirectional.className = 'Bidirectional';\nserialization.registerClass(Bidirectional);\n//# sourceMappingURL=wrappers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { InputLayer } from './engine/input_layer';\nimport { Layer } from './engine/topology';\nimport { input } from './exports';\nimport { ELU, LeakyReLU, PReLU, ReLU, Softmax, ThresholdedReLU } from './layers/advanced_activations';\nimport { Conv1D, Conv2D, Conv2DTranspose, Conv3D, Cropping2D, SeparableConv2D, UpSampling2D } from './layers/convolutional';\nimport { DepthwiseConv2D } from './layers/convolutional_depthwise';\nimport { ConvLSTM2D, ConvLSTM2DCell } from './layers/convolutional_recurrent';\nimport { Activation, Dense, Dropout, Flatten, Masking, Permute, RepeatVector, Reshape, SpatialDropout1D } from './layers/core';\nimport { Embedding } from './layers/embeddings';\nimport { Add, Average, Concatenate, Dot, Maximum, Minimum, Multiply } from './layers/merge';\nimport { AlphaDropout, GaussianDropout, GaussianNoise } from './layers/noise';\nimport { BatchNormalization, LayerNormalization } from './layers/normalization';\nimport { ZeroPadding2D } from './layers/padding';\nimport { AveragePooling1D, AveragePooling2D, AveragePooling3D, GlobalAveragePooling1D, GlobalAveragePooling2D, GlobalMaxPooling1D, GlobalMaxPooling2D, MaxPooling1D, MaxPooling2D, MaxPooling3D } from './layers/pooling';\nimport { GRU, GRUCell, LSTM, LSTMCell, RNN, RNNCell, SimpleRNN, SimpleRNNCell, StackedRNNCells } from './layers/recurrent';\nimport { Bidirectional, TimeDistributed } from './layers/wrappers';\n// TODO(cais): Add doc string to all the public static functions in this\n//   class; include exectuable JavaScript code snippets where applicable\n//   (b/74074458).\n// Input Layer.\n/**\n * An input layer is an entry point into a `tf.LayersModel`.\n *\n * `InputLayer` is generated automatically for `tf.Sequential`` models by\n * specifying the `inputshape` or `batchInputShape` for the first layer.  It\n * should not be specified explicitly. However, it can be useful sometimes,\n * e.g., when constructing a sequential model from a subset of another\n * sequential model's layers. Like the code snippet below shows.\n *\n * ```js\n * // Define a model which simply adds two inputs.\n * const model1 = tf.sequential();\n * model1.add(tf.layers.dense({inputShape: [4], units: 3, activation: 'relu'}));\n * model1.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));\n * model1.summary();\n * model1.predict(tf.zeros([1, 4])).print();\n *\n * // Construct another model, reusing the second layer of `model1` while\n * // not using the first layer of `model1`. Note that you cannot add the second\n * // layer of `model` directly as the first layer of the new sequential model,\n * // because doing so will lead to an error related to the fact that the layer\n * // is not an input layer. Instead, you need to create an `inputLayer` and add\n * // it to the new sequential model before adding the reused layer.\n * const model2 = tf.sequential();\n * // Use an inputShape that matches the input shape of `model1`'s second\n * // layer.\n * model2.add(tf.layers.inputLayer({inputShape: [3]}));\n * model2.add(model1.layers[1]);\n * model2.summary();\n * model2.predict(tf.zeros([1, 3])).print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Inputs', namespace: 'layers'}\n */\nexport function inputLayer(args) {\n    return new InputLayer(args);\n}\n// Advanced Activation Layers.\n/**\n * Exponetial Linear Unit (ELU).\n *\n * It follows:\n * `f(x) =  alpha * (exp(x) - 1.) for x < 0`,\n * `f(x) = x for x >= 0`.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * References:\n *   - [Fast and Accurate Deep Network Learning by Exponential Linear Units\n * (ELUs)](https://arxiv.org/abs/1511.07289v1)\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function elu(args) {\n    return new ELU(args);\n}\n/**\n * Rectified Linear Unit activation function.\n *\n * Input shape:\n *   Arbitrary. Use the config field `inputShape` (Array of integers, does\n *   not include the sample axis) when using this layer as the first layer\n *   in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function reLU(args) {\n    return new ReLU(args);\n}\n/**\n * Leaky version of a rectified linear unit.\n *\n * It allows a small gradient when the unit is not active:\n * `f(x) = alpha * x for x < 0.`\n * `f(x) = x for x >= 0.`\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function leakyReLU(args) {\n    return new LeakyReLU(args);\n}\n/**\n * Parameterized version of a leaky rectified linear unit.\n *\n * It follows\n * `f(x) = alpha * x for x < 0.`\n * `f(x) = x for x >= 0.`\n * wherein `alpha` is a trainable weight.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function prelu(args) {\n    return new PReLU(args);\n}\n/**\n * Softmax activation layer.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function softmax(args) {\n    return new Softmax(args);\n}\n/**\n * Thresholded Rectified Linear Unit.\n *\n * It follows:\n * `f(x) = x for x > theta`,\n * `f(x) = 0 otherwise`.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * References:\n *   - [Zero-Bias Autoencoders and the Benefits of Co-Adapting\n * Features](http://arxiv.org/abs/1402.3337)\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function thresholdedReLU(args) {\n    return new ThresholdedReLU(args);\n}\n// Convolutional Layers.\n/**\n * 1D convolution layer (e.g., temporal convolution).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input over a single spatial (or temporal) dimension\n * to produce a tensor of outputs.\n *\n * If `use_bias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model, provide an\n * `inputShape` argument `Array` or `null`.\n *\n * For example, `inputShape` would be:\n * - `[10, 128]` for sequences of 10 vectors of 128-dimensional vectors\n * - `[null, 128]` for variable-length sequences of 128-dimensional vectors.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional',  namespace: 'layers'}\n */\nexport function conv1d(args) {\n    return new Conv1D(args);\n}\n/**\n * 2D convolution layer (e.g. spatial convolution over images).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input to produce a tensor of outputs.\n *\n * If `useBias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model,\n * provide the keyword argument `inputShape`\n * (Array of integers, does not include the sample axis),\n * e.g. `inputShape=[128, 128, 3]` for 128x128 RGB pictures\n * in `dataFormat='channelsLast'`.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv2d(args) {\n    return new Conv2D(args);\n}\n/**\n * Transposed convolutional layer (sometimes called Deconvolution).\n *\n * The need for transposed convolutions generally arises\n * from the desire to use a transformation going in the opposite direction of\n * a normal convolution, i.e., from something that has the shape of the output\n * of some convolution to something that has the shape of its input while\n * maintaining a connectivity pattern that is compatible with said\n * convolution.\n *\n * When using this layer as the first layer in a model, provide the\n * configuration `inputShape` (`Array` of integers, does not include the\n * sample axis), e.g., `inputShape: [128, 128, 3]` for 128x128 RGB pictures in\n * `dataFormat: 'channelsLast'`.\n *\n * Input shape:\n *   4D tensor with shape:\n *   `[batch, channels, rows, cols]` if `dataFormat` is `'channelsFirst'`.\n *   or 4D tensor with shape\n *   `[batch, rows, cols, channels]` if `dataFormat` is `'channelsLast`.\n *\n * Output shape:\n *   4D tensor with shape:\n *   `[batch, filters, newRows, newCols]` if `dataFormat` is\n * `'channelsFirst'`. or 4D tensor with shape:\n *   `[batch, newRows, newCols, filters]` if `dataFormat` is `'channelsLast'`.\n *\n * References:\n *   - [A guide to convolution arithmetic for deep\n * learning](https://arxiv.org/abs/1603.07285v1)\n *   - [Deconvolutional\n * Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv2dTranspose(args) {\n    return new Conv2DTranspose(args);\n}\n/**\n * 3D convolution layer (e.g. spatial convolution over volumes).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input to produce a tensor of outputs.\n *\n * If `useBias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model,\n * provide the keyword argument `inputShape`\n * (Array of integers, does not include the sample axis),\n * e.g. `inputShape=[128, 128, 128, 1]` for 128x128x128 grayscale volumes\n * in `dataFormat='channelsLast'`.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv3d(args) {\n    return new Conv3D(args);\n}\n/**\n * Depthwise separable 2D convolution.\n *\n * Separable convolution consists of first performing\n * a depthwise spatial convolution\n * (which acts on each input channel separately)\n * followed by a pointwise convolution which mixes together the resulting\n * output channels. The `depthMultiplier` argument controls how many\n * output channels are generated per input channel in the depthwise step.\n *\n * Intuitively, separable convolutions can be understood as\n * a way to factorize a convolution kernel into two smaller kernels,\n * or as an extreme version of an Inception block.\n *\n * Input shape:\n *   4D tensor with shape:\n *     `[batch, channels, rows, cols]` if data_format='channelsFirst'\n *   or 4D tensor with shape:\n *     `[batch, rows, cols, channels]` if data_format='channelsLast'.\n *\n * Output shape:\n *   4D tensor with shape:\n *     `[batch, filters, newRows, newCols]` if data_format='channelsFirst'\n *   or 4D tensor with shape:\n *     `[batch, newRows, newCols, filters]` if data_format='channelsLast'.\n *     `rows` and `cols` values might have changed due to padding.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function separableConv2d(args) {\n    return new SeparableConv2D(args);\n}\n/**\n * Cropping layer for 2D input (e.g., image).\n *\n * This layer can crop an input\n * at the top, bottom, left and right side of an image tensor.\n *\n * Input shape:\n *   4D tensor with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, rows, cols, channels]`\n *   - If `data_format` is `\"channels_first\"`:\n *     `[batch, channels, rows, cols]`.\n *\n * Output shape:\n *   4D with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, croppedRows, croppedCols, channels]`\n *    - If `dataFormat` is `\"channelsFirst\"`:\n *     `[batch, channels, croppedRows, croppedCols]`.\n *\n * Examples\n * ```js\n *\n * const model = tf.sequential();\n * model.add(tf.layers.cropping2D({cropping:[[2, 2], [2, 2]],\n *                                inputShape: [128, 128, 3]}));\n * //now output shape is [batch, 124, 124, 3]\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function cropping2D(args) {\n    return new Cropping2D(args);\n}\n/**\n * Upsampling layer for 2D inputs.\n *\n * Repeats the rows and columns of the data\n * by size[0] and size[1] respectively.\n *\n *\n * Input shape:\n *    4D tensor with shape:\n *     - If `dataFormat` is `\"channelsLast\"`:\n *         `[batch, rows, cols, channels]`\n *     - If `dataFormat` is `\"channelsFirst\"`:\n *        `[batch, channels, rows, cols]`\n *\n * Output shape:\n *     4D tensor with shape:\n *     - If `dataFormat` is `\"channelsLast\"`:\n *        `[batch, upsampledRows, upsampledCols, channels]`\n *     - If `dataFormat` is `\"channelsFirst\"`:\n *         `[batch, channels, upsampledRows, upsampledCols]`\n *\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function upSampling2d(args) {\n    return new UpSampling2D(args);\n}\n// Convolutional(depthwise) Layers.\n/**\n * Depthwise separable 2D convolution.\n *\n * Depthwise Separable convolutions consists in performing just the first step\n * in a depthwise spatial convolution (which acts on each input channel\n * separately). The `depthMultplier` argument controls how many output channels\n * are generated per input channel in the depthwise step.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function depthwiseConv2d(args) {\n    return new DepthwiseConv2D(args);\n}\n// Basic Layers.\n/**\n * Applies an activation function to an output.\n *\n * This layer applies element-wise activation function.  Other layers, notably\n * `dense` can also apply activation functions.  Use this isolated activation\n * function to extract the values before and after the\n * activation. For instance:\n *\n * ```js\n * const input = tf.input({shape: [5]});\n * const denseLayer = tf.layers.dense({units: 1});\n * const activationLayer = tf.layers.activation({activation: 'relu6'});\n *\n * // Obtain the output symbolic tensors by applying the layers in order.\n * const denseOutput = denseLayer.apply(input);\n * const activationOutput = activationLayer.apply(denseOutput);\n *\n * // Create the model based on the inputs.\n * const model = tf.model({\n *     inputs: input,\n *     outputs: [denseOutput, activationOutput]\n * });\n *\n * // Collect both outputs and print separately.\n * const [denseOut, activationOut] = model.predict(tf.randomNormal([6, 5]));\n * denseOut.print();\n * activationOut.print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function activation(args) {\n    return new Activation(args);\n}\n/**\n * Creates a dense (fully connected) layer.\n *\n * This layer implements the operation:\n *   `output = activation(dot(input, kernel) + bias)`\n *\n * `activation` is the element-wise activation function\n *   passed as the `activation` argument.\n *\n * `kernel` is a weights matrix created by the layer.\n *\n * `bias` is a bias vector created by the layer (only applicable if `useBias`\n * is `true`).\n *\n * **Input shape:**\n *\n *   nD `tf.Tensor` with shape: `(batchSize, ..., inputDim)`.\n *\n *   The most common situation would be\n *   a 2D input with shape `(batchSize, inputDim)`.\n *\n * **Output shape:**\n *\n *   nD tensor with shape: `(batchSize, ..., units)`.\n *\n *   For instance, for a 2D input with shape `(batchSize, inputDim)`,\n *   the output would have shape `(batchSize, units)`.\n *\n * Note: if the input to the layer has a rank greater than 2, then it is\n * flattened prior to the initial dot product with the kernel.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function dense(args) {\n    return new Dense(args);\n}\n/**\n * Applies\n * [dropout](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf) to\n * the input.\n *\n * Dropout consists in randomly setting a fraction `rate` of input units to 0 at\n * each update during training time, which helps prevent overfitting.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function dropout(args) {\n    return new Dropout(args);\n}\n/**\n * Spatial 1D version of Dropout.\n *\n * This Layer type performs the same function as the Dropout layer, but it drops\n * entire 1D feature maps instead of individual elements. For example, if an\n * input example consists of 3 timesteps and the feature map for each timestep\n * has a size of 4, a `spatialDropout1d` layer may zero out the feature maps\n * of the 1st timesteps and 2nd timesteps completely while sparing all feature\n * elements of the 3rd timestep.\n *\n * If adjacent frames (timesteps) are strongly correlated (as is normally the\n * case in early convolution layers), regular dropout will not regularize the\n * activation and will otherwise just result in merely an effective learning\n * rate decrease. In this case, `spatialDropout1d` will help promote\n * independence among feature maps and should be used instead.\n *\n * **Arguments:**\n *   rate: A floating-point number >=0 and <=1. Fraction of the input elements\n *     to drop.\n *\n * **Input shape:**\n *   3D tensor with shape `(samples, timesteps, channels)`.\n *\n * **Output shape:**\n *   Same as the input shape.\n *\n * References:\n *   - [Efficient Object Localization Using Convolutional\n *      Networks](https://arxiv.org/abs/1411.4280)\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function spatialDropout1d(args) {\n    return new SpatialDropout1D(args);\n}\n/**\n * Flattens the input. Does not affect the batch size.\n *\n * A `Flatten` layer flattens each batch in its inputs to 1D (making the output\n * 2D).\n *\n * For example:\n *\n * ```js\n * const input = tf.input({shape: [4, 3]});\n * const flattenLayer = tf.layers.flatten();\n * // Inspect the inferred output shape of the flatten layer, which\n * // equals `[null, 12]`. The 2nd dimension is 4 * 3, i.e., the result of the\n * // flattening. (The 1st dimension is the undermined batch size.)\n * console.log(JSON.stringify(flattenLayer.apply(input).shape));\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function flatten(args) {\n    return new Flatten(args);\n}\n/**\n * Repeats the input n times in a new dimension.\n *\n * ```js\n *  const model = tf.sequential();\n *  model.add(tf.layers.repeatVector({n: 4, inputShape: [2]}));\n *  const x = tf.tensor2d([[10, 20]]);\n *  // Use the model to do inference on a data point the model hasn't see\n *  model.predict(x).print();\n *  // output shape is now [batch, 2, 4]\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function repeatVector(args) {\n    return new RepeatVector(args);\n}\n/**\n * Reshapes an input to a certain shape.\n *\n * ```js\n * const input = tf.input({shape: [4, 3]});\n * const reshapeLayer = tf.layers.reshape({targetShape: [2, 6]});\n * // Inspect the inferred output shape of the Reshape layer, which\n * // equals `[null, 2, 6]`. (The 1st dimension is the undermined batch size.)\n * console.log(JSON.stringify(reshapeLayer.apply(input).shape));\n * ```\n *\n * Input shape:\n *   Arbitrary, although all dimensions in the input shape must be fixed.\n *   Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n *\n * Output shape:\n *   [batchSize, targetShape[0], targetShape[1], ...,\n *    targetShape[targetShape.length - 1]].\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function reshape(args) {\n    return new Reshape(args);\n}\n/**\n * Permutes the dimensions of the input according to a given pattern.\n *\n * Useful for, e.g., connecting RNNs and convnets together.\n *\n * Example:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.permute({\n *   dims: [2, 1],\n *   inputShape: [10, 64]\n * }));\n * console.log(model.outputShape);\n * // Now model's output shape is [null, 64, 10], where null is the\n * // unpermuted sample (batch) dimension.\n * ```\n *\n * Input shape:\n *   Arbitrary. Use the configuration field `inputShape` when using this\n *   layer as the first layer in a model.\n *\n * Output shape:\n *   Same rank as the input shape, but with the dimensions re-ordered (i.e.,\n *   permuted) according to the `dims` configuration of this layer.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function permute(args) {\n    return new Permute(args);\n}\n/**\n * Maps positive integers (indices) into dense vectors of fixed size.\n * eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]\n *\n * **Input shape:** 2D tensor with shape: `[batchSize, sequenceLength]`.\n *\n * **Output shape:** 3D tensor with shape: `[batchSize, sequenceLength,\n * outputDim]`.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function embedding(args) {\n    return new Embedding(args);\n}\n// Merge Layers.\n/**\n * Layer that performs element-wise addition on an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape, and returns a\n * single tensor (also of the same shape). The inputs are specified as an\n * `Array` when the `apply` method of the `Add` layer instance is called. For\n * example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const addLayer = tf.layers.add();\n * const sum = addLayer.apply([input1, input2]);\n * console.log(JSON.stringify(sum.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function add(args) {\n    return new Add(args);\n}\n/**\n * Layer that performs element-wise averaging on an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape, and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const averageLayer = tf.layers.average();\n * const average = averageLayer.apply([input1, input2]);\n * console.log(JSON.stringify(average.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function average(args) {\n    return new Average(args);\n}\n/**\n * Layer that concatenates an `Array` of inputs.\n *\n * It takes a list of tensors, all of the same shape except for the\n * concatenation axis, and returns a single tensor, the concatenation\n * of all inputs. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 3]});\n * const concatLayer = tf.layers.concatenate();\n * const output = concatLayer.apply([input1, input2]);\n * console.log(JSON.stringify(output.shape));\n * // You get [null, 2, 5], with the first dimension as the undetermined batch\n * // dimension. The last dimension (5) is the result of concatenating the\n * // last dimensions of the inputs (2 and 3).\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function concatenate(args) {\n    return new Concatenate(args);\n}\n/**\n * Layer that computes the element-wise maximum an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const maxLayer = tf.layers.maximum();\n * const max = maxLayer.apply([input1, input2]);\n * console.log(JSON.stringify(max.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function maximum(args) {\n    return new Maximum(args);\n}\n/**\n * Layer that computes the element-wise minimum of an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const minLayer = tf.layers.minimum();\n * const min = minLayer.apply([input1, input2]);\n * console.log(JSON.stringify(min.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function minimum(args) {\n    return new Minimum(args);\n}\n/**\n * Layer that multiplies (element-wise) an `Array` of inputs.\n *\n * It takes as input an Array of tensors, all of the same\n * shape, and returns a single tensor (also of the same shape).\n * For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const input3 = tf.input({shape: [2, 2]});\n * const multiplyLayer = tf.layers.multiply();\n * const product = multiplyLayer.apply([input1, input2, input3]);\n * console.log(product.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function multiply(args) {\n    return new Multiply(args);\n}\n/**\n * Layer that computes a dot product between samples in two tensors.\n *\n * E.g., if applied to a list of two tensors `a` and `b` both of shape\n * `[batchSize, n]`, the output will be a tensor of shape `[batchSize, 1]`,\n * where each entry at index `[i, 0]` will be the dot product between\n * `a[i, :]` and `b[i, :]`.\n *\n * Example:\n *\n * ```js\n * const dotLayer = tf.layers.dot({axes: -1});\n * const x1 = tf.tensor2d([[10, 20], [30, 40]]);\n * const x2 = tf.tensor2d([[-1, -2], [-3, -4]]);\n *\n * // Invoke the layer's apply() method in eager (imperative) mode.\n * const y = dotLayer.apply([x1, x2]);\n * y.print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function dot(args) {\n    return new Dot(args);\n}\n// Normalization Layers.\n/**\n * Batch normalization layer (Ioffe and Szegedy, 2014).\n *\n * Normalize the activations of the previous layer at each batch,\n * i.e. applies a transformation that maintains the mean activation\n * close to 0 and the activation standard deviation close to 1.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape` (Array of integers, does\n *   not include the sample axis) when calling the constructor of this class,\n *   if this layer is used as a first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Batch Normalization: Accelerating Deep Network Training by Reducing\n * Internal Covariate Shift](https://arxiv.org/abs/1502.03167)\n *\n * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}\n */\nexport function batchNormalization(args) {\n    return new BatchNormalization(args);\n}\n/**\n * Layer-normalization layer (Ba et al., 2016).\n *\n * Normalizes the activations of the previous layer for each given example in a\n * batch independently, instead of across a batch like in `batchNormalization`.\n * In other words, this layer applies a transformation that maintanis the mean\n * activation within each example close to0 and activation variance close to 1.\n *\n * Input shape:\n *   Arbitrary. Use the argument `inputShape` when using this layer as the first\n *   layer in a model.\n *\n * Output shape:\n *   Same as input.\n *\n * References:\n *   - [Layer Normalization](https://arxiv.org/abs/1607.06450)\n *\n * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}\n */\nexport function layerNormalization(args) {\n    return new LayerNormalization(args);\n}\n// Padding Layers.\n/**\n * Zero-padding layer for 2D input (e.g., image).\n *\n * This layer can add rows and columns of zeros\n * at the top, bottom, left and right side of an image tensor.\n *\n * Input shape:\n *   4D tensor with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, rows, cols, channels]`\n *   - If `data_format` is `\"channels_first\"`:\n *     `[batch, channels, rows, cols]`.\n *\n * Output shape:\n *   4D with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, paddedRows, paddedCols, channels]`\n *    - If `dataFormat` is `\"channelsFirst\"`:\n *     `[batch, channels, paddedRows, paddedCols]`.\n *\n * @doc {heading: 'Layers', subheading: 'Padding', namespace: 'layers'}\n */\nexport function zeroPadding2d(args) {\n    return new ZeroPadding2D(args);\n}\n// Pooling Layers.\n/**\n * Average pooling operation for spatial data.\n *\n * Input shape: `[batchSize, inLength, channels]`\n *\n * Output shape: `[batchSize, pooledLength, channels]`\n *\n * `tf.avgPool1d` is an alias.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling1d(args) {\n    return new AveragePooling1D(args);\n}\nexport function avgPool1d(args) {\n    return averagePooling1d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling1d(args) {\n    return averagePooling1d(args);\n}\n/**\n * Average pooling operation for spatial data.\n *\n * Input shape:\n *  - If `dataFormat === CHANNEL_LAST`:\n *      4D tensor with shape:\n *      `[batchSize, rows, cols, channels]`\n *  - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *      `[batchSize, channels, rows, cols]`\n *\n * Output shape\n *  - If `dataFormat === CHANNEL_LAST`:\n *      4D tensor with shape:\n *      `[batchSize, pooleRows, pooledCols, channels]`\n *  - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *      `[batchSize, channels, pooleRows, pooledCols]`\n *\n * `tf.avgPool2d` is an alias.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling2d(args) {\n    return new AveragePooling2D(args);\n}\nexport function avgPool2d(args) {\n    return averagePooling2d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling2d(args) {\n    return averagePooling2d(args);\n}\n/**\n * Average pooling operation for 3D data.\n *\n * Input shape\n *   - If `dataFormat === channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, depths, rows, cols, channels]`\n *   - If `dataFormat === channelsFirst`:\n *      4D tensor with shape:\n *       `[batchSize, channels, depths, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`\n *   - If `dataFormat=channelsFirst`:\n *       5D tensor with shape:\n *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling3d(args) {\n    return new AveragePooling3D(args);\n}\nexport function avgPool3d(args) {\n    return averagePooling3d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling3d(args) {\n    return averagePooling3d(args);\n}\n/**\n * Global average pooling operation for temporal data.\n *\n * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.\n *\n * Output Shape:2D tensor with shape: `[batchSize, features]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalAveragePooling1d(args) {\n    return new GlobalAveragePooling1D(args);\n}\n/**\n * Global average pooling operation for spatial data.\n *\n * Input shape:\n *   - If `dataFormat` is `CHANNEL_LAST`:\n *       4D tensor with shape: `[batchSize, rows, cols, channels]`.\n *   - If `dataFormat` is `CHANNEL_FIRST`:\n *       4D tensor with shape: `[batchSize, channels, rows, cols]`.\n *\n * Output shape:\n *   2D tensor with shape: `[batchSize, channels]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalAveragePooling2d(args) {\n    return new GlobalAveragePooling2D(args);\n}\n/**\n * Global max pooling operation for temporal data.\n *\n * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.\n *\n * Output Shape:2D tensor with shape: `[batchSize, features]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalMaxPooling1d(args) {\n    return new GlobalMaxPooling1D(args);\n}\n/**\n * Global max pooling operation for spatial data.\n *\n * Input shape:\n *   - If `dataFormat` is `CHANNEL_LAST`:\n *       4D tensor with shape: `[batchSize, rows, cols, channels]`.\n *   - If `dataFormat` is `CHANNEL_FIRST`:\n *       4D tensor with shape: `[batchSize, channels, rows, cols]`.\n *\n * Output shape:\n *   2D tensor with shape: `[batchSize, channels]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalMaxPooling2d(args) {\n    return new GlobalMaxPooling2D(args);\n}\n/**\n * Max pooling operation for temporal data.\n *\n * Input shape:  `[batchSize, inLength, channels]`\n *\n * Output shape: `[batchSize, pooledLength, channels]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling1d(args) {\n    return new MaxPooling1D(args);\n}\n/**\n * Max pooling operation for spatial data.\n *\n * Input shape\n *   - If `dataFormat === CHANNEL_LAST`:\n *       4D tensor with shape:\n *       `[batchSize, rows, cols, channels]`\n *   - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *       `[batchSize, channels, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=CHANNEL_LAST`:\n *       4D tensor with shape:\n *       `[batchSize, pooleRows, pooledCols, channels]`\n *   - If `dataFormat=CHANNEL_FIRST`:\n *       4D tensor with shape:\n *       `[batchSize, channels, pooleRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling2d(args) {\n    return new MaxPooling2D(args);\n}\n/**\n * Max pooling operation for 3D data.\n *\n * Input shape\n *   - If `dataFormat === channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, depths, rows, cols, channels]`\n *   - If `dataFormat === channelsFirst`:\n *      5D tensor with shape:\n *       `[batchSize, channels, depths, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`\n *   - If `dataFormat=channelsFirst`:\n *       5D tensor with shape:\n *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling3d(args) {\n    return new MaxPooling3D(args);\n}\n// Recurrent Layers.\n/**\n * Gated Recurrent Unit - Cho et al. 2014.\n *\n * This is an `RNN` layer consisting of one `GRUCell`. However, unlike\n * the underlying `GRUCell`, the `apply` method of `SimpleRNN` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const rnn = tf.layers.gru({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `GRUCell`'s number of units.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function gru(args) {\n    return new GRU(args);\n}\n/**\n * Cell class for `GRU`.\n *\n * `GRUCell` is distinct from the `RNN` subclass `GRU` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `GRU` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.gruCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `GRUCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.gruCell({units: 4}),\n *   tf.layers.gruCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `gruCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `GRUCell`, use the\n * `tf.layers.gru`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function gruCell(args) {\n    return new GRUCell(args);\n}\n/**\n * Long-Short Term Memory layer - Hochreiter 1997.\n *\n * This is an `RNN` layer consisting of one `LSTMCell`. However, unlike\n * the underlying `LSTMCell`, the `apply` method of `LSTM` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const lstm = tf.layers.lstm({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = lstm.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `LSTMCell`'s number of units.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function lstm(args) {\n    return new LSTM(args);\n}\n/**\n * Cell class for `LSTM`.\n *\n * `LSTMCell` is distinct from the `RNN` subclass `LSTM` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `LSTM` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.lstmCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `LSTMCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.lstmCell({units: 4}),\n *   tf.layers.lstmCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `lstmCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `LSTMCell`, use the\n * `tf.layers.lstm`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function lstmCell(args) {\n    return new LSTMCell(args);\n}\n/**\n * Fully-connected RNN where the output is to be fed back to input.\n *\n * This is an `RNN` layer consisting of one `SimpleRNNCell`. However, unlike\n * the underlying `SimpleRNNCell`, the `apply` method of `SimpleRNN` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const rnn = tf.layers.simpleRNN({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `SimpleRNNCell`'s number of units.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function simpleRNN(args) {\n    return new SimpleRNN(args);\n}\n/**\n * Cell class for `SimpleRNN`.\n *\n * `SimpleRNNCell` is distinct from the `RNN` subclass `SimpleRNN` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `SimpleRNN` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.simpleRNNCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `SimpleRNNCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.simpleRNNCell({units: 4}),\n *   tf.layers.simpleRNNCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `SimpleRNNCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `SimpleRNNCell`, use the\n * `tf.layers.simpleRNN`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function simpleRNNCell(args) {\n    return new SimpleRNNCell(args);\n}\n/**\n * Convolutional LSTM layer - Xingjian Shi 2015.\n *\n * This is an `ConvRNN2D` layer consisting of one `ConvLSTM2DCell`. However,\n * unlike the underlying `ConvLSTM2DCell`, the `apply` method of `ConvLSTM2D`\n * operates on a sequence of inputs. The shape of the input (not including the\n * first, batch dimension) needs to be 4-D, with the first dimension being time\n * steps. For example:\n *\n * ```js\n * const filters = 3;\n * const kernelSize = 3;\n *\n * const batchSize = 4;\n * const sequenceLength = 2;\n * const size = 5;\n * const channels = 3;\n *\n * const inputShape = [batchSize, sequenceLength, size, size, channels];\n * const input = tf.ones(inputShape);\n *\n * const layer = tf.layers.convLstm2d({filters, kernelSize});\n *\n * const output = layer.apply(input);\n * ```\n */\n/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */\nexport function convLstm2d(args) {\n    return new ConvLSTM2D(args);\n}\n/**\n * Cell class for `ConvLSTM2D`.\n *\n * `ConvLSTM2DCell` is distinct from the `ConvRNN2D` subclass `ConvLSTM2D` in\n * that its `call` method takes the input data of only a single time step and\n * returns the cell's output at the time step, while `ConvLSTM2D` takes the\n * input data over a number of time steps. For example:\n *\n * ```js\n * const filters = 3;\n * const kernelSize = 3;\n *\n * const sequenceLength = 1;\n * const size = 5;\n * const channels = 3;\n *\n * const inputShape = [sequenceLength, size, size, channels];\n * const input = tf.ones(inputShape);\n *\n * const cell = tf.layers.convLstm2dCell({filters, kernelSize});\n *\n * cell.build(input.shape);\n *\n * const outputSize = size - kernelSize + 1;\n * const outShape = [sequenceLength, outputSize, outputSize, filters];\n *\n * const initialH = tf.zeros(outShape);\n * const initialC = tf.zeros(outShape);\n *\n * const [o, h, c] = cell.call([input, initialH, initialC], {});\n * ```\n */\n/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */\nexport function convLstm2dCell(args) {\n    return new ConvLSTM2DCell(args);\n}\n/**\n * Base class for recurrent layers.\n *\n * Input shape:\n *   3D tensor with shape `[batchSize, timeSteps, inputDim]`.\n *\n * Output shape:\n *   - if `returnState`, an Array of tensors (i.e., `tf.Tensor`s). The first\n *     tensor is the output. The remaining tensors are the states at the\n *     last time step, each with shape `[batchSize, units]`.\n *   - if `returnSequences`, the output will have shape\n *     `[batchSize, timeSteps, units]`.\n *   - else, the output will have shape `[batchSize, units]`.\n *\n * Masking:\n *   This layer supports masking for input data with a variable number\n *   of timesteps. To introduce masks to your data,\n *   use an embedding layer with the `mask_zero` parameter\n *   set to `True`.\n *\n * Notes on using statefulness in RNNs:\n *   You can set RNN layers to be 'stateful', which means that the states\n *   computed for the samples in one batch will be reused as initial states\n *   for the samples in the next batch. This assumes a one-to-one mapping\n *   between samples in different successive batches.\n *\n *   To enable statefulness:\n *     - specify `stateful: true` in the layer constructor.\n *     - specify a fixed batch size for your model, by passing\n *       if sequential model:\n *         `batchInputShape=[...]` to the first layer in your model.\n *       else for functional model with 1 or more Input layers:\n *         `batchShape=[...]` to all the first layers in your model.\n *       This is the expected shape of your inputs *including the batch size*.\n *       It should be a tuple of integers, e.g. `(32, 10, 100)`.\n *     - specify `shuffle=False` when calling fit().\n *\n *   To reset the states of your model, call `.resetStates()` on either\n *   a specific layer, or on your entire model.\n *\n * Note on specifying the initial state of RNNs\n *   You can specify the initial state of RNN layers symbolically by\n *   calling them with the option `initialState`. The value of\n *   `initialState` should be a tensor or list of tensors representing\n *   the initial state of the RNN layer.\n *\n *   You can specify the initial state of RNN layers numerically by\n *   calling `resetStates` with the keyword argument `states`. The value of\n *   `states` should be a numpy array or list of numpy arrays representing\n *   the initial state of the RNN layer.\n *\n * Note on passing external constants to RNNs\n *   You can pass \"external\" constants to the cell using the `constants`\n *   keyword argument of `RNN.call` method. This requires that the `cell.call`\n *   method accepts the same keyword argument `constants`. Such constants\n *   can be used to conditon the cell transformation on additional static inputs\n *   (not changing over time), a.k.a an attention mechanism.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function rnn(args) {\n    return new RNN(args);\n}\n/**\n * Wrapper allowing a stack of RNN cells to behave as a single cell.\n *\n * Used to implement efficient stacked RNNs.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function stackedRNNCells(args) {\n    return new StackedRNNCells(args);\n}\n// Wrapper Layers.\n/** @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'} */\nexport function bidirectional(args) {\n    return new Bidirectional(args);\n}\n/**\n * This wrapper applies a layer to every temporal slice of an input.\n *\n * The input should be at least 3D,  and the dimension of the index `1` will be\n * considered to be the temporal dimension.\n *\n * Consider a batch of 32 samples, where each sample is a sequence of 10 vectors\n * of 16 dimensions. The batch input shape of the layer is then `[32,  10,\n * 16]`, and the `inputShape`, not including the sample dimension, is\n * `[10, 16]`.\n *\n * You can then use `TimeDistributed` to apply a `Dense` layer to each of the 10\n * timesteps, independently:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.timeDistributed({\n *   layer: tf.layers.dense({units: 8}),\n *   inputShape: [10, 16],\n * }));\n *\n * // Now model.outputShape = [null, 10, 8].\n * // The output will then have shape `[32, 10, 8]`.\n *\n * // In subsequent layers, there is no need for `inputShape`:\n * model.add(tf.layers.timeDistributed({layer: tf.layers.dense({units: 32})}));\n * console.log(JSON.stringify(model.outputs[0].shape));\n * // Now model.outputShape = [null, 10, 32].\n * ```\n *\n * The output will then have shape `[32, 10, 32]`.\n *\n * `TimeDistributed` can be used with arbitrary layers, not just `Dense`, for\n * instance a `Conv2D` layer.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.timeDistributed({\n *   layer: tf.layers.conv2d({filters: 64, kernelSize: [3, 3]}),\n *   inputShape: [10, 299, 299, 3],\n * }));\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'}\n */\nexport function timeDistributed(args) {\n    return new TimeDistributed(args);\n}\n// Aliases for pooling.\nexport const globalMaxPool1d = globalMaxPooling1d;\nexport const globalMaxPool2d = globalMaxPooling2d;\nexport const maxPool1d = maxPooling1d;\nexport const maxPool2d = maxPooling2d;\nexport { Layer, RNN, RNNCell, input /* alias for tf.input */ };\n/**\n * Apply additive zero-centered Gaussian noise.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * This is useful to mitigate overfitting\n * (you could see it as a form of random data augmentation).\n * Gaussian Noise (GS) is a natural choice as corruption process\n * for real valued inputs.\n *\n * # Arguments\n *     stddev: float, standard deviation of the noise distribution.\n *\n * # Input shape\n *         Arbitrary. Use the keyword argument `input_shape`\n *         (tuple of integers, does not include the samples axis)\n *         when using this layer as the first layer in a model.\n *\n * # Output shape\n *         Same shape as input.\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function gaussianNoise(args) {\n    return new GaussianNoise(args);\n}\n/**\n * Apply multiplicative 1-centered Gaussian noise.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](\n *      http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function gaussianDropout(args) {\n    return new GaussianDropout(args);\n}\n/**\n * Applies Alpha Dropout to the input.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs\n * to their original values, in order to ensure the self-normalizing property\n * even after this dropout.\n * Alpha Dropout fits well to Scaled Exponential Linear Units\n * by randomly setting activations to the negative saturation value.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the\n *     shape for randomly generated keep/drop flags.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function alphaDropout(args) {\n    return new AlphaDropout(args);\n}\n/**\n * Masks a sequence by using a mask value to skip timesteps.\n *\n * If all features for a given sample timestep are equal to `mask_value`,\n * then the sample timestep will be masked (skipped) in all downstream layers\n * (as long as they support masking).\n *\n * If any downstream layer does not support masking yet receives such\n * an input mask, an exception will be raised.\n *\n * Arguments:\n *   - `maskValue`: Either None or mask value to skip.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * @doc {heading: 'Layers', subheading: 'Mask', namespace: 'layers'}\n */\nexport function masking(args) {\n    return new Masking(args);\n}\n//# sourceMappingURL=exports_layers.js.map", "import * as losses from './losses';\nimport * as metrics from './metrics';\n/**\n * Binary accuracy metric function.\n *\n * `yTrue` and `yPred` can have 0-1 values. Example:\n * ```js\n * const x = tf.tensor2d([[1, 1, 1, 1], [0, 0, 0, 0]], [2, 4]);\n * const y = tf.tensor2d([[1, 0, 1, 0], [0, 0, 0, 1]], [2, 4]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * `yTrue` and `yPred` can also have floating-number values between 0 and 1, in\n * which case the values will be thresholded at 0.5 to yield 0-1 values (i.e.,\n * a value >= 0.5 and <= 1.0 is interpreted as 1.\n * )\n * Example:\n * ```js\n * const x = tf.tensor1d([1, 1, 1, 1, 0, 0, 0, 0]);\n * const y = tf.tensor1d([0.2, 0.4, 0.6, 0.8, 0.2, 0.3, 0.4, 0.7]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryAccuracy(yTrue, yPred) {\n    return metrics.binaryAccuracy(yTrue, yPred);\n}\n/**\n * Binary crossentropy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0], [1], [1], [1]]);\n * const y = tf.tensor2d([[0], [0], [0.5], [1]]);\n * const crossentropy = tf.metrics.binaryCrossentropy(x, y);\n * crossentropy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction, probabilities for the `1` case.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryCrossentropy(yTrue, yPred) {\n    return metrics.binaryCrossentropy(yTrue, yPred);\n}\n/**\n * Sparse categorical accuracy metric function.\n *\n * Example:\n * ```js\n *\n * const yTrue = tf.tensor1d([1, 1, 2, 2, 0]);\n * const yPred = tf.tensor2d(\n *      [[0, 1, 0], [1, 0, 0], [0, 0.4, 0.6], [0, 0.6, 0.4], [0.7, 0.3, 0]]);\n * const crossentropy = tf.metrics.sparseCategoricalAccuracy(yTrue, yPred);\n * crossentropy.print();\n * ```\n *\n * @param yTrue True labels: indices.\n * @param yPred Predicted probabilities or logits.\n * @returns Accuracy tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function sparseCategoricalAccuracy(yTrue, yPred) {\n    return metrics.sparseCategoricalAccuracy(yTrue, yPred);\n}\n/**\n * Categorical accuracy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0, 0, 0, 1], [0, 0, 0, 1]]);\n * const y = tf.tensor2d([[0.1, 0.8, 0.05, 0.05], [0.1, 0.05, 0.05, 0.8]]);\n * const accuracy = tf.metrics.categoricalAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth: one-hot encoding of categories.\n * @param yPred Binary Tensor of prediction: probabilities or logits for the\n *   same categories as in `yTrue`.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalAccuracy(yTrue, yPred) {\n    return metrics.categoricalAccuracy(yTrue, yPred);\n}\n/**\n * Categorical crossentropy between an output tensor and a target tensor.\n *\n * @param target A tensor of the same shape as `output`.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalCrossentropy(yTrue, yPred) {\n    return metrics.categoricalCrossentropy(yTrue, yPred);\n}\n/**\n * Computes the precision of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const precision = tf.metrics.precision(x, y);\n * precision.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to be contain only 0-1 values.\n * @param yPred The predicted values. Expected to be contain only 0-1 values.\n * @return Precision Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function precision(yTrue, yPred) {\n    return metrics.precision(yTrue, yPred);\n}\n/**\n * Computes the recall of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const recall = tf.metrics.recall(x, y);\n * recall.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to be contain only 0-1 values.\n * @param yPred The predicted values. Expected to be contain only 0-1 values.\n * @return Recall Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function recall(yTrue, yPred) {\n    return metrics.recall(yTrue, yPred);\n}\n/**\n * Loss or metric function: Cosine proximity.\n *\n * Mathematically, cosine proximity is defined as:\n *   `-sum(l2Normalize(yTrue) * l2Normalize(yPred))`,\n * wherein `l2Normalize()` normalizes the L2 norm of the input to 1 and `*`\n * represents element-wise multiplication.\n *\n * ```js\n * const yTrue = tf.tensor2d([[1, 0], [1, 0]]);\n * const yPred = tf.tensor2d([[1 / Math.sqrt(2), 1 / Math.sqrt(2)], [0, 1]]);\n * const proximity = tf.metrics.cosineProximity(yTrue, yPred);\n * proximity.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Cosine proximity Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function cosineProximity(yTrue, yPred) {\n    return losses.cosineProximity(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean absolute error.\n *\n * Mathematically, mean absolute error is defined as:\n *   `mean(abs(yPred - yTrue))`,\n * wherein the `mean` is applied over feature dimensions.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [0, 0], [2, 3]]);\n * const yPred = tf.tensor2d([[0, 1], [0, 1], [-2, -3]]);\n * const mse = tf.metrics.meanAbsoluteError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsoluteError(yTrue, yPred) {\n    return losses.meanAbsoluteError(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean absolute percentage error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [10, 20]]);\n * const yPred = tf.tensor2d([[0, 1], [11, 24]]);\n * const mse = tf.metrics.meanAbsolutePercentageError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MAPE`, `tf.metrics.mape`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute percentage error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsolutePercentageError(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\nexport function MAPE(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\nexport function mape(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean squared error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [3, 4]]);\n * const yPred = tf.tensor2d([[0, 1], [-3, -4]]);\n * const mse = tf.metrics.meanSquaredError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MSE`, `tf.metrics.mse`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean squared error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanSquaredError(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\nexport function MSE(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\nexport function mse(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\n//# sourceMappingURL=exports_metrics.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport { modelFromJSON } from './models';\n//# sourceMappingURL=exports_models.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as regularizers from './regularizers';\n// tslint:disable-next-line:max-line-length\nimport { L1L2 } from './regularizers';\n/**\n * Regularizer for L1 and L2 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l1 * abs(x)) + sum(l2 * x^2)\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l1l2(config) {\n    return new L1L2(config);\n}\n/**\n * Regularizer for L1 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l1 * abs(x))\n * @param args l1 config.\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l1(config) {\n    return regularizers.l1(config);\n}\n/**\n * Regularizer for L2 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l2 * x^2)\n * @param args l2 config.\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l2(config) {\n    return regularizers.l2(config);\n}\n//# sourceMappingURL=exports_regularizers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/callbacks.py */\nimport { BaseCallback } from './base_callbacks';\nimport { LayersModel } from './engine/training';\nimport { NotImplementedError } from './errors';\nimport { resolveScalarsInLogs } from './logs';\nexport class Callback extends BaseCallback {\n    constructor() {\n        super(...arguments);\n        /** Instance of `keras.models.Model`. Reference of the model being trained. */\n        this.model = null;\n    }\n    setModel(model) {\n        if (!(model instanceof LayersModel)) {\n            throw new Error('model must be a LayersModel, not some other Container');\n        }\n        this.model = model;\n    }\n}\nfunction less(currVal, prevVal) {\n    return currVal < prevVal;\n}\nfunction greater(currVal, prevVal) {\n    return currVal > prevVal;\n}\n/**\n * A Callback that stops training when a monitored quantity has stopped\n * improving.\n */\nexport class EarlyStopping extends Callback {\n    constructor(args) {\n        super();\n        if (args == null) {\n            args = {};\n        }\n        if (args.restoreBestWeights) {\n            throw new NotImplementedError('restoreBestWeights = True is not implemented in EarlyStopping yet.');\n        }\n        this.monitor = args.monitor || 'val_loss';\n        this.minDelta = Math.abs(args.minDelta || 0);\n        this.patience = args.patience || 0;\n        this.verbose = args.verbose || 0;\n        this.mode = args.mode || 'auto';\n        this.baseline = args.baseline;\n        if (['auto', 'min', 'max'].indexOf(this.mode) === -1) {\n            console.warn(`EarlyStopping mode '${this.mode}' is invalid. ` +\n                `Falling back to mode 'auto'.`);\n            this.mode = 'auto';\n        }\n        if (this.mode === 'min') {\n            this.monitorFunc = less;\n        }\n        else if (this.mode === 'max') {\n            this.monitorFunc = greater;\n        }\n        else {\n            // For mode === 'auto'.\n            if (this.monitor.indexOf('acc') !== -1) {\n                this.monitorFunc = greater;\n            }\n            else {\n                this.monitorFunc = less;\n            }\n        }\n        if (this.monitorFunc === less) {\n            this.minDelta *= -1;\n        }\n    }\n    async onTrainBegin(logs) {\n        this.wait = 0;\n        this.stoppedEpoch = 0;\n        if (this.baseline != null) {\n            this.best = this.baseline;\n        }\n        else {\n            this.best = this.monitorFunc === less ? Infinity : -Infinity;\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        await resolveScalarsInLogs(logs);\n        const current = this.getMonitorValue(logs);\n        if (current == null) {\n            return;\n        }\n        if (this.monitorFunc(current - this.minDelta, this.best)) {\n            this.best = current;\n            this.wait = 0;\n            // TODO(cais): Logic for restoreBestWeights.\n        }\n        else {\n            this.wait++;\n            if (this.wait >= this.patience) {\n                this.stoppedEpoch = epoch;\n                this.model.stopTraining = true;\n            }\n            // TODO(cais): Logic for restoreBestWeights.\n        }\n    }\n    async onTrainEnd(logs) {\n        if (this.stoppedEpoch > 0 && this.verbose) {\n            console.log(`Epoch ${this.stoppedEpoch}: early stopping.`);\n        }\n    }\n    getMonitorValue(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        const monitorValue = logs[this.monitor];\n        if (monitorValue == null) {\n            console.warn(`Metric for EarlyStopping ${this.monitor} is not available. ` +\n                `Available metrics are: ${Object.keys(logs)}`);\n        }\n        return monitorValue;\n    }\n}\n/**\n * Factory function for a Callback that stops training when a monitored\n * quantity has stopped improving.\n *\n * Early stopping is a type of regularization, and protects model against\n * overfitting.\n *\n * The following example based on fake data illustrates how this callback\n * can be used during `tf.LayersModel.fit()`:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense({\n *   units: 3,\n *   activation: 'softmax',\n *   kernelInitializer: 'ones',\n *   inputShape: [2]\n * }));\n * const xs = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const ys = tf.tensor2d([[1, 0, 0], [0, 1, 0]], [2, 3]);\n * const xsVal = tf.tensor2d([4, 3, 2, 1], [2, 2]);\n * const ysVal = tf.tensor2d([[0, 0, 1], [0, 1, 0]], [2, 3]);\n * model.compile(\n *     {loss: 'categoricalCrossentropy', optimizer: 'sgd', metrics: ['acc']});\n *\n * // Without the EarlyStopping callback, the val_acc value would be:\n * //   0.5, 0.5, 0.5, 0.5, ...\n * // With val_acc being monitored, training should stop after the 2nd epoch.\n * const history = await model.fit(xs, ys, {\n *   epochs: 10,\n *   validationData: [xsVal, ysVal],\n *   callbacks: tf.callbacks.earlyStopping({monitor: 'val_acc'})\n * });\n *\n * // Expect to see a length-2 array.\n * console.log(history.history.val_acc);\n * ```\n *\n * @doc {\n *   heading: 'Callbacks',\n *   namespace: 'callbacks'\n * }\n */\nexport function earlyStopping(args) {\n    return new EarlyStopping(args);\n}\nexport const callbacks = { earlyStopping };\n//# sourceMappingURL=callbacks.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// This file lists all exports of TensorFlow.js Layers\nimport * as constraints from './exports_constraints';\nimport * as initializers from './exports_initializers';\nimport * as layers from './exports_layers';\nimport * as metrics from './exports_metrics';\nimport * as models from './exports_models';\nimport * as regularizers from './exports_regularizers';\nexport { CallbackList, CustomCallback, History } from './base_callbacks';\nexport { Callback, callbacks, EarlyStopping } from './callbacks';\nexport { InputSpec, SymbolicTensor } from './engine/topology';\nexport { LayersModel } from './engine/training';\nexport { input, loadLayersModel, model, registerCallbackConstructor, sequential } from './exports';\nexport { RNN } from './layers/recurrent';\nexport { Sequential } from './models';\nexport { LayerVariable } from './variables';\nexport { version as version_layers } from './version';\nexport { constraints, initializers, layers, metrics, models, regularizers };\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/** DataType enum. */\nexport var DataType;\n(function (DataType) {\n    DataType[DataType[\"DT_INVALID\"] = 0] = \"DT_INVALID\";\n    DataType[DataType[\"DT_FLOAT\"] = 1] = \"DT_FLOAT\";\n    DataType[DataType[\"DT_DOUBLE\"] = 2] = \"DT_DOUBLE\";\n    DataType[DataType[\"DT_INT32\"] = 3] = \"DT_INT32\";\n    DataType[DataType[\"DT_UINT8\"] = 4] = \"DT_UINT8\";\n    DataType[DataType[\"DT_INT16\"] = 5] = \"DT_INT16\";\n    DataType[DataType[\"DT_INT8\"] = 6] = \"DT_INT8\";\n    DataType[DataType[\"DT_STRING\"] = 7] = \"DT_STRING\";\n    DataType[DataType[\"DT_COMPLEX64\"] = 8] = \"DT_COMPLEX64\";\n    DataType[DataType[\"DT_INT64\"] = 9] = \"DT_INT64\";\n    DataType[DataType[\"DT_BOOL\"] = 10] = \"DT_BOOL\";\n    DataType[DataType[\"DT_QINT8\"] = 11] = \"DT_QINT8\";\n    DataType[DataType[\"DT_QUINT8\"] = 12] = \"DT_QUINT8\";\n    DataType[DataType[\"DT_QINT32\"] = 13] = \"DT_QINT32\";\n    DataType[DataType[\"DT_BFLOAT16\"] = 14] = \"DT_BFLOAT16\";\n    DataType[DataType[\"DT_FLOAT_REF\"] = 101] = \"DT_FLOAT_REF\";\n    DataType[DataType[\"DT_DOUBLE_REF\"] = 102] = \"DT_DOUBLE_REF\";\n    DataType[DataType[\"DT_INT32_REF\"] = 103] = \"DT_INT32_REF\";\n    DataType[DataType[\"DT_UINT8_REF\"] = 104] = \"DT_UINT8_REF\";\n    DataType[DataType[\"DT_INT16_REF\"] = 105] = \"DT_INT16_REF\";\n    DataType[DataType[\"DT_INT8_REF\"] = 106] = \"DT_INT8_REF\";\n    DataType[DataType[\"DT_STRING_REF\"] = 107] = \"DT_STRING_REF\";\n    DataType[DataType[\"DT_COMPLEX64_REF\"] = 108] = \"DT_COMPLEX64_REF\";\n    DataType[DataType[\"DT_INT64_REF\"] = 109] = \"DT_INT64_REF\";\n    DataType[DataType[\"DT_BOOL_REF\"] = 110] = \"DT_BOOL_REF\";\n    DataType[DataType[\"DT_QINT8_REF\"] = 111] = \"DT_QINT8_REF\";\n    DataType[DataType[\"DT_QUINT8_REF\"] = 112] = \"DT_QUINT8_REF\";\n    DataType[DataType[\"DT_QINT32_REF\"] = 113] = \"DT_QINT32_REF\";\n    DataType[DataType[\"DT_BFLOAT16_REF\"] = 114] = \"DT_BFLOAT16_REF\";\n})(DataType || (DataType = {}));\nexport var SaverDef;\n(function (SaverDef) {\n    /** CheckpointFormatVersion enum. */\n    let CheckpointFormatVersion;\n    (function (CheckpointFormatVersion) {\n        CheckpointFormatVersion[CheckpointFormatVersion[\"LEGACY\"] = 0] = \"LEGACY\";\n        CheckpointFormatVersion[CheckpointFormatVersion[\"V1\"] = 1] = \"V1\";\n        CheckpointFormatVersion[CheckpointFormatVersion[\"V2\"] = 2] = \"V2\";\n    })(CheckpointFormatVersion = SaverDef.CheckpointFormatVersion || (SaverDef.CheckpointFormatVersion = {}));\n})(SaverDef || (SaverDef = {}));\n//# sourceMappingURL=compiled_api.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst CUSTOM_OPS = {};\n/**\n * Register an Op for graph model executor. This allow you to register\n * TensorFlow custom op or override existing op.\n *\n * Here is an example of registering a new MatMul Op.\n * ```js\n * const customMatmul = (node) =>\n *    tf.matMul(\n *        node.inputs[0], node.inputs[1],\n *        node.attrs['transpose_a'], node.attrs['transpose_b']);\n *\n * tf.registerOp('MatMul', customMatmul);\n * ```\n * The inputs and attrs of the node object is based on the TensorFlow op\n * registry.\n *\n * @param name The Tensorflow Op name.\n * @param opFunc An op function which is called with the current graph node\n * during execution and needs to return a tensor or a list of tensors. The node\n * has the following attributes:\n *    - attr: A map from attribute name to its value\n *    - inputs: A list of input tensors\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function registerOp(name, opFunc) {\n    const opMapper = {\n        tfOpName: name,\n        category: 'custom',\n        inputs: [],\n        attrs: [],\n        customExecutor: opFunc\n    };\n    CUSTOM_OPS[name] = opMapper;\n}\n/**\n * Retrieve the OpMapper object for the registered op.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function getRegisteredOp(name) {\n    return CUSTOM_OPS[name];\n}\n/**\n * Deregister the Op for graph model executor.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function deregisterOp(name) {\n    delete CUSTOM_OPS[name];\n}\n//# sourceMappingURL=register.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { clone, util } from '@tensorflow/tfjs-core';\nexport function getParamValue(paramName, node, tensorMap, context) {\n    const inputParam = node.inputParams[paramName];\n    if (inputParam && inputParam.inputIndexStart !== undefined) {\n        const start = inputParam.inputIndexStart;\n        const end = inputParam.inputIndexEnd === 0 ?\n            undefined :\n            (inputParam.inputIndexEnd === undefined ? start + 1 :\n                inputParam.inputIndexEnd);\n        if (inputParam.type === 'tensor') {\n            return getTensor(node.inputNames[inputParam.inputIndexStart], tensorMap, context);\n        }\n        if (inputParam.type === 'tensors') {\n            const inputs = node.inputNames.slice(start, end);\n            return inputs.map(name => getTensor(name, tensorMap, context));\n        }\n        const tensor = getTensor(node.inputNames.slice(start)[0], tensorMap, context);\n        const data = tensor.dataSync();\n        return inputParam.type === 'number' ?\n            data[0] :\n            util.toNestedArray(tensor.shape, data);\n    }\n    const attrParam = node.attrParams[paramName];\n    return attrParam && attrParam.value;\n}\n/**\n * Retrieve the tensor based on input name by extracting the node name and\n * output index information.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n */\nexport function getTensor(name, tensorsMap, context) {\n    const [nodeName, index] = parseNodeName(name);\n    const contextId = context.currentContextIds.find(contextId => {\n        return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId)];\n    });\n    return contextId !== undefined ?\n        tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] :\n        undefined;\n}\n/**\n * Retrieve the tensors based on input name for current context.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n */\nexport function getTensorsForCurrentContenxt(name, tensorsMap, context) {\n    return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];\n}\n/**\n * Returns the node name and index from the Node input name.\n * @param inputName The input name of the node, in format of\n * node_name:output_index, i.e. MatMul:0, if the output_index is not set, it is\n * default to 0.\n */\nexport function getNodeNameAndIndex(inputName, context) {\n    const [nodeName, index] = parseNodeName(inputName);\n    return [\n        getNodeNameWithContextId(nodeName, context && context.currentContextId),\n        index\n    ];\n}\nfunction getNodeNameWithContextId(name, contextId) {\n    return !!contextId ? `${name}-${contextId}` : name;\n}\nexport function parseNodeName(name) {\n    const parts = name.split(':');\n    if (parts.length === 1) {\n        return [name, 0];\n    }\n    const nodeName = parts[0];\n    return [nodeName, Number(parts[parts.length - 1])];\n}\nexport function split(arr, size) {\n    const res = [];\n    for (let i = 0; i < arr.length; i += size) {\n        res.push(arr.slice(i, i + size));\n    }\n    return res;\n}\nexport function getPadding(node, tensorMap, context) {\n    let pad = getParamValue('pad', node, tensorMap, context);\n    if (pad === 'explicit') {\n        // This is 1d array, we need to convert it to 2d array\n        pad = getParamValue('explicitPaddings', node, tensorMap, context);\n        const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];\n        for (let i = 0; i < 4; i++) {\n            explicitPadding[i][0] = pad[i * 2];\n            explicitPadding[i][1] = pad[i * 2 + 1];\n        }\n        return explicitPadding;\n    }\n    return pad;\n}\n/**\n *  Reuse the tensor if it is marked as keep, otherwise clone the tensor to\n *  avoid disposal. This is important for TensorArray and TensorList ops, since\n *  internally they use a tensor as the id for TensorArray and TensorList, and\n * to simplify lookup, they also use Tensor.id as the key to the internal map.\n * These id tensors have been marked as kept in the backend, we need avoid clone\n * them in order to create new Tensor.id.\n * @param tensor\n */\nexport function cloneTensor(tensor) {\n    return tensor.kept ? tensor : clone(tensor);\n}\n//# sourceMappingURL=utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Add',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AddV2',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AddN',\n        'category': 'arithmetic',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'BiasAdd',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sub',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'RealDiv',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Div',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'DivNoNan',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'FloorDiv',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Mul',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Maximum',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Minimum',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Pow',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'SquaredDifference',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Mod',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'FloorMod',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    }\n];\n//# sourceMappingURL=arithmetic.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Abs',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Acos',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Asin',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atan',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atan2',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'y', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Ceil',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ClipByValue',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'clip_value_min', 'name': 'clipValueMin', 'type': 'number' },\n            { 'tfName': 'clip_value_max', 'name': 'clipValueMax', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Complex',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'real', 'type': 'tensor' },\n            { 'start': 1, 'name': 'imag', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ComplexAbs',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Cos',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Cosh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Elu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Exp',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Floor',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Log',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Imag',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'outputType',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Neg',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Real',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'outputType',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Prelu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'alpha', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Relu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Relu6',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'clipValueMin',\n                'name': 'clipValueMin',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'clipValueMax',\n                'name': 'clipValueMax',\n                'type': 'number',\n                'defaultValue': 6\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Selu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sigmoid',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sin',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sinh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sqrt',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Rsqrt',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Square',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Tan',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Tanh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sign',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Round',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Expm1',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Log1p',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Reciprocal',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Softplus',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Asinh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Acosh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atanh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Erf',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Prod',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axes', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'keep_dims',\n                'name': 'keepDims',\n                'type': 'bool',\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LeakyRelu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'alpha',\n                'name': 'alpha',\n                'type': 'number',\n                'defaultValue': 0.2\n            },\n            {\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=basic_math.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'LoopCond',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'name': 'pred', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Switch',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'data', 'type': 'tensor' },\n            { 'start': 1, 'name': 'pred', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Merge',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Enter',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'frame_name', 'name': 'frameName', 'type': 'string' },\n            { 'tfName': 'is_constant', 'name': 'isConstant', 'type': 'bool' }\n        ]\n    },\n    {\n        'tfOpName': 'Exit',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'NextIteration',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'size', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },\n            { 'tfName': 'dynamic_size', 'name': 'dynamicSize', 'type': 'bool' },\n            { 'tfName': 'clear_after_read', 'name': 'clearAfterRead', 'type': 'bool' },\n            {\n                'tfName': 'identical_element_shapes',\n                'name': 'identicalElementShapes',\n                'type': 'bool'\n            },\n            { 'tfName': 'tensor_array_name', 'name': 'name', 'type': 'string' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayWriteV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayReadV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{\n                'tfName': 'dtype',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    },\n    {\n        'tfOpName': 'TensorArrayGatherV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayScatterV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorArrayConcatV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }, {\n                'tfName': 'element_shape_except0',\n                'name': 'elementShapeExcept0',\n                'type': 'shape',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArraySplitV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 2, 'name': 'lengths', 'type': 'number[]' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorArraySizeV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'flowIn', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayCloseV3',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'StatelessIf',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'cond', 'type': 'tensor' },\n            { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }\n        ],\n        'attrs': [\n            { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },\n            { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'If',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'cond', 'type': 'tensor' },\n            { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }\n        ],\n        'attrs': [\n            { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },\n            { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'StatelessWhile',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },\n            { 'tfName': 'body', 'name': 'body', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'While',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },\n            { 'tfName': 'body', 'name': 'body', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListScatter',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' }\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListScatterV2',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 3, 'name': 'numElements', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListGather',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListGetItem',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListSetItem',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListReserve',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 1, 'name': 'numElements', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListFromTensor',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' }\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListStack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' },\n            { 'tfName': 'num_elements', 'name': 'numElements', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListSplit',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 2, 'name': 'lengths', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListConcat',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListPopBack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListPushBack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n];\n//# sourceMappingURL=control.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'AvgPool',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPool',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPoolWithArgmax',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' }, {\n                'tfName': 'include_batch_in_index',\n                'name': 'includeBatchInIndex',\n                'type': 'bool'\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AvgPool3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPool3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Conv1D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'stride', 'name': 'stride', 'type': 'number' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NWC'\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'dilation',\n                'name': 'dilation',\n                'type': 'number',\n                'defaultValue': 1\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Conv2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            { 'tfName': 'useCudnnOnGpu', 'name': 'useCudnnOnGpu', 'type': 'bool' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': '_FusedConv2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'use_cudnn_on_gpu',\n                'name': 'useCudnnOnGpu',\n                'type': 'bool',\n                'defaultValue': true\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'dilations',\n                'name': 'dilations',\n                'type': 'number[]',\n                'defaultValue': [1, 1, 1, 1]\n            },\n            {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.0001\n            },\n        ]\n    },\n    {\n        'tfOpName': 'Conv2DBackpropInput',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 2, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 0, 'name': 'outputShape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n        ]\n    },\n    {\n        'tfOpName': 'DepthwiseConv2d',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'input', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'DepthwiseConv2dNative',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'input', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'FusedDepthwiseConv2dNative',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'dilations',\n                'name': 'dilations',\n                'type': 'number[]',\n                'defaultValue': [1, 1, 1, 1]\n            },\n            {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Conv3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ],\n    },\n    {\n        'tfOpName': 'Dilation2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'rates', 'name': 'dilations', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }\n        ]\n    }\n];\n//# sourceMappingURL=convolution.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Fill',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n            { 'start': 1, 'name': 'value', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'LinSpace',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'start', 'type': 'number' },\n            { 'start': 1, 'name': 'stop', 'type': 'number' },\n            { 'start': 2, 'name': 'num', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'OneHot',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'depth', 'type': 'number' },\n            { 'start': 2, 'name': 'onValue', 'type': 'number', 'defaultValue': 1 },\n            { 'start': 3, 'name': 'offValue', 'type': 'number', 'defaultValue': 0 },\n        ],\n        'attrs': [\n            {\n                'tfName': 'axis',\n                'name': 'axis',\n                'type': 'number',\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Ones',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'OnesLike',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'RandomUniform',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'minval',\n                'name': 'minval',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'maxval',\n                'name': 'maxval',\n                'type': 'number',\n                'defaultValue': 1\n            },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'seed2',\n                'name': 'seed2',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Range',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'start', 'type': 'number' },\n            { 'start': 1, 'name': 'stop', 'type': 'number' },\n            { 'start': 2, 'name': 'step', 'type': 'number', 'defaultValue': 0 },\n        ],\n        'attrs': [{ 'tfName': 'Tidx', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TruncatedNormal',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'means',\n                'name': 'mean',\n                'type': 'number',\n                'defaultValue': 0.0\n            },\n            {\n                'tfName': 'stddev',\n                'name': 'stdDev',\n                'type': 'number',\n                'defaultValue': 1.0\n            },\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number' }, {\n                'tfName': 'seed2',\n                'name': 'seed2',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Zeros',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'ZerosLike',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'Multinomial',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'logits', 'type': 'tensor' },\n            { 'start': 1, 'name': 'numSamples', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number' },\n            { 'tfName': 'seed2', 'name': 'seed2', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'output_dtype', 'name': 'output_dtype', 'type': 'dtype' }\n        ]\n    }\n];\n//# sourceMappingURL=creation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'NonMaxSuppressionV2',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV3',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV4',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'T_threshold',\n                'name': 'threshold',\n                'type': 'dtype',\n                'notSupported': true\n            },\n            {\n                'tfName': 'pad_to_max_output_size',\n                'name': 'padToMaxOutputSize',\n                'type': 'bool'\n            }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV5',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' },\n            { 'start': 5, 'name': 'softNmsSigma', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Where',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ListDiff',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'y', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=dynamic.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'TopKV2',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'k', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'sorted', 'name': 'sorted', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Unique',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n    },\n    {\n        'tfOpName': 'UniqueV2',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' },\n        ],\n    },\n];\n//# sourceMappingURL=evaluation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'PlaceholderWithDefault',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'default', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'Placeholder',\n        'category': 'graph',\n        'attrs': [\n            { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    { 'tfOpName': 'Const', 'category': 'graph' }, {\n        'tfOpName': 'Identity',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'IdentityN',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Snapshot',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Rank',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Size',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Shape',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'ShapeN',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Print',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'data', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'message', 'name': 'message', 'type': 'string' }, {\n                'tfName': 'first_n',\n                'name': 'firstN',\n                'type': 'number',\n                'notSupported': true\n            },\n            {\n                'tfName': 'summarize',\n                'name': 'summarize',\n                'type': 'number',\n                'defaultValue': 3\n            }\n        ]\n    },\n    { 'tfOpName': 'NoOp', 'category': 'graph', 'inputs': [] }, {\n        'tfOpName': 'StopGradient',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'FakeQuantWithMinMaxVars',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'min', 'name': 'min', 'type': 'number' },\n            { 'tfName': 'max', 'name': 'max', 'type': 'number' }\n        ]\n    }\n];\n//# sourceMappingURL=graph.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'ResizeBilinear',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'images', 'type': 'tensor' },\n            { 'start': 1, 'name': 'size', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ResizeNearestNeighbor',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'images', 'type': 'tensor' },\n            { 'start': 1, 'name': 'size', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'CropAndResize',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'image', 'type': 'tensor' },\n            { 'start': 1, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 2, 'name': 'boxInd', 'type': 'tensor' },\n            { 'start': 3, 'name': 'cropSize', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'method', 'name': 'method', 'type': 'string' }, {\n                'tfName': 'extrapolation_value',\n                'name': 'extrapolationValue',\n                'type': 'number'\n            }\n        ]\n    }\n];\n//# sourceMappingURL=image.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Equal',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'NotEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Greater',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'GreaterEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Less',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LessEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalAnd',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalNot',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalOr',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Select',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n            { 'start': 1, 'name': 'a', 'type': 'tensor' },\n            { 'start': 2, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'SelectV2',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n            { 'start': 1, 'name': 'a', 'type': 'tensor' },\n            { 'start': 2, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=logical.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': '_FusedMatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' }, {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.0001\n            },\n            {\n                'tfName': 'transpose_a',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'transpose_b',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'transpose_a',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'transpose_b',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'BatchMatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'adj_x',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'adj_y',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'BatchMatMulV2',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'adj_x',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'adj_y',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Transpose',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'perm', 'type': 'number[]' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=matrices.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'FusedBatchNorm',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'FusedBatchNormV2',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'FusedBatchNormV3',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'LRN',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'depth_radius',\n                'name': 'radius',\n                'type': 'number',\n                'defaultValue': 5\n            },\n            { 'tfName': 'bias', 'name': 'bias', 'type': 'number', 'defaultValue': 1.0 },\n            {\n                'tfName': 'alpha',\n                'name': 'alpha',\n                'type': 'number',\n                'defaultValue': 1.0\n            },\n            {\n                'tfName': 'beta',\n                'name': 'beta',\n                'type': 'number',\n                'defaultValue': 0.5\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Softmax',\n        'category': 'normalization',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'LogSoftmax',\n        'category': 'normalization',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'SparseToDense',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },\n            { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'defaultValue': true,\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Max',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Mean',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Min',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Sum',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'All',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Any',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'ArgMax',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'ArgMin',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Prod',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Cumsum',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'exclusive', 'name': 'exclusive', 'type': 'bool' },\n            { 'tfName': 'reverse', 'name': 'reverse', 'type': 'bool' }\n        ]\n    }\n];\n//# sourceMappingURL=reduction.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'ConcatV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'end': -1, 'name': 'tensors', 'type': 'tensors' },\n            { 'start': -1, 'name': 'axis', 'type': 'number' }\n        ],\n        'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]\n    },\n    {\n        'tfOpName': 'Concat',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 1, 'end': 0, 'name': 'tensors', 'type': 'tensors' },\n            { 'start': 0, 'name': 'axis', 'type': 'number' }\n        ],\n        'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]\n    },\n    {\n        'tfOpName': 'GatherV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'Gather',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Reverse',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'dims', 'type': 'bool', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ReverseV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Slice',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'begin', 'type': 'number[]' },\n            { 'start': 2, 'name': 'size', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'StridedSlice',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'begin', 'type': 'number[]' },\n            { 'start': 2, 'name': 'end', 'type': 'number[]' },\n            { 'start': 3, 'name': 'strides', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'begin_mask',\n                'name': 'beginMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'end_mask',\n                'name': 'endMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'new_axis_mask',\n                'name': 'newAxisMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'ellipsis_mask',\n                'name': 'ellipsisMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'shrink_axis_mask',\n                'name': 'shrinkAxisMask',\n                'type': 'number',\n                'defaultValue': 0\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Pack',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'Unpack',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'num',\n                'name': 'num',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Tile',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'reps', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Split',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'axis', 'type': 'number', 'defaultValue': 0 },\n            { 'start': 1, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'num_split',\n                'name': 'numOrSizeSplits',\n                'type': 'number',\n                'defaultValue': 1\n            }]\n    },\n    {\n        'tfOpName': 'SplitV',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'numOrSizeSplits', 'type': 'number[]' },\n            { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'ScatterNd',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'values', 'type': 'tensor' },\n            { 'start': 2, 'name': 'shape', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'GatherNd',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'SparseToDense',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },\n            { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'defaultValue': false,\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=slice_join.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'FFT',\n        'category': 'spectral',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'IFFT',\n        'category': 'spectral',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'RFFT',\n        'category': 'spectral',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' }, {\n                'start': 1,\n                'name': 'fft_length',\n                'type': 'number',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'IRFFT',\n        'category': 'spectral',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' }, {\n                'start': 1,\n                'name': 'fft_length',\n                'type': 'number',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=spectral.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Cast',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'SrcT',\n                'name': 'sdtype',\n                'type': 'dtype',\n                'notSupported': true\n            },\n            { 'tfName': 'DstT', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'ExpandDims',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Pad',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' },\n        ],\n        'attrs': [{\n                'tfName': 'constant_value',\n                'name': 'constantValue',\n                'type': 'number',\n                'defaultValue': 0\n            }]\n    },\n    {\n        'tfOpName': 'PadV2',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' }, {\n                'start': 2,\n                'name': 'constantValue',\n                'type': 'number',\n                'defaultValue': 0\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Reshape',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'shape', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Squeeze',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'axis',\n                'tfDeprecatedName': 'squeeze_dims',\n                'name': 'axis',\n                'type': 'number[]'\n            }]\n    },\n    {\n        'tfOpName': 'SpaceToBatchND',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'paddings', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'BatchToSpaceND',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'crops', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'DepthToSpace',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'block_size', 'name': 'blockSize', 'type': 'number' },\n            { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string' }\n        ]\n    },\n    {\n        'tfOpName': 'BroadcastTo',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': []\n    }\n];\n//# sourceMappingURL=transformation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport * as tensorflow from '../data/compiled_api';\nimport { getRegisteredOp } from './custom_op/register';\nimport { getNodeNameAndIndex } from './executors/utils';\nimport * as arithmetic from './op_list/arithmetic';\nimport * as basicMath from './op_list/basic_math';\nimport * as control from './op_list/control';\nimport * as convolution from './op_list/convolution';\nimport * as creation from './op_list/creation';\nimport * as dynamic from './op_list/dynamic';\nimport * as evaluation from './op_list/evaluation';\nimport * as graph from './op_list/graph';\nimport * as image from './op_list/image';\nimport * as logical from './op_list/logical';\nimport * as matrices from './op_list/matrices';\nimport * as normalization from './op_list/normalization';\nimport * as reduction from './op_list/reduction';\nimport * as sliceJoin from './op_list/slice_join';\nimport * as spectral from './op_list/spectral';\nimport * as transformation from './op_list/transformation';\nexport class OperationMapper {\n    // Singleton instance for the mapper\n    static get Instance() {\n        return this._instance || (this._instance = new this());\n    }\n    // Loads the op mapping from the JSON file.\n    constructor() {\n        const ops = [\n            arithmetic, basicMath, control, convolution, creation, dynamic,\n            evaluation, logical, image, graph, matrices, normalization, reduction,\n            sliceJoin, spectral, transformation\n        ];\n        const mappersJson = [].concat(...ops.map(op => op.json));\n        this.opMappers = mappersJson.reduce((map, mapper) => {\n            map[mapper.tfOpName] = mapper;\n            return map;\n        }, {});\n    }\n    // Converts the model inference graph from Tensorflow GraphDef to local\n    // representation for TensorFlow.js API\n    transformGraph(graph, signature = {}) {\n        const tfNodes = graph.node;\n        const placeholders = [];\n        const weights = [];\n        const initNodes = [];\n        const nodes = tfNodes.reduce((map, node) => {\n            map[node.name] = this.mapNode(node);\n            if (node.op.startsWith('Placeholder')) {\n                placeholders.push(map[node.name]);\n            }\n            else if (node.op === 'Const') {\n                weights.push(map[node.name]);\n            }\n            else if (node.input == null || node.input.length === 0) {\n                initNodes.push(map[node.name]);\n            }\n            return map;\n        }, {});\n        let inputs = [];\n        const outputs = [];\n        let inputNodeNameToKey = {};\n        let outputNodeNameToKey = {};\n        if (signature != null) {\n            inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);\n            outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);\n        }\n        const allNodes = Object.keys(nodes);\n        allNodes.forEach(key => {\n            const node = nodes[key];\n            node.inputNames.forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                node.inputs.push(nodes[nodeName]);\n                nodes[nodeName].children.push(node);\n            });\n        });\n        // if signature has not outputs set, add any node that does not have\n        // outputs.\n        if (Object.keys(outputNodeNameToKey).length === 0) {\n            allNodes.forEach(key => {\n                const node = nodes[key];\n                if (node.children.length === 0) {\n                    outputs.push(node);\n                }\n            });\n        }\n        else {\n            Object.keys(outputNodeNameToKey).forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                const node = nodes[nodeName];\n                if (node != null) {\n                    node.signatureKey = outputNodeNameToKey[name];\n                    outputs.push(node);\n                }\n            });\n        }\n        if (Object.keys(inputNodeNameToKey).length > 0) {\n            Object.keys(inputNodeNameToKey).forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                const node = nodes[nodeName];\n                if (node) {\n                    node.signatureKey = inputNodeNameToKey[name];\n                    inputs.push(node);\n                }\n            });\n        }\n        else {\n            inputs = placeholders;\n        }\n        let functions = {};\n        if (graph.library != null && graph.library.function != null) {\n            functions = graph.library.function.reduce((functions, func) => {\n                functions[func.signature.name] = this.mapFunction(func);\n                return functions;\n            }, {});\n        }\n        const result = { nodes, inputs, outputs, weights, placeholders, signature, functions };\n        if (initNodes.length > 0) {\n            result.initNodes = initNodes;\n        }\n        return result;\n    }\n    mapSignatureEntries(entries) {\n        return Object.keys(entries || {})\n            .reduce((prev, curr) => {\n            prev[entries[curr].name] = curr;\n            return prev;\n        }, {});\n    }\n    mapNode(node) {\n        // Unsupported ops will cause an error at run-time (not parse time), since\n        // they may not be used by the actual execution subgraph.\n        const mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {};\n        if (node.attr == null) {\n            node.attr = {};\n        }\n        const newNode = {\n            name: node.name,\n            op: node.op,\n            category: mapper.category,\n            inputNames: (node.input ||\n                []).map(input => input.startsWith('^') ? input.substr(1) : input),\n            inputs: [],\n            children: [],\n            inputParams: {},\n            attrParams: {},\n            rawAttrs: node.attr\n        };\n        if (mapper.inputs != null) {\n            newNode.inputParams =\n                mapper.inputs.reduce((map, param) => {\n                    map[param.name] = {\n                        type: param.type,\n                        inputIndexStart: param.start,\n                        inputIndexEnd: param.end\n                    };\n                    return map;\n                }, {});\n        }\n        if (mapper.attrs != null) {\n            newNode.attrParams =\n                mapper.attrs.reduce((map, param) => {\n                    const type = param.type;\n                    let value = undefined;\n                    switch (param.type) {\n                        case 'string':\n                            value = getStringParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'string[]':\n                            value = getStringArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'number':\n                            value = getNumberParam(node.attr, param.tfName, (param.defaultValue || 0));\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'number[]':\n                            value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'bool':\n                            value = getBoolParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'bool[]':\n                            value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'shape':\n                            value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'shape[]':\n                            value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'dtype':\n                            value = getDtypeParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'dtype[]':\n                            value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'func':\n                            value = getFuncParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'tensor':\n                        case 'tensors':\n                            break;\n                        default:\n                            throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`);\n                    }\n                    map[param.name] = { value, type };\n                    return map;\n                }, {});\n        }\n        return newNode;\n    }\n    // map the TFunctionDef to TFJS graph object\n    mapFunction(functionDef) {\n        const tfNodes = functionDef.nodeDef;\n        const placeholders = [];\n        const weights = [];\n        let nodes = {};\n        if (tfNodes != null) {\n            nodes = tfNodes.reduce((map, node) => {\n                map[node.name] = this.mapNode(node);\n                if (node.op === 'Const') {\n                    weights.push(map[node.name]);\n                }\n                return map;\n            }, {});\n        }\n        const inputs = [];\n        const outputs = [];\n        functionDef.signature.inputArg.forEach(arg => {\n            const [nodeName,] = getNodeNameAndIndex(arg.name);\n            const node = {\n                name: nodeName,\n                op: 'Placeholder',\n                inputs: [],\n                inputNames: [],\n                category: 'graph',\n                inputParams: {},\n                attrParams: { dtype: { value: parseDtypeParam(arg.type), type: 'dtype' } },\n                children: []\n            };\n            node.signatureKey = arg.name;\n            inputs.push(node);\n            nodes[nodeName] = node;\n        });\n        const allNodes = Object.keys(nodes);\n        allNodes.forEach(key => {\n            const node = nodes[key];\n            node.inputNames.forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                node.inputs.push(nodes[nodeName]);\n                nodes[nodeName].children.push(node);\n            });\n        });\n        const returnNodeMap = functionDef.ret;\n        functionDef.signature.outputArg.forEach(output => {\n            const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);\n            const node = nodes[nodeName];\n            if (node != null) {\n                node.defaultOutput = index;\n                outputs.push(node);\n            }\n        });\n        const signature = this.mapArgsToSignature(functionDef);\n        return { nodes, inputs, outputs, weights, placeholders, signature };\n    }\n    mapArgsToSignature(functionDef) {\n        return {\n            methodName: functionDef.signature.name,\n            inputs: functionDef.signature.inputArg.reduce((map, arg) => {\n                map[arg.name] = this.mapArgToTensorInfo(arg);\n                return map;\n            }, {}),\n            outputs: functionDef.signature.outputArg.reduce((map, arg) => {\n                map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);\n                return map;\n            }, {}),\n        };\n    }\n    mapArgToTensorInfo(arg, nameMap) {\n        let name = arg.name;\n        if (nameMap != null) {\n            name = nameMap[name];\n        }\n        return { name, dtype: arg.type };\n    }\n}\nexport function decodeBase64(text) {\n    const global = env().global;\n    if (typeof global.atob !== 'undefined') {\n        return global.atob(text);\n    }\n    else if (typeof Buffer !== 'undefined') {\n        return new Buffer(text, 'base64').toString();\n    }\n    else {\n        throw new Error('Unable to decode base64 in this environment. ' +\n            'Missing built-in atob() or Buffer()');\n    }\n}\nexport function parseStringParam(s, keepCase) {\n    const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);\n    return keepCase ? value : value.toLowerCase();\n}\nexport function getStringParam(attrs, name, def, keepCase = false) {\n    const param = attrs[name];\n    if (param != null) {\n        return parseStringParam(param.s, keepCase);\n    }\n    return def;\n}\nexport function getBoolParam(attrs, name, def) {\n    const param = attrs[name];\n    return param ? param.b : def;\n}\nexport function getNumberParam(attrs, name, def) {\n    const param = attrs[name] || {};\n    const value = param['i'] != null ? param['i'] : (param['f'] != null ? param['f'] : def);\n    return (typeof value === 'number') ? value : parseInt(value, 10);\n}\nexport function parseDtypeParam(value) {\n    if (typeof (value) === 'string') {\n        // tslint:disable-next-line:no-any\n        value = tensorflow.DataType[value];\n    }\n    switch (value) {\n        case tensorflow.DataType.DT_FLOAT:\n            return 'float32';\n        case tensorflow.DataType.DT_INT32:\n        case tensorflow.DataType.DT_INT64:\n        case tensorflow.DataType.DT_INT8:\n        case tensorflow.DataType.DT_UINT8:\n            return 'int32';\n        case tensorflow.DataType.DT_BOOL:\n            return 'bool';\n        case tensorflow.DataType.DT_DOUBLE:\n            return 'float32';\n        case tensorflow.DataType.DT_STRING:\n            return 'string';\n        default:\n            // Unknown dtype error will happen at runtime (instead of parse time),\n            // since these nodes might not be used by the actual subgraph execution.\n            return null;\n    }\n}\nexport function getFuncParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.func) {\n        return param.func.name;\n    }\n    return def;\n}\nexport function getDtypeParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.type) {\n        return parseDtypeParam(param.type);\n    }\n    return def;\n}\nexport function getDtypeArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.type) {\n        return param.list.type.map(v => parseDtypeParam(v));\n    }\n    return def;\n}\nexport function parseTensorShapeParam(shape) {\n    if (shape.unknownRank) {\n        return undefined;\n    }\n    if (shape.dim != null) {\n        return shape.dim.map(dim => (typeof dim.size === 'number') ? dim.size : parseInt(dim.size, 10));\n    }\n    return [];\n}\nexport function getTensorShapeParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.shape) {\n        return parseTensorShapeParam(param.shape);\n    }\n    return def;\n}\nexport function getNumericArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param) {\n        return ((param.list.f && param.list.f.length ? param.list.f :\n            param.list.i) ||\n            [])\n            .map(v => (typeof v === 'number') ? v : parseInt(v, 10));\n    }\n    return def;\n}\nexport function getStringArrayParam(attrs, name, def, keepCase = false) {\n    const param = attrs[name];\n    if (param && param.list && param.list.s) {\n        return param.list.s.map((v) => {\n            return parseStringParam(v, keepCase);\n        });\n    }\n    return def;\n}\nexport function getTensorShapeArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.shape) {\n        return param.list.shape.map((v) => {\n            return parseTensorShapeParam(v);\n        });\n    }\n    return def;\n}\nexport function getBoolArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.b) {\n        return param.list.b;\n    }\n    return def;\n}\n//# sourceMappingURL=operation_mapper.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getTensor } from '../executors/utils';\nimport { getBoolArrayParam, getBoolParam, getDtypeArrayParam, getDtypeParam, getNumberParam, getNumericArrayParam, getStringArrayParam, getStringParam, getTensorShapeArrayParam, getTensorShapeParam } from '../operation_mapper';\n/**\n * Helper class for lookup inputs and params for nodes in the model graph.\n */\nexport class NodeValueImpl {\n    constructor(node, tensorMap, context) {\n        this.node = node;\n        this.tensorMap = tensorMap;\n        this.context = context;\n        this.inputs = [];\n        this.attrs = {};\n        this.inputs = node.inputNames.map(name => this.getInput(name));\n        if (node.rawAttrs != null) {\n            this.attrs = Object.keys(node.rawAttrs)\n                .reduce((attrs, key) => {\n                attrs[key] = this.getAttr(key);\n                return attrs;\n            }, {});\n        }\n    }\n    /**\n     * Return the value of the attribute or input param.\n     * @param name String: name of attribute or input param.\n     */\n    getInput(name) {\n        return getTensor(name, this.tensorMap, this.context);\n    }\n    /**\n     * Return the value of the attribute or input param.\n     * @param name String: name of attribute or input param.\n     */\n    getAttr(name, defaultValue) {\n        const value = this.node.rawAttrs[name];\n        if (value.tensor != null) {\n            return getTensor(name, this.tensorMap, this.context);\n        }\n        if (value.i != null || value.f != null) {\n            return getNumberParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.s != null) {\n            return getStringParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.b != null) {\n            return getBoolParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.shape != null) {\n            return getTensorShapeParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.type != null) {\n            return getDtypeParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.list != null) {\n            if (value.list.i != null || value.list.f != null) {\n                return getNumericArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.s != null) {\n                return getStringArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.shape != null) {\n                return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.b != null) {\n                return getBoolArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.type != null) {\n                return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n        }\n        return defaultValue;\n    }\n}\n//# sourceMappingURL=node_value_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This file exports ops used by the converters executors. By default it\n * re-exports all ops. In a custom build this is aliased to a file that will\n * only exports ops for a given model.json.\n */\nexport * from './ops';\n//# sourceMappingURL=ops_for_converter.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'BiasAdd':\n        case 'AddV2':\n        case 'Add': {\n            return [tfOps.add(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'AddN': {\n            return [tfOps.addN(getParamValue('tensors', node, tensorMap, context))];\n        }\n        case 'FloorMod':\n        case 'Mod':\n            return [tfOps.mod(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        case 'Mul':\n            return [tfOps.mul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        case 'RealDiv':\n        case 'Div': {\n            return [tfOps.div(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'DivNoNan': {\n            return [tfOps.divNoNan(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'FloorDiv': {\n            return [tfOps.floorDiv(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Sub': {\n            return [tfOps.sub(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Minimum': {\n            return [tfOps.minimum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Maximum': {\n            return [tfOps.maximum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Pow': {\n            return [tfOps.pow(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'SquaredDifference': {\n            return [tfOps.squaredDifference(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'arithmetic';\n//# sourceMappingURL=arithmetic_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Abs':\n        case 'ComplexAbs':\n            return [tfOps.abs(getParamValue('x', node, tensorMap, context))];\n        case 'Acos':\n            return [tfOps.acos(getParamValue('x', node, tensorMap, context))];\n        case 'Acosh':\n            return [tfOps.acosh(getParamValue('x', node, tensorMap, context))];\n        case 'Asin':\n            return [tfOps.asin(getParamValue('x', node, tensorMap, context))];\n        case 'Asinh':\n            return [tfOps.asinh(getParamValue('x', node, tensorMap, context))];\n        case 'Atan':\n            return [tfOps.atan(getParamValue('x', node, tensorMap, context))];\n        case 'Atan2':\n            return [tfOps.atan2(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context))];\n        case 'Atanh':\n            return [tfOps.atanh(getParamValue('x', node, tensorMap, context))];\n        case 'Ceil':\n            return [tfOps.ceil(getParamValue('x', node, tensorMap, context))];\n        case 'Complex':\n            return [tfOps.complex(getParamValue('real', node, tensorMap, context), getParamValue('imag', node, tensorMap, context))];\n        case 'Cos':\n            return [tfOps.cos(getParamValue('x', node, tensorMap, context))];\n        case 'Cosh':\n            return [tfOps.cosh(getParamValue('x', node, tensorMap, context))];\n        case 'Elu':\n            return [tfOps.elu(getParamValue('x', node, tensorMap, context))];\n        case 'Erf':\n            return [tfOps.erf(getParamValue('x', node, tensorMap, context))];\n        case 'Exp':\n            return [tfOps.exp(getParamValue('x', node, tensorMap, context))];\n        case 'Expm1': {\n            return [tfOps.expm1(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Floor':\n            return [tfOps.floor(getParamValue('x', node, tensorMap, context))];\n        case 'Log':\n            return [tfOps.log(getParamValue('x', node, tensorMap, context))];\n        case 'Log1p': {\n            return [tfOps.log1p(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Imag':\n            return [tfOps.imag(getParamValue('x', node, tensorMap, context))];\n        case 'Neg':\n            return [tfOps.neg(getParamValue('x', node, tensorMap, context))];\n        case 'Reciprocal': {\n            return [tfOps.reciprocal(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Real':\n            return [tfOps.real(getParamValue('x', node, tensorMap, context))];\n        case 'Relu':\n            return [tfOps.relu(getParamValue('x', node, tensorMap, context))];\n        case 'Round': {\n            return [tfOps.round(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Selu':\n            return [tfOps.selu(getParamValue('x', node, tensorMap, context))];\n        case 'Sigmoid':\n            return [tfOps.sigmoid(getParamValue('x', node, tensorMap, context))];\n        case 'Sin':\n            return [tfOps.sin(getParamValue('x', node, tensorMap, context))];\n        case 'Sign': {\n            return [tfOps.sign(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Sinh': {\n            return [tfOps.sinh(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Softplus': {\n            return [tfOps.softplus(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Sqrt': {\n            return [tfOps.sqrt(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Square': {\n            return [tfOps.square(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Tanh': {\n            return [tfOps.tanh(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Tan':\n            return [tfOps.tan(getParamValue('x', node, tensorMap, context))];\n        case 'Relu6':\n        case 'ClipByValue':\n            return [tfOps.clipByValue(getParamValue('x', node, tensorMap, context), getParamValue('clipValueMin', node, tensorMap, context), getParamValue('clipValueMax', node, tensorMap, context))];\n        case 'Rsqrt':\n            return [tfOps.rsqrt(getTensor(node.inputNames[0], tensorMap, context))];\n        case 'Prod':\n            return [tfOps.prod(getParamValue('x', node, tensorMap, context), getParamValue('axes', node, tensorMap, context))];\n        case 'LeakyRelu':\n            return [tfOps.leakyRelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n        case 'Prelu':\n            return [tfOps.prelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'basic_math';\n//# sourceMappingURL=basic_math_executor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This differs from util.assertShapesMatch in that it allows values of\n * negative one, an undefined size of a dimensinon, in a shape to match\n * anything.\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = '') {\n    util.assert(shapesEqualAllowUndefinedSize(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nexport function shapesEqualAllowUndefinedSize(n1, n2) {\n    if (n1.length !== n2.length) {\n        return false;\n    }\n    for (let i = 0; i < n1.length; i++) {\n        if (n1[i] !== -1 && n2[i] !== -1 && n1[i] !== n2[i]) {\n            return false;\n        }\n    }\n    return true;\n}\n//# sourceMappingURL=tensor_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize } from './tensor_utils';\n/**\n * The TensorArray object keeps an array of Tensors.  It\n * allows reading from the array and writing to the array.\n */\nexport class TensorArray {\n    constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) {\n        this.name = name;\n        this.dtype = dtype;\n        this.maxSize = maxSize;\n        this.elementShape = elementShape;\n        this.identicalElementShapes = identicalElementShapes;\n        this.dynamicSize = dynamicSize;\n        this.clearAfterRead = clearAfterRead;\n        this.tensors = [];\n        this.closed_ = false;\n        this.idTensor = scalar(0);\n        keep(this.idTensor);\n    }\n    get id() {\n        return this.idTensor.id;\n    }\n    get closed() {\n        return this.closed_;\n    }\n    /**\n     * Dispose the tensors and idTensor and mark the TensoryArray as closed.\n     */\n    clearAndClose(keepIds) {\n        this.tensors.forEach(tensor => {\n            if (keepIds == null || !keepIds.has(tensor.tensor.id)) {\n                tensor.tensor.dispose();\n            }\n        });\n        this.tensors = [];\n        this.closed_ = true;\n        this.idTensor.dispose();\n    }\n    size() {\n        return this.tensors.length;\n    }\n    /**\n     * Read the value at location index in the TensorArray.\n     * @param index Number the index to read from.\n     */\n    read(index) {\n        if (this.closed_) {\n            throw new Error(`TensorArray ${this.name} has already been closed.`);\n        }\n        if (index < 0 || index >= this.size()) {\n            throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);\n        }\n        const tensorWithState = this.tensors[index];\n        if (tensorWithState.cleared) {\n            throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read ` +\n                `(perhaps try setting clear_after_read = false?).`);\n        }\n        if (this.clearAfterRead) {\n            tensorWithState.cleared = true;\n        }\n        tensorWithState.read = true;\n        return tensorWithState.tensor;\n    }\n    /**\n     * Helper method to read multiple tensors from the specified indices.\n     */\n    readMany(indices) {\n        return indices.map(index => this.read(index));\n    }\n    /**\n     * Write value into the index of the TensorArray.\n     * @param index number the index to write to.\n     * @param tensor\n     */\n    write(index, tensor) {\n        if (this.closed_) {\n            throw new Error(`TensorArray ${this.name} has already been closed.`);\n        }\n        if (index < 0 || !this.dynamicSize && index >= this.maxSize) {\n            throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);\n        }\n        const t = this.tensors[index] || {};\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},\n          because the value dtype is ${tensor.dtype}, but TensorArray dtype is ${this.dtype}.`);\n        }\n        // Set the shape for the first time write to unknow shape tensor array\n        if (this.size() === 0 &&\n            (this.elementShape == null || this.elementShape.length === 0)) {\n            this.elementShape = tensor.shape;\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`);\n        if (t.read) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);\n        }\n        if (t.written) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);\n        }\n        t.tensor = tensor;\n        keep(tensor);\n        t.written = true;\n        this.tensors[index] = t;\n    }\n    /**\n     * Helper method to write multiple tensors to the specified indices.\n     */\n    writeMany(indices, tensors) {\n        if (indices.length !== tensors.length) {\n            throw new Error(`TensorArray ${this.name}: could not write multiple tensors,` +\n                `because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);\n        }\n        indices.forEach((i, index) => this.write(i, tensors[index]));\n    }\n    /**\n     * Return selected values in the TensorArray as a packed Tensor. All of\n     * selected values must have been written and their shapes must all match.\n     * @param [indices] number[] Optional. Taking values in [0, max_value). If the\n     *    TensorArray is not dynamic, max_value=size(). If not specified returns\n     *    all tensors in the original order.\n     * @param [dtype]\n     */\n    gather(indices, dtype) {\n        if (!!dtype && dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);\n        }\n        if (!indices) {\n            indices = [];\n            for (let i = 0; i < this.size(); i++) {\n                indices.push(i);\n            }\n        }\n        else {\n            indices = indices.slice(0, this.size());\n        }\n        if (indices.length === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        // Read all the PersistentTensors into a vector to keep track of\n        // their memory.\n        const tensors = this.readMany(indices);\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, 'TensorArray shape mismatch: ');\n        return stack(tensors, 0);\n    }\n    /**\n     * Return the values in the TensorArray as a concatenated Tensor.\n     */\n    concat(dtype) {\n        if (!!dtype && dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);\n        }\n        if (this.size() === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        const indices = [];\n        for (let i = 0; i < this.size(); i++) {\n            indices.push(i);\n        }\n        // Collect all the tensors from the tensors array.\n        const tensors = this.readMany(indices);\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);\n        return concat(tensors, 0);\n    }\n    /**\n     * Scatter the values of a Tensor in specific indices of a TensorArray.\n     * @param indices nummber[] values in [0, max_value). If the\n     *    TensorArray is not dynamic, max_value=size().\n     * @param tensor Tensor input tensor.\n     */\n    scatter(indices, tensor) {\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n        }\n        if (indices.length !== tensor.shape[0]) {\n            throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n        }\n        const maxIndex = Math.max(...indices);\n        if (!this.dynamicSize && maxIndex >= this.maxSize) {\n            throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);\n        }\n        this.writeMany(indices, unstack(tensor, 0));\n    }\n    /**\n     * Split the values of a Tensor into the TensorArray.\n     * @param length number[] with the lengths to use when splitting value along\n     *    its first dimension.\n     * @param tensor Tensor, the tensor to split.\n     */\n    split(length, tensor) {\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n        }\n        let totalLength = 0;\n        const cumulativeLengths = length.map(len => {\n            totalLength += len;\n            return totalLength;\n        });\n        if (totalLength !== tensor.shape[0]) {\n            throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n        }\n        if (!this.dynamicSize && length.length !== this.maxSize) {\n            throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), ` +\n                'and the TensorArray is not marked as dynamically resizeable');\n        }\n        const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n        const tensors = [];\n        tidy(() => {\n            tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n            for (let i = 0; i < length.length; ++i) {\n                const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n                const indices = [0, previousLength, 0];\n                const sizes = [1, length[i], elementPerRow];\n                tensors[i] = reshape(slice(tensor, indices, sizes), this.elementShape);\n            }\n            return tensors;\n        });\n        const indices = [];\n        for (let i = 0; i < length.length; i++) {\n            indices[i] = i;\n        }\n        this.writeMany(indices, tensors);\n    }\n}\n//# sourceMappingURL=tensor_array.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize } from './tensor_utils';\n/**\n * TensorList stores a container of `tf.Tensor` objects, which are accessible\n * via tensors field.\n *\n * In order to get a copy of the underlying list, use the copy method:\n * ```\n *    TensorList b = a.copy();\n *    b.tensors().pushBack(t);  // This does not modify a.tensors().\n * ```\n *\n * Note that this is not a deep copy: the memory locations of the underlying\n * tensors will still point to the same locations of the corresponding tensors\n * in the original.\n */\nexport class TensorList {\n    /**\n     *\n     * @param tensors list of tensors\n     * @param elementShape shape of each tensor\n     * @param elementDtype data type of each tensor\n     * @param maxNumElements The maximum allowed size of `tensors`. Defaults to -1\n     *   meaning that the size of `tensors` is unbounded.\n     */\n    constructor(tensors, elementShape, elementDtype, maxNumElements = -1) {\n        this.tensors = tensors;\n        this.elementShape = elementShape;\n        this.elementDtype = elementDtype;\n        if (tensors != null) {\n            tensors.forEach(tensor => {\n                if (elementDtype !== tensor.dtype) {\n                    throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor.dtype}`);\n                }\n                assertShapesMatchAllowUndefinedSize(elementShape, tensor.shape, 'TensorList shape mismatch: ');\n                keep(tensor);\n            });\n        }\n        this.idTensor = scalar(0);\n        this.maxNumElements = maxNumElements;\n        keep(this.idTensor);\n    }\n    get id() {\n        return this.idTensor.id;\n    }\n    /**\n     * Get a new TensorList containing a copy of the underlying tensor container.\n     */\n    copy() {\n        return new TensorList([...this.tensors], this.elementShape, this.elementDtype);\n    }\n    /**\n     * Dispose the tensors and idTensor and clear the tensor list.\n     */\n    clearAndClose(keepIds) {\n        this.tensors.forEach(tensor => {\n            if (keepIds == null || !keepIds.has(tensor.id)) {\n                tensor.dispose();\n            }\n        });\n        this.tensors.length = 0;\n        this.idTensor.dispose();\n    }\n    /**\n     * The size of the tensors in the tensor list.\n     */\n    size() {\n        return this.tensors.length;\n    }\n    /**\n     * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1)\n     * tf.Tensor.\n     * @param elementShape shape of each tensor\n     * @param elementDtype data type of each tensor\n     * @param numElements the number of elements to stack\n     */\n    stack(elementShape, elementDtype, numElements = -1) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (numElements !== -1 && this.tensors.length !== numElements) {\n            throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);\n        }\n        assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, 'TensorList shape mismatch: ');\n        return tidy(() => {\n            const reshapedTensors = this.tensors.map(tensor => reshape(tensor, elementShape));\n            return stack(reshapedTensors, 0);\n        });\n    }\n    /**\n     * Pop a tensor from the end of the list.\n     * @param elementShape shape of the tensor\n     * @param elementDtype data type of the tensor\n     */\n    popBack(elementShape, elementDtype) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (this.size() === 0) {\n            throw new Error('Trying to pop from an empty list.');\n        }\n        const tensor = this.tensors.pop();\n        assertShapesMatchAllowUndefinedSize(tensor.shape, elementShape, 'TensorList shape mismatch: ');\n        return reshape(tensor, elementShape);\n    }\n    /**\n     * Push a tensor to the end of the list.\n     * @param tensor Tensor to be pushed.\n     */\n    pushBack(tensor) {\n        if (tensor.dtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(tensor.shape, this.elementShape, 'TensorList shape mismatch: ');\n        if (this.maxNumElements === this.size()) {\n            throw new Error(`Trying to push element into a full list.`);\n        }\n        keep(tensor);\n        this.tensors.push(tensor);\n    }\n    /**\n     * Update the size of the list.\n     * @param size the new size of the list.\n     */\n    resize(size) {\n        if (size < 0) {\n            throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);\n        }\n        if (this.maxNumElements !== -1 && size > this.maxNumElements) {\n            throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);\n        }\n        this.tensors.length = size;\n    }\n    /**\n     * Retrieve the element at the provided index\n     * @param elementShape shape of the tensor\n     * @param elementDtype dtype of the tensor\n     * @param elementIndex index of the tensor\n     */\n    getItem(elementIndex, elementShape, elementDtype) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (elementIndex < 0 || elementIndex > this.tensors.length) {\n            throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);\n        }\n        if (this.tensors[elementIndex] == null) {\n            throw new Error(`element at index ${elementIndex} is null.`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, 'TensorList shape mismatch: ');\n        return this.tensors[elementIndex];\n    }\n    /**\n     * Set the tensor at the index\n     * @param elementIndex index of the tensor\n     * @param tensor the tensor to be inserted into the list\n     */\n    setItem(elementIndex, tensor) {\n        if (tensor.dtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n        }\n        if (elementIndex < 0 ||\n            this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {\n            throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, 'TensorList shape mismatch: ');\n        keep(tensor);\n        this.tensors[elementIndex] = tensor;\n    }\n    /**\n     * Return selected values in the TensorList as a stacked Tensor. All of\n     * selected values must have been written and their shapes must all match.\n     * @param indices indices of tensors to gather\n     * @param elementDtype output tensor dtype\n     * @param elementShape output tensor element shape\n     */\n    gather(indices, elementDtype, elementShape) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n        // When indices is greater than the size of the list, indices beyond the\n        // size of the list are ignored.\n        indices = indices.slice(0, this.size());\n        if (indices.length === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        return tidy(() => {\n            const tensors = indices.map(i => reshape(this.tensors[i], elementShape));\n            return stack(tensors, 0);\n        });\n    }\n    /**\n     * Return the values in the TensorList as a concatenated Tensor.\n     * @param elementDtype output tensor dtype\n     * @param elementShape output tensor element shape\n     */\n    concat(elementDtype, elementShape) {\n        if (!!elementDtype && elementDtype !== this.elementDtype) {\n            throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n        if (this.size() === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        return tidy(() => {\n            const tensors = this.tensors.map(t => reshape(t, elementShape));\n            return concat(tensors, 0);\n        });\n    }\n}\n/**\n * Creates a TensorList which, when stacked, has the value of tensor.\n * @param tensor from tensor\n * @param elementShape output tensor element shape\n */\nexport function fromTensor(tensor, elementShape, elementDtype) {\n    const dtype = tensor.dtype;\n    if (tensor.shape.length < 1) {\n        throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor.shape}`);\n    }\n    if (tensor.dtype !== elementDtype) {\n        throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${elementDtype}`);\n    }\n    const outputShape = tensor.shape.slice(1);\n    assertShapesMatchAllowUndefinedSize(outputShape, elementShape, 'TensorList shape mismatch: ');\n    const tensorList = unstack(tensor);\n    return new TensorList(tensorList, elementShape, dtype);\n}\n/**\n * Return a TensorList of the given size with empty elements.\n * @param elementShape the shape of the future elements of the list\n * @param elementDtype the desired type of elements in the list\n * @param numElements the number of elements to reserve\n */\nexport function reserve(elementShape, elementDtype, numElements) {\n    return new TensorList([], elementShape, elementDtype, numElements);\n}\n/**\n * Put tensors at specific indices of a stacked tensor into a TensorList.\n * @param indices list of indices on how to scatter the tensor.\n * @param tensor input tensor.\n * @param elementShape the shape of the future elements of the list\n * @param numElements the number of elements to scatter\n */\nexport function scatter(tensor, indices, elementShape, numElements) {\n    if (indices.length !== tensor.shape[0]) {\n        throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n    }\n    const maxIndex = Math.max(...indices);\n    if (numElements != null && numElements !== -1 && maxIndex >= numElements) {\n        throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);\n    }\n    const list = new TensorList([], elementShape, tensor.dtype, numElements);\n    const tensors = unstack(tensor, 0);\n    indices.forEach((value, index) => {\n        list.setItem(value, tensors[index]);\n    });\n    return list;\n}\n/**\n * Split the values of a Tensor into a TensorList.\n * @param length the lengths to use when splitting value along\n *    its first dimension.\n * @param tensor the tensor to split.\n * @param elementShape the shape of the future elements of the list\n */\nexport function split(tensor, length, elementShape) {\n    let totalLength = 0;\n    const cumulativeLengths = length.map(len => {\n        totalLength += len;\n        return totalLength;\n    });\n    if (totalLength !== tensor.shape[0]) {\n        throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n    }\n    const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n    const tensors = tidy(() => {\n        const tensors = [];\n        tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n        for (let i = 0; i < length.length; ++i) {\n            const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n            const indices = [0, previousLength, 0];\n            const sizes = [1, length[i], elementPerRow];\n            tensors[i] = reshape(slice(tensor, indices, sizes), elementShape);\n        }\n        tensor.dispose();\n        return tensors;\n    });\n    const list = new TensorList([], elementShape, tensor.dtype, length.length);\n    for (let i = 0; i < tensors.length; i++) {\n        list.setItem(i, tensors[i]);\n    }\n    return list;\n}\n//# sourceMappingURL=tensor_list.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { scalar } from '@tensorflow/tfjs-core';\nimport { TensorArray } from '../../executor/tensor_array';\nimport { fromTensor, reserve, scatter, split } from '../../executor/tensor_list';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = async (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'If':\n        case 'StatelessIf': {\n            const thenFunc = getParamValue('thenBranch', node, tensorMap, context);\n            const elseFunc = getParamValue('elseBranch', node, tensorMap, context);\n            const cond = getParamValue('cond', node, tensorMap, context);\n            const args = getParamValue('args', node, tensorMap, context);\n            const condValue = await cond.data();\n            if (condValue[0]) {\n                return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n            }\n            else {\n                return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n            }\n        }\n        case 'While':\n        case 'StatelessWhile': {\n            const bodyFunc = getParamValue('body', node, tensorMap, context);\n            const condFunc = getParamValue('cond', node, tensorMap, context);\n            const args = getParamValue('args', node, tensorMap, context);\n            // Calculate the condition of the loop\n            const condResult = (await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap));\n            const argIds = args.map(tensor => tensor.id);\n            let condValue = await condResult[0].data();\n            // Dispose the intermediate tensors for condition function\n            condResult.forEach(tensor => {\n                if (!tensor.kept && argIds.indexOf(tensor.id) === -1) {\n                    tensor.dispose();\n                }\n            });\n            let result = args;\n            while (condValue[0]) {\n                // Record the previous result for intermediate tensor tracking\n                const origResult = result;\n                // Execution the body of the loop\n                result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);\n                const resultIds = result.map(tensor => tensor.id);\n                // Dispose the intermediate tensor for body function that is not global\n                // kept, not input/output of the body function\n                origResult.forEach(tensor => {\n                    if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n                        resultIds.indexOf(tensor.id) === -1) {\n                        tensor.dispose();\n                    }\n                });\n                // Recalcuate the condition of the loop using the latest results.\n                const condResult = (await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap));\n                condValue = await condResult[0].data();\n                // Dispose the intermediate tensors for condition function\n                condResult.forEach(tensor => {\n                    if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n                        resultIds.indexOf(tensor.id) === -1) {\n                        tensor.dispose();\n                    }\n                });\n            }\n            return result;\n        }\n        case 'LoopCond': {\n            const pred = getParamValue('pred', node, tensorMap, context);\n            return [cloneTensor(pred)];\n        }\n        case 'Switch': {\n            const pred = getParamValue('pred', node, tensorMap, context);\n            let data = getParamValue('data', node, tensorMap, context);\n            if (!data.kept) {\n                data = cloneTensor(data);\n            }\n            // Outputs nodes :0 => false, :1 => true\n            return (await pred.data())[0] ? [undefined, data] : [data, undefined];\n        }\n        case 'Merge': {\n            const inputName = node.inputNames.find(name => getTensor(name, tensorMap, context) !== undefined);\n            if (inputName) {\n                const data = getTensor(inputName, tensorMap, context);\n                return [cloneTensor(data)];\n            }\n            return undefined;\n        }\n        case 'Enter': {\n            const frameId = getParamValue('frameName', node, tensorMap, context);\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.enterFrame(frameId);\n            return [cloneTensor(data)];\n        }\n        case 'Exit': {\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.exitFrame();\n            return [cloneTensor(data)];\n        }\n        case 'NextIteration': {\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.nextIteration();\n            return [cloneTensor(data)];\n        }\n        case 'TensorArrayV3': {\n            const size = getParamValue('size', node, tensorMap, context);\n            const dtype = getParamValue('dtype', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const dynamicSize = getParamValue('dynamicSize', node, tensorMap, context);\n            const clearAfterRead = getParamValue('clearAfterRead', node, tensorMap, context);\n            const identicalElementShapes = getParamValue('identicalElementShapes', node, tensorMap, context);\n            const name = getParamValue('name', node, tensorMap, context);\n            const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);\n            context.addTensorArray(tensorArray);\n            return [tensorArray.idTensor, scalar(1.0)];\n        }\n        case 'TensorArrayWriteV3': {\n            const id = getParamValue('tensorArrayId', node, tensorMap, context);\n            const index = getParamValue('index', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const writeTensorArray = context.getTensorArray(id.id);\n            writeTensorArray.write(index, writeTensor);\n            return [writeTensorArray.idTensor];\n        }\n        case 'TensorArrayReadV3': {\n            const readId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const readIndex = getParamValue('index', node, tensorMap, context);\n            const readTensorArray = context.getTensorArray(readId.id);\n            return [readTensorArray.read(readIndex)];\n        }\n        case 'TensorArrayGatherV3': {\n            const gatherId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const gatherIndices = getParamValue('indices', node, tensorMap, context);\n            const gatherDtype = getParamValue('dtype', node, tensorMap, context);\n            const gatherTensorArray = context.getTensorArray(gatherId.id);\n            return [gatherTensorArray.gather(gatherIndices, gatherDtype)];\n        }\n        case 'TensorArrayScatterV3': {\n            const scatterId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const scatterIndices = getParamValue('indices', node, tensorMap, context);\n            const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n            const scatterTensorArray = context.getTensorArray(scatterId.id);\n            scatterTensorArray.scatter(scatterIndices, scatterTensor);\n            return [scatterTensorArray.idTensor];\n        }\n        case 'TensorArrayConcatV3': {\n            const concatId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const concatTensorArray = context.getTensorArray(concatId.id);\n            const concatDtype = getParamValue('dtype', node, tensorMap, context);\n            return [concatTensorArray.concat(concatDtype)];\n        }\n        case 'TensorArraySplitV3': {\n            const splitId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const splitTensor = getParamValue('tensor', node, tensorMap, context);\n            const lengths = getParamValue('lengths', node, tensorMap, context);\n            const splitTensorArray = context.getTensorArray(splitId.id);\n            splitTensorArray.split(lengths, splitTensor);\n            return [splitTensorArray.idTensor];\n        }\n        case 'TensorArraySizeV3': {\n            const sizeId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const sizeTensorArray = context.getTensorArray(sizeId.id);\n            return [scalar(sizeTensorArray.size(), 'int32')];\n        }\n        case 'TensorArrayCloseV3': {\n            const closeId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const closeTensorArray = context.getTensorArray(closeId.id);\n            closeTensorArray.clearAndClose();\n            return [closeTensorArray.idTensor];\n        }\n        case 'TensorListSetItem': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const index = getParamValue('index', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            tensorList.setItem(index, writeTensor);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListGetItem': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const readIndex = getParamValue('index', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDType = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.getItem(readIndex, elementShape, elementDType)];\n        }\n        case 'TensorListScatterV2':\n        case 'TensorListScatter': {\n            const scatterIndices = getParamValue('indices', node, tensorMap, context);\n            const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListReserve': {\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = reserve(elementShape, elementDtype, numElements);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListGather': {\n            const gatherId = getParamValue('tensorListId', node, tensorMap, context);\n            const gatherIndices = getParamValue('indices', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(gatherId.id);\n            return [tensorList.gather(gatherIndices, elementDtype, elementShape)];\n        }\n        case 'TensorListStack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.stack(elementShape, elementDtype, numElements)];\n        }\n        case 'TensorListFromTensor': {\n            const tensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = fromTensor(tensor, elementShape, elementDtype);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListConcat': {\n            const concatId = getParamValue('tensorListId', node, tensorMap, context);\n            const tensorList = context.getTensorList(concatId.id);\n            const concatDtype = getParamValue('dtype', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            return [tensorList.concat(concatDtype, elementShape)];\n        }\n        case 'TensorListPushBack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            tensorList.pushBack(writeTensor);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListPopBack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDType = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.popBack(elementShape, elementDType)];\n        }\n        case 'TensorListSplit': {\n            const splitTensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const lengths = getParamValue('lengths', node, tensorMap, context);\n            const tensorList = split(splitTensor, lengths, elementShape);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'control';\n//# sourceMappingURL=control_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getPadding, getParamValue } from './utils';\nfunction fusedConvAndDepthWiseParams(node, tensorMap, context) {\n    const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n    const isBiasAdd = extraOp === 'biasadd';\n    const isPrelu = activationFunc === 'prelu';\n    const isBatchNorm = extraOp === 'fusedbatchnorm';\n    const numArgs = getParamValue('numArgs', node, tensorMap, context);\n    if (isBiasAdd) {\n        if (isPrelu && numArgs !== 2) {\n            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu ' +\n                'must have two extra arguments: bias and alpha.');\n        }\n        if (!isPrelu && numArgs !== 1) {\n            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd must have ' +\n                'one extra argument: bias.');\n        }\n    }\n    if (isBatchNorm) {\n        throw new Error('FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.');\n    }\n    const stride = getParamValue('strides', node, tensorMap, context);\n    const pad = getPadding(node, tensorMap, context);\n    const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n        .toUpperCase();\n    const dilations = getParamValue('dilations', node, tensorMap, context);\n    const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n    return {\n        stride,\n        pad,\n        dataFormat,\n        dilations,\n        biasArg,\n        preluArg,\n        activationFunc\n    };\n}\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Conv1D': {\n            const stride = getParamValue('stride', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilation = getParamValue('dilation', node, tensorMap, context);\n            return [tfOps.conv1d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), stride, pad, dataFormat, dilation)];\n        }\n        case 'Conv2D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            return [tfOps.conv2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n        }\n        case '_FusedConv2D': {\n            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n            return [tfOps.fused.conv2d({\n                    x: getParamValue('x', node, tensorMap, context),\n                    filter: getParamValue('filter', node, tensorMap, context),\n                    strides: [stride[1], stride[2]],\n                    pad: pad,\n                    dataFormat: dataFormat,\n                    dilations: [dilations[1], dilations[2]],\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        }\n        case 'FusedDepthwiseConv2dNative': {\n            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n            return [tfOps.fused.depthwiseConv2d({\n                    x: getParamValue('x', node, tensorMap, context),\n                    filter: getParamValue('filter', node, tensorMap, context),\n                    strides: [stride[1], stride[2]],\n                    pad: pad,\n                    dataFormat: dataFormat,\n                    dilations: [dilations[1], dilations[2]],\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        }\n        case 'Conv2DBackpropInput':\n        case 'Conv2dTranspose': {\n            const shape = getParamValue('outputShape', node, tensorMap, context);\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            return [tfOps.conv2dTranspose(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), shape, [stride[1], stride[2]], pad)];\n        }\n        case 'DepthwiseConv2dNative':\n        case 'DepthwiseConv2d': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            return [tfOps.depthwiseConv2d(getParamValue('input', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n        }\n        case 'Conv3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            return [tfOps.conv3d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2], stride[3]], pad, dataFormat, [dilations[1], dilations[2], dilations[3]])];\n        }\n        case 'AvgPool': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.avgPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n        }\n        case 'MaxPool': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.maxPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n        }\n        case 'MaxPoolWithArgmax': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            const includeBatchInIndex = getParamValue('includeBatchInIndex', node, tensorMap, context);\n            const { result, indexes } = tfOps.maxPoolWithArgmax(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad, includeBatchInIndex);\n            return [result, indexes];\n        }\n        case 'AvgPool3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.avgPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n        }\n        case 'MaxPool3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.maxPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n        }\n        case 'Dilation2D': {\n            const strides = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            // strides: [1, stride_height, stride_width, 1].\n            const strideHeight = strides[1];\n            const strideWidth = strides[2];\n            // dilations: [1, dilation_height, dilation_width, 1].\n            const dilationHeight = dilations[1];\n            const dilationWidth = dilations[2];\n            return [tfOps.dilation2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [strideHeight, strideWidth], pad, [dilationHeight, dilationWidth], 'NHWC' /* dataFormat */)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'convolution';\n//# sourceMappingURL=convolution_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Fill': {\n            const shape = getParamValue('shape', node, tensorMap, context);\n            const dtype = getParamValue('dtype', node, tensorMap, context);\n            const value = getParamValue('value', node, tensorMap, context);\n            return [tfOps.fill(shape, value, dtype)];\n        }\n        case 'LinSpace': {\n            const start = getParamValue('start', node, tensorMap, context);\n            const stop = getParamValue('stop', node, tensorMap, context);\n            const num = getParamValue('num', node, tensorMap, context);\n            return [tfOps.linspace(start, stop, num)];\n        }\n        case 'Multinomial': {\n            const logits = getParamValue('logits', node, tensorMap, context);\n            const numSamples = getParamValue('numSamples', node, tensorMap, context);\n            const seed = getParamValue('seed', node, tensorMap, context);\n            return [tfOps.multinomial(logits, numSamples, seed)];\n        }\n        case 'OneHot': {\n            const indices = getParamValue('indices', node, tensorMap, context);\n            const depth = getParamValue('depth', node, tensorMap, context);\n            const onValue = getParamValue('onValue', node, tensorMap, context);\n            const offValue = getParamValue('offValue', node, tensorMap, context);\n            return [tfOps.oneHot(indices, depth, onValue, offValue)];\n        }\n        case 'Ones': {\n            return [tfOps.ones(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'OnesLike': {\n            return [tfOps.onesLike(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'RandomUniform': {\n            return [tfOps.randomUniform(\n                // tslint:disable-next-line:no-any\n                getParamValue('shape', node, tensorMap, context), getParamValue('minval', node, tensorMap, context), getParamValue('maxval', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'Range': {\n            const start = getParamValue('start', node, tensorMap, context);\n            const stop = getParamValue('stop', node, tensorMap, context);\n            const step = getParamValue('step', node, tensorMap, context);\n            return [tfOps.range(start, stop, step, getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'TruncatedNormal': {\n            const shape = getParamValue('shape', node, tensorMap, context);\n            const mean = getParamValue('mean', node, tensorMap, context);\n            const stdDev = getParamValue('stdDev', node, tensorMap, context);\n            const seed = getParamValue('seed', node, tensorMap, context);\n            return [tfOps.truncatedNormal(shape, mean, stdDev, getParamValue('dtype', node, tensorMap, context), seed)];\n        }\n        case 'Zeros': {\n            return [tfOps.zeros(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'ZerosLike': {\n            return [tfOps.zerosLike(getParamValue('x', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'creation';\n//# sourceMappingURL=creation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nfunction nmsParams(node, tensorMap, context) {\n    const boxes = getParamValue('boxes', node, tensorMap, context);\n    const scores = getParamValue('scores', node, tensorMap, context);\n    const maxOutputSize = getParamValue('maxOutputSize', node, tensorMap, context);\n    const iouThreshold = getParamValue('iouThreshold', node, tensorMap, context);\n    const scoreThreshold = getParamValue('scoreThreshold', node, tensorMap, context);\n    const softNmsSigma = getParamValue('softNmsSigma', node, tensorMap, context);\n    return {\n        boxes,\n        scores,\n        maxOutputSize,\n        iouThreshold,\n        scoreThreshold,\n        softNmsSigma\n    };\n}\nexport const executeOp = async (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'NonMaxSuppressionV5': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node, tensorMap, context);\n            const result = await tfOps.image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n            return [result.selectedIndices, result.selectedScores];\n        }\n        case 'NonMaxSuppressionV4': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n            const padToMaxOutputSize = getParamValue('padToMaxOutputSize', node, tensorMap, context);\n            const result = await tfOps.image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n            return [result.selectedIndices, result.validOutputs];\n        }\n        case 'NonMaxSuppressionV3':\n        case 'NonMaxSuppressionV2': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n            return [await tfOps.image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];\n        }\n        case 'Where': {\n            const condition = tfOps.cast(getParamValue('condition', node, tensorMap, context), 'bool');\n            const result = [await tfOps.whereAsync(condition)];\n            condition.dispose();\n            return result;\n        }\n        case 'ListDiff': {\n            return tfOps.setdiff1dAsync(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context));\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'dynamic';\n//# sourceMappingURL=dynamic_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'TopKV2': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const k = getParamValue('k', node, tensorMap, context);\n            const sorted = getParamValue('sorted', node, tensorMap, context);\n            const result = tfOps.topk(x, k, sorted);\n            return [result.values, result.indices];\n        }\n        case 'Unique': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const result = tfOps.unique(x);\n            return [result.values, result.indices];\n        }\n        case 'UniqueV2': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const result = tfOps.unique(x, axis);\n            return [result.values, result.indices];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'evaluation';\n//# sourceMappingURL=evaluation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Const': {\n            return tensorMap[node.name];\n        }\n        case 'PlaceholderWithDefault':\n            const def = getParamValue('default', node, tensorMap, context);\n            return [getTensor(node.name, tensorMap, context) || def];\n        case 'Placeholder':\n            return [getTensor(node.name, tensorMap, context)];\n        case 'Identity':\n        case 'StopGradient':\n        case 'FakeQuantWithMinMaxVars': { // This op is currently ignored.\n            const data = getParamValue('x', node, tensorMap, context);\n            return [cloneTensor(data)];\n        }\n        case 'IdentityN':\n            return getParamValue('x', node, tensorMap, context)\n                .map((t) => cloneTensor(t));\n        case 'Snapshot':\n            const snapshot = getParamValue('x', node, tensorMap, context);\n            return [cloneTensor(snapshot)];\n        case 'Shape':\n            return [tfOps.tensor1d(getParamValue('x', node, tensorMap, context).shape, 'int32')];\n        case 'ShapeN':\n            return getParamValue('x', node, tensorMap, context)\n                .map((t) => tfOps.tensor1d(t.shape));\n        case 'Size':\n            return [tfOps.scalar(getParamValue('x', node, tensorMap, context).size, 'int32')];\n        case 'Rank':\n            return [tfOps.scalar(getParamValue('x', node, tensorMap, context).rank, 'int32')];\n        case 'NoOp':\n            return [tfOps.scalar(1)];\n        case 'Print':\n            const input = getParamValue('x', node, tensorMap, context);\n            const data = getParamValue('data', node, tensorMap, context);\n            const message = getParamValue('message', node, tensorMap, context);\n            const summarize = getParamValue('summarize', node, tensorMap, context);\n            console.warn('The graph has a tf.print() operation,' +\n                'usually used for debugging, which slows down performance.');\n            console.log(message);\n            for (let i = 0; i < data.length; i++) {\n                console.log(Array.prototype.slice.call(data[i].dataSync())\n                    .slice(0, summarize));\n            }\n            return [input];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'graph';\n//# sourceMappingURL=graph_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'ResizeBilinear': {\n            const images = getParamValue('images', node, tensorMap, context);\n            const size = getParamValue('size', node, tensorMap, context);\n            const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n            return [tfOps.image.resizeBilinear(images, [size[0], size[1]], alignCorners)];\n        }\n        case 'ResizeNearestNeighbor': {\n            const images = getParamValue('images', node, tensorMap, context);\n            const size = getParamValue('size', node, tensorMap, context);\n            const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n            return [tfOps.image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners)];\n        }\n        case 'CropAndResize': {\n            const image = getParamValue('image', node, tensorMap, context);\n            const boxes = getParamValue('boxes', node, tensorMap, context);\n            const boxInd = getParamValue('boxInd', node, tensorMap, context);\n            const cropSize = getParamValue('cropSize', node, tensorMap, context);\n            const method = getParamValue('method', node, tensorMap, context);\n            const extrapolationValue = getParamValue('extrapolationValue', node, tensorMap, context);\n            return [tfOps.image.cropAndResize(image, boxes, boxInd, cropSize, method, extrapolationValue)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'image';\n//# sourceMappingURL=image_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Equal': {\n            return [tfOps.equal(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'NotEqual': {\n            return [tfOps.notEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Greater': {\n            return [tfOps.greater(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'GreaterEqual': {\n            return [tfOps.greaterEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Less': {\n            return [tfOps.less(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LessEqual': {\n            return [tfOps.lessEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LogicalAnd': {\n            return [tfOps.logicalAnd(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LogicalNot': {\n            return [tfOps.logicalNot(getParamValue('a', node, tensorMap, context))];\n        }\n        case 'LogicalOr': {\n            return [tfOps.logicalOr(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Select':\n        case 'SelectV2': {\n            return [tfOps.where(getParamValue('condition', node, tensorMap, context), getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'logical';\n//# sourceMappingURL=logical_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'BatchMatMul':\n        case 'BatchMatMulV2':\n        case 'MatMul':\n            return [tfOps.matMul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context), getParamValue('transposeA', node, tensorMap, context), getParamValue('transposeB', node, tensorMap, context))];\n        case 'Transpose':\n            return [tfOps.transpose(getParamValue('x', node, tensorMap, context), getParamValue('perm', node, tensorMap, context))];\n        case '_FusedMatMul':\n            const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n            const isBiasAdd = extraOp === 'biasadd';\n            const isPrelu = activationFunc === 'prelu';\n            const numArgs = getParamValue('numArgs', node, tensorMap, context);\n            if (isBiasAdd) {\n                if (isPrelu && numArgs !== 2) {\n                    throw new Error('Fused MatMul with BiasAdd and Prelu must have two ' +\n                        'extra arguments: bias and alpha.');\n                }\n                if (!isPrelu && numArgs !== 1) {\n                    throw new Error('Fused MatMul with BiasAdd must have one extra argument: bias.');\n                }\n            }\n            const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n            return [tfOps.fused.matMul({\n                    a: getParamValue('a', node, tensorMap, context),\n                    b: getParamValue('b', node, tensorMap, context),\n                    transposeA: getParamValue('transposeA', node, tensorMap, context),\n                    transposeB: getParamValue('transposeB', node, tensorMap, context),\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'matrices';\n//# sourceMappingURL=matrices_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'FusedBatchNorm':\n        case 'FusedBatchNormV2': {\n            return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n        }\n        case 'FusedBatchNormV3': {\n            return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n        }\n        case 'LRN': {\n            return [tfOps.localResponseNormalization(getParamValue('x', node, tensorMap, context), getParamValue('radius', node, tensorMap, context), getParamValue('bias', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context), getParamValue('beta', node, tensorMap, context))];\n        }\n        case 'Softmax': {\n            return [tfOps.softmax(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'LogSoftmax': {\n            return [tfOps.logSoftmax(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'SparseToDense': {\n            return [tfOps.sparseToDense(getParamValue('sparseIndices', node, tensorMap, context), getParamValue('outputShape', node, tensorMap, context), getParamValue('sparseValues', node, tensorMap, context), getParamValue('defaultValue', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'normalization';\n//# sourceMappingURL=normalization_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Max': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.max(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Mean': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.mean(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Min': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.min(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Sum': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.sum(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'All': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.all(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Any': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.any(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'ArgMax': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.argMax(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'ArgMin': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.argMin(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Prod': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.prod(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Cumsum': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const exclusive = getParamValue('exclusive', node, tensorMap, context);\n            const reverse = getParamValue('reverse', node, tensorMap, context);\n            return [tfOps.cumsum(getParamValue('x', node, tensorMap, context), axis, exclusive, reverse)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'reduction';\n//# sourceMappingURL=reduction_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tidy, util } from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'ConcatV2':\n        case 'Concat': {\n            const n = getParamValue('n', node, tensorMap, context);\n            const axis = getParamValue('axis', node, tensorMap, context);\n            let inputs = getParamValue('tensors', node, tensorMap, context);\n            inputs = inputs.slice(0, n);\n            return [tfOps.concat(inputs, axis)];\n        }\n        case 'GatherV2':\n        case 'Gather': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const input = getParamValue('x', node, tensorMap, context);\n            const indices = getParamValue('indices', node, tensorMap, context);\n            return [tfOps.gather(input, tfOps.cast(indices, 'int32'), axis)];\n        }\n        case 'ReverseV2':\n        case 'Reverse': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const input = getParamValue('x', node, tensorMap, context);\n            return [tfOps.reverse(input, axis)];\n        }\n        case 'Slice': {\n            // tslint:disable-next-line:no-any\n            const begin = getParamValue('begin', node, tensorMap, context);\n            // tslint:disable-next-line:no-any\n            const size = getParamValue('size', node, tensorMap, context);\n            return [tfOps.slice(getParamValue('x', node, tensorMap, context), begin, size)];\n        }\n        case 'StridedSlice': {\n            const begin = getParamValue('begin', node, tensorMap, context);\n            const end = getParamValue('end', node, tensorMap, context);\n            const strides = getParamValue('strides', node, tensorMap, context);\n            const beginMask = getParamValue('beginMask', node, tensorMap, context);\n            const endMask = getParamValue('endMask', node, tensorMap, context);\n            const ellipsisMask = getParamValue('ellipsisMask', node, tensorMap, context);\n            const newAxisMask = getParamValue('newAxisMask', node, tensorMap, context);\n            const shrinkAxisMask = getParamValue('shrinkAxisMask', node, tensorMap, context);\n            const tensor = getParamValue('x', node, tensorMap, context);\n            return [tfOps.stridedSlice(tensor, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)];\n        }\n        case 'Pack': {\n            return tidy(() => {\n                const axis = getParamValue('axis', node, tensorMap, context);\n                const tensors = getParamValue('tensors', node, tensorMap, context);\n                // Reshape the tensors to the first tensor's shape if they don't\n                // match.\n                const shape = tensors[0].shape;\n                const squeezedShape = tfOps.squeeze(tensors[0]).shape;\n                const mapped = tensors.map(tensor => {\n                    const sameShape = util.arraysEqual(tensor.shape, shape);\n                    if (!sameShape &&\n                        !util.arraysEqual(tfOps.squeeze(tensor).shape, squeezedShape)) {\n                        throw new Error('the input tensors shape does not match');\n                    }\n                    return sameShape ? tensor : tfOps.reshape(tensor, shape);\n                });\n                return [tfOps.stack(mapped, axis)];\n            });\n        }\n        case 'Unpack': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const tensor = getParamValue('tensor', node, tensorMap, context);\n            return tfOps.unstack(tensor, axis);\n        }\n        case 'Tile': {\n            const reps = getParamValue('reps', node, tensorMap, context);\n            return [tfOps.tile(getParamValue('x', node, tensorMap, context), reps)];\n        }\n        case 'Split':\n        case 'SplitV': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const numOrSizeSplits = getParamValue('numOrSizeSplits', node, tensorMap, context);\n            const tensor = getParamValue('x', node, tensorMap, context);\n            return tfOps.split(tensor, numOrSizeSplits, axis);\n        }\n        case 'ScatterNd': {\n            const indices = getParamValue('indices', node, tensorMap, context);\n            const values = getParamValue('values', node, tensorMap, context);\n            const shape = getParamValue('shape', node, tensorMap, context);\n            return [tfOps.scatterND(indices, values, shape)];\n        }\n        case 'GatherNd': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const indices = getParamValue('indices', node, tensorMap, context);\n            return [tfOps.gatherND(x, indices)];\n        }\n        case 'SparseToDense': {\n            const indices = getParamValue('sparseIndices', node, tensorMap, context);\n            const shape = getParamValue('outputShape', node, tensorMap, context);\n            const sparseValues = getParamValue('sparseValues', node, tensorMap, context);\n            const defaultValue = getParamValue('defaultValue', node, tensorMap, context);\n            return [tfOps.sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ?\n                    defaultValue :\n                    tfOps.cast(defaultValue, sparseValues.dtype))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'slice_join';\n//# sourceMappingURL=slice_join_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'FFT': {\n            return [tfOps.fft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'IFFT': {\n            return [tfOps.ifft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'RFFT': {\n            return [tfOps.rfft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'IRFFT': {\n            return [tfOps.irfft(getParamValue('x', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'spectral';\n//# sourceMappingURL=spectral_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Cast': {\n            return [tfOps.cast(getParamValue('x', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'ExpandDims': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.expandDims(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Squeeze': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.squeeze(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Reshape': {\n            return [tfOps.reshape(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n        }\n        case 'PadV2':\n        case 'Pad': {\n            return [tfOps.pad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('constantValue', node, tensorMap, context))];\n        }\n        case 'SpaceToBatchND': {\n            const blockShape = getParamValue('blockShape', node, tensorMap, context);\n            const paddings = getParamValue('paddings', node, tensorMap, context);\n            return [tfOps.spaceToBatchND(getParamValue('x', node, tensorMap, context), blockShape, paddings)];\n        }\n        case 'BatchToSpaceND': {\n            const blockShape = getParamValue('blockShape', node, tensorMap, context);\n            const crops = getParamValue('crops', node, tensorMap, context);\n            return [tfOps.batchToSpaceND(getParamValue('x', node, tensorMap, context), blockShape, crops)];\n        }\n        case 'DepthToSpace': {\n            const blockSize = getParamValue('blockSize', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context).toUpperCase();\n            return [tfOps.depthToSpace(getParamValue('x', node, tensorMap, context), blockSize, dataFormat)];\n        }\n        case 'BroadcastTo': {\n            return [tfOps.broadcastTo(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'transformation';\n//# sourceMappingURL=transformation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { NodeValueImpl } from './custom_op/node_value_impl';\nimport { getRegisteredOp } from './custom_op/register';\nimport * as arithmetic from './executors/arithmetic_executor';\nimport * as basicMath from './executors/basic_math_executor';\nimport * as control from './executors/control_executor';\nimport * as convolution from './executors/convolution_executor';\nimport * as creation from './executors/creation_executor';\nimport * as dynamic from './executors/dynamic_executor';\nimport * as evaluation from './executors/evaluation_executor';\nimport * as graph from './executors/graph_executor';\nimport * as image from './executors/image_executor';\nimport * as logical from './executors/logical_executor';\nimport * as matrices from './executors/matrices_executor';\nimport * as normalization from './executors/normalization_executor';\nimport * as reduction from './executors/reduction_executor';\nimport * as sliceJoin from './executors/slice_join_executor';\nimport * as spectral from './executors/spectral_executor';\nimport * as transformation from './executors/transformation_executor';\n/**\n * Executes the op defined by the node object.\n * @param node\n * @param tensorMap contains tensors for executed nodes and weights\n */\nexport function executeOp(node, tensorMap, context) {\n    const value = ((node, tensorMap, context) => {\n        switch (node.category) {\n            case 'arithmetic':\n                return tfc.tidy(() => arithmetic.executeOp(node, tensorMap, context));\n            case 'basic_math':\n                return tfc.tidy(() => basicMath.executeOp(node, tensorMap, context));\n            case 'control':\n                return control.executeOp(node, tensorMap, context);\n            case 'convolution':\n                return tfc.tidy(() => convolution.executeOp(node, tensorMap, context));\n            case 'creation':\n                return tfc.tidy(() => creation.executeOp(node, tensorMap, context));\n            case 'dynamic':\n                return dynamic.executeOp(node, tensorMap, context);\n            case 'evaluation':\n                return tfc.tidy(() => evaluation.executeOp(node, tensorMap, context));\n            case 'image':\n                return tfc.tidy(() => image.executeOp(node, tensorMap, context));\n            case 'graph':\n                return tfc.tidy(() => graph.executeOp(node, tensorMap, context));\n            case 'logical':\n                return tfc.tidy(() => logical.executeOp(node, tensorMap, context));\n            case 'matrices':\n                return tfc.tidy(() => matrices.executeOp(node, tensorMap, context));\n            case 'normalization':\n                return tfc.tidy(() => normalization.executeOp(node, tensorMap, context));\n            case 'reduction':\n                return tfc.tidy(() => reduction.executeOp(node, tensorMap, context));\n            case 'slice_join':\n                return tfc.tidy(() => sliceJoin.executeOp(node, tensorMap, context));\n            case 'spectral':\n                return tfc.tidy(() => spectral.executeOp(node, tensorMap, context));\n            case 'transformation':\n                return tfc.tidy(() => transformation.executeOp(node, tensorMap, context));\n            case 'custom':\n                const opMapper = getRegisteredOp(node.op);\n                if (opMapper && opMapper.customExecutor) {\n                    return opMapper.customExecutor(new NodeValueImpl(node, tensorMap, context));\n                }\n                else {\n                    throw TypeError(`Custom op ${node.op} is not registered.`);\n                }\n            default:\n                throw TypeError(`Unknown op '${node.op}'. File an issue at ` +\n                    `https://github.com/tensorflow/tfjs/issues so we can add it` +\n                    `, or register a custom execution with tf.registerOp()`);\n        }\n    })(node, tensorMap, context);\n    if (value instanceof Promise) {\n        return value.then((data) => [].concat(data));\n    }\n    return [].concat(value);\n}\n//# sourceMappingURL=operation_executor.js.map", "/**\n * ExecutionContext captures the runtime environment of the node. It keeps\n * track of the current frame and iteration for the control flow ops.\n *\n * For example, typical Dynamic RNN model may contain loops, for which\n * TensorFlow will generate graphs with Enter/Exit nodes to control the\n * current execution frame, and NextIteration Nodes for iteration id increment.\n * For model with branch logic, TensorFLow will generate Switch/Merge ops.\n */\nexport class ExecutionContext {\n    constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}) {\n        this.weightMap = weightMap;\n        this.tensorArrayMap = tensorArrayMap;\n        this.tensorListMap = tensorListMap;\n        this.functionMap = functionMap;\n        this.rootContext = { id: 0, frameName: '', iterationId: 0 };\n        this.contexts = [this.rootContext];\n        this.lastId = 0;\n        this.generateCurrentContextIds();\n    }\n    newFrame(id, frameName) {\n        return { id, frameName, iterationId: 0 };\n    }\n    /**\n     * Set the current context\n     * @param contexts: ExecutionContextInfo[] the current path of execution\n     * frames\n     */\n    set currentContext(contexts) {\n        if (this.contexts !== contexts) {\n            this.contexts = contexts;\n            this.generateCurrentContextIds();\n        }\n    }\n    get currentContext() {\n        return this.contexts;\n    }\n    /**\n     * Returns the current context in string format.\n     */\n    get currentContextId() {\n        return this._currentContextIds[0];\n    }\n    /**\n     * Returns the current context and all parent contexts in string format.\n     * This allow access to the nodes in the current and parent frames.\n     */\n    get currentContextIds() {\n        return this._currentContextIds;\n    }\n    generateCurrentContextIds() {\n        const names = [];\n        for (let i = 0; i < this.contexts.length - 1; i++) {\n            const contexts = this.contexts.slice(0, this.contexts.length - i);\n            names.push(this.contextIdforContexts(contexts));\n        }\n        names.push('');\n        this._currentContextIds = names;\n    }\n    contextIdforContexts(contexts) {\n        return contexts ?\n            contexts\n                .map(context => (context.id === 0 && context.iterationId === 0) ?\n                '' :\n                `${context.frameName}-${context.iterationId}`)\n                .join('/') :\n            '';\n    }\n    /**\n     * Enter a new frame, a new context is pushed on the current context list.\n     * @param frameId new frame id\n     */\n    enterFrame(frameId) {\n        if (this.contexts) {\n            this.lastId++;\n            this.contexts = this.contexts.slice();\n            this.contexts.push(this.newFrame(this.lastId, frameId));\n            this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));\n        }\n    }\n    /**\n     * Exit the current frame, the last context is removed from the current\n     * context list.\n     */\n    exitFrame() {\n        if (this.contexts && this.contexts.length > 1) {\n            this.contexts = this.contexts.slice();\n            this.contexts.splice(-1);\n            this.currentContextIds.shift();\n        }\n        else {\n            throw new Error('Cannot exit frame, the context is empty');\n        }\n    }\n    /**\n     * Enter the next iteration of a loop, the iteration id of last context is\n     * increased.\n     */\n    nextIteration() {\n        if (this.contexts && this.contexts.length > 0) {\n            this.contexts = this.contexts.slice();\n            this.lastId++;\n            const context = Object.assign({}, this.contexts[this.contexts.length - 1]);\n            context.iterationId += 1;\n            context.id = this.lastId;\n            this.contexts.splice(-1, 1, context);\n            this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));\n        }\n        else {\n            throw new Error('Cannot increase frame iteration, the context is empty');\n        }\n    }\n    getWeight(name) {\n        return this.weightMap[name];\n    }\n    addTensorArray(tensorArray) {\n        this.tensorArrayMap[tensorArray.id] = tensorArray;\n    }\n    getTensorArray(id) {\n        return this.tensorArrayMap[id];\n    }\n    addTensorList(tensorList) {\n        this.tensorListMap[tensorList.id] = tensorList;\n    }\n    getTensorList(id) {\n        return this.tensorListMap[id];\n    }\n    dispose(keepIds) {\n        for (const key in this.tensorArrayMap) {\n            this.tensorArrayMap[key].clearAndClose(keepIds);\n        }\n        for (const key in this.tensorListMap) {\n            this.tensorListMap[key].clearAndClose(keepIds);\n        }\n    }\n}\n//# sourceMappingURL=execution_context.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { parseNodeName } from '../operations/executors/utils';\n/**\n * Given graph inputs and desired outputs, find the minimal set of nodes\n * to execute in order to compute the outputs. In addition return other useful\n * info such:\n * - Missing inputs needed to compute the output.\n * - Whether the subgraph contains dynamic ops (control flow, dynamic shape).\n * - Alternative inputs in order to avoid async (dynamic op) execution.\n */\nexport function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {\n    const usedNodes = new Set();\n    const missingInputs = [];\n    let dynamicNode = null;\n    let syncInputs = null;\n    // Start with the outputs, going backwards and find all the nodes that are\n    // needed to compute those outputs.\n    const seen = new Set();\n    const inputNodeNames = Object.keys(inputs).map(name => parseNodeName(name)[0]);\n    let initNodeNames = [];\n    if (initNodes != null) {\n        initNodeNames = initNodes.map(node => parseNodeName(node.name)[0]);\n    }\n    const frontier = [...outputs];\n    while (frontier.length > 0) {\n        const node = frontier.pop();\n        if (isControlFlow(node) || isDynamicShape(node)) {\n            if (dynamicNode == null) {\n                dynamicNode = node;\n                syncInputs = dynamicNode.children.map(child => child.name)\n                    .filter(name => usedNodes.has(name));\n            }\n        }\n        usedNodes.add(node.name);\n        // Weights are dead end since we already have their values.\n        if (weightMap[node.name] != null) {\n            continue;\n        }\n        // This node is a dead end since it's one of the user-provided inputs.\n        if (inputNodeNames.indexOf(node.name) !== -1) {\n            continue;\n        }\n        // This node is a dead end since it doesn't have any inputs.\n        if (initNodeNames.indexOf(node.name) !== -1) {\n            continue;\n        }\n        if (node.inputs.length === 0) {\n            missingInputs.push(node.name);\n            continue;\n        }\n        node.inputs.forEach(input => {\n            // Don't add to the frontier if it is already there.\n            if (seen.has(input.name)) {\n                return;\n            }\n            seen.add(input.name);\n            frontier.push(input);\n        });\n    }\n    return { inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs };\n}\n/**\n * Given the execution info, return a list of nodes in topological order that\n * need to be executed to compute the output.\n */\nexport function getNodesInTopologicalOrder(graph, weightMap, executionInfo) {\n    const { usedNodes, inputs } = executionInfo;\n    const frontier = [];\n    const inputNodes = Object.keys(inputs)\n        .map(name => parseNodeName(name)[0])\n        .map(name => graph.nodes[name]);\n    const initNodes = graph.initNodes;\n    inputNodes.forEach(input => {\n        if (usedNodes.has(input.name)) {\n            frontier.push(input);\n        }\n    });\n    graph.weights.forEach(weight => {\n        if (usedNodes.has(weight.name)) {\n            frontier.push(weight);\n        }\n    });\n    if (initNodes != null) {\n        initNodes.forEach(node => {\n            if (usedNodes.has(node.name)) {\n                frontier.push(node);\n            }\n        });\n    }\n    const seen = new Set();\n    const orderedNodes = [];\n    while (frontier.length > 0) {\n        const node = frontier.pop();\n        seen.add(node.name);\n        if (!weightMap[node.name]) {\n            orderedNodes.push(node);\n        }\n        node.children.forEach(child => {\n            if (!seen.has(child.name) && usedNodes.has(child.name) &&\n                child.inputs.every(input => seen.has(input.name))) {\n                frontier.push(child);\n            }\n        });\n    }\n    return orderedNodes;\n}\nconst CONTROL_FLOW_OPS = [\n    'Switch', 'Merge', 'Enter', 'Exit', 'NextIteration', 'StatelessIf',\n    'StatelessWhile', 'if', 'While'\n];\nconst DYNAMIC_SHAPE_OPS = [\n    'NonMaxSuppressionV2', 'NonMaxSuppressionV3', 'NonMaxSuppressionV5', 'Where'\n];\nexport function isControlFlow(node) {\n    return CONTROL_FLOW_OPS.indexOf(node.op) >= 0;\n}\nexport function isDynamicShape(node) {\n    return DYNAMIC_SHAPE_OPS.indexOf(node.op) >= 0;\n}\n//# sourceMappingURL=model_analysis.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tidy, util } from '@tensorflow/tfjs-core';\nimport { getNodeNameAndIndex, getParamValue, getTensor, getTensorsForCurrentContenxt, parseNodeName } from '../operations/executors/utils';\nimport { executeOp } from '../operations/operation_executor';\nimport { ExecutionContext } from './execution_context';\nimport { getExecutionSubgraph, getNodesInTopologicalOrder, isControlFlow } from './model_analysis';\nexport class GraphExecutor {\n    /**\n     *\n     * @param graph Graph the model or function graph to be executed.\n     * @param parent When building function exector you need to set the parent\n     * executor. Since the weights and function executor maps are set at parant\n     * level, that function executor can access the function maps and weight maps\n     * through the parent.\n     */\n    constructor(graph, parent) {\n        this.graph = graph;\n        this.parent = parent;\n        this.compiledMap = new Map();\n        this._weightMap = {};\n        this.SEPERATOR = ',';\n        this._functions = {};\n        this._functionExecutorMap = {};\n        this._outputs = graph.outputs;\n        this._inputs = graph.inputs;\n        this._initNodes = graph.initNodes;\n        this._signature = graph.signature;\n        this._functions = graph.functions;\n        // create sub-graph executors\n        if (graph.functions != null) {\n            Object.keys(graph.functions).forEach(name => {\n                this._functionExecutorMap[name] =\n                    new GraphExecutor(graph.functions[name], this);\n            });\n        }\n    }\n    get weightIds() {\n        return this.parent ? this.parent.weightIds : this._weightIds;\n    }\n    get functionExecutorMap() {\n        return this.parent ? this.parent.functionExecutorMap :\n            this._functionExecutorMap;\n    }\n    get weightMap() {\n        return this.parent ? this.parent.weightMap : this._weightMap;\n    }\n    set weightMap(weightMap) {\n        const weightIds = Object.keys(weightMap).map(key => weightMap[key].map(tensor => tensor.id));\n        this._weightIds = [].concat(...weightIds);\n        this._weightMap = weightMap;\n    }\n    get inputs() {\n        return this._inputs.map(node => {\n            return {\n                name: node.name,\n                shape: node.attrParams['shape'] ?\n                    node.attrParams['shape'].value :\n                    undefined,\n                dtype: node.attrParams['dtype'] ?\n                    node.attrParams['dtype'].value :\n                    undefined\n            };\n        });\n    }\n    get outputs() {\n        return this._outputs.map(node => {\n            return {\n                name: node.name,\n                shape: node.attrParams['shape'] ?\n                    node.attrParams['shape'].value :\n                    undefined,\n                dtype: node.attrParams['dtype'] ?\n                    node.attrParams['dtype'].value :\n                    undefined\n            };\n        });\n    }\n    get inputNodes() {\n        return this._inputs.map(node => node.signatureKey || node.name);\n    }\n    get outputNodes() {\n        return this._outputs.map((node) => {\n            const name = node.signatureKey || node.name;\n            return node.defaultOutput ? (`${name}:${node.defaultOutput}`) : name;\n        });\n    }\n    get functions() {\n        return Object.keys(this._functions).reduce((map, key) => {\n            map[key] = this._functions[key].signature;\n            return map;\n        }, {});\n    }\n    getCompilationKey(inputs, outputs) {\n        const sortedInputs = inputs.map(node => node.name).sort();\n        const sortedOutputs = outputs.map(node => node.name).sort();\n        return sortedInputs.join(this.SEPERATOR) + '--' +\n            sortedOutputs.join(this.SEPERATOR);\n    }\n    /**\n     * Compiles the inference graph and returns the minimal set of nodes that are\n     * required for execution, in the correct execution order.\n     */\n    compile(inputs, outputs) {\n        const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);\n        const { missingInputs, dynamicNode, syncInputs } = executionInfo;\n        if (dynamicNode != null) {\n            throw new Error(`This execution contains the node '${dynamicNode.name}', which has ` +\n                `the dynamic op '${dynamicNode.op}'. Please use ` +\n                `model.executeAsync() instead. Alternatively, to avoid the ` +\n                `dynamic ops, specify the inputs [${syncInputs}]`);\n        }\n        if (missingInputs.length > 0) {\n            const outNames = outputs.map(n => n.name);\n            const inNames = Object.keys(inputs);\n            throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs ` +\n                `[${inNames}]. Missing the following inputs: [${missingInputs}]`);\n        }\n        return getNodesInTopologicalOrder(this.graph, this.weightMap, executionInfo);\n    }\n    /**\n     * Executes the inference for given input tensors.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     */\n    execute(inputs, outputs) {\n        inputs = this.mapInputs(inputs);\n        const names = Object.keys(inputs).sort();\n        this.checkInputs(inputs);\n        this.checkInputShapeAndType(inputs);\n        outputs = this.mapOutputs(outputs);\n        this.checkOutputs(outputs);\n        const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n        const outputNodeNames = outputs.map(name => parseNodeName(name)[0]);\n        let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n        // If no outputs are specified, then use the default outputs of the model.\n        if (outputNodes.length === 0) {\n            outputNodes = this._outputs;\n        }\n        const compilationKey = this.getCompilationKey(inputNodes, outputNodes);\n        // Do nothing if the compiled graph cache contains the input.\n        let orderedNodes = this.compiledMap.get(compilationKey);\n        if (orderedNodes == null) {\n            orderedNodes = this.compile(inputs, outputNodes);\n            this.compiledMap.set(compilationKey, orderedNodes);\n        }\n        const tensorArrayMap = {};\n        const tensorListMap = {};\n        return tidy(() => {\n            const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n            const tensorsMap = Object.assign({}, this.weightMap);\n            Object.keys(inputs).forEach(name => {\n                const [nodeName, index] = parseNodeName(name);\n                const tensors = [];\n                tensors[index] = inputs[name];\n                tensorsMap[nodeName] = tensors;\n            });\n            const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n            const intermediateTensorConsumerCount = {};\n            for (let i = 0; i < orderedNodes.length; i++) {\n                const node = orderedNodes[i];\n                if (!tensorsMap[node.name]) {\n                    const tensors = executeOp(node, tensorsMap, context);\n                    if (tensors instanceof Promise) {\n                        throw new Error(`The execution of the op '${node.op}' returned a promise. ` +\n                            `Please use model.executeAsync() instead.`);\n                    }\n                    tensorsMap[node.name] = tensors;\n                    this.checkTensorForDisposal(node.name, node, tensorsMap, context, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount);\n                }\n            }\n            // dispose the context for the root executor\n            if (this.parent == null) {\n                context.dispose(tensorsToKeep);\n            }\n            return outputs.map(name => getTensor(name, tensorsMap, context));\n        });\n    }\n    getFrozenTensorIds(tensorMap) {\n        const ids = [].concat.apply([], Object.keys(tensorMap)\n            .map(key => tensorMap[key])\n            .map(tensors => tensors.map(tensor => tensor.id)));\n        return new Set(ids);\n    }\n    checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount) {\n        // Skip output nodes and any control flow nodes, since its dependency is\n        // tricky to track correctly.\n        if (node.category === 'control' || outputNames.indexOf(nodeName) !== -1) {\n            return;\n        }\n        tensorMap[nodeName].forEach(tensor => {\n            if (tensor != null) {\n                intermediateTensorConsumerCount[tensor.id] =\n                    (intermediateTensorConsumerCount[tensor.id] || 0) +\n                        node.children.length;\n            }\n        });\n        node.inputs.forEach(input => {\n            // Skip any control flow nodes, since its dependency is tricky to track\n            // correctly.\n            if (input.category !== 'control') {\n                const tensors = getTensorsForCurrentContenxt(input.name, tensorMap, context);\n                if (tensors != null) {\n                    tensors.forEach(tensor => {\n                        if (tensor && !tensorsToKeep.has(tensor.id)) {\n                            const count = intermediateTensorConsumerCount[tensor.id];\n                            if (count === 1) {\n                                tensor.dispose();\n                                delete intermediateTensorConsumerCount[tensor.id];\n                            }\n                            else if (count != null) {\n                                // only intermediate nodes has count set, inputs and weights are\n                                // not.\n                                intermediateTensorConsumerCount[tensor.id]--;\n                            }\n                        }\n                    });\n                }\n            }\n        });\n    }\n    /**\n     * Executes the inference for given input tensors in Async fashion.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     */\n    async executeAsync(inputs, outputs) {\n        return this._executeAsync(inputs, outputs);\n    }\n    /**\n     * Executes the inference for given input tensors in Async fashion.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     * @param isFunctionExecution Flag for executing a function.\n     * @param tensorArrayMap Optional, global TensorArray map by id. Used for\n     * function execution.\n     * @param tensorArrayMap Optinal global TensorList map by id. Used for\n     * function execution.\n     */\n    async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {\n        if (!isFunctionExecution) {\n            inputs = this.mapInputs(inputs);\n            this.checkInputs(inputs);\n            this.checkInputShapeAndType(inputs);\n            outputs = this.mapOutputs(outputs);\n            this.checkOutputs(outputs);\n        }\n        const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n        // Graph with control flow op requires runtime evaluation of the execution\n        // order, while without control flow the execution order is pre-determined\n        // in the compile method.\n        const tensorMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);\n        const results = outputs.map(name => getTensor(name, tensorMap, context));\n        // dispose all the intermediate tensors\n        const outputIds = results.map(t => t.id);\n        const inputIds = Object.keys(inputs).map(name => inputs[name].id);\n        const keepIds = new Set([...outputIds, ...inputIds, ...this.weightIds]);\n        Object.keys(tensorMap).forEach(key => {\n            const tensorArray = tensorMap[key];\n            tensorArray.forEach(tensor => {\n                if (tensor && !tensor.isDisposed && !keepIds.has(tensor.id)) {\n                    tensor.dispose();\n                }\n            });\n        });\n        // dispose the context for the root executor\n        if (this.parent == null) {\n            context.dispose(keepIds);\n        }\n        return results;\n    }\n    async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {\n        const mappedInputs = inputs.reduce((map, tensor, index) => {\n            map[this.inputs[index].name] = tensor;\n            return map;\n        }, {});\n        return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);\n    }\n    /**\n     * When there are control flow nodes in the graph, the graph execution use\n     * ExecutionContext to keep track of the frames and loop iterators.\n     * @param inputs placeholder tensors for the graph.\n     * @param context the execution context object for current execution.\n     * @param isFunctionExecution Flag for executing a function.\n     */\n    async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {\n        const names = Object.keys(inputs);\n        const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n        const outputNodeNames = outputNames.map(name => parseNodeName(name)[0]);\n        const outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n        const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes, this.weightMap);\n        const stack = [...inputNodes, ...this.graph.weights].map(node => {\n            return { node, contexts: context.currentContext };\n        });\n        const tensorsMap = Object.assign({}, this.weightMap);\n        Object.keys(inputs).forEach(name => {\n            const [nodeName, index] = parseNodeName(name);\n            const tensors = [];\n            tensors[index] = inputs[name];\n            tensorsMap[nodeName] = tensors;\n        });\n        const intermediateTensorConsumerCount = {};\n        const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n        const added = {};\n        while (stack.length > 0) {\n            const promises = this.processStack(inputNodes, stack, context, tensorsMap, added, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount, usedNodes);\n            await Promise.all(promises);\n        }\n        if (dynamicNode == null && !isFunctionExecution) {\n            console.warn(`This model execution did not contain any nodes with control flow ` +\n                `or dynamic output shapes. You can use model.execute() instead.`);\n        }\n        const missingOutputs = outputNodes\n            .filter(node => !isControlFlow(node) &&\n            !getTensor(node.name, tensorsMap, context))\n            .map(node => node.name);\n        if (missingOutputs.length > 0) {\n            let alternativeMsg = '';\n            if (dynamicNode != null) {\n                alternativeMsg =\n                    `Alternatively, to avoid the dynamic ops, use model.execute() ` +\n                        `and specify the inputs [${syncInputs}]`;\n            }\n            throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided ` +\n                `inputs [${names}]. Consider providing the following inputs: ` +\n                `[${missingInputs}]. ${alternativeMsg}`);\n        }\n        return tensorsMap;\n    }\n    processStack(inputNodes, stack, context, tensorMap, added, tensorsToKeep, outputNames, intermediateTensorConsumerCount, usedNodes) {\n        const promises = [];\n        while (stack.length > 0) {\n            const item = stack.pop();\n            context.currentContext = item.contexts;\n            let nodeName = '';\n            // The tensor of the Enter op with isConstant set should be set\n            // in the parent scope, so it will be available as constant for the\n            // whole loop.\n            if (item.node.op === 'Enter' &&\n                getParamValue('isConstant', item.node, tensorMap, context)) {\n                [nodeName] = getNodeNameAndIndex(item.node.name, context);\n            }\n            // only process nodes that are not provided as input nodes.\n            if (inputNodes.indexOf(item.node) === -1) {\n                const tensors = executeOp(item.node, tensorMap, context);\n                if (!nodeName) {\n                    [nodeName] = getNodeNameAndIndex(item.node.name, context);\n                }\n                const currentContext = context.currentContext;\n                if (tensors instanceof Promise) {\n                    promises.push(tensors.then(t => {\n                        tensorMap[nodeName] = t;\n                        context.currentContext = currentContext;\n                        this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n                        this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n                        return t;\n                    }));\n                }\n                else {\n                    tensorMap[nodeName] = tensors;\n                    this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n                    this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n                }\n            }\n            else {\n                this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n            }\n        }\n        return promises;\n    }\n    processChildNodes(node, stack, context, tensorMap, added, usedNodes) {\n        node.children.forEach((childNode) => {\n            const [nodeName,] = getNodeNameAndIndex(childNode.name, context);\n            if (added[nodeName] || !usedNodes.has(childNode.name)) {\n                return;\n            }\n            // Merge op can be pushed if any of its inputs has value.\n            if (childNode.op === 'Merge') {\n                if (childNode.inputNames.some(name => {\n                    return !!getTensor(name, tensorMap, context);\n                })) {\n                    added[nodeName] = true;\n                    stack.push({ contexts: context.currentContext, node: childNode });\n                }\n            }\n            else // Otherwise all inputs must to have value.\n             if (childNode.inputNames.every(name => {\n                return !!getTensor(name, tensorMap, context);\n            })) {\n                added[nodeName] = true;\n                stack.push({ contexts: context.currentContext, node: childNode });\n            }\n        });\n    }\n    /**\n     * Releases the memory used by the weight tensors.\n     */\n    dispose() {\n        Object.keys(this.weightMap)\n            .forEach(key => this.weightMap[key].forEach(tensor => tensor.dispose()));\n    }\n    checkInputShapeAndType(inputs) {\n        Object.keys(inputs).forEach(name => {\n            const input = inputs[name];\n            const [nodeName,] = parseNodeName(name);\n            const node = this.graph.nodes[nodeName];\n            if (node.attrParams['shape'] && node.attrParams['shape'].value) {\n                const shape = node.attrParams['shape'].value;\n                const match = shape.length === input.shape.length &&\n                    input.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim);\n                util.assert(match, () => `The shape of dict['${node.name}'] provided in ` +\n                    `model.execute(dict) must be [${shape}], but was ` +\n                    `[${input.shape}]`);\n            }\n            if (node.attrParams['dtype'] && node.attrParams['dtype'].value) {\n                util.assert(input.dtype === node.attrParams['dtype'].value, () => `The dtype of dict['${node.name}'] provided in ` +\n                    `model.execute(dict) must be ` +\n                    `${node.attrParams['dtype'].value}, but was ${input.dtype}`);\n            }\n        });\n    }\n    mapInputs(inputs) {\n        const result = {};\n        for (const inputName in inputs) {\n            if (this._signature != null && this._signature.inputs != null &&\n                this._signature.inputs[inputName] != null) {\n                const tensor = this._signature.inputs[inputName];\n                result[tensor.name] = inputs[inputName];\n            }\n            else {\n                result[inputName] = inputs[inputName];\n            }\n        }\n        return result;\n    }\n    checkInputs(inputs) {\n        const notInGraph = Object.keys(inputs).filter(name => {\n            const [nodeName] = parseNodeName(name);\n            return this.graph.nodes[nodeName] == null;\n        });\n        if (notInGraph.length > 0) {\n            throw new Error(`The dict provided in model.execute(dict) has ` +\n                `keys: [${notInGraph}] that are not part of graph`);\n        }\n    }\n    mapOutputs(outputs) {\n        return outputs.map(name => {\n            if (this._signature != null && this._signature.outputs != null &&\n                this._signature.outputs[name] != null) {\n                const tensor = this._signature.outputs[name];\n                return tensor.name;\n            }\n            return name;\n        }, {});\n    }\n    checkOutputs(outputs) {\n        outputs.forEach(name => {\n            const [normalizedName] = parseNodeName(name);\n            if (!this.graph.nodes[normalizedName]) {\n                throw new Error(`The output '${name}' is not found in the graph`);\n            }\n        });\n    }\n}\n//# sourceMappingURL=graph_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { io, Tensor } from '@tensorflow/tfjs-core';\nimport { OperationMapper } from '../operations/operation_mapper';\nimport { GraphExecutor } from './graph_executor';\nexport const TFHUB_SEARCH_PARAM = '?tfjs-format=file';\nexport const DEFAULT_MODEL_NAME = 'model.json';\n/**\n * A `tf.GraphModel` is a directed, acyclic graph built from a\n * SavedModel GraphDef and allows inference execution.\n *\n * A `tf.GraphModel` can only be created by loading from a model converted from\n * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using\n * the command line converter tool and loaded via `tf.loadGraphModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class GraphModel {\n    /**\n     * @param modelUrl url for the model, or an `io.IOHandler`.\n     * @param weightManifestUrl url for the weight file generated by\n     * scripts/convert.py script.\n     * @param requestOption options for Request, which allows to send credentials\n     * and custom headers.\n     * @param onProgress Optional, progress callback function, fired periodically\n     * before the load is completed.\n     */\n    constructor(modelUrl, loadOptions = {}) {\n        this.modelUrl = modelUrl;\n        this.loadOptions = loadOptions;\n        this.version = 'n/a';\n        if (loadOptions == null) {\n            this.loadOptions = {};\n        }\n    }\n    // Returns the version information for the tensorflow model GraphDef.\n    get modelVersion() {\n        return this.version;\n    }\n    get inputNodes() {\n        return this.executor.inputNodes;\n    }\n    get outputNodes() {\n        return this.executor.outputNodes;\n    }\n    get inputs() {\n        return this.executor.inputs;\n    }\n    get outputs() {\n        return this.executor.outputs;\n    }\n    get weights() {\n        return this.executor.weightMap;\n    }\n    findIOHandler() {\n        const path = this.modelUrl;\n        if (path.load != null) {\n            // Path is an IO Handler.\n            this.handler = path;\n        }\n        else if (this.loadOptions.requestInit != null) {\n            this.handler = io.browserHTTPRequest(path, this.loadOptions);\n        }\n        else {\n            const handlers = io.getLoadHandlers(path, this.loadOptions);\n            if (handlers.length === 0) {\n                // For backward compatibility: if no load handler can be found,\n                // assume it is a relative http path.\n                handlers.push(io.browserHTTPRequest(path, this.loadOptions));\n            }\n            else if (handlers.length > 1) {\n                throw new Error(`Found more than one (${handlers.length}) load handlers for ` +\n                    `URL '${[path]}'`);\n            }\n            this.handler = handlers[0];\n        }\n    }\n    /**\n     * Loads the model and weight files, construct the in memory weight map and\n     * compile the inference graph.\n     */\n    async load() {\n        this.findIOHandler();\n        if (this.handler.load == null) {\n            throw new Error('Cannot proceed with model loading because the IOHandler provided ' +\n                'does not have the `load` method implemented.');\n        }\n        const artifacts = await this.handler.load();\n        return this.loadSync(artifacts);\n    }\n    /**\n     * Synchronously construct the in memory weight map and\n     * compile the inference graph. Also initialize hashtable if any.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    loadSync(artifacts) {\n        this.artifacts = artifacts;\n        const graph = this.artifacts.modelTopology;\n        let signature = {};\n        if (this.artifacts.userDefinedMetadata != null) {\n            signature = // tslint:disable-next-line:no-any\n                this.artifacts.userDefinedMetadata.signature;\n        }\n        this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;\n        const weightMap = io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);\n        this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, signature));\n        this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);\n        if (artifacts.modelInitializer != null) {\n            const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer);\n            this.initializer = new GraphExecutor(initializer);\n            this.initializer.weightMap = this.executor.weightMap;\n            this.initializer.execute({}, []);\n        }\n        return true;\n    }\n    /**\n     * Save the configuration and/or weights of the GraphModel.\n     *\n     * An `IOHandler` is an object that has a `save` method of the proper\n     * signature defined. The `save` method manages the storing or\n     * transmission of serialized data (\"artifacts\") that represent the\n     * model's topology and weights onto or via a specific medium, such as\n     * file downloads, local storage, IndexedDB in the web browser and HTTP\n     * requests to a server. TensorFlow.js provides `IOHandler`\n     * implementations for a number of frequently used saving mediums, such as\n     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n     * for more details.\n     *\n     * This method also allows you to refer to certain types of `IOHandler`s\n     * as URL-like string shortcuts, such as 'localstorage://' and\n     * 'indexeddb://'.\n     *\n     * Example 1: Save `model`'s topology and weights to browser [local\n     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n     * then load it back.\n     *\n     * ```js\n     * const modelUrl =\n     *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n     * const model = await tf.loadGraphModel(modelUrl);\n     * const zeros = tf.zeros([1, 224, 224, 3]);\n     * model.predict(zeros).print();\n     *\n     * const saveResults = await model.save('localstorage://my-model-1');\n     *\n     * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * model.predict(zeros).print();\n     * ```\n     *\n     * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n     * scheme-based string shortcut for `IOHandler`.\n     * @param config Options for saving the model.\n     * @returns A `Promise` of `SaveResult`, which summarizes the result of\n     * the saving, such as byte sizes of the saved artifacts for the model's\n     *   topology and weight values.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async save(handlerOrURL, config) {\n        if (typeof handlerOrURL === 'string') {\n            const handlers = io.getSaveHandlers(handlerOrURL);\n            if (handlers.length === 0) {\n                throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n            }\n            else if (handlers.length > 1) {\n                throw new Error(`Found more than one (${handlers.length}) save handlers for ` +\n                    `URL '${handlerOrURL}'`);\n            }\n            handlerOrURL = handlers[0];\n        }\n        if (handlerOrURL.save == null) {\n            throw new Error('GraphModel.save() cannot proceed because the IOHandler ' +\n                'provided does not have the `save` attribute defined.');\n        }\n        return handlerOrURL.save(this.artifacts);\n    }\n    /**\n     * Execute the inference for the input tensors.\n     *\n     * @param input The input tensors, when there is single input for the model,\n     * inputs param should be a `tf.Tensor`. For models with mutliple inputs,\n     * inputs params should be in either `tf.Tensor`[] if the input order is\n     * fixed, or otherwise NamedTensorMap format.\n     *\n     * For model with multiple inputs, we recommend you use NamedTensorMap as the\n     * input type, if you use `tf.Tensor`[], the order of the array needs to\n     * follow the\n     * order of inputNodes array. @see {@link GraphModel.inputNodes}\n     *\n     * You can also feed any intermediate nodes using the NamedTensorMap as the\n     * input type. For example, given the graph\n     *    InputNode => Intermediate => OutputNode,\n     * you can execute the subgraph Intermediate => OutputNode by calling\n     *    model.execute('IntermediateNode' : tf.tensor(...));\n     *\n     * This is useful for models that uses tf.dynamic_rnn, where the intermediate\n     * state needs to be fed manually.\n     *\n     * For batch inference execution, the tensors for each input need to be\n     * concatenated together. For example with mobilenet, the required input shape\n     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].\n     * If we are provide a batched data of 100 images, the input tensor should be\n     * in the shape of [100, 244, 244, 3].\n     *\n     * @param config Prediction configuration for specifying the batch size and\n     * output node names. Currently the batch size option is ignored for graph\n     * model.\n     *\n     * @returns Inference result tensors. The output would be single `tf.Tensor`\n     * if model has single output node, otherwise Tensor[] or NamedTensorMap[]\n     * will be returned for model with multiple outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(inputs, config) {\n        return this.execute(inputs, this.outputNodes);\n    }\n    normalizeInputs(inputs) {\n        if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {\n            // The input is already a NamedTensorMap.\n            return inputs;\n        }\n        inputs = Array.isArray(inputs) ? inputs : [inputs];\n        if (inputs.length !== this.inputNodes.length) {\n            throw new Error('Input tensor count mismatch,' +\n                `the graph model has ${this.inputNodes.length} placeholders, ` +\n                `while there are ${inputs.length} input tensors.`);\n        }\n        return this.inputNodes.reduce((map, inputName, i) => {\n            map[inputName] = inputs[i];\n            return map;\n        }, {});\n    }\n    normalizeOutputs(outputs) {\n        outputs = outputs || this.outputNodes;\n        return !Array.isArray(outputs) ? [outputs] : outputs;\n    }\n    /**\n     * Executes inference for the model for given input tensors.\n     * @param inputs tensor, tensor array or tensor map of the inputs for the\n     * model, keyed by the input node names.\n     * @param outputs output node name from the Tensorflow model, if no\n     * outputs are specified, the default outputs of the model would be used.\n     * You can inspect intermediate nodes of the model by adding them to the\n     * outputs array.\n     *\n     * @returns A single tensor if provided with a single output or no outputs\n     * are provided and there is only one default output, otherwise return a\n     * tensor array. The order of the tensor array is the same as the outputs\n     * if provided, otherwise the order of outputNodes attribute of the model.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    execute(inputs, outputs) {\n        inputs = this.normalizeInputs(inputs);\n        outputs = this.normalizeOutputs(outputs);\n        const result = this.executor.execute(inputs, outputs);\n        return result.length > 1 ? result : result[0];\n    }\n    /**\n     * Executes inference for the model for given input tensors in async\n     * fashion, use this method when your model contains control flow ops.\n     * @param inputs tensor, tensor array or tensor map of the inputs for the\n     * model, keyed by the input node names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     *\n     * @returns A Promise of single tensor if provided with a single output or\n     * no outputs are provided and there is only one default output, otherwise\n     * return a tensor map.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async executeAsync(inputs, outputs) {\n        inputs = this.normalizeInputs(inputs);\n        outputs = this.normalizeOutputs(outputs);\n        const result = await this.executor.executeAsync(inputs, outputs);\n        return result.length > 1 ? result : result[0];\n    }\n    convertTensorMapToTensorsMap(map) {\n        return Object.keys(map).reduce((newMap, key) => {\n            newMap[key] = [map[key]];\n            return newMap;\n        }, {});\n    }\n    /**\n     * Releases the memory used by the weight tensors.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    dispose() {\n        this.executor.dispose();\n        if (this.initializer) {\n            this.initializer.dispose();\n        }\n    }\n}\n/**\n * Load a graph model given a URL to the model definition.\n *\n * Example of loading MobileNetV2 from a URL and making a prediction with a\n * zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n * const model = await tf.loadGraphModel(modelUrl);\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n *\n * Example of loading MobileNetV2 from a TF Hub URL and making a prediction with\n * a zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';\n * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n * @param modelUrl The url or an `io.IOHandler` that loads the model.\n * @param options Options for the HTTP request, which allows to send credentials\n *    and custom headers.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport async function loadGraphModel(modelUrl, options = {}) {\n    if (modelUrl == null) {\n        throw new Error('modelUrl in loadGraphModel() cannot be null. Please provide a url ' +\n            'or an IOHandler that loads the model');\n    }\n    if (options == null) {\n        options = {};\n    }\n    if (options.fromTFHub) {\n        if (modelUrl.load == null) {\n            if (!modelUrl.endsWith('/')) {\n                modelUrl = modelUrl + '/';\n            }\n            modelUrl = `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;\n        }\n    }\n    const model = new GraphModel(modelUrl, options);\n    await model.load();\n    return model;\n}\n//# sourceMappingURL=graph_model.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { GraphModel, loadGraphModel } from './executor/graph_model';\nexport { deregisterOp, registerOp } from './operations/custom_op/register';\nexport { version as version_converter } from './version';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\n/**\n * Apply a mapping function to a nested structure in a recursive manner.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapResult`.  The `DeepMapResult` either provides a\n *   replacement value for that node (i.e., replacing the subtree), or indicates\n *   that the node should be processed recursively.\n */\nexport function deepMap(input, mapFn) {\n    return deepMapInternal(input, mapFn);\n}\n/**\n * @param seen: A Map of known object mappings (i.e., memoized results of\n *   `mapFn()`)\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepMapInternal(input, mapFn, seen = new Map(), containedIn = new Set()) {\n    if (input == null) {\n        return null;\n    }\n    if (containedIn.has(input)) {\n        throw new Error('Circular references are not supported.');\n    }\n    if (seen.has(input)) {\n        return seen.get(input);\n    }\n    const result = mapFn(input);\n    if (result.recurse && result.value !== null) {\n        throw new Error('A deep map function may not return both a value and recurse=true.');\n    }\n    if (!result.recurse) {\n        seen.set(input, result.value);\n        return result.value;\n    }\n    else if (isIterable(input)) {\n        // tslint:disable-next-line:no-any\n        const mappedIterable = Array.isArray(input) ? [] : {};\n        containedIn.add(input);\n        for (const k in input) {\n            const child = input[k];\n            const childResult = deepMapInternal(child, mapFn, seen, containedIn);\n            mappedIterable[k] = childResult;\n        }\n        containedIn.delete(input);\n        return mappedIterable;\n    }\n    else {\n        throw new Error(`Can't recurse into non-iterable type: ${input}`);\n    }\n}\n// TODO(soergel, kangyizhang) Reconsider naming of deepZip() to avoid confusion\n// with zip()\n/**\n * Zip nested structures together in a recursive manner.\n *\n * This has the effect of transposing or pivoting data, e.g. converting it from\n * a row-major representation to a column-major representation.\n *\n * For example, `deepZip([{a: 1, b: 2}, {a: 3, b: 4}])` returns\n * `{a: [1, 3], b: [2, 4]}`.\n *\n * The inputs should all have the same nested structure (i.e., of arrays and\n * dicts).  The result is a single object with the same nested structure, where\n * the leaves are arrays collecting the values of the inputs at that location\n * (or, optionally, the result of a custom function applied to those arrays).\n *\n * @param inputs: An array of the objects to zip together.\n * @param zipFn: (optional) A function that expects an array of elements at a\n *   single node of the object tree, and returns a `DeepMapResult`.  The\n *   `DeepMapResult` either provides a result value for that node (i.e.,\n *   representing the subtree), or indicates that the node should be processed\n *   recursively.  The default zipFn recurses as far as possible and places\n *   arrays at the leaves.\n */\nexport function deepZip(inputs, zipFn = zipToList) {\n    return deepZipInternal(inputs, zipFn);\n}\n/**\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepZipInternal(inputs, zipFn, containedIn = new Set()) {\n    // The recursion follows the structure of input 0; it's assumed that all the\n    // other inputs have the same structure.\n    const input = inputs[0];\n    if (containedIn.has(input)) {\n        throw new Error('Circular references are not supported.');\n    }\n    const result = zipFn(inputs);\n    if (result.recurse && result.value !== null) {\n        throw new Error('A deep zip function may not return both a value and recurse=true.');\n    }\n    if (!result.recurse) {\n        return result.value;\n    }\n    else if (isIterable(input)) {\n        // tslint:disable-next-line:no-any\n        const mappedIterable = Array.isArray(input) ? [] : {};\n        containedIn.add(input);\n        for (const k in input) {\n            const children = inputs.map(x => x[k]);\n            const childResult = deepZipInternal(children, zipFn, containedIn);\n            mappedIterable[k] = childResult;\n        }\n        containedIn.delete(input);\n        return mappedIterable;\n    }\n    else {\n        throw new Error(`Can't recurse into non-iterable type: ${input}`);\n    }\n}\n// tslint:disable-next-line:no-any\nexport function zipToList(x) {\n    if (x === null) {\n        return null;\n    }\n    // TODO(soergel): validate array type?\n    if (isIterable(x[0])) {\n        return { value: null, recurse: true };\n    }\n    else {\n        return { value: x, recurse: false };\n    }\n}\n/**\n * Apply an async mapping function to a nested structure in a recursive manner.\n *\n * This first creates a nested structure of Promises, and then awaits all of\n * those, resulting in a single Promise for a resolved nested structure.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapAsyncResult`.  The `DeepMapAsyncResult` either provides\n *   a `Promise` for a replacement value for that node (i.e., replacing the\n *   subtree), or indicates that the node should be processed recursively.  Note\n *   that the decision whether or not to recurse must be made immediately; only\n *   the mapped value may be promised.\n */\nexport async function deepMapAndAwaitAll(input, mapFn) {\n    const seen = new Map();\n    // First do a normal deepMap, collecting Promises in 'seen' as a side effect.\n    deepMapInternal(input, mapFn, seen);\n    // Replace the Promises in 'seen' in place.\n    // Note TypeScript provides no async map iteration, and regular map iteration\n    // is broken too, so sadly we have to do Array.from() to make it work.\n    // (There's no advantage to Promise.all(), and that would be tricky anyway.)\n    for (const key of Array.from(seen.keys())) {\n        const value = seen.get(key);\n        if (value instanceof Promise) {\n            const mappedValue = await value;\n            seen.set(key, mappedValue);\n        }\n    }\n    // Normal deepMap again, this time filling in the resolved values.\n    // It's unfortunate that we have to do two passes.\n    // TODO(soergel): test performance and think harder about a fast solution.\n    const result = deepMapInternal(input, mapFn, seen);\n    return result;\n}\n/**\n * Determine whether the argument is iterable.\n *\n * @returns true if the argument is an array or any non-Tensor object.\n */\n// tslint:disable-next-line:no-any\nexport function isIterable(obj) {\n    return obj != null && (!ArrayBuffer.isView(obj)) &&\n        (Array.isArray(obj) ||\n            (typeof obj === 'object' && !(obj instanceof tf.Tensor)));\n}\n/**\n * Determine whether the argument can be converted to Tensor.\n *\n * Tensors, primitives, arrays, and TypedArrays all qualify; anything else does\n * not.\n *\n * @returns true if the argument can be converted to Tensor.\n */\n// tslint:disable-next-line:no-any\nexport function canTensorify(obj) {\n    return obj == null || isPrimitive(obj) || Array.isArray(obj) ||\n        (typeof obj === 'object' && (obj instanceof tf.Tensor)) ||\n        tf.util.isTypedArray(obj);\n}\n/**\n * Returns true if the given `value` is a primitive type. Otherwise returns\n * false. This is equivalant to node util.isPrimitive\n */\nfunction isPrimitive(value) {\n    return (value === null ||\n        (typeof value !== 'object' && typeof value !== 'function'));\n}\n//# sourceMappingURL=deep_map.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { deepMap, isIterable } from './deep_map';\nexport function deepClone(container) {\n    return deepMap(container, cloneIfTensor);\n}\n// tslint:disable-next-line: no-any\nfunction cloneIfTensor(item) {\n    if (item instanceof tf.Tensor) {\n        return ({ value: item.clone(), recurse: false });\n    }\n    else if (isIterable(item)) {\n        return { value: null, recurse: true };\n    }\n    else {\n        return { value: item, recurse: false };\n    }\n}\n//# sourceMappingURL=deep_clone.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * A ring buffer, providing O(1) FIFO, LIFO, and related operations.\n */\nexport class RingBuffer {\n    /**\n     * Constructs a `RingBuffer`.\n     * @param capacity The number of items that the buffer can accomodate.\n     */\n    constructor(capacity) {\n        this.capacity = capacity;\n        // Note we store the indices in the range 0 <= index < 2*capacity.\n        // This allows us to distinguish the full from the empty case.\n        // See https://www.snellman.net/blog/archive/2016-12-13-ring-buffers/\n        this.begin = 0; // inclusive\n        this.end = 0; // exclusive\n        if (capacity == null) {\n            throw new RangeError('Can\\'t create a ring buffer of unknown capacity.');\n        }\n        if (capacity < 1) {\n            throw new RangeError('Can\\'t create ring buffer of capacity < 1.');\n        }\n        this.data = new Array(capacity);\n        this.doubledCapacity = 2 * capacity;\n    }\n    /**\n     * Map any index into the range 0 <= index < 2*capacity.\n     */\n    wrap(index) {\n        // don't trust % on negative numbers\n        while (index < 0) {\n            index += this.doubledCapacity;\n        }\n        return index % this.doubledCapacity;\n    }\n    get(index) {\n        if (index < 0) {\n            throw new RangeError('Can\\'t get item at a negative index.');\n        }\n        return this.data[index % this.capacity];\n    }\n    set(index, value) {\n        if (index < 0) {\n            throw new RangeError('Can\\'t set item at a negative index.');\n        }\n        this.data[index % this.capacity] = value;\n    }\n    /**\n     * Returns the current number of items in the buffer.\n     */\n    length() {\n        let length = this.end - this.begin;\n        if (length < 0) {\n            length = this.doubledCapacity + length;\n        }\n        return length;\n    }\n    /**\n     * Reports whether the buffer is full.\n     * @returns true if the number of items in the buffer equals its capacity, and\n     *   false otherwise.\n     */\n    isFull() {\n        return this.length() === this.capacity;\n    }\n    /**\n     * Reports whether the buffer is empty.\n     * @returns true if the number of items in the buffer equals zero, and\n     *   false otherwise.\n     */\n    isEmpty() {\n        return this.length() === 0;\n    }\n    /**\n     * Adds an item to the end of the buffer.\n     */\n    push(value) {\n        if (this.isFull()) {\n            throw new RangeError('Ring buffer is full.');\n        }\n        this.set(this.end, value);\n        this.end = this.wrap(this.end + 1);\n    }\n    /**\n     * Adds many items to the end of the buffer, in order.\n     */\n    pushAll(values) {\n        for (const value of values) {\n            this.push(value);\n        }\n    }\n    /**\n     * Removes and returns the last item in the buffer.\n     */\n    pop() {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        this.end = this.wrap(this.end - 1);\n        const result = this.get(this.end);\n        this.set(this.end, undefined);\n        return result;\n    }\n    /**\n     * Adds an item to the beginning of the buffer.\n     */\n    unshift(value) {\n        if (this.isFull()) {\n            throw new RangeError('Ring buffer is full.');\n        }\n        this.begin = this.wrap(this.begin - 1);\n        this.set(this.begin, value);\n    }\n    /**\n     * Removes and returns the first item in the buffer.\n     */\n    shift() {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        const result = this.get(this.begin);\n        this.set(this.begin, undefined);\n        this.begin = this.wrap(this.begin + 1);\n        return result;\n    }\n    /**\n     * Removes and returns a specific item in the buffer, and moves the last item\n     * to the vacated slot.  This is useful for implementing a shuffling stream.\n     * Note that this operation necessarily scrambles the original order.\n     *\n     * @param relativeIndex: the index of the item to remove, relative to the\n     *   first item in the buffer (e.g., hiding the ring nature of the underlying\n     *   storage).\n     */\n    shuffleExcise(relativeIndex) {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        const index = this.wrap(this.begin + relativeIndex);\n        const result = this.get(index);\n        this.set(index, this.pop());\n        return result;\n    }\n}\n//# sourceMappingURL=ring_buffer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { RingBuffer } from './ring_buffer';\nexport class GrowingRingBuffer extends RingBuffer {\n    /**\n     * Constructs a `GrowingRingBuffer`.\n     */\n    constructor() {\n        super(GrowingRingBuffer.INITIAL_CAPACITY);\n    }\n    isFull() {\n        return false;\n    }\n    push(value) {\n        if (super.isFull()) {\n            this.expand();\n        }\n        super.push(value);\n    }\n    unshift(value) {\n        if (super.isFull()) {\n            this.expand();\n        }\n        super.unshift(value);\n    }\n    /**\n     * Doubles the capacity of the buffer.\n     */\n    expand() {\n        const newCapacity = this.capacity * 2;\n        const newData = new Array(newCapacity);\n        const len = this.length();\n        // Rotate the buffer to start at index 0 again, since we can't just\n        // allocate more space at the end.\n        for (let i = 0; i < len; i++) {\n            newData[i] = this.get(this.wrap(this.begin + i));\n        }\n        this.data = newData;\n        this.capacity = newCapacity;\n        this.doubledCapacity = 2 * this.capacity;\n        this.begin = 0;\n        this.end = len;\n    }\n}\nGrowingRingBuffer.INITIAL_CAPACITY = 32;\n//# sourceMappingURL=growing_ring_buffer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport * as seedrandom from 'seedrandom';\nimport { deepClone } from '../util/deep_clone';\nimport { deepMapAndAwaitAll, deepZip, zipToList } from '../util/deep_map';\nimport { GrowingRingBuffer } from '../util/growing_ring_buffer';\nimport { RingBuffer } from '../util/ring_buffer';\n// Here we implement a simple asynchronous iterator.\n// This lets us avoid using either third-party stream libraries or\n// recent TypeScript language support requiring polyfills.\n/**\n * Create a `LazyIterator` from an array of items.\n */\nexport function iteratorFromItems(items) {\n    return new ArrayIterator(items);\n}\n/**\n * Create a `LazyIterator` of incrementing integers.\n */\nexport function iteratorFromIncrementing(start) {\n    let i = start;\n    return iteratorFromFunction(() => ({ value: i++, done: false }));\n}\n/**\n * Create a `LazyIterator` from a function.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const iter = tf.data.iteratorFromFunction(func);\n * await iter.forEachAsync(e => console.log(e));\n * ```\n *\n * @param func A function that produces data on each call.\n */\nexport function iteratorFromFunction(func) {\n    return new FunctionCallIterator(func);\n}\n/**\n * Create a `LazyIterator` by concatenating underlying streams, which are\n * themselves provided as a stream.\n *\n * This can also be thought of as a \"stream flatten\" operation.\n *\n * @param baseIterators A stream of streams to be concatenated.\n * @param baseErrorHandler An optional function that can intercept `Error`s\n *   raised during a `next()` call on the base stream.  This function can decide\n *   whether the error should be propagated, whether the error should be\n *   ignored, or whether the base stream should be terminated.\n */\nexport function iteratorFromConcatenated(baseIterators, baseErrorHandler) {\n    return new ChainedIterator(baseIterators, baseErrorHandler);\n}\n/**\n * Create a `LazyIterator` by concatenating streams produced by calling a\n * stream-generating function a given number of times.\n *\n * Since a `LazyIterator` is read-once, it cannot be repeated, but this\n * function can be used to achieve a similar effect:\n *\n *   LazyIterator.ofConcatenatedFunction(() => new MyIterator(), 6);\n *\n * @param iteratorFunc: A function that produces a new stream on each call.\n * @param count: The number of times to call the function.\n * @param baseErrorHandler An optional function that can intercept `Error`s\n *   raised during a `next()` call on the base stream.  This function can decide\n *   whether the error should be propagated, whether the error should be\n *   ignored, or whether the base stream should be terminated.\n */\nexport function iteratorFromConcatenatedFunction(iteratorFunc, count, baseErrorHandler) {\n    return iteratorFromConcatenated(iteratorFromFunction(iteratorFunc).take(count), baseErrorHandler);\n}\n/**\n * Create a `LazyIterator` by zipping together an array, dict, or nested\n * structure of `LazyIterator`s (and perhaps additional constants).\n *\n * The underlying streams must provide elements in a consistent order such\n * that they correspond.\n *\n * Typically, the underlying streams should have the same number of\n * elements. If they do not, the behavior is determined by the\n * `mismatchMode` argument.\n *\n * The nested structure of the `iterators` argument determines the\n * structure of elements in the resulting iterator.\n *\n * @param iterators: An array or object containing LazyIterators at the\n * leaves.\n * @param mismatchMode: Determines what to do when one underlying iterator\n * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)\n * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`\n * causes the zipped iterator to terminate with the furst underlying\n * streams, so elements remaining on the longer streams are ignored.\n * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling\n * in nulls for the exhausted streams, until all streams are exhausted.\n */\nexport function iteratorFromZipped(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n    return new ZipIterator(iterators, mismatchMode);\n}\n/**\n * An asynchronous iterator, providing lazy access to a potentially\n * unbounded stream of elements.\n *\n * Iterator can be obtained from a dataset:\n * `const iter = await dataset.iterator();`\n */\nexport class LazyIterator {\n    /**\n     * Collect all remaining elements of a bounded stream into an array.\n     * Obviously this will succeed only for small streams that fit in memory.\n     * Useful for testing.\n     *\n     * @returns A Promise for an array of stream elements, which will resolve\n     *   when the stream is exhausted.\n     */\n    async toArray() {\n        const result = [];\n        let x = await this.next();\n        while (!x.done) {\n            result.push(x.value);\n            x = await this.next();\n        }\n        return result;\n    }\n    /**\n     * Collect all elements of this dataset into an array with prefetching 100\n     * elements. This is useful for testing, because the prefetch changes the\n     * order in which the Promises are resolved along the processing pipeline.\n     * This may help expose bugs where results are dependent on the order of\n     * Promise resolution rather than on the logical order of the stream (i.e.,\n     * due to hidden mutable state).\n     *\n     * @returns A Promise for an array of stream elements, which will resolve\n     *   when the stream is exhausted.\n     */\n    async toArrayForTest() {\n        const stream = this.prefetch(100);\n        const result = [];\n        let x = await stream.next();\n        while (!x.done) {\n            result.push(x.value);\n            x = await stream.next();\n        }\n        return result;\n    }\n    /**\n     * Draw items from the stream until it is exhausted.\n     *\n     * This can be useful when the stream has side effects but no output.  In\n     * that case, calling this function guarantees that the stream will be\n     * fully processed.\n     */\n    async resolveFully() {\n        let x = await this.next();\n        while (!x.done) {\n            x = await this.next();\n        }\n    }\n    /**\n     * Draw items from the stream until it is exhausted, or a predicate fails.\n     *\n     * This can be useful when the stream has side effects but no output.  In\n     * that case, calling this function guarantees that the stream will be\n     * fully processed.\n     */\n    async resolveWhile(predicate) {\n        let x = await this.next();\n        let shouldContinue = predicate(x.value);\n        while ((!x.done) && shouldContinue) {\n            x = await this.next();\n            shouldContinue = predicate(x.value);\n        }\n    }\n    /**\n     * Handles errors thrown on this stream using a provided handler function.\n     *\n     * @param handler A function that handles any `Error` thrown during a `next()`\n     *   call and returns true if the stream should continue (dropping the failed\n     *   call) or false if the stream should quietly terminate.  If the handler\n     *   itself throws (or rethrows) an `Error`, that will be propagated.\n     *\n     * @returns A `LazyIterator` of elements passed through from upstream,\n     *   possibly filtering or terminating on upstream `next()` calls that\n     *   throw an `Error`.\n     */\n    handleErrors(handler) {\n        return new ErrorHandlingLazyIterator(this, handler);\n    }\n    // TODO(soergel): Implement reduce() etc.\n    /**\n     * Filters this stream according to `predicate`.\n     *\n     * @param predicate A function mapping a stream element to a boolean or a\n     * `Promise` for one.\n     *\n     * @returns A `LazyIterator` of elements for which the predicate was true.\n     */\n    filter(predicate) {\n        return new FilterIterator(this, predicate);\n    }\n    /**\n     * Maps this stream through a 1-to-1 transform.\n     *\n     * @param transform A function mapping a stream element to a transformed\n     *   element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    map(transform) {\n        return new MapIterator(this, transform);\n    }\n    /**\n     * Maps this stream through an async 1-to-1 transform.\n     *\n     * @param transform A function mapping a stream element to a `Promise` for a\n     *   transformed stream element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    mapAsync(transform) {\n        return new AsyncMapIterator(this, transform);\n    }\n    /**\n     * Maps this stream through a 1-to-1 transform, forcing serial execution.\n     *\n     * @param transform A function mapping a stream element to a transformed\n     *   element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    serialMapAsync(transform) {\n        return new AsyncMapIterator(this, transform).serial();\n    }\n    /**\n     * Maps this stream through a 1-to-many transform.\n     *\n     * @param transform A function mapping a stream element to an array of\n     *   transformed elements.\n     *\n     * @returns A `DataStream` of transformed elements.\n     */\n    flatmap(transform) {\n        return new FlatmapIterator(this, transform);\n    }\n    /**\n     * Apply a function to every element of the stream.\n     *\n     * @param f A function to apply to each stream element.\n     */\n    async forEachAsync(f) {\n        return this.map(f).resolveFully();\n    }\n    /**\n     * Apply a function to every element of the stream, forcing serial execution.\n     *\n     * @param f A function to apply to each stream element.  Should return 'true'\n     *   to indicate that the stream should continue, or 'false' to cause it to\n     *   terminate.\n     */\n    async serialForEach(f) {\n        return this.serialMapAsync(f).resolveWhile(x => (x === true));\n    }\n    /**\n     * Groups elements into batches, represented as arrays of elements.\n     *\n     * We can think of the elements of this iterator as 'rows' (even if they are\n     * nested structures).  By the same token, consecutive values for a given\n     * key within the elements form a 'column'.  This matches the usual sense of\n     * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n     *\n     * Thus, \"Row-major\" means that the resulting batch is simply a collection of\n     * rows: `[row1, row2, row3, ...]`.  This is contrast to the column-major\n     * form, which is needed for vectorized computation.\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @returns A `LazyIterator` of batches of elements, represented as arrays\n     *   of the original element type.\n     */\n    rowMajorBatch(batchSize, smallLastBatch = true) {\n        return new RowMajorBatchIterator(this, batchSize, smallLastBatch);\n    }\n    /**\n     * Groups elements into batches, represented in column-major form.\n     *\n     * We can think of the elements of this iterator as 'rows' (even if they are\n     * nested structures).  By the same token, consecutive values for a given\n     * key within the elements form a 'column'.  This matches the usual sense of\n     * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n     *\n     * Thus, \"column-major\" means that the resulting batch is a (potentially\n     * nested) structure representing the columns.  Each column entry, then,\n     * contains a collection of the values found in that column for a range of\n     * input elements.  This representation allows for vectorized computation, in\n     * contrast to the row-major form.\n     *\n     * The inputs should all have the same nested structure (i.e., of arrays and\n     * dicts).  The result is a single object with the same nested structure,\n     * where the leaves are arrays collecting the values of the inputs at that\n     * location (or, optionally, the result of a custom function applied to those\n     * arrays).\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @param zipFn: (optional) A function that expects an array of elements at a\n     *   single node of the object tree, and returns a `DeepMapResult`.  The\n     *   `DeepMapResult` either provides a result value for that node (i.e.,\n     *   representing the subtree), or indicates that the node should be processed\n     *   recursively.  The default zipFn recurses as far as possible and places\n     *   arrays at the leaves.\n     * @returns A `LazyIterator` of batches of elements, represented as an object\n     *   with collections at the leaves.\n     */\n    columnMajorBatch(batchSize, smallLastBatch = true, \n    // tslint:disable-next-line:no-any\n    zipFn = zipToList) {\n        // First collect the desired number of input elements as a row-major batch.\n        const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch);\n        // Now 'rotate' or 'pivot' the data, collecting all values from each column\n        // in the batch (i.e., for each key within the elements) into an array.\n        return rowBatches.map(x => deepZip(x, zipFn));\n    }\n    /**\n     * Concatenate this `LazyIterator` with another.\n     *\n     * @param iterator A `LazyIterator` to be concatenated onto this one.\n     * @param baseErrorHandler An optional function that can intercept `Error`s\n     *   raised during a `next()` call on the base stream.  This function can\n     *   decide whether the error should be propagated, whether the error should\n     *   be ignored, or whether the base stream should be terminated.\n     * @returns A `LazyIterator`.\n     */\n    concatenate(iterator, baseErrorHandler) {\n        return new ChainedIterator(iteratorFromItems([this, iterator]), baseErrorHandler);\n    }\n    /**\n     * Limits this stream to return at most `count` items.\n     *\n     * @param count The maximum number of items to provide from the stream. If\n     * a negative or undefined value is given, the entire stream is returned\n     *   unaltered.\n     */\n    take(count) {\n        if (count < 0 || count == null) {\n            return this;\n        }\n        return new TakeIterator(this, count);\n    }\n    /**\n     * Skips the first `count` items in this stream.\n     *\n     * @param count The number of items to skip.  If a negative or undefined\n     * value is given, the entire stream is returned unaltered.\n     */\n    skip(count) {\n        if (count < 0 || count == null) {\n            return this;\n        }\n        return new SkipIterator(this, count);\n    }\n    /**\n     * Prefetch the first `bufferSize` items in this stream.\n     *\n     * Note this prefetches Promises, but makes no guarantees about when those\n     * Promises resolve.\n     *\n     * @param bufferSize: An integer specifying the number of elements to be\n     *   prefetched.\n     */\n    prefetch(bufferSize) {\n        return new PrefetchIterator(this, bufferSize);\n    }\n    // TODO(soergel): deep sharded shuffle, where supported\n    /**\n     * Randomly shuffles the elements of this stream.\n     *\n     * @param bufferSize: An integer specifying the number of elements from\n     * this stream from which the new stream will sample.\n     * @param seed: (Optional.) An integer specifying the random seed that\n     * will be used to create the distribution.\n     */\n    shuffle(windowSize, seed) {\n        return new ShuffleIterator(this, windowSize, seed);\n    }\n    /**\n     * Force an iterator to execute serially: each next() call will await the\n     * prior one, so that they cannot execute concurrently.\n     */\n    serial() {\n        return new SerialIterator(this);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on LazyIterator.  Unfortunately they can't be placed in separate files,\n// due to resulting trouble with circular imports.\n// ============================================================================\n// Iterators that just extend LazyIterator directly\n// ============================================================================\nclass ArrayIterator extends LazyIterator {\n    constructor(items) {\n        super();\n        this.items = items;\n        this.trav = 0;\n    }\n    summary() {\n        return `Array of ${this.items.length} items`;\n    }\n    async next() {\n        if (this.trav >= this.items.length) {\n            return { value: null, done: true };\n        }\n        const item = this.items[this.trav];\n        this.trav++;\n        return { value: deepClone(item), done: false };\n    }\n}\nclass FunctionCallIterator extends LazyIterator {\n    constructor(nextFn) {\n        super();\n        this.nextFn = nextFn;\n    }\n    summary() {\n        return `Function call`;\n    }\n    async next() {\n        try {\n            return this.nextFn();\n        }\n        catch (e) {\n            // Modify the error message but leave the stack trace intact\n            e.message =\n                `Error thrown while iterating through a dataset: ${e.message}`;\n            throw e;\n        }\n    }\n}\nclass SerialIterator extends LazyIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Serial`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        return this.upstream.next();\n    }\n}\nclass SkipIterator extends LazyIterator {\n    constructor(upstream, maxCount) {\n        super();\n        this.upstream = upstream;\n        this.maxCount = maxCount;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.count = 0;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Skip`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        // TODO(soergel): consider tradeoffs of reading in parallel, eg.\n        // collecting next() promises in an Array and then waiting for\n        // Promise.all() of those. Benefit: pseudo-parallel execution.  Drawback:\n        // maybe delayed GC.\n        while (this.count++ < this.maxCount) {\n            const skipped = await this.upstream.next();\n            // short-circuit if upstream is already empty\n            if (skipped.done) {\n                return skipped;\n            }\n            tf.dispose(skipped.value);\n        }\n        return this.upstream.next();\n    }\n}\nclass TakeIterator extends LazyIterator {\n    constructor(upstream, maxCount) {\n        super();\n        this.upstream = upstream;\n        this.maxCount = maxCount;\n        this.count = 0;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Take`;\n    }\n    async next() {\n        if (this.count++ >= this.maxCount) {\n            return { value: null, done: true };\n        }\n        return this.upstream.next();\n    }\n}\n// Note this batch just groups items into row-wise element arrays.\n// Rotating these to a column-wise representation happens only at the dataset\n// level.\nclass RowMajorBatchIterator extends LazyIterator {\n    constructor(upstream, batchSize, enableSmallLastBatch = true) {\n        super();\n        this.upstream = upstream;\n        this.batchSize = batchSize;\n        this.enableSmallLastBatch = enableSmallLastBatch;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> RowMajorBatch`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        const batch = [];\n        while (batch.length < this.batchSize) {\n            const item = await this.upstream.next();\n            if (item.done) {\n                if (this.enableSmallLastBatch && batch.length > 0) {\n                    return { value: batch, done: false };\n                }\n                return { value: null, done: true };\n            }\n            batch.push(item.value);\n        }\n        return { value: batch, done: false };\n    }\n}\nclass FilterIterator extends LazyIterator {\n    constructor(upstream, predicate) {\n        super();\n        this.upstream = upstream;\n        this.predicate = predicate;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Filter`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        while (true) {\n            const item = await this.upstream.next();\n            if (item.done || this.predicate(item.value)) {\n                return item;\n            }\n            tf.dispose(item.value);\n        }\n    }\n}\nclass MapIterator extends LazyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Map`;\n    }\n    async next() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return { value: null, done: true };\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // That's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying\n        // any intermediate Tensors.  Here we are concerned only about the\n        // inputs.\n        const mapped = this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mapped);\n        // TODO(soergel) faster intersection\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return { value: mapped, done: false };\n    }\n}\nclass ErrorHandlingLazyIterator extends LazyIterator {\n    constructor(upstream, handler) {\n        super();\n        this.upstream = upstream;\n        this.handler = handler;\n        this.count = 0;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> handleErrors`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        while (true) {\n            try {\n                return await this.upstream.next();\n            }\n            catch (e) {\n                if (!this.handler(e)) {\n                    return { value: null, done: true };\n                }\n                // If the handler returns true, loop and fetch the next upstream item.\n                // If the upstream iterator throws an endless stream of errors, and if\n                // the handler says to ignore them, then we loop forever here.  That is\n                // the correct behavior-- it's up to the handler to decide when to stop.\n            }\n        }\n    }\n}\nclass AsyncMapIterator extends LazyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> AsyncMap`;\n    }\n    async next() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return { value: null, done: true };\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // That's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying\n        // any intermediate Tensors.  Here we are concerned only about the\n        // inputs.\n        const mapped = await this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mapped);\n        // TODO(soergel) faster intersection\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return { value: mapped, done: false };\n    }\n}\n// Iterators that maintain a queue of pending items\n// ============================================================================\n/**\n * A base class for transforming streams that operate by maintaining an\n * output queue of elements that are ready to return via next().  This is\n * commonly required when the transformation is 1-to-many:  A call to next()\n * may trigger a call to the underlying stream, which will produce many\n * mapped elements of this stream-- of which we need to return only one, so\n * we have to queue the rest.\n */\nexport class OneToManyIterator extends LazyIterator {\n    constructor() {\n        super();\n        this.outputQueue = new GrowingRingBuffer();\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        // Fetch so that the queue contains at least one item if possible.\n        // If the upstream source is exhausted, AND there are no items left in\n        // the output queue, then this stream is also exhausted.\n        while (this.outputQueue.length() === 0) {\n            // TODO(soergel): consider parallel reads.\n            if (!await this.pump()) {\n                return { value: null, done: true };\n            }\n        }\n        return { value: this.outputQueue.shift(), done: false };\n    }\n}\nclass FlatmapIterator extends OneToManyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Flatmap`;\n    }\n    async pump() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return false;\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // that's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying any\n        // intermediate Tensors.  Here we are concerned only about the inputs.\n        const mappedArray = this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mappedArray);\n        this.outputQueue.pushAll(mappedArray);\n        // TODO(soergel) faster intersection, and deduplicate outputTensors\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return true;\n    }\n}\n/**\n * Provides a `LazyIterator` that concatenates a stream of underlying\n * streams.\n *\n * Doing this in a concurrency-safe way requires some trickery.  In\n * particular, we want this stream to return the elements from the\n * underlying streams in the correct order according to when next() was\n * called, even if the resulting Promises resolve in a different order.\n */\nexport class ChainedIterator extends LazyIterator {\n    constructor(iterators, baseErrorHandler) {\n        super();\n        this.baseErrorHandler = baseErrorHandler;\n        // Strict Promise execution order:\n        // a next() call may not even begin until the previous one completes.\n        this.lastRead = null;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.iterator = null;\n        this.moreIterators = iterators;\n    }\n    summary() {\n        const upstreamSummaries = 'TODO: fill in upstream of chained summaries';\n        return `${upstreamSummaries} -> Chained`;\n    }\n    async next() {\n        this.lastRead = this.readFromChain(this.lastRead);\n        return this.lastRead;\n    }\n    async readFromChain(lastRead) {\n        // Must await on the previous read since the previous read may have advanced\n        // the stream of streams, from which we need to read.\n        // This is unfortunate since we can't parallelize reads. Which means\n        // prefetching of chained streams is a no-op.\n        // One solution is to prefetch immediately upstream of this.\n        await lastRead;\n        if (this.iterator == null) {\n            const iteratorResult = await this.moreIterators.next();\n            if (iteratorResult.done) {\n                // No more streams to stream from.\n                return { value: null, done: true };\n            }\n            this.iterator = iteratorResult.value;\n            if (this.baseErrorHandler != null) {\n                this.iterator = this.iterator.handleErrors(this.baseErrorHandler);\n            }\n        }\n        const itemResult = await this.iterator.next();\n        if (itemResult.done) {\n            this.iterator = null;\n            return this.readFromChain(lastRead);\n        }\n        return itemResult;\n    }\n}\nexport var ZipMismatchMode;\n(function (ZipMismatchMode) {\n    ZipMismatchMode[ZipMismatchMode[\"FAIL\"] = 0] = \"FAIL\";\n    ZipMismatchMode[ZipMismatchMode[\"SHORTEST\"] = 1] = \"SHORTEST\";\n    ZipMismatchMode[ZipMismatchMode[\"LONGEST\"] = 2] = \"LONGEST\"; // use nulls for exhausted streams; use up the longest stream.\n})(ZipMismatchMode || (ZipMismatchMode = {}));\n/**\n * Provides a `LazyIterator` that zips together an array, dict, or nested\n * structure of `LazyIterator`s (and perhaps additional constants).\n *\n * The underlying streams must provide elements in a consistent order such\n * that they correspond.\n *\n * Typically, the underlying streams should have the same number of\n * elements. If they do not, the behavior is determined by the\n * `mismatchMode` argument.\n *\n * The nested structure of the `iterators` argument determines the\n * structure of elements in the resulting iterator.\n *\n * Doing this in a concurrency-safe way requires some trickery.  In\n * particular, we want this stream to return the elements from the\n * underlying streams in the correct order according to when next() was\n * called, even if the resulting Promises resolve in a different order.\n *\n * @param iterators: An array or object containing LazyIterators at the\n * leaves.\n * @param mismatchMode: Determines what to do when one underlying iterator\n * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)\n * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`\n * causes the zipped iterator to terminate with the furst underlying\n * streams, so elements remaining on the longer streams are ignored.\n * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling\n * in nulls for the exhausted streams, until all streams are exhausted.\n */\nclass ZipIterator extends LazyIterator {\n    constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n        super();\n        this.iterators = iterators;\n        this.mismatchMode = mismatchMode;\n        this.count = 0;\n        this.currentPromise = null;\n    }\n    summary() {\n        const upstreamSummaries = 'TODO: fill in upstream of zip summaries';\n        return `{${upstreamSummaries}} -> Zip`;\n    }\n    async nextState(afterState) {\n        // This chaining ensures that the underlying next() are not even called\n        // before the previous ones have resolved.\n        await afterState;\n        // Collect underlying iterator \"done\" signals as a side effect in\n        // getNext()\n        let numIterators = 0;\n        let iteratorsDone = 0;\n        function getNext(container) {\n            if (container instanceof LazyIterator) {\n                const result = container.next();\n                return {\n                    value: result.then(x => {\n                        numIterators++;\n                        if (x.done) {\n                            iteratorsDone++;\n                        }\n                        return x.value;\n                    }),\n                    recurse: false\n                };\n            }\n            else {\n                return { value: null, recurse: true };\n            }\n        }\n        const mapped = await deepMapAndAwaitAll(this.iterators, getNext);\n        if (numIterators === iteratorsDone) {\n            // The streams have all ended.\n            return { value: null, done: true };\n        }\n        if (iteratorsDone > 0) {\n            switch (this.mismatchMode) {\n                case ZipMismatchMode.FAIL:\n                    throw new Error('Zipped streams should have the same length. ' +\n                        `Mismatched at element ${this.count}.`);\n                case ZipMismatchMode.SHORTEST:\n                    return { value: null, done: true };\n                case ZipMismatchMode.LONGEST:\n                default:\n                // Continue.  The exhausted streams already produced value: null.\n            }\n        }\n        this.count++;\n        return { value: mapped, done: false };\n    }\n    async next() {\n        this.currentPromise = this.nextState(this.currentPromise);\n        return this.currentPromise;\n    }\n}\n// Iterators that maintain a ring buffer of pending promises\n// ============================================================================\n/**\n * A stream that prefetches a given number of items from an upstream source,\n * returning them in FIFO order.\n *\n * Note this prefetches Promises, but makes no guarantees about when those\n * Promises resolve.\n */\nexport class PrefetchIterator extends LazyIterator {\n    constructor(upstream, bufferSize) {\n        super();\n        this.upstream = upstream;\n        this.bufferSize = bufferSize;\n        this.buffer = new RingBuffer(bufferSize);\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Prefetch`;\n    }\n    /**\n     * Refill the prefetch buffer.  Returns only after the buffer is full, or\n     * the upstream source is exhausted.\n     */\n    refill() {\n        while (!this.buffer.isFull()) {\n            const v = this.upstream.next();\n            this.buffer.push(v);\n        }\n    }\n    next() {\n        this.refill();\n        // This shift will never throw an error because the buffer is always\n        // full after a refill. If the stream is exhausted, the buffer will be\n        // full of Promises that will resolve to the end-of-stream signal.\n        return this.buffer.shift();\n    }\n}\n/**\n * A stream that performs a sliding-window random shuffle on an upstream\n * source. This is like a `PrefetchIterator` except that the items are\n * returned in randomized order.  Mixing naturally improves as the buffer\n * size increases.\n */\nexport class ShuffleIterator extends PrefetchIterator {\n    constructor(upstream, windowSize, seed) {\n        super(upstream, windowSize);\n        this.upstream = upstream;\n        this.windowSize = windowSize;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.upstreamExhausted = false;\n        this.random = seedrandom.alea(seed || tf.util.now().toString());\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    randomInt(max) {\n        return Math.floor(this.random() * max);\n    }\n    chooseIndex() {\n        return this.randomInt(this.buffer.length());\n    }\n    async serialNext() {\n        // TODO(soergel): consider performance\n        if (!this.upstreamExhausted) {\n            this.refill();\n        }\n        while (!this.buffer.isEmpty()) {\n            const chosenIndex = this.chooseIndex();\n            const result = await this.buffer.shuffleExcise(chosenIndex);\n            if (result.done) {\n                this.upstreamExhausted = true;\n            }\n            else {\n                this.refill();\n                return result;\n            }\n        }\n        return { value: null, done: true };\n    }\n}\n//# sourceMappingURL=lazy_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport * as seedrandom from 'seedrandom';\nimport { iteratorFromConcatenated, iteratorFromFunction, iteratorFromItems, iteratorFromZipped, ZipMismatchMode } from './iterators/lazy_iterator';\nimport { canTensorify, deepMapAndAwaitAll, isIterable } from './util/deep_map';\n// TODO(soergel): consider vectorized operations within the pipeline.\n/**\n * Represents a potentially large list of independent data elements (typically\n * 'samples' or 'examples').\n *\n * A 'data example' may be a primitive, an array, a map from string keys to\n * values, or any nested structure of these.\n *\n * A `Dataset` represents an ordered collection of elements, together with a\n * chain of transformations to be performed on those elements. Each\n * transformation is a method of `Dataset` that returns another `Dataset`, so\n * these may be chained, e.g.\n * `const processedDataset = rawDataset.filter(...).map(...).batch(...)`.\n *\n * Data loading and transformation is done in a lazy, streaming fashion.  The\n * dataset may be iterated over multiple times; each iteration starts the data\n * loading anew and recapitulates the transformations.\n *\n * A `Dataset` is typically processed as a stream of unbatched examples --i.e.,\n * its transformations are applied one example at a time. Batching produces a\n * new `Dataset` where each element is a batch. Batching should usually come\n * last in a pipeline, because data transformations are easier to express on a\n * per-example basis than on a per-batch basis.\n *\n * The following code examples are calling `await dataset.forEachAsync(...)` to\n * iterate once over the entire dataset in order to print out the data.\n *\n * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}\n */\nexport class Dataset {\n    constructor() {\n        this.size = null;\n    }\n    // TODO(soergel): Make Datasets report whether repeated iterator() calls\n    // produce the same result (e.g., reading from a file) or different results\n    // (e.g., from the webcam).  Currently we don't make this distinction but it\n    // could be important for the user to know.\n    // abstract isDeterministic(): boolean;\n    /**\n     * Groups elements into batches.\n     *\n     * It is assumed that each of the incoming dataset elements has the same\n     * structure-- i.e. the same set of keys at each location in an object\n     * hierarchy.  For each key, the resulting `Dataset` provides a batched\n     * element collecting all of the incoming values for that key.\n     *\n     *  * Incoming primitives are grouped into a 1-D Tensor.\n     *  * Incoming Tensors are grouped into a new Tensor where the 0'th axis is\n     *    the batch dimension.\n     *  * Incoming arrays are converted to Tensor and then batched.\n     *  * A nested array is interpreted as an n-D Tensor, so the batched result\n     *    has n+1 dimensions.\n     *  * An array that cannot be converted to Tensor produces an error.\n     *\n     * If an array should not be batched as a unit, it should first be converted\n     * to an object with integer keys.\n     *\n     * Here are a few examples:\n     *\n     * Batch a dataset of numbers:\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8]).batch(4);\n     * await a.forEachAsync(e => e.print());\n     * ```\n     *\n     * Batch a dataset of arrays:\n     * ```js\n     * const b = tf.data.array([[1], [2], [3], [4], [5], [6], [7], [8]]).batch(4);\n     * await b.forEachAsync(e => e.print());\n     * ```\n     *\n     * Batch a dataset of objects:\n     * ```js\n     * const c = tf.data.array([{a: 1, b: 11}, {a: 2, b: 12}, {a: 3, b: 13},\n     *   {a: 4, b: 14}, {a: 5, b: 15}, {a: 6, b: 16}, {a: 7, b: 17},\n     *   {a: 8, b: 18}]).batch(4);\n     * await c.forEachAsync(e => {\n     *   console.log('{');\n     *   for(var key in e) {\n     *     console.log(key+':');\n     *     e[key].print();\n     *   }\n     *   console.log('}');\n     * })\n     * ```\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @returns A `Dataset`, from which a stream of batches can be obtained.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    batch(batchSize, smallLastBatch = true) {\n        const base = this;\n        tf.util.assert(batchSize > 0, () => `batchSize needs to be positive, but it is\n      ${batchSize}`);\n        let size;\n        if (this.size === Infinity || this.size == null) {\n            // If the size of this dataset is infinity or null, the new size keeps the\n            // same.\n            size = this.size;\n        }\n        else if (smallLastBatch) {\n            // If the size of this dataset is known and include small last batch, the\n            // new size is full batch count plus last batch.\n            size = Math.ceil(this.size / batchSize);\n        }\n        else {\n            // If the size of this dataset is known and not include small last batch,\n            // the new size is full batch count.\n            size = Math.floor(this.size / batchSize);\n        }\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator())\n                .columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat);\n        }, size);\n    }\n    /**\n     * Concatenates this `Dataset` with another.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]);\n     * const b = tf.data.array([4, 5, 6]);\n     * const c = a.concatenate(b);\n     * await c.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param dataset A `Dataset` to be concatenated onto this one.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    concatenate(dataset) {\n        const base = this;\n        let size;\n        if (this.size === Infinity || dataset.size === Infinity) {\n            // If the size of any of these two dataset is infinity, new size is\n            // infinity.\n            size = Infinity;\n        }\n        else if (this.size != null && dataset.size != null) {\n            // If the size of both datasets are known and not infinity, new size is\n            // sum the size of these two datasets.\n            size = this.size + dataset.size;\n        }\n        else {\n            // If neither of these two datasets has infinite size and any of these two\n            // datasets' size is null, the new size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).concatenate(await dataset.iterator()), size);\n    }\n    /**\n     * Filters this dataset according to `predicate`.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n     *   .filter(x => x%2 === 0);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param predicate A function mapping a dataset element to a boolean or a\n     * `Promise` for one.\n     *\n     * @returns A `Dataset` of elements for which the predicate was true.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    filter(predicate) {\n        const base = this;\n        let size;\n        if (this.size === Infinity) {\n            // If the size of this dataset is infinity, new size is infinity\n            size = Infinity;\n        }\n        else {\n            // If this dataset has limited elements, new size is null because it might\n            // exhausted randomly.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).filter(x => tf.tidy(() => predicate(x)));\n        }, size);\n    }\n    /**\n     * Apply a function to every element of the dataset.\n     *\n     * After the function is applied to a dataset element, any Tensors contained\n     * within that element are disposed.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param f A function to apply to each dataset element.\n     * @returns A `Promise` that resolves after all elements have been processed.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async forEachAsync(f) {\n        return (await this.iterator()).forEachAsync(f);\n    }\n    /**\n     * Maps this dataset through a 1-to-1 transform.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]).map(x => x*x);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param transform A function mapping a dataset element to a transformed\n     *   dataset element.\n     *\n     * @returns A `Dataset` of transformed elements.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    map(transform) {\n        const base = this;\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).map(x => tf.tidy(() => transform(x)));\n        }, this.size);\n    }\n    /**\n     * Maps this dataset through an async 1-to-1 transform.\n     *\n     * ```js\n     * const a =\n     *  tf.data.array([1, 2, 3]).mapAsync(x => new Promise(function(resolve){\n     *    setTimeout(() => {\n     *      resolve(x * x);\n     *    }, Math.random()*1000 + 500);\n     *  }));\n     * console.log(await a.toArray());\n     * ```\n     *\n     * @param transform A function mapping a dataset element to a `Promise` for a\n     *   transformed dataset element.  This transform is responsible for disposing\n     *   any intermediate `Tensor`s, i.e. by wrapping its computation in\n     *   `tf.tidy()`; that cannot be automated here (as it is in the synchronous\n     *   `map()` case).\n     *\n     * @returns A `Dataset` of transformed elements.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    mapAsync(transform) {\n        const base = this;\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).mapAsync(transform);\n        }, this.size);\n    }\n    /**\n     *  Creates a `Dataset` that prefetches elements from this dataset.\n     *\n     * @param bufferSize: An integer specifying the number of elements to be\n     *   prefetched.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    prefetch(bufferSize) {\n        if (bufferSize == null) {\n            throw new RangeError('`Dataset.prefetch()` requires bufferSize to be specified.');\n        }\n        const base = this;\n        return datasetFromIteratorFn(async () => (await base.iterator()).prefetch(bufferSize), this.size);\n    }\n    /**\n     * Repeats this dataset `count` times.\n     *\n     * NOTE: If this dataset is a function of global state (e.g. a random number\n     * generator), then different repetitions may produce different elements.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]).repeat(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: (Optional) An integer, representing the number of times\n     *   the dataset should be repeated. The default behavior (if `count` is\n     *   `undefined` or negative) is for the dataset be repeated indefinitely.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    repeat(count) {\n        const base = this;\n        let size;\n        if (this.size != null && count > 0) {\n            // If this dataset has size and count is positive, new size is current\n            // size multiply count. This also covers the case that current size is\n            // infinity.\n            size = this.size * count;\n        }\n        else if (count === 0) {\n            // If count is 0, new size is 0.\n            size = 0;\n        }\n        else if (this.size != null && (count === undefined || count < 0)) {\n            // If this dataset has size and count is undefined or negative, the\n            // dataset will be repeated indefinitely and new size is infinity.\n            size = Infinity;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => {\n            const iteratorIterator = iteratorFromFunction(async () => ({ value: await base.iterator(), done: false }));\n            return iteratorFromConcatenated(iteratorIterator.take(count));\n        }, size);\n    }\n    /**\n     * Creates a `Dataset` that skips `count` initial elements from this dataset.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).skip(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: The number of elements of this dataset that should be skipped\n     *   to form the new dataset.  If `count` is greater than the size of this\n     *   dataset, the new dataset will contain no elements.  If `count`\n     *   is `undefined` or negative, skips the entire dataset.\n     *\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    skip(count) {\n        const base = this;\n        let size;\n        if (this.size != null && count >= 0 && this.size >= count) {\n            // If the size of this dataset is greater than count, the new dataset's\n            // size is current size minus skipped size.This also covers the case that\n            // current size is infinity.\n            size = this.size - count;\n        }\n        else if (this.size != null &&\n            (this.size < count || count === undefined || count < 0)) {\n            // If the size of this dataset is smaller than count, or count is\n            // undefined or negative, skips the entire dataset and the new size is 0.\n            size = 0;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).skip(count), size);\n    }\n    /**\n     * Pseudorandomly shuffles the elements of this dataset. This is done in a\n     * streaming manner, by sampling from a given number of prefetched elements.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).shuffle(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param bufferSize: An integer specifying the number of elements from this\n     *   dataset from which the new dataset will sample.\n     * @param seed: (Optional) An integer specifying the random seed that will\n     *   be used to create the distribution.\n     * @param reshuffleEachIteration: (Optional) A boolean, which if true\n     *   indicates that the dataset should be pseudorandomly reshuffled each time\n     *   it is iterated over. If false, elements will be returned in the same\n     *   shuffled order on each iteration. (Defaults to `true`.)\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    shuffle(bufferSize, seed, reshuffleEachIteration = true) {\n        if (bufferSize == null || bufferSize < 0) {\n            if (this.size == null) {\n                throw new RangeError('`Dataset.shuffle()` requires bufferSize to be specified.');\n            }\n            else {\n                throw new RangeError('`Dataset.shuffle()` requires bufferSize to be specified.  ' +\n                    'If your data fits in main memory (for regular JS objects), ' +\n                    'and/or GPU memory (for `tf.Tensor`s), consider setting ' +\n                    `bufferSize to the dataset size (${this.size} elements)`);\n            }\n        }\n        const base = this;\n        const random = seedrandom.alea(seed || tf.util.now().toString());\n        return datasetFromIteratorFn(async () => {\n            let seed2 = random.int32();\n            if (reshuffleEachIteration) {\n                seed2 += random.int32();\n            }\n            return (await base.iterator()).shuffle(bufferSize, seed2.toString());\n        }, this.size);\n    }\n    /**\n     * Creates a `Dataset` with at most `count` initial elements from this\n     * dataset.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).take(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: The number of elements of this dataset that should be taken\n     *   to form the new dataset.  If `count` is `undefined` or negative, or if\n     *   `count` is greater than the size of this dataset, the new dataset will\n     *   contain all elements of this dataset.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    take(count) {\n        const base = this;\n        let size;\n        if (this.size != null && this.size > count) {\n            // If the size of this dataset is greater than count, the new dataset's\n            // size is count.\n            size = count;\n        }\n        else if (this.size != null && this.size <= count) {\n            // If the size of this dataset is equal or smaller than count, the new\n            // dataset's size is the size of this dataset.\n            size = this.size;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).take(count), size);\n    }\n    /**\n     * Collect all elements of this dataset into an array.\n     *\n     * Obviously this will succeed only for small datasets that fit in memory.\n     * Useful for testing and generally should be avoided if possible.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]);\n     * console.log(await a.toArray());\n     * ```\n     *\n     * @returns A Promise for an array of elements, which will resolve\n     *   when a new stream has been obtained and fully consumed.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async toArray() {\n        if (this.size === Infinity) {\n            throw new Error('Can not convert infinite data stream to array.');\n        }\n        return (await this.iterator()).toArray();\n    }\n    /**\n     * Collect all elements of this dataset into an array with prefetching 100\n     * elements. This is useful for testing, because the prefetch changes the\n     * order in which the Promises are resolved along the processing pipeline.\n     * This may help expose bugs where results are dependent on the order of\n     * Promise resolution rather than on the logical order of the stream (i.e.,\n     * due to hidden mutable state).\n     *\n     * @returns A Promise for an array of elements, which will resolve\n     *   when a new stream has been obtained and fully consumed.\n     */\n    async toArrayForTest() {\n        if (this.size === Infinity) {\n            throw new Error('Can not convert infinite data stream to array.');\n        }\n        return (await this.iterator()).toArrayForTest();\n    }\n}\n// TODO(soergel): deep sharded shuffle, where supported\nDataset.MAX_BUFFER_SIZE = 10000;\n/**\n * Create a `Dataset` defined by a provided iterator() function.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const iter = tf.data.iteratorFromFunction(func);\n * const ds = tf.data.datasetFromIteratorFn(iter);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n */\nexport function datasetFromIteratorFn(iteratorFn, size = null) {\n    return new class extends Dataset {\n        constructor() {\n            super(...arguments);\n            this.size = size;\n        }\n        /*\n         * Provide a new stream of elements.  Note this will also start new streams\n         * from any underlying `Dataset`s.\n         */\n        async iterator() {\n            return iteratorFn();\n        }\n    }();\n}\n/**\n * Create a `Dataset` from an array of elements.\n *\n * Create a Dataset from an array of objects:\n * ```js\n * const a = tf.data.array([{'item': 1}, {'item': 2}, {'item': 3}]);\n * await a.forEachAsync(e => console.log(e));\n * ```\n *\n * Create a Dataset from an array of numbers:\n * ```js\n * const a = tf.data.array([4, 5, 6]);\n * await a.forEachAsync(e => console.log(e));\n * ```\n * @param items An array of elements that will be parsed as items in a dataset.\n *\n * @doc {heading: 'Data', subheading: 'Creation', namespace: 'data'}\n */\nexport function array(items) {\n    return datasetFromIteratorFn(async () => iteratorFromItems(items), items.length);\n}\n/**\n * Create a `Dataset` by zipping together an array, dict, or nested\n * structure of `Dataset`s (and perhaps additional constants).\n * The underlying datasets must provide elements in a consistent order such that\n * they correspond.\n *\n * The number of elements in the resulting dataset is the same as the size of\n * the smallest dataset in datasets.\n *\n * The nested structure of the `datasets` argument determines the\n * structure of elements in the resulting iterator.\n *\n * Note this means that, given an array of two datasets that produce dict\n * elements, the result is a dataset that produces elements that are arrays\n * of two dicts:\n *\n * Zip an array of datasets:\n * ```js\n * console.log('Zip two datasets of objects:');\n * const ds1 = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);\n * const ds2 = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);\n * const ds3 = tf.data.zip([ds1, ds2]);\n * await ds3.forEachAsync(e => console.log(JSON.stringify(e)));\n *\n * // If the goal is to merge the dicts in order to produce elements like\n * // {a: ..., b: ...}, this requires a second step such as:\n * console.log('Merge the objects:');\n * const ds4 = ds3.map(x => {return {a: x[0].a, b: x[1].b}});\n * await ds4.forEachAsync(e => console.log(e));\n * ```\n *\n * Zip a dict of datasets:\n * ```js\n * const a = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);\n * const b = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);\n * const c = tf.data.zip({c: a, d: b});\n * await c.forEachAsync(e => console.log(JSON.stringify(e)));\n * ```\n *\n * @doc {heading: 'Data', subheading: 'Operations', namespace: 'data'}\n */\nexport function zip(datasets) {\n    // manually type-check the argument for JS users\n    if (!isIterable(datasets)) {\n        throw new Error('The argument to zip() must be an object or array.');\n    }\n    let size;\n    if (Array.isArray(datasets)) {\n        for (let i = 0; i < datasets.length; i++) {\n            size = size == null ? datasets[i].size :\n                Math.min(size, datasets[i].size);\n        }\n    }\n    else if (datasets instanceof Object) {\n        for (const ds in datasets) {\n            size = size == null ? datasets[ds].size :\n                Math.min(size, datasets[ds].size);\n        }\n    }\n    return datasetFromIteratorFn(async () => {\n        const streams = await deepMapAndAwaitAll(datasets, d => {\n            if (d instanceof Dataset) {\n                return { value: d.iterator(), recurse: false };\n            }\n            else if (isIterable(d)) {\n                return { value: null, recurse: true };\n            }\n            else {\n                throw new Error('Leaves of the structure passed to zip() must be Datasets, ' +\n                    'not primitives.');\n            }\n        });\n        return iteratorFromZipped(streams, ZipMismatchMode.SHORTEST);\n    }, size);\n}\n/**\n * A zip function for use with deepZip, passed via the columnMajorBatch call.\n *\n * Accepts an array of identically-structured nested elements and either batches\n * them (if they are primitives, numeric arrays, or Tensors) or requests\n * recursion (if not).\n */\n// tslint:disable-next-line:no-any\nfunction deepBatchConcat(rows) {\n    if (rows === null) {\n        return null;\n    }\n    // use the first item to decide whether to recurse or batch here.\n    const exampleRow = rows[0];\n    if (canTensorify(exampleRow)) {\n        // rows is an array of primitives, Tensors, or arrays.  Batch them.\n        const value = batchConcat(rows);\n        return { value, recurse: false };\n    }\n    // the example row is an object, so recurse into it.\n    return { value: null, recurse: true };\n}\n/**\n * Assembles a list of same-shaped numbers, number arrays, or Tensors\n * into a single new Tensor where axis 0 is the batch dimension.\n */\nfunction batchConcat(arrays) {\n    if (arrays.length === 0) {\n        // We can't return an empty Tensor because we don't know the element shape.\n        throw new Error('Can\\'t make a batch of zero elements.');\n    }\n    if (arrays[0] instanceof tf.Tensor) {\n        // Input is an array of Tensors\n        return tf.stack(arrays);\n    }\n    else {\n        // Input is a possibly-nested array of numbers.\n        return tf.tensor(arrays);\n    }\n}\n//# sourceMappingURL=dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { Dataset } from '../dataset';\n/**\n * Represents a potentially large collection of text lines.\n *\n * The results are not batched.\n */\nexport class TextLineDataset extends Dataset {\n    /**\n     * Create a `TextLineDataset`.\n     *\n     * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n     */\n    constructor(input) {\n        super();\n        this.input = input;\n    }\n    async iterator() {\n        const inputIterator = await this.input.iterator();\n        const utf8Iterator = inputIterator.decodeUTF8();\n        const lineIterator = utf8Iterator.split('\\n').map(line => {\n            // Windows/DOS format text file has extra line breaker at the end of line.\n            if (line.endsWith('\\r')) {\n                line = line.slice(0, -1);\n            }\n            return line;\n        });\n        return lineIterator;\n    }\n}\n//# sourceMappingURL=text_line_dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { Dataset } from '../dataset';\nimport { TextLineDataset } from './text_line_dataset';\nconst CODE_QUOTE = '\"';\nconst STATE_OUT = Symbol('out');\nconst STATE_FIELD = Symbol('field');\nconst STATE_QUOTE = Symbol('quote');\nconst STATE_QUOTE_AFTER_QUOTE = Symbol('quoteafterquote');\nconst STATE_WITHIN_QUOTE_IN_QUOTE = Symbol('quoteinquote');\n/**\n * Represents a potentially large collection of delimited text records.\n *\n * The produced `TensorContainer`s each contain one key-value pair for\n * every column of the table.  When a field is empty in the incoming data, the\n * resulting value is `undefined`, or throw error if it is required.  Values\n * that can be parsed as numbers are emitted as type `number`, other values\n * are parsed as `string`.\n *\n * The results are not batched.\n *\n * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}\n */\nexport class CSVDataset extends Dataset {\n    /**\n     * Create a `CSVDataset`.\n     *\n     * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n     * @param csvConfig (Optional) A CSVConfig object that contains configurations\n     *     of reading and decoding from CSV file(s).\n     *\n     *     hasHeader: (Optional) A boolean value that indicates whether the first\n     *     row of provided CSV file is a header line with column names, and should\n     *     not be included in the data. Defaults to `true`.\n     *\n     *     columnNames: (Optional) A list of strings that corresponds to\n     *     the CSV column names, in order. If provided, it ignores the column\n     *     names inferred from the header row. If not provided, infers the column\n     *     names from the first row of the records. If hasHeader is false and\n     *     columnNames is not provided, this method throws an error.\n     *\n     *     columnConfigs: (Optional) A dictionary whose key is column names, value\n     *     is an object stating if this column is required, column's data type,\n     *     default value, and if this column is label. If provided, keys must\n     *     correspond to names provided in columnNames or inferred from the file\n     *     header lines. If isLabel is true any column, returns an array of two\n     *     items: the first item is a dict of features key/value pairs, the second\n     *     item is a dict of labels key/value pairs. If no feature is marked as\n     *     label, returns a dict of features only.\n     *\n     *     configuredColumnsOnly (Optional) If true, only columns provided in\n     *     columnConfigs will be parsed and provided during iteration.\n     *\n     *     delimiter (Optional) The string used to parse each line of the input\n     *     file. Defaults to `,`.\n     */\n    constructor(input, csvConfig) {\n        super();\n        this.input = input;\n        this.hasHeader = true;\n        this.fullColumnNames = null;\n        this.columnNamesValidated = false;\n        this.columnConfigs = null;\n        this.configuredColumnsOnly = false;\n        this.delimiter = ',';\n        this.delimWhitespace = false;\n        this.base = new TextLineDataset(input);\n        if (!csvConfig) {\n            csvConfig = {};\n        }\n        this.hasHeader = csvConfig.hasHeader === false ? false : true;\n        this.fullColumnNames = csvConfig.columnNames;\n        this.columnConfigs = csvConfig.columnConfigs;\n        this.configuredColumnsOnly = csvConfig.configuredColumnsOnly;\n        if (csvConfig.delimWhitespace) {\n            util.assert(csvConfig.delimiter == null, () => 'Delimiter should not be provided when delimWhitespace is true.');\n            this.delimWhitespace = true;\n            this.delimiter = ' ';\n        }\n        else {\n            this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : ',';\n        }\n    }\n    /**\n     * Returns column names of the csv dataset. If `configuredColumnsOnly` is\n     * true, return column names in `columnConfigs`. If `configuredColumnsOnly` is\n     * false and `columnNames` is provided, `columnNames`. If\n     * `configuredColumnsOnly` is false and `columnNames` is not provided, return\n     * all column names parsed from the csv file. For example usage please go to\n     * `tf.data.csv`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async columnNames() {\n        if (!this.columnNamesValidated) {\n            await this.setColumnNames();\n        }\n        return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) :\n            this.fullColumnNames;\n    }\n    /* 1) If `columnNames` is provided as string[], use this string[] as output\n     * keys in corresponding order. The length must match the number of inferred\n     * columns if `hasHeader` is true .\n     * 2) If `columnNames` is not provided, parse header line as `columnNames` if\n     * hasHeader is true. If `hasHeader` is false, throw an error.\n     * 3) If `columnConfigs` is provided, all the keys in `columnConfigs` must\n     * exist in parsed `columnNames`.\n     */\n    async setColumnNames() {\n        const columnNamesFromFile = await this.maybeReadHeaderLine();\n        if (!this.fullColumnNames && !columnNamesFromFile) {\n            // Throw an error if columnNames is not provided and no header line.\n            throw new Error('Column names must be provided if there is no header line.');\n        }\n        else if (this.fullColumnNames && columnNamesFromFile) {\n            // Check provided columnNames match header line.\n            util.assert(columnNamesFromFile.length === this.fullColumnNames.length, () => 'The length of provided columnNames (' +\n                this.fullColumnNames.length.toString() +\n                ') does not match the length of the header line read from ' +\n                'file (' + columnNamesFromFile.length.toString() + ').');\n        }\n        if (!this.fullColumnNames) {\n            this.fullColumnNames = columnNamesFromFile;\n        }\n        // Check if there are duplicate column names.\n        const counts = this.fullColumnNames.reduce((countAcc, name) => {\n            countAcc[name] = (countAcc[name] + 1) || 1;\n            return countAcc;\n        }, {});\n        const duplicateNames = Object.keys(counts).filter((name) => (counts[name] > 1));\n        util.assert(duplicateNames.length === 0, () => 'Duplicate column names found: ' + duplicateNames.toString());\n        // Check if keys in columnConfigs match columnNames.\n        if (this.columnConfigs) {\n            for (const key of Object.keys(this.columnConfigs)) {\n                const index = this.fullColumnNames.indexOf(key);\n                if (index === -1) {\n                    throw new Error('The key \"' + key +\n                        '\" provided in columnConfigs does not match any of the column ' +\n                        'names (' + this.fullColumnNames.toString() + ').');\n                }\n            }\n        }\n        this.columnNamesValidated = true;\n    }\n    async maybeReadHeaderLine() {\n        if (this.hasHeader) {\n            const iter = await this.base.iterator();\n            const firstElement = await iter.next();\n            if (firstElement.done) {\n                throw new Error('No data was found for CSV parsing.');\n            }\n            const firstLine = firstElement.value;\n            const headers = this.parseRow(firstLine, false);\n            return headers;\n        }\n        else {\n            return null;\n        }\n    }\n    async iterator() {\n        if (!this.columnNamesValidated) {\n            await this.setColumnNames();\n        }\n        let lines = await this.base.iterator();\n        if (this.hasHeader) {\n            // We previously read the first line to get the columnNames.\n            // Now that we're providing data, skip it.\n            lines = lines.skip(1);\n        }\n        return lines.map(x => this.makeDataElement(x));\n    }\n    makeDataElement(line) {\n        const values = this.parseRow(line);\n        const features = {};\n        const labels = {};\n        for (let i = 0; i < this.fullColumnNames.length; i++) {\n            const key = this.fullColumnNames[i];\n            const config = this.columnConfigs ? this.columnConfigs[key] : null;\n            if (this.configuredColumnsOnly && !config) {\n                // This column is not selected.\n                continue;\n            }\n            else {\n                const value = values[i];\n                let parsedValue = null;\n                if (value === '') {\n                    // If default value is provided, use it. If default value is not\n                    // provided, set as undefined.\n                    if (config && config.default !== undefined) {\n                        parsedValue = config.default;\n                    }\n                    else if (config && (config.required || config.isLabel)) {\n                        throw new Error(`Required column ${key} is empty in this line: ${line}`);\n                    }\n                    else {\n                        parsedValue = undefined;\n                    }\n                }\n                else {\n                    // A value is present, so parse it based on type\n                    const valueAsNum = Number(value);\n                    if (isNaN(valueAsNum)) {\n                        // The value is a string and this column is declared as boolean\n                        // in config, parse it as boolean.\n                        if (config && config.dtype === 'bool') {\n                            parsedValue = this.getBoolean(value);\n                        }\n                        else {\n                            // Set value as string\n                            parsedValue = value;\n                        }\n                    }\n                    else if (!config || !config.dtype) {\n                        // If this value is a number and no type config is provided, return\n                        // it as number.\n                        parsedValue = valueAsNum;\n                    }\n                    else {\n                        // If this value is a number and data type is provided, parse it\n                        // according to provided data type.\n                        switch (config.dtype) {\n                            case 'float32':\n                                parsedValue = valueAsNum;\n                                break;\n                            case 'int32':\n                                parsedValue = Math.floor(valueAsNum);\n                                break;\n                            case 'bool':\n                                parsedValue = this.getBoolean(value);\n                                break;\n                            default:\n                                parsedValue = valueAsNum;\n                        }\n                    }\n                }\n                // Check if this column is label.\n                (config && config.isLabel) ? labels[key] = parsedValue :\n                    features[key] = parsedValue;\n            }\n        }\n        // If label exists, return an object of features and labels as {xs:features,\n        // ys:labels}, otherwise return features only.\n        if (Object.keys(labels).length === 0) {\n            return features;\n        }\n        else {\n            return { xs: features, ys: labels };\n        }\n    }\n    getBoolean(value) {\n        if (value === '1' || value.toLowerCase() === 'true') {\n            return 1;\n        }\n        else {\n            return 0;\n        }\n    }\n    // adapted from https://beta.observablehq.com/@mbostock/streaming-csv\n    parseRow(line, validateElementCount = true) {\n        const result = [];\n        let readOffset = 0;\n        const readLength = line.length;\n        let currentState = STATE_OUT;\n        // Goes through the line to parse quote.\n        for (let i = 0; i < readLength; i++) {\n            switch (currentState) {\n                // Before enter a new field\n                case STATE_OUT:\n                    switch (line.charAt(i)) {\n                        // Enter a quoted field\n                        case CODE_QUOTE:\n                            readOffset = i + 1;\n                            currentState = STATE_QUOTE;\n                            break;\n                        // Read an empty field\n                        case this.delimiter:\n                            readOffset = i + 1;\n                            // If delimiter is white space and configured to collapse\n                            // multiple white spaces, ignore this white space.\n                            if (this.delimiter === ' ' && this.delimWhitespace) {\n                                break;\n                            }\n                            result.push('');\n                            currentState = STATE_OUT;\n                            break;\n                        // Enter an unquoted field\n                        default:\n                            currentState = STATE_FIELD;\n                            readOffset = i;\n                            break;\n                    }\n                    break;\n                // In an unquoted field\n                case STATE_FIELD:\n                    switch (line.charAt(i)) {\n                        // Exit an unquoted field, add it to result\n                        case this.delimiter:\n                            result.push(line.substring(readOffset, i));\n                            currentState = STATE_OUT;\n                            readOffset = i + 1;\n                            break;\n                        default:\n                    }\n                    break;\n                // In a quoted field\n                case STATE_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Read a quote after a quote\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE_AFTER_QUOTE;\n                            break;\n                        default:\n                    }\n                    break;\n                // This state means it's right after a second quote in a field\n                case STATE_QUOTE_AFTER_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Finished a quoted field\n                        case this.delimiter:\n                            result.push(line.substring(readOffset, i - 1));\n                            currentState = STATE_OUT;\n                            readOffset = i + 1;\n                            break;\n                        // Finished a quoted part in a quoted field\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE;\n                            break;\n                        // In a quoted part in a quoted field\n                        default:\n                            currentState = STATE_WITHIN_QUOTE_IN_QUOTE;\n                            break;\n                    }\n                    break;\n                case STATE_WITHIN_QUOTE_IN_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Exit a quoted part in a quoted field\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE;\n                            break;\n                        default:\n                    }\n                    break;\n                default:\n            }\n        }\n        // Adds last item based on if it is quoted.\n        if (currentState === STATE_QUOTE_AFTER_QUOTE) {\n            result.push(line.substring(readOffset, readLength - 1));\n        }\n        else {\n            result.push(line.substring(readOffset));\n        }\n        // Check if each row has the same number of elements as column names.\n        if (validateElementCount && result.length !== this.fullColumnNames.length) {\n            throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);\n        }\n        return result;\n    }\n}\n// TODO(soergel): add more basic datasets for parity with tf.data\n// tf.data.FixedLengthRecordDataset()\n// tf.data.TFRecordDataset()\n//# sourceMappingURL=csv_dataset.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env, tensor, util } from '@tensorflow/tfjs-core';\nimport { LazyIterator } from './lazy_iterator';\n/**\n * Provide a stream of tensors from microphone audio stream. The tensors are\n * representing audio data as frequency-domain spectrogram generated with\n * browser's native FFT. Tensors representing time-domain waveform is available\n * based on configuration. Only works in browser environment.\n */\nexport class MicrophoneIterator extends LazyIterator {\n    constructor(microphoneConfig) {\n        super();\n        this.microphoneConfig = microphoneConfig;\n        this.isClosed = false;\n        this.fftSize = microphoneConfig.fftSize || 1024;\n        const fftSizeLog2 = Math.log2(this.fftSize);\n        if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 ||\n            !Number.isInteger(fftSizeLog2)) {\n            throw new Error(`Invalid fftSize: it must be a power of 2 between ` +\n                `2 to 4 and 2 to 14, but got ${this.fftSize}`);\n        }\n        this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43;\n        this.sampleRateHz = microphoneConfig.sampleRateHz;\n        this.columnTruncateLength =\n            microphoneConfig.columnTruncateLength || this.fftSize;\n        this.audioTrackConstraints = microphoneConfig.audioTrackConstraints;\n        this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0;\n        this.includeSpectrogram =\n            microphoneConfig.includeSpectrogram === false ? false : true;\n        this.includeWaveform =\n            microphoneConfig.includeWaveform === true ? true : false;\n        if (!this.includeSpectrogram && !this.includeWaveform) {\n            throw new Error('Both includeSpectrogram and includeWaveform are false. ' +\n                'At least one type of data should be returned.');\n        }\n    }\n    summary() {\n        return `microphone`;\n    }\n    // Construct a MicrophoneIterator and start the audio stream.\n    static async create(microphoneConfig = {}) {\n        if (env().get('IS_NODE')) {\n            throw new Error('microphone API is only supported in browser environment.');\n        }\n        const microphoneIterator = new MicrophoneIterator(microphoneConfig);\n        // Call async function start() to initialize the audio stream.\n        await microphoneIterator.start();\n        return microphoneIterator;\n    }\n    // Start the audio stream and FFT.\n    async start() {\n        try {\n            this.stream = await navigator.mediaDevices.getUserMedia({\n                audio: this.audioTrackConstraints == null ? true :\n                    this.audioTrackConstraints,\n                video: false\n            });\n        }\n        catch (e) {\n            throw new Error(`Error thrown while initializing video stream: ${e.message}`);\n        }\n        if (!this.stream) {\n            throw new Error('Could not obtain audio from microphone.');\n        }\n        const ctxConstructor = \n        // tslint:disable-next-line:no-any\n        window.AudioContext || window.webkitAudioContext;\n        this.audioContext = new ctxConstructor();\n        if (!this.sampleRateHz) {\n            // If sample rate is not provided, use the available sample rate on\n            // device.\n            this.sampleRateHz = this.audioContext.sampleRate;\n        }\n        else if (this.audioContext.sampleRate !== this.sampleRateHz) {\n            throw new Error(`Mismatch in sampling rate: ` +\n                `Expected: ${this.sampleRateHz}; ` +\n                `Actual: ${this.audioContext.sampleRate}`);\n        }\n        const streamSource = this.audioContext.createMediaStreamSource(this.stream);\n        this.analyser = this.audioContext.createAnalyser();\n        this.analyser.fftSize = this.fftSize * 2;\n        this.analyser.smoothingTimeConstant = this.smoothingTimeConstant;\n        streamSource.connect(this.analyser);\n        this.freqData = new Float32Array(this.fftSize);\n        this.timeData = new Float32Array(this.fftSize);\n        return;\n    }\n    async next() {\n        if (this.isClosed) {\n            return { value: null, done: true };\n        }\n        let spectrogramTensor;\n        let waveformTensor;\n        const audioDataQueue = await this.getAudioData();\n        if (this.includeSpectrogram) {\n            const freqData = this.flattenQueue(audioDataQueue.freqDataQueue);\n            spectrogramTensor = this.getTensorFromAudioDataArray(freqData, [this.numFrames, this.columnTruncateLength, 1]);\n        }\n        if (this.includeWaveform) {\n            const timeData = this.flattenQueue(audioDataQueue.timeDataQueue);\n            waveformTensor = this.getTensorFromAudioDataArray(timeData, [this.numFrames * this.fftSize, 1]);\n        }\n        return {\n            value: { 'spectrogram': spectrogramTensor, 'waveform': waveformTensor },\n            done: false\n        };\n    }\n    // Capture one result from the audio stream, and extract the value from\n    // iterator.next() result.\n    async capture() {\n        return (await this.next()).value;\n    }\n    async getAudioData() {\n        const freqDataQueue = [];\n        const timeDataQueue = [];\n        let currentFrames = 0;\n        return new Promise(resolve => {\n            const intervalID = setInterval(() => {\n                if (this.includeSpectrogram) {\n                    this.analyser.getFloatFrequencyData(this.freqData);\n                    // If the audio stream is initializing, return empty queue.\n                    if (this.freqData[0] === -Infinity) {\n                        resolve({ freqDataQueue, timeDataQueue });\n                    }\n                    freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength));\n                }\n                if (this.includeWaveform) {\n                    this.analyser.getFloatTimeDomainData(this.timeData);\n                    timeDataQueue.push(this.timeData.slice());\n                }\n                // Clean interval and return when all frames have been collected\n                if (++currentFrames === this.numFrames) {\n                    clearInterval(intervalID);\n                    resolve({ freqDataQueue, timeDataQueue });\n                }\n            }, this.fftSize / this.sampleRateHz * 1e3);\n        });\n    }\n    // Stop the audio stream and pause the iterator.\n    stop() {\n        if (!this.isClosed) {\n            this.isClosed = true;\n            this.analyser.disconnect();\n            this.audioContext.close();\n            if (this.stream != null && this.stream.getTracks().length > 0) {\n                this.stream.getTracks()[0].stop();\n            }\n        }\n    }\n    // Override toArray() function to prevent collecting.\n    toArray() {\n        throw new Error('Can not convert infinite audio stream to array.');\n    }\n    // Return audio sampling rate in Hz\n    getSampleRate() {\n        return this.sampleRateHz;\n    }\n    flattenQueue(queue) {\n        const frameSize = queue[0].length;\n        const freqData = new Float32Array(queue.length * frameSize);\n        queue.forEach((data, i) => freqData.set(data, i * frameSize));\n        return freqData;\n    }\n    getTensorFromAudioDataArray(freqData, shape) {\n        const vals = new Float32Array(util.sizeFromShape(shape));\n        // If the data is less than the output shape, the rest is padded with zeros.\n        vals.set(freqData, vals.length - freqData.length);\n        return tensor(vals, shape);\n    }\n}\n//# sourceMappingURL=microphone_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { browser, env, image, tensor1d, tensor2d, tidy, util } from '@tensorflow/tfjs-core';\nimport { LazyIterator } from './lazy_iterator';\n/**\n * Provide a stream of image tensors from webcam video stream. Only works in\n * browser environment.\n */\nexport class WebcamIterator extends LazyIterator {\n    constructor(webcamVideoElement, webcamConfig) {\n        super();\n        this.webcamVideoElement = webcamVideoElement;\n        this.webcamConfig = webcamConfig;\n        this.isClosed = true;\n        this.resize = false;\n        if (this.needToResize()) {\n            this.resize = true;\n            this.cropSize =\n                [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth];\n            this.cropBoxInd = tensor1d([0], 'int32');\n            if (this.webcamConfig.centerCrop) {\n                // Calculate the box based on resizing shape.\n                const widthCroppingRatio = this.webcamConfig.resizeWidth * 1.0 / this.webcamVideoElement.width;\n                const heightCroppingRatio = this.webcamConfig.resizeHeight * 1.0 /\n                    this.webcamVideoElement.height;\n                const widthCropStart = (1 - widthCroppingRatio) / 2;\n                const heightCropStart = (1 - heightCroppingRatio) / 2;\n                const widthCropEnd = widthCropStart + widthCroppingRatio;\n                const heightCropEnd = heightCroppingRatio + heightCropStart;\n                this.cropBox = tensor2d([heightCropStart, widthCropStart, heightCropEnd, widthCropEnd], [1, 4]);\n            }\n            else {\n                this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]);\n            }\n        }\n    }\n    summary() {\n        return `webcam`;\n    }\n    // Construct a WebcamIterator and start it's video stream.\n    static async create(webcamVideoElement, webcamConfig = {}) {\n        if (env().get('IS_NODE')) {\n            throw new Error('tf.data.webcam is only supported in browser environment.');\n        }\n        if (!webcamVideoElement) {\n            // If webcam video element is not provided, create a hidden video element\n            // with provided width and height.\n            webcamVideoElement = document.createElement('video');\n            if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) {\n                throw new Error('Please provide webcam video element, or resizeWidth and ' +\n                    'resizeHeight to create a hidden video element.');\n            }\n            webcamVideoElement.width = webcamConfig.resizeWidth;\n            webcamVideoElement.height = webcamConfig.resizeHeight;\n        }\n        const webcamIterator = new WebcamIterator(webcamVideoElement, webcamConfig);\n        // Call async function to initialize the video stream.\n        await webcamIterator.start();\n        return webcamIterator;\n    }\n    // Async function to start video stream.\n    async start() {\n        if (this.webcamConfig.facingMode) {\n            util.assert((this.webcamConfig.facingMode === 'user') ||\n                (this.webcamConfig.facingMode === 'environment'), () => `Invalid webcam facing mode: ${this.webcamConfig.facingMode}. ` +\n                `Please provide 'user' or 'environment'`);\n        }\n        try {\n            this.stream = await navigator.mediaDevices.getUserMedia({\n                video: {\n                    deviceId: this.webcamConfig.deviceId,\n                    facingMode: this.webcamConfig.facingMode ?\n                        this.webcamConfig.facingMode :\n                        'user',\n                    width: this.webcamVideoElement.width,\n                    height: this.webcamVideoElement.height\n                }\n            });\n        }\n        catch (e) {\n            // Modify the error message but leave the stack trace intact\n            e.message = `Error thrown while initializing video stream: ${e.message}`;\n            throw e;\n        }\n        if (!this.stream) {\n            throw new Error('Could not obtain video from webcam.');\n        }\n        // Older browsers may not have srcObject\n        try {\n            this.webcamVideoElement.srcObject = this.stream;\n        }\n        catch (error) {\n            console.log(error);\n            this.webcamVideoElement.src = window.URL.createObjectURL(this.stream);\n        }\n        // Start the webcam video stream\n        this.webcamVideoElement.play();\n        this.isClosed = false;\n        return new Promise(resolve => {\n            // Add event listener to make sure the webcam has been fully initialized.\n            this.webcamVideoElement.onloadedmetadata = () => {\n                resolve();\n            };\n        });\n    }\n    async next() {\n        if (this.isClosed) {\n            return { value: null, done: true };\n        }\n        let img;\n        try {\n            img = browser.fromPixels(this.webcamVideoElement);\n        }\n        catch (e) {\n            throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`);\n        }\n        if (this.resize) {\n            try {\n                return { value: this.cropAndResizeFrame(img), done: false };\n            }\n            catch (e) {\n                throw new Error(`Error thrown cropping the video: ${e.message}`);\n            }\n            finally {\n                img.dispose();\n            }\n        }\n        else {\n            return { value: img, done: false };\n        }\n    }\n    needToResize() {\n        // If resizeWidth and resizeHeight are provided, and different from the\n        // width and height of original HTMLVideoElement, then resizing and cropping\n        // is required.\n        if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight &&\n            (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth ||\n                this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) {\n            return true;\n        }\n        return false;\n    }\n    // Cropping and resizing each frame based on config\n    cropAndResizeFrame(img) {\n        return tidy(() => {\n            const expandedImage = img.toFloat().expandDims(0);\n            let resizedImage;\n            resizedImage = image.cropAndResize(expandedImage, this.cropBox, this.cropBoxInd, this.cropSize, 'bilinear');\n            // Extract image from batch cropping.\n            const shape = resizedImage.shape;\n            return resizedImage.reshape(shape.slice(1));\n        });\n    }\n    // Capture one frame from the video stream, and extract the value from\n    // iterator.next() result.\n    async capture() {\n        return (await this.next()).value;\n    }\n    // Stop the video stream and pause webcam iterator.\n    stop() {\n        const tracks = this.stream.getTracks();\n        tracks.forEach(track => track.stop());\n        try {\n            this.webcamVideoElement.srcObject = null;\n        }\n        catch (error) {\n            console.log(error);\n            this.webcamVideoElement.src = null;\n        }\n        this.isClosed = true;\n    }\n    // Override toArray() function to prevent collecting.\n    toArray() {\n        throw new Error('Can not convert infinite video stream to array.');\n    }\n}\n//# sourceMappingURL=webcam_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * Represents a data source readable as a stream of binary data chunks.\n *\n * Because `Dataset`s can be read repeatedly (via `Dataset.iterator()`), this\n * provides a means to repeatedly create streams from the underlying data\n * sources.\n */\nexport class DataSource {\n}\n// TODO(soergel): consider convenience factory functions here\n// in combination with chainable source->dataset above, e.g.:\n// tf.data.url(...).asCsvDataset().shuffle().batch()\n//# sourceMappingURL=datasource.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nexport class StringIterator extends LazyIterator {\n    /**\n     * Splits a string stream on a given separator.\n     *\n     * It is assumed that the incoming chunk boundaries have no semantic meaning,\n     * so conceptually the incoming stream is treated simply as the concatenation\n     * of its elements.\n     *\n     * The outgoing stream provides chunks corresponding to the results of the\n     * standard string split() operation (even if such a chunk spanned incoming\n     * chunks).  The separators are not included.\n     *\n     * A typical usage is to split a text file (represented as a stream with\n     * arbitrary chunk boundaries) into lines.\n     *\n     * @param upstream A readable stream of strings that can be treated as\n     *   concatenated.\n     * @param separator A character to split on.\n     */\n    split(separator) {\n        return new SplitIterator(this, separator);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on StringIterator.  Unfortunately they can't be placed in separate files, due\n// to resulting trouble with circular imports.\n// ============================================================================\n// We wanted multiple inheritance, e.g.\n//   class SplitIterator extends QueueIterator<string>, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\nclass SplitIterator extends StringIterator {\n    constructor(upstream, separator) {\n        super();\n        this.upstream = upstream;\n        this.impl = new SplitIteratorImpl(upstream, separator);\n    }\n    summary() {\n        return this.impl.summary();\n    }\n    async next() {\n        return this.impl.next();\n    }\n}\nclass SplitIteratorImpl extends OneToManyIterator {\n    constructor(upstream, separator) {\n        super();\n        this.upstream = upstream;\n        this.separator = separator;\n        // A partial string at the end of an upstream chunk\n        this.carryover = '';\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Split('${this.separator}')`;\n    }\n    async pump() {\n        const chunkResult = await this.upstream.next();\n        if (chunkResult.done) {\n            if (this.carryover === '') {\n                return false;\n            }\n            // Pretend that the pump succeeded in order to emit the small last batch.\n            // The next pump() call will actually fail.\n            this.outputQueue.push(this.carryover);\n            this.carryover = '';\n            return true;\n        }\n        const lines = chunkResult.value.split(this.separator);\n        // Note the behavior: \" ab \".split(' ') === ['', 'ab', '']\n        // Thus the carryover may be '' if the separator falls on a chunk\n        // boundary; this produces the correct result.\n        lines[0] = this.carryover + lines[0];\n        for (const line of lines.slice(0, -1)) {\n            this.outputQueue.push(line);\n        }\n        this.carryover = lines[lines.length - 1];\n        return true;\n    }\n}\n//# sourceMappingURL=string_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nimport { StringIterator } from './string_iterator';\nexport class ByteChunkIterator extends LazyIterator {\n    /**\n     * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n     *\n     * The byte arrays producetd from the ByteChunkIterator on which this is\n     * called will be interpreted as concatenated.  No assumptions are made about\n     * the boundaries of the incoming chunks, so a multi-byte UTF8 encoding of a\n     * character may span the boundary between chunks.  This naturally happens,\n     * for instance, when reading fixed-size byte arrays from a file.\n     */\n    decodeUTF8() {\n        return new Utf8Iterator(this);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on ByteChunkIterator.  Unfortunately they can't be placed in separate files,\n// due to resulting trouble with circular imports.\n// ============================================================================\n// We wanted multiple inheritance, e.g.\n//   class Utf8Iterator extends QueueIterator<string>, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\nclass Utf8Iterator extends StringIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        this.impl = new Utf8IteratorImpl(upstream);\n    }\n    summary() {\n        return this.impl.summary();\n    }\n    async next() {\n        return this.impl.next();\n    }\n}\n/**\n * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n *\n * This is tricky because the incoming byte array boundaries may disrupt a\n * multi-byte UTF8 character. Thus any incomplete character data at the end of\n * a chunk must be carried over and prepended to the next chunk before\n * decoding. Luckily with native decoder, TextDecoder in browser and\n * string_decoder in node, byte array boundaries are handled automatically.\n *\n * In the context of an input pipeline for machine learning, UTF8 decoding is\n * needed to parse text files containing training examples or prediction\n * requests (e.g., formatted as CSV or JSON). We cannot use the built-in\n * decoding provided by FileReader.readAsText() because here we are in a\n * streaming context, which FileReader does not support.\n *\n * @param upstream A `LazyIterator` of `Uint8Arrays` containing UTF8-encoded\n *   text, which should be interpreted as concatenated.  No assumptions are\n *   made about the boundaries of the incoming chunks, so a multi-byte UTF8\n *   encoding of a character may span the boundary between chunks.  This\n *   naturally happens, for instance, when reading fixed-size byte arrays from a\n *   file.\n */\nclass Utf8IteratorImpl extends OneToManyIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        if (env().get('IS_BROWSER')) {\n            this.decoder = new TextDecoder('utf-8');\n        }\n        else {\n            // tslint:disable-next-line:no-require-imports\n            const { StringDecoder } = require('string_decoder');\n            this.decoder = new StringDecoder('utf8');\n        }\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Utf8`;\n    }\n    async pump() {\n        const chunkResult = await this.upstream.next();\n        let chunk;\n        if (chunkResult.done) {\n            return false;\n        }\n        else {\n            chunk = chunkResult.value;\n        }\n        let text;\n        if (env().get('IS_BROWSER')) {\n            text = this.decoder.decode(chunk, { stream: true });\n        }\n        else {\n            text = this.decoder.write(Buffer.from(chunk.buffer));\n        }\n        this.outputQueue.push(text);\n        return true;\n    }\n}\n//# sourceMappingURL=byte_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n// inspired by https://github.com/maxogden/filereader-stream\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { ByteChunkIterator } from './byte_chunk_iterator';\n/**\n * Provide a stream of chunks from a File, Blob, or Uint8Array.\n * @param file The source File, Blob or Uint8Array.\n * @param options Optional settings controlling file reading.\n * @returns a lazy Iterator of Uint8Arrays containing sequential chunks of the\n *   input File, Blob or Uint8Array.\n */\nexport class FileChunkIterator extends ByteChunkIterator {\n    constructor(file, options = {}) {\n        super();\n        this.file = file;\n        this.options = options;\n        util.assert((file instanceof Uint8Array) ||\n            (env().get('IS_BROWSER') ?\n                (file instanceof File || file instanceof Blob) :\n                false), () => 'FileChunkIterator only supports File, Blob and Uint8Array ' +\n            'right now.');\n        this.offset = options.offset || 0;\n        // default 1MB chunk has tolerable perf on large files\n        this.chunkSize = options.chunkSize || 1024 * 1024;\n    }\n    summary() {\n        return `FileChunks ${this.file}`;\n    }\n    async next() {\n        if (this.offset >= ((this.file instanceof Uint8Array) ?\n            this.file.byteLength :\n            this.file.size)) {\n            return { value: null, done: true };\n        }\n        const chunk = new Promise((resolve, reject) => {\n            const end = this.offset + this.chunkSize;\n            if (this.file instanceof Uint8Array) {\n                // Note if end > this.uint8Array.byteLength, we just get a small last\n                // chunk.\n                resolve(new Uint8Array(this.file.slice(this.offset, end)));\n            }\n            else {\n                // This branch assumes that this.file type is File or Blob, which\n                // means it is in the browser environment.\n                // TODO(soergel): is this a performance issue?\n                const fileReader = new FileReader();\n                fileReader.onload = (event) => {\n                    let data = fileReader.result;\n                    // Not sure we can trust the return type of\n                    // FileReader.readAsArrayBuffer See e.g.\n                    // https://github.com/node-file-api/FileReader/issues/2\n                    if (data instanceof ArrayBuffer) {\n                        data = new Uint8Array(data);\n                    }\n                    if (!(data instanceof Uint8Array)) {\n                        return reject(new TypeError('FileReader returned unknown type.'));\n                    }\n                    resolve(data);\n                };\n                fileReader.onabort = (event) => {\n                    return reject(new Error('Aborted'));\n                };\n                fileReader.onerror = (event) => {\n                    return reject(new Error(event.type));\n                };\n                // TODO(soergel): better handle onabort, onerror\n                // Note if end > this.file.size, we just get a small last chunk.\n                const slice = this.file.slice(this.offset, end);\n                // We can't use readAsText here (even if we know the file is text)\n                // because the slice boundary may fall within a multi-byte character.\n                fileReader.readAsArrayBuffer(slice);\n            }\n            this.offset = end;\n        });\n        return { value: (await chunk), done: false };\n    }\n}\n//# sourceMappingURL=file_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { FileChunkIterator } from './file_chunk_iterator';\n/**\n * Provide a stream of chunks from a URL.\n *\n * Note this class first downloads the entire file into memory before providing\n * the first element from the stream.  This is because the Fetch API does not\n * yet reliably provide a reader stream for the response body.\n */\nexport async function urlChunkIterator(url, options = {}) {\n    let urlString;\n    let requestInit;\n    if ((typeof url) === 'string') {\n        urlString = url;\n    }\n    else {\n        urlString = url.url;\n        requestInit = getRequestInitFromRequest(url);\n    }\n    const response = await util.fetch(urlString, requestInit);\n    if (response.ok) {\n        const uint8Array = new Uint8Array(await response.arrayBuffer());\n        return new FileChunkIterator(uint8Array, options);\n    }\n    else {\n        throw new Error(response.statusText);\n    }\n}\n// Generate RequestInit from Request to match tf.util.fetch signature.\nconst getRequestInitFromRequest = (request) => {\n    const init = {\n        method: request.method,\n        headers: request.headers,\n        body: request.body,\n        mode: request.mode,\n        credentials: request.credentials,\n        cache: request.cache,\n        redirect: request.redirect,\n        referrer: request.referrer,\n        integrity: request.integrity,\n    };\n    return init;\n};\n//# sourceMappingURL=url_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n// Skip tslint any type check cause this method is aiming to check type of\n// input.\n// tslint:disable-next-line:no-any\nexport function isLocalPath(source) {\n    return (typeof source === 'string') && source.substr(0, 7) === 'file://';\n}\n//# sourceMappingURL=source_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { DataSource } from '../datasource';\nimport { FileChunkIterator } from '../iterators/file_chunk_iterator';\nimport { isLocalPath } from '../util/source_util';\n/**\n * Represents a file, blob, or Uint8Array readable as a stream of binary data\n * chunks.\n */\nexport class FileDataSource extends DataSource {\n    /**\n     * Create a `FileDataSource`.\n     *\n     * @param input Local file path, or `File`/`Blob`/`Uint8Array` object to\n     *     read. Local file only works in node environment.\n     * @param options Options passed to the underlying `FileChunkIterator`s,\n     *   such as {chunksize: 1024}.\n     */\n    constructor(input, options = {}) {\n        super();\n        this.input = input;\n        this.options = options;\n    }\n    async iterator() {\n        if (isLocalPath(this.input) && env().get('IS_NODE')) {\n            // tslint:disable-next-line:no-require-imports\n            const fs = require('fs');\n            this.input = fs.readFileSync(this.input.substr(7));\n        }\n        // TODO(kangyizhang): Add LocalFileChunkIterator to split local streaming\n        // with file in browser.\n        return new FileChunkIterator(this.input, this.options);\n    }\n}\n//# sourceMappingURL=file_data_source.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { DataSource } from '../datasource';\nimport { urlChunkIterator } from '../iterators/url_chunk_iterator';\nimport { isLocalPath } from '../util/source_util';\nimport { FileDataSource } from './file_data_source';\n/*\n * Represents a URL readable as a stream of binary data chunks.\n */\nexport class URLDataSource extends DataSource {\n    /**\n     * Create a `URLDataSource`.\n     *\n     * @param url A source URL string, or a `Request` object.\n     * @param options Options passed to the underlying `FileChunkIterator`s,\n     *   such as {chunksize: 1024}.\n     */\n    constructor(url, fileOptions = {}) {\n        super();\n        this.url = url;\n        this.fileOptions = fileOptions;\n    }\n    // TODO(soergel): provide appropriate caching options.  Currently this\n    // will download the URL anew for each call to iterator().  Since we have\n    // to treat the downloaded file as a blob/buffer anyway, we may as well retain\n    // it-- but that raises GC issues.  Also we may want a persistent disk cache.\n    async iterator() {\n        if (isLocalPath(this.url)) {\n            return (new FileDataSource(this.url, this.fileOptions))\n                .iterator();\n        }\n        else {\n            return urlChunkIterator(this.url, this.fileOptions);\n        }\n    }\n}\n//# sourceMappingURL=url_data_source.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { datasetFromIteratorFn } from './dataset';\nimport { CSVDataset } from './datasets/csv_dataset';\nimport { iteratorFromFunction } from './iterators/lazy_iterator';\nimport { MicrophoneIterator } from './iterators/microphone_iterator';\nimport { WebcamIterator } from './iterators/webcam_iterator';\nimport { URLDataSource } from './sources/url_data_source';\n/**\n * Create a `CSVDataset` by reading and decoding CSV file(s) from provided URL\n * or local path if it's in Node environment.\n *\n * Note: If isLabel in columnConfigs is `true` for at least one column, the\n * element in returned `CSVDataset` will be an object of\n * `{xs:features, ys:labels}`: xs is a dict of features key/value pairs, ys\n * is a dict of labels key/value pairs. If no column is marked as label,\n * returns a dict of features only.\n *\n * ```js\n * const csvUrl =\n * 'https://storage.googleapis.com/tfjs-examples/multivariate-linear-regression/data/boston-housing-train.csv';\n *\n * async function run() {\n *   // We want to predict the column \"medv\", which represents a median value of\n *   // a home (in $1000s), so we mark it as a label.\n *   const csvDataset = tf.data.csv(\n *     csvUrl, {\n *       columnConfigs: {\n *         medv: {\n *           isLabel: true\n *         }\n *       }\n *     });\n *\n *   // Number of features is the number of column names minus one for the label\n *   // column.\n *   const numOfFeatures = (await csvDataset.columnNames()).length - 1;\n *\n *   // Prepare the Dataset for training.\n *   const flattenedDataset =\n *     csvDataset\n *     .map(({xs, ys}) =>\n *       {\n *         // Convert xs(features) and ys(labels) from object form (keyed by\n *         // column name) to array form.\n *         return {xs:Object.values(xs), ys:Object.values(ys)};\n *       })\n *     .batch(10);\n *\n *   // Define the model.\n *   const model = tf.sequential();\n *   model.add(tf.layers.dense({\n *     inputShape: [numOfFeatures],\n *     units: 1\n *   }));\n *   model.compile({\n *     optimizer: tf.train.sgd(0.000001),\n *     loss: 'meanSquaredError'\n *   });\n *\n *   // Fit the model using the prepared Dataset\n *   return model.fitDataset(flattenedDataset, {\n *     epochs: 10,\n *     callbacks: {\n *       onEpochEnd: async (epoch, logs) => {\n *         console.log(epoch + ':' + logs.loss);\n *       }\n *     }\n *   });\n * }\n *\n * await run();\n * ```\n *\n * @param source URL or local path to get CSV file. If it's a local path, it\n * must have prefix `file://` and it only works in node environment.\n * @param csvConfig (Optional) A CSVConfig object that contains configurations\n *     of reading and decoding from CSV file(s).\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   configParamIndices: [1]\n *  }\n */\nexport function csv(source, csvConfig = {}) {\n    return new CSVDataset(new URLDataSource(source), csvConfig);\n}\n/**\n * Create a `Dataset` that produces each element by calling a provided function.\n *\n * Note that repeated iterations over this `Dataset` may produce different\n * results, because the function will be called anew for each element of each\n * iteration.\n *\n * Also, beware that the sequence of calls to this function may be out of order\n * in time with respect to the logical order of the Dataset. This is due to the\n * asynchronous lazy nature of stream processing, and depends on downstream\n * transformations (e.g. .shuffle()). If the provided function is pure, this is\n * no problem, but if it is a closure over a mutable state (e.g., a traversal\n * pointer), then the order of the produced elements may be scrambled.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const ds = tf.data.func(func);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * @param f A function that produces one data element on each call.\n */\nexport function func(f) {\n    const iter = iteratorFromFunction(f);\n    return datasetFromIteratorFn(async () => iter);\n}\n/**\n * Create a `Dataset` that produces each element from provided JavaScript\n * generator, which is a function*\n * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions),\n * or a function that returns an\n * iterator\n * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions).\n *\n * The returned iterator should have `.next()` function that returns element in\n * format of `{value: TensorContainer, done:boolean}`.\n *\n * Example of creating a dataset from an iterator factory:\n * ```js\n * function makeIterator() {\n *   const numElements = 10;\n *   let index = 0;\n *\n *   const iterator = {\n *     next: () => {\n *       let result;\n *       if (index < numElements) {\n *         result = {value: index, done: false};\n *         index++;\n *         return result;\n *       }\n *       return {value: index, done: true};\n *     }\n *   };\n *   return iterator;\n * }\n * const ds = tf.data.generator(makeIterator);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * Example of creating a dataset from a generator:\n * ```js\n * function* dataGenerator() {\n *   const numElements = 10;\n *   let index = 0;\n *   while (index < numElements) {\n *     const x = index;\n *     index++;\n *     yield x;\n *   }\n * }\n *\n * const ds = tf.data.generator(dataGenerator);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * @param generator A Javascript generator function that returns a JavaScript\n *     iterator.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   configParamIndices: [1]\n *  }\n */\nexport function generator(generator) {\n    return datasetFromIteratorFn(async () => {\n        const gen = await generator();\n        return iteratorFromFunction(() => gen.next());\n    });\n}\n/**\n * Create an iterator that generate `Tensor`s from webcam video stream. This API\n * only works in Browser environment when the device has webcam.\n *\n * Note: this code snippet only works when the device has a webcam. It will\n * request permission to open the webcam when running.\n * ```js\n * const videoElement = document.createElement('video');\n * videoElement.width = 100;\n * videoElement.height = 100;\n * const cam = await tf.data.webcam(videoElement);\n * const img = await cam.capture();\n * img.print();\n * cam.stop();\n * ```\n *\n * @param webcamVideoElement A `HTMLVideoElement` used to play video from\n *     webcam. If this element is not provided, a hidden `HTMLVideoElement` will\n *     be created. In that case, `resizeWidth` and `resizeHeight` must be\n *     provided to set the generated tensor shape.\n * @param webcamConfig A `WebcamConfig` object that contains configurations of\n *     reading and manipulating data from webcam video stream.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   ignoreCI: true\n *  }\n */\nexport async function webcam(webcamVideoElement, webcamConfig) {\n    return WebcamIterator.create(webcamVideoElement, webcamConfig);\n}\n/**\n * Create an iterator that generate frequency-domain spectrogram `Tensor`s from\n * microphone audio stream with browser's native FFT. This API only works in\n * browser environment when the device has microphone.\n *\n * Note: this code snippet only works when the device has a microphone. It will\n * request permission to open the microphone when running.\n * ```js\n * const mic = await tf.data.microphone({\n *   fftSize: 1024,\n *   columnTruncateLength: 232,\n *   numFramesPerSpectrogram: 43,\n *   sampleRateHz:44100,\n *   includeSpectrogram: true,\n *   includeWaveform: true\n * });\n * const audioData = await mic.capture();\n * const spectrogramTensor = audioData.spectrogram;\n * spectrogramTensor.print();\n * const waveformTensor = audioData.waveform;\n * waveformTensor.print();\n * mic.stop();\n * ```\n *\n * @param microphoneConfig A `MicrophoneConfig` object that contains\n *     configurations of reading audio data from microphone.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   ignoreCI: true\n *  }\n */\nexport async function microphone(microphoneConfig) {\n    return MicrophoneIterator.create(microphoneConfig);\n}\n//# sourceMappingURL=readers.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { array, Dataset, zip } from './dataset';\nexport { CSVDataset } from './datasets/csv_dataset';\nexport { TextLineDataset } from './datasets/text_line_dataset';\nexport { csv, func, generator, microphone, webcam } from './readers';\nexport { FileDataSource } from './sources/file_data_source';\nexport { URLDataSource } from './sources/url_data_source';\nexport { version as version_data } from './version';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function assertNotComplex(tensor, opName) {\n    if (!Array.isArray(tensor)) {\n        tensor = [tensor];\n    }\n    tensor.forEach(t => {\n        if (t != null) {\n            util.assert(t.dtype !== 'complex64', () => `${opName} does not support complex64 tensors in the CPU backend.`);\n        }\n    });\n}\n//# sourceMappingURL=cpu_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { backend_util, DataStorage, engine, env, kernel_impls, KernelBackend, max, slice_util, TensorBuffer, upcastType, util } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV3Impl = kernel_impls.nonMaxSuppressionV3Impl;\nconst split = kernel_impls.split;\nconst tile = kernel_impls.tile;\nconst topkImpl = kernel_impls.topkImpl;\nconst whereImpl = kernel_impls.whereImpl;\nimport * as seedrandom from 'seedrandom';\nimport { assertNotComplex } from './cpu_util';\nfunction mapActivation(backend, x, activation, preluActivationWeights) {\n    if (activation === 'linear') {\n        return backend.linear(x);\n    }\n    else if (activation === 'relu') {\n        return backend.relu(x);\n    }\n    else if (activation === 'elu') {\n        return tf.elu(x);\n    }\n    else if (activation === 'relu6') {\n        return backend.relu6(x);\n    }\n    else if (activation === 'prelu') {\n        return backend.prelu(x, preluActivationWeights);\n    }\n    throw new Error(`Activation ${activation} has not been implemented for the CPU backend.`);\n}\nexport class MathBackendCPU extends KernelBackend {\n    constructor() {\n        super();\n        this.blockSize = 48;\n        this.firstUse = true;\n        this.data = new DataStorage(this, engine());\n    }\n    write(values, shape, dtype) {\n        if (this.firstUse) {\n            this.firstUse = false;\n            if (env().get('IS_NODE')) {\n                backend_util.warn('\\n============================\\n' +\n                    'Hi there \uD83D\uDC4B. Looks like you are running TensorFlow.js in ' +\n                    'Node.js. To speed things up dramatically, install our node ' +\n                    'backend, which binds to TensorFlow C++, by running ' +\n                    'npm i @tensorflow/tfjs-node, ' +\n                    'or npm i @tensorflow/tfjs-node-gpu if you have CUDA. ' +\n                    'Then call require(\\'@tensorflow/tfjs-node\\'); (-gpu ' +\n                    'suffix for CUDA) at the start of your program. ' +\n                    'Visit https://github.com/tensorflow/tfjs-node for more details.' +\n                    '\\n============================');\n            }\n        }\n        const dataId = {};\n        this.data.set(dataId, { values, dtype, refCount: 1 });\n        return dataId;\n    }\n    /**\n     * Create a data bucket in cpu backend.\n     * @param shape Shape of the `TensorInfo`.\n     * @param dtype DType of the `TensorInfo`.\n     * @param values The value of the `TensorInfo` stored as a flattened array.\n     */\n    makeTensorInfo(shape, dtype, values) {\n        const outId = this.write(values, shape, dtype);\n        return { dataId: outId, shape, dtype };\n    }\n    /** Increase refCount of a `TensorData`. */\n    incRef(dataId) {\n        const tensorData = this.data.get(dataId);\n        tensorData.refCount++;\n    }\n    /** Decrease refCount of a `TensorData`. */\n    decRef(dataId) {\n        if (this.data.has(dataId)) {\n            const tensorData = this.data.get(dataId);\n            tensorData.refCount--;\n        }\n    }\n    move(dataId, values, shape, dtype) {\n        this.data.set(dataId, { values, dtype, refCount: 1 });\n    }\n    numDataIds() {\n        return this.data.numDataIds();\n    }\n    async read(dataId) {\n        return this.readSync(dataId);\n    }\n    readSync(dataId) {\n        const { dtype, complexTensorInfos } = this.data.get(dataId);\n        if (dtype === 'complex64') {\n            const realValues = this.readSync(complexTensorInfos.real.dataId);\n            const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n            return backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        return this.data.get(dataId).values;\n    }\n    bufferSync(t) {\n        const data = this.readSync(t.dataId);\n        let decodedData = data;\n        if (t.dtype === 'string') {\n            try {\n                // Decode the bytes into string.\n                decodedData = data.map(d => util.decodeString(d));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode encoded string bytes into utf-8');\n            }\n        }\n        return tf.buffer(t.shape, t.dtype, decodedData);\n    }\n    makeOutput(values, shape, dtype) {\n        const dataId = this.write(values, shape, dtype);\n        return engine().makeTensorFromDataId(dataId, shape, dtype, this);\n    }\n    disposeData(dataId) {\n        if (this.data.has(dataId)) {\n            const { complexTensorInfos } = this.data.get(dataId);\n            if (complexTensorInfos != null) {\n                this.disposeData(complexTensorInfos.real.dataId);\n                this.disposeData(complexTensorInfos.imag.dataId);\n            }\n            this.data.delete(dataId);\n        }\n    }\n    disposeIntermediateTensorInfo(tensorInfo) {\n        const dataId = tensorInfo.dataId;\n        if (this.data.has(dataId)) {\n            const tensorData = this.data.get(dataId);\n            tensorData.refCount--;\n            if (tensorData.refCount < 1) {\n                this.disposeData(dataId);\n            }\n        }\n    }\n    async time(f) {\n        const start = util.now();\n        f();\n        const kernelMs = util.now() - start;\n        return { kernelMs };\n    }\n    memory() {\n        return {\n            // Unreliable due to automatic gc. The numbers above are cumulative.\n            unreliable: true,\n            reasons: ['The reported memory is an upper bound. Due to automatic garbage ' +\n                    'collection, the true allocated memory may be less.']\n        };\n    }\n    stridedSlice(x, begin, end, strides) {\n        assertNotComplex(x, 'stridedSlice');\n        const outShape = slice_util.computeOutShape(begin, end, strides);\n        if (outShape.some(axis => axis === 0)) {\n            return tf.tensor([], outShape);\n        }\n        const buffer = tf.buffer(outShape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < buffer.size; i++) {\n            const loc = buffer.indexToLoc(i);\n            const newLoc = new Array(loc.length);\n            for (let j = 0; j < newLoc.length; j++) {\n                newLoc[j] = loc[j] * strides[j] + begin[j];\n            }\n            buffer.set(xBuf.get(...newLoc), ...loc);\n        }\n        return buffer.toTensor();\n    }\n    diag(x) {\n        const xVals = this.readSync(x.dataId);\n        const buffer = tf.buffer([x.size, x.size], x.dtype);\n        const vals = buffer.values;\n        for (let i = 0; i < xVals.length; i++) {\n            vals[i * x.size + i] = xVals[i];\n        }\n        return buffer.toTensor();\n    }\n    unstack(x, axis) {\n        const num = x.shape[axis];\n        const outShape = new Array(x.rank - 1);\n        let outIndex = 0;\n        for (let i = 0; i < x.rank; i++) {\n            if (i !== axis) {\n                outShape[outIndex++] = x.shape[i];\n            }\n        }\n        const begin = new Array(x.rank).fill(0);\n        const size = x.shape.slice();\n        size[axis] = 1;\n        const res = new Array(num);\n        for (let i = 0; i < res.length; i++) {\n            begin[axis] = i;\n            res[i] = tf.slice(x, begin, size).reshape(outShape);\n        }\n        return res;\n    }\n    reverse(x, axis) {\n        assertNotComplex(x, 'reverse');\n        const buffer = tf.buffer(x.shape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < buffer.size; i++) {\n            const outLoc = buffer.indexToLoc(i);\n            const inLoc = outLoc.slice();\n            axis.forEach(ax => inLoc[ax] = x.shape[ax] - 1 - inLoc[ax]);\n            buffer.set(xBuf.get(...inLoc), ...outLoc);\n        }\n        return buffer.toTensor();\n    }\n    neg(x) {\n        assertNotComplex(x, 'neg');\n        // TODO(lina128): Use mul directly once neg is modularized.\n        return tf.mul(tf.scalar(-1), x);\n    }\n    addN(tensors) {\n        assertNotComplex(tensors, 'addN');\n        const vals = tensors.map(t => this.readSync(t.dataId));\n        const result = tf.buffer(tensors[0].shape, tensors[0].dtype);\n        const resultVals = result.values;\n        for (let i = 0; i < tensors.length; i++) {\n            const currVals = vals[i];\n            for (let j = 0; j < resultVals.length; j++) {\n                resultVals[j] += currVals[j];\n            }\n        }\n        return result.toTensor();\n    }\n    softmax(logits, dim) {\n        const axes = util.parseAxisParam([dim], logits.shape);\n        // TODO(annxingyuan): Call maxImpl rather than op as part of softmax kernel\n        // modularization.\n        const maxLogit = max(logits, axes);\n        const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n        // TODO(lina128): Use sub directly once softmax is modularized.\n        const a = tf.sub(logits, maxLogit.reshape(expandedShape));\n        const b = tf.exp(a);\n        const sumExp = this.sum(b, axes).reshape(expandedShape);\n        // TODO(annxingyuan): Call divImpl rather than op as part of softmax\n        // kernel modularization.\n        return tf.div(b, sumExp);\n    }\n    pow(a, b) {\n        assertNotComplex([a, b], 'pow');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aValue, bValue) => Math.pow(aValue, bValue));\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        assertNotComplex([a, b], 'matMul');\n        const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n        const leftDim = transposeA ? a.shape[2] : a.shape[1];\n        const rightDim = transposeB ? b.shape[1] : b.shape[2];\n        const batchDim = a.shape[0];\n        const aValues = this.readSync(a.dataId);\n        const bValues = this.readSync(b.dataId);\n        const [aBatch, aOuterStep, aInnerStep] = transposeA ?\n            [a.strides[0], 1, a.strides[1]] :\n            [a.strides[0], a.strides[1], 1];\n        const [bInnerStep, bOuterStep, bBatch] = transposeB ?\n            [1, b.strides[1], b.strides[0]] :\n            [b.strides[1], 1, b.strides[0]];\n        const size = leftDim * rightDim;\n        const result = tf.buffer([batchDim, leftDim, rightDim], a.dtype);\n        const resVals = result.values;\n        const blockSize = this.blockSize;\n        for (let b = 0; b < batchDim; b++) {\n            for (let i0 = 0; i0 < leftDim; i0 += blockSize) {\n                for (let j0 = 0; j0 < rightDim; j0 += blockSize) {\n                    for (let k0 = 0; k0 < sharedDim; k0 += blockSize) {\n                        // for when blockSize doesn't evenly divide the input\n                        const iBlock = Math.min(i0 + blockSize, leftDim);\n                        const jBlock = Math.min(j0 + blockSize, rightDim);\n                        const kBlock = Math.min(k0 + blockSize, sharedDim);\n                        for (let i = i0; i < iBlock; i++) {\n                            for (let j = j0; j < jBlock; j++) {\n                                let sum = 0.0;\n                                for (let k = k0; k < kBlock; k++) {\n                                    sum += aValues[b * aBatch + i * aOuterStep + k * aInnerStep] *\n                                        bValues[k * bInnerStep + j * bOuterStep + b * bBatch];\n                                }\n                                resVals[b * size + (i * rightDim + j)] += sum;\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return result.toTensor();\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        let result = this.batchMatMul(a, b, transposeA, transposeB);\n        if (bias) {\n            // TODO(lina128): Use add directly once fusedBatchMatMul is modularized.\n            result = tf.add(result, bias);\n        }\n        if (activation) {\n            result =\n                mapActivation(this, result, activation, preluActivationWeights);\n        }\n        return result;\n    }\n    floorDiv(a, b) {\n        assertNotComplex([a, b], 'floorDiv');\n        const op = (a, b) => Math.floor(a / b);\n        const outputDtype = 'int32';\n        return this.broadcastedBinaryOp(a, b, outputDtype, op);\n    }\n    sum(x, axes) {\n        assertNotComplex(x, 'sum');\n        backend_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(outShape, resultDtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let sum = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                sum += aVals[offset + j];\n            }\n            vals[i] = sum;\n        }\n        return result;\n    }\n    prod(x, axes) {\n        assertNotComplex(x, 'sum');\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(outShape, resultDtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let prod = 1;\n            for (let j = 0; j < reduceSize; ++j) {\n                prod *= aVals[offset + j];\n            }\n            vals[i] = prod;\n        }\n        return result;\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        assertNotComplex(x, 'unsortedSegmentSum');\n        const res = [];\n        // Reshape the segment id's so that they can be broadcast with\n        // x. The new shape should be [segmentIds.shape, 1, ..., 1]\n        const numIters = x.rank - segmentIds.rank;\n        for (let i = 0; i < numIters; ++i) {\n            segmentIds = segmentIds.expandDims(i + 1);\n        }\n        for (let i = 0; i < numSegments; ++i) {\n            const segmentId = tf.scalar(i, 'int32');\n            const mask = tf.equal(segmentId, segmentIds).asType('float32');\n            const sum = mask.mul(x).sum(0);\n            res.push(sum);\n        }\n        return tf.stack(res);\n    }\n    argMin(x, axis) {\n        assertNotComplex(x, 'argMin');\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('argMin', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, 'int32');\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let min = aVals[offset];\n            let minIndex = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value < min) {\n                    min = value;\n                    minIndex = j;\n                }\n            }\n            vals[i] = minIndex;\n        }\n        return result;\n    }\n    argMax(x, axis) {\n        assertNotComplex(x, 'argMax');\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('argMax', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, 'int32');\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let max = aVals[offset];\n            let maxIndex = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value > max) {\n                    max = value;\n                    maxIndex = j;\n                }\n            }\n            vals[i] = maxIndex;\n        }\n        return result;\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        assertNotComplex(x, 'cumsum');\n        if (axis !== x.rank - 1) {\n            throw new Error(`backend.cumsum in CPU expects an inner-most axis=${x.rank - 1} ` +\n                `but got axis=${axis}`);\n        }\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(x.shape, resultDtype);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        const finalDim = x.shape[x.rank - 1];\n        const indexAdjuster = reverse ?\n            (i, j) => i + finalDim - j - 1 :\n            (i, j) => i + j;\n        for (let i = 0; i < aVals.length; i += finalDim) {\n            for (let j = 0; j < finalDim; j++) {\n                const idx = indexAdjuster(i, j);\n                if (j === 0) {\n                    vals[idx] = exclusive ? 0 : aVals[idx];\n                }\n                else {\n                    const prevIdx = indexAdjuster(i, j - 1);\n                    vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :\n                        aVals[idx] + vals[prevIdx];\n                }\n            }\n        }\n        return result;\n    }\n    equal(a, b) {\n        assertNotComplex([a, b], 'equal');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal === bVal) ? 1 : 0;\n        });\n    }\n    notEqual(a, b) {\n        assertNotComplex([a, b], 'notEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal !== bVal) ? 1 : 0;\n        });\n    }\n    less(a, b) {\n        assertNotComplex([a, b], 'less');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal < bVal) ? 1 : 0;\n        });\n    }\n    lessEqual(a, b) {\n        assertNotComplex([a, b], 'lessEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal <= bVal) ? 1 : 0;\n        });\n    }\n    greater(a, b) {\n        assertNotComplex([a, b], 'greater');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal > bVal) ? 1 : 0;\n        });\n    }\n    greaterEqual(a, b) {\n        assertNotComplex([a, b], 'greaterEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal >= bVal) ? 1 : 0;\n        });\n    }\n    logicalAnd(a, b) {\n        assertNotComplex([a, b], 'logicalAnd');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return aVal && bVal;\n        });\n    }\n    logicalOr(a, b) {\n        assertNotComplex([a, b], 'logicalOr');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return aVal || bVal;\n        });\n    }\n    select(condition, a, b) {\n        assertNotComplex([condition, a, b], 'select');\n        const values = this.readSync(condition.dataId);\n        const aValues = this.readSync(a.dataId);\n        const bValues = this.readSync(b.dataId);\n        const result = tf.zeros(a.shape, upcastType(a.dtype, b.dtype));\n        const newValues = this.readSync(result.dataId);\n        let index = 0;\n        const offset = condition.rank === 0 || condition.rank > 1 || a.rank === 1 ?\n            1 :\n            util.sizeFromShape(a.shape.slice(1));\n        for (let i = 0; i < values.length; i++) {\n            for (let j = 0; j < offset; j++) {\n                if (values[i] === 1) {\n                    newValues[index++] = aValues[i];\n                }\n                else {\n                    newValues[index++] = bValues[i];\n                }\n            }\n        }\n        return result;\n    }\n    where(condition) {\n        assertNotComplex([condition], 'where');\n        const condVals = this.readSync(condition.dataId);\n        return whereImpl(condition.shape, condVals);\n    }\n    topk(x, k, sorted) {\n        assertNotComplex(x, 'topk');\n        const xVals = this.readSync(x.dataId);\n        return topkImpl(xVals, x.shape, x.dtype, k, sorted);\n    }\n    min(x, axes) {\n        assertNotComplex(x, 'min');\n        backend_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let min = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value < min) {\n                    min = value;\n                }\n            }\n            vals[i] = min;\n        }\n        return result;\n    }\n    minimum(a, b) {\n        assertNotComplex([a, b], 'minimum');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => Math.min(aVal, bVal));\n    }\n    mod(a, b) {\n        assertNotComplex([a, b], 'mod');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n            const rem = aVal % bVal;\n            if ((aVal < 0 && bVal < 0) || (aVal >= 0 && bVal >= 0)) {\n                return rem;\n            }\n            else {\n                return (rem + bVal) % bVal;\n            }\n        });\n    }\n    maximum(a, b) {\n        assertNotComplex([a, b], 'maximum');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => Math.max(aVal, bVal));\n    }\n    all(x, axes) {\n        assertNotComplex(x, 'all');\n        backend_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let all = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                all = all && value;\n            }\n            vals[i] = all;\n        }\n        return result;\n    }\n    any(x, axes) {\n        assertNotComplex(x, 'any');\n        backend_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let anyVal = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                anyVal = anyVal || value;\n            }\n            vals[i] = anyVal;\n        }\n        return result;\n    }\n    squaredDifference(a, b) {\n        assertNotComplex([a, b], 'squaredDifference');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n            const diff = aVal - bVal;\n            return diff * diff;\n        });\n    }\n    linear(x) {\n        return x;\n    }\n    relu(x) {\n        assertNotComplex(x, 'relu');\n        const res = tf.zeros(x.shape, x.dtype);\n        const resVals = this.readSync(res.dataId);\n        const inVals = this.readSync(x.dataId);\n        for (let i = 0; i < inVals.length; ++i) {\n            resVals[i] = Math.max(0, inVals[i]);\n        }\n        return res;\n    }\n    relu6(x) {\n        assertNotComplex(x, 'relu');\n        const res = tf.zeros(x.shape, x.dtype);\n        const resVals = this.readSync(res.dataId);\n        const inVals = this.readSync(x.dataId);\n        for (let i = 0; i < inVals.length; ++i) {\n            resVals[i] = Math.min(Math.max(0, inVals[i]), 6);\n        }\n        return res;\n    }\n    prelu(x, a) {\n        assertNotComplex([x, a], 'prelu');\n        return this.broadcastedBinaryOp(x, a, x.dtype, (xValue, aValue) => xValue < 0 ? aValue * xValue : xValue);\n    }\n    eluDer(dy, y) {\n        assertNotComplex([dy, y], 'eluDer');\n        const resultValues = new Float32Array(y.size);\n        const values = this.readSync(y.dataId);\n        const dyValues = this.readSync(dy.dataId);\n        for (let i = 0; i < values.length; ++i) {\n            const v = values[i];\n            if (v >= 1) {\n                resultValues[i] = dyValues[i];\n            }\n            else {\n                resultValues[i] = dyValues[i] * (v + 1);\n            }\n        }\n        return this.makeOutput(resultValues, y.shape, 'float32');\n    }\n    atan2(a, b) {\n        assertNotComplex([a, b], 'atan2');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aValue, bValue) => Math.atan2(aValue, bValue));\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        let result = this.conv2d(input, filter, convInfo);\n        if (bias) {\n            // TODO(lina128): Use add directly once fusedConv2d is modularized.\n            result = tf.add(result, bias);\n        }\n        if (activation) {\n            result =\n                mapActivation(this, result, activation, preluActivationWeights);\n        }\n        return result;\n    }\n    conv2d(x, filter, convInfo) {\n        assertNotComplex([x, filter], 'conv2d');\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const padLeft = convInfo.padInfo.left;\n        const padTop = convInfo.padInfo.top;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const y = tf.buffer(convInfo.outShape, x.dtype);\n        const xBatchStride = x.strides[0];\n        const xRowStride = isChannelsLast ? x.strides[1] : x.strides[2];\n        const xColStride = isChannelsLast ? x.strides[2] : 1;\n        const xChannelStride = isChannelsLast ? 1 : x.strides[1];\n        const yBatchStride = y.strides[0];\n        const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];\n        const yColStride = isChannelsLast ? y.strides[2] : 1;\n        const yChannelStride = isChannelsLast ? 1 : y.strides[1];\n        const xVals = this.readSync(x.dataId);\n        const wVals = this.readSync(filter.dataId);\n        const yVals = y.values;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n            const xOffset1 = b * xBatchStride;\n            const yOffset1 = b * yBatchStride;\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const yOffset2 = yOffset1 + yR * yRowStride;\n                const xRCorner = yR * convInfo.strideHeight - padTop;\n                for (let wR = 0; wR < filterHeight; wR++) {\n                    const xR = xRCorner + wR * dilationHeight;\n                    if (xR < 0 || xR >= convInfo.inHeight) {\n                        continue;\n                    }\n                    const wOffset1 = wR * filter.strides[0];\n                    const xOffset2 = xOffset1 + xR * xRowStride;\n                    for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                        const yOffset3 = yOffset2 + yC * yColStride;\n                        const xCCorner = yC * convInfo.strideWidth - padLeft;\n                        for (let wC = 0; wC < filterWidth; wC++) {\n                            const xC = xCCorner + wC * dilationWidth;\n                            if (xC < 0 || xC >= convInfo.inWidth) {\n                                continue;\n                            }\n                            const wOffset2 = wOffset1 + wC * filter.strides[1];\n                            const xOffset3 = xOffset2 + xC * xColStride;\n                            let wOffset3 = wOffset2;\n                            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                const xVal = xVals[xOffset3 + d1 * xChannelStride];\n                                for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                                    yVals[yOffset3 + d2 * yChannelStride] +=\n                                        xVal * wVals[wOffset3 + d2];\n                                }\n                                wOffset3 += convInfo.outChannels;\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return y.toTensor();\n    }\n    conv3d(x, filter, convInfo) {\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const padFront = convInfo.padInfo.front;\n        const padLeft = convInfo.padInfo.left;\n        const padTop = convInfo.padInfo.top;\n        const y = tf.buffer(convInfo.outShape, x.dtype);\n        const xVals = this.readSync(x.dataId);\n        const wVals = this.readSync(filter.dataId);\n        const yVals = y.values;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n            const xOffset1 = b * x.strides[0];\n            const yOffset1 = b * y.strides[0];\n            for (let yF = 0; yF < convInfo.outDepth; ++yF) {\n                const yOffset2 = yOffset1 + yF * y.strides[1];\n                const xFCorner = yF * convInfo.strideDepth - padFront;\n                for (let wF = 0; wF < filterDepth; wF++) {\n                    const xF = xFCorner + wF * dilationDepth;\n                    if (xF < 0 || xF >= convInfo.inDepth) {\n                        continue;\n                    }\n                    const wOffset1 = wF * filter.strides[0];\n                    const xOffset2 = xOffset1 + xF * x.strides[1];\n                    for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                        const yOffset3 = yOffset2 + yR * y.strides[2];\n                        const xRCorner = yR * convInfo.strideHeight - padTop;\n                        for (let wR = 0; wR < filterHeight; wR++) {\n                            const xR = xRCorner + wR * dilationHeight;\n                            if (xR < 0 || xR >= convInfo.inHeight) {\n                                continue;\n                            }\n                            const wOffset2 = wOffset1 + wR * filter.strides[1];\n                            const xOffset3 = xOffset2 + xR * x.strides[2];\n                            for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                                const yOffset4 = yOffset3 + yC * convInfo.outChannels;\n                                const xCCorner = yC * convInfo.strideWidth - padLeft;\n                                for (let wC = 0; wC < filterWidth; wC++) {\n                                    const xC = xCCorner + wC * dilationWidth;\n                                    if (xC < 0 || xC >= convInfo.inWidth) {\n                                        continue;\n                                    }\n                                    const wOffset3 = wOffset2 + wC * filter.strides[2];\n                                    const xOffset4 = xOffset3 + xC * convInfo.inChannels;\n                                    let wOffset4 = wOffset3;\n                                    for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                        const xVal = xVals[xOffset4 + d1];\n                                        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                                            yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];\n                                        }\n                                        wOffset4 += convInfo.outChannels;\n                                    }\n                                }\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return y.toTensor();\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        assertNotComplex([dy, filter], 'conv2dDerInput');\n        const dx = tf.buffer(convInfo.inShape, 'float32');\n        const dxValues = dx.values;\n        const dyValues = this.readSync(dy.dataId);\n        const fltValues = this.readSync(filter.dataId);\n        const [fltS0, fltS1, fltS2] = filter.strides;\n        const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth, dataFormat } = convInfo;\n        const topPad = filterHeight - 1 - convInfo.padInfo.top;\n        const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const xBatchStride = dx.strides[0];\n        const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];\n        const xColStride = isChannelsLast ? dx.strides[2] : 1;\n        const xChannelStride = isChannelsLast ? 1 : dx.strides[1];\n        const yBatchStride = dy.strides[0];\n        const yRowStride = isChannelsLast ? dy.strides[1] : dy.strides[2];\n        const yColStride = isChannelsLast ? dy.strides[2] : 1;\n        const yChannelStride = isChannelsLast ? 1 : dy.strides[1];\n        for (let b = 0; b < batchSize; ++b) {\n            for (let d1 = 0; d1 < inChannels; ++d1) {\n                for (let xR = 0; xR < inHeight; ++xR) {\n                    const xRCorner = xR - topPad;\n                    const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                    const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                    for (let xC = 0; xC < inWidth; ++xC) {\n                        const xCCorner = xC - leftPad;\n                        const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                        const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                        let dotProd = 0;\n                        for (let yR = xRMin; yR < yRMax; ++yR) {\n                            const wR = yR * strideHeight - xRCorner;\n                            for (let yC = xCMin; yC < yCMax; ++yC) {\n                                const wC = yC * strideWidth - xCCorner;\n                                const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;\n                                const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n                                for (let d2 = 0; d2 < outChannels; ++d2) {\n                                    const pixel = dyValues[dyOffset + yChannelStride * d2];\n                                    const weight = fltValues[fltOffset + d2];\n                                    dotProd += pixel * weight;\n                                }\n                            }\n                        }\n                        const dxOffset = xBatchStride * b + xRowStride * xR +\n                            xColStride * xC + xChannelStride * d1;\n                        dxValues[dxOffset] = dotProd;\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        const dx = tf.buffer(convInfo.inShape, 'float32');\n        const dxValues = dx.values;\n        const [dxS0, dxS1, dxS2, dxS3] = dx.strides;\n        const dyValues = this.readSync(dy.dataId);\n        const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n        const fltValues = this.readSync(filter.dataId);\n        const [fltS0, fltS1, fltS2, fltS3] = filter.strides;\n        const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo;\n        const frontPad = filterDepth - 1 - convInfo.padInfo.front;\n        const topPad = filterHeight - 1 - convInfo.padInfo.top;\n        const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let d1 = 0; d1 < inChannels; ++d1) {\n                // Frames of depth\n                for (let xF = 0; xF < inDepth; ++xF) {\n                    const xFCorner = xF - frontPad;\n                    const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));\n                    const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);\n                    // Rows as per standard 2d matrix notation\n                    for (let xR = 0; xR < inHeight; ++xR) {\n                        const xRCorner = xR - topPad;\n                        const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                        const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                        // Columns as per standard 2d matrix notation\n                        for (let xC = 0; xC < inWidth; ++xC) {\n                            const xCCorner = xC - leftPad;\n                            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                            const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                            let dotProd = 0;\n                            for (let yF = xFMin; yF < yFMax; ++yF) {\n                                const wF = yF * strideDepth - xFCorner;\n                                for (let yR = xRMin; yR < yRMax; ++yR) {\n                                    const wR = yR * strideHeight - xRCorner;\n                                    for (let yC = xCMin; yC < yCMax; ++yC) {\n                                        const wC = yC * strideWidth - xCCorner;\n                                        const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;\n                                        const fltOffset = fltS0 * (filterDepth - 1 - wF) +\n                                            fltS1 * (filterHeight - 1 - wR) +\n                                            fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;\n                                        for (let d2 = 0; d2 < outChannels; ++d2) {\n                                            const pixel = dyValues[dyOffset + d2];\n                                            const weight = fltValues[fltOffset + d2];\n                                            dotProd += pixel * weight;\n                                        }\n                                    }\n                                }\n                            }\n                            dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =\n                                dotProd;\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    conv2dDerFilter(x, dy, convInfo) {\n        assertNotComplex([x, dy], 'conv2dDerFilter');\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const dW = tf.buffer(convInfo.filterShape, 'float32');\n        const leftPad = convInfo.padInfo.left;\n        const topPad = convInfo.padInfo.top;\n        const xBuf = this.bufferSync(x);\n        const dyBuf = this.bufferSync(dy);\n        for (let wR = 0; wR < filterHeight; ++wR) {\n            const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n            const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n            for (let wC = 0; wC < filterWidth; ++wC) {\n                const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                    for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                        // Need to convolve.\n                        let dotProd = 0;\n                        for (let b = 0; b < convInfo.batchSize; ++b) {\n                            for (let yR = yRMin; yR < yRMax; ++yR) {\n                                const xR = wR + yR * strideHeight - topPad;\n                                for (let yC = yCMin; yC < yCMax; ++yC) {\n                                    const xC = wC + yC * strideWidth - leftPad;\n                                    if (isChannelsLast) {\n                                        dotProd +=\n                                            xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n                                    }\n                                    else {\n                                        dotProd +=\n                                            xBuf.get(b, d1, xR, xC) * dyBuf.get(b, d2, yR, yC);\n                                    }\n                                }\n                            }\n                        }\n                        dW.set(dotProd, wR, wC, d1, d2);\n                    }\n                }\n            }\n        }\n        return dW.toTensor();\n    }\n    conv3dDerFilter(x, dy, convInfo) {\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dw = tf.buffer(convInfo.filterShape, 'float32');\n        const dwValues = dw.values;\n        const [dwS0, dwS1, dwS2, dwS3] = dw.strides;\n        const dyValues = this.readSync(dy.dataId);\n        const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n        const xValues = this.readSync(x.dataId);\n        const [xS0, xS1, xS2, xS3] = x.strides;\n        const frontPad = convInfo.padInfo.front;\n        const leftPad = convInfo.padInfo.left;\n        const topPad = convInfo.padInfo.top;\n        for (let wF = 0; wF < filterDepth; ++wF) {\n            const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));\n            const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);\n            const wOffset1 = wF * dwS0;\n            for (let wR = 0; wR < filterHeight; ++wR) {\n                const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n                const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n                const wOffset2 = wR * dwS1 + wOffset1;\n                for (let wC = 0; wC < filterWidth; ++wC) {\n                    const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                    const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                    const wOffset3 = wC * dwS2 + wOffset2;\n                    for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                        const wOffset4 = d1 * dwS3 + wOffset3;\n                        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                            let dotProd = 0;\n                            for (let b = 0; b < convInfo.batchSize; ++b) {\n                                const xOffset1 = b * xS0;\n                                const yOffset1 = b * dyS0;\n                                for (let yF = yFMin; yF < yFMax; ++yF) {\n                                    const xF = wF + yF * strideDepth - frontPad;\n                                    const xOffset2 = xF * xS1 + xOffset1;\n                                    const yOffset2 = yF * dyS1 + yOffset1;\n                                    for (let yR = yRMin; yR < yRMax; ++yR) {\n                                        const xR = wR + yR * strideHeight - topPad;\n                                        const xOffset3 = xR * xS2 + xOffset2;\n                                        const yOffset3 = yR * dyS2 + yOffset2;\n                                        for (let yC = yCMin; yC < yCMax; ++yC) {\n                                            const xC = wC + yC * strideWidth - leftPad;\n                                            const xOffset4 = xC * xS3 + xOffset3;\n                                            const yOffset4 = yC * dyS3 + yOffset3;\n                                            dotProd +=\n                                                xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];\n                                        }\n                                    }\n                                }\n                            }\n                            dwValues[wOffset4 + d2] = dotProd;\n                        }\n                    }\n                }\n            }\n        }\n        return dw.toTensor();\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        let result = this.depthwiseConv2D(input, filter, convInfo);\n        if (bias) {\n            // TODO(lina128): Use add directly once fusedDepthwiseConv2D is\n            // modularized.\n            result = tf.add(result, bias);\n        }\n        if (activation) {\n            result =\n                mapActivation(this, result, activation, preluActivationWeights);\n        }\n        return result;\n    }\n    depthwiseConv2D(x, filter, convInfo) {\n        assertNotComplex([x, filter], 'depthwiseConv2D');\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const padLeft = convInfo.padInfo.left;\n        const padTop = convInfo.padInfo.top;\n        const chMul = convInfo.outChannels / convInfo.inChannels;\n        const y = tf.buffer(convInfo.outShape, x.dtype);\n        const xVals = this.readSync(x.dataId);\n        const wVals = this.readSync(filter.dataId);\n        const yVals = y.values;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n            const xOffset1 = b * x.strides[0];\n            const yOffset1 = b * y.strides[0];\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const yOffset2 = yOffset1 + yR * y.strides[1];\n                const xRCorner = yR * convInfo.strideHeight - padLeft;\n                for (let wR = 0; wR < filterHeight; ++wR) {\n                    const xR = xRCorner + wR * dilationHeight;\n                    if (xR < 0 || xR >= convInfo.inHeight) {\n                        continue;\n                    }\n                    const wOffset1 = wR * filter.strides[0];\n                    const xOffset2 = xOffset1 + xR * x.strides[1];\n                    for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                        const yOffset3 = yOffset2 + yC * y.strides[2];\n                        const xCCorner = yC * convInfo.strideWidth - padTop;\n                        for (let wC = 0; wC < filterWidth; ++wC) {\n                            const xC = xCCorner + wC * dilationWidth;\n                            if (xC < 0 || xC >= convInfo.inWidth) {\n                                continue;\n                            }\n                            const wOffset2 = wOffset1 + wC * filter.strides[1];\n                            const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n                            let yOffset4 = yOffset3;\n                            let wOffset3 = wOffset2;\n                            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                const xVal = xVals[xOffset3 + d1];\n                                for (let q = 0; q < chMul; ++q) {\n                                    yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];\n                                }\n                                yOffset4 += chMul;\n                                wOffset3 += chMul;\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return y.toTensor();\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        assertNotComplex([dy, filter], 'depthwiseConv2DDerInput');\n        const dx = tf.buffer(convInfo.inShape, 'float32');\n        const dxValues = dx.values;\n        const [dxS0, dxS1, dxS2] = dx.strides;\n        const dyValues = this.readSync(dy.dataId);\n        const [dyS0, dyS1, dyS2] = dy.strides;\n        const fltValues = this.readSync(filter.dataId);\n        const [fltS0, fltS1, fltS2] = filter.strides;\n        const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n        const topPad = filterHeight - 1 - convInfo.padInfo.top;\n        const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n        const chMul = outChannels / inChannels;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let d1 = 0; d1 < inChannels; ++d1) {\n                for (let xR = 0; xR < inHeight; ++xR) {\n                    const xRCorner = xR - topPad;\n                    const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                    const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                    for (let xC = 0; xC < inWidth; ++xC) {\n                        const xCCorner = xC - leftPad;\n                        const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                        const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                        let dotProd = 0;\n                        for (let yR = xRMin; yR < yRMax; ++yR) {\n                            const wR = yR * strideHeight - xRCorner;\n                            for (let yC = xCMin; yC < yCMax; ++yC) {\n                                const wC = yC * strideWidth - xCCorner;\n                                const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n                                const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n                                for (let dm = 0; dm < chMul; ++dm) {\n                                    const d2 = d1 * chMul + dm;\n                                    const pixel = dyValues[dyOffset + d2];\n                                    const weight = fltValues[fltOffset + dm];\n                                    dotProd += pixel * weight;\n                                }\n                            }\n                        }\n                        dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    depthwiseConv2DDerFilter(x, dy, convInfo) {\n        assertNotComplex([x, dy], 'depthwiseConv2DDerFilter');\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dW = tf.buffer(convInfo.filterShape, 'float32');\n        const leftPad = convInfo.padInfo.left;\n        const topPad = convInfo.padInfo.top;\n        const chMul = convInfo.outChannels / convInfo.inChannels;\n        const xBuf = this.bufferSync(x);\n        const dyBuf = this.bufferSync(dy);\n        for (let wR = 0; wR < filterHeight; ++wR) {\n            const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n            const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n            for (let wC = 0; wC < filterWidth; ++wC) {\n                const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                    const d1 = Math.trunc(d2 / chMul);\n                    const dm = d2 % chMul;\n                    let dotProd = 0;\n                    for (let b = 0; b < convInfo.batchSize; ++b) {\n                        for (let yR = yRMin; yR < yRMax; ++yR) {\n                            const xR = wR + yR * strideHeight - topPad;\n                            for (let yC = yCMin; yC < yCMax; ++yC) {\n                                const xC = wC + yC * strideWidth - leftPad;\n                                dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n                            }\n                        }\n                    }\n                    dW.set(dotProd, wR, wC, d1, dm);\n                }\n            }\n        }\n        return dW.toTensor();\n    }\n    tile(x, reps) {\n        assertNotComplex(x, 'tile');\n        return tile(this.bufferSync(x), reps);\n    }\n    gather(x, indices, axis) {\n        assertNotComplex([x, indices], 'gather');\n        const newShape = x.shape.slice();\n        const indicesValues = this.readSync(indices.dataId);\n        newShape[axis] = indicesValues.length;\n        const result = tf.buffer(newShape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < result.size; ++i) {\n            const newLoc = result.indexToLoc(i);\n            const originalLoc = newLoc.slice();\n            originalLoc[axis] = indicesValues[newLoc[axis]];\n            const originalIndex = xBuf.locToIndex(originalLoc);\n            result.values[i] = xBuf.values[originalIndex];\n        }\n        return result.toTensor();\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        assertNotComplex([x], 'batchToSpaceND');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n        const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n        const reshapedPermuted = backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n        const sliceBeginCoords = backend_util.getSliceBeginCoords(crops, blockShape.length);\n        const sliceSize = backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n        return tf.transpose(x.reshape(reshaped), permuted)\n            .reshape(reshapedPermuted)\n            .slice(sliceBeginCoords, sliceSize);\n    }\n    pool3d(x, convInfo, poolType) {\n        assertNotComplex(x, 'pool3d');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n            Number.POSITIVE_INFINITY);\n        const xValues = this.readSync(x.dataId);\n        const output = tf.buffer(convInfo.outShape, x.dtype);\n        const outputVals = output.values;\n        const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] *\n            convInfo.outShape[3] * convInfo.outShape[4];\n        const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];\n        const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];\n        const outputColStrides = convInfo.outShape[4];\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            const outputBatchOffset = batch * outputBatchStrides;\n            const inputBatchOffset = batch * x.strides[0];\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n                    const xDepthCorner = yDepth * strideDepth - padFront;\n                    let xDepthMin = xDepthCorner;\n                    while (xDepthMin < 0) {\n                        xDepthMin += dilationDepth;\n                    }\n                    const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n                    const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides;\n                    for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n                        const xRowCorner = yRow * strideHeight - padTop;\n                        let xRowMin = xRowCorner;\n                        while (xRowMin < 0) {\n                            xRowMin += dilationHeight;\n                        }\n                        const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n                        const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;\n                        for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n                            const xColCorner = yCol * strideWidth - padLeft;\n                            let xColMin = xColCorner;\n                            while (xColMin < 0) {\n                                xColMin += dilationWidth;\n                            }\n                            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n                            // Shader code begins\n                            const outputColOffset = outputRowOffset + yCol * outputColStrides;\n                            let minMaxValue = initialValue;\n                            let avgValue = 0;\n                            let count = 0;\n                            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n                                const xDepthOffset = inputBatchOffset + xDepth * x.strides[1];\n                                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                                    const xRowOffset = xDepthOffset + xRow * x.strides[2];\n                                    for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                                        const xColOffset = xRowOffset + xCol * x.strides[3];\n                                        const pixel = xValues[xColOffset + channel];\n                                        if ((poolType === 'max' && pixel > minMaxValue)) {\n                                            minMaxValue = pixel;\n                                        }\n                                        else if (poolType === 'avg') {\n                                            avgValue += pixel;\n                                            count++;\n                                        }\n                                        if (isNaN(minMaxValue)) {\n                                            break;\n                                        }\n                                    }\n                                    if (isNaN(minMaxValue)) {\n                                        break;\n                                    }\n                                }\n                                if (isNaN(minMaxValue)) {\n                                    break;\n                                }\n                            }\n                            const outputOffset = outputColOffset + channel;\n                            outputVals[outputOffset] =\n                                poolType === 'avg' ? avgValue / count : minMaxValue;\n                        }\n                    }\n                }\n            }\n        }\n        return output.toTensor();\n    }\n    avgPool3d(x, convInfo) {\n        assertNotComplex(x, 'avgPool3d');\n        return this.pool3d(x, convInfo, 'avg').toFloat();\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        assertNotComplex([dy, x], 'avgPool3dBackprop');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const dx = tf.buffer(x.shape, 'float32');\n        const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n        const dyBuf = this.bufferSync(dy);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n                    for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n                        for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n                            // Shader code begins.\n                            const dyDepthCorner = dxDepth - padFront;\n                            const dyRowCorner = dxRow - padTop;\n                            const dyColCorner = dxCol - padLeft;\n                            let dotProd = 0;\n                            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n                                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n                                    Math.floor(dyDepth) !== dyDepth) {\n                                    continue;\n                                }\n                                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                                    const dyRow = (dyRowCorner + wRow) / strideHeight;\n                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n                                        Math.floor(dyRow) !== dyRow) {\n                                        continue;\n                                    }\n                                    for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                                        const dyCol = (dyColCorner + wCol) / strideWidth;\n                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n                                            Math.floor(dyCol) !== dyCol) {\n                                            continue;\n                                        }\n                                        const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        dotProd += pixel;\n                                    }\n                                }\n                            }\n                            dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    maxPool3d(x, convInfo) {\n        assertNotComplex(x, 'maxPool3d');\n        return this.pool3d(x, convInfo, 'max').toFloat();\n    }\n    maxPool3dPositions(x, convInfo) {\n        const maxPositions = tf.buffer(convInfo.outShape, 'int32');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const xBuf = this.bufferSync(x);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n                    const xDepthCorner = yDepth * strideDepth - padFront;\n                    let xDepthMin = xDepthCorner;\n                    while (xDepthMin < 0) {\n                        xDepthMin += dilationDepth;\n                    }\n                    const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n                    for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n                        const xRowCorner = yRow * strideHeight - padTop;\n                        let xRowMin = xRowCorner;\n                        while (xRowMin < 0) {\n                            xRowMin += dilationHeight;\n                        }\n                        const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n                        for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n                            const xColCorner = yCol * strideWidth - padLeft;\n                            let xColMin = xColCorner;\n                            while (xColMin < 0) {\n                                xColMin += dilationWidth;\n                            }\n                            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n                            // Shader code begins\n                            let maxValue = Number.NEGATIVE_INFINITY;\n                            let maxPosition = -1;\n                            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n                                const wDepth = xDepth - xDepthCorner;\n                                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                                    const wRow = xRow - xRowCorner;\n                                    for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                                        const wCol = xCol - xColCorner;\n                                        const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);\n                                        if (pixel >= maxValue) {\n                                            maxValue = pixel;\n                                            maxPosition = wDepth * effectiveFilterHeight *\n                                                effectiveFilterWidth +\n                                                wRow * effectiveFilterHeight + wCol;\n                                        }\n                                    }\n                                }\n                            }\n                            maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return maxPositions.toTensor();\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        assertNotComplex([x, y], 'maxPool3dBackprop');\n        const maxPositions = this.maxPool3dPositions(x, convInfo);\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const dx = tf.buffer(x.shape, 'float32');\n        const maxPosBuf = this.bufferSync(maxPositions);\n        const dyBuf = this.bufferSync(dy);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n                    for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n                        for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n                            // Shader code begins\n                            const dyDepthCorner = dxDepth - padFront;\n                            const dyRowCorner = dxRow - padTop;\n                            const dyColCorner = dxCol - padLeft;\n                            let dotProd = 0;\n                            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n                                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n                                    Math.floor(dyDepth) !== dyDepth) {\n                                    continue;\n                                }\n                                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                                    const dyRow = (dyRowCorner + wRow) / strideHeight;\n                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n                                        Math.floor(dyRow) !== dyRow) {\n                                        continue;\n                                    }\n                                    for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                                        const dyCol = (dyColCorner + wCol) / strideWidth;\n                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n                                            Math.floor(dyCol) !== dyCol) {\n                                            continue;\n                                        }\n                                        const maxPos = effectiveFilterDepth *\n                                            effectiveFilterHeight * effectiveFilterWidth -\n                                            1 -\n                                            maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth +\n                                            wRow * effectiveFilterWidth + wCol;\n                                        const mask = maxPos === curPos ? 1 : 0;\n                                        if (mask === 0) {\n                                            continue;\n                                        }\n                                        const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        dotProd += pixel * mask;\n                                    }\n                                }\n                            }\n                            dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        assertNotComplex(x, 'resizeBilinear');\n        const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n        const xValues = this.readSync(x.dataId);\n        const result = new Float32Array(util.sizeFromShape([batch, newHeight, newWidth, numChannels]));\n        const effectiveInputSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutputSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        let outputIdx = 0;\n        const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n        const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n        for (let b = 0; b < batch; b++) {\n            for (let r = 0; r < newHeight; r++) {\n                const sourceFracRow = effectiveRowSizeRatio * r;\n                const sourceRowFloor = Math.floor(sourceFracRow);\n                const rowFrac = sourceFracRow - sourceRowFloor;\n                const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));\n                const topRowOffset = b * x.strides[0] + sourceRowFloor * x.strides[1];\n                const botRowOffset = b * x.strides[0] + sourceRowCeil * x.strides[1];\n                for (let c = 0; c < newWidth; c++) {\n                    const sourceFracCol = effectiveColSizeRatio * c;\n                    const sourceColFloor = Math.floor(sourceFracCol);\n                    const colFrac = sourceFracCol - sourceColFloor;\n                    const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));\n                    const topLeftOffest = topRowOffset + sourceColFloor * x.strides[2];\n                    const botLeftOffset = botRowOffset + sourceColFloor * x.strides[2];\n                    const topRightOffset = topRowOffset + sourceColCeil * x.strides[2];\n                    const botRightOffest = botRowOffset + sourceColCeil * x.strides[2];\n                    for (let d = 0; d < numChannels; d++) {\n                        // Begin shader.\n                        // Compute the fractional index of the source.\n                        const topLeft = xValues[topLeftOffest + d];\n                        const bottomLeft = xValues[botLeftOffset + d];\n                        const topRight = xValues[topRightOffset + d];\n                        const bottomRight = xValues[botRightOffest + d];\n                        const top = topLeft + (topRight - topLeft) * colFrac;\n                        const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;\n                        const newValue = top + (bottom - top) * rowFrac;\n                        result[outputIdx++] = newValue;\n                    }\n                }\n            }\n        }\n        return tf.tensor(result, [batch, newHeight, newWidth, numChannels]);\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        assertNotComplex([dy, x], 'resizeBilinearBackprop');\n        const [batch, xHeight, xWidth, depth] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        const output = new Float32Array(batch * xHeight * xWidth * depth);\n        // In the backwards pass, we want to find the pixels that were generated\n        // for each pixel in the input image the forward pass and add the\n        // corresponding coefficient from dy to the gradient (with some\n        // interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/3039375c86a5bbc9610c7725dcaa95d635f87ba2/tensorflow/core/kernels/resize_bilinear_op.cc#L275\n        const dyValues = this.readSync(dy.dataId);\n        let offset = 0;\n        for (let b = 0; b < batch; b++) {\n            const bOffset = b * x.strides[0];\n            for (let r = 0; r < yHeight; r++) {\n                const dxR = r * heightScale;\n                const topDxRIndex = Math.floor(dxR);\n                const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);\n                const topDxROffset = bOffset + topDxRIndex * x.strides[1];\n                const bottomDxROffset = bOffset + bottomDxRIndex * x.strides[1];\n                const dxRLerp = dxR - topDxRIndex;\n                const inverseDxRLerp = 1.0 - dxRLerp;\n                for (let c = 0; c < yWidth; c++) {\n                    const dxC = c * widthScale;\n                    const leftDxCIndex = Math.floor(dxC);\n                    const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);\n                    const dxCLerp = dxC - leftDxCIndex;\n                    const inverseDxCLerp = 1.0 - dxCLerp;\n                    const topLeftRCOffset = topDxROffset + leftDxCIndex * x.strides[2];\n                    const topRightRCOffset = topDxROffset + rightDxCIndex * x.strides[2];\n                    const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * x.strides[2];\n                    const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * x.strides[2];\n                    const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;\n                    const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;\n                    const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;\n                    const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;\n                    for (let d = 0; d < depth; d++) {\n                        const dyVal = dyValues[offset++];\n                        output[topLeftRCOffset + d] +=\n                            dyVal * inverseDxRLerpTimesInverseDxCLerp;\n                        output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;\n                        output[bottomLeftRCOffset + d] +=\n                            dyVal * dxRLerpTimesInverseDxCLerp;\n                        output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(output, [batch, xWidth, xHeight, depth], x.dtype);\n    }\n    resizeNearestNeighbor(x, newHeight, newWidth, alignCorners) {\n        assertNotComplex(x, 'resizeNearestNeighbor');\n        const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n        const xValues = this.readSync(x.dataId);\n        const output = new Float32Array(batch * newHeight * newWidth * numChannels);\n        const effectiveInputSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutputSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n        const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n        let outputOffset = 0;\n        for (let b = 0; b < batch; b++) {\n            const batchOffset = b * x.strides[0];\n            for (let r = 0; r < newHeight; r++) {\n                const sourceFracRow = effectiveRowSizeRatio * r;\n                const sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) :\n                    Math.floor(sourceFracRow));\n                const rowOffset = batchOffset + sourceNearestRow * x.strides[1];\n                for (let c = 0; c < newWidth; c++) {\n                    const sourceFracCol = effectiveColSizeRatio * c;\n                    const sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) :\n                        Math.floor(sourceFracCol));\n                    const colOffset = rowOffset + sourceNearestCol * x.strides[2];\n                    for (let d = 0; d < numChannels; d++) {\n                        // Begin shader.\n                        // Compute the fractional index of the source.\n                        const newVal = xValues[colOffset + d];\n                        output[outputOffset++] = newVal;\n                    }\n                }\n            }\n        }\n        return tf.tensor(output, [batch, newHeight, newWidth, numChannels], x.dtype);\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        assertNotComplex([dy, x], 'resizeNearestNeighborBackprop');\n        const [batch, xHeight, xWidth, depth] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        const output = new Float32Array(batch * xHeight * xWidth * depth);\n        const dyValues = this.readSync(dy.dataId);\n        // In the backwards pass, we want to find the pixels that were generated\n        // for each pixel in the input image the forward pass\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        // Loop over the output space.\n        for (let b = 0; b < batch; b++) {\n            const batchOffset = b * x.strides[0];\n            for (let r = 0; r < xHeight; r++) {\n                const rowOffset = batchOffset + r * x.strides[1];\n                // Compute bounds for where in dy we will look\n                const startRLerp = Math.floor(r * invHeightScale);\n                const startDyR = Math.floor(startRLerp - (winHeight / 2));\n                for (let c = 0; c < xWidth; c++) {\n                    const colOffset = rowOffset + c * x.strides[2];\n                    // Compute bounds for where in dy we will look\n                    const startCLerp = Math.floor(c * invWidthScale);\n                    const startDyC = Math.floor(startCLerp - (winWidth / 2));\n                    for (let d = 0; d < depth; d++) {\n                        let accum = 0;\n                        // loop over dy\n                        for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {\n                            const dyR = dyRIndex + startDyR;\n                            // Guard against the window exceeding the bounds of dy\n                            if (dyR < 0 || dyR >= yHeight) {\n                                continue;\n                            }\n                            const dyROffset = batchOffset + dyR * dy.strides[1];\n                            const sourceFracRow = dyR * heightScale;\n                            const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) :\n                                Math.floor(sourceFracRow));\n                            if (r !== sourceNearestRow) {\n                                continue;\n                            }\n                            for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {\n                                const dyC = dyCIndex + startDyC;\n                                // Guard against the window exceeding the bounds of dy\n                                if (dyC < 0 || dyC >= yWidth) {\n                                    continue;\n                                }\n                                const dyCOffset = dyROffset + dyC * dy.strides[2];\n                                const sourceFracCol = dyC * widthScale;\n                                const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) :\n                                    Math.floor(sourceFracCol));\n                                if (c === sourceNearestCol) {\n                                    accum += dyValues[dyCOffset + d];\n                                }\n                            }\n                        }\n                        output[colOffset + d] = accum;\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(output, x.shape, x.dtype);\n    }\n    localResponseNormalization4D(x, depthRadius, bias, alpha, beta) {\n        assertNotComplex(x, 'localResponseNormalization4D');\n        const channels = x.shape[3];\n        const maxD = channels - 1;\n        const xValues = this.readSync(x.dataId);\n        const size = x.size;\n        const result = new Float32Array(size);\n        function sumAcrossChannels(offset) {\n            const currentChannel = offset % channels;\n            let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n            const endSumOffset = offset - currentChannel +\n                Math.min(currentChannel + depthRadius, maxD);\n            let sum = 0.0;\n            for (; beginSumOffset <= endSumOffset; beginSumOffset++) {\n                const z = xValues[beginSumOffset];\n                sum += z * z;\n            }\n            return sum;\n        }\n        for (let offset = 0; offset < size; offset++) {\n            const sum = sumAcrossChannels(offset);\n            const val = xValues[offset] * Math.pow(bias + alpha * sum, -beta);\n            result[offset] = val;\n        }\n        return tf.tensor4d(result, x.shape);\n    }\n    LRNGrad(dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {\n        assertNotComplex(dy, 'LRNGrad');\n        const channels = dy.shape[3];\n        const dyValues = this.readSync(dy.dataId);\n        const inputImageValues = this.readSync(inputImage.dataId);\n        const outputImageValues = this.readSync(outputImage.dataId);\n        const result = new Float32Array(dy.size);\n        const size = dy.size;\n        for (let offset = 0; offset < size; offset++) {\n            const currentChannel = offset % channels;\n            const depthBegin = (offset - currentChannel) + Math.max(0, currentChannel - depthRadius);\n            const depthEnd = (offset - currentChannel) +\n                Math.min(channels, currentChannel + depthRadius + 1);\n            let norm = 0;\n            for (let k = depthBegin; k < depthEnd; k++) {\n                norm += Math.pow(inputImageValues[k], 2);\n            }\n            norm = alpha * norm + bias;\n            for (let k = depthBegin; k < depthEnd; k++) {\n                let dyi = -2 * alpha * beta * inputImageValues[k] *\n                    outputImageValues[offset] / norm;\n                if (offset === k) {\n                    dyi += Math.pow(norm, -beta);\n                }\n                dyi *= dyValues[offset];\n                result[k] += dyi;\n            }\n        }\n        return tf.tensor4d(result, dy.shape);\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        assertNotComplex(logits, 'multinomial');\n        const probabilities = normalized ? logits : tf.softmax(logits);\n        const batchSize = probabilities.shape[0];\n        const numEvents = probabilities.shape[1];\n        const res = tf.zeros([batchSize, numSamples], 'int32');\n        const resVals = this.readSync(res.dataId);\n        const probVals = this.readSync(probabilities.dataId);\n        for (let b = 0; b < batchSize; ++b) {\n            const offset = b * numEvents;\n            // The cdf won't include the last event. It will be implicit if no other\n            // event happened.\n            const cdf = new Float32Array(numEvents - 1);\n            cdf[0] = probVals[offset];\n            for (let event = 1; event < cdf.length; ++event) {\n                cdf[event] = cdf[event - 1] + probVals[offset + event];\n            }\n            const random = seedrandom.alea(seed.toString());\n            const outOffset = b * numSamples;\n            for (let sampleId = 0; sampleId < numSamples; ++sampleId) {\n                const r = random();\n                // Assume last event happened by default.\n                resVals[outOffset + sampleId] = cdf.length;\n                for (let event = 0; event < cdf.length; event++) {\n                    if (r < cdf[event]) {\n                        resVals[outOffset + sampleId] = event;\n                        break;\n                    }\n                }\n            }\n        }\n        return res;\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        assertNotComplex(indices, 'oneHot');\n        const res = new Float32Array(indices.size * depth);\n        res.fill(offValue);\n        const indicesVal = this.readSync(indices.dataId);\n        for (let event = 0; event < indices.size; ++event) {\n            if (indicesVal[event] >= 0 && indicesVal[event] < depth) {\n                res[event * depth + indicesVal[event]] = onValue;\n            }\n        }\n        return tf.tensor2d(res, [indices.size, depth], 'int32');\n    }\n    nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n        assertNotComplex(boxes, 'nonMaxSuppression');\n        const boxesVals = this.readSync(boxes.dataId);\n        const scoresVals = this.readSync(scores.dataId);\n        return nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        util.assert(dataFormat === 'NHWC', () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`);\n        util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n        const batchSize = x.shape[0];\n        const inputHeight = x.shape[1];\n        const inputWidth = x.shape[2];\n        const inputDepth = x.shape[3];\n        const outputHeight = inputHeight * blockSize;\n        const outputWidth = inputWidth * blockSize;\n        const outputDepth = inputDepth / (blockSize * blockSize);\n        const xValues = this.readSync(x.dataId);\n        const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);\n        let outputIdx = 0;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let h = 0; h < outputHeight; ++h) {\n                const inH = Math.floor(h / blockSize);\n                const offsetH = (h % blockSize);\n                for (let w = 0; w < outputWidth; ++w) {\n                    const inW = Math.floor(w / blockSize);\n                    const offsetW = (w % blockSize);\n                    const offsetD = (offsetH * blockSize + offsetW) * outputDepth;\n                    for (let d = 0; d < outputDepth; ++d) {\n                        const inD = d + offsetD;\n                        const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));\n                        result[outputIdx++] = xValues[inputIdx];\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(result, [batchSize, outputHeight, outputWidth, outputDepth]);\n    }\n    broadcastedBinaryOp(a, b, dtype, op) {\n        const newShape = backend_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const result = tf.buffer(newShape, dtype);\n        const aVals = this.readSync(a.dataId);\n        const bVals = this.readSync(b.dataId);\n        const aBroadcastDims = backend_util.getBroadcastDims(a.shape, newShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(b.shape, newShape);\n        const resVals = result.values;\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < resVals.length; ++i) {\n                resVals[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n            }\n        }\n        else {\n            const aBuf = this.bufferSync(a);\n            const bBuf = this.bufferSync(b);\n            for (let i = 0; i < resVals.length; ++i) {\n                const loc = result.indexToLoc(i);\n                const aLoc = loc.slice(-a.rank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = aBuf.locToIndex(aLoc);\n                const bLoc = loc.slice(-b.rank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = bBuf.locToIndex(bLoc);\n                resVals[i] = op(aVals[aIndex], bVals[bIndex]);\n            }\n        }\n        return result.toTensor();\n    }\n    split(x, sizeSplits, axis) {\n        return split(x, sizeSplits, axis);\n    }\n    dispose() { }\n    floatPrecision() {\n        return 32;\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return super.epsilon();\n    }\n    cropAndResize(images, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        const [batch, imageHeight, imageWidth, numChannels] = images.shape;\n        const numBoxes = boxes.shape[0];\n        const [cropHeight, cropWidth] = cropSize;\n        const output = tf.buffer([numBoxes, cropHeight, cropWidth, numChannels], 'float32');\n        const boxVals = this.readSync(boxes.dataId);\n        const boxIndVals = this.readSync(boxIndex.dataId);\n        const imageVals = this.readSync(images.dataId);\n        const inStride = images.strides; // to calculate flat indexes into image\n        const outStride = output.strides; // to calculate flat indexes into output\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc\n        for (let b = 0; b < numBoxes; b++) {\n            const startInd = b * 4;\n            const y1 = boxVals[startInd];\n            const x1 = boxVals[startInd + 1];\n            const y2 = boxVals[startInd + 2];\n            const x2 = boxVals[startInd + 3];\n            const bInd = boxIndVals[b];\n            if (bInd >= batch) {\n                continue;\n            }\n            const heightScale = (cropHeight > 1) ?\n                (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) :\n                0;\n            const widthScale = (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;\n            for (let y = 0; y < cropHeight; y++) {\n                const yInd = (cropHeight > 1) ?\n                    y1 * (imageHeight - 1) + y * (heightScale) :\n                    0.5 * (y1 + y2) * (imageHeight - 1);\n                if (yInd < 0 || yInd > imageHeight - 1) {\n                    for (let x = 0; x < cropWidth; x++) {\n                        for (let c = 0; c < numChannels; c++) {\n                            const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[ind] = extrapolationValue;\n                        }\n                    }\n                    continue;\n                }\n                if (method === 'bilinear') {\n                    const topInd = Math.floor(yInd);\n                    const bottomInd = Math.ceil(yInd);\n                    const yLerp = yInd - topInd;\n                    for (let x = 0; x < cropWidth; x++) {\n                        const xInd = (cropWidth > 1) ?\n                            x1 * (imageWidth - 1) + x * widthScale :\n                            0.5 * (x1 + x2) * (imageWidth - 1);\n                        if (xInd < 0 || xInd > imageWidth - 1) {\n                            for (let c = 0; c < numChannels; c++) {\n                                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                                output.values[ind] = extrapolationValue;\n                            }\n                            continue;\n                        }\n                        const leftInd = Math.floor(xInd);\n                        const rightInd = Math.ceil(xInd);\n                        const xLerp = xInd - leftInd;\n                        for (let c = 0; c < numChannels; c++) {\n                            let ind = c + leftInd * inStride[2] + topInd * inStride[1] +\n                                bInd * inStride[0];\n                            const topLeft = imageVals[ind];\n                            ind = c + rightInd * inStride[2] + topInd * inStride[1] +\n                                bInd * inStride[0];\n                            const topRight = imageVals[ind];\n                            ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +\n                                bInd * inStride[0];\n                            const bottomLeft = imageVals[ind];\n                            ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +\n                                bInd * inStride[0];\n                            const bottomRight = imageVals[ind];\n                            const top = topLeft + (topRight - topLeft) * xLerp;\n                            const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;\n                            ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[ind] = top + ((bottom - top) * yLerp);\n                        }\n                    }\n                }\n                else { // method == \"nearest\"\n                    for (let x = 0; x < cropWidth; ++x) {\n                        const xInd = (cropWidth > 1) ?\n                            x1 * (imageWidth - 1) + x * widthScale :\n                            0.5 * (x1 + x2) * (imageWidth - 1);\n                        if (xInd < 0 || xInd > imageWidth - 1) {\n                            for (let c = 0; c < numChannels; c++) {\n                                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                                output.values[ind] = extrapolationValue;\n                            }\n                            continue;\n                        }\n                        const closestX = Math.round(xInd);\n                        const closestY = Math.round(yInd);\n                        for (let c = 0; c < numChannels; c++) {\n                            const inInd = c + closestX * inStride[2] +\n                                closestY * inStride[1] + bInd * inStride[0];\n                            const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[outInd] = imageVals[inInd];\n                        }\n                    }\n                }\n            }\n        }\n        return output.toTensor();\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n        const sumDupeIndices = false;\n        return this.scatter(sparseIndices, sparseValues, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n    }\n    gatherND(x, indices) {\n        const indicesShape = indices.shape;\n        const sliceRank = indicesShape[indicesShape.length - 1];\n        const [resultShape, numSlices, sliceSize, strides] = backend_util.prepareAndValidate(x, indices);\n        if (numSlices === 0) {\n            return tf.tensor([], resultShape, x.dtype);\n        }\n        const buffer = new TensorBuffer([numSlices, sliceSize], x.dtype);\n        const indicesData = this.readSync(indices.dataId);\n        const xData = this.readSync(x.dataId);\n        for (let i = 0; i < numSlices; i++) {\n            const index = [];\n            let flattenIndex = 0;\n            for (let j = 0; j < sliceRank; j++) {\n                const dim = indicesData[i * sliceRank + j];\n                flattenIndex += dim * strides[j];\n                index.push(dim);\n            }\n            if (flattenIndex < 0 || flattenIndex >= x.size / sliceSize) {\n                throw new Error(`Invalid indices: ${index} does not index into ${x.shape}`);\n            }\n            for (let k = 0; k < sliceSize; k++) {\n                buffer.values[i * sliceSize + k] = xData[flattenIndex * sliceSize + k];\n            }\n        }\n        return buffer.toTensor().reshape(resultShape);\n    }\n    scatterND(indices, updates, shape) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(updates, indices, shape);\n        const defaultValue = tf.scalar(0);\n        const sumDupeIndices = true;\n        return this.scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n    }\n    fill(shape, value, dtype) {\n        dtype = dtype || util.inferDtype(value);\n        const values = util.getArrayFromDType(dtype, util.sizeFromShape(shape));\n        values.fill(value);\n        return engine().makeTensor(values, shape, dtype, this);\n    }\n    onesLike(x) {\n        if (x.dtype === 'string') {\n            throw new Error('onesLike is not supported for string tensors');\n        }\n        else {\n            return this.fill(x.shape, 1, x.dtype);\n        }\n    }\n    zerosLike(x) {\n        const values = util.getArrayFromDType(x.dtype, util.sizeFromShape(x.shape));\n        return this.makeOutput(values, x.shape, x.dtype);\n    }\n    linspace(start, stop, num) {\n        return backend_util.linspaceImpl(start, stop, num);\n    }\n    scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {\n        const flattenShape = [outputSize / sliceSize, sliceSize];\n        const indicesData = this.readSync(indices.dataId);\n        const updatesData = this.readSync(updates.dataId);\n        if (outputSize === 0) {\n            return tf.tensor([], shape, updates.dtype);\n        }\n        const buffer = new TensorBuffer(flattenShape, updates.dtype);\n        buffer.values.fill(this.readSync(defaultValue.dataId)[0]);\n        for (let i = 0; i < numUpdates; i++) {\n            const index = [];\n            let flattenIndex = 0;\n            for (let j = 0; j < sliceRank; j++) {\n                const dim = indicesData[i * sliceRank + j];\n                index.push(dim);\n                flattenIndex += dim * strides[j];\n            }\n            if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {\n                throw new Error(`Invalid indices: ${index} does not index into ${shape}`);\n            }\n            for (let k = 0; k < sliceSize; k++) {\n                if (sumDupeIndices) {\n                    buffer.values[flattenIndex * sliceSize + k] +=\n                        updatesData[i * sliceSize + k];\n                }\n                else {\n                    buffer.values[flattenIndex * sliceSize + k] = updates.rank === 0 ?\n                        updatesData[0] :\n                        updatesData[i * sliceSize + k];\n                }\n            }\n        }\n        return buffer.toTensor().reshape(shape);\n    }\n}\n//# sourceMappingURL=backend_cpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs, util } from '@tensorflow/tfjs-core';\nexport function simpleAbsImpl(vals) {\n    const resultValues = new Float32Array(vals.length);\n    for (let i = 0; i < vals.length; ++i) {\n        resultValues[i] = Math.abs(vals[i]);\n    }\n    return resultValues;\n}\nexport const absKernelFunc = (args) => {\n    const { x } = args.inputs;\n    const cpuBackend = args.backend;\n    let resultValues = new Float32Array(util.sizeFromShape(x.shape));\n    if (x.dtype !== 'complex64') {\n        const values = cpuBackend.data.get(x.dataId).values;\n        resultValues = simpleAbsImpl(values);\n    }\n    else {\n        const complexVals = cpuBackend.data.get(x.dataId);\n        const real = complexVals.complexTensorInfos.real;\n        const imag = complexVals.complexTensorInfos.imag;\n        const realVals = cpuBackend.data.get(real.dataId).values;\n        const imagVals = cpuBackend.data.get(imag.dataId).values;\n        for (let i = 0; i < realVals.length; i++) {\n            const real = realVals[i];\n            const imag = imagVals[i];\n            resultValues[i] = Math.hypot(real, imag);\n        }\n    }\n    return cpuBackend.makeOutput(resultValues, x.shape, 'float32');\n};\nexport const absConfig = {\n    kernelName: Abs,\n    backendName: 'cpu',\n    kernelFunc: absKernelFunc,\n};\n//# sourceMappingURL=Abs.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for binary ops. Supports broadcast.\n */\nexport function createSimpleBinaryKernelImpl(op) {\n    return (aShape, bShape, aVals, bVals, dtype) => {\n        const newShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const resultRank = newShape.length;\n        const resultStrides = util.computeStrides(newShape);\n        const resultSize = util.sizeFromShape(newShape);\n        const result = util.getTypedArrayFromDType(dtype, resultSize);\n        const aRank = aShape.length;\n        const bRank = bShape.length;\n        const aStrides = util.computeStrides(aShape);\n        const bStrides = util.computeStrides(bShape);\n        const aBroadcastDims = backend_util.getBroadcastDims(aShape, newShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(bShape, newShape);\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < result.length; ++i) {\n                result[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n            }\n        }\n        else {\n            for (let i = 0; i < result.length; ++i) {\n                const loc = util.indexToLoc(i, resultRank, resultStrides);\n                const aLoc = loc.slice(-aRank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n                const bLoc = loc.slice(-bRank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n                result[i] = op(aVals[aIndex], bVals[bIndex]);\n            }\n        }\n        return [result, newShape];\n    };\n}\n//# sourceMappingURL=binary_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Complex } from '@tensorflow/tfjs-core';\nexport function complex(args) {\n    const { inputs, backend } = args;\n    const { real, imag } = inputs;\n    const realVals = backend.data.get(real.dataId).values;\n    const imagVals = backend.data.get(imag.dataId).values;\n    const complexInfo = backend.makeTensorInfo(real.shape, 'complex64');\n    const complex = backend.data.get(complexInfo.dataId);\n    // The complex tensor owns the underlying real and imag tensorInfos, only the\n    // complex tensor tracks refCount, when complexData is disposed the\n    // underlying tensorData will be disposed.\n    complex.complexTensorInfos = {\n        real: backend.makeTensorInfo(real.shape, 'float32', realVals),\n        imag: backend.makeTensorInfo(imag.shape, 'float32', imagVals)\n    };\n    return complexInfo;\n}\nexport const complexConfig = {\n    kernelName: Complex,\n    backendName: 'cpu',\n    kernelFunc: complex\n};\n//# sourceMappingURL=Complex.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '@tensorflow/tfjs-core';\nexport function identity(args) {\n    const { inputs, backend } = args;\n    const { x } = inputs;\n    backend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nexport const identityConfig = {\n    kernelName: Identity,\n    backendName: 'cpu',\n    kernelFunc: identity\n};\n//# sourceMappingURL=Identity.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Real } from '@tensorflow/tfjs-core';\nexport function real(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const real = backend.data.get(input.dataId).complexTensorInfos.real;\n    const realVal = backend.data.get(real.dataId).values;\n    // When complex tensor is disposed, its underlying parts will be disposed too.\n    // Make new tensor out of the real value of the complex. This makes sure the\n    // value is still accessible even if complex tensor is disposed.\n    return backend.makeTensorInfo(real.shape, real.dtype, realVal);\n}\nexport const realConfig = {\n    kernelName: Real,\n    backendName: 'cpu',\n    kernelFunc: real\n};\n//# sourceMappingURL=Real.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { Cast, util } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { complex } from './Complex';\nimport { identity } from './Identity';\nimport { real } from './Real';\nexport function cast(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { dtype } = attrs;\n    // Casting to complex64.\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return identity({ inputs: { x }, backend });\n        }\n        // TODO(lina128): Import kernel function once zeros is modularized.\n        const zerosTensor = tf.zeros(x.shape);\n        const floatX = cast({ inputs: { x }, backend, attrs: { dtype: 'float32' } });\n        const result = complex({ inputs: { real: floatX, imag: zerosTensor }, backend });\n        zerosTensor.dispose();\n        backend.disposeIntermediateTensorInfo(floatX);\n        return result;\n    }\n    // Casting from complex64\n    if (x.dtype === 'complex64') {\n        const realPart = real({ inputs: { input: x }, backend });\n        const result = cast({ inputs: { x: realPart }, backend, attrs: { dtype } });\n        backend.disposeIntermediateTensorInfo(realPart);\n        return result;\n    }\n    if (!util.hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        const result = identity({ inputs: { x }, backend });\n        return { dataId: result.dataId, shape: result.shape, dtype };\n    }\n    if (dtype === 'int32') {\n        const values = backend.data.get(x.dataId).values;\n        const resultValues = Int32Array.from(values);\n        return backend.makeTensorInfo(x.shape, 'int32', resultValues);\n    }\n    if (dtype === 'bool') {\n        // This is essentially the result of notEqual(x, 0). We avoid using\n        // kernel notEqual to avoid circular dependency, i.e. binary_utils ->\n        // cast -> notEqual -> binary_utils.\n        const xVals = backend.data.get(x.dataId).values;\n        const zero = util.toTypedArray([0], x.dtype);\n        const [resultData, resultShape] = createSimpleBinaryKernelImpl((a, b) => (a !== b) ? 1 : 0)(x.shape, [], xVals, zero, 'bool');\n        return backend.makeTensorInfo(resultShape, 'bool', resultData);\n    }\n    throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nexport const castConfig = {\n    kernelName: Cast,\n    backendName: 'cpu',\n    kernelFunc: cast\n};\n//# sourceMappingURL=Cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { cast } from '../kernels/Cast';\nimport { complex } from '../kernels/Complex';\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param name Kernel name.\n * @param binaryKernelImpl A `SimpleBinaryKernelImpl` for the kernel.\n * @param binaryKernelComplexImpl Optional. If exists, represents a\n *     `ComplexBinaryKernelImpl` for the kernel, will be used when input dtype\n *     is `complex64`.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc(name, simpleImpl, complexImpl, dtype) {\n    if (complexImpl == null) {\n        return ({ inputs, backend }) => {\n            const { a, b } = inputs;\n            const cpuBackend = backend;\n            assertNotComplex([a, b], name);\n            const aVals = cpuBackend.data.get(a.dataId).values;\n            const bVals = cpuBackend.data.get(b.dataId).values;\n            const $dtype = dtype || a.dtype;\n            const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n            return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n        };\n    }\n    return ({ inputs, backend }) => {\n        const { a, b } = inputs;\n        const cpuBackend = backend;\n        if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n            const $aComplex = cast({ inputs: { x: a }, backend: cpuBackend, attrs: { dtype: 'complex64' } });\n            const $aComplexVals = cpuBackend.data.get($aComplex.dataId);\n            const aReal = $aComplexVals.complexTensorInfos.real;\n            const aImag = $aComplexVals.complexTensorInfos.imag;\n            const aRealVals = cpuBackend.data.get(aReal.dataId).values;\n            const aImagVals = cpuBackend.data.get(aImag.dataId).values;\n            const $bComplex = cast({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: 'complex64' } });\n            const $bComplexVals = cpuBackend.data.get($bComplex.dataId);\n            const bReal = $bComplexVals.complexTensorInfos.real;\n            const bImag = $bComplexVals.complexTensorInfos.imag;\n            const bRealVals = cpuBackend.data.get(bReal.dataId).values;\n            const bImagVals = cpuBackend.data.get(bImag.dataId).values;\n            const [resultRealData, resultImagData, resultShape] = complexImpl(a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);\n            const resultReal = cpuBackend.makeTensorInfo(resultShape, 'float32', resultRealData);\n            const resultImag = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImagData);\n            const result = complex({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend });\n            cpuBackend.disposeIntermediateTensorInfo($aComplex);\n            cpuBackend.disposeIntermediateTensorInfo($bComplex);\n            cpuBackend.disposeIntermediateTensorInfo(resultReal);\n            cpuBackend.disposeIntermediateTensorInfo(resultImag);\n            return result;\n        }\n        else {\n            const aVals = cpuBackend.data.get(a.dataId).values;\n            const bVals = cpuBackend.data.get(b.dataId).values;\n            const $dtype = dtype || a.dtype;\n            const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n            return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n        }\n    };\n}\n/**\n * Template that creates the complex type implementation for binary ops.\n * Supports broadcast.\n */\nexport function createComplexBinaryKernelImpl(op) {\n    return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) => {\n        const resultShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const resultSize = util.sizeFromShape(resultShape);\n        const resultRank = resultShape.length;\n        const resultStrides = util.computeStrides(resultShape);\n        const resultRealVals = util.getTypedArrayFromDType('float32', resultSize);\n        const resultImagVals = util.getTypedArrayFromDType('float32', resultSize);\n        const aBroadcastDims = backend_util.getBroadcastDims(aShape, resultShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(bShape, resultShape);\n        const aVals = backend_util.mergeRealAndImagArrays(aRealVals, aImagVals);\n        const bVals = backend_util.mergeRealAndImagArrays(bRealVals, bImagVals);\n        const aRank = aShape.length;\n        const aStrides = util.computeStrides(aShape);\n        const bRank = bShape.length;\n        const bStrides = util.computeStrides(bShape);\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < resultRealVals.length; i++) {\n                const aIdx = i % aVals.length;\n                const bIdx = i % bVals.length;\n                const result = op(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);\n                resultRealVals[i] = result.real;\n                resultImagVals[i] = result.imag;\n            }\n        }\n        else {\n            for (let i = 0; i < resultRealVals.length; i++) {\n                const loc = util.indexToLoc(i, resultRank, resultStrides);\n                const aLoc = loc.slice(-aRank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n                const bLoc = loc.slice(-bRank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n                const opResult = op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);\n                resultRealVals[i] = opResult.real;\n                resultImagVals[i] = opResult.imag;\n            }\n        }\n        return [resultRealVals, resultImagVals, resultShape];\n    };\n}\n//# sourceMappingURL=kernel_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const addImpl = createSimpleBinaryKernelImpl(((a, b) => a + b));\nexport const addComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal + bReal, imag: aImag + bImag };\n}));\nexport const add = binaryKernelFunc(Add, addImpl, addComplexImpl);\nexport const addConfig = {\n    kernelName: Add,\n    backendName: 'cpu',\n    kernelFunc: add\n};\n//# sourceMappingURL=Add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for unary op.\n */\nexport function createSimpleUnaryImpl(op) {\n    return (values, dtype, attrs) => {\n        const newValues = util.getTypedArrayFromDType(dtype, values.length);\n        for (let i = 0; i < values.length; ++i) {\n            newValues[i] = op(values[i], attrs);\n        }\n        return newValues;\n    };\n}\n//# sourceMappingURL=unary_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param name Kernel name.\n * @param op A `SimpleUnaryOperation` for the kernel.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the input. This is mainly used in certain\n *     kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFunc(name, op, dtype) {\n    return ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        assertNotComplex(x, name);\n        if (x.dtype === 'string' || dtype === 'string') {\n            throw new Error('unaryKernelFunc does not support string input/output');\n        }\n        const cpuBackend = backend;\n        const values = cpuBackend.data.get(x.dataId).values;\n        const xSize = util.sizeFromShape(x.shape);\n        const $dtype = dtype || x.dtype;\n        const newValues = util.getArrayFromDType($dtype, xSize);\n        for (let i = 0; i < xSize; ++i) {\n            newValues[i] = op(values[i], attrs);\n        }\n        return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n    };\n}\n/**\n * Template that creates a `KernelFunc` for unary ops from the given\n * `SimpleUnaryImpl`..\n * @param name Kernel name.\n * @param unaryImpl A `SimpleUnaryImpl` that implements the op.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the input. This is mainly used in certain\n *     kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFuncFromImpl(name, unaryImpl, dtype) {\n    return ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        assertNotComplex(x, name);\n        if (x.dtype === 'string' || dtype === 'string') {\n            throw new Error('unaryKernelFunc does not support string input/output');\n        }\n        const cpuBackend = backend;\n        const values = cpuBackend.data.get(x.dataId).values;\n        const $dtype = dtype || x.dtype;\n        const newValues = unaryImpl(values, $dtype, attrs);\n        return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n    };\n}\n//# sourceMappingURL=unary_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));\nexport const ceilKernelFunc = unaryKernelFuncFromImpl(Ceil, ceilImpl);\nexport const ceilConfig = {\n    kernelName: Ceil,\n    backendName: 'cpu',\n    kernelFunc: ceilKernelFunc,\n};\n//# sourceMappingURL=Ceil.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));\nexport const expKernelFunc = unaryKernelFuncFromImpl(Exp, expImpl);\nexport const expConfig = {\n    kernelName: Exp,\n    backendName: 'cpu',\n    kernelFunc: expKernelFunc,\n};\n//# sourceMappingURL=Exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));\nexport const expm1KernelFunc = unaryKernelFuncFromImpl(Expm1, expm1Impl);\nexport const expm1Config = {\n    kernelName: Expm1,\n    backendName: 'cpu',\n    kernelFunc: expm1KernelFunc,\n};\n//# sourceMappingURL=Expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));\nexport const floorKernelFunc = unaryKernelFuncFromImpl(Floor, floorImpl);\nexport const floorConfig = {\n    kernelName: Floor,\n    backendName: 'cpu',\n    kernelFunc: floorKernelFunc,\n};\n//# sourceMappingURL=Floor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));\nexport const logKernelFunc = unaryKernelFuncFromImpl(Log, logImpl);\nexport const logConfig = {\n    kernelName: Log,\n    backendName: 'cpu',\n    kernelFunc: logKernelFunc,\n};\n//# sourceMappingURL=Log.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function maxImpl(aVals, reduceSize, outShape, dtype) {\n    const vals = util.getTypedArrayFromDType(dtype, util.sizeFromShape(outShape));\n    for (let i = 0; i < vals.length; ++i) {\n        const offset = i * reduceSize;\n        let max = aVals[offset];\n        for (let j = 0; j < reduceSize; ++j) {\n            const value = aVals[offset + j];\n            if (value > max) {\n                max = value;\n            }\n        }\n        vals[i] = max;\n    }\n    return vals;\n}\n//# sourceMappingURL=Max_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const multiplyImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue * bValue));\nexport const multiplyComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return {\n        real: aReal * bReal - aImag * bImag,\n        imag: aReal * bImag + aImag * bReal\n    };\n}));\nexport const multiply = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);\nexport const multiplyConfig = {\n    kernelName: Multiply,\n    backendName: 'cpu',\n    kernelFunc: multiply\n};\n//# sourceMappingURL=Multiply.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));\nexport const rsqrtKernelFunc = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);\nexport const rsqrtConfig = {\n    kernelName: Rsqrt,\n    backendName: 'cpu',\n    kernelFunc: rsqrtKernelFunc,\n};\n//# sourceMappingURL=Rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Slice, slice_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function sliceImpl(vals, begin, size, shape, dtype) {\n    const isContinous = slice_util.isSliceContinous(shape, begin, size);\n    const length = util.sizeFromShape(size);\n    const xStrides = util.computeStrides(shape);\n    if (isContinous) {\n        const flatOffset = slice_util.computeFlatOffset(begin, xStrides);\n        return vals.subarray(flatOffset, flatOffset + length);\n    }\n    const outVals = util.getTypedArrayFromDType(dtype, length);\n    for (let i = 0; i < length; ++i) {\n        const rank = size.length;\n        const strides = util.computeStrides(size);\n        const loc = util.indexToLoc(i, rank, strides);\n        const xLoc = loc.map((idx, j) => idx + begin[j]);\n        const xIndex = util.locToIndex(xLoc, shape.length, xStrides);\n        outVals[i] = vals[xIndex];\n    }\n    return outVals;\n}\nexport function slice(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { begin, size } = attrs;\n    assertNotComplex(x, 'slice');\n    const [$begin, $size] = slice_util.parseSliceParams(x, begin, size);\n    slice_util.assertParamsValid(x, $begin, $size);\n    const vals = backend.data.get(x.dataId).values;\n    const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);\n    return backend.makeTensorInfo($size, x.dtype, outVals);\n}\nexport const sliceConfig = {\n    kernelName: Slice,\n    backendName: 'cpu',\n    kernelFunc: slice\n};\n//# sourceMappingURL=Slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const subImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue - bValue));\nexport const subComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal - bReal, imag: aImag - bImag };\n}));\nexport const sub = binaryKernelFunc(Sub, subImpl, subComplexImpl);\nexport const subConfig = {\n    kernelName: Sub,\n    backendName: 'cpu',\n    kernelFunc: sub\n};\n//# sourceMappingURL=Sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function transposeImpl(xVals, xShape, dtype, perm, newShape) {\n    const xRank = xShape.length;\n    const xSize = util.sizeFromShape(xShape);\n    const xStrides = util.computeStrides(xShape);\n    const newStrides = util.computeStrides(newShape);\n    const result = util.getTypedArrayFromDType(dtype, util.sizeFromShape(newShape));\n    for (let i = 0; i < xSize; ++i) {\n        const loc = util.indexToLoc(i, xRank, xStrides);\n        // Permute location.\n        const newLoc = new Array(loc.length);\n        for (let i = 0; i < newLoc.length; i++) {\n            newLoc[i] = loc[perm[i]];\n        }\n        const newIndex = util.locToIndex(newLoc, xRank, newStrides);\n        result[newIndex] = xVals[i];\n    }\n    return result;\n}\n//# sourceMappingURL=Transpose_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer, util } from '@tensorflow/tfjs-core';\nexport function uniqueImpl(values, axis, shape, dtype) {\n    // Normalize and validate axis.\n    const $axis = util.parseAxisParam(axis, shape)[0];\n    // Calculate the new shape that is suitable for extracting data along the\n    // given axis.\n    //\n    // The rank is 3.\n    // The size of the 1st dimension is the size of all the axes < the given axis.\n    // The size of the 2nd dimension is the same as the size of the given axis.\n    // The size of the 3rd dimension is the size of all the axes > the given axis.\n    //\n    // For example, for a 4D tensor with shape=[2, 3, 5, 4] and axis=2, the\n    // newShape would be: [2*3, 5, 4].\n    //\n    // Note that this is not the final output shape. This will be the shape for an\n    // intermediate TensorBuffer (see inputBuffer below) to allow us to extract\n    // values along the given axis. To demonstrate how it works, consider the\n    // following example:\n    //\n    // Input: a 3D tensor, with shape [1, 2, 3]\n    // [\n    //   [\n    //      [1,2,3],\n    //      [4,5,6]\n    //   ]\n    // ]\n    // Axis: 2 (the last axis).\n    // Along axis 2, we expect to extract 3 tensors: [1,4], [2,5], [3,6].\n    //\n    // For this example, newShape would be: [2, 3, 1], where 2 is calculated from\n    // 1*2. The re-shaped data would look like:\n    //\n    // [\n    //   [\n    //     [1], [2], [3]\n    //   ],\n    //   [\n    //     [4], [5], [6]\n    //   ]\n    // ]\n    //\n    // Then, we can construct a 3-level nested loop by the following dimension\n    // order to extract the values along the axis (dimension1):\n    // i: dimension1       // 0,1,2 (newShape[1])\n    //   m: dimension0     // 0,1   (newShape[0])\n    //     n: dimension2   // 0     (newShape[2])\n    //\n    //                       m, i, n\n    //                      ---------\n    // Iteration 0: data at [0, 0, 0] => \"1\"\n    // Iteration 1: data at [1, 0, 0] => \"4\"\n    // We got [1,4].\n    // Iteration 2: data at [0, 1, 0] => \"2\"\n    // Iteration 3: data at [1, 1, 0] => \"5\"\n    // We got [2,5].\n    // Iteration 4: data at [0, 2, 0] => \"3\"\n    // Iteration 5: data at [1, 2, 0] => \"6\"\n    // We got [3,6].\n    const newShape = [1, shape[0], 1];\n    for (let i = 0; i < $axis; i++) {\n        newShape[0] *= shape[i];\n    }\n    newShape[1] = shape[$axis];\n    for (let i = $axis + 1; i < shape.length; i++) {\n        newShape[2] *= shape[i];\n    }\n    // A map from unique elements (their string representations) to their values\n    // in \"indices\" (below).\n    const uniqueElements = {};\n    // The indices of each unique element in the original tensor along the given\n    // axis. It is 1D and has the same size as the given axis.\n    const indices = new Int32Array(shape[$axis]);\n    // Create a buffer so we can easily extract value at a given location.\n    const inputBuffer = new TensorBuffer(newShape, dtype, values);\n    // The indices along the given axis that have unique elements. This is a\n    // de-duped version of \"indices\" above.\n    const uniqueIndices = [];\n    const is1DTensor = newShape[0] === 1 && newShape[2] === 1;\n    for (let i = 0; i < shape[$axis]; i++) {\n        // Extract values along the axis.\n        let element;\n        if (is1DTensor) {\n            // Fast path for 1D tensor input.\n            element = values[i].toString();\n        }\n        else {\n            const axisValues = [];\n            for (let m = 0; m < newShape[0]; m++) {\n                for (let n = 0; n < newShape[2]; n++) {\n                    axisValues.push(inputBuffer.get(m, i, n));\n                }\n            }\n            element = axisValues.join(',');\n        }\n        // Dedup and update various indices.\n        if (uniqueElements[element] !== undefined) {\n            indices[i] = uniqueElements[element];\n        }\n        else {\n            const uniqueIndex = Object.keys(uniqueElements).length;\n            uniqueElements[element] = uniqueIndex;\n            indices[i] = uniqueIndex;\n            uniqueIndices.push(i);\n        }\n    }\n    // Now we know where each of the unique elements are located along the axis\n    // (uniqueIndices). Extract them from input buffer and store them in the\n    // output buffer.\n    const outputTmpShape = newShape.slice();\n    outputTmpShape[1] = Object.keys(uniqueElements).length;\n    const outputBuffer = new TensorBuffer(outputTmpShape, dtype);\n    uniqueIndices.forEach((uniqueElementIndex, i) => {\n        for (let m = 0; m < newShape[0]; m++) {\n            for (let n = 0; n < newShape[2]; n++) {\n                outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);\n            }\n        }\n    });\n    // The output shape can be calculated from the input shape with the size of\n    // the given axis replaced by the number of unique elements along that axis.\n    const outputShape = shape.slice();\n    outputShape[$axis] = outputTmpShape[1];\n    return {\n        outputValues: outputBuffer.values,\n        outputShape,\n        indices,\n    };\n}\n//# sourceMappingURL=Unique_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Shared kernel impls for use in other backends.\nexport { simpleAbsImpl } from './kernels/Abs';\nexport { addImpl } from './kernels/Add';\nexport { ceilImpl } from './kernels/Ceil';\nexport { expImpl } from './kernels/Exp';\nexport { expm1Impl } from './kernels/Expm1';\nexport { floorImpl } from './kernels/Floor';\nexport { logImpl } from './kernels/Log';\nexport { maxImpl } from './kernels/Max_impl';\nexport { multiplyImpl } from './kernels/Multiply';\nexport { rsqrtImpl } from './kernels/Rsqrt';\nexport { sliceImpl } from './kernels/Slice';\nexport { subImpl } from './kernels/Sub';\nexport { transposeImpl } from './kernels/Transpose_impl';\nexport { uniqueImpl } from './kernels/Unique_impl';\n//# sourceMappingURL=shared.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/*\n * base.ts contains all the exports from tfjs-backend-cpu\n * without auto-kernel registration\n */\nimport { registerBackend } from '@tensorflow/tfjs-core';\nimport { MathBackendCPU } from './backend_cpu';\nimport * as shared from './shared';\nexport { MathBackendCPU } from './backend_cpu';\nexport { version as version_cpu } from './version';\nexport { shared };\n// Side effects for default initialization of MathBackendCPU\nregisterBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acos } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const acosKernelFunc = unaryKernelFunc(Acos, (xi) => Math.acos(xi));\nexport const acosConfig = {\n    kernelName: Acos,\n    backendName: 'cpu',\n    kernelFunc: acosKernelFunc,\n};\n//# sourceMappingURL=Acos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acosh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const acoshKernelFunc = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi));\nexport const acoshConfig = {\n    kernelName: Acosh,\n    backendName: 'cpu',\n    kernelFunc: acoshKernelFunc,\n};\n//# sourceMappingURL=Acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asin } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const asinKernelFunc = unaryKernelFunc(Asin, (xi) => Math.asin(xi));\nexport const asinConfig = {\n    kernelName: Asin,\n    backendName: 'cpu',\n    kernelFunc: asinKernelFunc,\n};\n//# sourceMappingURL=Asin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asinh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const asinhKernelFunc = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi));\nexport const asinhConfig = {\n    kernelName: Asinh,\n    backendName: 'cpu',\n    kernelFunc: asinhKernelFunc,\n};\n//# sourceMappingURL=Asinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const atanKernelFunc = unaryKernelFunc(Atan, (xi) => Math.atan(xi));\nexport const atanConfig = {\n    kernelName: Atan,\n    backendName: 'cpu',\n    kernelFunc: atanKernelFunc,\n};\n//# sourceMappingURL=Atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atanh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const atanhKernelFunc = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi));\nexport const atanhConfig = {\n    kernelName: Atanh,\n    backendName: 'cpu',\n    kernelFunc: atanhKernelFunc,\n};\n//# sourceMappingURL=Atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from '@tensorflow/tfjs-core';\nexport function pool(xValues, xShape, dtype, strides, convInfo, poolType) {\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n        Number.POSITIVE_INFINITY);\n    const output = buffer(convInfo.outShape, dtype);\n    const outputVals = output.values;\n    const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];\n    const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];\n    const outputColStrides = convInfo.outShape[3];\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        const outputBatchOffset = b * outputBatchStrides;\n        const inputBatchOffset = b * strides[0];\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const xRCorner = yR * strideHeight - padTop;\n                const xRMin = Math.max(0, xRCorner);\n                const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n                const outputRowOffset = outputBatchOffset + yR * outputRowStrides;\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const xCCorner = yC * strideWidth - padLeft;\n                    const xCMin = Math.max(0, xCCorner);\n                    const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n                    let minMaxValue = initialValue;\n                    let avgValue = 0;\n                    let count = 0;\n                    for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n                        const xROffset = inputBatchOffset + xR * strides[1];\n                        for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                            const xCOffset = xROffset + xC * strides[2];\n                            const pixel = xValues[xCOffset + d];\n                            if ((poolType === 'max' && pixel > minMaxValue)) {\n                                minMaxValue = pixel;\n                            }\n                            else if (poolType === 'avg') {\n                                avgValue += pixel;\n                                count++;\n                            }\n                        }\n                        if (isNaN(minMaxValue)) {\n                            break;\n                        }\n                    }\n                    const outputOffset = outputRowOffset + yC * outputColStrides + d;\n                    outputVals[outputOffset] =\n                        poolType === 'avg' ? avgValue / count : minMaxValue;\n                }\n            }\n        }\n    }\n    return output;\n}\nexport function maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) {\n    const maxPositions = buffer(convInfo.outShape, 'int32');\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const xBuf = buffer(xShape, dtype, xValues);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const xRCorner = yR * strideHeight - padTop;\n                let xRMin = xRCorner;\n                while (xRMin < 0) {\n                    xRMin += dilationHeight;\n                }\n                // const xRMin = Math.max(0, xRCorner);\n                const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const xCCorner = yC * strideWidth - padLeft;\n                    let xCMin = xCCorner;\n                    while (xCMin < 0) {\n                        xCMin += dilationWidth;\n                    }\n                    const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n                    let maxValue = Number.NEGATIVE_INFINITY;\n                    let maxPosition = -1;\n                    for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n                        const wR = xR - xRCorner;\n                        for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                            const wC = xC - xCCorner;\n                            const pixel = xBuf.get(b, xR, xC, d);\n                            if (pixel > maxValue) {\n                                maxValue = pixel;\n                                if (flattenPositions) {\n                                    maxPosition = includeBatchInIndex ?\n                                        ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) *\n                                            convInfo.inChannels +\n                                            d :\n                                        (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;\n                                }\n                                else {\n                                    maxPosition = wR * effectiveFilterWidth + wC;\n                                }\n                            }\n                        }\n                    }\n                    maxPositions.set(maxPosition, b, yR, yC, d);\n                }\n            }\n        }\n    }\n    return maxPositions;\n}\n//# sourceMappingURL=pool_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool, backend_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { pool } from '../utils/pool_utils';\nimport { identity } from './Identity';\nexport function avgPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'avgPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    let res;\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        res = identity({ inputs: { x }, backend });\n    }\n    else {\n        const xValues = backend.data.get(x.dataId).values;\n        const strides = util.computeStrides(x.shape);\n        const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'avg');\n        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);\n    }\n    return res;\n}\nexport const avgPoolConfig = {\n    kernelName: AvgPool,\n    backendName: 'cpu',\n    kernelFunc: avgPool\n};\n//# sourceMappingURL=AvgPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPoolBackprop, backend_util, buffer } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function avgPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input } = inputs;\n    const x = input;\n    assertNotComplex([dy, input], 'avgPoolBackprop');\n    const { filterSize, strides, pad } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = buffer(x.shape, 'float32');\n    const avgMultiplier = 1 / (filterHeight * filterWidth);\n    const dyData = backend.data.get(dy.dataId).values;\n    const dyBuf = buffer(dy.shape, 'float32', dyData);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n                for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n                    // Shader code begins.\n                    const dyRCorner = dxR - padTop;\n                    const dyCCorner = dxC - padLeft;\n                    let dotProd = 0;\n                    for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n                        const dyR = (dyRCorner + wR) / strideHeight;\n                        if (dyR < 0 || dyR >= convInfo.outHeight ||\n                            Math.floor(dyR) !== dyR) {\n                            continue;\n                        }\n                        for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                            const dyC = (dyCCorner + wC) / strideWidth;\n                            if (dyC < 0 || dyC >= convInfo.outWidth ||\n                                Math.floor(dyC) !== dyC) {\n                                continue;\n                            }\n                            const pixel = dyBuf.get(b, dyR, dyC, d);\n                            dotProd += pixel;\n                        }\n                    }\n                    dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const avgPoolBackpropConfig = {\n    kernelName: AvgPoolBackprop,\n    backendName: 'cpu',\n    kernelFunc: avgPoolBackprop\n};\n//# sourceMappingURL=AvgPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedBatchNorm, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function batchNormKernelFunc(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, scale, offset, mean, variance } = inputs;\n    util.assert(mean.shape.length === variance.shape.length, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert(offset == null || mean.shape.length === offset.shape.length, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert(scale == null || mean.shape.length === scale.shape.length, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    assertNotComplex([x, mean, variance, scale, offset], 'batchNorm');\n    let { varianceEpsilon } = attrs;\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const xVals = backend.data.get(x.dataId).values;\n    const mVals = backend.data.get(mean.dataId).values;\n    const varVals = backend.data.get(variance.dataId).values;\n    const sVals = scale ? backend.data.get(scale.dataId).values :\n        new Float32Array([1]);\n    const offVals = offset ?\n        backend.data.get(offset.dataId).values :\n        new Float32Array([0]);\n    const outVals = new Float32Array(xVals.length);\n    const offValsLength = offVals.length;\n    const sValsLength = sVals.length;\n    const varValsLength = varVals.length;\n    const mValsLength = mVals.length;\n    let offi = 0;\n    let mi = 0;\n    let si = 0;\n    let vi = 0;\n    for (let i = 0; i < xVals.length; ++i) {\n        outVals[i] = offVals[offi++] +\n            (xVals[i] - mVals[mi++]) * sVals[si++] /\n                Math.sqrt(varVals[vi++] + varianceEpsilon);\n        if (offi >= offValsLength) {\n            offi = 0;\n        }\n        if (mi >= mValsLength) {\n            mi = 0;\n        }\n        if (si >= sValsLength) {\n            si = 0;\n        }\n        if (vi >= varValsLength) {\n            vi = 0;\n        }\n    }\n    return backend.makeTensorInfo(x.shape, x.dtype, outVals);\n}\nexport const batchNormConfig = {\n    kernelName: FusedBatchNorm,\n    backendName: 'cpu',\n    kernelFunc: batchNormKernelFunc,\n};\n//# sourceMappingURL=BatchNorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ClipByValue } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const clipKernelFunc = unaryKernelFunc(ClipByValue, (xi, attrs) => {\n    const clipAttrs = attrs;\n    if (xi > clipAttrs.clipValueMax) {\n        return clipAttrs.clipValueMax;\n    }\n    return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;\n});\nexport const clipConfig = {\n    kernelName: ClipByValue,\n    backendName: 'cpu',\n    kernelFunc: clipKernelFunc,\n};\n//# sourceMappingURL=Clip.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Imag } from '@tensorflow/tfjs-core';\nexport function imag(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const imag = backend.data.get(input.dataId).complexTensorInfos.imag;\n    const imagVal = backend.data.get(imag.dataId).values;\n    // When complex tensor is disposed, its underlying parts will be disposed too.\n    // Make new tensor out of the imag value of the complex. This makes sure the\n    // value is still accessible even if complex tensor is disposed.\n    return backend.makeTensorInfo(imag.shape, imag.dtype, imagVal);\n}\nexport const imagConfig = {\n    kernelName: Imag,\n    backendName: 'cpu',\n    kernelFunc: imag\n};\n//# sourceMappingURL=Imag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape, util } from '@tensorflow/tfjs-core';\nexport function reshape(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { shape } = attrs;\n    const xSize = util.sizeFromShape(x.shape);\n    const $shape = util.inferFromImplicitShape(shape, xSize);\n    const $xSize = util.sizeFromShape($shape);\n    util.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n        `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n        `shape must have the same number of elements.`);\n    backend.incRef(x.dataId);\n    const xData = backend.data.get(x.dataId);\n    if (xData.complexTensorInfos != null) {\n        const real = xData.complexTensorInfos.real;\n        const imag = xData.complexTensorInfos.imag;\n        real.shape = $shape;\n        imag.shape = $shape;\n    }\n    return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nexport const reshapeConfig = {\n    kernelName: Reshape,\n    backendName: 'cpu',\n    kernelFunc: reshape\n};\n//# sourceMappingURL=Reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Concat, util } from '@tensorflow/tfjs-core';\nimport { complex } from './Complex';\nimport { imag } from './Imag';\nimport { real } from './Real';\nimport { reshape } from './Reshape';\nexport function concat(args) {\n    const { inputs, backend, attrs } = args;\n    const { axis } = attrs;\n    const $axis = util.parseAxisParam(axis, inputs[0].shape)[0];\n    let outShape = backend_util.computeOutShape(inputs.map(t => t.shape), $axis);\n    if (util.sizeFromShape(outShape) === 0) {\n        return backend.makeTensorInfo(outShape, inputs[0].dtype, []);\n    }\n    // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n    const $inputs = inputs.filter(t => util.sizeFromShape(t.shape) > 0);\n    if ($inputs.length === 1) {\n        return $inputs[0];\n    }\n    const shapes = $inputs.map(t => t.shape);\n    backend_util.assertParamsConsistent(shapes, $axis);\n    if ($inputs[0].dtype === 'complex64') {\n        const reals = $inputs.map((t) => real({ inputs: { input: t }, backend }));\n        const imags = $inputs.map((t) => imag({ inputs: { input: t }, backend }));\n        const realConcated = concat({ inputs: reals, backend, attrs: { axis } });\n        const imagConcated = concat({ inputs: imags, backend, attrs: { axis } });\n        const result = complex({ inputs: { real: realConcated, imag: imagConcated }, backend });\n        reals.forEach(r => backend.disposeIntermediateTensorInfo(r));\n        imags.forEach(i => backend.disposeIntermediateTensorInfo(i));\n        backend.disposeIntermediateTensorInfo(realConcated);\n        backend.disposeIntermediateTensorInfo(imagConcated);\n        return result;\n    }\n    // Any concat of n-dimensional tensors across any axis can be reduced to\n    // a concatenation of two-dimensional tensors across the axis 1 by first\n    // partitioning the axes of the original tensors into those less than the\n    // axis to be concatenated and the rest. Then reshape the tensors\n    // into a two-dimensional tensor by collapsing these two sets of axes and\n    // concatenate the resulting matrices across the axis 1, finally reshaping\n    // the result to have the proper shape.\n    const inputs2D = $inputs.map(t => {\n        const innerSize = util.sizeFromShape(t.shape.slice($axis));\n        const shape = [-1, innerSize];\n        return reshape({ inputs: { x: t }, backend, attrs: { shape } });\n    });\n    // Concats 2d tensors along axis=1.\n    outShape =\n        backend_util.computeOutShape(inputs2D.map(t => t.shape), 1 /* axis */);\n    const outVals = util.getTypedArrayFromDType($inputs[0].dtype, util.sizeFromShape(outShape));\n    if (inputs2D[0].shape[0] === 1) {\n        // Use built-in TypedArray.set() method for speed.\n        let offset = 0;\n        inputs2D.forEach(t => {\n            const val = backend.data.get(t.dataId).values;\n            const size = util.sizeFromShape(t.shape);\n            outVals.set(val, offset);\n            offset += size;\n        });\n    }\n    else {\n        let colOffset = 0;\n        inputs2D.forEach(t => {\n            const tVals = backend.data.get(t.dataId).values;\n            let tIdx = 0;\n            for (let row = 0; row < t.shape[0]; ++row) {\n                const resIdx = row * outShape[1] + colOffset;\n                for (let col = 0; col < t.shape[1]; ++col) {\n                    outVals[resIdx + col] = tVals[tIdx++];\n                }\n            }\n            colOffset += t.shape[1];\n        });\n    }\n    const finalOutShape = backend_util.computeOutShape($inputs.map(t => t.shape), $axis);\n    const outInfo = backend.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);\n    inputs2D.forEach(t => backend.disposeIntermediateTensorInfo(t));\n    return outInfo;\n}\nexport const concatConfig = {\n    kernelName: Concat,\n    backendName: 'cpu',\n    kernelFunc: concat\n};\n//# sourceMappingURL=Concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const cosKernelFunc = unaryKernelFunc(Cos, (xi) => Math.cos(xi));\nexport const cosConfig = {\n    kernelName: Cos,\n    backendName: 'cpu',\n    kernelFunc: cosKernelFunc,\n};\n//# sourceMappingURL=Cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cosh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const coshKernelFunc = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi));\nexport const coshConfig = {\n    kernelName: Cosh,\n    backendName: 'cpu',\n    kernelFunc: coshKernelFunc,\n};\n//# sourceMappingURL=Cosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2D, util } from '@tensorflow/tfjs-core';\nexport const dilation2dConfig = {\n    kernelName: Dilation2D,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const xVals = cpuBackend.data.get(x.dataId).values;\n        const xRank = x.shape.length;\n        const filterVals = cpuBackend.data.get(filter.dataId).values;\n        const filterRank = filter.shape.length;\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        const outSize = util.sizeFromShape(outShape);\n        const outRank = outShape.length;\n        const outputVals = util.getArrayFromDType(x.dtype, outSize);\n        // Upsampling the input by fill in `dilation size - 1` values between each\n        // input value.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const xIndex = util.locToIndex([b, hIn, wIn, d], xRank, util.computeStrides(x.shape));\n                                        const filterIndex = util.locToIndex([h, w, d], filterRank, util.computeStrides(filter.shape));\n                                        const val = xVals[xIndex] + filterVals[filterIndex];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        const outputIndex = util.locToIndex([b, hOut, wOut, d], outRank, util.computeStrides(outShape));\n                        outputVals[outputIndex] = curVal;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(outputVals, x.dtype), outShape, x.dtype);\n        return { dataId, shape: outShape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2DBackpropFilter, util } from '@tensorflow/tfjs-core';\nexport const dilation2dBackpropFilterConfig = {\n    kernelName: Dilation2DBackpropFilter,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter, dy } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const $x = util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n        const $filter = util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        util.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy ` +\n            `must have the same rank as output ${outShape.length}, but got ` +\n            `${dy.rank}`);\n        const $dy = util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n        // The computed filter gradients has the same dimensions as the filter:\n        // [filterHeight, filterWidth, depth]\n        const gradients = util.makeZerosNestedTypedArray(filter.shape, filter.dtype);\n        // In the case of multiple argmax branches, we only back-propagate along the\n        // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n        // similarly to the max-pooling backward routines.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        let hMax = 0;\n                        let wMax = 0;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                            hMax = h;\n                                            wMax = w;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);\n        return { dataId, shape: filter.shape, dtype: filter.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2DBackpropFilter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2DBackpropInput, util } from '@tensorflow/tfjs-core';\nexport const dilation2dBackpropInputConfig = {\n    kernelName: Dilation2DBackpropInput,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter, dy } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const $x = util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n        const $filter = util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        util.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy ` +\n            `must have the same rank as output ${outShape.length}, but got ` +\n            `${dy.rank}`);\n        const $dy = util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n        // The computed gradients has the same dimensions as the input:\n        // [batch, inputHeight, inputCols, inChannel]\n        const gradients = util.makeZerosNestedTypedArray(x.shape, x.dtype);\n        // In the case of multiple argmax branches, we only back-propagate along the\n        // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n        // similarly to the max-pooling backward routines.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        let hInMax = (hBeg < 0) ? 0 : hBeg;\n                        let wInMax = (wBeg < 0) ? 0 : wBeg;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                            hInMax = hIn;\n                                            wInMax = wIn;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(gradients, x.dtype), x.shape, x.dtype);\n        return { dataId, shape: x.shape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2DBackpropInput.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const divImpl = createSimpleBinaryKernelImpl((a, b) => a / b);\nexport const div = binaryKernelFunc(Div, divImpl);\nexport const divConfig = {\n    kernelName: Div,\n    backendName: 'cpu',\n    kernelFunc: div\n};\n//# sourceMappingURL=Div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Elu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const eluKernelFunc = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : (Math.exp(xi) - 1));\nexport const eluConfig = {\n    kernelName: Elu,\n    backendName: 'cpu',\n    kernelFunc: eluKernelFunc,\n};\n//# sourceMappingURL=Elu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Erf } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nconst p = backend_util.ERF_P;\nconst a1 = backend_util.ERF_A1;\nconst a2 = backend_util.ERF_A2;\nconst a3 = backend_util.ERF_A3;\nconst a4 = backend_util.ERF_A4;\nconst a5 = backend_util.ERF_A5;\nexport const erfKernelFunc = unaryKernelFunc(Erf, (xi) => {\n    const sign = Math.sign(xi);\n    const v = Math.abs(xi);\n    const t = 1.0 / (1.0 + p * v);\n    return sign *\n        (1.0 -\n            (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t *\n                Math.exp(-v * v));\n});\nexport const erfConfig = {\n    kernelName: Erf,\n    backendName: 'cpu',\n    kernelFunc: erfKernelFunc,\n};\n//# sourceMappingURL=Erf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { add } from '../kernels/Add';\nimport { complex } from '../kernels/Complex';\nimport { concat } from '../kernels/Concat';\nimport { divConfig } from '../kernels/Div';\nimport { identity } from '../kernels/Identity';\nimport { imag } from '../kernels/Imag';\nimport { multiply } from '../kernels/Multiply';\nimport { real } from '../kernels/Real';\nimport { slice } from '../kernels/Slice';\nimport { sub } from '../kernels/Sub';\n/**\n * Calculate FFT of inner most elements of batch tensor.\n */\nexport function fftBatch(input, inverse, cpuBackend) {\n    const inputShape = input.shape;\n    const batch = inputShape[0];\n    const innerDim = inputShape[1];\n    const inputVals = cpuBackend.data.get(input.dataId);\n    const real2D = inputVals.complexTensorInfos.real;\n    const imag2D = inputVals.complexTensorInfos.imag;\n    // Collects real and imaginary values separately.\n    const resultShape = [batch, innerDim];\n    const resultSize = util.sizeFromShape(resultShape);\n    const resultReal = util.getTypedArrayFromDType('float32', resultSize);\n    const resultImag = util.getTypedArrayFromDType('float32', resultSize);\n    for (let b = 0; b < batch; b++) {\n        // TODO: Support slice ops for complex type.\n        const r = slice({\n            inputs: { x: real2D },\n            backend: cpuBackend,\n            attrs: { begin: [b, 0], size: [1, innerDim] }\n        });\n        const i = slice({\n            inputs: { x: imag2D },\n            backend: cpuBackend,\n            attrs: { begin: [b, 0], size: [1, innerDim] }\n        });\n        const input = complex({ inputs: { real: r, imag: i }, backend: cpuBackend });\n        // Run FFT by batch element.\n        const { real, imag } = fftImpl(input, inverse, cpuBackend);\n        const res = backend_util.mergeRealAndImagArrays(real, imag);\n        for (let d = 0; d < innerDim; d++) {\n            const c = backend_util.getComplexWithIndex(res, d);\n            resultReal[b * innerDim + d] = c.real;\n            resultImag[b * innerDim + d] = c.imag;\n        }\n        cpuBackend.disposeIntermediateTensorInfo(r);\n        cpuBackend.disposeIntermediateTensorInfo(i);\n        cpuBackend.disposeIntermediateTensorInfo(input);\n    }\n    const $realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultReal);\n    const $imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImag);\n    const result = complex({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend });\n    cpuBackend.disposeIntermediateTensorInfo($realInfo);\n    cpuBackend.disposeIntermediateTensorInfo($imagInfo);\n    return result;\n}\nexport function fftImpl(input, inverse, cpuBackend) {\n    const inputSize = util.sizeFromShape(input.shape);\n    const inputVals = cpuBackend.data.get(input.dataId);\n    const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values;\n    const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;\n    if (isExponentOf2(inputSize)) {\n        const result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);\n        const resultShape = [input.shape[0], input.shape[1]];\n        if (inverse) {\n            const realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.real);\n            const imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.imag);\n            const sizeInfo = cpuBackend.makeTensorInfo([], 'float32', util.createScalarValue(inputSize, 'float32'));\n            const sizeInfoCopy = identity({ inputs: { x: sizeInfo }, backend: cpuBackend });\n            const divRealInfo = divConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend });\n            const divImagInfo = divConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend });\n            const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values;\n            const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values;\n            cpuBackend.disposeIntermediateTensorInfo(realInfo);\n            cpuBackend.disposeIntermediateTensorInfo(imagInfo);\n            cpuBackend.disposeIntermediateTensorInfo(sizeInfo);\n            cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);\n            cpuBackend.disposeIntermediateTensorInfo(divRealInfo);\n            cpuBackend.disposeIntermediateTensorInfo(divImagInfo);\n            return { real: divRealVals, imag: divImagVals };\n        }\n        return result;\n    }\n    else {\n        const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n        const rawOutput = fourierTransformByMatmul(data, inputSize, inverse);\n        return backend_util.splitRealAndImagArrays(rawOutput);\n    }\n}\nfunction isExponentOf2(size) {\n    return (size & size - 1) === 0;\n}\n// FFT using Cooley-Tukey algorithm on radix 2 dimensional input.\nfunction fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {\n    if (size === 1) {\n        return { real: realVals, imag: imagVals };\n    }\n    const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n    const half = size / 2;\n    const evenComplex = backend_util.complexWithEvenIndex(data);\n    const evenRealVals = evenComplex.real;\n    const evenImagVals = evenComplex.imag;\n    const evenShape = [evenRealVals.length];\n    const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenRealVals);\n    const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenImagVals);\n    const evenTensorInfo = complex({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend });\n    const oddComplex = backend_util.complexWithOddIndex(data);\n    const oddRealVals = oddComplex.real;\n    const oddImagVals = oddComplex.imag;\n    const oddShape = [oddRealVals.length];\n    const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddRealVals);\n    const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddImagVals);\n    const oddTensorInfo = complex({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend });\n    // Recursive call for half part of original input.\n    const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);\n    const $evenRealVals = $evenComplex.real;\n    const $evenImagVals = $evenComplex.imag;\n    const $evenShape = [$evenRealVals.length];\n    const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenRealVals);\n    const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenImagVals);\n    const $evenTensorInfo = complex({\n        inputs: { real: $evenRealInfo, imag: $evenImagInfo },\n        backend: cpuBackend\n    });\n    const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);\n    const $oddRealVals = $oddComplex.real;\n    const $oddImagVals = $oddComplex.imag;\n    const $oddShape = [$oddRealVals.length];\n    const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddRealVals);\n    const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddImagVals);\n    const $oddTensorInfo = complex({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend });\n    const e = backend_util.exponents(size, inverse);\n    const eShape = [e.real.length];\n    const eRealInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.real);\n    const eImagInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.imag);\n    const complexInfo = complex({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend });\n    const exponentInfo = multiply({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend });\n    const addPart = add({\n        inputs: { a: $evenTensorInfo, b: exponentInfo },\n        backend: cpuBackend\n    });\n    const subPart = sub({\n        inputs: { a: $evenTensorInfo, b: exponentInfo },\n        backend: cpuBackend\n    });\n    const addPartReal = real({ inputs: { input: addPart }, backend: cpuBackend });\n    const subPartReal = real({ inputs: { input: subPart }, backend: cpuBackend });\n    const addPartImag = imag({ inputs: { input: addPart }, backend: cpuBackend });\n    const subPartImag = imag({ inputs: { input: subPart }, backend: cpuBackend });\n    const $real = concat({\n        inputs: [addPartReal, subPartReal],\n        backend: cpuBackend,\n        attrs: { axis: 0 }\n    });\n    const $imag = concat({\n        inputs: [addPartImag, subPartImag],\n        backend: cpuBackend,\n        attrs: { axis: 0 }\n    });\n    const $realVals = cpuBackend.data.get($real.dataId).values;\n    const $imagVals = cpuBackend.data.get($imag.dataId).values;\n    cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo(eRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(eImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(complexInfo);\n    cpuBackend.disposeIntermediateTensorInfo(exponentInfo);\n    cpuBackend.disposeIntermediateTensorInfo(addPart);\n    cpuBackend.disposeIntermediateTensorInfo(subPart);\n    cpuBackend.disposeIntermediateTensorInfo(addPartReal);\n    cpuBackend.disposeIntermediateTensorInfo(addPartImag);\n    cpuBackend.disposeIntermediateTensorInfo(subPartReal);\n    cpuBackend.disposeIntermediateTensorInfo(subPartImag);\n    cpuBackend.disposeIntermediateTensorInfo($real);\n    cpuBackend.disposeIntermediateTensorInfo($imag);\n    return { real: $realVals, imag: $imagVals };\n}\n// Calculate fourier transform by multplying sinusoid matrix.\nfunction fourierTransformByMatmul(data, size, inverse) {\n    const ret = new Float32Array(size * 2);\n    // TODO: Use matmul instead once it supports complex64 type.\n    for (let r = 0; r < size; r++) {\n        let real = 0.0;\n        let imag = 0.0;\n        for (let c = 0; c < size; c++) {\n            const e = backend_util.exponent(r * c, size, inverse);\n            const term = backend_util.getComplexWithIndex(data, c);\n            real += term.real * e.real - term.imag * e.imag;\n            imag += term.real * e.imag + term.imag * e.real;\n        }\n        if (inverse) {\n            real /= size;\n            imag /= size;\n        }\n        backend_util.assignToTypedArray(ret, real, imag, r);\n    }\n    return ret;\n}\n//# sourceMappingURL=fft_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FFT, util } from '@tensorflow/tfjs-core';\nimport { fftBatch } from '../utils/fft_utils';\nimport { reshape } from './Reshape';\nexport function fft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputSize = util.sizeFromShape(input.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({\n        inputs: { x: input },\n        backend,\n        attrs: { shape: [batch, innerDimensionSize] }\n    });\n    const result = fftBatch(input2D, false, backend);\n    const resultReshaped = reshape({ inputs: { x: result }, backend, attrs: { shape: input.shape } });\n    backend.disposeIntermediateTensorInfo(input2D);\n    backend.disposeIntermediateTensorInfo(result);\n    return resultReshaped;\n}\nexport const fftConfig = {\n    kernelName: FFT,\n    backendName: 'cpu',\n    kernelFunc: fft\n};\n//# sourceMappingURL=FFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FlipLeftRight, util } from '@tensorflow/tfjs-core';\nexport const flipLeftRightConfig = {\n    kernelName: FlipLeftRight,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const cpuBackend = backend;\n        const output = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(image.shape));\n        const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n        const imageVals = cpuBackend.data.get(image.dataId).values;\n        for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n            for (let row = 0; row < imageHeight; row++) {\n                const rowOffset = row * (imageWidth * numChannels);\n                for (let col = 0; col < imageWidth; col++) {\n                    const colOffset = col * numChannels;\n                    for (let channel = 0; channel < numChannels; channel++) {\n                        const coords = [batch, row, col, channel];\n                        const x = coords[2];\n                        const coordX = Math.round(imageWidth - x);\n                        const outIdx = batchOffset + rowOffset + colOffset + channel;\n                        let outputValue = imageVals[outIdx];\n                        // If the coordinate position falls within the image boundaries...\n                        if (coordX >= 0 && coordX < imageWidth) {\n                            // set the output to the image value at the coordinate position.\n                            const rotatedColOffset = coordX * numChannels;\n                            const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel;\n                            outputValue = imageVals[imageIdx];\n                        }\n                        output[outIdx] = outputValue;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(output, image.shape, image.dtype);\n        return { dataId, shape: image.shape, dtype: image.dtype };\n    }\n};\n//# sourceMappingURL=FlipLeftRight.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IFFT, util } from '@tensorflow/tfjs-core';\nimport { fftBatch } from '../utils/fft_utils';\nimport { reshape } from './Reshape';\nexport function ifft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputSize = util.sizeFromShape(input.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({\n        inputs: { x: input },\n        backend,\n        attrs: { shape: [batch, innerDimensionSize] }\n    });\n    const result = fftBatch(input2D, true, backend);\n    const resultReshaped = reshape({ inputs: { x: result }, backend, attrs: { shape: input.shape } });\n    backend.disposeIntermediateTensorInfo(input2D);\n    backend.disposeIntermediateTensorInfo(result);\n    return resultReshaped;\n}\nexport const ifftConfig = {\n    kernelName: IFFT,\n    backendName: 'cpu',\n    kernelFunc: ifft\n};\n//# sourceMappingURL=IFFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsFinite } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isFiniteKernelFunc = unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, 'bool');\nexport const isFiniteConfig = {\n    kernelName: IsFinite,\n    backendName: 'cpu',\n    kernelFunc: isFiniteKernelFunc,\n};\n//# sourceMappingURL=IsFinite.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsInf } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isInfKernelFunc = unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, 'bool');\nexport const isInfConfig = {\n    kernelName: IsInf,\n    backendName: 'cpu',\n    kernelFunc: isInfKernelFunc,\n};\n//# sourceMappingURL=IsInf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsNan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isNaNKernelFunc = unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, 'bool');\nexport const isNaNConfig = {\n    kernelName: IsNan,\n    backendName: 'cpu',\n    kernelFunc: isNaNKernelFunc,\n};\n//# sourceMappingURL=IsNaN.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log1p } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const log1pKernelFunc = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi));\nexport const log1pConfig = {\n    kernelName: Log1p,\n    backendName: 'cpu',\n    kernelFunc: log1pKernelFunc,\n};\n//# sourceMappingURL=Log1p.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LogicalNot } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const logicalNotKernelFunc = unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, 'bool');\nexport const logicalNotConfig = {\n    kernelName: LogicalNot,\n    backendName: 'cpu',\n    kernelFunc: logicalNotKernelFunc,\n};\n//# sourceMappingURL=LogicalNot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '@tensorflow/tfjs-core';\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxImpl } from './Max_impl';\nimport { transposeImpl } from './Transpose_impl';\nexport const maxConfig = {\n    kernelName: Max,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { reductionIndices, keepDims } = attrs;\n        const cpuBackend = backend;\n        let xShape = x.shape;\n        const xRank = xShape.length;\n        const origAxes = util.parseAxisParam(reductionIndices, xShape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        let xVals = cpuBackend.data.get(x.dataId).values;\n        if (permutedAxes != null) {\n            const newShape = new Array(xRank);\n            for (let i = 0; i < newShape.length; i++) {\n                newShape[i] = xShape[permutedAxes[i]];\n            }\n            xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n            xShape = newShape;\n        }\n        assertNotComplex(x, 'max');\n        backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n        const [maxOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(xShape, axes);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);\n        const dataId = cpuBackend.write(result, maxOutShape, x.dtype);\n        let outShape = maxOutShape;\n        if (keepDims) {\n            // reshape\n            const newShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n            outShape = newShape;\n        }\n        return { dataId, shape: outShape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPool, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { pool } from '../utils/pool_utils';\nimport { identity } from './Identity';\nexport function maxPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'maxPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    let res;\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        res = identity({ inputs: { x }, backend });\n    }\n    else {\n        const xValues = backend.data.get(x.dataId).values;\n        const strides = util.computeStrides(x.shape);\n        const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'max');\n        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);\n    }\n    return res;\n}\nexport const maxPoolConfig = {\n    kernelName: MaxPool,\n    backendName: 'cpu',\n    kernelFunc: maxPool\n};\n//# sourceMappingURL=MaxPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, buffer, MaxPoolBackprop } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxPoolPositions } from '../utils/pool_utils';\nexport function maxPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input, output } = inputs;\n    const x = input;\n    assertNotComplex([input, output], 'maxPoolBackprop');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n    const xValues = backend.data.get(x.dataId).values;\n    const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = buffer(x.shape, 'float32');\n    const dyData = backend.data.get(dy.dataId).values;\n    const dyBuf = buffer(dy.shape, 'float32', dyData);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n                for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n                    // Shader code begins.\n                    const dyRCorner = dxR - padTop;\n                    const dyCCorner = dxC - padLeft;\n                    let dotProd = 0;\n                    for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n                        const dyR = (dyRCorner + wR) / strideHeight;\n                        if (dyR < 0 || dyR >= convInfo.outHeight ||\n                            Math.floor(dyR) !== dyR) {\n                            continue;\n                        }\n                        for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                            const dyC = (dyCCorner + wC) / strideWidth;\n                            if (dyC < 0 || dyC >= convInfo.outWidth ||\n                                Math.floor(dyC) !== dyC) {\n                                continue;\n                            }\n                            const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 -\n                                maxPosBuf.get(b, dyR, dyC, d);\n                            const curPos = wR * effectiveFilterWidth + wC;\n                            const mask = maxPos === curPos ? 1 : 0;\n                            if (mask === 0) {\n                                continue;\n                            }\n                            const pixel = dyBuf.get(b, dyR, dyC, d);\n                            dotProd += pixel * mask;\n                        }\n                    }\n                    dx.set(dotProd, b, dxR, dxC, d);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const maxPoolBackpropConfig = {\n    kernelName: MaxPoolBackprop,\n    backendName: 'cpu',\n    kernelFunc: maxPoolBackprop\n};\n//# sourceMappingURL=MaxPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { maxPoolPositions, pool } from '../utils/pool_utils';\nexport function maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) {\n    const strides = util.computeStrides(xShape);\n    const maxPools = pool(xValues, xShape, dtype, strides, convInfo, 'max');\n    const maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex);\n    return [maxPools.values, maxPositions.values];\n}\n//# sourceMappingURL=MaxPoolWithArgmax_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPoolWithArgmax } from '@tensorflow/tfjs-core';\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxPoolWithArgmaxImpl } from './MaxPoolWithArgmax_impl';\nexport const maxPoolWithArgmaxConfig = {\n    kernelName: MaxPoolWithArgmax,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { filterSize, strides, pad, includeBatchInIndex } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(x, 'MaxPoolWithArgmax');\n        const values = cpuBackend.data.get(x.dataId).values;\n        const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad);\n        const [pooled, indexes] = maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo);\n        const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype);\n        const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype);\n        return [\n            { dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype },\n            { dataId: indexesDataId, shape: convInfo.outShape, dtype: 'int32' }\n        ];\n    }\n};\n//# sourceMappingURL=MaxPoolWithArgmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NonMaxSuppressionV4 } from '@tensorflow/tfjs-core';\nimport { kernel_impls } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\nimport { assertNotComplex } from '../cpu_util';\nexport const nonMaxSuppressionV4Config = {\n    kernelName: NonMaxSuppressionV4,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(boxes, 'NonMaxSuppressionPadded');\n        const boxesVals = cpuBackend.data.get(boxes.dataId).values;\n        const scoresVals = cpuBackend.data.get(scores.dataId).values;\n        const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n        return [selectedIndices, validOutputs];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV4.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NonMaxSuppressionV5 } from '@tensorflow/tfjs-core';\nimport { kernel_impls } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nimport { assertNotComplex } from '../cpu_util';\nexport const nonMaxSuppressionV5Config = {\n    kernelName: NonMaxSuppressionV5,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(boxes, 'NonMaxSuppressionWithScore');\n        const boxesVals = cpuBackend.data.get(boxes.dataId).values;\n        const scoresVals = cpuBackend.data.get(scores.dataId).values;\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        const softNmsSigmaVal = softNmsSigma;\n        const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n        return [selectedIndices, selectedScores];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV5.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NotEqual } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const notEqualImpl = createSimpleBinaryKernelImpl(((a, b) => (a !== b) ? 1 : 0));\nexport const notEqual = binaryKernelFunc(NotEqual, notEqualImpl, null /* complexOp */, 'bool');\nexport const notEqualConfig = {\n    kernelName: NotEqual,\n    backendName: 'cpu',\n    kernelFunc: notEqual\n};\n//# sourceMappingURL=NotEqual.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { PadV2, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function padV2(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { paddings, constantValue } = attrs;\n    assertNotComplex(x, 'pad');\n    const outShape = paddings.map((p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);\n    const start = paddings.map(p => p[0]);\n    const xVals = backend.data.get(x.dataId).values;\n    const xSize = util.sizeFromShape(x.shape);\n    const xRank = x.shape.length;\n    const xStrides = util.computeStrides(x.shape);\n    const resultSize = util.sizeFromShape(outShape);\n    const resultRank = outShape.length;\n    const resultStrides = util.computeStrides(outShape);\n    const resVals = util.getTypedArrayFromDType(x.dtype, resultSize);\n    if (constantValue !== 0) {\n        resVals.fill(constantValue);\n    }\n    for (let i = 0; i < xSize; i++) {\n        const coords = util.indexToLoc(i, xRank, xStrides);\n        const outCoords = coords.map((c, i) => c + start[i]);\n        const outIndex = util.locToIndex(outCoords, resultRank, resultStrides);\n        resVals[outIndex] = xVals[i];\n    }\n    const outId = backend.write(resVals, outShape, x.dtype);\n    return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nexport const padV2Config = {\n    kernelName: PadV2,\n    backendName: 'cpu',\n    kernelFunc: padV2\n};\n//# sourceMappingURL=PadV2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reciprocal } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const reciprocalKernelFunc = unaryKernelFunc(Reciprocal, (xi) => 1 / xi);\nexport const reciprocalConfig = {\n    kernelName: Reciprocal,\n    backendName: 'cpu',\n    kernelFunc: reciprocalKernelFunc,\n};\n//# sourceMappingURL=Reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, RotateWithOffset, util } from '@tensorflow/tfjs-core';\nexport const rotateWithOffsetConfig = {\n    kernelName: RotateWithOffset,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const { radians, fillValue, center } = attrs;\n        const cpuBackend = backend;\n        const output = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(image.shape));\n        const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n        const [centerX, centerY] = backend_util.getImageCenter(center, imageHeight, imageWidth);\n        const fullOpacityValue = 255;\n        const sinFactor = Math.sin(radians);\n        const cosFactor = Math.cos(radians);\n        const imageVals = cpuBackend.data.get(image.dataId).values;\n        for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n            for (let row = 0; row < imageHeight; row++) {\n                const rowOffset = row * (imageWidth * numChannels);\n                for (let col = 0; col < imageWidth; col++) {\n                    const colOffset = col * numChannels;\n                    for (let channel = 0; channel < numChannels; channel++) {\n                        const coords = [batch, row, col, channel];\n                        const x = coords[2];\n                        const y = coords[1];\n                        // coordX/coordY are the result of rotating and translating x/y.\n                        let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;\n                        let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;\n                        coordX = Math.round(coordX + centerX);\n                        coordY = Math.round(coordY + centerY);\n                        let outputValue = fillValue;\n                        if (typeof fillValue !== 'number') {\n                            if (channel === 3) {\n                                outputValue = fullOpacityValue;\n                            }\n                            else {\n                                outputValue = fillValue[channel];\n                            }\n                        }\n                        // If the coordinate position falls within the image boundaries...\n                        if (coordX >= 0 && coordX < imageWidth && coordY >= 0 &&\n                            coordY < imageHeight) {\n                            // set the output to the image value at the coordinate position.\n                            const rotatedRowOffset = coordY * (imageWidth * numChannels);\n                            const rotatedColOffset = coordX * numChannels;\n                            const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;\n                            outputValue = imageVals[imageIdx];\n                        }\n                        const outIdx = batchOffset + rowOffset + colOffset + channel;\n                        output[outIdx] = outputValue;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(output, image.shape, image.dtype);\n        return { dataId, shape: image.shape, dtype: image.dtype };\n    }\n};\n//# sourceMappingURL=RotateWithOffset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Round } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const roundKernelFunc = unaryKernelFunc(Round, (xi) => {\n    // The algorithm is based on banker's rounding.\n    const base = Math.floor(xi);\n    if (xi - base < 0.5) {\n        return Math.floor(xi);\n    }\n    else if (xi - base > 0.5) {\n        return Math.ceil(xi);\n    }\n    else {\n        if (base % 2.0 === 0.0) {\n            return base;\n        }\n        else {\n            return base + 1.0;\n        }\n    }\n});\nexport const roundConfig = {\n    kernelName: Round,\n    backendName: 'cpu',\n    kernelFunc: roundKernelFunc,\n};\n//# sourceMappingURL=Round.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Selu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nconst scaleAlpha = backend_util.SELU_SCALEALPHA;\nconst scale = backend_util.SELU_SCALE;\nexport const seluKernelFunc = unaryKernelFunc(Selu, (xi) => {\n    if (xi >= 0) {\n        return scale * xi;\n    }\n    else {\n        return scaleAlpha * (Math.exp(xi) - 1);\n    }\n});\nexport const seluConfig = {\n    kernelName: Selu,\n    backendName: 'cpu',\n    kernelFunc: seluKernelFunc,\n};\n//# sourceMappingURL=Selu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sigmoid } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sigmoidKernelFunc = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));\nexport const sigmoidConfig = {\n    kernelName: Sigmoid,\n    backendName: 'cpu',\n    kernelFunc: sigmoidKernelFunc,\n};\n//# sourceMappingURL=Sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sign } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const signKernelFunc = unaryKernelFunc(Sign, (xi) => {\n    if (xi < 0) {\n        return -1;\n    }\n    else if (xi > 0) {\n        return 1;\n    }\n    else {\n        return 0;\n    }\n});\nexport const signConfig = {\n    kernelName: Sign,\n    backendName: 'cpu',\n    kernelFunc: signKernelFunc,\n};\n//# sourceMappingURL=Sign.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sinKernelFunc = unaryKernelFunc(Sin, (xi) => Math.sin(xi));\nexport const sinConfig = {\n    kernelName: Sin,\n    backendName: 'cpu',\n    kernelFunc: sinKernelFunc,\n};\n//# sourceMappingURL=Sin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sinh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sinhKernelFunc = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi));\nexport const sinhConfig = {\n    kernelName: Sinh,\n    backendName: 'cpu',\n    kernelFunc: sinhKernelFunc,\n};\n//# sourceMappingURL=Sinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softplus } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\n// mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n// epsilon is the difference between 1.0 and the next representable float.\n// For a single precision 32 bit float this should be 2^-23, see:\n// https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\nconst epsilon = 1.1920928955078125e-7;\nconst threshold = Math.log(epsilon) + 2.0;\nexport const softplusKernelFunc = unaryKernelFunc(Softplus, (xi) => {\n    // Value above which exp(x) may overflow, but softplus(x) == x\n    // is within machine epsilon.\n    const tooLarge = xi > -threshold;\n    // Value below which exp(x) may underflow, but softplus(x) == exp(x)\n    // is within machine epsilon.\n    const tooSmall = xi < threshold;\n    const expX = Math.exp(xi);\n    let result;\n    if (tooSmall) {\n        result = expX;\n    }\n    else if (tooLarge) {\n        result = xi;\n    }\n    else {\n        result = Math.log(1.0 + expX);\n    }\n    return result;\n});\nexport const softplusConfig = {\n    kernelName: Softplus,\n    backendName: 'cpu',\n    kernelFunc: softplusKernelFunc,\n};\n//# sourceMappingURL=Softplus.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { transposeImpl } from './Transpose_impl';\nexport function transpose(args) {\n    const { inputs, attrs, backend } = args;\n    const { x } = inputs;\n    const { perm } = attrs;\n    assertNotComplex(x, 'transpose');\n    const xRank = x.shape.length;\n    const newShape = new Array(xRank);\n    for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = x.shape[perm[i]];\n    }\n    const values = backend.data.get(x.dataId).values;\n    const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);\n    const dataId = backend.write(result, newShape, x.dtype);\n    return { dataId, shape: newShape, dtype: x.dtype };\n}\nexport const transposeConfig = {\n    kernelName: Transpose,\n    backendName: 'cpu',\n    kernelFunc: transpose\n};\n//# sourceMappingURL=Transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, SpaceToBatchND, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { padV2Config } from './PadV2';\nimport { reshape } from './Reshape';\nimport { transpose } from './Transpose';\nexport function spaceToBatchND(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { blockShape, paddings } = attrs;\n    assertNotComplex([x], 'spaceToBatchND');\n    const prod = util.sizeFromShape(blockShape);\n    const completePaddings = [[0, 0]];\n    completePaddings.push(...paddings);\n    for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n        completePaddings.push([0, 0]);\n    }\n    const paddedX = padV2Config.kernelFunc({\n        inputs: { x },\n        backend,\n        attrs: { paddings: completePaddings, constantValue: 0 }\n    });\n    const reshapedPaddedShape = backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n    const permutedReshapedPaddedPermutation = backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n    const flattenShape = backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n    const reshapeInputs = { x: paddedX };\n    const reshapeAttrs = { shape: reshapedPaddedShape };\n    const paddedXReshaped = reshape({ inputs: reshapeInputs, backend, attrs: reshapeAttrs });\n    const transposeInputs = { x: paddedXReshaped };\n    const transposeAttrs = { perm: permutedReshapedPaddedPermutation };\n    const paddedXT = transpose({ inputs: transposeInputs, backend, attrs: transposeAttrs });\n    const resultReshapeInputs = { x: paddedXT };\n    const resultReshapeAttrs = { shape: flattenShape };\n    const result = reshape({ inputs: resultReshapeInputs, backend, attrs: resultReshapeAttrs });\n    backend.disposeIntermediateTensorInfo(paddedX);\n    backend.disposeIntermediateTensorInfo(paddedXReshaped);\n    backend.disposeIntermediateTensorInfo(paddedXT);\n    return result;\n}\nexport const spaceToBatchNDConfig = {\n    kernelName: SpaceToBatchND,\n    backendName: 'cpu',\n    kernelFunc: spaceToBatchND\n};\n//# sourceMappingURL=SpaceToBatchND.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sqrt } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sqrtKernelFunc = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));\nexport const sqrtConfig = {\n    kernelName: Sqrt,\n    backendName: 'cpu',\n    kernelFunc: sqrtKernelFunc,\n};\n//# sourceMappingURL=Sqrt.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport const squareConfig = {\n    kernelName: Square,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend }) => {\n        const { x } = inputs;\n        const cpuBackend = backend;\n        assertNotComplex(x, 'square');\n        const values = cpuBackend.data.get(x.dataId).values;\n        const newValues = new Float32Array(values.length);\n        for (let i = 0; i < values.length; ++i) {\n            const value = values[i];\n            newValues[i] = value * value;\n        }\n        const dataId = cpuBackend.write(newValues, x.shape, x.dtype);\n        return { dataId, shape: x.shape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const squaredDifferenceImpl = createSimpleBinaryKernelImpl(((a, b) => {\n    const diff = a - b;\n    return diff * diff;\n}));\nexport const squaredDifference = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);\nexport const squaredDifferenceConfig = {\n    kernelName: SquaredDifference,\n    backendName: 'cpu',\n    kernelFunc: squaredDifference\n};\n//# sourceMappingURL=SquaredDifference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Step } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const stepKernelFunc = unaryKernelFunc(Step, (xi, attrs) => {\n    const stepAttrs = attrs;\n    if (isNaN(xi)) {\n        return NaN;\n    }\n    else {\n        return xi > 0 ? 1 : stepAttrs.alpha;\n    }\n});\nexport const stepConfig = {\n    kernelName: Step,\n    backendName: 'cpu',\n    kernelFunc: stepKernelFunc,\n};\n//# sourceMappingURL=Step.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const tanKernelFunc = unaryKernelFunc(Tan, (xi) => Math.tan(xi));\nexport const tanConfig = {\n    kernelName: Tan,\n    backendName: 'cpu',\n    kernelFunc: tanKernelFunc,\n};\n//# sourceMappingURL=Tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tanh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const tanhKernelFunc = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi));\nexport const tanhConfig = {\n    kernelName: Tanh,\n    backendName: 'cpu',\n    kernelFunc: tanhKernelFunc,\n};\n//# sourceMappingURL=Tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unique } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { uniqueImpl } from './Unique_impl';\nexport function unique(args) {\n    const { inputs, attrs, backend } = args;\n    const { axis } = attrs;\n    const { x } = inputs;\n    assertNotComplex(x, 'unique');\n    const values = backend.data.get(x.dataId).values;\n    const { outputValues, outputShape, indices } = uniqueImpl(values, axis, x.shape, x.dtype);\n    return [\n        backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n        backend.makeTensorInfo([indices.length], 'int32', indices),\n    ];\n}\nexport const uniqueConfig = {\n    kernelName: Unique,\n    backendName: 'cpu',\n    kernelFunc: unique,\n};\n//# sourceMappingURL=Unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// We explicitly import the modular kernels so they get registered in the\n// global registry when we compile the library. A modular build would replace\n// the contents of this file and import only the kernels that are needed.\nimport { registerKernel } from '@tensorflow/tfjs-core';\nimport { absConfig } from './kernels/Abs';\nimport { acosConfig } from './kernels/Acos';\nimport { acoshConfig } from './kernels/Acosh';\nimport { addConfig } from './kernels/Add';\nimport { asinConfig } from './kernels/Asin';\nimport { asinhConfig } from './kernels/Asinh';\nimport { atanConfig } from './kernels/Atan';\nimport { atanhConfig } from './kernels/Atanh';\nimport { avgPoolConfig } from './kernels/AvgPool';\nimport { avgPoolBackpropConfig } from './kernels/AvgPoolBackprop';\nimport { batchNormConfig } from './kernels/BatchNorm';\nimport { castConfig } from './kernels/Cast';\nimport { ceilConfig } from './kernels/Ceil';\nimport { clipConfig } from './kernels/Clip';\nimport { complexConfig } from './kernels/Complex';\nimport { concatConfig } from './kernels/Concat';\nimport { cosConfig } from './kernels/Cos';\nimport { coshConfig } from './kernels/Cosh';\nimport { dilation2dConfig } from './kernels/Dilation2D';\nimport { dilation2dBackpropFilterConfig } from './kernels/Dilation2DBackpropFilter';\nimport { dilation2dBackpropInputConfig } from './kernels/Dilation2DBackpropInput';\nimport { divConfig } from './kernels/Div';\nimport { eluConfig } from './kernels/Elu';\nimport { erfConfig } from './kernels/Erf';\nimport { expConfig } from './kernels/Exp';\nimport { expm1Config } from './kernels/Expm1';\nimport { fftConfig } from './kernels/FFT';\nimport { flipLeftRightConfig } from './kernels/FlipLeftRight';\nimport { floorConfig } from './kernels/Floor';\nimport { identityConfig } from './kernels/Identity';\nimport { ifftConfig } from './kernels/IFFT';\nimport { imagConfig } from './kernels/Imag';\nimport { isFiniteConfig } from './kernels/IsFinite';\nimport { isInfConfig } from './kernels/IsInf';\nimport { isNaNConfig } from './kernels/IsNaN';\nimport { logConfig } from './kernels/Log';\nimport { log1pConfig } from './kernels/Log1p';\nimport { logicalNotConfig } from './kernels/LogicalNot';\nimport { maxConfig } from './kernels/Max';\nimport { maxPoolConfig } from './kernels/MaxPool';\nimport { maxPoolBackpropConfig } from './kernels/MaxPoolBackprop';\nimport { maxPoolWithArgmaxConfig } from './kernels/MaxPoolWithArgmax';\nimport { multiplyConfig } from './kernels/Multiply';\nimport { nonMaxSuppressionV4Config } from './kernels/NonMaxSuppressionV4';\nimport { nonMaxSuppressionV5Config } from './kernels/NonMaxSuppressionV5';\nimport { notEqualConfig } from './kernels/NotEqual';\nimport { padV2Config } from './kernels/PadV2';\nimport { realConfig } from './kernels/Real';\nimport { reciprocalConfig } from './kernels/Reciprocal';\nimport { reshapeConfig } from './kernels/Reshape';\nimport { rotateWithOffsetConfig } from './kernels/RotateWithOffset';\nimport { roundConfig } from './kernels/Round';\nimport { rsqrtConfig } from './kernels/Rsqrt';\nimport { seluConfig } from './kernels/Selu';\nimport { sigmoidConfig } from './kernels/Sigmoid';\nimport { signConfig } from './kernels/Sign';\nimport { sinConfig } from './kernels/Sin';\nimport { sinhConfig } from './kernels/Sinh';\nimport { sliceConfig } from './kernels/Slice';\nimport { softplusConfig } from './kernels/Softplus';\nimport { spaceToBatchNDConfig } from './kernels/SpaceToBatchND';\nimport { sqrtConfig } from './kernels/Sqrt';\nimport { squareConfig } from './kernels/Square';\nimport { squaredDifferenceConfig } from './kernels/SquaredDifference';\nimport { stepConfig } from './kernels/Step';\nimport { subConfig } from './kernels/Sub';\nimport { tanConfig } from './kernels/Tan';\nimport { tanhConfig } from './kernels/Tanh';\nimport { transposeConfig } from './kernels/Transpose';\nimport { uniqueConfig } from './kernels/Unique';\n// List all kernel configs here\nconst kernelConfigs = [\n    absConfig,\n    acosConfig,\n    acoshConfig,\n    addConfig,\n    asinConfig,\n    asinhConfig,\n    atanConfig,\n    atanhConfig,\n    avgPoolConfig,\n    avgPoolBackpropConfig,\n    batchNormConfig,\n    castConfig,\n    ceilConfig,\n    clipConfig,\n    complexConfig,\n    concatConfig,\n    cosConfig,\n    coshConfig,\n    dilation2dConfig,\n    dilation2dBackpropInputConfig,\n    dilation2dBackpropFilterConfig,\n    divConfig,\n    eluConfig,\n    erfConfig,\n    expConfig,\n    expm1Config,\n    fftConfig,\n    flipLeftRightConfig,\n    floorConfig,\n    identityConfig,\n    ifftConfig,\n    imagConfig,\n    isFiniteConfig,\n    isInfConfig,\n    isNaNConfig,\n    logConfig,\n    log1pConfig,\n    logicalNotConfig,\n    maxPoolConfig,\n    maxPoolBackpropConfig,\n    maxPoolWithArgmaxConfig,\n    maxConfig,\n    multiplyConfig,\n    nonMaxSuppressionV4Config,\n    nonMaxSuppressionV5Config,\n    notEqualConfig,\n    padV2Config,\n    realConfig,\n    reciprocalConfig,\n    reshapeConfig,\n    rotateWithOffsetConfig,\n    roundConfig,\n    rsqrtConfig,\n    seluConfig,\n    sigmoidConfig,\n    signConfig,\n    sinConfig,\n    sinhConfig,\n    sliceConfig,\n    softplusConfig,\n    spaceToBatchNDConfig,\n    sqrtConfig,\n    squareConfig,\n    squaredDifferenceConfig,\n    stepConfig,\n    subConfig,\n    tanConfig,\n    tanhConfig,\n    transposeConfig,\n    uniqueConfig,\n];\nfor (const kernelConfig of kernelConfigs) {\n    registerKernel(kernelConfig);\n}\n//# sourceMappingURL=register_all_kernels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// All exports from this package should be in base.\nexport * from './base';\nimport './register_all_kernels';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst contexts = {};\nconst WEBGL_ATTRIBUTES = {\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: true\n};\nexport function setWebGLContext(webGLVersion, gl) {\n    contexts[webGLVersion] = gl;\n}\nexport function getWebGLContext(webGLVersion) {\n    if (!(webGLVersion in contexts)) {\n        const newCtx = getWebGLRenderingContext(webGLVersion);\n        if (newCtx !== null) {\n            contexts[webGLVersion] = newCtx;\n        }\n        else {\n            console.log('Could not get context for WebGL version', webGLVersion);\n            return null;\n        }\n    }\n    const gl = contexts[webGLVersion];\n    if (gl.isContextLost()) {\n        delete contexts[webGLVersion];\n        return getWebGLContext(webGLVersion);\n    }\n    gl.disable(gl.DEPTH_TEST);\n    gl.disable(gl.STENCIL_TEST);\n    gl.disable(gl.BLEND);\n    gl.disable(gl.DITHER);\n    gl.disable(gl.POLYGON_OFFSET_FILL);\n    gl.disable(gl.SAMPLE_COVERAGE);\n    gl.enable(gl.SCISSOR_TEST);\n    gl.enable(gl.CULL_FACE);\n    gl.cullFace(gl.BACK);\n    return contexts[webGLVersion];\n}\nfunction createCanvas(webGLVersion) {\n    if (typeof OffscreenCanvas !== 'undefined' && webGLVersion === 2) {\n        return new OffscreenCanvas(300, 150);\n    }\n    else if (typeof document !== 'undefined') {\n        return document.createElement('canvas');\n    }\n    else {\n        throw new Error('Cannot create a canvas in this context');\n    }\n}\nfunction getWebGLRenderingContext(webGLVersion) {\n    if (webGLVersion !== 1 && webGLVersion !== 2) {\n        throw new Error('Cannot get WebGL rendering context, WebGL is disabled.');\n    }\n    const canvas = createCanvas(webGLVersion);\n    canvas.addEventListener('webglcontextlost', (ev) => {\n        ev.preventDefault();\n        delete contexts[webGLVersion];\n    }, false);\n    if (webGLVersion === 1) {\n        return (canvas.getContext('webgl', WEBGL_ATTRIBUTES) ||\n            canvas.getContext('experimental-webgl', WEBGL_ATTRIBUTES));\n    }\n    return canvas.getContext('webgl2', WEBGL_ATTRIBUTES);\n}\n//# sourceMappingURL=canvas_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nexport var PackingScheme;\n(function (PackingScheme) {\n    /**\n     * All values in a single texel are densely packed without any constraints.\n     *\n     * This is how the shader encodes a tensor with shape = [2, 3, 4]\n     * (indices are [batch, row, col]).\n     *\n     * 000|001   010|011   020|021\n     * -------   -------   -------\n     * 002|003   012|013   022|023\n     *\n     * 100|101   110|111   120|121\n     * -------   -------   -------\n     * 102|103   112|113   122|123\n     *\n     */\n    PackingScheme[PackingScheme[\"DENSE\"] = 0] = \"DENSE\";\n    /**\n     * Single texels contain only values from the same batch, and from adjacent\n     * rows and columns.\n     *\n     * This is how the shader encodes a tensor with shape = [2, 3, 5]\n     * (indices are [batch, row, col]).\n     *\n     * 000|001   002|003   004|xxx   020|021   022|023   024|xxx\n     * -------   -------   -------   -------   -------   -------\n     * 010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n     *\n     * 100|101   102|103   104|xxx   120|121   122|123   124|xxx\n     * -------   -------   -------   -------   -------   -------\n     * 110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n     *\n     */\n    PackingScheme[PackingScheme[\"SHARED_BATCH\"] = 1] = \"SHARED_BATCH\";\n})(PackingScheme || (PackingScheme = {}));\nexport var TextureUsage;\n(function (TextureUsage) {\n    TextureUsage[TextureUsage[\"RENDER\"] = 0] = \"RENDER\";\n    TextureUsage[TextureUsage[\"UPLOAD\"] = 1] = \"UPLOAD\";\n    TextureUsage[TextureUsage[\"PIXELS\"] = 2] = \"PIXELS\";\n    TextureUsage[TextureUsage[\"DOWNLOAD\"] = 3] = \"DOWNLOAD\";\n})(TextureUsage || (TextureUsage = {}));\nexport var PhysicalTextureType;\n(function (PhysicalTextureType) {\n    PhysicalTextureType[PhysicalTextureType[\"UNPACKED_FLOAT16\"] = 0] = \"UNPACKED_FLOAT16\";\n    PhysicalTextureType[PhysicalTextureType[\"UNPACKED_FLOAT32\"] = 1] = \"UNPACKED_FLOAT32\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_4X1_UNSIGNED_BYTE\"] = 2] = \"PACKED_4X1_UNSIGNED_BYTE\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_2X2_FLOAT32\"] = 3] = \"PACKED_2X2_FLOAT32\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_2X2_FLOAT16\"] = 4] = \"PACKED_2X2_FLOAT16\";\n})(PhysicalTextureType || (PhysicalTextureType = {}));\nexport function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {\n    return [columns, rows];\n}\nexport function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {\n    return matrixSize * channelsPerTexture;\n}\nexport function getColorMatrixTextureShapeWidthHeight(rows, columns) {\n    return [columns * 4, rows];\n}\n/**\n * Get shape for densely packed RGBA texture.\n */\nexport function getDenseTexShape(shape) {\n    const size = util.sizeFromShape(shape);\n    const texelsNeeded = Math.ceil(size / 4);\n    return util.sizeToSquarishShape(texelsNeeded);\n}\nexport function getMatrixSizeFromUnpackedArraySize(unpackedSize, channelsPerTexture) {\n    if (unpackedSize % channelsPerTexture !== 0) {\n        throw new Error(`unpackedSize (${unpackedSize}) must be a multiple of ` +\n            `${channelsPerTexture}`);\n    }\n    return unpackedSize / channelsPerTexture;\n}\nexport function decodeMatrixFromUnpackedColorRGBAArray(unpackedArray, matrix, channels) {\n    const requiredSize = unpackedArray.length * channels / 4;\n    if (matrix.length < requiredSize) {\n        throw new Error(`matrix length (${matrix.length}) must be >= ${requiredSize}`);\n    }\n    let dst = 0;\n    for (let src = 0; src < unpackedArray.length; src += 4) {\n        for (let c = 0; c < channels; c++) {\n            matrix[dst++] = unpackedArray[src + c];\n        }\n    }\n}\nexport function getPackedMatrixTextureShapeWidthHeight(rows, columns) {\n    return [\n        Math.max(1, Math.ceil(columns / 2)), Math.max(1, Math.ceil(rows / 2))\n    ];\n}\nexport function getPackedRGBAArraySizeFromMatrixShape(rows, columns) {\n    const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return w * h * 4;\n}\nexport function getTextureConfig(\n// tslint:disable-next-line:no-any\ngl, textureHalfFloatExtension) {\n    // tslint:disable-next-line:no-any\n    const glany = gl;\n    let internalFormatFloat;\n    let internalFormatHalfFloat;\n    let internalFormatPackedHalfFloat;\n    let internalFormatPackedFloat;\n    let textureFormatFloat;\n    let downloadTextureFormat;\n    let downloadUnpackNumChannels;\n    let defaultNumChannels;\n    let textureTypeHalfFloat;\n    let textureTypeFloat;\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        internalFormatFloat = glany.R32F;\n        internalFormatHalfFloat = glany.R16F;\n        internalFormatPackedHalfFloat = glany.RGBA16F;\n        internalFormatPackedFloat = glany.RGBA32F;\n        textureFormatFloat = glany.RED;\n        downloadUnpackNumChannels = 4;\n        defaultNumChannels = 1;\n        textureTypeHalfFloat = glany.HALF_FLOAT;\n        textureTypeFloat = glany.FLOAT;\n    }\n    else {\n        internalFormatFloat = gl.RGBA;\n        internalFormatHalfFloat = gl.RGBA;\n        internalFormatPackedHalfFloat = gl.RGBA;\n        internalFormatPackedFloat = glany.RGBA;\n        textureFormatFloat = gl.RGBA;\n        downloadUnpackNumChannels = 4;\n        defaultNumChannels = 4;\n        textureTypeHalfFloat = textureHalfFloatExtension != null ?\n            textureHalfFloatExtension.HALF_FLOAT_OES :\n            null;\n        textureTypeFloat = gl.FLOAT;\n    }\n    downloadTextureFormat = gl.RGBA;\n    return {\n        internalFormatFloat,\n        internalFormatHalfFloat,\n        internalFormatPackedHalfFloat,\n        internalFormatPackedFloat,\n        textureFormatFloat,\n        downloadTextureFormat,\n        downloadUnpackNumChannels,\n        defaultNumChannels,\n        textureTypeHalfFloat,\n        textureTypeFloat\n    };\n}\n//# sourceMappingURL=tex_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { getWebGLContext } from './canvas_util';\nimport { getTextureConfig } from './tex_util';\nexport function callAndCheck(gl, func) {\n    const returnValue = func();\n    if (env().getBool('DEBUG')) {\n        checkWebGLError(gl);\n    }\n    return returnValue;\n}\nfunction checkWebGLError(gl) {\n    const error = gl.getError();\n    if (error !== gl.NO_ERROR) {\n        throw new Error('WebGL Error: ' + getWebGLErrorMessage(gl, error));\n    }\n}\n// https://en.wikipedia.org/wiki/Half-precision_floating-point_format\nconst MIN_FLOAT16 = 5.96e-8;\nconst MAX_FLOAT16 = 65504;\nexport function canBeRepresented(num) {\n    if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED') || num === 0 ||\n        (MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16)) {\n        return true;\n    }\n    return false;\n}\nexport function getWebGLErrorMessage(gl, status) {\n    switch (status) {\n        case gl.NO_ERROR:\n            return 'NO_ERROR';\n        case gl.INVALID_ENUM:\n            return 'INVALID_ENUM';\n        case gl.INVALID_VALUE:\n            return 'INVALID_VALUE';\n        case gl.INVALID_OPERATION:\n            return 'INVALID_OPERATION';\n        case gl.INVALID_FRAMEBUFFER_OPERATION:\n            return 'INVALID_FRAMEBUFFER_OPERATION';\n        case gl.OUT_OF_MEMORY:\n            return 'OUT_OF_MEMORY';\n        case gl.CONTEXT_LOST_WEBGL:\n            return 'CONTEXT_LOST_WEBGL';\n        default:\n            return `Unknown error code ${status}`;\n    }\n}\nexport function getExtensionOrThrow(gl, extensionName) {\n    return throwIfNull(gl, () => gl.getExtension(extensionName), 'Extension \"' + extensionName + '\" not supported on this browser.');\n}\nexport function createVertexShader(gl, vertexShaderSource) {\n    const vertexShader = throwIfNull(gl, () => gl.createShader(gl.VERTEX_SHADER), 'Unable to create vertex WebGLShader.');\n    callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));\n    callAndCheck(gl, () => gl.compileShader(vertexShader));\n    if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {\n        console.log(gl.getShaderInfoLog(vertexShader));\n        throw new Error('Failed to compile vertex shader.');\n    }\n    return vertexShader;\n}\nexport function createFragmentShader(gl, fragmentShaderSource) {\n    const fragmentShader = throwIfNull(gl, () => gl.createShader(gl.FRAGMENT_SHADER), 'Unable to create fragment WebGLShader.');\n    callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));\n    callAndCheck(gl, () => gl.compileShader(fragmentShader));\n    if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {\n        logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));\n        throw new Error('Failed to compile fragment shader.');\n    }\n    return fragmentShader;\n}\nconst lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;\nfunction logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {\n    const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);\n    if (lineNumberRegexResult == null) {\n        console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);\n        console.log(shaderSource);\n        return;\n    }\n    const lineNumber = +lineNumberRegexResult[1];\n    const shaderLines = shaderSource.split('\\n');\n    const pad = shaderLines.length.toString().length + 2;\n    const linesWithLineNumbers = shaderLines.map((line, lineNumber) => util.rightPad((lineNumber + 1).toString(), pad) + line);\n    let maxLineLength = 0;\n    for (let i = 0; i < linesWithLineNumbers.length; i++) {\n        maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);\n    }\n    const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);\n    const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);\n    const afterErrorLines = linesWithLineNumbers.slice(lineNumber);\n    console.log(beforeErrorLines.join('\\n'));\n    console.log(shaderInfoLog.split('\\n')[0]);\n    console.log(`%c ${util.rightPad(errorLine[0], maxLineLength)}`, 'border:1px solid red; background-color:#e3d2d2; color:#a61717');\n    console.log(afterErrorLines.join('\\n'));\n}\nexport function createProgram(gl) {\n    return throwIfNull(gl, () => gl.createProgram(), 'Unable to create WebGLProgram.');\n}\nexport function linkProgram(gl, program) {\n    callAndCheck(gl, () => gl.linkProgram(program));\n    if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {\n        console.log(gl.getProgramInfoLog(program));\n        throw new Error('Failed to link vertex and fragment shaders.');\n    }\n}\nexport function validateProgram(gl, program) {\n    callAndCheck(gl, () => gl.validateProgram(program));\n    if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {\n        console.log(gl.getProgramInfoLog(program));\n        throw new Error('Shader program validation failed.');\n    }\n}\nexport function createStaticVertexBuffer(gl, data) {\n    const buffer = throwIfNull(gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));\n    return buffer;\n}\nexport function createStaticIndexBuffer(gl, data) {\n    const buffer = throwIfNull(gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n    callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));\n    return buffer;\n}\nexport function getNumChannels() {\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        return 1;\n    }\n    return 4;\n}\nexport function createTexture(gl) {\n    return throwIfNull(gl, () => gl.createTexture(), 'Unable to create WebGLTexture.');\n}\nexport function validateTextureSize(width, height) {\n    const maxTextureSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n    if ((width <= 0) || (height <= 0)) {\n        const requested = `[${width}x${height}]`;\n        throw new Error('Requested texture size ' + requested + ' is invalid.');\n    }\n    if ((width > maxTextureSize) || (height > maxTextureSize)) {\n        const requested = `[${width}x${height}]`;\n        const max = `[${maxTextureSize}x${maxTextureSize}]`;\n        throw new Error('Requested texture size ' + requested +\n            ' greater than WebGL maximum on this browser / GPU ' + max + '.');\n    }\n}\nexport function createFramebuffer(gl) {\n    return throwIfNull(gl, () => gl.createFramebuffer(), 'Unable to create WebGLFramebuffer.');\n}\nexport function bindVertexBufferToProgramAttribute(gl, program, attribute, buffer, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {\n    const loc = gl.getAttribLocation(program, attribute);\n    if (loc === -1) {\n        // The GPU compiler decided to strip out this attribute because it's unused,\n        // thus no need to bind.\n        return false;\n    }\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes));\n    callAndCheck(gl, () => gl.enableVertexAttribArray(loc));\n    return true;\n}\nexport function bindTextureUnit(gl, texture, textureUnit) {\n    validateTextureUnit(gl, textureUnit);\n    callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n}\nexport function unbindTextureUnit(gl, textureUnit) {\n    validateTextureUnit(gl, textureUnit);\n    callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function getProgramUniformLocationOrThrow(gl, program, uniformName) {\n    return throwIfNull(gl, () => gl.getUniformLocation(program, uniformName), 'uniform \"' + uniformName + '\" not present in program.');\n}\nexport function getProgramUniformLocation(gl, program, uniformName) {\n    return gl.getUniformLocation(program, uniformName);\n}\nexport function bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) {\n    callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));\n    callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));\n}\nexport function bindCanvasToFramebuffer(gl) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n    callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));\n    callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));\n}\nexport function bindColorTextureToFramebuffer(gl, texture, framebuffer) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));\n}\nexport function unbindColorTextureFromFramebuffer(gl, framebuffer) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));\n}\nexport function validateFramebuffer(gl) {\n    const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);\n    if (status !== gl.FRAMEBUFFER_COMPLETE) {\n        throw new Error('Error binding framebuffer: ' + getFramebufferErrorMessage(gl, status));\n    }\n}\nexport function getFramebufferErrorMessage(gl, status) {\n    switch (status) {\n        case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:\n            return 'FRAMEBUFFER_INCOMPLETE_ATTACHMENT';\n        case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:\n            return 'FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT';\n        case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:\n            return 'FRAMEBUFFER_INCOMPLETE_DIMENSIONS';\n        case gl.FRAMEBUFFER_UNSUPPORTED:\n            return 'FRAMEBUFFER_UNSUPPORTED';\n        default:\n            return `unknown error ${status}`;\n    }\n}\nfunction throwIfNull(gl, returnTOrNull, failureMessage) {\n    const tOrNull = callAndCheck(gl, () => returnTOrNull());\n    if (tOrNull == null) {\n        throw new Error(failureMessage);\n    }\n    return tOrNull;\n}\nfunction validateTextureUnit(gl, textureUnit) {\n    const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;\n    const glTextureUnit = textureUnit + gl.TEXTURE0;\n    if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {\n        const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;\n        throw new Error(`textureUnit must be in ${textureUnitRange}.`);\n    }\n}\nexport function getBatchDim(shape, dimsToSkip = 2) {\n    return util.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));\n}\nexport function getRowsCols(shape) {\n    if (shape.length === 0) {\n        throw Error('Cannot get rows and columns of an empty shape array.');\n    }\n    return [\n        shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]\n    ];\n}\nexport function getShapeAs3D(shape) {\n    let shapeAs3D = [1, 1, 1];\n    const isScalar = shape.length === 0 || (shape.length === 1 && shape[0] === 1);\n    if (!isScalar) {\n        shapeAs3D =\n            [getBatchDim(shape), ...getRowsCols(shape)];\n    }\n    return shapeAs3D;\n}\nexport function getTextureShapeFromLogicalShape(logShape, isPacked = false) {\n    let maxTexSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n    if (isPacked) {\n        maxTexSize = maxTexSize * 2;\n        // This logic ensures we accurately count the number of packed texels needed\n        // to accommodate the tensor. We can only pack values in the same texel if\n        // they are from adjacent pairs of rows/cols within the same batch. So if a\n        // tensor has 3 rows, we pretend it has 4 rows in order to account for the\n        // fact that the texels containing the third row are half empty.\n        logShape = logShape.map((d, i) => i >= logShape.length - 2 ?\n            util.nearestLargerEven(logShape[i]) :\n            logShape[i]);\n        // Packed texture height is at least 2 (the channel height of a single\n        // texel).\n        if (logShape.length === 1) {\n            logShape = [2, logShape[0]];\n        }\n    }\n    // If logical shape is 2, we don't squeeze, since we want to match physical.\n    if (logShape.length !== 2) {\n        const squeezeResult = util.squeezeShape(logShape);\n        logShape = squeezeResult.newShape;\n    }\n    let size = util.sizeFromShape(logShape);\n    if (logShape.length <= 1 && size <= maxTexSize) {\n        return [1, size];\n    }\n    else if (logShape.length === 2 && logShape[0] <= maxTexSize &&\n        logShape[1] <= maxTexSize) {\n        return logShape;\n    }\n    else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize &&\n        logShape[2] <= maxTexSize) {\n        return [logShape[0] * logShape[1], logShape[2]];\n    }\n    else if (logShape.length === 3 && logShape[0] <= maxTexSize &&\n        logShape[1] * logShape[2] <= maxTexSize) {\n        return [logShape[0], logShape[1] * logShape[2]];\n    }\n    else if (logShape.length === 4 &&\n        logShape[0] * logShape[1] * logShape[2] <= maxTexSize &&\n        logShape[3] <= maxTexSize) {\n        return [logShape[0] * logShape[1] * logShape[2], logShape[3]];\n    }\n    else if (logShape.length === 4 && logShape[0] <= maxTexSize &&\n        logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {\n        return [logShape[0], logShape[1] * logShape[2] * logShape[3]];\n    }\n    else {\n        if (isPacked) {\n            // For packed textures size equals the number of channels required to\n            // accommodate the texture data. However in order to squarify such that\n            // inner dimensions stay even, we rewrite size to equal the number of\n            // texels. Then in the return statement we rehydrate the squarified\n            // dimensions to channel units.\n            const batchDim = getBatchDim(logShape);\n            let rows = 2, cols = 2;\n            if (logShape.length) {\n                [rows, cols] = getRowsCols(logShape);\n            }\n            size = batchDim * (rows / 2) * (cols / 2);\n            return util.sizeToSquarishShape(size).map(d => d * 2);\n        }\n        return util.sizeToSquarishShape(size);\n    }\n}\nfunction isEven(n) {\n    return n % 2 === 0;\n}\n/**\n * This determines whether reshaping a packed texture requires rearranging\n * the data within the texture, assuming 2x2 packing.\n */\nexport function isReshapeFree(shape1, shape2) {\n    shape1 = shape1.slice(-2);\n    shape2 = shape2.slice(-2);\n    if (util.arraysEqual(shape1, shape2)) {\n        return true;\n    }\n    if (!shape1.length || !shape2.length) { // One of the shapes is a scalar.\n        return true;\n    }\n    if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 ||\n        shape2[1] === 0) {\n        return true;\n    }\n    if (shape1.length !== shape2.length) { // One of the shapes is a vector.\n        const shape1Cols = shape1.slice(-1)[0];\n        const shape2Cols = shape2.slice(-1)[0];\n        if (shape1Cols === shape2Cols) {\n            return true;\n        }\n        if (isEven(shape1Cols) && isEven(shape2Cols) &&\n            (shape1[0] === 1 || shape2[0] === 1)) {\n            return true;\n        }\n    }\n    return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);\n}\n// We cache webgl params because the environment gets reset between\n// unit tests and we don't want to constantly query the WebGLContext for\n// MAX_TEXTURE_SIZE.\nlet MAX_TEXTURE_SIZE;\nlet MAX_TEXTURES_IN_SHADER;\nexport function getWebGLMaxTextureSize(webGLVersion) {\n    if (MAX_TEXTURE_SIZE == null) {\n        const gl = getWebGLContext(webGLVersion);\n        MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n    }\n    return MAX_TEXTURE_SIZE;\n}\nexport function resetMaxTextureSize() {\n    MAX_TEXTURE_SIZE = null;\n}\nexport function resetMaxTexturesInShader() {\n    MAX_TEXTURES_IN_SHADER = null;\n}\nexport function getMaxTexturesInShader(webGLVersion) {\n    if (MAX_TEXTURES_IN_SHADER == null) {\n        const gl = getWebGLContext(webGLVersion);\n        MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);\n    }\n    // We cap at 16 to avoid spurious runtime \"memory exhausted\" error.\n    return Math.min(16, MAX_TEXTURES_IN_SHADER);\n}\nexport function getWebGLDisjointQueryTimerVersion(webGLVersion) {\n    if (webGLVersion === 0) {\n        return 0;\n    }\n    let queryTimerVersion;\n    const gl = getWebGLContext(webGLVersion);\n    if (hasExtension(gl, 'EXT_disjoint_timer_query_webgl2') &&\n        webGLVersion === 2) {\n        queryTimerVersion = 2;\n    }\n    else if (hasExtension(gl, 'EXT_disjoint_timer_query')) {\n        queryTimerVersion = 1;\n    }\n    else {\n        queryTimerVersion = 0;\n    }\n    return queryTimerVersion;\n}\nexport function hasExtension(gl, extensionName) {\n    const ext = gl.getExtension(extensionName);\n    return ext != null;\n}\nexport function isWebGLVersionEnabled(webGLVersion) {\n    try {\n        const gl = getWebGLContext(webGLVersion);\n        if (gl != null) {\n            return true;\n        }\n    }\n    catch (e) {\n        console.log('Error when getting WebGL context: ', e);\n        return false;\n    }\n    return false;\n}\nexport function isCapableOfRenderingToFloatTexture(webGLVersion) {\n    if (webGLVersion === 0) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    if (webGLVersion === 1) {\n        if (!hasExtension(gl, 'OES_texture_float')) {\n            return false;\n        }\n    }\n    else {\n        if (!hasExtension(gl, 'EXT_color_buffer_float')) {\n            return false;\n        }\n    }\n    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n    return isFrameBufferComplete;\n}\n/**\n * Check if we can download values from a float/half-float texture.\n *\n * Note that for performance reasons we use binding a texture to a framebuffer\n * as a proxy for ability to download float values later using readPixels. The\n * texture params of this texture will not match those in readPixels exactly\n * but if we are unable to bind some kind of float texture to the frameBuffer\n * then we definitely will not be able to read float values from it.\n */\nexport function isDownloadFloatTextureEnabled(webGLVersion) {\n    if (webGLVersion === 0) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    if (webGLVersion === 1) {\n        if (!hasExtension(gl, 'OES_texture_float')) {\n            return false;\n        }\n        if (!hasExtension(gl, 'WEBGL_color_buffer_float')) {\n            return false;\n        }\n    }\n    else {\n        if (hasExtension(gl, 'EXT_color_buffer_float')) {\n            return createFloatTextureAndBindToFramebuffer(gl);\n        }\n        const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n        if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {\n            const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n            return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);\n        }\n        return false;\n    }\n    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n    return isFrameBufferComplete;\n}\nfunction createFloatTextureAndBindToFramebuffer(gl) {\n    const texConfig = getTextureConfig(gl);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    const width = 1;\n    const height = 1;\n    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);\n    const frameBuffer = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    gl.deleteTexture(texture);\n    gl.deleteFramebuffer(frameBuffer);\n    return isFrameBufferComplete;\n}\nfunction createHalfFloatTextureAndBindToFramebuffer(\n// tslint:disable-next-line:no-any\ngl, textureHalfFloatExtension) {\n    const texConfig = getTextureConfig(gl, textureHalfFloatExtension);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    const width = 1;\n    const height = 1;\n    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);\n    const frameBuffer = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    gl.deleteTexture(texture);\n    gl.deleteFramebuffer(frameBuffer);\n    return isFrameBufferComplete;\n}\nexport function isWebGLFenceEnabled(webGLVersion) {\n    if (webGLVersion !== 2) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    // tslint:disable-next-line:no-any\n    const isEnabled = gl.fenceSync != null;\n    return isEnabled;\n}\nexport function assertNotComplex(tensor, opName) {\n    if (!Array.isArray(tensor)) {\n        tensor = [tensor];\n    }\n    tensor.forEach(t => {\n        if (t != null) {\n            util.assert(t.dtype !== 'complex64', () => `${opName} does not support complex64 tensors ` +\n                'in the WebGL backend.');\n        }\n    });\n}\n//# sourceMappingURL=webgl_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { device_util, env } from '@tensorflow/tfjs-core';\nimport { getMaxTexturesInShader, getWebGLDisjointQueryTimerVersion, getWebGLMaxTextureSize, isCapableOfRenderingToFloatTexture, isDownloadFloatTextureEnabled, isWebGLFenceEnabled, isWebGLVersionEnabled } from './webgl_util';\nconst ENV = env();\n/**\n * This file contains WebGL-specific flag registrations.\n */\n/**\n * True if WebGL is supported.\n */\nENV.registerFlag('HAS_WEBGL', () => ENV.getNumber('WEBGL_VERSION') > 0);\n/** 0: No WebGL, 1: WebGL 1.0, 2: WebGL 2.0. */\nENV.registerFlag('WEBGL_VERSION', () => {\n    if (isWebGLVersionEnabled(2)) {\n        return 2;\n    }\n    else if (isWebGLVersionEnabled(1)) {\n        return 1;\n    }\n    return 0;\n});\n/** Whether to check for numerical representation problems. */\nENV.registerFlag('WEBGL_CHECK_NUMERICAL_PROBLEMS', () => false);\nENV.registerFlag('WEBGL_BUFFER_SUPPORTED', () => ENV.get('WEBGL_VERSION') === 2);\n/** Whether the WebGL backend will sometimes forward ops to the CPU. */\nENV.registerFlag('WEBGL_CPU_FORWARD', () => true);\n/** Whether the WebGL backend will always use f16 textures for rendering. */\nENV.registerFlag('WEBGL_FORCE_F16_TEXTURES', () => false);\n/** Whether to turn all packing related flags on. */\nENV.registerFlag('WEBGL_PACK', () => ENV.getBool('HAS_WEBGL'));\n/** Whether we will pack the batchnormalization op. */\nENV.registerFlag('WEBGL_PACK_NORMALIZATION', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack the clip op. */\nENV.registerFlag('WEBGL_PACK_CLIP', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack the depthwise conv op. */\n// TODO: https://github.com/tensorflow/tfjs/issues/1679\nENV.registerFlag('WEBGL_PACK_DEPTHWISECONV', () => false);\n/** Whether we will pack binary ops. */\nENV.registerFlag('WEBGL_PACK_BINARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack unary ops. */\nENV.registerFlag('WEBGL_PACK_UNARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack array ops. */\nENV.registerFlag('WEBGL_PACK_ARRAY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack image ops. */\nENV.registerFlag('WEBGL_PACK_IMAGE_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack reduce ops. */\nENV.registerFlag('WEBGL_PACK_REDUCE', () => ENV.getBool('WEBGL_PACK'));\n/** Whether packed WebGL kernels lazily unpack their outputs. */\nENV.registerFlag('WEBGL_LAZILY_UNPACK', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will use the im2col algorithm to speed up convolutions. */\nENV.registerFlag('WEBGL_CONV_IM2COL', () => ENV.getBool('WEBGL_PACK'));\n/** The maximum texture dimension. */\nENV.registerFlag('WEBGL_MAX_TEXTURE_SIZE', () => getWebGLMaxTextureSize(ENV.getNumber('WEBGL_VERSION')));\n/** The maximum texture dimension. */\nENV.registerFlag('WEBGL_MAX_TEXTURES_IN_SHADER', () => getMaxTexturesInShader(ENV.getNumber('WEBGL_VERSION')));\n/**\n * The disjoint_query_timer extension version.\n * 0: disabled, 1: EXT_disjoint_timer_query, 2:\n * EXT_disjoint_timer_query_webgl2.\n * In Firefox with WebGL 2.0,\n * EXT_disjoint_timer_query_webgl2 is not available, so we must use the\n * WebGL 1.0 extension.\n */\nENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', () => {\n    const webGLVersion = ENV.getNumber('WEBGL_VERSION');\n    if (webGLVersion === 0) {\n        return 0;\n    }\n    return getWebGLDisjointQueryTimerVersion(webGLVersion);\n});\n/**\n * Whether the timer object from the disjoint_query_timer extension gives\n * timing information that is reliable.\n */\nENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', () => ENV.getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&\n    !device_util.isMobile());\n/**\n * Whether the device is physically capable of rendering to float32 textures.\n */\nENV.registerFlag('WEBGL_RENDER_FLOAT32_CAPABLE', () => isCapableOfRenderingToFloatTexture(ENV.getNumber('WEBGL_VERSION')));\n/**\n * Whether rendering to float32 textures is enabled. If disabled, renders to\n * float16 textures.\n */\nENV.registerFlag('WEBGL_RENDER_FLOAT32_ENABLED', () => {\n    return ENV.getBool('WEBGL_FORCE_F16_TEXTURES') ?\n        false :\n        ENV.getBool('WEBGL_RENDER_FLOAT32_CAPABLE');\n});\n/**\n * Whether downloading float textures is enabled (16 or 32 bit). If disabled,\n * uses IEEE 754 encoding of the float32 values to 4 uint8 when downloading.\n */\nENV.registerFlag('WEBGL_DOWNLOAD_FLOAT_ENABLED', () => isDownloadFloatTextureEnabled(ENV.getNumber('WEBGL_VERSION')));\n/** Whether the fence API is available. */\nENV.registerFlag('WEBGL_FENCE_API_ENABLED', () => isWebGLFenceEnabled(ENV.getNumber('WEBGL_VERSION')));\n/**\n * Tensors with size <= than this will be uploaded as uniforms, not textures.\n */\nENV.registerFlag('WEBGL_SIZE_UPLOAD_UNIFORM', () => {\n    // Use uniform uploads only when 32bit floats are supported. In\n    // 16bit\n    // environments there are problems with comparing a 16bit texture value\n    // with a 32bit uniform value.\n    const useUniforms = ENV.getBool('WEBGL_RENDER_FLOAT32_ENABLED');\n    return useUniforms ? 4 : 0;\n});\n/**\n * If the total number of bytes allocated on the GPU is greater than this\n * number, we will aggressively delete textures upon disposal with\n * gl.deleteMatrixTexture, rather than making them available for reuse.\n *\n * Default value -1 indicates that we will never aggressively delete textures.\n */\nENV.registerFlag('WEBGL_DELETE_TEXTURE_THRESHOLD', () => {\n    return -1;\n}, threshold => {\n    if (threshold < 0 && threshold !== -1) {\n        throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never ` +\n            `delete) or at least 0, but got ${threshold}.`);\n    }\n});\n//# sourceMappingURL=flags_webgl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Import shared functionality from tfjs-backend-cpu without triggering\n// side effects.\n// tslint:disable-next-line: no-imports-from-dist\nimport * as shared from '@tensorflow/tfjs-backend-cpu/dist/shared';\nconst { simpleAbsImpl: simpleAbsImplCPU, addImpl: addImplCPU, ceilImpl: ceilImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, multiplyImpl: multiplyImplCPU, rsqrtImpl: rsqrtImplCPU, sliceImpl: sliceImplCPU, subImpl: subImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU, } = shared;\nexport { simpleAbsImplCPU, addImplCPU, ceilImplCPU, expImplCPU, expm1ImplCPU, logImplCPU, multiplyImplCPU, sliceImplCPU, subImplCPU, floorImplCPU, maxImplCPU, rsqrtImplCPU, transposeImplCPU, uniqueImplCPU, };\n//# sourceMappingURL=shared.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AddNProgram {\n    constructor(outputShape, shapes) {\n        this.outputShape = [];\n        this.outputShape = outputShape;\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const snippets = [];\n        // Get target elements from every input tensor.\n        this.variableNames.forEach(variable => {\n            snippets.push(`float v${variable} = get${variable}AtOutCoords();`);\n        });\n        // Calculate the sum of all elements.\n        const operation = this.variableNames\n            .map(variable => {\n            return `v${variable}`;\n        })\n            .join(' + ');\n        this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        float result = ${operation};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=addn_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AddNPackedProgram {\n    constructor(outputShape, shapes) {\n        this.outputShape = [];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const snippets = [];\n        // Get target elements from every input tensor.\n        this.variableNames.forEach(variable => {\n            snippets.push(`vec4 v${variable} = get${variable}AtOutCoords();`);\n        });\n        // Calculate the sum of all elements.\n        const operation = this.variableNames\n            .map(variable => {\n            return `v${variable}`;\n        })\n            .join(' + ');\n        this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        vec4 result = ${operation};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=addn_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ArgMinMaxProgram {\n    constructor(reduceInfo, op, firstPass) {\n        this.variableNames = ['A'];\n        const { windowSize, batchSize, outSize } = reduceInfo;\n        if (!firstPass) {\n            this.variableNames.push('bestIndicesA');\n        }\n        this.outputShape = [batchSize, outSize];\n        const compOp = (op === 'max') ? '>' : '<';\n        const indexSnippet = firstPass ?\n            'inOffset + i;' :\n            'round(getBestIndicesA(batch, inOffset + i));';\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          int inIdx = ${indexSnippet};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${compOp} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=argminmax_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport function getVecChannels(name, rank) {\n    return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank).map(d => `${name}.${d}`);\n}\nexport function getChannels(name, rank) {\n    if (rank === 1) {\n        return [name];\n    }\n    return getVecChannels(name, rank);\n}\nexport function getSourceCoords(rank, dims) {\n    if (rank === 1) {\n        return 'rc';\n    }\n    let coords = '';\n    for (let i = 0; i < rank; i++) {\n        coords += dims[i];\n        if (i < rank - 1) {\n            coords += ',';\n        }\n    }\n    return coords;\n}\n//# sourceMappingURL=packing_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nexport function getGlslDifferences() {\n    let version;\n    let attribute;\n    let varyingVs;\n    let varyingFs;\n    let texture2D;\n    let output;\n    let defineOutput;\n    let defineSpecialNaN;\n    let defineSpecialInf;\n    let defineRound;\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        version = '#version 300 es';\n        attribute = 'in';\n        varyingVs = 'out';\n        varyingFs = 'in';\n        texture2D = 'texture';\n        output = 'outputColor';\n        defineOutput = 'out vec4 outputColor;';\n        // Use custom isnan definition to work across differences between\n        // implementations on various platforms. While this should happen in ANGLE\n        // we still see differences between android and windows (on chrome) when\n        // using isnan directly.\n        defineSpecialNaN = `\n      bool isnan_custom(float val) {\n        return (val > 0.0 || val < 0.0) ? false : val != 0.0;\n      }\n\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan_custom(val.x),\n          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n      }\n\n      #define isnan(value) isnan_custom(value)\n    `;\n        // In webgl 2 we do not need to specify a custom isinf so there is no\n        // need for a special INFINITY constant.\n        defineSpecialInf = ``;\n        defineRound = `\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n    }\n    else {\n        version = '';\n        attribute = 'attribute';\n        varyingVs = 'varying';\n        varyingFs = 'varying';\n        texture2D = 'texture2D';\n        output = 'gl_FragColor';\n        defineOutput = '';\n        // WebGL1 has no built in isnan so we define one here.\n        defineSpecialNaN = `\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    `;\n        defineSpecialInf = `\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    `;\n        defineRound = `\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n    }\n    return {\n        version,\n        attribute,\n        varyingVs,\n        varyingFs,\n        texture2D,\n        output,\n        defineOutput,\n        defineSpecialNaN,\n        defineSpecialInf,\n        defineRound\n    };\n}\n//# sourceMappingURL=glsl_version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Produces GLSL code that derives logical coordinates from a flat\n * index. The code performs integer division with each stride and decrements\n * the index until the index equals the final dimension coordinate.\n */\nexport function getLogicalCoordinatesFromFlatIndex(coords, shape, index = 'index') {\n    const strides = util.computeStrides(shape);\n    return strides\n        .map((stride, i) => {\n        const line1 = `int ${coords[i]} = ${index} / ${stride}`;\n        const line2 = i === strides.length - 1 ?\n            `int ${coords[i + 1]} = ${index} - ${coords[i]} * ${stride}` :\n            `index -= ${coords[i]} * ${stride}`;\n        return `${line1}; ${line2};`;\n    })\n        .join('');\n}\nfunction buildVec(x) {\n    if (x.length === 1) {\n        return `${x[0]}`;\n    }\n    return `vec${x.length}(${x.join(',')})`;\n}\n/**\n * Produces GLSL code that computes the dot product of the input x and y\n * vectors. Handles splitting inputs into increments of vec4s when necessary.\n */\nexport function dotify(x, y) {\n    if (x.length !== y.length) {\n        throw new Error(`Vectors to be dotted must be of the same length -` +\n            `got ${x.length} and ${y.length}`);\n    }\n    const slices = [];\n    const nearestVec4 = Math.floor(x.length / 4);\n    const nearestVec4Remainder = x.length % 4;\n    for (let i = 0; i < nearestVec4; i++) {\n        const xSlice = x.slice(i * 4, i * 4 + 4);\n        const ySlice = y.slice(i * 4, i * 4 + 4);\n        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n    }\n    if (nearestVec4Remainder !== 0) {\n        let xSlice = x.slice(nearestVec4 * 4);\n        let ySlice = y.slice(nearestVec4 * 4);\n        if (xSlice.length === 1) {\n            xSlice = xSlice.map(d => `float(${d})`);\n            ySlice = ySlice.map(d => `float(${d})`);\n        }\n        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n    }\n    return slices.map((d, i) => `dot(${d})`).join('+');\n}\n/**\n * Produces GLSL that computes the flat index from 3D coordinates.\n */\nexport function getFlatIndexFrom3D(shape) {\n    const strides = util.computeStrides(shape).map(d => d.toString());\n    return `\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;\n  }\n`;\n}\nexport const ENCODE_FLOAT_SNIPPET = `\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`;\n//# sourceMappingURL=shader_compiler_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nconst { getBroadcastDims } = backend_util;\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nexport function makeShader(inputsInfo, outputShape, userCode, usesPackedTextures) {\n    const prefixSnippets = [];\n    inputsInfo.forEach(x => {\n        const size = util.sizeFromShape(x.shapeInfo.logicalShape);\n        // Snippet when we decided to upload the values as uniform.\n        if (x.shapeInfo.isUniform) {\n            prefixSnippets.push(`uniform float ${x.name}${size > 1 ? `[${size}]` : ''};`);\n        }\n        else {\n            prefixSnippets.push(`uniform sampler2D ${x.name};`);\n            prefixSnippets.push(`uniform int offset${x.name};`);\n        }\n    });\n    const inputPrefixSnippet = prefixSnippets.join('\\n');\n    const inputSamplingSnippet = inputsInfo\n        .map(x => getInputSamplingSnippet(x, outputShape, usesPackedTextures))\n        .join('\\n');\n    const outTexShape = outputShape.texShape;\n    const glsl = getGlslDifferences();\n    const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);\n    let outputSamplingSnippet;\n    let floatTextureSetOutputSnippet;\n    let shaderPrefix = getShaderPrefix(glsl);\n    if (outputShape.isPacked) {\n        outputSamplingSnippet =\n            getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n        floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);\n    }\n    else {\n        outputSamplingSnippet =\n            getOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n        floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);\n    }\n    if (usesPackedTextures) {\n        shaderPrefix += SHADER_PACKED_PREFIX;\n    }\n    const source = [\n        shaderPrefix, floatTextureSampleSnippet, floatTextureSetOutputSnippet,\n        inputPrefixSnippet, outputSamplingSnippet, inputSamplingSnippet, userCode\n    ].join('\\n');\n    return source;\n}\nfunction getSamplerFromInInfo(inInfo) {\n    const shape = inInfo.shapeInfo.logicalShape;\n    switch (shape.length) {\n        case 0:\n            return getSamplerScalar(inInfo);\n        case 1:\n            return getSampler1D(inInfo);\n        case 2:\n            return getSampler2D(inInfo);\n        case 3:\n            return getSampler3D(inInfo);\n        case 4:\n            return getSampler4D(inInfo);\n        case 5:\n            return getSampler5D(inInfo);\n        case 6:\n            return getSampler6D(inInfo);\n        default:\n            throw new Error(`${shape.length}-D input sampling` +\n                ` is not yet supported`);\n    }\n}\nfunction getPackedSamplerFromInInfo(inInfo) {\n    const shape = inInfo.shapeInfo.logicalShape;\n    switch (shape.length) {\n        case 0:\n            return getPackedSamplerScalar(inInfo);\n        case 1:\n            return getPackedSampler1D(inInfo);\n        case 2:\n            return getPackedSampler2D(inInfo);\n        case 3:\n            return getPackedSampler3D(inInfo);\n        default:\n            return getPackedSamplerND(inInfo);\n    }\n}\nfunction getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false) {\n    let res = '';\n    if (usesPackedTextures) {\n        res += getPackedSamplerFromInInfo(inInfo);\n    }\n    else {\n        res += getSamplerFromInInfo(inInfo);\n    }\n    const inShape = inInfo.shapeInfo.logicalShape;\n    const outShape = outShapeInfo.logicalShape;\n    if (inShape.length <= outShape.length) {\n        if (usesPackedTextures) {\n            res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);\n        }\n        else {\n            res += getSamplerAtOutputCoords(inInfo, outShapeInfo);\n        }\n    }\n    return res;\n}\nfunction getPackedOutputSamplingSnippet(outShape, outTexShape) {\n    switch (outShape.length) {\n        case 0:\n            return getOutputScalarCoords();\n        case 1:\n            return getOutputPacked1DCoords(outShape, outTexShape);\n        case 2:\n            return getOutputPacked2DCoords(outShape, outTexShape);\n        case 3:\n            return getOutputPacked3DCoords(outShape, outTexShape);\n        default:\n            return getOutputPackedNDCoords(outShape, outTexShape);\n    }\n}\nfunction getOutputSamplingSnippet(outShape, outTexShape) {\n    switch (outShape.length) {\n        case 0:\n            return getOutputScalarCoords();\n        case 1:\n            return getOutput1DCoords(outShape, outTexShape);\n        case 2:\n            return getOutput2DCoords(outShape, outTexShape);\n        case 3:\n            return getOutput3DCoords(outShape, outTexShape);\n        case 4:\n            return getOutput4DCoords(outShape, outTexShape);\n        case 5:\n            return getOutput5DCoords(outShape, outTexShape);\n        case 6:\n            return getOutput6DCoords(outShape, outTexShape);\n        default:\n            throw new Error(`${outShape.length}-D output sampling is not yet supported`);\n    }\n}\nfunction getFloatTextureSampleSnippet(glsl) {\n    return `\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${glsl.texture2D}(textureSampler, uv).r;\n    }\n  `;\n}\nfunction getFloatTextureSetRSnippet(glsl) {\n    return `\n    void setOutput(float val) {\n      ${glsl.output} = vec4(val, 0, 0, 0);\n    }\n  `;\n}\nfunction getFloatTextureSetRGBASnippet(glsl) {\n    return `\n    void setOutput(vec4 val) {\n      ${glsl.output} = val;\n    }\n  `;\n}\nfunction getShaderPrefix(glsl) {\n    const SHADER_PREFIX = `${glsl.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${glsl.varyingFs} vec2 resultUV;\n    ${glsl.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${glsl.defineSpecialNaN}\n    ${glsl.defineSpecialInf}\n    ${glsl.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${SAMPLE_1D_SNIPPET}\n    ${SAMPLE_2D_SNIPPET}\n    ${SAMPLE_3D_SNIPPET}\n  `;\n    return SHADER_PREFIX;\n}\nconst SAMPLE_1D_SNIPPET = `\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SAMPLE_2D_SNIPPET = `\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SAMPLE_3D_SNIPPET = `\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SHADER_PACKED_PREFIX = `\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;\nfunction getOutputScalarCoords() {\n    return `\n    int getOutputCoords() {\n      return 0;\n    }\n  `;\n}\nfunction getOutputPacked1DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (packedTexShape[0] === 1) {\n        return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);\n      }\n    `;\n    }\n    if (packedTexShape[1] === 1) {\n        return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);\n      }\n    `;\n    }\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);\n    }\n  `;\n}\nfunction getOutput1DCoords(shape, texShape) {\n    if (texShape[0] === 1) {\n        return `\n      int getOutputCoords() {\n        return int(resultUV.x * ${texShape[1]}.0);\n      }\n    `;\n    }\n    if (texShape[1] === 1) {\n        return `\n      int getOutputCoords() {\n        return int(resultUV.y * ${texShape[0]}.0);\n      }\n    `;\n    }\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      return resTexRC.x * ${texShape[1]} + resTexRC.y;\n    }\n  `;\n}\nfunction getOutputPacked3DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texelsInLogicalRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;\n}\nfunction getOutput3DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\nfunction getOutputPackedNDCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);\n    let texelsInBatchN = texelsInBatch;\n    let batches = ``;\n    let coords = 'b, r, c';\n    for (let b = 2; b < shape.length - 1; b++) {\n        texelsInBatchN *= shape[shape.length - b - 1];\n        batches = `\n      int b${b} = index / ${texelsInBatchN};\n      index -= b${b} * ${texelsInBatchN};\n    ` + batches;\n        coords = `b${b}, ` + coords;\n    }\n    return `\n    ivec${shape.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      ${batches}\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec${shape.length}(${coords});\n    }\n  `;\n}\nfunction getOutput4DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2'], shape);\n    return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec4(r, c, d, d2);\n    }\n  `;\n}\nfunction getOutput5DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3'], shape);\n    return `\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},\n                             ${texShape[1]}));\n\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `;\n}\nfunction getOutput6DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3', 'd4'], shape);\n    return `\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `;\n}\nfunction getOutputPacked2DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (util.arraysEqual(shape, texShape)) {\n        return `\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      }\n    `;\n    }\n    // texels needed to accommodate a logical row\n    const texelsInLogicalRow = Math.ceil(shape[1] / 2);\n    /**\n     * getOutputCoords\n     *\n     * resTexRC: The rows and columns of the texels. If you move over one\n     * texel to the right in the packed texture, you are moving over one column\n     * (not two).\n     *\n     * index: The texel index\n     */\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getOutput2DCoords(shape, texShape) {\n    if (util.arraysEqual(shape, texShape)) {\n        return `\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));\n      }\n    `;\n    }\n    if (shape[1] === 1) {\n        return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;\n    }\n    if (shape[0] === 1) {\n        return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;\n    }\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      int r = index / ${shape[1]};\n      int c = index - r * ${shape[1]};\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getFlatOffsetUniformName(texName) {\n    return `offset${texName}`;\n}\nfunction getPackedSamplerScalar(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}() {\n      return ${glsl.texture2D}(${texName}, halfCR);\n    }\n  `;\n}\nfunction getSamplerScalar(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    if (inputInfo.shapeInfo.isUniform) {\n        return `float ${funcName}() {return ${texName};}`;\n    }\n    const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;\n    if (texNumR === 1 && texNumC === 1) {\n        return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n    }\n    const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}() {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler1D(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${packedTexShape[0]}, ${packedTexShape[1]}, index);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler1D(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int index) {\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texShape = inputInfo.shapeInfo.texShape;\n    const tNumR = texShape[0];\n    const tNumC = texShape[1];\n    if (tNumC === 1 && tNumR === 1) {\n        return `\n      float ${funcName}(int index) {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    if (tNumC === 1) {\n        return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (tNumR === 1) {\n        return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n    float ${funcName}(int index) {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler2D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const glsl = getGlslDifferences();\n    if (texShape != null && util.arraysEqual(shape, texShape)) {\n        return `\n      vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n\n        return ${glsl.texture2D}(${texName}, uv);\n      }\n    `;\n    }\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const valuesPerRow = Math.ceil(shape[1] / 2);\n    return `\n    vec4 ${funcName}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler2D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    if (texShape != null && util.arraysEqual(shape, texShape)) {\n        const texNumR = texShape[0];\n        const texNumC = texShape[1];\n        return `\n    float ${funcName}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['row', 'col'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const offset = getFlatOffsetUniformName(texName);\n    if (texNumC === 1) {\n        // index is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    if (texNumR === 1) {\n        // index is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    return `\n  float ${funcName}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${shape[1]} + col + ${offset};\n    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n    return sampleTexture(${texName}, uv);\n  }\n`;\n}\nfunction getPackedSampler3D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (shape[0] === 1) {\n        const squeezedShape = shape.slice(1);\n        const keptDims = [1, 2];\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['b', 'row', 'col'];\n        return `\n        ${getPackedSamplerFromInInfo(newInputInfo)}\n        vec4 ${funcName}(int b, int row, int col) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n    }\n    const texNumR = packedTexShape[0];\n    const texNumC = packedTexShape[1];\n    const valuesPerRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler3D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride0 = shape[1] * shape[2];\n    const stride1 = shape[2];\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['row', 'col', 'depth'];\n        return `\n        ${getSamplerFromInInfo(newInputInfo)}\n        float ${funcName}(int row, int col, int depth) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${stride0}, ${stride1}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n        float ${funcName}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${texNumC}.0, ${texNumR}.0);\n          return sampleTexture(${texName}, uv);\n        }\n      `;\n    }\n    if (texNumC === stride1 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n      float ${funcName}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};\n        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n        return sampleTexture(${texName}, uv);\n      }\n  `;\n}\nfunction getPackedSamplerND(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const rank = shape.length;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texNumR = packedTexShape[0];\n    const texNumC = packedTexShape[1];\n    const valuesPerRow = Math.ceil(shape[rank - 1] / 2);\n    let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);\n    let params = `int b, int row, int col`;\n    let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;\n    for (let b = 2; b < rank - 1; b++) {\n        params = `int b${b}, ` + params;\n        texelsInBatch *= shape[rank - b - 1];\n        index = `b${b} * ${texelsInBatch} + ` + index;\n    }\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(${params}) {\n      int index = ${index};\n      int texR = index / ${texNumC};\n      int texC = index - texR * ${texNumC};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler4D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride2 = shape[3];\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${stride1}, ${stride2}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride2 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} +\n          depth * ${stride2} + depth2;\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler5D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride3 = shape[4];\n    const stride2 = shape[3] * stride3;\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2', 'depth3'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          depth3;\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${stride1}, ${stride2}, ${stride3}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride3 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3]},\n               ${shape[2] * shape[3]}, ${shape[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler6D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    const stride4 = shape[5];\n    const stride3 = shape[4] * stride4;\n    const stride2 = shape[3] * stride3;\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${stride4}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride4 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},\n               ${shape[2] * shape[3] * shape[4]},\n               ${shape[3] * shape[4]},\n               ${shape[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getUniformSampler(inputInfo) {\n    const texName = inputInfo.name;\n    const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n    if (inSize < 2) {\n        return `return ${texName};`;\n    }\n    return `\n    for (int i = 0; i < ${inSize}; i++) {\n      if (i == index) {\n        return ${texName}[i];\n      }\n    }\n  `;\n}\nfunction getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n    const texName = inputInfo.name;\n    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n    const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n    const inRank = inputInfo.shapeInfo.logicalShape.length;\n    const outRank = outShapeInfo.logicalShape.length;\n    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n    const type = getCoordsDataType(outRank);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet;\n    const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n    if (inRank === 0) {\n        coordsSnippet = '';\n    }\n    else if (outRank < 2 && broadcastDims.length >= 1) {\n        coordsSnippet = 'coords = 0;';\n    }\n    else {\n        coordsSnippet =\n            broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n                .join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n        unpackedCoordsSnippet = 'coords';\n    }\n    else {\n        unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n            .map((s, i) => `coords.${fields[i + rankDiff]}`)\n            .join(', ');\n    }\n    let output = `return outputValue;`;\n    const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n    const isInputScalar = inSize === 1;\n    const outSize = util.sizeFromShape(outShapeInfo.logicalShape);\n    const isOutputScalar = outSize === 1;\n    if (inRank === 1 && !isInputScalar && !isOutputScalar) {\n        output = `\n      return vec4(outputValue.xy, outputValue.xy);\n    `;\n    }\n    else if (isInputScalar && !isOutputScalar) {\n        if (outRank === 1) {\n            output = `\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `;\n        }\n        else {\n            output = `\n        return vec4(outputValue.x);\n      `;\n        }\n    }\n    else if (broadcastDims.length) {\n        const rows = inRank - 2;\n        const cols = inRank - 1;\n        if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {\n            output = `return vec4(outputValue.x);`;\n        }\n        else if (broadcastDims.indexOf(rows) > -1) {\n            output = `return vec4(outputValue.x, outputValue.y, ` +\n                `outputValue.x, outputValue.y);`;\n        }\n        else if (broadcastDims.indexOf(cols) > -1) {\n            output = `return vec4(outputValue.xx, outputValue.zz);`;\n        }\n    }\n    return `\n    vec4 ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});\n      ${output}\n    }\n  `;\n}\nfunction getSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n    const texName = inputInfo.name;\n    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n    const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n    const outTexShape = outShapeInfo.texShape;\n    const inTexShape = inputInfo.shapeInfo.texShape;\n    const inRank = inputInfo.shapeInfo.logicalShape.length;\n    const outRank = outShapeInfo.logicalShape.length;\n    if (!inputInfo.shapeInfo.isUniform && inRank === outRank &&\n        inputInfo.shapeInfo.flatOffset == null &&\n        util.arraysEqual(inTexShape, outTexShape)) {\n        return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, resultUV);\n      }\n    `;\n    }\n    const type = getCoordsDataType(outRank);\n    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet;\n    const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n    if (inRank === 0) {\n        coordsSnippet = '';\n    }\n    else if (outRank < 2 && broadcastDims.length >= 1) {\n        coordsSnippet = 'coords = 0;';\n    }\n    else {\n        coordsSnippet =\n            broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n                .join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n        unpackedCoordsSnippet = 'coords';\n    }\n    else {\n        unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n            .map((s, i) => `coords.${fields[i + rankDiff]}`)\n            .join(', ');\n    }\n    return `\n    float ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      return get${texFuncSnippet}(${unpackedCoordsSnippet});\n    }\n  `;\n}\nexport function getCoordsDataType(rank) {\n    if (rank <= 1) {\n        return 'int';\n    }\n    else if (rank === 2) {\n        return 'ivec2';\n    }\n    else if (rank === 3) {\n        return 'ivec3';\n    }\n    else if (rank === 4) {\n        return 'ivec4';\n    }\n    else if (rank === 5) {\n        return 'ivec5';\n    }\n    else if (rank === 6) {\n        return 'ivec6';\n    }\n    else {\n        throw Error(`GPU for rank ${rank} is not yet supported`);\n    }\n}\n/** Returns a new input info (a copy) that has a squeezed logical shape. */\nfunction squeezeInputInfo(inInfo, squeezedShape) {\n    // Deep copy.\n    const newInputInfo = JSON.parse(JSON.stringify(inInfo));\n    newInputInfo.shapeInfo.logicalShape = squeezedShape;\n    return newInputInfo;\n}\nfunction getSqueezedParams(params, keptDims) {\n    return keptDims.map(d => params[d]).join(', ');\n}\n//# sourceMappingURL=shader_compiler.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ArgMinMaxPackedProgram {\n    constructor(shape, windowSize, op, firstPass) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        util.assert(shape.length > 2, () => `Packed arg${op.charAt(0).toUpperCase() +\n            op.slice(1)} supports only inputs with rank above 2.`);\n        const inSize = shape[shape.length - 1];\n        const outSize = Math.ceil(inSize / windowSize);\n        this.outputShape = shape.slice(0, -1);\n        if (outSize > 1) {\n            this.outputShape.push(outSize);\n        }\n        if (!firstPass) {\n            this.variableNames.push('bestIndicesA');\n        }\n        const outShape = this.outputShape;\n        const rank = outShape.length;\n        const dtype = getCoordsDataType(rank);\n        const coords = getChannels('coords', rank);\n        let sourceLocSetup;\n        let sourceRank;\n        if (outSize === 1) {\n            sourceRank = rank + 1;\n            const sourceLocDType = getCoordsDataType(sourceRank);\n            sourceLocSetup = `\n        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 1]};\n        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 2]};\n        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 1]};\n        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 2]};`;\n        }\n        else {\n            sourceRank = rank;\n            sourceLocSetup = `\n        ${dtype} sourceLocR = coords;\n        ++${coords[rank - 1]};\n        ${dtype} sourceLocG = coords;\n        ++${coords[rank - 2]};\n        ${dtype} sourceLocA = coords;\n        --${coords[rank - 1]};\n        ${dtype} sourceLocB = coords;\n        --${coords[rank - 2]};`;\n        }\n        const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, sourceRank);\n        const inChannel = '.' + channels[sourceRank - 1]; // e.g. \".b\" for rank 3.\n        const intChannels = channels.map(x => 'int ' + x);\n        const srcRCoords = getChannels('sourceLocR', sourceRank - 1).concat('inIdx.r');\n        const srcGCoords = getChannels('sourceLocG', sourceRank - 1).concat('inIdx.g');\n        const srcBCoords = getChannels('sourceLocB', sourceRank - 1).concat('inIdx.b');\n        const srcACoords = getChannels('sourceLocA', sourceRank - 1).concat('inIdx.a');\n        const compOp = (op === 'max') ? 'greaterThan' : 'lessThan';\n        const fetchCandidateIdx = firstPass ? '' : `\n          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),\n                             getBestIndicesAChannel(${srcGCoords.join()}),\n                             getBestIndicesAChannel(${srcBCoords.join()}),\n                             getBestIndicesAChannel(${srcACoords.join()})));`;\n        const fetchValue = `vec4(\n            getAChannel(${srcRCoords.join()}),\n            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,\n            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;\n        const getBestIndicesAChannelSnippet = firstPass ? '' : `\n      float getBestIndicesAChannel(${intChannels.join()}) {\n        return getChannel(getBestIndicesA(${channels.join()}),\n                                          vec2(${channels.slice(-2).join()}));\n      }`;\n        this.userCode = `\n      float getAChannel(${intChannels.join()}) {\n        return getChannel(getA(${channels.join()}),\n                               vec2(${channels.slice(-2).join()}));\n      }\n      ${getBestIndicesAChannelSnippet}\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        bool hasNextCol = ${coords[rank - 1]} < ${outShape[rank - 1] - 1};\n        bool hasNextRow = ${coords[rank - 2]} < ${outShape[rank - 2] - 1};\n        ${sourceLocSetup}\n        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},\n          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${fetchValue};\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          inIdx = srcIdx;\n          ${fetchCandidateIdx}\n          vec4 candidate = ${fetchValue};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=argminmax_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AvgPool2DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const avgMultiplier = 1 / (filterHeight * filterWidth);\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth};\n            wC+= ${dilationWidth}) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class AvgPool3DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy'];\n        this.outputShape = convInfo.inShape;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=avg_pool_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\n// (Ar + Ai)(Br + Bi) =\n// ArBr + ArBi + AiBr + AiBi = ArBr - AB + ArBi + AiBr\n// Yr = ArBr - AB\n// Yi = ArBi + AiBr\nexport const COMPLEX_MULTIPLY = {\n    REAL: 'return areal * breal - aimag * bimag;',\n    IMAG: 'return areal * bimag + aimag * breal;'\n};\nexport class BinaryOpComplexProgram {\n    constructor(op, aShape, bShape) {\n        this.variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        this.userCode = `\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${op}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_complex_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nconst CHECK_NAN_SNIPPET = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nexport const ADD = 'return a + b;';\nexport const SUB = 'return a - b;';\nexport const MUL = 'return a * b;';\n// We use native integer division to deal with floating point imprecision. Since\n// we implement floor division and glsl implements truncated division, we\n// correct for this by subtracting 1 from result when the result is negative and\n// there is a remainder.\nexport const INT_DIV = `\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`;\nexport const POW = `\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nif (b == 0.0) {\n  return 1.0;\n}\nreturn (round(mod(b, 2.0)) != 1) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`;\nexport const SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport const EQUAL = `return float(a == b);`;\nexport const NOT_EQUAL = `return float(a != b);`;\nexport const LESS = `return float(a < b);`;\nexport const LESS_EQUAL = `return float(a <= b);`;\nexport const GREATER = `return float(a > b);`;\nexport const GREATER_EQUAL = `return float(a >= b);`;\nexport const LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;\nexport const LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;\nexport const MAX = CHECK_NAN_SNIPPET + `\n  return max(a, b);\n`;\nexport const MIN = CHECK_NAN_SNIPPET + `\n  return min(a, b);\n`;\nexport const MOD = `if (b == 0.0) return NAN;\n  return mod(a, b);`;\nexport const ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;\nexport const PRELU = `return (a < 0.) ? b * a : a;`;\nexport class BinaryOpProgram {\n    constructor(op, aShape, bShape) {\n        this.variableNames = ['A', 'B'];\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        this.userCode = `\n      float binaryOperation(float a, float b) {\n        ${op}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nconst CHECK_NAN_SNIPPET = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\nexport const INT_DIV = `\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n`;\nexport const POW = `\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const PRELU = `\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;\nexport const ELU_DER = `\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`;\nexport const EQUAL = `\n  return vec4(equal(a, b));\n`;\nexport const NOT_EQUAL = `\n  return vec4(notEqual(a, b));\n`;\nexport const LESS = `\n  return vec4(lessThan(a, b));\n`;\nexport const LESS_EQUAL = `\n  return vec4(lessThanEqual(a, b));\n`;\nexport const GREATER = `\n  return vec4(greaterThan(a, b));\n`;\nexport const GREATER_EQUAL = `\n  return vec4(greaterThanEqual(a, b));\n`;\nexport const LOGICAL_AND = `\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`;\nexport const LOGICAL_OR = `\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`;\nexport const MAX = `\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const MIN = `\n  vec4 result = vec4(min(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const MOD = `\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport class BinaryOpPackedProgram {\n    constructor(op, aShape, bShape, checkOutOfBounds = false) {\n        this.variableNames = ['A', 'B'];\n        this.supportsBroadcasting = true;\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const rank = this.outputShape.length;\n        let checkOutOfBoundsString = '';\n        if (checkOutOfBounds) {\n            if (rank === 0 || util.sizeFromShape(this.outputShape) === 1) {\n                checkOutOfBoundsString = `\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;\n            }\n            else {\n                const dtype = getCoordsDataType(rank);\n                checkOutOfBoundsString = `\n          ${dtype} coords = getOutputCoords();\n        `;\n                if (rank === 1) {\n                    checkOutOfBoundsString += `\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;\n                }\n                else {\n                    const channels = getChannels('coords', rank);\n                    checkOutOfBoundsString += `\n            bool nextRowOutOfBounds =\n              (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};\n            bool nextColOutOfBounds =\n              (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `;\n                }\n            }\n        }\n        this.userCode = `\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${op}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${checkOutOfBoundsString}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ClipProgram {\n    constructor(aShape) {\n        this.variableNames = ['A'];\n        this.outputShape = aShape;\n        this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    `;\n    }\n    getCustomSetupFunc(min, max) {\n        return (gpgpu, webGLProgram) => {\n            if (this.minLoc == null) {\n                this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n                this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n            }\n            gpgpu.gl.uniform1f(this.minLoc, min);\n            gpgpu.gl.uniform1f(this.maxLoc, max);\n        };\n    }\n}\n//# sourceMappingURL=clip_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ClipPackedProgram {\n    constructor(aShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = aShape;\n        this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    `;\n    }\n    getCustomSetupFunc(min, max) {\n        return (gpgpu, webGLProgram) => {\n            if (this.minLoc == null) {\n                this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n                this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n            }\n            gpgpu.gl.uniform1f(this.minLoc, min);\n            gpgpu.gl.uniform1f(this.maxLoc, max);\n        };\n    }\n}\n//# sourceMappingURL=clip_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ComplexAbsProgram {\n    constructor(shape) {\n        this.variableNames = ['real', 'imag'];\n        this.outputShape = shape;\n        this.userCode = `\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `;\n    }\n}\n//# sourceMappingURL=complex_abs_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class ConcatProgram {\n    // Concats 2d tensors along axis=1. See comments in MathBackendWebGL.concat().\n    constructor(shapes) {\n        this.outputShape = [];\n        this.outputShape = backend_util.computeOutShape(shapes, 1 /* axis */);\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const offsets = new Array(shapes.length - 1);\n        offsets[0] = shapes[0][1];\n        for (let i = 1; i < offsets.length; i++) {\n            offsets[i] = offsets[i - 1] + shapes[i][1];\n        }\n        const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];\n        for (let i = 1; i < offsets.length; i++) {\n            const shift = offsets[i - 1];\n            snippets.push(`else if (yC < ${offsets[i]}) ` +\n                `setOutput(getT${i}(yR, yC-${shift}));`);\n        }\n        const lastIndex = offsets.length;\n        const lastShift = offsets[offsets.length - 1];\n        snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${snippets.join('\\n        ')}\n      }\n    `;\n    }\n}\n//# sourceMappingURL=concat_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ConcatPackedProgram {\n    constructor(shapes, axis) {\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = [];\n        this.outputShape = backend_util.computeOutShape(shapes, axis);\n        const shape = this.outputShape;\n        const rank = shape.length;\n        const dtype = getCoordsDataType(rank);\n        const coords = getChannels('coords', rank);\n        const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank);\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const offsets = new Array(shapes.length - 1);\n        offsets[0] = shapes[0][axis];\n        for (let i = 1; i < offsets.length; i++) {\n            offsets[i] = offsets[i - 1] + shapes[i][axis];\n        }\n        const channel = channels[axis];\n        const lastChannels = channels.slice(-2);\n        const allChannels = channels.join();\n        let getValueSnippet = `if (${channel} < ${offsets[0]}) {\n        return getChannel(\n            getT0(${allChannels}), vec2(${lastChannels.join()}));\n        }`;\n        for (let i = 1; i < offsets.length; i++) {\n            const shift = offsets[i - 1];\n            // Note: the >= comparison below may seem unnecessary given the check\n            // above but is needed to workaround branch execution issues on some\n            // devices. It makes all the conditions exclusive without relying on\n            // execution order.\n            getValueSnippet += `\n        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i - 1]}) {\n          return getChannel(\n            getT${i}(${shiftedChannels(channels, channel, shift)}),\n            vec2(${shiftedChannels(lastChannels, channel, shift)}));\n        }`;\n        }\n        const lastIndex = offsets.length;\n        const shift = offsets[offsets.length - 1];\n        getValueSnippet += `\n        return getChannel(\n          getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),\n          vec2(${shiftedChannels(lastChannels, channel, shift)}));`;\n        this.userCode = `\n      float getValue(${channels.map(x => 'int ' + x)}) {\n        ${getValueSnippet}\n      }\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${coords}), 0., 0., 0.);\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} + 1;\n        if (${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.g = getValue(${coords});\n        }\n\n        ${coords[rank - 2]} = ${coords[rank - 2]} + 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]}) {\n          result.a = getValue(${coords});\n        }\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} - 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]} &&\n            ${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.b = getValue(${coords});\n        }\n        setOutput(result);\n      }\n    `;\n    }\n}\n/**\n * Return an expression for coordinates into a vector where a given channel\n * will be offset by [shift].\n *\n * @param channels the channels to consider\n * @param channel the channel we want shifted\n * @param shift  the amount to subtract from the channel.\n *\n * @returns a string of the form 'x, y-[shift], z' where any one channel can\n * have the shift applied.\n */\nfunction shiftedChannels(channels, channel, shift) {\n    const channelIdx = channels.indexOf(channel);\n    const res = channels.map((c, idx) => {\n        if (idx === channelIdx) {\n            return `${c} - ${shift}`;\n        }\n        else {\n            return c;\n        }\n    });\n    return res.join();\n}\n//# sourceMappingURL=concat_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Conv2DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              if (${isChannelsLast}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv2DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        const rowDim = isChannelsLast ? 1 : 2;\n        const colDim = isChannelsLast ? 2 : 3;\n        const channelDim = isChannelsLast ? 3 : 1;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${channelDim}];\n\n        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n\n              if (${isChannelsLast}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv3DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.userCode = `\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {\n            int xF = wF + yF * ${strideDepth} - ${padFront};\n\n            if (xF < 0 || xF >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n              int xR = wR + yR * ${strideHeight} - ${padTop};\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n                int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv3DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padFront = filterDepth - 1 - convInfo.padInfo.front;\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;\n\n          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${filterDepth} - 1 - wF;\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${filterHeight} - 1 - wR;\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${filterWidth} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthwiseConv2DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${channelMul} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class DepthwiseConv2DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${channelMul}; dm++) {\n              int d2 = d1 * ${channelMul} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_backprop_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Conv2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivationWeights = false) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n        const inputDepthVec4Remainder = convInfo.inChannels % 4;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const rowDim = isChannelsLast ? 1 : 2;\n        const colDim = isChannelsLast ? 2 : 3;\n        const channelDim = isChannelsLast ? 3 : 1;\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivationWeights) {\n                activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivationWeights) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${channelDim}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${convInfo.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${inputDepthVec4Remainder === 1}) {\n\n              if (${isChannelsLast}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              }\n\n            } else if (${inputDepthVec4Remainder === 2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${inputDepthVec4Remainder === 3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\nexport class Conv3DProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n        const inputDepthVec4Remainder = convInfo.inChannels % 4;\n        this.userCode = `\n      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          int xF = xFCorner + wF * ${dilationDepth};\n\n          if (xF < 0 || xF >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            int xR = xRCorner + wR * ${dilationHeight};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${inputDepthVec4Remainder === 1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);\n              } else if (${inputDepthVec4Remainder === 2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${inputDepthVec4Remainder === 3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthwiseConv2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const xNumRows = convInfo.inHeight;\n        const xNumCols = convInfo.inWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${xNumRows}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${xNumCols}) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport class DepthwiseConvPacked2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['x', 'W'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = convInfo.outShape;\n        const xNumRows = convInfo.inHeight;\n        const xNumCols = convInfo.inWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const texelsAcross = filterWidth;\n        let mainLoop = `int xR; int xC; int xCOffset;`;\n        for (let r = 0; r < filterHeight; r++) {\n            for (let c = 0; c < filterWidth; c++) {\n                mainLoop += `\n          vec4 xTexelR${r}C${c * 2} = vec4(0.);\n          vec4 wR${r}C${c} = vec4(0.);\n          vec4 xR${r}C${c} = vec4(0.);`;\n            }\n        }\n        /**\n         * This vectorized implementation works by gathering the values needed for\n         * each output channel's dot product into vec4's and then multiplying them\n         * all together (this happens in the final double for-loop below). Most of\n         * the main loop consists of constructing these vec4's with the minimum\n         * number of texture2D calls, which means making use of all four returned\n         * values from a texture2D call at once.\n         */\n        for (let r = 0; r < filterHeight; r++) {\n            for (let texelC = 0; texelC < texelsAcross; texelC++) {\n                const c = texelC * 2;\n                mainLoop += `\n          xR = xRCorner + ${r * dilationHeight};\n          xC = xCCorner + ${c * dilationWidth};\n        `;\n                if (strideWidth === 1) {\n                    if (c < filterWidth) {\n                        // If padding is odd, the outer texels have to be composed.\n                        if (padLeft % 2 === 1) {\n                            // TODO: Ensure vec4 previous does not result in redundant sample,\n                            // and avoid setting xTexelRC's that exceed the boundary in the\n                            // first place rather than resetting them to vec4(0)).\n                            // To compute xCOffset:\n                            // - If padding is odd, we must add 1 to ensure we ask for an\n                            // even-numbered row.\n                            // - We subtract 2 to access the previous texel.\n                            mainLoop += `\n                xCOffset = xC + 1;\n                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= ${xNumCols}) {\n                    xTexelR${r}C${c}.zw = vec2(0.);\n                  }\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xCOffset = xC + 1 - 2;\n                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  vec4 previous = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= ${xNumCols}) {\n                    previous.zw = vec2(0.);\n                  }\n\n                  xR${r}C${c} = vec4(previous.zw, xTexelR${r}C${c}.xy);\n                } else {\n                  xR${r}C${c} = vec4(0, 0, xTexelR${r}C${c}.xy);\n                }\n              `;\n                        }\n                        else {\n                            // Padding is even, so xRC corresponds to a single texel.\n                            mainLoop += `\n                if(xR >= 0 && xR < ${xNumRows} && xC >= 0 && xC < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xR${r}C${c} = xTexelR${r}C${c};\n              `;\n                        }\n                        if (c + 1 < filterWidth) {\n                            // If dilation is even, the second entry should match the first\n                            // (either both are composed or both are single samples). But if\n                            // dilation is odd, then the second entry should be the opposite\n                            // of the first (if the first is composed, the second is a single\n                            // sample, and vice versa.)\n                            const nextTexelOffset = padLeft % 2 === 0 ?\n                                util.nearestLargerEven(dilationWidth) :\n                                dilationWidth;\n                            if ((dilationWidth % 2 === 0 && padLeft % 2 === 1) ||\n                                (dilationWidth % 2 !== 0 && padLeft % 2 !== 1)) {\n                                mainLoop += `\n                  xCOffset = xC + ${padLeft % 2} + ${nextTexelOffset};\n\n                  if(xR >= 0 && xR < ${xNumRows} &&\n                    xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                  }\n                `;\n                                // If dilation > 1 then the xRC's will not be able to share any\n                                // values, so each xRC will require two unique calls to getX.\n                                if (dilationWidth > 1) {\n                                    mainLoop += `\n                    xCOffset -= 2;\n                    if(xR >= 0 && xR < ${xNumRows} &&\n                      xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                      xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n                    } else {\n                      xTexelR${r}C${c} = vec4(0.);\n                    }\n                  `;\n                                }\n                                mainLoop += `\n                  xR${r}C${c + 1} = vec4(\n                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.xy);\n                `;\n                            }\n                            else {\n                                mainLoop += `\n                  xCOffset = xC + ${nextTexelOffset};\n\n                  if(xR >= 0 && xR < ${xNumRows} &&\n                    xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                  }\n\n                  xR${r}C${c + 1} = xTexelR${r}C${c + 2};\n                `;\n                            }\n                        }\n                    }\n                }\n                else { // stride > 1\n                    if (c < filterWidth) {\n                        mainLoop += `\n              if(xR >= 0 && xR < ${xNumRows}) {\n            `;\n                        // Depending on whether padLeft is even or odd, we want either the\n                        // xy or zw channels from X texels for xR${r}C${c}. If padLeft is\n                        // even, xR${r}C${c + 1} is simply the zw channels of texels we've\n                        // already sampled. But if padLeft is odd, xR${r}C{$c + 1}.zw will\n                        // need to come from the xy channels of a new texel, hence the `vec4\n                        // final` initialized below.\n                        if (padLeft % 2 === 1) {\n                            mainLoop += `\n                xCOffset = xC + 1 - ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < ${xNumCols}) {\n                  xTexelR${r}C${c + 2} = getX(batch, xR, xC + 1, d1);\n                } else {\n                  xTexelR${r}C${c + 2} = vec4(0.);\n                }\n\n                xR${r}C${c} = vec4(\n                  xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n              `;\n                            if (c + 1 < filterWidth) {\n                                mainLoop += `\n                  vec4 final = vec4(0.);\n                  xCOffset = xC + 1 + ${strideWidth};\n                  if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xR${r}C${c + 1} = vec4(xTexelR${r}C${c + 2}.xy, final.xy);\n                `;\n                            }\n                        }\n                        else {\n                            mainLoop += `\n                if(xC >= 0 && xC < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xCOffset = xC + ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${r}C${c + 2} = vec4(0.);\n                }\n\n                xR${r}C${c} = vec4(\n                  xTexelR${r}C${c}.xy, xTexelR${r}C${c + 2}.xy);\n              `;\n                            if (c + 1 < filterWidth) {\n                                mainLoop += `\n                  xR${r}C${c + 1} = vec4(\n                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n                `;\n                            }\n                        }\n                        mainLoop += `}`;\n                    }\n                }\n                if (c < filterWidth) {\n                    mainLoop += `\n            vec4 wTexelR${r}C${c} = getW(${r}, ${c}, d1, q);\n            wR${r}C${c} = vec4(wTexelR${r}C${c}.xz, wTexelR${r}C${c}.xz);\n          `;\n                    if (c + 1 < filterWidth) {\n                        mainLoop += `\n              vec4 wTexelR${r}C${c + 1} = getW(${r}, ${c + 1}, d1, q);\n              wR${r}C${c + 1} =\n                vec4(wTexelR${r}C${c + 1}.xz, wTexelR${r}C${c + 1}.xz);`;\n                    }\n                }\n            }\n        }\n        for (let r = 0; r < filterHeight; r++) {\n            for (let c = 0; c < filterWidth; c++) {\n                mainLoop += `dotProd += xR${r}C${c} * wR${r}C${c};`;\n            }\n        }\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 dotProd = vec4(0.);\n\n        ${mainLoop}\n\n        vec4 result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_packed_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class CropAndResizeProgram {\n    constructor(imageShape, boxShape, cropSize, method, extrapolationValue) {\n        this.variableNames = ['Image', 'Boxes', 'BoxInd'];\n        this.outputShape = [];\n        const [batch, imageHeight, imageWidth, depth] = imageShape;\n        const [numBoxes,] = boxShape;\n        const [cropHeight, cropWidth] = cropSize;\n        this.outputShape = [numBoxes, cropHeight, cropWidth, depth];\n        const methodId = method === 'bilinear' ? 1 : 0;\n        const [inputHeightFloat, inputWidthFloat] = [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];\n        const [heightRatio, heightScale, inY] = cropHeight > 1 ?\n            [\n                `${(imageHeight - 1) / (cropHeight - 1)}`,\n                '(y2-y1) * height_ratio',\n                `y1*${inputHeightFloat} + float(y)*(height_scale)`,\n            ] :\n            [\n                '0.0',\n                '0.0',\n                `0.5 * (y1+y2) * ${inputHeightFloat}`,\n            ];\n        const [widthRatio, widthScale, inX] = cropWidth > 1 ?\n            [\n                `${(imageWidth - 1) / (cropWidth - 1)}`,\n                '(x2-x1) * width_ratio',\n                `x1*${inputWidthFloat} + float(x)*(width_scale)`,\n            ] :\n            [\n                '0.0',\n                '0.0',\n                `0.5 * (x1+x2) * ${inputWidthFloat}`,\n            ];\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc\n        this.userCode = `\n      const float height_ratio = float(${heightRatio});\n      const float width_ratio = float(${widthRatio});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${batch}) {\n          return;\n        }\n\n        float height_scale = ${heightScale};\n        float width_scale = ${widthScale};\n\n        float in_y = ${inY};\n        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n        float in_x = ${inX};\n        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${methodId} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=crop_and_resize_gpu.js.map", "import { getCoordsDataType } from './shader_compiler';\nexport class CumSumProgram {\n    constructor(shape, exclusive, reverse) {\n        this.variableNames = ['x'];\n        this.outputShape = shape;\n        const rank = shape.length;\n        const val = exclusive ? '0.0' : `getX(${getCoords(rank, 'coords')})`;\n        const length = shape[shape.length - 1];\n        let condition = '';\n        let idxString = '';\n        // When exclusive is set, the cumsum op becomes roll op that copies the\n        // value from the previous index based on the direction specified by the\n        // reverse flag.\n        if (exclusive) {\n            condition = reverse ? `end != ${length - 1}` : 'end != 0';\n            idxString = reverse ? 'end + 1' : 'end - 1';\n        }\n        else {\n            condition = reverse ? `end + pow2 < ${length}` : 'end >= pow2';\n            idxString = (reverse ? 'end + pow2' : 'end - pow2');\n        }\n        this.userCode = `\n      uniform float index;\n      void main() {\n        ${getCoordsDataType(rank)} coords = getOutputCoords();\n        int end = ${getFinalCoord(rank, 'coords')};\n        float val = ${val};\n        int pow2 = int(pow(2.0, index));\n        if (${condition}) {\n          int idx = ${idxString};\n          ${getFinalCoord(rank, 'coords')} = idx;\n          val += getX(${getCoords(rank, 'coords')});\n        }\n        setOutput(val);\n      }\n    `;\n    }\n    getCustomSetupFunc(index) {\n        return (gpgpu, webGLProgram) => {\n            if (this.index == null) {\n                this.index = gpgpu.getUniformLocation(webGLProgram, 'index');\n            }\n            gpgpu.gl.uniform1f(this.index, index);\n        };\n    }\n}\nfunction getCoords(rank, name) {\n    if (rank === 1) {\n        return `${name}`;\n    }\n    else if (rank === 2) {\n        return `${name}.x, ${name}.y`;\n    }\n    else if (rank === 3) {\n        return `${name}.x, ${name}.y, ${name}.z`;\n    }\n    else if (rank === 4) {\n        return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n    }\n    else {\n        throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n    }\n}\nfunction getFinalCoord(rank, name) {\n    if (rank === 1) {\n        return `${name}`;\n    }\n    else if (rank === 2) {\n        return `${name}.y`;\n    }\n    else if (rank === 3) {\n        return `${name}.z`;\n    }\n    else if (rank === 4) {\n        return `${name}.w`;\n    }\n    else {\n        throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n    }\n}\n//# sourceMappingURL=cumsum_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nimport { getDenseTexShape, PackingScheme } from './tex_util';\nexport class DecodeMatrixProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        this.outPackingScheme = PackingScheme.DENSE;\n        const texShape = getDenseTexShape(outputShape);\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=decode_matrix_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nimport { getDenseTexShape, PackingScheme } from './tex_util';\nexport class DecodeMatrixPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outPackingScheme = PackingScheme.DENSE;\n        const texShape = getDenseTexShape(outputShape);\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=decode_matrix_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthToSpaceProgram {\n    constructor(outputShape, blockSize, dataFormat) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        this.outputShape = outputShape;\n        this.blockSize = blockSize;\n        this.dataFormat = dataFormat;\n        this.userCode = `\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${blockSize};\n      int offset_h = imod(h, ${blockSize});\n      int in_w = w / ${blockSize};\n      int offset_w = imod(w, ${blockSize});\n      int offset_d = (offset_h * ${blockSize} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `;\n    }\n    getHeightCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[1]`;\n        }\n        else {\n            return `coords[2]`;\n        }\n    }\n    getWidthCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[2]`;\n        }\n        else {\n            return `coords[3]`;\n        }\n    }\n    getDepthCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[3]`;\n        }\n        else {\n            return `coords[1]`;\n        }\n    }\n    getOutputDepthSize() {\n        if (this.dataFormat === 'NHWC') {\n            return this.outputShape[3];\n        }\n        else {\n            return this.outputShape[1];\n        }\n    }\n    getInputSamplingString() {\n        if (this.dataFormat === 'NHWC') {\n            return `getX(b, in_h, in_w, in_d)`;\n        }\n        else {\n            return `getX(b, in_d, in_h, in_w)`;\n        }\n    }\n}\n//# sourceMappingURL=depth_to_space_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DiagProgram {\n    constructor(size) {\n        this.variableNames = ['X'];\n        this.outputShape = [size, size];\n        this.userCode = `\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=diag_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport { ENCODE_FLOAT_SNIPPET } from './shader_compiler_util';\nimport { TextureUsage } from './tex_util';\nexport class EncodeFloatProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.outTexUsage = TextureUsage.DOWNLOAD;\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_float_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport { ENCODE_FLOAT_SNIPPET } from './shader_compiler_util';\nimport { TextureUsage } from './tex_util';\nexport class EncodeFloatPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = false;\n        this.outTexUsage = TextureUsage.DOWNLOAD;\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_float_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nexport class EncodeMatrixProgram {\n    constructor(outputShape, texShape, inputIsUnsignedByte = false) {\n        this.variableNames = ['A'];\n        const glsl = getGlslDifferences();\n        const [height, width] = texShape;\n        this.outputShape = outputShape;\n        let output = `result`;\n        if (inputIsUnsignedByte) {\n            output = `floor(result * 255. + 0.5)`;\n        }\n        this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / ${width};\n        int c = imod(flatIndex, ${width});\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n        vec4 values = ${glsl.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${glsl.output} = vec4(${output}, 0., 0., 0.);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_matrix_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\n/*\nThis is how the shader encodes a tensor with shape = [2, 3, 5]\n(indices are [batch, row, col]).\n\n000|001   002|003   004|xxx   020|021   022|023   024|xxx\n-------   -------   -------   -------   -------   -------\n010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\n100|101   102|103   104|xxx   120|121   122|123   124|xxx\n-------   -------   -------   -------   -------   -------\n110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\nSingle texels contain only values from the same batch, and from adjacent rows\nand columns.\n */\nexport class EncodeMatrixPackedProgram {\n    constructor(outputShape, texShape, inputIsUnsignedByte = false) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        const glsl = getGlslDifferences();\n        const [height, width] = texShape;\n        this.outputShape = outputShape;\n        let mainLoop = '';\n        let output = 'result';\n        if (inputIsUnsignedByte) {\n            output = 'floor(result * 255. + 0.5)';\n        }\n        for (let row = 0; row <= 1; row++) {\n            for (let col = 0; col <= 1; col++) {\n                const channel = row * 2 + col;\n                mainLoop += `\n          localCoords = coords;\n          if(localCoords[2] + ${col} < ${outputShape[2]}) {\n            localCoords[2] += ${col};\n            if(localCoords[1] + ${row} < ${outputShape[1]}) {\n              localCoords[1] += ${row};\n\n              flatIndex = getFlatIndex(localCoords);\n              offset = imod(flatIndex, 4);\n\n              flatIndex = idiv(flatIndex, 4, 1.);\n\n              r = flatIndex / ${width};\n              c = imod(flatIndex, ${width});\n              uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n              values = ${glsl.texture2D}(A, uv);\n\n              if(offset == 0) {\n                result[${channel}] = values[0];\n              } else if(offset == 1) {\n                result[${channel}] = values[1];\n              } else if(offset == 2) {\n                result[${channel}] = values[2];\n              } else {\n                result[${channel}] = values[3];\n              }\n            }\n          }\n        `;\n            }\n        }\n        this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        vec4 result = vec4(0.);\n        int flatIndex, r, c, offset;\n        ivec3 localCoords;\n        vec2 uv;\n        vec4 values;\n\n        ${mainLoop}\n\n        ${glsl.output} = ${output};\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_matrix_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const COMPLEX_FFT = {\n    REAL: 'return real * expR - imag * expI;',\n    IMAG: 'return real * expI + imag * expR;'\n};\nexport class FFTProgram {\n    constructor(op, inputShape, inverse) {\n        this.variableNames = ['real', 'imag'];\n        const innerDim = inputShape[1];\n        this.outputShape = inputShape;\n        const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;\n        const resultDenominator = inverse ? `${innerDim}.0` : '1.0';\n        this.userCode = `\n      const float exponentMultiplier = ${exponentMultiplierSnippet};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${op}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${innerDim});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${innerDim}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=fft_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FillProgram {\n    constructor(shape, value) {\n        this.outputShape = [];\n        this.variableNames = ['x'];\n        this.outputShape = shape;\n        this.userCode = `\n      uniform float value;\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    `;\n    }\n    getCustomSetupFunc(value) {\n        return (gpgpu, webGLProgram) => {\n            if (this.valueLoc == null) {\n                this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'value');\n            }\n            gpgpu.gl.uniform1f(this.valueLoc, value);\n        };\n    }\n}\n//# sourceMappingURL=fill_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class GatherProgram {\n    constructor(aShape, indicesLength, axis) {\n        this.variableNames = ['A', 'indices'];\n        const outputShape = aShape.slice();\n        outputShape[axis] = indicesLength;\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const sourceCoords = getSourceCoords(aShape, axis);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n    }\n}\nfunction getSourceCoords(aShape, axis) {\n    const rank = aShape.length;\n    if (rank > 4) {\n        throw Error(`Gather for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n        return `int(getIndices(resRC))`;\n    }\n    const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n    const sourceCoords = [];\n    for (let i = 0; i < aShape.length; i++) {\n        if (i === axis) {\n            sourceCoords.push(`int(getIndices(${currentCoords[i]}))`);\n        }\n        else {\n            sourceCoords.push(`${currentCoords[i]}`);\n        }\n    }\n    return sourceCoords.join();\n}\n//# sourceMappingURL=gather_gpu.js.map", "import { getCoordsDataType } from './shader_compiler';\nexport class GatherNDProgram {\n    constructor(sliceDim, strides, shape) {\n        this.sliceDim = sliceDim;\n        this.strides = strides;\n        this.variableNames = ['x', 'indices'];\n        this.outputShape = shape;\n        const stridesType = getCoordsDataType(strides.length);\n        const dtype = getCoordsDataType(shape.length);\n        const strideString = this.sliceDim > 1 ? 'strides[j]' : 'strides';\n        this.userCode = `\n        ${stridesType} strides = ${stridesType}(${this.strides});\n         void main() {\n          ${dtype} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${strideString};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `;\n    }\n}\n//# sourceMappingURL=gather_nd_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\nexport function createVertexShader(gl) {\n    const glsl = getGlslDifferences();\n    const vertexShaderSource = `${glsl.version}\n    precision highp float;\n    ${glsl.attribute} vec3 clipSpacePos;\n    ${glsl.attribute} vec2 uv;\n    ${glsl.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;\n    return webgl_util.createVertexShader(gl, vertexShaderSource);\n}\nexport function createVertexBuffer(gl) {\n    // [x y z u v] * [upper-left, lower-left, upper-right, lower-right]\n    const vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);\n    return webgl_util.createStaticVertexBuffer(gl, vertexArray);\n}\nexport function createIndexBuffer(gl) {\n    // OpenGL (and WebGL) have \"CCW == front\" winding\n    const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);\n    return webgl_util.createStaticIndexBuffer(gl, triangleVertexIndices);\n}\nfunction createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {\n    webgl_util.validateTextureSize(width, height);\n    const texture = webgl_util.createTexture(gl);\n    const tex2d = gl.TEXTURE_2D;\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(tex2d, texture));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));\n    webgl_util.callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null));\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n    return texture;\n}\nexport function getInternalFormatForFloat32MatrixTexture(textureConfig) {\n    return textureConfig.internalFormatFloat;\n}\nexport function createFloat32MatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT);\n}\nexport function getInternalFormatForFloat16MatrixTexture(textureConfig) {\n    return textureConfig.internalFormatHalfFloat;\n}\nexport function createFloat16MatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);\n}\nexport function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) {\n    return textureConfig.downloadTextureFormat;\n}\nexport function createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE);\n}\nexport function getInternalFormatForPackedMatrixTexture(textureConfig) {\n    return textureConfig.internalFormatPackedFloat;\n}\nexport function createPackedMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT);\n}\nexport function getInternalFormatForFloat16PackedMatrixTexture(textureConfig) {\n    return textureConfig.internalFormatPackedHalfFloat;\n}\nexport function createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat);\n}\nexport function bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {\n    const posOffset = 0; // x is the first buffer element\n    const uvOffset = 3 * 4; // uv comes after [x y z]\n    const stride = (3 * 4) + (2 * 4); // xyz + uv, each entry is 4-byte float.\n    webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));\n    const success = webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'clipSpacePos', vertexBuffer, 3, stride, posOffset);\n    return success &&\n        webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'uv', vertexBuffer, 2, stride, uvOffset);\n}\nexport function uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) {\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n    let dataForUpload, texelDataType, internalFormat;\n    if (data instanceof Uint8Array) {\n        dataForUpload = new Uint8Array(width * height * 4);\n        texelDataType = gl.UNSIGNED_BYTE;\n        internalFormat = gl.RGBA;\n    }\n    else {\n        dataForUpload = new Float32Array(width * height * 4);\n        texelDataType = gl.FLOAT;\n        internalFormat = textureConfig.internalFormatPackedFloat;\n    }\n    dataForUpload.set(data);\n    webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload));\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function uploadPixelDataToTexture(gl, texture, pixels) {\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n    if (pixels.data instanceof Uint8Array) {\n        webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));\n    }\n    else {\n        webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n    }\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function createBufferFromOutputTexture(gl2, rows, columns, textureConfig) {\n    // Create and bind the buffer.\n    const buffer = gl2.createBuffer();\n    webgl_util.callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer));\n    // Initialize the buffer to the size of the texture in bytes.\n    const bytesPerFloat = 4;\n    const valuesPerTexel = 4;\n    const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;\n    webgl_util.callAndCheck(gl2, () => gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));\n    // Enqueue a command on the GPU command queue to copy of texture into the\n    // buffer.\n    webgl_util.callAndCheck(gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));\n    webgl_util.callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));\n    return buffer;\n}\nexport function downloadFloat32MatrixFromBuffer(gl, buffer, size) {\n    const gl2 = gl;\n    const downloadTarget = new Float32Array(size);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n    return downloadTarget;\n}\nexport function downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {\n    const [w, h] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    const numChannels = 4;\n    const downloadTarget = new Uint8Array(tex_util.getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));\n    webgl_util.callAndCheck(gl, () => gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget));\n    // By wrapping the buffer in a Float32Array, we use native browser IEEE 754\n    // decoding of the 4 bytes that back each 32 bit float.\n    return new Float32Array(downloadTarget.buffer);\n}\nexport function downloadPackedMatrixFromBuffer(gl, buffer, batch, rows, cols, physicalRows, physicalCols, textureConfig) {\n    const gl2 = gl;\n    const downloadTarget = new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n    return downloadTarget;\n}\nexport function downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {\n    const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);\n    webgl_util.callAndCheck(gl, () => gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));\n    return packedRGBA;\n}\n//# sourceMappingURL=gpgpu_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { getWebGLContext, setWebGLContext } from './canvas_util';\nimport * as gpgpu_util from './gpgpu_util';\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\nexport class GPGPUContext {\n    constructor(gl) {\n        this.outputTexture = null;\n        this.program = null;\n        this.disposed = false;\n        this.vertexAttrsAreBound = false;\n        this.itemsToPoll = [];\n        const glVersion = env().getNumber('WEBGL_VERSION');\n        if (gl != null) {\n            this.gl = gl;\n            setWebGLContext(glVersion, gl);\n        }\n        else {\n            this.gl = getWebGLContext(glVersion);\n        }\n        // WebGL 2.0 enables texture floats without an extension.\n        let COLOR_BUFFER_FLOAT = 'WEBGL_color_buffer_float';\n        const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n        if (env().getNumber('WEBGL_VERSION') === 1) {\n            const TEXTURE_FLOAT = 'OES_texture_float';\n            const TEXTURE_HALF_FLOAT = 'OES_texture_half_float';\n            this.textureFloatExtension =\n                webgl_util.getExtensionOrThrow(this.gl, TEXTURE_FLOAT);\n            if (webgl_util.hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {\n                this.textureHalfFloatExtension =\n                    webgl_util.getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);\n            }\n            else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n                throw new Error('GL context does not support half float textures, yet the ' +\n                    'environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n            }\n            this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);\n            if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n                this.colorBufferHalfFloatExtension =\n                    webgl_util.getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);\n            }\n            else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n                throw new Error('GL context does not support color renderable half floats, yet ' +\n                    'the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n            }\n        }\n        else {\n            COLOR_BUFFER_FLOAT = 'EXT_color_buffer_float';\n            if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {\n                this.colorBufferFloatExtension =\n                    this.gl.getExtension(COLOR_BUFFER_FLOAT);\n            }\n            else if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n                this.colorBufferHalfFloatExtension =\n                    this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n            }\n            else {\n                throw new Error('GL context does not support color renderable floats');\n            }\n        }\n        this.vertexBuffer = gpgpu_util.createVertexBuffer(this.gl);\n        this.indexBuffer = gpgpu_util.createIndexBuffer(this.gl);\n        this.framebuffer = webgl_util.createFramebuffer(this.gl);\n        this.textureConfig =\n            tex_util.getTextureConfig(this.gl, this.textureHalfFloatExtension);\n    }\n    get debug() {\n        return env().getBool('DEBUG');\n    }\n    dispose() {\n        if (this.disposed) {\n            return;\n        }\n        if (this.program != null) {\n            console.warn('Disposing a GPGPUContext that still has a bound WebGLProgram.' +\n                ' This is probably a resource leak, delete the program with ' +\n                'GPGPUContext.deleteProgram before disposing.');\n        }\n        if (this.outputTexture != null) {\n            console.warn('Disposing a GPGPUContext that still has a bound output matrix ' +\n                'texture.  This is probably a resource leak, delete the output ' +\n                'matrix texture with GPGPUContext.deleteMatrixTexture before ' +\n                'disposing.');\n        }\n        const gl = this.gl;\n        webgl_util.callAndCheck(gl, () => gl.finish());\n        webgl_util.callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));\n        webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));\n        this.disposed = true;\n    }\n    createFloat32MatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createFloat16MatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createUnsignedBytesMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    uploadPixelDataToTexture(texture, pixels) {\n        this.throwIfDisposed();\n        gpgpu_util.uploadPixelDataToTexture(this.gl, texture, pixels);\n    }\n    uploadDenseMatrixToTexture(texture, width, height, data) {\n        this.throwIfDisposed();\n        gpgpu_util.uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig);\n    }\n    createFloat16PackedMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createPackedMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    deleteMatrixTexture(texture) {\n        this.throwIfDisposed();\n        if (this.outputTexture === texture) {\n            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n            this.outputTexture = null;\n        }\n        webgl_util.callAndCheck(this.gl, () => this.gl.deleteTexture(texture));\n    }\n    downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) {\n        return this.downloadMatrixDriver(texture, () => gpgpu_util.downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig));\n    }\n    downloadPackedMatrixFromBuffer(buffer, batch, rows, columns, physicalRows, physicalCols) {\n        return gpgpu_util.downloadPackedMatrixFromBuffer(this.gl, buffer, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);\n    }\n    downloadFloat32MatrixFromBuffer(buffer, size) {\n        return gpgpu_util.downloadFloat32MatrixFromBuffer(this.gl, buffer, size);\n    }\n    createBufferFromTexture(texture, rows, columns) {\n        this.bindTextureToFrameBuffer(texture);\n        const result = gpgpu_util.createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig);\n        this.unbindTextureToFrameBuffer();\n        return result;\n    }\n    createAndWaitForFence() {\n        const fenceContext = this.createFence(this.gl);\n        return this.pollFence(fenceContext);\n    }\n    createFence(gl) {\n        let query;\n        let isFencePassed;\n        if (env().getBool('WEBGL_FENCE_API_ENABLED')) {\n            const gl2 = gl;\n            const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);\n            gl.flush();\n            isFencePassed = () => {\n                const status = gl2.clientWaitSync(sync, 0, 0);\n                return status === gl2.ALREADY_SIGNALED ||\n                    status === gl2.CONDITION_SATISFIED;\n            };\n            query = sync;\n        }\n        else if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n            query = this.beginQuery();\n            this.endQuery();\n            isFencePassed = () => this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n        }\n        else {\n            // If we have no way to fence, return true immediately. This will fire in\n            // WebGL 1.0 when there is no disjoint query timer. In this case, because\n            // the fence passes immediately, we'll immediately ask for a download of\n            // the texture, which will cause the UI thread to hang.\n            isFencePassed = () => true;\n        }\n        return { query, isFencePassed };\n    }\n    downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {\n        return this.downloadMatrixDriver(texture, () => gpgpu_util.downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));\n    }\n    createProgram(fragmentShaderSource) {\n        this.throwIfDisposed();\n        const gl = this.gl;\n        const fragmentShader = webgl_util.createFragmentShader(gl, fragmentShaderSource);\n        const vertexShader = gpgpu_util.createVertexShader(gl);\n        const program = webgl_util.createProgram(gl);\n        webgl_util.callAndCheck(gl, () => gl.attachShader(program, vertexShader));\n        webgl_util.callAndCheck(gl, () => gl.attachShader(program, fragmentShader));\n        webgl_util.linkProgram(gl, program);\n        if (this.debug) {\n            webgl_util.validateProgram(gl, program);\n        }\n        if (!this.vertexAttrsAreBound) {\n            this.setProgram(program);\n            this.vertexAttrsAreBound = gpgpu_util.bindVertexProgramAttributeStreams(gl, this.program, this.vertexBuffer);\n        }\n        return program;\n    }\n    deleteProgram(program) {\n        this.throwIfDisposed();\n        if (program === this.program) {\n            this.program = null;\n        }\n        if (program != null) {\n            webgl_util.callAndCheck(this.gl, () => this.gl.deleteProgram(program));\n        }\n    }\n    setProgram(program) {\n        this.throwIfDisposed();\n        this.program = program;\n        if ((this.program != null) && this.debug) {\n            webgl_util.validateProgram(this.gl, this.program);\n        }\n        webgl_util.callAndCheck(this.gl, () => this.gl.useProgram(program));\n    }\n    getUniformLocation(program, uniformName, shouldThrow = true) {\n        this.throwIfDisposed();\n        if (shouldThrow) {\n            return webgl_util.getProgramUniformLocationOrThrow(this.gl, program, uniformName);\n        }\n        else {\n            return webgl_util.getProgramUniformLocation(this.gl, program, uniformName);\n        }\n    }\n    getAttributeLocation(program, attribute) {\n        this.throwIfDisposed();\n        return webgl_util.callAndCheck(this.gl, () => this.gl.getAttribLocation(program, attribute));\n    }\n    getUniformLocationNoThrow(program, uniformName) {\n        this.throwIfDisposed();\n        return this.gl.getUniformLocation(program, uniformName);\n    }\n    setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) {\n        this.throwIfDisposed();\n        this.throwIfNoProgram();\n        webgl_util.bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit);\n    }\n    setOutputMatrixTexture(outputMatrixTexture, rows, columns) {\n        this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);\n    }\n    setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) {\n        this.throwIfDisposed();\n        const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n        this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);\n    }\n    setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n        this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);\n    }\n    setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n        throw new Error('setOutputPackedMatrixWriteRegion not implemented.');\n    }\n    debugValidate() {\n        if (this.program != null) {\n            webgl_util.validateProgram(this.gl, this.program);\n        }\n        webgl_util.validateFramebuffer(this.gl);\n    }\n    executeProgram() {\n        this.throwIfDisposed();\n        this.throwIfNoProgram();\n        const gl = this.gl;\n        if (this.debug) {\n            this.debugValidate();\n        }\n        webgl_util.callAndCheck(gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));\n    }\n    blockUntilAllProgramsCompleted() {\n        this.throwIfDisposed();\n        webgl_util.callAndCheck(this.gl, () => this.gl.finish());\n    }\n    getQueryTimerExtension() {\n        if (this.disjointQueryTimerExtension == null) {\n            this.disjointQueryTimerExtension =\n                webgl_util.getExtensionOrThrow(this.gl, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2 ?\n                    'EXT_disjoint_timer_query_webgl2' :\n                    'EXT_disjoint_timer_query');\n        }\n        return this.disjointQueryTimerExtension;\n    }\n    getQueryTimerExtensionWebGL2() {\n        return this.getQueryTimerExtension();\n    }\n    getQueryTimerExtensionWebGL1() {\n        return this.getQueryTimerExtension();\n    }\n    beginQuery() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            const query = gl2.createQuery();\n            gl2.beginQuery(ext.TIME_ELAPSED_EXT, query);\n            return query;\n        }\n        const ext = this.getQueryTimerExtensionWebGL1();\n        const query = ext.createQueryEXT();\n        ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);\n        return query;\n    }\n    endQuery() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            gl2.endQuery(ext.TIME_ELAPSED_EXT);\n            return;\n        }\n        const ext = this.getQueryTimerExtensionWebGL1();\n        ext.endQueryEXT(ext.TIME_ELAPSED_EXT);\n    }\n    async waitForQueryAndGetTime(query) {\n        await util.repeatedTry(() => this.disposed || // while testing contexts are created / disposed\n            // in rapid succession, so without this check we\n            // may poll for the query timer indefinitely\n            this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')));\n        return this.getQueryTime(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n    }\n    getQueryTime(query, queryTimerVersion) {\n        if (queryTimerVersion === 0) {\n            return null;\n        }\n        if (queryTimerVersion === 2) {\n            const gl2 = this.gl;\n            const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);\n            // Return milliseconds.\n            return timeElapsedNanos / 1000000;\n        }\n        else {\n            const ext = this.getQueryTimerExtensionWebGL1();\n            const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);\n            // Return milliseconds.\n            return timeElapsedNanos / 1000000;\n        }\n    }\n    isQueryAvailable(query, queryTimerVersion) {\n        if (queryTimerVersion === 0) {\n            return true;\n        }\n        if (queryTimerVersion === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);\n            if (this.disjoint == null) {\n                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n            }\n            return available && !this.disjoint;\n        }\n        else {\n            const ext = this.getQueryTimerExtensionWebGL1();\n            const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);\n            if (this.disjoint == null) {\n                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n            }\n            return available && !this.disjoint;\n        }\n    }\n    pollFence(fenceContext) {\n        return new Promise(resolve => {\n            this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());\n        });\n    }\n    pollItems() {\n        // Find the last query that has finished.\n        const index = linearSearchLastTrue(this.itemsToPoll.map(x => x.isDoneFn));\n        for (let i = 0; i <= index; ++i) {\n            const { resolveFn } = this.itemsToPoll[i];\n            resolveFn();\n        }\n        this.itemsToPoll = this.itemsToPoll.slice(index + 1);\n    }\n    addItemToPoll(isDoneFn, resolveFn) {\n        this.itemsToPoll.push({ isDoneFn, resolveFn });\n        if (this.itemsToPoll.length > 1) {\n            // We already have a running loop that polls.\n            return;\n        }\n        // Start a new loop that polls.\n        util.repeatedTry(() => {\n            this.pollItems();\n            // End the loop if no more items to poll.\n            return this.itemsToPoll.length === 0;\n        });\n    }\n    bindTextureToFrameBuffer(texture) {\n        this.throwIfDisposed();\n        webgl_util.bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);\n        if (this.debug) {\n            webgl_util.validateFramebuffer(this.gl);\n        }\n    }\n    unbindTextureToFrameBuffer() {\n        if (this.outputTexture != null) {\n            webgl_util.bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);\n            if (this.debug) {\n                webgl_util.validateFramebuffer(this.gl);\n            }\n        }\n        else {\n            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n        }\n    }\n    downloadMatrixDriver(texture, downloadAndDecode) {\n        this.bindTextureToFrameBuffer(texture);\n        const result = downloadAndDecode();\n        this.unbindTextureToFrameBuffer();\n        return result;\n    }\n    setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) {\n        this.throwIfDisposed();\n        const gl = this.gl;\n        webgl_util.bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);\n        if (this.debug) {\n            webgl_util.validateFramebuffer(gl);\n        }\n        this.outputTexture = outputMatrixTextureMaybePacked;\n        webgl_util.callAndCheck(gl, () => gl.viewport(0, 0, width, height));\n        webgl_util.callAndCheck(gl, () => gl.scissor(0, 0, width, height));\n    }\n    setOutputMatrixWriteRegionDriver(x, y, width, height) {\n        this.throwIfDisposed();\n        webgl_util.callAndCheck(this.gl, () => this.gl.scissor(x, y, width, height));\n    }\n    throwIfDisposed() {\n        if (this.disposed) {\n            throw new Error('Attempted to use disposed GPGPUContext.');\n        }\n    }\n    throwIfNoProgram() {\n        if (this.program == null) {\n            throw new Error('No GPU program is currently set.');\n        }\n    }\n}\n/**\n * Finds the index of the last true element using linear search.\n * Note: We can't do binary search because Chrome expects us to explicitly\n * test all fences before download:\n * https://github.com/tensorflow/tfjs/issues/1145\n */\nexport function linearSearchLastTrue(arr) {\n    let i = 0;\n    for (; i < arr.length; ++i) {\n        const isDone = arr[i]();\n        if (!isDone) {\n            break;\n        }\n    }\n    return i - 1;\n}\n//# sourceMappingURL=gpgpu_context.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport * as shader_compiler from './shader_compiler';\nexport function compileProgram(gpgpu, program, inputs, output) {\n    const userCode = program.userCode;\n    const inputInfos = inputs.map((input, i) => {\n        const shapeInfo = {\n            logicalShape: input.shape,\n            texShape: input.isUniform ? null : input.texData.texShape,\n            isUniform: input.isUniform,\n            isPacked: input.isUniform ? false : input.texData.isPacked,\n            flatOffset: null\n        };\n        if (input.texData != null && input.texData.slice != null &&\n            input.texData.slice.flatOffset > 0) {\n            shapeInfo.flatOffset = input.texData.slice.flatOffset;\n        }\n        return { name: program.variableNames[i], shapeInfo };\n    });\n    const inShapeInfos = inputInfos.map(x => x.shapeInfo);\n    const outShapeInfo = {\n        logicalShape: output.shape,\n        texShape: output.texData.texShape,\n        isUniform: false,\n        isPacked: output.texData.isPacked,\n        flatOffset: null\n    };\n    const source = shader_compiler.makeShader(inputInfos, outShapeInfo, userCode, program.packedInputs);\n    const webGLProgram = gpgpu.createProgram(source);\n    // Add special uniforms (NAN, INFINITY)\n    let infLoc = null;\n    const nanLoc = gpgpu.getUniformLocation(webGLProgram, 'NAN', false);\n    if (env().getNumber('WEBGL_VERSION') === 1) {\n        infLoc = gpgpu.getUniformLocation(webGLProgram, 'INFINITY', false);\n    }\n    // Add user-defined uniforms\n    const uniformLocations = {};\n    for (let i = 0; i < program.variableNames.length; i++) {\n        const varName = program.variableNames[i];\n        const shouldThrow = false;\n        uniformLocations[varName] =\n            gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);\n        uniformLocations[`offset${varName}`] =\n            gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);\n    }\n    return {\n        program,\n        source,\n        webGLProgram,\n        uniformLocations,\n        inShapeInfos,\n        outShapeInfo,\n        infLoc,\n        nanLoc,\n    };\n}\nfunction validateBinaryAndProgram(shapeInfos, inputs) {\n    if (shapeInfos.length !== inputs.length) {\n        throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but ` +\n            `was executed with ${inputs.length} inputs`);\n    }\n    shapeInfos.forEach((s, i) => {\n        const shapeA = s.logicalShape;\n        const input = inputs[i];\n        const shapeB = input.shape;\n        if (!util.arraysEqual(shapeA, shapeB)) {\n            throw Error(`Binary was compiled with different shapes than ` +\n                `the current args. Shapes ${shapeA} and ${shapeB} must match`);\n        }\n        // The input is uploaded as uniform.\n        if (s.isUniform && input.isUniform) {\n            return;\n        }\n        const texShapeA = s.texShape;\n        const texShapeB = input.isUniform ? null : input.texData.texShape;\n        if (!util.arraysEqual(texShapeA, texShapeB)) {\n            throw Error(`Binary was compiled with different texture shapes than the` +\n                ` current args. Shape ${texShapeA} and ${texShapeB} must match`);\n        }\n    });\n}\nexport function runProgram(gpgpu, binary, inputs, output, customSetup) {\n    validateBinaryAndProgram(binary.inShapeInfos, inputs);\n    validateBinaryAndProgram([binary.outShapeInfo], [output]);\n    const outTex = output.texData.texture;\n    const outTexShape = output.texData.texShape;\n    if (output.texData.isPacked) {\n        gpgpu.setOutputPackedMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n    }\n    else {\n        gpgpu.setOutputMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n    }\n    gpgpu.setProgram(binary.webGLProgram);\n    // Set special uniforms (NAN, INFINITY)\n    if (env().getNumber('WEBGL_VERSION') === 1) {\n        if (binary.infLoc !== null) {\n            gpgpu.gl.uniform1f(binary.infLoc, Infinity);\n        }\n    }\n    if (binary.nanLoc !== null) {\n        gpgpu.gl.uniform1f(binary.nanLoc, NaN);\n    }\n    // Set user-defined inputs\n    inputs.forEach((input, i) => {\n        const varName = binary.program.variableNames[i];\n        const varLoc = binary.uniformLocations[varName];\n        const varOffsetLoc = binary.uniformLocations[`offset${varName}`];\n        if (varLoc == null) {\n            // The compiler inferred that this variable is not used in this shader.\n            return;\n        }\n        if (input.isUniform) {\n            // Upload the values of the tensor as uniform.\n            if (util.sizeFromShape(input.shape) < 2) {\n                gpgpu.gl.uniform1f(varLoc, input.uniformValues[0]);\n            }\n            else {\n                let vals = input.uniformValues;\n                if (!(vals instanceof Float32Array)) {\n                    vals = new Float32Array(vals);\n                }\n                gpgpu.gl.uniform1fv(varLoc, vals);\n            }\n            return;\n        }\n        // If the input was sliced, upload the flat offset index.\n        if (input.texData.slice != null && varOffsetLoc != null) {\n            gpgpu.gl.uniform1i(varOffsetLoc, input.texData.slice.flatOffset);\n        }\n        gpgpu.setInputMatrixTexture(input.texData.texture, varLoc, i);\n    });\n    if (customSetup != null) {\n        customSetup(gpgpu, binary.webGLProgram);\n    }\n    gpgpu.executeProgram();\n}\nexport function makeShaderKey(program, inputs, output) {\n    let keyInputs = '';\n    inputs.concat(output).forEach(x => {\n        const hasOffset = x.texData != null && x.texData.slice != null &&\n            x.texData.slice.flatOffset > 0;\n        const texShape = x.isUniform ? 'uniform' : x.texData.texShape;\n        keyInputs += `${x.shape}_${texShape}_${hasOffset}`;\n    });\n    const keyUserCode = program.userCode;\n    let key = program.constructor.name;\n    // Fast string concat. See https://jsperf.com/string-concatenation/14.\n    key += '_' + keyInputs + '_' + keyUserCode;\n    return key;\n}\n//# sourceMappingURL=gpgpu_math.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nexport class Im2ColPackedProgram {\n    constructor(outputShape, inputShape, convInfo) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        const { filterWidth, inChannels, strideWidth, strideHeight, padInfo, outWidth, dilationWidth, dilationHeight, dataFormat } = convInfo;\n        const { left, top } = padInfo;\n        const itemsPerBlockRow = inChannels * filterWidth;\n        const glsl = getGlslDifferences();\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const rowDim = isChannelsLast ? 0 : 1;\n        const colDim = isChannelsLast ? 1 : 2;\n        let unrolled = ``;\n        for (let row = 0; row <= 1; row++) {\n            for (let col = 0; col <= 1; col++) {\n                unrolled += `\n          blockIndex = rc.y + ${col};\n          pos = rc.x + ${row};\n\n          if(blockIndex < ${outputShape[1]} && pos < ${outputShape[0]}) {\n            offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${top};\n            d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});\n\n            if(d0 < ${inputShape[rowDim]} && d0 >= 0) {\n\n              offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${strideWidth}. - ${left}.);\n              d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${itemsPerBlockRow}.) / ${inChannels}.));\n\n              if(d1 < ${inputShape[colDim]} && d1 >= 0) {\n\n                ch = int(mod(float(pos), ${inChannels}.));\n\n                if (${isChannelsLast}) {\n                  innerDims = vec2(d1, ch);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;\n            }\n        }\n        this.userCode = `\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${unrolled}\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=im2col_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNProgram {\n    constructor(xShape, radius, bias, alpha, beta) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        const rad = radius;\n        const maxD = xShape[3] - 1;\n        this.outputShape = xShape;\n        // optimize pow(bias + alpha * sum, -beta)\n        // src: https://github.com/tensorflow/tensorflow/..\n        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n        // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n        let powOperator;\n        const basis = `float(${bias}) + float(${alpha}) * sum`;\n        if (beta === 0.5) {\n            powOperator = `inversesqrt(${basis})`;\n        }\n        else if (beta === 1.0) {\n            powOperator = `1.0/(${basis})`;\n        }\n        else {\n            powOperator = `exp(log(${basis}) * float(-${beta}));`;\n        }\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${rad}; j <= ${rad}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${maxD}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${powOperator};\n        setOutput(val);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNGradProgram {\n    constructor(inputShape, depthRadius, bias, alpha, beta) {\n        this.variableNames = ['inputImage', 'outputImage', 'dy'];\n        this.outputShape = [];\n        this.outputShape = inputShape;\n        this.depth = inputShape[3];\n        this.depthRadius = depthRadius;\n        this.bias = bias;\n        this.alpha = alpha;\n        this.beta = beta;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${depthRadius})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${depthRadius} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${alpha}) * norm + float(${bias});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${alpha})\n                * float(${beta})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${beta});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_grad_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNPackedProgram {\n    constructor(xShape, radius, bias, alpha, beta) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const rad = radius;\n        const maxD = xShape[3] - 1;\n        this.outputShape = xShape;\n        // optimize pow(bias + alpha * sum, -beta)\n        // src: https://github.com/tensorflow/tensorflow/..\n        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n        // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n        let powOperator;\n        const basis = `float(${bias}) + float(${alpha}) * sum`;\n        if (beta === 0.5) {\n            powOperator = `inversesqrt(${basis})`;\n        }\n        else if (beta === 1.0) {\n            powOperator = `1.0/(${basis})`;\n        }\n        else {\n            powOperator = `exp(log(${basis}) * float(-${beta}));`;\n        }\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${rad};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${rad}; j <= ${rad}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${powOperator};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MaxPool2DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'maxPos'];\n        this.outputShape = convInfo.inShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n          wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${effectiveFilterWidth} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class MaxPool3DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'maxPos'];\n        this.outputShape = convInfo.inShape;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n           wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${lastIndex} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                  wR * ${effectiveFilterWidth} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=max_pool_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MatMulPackedProgram {\n    constructor(aShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['matrixA', 'matrixB'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        const sharedDim = transposeA ? aShape[1] : aShape[2];\n        const sharedDimensionPacked = Math.ceil(sharedDim / 2);\n        const aSample = transposeA ? 'i * 2, rc.y' : 'rc.y, i * 2';\n        const bSample = transposeB ? 'rc.z, i * 2' : 'i * 2, rc.z';\n        const aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];\n        const bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const float sharedDimension = ${sharedDimensionPacked}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${sharedDimensionPacked}; i++) {\n          vec4 a = getMatrixA(rc.x, ${aSample});\n          vec4 b = getMatrixB(rc.x, ${bSample});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${aSwizzle[0]} * ${bSwizzle[0]});\n          result += (${aSwizzle[1]} * ${bSwizzle[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${addBiasSnippet}\n\n        ${applyActivationSnippet}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=mulmat_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MultinomialProgram {\n    constructor(batchSize, numOutcomes, numSamples) {\n        this.variableNames = ['probs'];\n        this.outputShape = [batchSize, numSamples];\n        this.userCode = `\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < ${numOutcomes - 1}; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(${numOutcomes - 1}));\n      }\n    `;\n    }\n    getCustomSetupFunc(seed) {\n        return (gpgpu, webGLProgram) => {\n            if (this.seedLoc == null) {\n                this.seedLoc = gpgpu.getUniformLocation(webGLProgram, 'seed');\n            }\n            gpgpu.gl.uniform1f(this.seedLoc, seed);\n        };\n    }\n}\n//# sourceMappingURL=multinomial_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class OneHotProgram {\n    constructor(numIndices, depth, onValue, offValue) {\n        this.variableNames = ['indices'];\n        this.outputShape = [numIndices, depth];\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${offValue}), float(${onValue}),\n                      float(index == coords.y)));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=onehot_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class PackProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        // Only input / output 3D tensors.\n        this.outputShape = outputShape;\n        const rank = outputShape.length;\n        if (rank === 0) {\n            this.userCode = `\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;\n        }\n        else {\n            const channels = getChannels('rc', rank);\n            const dtype = getCoordsDataType(rank);\n            const outOfBoundsCondition = getOutOfBoundsCondition(rank, outputShape, channels);\n            const setup = getSetup(rank, outputShape[outputShape.length - 1], outputShape[outputShape.length - 2], channels);\n            const output = getOutput(outputShape, channels);\n            this.userCode = `\n        void main() {\n          ${dtype} rc = getOutputCoords();\n\n          if(${outOfBoundsCondition}) {\n            setOutput(vec4(0));\n          } else {\n            ${setup}\n\n            setOutput(vec4(${output}));\n          }\n        }\n      `;\n        }\n    }\n}\nfunction getSourceCoordsArr(rank, dims) {\n    const coords = [];\n    for (let row = 0; row <= 1; row++) {\n        for (let col = 0; col <= 1; col++) {\n            let coord = `${row === 0 ? 'r' : 'rp1'}, ${col === 0 ? 'c' : 'cp1'}`;\n            for (let d = 2; d < rank; d++) {\n                coord = `${dims[dims.length - 1 - d]},` + coord;\n            }\n            coords.push(coord);\n        }\n    }\n    return coords;\n}\nfunction getOutOfBoundsCondition(rank, shape, dims) {\n    if (rank === 1) {\n        return `rc > ${shape[0]}`;\n    }\n    let cond = '';\n    for (let i = rank - 2; i < rank; i++) {\n        cond += `${dims[i]} >= ${shape[i]}`;\n        if (i < rank - 1) {\n            cond += '||';\n        }\n    }\n    return cond;\n}\nfunction getSetup(rank, cols, rows, dims) {\n    if (rank === 1) {\n        return '';\n    }\n    const innerDims = dims.slice(-2);\n    return `\n    int r = ${innerDims[0]};\n    int c = ${innerDims[1]};\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= ${cols};\n    bool rEdge = rp1 >= ${rows};\n  `;\n}\nfunction getOutput(shape, dims) {\n    const rank = shape.length;\n    const sourceCoords = getSourceCoordsArr(rank, dims);\n    if (rank === 1) {\n        return `getA(rc),\n            rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),\n            0, 0`;\n    }\n    return `getA(${sourceCoords[0]}),\n          cEdge ? 0. : getA(${sourceCoords[1]}),\n          rEdge ? 0. : getA(${sourceCoords[2]}),\n          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;\n}\n//# sourceMappingURL=pack_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class PadProgram {\n    constructor(xShape, paddings, constantValue) {\n        this.variableNames = ['x'];\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const type = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const unpackedCoords = ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);\n        if (rank === 1) {\n            this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float(${constantValue}));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `;\n            return;\n        }\n        this.userCode = `\n      ${type} start = ${type}(${start});\n      ${type} end = ${type}(${end});\n\n      void main() {\n        ${type} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float(${constantValue}));\n        } else {\n          ${type} coords = outC - start;\n          setOutput(getX(${unpackedCoords}));\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pad_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class PadPackedProgram {\n    constructor(xShape, paddings, constantValue) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const dtype = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const coords = getChannels('rc', rank);\n        const source = getChannels('source', rank);\n        const cLimit = `${coords[rank - 1]} < ${this.outputShape[rank - 1]}`;\n        const innerDims = rank === 1 ? 'source' : `vec2(${source.slice(-2).join()})`;\n        const componentSetup = [\n            `${dtype} rc = outputLoc;`, `${coords[rank - 1]} += 1;\n       if(${cLimit}) {\n      `,\n            rank === 1 ? '' : `}\n       rc = outputLoc;\n       ${coords[rank - 2]} += 1;\n       if(${coords[rank - 2]} < ${this.outputShape[rank - 2]}) {`,\n            rank === 1 ? '' : `  ${coords[rank - 1]} += 1;\n         if(${cLimit}) {`\n        ];\n        const paddingArea = rank === 1 ?\n            'rc < start || rc >= end' :\n            'any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))';\n        let mainLoop = '';\n        for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {\n            mainLoop += `\n        ${componentSetup[i]}\n        if (${paddingArea}) {\n          result[${i}] = float(${constantValue});\n        } else {\n          ${dtype} source = rc - start;\n          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n        }\n        mainLoop += (rank === 1 ? `} ` : `}}`);\n        this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pad_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Pool2DProgram {\n    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n        this.variableNames = ['x'];\n        if (poolType === 'avg' && computePositions) {\n            throw new Error('Cannot compute positions for average pool.');\n        }\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.outputShape = convInfo.outShape;\n        const isAvgPool = poolType === 'avg';\n        const batchFlattenPositionStr = `((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n        const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n        let initializationValue = '0.0';\n        if (!isAvgPool) {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n        }\n        if (computePositions) {\n            const compareOp = '>=';\n            this.userCode = `\n        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n        const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${compareOp} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${flattenPositions ? (includeBatchInIndex ? batchFlattenPositionStr :\n                flattenPositionStr) :\n                `wR * ${effectiveFilterWidth} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n            return;\n        }\n        const compareOp = 'max';\n        let returnValue = `${poolType}(${poolType}(${poolType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (poolType === 'avg') {\n            returnValue = `avgValue / count`;\n        }\n        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n        const filterWidthVec4Remainder = filterWidth % 4;\n        const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)\n            );\n\n            ${updateSnippet}\n          }\n\n          int xC = xCCorner + ${filterWidthNearestVec4};\n          if (${filterWidthVec4Remainder === 1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              initializationValue\n            );\n\n            ${updateSnippet}\n          }\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\nexport class Pool3DProgram {\n    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n        this.variableNames = ['x'];\n        if (poolType === 'avg' && computePositions) {\n            throw new Error('Cannot compute positions for average pool.');\n        }\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.outputShape = convInfo.outShape;\n        const isAvgPool = poolType === 'avg';\n        let initializationValue = '0.0';\n        if (!isAvgPool) {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n        }\n        if (computePositions) {\n            const compareOp = '>=';\n            this.userCode = `\n        const ivec3 strides =\n            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${effectiveFilterDepth};\n              wD += ${dilationDepth}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${effectiveFilterHeight};\n                wR += ${dilationHeight}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${effectiveFilterWidth};\n                  wC += ${dilationWidth}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${compareOp} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${flattenPositions ?\n                (includeBatchInIndex ?\n                    `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` :\n                    `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch`) :\n                `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                      wR * ${effectiveFilterWidth} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n            return;\n        }\n        const compareOp = 'max';\n        let returnValue = `${poolType}(${poolType}(${poolType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (poolType === 'avg') {\n            returnValue = `avgValue / count`;\n        }\n        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n        const filterWidthVec4Remainder = filterWidth % 4;\n        const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        this.userCode = `\n      const ivec3 strides =\n        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)\n              );\n\n              ${updateSnippet}\n            }\n\n            int xC = xCCorner + ${filterWidthNearestVec4};\n            if (${filterWidthVec4Remainder === 1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                initializationValue\n              );\n\n              ${updateSnippet}\n            }\n          }\n          setOutput(${returnValue});\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pool_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ReduceProgram {\n    constructor(reduceInfo, reduceType) {\n        this.variableNames = ['x'];\n        const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n        this.outputShape = [batchSize, outSize];\n        let initializationValue = '0.0';\n        let compareOp = ``;\n        if (reduceType === 'prod') {\n            initializationValue = '1.0';\n        }\n        else if (reduceType === 'min') {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '1.0 / 1e-20';\n            compareOp = `min`;\n        }\n        else if (reduceType === 'max') {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n            compareOp = `max`;\n        }\n        let returnValue = `${reduceType}(${reduceType}(${reduceType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (reduceType === 'sum') {\n            returnValue = `sumValue`;\n        }\n        else if (reduceType === 'prod') {\n            returnValue = `prodValue`;\n        }\n        else if (reduceType === 'all') {\n            returnValue = `allValue`;\n        }\n        else if (reduceType === 'any') {\n            returnValue = `anyValue`;\n        }\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        let updateSnippet = `\n      if (${reduceType === 'sum'}) {\n        sumValue += dot(values, ones);\n      } else if (${reduceType === 'prod'}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        let vecType = `vec4`;\n        if (reduceType === 'all') {\n            initializationValue = '1.0';\n            updateSnippet = `\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `;\n            vecType = `bvec4`;\n        }\n        else if (reduceType === 'any') {\n            initializationValue = '0.0';\n            updateSnippet = `\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `;\n            vecType = `bvec4`;\n        }\n        let checkOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n        }\n        this.userCode = `\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        vec4 minMaxValue = vec4(${initializationValue});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\n//# sourceMappingURL=reduce_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as shader_util from './shader_compiler_util';\nexport class ReshapePackedProgram {\n    constructor(outputShape, inputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        let mainLoop = ``;\n        for (let i = 0; i < 4; i++) {\n            let thisRC = `thisRC = rc;`;\n            if (i % 2 === 1) {\n                thisRC += `thisRC.z += 1;`;\n            }\n            if (i > 1) {\n                thisRC += `thisRC.y += 1;`;\n            }\n            mainLoop += `\n        ${thisRC}\n        ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ''}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${i}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${i > 0 ? '}' : ''}\n      `;\n        }\n        this.userCode = `\n      ${getReshapedInputCoords(inputShape)}\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${outputShape[1]};\n        int cols = ${outputShape[2]};\n\n        ${mainLoop}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\nfunction getReshapedInputCoords(shape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n    return `\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\n//# sourceMappingURL=reshape_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearBackpropProgram {\n    constructor(dy, x, alignCorners) {\n        this.variableNames = ['dy'];\n        this.outputShape = [];\n        this.outputShape = x.shape;\n        const [, xHeight, xWidth,] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        // In the backwards pass, we want to find the pixels that were generated for\n        // each pixel in the input image the forward pass and add the corresponding\n        // coefficient from dy to the gradient (with some interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearPackedProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        this.userCode = `\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,\n                                     ${oldWidth}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${depth - 1};\n        bool hasNextRow = coords.z < ${newWidth - 1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeNearestNeigborBackpropProgram {\n    constructor(dy, x, alignCorners) {\n        this.variableNames = ['dy'];\n        this.outputShape = [];\n        this.outputShape = x.shape;\n        const [, xHeight, xWidth,] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        // In the backwards pass, we want to find the pixels that were generated for\n        // each pixel in the input image the forward pass and add the corresponding\n        // coefficient from dy to the gradient (with some interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${effectiveXSize[0]}) *\n                (float(dyR) / float(${effectiveYSize[0]}));\n\n            float sourceFracCol =\n                float(${effectiveXSize[1]}) *\n                  (float(dyC) / float(${effectiveYSize[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${xHeight}) - 1),\n                ${alignCorners} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${xWidth}) - 1),\n                ${alignCorners} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_nearest_neighbor_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeNearestNeighborProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        // When align corners is false, we rounds the value with floor.\n        const roundBase = alignCorners ? '0.5' : '0.0';\n        this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_nearest_neighbor_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class ReverseProgram {\n    constructor(xShape, axis) {\n        this.variableNames = ['x'];\n        const rank = xShape.length;\n        if (rank > 4) {\n            throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n        }\n        this.outputShape = xShape;\n        if (rank === 1) {\n            this.userCode = `\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${xShape[0]} - coord - 1));\n        }\n      `;\n            return;\n        }\n        const getInCoord = (i) => {\n            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n                return `${xShape[i]} - coords[${i}] - 1`;\n            }\n            return `coords[${i}]`;\n        };\n        const inCoords = xShape.map((_, i) => getInCoord(i)).join(',');\n        const type = getCoordsDataType(rank);\n        this.userCode = `\n      void main() {\n        ${type} coords = getOutputCoords();\n        setOutput(getX(${inCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=reverse_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ReversePackedProgram {\n    constructor(xShape, axis) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const rank = xShape.length;\n        if (rank > 4) {\n            throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n        }\n        this.outputShape = xShape;\n        const channels = getChannels('rc', rank);\n        const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;\n        const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;\n        const type = getCoordsDataType(rank);\n        if (rank === 1) {\n            this.userCode = `\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${xShape[0]} - rc - 1),\n            ${xShape[0]} - rc - 1);\n          if(${nextColumn}){\n              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),\n                ${xShape[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `;\n        }\n        else {\n            this.userCode = `\n        void main() {\n          ${type} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${getR(channels.slice())};\n          if(${nextColumn}){\n            result.g = ${getG(channels.slice())};\n          }\n          if(${nextRow}) {\n            result.b = ${getB(channels.slice())};\n            if(${nextColumn}) {\n              result.a = ${getA(channels.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `;\n        }\n        function getR(channels) {\n            return getChannel(channels);\n        }\n        function getG(channels) {\n            channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getB(channels) {\n            channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getA(channels) {\n            channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n            channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getChannel(channels) {\n            const inCoordsArray = xShape.map((_, i) => getInCoord(i, channels));\n            const inCoords = inCoordsArray.join(',');\n            const innerDims = inCoordsArray.slice(-2).join(',');\n            return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;\n        }\n        function getInCoord(i, channels1) {\n            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n                return `${xShape[i]} - ${channels1[i]} - 1`;\n            }\n            else {\n                return `${channels1[i]}`;\n            }\n        }\n    }\n}\n//# sourceMappingURL=reverse_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class ScatterProgram {\n    constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true) {\n        this.variableNames = ['updates', 'indices', 'defaultValue'];\n        this.outputShape = shape;\n        const stridesType = getCoordsDataType(strides.length);\n        const dtype = getCoordsDataType(shape.length);\n        let indicesString = '';\n        if (indicesRank === 1) {\n            indicesString = 'i';\n        }\n        else if (indicesRank === 2) {\n            indicesString = 'i, j';\n        }\n        const indicesSnippet = `getIndices(${indicesString})`;\n        let updatesString = '';\n        if (updatesRank === 1) {\n            updatesString = 'i';\n        }\n        else if (updatesRank === 2) {\n            updatesString = 'i, coords[1]';\n        }\n        const updatesSnippet = `getUpdates(${updatesString})`;\n        const strideString = sliceDim > 1 ? 'strides[j]' : 'strides';\n        this.userCode = `\n        ${stridesType} strides = ${stridesType}(${strides});\n\n        void main() {\n          ${dtype} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${updateSize}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${sliceDim}; j++) {\n              int index = round(${indicesSnippet});\n              flattenedIndex += index * ${strideString};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${updatesSnippet};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `;\n    }\n}\n//# sourceMappingURL=scatter_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class SegmentOpProgram {\n    constructor(segOpInfo, segOpType) {\n        this.variableNames = ['x', 'segmentIds'];\n        const windowSize = segOpInfo.windowSize;\n        const batchSize = segOpInfo.batchSize;\n        const inSize = segOpInfo.inSize;\n        const numSegments = segOpInfo.numSegments;\n        const outSize = numSegments * Math.ceil(inSize / windowSize);\n        this.outputShape = [batchSize, outSize];\n        const initializationValue = '0.0';\n        const returnValue = `sumValue`;\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        const updateSnippet = `\n        sumValue += dot(values, segFilter);\n    `;\n        let checkValueOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkValueOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n        }\n        let checkSegmentIdOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkSegmentIdOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return -1.0;\n        }\n      `;\n        }\n        this.userCode = `\n      const float initializationValue = ${initializationValue};\n\n      float getValue(int batch, int inIdx) {\n        ${checkValueOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${checkSegmentIdOutOfBounds}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${numSegments})) * float(${windowSize}));\n        int currentSeg = int(mod(float(outIdx), float(${numSegments})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\n//# sourceMappingURL=segment_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class SelectProgram {\n    constructor(cRank, shape, rank) {\n        this.variableNames = ['c', 'a', 'b'];\n        this.outputShape = shape;\n        let cCoords;\n        let abCoords;\n        if (rank > 4) {\n            throw Error(`Where for rank ${rank} is not yet supported`);\n        }\n        if (rank === 1) {\n            abCoords = `resRC`;\n            cCoords = `resRC`;\n        }\n        else {\n            const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n            const cCoordVars = [];\n            const abCoordVars = [];\n            for (let i = 0; i < shape.length; i++) {\n                abCoordVars.push(`${currentCoords[i]}`);\n                if (i < cRank) {\n                    cCoordVars.push(`${currentCoords[i]}`);\n                }\n            }\n            cCoords = cCoordVars.join();\n            abCoords = abCoordVars.join();\n        }\n        const dtype = getCoordsDataType(rank);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        float cVal = getC(${cCoords});\n        if (cVal >= 1.0) {\n          setOutput(getA(${abCoords}));\n        } else {\n          setOutput(getB(${abCoords}));\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=select_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class SliceProgram {\n    constructor(destSize) {\n        this.variableNames = ['source'];\n        this.outputShape = destSize;\n        this.rank = destSize.length;\n        const dtype = getCoordsDataType(this.rank);\n        const uniformPart = `uniform int start[${this.rank}];`;\n        const sourceCoords = getCoords(this.rank);\n        let body;\n        const coordSum = destSize.map((_, i) => {\n            return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`;\n        });\n        body = `\n        ${dtype} sourceLoc;\n        ${dtype} coords = getOutputCoords();\n        ${coordSum.join('\\n')}\n      `;\n        this.userCode = `\n      ${uniformPart}\n      void main() {\n        ${body}\n        setOutput(getSource(${sourceCoords}));\n      }\n    `;\n    }\n    getCustomSetupFunc(start) {\n        if (start.length !== this.rank) {\n            throw Error(`The rank (${this.rank}) of the program must match the ` +\n                `length of start (${start.length})`);\n        }\n        return (gpgpu, webGLProgram) => {\n            if (this.startLoc == null) {\n                this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n                if (this.startLoc == null) {\n                    // This means the compiler has optimized and realized it doesn't need\n                    // the uniform.\n                    return;\n                }\n            }\n            gpgpu.gl.uniform1iv(this.startLoc, start);\n        };\n    }\n}\nconst coords = ['x', 'y', 'z', 'w', 'u', 'v'];\nfunction getCoords(rank) {\n    if (rank === 1) {\n        return 'sourceLoc';\n    }\n    else if (rank <= 6) {\n        return coords.slice(0, rank).map(x => 'sourceLoc.' + x).join(',');\n    }\n    else {\n        throw Error(`Slicing for rank ${rank} is not yet supported`);\n    }\n}\n//# sourceMappingURL=slice_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class SlicePackedProgram {\n    constructor(destSize) {\n        this.variableNames = ['source'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = destSize;\n        this.rank = destSize.length;\n        const dtype = getCoordsDataType(this.rank);\n        const coords = getChannels('coords', this.rank);\n        const sourceLoc = getChannels('sourceLoc', this.rank);\n        const innerDims = this.rank === 1 ? 'sourceLoc' : `vec2(${sourceLoc.slice(-2).join()})`;\n        const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;\n        const upperRow = `\n      result.x = ${getChannel};\n      if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n        ++${sourceLoc[this.rank - 1]};\n        result.y = ${getChannel};\n        --${sourceLoc[this.rank - 1]};\n      }\n    `;\n        const lowerRow = this.rank === 1 ? '' : `\n      --${coords[this.rank - 1]};\n      if (++${coords[this.rank - 2]} < ${destSize[this.rank - 2]}) {\n        ++${sourceLoc[this.rank - 2]};\n        result.z = ${getChannel};\n        if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n          ++${sourceLoc[this.rank - 1]};\n          result.w = ${getChannel};\n        }\n      }\n    `;\n        const sourceLocSetup = this.rank <= 4 ?\n            `sourceLoc = coords +\n            ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` :\n            destSize.map((_, i) => `${sourceLoc[i]} = ${coords[i]} + start[${i}];`)\n                .join('\\n');\n        this.userCode = `\n      uniform int start[${this.rank}];\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        ${dtype} sourceLoc;\n        ${sourceLocSetup}\n        vec4 result = vec4(0.);\n        ${upperRow}\n        ${lowerRow}\n        setOutput(result);\n      }\n    `;\n    }\n    getCustomSetupFunc(start) {\n        if (start.length !== this.rank) {\n            throw Error(`The rank (${this.rank}) of the program must match the ` +\n                `length of start (${start.length})`);\n        }\n        return (gpgpu, webGLProgram) => {\n            if (this.startLoc == null) {\n                this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n                if (this.startLoc == null) {\n                    // This means the compiler has optimized and realized it doesn't need\n                    // the uniform.\n                    return;\n                }\n            }\n            gpgpu.gl.uniform1iv(this.startLoc, start);\n        };\n    }\n}\n//# sourceMappingURL=slice_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class StridedSliceProgram {\n    constructor(begin, strides, size) {\n        this.variableNames = ['x'];\n        this.outputShape = size;\n        const rank = size.length;\n        const inputDtype = getCoordsDataType(size.length);\n        const dtype = getCoordsDataType(size.length);\n        let newCoords = '';\n        if (rank === 1) {\n            newCoords = 'coords * strides + begin';\n        }\n        else {\n            let outputAxis = 0;\n            newCoords =\n                size.map((_, i) => {\n                    outputAxis++;\n                    return size.length === 1 ?\n                        `coords * strides[${i}] + begin[${i}]` :\n                        `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;\n                })\n                    .join(',');\n        }\n        this.userCode = `\n      ${inputDtype} begin = ${inputDtype}(${begin});\n      ${inputDtype} strides = ${inputDtype}(${strides});\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        setOutput(getX(${newCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=strided_slice_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { getInternalFormatForFloat16MatrixTexture, getInternalFormatForFloat16PackedMatrixTexture, getInternalFormatForFloat32MatrixTexture, getInternalFormatForPackedMatrixTexture, getInternalFormatForUnsignedBytesMatrixTexture } from './gpgpu_util';\nimport { getPackedMatrixTextureShapeWidthHeight, getUnpackedMatrixTextureShapeWidthHeight, PhysicalTextureType, TextureUsage } from './tex_util';\nexport class TextureManager {\n    constructor(gpgpu) {\n        this.gpgpu = gpgpu;\n        this.numUsedTextures = 0;\n        this.numFreeTextures = 0;\n        this._numBytesAllocated = 0;\n        this._numBytesFree = 0; // How many bytes that have been allocated\n        // are available for reuse.\n        this.freeTextures = {};\n        this.logEnabled = false;\n        this.usedTextures = {};\n    }\n    acquireTexture(shapeRC, usage, isPacked) {\n        const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);\n        const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);\n        if (!(shapeKey in this.freeTextures)) {\n            this.freeTextures[shapeKey] = [];\n        }\n        if (!(shapeKey in this.usedTextures)) {\n            this.usedTextures[shapeKey] = [];\n        }\n        const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n        if (this.freeTextures[shapeKey].length > 0) {\n            this.numFreeTextures--;\n            this.numUsedTextures++;\n            this._numBytesFree -= texBytes;\n            this.log();\n            const newTexture = this.freeTextures[shapeKey].shift();\n            this.usedTextures[shapeKey].push(newTexture);\n            return newTexture;\n        }\n        let newTexture;\n        if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {\n            newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {\n            newTexture =\n                this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {\n            newTexture =\n                this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {\n            newTexture =\n                this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {\n            newTexture =\n                this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        this.usedTextures[shapeKey].push(newTexture);\n        this.numUsedTextures++;\n        this._numBytesAllocated += texBytes;\n        this.log();\n        return newTexture;\n    }\n    releaseTexture(texture, shape, logicalTexType, isPacked) {\n        if (this.freeTextures == null) {\n            // Already disposed.\n            return;\n        }\n        const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);\n        const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);\n        if (!(shapeKey in this.freeTextures)) {\n            this.freeTextures[shapeKey] = [];\n        }\n        const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n        const deleteTexThreshold = env().get('WEBGL_DELETE_TEXTURE_THRESHOLD');\n        if (deleteTexThreshold !== -1 &&\n            this._numBytesAllocated > deleteTexThreshold) {\n            this.gpgpu.deleteMatrixTexture(texture);\n            this._numBytesAllocated -= texBytes;\n        }\n        else {\n            this.freeTextures[shapeKey].push(texture);\n            this.numFreeTextures++;\n            this._numBytesFree += texBytes;\n        }\n        this.numUsedTextures--;\n        const texList = this.usedTextures[shapeKey];\n        const texIndex = texList.indexOf(texture);\n        if (texIndex < 0) {\n            throw new Error('Cannot release a texture that was never provided by this ' +\n                'texture manager');\n        }\n        texList.splice(texIndex, 1);\n        this.log();\n    }\n    log() {\n        if (!this.logEnabled) {\n            return;\n        }\n        const total = this.numFreeTextures + this.numUsedTextures;\n        console.log('Free/Used', `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`);\n        const freeRatio = this._numBytesFree / this._numBytesAllocated;\n        console.log(`Bytes allocated: ${this._numBytesAllocated}`);\n        console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`);\n    }\n    get numBytesAllocated() {\n        return this._numBytesAllocated;\n    }\n    get numBytesFree() {\n        return this._numBytesFree;\n    }\n    getNumUsedTextures() {\n        return this.numUsedTextures;\n    }\n    getNumFreeTextures() {\n        return this.numFreeTextures;\n    }\n    dispose() {\n        if (this.freeTextures == null) {\n            // Already disposed.\n            return;\n        }\n        for (const texShape in this.freeTextures) {\n            this.freeTextures[texShape].forEach(tex => {\n                this.gpgpu.deleteMatrixTexture(tex);\n            });\n        }\n        for (const texShape in this.usedTextures) {\n            this.usedTextures[texShape].forEach(tex => {\n                this.gpgpu.deleteMatrixTexture(tex);\n            });\n        }\n        this.freeTextures = null;\n        this.usedTextures = null;\n        this.numUsedTextures = 0;\n        this.numFreeTextures = 0;\n        this._numBytesAllocated = 0;\n        this._numBytesFree = 0;\n    }\n}\nfunction numBytesForInternalFormat(gl, internalFormat) {\n    // tslint:disable-next-line:no-any\n    const glany = gl;\n    if (internalFormat === glany.R32F) {\n        return 4;\n    }\n    else if (internalFormat === glany.R16F) {\n        return 2;\n    }\n    else if (internalFormat === glany.RGBA32F) {\n        return 16;\n    }\n    else if (internalFormat === gl.RGBA) {\n        return 16;\n    }\n    else if (internalFormat === glany.RGBA16F) {\n        return 8;\n    }\n    throw new Error(`Unknown internal format ${internalFormat}`);\n}\nexport function computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) {\n    // It is not possible to infer packed status from the texture type because\n    // depending on the textureConfig, different  texture types may resolve to the\n    // same internal format (e.g. in WebGL1, the internal format for\n    // UNPACKED_FLOAT16 textures is gl.RGBA). Therefore we pass in `isPacked`\n    // explicitly.\n    const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig);\n    let numElements;\n    if (isPacked) {\n        const [packedWidth, packedHeight] = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n        numElements = packedWidth * packedHeight;\n    }\n    else {\n        const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n        numElements = width * height;\n    }\n    const bytesPerElement = numBytesForInternalFormat(gl, internalFormat);\n    return numElements * bytesPerElement;\n}\nfunction internalFormatForPhysicalTexType(physicalTexType, textureConfig) {\n    switch (physicalTexType) {\n        case PhysicalTextureType.PACKED_2X2_FLOAT32:\n            return getInternalFormatForPackedMatrixTexture(textureConfig);\n        case PhysicalTextureType.PACKED_2X2_FLOAT16:\n            return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);\n        case PhysicalTextureType.UNPACKED_FLOAT32:\n            return getInternalFormatForFloat32MatrixTexture(textureConfig);\n        case PhysicalTextureType.UNPACKED_FLOAT16:\n            return getInternalFormatForFloat16MatrixTexture(textureConfig);\n        case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:\n            return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);\n        default:\n            throw new Error(`Unknown physical texture type ${physicalTexType}`);\n    }\n}\nfunction getPhysicalTextureForRendering(isPacked) {\n    if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED')) {\n        if (isPacked) {\n            return PhysicalTextureType.PACKED_2X2_FLOAT32;\n        }\n        return PhysicalTextureType.UNPACKED_FLOAT32;\n    }\n    if (isPacked) {\n        return PhysicalTextureType.PACKED_2X2_FLOAT16;\n    }\n    return PhysicalTextureType.UNPACKED_FLOAT16;\n}\nfunction getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {\n    if (logicalTexType === TextureUsage.UPLOAD) {\n        return PhysicalTextureType.PACKED_2X2_FLOAT32;\n    }\n    else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {\n        return getPhysicalTextureForRendering(isPacked);\n    }\n    else if (logicalTexType === TextureUsage.DOWNLOAD ||\n        logicalTexType === TextureUsage.PIXELS) {\n        return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;\n    }\n    throw new Error(`Unknown logical texture type ${logicalTexType}`);\n}\nfunction getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {\n    return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;\n}\n//# sourceMappingURL=texture_manager.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class TileProgram {\n    constructor(aShape, reps) {\n        this.variableNames = ['A'];\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[i] * reps[i];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const sourceCoords = getSourceCoords(aShape);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n    }\n}\nfunction getSourceCoords(aShape) {\n    const rank = aShape.length;\n    if (rank > 5) {\n        throw Error(`Tile for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n        return `imod(resRC, ${aShape[0]})`;\n    }\n    const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u'];\n    const sourceCoords = [];\n    for (let i = 0; i < aShape.length; i++) {\n        sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);\n    }\n    return sourceCoords.join();\n}\n//# sourceMappingURL=tile_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class UnaryOpProgram {\n    constructor(aShape, opSnippet) {\n        this.variableNames = ['A'];\n        this.outputShape = aShape;\n        this.userCode = `\n      float unaryOperation(float x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n    }\n}\nconst CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;\nexport const LINEAR = `return x;`;\nexport const ABS = `return abs(x);`;\nexport const RELU = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\nexport const RELU6 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`;\nexport const ELU = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\nexport const SELU = `\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${backend_util.SELU_SCALEALPHA};\n  float scale = ${backend_util.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`;\nexport function STEP(alpha = 0.0) {\n    return CHECK_NAN_SNIPPET + `\n    return x > 0.0 ? 1.0 : float(${alpha});\n  `;\n}\nexport const NEG = `return -x;`;\nexport const CEIL = `return ceil(x);`;\nexport const FLOOR = `return floor(x);`;\nexport const SIGN = `\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n`;\nexport const IS_NAN = `return float(isnan(x));`;\nexport const IS_INF = `return float(isinf(x));`;\nexport const IS_FINITE = `return float(!isnan(x) && !isinf(x));`;\nexport const ROUND = `\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`;\nexport const EXP = `return exp(x);`;\nexport const EXPM1 = `return exp(x) - 1.0;`;\nexport const LOG = `if (x < 0.0) return NAN;\n  return log(x);`;\nexport const LOG1P = `return log(1.0 + x);`;\nexport const SQRT = `return sqrt(x);`;\nexport const RSQRT = `return inversesqrt(x);`;\nexport const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n/**\n * mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n *\n * epsilon is the difference between 1.0 and the next representable\n * float. For a single precision 32 bit float this should be 2^-23, see:\n * https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\n *\n * too_large = (x > -threshold) is value above which exp(x) may overflow\n * but softplus(x) == x is within machine epsilon\n *\n * too_small = (x < threshold) is value below which exp(x) may underflow,\n * but softplus(x) == exp(x) is within machine epsilon.\n */\nexport const SOFTPLUS = `\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`;\nexport const ASIN = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`;\nexport const ACOS = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`;\nexport const ATAN = CHECK_NAN_SNIPPET + `\n  return atan(x);\n`;\nexport const SINH = `\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`;\nexport const COSH = `\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`;\nexport const TANH = `\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`;\nexport const ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`;\nexport const ACOSH = CHECK_NAN_SNIPPET + `\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));`;\nexport const ATANH = CHECK_NAN_SNIPPET + `\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;\nexport const ERF = `\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = ${backend_util.ERF_P};\n  float a1 = ${backend_util.ERF_A1};\n  float a2 = ${backend_util.ERF_A2};\n  float a3 = ${backend_util.ERF_A3};\n  float a4 = ${backend_util.ERF_A4};\n  float a5 = ${backend_util.ERF_A5};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`;\nexport const RECIPROCAL = `return 1.0 / x;`;\nexport const LOGICAL_NOT = `return float(!(x >= 1.0));`;\nexport const TO_INT = `return float(int(x));`;\nexport const CLONE = 'return x;';\n//# sourceMappingURL=unaryop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const LINEAR = `return x;`;\nexport const LOG = `\n  vec4 result = log(x);\n  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\n  result.r = isNaN.r == 1.0 ? NAN : result.r;\n  result.g = isNaN.g == 1.0 ? NAN : result.g;\n  result.b = isNaN.b == 1.0 ? NAN : result.b;\n  result.a = isNaN.a == 1.0 ? NAN : result.a;\n\n  return result;\n`;\nexport const RELU = `\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nexport const RELU6 = `\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nexport const ELU = `\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`;\nexport class UnaryOpPackedProgram {\n    constructor(aShape, opSnippet) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = aShape;\n        this.userCode = `\n      vec4 unaryOperation(vec4 x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=unaryop_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels, getSourceCoords } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class UnpackProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = false;\n        this.outputShape = outputShape;\n        const rank = outputShape.length;\n        const channels = getChannels('rc', rank);\n        const dtype = getCoordsDataType(rank);\n        const sourceCoords = getSourceCoords(rank, channels);\n        const innerDims = channels.slice(-2);\n        const coords = rank <= 1 ? 'rc' : `vec2(${innerDims.join(',')})`;\n        this.userCode = `\n      void main() {\n        ${dtype} rc = getOutputCoords();\n        vec4 packedInput = getA(${sourceCoords});\n\n        setOutput(getChannel(packedInput, ${coords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=unpack_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Import webgl flags.\nimport './flags_webgl';\nimport * as tf from '@tensorflow/tfjs-core';\nimport { complex, div, engine, env, imag, max, range, real, reshape, scalar, softmax, tensor, tidy, transpose } from '@tensorflow/tfjs-core';\nimport { backend_util, buffer, kernel_impls, slice_util, util } from '@tensorflow/tfjs-core';\nimport { DataStorage, KernelBackend, upcastType } from '@tensorflow/tfjs-core';\nimport { addImplCPU, ceilImplCPU, expImplCPU, expm1ImplCPU, floorImplCPU, logImplCPU, multiplyImplCPU, rsqrtImplCPU, simpleAbsImplCPU, sliceImplCPU, subImplCPU } from './kernel_utils/shared';\nconst { segment_util } = backend_util;\nconst split = kernel_impls.split;\nconst tile = kernel_impls.tile;\nconst topkImpl = kernel_impls.topkImpl;\nconst whereImpl = kernel_impls.whereImpl;\nimport { AddNProgram } from './addn_gpu';\nimport { AddNPackedProgram } from './addn_packed_gpu';\nimport { ArgMinMaxProgram } from './argminmax_gpu';\nimport { ArgMinMaxPackedProgram } from './argminmax_packed_gpu';\nimport { AvgPool3DBackpropProgram } from './avg_pool_backprop_gpu';\nimport * as binaryop_complex_gpu from './binaryop_complex_gpu';\nimport { BinaryOpComplexProgram } from './binaryop_complex_gpu';\nimport * as binaryop_gpu from './binaryop_gpu';\nimport { BinaryOpProgram } from './binaryop_gpu';\nimport * as binaryop_packed_gpu from './binaryop_packed_gpu';\nimport { BinaryOpPackedProgram } from './binaryop_packed_gpu';\nimport { getWebGLContext } from './canvas_util';\nimport { ClipProgram } from './clip_gpu';\nimport { ClipPackedProgram } from './clip_packed_gpu';\nimport { ComplexAbsProgram } from './complex_abs_gpu';\nimport { ConcatProgram } from './concat_gpu';\nimport { ConcatPackedProgram } from './concat_packed_gpu';\nimport { Conv2DDerFilterProgram, Conv2DDerInputProgram, Conv3DDerFilterProgram, Conv3DDerInputProgram } from './conv_backprop_gpu';\nimport { DepthwiseConv2DDerFilterProgram, DepthwiseConv2DDerInputProgram } from './conv_backprop_gpu_depthwise';\nimport { Conv2DProgram, Conv3DProgram } from './conv_gpu';\nimport { DepthwiseConv2DProgram } from './conv_gpu_depthwise';\nimport { DepthwiseConvPacked2DProgram } from './conv_packed_gpu_depthwise';\nimport { CropAndResizeProgram } from './crop_and_resize_gpu';\nimport { CumSumProgram } from './cumsum_gpu';\nimport { DecodeMatrixProgram } from './decode_matrix_gpu';\nimport { DecodeMatrixPackedProgram } from './decode_matrix_packed_gpu';\nimport { DepthToSpaceProgram } from './depth_to_space_gpu';\nimport { DiagProgram } from './diag_gpu';\nimport { EncodeFloatProgram } from './encode_float_gpu';\nimport { EncodeFloatPackedProgram } from './encode_float_packed_gpu';\nimport { EncodeMatrixProgram } from './encode_matrix_gpu';\nimport { EncodeMatrixPackedProgram } from './encode_matrix_packed_gpu';\nimport * as fft_gpu from './fft_gpu';\nimport { FFTProgram } from './fft_gpu';\nimport { FillProgram } from './fill_gpu';\nimport { GatherProgram } from './gather_gpu';\nimport { GatherNDProgram } from './gather_nd_gpu';\nimport { GPGPUContext } from './gpgpu_context';\nimport * as gpgpu_math from './gpgpu_math';\nimport { Im2ColPackedProgram } from './im2col_packed_gpu';\nimport { LRNProgram } from './lrn_gpu';\nimport { LRNGradProgram } from './lrn_grad_gpu';\nimport { LRNPackedProgram } from './lrn_packed_gpu';\nimport { MaxPool3DBackpropProgram } from './max_pool_backprop_gpu';\nimport { MatMulPackedProgram } from './mulmat_packed_gpu';\nimport { MultinomialProgram } from './multinomial_gpu';\nimport { OneHotProgram } from './onehot_gpu';\nimport { PackProgram } from './pack_gpu';\nimport { PadProgram } from './pad_gpu';\nimport { PadPackedProgram } from './pad_packed_gpu';\nimport { Pool3DProgram } from './pool_gpu';\nimport { ReduceProgram } from './reduce_gpu';\nimport { ReshapePackedProgram } from './reshape_packed_gpu';\nimport { ResizeBilinearBackpropProgram } from './resize_bilinear_backprop_gpu';\nimport { ResizeBilinearProgram } from './resize_bilinear_gpu';\nimport { ResizeBilinearPackedProgram } from './resize_bilinear_packed_gpu';\nimport { ResizeNearestNeigborBackpropProgram } from './resize_nearest_neighbor_backprop_gpu';\nimport { ResizeNearestNeighborProgram } from './resize_nearest_neighbor_gpu';\nimport { ReverseProgram } from './reverse_gpu';\nimport { ReversePackedProgram } from './reverse_packed_gpu';\nimport { ScatterProgram } from './scatter_gpu';\nimport { SegmentOpProgram } from './segment_gpu';\nimport { SelectProgram } from './select_gpu';\nimport { SliceProgram } from './slice_gpu';\nimport { SlicePackedProgram } from './slice_packed_gpu';\nimport { StridedSliceProgram } from './strided_slice_gpu';\nimport * as tex_util from './tex_util';\nimport { TextureUsage } from './tex_util';\nimport { TextureManager } from './texture_manager';\nimport { TileProgram } from './tile_gpu';\nimport * as unary_op from './unaryop_gpu';\nimport { UnaryOpProgram } from './unaryop_gpu';\nimport * as unary_packed_op from './unaryop_packed_gpu';\nimport { UnaryOpPackedProgram } from './unaryop_packed_gpu';\nimport { UnpackProgram } from './unpack_gpu';\nimport * as webgl_util from './webgl_util';\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\nconst binaryCaches = {};\nexport function getBinaryCache(webGLVersion) {\n    if (webGLVersion in binaryCaches) {\n        return binaryCaches[webGLVersion];\n    }\n    binaryCaches[webGLVersion] = {};\n    return binaryCaches[webGLVersion];\n}\nfunction mapActivationToShaderProgram(activation, packed = false) {\n    if (activation === 'linear') {\n        if (packed) {\n            return unary_packed_op.LINEAR;\n        }\n        return unary_op.LINEAR;\n    }\n    else if (activation === 'relu') {\n        if (packed) {\n            return unary_packed_op.RELU;\n        }\n        return unary_op.RELU;\n    }\n    else if (activation === 'elu') {\n        if (packed) {\n            return unary_packed_op.ELU;\n        }\n        return unary_op.ELU;\n    }\n    else if (activation === 'relu6') {\n        if (packed) {\n            return unary_packed_op.RELU6;\n        }\n        return unary_op.RELU6;\n    }\n    else if (activation === 'prelu') {\n        if (packed) {\n            return binaryop_packed_gpu.PRELU;\n        }\n        return binaryop_gpu.PRELU;\n    }\n    throw new Error(`Activation ${activation} has not been implemented for the WebGL backend.`);\n}\n// Empirically determined constant used to determine size threshold for handing\n// off execution to the CPU.\nconst CPU_HANDOFF_SIZE_THRESHOLD = 128;\n// Empirically determined constant used to decide the number of MB on GPU\n// before we warn about high memory use. The MB are this constant * screen area\n// * dpi / 1024 / 1024.\nconst BEFORE_PAGING_CONSTANT = 600;\nfunction numMBBeforeWarning() {\n    if (env().global.screen == null) {\n        return 1024; // 1 GB.\n    }\n    return (env().global.screen.height * env().global.screen.width *\n        window.devicePixelRatio) *\n        BEFORE_PAGING_CONSTANT / 1024 / 1024;\n}\n// Empirically determined minimal shared dimension in matmul before we forward\n// to a.mul(b).sum() in order to take advantage of GPU parallelism. See\n// https://github.com/tensorflow/tfjs-core/pull/1379 for benchmarks.\nexport const MATMUL_SHARED_DIM_THRESHOLD = 1000;\nexport class MathBackendWebGL extends KernelBackend {\n    constructor(gpgpu) {\n        super();\n        // Maps data ids that have a pending read operation, to list of subscribers.\n        this.pendingRead = new WeakMap();\n        // List of data ids that are scheduled for disposal, but are waiting on a\n        // pending read operation.\n        this.pendingDisposal = new WeakSet();\n        // Used to count the number of 'shallow' sliced tensors that point to the\n        // same data id.\n        this.dataRefCount = new WeakMap();\n        this.numBytesInGPU = 0;\n        // Accumulated time spent (including blocking) in uploading data to webgl.\n        this.uploadWaitMs = 0;\n        // Accumulated time spent (including blocking in downloading data from webgl.\n        this.downloadWaitMs = 0;\n        this.warnedAboutMemory = false;\n        this.warnedAboutCPUBackend = false;\n        this.pendingDeletes = 0;\n        this.disposed = false;\n        if (!env().getBool('HAS_WEBGL')) {\n            throw new Error('WebGL is not supported on this device');\n        }\n        if (gpgpu == null) {\n            const gl = getWebGLContext(env().getNumber('WEBGL_VERSION'));\n            this.binaryCache = getBinaryCache(env().getNumber('WEBGL_VERSION'));\n            this.gpgpu = new GPGPUContext(gl);\n            this.canvas = gl.canvas;\n            this.gpgpuCreatedLocally = true;\n        }\n        else {\n            this.gpgpu = gpgpu;\n            this.binaryCache = {};\n            this.gpgpuCreatedLocally = false;\n            this.canvas = gpgpu.gl.canvas;\n        }\n        this.textureManager = new TextureManager(this.gpgpu);\n        this.numMBBeforeWarning = numMBBeforeWarning();\n        this.texData = new DataStorage(this, engine());\n    }\n    numDataIds() {\n        return this.texData.numDataIds() +\n            (this.cpuBackend ? this.cpuBackend.numDataIds() : 0) -\n            this.pendingDeletes;\n    }\n    write(values, shape, dtype) {\n        if (env().getBool('WEBGL_CHECK_NUMERICAL_PROBLEMS') ||\n            env().getBool('DEBUG')) {\n            this.checkNumericalProblems(values);\n        }\n        if (dtype === 'complex64' && values != null) {\n            throw new Error(`Cannot write to a complex64 dtype. ` +\n                `Please use tf.complex(real, imag).`);\n        }\n        const dataId = {};\n        this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });\n        return dataId;\n    }\n    /** Increase refCount of a `TextureData`. */\n    incRef(dataId) {\n        const texData = this.texData.get(dataId);\n        texData.refCount++;\n    }\n    /** Decrease refCount of a `TextureData`. */\n    decRef(dataId) {\n        if (this.texData.has(dataId)) {\n            const texData = this.texData.get(dataId);\n            texData.refCount--;\n        }\n    }\n    move(dataId, values, shape, dtype) {\n        if (env().getBool('DEBUG')) {\n            this.checkNumericalProblems(values);\n        }\n        if (dtype === 'complex64') {\n            throw new Error(`Cannot write to a complex64 dtype. ` +\n                `Please use tf.complex(real, imag).`);\n        }\n        this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });\n    }\n    disposeIntermediateTensorInfo(tensorInfo) {\n        const dataId = tensorInfo.dataId;\n        if (this.texData.has(dataId)) {\n            const textureData = this.texData.get(dataId);\n            textureData.refCount--;\n            if (textureData.refCount < 1) {\n                this.disposeData(dataId);\n            }\n        }\n    }\n    readSync(dataId) {\n        const texData = this.texData.get(dataId);\n        const { values, dtype, complexTensors, slice, shape, isPacked } = texData;\n        // The presence of `slice` indicates this tensor is a shallow slice of a\n        // different tensor, and is using that original tensor's texture. Run\n        // `clone` in order to copy that texture and read from it.\n        if (slice != null) {\n            let program;\n            if (isPacked) {\n                program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n            }\n            else {\n                program = new UnaryOpProgram(shape, unary_op.CLONE);\n            }\n            const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n            const data = this.readSync(res.dataId);\n            this.disposeIntermediateTensorInfo(res);\n            return data;\n        }\n        if (values != null) {\n            return this.convertAndCacheOnCPU(dataId);\n        }\n        if (dtype === 'string') {\n            return values;\n        }\n        const shouldTimeProgram = this.activeTimers != null;\n        let start;\n        if (shouldTimeProgram) {\n            start = util.now();\n        }\n        let result;\n        if (dtype === 'complex64') {\n            const realValues = complexTensors.real.dataSync();\n            const imagValues = complexTensors.imag.dataSync();\n            result = backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        else {\n            result = this.getValuesFromTexture(dataId);\n        }\n        if (shouldTimeProgram) {\n            this.downloadWaitMs += util.now() - start;\n        }\n        return this.convertAndCacheOnCPU(dataId, result);\n    }\n    async read(dataId) {\n        if (this.pendingRead.has(dataId)) {\n            const subscribers = this.pendingRead.get(dataId);\n            return new Promise(resolve => subscribers.push(resolve));\n        }\n        const texData = this.texData.get(dataId);\n        const { values, shape, slice, dtype, complexTensors, isPacked } = texData;\n        // The presence of `slice` indicates this tensor is a shallow slice of a\n        // different tensor, and is using that original tensor's texture. Run\n        // `clone` in order to copy that texture and read from it.\n        if (slice != null) {\n            let program;\n            if (isPacked) {\n                program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n            }\n            else {\n                program = new UnaryOpProgram(shape, unary_op.CLONE);\n            }\n            const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n            const data = this.read(res.dataId);\n            this.disposeIntermediateTensorInfo(res);\n            return data;\n        }\n        if (values != null) {\n            return this.convertAndCacheOnCPU(dataId);\n        }\n        if (!env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED') &&\n            env().getNumber('WEBGL_VERSION') === 2) {\n            throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and ` +\n                `WEBGL_VERSION=2 not yet supported.`);\n        }\n        let buffer = null;\n        let tmpDownloadTarget;\n        if (dtype !== 'complex64' && env().get('WEBGL_BUFFER_SUPPORTED')) {\n            // Possibly copy the texture into a buffer before inserting a fence.\n            tmpDownloadTarget = this.decode(dataId);\n            const tmpData = this.texData.get(tmpDownloadTarget.dataId);\n            buffer = this.gpgpu.createBufferFromTexture(tmpData.texture, ...tex_util.getDenseTexShape(shape));\n        }\n        this.pendingRead.set(dataId, []);\n        if (dtype !== 'complex64') {\n            // Create a fence and wait for it to resolve.\n            await this.gpgpu.createAndWaitForFence();\n        }\n        // Download the values from the GPU.\n        let vals;\n        if (dtype === 'complex64') {\n            const ps = await Promise.all([complexTensors.real.data(), complexTensors.imag.data()]);\n            const realValues = ps[0];\n            const imagValues = ps[1];\n            vals = backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        else if (buffer == null) {\n            vals = this.getValuesFromTexture(dataId);\n        }\n        else {\n            const size = util.sizeFromShape(shape);\n            vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer, size);\n        }\n        if (tmpDownloadTarget != null) {\n            this.disposeIntermediateTensorInfo(tmpDownloadTarget);\n        }\n        const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);\n        const subscribers = this.pendingRead.get(dataId);\n        this.pendingRead.delete(dataId);\n        // Notify all pending reads.\n        subscribers.forEach(resolve => resolve(dTypeVals));\n        if (this.pendingDisposal.has(dataId)) {\n            this.pendingDisposal.delete(dataId);\n            this.disposeData(dataId);\n            this.pendingDeletes--;\n        }\n        return dTypeVals;\n    }\n    checkNumericalProblems(values) {\n        if (values == null) {\n            return;\n        }\n        for (let i = 0; i < values.length; i++) {\n            const num = values[i];\n            if (!webgl_util.canBeRepresented(num)) {\n                if (env().getBool('WEBGL_RENDER_FLOAT32_CAPABLE')) {\n                    throw Error(`The value ${num} cannot be represented with your ` +\n                        `current settings. Consider enabling float32 rendering: ` +\n                        `'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);\n                }\n                throw Error(`The value ${num} cannot be represented on this device.`);\n            }\n        }\n    }\n    getValuesFromTexture(dataId) {\n        const { shape, dtype, isPacked } = this.texData.get(dataId);\n        const size = util.sizeFromShape(shape);\n        if (env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {\n            const tmpTarget = this.decode(dataId);\n            const tmpData = this.texData.get(tmpTarget.dataId);\n            const vals = this.gpgpu\n                .downloadMatrixFromPackedTexture(tmpData.texture, ...tex_util.getDenseTexShape(shape))\n                .subarray(0, size);\n            this.disposeIntermediateTensorInfo(tmpTarget);\n            return vals;\n        }\n        const shouldUsePackedProgram = env().getBool('WEBGL_PACK') && isPacked === true;\n        const outputShape = shouldUsePackedProgram ? webgl_util.getShapeAs3D(shape) : shape;\n        const program = shouldUsePackedProgram ?\n            new EncodeFloatPackedProgram(outputShape) :\n            new EncodeFloatProgram(outputShape);\n        const output = this.runWebGLProgram(program, [{ shape: outputShape, dtype, dataId }], 'float32');\n        const tmpData = this.texData.get(output.dataId);\n        const vals = this.gpgpu\n            .downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture, tmpData.texShape[0], tmpData.texShape[1])\n            .subarray(0, size);\n        this.disposeIntermediateTensorInfo(output);\n        return vals;\n    }\n    async time(f) {\n        const oldActiveTimers = this.activeTimers;\n        const newActiveTimers = [];\n        let outerMostTime = false;\n        if (this.programTimersStack == null) {\n            this.programTimersStack = newActiveTimers;\n            outerMostTime = true;\n        }\n        else {\n            this.activeTimers.push(newActiveTimers);\n        }\n        this.activeTimers = newActiveTimers;\n        f();\n        // needing to split these up because util.flatten only accepts certain types\n        const flattenedActiveTimerQueries = util.flatten(this.activeTimers.map((d) => d.query))\n            .filter(d => d != null);\n        const flattenedActiveTimerNames = util.flatten(this.activeTimers.map((d) => d.name))\n            .filter(d => d != null);\n        this.activeTimers = oldActiveTimers;\n        if (outerMostTime) {\n            this.programTimersStack = null;\n        }\n        const res = {\n            uploadWaitMs: this.uploadWaitMs,\n            downloadWaitMs: this.downloadWaitMs,\n            kernelMs: null,\n            wallMs: null // will be filled by the engine\n        };\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n            res['kernelMs'] = util.sum(kernelMs);\n            res['getExtraProfileInfo'] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d }))\n                .map(d => `${d.name}: ${d.ms}`)\n                .join(', ');\n        }\n        else {\n            res['kernelMs'] = {\n                error: 'WebGL query timers are not supported in this environment.'\n            };\n        }\n        this.uploadWaitMs = 0;\n        this.downloadWaitMs = 0;\n        return res;\n    }\n    memory() {\n        return {\n            unreliable: false,\n            numBytesInGPU: this.numBytesInGPU,\n            numBytesInGPUAllocated: this.textureManager.numBytesAllocated,\n            numBytesInGPUFree: this.textureManager.numBytesFree\n        };\n    }\n    startTimer() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            return this.gpgpu.beginQuery();\n        }\n        return { startMs: util.now(), endMs: null };\n    }\n    endTimer(query) {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            this.gpgpu.endQuery();\n            return query;\n        }\n        query.endMs = util.now();\n        return query;\n    }\n    async getQueryTime(query) {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            return this.gpgpu.waitForQueryAndGetTime(query);\n        }\n        const timerQuery = query;\n        return timerQuery.endMs - timerQuery.startMs;\n    }\n    disposeData(dataId) {\n        if (this.pendingDisposal.has(dataId)) {\n            return;\n        }\n        if (this.pendingRead.has(dataId)) {\n            this.pendingDisposal.add(dataId);\n            this.pendingDeletes++;\n            return;\n        }\n        // No-op if already disposed.\n        if (!this.texData.has(dataId)) {\n            return;\n        }\n        this.releaseGPUData(dataId);\n        const { complexTensors } = this.texData.get(dataId);\n        if (complexTensors != null) {\n            complexTensors.real.dispose();\n            complexTensors.imag.dispose();\n        }\n        this.texData.delete(dataId);\n    }\n    releaseGPUData(dataId) {\n        const { texture, dtype, texShape, usage, isPacked, slice } = this.texData.get(dataId);\n        const key = slice && slice.origDataId || dataId;\n        const refCount = this.dataRefCount.get(key);\n        if (refCount > 1) {\n            this.dataRefCount.set(key, refCount - 1);\n        }\n        else {\n            this.dataRefCount.delete(key);\n            if (texture != null) {\n                this.numBytesInGPU -= this.computeBytes(texShape, dtype);\n                this.textureManager.releaseTexture(texture, texShape, usage, isPacked);\n            }\n        }\n        const texData = this.texData.get(dataId);\n        texData.texture = null;\n        texData.texShape = null;\n        texData.isPacked = false;\n        texData.slice = null;\n    }\n    getTexture(dataId) {\n        this.uploadToGPU(dataId);\n        return this.texData.get(dataId).texture;\n    }\n    /**\n     * Returns internal information for the specific data bucket. Used in unit\n     * tests.\n     */\n    getDataInfo(dataId) {\n        return this.texData.get(dataId);\n    }\n    getCPUBackend() {\n        if (!env().getBool('WEBGL_CPU_FORWARD')) {\n            return null;\n        }\n        if (this.cpuBackend == null) {\n            this.cpuBackend = engine().findBackend('cpu');\n        }\n        return this.cpuBackend;\n    }\n    /*\n    Tests whether all the inputs to an op are small and on the CPU. This heuristic\n    determines when it would be faster to execute a kernel on the CPU. WebGL\n    kernels opt into running this check and forwarding when appropriate.\n    TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more\n    sustainable strategy for optimizing backend execution of ops.\n     */\n    shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) {\n        const cpuBackend = this.getCPUBackend();\n        if (!this.warnedAboutCPUBackend && cpuBackend == null) {\n            console.warn('Your application contains ops that are small enough to be ' +\n                'executed on the CPU backend, however the CPU backend cannot ' +\n                'be found. Consider importing the CPU backend ' +\n                '(@tensorflow/tfjs-backend-cpu) for better performance.');\n            this.warnedAboutCPUBackend = true;\n        }\n        return cpuBackend != null &&\n            inputs.every(input => this.texData.get(input.dataId).texture == null &&\n                util.sizeFromShape(input.shape) < sizeThreshold);\n    }\n    getGPGPUContext() {\n        return this.gpgpu;\n    }\n    complex(real, imag) {\n        const result = this.makeOutput(real.shape, 'complex64');\n        const resultData = this.texData.get(result.dataId);\n        // The backend owns the reference to the underlying real and imaginary\n        // clones. These will explicitly get disposed when the complex tensor is\n        // disposed.\n        resultData.complexTensors = {\n            real: engine().keep(real.clone()),\n            imag: engine().keep(imag.clone())\n        };\n        return result;\n    }\n    real(input) {\n        const resultData = this.texData.get(input.dataId);\n        return resultData.complexTensors.real.clone();\n    }\n    imag(input) {\n        const resultData = this.texData.get(input.dataId);\n        return resultData.complexTensors.imag.clone();\n    }\n    slice(x, begin, size) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = sliceImplCPU(this.texData.get(x.dataId).values, begin, size, x.shape, x.dtype);\n            return this.makeOutput(size, x.dtype, outValues);\n        }\n        // Short-circuit computation if the slice is zero-sized.\n        if (util.sizeFromShape(size) === 0) {\n            return tensor([], size, x.dtype);\n        }\n        const { isPacked } = this.texData.get(x.dataId);\n        const isContinous = slice_util.isSliceContinous(x.shape, begin, size);\n        if (isPacked || !isContinous) {\n            const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n                new SlicePackedProgram(size) :\n                new SliceProgram(size);\n            const customSetup = program.getCustomSetupFunc(begin);\n            return this.compileAndRun(program, [x], null, customSetup);\n        }\n        this.uploadToGPU(x.dataId);\n        return this.shallowSlice(x, begin, size);\n    }\n    shallowSlice(x, begin, size) {\n        const xTexData = this.texData.get(x.dataId);\n        const t = this.makeOutput(size, x.dtype);\n        const newTexData = this.texData.get(t.dataId);\n        // Copy texture data from the original tensor.\n        Object.assign(newTexData, xTexData);\n        newTexData.shape = size;\n        newTexData.dtype = x.dtype;\n        let flatOffset = slice_util.computeFlatOffset(begin, x.strides);\n        if (xTexData.slice) {\n            // We are slicing an already sliced tensor, so we have to accumulate\n            // the offset.\n            flatOffset += xTexData.slice.flatOffset;\n        }\n        newTexData.slice = {\n            flatOffset,\n            // Point to the original dataId, which is used to do ref counting.\n            origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId\n        };\n        // Increase the ref count for that data bucket.\n        const refCount = this.dataRefCount.get(newTexData.slice.origDataId) || 1;\n        this.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);\n        return t;\n    }\n    stridedSlice(x, begin, end, strides) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.stridedSlice(x, begin, end, strides));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const outShape = slice_util.computeOutShape(begin, end, strides);\n        if (outShape.some(axis => axis === 0)) {\n            return tensor([], outShape);\n        }\n        const program = new StridedSliceProgram(begin, strides, outShape);\n        return this.compileAndRun(program, [x]);\n    }\n    reverse(x, axis) {\n        const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n            new ReversePackedProgram(x.shape, axis) :\n            new ReverseProgram(x.shape, axis);\n        return this.compileAndRun(program, [x]);\n    }\n    concat(tensors, axis) {\n        if (tensors[0].dtype === 'complex64') {\n            const reals = tensors.map((t) => real(t));\n            const imags = tensors.map((t) => imag(t));\n            return complex(this.concat(reals, axis), this.concat(imags, axis));\n        }\n        if (tensors.length === 1) {\n            return tensors[0];\n        }\n        if (tensors.length > env().getNumber('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n            const midIndex = Math.floor(tensors.length / 2);\n            const leftSide = this.concat(tensors.slice(0, midIndex), axis);\n            const rightSide = this.concat(tensors.slice(midIndex), axis);\n            return this.concat([leftSide, rightSide], axis);\n        }\n        if (env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') && tensors[0].rank > 1) {\n            const program = new ConcatPackedProgram(tensors.map(t => t.shape), axis);\n            return this.compileAndRun(program, tensors);\n        }\n        // Any concat of n-dimensional tensors across any axis can be reduced to\n        // a concatenation of two-dimensional tensors across the axis 1 by first\n        // partitioning the axes of the original tensors into those less than the\n        // axis to be concatenated and the rest. Then reshape the tensors\n        // into a two-dimensional tensor by collapsing these two sets of axes and\n        // concatenate the resulting matrices across the axis 1, finally reshaping\n        // the result to have the proper shape.\n        const outShape = backend_util.computeOutShape(tensors.map(t => t.shape), axis);\n        const tensors2D = tensors.map(t => t.as2D(-1, util.sizeFromShape(t.shape.slice(axis))));\n        const program = new ConcatProgram(tensors2D.map(t => t.shape));\n        const res = this.compileAndRun(program, tensors2D);\n        return res.reshape(outShape);\n    }\n    neg(x) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.neg(x));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.NEG, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.NEG);\n        return this.compileAndRun(program, [x]);\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n        const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n        const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n        const [batch, ,] = a.shape;\n        // Since the matrices are vectors, it is faster to call mul().sum()\n        // because sum() is O(sqrt(N)) due to divide-and-conquer.\n        if ((outerShapeA === 1 || outerShapeB === 1) &&\n            sharedDim > MATMUL_SHARED_DIM_THRESHOLD) {\n            if (transposeA) {\n                a = transpose(a, [0, 2, 1]);\n            }\n            if (transposeB) {\n                b = transpose(b, [0, 2, 1]);\n            }\n            const a3D = outerShapeB === 1 ? a : a.as3D(batch, sharedDim, 1);\n            const axis = outerShapeB === 1 ? 2 : 1;\n            const b3D = outerShapeB === 1 ? b.as3D(batch, 1, sharedDim) : b;\n            return this.multiply(a3D, b3D).sum(axis, true /* keepDims */);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        const program = new MatMulPackedProgram(a.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n        const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n        const [batch, ,] = a.shape;\n        const dtype = upcastType(a.dtype, b.dtype);\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;\n        const program = new MatMulPackedProgram(a.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [a, b];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (preluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        return this.compileAndRun(program, inputs, dtype);\n    }\n    multiply(a, b) {\n        if (a.dtype === 'complex64') {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const realProgram = new BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.REAL, a.shape, b.shape);\n            const imagProgram = new BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);\n            const inputs = [\n                this.makeComplexComponentTensorInfo(a, aData.complexTensors.real),\n                this.makeComplexComponentTensorInfo(a, aData.complexTensors.imag),\n                this.makeComplexComponentTensorInfo(b, bData.complexTensors.real),\n                this.makeComplexComponentTensorInfo(b, bData.complexTensors.imag)\n            ];\n            const real = this.compileAndRun(realProgram, inputs);\n            const imag = this.compileAndRun(imagProgram, inputs);\n            const complex = this.complex(real, imag);\n            real.dispose();\n            imag.dispose();\n            return complex;\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        if (this.shouldExecuteOnCPU([a, b])) {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const [outValues, outShape] = multiplyImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n            return this.makeOutput(outShape, dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_gpu.MUL, a.dtype);\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.MUL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], a.dtype);\n    }\n    localResponseNormalization4D(x, radius, bias, alpha, beta) {\n        const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n            new LRNPackedProgram(x.shape, radius, bias, alpha, beta) :\n            new LRNProgram(x.shape, radius, bias, alpha, beta);\n        return this.compileAndRun(program, [x]);\n    }\n    LRNGrad(dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {\n        const program = new LRNGradProgram(inputImage.shape, depthRadius, bias, alpha, beta);\n        return this.compileAndRun(program, [inputImage, outputImage, dy]);\n    }\n    tile(x, reps) {\n        if (x.dtype === 'string') {\n            const data = this.readSync(x.dataId);\n            const decodedData = data.map(d => util.decodeString(d));\n            const buf = buffer(x.shape, x.dtype, decodedData);\n            return tile(buf, reps);\n        }\n        const program = new TileProgram(x.shape, reps);\n        return this.compileAndRun(program, [x]);\n    }\n    pad(x, paddings, constantValue) {\n        const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n            new PadPackedProgram(x.shape, paddings, constantValue) :\n            new PadProgram(x.shape, paddings, constantValue);\n        return this.compileAndRun(program, [x]);\n    }\n    gather(x, indices, axis) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x, indices], () => this.cpuBackend.gather(x, indices, axis));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = new GatherProgram(x.shape, indices.size, axis);\n        return this.compileAndRun(program, [x, indices]);\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        util.assert(x.rank <= 4, () => 'batchToSpaceND for rank > 4 with a WebGL backend not ' +\n            'implemented yet');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n        const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n        const reshapedPermuted = backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n        const sliceBeginCoords = backend_util.getSliceBeginCoords(crops, blockShape.length);\n        const sliceSize = backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n        return transpose(x.reshape(reshaped), permuted)\n            .reshape(reshapedPermuted)\n            .slice(sliceBeginCoords, sliceSize);\n    }\n    spaceToBatchND(x, blockShape, paddings) {\n        util.assert(x.rank <= 4, () => 'spaceToBatchND for rank > 4 with a WebGL backend not ' +\n            'implemented yet');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const completePaddings = [[0, 0]];\n        completePaddings.push(...paddings);\n        for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n            completePaddings.push([0, 0]);\n        }\n        const paddedX = x.pad(completePaddings);\n        const reshapedPaddedShape = backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n        const permutedReshapedPaddedPermutation = backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n        const flattenShape = backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n        const paddedXT = transpose(paddedX.reshape(reshapedPaddedShape), permutedReshapedPaddedPermutation);\n        return reshape(paddedXT, flattenShape);\n    }\n    reduce(x, reduceType, dtype) {\n        const batchSize = x.shape[0];\n        const inSize = x.shape[1];\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const outSize = Math.ceil(inSize / windowSize);\n        const reduceInfo = { windowSize, inSize, batchSize, outSize };\n        const program = new ReduceProgram(reduceInfo, reduceType);\n        const output = this.compileAndRun(program, [x], dtype);\n        // No need to run another GPGPU program.\n        if (output.shape[1] === 1) {\n            return output;\n        }\n        return this.reduce(output, reduceType, dtype);\n    }\n    argReduce(x, reduceType, bestIndicesA = null) {\n        let batchSize = x.shape[0];\n        let inSize = x.shape[1];\n        if (bestIndicesA != null) {\n            batchSize = bestIndicesA.shape[0];\n            inSize = bestIndicesA.shape[1];\n        }\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const reduceInfo = {\n            windowSize,\n            inSize,\n            batchSize,\n            outSize: Math.ceil(inSize / windowSize)\n        };\n        const program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);\n        const inputs = [x];\n        if (bestIndicesA != null) {\n            inputs.push(bestIndicesA);\n        }\n        const output = this.compileAndRun(program, inputs, 'int32');\n        // No need to run another GPGPU program.\n        if (output.shape[1] === 1) {\n            return output;\n        }\n        return this.argReduce(x, reduceType, output);\n    }\n    argReducePacked(x, reduceType, bestIndicesA = null) {\n        const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;\n        const inSize = inShape[inShape.length - 1];\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null);\n        const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];\n        const output = this.compileAndRun(program, inputs, 'int32');\n        if (output.rank === x.rank) {\n            return this.argReducePacked(x, reduceType, output);\n        }\n        return output;\n    }\n    sum(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        return this.reduce(a2D, 'sum', outputDType).reshape(outShape);\n    }\n    prod(x, axes) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.prod(x, axes));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        return this.reduce(a2D, 'prod', outputDType).reshape(outShape);\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        let axis = 0;\n        const permutation = backend_util.getAxesPermutation([axis], x.rank);\n        let permutedX = x;\n        if (permutation != null) {\n            permutedX = transpose(x, permutation);\n            axis = backend_util.getInnerMostAxes(1, x.rank)[0];\n        }\n        const outShape = segment_util.computeOutShape(permutedX.shape, axis, numSegments);\n        const inSize = util.sizeFromShape([permutedX.shape[axis]]);\n        const a2D = permutedX.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        let result = this.segOpCompute(a2D, 'unsortedSegmentSum', segmentIds, outputDType, numSegments)\n            .reshape(outShape);\n        if (permutation != null) {\n            result =\n                transpose(result, backend_util.getUndoAxesPermutation(permutation));\n        }\n        return result;\n    }\n    segOpCompute(x, segOpType, segmentIds, dtype, numSegments) {\n        const batchSize = x.shape[0];\n        const inSize = x.shape[1];\n        const windowSize = segment_util.segOpComputeOptimalWindowSize(inSize, numSegments);\n        const segOpInfo = { windowSize, inSize, batchSize, numSegments };\n        const program = new SegmentOpProgram(segOpInfo, segOpType);\n        const output = this.compileAndRun(program, [x, segmentIds], dtype);\n        // No need to run another GPGPU program.\n        if (output.shape[1] === numSegments) {\n            return output;\n        }\n        segmentIds = range(0, numSegments).tile([inSize / windowSize]);\n        return this.segOpCompute(output, segOpType, segmentIds, dtype, numSegments);\n    }\n    argMinMaxReduce(x, axis, reduceType) {\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('arg' + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.rank);\n        if (!env().getBool('WEBGL_PACK_REDUCE') || x.rank <= 2) {\n            const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n            const inSize = util.sizeFromShape(reduceShape);\n            const a2D = x.as2D(-1, inSize);\n            return this.argReduce(a2D, reduceType).reshape(outShape);\n        }\n        return this.argReducePacked(x, reduceType);\n    }\n    argMin(x, axis) {\n        return this.argMinMaxReduce(x, axis, 'min');\n    }\n    argMax(x, axis) {\n        return this.argMinMaxReduce(x, axis, 'max');\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        if (axis !== x.rank - 1) {\n            throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.rank - 1} ` +\n                `but got axis=${axis}`);\n        }\n        const size = x.shape[axis];\n        let result = x;\n        // Use cumsum parallel algorithm, ref:\n        // https://developer.nvidia.com/gpugems/gpugems3/part-vi-gpu-computing/chapter-39-parallel-prefix-sum-scan-cuda\n        for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {\n            const program = new CumSumProgram(x.shape, false, reverse);\n            const customSetup = program.getCustomSetupFunc(i);\n            const prevResult = result;\n            result = this.compileAndRun(program, [result], result.dtype, customSetup);\n            prevResult.dispose();\n        }\n        // For exclusive cumsum, shift the end result in the direction of sum and\n        // add 0 to the front index.\n        if (exclusive) {\n            const program = new CumSumProgram(x.shape, exclusive, reverse);\n            const prevResult = result;\n            result = this.compileAndRun(program, [result]);\n            prevResult.dispose();\n        }\n        return result;\n    }\n    equal(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    notEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.NOT_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.NOT_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    less(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.less(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LESS, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LESS, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    lessEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LESS_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LESS_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    greater(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.greater(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.GREATER, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.GREATER, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    greaterEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.GREATER_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.GREATER_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    logicalNot(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.LOGICAL_NOT);\n        return this.compileAndRun(program, [x]);\n    }\n    logicalAnd(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LOGICAL_AND, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LOGICAL_AND, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    logicalOr(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LOGICAL_OR, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LOGICAL_OR, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    select(condition, a, b) {\n        const program = new SelectProgram(condition.rank, a.shape, a.rank);\n        return this.compileAndRun(program, [condition, a, b], upcastType(a.dtype, b.dtype));\n    }\n    where(condition) {\n        backend_util.warn('tf.where() in webgl locks the UI thread. ' +\n            'Call tf.whereAsync() instead');\n        const condVals = condition.dataSync();\n        return whereImpl(condition.shape, condVals);\n    }\n    topk(x, k, sorted) {\n        const xVals = x.dataSync();\n        return topkImpl(xVals, x.shape, x.dtype, k, sorted);\n    }\n    min(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'min', a2D.dtype).reshape(outShape);\n    }\n    minimum(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.minimum(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MIN, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MIN, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    mod(a, b) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MOD, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MOD, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    maximum(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.maximum(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MAX, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MAX, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    all(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'all', a2D.dtype).reshape(outShape);\n    }\n    any(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'any', a2D.dtype).reshape(outShape);\n    }\n    floorDiv(a, b) {\n        const op = binaryop_gpu.INT_DIV;\n        const outputDtype = 'int32';\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.INT_DIV, outputDtype);\n        }\n        const program = new BinaryOpProgram(op, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], outputDtype);\n    }\n    add(a, b) {\n        if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n            return this.complexSeparableBinaryOp(a, b, binaryop_gpu.ADD);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        if (this.shouldExecuteOnCPU([a, b])) {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const [outValues, outShape] = addImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n            return this.makeOutput(outShape, dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_gpu.ADD, dtype);\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.ADD, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    packedUnaryOp(x, op, dtype) {\n        const program = new UnaryOpPackedProgram(x.shape, op);\n        return this.compileAndRun(program, [x], dtype);\n    }\n    packedBinaryOp(a, b, op, dtype, checkOutOfBounds = false) {\n        const program = new BinaryOpPackedProgram(op, a.shape, b.shape, checkOutOfBounds);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    /**\n     * Computes a complex binary operation that can be decomposed into a simple\n     * binary operation on both the real and imagary parts.\n     */\n    complexSeparableBinaryOp(a, b, op) {\n        const aData = this.texData.get(a.dataId);\n        const bData = this.texData.get(b.dataId);\n        const [real, imag] = [\n            [aData.complexTensors.real, bData.complexTensors.real],\n            [aData.complexTensors.imag, bData.complexTensors.imag]\n        ].map(complexParts => {\n            const [aPart, bPart] = complexParts;\n            const aHandle = this.makeComplexComponentTensorInfo(a, aPart);\n            const bHandle = this.makeComplexComponentTensorInfo(b, bPart);\n            const program = new BinaryOpProgram(op, a.shape, b.shape);\n            return this.compileAndRun(program, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));\n        });\n        const complex = this.complex(real, imag);\n        real.dispose();\n        imag.dispose();\n        return complex;\n    }\n    // Returns a TensorInfo with the complex shape and the dataId of the\n    // underlying part. We need to do this because a reshaped complex tensor is\n    // not reflected in its parts.\n    makeComplexComponentTensorInfo(complexTensor, complexPart) {\n        return {\n            dataId: complexPart.dataId,\n            dtype: complexPart.dtype,\n            shape: complexTensor.shape\n        };\n    }\n    addN(tensors) {\n        if (tensors.length === 1) {\n            return tensors[0];\n        }\n        // Limit the number of uploaded textures for optimization.\n        if (tensors.length > env().get('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n            const midIndex = Math.floor(tensors.length / 2);\n            const leftSide = this.addN(tensors.slice(0, midIndex));\n            const rightSide = this.addN(tensors.slice(midIndex));\n            return this.addN([leftSide, rightSide]);\n        }\n        const dtype = tensors.map(t => t.dtype).reduce((d1, d2) => upcastType(d1, d2));\n        const shapes = tensors.map(t => t.shape);\n        // We can make sure shapes are identical in op level.\n        const usePackedOp = env().getBool('WEBGL_PACK');\n        const program = usePackedOp ?\n            new AddNPackedProgram(tensors[0].shape, shapes) :\n            new AddNProgram(tensors[0].shape, shapes);\n        return this.compileAndRun(program, tensors, dtype);\n    }\n    subtract(a, b) {\n        if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n            return this.complexSeparableBinaryOp(a, b, binaryop_gpu.SUB);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        if (this.shouldExecuteOnCPU([a, b])) {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const [outValues, outShape] = subImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n            return this.makeOutput(outShape, dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_gpu.SUB, a.dtype);\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.SUB, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    pow(a, b) {\n        const usePackedOp = env().getBool('WEBGL_PACK_BINARY_OPERATIONS');\n        const program = usePackedOp ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.POW, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.POW, a.shape, b.shape);\n        const dtype = upcastType(a.dtype, b.dtype);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    ceil(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = ceilImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.CEIL, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.CEIL);\n        return this.compileAndRun(program, [x]);\n    }\n    floor(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = floorImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.FLOOR, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.FLOOR);\n        return this.compileAndRun(program, [x]);\n    }\n    sign(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SIGN);\n        return this.compileAndRun(program, [x]);\n    }\n    isNaN(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_NAN);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    isInf(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_INF);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    isFinite(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_FINITE);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    round(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ROUND);\n        return this.compileAndRun(program, [x]);\n    }\n    exp(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = expImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.EXP, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.EXP);\n        return this.compileAndRun(program, [x]);\n    }\n    expm1(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = expm1ImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.EXPM1, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.EXPM1);\n        return this.compileAndRun(program, [x]);\n    }\n    softmax(logits, dim) {\n        const axes = util.parseAxisParam([dim], logits.shape);\n        // TODO(annxingyuan): Call maxImpl rather than op as part of softmax kernel\n        // modularization.\n        const maxLogit = max(logits, axes);\n        const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n        const a = this.subtract(logits, maxLogit.reshape(expandedShape));\n        const b = this.exp(a);\n        const sumExp = this.sum(b, axes).reshape(expandedShape);\n        // TODO(annxingyuan): Call divImpl rather than op as part of softmax kernel\n        // modularization.\n        return div(b, sumExp);\n    }\n    log(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = logImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_packed_op.LOG, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.LOG);\n        return this.compileAndRun(program, [x]);\n    }\n    log1p(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.LOG1P);\n        return this.compileAndRun(program, [x]);\n    }\n    sqrt(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SQRT);\n        return this.compileAndRun(program, [x]);\n    }\n    rsqrt(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = rsqrtImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.RSQRT);\n        return this.compileAndRun(program, [x]);\n    }\n    reciprocal(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.RECIPROCAL);\n        return this.compileAndRun(program, [x]);\n    }\n    relu(x) {\n        let program;\n        if (env().getBool('WEBGL_PACK')) {\n            program = new UnaryOpPackedProgram(x.shape, unary_packed_op.RELU);\n        }\n        else {\n            program = new UnaryOpProgram(x.shape, unary_op.RELU);\n        }\n        return this.compileAndRun(program, [x]);\n    }\n    relu6(x) {\n        let program;\n        if (env().getBool('WEBGL_PACK')) {\n            program = new UnaryOpPackedProgram(x.shape, unary_packed_op.RELU6);\n        }\n        else {\n            program = new UnaryOpProgram(x.shape, unary_op.RELU6);\n        }\n        return this.compileAndRun(program, [x]);\n    }\n    prelu(x, alpha) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.PRELU, x.shape, alpha.shape) :\n            new BinaryOpProgram(binaryop_gpu.PRELU, x.shape, alpha.shape);\n        return this.compileAndRun(program, [x, alpha]);\n    }\n    elu(x) {\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_packed_op.ELU, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.ELU);\n        return this.compileAndRun(program, [x]);\n    }\n    eluDer(dy, y) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.ELU_DER, dy.shape, y.shape) :\n            new BinaryOpProgram(binaryop_gpu.ELU_DER, dy.shape, y.shape);\n        return this.compileAndRun(program, [dy, y]);\n    }\n    selu(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SELU);\n        return this.compileAndRun(program, [x]);\n    }\n    int(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.TO_INT);\n        return this.compileAndRun(program, [x], 'int32');\n    }\n    clip(x, min, max) {\n        let program;\n        if (env().getBool('WEBGL_PACK_CLIP')) {\n            program = new ClipPackedProgram(x.shape);\n        }\n        else {\n            program = new ClipProgram(x.shape);\n        }\n        const customSetup = program.getCustomSetupFunc(min, max);\n        return this.compileAndRun(program, [x], null, customSetup);\n    }\n    abs(x) {\n        // TODO: handle cases when x is complex.\n        if (this.shouldExecuteOnCPU([x]) && x.dtype !== 'complex64') {\n            const outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.ABS, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.ABS);\n        return this.compileAndRun(program, [x]);\n    }\n    complexAbs(x) {\n        const xData = this.texData.get(x.dataId);\n        const program = new ComplexAbsProgram(x.shape);\n        const inputs = [\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.real),\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.imag),\n        ];\n        return this.compileAndRun(program, inputs);\n    }\n    sigmoid(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SIGMOID);\n        return this.compileAndRun(program, [x]);\n    }\n    softplus(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SOFTPLUS);\n        return this.compileAndRun(program, [x]);\n    }\n    asin(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ASIN);\n        return this.compileAndRun(program, [x]);\n    }\n    acos(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ACOS);\n        return this.compileAndRun(program, [x]);\n    }\n    atan(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ATAN);\n        return this.compileAndRun(program, [x]);\n    }\n    sinh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SINH);\n        return this.compileAndRun(program, [x]);\n    }\n    cosh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.COSH);\n        return this.compileAndRun(program, [x]);\n    }\n    tanh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.TANH);\n        return this.compileAndRun(program, [x]);\n    }\n    asinh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ASINH);\n        return this.compileAndRun(program, [x]);\n    }\n    acosh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ACOSH);\n        return this.compileAndRun(program, [x]);\n    }\n    atanh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ATANH);\n        return this.compileAndRun(program, [x]);\n    }\n    erf(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ERF);\n        return this.compileAndRun(program, [x]);\n    }\n    step(x, alpha) {\n        const program = new UnaryOpProgram(x.shape, unary_op.STEP(alpha));\n        return this.compileAndRun(program, [x]);\n    }\n    conv2dByMatMul(x, filter, convInfo, bias, activation, preluActivationWeights) {\n        // Reshapes conv2D input to 2D tensors, uses matMul and then reshape the\n        // result from 2D to 4D.\n        const xShape = x.shape;\n        const xTexData = this.texData.get(x.dataId);\n        const sharedMatMulDim = convInfo.inChannels;\n        const outerShapeX = xShape[0] * xShape[1] * xShape[2];\n        const outerShapeFilter = convInfo.outChannels;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const transposeA = false;\n        const transposeB = false;\n        // TODO: Once reduction ops are packed, batchMatMul will always be packed\n        // and we can remove this condition.\n        const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) &&\n            sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;\n        const reshapeWillBeExpensive = xShape[2] % 2 !== 0 && !!xTexData.isPacked;\n        if (batchMatMulWillBeUnpacked || !env().getBool('WEBGL_LAZILY_UNPACK') ||\n            !env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ||\n            !reshapeWillBeExpensive) {\n            const targetShape = isChannelsLast ? xShape[0] * xShape[1] * xShape[2] :\n                xShape[0] * xShape[2] * xShape[3];\n            const xReshaped = reshape(x, [1, targetShape, convInfo.inChannels]);\n            const filterReshaped = reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);\n            const result = this.fusedBatchMatMul({\n                a: xReshaped,\n                b: filterReshaped,\n                transposeA,\n                transposeB,\n                bias,\n                activation,\n                preluActivationWeights\n            });\n            return reshape(result, convInfo.outShape);\n        }\n        // Following optimization is specific to packed |x| with odd row count\n        // (For example, in channelLast mode, 'row count' refers to x.shape[2]):\n        // we avoid expensive packed 2x2 reshape by padding row count to next,\n        // even number. When x.shape[2] is odd, the result of packed batchMatMul is\n        // the same (has the same texture layout and and values in the texture) as\n        // it is for even x.shape[2] + 1. We make the odd-rows tensor to look like\n        // even-rows tensor before the operation and, after the batchMatMul,\n        // fix the even-rows result to have odd number of rows.\n        const targetShape = isChannelsLast ?\n            xShape[0] * xShape[1] * (xShape[2] + 1) :\n            xShape[0] * xShape[2] * (xShape[3] + 1);\n        const xReshaped = {\n            dataId: x.dataId,\n            shape: [1, targetShape, convInfo.inChannels],\n            dtype: x.dtype\n        };\n        // xTexData.shape gets referenced from GPGPUBinary.inShapeInfos.\n        // Decrementing row count, after batchMatMul->...->compileProgram leads to\n        // invalid row count within the reference in GPGPUBinary.inShapeInfos.\n        // Alternative fix would be to provide a copy to GPGPUBinary.inShapeInfos\n        // in compileProgram method, but that would affect compilation of all\n        // programs - instead, provide a copy here, with even row count, before\n        // calling batchMatMul->...->compileProgram and after that, the original\n        // xTexData.shape is restored.\n        const originalXTexDataShape = xTexData.shape;\n        xTexData.shape = xTexData.shape.slice();\n        xTexData.shape[xTexData.shape.length - 2]++;\n        util.assert(webgl_util.isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);\n        const filterReshaped = reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);\n        const pointwiseConv = this.fusedBatchMatMul({\n            a: xReshaped,\n            b: filterReshaped,\n            transposeA,\n            transposeB,\n            bias,\n            activation,\n            preluActivationWeights\n        });\n        const pointwiseConvTexData = this.texData.get(pointwiseConv.dataId);\n        util.assert(pointwiseConvTexData.isPacked, () => 'batchMatMul result is expected to be packed');\n        // Restore the input shape to original.\n        xTexData.shape = originalXTexDataShape;\n        // Set the output shape - there is no need for expensive reshape as data\n        // layout is already correct.\n        pointwiseConvTexData.shape = convInfo.outShape;\n        return engine().makeTensorFromDataId(pointwiseConv.dataId, convInfo.outShape, pointwiseConv.dtype);\n    }\n    conv2dWithIm2Row(x, filter, convInfo, bias, activation, preluActivationWeights) {\n        // Rearranges conv2d input so each block to be convolved over forms the\n        // column of a new matrix with shape [filterWidth * filterHeight *\n        // inChannels, outHeight * outWidth]. The filter is also rearranged so each\n        // output channel forms a row of a new matrix with shape [outChannels,\n        // filterWidth * filterHeight * inChannels]. The convolution is then\n        // computed by multiplying these matrices and reshaping the result.\n        const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo;\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const sharedDim = filterWidth * filterHeight * inChannels;\n        const numCols = outHeight * outWidth;\n        const x2ColShape = [sharedDim, numCols];\n        const transposeA = true;\n        const transposeB = false;\n        const xSqueezed = x.squeeze([0]);\n        const w2Row = filter.reshape([1, sharedDim, -1]);\n        const im2ColProgram = new Im2ColPackedProgram(x2ColShape, xSqueezed.shape, convInfo);\n        const im2Col = this.compileAndRun(im2ColProgram, [xSqueezed]).reshape([\n            1, x2ColShape[0], x2ColShape[1]\n        ]);\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;\n        const matmulProgram = new MatMulPackedProgram(im2Col.shape, [1, numCols, convInfo.outChannels], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [im2Col, w2Row];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (hasPreluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        const product = this.compileAndRun(matmulProgram, inputs);\n        if (isChannelsLast) {\n            return product.reshape([1, outHeight, outWidth, convInfo.outChannels]);\n        }\n        else {\n            return product.reshape([1, convInfo.outChannels, outHeight, outWidth]);\n        }\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n            (convInfo.padInfo.type === 'SAME' ||\n                convInfo.padInfo.type === 'VALID')) {\n            return this.conv2dByMatMul(input, filter, convInfo, bias, activation, preluActivationWeights);\n        }\n        if (env().getBool('WEBGL_CONV_IM2COL') && input.shape[0] === 1) {\n            return this.conv2dWithIm2Row(input, filter, convInfo, bias, activation, preluActivationWeights);\n        }\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, false) : null;\n        const program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [input, filter];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (preluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        return this.compileAndRun(program, inputs);\n    }\n    conv2d(x, filter, convInfo) {\n        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n            (convInfo.padInfo.type === 'SAME' ||\n                convInfo.padInfo.type === 'VALID')) {\n            return this.conv2dByMatMul(x, filter, convInfo);\n        }\n        if (env().getBool('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {\n            return this.conv2dWithIm2Row(x, filter, convInfo);\n        }\n        const program = new Conv2DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        const program = new Conv2DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    conv2dDerFilter(x, dy, convInfo) {\n        const program = new Conv2DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        const shouldPackDepthwiseConv = env().getBool('WEBGL_PACK_DEPTHWISECONV') &&\n            convInfo.strideWidth <= 2 &&\n            convInfo.outChannels / convInfo.inChannels === 1;\n        const fusedActivation = activation ?\n            mapActivationToShaderProgram(activation, shouldPackDepthwiseConv) :\n            null;\n        const inputs = [input, filter];\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        if (hasBias) {\n            inputs.push(bias);\n        }\n        if (hasPreluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        let program;\n        if (shouldPackDepthwiseConv) {\n            program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n            return this.compileAndRun(program, inputs);\n        }\n        program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n        return this.compileAndRun(program, inputs);\n    }\n    depthwiseConv2D(x, filter, convInfo) {\n        let program;\n        if (env().getBool('WEBGL_PACK_DEPTHWISECONV') &&\n            convInfo.strideWidth <= 2 &&\n            convInfo.outChannels / convInfo.inChannels === 1) {\n            program = new DepthwiseConvPacked2DProgram(convInfo);\n            return this.compileAndRun(program, [x, filter]);\n        }\n        program = new DepthwiseConv2DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        const program = new DepthwiseConv2DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    depthwiseConv2DDerFilter(x, dy, convInfo) {\n        const program = new DepthwiseConv2DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    conv3d(x, filter, convInfo) {\n        const program = new Conv3DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        const program = new Conv3DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    conv3dDerFilter(x, dy, convInfo) {\n        const program = new Conv3DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    cast(x, dtype) {\n        return backend_util.castTensor(x, dtype, this);\n    }\n    unstack(x, axis) {\n        const num = x.shape[axis];\n        const outShape = new Array(x.rank - 1);\n        let outIndex = 0;\n        for (let i = 0; i < x.rank; i++) {\n            if (i !== axis) {\n                outShape[outIndex++] = x.shape[i];\n            }\n        }\n        const begin = new Array(x.rank).fill(0);\n        const size = x.shape.slice();\n        size[axis] = 1;\n        const res = new Array(num);\n        for (let i = 0; i < res.length; i++) {\n            begin[axis] = i;\n            res[i] = this.slice(x, begin, size).reshape(outShape);\n        }\n        return res;\n    }\n    avgPool3d(x, convInfo) {\n        const program = new Pool3DProgram(convInfo, 'avg', false);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        const avgPool3dBackpropProgram = new AvgPool3DBackpropProgram(convInfo);\n        return this.compileAndRun(avgPool3dBackpropProgram, [dy], x.dtype);\n    }\n    maxPool3d(x, convInfo) {\n        const program = new Pool3DProgram(convInfo, 'max', false);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        const getPositions = true;\n        const maxPool3dPositionsProgram = new Pool3DProgram(convInfo, 'max', getPositions);\n        const maxPool3dPositions = this.compileAndRun(maxPool3dPositionsProgram, [x]);\n        const maxPool3dBackPropProgram = new MaxPool3DBackpropProgram(convInfo);\n        const result = this.compileAndRun(maxPool3dBackPropProgram, [dy, maxPool3dPositions], x.dtype);\n        maxPool3dPositions.dispose();\n        return result;\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        const program = env().getBool('WEBGL_PACK_IMAGE_OPERATIONS') ?\n            new ResizeBilinearPackedProgram(x.shape, newHeight, newWidth, alignCorners) :\n            new ResizeBilinearProgram(x.shape, newHeight, newWidth, alignCorners);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        const program = new ResizeBilinearBackpropProgram(dy, x, alignCorners);\n        return this.compileAndRun(program, [dy]);\n    }\n    resizeNearestNeighbor(x, newHeight, newWidth, alignCorners) {\n        const program = new ResizeNearestNeighborProgram(x.shape, newHeight, newWidth, alignCorners);\n        return this.compileAndRun(program, [x]);\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        const program = new ResizeNearestNeigborBackpropProgram(dy, x, alignCorners);\n        return this.compileAndRun(program, [dy]);\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        const probs = normalized ? logits : softmax(logits);\n        const batchSize = probs.shape[0];\n        const numOutcomes = probs.shape[1];\n        const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);\n        const customSetup = program.getCustomSetupFunc(seed);\n        return this.compileAndRun(program, [probs], 'int32', customSetup);\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        const program = new OneHotProgram(indices.size, depth, onValue, offValue);\n        return this.compileAndRun(program, [indices]);\n    }\n    diag(x) {\n        const program = new DiagProgram(x.size);\n        return this.compileAndRun(program, [x]);\n    }\n    cropAndResize(image, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        const program = new CropAndResizeProgram(image.shape, boxes.shape, cropSize, method, extrapolationValue);\n        return this.compileAndRun(program, [image, boxes, boxIndex], 'float32');\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n        const batchSize = x.shape[0];\n        const inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];\n        const inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];\n        const inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];\n        const outputHeight = inputHeight * blockSize;\n        const outputWidth = inputWidth * blockSize;\n        const outputDepth = inputDepth / (blockSize * blockSize);\n        const outputShape = (dataFormat === 'NHWC') ?\n            [batchSize, outputHeight, outputWidth, outputDepth] :\n            [batchSize, outputDepth, outputHeight, outputWidth];\n        const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);\n        return this.compileAndRun(program, [x]);\n    }\n    split(x, sizeSplits, axis) {\n        return split(x, sizeSplits, axis);\n    }\n    scatterND(indices, updates, shape) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(updates, indices, shape);\n        const flattenShape = [outputSize / sliceSize, sliceSize];\n        const flattenIndices = indices.reshape([numUpdates, sliceRank]);\n        const flattenX = updates.reshape([numUpdates, sliceSize]);\n        if (outputSize === 0) {\n            return backend_util.reshapeTensor(tensor([]), shape);\n        }\n        const defaultValue = scalar(0);\n        const program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.rank, flattenX.rank, strides, flattenShape);\n        const res = this.compileAndRun(program, [flattenX, flattenIndices, defaultValue]);\n        return res.reshape(shape);\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        const { sliceRank, numUpdates, strides, outputSize } = backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n        const sumDupeIndices = false;\n        const program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.rank, sparseValues.rank, strides, [outputSize, 1], sumDupeIndices);\n        const res = this.compileAndRun(program, [sparseValues, sparseIndices, defaultValue]);\n        return res.reshape(outputShape);\n    }\n    fft(x) {\n        const inverse = false;\n        return this.fftImpl(x, inverse);\n    }\n    ifft(x) {\n        const inverse = true;\n        return this.fftImpl(x, inverse);\n    }\n    fftImpl(x, inverse) {\n        const xData = this.texData.get(x.dataId);\n        const realProgram = new FFTProgram(fft_gpu.COMPLEX_FFT.REAL, x.shape, inverse);\n        const imagProgram = new FFTProgram(fft_gpu.COMPLEX_FFT.IMAG, x.shape, inverse);\n        const inputs = [\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.real),\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.imag),\n        ];\n        const real = this.compileAndRun(realProgram, inputs);\n        const imag = this.compileAndRun(imagProgram, inputs);\n        const complex = this.complex(real, imag).as2D(x.shape[0], x.shape[1]);\n        real.dispose();\n        imag.dispose();\n        return complex;\n    }\n    gatherND(x, indices) {\n        const indicesShape = indices.shape;\n        const sliceRank = indicesShape[indicesShape.length - 1];\n        const [resultShape, numSlices, sliceSize, strides] = backend_util.prepareAndValidate(x, indices);\n        const flattenIndices = indices.reshape([numSlices, sliceRank]);\n        const flattenX = x.reshape([x.size / sliceSize, sliceSize]);\n        const program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);\n        const res = this.compileAndRun(program, [flattenX, flattenIndices]);\n        return res.reshape(resultShape);\n    }\n    fill(shape, value, dtype) {\n        dtype = dtype || util.inferDtype(value);\n        if (dtype === 'string') {\n            // String type should be handled in CPU memory.\n            const values = util.getArrayFromDType(dtype, util.sizeFromShape(shape));\n            values.fill(value);\n            return engine().makeTensor(values, shape, dtype, this);\n        }\n        else {\n            const program = new FillProgram(shape, value);\n            const customSetup = program.getCustomSetupFunc(value);\n            return this.compileAndRun(program, [], dtype, customSetup);\n        }\n    }\n    onesLike(x) {\n        if (x.dtype === 'string') {\n            throw new Error('onesLike is not supported under string dtype');\n        }\n        else {\n            // TODO(cais, smilkov): Add WebGL shader for onesLike:\n            //   https://github.com/tensorflow/tfjs/issues/1293\n            return this.fill(x.shape, 1, x.dtype);\n        }\n    }\n    zerosLike(x) {\n        return this.fill(x.shape, x.dtype === 'string' ? '' : 0, x.dtype);\n    }\n    linspace(start, stop, num) {\n        // TODO: Use CPU implementation due to the precision problem in Safari.\n        return backend_util.linspaceImpl(start, stop, num);\n    }\n    makeTensorInfo(shape, dtype, values) {\n        const dataId = this.write(values, shape, dtype);\n        this.texData.get(dataId).usage = null;\n        return { dataId, shape, dtype };\n    }\n    makeOutput(shape, dtype, values) {\n        const { dataId } = this.makeTensorInfo(shape, dtype, values);\n        return engine().makeTensorFromDataId(dataId, shape, dtype, this);\n    }\n    unpackTensor(input) {\n        const program = new UnpackProgram(input.shape);\n        return this.runWebGLProgram(program, [input], input.dtype);\n    }\n    packTensor(input) {\n        const program = new PackProgram(input.shape);\n        const preventEagerUnpackingOutput = true;\n        return this.runWebGLProgram(program, [input], input.dtype, null /* customSetup */, preventEagerUnpackingOutput);\n    }\n    packedReshape(input, afterShape) {\n        const input3DShape = [\n            webgl_util.getBatchDim(input.shape),\n            ...webgl_util.getRowsCols(input.shape)\n        ];\n        const input3D = {\n            dtype: input.dtype,\n            shape: input3DShape,\n            dataId: input.dataId\n        };\n        const afterShapeAs3D = [\n            webgl_util.getBatchDim(afterShape), ...webgl_util.getRowsCols(afterShape)\n        ];\n        const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n        const preventEagerUnpackingOfOutput = true;\n        const output = this.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n        return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n    }\n    decode(dataId) {\n        const texData = this.texData.get(dataId);\n        const { isPacked, shape, dtype } = texData;\n        const shapeAs3D = webgl_util.getShapeAs3D(shape);\n        let program;\n        if (isPacked) {\n            program = new DecodeMatrixPackedProgram(shapeAs3D);\n        }\n        else {\n            program = new DecodeMatrixProgram(shapeAs3D);\n        }\n        const preventEagerUnpackingOfOutput = true;\n        const out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype, dataId }], dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n        return { dtype, shape, dataId: out.dataId };\n    }\n    runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {\n        const output = this.makeTensorInfo(program.outputShape, outputDtype);\n        const outData = this.texData.get(output.dataId);\n        if (program.packedOutput) {\n            outData.isPacked = true;\n        }\n        if (program.outPackingScheme === tex_util.PackingScheme.DENSE) {\n            const texelShape = tex_util.getDenseTexShape(program.outputShape);\n            // For a densely packed output, we explicitly set texShape\n            // so it doesn't get assigned later according to our typical packing\n            // scheme wherein a single texel can only contain values from adjacent\n            // rows/cols.\n            outData.texShape = texelShape.map(d => d * 2);\n        }\n        if (program.outTexUsage != null) {\n            outData.usage = program.outTexUsage;\n        }\n        if (util.sizeFromShape(output.shape) === 0) {\n            // Short-circuit the computation since the result is empty (has 0 in its\n            // shape).\n            outData.values =\n                util.getTypedArrayFromDType(output.dtype, 0);\n            return output;\n        }\n        const dataToDispose = [];\n        const inputsData = inputs.map(input => {\n            if (input.dtype === 'complex64') {\n                throw new Error(`GPGPUProgram does not support complex64 input. For complex64 ` +\n                    `dtypes, please separate the program into real and imaginary ` +\n                    `parts.`);\n            }\n            let texData = this.texData.get(input.dataId);\n            if (texData.texture == null) {\n                if (!program.packedInputs &&\n                    util.sizeFromShape(input.shape) <=\n                        env().getNumber('WEBGL_SIZE_UPLOAD_UNIFORM')) {\n                    // Upload small tensors that live on the CPU as uniforms, not as\n                    // textures. Do this only when the environment supports 32bit floats\n                    // due to problems when comparing 16bit floats with 32bit floats.\n                    // TODO(https://github.com/tensorflow/tfjs/issues/821): Make it\n                    // possible for packed shaders to sample from uniforms.\n                    return {\n                        shape: input.shape,\n                        texData: null,\n                        isUniform: true,\n                        uniformValues: texData.values\n                    };\n                }\n                // This ensures that if a packed program's inputs have not yet been\n                // uploaded to the GPU, they get uploaded as packed right off the bat.\n                if (program.packedInputs) {\n                    texData.isPacked = true;\n                    texData.shape = input.shape;\n                }\n            }\n            else if (!!texData.isPacked !== !!program.packedInputs) {\n                input = texData.isPacked ? this.unpackTensor(input) :\n                    this.packTensor(input);\n                dataToDispose.push(input);\n                texData = this.texData.get(input.dataId);\n            }\n            else if (texData.isPacked &&\n                !webgl_util.isReshapeFree(texData.shape, input.shape)) {\n                // This is a special case where a texture exists for a tensor\n                // but the shapes are incompatible (due to packing constraints) because\n                // the tensor did not have a chance to go through the packed reshape\n                // shader. This only happens when we reshape the *same* tensor to form\n                // *distinct* inputs to an op, e.g. dotting a vector with itself. This\n                // case will disappear once packed uploading is the default.\n                const savedInput = input;\n                const targetShape = input.shape;\n                input.shape = texData.shape;\n                input = this.packedReshape(input, targetShape);\n                dataToDispose.push(input);\n                texData = this.texData.get(input.dataId);\n                savedInput.shape = targetShape;\n            }\n            this.uploadToGPU(input.dataId);\n            return { shape: input.shape, texData, isUniform: false };\n        });\n        this.uploadToGPU(output.dataId);\n        const outputData = { shape: output.shape, texData: outData, isUniform: false };\n        const key = gpgpu_math.makeShaderKey(program, inputsData, outputData);\n        const binary = this.getAndSaveBinary(key, () => {\n            return gpgpu_math.compileProgram(this.gpgpu, program, inputsData, outputData);\n        });\n        const shouldTimeProgram = this.activeTimers != null;\n        let query;\n        if (shouldTimeProgram) {\n            query = this.startTimer();\n        }\n        gpgpu_math.runProgram(this.gpgpu, binary, inputsData, outputData, customSetup);\n        dataToDispose.forEach(info => this.disposeIntermediateTensorInfo(info));\n        if (shouldTimeProgram) {\n            query = this.endTimer(query);\n            this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });\n        }\n        if (!env().getBool('WEBGL_LAZILY_UNPACK') && outData.isPacked &&\n            preventEagerUnpackingOfOutput === false) {\n            const unpacked = this.unpackTensor(output);\n            this.disposeIntermediateTensorInfo(output);\n            return unpacked;\n        }\n        return output;\n    }\n    compileAndRun(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {\n        outputDtype = outputDtype || inputs[0].dtype;\n        const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput);\n        return engine().makeTensorFromDataId(outInfo.dataId, outInfo.shape, outInfo.dtype);\n    }\n    getAndSaveBinary(key, getBinary) {\n        if (!(key in this.binaryCache)) {\n            this.binaryCache[key] = getBinary();\n        }\n        return this.binaryCache[key];\n    }\n    getTextureManager() {\n        return this.textureManager;\n    }\n    dispose() {\n        if (this.disposed) {\n            return;\n        }\n        // Avoid disposing the compiled webgl programs during unit testing because\n        // it slows down test execution.\n        if (!env().getBool('IS_TEST')) {\n            const allKeys = Object.keys(this.binaryCache);\n            allKeys.forEach(key => {\n                this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);\n                delete this.binaryCache[key];\n            });\n        }\n        this.textureManager.dispose();\n        if (this.canvas != null &&\n            (typeof (HTMLCanvasElement) !== 'undefined' &&\n                this.canvas instanceof HTMLCanvasElement)) {\n            this.canvas.remove();\n        }\n        else {\n            this.canvas = null;\n        }\n        if (this.gpgpuCreatedLocally) {\n            this.gpgpu.program = null;\n            this.gpgpu.dispose();\n        }\n        this.disposed = true;\n    }\n    floatPrecision() {\n        if (this.floatPrecisionValue == null) {\n            this.floatPrecisionValue = tidy(() => {\n                if (!env().get('WEBGL_RENDER_FLOAT32_ENABLED')) {\n                    // Momentarily switching DEBUG flag to false so we don't throw an\n                    // error trying to upload a small value.\n                    const debugFlag = env().getBool('DEBUG');\n                    env().set('DEBUG', false);\n                    const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];\n                    env().set('DEBUG', debugFlag);\n                    if (underflowCheckValue > 0) {\n                        return 32;\n                    }\n                }\n                return 16;\n            });\n        }\n        return this.floatPrecisionValue;\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n    }\n    uploadToGPU(dataId) {\n        const texData = this.texData.get(dataId);\n        const { shape, dtype, values, texture, usage, isPacked } = texData;\n        if (texture != null) {\n            // Array is already on GPU. No-op.\n            return;\n        }\n        const shouldTimeProgram = this.activeTimers != null;\n        let start;\n        if (shouldTimeProgram) {\n            start = util.now();\n        }\n        let texShape = texData.texShape;\n        if (texShape == null) {\n            texShape = webgl_util.getTextureShapeFromLogicalShape(shape, isPacked);\n            texData.texShape = texShape;\n        }\n        if (values != null) {\n            const shapeAs3D = webgl_util.getShapeAs3D(shape);\n            let program;\n            let width = texShape[1], height = texShape[0];\n            const isByteArray = values instanceof Uint8Array;\n            if (isPacked) {\n                [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]);\n                program = new EncodeMatrixPackedProgram(shapeAs3D, [height, width], isByteArray);\n            }\n            else {\n                program =\n                    new EncodeMatrixProgram(shapeAs3D, [height, width], isByteArray);\n            }\n            const tempDenseInputHandle = this.makeTensorInfo([height, width], dtype);\n            if (isByteArray) {\n                this.texData.get(tempDenseInputHandle.dataId).usage =\n                    TextureUsage.PIXELS;\n            }\n            else {\n                this.texData.get(tempDenseInputHandle.dataId).usage =\n                    TextureUsage.UPLOAD;\n            }\n            this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);\n            // We want the output to remain packed regardless of the value of\n            // WEBGL_PACK.\n            const preventEagerUnpacking = true;\n            const encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, null, preventEagerUnpacking);\n            // Have the original texture assume the identity of the encoded output.\n            const outputTexData = this.texData.get(encodedOutputTarget.dataId);\n            texData.texture = outputTexData.texture;\n            texData.texShape = outputTexData.texShape;\n            texData.isPacked = outputTexData.isPacked;\n            texData.usage = outputTexData.usage;\n            this.disposeIntermediateTensorInfo(tempDenseInputHandle);\n            this.texData.delete(encodedOutputTarget.dataId);\n            // Once uploaded, don't store the values on cpu.\n            texData.values = null;\n            if (shouldTimeProgram) {\n                this.uploadWaitMs += util.now() - start;\n            }\n        }\n        else {\n            const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);\n            texData.texture = newTexture;\n        }\n    }\n    convertAndCacheOnCPU(dataId, float32Values) {\n        const texData = this.texData.get(dataId);\n        const { dtype } = texData;\n        this.releaseGPUData(dataId);\n        if (float32Values != null) {\n            texData.values = float32ToTypedArray(float32Values, dtype);\n        }\n        return texData.values;\n    }\n    acquireTexture(texShape, texType, dtype, isPacked) {\n        this.numBytesInGPU += this.computeBytes(texShape, dtype);\n        if (!this.warnedAboutMemory &&\n            this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {\n            const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);\n            this.warnedAboutMemory = true;\n            console.warn(`High memory usage in GPU: ${mb} MB, ` +\n                `most likely due to a memory leak`);\n        }\n        return this.textureManager.acquireTexture(texShape, texType, isPacked);\n    }\n    computeBytes(shape, dtype) {\n        return shape[0] * shape[1] * util.bytesPerElement(dtype);\n    }\n    tryRunOnCpuOrThrow(inputs, fn) {\n        if (this.shouldExecuteOnCPU(inputs)) {\n            try {\n                return fn();\n            }\n            catch (e) {\n                if (env().getBool('IS_TEST')) {\n                    throw new Error('CPU forwarding failed');\n                }\n            }\n        }\n        return null;\n    }\n}\nfunction float32ToTypedArray(a, dtype) {\n    if (dtype === 'float32' || dtype === 'complex64') {\n        return a;\n    }\n    else if (dtype === 'int32' || dtype === 'bool') {\n        const result = (dtype === 'int32') ? new Int32Array(a.length) :\n            new Uint8Array(a.length);\n        for (let i = 0; i < result.length; ++i) {\n            result[i] = Math.round(a[i]);\n        }\n        return result;\n    }\n    else {\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n}\n//# sourceMappingURL=backend_webgl.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport * as gpgpu_util from './gpgpu_util';\nimport * as webgl_util from './webgl_util';\nexport { MathBackendWebGL } from './backend_webgl';\nexport { setWebGLContext } from './canvas_util';\nexport { GPGPUContext } from './gpgpu_context';\n// WebGL specific utils.\nexport { gpgpu_util, webgl_util };\n/**\n * Enforce use of half precision textures if available on the platform.\n *\n * @doc {heading: 'Environment', namespace: 'webgl'}\n */\nexport function forceHalfFloat() {\n    env().set('WEBGL_FORCE_F16_TEXTURES', true);\n}\n//# sourceMappingURL=webgl.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// base.ts is the webgl backend without auto kernel registration.\nimport { device_util, registerBackend } from '@tensorflow/tfjs-core';\nimport { MathBackendWebGL } from './backend_webgl';\nexport { version as version_webgl } from './version';\nif (device_util.isBrowser()) {\n    registerBackend('webgl', () => new MathBackendWebGL(), 2 /* priority */);\n}\n// Export webgl utilities\nexport * from './webgl';\n// Export forceHalfFlost under webgl namespace for the union bundle.\nimport { forceHalfFloat } from './webgl';\nexport const webgl = { forceHalfFloat };\n//# sourceMappingURL=base.js.map", "import { env } from '@tensorflow/tfjs-core';\nimport { BinaryOpProgram } from '../binaryop_gpu';\nimport { BinaryOpPackedProgram } from '../binaryop_packed_gpu';\nimport { UnaryOpProgram } from '../unaryop_gpu';\nexport const CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;\nexport const CHECK_NAN_SNIPPET_BINARY = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nexport const CHECK_NAN_SNIPPET_BINARY_PACKED = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param opSnippets Op snippet to create `UnaryOpProgram`.\n */\nexport function unaryKernelFunc(opSnippet) {\n    return ({ inputs, backend }) => {\n        const { x } = inputs;\n        const webglBackend = backend;\n        const program = new UnaryOpProgram(x.shape, opSnippet);\n        return webglBackend.runWebGLProgram(program, [x], x.dtype);\n    };\n}\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param opSnippet Op snippet to create `BinaryOpProgram`.\n * @param packedOpSnippet Op snippet to create `BinaryOpPackedProgram`.\n * @param checkOutOfBoundsForPackedProgram Whether to set checkOutOfBounds=true\n *     when creating BinaryOpPackedProgram.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc(opSnippet, packedOpSnippet, checkOutOfBoundsForPackedProgram, dtype) {\n    // TODO(jingjin): handle complex64.\n    return ({ inputs, backend }) => {\n        const { a, b } = inputs;\n        const webglBackend = backend;\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(packedOpSnippet, a.shape, b.shape, !!checkOutOfBoundsForPackedProgram) :\n            new BinaryOpProgram(opSnippet, a.shape, b.shape);\n        const $dtype = dtype || a.dtype;\n        const output = webglBackend.runWebGLProgram(program, [a, b], $dtype);\n        return output;\n    };\n}\n//# sourceMappingURL=kernel_funcs_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan2 } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc, CHECK_NAN_SNIPPET_BINARY, CHECK_NAN_SNIPPET_BINARY_PACKED } from '../kernel_utils/kernel_funcs_utils';\nconst ATAN2 = CHECK_NAN_SNIPPET_BINARY + `\n  return atan(a, b);\n`;\nconst ATAN2_PACKED = `\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET_BINARY_PACKED + `\n  return result;\n`;\nexport const atan2KernelFunc = binaryKernelFunc(ATAN2, ATAN2_PACKED);\nexport const atan2Config = {\n    kernelName: Atan2,\n    backendName: 'webgl',\n    kernelFunc: atan2KernelFunc,\n};\n//# sourceMappingURL=Atan2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '@tensorflow/tfjs-core';\nexport function identity(args) {\n    const { inputs, backend } = args;\n    const { x } = inputs;\n    backend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nexport const identityConfig = {\n    kernelName: Identity,\n    backendName: 'webgl',\n    kernelFunc: identity\n};\n//# sourceMappingURL=Identity.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool, backend_util, util } from '@tensorflow/tfjs-core';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nimport { identity } from './Identity';\nexport function avgPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'avgPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        return identity({ inputs: { x }, backend });\n    }\n    const avgPoolProgram = new Pool2DProgram(convInfo, 'avg', false);\n    return backend.runWebGLProgram(avgPoolProgram, [x], 'float32');\n}\nexport const avgPoolConfig = {\n    kernelName: AvgPool,\n    backendName: 'webgl',\n    kernelFunc: avgPool\n};\n//# sourceMappingURL=AvgPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPoolBackprop, backend_util } from '@tensorflow/tfjs-core';\nimport { AvgPool2DBackpropProgram } from '../avg_pool_backprop_gpu';\nimport { assertNotComplex } from '../webgl_util';\nexport function avgPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input } = inputs;\n    const x = input;\n    assertNotComplex([dy, input], 'avgPoolBackprop');\n    const { filterSize, strides, pad } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);\n    const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);\n    return backend.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);\n}\nexport const avgPoolBackpropConfig = {\n    kernelName: AvgPoolBackprop,\n    backendName: 'webgl',\n    kernelFunc: avgPoolBackprop\n};\n//# sourceMappingURL=AvgPoolBackprop.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class BatchNormProgram {\n    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n        this.outputShape = [];\n        this.variableNames = ['x', 'mean', 'variance'];\n        backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n        backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n        let offsetSnippet = '0.0';\n        if (offsetShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n            this.variableNames.push('offset');\n            offsetSnippet = 'getOffsetAtOutCoords()';\n        }\n        let scaleSnippet = '1.0';\n        if (scaleShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n            this.variableNames.push('scale');\n            scaleSnippet = 'getScaleAtOutCoords()';\n        }\n        this.outputShape = xShape;\n        this.userCode = `\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${offsetSnippet};\n        float scale = ${scaleSnippet};\n        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=batchnorm_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class BatchNormPackedProgram {\n    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.variableNames = ['x', 'mean', 'variance'];\n        backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n        backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n        let offsetSnippet = 'vec4(0.0)';\n        if (offsetShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n            this.variableNames.push('offset');\n            offsetSnippet = 'getOffsetAtOutCoords()';\n        }\n        let scaleSnippet = 'vec4(1.0)';\n        if (scaleShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n            this.variableNames.push('scale');\n            scaleSnippet = 'getScaleAtOutCoords()';\n        }\n        this.outputShape = xShape;\n        this.userCode = `\n      void main() {\n        vec4 offset = ${offsetSnippet};\n        vec4 scale = ${scaleSnippet};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=batchnorm_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, FusedBatchNorm, util } from '@tensorflow/tfjs-core';\nimport { BatchNormProgram } from '../batchnorm_gpu';\nimport { BatchNormPackedProgram } from '../batchnorm_packed_gpu';\nexport const batchNormKernelFunc = ({ inputs, backend, attrs }) => {\n    const { x, mean, variance, offset, scale } = inputs;\n    util.assert(mean.shape.length === variance.shape.length, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert(offset == null || mean.shape.length === offset.shape.length, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert(scale == null || mean.shape.length === scale.shape.length, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    let { varianceEpsilon } = attrs;\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const finalInputs = [x, mean, variance];\n    let offsetShape = null;\n    if (offset != null) {\n        offsetShape = offset.shape;\n        finalInputs.push(offset);\n    }\n    let scaleShape = null;\n    if (scale != null) {\n        scaleShape = scale.shape;\n        finalInputs.push(scale);\n    }\n    const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n        new BatchNormPackedProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) :\n        new BatchNormProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);\n    const output = backend.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);\n    return output;\n};\nexport const batchNormConfig = {\n    kernelName: FusedBatchNorm,\n    backendName: 'webgl',\n    kernelFunc: batchNormKernelFunc,\n};\n//# sourceMappingURL=BatchNorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '@tensorflow/tfjs-core';\nimport { CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst COS = CHECK_NAN_SNIPPET_UNARY + `\n  return cos(x);\n`;\nexport const cosKernelFunc = unaryKernelFunc(COS);\nexport const cosConfig = {\n    kernelName: Cos,\n    backendName: 'webgl',\n    kernelFunc: cosKernelFunc,\n};\n//# sourceMappingURL=Cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\n// Without the equality check div produces 0.9999 for a = b, which when\n// floored can cause errors.\nconst DIV = `\nif (a == b) {\n  return 1.0;\n};\nreturn a / b;`;\n// We do the same as in ./binaryop_gpu, with vec4 and ivec4.\n// On Linux, the vectorized implementation produces NaNs when a and b are 0.\nconst DIV_PACKED = `\n  // vec4 one = vec4(equal(a, b));\n  // return one + (vec4(1.0) - one) * a / b;\n  vec4 result = a / b;\n  if(a.x == b.x) {\n    result.x = 1.;\n  }\n  if(a.y == b.y) {\n    result.y = 1.;\n  }\n  if(a.z == b.z) {\n    result.z = 1.;\n  }\n  if(a.w == b.w) {\n    result.w = 1.;\n  }\n\n  return result;\n`;\nexport const divKernelFunc = binaryKernelFunc(DIV, DIV_PACKED, true /* checkOutOfBoundsForPackedProgram */);\nexport const divConfig = {\n    kernelName: Div,\n    backendName: 'webgl',\n    kernelFunc: divKernelFunc,\n};\n//# sourceMappingURL=Div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FlipLeftRightProgram {\n    constructor(imageShape) {\n        this.variableNames = ['Image'];\n        this.outputShape = [];\n        const imageWidth = imageShape[2];\n        this.outputShape = imageShape;\n        this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${imageWidth} - x;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${imageWidth}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n    }\n}\n//# sourceMappingURL=flip_left_right_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FlipLeftRight } from '@tensorflow/tfjs-core';\nimport { FlipLeftRightProgram } from '../flip_left_right_gpu';\nexport const flipLeftRightConfig = {\n    kernelName: FlipLeftRight,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend }) => {\n        const { image } = inputs;\n        const webglBackend = backend;\n        const program = new FlipLeftRightProgram(image.shape);\n        const output = webglBackend.runWebGLProgram(program, [image], image.dtype);\n        return output;\n    }\n};\n//# sourceMappingURL=FlipLeftRight.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from '../../glsl_version';\nexport class FromPixelsProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        const glsl = getGlslDifferences();\n        const [height, width,] = outputShape;\n        this.outputShape = outputShape;\n        this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);\n\n        vec4 values = ${glsl.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=from_pixels_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from '../../glsl_version';\nexport class FromPixelsPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        const glsl = getGlslDifferences();\n        const [height, width,] = outputShape;\n        this.outputShape = outputShape;\n        this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(${width}.0, ${height}.0);\n            vec4 values = ${glsl.texture2D}(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=from_pixels_packed_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { FromPixels } from '@tensorflow/tfjs-core';\nimport { TextureUsage } from '../tex_util';\nimport { FromPixelsProgram } from './FromPixels_utils/from_pixels_gpu';\nimport { FromPixelsPackedProgram } from './FromPixels_utils/from_pixels_packed_gpu';\nexport const fromPixelsConfig = {\n    kernelName: FromPixels,\n    backendName: 'webgl',\n    kernelFunc: fromPixels,\n};\nlet fromPixels2DContext;\nfunction fromPixels(args) {\n    const { inputs, backend, attrs } = args;\n    let { pixels } = inputs;\n    const { numChannels } = attrs;\n    const isVideo = typeof (HTMLVideoElement) !== 'undefined' &&\n        pixels instanceof HTMLVideoElement;\n    const isImage = typeof (HTMLImageElement) !== 'undefined' &&\n        pixels instanceof HTMLImageElement;\n    const [width, height] = isVideo ?\n        [\n            pixels.videoWidth,\n            pixels.videoHeight\n        ] :\n        [pixels.width, pixels.height];\n    const texShape = [height, width];\n    const outShape = [height, width, numChannels];\n    if (isImage || isVideo) {\n        if (fromPixels2DContext == null) {\n            fromPixels2DContext = document.createElement('canvas').getContext('2d');\n        }\n        fromPixels2DContext.canvas.width = width;\n        fromPixels2DContext.canvas.height = height;\n        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n        pixels = fromPixels2DContext.canvas;\n    }\n    const tempPixelHandle = backend.makeTensorInfo(texShape, 'int32');\n    // This is a byte texture with pixels.\n    backend.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;\n    backend.gpgpu.uploadPixelDataToTexture(backend.getTexture(tempPixelHandle.dataId), pixels);\n    const program = env().getBool('WEBGL_PACK') ?\n        new FromPixelsPackedProgram(outShape) :\n        new FromPixelsProgram(outShape);\n    const res = backend.runWebGLProgram(program, [tempPixelHandle], 'int32');\n    backend.disposeData(tempPixelHandle.dataId);\n    return res;\n}\n//# sourceMappingURL=FromPixels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { ReduceProgram } from '../reduce_gpu';\n// Returns an array of configuration objects that describe each stage of the\n// reduction.\nfunction getReductionStages(inShape) {\n    const stages = [];\n    while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {\n        const outSize = stages.length ? stages[stages.length - 1].outSize : inShape[1];\n        const windowSize = backend_util.computeOptimalWindowSize(outSize);\n        stages.push({\n            inSize: outSize,\n            windowSize,\n            outSize: Math.ceil(outSize / windowSize)\n        });\n    }\n    return stages;\n}\nexport function reduce(x, dtype, reductionType, backend) {\n    const reductionStages = getReductionStages(x.shape);\n    let result = x;\n    for (let i = 0; i < reductionStages.length; i++) {\n        const { inSize, windowSize, outSize } = reductionStages[i];\n        const program = new ReduceProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, reductionType);\n        const previousResult = result;\n        result = backend.runWebGLProgram(program, [result], dtype);\n        if (previousResult.dataId !== x.dataId) {\n            backend.disposeData(previousResult.dataId);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=reduce.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ReshapePackedProgram } from '../reshape_packed_gpu';\nimport { getBatchDim, getRowsCols } from '../webgl_util';\nexport function packedReshape(input, afterShape, backend) {\n    const input3DShape = [getBatchDim(input.shape),\n        ...getRowsCols(input.shape)];\n    const input3D = {\n        dtype: input.dtype,\n        shape: input3DShape,\n        dataId: input.dataId\n    };\n    const afterShapeAs3D = [getBatchDim(afterShape),\n        ...getRowsCols(afterShape)];\n    const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n    const preventEagerUnpackingOfOutput = true;\n    const output = backend.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n    return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n}\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape, util } from '@tensorflow/tfjs-core';\nimport { packedReshape } from '../kernel_utils/reshape';\nimport { isReshapeFree } from '../webgl_util';\nexport function reshape(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { shape } = attrs;\n    const webglBackend = backend;\n    const xSize = util.sizeFromShape(x.shape);\n    const $shape = util.inferFromImplicitShape(shape, xSize);\n    const $xSize = util.sizeFromShape($shape);\n    util.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n        `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n        `shape must have the same number of elements.`);\n    const xTexData = webglBackend.texData.get(x.dataId);\n    if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) &&\n        !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {\n        return packedReshape(x, $shape, webglBackend);\n    }\n    webglBackend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nexport const reshapeConfig = {\n    kernelName: Reshape,\n    backendName: 'webgl',\n    kernelFunc: reshape\n};\n//# sourceMappingURL=Reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { reduce } from '../kernel_utils/reduce';\nimport { reshape } from '../kernels/Reshape';\nexport function maxImpl(x, reduceShape, outShape, backend) {\n    const inSize = util.sizeFromShape(reduceShape);\n    const xSize = util.sizeFromShape(x.shape);\n    const batchSize = xSize / inSize;\n    const reshapedInput = reshape({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend });\n    const reduced = reduce(reshapedInput, x.dtype, 'max', backend);\n    const reshapedOutput = reshape({ inputs: { x: reduced }, attrs: { shape: outShape }, backend });\n    backend.disposeIntermediateTensorInfo(reshapedInput);\n    backend.disposeIntermediateTensorInfo(reduced);\n    return reshapedOutput;\n}\n//# sourceMappingURL=Max_impl.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class TransposeProgram {\n    constructor(aShape, newDim) {\n        this.variableNames = ['A'];\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[newDim[i]];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const switched = getSwitchedCoords(newDim);\n        this.userCode = `\n    void main() {\n      ${dtype} resRC = getOutputCoords();\n      setOutput(getA(${switched}));\n    }\n    `;\n    }\n}\nfunction getSwitchedCoords(newDim) {\n    const rank = newDim.length;\n    if (rank > 6) {\n        throw Error(`Transpose for rank ${rank} is not yet supported`);\n    }\n    const originalOrder = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u', 'resRC.v'];\n    const switchedCoords = new Array(rank);\n    for (let i = 0; i < newDim.length; i++) {\n        switchedCoords[newDim[i]] = originalOrder[i];\n    }\n    return switchedCoords.join();\n}\n//# sourceMappingURL=transpose_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getVecChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class TransposePackedProgram {\n    constructor(aShape, newDim) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[newDim[i]];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        if (this.rank > 6) {\n            throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);\n        }\n        const dtype = getCoordsDataType(this.rank);\n        const outputOrder = getVecChannels('rc', this.rank);\n        const switchedOrder = new Array(this.rank);\n        for (let i = 0; i < newDim.length; i++) {\n            switchedOrder[newDim[i]] = outputOrder[i];\n        }\n        const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;\n        const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;\n        const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;\n        this.userCode = `\n    void main() {\n      ${dtype} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${getc};\n      if(${nextColumn}) {\n        result[1] = ${getc};\n      }\n      --${outputOrder[this.rank - 1]};\n      if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {\n        result[2] = ${getc};\n        if(${nextColumn}) {\n          result[3] = ${getc};\n        }\n      }\n      setOutput(result);\n    }\n    `;\n    }\n}\n//# sourceMappingURL=transpose_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { transposeImplCPU } from '../kernel_utils/shared';\nimport { TransposeProgram } from '../transpose_gpu';\nimport { TransposePackedProgram } from '../transpose_packed_gpu';\nexport function transposeImpl(x, perm, backend) {\n    const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n        new TransposePackedProgram(x.shape, perm) :\n        new TransposeProgram(x.shape, perm);\n    return backend.runWebGLProgram(program, [x], x.dtype);\n}\nexport { transposeImplCPU };\n//# sourceMappingURL=Transpose_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '@tensorflow/tfjs-core';\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { maxImplCPU } from '../kernel_utils/shared';\nimport { maxImpl } from './Max_impl';\nimport { transposeImpl, transposeImplCPU } from './Transpose_impl';\nexport const maxConfig = {\n    kernelName: Max,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { reductionIndices, keepDims } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        const maxInputIsTransposed = permutedAxes != null;\n        const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);\n        let maxInput = x;\n        if (maxInputIsTransposed) {\n            if (shouldExecuteOnCPU) {\n                const xTexData = webglBackend.texData.get(maxInput.dataId);\n                const values = xTexData.values;\n                const newShape = new Array(xRank);\n                for (let i = 0; i < newShape.length; i++) {\n                    newShape[i] = x.shape[permutedAxes[i]];\n                }\n                const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n                maxInput = webglBackend.makeTensorInfo(newShape, x.dtype);\n                const maxInputData = webglBackend.texData.get(maxInput.dataId);\n                maxInputData.values = maxInputValues;\n            }\n            else {\n                maxInput = transposeImpl(x, permutedAxes, webglBackend);\n            }\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n        }\n        backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n        const [maxOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(maxInput.shape, axes);\n        let outShape = maxOutShape;\n        if (keepDims) {\n            // rather than reshape at the end, set the target shape here.\n            outShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n        }\n        let out;\n        if (shouldExecuteOnCPU) {\n            const xTexData = webglBackend.texData.get(maxInput.dataId);\n            const values = xTexData.values;\n            const outValues = maxImplCPU(values, util.sizeFromShape(reduceShape), outShape, x.dtype);\n            out = webglBackend.makeTensorInfo(outShape, x.dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n        }\n        else {\n            out = maxImpl(maxInput, reduceShape, outShape, webglBackend);\n        }\n        if (maxInputIsTransposed) {\n            webglBackend.disposeIntermediateTensorInfo(maxInput);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPool, util } from '@tensorflow/tfjs-core';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nimport { identity } from './Identity';\nexport function maxPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'maxPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        return identity({ inputs: { x }, backend });\n    }\n    const maxPoolProgram = new Pool2DProgram(convInfo, 'max', false);\n    return backend.runWebGLProgram(maxPoolProgram, [x], x.dtype);\n}\nexport const maxPoolConfig = {\n    kernelName: MaxPool,\n    backendName: 'webgl',\n    kernelFunc: maxPool\n};\n//# sourceMappingURL=MaxPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPoolBackprop } from '@tensorflow/tfjs-core';\nimport { MaxPool2DBackpropProgram } from '../max_pool_backprop_gpu';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nexport function maxPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input, output } = inputs;\n    const x = input;\n    assertNotComplex([input, output], 'maxPoolBackprop');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n    const getPositions = true;\n    const maxPoolPositionsProgram = new Pool2DProgram(convInfo, 'max', getPositions);\n    const maxPoolPositions = backend.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);\n    const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);\n    const result = backend.runWebGLProgram(maxPoolBackPropProgram, [dy, maxPoolPositions], x.dtype);\n    backend.disposeIntermediateTensorInfo(maxPoolPositions);\n    return result;\n}\nexport const maxPoolBackpropConfig = {\n    kernelName: MaxPoolBackprop,\n    backendName: 'webgl',\n    kernelFunc: maxPoolBackprop\n};\n//# sourceMappingURL=MaxPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Pool2DProgram } from '../pool_gpu';\nexport function maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, backend) {\n    let program = new Pool2DProgram(convInfo, 'max', false);\n    const poolOutput = backend.runWebGLProgram(program, [x], 'float32');\n    program = new Pool2DProgram(convInfo, 'max', true, true, includeBatchInIndex);\n    const indexOutput = backend.runWebGLProgram(program, [x], 'float32');\n    return [poolOutput, indexOutput];\n}\n//# sourceMappingURL=MaxPoolWithArgmax_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPoolWithArgmax } from '@tensorflow/tfjs-core';\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { maxPoolWithArgmaxImpl } from './MaxPoolWithArgmax_impl';\nexport const maxPoolWithArgmaxConfig = {\n    kernelName: MaxPoolWithArgmax,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { filterSize, strides, pad, includeBatchInIndex } = attrs;\n        const webglBackend = backend;\n        util.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);\n        const dilations = [1, 1];\n        util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n            `Got strides ${strides} and dilations '${dilations}'`);\n        const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad);\n        const [result, indexes] = maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, webglBackend);\n        return [result, indexes];\n    }\n};\n//# sourceMappingURL=MaxPoolWithArgmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls, NonMaxSuppressionV3 } from '@tensorflow/tfjs-core';\nexport const nonMaxSuppressionV3Config = {\n    kernelName: NonMaxSuppressionV3,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        return kernel_impls.nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal);\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV3.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls, NonMaxSuppressionV4 } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\nexport const nonMaxSuppressionV4Config = {\n    kernelName: NonMaxSuppressionV4,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n        return [selectedIndices, validOutputs];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV4.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls } from '@tensorflow/tfjs-core';\nimport { NonMaxSuppressionV5 } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nexport const nonMaxSuppressionV5Config = {\n    kernelName: NonMaxSuppressionV5,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        const softNmsSigmaVal = softNmsSigma;\n        const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n        return [selectedIndices, selectedScores];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV5.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class RotateProgram {\n    constructor(imageShape, radians, fillValue, center) {\n        this.variableNames = ['Image'];\n        this.outputShape = [];\n        const imageHeight = imageShape[1];\n        const imageWidth = imageShape[2];\n        const sinFactor = Math.sin(radians).toFixed(3);\n        const cosFactor = Math.cos(radians).toFixed(3);\n        this.outputShape = imageShape;\n        const [centerX, centerY] = backend_util.getImageCenter(center, imageHeight, imageWidth);\n        const centerXString = centerX.toFixed(3);\n        const centerYString = centerY.toFixed(3);\n        let fillSnippet = '';\n        if (typeof fillValue === 'number') {\n            fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;\n        }\n        else {\n            fillSnippet = `\n        vec3 fill = vec3(${fillValue.join(',')});\n        float outputValue = fill[coords[3]];`;\n        }\n        this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - ${centerXString}) * ${cosFactor} - (float(y) - ${centerYString}) * ${sinFactor};\n          float coordYFloat = (float(x) - ${centerXString}) * ${sinFactor} + (float(y) - ${centerYString}) * ${cosFactor};\n          int coordX = int(round(coordXFloat + ${centerXString}));\n          int coordY = int(round(coordYFloat + ${centerYString}));\n          ${fillSnippet}\n          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n    }\n}\n//# sourceMappingURL=rotate_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { RotateWithOffset } from '@tensorflow/tfjs-core';\nimport { RotateProgram } from '../rotate_gpu';\nexport const rotateWithOffsetConfig = {\n    kernelName: RotateWithOffset,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const { radians, fillValue, center } = attrs;\n        const webglBackend = backend;\n        const program = new RotateProgram(image.shape, radians, fillValue, center);\n        const output = webglBackend.runWebGLProgram(program, [image], image.dtype);\n        return output;\n    }\n};\n//# sourceMappingURL=RotateWithOffset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '@tensorflow/tfjs-core';\nimport { CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SIN = CHECK_NAN_SNIPPET_UNARY + `\n  return sin(x);\n`;\nexport const sinKernelFunc = unaryKernelFunc(SIN);\nexport const sinConfig = {\n    kernelName: Sin,\n    backendName: 'webgl',\n    kernelFunc: sinKernelFunc,\n};\n//# sourceMappingURL=Sin.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SQUARE = `return x * x;`;\nexport const squareKernelFunc = unaryKernelFunc(SQUARE);\nexport const squareConfig = {\n    kernelName: Square,\n    backendName: 'webgl',\n    kernelFunc: squareKernelFunc,\n};\n//# sourceMappingURL=Square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport const squaredDifferenceKernelFunc = binaryKernelFunc(SQUARED_DIFFERENCE, SQUARED_DIFFERENCE);\nexport const squaredDifferenceConfig = {\n    kernelName: SquaredDifference,\n    backendName: 'webgl',\n    kernelFunc: squaredDifferenceKernelFunc,\n};\n//# sourceMappingURL=SquaredDifference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst TAN = `return tan(x);`;\nexport const tanKernelFunc = unaryKernelFunc(TAN);\nexport const tanConfig = {\n    kernelName: Tan,\n    backendName: 'webgl',\n    kernelFunc: tanKernelFunc,\n};\n//# sourceMappingURL=Tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '@tensorflow/tfjs-core';\nimport { transposeImpl } from './Transpose_impl';\nimport { transposeImplCPU as cpuTranspose } from './Transpose_impl';\nexport const transposeConfig = {\n    kernelName: Transpose,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { perm } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const newShape = new Array(xRank);\n        for (let i = 0; i < newShape.length; i++) {\n            newShape[i] = x.shape[perm[i]];\n        }\n        let out;\n        if (webglBackend.shouldExecuteOnCPU([x])) {\n            const xTexData = webglBackend.texData.get(x.dataId);\n            const values = xTexData.values;\n            const outValues = cpuTranspose(values, x.shape, x.dtype, perm, newShape);\n            out = webglBackend.makeTensorInfo(newShape, x.dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n        }\n        else {\n            out = transposeImpl(x, perm, webglBackend);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unique } from '@tensorflow/tfjs-core';\nimport { uniqueImplCPU } from '../kernel_utils/shared';\nimport { assertNotComplex } from '../webgl_util';\nexport function unique(args) {\n    const { inputs, attrs, backend } = args;\n    const { axis } = attrs;\n    const { x } = inputs;\n    assertNotComplex(x, 'unique');\n    // For now, always forward calculation to the CPU backend.\n    console.warn('WARNING: ', 'UI might be locked temporarily as data is being downloaded');\n    const values = backend.readSync(x.dataId);\n    const { outputValues, outputShape, indices } = uniqueImplCPU(values, axis, x.shape, x.dtype);\n    return [\n        backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n        backend.makeTensorInfo([indices.length], 'int32', indices),\n    ];\n}\nexport const uniqueConfig = {\n    kernelName: Unique,\n    backendName: 'webgl',\n    kernelFunc: unique,\n};\n//# sourceMappingURL=Unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { registerKernel } from '@tensorflow/tfjs-core';\nimport { atan2Config } from './kernels/Atan2';\nimport { avgPoolConfig } from './kernels/AvgPool';\nimport { avgPoolBackpropConfig } from './kernels/AvgPoolBackprop';\nimport { batchNormConfig } from './kernels/BatchNorm';\nimport { cosConfig } from './kernels/Cos';\nimport { divConfig } from './kernels/Div';\nimport { flipLeftRightConfig } from './kernels/FlipLeftRight';\nimport { fromPixelsConfig } from './kernels/FromPixels';\nimport { identityConfig } from './kernels/Identity';\nimport { maxConfig } from './kernels/Max';\nimport { maxPoolConfig } from './kernels/MaxPool';\nimport { maxPoolBackpropConfig } from './kernels/MaxPoolBackprop';\nimport { maxPoolWithArgmaxConfig } from './kernels/MaxPoolWithArgmax';\nimport { nonMaxSuppressionV3Config } from './kernels/NonMaxSuppressionV3';\nimport { nonMaxSuppressionV4Config } from './kernels/NonMaxSuppressionV4';\nimport { nonMaxSuppressionV5Config } from './kernels/NonMaxSuppressionV5';\nimport { reshapeConfig } from './kernels/Reshape';\nimport { rotateWithOffsetConfig } from './kernels/RotateWithOffset';\nimport { sinConfig } from './kernels/Sin';\nimport { squareConfig } from './kernels/Square';\nimport { squaredDifferenceConfig } from './kernels/SquaredDifference';\nimport { tanConfig } from './kernels/Tan';\nimport { transposeConfig } from './kernels/Transpose';\nimport { uniqueConfig } from './kernels/Unique';\n// List all kernel configs here\nconst kernelConfigs = [\n    atan2Config,\n    avgPoolConfig,\n    avgPoolBackpropConfig,\n    batchNormConfig,\n    cosConfig,\n    divConfig,\n    flipLeftRightConfig,\n    fromPixelsConfig,\n    identityConfig,\n    maxConfig,\n    maxPoolConfig,\n    maxPoolBackpropConfig,\n    maxPoolWithArgmaxConfig,\n    nonMaxSuppressionV3Config,\n    nonMaxSuppressionV4Config,\n    nonMaxSuppressionV5Config,\n    reshapeConfig,\n    rotateWithOffsetConfig,\n    sinConfig,\n    squareConfig,\n    squaredDifferenceConfig,\n    tanConfig,\n    transposeConfig,\n    uniqueConfig,\n];\nfor (const kernelConfig of kernelConfigs) {\n    registerKernel(kernelConfig);\n}\n//# sourceMappingURL=register_all_kernels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// All exports from this package should be in base.\nexport * from './base';\nimport './register_all_kernels';\n//# sourceMappingURL=index.js.map", "/** @license See the LICENSE file. */\n\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport {version};\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport * from '@tensorflow/tfjs-core';\nexport * from '@tensorflow/tfjs-layers';\nexport * from '@tensorflow/tfjs-converter';\n\n// Export data api as tf.data\nimport * as data from '@tensorflow/tfjs-data';\nexport {data};\n\n// Import and register backends.\nimport '@tensorflow/tfjs-backend-cpu';\nimport '@tensorflow/tfjs-backend-webgl';\n\n// Import versions of all sub-packages.\nimport {version_core} from '@tensorflow/tfjs-core';\nimport {version_cpu} from '@tensorflow/tfjs-backend-cpu';\nimport {version_webgl} from '@tensorflow/tfjs-backend-webgl';\nimport {version_data} from '@tensorflow/tfjs-data';\nimport {version_layers} from '@tensorflow/tfjs-layers';\nimport {version_converter} from '@tensorflow/tfjs-converter';\nimport {version as version_union} from './version';\n\nexport const version = {\n  'tfjs-core': version_core,\n  'tfjs-backend-cpu': version_cpu,\n  'tfjs-backend-webgl': version_webgl,\n  'tfjs-data': version_data,\n  'tfjs-layers': version_layers,\n  'tfjs-converter': version_converter,\n  'tfjs': version_union\n};\n", "export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r)\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g)\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b)\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0)\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = input.toBatchTensor(112, true)\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = input.toBatchTensor(112, true)\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([\n          tf.fill([68], fillX),\n          tf.fill([68], fillY)\n        ], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = input.toBatchTensor(112, true)\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}"],
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+  "sourcesContent": ["\"use strict\";\n\n// ref: https://github.com/tc39/proposal-global\nvar getGlobal = function () {\n\t// the only reliable means to get the global object is\n\t// `Function('return this')()`\n\t// However, this causes CSP violations in Chrome apps.\n\tif (typeof self !== 'undefined') { return self; }\n\tif (typeof window !== 'undefined') { return window; }\n\tif (typeof global !== 'undefined') { return global; }\n\tthrow new Error('unable to locate global object');\n}\n\nvar global = getGlobal();\n\nmodule.exports = exports = global.fetch;\n\n// Needed for TypeScript and Webpack.\nif (global.fetch) {\n\texports.default = global.fetch.bind(global);\n}\n\nexports.Headers = global.Headers;\nexports.Request = global.Request;\nexports.Response = global.Response;", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage {\n    constructor(backend, dataMover) {\n        this.backend = backend;\n        this.dataMover = dataMover;\n        this.data = new WeakMap();\n        this.dataIdsCount = 0;\n    }\n    get(dataId) {\n        if (!this.data.has(dataId)) {\n            this.dataMover.moveData(this.backend, dataId);\n        }\n        return this.data.get(dataId);\n    }\n    set(dataId, value) {\n        this.dataIdsCount++;\n        this.data.set(dataId, value);\n    }\n    has(dataId) {\n        return this.data.has(dataId);\n    }\n    delete(dataId) {\n        this.dataIdsCount--;\n        return this.data.delete(dataId);\n    }\n    numDataIds() {\n        return this.dataIdsCount;\n    }\n}\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend {\n    time(f) {\n        return notYetImplemented('time');\n    }\n    read(dataId) {\n        return notYetImplemented('read');\n    }\n    readSync(dataId) {\n        return notYetImplemented('readSync');\n    }\n    numDataIds() {\n        return notYetImplemented('numDataIds');\n    }\n    disposeData(dataId) {\n        return notYetImplemented('disposeData');\n    }\n    write(values, shape, dtype) {\n        return notYetImplemented('write');\n    }\n    move(dataId, values, shape, dtype) {\n        return notYetImplemented('move');\n    }\n    memory() {\n        return notYetImplemented('memory');\n    }\n    /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n    floatPrecision() {\n        return notYetImplemented('floatPrecision');\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        return notYetImplemented('batchMatMul');\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedBatchMatMul');\n    }\n    slice(x, begin, size) {\n        return notYetImplemented('slice');\n    }\n    stridedSlice(x, begin, end, strides) {\n        return notYetImplemented('stridedSlice');\n    }\n    unstack(x, axis) {\n        return notYetImplemented('unstack');\n    }\n    reverse(a, axis) {\n        return notYetImplemented('reverse');\n    }\n    concat(tensors, axis) {\n        return notYetImplemented('concat');\n    }\n    neg(a) {\n        return notYetImplemented('neg');\n    }\n    add(a, b) {\n        return notYetImplemented('add');\n    }\n    addN(tensors) {\n        return notYetImplemented('addN');\n    }\n    subtract(a, b) {\n        return notYetImplemented('subtract');\n    }\n    multiply(a, b) {\n        return notYetImplemented('multiply');\n    }\n    realDivide(a, b) {\n        return notYetImplemented('realDivide');\n    }\n    floorDiv(a, b) {\n        return notYetImplemented('floorDiv');\n    }\n    sum(x, axes) {\n        return notYetImplemented('sum');\n    }\n    prod(x, axes) {\n        return notYetImplemented('prod');\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        return notYetImplemented('unsortedSegmentSum');\n    }\n    argMin(x, axis) {\n        return notYetImplemented('argMin');\n    }\n    argMax(x, axis) {\n        return notYetImplemented('argMax');\n    }\n    equal(a, b) {\n        return notYetImplemented('equal');\n    }\n    notEqual(a, b) {\n        return notYetImplemented('notEqual');\n    }\n    less(a, b) {\n        return notYetImplemented('less');\n    }\n    lessEqual(a, b) {\n        return notYetImplemented('lessEqual');\n    }\n    greater(a, b) {\n        return notYetImplemented('greater');\n    }\n    greaterEqual(a, b) {\n        return notYetImplemented('greaterEqual');\n    }\n    logicalNot(a) {\n        return notYetImplemented('logicalNot');\n    }\n    logicalAnd(a, b) {\n        return notYetImplemented('logicalAnd');\n    }\n    logicalOr(a, b) {\n        return notYetImplemented('logicalOr');\n    }\n    where(condition) {\n        return notYetImplemented('where');\n    }\n    select(condition, a, b) {\n        return notYetImplemented('select');\n    }\n    topk(x, k, sorted) {\n        return notYetImplemented('topk');\n    }\n    min(x, axes) {\n        return notYetImplemented('min');\n    }\n    minimum(a, b) {\n        return notYetImplemented('minimum');\n    }\n    mod(a, b) {\n        return notYetImplemented('mod');\n    }\n    max(x, axes) {\n        return notYetImplemented('max');\n    }\n    maximum(a, b) {\n        return notYetImplemented('maximum');\n    }\n    all(x, axes) {\n        return notYetImplemented('all');\n    }\n    any(x, axes) {\n        return notYetImplemented('any');\n    }\n    squaredDifference(a, b) {\n        return notYetImplemented('squaredDifference');\n    }\n    ceil(x) {\n        return notYetImplemented('ceil');\n    }\n    floor(x) {\n        return notYetImplemented('floor');\n    }\n    round(x) {\n        return notYetImplemented('round');\n    }\n    sign(x) {\n        return notYetImplemented('sign');\n    }\n    isNaN(x) {\n        return notYetImplemented('isNaN');\n    }\n    isInf(x) {\n        return notYetImplemented('isInf');\n    }\n    isFinite(x) {\n        return notYetImplemented('isFinite');\n    }\n    pow(a, b) {\n        return notYetImplemented('pow');\n    }\n    exp(x) {\n        return notYetImplemented('exp');\n    }\n    expm1(x) {\n        return notYetImplemented('expm1');\n    }\n    softmax(x, dim) {\n        return notYetImplemented('softmax');\n    }\n    log(x) {\n        return notYetImplemented('log');\n    }\n    log1p(x) {\n        return notYetImplemented('log1p');\n    }\n    sqrt(x) {\n        return notYetImplemented('sqrt');\n    }\n    rsqrt(x) {\n        return notYetImplemented('rsqrt');\n    }\n    square(x) {\n        return notYetImplemented('square');\n    }\n    reciprocal(x) {\n        return notYetImplemented('reciprocal');\n    }\n    relu(x) {\n        return notYetImplemented('relu');\n    }\n    relu6(x) {\n        return notYetImplemented('relu6');\n    }\n    prelu(x, a) {\n        return notYetImplemented('prelu');\n    }\n    elu(x) {\n        return notYetImplemented('elu');\n    }\n    eluDer(dy, y) {\n        return notYetImplemented('eluDer');\n    }\n    selu(x) {\n        return notYetImplemented('selu');\n    }\n    int(x) {\n        return notYetImplemented('int');\n    }\n    clip(x, min, max) {\n        return notYetImplemented('clip');\n    }\n    abs(x) {\n        return notYetImplemented('abs');\n    }\n    complexAbs(x) {\n        return notYetImplemented('complexAbs');\n    }\n    sigmoid(x) {\n        return notYetImplemented('sigmoid');\n    }\n    softplus(x) {\n        return notYetImplemented('softplus');\n    }\n    sin(x) {\n        return notYetImplemented('sin');\n    }\n    cos(x) {\n        return notYetImplemented('cos');\n    }\n    tan(x) {\n        return notYetImplemented('tan');\n    }\n    asin(x) {\n        return notYetImplemented('asin');\n    }\n    acos(x) {\n        return notYetImplemented('acos');\n    }\n    atan(x) {\n        return notYetImplemented('atan');\n    }\n    atan2(a, b) {\n        return notYetImplemented('atan2');\n    }\n    sinh(x) {\n        return notYetImplemented('sinh');\n    }\n    cosh(x) {\n        return notYetImplemented('cosh');\n    }\n    tanh(x) {\n        return notYetImplemented('tanh');\n    }\n    asinh(x) {\n        return notYetImplemented('asinh');\n    }\n    acosh(x) {\n        return notYetImplemented('acosh');\n    }\n    atanh(x) {\n        return notYetImplemented('atanh');\n    }\n    erf(x) {\n        return notYetImplemented('erf');\n    }\n    step(x, alpha) {\n        return notYetImplemented('step');\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedConv2d');\n    }\n    conv2d(x, filter, convInfo) {\n        return notYetImplemented('conv2d');\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        return notYetImplemented('conv2dDerInput');\n    }\n    conv2dDerFilter(x, dY, convInfo) {\n        return notYetImplemented('conv2dDerFilter');\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedDepthwiseConv2D');\n    }\n    depthwiseConv2D(input, filter, convInfo) {\n        return notYetImplemented('depthwiseConv2D');\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        return notYetImplemented('depthwiseConv2DDerInput');\n    }\n    depthwiseConv2DDerFilter(x, dY, convInfo) {\n        return notYetImplemented('depthwiseConv2DDerFilter');\n    }\n    conv3d(x, filter, convInfo) {\n        return notYetImplemented('conv3d');\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        return notYetImplemented('conv3dDerInput');\n    }\n    conv3dDerFilter(x, dY, convInfo) {\n        return notYetImplemented('conv3dDerFilter');\n    }\n    maxPool(x, convInfo) {\n        return notYetImplemented('maxPool');\n    }\n    maxPoolBackprop(dy, x, y, convInfo) {\n        return notYetImplemented('maxPoolBackprop');\n    }\n    avgPool(x, convInfo) {\n        return notYetImplemented('avgPool');\n    }\n    avgPoolBackprop(dy, x, convInfo) {\n        return notYetImplemented('avgPoolBackprop');\n    }\n    avgPool3d(x, convInfo) {\n        return notYetImplemented('avgPool3d');\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        return notYetImplemented('avgPool3dBackprop');\n    }\n    maxPool3d(x, convInfo) {\n        return notYetImplemented('maxPool3d');\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        return notYetImplemented('maxPool3dBackprop');\n    }\n    reshape(x, shape) {\n        return notYetImplemented('reshape');\n    }\n    cast(x, dtype) {\n        return notYetImplemented('cast');\n    }\n    tile(x, reps) {\n        return notYetImplemented('tile');\n    }\n    pad(x, paddings, constantValue) {\n        return notYetImplemented('pad');\n    }\n    transpose(x, perm) {\n        return notYetImplemented('transpose');\n    }\n    gather(x, indices, axis) {\n        return notYetImplemented('gather');\n    }\n    gatherND(x, indices) {\n        return notYetImplemented('gatherND');\n    }\n    scatterND(indices, updates, shape) {\n        return notYetImplemented('scatterND');\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        return notYetImplemented('batchToSpaceND');\n    }\n    spaceToBatchND(x, blockShape, paddings) {\n        return notYetImplemented('spaceToBatchND');\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        return notYetImplemented('resizeBilinear');\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        return notYetImplemented('resizeBilinearBackprop');\n    }\n    resizeNearestNeighbor(x, newHEight, newWidth, alignCorners) {\n        return notYetImplemented('resizeNearestNeighbor');\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        return notYetImplemented('resizeNearestNeighborBackprop');\n    }\n    batchNorm(x, mean, variance, offset, scale, varianceEpsilon) {\n        return notYetImplemented('batchNorm');\n    }\n    localResponseNormalization4D(x, radius, bias, alpha, beta) {\n        return notYetImplemented('localResponseNormalization4D');\n    }\n    LRNGrad(dy, inputImage, outputImage, radius, bias, alpha, beta) {\n        return notYetImplemented('LRNGrad');\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        return notYetImplemented('multinomial');\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        return notYetImplemented('oneHot');\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        return notYetImplemented('cumsum');\n    }\n    nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n        return notYetImplemented('nonMaxSuppression');\n    }\n    fft(x) {\n        return notYetImplemented('fft');\n    }\n    ifft(x) {\n        return notYetImplemented('ifft');\n    }\n    complex(real, imag) {\n        return notYetImplemented('complex');\n    }\n    real(input) {\n        return notYetImplemented('real');\n    }\n    imag(input) {\n        return notYetImplemented('imag');\n    }\n    cropAndResize(image, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        return notYetImplemented('cropAndResize');\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        return notYetImplemented('depthToSpace');\n    }\n    // Aligns with the \"SplitV\" kernel in TensorFlow.\n    split(value, sizeSplits, axis) {\n        return notYetImplemented('split');\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        return notYetImplemented('sparseToDense');\n    }\n    diag(x) {\n        return notYetImplemented('diag');\n    }\n    fill(shape, value, dtype) {\n        return notYetImplemented('fill');\n    }\n    onesLike(x) {\n        return notYetImplemented('onesLike');\n    }\n    zerosLike(x) {\n        return notYetImplemented('zerosLike');\n    }\n    linspace(start, stop, num) {\n        return notYetImplemented('linspace');\n    }\n    dispose() {\n        return notYetImplemented('dispose');\n    }\n}\nfunction notYetImplemented(kernelName) {\n    throw new Error(`'${kernelName}' not yet implemented or not found in the registry. ` +\n        `This kernel may not be supported by the tfjs backend you have chosen`);\n}\n//# sourceMappingURL=backend.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.\nconst TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';\n/**\n * The environment contains evaluated flags as well as the registered platform.\n * This is always used as a global singleton and can be retrieved with\n * `tf.env()`.\n *\n * @doc {heading: 'Environment'}\n */\nexport class Environment {\n    // tslint:disable-next-line: no-any\n    constructor(global) {\n        this.global = global;\n        this.flags = {};\n        this.flagRegistry = {};\n        this.urlFlags = {};\n        this.populateURLFlags();\n    }\n    setPlatform(platformName, platform) {\n        if (this.platform != null) {\n            console.warn(`Platform ${this.platformName} has already been set. ` +\n                `Overwriting the platform with ${platform}.`);\n        }\n        this.platformName = platformName;\n        this.platform = platform;\n    }\n    registerFlag(flagName, evaluationFn, setHook) {\n        this.flagRegistry[flagName] = { evaluationFn, setHook };\n        // Override the flag value from the URL. This has to happen here because the\n        // environment is initialized before flags get registered.\n        if (this.urlFlags[flagName] != null) {\n            const flagValue = this.urlFlags[flagName];\n            console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);\n            this.set(flagName, flagValue);\n        }\n    }\n    async getAsync(flagName) {\n        if (flagName in this.flags) {\n            return this.flags[flagName];\n        }\n        this.flags[flagName] = await this.evaluateFlag(flagName);\n        return this.flags[flagName];\n    }\n    get(flagName) {\n        if (flagName in this.flags) {\n            return this.flags[flagName];\n        }\n        const flagValue = this.evaluateFlag(flagName);\n        if (flagValue instanceof Promise) {\n            throw new Error(`Flag ${flagName} cannot be synchronously evaluated. ` +\n                `Please use getAsync() instead.`);\n        }\n        this.flags[flagName] = flagValue;\n        return this.flags[flagName];\n    }\n    getNumber(flagName) {\n        return this.get(flagName);\n    }\n    getBool(flagName) {\n        return this.get(flagName);\n    }\n    getFlags() {\n        return this.flags;\n    }\n    // For backwards compatibility.\n    get features() {\n        return this.flags;\n    }\n    set(flagName, value) {\n        if (this.flagRegistry[flagName] == null) {\n            throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);\n        }\n        this.flags[flagName] = value;\n        if (this.flagRegistry[flagName].setHook != null) {\n            this.flagRegistry[flagName].setHook(value);\n        }\n    }\n    evaluateFlag(flagName) {\n        if (this.flagRegistry[flagName] == null) {\n            throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);\n        }\n        return this.flagRegistry[flagName].evaluationFn();\n    }\n    setFlags(flags) {\n        this.flags = Object.assign({}, flags);\n    }\n    reset() {\n        this.flags = {};\n        this.urlFlags = {};\n        this.populateURLFlags();\n    }\n    populateURLFlags() {\n        if (typeof this.global === 'undefined' ||\n            typeof this.global.location === 'undefined' ||\n            typeof this.global.location.search === 'undefined') {\n            return;\n        }\n        const urlParams = getQueryParams(this.global.location.search);\n        if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n            const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');\n            keyValues.forEach(keyValue => {\n                const [key, value] = keyValue.split(':');\n                this.urlFlags[key] = parseValue(key, value);\n            });\n        }\n    }\n}\nexport function getQueryParams(queryString) {\n    const params = {};\n    queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n        decodeParam(params, t[0], t[1]);\n        return t.join('=');\n    });\n    return params;\n}\nfunction decodeParam(params, name, value) {\n    params[decodeURIComponent(name)] = decodeURIComponent(value || '');\n}\nfunction parseValue(flagName, value) {\n    value = value.toLowerCase();\n    if (value === 'true' || value === 'false') {\n        return value === 'true';\n    }\n    else if (`${+value}` === value) {\n        return +value;\n    }\n    throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);\n}\n/**\n * Returns the current environment (a global singleton).\n *\n * The environment object contains the evaluated feature values as well as the\n * active platform.\n *\n * @doc {heading: 'Environment'}\n */\nexport function env() {\n    return ENV;\n}\nexport let ENV = null;\nexport function setEnvironmentGlobal(environment) {\n    ENV = environment;\n}\n//# sourceMappingURL=environment.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Note that the identifier globalNameSpace is scoped to this module, but will\n// always resolve to the same global object regardless of how the module is\n// resolved.\n// tslint:disable-next-line:no-any\nlet globalNameSpace;\n// tslint:disable-next-line:no-any\nexport function getGlobalNamespace() {\n    if (globalNameSpace == null) {\n        // tslint:disable-next-line:no-any\n        let ns;\n        if (typeof (window) !== 'undefined') {\n            ns = window;\n        }\n        else if (typeof (global) !== 'undefined') {\n            ns = global;\n        }\n        else if (typeof (process) !== 'undefined') {\n            ns = process;\n        }\n        else if (typeof (self) !== 'undefined') {\n            ns = self;\n        }\n        else {\n            throw new Error('Could not find a global object');\n        }\n        globalNameSpace = ns;\n    }\n    return globalNameSpace;\n}\n// tslint:disable-next-line:no-any\nfunction getGlobalMap() {\n    const ns = getGlobalNamespace();\n    if (ns._tfGlobals == null) {\n        ns._tfGlobals = new Map();\n    }\n    return ns._tfGlobals;\n}\n/**\n * Returns a globally accessible 'singleton' object.\n *\n * @param key the name of the object\n * @param init a function to initialize to initialize this object\n *             the first time it is fetched.\n */\nexport function getGlobal(key, init) {\n    const globalMap = getGlobalMap();\n    if (globalMap.has(key)) {\n        return globalMap.get(key);\n    }\n    else {\n        const singleton = init();\n        globalMap.set(key, singleton);\n        return globalMap.get(key);\n    }\n}\n//# sourceMappingURL=global_util.js.map", "export const Abs = 'Abs';\nexport const Acos = 'Acos';\nexport const Acosh = 'Acosh';\nexport const Add = 'Add';\nexport const AddN = 'AddN';\nexport const All = 'All';\nexport const Any = 'Any';\nexport const ArgMax = 'ArgMax';\nexport const ArgMin = 'ArgMin';\nexport const Asin = 'Asin';\nexport const Asinh = 'Asinh';\nexport const Atan = 'Atan';\nexport const Atanh = 'Atanh';\nexport const Atan2 = 'Atan2';\nexport const AvgPool = 'AvgPool';\nexport const AvgPoolBackprop = 'AvgPoolBackprop';\nexport const AvgPool3D = 'AvgPool3D';\nexport const AvgPool3DBackprop = 'AvgPool3DBackprop';\nexport const BatchMatMul = 'BatchMatMul';\nexport const BatchToSpaceND = 'BatchToSpaceND';\nexport const BroadcastTo = 'BroadcastTo';\nexport const Cast = 'Cast';\nexport const Ceil = 'Ceil';\nexport const ClipByValue = 'ClipByValue';\nexport const Complex = 'Complex';\nexport const Concat = 'Concat';\nexport const Conv2D = 'Conv2D';\nexport const Conv2DBackpropFilter = 'Conv2DBackpropFilter';\nexport const Conv2DBackpropInput = 'Conv2DBackpropInput';\nexport const Conv3D = 'Conv3D';\nexport const Conv3DBackpropFilterV2 = 'Conv3DBackpropFilterV2';\nexport const Conv3DBackpropInputV2 = 'Conv3DBackpropInputV2';\nexport const Cos = 'Cos';\nexport const Cosh = 'Cosh';\nexport const Cumsum = 'Cumsum';\nexport const CropAndResize = 'CropAndResize';\nexport const DepthToSpace = 'DepthToSpace';\nexport const DepthwiseConv2dNative = 'DepthwiseConv2dNative';\nexport const DepthwiseConv2dNativeBackpropFilter = 'DepthwiseConv2dNativeBackpropFilter';\nexport const DepthwiseConv2dNativeBackpropInput = 'DepthwiseConv2dNativeBackpropInput';\nexport const Diag = 'Diag';\nexport const Dilation2D = 'Dilation2D';\nexport const Dilation2DBackpropInput = 'Dilation2DBackpropInput';\nexport const Dilation2DBackpropFilter = 'Dilation2DBackpropFilter';\nexport const Div = 'Div';\nexport const Elu = 'Elu';\nexport const EluGrad = 'EluGrad';\nexport const Erf = 'Erf';\nexport const Equal = 'Equal';\nexport const Exp = 'Exp';\nexport const Expm1 = 'Expm1';\nexport const FFT = 'FFT';\nexport const Fill = 'Fill';\nexport const FlipLeftRight = 'FlipLeftRight';\nexport const Floor = 'Floor';\nexport const FloorDiv = 'FloorDiv';\nexport const FusedBatchNorm = 'FusedBatchNorm';\nexport const GatherV2 = 'GatherV2';\nexport const GatherNd = 'GatherNd';\nexport const Greater = 'Greater';\nexport const GreaterEqual = 'GreaterEqual';\nexport const Identity = 'Identity';\nexport const IFFT = 'IFFT';\nexport const Imag = 'Imag';\nexport const IsFinite = 'IsFinite';\nexport const IsInf = 'IsInf';\nexport const IsNan = 'IsNan';\nexport const Less = 'Less';\nexport const LessEqual = 'LessEqual';\nexport const LinSpace = 'LinSpace';\nexport const Log = 'Log';\nexport const Log1p = 'Log1p';\nexport const LogicalAnd = 'LogicalAnd';\nexport const LogicalNot = 'LogicalNot';\nexport const LogicalOr = 'LogicalOr';\nexport const LogSoftmax = 'LogSoftmax';\nexport const LRN = 'LRN';\nexport const LRNBackprop = 'LRNBackprop';\nexport const Max = 'Max';\nexport const Maximum = 'Maximum';\nexport const MaxPool = 'MaxPool';\nexport const MaxPoolBackprop = 'MaxPoolBackprop';\nexport const MaxPool3D = 'MaxPool3D';\nexport const MaxPool3DBackprop = 'MaxPool3DBackprop';\nexport const MaxPoolWithArgmax = 'MaxPoolWithArgmax';\nexport const Mean = 'Mean';\nexport const Min = 'Min';\nexport const Minimum = 'Minimum';\nexport const Mod = 'Mod';\nexport const Multiply = 'Multiply';\nexport const Negate = 'Negate';\nexport const NotEqual = 'NotEqual';\nexport const NonMaxSuppressionV3 = 'NonMaxSuppressionV3';\nexport const NonMaxSuppressionV4 = 'NonMaxSuppressionV4';\nexport const NonMaxSuppressionV5 = 'NonMaxSuppressionV5';\nexport const OnesLike = 'OnesLike';\nexport const OneHot = 'OneHot';\nexport const PadV2 = 'PadV2';\nexport const Pool = 'Pool';\nexport const Pow = 'Pow';\nexport const Prelu = 'Prelu';\nexport const Prod = 'Prod';\nexport const Range = 'Range';\nexport const Real = 'Real';\nexport const Reciprocal = 'Reciprocal';\nexport const Relu = 'Relu';\nexport const Reshape = 'Reshape';\nexport const ResizeNearestNeighbor = 'ResizeNearestNeighbor';\nexport const ResizeNearestNeighborGrad = 'ResizeNearestNeighborGrad';\nexport const ResizeBilinear = 'ResizeBilinear';\nexport const ResizeBilinearGrad = 'ResizeBilinearGrad';\nexport const Relu6 = 'Relu6';\nexport const Reverse = 'Reverse';\nexport const Round = 'Round';\nexport const Rsqrt = 'Rsqrt';\nexport const ScatterNd = 'ScatterNd';\nexport const SelectV2 = 'SelectV2';\nexport const Selu = 'Selu';\nexport const Slice = 'Slice';\nexport const Sin = 'Sin';\nexport const Sinh = 'Sinh';\nexport const Sign = 'Sign';\nexport const Sigmoid = 'Sigmoid';\nexport const Softplus = 'Softplus';\nexport const Sqrt = 'Sqrt';\nexport const Sum = 'Sum';\nexport const SpaceToBatchND = 'SpaceToBatchND';\nexport const SplitV = 'SplitV';\nexport const Softmax = 'Softmax';\nexport const SquaredDifference = 'SquaredDifference';\nexport const Square = 'Square';\nexport const Sub = 'Sub';\nexport const SparseToDense = 'SparseToDense';\nexport const StridedSlice = 'StridedSlice';\nexport const Tan = 'Tan';\nexport const Tanh = 'Tanh';\nexport const Tile = 'Tile';\nexport const TopK = 'TopK';\nexport const Transpose = 'Transpose';\nexport const Unique = 'Unique';\nexport const Unpack = 'Unpack';\nexport const UnsortedSegmentSum = 'UnsortedSegmentSum';\nexport const ZerosLike = 'ZerosLike';\n/**\n * TensorFlow.js-only kernels\n */\nexport const Step = 'Step';\nexport const FromPixels = 'FromPixels';\nexport const RotateWithOffset = 'RotateWithOffset';\nexport const _FusedMatMul = '_FusedMatMul';\nexport const FusedConv2D = 'FusedConv2D';\nexport const FusedDepthwiseConv2D = 'FusedDepthwiseConv2D';\n//# sourceMappingURL=kernel_names.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nimport { getGlobal } from './global_util';\nconst kernelRegistry = getGlobal('kernelRegistry', () => new Map());\nconst gradRegistry = getGlobal('gradRegistry', () => new Map());\n/**\n * Returns the kernel function (code) associated with the provided names.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n */\nexport function getKernel(kernelName, backendName) {\n    const key = makeKey(kernelName, backendName);\n    return kernelRegistry.get(key);\n}\n/**\n * Returns the registered gradient info associated with the provided kernel.\n * @param kernelName The official TF kernel name.\n */\nexport function getGradient(kernelName) {\n    return gradRegistry.get(kernelName);\n}\nexport function getKernelsForBackend(backendName) {\n    const it = kernelRegistry.entries();\n    const result = [];\n    while (true) {\n        const { done, value } = it.next();\n        if (done) {\n            break;\n        }\n        const [key, config] = value;\n        const [backend,] = key.split('_');\n        if (backend === backendName) {\n            result.push(config);\n        }\n    }\n    return result;\n}\n/**\n * Registers the function (forward pass) for the kernel in a global registry.\n *\n * @param config A config object with the following properties:\n * - `kernelName` The official name of the kernel.\n * - `backendName` The official name of the backend.\n * - `kernelFunc` The function to run during the forward pass of the kernel.\n * - `setupFunc` Optional. Gets called once, after the backend initializes.\n * - `disposeFunc` Optional. Gets called once, right before the backend is\n * disposed.\n */\nexport function registerKernel(config) {\n    const { kernelName, backendName } = config;\n    const key = makeKey(kernelName, backendName);\n    if (kernelRegistry.has(key)) {\n        console.warn(`The kernel '${kernelName}' for backend ` +\n            `'${backendName}' is already registered`);\n    }\n    kernelRegistry.set(key, config);\n}\n/**\n * Registers a gradient function for a given kernel in the global registry,\n * to be used during the back-propagation of that kernel.\n *\n * @param config An object with the following properties:\n * - `kernelName` The name of the kernel that the gradient function is for.\n * - `gradFunc` The function to run during back-propagation.\n */\nexport function registerGradient(config) {\n    const { kernelName } = config;\n    if (gradRegistry.has(kernelName)) {\n        // TODO (yassogba) after 3.0 assess whether we need to keep this gated\n        // to debug mode.\n        if (env().getBool('DEBUG')) {\n            console.warn(`Overriding the gradient for '${kernelName}'`);\n        }\n    }\n    gradRegistry.set(kernelName, config);\n}\n/**\n * Removes the kernel function from the registry.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n *\n */\nexport function unregisterKernel(kernelName, backendName) {\n    const key = makeKey(kernelName, backendName);\n    if (!kernelRegistry.has(key)) {\n        throw new Error(`The kernel '${kernelName}' for backend ` +\n            `'${backendName}' is not registered`);\n    }\n    kernelRegistry.delete(key);\n}\n/** Removes the registered gradient from the global registry. */\nexport function unregisterGradient(kernelName) {\n    if (!gradRegistry.has(kernelName)) {\n        throw new Error(`The gradient '${kernelName}' for backend is not registered`);\n    }\n    gradRegistry.delete(kernelName);\n}\n/**\n * Finds kernels that have already been registered to a backend and re-registers\n * them for a new backend. Useful for registering custom backends.\n * @param registeredBackendName Already registered backend.\n * @param newBackendName New backend.\n */\nexport function copyRegisteredKernels(registeredBackendName, newBackendName) {\n    const kernels = getKernelsForBackend(registeredBackendName);\n    kernels.forEach(kernelConfig => {\n        const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName });\n        registerKernel(newKernelConfig);\n    });\n}\nfunction makeKey(kernelName, backendName) {\n    return `${backendName}_${kernelName}`;\n}\n//# sourceMappingURL=kernel_registry.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\n/**\n * Shuffles the array in-place using Fisher-Yates algorithm.\n *\n * ```js\n * const a = [1, 2, 3, 4, 5];\n * tf.util.shuffle(a);\n * console.log(a);\n * ```\n *\n * @param array The array to shuffle in-place.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\n// tslint:disable-next-line:no-any\nexport function shuffle(array) {\n    let counter = array.length;\n    let temp = 0;\n    let index = 0;\n    // While there are elements in the array\n    while (counter > 0) {\n        // Pick a random index\n        index = (Math.random() * counter) | 0;\n        // Decrease counter by 1\n        counter--;\n        // And swap the last element with it\n        temp = array[counter];\n        array[counter] = array[index];\n        array[index] = temp;\n    }\n}\n/** Clamps a value to a specified range. */\nexport function clamp(min, x, max) {\n    return Math.max(min, Math.min(x, max));\n}\nexport function nearestLargerEven(val) {\n    return val % 2 === 0 ? val : val + 1;\n}\nexport function sum(arr) {\n    let sum = 0;\n    for (let i = 0; i < arr.length; i++) {\n        sum += arr[i];\n    }\n    return sum;\n}\n/**\n * Returns a sample from a uniform [a, b) distribution.\n *\n * @param a The minimum support (inclusive).\n * @param b The maximum support (exclusive).\n * @return A pseudorandom number on the half-open interval [a,b).\n */\nexport function randUniform(a, b) {\n    const r = Math.random();\n    return (b * r) + (1 - r) * a;\n}\n/** Returns the squared Euclidean distance between two vectors. */\nexport function distSquared(a, b) {\n    let result = 0;\n    for (let i = 0; i < a.length; i++) {\n        const diff = Number(a[i]) - Number(b[i]);\n        result += diff * diff;\n    }\n    return result;\n}\n/**\n * Asserts that the expression is true. Otherwise throws an error with the\n * provided message.\n *\n * ```js\n * const x = 2;\n * tf.util.assert(x === 2, 'x is not 2');\n * ```\n *\n * @param expr The expression to assert (as a boolean).\n * @param msg A function that returns the message to report when throwing an\n *     error. We use a function for performance reasons.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function assert(expr, msg) {\n    if (!expr) {\n        throw new Error(typeof msg === 'string' ? msg : msg());\n    }\n}\nexport function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = '') {\n    assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nexport function assertNonNull(a) {\n    assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);\n}\n// NOTE: We explicitly type out what T extends instead of any so that\n// util.flatten on a nested array of number doesn't try to infer T as a\n// number[][], causing us to explicitly type util.flatten<number>().\n/**\n *  Flattens an arbitrarily nested array.\n *\n * ```js\n * const a = [[1, 2], [3, 4], [5, [6, [7]]]];\n * const flat = tf.util.flatten(a);\n * console.log(flat);\n * ```\n *\n *  @param arr The nested array to flatten.\n *  @param result The destination array which holds the elements.\n *  @param skipTypedArray If true, avoids flattening the typed arrays. Defaults\n *      to false.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function flatten(arr, result = [], skipTypedArray = false) {\n    if (result == null) {\n        result = [];\n    }\n    if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {\n        for (let i = 0; i < arr.length; ++i) {\n            flatten(arr[i], result, skipTypedArray);\n        }\n    }\n    else {\n        result.push(arr);\n    }\n    return result;\n}\n/**\n * Returns the size (number of elements) of the tensor given its shape.\n *\n * ```js\n * const shape = [3, 4, 2];\n * const size = tf.util.sizeFromShape(shape);\n * console.log(size);\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function sizeFromShape(shape) {\n    if (shape.length === 0) {\n        // Scalar.\n        return 1;\n    }\n    let size = shape[0];\n    for (let i = 1; i < shape.length; i++) {\n        size *= shape[i];\n    }\n    return size;\n}\nexport function isScalarShape(shape) {\n    return shape.length === 0;\n}\nexport function arraysEqual(n1, n2) {\n    if (n1 === n2) {\n        return true;\n    }\n    if (n1 == null || n2 == null) {\n        return false;\n    }\n    if (n1.length !== n2.length) {\n        return false;\n    }\n    for (let i = 0; i < n1.length; i++) {\n        if (n1[i] !== n2[i]) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function isInt(a) {\n    return a % 1 === 0;\n}\nexport function tanh(x) {\n    // tslint:disable-next-line:no-any\n    if (Math.tanh != null) {\n        // tslint:disable-next-line:no-any\n        return Math.tanh(x);\n    }\n    if (x === Infinity) {\n        return 1;\n    }\n    else if (x === -Infinity) {\n        return -1;\n    }\n    else {\n        const e2x = Math.exp(2 * x);\n        return (e2x - 1) / (e2x + 1);\n    }\n}\nexport function sizeToSquarishShape(size) {\n    const width = Math.ceil(Math.sqrt(size));\n    return [width, Math.ceil(size / width)];\n}\n/**\n * Creates a new array with randomized indicies to a given quantity.\n *\n * ```js\n * const randomTen = tf.util.createShuffledIndices(10);\n * console.log(randomTen);\n * ```\n *\n * @param number Quantity of how many shuffled indicies to create.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function createShuffledIndices(n) {\n    const shuffledIndices = new Uint32Array(n);\n    for (let i = 0; i < n; ++i) {\n        shuffledIndices[i] = i;\n    }\n    shuffle(shuffledIndices);\n    return shuffledIndices;\n}\nexport function rightPad(a, size) {\n    if (size <= a.length) {\n        return a;\n    }\n    return a + ' '.repeat(size - a.length);\n}\nexport function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {\n    return new Promise((resolve, reject) => {\n        let tryCount = 0;\n        const tryFn = () => {\n            if (checkFn()) {\n                resolve();\n                return;\n            }\n            tryCount++;\n            const nextBackoff = delayFn(tryCount);\n            if (maxCounter != null && tryCount >= maxCounter) {\n                reject();\n                return;\n            }\n            setTimeout(tryFn, nextBackoff);\n        };\n        tryFn();\n    });\n}\n/**\n * Given the full size of the array and a shape that may contain -1 as the\n * implicit dimension, returns the inferred shape where -1 is replaced.\n * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].\n *\n * @param shape The shape, which may contain -1 in some dimension.\n * @param size The full size (number of elements) of the array.\n * @return The inferred shape where -1 is replaced with the inferred size.\n */\nexport function inferFromImplicitShape(shape, size) {\n    let shapeProd = 1;\n    let implicitIdx = -1;\n    for (let i = 0; i < shape.length; ++i) {\n        if (shape[i] >= 0) {\n            shapeProd *= shape[i];\n        }\n        else if (shape[i] === -1) {\n            if (implicitIdx !== -1) {\n                throw Error(`Shapes can only have 1 implicit size. ` +\n                    `Found -1 at dim ${implicitIdx} and dim ${i}`);\n            }\n            implicitIdx = i;\n        }\n        else if (shape[i] < 0) {\n            throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n        }\n    }\n    if (implicitIdx === -1) {\n        if (size > 0 && size !== shapeProd) {\n            throw Error(`Size(${size}) must match the product of shape ${shape}`);\n        }\n        return shape;\n    }\n    if (shapeProd === 0) {\n        throw Error(`Cannot infer the missing size in [${shape}] when ` +\n            `there are 0 elements`);\n    }\n    if (size % shapeProd !== 0) {\n        throw Error(`The implicit shape can't be a fractional number. ` +\n            `Got ${size} / ${shapeProd}`);\n    }\n    const newShape = shape.slice();\n    newShape[implicitIdx] = size / shapeProd;\n    return newShape;\n}\nexport function parseAxisParam(axis, shape) {\n    const rank = shape.length;\n    // Normalize input\n    axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n    // Check for valid range\n    assert(axis.every(ax => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but ` +\n        `got axis ${axis}`);\n    // Check for only integers\n    assert(axis.every(ax => isInt(ax)), () => `All values in axis param must be integers but ` +\n        `got axis ${axis}`);\n    // Handle negative axis.\n    return axis.map(a => a < 0 ? rank + a : a);\n}\n/** Reduces the shape by removing all dimensions of shape 1. */\nexport function squeezeShape(shape, axis) {\n    const newShape = [];\n    const keptDims = [];\n    const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;\n    const axes = (axis == null || isEmptyArray) ?\n        null :\n        parseAxisParam(axis, shape).sort();\n    let j = 0;\n    for (let i = 0; i < shape.length; ++i) {\n        if (axes != null) {\n            if (axes[j] === i && shape[i] !== 1) {\n                throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n            }\n            if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {\n                newShape.push(shape[i]);\n                keptDims.push(i);\n            }\n            if (axes[j] <= i) {\n                j++;\n            }\n        }\n        if (shape[i] !== 1) {\n            newShape.push(shape[i]);\n            keptDims.push(i);\n        }\n    }\n    return { newShape, keptDims };\n}\nexport function getTypedArrayFromDType(dtype, size) {\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    return values;\n}\nexport function getArrayFromDType(dtype, size) {\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else if (dtype === 'string') {\n        values = new Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    return values;\n}\nexport function checkConversionForErrors(vals, dtype) {\n    for (let i = 0; i < vals.length; i++) {\n        const num = vals[i];\n        if (isNaN(num) || !isFinite(num)) {\n            throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n        }\n    }\n}\n/** Returns true if the dtype is valid. */\nexport function isValidDtype(dtype) {\n    return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' ||\n        dtype === 'int32' || dtype === 'string';\n}\n/**\n * Returns true if the new type can't encode the old type without loss of\n * precision.\n */\nexport function hasEncodingLoss(oldType, newType) {\n    if (newType === 'complex64') {\n        return false;\n    }\n    if (newType === 'float32' && oldType !== 'complex64') {\n        return false;\n    }\n    if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {\n        return false;\n    }\n    if (newType === 'bool' && oldType === 'bool') {\n        return false;\n    }\n    return true;\n}\nexport function isTypedArray(a) {\n    return a instanceof Float32Array || a instanceof Int32Array ||\n        a instanceof Uint8Array;\n}\nexport function bytesPerElement(dtype) {\n    if (dtype === 'float32' || dtype === 'int32') {\n        return 4;\n    }\n    else if (dtype === 'complex64') {\n        return 8;\n    }\n    else if (dtype === 'bool') {\n        return 1;\n    }\n    else {\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n}\n/**\n * Returns the approximate number of bytes allocated in the string array - 2\n * bytes per character. Computing the exact bytes for a native string in JS is\n * not possible since it depends on the encoding of the html page that serves\n * the website.\n */\nexport function bytesFromStringArray(arr) {\n    if (arr == null) {\n        return 0;\n    }\n    let bytes = 0;\n    arr.forEach(x => bytes += x.length);\n    return bytes;\n}\n/** Returns true if the value is a string. */\nexport function isString(value) {\n    return typeof value === 'string' || value instanceof String;\n}\nexport function isBoolean(value) {\n    return typeof value === 'boolean';\n}\nexport function isNumber(value) {\n    return typeof value === 'number';\n}\nexport function inferDtype(values) {\n    if (Array.isArray(values)) {\n        return inferDtype(values[0]);\n    }\n    if (values instanceof Float32Array) {\n        return 'float32';\n    }\n    else if (values instanceof Int32Array || values instanceof Uint8Array) {\n        return 'int32';\n    }\n    else if (isNumber(values)) {\n        return 'float32';\n    }\n    else if (isString(values)) {\n        return 'string';\n    }\n    else if (isBoolean(values)) {\n        return 'bool';\n    }\n    return 'float32';\n}\nexport function isFunction(f) {\n    return !!(f && f.constructor && f.call && f.apply);\n}\nexport function nearestDivisor(size, start) {\n    for (let i = start; i < size; ++i) {\n        if (size % i === 0) {\n            return i;\n        }\n    }\n    return size;\n}\nexport function computeStrides(shape) {\n    const rank = shape.length;\n    if (rank < 2) {\n        return [];\n    }\n    // Last dimension has implicit stride of 1, thus having D-1 (instead of D)\n    // strides.\n    const strides = new Array(rank - 1);\n    strides[rank - 2] = shape[rank - 1];\n    for (let i = rank - 3; i >= 0; --i) {\n        strides[i] = strides[i + 1] * shape[i + 1];\n    }\n    return strides;\n}\n/**\n * Create typed array for scalar value. Used for storing in `DataStorage`.\n */\nexport function createScalarValue(value, dtype) {\n    if (dtype === 'string') {\n        return encodeString(value);\n    }\n    return toTypedArray([value], dtype);\n}\nexport function toTypedArray(a, dtype) {\n    if (dtype === 'string') {\n        throw new Error('Cannot convert a string[] to a TypedArray');\n    }\n    if (Array.isArray(a)) {\n        a = flatten(a);\n    }\n    if (env().getBool('DEBUG')) {\n        checkConversionForErrors(a, dtype);\n    }\n    if (noConversionNeeded(a, dtype)) {\n        return a;\n    }\n    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n        return new Float32Array(a);\n    }\n    else if (dtype === 'int32') {\n        return new Int32Array(a);\n    }\n    else if (dtype === 'bool') {\n        const bool = new Uint8Array(a.length);\n        for (let i = 0; i < bool.length; ++i) {\n            if (Math.round(a[i]) !== 0) {\n                bool[i] = 1;\n            }\n        }\n        return bool;\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\nfunction createNestedArray(offset, shape, a) {\n    const ret = new Array();\n    if (shape.length === 1) {\n        const d = shape[0];\n        for (let i = 0; i < d; i++) {\n            ret[i] = a[offset + i];\n        }\n    }\n    else {\n        const d = shape[0];\n        const rest = shape.slice(1);\n        const len = rest.reduce((acc, c) => acc * c);\n        for (let i = 0; i < d; i++) {\n            ret[i] = createNestedArray(offset + i * len, rest, a);\n        }\n    }\n    return ret;\n}\n// Provide a nested array of TypedArray in given shape.\nexport function toNestedArray(shape, a) {\n    if (shape.length === 0) {\n        // Scalar type should return a single number.\n        return a[0];\n    }\n    const size = shape.reduce((acc, c) => acc * c);\n    if (size === 0) {\n        // A tensor with shape zero should be turned into empty list.\n        return [];\n    }\n    if (size !== a.length) {\n        throw new Error(`[${shape}] does not match the input size ${a.length}.`);\n    }\n    return createNestedArray(0, shape, a);\n}\nfunction noConversionNeeded(a, dtype) {\n    return (a instanceof Float32Array && dtype === 'float32') ||\n        (a instanceof Int32Array && dtype === 'int32') ||\n        (a instanceof Uint8Array && dtype === 'bool');\n}\nexport function makeOnesTypedArray(size, dtype) {\n    const array = makeZerosTypedArray(size, dtype);\n    for (let i = 0; i < array.length; i++) {\n        array[i] = 1;\n    }\n    return array;\n}\nexport function makeZerosTypedArray(size, dtype) {\n    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n        return new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        return new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        return new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\n/**\n * Make nested `TypedArray` filled with zeros.\n * @param shape The shape information for the nested array.\n * @param dtype dtype of the array element.\n */\nexport function makeZerosNestedTypedArray(shape, dtype) {\n    const size = shape.reduce((prev, curr) => prev * curr, 1);\n    if (dtype == null || dtype === 'float32') {\n        return toNestedArray(shape, new Float32Array(size));\n    }\n    else if (dtype === 'int32') {\n        return toNestedArray(shape, new Int32Array(size));\n    }\n    else if (dtype === 'bool') {\n        return toNestedArray(shape, new Uint8Array(size));\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\n/**\n * Returns the current high-resolution time in milliseconds relative to an\n * arbitrary time in the past. It works across different platforms (node.js,\n * browsers).\n *\n * ```js\n * console.log(tf.util.now());\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function now() {\n    return env().platform.now();\n}\nexport function assertNonNegativeIntegerDimensions(shape) {\n    shape.forEach(dimSize => {\n        assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got ` +\n            `shape [${shape}].`);\n    });\n}\n/**\n * Returns a platform-specific implementation of\n * [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n *\n * If `fetch` is defined on the global object (`window`, `process`, etc.),\n * `tf.util.fetch` returns that function.\n *\n * If not, `tf.util.fetch` returns a platform-specific solution.\n *\n * ```js\n * const resource = await tf.util.fetch('https://unpkg.com/@tensorflow/tfjs');\n * // handle response\n * ```\n *\n * @doc {heading: 'Util'}\n */\nexport function fetch(path, requestInits) {\n    return env().platform.fetch(path, requestInits);\n}\n/**\n * Encodes the provided string into bytes using the provided encoding scheme.\n *\n * @param s The string to encode.\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function encodeString(s, encoding = 'utf-8') {\n    encoding = encoding || 'utf-8';\n    return env().platform.encode(s, encoding);\n}\n/**\n * Decodes the provided bytes into a string using the provided encoding scheme.\n * @param bytes The bytes to decode.\n *\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function decodeString(bytes, encoding = 'utf-8') {\n    encoding = encoding || 'utf-8';\n    return env().platform.decode(bytes, encoding);\n}\n/**\n * Computes flat index for a given location (multidimentionsal index) in a\n * Tensor/multidimensional array.\n *\n * @param locs Location in the tensor.\n * @param rank Rank of the tensor.\n * @param strides Tensor strides.\n */\nexport function locToIndex(locs, rank, strides) {\n    if (rank === 0) {\n        return 0;\n    }\n    else if (rank === 1) {\n        return locs[0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n        index += strides[i] * locs[i];\n    }\n    return index;\n}\n/**\n * Computes the location (multidimensional index) in a tensor/multidimentional\n * array for a given flat index.\n *\n * @param index Index in flat array.\n * @param rank Rank of tensor.\n * @param strides Strides of tensor.\n */\nexport function indexToLoc(index, rank, strides) {\n    if (rank === 0) {\n        return [];\n    }\n    else if (rank === 1) {\n        return [index];\n    }\n    const locs = new Array(rank);\n    for (let i = 0; i < locs.length - 1; ++i) {\n        locs[i] = Math.floor(index / strides[i]);\n        index -= locs[i] * strides[i];\n    }\n    locs[locs.length - 1] = index;\n    return locs;\n}\n//# sourceMappingURL=util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from './util';\nexport class Profiler {\n    constructor(backendTimer, logger) {\n        this.backendTimer = backendTimer;\n        this.logger = logger;\n        if (logger == null) {\n            this.logger = new Logger();\n        }\n    }\n    profileKernel(kernelName, inputs, f) {\n        let outputs;\n        const holdResultWrapperFn = () => {\n            outputs = f();\n        };\n        const timer = this.backendTimer.time(holdResultWrapperFn);\n        for (let i = 0; i < outputs.length; i++) {\n            const output = outputs[i];\n            // Dangling promise here because we don't want to propagate up\n            // asynchronicity.\n            output.data().then(tensorVals => {\n                checkComputationForErrors(tensorVals, output.dtype, kernelName);\n            });\n        }\n        const kernelProfile = {\n            kernelName,\n            outputs,\n            inputs,\n            timeMs: timer.then(timing => timing.kernelMs),\n            extraInfo: timer.then(timing => timing.getExtraProfileInfo != null ?\n                timing.getExtraProfileInfo() :\n                '')\n        };\n        return kernelProfile;\n    }\n    logKernelProfile(kernelProfile) {\n        const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile;\n        outputs.forEach(result => {\n            Promise.all([result.data(), timeMs, extraInfo]).then(valueContainer => {\n                this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);\n            });\n        });\n    }\n}\nexport function checkComputationForErrors(vals, dtype, kernelName) {\n    if (dtype !== 'float32') {\n        // Only floating point computations will generate NaN values\n        return false;\n    }\n    for (let i = 0; i < vals.length; i++) {\n        const num = vals[i];\n        if (isNaN(num) || !isFinite(num)) {\n            // Throwing custom exception so behavior is testable.\n            console.warn(`Found ${num} in the result of '${kernelName}'`);\n            return true;\n        }\n    }\n    return false;\n}\nexport class Logger {\n    logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {\n        const time = typeof timeMs === 'number' ? util.rightPad(`${timeMs}ms`, 9) :\n            timeMs['error'];\n        const paddedName = util.rightPad(name, 25);\n        const rank = result.rank;\n        const size = result.size;\n        const shape = util.rightPad(result.shape.toString(), 14);\n        let inputShapesDescription = '';\n        for (const name in inputs) {\n            const input = inputs[name];\n            if (input != null) {\n                // The input might be a non-tensor (e.g HTMLImageElement), in which case\n                // we claim the output shape as input shape.\n                const inputShape = input.shape || result.shape;\n                const inputRank = inputShape.length;\n                inputShapesDescription +=\n                    `${name}: ${inputRank}D ${inputRank > 0 ? inputShape : ''} `;\n            }\n        }\n        console.log(`%c${paddedName}\\t%c${time}\\t%c${rank}D ${shape}\\t%c${size}\\t%c${inputShapesDescription}\\t%c${extraInfo}`, 'font-weight:bold', 'color:red', 'color:blue', 'color: orange', 'color: green', 'color: steelblue');\n    }\n}\n//# sourceMappingURL=profiler.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from './util';\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nexport function getFilteredNodesXToY(tape, xs, y) {\n    // Forward pass to compute all the nodes and Tensors that are transitively a\n    // function of x.\n    const tensorsFromX = {};\n    const nodesFromX = {};\n    for (let i = 0; i < xs.length; i++) {\n        tensorsFromX[xs[i].id] = true;\n    }\n    for (let i = 0; i < tape.length; i++) {\n        const node = tape[i];\n        const nodeInputs = node.inputs;\n        for (const inputName in nodeInputs) {\n            const input = nodeInputs[inputName];\n            let anyInputFromX = false;\n            for (let j = 0; j < xs.length; j++) {\n                if (tensorsFromX[input.id]) {\n                    node.outputs.forEach(output => tensorsFromX[output.id] = true);\n                    anyInputFromX = true;\n                    nodesFromX[node.id] = true;\n                    break;\n                }\n            }\n            if (anyInputFromX) {\n                break;\n            }\n        }\n    }\n    // Backward pass to find all of the nodes and Tensors that lead to y.\n    const tensorsLeadToY = {};\n    tensorsLeadToY[y.id] = true;\n    const nodesToY = {};\n    for (let i = tape.length - 1; i >= 0; i--) {\n        const node = tape[i];\n        const nodeInputs = node.inputs;\n        // If any of the outputs lead to y, mark all of the inputs as leading to y.\n        for (let j = 0; j < node.outputs.length; j++) {\n            if (tensorsLeadToY[node.outputs[j].id]) {\n                for (const inputName in nodeInputs) {\n                    tensorsLeadToY[nodeInputs[inputName].id] = true;\n                    nodesToY[node.id] = true;\n                }\n                break;\n            }\n        }\n    }\n    // Return the paths that come from x and lead to y.\n    const filteredTape = [];\n    for (let i = 0; i < tape.length; i++) {\n        const node = tape[i];\n        if (nodesFromX[node.id] && nodesToY[node.id]) {\n            // Prune the inputs from the node that aren't a function of x.\n            const prunedInputs = {};\n            for (const inputName in node.inputs) {\n                const nodeInput = node.inputs[inputName];\n                if (tensorsFromX[nodeInput.id]) {\n                    prunedInputs[inputName] = nodeInput;\n                }\n            }\n            // Copy the node and overwrite inputsAndArgs to the pruned version.\n            const prunedNode = Object.assign({}, node);\n            prunedNode.inputs = prunedInputs;\n            prunedNode.outputs = node.outputs;\n            filteredTape.push(prunedNode);\n        }\n    }\n    return filteredTape;\n}\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nexport function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy, add) {\n    // Walk the tape backward and keep a map of Tensor to its gradient.\n    for (let i = filteredTape.length - 1; i >= 0; i--) {\n        const node = filteredTape[i];\n        const dys = [];\n        node.outputs.forEach(o => {\n            const gradTensor = tensorAccumulatedGradientMap[o.id];\n            if (gradTensor != null) {\n                dys.push(gradTensor);\n            }\n            else {\n                // This particular output is not in the back-propagation subgraph, so it\n                // does not affect the final output, thus we put null for its dy.\n                dys.push(null);\n            }\n        });\n        if (node.gradient == null) {\n            throw new Error(`Cannot compute gradient: gradient function not found ` +\n                `for ${node.kernelName}.`);\n        }\n        // Backprop dy through this node and accumulate gradients over the inputs.\n        const inputGradients = node.gradient(dys);\n        for (const inputName in node.inputs) {\n            if (!(inputName in inputGradients)) {\n                throw new Error(`Cannot backprop through input ${inputName}. ` +\n                    `Available gradients found: ${Object.keys(inputGradients)}.`);\n            }\n            // Call the gradient function.\n            const dx = tidy(() => inputGradients[inputName]());\n            if (dx.dtype !== 'float32') {\n                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n                    `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n            }\n            const x = node.inputs[inputName];\n            if (!util.arraysEqual(dx.shape, x.shape)) {\n                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n                    `'${inputName}' has shape '${dx.shape}', which does not match ` +\n                    `the shape of the input '${x.shape}'`);\n            }\n            if (tensorAccumulatedGradientMap[x.id] == null) {\n                tensorAccumulatedGradientMap[x.id] = dx;\n            }\n            else {\n                const curGradient = tensorAccumulatedGradientMap[x.id];\n                tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);\n                curGradient.dispose();\n            }\n        }\n    }\n}\n//# sourceMappingURL=tape.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { computeStrides, isString, rightPad, sizeFromShape } from './util';\n// Maximum number of values before we decide to show ellipsis.\nconst FORMAT_LIMIT_NUM_VALS = 20;\n// Number of first and last values to show when displaying a, b,...,y, z.\nconst FORMAT_NUM_FIRST_LAST_VALS = 3;\n// Number of significant digits to show.\nconst FORMAT_NUM_SIG_DIGITS = 7;\nexport function tensorToString(vals, shape, dtype, verbose) {\n    const strides = computeStrides(shape);\n    const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n    const rank = shape.length;\n    const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n    const lines = ['Tensor'];\n    if (verbose) {\n        lines.push(`  dtype: ${dtype}`);\n        lines.push(`  rank: ${rank}`);\n        lines.push(`  shape: [${shape}]`);\n        lines.push(`  values:`);\n    }\n    lines.push(valsLines.map(l => '    ' + l).join('\\n'));\n    return lines.join('\\n');\n}\nfunction computeMaxSizePerColumn(vals, shape, dtype, strides) {\n    const n = sizeFromShape(shape);\n    const numCols = strides[strides.length - 1];\n    const padPerCol = new Array(numCols).fill(0);\n    const rank = shape.length;\n    const valuesOrTuples = dtype === 'complex64' ? createComplexTuples(vals) : vals;\n    if (rank > 1) {\n        for (let row = 0; row < n / numCols; row++) {\n            const offset = row * numCols;\n            for (let j = 0; j < numCols; j++) {\n                padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);\n            }\n        }\n    }\n    return padPerCol;\n}\nfunction valToString(val, pad, dtype) {\n    let valStr;\n    if (Array.isArray(val)) {\n        valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +\n            `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n    }\n    else if (isString(val)) {\n        valStr = `'${val}'`;\n    }\n    else if (dtype === 'bool') {\n        valStr = boolNumToString(val);\n    }\n    else {\n        valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n    }\n    return rightPad(valStr, pad);\n}\nfunction boolNumToString(v) {\n    return v === 0 ? 'false' : 'true';\n}\nfunction subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {\n    const storagePerElement = dtype === 'complex64' ? 2 : 1;\n    const size = shape[0];\n    const rank = shape.length;\n    if (rank === 0) {\n        if (dtype === 'complex64') {\n            const complexTuple = createComplexTuples(vals);\n            return [valToString(complexTuple[0], 0, dtype)];\n        }\n        if (dtype === 'bool') {\n            return [boolNumToString(vals[0])];\n        }\n        return [vals[0].toString()];\n    }\n    if (rank === 1) {\n        if (size > FORMAT_LIMIT_NUM_VALS) {\n            const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n            let firstVals = Array.from(vals.slice(0, firstValsSize));\n            let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));\n            if (dtype === 'complex64') {\n                firstVals = createComplexTuples(firstVals);\n                lastVals = createComplexTuples(lastVals);\n            }\n            return [\n                '[' +\n                    firstVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n                        .join(', ') +\n                    ', ..., ' +\n                    lastVals\n                        .map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype))\n                        .join(', ') +\n                    ']'\n            ];\n        }\n        const displayVals = dtype === 'complex64' ? createComplexTuples(vals) :\n            Array.from(vals);\n        return [\n            '[' +\n                displayVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n                    .join(', ') +\n                ']'\n        ];\n    }\n    // The array is rank 2 or more.\n    const subshape = shape.slice(1);\n    const substrides = strides.slice(1);\n    const stride = strides[0] * storagePerElement;\n    const lines = [];\n    if (size > FORMAT_LIMIT_NUM_VALS) {\n        for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false /* isLast */));\n        }\n        lines.push('...');\n        for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n        }\n    }\n    else {\n        for (let i = 0; i < size; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n        }\n    }\n    const sep = rank === 2 ? ',' : '';\n    lines[0] = '[' + lines[0] + sep;\n    for (let i = 1; i < lines.length - 1; i++) {\n        lines[i] = ' ' + lines[i] + sep;\n    }\n    let newLineSep = ',\\n';\n    for (let i = 2; i < rank; i++) {\n        newLineSep += '\\n';\n    }\n    lines[lines.length - 1] =\n        ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);\n    return lines;\n}\nfunction createComplexTuples(vals) {\n    const complexTuples = [];\n    for (let i = 0; i < vals.length; i += 2) {\n        complexTuples.push([vals[i], vals[i + 1]]);\n    }\n    return complexTuples;\n}\n//# sourceMappingURL=tensor_format.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensorToString } from './tensor_format';\nimport * as util from './util';\nimport { computeStrides, toNestedArray } from './util';\n/**\n * A mutable object, similar to `tf.Tensor`, that allows users to set values\n * at locations before converting to an immutable `tf.Tensor`.\n *\n * See `tf.buffer` for creating a tensor buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class TensorBuffer {\n    constructor(shape, dtype, values) {\n        this.dtype = dtype;\n        this.shape = shape.slice();\n        this.size = util.sizeFromShape(shape);\n        if (values != null) {\n            const n = values.length;\n            util.assert(n === this.size, () => `Length of values '${n}' does not match the size ` +\n                `inferred by the shape '${this.size}'.`);\n        }\n        if (dtype === 'complex64') {\n            throw new Error(`complex64 dtype TensorBuffers are not supported. Please create ` +\n                `a TensorBuffer for the real and imaginary parts separately and ` +\n                `call tf.complex(real, imag).`);\n        }\n        this.values = values || util.getArrayFromDType(dtype, this.size);\n        this.strides = computeStrides(shape);\n    }\n    /**\n     * Sets a value in the buffer at a given location.\n     *\n     * @param value The value to set.\n     * @param locs  The location indices.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    set(value, ...locs) {\n        if (locs.length === 0) {\n            locs = [0];\n        }\n        util.assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must ` +\n            `match the rank (${this.rank})`);\n        const index = this.locToIndex(locs);\n        this.values[index] = value;\n    }\n    /**\n     * Returns the value in the buffer at the provided location.\n     *\n     * @param locs The location indices.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    get(...locs) {\n        if (locs.length === 0) {\n            locs = [0];\n        }\n        let i = 0;\n        for (const loc of locs) {\n            if (loc < 0 || loc >= this.shape[i]) {\n                const msg = `Requested out of range element at ${locs}. ` +\n                    `  Buffer shape=${this.shape}`;\n                throw new Error(msg);\n            }\n            i++;\n        }\n        let index = locs[locs.length - 1];\n        for (let i = 0; i < locs.length - 1; ++i) {\n            index += this.strides[i] * locs[i];\n        }\n        return this.values[index];\n    }\n    locToIndex(locs) {\n        if (this.rank === 0) {\n            return 0;\n        }\n        else if (this.rank === 1) {\n            return locs[0];\n        }\n        let index = locs[locs.length - 1];\n        for (let i = 0; i < locs.length - 1; ++i) {\n            index += this.strides[i] * locs[i];\n        }\n        return index;\n    }\n    indexToLoc(index) {\n        if (this.rank === 0) {\n            return [];\n        }\n        else if (this.rank === 1) {\n            return [index];\n        }\n        const locs = new Array(this.shape.length);\n        for (let i = 0; i < locs.length - 1; ++i) {\n            locs[i] = Math.floor(index / this.strides[i]);\n            index -= locs[i] * this.strides[i];\n        }\n        locs[locs.length - 1] = index;\n        return locs;\n    }\n    get rank() {\n        return this.shape.length;\n    }\n    /**\n     * Creates an immutable `tf.Tensor` object from the buffer.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    toTensor() {\n        return trackerFn().makeTensor(this.values, this.shape, this.dtype);\n    }\n}\n// For tracking tensor creation and disposal.\nlet trackerFn = null;\n// Used by chaining methods to call into ops.\nlet opHandler = null;\n// Used to warn about deprecated methods.\nlet deprecationWarningFn = null;\n// This here so that we can use this method on dev branches and keep the\n// functionality at master.\n// tslint:disable-next-line:no-unused-expression\n[deprecationWarningFn];\n/**\n * An external consumer can register itself as the tensor tracker. This way\n * the Tensor class can notify the tracker for every tensor created and\n * disposed.\n */\nexport function setTensorTracker(fn) {\n    trackerFn = fn;\n}\n/**\n * An external consumer can register itself as the op handler. This way the\n * Tensor class can have chaining methods that call into ops via the op\n * handler.\n */\nexport function setOpHandler(handler) {\n    opHandler = handler;\n}\n/**\n * Sets the deprecation warning function to be used by this file. This way the\n * Tensor class can be a leaf but still use the environment.\n */\nexport function setDeprecationWarningFn(fn) {\n    deprecationWarningFn = fn;\n}\n/**\n * A `tf.Tensor` object represents an immutable, multidimensional array of\n * numbers that has a shape and a data type.\n *\n * See `tf.tensor` for details on how to create a `tf.Tensor`.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Tensor {\n    constructor(shape, dtype, dataId, id) {\n        /** Whether this tensor has been globally kept. */\n        this.kept = false;\n        this.isDisposedInternal = false;\n        this.shape = shape.slice();\n        this.dtype = dtype || 'float32';\n        this.size = util.sizeFromShape(shape);\n        this.strides = computeStrides(shape);\n        this.dataId = dataId;\n        this.id = id;\n        this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher');\n    }\n    get rank() {\n        return this.shape.length;\n    }\n    /**\n     * Returns a promise of `tf.TensorBuffer` that holds the underlying data.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async buffer() {\n        const vals = await this.data();\n        return opHandler.buffer(this.shape, this.dtype, vals);\n    }\n    /**\n     * Returns a `tf.TensorBuffer` that holds the underlying data.\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    bufferSync() {\n        return opHandler.buffer(this.shape, this.dtype, this.dataSync());\n    }\n    /**\n     * Returns the tensor data as a nested array. The transfer of data is done\n     * asynchronously.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async array() {\n        const vals = await this.data();\n        return toNestedArray(this.shape, vals);\n    }\n    /**\n     * Returns the tensor data as a nested array. The transfer of data is done\n     * synchronously.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    arraySync() {\n        return toNestedArray(this.shape, this.dataSync());\n    }\n    /**\n     * Asynchronously downloads the values from the `tf.Tensor`. Returns a\n     * promise of `TypedArray` that resolves when the computation has finished.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async data() {\n        this.throwIfDisposed();\n        const data = trackerFn().read(this.dataId);\n        if (this.dtype === 'string') {\n            const bytes = await data;\n            try {\n                return bytes.map(b => util.decodeString(b));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode the string bytes into utf-8. ' +\n                    'To get the original bytes, call tensor.bytes().');\n            }\n        }\n        return data;\n    }\n    /**\n     * Synchronously downloads the values from the `tf.Tensor`. This blocks the\n     * UI thread until the values are ready, which can cause performance issues.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    dataSync() {\n        this.throwIfDisposed();\n        const data = trackerFn().readSync(this.dataId);\n        if (this.dtype === 'string') {\n            try {\n                return data.map(b => util.decodeString(b));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode the string bytes into utf-8. ' +\n                    'To get the original bytes, call tensor.bytes().');\n            }\n        }\n        return data;\n    }\n    /** Returns the underlying bytes of the tensor's data. */\n    async bytes() {\n        this.throwIfDisposed();\n        const data = await trackerFn().read(this.dataId);\n        if (this.dtype === 'string') {\n            return data;\n        }\n        else {\n            return new Uint8Array(data.buffer);\n        }\n    }\n    /**\n     * Disposes `tf.Tensor` from memory.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    dispose() {\n        if (this.isDisposed) {\n            return;\n        }\n        trackerFn().disposeTensor(this);\n        this.isDisposedInternal = true;\n    }\n    get isDisposed() {\n        return this.isDisposedInternal;\n    }\n    throwIfDisposed() {\n        if (this.isDisposed) {\n            throw new Error(`Tensor is disposed.`);\n        }\n    }\n    /**\n     * Prints the `tf.Tensor`. See `tf.print` for details.\n     *\n     * @param verbose Whether to print verbose information about the tensor,\n     *    including dtype and size.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    print(verbose = false) {\n        return opHandler.print(this, verbose);\n    }\n    /**\n     * Returns a copy of the tensor. See `tf.clone` for details.\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    clone() {\n        this.throwIfDisposed();\n        return opHandler.clone(this);\n    }\n    /**\n     * Returns a human-readable description of the tensor. Useful for logging.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    toString(verbose = false) {\n        const vals = this.dataSync();\n        return tensorToString(vals, this.shape, this.dtype, verbose);\n    }\n    cast(dtype) {\n        this.throwIfDisposed();\n        return opHandler.cast(this, dtype);\n    }\n    variable(trainable = true, name, dtype) {\n        this.throwIfDisposed();\n        return trackerFn().makeVariable(this, trainable, name, dtype);\n    }\n}\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n    value: (instance) => {\n        // Implementation note: we should use properties of the object that will be\n        // defined before the constructor body has finished executing (methods).\n        // This is because when this code is transpiled by babel, babel will call\n        // classCallCheck before the constructor body is run.\n        // See https://github.com/tensorflow/tfjs/issues/3384 for backstory.\n        return !!instance && instance.data != null && instance.dataSync != null &&\n            instance.throwIfDisposed != null;\n    }\n});\n/**\n * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Variable extends Tensor {\n    constructor(initialValue, trainable, name, tensorId) {\n        super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);\n        this.trainable = trainable;\n        this.name = name;\n    }\n    /**\n     * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n     * the same shape and dtype as the old `tf.Tensor`.\n     *\n     * @param newValue New tensor to be assigned to this variable.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    assign(newValue) {\n        if (newValue.dtype !== this.dtype) {\n            throw new Error(`dtype of the new value (${newValue.dtype}) and ` +\n                `previous value (${this.dtype}) must match`);\n        }\n        if (!util.arraysEqual(newValue.shape, this.shape)) {\n            throw new Error(`shape of the new value (${newValue.shape}) and ` +\n                `previous value (${this.shape}) must match`);\n        }\n        trackerFn().disposeTensor(this);\n        this.dataId = newValue.dataId;\n        trackerFn().incRef(this, null /* backend */);\n    }\n    dispose() {\n        trackerFn().disposeVariable(this);\n        this.isDisposedInternal = true;\n    }\n}\nObject.defineProperty(Variable, Symbol.hasInstance, {\n    value: (instance) => {\n        return instance instanceof Tensor && instance.assign != null &&\n            instance.assign instanceof Function;\n    }\n});\n//# sourceMappingURL=tensor.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Rank;\n(function (Rank) {\n    Rank[\"R0\"] = \"R0\";\n    Rank[\"R1\"] = \"R1\";\n    Rank[\"R2\"] = \"R2\";\n    Rank[\"R3\"] = \"R3\";\n    Rank[\"R4\"] = \"R4\";\n    Rank[\"R5\"] = \"R5\";\n    Rank[\"R6\"] = \"R6\";\n})(Rank || (Rank = {}));\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nvar UpcastInt32AndMap;\n(function (UpcastInt32AndMap) {\n    UpcastInt32AndMap[\"float32\"] = \"float32\";\n    UpcastInt32AndMap[\"int32\"] = \"int32\";\n    UpcastInt32AndMap[\"bool\"] = \"int32\";\n    UpcastInt32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));\nvar UpcastBoolAndMap;\n(function (UpcastBoolAndMap) {\n    UpcastBoolAndMap[\"float32\"] = \"float32\";\n    UpcastBoolAndMap[\"int32\"] = \"int32\";\n    UpcastBoolAndMap[\"bool\"] = \"bool\";\n    UpcastBoolAndMap[\"complex64\"] = \"complex64\";\n})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));\nvar UpcastFloat32AndMap;\n(function (UpcastFloat32AndMap) {\n    UpcastFloat32AndMap[\"float32\"] = \"float32\";\n    UpcastFloat32AndMap[\"int32\"] = \"float32\";\n    UpcastFloat32AndMap[\"bool\"] = \"float32\";\n    UpcastFloat32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));\nvar UpcastComplex64AndMap;\n(function (UpcastComplex64AndMap) {\n    UpcastComplex64AndMap[\"float32\"] = \"complex64\";\n    UpcastComplex64AndMap[\"int32\"] = \"complex64\";\n    UpcastComplex64AndMap[\"bool\"] = \"complex64\";\n    UpcastComplex64AndMap[\"complex64\"] = \"complex64\";\n})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));\nconst upcastTypeMap = {\n    'float32': UpcastFloat32AndMap,\n    'int32': UpcastInt32AndMap,\n    'bool': UpcastBoolAndMap,\n    'complex64': UpcastComplex64AndMap\n};\nexport function upcastType(typeA, typeB) {\n    if (typeA === 'string' || typeB === 'string') {\n        if (typeA === 'string' && typeB === 'string') {\n            return 'string';\n        }\n        throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n    }\n    return upcastTypeMap[typeA][typeB];\n}\n/** Returns the output type after summation. */\nexport function sumOutType(type) {\n    return upcastType(type, 'int32');\n}\n//# sourceMappingURL=types.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from './tensor';\nimport { upcastType } from './types';\nimport { assert } from './util';\nexport function makeTypesMatch(a, b) {\n    if (a.dtype === b.dtype) {\n        return [a, b];\n    }\n    const dtype = upcastType(a.dtype, b.dtype);\n    return [a.cast(dtype), b.cast(dtype)];\n}\nexport function assertTypesMatch(a, b) {\n    assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and` +\n        ` second(${b.dtype}) input must match`);\n}\nexport function isTensorInList(tensor, tensorList) {\n    return tensorList.some(x => x.id === tensor.id);\n}\n/**\n * Extracts any `Tensor`s found within the provided object.\n *\n * @param container an object that may be a `Tensor` or may directly contain\n *   `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it\n *   is safe to pass any object here, except that `Promise`s are not\n *   supported.\n * @returns An array of `Tensors` found within the passed object. If the\n *   argument is simply a `Tensor', a list containing that `Tensor` is\n *   returned. If the object is not a `Tensor` or does not\n *   contain `Tensors`, an empty list is returned.\n */\nexport function getTensorsInContainer(result) {\n    const list = [];\n    const seen = new Set();\n    walkTensorContainer(result, list, seen);\n    return list;\n}\nfunction walkTensorContainer(container, list, seen) {\n    if (container == null) {\n        return;\n    }\n    if (container instanceof Tensor) {\n        list.push(container);\n        return;\n    }\n    if (!isIterable(container)) {\n        return;\n    }\n    // Iteration over keys works also for arrays.\n    const iterable = container;\n    for (const k in iterable) {\n        const val = iterable[k];\n        if (!seen.has(val)) {\n            seen.add(val);\n            walkTensorContainer(val, list, seen);\n        }\n    }\n}\n// tslint:disable-next-line:no-any\nfunction isIterable(obj) {\n    return Array.isArray(obj) || typeof obj === 'object';\n}\n//# sourceMappingURL=tensor_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { KernelBackend } from './backends/backend';\nimport { Environment, setEnvironmentGlobal } from './environment';\nimport { getGlobalNamespace } from './global_util';\nimport { Add, Cast } from './kernel_names';\nimport { getGradient, getKernel, getKernelsForBackend } from './kernel_registry';\nimport { Profiler } from './profiler';\nimport { backpropagateGradients, getFilteredNodesXToY } from './tape';\nimport { setTensorTracker, Tensor, Variable } from './tensor';\nimport { getTensorsInContainer } from './tensor_util';\nimport * as util from './util';\nimport { bytesFromStringArray, makeOnesTypedArray, now, sizeFromShape } from './util';\nclass EngineState {\n    constructor() {\n        // Public since optimizers will use it.\n        this.registeredVariables = {};\n        this.nextTapeNodeId = 0;\n        this.numBytes = 0;\n        this.numTensors = 0;\n        this.numStringTensors = 0;\n        this.numDataBuffers = 0;\n        // Number of nested tf.grad() statements when computing higher-order\n        // gradients. E.g. `1` for first-order gradients and `2` for second-order\n        // gradients. Used to track if the tape should be removed after a backprop.\n        this.gradientDepth = 0;\n        // Number of nested kernel calls. When kernel depth is greater than 1, we turn\n        // off the tape.\n        this.kernelDepth = 0;\n        this.scopeStack = [];\n        /**\n         * Keeps track of the number of data moves during a kernel execution. We\n         * maintain a stack since kernels can call other kernels, recursively.\n         */\n        this.numDataMovesStack = [];\n        this.nextScopeId = 0;\n        this.tensorInfo = new WeakMap();\n        this.profiling = false;\n        this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null };\n    }\n    dispose() {\n        for (const variableName in this.registeredVariables) {\n            this.registeredVariables[variableName].dispose();\n        }\n    }\n}\nexport class Engine {\n    constructor(ENV) {\n        this.ENV = ENV;\n        this.registry = {};\n        this.registryFactory = {};\n        this.pendingBackendInitId = 0;\n        this.state = new EngineState();\n    }\n    async ready() {\n        if (this.pendingBackendInit != null) {\n            return this.pendingBackendInit.then(() => { });\n        }\n        if (this.backendInstance != null) {\n            return;\n        }\n        const sortedBackends = this.getSortedBackends();\n        for (let i = 0; i < sortedBackends.length; i++) {\n            const backendName = sortedBackends[i];\n            const success = await this.initializeBackend(backendName).success;\n            if (success) {\n                await this.setBackend(backendName);\n                return;\n            }\n        }\n        throw new Error(`Could not initialize any backends, all backend initializations ` +\n            `failed.`);\n    }\n    get backend() {\n        if (this.pendingBackendInit != null) {\n            throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make ` +\n                `sure to await tf.ready() or await tf.setBackend() before calling ` +\n                `other methods`);\n        }\n        if (this.backendInstance == null) {\n            const { name, asyncInit } = this.initializeBackendsAndReturnBest();\n            if (asyncInit) {\n                throw new Error(`The highest priority backend '${name}' has not yet been ` +\n                    `initialized. Make sure to await tf.ready() or ` +\n                    `await tf.setBackend() before calling other methods`);\n            }\n            this.setBackend(name);\n        }\n        return this.backendInstance;\n    }\n    backendNames() {\n        return Object.keys(this.registryFactory);\n    }\n    findBackend(backendName) {\n        if (!(backendName in this.registry)) {\n            // If the backend hasn't been initialized but we have a registry entry for\n            // it, initialize it and return it.\n            if (backendName in this.registryFactory) {\n                const { asyncInit } = this.initializeBackend(backendName);\n                if (asyncInit) {\n                    // Backend is not ready yet.\n                    return null;\n                }\n            }\n            else {\n                return null;\n            }\n        }\n        return this.registry[backendName];\n    }\n    findBackendFactory(backendName) {\n        if (!(backendName in this.registryFactory)) {\n            return null;\n        }\n        return this.registryFactory[backendName].factory;\n    }\n    registerBackend(backendName, factory, priority = 1) {\n        if (backendName in this.registryFactory) {\n            console.warn(`${backendName} backend was already registered. ` +\n                `Reusing existing backend factory.`);\n            return false;\n        }\n        this.registryFactory[backendName] = { factory, priority };\n        return true;\n    }\n    async setBackend(backendName) {\n        if (this.registryFactory[backendName] == null) {\n            throw new Error(`Backend name '${backendName}' not found in registry`);\n        }\n        this.backendName = backendName;\n        if (this.registry[backendName] == null) {\n            this.backendInstance = null;\n            const { success, asyncInit } = this.initializeBackend(backendName);\n            const result = asyncInit ? await success : success;\n            if (!result) {\n                return false;\n            }\n        }\n        this.backendInstance = this.registry[backendName];\n        this.setupRegisteredKernels();\n        // Reset the profiler.\n        this.profiler = new Profiler(this.backendInstance);\n        return true;\n    }\n    setupRegisteredKernels() {\n        const kernels = getKernelsForBackend(this.backendName);\n        kernels.forEach(kernel => {\n            if (kernel.setupFunc != null) {\n                kernel.setupFunc(this.backendInstance);\n            }\n        });\n    }\n    disposeRegisteredKernels(backendName) {\n        const kernels = getKernelsForBackend(backendName);\n        kernels.forEach(kernel => {\n            if (kernel.disposeFunc != null) {\n                kernel.disposeFunc(this.registry[backendName]);\n            }\n        });\n    }\n    /**\n     * Initializes a backend by looking up the backend name in the factory\n     * registry and calling the factory method. Returns a boolean representing\n     * whether the initialization of the backend suceeded. Throws an error if\n     * there is no backend in the factory registry.\n     */\n    initializeBackend(backendName) {\n        const registryFactoryEntry = this.registryFactory[backendName];\n        if (registryFactoryEntry == null) {\n            throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);\n        }\n        try {\n            const backend = registryFactoryEntry.factory();\n            /* Test if the factory returns a promise.\n            Done in a more liberal way than\n            previous 'Promise.resolve(backend)===backend'\n            as we needed to account for custom Promise\n            implementations (e.g. Angular) */\n            if (backend && !(backend instanceof KernelBackend)\n                && typeof backend.then === 'function') {\n                const promiseId = ++this.pendingBackendInitId;\n                const success = backend\n                    .then(backendInstance => {\n                    // Outdated promise. Another backend was set in the meantime.\n                    if (promiseId < this.pendingBackendInitId) {\n                        return false;\n                    }\n                    this.registry[backendName] = backendInstance;\n                    this.pendingBackendInit = null;\n                    return true;\n                })\n                    .catch(err => {\n                    // Outdated promise. Another backend was set in the meantime.\n                    if (promiseId < this.pendingBackendInitId) {\n                        return false;\n                    }\n                    this.pendingBackendInit = null;\n                    console.warn(`Initialization of backend ${backendName} failed`);\n                    console.warn(err.stack || err.message);\n                    return false;\n                });\n                this.pendingBackendInit = success;\n                return { success, asyncInit: true };\n            }\n            else {\n                this.registry[backendName] = backend;\n                return { success: true, asyncInit: false };\n            }\n        }\n        catch (err) {\n            console.warn(`Initialization of backend ${backendName} failed`);\n            console.warn(err.stack || err.message);\n            return { success: false, asyncInit: false };\n        }\n    }\n    removeBackend(backendName) {\n        if (!(backendName in this.registryFactory)) {\n            throw new Error(`${backendName} backend not found in registry`);\n        }\n        if (this.backendName === backendName && this.pendingBackendInit != null) {\n            // There is a pending promise of the backend we want to remove. Make it\n            // obsolete.\n            this.pendingBackendInitId++;\n        }\n        if (backendName in this.registry) {\n            this.disposeRegisteredKernels(backendName);\n            this.registry[backendName].dispose();\n            delete this.registry[backendName];\n        }\n        delete this.registryFactory[backendName];\n        // Unset the backend if it is active.\n        if (this.backendName === backendName) {\n            this.pendingBackendInit = null;\n            this.backendName = null;\n            this.backendInstance = null;\n        }\n    }\n    getSortedBackends() {\n        if (Object.keys(this.registryFactory).length === 0) {\n            throw new Error('No backend found in registry.');\n        }\n        return Object.keys(this.registryFactory).sort((a, b) => {\n            // Highest priority comes first.\n            return this.registryFactory[b].priority -\n                this.registryFactory[a].priority;\n        });\n    }\n    initializeBackendsAndReturnBest() {\n        const sortedBackends = this.getSortedBackends();\n        for (let i = 0; i < sortedBackends.length; i++) {\n            const backendName = sortedBackends[i];\n            const { success, asyncInit } = this.initializeBackend(backendName);\n            if (asyncInit || success) {\n                return { name: backendName, asyncInit };\n            }\n        }\n        throw new Error(`Could not initialize any backends, all backend initializations ` +\n            `failed.`);\n    }\n    moveData(backend, dataId) {\n        const info = this.state.tensorInfo.get(dataId);\n        const srcBackend = info.backend;\n        const values = this.readSync(dataId);\n        // Delete the tensor from the old backend and move it to the new\n        // backend.\n        srcBackend.disposeData(dataId);\n        info.backend = backend;\n        backend.move(dataId, values, info.shape, info.dtype);\n        if (this.shouldCheckForMemLeaks()) {\n            // Track the number of moves during a kernel execution to correctly\n            // detect memory leaks.\n            this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;\n        }\n    }\n    tidy(nameOrFn, fn) {\n        let name = null;\n        if (fn == null) {\n            // Called with only 1 argument.\n            if (typeof nameOrFn !== 'function') {\n                throw new Error('Please provide a function to tidy()');\n            }\n            fn = nameOrFn;\n        }\n        else {\n            // Called with 2 arguments.\n            if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {\n                throw new Error('When calling with two arguments, the first argument ' +\n                    'to tidy() must be a string');\n            }\n            if (typeof fn !== 'function') {\n                throw new Error('When calling with two arguments, the 2nd argument ' +\n                    'to tidy() must be a function');\n            }\n            name = nameOrFn;\n            // TODO(nsthorat,smilkov): Do operation logging and performance\n            // profiling.\n        }\n        let result;\n        return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {\n            result = fn();\n            if (result instanceof Promise) {\n                console.error('Cannot return a Promise inside of tidy.');\n            }\n            return result;\n        });\n    }\n    scopedRun(start, end, f) {\n        start();\n        try {\n            const res = f();\n            end();\n            return res;\n        }\n        catch (ex) {\n            end();\n            throw ex;\n        }\n    }\n    nextTensorId() {\n        return Engine.nextTensorId++;\n    }\n    nextVariableId() {\n        return Engine.nextVariableId++;\n    }\n    /**\n     * This method is called instead of the public-facing tensor.clone() when\n     * saving a tensor for backwards pass. It makes sure to add the clone\n     * operation to the tape regardless of being called inside a kernel\n     * execution.\n     *\n     * This method will go away once all kernels are modularized since we won't\n     * need to turn off the tape inside runKernel().\n     */\n    clone(x) {\n        const y = this.makeTensorFromDataId(x.dataId, x.shape, x.dtype);\n        const inputs = { x };\n        const grad = (dy) => ({\n            x: () => {\n                const dtype = 'float32';\n                const gradInputs = { x: dy };\n                const attrs = { dtype };\n                return ENGINE.runKernelFunc(backend => backend.cast(dy, dtype), gradInputs, null /* grad */, Cast, attrs);\n            }\n        });\n        const saved = [];\n        this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {});\n        return y;\n    }\n    /**\n     * Execute a kernel with the given name and return the output tensor.\n     *\n     * @param kernelName The name of the kernel to execute.\n     * @param inputs A map of input names to tensors.\n     * @param attrs A map of attribute names to their values. An attribute is a\n     *     primitive (non-tensor) input to the kernel.\n     * @param inputsToSave A list of tensors, inputs to save for the backprop\n     *     computation.\n     * @param outputsToSave A list of booleans, specifying which output to save\n     *     for the backprop computation. These are booleans since the output\n     * tensors are not visible to the user.\n     */\n    runKernel(kernelName, inputs, attrs, inputsToSave, outputsToSave) {\n        const forwardFunc = null;\n        const backwardsFunc = null;\n        // Call runKernel as a stop-gap until we modularize all kernels.\n        // Once we modularize all kernels, we will remove the existing\n        // `runKernelFunc`.\n        return this.runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave);\n    }\n    shouldCheckForMemLeaks() {\n        return this.ENV.getBool('IS_TEST');\n    }\n    checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {\n        const numDataIdsAfter = this.backend.numDataIds();\n        // Count the number of data ids associated with the result of the kernel.\n        let numOutputDataIds = 0;\n        outInfos.forEach(info => {\n            // Complex numbers allocate 3 data ids, one for 'real', one for\n            // 'imaginary', and one for the container that holds the former two.\n            numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1);\n        });\n        // Account for the number of moves during kernel execution. A \"data move\"\n        // can happen in the middle of a kernel execution, placing a new (key,value)\n        // pair in the data storage. Since data moves have net zero effect (we\n        // always remove the data from the old backend), we have to cancel them out\n        // when detecting memory leaks.\n        const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];\n        const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;\n        if (dataIdsLeaked > 0) {\n            throw new Error(`Backend '${this.backendName}' has an internal memory leak ` +\n                `(${dataIdsLeaked} data ids) after running '${kernelName}'`);\n        }\n    }\n    /**\n     * @deprecated Use `runKernel` for newly added kernels. Keep using this method\n     *     only for kernels that are not yet fully modularized.\n     */\n    runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave) {\n        let outputs;\n        let saved = [];\n        const isTapeOn = this.isTapeOn();\n        if (kernelName == null) {\n            kernelName =\n                this.state.activeScope != null ? this.state.activeScope.name : '';\n        }\n        const startingBytecount = this.state.numBytes;\n        const startingNumTensors = this.state.numTensors;\n        if (this.shouldCheckForMemLeaks()) {\n            this.state.numDataMovesStack.push(0);\n        }\n        let kernelFunc;\n        const kernel = getKernel(kernelName, this.backendName);\n        let out;\n        if (kernel != null) {\n            kernelFunc = () => {\n                const numDataIdsBefore = this.backend.numDataIds();\n                out = kernel.kernelFunc({ inputs, attrs, backend: this.backend });\n                const outInfos = Array.isArray(out) ? out : [out];\n                if (this.shouldCheckForMemLeaks()) {\n                    this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);\n                }\n                const outTensors = outInfos.map(({ dataId, shape, dtype }) => this.makeTensorFromDataId(dataId, shape, dtype));\n                // Save the inputs and outputs.\n                // Do not save unless we are recording to the tape. Otherwise it would\n                // cause a mem leak since we would never run backprop, which disposes\n                // the kept tensors.\n                if (isTapeOn) {\n                    let tensorsToSave = this.getTensorsForGradient(kernelName, inputs, outTensors);\n                    if (tensorsToSave == null) {\n                        // Fallback for ops that call runKernelFunc and pass in\n                        // inputsToSave and outputsToSave. Currently this is the set of ops\n                        // with kernel support in the WASM backend. Once those ops and\n                        // respective gradients are modularised we can remove this path.\n                        if (outputsToSave == null) {\n                            outputsToSave = [];\n                        }\n                        const outsToSave = outTensors.filter((_, i) => outputsToSave[i]);\n                        tensorsToSave = (inputsToSave || []).slice().concat(outsToSave);\n                    }\n                    saved = this.saveTensorsForBackwardMode(tensorsToSave);\n                }\n                return outTensors;\n            };\n        }\n        else {\n            const saveFunc = (tensors) => {\n                // Do not save unless we are recording to the tape. Otherwise it would\n                // cause a mem leak since we would never run backprop, which disposes\n                // the kept tensors.\n                if (!isTapeOn) {\n                    return;\n                }\n                saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n            };\n            kernelFunc = () => {\n                const numDataIdsBefore = this.backend.numDataIds();\n                out = this.tidy(() => forwardFunc(this.backend, saveFunc));\n                const outs = (Array.isArray(out) ? out : [out]);\n                if (this.shouldCheckForMemLeaks()) {\n                    this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outs);\n                }\n                return outs;\n            };\n        }\n        // Stop recording to a tape when running a kernel.\n        let kernelProfile;\n        this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {\n            if (!this.ENV.getBool('DEBUG') && !this.state.profiling) {\n                outputs = kernelFunc();\n            }\n            else {\n                kernelProfile = this.profiler.profileKernel(kernelName, inputs, () => kernelFunc());\n                if (this.ENV.getBool('DEBUG')) {\n                    this.profiler.logKernelProfile(kernelProfile);\n                }\n                outputs = kernelProfile.outputs;\n            }\n        });\n        if (isTapeOn) {\n            this.addTapeNode(kernelName, inputs, outputs, backwardsFunc, saved, attrs);\n        }\n        if (this.state.profiling) {\n            this.state.activeProfile.kernels.push({\n                name: kernelName,\n                bytesAdded: this.state.numBytes - startingBytecount,\n                totalBytesSnapshot: this.state.numBytes,\n                tensorsAdded: this.state.numTensors - startingNumTensors,\n                totalTensorsSnapshot: this.state.numTensors,\n                inputShapes: Object.keys(inputs).map(key => inputs[key] != null ? inputs[key].shape : null),\n                outputShapes: outputs.map(item => item.shape),\n                kernelTimeMs: kernelProfile.timeMs,\n                extraInfo: kernelProfile.extraInfo\n            });\n        }\n        return (Array.isArray(out) ? outputs : outputs[0]);\n    }\n    /**\n     * Saves tensors used in forward mode for use in backward mode.\n     *\n     * @param tensors the list of tensors to save.\n     */\n    saveTensorsForBackwardMode(tensors) {\n        const saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n        return saved;\n    }\n    /**\n     * Returns a list of tensors to save for a given gradient calculation.\n     *\n     * Returns undefined if their is no registered gradient for this kernel in the\n     * gradient registry.\n     *\n     * @param kernelName name of kernel to look up gradient for.\n     * @param inputs a map of input tensors.\n     * @param outputs an array of output tensors from forward mode of kernel.\n     */\n    getTensorsForGradient(kernelName, inputs, outputs) {\n        const gradConfig = getGradient(kernelName);\n        if (gradConfig != null) {\n            const inputsToSave = gradConfig.inputsToSave || [];\n            const outputsToSave = gradConfig.outputsToSave || [];\n            // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs\n            // specified in inputsToSave will be saved.\n            let inputTensorsToSave;\n            if (gradConfig.saveAllInputs) {\n                util.assert(Array.isArray(inputs), () => 'saveAllInputs is true, expected inputs to be an array.');\n                inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);\n            }\n            else {\n                inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);\n            }\n            const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);\n            return inputTensorsToSave.concat(outputTensorsToSave);\n        }\n        // TODO(yassogba) throw exception here once all runkernelFunc calls with\n        // inputsToSave/outputsToSave are removed\n        return null;\n    }\n    /**\n     * Internal method used by public APIs for tensor creation. Makes a new\n     * tensor with the provided shape, dtype and values. It always\n     * creates a new data id and writes the values to the underlying backend.\n     */\n    makeTensor(values, shape, dtype, backend) {\n        if (values == null) {\n            throw new Error('Values passed to engine.makeTensor() are null');\n        }\n        dtype = dtype || 'float32';\n        backend = backend || this.backend;\n        let backendVals = values;\n        if (dtype === 'string' && util.isString(values[0])) {\n            backendVals = values.map(d => util.encodeString(d));\n        }\n        const dataId = backend.write(backendVals, shape, dtype);\n        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n        this.incRef(t, backend);\n        // Count bytes for string tensors.\n        if (dtype === 'string') {\n            const info = this.state.tensorInfo.get(dataId);\n            const newBytes = bytesFromStringArray(backendVals);\n            this.state.numBytes += newBytes - info.bytes;\n            info.bytes = newBytes;\n        }\n        return t;\n    }\n    /**\n     * Internal method used by backends. Makes a new tensor\n     * that is a wrapper around an existing data id. It doesn't create\n     * a new data id, only increments the ref count used in memory tracking.\n     */\n    makeTensorFromDataId(dataId, shape, dtype, backend) {\n        dtype = dtype || 'float32';\n        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n        this.incRef(t, backend);\n        return t;\n    }\n    makeVariable(initialValue, trainable = true, name, dtype) {\n        name = name || this.nextVariableId().toString();\n        if (dtype != null && dtype !== initialValue.dtype) {\n            initialValue = initialValue.cast(dtype);\n        }\n        const v = new Variable(initialValue, trainable, name, this.nextTensorId());\n        if (this.state.registeredVariables[v.name] != null) {\n            throw new Error(`Variable with name ${v.name} was already registered`);\n        }\n        this.state.registeredVariables[v.name] = v;\n        this.incRef(v, this.backend);\n        return v;\n    }\n    incRef(a, backend) {\n        const refCount = this.state.tensorInfo.has(a.dataId) ?\n            this.state.tensorInfo.get(a.dataId).refCount :\n            0;\n        this.state.numTensors++;\n        if (a.dtype === 'string') {\n            this.state.numStringTensors++;\n        }\n        if (refCount === 0) {\n            this.state.numDataBuffers++;\n            // Bytes for complex numbers are counted by their components. Bytes for\n            // string tensors are counted when writing values.\n            let bytes = 0;\n            if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n                bytes = a.size * util.bytesPerElement(a.dtype);\n            }\n            this.state.tensorInfo.set(a.dataId, {\n                backend: backend || this.backend,\n                dtype: a.dtype,\n                shape: a.shape,\n                bytes,\n                refCount: 0\n            });\n            this.state.numBytes += bytes;\n        }\n        this.state.tensorInfo.get(a.dataId).refCount++;\n        if (!(a instanceof Variable)) {\n            this.track(a);\n        }\n    }\n    disposeTensor(a) {\n        if (!this.state.tensorInfo.has(a.dataId)) {\n            return;\n        }\n        this.state.numTensors--;\n        if (a.dtype === 'string') {\n            this.state.numStringTensors--;\n        }\n        const info = this.state.tensorInfo.get(a.dataId);\n        const refCount = info.refCount;\n        if (refCount <= 1) {\n            // Don't count bytes for complex numbers as they are counted by their\n            // components.\n            if (a.dtype !== 'complex64') {\n                this.state.numBytes -= info.bytes;\n            }\n            this.state.numDataBuffers--;\n            info.backend.disposeData(a.dataId);\n            this.state.tensorInfo.delete(a.dataId);\n        }\n        else {\n            this.state.tensorInfo.get(a.dataId).refCount--;\n        }\n        // TODO(nsthorat): Construct an error and save the stack trace for\n        // debugging when in debug mode. Creating a stack trace is too expensive\n        // to do unconditionally.\n    }\n    disposeVariables() {\n        for (const varName in this.state.registeredVariables) {\n            const v = this.state.registeredVariables[varName];\n            this.disposeVariable(v);\n        }\n    }\n    disposeVariable(v) {\n        this.disposeTensor(v);\n        if (this.state.registeredVariables[v.name] != null) {\n            delete this.state.registeredVariables[v.name];\n        }\n    }\n    memory() {\n        const info = this.backend.memory();\n        info.numTensors = this.state.numTensors;\n        info.numDataBuffers = this.state.numDataBuffers;\n        info.numBytes = this.state.numBytes;\n        if (this.state.numStringTensors > 0) {\n            info.unreliable = true;\n            if (info.reasons == null) {\n                info.reasons = [];\n            }\n            info.reasons.push('Memory usage by string tensors is approximate ' +\n                '(2 bytes per character)');\n        }\n        return info;\n    }\n    async profile(query) {\n        this.state.profiling = true;\n        const startBytes = this.state.numBytes;\n        const startNumTensors = this.state.numTensors;\n        this.state.activeProfile.kernels = [];\n        this.state.activeProfile.result = await query();\n        this.state.profiling = false;\n        this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map(d => d.totalBytesSnapshot));\n        this.state.activeProfile.newBytes = this.state.numBytes - startBytes;\n        this.state.activeProfile.newTensors =\n            this.state.numTensors - startNumTensors;\n        for (const kernel of this.state.activeProfile.kernels) {\n            kernel.kernelTimeMs = await kernel.kernelTimeMs;\n            kernel.extraInfo = await kernel.extraInfo;\n        }\n        return this.state.activeProfile;\n    }\n    isTapeOn() {\n        return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;\n    }\n    addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {\n        const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved };\n        const gradConfig = getGradient(kernelName);\n        if (gradConfig != null) {\n            gradientsFunc = gradConfig.gradFunc;\n        }\n        if (gradientsFunc != null) {\n            tapeNode.gradient = (dys) => {\n                // TODO(smilkov): To optimize back-prop, pass dys that are not used in\n                // the backprop graph to the user as null instead of zeros\n                dys = dys.map((dy, i) => {\n                    if (dy == null) {\n                        const output = outputs[i];\n                        const vals = util.makeZerosTypedArray(output.size, output.dtype);\n                        return this.makeTensor(vals, output.shape, output.dtype);\n                    }\n                    return dy;\n                });\n                // Grad functions of ops with single outputs expect a dy, while ops\n                // with multiple outputs expect dys (array of dy).\n                return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);\n            };\n        }\n        this.state.activeTape.push(tapeNode);\n    }\n    keep(result) {\n        result.kept = true;\n        return result;\n    }\n    startTape() {\n        if (this.state.gradientDepth === 0) {\n            this.state.activeTape = [];\n        }\n        this.state.gradientDepth++;\n    }\n    endTape() {\n        this.state.gradientDepth--;\n    }\n    /**\n     * Start a scope. Use this with endScope() to achieve the same functionality\n     * as scope() without the need for a function closure.\n     */\n    startScope(name) {\n        const scopeInfo = {\n            track: [],\n            name: 'unnamed scope',\n            id: this.state.nextScopeId++\n        };\n        if (name) {\n            scopeInfo.name = name;\n        }\n        this.state.scopeStack.push(scopeInfo);\n        this.state.activeScope = scopeInfo;\n    }\n    /**\n     * End a scope. Use this with startScope() to achieve the same functionality\n     * as scope() without the need for a function closure.\n     */\n    endScope(result) {\n        const tensorsToTrackInParent = getTensorsInContainer(result);\n        const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map(t => t.id));\n        // Dispose the arrays tracked in this scope.\n        for (let i = 0; i < this.state.activeScope.track.length; i++) {\n            const tensor = this.state.activeScope.track[i];\n            if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) {\n                tensor.dispose();\n            }\n        }\n        const oldScope = this.state.scopeStack.pop();\n        this.state.activeScope = this.state.scopeStack.length === 0 ?\n            null :\n            this.state.scopeStack[this.state.scopeStack.length - 1];\n        // Track the current result in the parent scope.\n        tensorsToTrackInParent.forEach(tensor => {\n            // Only track the tensor if was allocated in the inner scope and is not\n            // globally kept.\n            if (!tensor.kept && tensor.scopeId === oldScope.id) {\n                this.track(tensor);\n            }\n        });\n    }\n    /**\n     * Returns gradients of `f` with respect to each of the `xs`. The gradients\n     * returned are of the same length as `xs`, but some might be null if `f`\n     * was not a function of that `x`. It also takes optional dy to multiply the\n     * gradient, which defaults to `1`.\n     */\n    gradients(f, xs, dy, allowNoGradients = false) {\n        util.assert(xs.length > 0, () => 'gradients() received an empty list of xs.');\n        if (dy != null && dy.dtype !== 'float32') {\n            throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n        }\n        const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy('forward', f));\n        util.assert(y instanceof Tensor, () => 'The result y returned by f() must be a tensor.');\n        // Filter out the nodes that don't connect x => y.\n        const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);\n        if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n            throw new Error('Cannot compute gradient of y=f(x) with respect to x. Make sure ' +\n                'that the f you passed encloses all operations that lead from x ' +\n                'to y.');\n        }\n        return this.tidy('backward', () => {\n            const accumulatedGradientMap = {};\n            accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;\n            // Backprop gradients through the filtered nodes.\n            backpropagateGradients(accumulatedGradientMap, filteredTape, \n            // Pass the tidy function to avoid circular dep with `tape.ts`.\n            f => this.tidy(f), \n            // Pass an add function to avoide a circular dep with `tape.ts`.\n            add);\n            const grads = xs.map(x => accumulatedGradientMap[x.id]);\n            if (this.state.gradientDepth === 0) {\n                // This means that we are not computing higher-order gradients\n                // and can clean up the tape.\n                this.state.activeTape.forEach(node => {\n                    for (const tensor of node.saved) {\n                        tensor.dispose();\n                    }\n                });\n                this.state.activeTape = null;\n            }\n            return { value: y, grads };\n        });\n    }\n    customGrad(f) {\n        util.assert(util.isFunction(f), () => 'The f passed in customGrad(f) must be a function.');\n        return (...inputs) => {\n            util.assert(inputs.every(t => t instanceof Tensor), () => 'The args passed in customGrad(f)(x1, x2,...) must all be ' +\n                'tensors');\n            let res;\n            const inputMap = {};\n            inputs.forEach((input, i) => {\n                inputMap[i] = input;\n            });\n            return this.runKernelFunc((_, save) => {\n                res = f(...[...inputs, save]);\n                util.assert(res.value instanceof Tensor, () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.value` is a tensor');\n                util.assert(util.isFunction(res.gradFunc), () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function.');\n                return res.value;\n            }, inputMap, (dy, saved) => {\n                const gradRes = res.gradFunc(dy, saved);\n                const grads = Array.isArray(gradRes) ? gradRes : [gradRes];\n                util.assert(grads.length === inputs.length, () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'the same number of tensors as inputs passed to f(...).');\n                util.assert(grads.every(t => t instanceof Tensor), () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'a list of only tensors.');\n                const gradMap = {};\n                grads.forEach((grad, i) => {\n                    gradMap[i] = () => grad;\n                });\n                return gradMap;\n            });\n        };\n    }\n    readSync(dataId) {\n        // Route the read to the correct backend.\n        const info = this.state.tensorInfo.get(dataId);\n        return info.backend.readSync(dataId);\n    }\n    read(dataId) {\n        // Route the read to the correct backend.\n        const info = this.state.tensorInfo.get(dataId);\n        return info.backend.read(dataId);\n    }\n    async time(query) {\n        const start = now();\n        const timingInfo = await this.backend.time(query);\n        timingInfo.wallMs = now() - start;\n        return timingInfo;\n    }\n    /**\n     * Tracks a Tensor in the current scope to be automatically cleaned up\n     * when the current scope ends, and returns the value.\n     *\n     * @param result The Tensor to track in the current scope.\n     */\n    track(result) {\n        if (this.state.activeScope != null) {\n            result.scopeId = this.state.activeScope.id;\n            this.state.activeScope.track.push(result);\n        }\n        return result;\n    }\n    get registeredVariables() {\n        return this.state.registeredVariables;\n    }\n    /**\n     * Resets the engine state. Removes all backends but does not remove\n     * registered backend factories.\n     */\n    reset() {\n        // Make any pending promise obsolete.\n        this.pendingBackendInitId++;\n        this.state.dispose();\n        this.ENV.reset();\n        this.state = new EngineState();\n        for (const backendName in this.registry) {\n            this.disposeRegisteredKernels(backendName);\n            this.registry[backendName].dispose();\n            delete this.registry[backendName];\n        }\n        this.backendName = null;\n        this.backendInstance = null;\n        this.pendingBackendInit = null;\n    }\n}\nEngine.nextTensorId = 0;\nEngine.nextVariableId = 0;\nfunction ones(shape) {\n    const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');\n    return ENGINE.makeTensor(values, shape, 'float32');\n}\nexport function getOrMakeEngine() {\n    const ns = getGlobalNamespace();\n    if (ns._tfengine == null) {\n        const environment = new Environment(ns);\n        ns._tfengine = new Engine(environment);\n    }\n    setEnvironmentGlobal(ns._tfengine.ENV);\n    // Tell the current tensor interface that the global engine is responsible\n    // for tracking.\n    setTensorTracker(() => ns._tfengine);\n    return ns._tfengine;\n}\nexport const ENGINE = getOrMakeEngine();\n/**\n * A implementation of the add op for use within engine and tape.\n *\n * This allows us to avoid a circular dependency between add.ts and engine.\n * It is exported to be available in tape tests.\n */\nexport function add(a, b) {\n    // We duplicate Add here to avoid a circular dependency with add.ts.\n    const inputs = { a, b };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.add(a, b);\n        save([a, b]);\n        return res;\n    }, inputs, null /* gradient */, Add);\n}\n//# sourceMappingURL=engine.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line:no-any\nfunction _isNavigatorDefined() {\n    return typeof navigator !== 'undefined' && navigator != null;\n}\nexport function isMobile() {\n    if (_isNavigatorDefined()) {\n        // tslint:disable-next-line:no-any\n        const a = navigator.userAgent || navigator.vendor || window.opera;\n        // tslint:disable-next-line:max-line-length\n        return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n            .test(a) ||\n            // tslint:disable-next-line:max-line-length\n            /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i\n                .test(a.substr(0, 4));\n    }\n    return false;\n}\nexport function isBrowser() {\n    return (typeof window !== 'undefined' && window.document != null) ||\n        //@ts-ignore\n        (typeof WorkerGlobalScope !== 'undefined');\n}\n//# sourceMappingURL=device_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './engine';\nimport * as device_util from './device_util';\nimport { env } from './environment';\nconst ENV = env();\n/**\n * This file contains environment-related flag registrations.\n */\n/** Whether to enable debug mode. */\nENV.registerFlag('DEBUG', () => false, debugValue => {\n    if (debugValue) {\n        console.warn('Debugging mode is ON. The output of every math call will ' +\n            'be downloaded to CPU and checked for NaNs. ' +\n            'This significantly impacts performance.');\n    }\n});\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_BROWSER', () => device_util.isBrowser());\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_NODE', () => (typeof process !== 'undefined') &&\n    (typeof process.versions !== 'undefined') &&\n    (typeof process.versions.node !== 'undefined'));\n/** Whether this browser is Chrome. */\nENV.registerFlag('IS_CHROME', () => typeof navigator !== 'undefined' && navigator != null &&\n    navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&\n    /Google Inc/.test(navigator.vendor));\n/**\n * True when the environment is \"production\" where we disable safety checks\n * to gain performance.\n */\nENV.registerFlag('PROD', () => false);\n/**\n * Whether to do sanity checks when inferring a shape from user-provided\n * values, used when creating a new tensor.\n */\nENV.registerFlag('TENSORLIKE_CHECK_SHAPE_CONSISTENCY', () => ENV.getBool('DEBUG'));\n/** Whether deprecation warnings are enabled. */\nENV.registerFlag('DEPRECATION_WARNINGS_ENABLED', () => true);\n/** True if running unit tests. */\nENV.registerFlag('IS_TEST', () => false);\n//# sourceMappingURL=flags.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { env } from './environment';\nimport { Tensor } from './tensor';\nimport { assert, flatten, inferDtype, isTypedArray, toTypedArray } from './util';\nexport function inferShape(val, dtype) {\n    let firstElem = val;\n    if (isTypedArray(val)) {\n        return dtype === 'string' ? [] : [val.length];\n    }\n    if (!Array.isArray(val)) {\n        return []; // Scalar.\n    }\n    const shape = [];\n    while (Array.isArray(firstElem) ||\n        isTypedArray(firstElem) && dtype !== 'string') {\n        shape.push(firstElem.length);\n        firstElem = firstElem[0];\n    }\n    if (Array.isArray(val) &&\n        env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {\n        deepAssertShapeConsistency(val, shape, []);\n    }\n    return shape;\n}\nfunction deepAssertShapeConsistency(val, shape, indices) {\n    indices = indices || [];\n    if (!(Array.isArray(val)) && !isTypedArray(val)) {\n        assert(shape.length === 0, () => `Element arr[${indices.join('][')}] is a primitive, ` +\n            `but should be an array/TypedArray of ${shape[0]} elements`);\n        return;\n    }\n    assert(shape.length > 0, () => `Element arr[${indices.join('][')}] should be a primitive, ` +\n        `but is an array of ${val.length} elements`);\n    assert(val.length === shape[0], () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +\n        `elements, but has ${val.length} elements`);\n    const subShape = shape.slice(1);\n    for (let i = 0; i < val.length; ++i) {\n        deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n    }\n}\nfunction assertDtype(expectedDtype, actualDType, argName, functionName) {\n    if (expectedDtype == null) {\n        return;\n    }\n    if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||\n        expectedDtype === 'numeric' && actualDType === 'string') {\n        throw new Error(`Argument '${argName}' passed to '${functionName}' must ` +\n            `be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n    }\n}\nexport function convertToTensor(x, argName, functionName, parseAsDtype = 'numeric') {\n    if (x instanceof Tensor) {\n        assertDtype(parseAsDtype, x.dtype, argName, functionName);\n        return x;\n    }\n    let inferredDtype = inferDtype(x);\n    // If the user expects a bool/int/float, use that info to update the\n    // inferredDtype when it is not a string.\n    if (inferredDtype !== 'string' &&\n        ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {\n        inferredDtype = parseAsDtype;\n    }\n    assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n    if ((x == null) ||\n        (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&\n            typeof x !== 'boolean' && typeof x !== 'string')) {\n        const type = x == null ? 'null' : x.constructor.name;\n        throw new Error(`Argument '${argName}' passed to '${functionName}' must be a ` +\n            `Tensor or TensorLike, but got '${type}'`);\n    }\n    const inferredShape = inferShape(x, inferredDtype);\n    if (!isTypedArray(x) && !Array.isArray(x)) {\n        x = [x];\n    }\n    const skipTypedArray = true;\n    const values = inferredDtype !== 'string' ?\n        toTypedArray(x, inferredDtype) :\n        flatten(x, [], skipTypedArray);\n    return ENGINE.makeTensor(values, inferredShape, inferredDtype);\n}\nexport function convertToTensorArray(arg, argName, functionName, parseAsDtype = 'numeric') {\n    if (!Array.isArray(arg)) {\n        throw new Error(`Argument ${argName} passed to ${functionName} must be a ` +\n            '`Tensor[]` or `TensorLike[]`');\n    }\n    const tensors = arg;\n    return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName), parseAsDtype);\n}\n//# sourceMappingURL=tensor_util_env.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nexport const OP_SCOPE_SUFFIX = '__op';\n/**\n * Used for wrapping functions that perform math operations on\n * Tensors. The function will be wrapped in a named scope that cleans all\n * memory usage after the function is done.\n */\nexport function op(f) {\n    const keys = Object.keys(f);\n    if (keys.length !== 1) {\n        throw new Error(`Please provide an object with a single key ` +\n            `(operation name) mapping to a function. Got an object with ` +\n            `${keys.length} keys.`);\n    }\n    let opName = keys[0];\n    const fn = f[opName];\n    // Strip the underscore from the end of the function name.\n    if (opName.endsWith('_')) {\n        opName = opName.substring(0, opName.length - 1);\n    }\n    // add an __op suffix to distinguish ops from kernels in tf.profile\n    opName = opName + OP_SCOPE_SUFFIX;\n    // tslint:disable-next-line:no-any\n    const f2 = (...args) => {\n        ENGINE.startScope(opName);\n        try {\n            const result = fn(...args);\n            if (result instanceof Promise) {\n                console.error('Cannot return a Promise inside of tidy.');\n            }\n            ENGINE.endScope(result);\n            return result;\n        }\n        catch (ex) {\n            ENGINE.endScope(null);\n            throw ex;\n        }\n    };\n    Object.defineProperty(f2, 'name', { value: opName, configurable: true });\n    // tslint:disable-next-line:no-any\n    return f2;\n}\n//# sourceMappingURL=operation.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Complex } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction complex_(real, imag) {\n    const $real = convertToTensor(real, 'real', 'complex');\n    const $imag = convertToTensor(imag, 'imag', 'complex');\n    util.assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n        `must match in call to tf.complex().`);\n    const forward = (backend) => {\n        return backend.complex($real, $imag);\n    };\n    const inputs = { real: $real, imag: $imag };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Complex);\n}\nexport const complex = op({ complex_ });\n//# sourceMappingURL=complex.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { assert, assertNonNegativeIntegerDimensions, flatten, inferDtype, isTypedArray, sizeFromShape, toTypedArray } from '../util';\n/** This is shared code across all tensor creation methods. */\nexport function makeTensor(values, shape, inferredShape, dtype) {\n    if (dtype == null) {\n        dtype = inferDtype(values);\n    }\n    if (dtype === 'complex64') {\n        throw new Error(`Cannot construct a complex64 tensor directly. ` +\n            `Please use tf.complex(real, imag).`);\n    }\n    if (!isTypedArray(values) && !Array.isArray(values) &&\n        typeof values !== 'number' && typeof values !== 'boolean' &&\n        typeof values !== 'string') {\n        throw new Error('values passed to tensor(values) must be a number/boolean/string or ' +\n            'an array of numbers/booleans/strings, or a TypedArray');\n    }\n    if (shape != null) {\n        assertNonNegativeIntegerDimensions(shape);\n        const providedSize = sizeFromShape(shape);\n        const inferredSize = sizeFromShape(inferredShape);\n        assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ` +\n            `${providedSize} values but has ${inferredSize}`);\n        for (let i = 0; i < inferredShape.length; ++i) {\n            const inferred = inferredShape[i];\n            const flatDimsDontMatch = i === inferredShape.length - 1 ?\n                inferred !== sizeFromShape(shape.slice(i)) :\n                true;\n            assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape ` +\n                `(${inferredShape}) does not match the provided ` +\n                `shape (${shape}). `);\n        }\n    }\n    if (!isTypedArray(values) && !Array.isArray(values)) {\n        values = [values];\n    }\n    shape = shape || inferredShape;\n    values = dtype !== 'string' ?\n        toTypedArray(values, dtype) :\n        flatten(values, [], true);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=tensor_ops_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`. If the values are strings,\n *     they will be encoded as utf-8 and kept as `Uint8Array[]`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor(values, shape, dtype) {\n    const inferredShape = inferShape(values, dtype);\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/* Type definitions for exporting and importing of models. */\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP = {\n    'float32': 4,\n    'float16': 2,\n    'int32': 4,\n    'uint16': 2,\n    'uint8': 1,\n    'bool': 1,\n    'complex64': 8\n};\n//# sourceMappingURL=types.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../ops/complex';\nimport { tensor } from '../ops/tensor';\nimport { sizeFromShape } from '../util';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/** Number of bytes reserved for the length of the string. (32bit integer). */\nconst NUM_BYTES_STRING_LENGTH = 4;\n/**\n * Encode a map from names to weight values as an ArrayBuffer, along with an\n * `Array` of `WeightsManifestEntry` as specification of the encoded weights.\n *\n * This function does not perform sharding.\n *\n * This function is the reverse of `decodeWeights`.\n *\n * @param tensors A map (\"dict\") from names to tensors.\n * @param group Group to which the weights belong (optional).\n * @returns A `Promise` of\n *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s\n *     concatenated.\n *   - An `Array` of `WeightManifestEntry`s, carrying information including\n *     tensor names, `dtype`s and shapes.\n * @throws Error: on unsupported tensor `dtype`.\n */\nexport async function encodeWeights(tensors, group) {\n    // TODO(adarob, cais): Support quantization.\n    const specs = [];\n    const dataPromises = [];\n    const names = Array.isArray(tensors) ?\n        tensors.map(tensor => tensor.name) :\n        Object.keys(tensors);\n    for (let i = 0; i < names.length; ++i) {\n        const name = names[i];\n        const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];\n        if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool' &&\n            t.dtype !== 'string' && t.dtype !== 'complex64') {\n            throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n        }\n        const spec = { name, shape: t.shape, dtype: t.dtype };\n        if (t.dtype === 'string') {\n            const utf8bytes = new Promise(async (resolve) => {\n                const vals = await t.bytes();\n                const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) +\n                    NUM_BYTES_STRING_LENGTH * vals.length;\n                const bytes = new Uint8Array(totalNumBytes);\n                let offset = 0;\n                for (let i = 0; i < vals.length; i++) {\n                    const val = vals[i];\n                    const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);\n                    bytes.set(bytesOfLength, offset);\n                    offset += NUM_BYTES_STRING_LENGTH;\n                    bytes.set(val, offset);\n                    offset += val.length;\n                }\n                resolve(bytes);\n            });\n            dataPromises.push(utf8bytes);\n        }\n        else {\n            dataPromises.push(t.data());\n        }\n        if (group != null) {\n            spec.group = group;\n        }\n        specs.push(spec);\n    }\n    const tensorValues = await Promise.all(dataPromises);\n    return { data: concatenateTypedArrays(tensorValues), specs };\n}\n/**\n * Decode flat ArrayBuffer as weights.\n *\n * This function does not handle sharding.\n *\n * This function is the reverse of `encodeWeights`.\n *\n * @param buffer A flat ArrayBuffer carrying the binary values of the tensors\n *   concatenated in the order specified in `specs`.\n * @param specs Specifications of the names, dtypes and shapes of the tensors\n *   whose value are encoded by `buffer`.\n * @return A map from tensor name to tensor value, with the names corresponding\n *   to names in `specs`.\n * @throws Error, if any of the tensors has unsupported dtype.\n */\nexport function decodeWeights(buffer, specs) {\n    // TODO(adarob, cais): Support quantization.\n    const out = {};\n    let float16Decode;\n    let offset = 0;\n    for (const spec of specs) {\n        const name = spec.name;\n        const dtype = spec.dtype;\n        const shape = spec.shape;\n        const size = sizeFromShape(shape);\n        let values;\n        if ('quantization' in spec) {\n            const quantization = spec.quantization;\n            if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n                if (!('min' in quantization && 'scale' in quantization)) {\n                    throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} ` +\n                        `doesn't have corresponding metadata min and scale.`);\n                }\n            }\n            else if (quantization.dtype === 'float16') {\n                if (dtype !== 'float32') {\n                    throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} ` +\n                        `which only supports weights of type float32 not ${dtype}.`);\n                }\n            }\n            else {\n                throw new Error(`Weight ${spec.name} has unknown ` +\n                    `quantization dtype ${quantization.dtype}. ` +\n                    `Supported quantization dtypes are: ` +\n                    `'uint8', 'uint16', and 'float16'.`);\n            }\n            const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n            const byteBuffer = buffer.slice(offset, offset + size * quantizationSizeFactor);\n            const quantizedArray = (quantization.dtype === 'uint8') ?\n                new Uint8Array(byteBuffer) :\n                new Uint16Array(byteBuffer);\n            if (dtype === 'float32') {\n                if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n                    values = new Float32Array(quantizedArray.length);\n                    for (let i = 0; i < quantizedArray.length; i++) {\n                        const v = quantizedArray[i];\n                        values[i] = v * quantization.scale + quantization.min;\n                    }\n                }\n                else if (quantization.dtype === 'float16') {\n                    if (float16Decode === undefined) {\n                        float16Decode = getFloat16Decoder();\n                    }\n                    values = float16Decode(quantizedArray);\n                }\n                else {\n                    throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n                        `for weight type float32.`);\n                }\n            }\n            else if (dtype === 'int32') {\n                if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {\n                    throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n                        `for weight type int32.`);\n                }\n                values = new Int32Array(quantizedArray.length);\n                for (let i = 0; i < quantizedArray.length; i++) {\n                    const v = quantizedArray[i];\n                    values[i] = Math.round(v * quantization.scale + quantization.min);\n                }\n            }\n            else {\n                throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n            }\n            offset += size * quantizationSizeFactor;\n        }\n        else if (dtype === 'string') {\n            const size = sizeFromShape(spec.shape);\n            values = [];\n            for (let i = 0; i < size; i++) {\n                const byteLength = new Uint32Array(buffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];\n                offset += NUM_BYTES_STRING_LENGTH;\n                const bytes = new Uint8Array(buffer.slice(offset, offset + byteLength));\n                values.push(bytes);\n                offset += byteLength;\n            }\n        }\n        else {\n            const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n            const byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);\n            if (dtype === 'float32') {\n                values = new Float32Array(byteBuffer);\n            }\n            else if (dtype === 'int32') {\n                values = new Int32Array(byteBuffer);\n            }\n            else if (dtype === 'bool') {\n                values = new Uint8Array(byteBuffer);\n            }\n            else if (dtype === 'complex64') {\n                values = new Float32Array(byteBuffer);\n                const real = new Float32Array(values.length / 2);\n                const image = new Float32Array(values.length / 2);\n                for (let i = 0; i < real.length; i++) {\n                    real[i] = values[i * 2];\n                    image[i] = values[i * 2 + 1];\n                }\n                const realTensor = tensor(real, shape, 'float32');\n                const imageTensor = tensor(image, shape, 'float32');\n                out[name] = complex(realTensor, imageTensor);\n            }\n            else {\n                throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n            }\n            offset += size * dtypeFactor;\n        }\n        if (dtype !== 'complex64') {\n            out[name] = tensor(values, shape, dtype);\n        }\n    }\n    return out;\n}\n/**\n * Concatenate TypedArrays into an ArrayBuffer.\n */\nexport function concatenateTypedArrays(xs) {\n    // TODO(adarob, cais): Support quantization.\n    if (xs === null) {\n        throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n    }\n    let totalByteLength = 0;\n    // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'\n    // can have a different byte length from that of the `TypedArray` itself,\n    // for example, when the `TypedArray` is created from an offset in an\n    // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match\n    // the `TypedArray` in byte length. If an element of `xs` does not show\n    // this property, a new `TypedArray` that satisfy this property will be\n    // constructed and pushed into `normalizedXs`.\n    const normalizedXs = [];\n    xs.forEach((x) => {\n        totalByteLength += x.byteLength;\n        // tslint:disable:no-any\n        normalizedXs.push(x.byteLength === x.buffer.byteLength ? x :\n            new x.constructor(x));\n        if (!(x instanceof Float32Array || x instanceof Int32Array ||\n            x instanceof Uint8Array)) {\n            throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n        }\n        // tslint:enable:no-any\n    });\n    const y = new Uint8Array(totalByteLength);\n    let offset = 0;\n    normalizedXs.forEach((x) => {\n        y.set(new Uint8Array(x.buffer), offset);\n        offset += x.byteLength;\n    });\n    return y.buffer;\n}\n// Use Buffer on Node.js instead of Blob/atob/btoa\nconst useNodeBuffer = typeof Buffer !== 'undefined' &&\n    (typeof Blob === 'undefined' || typeof atob === 'undefined' ||\n        typeof btoa === 'undefined');\n/**\n * Calculate the byte length of a JavaScript string.\n *\n * Note that a JavaScript string can contain wide characters, therefore the\n * length of the string is not necessarily equal to the byte length.\n *\n * @param str Input string.\n * @returns Byte length.\n */\nexport function stringByteLength(str) {\n    if (useNodeBuffer) {\n        return Buffer.byteLength(str);\n    }\n    return new Blob([str]).size;\n}\n/**\n * Encode an ArrayBuffer as a base64 encoded string.\n *\n * @param buffer `ArrayBuffer` to be converted.\n * @returns A string that base64-encodes `buffer`.\n */\nexport function arrayBufferToBase64String(buffer) {\n    if (useNodeBuffer) {\n        return Buffer.from(buffer).toString('base64');\n    }\n    const buf = new Uint8Array(buffer);\n    let s = '';\n    for (let i = 0, l = buf.length; i < l; i++) {\n        s += String.fromCharCode(buf[i]);\n    }\n    return btoa(s);\n}\n/**\n * Decode a base64 string as an ArrayBuffer.\n *\n * @param str Base64 string.\n * @returns Decoded `ArrayBuffer`.\n */\nexport function base64StringToArrayBuffer(str) {\n    if (useNodeBuffer) {\n        const buf = Buffer.from(str, 'base64');\n        return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n    }\n    const s = atob(str);\n    const buffer = new Uint8Array(s.length);\n    for (let i = 0; i < s.length; ++i) {\n        buffer.set([s.charCodeAt(i)], i);\n    }\n    return buffer.buffer;\n}\n/**\n * Concatenate a number of ArrayBuffers into one.\n *\n * @param buffers A number of array buffers to concatenate.\n * @returns Result of concatenating `buffers` in order.\n */\nexport function concatenateArrayBuffers(buffers) {\n    if (buffers.length === 1) {\n        return buffers[0];\n    }\n    let totalByteLength = 0;\n    buffers.forEach((buffer) => {\n        totalByteLength += buffer.byteLength;\n    });\n    const temp = new Uint8Array(totalByteLength);\n    let offset = 0;\n    buffers.forEach((buffer) => {\n        temp.set(new Uint8Array(buffer), offset);\n        offset += buffer.byteLength;\n    });\n    return temp.buffer;\n}\n/**\n * Get the basename of a path.\n *\n * Behaves in a way analogous to Linux's basename command.\n *\n * @param path\n */\nexport function basename(path) {\n    const SEPARATOR = '/';\n    path = path.trim();\n    while (path.endsWith(SEPARATOR)) {\n        path = path.slice(0, path.length - 1);\n    }\n    const items = path.split(SEPARATOR);\n    return items[items.length - 1];\n}\n/**\n * Populate ModelArtifactsInfo fields for a model with JSON topology.\n * @param modelArtifacts\n * @returns A ModelArtifactsInfo object.\n */\nexport function getModelArtifactsInfoForJSON(modelArtifacts) {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n        throw new Error('Expected JSON model topology, received ArrayBuffer.');\n    }\n    return {\n        dateSaved: new Date(),\n        modelTopologyType: 'JSON',\n        modelTopologyBytes: modelArtifacts.modelTopology == null ?\n            0 :\n            stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n        weightSpecsBytes: modelArtifacts.weightSpecs == null ?\n            0 :\n            stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n        weightDataBytes: modelArtifacts.weightData == null ?\n            0 :\n            modelArtifacts.weightData.byteLength,\n    };\n}\n/**\n * Computes mantisa table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 2048 mantissa lookup values.\n */\nfunction computeFloat16MantisaTable() {\n    const convertMantissa = (i) => {\n        let m = i << 13;\n        let e = 0;\n        while ((m & 0x00800000) === 0) {\n            e -= 0x00800000;\n            m <<= 1;\n        }\n        m &= ~0x00800000;\n        e += 0x38800000;\n        return m | e;\n    };\n    const mantisaTable = new Uint32Array(2048);\n    mantisaTable[0] = 0;\n    for (let i = 1; i < 1024; i++) {\n        mantisaTable[i] = convertMantissa(i);\n    }\n    for (let i = 1024; i < 2048; i++) {\n        mantisaTable[i] = 0x38000000 + ((i - 1024) << 13);\n    }\n    return mantisaTable;\n}\n/**\n * Computes exponent table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 64 exponent lookup values.\n */\nfunction computeFloat16ExponentTable() {\n    const exponentTable = new Uint32Array(64);\n    exponentTable[0] = 0;\n    exponentTable[31] = 0x47800000;\n    exponentTable[32] = 0x80000000;\n    exponentTable[63] = 0xc7800000;\n    for (let i = 1; i < 31; i++) {\n        exponentTable[i] = i << 23;\n    }\n    for (let i = 33; i < 63; i++) {\n        exponentTable[i] = 0x80000000 + ((i - 32) << 23);\n    }\n    return exponentTable;\n}\n/**\n * Computes offset table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 6d offset values.\n */\nfunction computeFloat16OffsetTable() {\n    const offsetTable = new Uint32Array(64);\n    for (let i = 0; i < 64; i++) {\n        offsetTable[i] = 1024;\n    }\n    offsetTable[0] = offsetTable[32] = 0;\n    return offsetTable;\n}\n/**\n * Retrieve a Float16 decoder which will decode a ByteArray of Float16 values\n * to a Float32Array.\n *\n * @returns Function (buffer: Uint16Array) => Float32Array which decodes\n *          the Uint16Array of Float16 bytes to a Float32Array.\n */\nexport function getFloat16Decoder() {\n    // Algorithm is based off of\n    // http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n    // Cache lookup tables\n    const mantisaTable = computeFloat16MantisaTable();\n    const exponentTable = computeFloat16ExponentTable();\n    const offsetTable = computeFloat16OffsetTable();\n    return (quantizedArray) => {\n        const buffer = new ArrayBuffer(4 * quantizedArray.length);\n        const bufferUint32View = new Uint32Array(buffer);\n        for (let index = 0; index < quantizedArray.length; index++) {\n            const float16Bits = quantizedArray[index];\n            const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 0x3ff)] +\n                exponentTable[float16Bits >> 10];\n            bufferUint32View[index] = float32Bits;\n        }\n        return new Float32Array(buffer);\n    };\n}\n//# sourceMappingURL=io_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class IORouterRegistry {\n    constructor() {\n        this.saveRouters = [];\n        this.loadRouters = [];\n    }\n    static getInstance() {\n        if (IORouterRegistry.instance == null) {\n            IORouterRegistry.instance = new IORouterRegistry();\n        }\n        return IORouterRegistry.instance;\n    }\n    /**\n     * Register a save-handler router.\n     *\n     * @param saveRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `save` method defined or `null`.\n     */\n    static registerSaveRouter(saveRouter) {\n        IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n    }\n    /**\n     * Register a load-handler router.\n     *\n     * @param loadRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `load` method defined or `null`.\n     */\n    static registerLoadRouter(loadRouter) {\n        IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n    }\n    /**\n     * Look up IOHandler for saving, given a URL-like string.\n     *\n     * @param url\n     * @returns If only one match is found, an instance of IOHandler with the\n     * `save` method defined. If no match is found, `null`.\n     * @throws Error, if more than one match is found.\n     */\n    static getSaveHandlers(url) {\n        return IORouterRegistry.getHandlers(url, 'save');\n    }\n    /**\n     * Look up IOHandler for loading, given a URL-like string.\n     *\n     * @param url\n     * @param loadOptions Optional, custom load options.\n     * @returns All valid handlers for `url`, given the currently registered\n     *   handler routers.\n     */\n    static getLoadHandlers(url, loadOptions) {\n        return IORouterRegistry.getHandlers(url, 'load', loadOptions);\n    }\n    static getHandlers(url, handlerType, loadOptions) {\n        const validHandlers = [];\n        const routers = handlerType === 'load' ?\n            IORouterRegistry.getInstance().loadRouters :\n            IORouterRegistry.getInstance().saveRouters;\n        routers.forEach(router => {\n            const handler = router(url, loadOptions);\n            if (handler !== null) {\n                validHandlers.push(handler);\n            }\n        });\n        return validHandlers;\n    }\n}\nexport const registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);\nexport const registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);\nexport const getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);\nexport const getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);\n//# sourceMappingURL=router_registry.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DATABASE_NAME = 'tensorflowjs';\nconst DATABASE_VERSION = 1;\n// Model data and ModelArtifactsInfo (metadata) are stored in two separate\n// stores for efficient access of the list of stored models and their metadata.\n// 1. The object store for model data: topology, weights and weight manifests.\nconst MODEL_STORE_NAME = 'models_store';\n// 2. The object store for ModelArtifactsInfo, including meta-information such\n//    as the type of topology (JSON vs binary), byte size of the topology, byte\n//    size of the weights, etc.\nconst INFO_STORE_NAME = 'model_info_store';\n/**\n * Delete the entire database for tensorflow.js, including the models store.\n */\nexport async function deleteDatabase() {\n    const idbFactory = getIndexedDBFactory();\n    return new Promise((resolve, reject) => {\n        const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);\n        deleteRequest.onsuccess = () => resolve();\n        deleteRequest.onerror = error => reject(error);\n    });\n}\nfunction getIndexedDBFactory() {\n    if (!env().getBool('IS_BROWSER')) {\n        // TODO(cais): Add more info about what IOHandler subtypes are available.\n        //   Maybe point to a doc page on the web and/or automatically determine\n        //   the available IOHandlers and print them in the error message.\n        throw new Error('Failed to obtain IndexedDB factory because the current environment' +\n            'is not a web browser.');\n    }\n    // tslint:disable-next-line:no-any\n    const theWindow = typeof window === 'undefined' ? self : window;\n    const factory = theWindow.indexedDB || theWindow.mozIndexedDB ||\n        theWindow.webkitIndexedDB || theWindow.msIndexedDB ||\n        theWindow.shimIndexedDB;\n    if (factory == null) {\n        throw new Error('The current browser does not appear to support IndexedDB.');\n    }\n    return factory;\n}\nfunction setUpDatabase(openRequest) {\n    const db = openRequest.result;\n    db.createObjectStore(MODEL_STORE_NAME, { keyPath: 'modelPath' });\n    db.createObjectStore(INFO_STORE_NAME, { keyPath: 'modelPath' });\n}\n/**\n * IOHandler subclass: Browser IndexedDB.\n *\n * See the doc string of `browserIndexedDB` for more details.\n */\nexport class BrowserIndexedDB {\n    constructor(modelPath) {\n        this.indexedDB = getIndexedDBFactory();\n        if (modelPath == null || !modelPath) {\n            throw new Error('For IndexedDB, modelPath must not be null, undefined or empty.');\n        }\n        this.modelPath = modelPath;\n    }\n    async save(modelArtifacts) {\n        // TODO(cais): Support saving GraphDef models.\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        return this.databaseAction(this.modelPath, modelArtifacts);\n    }\n    async load() {\n        return this.databaseAction(this.modelPath);\n    }\n    /**\n     * Perform database action to put model artifacts into or read model artifacts\n     * from IndexedDB object store.\n     *\n     * Whether the action is put or get depends on whether `modelArtifacts` is\n     * specified. If it is specified, the action will be put; otherwise the action\n     * will be get.\n     *\n     * @param modelPath A unique string path for the model.\n     * @param modelArtifacts If specified, it will be the model artifacts to be\n     *   stored in IndexedDB.\n     * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`\n     *   of `ModelArtifacts`, if the action is get.\n     */\n    databaseAction(modelPath, modelArtifacts) {\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                if (modelArtifacts == null) {\n                    // Read model out from object store.\n                    const modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');\n                    const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                    const getRequest = modelStore.get(this.modelPath);\n                    getRequest.onsuccess = () => {\n                        if (getRequest.result == null) {\n                            db.close();\n                            return reject(new Error(`Cannot find model with path '${this.modelPath}' ` +\n                                `in IndexedDB.`));\n                        }\n                        else {\n                            resolve(getRequest.result.modelArtifacts);\n                        }\n                    };\n                    getRequest.onerror = error => {\n                        db.close();\n                        return reject(getRequest.error);\n                    };\n                    modelTx.oncomplete = () => db.close();\n                }\n                else {\n                    // Put model into object store.\n                    const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n                    // First, put ModelArtifactsInfo into info store.\n                    const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n                    let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                    const putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo });\n                    let modelTx;\n                    putInfoRequest.onsuccess = () => {\n                        // Second, put model data into model store.\n                        modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n                        const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                        const putModelRequest = modelStore.put({\n                            modelPath: this.modelPath,\n                            modelArtifacts,\n                            modelArtifactsInfo\n                        });\n                        putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo });\n                        putModelRequest.onerror = error => {\n                            // If the put-model request fails, roll back the info entry as\n                            // well.\n                            infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                            const deleteInfoRequest = infoStore.delete(this.modelPath);\n                            deleteInfoRequest.onsuccess = () => {\n                                db.close();\n                                return reject(putModelRequest.error);\n                            };\n                            deleteInfoRequest.onerror = error => {\n                                db.close();\n                                return reject(putModelRequest.error);\n                            };\n                        };\n                    };\n                    putInfoRequest.onerror = error => {\n                        db.close();\n                        return reject(putInfoRequest.error);\n                    };\n                    infoTx.oncomplete = () => {\n                        if (modelTx == null) {\n                            db.close();\n                        }\n                        else {\n                            modelTx.oncomplete = () => db.close();\n                        }\n                    };\n                }\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n}\nBrowserIndexedDB.URL_SCHEME = 'indexeddb://';\nexport const indexedDBRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n            return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\n/**\n * Creates a browser IndexedDB IOHandler for saving and loading models.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save('indexeddb://MyModel'));\n * console.log(saveResult);\n * ```\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),\n *   which can be used with, e.g., `tf.Model.save`.\n */\nexport function browserIndexedDB(modelPath) {\n    return new BrowserIndexedDB(modelPath);\n}\nfunction maybeStripScheme(key) {\n    return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?\n        key.slice(BrowserIndexedDB.URL_SCHEME.length) :\n        key;\n}\nexport class BrowserIndexedDBManager {\n    constructor() {\n        this.indexedDB = getIndexedDBFactory();\n    }\n    async listModels() {\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                const tx = db.transaction(INFO_STORE_NAME, 'readonly');\n                const store = tx.objectStore(INFO_STORE_NAME);\n                // tslint:disable:max-line-length\n                // Need to cast `store` as `any` here because TypeScript's DOM\n                // library does not have the `getAll()` method even though the\n                // method is supported in the latest version of most mainstream\n                // browsers:\n                // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll\n                // tslint:enable:max-line-length\n                // tslint:disable-next-line:no-any\n                const getAllInfoRequest = store.getAll();\n                getAllInfoRequest.onsuccess = () => {\n                    const out = {};\n                    for (const item of getAllInfoRequest.result) {\n                        out[item.modelPath] = item.modelArtifactsInfo;\n                    }\n                    resolve(out);\n                };\n                getAllInfoRequest.onerror = error => {\n                    db.close();\n                    return reject(getAllInfoRequest.error);\n                };\n                tx.oncomplete = () => db.close();\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n    async removeModel(path) {\n        path = maybeStripScheme(path);\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n                const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                const getInfoRequest = infoStore.get(path);\n                let modelTx;\n                getInfoRequest.onsuccess = () => {\n                    if (getInfoRequest.result == null) {\n                        db.close();\n                        return reject(new Error(`Cannot find model with path '${path}' ` +\n                            `in IndexedDB.`));\n                    }\n                    else {\n                        // First, delete the entry in the info store.\n                        const deleteInfoRequest = infoStore.delete(path);\n                        const deleteModelData = () => {\n                            // Second, delete the entry in the model store.\n                            modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n                            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                            const deleteModelRequest = modelStore.delete(path);\n                            deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo);\n                            deleteModelRequest.onerror = error => reject(getInfoRequest.error);\n                        };\n                        // Proceed with deleting model data regardless of whether deletion\n                        // of info data succeeds or not.\n                        deleteInfoRequest.onsuccess = deleteModelData;\n                        deleteInfoRequest.onerror = error => {\n                            deleteModelData();\n                            db.close();\n                            return reject(getInfoRequest.error);\n                        };\n                    }\n                };\n                getInfoRequest.onerror = error => {\n                    db.close();\n                    return reject(getInfoRequest.error);\n                };\n                infoTx.oncomplete = () => {\n                    if (modelTx == null) {\n                        db.close();\n                    }\n                    else {\n                        modelTx.oncomplete = () => db.close();\n                    }\n                };\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n}\n//# sourceMappingURL=indexed_db.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { arrayBufferToBase64String, base64StringToArrayBuffer, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst PATH_SEPARATOR = '/';\nconst PATH_PREFIX = 'tensorflowjs_models';\nconst INFO_SUFFIX = 'info';\nconst MODEL_TOPOLOGY_SUFFIX = 'model_topology';\nconst WEIGHT_SPECS_SUFFIX = 'weight_specs';\nconst WEIGHT_DATA_SUFFIX = 'weight_data';\nconst MODEL_METADATA_SUFFIX = 'model_metadata';\n/**\n * Purge all tensorflow.js-saved model artifacts from local storage.\n *\n * @returns Paths of the models purged.\n */\nexport function purgeLocalStorageArtifacts() {\n    if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n        typeof window.localStorage === 'undefined') {\n        throw new Error('purgeLocalStorageModels() cannot proceed because local storage is ' +\n            'unavailable in the current environment.');\n    }\n    const LS = window.localStorage;\n    const purgedModelPaths = [];\n    for (let i = 0; i < LS.length; ++i) {\n        const key = LS.key(i);\n        const prefix = PATH_PREFIX + PATH_SEPARATOR;\n        if (key.startsWith(prefix) && key.length > prefix.length) {\n            LS.removeItem(key);\n            const modelName = getModelPathFromKey(key);\n            if (purgedModelPaths.indexOf(modelName) === -1) {\n                purgedModelPaths.push(modelName);\n            }\n        }\n    }\n    return purgedModelPaths;\n}\nfunction getModelKeys(path) {\n    return {\n        info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n        topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n        weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n        weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),\n        modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)\n    };\n}\n/**\n * Get model path from a local-storage key.\n *\n * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'\n *\n * @param key\n */\nfunction getModelPathFromKey(key) {\n    const items = key.split(PATH_SEPARATOR);\n    if (items.length < 3) {\n        throw new Error(`Invalid key format: ${key}`);\n    }\n    return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\nfunction maybeStripScheme(key) {\n    return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?\n        key.slice(BrowserLocalStorage.URL_SCHEME.length) :\n        key;\n}\n/**\n * IOHandler subclass: Browser Local Storage.\n *\n * See the doc string to `browserLocalStorage` for more details.\n */\nexport class BrowserLocalStorage {\n    constructor(modelPath) {\n        if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n            typeof window.localStorage === 'undefined') {\n            // TODO(cais): Add more info about what IOHandler subtypes are\n            // available.\n            //   Maybe point to a doc page on the web and/or automatically determine\n            //   the available IOHandlers and print them in the error message.\n            throw new Error('The current environment does not support local storage.');\n        }\n        this.LS = window.localStorage;\n        if (modelPath == null || !modelPath) {\n            throw new Error('For local storage, modelPath must not be null, undefined or empty.');\n        }\n        this.modelPath = modelPath;\n        this.keys = getModelKeys(this.modelPath);\n    }\n    /**\n     * Save model artifacts to browser local storage.\n     *\n     * See the documentation to `browserLocalStorage` for details on the saved\n     * artifacts.\n     *\n     * @param modelArtifacts The model artifacts to be stored.\n     * @returns An instance of SaveResult.\n     */\n    async save(modelArtifacts) {\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        else {\n            const topology = JSON.stringify(modelArtifacts.modelTopology);\n            const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n            const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n            try {\n                this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n                this.LS.setItem(this.keys.topology, topology);\n                this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n                this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData));\n                this.LS.setItem(this.keys.modelMetadata, JSON.stringify({\n                    format: modelArtifacts.format,\n                    generatedBy: modelArtifacts.generatedBy,\n                    convertedBy: modelArtifacts.convertedBy,\n                    userDefinedMetadata: modelArtifacts.userDefinedMetadata\n                }));\n                return { modelArtifactsInfo };\n            }\n            catch (err) {\n                // If saving failed, clean up all items saved so far.\n                this.LS.removeItem(this.keys.info);\n                this.LS.removeItem(this.keys.topology);\n                this.LS.removeItem(this.keys.weightSpecs);\n                this.LS.removeItem(this.keys.weightData);\n                this.LS.removeItem(this.keys.modelMetadata);\n                throw new Error(`Failed to save model '${this.modelPath}' to local storage: ` +\n                    `size quota being exceeded is a possible cause of this failure: ` +\n                    `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` +\n                    `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` +\n                    `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n            }\n        }\n    }\n    /**\n     * Load a model from local storage.\n     *\n     * See the documentation to `browserLocalStorage` for details on the saved\n     * artifacts.\n     *\n     * @returns The loaded model (if loading succeeds).\n     */\n    async load() {\n        const info = JSON.parse(this.LS.getItem(this.keys.info));\n        if (info == null) {\n            throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);\n        }\n        if (info.modelTopologyType !== 'JSON') {\n            throw new Error('BrowserLocalStorage does not support loading non-JSON model ' +\n                'topology yet.');\n        }\n        const out = {};\n        // Load topology.\n        const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n        if (topology == null) {\n            throw new Error(`In local storage, the topology of model '${this.modelPath}' ` +\n                `is missing.`);\n        }\n        out.modelTopology = topology;\n        // Load weight specs.\n        const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n        if (weightSpecs == null) {\n            throw new Error(`In local storage, the weight specs of model '${this.modelPath}' ` +\n                `are missing.`);\n        }\n        out.weightSpecs = weightSpecs;\n        // Load meta-data fields.\n        const metadataString = this.LS.getItem(this.keys.modelMetadata);\n        if (metadataString != null) {\n            const metadata = JSON.parse(metadataString);\n            out.format = metadata['format'];\n            out.generatedBy = metadata['generatedBy'];\n            out.convertedBy = metadata['convertedBy'];\n            out.userDefinedMetadata = metadata['userDefinedMetadata'];\n        }\n        // Load weight data.\n        const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n        if (weightDataBase64 == null) {\n            throw new Error(`In local storage, the binary weight values of model ` +\n                `'${this.modelPath}' are missing.`);\n        }\n        out.weightData = base64StringToArrayBuffer(weightDataBase64);\n        return out;\n    }\n}\nBrowserLocalStorage.URL_SCHEME = 'localstorage://';\nexport const localStorageRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n            return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\n/**\n * Factory function for local storage IOHandler.\n *\n * This `IOHandler` supports both `save` and `load`.\n *\n * For each model's saved artifacts, four items are saved to local storage.\n *   - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the\n *     model, such as date saved, type of the topology, size in bytes, etc.\n *   - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-\n *     style models, this is a stringized JSON.\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the\n *     model, can be used to decode the saved binary weight values (see\n *     item below).\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary\n *     weight values, stored as a base64-encoded string.\n *\n * Saving may throw an `Error` if the total size of the artifacts exceed the\n * browser-specific quota.\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `IOHandler`, which can be used with, e.g.,\n *   `tf.Model.save`.\n */\nexport function browserLocalStorage(modelPath) {\n    return new BrowserLocalStorage(modelPath);\n}\nexport class BrowserLocalStorageManager {\n    constructor() {\n        assert(env().getBool('IS_BROWSER'), () => 'Current environment is not a web browser');\n        assert(typeof window === 'undefined' ||\n            typeof window.localStorage !== 'undefined', () => 'Current browser does not appear to support localStorage');\n        this.LS = window.localStorage;\n    }\n    async listModels() {\n        const out = {};\n        const prefix = PATH_PREFIX + PATH_SEPARATOR;\n        const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n        for (let i = 0; i < this.LS.length; ++i) {\n            const key = this.LS.key(i);\n            if (key.startsWith(prefix) && key.endsWith(suffix)) {\n                const modelPath = getModelPathFromKey(key);\n                out[modelPath] = JSON.parse(this.LS.getItem(key));\n            }\n        }\n        return out;\n    }\n    async removeModel(path) {\n        path = maybeStripScheme(path);\n        const keys = getModelKeys(path);\n        if (this.LS.getItem(keys.info) == null) {\n            throw new Error(`Cannot find model at path '${path}'`);\n        }\n        const info = JSON.parse(this.LS.getItem(keys.info));\n        this.LS.removeItem(keys.info);\n        this.LS.removeItem(keys.topology);\n        this.LS.removeItem(keys.weightSpecs);\n        this.LS.removeItem(keys.weightData);\n        return info;\n    }\n}\n//# sourceMappingURL=local_storage.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Classes and functions for model management across multiple storage mediums.\n *\n * Supported client actions:\n * - Listing models on all registered storage mediums.\n * - Remove model by URL from any registered storage mediums, by using URL\n *   string.\n * - Moving or copying model from one path to another in the same medium or from\n *   one medium to another, by using URL strings.\n */\nimport { assert } from '../util';\nimport { IORouterRegistry } from './router_registry';\nconst URL_SCHEME_SUFFIX = '://';\nexport class ModelStoreManagerRegistry {\n    constructor() {\n        this.managers = {};\n    }\n    static getInstance() {\n        if (ModelStoreManagerRegistry.instance == null) {\n            ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n        }\n        return ModelStoreManagerRegistry.instance;\n    }\n    /**\n     * Register a save-handler router.\n     *\n     * @param saveRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `save` method defined or `null`.\n     */\n    static registerManager(scheme, manager) {\n        assert(scheme != null, () => 'scheme must not be undefined or null.');\n        if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n            scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n        }\n        assert(scheme.length > 0, () => 'scheme must not be an empty string.');\n        const registry = ModelStoreManagerRegistry.getInstance();\n        assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);\n        registry.managers[scheme] = manager;\n    }\n    static getManager(scheme) {\n        const manager = this.getInstance().managers[scheme];\n        if (manager == null) {\n            throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n        }\n        return manager;\n    }\n    static getSchemes() {\n        return Object.keys(this.getInstance().managers);\n    }\n}\n/**\n * Helper method for parsing a URL string into a scheme and a path.\n *\n * @param url E.g., 'localstorage://my-model'\n * @returns A dictionary with two fields: scheme and path.\n *   Scheme: e.g., 'localstorage' in the example above.\n *   Path: e.g., 'my-model' in the example above.\n */\nfunction parseURL(url) {\n    if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n        throw new Error(`The url string provided does not contain a scheme. ` +\n            `Supported schemes are: ` +\n            `${ModelStoreManagerRegistry.getSchemes().join(',')}`);\n    }\n    return {\n        scheme: url.split(URL_SCHEME_SUFFIX)[0],\n        path: url.split(URL_SCHEME_SUFFIX)[1],\n    };\n}\nasync function cloneModelInternal(sourceURL, destURL, deleteSource = false) {\n    assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);\n    const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n    assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);\n    assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n        `load handlers for source URL ${sourceURL}.`);\n    const loadHandler = loadHandlers[0];\n    const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n    assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination ` +\n        `URL ${destURL}.`);\n    assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n        `save handlers for destination URL ${destURL}.`);\n    const saveHandler = saveHandlers[0];\n    const sourceScheme = parseURL(sourceURL).scheme;\n    const sourcePath = parseURL(sourceURL).path;\n    const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n    const modelArtifacts = await loadHandler.load();\n    // If moving within the same storage medium, remove the old model as soon as\n    // the loading is done. Without doing this, it is possible that the combined\n    // size of the two models will cause the cloning to fail.\n    if (deleteSource && sameMedium) {\n        await ModelStoreManagerRegistry.getManager(sourceScheme)\n            .removeModel(sourcePath);\n    }\n    const saveResult = await saveHandler.save(modelArtifacts);\n    // If moving between mediums, the deletion is done after the save succeeds.\n    // This guards against the case in which saving to the destination medium\n    // fails.\n    if (deleteSource && !sameMedium) {\n        await ModelStoreManagerRegistry.getManager(sourceScheme)\n            .removeModel(sourcePath);\n    }\n    return saveResult.modelArtifactsInfo;\n}\n/**\n * List all models stored in registered storage mediums.\n *\n * For a web browser environment, the registered mediums are Local Storage and\n * IndexedDB.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @returns A `Promise` of a dictionary mapping URLs of existing models to\n * their model artifacts info. URLs include medium-specific schemes, e.g.,\n *   'indexeddb://my/model/1'. Model artifacts info include type of the\n * model's topology, byte sizes of the topology, weights, etc.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function listModels() {\n    const schemes = ModelStoreManagerRegistry.getSchemes();\n    const out = {};\n    for (const scheme of schemes) {\n        const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();\n        for (const path in schemeOut) {\n            const url = scheme + URL_SCHEME_SUFFIX + path;\n            out[url] = schemeOut[path];\n        }\n    }\n    return out;\n}\n/**\n * Remove a model specified by URL from a reigstered storage medium.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @param url A URL to a stored model, with a scheme prefix, e.g.,\n *   'localstorage://my-model-1', 'indexeddb://my/model/2'.\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n *   is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function removeModel(url) {\n    const schemeAndPath = parseURL(url);\n    const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n    return manager.removeModel(schemeAndPath.path);\n}\n/**\n * Copy a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Copying within a storage medium, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Copying between two storage mediums, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Copy the model, from Local Storage to IndexedDB.\n * await tf.io.copyModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove both models.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of copying.\n * @param destURL Destination URL of copying.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function copyModel(sourceURL, destURL) {\n    const deleteSource = false;\n    return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n/**\n * Move a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Moving within a storage medium, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Moving between two storage mediums, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Move the model, from Local Storage to IndexedDB.\n * await tf.io.moveModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove the moved model.\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of moving.\n * @param destURL Destination URL of moving.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function moveModel(sourceURL, destURL) {\n    const deleteSource = true;\n    return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\nexport { moveModel, copyModel, removeModel, listModels };\n//# sourceMappingURL=model_management.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { BrowserIndexedDB, BrowserIndexedDBManager } from '../io/indexed_db';\nimport { BrowserLocalStorage, BrowserLocalStorageManager } from '../io/local_storage';\nimport { ModelStoreManagerRegistry } from '../io/model_management';\nexport class PlatformBrowser {\n    fetch(path, init) {\n        return fetch(path, init);\n    }\n    now() {\n        return performance.now();\n    }\n    encode(text, encoding) {\n        if (encoding !== 'utf-8' && encoding !== 'utf8') {\n            throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);\n        }\n        if (this.textEncoder == null) {\n            this.textEncoder = new TextEncoder();\n        }\n        return this.textEncoder.encode(text);\n    }\n    decode(bytes, encoding) {\n        return new TextDecoder(encoding).decode(bytes);\n    }\n}\nif (env().get('IS_BROWSER')) {\n    env().setPlatform('browser', new PlatformBrowser());\n    // Register LocalStorage IOHandler\n    try {\n        ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n    }\n    catch (err) {\n    }\n    // Register IndexedDB IOHandler\n    try {\n        ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n    }\n    catch (err) {\n    }\n}\n//# sourceMappingURL=platform_browser.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\n// We are wrapping this within an object so it can be stubbed by Jasmine.\nexport const getNodeFetch = {\n    // tslint:disable-next-line:no-require-imports\n    importFetch: () => require('node-fetch')\n};\nlet systemFetch;\n// These getters and setters are for testing so we don't export a mutable\n// variable.\nexport function resetSystemFetch() {\n    systemFetch = null;\n}\nexport function setSystemFetch(fetchFn) {\n    systemFetch = fetchFn;\n}\nexport function getSystemFetch() {\n    return systemFetch;\n}\nexport class PlatformNode {\n    constructor() {\n        // tslint:disable-next-line:no-require-imports\n        this.util = require('util');\n        // According to the spec, the built-in encoder can do only UTF-8 encoding.\n        // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder\n        this.textEncoder = new this.util.TextEncoder();\n    }\n    fetch(path, requestInits) {\n        if (env().global.fetch != null) {\n            return env().global.fetch(path, requestInits);\n        }\n        if (systemFetch == null) {\n            systemFetch = getNodeFetch.importFetch();\n        }\n        return systemFetch(path, requestInits);\n    }\n    now() {\n        const time = process.hrtime();\n        return time[0] * 1000 + time[1] / 1000000;\n    }\n    encode(text, encoding) {\n        if (encoding !== 'utf-8' && encoding !== 'utf8') {\n            throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);\n        }\n        return this.textEncoder.encode(text);\n    }\n    decode(bytes, encoding) {\n        if (bytes.length === 0) {\n            return '';\n        }\n        return new this.util.TextDecoder(encoding).decode(bytes);\n    }\n}\nif (env().get('IS_NODE')) {\n    env().setPlatform('node', new PlatformNode());\n}\n//# sourceMappingURL=platform_node.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport * as util from '../util';\n/**\n * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.\n *\n * The values are stored in CPU as `TypedArray`. Fill the buffer using\n * `buffer.set()`, or by modifying directly `buffer.values`.\n *\n * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with\n * those values.\n *\n * ```js\n * // Create a buffer and set values at particular indices.\n * const buffer = tf.buffer([2, 2]);\n * buffer.set(3, 0, 0);\n * buffer.set(5, 1, 0);\n *\n * // Convert the buffer back to a tensor.\n * buffer.toTensor().print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The dtype of the buffer. Defaults to 'float32'.\n * @param values The values of the buffer as `TypedArray`. Defaults to\n * zeros.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function buffer(shape, dtype = 'float32', values) {\n    dtype = dtype || 'float32';\n    util.assertNonNegativeIntegerDimensions(shape);\n    return new TensorBuffer(shape, dtype, values);\n}\n//# sourceMappingURL=buffer.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cast } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Casts a `tf.Tensor` to a new dtype.\n *\n * ```js\n * const x = tf.tensor1d([1.5, 2.5, 3]);\n * tf.cast(x, 'int32').print();\n * ```\n * @param x The input tensor to be casted.\n * @param dtype The dtype to cast the input tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction cast_(x, dtype) {\n    const $x = convertToTensor(x, 'x', 'cast');\n    // Sanity checks.\n    if (!util.isValidDtype(dtype)) {\n        throw new Error(`Failed to cast to unknown dtype ${dtype}`);\n    }\n    if (dtype === 'string' && $x.dtype !== 'string' ||\n        dtype !== 'string' && $x.dtype === 'string') {\n        throw new Error('Only strings can be casted to strings');\n    }\n    const inputs = { x: $x };\n    const attrs = { dtype };\n    return ENGINE.runKernelFunc(backend => backend.cast($x, dtype), inputs, null /* grad */, Cast, attrs);\n}\nexport const cast = op({ cast_ });\n//# sourceMappingURL=cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Identity } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction clone_(x) {\n    const $x = convertToTensor(x, 'x', 'clone', null);\n    const forward = () => ENGINE.makeTensorFromDataId($x.dataId, $x.shape, $x.dtype);\n    const inputs = { x: $x };\n    // Note this op is called tf.identity in python. Hence the kernel name used\n    // here.\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Identity);\n}\nexport const clone = op({ clone_ });\n//# sourceMappingURL=clone.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function print(x, verbose = false) {\n    console.log(x.toString(verbose));\n}\n//# sourceMappingURL=print.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Required side effectful code for tfjs-core\n// Set up Engine and ENV\nimport { getOrMakeEngine } from './engine';\ngetOrMakeEngine();\n// Register backend-agnostic flags.\nimport './flags';\n// Register platforms\nimport './platforms/platform_browser';\nimport './platforms/platform_node';\n// Set up OpHandler\nimport { buffer } from './ops/buffer';\nimport { cast } from './ops/cast';\nimport { clone } from './ops/clone';\nimport { print } from './ops/print';\nimport { setOpHandler } from './tensor';\nconst opHandler = {\n    buffer,\n    cast,\n    clone,\n    print\n};\nsetOpHandler(opHandler);\n//# sourceMappingURL=base_side_effects.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandlers related to files, such as browser-triggered file downloads,\n * user-selected files in browser.\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { basename, concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DEFAULT_FILE_NAME_PREFIX = 'model';\nconst DEFAULT_JSON_EXTENSION_NAME = '.json';\nconst DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';\nfunction defer(f) {\n    return new Promise(resolve => setTimeout(resolve)).then(f);\n}\nexport class BrowserDownloads {\n    constructor(fileNamePrefix) {\n        if (!env().getBool('IS_BROWSER')) {\n            // TODO(cais): Provide info on what IOHandlers are available under the\n            //   current environment.\n            throw new Error('browserDownloads() cannot proceed because the current environment ' +\n                'is not a browser.');\n        }\n        if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n            fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n        }\n        if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n            fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n        }\n        this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n        this.weightDataFileName =\n            fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n    }\n    async save(modelArtifacts) {\n        if (typeof (document) === 'undefined') {\n            throw new Error('Browser downloads are not supported in ' +\n                'this environment since `document` is not present');\n        }\n        const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: 'application/octet-stream' }));\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserDownloads.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        else {\n            const weightsManifest = [{\n                    paths: ['./' + this.weightDataFileName],\n                    weights: modelArtifacts.weightSpecs\n                }];\n            const modelTopologyAndWeightManifest = {\n                modelTopology: modelArtifacts.modelTopology,\n                format: modelArtifacts.format,\n                generatedBy: modelArtifacts.generatedBy,\n                convertedBy: modelArtifacts.convertedBy,\n                weightsManifest\n            };\n            const modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: 'application/json' }));\n            // If anchor elements are not provided, create them without attaching them\n            // to parents, so that the downloaded file names can be controlled.\n            const jsonAnchor = this.jsonAnchor == null ? document.createElement('a') :\n                this.jsonAnchor;\n            jsonAnchor.download = this.modelTopologyFileName;\n            jsonAnchor.href = modelTopologyAndWeightManifestURL;\n            // Trigger downloads by evoking a click event on the download anchors.\n            // When multiple downloads are started synchronously, Firefox will only\n            // save the last one.\n            await defer(() => jsonAnchor.dispatchEvent(new MouseEvent('click')));\n            if (modelArtifacts.weightData != null) {\n                const weightDataAnchor = this.weightDataAnchor == null ?\n                    document.createElement('a') :\n                    this.weightDataAnchor;\n                weightDataAnchor.download = this.weightDataFileName;\n                weightDataAnchor.href = weightsURL;\n                await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent('click')));\n            }\n            return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) };\n        }\n    }\n}\nBrowserDownloads.URL_SCHEME = 'downloads://';\nclass BrowserFiles {\n    constructor(files) {\n        if (files == null || files.length < 1) {\n            throw new Error(`When calling browserFiles, at least 1 file is required, ` +\n                `but received ${files}`);\n        }\n        this.files = files;\n    }\n    async load() {\n        const jsonFile = this.files[0];\n        const weightFiles = this.files.slice(1);\n        return new Promise((resolve, reject) => {\n            const jsonReader = new FileReader();\n            jsonReader.onload = (event) => {\n                // tslint:disable-next-line:no-any\n                const modelJSON = JSON.parse(event.target.result);\n                const modelTopology = modelJSON.modelTopology;\n                if (modelTopology == null) {\n                    reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));\n                    return;\n                }\n                if (weightFiles.length === 0) {\n                    resolve({ modelTopology });\n                }\n                const weightsManifest = modelJSON.weightsManifest;\n                if (weightsManifest == null) {\n                    reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));\n                    return;\n                }\n                let pathToFile;\n                try {\n                    pathToFile =\n                        this.checkManifestAndWeightFiles(weightsManifest, weightFiles);\n                }\n                catch (err) {\n                    reject(err);\n                    return;\n                }\n                const weightSpecs = [];\n                const paths = [];\n                const perFileBuffers = [];\n                weightsManifest.forEach(weightsGroup => {\n                    weightsGroup.paths.forEach(path => {\n                        paths.push(path);\n                        perFileBuffers.push(null);\n                    });\n                    weightSpecs.push(...weightsGroup.weights);\n                });\n                weightsManifest.forEach(weightsGroup => {\n                    weightsGroup.paths.forEach(path => {\n                        const weightFileReader = new FileReader();\n                        weightFileReader.onload = (event) => {\n                            // tslint:disable-next-line:no-any\n                            const weightData = event.target.result;\n                            const index = paths.indexOf(path);\n                            perFileBuffers[index] = weightData;\n                            if (perFileBuffers.indexOf(null) === -1) {\n                                resolve({\n                                    modelTopology,\n                                    weightSpecs,\n                                    weightData: concatenateArrayBuffers(perFileBuffers),\n                                    format: modelJSON.format,\n                                    generatedBy: modelJSON.generatedBy,\n                                    convertedBy: modelJSON.convertedBy,\n                                    userDefinedMetadata: modelJSON.userDefinedMetadata\n                                });\n                            }\n                        };\n                        weightFileReader.onerror = error => reject(`Failed to weights data from file of path '${path}'.`);\n                        weightFileReader.readAsArrayBuffer(pathToFile[path]);\n                    });\n                });\n            };\n            jsonReader.onerror = error => reject(`Failed to read model topology and weights manifest JSON ` +\n                `from file '${jsonFile.name}'. BrowserFiles supports loading ` +\n                `Keras-style tf.Model artifacts only.`);\n            jsonReader.readAsText(jsonFile);\n        });\n    }\n    /**\n     * Check the compatibility between weights manifest and weight files.\n     */\n    checkManifestAndWeightFiles(manifest, files) {\n        const basenames = [];\n        const fileNames = files.map(file => basename(file.name));\n        const pathToFile = {};\n        for (const group of manifest) {\n            group.paths.forEach(path => {\n                const pathBasename = basename(path);\n                if (basenames.indexOf(pathBasename) !== -1) {\n                    throw new Error(`Duplicate file basename found in weights manifest: ` +\n                        `'${pathBasename}'`);\n                }\n                basenames.push(pathBasename);\n                if (fileNames.indexOf(pathBasename) === -1) {\n                    throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);\n                }\n                else {\n                    pathToFile[path] = files[fileNames.indexOf(pathBasename)];\n                }\n            });\n        }\n        if (basenames.length !== files.length) {\n            throw new Error(`Mismatch in the number of files in weights manifest ` +\n                `(${basenames.length}) and the number of weight files provided ` +\n                `(${files.length}).`);\n        }\n        return pathToFile;\n    }\n}\nexport const browserDownloadsRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n            return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\n/**\n * Creates an IOHandler that triggers file downloads from the browser.\n *\n * The returned `IOHandler` instance can be used as model exporting methods such\n * as `tf.Model.save` and supports only saving.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * const saveResult = await model.save('downloads://mymodel');\n * // This will trigger downloading of two files:\n * //   'mymodel.json' and 'mymodel.weights.bin'.\n * console.log(saveResult);\n * ```\n *\n * @param fileNamePrefix Prefix name of the files to be downloaded. For use with\n *   `tf.Model`, `fileNamePrefix` should follow either of the following two\n *   formats:\n *   1. `null` or `undefined`, in which case the default file\n *      names will be used:\n *      - 'model.json' for the JSON file containing the model topology and\n *        weights manifest.\n *      - 'model.weights.bin' for the binary file containing the binary weight\n *        values.\n *   2. A single string or an Array of a single string, as the file name prefix.\n *      For example, if `'foo'` is provided, the downloaded JSON\n *      file and binary weights file will be named 'foo.json' and\n *      'foo.weights.bin', respectively.\n * @param config Additional configuration for triggering downloads.\n * @returns An instance of `BrowserDownloads` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserDownloads(fileNamePrefix = 'model') {\n    return new BrowserDownloads(fileNamePrefix);\n}\n/**\n * Creates an IOHandler that loads model artifacts from user-selected files.\n *\n * This method can be used for loading from files such as user-selected files\n * in the browser.\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * // Note: This code snippet won't run properly without the actual file input\n * //   elements in the HTML DOM.\n *\n * // Suppose there are two HTML file input (`<input type=\"file\" ...>`)\n * // elements.\n * const uploadJSONInput = document.getElementById('upload-json');\n * const uploadWeightsInput = document.getElementById('upload-weights');\n * const model = await tf.loadLayersModel(tf.io.browserFiles(\n *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));\n * ```\n *\n * @param files `File`s to load from. Currently, this function supports only\n *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for\n *   which an `Array` of `File`s is expected (in that order):\n *   - A JSON file containing the model topology and weight manifest.\n *   - Optionally, One or more binary files containing the binary weights.\n *     These files must have names that match the paths in the `weightsManifest`\n *     contained by the aforementioned JSON file, or errors will be thrown\n *     during loading. These weights files have the same format as the ones\n *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`\n *     Python PIP package. If no weights files are provided, only the model\n *     topology will be loaded from the JSON file above.\n * @returns An instance of `Files` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserFiles(files) {\n    return new BrowserFiles(files);\n}\n//# sourceMappingURL=browser_files.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../util';\n/**\n * Monitor Promise.all progress, fire onProgress callback function.\n *\n * @param promises Promise list going to be monitored\n * @param onProgress Callback function. Fired when a promise resolved.\n * @param startFraction Optional fraction start. Default to 0.\n * @param endFraction Optional fraction end. Default to 1.\n */\nexport function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {\n    checkPromises(promises);\n    startFraction = startFraction == null ? 0 : startFraction;\n    endFraction = endFraction == null ? 1 : endFraction;\n    checkFraction(startFraction, endFraction);\n    let resolvedPromise = 0;\n    const registerMonitor = (promise) => {\n        promise.then(value => {\n            const fraction = startFraction +\n                ++resolvedPromise / promises.length * (endFraction - startFraction);\n            // pass fraction as parameter to callback function.\n            onProgress(fraction);\n            return value;\n        });\n        return promise;\n    };\n    function checkPromises(promises) {\n        assert(promises != null && Array.isArray(promises) && promises.length > 0, () => 'promises must be a none empty array');\n    }\n    function checkFraction(startFraction, endFraction) {\n        assert(startFraction >= 0 && startFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n            `got startFraction ${startFraction}`);\n        assert(endFraction >= 0 && endFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n            `got endFraction ${endFraction}`);\n        assert(endFraction >= startFraction, () => `startFraction must be no more than endFraction, but ` +\n            `got startFraction ${startFraction} and endFraction ` +\n            `${endFraction}`);\n    }\n    return Promise.all(promises.map(registerMonitor));\n}\n//# sourceMappingURL=progress.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\nimport * as util from '../util';\nimport { decodeWeights } from './io_utils';\nimport { monitorPromisesProgress } from './progress';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/**\n * Reads binary weights data from a number of URLs.\n *\n * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.\n * @param requestOptions RequestInit (options) for the HTTP requests.\n * @param fetchFunc Optional overriding value for the `window.fetch` function.\n * @param onProgress Optional, progress callback function, fired periodically\n *   before the load is completed.\n * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same\n *   length as `fetchURLs`.\n */\nexport async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {\n    if (loadOptions == null) {\n        loadOptions = {};\n    }\n    const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch :\n        loadOptions.fetchFunc;\n    // Create the requests for all of the weights in parallel.\n    const requests = fetchURLs.map(fetchURL => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true }));\n    const fetchStartFraction = 0;\n    const fetchEndFraction = 0.5;\n    const responses = loadOptions.onProgress == null ?\n        await Promise.all(requests) :\n        await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);\n    const bufferPromises = responses.map(response => response.arrayBuffer());\n    const bufferStartFraction = 0.5;\n    const bufferEndFraction = 1;\n    const buffers = loadOptions.onProgress == null ?\n        await Promise.all(bufferPromises) :\n        await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);\n    return buffers;\n}\n/**\n * Reads a weights manifest JSON configuration, fetches the weights and\n * returns them as `Tensor`s.\n *\n * @param manifest The weights manifest JSON.\n * @param filePathPrefix The path prefix for filenames given in the manifest.\n *     Defaults to the empty string.\n * @param weightNames The names of the weights to be fetched.\n */\nexport async function loadWeights(manifest, filePathPrefix = '', weightNames, requestInit) {\n    // TODO(nsthorat): Groups are currently fetched atomically. If you need a\n    // single weight from a group, the whole group will be fetched. At a future\n    // date, we should support fetching only the individual shards within a\n    // group that are needed to reconstruct the requested weight.\n    // TODO(cais): Use `decodeWeights` for implementation.\n    const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit });\n    const loadWeights = weightsLoaderFactory(fetchWeights);\n    return loadWeights(manifest, filePathPrefix, weightNames);\n}\n/**\n * Creates a function, which reads a weights manifest JSON configuration,\n * fetches the weight files using the specified function and returns them as\n * `Tensor`s.\n *\n * ```js\n * // example for creating a nodejs weight loader, which reads the weight files\n * // from disk using fs.readFileSync\n *\n * import * as fs from 'fs'\n *\n * const fetchWeightsFromDisk = (filePaths: string[]) =>\n *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)\n *\n * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n *\n * const manifest = JSON.parse(\n *   fs.readFileSync('./my_model-weights_manifest').toString()\n * )\n * const weightMap = await loadWeights(manifest, './')\n * ```\n * @param fetchWeightsFunction The function used for fetching the weight files.\n * @returns Weight loading function.\n */\nexport function weightsLoaderFactory(fetchWeightsFunction) {\n    return async (manifest, filePathPrefix = '', weightNames) => {\n        // Collect all the groups, weights, and their relative offsets to be\n        // fetched.\n        const groupIndicesToFetchMap = manifest.map(() => false);\n        const groupWeightsToFetch = {};\n        const weightsFound = weightNames != null ? weightNames.map(() => false) : [];\n        const allManifestWeightNames = [];\n        manifest.forEach((manifestGroupConfig, groupIndex) => {\n            let groupOffset = 0;\n            manifestGroupConfig.weights.forEach(weightsEntry => {\n                const rawDtype = ('quantization' in weightsEntry) ?\n                    weightsEntry.quantization.dtype :\n                    weightsEntry.dtype;\n                const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *\n                    util.sizeFromShape(weightsEntry.shape);\n                const enqueueWeightsForFetchingFn = () => {\n                    groupIndicesToFetchMap[groupIndex] = true;\n                    if (groupWeightsToFetch[groupIndex] == null) {\n                        groupWeightsToFetch[groupIndex] = [];\n                    }\n                    groupWeightsToFetch[groupIndex].push({\n                        manifestEntry: weightsEntry,\n                        groupOffset,\n                        sizeBytes: weightsBytes\n                    });\n                };\n                if (weightNames != null) {\n                    weightNames.forEach((weightName, weightIndex) => {\n                        if (weightName === weightsEntry.name) {\n                            enqueueWeightsForFetchingFn();\n                            weightsFound[weightIndex] = true;\n                        }\n                    });\n                }\n                else {\n                    enqueueWeightsForFetchingFn();\n                }\n                allManifestWeightNames.push(weightsEntry.name);\n                groupOffset += weightsBytes;\n            });\n        });\n        if (!weightsFound.every(found => found)) {\n            const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n            throw new Error(`Could not find weights in manifest with names: ` +\n                `${weightsNotFound.join(', ')}. \\n` +\n                `Manifest JSON has weights with names: ` +\n                `${allManifestWeightNames.join(', ')}.`);\n        }\n        // Convert the one-hot boolean groupId => shouldFetch map to a list of group\n        // IDs.\n        const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n            if (shouldFetch) {\n                accumulator.push(i);\n            }\n            return accumulator;\n        }, []);\n        const fetchUrls = [];\n        groupIndicesToFetch.forEach(i => {\n            manifest[i].paths.forEach(filepath => {\n                const fetchUrl = filePathPrefix +\n                    (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;\n                fetchUrls.push(fetchUrl);\n            });\n        });\n        const buffers = await fetchWeightsFunction(fetchUrls);\n        const weightsTensorMap = {};\n        let bufferIndexOffset = 0;\n        groupIndicesToFetch.forEach(i => {\n            const numBuffers = manifest[i].paths.length;\n            let groupBytes = 0;\n            for (let i = 0; i < numBuffers; i++) {\n                groupBytes += buffers[bufferIndexOffset + i].byteLength;\n            }\n            // Create a buffer for the whole group.\n            const groupBuffer = new ArrayBuffer(groupBytes);\n            const groupByteBuffer = new Uint8Array(groupBuffer);\n            let groupBufferOffset = 0;\n            for (let i = 0; i < numBuffers; i++) {\n                const buffer = new Uint8Array(buffers[bufferIndexOffset + i]);\n                groupByteBuffer.set(buffer, groupBufferOffset);\n                groupBufferOffset += buffer.byteLength;\n            }\n            const weightsEntries = groupWeightsToFetch[i];\n            weightsEntries.forEach(weightsEntry => {\n                const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);\n                const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n                for (const name in nameToTensorMap) {\n                    weightsTensorMap[name] = nameToTensorMap[name];\n                }\n            });\n            bufferIndexOffset += numBuffers;\n        });\n        return weightsTensorMap;\n    };\n}\n//# sourceMappingURL=weights_loader.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandler implementations based on HTTP requests in the web browser.\n *\n * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n */\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nimport { loadWeightsAsArrayBuffer } from './weights_loader';\nconst OCTET_STREAM_MIME_TYPE = 'application/octet-stream';\nconst JSON_TYPE = 'application/json';\nexport class HTTPRequest {\n    constructor(path, loadOptions) {\n        this.DEFAULT_METHOD = 'POST';\n        if (loadOptions == null) {\n            loadOptions = {};\n        }\n        this.weightPathPrefix = loadOptions.weightPathPrefix;\n        this.onProgress = loadOptions.onProgress;\n        this.weightUrlConverter = loadOptions.weightUrlConverter;\n        if (loadOptions.fetchFunc != null) {\n            assert(typeof loadOptions.fetchFunc === 'function', () => 'Must pass a function that matches the signature of ' +\n                '`fetch` (see ' +\n                'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');\n            this.fetch = loadOptions.fetchFunc;\n        }\n        else {\n            this.fetch = env().platform.fetch;\n        }\n        assert(path != null && path.length > 0, () => 'URL path for http must not be null, undefined or ' +\n            'empty.');\n        if (Array.isArray(path)) {\n            assert(path.length === 2, () => 'URL paths for http must have a length of 2, ' +\n                `(actual length is ${path.length}).`);\n        }\n        this.path = path;\n        if (loadOptions.requestInit != null &&\n            loadOptions.requestInit.body != null) {\n            throw new Error('requestInit is expected to have no pre-existing body, but has one.');\n        }\n        this.requestInit = loadOptions.requestInit || {};\n    }\n    async save(modelArtifacts) {\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserHTTPRequest.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        const init = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);\n        init.body = new FormData();\n        const weightsManifest = [{\n                paths: ['./model.weights.bin'],\n                weights: modelArtifacts.weightSpecs,\n            }];\n        const modelTopologyAndWeightManifest = {\n            modelTopology: modelArtifacts.modelTopology,\n            format: modelArtifacts.format,\n            generatedBy: modelArtifacts.generatedBy,\n            convertedBy: modelArtifacts.convertedBy,\n            userDefinedMetadata: modelArtifacts.userDefinedMetadata,\n            weightsManifest\n        };\n        init.body.append('model.json', new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), 'model.json');\n        if (modelArtifacts.weightData != null) {\n            init.body.append('model.weights.bin', new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), 'model.weights.bin');\n        }\n        const response = await this.fetch(this.path, init);\n        if (response.ok) {\n            return {\n                modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n                responses: [response],\n            };\n        }\n        else {\n            throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ` +\n                `${response.status}.`);\n        }\n    }\n    /**\n     * Load model artifacts via HTTP request(s).\n     *\n     * See the documentation to `tf.io.http` for details on the saved\n     * artifacts.\n     *\n     * @returns The loaded model artifacts (if loading succeeds).\n     */\n    async load() {\n        const modelConfigRequest = await this.fetch(this.path, this.requestInit);\n        if (!modelConfigRequest.ok) {\n            throw new Error(`Request to ${this.path} failed with status code ` +\n                `${modelConfigRequest.status}. Please verify this URL points to ` +\n                `the model JSON of the model to load.`);\n        }\n        let modelConfig;\n        try {\n            modelConfig = await modelConfigRequest.json();\n        }\n        catch (e) {\n            let message = `Failed to parse model JSON of response from ${this.path}.`;\n            // TODO(nsthorat): Remove this after some time when we're comfortable that\n            // .pb files are mostly gone.\n            if (this.path.endsWith('.pb')) {\n                message += ' Your path contains a .pb file extension. ' +\n                    'Support for .pb models have been removed in TensorFlow.js 1.0 ' +\n                    'in favor of .json models. You can re-convert your Python ' +\n                    'TensorFlow model using the TensorFlow.js 1.0 conversion scripts ' +\n                    'or you can convert your.pb models with the \\'pb2json\\'' +\n                    'NPM script in the tensorflow/tfjs-converter repository.';\n            }\n            else {\n                message += ' Please make sure the server is serving valid ' +\n                    'JSON for this request.';\n            }\n            throw new Error(message);\n        }\n        const modelTopology = modelConfig.modelTopology;\n        const weightsManifest = modelConfig.weightsManifest;\n        const generatedBy = modelConfig.generatedBy;\n        const convertedBy = modelConfig.convertedBy;\n        const format = modelConfig.format;\n        const userDefinedMetadata = modelConfig.userDefinedMetadata;\n        // We do not allow both modelTopology and weightsManifest to be missing.\n        if (modelTopology == null && weightsManifest == null) {\n            throw new Error(`The JSON from HTTP path ${this.path} contains neither model ` +\n                `topology or manifest for weights.`);\n        }\n        let weightSpecs;\n        let weightData;\n        if (weightsManifest != null) {\n            const results = await this.loadWeights(weightsManifest);\n            [weightSpecs, weightData] = results;\n        }\n        const artifacts = {\n            modelTopology,\n            weightSpecs,\n            weightData,\n            userDefinedMetadata,\n            generatedBy,\n            convertedBy,\n            format\n        };\n        const initializer = modelConfig.modelInitializer;\n        if (initializer) {\n            artifacts.modelInitializer = initializer;\n        }\n        return artifacts;\n    }\n    async loadWeights(weightsManifest) {\n        const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n        const [prefix, suffix] = parseUrl(weightPath);\n        const pathPrefix = this.weightPathPrefix || prefix;\n        const weightSpecs = [];\n        for (const entry of weightsManifest) {\n            weightSpecs.push(...entry.weights);\n        }\n        const fetchURLs = [];\n        const urlPromises = [];\n        for (const weightsGroup of weightsManifest) {\n            for (const path of weightsGroup.paths) {\n                if (this.weightUrlConverter != null) {\n                    urlPromises.push(this.weightUrlConverter(path));\n                }\n                else {\n                    fetchURLs.push(pathPrefix + path + suffix);\n                }\n            }\n        }\n        if (this.weightUrlConverter) {\n            fetchURLs.push(...await Promise.all(urlPromises));\n        }\n        const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {\n            requestInit: this.requestInit,\n            fetchFunc: this.fetch,\n            onProgress: this.onProgress\n        });\n        return [weightSpecs, concatenateArrayBuffers(buffers)];\n    }\n}\nHTTPRequest.URL_SCHEME_REGEX = /^https?:\\/\\//;\n/**\n * Extract the prefix and suffix of the url, where the prefix is the path before\n * the last file, and suffix is the search params after the last file.\n * ```\n * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'\n * [prefix, suffix] = parseUrl(url)\n * // prefix = 'http://tfhub.dev/model/1/'\n * // suffix = '?tfjs-format=file'\n * ```\n * @param url the model url to be parsed.\n */\nexport function parseUrl(url) {\n    const lastSlash = url.lastIndexOf('/');\n    const lastSearchParam = url.lastIndexOf('?');\n    const prefix = url.substring(0, lastSlash);\n    const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';\n    return [prefix + '/', suffix];\n}\nexport function isHTTPScheme(url) {\n    return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;\n}\nexport const httpRouter = (url, loadOptions) => {\n    if (typeof fetch === 'undefined' &&\n        (loadOptions == null || loadOptions.fetchFunc == null)) {\n        // `http` uses `fetch` or `node-fetch`, if one wants to use it in\n        // an environment that is not the browser or node they have to setup a\n        // global fetch polyfill.\n        return null;\n    }\n    else {\n        let isHTTP = true;\n        if (Array.isArray(url)) {\n            isHTTP = url.every(urlItem => isHTTPScheme(urlItem));\n        }\n        else {\n            isHTTP = isHTTPScheme(url);\n        }\n        if (isHTTP) {\n            return http(url, loadOptions);\n        }\n    }\n    return null;\n};\nIORouterRegistry.registerSaveRouter(httpRouter);\nIORouterRegistry.registerLoadRouter(httpRouter);\n/**\n * Creates an IOHandler subtype that sends model artifacts to HTTP server.\n *\n * An HTTP request of the `multipart/form-data` mime type will be sent to the\n * `path` URL. The form data includes artifacts that represent the topology\n * and/or weights of the model. In the case of Keras-style `tf.Model`, two\n * blobs (files) exist in form-data:\n *   - A JSON file consisting of `modelTopology` and `weightsManifest`.\n *   - A binary weights file consisting of the concatenated weight values.\n * These files are in the same format as the one generated by\n * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).\n *\n * The following code snippet exemplifies the client-side code that uses this\n * function:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save(tf.io.http(\n *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));\n * console.log(saveResult);\n * ```\n *\n * If the default `POST` method is to be used, without any custom parameters\n * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:\n *\n * ```js\n * const saveResult = await model.save('http://model-server:5000/upload');\n * ```\n *\n * The following GitHub Gist\n * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864\n * implements a server based on [flask](https://github.com/pallets/flask) that\n * can receive the request. Upon receiving the model artifacts via the requst,\n * this particular server reconsistutes instances of [Keras\n * Models](https://keras.io/models/model/) in memory.\n *\n *\n * @param path A URL path to the model.\n *   Can be an absolute HTTP path (e.g.,\n *   'http://localhost:8000/model-upload)') or a relative path (e.g.,\n *   './model-upload').\n * @param requestInit Request configurations to be used when sending\n *    HTTP request to server using `fetch`. It can contain fields such as\n *    `method`, `credentials`, `headers`, `mode`, etc. See\n *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request\n *    for more information. `requestInit` must not have a body, because the\n * body will be set by TensorFlow.js. File blobs representing the model\n * topology (filename: 'model.json') and the weights of the model (filename:\n * 'model.weights.bin') will be appended to the body. If `requestInit` has a\n * `body`, an Error will be thrown.\n * @param loadOptions Optional configuration for the loading. It includes the\n *   following fields:\n *   - weightPathPrefix Optional, this specifies the path prefix for weight\n *     files, by default this is calculated from the path param.\n *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,\n *     the `fetch` from node-fetch can be used here.\n *   - onProgress Optional, progress callback function, fired periodically\n *     before the load is completed.\n * @returns An instance of `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function http(path, loadOptions) {\n    return new HTTPRequest(path, loadOptions);\n}\n/**\n * Deprecated. Use `tf.io.http`.\n * @param path\n * @param loadOptions\n */\nexport function browserHTTPRequest(path, loadOptions) {\n    return http(path, loadOptions);\n}\n//# sourceMappingURL=http.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nclass PassthroughLoader {\n    constructor(modelArtifacts) {\n        this.modelArtifacts = modelArtifacts;\n    }\n    async load() {\n        return this.modelArtifacts;\n    }\n}\nclass PassthroughSaver {\n    constructor(saveHandler) {\n        this.saveHandler = saveHandler;\n    }\n    async save(modelArtifacts) {\n        return this.saveHandler(modelArtifacts);\n    }\n}\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadLayersModel(tf.io.fromMemory(\n *     modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelArtifacts a object containing model topology (i.e., parsed from\n *   the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n *   names, shapes, types, and quantization of the weight data.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n *   concatenated in the order described by the weightSpecs.\n * @param trainingConfig Model training configuration. Optional.\n *\n * @returns A passthrough `IOHandler` that simply loads the provided data.\n */\nexport function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {\n    if (arguments.length === 1) {\n        const isModelArtifacts = modelArtifacts.modelTopology != null ||\n            modelArtifacts.weightSpecs != null;\n        if (isModelArtifacts) {\n            return new PassthroughLoader(modelArtifacts);\n        }\n        else {\n            // Legacy support: with only modelTopology.\n            // TODO(cais): Remove this deprecated API.\n            console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n                'The argument should be of type ModelArtifacts. ' +\n                'The multi-argument signature of tf.io.fromMemory() has been ' +\n                'deprecated and will be removed in a future release.');\n            return new PassthroughLoader({ modelTopology: modelArtifacts });\n        }\n    }\n    else {\n        // Legacy support.\n        // TODO(cais): Remove this deprecated API.\n        console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n            'The argument should be of type ModelArtifacts. ' +\n            'The multi-argument signature of tf.io.fromMemory() has been ' +\n            'deprecated and will be removed in a future release.');\n        return new PassthroughLoader({\n            modelTopology: modelArtifacts,\n            weightSpecs,\n            weightData,\n            trainingConfig\n        });\n    }\n}\n/**\n * Creates an IOHandler that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n *   // ... do something with the artifacts ...\n *   return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n *     `SaveResult`.\n */\nexport function withSaveHandler(saveHandler) {\n    return new PassthroughSaver(saveHandler);\n}\n//# sourceMappingURL=passthrough.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Importing local_storage and indexed_db is necessary for the routers to be\n// registered.\nimport './indexed_db';\nimport './local_storage';\nimport { browserFiles } from './browser_files';\nimport { browserHTTPRequest, http, isHTTPScheme } from './http';\nimport { concatenateArrayBuffers, decodeWeights, encodeWeights, getModelArtifactsInfoForJSON } from './io_utils';\nimport { fromMemory, withSaveHandler } from './passthrough';\nimport { getLoadHandlers, getSaveHandlers, registerLoadRouter, registerSaveRouter } from './router_registry';\nimport { loadWeights, weightsLoaderFactory } from './weights_loader';\nexport { copyModel, listModels, moveModel, removeModel } from './model_management';\nexport { browserFiles, browserHTTPRequest, concatenateArrayBuffers, decodeWeights, encodeWeights, fromMemory, getLoadHandlers, getModelArtifactsInfoForJSON, getSaveHandlers, http, isHTTPScheme, loadWeights, registerLoadRouter, registerSaveRouter, weightsLoaderFactory, withSaveHandler };\n//# sourceMappingURL=io.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reshape } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Reshapes a `tf.Tensor` to a given shape.\n *\n * Given an input tensor, returns a new tensor with the same values as the\n * input tensor with shape `shape`.\n *\n * If one component of shape is the special value -1, the size of that\n * dimension is computed so that the total size remains constant. In\n * particular, a shape of [-1] flattens into 1-D. At most one component of\n * shape can be -1.\n *\n * If shape is 1-D or higher, then the operation returns a tensor with shape\n * shape filled with the values of tensor. In this case, the number of\n * elements implied by shape must be the same as the number of elements in\n * tensor.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.reshape([2, 2]).print();\n * ```\n *\n * @param x The input tensor to be reshaped.\n * @param shape An array of integers defining the output tensor shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction reshape_(x, shape) {\n    const $x = convertToTensor(x, 'x', 'reshape', null);\n    const inputs = { x: $x };\n    const attrs = { shape };\n    const forward = (backend, save) => {\n        shape = util.inferFromImplicitShape(shape, $x.size);\n        util.assert($x.size === util.sizeFromShape(shape), () => 'new shape and old shape must have the same number of elements.');\n        save([$x]);\n        return backend.reshape($x, shape);\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Reshape, attrs);\n}\nexport const reshape = op({ reshape_ });\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchMatMul } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the dot product of two matrices, A * B. These must be matrices.\n *\n * ```js\n * const a = tf.tensor2d([1, 2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.matMul(b).print();  // or tf.matMul(a, b)\n * ```\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction matMul_(a, b, transposeA = false, transposeB = false) {\n    let $a = convertToTensor(a, 'a', 'matMul');\n    let $b = convertToTensor(b, 'b', 'matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    util.assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in matMul: inputs must have the same rank of at least 2, ` +\n        `got ranks ${$a.rank} and ${$b.rank}.`);\n    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n    const outerDimsA = $a.shape.slice(0, -2);\n    const outerDimsB = $b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n    util.assert(util.arraysEqual(outerDimsA, outerDimsB), () => `Error in matMul: outer dimensions (${outerDimsA}) and (` +\n        `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} must match.`);\n    util.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (` +\n        `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} and transposeA=${transposeA}` +\n        ` and transposeB=${transposeB} must match.`);\n    const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n    const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n        reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n    const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n        reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n    const forward = (backend, save) => {\n        save([a3D, b3D]);\n        return backend.batchMatMul(a3D, b3D, transposeA, transposeB);\n    };\n    const inputs = { a: a3D, b: b3D };\n    const attrs = { transposeA, transposeB };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, BatchMatMul, attrs);\n    return reshape(res, outShape);\n}\nexport const matMul = op({ matMul_ });\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OneHot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank\n * `R+1` with the last axis of size `depth`.\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor` of indices with dtype `int32`.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n *     not match the location.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction oneHot_(indices, depth, onValue = 1, offValue = 0) {\n    if (depth < 2) {\n        throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n    }\n    const $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');\n    const outShape = [...$indices.shape, depth];\n    const forward = (backend, save) => {\n        save([$indices]);\n        return reshape(backend.oneHot(reshape($indices, [$indices.size]), depth, onValue, offValue), outShape);\n    };\n    const inputs = { indices: $indices };\n    const attrs = { depth, onValue, offValue };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, OneHot, attrs);\n}\nexport const oneHot = op({ oneHot_ });\n//# sourceMappingURL=one_hot.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Transpose } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print();  // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction transpose_(x, perm) {\n    const $x = convertToTensor(x, 'x', 'transpose');\n    if (perm == null) {\n        perm = $x.shape.map((s, i) => i).reverse();\n    }\n    util.assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n        `must match length of perm ${perm}.`);\n    perm.forEach(axis => {\n        util.assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n            ` but got ${perm}`);\n    });\n    if ($x.rank <= 1) {\n        return $x.clone();\n    }\n    const inputs = { x: $x };\n    const attrs = { perm };\n    return ENGINE.runKernelFunc(backend => backend.transpose($x, perm), inputs, null /* gradient */, Transpose, attrs);\n}\nexport const transpose = op({ transpose_ });\n//# sourceMappingURL=transpose.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { matMul } from './mat_mul';\nimport { oneHot } from './one_hot';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Computes the confusion matrix from true labels and predicted labels.\n *\n * ```js\n * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n * const numClasses = 3;\n * const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n * out.print();\n * // Expected output matrix:\n * // [[2, 0, 0],\n * //  [0, 1, 1],\n * //  [0, 0, 1]]\n * ```\n *\n * @param labels The target labels, assumed to be 0-based integers\n *   for the classes. The shape is `[numExamples]`, where\n *   `numExamples` is the number of examples included.\n * @param predictions The predicted classes, assumed to be\n *   0-based integers for the classes. Must have the same shape as `labels`.\n * @param numClasses Number of all classes, as an integer.\n *   Its value must be larger than the largest element in `labels` and\n *   `predictions`.\n * @returns The confusion matrix as a int32-type 2D tensor. The value at\n *   row `r` and column `c` is the number of times examples of actual class\n *   `r` were predicted as class `c`.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nexport function confusionMatrix_(labels, predictions, numClasses) {\n    const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');\n    const $predictions = convertToTensor(predictions, 'predictions', 'confusionMatrix');\n    util.assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, ` +\n        `but got ${numClasses}`);\n    util.assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n    util.assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, ` +\n        `but got ${$predictions.rank}`);\n    util.assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ` +\n        `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +\n        `Labels and predictions should have the same number of elements.`);\n    util.assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ` +\n        `${numClasses}`);\n    // TODO(cais): In the future, if oneHot supports tensors inputs for\n    //   `numClasses`, `confusionMatrix` can make `numClasses` optional.\n    const oneHotLabels = oneHot(cast($labels, 'int32'), numClasses);\n    const oneHotPredictions = oneHot(cast($predictions, 'int32'), numClasses);\n    const oneHotLabelsT = transpose(oneHotLabels);\n    return cast(matMul(oneHotLabelsT, oneHotPredictions), 'int32');\n}\nexport const confusionMatrix = op({ confusionMatrix_ });\n//# sourceMappingURL=confusion_matrix.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Exports under the tf.math.* namespace.\n */\nimport { confusionMatrix } from './ops/confusion_matrix';\nexport { confusionMatrix };\n//# sourceMappingURL=math.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor3d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided,  it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor3d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 3) {\n        throw new Error('tensor3d() requires shape to have three numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n        throw new Error('tensor3d() requires values to be number[][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor3d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor3d.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FromPixels } from '../kernel_names';\nimport { getKernel } from '../kernel_registry';\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { tensor3d } from './tensor3d';\nlet fromPixels2DContext;\n/**\n * Creates a `tf.Tensor` from an image.\n *\n * ```js\n * const image = new ImageData(1, 1);\n * image.data[0] = 100;\n * image.data[1] = 150;\n * image.data[2] = 200;\n * image.data[3] = 255;\n *\n * tf.browser.fromPixels(image).print();\n * ```\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel. You can also pass in an image\n * object with following attributes:\n * `{data: Uint8Array; width: number; height: number}`\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n *\n * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}\n */\nfunction fromPixels_(pixels, numChannels = 3) {\n    // Sanity checks.\n    if (numChannels > 4) {\n        throw new Error('Cannot construct Tensor with more than 4 channels from pixels.');\n    }\n    if (pixels == null) {\n        throw new Error('pixels passed to tf.browser.fromPixels() can not be null');\n    }\n    let isPixelData = false;\n    let isImageData = false;\n    let isVideo = false;\n    let isImage = false;\n    let isCanvasLike = false;\n    if (pixels.data instanceof Uint8Array) {\n        isPixelData = true;\n    }\n    else if (typeof (ImageData) !== 'undefined' && pixels instanceof ImageData) {\n        isImageData = true;\n    }\n    else if (typeof (HTMLVideoElement) !== 'undefined' &&\n        pixels instanceof HTMLVideoElement) {\n        isVideo = true;\n    }\n    else if (typeof (HTMLImageElement) !== 'undefined' &&\n        pixels instanceof HTMLImageElement) {\n        isImage = true;\n        // tslint:disable-next-line: no-any\n    }\n    else if (pixels.getContext != null) {\n        isCanvasLike = true;\n    }\n    else {\n        throw new Error('pixels passed to tf.browser.fromPixels() must be either an ' +\n            `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData ` +\n            `in browser, or OffscreenCanvas, ImageData in webworker` +\n            ` or {data: Uint32Array, width: number, height: number}, ` +\n            `but was ${pixels.constructor.name}`);\n    }\n    if (isVideo) {\n        const HAVE_CURRENT_DATA_READY_STATE = 2;\n        if (isVideo &&\n            pixels.readyState <\n                HAVE_CURRENT_DATA_READY_STATE) {\n            throw new Error('The video element has not loaded data yet. Please wait for ' +\n                '`loadeddata` event on the <video> element.');\n        }\n    }\n    // If the current backend has 'FromPixels' registered, it has a more\n    // efficient way of handling pixel uploads, so we call that.\n    const kernel = getKernel(FromPixels, ENGINE.backendName);\n    if (kernel != null) {\n        const inputs = { pixels };\n        const attrs = { numChannels };\n        return ENGINE.runKernel(FromPixels, inputs, attrs);\n    }\n    const [width, height] = isVideo ?\n        [\n            pixels.videoWidth,\n            pixels.videoHeight\n        ] :\n        [pixels.width, pixels.height];\n    let vals;\n    if (isCanvasLike) {\n        vals =\n            // tslint:disable-next-line:no-any\n            pixels.getContext('2d').getImageData(0, 0, width, height).data;\n    }\n    else if (isImageData || isPixelData) {\n        vals = pixels.data;\n    }\n    else if (isImage || isVideo) {\n        if (fromPixels2DContext == null) {\n            fromPixels2DContext = document.createElement('canvas').getContext('2d');\n        }\n        fromPixels2DContext.canvas.width = width;\n        fromPixels2DContext.canvas.height = height;\n        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n        vals = fromPixels2DContext.getImageData(0, 0, width, height).data;\n    }\n    let values;\n    if (numChannels === 4) {\n        values = new Int32Array(vals);\n    }\n    else {\n        const numPixels = width * height;\n        values = new Int32Array(numPixels * numChannels);\n        for (let i = 0; i < numPixels; i++) {\n            for (let channel = 0; channel < numChannels; ++channel) {\n                values[i * numChannels + channel] = vals[i * 4 + channel];\n            }\n        }\n    }\n    const outShape = [height, width, numChannels];\n    return tensor3d(values, outShape, 'int32');\n}\n/**\n * Draws a `tf.Tensor` of pixel values to a byte array or optionally a\n * canvas.\n *\n * When the dtype of the input is 'float32', we assume values in the range\n * [0-1]. Otherwise, when input is 'int32', we assume values in the range\n * [0-255].\n *\n * Returns a promise that resolves when the canvas has been drawn to.\n *\n * @param img A rank-2 or rank-3 tensor. If rank-2, draws grayscale. If\n *     rank-3, must have depth of 1, 3 or 4. When depth of 1, draws\n * grayscale. When depth of 3, we draw with the first three components of\n * the depth dimension corresponding to r, g, b and alpha = 1. When depth of\n * 4, all four components of the depth dimension correspond to r, g, b, a.\n * @param canvas The canvas to draw to.\n *\n * @doc {heading: 'Browser', namespace: 'browser'}\n */\nexport async function toPixels(img, canvas) {\n    let $img = convertToTensor(img, 'img', 'toPixels');\n    if (!(img instanceof Tensor)) {\n        // Assume int32 if user passed a native array.\n        const originalImgTensor = $img;\n        $img = cast(originalImgTensor, 'int32');\n        originalImgTensor.dispose();\n    }\n    if ($img.rank !== 2 && $img.rank !== 3) {\n        throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);\n    }\n    const [height, width] = $img.shape.slice(0, 2);\n    const depth = $img.rank === 2 ? 1 : $img.shape[2];\n    if (depth > 4 || depth === 2) {\n        throw new Error(`toPixels only supports depth of size ` +\n            `1, 3 or 4 but got ${depth}`);\n    }\n    if ($img.dtype !== 'float32' && $img.dtype !== 'int32') {\n        throw new Error(`Unsupported type for toPixels: ${$img.dtype}.` +\n            ` Please use float32 or int32 tensors.`);\n    }\n    const data = await $img.data();\n    const multiplier = $img.dtype === 'float32' ? 255 : 1;\n    const bytes = new Uint8ClampedArray(width * height * 4);\n    for (let i = 0; i < height * width; ++i) {\n        const rgba = [0, 0, 0, 255];\n        for (let d = 0; d < depth; d++) {\n            const value = data[i * depth + d];\n            if ($img.dtype === 'float32') {\n                if (value < 0 || value > 1) {\n                    throw new Error(`Tensor values for a float32 Tensor must be in the ` +\n                        `range [0 - 1] but encountered ${value}.`);\n                }\n            }\n            else if ($img.dtype === 'int32') {\n                if (value < 0 || value > 255) {\n                    throw new Error(`Tensor values for a int32 Tensor must be in the ` +\n                        `range [0 - 255] but encountered ${value}.`);\n                }\n            }\n            if (depth === 1) {\n                rgba[0] = value * multiplier;\n                rgba[1] = value * multiplier;\n                rgba[2] = value * multiplier;\n            }\n            else {\n                rgba[d] = value * multiplier;\n            }\n        }\n        const j = i * 4;\n        bytes[j + 0] = Math.round(rgba[0]);\n        bytes[j + 1] = Math.round(rgba[1]);\n        bytes[j + 2] = Math.round(rgba[2]);\n        bytes[j + 3] = Math.round(rgba[3]);\n    }\n    if (canvas != null) {\n        canvas.width = width;\n        canvas.height = height;\n        const ctx = canvas.getContext('2d');\n        const imageData = new ImageData(bytes, width, height);\n        ctx.putImageData(imageData, 0, 0);\n    }\n    if ($img !== img) {\n        $img.dispose();\n    }\n    return bytes;\n}\nexport const fromPixels = op({ fromPixels_ });\n//# sourceMappingURL=browser.js.map", "import { computeStrides } from '../util';\n/**\n * Validate gather nd inputs.\n *\n * @param tensor The tensor contains the source values.\n * @param indices The tensor contains the indices to slice the source.\n *\n * @returns [resultShape, numUpdates, sliceSize, strides]\n */\nexport function prepareAndValidate(tensor, indices) {\n    if (tensor.rank < 1) {\n        throw new Error('tf.gatherND() expects the input to be rank 1 or higher,' +\n            ` but the rank was ${tensor.rank}.`);\n    }\n    if (indices.rank < 1) {\n        throw new Error('tf.gatherND() expects the indices to be rank 1 or higher,' +\n            ` but the rank was ${indices.rank}.`);\n    }\n    if (indices.dtype !== 'int32') {\n        throw new Error('tf.gatherND() expects the indices to be int32 type,' +\n            ` but the dtype was ${indices.dtype}.`);\n    }\n    if (indices.shape[indices.rank - 1] > tensor.rank) {\n        throw new Error('index innermost dimension length must be <= tensor rank; saw: ' +\n            `${indices.shape[indices.rank - 1]} vs. ${tensor.rank}`);\n    }\n    if (tensor.size === 0) {\n        throw new Error('Requested more than 0 entries, but input is empty.' +\n            ` Input shape: ${tensor.shape}.`);\n    }\n    const indicesShape = indices.shape;\n    const sliceRank = indicesShape[indicesShape.length - 1];\n    // The result shape is\n    //   indices.shape[:-1] + params.shape[indices.shape[-1]:]\n    let nResult = 1;\n    for (let i = 0; i < indicesShape.length - 1; ++i) {\n        nResult *= indicesShape[i];\n    }\n    const inputShape = tensor.shape;\n    const resultShape = indicesShape.slice();\n    resultShape.pop();\n    let sliceSize = 1;\n    for (let i = sliceRank; i < tensor.rank; ++i) {\n        sliceSize *= inputShape[i];\n        resultShape.push(inputShape[i]);\n    }\n    const strides = [...computeStrides(tensor.shape).map(stride => stride / sliceSize),\n        1].slice(0, sliceRank);\n    return [resultShape, nResult, sliceSize, strides];\n}\n//# sourceMappingURL=gather_nd_util.js.map", "import { computeStrides, sizeFromShape } from '../util';\n/**\n * Check whether updates.shape = indices.shape[:batchDim] +\n * shape[sliceDim:]\n *\n * @param x The input tensor.\n */\nexport function validateUpdateShape(shape, indices, updates) {\n    const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n    const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;\n    const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +\n        `shape[sliceDim:], got updates.shape: ${updates.shape}` +\n        `, indices.shape: ${indices.shape}, shape: ${shape}` +\n        `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n    if (updates.rank < batchDim) {\n        throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n    }\n    if (shape.length < sliceDim + (updates.rank - batchDim)) {\n        throw new Error(shapeError +\n            ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n    }\n    if (updates.rank !== batchDim + shape.length - sliceDim) {\n        throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n    }\n    for (let d = 0; d < batchDim; ++d) {\n        if (updates.shape[d] !== indices.shape[d]) {\n            throw new Error(shapeError +\n                ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);\n        }\n    }\n    for (let d = 0; d < updates.rank - batchDim; ++d) {\n        if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n            throw new Error(shapeError +\n                ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);\n        }\n    }\n}\n/**\n * Validate scatter nd inputs.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n */\nexport function validateInput(updates, indices, shape) {\n    if (indices.rank < 1) {\n        throw new Error('tf.scatterND() expects the indices to be rank 1 or higher,' +\n            ` but the rank was ${indices.rank}.`);\n    }\n    if (updates.rank < 1) {\n        throw new Error('tf.scatterND() expects the updates to be rank 1 or higher,' +\n            ` but the rank was ${updates.rank}.`);\n    }\n    if (indices.dtype !== 'int32') {\n        throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);\n    }\n    if (shape.length < 1) {\n        throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);\n    }\n    if (shape.length === 0) {\n        if (indices.size === 0) {\n            throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);\n        }\n        if (updates.size === 0) {\n            throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);\n        }\n    }\n    validateUpdateShape(shape, indices, updates);\n}\n/**\n * Calculate the shape information for the output.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n *\n * @returns ScatterShapeInfo\n */\nexport function calculateShapes(updates, indices, shape) {\n    // Calculate the number of dimensions in indices\n    const indicesRank = indices.shape.length;\n    const sliceRank = (indicesRank > 1) ? indices.shape[indicesRank - 1] : 1;\n    // Calculate the number of elements that make up each slice of our updated\n    // tensor. This allows us to work with flattened tensors and copy over whole\n    // slices at a time.\n    const totalNd = shape.length;\n    let sliceSize = 1;\n    for (let i = sliceRank; i < totalNd; ++i) {\n        sliceSize *= shape[i];\n    }\n    const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;\n    const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;\n    const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];\n    const outputSize = sizeFromShape(shape);\n    return { sliceRank, numUpdates, sliceSize, strides, outputSize };\n}\n//# sourceMappingURL=scatter_nd_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nexport function assertParamsValid(input, begin, size) {\n    const inputRank = input.shape.length;\n    util.assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must ` +\n        `match the rank of the array (${inputRank}).`);\n    util.assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must ` +\n        `match the rank of the array (${inputRank}).`);\n    for (let i = 0; i < inputRank; ++i) {\n        util.assert(begin[i] + size[i] <= input.shape[i], () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] ` +\n            `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${input.shape[i]})`);\n    }\n}\n/** Converts a binary mask to an array of axes. Used in stridedSlice(). */\nexport function maskToAxes(mask) {\n    const axes = [];\n    let axis = 0;\n    while (mask > 0) {\n        if (mask & 1) {\n            axes.push(axis);\n        }\n        mask /= 2;\n        axis++;\n    }\n    return axes;\n}\n/** Computes the output shape given the strided slice params. */\nexport function computeOutShape(begin, end, strides) {\n    const size = [];\n    for (let axis = 0; axis < begin.length; axis++) {\n        size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);\n    }\n    return size;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stride value. Otherwise, insert.\nexport function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {\n    const newStrides = [...strides];\n    for (let i = newStrides.length; i < inputShape.length; i++) {\n        newStrides.push(1);\n    }\n    for (let i = 0; i < numElidedAxes; i++) {\n        if (i === 0) {\n            newStrides[ellipsisInsertionIndex] = 1;\n        }\n        else {\n            newStrides.splice(ellipsisInsertionIndex, 0 /* num elements to delete */, 1 /* element to add */);\n            newStrides.pop();\n        }\n    }\n    return newStrides;\n}\nfunction unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {\n    if (normalizedAxis <= ellipsisInsertionIndex) {\n        return normalizedAxis;\n    }\n    return normalizedAxis - (numElidedAxes - 1);\n}\nfunction getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {\n    const elidedAxes = [];\n    for (let i = 0; i < numElidedAxes; i++) {\n        elidedAxes.push(ellipsisInsertionIndex + i);\n    }\n    return elidedAxes;\n}\n// Normalize the start, end and strides.\nexport function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {\n    const inputRank = inputShape.length;\n    let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);\n    if (ellipsisAxes.length && numInterpolatedAxes > 0) {\n        const fullIndex = ellipsisAxes[0];\n        // The ellipsis applies to the masked index as well as any dimensions\n        // that are interpolated.\n        const numElidedAxes = numInterpolatedAxes + 1;\n        normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);\n        normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);\n        normalizedStrides =\n            stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);\n    }\n    else {\n        for (let axis = 0; axis < inputRank; axis++) {\n            normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);\n            normalizedEnd[axis] =\n                stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);\n            normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);\n        }\n    }\n    return {\n        begin: normalizedBegin,\n        end: normalizedEnd,\n        strides: normalizedStrides\n    };\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current start value. Otherwise, insert.\nexport function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {\n    const newIndices = [...inputShape];\n    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n    for (let axis = 0; axis < newIndices.length; axis++) {\n        if (elidedAxes.indexOf(axis) > -1) {\n            newIndices[axis] = 0;\n        }\n        else {\n            const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n            let originalValue = originalBegin[originalAxis];\n            if (beginMask & 1 << originalAxis) {\n                originalValue = 0;\n            }\n            newIndices[axis] = originalValue;\n        }\n    }\n    return newIndices;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stop value. Otherwise, insert.\nexport function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {\n    const newIndices = [...inputShape];\n    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n    for (let axis = 0; axis < newIndices.length; axis++) {\n        if (elidedAxes.indexOf(axis) > -1) {\n            newIndices[axis] = Number.MAX_SAFE_INTEGER;\n        }\n        else {\n            const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n            let originalValue = originalEnd[originalAxis];\n            if (endMask & 1 << originalAxis) {\n                originalValue = Number.MAX_SAFE_INTEGER;\n            }\n            newIndices[axis] = originalValue;\n        }\n    }\n    for (let i = 0; i < newIndices.length; i++) {\n        // Handle negative indices\n        const axisSize = inputShape[i];\n        if (newIndices[i] < 0) {\n            newIndices[i] += axisSize;\n        }\n        newIndices[i] = util.clamp(0, newIndices[i], inputShape[i]);\n    }\n    return newIndices;\n}\nexport function stridesForAxis(strides, axis, ellipsisMask) {\n    let stride = strides[axis];\n    if (ellipsisMask & (1 << axis) || stride == null) {\n        stride = 1;\n    }\n    return stride;\n}\nexport function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {\n    // Begin with the specified index\n    let start = startIndices[axis];\n    const stride = strides[axis] || 1;\n    // Check the axis bit from right of masked axes, or the begin index is not set\n    // for the axis.\n    if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {\n        if (stride > 0) {\n            // Forward iteration - use the first element. These values will get\n            // clamped below (Note: We could have set them to 0 and axis_size-1, but\n            // use lowest() and max() to maintain symmetry with StopForAxis())\n            start = Number.MIN_SAFE_INTEGER;\n        }\n        else {\n            // Backward iteration - use the last element.\n            start = Number.MAX_SAFE_INTEGER;\n        }\n    }\n    // Handle negative indices\n    const axisSize = inputShape[axis];\n    if (start < 0) {\n        start += axisSize;\n    }\n    // Clamping\n    start = util.clamp(0, start, axisSize - 1);\n    return start;\n}\nexport function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {\n    // Begin with the specified index\n    let stop = stopIndices[axis];\n    const stride = strides[axis] || 1;\n    // Check the axis bit from right of masked axes, or if the stop index is not\n    // set for this axis.\n    if (endMask & (1 << axis) || ellipsisMask & (1 << axis) || stop == null) {\n        if (stride > 0) {\n            // Forward iteration - use the last element. These values will get\n            // clamped below\n            stop = Number.MAX_SAFE_INTEGER;\n        }\n        else {\n            // Backward iteration - use the first element.\n            stop = Number.MIN_SAFE_INTEGER;\n        }\n    }\n    // Handle negative indices\n    const axisSize = inputShape[axis];\n    if (stop < 0) {\n        stop += axisSize;\n    }\n    // Clamping\n    // Because the end index points one past the last element, we need slightly\n    // different clamping ranges depending on the direction.\n    if (stride > 0) {\n        // Forward iteration\n        stop = util.clamp(0, stop, axisSize);\n    }\n    else {\n        // Backward iteration\n        stop = util.clamp(-1, stop, axisSize - 1);\n    }\n    return stop;\n}\n/**\n * Returns true if the slice occupies a continous set of elements in the\n * 'flat' space.\n */\nexport function isSliceContinous(shape, begin, size) {\n    // Index of the first axis that has size > 1.\n    let firstNonOneAxis = size.length;\n    for (let i = 0; i < size.length; i++) {\n        if (size[i] > 1) {\n            firstNonOneAxis = i;\n            break;\n        }\n    }\n    for (let i = firstNonOneAxis + 1; i < size.length; i++) {\n        if (begin[i] > 0 || size[i] !== shape[i]) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function computeFlatOffset(begin, strides) {\n    let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;\n    for (let i = 0; i < begin.length - 1; i++) {\n        flatOffset += begin[i] * strides[i];\n    }\n    return flatOffset;\n}\nexport function parseSliceParams(x, begin, size) {\n    // The following logic allows for more ergonomic calls.\n    let begin_;\n    const xRank = x.shape.length;\n    if (typeof begin === 'number') {\n        begin_ = [begin, ...new Array(xRank - 1).fill(0)];\n    }\n    else if (begin.length < xRank) {\n        begin_ = begin.concat(new Array(xRank - begin.length).fill(0));\n    }\n    else {\n        begin_ = begin.slice();\n    }\n    begin_.forEach(d => {\n        util.assert(d !== -1, () => 'slice() does not support negative begin indexing.');\n    });\n    let size_;\n    if (size == null) {\n        size_ = new Array(xRank).fill(-1);\n    }\n    else if (typeof size === 'number') {\n        size_ = [size, ...new Array(xRank - 1).fill(-1)];\n    }\n    else if (size.length < xRank) {\n        size_ = size.concat(new Array(xRank - size.length).fill(-1));\n    }\n    else {\n        size_ = size;\n    }\n    size_ = size_.map((d, i) => {\n        if (d >= 0) {\n            return d;\n        }\n        else {\n            util.assert(d === -1, () => `Negative size values should be exactly -1 but got ` +\n                `${d} for the slice() size at index ${i}.`);\n            return x.shape[i] - begin_[i];\n        }\n    });\n    return [begin_, size_];\n}\n//# sourceMappingURL=slice_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from './util';\n/**\n * Serializable defines the serialization contract.\n *\n * TFJS requires serializable classes to return their className when asked\n * to avoid issues with minification.\n */\nexport class Serializable {\n    /**\n     * Return the class name for this class to use in serialization contexts.\n     *\n     * Generally speaking this will be the same thing that constructor.name\n     * would have returned.  However, the class name needs to be robust\n     * against minification for serialization/deserialization to work properly.\n     *\n     * There's also places such as initializers.VarianceScaling, where\n     * implementation details between different languages led to different\n     * class hierarchies and a non-leaf node is used for serialization purposes.\n     */\n    getClassName() {\n        return this.constructor\n            .className;\n    }\n    /**\n     * Creates an instance of T from a ConfigDict.\n     *\n     * This works for most descendants of serializable.  A few need to\n     * provide special handling.\n     * @param cls A Constructor for the class to instantiate.\n     * @param config The Configuration for the object.\n     */\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/**\n * Maps string keys to class constructors.\n *\n * Used during (de)serialization from the cross-language JSON format, which\n * requires the class name in the serialization format matches the class\n * names as used in Python, should it exist.\n */\nexport class SerializationMap {\n    constructor() {\n        this.classNameMap = {};\n    }\n    /**\n     * Returns the singleton instance of the map.\n     */\n    static getMap() {\n        if (SerializationMap.instance == null) {\n            SerializationMap.instance = new SerializationMap();\n        }\n        return SerializationMap.instance;\n    }\n    /**\n     * Registers the class as serializable.\n     */\n    static register(cls) {\n        SerializationMap.getMap().classNameMap[cls.className] =\n            [cls, cls.fromConfig];\n    }\n}\n/**\n * Register a class with the serialization map of TensorFlow.js.\n *\n * This is often used for registering custom Layers, so they can be\n * serialized and deserialized.\n *\n * Example:\n *\n * ```js\n * class MyCustomLayer extends tf.layers.Layer {\n *   static className = 'MyCustomLayer';\n *\n *   constructor(config) {\n *     super(config);\n *   }\n * }\n * tf.serialization.registerClass(MyCustomLayer);\n * ```\n *\n * @param cls The class to be registered. It must have a public static member\n *   called `className` defined and the value must be a non-empty string.\n *\n * @doc {heading: 'Models', subheading: 'Serialization', ignoreCI: true}\n */\nexport function registerClass(cls) {\n    assert(cls.className != null, () => `Class being registered does not have the static className ` +\n        `property defined.`);\n    assert(typeof cls.className === 'string', () => `className is required to be a string, but got type ` +\n        typeof cls.className);\n    assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, ` +\n        `which is disallowed.`);\n    SerializationMap.register(cls);\n}\n//# sourceMappingURL=serialization.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { inferShape } from './tensor_util_env';\nimport { arraysEqual, flatten, isString, isTypedArray } from './util';\nconst TEST_EPSILON_FLOAT32 = 1e-3;\nexport const TEST_EPSILON_FLOAT16 = 1e-1;\nexport function expectArraysClose(actual, expected, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon));\n}\nexport function testEpsilon() {\n    return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :\n        TEST_EPSILON_FLOAT16;\n}\nfunction expectArraysPredicate(actual, expected, predicate) {\n    let checkClassType = true;\n    if (isTypedArray(actual) || isTypedArray(expected)) {\n        checkClassType = false;\n    }\n    if (isTypedArray(actual) && isTypedArray(expected)) {\n        checkClassType = true;\n    }\n    if (checkClassType) {\n        const aType = actual.constructor.name;\n        const bType = expected.constructor.name;\n        if (aType !== bType) {\n            throw new Error(`Arrays are of different type. Actual: ${aType}. ` +\n                `Expected: ${bType}`);\n        }\n    }\n    if (Array.isArray(actual) && Array.isArray(expected)) {\n        const actualShape = inferShape(actual);\n        const expectedShape = inferShape(expected);\n        if (!arraysEqual(actualShape, expectedShape)) {\n            throw new Error(`Arrays have different shapes. ` +\n                `Actual: [${actualShape}]. Expected: [${expectedShape}]`);\n        }\n    }\n    const actualFlat = isTypedArray(actual) ? actual : flatten(actual);\n    const expectedFlat = isTypedArray(expected) ?\n        expected :\n        flatten(expected);\n    if (actualFlat.length !== expectedFlat.length) {\n        throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs ` +\n            `expected: ${expectedFlat.length}.\\n` +\n            `Actual:   ${actualFlat}.\\n` +\n            `Expected: ${expectedFlat}.`);\n    }\n    for (let i = 0; i < expectedFlat.length; ++i) {\n        const a = actualFlat[i];\n        const e = expectedFlat[i];\n        if (!predicate(a, e)) {\n            throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\\n` +\n                `Actual:   ${actualFlat}.\\n` +\n                `Expected: ${expectedFlat}.`);\n        }\n    }\n}\nexport function expectPromiseToFail(fn, done) {\n    fn().then(() => done.fail(), () => done());\n}\nexport function expectArraysEqual(actual, expected) {\n    const exp = typeof expected === 'string' || typeof expected === 'number' ||\n        typeof expected === 'boolean' ?\n        [expected] :\n        expected;\n    if (isString(actual) || isString(actual[0]) ||\n        isString(expected) || isString(expected[0])) {\n        // tslint:disable-next-line: triple-equals\n        return expectArraysPredicate(actual, exp, (a, b) => a == b);\n    }\n    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));\n}\nexport function expectNumbersClose(a, e, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    if (!areClose(a, e, epsilon)) {\n        throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n    }\n}\nfunction areClose(a, e, epsilon) {\n    if (!isFinite(a) && !isFinite(e)) {\n        return true;\n    }\n    if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {\n        return false;\n    }\n    return true;\n}\nexport function expectValuesInRange(actual, low, high) {\n    for (let i = 0; i < actual.length; i++) {\n        if (actual[i] < low || actual[i] > high) {\n            throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);\n        }\n    }\n}\nexport function expectArrayBuffersEqual(actual, expected) {\n    // Safari & Jasmine don't like comparing ArrayBuffers directly. Wrapping in\n    // a Float32Array solves this issue.\n    expect(new Float32Array(actual)).toEqual(new Float32Array(expected));\n}\n//# sourceMappingURL=test_util.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { env } from './environment';\nimport { setDeprecationWarningFn } from './tensor';\nimport { getTensorsInContainer } from './tensor_util';\n/**\n * Enables production mode which disables correctness checks in favor of\n * performance.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableProdMode() {\n    env().set('PROD', true);\n}\n/**\n * Enables debug mode which will log information about all executed kernels:\n * the elapsed time of the kernel execution, as well as the rank, shape, and\n * size of the output tensor.\n *\n * Debug mode will significantly slow down your application as it will\n * download the result of every operation to the CPU. This should not be used in\n * production. Debug mode does not affect the timing information of the kernel\n * execution as we do not measure download time in the kernel execution time.\n *\n * See also: `tf.profile`, `tf.memory`.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableDebugMode() {\n    env().set('DEBUG', true);\n}\n/** Globally disables deprecation warnings */\nexport function disableDeprecationWarnings() {\n    env().set('DEPRECATION_WARNINGS_ENABLED', false);\n    console.warn(`TensorFlow.js deprecation warnings have been disabled.`);\n}\n/** Warn users about deprecated functionality. */\nexport function deprecationWarn(msg) {\n    if (env().getBool('DEPRECATION_WARNINGS_ENABLED')) {\n        console.warn(msg + ' You can disable deprecation warnings with ' +\n            'tf.disableDeprecationWarnings().');\n    }\n}\nsetDeprecationWarningFn(deprecationWarn);\n/**\n * Dispose all variables kept in backend engine.\n *\n * @doc {heading: 'Environment'}\n */\nexport function disposeVariables() {\n    ENGINE.disposeVariables();\n}\n/**\n * It returns the global engine that keeps track of all tensors and backends.\n *\n * @doc {heading: 'Environment'}\n */\nexport function engine() {\n    return ENGINE;\n}\n/**\n * Returns memory info at the current time in the program. The result is an\n * object with the following properties:\n *\n * - `numBytes`: Number of bytes allocated (undisposed) at this time.\n * - `numTensors`: Number of unique tensors allocated.\n * - `numDataBuffers`: Number of unique data buffers allocated\n *   (undisposed) at this time, which is \u2264 the number of tensors\n *   (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same\n *   data buffer with `a`).\n * - `unreliable`: True if the memory usage is unreliable. See `reasons` when\n *    `unreliable` is true.\n * - `reasons`: `string[]`, reasons why the memory is unreliable, present if\n *    `unreliable` is true.\n *\n * WebGL Properties:\n * - `numBytesInGPU`: Number of bytes allocated (undisposed) in the GPU only at\n *     this time.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function memory() {\n    return ENGINE.memory();\n}\n/**\n * Executes the provided function `f()` and returns a promise that resolves\n * with information about the function's memory use:\n * - `newBytes`: the number of new bytes allocated\n * - `newTensors`: the number of new tensors created\n * - `peakBytes`: the peak number of bytes allocated\n * - `kernels`: an array of objects for each kernel involved that reports\n * their input and output shapes, number of bytes used, and number of new\n * tensors created.\n *\n * ```js\n * const profile = await tf.profile(() => {\n *   const x = tf.tensor1d([1, 2, 3]);\n *   let x2 = x.square();\n *   x2.dispose();\n *   x2 = x.square();\n *   x2.dispose();\n *   return x;\n * });\n *\n * console.log(`newBytes: ${profile.newBytes}`);\n * console.log(`newTensors: ${profile.newTensors}`);\n * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>\n * k.totalBytesSnapshot)}`);\n * ```\n *\n *\n * @doc {heading: 'Performance', subheading: 'Profile'}\n */\nexport function profile(f) {\n    return ENGINE.profile(f);\n}\n/**\n * Executes the provided function `fn` and after it is executed, cleans up all\n * intermediate tensors allocated by `fn` except those returned by `fn`.\n * `fn` must not return a Promise (async functions not allowed). The returned\n * result can be a complex object.\n *\n * Using this method helps avoid memory leaks. In general, wrap calls to\n * operations in `tf.tidy` for automatic memory cleanup.\n *\n * NOTE: Variables do *not* get cleaned up when inside a tidy(). If you want to\n * dispose variables, please use `tf.disposeVariables` or call dispose()\n * directly on variables.\n *\n * ```js\n * // y = 2 ^ 2 + 1\n * const y = tf.tidy(() => {\n *   // a, b, and one will be cleaned up when the tidy ends.\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *   const b = a.square();\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * y.print();\n * ```\n *\n * @param nameOrFn The name of the closure, or the function to execute.\n *     If a name is provided, the 2nd argument should be the function.\n *     If debug mode is on, the timing and the memory usage of the function\n *     will be tracked and displayed on the console using the provided name.\n * @param fn The function to execute.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function tidy(nameOrFn, fn) {\n    return ENGINE.tidy(nameOrFn, fn);\n}\n/**\n * Disposes any `tf.Tensor`s found within the provided object.\n *\n * @param container an object that may be a `tf.Tensor` or may directly\n *     contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If\n *     the object is not a `tf.Tensor` or does not contain `Tensors`, nothing\n *     happens. In general it is safe to pass any object here, except that\n *     `Promise`s are not supported.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function dispose(container) {\n    const tensors = getTensorsInContainer(container);\n    tensors.forEach(tensor => tensor.dispose());\n}\n/**\n * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed\n * automatically.\n *\n * ```js\n * let b;\n * const y = tf.tidy(() => {\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *\n *   // b will not be cleaned up by the tidy. a and one will be cleaned up\n *   // when the tidy ends.\n *   b = tf.keep(a.square());\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * console.log('y:');\n * y.print();\n * console.log('b:');\n * b.print();\n * ```\n *\n * @param result The tensor to keep from being disposed.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function keep(result) {\n    return ENGINE.keep(result);\n}\n/**\n * Executes `f()` and returns a promise that resolves with timing\n * information.\n *\n * The result is an object with the following properties:\n *\n * - `wallMs`: Wall execution time.\n * - `kernelMs`: Kernel execution time, ignoring data transfer. If using the\n * WebGL backend and the query timer extension is not available, this will\n * return an error object.\n * - On `WebGL` The following additional properties exist:\n *   - `uploadWaitMs`: CPU blocking time on texture uploads.\n *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).\n *\n * ```js\n * const x = tf.randomNormal([20, 20]);\n * const time = await tf.time(() => x.matMul(x));\n *\n * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);\n * ```\n *\n * @param f The function to execute and time.\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nexport function time(f) {\n    return ENGINE.time(f);\n}\n/**\n * Sets the backend (cpu, webgl, wasm, etc) responsible for creating tensors and\n * executing operations on those tensors. Returns a promise that resolves\n * to a boolean if the backend initialization was successful.\n *\n * Note this disposes the current backend, if any, as well as any tensors\n * associated with it. A new backend is initialized, even if it is of the\n * same type as the previous one.\n *\n * @param backendName The name of the backend. Currently supports\n *     `'webgl'|'cpu'` in the browser, `'tensorflow'` under node.js\n *     (requires tfjs-node), and `'wasm'` (requires tfjs-backend-wasm).\n *\n * @doc {heading: 'Backends'}\n */\nexport function setBackend(backendName) {\n    return ENGINE.setBackend(backendName);\n}\n/**\n * Returns a promise that resolves when the currently selected backend (or the\n * highest priority one) has initialized. Await this promise when you are using\n * a backend that has async initialization.\n *\n * @doc {heading: 'Backends'}\n */\nexport function ready() {\n    return ENGINE.ready();\n}\n/**\n * Returns the current backend name (cpu, webgl, etc). The backend is\n * responsible for creating tensors and executing operations on those tensors.\n *\n * @doc {heading: 'Backends'}\n */\nexport function getBackend() {\n    return ENGINE.backendName;\n}\n/**\n * Removes a backend and the registered factory.\n *\n * @doc {heading: 'Backends'}\n */\nexport function removeBackend(name) {\n    ENGINE.removeBackend(name);\n}\n/**\n * Finds the backend registered under the provided name. Returns null if the\n * name is not in the registry, or the registration hasn't finished yet.\n */\nexport function findBackend(name) {\n    return ENGINE.findBackend(name);\n}\n/**\n * Finds the backend factory registered under the provided name. Returns a\n * function that produces a new backend when called. Returns null if the name\n * is not in the registry.\n */\nexport function findBackendFactory(name) {\n    return ENGINE.findBackendFactory(name);\n}\n/**\n * Registers a global backend. The registration should happen when importing\n * a module file (e.g. when importing `backend_webgl.ts`), and is used for\n * modular builds (e.g. custom tfjs bundle with only webgl support).\n *\n * @param factory The backend factory function. When called, it should\n * return a backend instance, or a promise of an instance.\n * @param priority The priority of the backend (higher = more important).\n *     In case multiple backends are registered, the priority is used to find\n *     the best backend. Defaults to 1.\n * @return False if there is already a registered backend under this name, true\n *     if not.\n *\n * @doc {heading: 'Backends'}\n */\nexport function registerBackend(name, factory, priority = 1) {\n    return ENGINE.registerBackend(name, factory, priority);\n}\n/**\n * Gets the current backend. If no backends have been initialized, this will\n * attempt to initialize the best backend. Will throw an error if the highest\n * priority backend has async initialization, in which case, you should call\n * 'await tf.ready()' before running other code.\n *\n * @doc {heading: 'Backends'}\n */\nexport function backend() {\n    return ENGINE.backend;\n}\n/**\n * Sets the global platform.\n *\n * @param platformName The name of this platform.\n * @param platform A platform implementation.\n */\nexport function setPlatform(platformName, platform) {\n    env().setPlatform(platformName, platform);\n}\n//# sourceMappingURL=globals.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Add } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n *\n * ```js\n * // Broadcast add a with b.\n * const a = tf.scalar(5);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n * @param a The first `tf.Tensor` to add.\n * @param b The second `tf.Tensor` to add. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction add_(a, b) {\n    let $a = convertToTensor(a, 'a', 'add');\n    let $b = convertToTensor(b, 'b', 'add');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.add($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Add);\n}\nexport const add = op({ add_ });\n//# sourceMappingURL=add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FloorDiv } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n * The result is rounded with floor function.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.floorDiv(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.floorDiv(b).print();  // or tf.floorDiv(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction floorDiv_(a, b) {\n    let $a = convertToTensor(a, 'a', 'floorDiv');\n    let $b = convertToTensor(b, 'b', 'floorDiv');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.floorDiv($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, FloorDiv);\n}\nexport const floorDiv = op({ floorDiv_ });\n//# sourceMappingURL=floorDiv.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Div } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { floorDiv } from './floorDiv';\nimport { op } from './operation';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction div_(a, b) {\n    let $a = convertToTensor(a, 'a', 'div');\n    let $b = convertToTensor(b, 'b', 'div');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'int32' && $b.dtype === 'int32') {\n        return floorDiv($a, $b);\n    }\n    const forward = (backend, save) => {\n        const res = backend.realDivide($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = {};\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Div, attrs);\n}\nexport const div = op({ div_ });\n//# sourceMappingURL=div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Multiply } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.\n *\n * We also expose `tf.mulStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([2, 3, 4, 5]);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n *\n * ```js\n * // Broadcast mul a with b.\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.scalar(5);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n * @param a The first tensor to multiply.\n * @param b The second tensor to multiply. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mul_(a, b) {\n    let $a = convertToTensor(a, 'a', 'mul');\n    let $b = convertToTensor(b, 'b', 'mul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.multiply($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Multiply);\n}\nexport const mul = op({ mul_ });\n//# sourceMappingURL=mul.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Abs } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes absolute value element-wise: `abs(x)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.abs().print();  // or tf.abs(x)\n * ```\n * @param x The input `tf.Tensor`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction abs_(x) {\n    const $x = convertToTensor(x, 'x', 'abs');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'complex64') {\n            return backend.complexAbs($x);\n        }\n        return backend.abs($x);\n    }, inputs, null /* grad */, Abs);\n}\nexport const abs = op({ abs_ });\n//# sourceMappingURL=abs.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.acos().print();  // or tf.acos(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acos_(x) {\n    const $x = convertToTensor(x, 'x', 'acos');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.acos($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Acos);\n}\nexport const acos = op({ acos_ });\n//# sourceMappingURL=acos.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:\n * `acosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([10, 1, 3, 5.7]);\n *\n * x.acosh().print();  // or tf.acosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acosh_(x) {\n    const $x = convertToTensor(x, 'x', 'acosh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.acosh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Acosh);\n}\nexport const acosh = op({ acosh_ });\n//# sourceMappingURL=acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AddN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n *\n * tf.addN([a, b, c]).print();\n * ```\n * @param tensors A list of tensors with the same shape and dtype.\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction addN_(tensors) {\n    util.assert(Array.isArray(tensors), () => 'The argument passed to tf.addN() must be a list of tensors');\n    util.assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ` +\n        `${tensors.length}`);\n    const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));\n    const firstTensor = $tensors[0];\n    $tensors.forEach(t => {\n        if (t.dtype !== firstTensor.dtype) {\n            throw new Error('All tensors passed to tf.addN() must have the same dtype');\n        }\n    });\n    $tensors.forEach(t => {\n        if (!util.arraysEqual(t.shape, firstTensor.shape)) {\n            throw new Error('All tensors passed to tf.addN() must have the same shape');\n        }\n    });\n    const forward = (backend, save) => {\n        const res = backend.addN($tensors);\n        save($tensors);\n        return res;\n    };\n    const inputs = $tensors;\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, AddN);\n}\nexport const addN = op({ addN_ });\n//# sourceMappingURL=add_n.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n * Returns true if the axis specifies the inner most dimensions of the\n * array.\n */\nexport function axesAreInnerMostDims(axes, rank) {\n    for (let i = 0; i < axes.length; ++i) {\n        if (axes[axes.length - i - 1] !== rank - 1 - i) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function combineLocations(outputLoc, reduceLoc, axes) {\n    const rank = outputLoc.length + reduceLoc.length;\n    const loc = [];\n    let outIdx = 0;\n    let reduceIdx = 0;\n    for (let dim = 0; dim < rank; dim++) {\n        if (axes.indexOf(dim) === -1) {\n            loc.push(outputLoc[outIdx++]);\n        }\n        else {\n            loc.push(reduceLoc[reduceIdx++]);\n        }\n    }\n    return loc;\n}\nexport function computeOutAndReduceShapes(aShape, axes) {\n    const outShape = [];\n    const rank = aShape.length;\n    for (let dim = 0; dim < rank; dim++) {\n        if (axes.indexOf(dim) === -1) {\n            outShape.push(aShape[dim]);\n        }\n    }\n    const reduceShape = axes.map(dim => aShape[dim]);\n    return [outShape, reduceShape];\n}\nexport function expandShapeToKeepDim(shape, axes) {\n    const reduceSubShape = axes.map(x => 1);\n    return combineLocations(shape, reduceSubShape, axes);\n}\nexport function assertAxesAreInnerMostDims(msg, axes, rank) {\n    util.assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. ` +\n        `Got axes ${axes} and rank-${rank} input.`);\n}\n/**\n * Returns the axes permutation to be used with `tf.transpose`, if such\n * permutation is necessary. Otherwise it returns null. This method is used by\n * operations that operate only on inner-most axes.\n */\nexport function getAxesPermutation(axes, rank) {\n    if (axesAreInnerMostDims(axes, rank)) {\n        return null;\n    }\n    const result = [];\n    for (let i = 0; i < rank; ++i) {\n        if (axes.indexOf(i) === -1) {\n            result.push(i);\n        }\n    }\n    axes.forEach(axis => result.push(axis));\n    return result;\n}\n/** Returns the axes permutation that undoes the original permutation. */\nexport function getUndoAxesPermutation(axes) {\n    return axes.map((axis, i) => [i, axis])\n        .sort((a, b) => a[1] - b[1])\n        .map(x => x[0]);\n}\nexport function getInnerMostAxes(numAxes, rank) {\n    const res = [];\n    for (let i = rank - numAxes; i < rank; ++i) {\n        res.push(i);\n    }\n    return res;\n}\n//# sourceMappingURL=axis_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { All } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the logical and of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.all().print();  // or tf.all(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.all(axis).print();  // or tf.all(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction all_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'all', 'bool');\n    const forward = (backend) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = getInnerMostAxes(axes.length, $x.rank);\n        }\n        const res = backend.all($x, axes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(res.shape, origAxes);\n            return reshape(res, newShape);\n        }\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, All, attrs);\n}\nexport const all = op({ all_ });\n//# sourceMappingURL=all.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Any } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the logical or of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.any().print();  // or tf.any(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.any(axis).print();  // or tf.any(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction any_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'any', 'bool');\n    const forward = (backend) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = getInnerMostAxes(axes.length, $x.rank);\n        }\n        const res = backend.any($x, axes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(res.shape, origAxes);\n            return reshape(res, newShape);\n        }\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Any, attrs);\n}\n// tslint:disable-next-line:variable-name\nexport const any = op({ any_ });\n//# sourceMappingURL=any.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Returns the indices of the maximum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMax().print();  // or tf.argMax(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMax(axis).print();  // or tf.argMax(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMax_(x, axis = 0) {\n    let $x = convertToTensor(x, 'x', 'argMax');\n    const forward = (backend, save) => {\n        save([$x]);\n        let axes = util.parseAxisParam(axis, $x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        return backend.argMax($x, axes[0]);\n    };\n    const inputs = { x: $x };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ArgMax, attrs);\n}\nexport const argMax = op({ argMax_ });\n//# sourceMappingURL=arg_max.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Returns the indices of the minimum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMin().print();  // or tf.argMin(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMin(axis).print();  // or tf.argMin(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMin_(x, axis = 0) {\n    let $x = convertToTensor(x, 'x', 'argMin');\n    const forward = (backend, save) => {\n        save([$x]);\n        if (axis == null) {\n            axis = 0;\n        }\n        let axes = util.parseAxisParam(axis, $x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        return backend.argMin($x, axes[0]);\n    };\n    const inputs = { x: $x };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ArgMin, attrs);\n}\nexport const argMin = op({ argMin_ });\n//# sourceMappingURL=arg_min.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asin().print();  // or tf.asin(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asin_(x) {\n    const $x = convertToTensor(x, 'x', 'asin');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.asin($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Asin);\n}\nexport const asin = op({ asin_ });\n//# sourceMappingURL=asin.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:\n * `asinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asinh().print();  // or tf.asinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asinh_(x) {\n    const $x = convertToTensor(x, 'x', 'asinh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.asinh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Asinh);\n}\nexport const asinh = op({ asinh_ });\n//# sourceMappingURL=asinh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.atan().print();  // or tf.atan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan_(x) {\n    const $x = convertToTensor(x, 'x', 'atan');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.atan($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Atan);\n}\nexport const atan = op({ atan_ });\n//# sourceMappingURL=atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan2 } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);\n * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);\n *\n * tf.atan2(a, b).print()\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan2_(a, b) {\n    let $a = convertToTensor(a, 'a', 'atan2');\n    let $b = convertToTensor(b, 'b', 'atan2');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.atan2($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Atan2);\n}\nexport const atan2 = op({ atan2_ });\n//# sourceMappingURL=atan2.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:\n * `atanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.atanh().print();  // or tf.atanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atanh_(x) {\n    const $x = convertToTensor(x, 'x', 'atanh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.atanh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Atanh);\n}\nexport const atanh = op({ atanh_ });\n//# sourceMappingURL=atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n *\n * @param inputShape Input tensor shape is of the following dimensions:\n *     `[batch, height, width, inChannels]`.\n * @param filterShape The filter shape is of the following dimensions:\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat The data format of the input and output data.\n *     Defaults to 'NHWC'.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`.\n *     Defaults to `[1, 1]`. If `dilations` is a single number, then\n *     `dilationHeight == dilationWidth`.\n */\nexport function computeDilation2DInfo(inputShape, filterShape, strides, pad, dataFormat = 'NHWC', dilations) {\n    // `computerConv2DInfo` require filterShape to be in the dimension of:\n    // `[filterHeight, filterWidth, depth, outDepth]`, dilation2d doesn't have\n    // outDepth, it should have the same depth as the input.\n    // Input shape: [batch, height, width, inChannels]\n    const inputChannels = inputShape[3];\n    const $filterShape = [...filterShape, inputChannels];\n    const $dataFormat = convertConv2DDataFormat(dataFormat);\n    return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad, null /* roundingMode */, null /* depthWise */, $dataFormat);\n}\nexport function computePool2DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'channelsLast') {\n    const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n    let filterShape;\n    if (dataFormat === 'channelsLast') {\n        filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n    }\n    else if (dataFormat === 'channelsFirst') {\n        filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    return computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, false, dataFormat);\n}\n/**\n * Computes the information for a forward pass of a pooling3D operation.\n */\nexport function computePool3DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'NDHWC') {\n    const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);\n    let filterShape;\n    let $dataFormat;\n    if (dataFormat === 'NDHWC') {\n        $dataFormat = 'channelsLast';\n        filterShape =\n            [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];\n    }\n    else if (dataFormat === 'NCDHW') {\n        $dataFormat = 'channelsFirst';\n        filterShape =\n            [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    return computeConv3DInfo(inShape, filterShape, strides, dilations, pad, false, $dataFormat, roundingMode);\n}\n/**\n * Computes the information for a forward pass of a convolution/pooling\n * operation.\n */\nexport function computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, depthwise = false, dataFormat = 'channelsLast') {\n    let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n    if (dataFormat === 'channelsLast') {\n        [batchSize, inHeight, inWidth, inChannels] = inShape;\n    }\n    else if (dataFormat === 'channelsFirst') {\n        [batchSize, inChannels, inHeight, inWidth] = inShape;\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    const [filterHeight, filterWidth, , filterChannels] = filterShape;\n    const [strideHeight, strideWidth] = parseTupleParam(strides);\n    const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n    const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);\n    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n    let outShape;\n    if (dataFormat === 'channelsFirst') {\n        outShape = [batchSize, outChannels, outHeight, outWidth];\n    }\n    else if (dataFormat === 'channelsLast') {\n        outShape = [batchSize, outHeight, outWidth, outChannels];\n    }\n    return {\n        batchSize,\n        dataFormat,\n        inHeight,\n        inWidth,\n        inChannels,\n        outHeight,\n        outWidth,\n        outChannels,\n        padInfo,\n        strideHeight,\n        strideWidth,\n        filterHeight,\n        filterWidth,\n        effectiveFilterHeight,\n        effectiveFilterWidth,\n        dilationHeight,\n        dilationWidth,\n        inShape,\n        outShape,\n        filterShape\n    };\n}\n/**\n * Computes the information for a forward pass of a 3D convolution/pooling\n * operation.\n */\nexport function computeConv3DInfo(inShape, filterShape, strides, dilations, pad, depthwise = false, dataFormat = 'channelsLast', roundingMode) {\n    let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];\n    if (dataFormat === 'channelsLast') {\n        [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n    }\n    else if (dataFormat === 'channelsFirst') {\n        [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;\n    const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n    const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);\n    const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);\n    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n    const { padInfo, outDepth, outHeight, outWidth } = get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);\n    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n    let outShape;\n    if (dataFormat === 'channelsFirst') {\n        outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n    }\n    else if (dataFormat === 'channelsLast') {\n        outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n    }\n    return {\n        batchSize,\n        dataFormat,\n        inDepth,\n        inHeight,\n        inWidth,\n        inChannels,\n        outDepth,\n        outHeight,\n        outWidth,\n        outChannels,\n        padInfo,\n        strideDepth,\n        strideHeight,\n        strideWidth,\n        filterDepth,\n        filterHeight,\n        filterWidth,\n        effectiveFilterDepth,\n        effectiveFilterHeight,\n        effectiveFilterWidth,\n        dilationDepth,\n        dilationHeight,\n        dilationWidth,\n        inShape,\n        outShape,\n        filterShape\n    };\n}\nfunction computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {\n    if (zeroPad == null) {\n        zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n    }\n    const inputRows = inShape[0];\n    const inputCols = inShape[1];\n    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    return [outputRows, outputCols];\n}\nfunction computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {\n    if (zeroPad == null) {\n        zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n    }\n    const inputDepth = inShape[0];\n    const inputRows = inShape[1];\n    const inputCols = inShape[2];\n    const outputDepths = conditionalRound((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputDepths), () => `The output # of depths (${outputDepths}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    return [outputDepths, outputRows, outputCols, outChannels];\n}\nexport function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {\n    const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n    return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\nfunction parseTupleParam(param) {\n    if (typeof param === 'number') {\n        return [param, param, param];\n    }\n    if (param.length === 2) {\n        return [param[0], param[1], 1];\n    }\n    return param;\n}\nfunction parse3TupleParam(param) {\n    return typeof param === 'number' ? [param, param, param] : param;\n}\n/* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d\n * Atrous convolution is equivalent to standard convolution with upsampled\n * filters with effective_filter_height =\n * filter_height + (filter_height - 1) * (dilation - 1)\n * and effective_filter_width =\n * filter_width + (filter_width - 1) * (dilation - 1),\n * produced by inserting dilation - 1 zeros along consecutive elements across\n * the filters' spatial dimensions.\n * When there is a dilation, this converts a filter dimension to the\n * effective filter dimension, so it can be used in a standard convolution.\n */\nfunction getEffectiveFilterSize(filterSize, dilation) {\n    if (dilation <= 1) {\n        return filterSize;\n    }\n    return filterSize + (filterSize - 1) * (dilation - 1);\n}\nfunction getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {\n    let padInfo;\n    let outHeight;\n    let outWidth;\n    if (typeof pad === 'number') {\n        const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n        padInfo = { top: pad, bottom: pad, left: pad, right: pad, type: padType };\n        const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad, roundingMode);\n        outHeight = outShape[0];\n        outWidth = outShape[1];\n    }\n    else if (pad === 'same') {\n        outHeight = Math.ceil(inHeight / strideHeight);\n        outWidth = Math.ceil(inWidth / strideWidth);\n        const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);\n        const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);\n        const top = Math.floor(padAlongHeight / 2);\n        const bottom = padAlongHeight - top;\n        const left = Math.floor(padAlongWidth / 2);\n        const right = padAlongWidth - left;\n        padInfo = { top, bottom, left, right, type: 'SAME' };\n    }\n    else if (pad === 'valid') {\n        padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: 'VALID' };\n        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n    }\n    else if (typeof pad === 'object') {\n        const top = dataFormat === 'channelsLast' ? pad[1][0] : pad[2][0];\n        const bottom = dataFormat === 'channelsLast' ? pad[1][1] : pad[2][1];\n        const left = dataFormat === 'channelsLast' ? pad[2][0] : pad[3][0];\n        const right = dataFormat === 'channelsLast' ? pad[2][1] : pad[3][1];\n        const padType = (top === 0 && bottom === 0 && left === 0 && right === 0) ?\n            'VALID' :\n            'EXPLICIT';\n        padInfo = { top, bottom, left, right, type: padType };\n        outHeight = conditionalRound((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);\n        outWidth = conditionalRound((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);\n    }\n    else {\n        throw Error(`Unknown padding parameter: ${pad}`);\n    }\n    return { padInfo, outHeight, outWidth };\n}\nfunction get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {\n    let padInfo;\n    let outDepth;\n    let outHeight;\n    let outWidth;\n    if (typeof pad === 'number') {\n        const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n        padInfo = {\n            top: pad,\n            bottom: pad,\n            left: pad,\n            right: pad,\n            front: pad,\n            back: pad,\n            type: padType\n        };\n        const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad, roundingMode);\n        outDepth = outShape[0];\n        outHeight = outShape[1];\n        outWidth = outShape[2];\n    }\n    else if (pad === 'same') {\n        outDepth = Math.ceil(inDepth / strideDepth);\n        outHeight = Math.ceil(inHeight / strideHeight);\n        outWidth = Math.ceil(inWidth / strideWidth);\n        const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n        const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;\n        const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n        const front = Math.floor(padAlongDepth / 2);\n        const back = padAlongDepth - front;\n        const top = Math.floor(padAlongHeight / 2);\n        const bottom = padAlongHeight - top;\n        const left = Math.floor(padAlongWidth / 2);\n        const right = padAlongWidth - left;\n        padInfo = { top, bottom, left, right, front, back, type: 'SAME' };\n    }\n    else if (pad === 'valid') {\n        padInfo = {\n            top: 0,\n            bottom: 0,\n            left: 0,\n            right: 0,\n            front: 0,\n            back: 0,\n            type: 'VALID'\n        };\n        outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);\n        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n    }\n    else {\n        throw Error(`Unknown padding parameter: ${pad}`);\n    }\n    return { padInfo, outDepth, outHeight, outWidth };\n}\n/**\n * Rounds a value depending on the rounding mode\n * @param value\n * @param roundingMode\n */\nfunction conditionalRound(value, roundingMode) {\n    if (!roundingMode) {\n        return value;\n    }\n    switch (roundingMode) {\n        case 'round':\n            // used for Caffe Conv\n            return Math.round(value);\n        case 'ceil':\n            // used for Caffe Pool\n            return Math.ceil(value);\n        case 'floor':\n            return Math.floor(value);\n        default:\n            throw new Error(`Unknown roundingMode ${roundingMode}`);\n    }\n}\nexport function tupleValuesAreOne(param) {\n    const [dimA, dimB, dimC] = parseTupleParam(param);\n    return dimA === 1 && dimB === 1 && dimC === 1;\n}\nexport function eitherStridesOrDilationsAreOne(strides, dilations) {\n    return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n/**\n * Convert Conv2D dataFormat from 'NHWC'|'NCHW' to\n *    'channelsLast'|'channelsFirst'\n * @param dataFormat in 'NHWC'|'NCHW' mode\n * @return dataFormat in 'channelsLast'|'channelsFirst' mode\n * @throws unknown dataFormat\n */\nexport function convertConv2DDataFormat(dataFormat) {\n    if (dataFormat === 'NHWC') {\n        return 'channelsLast';\n    }\n    else if (dataFormat === 'NCHW') {\n        return 'channelsFirst';\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n}\n//# sourceMappingURL=conv_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction avgPool_(x, filterSize, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'avgPool', 'float32');\n    const dilations = 1;\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in avgPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        save([x4D]);\n        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n            util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n            return x4D.clone();\n        }\n        return backend.avgPool(x4D, convInfo);\n    };\n    const inputs = { x: x4D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool, attrs);\n    res = cast(res, $x.dtype);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const avgPool = op({ avgPool_ });\n//# sourceMappingURL=avg_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { deprecationWarn } from '../globals';\nimport { AvgPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D average pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.avgPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates:\n *     `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction avgPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat = 'NDHWC', dilations) {\n    if (dilations == null) {\n        dilations = [1, 1, 1];\n    }\n    else {\n        deprecationWarn('dilations is deprecated, this field will be gone in ' +\n            'v3.0.0.');\n    }\n    const $x = convertToTensor(x, 'x', 'avgPool3d', 'float32');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in avgPool3d: Only NDHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool3d: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in avgPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1, 1];\n        }\n        const convInfo = conv_util.computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);\n        save([x5D]);\n        return backend.avgPool3d(x5D, convInfo);\n    };\n    const inputs = { x: x5D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat, dilations };\n    let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool3D, attrs);\n    res = cast(res, x5D.dtype);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const avgPool3d = op({ avgPool3d_ });\n//# sourceMappingURL=avg_pool_3d.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nexport function assertParamsConsistent(shapes, axis) {\n    const rank = shapes[0].length;\n    shapes.forEach((shape, i) => {\n        util.assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same ` +\n            `as the rank of the rest (${rank})`);\n    });\n    util.assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);\n    const firstShape = shapes[0];\n    shapes.forEach((shape, i) => {\n        for (let r = 0; r < rank; r++) {\n            util.assert((r === axis) || (shape[r] === firstShape[r]), () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) ` +\n                `does not match the shape of the rest (${firstShape}) ` +\n                `along the non-concatenated axis ${i}.`);\n        }\n    });\n}\nexport function computeOutShape(shapes, axis) {\n    const outputShape = shapes[0].slice();\n    for (let i = 1; i < shapes.length; i++) {\n        outputShape[axis] += shapes[i][axis];\n    }\n    return outputShape;\n}\n//# sourceMappingURL=concat_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Concat } from '../kernel_names';\nimport { convertToTensorArray } from '../tensor_util_env';\nimport { assert, parseAxisParam, sizeFromShape } from '../util';\nimport { assertParamsConsistent, computeOutShape } from './concat_util';\nimport { op } from './operation';\nimport { tensor } from './tensor';\n/**\n * Concatenates a list of `tf.Tensor`s along a given axis.\n *\n * The tensors ranks and types must match, and their sizes must match in all\n * dimensions except `axis`.\n *\n * Also available are stricter rank-specific methods that assert that\n * `tensors` are of the given rank:\n *   - `tf.concat1d`\n *   - `tf.concat2d`\n *   - `tf.concat3d`\n *   - `tf.concat4d`\n *\n * Except `tf.concat1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * a.concat(b).print();  // or a.concat(b)\n * ```\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.concat([a, b, c]).print();\n * ```\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [10, 20]]);\n * const b = tf.tensor2d([[3, 4], [30, 40]]);\n * const axis = 1;\n * tf.concat([a, b], axis).print();\n * ```\n * @param tensors A list of tensors to concatenate.\n * @param axis The axis to concate along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction concat_(tensors, axis = 0) {\n    assert(tensors.length >= 1, () => 'Pass at least one tensor to concat');\n    let $tensors = convertToTensorArray(tensors, 'tensors', 'concat');\n    if ($tensors[0].dtype === 'complex64') {\n        $tensors.forEach(tensor => {\n            if (tensor.dtype !== 'complex64') {\n                throw new Error(`Cannot concatenate complex64 tensors with a tensor\n          with dtype ${tensor.dtype}. `);\n            }\n        });\n    }\n    const forward = (backend, save) => {\n        const $axis = parseAxisParam(axis, $tensors[0].shape)[0];\n        const outShape = computeOutShape($tensors.map(t => t.shape), $axis);\n        if (sizeFromShape(outShape) === 0) {\n            return tensor([], outShape);\n        }\n        // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n        $tensors = $tensors.filter(t => t.size > 0);\n        if ($tensors.length === 1) {\n            return $tensors[0];\n        }\n        const shapes = $tensors.map(t => t.shape);\n        assertParamsConsistent(shapes, $axis);\n        const res = backend.concat($tensors, $axis);\n        save($tensors);\n        return res;\n    };\n    const inputs = $tensors;\n    const attr = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Concat, attr);\n}\nexport const concat = op({ concat_ });\n//# sourceMappingURL=concat.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sigmoid } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sigmoid element-wise, `1 / (1 + exp(-x))`\n *\n * ```js\n * const x = tf.tensor1d([0, -1, 2, -3]);\n *\n * x.sigmoid().print();  // or tf.sigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sigmoid_(x) {\n    const $x = convertToTensor(x, 'x', 'sigmoid');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sigmoid($x);\n        save([res]);\n        return res;\n    }, inputs, null /* grad */, Sigmoid);\n}\nexport const sigmoid = op({ sigmoid_ });\n//# sourceMappingURL=sigmoid.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Slice } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport * as slice_util from './slice_util';\n/**\n * Extracts a slice from a `tf.Tensor` starting at coordinates `begin`\n * and is of size `size`.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `x` is of the given rank:\n *   - `tf.slice1d`\n *   - `tf.slice2d`\n *   - `tf.slice3d`\n *   - `tf.slice4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.slice([1], [2]).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * x.slice([1, 0], [1, 2]).print();\n * ```\n * @param x The input `tf.Tensor` to slice from.\n * @param begin The coordinates to start the slice from. The length can be\n *     less than the rank of x - the rest of the axes will have implicit 0 as\n *     start. Can also be a single number, in which case it specifies the\n *     first axis.\n * @param size The size of the slice. The length can be less than the rank of\n *     x - the rest of the axes will have implicit -1. A value of -1 requests\n *     the rest of the dimensions in the axis. Can also be a single number,\n *     in which case it specifies the size of the first axis.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction slice_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice');\n    if ($x.rank === 0) {\n        throw new Error('Slicing scalar is not possible');\n    }\n    const forward = (backend, save) => {\n        const [begin_, size_] = slice_util.parseSliceParams($x, begin, size);\n        slice_util.assertParamsValid($x, begin_, size_);\n        save([$x]);\n        return backend.slice($x, begin_, size_);\n    };\n    const inputs = { x: $x };\n    const attrs = { begin, size };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Slice, attrs);\n}\nexport const slice = op({ slice_ });\n//# sourceMappingURL=slice.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, 70]);\n *\n * x.tanh().print();  // or tf.tanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tanh_(x) {\n    const $x = convertToTensor(x, 'x', 'tanh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const y = backend.tanh($x);\n        save([y]);\n        return y;\n    }, inputs, null /* grad */, Tanh);\n}\nexport const tanh = op({ tanh_ });\n//# sourceMappingURL=tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { add } from './add';\nimport { concat } from './concat';\nimport { matMul } from './mat_mul';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { slice } from './slice';\nimport { tanh } from './tanh';\n/**\n * Computes the next state and output of a BasicLSTMCell.\n *\n * Returns `[newC, newH]`.\n *\n * Derived from tf.contrib.rnn.BasicLSTMCell.\n *\n * @param forgetBias Forget bias for the cell.\n * @param lstmKernel The weights for the cell.\n * @param lstmBias The bias for the cell.\n * @param data The input to the cell.\n * @param c Previous cell state.\n * @param h Previous cell output.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {\n    const $forgetBias = convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');\n    const $lstmKernel = convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');\n    const $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');\n    const $data = convertToTensor(data, 'data', 'basicLSTMCell');\n    const $c = convertToTensor(c, 'c', 'basicLSTMCell');\n    const $h = convertToTensor(h, 'h', 'basicLSTMCell');\n    const combined = concat([$data, $h], 1);\n    const weighted = matMul(combined, $lstmKernel);\n    const res = add(weighted, $lstmBias);\n    // i = input_gate, j = new_input, f = forget_gate, o = output_gate\n    const batchSize = res.shape[0];\n    const sliceCols = res.shape[1] / 4;\n    const sliceSize = [batchSize, sliceCols];\n    const i = slice(res, [0, 0], sliceSize);\n    const j = slice(res, [0, sliceCols], sliceSize);\n    const f = slice(res, [0, sliceCols * 2], sliceSize);\n    const o = slice(res, [0, sliceCols * 3], sliceSize);\n    const newC = add(mul(sigmoid(i), tanh(j)), mul($c, sigmoid(add($forgetBias, f))));\n    const newH = mul(tanh(newC), sigmoid(o));\n    return [newC, newH];\n}\nexport const basicLSTMCell = op({ basicLSTMCell_ });\n//# sourceMappingURL=basic_lstm_cell.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchToSpaceND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation reshapes the \"batch\" dimension 0 into `M + 1` dimensions of\n * shape `blockShape + [batch]`, interleaves these blocks back into the grid\n * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with\n * the same rank as the input. The spatial dimensions of this intermediate\n * result are then optionally cropped according to `crops` to produce the\n * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise\n * description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n * const blockShape = [2, 2];\n * const crops = [[0, 0], [0, 0]];\n *\n * x.batchToSpaceND(blockShape, crops).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param crops A 2-D array.  Must have shape `[M, 2]`, all values must be >= 0.\n * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input\n * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required\n * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,\n * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,\n * x.shape[N-1]]`\n *\n * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /\n * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`\n * according to `crops` to produce the output of shape: `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],\n * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -\n * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction batchToSpaceND_(x, blockShape, crops) {\n    const $x = convertToTensor(x, 'x', 'batchToSpaceND');\n    const prod = blockShape.reduce((a, b) => a * b);\n    util.assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);\n    util.assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);\n    util.assert($x.shape[0] % prod === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of ` +\n        `the elements of blockShape ${blockShape.join(' * ')} === ${prod}`);\n    const forward = backend => {\n        return backend.batchToSpaceND($x, blockShape, crops);\n    };\n    const inputs = { x: $x };\n    const attrs = { blockShape, crops };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, BatchToSpaceND, attrs);\n}\nexport const batchToSpaceND = op({ batchToSpaceND_ });\n//# sourceMappingURL=batch_to_space_nd.js.map", "import { reshape } from './reshape';\nexport function xAs4D(x) {\n    let x4D;\n    if (x.rank === 0 || x.rank === 1) {\n        x4D = reshape(x, [1, 1, 1, x.size]);\n    }\n    else if (x.rank === 2) {\n        x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n    }\n    else if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    else {\n        x4D = x;\n    }\n    return x4D;\n}\n//# sourceMappingURL=batchnorm_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FusedBatchNorm } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { xAs4D } from './batchnorm_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Batch normalization.\n *\n * As described in\n * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).\n *\n * Mean, variance, scale, and offset can be of two shapes:\n *   - The same shape as the input.\n *   - In the common case, the depth dimension is the last dimension of x, so\n *     the values would be an `tf.Tensor1D` of shape [depth].\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that parameters passed are of given rank\n *   - `tf.batchNorm2d`\n *   - `tf.batchNorm3d`\n *   - `tf.batchNorm4d`\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction batchNorm_(x, mean, variance, offset, scale, varianceEpsilon) {\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($mean.rank === $variance.rank, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert($offset == null || $mean.rank === $offset.rank, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert($scale == null || $mean.rank === $scale.rank, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    const x4D = xAs4D($x);\n    const forward = (backend, save) => {\n        save([x4D, $mean, $variance, $scale]);\n        return backend.batchNorm(x4D, as1DOr4D($mean), as1DOr4D($variance), as1DOr4D($offset), as1DOr4D($scale), varianceEpsilon);\n    };\n    const inputs = {\n        x: x4D,\n        scale: $scale,\n        offset: $offset,\n        mean: $mean,\n        variance: $variance\n    };\n    const attrs = { varianceEpsilon };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, FusedBatchNorm, attrs);\n    return reshape(res, $x.shape);\n}\nfunction as1DOr4D(x) {\n    if (x == null) {\n        return null;\n    }\n    if (x.rank === 0) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [x.size]);\n    }\n    else if (x.rank === 1) {\n        return x;\n    }\n    else if (x.rank === 2) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n    }\n    else if (x.rank === 3) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    return x;\n}\nexport const batchNorm = op({ batchNorm_ });\n//# sourceMappingURL=batchnorm.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 2D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm2d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm2d = op({ batchNorm2d_ });\n//# sourceMappingURL=batchnorm2d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 3D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm3d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm3d = op({ batchNorm3d_ });\n//# sourceMappingURL=batchnorm3d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 4D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm4d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm4d = op({ batchNorm4d_ });\n//# sourceMappingURL=batchnorm4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BroadcastTo } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { clone } from './clone';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Broadcast an array to a compatible shape NumPy-style.\n *\n * The tensor's shape is compared to the broadcast shape from end to beginning.\n * Ones are prepended to the tensor's shape until is has the same length as\n * the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is\n * already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then\n * the input tensor is tiled N times along that axis (using tf.tile).\n *\n * @param input The tensor that is to be broadcasted.\n * @param shape The input is to be broadcast to this shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction broadcastTo_(x, shape) {\n    let input = convertToTensor(x, 'broadcastTo', 'x');\n    const xShape = input.shape;\n    if (shape.some(d => !(d > 0) || d % 1 !== 0)) {\n        throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);\n    }\n    if (shape.length < input.rank) {\n        throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input.rank}.`);\n    }\n    if (shape.length > input.rank) {\n        const newShape = input.shape.slice();\n        while (newShape.length < shape.length) {\n            newShape.unshift(1);\n        }\n        input = reshape(input, newShape);\n    }\n    const inputShape = input.shape;\n    const reps = Array.from(shape);\n    for (let i = shape.length - 1; i >= 0; i--) {\n        if (inputShape[i] === shape[i]) {\n            reps[i] = 1;\n        }\n        else if (input.shape[i] !== 1) {\n            throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);\n        }\n    }\n    const axes = reps.map((n, i) => n > 1 ? i : -1).filter(i => i >= 0);\n    if (axes.length === 0) {\n        return clone(input);\n    }\n    const forward = (backend) => backend.tile(input, reps);\n    const inputs = { x: input };\n    const attrs = { shape, inputShape };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, BroadcastTo, attrs);\n}\nexport const broadcastTo = op({ broadcastTo_ });\n//# sourceMappingURL=broadcast_to.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Ceil } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.ceil().print();  // or tf.ceil(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction ceil_(x) {\n    const $x = convertToTensor(x, 'x', 'ceil');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.ceil($x), inputs, null /* grad */, Ceil);\n}\nexport const ceil = op({ ceil_ });\n//# sourceMappingURL=ceil.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ClipByValue } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)\n * ```\n * @param x The input tensor.\n * @param clipValueMin Lower-bound of range to be clipped to.\n * @param clipValueMax Upper-bound of range to be clipped to.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction clipByValue_(x, clipValueMin, clipValueMax) {\n    const $x = convertToTensor(x, 'x', 'clipByValue');\n    util.assert((clipValueMin <= clipValueMax), () => `Error in clip: min (${clipValueMin}) must be ` +\n        `less than or equal to max (${clipValueMax}).`);\n    const inputs = { x: $x };\n    const attrs = { clipValueMin, clipValueMax };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.clip($x, clipValueMin, clipValueMax);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, ClipByValue, attrs);\n}\nexport const clipByValue = op({ clipByValue_ });\n//# sourceMappingURL=clip_by_value.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(3) = |r1, g1, b1|\n * B: shape(2) = |r2, g2|\n * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @return The concatenated array.\n */\nfunction concat1d_(tensors) {\n    return concat(tensors, 0 /* axis */);\n}\nexport const concat1d = op({ concat1d_ });\n//# sourceMappingURL=concat_1d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *\n * B: shape(2, 3) = | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * C = tf.concat2d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *                  | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * if axis = 1:\n * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |\n *                   | r2, g2, b2, r4, g4, b4 |\n *\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concatenate along.\n * @return The concatenated array.\n */\nfunction concat2d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat2d = op({ concat2d_ });\n//# sourceMappingURL=concat_2d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor3D`s along an axis.\n * See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *\n * B: shape(2, 1, 3) = | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * C = tf.concat3d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *                     | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * if axis = 1:\n * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |\n *                     | r2, g2, b2, r4, g4, b4 |\n *\n * if axis = 2:\n * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |\n *                      | r2, g2, b2, r4, g4, b4 |\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat3d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat3d = op({ concat3d_ });\n//# sourceMappingURL=concat_3d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor4D`s along an axis.\n * See `concat` for details.\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat4d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat4d = op({ concat4d_ });\n//# sourceMappingURL=concat_4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 2D convolution over the input x.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv2d');\n    const $filter = convertToTensor(filter, 'filter', 'conv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n    util.assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match ` +\n        `input depth for filter ${$filter.shape[2]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const forward = (backend, save) => {\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        const res = backend.conv2d(x4D, $filter, convInfo);\n        save([x4D, $filter]);\n        return res;\n    };\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv2D, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const conv2d = op({ conv2d_ });\n//# sourceMappingURL=conv2d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 1D convolution over the input x.\n *\n * @param x The input tensor, of rank 3 or rank 2, of shape\n *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.\n * @param filter The filter, rank 3, of shape\n *     `[filterWidth, inDepth, outDepth]`.\n * @param stride The number of entries by which the filter is moved right at\n *     each step.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from \"NWC\", \"NCW\". Defaults to \"NWC\",\n *     the data is stored in the order of [batch, in_width, in_channels]. Only\n *     \"NWC\" is currently supported.\n * @param dilation The dilation rate in which we sample input values in\n *     atrous convolution. Defaults to `1`. If it is greater than 1, then\n *     stride must be `1`.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv1d_(x, filter, stride, pad, dataFormat = 'NWC', dilation = 1, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv1d');\n    const $filter = convertToTensor(filter, 'filter', 'conv1d');\n    let x3D = $x;\n    let reshapedTo3D = false;\n    if ($x.rank === 2) {\n        reshapedTo3D = true;\n        x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);\n    }\n    util.assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n    util.assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv1d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    util.assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +\n        `input depth for filter ${$filter.shape[1]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(stride, dilation), () => 'Error in conv1D: Either stride or dilation must be 1. ' +\n        `Got stride ${stride} and dilation '${dilation}'`);\n    util.assert(dataFormat === 'NWC', () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);\n    const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);\n    const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);\n    const strides = [1, stride];\n    const dilations = [1, dilation];\n    const conv2dDataFormat = 'NHWC';\n    const res = conv2d(input4D, filter4D, strides, pad, conv2dDataFormat, dilations, dimRoundingMode);\n    if (reshapedTo3D) {\n        return reshape(res, [res.shape[2], res.shape[3]]);\n    }\n    return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);\n}\nexport const conv1d = op({ conv1d_ });\n//# sourceMappingURL=conv1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropInput } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 2D convolution.\n *\n * @param xShape The shape of the input: [batch, height, width, inDepth].\n * If length of 3, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 4 or rank 3 of shape\n *   `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction conv2DBackpropInput_(xShape, dy, filter, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n    util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n        `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n    let xShape4D = xShape;\n    let dy4D = dy;\n    let reshapedTo4D = false;\n    if (dy.rank === 3) {\n        reshapedTo4D = true;\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n        xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n    }\n    util.assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ` +\n        `${xShape4D.length}.`);\n    util.assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got ` +\n        `rank ${dy4D.rank}`);\n    util.assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got ` +\n        `rank ${filter.rank}`);\n    const inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1];\n    const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n    util.assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must ` +\n        `match input depth for filter ${filter.shape[2]}.`);\n    util.assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must ` +\n        `match output depth for filter ${filter.shape[3]}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2dDerInput: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const dilations = 1;\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(xShape4D, filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        const res = backend.conv2dDerInput(dy4D, filter, convInfo);\n        save([dy4D, filter]);\n        return res;\n    };\n    const inputs = { dy: dy4D, filter };\n    const attrs = { strides, pad, dataFormat, dimRoundingMode, inputShape: xShape4D };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv2DBackpropInput, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const conv2DBackpropInput = op({ conv2DBackpropInput_ });\n//# sourceMappingURL=conv2d_backprop_input.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport { conv2DBackpropInput } from './conv2d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 2D convolution of an image, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 4 or rank 3, of shape\n *   `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 4 or rank 3:\n *     `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.\n * @param strides The strides of the original convolution:\n *     `[strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n * @param dimRoundingMode The rounding mode used when computing output\n *    dimensions if pad is a number. If none is provided, it will not round\n *    and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2dTranspose_(x, filter, outputShape, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv2dTranspose');\n    const $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');\n    return conv2DBackpropInput(outputShape, $x, $filter, strides, pad, 'NHWC', dimRoundingMode);\n}\nexport const conv2dTranspose = op({ conv2dTranspose_ });\n//# sourceMappingURL=conv2d_transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { eitherStridesOrDilationsAreOne } from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 3D convolution over the input x.\n *\n * @param x The input tensor, of rank 5 or rank 4, of shape\n *     `[batch, depth, height, width, channels]`. If rank 4,\n * batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.\n *      inChannels must match between input and filter.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationDepth, dilationHeight,\n *     dilationWidth]` in which we sample input values across the height\n *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.\n *     If `dilations` is a single number, then\n *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater\n *     than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3d_(x, filter, strides, pad, dataFormat = 'NDHWC', dilations = [1, 1, 1]) {\n    const $x = convertToTensor(x, 'x', 'conv3d');\n    const $filter = convertToTensor(filter, 'filter', 'conv3d');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n    util.assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +\n        `input depth for filter ${$filter.shape[3]}.`);\n    util.assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv3D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computeConv3DInfo(x5D.shape, $filter.shape, strides, dilations, pad);\n        const res = backend.conv3d(x5D, $filter, convInfo);\n        save([x5D, $filter]);\n        return res;\n    };\n    const inputs = { x: x5D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv3D, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const conv3d = op({ conv3d_ });\n//# sourceMappingURL=conv3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3DBackpropInputV2 } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 3D convolution.\n *\n * @param xShape The shape of the input: [batch, depth, height, width,\n * in_channels]. If length of 4, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 5 or rank 4 of shape\n *   `[batch, outDepth, outHeight, outWidth, in_channels]`.\n * If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n */\nfunction conv3DBackpropInput_(xShape, dy, filter, strides, pad) {\n    util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n        `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n    let xShape5D = xShape;\n    let dy5D = dy;\n    let reshapedTo5D = false;\n    if (dy.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n        xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n    }\n    const inDepth = xShape5D[4];\n    const outDepth = dy5D.shape[4];\n    util.assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ` +\n        `${xShape5D.length}.`);\n    util.assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got ` +\n        `rank ${dy5D.rank}`);\n    util.assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got ` +\n        `rank ${filter.rank}`);\n    util.assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must ` +\n        `match input depth for filter ${filter.shape[3]}.`);\n    util.assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must ` +\n        `match output depth for filter ${filter.shape[4]}.`);\n    const forward = backend => {\n        const dilations = 1;\n        const convInfo = conv_util.computeConv3DInfo(xShape5D, filter.shape, strides, dilations, pad);\n        return backend.conv3dDerInput(dy5D, filter, convInfo);\n    };\n    const inputs = { dy: dy5D };\n    const attrs = { pad };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropInputV2, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const conv3DBackpropInput = op({ conv3DBackpropInput_ });\n//# sourceMappingURL=conv3d_backprop_input.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport { conv3DBackpropInput } from './conv3d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 3D convolution of a volume, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 5 or rank 4, of shape\n *   `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[depth, filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 5 or rank 4:\n *     `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is\n *    assumed.\n * @param strides The strides of the original convolution:\n *     `[strideDepth, strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3dTranspose_(x, filter, outputShape, strides, pad) {\n    const $x = convertToTensor(x, 'x', 'conv3dTranspose');\n    const $filter = convertToTensor(filter, 'filter', 'conv3dTranspose');\n    return conv3DBackpropInput(outputShape, $x, $filter, strides, pad);\n}\nexport const conv3dTranspose = op({ conv3dTranspose_ });\n//# sourceMappingURL=conv3d_transpose.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.cos().print();  // or tf.cos(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cos_(x) {\n    const $x = convertToTensor(x, 'x', 'cos');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.cos($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Cos);\n}\nexport const cos = op({ cos_ });\n//# sourceMappingURL=cos.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.cosh().print();  // or tf.cosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cosh_(x) {\n    const $x = convertToTensor(x, 'x', 'cosh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.cosh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Cosh);\n}\nexport const cosh = op({ cosh_ });\n//# sourceMappingURL=cosh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cumsum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { getAxesPermutation, getInnerMostAxes, getUndoAxesPermutation } from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Computes the cumulative sum of a `tf.Tensor` along `axis`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4]);\n * x.cumsum().print();\n * ```\n * ```js\n * const x = tf.tensor([[1, 2], [3, 4]]);\n * x.cumsum().print();\n * ```\n *\n * @param x The input tensor to be summed.\n * @param axis The axis along which to sum. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative sum. Optional.\n *     Defaults to false. If set to true then the sum of each tensor entry\n *     does not include its own value, but only the values previous to it\n *     along the specified axis.\n * @param reverse Whether to sum in the opposite direction. Optional.\n *     Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Scan'}\n */\nfunction cumsum_(x, axis = 0, exclusive = false, reverse = false) {\n    const $x = convertToTensor(x, 'x', 'cumsum');\n    const forward = (backend, save) => {\n        const permutation = getAxesPermutation([axis], $x.rank);\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n        }\n        const permutedAxis = getInnerMostAxes(1, $x.rank)[0];\n        let value = backend.cumsum(permutedX, permutedAxis, exclusive, reverse);\n        save([$x]);\n        if (permutation != null) {\n            const reversePermutation = getUndoAxesPermutation(permutation);\n            value = transpose(value, reversePermutation);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, exclusive, reverse };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Cumsum, attrs);\n}\nexport const cumsum = op({ cumsum_ });\n//# sourceMappingURL=cumsum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthToSpace } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Rearranges data from depth into blocks of spatial data. More specifically,\n * this op outputs a copy of the input tensor where values from the `depth`\n * dimension are moved in spatial blocks to the `height` and `width` dimensions.\n * The attr `blockSize` indicates the input block size and how the data is\n * moved.\n *\n *  - Chunks of data of size `blockSize * blockSize` from depth are rearranged\n * into non-overlapping blocks of size `blockSize x blockSize`\n *\n *  - The width the output tensor is `inputWidth * blockSize`, whereas the\n * height is `inputHeight * blockSize`\n *\n *  - The Y, X coordinates within each block of the output image are determined\n * by the high order component of the input channel index\n *\n *  - The depth of the input tensor must be divisible by `blockSize *\n * blockSize`\n *\n * The `dataFormat` attr specifies the layout of the input and output tensors\n * with the following options: \"NHWC\": [ `batch, height, width, channels` ]\n * \"NCHW\": [ `batch, channels, height, width` ]\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);\n * const blockSize = 2;\n * const dataFormat = \"NHWC\";\n *\n * tf.depthToSpace(x, blockSize, dataFormat).print();\n * ```\n *\n * @param x The input tensor of rank 4\n * @param blockSIze  An `int` that is `>= 2`. The size of the spatial block\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to \"NHWC\"\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction depthToSpace_(x, blockSize, dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'depthToSpace');\n    const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];\n    const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];\n    const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];\n    util.assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputHeight} and ${blockSize}  for depthToSpace with input shape\n    ${$x.shape}`);\n    util.assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputWidth} and ${blockSize} for depthToSpace with input shape\n        ${$x.shape}`);\n    util.assert((inputDepth % (blockSize * blockSize) === 0), () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);\n    const forward = backend => backend.depthToSpace($x, blockSize, dataFormat);\n    const inputs = { x: $x };\n    const attrs = { blockSize, dataFormat };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, DepthToSpace, attrs);\n}\nexport const depthToSpace = op({ depthToSpace_ });\n//# sourceMappingURL=depth_to_space.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNative } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Depthwise 2D convolution.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction depthwiseConv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n    const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels ` +\n        `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n        `filter ${$filter.shape[2]}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1];\n        }\n        util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +\n            `1. Got strides ${strides} and dilations '${dilations}'`);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n        const res = backend.depthwiseConv2D(x4D, $filter, convInfo);\n        save([x4D, $filter]);\n        return res;\n    };\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, DepthwiseConv2dNative, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const depthwiseConv2d = op({ depthwiseConv2d_ });\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Diag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Returns a diagonal tensor with a given diagonal values.\n *\n * Given a diagonal, this operation returns a tensor with the diagonal and\n * everything else padded with zeros.\n *\n * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor\n * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * tf.diag(x).print()\n * ```\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4, 5, 6, 6, 8], [4, 2])\n *\n * tf.diag(x).print()\n * ```\n * @param x The input tensor.\n */\nfunction diag_(x) {\n    const $x = convertToTensor(x, 'x', 'diag');\n    const forward = backend => {\n        const flat = reshape($x, [$x.size]);\n        const result = backend.diag(flat);\n        const outShape = [...x.shape, ...x.shape];\n        return reshape(result, outShape);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Diag);\n}\nexport const diag = op({ diag_ });\n//# sourceMappingURL=diag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Dilation2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the grayscale dilation over the input `x`.\n *\n * @param x The input tensor, rank 3 or rank 4 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter tensor, rank 3, of shape\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat Specify the data format of the input and output data.\n *      Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the\n *      default format \"NHWC\", the data is stored in the order of: [batch,\n *      height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations`\n *     is a single number, then `dilationHeight == dilationWidth`. If it is\n *     greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction dilation2d_(x, filter, strides, pad, dilations = [1, 1], dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'dilation2d');\n    const $filter = convertToTensor(filter, 'filter', 'dilation2d');\n    util.assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ` +\n        `${$x.rank}.`);\n    util.assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(dataFormat === 'NHWC', () => `Error in dilation2d: Only NHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n        reshapedTo4D = true;\n    }\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dilations };\n    const res = ENGINE.runKernel(Dilation2D, inputs, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const dilation2d = op({ dilation2d_ });\n//# sourceMappingURL=dilation2d.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the dimensions in the input shape that are broadcasted to\n * produce the provided output shape.\n *\n * The returned dimensions are 0-indexed and sorted. An example:\n * inShape = [4, 1, 3]\n * outShape = [5, 4, 3, 3]\n * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.\n */\nexport function getBroadcastDims(inShape, outShape) {\n    const inRank = inShape.length;\n    const dims = [];\n    for (let i = 0; i < inRank; i++) {\n        const dim = inRank - 1 - i;\n        const a = inShape[dim] || 1;\n        const b = outShape[outShape.length - 1 - i] || 1;\n        if (b > 1 && a === 1) {\n            dims.unshift(dim);\n        }\n    }\n    return dims;\n}\n/**\n * Returns the axes in the output space that should be reduced to produce\n * the input space.\n */\nexport function getReductionAxes(inShape, outShape) {\n    const result = [];\n    for (let i = 0; i < outShape.length; i++) {\n        const inDim = inShape[inShape.length - i - 1];\n        const outAxis = outShape.length - i - 1;\n        const outDim = outShape[outAxis];\n        if (inDim == null || (inDim === 1 && outDim > 1)) {\n            result.unshift(outAxis);\n        }\n    }\n    return result;\n}\nexport function assertAndGetBroadcastShape(shapeA, shapeB) {\n    const result = [];\n    const l = Math.max(shapeA.length, shapeB.length);\n    for (let i = 0; i < l; i++) {\n        let a = shapeA[shapeA.length - i - 1];\n        if (a == null) {\n            a = 1;\n        }\n        let b = shapeB[shapeB.length - i - 1];\n        if (b == null) {\n            b = 1;\n        }\n        if (a === 1) {\n            result.unshift(b);\n        }\n        else if (b === 1) {\n            result.unshift(a);\n        }\n        else if (a !== b) {\n            const errMsg = `Operands could not be broadcast together with shapes ` +\n                `${shapeA} and ${shapeB}.`;\n            throw Error(errMsg);\n        }\n        else {\n            result.unshift(a);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=broadcast_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Equal } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a == b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.equal(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction equal_(a, b) {\n    let $a = convertToTensor(a, 'a', 'equal');\n    let $b = convertToTensor(b, 'b', 'equal');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.equal($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null, Equal);\n}\nexport const equal = op({ equal_ });\n//# sourceMappingURL=equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SelectV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, assertShapesMatch } from '../util';\nimport { broadcastTo } from './broadcast_to';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the elements, either `a` or `b` depending on the `condition`.\n *\n * If the condition is true, select from `a`, otherwise select from `b`.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const a = tf.tensor1d([1 , 2, 3]);\n * const b = tf.tensor1d([-1, -2, -3]);\n *\n * a.where(cond, b).print();\n * ```\n *\n * @param condition The input condition. Must be of dtype bool.\n * @param a If `condition` is rank 1, `a` may have a higher rank but\n *     its first dimension must match the size of `condition`.\n * @param b A tensor with the same dtype as `a` and with shape that is\n *     compatible with `a`.\n * @return A tensor with same dtype as `a` and `b`, and shape that is\n *     broadcastable from `a` and `b`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction where_(condition, a, b) {\n    const $a = convertToTensor(a, 'a', 'where');\n    const $b = convertToTensor(b, 'b', 'where');\n    const $condition = convertToTensor(condition, 'condition', 'where', 'bool');\n    // TODO: move this logic to forward function when the broadcastTo op is\n    // implemented in WASM.\n    // Find the broadcastable shape for $a and $b.\n    const broadcastShape = assertAndGetBroadcastShape($a.shape, $b.shape);\n    const $broadcastedA = broadcastTo($a, broadcastShape);\n    const $broadcastedB = broadcastTo($b, broadcastShape);\n    if ($condition.rank === 1) {\n        // If condition rank is 1, then the first dimension must match the size of\n        // condition.\n        assert($condition.shape[0] === $a.shape[0], () => 'The first dimension of `a` must match the size of `condition`.');\n    }\n    if ($condition.rank !== 1) {\n        // A must have the same shape as condition.\n        assertShapesMatch($condition.shape, $broadcastedB.shape, 'Error in where: ');\n    }\n    const forward = (backend, save) => {\n        const res = backend.select($condition, $broadcastedA, $broadcastedB);\n        save([$condition]);\n        return res;\n    };\n    const inputs = {\n        condition: $condition,\n        t: $broadcastedA,\n        e: $broadcastedB\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, SelectV2);\n}\nexport const where = op({ where_ });\n//# sourceMappingURL=where.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ZerosLike } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.zerosLike(x).print();\n * ```\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction zerosLike_(x) {\n    const $x = convertToTensor(x, 'x', 'zerosLike');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.zerosLike($x), inputs, null /* grad */, ZerosLike);\n}\nexport const zerosLike = op({ zerosLike_ });\n//# sourceMappingURL=zeros_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { div } from './div';\nimport { equal } from './equal';\nimport { op } from './operation';\nimport { where } from './where';\nimport { zerosLike } from './zeros_like';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0\n * if denominator is 0.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n * const c = tf.tensor1d([0, 0, 0, 0]);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n * const c = tf.scalar(0);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction divNoNan_(a, b) {\n    // TODO: Make this into its own kernel.\n    let $a = convertToTensor(a, 'a', 'div');\n    let $b = convertToTensor(b, 'b', 'div');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const divResult = div($a, $b);\n    const zeros = zerosLike(divResult);\n    const bEqualsZero = equal($b, zeros);\n    return where(bEqualsZero, zeros, divResult);\n}\nexport const divNoNan = op({ divNoNan_ });\n//# sourceMappingURL=div_no_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor2d([[1, 2], [3, 4]]);\n * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n *\n * a.dot(b).print();  // or tf.dot(a, b)\n * b.dot(a).print();\n * b.dot(c).print();\n * ```\n * @param t1 The first tensor in the dot operation.\n * @param t2 The second tensor in the dot operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction dot_(t1, t2) {\n    const $t1 = convertToTensor(t1, 't1', 'dot');\n    const $t2 = convertToTensor(t2, 't2', 'dot');\n    util.assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ` +\n        `${$t1.rank} and ${$t2.rank}.`);\n    const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);\n    const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);\n    util.assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ` +\n        `${t1Inner} and ${t2Inner}.`);\n    if ($t1.rank === 1 && $t2.rank === 1) {\n        const t12D = reshape($t1, [1, -1]);\n        const t22D = reshape($t2, [-1, 1]);\n        const t1t2 = matMul(t12D, t22D);\n        return reshape(t1t2, []);\n    }\n    else if ($t1.rank === 1 && $t2.rank === 2) {\n        const t12D = reshape($t1, [1, -1]);\n        const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n        const t1t2 = matMul(t12D, t22D);\n        return reshape(t1t2, [t1t2.size]);\n    }\n    else if ($t1.rank === 2 && $t2.rank === 1) {\n        const t22D = reshape($t2, [-1, 1]);\n        const t1t2 = matMul($t1, t22D);\n        return reshape(t1t2, [t1t2.size]);\n    }\n    else {\n        const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n        const t1t2 = matMul($t1, t22D);\n        return t1t2;\n    }\n}\nexport const dot = op({ dot_ });\n//# sourceMappingURL=dot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Elu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential linear element-wise: `x > 0 ? e ^ x - 1 : 0`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 1, -3, 2]);\n *\n * x.elu().print();  // or tf.elu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction elu_(x) {\n    const $x = convertToTensor(x, 'x', 'elu');\n    const forward = (backend, save) => {\n        const y = backend.elu($x);\n        save([y]);\n        return y;\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Elu);\n}\nexport const elu = op({ elu_ });\n//# sourceMappingURL=elu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Erf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes gause error function of the input `tf.Tensor` element-wise:\n * `erf(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.erf().print(); // or tf.erf(x);\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction erf_(x) {\n    let $x = convertToTensor(x, 'x', 'erf');\n    util.assert($x.dtype === 'int32' || $x.dtype === 'float32', () => 'Input dtype must be `int32` or `float32`.');\n    if ($x.dtype === 'int32') {\n        $x = cast($x, 'float32');\n    }\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.erf($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Erf);\n}\nexport const erf = op({ erf_ });\n//# sourceMappingURL=erf.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Exp } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.exp().print();  // or tf.exp(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction exp_(x) {\n    const $x = convertToTensor(x, 'x', 'exp');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.exp($x);\n        save([res]);\n        return res;\n    }, inputs, null /* grad */, Exp);\n}\nexport const exp = op({ exp_ });\n//# sourceMappingURL=exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n * into the tensor's shape.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const axis = 1;\n * x.expandDims(axis).print();\n * ```\n *\n * @param x The input tensor whose dimensions to be expanded.\n * @param axis The dimension index at which to insert shape of `1`. Defaults\n *     to 0 (the first dimension).\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction expandDims_(x, axis = 0) {\n    const parseAs = null;\n    const $x = convertToTensor(x, 'x', 'expandDims', parseAs);\n    util.assert(axis <= $x.rank, () => 'Axis must be <= rank of the tensor');\n    const newShape = $x.shape.slice();\n    if (axis < 0) {\n        // Negative value is counted from the tail of rank.\n        util.assert(-($x.rank + 1) <= axis, () => `Axis must be in the interval [${-($x.rank + 1)}, ${$x.rank}]`);\n        axis = $x.rank + axis + 1;\n    }\n    newShape.splice(axis, 0, 1);\n    return reshape($x, newShape);\n}\nexport const expandDims = op({ expandDims_ });\n//# sourceMappingURL=expand_dims.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Expm1 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential of the input `tf.Tensor` minus one element-wise.\n * `e ^ x - 1`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.expm1().print();  // or tf.expm1(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction expm1_(x) {\n    const $x = convertToTensor(x, 'x', 'expm1');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.expm1($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Expm1);\n}\nexport const expm1 = op({ expm1_ });\n//# sourceMappingURL=expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tile } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Construct a tensor by repeating it the number of times given by reps.\n *\n * This operation creates a new tensor by replicating `input` `reps`\n * times. The output tensor's i'th dimension has `input.shape[i] *\n * reps[i]` elements, and the values of `input` are replicated\n * `reps[i]` times along the i'th dimension. For example, tiling\n * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n *\n * a.tile([2]).print();    // or a.tile([2])\n * ```\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.tile([1, 2]).print();  // or a.tile([1, 2])\n * ```\n * @param x The tensor to tile.\n * @param reps Determines the number of replications per dimension.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction tile_(x, reps) {\n    const parseAs = null;\n    const $x = convertToTensor(x, 'x', 'tile', parseAs);\n    util.assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n        `must match length of reps ${reps}.`);\n    const forward = (backend, save) => {\n        const res = backend.tile($x, reps);\n        save([$x]);\n        return res;\n    };\n    const inputsToSave = [$x];\n    const inputs = { x: $x };\n    const attrs = { reps };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Tile, attrs, inputsToSave);\n}\nexport const tile = op({ tile_ });\n//# sourceMappingURL=tile.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { expandDims } from './expand_dims';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { tile } from './tile';\n/**\n * Create an identity matrix.\n *\n * @param numRows Number of rows.\n * @param numColumns Number of columns. Defaults to `numRows`.\n * @param batchShape If provided, will add the batch shape to the beginning\n *   of the shape of the returned `tf.Tensor` by repeating the identity\n *   matrix.\n * @param dtype Data type.\n * @returns Identity matrix of the specified size and data type, possibly\n *   with batch repetition if `batchShape` is specified.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction eye_(numRows, numColumns, batchShape, dtype = 'float32') {\n    if (numColumns == null) {\n        numColumns = numRows;\n    }\n    const buff = buffer([numRows, numColumns], dtype);\n    const n = numRows <= numColumns ? numRows : numColumns;\n    for (let i = 0; i < n; ++i) {\n        buff.set(1, i, i);\n    }\n    const out = reshape(buff.toTensor(), [numRows, numColumns]);\n    if (batchShape == null) {\n        return out;\n    }\n    else {\n        if (batchShape.length === 1) {\n            return tile(expandDims(out, 0), [batchShape[0], 1, 1]);\n        }\n        else if (batchShape.length === 2) {\n            // tslint:disable-next-line:no-unnecessary-type-assertion\n            return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);\n        }\n        else if (batchShape.length === 3) {\n            // tslint:disable-next-line:no-unnecessary-type-assertion\n            return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [\n                batchShape[0], batchShape[1], batchShape[2], 1, 1\n            ]);\n        }\n        else {\n            throw new Error(`eye() currently supports only 1D and 2D ` +\n                // tslint:disable-next-line:no-any\n                `batchShapes, but received ${batchShape.length}D.`);\n        }\n    }\n}\nexport const eye = op({ eye_ });\n//# sourceMappingURL=eye.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Fill } from '../kernel_names';\n/**\n * Creates a `tf.Tensor` filled with a scalar value.\n *\n * ```js\n * tf.fill([2, 2], 4).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param value The scalar value to fill the tensor with.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction fill(shape, value, dtype) {\n    const attrs = { shape, value, dtype };\n    return ENGINE.runKernelFunc(backend => backend.fill(shape, value, dtype), {}, null, Fill, attrs);\n}\nexport { fill };\n//# sourceMappingURL=fill.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Floor } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.floor().print();  // or tf.floor(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction floor_(x) {\n    const $x = convertToTensor(x, 'x', 'floor');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.floor($x), inputs, null /* grad */, Floor);\n}\nexport const floor = op({ floor_ });\n//# sourceMappingURL=floor.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inputs of size above this threshold will be parallelized by calling multiple\n * shader programs.\n */\nimport { nearestDivisor } from '../util';\nexport const PARALLELIZE_THRESHOLD = 30;\nexport function computeOptimalWindowSize(inSize) {\n    if (inSize <= PARALLELIZE_THRESHOLD) {\n        return inSize;\n    }\n    return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n}\n//# sourceMappingURL=reduce_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nearestDivisor } from '../util';\nimport { PARALLELIZE_THRESHOLD } from './reduce_util';\nexport function segOpComputeOptimalWindowSize(inSize, numSegments) {\n    let done = false;\n    let res;\n    if (inSize <= PARALLELIZE_THRESHOLD) {\n        res = inSize;\n        done = true;\n    }\n    else {\n        res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n    }\n    while (!done) {\n        if (res > numSegments || res === inSize) {\n            done = true;\n        }\n        else {\n            res = nearestDivisor(inSize, res + 1);\n        }\n    }\n    return res;\n}\nexport function computeOutShape(aShape, axis, numSegments) {\n    const outShape = [];\n    const rank = aShape.length;\n    for (let dim = 0; dim < rank; dim++) {\n        if (dim !== axis) {\n            outShape.push(aShape[dim]);\n        }\n        else {\n            outShape.push(numSegments);\n        }\n    }\n    return outShape;\n}\nexport function collectGatherOpShapeInfo(x, indices, axis) {\n    const dimSize = x.shape[axis];\n    const outputShape = [];\n    let batchSize = 1;\n    let sliceSize = 1;\n    for (let i = 0; i < axis; i++) {\n        outputShape.push(x.shape[i]);\n        batchSize *= x.shape[i];\n    }\n    for (let i = 0; i < indices.rank; i++) {\n        outputShape.push(indices.shape[i]);\n    }\n    for (let i = axis + 1; i < x.rank; i++) {\n        outputShape.push(x.shape[i]);\n        sliceSize *= x.shape[i];\n    }\n    return { batchSize, sliceSize, dimSize, outputShape };\n}\n//# sourceMappingURL=segment_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { collectGatherOpShapeInfo } from './segment_util';\n/**\n * Gather slices from tensor `x`'s axis `axis` according to `indices`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const indices = tf.tensor1d([1, 3, 3], 'int32');\n *\n * x.gather(indices).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const indices = tf.tensor1d([1, 1, 0], 'int32');\n *\n * x.gather(indices).print();\n * ```\n * @param x The input tensor whose slices to be gathered.\n * @param indices The indices of the values to extract.\n * @param axis The axis over which to select values. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction gather_(x, indices, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'gather');\n    const $indices = convertToTensor(indices, 'indices', 'gather', 'int32');\n    const inputs = { x: $x, indices: $indices };\n    const attrs = { axis };\n    const forward = (backend, save) => {\n        const parsedAxis = parseAxisParam(axis, $x.shape)[0];\n        const shapeInfo = collectGatherOpShapeInfo($x, $indices, parsedAxis);\n        const res = backend.gather($x, reshape($indices, [$indices.size]), parsedAxis);\n        save([$x, $indices]);\n        return reshape(res, shapeInfo.outputShape);\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, GatherV2, attrs);\n}\nexport const gather = op({ gather_ });\n//# sourceMappingURL=gather.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Greater } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a > b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greater(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greater_(a, b) {\n    let $a = convertToTensor(a, 'a', 'greater');\n    let $b = convertToTensor(b, 'b', 'greater');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.greater($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Greater);\n}\nexport const greater = op({ greater_ });\n//# sourceMappingURL=greater.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GreaterEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a >= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greaterEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greaterEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'greaterEqual');\n    let $b = convertToTensor(b, 'b', 'greaterEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.greaterEqual($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, GreaterEqual);\n}\nexport const greaterEqual = op({ greaterEqual_ });\n//# sourceMappingURL=greater_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Imag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the imaginary part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the imaginary part of each element in input considered as a complex number.\n * If input is real, a tensor of all zeros is returned.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.imag(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction imag_(input) {\n    const $input = convertToTensor(input, 'input', 'imag');\n    const forward = (backend) => {\n        return backend.imag($input);\n    };\n    const inputs = { input: $input };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Imag);\n}\nexport const imag = op({ imag_ });\n//# sourceMappingURL=imag.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsFinite } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are finite.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isFinite().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isFinite_(x) {\n    const $x = convertToTensor(x, 'x', 'isFinite');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.isFinite($x), inputs, null /* grad */, IsFinite);\n}\nexport const isFinite = op({ isFinite_ });\n//# sourceMappingURL=is_finite.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsInf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are Infinity or -Infinity.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isInf().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isInf_(x) {\n    const $x = convertToTensor(x, 'x', 'isInf');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.isInf($x), inputs, null /* grad */, IsInf);\n}\nexport const isInf = op({ isInf_ });\n//# sourceMappingURL=is_inf.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsNan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * RReturns which elements of x are NaN.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isNaN().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isNaN_(x) {\n    const $x = convertToTensor(x, 'x', 'isNaN');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.isNaN($x), inputs, null /* grad */, IsNan);\n}\nexport const isNaN = op({ isNaN_ });\n//# sourceMappingURL=is_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Maximum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.maximumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * ```js\n * // Broadcast maximum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction maximum_(a, b) {\n    let $a = convertToTensor(a, 'a', 'maximum');\n    let $b = convertToTensor(b, 'b', 'maximum');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'bool') {\n        $a = cast($a, 'int32');\n        $b = cast($b, 'int32');\n    }\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.maximum($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Maximum);\n}\nexport const maximum = op({ maximum_ });\n//# sourceMappingURL=maximum.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { isTypedArray } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.scalar` as it makes the code more readable.\n *\n * ```js\n * tf.scalar(3.14).print();\n * ```\n *\n * @param value The value of the scalar.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function scalar(value, dtype) {\n    if (((isTypedArray(value) && dtype !== 'string') || Array.isArray(value)) &&\n        dtype !== 'complex64') {\n        throw new Error('Error creating a new Scalar: value must be a primitive ' +\n            '(number|boolean|string)');\n    }\n    if (dtype === 'string' && isTypedArray(value) &&\n        !(value instanceof Uint8Array)) {\n        throw new Error('When making a scalar from encoded string, ' +\n            'the value must be `Uint8Array`.');\n    }\n    const shape = [];\n    const inferredShape = [];\n    return makeTensor(value, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=scalar.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { maximum } from './maximum';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { scalar } from './scalar';\n/**\n * Computes leaky rectified linear element-wise.\n *\n * See\n * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](\n *     http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.leakyRelu(0.1).print();  // or tf.leakyRelu(x, 0.1)\n * ```\n * @param x The input tensor.\n * @param alpha The scaling factor for negative values, defaults to 0.2.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction leakyRelu_(x, alpha = 0.2) {\n    const $x = convertToTensor(x, 'x', 'leakyRelu');\n    return maximum(mul(scalar(alpha), $x), $x);\n}\nexport const leakyRelu = op({ leakyRelu_ });\n//# sourceMappingURL=leaky_relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Less } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a < b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.less(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction less_(a, b) {\n    let $a = convertToTensor(a, 'a', 'less');\n    let $b = convertToTensor(b, 'b', 'less');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.less($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Less);\n}\nexport const less = op({ less_ });\n//# sourceMappingURL=less.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LessEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a <= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.lessEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction lessEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'lessEqual');\n    let $b = convertToTensor(b, 'b', 'lessEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.lessEqual($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LessEqual);\n}\nexport const lessEqual = op({ lessEqual_ });\n//# sourceMappingURL=less_equal.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LinSpace } from '../kernel_names';\n/**\n * Return an evenly spaced sequence of numbers over the given interval.\n *\n * ```js\n * tf.linspace(0, 9, 10).print();\n * ```\n * @param start The start value of the sequence.\n * @param stop The end value of the sequence.\n * @param num The number of values to generate.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function linspace(start, stop, num) {\n    if (num <= 0) {\n        throw new Error('The number of values should be positive.');\n    }\n    const attrs = { start, stop, num };\n    return ENGINE.runKernelFunc(backend => backend.linspace(start, stop, num), {} /* inputs */, null /* grad */, LinSpace, attrs);\n}\n//# sourceMappingURL=linspace.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LRN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Normalizes the activation of a local neighborhood across or within\n * channels.\n *\n * @param x The input tensor. The 4-D input tensor is treated as a 3-D array\n *     of 1D vectors (along the last dimension), and each vector is\n *     normalized independently.\n * @param depthRadius The number of adjacent channels in the 1D normalization\n *     window.\n * @param bias A constant bias term for the basis.\n * @param alpha A scale factor, usually positive.\n * @param beta An exponent.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n    const $x = convertToTensor(x, 'x', 'localResponseNormalization');\n    util.assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${$x.rank}.`);\n    util.assert(util.isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an ` +\n        `integer but got depthRadius ${depthRadius}.`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    const forward = (backend, save) => {\n        const y = backend.localResponseNormalization4D(x4D, depthRadius, bias, alpha, beta);\n        save([x4D, y]);\n        return y;\n    };\n    const inputs = { x: x4D };\n    const attrs = { depthRadius, bias, alpha, beta };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, LRN, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    else {\n        return res;\n    }\n}\nexport const localResponseNormalization = op({ localResponseNormalization_ });\n//# sourceMappingURL=local_response_normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E]);\n *\n * x.log().print();  // or tf.log(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log_(x) {\n    const $x = convertToTensor(x, 'x', 'log');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.log($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Log);\n}\nexport const log = op({ log_ });\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log1p } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` plus one\n * element-wise: `ln(1 + x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E - 1]);\n *\n * x.log1p().print();  // or tf.log1p(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log1p_(x) {\n    const $x = convertToTensor(x, 'x', 'log1p');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.log1p($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Log1p);\n}\nexport const log1p = op({ log1p_ });\n//# sourceMappingURL=log1p.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { Tensor, Variable } from './tensor';\nimport { convertToTensor, convertToTensorArray } from './tensor_util_env';\nimport * as util from './util';\n/**\n * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the\n * gradient of `f(x)` with respect to `x`.\n *\n * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to\n * `x` is computed instead. `f(x)` must take a single tensor `x` and return a\n * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.grad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * g(x).print();\n * ```\n *\n * ```js\n * // f(x) = x ^ 3\n * const f = x => x.pow(tf.scalar(3, 'int32'));\n * // f'(x) = 3x ^ 2\n * const g = tf.grad(f);\n * // f''(x) = 6x\n * const gg = tf.grad(g);\n *\n * const x = tf.tensor1d([2, 3]);\n * gg(x).print();\n * ```\n *\n * @param f The function f(x), to compute gradient for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grad(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in grad(f) must be a function');\n    return (x, dy) => {\n        // x can be of any dtype, thus null as the last argument.\n        const $x = convertToTensor(x, 'x', 'tf.grad', null);\n        const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grad') : null;\n        return ENGINE.tidy(() => {\n            const { value, grads } = ENGINE.gradients(() => f($x), [$x], $dy);\n            if ($dy != null) {\n                util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grad(f)(x, dy) must match the shape ' +\n                    'returned by f(x)');\n            }\n            checkGrads(grads);\n            return grads[0];\n        });\n    };\n}\n/**\n * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,\n * which gives an array of gradients of `f()` with respect to each input\n * [`x1`,`x2`,...].\n *\n * If `dy` is passed when calling `g()`, the gradient of\n * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.\n * The provided `f` must take one or more tensors and return a single tensor\n * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df / da = b, df / db = a\n * const g = tf.grads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const [da, db] = g([a, b]);\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @param f The function `f(x1, x2,...)` to compute gradients for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grads(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in grads(f) must be a function');\n    return (args, dy) => {\n        util.assert(Array.isArray(args), () => 'The args passed in grads(f)(args) must be an array ' +\n            'of `Tensor`s or `TensorLike`s');\n        // args can be of any dtype, thus null as the last argument.\n        const $args = convertToTensorArray(args, 'args', 'tf.grads', null);\n        const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grads') : null;\n        return ENGINE.tidy(() => {\n            const { value, grads } = ENGINE.gradients(() => f(...$args), $args, $dy);\n            if ($dy != null) {\n                util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grads(f)([x1,...], dy) must ' +\n                    'match the shape returned by f([x1,...])');\n            }\n            checkGrads(grads);\n            return grads;\n        });\n    };\n}\n/**\n * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.valueAndGrad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * const {value, grad} = g(x);\n *\n * console.log('value');\n * value.print();\n * console.log('grad');\n * grad.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrad(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in valueAndGrad(f) must be a function');\n    return (x, dy) => {\n        util.assert(x instanceof Tensor, () => 'The x passed in valueAndGrad(f)(x) must be a tensor');\n        util.assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrad(f)(x, dy) must be a tensor');\n        const { grads, value } = ENGINE.gradients(() => f(x), [x], dy);\n        checkGrads(grads);\n        return { grad: grads[0], value };\n    };\n}\n/**\n * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df/da = b, df/db = a\n * const g = tf.valueAndGrads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const {value, grads} = g([a, b]);\n *\n * const [da, db] = grads;\n *\n * console.log('value');\n * value.print();\n *\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrads(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in valueAndGrads(f) must be a function');\n    return (args, dy) => {\n        util.assert(Array.isArray(args) && args.every(arg => arg instanceof Tensor), () => 'The args passed in valueAndGrads(f)(args) must be array of ' +\n            'tensors');\n        util.assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrads(f)(args, dy) must be a tensor');\n        const res = ENGINE.gradients(() => f(...args), args, dy);\n        if (dy != null) {\n            util.assertShapesMatch(res.value.shape, dy.shape, 'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +\n                'match the shape returned by f([x1,...])');\n        }\n        checkGrads(res.grads);\n        return res;\n    };\n}\n/**\n * Computes and returns the gradient of f(x) with respect to the list of\n * trainable variables provided by `varList`. If no list is provided, it\n * defaults to all trainable variables.\n *\n * ```js\n * const a = tf.variable(tf.tensor1d([3, 4]));\n * const b = tf.variable(tf.tensor1d([5, 6]));\n * const x = tf.tensor1d([1, 2]);\n *\n * // f(a, b) = a * x ^ 2 + b * x\n * const f = () => a.mul(x.square()).add(b.mul(x)).sum();\n * // df/da = x ^ 2, df/db = x\n * const {value, grads} = tf.variableGrads(f);\n *\n * Object.keys(grads).forEach(varName => grads[varName].print());\n * ```\n *\n * @param f The function to execute. f() should return a scalar.\n * @param varList The list of variables to compute the gradients with respect\n *     to. Defaults to all trainable variables.\n * @returns An object with the following keys and values:\n *   - `value`: The value of the function `f`.\n *   - `grads`: A map from the names of the variables to the gradients.\n *     If the `varList` argument is provided explicitly and contains a subset of\n *     non-trainable variables, this map in the return value will contain keys\n *     that map the names of the non-trainable variables to `null`.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction variableGrads(f, varList) {\n    util.assert(util.isFunction(f), () => 'The f passed in variableGrads(f) must be a function');\n    util.assert(varList == null ||\n        Array.isArray(varList) && varList.every(v => v instanceof Variable), () => 'The varList passed in variableGrads(f, varList) must be an array ' +\n        'of variables');\n    const specifiedVarList = varList != null;\n    if (!specifiedVarList) {\n        // Get all of the trainable variables.\n        varList = [];\n        for (const varName in ENGINE.registeredVariables) {\n            varList.push(ENGINE.registeredVariables[varName]);\n        }\n    }\n    const specifiedNonTrainable = specifiedVarList ? varList.filter(variable => !variable.trainable) : null;\n    // Prune non-trainable variables.\n    const originalVarCount = varList.length;\n    varList = varList.filter(variable => variable.trainable);\n    util.assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to ` +\n        `be trainable, but none of the ${originalVarCount} variables is ` +\n        `trainable.`);\n    const allowNoGradients = true;\n    const { value, grads } = ENGINE.gradients(f, varList, null, allowNoGradients);\n    util.assert(grads.some(g => g != null), () => 'Cannot find a connection between any variable and the result of ' +\n        'the loss function y=f(x). Please make sure the operations that ' +\n        'use variables are inside the function f passed to minimize().');\n    util.assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it ` +\n        `returned a rank-${value.rank} tensor`);\n    const namedGrads = {};\n    varList.forEach((v, i) => {\n        if (grads[i] != null) {\n            namedGrads[v.name] = grads[i];\n        }\n    });\n    if (specifiedNonTrainable != null) {\n        // If varList is explicitly provided and contains non-trainable values,\n        // add them to the returned gradients with `null` values.\n        specifiedNonTrainable.forEach(v => namedGrads[v.name] = null);\n    }\n    return { value, grads: namedGrads };\n}\n/**\n * Overrides the gradient computation of a function `f`.\n *\n * Takes a function\n * `f(...inputs, save) => {value: Tensor, gradFunc: (dy, saved) => Tensor[]}`\n * and returns another function `g(...inputs)` which takes the same inputs as\n * `f`. When called, `g` returns `f().value`. In backward mode, custom gradients\n * with respect to each input of `f` are computed using `f().gradFunc`.\n *\n * The `save` function passsed to `f` should be used for saving tensors needed\n * in the gradient. And the `saved` passed to the `gradFunc` is a\n * `NamedTensorMap`, which contains those saved tensor.\n *\n * ```js\n * const customOp = tf.customGrad((x, save) => {\n *   // Save x to make sure it's available later for the gradient.\n *   save([x]);\n *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);\n *   return {\n *     value: x.square(),\n *     // Note `saved.x` which points to the `x` we saved earlier.\n *     gradFunc: (dy, saved) => [dy.mul(saved[0].abs())]\n *   };\n * });\n *\n * const x = tf.tensor1d([-1, -2, 3]);\n * const dx = tf.grad(x => customOp(x));\n *\n * console.log(`f(x):`);\n * customOp(x).print();\n * console.log(`f'(x):`);\n * dx(x).print();\n * ```\n *\n * @param f The function to evaluate in forward mode, which should return\n *     `{value: Tensor, gradFunc: (dy, saved) => Tensor[]}`, where `gradFunc`\n *     returns the custom gradients of `f` with respect to its inputs.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction customGrad(f) {\n    return ENGINE.customGrad(f);\n}\nfunction checkGrads(grads) {\n    const numNullGradients = grads.filter(g => g == null).length;\n    if (numNullGradients > 0) {\n        throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.`);\n    }\n}\nexport { customGrad, variableGrads, valueAndGrad, valueAndGrads, grad, grads, };\n//# sourceMappingURL=gradients.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Negate } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes `-1 * x` element-wise.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);\n *\n * x.neg().print();  // or tf.neg(x)\n * ```\n *\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction neg_(x) {\n    const $x = convertToTensor(x, 'x', 'neg');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.neg($x), inputs, null /* grad */, Negate);\n}\nexport const neg = op({ neg_ });\n//# sourceMappingURL=neg.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softplus } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.softplus().print();  // or tf.softplus(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction softplus_(x) {\n    const $x = convertToTensor(x, 'x', 'softplus');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.softplus($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Softplus);\n}\nexport const softplus = op({ softplus_ });\n//# sourceMappingURL=softplus.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../gradients';\nimport { convertToTensor } from '../tensor_util_env';\nimport { mul } from './mul';\nimport { neg } from './neg';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { softplus } from './softplus';\n/**\n * Computes log sigmoid of the input `tf.Tensor` element-wise:\n * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.logSigmoid().print();  // or tf.logSigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction logSigmoid_(x) {\n    const $x = convertToTensor(x, 'x', 'logSigmoid');\n    // Use a custom gradient to maintain previous implementation.\n    // There is no LogSigmoid kernel in TF so we can't use engine.runKernel\n    // directly\n    const customOp = customGrad((x) => {\n        // TODO(yassogba) we can remove the chained softplus call here only\n        // after backends have modualrized softplus at which point we can call\n        // engine runKernel(..., Sotfplus, ...) directly.\n        const value = neg(softplus(neg(x)));\n        const gradFunc = (dy) => {\n            const derX = mul(dy, sigmoid(neg(x)));\n            return derX;\n        };\n        return { value, gradFunc };\n    });\n    return customOp($x);\n}\nexport const logSigmoid = op({ logSigmoid_ });\n//# sourceMappingURL=log_sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Max } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the maximum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.max().print();  // or tf.max(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.max(axis).print();  // or tf.max(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction max_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'max');\n    const forward = (backend, save) => {\n        const origAxes = util.parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let maxInput = $x;\n        if (permutedAxes != null) {\n            maxInput = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, maxInput.rank);\n        }\n        const y = backend.max(maxInput, axes);\n        if (permutedAxes != null) {\n            maxInput.dispose();\n        }\n        let res = y;\n        if (keepDims) {\n            const expandedShape = axis_util.expandShapeToKeepDim(res.shape, util.parseAxisParam(axis, $x.shape));\n            res = reshape(res, expandedShape);\n            y.dispose();\n        }\n        save([$x, res]);\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { reductionIndices: axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Max, attrs);\n}\nexport const max = op({ max_ });\n//# sourceMappingURL=max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sub } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n *\n * ```js\n * // Broadcast subtract a with b.\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.scalar(5);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n * @param a The first `tf.Tensor` to subtract from.\n * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction sub_(a, b) {\n    let $a = convertToTensor(a, 'a', 'sub');\n    let $b = convertToTensor(b, 'b', 'sub');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.subtract($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Sub);\n}\nexport const sub = op({ sub_ });\n//# sourceMappingURL=sub.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the sum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If axes has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.sum().print();  // or tf.sum(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.sum(axis).print();  // or tf.sum(x, axis)\n * ```\n *\n * @param x The input tensor to compute the sum over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction sum_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'sum');\n    if ($x.dtype === 'bool') {\n        $x = cast($x, 'int32');\n    }\n    const forward = (backend, save) => {\n        save([$x]);\n        const axes = parseAxisParam(axis, $x.shape);\n        const permutation = getAxesPermutation(axes, $x.rank);\n        let reductionAxes = axes;\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n        }\n        let value = backend.sum(permutedX, reductionAxes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(value.shape, axes);\n            value = reshape(value, newShape);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Sum, attrs);\n}\nexport const sum = op({ sum_ });\n//# sourceMappingURL=sum.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogSoftmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { op } from './operation';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log softmax.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param axis The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction logSoftmax_(logits, axis = -1) {\n    const $logits = convertToTensor(logits, 'logits', 'logSoftmax');\n    if (axis === -1) {\n        axis = $logits.rank - 1;\n    }\n    if (axis !== $logits.rank - 1) {\n        throw Error('Log Softmax along a non-last dimension is not yet supported. ' +\n            `Logits was rank ${$logits.rank} and axis was ${axis}`);\n    }\n    const forward = (backend, save) => {\n        const keepDims = true;\n        const xMax = max(logits, axis, true);\n        const shifted = sub(logits, xMax);\n        const value = sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis, keepDims)));\n        save([value]);\n        return value;\n    };\n    const inputs = { logits: $logits };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LogSoftmax, attrs);\n}\nexport const logSoftmax = op({ logSoftmax_ });\n//# sourceMappingURL=log_softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { add } from './add';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log(sum(exp(elements across the reduction dimensions)).\n *\n * Reduces the input along the dimensions given in `axis`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.logSumExp().print();  // or tf.logSumExp(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.logSumExp(axis).print();  // or tf.logSumExp(a, axis)\n * ```\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. If null (the default),\n *     reduces all dimensions.\n * @param keepDims If true, retains reduced dimensions with length\n *     of 1. Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction logSumExp_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'logSumExp');\n    const axes = parseAxisParam(axis, $x.shape);\n    const xMax = max($x, axes, true /* keepDims */);\n    const a = sub($x, xMax);\n    const b = exp(a);\n    const c = sum(b, axes);\n    const d = log(c);\n    const res = add(reshape(xMax, d.shape), d);\n    if (keepDims) {\n        const newShape = expandShapeToKeepDim(res.shape, axes);\n        return reshape(res, newShape);\n    }\n    return res;\n}\nexport const logSumExp = op({ logSumExp_ });\n//# sourceMappingURL=log_sum_exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalAnd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a AND b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalAnd(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalAnd_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(backend => backend.logicalAnd($a, $b), inputs, null /* grad */, LogicalAnd);\n}\nexport const logicalAnd = op({ logicalAnd_ });\n//# sourceMappingURL=logical_and.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalNot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the truth value of `NOT x` element-wise.\n *\n * ```js\n * const a = tf.tensor1d([false, true], 'bool');\n *\n * a.logicalNot().print();\n * ```\n *\n * @param x The input tensor. Must be of dtype 'bool'.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalNot_(x) {\n    const $x = convertToTensor(x, 'x', 'logicalNot', 'bool');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.logicalNot($x), inputs, null /* grad */, LogicalNot);\n}\nexport const logicalNot = op({ logicalNot_ });\n//# sourceMappingURL=logical_not.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalOr } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a OR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalOr(b).print();\n * ```\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalOr_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalOr', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalOr', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(backend => backend.logicalOr($a, $b), inputs, null /* grad */, LogicalOr);\n}\nexport const logicalOr = op({ logicalOr_ });\n//# sourceMappingURL=logical_or.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { logicalAnd } from './logical_and';\nimport { logicalNot } from './logical_not';\nimport { logicalOr } from './logical_or';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalXor(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalXor_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalXor', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalXor', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    // x ^ y = (x | y) & ~(x & y)\n    return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));\n}\nexport const logicalXor = op({ logicalXor_ });\n//# sourceMappingURL=logical_xor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction maxPool_(x, filterSize, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'maxPool');\n    const dilations = 1;\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        let y;\n        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n            util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n            y = x4D.clone();\n        }\n        else {\n            y = backend.maxPool(x4D, convInfo);\n        }\n        save([x4D, y]);\n        return y;\n    };\n    const inputs = { x: x4D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const maxPool = op({ maxPool_ });\n//# sourceMappingURL=max_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { deprecationWarn } from '../globals';\nimport { MaxPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D max pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.maxPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPool3d_(x, filterSize = [1, 1, 1], strides, pad, dimRoundingMode, dataFormat = 'NDHWC', dilations) {\n    if (dilations == null) {\n        dilations = [1, 1, 1];\n    }\n    else {\n        deprecationWarn('dilations is deprecated, this field will be gone in ' +\n            'v3.0.0.');\n    }\n    const $x = convertToTensor(x, 'x', 'maxPool3d');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in maxPool3d: Only NDHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool3d: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1, 1];\n        }\n        const convInfo = conv_util.computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);\n        const y = backend.maxPool3d(x5D, convInfo);\n        save([x5D, y]);\n        return y;\n    };\n    const inputs = { x: x5D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat, dilations };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool3D, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const maxPool3d = op({ maxPool3d_ });\n//# sourceMappingURL=max_pool_3d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPoolWithArgmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the 2D max pooling of an image with Argmax index.\n * The indices in argmax are flattened, so that a maximum value at position `[b,\n * y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if\n * include_batch_in_index is False; `((b * height + y) * width + x) * channels\n * +c` if include_batch_in_index is True.\n *\n * The indices returned are always in `[0, height) x [0, width)` before\n * flattening.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param includeBatchIndex Defaults to False. Whether to include batch\n *    dimension in flattened index of argmax.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPoolWithArgmax_(x, filterSize, strides, pad, includeBatchInIndex = false) {\n    const $x = convertToTensor(x, 'x', 'maxPoolWithArgmax');\n    const inputs = { x: $x };\n    const attrs = { filterSize, strides, pad, includeBatchInIndex };\n    const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);\n    return { result: result[0], indexes: result[1] };\n}\nexport const maxPoolWithArgmax = op({ maxPoolWithArgmax_ });\n//# sourceMappingURL=max_pool_with_argmax.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeZerosTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\n/**\n * Creates a `tf.Tensor` with all elements set to 0.\n *\n * ```js\n * tf.zeros([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Can\n *     be 'float32', 'int32' or 'bool'. Defaults to 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function zeros(shape, dtype = 'float32') {\n    if (dtype === 'complex64') {\n        const real = zeros(shape, 'float32');\n        const imag = zeros(shape, 'float32');\n        return complex(real, imag);\n    }\n    const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=zeros.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeOnesTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\nimport { zeros } from './zeros';\n/**\n * Creates a `tf.Tensor` with all elements set to 1.\n *\n * ```js\n * tf.ones([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n *     'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function ones(shape, dtype = 'float32') {\n    if (dtype === 'complex64') {\n        const real = ones(shape, 'float32');\n        const imag = zeros(shape, 'float32');\n        return complex(real, imag);\n    }\n    const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=ones.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../gradients';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam, sizeFromShape } from '../util';\nimport { computeOutAndReduceShapes } from './axis_util';\nimport { cast } from './cast';\nimport { div } from './div';\nimport { mul } from './mul';\nimport { ones } from './ones';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { scalar } from './scalar';\nimport { sum } from './sum';\n/**\n * Computes the mean of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is\n * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with\n * a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.mean().print();  // or tf.mean(a)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.mean(axis).print();  // or tf.mean(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction mean_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'mean');\n    const axes = parseAxisParam(axis, $x.shape);\n    const shapes = computeOutAndReduceShapes($x.shape, axes);\n    const reduceShape = shapes[1];\n    const reduceSize = sizeFromShape(reduceShape);\n    // Use a custom gradient to bypass 2 gradient backprops since mean is used\n    // extremely often.\n    const customOp = customGrad((x) => {\n        const reduceSizeScalar = scalar(reduceSize);\n        // Cast if needed.\n        const xReduce = reduceSizeScalar.dtype === x.dtype ?\n            x :\n            cast(x, reduceSizeScalar.dtype);\n        const res = div(xReduce, reduceSizeScalar);\n        const value = sum(res, axis, keepDims);\n        const gradFunc = (dy) => {\n            const expandedDyShape = x.shape.slice();\n            axes.forEach(axis => {\n                expandedDyShape[axis] = 1;\n            });\n            const expandedDy = reshape(dy, expandedDyShape);\n            const derX = div(mul(expandedDy, ones(x.shape, 'float32')), reduceSize);\n            return derX;\n        };\n        return { value, gradFunc };\n    });\n    return customOp($x);\n}\nexport const mean = op({ mean_ });\n//# sourceMappingURL=mean.js.map", "import { ENGINE } from '../engine';\nimport { Min } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the minimum value from the input.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axes`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axes` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.min().print();  // or tf.min(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.min(axis).print();  // or tf.min(x, axis)\n * ```\n *\n * @param x The input Tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction min_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'min');\n    const forward = (backend, save) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let minInput = $x;\n        if (permutedAxes != null) {\n            minInput = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        const y = backend.min(minInput, axes);\n        if (permutedAxes != null) {\n            minInput.dispose();\n        }\n        let res = y;\n        if (keepDims) {\n            const expandedShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n            res = reshape(y, expandedShape);\n            y.dispose();\n        }\n        save([$x, res]);\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Min, attrs);\n}\nexport const min = op({ min_ });\n//# sourceMappingURL=min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Minimum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `minimumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * ```js\n * // Broadcast minimum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction minimum_(a, b) {\n    let $a = convertToTensor(a, 'a', 'minimum');\n    let $b = convertToTensor(b, 'b', 'minimum');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'bool') {\n        $a = cast($a, 'int32');\n        $b = cast($b, 'int32');\n    }\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.minimum($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Minimum);\n}\nexport const minimum = op({ minimum_ });\n//# sourceMappingURL=minimum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Mod } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the mod of a and b element-wise.\n * `floor(x / y) * y + mod(x, y) = x`\n * Supports broadcasting.\n *\n * We also expose `tf.modStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * ```js\n * // Broadcast a mod b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mod_(a, b) {\n    let $a = convertToTensor(a, 'a', 'mod');\n    let $b = convertToTensor(b, 'b', 'mod');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.mod($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Mod);\n}\nexport const mod = op({ mod_ });\n//# sourceMappingURL=mod.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes square of `x` element-wise: `x ^ 2`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);\n *\n * x.square().print();  // or tf.square(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction square_(x) {\n    const $x = convertToTensor(x, 'x', 'square');\n    const attrs = {};\n    const inputsToSave = [$x];\n    const outputsToSave = [];\n    return ENGINE.runKernelFunc((backend, save) => {\n        save([$x]);\n        return backend.square($x);\n    }, { x: $x }, null /* grad */, 'Square', attrs, inputsToSave, outputsToSave);\n}\nexport const square = op({ square_ });\n//# sourceMappingURL=square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { cast } from './cast';\nimport { mean } from './mean';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { square } from './square';\nimport { sub } from './sub';\n/**\n * Calculates the mean and variance of `x`. The mean and variance are\n * calculated by aggregating the contents of `x` across `axes`. If `x` is\n * 1-D and `axes = [0]` this is just the mean and variance of a vector.\n *\n * @param x The input tensor.\n * @param axis The dimension(s) along with to compute mean and\n *     variance. By default it reduces all dimensions.\n * @param keepDims If true, the moments have the same dimensionality as the\n *     input.\n * @return An object with two keys: `mean` and `variance`.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction moments_(x, axis = null, keepDims = false) {\n    x = convertToTensor(x, 'x', 'moments');\n    const axes = parseAxisParam(axis, x.shape);\n    const xMean = mean(x, axes, keepDims);\n    let keepDimsShape = xMean.shape;\n    if (!keepDims) {\n        keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);\n    }\n    const devSquared = square(sub(cast(x, 'float32'), reshape(xMean, keepDimsShape)));\n    const variance = mean(devSquared, axes, keepDims);\n    return { mean: xMean, variance };\n}\nexport const moments = op({ moments_ });\n//# sourceMappingURL=moments.js.map", "import { convertToTensor, convertToTensorArray } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the next states and outputs of a stack of LSTMCells.\n *\n * Each cell output is used as input to the next cell.\n *\n * Returns `[cellState, cellOutput]`.\n *\n * Derived from tf.contrib.rn.MultiRNNCell.\n *\n * @param lstmCells Array of LSTMCell functions.\n * @param data The input to the cell.\n * @param c Array of previous cell states.\n * @param h Array of previous cell outputs.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction multiRNNCell_(lstmCells, data, c, h) {\n    const $data = convertToTensor(data, 'data', 'multiRNNCell');\n    const $c = convertToTensorArray(c, 'c', 'multiRNNCell');\n    const $h = convertToTensorArray(h, 'h', 'multiRNNCell');\n    let input = $data;\n    const newStates = [];\n    for (let i = 0; i < lstmCells.length; i++) {\n        const output = lstmCells[i](input, $c[i], $h[i]);\n        newStates.push(output[0]);\n        newStates.push(output[1]);\n        input = output[1];\n    }\n    const newC = [];\n    const newH = [];\n    for (let i = 0; i < newStates.length; i += 2) {\n        newC.push(newStates[i]);\n        newH.push(newStates[i + 1]);\n    }\n    return [newC, newH];\n}\nexport const multiRNNCell = op({ multiRNNCell_ });\n//# sourceMappingURL=multi_rnn_cell.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Creates a `tf.Tensor` with values drawn from a multinomial distribution.\n *\n * ```js\n * const probs = tf.tensor([.75, .25]);\n * tf.multinomial(probs, 3).print();\n * ```\n *\n * @param logits 1D array with unnormalized log-probabilities, or\n *     2D array of shape `[batchSize, numOutcomes]`. See the `normalized`\n *     parameter.\n * @param numSamples Number of samples to draw for each row slice.\n * @param seed The seed number.\n * @param normalized Whether the provided `logits` are normalized true\n *     probabilities (sum to 1). Defaults to false.\n * @return 1D array of shape `[numSamples]`, or 2D array of shape\n *     `[batchSize, numSamples]`, depending on the rank of the input.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction multinomial_(logits, numSamples, seed, normalized = false) {\n    const $logits = convertToTensor(logits, 'logits', 'multinomial');\n    const numOutcomes = $logits.size;\n    const origRank = $logits.rank;\n    if (numOutcomes < 2) {\n        throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ` +\n            `${numOutcomes}.`);\n    }\n    if (origRank > 2) {\n        throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n    }\n    seed = seed || Math.random();\n    const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;\n    const res = ENGINE.runKernelFunc(backend => backend.multinomial(logits2D, normalized, numSamples, seed), { logits2D });\n    // tslint:disable-next-line:no-unnecessary-type-assertion\n    return origRank === 1 ? reshape(res, [res.size]) : res;\n}\nexport const multinomial = op({ multinomial_ });\n//# sourceMappingURL=multinomial.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { NotEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a != b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([0, 2, 3]);\n *\n * a.notEqual(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction notEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'notEqual');\n    let $b = convertToTensor(b, 'b', 'notEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend) => backend.notEqual($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, NotEqual);\n}\nexport const notEqual = op({ notEqual_ });\n//# sourceMappingURL=not_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Real } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the real part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the real part of each element in input considered as a complex number.\n *\n * If the input is real, it simply makes a clone.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.real(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction real_(input) {\n    const $input = convertToTensor(input, 'input', 'real');\n    const forward = (backend) => {\n        return backend.real($input);\n    };\n    const inputs = { input: $input };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Real);\n}\nexport const real = op({ real_ });\n//# sourceMappingURL=real.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OnesLike } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { complex } from './complex';\nimport { imag } from './imag';\nimport { op } from './operation';\nimport { real } from './real';\nimport { zerosLike } from './zeros_like';\n/**\n * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.onesLike(x).print();\n * ```\n * @param x A tensor.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction onesLike_(x) {\n    const $x = convertToTensor(x, 'x', 'onesLike');\n    const forward = (backend, save) => {\n        if ($x.dtype === 'complex64') {\n            const r = onesLike(real($x));\n            const i = zerosLike(imag($x));\n            return complex(r, i);\n        }\n        return backend.onesLike($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, OnesLike);\n}\nexport const onesLike = op({ onesLike_ });\n//# sourceMappingURL=ones_like.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the outer product of two vectors, `v1` and `v2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([3, 4, 5]);\n *\n * tf.outerProduct(a, b).print();\n * ```\n * @param v1 The first vector in the outer product operation.\n * @param v2 The second vector in the outer product operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction outerProduct_(v1, v2) {\n    const $v1 = convertToTensor(v1, 'v1', 'outerProduct');\n    const $v2 = convertToTensor(v2, 'v2', 'outerProduct');\n    util.assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ` +\n        `${$v1.rank} and ${$v2.rank}.`);\n    const v12D = reshape($v1, [-1, 1]);\n    const v22D = reshape($v2, [1, -1]);\n    return matMul(v12D, v22D);\n}\nexport const outerProduct = op({ outerProduct_ });\n//# sourceMappingURL=outer_product.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { PadV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Pads a `tf.Tensor` with a given value and paddings.\n *\n * This operation currently only implements the `CONSTANT` mode.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `paddings` is of given length.\n *   - `tf.pad1d`\n *   - `tf.pad2d`\n *   - `tf.pad3d`\n *   - `tf.pad4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.pad([[1, 2]]).print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * @param constantValue The pad value to use. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction pad_(x, paddings, constantValue = 0) {\n    const $x = convertToTensor(x, 'x', 'pad');\n    if ($x.rank === 0) {\n        throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');\n    }\n    const forward = (backend, save) => {\n        save([$x]);\n        return backend.pad($x, paddings, constantValue);\n    };\n    const attrs = { paddings, constantValue };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, PadV2, attrs);\n}\nexport const pad = op({ pad_ });\n//# sourceMappingURL=pad.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.\n */\nfunction pad1d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 2, () => 'Invalid number of paddings. Must be length of 2.');\n    return pad(x, [paddings], constantValue);\n}\nexport const pad1d = op({ pad1d_ });\n//# sourceMappingURL=pad1d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.\n */\nfunction pad2d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 2 && paddings[0].length === 2 &&\n        paddings[1].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad2d = op({ pad2d_ });\n//# sourceMappingURL=pad2d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.\n */\nfunction pad3d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 3 && paddings[0].length === 2 &&\n        paddings[1].length === 2 && paddings[2].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad3d = op({ pad3d_ });\n//# sourceMappingURL=pad3d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.\n */\nfunction pad4d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 4 && paddings[0].length === 2 &&\n        paddings[1].length === 2 && paddings[2].length === 2 &&\n        paddings[3].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad4d = op({ pad4d_ });\n//# sourceMappingURL=pad4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SpaceToBatchND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation divides \"spatial\" dimensions `[1, ..., M]` of the input into\n * a grid of blocks of shape `blockShape`, and interleaves these blocks with\n * the \"batch\" dimension (0) such that in the output, the spatial\n * dimensions `[1, ..., M]` correspond to the position within the grid,\n * and the batch dimension combines both the position within a spatial block\n * and the original batch position. Prior to division into blocks,\n * the spatial dimensions of the input are optionally zero padded\n * according to `paddings`. See below for a precise description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n * const blockShape = [2, 2];\n * const paddings = [[0, 0], [0, 0]];\n *\n * x.spaceToBatchND(blockShape, paddings).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=\n *     0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad\n * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It\n * is required that\n * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input\n * according to `paddings` to produce `padded` of shape paddedShape.\n *\n * 2. Reshape `padded` to `reshapedPadded` of shape:\n * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`\n *\n * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`\n * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the\n * batch dimension, producing an output tensor of shape:\n * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction spaceToBatchND_(x, blockShape, paddings) {\n    const $x = convertToTensor(x, 'x', 'spaceToBatchND');\n    util.assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);\n    util.assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);\n    util.assert($x.shape.reduce((a, b, i) => {\n        if (i > 0 && i <= blockShape.length) {\n            return a &&\n                ((b + paddings[i - 1][0] + paddings[i - 1][1]) %\n                    blockShape[i - 1] ===\n                    0);\n        }\n        return a;\n    }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);\n    const forward = backend => backend.spaceToBatchND($x, blockShape, paddings);\n    const inputs = { x: $x };\n    const attrs = { blockShape, paddings };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, SpaceToBatchND, attrs);\n}\nexport const spaceToBatchND = op({ spaceToBatchND_ });\n//# sourceMappingURL=space_to_batch_nd.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { avgPool } from './avg_pool';\nimport { batchToSpaceND } from './batch_to_space_nd';\nimport * as conv_util from './conv_util';\nimport { maxPool } from './max_pool';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { spaceToBatchND } from './space_to_batch_nd';\n/**\n * Performs an N-D pooling operation\n *\n * @param input The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param windowShape The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param poolingType The type of pooling, either 'max' or 'avg'.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction pool_(input, windowShape, poolingType, pad, dilations, strides) {\n    if (dilations == null) {\n        dilations = [1, 1];\n    }\n    if (strides == null) {\n        strides = 1;\n    }\n    if (pad === 0) {\n        pad = 'valid';\n    }\n    const $x = convertToTensor(input, 'x', 'maxPool');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in pool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = conv_util.computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad);\n    const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];\n    // The following implementation does batchToSpace(pool(spaceToBatch(x)))\n    // whenever dilation > 1 since the TF kernels do not support dilation > 1.\n    // tslint:disable-next-line:max-line-length\n    // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037\n    let basePadding;\n    if (pad === 'same') {\n        basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);\n    }\n    else {\n        basePadding = [[0, 0], [0, 0]];\n    }\n    const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n    const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n    const convertedPad = isDilationOne ? pad : 'valid';\n    const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n    const forwardOp = poolingType === 'avg' ?\n        () => avgPool(convertedX, windowShape, strides, convertedPad) :\n        () => maxPool(convertedX, windowShape, strides, convertedPad);\n    const y = forwardOp();\n    const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\n// Helper function to compute crops and paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184\nfunction requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {\n    const padStart = basePadding.map(b => b[0]);\n    const origPadEnd = basePadding.map(b => b[1]);\n    const fullInputShape = inputShape.concat(padStart, origPadEnd);\n    const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n    const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n    const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n    const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n    return [paddings, crops];\n}\n// Helper function to compute base paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524\nfunction withSpaceToBatchBasePaddings(filterShape, dilation) {\n    // Spatial dimensions of the filters and the upsampled filters in which we\n    // introduce (rate - 1) zeros between consecutive filter values.\n    const dilatedFilterShape = filterShape.map((s, i) => {\n        return s + (s - 1) * (dilation[i] - 1);\n    });\n    const padExtraShape = dilatedFilterShape.map(s => s - 1);\n    // When padding is odd, we pad more at end, following the same\n    // convention as conv2d.\n    const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));\n    const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n    return padExtraShape.map((_, i) => {\n        return [padExtraStart[i], padExtraEnd[i]];\n    });\n}\nexport const pool = op({ pool_ });\n//# sourceMappingURL=pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Pow } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the power of one `tf.Tensor` to another. Supports broadcasting.\n *\n * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for\n * corresponding elements in x and y. The result's dtype will be the upcasted\n * type of the `base` and `exp` dtypes.\n *\n * ```js\n * const a = tf.tensor([[2, 3], [4, 5]])\n * const b = tf.tensor([[1, 2], [3, 0]]).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n *\n * ```js\n * const a = tf.tensor([[1, 2], [3, 4]])\n * const b = tf.tensor(2).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n * We also expose `powStrict` which has the same signature as this op and\n * asserts that `base` and `exp` are the same shape (does not broadcast).\n *\n * @param base The base `tf.Tensor` to pow element-wise.\n * @param exp The exponent `tf.Tensor` to pow element-wise.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction pow_(base, exp) {\n    let $base = convertToTensor(base, 'base', 'pow');\n    let $exp = convertToTensor(exp, 'exp', 'pow');\n    [$base, $exp] = makeTypesMatch($base, $exp);\n    const inputs = { a: $base, b: $exp };\n    const forward = (backend, save) => {\n        const y = backend.pow($base, $exp);\n        save([$base, $exp, y]);\n        return y;\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Pow);\n}\nexport const pow = op({ pow_ });\n//# sourceMappingURL=pow.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prelu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes leaky rectified linear element-wise with parametric alphas.\n *\n * `x < 0 ? alpha * x : f(x) = x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n * const alpha = tf.scalar(0.1);\n *\n * x.prelu(alpha).print();  // or tf.prelu(x, alpha)\n * ```\n * @param x The input tensor.\n * @param alpha Scaling factor for negative values.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction prelu_(x, alpha) {\n    const $x = convertToTensor(x, 'x', 'prelu');\n    const $alpha = convertToTensor(alpha, 'alpha', 'prelu');\n    const forward = (backend, save) => {\n        const res = backend.prelu($x, $alpha);\n        save([$x, $alpha]);\n        return res;\n    };\n    const inputs = { x: $x, alpha: $alpha };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Prelu);\n}\nexport const prelu = op({ prelu_ });\n//# sourceMappingURL=prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prod } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the product of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.prod().print();  // or tf.prod(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.prod(axis).print();  // or tf.prod(x, axis)\n * ```\n *\n * @param x The input tensor to compute the product over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction prod_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'prod');\n    const forward = (backend) => {\n        if ($x.dtype === 'bool') {\n            $x = cast($x, 'int32');\n        }\n        const axes = parseAxisParam(axis, $x.shape);\n        const permutation = getAxesPermutation(axes, $x.rank);\n        let reductionAxes = axes;\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n        }\n        let value = backend.prod(permutedX, reductionAxes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(value.shape, axes);\n            value = reshape(value, newShape);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Prod, attrs);\n}\nexport const prod = op({ prod_ });\n//# sourceMappingURL=prod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { sizeFromShape } from '../util';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with values sampled from a random number generator\n * function defined by the user.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param randFunction A random number generator function which is called\n * for each element in the output tensor.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\nfunction rand_(shape, randFunction, dtype) {\n    const size = sizeFromShape(shape);\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    for (let i = 0; i < size; i++) {\n        values[i] = randFunction();\n    }\n    return ENGINE.makeTensor(values, shape, dtype);\n}\nexport const rand = op({ rand_ });\n//# sourceMappingURL=rand.js.map", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as seedrandom from 'seedrandom';\nimport { expectNumbersClose, testEpsilon } from '../test_util';\n// https://en.wikipedia.org/wiki/Marsaglia_polar_method\nexport class MPRandGauss {\n    constructor(mean, stdDeviation, dtype, truncated, seed) {\n        this.mean = mean;\n        this.stdDev = stdDeviation;\n        this.dtype = dtype;\n        this.nextVal = NaN;\n        this.truncated = truncated;\n        if (this.truncated) {\n            this.upper = this.mean + this.stdDev * 2;\n            this.lower = this.mean - this.stdDev * 2;\n        }\n        const seedValue = seed ? seed : Math.random();\n        this.random = seedrandom.alea(seedValue.toString());\n    }\n    /** Returns next sample from a Gaussian distribution. */\n    nextValue() {\n        if (!isNaN(this.nextVal)) {\n            const value = this.nextVal;\n            this.nextVal = NaN;\n            return value;\n        }\n        let resultX, resultY;\n        let isValid = false;\n        while (!isValid) {\n            let v1, v2, s;\n            do {\n                v1 = 2 * this.random() - 1;\n                v2 = 2 * this.random() - 1;\n                s = v1 * v1 + v2 * v2;\n            } while (s >= 1 || s === 0);\n            const mul = Math.sqrt(-2.0 * Math.log(s) / s);\n            resultX = this.mean + this.stdDev * v1 * mul;\n            resultY = this.mean + this.stdDev * v2 * mul;\n            if (!this.truncated || this.isValidTruncated(resultX)) {\n                isValid = true;\n            }\n        }\n        if (!this.truncated || this.isValidTruncated(resultY)) {\n            this.nextVal = this.convertValue(resultY);\n        }\n        return this.convertValue(resultX);\n    }\n    /** Handles proper rounding for non-floating-point numbers. */\n    convertValue(value) {\n        if (this.dtype == null || this.dtype === 'float32') {\n            return value;\n        }\n        return Math.round(value);\n    }\n    /** Returns true if less than 2-standard-deviations from the mean. */\n    isValidTruncated(value) {\n        return value <= this.upper && value >= this.lower;\n    }\n}\n// Marsaglia, George, and Wai Wan Tsang. 2000. \"A Simple Method for Generating\n// Gamma Variables.\"\nexport class RandGamma {\n    constructor(alpha, beta, dtype, seed) {\n        this.alpha = alpha;\n        this.beta = 1 / beta; // convert rate to scale parameter\n        this.dtype = dtype;\n        const seedValue = seed ? seed : Math.random();\n        this.randu = seedrandom.alea(seedValue.toString());\n        this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());\n        if (alpha < 1) {\n            this.d = alpha + (2 / 3);\n        }\n        else {\n            this.d = alpha - (1 / 3);\n        }\n        this.c = 1 / Math.sqrt(9 * this.d);\n    }\n    /** Returns next sample from a gamma distribution. */\n    nextValue() {\n        let x2, v0, v1, x, u, v;\n        while (true) {\n            do {\n                x = this.randn.nextValue();\n                v = 1 + (this.c * x);\n            } while (v <= 0);\n            v *= v * v;\n            x2 = x * x;\n            v0 = 1 - (0.331 * x2 * x2);\n            v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v)));\n            u = this.randu();\n            if (u < v0 || Math.log(u) < v1) {\n                break;\n            }\n        }\n        v = (1 / this.beta) * this.d * v;\n        if (this.alpha < 1) {\n            v *= Math.pow(this.randu(), 1 / this.alpha);\n        }\n        return this.convertValue(v);\n    }\n    /** Handles proper rounding for non-floating-point numbers. */\n    convertValue(value) {\n        if (this.dtype === 'float32') {\n            return value;\n        }\n        return Math.round(value);\n    }\n}\nexport class UniformRandom {\n    constructor(min = 0, max = 1, dtype, seed) {\n        /** Handles proper rounding for non floating point numbers. */\n        this.canReturnFloat = () => (this.dtype == null || this.dtype === 'float32');\n        this.min = min;\n        this.range = max - min;\n        this.dtype = dtype;\n        if (seed == null) {\n            seed = Math.random();\n        }\n        if (typeof seed === 'number') {\n            seed = seed.toString();\n        }\n        if (!this.canReturnFloat() && this.range <= 1) {\n            throw new Error(`The difference between ${min} - ${max} <= 1 and dtype is not float`);\n        }\n        this.random = seedrandom.alea(seed);\n    }\n    convertValue(value) {\n        if (this.canReturnFloat()) {\n            return value;\n        }\n        return Math.round(value);\n    }\n    nextValue() {\n        return this.convertValue(this.min + this.range * this.random());\n    }\n}\nexport function jarqueBeraNormalityTest(values) {\n    // https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test\n    const n = values.length;\n    const s = skewness(values);\n    const k = kurtosis(values);\n    const jb = n / 6 * (Math.pow(s, 2) + 0.25 * Math.pow(k - 3, 2));\n    // JB test requires 2-degress of freedom from Chi-Square @ 0.95:\n    // http://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm\n    const CHI_SQUARE_2DEG = 5.991;\n    if (jb > CHI_SQUARE_2DEG) {\n        throw new Error(`Invalid p-value for JB: ${jb}`);\n    }\n}\nexport function expectArrayInMeanStdRange(actual, expectedMean, expectedStdDev, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    const actualMean = mean(actual);\n    expectNumbersClose(actualMean, expectedMean, epsilon);\n    expectNumbersClose(standardDeviation(actual, actualMean), expectedStdDev, epsilon);\n}\nfunction mean(values) {\n    let sum = 0;\n    for (let i = 0; i < values.length; i++) {\n        sum += values[i];\n    }\n    return sum / values.length;\n}\nfunction standardDeviation(values, mean) {\n    let squareDiffSum = 0;\n    for (let i = 0; i < values.length; i++) {\n        const diff = values[i] - mean;\n        squareDiffSum += diff * diff;\n    }\n    return Math.sqrt(squareDiffSum / values.length);\n}\nfunction kurtosis(values) {\n    // https://en.wikipedia.org/wiki/Kurtosis\n    const valuesMean = mean(values);\n    const n = values.length;\n    let sum2 = 0;\n    let sum4 = 0;\n    for (let i = 0; i < n; i++) {\n        const v = values[i] - valuesMean;\n        sum2 += Math.pow(v, 2);\n        sum4 += Math.pow(v, 4);\n    }\n    return (1 / n) * sum4 / Math.pow((1 / n) * sum2, 2);\n}\nfunction skewness(values) {\n    // https://en.wikipedia.org/wiki/Skewness\n    const valuesMean = mean(values);\n    const n = values.length;\n    let sum2 = 0;\n    let sum3 = 0;\n    for (let i = 0; i < n; i++) {\n        const v = values[i] - valuesMean;\n        sum2 += Math.pow(v, 2);\n        sum3 += Math.pow(v, 3);\n    }\n    return (1 / n) * sum3 / Math.pow((1 / (n - 1)) * sum2, 3 / 2);\n}\n//# sourceMappingURL=rand_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { RandGamma } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a gamma distribution.\n *\n * ```js\n * tf.randomGamma([2, 2], 1).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param alpha The shape parameter of the gamma distribution.\n * @param beta The inverse scale parameter of the gamma distribution. Defaults\n *     to 1.\n * @param dtype The data type of the output. Defaults to float32.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomGamma_(shape, alpha, beta = 1, dtype = 'float32', seed) {\n    if (beta == null) {\n        beta = 1;\n    }\n    if (dtype == null) {\n        dtype = 'float32';\n    }\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new Error(`Unsupported data type ${dtype}`);\n    }\n    const rgamma = new RandGamma(alpha, beta, dtype, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = rgamma.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomGamma = op({ randomGamma_ });\n//# sourceMappingURL=random_gamma.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a normal distribution.\n *\n * ```js\n * tf.randomNormal([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n    if (dtype != null && dtype === 'bool') {\n        throw new Error(`Unsupported data type ${dtype}`);\n    }\n    const randGauss = new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = randGauss.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomNormal = op({ randomNormal_ });\n//# sourceMappingURL=random_normal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { UniformRandom } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a uniform distribution.\n *\n * The generated values follow a uniform distribution in the range [minval,\n * maxval). The lower bound minval is included in the range, while the upper\n * bound maxval is excluded.\n *\n * ```js\n * tf.randomUniform([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param minval The lower bound on the range of random values to generate.\n *   Defaults to 0.\n * @param maxval The upper bound on the range of random values to generate.\n *   Defaults to 1.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomUniform_(shape, minval = 0, maxval = 1, dtype = 'float32', seed) {\n    const res = buffer(shape, dtype);\n    const random = new UniformRandom(minval, maxval, null, seed);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = random.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomUniform = op({ randomUniform_ });\n//# sourceMappingURL=random_uniform.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor1d` as it makes the code more readable.\n *\n * ```js\n * tf.tensor1d([1, 2, 3]).print();\n * ```\n *\n * @param values The values of the tensor. Can be array of numbers,\n *     or a `TypedArray`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor1d(values, dtype) {\n    assertNonNull(values);\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 1) {\n        throw new Error('tensor1d() requires values to be a flat/TypedArray');\n    }\n    const shape = null;\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor1d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Range } from '../kernel_names';\nimport { makeZerosTypedArray } from '../util';\nimport { tensor1d } from './tensor1d';\nimport { zeros } from './zeros';\n/**\n * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.\n *\n * The tensor is a is half-open interval meaning it includes start, but\n * excludes stop. Decrementing ranges and negative step values are also\n * supported.sv\n *\n *\n * ```js\n * tf.range(0, 9, 2).print();\n * ```\n *\n * @param start An integer start value\n * @param stop An integer stop value\n * @param step An integer increment (will default to 1 or -1)\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function range(start, stop, step = 1, dtype = 'float32') {\n    if (step === 0) {\n        throw new Error('Cannot have a step of zero');\n    }\n    const forward = () => {\n        const sameStartStop = start === stop;\n        const increasingRangeNegativeStep = start < stop && step < 0;\n        const decreasingRangePositiveStep = stop < start && step > 1;\n        if (sameStartStop || increasingRangeNegativeStep ||\n            decreasingRangePositiveStep) {\n            return zeros([0], dtype);\n        }\n        const numElements = Math.abs(Math.ceil((stop - start) / step));\n        const values = makeZerosTypedArray(numElements, dtype);\n        if (stop < start && step === 1) {\n            // Auto adjust the step's sign if it hasn't been set\n            // (or was set to 1)\n            step = -1;\n        }\n        values[0] = start;\n        for (let i = 1; i < values.length; i++) {\n            values[i] = values[i - 1] + step;\n        }\n        return tensor1d(values, dtype);\n    };\n    const attrs = { start, stop, step, dtype };\n    return ENGINE.runKernelFunc(forward, {} /* inputs */, null /* grad */, Range, attrs);\n}\n//# sourceMappingURL=range.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reciprocal } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of x element-wise: `1 / x`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, 2]);\n *\n * x.reciprocal().print();  // or tf.reciprocal(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction reciprocal_(x) {\n    const $x = convertToTensor(x, 'x', 'reciprocal');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.reciprocal($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Reciprocal);\n}\nexport const reciprocal = op({ reciprocal_ });\n//# sourceMappingURL=reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { cast } from './cast';\n/**\n * Computes rectified linear element-wise: `max(x, 0)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.relu().print();  // or tf.relu(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu_(x) {\n    const $x = convertToTensor(x, 'x', 'relu');\n    const forward = (backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'bool') {\n            return cast($x, 'int32');\n        }\n        return backend.relu($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Relu);\n}\nexport const relu = op({ relu_ });\n//# sourceMappingURL=relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu6 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes rectified linear 6 element-wise: `min(max(x, 0), 6)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 8]);\n *\n * x.relu6().print();  // or tf.relu6(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu6_(x) {\n    const $x = convertToTensor(x, 'x', 'relu6');\n    const forward = (backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'bool') {\n            return cast($x, 'int32');\n        }\n        return backend.relu6($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Relu6);\n}\nexport const relu6 = op({ relu6_ });\n//# sourceMappingURL=relu6.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reverse } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { clone } from './clone';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Reverses a `tf.Tensor` along a specified axis.\n *\n * Also available are stricter rank-specific methods that assert that `x` is\n * of the given rank:\n *   - `tf.reverse1d`\n *   - `tf.reverse2d`\n *   - `tf.reverse3d`\n *   - `tf.reverse4d`\n *\n * Except `tf.reverse1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.reverse().print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.reverse(axis).print();\n * ```\n * @param x The input tensor to be reversed.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction reverse_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    const forward = (backend) => {\n        const axes = parseAxisParam(axis, $x.shape);\n        if ($x.rank === 0) {\n            return clone($x);\n        }\n        const res = backend.reverse($x, axes);\n        return reshape(res, $x.shape);\n    };\n    const inputs = { x: $x };\n    const attrs = { dims: axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Reverse, attrs);\n}\nexport const reverse = op({ reverse_ });\n//# sourceMappingURL=reverse.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor1D`.\n *\n * @param x The input tensor.\n */\nfunction reverse1d_(x) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);\n    return reverse($x, 0);\n}\nexport const reverse1d = op({ reverse1d_ });\n//# sourceMappingURL=reverse_1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor2D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse2d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse2d = op({ reverse2d_ });\n//# sourceMappingURL=reverse_2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor3D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse3d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse3d = op({ reverse3d_ });\n//# sourceMappingURL=reverse_3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor4D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse4d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse4d = op({ reverse4d_ });\n//# sourceMappingURL=reverse_4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Round } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes round of input `tf.Tensor` element-wise: `round(x)`.\n * It implements banker's rounding.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.round().print();  // or tf.round(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction round_(x) {\n    const $x = convertToTensor(x, 'x', 'round');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.round($x), inputs, null /* grad */, Round);\n}\nexport const round = op({ round_ });\n//# sourceMappingURL=round.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Rsqrt } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of square root of the input `tf.Tensor` element-wise:\n * `y = 1 / sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.rsqrt().print();  // or tf.rsqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction rsqrt_(x) {\n    const $x = convertToTensor(x, 'x', 'rsqrt');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.rsqrt($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Rsqrt);\n}\nexport const rsqrt = op({ rsqrt_ });\n//# sourceMappingURL=rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Selu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes scaled exponential linear element-wise.\n *\n * `x < 0 ? scale * alpha * (exp(x) - 1) : x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.selu().print();  // or tf.selu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction selu_(x) {\n    const $x = convertToTensor(x, 'x', 'selu');\n    const forward = (backend, save) => {\n        const res = backend.selu($x);\n        save([$x]);\n        return res;\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Selu);\n}\nexport const selu = op({ selu_ });\n//# sourceMappingURL=selu.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport { depthwiseConv2d } from './depthwise_conv2d';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * 2-D convolution with separable filters.\n *\n * Performs a depthwise convolution that acts separately on channels followed\n * by a pointwise convolution that mixes channels. Note that this is\n * separability between dimensions [1, 2] and 3, not spatial separability\n * between dimensions 1 and 2.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is\n *     the filter used in the first step.\n * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape\n *     `[1, 1, inChannels * channelMultiplier, outChannels]`. This is\n *     the filter used in the second step.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation = [1, 1], dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'separableConv2d');\n    const $depthwiseFilter = convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');\n    const $pointwiseFilter = convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    if (dataFormat === 'NCHW') {\n        throw new Error('separableConv2d currently does not support dataFormat NCHW; only ' +\n            'NHWC is supported');\n    }\n    util.assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but ` +\n        `got rank ${$depthwiseFilter.rank}.`);\n    util.assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but ` +\n        `got rank ${$depthwiseFilter.rank}.`);\n    util.assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter ` +\n        ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n    util.assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise ` +\n        `filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n    const inChannels = $depthwiseFilter.shape[2];\n    const channelMultiplier = $depthwiseFilter.shape[3];\n    util.assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter ` +\n        `must be ${inChannels * channelMultiplier}, ` +\n        `but got ${$pointwiseFilter.shape[2]}.`);\n    const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);\n    const pointwiseStride = 1;\n    const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const separableConv2d = op({ separableConv2d_ });\n//# sourceMappingURL=separable_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\n/**\n * Computes the difference between two lists of numbers.\n *\n * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`\n * that represents all values that are in `x` but not in `y`. The returned\n * Tensor `out` is sorted in the same order that the numbers appear in `x`\n * (duplicates are preserved). This operation also returns a Tensor indices that\n * represents the position of each out element in `x`. In other words:\n *\n * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`\n *\n * ```js\n * const x = [1, 2, 3, 4, 5, 6];\n * const y = [1, 3, 5];\n *\n * const [out, indices] = await tf.setdiff1dAsync(x, y);\n * out.print(); // [2, 4, 6]\n * indices.print(); // [1, 3, 5]\n * ```\n *\n * @param x 1-D Tensor. Values to keep.\n * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the\n *     output.\n * @returns Promise of Tensor tuple [out, indices].\n *  out: Tensor with the same type as x.\n *  indices: A Tensor of type int32.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nasync function setdiff1dAsync_(x, y) {\n    const $x = convertToTensor(x, 'x', 'setdiff1d');\n    const $y = convertToTensor(y, 'y', 'setdiff1d');\n    util.assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);\n    util.assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);\n    util.assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);\n    const xVals = await $x.data();\n    const yVals = await $y.data();\n    const ySet = new Set(yVals);\n    let outputSize = 0;\n    for (let i = 0; i < xVals.length; i++) {\n        if (!ySet.has(xVals[i])) {\n            outputSize++;\n        }\n    }\n    const buffer = new TensorBuffer([outputSize], $x.dtype);\n    const indices = new TensorBuffer([outputSize], 'int32');\n    for (let i = 0, p = 0; i < xVals.length; i++) {\n        if (!ySet.has(xVals[i])) {\n            buffer.values[p] = xVals[i];\n            indices.values[p] = i;\n            p++;\n        }\n    }\n    return [buffer.toTensor(), indices.toTensor()];\n}\nexport const setdiff1dAsync = setdiff1dAsync_;\n//# sourceMappingURL=setdiff1d_async.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sign } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns an element-wise indication of the sign of a number.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);\n *\n * x.sign().print();  // or tf.sign(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sign_(x) {\n    const $x = convertToTensor(x, 'x', 'sign');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.sign($x), inputs, null /* grad */, Sign);\n}\nexport const sign = op({ sign_ });\n//# sourceMappingURL=sign.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sin of the input Tensor element-wise: `sin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.sin().print();  // or tf.sin(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sin_(x) {\n    const $x = convertToTensor(x, 'x', 'sin');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sin($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sin);\n}\nexport const sin = op({ sin_ });\n//# sourceMappingURL=sin.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.sinh().print();  // or tf.sinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sinh_(x) {\n    const $x = convertToTensor(x, 'x', 'sinh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sinh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sinh);\n}\nexport const sinh = op({ sinh_ });\n//# sourceMappingURL=sinh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 1D slice from 1D array starting at coordinates `begin` and is\n * of length `size`. See `slice` for details.\n */\nfunction slice1d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice1d');\n    util.assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, [begin], [size]);\n}\nexport const slice1d = op({ slice1d_ });\n//# sourceMappingURL=slice1d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 2D slice from a 2D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice2d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice2d');\n    util.assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice2d = op({ slice2d_ });\n//# sourceMappingURL=slice2d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 3D slice from a 3D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice3d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice3d');\n    util.assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice3d = op({ slice3d_ });\n//# sourceMappingURL=slice3d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 4D slice from a 4D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice4d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice4d');\n    util.assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice4d = op({ slice4d_ });\n//# sourceMappingURL=slice4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the softmax normalized vector given the logits.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction softmax_(logits, dim = -1) {\n    const $logits = convertToTensor(logits, 'logits', 'softmax', 'float32');\n    if (dim === -1) {\n        dim = $logits.rank - 1;\n    }\n    if (dim !== $logits.rank - 1) {\n        throw Error('Softmax along a non-last dimension is not yet supported. ' +\n            `Logits was rank ${$logits.rank} and dim was ${dim}`);\n    }\n    const inputs = { logits: $logits };\n    const attrs = { dim };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const y = backend.softmax($logits, dim);\n        save([y]);\n        return y;\n    }, inputs, null /* grad */, Softmax, attrs);\n}\nexport const softmax = op({ softmax_ });\n//# sourceMappingURL=softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\n/**\n * Fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the inner-most\n * dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.fft().print();  // tf.spectral.fft(x).print();\n * ```\n * @param input The complex input to compute an fft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction fft_(input) {\n    assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.fft() must be complex64 ` +\n        `but got ${input.dtype}.`);\n    const inputs = { input };\n    return ENGINE.runKernelFunc(backend => {\n        // Collapse all outer dimensions to a single batch dimension.\n        const innerDimensionSize = input.shape[input.shape.length - 1];\n        const batch = input.size / innerDimensionSize;\n        const input2D = input.as2D(batch, innerDimensionSize);\n        const result = backend.fft(input2D);\n        return result.reshape(input.shape);\n    }, inputs, null /* gradient */, FFT);\n}\nexport const fft = op({ fft_ });\n//# sourceMappingURL=fft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { IFFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Inverse fast Fourier transform.\n *\n * Computes the inverse 1-dimensional discrete Fourier transform over the\n * inner-most dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.ifft().print();  // tf.spectral.ifft(x).print();\n * ```\n * @param input The complex input to compute an ifft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction ifft_(input) {\n    assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.ifft() must be complex64 ` +\n        `but got ${input.dtype}.`);\n    const inputs = { input };\n    return ENGINE.runKernelFunc(backend => {\n        // Collapse all outer dimensions to a single batch dimension.\n        const innerDimensionSize = input.shape[input.shape.length - 1];\n        const batch = input.size / innerDimensionSize;\n        const input2D = reshape(input, [batch, innerDimensionSize]);\n        const result = backend.ifft(input2D);\n        return reshape(result, input.shape);\n    }, inputs, null /* gradient */, IFFT);\n}\nexport const ifft = op({ ifft_ });\n//# sourceMappingURL=ifft.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { reverse } from '../reverse';\nimport { scalar } from '../scalar';\nimport { slice } from '../slice';\nimport { ifft } from './ifft';\n/**\n * Inversed real value input fast Fourier transform.\n *\n * Computes the 1-dimensional inversed discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([0, 0, 0]);\n * const x = tf.complex(real, imag);\n *\n * x.irfft().print();\n * ```\n * @param input The real value input to compute an irfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction irfft_(input) {\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n    let ret;\n    if (innerDimensionSize <= 2) {\n        const complexInput = reshape(input, [batch, innerDimensionSize]);\n        ret = ifft(complexInput);\n    }\n    else {\n        // The length of unique components of the DFT of a real-valued signal\n        // is 2 * (input_len - 1)\n        const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n        const realInput = reshape(real(input), [batch, innerDimensionSize]);\n        const imagInput = reshape(imag(input), [batch, innerDimensionSize]);\n        const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);\n        const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));\n        const r = concat([realInput, realConjugate], 1);\n        const i = concat([imagInput, imagConjugate], 1);\n        const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);\n        ret = ifft(complexInput);\n    }\n    ret = real(ret);\n    // reshape the result if the input is 3D tensor.\n    if (input.rank === 3 && input.shape[0] !== 0) {\n        const temp = ret;\n        const batch = input.shape[0];\n        ret = reshape(ret, [batch, ret.shape[0] / batch, ret.shape[1]]);\n        temp.dispose();\n    }\n    return ret;\n}\nexport const irfft = op({ irfft_ });\n//# sourceMappingURL=irfft.js.map", "import { assert } from '../util';\n/**\n * Prepare the split size array. When the input is a number, the axis is evenly\n * divided among the split size. When the input contains the negative value, the\n * rest of the axis is allocated toward that.\n */\nexport function prepareSplitSize(x, numOrSizeSplits, axis = 0) {\n    let splitSizes = [];\n    if (typeof (numOrSizeSplits) === 'number') {\n        assert(x.shape[axis] % numOrSizeSplits === 0, () => 'Number of splits must evenly divide the axis.');\n        splitSizes =\n            new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);\n    }\n    else {\n        const numOfNegs = numOrSizeSplits.reduce((count, value) => {\n            if (value === -1) {\n                count += 1;\n            }\n            return count;\n        }, 0);\n        assert(numOfNegs <= 1, () => 'There should be only one negative value in split array.');\n        const negIndex = numOrSizeSplits.indexOf(-1);\n        // Allow the number of split array to be -1, which indicates the rest\n        // of dimension is allocated to that split.\n        if (negIndex !== -1) {\n            const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);\n            numOrSizeSplits[negIndex] = x.shape[axis] - total;\n        }\n        assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => 'The sum of sizes must match the size of the axis dimension.');\n        splitSizes = numOrSizeSplits;\n    }\n    return splitSizes;\n}\n//# sourceMappingURL=split_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SplitV } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { op } from './operation';\nimport { prepareSplitSize } from './split_util';\n/**\n * Splits a `tf.Tensor` into sub tensors.\n *\n * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`\n * into `numOrSizeSplits` smaller tensors.\n * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.\n *\n * If `numOrSizeSplits` is a number array, splits `x` into\n * `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the\n * same size as `x` except along dimension `axis` where the size is\n * `numOrSizeSplits[i]`.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);\n * const [a, b] = tf.split(x, 2, 1);\n * a.print();\n * b.print();\n *\n * const [c, d, e] = tf.split(x, [1, 2, 1], 1);\n * c.print();\n * d.print();\n * e.print();\n * ```\n *\n * @param x The input tensor to split.\n * @param numOrSizeSplits Either an integer indicating the number of\n * splits along the axis or an array of integers containing the sizes of\n * each output tensor along the axis. If a number then it must evenly divide\n * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.\n * Can contain one -1 indicating that dimension is to be inferred.\n * @param axis The dimension along which to split. Defaults to 0 (the first\n * dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction split_(x, numOrSizeSplits, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'split');\n    const forward = (backend, _) => {\n        const $axis = parseAxisParam(axis, $x.shape)[0];\n        const splitSizes = prepareSplitSize($x, numOrSizeSplits, $axis);\n        return backend.split($x, splitSizes, $axis);\n    };\n    const inputs = { x: $x };\n    const attr = { numOrSizeSplits, axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, SplitV, attr);\n}\nexport const split = op({ split_ });\n//# sourceMappingURL=split.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../../util';\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { split } from '../split';\nimport { zeros } from '../zeros';\nimport { zerosLike } from '../zeros_like';\nimport { fft } from './fft';\n/**\n * Real value input fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * real.rfft().print();\n * ```\n * @param input The real value input to compute an rfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction rfft_(input, fftLength) {\n    assert(input.dtype === 'float32', () => `The dtype for rfft() must be real value but got ${input.dtype}`);\n    let innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n    let adjustedInput;\n    if (fftLength != null && fftLength < innerDimensionSize) {\n        // Need to crop\n        const begin = input.shape.map(v => 0);\n        const size = input.shape.map(v => v);\n        size[input.shape.length - 1] = fftLength;\n        adjustedInput = slice(input, begin, size);\n        innerDimensionSize = fftLength;\n    }\n    else if (fftLength != null && fftLength > innerDimensionSize) {\n        // Need to pad with zeros\n        const zerosShape = input.shape.map(v => v);\n        zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;\n        adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);\n        innerDimensionSize = fftLength;\n    }\n    else {\n        adjustedInput = input;\n    }\n    // Complement the input with zero imaginary numbers.\n    const zerosInput = zerosLike(adjustedInput);\n    const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);\n    const ret = fft(complexInput);\n    // Exclude complex conjugations. These conjugations are put symmetrically.\n    const half = Math.floor(innerDimensionSize / 2) + 1;\n    const realValues = real(ret);\n    const imagValues = imag(ret);\n    const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);\n    const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);\n    const outputShape = adjustedInput.shape.slice();\n    outputShape[adjustedInput.shape.length - 1] = half;\n    return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);\n}\nexport const rfft = op({ rfft_ });\n//# sourceMappingURL=rfft.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sqrt } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.sqrt().print();  // or tf.sqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sqrt_(x) {\n    const $x = convertToTensor(x, 'x', 'sqrt');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sqrt($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sqrt);\n}\nexport const sqrt = op({ sqrt_ });\n//# sourceMappingURL=sqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SquaredDifference } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns (a - b) * (a - b) element-wise.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * ```js\n * // Broadcast squared difference  a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction squaredDifference_(a, b) {\n    let $a = convertToTensor(a, 'a', 'squaredDifference');\n    let $b = convertToTensor(b, 'b', 'squaredDifference');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.squaredDifference($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = {};\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, SquaredDifference, attrs);\n}\nexport const squaredDifference = op({ squaredDifference_ });\n//# sourceMappingURL=squared_difference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { squeezeShape } from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Removes dimensions of size 1 from the shape of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);\n * x.squeeze().print();\n * ```\n *\n * @param x The input tensor to be squeezed.\n * @param axis An optional list of numbers. If specified, only\n *     squeezes the dimensions listed. The dimension index starts at 0. It\n * is an error to squeeze a dimension that is not 1.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction squeeze_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'squeeze');\n    return reshape($x, squeezeShape($x.shape, axis).newShape);\n}\nexport const squeeze = op({ squeeze_ });\n//# sourceMappingURL=squeeze.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensorArray } from '../tensor_util_env';\nimport * as util from '../util';\nimport { concat } from './concat';\nimport { expandDims } from './expand_dims';\nimport { op } from './operation';\n/**\n * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.stack([a, b, c]).print();\n * ```\n *\n * @param tensors A list of tensor objects with the same shape and dtype.\n * @param axis The axis to stack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction stack_(tensors, axis = 0) {\n    const $tensors = convertToTensorArray(tensors, 'tensors', 'stack');\n    util.assert($tensors.length >= 1, () => 'Pass at least one tensor to tf.stack');\n    if ($tensors.length === 1) {\n        return expandDims($tensors[0], axis);\n    }\n    const rank = $tensors[0].rank;\n    const shape = $tensors[0].shape;\n    const dtype = $tensors[0].dtype;\n    util.assert(axis <= rank, () => 'Axis must be <= rank of the tensor');\n    $tensors.forEach(t => {\n        util.assertShapesMatch(shape, t.shape, 'All tensors passed to stack must have matching shapes');\n        util.assert(dtype === t.dtype, () => 'All tensors passed to stack must have matching dtypes');\n    });\n    const expandedTensors = $tensors.map(t => expandDims(t, axis));\n    // Stack exists in the TensorFlow C++ API\n    // (https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/stack) but not\n    // in\n    // https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/core/ops/ops.pbtxt.\n    // Therefore we are treating it like a high-level op rather than\n    // creating a dedicated stack kernel.\n    return concat(expandedTensors, axis);\n}\nexport const stack = op({ stack_ });\n//# sourceMappingURL=stack.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Step } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha * x`\n *\n * ```js\n * const x = tf.tensor1d([0, 2, -1, -3]);\n *\n * x.step(.5).print();  // or tf.step(x, .5)\n * ```\n * @param x The input tensor.\n * @param alpha The gradient when input is negative.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction step_(x, alpha = 0.0) {\n    const $x = convertToTensor(x, 'x', 'step');\n    const inputs = { x: $x };\n    const attrs = { alpha };\n    return ENGINE.runKernelFunc(backend => backend.step($x, alpha), inputs, null /* grad */, Step, attrs);\n}\nexport const step = op({ step_ });\n//# sourceMappingURL=step.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { StridedSlice } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { slice } from './slice';\nimport { computeOutShape, getNormalizedAxes, maskToAxes } from './slice_util';\n/**\n * Extracts a strided slice of a tensor.\n *\n * Roughly speaking, this op extracts a slice of size (end-begin)/stride from\n * the given input tensor (x). Starting at the location specified by begin the\n * slice continues by adding stride to the index until all dimensions are not\n * less than end. Note that a stride can be negative, which causes a reverse\n * slice.\n *\n * ```js\n * const t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],\n *    [3, 2, 3]);\n * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print()  // [[[3, 3, 3]]]\n * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print()  // [[[3, 3, 3],\n *                                                     // [4, 4, 4]]]\n * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],\n *                                                     // [3, 3, 3]]]\n * ```\n *\n * @param x The tensor to stride slice.\n * @param begin The coordinates to start the slice from.\n * @param end: The coordinates to end the slice at.\n * @param strides: The size of the slice.\n * @param beginMask: If the ith bit of beginMask is set, begin[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param endMask: If the ith bit of endMask is set, end[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param shrinkAxisMask: a bitmask where bit i implies that\n * the ith specification should shrink the dimensionality. begin and end must\n * imply a slice of size 1 in the dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {\n    let $x = convertToTensor(x, 'x', 'stridedSlice');\n    const forward = (backend) => {\n        if (strides == null) {\n            strides = new Array(begin.length);\n        }\n        const ellipsisAxes = maskToAxes(ellipsisMask);\n        if (ellipsisAxes.length > 1) {\n            throw new Error('Multiple ellipses in slice is not allowed.');\n        }\n        if (ellipsisMask !== 0 && newAxisMask !== 0) {\n            throw new Error('Using both ellipsisMask and newAxisMask is not yet supported.');\n        }\n        if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {\n            throw new Error('Using both ellipsisMask and shrinkAxisMask is not yet supported.');\n        }\n        const numInterpolatedAxes = $x.rank - begin.length;\n        // Expand the dims of x based on the newAxisMask.\n        const expandAxes = maskToAxes(newAxisMask);\n        const newShape = $x.shape.slice();\n        expandAxes.forEach(axis => {\n            begin[axis] = 0;\n            end[axis] = 1;\n            newShape.splice(axis, 0, 1);\n        });\n        $x = reshape($x, newShape);\n        const { begin: normalizedBegin, end: normalizedEnd, strides: normalizedStrides } = getNormalizedAxes($x.shape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask);\n        begin = normalizedBegin;\n        end = normalizedEnd;\n        strides = normalizedStrides;\n        const shrinkAxes = maskToAxes(shrinkAxisMask);\n        // Adjust the ends based on the shrink mask.\n        shrinkAxes.forEach(axis => {\n            end[axis] = begin[axis] + 1;\n            strides[axis] = 1;\n        });\n        // Figure out the output shape.\n        const size = computeOutShape(begin, end, strides);\n        // Remove the axes based on shrinkMask.\n        const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);\n        const nonStrided = strides.every(v => v === 1);\n        if (nonStrided) {\n            return reshape(slice($x, begin, size), outShape);\n        }\n        const res = backend.stridedSlice($x, begin, end, strides);\n        return reshape(res, outShape);\n    };\n    const inputs = { x: $x };\n    const attrs = {\n        begin,\n        end,\n        strides,\n        beginMask,\n        endMask,\n        ellipsisMask,\n        newAxisMask,\n        shrinkAxisMask\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, StridedSlice, attrs);\n}\nexport const stridedSlice = op({ stridedSlice_ });\n//# sourceMappingURL=strided_slice.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.tan().print();  // or tf.tan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tan_(x) {\n    const $x = convertToTensor(x, 'x', 'tan');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.tan($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Tan);\n}\nexport const tan = op({ tan_ });\n//# sourceMappingURL=tan.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor2d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor2d([[1, 2], [3, 4]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor2d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 2) {\n        throw new Error('tensor2d() requires shape to have two numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n        throw new Error('tensor2d() requires values to be number[][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor2d() requires shape to be provided when `values` ' +\n            'are a flat/TypedArray');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor2d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor4d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor4d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 4) {\n        throw new Error('tensor4d() requires shape to have four numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n        throw new Error('tensor4d() requires values to be number[][][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor4d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor5d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor5d([[[[[1], [2]], [[3], [4]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor5d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 5) {\n        throw new Error('tensor5d() requires shape to have five numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n        throw new Error('tensor5d() requires values to be ' +\n            'number[][][][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor5d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor5d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor6d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor6d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 6) {\n        throw new Error('tensor6d() requires shape to have six numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n        throw new Error('tensor6d() requires values to be number[][][][][][] or ' +\n            'flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor6d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    shape = shape ||\n        inferredShape;\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor6d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { TopK } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Finds the values and indices of the `k` largest entries along the last\n * dimension.\n *\n * If the input is a vector (rank=1), finds the k largest entries in the vector\n * and outputs their values and indices as vectors. Thus values[j] is the j-th\n * largest entry in input, and its index is indices[j].\n * For higher rank inputs, computes the top k entries along the last dimension.\n *\n * If two elements are equal, the lower-index element appears first.\n *\n * ```js\n * const a = tf.tensor2d([[1, 5], [4, 3]]);\n * const {values, indices} = tf.topk(a);\n * values.print();\n * indices.print();\n * ```\n * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.\n * @param k Number of top elements to look for along the last dimension.\n * @param sorted If true, the resulting `k` elements will be sorted by the\n *     values in descending order.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction topk_(x, k = 1, sorted = true) {\n    const $x = convertToTensor(x, 'x', 'topk');\n    if ($x.rank === 0) {\n        throw new Error('topk() expects the input to be of rank 1 or higher');\n    }\n    const lastDim = $x.shape[$x.shape.length - 1];\n    if (k > lastDim) {\n        throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) ` +\n            `but got ${k}`);\n    }\n    const inputs = { x: $x };\n    const attrs = { k, sorted };\n    const [values, indices] = ENGINE.runKernelFunc(b => b.topk($x, k, sorted), inputs, null /* grad */, TopK, attrs);\n    return { values, indices };\n}\nexport const topk = op({ topk_ });\n//# sourceMappingURL=topk.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a truncated normal\n * distribution.\n *\n * ```js\n * tf.truncatedNormal([2, 2]).print();\n * ```\n *\n * The generated values follow a normal distribution with specified mean and\n * standard deviation, except that values whose magnitude is more than 2\n * standard deviations from the mean are dropped and re-picked.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output tensor.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction truncatedNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n    if (dtype != null && dtype === 'bool') {\n        throw new Error(`Unsupported data type $ { dtype }`);\n    }\n    const randGauss = new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = randGauss.nextValue();\n    }\n    return res.toTensor();\n}\nexport const truncatedNormal = op({ truncatedNormal_ });\n//# sourceMappingURL=truncated_normal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unique } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert } from '../util';\nimport { op } from './operation';\n/**\n * Finds unique elements along an axis of a tensor.\n *\n * It returns a tensor `values` containing all of the unique elements along the\n * `axis` of the given tensor `x` in the same order that they occur along the\n * `axis` in `x`; `x` does not need to be sorted. It also returns a tensor\n * `indices` the same size as the number of the elements in `x` along the `axis`\n * dimension. It contains the index in the unique output `values`.\n *\n * ```js\n * // A 1-D tensor\n * const a = tf.tensor1d([1, 1, 2, 4, 4, 4, 7, 8, 8]);\n * const {values, indices} = tf.unique(a);\n * values.print();   // [1, 2, 4, 7, 8,]\n * indices.print();  // [0, 0, 1, 2, 2, 2, 3, 4, 4]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=0\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 0)\n * values.print();   // [[1, 0, 0],\n *                   //  [2, 0, 0]]\n * indices.print();  // [0, 0, 1]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=1\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 1)\n * values.print();   // [[1, 0],\n *                   //  [1, 0],\n *                   //  [2, 0]]\n * indices.print();  // [0, 1, 1]\n * ```\n * @param x A tensor (int32, string, bool).\n * @param axis The axis of the tensor to find the unique elements.\n * @returns [uniqueElements, indices] (see above for details)\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction unique_(x, axis = 0) {\n    // x can be of any dtype, thus null as the last argument.\n    const $x = convertToTensor(x, 'x', 'unique', null);\n    assert($x.rank > 0, () => 'The input tensor must be at least 1D');\n    const inputs = { x: $x };\n    const attrs = { axis };\n    const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs);\n    return { values, indices };\n}\nexport const unique = op({ unique_ });\n//# sourceMappingURL=unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { UnsortedSegmentSum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, isInt } from '../util';\nimport { op } from './operation';\n/**\n * Computes the sum along segments of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');\n * const numSegments = 3;\n *\n * x.unsortedSegmentSum(segmentIds, numSegments).print()\n * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)\n * ```\n * @param x The `tf.Tensor` that will be summed along its segments.\n * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s\n * dimension along the `axis`.  Maps each element of `x` to a segment.\n * @param numSegments The number of distinct `segmentIds`.\n *\n * @doc {heading: 'Operations', subheading: 'Segment'}\n */\nfunction unsortedSegmentSum_(x, segmentIds, numSegments) {\n    const $x = convertToTensor(x, 'x', 'unsortedSegmentSum');\n    const $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');\n    assert(isInt(numSegments), () => 'numSegments must be of dtype int');\n    const inputs = { x: $x, segmentIds: $segmentIds };\n    const attrs = { numSegments };\n    const forward = (backend, save) => {\n        const res = backend.unsortedSegmentSum($x, $segmentIds, numSegments);\n        save([$segmentIds]);\n        return res;\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, UnsortedSegmentSum, attrs);\n}\nexport const unsortedSegmentSum = op({ unsortedSegmentSum_ });\n//# sourceMappingURL=unsorted_segment_sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unpack } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * tf.unstack(a).forEach(tensor => tensor.print());\n * ```\n *\n * @param x A tensor object.\n * @param axis The axis to unstack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction unstack_(x, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'unstack');\n    util.assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);\n    if (axis < 0) {\n        axis += $x.shape.length;\n    }\n    const inputs = { value: $x };\n    const attrs = { axis };\n    const forward = (backend) => backend.unstack($x, axis);\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Unpack, attrs);\n}\nexport const unstack = op({ unstack_ });\n//# sourceMappingURL=unstack.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\n/**\n * Creates a new variable with the provided initial value.\n * ```js\n * const x = tf.variable(tf.tensor([1, 2, 3]));\n * x.assign(tf.tensor([4, 5, 6]));\n *\n * x.print();\n * ```\n *\n * @param initialValue Initial value for the tensor.\n * @param trainable If true, optimizers are allowed to update it.\n * @param name Name of the variable. Defaults to a unique id.\n * @param dtype If set, initialValue will be converted to the given type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function variable(initialValue, trainable = true, name, dtype) {\n    return ENGINE.makeVariable(initialValue, trainable, name, dtype);\n}\n//# sourceMappingURL=variable.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the Where kernel shared between cpu and webgl */\nimport { buffer } from '../ops/buffer';\nexport function whereImpl(condShape, condVals) {\n    const indices = [];\n    for (let i = 0; i < condVals.length; i++) {\n        if (condVals[i]) {\n            indices.push(i);\n        }\n    }\n    const inBuffer = buffer(condShape, 'int32');\n    const out = buffer([indices.length, condShape.length], 'int32');\n    for (let i = 0; i < indices.length; i++) {\n        const loc = inBuffer.indexToLoc(indices[i]);\n        const offset = i * condShape.length;\n        out.values.set(loc, offset);\n    }\n    return out.toTensor();\n}\n//# sourceMappingURL=where_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { whereImpl } from '../backends/where_impl';\nimport { convertToTensor } from '../tensor_util_env';\n/**\n * Returns the coordinates of true elements of condition.\n *\n * The coordinates are returned in a 2-D tensor where the first dimension (rows)\n * represents the number of true elements, and the second dimension (columns)\n * represents the coordinates of the true elements. Keep in mind, the shape of\n * the output tensor can vary depending on how many true values there are in\n * input. Indices are output in row-major order. The resulting tensor has the\n * shape `[numTrueElems, condition.rank]`.\n *\n * This is analogous to calling the python `tf.where(cond)` without an x or y.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const result = await tf.whereAsync(cond);\n * result.print();\n * ```\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nasync function whereAsync_(condition) {\n    const $condition = convertToTensor(condition, 'condition', 'whereAsync', 'bool');\n    const vals = await $condition.data();\n    const res = whereImpl($condition.shape, vals);\n    if (condition !== $condition) {\n        $condition.dispose();\n    }\n    return res;\n}\nexport const whereAsync = whereAsync_;\n//# sourceMappingURL=where_async.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { gather } from './gather';\nimport { reshape } from './reshape';\nimport { squeeze } from './squeeze';\nimport { whereAsync } from './where_async';\n/**\n * Apply boolean mask to tensor.\n *\n * ```js\n * const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);\n * const mask = tf.tensor1d([1, 0, 1], 'bool');\n * const result = await tf.booleanMaskAsync(tensor, mask);\n * result.print();\n * ```\n *\n * @param tensor N-D tensor.\n * @param mask K-D boolean tensor, K <= N and K must be known statically.\n * @param axis A 0-D int Tensor representing the axis in tensor to mask from.\n *     By default, axis is 0 which will mask from the first dimension.\n *     Otherwise K + axis <= N.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nasync function booleanMaskAsync_(tensor, mask, axis) {\n    const $tensor = convertToTensor(tensor, 'tensor', 'boolMask');\n    const $mask = convertToTensor(mask, 'mask', 'boolMask', 'bool');\n    const axisFrom = axis == null ? 0 : axis;\n    const maskDim = $mask.rank;\n    const tensorShape = $tensor.shape;\n    util.assert(maskDim > 0, () => 'mask cannot be scalar');\n    util.assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);\n    let leadingSize = 1;\n    for (let i = axisFrom; i < axisFrom + maskDim; i++) {\n        leadingSize *= tensorShape[i];\n    }\n    const targetTensorShape = tensorShape.slice(0, axisFrom)\n        .concat([leadingSize], tensorShape.slice(axisFrom + maskDim));\n    const reshapedTensor = reshape($tensor, targetTensorShape);\n    const reshapedMask = reshape($mask, [-1]);\n    const positivePositions = await whereAsync(reshapedMask);\n    const indices = squeeze(positivePositions, [1]);\n    const res = gather(reshapedTensor, indices, axisFrom);\n    // Ensure no memory leak.\n    if (tensor !== $tensor) {\n        $tensor.dispose();\n    }\n    if (mask !== $mask) {\n        $mask.dispose();\n    }\n    indices.dispose();\n    reshapedTensor.dispose();\n    reshapedMask.dispose();\n    positivePositions.dispose();\n    return res;\n}\nexport const booleanMaskAsync = booleanMaskAsync_;\n//# sourceMappingURL=boolean_mask.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../globals';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertShapesMatch } from '../util';\nimport { equal } from './equal';\nimport { greater } from './greater';\nimport { greaterEqual } from './greater_equal';\nimport { less } from './less';\nimport { lessEqual } from './less_equal';\nimport { notEqual } from './not_equal';\nimport { op } from './operation';\n/**\n * @deprecated\n * Strict version of `tf.notEqual` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction notEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'notEqualStrict');\n    const $b = convertToTensor(b, 'b', 'notEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in notEqualStrict: ');\n    return notEqual($a, $b);\n}\n/**\n * @deprecated\n * Strict version of `tf.less` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction lessStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'lessStrict');\n    const $b = convertToTensor(b, 'b', 'lessStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in lessStrict: ');\n    return less($a, $b);\n}\nfunction equalStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'equalStrict');\n    const $b = convertToTensor(b, 'b', 'equalStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in equalStrict: ');\n    return equal($a, $b);\n}\nfunction lessEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'lessEqualStrict');\n    const $b = convertToTensor(b, 'b', 'lessEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in lessEqualStrict: ');\n    return lessEqual($a, $b);\n}\nfunction greaterStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'greaterStrict');\n    const $b = convertToTensor(b, 'b', 'greaterStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in greaterStrict: ');\n    return greater($a, $b);\n}\nfunction greaterEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'greaterEqualStrict');\n    const $b = convertToTensor(b, 'b', 'greaterEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in greaterEqualStrict: ');\n    return greaterEqual($a, $b);\n}\nexport const equalStrict = op({ equalStrict_ });\nexport const greaterEqualStrict = op({ greaterEqualStrict_ });\nexport const greaterStrict = op({ greaterStrict_ });\nexport const lessEqualStrict = op({ lessEqualStrict_ });\nexport const lessStrict = op({ lessStrict_ });\nexport const notEqualStrict = op({ notEqualStrict_ });\n//# sourceMappingURL=compare.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../globals';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { maximum } from './maximum';\nimport { minimum } from './minimum';\nimport { mod } from './mod';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { squaredDifference } from './squared_difference';\nimport { sub } from './sub';\n/**\n * @deprecated\n * Adds two `tf.Tensor`s element-wise, A + B.\n *\n * Inputs must be the same shape. For broadcasting support, use add() instead.\n *\n * @param a The first Tensor to add element-wise.\n * @param b The second Tensor to add element-wise.\n */\nfunction addStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'addStrict');\n    const $b = convertToTensor(b, 'b', 'addStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in addStrict: ');\n    return add($a, $b);\n}\n/**\n * @deprecated\n * Subtracts two `tf.Tensor`s element-wise, A - B. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.sub` instead.\n *\n * @param a The first Tensor to subtract element-wise.\n * @param b The second Tensor to subtract element-wise.\n */\nfunction subStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'subStrict');\n    const $b = convertToTensor(b, 'b', 'subStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in subStrict: ');\n    return sub($a, $b);\n}\n/**\n * @deprecated\n * Computes the power of one `tf.Tensor` to another. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.pow` instead.\n *\n * @param base The base tensor to pow element-wise.\n * @param exp The exponent tensor to pow element-wise.\n */\nfunction powStrict_(base, exp) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    util.assertShapesMatch(base.shape, exp.shape, 'Error in powStrict: ');\n    return pow(base, exp);\n}\n/**\n * @deprecated\n * Multiplies two `tf.Tensor`s element-wise, A * B.\n *\n * Inputs must be the same shape. For broadcasting support, use `tf.mul`.\n *\n * @param a The first tensor to multiply.\n * @param b The first tensor to multiply. Must have the same\n *    dtype as `a`.\n */\nfunction mulStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'mul');\n    const $b = convertToTensor(b, 'b', 'mul');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in multiplyStrict: ');\n    return mul($a, $b);\n}\n/**\n * @deprecated\n * Divides two `tf.Tensor`s element-wise, A / B. Inputs must\n * be the same shape.\n *\n * @param a The first tensor as the numerator for element-wise division.\n * @param b The second tensor as the denominator for element-wise division.\n */\nfunction divStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'div');\n    const $b = convertToTensor(b, 'b', 'div');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in divideStrict: ');\n    return div($a, $b);\n}\n/**\n * @deprecated\n * Returns the mod of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use mod().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction modStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'modStrict');\n    const $b = convertToTensor(b, 'b', 'modStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in modStrict: ');\n    return mod($a, $b);\n}\n/**\n * @deprecated\n * Returns the min of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use minimum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction minimumStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'minimumStrict');\n    const $b = convertToTensor(b, 'b', 'minimumStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in minimumStrict: ');\n    return minimum($a, $b);\n}\n/**\n * @deprecated\n * Returns the max of a and b (`a > b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use maximum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction maximumStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'maximumStrict');\n    const $b = convertToTensor(b, 'b', 'maximumStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in maximumStrict: ');\n    return maximum($a, $b);\n}\n/**\n * @deprecated\n * Returns (a - b) * (a - b) element-wise.\n *\n * Inputs must be the same shape. For broadcasting support, use\n * `tf.squaredDifference` instead.\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\nfunction squaredDifferenceStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'squaredDifferenceStrict');\n    const $b = convertToTensor(b, 'b', 'squaredDifferenceStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in squaredDifferenceStrict: ');\n    return squaredDifference($a, $b);\n}\nexport const addStrict = op({ addStrict_ });\nexport const divStrict = op({ divStrict_ });\nexport const maximumStrict = op({ maximumStrict_ });\nexport const minimumStrict = op({ minimumStrict_ });\nexport const modStrict = op({ modStrict_ });\nexport const mulStrict = op({ mulStrict_ });\nexport const powStrict = op({ powStrict_ });\nexport const squaredDifferenceStrict = op({ squaredDifferenceStrict_ });\nexport const subStrict = op({ subStrict_ });\n//# sourceMappingURL=binary_ops.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { abs } from './abs';\nimport * as axis_util from './axis_util';\nimport { max } from './max';\nimport { min } from './min';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { reshape } from './reshape';\nimport { scalar } from './scalar';\nimport { sqrt } from './sqrt';\nimport { square } from './square';\nimport { sum } from './sum';\n/**\n * Computes the norm of scalar, vectors, and matrices.\n * This function can compute several different vector norms (the 1-norm, the\n * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)\n * and matrix norms (Frobenius, 1-norm, and inf-norm).\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.norm().print();  // or tf.norm(x)\n * ```\n *\n * @param x The input array.\n * @param ord Optional. Order of the norm. Supported norm types are\n * following:\n *\n *  | ord        | norm for matrices         | norm for vectors\n *  |------------|---------------------------|---------------------\n *  |'euclidean' |Frobenius norm             |2-norm\n *  |'fro'       |Frobenius norm\t           |\n *  |Infinity    |max(sum(abs(x), axis=1))   |max(abs(x))\n *  |-Infinity   |min(sum(abs(x), axis=1))   |min(abs(x))\n *  |1           |max(sum(abs(x), axis=0))   |sum(abs(x))\n *  |2           |                           |sum(abs(x)^2)^1/2*\n *\n * @param axis Optional. If axis is null (the default), the input is\n * considered a vector and a single vector norm is computed over the entire\n * set of values in the Tensor, i.e. norm(x, ord) is equivalent\n * to norm(x.reshape([-1]), ord). If axis is a integer, the input\n * is considered a batch of vectors, and axis determines the axis in x\n * over which to compute vector norms. If axis is a 2-tuple of integer it is\n * considered a batch of matrices and axis determines the axes in NDArray\n * over which to compute a matrix norm.\n * @param keepDims Optional. If true, the norm have the same dimensionality\n * as the input.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction norm_(x, ord = 'euclidean', axis = null, keepDims = false) {\n    x = convertToTensor(x, 'x', 'norm');\n    const norm = normImpl(x, ord, axis);\n    let keepDimsShape = norm.shape;\n    if (keepDims) {\n        const axes = parseAxisParam(axis, x.shape);\n        keepDimsShape = axis_util.expandShapeToKeepDim(norm.shape, axes);\n    }\n    return reshape(norm, keepDimsShape);\n}\nfunction normImpl(x, p, axis = null) {\n    if (x.rank === 0) {\n        return abs(x);\n    }\n    // consider vector when no axis is specified\n    if (x.rank !== 1 && axis === null) {\n        return normImpl(reshape(x, [-1]), p, axis);\n    }\n    // vector\n    if (x.rank === 1 || typeof axis === 'number' ||\n        Array.isArray(axis) && axis.length === 1) {\n        if (p === 1) {\n            return sum(abs(x), axis);\n        }\n        if (p === Infinity) {\n            return max(abs(x), axis);\n        }\n        if (p === -Infinity) {\n            return min(abs(x), axis);\n        }\n        if (p === 'euclidean' || p === 2) {\n            // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2\n            return sqrt(sum(pow(abs(x), scalar(2, 'int32')), axis));\n        }\n        throw new Error(`Error in norm: invalid ord value: ${p}`);\n    }\n    // matrix (assumption axis[0] < axis[1])\n    if (Array.isArray(axis) && axis.length === 2) {\n        if (p === 1) {\n            return max(sum(abs(x), axis[0]), axis[1] - 1);\n        }\n        if (p === Infinity) {\n            return max(sum(abs(x), axis[1]), axis[0]);\n        }\n        if (p === -Infinity) {\n            return min(sum(abs(x), axis[1]), axis[0]);\n        }\n        if (p === 'fro' || p === 'euclidean') {\n            // norm(x) = sqrt(sum(pow(x, 2)))\n            return sqrt(sum(square(x), axis));\n        }\n        throw new Error(`Error in norm: invalid ord value: ${p}`);\n    }\n    throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\nexport const norm = op({ norm_ });\n//# sourceMappingURL=norm.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assertTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { scalar } from './scalar';\nimport { sub } from './sub';\n/**\n * Compute the moving average of a variable.\n *\n * Without zeroDebias, the moving average operation is defined by:\n *   `v += delta`\n * where\n *   `delta = (1 - decay) * (x - v)`\n *\n * With zeroDebias (default), the `delta` term is scaled to debias the\n * effect of the (assumed) zero-initialization of `v`.\n *   `delta /= (1 - decay ^ step)`\n *\n * For more details on the zero-debiasing algorithm, see:\n *   https://arxiv.org/abs/1412.6980\n *\n * Note that this function is completely stateless and does not keep track of\n * step count. The step count needs to be maintained by the caller and passed\n * in as `step`.\n *\n * @param v The current moving average value.\n * @param x New input value, must have the same shape and dtype as `v`.\n * @param decay The decay factor. Typical values are 0.95 and 0.99.\n * @param step Step count.\n * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).\n * @returns The new moving average value.\n *\n * @doc {heading: 'Operations', subheading: 'Moving Average'}\n */\nfunction movingAverage_(v, x, decay, step, zeroDebias = true) {\n    const $v = convertToTensor(v, 'v', 'movingAverage');\n    const $x = convertToTensor(x, 'x', 'movingAverage');\n    const $decay = convertToTensor(decay, 'decay', 'movingAverage');\n    assertTypesMatch($v, $x);\n    util.assert(util.arraysEqual($v.shape, $x.shape), () => 'Shape mismatch in v and x');\n    const one = scalar(1);\n    const oneMinusDecay = sub(one, $decay);\n    let update = mul(sub($x, $v), oneMinusDecay);\n    if (zeroDebias) {\n        util.assert(step != null, () => 'When using zeroDebias: true, step is required.');\n        const $step = convertToTensor(step, 'step', 'movingAverage');\n        update = div(update, sub(one, pow($decay, $step)));\n    }\n    return add($v, update);\n}\nexport const movingAverage = op({ movingAverage_ });\n//# sourceMappingURL=moving_average.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ScatterNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport * as scatter_nd_util from './scatter_nd_util';\n/**\n * Creates a new tensor by applying sparse updates to individual\n * values or slices within a zero tensor of the given shape tensor according to\n * indices. This operator is the inverse of the `tf.gatherND` operator which\n * extracts values or slices from a given tensor.\n *\n * ```js\n * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32');\n * const updates = tf.tensor1d([9, 10, 11, 12]);\n * const shape = [8];\n * tf.scatterND(indices, updates, shape).print() //[0, 11, 0, 10, 9, 0, 0, 12]\n * ```\n *\n * @param indices The tensor contains the indices into the output tensor.\n * @param updates The tensor contains the value for the indices.\n * @param shape: The shape of the output tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction scatterND_(indices, updates, shape) {\n    const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');\n    const $updates = convertToTensor(updates, 'updates', 'scatterND');\n    scatter_nd_util.validateInput($updates, $indices, shape);\n    const forward = (backend) => {\n        return backend.scatterND($indices, $updates, shape);\n    };\n    const inputs = { indices: $indices, updates: $updates };\n    const attrs = { shape };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ScatterNd, attrs);\n}\nexport const scatterND = op({ scatterND_ });\n//# sourceMappingURL=scatter_nd.js.map", "/**\n * Validate sparseToDense inputs.\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape number[]. Shape of the dense output tensor.\n * @param validateIndices boolean. indice validation is not supported, error\n * will be thrown if it is set.\n */\nexport function validateInput(sparseIndices, sparseValues, outputShape, defaultValues) {\n    if (sparseIndices.dtype !== 'int32') {\n        throw new Error('tf.sparseToDense() expects the indices to be int32 type,' +\n            ` but the dtype was ${sparseIndices.dtype}.`);\n    }\n    if (sparseIndices.rank > 2) {\n        throw new Error('sparseIndices should be a scalar, vector, or matrix,' +\n            ` but got shape ${sparseIndices.shape}.`);\n    }\n    const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n    const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n    if (outputShape.length !== numDims) {\n        throw new Error('outputShape has incorrect number of elements:,' +\n            ` ${outputShape.length}, should be: ${numDims}.`);\n    }\n    const numValues = sparseValues.size;\n    if (!(sparseValues.rank === 0 ||\n        sparseValues.rank === 1 && numValues === numElems)) {\n        throw new Error('sparseValues has incorrect shape ' +\n            `${sparseValues.shape}, should be [] or [${numElems}]`);\n    }\n    if (sparseValues.dtype !== defaultValues.dtype) {\n        throw new Error('sparseValues.dtype must match defaultValues.dtype');\n    }\n}\n//# sourceMappingURL=sparse_to_dense_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SparseToDense } from '../kernel_names';\nimport * as sparse_to_dense from '../ops/sparse_to_dense_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Converts a sparse representation into a dense tensor.\n *\n * Builds an array dense with shape outputShape such that:\n *\n * // If sparseIndices is scalar\n * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)\n *\n * // If sparseIndices is a vector, then for each i\n * dense[sparseIndices[i]] = sparseValues[i]\n *\n * // If sparseIndices is an n by d matrix, then for each i in [0, n)\n * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]\n * All other values in dense are set to defaultValue. If sparseValues is a\n * scalar, all sparse indices are set to this single value.\n *\n * If indices are repeated the final value is summed over all values for those\n * indices.\n *\n * ```js\n * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');\n * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');\n * const shape = [8];\n * tf.sparseToDense(indices, values, shape).print();\n * ```\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape Shape of the dense output tensor. the type is inferred.\n * @param defaultValue Scalar. Value to set for indices not specified in\n * sparseIndices. Defaults to zero.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {\n    const $sparseIndices = convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');\n    const $sparseValues = convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');\n    const $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);\n    sparse_to_dense.validateInput($sparseIndices, $sparseValues, outputShape, $defaultValue);\n    const inputs = {\n        sparseIndices: $sparseIndices,\n        sparseValues: $sparseValues,\n        defaultValue: $defaultValue\n    };\n    const attrs = { outputShape };\n    return ENGINE.runKernelFunc(backend => backend.sparseToDense($sparseIndices, $sparseValues, outputShape, $defaultValue), inputs, null /* grad */, SparseToDense, attrs);\n}\nexport const sparseToDense = op({ sparseToDense_ });\n//# sourceMappingURL=sparse_to_dense.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Gather slices from input tensor into a Tensor with shape specified by\n * `indices`.\n *\n * `indices` is an K-dimensional integer tensor, best thought of as a\n * (K-1)-dimensional tensor of indices into input, where each element defines a\n * slice of input:\n * output[\\\\(i_0, ..., i_{K-2}\\\\)] = input[indices[\\\\(i_0, ..., i_{K-2}\\\\)]]\n *\n * Whereas in `tf.gather`, `indices` defines slices into the first dimension of\n * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions\n * of input, where N = indices.shape[-1].\n *\n * The last dimension of indices can be at most the rank of input:\n * indices.shape[-1] <= input.rank\n *\n * The last dimension of `indices` corresponds to elements\n * (if indices.shape[-1] == input.rank) or slices\n * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of\n * input.\n * The output tensor has shape\n * indices.shape[:-1] + input.shape[indices.shape[-1]:]\n *\n * Note that on CPU, if an out of bound index is found, an error is returned. On\n * GPU, if an out of bound index is found, a 0 is stored in the corresponding\n * output value.\n *\n * ```js\n * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');\n * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);\n * tf.gatherND(input, indices).print() // [10, 11]\n * ```\n *\n * @param x The tensor from which to gather values.\n * @param indices Index tensor, must be of type int32.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction gatherND_(x, indices) {\n    const $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');\n    const $x = convertToTensor(x, 'x', 'gatherND');\n    const forward = (backend) => {\n        return backend.gatherND($x, $indices);\n    };\n    const inputs = { params: $x, indices: $indices };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, GatherNd);\n}\nexport const gatherND = op({ gatherND_ });\n//# sourceMappingURL=gather_nd.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n * Normalize noise shape based on provided tensor and noise shape.\n *\n * @param x Tensor.\n * @param noiseShape The shape for the randomly generated keep/drop flags, as\n *   an array of numbers. Optional.\n * @returns Normalized noise shape.\n */\nexport function getNoiseShape(x, noiseShape) {\n    if (noiseShape == null) {\n        return x.shape.slice();\n    }\n    if (util.arraysEqual(x.shape, noiseShape)) {\n        return noiseShape;\n    }\n    if (x.shape.length === noiseShape.length) {\n        const newDimension = [];\n        for (let i = 0; i < x.shape.length; i++) {\n            if (noiseShape[i] == null && x.shape[i] != null) {\n                newDimension.push(x.shape[i]);\n            }\n            else {\n                newDimension.push(noiseShape[i]);\n            }\n        }\n        return newDimension;\n    }\n    return noiseShape;\n}\n//# sourceMappingURL=dropout_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { getNoiseShape } from './dropout_util';\nimport { floor } from './floor';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { randomUniform } from './random_uniform';\n/**\n * Computes dropout.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 2, 1]);\n * const rate = 0.75;\n * const output = tf.dropout(x, rate);\n * output.print();\n * ```\n *\n * @param x A floating point Tensor or TensorLike.\n * @param rate A float in the range [0, 1). The probability that each element\n *   of x is discarded.\n * @param noiseShape An array of numbers of type int32, representing the\n * shape for randomly generated keep/drop flags. If the noiseShape has null\n * value, it will be automatically replaced with the x's relative dimension\n * size. Optional.\n * @param seed Used to create random seeds. Optional.\n * @returns A Tensor of the same shape of x.\n *\n * @doc {heading: 'Operations', subheading: 'Dropout'}\n */\nfunction dropout_(x, rate, noiseShape, seed) {\n    const $x = convertToTensor(x, 'x', 'dropout');\n    util.assert($x.dtype === 'float32', () => `x has to be a floating point tensor since it's going to be ` +\n        `scaled, but got a ${$x.dtype} tensor instead.`);\n    util.assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);\n    if (rate === 0) {\n        return x instanceof Tensor ? $x.clone() : $x;\n    }\n    const $noiseShape = getNoiseShape($x, noiseShape);\n    const keepProb = 1 - rate;\n    const multiplier = div(floor(add(randomUniform($noiseShape, 0, 1, 'float32', seed), keepProb)), keepProb);\n    return mul($x, multiplier);\n}\nexport const dropout = op({ dropout_ });\n//# sourceMappingURL=dropout.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensor1d } from './tensor1d';\nexport function enclosingPowerOfTwo(value) {\n    // Return 2**N for integer N such that 2**N >= value.\n    return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2.0))));\n}\nexport function cosineWindow(windowLength, a, b) {\n    const even = 1 - windowLength % 2;\n    const newValues = new Float32Array(windowLength);\n    for (let i = 0; i < windowLength; ++i) {\n        const cosArg = (2.0 * Math.PI * i) / (windowLength + even - 1);\n        newValues[i] = a - b * Math.cos(cosArg);\n    }\n    return tensor1d(newValues, 'float32');\n}\n//# sourceMappingURL=signal_ops_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, assertShapesMatch, getTypedArrayFromDType } from '../util';\nimport { tensor } from './tensor';\n/**\n * Returns whether the targets are in the top K predictions.\n *\n * ```js\n * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);\n * const targets = tf.tensor1d([2, 0]);\n * const precision = await tf.inTopKAsync(predictions, targets);\n * precision.print();\n * ```\n * @param predictions 2-D or higher `tf.Tensor` with last dimension being\n *     at least `k`.\n * @param targets 1-D or higher `tf.Tensor`.\n * @param k Optional Number of top elements to look at for computing precision,\n *     default to 1.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nasync function inTopKAsync_(predictions, targets, k = 1) {\n    const $predictions = convertToTensor(predictions, 'predictions', 'inTopK');\n    const $targets = convertToTensor(targets, 'targets', 'inTopK');\n    assert($predictions.rank > 1, () => 'inTopK() expects the predictions to be of rank 2 or higher, ' +\n        `but got ${$predictions.rank}`);\n    assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than ` +\n        `targets rank, but got predictions rank ` +\n        `${$predictions.rank} and targets rank ${$targets.rank}`);\n    assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, ` +\n        'except the last dimension.');\n    const lastDim = $predictions.shape[$predictions.shape.length - 1];\n    assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last ` +\n        `dimension (${lastDim}), but got ${k}`);\n    const predictionsVals = await $predictions.data();\n    const targetsVals = await $targets.data();\n    // Reshape predictionsVals into a 2d tensor [batch, lastDim]\n    // and look up topK along lastDim.\n    const [batch, size] = [predictionsVals.length / lastDim, lastDim];\n    const precision = getTypedArrayFromDType('bool', batch);\n    for (let b = 0; b < batch; b++) {\n        const offset = b * size;\n        const vals = predictionsVals.subarray(offset, offset + size);\n        const valAndInd = [];\n        for (let i = 0; i < vals.length; i++) {\n            valAndInd.push({ value: vals[i], index: i });\n        }\n        valAndInd.sort((a, b) => b.value - a.value);\n        precision[b] = 0;\n        for (let i = 0; i < k; i++) {\n            if (valAndInd[i].index === targetsVals[b]) {\n                precision[b] = 1;\n                break;\n            }\n        }\n    }\n    if (predictions !== $predictions) {\n        $predictions.dispose();\n    }\n    if (targets !== $targets) {\n        $targets.dispose();\n    }\n    // Output precision has the same shape as targets.\n    return tensor(precision, $targets.shape, 'bool');\n}\nexport const inTopKAsync = inTopKAsync_;\n//# sourceMappingURL=in_top_k.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropFilter } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the filter of a 2D convolution.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.\n * @param dy The dy image, of rank 4 or rank 3, of shape\n *     [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.\n * @param filterShape The shape of the filter, length 4,\n *     [filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction conv2DBackpropFilter_(x, dy, filterShape, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n    let x4D = x;\n    if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    let dy4D = dy;\n    if (dy4D.rank === 3) {\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ` +\n        `${x4D.shape}.`);\n    util.assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ` +\n        `${dy4D.shape}.`);\n    util.assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ` +\n        `${filterShape}.`);\n    const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n    const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n    util.assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must ` +\n        `match input depth in filter (${filterShape[2]}.`);\n    util.assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must ` +\n        `match output depth for filter (${filterShape[3]}).`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2dDerFilter: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const dilations = 1;\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, filterShape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        return backend.conv2dDerFilter(x4D, dy4D, convInfo);\n    };\n    const inputs = { x: x4D, dy: dy4D };\n    const attrs = { strides, pad, dataFormat, dimRoundingMode };\n    return ENGINE.runKernelFunc(forward, inputs, null, Conv2DBackpropFilter, attrs);\n}\nexport const conv2DBackpropFilter = op({ conv2DBackpropFilter_ });\n//# sourceMappingURL=conv2d_backprop_filter.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as broadcast_util from './broadcast_util';\nimport { elu } from './elu';\nimport { mul } from './mul';\nimport { prelu } from './prelu';\nimport { relu } from './relu';\nimport { relu6 } from './relu6';\nimport { reshape } from './reshape';\nimport { step } from './step';\nimport { sum } from './sum';\n// Returns gradient for fused activation.\nexport function getFusedDyActivation(dy, y, activation) {\n    if (activation == null || activation === 'linear') {\n        return dy;\n    }\n    if (activation === 'relu') {\n        return mul(dy, step(y));\n    }\n    throw new Error(`Cannot compute gradient for fused activation ${activation}.`);\n}\n// Returns gradient for fused bias.\nexport function getFusedBiasGradient(bias, dyActivation) {\n    let res = dyActivation;\n    const reduceAxes = broadcast_util.getReductionAxes(bias.shape, dyActivation.shape);\n    if (reduceAxes.length > 0) {\n        res = sum(res, reduceAxes);\n    }\n    return reshape(res, bias.shape);\n}\nexport function applyActivation(x, activation, preluActivationWeights) {\n    if (activation === 'linear') {\n        return x;\n    }\n    else if (activation === 'relu') {\n        return relu(x);\n    }\n    else if (activation === 'elu') {\n        return elu(x);\n    }\n    else if (activation === 'relu6') {\n        return relu6(x);\n    }\n    else if (activation === 'prelu') {\n        return prelu(x, preluActivationWeights);\n    }\n    throw new Error(`Unknown fused activation ${activation}.`);\n}\n// Whether we should call fused ops.\nexport const shouldFuse = (gradientDepth, activation) => {\n    const gradientMode = gradientDepth > 0;\n    return !gradientMode || activation === 'linear';\n};\n//# sourceMappingURL=fused_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { conv2d as unfusedConv2d } from '../conv2d';\nimport { conv2DBackpropFilter } from '../conv2d_backprop_filter';\nimport { conv2DBackpropInput } from '../conv2d_backprop_input';\nimport * as conv_util from '../conv_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes a 2D convolution over the input x, optionally fused with adding a\n * bias and applying an activation.\n *\n * ```js\n * const inputDepth = 2;\n * const inShape = [2, 2, 2, inputDepth];\n * const outputDepth = 2;\n * const fSize = 1;\n * const pad = 0;\n * const strides = 1;\n *\n * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n * 16], inShape);\n * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,\n * outputDepth]);\n *\n * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',\n * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();\n * ```\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid` output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`) to be\n *     applied\n *      after biasAdd.\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights }) {\n    activation = activation || 'linear';\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    const $x = convertToTensor(x, 'x', 'conv2d');\n    const $filter = convertToTensor(filter, 'filter', 'conv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ` +\n        `${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in fused conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match ` +\n        `input depth for filter ${$filter.shape[2]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    util.assert(dataFormat === 'NHWC', () => `Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);\n    const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n        [$bias] = makeTypesMatch($bias, $x);\n        broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused conv2d');\n    }\n    const grad = (dy, saved) => {\n        const [$filter, x4D, y, $bias] = saved;\n        const dyActivation = getFusedDyActivation(dy, y, activation);\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused conv2D: ' +\n            `dilation rates greater than 1 ` +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n        const xDer = conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad);\n        const filterDer = conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad);\n        const der = [xDer, filterDer];\n        if ($bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            der.push(biasDer);\n        }\n        return der;\n    };\n    const forward = (backend) => {\n        const res = backend.fusedConv2d({\n            input: x4D,\n            filter: $filter,\n            convInfo,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return res;\n    };\n    const inputs = {\n        x: x4D,\n        filter: $filter,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((x4D, filter, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedConv2D, attrs);\n            save([filter, x4D, res]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOp(x4D, $filter);\n    }\n    else {\n        const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedConv2D, attrs);\n            save([filter, x4D, res, bias]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOpWithBias(x4D, $filter, $bias);\n    }\n}\nexport const conv2d = op({ fusedConv2d_ });\n//# sourceMappingURL=conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropFilter } from '../kernel_names';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, convInfo) {\n    let x4D = x;\n    if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    let dy4D = dy;\n    if (dy4D.rank === 3) {\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    const forward = backend => backend.depthwiseConv2DDerFilter(x4D, dy4D, convInfo);\n    const inputs = { x: x4D, dy: dy4D };\n    return ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropFilter);\n}\nexport const depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ });\n//# sourceMappingURL=depthwise_conv2d_native_backprop_filter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropInput } from '../kernel_names';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, convInfo) {\n    let dy4D = dy;\n    let reshapedTo4D = false;\n    if (dy.rank === 3) {\n        reshapedTo4D = true;\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    const forward = backend => backend.depthwiseConv2DDerInput(dy4D, filter, convInfo);\n    const inputs = { dy: dy4D };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropInput);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ });\n//# sourceMappingURL=depthwise_conv2d_native_backprop_input.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedDepthwiseConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport * as conv_util from '../conv_util';\nimport { depthwiseConv2d as unfusedDepthwiseConv2d } from '../depthwise_conv2d';\nimport { depthwiseConv2dNativeBackpropFilter } from '../depthwise_conv2d_native_backprop_filter';\nimport { depthwiseConv2dNativeBackpropInput } from '../depthwise_conv2d_native_backprop_input';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes depthwise 2D convolution, optionally fused with adding a\n * bias and applying an activation.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`).\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedDepthwiseConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights }) {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedDepthwiseConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n    const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, ` +\n        `but got rank ${$filter.rank}.`);\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels ` +\n        `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n        `filter ${$filter.shape[2]}.`);\n    if (dilations == null) {\n        dilations = [1, 1];\n    }\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in fused depthwiseConv2d: Either strides or dilations must ' +\n        `be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in fused depthwiseConv2d: pad must be an integer when ` +\n            `using dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n        [$bias] = makeTypesMatch($bias, $x);\n        broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');\n    }\n    const grad = (dy, saved) => {\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused depthwiseConv2d: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${dilations}'`);\n        const [$filter, x4D, y, bias] = saved;\n        const dyActivation = getFusedDyActivation(dy, y, activation);\n        const xDer = depthwiseConv2dNativeBackpropInput(x4D.shape, dyActivation, $filter, convInfo);\n        const filterDer = depthwiseConv2dNativeBackpropFilter(x4D, dyActivation, $filter.shape, convInfo);\n        if (bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            return [xDer, filterDer, biasDer];\n        }\n        return [xDer, filterDer];\n    };\n    const forward = (backend) => {\n        const res = backend.fusedDepthwiseConv2D({\n            input: x4D,\n            filter: $filter,\n            convInfo,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return res;\n    };\n    const inputs = {\n        x: x4D,\n        filter: $filter,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((x4D, filter, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedDepthwiseConv2D, attrs);\n            save([filter, x4D, res]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOp(x4D, $filter);\n    }\n    else {\n        const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedDepthwiseConv2D, attrs);\n            save([filter, x4D, res, bias]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOpWithBias(x4D, $filter, $bias);\n    }\n}\nexport const depthwiseConv2d = op({ fusedDepthwiseConv2d_ });\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { _FusedMatMul } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { matMul as unfusedMatMul } from '../mat_mul';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes the dot product of two matrices with optional activation and bias.\n *\n * ```js\n * const a = tf.tensor2d([-1, -2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const bias = tf.tensor2d([1, 2], [1, 2]);\n *\n * tf.fused.matMul({a, b, bias, activation: 'relu'}).print();\n * ```\n *\n * @param obj An object with the following properties:\n * - `a` First matrix in dot product operation.\n * - `b` Second matrix in dot product operation.\n * - `transposeA` If true, `a` is transposed before multiplication.\n * - `transposeB` If true, `b` is transposed before multiplication.\n * - `bias` Matrix to be added to the result.\n * - `activation` Name of activation kernel (defaults to `linear`).\n * - `preluActivationWeights` Tensor of prelu weights.\n */\nfunction fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation = 'linear', preluActivationWeights }) {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedMatMul(a, b, transposeA, transposeB);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    let $a = convertToTensor(a, 'a', 'fused matMul');\n    let $b = convertToTensor(b, 'b', 'fused matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n    const outerDimsA = $a.shape.slice(0, -2);\n    const outerDimsB = $b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n    util.assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in fused matMul: inputs must have the same rank of at least ` +\n        `2, got ranks ${$a.rank} and ${$b.rank}.`);\n    util.assert(util.arraysEqual(outerDimsA, outerDimsB), () => `Error in fused matMul: outer dimensions (${outerDimsA}) and (` +\n        `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} must match.`);\n    util.assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (` +\n        `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} and transposeA=${transposeA}` +\n        ` and transposeB=${transposeB} must match.`);\n    const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n    const a3D = transposeA ?\n        reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n        reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n    const b3D = transposeB ?\n        reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n        reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused matMul');\n        [$bias] = makeTypesMatch($bias, $a);\n        broadcast_util.assertAndGetBroadcastShape(outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused matMul');\n    }\n    const grad = (dy, saved) => {\n        const [a3D, b3D, y, $bias] = saved;\n        // we reshape dy because the result of the forward is not\n        // necessarily going to be a 3d tensor due to a reshape done at the end of\n        // the customOp.\n        const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation);\n        let aDer;\n        let bDer;\n        if (!transposeA && !transposeB) {\n            aDer = unfusedMatMul(dyActivation, b3D, false, true);\n            bDer = unfusedMatMul(a3D, dyActivation, true, false);\n        }\n        else if (!transposeA && transposeB) {\n            aDer = unfusedMatMul(dyActivation, b3D, false, false);\n            bDer = unfusedMatMul(dyActivation, a3D, true, false);\n        }\n        else if (transposeA && !transposeB) {\n            aDer = unfusedMatMul(b3D, dyActivation, false, true);\n            bDer = unfusedMatMul(a3D, dyActivation, false, false);\n        }\n        else {\n            aDer = unfusedMatMul(b3D, dyActivation, true, true);\n            bDer = unfusedMatMul(dyActivation, a3D, true, true);\n        }\n        if (bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            return [aDer, bDer, biasDer];\n        }\n        else {\n            return [aDer, bDer];\n        }\n    };\n    const forward = (backend) => {\n        const y = backend.fusedBatchMatMul({\n            a: a3D,\n            b: b3D,\n            transposeA,\n            transposeB,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return y;\n    };\n    const inputs = {\n        a: a3D,\n        b: b3D,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { transposeA, transposeB, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((a3D, b3D, save) => {\n            const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, _FusedMatMul, attrs);\n            save([a3D, b3D, res]);\n            return { value: reshape(res, outShape), gradFunc: grad };\n        });\n        return customOp(a3D, b3D);\n    }\n    else {\n        const customOpWithBias = customGrad((a3D, b3D, $bias, save) => {\n            const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, _FusedMatMul, attrs);\n            save([a3D, b3D, res, $bias]);\n            return { value: reshape(res, outShape), gradFunc: grad };\n        });\n        return customOpWithBias(a3D, b3D, $bias);\n    }\n}\nexport const matMul = op({ fusedMatMul_ });\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2d } from './fused/conv2d';\nimport { depthwiseConv2d } from './fused/depthwise_conv2d';\nimport { matMul } from './fused/mat_mul';\nexport { conv2d, depthwiseConv2d, matMul };\n//# sourceMappingURL=fused_ops.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a hamming window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hammingWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hammingWindow_(windowLength) {\n    return cosineWindow(windowLength, 0.54, 0.46);\n}\nexport const hammingWindow = op({ hammingWindow_ });\n//# sourceMappingURL=hamming_window.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a Hann window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hannWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hannWindow_(windowLength) {\n    return cosineWindow(windowLength, 0.5, 0.5);\n}\nexport const hannWindow = op({ hannWindow_ });\n//# sourceMappingURL=hann_window.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../concat';\nimport { fill } from '../fill';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { tensor2d } from '../tensor2d';\n/**\n * Expands input into frames of frameLength.\n * Slides a window size with frameStep.\n *\n * ```js\n * tf.signal.frame([1, 2, 3], 2, 1).print();\n * ```\n * @param signal The input tensor to be expanded\n * @param frameLength Length of each frame\n * @param frameStep The frame hop size in samples.\n * @param padEnd Whether to pad the end of signal with padValue.\n * @param padValue An number to use where the input signal does\n *     not exist when padEnd is True.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction frame_(signal, frameLength, frameStep, padEnd = false, padValue = 0) {\n    let start = 0;\n    const output = [];\n    while (start + frameLength <= signal.size) {\n        output.push(slice(signal, start, frameLength));\n        start += frameStep;\n    }\n    if (padEnd) {\n        while (start < signal.size) {\n            const padLen = (start + frameLength) - signal.size;\n            const pad = concat([\n                slice(signal, start, frameLength - padLen), fill([padLen], padValue)\n            ]);\n            output.push(pad);\n            start += frameStep;\n        }\n    }\n    if (output.length === 0) {\n        return tensor2d([], [0, frameLength]);\n    }\n    return reshape(concat(output), [output.length, frameLength]);\n}\nexport const frame = op({ frame_ });\n//# sourceMappingURL=frame.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../concat';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { enclosingPowerOfTwo } from '../signal_ops_util';\nimport { slice } from '../slice';\nimport { rfft } from '../spectral/rfft';\nimport { frame } from './frame';\nimport { hannWindow } from './hann_window';\n/**\n * Computes the Short-time Fourier Transform of signals\n * See: https://en.wikipedia.org/wiki/Short-time_Fourier_transform\n *\n * ```js\n * const input = tf.tensor1d([1, 1, 1, 1, 1])\n * tf.signal.stft(input, 3, 1).print();\n * ```\n * @param signal 1-dimensional real value tensor.\n * @param frameLength The window length of samples.\n * @param frameStep The number of samples to step.\n * @param fftLength The size of the FFT to apply.\n * @param windowFn A callable that takes a window length and returns 1-d tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction stft_(signal, frameLength, frameStep, fftLength, windowFn = hannWindow) {\n    if (fftLength == null) {\n        fftLength = enclosingPowerOfTwo(frameLength);\n    }\n    const framedSignal = frame(signal, frameLength, frameStep);\n    const windowedSignal = mul(framedSignal, windowFn(frameLength));\n    const output = [];\n    for (let i = 0; i < framedSignal.shape[0]; i++) {\n        output.push(rfft(slice(windowedSignal, [i, 0], [1, frameLength]), fftLength));\n    }\n    return concat(output);\n}\nexport const stft = op({ stft_ });\n//# sourceMappingURL=stft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { CropAndResize } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Extracts crops from the input image tensor and resizes them using bilinear\n * sampling or nearest neighbor sampling (possibly with aspect ratio change)\n * to a common output size specified by crop_size.\n *\n * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,\n *     where imageHeight and imageWidth must be positive, specifying the\n *     batch of images from which to take crops\n * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized\n *     coordinates of the box in the boxInd[i]'th image in the batch\n * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range\n *     `[0, batch)` that specifies the image that the `i`-th box refers to.\n * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`\n *     specifying the size to which all crops are resized to.\n * @param method Optional string from `'bilinear' | 'nearest'`,\n *     defaults to bilinear, which specifies the sampling method for resizing\n * @param extrapolationValue A threshold for deciding when to remove boxes based\n *     on score. Defaults to 0.\n * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction cropAndResize_(image, boxes, boxInd, cropSize, method, extrapolationValue) {\n    const $image = convertToTensor(image, 'image', 'cropAndResize');\n    const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');\n    const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');\n    method = method || 'bilinear';\n    extrapolationValue = extrapolationValue || 0;\n    const numBoxes = $boxes.shape[0];\n    util.assert($image.rank === 4, () => 'Error in cropAndResize: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    util.assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +\n        `but had shape ${$boxes.shape}.`);\n    util.assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +\n        `but had shape ${$boxes.shape}.`);\n    util.assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got ` +\n        `length ${cropSize.length}.`);\n    util.assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);\n    util.assert(method === 'bilinear' || method === 'nearest', () => `method must be bilinear or nearest, but was ${method}`);\n    const forward = (backend) => backend.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);\n    const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };\n    const attrs = { method, extrapolationValue, cropSize };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, CropAndResize, attrs);\n    return res;\n}\nexport const cropAndResize = op({ cropAndResize_ });\n//# sourceMappingURL=crop_and_resize.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FlipLeftRight } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Flips the image left to right. Currently available in the CPU, WebGL, and\n * WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction flipLeftRight_(image) {\n    const $image = convertToTensor(image, 'image', 'flipLeftRight', 'float32');\n    util.assert($image.rank === 4, () => 'Error in flipLeftRight: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    const inputs = { image: $image };\n    const res = ENGINE.runKernel(FlipLeftRight, inputs, {});\n    return res;\n}\nexport const flipLeftRight = op({ flipLeftRight_ });\n//# sourceMappingURL=flip_left_right.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { RotateWithOffset } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Rotates the input image tensor counter-clockwise with an optional offset\n * center of rotation. Currently available in the CPU, WebGL, and WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n * @param radians The amount of rotation.\n * @param fillValue The value to fill in the empty space leftover\n *     after rotation. Can be either a single grayscale value (0-255), or an\n *     array of three numbers `[red, green, blue]` specifying the red, green,\n *     and blue channels. Defaults to `0` (black).\n * @param center The center of rotation. Can be either a single value (0-1), or\n *     an array of two numbers `[centerX, centerY]`. Defaults to `0.5` (rotates\n *     the image around its center).\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction rotateWithOffset_(image, radians, fillValue = 0, center = 0.5) {\n    const $image = convertToTensor(image, 'image', 'rotateWithOffset', 'float32');\n    util.assert($image.rank === 4, () => 'Error in rotateWithOffset: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    const inputs = { image: $image };\n    const attrs = { radians, fillValue, center };\n    const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);\n    return res;\n}\nexport const rotateWithOffset = op({ rotateWithOffset_ });\n//# sourceMappingURL=rotate_with_offset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nfunction nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n    if (iouThreshold == null) {\n        iouThreshold = 0.5;\n    }\n    if (scoreThreshold == null) {\n        scoreThreshold = Number.NEGATIVE_INFINITY;\n    }\n    if (softNmsSigma == null) {\n        softNmsSigma = 0.0;\n    }\n    const numBoxes = boxes.shape[0];\n    maxOutputSize = Math.min(maxOutputSize, numBoxes);\n    util.assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n    util.assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n    util.assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n    util.assert(scores.rank === 1, () => 'scores must be a 1D tensor');\n    util.assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, ` +\n        `but was ${scores.shape[0]}`);\n    util.assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);\n    return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n}\nexport { nonMaxSuppSanityCheck };\n//# sourceMappingURL=nonmax_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV3 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\nfunction nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n    maxOutputSize = inputs.maxOutputSize;\n    iouThreshold = inputs.iouThreshold;\n    scoreThreshold = inputs.scoreThreshold;\n    const attrs = { maxOutputSize, iouThreshold, scoreThreshold };\n    return ENGINE.runKernelFunc(b => b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold), { boxes: $boxes, scores: $scores }, null /* grad */, NonMaxSuppressionV3, attrs);\n}\nexport const nonMaxSuppression = op({ nonMaxSuppression_ });\n//# sourceMappingURL=non_max_suppression.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inserts a value into a sorted array. This method allows duplicate, meaning it\n * allows inserting duplicate value, in which case, the element will be inserted\n * at the lowest index of the value.\n * @param arr The array to modify.\n * @param element The element to insert.\n * @param comparator Optional. If no comparator is specified, elements are\n * compared using array_util.defaultComparator, which is suitable for Strings\n * and Numbers in ascending arrays. If the array contains multiple instances of\n * the target value, the left-most instance will be returned. To provide a\n * comparator, it should take 2 arguments to compare and return a negative,\n * zero, or a positive number.\n */\nexport function binaryInsert(arr, element, comparator) {\n    const index = binarySearch(arr, element, comparator);\n    const insertionPoint = index < 0 ? -(index + 1) : index;\n    arr.splice(insertionPoint, 0, element);\n}\n/**\n * Searches the array for the target using binary search, returns the index\n * of the found element, or position to insert if element not found. If no\n * comparator is specified, elements are compared using array_\n * util.defaultComparator, which is suitable for Strings and Numbers in\n * ascending arrays. If the array contains multiple instances of the target\n * value, the left-most instance will be returned.\n * @param arr The array to be searched in.\n * @param target The target to be searched for.\n * @param comparator Should take 2 arguments to compare and return a negative,\n *    zero, or a positive number.\n * @return Lowest index of the target value if found, otherwise the insertion\n *    point where the target should be inserted, in the form of\n *    (-insertionPoint - 1).\n */\nexport function binarySearch(arr, target, comparator) {\n    return binarySearch_(arr, target, comparator || defaultComparator);\n}\n/**\n * Compares its two arguments for order.\n * @param a The first element to be compared.\n * @param b The second element to be compared.\n * @return A negative number, zero, or a positive number as the first\n *     argument is less than, equal to, or greater than the second.\n */\nfunction defaultComparator(a, b) {\n    return a > b ? 1 : a < b ? -1 : 0;\n}\nfunction binarySearch_(arr, target, comparator) {\n    let left = 0;\n    let right = arr.length;\n    let middle = 0;\n    let found = false;\n    while (left < right) {\n        middle = left + ((right - left) >>> 1);\n        const compareResult = comparator(target, arr[middle]);\n        if (compareResult > 0) {\n            left = middle + 1;\n        }\n        else {\n            right = middle;\n            // If compareResult is 0, the value is found. We record it is found,\n            // and then keep looking because there may be duplicate.\n            found = !compareResult;\n        }\n    }\n    return found ? left : -left - 1;\n}\n//# sourceMappingURL=array_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Implementation of the NonMaxSuppression kernel shared between webgl and cpu.\n */\nimport { scalar } from '../ops/scalar';\nimport { tensor1d } from '../ops/tensor1d';\nimport { binaryInsert } from './array_util';\nexport function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */)\n        .selectedIndices;\n}\nexport function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */, false /* returnScoresTensor */, padToMaxOutputSize /* padToMaxOutputSize */, true\n    /* returnValidOutputs */ );\n}\nexport function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true /* returnScoresTensor */);\n}\nfunction nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {\n    // The list is sorted in ascending order, so that we can always pop the\n    // candidate with the largest score in O(1) time.\n    const candidates = [];\n    for (let i = 0; i < scores.length; i++) {\n        if (scores[i] > scoreThreshold) {\n            candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 });\n        }\n    }\n    candidates.sort(ascendingComparator);\n    // If softNmsSigma is 0, the outcome of this algorithm is exactly same as\n    // before.\n    const scale = softNmsSigma > 0 ? (-0.5 / softNmsSigma) : 0.0;\n    const selectedIndices = [];\n    const selectedScores = [];\n    while (selectedIndices.length < maxOutputSize && candidates.length > 0) {\n        const candidate = candidates.pop();\n        const { score: originalScore, boxIndex, suppressBeginIndex } = candidate;\n        if (originalScore < scoreThreshold) {\n            break;\n        }\n        // Overlapping boxes are likely to have similar scores, therefore we\n        // iterate through the previously selected boxes backwards in order to\n        // see if candidate's score should be suppressed. We use\n        // suppressBeginIndex to track and ensure a candidate can be suppressed\n        // by a selected box no more than once. Also, if the overlap exceeds\n        // iouThreshold, we simply ignore the candidate.\n        let ignoreCandidate = false;\n        for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {\n            const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);\n            if (iou >= iouThreshold) {\n                ignoreCandidate = true;\n                break;\n            }\n            candidate.score =\n                candidate.score * suppressWeight(iouThreshold, scale, iou);\n            if (candidate.score <= scoreThreshold) {\n                break;\n            }\n        }\n        // At this point, if `candidate.score` has not dropped below\n        // `scoreThreshold`, then we know that we went through all of the\n        // previous selections and can safely update `suppressBeginIndex` to the\n        // end of the selected array. Then we can re-insert the candidate with\n        // the updated score and suppressBeginIndex back in the candidate list.\n        // If on the other hand, `candidate.score` has dropped below the score\n        // threshold, we will not add it back to the candidates list.\n        candidate.suppressBeginIndex = selectedIndices.length;\n        if (!ignoreCandidate) {\n            // Candidate has passed all the tests, and is not suppressed, so\n            // select the candidate.\n            if (candidate.score === originalScore) {\n                selectedIndices.push(boxIndex);\n                selectedScores.push(candidate.score);\n            }\n            else if (candidate.score > scoreThreshold) {\n                // Candidate's score is suppressed but is still high enough to be\n                // considered, so add back to the candidates list.\n                binaryInsert(candidates, candidate, ascendingComparator);\n            }\n        }\n    }\n    // NonMaxSuppressionV4 feature: padding output to maxOutputSize.\n    const validOutputs = selectedIndices.length;\n    const elemsToPad = maxOutputSize - validOutputs;\n    if (padToMaxOutputSize && elemsToPad > 0) {\n        selectedIndices.push(...new Array(elemsToPad).fill(0));\n        selectedScores.push(...new Array(elemsToPad).fill(0.0));\n    }\n    const result = { selectedIndices: tensor1d(selectedIndices, 'int32') };\n    if (returnScoresTensor) {\n        result['selectedScores'] = tensor1d(selectedScores, 'float32');\n    }\n    if (returnValidOutputs) {\n        result['validOutputs'] = scalar(validOutputs, 'int32');\n    }\n    return result;\n}\nfunction intersectionOverUnion(boxes, i, j) {\n    const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n    const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n    const yminI = Math.min(iCoord[0], iCoord[2]);\n    const xminI = Math.min(iCoord[1], iCoord[3]);\n    const ymaxI = Math.max(iCoord[0], iCoord[2]);\n    const xmaxI = Math.max(iCoord[1], iCoord[3]);\n    const yminJ = Math.min(jCoord[0], jCoord[2]);\n    const xminJ = Math.min(jCoord[1], jCoord[3]);\n    const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n    const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n    const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n    const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n    if (areaI <= 0 || areaJ <= 0) {\n        return 0.0;\n    }\n    const intersectionYmin = Math.max(yminI, yminJ);\n    const intersectionXmin = Math.max(xminI, xminJ);\n    const intersectionYmax = Math.min(ymaxI, ymaxJ);\n    const intersectionXmax = Math.min(xmaxI, xmaxJ);\n    const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *\n        Math.max(intersectionXmax - intersectionXmin, 0.0);\n    return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n// A Gaussian penalty function, this method always returns values in [0, 1].\n// The weight is a function of similarity, the more overlap two boxes are, the\n// smaller the weight is, meaning highly overlapping boxe will be significantly\n// penalized. On the other hand, a non-overlapping box will not be penalized.\nfunction suppressWeight(iouThreshold, scale, iou) {\n    const weight = Math.exp(scale * iou * iou);\n    return iou <= iouThreshold ? weight : 0.0;\n}\nfunction ascendingComparator(c1, c2) {\n    // For objects with same scores, we make the object with the larger index go\n    // first. In an array that pops from the end, this means that the object with\n    // the smaller index will be popped first. This ensures the same output as\n    // the TensorFlow python version.\n    return (c1.score - c2.score) ||\n        ((c1.score === c2.score) && (c2.boxIndex - c1.boxIndex));\n}\n//# sourceMappingURL=non_max_suppression_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV3Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This is the async version of `nonMaxSuppression`\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n    maxOutputSize = inputs.maxOutputSize;\n    iouThreshold = inputs.iouThreshold;\n    scoreThreshold = inputs.scoreThreshold;\n    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n    const boxesVals = boxesAndScores[0];\n    const scoresVals = boxesAndScores[1];\n    // We call a cpu based impl directly with the typedarray data  here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\n//# sourceMappingURL=non_max_suppression_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV5 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    maxOutputSize = params.maxOutputSize;\n    iouThreshold = params.iouThreshold;\n    scoreThreshold = params.scoreThreshold;\n    softNmsSigma = params.softNmsSigma;\n    const inputs = { boxes: $boxes, scores: $scores };\n    const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n    const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);\n    return { selectedIndices: result[0], selectedScores: result[1] };\n}\nexport const nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ });\n//# sourceMappingURL=non_max_suppression_with_score.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV5Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    maxOutputSize = params.maxOutputSize;\n    iouThreshold = params.iouThreshold;\n    scoreThreshold = params.scoreThreshold;\n    softNmsSigma = params.softNmsSigma;\n    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n    const boxesVals = boxesAndScores[0];\n    const scoresVals = boxesAndScores[1];\n    // We call a cpu based impl directly with the typedarray data  here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;\n//# sourceMappingURL=non_max_suppression_with_score_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV4 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n    const $maxOutputSize = params.maxOutputSize;\n    const $iouThreshold = params.iouThreshold;\n    const $scoreThreshold = params.scoreThreshold;\n    const inputs = { boxes: $boxes, scores: $scores };\n    const attrs = {\n        maxOutputSize: $maxOutputSize,\n        iouThreshold: $iouThreshold,\n        scoreThreshold: $scoreThreshold,\n        padToMaxOutputSize\n    };\n    const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);\n    return { selectedIndices: result[0], validOutputs: result[1] };\n}\nexport const nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ });\n//# sourceMappingURL=non_max_suppression_padded.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV4Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n    const $maxOutputSize = params.maxOutputSize;\n    const $iouThreshold = params.iouThreshold;\n    const $scoreThreshold = params.scoreThreshold;\n    const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);\n    // We call a cpu based impl directly with the typedarray data here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;\n//# sourceMappingURL=non_max_suppression_padded_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeBilinear } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Bilinear resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeBilinear_(images, size, alignCorners = false) {\n    const $images = convertToTensor(images, 'images', 'resizeBilinear');\n    util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got ` +\n        `rank ${$images.rank}.`);\n    util.assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ` +\n        `${size}.`);\n    let batchImages = $images;\n    let reshapedTo4D = false;\n    if ($images.rank === 3) {\n        reshapedTo4D = true;\n        batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n    }\n    const [newHeight, newWidth] = size;\n    const forward = (backend, save) => {\n        save([batchImages]);\n        return backend.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);\n    };\n    const inputs = { images: batchImages };\n    const attrs = { alignCorners, size };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, ResizeBilinear, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const resizeBilinear = op({ resizeBilinear_ });\n//# sourceMappingURL=resize_bilinear.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeNearestNeighbor } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * NearestNeighbor resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeNearestNeighbor_(images, size, alignCorners = false) {\n    const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');\n    util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` +\n        `rank ${$images.rank}.`);\n    util.assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ` +\n        `${size}.`);\n    util.assert($images.dtype === 'float32' || $images.dtype === 'int32', () => '`images` must have `int32` or `float32` as dtype');\n    let batchImages = $images;\n    let reshapedTo4D = false;\n    if ($images.rank === 3) {\n        reshapedTo4D = true;\n        batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n    }\n    const [newHeight, newWidth] = size;\n    const inputs = { images: batchImages };\n    const attrs = { alignCorners, size };\n    const forward = (backend, save) => {\n        save([batchImages]);\n        return backend.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);\n    };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, ResizeNearestNeighbor, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const resizeNearestNeighbor = op({ resizeNearestNeighbor_ });\n//# sourceMappingURL=resize_nearest_neighbor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assert } from '../../util';\nimport { greaterEqual } from '../greater_equal';\nimport { lessEqual } from '../less_equal';\nimport { logicalAnd } from '../logical_and';\nimport { op } from '../operation';\nimport { range } from '../range';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\nimport { zeros } from '../zeros';\n/**\n * Copy a tensor setting everything outside a central band in each innermost\n * matrix to zero.\n *\n * The band part is computed as follows: Assume input has `k` dimensions\n * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where\n * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.\n * The indicator function\n * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower))`\n * `&& (num_upper < 0 || (n-m) <= num_upper)`\n *\n * ```js\n * const x = tf.tensor2d([[ 0,  1,  2, 3],\n *                        [-1,  0,  1, 2],\n *                        [-2, -1,  0, 1],\n *                        [-3, -2, -1, 0]]);\n * let y = tf.linalg.bandPart(x, 1, -1);\n * y.print(); // [[ 0,  1,  2, 3],\n *            //  [-1,  0,  1, 2],\n *            //  [ 0, -1,  0, 1],\n *            //  [ 0, 0 , -1, 0]]\n * let z = tf.linalg.bandPart(x, 2, 1);\n * z.print(); // [[ 0,  1,  0, 0],\n *            //  [-1,  0,  1, 0],\n *            //  [-2, -1,  0, 1],\n *            //  [ 0, -2, -1, 0]]\n * ```\n *\n * @param x Rank `k` tensor\n * @param numLower Number of subdiagonals to keep.\n *   If negative, keep entire lower triangle.\n * @param numUpper Number of subdiagonals to keep.\n *   If negative, keep entire upper triangle.\n * @returns Rank `k` tensor of the same shape as input.\n *   The extracted banded tensor.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction bandPart_(a, numLower, numUpper) {\n    assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);\n    assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);\n    const $a = convertToTensor(a, 'a', 'bandPart');\n    assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);\n    const shape = $a.shape;\n    const [M, N] = $a.shape.slice(-2);\n    if (!(numLower <= M)) {\n        throw new Error(`bandPart(): numLower (${numLower})` +\n            ` must not be greater than the number of rows (${M}).`);\n    }\n    if (!(numUpper <= N)) {\n        throw new Error(`bandPart(): numUpper (${numUpper})` +\n            ` must not be greater than the number of columns (${N}).`);\n    }\n    if (numLower < 0) {\n        numLower = M;\n    }\n    if (numUpper < 0) {\n        numUpper = N;\n    }\n    const i = reshape(range(0, M, 1, 'int32'), [-1, 1]);\n    const j = range(0, N, 1, 'int32');\n    const ij = sub(i, j);\n    const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, 'int32')), greaterEqual(ij, scalar(-numUpper, 'int32')));\n    const zero = zeros([M, N], $a.dtype);\n    return reshape(stack(unstack(reshape($a, [-1, M, N]))\n        .map(mat => where(inBand, mat, zero))), shape);\n}\nexport const bandPart = op({ bandPart_ });\n//# sourceMappingURL=band_part.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { assert } from '../../util';\nimport { div } from '../div';\nimport { mul } from '../mul';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { split } from '../split';\nimport { squeeze } from '../squeeze';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\n/**\n * Gram-Schmidt orthogonalization.\n *\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * let y = tf.linalg.gramSchmidt(x);\n * y.print();\n * console.log('Othogonalized:');\n * y.dot(y.transpose()).print();  // should be nearly the identity matrix.\n * console.log('First row direction maintained:');\n * const data = await y.array();\n * console.log(data[0][1] / data[0][0]);  // should be nearly 2.\n * ```\n *\n * @param xs The vectors to be orthogonalized, in one of the two following\n *   formats:\n *   - An Array of `tf.Tensor1D`.\n *   - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows\n *     of `xs`.\n *   In each case, all the vectors must have the same length and the length\n *   must be greater than or equal to the number of vectors.\n * @returns The orthogonalized and normalized vectors or matrix.\n *   Orthogonalization means that the vectors or the rows of the matrix\n *   are orthogonal (zero inner products). Normalization means that each\n *   vector or each row of the matrix has an L2 norm that equals `1`.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction gramSchmidt_(xs) {\n    let inputIsTensor2D;\n    if (Array.isArray(xs)) {\n        inputIsTensor2D = false;\n        assert(xs != null && xs.length > 0, () => 'Gram-Schmidt process: input must not be null, undefined, or ' +\n            'empty');\n        const dim = xs[0].shape[0];\n        for (let i = 1; i < xs.length; ++i) {\n            assert(xs[i].shape[0] === dim, () => 'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +\n                `(${xs[i].shape[0]} vs. ${dim})`);\n        }\n    }\n    else {\n        inputIsTensor2D = true;\n        xs = split(xs, xs.shape[0], 0).map(x => squeeze(x, [0]));\n    }\n    assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds ` +\n        `number of dimensions (${xs[0].shape[0]}).`);\n    const ys = [];\n    const xs1d = xs;\n    for (let i = 0; i < xs.length; ++i) {\n        ys.push(ENGINE.tidy(() => {\n            let x = xs1d[i];\n            if (i > 0) {\n                for (let j = 0; j < i; ++j) {\n                    const proj = mul(sum(mul(ys[j], x)), ys[j]);\n                    x = sub(x, proj);\n                }\n            }\n            return div(x, norm(x, 'euclidean'));\n        }));\n    }\n    if (inputIsTensor2D) {\n        return stack(ys, 0);\n    }\n    else {\n        return ys;\n    }\n}\nexport const gramSchmidt = op({ gramSchmidt_ });\n//# sourceMappingURL=gram_schmidt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { dispose } from '../../globals';\nimport { assert } from '../../util';\nimport { clone } from '../clone';\nimport { concat } from '../concat';\nimport { div } from '../div';\nimport { eye } from '../eye';\nimport { greater } from '../greater';\nimport { matMul } from '../mat_mul';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { tensor2d } from '../tensor2d';\nimport { transpose } from '../transpose';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\n/**\n * Compute QR decomposition of m-by-n matrix using Householder transformation.\n *\n * Implementation based on\n *   [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]\n * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [3, 4]]);\n * let [q, r] = tf.linalg.qr(a);\n * console.log('Q');\n * q.print();\n * console.log('R');\n * r.print();\n * console.log('Orthogonalized');\n * q.dot(q.transpose()).print()  // should be nearly the identity matrix.\n * console.log('Reconstructed');\n * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];\n * ```\n *\n * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose\n *   it has the shape `[..., M, N]`.\n * @param fullMatrices An optional boolean parameter. Defaults to `false`.\n *   If `true`, compute full-sized `Q`. If `false` (the default),\n *   compute only the leading N columns of `Q` and `R`.\n * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,\n *   i.e., its columns all have unit norm and are mutually orthogonal.\n *   If `M >= N`,\n *     If `fullMatrices` is `false` (default),\n *       - `Q` has a shape of `[..., M, N]`,\n *       - `R` has a shape of `[..., N, N]`.\n *     If `fullMatrices` is `true` (default),\n *       - `Q` has a shape of `[..., M, M]`,\n *       - `R` has a shape of `[..., M, N]`.\n *   If `M < N`,\n *     - `Q` has a shape of `[..., M, M]`,\n *     - `R` has a shape of `[..., M, N]`.\n * @throws If the rank of `x` is less than 2.\n *\n * @doc {heading:'Operations',\n *       subheading:'Linear Algebra',\n *       namespace:'linalg'}\n */\nfunction qr_(x, fullMatrices = false) {\n    assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);\n    if (x.rank === 2) {\n        return qr2d(x, fullMatrices);\n    }\n    else {\n        // Rank > 2.\n        // TODO(cais): Below we split the input into individual 2D tensors,\n        //   perform QR decomposition on them and then stack the results back\n        //   together. We should explore whether this can be parallelized.\n        const outerDimsProd = x.shape.slice(0, x.shape.length - 2)\n            .reduce((value, prev) => value * prev);\n        const x2ds = unstack(reshape(x, [\n            outerDimsProd, x.shape[x.shape.length - 2],\n            x.shape[x.shape.length - 1]\n        ]), 0);\n        const q2ds = [];\n        const r2ds = [];\n        x2ds.forEach(x2d => {\n            const [q2d, r2d] = qr2d(x2d, fullMatrices);\n            q2ds.push(q2d);\n            r2ds.push(r2d);\n        });\n        const q = reshape(stack(q2ds, 0), x.shape);\n        const r = reshape(stack(r2ds, 0), x.shape);\n        return [q, r];\n    }\n}\nfunction qr2d(x, fullMatrices = false) {\n    return ENGINE.tidy(() => {\n        assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);\n        const m = x.shape[0];\n        const n = x.shape[1];\n        let q = eye(m); // Orthogonal transform so far.\n        let r = clone(x); // Transformed matrix so far.\n        const one2D = tensor2d([[1]], [1, 1]);\n        let w = clone(one2D);\n        const iters = m >= n ? n : m;\n        for (let j = 0; j < iters; ++j) {\n            // This tidy within the for-loop ensures we clean up temporary\n            // tensors as soon as they are no longer needed.\n            const rTemp = r;\n            const wTemp = w;\n            const qTemp = q;\n            [w, r, q] = ENGINE.tidy(() => {\n                // Find H = I - tau * w * w', to put zeros below R(j, j).\n                const rjEnd1 = slice(r, [j, j], [m - j, 1]);\n                const normX = norm(rjEnd1);\n                const rjj = slice(r, [j, j], [1, 1]);\n                // The sign() function returns 0 on 0, which causes division by zero.\n                const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));\n                const u1 = sub(rjj, mul(s, normX));\n                const wPre = div(rjEnd1, u1);\n                if (wPre.shape[0] === 1) {\n                    w = clone(one2D);\n                }\n                else {\n                    w = concat([\n                        one2D,\n                        slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])\n                    ], 0);\n                }\n                const tau = neg(div(matMul(s, u1), normX));\n                // -- R := HR, Q := QH.\n                const rjEndAll = slice(r, [j, 0], [m - j, n]);\n                const tauTimesW = mul(tau, w);\n                const wT = transpose(w);\n                if (j === 0) {\n                    r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n                }\n                else {\n                    const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n                    r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);\n                }\n                const tawTimesWT = transpose(tauTimesW);\n                const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);\n                if (j === 0) {\n                    q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n                }\n                else {\n                    const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n                    q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);\n                }\n                return [w, r, q];\n            });\n            dispose([rTemp, wTemp, qTemp]);\n        }\n        if (!fullMatrices && m > n) {\n            q = slice(q, [0, 0], [m, n]);\n            r = slice(r, [0, 0], [n, n]);\n        }\n        return [q, r];\n    });\n}\nexport const qr = op({ qr_ });\n//# sourceMappingURL=qr.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Reduction;\n(function (Reduction) {\n    Reduction[Reduction[\"NONE\"] = 0] = \"NONE\";\n    Reduction[Reduction[\"MEAN\"] = 1] = \"MEAN\";\n    Reduction[Reduction[\"SUM\"] = 2] = \"SUM\";\n    Reduction[Reduction[\"SUM_BY_NONZERO_WEIGHTS\"] = 3] = \"SUM_BY_NONZERO_WEIGHTS\";\n})(Reduction || (Reduction = {}));\n//# sourceMappingURL=loss_ops_utils.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { Reduction } from '../loss_ops_utils';\nimport { mean } from '../mean';\nimport { mul } from '../mul';\nimport { notEqual } from '../not_equal';\nimport { ones } from '../ones';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sum } from '../sum';\n/**\n * Computes the weighted loss between two tensors.\n *\n * @param losses Tensor of shape `[batch_size, d1, ... dN]`.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `losses`, and must be broadcastable to `losses` (i.e., all\n *    dimensions must be either `1`, or the same as the corresponding\n *    `losses` dimension).\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction computeWeightedLoss_(losses, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');\n    }\n    const weightedLoss = ($weights == null) ? $losses : mul($losses, $weights);\n    if (reduction === Reduction.NONE) {\n        return weightedLoss;\n    }\n    if (reduction === Reduction.SUM) {\n        return sum(weightedLoss);\n    }\n    if (reduction === Reduction.MEAN) {\n        if ($weights == null) {\n            return mean(weightedLoss);\n        }\n        else {\n            const broadcastFactor = $losses.size / $weights.size;\n            const result = div(sum(weightedLoss), sum($weights));\n            return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) :\n                result;\n        }\n    }\n    if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n        if ($weights == null) {\n            return div(sum(weightedLoss), scalar($losses.size));\n        }\n        else {\n            const broadcastedWeights = mul($weights, ones($losses.shape));\n            const numNonZeros = cast(sum(notEqual(broadcastedWeights, scalar(0))), 'float32');\n            return div(sum(weightedLoss), numNonZeros);\n        }\n    }\n    throw Error(`Unknown reduction: ${reduction}`);\n}\nexport const computeWeightedLoss = op({ computeWeightedLoss_ });\n//# sourceMappingURL=compute_weighted_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the absolute difference loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'absoluteDifference');\n    const $predictions = convertToTensor(predictions, 'predictions', 'absoluteDifference');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'absoluteDifference');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in absoluteDifference: ');\n    const losses = abs(sub($labels, $predictions));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const absoluteDifference = op({ absoluteDifference_ });\n//# sourceMappingURL=absolute_difference.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the cosine distance loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param axis The dimension along which the cosine distance is computed.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'cosineDistance');\n    const $predictions = convertToTensor(predictions, 'predictions', 'cosineDistance');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'cosineDistance');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in cosineDistance: ');\n    const one = scalar(1);\n    const losses = sub(one, sum(mul($labels, $predictions), axis, true));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const cosineDistance = op({ cosineDistance_ });\n//# sourceMappingURL=cosine_distance.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the Hinge loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $labels = convertToTensor(labels, 'labels', 'hingeLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'hingeLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');\n    const one = scalar(1);\n    // Convert binary labels to (-1, 1)\n    $labels = sub(mul(scalar(2), $labels), one);\n    const losses = relu(sub(one, mul($labels, $predictions)));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const hingeLoss = op({ hingeLoss_ });\n//# sourceMappingURL=hinge_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { Reduction } from '../loss_ops_utils';\nimport { minimum } from '../minimum';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { square } from '../square';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the huber loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param delta Point where huber loss changes from quadratic to linear.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`.\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction huberLoss_(labels, predictions, weights, delta = 1.0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'huberLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'huberLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');\n    const deltaScalar = scalar(delta);\n    const error = abs(sub($predictions, $labels));\n    const quadratic = minimum(error, deltaScalar);\n    const linear = sub(error, quadratic);\n    const losses = add(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const huberLoss = op({ huberLoss_ });\n//# sourceMappingURL=huber_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { log } from '../log';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the log loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param epsilon A small increment to avoid taking log of zero\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction logLoss_(labels, predictions, weights, epsilon = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'logLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'logLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'logLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');\n    const one = scalar(1);\n    const epsilonScalar = scalar(epsilon);\n    const l1 = neg(mul($labels, log(add($predictions, epsilonScalar))));\n    const l2 = mul(sub(one, $labels), log(add(sub(one, $predictions), epsilonScalar)));\n    const losses = sub(l1, l2);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const logLoss = op({ logLoss_ });\n//# sourceMappingURL=log_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { squaredDifference } from '../squared_difference';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the mean squared error between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'meanSquaredError');\n    const $predictions = convertToTensor(predictions, 'predictions', 'meanSquaredError');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'meanSquaredError');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in meanSquaredError: ');\n    const losses = squaredDifference($labels, $predictions);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const meanSquaredError = op({ meanSquaredError_ });\n//# sourceMappingURL=mean_squared_error.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { exp } from '../exp';\nimport { log1p } from '../log1p';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\nfunction sigmoidCrossEntropyWithLogits_(labels, logits) {\n    const $labels = convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');\n    const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');\n    assertShapesMatch($labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');\n    /**\n     * Implementation Details:\n     *\n     * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n     *     z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n     *   = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n     *   = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n     *   = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n     *   = (1 - z) * x + log(1 + exp(-x))\n     *   = x - x * z + log(1 + exp(-x))\n     *\n     *   For x < 0, to avoid overflow in exp(-x), we reformulate the above\n     *     x - x * z + log(1 + exp(-x))\n     *   = log(exp(x)) - x * z + log(1 + exp(-x))\n     *   = - x * z + log(1 + exp(x))\n     *\n     * Hence, to ensure stability and avoid overflow, the implementation uses\n     * this equivalent formulation:\n     *     max(x, 0) - x * z + log(1 + exp(-abs(x)))\n     */\n    const maxOutput = relu($logits);\n    const outputXTarget = mul($logits, $labels);\n    const sigmoidOutput = log1p(exp(neg(abs($logits))));\n    return add(sub(maxOutput, outputXTarget), sigmoidOutput);\n}\n/**\n * Computes the sigmoid cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)\n *                         + 0.5 * labelSmoothing\n *\n * @param multiClassLabels The ground truth output tensor of shape\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $multiClassLabels = convertToTensor(multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');\n    const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');\n    }\n    assertShapesMatch($multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');\n    if (labelSmoothing > 0) {\n        const labelSmoothingScalar = scalar(labelSmoothing);\n        const one = scalar(1);\n        const half = scalar(0.5);\n        $multiClassLabels =\n            add(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));\n    }\n    const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ });\n//# sourceMappingURL=sigmoid_cross_entropy.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../../gradients';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { expandShapeToKeepDim } from '../axis_util';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { exp } from '../exp';\nimport { logSumExp } from '../log_sum_exp';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes softmax cross entropy between logits and labels.\n *\n * Measures the probability error in discrete classification tasks in which\n * the classes are mutually exclusive (each entry is in exactly one class).\n * For example, each CIFAR-10 image is labeled with one and only one label: an\n * image can be a dog or a truck, but not both.\n *\n * `NOTE`: While the classes are mutually exclusive, their probabilities need\n * not be. All that is required is that each row of labels is a valid\n * probability distribution. If they are not, the computation of the gradient\n * will be incorrect.\n *\n * `WARNING`: This op expects unscaled logits, since it performs a softmax on\n * logits internally for efficiency. Do not call this op with the output of\n * softmax, as it will produce incorrect results.\n *\n * logits and labels must have the same shape, e.g. [batch_size, num_classes]\n * and the same dtype.\n * @param labels The labels array.\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\nfunction softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {\n    if (dim === -1) {\n        dim = logits.rank - 1;\n    }\n    if (dim !== logits.rank - 1) {\n        throw Error(`Softmax cross entropy along a non-last dimension is not yet ` +\n            `supported. Labels / logits was rank ${logits.rank} ` +\n            `and dim was ${dim}`);\n    }\n    // Use a custom gradient for numerical stability.\n    const customOp = customGrad((labels, logits, save) => {\n        // Reference:\n        //   1. http://cs231n.github.io/linear-classify/#softmax\n        //   2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/\n        const keepDims = true;\n        const lse = logSumExp(logits, [dim], keepDims);\n        const logResult = sub(cast(logits, 'float32'), lse);\n        save([labels, logResult]);\n        const costVector = neg(mul(logResult, labels));\n        const value = sum(costVector, [dim]);\n        const gradFunc = (dy, saved) => {\n            const [labels, logResult] = saved;\n            const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n            return [\n                mul(reshape(dy, dyShape), sub(cast(labels, 'float32'), exp(logResult))),\n                mul(reshape(dy, dyShape), sub(exp(logResult), cast(labels, 'float32'))),\n            ];\n        };\n        return { value, gradFunc };\n    });\n    return customOp(labels, logits);\n}\n/**\n * Computes the softmax cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newOnehotLabels = onehotLabels * (1 - labelSmoothing)\n *                         + labelSmoothing / numClasses\n *\n * @param onehotLabels One hot encoded labels\n *    [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or 1, and must be\n *    broadcastable to `loss`  of shape [batch_size]\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $onehotLabels = convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');\n    const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');\n    }\n    assertShapesMatch($onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');\n    if (labelSmoothing > 0) {\n        const labelSmoothingScalar = scalar(labelSmoothing);\n        const one = scalar(1);\n        const numClasses = scalar($onehotLabels.shape[1]);\n        $onehotLabels =\n            add(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));\n    }\n    const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const softmaxCrossEntropy = op({ softmaxCrossEntropy_ });\n//# sourceMappingURL=softmax_cross_entropy.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Modularized ops.\nexport { abs } from './abs';\nexport { acos } from './acos';\nexport { acosh } from './acosh';\nexport { add } from './add';\nexport { addN } from './add_n';\nexport { all } from './all';\nexport { any } from './any';\nexport { argMax } from './arg_max';\nexport { argMin } from './arg_min';\nexport { asin } from './asin';\nexport { asinh } from './asinh';\nexport { atan } from './atan';\nexport { atan2 } from './atan2';\nexport { atanh } from './atanh';\nexport { avgPool } from './avg_pool';\nexport { avgPool3d } from './avg_pool_3d';\nexport { basicLSTMCell } from './basic_lstm_cell';\nexport { batchToSpaceND } from './batch_to_space_nd';\nexport { batchNorm } from './batchnorm';\nexport { batchNorm2d } from './batchnorm2d';\nexport { batchNorm3d } from './batchnorm3d';\nexport { batchNorm4d } from './batchnorm4d';\nexport { broadcastTo } from './broadcast_to';\nexport { buffer } from './buffer';\nexport { cast } from './cast';\nexport { ceil } from './ceil';\nexport { clipByValue } from './clip_by_value';\nexport { clone } from './clone';\nexport { complex } from './complex';\nexport { concat } from './concat';\nexport { concat1d } from './concat_1d';\nexport { concat2d } from './concat_2d';\nexport { concat3d } from './concat_3d';\nexport { concat4d } from './concat_4d';\nexport { conv1d } from './conv1d';\nexport { conv2d } from './conv2d';\nexport { conv2dTranspose } from './conv2d_transpose';\nexport { conv3d } from './conv3d';\nexport { conv3dTranspose } from './conv3d_transpose';\nexport { cos } from './cos';\nexport { cosh } from './cosh';\nexport { cumsum } from './cumsum';\nexport { depthToSpace } from './depth_to_space';\nexport { depthwiseConv2d } from './depthwise_conv2d';\nexport { diag } from './diag';\nexport { dilation2d } from './dilation2d';\nexport { div } from './div';\nexport { divNoNan } from './div_no_nan';\nexport { dot } from './dot';\nexport { elu } from './elu';\nexport { equal } from './equal';\nexport { erf } from './erf';\nexport { exp } from './exp';\nexport { expandDims } from './expand_dims';\nexport { expm1 } from './expm1';\nexport { eye } from './eye';\nexport { fill } from './fill';\nexport { floor } from './floor';\nexport { floorDiv } from './floorDiv';\nexport { gather } from './gather';\nexport { greater } from './greater';\nexport { greaterEqual } from './greater_equal';\nexport { imag } from './imag';\nexport { isFinite } from './is_finite';\nexport { isInf } from './is_inf';\nexport { isNaN } from './is_nan';\nexport { leakyRelu } from './leaky_relu';\nexport { less } from './less';\nexport { lessEqual } from './less_equal';\nexport { linspace } from './linspace';\nexport { localResponseNormalization } from './local_response_normalization';\nexport { log } from './log';\nexport { log1p } from './log1p';\nexport { logSigmoid } from './log_sigmoid';\nexport { logSoftmax } from './log_softmax';\nexport { logSumExp } from './log_sum_exp';\nexport { logicalAnd } from './logical_and';\nexport { logicalNot } from './logical_not';\nexport { logicalOr } from './logical_or';\nexport { logicalXor } from './logical_xor';\nexport { matMul } from './mat_mul';\nexport { max } from './max';\nexport { maxPool } from './max_pool';\nexport { maxPool3d } from './max_pool_3d';\nexport { maxPoolWithArgmax } from './max_pool_with_argmax';\nexport { maximum } from './maximum';\nexport { mean } from './mean';\nexport { min } from './min';\nexport { minimum } from './minimum';\nexport { mod } from './mod';\nexport { moments } from './moments';\nexport { mul } from './mul';\nexport { multiRNNCell } from './multi_rnn_cell';\nexport { multinomial } from './multinomial';\nexport { neg } from './neg';\nexport { notEqual } from './not_equal';\nexport { oneHot } from './one_hot';\nexport { ones } from './ones';\nexport { onesLike } from './ones_like';\nexport { outerProduct } from './outer_product';\nexport { pad } from './pad';\nexport { pad1d } from './pad1d';\nexport { pad2d } from './pad2d';\nexport { pad3d } from './pad3d';\nexport { pad4d } from './pad4d';\nexport { pool } from './pool';\nexport { pow } from './pow';\nexport { prelu } from './prelu';\nexport { print } from './print';\nexport { prod } from './prod';\nexport { rand } from './rand';\nexport { randomGamma } from './random_gamma';\nexport { randomNormal } from './random_normal';\nexport { randomUniform } from './random_uniform';\nexport { range } from './range';\nexport { real } from './real';\nexport { reciprocal } from './reciprocal';\nexport { relu } from './relu';\nexport { relu6 } from './relu6';\nexport { reshape } from './reshape';\nexport { reverse } from './reverse';\nexport { reverse1d } from './reverse_1d';\nexport { reverse2d } from './reverse_2d';\nexport { reverse3d } from './reverse_3d';\nexport { reverse4d } from './reverse_4d';\nexport { round } from './round';\nexport { rsqrt } from './rsqrt';\nexport { scalar } from './scalar';\nexport { selu } from './selu';\nexport { separableConv2d } from './separable_conv2d';\nexport { setdiff1dAsync } from './setdiff1d_async';\nexport { sigmoid } from './sigmoid';\nexport { sign } from './sign';\nexport { sin } from './sin';\nexport { sinh } from './sinh';\nexport { slice } from './slice';\nexport { slice1d } from './slice1d';\nexport { slice2d } from './slice2d';\nexport { slice3d } from './slice3d';\nexport { slice4d } from './slice4d';\nexport { softmax } from './softmax';\nexport { softplus } from './softplus';\nexport { spaceToBatchND } from './space_to_batch_nd';\nexport { fft } from './spectral/fft';\nexport { ifft } from './spectral/ifft';\nexport { irfft } from './spectral/irfft';\nexport { rfft } from './spectral/rfft';\nexport { split } from './split';\nexport { sqrt } from './sqrt';\nexport { square } from './square';\nexport { squaredDifference } from './squared_difference';\nexport { squeeze } from './squeeze';\nexport { stack } from './stack';\nexport { step } from './step';\nexport { stridedSlice } from './strided_slice';\nexport { sub } from './sub';\nexport { sum } from './sum';\nexport { tan } from './tan';\nexport { tanh } from './tanh';\nexport { tensor } from './tensor';\nexport { tensor1d } from './tensor1d';\nexport { tensor2d } from './tensor2d';\nexport { tensor3d } from './tensor3d';\nexport { tensor4d } from './tensor4d';\nexport { tensor5d } from './tensor5d';\nexport { tensor6d } from './tensor6d';\nexport { tile } from './tile';\nexport { topk } from './topk';\nexport { truncatedNormal } from './truncated_normal';\nexport { unique } from './unique';\nexport { unsortedSegmentSum } from './unsorted_segment_sum';\nexport { unstack } from './unstack';\nexport { variable } from './variable';\nexport { where } from './where';\nexport { whereAsync } from './where_async';\nexport { zeros } from './zeros';\nexport { zerosLike } from './zeros_like';\nexport * from './boolean_mask';\nexport * from './compare';\nexport * from './binary_ops';\nexport * from './transpose';\nexport * from './norm';\nexport * from './moving_average';\nexport * from './scatter_nd';\nexport * from './sparse_to_dense';\nexport * from './gather_nd';\nexport * from './dropout';\nexport * from './signal_ops_util';\nexport * from './in_top_k';\nexport { op, OP_SCOPE_SUFFIX } from './operation';\nimport { rfft } from './spectral/rfft';\nimport { fft } from './spectral/fft';\nimport { ifft } from './spectral/ifft';\nimport { irfft } from './spectral/irfft';\nconst spectral = {\n    fft,\n    ifft,\n    rfft,\n    irfft\n};\nimport * as fused from './fused_ops';\nimport { hammingWindow } from './signal/hamming_window';\nimport { hannWindow } from './signal/hann_window';\nimport { frame } from './signal/frame';\nimport { stft } from './signal/stft';\nconst signal = {\n    hammingWindow,\n    hannWindow,\n    frame,\n    stft,\n};\n// Image Ops namespace\nimport { cropAndResize } from './image/crop_and_resize';\nimport { flipLeftRight } from './image/flip_left_right';\nimport { rotateWithOffset } from './image/rotate_with_offset';\nimport { nonMaxSuppression } from './image/non_max_suppression';\nimport { nonMaxSuppressionAsync } from './image/non_max_suppression_async';\nimport { nonMaxSuppressionWithScore } from './image/non_max_suppression_with_score';\nimport { nonMaxSuppressionWithScoreAsync } from './image/non_max_suppression_with_score_async';\nimport { nonMaxSuppressionPadded } from './image/non_max_suppression_padded';\nimport { nonMaxSuppressionPaddedAsync } from './image/non_max_suppression_padded_async';\nimport { resizeBilinear } from './image/resize_bilinear';\nimport { resizeNearestNeighbor } from './image/resize_nearest_neighbor';\nconst image = {\n    flipLeftRight,\n    resizeNearestNeighbor,\n    resizeBilinear,\n    rotateWithOffset,\n    cropAndResize,\n    nonMaxSuppression,\n    nonMaxSuppressionAsync,\n    nonMaxSuppressionWithScore,\n    nonMaxSuppressionWithScoreAsync,\n    nonMaxSuppressionPadded,\n    nonMaxSuppressionPaddedAsync\n};\n// linalg namespace\nimport { bandPart } from './linalg/band_part';\nimport { gramSchmidt } from './linalg/gram_schmidt';\nimport { qr } from './linalg/qr';\nconst linalg = {\n    bandPart,\n    gramSchmidt,\n    qr\n};\n// losses namespace;\nimport { absoluteDifference } from './losses/absolute_difference';\nimport { computeWeightedLoss } from './losses/compute_weighted_loss';\nimport { cosineDistance } from './losses/cosine_distance';\nimport { hingeLoss } from './losses/hinge_loss';\nimport { huberLoss } from './losses/huber_loss';\nimport { logLoss } from './losses/log_loss';\nimport { meanSquaredError } from './losses/mean_squared_error';\nimport { sigmoidCrossEntropy } from './losses/sigmoid_cross_entropy';\nimport { softmaxCrossEntropy } from './losses/softmax_cross_entropy';\nconst losses = {\n    absoluteDifference,\n    computeWeightedLoss,\n    cosineDistance,\n    hingeLoss,\n    huberLoss,\n    logLoss,\n    meanSquaredError,\n    sigmoidCrossEntropy,\n    softmaxCrossEntropy\n};\n// Second level exports.\nexport { image, linalg, losses, spectral, fused, signal };\n//# sourceMappingURL=ops.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dispose } from '../globals';\nimport { variableGrads } from '../gradients';\nimport { scalar } from '../ops/ops';\nimport { Serializable } from '../serialization';\n/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */\nexport class Optimizer extends Serializable {\n    /**\n     * Executes `f()` and minimizes the scalar output of `f()` by computing\n     * gradients of y with respect to the list of trainable variables provided by\n     * `varList`. If no list is provided, it defaults to all trainable variables.\n     *\n     * @param f The function to execute and whose output to minimize.\n     * @param returnCost Whether to return the scalar cost value produced by\n     * executing `f()`.\n     * @param varList An optional list of variables to update. If specified, only\n     * the trainable variables in varList will be updated by minimize. Defaults to\n     * all trainable variables.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers'}\n     */\n    minimize(f, returnCost = false, varList) {\n        const { value, grads } = this.computeGradients(f, varList);\n        if (varList != null) {\n            const gradArray = varList.map(v => ({ name: v.name, tensor: grads[v.name] }));\n            this.applyGradients(gradArray);\n        }\n        else {\n            this.applyGradients(grads);\n        }\n        // Dispose gradients.\n        dispose(grads);\n        if (returnCost) {\n            return value;\n        }\n        else {\n            value.dispose();\n            return null;\n        }\n    }\n    /**\n     * The number of iterations that this optimizer instance has been invoked for.\n     */\n    get iterations() {\n        if (this.iterations_ == null) {\n            this.iterations_ = 0;\n        }\n        return this.iterations_;\n    }\n    incrementIterations() {\n        this.iterations_ = this.iterations + 1;\n    }\n    /**\n     * Executes f() and computes the gradient of the scalar output of f() with\n     * respect to the list of trainable variables provided by `varList`. If no\n     * list is provided, it defaults to all trainable variables.\n     *\n     * @param f The function to execute and whose output to use for computing\n     * gradients with respect to variables.\n     * @param varList An optional list of variables to compute gradients with\n     * respect to. If specified, only the trainable variables in varList will have\n     * gradients computed with respect to. Defaults to all trainable variables.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers'}\n     */\n    computeGradients(f, varList) {\n        return variableGrads(f, varList);\n    }\n    /**\n     * Dispose the variables (if any) owned by this optimizer instance.\n     */\n    dispose() {\n        if (this.iterations_ != null) {\n            dispose(this.iterations_);\n        }\n    }\n    async saveIterations() {\n        if (this.iterations_ == null) {\n            this.iterations_ = 0;\n        }\n        return {\n            name: 'iter',\n            // TODO(cais): Use 'int64' type when available.\n            tensor: scalar(this.iterations_, 'int32')\n        };\n    }\n    async getWeights() {\n        throw new Error('getWeights() is not implemented for this optimizer yet.');\n    }\n    async setWeights(weightValues) {\n        throw new Error(`setWeights() is not implemented for this optimizer class ` +\n            `${this.getClassName()}`);\n    }\n    /**\n     * Extract the first element of the weight values and set it\n     * as the iterations counter variable of this instance of optimizer.\n     *\n     * @param weightValues\n     * @returns Weight values with the first element consumed and excluded.\n     */\n    async extractIterations(weightValues) {\n        this.iterations_ = (await weightValues[0].tensor.data())[0];\n        return weightValues.slice(1);\n    }\n}\nObject.defineProperty(Optimizer, Symbol.hasInstance, {\n    value: (instance) => {\n        return instance.minimize != null && instance.computeGradients != null &&\n            instance.applyGradients != null;\n    }\n});\n//# sourceMappingURL=optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/ops';\nimport { square } from '../ops/square';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class AdadeltaOptimizer extends Optimizer {\n    constructor(learningRate, rho, epsilon = null) {\n        super();\n        this.learningRate = learningRate;\n        this.rho = rho;\n        this.epsilon = epsilon;\n        this.accumulatedGrads = [];\n        this.accumulatedUpdates = [];\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            const trainable = false;\n            if (this.accumulatedGrads[i] == null) {\n                this.accumulatedGrads[i] = {\n                    originalName: `${name}/accum_grad`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedUpdates[i] == null) {\n                this.accumulatedUpdates[i] = {\n                    originalName: `${name}/accum_var`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedGrad = this.accumulatedGrads[i].variable;\n            const accumulatedUpdate = this.accumulatedUpdates[i].variable;\n            tidy(() => {\n                const newAccumulatedGrad = add(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));\n                const updates = mul(div(sqrt(add(accumulatedUpdate, this.epsilon)), sqrt(add(accumulatedGrad, this.epsilon))), gradient);\n                const newAccumulatedUpdate = add(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));\n                accumulatedGrad.assign(newAccumulatedGrad);\n                accumulatedUpdate.assign(newAccumulatedUpdate);\n                const newValue = add(mul(updates, -this.learningRate), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedUpdates != null) {\n            dispose(this.accumulatedGrads.map(v => v.variable));\n            dispose(this.accumulatedUpdates.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const variableCount = weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedGrads =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedUpdates =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'rho': this.rho,\n            'epsilon': this.epsilon\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['rho'], config['epsilon']);\n    }\n}\n/** @nocollapse */\nAdadeltaOptimizer.className = 'Adadelta'; // Name matters for Python compatibility.\nregisterClass(AdadeltaOptimizer);\n//# sourceMappingURL=adadelta_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { fill } from '../ops/fill';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class AdagradOptimizer extends Optimizer {\n    constructor(learningRate, initialAccumulatorValue = 0.1) {\n        super();\n        this.learningRate = learningRate;\n        this.initialAccumulatorValue = initialAccumulatorValue;\n        this.accumulatedGrads = [];\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            if (this.accumulatedGrads[i] == null) {\n                const trainable = false;\n                this.accumulatedGrads[i] = {\n                    originalName: `${name}/accumulator`,\n                    variable: tidy(() => fill(value.shape, this.initialAccumulatorValue)\n                        .variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedGrad = this.accumulatedGrads[i].variable;\n            tidy(() => {\n                const newAccumulatedGrad = add(accumulatedGrad, square(gradient));\n                accumulatedGrad.assign(newAccumulatedGrad);\n                const newValue = add(mul(div(gradient, sqrt(add(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedGrads != null) {\n            dispose(this.accumulatedGrads.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        return [await this.saveIterations()].concat(this.accumulatedGrads.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const trainable = false;\n        this.accumulatedGrads = weightValues.map(v => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'initialAccumulatorValue': this.initialAccumulatorValue,\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['initialAccumulatorValue']);\n    }\n}\n/** @nocollapse */\nAdagradOptimizer.className = 'Adagrad'; // Note: Name matters for Python compatibility.\nregisterClass(AdagradOptimizer);\n//# sourceMappingURL=adagrad_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { pow } from '../ops/pow';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\nexport class AdamOptimizer extends Optimizer {\n    constructor(learningRate, beta1, beta2, epsilon = null) {\n        super();\n        this.learningRate = learningRate;\n        this.beta1 = beta1;\n        this.beta2 = beta2;\n        this.epsilon = epsilon;\n        this.accumulatedFirstMoment = [];\n        this.accumulatedSecondMoment = [];\n        tidy(() => {\n            // accB* will be updated by batch.\n            this.accBeta1 = scalar(beta1).variable();\n            this.accBeta2 = scalar(beta2).variable();\n        });\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const varNames = Array.isArray(variableGradients) ?\n            variableGradients.map(v => v.name) :\n            Object.keys(variableGradients);\n        tidy(() => {\n            const oneMinusAccBeta1 = sub(1, this.accBeta1);\n            const oneMinusAccBeta2 = sub(1, this.accBeta2);\n            varNames.forEach((name, i) => {\n                const value = ENGINE.registeredVariables[name];\n                const trainable = false;\n                if (this.accumulatedFirstMoment[i] == null) {\n                    this.accumulatedFirstMoment[i] = {\n                        originalName: `${name}/m`,\n                        variable: tidy(() => zerosLike(value).variable(trainable))\n                    };\n                }\n                if (this.accumulatedSecondMoment[i] == null) {\n                    this.accumulatedSecondMoment[i] = {\n                        originalName: `${name}/v`,\n                        variable: tidy(() => zerosLike(value).variable(trainable))\n                    };\n                }\n                const gradient = Array.isArray(variableGradients) ?\n                    variableGradients[i].tensor :\n                    variableGradients[name];\n                if (gradient == null) {\n                    return;\n                }\n                const firstMoment = this.accumulatedFirstMoment[i].variable;\n                const secondMoment = this.accumulatedSecondMoment[i].variable;\n                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n                const newSecondMoment = add(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));\n                const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);\n                const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);\n                firstMoment.assign(newFirstMoment);\n                secondMoment.assign(newSecondMoment);\n                const newValue = add(mul(div(biasCorrectedFirstMoment, add(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);\n                value.assign(newValue);\n            });\n            this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n            this.accBeta2.assign(mul(this.accBeta2, this.beta2));\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.accBeta1.dispose();\n        this.accBeta2.dispose();\n        if (this.accumulatedFirstMoment != null) {\n            dispose(this.accumulatedFirstMoment.map(v => v.variable));\n        }\n        if (this.accumulatedSecondMoment != null) {\n            dispose(this.accumulatedSecondMoment.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        tidy(() => {\n            this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));\n            this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));\n        });\n        const variableCount = weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedFirstMoment =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedSecondMoment =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'beta1': this.beta1,\n            'beta2': this.beta2,\n            'epsilon': this.epsilon,\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon']);\n    }\n}\n/** @nocollapse */\nAdamOptimizer.className = 'Adam'; // Note: Name matters for Python compatibility.\nregisterClass(AdamOptimizer);\n//# sourceMappingURL=adam_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { abs } from '../ops/abs';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { maximum } from '../ops/maximum';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\nexport class AdamaxOptimizer extends Optimizer {\n    constructor(learningRate, beta1, beta2, epsilon = null, decay = 0.0) {\n        super();\n        this.learningRate = learningRate;\n        this.beta1 = beta1;\n        this.beta2 = beta2;\n        this.epsilon = epsilon;\n        this.decay = decay;\n        this.accumulatedFirstMoment = [];\n        this.accumulatedWeightedInfNorm = [];\n        tidy(() => {\n            this.iteration = scalar(0).variable();\n            this.accBeta1 = scalar(beta1).variable();\n        });\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        tidy(() => {\n            const oneMinusAccBeta1 = sub(1, this.accBeta1);\n            const lr = div(-this.learningRate, add(mul(this.iteration, this.decay), 1));\n            variableNames.forEach((name, i) => {\n                const value = ENGINE.registeredVariables[name];\n                const trainable = false;\n                if (this.accumulatedFirstMoment[i] == null) {\n                    this.accumulatedFirstMoment[i] = {\n                        originalName: `${name}/m`,\n                        variable: zerosLike(value).variable(trainable)\n                    };\n                }\n                if (this.accumulatedWeightedInfNorm[i] == null) {\n                    this.accumulatedWeightedInfNorm[i] = {\n                        originalName: `${name}/v`,\n                        variable: zerosLike(value).variable(trainable)\n                    };\n                }\n                const gradient = Array.isArray(variableGradients) ?\n                    variableGradients[i].tensor :\n                    variableGradients[name];\n                if (gradient == null) {\n                    return;\n                }\n                const firstMoment = this.accumulatedFirstMoment[i].variable;\n                const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;\n                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n                const ut0 = mul(weightedInfNorm, this.beta2);\n                const ut1 = abs(gradient);\n                const newWeightedInfNorm = maximum(ut0, ut1);\n                firstMoment.assign(newFirstMoment);\n                weightedInfNorm.assign(newWeightedInfNorm);\n                const newValue = add(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add(newWeightedInfNorm, this.epsilon))), value);\n                value.assign(newValue);\n            });\n            this.iteration.assign(add(this.iteration, 1));\n            this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.accBeta1.dispose();\n        this.iteration.dispose();\n        if (this.accumulatedFirstMoment != null) {\n            dispose(this.accumulatedFirstMoment.map(v => v.variable));\n        }\n        if (this.accumulatedWeightedInfNorm != null) {\n            dispose(this.accumulatedWeightedInfNorm.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        throw new Error('getWeights() is not implemented for Adamax yet.');\n    }\n    async setWeights(weightValues) {\n        throw new Error('setWeights() is not implemented for Adamax yet.');\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'beta1': this.beta1,\n            'beta2': this.beta2,\n            'epsilon': this.epsilon,\n            'decay': this.decay\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon'], config['decay']);\n    }\n}\n/** @nocollapse */\nAdamaxOptimizer.className = 'Adamax'; // Note: Name matters for Python compatbility.\nregisterClass(AdamaxOptimizer);\n//# sourceMappingURL=adamax_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { keep, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class SGDOptimizer extends Optimizer {\n    constructor(learningRate) {\n        super();\n        this.learningRate = learningRate;\n        this.setLearningRate(learningRate);\n    }\n    applyGradients(variableGradients) {\n        const varNames = Array.isArray(variableGradients) ?\n            variableGradients.map(v => v.name) :\n            Object.keys(variableGradients);\n        varNames.forEach((name, i) => {\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const value = ENGINE.registeredVariables[name];\n            tidy(() => {\n                const newValue = add(mul(this.c, gradient), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    /**\n     * Sets the learning rate of the optimizer.\n     */\n    setLearningRate(learningRate) {\n        this.learningRate = learningRate;\n        if (this.c != null) {\n            this.c.dispose();\n        }\n        this.c = keep(scalar(-learningRate));\n    }\n    dispose() {\n        this.c.dispose();\n    }\n    async getWeights() {\n        return [await this.saveIterations()];\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        if (weightValues.length !== 0) {\n            throw new Error('SGD optimizer does not have settable weights.');\n        }\n    }\n    getConfig() {\n        return { 'learningRate': this.learningRate };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate']);\n    }\n}\n/** @nocollapse */\nSGDOptimizer.className = 'SGD'; // Note: Name matters for Python compatibility.\nregisterClass(SGDOptimizer);\n//# sourceMappingURL=sgd_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { SGDOptimizer } from './sgd_optimizer';\n/** @doclink Optimizer */\nexport class MomentumOptimizer extends SGDOptimizer {\n    constructor(learningRate, momentum, useNesterov = false) {\n        super(learningRate);\n        this.learningRate = learningRate;\n        this.momentum = momentum;\n        this.useNesterov = useNesterov;\n        this.accumulations = [];\n        this.m = scalar(this.momentum);\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            if (this.accumulations[i] == null) {\n                const trainable = false;\n                this.accumulations[i] = {\n                    originalName: `${name}/momentum`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const accumulation = this.accumulations[i].variable;\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            tidy(() => {\n                let newValue;\n                const newAccumulation = add(mul(this.m, accumulation), gradient);\n                if (this.useNesterov) {\n                    newValue = add(mul(this.c, add(gradient, mul(newAccumulation, this.m))), value);\n                }\n                else {\n                    newValue = add(mul(this.c, newAccumulation), value);\n                }\n                accumulation.assign(newAccumulation);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.m.dispose();\n        if (this.accumulations != null) {\n            dispose(this.accumulations.map(v => v.variable));\n        }\n    }\n    /**\n     * Sets the momentum of the optimizer.\n     *\n     * @param momentum\n     */\n    setMomentum(momentum) {\n        this.momentum = momentum;\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        return [await this.saveIterations()].concat(this.accumulations.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const trainable = false;\n        this.accumulations = weightValues.map(v => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'momentum': this.momentum,\n            'useNesterov': this.useNesterov\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['momentum'], config['useNesterov']);\n    }\n}\n/** @nocollapse */\nMomentumOptimizer.className = 'Momentum'; // Name matters for Python compatibility.\nregisterClass(MomentumOptimizer);\n//# sourceMappingURL=momentum_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class RMSPropOptimizer extends Optimizer {\n    constructor(learningRate, decay = 0.9, momentum = 0.0, epsilon = null, centered = false) {\n        super();\n        this.learningRate = learningRate;\n        this.decay = decay;\n        this.momentum = momentum;\n        this.epsilon = epsilon;\n        this.accumulatedMeanSquares = [];\n        this.accumulatedMoments = [];\n        this.accumulatedMeanGrads = [];\n        this.centered = centered;\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n        if (learningRate == null) {\n            throw new Error(`learningRate for RMSPropOptimizer must be defined.`);\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            const trainable = false;\n            if (this.accumulatedMeanSquares[i] == null) {\n                this.accumulatedMeanSquares[i] = {\n                    originalName: `${name}/rms`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedMoments[i] == null) {\n                this.accumulatedMoments[i] = {\n                    originalName: `${name}/momentum`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedMeanGrads[i] == null && this.centered) {\n                this.accumulatedMeanGrads[i] = {\n                    originalName: `${name}/mg`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;\n            const accumulatedMoments = this.accumulatedMoments[i].variable;\n            tidy(() => {\n                const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n                if (this.centered) {\n                    const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;\n                    // Centered gradient\n                    const newAccumulatedMeanGrad = add(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));\n                    const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add(square(newAccumulatedMeanGrad), this.epsilon))));\n                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), gradContribution);\n                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n                    accumulatedMeanGrad.assign(newAccumulatedMeanGrad);\n                    accumulatedMoments.assign(newAccumulatedMoments);\n                    const newValue = sub(value, newAccumulatedMoments);\n                    value.assign(newValue);\n                }\n                else {\n                    // Plain gradient\n                    const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add(newAccumulatedMeanSquare, this.epsilon))));\n                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n                    accumulatedMoments.assign(newAccumulatedMoments);\n                    const newValue = sub(value, newAccumulatedMoments);\n                    value.assign(newValue);\n                }\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedMeanSquares != null) {\n            dispose(this.accumulatedMeanSquares.map(v => v.variable));\n        }\n        if (this.accumulatedMeanGrads != null && this.centered) {\n            dispose(this.accumulatedMeanGrads.map(v => v.variable));\n        }\n        if (this.accumulatedMoments != null) {\n            dispose(this.accumulatedMoments.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];\n        if (this.centered) {\n            variables.push(...this.accumulatedMeanGrads);\n        }\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedMeanSquares =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedMoments =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        if (this.centered) {\n            this.accumulatedMeanGrads =\n                weightValues.slice(variableCount * 2, variableCount * 3)\n                    .map(v => ({\n                    originalName: v.name,\n                    variable: v.tensor.variable(trainable)\n                }));\n        }\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'decay': this.decay,\n            'momentum': this.momentum,\n            'epsilon': this.epsilon,\n            'centered': this.centered\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['decay'], config['momentum'], config['epsilon'], config['centered']);\n    }\n}\n/** @nocollapse */\nRMSPropOptimizer.className = 'RMSProp'; // Note: Name matters for Python compatibility.\nregisterClass(RMSPropOptimizer);\n//# sourceMappingURL=rmsprop_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AdadeltaOptimizer } from './adadelta_optimizer';\nimport { AdagradOptimizer } from './adagrad_optimizer';\nimport { AdamOptimizer } from './adam_optimizer';\nimport { AdamaxOptimizer } from './adamax_optimizer';\nimport { MomentumOptimizer } from './momentum_optimizer';\nimport { RMSPropOptimizer } from './rmsprop_optimizer';\nimport { SGDOptimizer } from './sgd_optimizer';\nexport class OptimizerConstructors {\n    /**\n     * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.\n     *\n     * ```js\n     * // Fit a quadratic function by learning the coefficients a, b, c.\n     * const xs = tf.tensor1d([0, 1, 2, 3]);\n     * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);\n     *\n     * const a = tf.scalar(Math.random()).variable();\n     * const b = tf.scalar(Math.random()).variable();\n     * const c = tf.scalar(Math.random()).variable();\n     *\n     * // y = a * x^2 + b * x + c.\n     * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);\n     * const loss = (pred, label) => pred.sub(label).square().mean();\n     *\n     * const learningRate = 0.01;\n     * const optimizer = tf.train.sgd(learningRate);\n     *\n     * // Train the model.\n     * for (let i = 0; i < 10; i++) {\n     *   optimizer.minimize(() => loss(f(xs), ys));\n     * }\n     *\n     * // Make predictions.\n     * console.log(\n     *     `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);\n     * const preds = f(xs).dataSync();\n     * preds.forEach((pred, i) => {\n     *   console.log(`x: ${i}, pred: ${pred}`);\n     * });\n     * ```\n     *\n     * @param learningRate The learning rate to use for the SGD algorithm.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static sgd(learningRate) {\n        return new SGDOptimizer(learningRate);\n    }\n    /**\n     * Constructs a `tf.MomentumOptimizer` that uses momentum gradient\n     * descent.\n     *\n     * See\n     * [http://proceedings.mlr.press/v28/sutskever13.pdf](\n     * http://proceedings.mlr.press/v28/sutskever13.pdf)\n     *\n     * @param learningRate The learning rate to use for the Momentum gradient\n     * descent algorithm.\n     * @param momentum The momentum to use for the momentum gradient descent\n     * algorithm.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static momentum(learningRate, momentum, useNesterov = false) {\n        return new MomentumOptimizer(learningRate, momentum, useNesterov);\n    }\n    /**\n     * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient\n     * descent. This implementation uses plain momentum and is not centered\n     * version of RMSProp.\n     *\n     * See\n     * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](\n     * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\n     *\n     * @param learningRate The learning rate to use for the RMSProp gradient\n     * descent algorithm.\n     * @param decay The discounting factor for the history/coming gradient.\n     * @param momentum The momentum to use for the RMSProp gradient descent\n     * algorithm.\n     * @param epsilon Small value to avoid zero denominator.\n     * @param centered If true, gradients are normalized by the estimated\n     * variance of the gradient.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static rmsprop(learningRate, decay = .9, momentum = 0.0, epsilon = null, centered = false) {\n        return new RMSPropOptimizer(learningRate, decay, momentum, epsilon, centered);\n    }\n    /**\n     * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.\n     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n     *\n     * @param learningRate The learning rate to use for the Adam gradient\n     * descent algorithm.\n     * @param beta1 The exponential decay rate for the 1st moment estimates.\n     * @param beta2 The exponential decay rate for the 2nd moment estimates.\n     * @param epsilon A small constant for numerical stability.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adam(learningRate = 0.001, beta1 = 0.9, beta2 = 0.999, epsilon = null) {\n        return new AdamOptimizer(learningRate, beta1, beta2, epsilon);\n    }\n    /**\n     * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.\n     * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)\n     *\n     * @param learningRate The learning rate to use for the Adadelta gradient\n     * descent algorithm.\n     * @param rho The learning rate decay over each update.\n     * @param epsilon A constant epsilon used to better condition the grad\n     * update.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adadelta(learningRate = .001, rho = .95, epsilon = null) {\n        return new AdadeltaOptimizer(learningRate, rho, epsilon);\n    }\n    /**\n     * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.\n     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n     *\n     * @param learningRate The learning rate to use for the Adamax gradient\n     * descent algorithm.\n     * @param beta1 The exponential decay rate for the 1st moment estimates.\n     * @param beta2 The exponential decay rate for the 2nd moment estimates.\n     * @param epsilon A small constant for numerical stability.\n     * @param decay The learning rate decay over each update.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adamax(learningRate = 0.002, beta1 = 0.9, beta2 = 0.999, epsilon = null, decay = 0.0) {\n        return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);\n    }\n    /**\n     * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.\n     * See\n     * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](\n     * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\n     * or\n     * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](\n     * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)\n     *\n     * @param learningRate The learning rate to use for the Adagrad gradient\n     * descent algorithm.\n     * @param initialAccumulatorValue Starting value for the accumulators, must be\n     * positive.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adagrad(learningRate, initialAccumulatorValue = 0.1) {\n        return new AdagradOptimizer(learningRate, initialAccumulatorValue);\n    }\n}\n//# sourceMappingURL=optimizer_constructors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// So typings can propagate.\nimport { AdadeltaOptimizer } from './optimizers/adadelta_optimizer';\nimport { AdagradOptimizer } from './optimizers/adagrad_optimizer';\nimport { AdamOptimizer } from './optimizers/adam_optimizer';\nimport { AdamaxOptimizer } from './optimizers/adamax_optimizer';\nimport { MomentumOptimizer } from './optimizers/momentum_optimizer';\nimport { OptimizerConstructors } from './optimizers/optimizer_constructors';\nimport { RMSPropOptimizer } from './optimizers/rmsprop_optimizer';\nimport { SGDOptimizer } from './optimizers/sgd_optimizer';\n// tslint:disable-next-line:no-unused-expression\n[MomentumOptimizer, SGDOptimizer, AdadeltaOptimizer, AdagradOptimizer,\n    RMSPropOptimizer, AdamaxOptimizer, AdamOptimizer];\nexport const train = {\n    sgd: OptimizerConstructors.sgd,\n    momentum: OptimizerConstructors.momentum,\n    adadelta: OptimizerConstructors.adadelta,\n    adagrad: OptimizerConstructors.adagrad,\n    rmsprop: OptimizerConstructors.rmsprop,\n    adamax: OptimizerConstructors.adamax,\n    adam: OptimizerConstructors.adam\n};\n//# sourceMappingURL=train.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst delayCallback = (() => {\n    if (typeof requestAnimationFrame !== 'undefined') {\n        return requestAnimationFrame;\n    }\n    else if (typeof setImmediate !== 'undefined') {\n        return setImmediate;\n    }\n    return (f) => f(); // no delays\n})();\n/**\n * Returns a promise that resolve when a requestAnimationFrame has completed.\n *\n * On Node.js this uses setImmediate instead of requestAnimationFrame.\n *\n * This is simply a sugar method so that users can do the following:\n * `await tf.nextFrame();`\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nfunction nextFrame() {\n    return new Promise(resolve => delayCallback(() => resolve()));\n}\nexport { nextFrame };\n//# sourceMappingURL=browser_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Returns the image center in pixels.\nexport function getImageCenter(center, imageHeight, imageWidth) {\n    const centerX = imageWidth * (typeof center === 'number' ? center : center[0]);\n    const centerY = imageHeight * (typeof center === 'number' ? center : center[1]);\n    return [centerX, centerY];\n}\n//# sourceMappingURL=rotate_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Gets the new shape of the input Tensor after it's been reshaped\n * to:\n * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),\n * inputShape[1], ..., inputShape[N-1]]\n *\n * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshaped(inputShape, blockShape, prod, batchToSpace = true) {\n    let reshaped = [];\n    if (batchToSpace) {\n        reshaped = reshaped.concat(blockShape.slice(0));\n        reshaped.push(inputShape[0] / prod);\n        reshaped = reshaped.concat(inputShape.slice(1));\n    }\n    else {\n        reshaped = reshaped.concat(inputShape[0]);\n        const spatialLength = blockShape.length;\n        for (let i = 0; i < spatialLength; ++i) {\n            reshaped =\n                reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);\n        }\n        reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n    }\n    return reshaped;\n}\n/**\n * Gets the permutation that will transpose the dimensions of the\n * reshaped tensor to shape:\n *\n * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,\n * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {\n    const permuted = [];\n    if (batchToSpace) {\n        permuted.push(blockShapeRank);\n        for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {\n            if (i <= 2 * blockShapeRank) {\n                permuted.push(i);\n                permuted.push(i - (blockShapeRank + 1));\n            }\n            else {\n                permuted.push(i);\n            }\n        }\n    }\n    else {\n        const permutedBeforeBatch = [];\n        const permutedAfterBatch = [];\n        for (let i = 1; i < reshapedRank; ++i) {\n            if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {\n                permutedAfterBatch.push(i);\n            }\n            else {\n                permutedBeforeBatch.push(i);\n            }\n        }\n        permuted.push(...permutedBeforeBatch);\n        permuted.push(0);\n        permuted.push(...permutedAfterBatch);\n    }\n    return permuted;\n}\n/**\n * Gets the shape of the reshaped and permuted input Tensor before any cropping\n * is applied.  The new shape will be:\n *\n * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,\n * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshapedPermuted(inputShape, blockShape, prod, batchToSpace = true) {\n    const reshapedPermuted = [];\n    if (batchToSpace) {\n        reshapedPermuted.push(inputShape[0] / prod);\n    }\n    else {\n        reshapedPermuted.push(inputShape[0] * prod);\n    }\n    for (let i = 1; i < inputShape.length; ++i) {\n        if (i <= blockShape.length) {\n            if (batchToSpace) {\n                reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);\n            }\n            else {\n                reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);\n            }\n        }\n        else {\n            reshapedPermuted.push(inputShape[i]);\n        }\n    }\n    return reshapedPermuted;\n}\n/**\n * Converts the crops argument into the beginning coordinates of a slice\n * operation.\n */\nexport function getSliceBeginCoords(crops, blockShape) {\n    const sliceBeginCoords = [0];\n    for (let i = 0; i < blockShape; ++i) {\n        sliceBeginCoords.push(crops[i][0]);\n    }\n    return sliceBeginCoords;\n}\n/**\n * Converts the crops argument into the size of a slice operation.  When\n * combined with getSliceBeginCoords this function allows the reshaped and\n * permuted Tensor to be cropped to its final output shape of:\n *\n * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,\n * inputShape[M] * blockShape[M-1] -crops[M-1,0] -\n * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getSliceSize(uncroppedShape, crops, blockShape) {\n    const sliceSize = uncroppedShape.slice(0, 1);\n    for (let i = 0; i < blockShape; ++i) {\n        sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);\n    }\n    return sliceSize;\n}\n//# sourceMappingURL=array_ops_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const SELU_SCALEALPHA = 1.7580993408473768599402175208123;\nexport const SELU_SCALE = 1.0507009873554804934193349852946;\n//# sourceMappingURL=selu_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const ERF_P = 0.3275911;\nexport const ERF_A1 = 0.254829592;\nexport const ERF_A2 = -0.284496736;\nexport const ERF_A3 = 1.421413741;\nexport const ERF_A4 = -1.453152027;\nexport const ERF_A5 = 1.061405429;\n//# sourceMappingURL=erf_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nexport function warn(...msg) {\n    if (!env().getBool('IS_TEST')) {\n        console.warn(...msg);\n    }\n}\nexport function log(...msg) {\n    if (!env().getBool('IS_TEST')) {\n        console.log(...msg);\n    }\n}\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Merges real and imaginary Float32Arrays into a single complex Float32Array.\n *\n * The memory layout is interleaved as follows:\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n * complex: [r0, i0, r1, i1, r2, i2]\n *\n * This is the inverse of splitRealAndImagArrays.\n *\n * @param real The real values of the complex tensor values.\n * @param imag The imag values of the complex tensor values.\n * @returns A complex tensor as a Float32Array with merged values.\n */\nexport function mergeRealAndImagArrays(real, imag) {\n    if (real.length !== imag.length) {\n        throw new Error(`Cannot merge real and imag arrays of different lengths. real:` +\n            `${real.length}, imag: ${imag.length}.`);\n    }\n    const result = new Float32Array(real.length * 2);\n    for (let i = 0; i < result.length; i += 2) {\n        result[i] = real[i / 2];\n        result[i + 1] = imag[i / 2];\n    }\n    return result;\n}\n/**\n * Splits a complex Float32Array into real and imag parts.\n *\n * The memory layout is interleaved as follows:\n * complex: [r0, i0, r1, i1, r2, i2]\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n *\n * This is the inverse of mergeRealAndImagArrays.\n *\n * @param complex The complex tensor values.\n * @returns An object with real and imag Float32Array components of the complex\n *     tensor.\n */\nexport function splitRealAndImagArrays(complex) {\n    const real = new Float32Array(complex.length / 2);\n    const imag = new Float32Array(complex.length / 2);\n    for (let i = 0; i < complex.length; i += 2) {\n        real[i / 2] = complex[i];\n        imag[i / 2] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Extracts even indexed complex values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithEvenIndex(complex) {\n    const len = Math.ceil(complex.length / 4);\n    const real = new Float32Array(len);\n    const imag = new Float32Array(len);\n    for (let i = 0; i < complex.length; i += 4) {\n        real[Math.floor(i / 4)] = complex[i];\n        imag[Math.floor(i / 4)] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Extracts odd indexed comple values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithOddIndex(complex) {\n    const len = Math.floor(complex.length / 4);\n    const real = new Float32Array(len);\n    const imag = new Float32Array(len);\n    for (let i = 2; i < complex.length; i += 4) {\n        real[Math.floor(i / 4)] = complex[i];\n        imag[Math.floor(i / 4)] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Get the map representing a complex value in the given array.\n * @param complex The complex tensor values.\n * @param index An index of the target complex value.\n */\nexport function getComplexWithIndex(complex, index) {\n    const real = complex[index * 2];\n    const imag = complex[index * 2 + 1];\n    return { real, imag };\n}\n/**\n * Insert a given complex value into the TypedArray.\n * @param data The array in which the complex value is inserted.\n * @param c The complex value to be inserted.\n * @param index An index of the target complex value.\n */\nexport function assignToTypedArray(data, real, imag, index) {\n    data[index * 2] = real;\n    data[index * 2 + 1] = imag;\n}\n/**\n * Make the list of exponent terms used by FFT.\n */\nexport function exponents(n, inverse) {\n    const real = new Float32Array(n / 2);\n    const imag = new Float32Array(n / 2);\n    for (let i = 0; i < Math.ceil(n / 2); i++) {\n        const x = (inverse ? 2 : -2) * Math.PI * (i / n);\n        real[i] = Math.cos(x);\n        imag[i] = Math.sin(x);\n    }\n    return { real, imag };\n}\n/**\n * Make the exponent term used by FFT.\n */\nexport function exponent(k, n, inverse) {\n    const x = (inverse ? 2 : -2) * Math.PI * (k / n);\n    const real = Math.cos(x);\n    const imag = Math.sin(x);\n    return { real, imag };\n}\n//# sourceMappingURL=complex_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { cast } from '../ops/cast';\nimport { scalar } from '../ops/scalar';\nimport { tensor1d } from '../ops/tensor1d';\nimport { zeros } from '../ops/zeros';\nimport { hasEncodingLoss, makeZerosTypedArray } from '../util';\n// Utilities needed by backend consumers of tf-core.\nexport * from '../ops/axis_util';\nexport * from '../ops/broadcast_util';\nexport * from '../ops/concat_util';\nexport * from '../ops/conv_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/reduce_util';\nimport * as slice_util from '../ops/slice_util';\nexport { slice_util };\nexport { upcastType } from '../types';\nexport * from '../ops/rotate_util';\nexport * from '../ops/array_ops_util';\nexport * from '../ops/gather_nd_util';\nexport * from '../ops/scatter_nd_util';\nexport * from '../ops/selu_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/erf_util';\nexport * from '../log';\nexport * from '../backends/complex_util';\nexport * from '../ops/split_util';\nimport * as segment_util from '../ops/segment_util';\nexport { segment_util };\nexport function castTensor(x, dtype, backend) {\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return x.clone();\n        }\n        const zerosTensor = zeros(x.shape);\n        const floatX = cast(x, 'float32');\n        const result = backend.complex(floatX, zerosTensor);\n        zerosTensor.dispose();\n        floatX.dispose();\n        return result;\n    }\n    if (!hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        return ENGINE.makeTensorFromDataId(x.dataId, x.shape, dtype);\n    }\n    if (x.dtype === 'complex64') {\n        const real = backend.real(x);\n        const result = cast(real, dtype);\n        real.dispose();\n        return result;\n    }\n    if (dtype === 'int32') {\n        return backend.int(x);\n    }\n    else if (dtype === 'bool') {\n        const zero = scalar(0, x.dtype);\n        const result = backend.notEqual(x, zero);\n        zero.dispose();\n        return result;\n    }\n    else {\n        throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n    }\n}\nexport function reshapeTensor(x, shape) {\n    return ENGINE.makeTensorFromDataId(x.dataId, shape, x.dtype);\n}\nexport function linspaceImpl(start, stop, num) {\n    const step = (stop - start) / (num - 1);\n    const values = makeZerosTypedArray(num, 'float32');\n    values[0] = start;\n    for (let i = 1; i < values.length; i++) {\n        values[i] = values[i - 1] + step;\n    }\n    return tensor1d(values, 'float32');\n}\n//# sourceMappingURL=backend_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { slice } from '../ops/slice';\n// TODO(annxingyuan): Use this helper in WASM Split kernel once intermediate\n// kernels have been modularized in WebGL and CPU\n// https://github.com/tensorflow/tfjs/issues/2822.\n/** Shared implementation of the split kernel across WebGL and CPU. */\nexport function split(x, sizeSplits, axis) {\n    const begin = new Array(x.rank).fill(0);\n    const size = x.shape.slice();\n    return sizeSplits.map(s => {\n        const sliceSize = [...size];\n        sliceSize[axis] = s;\n        const sliceT = slice(x, begin, sliceSize);\n        begin[axis] += s;\n        return sliceT;\n    });\n}\n//# sourceMappingURL=split_shared.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * An implementation of the tile kernel shared between webgl and cpu for string\n * tensors only.\n */\nimport { buffer } from '../ops/buffer';\nexport function tile(xBuf, reps) {\n    const newShape = new Array(xBuf.rank);\n    for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = xBuf.shape[i] * reps[i];\n    }\n    const result = buffer(newShape, xBuf.dtype);\n    for (let i = 0; i < result.values.length; ++i) {\n        const newLoc = result.indexToLoc(i);\n        const originalLoc = new Array(xBuf.rank);\n        for (let j = 0; j < originalLoc.length; j++) {\n            originalLoc[j] = newLoc[j] % xBuf.shape[j];\n        }\n        const originalIndex = xBuf.locToIndex(originalLoc);\n        result.values[i] = xBuf.values[originalIndex];\n    }\n    return result.toTensor();\n}\n//# sourceMappingURL=tile_impl.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the TopK kernel shared between webgl and cpu. */\nimport { tensor } from '../ops/tensor';\nimport { getTypedArrayFromDType } from '../util';\nexport function topkImpl(x, xShape, xDtype, k, sorted) {\n    // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.\n    const lastDim = xShape[xShape.length - 1];\n    const [batch, size] = [x.length / lastDim, lastDim];\n    const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);\n    const allTopKIndices = getTypedArrayFromDType('int32', batch * k);\n    for (let b = 0; b < batch; b++) {\n        const offset = b * size;\n        const vals = x.subarray(offset, offset + size);\n        const valAndInd = [];\n        for (let i = 0; i < vals.length; i++) {\n            valAndInd.push({ value: vals[i], index: i });\n        }\n        valAndInd.sort((a, b) => b.value - a.value);\n        const outOffset = b * k;\n        const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n        const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n        for (let i = 0; i < k; i++) {\n            topKVals[i] = valAndInd[i].value;\n            topKIndices[i] = valAndInd[i].index;\n        }\n    }\n    // Reshape back to the original input shape, except that the last\n    // dimension is k.\n    const outputShape = xShape.slice();\n    outputShape[outputShape.length - 1] = k;\n    return [\n        tensor(allTopKVals, outputShape, xDtype),\n        tensor(allTopKIndices, outputShape, 'int32')\n    ];\n}\n//# sourceMappingURL=topk_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { nonMaxSuppressionV3Impl, nonMaxSuppressionV4Impl, nonMaxSuppressionV5Impl } from './non_max_suppression_impl';\nexport { split } from './split_shared';\nexport { tile } from './tile_impl';\nexport { topkImpl } from './topk_impl';\nexport { whereImpl } from './where_impl';\n//# sourceMappingURL=kernel_impls.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// base.ts is tfjs-core without auto registration of things like flags,\n// gradients, chained ops or the opHandler. See base_side_effects.ts for parts\n// tfjs core that are required side effects.\n/**\n * @fileoverview\n * @suppress {partialAlias} Optimization disabled due to passing the module\n * object into a function below:\n *\n *   import * as ops from './ops/ops';\n *   setOpHandler(ops);\n */\n// Serialization.\nimport * as io from './io/io';\nimport * as math from './math';\nimport * as browser from './ops/browser';\nimport * as gather_util from './ops/gather_nd_util';\nimport * as scatter_util from './ops/scatter_nd_util';\nimport * as slice_util from './ops/slice_util';\nimport * as serialization from './serialization';\nimport * as tensor_util from './tensor_util';\nimport * as test_util from './test_util';\nimport * as util from './util';\nimport { version } from './version';\n// Optimizers.\nexport { AdadeltaOptimizer } from './optimizers/adadelta_optimizer';\nexport { AdagradOptimizer } from './optimizers/adagrad_optimizer';\nexport { AdamOptimizer } from './optimizers/adam_optimizer';\nexport { AdamaxOptimizer } from './optimizers/adamax_optimizer';\nexport { MomentumOptimizer } from './optimizers/momentum_optimizer';\nexport { Optimizer } from './optimizers/optimizer';\nexport { RMSPropOptimizer } from './optimizers/rmsprop_optimizer';\nexport { SGDOptimizer } from './optimizers/sgd_optimizer';\nexport { Tensor, TensorBuffer, Variable } from './tensor';\nexport { Rank, sumOutType, upcastType } from './types';\nexport * from './ops/ops';\nexport { Reduction } from './ops/loss_ops_utils';\nexport * from './train';\nexport * from './globals';\nexport * from './kernel_registry';\nexport { customGrad, grad, grads, valueAndGrad, valueAndGrads, variableGrads } from './gradients';\nexport { Environment, env, ENV } from './environment';\nexport { version as version_core };\n// Top-level method exports.\nexport { nextFrame } from './browser_util';\n// Second level exports.\nimport * as backend_util from './backends/backend_util';\nimport * as device_util from './device_util';\nexport { browser, io, math, serialization, test_util, util, backend_util, tensor_util, slice_util, gather_util, scatter_util, device_util };\nimport * as kernel_impls from './backends/kernel_impls';\nexport { kernel_impls };\n// Backend specific.\nexport { KernelBackend, DataStorage } from './backends/backend';\n// Export all kernel names / info.\nexport * from './kernel_names';\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const absGradConfig = {\n    kernelName: Abs,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, step(cast(x, 'float32'), -1)) };\n    }\n};\n//# sourceMappingURL=Abs_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acos } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { neg } from '../ops/neg';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const acosGradConfig = {\n    kernelName: Acos,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = square(cast(x, 'float32'));\n                const b = sqrt(sub(scalar(1), a));\n                return neg(div(dy, b));\n            }\n        };\n    }\n};\n//# sourceMappingURL=Acos_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acosh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const acoshGradConfig = {\n    kernelName: Acosh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = sqrt(sub(square(cast(x, 'float32')), 1));\n                return div(dy, a);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Acosh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const addGradConfig = {\n    kernelName: Add,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Add_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AddN } from '../kernel_names';\nexport const addNGradConfig = {\n    kernelName: AddN,\n    saveAllInputs: true,\n    gradFunc: (dy, saved) => {\n        const ders = {};\n        saved.forEach((_, i) => {\n            ders[i] = () => dy.clone();\n        });\n        return ders;\n    }\n};\n//# sourceMappingURL=AddN_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ArgMax } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const argMaxGradConfig = {\n    kernelName: ArgMax,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => zerosLike(x) };\n    }\n};\n//# sourceMappingURL=ArgMax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ArgMin } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const argMinGradConfig = {\n    kernelName: ArgMin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => zerosLike(x) };\n    }\n};\n//# sourceMappingURL=ArgMin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asin } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const asinGradConfig = {\n    kernelName: Asin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, 'float32'))))) };\n    }\n};\n//# sourceMappingURL=Asin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asinh } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nexport const asinhGradConfig = {\n    kernelName: Asinh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = sqrt(add(scalar(1), square(cast(x, 'float32'))));\n                return div(dy, a);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Asinh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan2 } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const atan2GradConfig = {\n    kernelName: Atan2,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const d = add(square(a), square(b));\n            let res = mul(dy, div(b, d));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            const d = add(square(a), square(b));\n            let res = neg(mul(dy, div(a, d)));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Atan2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nexport const atanGradConfig = {\n    kernelName: Atan,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, add(square(cast(x, 'float32')), 1)) };\n    }\n};\n//# sourceMappingURL=Atan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atanh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { scalar } from '../ops/scalar';\nexport const atanhGradConfig = {\n    kernelName: Atanh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, sub(scalar(1), square(cast(x, 'float32')))) };\n    }\n};\n//# sourceMappingURL=Atanh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool3DBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of a 3d avg pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0. The dilation\n *     rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction avgPool3dBackprop_(dy, input, filterSize, strides, dilations = [1, 1, 1], pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'avgPool3dBackprop');\n    const $input = convertToTensor(input, 'input', 'avgPool3dBackprop');\n    let dy5D = $dy;\n    let input5D = $input;\n    let reshapedTo5D = false;\n    if ($input.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n        input5D = reshape($input, [\n            1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n        ]);\n    }\n    util.assert(dy5D.rank === 5, () => `Error in avgPool3dBackprop: dy must be rank 5 but got rank ` +\n        `${dy5D.rank}.`);\n    util.assert(input5D.rank === 5, () => `Error in avgPool3dBackprop: input must be rank 5 but got rank ` +\n        `${input5D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool3dBackprop: Either strides or dilations ' +\n        `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n        return backend.avgPool3dBackprop(dy5D, input5D, convInfo);\n    };\n    const inputs = { dy: dy5D, input: input5D };\n    const attrs = { filterSize, strides, dilations, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool3DBackprop, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const avgPool3dBackprop = op({ avgPool3dBackprop_ });\n//# sourceMappingURL=avg_pool_3d_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool3D } from '../kernel_names';\nimport { avgPool3dBackprop } from '../ops/avg_pool_3d_backprop';\nexport const avgPool3DGradConfig = {\n    kernelName: AvgPool3D,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { filterSize, strides, dilations, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1, 1] : dilations;\n        return {\n            x: () => avgPool3dBackprop(dy, x, filterSize, strides, $dilations, pad, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=AvgPool3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPoolBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of an 2D avg pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The input image, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction avgPoolBackprop_(dy, input, filterSize, strides, pad) {\n    const $dy = convertToTensor(dy, 'dy', 'avgPoolBackprop');\n    const $input = convertToTensor(input, 'input', 'avgPoolBackprop');\n    util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n    let input4D = $input;\n    let dy4D = $dy;\n    let reshapedTo4D = false;\n    if ($input.rank === 3) {\n        reshapedTo4D = true;\n        input4D =\n            reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);\n        dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);\n    }\n    util.assert(dy4D.rank === 4, () => `Error in avgPoolBackprop: dy must be rank 4 but got rank ` +\n        `${dy4D.rank}.`);\n    util.assert(input4D.rank === 4, () => `Error in avgPoolBackprop: input must be rank 4 but got rank ` +\n        `${input4D.rank}.`);\n    const forward = backend => {\n        const convInfo = conv_util.computePool2DInfo(input4D.shape, filterSize, strides, 1 /* dilations */, pad);\n        return backend.avgPoolBackprop(dy4D, input4D, convInfo);\n    };\n    const inputs = { dy: dy4D, input: input4D };\n    const attrs = { filterSize, strides, pad };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, AvgPoolBackprop, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const avgPoolBackprop = op({ avgPoolBackprop_ });\n//# sourceMappingURL=avg_pool_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool } from '../kernel_names';\nimport { avgPoolBackprop } from '../ops/avg_pool_backprop';\nexport const avgPoolGradConfig = {\n    kernelName: AvgPool,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { filterSize, strides, pad } = attrs;\n        return {\n            x: () => avgPoolBackprop(dy, x, filterSize, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=AvgPool_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchMatMul } from '../kernel_names';\nimport { matMul } from '../ops/mat_mul';\nexport const batchMatMulGradConfig = {\n    kernelName: BatchMatMul,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved, attrs) => {\n        const [a, b] = saved;\n        const { transposeA, transposeB } = attrs;\n        if (!transposeA && !transposeB) {\n            return {\n                a: () => matMul(dy, b, false, true),\n                b: () => matMul(a, dy, true, false)\n            };\n        }\n        else if (!transposeA && transposeB) {\n            return {\n                a: () => matMul(dy, b, false, false),\n                b: () => matMul(dy, a, true, false)\n            };\n        }\n        else if (transposeA && !transposeB) {\n            return {\n                a: () => matMul(b, dy, false, true),\n                b: () => matMul(a, dy, false, false)\n            };\n        }\n        else {\n            return {\n                a: () => matMul(b, dy, true, true),\n                b: () => matMul(dy, a, true, true)\n            };\n        }\n    }\n};\n//# sourceMappingURL=BatchMatMul_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchToSpaceND } from '../kernel_names';\nimport { spaceToBatchND } from '../ops/space_to_batch_nd';\nexport const batchToSpaceNDGradConfig = {\n    kernelName: BatchToSpaceND,\n    gradFunc: (dy, saved, attrs) => {\n        const { blockShape, crops } = attrs;\n        return { x: () => spaceToBatchND(dy, blockShape, crops) };\n    }\n};\n//# sourceMappingURL=BatchToSpaceND_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BroadcastTo } from '../kernel_names';\nimport { sum } from '../ops/sum';\nexport const broadcastToGradConfig = {\n    kernelName: BroadcastTo,\n    gradFunc: (dy, saved, attrs) => {\n        const broadCastToAttrs = attrs;\n        const inputShape = broadCastToAttrs.inputShape;\n        const outputShape = broadCastToAttrs.shape;\n        const reps = Array.from(outputShape);\n        for (let i = inputShape.length - 1; i >= 0; i--) {\n            if (inputShape[i] === outputShape[i]) {\n                reps[i] = 1;\n            }\n            else if (inputShape[i] !== 1) {\n                throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);\n            }\n        }\n        const axes = [];\n        for (let i = 0; i < reps.length; i++) {\n            if (reps[i] > 1) {\n                axes.push(i);\n            }\n        }\n        return { x: () => sum(dy, axes, true /* keepDims */) };\n    }\n};\n//# sourceMappingURL=BroadcastTo_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cast } from '../kernel_names';\nexport const castGradConfig = {\n    kernelName: Cast,\n    gradFunc: (dy) => {\n        return { x: () => dy.clone() };\n    }\n};\n//# sourceMappingURL=Cast_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const ceilGradConfig = {\n    kernelName: Ceil,\n    gradFunc: (dy) => {\n        // TODO(manrajgrover): Return null for gradients when backprop supports it.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Ceil_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ClipByValue } from '../kernel_names';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { lessEqual } from '../ops/less_equal';\nimport { logicalAnd } from '../ops/logical_and';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const clipByValueGradConfig = {\n    kernelName: ClipByValue,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { clipValueMin, clipValueMax } = attrs;\n        return {\n            x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy)),\n        };\n    }\n};\n//# sourceMappingURL=ClipByValue_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Concat } from '../kernel_names';\nimport { split } from '../ops/split';\nimport { parseAxisParam } from '../util';\nexport const concatGradConfig = {\n    kernelName: Concat,\n    saveAllInputs: true,\n    gradFunc: (dy, saved, attrs) => {\n        const shapes = saved.map(t => t.shape);\n        const { axis } = attrs;\n        const $axis = parseAxisParam(axis, saved[0].shape)[0];\n        const sizeSplits = shapes.map(s => s[$axis]);\n        const derTensors = split(dy, sizeSplits, $axis);\n        return derTensors.map(t => () => t);\n    }\n};\n//# sourceMappingURL=Concat_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv2D } from '../kernel_names';\nimport { conv2DBackpropFilter } from '../ops/conv2d_backprop_filter';\nimport { conv2DBackpropInput } from '../ops/conv2d_backprop_input';\nimport * as conv_util from '../ops/conv_util';\nimport * as util from '../util';\nexport const conv2DGradConfig = {\n    kernelName: Conv2D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x4D, $filter] = saved;\n        const { dilations, strides, pad, dataFormat } = attrs;\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of conv2D: dilation rates greater than 1 ' +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n        return {\n            x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad, dataFormat),\n            filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad, dataFormat)\n        };\n    }\n};\n//# sourceMappingURL=Conv2D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv2DBackpropInput } from '../kernel_names';\nimport { conv2d } from '../ops/conv2d';\nimport { conv2DBackpropFilter } from '../ops/conv2d_backprop_filter';\nexport const conv2DBackpropInputGradConfig = {\n    kernelName: Conv2DBackpropInput,\n    inputsToSave: ['dy', 'filter'],\n    gradFunc: (ddx, saved, attrs) => {\n        const [dy, filter] = saved;\n        const { strides, pad, dataFormat, dimRoundingMode } = attrs;\n        return {\n            dy: () => conv2d(ddx, filter, strides, pad, dataFormat, 1 /* dilations */, dimRoundingMode),\n            filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad, dataFormat, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=Conv2DBackpropInput_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3DBackpropFilterV2 } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the filter of a 3D convolution.\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     [batch, depth, height, width, inChannels]. If rank 4, batch of 1 is\n *     assumed.\n * @param dy The dy image, of rank 5 or rank 4, of shape\n *     [batch, depth, height, width, outDepth]. If rank 4, batch of 1 is\n *     assumed.\n * @param filterShape The shape of the filter, length 5,\n *     [filterDepth, filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideDepth, strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction conv3DBackpropFilter_(x, dy, filterShape, strides, pad) {\n    let x5D = x;\n    if (x.rank === 4) {\n        x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);\n    }\n    let dy5D = dy;\n    if (dy5D.rank === 4) {\n        dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ` +\n        `${x5D.shape}.`);\n    util.assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ` +\n        `${dy5D.shape}.`);\n    util.assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ` +\n        `${filterShape}.`);\n    util.assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must ` +\n        `match input depth in filter (${filterShape[3]}.`);\n    util.assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must ` +\n        `match output depth for filter (${filterShape[4]}).`);\n    const forward = backend => {\n        const dilations = 1;\n        const convInfo = conv_util.computeConv3DInfo(x5D.shape, filterShape, strides, dilations, pad);\n        return backend.conv3dDerFilter(x5D, dy5D, convInfo);\n    };\n    const inputs = { x: x5D, y: dy5D };\n    const attrs = { strides, pad };\n    return ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropFilterV2, attrs);\n}\nexport const conv3DBackpropFilter = op({ conv3DBackpropFilter_ });\n//# sourceMappingURL=conv3d_backprop_filter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv3D } from '../kernel_names';\nimport { conv3DBackpropFilter } from '../ops/conv3d_backprop_filter';\nimport { conv3DBackpropInput } from '../ops/conv3d_backprop_input';\nimport { tupleValuesAreOne } from '../ops/conv_util';\nimport * as util from '../util';\nexport const conv3DGradConfig = {\n    kernelName: Conv3D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const { dilations, strides, pad } = attrs;\n        util.assert(tupleValuesAreOne(dilations), () => 'Error in gradient of conv3D: dilation rates greater than 1 are ' +\n            `not yet supported in gradients. Got dilations '${dilations}'`);\n        const [x5D, $filter] = saved;\n        return {\n            x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad),\n            filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=Conv3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { sin } from '../ops/sin';\nexport const cosGradConfig = {\n    kernelName: Cos,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(neg(sin(cast(x, 'float32'))), dy) };\n    }\n};\n//# sourceMappingURL=Cos_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cosh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { sinh } from '../ops/sinh';\nexport const coshGradConfig = {\n    kernelName: Cosh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(sinh(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Cosh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cumsum } from '../kernel_names';\nimport { getAxesPermutation } from '../ops/axis_util';\nimport { cumsum } from '../ops/cumsum';\nimport { transpose } from '../ops/transpose';\nexport const cumsumGradConfig = {\n    kernelName: Cumsum,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { axis, exclusive, reverse } = attrs;\n        return {\n            x: () => {\n                const permutation = getAxesPermutation([axis], x.rank);\n                let out = cumsum(dy, axis, exclusive, !reverse);\n                if (permutation != null) {\n                    out = transpose(out, permutation);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Cumsum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { DepthwiseConv2dNative } from '../kernel_names';\nimport * as conv_util from '../ops/conv_util';\nimport { depthwiseConv2dNativeBackpropFilter } from '../ops/depthwise_conv2d_native_backprop_filter';\nimport { depthwiseConv2dNativeBackpropInput } from '../ops/depthwise_conv2d_native_backprop_input';\nimport * as util from '../util';\nexport const depthwiseConv2dNativeGradConfig = {\n    kernelName: DepthwiseConv2dNative,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const { dilations, strides, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1] : dilations;\n        util.assert(conv_util.tupleValuesAreOne($dilations), () => 'Error in gradient of depthwiseConv2dNative: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${$dilations}'`);\n        const [x, filter] = saved;\n        util.assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be ` +\n            `rank 4, but got rank ${x.rank}.`);\n        util.assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be ` +\n            `rank 4, but got rank ${filter.rank}.`);\n        util.assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input ` +\n            `channels (${x.shape[3]}) must match the inChannels dimension ` +\n            `in filter ${filter.shape[2]}.`);\n        util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, $dilations), () => 'Error in gradient of depthwiseConv2d: Either strides or ' +\n            `dilations must be  1. Got strides ${strides} and dilations ` +\n            `'${$dilations}'.`);\n        if (dimRoundingMode != null) {\n            util.assert(util.isInt(pad), () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n                `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n        }\n        const convInfo = conv_util.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad, dimRoundingMode, true /* depthwise */);\n        return {\n            x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, convInfo),\n            filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, convInfo),\n        };\n    }\n};\n//# sourceMappingURL=DepthwiseConv2dNative_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput } from '../kernel_names';\nexport const dilation2dGradConfig = {\n    kernelName: Dilation2D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, filter] = saved;\n        const inputInputs = { x, filter, dy };\n        const filterInputs = { x, filter, dy };\n        return {\n            x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),\n            filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)\n        };\n    }\n};\n//# sourceMappingURL=Dilation2D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const divGradConfig = {\n    kernelName: Div,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = div(dy, cast(b, 'float32'));\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            let res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = reshape(sum(res, reduceAxes), b.shape);\n            }\n            const tmp = square(b);\n            return neg(div(res, cast(tmp, 'float32')));\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Div_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Elu, EluGrad } from '../kernel_names';\nexport const eluGradConfig = {\n    kernelName: Elu,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        const backPropKernelFunc = (backend) => {\n            return backend.eluDer(dy, y);\n        };\n        const inputs = { dy, y };\n        return {\n            x: () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* grad */, EluGrad)\n        };\n    }\n};\n//# sourceMappingURL=Elu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Erf } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { square } from '../ops/square';\nexport const erfGradConfig = {\n    kernelName: Erf,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));\n        return { x: () => mul(dy, a) };\n    }\n};\n//# sourceMappingURL=Erf_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '../kernel_names';\nimport { mul } from '../ops/mul';\nexport const expGradConfig = {\n    kernelName: Exp,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(dy, y) };\n    }\n};\n//# sourceMappingURL=Exp_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nexport const expm1GradConfig = {\n    kernelName: Expm1,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, exp(x)) };\n    }\n};\n//# sourceMappingURL=Expm1_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const floorGradConfig = {\n    kernelName: Floor,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Floor_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FloorDiv } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const floorDivGradConfig = {\n    kernelName: FloorDiv,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = div(dy, cast(b, 'float32'));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            let res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = reshape(sum(res, reduceAxes), b.shape);\n            }\n            const tmp = square(b);\n            return neg(div(res, cast(tmp, 'float32')));\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=FloorDiv_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedBatchNorm } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { getReductionAxes } from '../ops/broadcast_util';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { rsqrt } from '../ops/rsqrt';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nimport { tile } from '../ops/tile';\nexport const fusedBatchNormGradConfig = {\n    kernelName: FusedBatchNorm,\n    inputsToSave: ['x', 'mean', 'variance', 'scale'],\n    gradFunc: (dy, saved, attrs) => {\n        const { varianceEpsilon } = attrs;\n        const [x, mean, variance, scale] = saved;\n        const scaleValue = scale == null ? scalar(1) : scale;\n        const reductionAxes = getReductionAxes(mean.shape, x.shape);\n        const tileShape = [];\n        if (mean.rank === 1) {\n            for (let i = 0; i < x.shape.length - 1; ++i) {\n                tileShape.push(x.shape[i]);\n            }\n            tileShape.push(1);\n        }\n        const xMinusMean = sub(x, mean);\n        const dyTimesScaleValue = mul(dy, scaleValue);\n        const oneOverSqrtVariance = rsqrt(add(variance, scalar(varianceEpsilon)));\n        const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));\n        const derX = () => {\n            if (mean.rank === 1) {\n                return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean.shape[0]]), tileShape)), scaleValue), x.shape);\n            }\n            else {\n                return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);\n            }\n        };\n        const derMean = () => {\n            let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);\n            if (mean.rank === 1) {\n                meanDer = sum(meanDer, reductionAxes);\n            }\n            return reshape(meanDer, mean.shape);\n        };\n        const derVariance = () => {\n            let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);\n            if (mean.rank === 1) {\n                varianceDer = sum(varianceDer, reductionAxes);\n            }\n            return reshape(varianceDer, mean.shape);\n        };\n        const derScale = () => {\n            const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);\n            let scaleDer = mul(dy, xMinusMean2TimesRsqrt);\n            if (mean.rank === 1) {\n                scaleDer = sum(scaleDer, reductionAxes);\n            }\n            return reshape(scaleDer, mean.shape);\n        };\n        const derOffset = () => {\n            let offsetDer = dy;\n            if (mean.rank === 1) {\n                offsetDer = sum(offsetDer, reductionAxes);\n            }\n            return reshape(offsetDer, mean.shape);\n        };\n        return {\n            x: derX,\n            mean: derMean,\n            variance: derVariance,\n            scale: derScale,\n            offset: derOffset\n        };\n    }\n};\n//# sourceMappingURL=FusedBatchNorm_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GatherV2 } from '../kernel_names';\nimport { getUndoAxesPermutation } from '../ops/axis_util';\nimport { reshape } from '../ops/reshape';\nimport { transpose } from '../ops/transpose';\nimport { unsortedSegmentSum } from '../ops/unsorted_segment_sum';\nimport { parseAxisParam } from '../util';\nexport const gatherGradConfig = {\n    kernelName: GatherV2,\n    inputsToSave: ['x', 'indices'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, indices] = saved;\n        const { axis } = attrs;\n        const parsedAxis = parseAxisParam(axis, x.shape)[0];\n        const derX = () => {\n            const paramsShape = x.shape;\n            const indicesSize = indices.size;\n            const outerShape = paramsShape.slice(0, parsedAxis);\n            const outerDims = outerShape.length;\n            const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);\n            const innerDims = innerShape.length;\n            const outerAxesIndices = arrayRange(0, outerDims);\n            const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);\n            const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);\n            const values = reshape(dy, valuesShape);\n            const reshapedIndices = reshape(indices, [indicesSize]);\n            const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);\n            const valuesTranspose = transpose(values, transposeDims);\n            let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);\n            const invertTransposeDims = getUndoAxesPermutation(transposeDims);\n            paramsGrad = transpose(paramsGrad, invertTransposeDims);\n            return paramsGrad;\n        };\n        return { x: derX, indices: () => indices };\n    }\n};\nfunction arrayRange(start, stop) {\n    const result = [];\n    for (let i = start; i < stop; ++i) {\n        result.push(i);\n    }\n    return result;\n}\nfunction arrayConcat(arrays) {\n    const result = [];\n    for (let i = 0; i < arrays.length; ++i) {\n        for (let j = 0; j < arrays[i].length; ++j) {\n            result.push(arrays[i][j]);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=GatherV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GreaterEqual } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const greaterEqualGradConfig = {\n    kernelName: GreaterEqual,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        return { a: () => zerosLike(a), b: () => zerosLike(b) };\n    }\n};\n//# sourceMappingURL=GreaterEqual_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '../kernel_names';\nimport { cast } from '../ops/cast';\nexport const identityGradConfig = {\n    kernelName: Identity,\n    gradFunc: (dy) => {\n        return { x: () => cast(dy, 'float32') };\n    }\n};\n//# sourceMappingURL=Identity_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsFinite } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isFiniteGradConfig = {\n    kernelName: IsFinite,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsFinite_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsInf } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isInfGradConfig = {\n    kernelName: IsInf,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsInf_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsNan } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isNanGradConfig = {\n    kernelName: IsNan,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsNan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log1p } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nexport const log1pGradConfig = {\n    kernelName: Log1p,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, add(x, 1)) };\n    }\n};\n//# sourceMappingURL=Log1p_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nexport const logGradConfig = {\n    kernelName: Log,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, cast(x, 'float32')) };\n    }\n};\n//# sourceMappingURL=Log_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LogSoftmax } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nexport const logSoftmaxGradConfig = {\n    kernelName: LogSoftmax,\n    inputsToSave: [],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [value] = saved;\n        const { axis } = attrs;\n        return {\n            logits: () => {\n                const keepDims = true;\n                const softmax = exp(value);\n                return sub(dy, mul(sum(dy, axis, keepDims), softmax));\n            }\n        };\n    }\n};\n//# sourceMappingURL=LogSoftmax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LRNBackprop } from '../kernel_names';\nimport { op } from './operation';\nfunction localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n    const forward = backend => backend.LRNGrad(dy, x, y, depthRadius, bias, alpha, beta);\n    const inputs = { x, y, dy };\n    const attrs = { depthRadius, bias, alpha, beta };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LRNBackprop, attrs);\n}\nexport const localResponseNormalizationBackprop = op({ localResponseNormalizationBackprop_ });\n//# sourceMappingURL=local_response_normalization_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LRN } from '../kernel_names';\nimport { localResponseNormalizationBackprop } from '../ops/local_response_normalization_backprop';\nexport const lrnGradConfig = {\n    kernelName: LRN,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { depthRadius, bias, alpha, beta } = attrs;\n        return {\n            x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)\n        };\n    }\n};\n//# sourceMappingURL=LRN_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as axis_util from '../ops/axis_util';\nimport { cast } from '../ops/cast';\nimport { equal } from '../ops/equal';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { transpose } from '../ops/transpose';\n/**\n * Gradient helper function for the min and max operations.\n */\nexport function gradForMinAndMax(dy, y, xOrig, origAxes, permutedAxes) {\n    if (y.rank < xOrig.rank) {\n        y = reshape(y, axis_util.expandShapeToKeepDim(y.shape, origAxes));\n    }\n    if (dy.rank < xOrig.rank) {\n        dy = reshape(dy, axis_util.expandShapeToKeepDim(dy.shape, origAxes));\n    }\n    return {\n        x: () => {\n            const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));\n            return permutedAxes == null ? dx : transpose(dx, permutedAxes);\n        }\n    };\n}\n//# sourceMappingURL=min_max_grad_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nimport * as util from '../util';\nimport { gradForMinAndMax } from './min_max_grad_util';\nexport const maxGradConfig = {\n    kernelName: Max,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const maxAttrs = attrs;\n        const { reductionIndices } = maxAttrs;\n        const [x, y] = saved;\n        const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(origAxes, x.rank);\n        const maxGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);\n        return {\n            x: () => {\n                let out = maxGrad['x']();\n                if (permutedAxes != null) {\n                    out = transpose(out);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Max_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Maximum } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { less } from '../ops/less';\nimport { mul } from '../ops/mul';\nexport const maximumGradConfig = {\n    kernelName: Maximum,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const derA = () => mul(dy, cast(greaterEqual(a, b), 'float32'));\n        const derB = () => mul(dy, cast(less(a, b), 'float32'));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Maximum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool3DBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of a 3d max pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank 4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param output The original output image, of rank 5 of shape\n *     [batchSize, outDepth, outHeight, outWidth, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPool3dBackprop_(dy, input, output, filterSize, strides, dilations = [1, 1, 1], pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'maxPool3dBackprop');\n    const $input = convertToTensor(input, 'input', 'maxPool3dBackprop');\n    const $output = convertToTensor(output, 'output', 'maxPool3dBackprop');\n    let dy5D = $dy;\n    let input5D = $input;\n    let output5D = $output;\n    let reshapedTo5D = false;\n    if ($input.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n        input5D = reshape($input, [\n            1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n        ]);\n        output5D = reshape($output, [\n            1, $output.shape[0], $output.shape[1], $output.shape[2], $output.shape[3]\n        ]);\n    }\n    util.assert(dy5D.rank === 5, () => `Error in maxPool3dBackprop: dy must be rank 5 but got rank ` +\n        `${dy5D.rank}.`);\n    util.assert(input5D.rank === 5, () => `Error in maxPool3dBackprop: input must be rank 5 but got rank ` +\n        `${input5D.rank}.`);\n    util.assert(output5D.rank === 5, () => `Error in maxPool3dBackprop: output must be rank 5 but got rank ` +\n        `${output5D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool3dBackprop: Either strides or dilations ' +\n        `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n        return backend.maxPool3dBackprop(dy5D, input5D, output5D, convInfo);\n    };\n    const inputs = { dy: dy5D, input: input5D, output: output5D };\n    const attrs = { filterSize, strides, dilations, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool3DBackprop, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const maxPool3dBackprop = op({ maxPool3dBackprop_ });\n//# sourceMappingURL=max_pool_3d_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPool3D } from '../kernel_names';\nimport { maxPool3dBackprop } from '../ops/max_pool_3d_backprop';\nexport const maxPool3DGradConfig = {\n    kernelName: MaxPool3D,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { filterSize, strides, dilations, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1, 1] : dilations;\n        return {\n            x: () => maxPool3dBackprop(dy, x, y, filterSize, strides, $dilations, pad, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=MaxPool3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPoolBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\n/**\n * Computes the backprop of a 2D max pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The original input image, of rank 4, of shape\n *     [batchSize, height, width, channels].\n * @param output The original output image, of rank 4, of shape\n *     [batchSize, outHeight, outWidth, channels].\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPoolBackprop_(dy, input, output, filterSize, strides, pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'maxPoolBackprop');\n    const $input = convertToTensor(input, 'input', 'maxPoolBackprop');\n    const $output = convertToTensor(output, 'output', 'maxPoolBackprop');\n    util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy ` +\n        `(${$dy.rank})`);\n    util.assert($dy.rank === 4, () => `Error in maxPoolBackprop: dy must be rank 4 but got rank ` +\n        `${$dy.rank}.`);\n    util.assert($input.rank === 4, () => `Error in maxPoolBackprop: input must be rank 4 but got rank ` +\n        `${$input.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPoolBackprop: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool2DInfo($input.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        return backend.maxPoolBackprop($dy, $input, $output, convInfo);\n    };\n    const inputs = { dy: $dy, input: $input, output: $output };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    return ENGINE.runKernelFunc(forward, inputs, null, MaxPoolBackprop, attrs);\n}\nexport const maxPoolBackprop = op({ maxPoolBackprop_ });\n//# sourceMappingURL=max_pool_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPool } from '../kernel_names';\nimport { maxPoolBackprop } from '../ops/max_pool_backprop';\nexport const maxPoolGradConfig = {\n    kernelName: MaxPool,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { filterSize, strides, pad } = attrs;\n        return {\n            x: () => maxPoolBackprop(dy, x, y, filterSize, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=MaxPool_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Min } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nimport * as util from '../util';\nimport { gradForMinAndMax } from './min_max_grad_util';\nexport const minGradConfig = {\n    kernelName: Min,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const minAttrs = attrs;\n        const { axis } = minAttrs;\n        const [x, y] = saved;\n        const origAxes = util.parseAxisParam(axis, x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(origAxes, x.rank);\n        const minGrad = gradForMinAndMax(dy, y, x, origAxes, permutedAxes);\n        return {\n            x: () => {\n                let out = minGrad['x']();\n                if (permutedAxes != null) {\n                    out = transpose(out);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Min_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Minimum } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { greater } from '../ops/greater';\nimport { lessEqual } from '../ops/less_equal';\nimport { mul } from '../ops/mul';\nexport const minimumGradConfig = {\n    kernelName: Minimum,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const derA = () => mul(dy, cast(lessEqual(a, b), 'float32'));\n        const derB = () => mul(dy, cast(greater(a, b), 'float32'));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Minimum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Mod } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { div } from '../ops/div';\nimport { floor } from '../ops/floor';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const modGradConfig = {\n    kernelName: Mod,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(dy, reduceAxes), a.shape);\n            }\n            return dy;\n        };\n        const derB = () => {\n            const res = mul(dy, neg(floor(div(a, b))));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), b.shape);\n            }\n            return res;\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Mod_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const multiplyGradConfig = {\n    kernelName: Multiply,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = mul(dy, cast(b, 'float32'));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            const res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), b.shape);\n            }\n            return res;\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Multiply_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Negate } from '../kernel_names';\nimport { neg } from '../ops/neg';\nexport const negateGradConfig = {\n    kernelName: Negate,\n    gradFunc: (dy) => {\n        return { x: () => neg(dy) };\n    }\n};\n//# sourceMappingURL=Negate_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { OneHot } from '../kernel_names';\nimport { zeros } from '../ops/zeros';\nexport const oneHotGradConfig = {\n    kernelName: OneHot,\n    inputsToSave: ['indices'],\n    gradFunc: (dy, saved) => {\n        const indices = saved[0];\n        return { indices: () => zeros(indices.shape, 'float32') };\n    }\n};\n//# sourceMappingURL=OneHot_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { OnesLike } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const onesLikeGradConfig = {\n    kernelName: OnesLike,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=OnesLike_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { PadV2 } from '../kernel_names';\nimport { slice } from '../ops/slice';\nexport const padV2GradConfig = {\n    kernelName: PadV2,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        // Pad introduces values around the original tensor, so the gradient\n        // slices the original shape out of the gradient.\n        const x = saved[0];\n        const { paddings } = attrs;\n        const begin = paddings.map(p => p[0]);\n        return { x: () => slice(dy, begin, x.shape) };\n    }\n};\n//# sourceMappingURL=PadV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Pow } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { greater } from '../ops/greater';\nimport { log } from '../ops/log';\nimport { mul } from '../ops/mul';\nimport { pow } from '../ops/pow';\nimport { reshape } from '../ops/reshape';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const powGradConfig = {\n    kernelName: Pow,\n    inputsToSave: ['a', 'b'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [a, b, y] = saved;\n        const base = a;\n        const exp = b;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(base.shape, exp.shape);\n        const derBase = () => {\n            const expFloat = cast(exp, 'float32');\n            let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));\n            const reduceAxes = broadcast_util.getReductionAxes(base.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, base.shape);\n        };\n        const derExp = () => {\n            const condition = greater(base, 0);\n            const logBase = where(condition, log(base), zerosLike(base));\n            let res = mul(dy, mul(y, logBase));\n            const reduceAxes = broadcast_util.getReductionAxes(exp.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, exp.shape);\n        };\n        return { a: derBase, b: derExp };\n    }\n};\n//# sourceMappingURL=Pow_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Prelu } from '../kernel_names';\nimport { getReductionAxes } from '../ops/broadcast_util';\nimport { greater } from '../ops/greater';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const preluGradConfig = {\n    kernelName: Prelu,\n    inputsToSave: ['x', 'alpha'],\n    gradFunc: (dy, saved) => {\n        const [x, alpha] = saved;\n        const mask = greater(x, 0);\n        return {\n            x: () => where(mask, dy, mul(dy, alpha)),\n            alpha: () => {\n                let res = where(mask, zerosLike(dy), mul(dy, x));\n                const reduceAxes = getReductionAxes(alpha.shape, dy.shape);\n                if (reduceAxes.length > 0) {\n                    res = sum(res, reduceAxes);\n                }\n                return reshape(res, alpha.shape);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Prelu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reciprocal } from '../kernel_names';\nimport { div } from '../ops/div';\nimport { neg } from '../ops/neg';\nimport { square } from '../ops/square';\nexport const reciprocalGradConfig = {\n    kernelName: Reciprocal,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, neg(square(x))) };\n    }\n};\n//# sourceMappingURL=Reciprocal_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu6 } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { lessEqual } from '../ops/less_equal';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const relu6GradConfig = {\n    kernelName: Relu6,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        const mask = mul(lessEqual(x, 6), step(x));\n        return { x: () => mul(dy, cast(mask, 'float32')) };\n    }\n};\n//# sourceMappingURL=Relu6_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const reluGradConfig = {\n    kernelName: Relu,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, cast(step(x), 'float32')) };\n    }\n};\n//# sourceMappingURL=Relu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape } from '../kernel_names';\nimport { reshape } from '../ops/reshape';\nexport const reshapeGradConfig = {\n    kernelName: Reshape,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => reshape(dy, x.shape) };\n    }\n};\n//# sourceMappingURL=Reshape_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ResizeBilinear, ResizeBilinearGrad } from '../kernel_names';\nexport const resizeBilinearGradConfig = {\n    kernelName: ResizeBilinear,\n    inputsToSave: ['images'],\n    gradFunc: (dy, saved, attrs) => {\n        const [images] = saved;\n        const backPropKernelFunc = (backend) => {\n            const { alignCorners } = attrs;\n            return backend.resizeBilinearBackprop(dy, images, alignCorners);\n        };\n        const inputs = { images };\n        const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* gradient */, ResizeBilinearGrad, attrs);\n        return { images: imagesDer };\n    }\n};\n//# sourceMappingURL=ResizeBilinear_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ResizeNearestNeighbor, ResizeNearestNeighborGrad } from '../kernel_names';\nexport const resizeNearestNeighborGradConfig = {\n    kernelName: ResizeNearestNeighbor,\n    inputsToSave: ['images'],\n    gradFunc: (dy, saved, attrs) => {\n        const [images] = saved;\n        const backPropKernelFunc = (backend) => {\n            const { alignCorners } = attrs;\n            return backend.resizeNearestNeighborBackprop(dy, images, alignCorners);\n        };\n        const inputs = { images };\n        const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* gradient */, ResizeNearestNeighborGrad, attrs);\n        return { images: imagesDer };\n    }\n};\n//# sourceMappingURL=ResizeNearestNeighbor_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reverse } from '../kernel_names';\nimport { reverse } from '../ops/reverse';\nimport { parseAxisParam } from '../util';\nexport const reverseGradConfig = {\n    kernelName: Reverse,\n    gradFunc: (dy, saved, attrs) => {\n        const { dims } = attrs;\n        const axes = parseAxisParam(dims, dy.shape);\n        return { x: () => reverse(dy, axes) };\n    }\n};\n//# sourceMappingURL=Reverse_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Round } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const roundGradConfig = {\n    kernelName: Round,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Round_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '../kernel_names';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { pow } from '../ops/pow';\nexport const rsqrtGradConfig = {\n    kernelName: Rsqrt,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => neg(div(dy, mul(pow(x, 1.5), 2))) };\n    }\n};\n//# sourceMappingURL=Rsqrt_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SelectV2 } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { logicalNot } from '../ops/logical_not';\nimport { mul } from '../ops/mul';\nimport { zerosLike } from '../ops/zeros_like';\nexport const selectV2PoolGradConfig = {\n    kernelName: SelectV2,\n    inputsToSave: ['condition'],\n    gradFunc: (dy, saved) => {\n        const [condition] = saved;\n        return {\n            // TODO(julianoks): Return null for condition gradient\n            // when backprop supports it.\n            condition: () => cast(zerosLike(condition), 'float32'),\n            t: () => mul(dy, cast(condition, dy.dtype)),\n            e: () => mul(dy, cast(logicalNot(condition), dy.dtype))\n        };\n    }\n};\n//# sourceMappingURL=SelectV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Selu } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { exp } from '../ops/exp';\nimport { greater } from '../ops/greater';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { SELU_SCALE, SELU_SCALEALPHA } from '../ops/selu_util';\nimport { where } from '../ops/where';\nexport const seluGradConfig = {\n    kernelName: Selu,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const mask = greater(x, scalar(0));\n                const scaleAlpha = scalar(SELU_SCALEALPHA);\n                const scale = scalar(SELU_SCALE);\n                const greaterThanZeroDer = mul(dy, scale);\n                const lessEqualZeroDer = mul(mul(dy, scaleAlpha), exp(cast(x, 'float32')));\n                return where(mask, greaterThanZeroDer, lessEqualZeroDer);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Selu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sigmoid } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nexport const sigmoidGradConfig = {\n    kernelName: Sigmoid,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(dy, mul(y, sub(scalar(1), y))) };\n    }\n};\n//# sourceMappingURL=Sigmoid_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sign } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const signGradConfig = {\n    kernelName: Sign,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Sign_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { cos } from '../ops/cos';\nimport { mul } from '../ops/mul';\nexport const sinGradConfig = {\n    kernelName: Sin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(cos(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Sin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sinh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { cosh } from '../ops/cosh';\nimport { mul } from '../ops/mul';\nexport const sinhGradConfig = {\n    kernelName: Sinh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(cosh(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Sinh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Slice } from '../kernel_names';\nimport { pad } from '../ops/pad';\nimport { parseSliceParams } from '../ops/slice_util';\nexport const sliceGradConfig = {\n    kernelName: Slice,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { begin, size } = attrs;\n        const inputShape = x.shape;\n        const [begin_, size_] = parseSliceParams(x, begin, size);\n        // Create an Nx2 padding where the first column represents how many\n        // zeros are prepended (at start) for each dimension, and the second\n        // column indicates how many zeros are appended (at end).\n        // The number of zeros to append is the shape of the input\n        // elementwise-subtracted by both the begin vector and sizes vector.\n        const paddings = [];\n        for (let i = 0; i < dy.rank; i++) {\n            paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);\n        }\n        return { x: () => pad(dy, paddings) };\n    }\n};\n//# sourceMappingURL=Slice_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softmax } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nexport const softmaxGradConfig = {\n    kernelName: Softmax,\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [y] = saved;\n        const { dim } = attrs;\n        const keepDims = true;\n        const dyTimesY = mul(dy, y);\n        return {\n            logits: () => sub(dyTimesY, mul(sum(dyTimesY, [dim], keepDims), y))\n        };\n    }\n};\n//# sourceMappingURL=Softmax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softplus } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { sigmoid } from '../ops/sigmoid';\nexport const softplusGradConfig = {\n    kernelName: Softplus,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, sigmoid(x)) };\n    }\n};\n//# sourceMappingURL=Softplus_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SpaceToBatchND } from '../kernel_names';\nimport { batchToSpaceND } from '../ops/batch_to_space_nd';\nexport const spaceToBatchNDGradConfig = {\n    kernelName: SpaceToBatchND,\n    gradFunc: (dy, saved, attrs) => {\n        const { blockShape, paddings } = attrs;\n        return { x: () => batchToSpaceND(dy, blockShape, paddings) };\n    }\n};\n//# sourceMappingURL=SpaceToBatchND_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SplitV } from '../kernel_names';\nimport { concat } from '../ops/concat';\nexport const splitVGradConfig = {\n    kernelName: SplitV,\n    gradFunc: (dy, saved, attrs) => {\n        const { axis } = attrs;\n        return { x: () => concat(dy, axis) };\n    }\n};\n//# sourceMappingURL=SplitV_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sqrt } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nexport const sqrtGradConfig = {\n    kernelName: Sqrt,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, mul(sqrt(cast(x, 'float32')), 2)) };\n    }\n};\n//# sourceMappingURL=Sqrt_grad.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nexport const squareGradConfig = {\n    kernelName: Square,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, mul(cast(x, 'float32'), 2)) };\n    }\n};\n//# sourceMappingURL=Square_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nexport const squaredDifferenceGradConfig = {\n    kernelName: SquaredDifference,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const two = scalar(2);\n        const derA = () => mul(dy, mul(two, sub(a, b)));\n        const derB = () => mul(dy, mul(two, sub(b, a)));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=SquaredDifference_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Step } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const stepGradConfig = {\n    kernelName: Step,\n    gradFunc: (dy) => {\n        // TODO(manrajgrover): Return null for gradients when backprop supports\n        // it.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Step_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const subGradConfig = {\n    kernelName: Sub,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(neg(res), b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Sub_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sum } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { ones } from '../ops/ones';\nimport { reshape } from '../ops/reshape';\nimport { parseAxisParam } from '../util';\nexport const sumGradConfig = {\n    kernelName: Sum,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const expandedDyShape = x.shape.slice();\n        const { axis } = attrs;\n        const axes = parseAxisParam(axis, x.shape);\n        axes.forEach(axis => {\n            expandedDyShape[axis] = 1;\n        });\n        const expandedDy = reshape(dy, expandedDyShape);\n        const derX = mul(expandedDy, ones(x.shape, 'float32'));\n        return { x: () => derX };\n    }\n};\n//# sourceMappingURL=Sum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '../kernel_names';\nimport { cos } from '../ops/cos';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nexport const tanGradConfig = {\n    kernelName: Tan,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, square(cos(x))) };\n    }\n};\n//# sourceMappingURL=Tan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tanh } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const tanhGradConfig = {\n    kernelName: Tanh,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(sub(scalar(1), square(y)), dy) };\n    }\n};\n//# sourceMappingURL=Tanh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tile } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { slice } from '../ops/slice';\nimport { zerosLike } from '../ops/zeros_like';\nexport const tileGradConfig = {\n    kernelName: Tile,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { reps } = attrs;\n        const derX = () => {\n            let xGrad = zerosLike(x);\n            // TODO(cais): Maybe reduce memory footprint by avoiding repeated\n            // slicing.\n            if (x.rank === 1) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    xGrad = add(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));\n                }\n            }\n            else if (x.rank === 2) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        xGrad = add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [\n                            x.shape[0], x.shape[1]\n                        ]));\n                    }\n                }\n            }\n            else if (x.rank === 3) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        for (let k = 0; k < reps[2]; ++k) {\n                            xGrad =\n                                add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));\n                        }\n                    }\n                }\n            }\n            else if (x.rank === 4) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        for (let k = 0; k < reps[2]; ++k) {\n                            for (let l = 0; l < reps[3]; ++l) {\n                                xGrad =\n                                    add(xGrad, slice(dy, [\n                                        i * x.shape[0], j * x.shape[1], k * x.shape[2],\n                                        l * x.shape[3]\n                                    ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));\n                            }\n                        }\n                    }\n                }\n            }\n            else {\n                throw new Error(`Gradient for tile operation is not implemented for rank-` +\n                    `${x.rank} tensors yet.`);\n            }\n            return xGrad;\n        };\n        return { x: derX };\n    },\n};\n//# sourceMappingURL=Tile_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nexport const transposeGradConfig = {\n    kernelName: Transpose,\n    gradFunc: (dy, saved, attrs) => {\n        const transposeAttrs = attrs;\n        const { perm } = transposeAttrs;\n        const undoPerm = axis_util.getUndoAxesPermutation(perm);\n        return { x: () => transpose(dy, undoPerm) };\n    }\n};\n//# sourceMappingURL=Transpose_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unpack } from '../kernel_names';\nimport { stack } from '../ops/stack';\nexport const unpackGradConfig = {\n    kernelName: Unpack,\n    gradFunc: (dy, saved, attrs) => {\n        const unpackAttrs = attrs;\n        const { axis } = unpackAttrs;\n        return { value: () => stack(dy, axis) };\n    }\n};\n//# sourceMappingURL=Unpack_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { UnsortedSegmentSum } from '../kernel_names';\nimport { expandDims } from '../ops/expand_dims';\nimport { gather } from '../ops/gather';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { logicalAnd } from '../ops/logical_and';\nimport { maximum } from '../ops/maximum';\nimport { ones } from '../ops/ones';\nimport { scalar } from '../ops/scalar';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const unsortedSegmentSumGradConfig = {\n    kernelName: UnsortedSegmentSum,\n    inputsToSave: ['segmentIds'],\n    gradFunc: (dy, saved) => {\n        const [segmentIds] = saved;\n        const derX = () => {\n            return gatherDropNegatives(dy, segmentIds);\n        };\n        return { x: derX };\n    }\n};\nfunction gatherDropNegatives(x, indices) {\n    // Helper function for unsorted segment ops. Gathers params for\n    // positive segment ids and gathers 0 for inputs with negative segment id.\n    // Mirrors _GatherDropNegatives from tensorflow/python/ops/math_grad.py\n    const zeroClippedIndices = maximum(indices, zerosLike(indices));\n    const gathered = gather(x, zeroClippedIndices);\n    let isPositive = greaterEqual(indices, scalar(0, 'int32'));\n    const numIters = gathered.rank - isPositive.rank;\n    for (let i = 0; i < numIters; ++i) {\n        isPositive = expandDims(isPositive, i + 1);\n    }\n    isPositive = logicalAnd(isPositive, ones(gathered.shape, 'bool'));\n    const zeroSlice = zerosLike(gathered);\n    return where(isPositive, gathered, zeroSlice);\n}\n//# sourceMappingURL=UnsortedSegmentSum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ZerosLike } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const zerosLikeGradConfig = {\n    kernelName: ZerosLike,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=ZerosLike_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { absGradConfig } from './gradients/Abs_grad';\nimport { acosGradConfig } from './gradients/Acos_grad';\nimport { acoshGradConfig } from './gradients/Acosh_grad';\nimport { addGradConfig } from './gradients/Add_grad';\nimport { addNGradConfig } from './gradients/AddN_grad';\nimport { argMaxGradConfig } from './gradients/ArgMax_grad';\nimport { argMinGradConfig } from './gradients/ArgMin_grad';\nimport { asinGradConfig } from './gradients/Asin_grad';\nimport { asinhGradConfig } from './gradients/Asinh_grad';\nimport { atan2GradConfig } from './gradients/Atan2_grad';\nimport { atanGradConfig } from './gradients/Atan_grad';\nimport { atanhGradConfig } from './gradients/Atanh_grad';\nimport { avgPool3DGradConfig } from './gradients/AvgPool3D_grad';\nimport { avgPoolGradConfig } from './gradients/AvgPool_grad';\nimport { batchMatMulGradConfig } from './gradients/BatchMatMul_grad';\nimport { batchToSpaceNDGradConfig } from './gradients/BatchToSpaceND_grad';\nimport { broadcastToGradConfig } from './gradients/BroadcastTo_grad';\nimport { castGradConfig } from './gradients/Cast_grad';\nimport { ceilGradConfig } from './gradients/Ceil_grad';\nimport { clipByValueGradConfig } from './gradients/ClipByValue_grad';\nimport { concatGradConfig } from './gradients/Concat_grad';\nimport { conv2DGradConfig } from './gradients/Conv2D_grad';\nimport { conv2DBackpropInputGradConfig } from './gradients/Conv2DBackpropInput_grad';\nimport { conv3DGradConfig } from './gradients/Conv3D_grad';\nimport { cosGradConfig } from './gradients/Cos_grad';\nimport { coshGradConfig } from './gradients/Cosh_grad';\nimport { cumsumGradConfig } from './gradients/Cumsum_grad';\nimport { depthwiseConv2dNativeGradConfig } from './gradients/DepthwiseConv2dNative_grad';\nimport { dilation2dGradConfig } from './gradients/Dilation2D_grad';\nimport { divGradConfig } from './gradients/Div_grad';\nimport { eluGradConfig } from './gradients/Elu_grad';\nimport { erfGradConfig } from './gradients/Erf_grad';\nimport { expGradConfig } from './gradients/Exp_grad';\nimport { expm1GradConfig } from './gradients/Expm1_grad';\nimport { floorGradConfig } from './gradients/Floor_grad';\nimport { floorDivGradConfig } from './gradients/FloorDiv_grad';\nimport { fusedBatchNormGradConfig } from './gradients/FusedBatchNorm_grad';\nimport { gatherGradConfig } from './gradients/GatherV2_grad';\nimport { greaterEqualGradConfig } from './gradients/GreaterEqual_grad';\nimport { identityGradConfig } from './gradients/Identity_grad';\nimport { isFiniteGradConfig } from './gradients/IsFinite_grad';\nimport { isInfGradConfig } from './gradients/IsInf_grad';\nimport { isNanGradConfig } from './gradients/IsNan_grad';\nimport { log1pGradConfig } from './gradients/Log1p_grad';\nimport { logGradConfig } from './gradients/Log_grad';\nimport { logSoftmaxGradConfig } from './gradients/LogSoftmax_grad';\nimport { lrnGradConfig } from './gradients/LRN_grad';\nimport { maxGradConfig } from './gradients/Max_grad';\nimport { maximumGradConfig } from './gradients/Maximum_grad';\nimport { maxPool3DGradConfig } from './gradients/MaxPool3D_grad';\nimport { maxPoolGradConfig } from './gradients/MaxPool_grad';\nimport { minGradConfig } from './gradients/Min_grad';\nimport { minimumGradConfig } from './gradients/Minimum_grad';\nimport { modGradConfig } from './gradients/Mod_grad';\nimport { multiplyGradConfig } from './gradients/Multiply_grad';\nimport { negateGradConfig } from './gradients/Negate_grad';\nimport { oneHotGradConfig } from './gradients/OneHot_grad';\nimport { onesLikeGradConfig } from './gradients/OnesLike_grad';\nimport { padV2GradConfig } from './gradients/PadV2_grad';\nimport { powGradConfig } from './gradients/Pow_grad';\nimport { preluGradConfig } from './gradients/Prelu_grad';\nimport { reciprocalGradConfig } from './gradients/Reciprocal_grad';\nimport { relu6GradConfig } from './gradients/Relu6_grad';\nimport { reluGradConfig } from './gradients/Relu_grad';\nimport { reshapeGradConfig } from './gradients/Reshape_grad';\nimport { resizeBilinearGradConfig } from './gradients/ResizeBilinear_grad';\nimport { resizeNearestNeighborGradConfig } from './gradients/ResizeNearestNeighbor_grad';\nimport { reverseGradConfig } from './gradients/Reverse_grad';\nimport { roundGradConfig } from './gradients/Round_grad';\nimport { rsqrtGradConfig } from './gradients/Rsqrt_grad';\nimport { selectV2PoolGradConfig } from './gradients/SelectV2_grad';\nimport { seluGradConfig } from './gradients/Selu_grad';\nimport { sigmoidGradConfig } from './gradients/Sigmoid_grad';\nimport { signGradConfig } from './gradients/Sign_grad';\nimport { sinGradConfig } from './gradients/Sin_grad';\nimport { sinhGradConfig } from './gradients/Sinh_grad';\nimport { sliceGradConfig } from './gradients/Slice_grad';\nimport { softmaxGradConfig } from './gradients/Softmax_grad';\nimport { softplusGradConfig } from './gradients/Softplus_grad';\nimport { spaceToBatchNDGradConfig } from './gradients/SpaceToBatchND_grad';\nimport { splitVGradConfig } from './gradients/SplitV_grad';\nimport { sqrtGradConfig } from './gradients/Sqrt_grad';\nimport { squareGradConfig } from './gradients/Square_grad';\nimport { squaredDifferenceGradConfig } from './gradients/SquaredDifference_grad';\nimport { stepGradConfig } from './gradients/Step_grad';\nimport { subGradConfig } from './gradients/Sub_grad';\nimport { sumGradConfig } from './gradients/Sum_grad';\nimport { tanGradConfig } from './gradients/Tan_grad';\nimport { tanhGradConfig } from './gradients/Tanh_grad';\nimport { tileGradConfig } from './gradients/Tile_grad';\nimport { transposeGradConfig } from './gradients/Transpose_grad';\nimport { unpackGradConfig } from './gradients/Unpack_grad';\nimport { unsortedSegmentSumGradConfig } from './gradients/UnsortedSegmentSum_grad';\nimport { zerosLikeGradConfig } from './gradients/ZerosLike_grad';\nimport { registerGradient } from './kernel_registry';\n// Export all kernel configs here so that the package can auto register them\nconst gradConfigs = [\n    absGradConfig,\n    acosGradConfig,\n    acoshGradConfig,\n    addGradConfig,\n    addNGradConfig,\n    argMaxGradConfig,\n    argMinGradConfig,\n    asinGradConfig,\n    asinhGradConfig,\n    atan2GradConfig,\n    atanGradConfig,\n    atanhGradConfig,\n    avgPool3DGradConfig,\n    avgPoolGradConfig,\n    batchMatMulGradConfig,\n    batchToSpaceNDGradConfig,\n    broadcastToGradConfig,\n    castGradConfig,\n    ceilGradConfig,\n    clipByValueGradConfig,\n    concatGradConfig,\n    conv2DBackpropInputGradConfig,\n    conv2DGradConfig,\n    conv3DGradConfig,\n    cosGradConfig,\n    coshGradConfig,\n    cumsumGradConfig,\n    depthwiseConv2dNativeGradConfig,\n    dilation2dGradConfig,\n    divGradConfig,\n    eluGradConfig,\n    erfGradConfig,\n    expGradConfig,\n    expm1GradConfig,\n    floorDivGradConfig,\n    floorGradConfig,\n    fusedBatchNormGradConfig,\n    gatherGradConfig,\n    greaterEqualGradConfig,\n    identityGradConfig,\n    isFiniteGradConfig,\n    isInfGradConfig,\n    isNanGradConfig,\n    log1pGradConfig,\n    logGradConfig,\n    logSoftmaxGradConfig,\n    lrnGradConfig,\n    maxGradConfig,\n    maxGradConfig,\n    maximumGradConfig,\n    maxPool3DGradConfig,\n    maxPoolGradConfig,\n    minGradConfig,\n    minimumGradConfig,\n    modGradConfig,\n    multiplyGradConfig,\n    negateGradConfig,\n    oneHotGradConfig,\n    onesLikeGradConfig,\n    padV2GradConfig,\n    padV2GradConfig,\n    powGradConfig,\n    preluGradConfig,\n    reciprocalGradConfig,\n    relu6GradConfig,\n    reluGradConfig,\n    reshapeGradConfig,\n    resizeBilinearGradConfig,\n    resizeNearestNeighborGradConfig,\n    reverseGradConfig,\n    roundGradConfig,\n    rsqrtGradConfig,\n    selectV2PoolGradConfig,\n    seluGradConfig,\n    sigmoidGradConfig,\n    signGradConfig,\n    sinGradConfig,\n    sinhGradConfig,\n    sliceGradConfig,\n    softmaxGradConfig,\n    softplusGradConfig,\n    spaceToBatchNDGradConfig,\n    spaceToBatchNDGradConfig,\n    splitVGradConfig,\n    splitVGradConfig,\n    sqrtGradConfig,\n    squaredDifferenceGradConfig,\n    squareGradConfig,\n    stepGradConfig,\n    subGradConfig,\n    sumGradConfig,\n    tanGradConfig,\n    tanhGradConfig,\n    tileGradConfig,\n    transposeGradConfig,\n    unpackGradConfig,\n    unsortedSegmentSumGradConfig,\n    zerosLikeGradConfig\n];\nfor (const gradientConfig of gradConfigs) {\n    registerGradient(gradientConfig);\n}\n//# sourceMappingURL=register_all_gradients.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { abs } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.abs = function () {\n    this.throwIfDisposed();\n    return abs(this);\n};\n//# sourceMappingURL=abs.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { acos } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.acos = function () {\n    this.throwIfDisposed();\n    return acos(this);\n};\n//# sourceMappingURL=acos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { acosh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.acosh = function () {\n    this.throwIfDisposed();\n    return acosh(this);\n};\n//# sourceMappingURL=acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { addStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.addStrict = function (x) {\n    this.throwIfDisposed();\n    return addStrict(this, x);\n};\n//# sourceMappingURL=add_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { add } from '../../ops/add';\nimport { Tensor } from '../../tensor';\nTensor.prototype.add = function (b) {\n    this.throwIfDisposed();\n    return add(this, b);\n};\n//# sourceMappingURL=add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { all } from '../../ops/all';\nimport { Tensor } from '../../tensor';\nTensor.prototype.all = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return all(this, axis, keepDims);\n};\n//# sourceMappingURL=all.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { any } from '../../ops/any';\nimport { Tensor } from '../../tensor';\nTensor.prototype.any = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return any(this, axis, keepDims);\n};\n//# sourceMappingURL=any.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { argMax } from '../../ops/arg_max';\nimport { Tensor } from '../../tensor';\nTensor.prototype.argMax = function (axis) {\n    this.throwIfDisposed();\n    return argMax(this, axis);\n};\n//# sourceMappingURL=arg_max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { argMin } from '../../ops/arg_min';\nimport { Tensor } from '../../tensor';\nTensor.prototype.argMin = function (axis) {\n    this.throwIfDisposed();\n    return argMin(this, axis);\n};\n//# sourceMappingURL=arg_min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\nimport { assert } from '../../util';\n/** Converts a size-1 `tf.Tensor` to a `tf.Scalar`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asScalar = function () {\n    this.throwIfDisposed();\n    assert(this.size === 1, () => 'The array must have only 1 element.');\n    return reshape(this, []);\n};\n//# sourceMappingURL=as_scalar.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * Casts a `tf.Tensor` to a specified dtype.\n *\n * @param dtype Data-type to cast the tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asType = function (dtype) {\n    this.throwIfDisposed();\n    return cast(this, dtype);\n};\n//# sourceMappingURL=as_type.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/** Converts a `tf.Tensor` to a `tf.Tensor1D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as1D = function () {\n    this.throwIfDisposed();\n    return reshape(this, [this.size]);\n};\n//# sourceMappingURL=as1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor2D`.\n *\n * @param rows Number of rows in `tf.Tensor2D`.\n * @param columns Number of columns in `tf.Tensor2D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as2D = function (rows, columns) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns]);\n};\n//# sourceMappingURL=as2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor3D`.\n *\n * @param rows Number of rows in `tf.Tensor3D`.\n * @param columns Number of columns in `tf.Tensor3D`.\n * @param depth Depth of `tf.Tensor3D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as3D = function (rows, columns, depth) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth]);\n};\n//# sourceMappingURL=as3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor4D`.\n *\n * @param rows Number of rows in `tf.Tensor4D`.\n * @param columns Number of columns in `tf.Tensor4D`.\n * @param depth Depth of `tf.Tensor4D`.\n * @param depth2 4th dimension of `tf.Tensor4D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as4D = function (rows, columns, depth, depth2) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth, depth2]);\n};\n//# sourceMappingURL=as4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor5D`.\n *\n * @param rows Number of rows in `tf.Tensor5D`.\n * @param columns Number of columns in `tf.Tensor5D`.\n * @param depth Depth of `tf.Tensor5D`.\n * @param depth2 4th dimension of `tf.Tensor5D`.\n * @param depth3 5th dimension of 'tf.Tensor5D'\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as5D = function (rows, columns, depth, depth2, depth3) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth, depth2, depth3]);\n};\n//# sourceMappingURL=as5d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { asin } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.asin = function () {\n    this.throwIfDisposed();\n    return asin(this);\n};\n//# sourceMappingURL=asin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { asinh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.asinh = function () {\n    this.throwIfDisposed();\n    return asinh(this);\n};\n//# sourceMappingURL=asinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { atan } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atan = function () {\n    this.throwIfDisposed();\n    return atan(this);\n};\n//# sourceMappingURL=atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { atan2 } from '../../ops/atan2';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atan2 = function (b) {\n    this.throwIfDisposed();\n    return atan2(this, b);\n};\n//# sourceMappingURL=atan2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { atanh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atanh = function () {\n    this.throwIfDisposed();\n    return atanh(this);\n};\n//# sourceMappingURL=atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { avgPool } from '../../ops/avg_pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.avgPool = function (filterSize, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return avgPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=avg_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { batchToSpaceND } from '../../ops/batch_to_space_nd';\nimport { Tensor } from '../../tensor';\nTensor.prototype.batchToSpaceND = function (blockShape, crops) {\n    this.throwIfDisposed();\n    return batchToSpaceND(this, blockShape, crops);\n};\n//# sourceMappingURL=batch_to_space_nd.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { batchNorm } from '../../ops/batchnorm';\nimport { Tensor } from '../../tensor';\nTensor.prototype.batchNorm = function (mean, variance, offset, scale, varianceEpsilon) {\n    this.throwIfDisposed();\n    return batchNorm(this, mean, variance, offset, scale, varianceEpsilon);\n};\n//# sourceMappingURL=batchnorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { broadcastTo } from '../../ops/broadcast_to';\nimport { Tensor } from '../../tensor';\nTensor.prototype.broadcastTo = function (shape) {\n    this.throwIfDisposed();\n    return broadcastTo(this, shape);\n};\n//# sourceMappingURL=broadcast_to.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cast = function (dtype) {\n    this.throwIfDisposed();\n    return cast(this, dtype);\n};\n//# sourceMappingURL=cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { ceil } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.ceil = function () {\n    this.throwIfDisposed();\n    return ceil(this);\n};\n//# sourceMappingURL=ceil.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { clipByValue } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.clipByValue = function (min, max) {\n    this.throwIfDisposed();\n    return clipByValue(this, min, max);\n};\n//# sourceMappingURL=clip_by_value.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../../ops/concat';\nimport { Tensor } from '../../tensor';\nTensor.prototype.concat = function (x, axis) {\n    this.throwIfDisposed();\n    if (x instanceof Tensor) {\n        x = [x];\n    }\n    return concat([this, ...x], axis);\n};\n//# sourceMappingURL=concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv1d } from '../../ops/conv1d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv1d = function (filter, stride, pad, dataFormat, dilation, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv1d(this, filter, stride, pad, dataFormat, dilation, dimRoundingMode);\n};\n//# sourceMappingURL=conv1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2dTranspose } from '../../ops/conv2d_transpose';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv2dTranspose = function (filter, outputShape, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv2dTranspose(this, filter, outputShape, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=conv2d_transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2d } from '../../ops/conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cos } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cos = function () {\n    this.throwIfDisposed();\n    return cos(this);\n};\n//# sourceMappingURL=cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cosh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cosh = function () {\n    this.throwIfDisposed();\n    return cosh(this);\n};\n//# sourceMappingURL=cosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { cumsum } from '../../ops/cumsum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cumsum = function (axis, exclusive, reverse) {\n    this.throwIfDisposed();\n    return cumsum(this, axis, exclusive, reverse);\n};\n//# sourceMappingURL=cumsum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { depthToSpace } from '../../ops/depth_to_space';\nimport { Tensor } from '../../tensor';\nTensor.prototype.depthToSpace = function (blockSize, dataFormat) {\n    this.throwIfDisposed();\n    return depthToSpace(this, blockSize, dataFormat);\n};\n//# sourceMappingURL=depth_to_space.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../../globals';\nimport { depthwiseConv2d } from '../../ops/depthwise_conv2d';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated Use `depthwiseConv2d` instead.\n */\nTensor.prototype.depthwiseConv2D = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    deprecationWarn('depthwiseConv2D is deprecated, use depthwiseConv2d instead');\n    this.throwIfDisposed();\n    return depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=depthwise_conv2D_deprecated.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { depthwiseConv2d } from '../../ops/depthwise_conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.depthwiseConv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    this.throwIfDisposed();\n    return depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dilation2d } from '../../ops/dilation2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.dilation2d = function (filter, strides, pad, dilations, dataFormat) {\n    this.throwIfDisposed();\n    return dilation2d(this, filter, strides, pad, dilations, dataFormat);\n};\n//# sourceMappingURL=dilation2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { divNoNan } from '../../ops/div_no_nan';\nimport { Tensor } from '../../tensor';\nTensor.prototype.divNoNan = function (b) {\n    this.throwIfDisposed();\n    return divNoNan(this, b);\n};\n//# sourceMappingURL=div_no_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { divStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.divStrict = function (x) {\n    this.throwIfDisposed();\n    return divStrict(this, x);\n};\n//# sourceMappingURL=div_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { div } from '../../ops/div';\nimport { Tensor } from '../../tensor';\nTensor.prototype.div = function (b) {\n    this.throwIfDisposed();\n    return div(this, b);\n};\n//# sourceMappingURL=div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dot } from '../../ops/dot';\nimport { Tensor } from '../../tensor';\nTensor.prototype.dot = function (b) {\n    this.throwIfDisposed();\n    return dot(this, b);\n};\n//# sourceMappingURL=dot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { elu } from '../../ops/elu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.elu = function () {\n    this.throwIfDisposed();\n    return elu(this);\n};\n//# sourceMappingURL=elu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { equalStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.equalStrict = function (x) {\n    this.throwIfDisposed();\n    return equalStrict(this, x);\n};\n//# sourceMappingURL=equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { equal } from '../../ops/equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.equal = function (b) {\n    this.throwIfDisposed();\n    return equal(this, b);\n};\n//# sourceMappingURL=equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { erf } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.erf = function () {\n    this.throwIfDisposed();\n    return erf(this);\n};\n//# sourceMappingURL=erf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { exp } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.exp = function () {\n    this.throwIfDisposed();\n    return exp(this);\n};\n//# sourceMappingURL=exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { expandDims } from '../../ops/expand_dims';\nimport { Tensor } from '../../tensor';\nTensor.prototype.expandDims = function (axis) {\n    this.throwIfDisposed();\n    return expandDims(this, axis);\n};\n//# sourceMappingURL=expand_dims.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { expm1 } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.expm1 = function () {\n    this.throwIfDisposed();\n    return expm1(this);\n};\n//# sourceMappingURL=expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { fft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.fft = function () {\n    this.throwIfDisposed();\n    return fft(this);\n};\n//# sourceMappingURL=fft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/** Flatten a Tensor to a 1D array.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.flatten = function () {\n    this.throwIfDisposed();\n    return reshape(this, [this.size]);\n};\n//# sourceMappingURL=flatten.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { floor } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.floor = function () {\n    this.throwIfDisposed();\n    return floor(this);\n};\n//# sourceMappingURL=floor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { floorDiv } from '../../ops/floorDiv';\nimport { Tensor } from '../../tensor';\nTensor.prototype.floorDiv = function (b) {\n    this.throwIfDisposed();\n    return floorDiv(this, b);\n};\n//# sourceMappingURL=floorDiv.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { gather } from '../../ops/gather';\nimport { Tensor } from '../../tensor';\nTensor.prototype.gather = function (indices, axis) {\n    this.throwIfDisposed();\n    return gather(this, indices, axis);\n};\n//# sourceMappingURL=gather.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { greaterEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return greaterEqualStrict(this, x);\n};\n//# sourceMappingURL=greater_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { greaterEqual } from '../../ops/greater_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.greaterEqual = function (b) {\n    this.throwIfDisposed();\n    return greaterEqual(this, b);\n};\n//# sourceMappingURL=greater_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { greaterStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterStrict = function (x) {\n    this.throwIfDisposed();\n    return greaterStrict(this, x);\n};\n//# sourceMappingURL=greater_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { greater } from '../../ops/greater';\nimport { Tensor } from '../../tensor';\nTensor.prototype.greater = function (b) {\n    this.throwIfDisposed();\n    return greater(this, b);\n};\n//# sourceMappingURL=greater.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { ifft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.ifft = function () {\n    this.throwIfDisposed();\n    return ifft(this);\n};\n//# sourceMappingURL=ifft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { irfft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.irfft = function () {\n    this.throwIfDisposed();\n    return irfft(this);\n};\n//# sourceMappingURL=irfft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isFinite } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isFinite = function () {\n    this.throwIfDisposed();\n    return isFinite(this);\n};\n//# sourceMappingURL=is_finite.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isInf } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isInf = function () {\n    this.throwIfDisposed();\n    return isInf(this);\n};\n//# sourceMappingURL=is_inf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isNaN } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isNaN = function () {\n    this.throwIfDisposed();\n    return isNaN(this);\n};\n//# sourceMappingURL=is_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { leakyRelu } from '../../ops/leaky_relu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.leakyRelu = function (alpha) {\n    this.throwIfDisposed();\n    return leakyRelu(this, alpha);\n};\n//# sourceMappingURL=leaky_relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { lessEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.lessEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return lessEqualStrict(this, x);\n};\n//# sourceMappingURL=less_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { lessEqual } from '../../ops/less_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.lessEqual = function (b) {\n    this.throwIfDisposed();\n    return lessEqual(this, b);\n};\n//# sourceMappingURL=less_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { lessStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.lessStrict = function (x) {\n    this.throwIfDisposed();\n    return lessStrict(this, x);\n};\n//# sourceMappingURL=less_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { less } from '../../ops/less';\nimport { Tensor } from '../../tensor';\nTensor.prototype.less = function (b) {\n    this.throwIfDisposed();\n    return less(this, b);\n};\n//# sourceMappingURL=less.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { localResponseNormalization } from '../../ops/local_response_normalization';\nimport { Tensor } from '../../tensor';\nTensor.prototype.localResponseNormalization = function (depthRadius, bias, alpha, beta) {\n    this.throwIfDisposed();\n    return localResponseNormalization(this, depthRadius, bias, alpha, beta);\n};\n//# sourceMappingURL=local_response_normalization.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { logSigmoid } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSigmoid = function () {\n    this.throwIfDisposed();\n    return logSigmoid(this);\n};\n//# sourceMappingURL=log_sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { logSoftmax } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSoftmax = function (axis) {\n    this.throwIfDisposed();\n    return logSoftmax(this, axis);\n};\n//# sourceMappingURL=log_softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logSumExp } from '../../ops/log_sum_exp';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSumExp = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return logSumExp(this, axis, keepDims);\n};\n//# sourceMappingURL=log_sum_exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { log } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.log = function () {\n    this.throwIfDisposed();\n    return log(this);\n};\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { log1p } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.log1p = function () {\n    this.throwIfDisposed();\n    return log1p(this);\n};\n//# sourceMappingURL=log1p.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalAnd } from '../../ops/logical_and';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalAnd = function (b) {\n    this.throwIfDisposed();\n    return logicalAnd(this, b);\n};\n//# sourceMappingURL=logical_and.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalNot } from '../../ops/logical_not';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalNot = function () {\n    this.throwIfDisposed();\n    return logicalNot(this);\n};\n//# sourceMappingURL=logical_not.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalOr } from '../../ops/logical_or';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalOr = function (b) {\n    this.throwIfDisposed();\n    return logicalOr(this, b);\n};\n//# sourceMappingURL=logical_or.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalXor } from '../../ops/logical_xor';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalXor = function (b) {\n    this.throwIfDisposed();\n    return logicalXor(this, b);\n};\n//# sourceMappingURL=logical_xor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { matMul } from '../../ops/mat_mul';\nimport { Tensor } from '../../tensor';\nTensor.prototype.matMul = function (b, transposeA, transposeB) {\n    this.throwIfDisposed();\n    return matMul(this, b, transposeA, transposeB);\n};\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { maxPool } from '../../ops/max_pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.maxPool = function (filterSize, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return maxPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=max_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { max } from '../../ops/max';\nimport { Tensor } from '../../tensor';\nTensor.prototype.max = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return max(this, axis, keepDims);\n};\n//# sourceMappingURL=max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { maximumStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.maximumStrict = function (x) {\n    this.throwIfDisposed();\n    return maximumStrict(this, x);\n};\n//# sourceMappingURL=maximum_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { maximum } from '../../ops/maximum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.maximum = function (b) {\n    this.throwIfDisposed();\n    return maximum(this, b);\n};\n//# sourceMappingURL=maximum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mean } from '../../ops/mean';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mean = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return mean(this, axis, keepDims);\n};\n//# sourceMappingURL=mean.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { min } from '../../ops/min';\nimport { Tensor } from '../../tensor';\nTensor.prototype.min = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return min(this, axis, keepDims);\n};\n//# sourceMappingURL=min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { minimumStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.minimumStrict = function (x) {\n    this.throwIfDisposed();\n    return minimumStrict(this, x);\n};\n//# sourceMappingURL=minimum_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { minimum } from '../../ops/minimum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.minimum = function (b) {\n    this.throwIfDisposed();\n    return minimum(this, b);\n};\n//# sourceMappingURL=minimum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { modStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.modStrict = function (x) {\n    this.throwIfDisposed();\n    return modStrict(this, x);\n};\n//# sourceMappingURL=mod_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mod } from '../../ops/mod';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mod = function (b) {\n    this.throwIfDisposed();\n    return mod(this, b);\n};\n//# sourceMappingURL=mod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { mulStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.mulStrict = function (x) {\n    this.throwIfDisposed();\n    return mulStrict(this, x);\n};\n//# sourceMappingURL=mul_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mul } from '../../ops/mul';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mul = function (b) {\n    this.throwIfDisposed();\n    return mul(this, b);\n};\n//# sourceMappingURL=mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { neg } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.neg = function () {\n    this.throwIfDisposed();\n    return neg(this);\n};\n//# sourceMappingURL=neg.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { norm } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.norm = function (ord, axis, keepDims) {\n    this.throwIfDisposed();\n    return norm(this, ord, axis, keepDims);\n};\n//# sourceMappingURL=norm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { notEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.notEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return notEqualStrict(this, x);\n};\n//# sourceMappingURL=not_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { notEqual } from '../../ops/not_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.notEqual = function (b) {\n    this.throwIfDisposed();\n    return notEqual(this, b);\n};\n//# sourceMappingURL=not_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { oneHot } from '../../ops/one_hot';\nimport { Tensor } from '../../tensor';\nTensor.prototype.oneHot = function (depth, onValue = 1, offValue = 0) {\n    this.throwIfDisposed();\n    return oneHot(this, depth, onValue, offValue);\n};\n//# sourceMappingURL=one_hot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { onesLike } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.onesLike = function () {\n    this.throwIfDisposed();\n    return onesLike(this);\n};\n//# sourceMappingURL=ones_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pad } from '../../ops/pad';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pad = function (paddings, constantValue) {\n    this.throwIfDisposed();\n    return pad(this, paddings, constantValue);\n};\n//# sourceMappingURL=pad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pool } from '../../ops/pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pool = function (windowShape, poolingType, padding, dilationRate, strides) {\n    this.throwIfDisposed();\n    return pool(this, windowShape, poolingType, padding, dilationRate, strides);\n};\n//# sourceMappingURL=pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { powStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.powStrict = function (exp) {\n    this.throwIfDisposed();\n    return powStrict(this, exp);\n};\n//# sourceMappingURL=pow_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pow } from '../../ops/pow';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pow = function (exp) {\n    this.throwIfDisposed();\n    return pow(this, exp);\n};\n//# sourceMappingURL=pow.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { prelu } from '../../ops/prelu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.prelu = function (alpha) {\n    this.throwIfDisposed();\n    return prelu(this, alpha);\n};\n//# sourceMappingURL=prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { prod } from '../../ops/prod';\nimport { Tensor } from '../../tensor';\nTensor.prototype.prod = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return prod(this, axis, keepDims);\n};\n//# sourceMappingURL=prod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { reciprocal } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reciprocal = function () {\n    this.throwIfDisposed();\n    return reciprocal(this);\n};\n//# sourceMappingURL=reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { relu } from '../../ops/relu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.relu = function () {\n    this.throwIfDisposed();\n    return relu(this);\n};\n//# sourceMappingURL=relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { relu6 } from '../../ops/relu6';\nimport { Tensor } from '../../tensor';\nTensor.prototype.relu6 = function () {\n    this.throwIfDisposed();\n    return relu6(this);\n};\n//# sourceMappingURL=relu6.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Reshapes the tensor into the shape of the provided tensor.\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.reshapeAs = function (x) {\n    this.throwIfDisposed();\n    return reshape(this, x.shape);\n};\n//# sourceMappingURL=reshape_as.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reshape = function (shape) {\n    this.throwIfDisposed();\n    return reshape(this, shape);\n};\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { resizeBilinear } from '../../ops/image/resize_bilinear';\nimport { Tensor } from '../../tensor';\nTensor.prototype.resizeBilinear = function (newShape2D, alignCorners) {\n    this.throwIfDisposed();\n    return resizeBilinear(this, newShape2D, alignCorners);\n};\n//# sourceMappingURL=resize_bilinear.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { resizeNearestNeighbor } from '../../ops/image/resize_nearest_neighbor';\nimport { Tensor } from '../../tensor';\nTensor.prototype.resizeNearestNeighbor = function (newShape2D, alignCorners) {\n    this.throwIfDisposed();\n    return resizeNearestNeighbor(this, newShape2D, alignCorners);\n};\n//# sourceMappingURL=resize_nearest_neighbor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reverse } from '../../ops/reverse';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reverse = function (axis) {\n    this.throwIfDisposed();\n    return reverse(this, axis);\n};\n//# sourceMappingURL=reverse.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { rfft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.rfft = function () {\n    this.throwIfDisposed();\n    return rfft(this);\n};\n//# sourceMappingURL=rfft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { round } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.round = function () {\n    this.throwIfDisposed();\n    return round(this);\n};\n//# sourceMappingURL=round.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { rsqrt } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.rsqrt = function () {\n    this.throwIfDisposed();\n    return rsqrt(this);\n};\n//# sourceMappingURL=rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { selu } from '../../ops/selu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.selu = function () {\n    this.throwIfDisposed();\n    return selu(this);\n};\n//# sourceMappingURL=selu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { separableConv2d } from '../../ops/separable_conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.separableConv2d = function (depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat) {\n    this.throwIfDisposed();\n    return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat);\n};\n//# sourceMappingURL=separable_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sigmoid } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sigmoid = function () {\n    this.throwIfDisposed();\n    return sigmoid(this);\n};\n//# sourceMappingURL=sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sign } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sign = function () {\n    this.throwIfDisposed();\n    return sign(this);\n};\n//# sourceMappingURL=sign.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sin } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sin = function () {\n    this.throwIfDisposed();\n    return sin(this);\n};\n//# sourceMappingURL=sin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sinh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sinh = function () {\n    this.throwIfDisposed();\n    return sinh(this);\n};\n//# sourceMappingURL=sinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { slice } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.slice = function (begin, size) {\n    this.throwIfDisposed();\n    return slice(this, begin, size);\n};\n//# sourceMappingURL=slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { softmax } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.softmax = function (dim) {\n    this.throwIfDisposed();\n    return softmax(this, dim);\n};\n//# sourceMappingURL=softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { softplus } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.softplus = function () {\n    this.throwIfDisposed();\n    return softplus(this);\n};\n//# sourceMappingURL=softplus.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { spaceToBatchND } from '../../ops/space_to_batch_nd';\nimport { Tensor } from '../../tensor';\nTensor.prototype.spaceToBatchND = function (blockShape, paddings) {\n    this.throwIfDisposed();\n    return spaceToBatchND(this, blockShape, paddings);\n};\n//# sourceMappingURL=space_to_batch_nd.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { split } from '../../ops/split';\nimport { Tensor } from '../../tensor';\nTensor.prototype.split = function (numOrSizeSplits, axis) {\n    this.throwIfDisposed();\n    return split(this, numOrSizeSplits, axis);\n};\n//# sourceMappingURL=split.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sqrt } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sqrt = function () {\n    this.throwIfDisposed();\n    return sqrt(this);\n};\n//# sourceMappingURL=sqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { square } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.square = function () {\n    this.throwIfDisposed();\n    return square(this);\n};\n//# sourceMappingURL=square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { squaredDifference } from '../../ops/squared_difference';\nimport { Tensor } from '../../tensor';\nTensor.prototype.squaredDifference = function (b) {\n    this.throwIfDisposed();\n    return squaredDifference(this, b);\n};\n//# sourceMappingURL=squared_difference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { squaredDifferenceStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.squaredDifferenceStrict = function (x) {\n    this.throwIfDisposed();\n    return squaredDifferenceStrict(this, x);\n};\n//# sourceMappingURL=squared_difference_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { squeeze } from '../../ops/squeeze';\nimport { Tensor } from '../../tensor';\nTensor.prototype.squeeze = function (axis) {\n    this.throwIfDisposed();\n    return squeeze(this, axis);\n};\n//# sourceMappingURL=squeeze.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { stack } from '../../ops/stack';\nimport { Tensor } from '../../tensor';\nTensor.prototype.stack = function (x, axis) {\n    this.throwIfDisposed();\n    const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];\n    return stack(tensorsToBeStacked, axis);\n};\n//# sourceMappingURL=stack.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { step } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.step = function (alpha) {\n    this.throwIfDisposed();\n    return step(this, alpha);\n};\n//# sourceMappingURL=step.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { stridedSlice } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.stridedSlice = function (begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n    this.throwIfDisposed();\n    return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n};\n//# sourceMappingURL=strided_slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { subStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.subStrict = function (x) {\n    this.throwIfDisposed();\n    return subStrict(this, x);\n};\n//# sourceMappingURL=sub_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { sub } from '../../ops/sub';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sub = function (b) {\n    this.throwIfDisposed();\n    return sub(this, b);\n};\n//# sourceMappingURL=sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { sum } from '../../ops/sum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sum = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return sum(this, axis, keepDims);\n};\n//# sourceMappingURL=sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { tan } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tan = function () {\n    this.throwIfDisposed();\n    return tan(this);\n};\n//# sourceMappingURL=tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { tanh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tanh = function () {\n    this.throwIfDisposed();\n    return tanh(this);\n};\n//# sourceMappingURL=tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tile } from '../../ops/tile';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tile = function (reps) {\n    this.throwIfDisposed();\n    return tile(this, reps);\n};\n//# sourceMappingURL=tile.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `bool`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toBool = function () {\n    this.throwIfDisposed();\n    return cast(this, 'bool');\n};\n//# sourceMappingURL=to_bool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `float32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toFloat = function () {\n    this.throwIfDisposed();\n    return cast(this, 'float32');\n};\n//# sourceMappingURL=to_float.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `int32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toInt = function () {\n    this.throwIfDisposed();\n    return cast(this, 'int32');\n};\n//# sourceMappingURL=to_int.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { topk } from '../../ops/topk';\nimport { Tensor } from '../../tensor';\nTensor.prototype.topk = function (k, sorted) {\n    this.throwIfDisposed();\n    return topk(this, k, sorted);\n};\n//# sourceMappingURL=topk.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { transpose } from '../../ops/transpose';\nimport { Tensor } from '../../tensor';\nTensor.prototype.transpose = function (perm) {\n    this.throwIfDisposed();\n    return transpose(this, perm);\n};\n//# sourceMappingURL=transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unique } from '../../ops/unique';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unique = function (axis) {\n    this.throwIfDisposed();\n    return unique(this, axis);\n};\n//# sourceMappingURL=unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unsortedSegmentSum } from '../../ops/unsorted_segment_sum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unsortedSegmentSum = function (segmentIds, numSegments) {\n    this.throwIfDisposed();\n    return unsortedSegmentSum(this, segmentIds, numSegments);\n};\n//# sourceMappingURL=unsorted_segment_sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unstack } from '../../ops/unstack';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unstack = function (axis) {\n    this.throwIfDisposed();\n    return unstack(this, axis);\n};\n//# sourceMappingURL=unstack.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { where } from '../../ops/where';\nimport { Tensor } from '../../tensor';\nTensor.prototype.where = function (condition, x) {\n    this.throwIfDisposed();\n    return where(condition, this, x);\n};\n//# sourceMappingURL=where.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { zerosLike } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.zerosLike = function () {\n    this.throwIfDisposed();\n    return zerosLike(this);\n};\n//# sourceMappingURL=zeros_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './abs';\nimport './acos';\nimport './acosh';\nimport './add_strict';\nimport './add';\nimport './all';\nimport './any';\nimport './arg_max';\nimport './arg_min';\nimport './as_scalar';\nimport './as_type';\nimport './as1d';\nimport './as2d';\nimport './as3d';\nimport './as4d';\nimport './as5d';\nimport './asin';\nimport './asinh';\nimport './atan';\nimport './atan2';\nimport './atanh';\nimport './avg_pool';\nimport './batch_to_space_nd';\nimport './batchnorm';\nimport './broadcast_to';\nimport './cast';\nimport './ceil';\nimport './clip_by_value';\nimport './concat';\nimport './conv1d';\nimport './conv2d_transpose';\nimport './conv2d';\nimport './cos';\nimport './cosh';\nimport './cumsum';\nimport './depth_to_space';\nimport './depthwise_conv2D_deprecated';\nimport './depthwise_conv2d';\nimport './dilation2d';\nimport './div_no_nan';\nimport './div_strict';\nimport './div';\nimport './dot';\nimport './elu';\nimport './equal_strict';\nimport './equal';\nimport './erf';\nimport './exp';\nimport './expand_dims';\nimport './expm1';\nimport './fft';\nimport './flatten';\nimport './floor';\nimport './floorDiv';\nimport './gather';\nimport './greater_equal_strict';\nimport './greater_equal';\nimport './greater_strict';\nimport './greater';\nimport './ifft';\nimport './irfft';\nimport './is_finite';\nimport './is_inf';\nimport './is_nan';\nimport './leaky_relu';\nimport './less_equal_strict';\nimport './less_equal';\nimport './less_strict';\nimport './less';\nimport './local_response_normalization';\nimport './log_sigmoid';\nimport './log_softmax';\nimport './log_sum_exp';\nimport './log';\nimport './log1p';\nimport './logical_and';\nimport './logical_not';\nimport './logical_or';\nimport './logical_xor';\nimport './mat_mul';\nimport './max_pool';\nimport './max';\nimport './maximum_strict';\nimport './maximum';\nimport './mean';\nimport './min';\nimport './minimum_strict';\nimport './minimum';\nimport './mod_strict';\nimport './mod';\nimport './mul_strict';\nimport './mul';\nimport './neg';\nimport './norm';\nimport './not_equal_strict';\nimport './not_equal';\nimport './one_hot';\nimport './ones_like';\nimport './pad';\nimport './pool';\nimport './pow_strict';\nimport './pow';\nimport './prelu';\nimport './prod';\nimport './reciprocal';\nimport './relu';\nimport './relu6';\nimport './reshape_as';\nimport './reshape';\nimport './resize_bilinear';\nimport './resize_nearest_neighbor';\nimport './reverse';\nimport './rfft';\nimport './round';\nimport './rsqrt';\nimport './selu';\nimport './separable_conv2d';\nimport './sigmoid';\nimport './sign';\nimport './sin';\nimport './sinh';\nimport './slice';\nimport './softmax';\nimport './softplus';\nimport './space_to_batch_nd';\nimport './split';\nimport './sqrt';\nimport './square';\nimport './squared_difference';\nimport './squared_difference_strict';\nimport './squeeze';\nimport './stack';\nimport './step';\nimport './strided_slice';\nimport './sub_strict';\nimport './sub';\nimport './sum';\nimport './tan';\nimport './tanh';\nimport './tile';\nimport './to_bool';\nimport './to_float';\nimport './to_int';\nimport './topk';\nimport './transpose';\nimport './unique';\nimport './unsorted_segment_sum';\nimport './unstack';\nimport './where';\nimport './zeros_like';\n//# sourceMappingURL=register_all_chained_ops.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Required side effectful code.\nimport './base_side_effects';\n// All exports from this package should be in base.\nexport * from './base';\n// Register all the gradients.\nimport './register_all_gradients';\n// Import all op chainers and add type info to Tensor.\nimport './public/chained_ops/register_all_chained_ops';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { backend } from '@tensorflow/tfjs-core';\nlet _epsilon;\n/**\n * Returns the value of the fuzz factor used in numeric expressions.\n */\nexport function epsilon() {\n    if (_epsilon == null) {\n        _epsilon = backend().epsilon();\n    }\n    return _epsilon;\n}\n/**\n * Sets the value of the fuzz factor used in numeric expressions.\n * @param e New value of epsilon.\n */\nexport function setEpsilon(e) {\n    _epsilon = e;\n}\n/**\n * Returns the default image data format convention.\n */\nexport function imageDataFormat() {\n    return 'channelsLast';\n}\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Explicit error types.\n *\n * See the following link for more information about why the code includes\n * calls to setPrototypeOf:\n *\n * https://github.com/Microsoft/TypeScript-wiki/blob/master/Breaking-Changes.md#extending-built-ins-like-error-array-and-map-may-no-longer-work\n */\n// tslint:enable\n/**\n * Equivalent of Python's AttributeError.\n */\nexport class AttributeError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, AttributeError.prototype);\n    }\n}\n/**\n * Equivalent of Python's RuntimeError.\n */\nexport class RuntimeError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, RuntimeError.prototype);\n    }\n}\n/**\n * Equivalent of Python's ValueError.\n */\nexport class ValueError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, ValueError.prototype);\n    }\n}\n/**\n * Equivalent of Python's NotImplementedError.\n */\nexport class NotImplementedError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, NotImplementedError.prototype);\n    }\n}\n/**\n * Equivalent of Python's AssertionError.\n */\nexport class AssertionError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, AssertionError.prototype);\n    }\n}\n/**\n * Equivalent of Python's IndexError.\n */\nexport class IndexError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, IndexError.prototype);\n    }\n}\n//# sourceMappingURL=errors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: utils/generic_utils.py */\nimport { util } from '@tensorflow/tfjs-core';\nimport { AssertionError, ValueError } from '../errors';\n// tslint:enable\n/**\n * If `value` is an Array, equivalent to Python's `value * numValues`.\n * If `value` is not an Array, equivalent to Python's `[value] * numValues`\n */\n// tslint:disable-next-line:no-any\nexport function pyListRepeat(value, numValues) {\n    if (Array.isArray(value)) {\n        // tslint:disable-next-line:no-any\n        let newArray = [];\n        for (let i = 0; i < numValues; i++) {\n            newArray = newArray.concat(value);\n        }\n        return newArray;\n    }\n    else {\n        const newArray = new Array(numValues);\n        newArray.fill(value);\n        return newArray;\n    }\n}\nexport function assert(val, message) {\n    if (!val) {\n        throw new AssertionError(message);\n    }\n}\n/**\n * Count the number of elements of the `array` that are equal to `reference`.\n */\nexport function count(array, refernce) {\n    let counter = 0;\n    for (const item of array) {\n        if (item === refernce) {\n            counter++;\n        }\n    }\n    return counter;\n}\n/**\n * If an array is of length 1, just return the first element. Otherwise, return\n * the full array.\n * @param tensors\n */\nexport function singletonOrArray(xs) {\n    if (xs.length === 1) {\n        return xs[0];\n    }\n    return xs;\n}\n/**\n * Normalizes a list/tensor into a list.\n *\n * If a tensor is passed, we return\n * a list of size 1 containing the tensor.\n *\n * @param x target object to be normalized.\n */\n// tslint:disable-next-line:no-any\nexport function toList(x) {\n    if (Array.isArray(x)) {\n        return x;\n    }\n    return [x];\n}\n/**\n * Generate a UID for a list\n */\n// tslint:disable-next-line:no-any\nexport function objectListUid(objs) {\n    const objectList = toList(objs);\n    let retVal = '';\n    for (const obj of objectList) {\n        if (obj.id == null) {\n            throw new ValueError(`Object ${obj} passed to objectListUid without an id`);\n        }\n        if (retVal !== '') {\n            retVal = retVal + ', ';\n        }\n        retVal = `${retVal}${Math.abs(obj.id)}`;\n    }\n    return retVal;\n}\n/**\n * Converts string to snake-case.\n * @param name\n */\nexport function toSnakeCase(name) {\n    const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, '$1_$2');\n    const insecure = intermediate.replace(/([a-z])([A-Z])/g, '$1_$2').toLowerCase();\n    /*\n     If the class is private the name starts with \"_\" which is not secure\n     for creating scopes. We prefix the name with \"private\" in this case.\n     */\n    if (insecure[0] !== '_') {\n        return insecure;\n    }\n    return 'private' + insecure;\n}\nexport function toCamelCase(identifier) {\n    // quick return for empty string or single character strings\n    if (identifier.length <= 1) {\n        return identifier;\n    }\n    // Check for the underscore indicating snake_case\n    if (identifier.indexOf('_') === -1) {\n        return identifier;\n    }\n    return identifier.replace(/[_]+(\\w|$)/g, (m, p1) => p1.toUpperCase());\n}\n// tslint:disable-next-line:no-any\nlet _GLOBAL_CUSTOM_OBJECTS = {};\nexport function serializeKerasObject(instance) {\n    if (instance === null || instance === undefined) {\n        return null;\n    }\n    const dict = {};\n    dict['className'] = instance.getClassName();\n    dict['config'] = instance.getConfig();\n    return dict;\n}\n/**\n * Replace ndarray-style scalar objects in serialization objects with numbers.\n *\n * Background: In some versions of tf.keras, certain scalar values in the HDF5\n * model save file can be serialized as: `{'type': 'ndarray', 'value': num}`,\n * where in `num` is a plain number. This method converts such serialization\n * to a `number`.\n *\n * @param config The keras-format serialization object to be processed\n *   (in place).\n */\nfunction convertNDArrayScalarsInConfig(config) {\n    if (config == null || typeof config !== 'object') {\n        return;\n    }\n    else if (Array.isArray(config)) {\n        config.forEach(configItem => convertNDArrayScalarsInConfig(configItem));\n    }\n    else {\n        const fields = Object.keys(config);\n        for (const field of fields) {\n            const value = config[field];\n            if (value != null && typeof value === 'object') {\n                if (!Array.isArray(value) && value['type'] === 'ndarray' &&\n                    typeof value['value'] === 'number') {\n                    config[field] = value['value'];\n                }\n                else {\n                    convertNDArrayScalarsInConfig(value);\n                }\n            }\n        }\n    }\n}\n/**\n * Deserialize a saved Keras Object\n * @param identifier either a string ID or a saved Keras dictionary\n * @param moduleObjects a list of Python class names to object constructors\n * @param customObjects a list of Python class names to object constructors\n * @param printableModuleName debug text for the object being reconstituted\n * @param fastWeightInit Optional flag to use fast weight initialization\n *   during deserialization. This is applicable to cases in which\n *   the initialization will be immediately overwritten by loaded weight\n *   values. Default: `false`.\n * @returns a TensorFlow.js Layers object\n */\n// tslint:disable:no-any\nexport function deserializeKerasObject(identifier, moduleObjects = {}, customObjects = {}, printableModuleName = 'object', fastWeightInit = false) {\n    // tslint:enable\n    if (typeof identifier === 'string') {\n        const functionName = identifier;\n        let fn;\n        if (functionName in customObjects) {\n            fn = customObjects[functionName];\n        }\n        else if (functionName in _GLOBAL_CUSTOM_OBJECTS) {\n            fn = _GLOBAL_CUSTOM_OBJECTS[functionName];\n        }\n        else {\n            fn = moduleObjects[functionName];\n            if (fn == null) {\n                throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. ` +\n                    `This may be due to one of the following reasons:\\n` +\n                    `1. The ${printableModuleName} is defined in Python, in which ` +\n                    `case it needs to be ported to TensorFlow.js or your JavaScript ` +\n                    `code.\\n` +\n                    `2. The custom ${printableModuleName} is defined in JavaScript, ` +\n                    `but is not registered properly with ` +\n                    `tf.serialization.registerClass().`);\n                // TODO(cais): Add link to tutorial page on custom layers.\n            }\n        }\n        return fn;\n    }\n    else {\n        // In this case we are dealing with a Keras config dictionary.\n        const config = identifier;\n        if (config['className'] == null || config['config'] == null) {\n            throw new ValueError(`${printableModuleName}: Improper config format: ` +\n                `${JSON.stringify(config)}.\\n` +\n                `'className' and 'config' must set.`);\n        }\n        const className = config['className'];\n        let cls, fromConfig;\n        if (className in customObjects) {\n            [cls, fromConfig] = customObjects[className];\n        }\n        else if (className in _GLOBAL_CUSTOM_OBJECTS) {\n            [cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS['className'];\n        }\n        else if (className in moduleObjects) {\n            [cls, fromConfig] = moduleObjects[className];\n        }\n        if (cls == null) {\n            throw new ValueError(`Unknown ${printableModuleName}: ${className}. ` +\n                `This may be due to one of the following reasons:\\n` +\n                `1. The ${printableModuleName} is defined in Python, in which ` +\n                `case it needs to be ported to TensorFlow.js or your JavaScript ` +\n                `code.\\n` +\n                `2. The custom ${printableModuleName} is defined in JavaScript, ` +\n                `but is not registered properly with ` +\n                `tf.serialization.registerClass().`);\n            // TODO(cais): Add link to tutorial page on custom layers.\n        }\n        if (fromConfig != null) {\n            // Porting notes: Instead of checking to see whether fromConfig accepts\n            // customObjects, we create a customObjects dictionary and tack it on to\n            // config['config'] as config['config'].customObjects. Objects can use it,\n            // if they want.\n            // tslint:disable-next-line:no-any\n            const customObjectsCombined = {};\n            for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) {\n                customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key];\n            }\n            for (const key of Object.keys(customObjects)) {\n                customObjectsCombined[key] = customObjects[key];\n            }\n            // Add the customObjects to config\n            const nestedConfig = config['config'];\n            nestedConfig['customObjects'] = customObjectsCombined;\n            const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n            for (const key of Object.keys(customObjects)) {\n                _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n            }\n            convertNDArrayScalarsInConfig(config['config']);\n            const returnObj = fromConfig(cls, config['config'], customObjects, fastWeightInit);\n            _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n            return returnObj;\n        }\n        else {\n            // Then `cls` may be a function returning a class.\n            // In this case by convention `config` holds\n            // the kwargs of the function.\n            const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n            for (const key of Object.keys(customObjects)) {\n                _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n            }\n            // In python this is **config['config'], for tfjs-layers we require\n            // classes that use this fall-through construction method to take\n            // a config interface that mimics the expansion of named parameters.\n            const returnObj = new cls(config['config']);\n            _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n            return returnObj;\n        }\n    }\n}\n/**\n * Compares two numbers for sorting.\n * @param a\n * @param b\n */\nexport function numberCompare(a, b) {\n    return (a < b) ? -1 : ((a > b) ? 1 : 0);\n}\n/**\n * Comparison of two numbers for reverse sorting.\n * @param a\n * @param b\n */\nexport function reverseNumberCompare(a, b) {\n    return -1 * numberCompare(a, b);\n}\n/**\n * Convert a string into the corresponding DType.\n * @param dtype\n * @returns An instance of DType.\n */\nexport function stringToDType(dtype) {\n    switch (dtype) {\n        case 'float32':\n            return 'float32';\n        default:\n            throw new ValueError(`Invalid dtype: ${dtype}`);\n    }\n}\n/**\n * Test the element-by-element equality of two Arrays of strings.\n * @param xs First array of strings.\n * @param ys Second array of strings.\n * @returns Wether the two arrays are all equal, element by element.\n */\nexport function stringsEqual(xs, ys) {\n    if (xs == null || ys == null) {\n        return xs === ys;\n    }\n    if (xs.length !== ys.length) {\n        return false;\n    }\n    for (let i = 0; i < xs.length; ++i) {\n        if (xs[i] !== ys[i]) {\n            return false;\n        }\n    }\n    return true;\n}\n/**\n * Get the unique elements of an array.\n * @param xs Array.\n * @returns An Array consisting of the unique elements in `xs`.\n */\nexport function unique(xs) {\n    if (xs == null) {\n        return xs;\n    }\n    const out = [];\n    // TODO(cais): Maybe improve performance by sorting.\n    for (const x of xs) {\n        if (out.indexOf(x) === -1) {\n            out.push(x);\n        }\n    }\n    return out;\n}\n/**\n * Determine if an Object is empty (i.e., does not have own properties).\n * @param obj Object\n * @returns Whether the Object is empty.\n * @throws ValueError: If object is `null` or `undefined`.\n */\nexport function isObjectEmpty(obj) {\n    if (obj == null) {\n        throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);\n    }\n    for (const key in obj) {\n        if (obj.hasOwnProperty(key)) {\n            return false;\n        }\n    }\n    return true;\n}\n/**\n * Helper function used to build type union/enum run-time checkers.\n * @param values The list of allowed values.\n * @param label A string name for the type\n * @param value The value to test.\n * @throws ValueError: If the value is not in values nor `undefined`/`null`.\n */\nexport function checkStringTypeUnionValue(values, label, value) {\n    if (value == null) {\n        return;\n    }\n    if (values.indexOf(value) < 0) {\n        throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`);\n    }\n}\n/**\n * Helper function for verifying the types of inputs.\n *\n * Ensures that the elements of `x` are all of type `expectedType`.\n * Also verifies that the length of `x` is within bounds.\n *\n * @param x Object to test.\n * @param expectedType The string expected type of all of the elements in the\n * Array.\n * @param minLength Return false if x.length is less than this.\n * @param maxLength Return false if x.length is greater than this.\n * @returns true if and only if `x` is an `Array<expectedType>` with\n * length >= `minLength` and <= `maxLength`.\n */\n// tslint:disable:no-any\nexport function checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) {\n    assert(minLength >= 0);\n    assert(maxLength >= minLength);\n    return (Array.isArray(x) && x.length >= minLength && x.length <= maxLength &&\n        x.every(e => typeof e === expectedType));\n}\n// tslint:enable:no-any\n/**\n * Assert that a value or an array of value are positive integer.\n *\n * @param value The value being asserted on. May be a single number or an array\n *   of numbers.\n * @param name Name of the value, used to make the error message.\n */\nexport function assertPositiveInteger(value, name) {\n    if (Array.isArray(value)) {\n        util.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`);\n        value.forEach((v, i) => assertPositiveInteger(v, `element ${i + 1} of ${name}`));\n    }\n    else {\n        util.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ` +\n            `${formatAsFriendlyString(value)}.`);\n    }\n}\n/**\n * Format a value into a display-friendly, human-readable fashion.\n *\n * - `null` is formatted as `'null'`\n * - Strings are formated with flanking pair of quotes.\n * - Arrays are formatted with flanking pair of square brackets.\n *\n * @param value The value to display.\n * @return Formatted string.\n */\n// tslint:disable-next-line:no-any\nexport function formatAsFriendlyString(value) {\n    if (value === null) {\n        return 'null';\n    }\n    else if (Array.isArray(value)) {\n        return '[' + value.map(v => formatAsFriendlyString(v)).join(',') + ']';\n    }\n    else if (typeof value === 'string') {\n        return `\"${value}\"`;\n    }\n    else {\n        return `${value}`;\n    }\n}\n/**\n * Returns a function `f2` (decorator) which wraps the original function\n * `f`. `f2` guarantees that `f` can be called at most once\n * every `waitMs` ms. If `f2` is called more often, it will return\n * the last returned result of `f`.\n *\n * @param f The original function `f` to wrap.\n * @param waitMs The time between two consecutive calls to `f` in ms.\n */\nexport function debounce(f, waitMs) {\n    let lastTime = util.now();\n    let lastResult;\n    const f2 = (...args) => {\n        const now = util.now();\n        if (now - lastTime < waitMs) {\n            return lastResult;\n        }\n        lastTime = now;\n        lastResult = f(...args);\n        return lastResult;\n    };\n    return f2;\n}\n/**\n * Returns the fusable activation given a layers identifier.\n *\n * @param activationName The layers identifier string.\n * @return The name of the fusable activation.\n */\nexport function mapActivationToFusedKernel(activationName) {\n    if (activationName === 'relu') {\n        return 'relu';\n    }\n    if (activationName === 'linear') {\n        return 'linear';\n    }\n    if (activationName === 'elu') {\n        return 'elu';\n    }\n    return null;\n}\n/**\n * Returns the cartesian product of sets of values.\n * This works the same as itertools.product in Python.\n *\n * Example:\n *\n * filters = [128, 256, 512]\n * paddings = ['same', 'valid']\n *\n * product = [ [128, 'same'], [128, 'valid'], [256, 'same'], [256, 'valid'],\n * [512, 'same'], [512, 'valid']]\n *\n * @param arrayOfValues List/array of values.\n * @return The cartesian product.\n */\nexport function getCartesianProductOfValues(...arrayOfValues) {\n    assert(arrayOfValues.length > 0, 'arrayOfValues is empty');\n    for (const values of arrayOfValues) {\n        assert(Array.isArray(values), 'one of the values is not an array');\n        assert(values.length > 0, 'one of the values is empty');\n    }\n    return arrayOfValues.reduce((products, values) => {\n        if (products.length === 0) {\n            return values.map(value => [value]);\n        }\n        return values\n            .map(value => {\n            return products.map((prevValue) => [...prevValue, value]);\n        })\n            .reduce((flattenedProduct, unflattenedProduct) => {\n            return flattenedProduct.concat(unflattenedProduct);\n        }, []);\n    }, []);\n}\n//# sourceMappingURL=generic_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/contraints.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport { deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\n/**\n * Helper function used by many of the Constraints to find the L2Norms.\n */\nfunction calcL2Norms(w, axis) {\n    return tidy(() => tfc.sqrt(tfc.sum(tfc.mul(w, w), axis, true)));\n}\n/**\n * Base class for functions that impose constraints on weight values\n *\n * @doc {\n *   heading: 'Constraints',\n *   subheading: 'Classes',\n *   namespace: 'constraints'\n * }\n */\nexport class Constraint extends serialization.Serializable {\n    getConfig() {\n        return {};\n    }\n}\nexport class MaxNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultMaxValue = 2;\n        this.defaultAxis = 0;\n        this.maxValue =\n            args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => {\n            const norms = calcL2Norms(w, this.axis);\n            const desired = tfc.clipByValue(norms, 0, this.maxValue);\n            return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));\n        });\n    }\n    getConfig() {\n        return { maxValue: this.maxValue, axis: this.axis };\n    }\n}\n/** @nocollapse */\nMaxNorm.className = 'MaxNorm';\nserialization.registerClass(MaxNorm);\nexport class UnitNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultAxis = 0;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => tfc.div(w, tfc.add(epsilon(), calcL2Norms(w, this.axis))));\n    }\n    getConfig() {\n        return { axis: this.axis };\n    }\n}\n/** @nocollapse */\nUnitNorm.className = 'UnitNorm';\nserialization.registerClass(UnitNorm);\nexport class NonNeg extends Constraint {\n    apply(w) {\n        return tfc.relu(w);\n    }\n}\n/** @nocollapse */\nNonNeg.className = 'NonNeg';\nserialization.registerClass(NonNeg);\nexport class MinMaxNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultMinValue = 0.0;\n        this.defaultMaxValue = 1.0;\n        this.defaultRate = 1.0;\n        this.defaultAxis = 0;\n        this.minValue =\n            args.minValue != null ? args.minValue : this.defaultMinValue;\n        this.maxValue =\n            args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n        this.rate = args.rate != null ? args.rate : this.defaultRate;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => {\n            const norms = calcL2Norms(w, this.axis);\n            const desired = tfc.add(tfc.mul(this.rate, tfc.clipByValue(norms, this.minValue, this.maxValue)), tfc.mul(1.0 - this.rate, norms));\n            return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));\n        });\n    }\n    getConfig() {\n        return {\n            minValue: this.minValue,\n            maxValue: this.maxValue,\n            rate: this.rate,\n            axis: this.axis\n        };\n    }\n}\n/** @nocollapse */\nMinMaxNorm.className = 'MinMaxNorm';\nserialization.registerClass(MinMaxNorm);\n// Maps the JavaScript-like identifier keys to the corresponding registry\n// symbols.\nexport const CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'maxNorm': 'MaxNorm',\n    'minMaxNorm': 'MinMaxNorm',\n    'nonNeg': 'NonNeg',\n    'unitNorm': 'UnitNorm'\n};\nexport function serializeConstraint(constraint) {\n    return serializeKerasObject(constraint);\n}\nexport function deserializeConstraint(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'constraint');\n}\nexport function getConstraint(identifier) {\n    if (identifier == null) {\n        return null;\n    }\n    if (typeof identifier === 'string') {\n        const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        const config = { className, config: {} };\n        return deserializeConstraint(config);\n    }\n    else if (identifier instanceof Constraint) {\n        return identifier;\n    }\n    else {\n        return deserializeConstraint(identifier);\n    }\n}\n//# sourceMappingURL=constraints.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// tslint:disable-next-line:max-line-length\nimport { MaxNorm, MinMaxNorm, NonNeg, UnitNorm } from './constraints';\n/**\n * MaxNorm weight constraint.\n *\n * Constrains the weights incident to each hidden unit\n * to have a norm less than or equal to a desired value.\n *\n * References\n *       - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting\n * Srivastava, Hinton, et al.\n * 2014](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)\n *\n * @doc {heading: 'Constraints',namespace: 'constraints'}\n */\nexport function maxNorm(args) {\n    return new MaxNorm(args);\n}\n/**\n * Constrains the weights incident to each hidden unit to have unit norm.\n *\n * @doc {heading: 'Constraints', namespace: 'constraints'}\n */\nexport function unitNorm(args) {\n    return new UnitNorm(args);\n}\n/**\n * Constains the weight to be non-negative.\n *\n * @doc {heading: 'Constraints', namespace: 'constraints'}\n */\nexport function nonNeg() {\n    return new NonNeg();\n}\n/** @doc {heading: 'Constraints', namespace: 'constraints'} */\nexport function minMaxNorm(config) {\n    return new MinMaxNorm(config);\n}\n//# sourceMappingURL=exports_constraints.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport const VALID_DATA_FORMAT_VALUES = ['channelsFirst', 'channelsLast'];\nexport const VALID_PADDING_MODE_VALUES = ['valid', 'same', 'causal'];\nexport const VALID_POOL_MODE_VALUES = ['max', 'avg'];\nexport const VALID_BIDIRECTIONAL_MERGE_MODES = ['sum', 'mul', 'concat', 'ave'];\nexport const VALID_SAMPLE_WEIGHT_MODES = ['temporal'];\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Common functions for TensorFlow.js Layers.\n */\nimport { VALID_DATA_FORMAT_VALUES, VALID_PADDING_MODE_VALUES, VALID_POOL_MODE_VALUES } from './keras_format/common';\nimport { checkStringTypeUnionValue } from './utils/generic_utils';\n// A map from the requested scoped name of a Tensor to the number of Tensors\n// wanting that name so far.  This allows enforcing name uniqueness by appending\n// an incrementing index, e.g. scope/name, scope/name_1, scope/name_2, etc.\nconst nameMap = new Map();\nexport function checkDataFormat(value) {\n    checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, 'DataFormat', value);\n}\nexport function checkPaddingMode(value) {\n    checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, 'PaddingMode', value);\n}\nexport function checkPoolMode(value) {\n    checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, 'PoolMode', value);\n}\nconst _nameScopeStack = [];\nconst _nameScopeDivider = '/';\n/**\n * Enter namescope, which can be nested.\n */\nexport function nameScope(name, fn) {\n    _nameScopeStack.push(name);\n    try {\n        const val = fn();\n        _nameScopeStack.pop();\n        return val;\n    }\n    catch (e) {\n        _nameScopeStack.pop();\n        throw e;\n    }\n}\n/**\n * Get the current namescope as a flat, concatenated string.\n */\nfunction currentNameScopePrefix() {\n    if (_nameScopeStack.length === 0) {\n        return '';\n    }\n    else {\n        return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider;\n    }\n}\n/**\n * Get the name a Tensor (or Variable) would have if not uniqueified.\n * @param tensorName\n * @return Scoped name string.\n */\nexport function getScopedTensorName(tensorName) {\n    if (!isValidTensorName(tensorName)) {\n        throw new Error('Not a valid tensor name: \\'' + tensorName + '\\'');\n    }\n    return currentNameScopePrefix() + tensorName;\n}\n/**\n * Get unique names for Tensors and Variables.\n * @param scopedName The fully-qualified name of the Tensor, i.e. as produced by\n *  `getScopedTensorName()`.\n * @return A unique version of the given fully scoped name.\n *   If this is the first time that the scoped name is seen in this session,\n *   then the given `scopedName` is returned unaltered.  If the same name is\n *   seen again (producing a collision), an incrementing suffix is added to the\n *   end of the name, so it takes the form 'scope/name_1', 'scope/name_2', etc.\n */\nexport function getUniqueTensorName(scopedName) {\n    if (!isValidTensorName(scopedName)) {\n        throw new Error('Not a valid tensor name: \\'' + scopedName + '\\'');\n    }\n    if (!nameMap.has(scopedName)) {\n        nameMap.set(scopedName, 0);\n    }\n    const index = nameMap.get(scopedName);\n    nameMap.set(scopedName, nameMap.get(scopedName) + 1);\n    if (index > 0) {\n        const result = `${scopedName}_${index}`;\n        // Mark the composed name as used in case someone wants\n        // to call getUniqueTensorName(\"name_1\").\n        nameMap.set(result, 1);\n        return result;\n    }\n    else {\n        return scopedName;\n    }\n}\nconst tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\\._\\/]*$/);\n/**\n * Determine whether a string is a valid tensor name.\n * @param name\n * @returns A Boolean indicating whether `name` is a valid tensor name.\n */\nexport function isValidTensorName(name) {\n    return !!name.match(tensorNameRegex);\n}\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Math utility functions.\n *\n * This file contains some frequently used math function that operates on\n * number[] or Float32Array and return a number. Many of these functions are\n * not-so-thick wrappers around TF.js Core functions. But they offer the\n * convenience of\n * 1) not having to convert the inputs into Tensors,\n * 2) not having to convert the returned Tensors to numbers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { scalar, tensor1d } from '@tensorflow/tfjs-core';\nimport { ValueError } from '../errors';\n/**\n * Determine if a number is an integer.\n */\nexport function isInteger(x) {\n    return x === parseInt(x.toString(), 10);\n}\n/**\n * Calculate the product of an array of numbers.\n * @param array The array to calculate the product over.\n * @param begin Beginning index, inclusive.\n * @param end Ending index, exclusive.\n * @return The product.\n */\nexport function arrayProd(array, begin, end) {\n    if (begin == null) {\n        begin = 0;\n    }\n    if (end == null) {\n        end = array.length;\n    }\n    let prod = 1;\n    for (let i = begin; i < end; ++i) {\n        prod *= array[i];\n    }\n    return prod;\n}\n/**\n * A helper function transforms the two input types to an instance of Tensor1D,\n * so the return value can be fed directly into various TF.js Core functions.\n * @param array\n */\nfunction toArray1D(array) {\n    array = Array.isArray(array) ? new Float32Array(array) : array;\n    return tensor1d(array);\n}\n/**\n * Compute minimum value.\n * @param array\n * @return minimum value.\n */\nexport function min(array) {\n    return tfc.min(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute maximum value.\n * @param array\n * @return maximum value\n */\nexport function max(array) {\n    return tfc.max(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute sum of array.\n * @param array\n * @return The sum.\n */\nexport function sum(array) {\n    return tfc.sum(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute mean of array.\n * @param array\n * @return The mean.\n */\nexport function mean(array) {\n    return sum(array) / array.length;\n}\n/**\n * Compute variance of array.\n * @param array\n * @return The variance.\n */\nexport function variance(array) {\n    const demeaned = tfc.sub(toArray1D(array), scalar(mean(array)));\n    const sumSquare = tfc.sum(tfc.mul(demeaned, demeaned)).dataSync()[0];\n    return sumSquare / array.length;\n}\n/**\n * Compute median of array.\n * @param array\n * @return The median value.\n */\nexport function median(array) {\n    const arraySorted = array.slice().sort((a, b) => a - b);\n    const lowIdx = Math.floor((arraySorted.length - 1) / 2);\n    const highIdx = Math.ceil((arraySorted.length - 1) / 2);\n    if (lowIdx === highIdx) {\n        return arraySorted[lowIdx];\n    }\n    return (arraySorted[lowIdx] + arraySorted[highIdx]) / 2;\n}\n/**\n * Generate an array of integers in [begin, end).\n * @param begin Beginning integer, inclusive.\n * @param end Ending integer, exclusive.\n * @returns Range array.\n * @throws ValueError, iff `end` < `begin`.\n */\nexport function range(begin, end) {\n    if (end < begin) {\n        throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);\n    }\n    const out = [];\n    for (let i = begin; i < end; ++i) {\n        out.push(i);\n    }\n    return out;\n}\n//# sourceMappingURL=math_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * deeplearn.js backend.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { onesLike as coreOnesLike, scalar, tensor1d, tidy, where, zerosLike as coreZerosLike } from '@tensorflow/tfjs-core';\nimport { checkDataFormat } from '../common';\nimport { NotImplementedError, ValueError } from '../errors';\nimport * as math_utils from '../utils/math_utils';\nimport { imageDataFormat } from './common';\n// tslint:enable\n/* Setting and getting backend from deeplearn.js. */\n// Default deeplearn.js backend is WebGL (GPU).\nlet backend = 'webgl';\nexport function setBackend(requestedBackend) {\n    tfc.setBackend(requestedBackend);\n    backend = requestedBackend;\n}\nexport function getBackend() {\n    return backend;\n}\n/**\n * Indicates whether the backend is operating symbolically.\n *\n * This function will be used to determine how to interpret user code. If\n * it returns true, calls to the backend construct a symbolic graph; if\n * it returns false, calls to the backend execute immediately.\n */\nexport function isBackendSymbolic() {\n    return false;\n}\n/**\n * Get the number of elements in a Tensor.\n * @param x The Tensor.\n * @return Number of elements in `x`.\n */\nexport function countParams(x) {\n    const shape = x.shape;\n    if (shape.length > 0) {\n        return shape.reduce((a, b) => a * b);\n    }\n    else {\n        // Scalar.\n        return 1;\n    }\n}\n/**\n * Casts a tensor to a different dtype and returns it.\n * @param x Input tensor.\n * @param dtype String: 'float32'|'int32'|'bool'.\n * @returns Tensor of the specified `dtype`.\n */\nexport function cast(x, dtype) {\n    return x.asType(dtype);\n}\n/**\n * Adds a 1-sized dimension at index \"axis\".\n * @param x Input tensor.\n * @param axis Position where to add the new axis.\n * @returns Result of the dimension expansion.\n */\nexport function expandDims(x, axis = -1) {\n    const outShape = x.shape.slice();\n    if (axis < 0) {\n        axis = outShape.length + axis + 1;\n    }\n    outShape.splice(axis, 0, 1);\n    return x.reshape(outShape);\n}\n/**\n * Repeats a 2D tensor.\n *\n * If `x` has shape `[samples, dim]` and `n` is 2, for example, the output\n * will have shape `[samples, 2, dim]`.\n *\n * @param x Input tensor.\n * @param n Integer, number of times to repeat.\n * @returns The result of the repeat operation.\n * @throws ValueError: If input tensor is not 2D.\n */\nexport function repeat(x, n) {\n    return tidy(() => {\n        if (x.shape.length !== 2) {\n            throw new ValueError(`repeat() expects a rank-2 tensor, but received a ` +\n                `rank-${x.shape.length} tensor.`);\n        }\n        const y = expandDims(x, 1);\n        return tile(y, [1, n, 1]);\n    });\n}\n/**\n * Flatten a Tensor into 1D.\n * @param x Input tensor.\n * @return The result of the flattening `x`.\n */\nexport function flatten(x) {\n    const newShape = [math_utils.arrayProd(x.shape)];\n    return x.reshape(newShape);\n}\n/**\n * Turn a nD tensor into a 2D tensor with same 0th dimension.\n * In other words, it flattens each data samples of a batch.\n *\n * @param x The tensor to flatten. The rank of this tensor is required to be 2\n *   or higher.\n * @return The result of the flattening.\n */\nexport function batchFlatten(x) {\n    if (x.rank <= 1) {\n        throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);\n    }\n    const newShape = [x.shape[0], math_utils.arrayProd(x.shape, 1)];\n    return x.reshape(newShape);\n}\n/**\n * Do slicing along the first axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size size of the slice along the first axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongFirstAxis(array, start, size) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                return tfc.slice2d(array, [start, 0], [size, array.shape[1]]);\n            case 3:\n                return tfc.slice3d(array, [start, 0, 0], [size, array.shape[1], array.shape[2]]);\n            case 4:\n                return tfc.slice4d(array, [start, 0, 0, 0], [size, array.shape[1], array.shape[2], array.shape[3]]);\n            case 5:\n                return tfc.slice(array, [start, 0, 0, 0, 0], [\n                    size, array.shape[1], array.shape[2], array.shape[3], array.shape[4]\n                ]);\n            case 6:\n                return tfc.slice(array, [start, 0, 0, 0, 0, 0], [\n                    size, array.shape[1], array.shape[2], array.shape[3], array.shape[4],\n                    array.shape[5]\n                ]);\n            default:\n                throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Do slicing along the last axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size size of the slice along the last axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongLastAxis(array, start, size) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                return tfc.slice2d(array, [0, start], [array.shape[0], size]);\n            case 3:\n                return tfc.slice3d(array, [0, 0, start], [array.shape[0], array.shape[1], size]);\n            case 4:\n                return tfc.slice4d(array, [0, 0, 0, start], [array.shape[0], array.shape[1], array.shape[2], size]);\n            default:\n                throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Do slicing along the sepcified axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size of the slice along the chosen axis.\n * @param choose an axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongAxis(array, start, size, axis) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            case 3:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return tfc.slice3d(array, [0, start, 0], [array.shape[0], size, array.shape[2]]);\n                    case 3:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            case 4:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return tfc.slice4d(array, [0, start, 0, 0], [array.shape[0], size, array.shape[2], array.shape[3]]);\n                    case 3:\n                        return tfc.slice4d(array, [0, 0, start, 0], [array.shape[0], array.shape[1], size, array.shape[3]]);\n                    case 4:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            default:\n                throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Concatenates a list of tensors alongside the specified axis.\n * @param tensors `Array` of tensors to concatenate.\n * @param axis Concatenation axis.\n * @returns The result of the concatenation.\n */\nexport function concatenate(tensors, axis = -1) {\n    let rank;\n    if (axis < 0) {\n        rank = tensors[0].rank;\n        if (rank !== 0) {\n            axis = rank;\n        }\n        else {\n            axis = 0;\n        }\n    }\n    if (axis === tensors[0].rank) {\n        // Porting Note: This is necessary because tfc.concat() requires axis to be\n        //   in the interval [-rank, rank).\n        axis = -1;\n    }\n    // Porting Note: Sparse concat is not supported yet.\n    return tfc.concat(tensors, axis);\n}\n/**\n * Concatenate two arrays along the first dimension.\n * @param a The 1st `tf.Tensor` to concatenate.\n * @param b The 2nd `tf.Tensor` to concatenate.\n * @returns Result of the concatenation.\n * @throws ValueError: If `a` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function concatAlongFirstAxis(a, b) {\n    switch (a.rank) {\n        case 1:\n            return tfc.concat1d([a, b]);\n        case 2:\n            return tfc.concat2d([a, b], 0);\n        case 3:\n            return tfc.concat3d([a, b], 0);\n        case 4:\n            return tfc.concat4d([a, b], 0);\n        default:\n            throw new ValueError(`concatAlongFirstAxis() received an unsupported ` +\n                `tensor rank: ${a.rank}`);\n    }\n}\n/**\n * Creates a tensor by tiling `x` by `n`.\n * @param x A tensor.\n * @param n An Array of integers or a single integer. If an Array, the length\n *   must be the same as the number of dimensions in `x`. If a single integer,\n *   it will be treated as an Array of length 1.\n */\nexport function tile(x, n) {\n    if (!Array.isArray(n)) {\n        n = [n];\n    }\n    if (x.rank !== n.length) {\n        throw new ValueError(`The length of input n (${n.length}) does not match ` +\n            `the number of dimensions in input x (${x.rank})`);\n    }\n    return tfc.tile(x, n);\n}\n/* Creation of random tensors. */\n/**\n * Get a tensor with normal distribution of values.\n *\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @return The normal tensor.\n */\nexport function randomNormal(shape, mean = 0.0, stddev = 1.0, dtype, seed) {\n    return tfc.randomNormal(shape, mean, stddev, dtype, seed);\n}\n/* Linear Algebra */\n/**\n * Multiply two tensors and returns the result as a tensor.\n *\n * For 2D tensors, this is equivalent to matrix multiplication (matMul).\n * For tensors of higher ranks, it follows the Theano behavior,\n * (e.g. `(2, 3) * (4, 3, 5) -> (2, 4, 5)`).  From the Theano documentation:\n *\n * For N dimensions it is a sum product over the last axis of x and the\n * second-to-last of y:\n *\n * @param a A tensor of at least rank 2.\n * @param b A tensor of at least rank 2.\n * @param activation (optional) A string identifying the activation\n *   function.\n * @return Result of the dot operation.\n */\nexport function dot(a, b, activation, bias) {\n    if ((a.rank < 2) || (b.rank < 2)) {\n        throw new NotImplementedError(`dot requires both inputs to be rank >= 2` +\n            ` but got x shape = ${a.shape} and y shape = ${b.shape}`);\n    }\n    if (b.rank >= 3) {\n        const xLastDim = a.shape.slice(-1)[0];\n        const ySecondLastDim = b.shape.slice(-2)[0];\n        if (xLastDim !== ySecondLastDim) {\n            throw new NotImplementedError(`If rank y >= 3, then the second last dim` +\n                ` of y must equal the last dim of x but got x shape = ${a.shape} and ` +\n                ` y shape = ${b.shape}`);\n        }\n    }\n    // Handle basic 2D x 2D case.\n    if ((a.rank === 2) && (b.rank === 2)) {\n        const transposeA = false;\n        const transposeB = false;\n        // tfc.fused.matMul only fuses certain activation functions. Unsupported\n        // activation functions are treated as 'linear' activations, which is\n        // equivalent to a no-op.\n        return tfc.fused.matMul({\n            a,\n            b: b,\n            transposeA,\n            transposeB,\n            bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n            activation\n        });\n    }\n    else {\n        // Reshape x into the analogous 2D Tensor.\n        const aFirstDims = a.shape.slice(); // Holds all but the last dim of x.\n        const aLastDim = aFirstDims.pop();\n        a = a.reshape([-1, aLastDim]);\n        // Reshape y into the analogous 2D Tensor, and keep track of the\n        // required dimensions to reproduce the output shape.\n        const bShape = b.shape.slice();\n        const bLastDim = bShape.pop();\n        const ySecondLastDim = bShape.pop();\n        const yOtherDims = [...bShape, bLastDim];\n        // permutation should be like [r-2, 0, 1, 2, ... r-4, r-3, r-1]\n        // where r is the rank of y.\n        const perm = Array.from({ length: b.rank }, (_, i) => {\n            if (i === 0) {\n                return b.rank - 2;\n            }\n            else if (i <= b.rank - 2) {\n                return i - 1;\n            }\n            return i;\n        });\n        b = b.transpose(perm).reshape([ySecondLastDim, -1]);\n        // Multiply x and y as 2D Tensors, and then reshape back to original.\n        const outputShape = [...aFirstDims, ...yOtherDims];\n        const transposeA = false;\n        const transposeB = false;\n        return tfc.fused\n            .matMul({\n            a,\n            b,\n            transposeA,\n            transposeB,\n            bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n            activation\n        })\n            .reshape(outputShape);\n    }\n}\n/**\n * Compute the sign Tensor of an input Tensor.\n *\n * Elements of the input `tf.Tensor` that are === 0 are mapped to 0.\n * Elements of the input `tf.Tensor` that are > 0 are mapped to 1.\n * Elements of the input `tf.Tensor` that are < 0 are mapped to -1.\n *\n * @param x Input `tf.Tensor`.\n * @return The sign `tf.Tensor`.\n */\nexport function sign(x) {\n    // TODO(cais): Move to the core.\n    return tidy(() => {\n        const zerosLikeX = coreZerosLike(x);\n        const onesLikeX = coreOnesLike(x);\n        return where(tfc.equal(x, zerosLikeX), zerosLikeX, where(tfc.greater(x, coreZerosLike(x)), onesLikeX, tfc.mul(-1, onesLikeX)));\n    });\n}\n/**\n * Computes the one-hot representation of an integer tensor.\n * @param indices nD integer tensor of shape\n *   `(batch_size, dim1, dim2, ... dim(n-1))`\n * @param numClasses Integer, number of classes to consider.\n * @returns (n + 1)D one hot representation of the input\n *   with shape `(batch_size, dim1, dim2, ... dim(n-1), num_classes)`\n */\nexport function oneHot(indices, numClasses) {\n    return tidy(() => {\n        if (indices.rank !== 1) {\n            throw new Error('Only 1D one-hot tensors are supported in the ' +\n                'deeplearn backend, at present.');\n        }\n        indices = indices.toInt();\n        return tfc.oneHot(indices, numClasses).toFloat();\n    });\n}\n/* Elementary math functions. */\n/**\n * Retrieves the elements of indices `indices` in the tensor `reference`.\n * @param reference A tensor.\n * @param indices An integer tensor of indices or an `Array` of integers.\n * @param axis Axis along which to perform the gather operation.\n * @returns The result of the gathering as a tensor.\n */\nexport function gather(reference, indices, axis) {\n    return tidy(() => {\n        if (Array.isArray(indices)) {\n            indices = tensor1d(indices, 'int32');\n        }\n        else {\n            indices = indices.toInt();\n        }\n        return tfc.gather(reference, indices, axis);\n    });\n}\n/**\n * Element-wise square.\n * @param x Input tensor.\n * @return element-wise x^2\n */\nexport function square(x) {\n    return tfc.mul(x, x);\n}\n/**\n * Element-wise exponentiation.\n *\n * Porting Note: In PyKeras, `a` (the exponent) is a Python integer, which\n *   takes advatnage of the backend's (e.g., TensorFlow's) automatic\n * conversion to tensor. Here we allow `a` to be either a number or a tensor.\n *\n * @param x The base tensor.\n * @param a The exponent, tensor or number. If a number, it is rounded to the\n *   nearest integer and converted to a tensor.\n * @returns A tensor of the same shape as `x`.\n */\nexport function pow(x, a) {\n    return tidy(() => {\n        if (typeof (a) === 'number') {\n            a = scalar(Math.round(a), 'int32');\n        }\n        if (a.dtype !== 'int32') {\n            throw new NotImplementedError(`Non-int32 dtype (${a.dtype}) is not supported by pow() yet`);\n        }\n        return tfc.pow(x, a);\n    });\n}\n/**\n * Reshapes bias tensor according to rank of x.\n */\nfunction reshapeBias(xRank, bias, dataFormat) {\n    const biasShape = bias.shape;\n    if (bias.rank !== 1 && bias.rank !== xRank) {\n        throw new ValueError(`Unexpected bias dimensions: ${bias.rank}` +\n            `; expected it to be 1 or ${xRank}`);\n    }\n    if (xRank === 5) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1, 1, 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, 1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank === 4) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1, 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[2], biasShape[0], biasShape[1]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank === 3) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[1], biasShape[0]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank < 3) {\n        return bias;\n    }\n    throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);\n}\n/* Neural-network operations. */\n/**\n * Add a bias to a tensor.\n *\n * @param x The tensor to add the bias to.\n * @param bias The bias to add to `x`. Must be 1D or the same rank as `x`.\n * @return Result of the bias adding.\n * @throws ValueError: If the rank of `bias` is incorrect.\n */\nexport function biasAdd(x, bias, dataFormat) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        return x.add(reshapeBias(x.rank, bias, dataFormat));\n    });\n}\n/**\n * Exponential linear unit (ELU).\n * @param x A tensor or variable to compute the activation function for.\n * @param alpha: A scalar, a scaling factor for the negative section.\n * @return Output of the ELU operation.\n */\nexport function elu(x, alpha = 1) {\n    // TODO(cais): Add support for alpha values other than 1.\n    if (alpha !== 1) {\n        throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented ` +\n            `yet.`);\n    }\n    return tfc.elu(x);\n}\n/**\n * Softsign of a tensor.\n *\n * Defined as x / (abs(x) + 1), element-wise.\n *\n * @param x: Input.\n * @returns Output.\n */\nexport function softsign(x) {\n    return tidy(() => tfc.div(x, tfc.abs(x).add(1)));\n}\n/**\n * Sets entries in `x` to zero at random, while scaling the entire tensor.\n *\n * @param x input tensor.\n * @param level fraction of the entries in the tensor that will be set to 0.\n * @param noiseShape shape of randomly generated keep/drop flags, must be\n *   broadcastable to the shape of `x`. Optional.\n * @param seed random seed to ensure determinism. Optional.\n * @returns Result of the dropout operation.\n */\nexport function dropout(x, level, noiseShape, seed) {\n    return tidy(() => tfc.dropout(x, level, noiseShape, seed));\n}\n/**\n * Element-wise, segment-wise linear approximation of sigmoid.\n *\n * Returns `0.` if `x < -2.5`, `1.` if `x > 2.5`.\n * In `-2.5 <= x <= 2.5`, returns `0.2 * x + 0.5`.\n *\n * @param x Input tensor.\n * @returns Output tensor.\n */\nexport function hardSigmoid(x) {\n    return tidy(() => {\n        const y = tfc.add(.5, tfc.mul(.2, x));\n        return tfc.clipByValue(y, 0, 1);\n    });\n}\n/**\n * Invoke `x` in the training phase, and `alt` otherwise.\n *\n * Porting Note: We do not create placeholder tensors for the `training`\n * boolean flag here, because there is no such thing in the TF.js imperative\n * backend.\n *\n * @param x The function to invoke iff `training` is `true`.\n * @param alt The function to invoke iff `training` is `false`.\n * @param training Boolean flag for whether training phase is active.\n * @returns The return value of `x()` if `training` is `true`, or the return\n *   value of `alt()` if `training` is `false`.\n */\nexport function inTrainPhase(x, alt, training = false) {\n    return training ? x() : alt();\n}\n//# sourceMappingURL=tfjs_backend.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport const VALID_FAN_MODE_VALUES = ['fanIn', 'fanOut', 'fanAvg'];\nexport const VALID_DISTRIBUTION_VALUES = ['normal', 'uniform', 'truncatedNormal'];\n// We can't easily extract a string[] from the string union type, but we can\n// recapitulate the list, enforcing at compile time that the values are valid\n// and that we have the right number of them.\n/**\n * A string array of valid Initializer class names.\n *\n * This is guaranteed to match the `InitializerClassName` union type.\n */\nexport const initializerClassNames = [\n    'Zeros', 'Ones', 'Constant', 'RandomNormal', 'RandomUniform',\n    'TruncatedNormal', 'VarianceScaling', 'Orthogonal', 'Identity'\n];\n//# sourceMappingURL=initializer_config.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { eye, linalg, mul, ones, randomUniform, scalar, serialization, tidy, truncatedNormal, zeros } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { checkDataFormat } from './common';\nimport { NotImplementedError, ValueError } from './errors';\nimport { VALID_DISTRIBUTION_VALUES, VALID_FAN_MODE_VALUES } from './keras_format/initializer_config';\nimport { checkStringTypeUnionValue, deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\nimport { arrayProd } from './utils/math_utils';\nexport function checkFanMode(value) {\n    checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, 'FanMode', value);\n}\nexport function checkDistribution(value) {\n    checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, 'Distribution', value);\n}\n/**\n * Initializer base class.\n *\n * @doc {\n *   heading: 'Initializers', subheading: 'Classes', namespace: 'initializers'}\n */\nexport class Initializer extends serialization.Serializable {\n    fromConfigUsesCustomObjects() {\n        return false;\n    }\n    getConfig() {\n        return {};\n    }\n}\nexport class Zeros extends Initializer {\n    apply(shape, dtype) {\n        return zeros(shape, dtype);\n    }\n}\n/** @nocollapse */\nZeros.className = 'Zeros';\nserialization.registerClass(Zeros);\nexport class Ones extends Initializer {\n    apply(shape, dtype) {\n        return ones(shape, dtype);\n    }\n}\n/** @nocollapse */\nOnes.className = 'Ones';\nserialization.registerClass(Ones);\nexport class Constant extends Initializer {\n    constructor(args) {\n        super();\n        if (typeof args !== 'object') {\n            throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);\n        }\n        if (args.value === undefined) {\n            throw new ValueError(`config must have value set but got ${args}`);\n        }\n        this.value = args.value;\n    }\n    apply(shape, dtype) {\n        return tidy(() => mul(scalar(this.value), ones(shape, dtype)));\n    }\n    getConfig() {\n        return {\n            value: this.value,\n        };\n    }\n}\n/** @nocollapse */\nConstant.className = 'Constant';\nserialization.registerClass(Constant);\nexport class RandomUniform extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MINVAL = -0.05;\n        this.DEFAULT_MAXVAL = 0.05;\n        this.minval = args.minval || this.DEFAULT_MINVAL;\n        this.maxval = args.maxval || this.DEFAULT_MAXVAL;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        return randomUniform(shape, this.minval, this.maxval, dtype);\n    }\n    getConfig() {\n        return { minval: this.minval, maxval: this.maxval, seed: this.seed };\n    }\n}\n/** @nocollapse */\nRandomUniform.className = 'RandomUniform';\nserialization.registerClass(RandomUniform);\nexport class RandomNormal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MEAN = 0.;\n        this.DEFAULT_STDDEV = 0.05;\n        this.mean = args.mean || this.DEFAULT_MEAN;\n        this.stddev = args.stddev || this.DEFAULT_STDDEV;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        dtype = dtype || 'float32';\n        if (dtype !== 'float32' && dtype !== 'int32') {\n            throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n        }\n        return K.randomNormal(shape, this.mean, this.stddev, dtype, this.seed);\n    }\n    getConfig() {\n        return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n    }\n}\n/** @nocollapse */\nRandomNormal.className = 'RandomNormal';\nserialization.registerClass(RandomNormal);\nexport class TruncatedNormal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MEAN = 0.;\n        this.DEFAULT_STDDEV = 0.05;\n        this.mean = args.mean || this.DEFAULT_MEAN;\n        this.stddev = args.stddev || this.DEFAULT_STDDEV;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        dtype = dtype || 'float32';\n        if (dtype !== 'float32' && dtype !== 'int32') {\n            throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);\n        }\n        return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed);\n    }\n    getConfig() {\n        return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n    }\n}\n/** @nocollapse */\nTruncatedNormal.className = 'TruncatedNormal';\nserialization.registerClass(TruncatedNormal);\nexport class Identity extends Initializer {\n    constructor(args) {\n        super();\n        this.gain = args.gain != null ? args.gain : 1.0;\n    }\n    apply(shape, dtype) {\n        return tidy(() => {\n            if (shape.length !== 2 || shape[0] !== shape[1]) {\n                throw new ValueError('Identity matrix initializer can only be used for' +\n                    ' 2D square matrices.');\n            }\n            else {\n                return mul(this.gain, eye(shape[0]));\n            }\n        });\n    }\n    getConfig() {\n        return { gain: this.gain };\n    }\n}\n/** @nocollapse */\nIdentity.className = 'Identity';\nserialization.registerClass(Identity);\n/**\n * Computes the number of input and output units for a weight shape.\n * @param shape Shape of weight.\n * @param dataFormat data format to use for convolution kernels.\n *   Note that all kernels in Keras are standardized on the\n *   CHANNEL_LAST ordering (even when inputs are set to CHANNEL_FIRST).\n * @return An length-2 array: fanIn, fanOut.\n */\nfunction computeFans(shape, dataFormat = 'channelsLast') {\n    let fanIn;\n    let fanOut;\n    checkDataFormat(dataFormat);\n    if (shape.length === 2) {\n        fanIn = shape[0];\n        fanOut = shape[1];\n    }\n    else if ([3, 4, 5].indexOf(shape.length) !== -1) {\n        if (dataFormat === 'channelsFirst') {\n            const receptiveFieldSize = arrayProd(shape, 2);\n            fanIn = shape[1] * receptiveFieldSize;\n            fanOut = shape[0] * receptiveFieldSize;\n        }\n        else if (dataFormat === 'channelsLast') {\n            const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2);\n            fanIn = shape[shape.length - 2] * receptiveFieldSize;\n            fanOut = shape[shape.length - 1] * receptiveFieldSize;\n        }\n    }\n    else {\n        const shapeProd = arrayProd(shape);\n        fanIn = Math.sqrt(shapeProd);\n        fanOut = Math.sqrt(shapeProd);\n    }\n    return [fanIn, fanOut];\n}\nexport class VarianceScaling extends Initializer {\n    /**\n     * Constructor of VarianceScaling.\n     * @throws ValueError for invalid value in scale.\n     */\n    constructor(args) {\n        super();\n        if (args.scale < 0.0) {\n            throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);\n        }\n        this.scale = args.scale == null ? 1.0 : args.scale;\n        this.mode = args.mode == null ? 'fanIn' : args.mode;\n        checkFanMode(this.mode);\n        this.distribution =\n            args.distribution == null ? 'normal' : args.distribution;\n        checkDistribution(this.distribution);\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        const fans = computeFans(shape);\n        const fanIn = fans[0];\n        const fanOut = fans[1];\n        let scale = this.scale;\n        if (this.mode === 'fanIn') {\n            scale /= Math.max(1, fanIn);\n        }\n        else if (this.mode === 'fanOut') {\n            scale /= Math.max(1, fanOut);\n        }\n        else {\n            scale /= Math.max(1, (fanIn + fanOut) / 2);\n        }\n        if (this.distribution === 'normal') {\n            const stddev = Math.sqrt(scale);\n            dtype = dtype || 'float32';\n            if (dtype !== 'float32' && dtype !== 'int32') {\n                throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);\n            }\n            return truncatedNormal(shape, 0, stddev, dtype, this.seed);\n        }\n        else {\n            const limit = Math.sqrt(3 * scale);\n            return randomUniform(shape, -limit, limit, dtype);\n        }\n    }\n    getConfig() {\n        return {\n            scale: this.scale,\n            mode: this.mode,\n            distribution: this.distribution,\n            seed: this.seed\n        };\n    }\n}\n/** @nocollapse */\nVarianceScaling.className = 'VarianceScaling';\nserialization.registerClass(VarianceScaling);\nexport class GlorotUniform extends VarianceScaling {\n    /**\n     * Constructor of GlorotUniform\n     * @param scale\n     * @param mode\n     * @param distribution\n     * @param seed\n     */\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanAvg',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, GlorotUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nGlorotUniform.className = 'GlorotUniform';\nserialization.registerClass(GlorotUniform);\nexport class GlorotNormal extends VarianceScaling {\n    /**\n     * Constructor of GlorotNormal.\n     * @param scale\n     * @param mode\n     * @param distribution\n     * @param seed\n     */\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanAvg',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, GlorotNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nGlorotNormal.className = 'GlorotNormal';\nserialization.registerClass(GlorotNormal);\nexport class HeNormal extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 2.0,\n            mode: 'fanIn',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, HeNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nHeNormal.className = 'HeNormal';\nserialization.registerClass(HeNormal);\nexport class HeUniform extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 2.0,\n            mode: 'fanIn',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, HeUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nHeUniform.className = 'HeUniform';\nserialization.registerClass(HeUniform);\nexport class LeCunNormal extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanIn',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, LeCunNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nLeCunNormal.className = 'LeCunNormal';\nserialization.registerClass(LeCunNormal);\nexport class LeCunUniform extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanIn',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, LeCunUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nLeCunUniform.className = 'LeCunNormal';\nserialization.registerClass(LeCunUniform);\nexport class Orthogonal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_GAIN = 1;\n        this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain;\n        this.seed = args.seed;\n        if (this.seed != null) {\n            throw new NotImplementedError('Random seed is not implemented for Orthogonal Initializer yet.');\n        }\n    }\n    apply(shape, dtype) {\n        return tidy(() => {\n            if (shape.length < 2) {\n                throw new NotImplementedError('Shape must be at least 2D.');\n            }\n            if (shape[0] * shape[1] > 2000) {\n                console.warn(`Orthogonal initializer is being called on a matrix with more ` +\n                    `than 2000 (${shape[0] * shape[1]}) elements: ` +\n                    `Slowness may result.`);\n            }\n            // TODO(cais): Add seed support.\n            const normalizedShape = shape[0] > shape[1] ? [shape[1], shape[0]] : shape;\n            const a = K.randomNormal(normalizedShape, 0, 1, 'float32');\n            let q = linalg.gramSchmidt(a);\n            if (shape[0] > shape[1]) {\n                q = q.transpose();\n            }\n            return mul(this.gain, q);\n        });\n    }\n    getConfig() {\n        return {\n            gain: this.gain,\n            seed: this.seed,\n        };\n    }\n}\n/** @nocollapse */\nOrthogonal.className = 'Orthogonal';\nserialization.registerClass(Orthogonal);\n// Maps the JavaScript-like identifier keys to the corresponding registry\n// symbols.\nexport const INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'constant': 'Constant',\n    'glorotNormal': 'GlorotNormal',\n    'glorotUniform': 'GlorotUniform',\n    'heNormal': 'HeNormal',\n    'heUniform': 'HeUniform',\n    'identity': 'Identity',\n    'leCunNormal': 'LeCunNormal',\n    'leCunUniform': 'LeCunUniform',\n    'ones': 'Ones',\n    'orthogonal': 'Orthogonal',\n    'randomNormal': 'RandomNormal',\n    'randomUniform': 'RandomUniform',\n    'truncatedNormal': 'TruncatedNormal',\n    'varianceScaling': 'VarianceScaling',\n    'zeros': 'Zeros'\n};\nfunction deserializeInitializer(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'initializer');\n}\nexport function serializeInitializer(initializer) {\n    return serializeKerasObject(initializer);\n}\nexport function getInitializer(identifier) {\n    if (typeof identifier === 'string') {\n        const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        /* We have four 'helper' classes for common initializers that\n        all get serialized as 'VarianceScaling' and shouldn't go through\n        the deserializeInitializer pathway. */\n        if (className === 'GlorotNormal') {\n            return new GlorotNormal();\n        }\n        else if (className === 'GlorotUniform') {\n            return new GlorotUniform();\n        }\n        else if (className === 'HeNormal') {\n            return new HeNormal();\n        }\n        else if (className === 'HeUniform') {\n            return new HeUniform();\n        }\n        else if (className === 'LeCunNormal') {\n            return new LeCunNormal();\n        }\n        else if (className === 'LeCunUniform') {\n            return new LeCunUniform();\n        }\n        else {\n            const config = {};\n            config['className'] = className;\n            config['config'] = {};\n            return deserializeInitializer(config);\n        }\n    }\n    else if (identifier instanceof Initializer) {\n        return identifier;\n    }\n    else {\n        return deserializeInitializer(identifier);\n    }\n}\n//# sourceMappingURL=initializers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// tslint:disable-next-line:max-line-length\nimport { Constant, GlorotNormal, GlorotUniform, HeNormal, HeUniform, Identity, LeCunNormal, LeCunUniform, Ones, Orthogonal, RandomNormal, RandomUniform, TruncatedNormal, VarianceScaling, Zeros } from './initializers';\n/**\n * Initializer that generates tensors initialized to 0.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function zeros() {\n    return new Zeros();\n}\n/**\n * Initializer that generates tensors initialized to 1.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function ones() {\n    return new Ones();\n}\n/**\n * Initializer that generates values initialized to some constant.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function constant(args) {\n    return new Constant(args);\n}\n/**\n * Initializer that generates random values initialized to a uniform\n * distribution.\n *\n * Values will be distributed uniformly between the configured minval and\n * maxval.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function randomUniform(args) {\n    return new RandomUniform(args);\n}\n/**\n * Initializer that generates random values initialized to a normal\n * distribution.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function randomNormal(args) {\n    return new RandomNormal(args);\n}\n/**\n * Initializer that generates random values initialized to a truncated normal.\n * distribution.\n *\n * These values are similar to values from a `RandomNormal` except that values\n * more than two standard deviations from the mean are discarded and re-drawn.\n * This is the recommended initializer for neural network weights and filters.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function truncatedNormal(args) {\n    return new TruncatedNormal(args);\n}\n/**\n * Initializer that generates the identity matrix.\n * Only use for square 2D matrices.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function identity(args) {\n    return new Identity(args);\n}\n/**\n * Initializer capable of adapting its scale to the shape of weights.\n * With distribution=NORMAL, samples are drawn from a truncated normal\n * distribution centered on zero, with `stddev = sqrt(scale / n)` where n is:\n *   - number of input units in the weight tensor, if mode = FAN_IN.\n *   - number of output units, if mode = FAN_OUT.\n *   - average of the numbers of input and output units, if mode = FAN_AVG.\n * With distribution=UNIFORM,\n * samples are drawn from a uniform distribution\n * within [-limit, limit], with `limit = sqrt(3 * scale / n)`.\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function varianceScaling(config) {\n    return new VarianceScaling(config);\n}\n/**\n * Glorot uniform initializer, also called Xavier uniform initializer.\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotUniform(args) {\n    return new GlorotUniform(args);\n}\n/**\n * Glorot normal initializer, also called Xavier normal initializer.\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor.\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotNormal(args) {\n    return new GlorotNormal(args);\n}\n/**\n * He normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function heNormal(args) {\n    return new HeNormal(args);\n}\n/**\n * He uniform initializer.\n *\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / fan_in)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function heUniform(args) {\n    return new HeUniform(args);\n}\n/**\n * LeCun normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(1 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * References:\n *   [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n *   [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunNormal(args) {\n    return new LeCunNormal(args);\n}\n/**\n * LeCun uniform initializer.\n *\n * It draws samples from a uniform distribution in the interval\n * `[-limit, limit]` with `limit = sqrt(3 / fanIn)`,\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunUniform(args) {\n    return new LeCunUniform(args);\n}\n/**\n * Initializer that generates a random orthogonal matrix.\n *\n * Reference:\n * [Saxe et al., http://arxiv.org/abs/1312.6120](http://arxiv.org/abs/1312.6120)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function orthogonal(args) {\n    return new Orthogonal(args);\n}\n//# sourceMappingURL=exports_initializers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Utilities related to persistent state in the backend.\n */\n/**\n * An ID to track `tf.SymbolicTensor`s and derived classes.\n * Required in different places in engine/topology.ts to identify unique\n * tensors.\n */\nlet _nextUniqueTensorId = 0;\nexport function getNextUniqueTensorId() {\n    return _nextUniqueTensorId++;\n}\nconst _uidPrefixes = {};\n/**\n * Provides a unique UID given a string prefix.\n *\n * @param prefix\n */\nexport function getUid(prefix = '') {\n    if (!(prefix in _uidPrefixes)) {\n        _uidPrefixes[prefix] = 0;\n    }\n    _uidPrefixes[prefix] += 1;\n    return prefix + _uidPrefixes[prefix].toString();\n}\n//# sourceMappingURL=state.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { ValueError } from '../errors';\n// tslint:enable\n/**\n * Determine whether the input is an Array of Shapes.\n */\nexport function isArrayOfShapes(x) {\n    return Array.isArray(x) && Array.isArray(x[0]);\n}\n/**\n * Special case of normalizing shapes to lists.\n *\n * @param x A shape or list of shapes to normalize into a list of Shapes.\n * @return A list of Shapes.\n */\nexport function normalizeShapeList(x) {\n    if (x.length === 0) {\n        return [];\n    }\n    if (!Array.isArray(x[0])) {\n        return [x];\n    }\n    return x;\n}\n/**\n * Helper function to obtain exactly one Tensor.\n * @param xs: A single `tf.Tensor` or an `Array` of `tf.Tensor`s.\n * @return A single `tf.Tensor`. If `xs` is an `Array`, return the first one.\n * @throws ValueError: If `xs` is an `Array` and its length is not 1.\n */\nexport function getExactlyOneTensor(xs) {\n    let x;\n    if (Array.isArray(xs)) {\n        if (xs.length !== 1) {\n            throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);\n        }\n        x = xs[0];\n    }\n    else {\n        x = xs;\n    }\n    return x;\n}\n/**\n * Helper function to obtain exactly on instance of Shape.\n *\n * @param shapes Input single `Shape` or Array of `Shape`s.\n * @returns If input is a single `Shape`, return it unchanged. If the input is\n *   an `Array` containing exactly one instance of `Shape`, return the instance.\n *   Otherwise, throw a `ValueError`.\n * @throws ValueError: If input is an `Array` of `Shape`s, and its length is not\n *   1.\n */\nexport function getExactlyOneShape(shapes) {\n    if (Array.isArray(shapes) && Array.isArray(shapes[0])) {\n        if (shapes.length === 1) {\n            shapes = shapes;\n            return shapes[0];\n        }\n        else {\n            throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`);\n        }\n    }\n    else {\n        return shapes;\n    }\n}\n//# sourceMappingURL=types_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Count the elements in an Array of LayerVariables.\n *\n * @param weights: The LayerVariables of which the constituent numbers are to\n *   be counted.\n * @returns A count of the elements in all the LayerVariables\n */\nexport function countParamsInWeights(weights) {\n    let count = 0;\n    for (const weight of weights) {\n        if (weight.shape.length === 0) {\n            count += 1;\n        }\n        else {\n            count += weight.shape.reduce((a, b) => a * b);\n        }\n    }\n    return count;\n}\n//# sourceMappingURL=variable_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { variableGrads } from '@tensorflow/tfjs-core';\nimport { getNextUniqueTensorId } from './backend/state';\nimport { getScopedTensorName, getUniqueTensorName } from './common';\nimport { NotImplementedError } from './errors';\nconst DEFAULT_VARIABLE_NAME_PREFIX = 'Variable';\n/**\n * A `tf.layers.LayerVariable` is similar to a `tf.Tensor` in that it has a\n * dtype and shape, but its value is mutable.  The value is itself represented\n * as a`tf.Tensor`, and can be read with the `read()` method and updated with\n * the `write()` method.\n */\nexport class LayerVariable {\n    /**\n     * Construct Variable from a `tf.Tensor`.\n     *\n     * If not explicitly named, the Variable will be given a name with the\n     * prefix 'Variable'. Variable names are unique. In the case of name\n     * collision, suffixies '_<num>' will be added to the name.\n     *\n     * @param val Initial value of the Variable.\n     * @param name Name of the variable. If `null` or `undefined` is provided, it\n     *   will default a name with the prefix 'Variable'.\n     * @param constraint Optional, projection function to be applied to the\n     * variable after optimize updates\n     * @throws ValueError if `name` is `null` or `undefined`.\n     */\n    constructor(val, dtype = 'float32', name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true, constraint = null) {\n        this.dtype = dtype == null ? 'float32' : dtype;\n        this.shape = val.shape;\n        this.id = getNextUniqueTensorId();\n        name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name;\n        this.originalName = getScopedTensorName(name);\n        this.name = getUniqueTensorName(this.originalName);\n        this.trainable_ = trainable;\n        this.constraint = constraint;\n        this.val = tfc.variable(val, this.trainable_, this.name, this.dtype);\n    }\n    /**\n     * Get a snapshot of the Variable's value.\n     *\n     * The returned value is a snapshot of the Variable's value at the time of\n     * the invocation. Future mutations in the value of the tensor will only\n     * be reflected by future calls to this method.\n     */\n    read() {\n        this.assertNotDisposed();\n        return this.val;\n    }\n    /**\n     * Update the value of the Variable.\n     *\n     * @param newVal: The new value to update to. Must be consistent with the\n     *   dtype and shape of the Variable.\n     * @return This Variable.\n     */\n    write(newVal) {\n        // TODO(cais): Once  TF.js Core supports Tensor.dtype, check dtype match.\n        this.assertNotDisposed();\n        checkShapesMatch(this.val, newVal);\n        // Skip updating if this is the exact same tensor.\n        if (this.val.id !== newVal.id) {\n            this.val.assign(newVal);\n            if (this.constraint != null) {\n                this.val.assign(this.constraint.apply(this.val));\n            }\n        }\n        return this;\n    }\n    /**\n     * Dispose this LayersVariable instance from memory.\n     */\n    dispose() {\n        this.assertNotDisposed();\n        this.val.dispose();\n    }\n    assertNotDisposed() {\n        if (this.val.isDisposed) {\n            throw new Error(`LayersVariable ${this.name} is already disposed.`);\n        }\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this.trainable_ = trainable;\n        this.val.trainable = trainable;\n    }\n}\nfunction checkShapesMatch(x, y) {\n    if (x.shape.toString() !== y.shape.toString()) {\n        throw new Error('Shape mismatch: ' + JSON.stringify(x.shape) + ' vs. ' +\n            JSON.stringify(y.shape));\n    }\n}\n/**\n * Create a Variable.\n * @param x The initial value of the `Variable`.\n * @param dtype optional, the type of the variable.\n * @param name optional, the name of the variable, default provided by\n * Variable.\n * @param constraint optional, a constraint to be applied after every update.\n * @return The newly instantiated `Variable`.\n */\nexport function variable(x, dtype, name, constraint) {\n    return new LayerVariable(x, dtype, name, true, constraint);\n}\n/**\n * Instantiates an all-zeros Variable and returns it.\n *\n * @param shape Shape of the tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return An all-zero Variable.\n */\nexport function zerosVariable(shape, dtype, name) {\n    // TODO(cais): Implement logic for dtype.\n    return new LayerVariable(tfc.zeros(shape), dtype, name);\n}\n/**\n * Instantiates an all-zeros tensor of the same shape as another tensor.\n *\n * @param x The other tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return A newly instantiated Variable.\n */\nexport function zerosLike(x, dtype, name) {\n    return new LayerVariable(tfc.zerosLike(x), dtype, name);\n}\n/**\n * Instantiates an all-ones tensor and returns it.\n *\n * @param shape Shape of the tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return An all-ones Variable.\n */\nexport function onesVariable(shape, dtype, name) {\n    // TODO(cais): Implement logic for dtype.\n    const allocated = tfc.ones(shape);\n    return new LayerVariable(allocated, dtype, name);\n}\n/**\n * Instantiates an all-ones tensor of the same shape as another tensor.\n *\n * @param x The other tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return A newly instantiated Variable.\n */\nexport function onesLike(x, dtype, name) {\n    const allocated = tfc.onesLike(x);\n    return new LayerVariable(allocated, dtype, name);\n}\n/**\n * Instantiate an identity matrix and returns it, as a Variable\n *\n * @param size Number of rows/columns.\n * @param dtype Data type of returned Variable.\n * @param name Name of returned Variable.\n * @return A Variable, an identity matrix.\n */\nexport function eyeVariable(size, dtype, name) {\n    return new LayerVariable(tfc.eye(size), dtype, name);\n}\n/**\n * Get a Variable with uniform distribution of values.\n * @param shape Shape of the tensor.\n * @param minval Lower bound of the uniform distribution.\n * @param maxval Upper bound of the uniform distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The uniform-random Variable.\n */\nexport function randomUniformVariable(shape, minval, maxval, dtype, seed, name = 'randomUniform') {\n    return new LayerVariable(tfc.randomUniform(shape, minval, maxval, dtype), dtype, name);\n}\n/**\n * Get a Variable with truncated-normal distribution of values.\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The truncated-normal-random Variable.\n */\nexport function truncatedNormalVariable(shape, mean = 0.0, stddev = 1.0, dtype, seed, name = 'truncatedNormal') {\n    // TODO(cais): Implement logic for dtype and seed once they are supported\n    // by deeplearn.js.\n    dtype = dtype || 'float32';\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n    }\n    return new LayerVariable(tfc.truncatedNormal(shape, mean, stddev, dtype, seed), dtype, name);\n}\n/**\n * Get a Variable with normal distribution of values.\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The truncated-normal-random Variable.\n */\nexport function randomNormalVariable(shape, mean = 0.0, stddev = 1.0, dtype, seed, name = 'randomNormal') {\n    dtype = dtype || 'float32';\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new NotImplementedError(`randomNormalVariable does not support dType ${dtype}.`);\n    }\n    return new LayerVariable(tfc.randomNormal(shape, mean, stddev, dtype, seed), dtype, name);\n}\n/**\n * Update the value of a Variable.\n * @param x The Variable to be updated.\n * @param xNew The new value to update to.\n * @return The Variable updated.\n */\nexport function update(x, xNew) {\n    return x.write(xNew);\n}\n/**\n * Update the value of a Variable by adding an increment.\n * @param x The Variable to be updated.\n * @param increment The incrment to add to `x`.\n * @return The Variable updated.\n */\nexport function updateAdd(x, increment) {\n    return x.write(tfc.add(x.read(), increment));\n}\n/**\n * Update the value of a Variable by subtracting a decrement.\n * @param x The Variable to be updated.\n * @param decrement The decrement to subtract from `x`.\n * @return The Variable updated.\n */\nexport function updateSub(x, decrement) {\n    return x.write(tfc.sub(x.read(), decrement));\n}\n/**\n * Get the values of an array of Variables.\n *\n * @param tensors An `Array` of `Variable`s to get the values of.\n * @return The values of the inputs, as an `Array` of`tf.Tensor`s.\n */\nexport function batchGetValue(xs) {\n    return xs.map(x => x.read());\n}\n/**\n * Update the value of multiple Variables at once.\n *\n * @param variablesAndValues An `Array`, each element is of type\n *   [Variable, Tensor]. The first item is the\n *   `Variable` of which the value is to be updated. The second item\n *   carries the new value.\n */\nexport function batchSetValue(variablesAndValues) {\n    variablesAndValues.forEach(variableAndValue => {\n        const variable = variableAndValue[0];\n        variable.write(variableAndValue[1]);\n    });\n}\n/**\n * Returns the gradients of `variables` w.r.t. the return value of `lossFn`.\n * @param lossFn A function which returns a Scalar to be used as the function\n *   value (i.e., numerator) for differentiation.\n * @param variables List of variables to be used as the independent variables\n *   (i.e., denominator) for differentiation.\n * @returns An Array of gradients tensors.\n */\nexport function gradients(lossFn, variables) {\n    // TODO(cais): The return type signature can be simplified if deeplearn makes\n    //   the corresponding type public.\n    const variableList = variables.map(variable => variable.read());\n    const valudAndGrads = variableGrads(lossFn, variableList);\n    return variables.map(variable => valudAndGrads.grads[variable.name]);\n}\n//# sourceMappingURL=variables.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/engine/topology.py */\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getNextUniqueTensorId, getUid } from '../backend/state';\nimport { getScopedTensorName, getUniqueTensorName, nameScope } from '../common';\nimport { AttributeError, NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { getInitializer } from '../initializers';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as types_utils from '../utils/types_utils';\nimport * as variable_utils from '../utils/variable_utils';\nimport { batchGetValue, batchSetValue, LayerVariable } from '../variables';\n/**\n * Specifies the ndim, dtype and shape of every input to a layer.\n *\n * Every layer should expose (if appropriate) an `inputSpec` attribute:\n * a list of instances of InputSpec (one per input tensor).\n *\n * A null entry in a shape is compatible with any dimension,\n * a null shape is compatible with any shape.\n */\nexport class InputSpec {\n    constructor(args) {\n        this.dtype = args.dtype;\n        this.shape = args.shape;\n        /*\n          TODO(michaelterry): Could throw error if ndim and shape are both defined\n            (then backport).\n        */\n        if (args.shape != null) {\n            this.ndim = args.shape.length;\n        }\n        else {\n            this.ndim = args.ndim;\n        }\n        this.maxNDim = args.maxNDim;\n        this.minNDim = args.minNDim;\n        this.axes = args.axes || {};\n    }\n}\n/**\n * `tf.SymbolicTensor` is a placeholder for a Tensor without any concrete value.\n *\n * They are most often encountered when building a graph of `Layer`s for a\n * a `tf.LayersModel` and the input data's shape, but not values are known.\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\nexport class SymbolicTensor {\n    /**\n     *\n     * @param dtype\n     * @param shape\n     * @param sourceLayer The Layer that produced this symbolic tensor.\n     * @param inputs The inputs passed to sourceLayer's __call__() method.\n     * @param nodeIndex\n     * @param tensorIndex\n     * @param callArgs The keyword arguments passed to the __call__() method.\n     * @param name\n     * @param outputTensorIndex The index of this tensor in the list of outputs\n     *   returned by apply().\n     */\n    constructor(dtype, shape, sourceLayer, inputs, callArgs, name, outputTensorIndex) {\n        this.dtype = dtype;\n        this.shape = shape;\n        this.sourceLayer = sourceLayer;\n        this.inputs = inputs;\n        this.callArgs = callArgs;\n        this.outputTensorIndex = outputTensorIndex;\n        this.id = getNextUniqueTensorId();\n        if (name != null) {\n            this.originalName = getScopedTensorName(name);\n            this.name = getUniqueTensorName(this.originalName);\n        }\n        this.rank = shape.length;\n    }\n}\nlet _nextNodeID = 0;\n/**\n * A `Node` describes the connectivity between two layers.\n *\n * Each time a layer is connected to some new input,\n * a node is added to `layer.inboundNodes`.\n *\n * Each time the output of a layer is used by another layer,\n * a node is added to `layer.outboundNodes`.\n *\n * `nodeIndices` and `tensorIndices` are basically fine-grained coordinates\n * describing the origin of the `inputTensors`, verifying the following:\n *\n * `inputTensors[i] ==\n * inboundLayers[i].inboundNodes[nodeIndices[i]].outputTensors[\n *   tensorIndices[i]]`\n *\n * A node from layer A to layer B is added to:\n *     A.outboundNodes\n *     B.inboundNodes\n */\nexport class Node {\n    constructor(args, \n    // TODO(michaelterry): Define actual type for this.\n    callArgs) {\n        this.callArgs = callArgs;\n        this.id = _nextNodeID++;\n        /*\n          Layer instance (NOT a list).\n          this is the layer that takes a list of input tensors\n          and turns them into a list of output tensors.\n          the current node will be added to\n          the inboundNodes of outboundLayer.\n        */\n        this.outboundLayer = args.outboundLayer;\n        /*\n            The following 3 properties describe where\n            the input tensors come from: which layers,\n            and for each layer, which node and which\n            tensor output of each node.\n        */\n        // List of layer instances.\n        this.inboundLayers = args.inboundLayers;\n        // List of integers, 1:1 mapping with inboundLayers.\n        this.nodeIndices = args.nodeIndices;\n        // List of integers, 1:1 mapping with inboundLayers.\n        this.tensorIndices = args.tensorIndices;\n        /*\n            Following 2 properties:\n            tensor inputs and outputs of outboundLayer.\n        */\n        // List of tensors. 1:1 mapping with inboundLayers.\n        this.inputTensors = args.inputTensors;\n        // List of tensors, created by outboundLayer.call().\n        this.outputTensors = args.outputTensors;\n        /*\n            Following 2 properties: input and output masks.\n            List of tensors, 1:1 mapping with inputTensor.\n        */\n        this.inputMasks = args.inputMasks;\n        // List of tensors, created by outboundLayer.computeMask().\n        this.outputMasks = args.outputMasks;\n        // Following 2 properties: input and output shapes.\n        // List of shape tuples, shapes of inputTensors.\n        this.inputShapes = args.inputShapes;\n        // List of shape tuples, shapes of outputTensors.\n        this.outputShapes = args.outputShapes;\n        // Add nodes to all layers involved.\n        for (const layer of args.inboundLayers) {\n            if (layer != null) {\n                layer.outboundNodes.push(this);\n            }\n        }\n        args.outboundLayer.inboundNodes.push(this);\n    }\n    getConfig() {\n        const inboundNames = [];\n        for (const layer of this.inboundLayers) {\n            if (layer != null) {\n                inboundNames.push(layer.name);\n            }\n            else {\n                inboundNames.push(null);\n            }\n        }\n        return {\n            outboundLayer: this.outboundLayer ? this.outboundLayer.name : null,\n            inboundLayers: inboundNames,\n            nodeIndices: this.nodeIndices,\n            tensorIndices: this.tensorIndices\n        };\n    }\n}\nlet _nextLayerID = 0;\n/**\n * A layer is a grouping of operations and weights that can be composed to\n * create a `tf.LayersModel`.\n *\n * Layers are constructed by using the functions under the\n * [tf.layers](#Layers-Basic) namespace.\n *\n * @doc {heading: 'Layers', subheading: 'Classes', namespace: 'layers'}\n */\nexport class Layer extends serialization.Serializable {\n    constructor(args = {}) {\n        super();\n        this._callHook = null;\n        this._addedWeightNames = [];\n        // Porting Notes: PyKeras does not have this property in this base Layer\n        //   class. Instead lets Layer subclass set it dynamically and checks the\n        //   value with `hasattr`. In tfjs-layers, we let this be a member of this\n        //   base class.\n        this._stateful = false;\n        this.id = _nextLayerID++;\n        this.activityRegularizer = null;\n        this.inputSpec = null;\n        this.supportsMasking = false;\n        // These properties will be set upon call of this.build()\n        this._trainableWeights = [];\n        this._nonTrainableWeights = [];\n        this._losses = [];\n        this._updates = [];\n        this._built = false;\n        /*\n          These lists will be filled via successive calls\n          to this.addInboundNode().\n         */\n        this.inboundNodes = [];\n        this.outboundNodes = [];\n        let name = args.name;\n        if (!name) {\n            const prefix = this.getClassName();\n            name = generic_utils.toSnakeCase(prefix) + '_' + getUid(prefix);\n        }\n        this.name = name;\n        this.trainable_ = args.trainable == null ? true : args.trainable;\n        if (args.inputShape != null || args.batchInputShape != null) {\n            /*\n              In this case we will later create an input layer\n              to insert before the current layer\n             */\n            let batchInputShape;\n            if (args.batchInputShape != null) {\n                batchInputShape = args.batchInputShape;\n            }\n            else if (args.inputShape != null) {\n                let batchSize = null;\n                if (args.batchSize != null) {\n                    batchSize = args.batchSize;\n                }\n                batchInputShape = [batchSize].concat(args.inputShape);\n            }\n            this.batchInputShape = batchInputShape;\n            // Set dtype.\n            let dtype = args.dtype;\n            if (dtype == null) {\n                dtype = args.inputDType;\n            }\n            if (dtype == null) {\n                dtype = 'float32';\n            }\n            this.dtype = dtype;\n        }\n        if (args.weights != null) {\n            this.initialWeights = args.weights;\n        }\n        else {\n            this.initialWeights = null;\n        }\n        // The value of `_refCount` is initialized to null. When the layer is used\n        // in a symbolic way for the first time, it will be set to 1.\n        this._refCount = null;\n        this.fastWeightInitDuringBuild = false;\n    }\n    /**\n     * Converts a layer and its index to a unique (immutable type) name.\n     * This function is used internally with `this.containerNodes`.\n     * @param layer The layer.\n     * @param nodeIndex The layer's position (e.g. via enumerate) in a list of\n     *   nodes.\n     *\n     * @returns The unique name.\n     */\n    static nodeKey(layer, nodeIndex) {\n        return layer.name + '_ib-' + nodeIndex.toString();\n    }\n    /**\n     * Returns this.inboundNode at index nodeIndex.\n     *\n     * Porting note: This is a replacement for _get_node_attribute_at_index()\n     * @param nodeIndex\n     * @param attrName The name of the attribute related to request for this node.\n     */\n    getNodeAtIndex(nodeIndex, attrName) {\n        if (this.inboundNodes.length === 0) {\n            throw new RuntimeError('The layer has never been called ' +\n                `and thus has no defined ${attrName}.`);\n        }\n        if (this.inboundNodes.length <= nodeIndex) {\n            throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, ` +\n                `but the layer has only ${this.inboundNodes.length} inbound nodes.`);\n        }\n        return this.inboundNodes[nodeIndex];\n    }\n    /**\n     * Retrieves the input tensor(s) of a layer at a given node.\n     *\n     * @param nodeIndex Integer, index of the node from which to retrieve the\n     *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n     *   was called.\n     *\n     * @return A tensor (or list of tensors if the layer has multiple inputs).\n     */\n    getInputAt(nodeIndex) {\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(nodeIndex, 'input').inputTensors);\n    }\n    /**\n     * Retrieves the output tensor(s) of a layer at a given node.\n     *\n     * @param nodeIndex Integer, index of the node from which to retrieve the\n     *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n     *   was called.\n     *\n     * @return A tensor (or list of tensors if the layer has multiple outputs).\n     */\n    getOutputAt(nodeIndex) {\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(nodeIndex, 'output').outputTensors);\n    }\n    // Properties\n    /**\n     * Retrieves the input tensor(s) of a layer.\n     *\n     * Only applicable if the layer has exactly one inbound node,\n     * i.e. if it is connected to one incoming layer.\n     *\n     * @return Input tensor or list of input tensors.\n     *\n     * @exception AttributeError if the layer is connected to more than one\n     *   incoming layers.\n     */\n    get input() {\n        if (this.inboundNodes.length > 1) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has multiple inbound nodes, ' +\n                'hence the notion of \"layer input\" ' +\n                'is ill-defined. ' +\n                'Use `getInputAt(nodeIndex)` instead.');\n        }\n        else if (this.inboundNodes.length === 0) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' is not connected, no input to return.');\n        }\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(0, 'input').inputTensors);\n    }\n    /**\n     * Retrieves the output tensor(s) of a layer.\n     *\n     * Only applicable if the layer has exactly one inbound node,\n     * i.e. if it is connected to one incoming layer.\n     *\n     * @return Output tensor or list of output tensors.\n     *\n     * @exception AttributeError if the layer is connected to more than one\n     *   incoming layers.\n     */\n    get output() {\n        if (this.inboundNodes.length === 0) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has no inbound nodes.');\n        }\n        if (this.inboundNodes.length > 1) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has multiple inbound nodes, ' +\n                'hence the notion of \"layer output\" ' +\n                'is ill-defined. ' +\n                'Use `getOutputAt(nodeIndex)` instead.');\n        }\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(0, 'output').outputTensors);\n    }\n    get losses() {\n        return this._losses;\n    }\n    /**\n     * Retrieves the Layer's current loss values.\n     *\n     * Used for regularizers during training.\n     */\n    calculateLosses() {\n        // Porting Node: This is an augmentation to Layer.loss in PyKeras.\n        //   In PyKeras, Layer.loss returns symbolic tensors. Here a concrete\n        //   Tensor (specifically Scalar) values are returned. This is due to the\n        //   imperative backend.\n        return this.losses.map(lossFn => lossFn());\n    }\n    get updates() {\n        return this._updates;\n    }\n    get built() {\n        return this._built;\n    }\n    set built(built) {\n        this._built = built;\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this._trainableWeights.forEach(w => w.trainable = trainable);\n        this.trainable_ = trainable;\n    }\n    get trainableWeights() {\n        if (this.trainable_) {\n            return this._trainableWeights.filter(w => w.trainable);\n        }\n        else {\n            return [];\n        }\n    }\n    set trainableWeights(weights) {\n        this._trainableWeights = weights;\n    }\n    get nonTrainableWeights() {\n        if (this.trainable) {\n            return this._trainableWeights.filter(w => !w.trainable)\n                .concat(this._nonTrainableWeights);\n        }\n        else {\n            return this._trainableWeights.concat(this._nonTrainableWeights);\n        }\n    }\n    set nonTrainableWeights(weights) {\n        this._nonTrainableWeights = weights;\n    }\n    /**\n     * The concatenation of the lists trainableWeights and nonTrainableWeights\n     * (in this order).\n     */\n    get weights() {\n        return this.trainableWeights.concat(this.nonTrainableWeights);\n    }\n    get stateful() {\n        return this._stateful;\n    }\n    /**\n     * Reset the states of the layer.\n     *\n     * This method of the base Layer class is essentially a no-op.\n     * Subclasses that are stateful (e.g., stateful RNNs) should override this\n     * method.\n     */\n    resetStates() {\n        if (!this.stateful) {\n            throw new Error('Cannot call the resetStates() method of a non-stateful Layer ' +\n                'object.');\n        }\n    }\n    /**\n     * Checks compatibility between the layer and provided inputs.\n     *\n     * This checks that the tensor(s) `input`\n     * verify the input assumptions of the layer\n     * (if any). If not, exceptions are raised.\n     *\n     * @param inputs Input tensor or list of input tensors.\n     *\n     * @exception ValueError in case of mismatch between\n     *   the provided inputs and the expectations of the layer.\n     */\n    assertInputCompatibility(inputs) {\n        inputs = generic_utils.toList(inputs);\n        if (this.inputSpec == null || this.inputSpec.length === 0) {\n            return;\n        }\n        const inputSpec = generic_utils.toList(this.inputSpec);\n        if (inputs.length !== inputSpec.length) {\n            throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, ` +\n                `but it received ${inputs.length} input tensors. ` +\n                `Input received: ${inputs}`);\n        }\n        for (let inputIndex = 0; inputIndex < inputs.length; inputIndex++) {\n            const x = inputs[inputIndex];\n            const spec = inputSpec[inputIndex];\n            if (spec == null) {\n                continue;\n            }\n            // Check ndim.\n            const ndim = x.rank;\n            if (spec.ndim != null) {\n                if (ndim !== spec.ndim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: ` +\n                        `expected ndim=${spec.ndim}, found ndim=${ndim}`);\n                }\n            }\n            if (spec.maxNDim != null) {\n                if (ndim > spec.maxNDim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}` +\n                        `: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);\n                }\n            }\n            if (spec.minNDim != null) {\n                if (ndim < spec.minNDim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}` +\n                        `: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);\n                }\n            }\n            // Check dtype.\n            if (spec.dtype != null) {\n                if (x.dtype !== spec.dtype) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} ` +\n                        `: expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);\n                }\n            }\n            // Check specific shape axes.\n            if (spec.axes) {\n                const xShape = x.shape;\n                for (const key in spec.axes) {\n                    const axis = Number(key);\n                    const value = spec.axes[key];\n                    // Perform Python-style slicing in case axis < 0;\n                    // TODO(cais): Use https://github.com/alvivi/typescript-underscore to\n                    // ensure type safety through Underscore calls.\n                    const xShapeAtAxis = axis >= 0 ? xShape[axis] : xShape[xShape.length + axis];\n                    if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) {\n                        throw new ValueError(`Input ${inputIndex} is incompatible with layer ` +\n                            `${this.name}: expected axis ${axis} of input shape to ` +\n                            `have value ${value} but got shape ${xShape}.`);\n                    }\n                }\n            }\n            // Check shape.\n            if (spec.shape != null) {\n                for (let i = 0; i < spec.shape.length; ++i) {\n                    const specDim = spec.shape[i];\n                    const dim = x.shape[i];\n                    if (specDim != null && dim != null) {\n                        if (specDim !== dim) {\n                            throw new ValueError(`Input ${inputIndex} is incompatible with layer ` +\n                                `${this.name}: expected shape=${spec.shape}, ` +\n                                `found shape=${x.shape}.`);\n                        }\n                    }\n                }\n            }\n        }\n    }\n    /**\n     * This is where the layer's logic lives.\n     *\n     * @param inputs Input tensor, or list/tuple of input tensors.\n     * @param kwargs Additional keyword arguments.\n     *\n     * @return A tensor or list/tuple of tensors.\n     */\n    call(inputs, kwargs) {\n        return inputs;\n    }\n    invokeCallHook(inputs, kwargs) {\n        if (this._callHook != null) {\n            this._callHook(inputs, kwargs);\n        }\n    }\n    /**\n     * Set call hook.\n     * This is currently used for testing only.\n     * @param callHook\n     */\n    setCallHook(callHook) {\n        this._callHook = callHook;\n    }\n    /**\n     * Clear call hook.\n     * This is currently used for testing only.\n     */\n    clearCallHook() {\n        this._callHook = null;\n    }\n    /**\n     * Builds or executes a `Layer's logic.\n     *\n     * When called with `tf.Tensor`(s), execute the `Layer`s computation and\n     * return Tensor(s). For example:\n     *\n     * ```js\n     * const denseLayer = tf.layers.dense({\n     *   units: 1,\n     *   kernelInitializer: 'zeros',\n     *   useBias: false\n     * });\n     *\n     * // Invoke the layer's apply() method with a `tf.Tensor` (with concrete\n     * // numeric values).\n     * const input = tf.ones([2, 2]);\n     * const output = denseLayer.apply(input);\n     *\n     * // The output's value is expected to be [[0], [0]], due to the fact that\n     * // the dense layer has a kernel initialized to all-zeros and does not have\n     * // a bias.\n     * output.print();\n     * ```\n     *\n     * When called with `tf.SymbolicTensor`(s), this will prepare the layer for\n     * future execution.  This entails internal book-keeping on shapes of\n     * expected Tensors, wiring layers together, and initializing weights.\n     *\n     * Calling `apply` with `tf.SymbolicTensor`s are typically used during the\n     * building of non-`tf.Sequential` models. For example:\n     *\n     * ```js\n     * const flattenLayer = tf.layers.flatten();\n     * const denseLayer = tf.layers.dense({units: 1});\n     *\n     * // Use tf.layers.input() to obtain a SymbolicTensor as input to apply().\n     * const input = tf.input({shape: [2, 2]});\n     * const output1 = flattenLayer.apply(input);\n     *\n     * // output1.shape is [null, 4]. The first dimension is the undetermined\n     * // batch size. The second dimension comes from flattening the [2, 2]\n     * // shape.\n     * console.log(JSON.stringify(output1.shape));\n     *\n     * // The output SymbolicTensor of the flatten layer can be used to call\n     * // the apply() of the dense layer:\n     * const output2 = denseLayer.apply(output1);\n     *\n     * // output2.shape is [null, 1]. The first dimension is the undetermined\n     * // batch size. The second dimension matches the number of units of the\n     * // dense layer.\n     * console.log(JSON.stringify(output2.shape));\n     *\n     * // The input and output and be used to construct a model that consists\n     * // of the flatten and dense layers.\n     * const model = tf.model({inputs: input, outputs: output2});\n     * ```\n     *\n     * @param inputs a `tf.Tensor` or `tf.SymbolicTensor` or an Array of them.\n     * @param kwargs Additional keyword arguments to be passed to `call()`.\n     *\n     * @return Output of the layer's `call` method.\n     *\n     * @exception ValueError error in case the layer is missing shape information\n     *   for its `build` call.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    // Porting Note: This is a replacement for __call__() in Python.\n    apply(inputs, kwargs) {\n        kwargs = kwargs || {};\n        this.assertNotDisposed();\n        // Ensure inputs are all the same type.\n        const inputsList = generic_utils.toList(inputs);\n        let allAreSymbolic = true;\n        for (const input of inputsList) {\n            if (!(input instanceof SymbolicTensor)) {\n                allAreSymbolic = false;\n                break;\n            }\n        }\n        let noneAreSymbolic = true;\n        for (const input of inputsList) {\n            if (input instanceof SymbolicTensor) {\n                noneAreSymbolic = false;\n                break;\n            }\n        }\n        if (allAreSymbolic === noneAreSymbolic) {\n            throw new ValueError('Arguments to apply() must be all ' +\n                'SymbolicTensors or all Tensors');\n        }\n        // TODO(michaelterry): nameScope() may not be necessary.\n        return nameScope(this.name, () => {\n            // Handle laying building (weight creating, input spec locking).\n            if (!this.built) {\n                /*\n                  Throw exceptions in case the input is not compatible\n                  with the inputSpec specified in the layer constructor.\n                 */\n                this.assertInputCompatibility(inputs);\n                // Collect input shapes to build layer.\n                const inputShapes = [];\n                for (const xElem of generic_utils.toList(inputs)) {\n                    inputShapes.push(xElem.shape);\n                }\n                this.build(generic_utils.singletonOrArray(inputShapes));\n                this.built = true;\n                // Load weights that were specified at layer instantiation.\n                if (this.initialWeights) {\n                    this.setWeights(this.initialWeights);\n                }\n                if (this._refCount === null && noneAreSymbolic) {\n                    // The first use of this layer is a non-symbolic call, set ref count\n                    // to 1 so the Layer can be properly disposed if its dispose() method\n                    // is called.\n                    this._refCount = 1;\n                }\n            }\n            /*\n              Throw exceptions in case the input is not compatible\n              with the inputSpec set at build time.\n            */\n            this.assertInputCompatibility(inputs);\n            // Handle mask propagation.\n            // TODO(michaelterry): Mask propagation not currently implemented.\n            // Actually call the layer, collecting output(s), mask(s), and shape(s).\n            if (noneAreSymbolic) {\n                let output = this.call(inputs, kwargs);\n                // TODO(michaelterry): Compute the outputMask\n                // If the layer returns tensors from its inputs, unmodified,\n                // we copy them to avoid loss of tensor metadata.\n                const outputList = generic_utils.toList(output);\n                const outputListCopy = [];\n                // TODO(michaelterry): This copying may not be necessary given our eager\n                // backend.\n                for (let x of outputList) {\n                    if (inputsList.indexOf(x) !== -1) {\n                        x = x.clone();\n                    }\n                    outputListCopy.push(x);\n                }\n                output = generic_utils.singletonOrArray(outputListCopy);\n                if (this.activityRegularizer != null) {\n                    throw new NotImplementedError('Layer invocation in the presence of activity ' +\n                        'regularizer(s) is not supported yet.');\n                }\n                // TODO(michaelterry): Call addInboundNode()?\n                return output;\n            }\n            else {\n                const inputShape = collectInputShape(inputs);\n                const outputShape = this.computeOutputShape(inputShape);\n                let output;\n                const outputDType = guessOutputDType(inputs);\n                this.warnOnIncompatibleInputShape(Array.isArray(inputs) ? inputShape[0] :\n                    inputShape);\n                if (outputShape != null && outputShape.length > 0 &&\n                    Array.isArray(outputShape[0])) {\n                    // We have multiple output shapes. Create multiple output tensors.\n                    output = outputShape\n                        .map((shape, index) => new SymbolicTensor(outputDType, shape, this, generic_utils.toList(inputs), kwargs, this.name, index));\n                }\n                else {\n                    output = new SymbolicTensor(outputDType, outputShape, this, generic_utils.toList(inputs), kwargs, this.name);\n                }\n                /*\n                  Add an inbound node to the layer, so that it keeps track\n                  of the call and of all new variables created during the call.\n                  This also updates the layer history of the output tensor(s).\n                  If the input tensor(s) had no previous history,\n                  this does nothing.\n                */\n                this.addInboundNode(inputs, output, null, null, inputShape, outputShape, kwargs);\n                this._refCount++;\n                if (this.activityRegularizer != null) {\n                    throw new NotImplementedError('Layer invocation in the presence of activity ' +\n                        'regularizer(s) is not supported yet.');\n                }\n                return output;\n            }\n        });\n    }\n    /**\n     * Check compatibility between input shape and this layer's batchInputShape.\n     *\n     * Print warning if any incompatibility is found.\n     *\n     * @param inputShape Input shape to be checked.\n     */\n    warnOnIncompatibleInputShape(inputShape) {\n        if (this.batchInputShape == null) {\n            return;\n        }\n        else if (inputShape.length !== this.batchInputShape.length) {\n            console.warn(`The rank of the input tensor provided (shape: ` +\n                `${JSON.stringify(inputShape)}) does not match that of the ` +\n                `batchInputShape (${JSON.stringify(this.batchInputShape)}) ` +\n                `of the layer ${this.name}`);\n        }\n        else {\n            let dimMismatch = false;\n            this.batchInputShape.forEach((dimension, i) => {\n                if (dimension != null && inputShape[i] != null &&\n                    inputShape[i] !== dimension) {\n                    dimMismatch = true;\n                }\n            });\n            if (dimMismatch) {\n                console.warn(`The shape of the input tensor ` +\n                    `(${JSON.stringify(inputShape)}) does not ` +\n                    `match the expectation of layer ${this.name}: ` +\n                    `${JSON.stringify(this.batchInputShape)}`);\n            }\n        }\n    }\n    /**\n     * Retrieves the output shape(s) of a layer.\n     *\n     * Only applicable if the layer has only one inbound node, or if all inbound\n     * nodes have the same output shape.\n     *\n     * @returns Output shape or shapes.\n     * @throws AttributeError: if the layer is connected to more than one incoming\n     *   nodes.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    get outputShape() {\n        if (this.inboundNodes == null || this.inboundNodes.length === 0) {\n            throw new AttributeError(`The layer ${this.name} has never been called and thus has no ` +\n                `defined output shape.`);\n        }\n        const allOutputShapes = [];\n        for (const node of this.inboundNodes) {\n            const shapeString = JSON.stringify(node.outputShapes);\n            if (allOutputShapes.indexOf(shapeString) === -1) {\n                allOutputShapes.push(shapeString);\n            }\n        }\n        if (allOutputShapes.length === 1) {\n            const outputShapes = this.inboundNodes[0].outputShapes;\n            if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) &&\n                outputShapes.length === 1) {\n                return outputShapes[0];\n            }\n            else {\n                return outputShapes;\n            }\n        }\n        else {\n            throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different ` +\n                `output shapes. Hence the notion of \"output shape\" is ill-defined ` +\n                `for the layer.`);\n            // TODO(cais): Implement getOutputShapeAt().\n        }\n    }\n    /**\n     * Counts the total number of numbers (e.g., float32, int32) in the\n     * weights.\n     *\n     * @returns An integer count.\n     * @throws RuntimeError: If the layer is not built yet (in which case its\n     *   weights are not defined yet.)\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    countParams() {\n        if (!this.built) {\n            throw new RuntimeError(`You tried to call countParams() on ${this.name}, ` +\n                `but the layer is not built yet. Build it first by calling ` +\n                `build(batchInputShape).`);\n        }\n        return variable_utils.countParamsInWeights(this.weights);\n    }\n    /**\n     * Creates the layer weights.\n     *\n     * Must be implemented on all layers that have weights.\n     *\n     * Called when apply() is called to construct the weights.\n     *\n     * @param inputShape A `Shape` or array of `Shape` (unused).\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    build(inputShape) {\n        this.built = true;\n    }\n    /**\n     * Returns the current values of the weights of the layer.\n     *\n     * @param trainableOnly Whether to get the values of only trainable weights.\n     * @returns Weight values as an `Array` of `tf.Tensor`s.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    getWeights(trainableOnly = false) {\n        return batchGetValue(trainableOnly ? this.trainableWeights : this.weights);\n    }\n    /**\n     * Sets the weights of the layer, from Tensors.\n     *\n     * @param weights a list of Tensors. The number of arrays and their shape\n     *   must match number of the dimensions of the weights of the layer (i.e.\n     *   it should match the output of `getWeights`).\n     *\n     * @exception ValueError If the provided weights list does not match the\n     *   layer's specifications.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    setWeights(weights) {\n        tidy(() => {\n            const params = this.weights;\n            if (params.length !== weights.length) {\n                // TODO(cais): Restore the following and use `providedWeights`, instead\n                // of `weights` in the error message, once the deeplearn.js bug is\n                // fixed: https://github.com/PAIR-code/deeplearnjs/issues/498 const\n                // providedWeights = JSON.stringify(weights).substr(0, 50);\n                throw new ValueError(`You called setWeights(weights) on layer \"${this.name}\" ` +\n                    `with a weight list of length ${weights.length}, ` +\n                    `but the layer was expecting ${params.length} weights. ` +\n                    `Provided weights: ${weights}...`);\n            }\n            if (params.length === 0) {\n                return;\n            }\n            const weightValueTuples = [];\n            const paramValues = batchGetValue(params);\n            for (let i = 0; i < paramValues.length; ++i) {\n                const pv = paramValues[i];\n                const p = params[i];\n                const w = weights[i];\n                if (!util.arraysEqual(pv.shape, w.shape)) {\n                    throw new ValueError(`Layer weight shape ${pv.shape} ` +\n                        `not compatible with provided weight shape ${w.shape}`);\n                }\n                weightValueTuples.push([p, w]);\n            }\n            batchSetValue(weightValueTuples);\n        });\n    }\n    /**\n     * Adds a weight variable to the layer.\n     *\n     * @param name Name of the new weight variable.\n     * @param shape The shape of the weight.\n     * @param dtype The dtype of the weight.\n     * @param initializer An initializer instance.\n     * @param regularizer A regularizer instance.\n     * @param trainable Whether the weight should be trained via backprop or not\n     *   (assuming that the layer itself is also trainable).\n     * @param constraint An optional trainable.\n     * @return The created weight variable.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint) {\n        // Reject duplicate weight names.\n        if (this._addedWeightNames.indexOf(name) !== -1) {\n            throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);\n        }\n        this._addedWeightNames.push(name);\n        if (dtype == null) {\n            dtype = 'float32';\n        }\n        if (this.fastWeightInitDuringBuild) {\n            initializer = getInitializer('zeros');\n        }\n        const initValue = initializer.apply(shape, dtype);\n        const weight = new LayerVariable(initValue, dtype, name, trainable, constraint);\n        initValue.dispose();\n        // Request backend not to dispose the weights of the model on scope() exit.\n        if (regularizer != null) {\n            this.addLoss(() => regularizer.apply(weight.read()));\n        }\n        if (trainable == null) {\n            trainable = true;\n        }\n        if (trainable) {\n            this._trainableWeights.push(weight);\n        }\n        else {\n            this._nonTrainableWeights.push(weight);\n        }\n        return weight;\n    }\n    /**\n     * Set the fast-weight-initialization flag.\n     *\n     * In cases where the initialized weight values will be immediately\n     * overwritten by loaded weight values during model loading, setting\n     * the flag to `true` saves unnecessary calls to potentially expensive\n     * initializers and speeds up the loading process.\n     *\n     * @param value Target value of the flag.\n     */\n    setFastWeightInitDuringBuild(value) {\n        this.fastWeightInitDuringBuild = value;\n    }\n    /**\n     * Add losses to the layer.\n     *\n     * The loss may potentionally be conditional on some inputs tensors,\n     * for instance activity losses are conditional on the layer's inputs.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    addLoss(losses) {\n        if (losses == null || Array.isArray(losses) && losses.length === 0) {\n            return;\n        }\n        // Update this.losses\n        losses = generic_utils.toList(losses);\n        if (this._losses !== undefined && this._losses !== null) {\n            this.losses.push(...losses);\n        }\n    }\n    /**\n     * Computes the output shape of the layer.\n     *\n     * Assumes that the layer will be built to match that input shape provided.\n     *\n     * @param inputShape A shape (tuple of integers) or a list of shape tuples\n     *   (one per output tensor of the layer). Shape tuples can include null for\n     *   free dimensions, instead of an integer.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    /**\n     * Computes an output mask tensor.\n     *\n     * @param inputs Tensor or list of tensors.\n     * @param mask Tensor or list of tensors.\n     *\n     * @return null or a tensor (or list of tensors, one per output tensor of the\n     * layer).\n     */\n    computeMask(inputs, mask) {\n        if (!this.supportsMasking) {\n            if (mask != null) {\n                if (Array.isArray(mask)) {\n                    mask.forEach(maskElement => {\n                        if (maskElement != null) {\n                            throw new TypeError(`Layer ${this.name} does not support masking, ` +\n                                'but was passed an inputMask.');\n                        }\n                    });\n                }\n                else {\n                    throw new TypeError(`Layer ${this.name} does not support masking, ` +\n                        'but was passed an inputMask.');\n                }\n            }\n            // masking not explicitly supported: return null as mask\n            return null;\n        }\n        // if masking is explictly supported, by default\n        // carry over the input mask\n        return mask;\n    }\n    /**\n     * Internal method to create an inbound node for the layer.\n     *\n     * @param inputTensors List of input tensors.\n     * @param outputTensors List of output tensors.\n     * @param inputMasks List of input masks (a mask can be a tensor, or null).\n     * @param outputMasks List of output masks (a mask can be a tensor, or null).\n     * @param inputShapes List of input shape tuples.\n     * @param outputShapes List of output shape tuples.\n     * @param kwargs Dictionary of keyword arguments that were passed to the\n     *   `call` method of the layer at the call that created the node.\n     */\n    addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) {\n        const inputTensorList = generic_utils.toList(inputTensors);\n        outputTensors = generic_utils.toList(outputTensors);\n        inputMasks = generic_utils.toList(inputMasks);\n        outputMasks = generic_utils.toList(outputMasks);\n        inputShapes = types_utils.normalizeShapeList(inputShapes);\n        outputShapes = types_utils.normalizeShapeList(outputShapes);\n        // Collect input tensor(s) coordinates.\n        const inboundLayers = [];\n        const nodeIndices = [];\n        const tensorIndices = [];\n        for (const x of inputTensorList) {\n            /*\n             * TODO(michaelterry): Keras adds this value to tensors; it's not\n             * clear whether we'll use this or not.\n             */\n            inboundLayers.push(x.sourceLayer);\n            nodeIndices.push(x.nodeIndex);\n            tensorIndices.push(x.tensorIndex);\n        }\n        // Create node, add it to inbound nodes.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers,\n            nodeIndices,\n            tensorIndices,\n            inputTensors: inputTensorList,\n            outputTensors,\n            inputMasks,\n            outputMasks,\n            inputShapes,\n            outputShapes\n        }, kwargs);\n        // Update tensor history\n        for (let i = 0; i < outputTensors.length; i++) {\n            // TODO(michaelterry: _uses_learning_phase not tracked.\n            outputTensors[i].sourceLayer = this;\n            outputTensors[i].nodeIndex = this.inboundNodes.length - 1;\n            outputTensors[i].tensorIndex = i;\n        }\n    }\n    /**\n     * Returns the config of the layer.\n     *\n     * A layer config is a TS dictionary (serializable)\n     * containing the configuration of a layer.\n     * The same layer can be reinstantiated later\n     * (without its trained weights) from this configuration.\n     *\n     * The config of a layer does not include connectivity\n     * information, nor the layer class name.  These are handled\n     * by 'Container' (one layer of abstraction above).\n     *\n     * Porting Note: The TS dictionary follows TS naming standrds for\n     * keys, and uses tfjs-layers type-safe Enums.  Serialization methods\n     * should use a helper function to convert to the pythonic storage\n     * standard. (see serialization_utils.convertTsToPythonic)\n     *\n     * @returns TS dictionary of configuration.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    getConfig() {\n        const config = { name: this.name, trainable: this.trainable };\n        if (this.batchInputShape != null) {\n            config['batchInputShape'] = this.batchInputShape;\n        }\n        if (this.dtype != null) {\n            config['dtype'] = this.dtype;\n        }\n        return config;\n    }\n    /**\n     * Dispose the weight variables that this Layer instance holds.\n     *\n     * @returns {number} Number of disposed variables.\n     */\n    disposeWeights() {\n        this.weights.forEach(weight => weight.dispose());\n        return this.weights.length;\n    }\n    assertNotDisposed() {\n        if (this._refCount === 0) {\n            throw new Error(`Layer '${this.name}' is already disposed.`);\n        }\n    }\n    /**\n     * Attempt to dispose layer's weights.\n     *\n     * This method decrease the reference count of the Layer object by 1.\n     *\n     * A Layer is reference-counted. Its reference count is incremented by 1\n     * the first item its `apply()` method is called and when it becomes a part\n     * of a new `Node` (through calling the `apply()`) method on a\n     * `tf.SymbolicTensor`).\n     *\n     * If the reference count of a Layer becomes 0, all the weights will be\n     * disposed and the underlying memory (e.g., the textures allocated in WebGL)\n     * will be freed.\n     *\n     * Note: If the reference count is greater than 0 after the decrement, the\n     * weights of the Layer will *not* be disposed.\n     *\n     * After a Layer is disposed, it cannot be used in calls such as `apply()`,\n     * `getWeights()` or `setWeights()` anymore.\n     *\n     * @returns A DisposeResult Object with the following fields:\n     *   - refCountAfterDispose: The reference count of the Container after this\n     *     `dispose()` call.\n     *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n     *     during this `dispose()` call.\n     * @throws {Error} If the layer is not built yet, or if the layer has already\n     *   been disposed.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    dispose() {\n        if (!this.built) {\n            throw new Error(`Cannot dispose Layer ${this.name} because it has not been ` +\n                `built yet.`);\n        }\n        if (this._refCount === null) {\n            throw new Error(`Cannot dispose Layer ${this.name} because it has not been used ` +\n                `yet.`);\n        }\n        this.assertNotDisposed();\n        let numDisposedVariables = 0;\n        if (--this._refCount === 0) {\n            numDisposedVariables = this.disposeWeights();\n        }\n        return { refCountAfterDispose: this._refCount, numDisposedVariables };\n    }\n}\n/**\n * Collects the input shape(s) of a list of `tf.Tensor`s or\n * `tf.SymbolicTensor`s.\n *\n * TODO(michaelterry): Update PyKeras docs (backport).\n *\n * @param inputTensors List of input tensors (or single input tensor).\n *\n * @return List of shape tuples (or single tuple), one tuple per input.\n */\nfunction collectInputShape(inputTensors) {\n    inputTensors =\n        generic_utils.toList(inputTensors);\n    const shapes = [];\n    for (const x of inputTensors) {\n        shapes.push(x.shape);\n    }\n    return generic_utils.singletonOrArray(shapes);\n}\n/**\n * Guesses output dtype based on inputs.\n *\n * At present, just returns 'float32' for any input.\n *\n * @param inputTensors List of input tensors (or single input tensor).\n *\n * @return The guessed DType. At present, always returns 'float32'.\n */\nfunction guessOutputDType(inputTensors) {\n    return 'float32';\n}\n/**\n * Returns the list of input tensors necessary to compute `tensor`.\n *\n * Output will always be a list of tensors (potentially with 1 element).\n *\n * @param tensor The tensor to start from.\n * @param layer Origin layer of the tensor.\n * @param nodeIndex Origin node index of the tensor.\n *\n * @return Array of input tensors.\n */\nexport function getSourceInputs(tensor, layer, nodeIndex) {\n    if (layer == null || (nodeIndex != null && nodeIndex > 0)) {\n        layer = tensor.sourceLayer;\n        nodeIndex = tensor.nodeIndex;\n    }\n    if (layer.inboundNodes.length === 0) {\n        return [tensor];\n    }\n    else {\n        const node = layer.inboundNodes[nodeIndex];\n        if (node.inboundLayers.length === 0) {\n            return node.inputTensors;\n        }\n        else {\n            const sourceTensors = [];\n            for (let i = 0; i < node.inboundLayers.length; i++) {\n                const x = node.inputTensors[i];\n                const layer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const previousSources = getSourceInputs(x, layer, nodeIndex);\n                // Avoid input redundancy.\n                for (const x of previousSources) {\n                    if (sourceTensors.indexOf(x) === -1) {\n                        sourceTensors.push(x);\n                    }\n                }\n            }\n            return sourceTensors;\n        }\n    }\n}\n//# sourceMappingURL=topology.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { serialization } from '@tensorflow/tfjs-core';\nimport { getUid } from '../backend/state';\nimport { ValueError } from '../errors';\nimport { Layer, Node, SymbolicTensor } from './topology';\nexport class InputLayer extends Layer {\n    constructor(args) {\n        super({\n            dtype: args.dtype,\n            name: args.name != null ? args.name : getUid('input').toString()\n        });\n        // Normalize config.batchSize and config.sparse\n        if (args.batchSize == null) {\n            args.batchSize = null;\n        }\n        if (args.sparse == null) {\n            args.sparse = false;\n        }\n        this.trainable = false;\n        this.built = true;\n        this.sparse = args.sparse;\n        if (args.inputShape != null && args.batchInputShape != null) {\n            throw new ValueError('Only provide the inputShape OR ' +\n                'batchInputShape argument to inputLayer, not both at the same time.');\n        }\n        let batchInputShape = args.batchInputShape;\n        if (batchInputShape == null) {\n            if (args.inputShape == null) {\n                throw new ValueError('An InputLayer should be passed either a ' +\n                    '`batchInputShape` or an `inputShape`.');\n            }\n            else {\n                batchInputShape = [args.batchSize].concat(args.inputShape);\n            }\n        }\n        else {\n            // TODO(michaelterry): Backport to PyKeras\n            if (args.batchSize != null) {\n                throw new ValueError('Cannot specify batchSize if batchInputShape is ' +\n                    'specified when creating an InputLayer.');\n            }\n        }\n        const dtype = args.dtype || 'float32';\n        this.batchInputShape = batchInputShape;\n        this.dtype = dtype;\n        // TODO(michaelterry): Backport this to PyKeras?\n        this.inputSpec = [{ shape: batchInputShape }];\n        const inputTensor = new SymbolicTensor(this.dtype, this.batchInputShape, this, [], {}, this.name);\n        inputTensor.nodeIndex = 0;\n        inputTensor.tensorIndex = 0;\n        // Create an input node to add to this.outboundNode.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers: [],\n            nodeIndices: [],\n            tensorIndices: [],\n            inputTensors: [inputTensor],\n            outputTensors: [inputTensor],\n            inputMasks: [null],\n            outputMasks: [null],\n            inputShapes: [batchInputShape],\n            outputShapes: [batchInputShape]\n        });\n    }\n    apply(inputs, kwargs) {\n        throw new ValueError('Cannot pass any input to an ' +\n            `InputLayer's apply() method. InputLayer name: ${this.name}`);\n    }\n    dispose() {\n        // dispose() for InputLayer is overridden as no-op.\n        return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 };\n    }\n    getConfig() {\n        return {\n            batchInputShape: this.batchInputShape,\n            dtype: this.dtype,\n            sparse: this.sparse,\n            name: this.name\n        };\n    }\n}\n/** @nocollapse */\nInputLayer.className = 'InputLayer';\nserialization.registerClass(InputLayer);\nexport function Input(config) {\n    if (config.batchShape == null && config.shape == null) {\n        throw new Error('Please provide to Input either a `shape`' +\n            ' or a `batchShape` argument. Note that ' +\n            '`shape` does not include the batch ' +\n            'dimension.');\n    }\n    if (config.batchShape != null && config.shape != null) {\n        // TODO(michaelterry): Backport to PyKeras.\n        throw new ValueError('Please provide either a `shape` or `batchShape` ' +\n            'argument to Input, but not both.');\n    }\n    let batchShape = config.batchShape;\n    if (config.shape != null && batchShape == null) {\n        batchShape = [null].concat(config.shape);\n    }\n    let dtype = config.dtype;\n    if (dtype == null) {\n        dtype = 'float32';\n    }\n    const inputLayer = new InputLayer({\n        batchInputShape: batchShape,\n        name: config.name,\n        dtype,\n        sparse: config.sparse\n    });\n    const outputs = inputLayer.inboundNodes[0].outputTensors;\n    return outputs[0];\n}\n//# sourceMappingURL=input_layer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { dispose } from '@tensorflow/tfjs-core';\n/**\n * Turn any Scalar values in a Logs object into actual number values.\n *\n * @param logs The `Logs` object to be resolved in place.\n */\nexport async function resolveScalarsInLogs(logs) {\n    if (logs == null) {\n        return;\n    }\n    const promises = [];\n    const keys = [];\n    const scalarsToDispose = [];\n    for (const key in logs) {\n        const value = logs[key];\n        if (typeof value !== 'number') {\n            const valueScalar = value;\n            promises.push(valueScalar.data());\n            keys.push(key);\n            scalarsToDispose.push(valueScalar);\n        }\n    }\n    if (promises.length > 0) {\n        const values = await Promise.all(promises);\n        for (let i = 0; i < values.length; ++i) {\n            logs[keys[i]] = values[i][0];\n        }\n        // Dispose the original scalar tensors.\n        dispose(scalarsToDispose);\n    }\n}\n/**\n * Dispose all Tensors in an UnresolvedLogs object.\n *\n * @param logs An `UnresolvedLogs` object potentially containing `tf.Tensor`s in\n *   places where the values can be `tf.Tensor` or `number`.\n */\nexport function disposeTensorsInLogs(logs) {\n    if (logs == null) {\n        return;\n    }\n    for (const key in logs) {\n        const value = logs[key];\n        if (typeof value !== 'number') {\n            value.dispose();\n        }\n    }\n}\n//# sourceMappingURL=logs.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/callbacks.py */\nimport { add, div, keep, mul, nextFrame, tidy, util } from '@tensorflow/tfjs-core';\nimport { ValueError } from './errors';\nimport { resolveScalarsInLogs } from './logs';\nimport * as generic_utils from './utils/generic_utils';\n/** Verbosity logging level when fitting a model. */\nexport var ModelLoggingVerbosity;\n(function (ModelLoggingVerbosity) {\n    ModelLoggingVerbosity[ModelLoggingVerbosity[\"SILENT\"] = 0] = \"SILENT\";\n    ModelLoggingVerbosity[ModelLoggingVerbosity[\"VERBOSE\"] = 1] = \"VERBOSE\";\n})(ModelLoggingVerbosity || (ModelLoggingVerbosity = {}));\n/** How often to yield to the main thread when training (in ms). */\nexport const DEFAULT_YIELD_EVERY_MS = 125;\n/**\n * Abstract base class used to build new callbacks.\n *\n * The `logs` dictionary that callback methods take as argument will contain\n * keys for quantities relevant to the current batch or epoch.\n *\n * Currently, the `.fit()` method of the `Sequential` model class\n * will include the following quantities in the `logs` that\n * it passes to its callbacks:\n *\n * onEpochEnd: Logs include `acc` and `loss`, and optionally include `valLoss`\n *   (if validation is enabled in `fit`), and `valAcc` (if validation and\n *   accuracy monitoring are enabled).\n * onBatchBegin: Logs include `size`, the number of samples in the current\n *   batch.\n * onBatchEnd: Logs include `loss`, and optionally `acc` (if accuracy monitoring\n *   is enabled).\n */\nexport class BaseCallback {\n    constructor() {\n        // TODO(michaelterry): This type is a best guess.\n        this.validationData = null;\n    }\n    setParams(params) {\n        this.params = params;\n    }\n    async onEpochBegin(epoch, logs) { }\n    async onEpochEnd(epoch, logs) { }\n    async onBatchBegin(batch, logs) { }\n    async onBatchEnd(batch, logs) { }\n    async onTrainBegin(logs) { }\n    async onTrainEnd(logs) { }\n    // LayersModel needs to call Callback.setModel(), but cannot actually depend\n    // on Callback because that creates a cyclic dependency.  Providing this no-op\n    // method on BaseCallback breaks the cycle: this way LayersModel can depend on\n    // BaseCallback but not on Callback.  The argument is typed as `Container`\n    // (the superclass of LayersModel) to avoid recapitulating the cycle. Callback\n    // overrides this method and enforces that the argument is really a\n    // LayersModel.\n    setModel(model) {\n        // Do nothing. Use Callback instead of BaseCallback to track the model.\n    }\n}\n/**\n * Container abstracting a list of callbacks.\n */\nexport class CallbackList {\n    // TODO(cais): When the need arises, uncomment the following lines and\n    // implement the queue for time values.\n    // private deltaTBatch: number;\n    // private deltaTsBatchBegin: Array<number>;\n    // private deltaTsBatchEnd: Array<number>;\n    /**\n     * Constructor of CallbackList.\n     * @param callbacks Array of `Callback` instances.\n     * @param queueLength Queue length for keeping running statistics over\n     *   callback execution time.\n     */\n    constructor(callbacks, queueLength = 10) {\n        // TODO(cais): Make use of queueLength when implementing the queue for time\n        // values.\n        if (callbacks == null) {\n            callbacks = [];\n        }\n        this.callbacks = callbacks;\n        this.queueLength = queueLength;\n    }\n    append(callback) {\n        this.callbacks.push(callback);\n    }\n    setParams(params) {\n        for (const callback of this.callbacks) {\n            callback.setParams(params);\n        }\n    }\n    setModel(model) {\n        for (const callback of this.callbacks) {\n            callback.setModel(model);\n        }\n    }\n    /**\n     * Called at the start of an epoch.\n     * @param epoch Index of epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onEpochBegin(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onEpochBegin(epoch, logs);\n        }\n    }\n    /**\n     * Called at the end of an epoch.\n     * @param epoch Index of epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onEpochEnd(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onEpochEnd(epoch, logs);\n        }\n    }\n    /**\n     * Called  right before processing a batch.\n     * @param batch Index of batch within the current epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onBatchBegin(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onBatchBegin(batch, logs);\n        }\n    }\n    /**\n     * Called at the end of a batch.\n     * @param batch Index of batch within the current epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onBatchEnd(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onBatchEnd(batch, logs);\n        }\n    }\n    /**\n     * Called at the beginning of training.\n     * @param logs Dictionary of logs.\n     */\n    async onTrainBegin(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onTrainBegin(logs);\n        }\n    }\n    /**\n     * Called at the end of training.\n     * @param logs Dictionary of logs.\n     */\n    async onTrainEnd(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onTrainEnd(logs);\n        }\n    }\n}\n/**\n * Callback that accumulates epoch averages of metrics.\n *\n * This callback is automatically applied to every LayersModel.\n */\nexport class BaseLogger extends BaseCallback {\n    constructor() {\n        super();\n    }\n    async onEpochBegin(epoch) {\n        this.seen = 0;\n        this.totals = {};\n    }\n    async onBatchEnd(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        const batchSize = logs['size'] == null ? 0 : logs['size'];\n        this.seen += batchSize;\n        for (const key in logs) {\n            const value = logs[key];\n            if (typeof value === 'number') {\n                if (!this.totals.hasOwnProperty(key)) {\n                    this.totals[key] = 0;\n                }\n                this.totals[key] = this.totals[key] + value * batchSize;\n            }\n            else {\n                let oldTotalsToDispose;\n                if (key in this.totals) {\n                    oldTotalsToDispose = this.totals[key];\n                }\n                else {\n                    this.totals[key] = 0;\n                }\n                const total = tidy(() => add((this.totals[key]), mul(value, batchSize)));\n                this.totals[key] = total;\n                if (oldTotalsToDispose != null) {\n                    oldTotalsToDispose.dispose();\n                }\n            }\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        if (logs != null) {\n            for (const key of this.params['metrics']) {\n                if (this.totals[key] == null) {\n                    continue;\n                }\n                if (typeof this.totals[key] === 'number') {\n                    logs[key] = this.totals[key] / this.seen;\n                }\n                else {\n                    tidy(() => {\n                        const log = mul(div(1, this.seen), this.totals[key]);\n                        logs[key] = log;\n                        this.totals[key].dispose();\n                        keep(logs[key]);\n                    });\n                }\n            }\n        }\n    }\n}\n/**\n * Callback that records events into a `History` object. This callback is\n * automatically applied to every TF.js Layers model. The `History` object\n * gets returned by the `fit` method of models.\n */\nexport class History extends BaseCallback {\n    async onTrainBegin(logs) {\n        this.epoch = [];\n        this.history = {};\n    }\n    async onEpochEnd(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        this.epoch.push(epoch);\n        for (const key in logs) {\n            if (this.history[key] == null) {\n                this.history[key] = [];\n            }\n            this.history[key].push(logs[key]);\n        }\n    }\n    /**\n     * Await the values of all losses and metrics.\n     */\n    async syncData() {\n        const promises = [];\n        const keys = [];\n        const indices = [];\n        for (const key in this.history) {\n            const valueArray = this.history[key];\n            for (let i = 0; i < valueArray.length; ++i) {\n                if (typeof valueArray[i] !== 'number') {\n                    const valueScalar = valueArray[i];\n                    promises.push(valueScalar.data());\n                    keys.push(key);\n                    indices.push(i);\n                }\n            }\n        }\n        const values = await Promise.all(promises);\n        for (let n = 0; n < values.length; ++n) {\n            const tensorToDispose = this.history[keys[n]][indices[n]];\n            tensorToDispose.dispose();\n            this.history[keys[n]][indices[n]] = values[n][0];\n        }\n    }\n}\n/**\n * Custom callback for training.\n */\nexport class CustomCallback extends BaseCallback {\n    constructor(args, yieldEvery) {\n        super();\n        this.currentEpoch = 0;\n        this.yieldEvery = yieldEvery || 'auto';\n        if (this.yieldEvery === 'auto') {\n            this.yieldEvery = DEFAULT_YIELD_EVERY_MS;\n        }\n        if (this.yieldEvery === 'never' && args.onYield != null) {\n            throw new Error('yieldEvery is `never` but you provided an `onYield` callback. ' +\n                'Either change `yieldEvery` or remove the callback');\n        }\n        if (util.isNumber(this.yieldEvery)) {\n            // Decorate `maybeWait` so it will be called at most once every\n            // `yieldEvery` ms.\n            this.maybeWait = generic_utils.debounce(this.maybeWait.bind(this), this.yieldEvery);\n        }\n        this.trainBegin = args.onTrainBegin;\n        this.trainEnd = args.onTrainEnd;\n        this.epochBegin = args.onEpochBegin;\n        this.epochEnd = args.onEpochEnd;\n        this.batchBegin = args.onBatchBegin;\n        this.batchEnd = args.onBatchEnd;\n        this.yield = args.onYield;\n    }\n    async maybeWait(epoch, batch, logs) {\n        const ps = [];\n        if (this.yield != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.yield(epoch, batch, logs));\n        }\n        ps.push(nextFrame());\n        await Promise.all(ps);\n    }\n    async onEpochBegin(epoch, logs) {\n        this.currentEpoch = epoch;\n        if (this.epochBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.epochBegin(epoch, logs);\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        const ps = [];\n        if (this.epochEnd != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.epochEnd(epoch, logs));\n        }\n        if (this.yieldEvery === 'epoch') {\n            ps.push(nextFrame());\n        }\n        await Promise.all(ps);\n    }\n    async onBatchBegin(batch, logs) {\n        if (this.batchBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.batchBegin(batch, logs);\n        }\n    }\n    async onBatchEnd(batch, logs) {\n        const ps = [];\n        if (this.batchEnd != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.batchEnd(batch, logs));\n        }\n        if (this.yieldEvery === 'batch') {\n            ps.push(nextFrame());\n        }\n        else if (util.isNumber(this.yieldEvery)) {\n            ps.push(this.maybeWait(this.currentEpoch, batch, logs));\n        }\n        await Promise.all(ps);\n    }\n    async onTrainBegin(logs) {\n        if (this.trainBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.trainBegin(logs);\n        }\n    }\n    async onTrainEnd(logs) {\n        if (this.trainEnd != null) {\n            await resolveScalarsInLogs(logs);\n            await this.trainEnd(logs);\n        }\n    }\n}\n/**\n * Standardize callbacks or configurations of them to an Array of callbacks.\n */\nexport function standardizeCallbacks(callbacks, yieldEvery) {\n    if (callbacks == null) {\n        callbacks = {};\n    }\n    if (callbacks instanceof BaseCallback) {\n        return [callbacks];\n    }\n    if (Array.isArray(callbacks) && callbacks[0] instanceof BaseCallback) {\n        return callbacks;\n    }\n    // Convert custom callback configs to custom callback objects.\n    const callbackConfigs = generic_utils.toList(callbacks);\n    return callbackConfigs.map(callbackConfig => new CustomCallback(callbackConfig, yieldEvery));\n}\n/**\n * A global registry for callback constructors to be used during\n * LayersModel.fit().\n */\nexport class CallbackConstructorRegistry {\n    /**\n     * Blocks public access to constructor.\n     */\n    constructor() { }\n    /**\n     * Register a tf.LayersModel.fit() callback constructor.\n     *\n     * The registered callback constructor will be used to instantiate\n     * callbacks for every tf.LayersModel.fit() call afterwards.\n     *\n     * @param verbosityLevel Level of verbosity at which the `callbackConstructor`\n     *   is to be reigstered.\n     * @param callbackConstructor A no-arg constructor for `tf.Callback`.\n     * @throws Error, if the same callbackConstructor has been registered before,\n     *   either at the same or a different `verbosityLevel`.\n     */\n    static registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n        util.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, ` +\n            `but got ${verbosityLevel}`);\n        CallbackConstructorRegistry.checkForDuplicate(callbackConstructor);\n        if (CallbackConstructorRegistry.constructors[verbosityLevel] == null) {\n            CallbackConstructorRegistry.constructors[verbosityLevel] = [];\n        }\n        CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor);\n    }\n    static checkForDuplicate(callbackConstructor) {\n        for (const levelName in CallbackConstructorRegistry.constructors) {\n            const constructors = CallbackConstructorRegistry.constructors[+levelName];\n            constructors.forEach(ctor => {\n                if (ctor === callbackConstructor) {\n                    throw new ValueError('Duplicate callback constructor.');\n                }\n            });\n        }\n    }\n    /**\n     * Clear all registered callback constructors.\n     */\n    static clear() {\n        CallbackConstructorRegistry.constructors = {};\n    }\n    /**\n     * Create callbacks using the registered callback constructors.\n     *\n     * Given `verbosityLevel`, all constructors registered at that level or above\n     * will be called and the instantiated callbacks will be used.\n     *\n     * @param verbosityLevel: Level of verbosity.\n     */\n    static createCallbacks(verbosityLevel) {\n        const constructors = [];\n        for (const levelName in CallbackConstructorRegistry.constructors) {\n            const level = +levelName;\n            if (verbosityLevel >= level) {\n                constructors.push(...CallbackConstructorRegistry.constructors[level]);\n            }\n        }\n        return constructors.map(ctor => new ctor());\n    }\n}\nCallbackConstructorRegistry.constructors = {};\nexport function configureCallbacks(callbacks, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) {\n    const history = new History();\n    const actualCallbacks = [\n        new BaseLogger(), ...CallbackConstructorRegistry.createCallbacks(verbose)\n    ];\n    if (callbacks != null) {\n        actualCallbacks.push(...callbacks);\n    }\n    actualCallbacks.push(history);\n    const callbackList = new CallbackList(actualCallbacks);\n    // TODO(cais): Figure out when this LayersModel instance can have a\n    // dynamically\n    //   set property called 'callback_model' as in PyKeras.\n    callbackList.setParams({\n        epochs,\n        initialEpoch,\n        samples: numTrainSamples,\n        steps: stepsPerEpoch,\n        batchSize,\n        verbose,\n        doValidation,\n        metrics: callbackMetrics,\n    });\n    return { callbackList, history };\n}\n//# sourceMappingURL=base_callbacks.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source layers/__init__.py */\nimport { serialization } from '@tensorflow/tfjs-core';\nimport { deserializeKerasObject } from '../utils/generic_utils';\n/**\n * Instantiate a layer from a config dictionary.\n * @param config dict of the form {class_name: str, config: dict}\n * @param customObjects dict mapping class names (or function names)\n *   of custom (non-Keras) objects to class/functions\n * @param fastWeightInit Optional flag to use fast weight initialization\n *   during deserialization. This is applicable to cases in which\n *   the initialization will be immediately overwritten by loaded weight\n *   values. Default: `false`.\n * @returns Layer instance (may be LayersModel, Sequential, Layer...)\n */\nexport function deserialize(config, customObjects = {}, fastWeightInit = false) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'layer', fastWeightInit);\n}\n//# sourceMappingURL=serialization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source: losses.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { tidy, util } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport * as K from './backend/tfjs_backend';\nimport { ValueError } from './errors';\n/**\n * Normalizes a tensor wrt the L2 norm alongside the specified axis.\n * @param x\n * @param axis Axis along which to perform normalization.\n */\nexport function l2Normalize(x, axis) {\n    return tidy(() => {\n        if (x.dtype !== 'float32') {\n            x = x.asType('float32');\n        }\n        const squareSum = tfc.sum(K.square(x), axis, true);\n        const epsilonTensor = tfc.fill(squareSum.shape, epsilon());\n        const norm = tfc.sqrt(tfc.maximum(squareSum, epsilonTensor));\n        return tfc.div(x, norm);\n    });\n}\nexport function meanSquaredError(yTrue, yPred) {\n    return tidy(() => tfc.mean(K.square(tfc.sub(yPred, yTrue)), -1));\n}\nexport function meanAbsoluteError(yTrue, yPred) {\n    return tidy(() => tfc.mean(tfc.abs(tfc.sub(yPred, yTrue)), -1));\n}\nexport function meanAbsolutePercentageError(yTrue, yPred) {\n    return tidy(() => {\n        const diff = tfc.sub(yTrue, yPred);\n        const clippedTrue = tfc.clipByValue(tfc.abs(yTrue), epsilon(), Number.MAX_VALUE);\n        const absResult = tfc.abs(tfc.div(diff, clippedTrue));\n        return tfc.mul(100, tfc.mean(absResult, -1));\n    });\n}\nexport function meanSquaredLogarithmicError(yTrue, yPred) {\n    return tidy(() => {\n        const clippedPred = tfc.clipByValue(yPred, epsilon(), Number.MAX_VALUE);\n        const firstLog = tfc.log(tfc.add(1, clippedPred));\n        const clippedTrue = tfc.clipByValue(yTrue, epsilon(), Number.MAX_VALUE);\n        const secondLog = tfc.log(tfc.add(1, clippedTrue));\n        return tfc.mean(K.square(tfc.sub(firstLog, secondLog)), -1);\n    });\n}\nexport function squaredHinge(yTrue, yPred) {\n    return tidy(() => {\n        const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n        return tfc.mean(K.square(maxResult), -1);\n    });\n}\nexport function hinge(yTrue, yPred) {\n    return tidy(() => {\n        const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n        return tfc.mean(maxResult, -1);\n    });\n}\nexport function categoricalHinge(yTrue, yPred) {\n    return tidy(() => {\n        const pos = tfc.sum(tfc.mul(yTrue, yPred), -1);\n        const neg = tfc.max(tfc.mul(tfc.sub(1, yTrue), yPred), -1);\n        return tfc.maximum(0, tfc.add(1, tfc.sub(neg, pos)));\n    });\n}\n/**\n * Logarithm of the hyperbolic cosine of the prediction error.\n *\n * `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and\n * to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly\n * like the mean squared error, but will not be so strongly affected by the\n * occasional wildly incorrect prediction.\n */\nexport function logcosh(yTrue, yPred) {\n    return tidy(() => {\n        const log2 = Math.log(2);\n        const predictionDiff = tfc.sub(yPred, yTrue);\n        const logcoshResult = tfc.sub(tfc.add(predictionDiff, tfc.softplus(tfc.mul(-2, predictionDiff))), log2);\n        return tfc.mean(logcoshResult, -1);\n    });\n}\nexport function categoricalCrossentropy(target, output, fromLogits = false) {\n    return tidy(() => {\n        if (fromLogits) {\n            output = tfc.softmax(output);\n        }\n        else {\n            // scale preds so that the class probabilities of each sample sum to 1.\n            const outputSum = tfc.sum(output, output.shape.length - 1, true);\n            output = tfc.div(output, outputSum);\n        }\n        output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n        return tfc.neg(tfc.sum(tfc.mul(target.toFloat(), tfc.log(output)), output.shape.length - 1));\n    });\n}\n/**\n * Categorical crossentropy with integer targets.\n *\n * @param target An integer tensor.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n */\nexport function sparseCategoricalCrossentropy(target, output, fromLogits = false) {\n    return tidy(() => {\n        const flatTarget = tfc.floor(K.flatten(target)).toInt();\n        output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n        const outputShape = output.shape;\n        const oneHotTarget = tfc.oneHot(flatTarget, outputShape[outputShape.length - 1])\n            .reshape(outputShape);\n        return categoricalCrossentropy(oneHotTarget, output, fromLogits);\n    });\n}\n/**\n * From TensorFlow's implementation in nn_impl.py:\n *\n * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n *      z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n *    = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n *    = (1 - z) * x + log(1 + exp(-x))\n *    = x - x * z + log(1 + exp(-x))\n * For x < 0, to avoid overflow in exp(-x), we reformulate the above\n *      x - x * z + log(1 + exp(-x))\n *    = log(exp(x)) - x * z + log(1 + exp(-x))\n *    = - x * z + log(1 + exp(x))\n * Hence, to ensure stability and avoid overflow, the implementation uses this\n * equivalent formulation\n *    max(x, 0) - x * z + log(1 + exp(-abs(x)))\n *\n * @param labels The labels.\n * @param logits The logits.\n */\nexport function sigmoidCrossEntropyWithLogits(labels, logits) {\n    if (!util.arraysEqual(labels.shape, logits.shape)) {\n        throw new ValueError(`logits and labels must have the same shape, but got shapes ` +\n            `${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);\n    }\n    return tidy(() => {\n        // The logistic loss formula from above is\n        //   x - x * z + log(1 + exp(-x))\n        // For x < 0, a more numerically stable formula is\n        //   -x * z + log(1 + exp(x))\n        // Note that these two expressions can be combined into the following:\n        //   max(x, 0) - x * z + log(1 + exp(-abs(x)))\n        const reluLogits = logits.relu();\n        const negAbsLogits = logits.abs().neg();\n        return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p());\n    });\n}\nexport function binaryCrossentropy(yTrue, yPred) {\n    return tidy(() => {\n        let y;\n        y = tfc.clipByValue(yPred, epsilon(), 1 - epsilon());\n        y = tfc.log(tfc.div(y, tfc.sub(1, y)));\n        return tfc.mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);\n    });\n}\nexport function kullbackLeiblerDivergence(yTrue, yPred) {\n    return tidy(() => {\n        const clippedTrue = tfc.clipByValue(yTrue, epsilon(), 1);\n        const clippedPred = tfc.clipByValue(yPred, epsilon(), 1);\n        return tfc.sum(tfc.mul(yTrue, tfc.log(tfc.div(clippedTrue, clippedPred))), -1);\n    });\n}\nexport function poisson(yTrue, yPred) {\n    return tidy(() => {\n        const logPred = tfc.log(tfc.add(epsilon(), yPred));\n        return tfc.mean(tfc.sub(yPred, tfc.mul(yTrue, logPred)), -1);\n    });\n}\nexport function cosineProximity(yTrue, yPred) {\n    return tidy(() => {\n        const trueNormalized = l2Normalize(yTrue, -1);\n        const predNormalized = l2Normalize(yPred, -1);\n        const trueXPred = tfc.mul(trueNormalized, predNormalized);\n        return tfc.neg(tfc.sum(trueXPred, -1));\n    });\n}\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const msle = meanSquaredLogarithmicError;\nexport const MSLE = meanSquaredLogarithmicError;\nexport const kld = kullbackLeiblerDivergence;\nexport const KLD = kullbackLeiblerDivergence;\nexport const cosine = cosineProximity;\n// TODO(michaelterry): Add deserialize() function.\nexport const lossesMap = {\n    meanSquaredError,\n    meanAbsoluteError,\n    meanAbsolutePercentageError,\n    meanSquaredLogarithmicError,\n    squaredHinge,\n    hinge,\n    categoricalHinge,\n    logcosh,\n    categoricalCrossentropy,\n    sparseCategoricalCrossentropy,\n    binaryCrossentropy,\n    kullbackLeiblerDivergence,\n    poisson,\n    cosineProximity\n};\n// Porting note: This diverges from the PyKeras implementation and may need to\n// change based on (de)serialization requirements.\nexport function get(identifierOrFn) {\n    if (typeof identifierOrFn === 'string') {\n        if (identifierOrFn in lossesMap) {\n            return lossesMap[identifierOrFn];\n        }\n        let errMsg = `Unknown loss ${identifierOrFn}`;\n        if (identifierOrFn.toLowerCase().includes('softmaxcrossentropy')) {\n            errMsg = `Unknown loss ${identifierOrFn}. ` +\n                'Use \"categoricalCrossentropy\" as the string name for ' +\n                'tf.losses.softmaxCrossEntropy';\n        }\n        throw new ValueError(errMsg);\n    }\n    else {\n        return identifierOrFn;\n    }\n}\n//# sourceMappingURL=losses.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Built-in metrics.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { tidy } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { NotImplementedError, ValueError } from './errors';\nimport { categoricalCrossentropy as categoricalCrossentropyLoss, cosineProximity, meanAbsoluteError, meanAbsolutePercentageError, meanSquaredError, sparseCategoricalCrossentropy as sparseCategoricalCrossentropyLoss } from './losses';\nimport { binaryCrossentropy as lossBinaryCrossentropy } from './losses';\nimport { lossesMap } from './losses';\nimport * as util from './utils/generic_utils';\nexport function binaryAccuracy(yTrue, yPred) {\n    return tidy(() => {\n        const threshold = tfc.mul(.5, tfc.onesLike(yPred));\n        const yPredThresholded = K.cast(tfc.greater(yPred, threshold), yTrue.dtype);\n        return tfc.mean(tfc.equal(yTrue, yPredThresholded), -1);\n    });\n}\nexport function categoricalAccuracy(yTrue, yPred) {\n    return tidy(() => K.cast(tfc.equal(tfc.argMax(yTrue, -1), tfc.argMax(yPred, -1)), 'float32'));\n}\nfunction truePositives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(1), yPred.equal(1)).sum().cast('float32');\n    });\n}\nfunction falseNegatives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(1), yPred.equal(0)).sum().cast('float32');\n    });\n}\nfunction falsePositives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(0), yPred.equal(1)).sum().cast('float32');\n    });\n}\nexport function precision(yTrue, yPred) {\n    return tidy(() => {\n        const tp = truePositives(yTrue, yPred);\n        const fp = falsePositives(yTrue, yPred);\n        const denominator = tp.add(fp);\n        return tfc.where(tfc.greater(denominator, 0), tp.div(denominator), 0)\n            .cast('float32');\n    });\n}\nexport function recall(yTrue, yPred) {\n    return tidy(() => {\n        const tp = truePositives(yTrue, yPred);\n        const fn = falseNegatives(yTrue, yPred);\n        const denominator = tp.add(fn);\n        return tfc.where(tfc.greater(denominator, 0), tp.div(denominator), 0)\n            .cast('float32');\n    });\n}\nexport function binaryCrossentropy(yTrue, yPred) {\n    return lossBinaryCrossentropy(yTrue, yPred);\n}\nexport function sparseCategoricalAccuracy(yTrue, yPred) {\n    if (yTrue.rank === yPred.rank) {\n        yTrue = yTrue.squeeze([yTrue.rank - 1]);\n    }\n    yPred = yPred.argMax(-1);\n    if (yPred.dtype !== yTrue.dtype) {\n        yPred = yPred.asType(yTrue.dtype);\n    }\n    return tfc.equal(yTrue, yPred).asType('float32');\n}\nexport function topKCategoricalAccuracy(yTrue, yPred) {\n    throw new NotImplementedError();\n}\nexport function sparseTopKCategoricalAccuracy(yTrue, yPred) {\n    throw new NotImplementedError();\n}\n// Aliases.\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const categoricalCrossentropy = categoricalCrossentropyLoss;\nexport const cosine = cosineProximity;\nexport const sparseCategoricalCrossentropy = sparseCategoricalCrossentropyLoss;\n// TODO(cais, nielsene): Add serialize().\nexport const metricsMap = {\n    binaryAccuracy,\n    categoricalAccuracy,\n    precision,\n    categoricalCrossentropy,\n    sparseCategoricalCrossentropy,\n    mse,\n    MSE,\n    mae,\n    MAE,\n    mape,\n    MAPE,\n    cosine\n};\nexport function get(identifier) {\n    if (typeof identifier === 'string' && identifier in metricsMap) {\n        return metricsMap[identifier];\n    }\n    else if (typeof identifier !== 'string' && identifier != null) {\n        return identifier;\n    }\n    else {\n        throw new ValueError(`Unknown metric ${identifier}`);\n    }\n}\n/**\n * Get the shortcut function name.\n *\n * If the fn name is a string,\n *   directly return the string name.\n * If the function is included in metricsMap or lossesMap,\n *   return key of the map.\n *   - If the function relative to multiple keys,\n *     return the first found key as the function name.\n *   - If the function exists in both lossesMap and metricsMap,\n *     search lossesMap first.\n * If the function is not included in metricsMap or lossesMap,\n *   return the function name.\n *\n * @param fn loss function, metric function, or short cut name.\n * @returns Loss or Metric name in string.\n */\nexport function getLossOrMetricName(fn) {\n    util.assert(fn !== null, `Unknown LossOrMetricFn ${fn}`);\n    if (typeof fn === 'string') {\n        return fn;\n    }\n    else {\n        let fnName;\n        for (const key of Object.keys(lossesMap)) {\n            if (lossesMap[key] === fn) {\n                fnName = key;\n                break;\n            }\n        }\n        if (fnName !== undefined) {\n            return fnName;\n        }\n        for (const key of Object.keys(metricsMap)) {\n            if (metricsMap[key] === fn) {\n                fnName = key;\n                break;\n            }\n        }\n        if (fnName !== undefined) {\n            return fnName;\n        }\n        return fn.name;\n    }\n}\n//# sourceMappingURL=metrics.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Optimizers.\n */\nimport { train } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport { ValueError } from './errors';\n// Add (de)serialize()\n// Porting note: This diverges from the PyKeras implementation and may need to\n// change based on (de)serialization requirements.\nexport function getOptimizer(identifier) {\n    const optimizerMap = {\n        'Adagrad': () => train.adagrad(0.01),\n        'Adadelta': () => train.adadelta(1, 0.95, epsilon()),\n        'Adam': () => train.adam(0.001, 0.9, 0.999, epsilon()),\n        'Adamax': () => train.adamax(0.002, 0.9, 0.999, epsilon(), 0),\n        'RMSProp': () => train.rmsprop(0.001, 0.9, 0, epsilon()),\n        'SGD': () => train.sgd(0.01)\n    };\n    optimizerMap['adagrad'] = optimizerMap['Adagrad'];\n    optimizerMap['adadelta'] = optimizerMap['Adadelta'];\n    optimizerMap['adam'] = optimizerMap['Adam'];\n    optimizerMap['adamax'] = optimizerMap['Adamax'];\n    optimizerMap['rmsprop'] = optimizerMap['RMSProp'];\n    optimizerMap['sgd'] = optimizerMap['SGD'];\n    if (identifier in optimizerMap) {\n        return optimizerMap[identifier]();\n    }\n    throw new ValueError(`Unknown Optimizer ${identifier}`);\n}\n//# sourceMappingURL=optimizers.js.map", "/**\n * @license\n * Copyright 2019 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/** Utility functions related to user-defined metadata. */\n// Maximum recommended serialized size for user-defined metadata.\n// Beyond this limit, a warning message will be printed during model loading and\n// saving.\nexport const MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024;\n/**\n * Check validity of user-defined metadata.\n *\n * @param userDefinedMetadata\n * @param modelName Name of the model that the user-defined metadata belongs to.\n *   Used during construction of error messages.\n * @param checkSize Whether to check the size of the metadata is under\n *   recommended limit. Default: `false`. If `true`, will try stringify the\n *   JSON object and print a console warning if the serialzied size is above the\n *   limit.\n * @throws Error if `userDefinedMetadata` is not a plain JSON object.\n */\nexport function checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) {\n    if (userDefinedMetadata == null ||\n        typeof userDefinedMetadata !== 'object' ||\n        Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype ||\n        !plainObjectCheck(userDefinedMetadata)) {\n        throw new Error('User-defined metadata is expected to be a JSON object, but is not.');\n    }\n    if (checkSize) {\n        const out = JSON.stringify(userDefinedMetadata);\n        if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) {\n            console.warn(`User-defined metadata of model \"${modelName}\" is too large in ` +\n                `size (length=${out.length} when serialized). It is not ` +\n                `recommended to store such large objects in user-defined metadata. ` +\n                `Please make sure its serialized length is <= ` +\n                `${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`);\n        }\n    }\n}\n/**\n * Check if an input is plain JSON object or any valid subfield of it.\n *\n * @param x The input to be checked.\n * @param assertObject Whether to assert `x` is a JSON object, i.e., reject\n *   cases of arrays and primitives.\n * @return Returns `true` if and only if `x` is a plain JSON object,\n *   a JSON-valid primitive including string, number, boolean and null,\n *   or an array of the said types.\n */\n// tslint:disable-next-line:no-any\nexport function plainObjectCheck(x) {\n    if (x === null) {\n        // Note: typeof `null` is 'object', and `null` is valid in JSON.\n        return true;\n    }\n    else if (typeof x === 'object') {\n        if (Object.getPrototypeOf(x) === Object.prototype) {\n            // `x` is a JavaScript object and its prototype is Object.\n            const keys = Object.keys(x);\n            for (const key of keys) {\n                if (typeof key !== 'string') {\n                    // JSON keys must be strings.\n                    return false;\n                }\n                if (!plainObjectCheck(x[key])) { // Recursive call.\n                    return false;\n                }\n            }\n            return true;\n        }\n        else {\n            // `x` is a JavaScript object but its prototype is not Object.\n            if (Array.isArray(x)) {\n                // `x` is a JavaScript array.\n                for (const item of x) {\n                    if (!plainObjectCheck(item)) { // Recursive call.\n                        return false;\n                    }\n                }\n                return true;\n            }\n            else {\n                // `x` is a JavaScript object and its prototype is not Object,\n                // and it's not an Array. I.e., it's a complex object such as\n                // `Error` and `Date`.\n                return false;\n            }\n        }\n    }\n    else {\n        // `x` is not a JavaScript object or `null`.\n        const xType = typeof x;\n        return xType === 'string' || xType === 'number' || xType === 'boolean';\n    }\n}\n//# sourceMappingURL=user_defined_metadata.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { countParamsInWeights } from './variable_utils';\n/**\n * Print the summary of a LayersModel object.\n *\n * @param model tf.LayersModel instance.\n * @param lineLength Total length of printed lines. Set this to adapt to the\n *   display to different terminal or console sizes.\n * @param positions Relative or absolute positions of log elements in each\n *   line. Each number corresponds to right-most (i.e., ending) position of a\n *   column.\n *   If not provided, defaults to `[0.45, 0.85, 1]` for sequential-like\n *   models and `[0.33, 0.55, 0.67, 1]` for non-sequential like models.\n * @param printFn Print function to use.\n *   It will be called on each line of the summary. You can provide a custom\n *   function in order to capture the string summary. Defaults to `console.log`.\n */\nexport function printSummary(model, lineLength, positions, \n// tslint:disable-next-line:no-any\nprintFn = console.log) {\n    const sequentialLike = isModelSequentialLike(model);\n    // Header names for different log elements.\n    const toDisplay = ['Layer (type)', 'Output shape', 'Param #'];\n    if (sequentialLike) {\n        lineLength = lineLength || 65;\n        positions = positions || [0.45, 0.85, 1];\n    }\n    else {\n        lineLength = lineLength || 98;\n        positions = positions || [0.33, 0.55, 0.67, 1];\n        // Header names for different log elements.\n    }\n    if (positions[positions.length - 1] <= 1) {\n        // `positions` is relative. Convert it to absolute positioning.\n        positions = positions.map(p => Math.floor(lineLength * p));\n    }\n    let relevantNodes;\n    if (!sequentialLike) {\n        toDisplay.push('Receives inputs');\n        relevantNodes = [];\n        for (const depth in model.nodesByDepth) {\n            relevantNodes.push(...model.nodesByDepth[depth]);\n        }\n    }\n    printFn('_'.repeat(lineLength));\n    printRow(toDisplay, positions, printFn);\n    printFn('='.repeat(lineLength));\n    const layers = model.layers;\n    for (let i = 0; i < layers.length; ++i) {\n        if (sequentialLike) {\n            printLayerSummary(layers[i], positions, printFn);\n        }\n        else {\n            printLayerSummaryWithConnections(layers[i], positions, relevantNodes, printFn);\n        }\n        printFn((i === layers.length - 1 ? '=' : '_').repeat(lineLength));\n    }\n    // tslint:disable-next-line:no-any\n    model.checkTrainableWeightsConsistency();\n    const trainableCount = countTrainableParams(model);\n    const nonTrainableCount = countParamsInWeights(model.nonTrainableWeights);\n    printFn(`Total params: ${trainableCount + nonTrainableCount}`);\n    printFn(`Trainable params: ${trainableCount}`);\n    printFn(`Non-trainable params: ${nonTrainableCount}`);\n    printFn('_'.repeat(lineLength));\n}\nfunction countTrainableParams(model) {\n    let trainableCount;\n    // tslint:disable:no-any\n    if (model.collectedTrainableWeights != null) {\n        trainableCount =\n            countParamsInWeights(model.collectedTrainableWeights);\n    }\n    else {\n        trainableCount = countParamsInWeights(model.trainableWeights);\n    }\n    // tslint:enable:no-any\n    return trainableCount;\n}\nfunction isModelSequentialLike(model) {\n    let sequentialLike = true;\n    const nodesByDepth = [];\n    const nodes = [];\n    for (const depth in model.nodesByDepth) {\n        nodesByDepth.push(model.nodesByDepth[depth]);\n    }\n    for (const depthNodes of nodesByDepth) {\n        if (depthNodes.length > 1 ||\n            depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) {\n            sequentialLike = false;\n            break;\n        }\n        nodes.push(...depthNodes);\n    }\n    if (sequentialLike) {\n        // Search for shared layers.\n        for (const layer of model.layers) {\n            let flag = false;\n            for (const node of layer.inboundNodes) {\n                if (nodes.indexOf(node) !== -1) {\n                    if (flag) {\n                        sequentialLike = false;\n                        break;\n                    }\n                    else {\n                        flag = true;\n                    }\n                }\n            }\n            if (!sequentialLike) {\n                break;\n            }\n        }\n    }\n    return sequentialLike;\n}\nfunction printRow(fields, positions, \n// tslint:disable-next-line:no-any\nprintFn = console.log) {\n    let line = '';\n    for (let i = 0; i < fields.length; ++i) {\n        if (i > 0) {\n            line = line.slice(0, line.length - 1) + ' ';\n        }\n        line += fields[i];\n        line = line.slice(0, positions[i]);\n        line += ' '.repeat(positions[i] - line.length);\n    }\n    printFn(line);\n}\n/**\n * Prints a summary for a single Layer, without connectivity information.\n *\n * @param layer: Layer instance to print.\n */\nfunction printLayerSummary(layer, positions, \n// tslint:disable-next-line:no-any\nprintFn) {\n    let outputShape;\n    try {\n        outputShape = JSON.stringify(layer.outputShape);\n    }\n    catch (err) {\n        outputShape = 'multiple';\n    }\n    const name = layer.name;\n    const className = layer.getClassName();\n    const fields = [`${name} (${className})`, outputShape, layer.countParams().toString()];\n    printRow(fields, positions, printFn);\n}\n/**\n * Prints a summary for a single Layer, with connectivity information.\n */\nfunction printLayerSummaryWithConnections(layer, positions, relevantNodes, \n// tslint:disable-next-line:no-any\nprintFn) {\n    let outputShape;\n    try {\n        outputShape = JSON.stringify(layer.outputShape);\n    }\n    catch (err) {\n        outputShape = 'multiple';\n    }\n    const connections = [];\n    for (const node of layer.inboundNodes) {\n        if (relevantNodes != null && relevantNodes.length > 0 &&\n            relevantNodes.indexOf(node) === -1) {\n            continue;\n        }\n        for (let i = 0; i < node.inboundLayers.length; ++i) {\n            const inboundLayer = node.inboundLayers[i].name;\n            const inboundLayerIndex = node.nodeIndices[i];\n            const inboundTensorIndex = node.tensorIndices[i];\n            connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);\n        }\n    }\n    const name = layer.name;\n    const className = layer.getClassName();\n    const firstConnection = connections.length === 0 ? '' : connections[0];\n    const fields = [\n        `${name} (${className})`, outputShape, layer.countParams().toString(),\n        firstConnection\n    ];\n    printRow(fields, positions, printFn);\n    for (let i = 1; i < connections.length; ++i) {\n        printRow(['', '', '', connections[i]], positions, printFn);\n    }\n}\n//# sourceMappingURL=layer_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as generic_utils from '../utils/generic_utils';\n// tslint:enable\n/**\n * Test whether a value in an array is the name of a LayersModel or Layer.\n * @param key The key name that the value is found under. Note that the key\n *   may not be at the level immediately above the value, if the value is in a\n *   nested array.\n * @param index Index of the value in the Array that it is found in.\n * @param value The value object.\n * @returns A boolean indicating whether value is a name.\n */\nfunction isArrayItemInputOrOutputName(key, index, value) {\n    return (key === 'inboundNodes' || key === 'outputLayers' ||\n        key === 'inputLayers') &&\n        index === 0 && typeof value === 'string';\n}\n/**\n * Convert a Pythonic config object to TypeScript config object.\n * @param pythonicConfig The config object to convert.\n * @param key Optional key name of the object being converted.\n * @returns Result of the conversion.\n */\nexport function convertPythonicToTs(pythonicConfig, key) {\n    if (pythonicConfig === null) {\n        return null;\n    }\n    else if (typeof pythonicConfig === 'string') {\n        return generic_utils.toCamelCase(pythonicConfig);\n    }\n    else if ((typeof pythonicConfig === 'number') ||\n        (typeof pythonicConfig === 'boolean')) {\n        return pythonicConfig;\n    }\n    else if (pythonicConfig instanceof Array) {\n        const tsArray = [];\n        const arrayLength = pythonicConfig.length;\n        for (let i = 0; i < arrayLength; ++i) {\n            const item = pythonicConfig[i];\n            if (isArrayItemInputOrOutputName(key, i, item)) {\n                tsArray.push(item);\n            }\n            else {\n                tsArray.push(convertPythonicToTs(item, key));\n            }\n        }\n        return tsArray;\n    }\n    else {\n        const tsDict = {};\n        for (const pythonicKey of Object.keys(pythonicConfig)) {\n            const pythonicValue = pythonicConfig[pythonicKey];\n            if (pythonicKey === 'name' && typeof pythonicValue === 'string') {\n                // Special case the 'name' key with a string value. Name values, such as\n                // the names of LayersModel and Layer instances, should not undergo the\n                // camel-case conversion.\n                tsDict[pythonicKey] = pythonicValue;\n            }\n            else {\n                const tsKey = generic_utils.toCamelCase(pythonicKey);\n                tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey);\n            }\n        }\n        return tsDict;\n    }\n}\n/**\n * Convert a TypeScript config object to Python config object.\n * @param tsConfig The config object to convert.\n * @param key Optional key name of the object being converted.\n * @returns Result of the conversion.\n */\nexport function convertTsToPythonic(tsConfig, key) {\n    if (tsConfig === null || tsConfig === undefined) {\n        return null;\n    }\n    else if (typeof tsConfig === 'string') {\n        return generic_utils.toSnakeCase(tsConfig);\n    }\n    else if ((typeof tsConfig === 'number') || (typeof tsConfig === 'boolean')) {\n        return tsConfig;\n    }\n    else if (tsConfig instanceof Array) {\n        const pyArray = [];\n        const arrayLength = tsConfig.length;\n        for (let i = 0; i < arrayLength; ++i) {\n            const item = tsConfig[i];\n            if (isArrayItemInputOrOutputName(key, i, item)) {\n                pyArray.push(item);\n            }\n            else {\n                pyArray.push(convertTsToPythonic(item, key));\n            }\n        }\n        return pyArray;\n    }\n    else {\n        const pyDict = {};\n        for (const tsKey of Object.keys(tsConfig)) {\n            const tsValue = tsConfig[tsKey];\n            const pyKey = generic_utils.toSnakeCase(tsKey);\n            if ((tsKey === 'name' || tsKey === 'className') &&\n                typeof tsValue === 'string') {\n                // Special case the 'name' key with a string value. Name values, such as\n                // the names of LayersModel and Layer instances, should not undergo the\n                // snake-case conversion.\n                pyDict[pyKey] = tsValue;\n            }\n            else {\n                pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey);\n            }\n        }\n        return pyDict;\n    }\n}\n//# sourceMappingURL=serialization_utils.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Executor: Evaluates SymbolicTensor based on feeds.\n */\nimport { cast, dispose, memory, util } from '@tensorflow/tfjs-core';\nimport { ValueError } from '../errors';\nimport { toList } from '../utils/generic_utils';\nimport { InputLayer } from './input_layer';\nimport { SymbolicTensor } from './topology';\n/**\n * Helper function to check the dtype and shape compatibility of a feed value.\n */\nfunction assertFeedCompatibility(key, val) {\n    // Check dtype compatibility.\n    if (key.dtype == null || key.dtype === val.dtype) {\n        //  a.  If types match, return val tensor as is.\n        return val;\n    }\n    try {\n        //  b. Attempt to convert to expected type.\n        return cast(val, key.dtype);\n    }\n    catch (err) {\n        //  c. If conversion fails, return helpful error.\n        throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype ` +\n            `of the key '${key.name}' (${key.dtype}).`);\n    }\n}\n/**\n * FeedDict: A mapping from unique SymbolicTensors to feed values for them.\n * A feed value is a concrete value represented as an `Tensor`.\n */\nexport class FeedDict {\n    /**\n     * Constructor, optionally does copy-construction.\n     * @param feeds An Array of `Feed`s, or another `FeedDict`, in which case\n     *   copy-construction will be performed.\n     */\n    constructor(feeds) {\n        this.id2Value = {};\n        this.id2Mask = {};\n        this.name2Id = {};\n        if (feeds instanceof FeedDict) {\n            for (const id in feeds.id2Value) {\n                this.id2Value[id] = feeds.id2Value[id];\n                if (id in feeds.id2Mask) {\n                    this.id2Mask[id] = feeds.id2Mask[id];\n                }\n            }\n        }\n        else {\n            if (feeds == null) {\n                return;\n            }\n            for (const feed of feeds) {\n                this.add(feed.key, feed.value);\n            }\n        }\n    }\n    /**\n     * Add a key-value pair to the FeedDict.\n     *\n     * @param key The key of the feed.\n     * @param value The value of the tensor feed.\n     * @param mask The value of the mask feed (optional).\n     * @returns This `FeedDict`.\n     * @throws ValueError: If the key `SymbolicTensor` already exists in the\n     *   `FeedDict`.\n     */\n    add(key, value, mask) {\n        if (this.id2Value[key.id] == null) {\n            this.id2Value[key.id] = assertFeedCompatibility(key, value);\n            this.name2Id[key.name] = key.id;\n            if (mask != null) {\n                this.id2Mask[key.id] = mask;\n            }\n        }\n        else {\n            throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);\n        }\n        return this;\n    }\n    /**\n     * Add a Feed to the FeedDict.\n     * @param feed The new `Feed` to add.\n     * @returns This `FeedDict`.\n     */\n    addFeed(feed) {\n        this.add(feed.key, feed.value);\n    }\n    /**\n     * Probe whether a key already exists in the FeedDict.\n     * @param key\n     */\n    hasKey(key) {\n        return this.id2Value[key.id] != null;\n    }\n    /**\n     * Get all the SymbolicTensor available in this FeedDict.\n     */\n    names() {\n        return Object.keys(this.name2Id);\n    }\n    /**\n     * Get the feed value for given key.\n     * @param key The SymbolicTensor, or its name (as a string), of which the\n     *     value is sought.\n     * @returns If `key` exists, the corresponding feed value.\n     * @throws ValueError: If `key` does not exist in this `FeedDict`.\n     */\n    getValue(key) {\n        if (key instanceof SymbolicTensor) {\n            if (this.id2Value[key.id] == null) {\n                throw new ValueError(`Nonexistent key: ${key.name}`);\n            }\n            else {\n                return this.id2Value[key.id];\n            }\n        }\n        else {\n            const id = this.name2Id[key];\n            if (id == null) {\n                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n            }\n            return this.id2Value[id];\n        }\n    }\n    /**\n     * Get the feed mask for given key.\n     * @param key The SymbolicTensor, or its name (as a string), of which the\n     *     value is sought.\n     * @returns If `key` exists, the corresponding feed mask.\n     * @throws ValueError: If `key` does not exist in this `FeedDict`.\n     */\n    getMask(key) {\n        if (key instanceof SymbolicTensor) {\n            if (this.id2Value[key.id] == null) {\n                throw new ValueError(`Nonexistent key: ${key.name}`);\n            }\n            else {\n                return this.id2Mask[key.id];\n            }\n        }\n        else {\n            const id = this.name2Id[key];\n            if (id == null) {\n                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n            }\n            return this.id2Mask[id];\n        }\n    }\n    /** Dispose all mask Tensors held by this object. */\n    disposeMasks() {\n        if (this.id2Mask != null) {\n            dispose(this.id2Mask);\n        }\n    }\n}\n// Cache for topologically sorted SymbolicTensors for given execution\n// targets (i.e., fetches).\nconst cachedSorted = {};\n// Cache for recipient count maps for given execution targets (i.e., fetches).\nconst cachedRecipientCounts = {};\n/**\n * Execute a SymbolicTensor by using concrete feed values.\n *\n * A `SymbolicTensor` object is a node in a computation graph of TF.js\n * Layers. The object is backed by a source layer and input\n * `SymbolicTensor`s to the source layer. This method evaluates\n * the `call()` method of the source layer, using concrete values of the\n * inputs obtained from either\n * * `feedDict`, if the input key exists in `feedDict`, or else,\n * * a recursive call to `execute()` itself.\n *\n * @param x: The `SymbolicTensor` to execute.\n * @param feedDict: The feed values, as base condition of the recursion.\n *   execution.\n * @param kwargs: Optional keyword arguments.\n * @param probe: A probe object (of interface `ExecutionProbe`) used for\n *   testing memory footprint of `execute` calls.\n * @returns Result of the execution.\n * @throws ValueError: If any `SymbolicTensor`s from `InputLayer`s\n *   encountered during the execution lacks a feed value in `feedDict`.\n */\nexport function execute(fetches, feedDict, kwargs, probe) {\n    const training = kwargs == null ? false : kwargs['training'];\n    const arrayFetches = Array.isArray(fetches);\n    const fetchArray = arrayFetches ? fetches : [fetches];\n    const outputNames = fetchArray.map(t => t.name);\n    const finalOutputs = [];\n    const feedNames = feedDict.names();\n    for (const outputName of outputNames) {\n        if (feedNames.indexOf(outputName) !== -1) {\n            finalOutputs.push(feedDict.getValue(outputName));\n        }\n        else {\n            finalOutputs.push(null);\n        }\n    }\n    if (probe != null) {\n        // For optional probing of memory footprint during execution.\n        probe.maxNumTensors = -Infinity;\n        probe.minNumTensors = Infinity;\n    }\n    // Check cache.\n    const fetchAndFeedKey = outputNames.join(',') + '|' + feedDict.names().join(',');\n    let sorted;\n    let recipientCounts;\n    if (cachedSorted[fetchAndFeedKey] == null) {\n        // Cache doesn't contain the desired combination of fetches. Compute\n        // topological sort for the combination for the first time.\n        const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);\n        sorted = out.sorted;\n        recipientCounts = out.recipientCounts;\n        // Store results in cache for future use.\n        cachedSorted[fetchAndFeedKey] = sorted;\n        cachedRecipientCounts[fetchAndFeedKey] = recipientCounts;\n    }\n    sorted = cachedSorted[fetchAndFeedKey];\n    recipientCounts = {};\n    if (!training) {\n        Object.assign(recipientCounts, cachedRecipientCounts[fetchAndFeedKey]);\n    }\n    const internalFeedDict = new FeedDict(feedDict);\n    // Start iterative execution on the topologically-sorted SymbolicTensors.\n    for (let i = 0; i < sorted.length; ++i) {\n        if (probe != null) {\n            // For optional probing of memory usage during execution.\n            const numTensors = memory().numTensors;\n            if (numTensors > probe.maxNumTensors) {\n                probe.maxNumTensors = numTensors;\n            }\n            if (numTensors < probe.minNumTensors) {\n                probe.minNumTensors = numTensors;\n            }\n        }\n        const symbolic = sorted[i];\n        const srcLayer = symbolic.sourceLayer;\n        if (srcLayer instanceof InputLayer) {\n            continue;\n        }\n        const inputValues = [];\n        const inputMasks = [];\n        const tensorsToDispose = [];\n        let maskExists = false;\n        for (const input of symbolic.inputs) {\n            const value = internalFeedDict.getValue(input);\n            const mask = internalFeedDict.getMask(input);\n            inputValues.push(value);\n            inputMasks.push(mask);\n            if (mask != null) {\n                maskExists = true;\n            }\n            if (!training) {\n                recipientCounts[input.name]--;\n                if (recipientCounts[input.name] === 0 && !feedDict.hasKey(input) &&\n                    outputNames.indexOf(input.name) === -1 && !value.isDisposed &&\n                    input.sourceLayer.stateful !== true) {\n                    tensorsToDispose.push(value);\n                }\n            }\n        }\n        if (maskExists) {\n            kwargs = kwargs || {};\n            kwargs['mask'] = inputMasks[0];\n        }\n        const outputTensors = toList(srcLayer.apply(inputValues, kwargs));\n        let outputMask = null;\n        if (srcLayer.supportsMasking) {\n            outputMask = srcLayer.computeMask(inputValues, inputMasks);\n        }\n        const layerOutputs = getNodeOutputs(symbolic);\n        const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];\n        for (let i = 0; i < outputSymbolicTensors.length; ++i) {\n            if (!internalFeedDict.hasKey(outputSymbolicTensors[i])) {\n                internalFeedDict.add(outputSymbolicTensors[i], outputTensors[i], Array.isArray(outputMask) ? outputMask[0] : outputMask);\n            }\n            const index = outputNames.indexOf(outputSymbolicTensors[i].name);\n            if (index !== -1) {\n                finalOutputs[index] = outputTensors[i];\n            }\n        }\n        if (!training) {\n            // Clean up Tensors that are no longer needed.\n            dispose(tensorsToDispose);\n        }\n    }\n    // NOTE(cais): Unlike intermediate tensors, we don't discard mask\n    // tensors as we go, because these tensors are sometimes passed over a\n    // series of mutliple layers, i.e., not obeying the immediate input\n    // relations in the graph. If this becomes a memory-usage concern,\n    // we can improve this in the future.\n    internalFeedDict.disposeMasks();\n    return arrayFetches ? finalOutputs : finalOutputs[0];\n}\n/**\n * Sort the `SymbolicTensor`s topologically, for an array of fetches.\n *\n * This function calls getTopologicalSortAndRecipientCountsForOneFetch and\n * merges their results.\n *\n * @param fetch The array of fetches requested. Must be a non-empty array.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientCounts: Recipient counts for all SymbolicTensors in `sorted`.\n */\nfunction getTopologicalSortAndRecipientCounts(fetches, feedDict) {\n    util.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`);\n    let finalSorted = [];\n    let finalRecipientMap = {};\n    if (fetches.length === 1) {\n        // Special-casing 1 fetch for efficiency.\n        const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);\n        finalSorted = out.sorted;\n        finalRecipientMap = out.recipientMap;\n    }\n    else {\n        const visited = new Set();\n        for (const fetch of fetches) {\n            const { sorted, recipientMap } = getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict);\n            // Merge sorted SymbolicTensor Arrays.\n            for (const symbolicTensor of sorted) {\n                if (!visited.has(symbolicTensor.name)) {\n                    finalSorted.push(symbolicTensor);\n                    visited.add(symbolicTensor.name);\n                }\n            }\n            // Merge recipient maps.\n            for (const name in recipientMap) {\n                if (finalRecipientMap[name] == null) {\n                    finalRecipientMap[name] = new Set();\n                }\n                recipientMap[name].forEach(recipient => finalRecipientMap[name].add(recipient));\n            }\n        }\n    }\n    return {\n        sorted: finalSorted,\n        recipientCounts: recipientMap2Counts(finalRecipientMap)\n    };\n}\nfunction recipientMap2Counts(recipientMap) {\n    const recipientCounts = {};\n    for (const name in recipientMap) {\n        recipientCounts[name] = recipientMap[name].size;\n    }\n    return recipientCounts;\n}\n/**\n * Sort the `SymbolicTensor`s topologically, for a single fetch.\n *\n * This helper function processes the upstream SymbolicTensors of a single\n * fetch.\n *\n * @param fetch The single fetch requested.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientMap: Recipient names for all SymbolicTensors in `sorted`.\n */\nexport function getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict) {\n    const visited = new Set();\n    const sorted = [];\n    const recipientMap = {};\n    // Put keys of the feedDict into visited first, so they don't have to be\n    // walked. This is needed in case where there are feeds for intermediate\n    // SymbolicTensors of the graph.\n    for (const key of feedDict.names()) {\n        visited.add(key);\n    }\n    const stack = [];\n    const marks = [];\n    // Initial population of stack and marks.\n    stack.push(fetch);\n    while (stack.length > 0) {\n        const top = stack[stack.length - 1];\n        if (visited.has(top.name)) {\n            stack.pop();\n            continue;\n        }\n        const topIsMarked = marks[marks.length - 1] === stack.length - 1;\n        if (top.inputs.length === 0 || topIsMarked) {\n            // Input SymbolicTensor or all children have been visited.\n            stack.pop();\n            sorted.push(top);\n            visited.add(top.name);\n            if (topIsMarked) {\n                marks.pop();\n            }\n        }\n        else {\n            // A non-input SymbolicTensor whose upstream SymbolicTensors haven't\n            // been visited yet. Push them onto the stack.\n            marks.push(stack.length - 1);\n            for (const input of top.inputs) {\n                // Increment the recipient count. Note that this needs to happen\n                // regardless of whether the SymbolicTensor has been visited before.\n                if (recipientMap[input.name] == null) {\n                    recipientMap[input.name] = new Set();\n                }\n                recipientMap[input.name].add(top.name);\n                if (visited.has(input.name)) {\n                    continue; // Avoid repeated visits to the same SymbolicTensor.\n                }\n                stack.push(input);\n            }\n        }\n    }\n    return { sorted, recipientMap };\n}\n/**\n * Get the symbolic output tensors of the node to which a given fetch belongs.\n * @param fetch The fetched symbolic tensor.\n * @returns The Array of symbolic tensors output by the node to which `fetch`\n *   belongs.\n */\nfunction getNodeOutputs(fetch) {\n    let layerOutputs;\n    if (fetch.sourceLayer.inboundNodes.length === 1) {\n        layerOutputs = fetch.sourceLayer.output;\n    }\n    else {\n        let nodeIndex = null;\n        for (let i = 0; i < fetch.sourceLayer.inboundNodes.length; ++i) {\n            for (const outputTensor of fetch.sourceLayer.inboundNodes[i]\n                .outputTensors) {\n                if (outputTensor.id === fetch.id) {\n                    nodeIndex = i;\n                    break;\n                }\n            }\n        }\n        layerOutputs = fetch.sourceLayer.getOutputAt(nodeIndex);\n    }\n    return layerOutputs;\n}\n//# sourceMappingURL=executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/engine/topology.py */\nimport { tidy } from '@tensorflow/tfjs-core';\nimport { getUid } from '../backend/state';\nimport { NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { deserialize as deserializeLayer } from '../layers/serialization';\nimport * as generic_utils from '../utils/generic_utils';\nimport { convertTsToPythonic } from '../utils/serialization_utils';\nimport * as types_utils from '../utils/types_utils';\nimport { batchSetValue } from '../variables';\nimport { version as layersVersion } from '../version';\nimport { execute, FeedDict } from './executor';\nimport { InputLayer } from './input_layer';\nimport { Layer, Node } from './topology';\n/**\n * A Container is a directed acyclic graph of layers.\n *\n * It is the topological form of a \"model\". A LayersModel\n * is simply a Container with added training routines.\n *\n */\nexport class Container extends Layer {\n    constructor(args) {\n        // No args passed to super's constructor.\n        super({});\n        this.containerNodes = new Set();\n        this.name = args.name;\n        if (this.name == null) {\n            const prefix = this.getClassName().toLowerCase();\n            this.name = getUid(prefix);\n        }\n        this.supportsMasking = false;\n        this.trainable_ = true;\n        // TODO(michaelterry): Initialize perInputLosses/Updates here.\n        // Container-specific properties.\n        if (Array.isArray(args.inputs)) {\n            this.inputs = args.inputs.slice();\n        }\n        else {\n            this.inputs = [args.inputs];\n        }\n        if (Array.isArray(args.outputs)) {\n            this.outputs = args.outputs.slice();\n        }\n        else {\n            this.outputs = [args.outputs];\n        }\n        // Check for redundancy in inputs.\n        if (generic_utils.unique(this.inputs).length !== this.inputs.length) {\n            throw new ValueError('The list of inputs passed to the model is ' +\n                'redundant. All inputs should only appear once. Found: ' +\n                `${this.inputs.map(x => x.name)}`);\n        }\n        // Check for redundancy in outputs.\n        if (generic_utils.unique(this.outputs).length !== this.outputs.length) {\n            console.warn('The list of outputs passed to the model is redundant. ' +\n                'All outputs should only appear once. Found: ' +\n                `${this.outputs.map(x => x.name)}`);\n        }\n        /*\n          List of initial layers (1 to 1 mapping with this.inputs, hence the same\n          layer might appear twice)\n        */\n        this.inputLayers = [];\n        this.inputLayersNodeIndices = [];\n        this.inputLayersTensorIndices = [];\n        /*\n          List of layers (1 to 1 mapping with this.outputs, hence the same layer\n          might appear twice)\n        */\n        this.outputLayers = [];\n        this.outputLayersNodeIndices = [];\n        this.outputLayersTensorIndices = [];\n        /*\n          All layers in order of horizontal graph traversal. Entries are unique.\n          Includes input and output layers.\n        */\n        this.layers = [];\n        /*\n          References to container layers that were constructed internally. We need\n          these to properly dispose of tensors from nested containers.\n        */\n        this.internalContainerRefs = [];\n        // TODO(michaelterry): Determine if caching still needed with eager\n        // backend.\n        /*\n          This is for performance optimization when calling the Container on new\n          inputs. Every time the Container is called on a set on input tensors,\n          we compute the output tensors, output masks and output shapes in one pass,\n          then cache them here. When one of these outputs is queried later,\n          we retrieve it from there instead of recomputing it.\n        */\n        // this.outputTensorCache = {};\n        // this.outputShapeCache = {};\n        // Build this.outputLayers:\n        for (const x of this.outputs) {\n            const layer = x.sourceLayer;\n            const nodeIndex = x.nodeIndex;\n            const tensorIndex = x.tensorIndex;\n            this.outputLayers.push(layer);\n            this.outputLayersNodeIndices.push(nodeIndex);\n            this.outputLayersTensorIndices.push(tensorIndex);\n        }\n        // TODO(michaelterry): Add output mask cache code.\n        // Build this.inputLayers:\n        for (const x of this.inputs) {\n            const layer = x.sourceLayer;\n            const nodeIndex = x.nodeIndex;\n            const tensorIndex = x.tensorIndex;\n            /*\n              It's supposed to be an input layer, so only one node\n              and one tensor output.\n            */\n            generic_utils.assert(nodeIndex === 0, 'input layer has >1 nodes');\n            generic_utils.assert(tensorIndex === 0, 'input layer has >1 tensors');\n            this.inputLayers.push(layer);\n            this.inputLayersNodeIndices.push(nodeIndex);\n            this.inputLayersTensorIndices.push(tensorIndex);\n        }\n        // Build this.inputNames and this.outputNames.\n        this.inputNames = [];\n        this.outputNames = [];\n        this.feedInputShapes = [];\n        this.feedInputNames = [];\n        this.feedOutputNames = [];\n        for (let i = 0; i < this.inputLayers.length; i++) {\n            const layer = this.inputLayers[i];\n            // Check that layer is an InputLayer.\n            if (!(layer instanceof InputLayer)) {\n                throw new TypeError('Input layers to a LayersModel must be InputLayer objects. ' +\n                    `Received inputs: ${args.inputs}. ` +\n                    `Input ${i} (0-based) originates ` +\n                    `from layer type ${layer.getClassName()}.`);\n            }\n            this.inputNames.push(layer.name);\n            this.feedInputShapes.push(layer.batchInputShape);\n            this.feedInputNames.push(layer.name);\n        }\n        for (const layer of this.outputLayers) {\n            this.outputNames.push(layer.name);\n        }\n        this.internalInputShapes = this.inputs.map(x => x.shape);\n        this.internalOutputShapes = this.outputs.map(x => x.shape);\n        /*\n          Container_nodes: set of nodes included in the graph (not all nodes\n          included in the layers are relevant to the current graph).\n        */\n        // ids of all nodes relevant to the Container:\n        const nodesDepths = {};\n        // To recover nodes from their ID.\n        const nodeIDToNode = {};\n        const layersDepths = {};\n        // To layers from their ID.\n        const layerIDToLayer = {};\n        const layerIndices = {};\n        const nodesInDecreasingDepth = [];\n        /**\n         * Builds a map of the graph of layers.\n         *\n         * This recursively updates the map `layerIndices`,\n         * the list `nodesInDecreasingDepth` and the set `containerNodes`.\n         *\n         * @param tensor Some tensor in a graph.\n         * @param finishedNodes Set of nodes whose subgraphs have been traversed\n         *         completely. Useful to prevent duplicated work.\n         * @param nodesInProgress Set of nodes that are currently active on the\n         *         recursion stack. Useful to detect cycles.\n         * @param layer Layer from which `tensor` comes from. If not provided,\n         *   will be obtained from tensor.sourceLayer.\n         * @param nodeIndex Node index from which `tensor` comes from.\n         * @param tensorIndex TensorIndex from which `tensor` comes from.\n         *\n         * @exception RuntimeError if a cycle is detected.\n         */\n        const buildMapOfGraph = (tensor, finishedNodes, nodesInProgress, layer, nodeIndex, tensorIndex) => {\n            if (layer == null || nodeIndex == null || tensorIndex == null) {\n                layer = tensor.sourceLayer;\n                nodeIndex = tensor.nodeIndex;\n                tensorIndex = tensor.tensorIndex;\n            }\n            const node = layer.inboundNodes[nodeIndex];\n            // Prevent cycles.\n            if (nodesInProgress.indexOf(node) !== -1) {\n                throw new RuntimeError(`The tensor ${tensor.name} at layer \"${layer.name}\" ` +\n                    'is part of a cycle.');\n            }\n            // Don't repeat work for shared subgraphs\n            if (finishedNodes.indexOf(node) !== -1) {\n                return;\n            }\n            // Update containerNodes.\n            this.containerNodes.add(Container.nodeKey(layer, nodeIndex));\n            // Store the traversal order for layer sorting.\n            if (!(layer.id in layerIndices)) {\n                layerIndices[layer.id] = Object.keys(layerIndices).length;\n            }\n            if (nodesInProgress.indexOf(node) === -1) {\n                nodesInProgress.push(node);\n            }\n            // Propagate to all previous tensors connected to this node.\n            const numInboundLayers = node.inboundLayers.length;\n            for (let i = 0; i < numInboundLayers; i++) {\n                const x = node.inputTensors[i];\n                const layer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const tensorIndex = node.tensorIndices[i];\n                buildMapOfGraph(x, finishedNodes, nodesInProgress, layer, nodeIndex, tensorIndex);\n            }\n            finishedNodes.push(node);\n            while (nodesInProgress.indexOf(node) >= 0) {\n                nodesInProgress.splice(nodesInProgress.indexOf(node), 1);\n            }\n            nodesInDecreasingDepth.push(node);\n        };\n        const finishedNodes = [];\n        const nodesInProgress = [];\n        for (const x of this.outputs) {\n            buildMapOfGraph(x, finishedNodes, nodesInProgress);\n        }\n        const reversedNodesInDecreasingDepth = nodesInDecreasingDepth.slice().reverse();\n        for (const node of reversedNodesInDecreasingDepth) {\n            nodeIDToNode[node.id] = node;\n            // If the depth is not set, the node has no outbound nodes (depth 0).\n            if (!(node.id in nodesDepths)) {\n                nodesDepths[node.id] = 0;\n            }\n            let depth = nodesDepths[node.id];\n            // Update the depth of the corresponding layer\n            const previousDepth = (layersDepths[node.outboundLayer.id] == null ?\n                0 :\n                layersDepths[node.outboundLayer.id]);\n            /*\n              If we've seen this layer before at a higher depth, we should use that\n              depth instead of the node depth.  This is necessary for shared layers\n              that have inputs at different depth levels in the graph.\n            */\n            depth = Math.max(depth, previousDepth);\n            layersDepths[node.outboundLayer.id] = depth;\n            layerIDToLayer[node.outboundLayer.id] = node.outboundLayer;\n            nodesDepths[node.id] = depth;\n            // Update the depth of inbound nodes.\n            for (let i = 0; i < node.inboundLayers.length; i++) {\n                const inboundLayer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const inboundNode = inboundLayer.inboundNodes[nodeIndex];\n                const previousDepth = (nodesDepths[inboundNode.id] == null ? 0 :\n                    nodesDepths[inboundNode.id]);\n                nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth);\n                nodeIDToNode[inboundNode.id] = inboundNode;\n            }\n        }\n        // Build a dict {depth: list of nodes with this depth}\n        const nodesByDepth = {};\n        for (const nodeID in nodesDepths) {\n            const depth = nodesDepths[nodeID];\n            if (!(depth in nodesByDepth)) {\n                nodesByDepth[depth] = [];\n            }\n            nodesByDepth[depth].push(nodeIDToNode[nodeID]);\n        }\n        // Build a dict {depth: list of layers with this depth}\n        const layersByDepth = {};\n        for (const layerID in layersDepths) {\n            const depth = layersDepths[layerID];\n            if (!(depth in layersByDepth)) {\n                layersByDepth[depth] = [];\n            }\n            layersByDepth[depth].push(layerIDToLayer[layerID]);\n        }\n        // Get sorted list of layer depths.\n        let depthKeys = Object.keys(layersByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Set this.layers and this.layersByDepth.\n        this.layers = [];\n        for (const depth of depthKeys) {\n            const layersForDepth = layersByDepth[depth];\n            // Container.layers needs to have a deterministic order:\n            // here we order them by traversal order.\n            layersForDepth.sort((a, b) => {\n                const aIndex = layerIndices[a.id];\n                const bIndex = layerIndices[b.id];\n                if (aIndex < bIndex) {\n                    return -1;\n                }\n                if (aIndex > bIndex) {\n                    return 1;\n                }\n                return 0;\n            });\n            for (const layer of layersForDepth) {\n                if (layer instanceof Container) {\n                    this.internalContainerRefs.push(layer);\n                }\n                this.layers.push(layer);\n            }\n        }\n        this.layersByDepth = layersByDepth;\n        // Get sorted list of node depths;\n        depthKeys = Object.keys(nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Check that all tensors required are computable.\n        // computable_tensors: all tensors in the graph\n        // that can be computed from the inputs provided.\n        const computableTensors = this.inputs.slice();\n        // To provide a better error msg.\n        const layersWithCompleteInput = [];\n        for (const depth of depthKeys) {\n            for (const node of nodesByDepth[depth]) {\n                const layer = node.outboundLayer;\n                if (layer != null) {\n                    for (const x of node.inputTensors) {\n                        if (computableTensors.indexOf(x) === -1) {\n                            throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x}` +\n                                ` at layer \"${layer.name}\". ` +\n                                'The following previous layers were accessed without ' +\n                                `issue: ${layersWithCompleteInput}`);\n                        }\n                    }\n                    for (const x of node.outputTensors) {\n                        computableTensors.push(x);\n                    }\n                    layersWithCompleteInput.push(layer.name);\n                }\n            }\n        }\n        // Set this.containerNodes and this.nodesByDepth.\n        this.nodesByDepth = nodesByDepth;\n        // Ensure name unicity, which will be crucial for serialization\n        // (since serialized nodes refer to layers by their name).\n        const allNames = this.layers.map(x => x.name);\n        for (const name of allNames) {\n            const numOccurrences = allNames.filter(x => x === name).length;\n            if (numOccurrences !== 1) {\n                throw new RuntimeError(`The name \"${name}\" is used ${numOccurrences} times ` +\n                    'in the model. All layer names should be unique. Layer names: ' +\n                    JSON.stringify(allNames));\n            }\n        }\n        // Layer parameters.\n        // The new container starts with a single inbound node\n        // for its inputs, and no outbound nodes.\n        // Will be appended to by future calls to apply().\n        this.outboundNodes = [];\n        // Will be appended to below, and by future calls to apply().\n        this.inboundNodes = [];\n        // Create the node linking internal inputs to internal outputs.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers: [],\n            nodeIndices: [],\n            tensorIndices: [],\n            inputTensors: this.inputs,\n            outputTensors: this.outputs,\n            inputMasks: this.inputs.map(x => null),\n            outputMasks: this.outputs.map(x => null),\n            inputShapes: this.inputs.map(x => x.shape),\n            outputShapes: this.outputs.map(x => x.shape)\n        });\n        this.built = true;\n        this._refCount = 1; // The ref count of a container always start at 1.\n    }\n    assertNotDisposed() {\n        if (this._refCount === 0) {\n            throw new Error(`Container '${this.name}' is already disposed.`);\n        }\n    }\n    /**\n     * Attempt to dispose a LayersModel's weights.\n     *\n     * This method decrease the reference count of the LayersModel object by 1.\n     *\n     * A LayersModel is reference-counted. Its reference count is incremented by 1\n     * when it is first constructed and when it is used as a Layer of another\n     * LayersModel.\n     *\n     * If the reference count of a LayersModel becomes 0, the `dispose` method of\n     * all its constituent `Layer`s will be called.\n     *\n     * Note: If the reference count is greater than 0 after the decrement, the\n     * `dispose` method of its constituent `Layer`s will *not* be called.\n     *\n     * After a LayersModel is disposed, it cannot be used in calls such as\n     * 'predict`, `evaluate` or `fit` anymore.\n     *\n     * @returns A DisposeResult Object with the following fields:\n     *   - refCountAfterDispose: The reference count of the LayersModel after this\n     *     `dispose()` call.\n     *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n     *     during this `dispose()` call.\n     * @throws {Error} If the layer is not built yet, or if the LayersModel has\n     *   already been disposed.\n     */\n    dispose() {\n        this.assertNotDisposed();\n        const result = { refCountAfterDispose: null, numDisposedVariables: 0 };\n        if (--this._refCount === 0) {\n            for (const layer of this.layers) {\n                result.numDisposedVariables += layer.dispose().numDisposedVariables;\n            }\n            // Call dispose on each internally created container layer again to ensure\n            // their refCounts hit zero and their tensors are subsequently deleted.\n            for (const container of this.internalContainerRefs) {\n                result.numDisposedVariables += container.dispose().numDisposedVariables;\n            }\n        }\n        result.refCountAfterDispose = this._refCount;\n        return result;\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this.layers.forEach(layer => {\n            // tslint:disable-next-line:no-any\n            layer._trainableWeights\n                .forEach(w => w.trainable = trainable);\n        });\n        this.trainable_ = trainable;\n    }\n    get trainableWeights() {\n        // Porting Note: This check below is to prevent errors where the\n        //   _trainableWeights inherited from the parent class (Layer) gets\n        //   inadvertently used.\n        if (this._trainableWeights.length > 0) {\n            throw new ValueError('Container instance unexpectedly contains _trainableWeights.' +\n                'The trainable weights of a Container are a union of the ' +\n                'trainable weights of its consituent Layers. Its own ' +\n                '_trainableWeights must remain an empty Array.');\n        }\n        if (!this.trainable) {\n            return [];\n        }\n        let weights = [];\n        for (const layer of this.layers) {\n            weights = weights.concat(layer.trainableWeights);\n        }\n        return weights;\n    }\n    get nonTrainableWeights() {\n        const weights = [];\n        for (const layer of this.layers) {\n            weights.push(...layer.nonTrainableWeights);\n        }\n        if (!this.trainable) {\n            const trainableWeights = [];\n            for (const layer of this.layers) {\n                trainableWeights.push(...layer.trainableWeights);\n            }\n            return trainableWeights.concat(weights);\n        }\n        return weights;\n    }\n    get weights() {\n        return this.trainableWeights.concat(this.nonTrainableWeights);\n    }\n    /**\n     * Loads all layer weights from a JSON object.\n     *\n     * Porting Note: HDF5 weight files cannot be directly loaded in JavaScript /\n     *   TypeScript. The utility script at `scripts/pykeras.py` offers means\n     *   to convert them into JSON strings compatible with this method.\n     * Porting Note: TensorFlow.js Layers supports only loading by name currently.\n     *\n     * @param weights A JSON mapping weight names to weight values as nested\n     *   arrays of numbers, or a `NamedTensorMap`, i.e., a JSON mapping weight\n     *   names to `tf.Tensor` objects.\n     * @param strict Require that the provided weights exactly match those\n     *   required by the container.  Default: `true`.  Passing `false` means that\n     *   extra weights and missing weights will be silently ignored.\n     */\n    loadWeights(weights, strict = true) {\n        const nameToWeight = {};\n        let totalWeightsCount = 0;\n        for (const layer of this.layers) {\n            for (const weight of layer.weights) {\n                if (nameToWeight[weight.originalName] != null) {\n                    throw new ValueError(`Duplicate weight name: ${weight.originalName}`);\n                }\n                nameToWeight[weight.originalName] = weight;\n                totalWeightsCount++;\n            }\n        }\n        const weightValueTuples = [];\n        for (const name in weights) {\n            // TF 2.2.0 added cell name to the weight name in the format of\n            // layer_name/cell_name/weight_name, we need to remove\n            // the inner cell name.\n            let validatedName = name;\n            if (nameToWeight[name] == null) {\n                const tokens = name.split('/');\n                const shortenNameArray = tokens.slice(0, -2).concat([tokens[tokens.length - 1]]);\n                validatedName = shortenNameArray.join('/');\n            }\n            if (nameToWeight[validatedName] != null) {\n                weightValueTuples.push([nameToWeight[validatedName], weights[name]]);\n            }\n            else if (strict) {\n                throw new ValueError(`Provided weight data has no target variable: ${name}`);\n            }\n            delete nameToWeight[validatedName];\n        }\n        if (strict) {\n            // Check that all weights are set.\n            const unsetNames = [];\n            for (const name in nameToWeight) {\n                unsetNames.push(name);\n            }\n            if (unsetNames.length > 0) {\n                throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ` +\n                    `${unsetNames}`);\n            }\n        }\n        batchSetValue(weightValueTuples);\n    }\n    /**\n     * Util shared between different serialization methods.\n     * @returns LayersModel config with Keras version information added.\n     */\n    updatedConfig() {\n        const theConfig = this.getConfig();\n        const modelConfig = {};\n        modelConfig['className'] = this.getClassName();\n        modelConfig['config'] = theConfig;\n        modelConfig['kerasVersion'] = `tfjs-layers ${layersVersion}`;\n        // TODO(nielsene): Replace something like K.backend() once\n        // possible.\n        modelConfig['backend'] = 'TensorFlow.js';\n        return modelConfig;\n    }\n    /**\n     * Returns a JSON string containing the network configuration.\n     *\n     * To load a network from a JSON save file, use\n     * models.modelFromJSON(jsonString);\n     * @param extraJsonArgs Unused in tfjs-layers, maintained for PyKeras\n     * @param returnString Whether the return value should be stringified\n     *    (default: `true`).\n     * @returns a JSON string if `returnString` (default), or a JSON object if\n     *   `!returnString`.\n     */\n    // tslint:disable-next-line:no-any\n    toJSON(unused, returnString = true) {\n        const modelConfig = convertTsToPythonic(this.updatedConfig());\n        return returnString ? JSON.stringify(modelConfig) : modelConfig;\n    }\n    /**\n     * Call the model on new inputs.\n     *\n     * In this case `call` just reapplies all ops in the graph to the new inputs\n     * (e.g. build a new computational graph from the provided inputs).\n     *\n     * @param inputs A tensor or list of tensors.\n     * @param mask A mask or list of masks. A mask can be either a tensor or null\n     *   (no mask).\n     *\n     * @return A tensor if there is a single output, or a list of tensors if there\n     *   are more than one outputs.\n     */\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = generic_utils.toList(inputs);\n            const feedDict = new FeedDict();\n            for (let i = 0; i < this.inputs.length; ++i) {\n                feedDict.add(this.inputs[i], inputs[i]);\n            }\n            return execute(this.outputs, feedDict, kwargs);\n        });\n    }\n    /**\n     * Computes an output mask tensor.\n     *\n     * @param inputs Tensor or list of tensors.\n     * @param mask Tensor or list of tensors.\n     *\n     * @return null or a tensor (or list of tensors, one per output tensor of the\n     * layer).\n     */\n    computeMask(inputs, mask) {\n        return tidy(() => {\n            inputs = generic_utils.toList(inputs);\n            let masks;\n            if (mask == null) {\n                masks = generic_utils.pyListRepeat(null, inputs.length);\n            }\n            else {\n                masks = generic_utils.toList(mask);\n            }\n            // TODO(michaelterry): Add support for mask caching.\n            return this.runInternalGraph(inputs, masks)[1];\n        });\n    }\n    /**\n     * Computes the output shape of the layer.\n     *\n     * Assumes that the layer will be built to match that input shape provided.\n     *\n     * @param inputShape A shape (tuple of integers) or a list of shape tuples\n     *   (one per output tensor of the layer). Shape tuples can include null for\n     *   free dimensions, instead of an integer.\n     */\n    computeOutputShape(inputShape) {\n        const inputShapes = types_utils.normalizeShapeList(inputShape);\n        if (inputShapes.length !== this.inputLayers.length) {\n            throw new ValueError(`Invalid inputShape argument ${inputShape}: ` +\n                `model has ${this.inputLayers.length} tensor inputs.`);\n        }\n        // TODO(michaelterry): Add caching\n        const layersToOutputShapes = {};\n        for (let i = 0; i < inputShapes.length; i++) {\n            const layer = this.inputLayers[i];\n            const inputShape = inputShapes[i];\n            // It's an input layer: computeOutputShape is identity,\n            // and there is only one node and one tensor output.\n            const shapeKey = layer.name + '_0_0';\n            layersToOutputShapes[shapeKey] = inputShape;\n        }\n        const depthKeys = Object.keys(this.nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Iterate over nodes, by depth level.\n        if (depthKeys.length > 1) {\n            for (const depth of depthKeys) {\n                const nodes = this.nodesByDepth[depth];\n                for (const node of nodes) {\n                    // This is always a single layer, never a list.\n                    const layer = node.outboundLayer;\n                    if (this.inputLayers.map(x => x.id).indexOf(layer.id) !== -1) {\n                        // We've already covered the input layers a few lines above.\n                        continue;\n                    }\n                    // Potentially redundant list, same size of node.inputTensors.\n                    const inputShapes = [];\n                    for (let j = 0; j < node.inboundLayers.length; j++) {\n                        const inboundLayer = node.inboundLayers[j];\n                        const nodeIndex = node.nodeIndices[j];\n                        const tensorIndex = node.tensorIndices[j];\n                        const shapeKey = `${inboundLayer.name}_${nodeIndex}_${tensorIndex}`;\n                        const inputShape = layersToOutputShapes[shapeKey];\n                        inputShapes.push(inputShape);\n                    }\n                    const outputShape = layer.computeOutputShape(generic_utils.singletonOrArray(inputShapes));\n                    const outputShapes = types_utils.normalizeShapeList(outputShape);\n                    const nodeIndex = layer.inboundNodes.indexOf(node);\n                    for (let j = 0; j < outputShapes.length; j++) {\n                        const shapeKey = `${layer.name}_${nodeIndex}_${j}`;\n                        layersToOutputShapes[shapeKey] = outputShapes[j];\n                    }\n                }\n            }\n        }\n        // Read final output shapes from layersToOutputShapes.\n        const outputShapes = [];\n        const outputShapeKeys = [];\n        for (let i = 0; i < this.outputLayers.length; i++) {\n            const layer = this.outputLayers[i];\n            const nodeIndex = this.outputLayersNodeIndices[i];\n            const tensorIndex = this.outputLayersTensorIndices[i];\n            const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;\n            outputShapeKeys.push(shapeKey);\n        }\n        for (let i = 0; i < outputShapeKeys.length; i++) {\n            const key = outputShapeKeys[i];\n            generic_utils.assert(key in layersToOutputShapes);\n            outputShapes.push(layersToOutputShapes[key]);\n        }\n        // TODO(michaelterry): Update cache\n        return generic_utils.singletonOrArray(outputShapes);\n    }\n    /**\n     * Computes output tensors for new inputs.\n     *\n     * Note:\n     *   - Expects `inputs` to be a list (potentially with 1 element).\n     *\n     * @param inputs List of tensors\n     * @param masks List of masks (tensors or null).\n     * @return Three lists: outputTensors, outputMasks, outputShapes\n     */\n    runInternalGraph(inputs, masks) {\n        if (masks == null) {\n            masks = generic_utils.pyListRepeat(null, inputs.length);\n        }\n        // Dictionary mapping reference tensors to tuples\n        // (computed tensor, compute mask)\n        // we assume a 1:1 mapping from tensor to mask\n        // TODO: raise exception when a `.computeMask()` call\n        // does not return a list the same size as `call`\n        const tensorMap = {};\n        for (let i = 0; i < this.inputs.length; ++i) {\n            const x = this.inputs[i];\n            const y = inputs[i];\n            const mask = masks[i];\n            tensorMap[x.id] = [y, mask];\n        }\n        const depthKeys = Object.keys(this.nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        for (const depth of depthKeys) {\n            const nodes = this.nodesByDepth[depth];\n            for (const node of nodes) {\n                // This is always a single layer, never a list.\n                const layer = node.outboundLayer;\n                const referenceInputTensors = node.inputTensors;\n                const referenceOutputTensors = node.outputTensors;\n                // If all previous input tensors are available in tensorMap,\n                // then call node.inboundLayer on them.\n                // List of tuples [input, mask]:\n                const computedData = new Array();\n                for (const x of referenceInputTensors) {\n                    if (x.id in tensorMap) {\n                        computedData.push(tensorMap[x.id]);\n                    }\n                }\n                if (computedData.length === referenceInputTensors.length) {\n                    // TODO(michaelterry): Add K.name_scope here, if we need it.\n                    let kwargs = {};\n                    let computedTensors;\n                    let computedMasks;\n                    let outputTensors;\n                    let outputMasks;\n                    // call layer\n                    if (node.callArgs != null) {\n                        kwargs = node.callArgs;\n                    }\n                    if (computedData.length === 1) {\n                        const [computedTensor, computedMask] = computedData[0];\n                        if (kwargs['mask'] == null) {\n                            kwargs['mask'] = computedMask;\n                        }\n                        outputTensors =\n                            generic_utils.toList(layer.call(computedTensor, kwargs));\n                        outputMasks = generic_utils.toList(layer.computeMask(computedTensor, computedMask));\n                        computedTensors = [computedTensor];\n                        computedMasks = [computedMask];\n                    }\n                    else {\n                        computedTensors = computedData.map(x => x[0]);\n                        computedMasks = computedData.map(x => x[1]);\n                        if (kwargs['mask'] == null) {\n                            kwargs['mask'] = computedMasks;\n                        }\n                        outputTensors =\n                            generic_utils.toList(layer.call(computedTensors, kwargs));\n                        outputMasks = generic_utils.toList(layer.computeMask(computedTensors, computedMasks));\n                    }\n                    if (layer.activityRegularizer) {\n                        throw new NotImplementedError('LayersModel invocation with concrete Tensor value(s) in the ' +\n                            'presence of activity regularizer(s) is not supported yet.');\n                    }\n                    // TODO(michaelterry): Add model updates and losses\n                    // Update tensor map.\n                    for (let i = 0; i < referenceOutputTensors.length; ++i) {\n                        const x = referenceOutputTensors[i];\n                        const y = outputTensors[i];\n                        const mask = outputMasks[i];\n                        tensorMap[x.id] = [y, mask];\n                    }\n                }\n            }\n        }\n        const outputTensors = [];\n        const outputMasks = [];\n        const outputShapes = [];\n        for (const x of this.outputs) {\n            generic_utils.assert(x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`);\n            const [tensor, mask] = tensorMap[x.id];\n            outputShapes.push(tensor.shape);\n            outputTensors.push(tensor);\n            outputMasks.push(mask);\n        }\n        // TODO(michaelterry): Add support for caches.\n        return [outputTensors, outputMasks, outputShapes];\n    }\n    /**\n     * Builds a map of internal node keys to node ordering.\n     * Used in serializaion a node orderings may change as unused nodes are\n     * dropped. Porting Note:  This helper method was pulled out of getConfig to\n     * improve readability.\n     * @param layers An array of Layers in the model.\n     * @returns Map of Node Keys to index order within the layer.\n     */\n    buildNodeConversionMap(layers) {\n        const nodeConversionMap = {};\n        let keptNodes;\n        for (const layer of this.layers) {\n            keptNodes = layer instanceof Container ? 1 : 0;\n            for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n                const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n                if (this.containerNodes.has(nodeKey)) {\n                    // i.e. we mark it to be saved\n                    nodeConversionMap[nodeKey] = keptNodes;\n                    keptNodes += 1;\n                }\n            }\n        }\n        return nodeConversionMap;\n    }\n    /**\n     * Retrieves a layer based on either its name (unique) or index.\n     *\n     * Indices are based on order of horizontal graph traversal (bottom-up).\n     *\n     * If both `name` and `index` are specified, `index` takes precedence.\n     *\n     * @param name Name of layer.\n     * @param index Index of layer.\n     * @returns A Layer instance.\n     * @throws ValueError: In case of invalid layer name or index.\n     *\n     * @doc {\n     *    heading: 'Layers',\n     *    subheading: 'Classes',\n     *    namespace: 'layers',\n     *    subclasses: ['LayersModel']\n     * }\n     */\n    getLayer(name, index) {\n        if (index != null) {\n            if (this.layers.length <= index) {\n                throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only ` +\n                    `has ${this.layers.length} layer(s).`);\n            }\n            else {\n                return this.layers[index];\n            }\n        }\n        else {\n            if (name == null) {\n                throw new ValueError('Provide either a layer name or layer index');\n            }\n        }\n        for (const layer of this.layers) {\n            if (layer.name === name) {\n                return layer;\n            }\n        }\n        throw new ValueError(`No such layer: ${name}`);\n    }\n    /**\n     * Retrieves the Container's current loss values.\n     *\n     * Used for regularizers during training.\n     */\n    calculateLosses() {\n        // Porting Node: This is an augmentation to Container.loss in PyKeras.\n        //   In PyKeras, Container.loss returns symbolic tensors. Here a concrete\n        //   Tensor (specifically Scalar) values are returned. This is due to the\n        //   imperative backend.\n        return tidy(() => {\n            const losses = [];\n            for (const layer of this.layers) {\n                for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length; ++nodeIndex) {\n                    const nodeKey = Container.nodeKey(layer, nodeIndex);\n                    if (this.containerNodes.has(nodeKey)) {\n                        losses.push(...layer.calculateLosses());\n                    }\n                }\n            }\n            // TODO(cais): Add any unconditional model-level losses?\n            return losses;\n        });\n    }\n    getConfig() {\n        const config = { name: this.name };\n        // Build a map from layer unique name (self._node_key)\n        // to the index of the nodes that are saved in the config.\n        // Only nodes in container_nodes are saved.\n        const nodeConversionMap = this.buildNodeConversionMap(this.layers);\n        // Serialize and save the layers in layerConfigs\n        const layerConfigs = [];\n        for (const layer of this.layers) {\n            const layerClassName = layer.getClassName();\n            const layerConfig = layer.getConfig();\n            const filteredInboundNodes = [];\n            for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n                const node = layer.inboundNodes[originalNodeIndex];\n                const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n                let kwargs = {};\n                if (this.containerNodes.has(nodeKey)) {\n                    // The node is relevant to the model:\n                    // add to filteredInboundNodes.\n                    if (node.callArgs) {\n                        try {\n                            JSON.stringify(node.callArgs);\n                            kwargs = node.callArgs;\n                        }\n                        catch (err) {\n                            console.warn(`Layer ${layer.name} was passed ` +\n                                `non-serializable keyword arguments: ` +\n                                `${node.callArgs}. They will not be included ` +\n                                `in the serialized model (and thus will be ` +\n                                `missing at deserialization time).`);\n                            kwargs = {};\n                        }\n                    }\n                    if (node.inboundLayers.length > 0) {\n                        const nodeData = [];\n                        for (let i = 0; i < node.inboundLayers.length; i++) {\n                            const inboundLayer = node.inboundLayers[i];\n                            const nodeIndex = node.nodeIndices[i];\n                            const tensorIndex = node.tensorIndices[i];\n                            const nodeKey = Container.nodeKey(inboundLayer, nodeIndex);\n                            let newNodeIndex = nodeConversionMap[nodeKey];\n                            if (newNodeIndex == null) {\n                                newNodeIndex = 0;\n                            }\n                            nodeData.push([inboundLayer.name, newNodeIndex, tensorIndex, kwargs]);\n                        }\n                        filteredInboundNodes.push(nodeData);\n                    }\n                }\n            }\n            const dict = {};\n            dict['name'] = layer.name;\n            dict['className'] = layerClassName;\n            dict['config'] = layerConfig;\n            dict['inboundNodes'] = filteredInboundNodes;\n            layerConfigs.push(dict);\n        }\n        config['layers'] = layerConfigs;\n        // Gather info about inputs and outputs\n        const modelInputs = [];\n        for (let i = 0; i < this.inputLayers.length; i++) {\n            const layer = this.inputLayers[i];\n            const nodeIndex = this.inputLayersNodeIndices[i];\n            const nodeKey = Container.nodeKey(layer, nodeIndex);\n            if (!this.containerNodes.has(nodeKey)) {\n                continue;\n            }\n            let newNodeIndex = nodeConversionMap[nodeKey];\n            if (newNodeIndex === null || newNodeIndex === undefined) {\n                newNodeIndex = 0;\n            }\n            const tensorIndex = this.inputLayersTensorIndices[i];\n            modelInputs.push([layer.name, newNodeIndex, tensorIndex]);\n        }\n        config['inputLayers'] = modelInputs;\n        const modelOutputs = [];\n        for (let i = 0; i < this.outputLayers.length; i++) {\n            const layer = this.outputLayers[i];\n            const nodeIndex = this.outputLayersNodeIndices[i];\n            const nodeKey = Container.nodeKey(layer, nodeIndex);\n            if (!this.containerNodes.has(nodeKey)) {\n                continue;\n            }\n            let newNodeIndex = nodeConversionMap[nodeKey];\n            if (newNodeIndex === null || newNodeIndex === undefined) {\n                newNodeIndex = 0;\n            }\n            const tensorIndex = this.outputLayersTensorIndices[i];\n            modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);\n        }\n        config['outputLayers'] = modelOutputs;\n        return config;\n    }\n    /**\n     * Instantiates a LayersModel from its config (output of `get_config()`).\n     * @param cls the class to create\n     * @param config LayersModel config dictionary.\n     * @param customObjects An optional dictionary of custom objects.\n     * @param fastWeightInit Optional flag to use fast weight initialization\n     *   during deserialization. This is applicable to cases in which\n     *   the initialization will be immediately overwritten by loaded weight\n     *   values. Default: `false`.\n     * @returns A LayersModel instance.\n     * @throws ValueError: In case of improperly formatted config dict.\n     */\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n        // Layer instances created during\n        // the graph reconstruction process\n        const createdLayers = {};\n        // Dictionary mapping layer instances to\n        // node data that specifies a layer call.\n        // It acts as a queue that maintains any unprocessed\n        // layer call until it becomes possible to process it\n        // (i.e. until the input tensors to the call all exist).\n        const unprocessedNodes = {};\n        function addUnprocessedNode(layer, nodeData) {\n            if (!(layer.name in unprocessedNodes)) {\n                unprocessedNodes[layer.name] = [nodeData];\n            }\n            else {\n                unprocessedNodes[layer.name].push(nodeData);\n            }\n        }\n        function processNode(layer, nodeData) {\n            const inputTensors = [];\n            let kwargs;\n            for (const inputData of nodeData) {\n                const inboundLayerName = inputData[0];\n                const inboundNodeIndex = inputData[1];\n                const inboundTensorIndex = inputData[2];\n                kwargs = inputData[3] == null ?\n                    {} :\n                    inputData[3];\n                if (!(inboundLayerName in createdLayers)) {\n                    addUnprocessedNode(layer, nodeData);\n                    return;\n                }\n                const inboundLayer = createdLayers[inboundLayerName];\n                if (inboundLayer.inboundNodes.length <= inboundNodeIndex) {\n                    addUnprocessedNode(layer, nodeData);\n                    return;\n                }\n                const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex];\n                inputTensors.push(inboundNode.outputTensors[inboundTensorIndex]);\n            }\n            // Call layer on its inputs, thus creating the node\n            // and building the layer if needed.\n            // Note: This has Eager vs Graph Implications.\n            if (inputTensors.length > 0) {\n                layer.apply(generic_utils.singletonOrArray(inputTensors), kwargs); // was ** kwargs\n            }\n        }\n        /**\n         * Deserialize a layer, then call it on appropriate inputs.\n         * @param layerData: layer config dict.\n         * @throws ValueError: In case of improperly formatted `layer_data`\n         * dict.\n         */\n        function processLayer(layerData) {\n            const layerName = layerData['name'];\n            // Instantiate layer.\n            const layer = deserializeLayer(layerData, config['customObjects'] != null ?\n                config['customObjects'] :\n                {});\n            layer.setFastWeightInitDuringBuild(fastWeightInit);\n            createdLayers[layerName] = layer;\n            // Gather layer inputs.\n            const inboundNodesData = layerData['inboundNodes'];\n            inboundNodesData.forEach(nodeData => {\n                if (!(nodeData instanceof Array)) {\n                    throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);\n                }\n                // We don't process nodes (i.e. make layer calls)\n                // on the fly because the inbound node may not yet exist,\n                // in case of layer shared at different topological depths\n                // (e.g.a model such as A(B(A(B(x)))))\n                addUnprocessedNode(layer, nodeData);\n            });\n        }\n        // First, we create all layers and enqueue nodes to be processed.\n        const name = config['name'];\n        const layersFromConfig = config['layers'];\n        for (const layerData of layersFromConfig) {\n            processLayer(layerData);\n        }\n        // Then we process nodes in order of layer depth.\n        // Nodes that cannot yet be processed(if the inbound node\n        // does not yet exist) are re - enqueued, and the process\n        // is repeated until all nodes are processed.\n        while (!generic_utils.isObjectEmpty(unprocessedNodes)) {\n            for (const layerData of layersFromConfig) {\n                const layer = createdLayers[layerData['name']];\n                if (layer.name in unprocessedNodes) {\n                    const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name];\n                    delete unprocessedNodes[layer.name];\n                    for (const nodeData of currentUnprocessedNodesForLayer) {\n                        processNode(layer, nodeData);\n                    }\n                }\n            }\n        }\n        const inputTensors = [];\n        const outputTensors = [];\n        const inputLayersFromConfig = config['inputLayers'];\n        for (const layerData of inputLayersFromConfig) {\n            const layerName = layerData[0];\n            const nodeIndex = layerData[1];\n            const tensorIndex = layerData[2];\n            generic_utils.assert(layerName in createdLayers);\n            const layer = createdLayers[layerName];\n            const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n            inputTensors.push(layerOutputTensors[tensorIndex]);\n        }\n        const outputLayersFromConfig = config['outputLayers'];\n        for (const layerData of outputLayersFromConfig) {\n            const layerName = layerData[0];\n            const nodeIndex = layerData[1];\n            const tensorIndex = layerData[2];\n            generic_utils.assert(layerName in createdLayers);\n            const layer = createdLayers[layerName];\n            const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n            outputTensors.push(layerOutputTensors[tensorIndex]);\n        }\n        return new cls({ inputs: inputTensors, outputs: outputTensors, name });\n    }\n    /**\n     * Determine whether the container is stateful.\n     *\n     * Porting Note: this is the equivalent of the stateful @property of\n     *   the Container class in PyKeras.\n     */\n    get stateful() {\n        // Porting Note: This check is to prevent inadvertent setting of the\n        //   _stateful property of the Container instance.\n        if (this._stateful) {\n            throw new ValueError('Container instance unexpectedly has _stateful = true. The ' +\n                'statefulness of a Container is determined by the Layers it ' +\n                'contains. Its _stateful property must remain the default false.');\n        }\n        for (const layer of this.layers) {\n            if (layer.stateful) {\n                return true;\n            }\n        }\n        return false;\n    }\n    /**\n     * Reset the state of all stateful constituent layers (if any).\n     *\n     * Examples of stateful layers include RNN layers whose `stateful` property\n     * is set as `true`.\n     */\n    resetStates() {\n        tidy(() => {\n            this.layers.forEach(layer => {\n                // tslint:disable:no-any\n                if (layer.stateful) {\n                    layer.resetStates();\n                }\n                // tslint:enable:no-any\n            });\n        });\n    }\n}\n//# sourceMappingURL=container.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { dispose, mul, tensor1d, tidy } from '@tensorflow/tfjs-core';\nfunction standardizeSampleOrClassWeights(xWeight, outputNames, weightType) {\n    const numOutputs = outputNames.length;\n    if (xWeight == null || (Array.isArray(xWeight) && xWeight.length === 0)) {\n        return outputNames.map(name => null);\n    }\n    if (numOutputs === 1) {\n        if (Array.isArray(xWeight) && xWeight.length === 1) {\n            return xWeight;\n        }\n        else if (typeof xWeight === 'object' && outputNames[0] in xWeight) {\n            return [xWeight[outputNames[0]]];\n        }\n        else {\n            return [xWeight];\n        }\n    }\n    if (Array.isArray(xWeight)) {\n        if (xWeight.length !== numOutputs) {\n            throw new Error(`Provided ${weightType} is an array of ${xWeight.length} ` +\n                `element(s), but the model has ${numOutputs} outputs. ` +\n                `Make sure a set of weights is provided for each model output.`);\n        }\n        return xWeight;\n    }\n    else if (typeof xWeight === 'object' && Object.keys(xWeight).length > 0 &&\n        typeof xWeight[Object.keys(xWeight)[0]] ===\n            'object') {\n        const output = [];\n        outputNames.forEach(outputName => {\n            if (outputName in xWeight) {\n                output.push(xWeight[outputName]);\n            }\n            else {\n                output.push(null);\n            }\n        });\n        return output;\n    }\n    else {\n        throw new Error(`The model has multiple (${numOutputs}) outputs, ` +\n            `so ${weightType} must be either an array with ` +\n            `${numOutputs} elements or an object with ${outputNames} keys. ` +\n            `Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);\n    }\n}\n/**\n * Standardize class weighting objects.\n *\n * This function takes a single class-weighting object, an array of them,\n * or a map from output name to class-weighting object. It compares it to the\n * output name(s) of the model, base on which it outputs an array of\n * class-weighting objects of which the length matches the number of outputs.\n *\n * @param classWeight Input class-weighting object(s).\n * @param outputNames All output name(s) of the model.\n * @return An array of class-weighting objects. The length of the array matches\n *   the model's number of outputs.\n */\nexport function standardizeClassWeights(classWeight, outputNames) {\n    return standardizeSampleOrClassWeights(classWeight, outputNames, 'classWeight');\n}\nexport function standardizeSampleWeights(classWeight, outputNames) {\n    return standardizeSampleOrClassWeights(classWeight, outputNames, 'sampleWeight');\n}\n/**\n * Standardize by-sample and/or by-class weights for training.\n *\n * Note that this function operates on one model output at a time. For a model\n * with multiple outputs, you must call this function multiple times.\n *\n * @param y The target tensor that the by-sample and/or by-class weight is for.\n *     The values of y are assumed to encode the classes, either directly\n *     as an integer index, or as one-hot encoding.\n * @param sampleWeight By-sample weights.\n * @param classWeight By-class weights: an object mapping class indices\n *     (integers) to a weight (float) to apply to the model's loss for the\n *     samples from this class during training. This can be useful to tell the\n *     model to \"pay more attention\" to samples from an under-represented class.\n * @param sampleWeightMode The mode for the sample weights.\n * @return A Promise of weight tensor, of which the size of the first dimension\n *     matches that of `y`.\n */\nexport async function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) {\n    if (sampleWeight != null || sampleWeightMode != null) {\n        // TODO(cais): Once 'temporal' mode is implemented, document it in the doc\n        // string.\n        throw new Error('Support sampleWeight is not implemented yet');\n    }\n    if (classWeight != null) {\n        // Apply class weights per sample.\n        const yClasses = tidy(() => {\n            if (y.shape.length === 1) {\n                // Assume class indices.\n                return y.clone();\n            }\n            else if (y.shape.length === 2) {\n                if (y.shape[1] > 1) {\n                    // Assume one-hot encoding of classes.\n                    const axis = 1;\n                    return y.argMax(axis);\n                }\n                else if (y.shape[1] === 1) {\n                    // Class index.\n                    return y.reshape([y.shape[0]]);\n                }\n                else {\n                    throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) ` +\n                        `during handling of class weights. The size is expected to be ` +\n                        `>= 1.`);\n                }\n            }\n            else {\n                throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during ` +\n                    `handling of class weights. The rank is expected to be 1 or 2.`);\n            }\n        });\n        const yClassIndices = Array.from(await yClasses.data());\n        dispose(yClasses);\n        const classSampleWeight = [];\n        yClassIndices.forEach(classIndex => {\n            if (classWeight[classIndex] == null) {\n                throw new Error(`classWeight must contain all classes in the training data. ` +\n                    `The class ${classIndex} exists in the data but not in ` +\n                    `classWeight`);\n            }\n            else {\n                classSampleWeight.push(classWeight[classIndex]);\n            }\n        });\n        return tensor1d(classSampleWeight, 'float32');\n    }\n    else {\n        return null;\n    }\n}\n/**\n * Apply per-sample weights on the loss values from a number of samples.\n *\n * @param losses Loss tensor of shape `[batchSize]`.\n * @param sampleWeights Per-sample weight tensor of shape `[batchSize]`.\n * @returns Tensor of the same shape as`losses`.\n */\nexport function computeWeightedLoss(losses, sampleWeights) {\n    return mul(losses, sampleWeights);\n}\n//# sourceMappingURL=training_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Interfaces and methods for training models using TensorFlow.js datasets.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { scalar } from '@tensorflow/tfjs-core';\nimport { configureCallbacks, standardizeCallbacks } from '../base_callbacks';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { disposeTensorsInLogs } from '../logs';\nimport { singletonOrArray, toList } from '../utils/generic_utils';\nimport { standardizeClassWeights, standardizeWeights } from './training_utils';\n// Default batch size used during tensor-based validation.\nconst DEFAULT_VALIDATION_BATCH_SIZE = 32;\n/**\n * Standardize the output of a dataset iterator for use by\n * LayersModel.fitDataset().\n *\n * @param model: A `tf.LayersModel` object.\n * @param iteratorOut The output of a dataset iterator. It is required to be\n *   an object of the form `{xs: TensorOrArrayOrMap, ys:\n * TensorOrArrayOrMap}`, where `TensorOrArrayOrMap` is a single `tf.Tensor`,\n * a `tf.Tensor[]`, or a flat map from string names to `tf.Tensor`s.\n * @returns A flat array of `tf.Tensor` objects: the input `tf.Tensor`s\n *   followed by the target `tf.Tensor`s.  When `tf.Tensor`s are provided\n *   as a map, the order in the resulting array is taken from the `inputNames`\n *   and `outputNames` of the model.\n */\nfunction standardizeDataIteratorOutput(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, iteratorOut) {\n    let xs;\n    let ys;\n    const iteratorOutObj = iteratorOut;\n    xs = iteratorOutObj['xs'];\n    ys = iteratorOutObj['ys'];\n    tfc.util.assert(xs != null && ys != null, () => 'A Dataset iterator for fitDataset() is expected to generate ' +\n        'objects of the form `{xs: xVal, ys: yVal}`, where the two ' +\n        'values may be `tf.Tensor`, an array of Tensors, or a map of ' +\n        'string to Tensor.  The provided Dataset instead generates ' +\n        `${iteratorOut}`);\n    const flattenedXs = flattenTensorOrArrayOrMap('input', model.inputNames, xs);\n    const flattenedYs = flattenTensorOrArrayOrMap('output', model.outputNames, ys);\n    const batchSize = flattenedXs[0].shape[0];\n    tfc.util.assert(flattenedXs.length === model.inputs.length, () => `LayersModel has ${model.inputs.length} inputs, but the dataset ` +\n        `provides ${flattenedXs.length} inputs.  (Expected input keys: ` +\n        `${JSON.stringify(model.inputNames)})`);\n    tfc.util.assert(flattenedYs.length === model.outputs.length, () => `LayersModel has ${model.outputs.length} outputs, but the dataset ` +\n        `provides ${flattenedYs.length} outputs.  (Expected output keys: ` +\n        `${JSON.stringify(model.outputNames)})`);\n    for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {\n        tfc.util.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ` +\n            `${model.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; ` +\n            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);\n    }\n    for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {\n        tfc.util.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ` +\n            `${model.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; ` +\n            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);\n    }\n    return { xs: flattenedXs, ys: flattenedYs };\n}\nfunction flattenTensorOrArrayOrMap(inputOrOutput, names, values) {\n    if (values instanceof tfc.Tensor) {\n        return [values];\n    }\n    else if (Array.isArray(values)) {\n        tfc.util.assert(values.length === names.length, () => `Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`);\n        return values;\n    }\n    else {\n        const result = [];\n        // Check that all the required keys are available.\n        for (const name of names) {\n            if (values[name] == null) {\n                throw new ValueError(`The feature data generated by the dataset lacks the required ` +\n                    `${inputOrOutput} key '${name}'.`);\n            }\n            result.push(values[name]);\n        }\n        return result;\n    }\n}\nfunction standardizeTensorValidationData(data) {\n    if (data.length === 3) {\n        throw new NotImplementedError('Validation with sample weights is not implemented yet.');\n    }\n    return { xs: data[0], ys: data[1] };\n}\nexport async function fitDataset(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, dataset, args) {\n    const hasBatchesPerEpoch = args.batchesPerEpoch != null;\n    tfc.util.assert(model.optimizer != null, () => 'You must compile a model before training/testing. Use ' +\n        'LayersModel.compile(modelCompileConfig).');\n    tfc.util.assert(args != null, () => `For fitDataset(), the 2nd argument (config) is required, ` +\n        `but it is not provided in this call.`);\n    tfc.util.assert(args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs), () => `For fitDataset(), config.epochs is expected to be a positive ` +\n        `integer, but got ${args.epochs}`);\n    tfc.util.assert(!hasBatchesPerEpoch ||\n        (args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch)), () => `For fitDataset(), config.batchesPerEpoch is expected to be a ` +\n        `positive integer if specified, but got ${args.batchesPerEpoch}`);\n    tfc.util.assert(\n    // tslint:disable-next-line:no-any\n    args['validationSplit'] == null, () => '`validationSplit` is not supported by `fitDataset()`. ' +\n        'Use validationData instead.');\n    if (model.isTraining) {\n        throw new Error('Cannot start training because another fit() call is ongoing.');\n    }\n    model.isTraining = true;\n    try {\n        const doValidation = args.validationData != null;\n        let valXs;\n        let valYs;\n        if (doValidation) {\n            if (isDatasetObject(args.validationData)) {\n                tfc.util.assert(args.validationBatches == null ||\n                    (args.validationBatches > 0 &&\n                        Number.isInteger(args.validationBatches)), () => `For fitDataset() with dataset-based validation, ` +\n                    `config.validationBatches is expected not to be provided, ` +\n                    `or to be a positive integer, ` +\n                    `but got ${args.validationBatches}`);\n            }\n            else {\n                const validationData = standardizeTensorValidationData(args.validationData);\n                valXs = validationData.xs;\n                valYs = validationData.ys;\n            }\n        }\n        const trainFunction = model.makeTrainFunction();\n        const outLabels = model.getDedupedMetricsNames();\n        let callbackMetrics;\n        if (doValidation) {\n            callbackMetrics =\n                outLabels.slice().concat(outLabels.map(n => 'val_' + n));\n        }\n        else {\n            callbackMetrics = outLabels.slice();\n        }\n        const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);\n        const verbose = args.verbose == null ? 1 : args.verbose;\n        const { callbackList, history } = configureCallbacks(callbacks, verbose, args.epochs, null, null, getStepsPerEpoch(dataset, args), null, // Batch size determined by the dataset itself.\n        doValidation, callbackMetrics);\n        callbackList.setModel(model);\n        model.history = history;\n        await callbackList.onTrainBegin();\n        model.stopTraining_ = false;\n        let epoch = args.initialEpoch == null ? 0 : args.initialEpoch;\n        let dataIterator = await dataset.iterator();\n        while (epoch < args.epochs) {\n            const epochLogs = {};\n            await callbackList.onEpochBegin(epoch);\n            let stepsDone = 0;\n            let batchIndex = 0;\n            if (!hasBatchesPerEpoch) {\n                dataIterator = await dataset.iterator();\n            }\n            while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) {\n                const iteratorOut = await dataIterator.next();\n                // If `batchesPerEpoch` is specified, the dataset should not be\n                // exhausted until all epoches are done.\n                if (hasBatchesPerEpoch && iteratorOut.done) {\n                    console.warn('You provided `batchesPerEpoch` as ' +\n                        `${args.batchesPerEpoch}, ` +\n                        'but your dataset iterator ran out of data after ' +\n                        `${stepsDone} batches; ` +\n                        'interrupting training. Make sure that your ' +\n                        'dataset can generate at least `batchesPerEpoch * epochs` ' +\n                        'batches (in this case, ' +\n                        `${args.batchesPerEpoch * args.epochs} batches). ` +\n                        'You may need to use the repeat() function when building ' +\n                        'your dataset.');\n                    break;\n                }\n                if (iteratorOut.value != null) {\n                    const { xs, ys } = standardizeDataIteratorOutput(model, iteratorOut.value);\n                    const batchLogs = {};\n                    batchLogs['batch'] = batchIndex;\n                    batchLogs['size'] = xs[0].shape[0];\n                    await callbackList.onBatchBegin(batchIndex, batchLogs);\n                    const sampleWeights = [];\n                    if (args.classWeight != null) {\n                        const standardClassWeights = standardizeClassWeights(args.classWeight, model.outputNames);\n                        for (let i = 0; i < standardClassWeights.length; ++i) {\n                            sampleWeights.push(await standardizeWeights(ys[i], null, standardClassWeights[i]));\n                        }\n                    }\n                    // Train on batch.\n                    const ins = xs.concat(ys).concat(sampleWeights);\n                    const outs = trainFunction(ins);\n                    tfc.dispose(ins);\n                    for (let i = 0; i < outLabels.length; ++i) {\n                        const label = outLabels[i];\n                        const out = outs[i];\n                        batchLogs[label] = out;\n                        tfc.keep(out);\n                    }\n                    await callbackList.onBatchEnd(batchIndex, batchLogs);\n                    disposeTensorsInLogs(batchLogs);\n                    batchIndex++;\n                    stepsDone++;\n                }\n                if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch :\n                    iteratorOut.done) {\n                    // Epoch finished. Perform validation.\n                    if (doValidation) {\n                        let valOuts;\n                        if (isDatasetObject(args.validationData)) {\n                            valOuts = toList(await model.evaluateDataset(args.validationData, { batches: args.validationBatches }));\n                        }\n                        else {\n                            valOuts = toList(model.evaluate(valXs, valYs, {\n                                batchSize: args.validationBatchSize == null ?\n                                    DEFAULT_VALIDATION_BATCH_SIZE :\n                                    args.validationBatchSize,\n                                verbose: 0\n                            }));\n                        }\n                        for (let i = 0; i < model.metricsNames.length; ++i) {\n                            epochLogs[`val_${model.metricsNames[i]}`] = valOuts[i];\n                        }\n                    }\n                    // Call `break` to exit one epoch lopp after validation is done. If\n                    // config.batchesPerEpoch is specified, an epoch while loop will\n                    // stop when `stepsDone >= config.batchesPerEpoch`. When\n                    // config.batchesPerEpoch is not provided, the following `break` is\n                    // required to exit the while lopp after dataset is exhausted.\n                    break;\n                }\n                if (model.stopTraining_) {\n                    break;\n                }\n            }\n            await callbackList.onEpochEnd(epoch, epochLogs);\n            epoch++;\n            if (model.stopTraining_) {\n                break;\n            }\n        }\n        await callbackList.onTrainEnd();\n        await model.history.syncData();\n        return model.history;\n    }\n    finally {\n        model.isTraining = false;\n    }\n}\n/** Helper function that determines number of steps (batches) per epoch. */\nfunction getStepsPerEpoch(dataset, args) {\n    // Attempt to determine # of batches in an epoch.\n    let stepsPerEpoch = null;\n    if (args.batchesPerEpoch != null) {\n        stepsPerEpoch = args.batchesPerEpoch;\n    }\n    else if (Number.isFinite(dataset.size)) {\n        stepsPerEpoch = dataset.size;\n    }\n    return stepsPerEpoch;\n}\n// Check if provided object is a Dataset object by checking its .iterator\n// element.\nfunction isDatasetObject(dataset) {\n    return (typeof dataset.iterator === 'function');\n}\n// Check if provided object is a LazyIterator object by checking it's .next\n// element.\nfunction isLazyIteratorObject(iterator) {\n    return (typeof iterator.next === 'function');\n}\nexport async function evaluateDataset(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, dataset, args) {\n    args = args || {};\n    const hasBatches = args.batches != null;\n    const f = model.testFunction;\n    let outs = [];\n    if (args.verbose > 0) {\n        throw new NotImplementedError('Verbose mode is not implemented yet.');\n    }\n    tfc.util.assert(!hasBatches || (args.batches > 0 && Number.isInteger(args.batches)), () => 'Test loop expects `batches` to be a positive integer, but ' +\n        `received ${JSON.stringify(args.batches)}`);\n    const dataIterator = isLazyIteratorObject(dataset) ?\n        dataset :\n        await dataset.iterator();\n    // Keeps track of number of examples used in this evaluation.\n    let numExamples = 0;\n    let batch = 0;\n    while (hasBatches ? batch < args.batches : true) {\n        const iteratorOut = await dataIterator.next();\n        outs = tfc.tidy(() => {\n            if (iteratorOut.value) {\n                // TODO(cais): Once real dataset is available, use\n                //   `map(x => standardizeDataIteratorOutput(model, x).map(f)`.\n                const { xs, ys } = standardizeDataIteratorOutput(model, iteratorOut.value);\n                const xsAndYs = xs.concat(ys);\n                const batchOuts = tfc.tidy(() => f(xsAndYs));\n                tfc.dispose(xsAndYs);\n                if (batch === 0) {\n                    for (let i = 0; i < batchOuts.length; ++i) {\n                        outs.push(scalar(0));\n                    }\n                }\n                const batchSize = xsAndYs[0].shape[0];\n                for (let i = 0; i < batchOuts.length; ++i) {\n                    const batchOut = batchOuts[i];\n                    const oldScalar = outs[i];\n                    outs[i] =\n                        tfc.tidy(() => tfc.add(outs[i], tfc.mul(batchSize, batchOut)));\n                    if (batch > 0) {\n                        tfc.dispose(oldScalar);\n                    }\n                }\n                tfc.dispose(batchOuts);\n                numExamples += batchSize;\n                ++batch;\n            }\n            return outs;\n        });\n        if (iteratorOut.done) {\n            if (hasBatches) {\n                console.warn('Your dataset iterator ran out of data during evaluateDataset(). ' +\n                    'Interrupting evalution. Make sure that your ' +\n                    'dataset can generate at least `batches` ' +\n                    `batches (in this case, ${args.batches} batches). ` +\n                    'You may need to use the repeat() function when building ' +\n                    'your dataset.');\n            }\n            break;\n        }\n    }\n    for (let i = 0; i < outs.length; ++i) {\n        const oldScalar = outs[i];\n        outs[i] = tfc.div(outs[i], numExamples);\n        tfc.dispose(oldScalar);\n    }\n    return singletonOrArray(outs);\n}\n//# sourceMappingURL=training_dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Interfaces and methods for training models using tf.Tensor objects.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { Tensor, tensor1d, util } from '@tensorflow/tfjs-core';\nimport { expandDims, gather, sliceAlongFirstAxis } from '../backend/tfjs_backend';\nimport { configureCallbacks, standardizeCallbacks } from '../base_callbacks';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { disposeTensorsInLogs } from '../logs';\nimport { range } from '../utils/math_utils';\nexport function checkBatchSize(batchSize) {\n    tfc.util.assert(batchSize > 0 && Number.isInteger(batchSize), () => `batchSize is required to be a positive integer, but got ${batchSize}`);\n}\n/**\n * Slice a Tensor or an Array of Tensors, by start and stop indices.\n *\n * Porting Note: The `_slice_arrays` function in PyKeras is covered by this\n *   function and `sliceArraysByIndices()` together.\n *\n * @param arrays: the input.\n * @param start: the starting index (inclusive).\n * @param stop: the stopping index (exclusive).\n * @returns The result of the slicing. If `arrays` is an `Array` of\n *   `tf.Tensor`s, the slicing will be applied to all elements of the `Array`\n *   in the same way.\n */\nexport function sliceArrays(arrays, start, stop) {\n    if (arrays == null) {\n        return [null];\n    }\n    else if (Array.isArray(arrays)) {\n        return arrays.map(array => sliceAlongFirstAxis(array, start, stop - start));\n    }\n    else { // Tensor.\n        return sliceAlongFirstAxis(arrays, start, stop - start);\n    }\n}\n/**\n * Slice a Tensor or an Array of Tensors, by random-order indices.\n *\n * Porting Note: The `_slice_arrays` function in PyKeras is covered by this\n *   function and `sliceArrays()` together.\n *\n * @param arrays The input `tf.Tensor` or `Array` of `tf.Tensor`s to slice.\n *   If an `Array` of `tf.Tensor`s, all `tf.Tensor`s will be sliced in the\n *   same fashion.\n * @param indices The indices to use for slicing along the first (batch)\n *   dimension.\n * @returns Result(s) of the slicing.\n */\nexport function sliceArraysByIndices(arrays, indices) {\n    return tfc.tidy(() => {\n        if (arrays == null) {\n            return null;\n        }\n        else if (Array.isArray(arrays)) {\n            return arrays.map(array => sliceArraysByIndices(array, indices));\n        }\n        else {\n            // TODO(cais): indices should be a pre-constructed Tensor1D to avoid\n            //   tensor1d() calls.\n            return gather(arrays, indices.dtype === 'int32' ? indices : indices.toInt());\n        }\n    });\n}\n/**\n * Returns a list of batch indices (tuples of indices).\n * @param size: Integer, total size of the data to slice into batches.\n * @param batchSize: Integer, batch size.\n * @returns An Array of [batchStart, batchEnd] tuples. batchStart is\n *   inclusive; batchEnd is exclusive. I.e., each batch consists of indices x\n *   that satisfy batchStart <= x < batchEnd.\n */\nexport function makeBatches(size, batchSize) {\n    const output = [];\n    let batchStart = 0;\n    let batchEnd = null;\n    while (batchStart < size) {\n        batchEnd = batchStart + batchSize;\n        if (batchEnd >= size) {\n            batchEnd = size;\n        }\n        output.push([batchStart, batchEnd]);\n        batchStart = batchEnd;\n    }\n    return output;\n}\n/**\n * Abstract fit function for `f(ins)`.\n * @param f A Function returning a list of tensors. For training, this\n *   function is expected to perform the updates to the variables.\n * @param ins List of tensors to be fed to `f`.\n * @param outLabels List of strings, display names of the outputs of `f`.\n * @param batchSize Integer batch size or `== null` if unknown. Default : 32.\n * @param epochs Number of times to iterate over the data. Default : 1.\n * @param verbose Verbosity mode: 0, 1, or 2. Default: 1.\n * @param callbacks List of callbacks to be called during training.\n * @param valF Function to call for validation.\n * @param valIns List of tensors to be fed to `valF`.\n * @param shuffle Whether to shuffle the data at the beginning of every\n * epoch. Default : true.\n * @param callbackMetrics List of strings, the display names of the metrics\n *   passed to the callbacks. They should be the concatenation of the\n *   display names of the outputs of `f` and the list of display names\n *   of the outputs of `valF`.\n * @param initialEpoch Epoch at which to start training (useful for\n *   resuming a previous training run). Default : 0.\n * @param stepsPerEpoch Total number of steps (batches on samples) before\n *   declaring one epoch finished and starting the next epoch. Ignored with\n *   the default value of `undefined` or `null`.\n * @param validationSteps Number of steps to run validation for (only if\n *   doing validation from data tensors). Not applicable for tfjs-layers.\n * @returns A `History` object.\n */\nasync function fitLoop(\n// Type `model` as `any` here to avoid circular dependency w/ training.ts.\n// tslint:disable-next-line:no-any\nmodel, f, ins, outLabels, batchSize, epochs, verbose, callbacks, valF, valIns, shuffle, callbackMetrics, initialEpoch, stepsPerEpoch, validationSteps) {\n    if (batchSize == null) {\n        batchSize = 32;\n    }\n    if (epochs == null) {\n        epochs = 1;\n    }\n    if (shuffle == null) {\n        shuffle = true;\n    }\n    if (initialEpoch == null) {\n        initialEpoch = 0;\n    }\n    // TODO(cais): Change const to let below when implementing validation.\n    let doValidation = false;\n    if (valF != null && valIns != null) {\n        doValidation = true;\n        // TODO(cais): verbose message.\n    }\n    if (validationSteps != null) {\n        doValidation = true;\n        if (stepsPerEpoch == null) {\n            throw new ValueError('Can only use `validationSteps` when doing step-wise training, ' +\n                'i.e., `stepsPerEpoch` must be set.');\n        }\n    }\n    const numTrainSamples = model.checkNumSamples(ins, batchSize, stepsPerEpoch, 'steps_per_epoch');\n    let indexArray;\n    if (numTrainSamples != null) {\n        indexArray = range(0, numTrainSamples);\n    }\n    if (verbose == null) {\n        verbose = 1;\n    }\n    const { callbackList, history } = configureCallbacks(callbacks, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics);\n    callbackList.setModel(model);\n    model.history = history;\n    await callbackList.onTrainBegin();\n    model.stopTraining_ = false;\n    // TODO(cais): Take care of callbacks.validation_data as in PyKeras.\n    // TODO(cais): Pre-convert feeds for performance as in PyKeras.\n    for (let epoch = initialEpoch; epoch < epochs; ++epoch) {\n        await callbackList.onEpochBegin(epoch);\n        const epochLogs = {};\n        if (stepsPerEpoch != null) {\n            throw new NotImplementedError('stepsPerEpoch mode is not implemented yet.');\n        }\n        else {\n            if (shuffle === 'batch') {\n                throw new NotImplementedError('batch shuffling is not implemneted yet');\n            }\n            else if (shuffle) {\n                util.shuffle(indexArray);\n            }\n            // Convert the potentially shuffled indices to Tensor1D, to avoid the\n            // cost of repeated creation of Array1Ds later on.\n            const epochIndexArray1D = tensor1d(indexArray);\n            const batches = makeBatches(numTrainSamples, batchSize);\n            for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                const batchLogs = {};\n                await callbackList.onBatchBegin(batchIndex, batchLogs);\n                tfc.tidy(() => {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    const batchIds = sliceAlongFirstAxis(epochIndexArray1D, batchStart, batchEnd - batchStart);\n                    batchLogs['batch'] = batchIndex;\n                    batchLogs['size'] = batchEnd - batchStart;\n                    // TODO(cais): In ins, train flag can be a number, instead of an\n                    //   Tensor? Do we need to handle this in tfjs-layers?\n                    const insBatch = sliceArraysByIndices(ins, batchIds);\n                    const outs = f(insBatch);\n                    for (let i = 0; i < outLabels.length; ++i) {\n                        const label = outLabels[i];\n                        const out = outs[i];\n                        batchLogs[label] = out;\n                        tfc.keep(out);\n                        // TODO(cais): Use scope() to avoid ownership.\n                    }\n                    if (batchIndex === batches.length - 1) { // Last batch.\n                        if (doValidation) {\n                            const valOuts = model.testLoop(valF, valIns, batchSize);\n                            // Porting Notes: In tfjs-layers, valOuts is always an Array.\n                            for (let i = 0; i < outLabels.length; ++i) {\n                                const label = outLabels[i];\n                                const out = valOuts[i];\n                                tfc.keep(out);\n                                // TODO(cais): Use scope() to avoid ownership.\n                                epochLogs['val_' + label] = out;\n                            }\n                        }\n                    }\n                });\n                await callbackList.onBatchEnd(batchIndex, batchLogs);\n                disposeTensorsInLogs(batchLogs);\n                if (model.stopTraining_) {\n                    break;\n                }\n                // TODO(cais): return outs as list of Tensor.\n            }\n            epochIndexArray1D.dispose();\n        }\n        // TODO(cais): Run validation at the end of the epoch.\n        await callbackList.onEpochEnd(epoch, epochLogs);\n        if (model.stopTraining_) {\n            break;\n        }\n    }\n    await callbackList.onTrainEnd();\n    await model.history.syncData();\n    return model.history;\n}\nexport async function fitTensors(\n// Type `model` as `any` here to avoid circular dependency w/ training.ts.\n// tslint:disable-next-line:no-any\nmodel, x, y, args = {}) {\n    if (model.isTraining) {\n        throw new Error('Cannot start training because another fit() call is ongoing.');\n    }\n    model.isTraining = true;\n    let inputs;\n    let targets;\n    let inputValX;\n    let inputValY;\n    let valX;\n    let valY;\n    let sampleWeights;\n    try {\n        const batchSize = args.batchSize == null ? 32 : args.batchSize;\n        checkBatchSize(batchSize);\n        // Validate user data.\n        // TODO(cais): Support sampleWeight.\n        const checkBatchAxis = false;\n        const standardizedOuts = await model.standardizeUserData(x, y, args.sampleWeight, args.classWeight, checkBatchAxis, batchSize);\n        inputs = standardizedOuts[0];\n        targets = standardizedOuts[1];\n        sampleWeights = standardizedOuts[2];\n        // Prepare validation data.\n        let doValidation = false;\n        let valIns;\n        if (args.validationData != null && args.validationData.length > 0) {\n            doValidation = true;\n            if (args.validationData.length === 2) {\n                // config.validationData consists of valX and valY.\n                inputValX = args.validationData[0];\n                inputValY = args.validationData[1];\n            }\n            else if (args.validationData.length === 3) {\n                throw new NotImplementedError('validationData including sample weights is not supported yet.');\n            }\n            else {\n                throw new ValueError(`When passing validation data, it must contain 2 (valX, valY) ` +\n                    `or 3 (valX, valY, valSampleWeight) items; ` +\n                    `${args.validationData} is invalid.`);\n            }\n            const checkBatchAxis = true;\n            const valStandardized = await model.standardizeUserData(inputValX, inputValY, null, /** Unused sample weights. */ null, /** Unused class weights. */ checkBatchAxis, batchSize);\n            valX = valStandardized[0];\n            valY = valStandardized[1];\n            valIns = valX.concat(valY);\n            // TODO(cais): Add useLearningPhase data properly.\n        }\n        else if (args.validationSplit != null && args.validationSplit > 0 &&\n            args.validationSplit < 1) {\n            doValidation = true;\n            // Porting Note: In tfjs-layers, inputs[0] is always a Tensor.\n            const splitAt = Math.floor(inputs[0].shape[0] * (1 - args.validationSplit));\n            const originalBatchSize = inputs[0].shape[0];\n            valX = sliceArrays(inputs, splitAt, originalBatchSize);\n            inputs = sliceArrays(inputs, 0, splitAt);\n            valY = sliceArrays(targets, splitAt, originalBatchSize);\n            targets = sliceArrays(targets, 0, splitAt);\n            // TODO(cais): Once sampleWeights becomes available, slice it to get\n            //   valSampleWeights.\n            valIns = valX.concat(valY);\n            // TODO(cais): Add useLearningPhase data properly.\n        }\n        else if (args.validationSteps != null) {\n            doValidation = true;\n            // TODO(cais): Add useLearningPhase.\n        }\n        const ins = inputs.concat(targets).concat(sampleWeights);\n        model.checkTrainableWeightsConsistency();\n        // TODO(cais): Handle use_learning_phase and learning_phase?\n        // Porting Note: Here we see a key deviation of tfjs-layers from\n        // Keras.\n        //  Due to the imperative nature of tfjs-layers' backend (tfjs-core),\n        //  we do not construct symbolic computation graphs to embody the\n        //  training process. Instead, we define a function that performs the\n        //  training action. In PyKeras, the data (inputs and targets) are fed\n        //  through graph placeholders. In tfjs-layers, the data are fed as\n        //  function arguments. Since the function are defined below in the\n        //  scope, we don't have equivalents of PyKeras's\n        //  `_make_train_funciton`.\n        const trainFunction = model.makeTrainFunction();\n        const outLabels = model.getDedupedMetricsNames();\n        let valFunction;\n        let callbackMetrics;\n        if (doValidation) {\n            model.makeTestFunction();\n            valFunction = model.testFunction;\n            callbackMetrics =\n                outLabels.slice().concat(outLabels.map(n => 'val_' + n));\n        }\n        else {\n            valFunction = null;\n            valIns = [];\n            callbackMetrics = outLabels.slice();\n        }\n        const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);\n        const out = await fitLoop(model, trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null);\n        return out;\n    }\n    finally {\n        model.isTraining = false;\n        // Memory clean up.\n        disposeNewTensors(inputs, x);\n        disposeNewTensors(targets, y);\n        disposeNewTensors(valX, inputValX);\n        disposeNewTensors(valY, inputValY);\n        if (sampleWeights != null) {\n            tfc.dispose(sampleWeights);\n        }\n    }\n    // TODO(cais): Add value to outLabels.\n}\n/**\n * Ensure tensors all have a rank of at least 2.\n *\n * If a tensor has a rank of 1, it is dimension-expanded to rank 2.\n * If any tensor has a rank of 0 (i.e., is a scalar), an error will be thrown.\n */\nexport function ensureTensorsRank2OrHigher(tensors) {\n    const outs = [];\n    if (tensors instanceof Tensor) {\n        tensors = [tensors];\n    }\n    // Make Tensors at least 2D.\n    for (let i = 0; i < tensors.length; ++i) {\n        const tensor = tensors[i];\n        if (tensor.rank === 1) {\n            outs.push(expandDims(tensor, 1));\n        }\n        else if (tensor.rank === 0) {\n            throw new Error('Expected tensor to be at least 1D, but received a 0D tensor ' +\n                '(scalar).');\n        }\n        else {\n            outs.push(tensor);\n        }\n    }\n    return outs;\n}\n/**\n * Compare a set of tensors with a reference (old) set, discard the ones\n * in the new set that are not present in the reference set.\n *\n * This method is used for memory clenaup during calls such as\n * LayersModel.fit().\n *\n * @param tensors New set which may contain Tensors not present in\n *   `refTensors`.\n * @param refTensors Reference Tensor set.\n */\n// TODO(cais, kangyizhang): Deduplicate with tfjs-data.\nexport function disposeNewTensors(tensors, refTensors) {\n    if (tensors == null) {\n        return;\n    }\n    const oldTensorIds = [];\n    if (refTensors instanceof Tensor) {\n        oldTensorIds.push(refTensors.id);\n    }\n    else if (Array.isArray(refTensors)) {\n        refTensors.forEach(t => oldTensorIds.push(t.id));\n    }\n    else if (refTensors != null) {\n        // `oldTensors` is a map from string name to Tensor.\n        for (const name in refTensors) {\n            const oldTensor = refTensors[name];\n            oldTensorIds.push(oldTensor.id);\n        }\n    }\n    const tensorsToDispose = [];\n    if (tensors instanceof Tensor) {\n        if (oldTensorIds.indexOf(tensors.id) === -1) {\n            tensorsToDispose.push(tensors);\n        }\n    }\n    else if (Array.isArray(tensors)) {\n        tensors.forEach(t => {\n            if (oldTensorIds.indexOf(t.id) === -1) {\n                tensorsToDispose.push(t);\n            }\n        });\n    }\n    else if (tensors != null) {\n        // `oldTensors` is a map from string name to Tensor.\n        for (const name in tensors) {\n            const tensor = tensors[name];\n            if (oldTensorIds.indexOf(tensor.id) === -1) {\n                tensorsToDispose.push(tensor);\n            }\n        }\n    }\n    tensorsToDispose.forEach(t => {\n        if (!t.isDisposed) {\n            t.dispose();\n        }\n    });\n}\n//# sourceMappingURL=training_tensors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source: engine/training.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { io, Optimizer, scalar, serialization, Tensor, tensor1d, util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { deserialize } from '../layers/serialization';\nimport * as losses from '../losses';\nimport * as Metrics from '../metrics';\nimport * as optimizers from '../optimizers';\nimport { checkUserDefinedMetadata } from '../user_defined_metadata';\nimport { count, pyListRepeat, singletonOrArray, toCamelCase, toSnakeCase, unique } from '../utils/generic_utils';\nimport { printSummary } from '../utils/layer_utils';\nimport { range } from '../utils/math_utils';\nimport { convertPythonicToTs } from '../utils/serialization_utils';\nimport { version } from '../version';\nimport { Container } from './container';\nimport { execute, FeedDict } from './executor';\nimport { evaluateDataset, fitDataset } from './training_dataset';\nimport { checkBatchSize, disposeNewTensors, ensureTensorsRank2OrHigher, fitTensors, makeBatches, sliceArrays, sliceArraysByIndices } from './training_tensors';\nimport { computeWeightedLoss, standardizeClassWeights, standardizeWeights } from './training_utils';\n/**\n * Helper function for polymorphic input data: 1. singleton Tensor.\n */\nexport function isDataTensor(x) {\n    return x instanceof Tensor;\n}\n/**\n * Helper function for polymorphic input data: 2. Array of Tensor.\n */\nexport function isDataArray(x) {\n    return Array.isArray(x);\n}\n/**\n * Helper function for polymorphic input data: 3. \"dict\" of Tensor.\n */\nexport function isDataDict(x) {\n    return !isDataTensor(x) && !isDataArray(x);\n}\n/**\n * Normalizes inputs and targets provided by users.\n * @param data User-provided input data (polymorphic).\n * @param names An Array of expected Tensor names.\n * @param shapes Optional Array of expected Tensor shapes.\n * @param checkBatchAxis Whether to check that the batch axis of the arrays\n *   match  the expected value found in `shapes`.\n * @param exceptionPrefix String prefix used for exception formatting.\n * @returns List of standardized input Tensors (one Tensor per model input).\n * @throws ValueError: in case of improperly formatted user data.\n */\nexport function standardizeInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = '') {\n    if (names == null || names.length === 0) {\n        // Check for the case where the model expected no data, but some data got\n        // sent.\n        if (data != null) {\n            let gotUnexpectedData = false;\n            if (isDataArray(data) && data.length > 0) {\n                gotUnexpectedData = true;\n            }\n            else if (isDataDict(data)) {\n                for (const key in data) {\n                    if (data.hasOwnProperty(key)) {\n                        gotUnexpectedData = true;\n                        break;\n                    }\n                }\n            }\n            else {\n                // `data` is a singleton Tensor in this case.\n                gotUnexpectedData = true;\n            }\n            if (gotUnexpectedData) {\n                throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, ` +\n                    `but got ${data}`);\n            }\n        }\n        return [];\n    }\n    if (data == null) {\n        return names.map(name => null);\n    }\n    let arrays;\n    if (isDataDict(data)) {\n        data = data;\n        arrays = [];\n        for (const name of names) {\n            if (data[name] == null) {\n                throw new ValueError(`No data provided for \"${name}\". Need data for each key in: ` +\n                    `${names}`);\n            }\n            arrays.push(data[name]);\n        }\n    }\n    else if (isDataArray(data)) {\n        data = data;\n        if (data.length !== names.length) {\n            throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of ` +\n                `Tensors that you are passing to your model is not the size the ` +\n                `model expected. Expected to see ${names.length} Tensor(s), but ` +\n                `instead got the following list of Tensor(s): ${data}`);\n        }\n        arrays = data;\n    }\n    else {\n        data = data;\n        if (names.length > 1) {\n            throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), ` +\n                `but only received one Tensor. Found: Tensor with shape ${data.shape}`);\n        }\n        arrays = [data];\n    }\n    arrays = ensureTensorsRank2OrHigher(arrays);\n    // Check shape compatibility.\n    if (shapes != null) {\n        for (let i = 0; i < names.length; ++i) {\n            if (shapes[i] == null) {\n                continue;\n            }\n            const array = arrays[i];\n            if (array.shape.length !== shapes[i].length) {\n                throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                    `to have ${shapes[i].length} dimension(s). but got array with ` +\n                    `shape ${array.shape}`);\n            }\n            for (let j = 0; j < shapes[i].length; ++j) {\n                if (j === 0 && !checkBatchAxis) {\n                    // Skip the first (batch) axis.\n                    continue;\n                }\n                const dim = array.shape[j];\n                const refDim = shapes[i][j];\n                if (refDim != null && refDim >= 0 && dim !== refDim) {\n                    throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                        `to have shape [${shapes[i]}], but got array with shape ` +\n                        `[${array.shape}].`);\n                }\n            }\n        }\n    }\n    return arrays;\n}\n/**\n * User input validation for Tensors.\n * @param inputs `Array` of `tf.Tensor`s for inputs.\n * @param targets `Array` of `tf.Tensor`s for targets.\n * @param weights Optional `Array` of `tf.Tensor`s for sample weights.\n * @throws ValueError: in case of incorrectly formatted data.\n */\nexport function checkArrayLengths(inputs, targets, weights) {\n    const setX = unique(inputs.map(input => input.shape[0]));\n    setX.sort();\n    const setY = unique(targets.map(target => target.shape[0]));\n    setY.sort();\n    // TODO(cais): Check `weights` as well.\n    if (setX.length > 1) {\n        throw new ValueError(`All input Tensors (x) should have the same number of samples. ` +\n            `Got array shapes: ` +\n            `${JSON.stringify(inputs.map(input => input.shape))}`);\n    }\n    if (setY.length > 1) {\n        throw new ValueError(`All target Tensors (y) should have the same number of samples. ` +\n            `Got array shapes: ` +\n            `${JSON.stringify(targets.map(target => target.shape))}`);\n    }\n    if (setX.length > 0 && setY.length > 0 && !util.arraysEqual(setX, setY)) {\n        throw new ValueError(`Input Tensors should have the same number of samples as target ` +\n            `Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target ` +\n            `sample(s).`);\n    }\n}\n/**\n * Validation on the compatibility of targes and loss functions.\n *\n * This helps prevent users from using loss functions incorrectly.\n *\n * @param targets `Array` of `tf.Tensor`s of targets.\n * @param lossFns `Array` of loss functions.\n * @param outputShapes `Array` of shapes of model outputs.\n */\nfunction checkLossAndTargetCompatibility(targets, lossFns, outputShapes) {\n    // TODO(cais): Dedicated test coverage?\n    const keyLosses = [\n        losses.meanSquaredError, losses.binaryCrossentropy,\n        losses.categoricalCrossentropy\n    ];\n    for (let i = 0; i < targets.length; ++i) {\n        const y = targets[i];\n        const loss = lossFns[i];\n        const shape = outputShapes[i];\n        if (loss == null) {\n            continue;\n        }\n        if (loss === losses.categoricalCrossentropy) {\n            if (y.shape[y.shape.length - 1] === 1) {\n                throw new ValueError(`You are passing a target array of shape ${y.shape} while using ` +\n                    `a loss 'categorical_crossentropy'. 'categorical_crossentropy'` +\n                    `expects targets to be binary matrices (1s and 0s) of shape ` +\n                    `[samples, classes].`);\n                // TODO(cais): Example code in error message.\n            }\n        }\n        if (keyLosses.indexOf(loss) !== -1) {\n            const slicedYShape = y.shape.slice(1);\n            const slicedShape = shape.slice(1);\n            for (let j = 0; j < slicedYShape.length; ++j) {\n                const targetDim = slicedYShape[j];\n                const outDim = slicedShape[j];\n                if (outDim != null && targetDim !== outDim) {\n                    throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an ` +\n                        `output of shape ${shape}, while using a loss function that ` +\n                        `expects targets to have the same shape as the output.`);\n                }\n            }\n        }\n    }\n}\n/**\n * Check inputs provided by the user.\n *\n * Porting Note: This corresponds to _standardize_input_data() in Python\n *   Keras. Because of the strong typing in TF.js, we do not need to convert\n *   the data. Specifically:\n *   1) in PyKeras, `data` can be `DataFrame` instances from pandas, for\n *      example. We don't need to worry about that here because there is no\n *      widely popular javascript/typesdcript equivalent of pandas (so far).\n *      If one becomes available in the future, we can add support.\n *   2) in PyKeras, inputs can be Python dict. But here we are stipulating\n * that the data is either a single `tf.Tensor` or an Array of `tf.Tensor`s. We\n * may add support for `Object` data inputs in the future when the need\n * arises.\n *\n * Instead, we perform basic checks for number of parameters and shapes.\n *\n * @param data: The input data.\n * @param names: Name for the inputs, from the model.\n * @param shapes: Expected shapes for the input data, from the model.\n * @param checkBatchAxis: Whether the size along the batch axis (i.e., the\n *   first dimension) will be checked for matching.\n * @param exceptionPrefix: Execption prefix message, used in generating error\n *   messages.\n * @throws ValueError: on incorrect number of inputs or mismatches in shapes.\n */\nfunction checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = '') {\n    let arrays;\n    if (Array.isArray(data)) {\n        if (data.length !== names.length) {\n            throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of ` +\n                `Tensors that you are passing to your model is not the size the ` +\n                `the model expected. Expected to see ${names.length} Tensor(s),` +\n                ` but instead got ${data.length} Tensors(s).`);\n        }\n        arrays = data;\n    }\n    else {\n        if (names.length > 1) {\n            throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, ` +\n                `but only received one Tensor. Found: array with shape ` +\n                `${JSON.stringify(data.shape)}.`);\n        }\n        arrays = [data];\n    }\n    if (shapes != null) {\n        for (let i = 0; i < names.length; ++i) {\n            if (shapes[i] == null) {\n                continue;\n            }\n            const array = arrays[i];\n            if (array.shape.length !== shapes[i].length) {\n                throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                    `to have ${shapes[i].length} dimension(s), but got array with ` +\n                    `shape ${JSON.stringify(array.shape)}`);\n            }\n            for (let j = 0; j < shapes[i].length; ++j) {\n                if (j === 0 && !checkBatchAxis) {\n                    continue;\n                }\n                const dim = array.shape[j];\n                const refDim = shapes[i][j];\n                if (refDim != null) {\n                    if (refDim !== dim) {\n                        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ` +\n                            `${names[i]} to have shape ${JSON.stringify(shapes[i])} but ` +\n                            `got array with shape ${JSON.stringify(array.shape)}.`);\n                    }\n                }\n            }\n        }\n    }\n}\n/**\n * Maps metric functions to model outputs.\n * @param metrics An shortcut strings name, metric function, `Array` or dict\n *   (`Object`) of metric functions.\n * @param outputNames An `Array` of the names of model outputs.\n * @returns An `Array` (one entry per model output) of `Array` of metric\n *   functions. For instance, if the model has 2 outputs, and for the first\n *   output we want to compute `binaryAccuracy` and `binaryCrossentropy`,\n *   and just `binaryAccuracy` for the second output, the `Array` would look\n *   like:\n *     `[[binaryAccuracy, binaryCrossentropy],  [binaryAccuracy]]`\n * @throws TypeError: incompatible metrics format.\n */\nexport function collectMetrics(metrics, outputNames) {\n    if (metrics == null || Array.isArray(metrics) && metrics.length === 0) {\n        return outputNames.map(name => []);\n    }\n    let wrappedMetrics;\n    if (typeof metrics === 'string' || typeof metrics === 'function') {\n        wrappedMetrics = [metrics];\n    }\n    else if (Array.isArray(metrics) || typeof metrics === 'object') {\n        wrappedMetrics = metrics;\n    }\n    else {\n        throw new TypeError('Type of metrics argument not understood. Expected an string,' +\n            `function, Array, or Object, found: ${metrics}`);\n    }\n    if (Array.isArray(wrappedMetrics)) {\n        // We then apply all metrics to all outputs.\n        return outputNames.map(name => wrappedMetrics);\n    }\n    else {\n        // In this case, metrics is a dict.\n        const nestedMetrics = [];\n        for (const name of outputNames) {\n            let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : [];\n            if (!Array.isArray(outputMetrics)) {\n                outputMetrics = [outputMetrics];\n            }\n            nestedMetrics.push(outputMetrics);\n        }\n        return nestedMetrics;\n    }\n}\nconst LAYERS_MODEL_FORMAT_NAME = 'layers-model';\n/**\n * A `tf.LayersModel` is a directed, acyclic graph of `tf.Layer`s plus methods\n * for training, evaluation, prediction and saving.\n *\n * `tf.LayersModel` is the basic unit of training, inference and evaluation in\n * TensorFlow.js. To create a `tf.LayersModel`, use `tf.LayersModel`.\n *\n * See also:\n *   `tf.Sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class LayersModel extends Container {\n    constructor(args) {\n        super(args);\n        this.isTraining = false;\n    }\n    /**\n     * Print a text summary of the model's layers.\n     *\n     * The summary includes\n     * - Name and type of all layers that comprise the model.\n     * - Output shape(s) of the layers\n     * - Number of weight parameters of each layer\n     * - If the model has non-sequential-like topology, the inputs each layer\n     *   receives\n     * - The total number of trainable and non-trainable parameters of the model.\n     *\n     * ```js\n     * const input1 = tf.input({shape: [10]});\n     * const input2 = tf.input({shape: [20]});\n     * const dense1 = tf.layers.dense({units: 4}).apply(input1);\n     * const dense2 = tf.layers.dense({units: 8}).apply(input2);\n     * const concat = tf.layers.concatenate().apply([dense1, dense2]);\n     * const output =\n     *     tf.layers.dense({units: 3, activation: 'softmax'}).apply(concat);\n     *\n     * const model = tf.model({inputs: [input1, input2], outputs: output});\n     * model.summary();\n     * ```\n     *\n     * @param lineLength Custom line length, in number of characters.\n     * @param positions Custom widths of each of the columns, as either\n     *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n     *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n     *   right-most (i.e., ending) position of a column.\n     * @param printFn Custom print function. Can be used to replace the default\n     *   `console.log`. For example, you can use `x => {}` to mute the printed\n     *   messages in the console.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    summary(lineLength, positions, printFn = console.log) {\n        if (!this.built) {\n            throw new ValueError(`This model has never been called, thus its weights have not been ` +\n                `created yet. So no summary can be displayed. Build the model ` +\n                `first (e.g., by calling it on some test data).`);\n        }\n        printSummary(this, lineLength, positions, printFn);\n    }\n    /**\n     * Configures and prepares the model for training and evaluation.  Compiling\n     * outfits the model with an optimizer, loss, and/or metrics.  Calling `fit`\n     * or `evaluate` on an un-compiled model will throw an error.\n     *\n     * @param args a `ModelCompileArgs` specifying the loss, optimizer, and\n     * metrics to be used for fitting and evaluating this model.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    compile(args) {\n        if (args.loss == null) {\n            args.loss = [];\n        }\n        this.loss = args.loss;\n        if (typeof args.optimizer === 'string') {\n            this.optimizer_ = optimizers.getOptimizer(args.optimizer);\n            this.isOptimizerOwned = true;\n        }\n        else {\n            if (!(args.optimizer instanceof Optimizer)) {\n                throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`);\n            }\n            this.optimizer_ = args.optimizer;\n            this.isOptimizerOwned = false;\n        }\n        // TODO(cais): Add lossWeights.\n        // TODO(cais): Add sampleWeightMode.\n        // Prepare loss functions.\n        let lossFunctions = [];\n        if (!Array.isArray(args.loss) && typeof args.loss !== 'string' &&\n            typeof args.loss !== 'function') {\n            args.loss = args.loss;\n            for (const name in args.loss) {\n                if (this.outputNames.indexOf(name) === -1) {\n                    throw new ValueError(`Unknown entry in loss dictionary: \"${name}\". ` +\n                        `Only expected the following keys: ${this.outputNames}`);\n                }\n            }\n            for (const name of this.outputNames) {\n                if (args.loss[name] == null) {\n                    console.warn(`Output \"${name}\" is missing from loss dictionary. We assume ` +\n                        `this was done on purpose, and we will not be expecting data ` +\n                        `to be passed to ${name} during training`);\n                }\n                lossFunctions.push(losses.get(args.loss[name]));\n            }\n        }\n        else if (Array.isArray(args.loss)) {\n            if (args.loss.length !== this.outputs.length) {\n                throw new ValueError(`When passing an Array as loss, it should have one entry per ` +\n                    `model output. The model has ${this.outputs.length} output(s), ` +\n                    `but you passed loss=${args.loss}.`);\n            }\n            const theLosses = args.loss;\n            lossFunctions = theLosses.map(l => losses.get(l));\n        }\n        else {\n            const lossFunction = losses.get(args.loss);\n            this.outputs.forEach(_ => {\n                lossFunctions.push(lossFunction);\n            });\n        }\n        this.lossFunctions = lossFunctions;\n        this.feedOutputNames = [];\n        this.feedOutputShapes = [];\n        this.feedLossFns = [];\n        for (let i = 0; i < this.outputs.length; ++i) {\n            // TODO(cais): Logic for skipping target(s).\n            const shape = this.internalOutputShapes[i];\n            const name = this.outputNames[i];\n            this.feedOutputNames.push(name);\n            this.feedOutputShapes.push(shape);\n            this.feedLossFns.push(this.lossFunctions[i]);\n        }\n        // TODO(cais): Add logic for output masks.\n        // TODO(cais): Add logic for sample weights.\n        const skipTargetIndices = [];\n        // Prepare metrics.\n        this.metrics = args.metrics;\n        // TODO(cais): Add weightedMetrics.\n        this.metricsNames = ['loss'];\n        this.metricsTensors = [];\n        // Compute total loss.\n        // Porting Note: In PyKeras, metrics_tensors are symbolic tensor objects.\n        //   Here, metricsTensors are TypeScript functions. This difference is due\n        //   to the difference in symbolic/imperative property of the backends.\n        nameScope('loss', () => {\n            for (let i = 0; i < this.outputs.length; ++i) {\n                if (skipTargetIndices.indexOf(i) !== -1) {\n                    continue;\n                }\n                // TODO(cais): Add weightedLoss, sampleWeight and mask.\n                //   The following line should be weightedLoss\n                const weightedLoss = this.lossFunctions[i];\n                if (this.outputs.length > 1) {\n                    this.metricsTensors.push([weightedLoss, i]);\n                    this.metricsNames.push(this.outputNames[i] + '_loss');\n                }\n            }\n            // Porting Note: Due to the imperative nature of the backend, we calculate\n            //   the regularizer penalties in the totalLossFunction, instead of here.\n        });\n        const nestedMetrics = collectMetrics(args.metrics, this.outputNames);\n        // TODO(cais): Add nestedWeightedMetrics.\n        /**\n         * Helper function used in loop below.\n         */\n        const appendMetric = (outputIndex, metricName, metricTensor) => {\n            if (this.outputNames.length > 1) {\n                metricName = this.outputNames[outputIndex] + '_' + metricName;\n            }\n            this.metricsNames.push(metricName);\n            this.metricsTensors.push([metricTensor, outputIndex]);\n        };\n        nameScope('metric', () => {\n            for (let i = 0; i < this.outputs.length; ++i) {\n                if (skipTargetIndices.indexOf(i) !== -1) {\n                    continue;\n                }\n                const outputMetrics = nestedMetrics[i];\n                // TODO(cais): Add weights and outputWeightedMetrics.\n                // TODO(cais): Add optional arg `weights` to the following function.\n                const handleMetrics = (metrics) => {\n                    const metricNamePrefix = '';\n                    let metricName;\n                    let accFn;\n                    let weightedMetricFn;\n                    //  TODO(cais): Use 'weights_' for weighted metrics.\n                    for (const metric of metrics) {\n                        if (typeof metric === 'string' &&\n                            ['accuracy', 'acc', 'crossentropy', 'ce'].indexOf(metric) !==\n                                -1) {\n                            const outputShape = this.internalOutputShapes[i];\n                            if (outputShape[outputShape.length - 1] === 1 ||\n                                this.lossFunctions[i] === losses.binaryCrossentropy) {\n                                // case: binary accuracy/crossentropy.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.binaryAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.binaryCrossentropy;\n                                }\n                            }\n                            else if (this.lossFunctions[i] ===\n                                losses.sparseCategoricalCrossentropy) {\n                                // case: categorical accuracy / crossentropy with sparse\n                                // targets.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.sparseCategoricalAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.sparseCategoricalCrossentropy;\n                                }\n                            }\n                            else {\n                                // case: categorical accuracy / crossentropy.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.categoricalAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.categoricalCrossentropy;\n                                }\n                            }\n                            let suffix;\n                            if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                suffix = 'acc';\n                            }\n                            else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                suffix = 'ce';\n                            }\n                            // TODO(cais): Add weighting actually.\n                            weightedMetricFn = accFn;\n                            metricName = metricNamePrefix + suffix;\n                        }\n                        else {\n                            const metricFn = Metrics.get(metric);\n                            // TODO(cais): Add weighting actually.\n                            weightedMetricFn = metricFn;\n                            metricName =\n                                metricNamePrefix + Metrics.getLossOrMetricName(metric);\n                        }\n                        // TODO(cais): Add weighting and masking to metricResult.\n                        let metricResult;\n                        nameScope(metricName, () => {\n                            metricResult = weightedMetricFn;\n                        });\n                        appendMetric(i, metricName, metricResult);\n                    }\n                };\n                handleMetrics(outputMetrics);\n                // TODO(cais): Call handleMetrics with weights.\n            }\n        });\n        // Porting Notes: Given the imperative backend of tfjs-core,\n        //   there is no need for constructing the symbolic graph and placeholders.\n        this.collectedTrainableWeights = this.trainableWeights;\n    }\n    /**\n     * Check trainable weights count consistency.\n     *\n     * This will raise a warning if `this.trainableWeights` and\n     * `this.collectedTrainableWeights` are inconsistent (i.e., have different\n     * numbers of parameters).\n     * Inconsistency will typically arise when one modifies `model.trainable`\n     * without calling `model.compile()` again.\n     */\n    checkTrainableWeightsConsistency() {\n        if (this.collectedTrainableWeights == null) {\n            return;\n        }\n        if (this.trainableWeights.length !==\n            this.collectedTrainableWeights.length) {\n            console.warn('Discrepancy between trainableweights and collected trainable ' +\n                'weights. Did you set `model.trainable` without calling ' +\n                '`model.compile()` afterwards?');\n        }\n    }\n    /**\n     * Returns the loss value & metrics values for the model in test mode.\n     *\n     * Loss and metrics are specified during `compile()`, which needs to happen\n     * before calls to `evaluate()`.\n     *\n     * Computation is done in batches.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const result = model.evaluate(\n     *     tf.ones([8, 10]), tf.ones([8, 1]), {batchSize: 4});\n     * result.print();\n     * ```\n     *\n     * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple inputs.\n     * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple outputs.\n     * @param args A `ModelEvaluateArgs`, containing optional fields.\n     *\n     * @return `Scalar` test loss (if the model has a single output and no\n     *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n     *   and/or metrics). The attribute `model.metricsNames`\n     *   will give you the display labels for the scalar outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    evaluate(x, y, args = {}) {\n        const batchSize = args.batchSize == null ? 32 : args.batchSize;\n        checkBatchSize(batchSize);\n        // TODO(cais): Standardize `config.sampleWeights` as well.\n        // Validate user data.\n        const checkBatchAxis = true;\n        const standardizedOuts = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n        try {\n            // TODO(cais): If uses `useLearningPhase`, set the corresponding element\n            // of the input to 0.\n            const ins = standardizedOuts[0].concat(standardizedOuts[1]);\n            this.makeTestFunction();\n            const f = this.testFunction;\n            const testOuts = this.testLoop(f, ins, batchSize, args.verbose, args.steps);\n            return singletonOrArray(testOuts);\n        }\n        finally {\n            disposeNewTensors(standardizedOuts[0], x);\n            disposeNewTensors(standardizedOuts[1], y);\n        }\n    }\n    // TODO(cais): Add code snippet below once real dataset objects are\n    //   available.\n    /**\n     * Evaluate model using a dataset object.\n     *\n     * Note: Unlike `evaluate()`, this method is asynchronous (`async`);\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for evaluation. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs. Of the two items in the array, the\n     *   first is the input feature(s) and the second is the output target(s).\n     * @param args A configuration object for the dataset-based evaluation.\n     * @returns Loss and metric values as an Array of `Scalar` objects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async evaluateDataset(dataset, args) {\n        this.makeTestFunction();\n        return evaluateDataset(this, dataset, args);\n    }\n    /**\n     * Get number of samples provided for training, evaluation or prediction.\n     *\n     * @param ins Input `tf.Tensor`.\n     * @param batchSize Integer batch size, optional.\n     * @param steps Total number of steps (batches of samples) before\n     * declaring loop finished. Optional.\n     * @param stepsName The public API's parameter name for `steps`.\n     * @returns Number of samples provided.\n     */\n    checkNumSamples(ins, batchSize, steps, stepsName = 'steps') {\n        let numSamples;\n        if (steps != null) {\n            numSamples = null;\n            if (batchSize != null) {\n                throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.` +\n                    `Got batchSize = ${batchSize}`);\n            }\n        }\n        else if (ins != null) {\n            if (Array.isArray(ins)) {\n                numSamples = ins[0].shape[0];\n            }\n            else {\n                numSamples = ins.shape[0];\n            }\n        }\n        else {\n            throw new ValueError(`Either the input data should have a defined shape, or ` +\n                `${stepsName} shoud be specified.`);\n        }\n        return numSamples;\n    }\n    /**\n     * Execute internal tensors of the model with input data feed.\n     * @param inputs Input data feed. Must match the inputs of the model.\n     * @param outputs Names of the output tensors to be fetched. Must match\n     *   names of the SymbolicTensors that belong to the graph.\n     * @returns Fetched values for `outputs`.\n     */\n    execute(inputs, outputs) {\n        if (Array.isArray(outputs) && outputs.length === 0) {\n            throw new ValueError('`outputs` is an empty Array, which is not allowed.');\n        }\n        const outputsIsArray = Array.isArray(outputs);\n        const outputNames = (outputsIsArray ? outputs : [outputs]);\n        const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames);\n        // Format the input into a FeedDict.\n        const feedDict = new FeedDict();\n        if (inputs instanceof Tensor) {\n            inputs = [inputs];\n        }\n        if (Array.isArray(inputs)) {\n            if (inputs.length !== this.inputs.length) {\n                throw new ValueError(`The number of inputs provided (${inputs.length}) ` +\n                    `does not match the number of inputs of this model ` +\n                    `(${this.inputs.length}).`);\n            }\n            for (let i = 0; i < this.inputs.length; ++i) {\n                feedDict.add(this.inputs[i], inputs[i]);\n            }\n        }\n        else {\n            for (const input of this.inputs) {\n                const tensorValue = inputs[input.name];\n                if (tensorValue == null) {\n                    throw new ValueError(`No value is provided for the model's input ${input.name}`);\n                }\n                feedDict.add(input, tensorValue);\n            }\n        }\n        // Run execution.\n        const executeOutputs = execute(outputSymbolicTensors, feedDict);\n        return outputsIsArray ? executeOutputs : executeOutputs[0];\n    }\n    /**\n     * Retrieve the model's internal symbolic tensors from symbolic-tensor names.\n     */\n    retrieveSymbolicTensors(symbolicTensorNames) {\n        const outputSymbolicTensors = pyListRepeat(null, symbolicTensorNames.length);\n        let outputsRemaining = symbolicTensorNames.length;\n        for (const layer of this.layers) {\n            const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output];\n            const layerOutputNames = layerOutputs.map(output => output.name);\n            for (let i = 0; i < symbolicTensorNames.length; ++i) {\n                const index = layerOutputNames.indexOf(symbolicTensorNames[i]);\n                if (index !== -1) {\n                    outputSymbolicTensors[i] = layerOutputs[index];\n                    outputsRemaining--;\n                }\n                if (outputsRemaining === 0) {\n                    break;\n                }\n            }\n            if (outputsRemaining === 0) {\n                break;\n            }\n        }\n        if (outputsRemaining > 0) {\n            const remainingNames = [];\n            outputSymbolicTensors.forEach((tensor, i) => {\n                if (tensor == null) {\n                    remainingNames.push(symbolicTensorNames[i]);\n                }\n            });\n            throw new ValueError(`Cannot find SymbolicTensors for output name(s): ` +\n                `${JSON.stringify(remainingNames)}`);\n        }\n        return outputSymbolicTensors;\n    }\n    /**\n     * Helper method to loop over some data in batches.\n     *\n     * Porting Note: Not using the functional approach in the Python equivalent\n     *   due to the imperative backend.\n     * Porting Note: Does not support step mode currently.\n     *\n     * @param ins: input data\n     * @param batchSize: integer batch size.\n     * @param verbose: verbosity model\n     * @returns: Predictions as `tf.Tensor` (if a single output) or an `Array` of\n     *   `tf.Tensor` (if multipe outputs).\n     */\n    predictLoop(ins, batchSize = 32, verbose = false) {\n        return tfc.tidy(() => {\n            const numSamples = this.checkNumSamples(ins);\n            if (verbose) {\n                throw new NotImplementedError('Verbose predictLoop() is not implemented yet.');\n            }\n            // Sample-based predictions.\n            // Porting Note: Tensor currently does not support sliced assignments as\n            //   in numpy, e.g., x[1:3] = y. Therefore we use concatenation while\n            //   iterating over the batches.\n            const batches = makeBatches(numSamples, batchSize);\n            const outsBatches = this.outputs.map(output => []);\n            // TODO(cais): Can the scope() be pushed down inside the for loop?\n            for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                const batchOuts = tfc.tidy(() => {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    // TODO(cais): Take care of the case of the last element is a flag for\n                    //   training/test.\n                    const insBatch = sliceArrays(ins, batchStart, batchEnd);\n                    // Construct the feeds for execute();\n                    const feeds = [];\n                    if (Array.isArray(insBatch)) {\n                        for (let i = 0; i < insBatch.length; ++i) {\n                            feeds.push({ key: this.inputs[i], value: insBatch[i] });\n                        }\n                    }\n                    else {\n                        feeds.push({ key: this.inputs[0], value: insBatch });\n                    }\n                    const feedDict = new FeedDict(feeds);\n                    return execute(this.outputs, feedDict);\n                });\n                batchOuts.forEach((batchOut, i) => outsBatches[i].push(batchOut));\n            }\n            return singletonOrArray(outsBatches.map(batches => tfc.concat(batches, 0)));\n        });\n    }\n    /**\n     * Generates output predictions for the input samples.\n     *\n     * Computation is done in batches.\n     *\n     * Note: the \"step\" mode of predict() is currently not supported.\n     *   This is because the TensorFlow.js core backend is imperative only.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predict(tf.ones([8, 10]), {batchSize: 4}).print();\n     * ```\n     *\n     * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n     *   the model has multiple inputs.\n     * @param args A `ModelPredictArgs` object containing optional fields.\n     *\n     * @return Prediction results as a `tf.Tensor`(s).\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and the model's expectations, or in case a stateful model receives a\n     *   number of samples that is not a multiple of the batch size.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(x, args = {}) {\n        const xsRank2OrHigher = ensureTensorsRank2OrHigher(x);\n        checkInputData(xsRank2OrHigher, this.inputNames, this.feedInputShapes, false);\n        try {\n            // TODO(cais): Take care of stateful models.\n            //   if (this.stateful) ...\n            // TODO(cais): Take care of the learning_phase boolean flag.\n            //   if (this.useLearningPhase) ...\n            const batchSize = args.batchSize == null ? 32 : args.batchSize;\n            checkBatchSize(batchSize);\n            return this.predictLoop(xsRank2OrHigher, batchSize);\n        }\n        finally {\n            disposeNewTensors(xsRank2OrHigher, x);\n        }\n    }\n    /**\n     * Returns predictions for a single batch of samples.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predictOnBatch(tf.ones([8, 10])).print();\n     * ```\n     * @param x: Input samples, as a Tensor (for models with exactly one\n     *   input) or an array of Tensors (for models with more than one input).\n     * @return Tensor(s) of predictions\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predictOnBatch(x) {\n        checkInputData(x, this.inputNames, this.feedInputShapes, true);\n        // TODO(cais): Take care of the learning_phase boolean flag.\n        //   if (this.useLearningPhase) ...\n        const batchSize = (Array.isArray(x) ? x[0] : x).shape[0];\n        return this.predictLoop(x, batchSize);\n    }\n    standardizeUserDataXY(x, y, checkBatchAxis = true, batchSize) {\n        // TODO(cais): Add sampleWeight, classWeight\n        if (this.optimizer_ == null) {\n            throw new RuntimeError('You must compile a model before training/testing. Use ' +\n                'LayersModel.compile(modelCompileArgs).');\n        }\n        const outputShapes = [];\n        for (let i = 0; i < this.feedOutputShapes.length; ++i) {\n            const outputShape = this.feedOutputShapes[i];\n            const lossFn = this.feedLossFns[i];\n            if (lossFn === losses.sparseCategoricalCrossentropy) {\n                outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1]));\n            }\n            else {\n                // Porting Note: Because of strong typing `lossFn` must be a function.\n                outputShapes.push(outputShape);\n            }\n        }\n        x = standardizeInputData(x, this.feedInputNames, this.feedInputShapes, false, 'input');\n        y = standardizeInputData(y, this.feedOutputNames, outputShapes, false, 'target');\n        // TODO(cais): Standardize sampleWeights & classWeights.\n        checkArrayLengths(x, y, null);\n        // TODO(cais): Check sampleWeights as well.\n        checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes);\n        if (this.stateful && batchSize != null && batchSize > 0) {\n            if (x[0].shape[0] % batchSize !== 0) {\n                throw new ValueError(`In a stateful network, you should only pass inputs with a ` +\n                    `number of samples that is divisible by the batch size ` +\n                    `${batchSize}. Found: ${x[0].shape[0]} sample(s).`);\n            }\n        }\n        return [x, y];\n    }\n    async standardizeUserData(x, y, sampleWeight, classWeight, checkBatchAxis = true, batchSize) {\n        const [standardXs, standardYs] = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n        // TODO(cais): Handle sampleWeights.\n        if (sampleWeight != null) {\n            throw new Error('sample weight is not supported yet.');\n        }\n        let standardSampleWeights = null;\n        if (classWeight != null) {\n            const classWeights = standardizeClassWeights(classWeight, this.outputNames);\n            standardSampleWeights = [];\n            for (let i = 0; i < classWeights.length; ++i) {\n                standardSampleWeights.push(await standardizeWeights(standardYs[i], null, classWeights[i]));\n            }\n        }\n        // TODO(cais): Deal with the case of model.stateful == true.\n        return [standardXs, standardYs, standardSampleWeights];\n    }\n    /**\n     * Loop over some test data in batches.\n     * @param f A Function returning a list of tensors.\n     * @param ins Array of tensors to be fed to `f`.\n     * @param batchSize Integer batch size or `null` / `undefined`.\n     * @param verbose verbosity mode.\n     * @param steps Total number of steps (batches of samples) before\n     * declaring test finished. Ignored with the default value of `null` /\n     * `undefined`.\n     * @returns Array of Scalars.\n     */\n    testLoop(f, ins, batchSize, verbose = 0, steps) {\n        return tfc.tidy(() => {\n            const numSamples = this.checkNumSamples(ins, batchSize, steps, 'steps');\n            const outs = [];\n            if (verbose > 0) {\n                throw new NotImplementedError('Verbose mode is not implemented yet.');\n            }\n            // TODO(cais): Use `indicesForConversionToDense' to prevent slow down.\n            if (steps != null) {\n                throw new NotImplementedError('steps mode in testLoop() is not implemented yet');\n            }\n            else {\n                const batches = makeBatches(numSamples, batchSize);\n                const indexArray = tensor1d(range(0, numSamples));\n                for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    const batchIds = K.sliceAlongFirstAxis(indexArray, batchStart, batchEnd - batchStart);\n                    // TODO(cais): In ins, train flag can be a number, instead of an\n                    //   Tensor? Do we need to handle this in tfjs-layers?\n                    const insBatch = sliceArraysByIndices(ins, batchIds);\n                    const batchOuts = f(insBatch);\n                    if (batchIndex === 0) {\n                        for (let i = 0; i < batchOuts.length; ++i) {\n                            outs.push(scalar(0));\n                        }\n                    }\n                    for (let i = 0; i < batchOuts.length; ++i) {\n                        const batchOut = batchOuts[i];\n                        outs[i] =\n                            tfc.add(outs[i], tfc.mul(batchEnd - batchStart, batchOut));\n                    }\n                }\n                for (let i = 0; i < outs.length; ++i) {\n                    outs[i] = tfc.div(outs[i], numSamples);\n                }\n            }\n            return outs;\n        });\n    }\n    getDedupedMetricsNames() {\n        const outLabels = this.metricsNames;\n        // Rename duplicated metrics names (can happen with an output layer\n        // shared among multiple dataflows).\n        const dedupedOutLabels = [];\n        for (let i = 0; i < outLabels.length; ++i) {\n            const label = outLabels[i];\n            let newLabel = label;\n            if (count(outLabels, label) > 1) {\n                const dupIndex = count(outLabels.slice(0, i), label);\n                newLabel += `_${dupIndex}`;\n            }\n            dedupedOutLabels.push(newLabel);\n        }\n        return dedupedOutLabels;\n    }\n    /**\n     * Creates a function that performs the following actions:\n     *\n     * 1. computes the losses\n     * 2. sums them to get the total loss\n     * 3. call the optimizer computes the gradients of the LayersModel's\n     *    trainable weights w.r.t. the total loss and update the variables\n     * 4. calculates the metrics\n     * 5. returns the values of the losses and metrics.\n     */\n    makeTrainFunction() {\n        return (data) => {\n            const lossValues = [];\n            const inputs = data.slice(0, this.inputs.length);\n            const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n            const sampleWeights = data.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2);\n            const metricsValues = [];\n            // Create a function that computes the total loss based on the\n            // inputs. This function is used for obtaining gradients through\n            // backprop.\n            const totalLossFunction = () => {\n                const feeds = [];\n                for (let i = 0; i < this.inputs.length; ++i) {\n                    feeds.push({ key: this.inputs[i], value: inputs[i] });\n                }\n                const feedDict = new FeedDict(feeds);\n                const outputs = execute(this.outputs, feedDict, { 'training': true });\n                // TODO(cais): Take care of the case of multiple outputs from a\n                //   single layer?\n                let totalLoss;\n                for (let i = 0; i < this.lossFunctions.length; ++i) {\n                    const lossFunction = this.lossFunctions[i];\n                    let loss = lossFunction(targets[i], outputs[i]);\n                    if (sampleWeights[i] != null) {\n                        loss = computeWeightedLoss(loss, sampleWeights[i]);\n                    }\n                    // TODO(cais): push Scalar instead.\n                    const meanLoss = tfc.mean(loss);\n                    // TODO(cais): Use a scope() instead, to avoid ownership.\n                    lossValues.push(meanLoss);\n                    if (i === 0) {\n                        totalLoss = loss;\n                    }\n                    else {\n                        totalLoss = tfc.add(totalLoss, loss);\n                    }\n                }\n                // Compute the metrics.\n                // TODO(cais): These should probably be calculated outside\n                //   totalLossFunction to benefit speed?\n                for (let i = 0; i < this.metricsTensors.length; ++i) {\n                    let weightedMetric;\n                    if (this.outputs.length > 1 && i < this.outputs.length) {\n                        weightedMetric = lossValues[i];\n                    }\n                    else {\n                        const metric = this.metricsTensors[i][0];\n                        const outputIndex = this.metricsTensors[i][1];\n                        weightedMetric =\n                            tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));\n                    }\n                    tfc.keep(weightedMetric);\n                    // TODO(cais): Use a scope() instead, to avoid ownership.\n                    metricsValues.push(weightedMetric);\n                }\n                totalLoss = tfc.mean(totalLoss);\n                // Add regularizer penalties.\n                this.calculateLosses().forEach(regularizerLoss => {\n                    totalLoss = tfc.add(totalLoss, regularizerLoss);\n                });\n                return totalLoss;\n            };\n            const variables = this.collectedTrainableWeights.map(param => param.read());\n            const returnCost = true;\n            const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables);\n            return [totalLossValue].concat(metricsValues);\n        };\n    }\n    /**\n     * Create a function which, when invoked with an array of `tf.Tensor`s as a\n     * batch of inputs, returns the prespecified loss and metrics of the model\n     * under the batch of input data.\n     */\n    makeTestFunction() {\n        this.testFunction = (data) => {\n            return tfc.tidy(() => {\n                const valOutputs = [];\n                let totalLoss;\n                const inputs = data.slice(0, this.inputs.length);\n                const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n                const feeds = [];\n                for (let i = 0; i < this.inputs.length; ++i) {\n                    feeds.push({ key: this.inputs[i], value: inputs[i] });\n                }\n                const feedDict = new FeedDict(feeds);\n                const outputs = execute(this.outputs, feedDict);\n                // Compute total loss.\n                for (let i = 0; i < this.lossFunctions.length; ++i) {\n                    const lossFunction = this.lossFunctions[i];\n                    // TODO(cais): Add sample weighting and replace the simple\n                    // averaging.\n                    const loss = tfc.mean(lossFunction(targets[i], outputs[i]));\n                    if (i === 0) {\n                        totalLoss = loss;\n                    }\n                    else {\n                        totalLoss = tfc.add(totalLoss, loss);\n                    }\n                    valOutputs.push(totalLoss);\n                }\n                // Compute the metrics.\n                for (let i = 0; i < this.metricsTensors.length; ++i) {\n                    const metric = this.metricsTensors[i][0];\n                    const outputIndex = this.metricsTensors[i][1];\n                    // TODO(cais): Replace K.mean() with a proper weighting function.\n                    const meanMetric = tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));\n                    valOutputs.push(meanMetric);\n                }\n                return valOutputs;\n            });\n        };\n    }\n    /**\n     * Trains the model for a fixed number of epochs (iterations on a\n     * dataset).\n     *\n     * ```js\n     * const model = tf.sequential({\n     *     layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * for (let i = 1; i < 5 ; ++i) {\n     *   const h = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *       batchSize: 4,\n     *       epochs: 3\n     *   });\n     *   console.log(\"Loss after Epoch \" + i + \" : \" + h.history.loss[0]);\n     * }\n     * ```\n     *\n     * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n     * model has multiple inputs. If all inputs in the model are named, you\n     * can also pass a dictionary mapping input names to `tf.Tensor`s.\n     * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n     * the model has multiple outputs. If all outputs in the model are named,\n     * you can also pass a dictionary mapping output names to `tf.Tensor`s.\n     * @param args A `ModelFitArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @exception ValueError In case of mismatch between the provided input\n     * data and what the model expects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fit(x, y, args = {}) {\n        return fitTensors(this, x, y, args);\n    }\n    // TODO(cais): Add code snippet below when it's possible to instantiate\n    //   actual dataset objects.\n    /**\n     * Trains the model using a dataset object.\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for training. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs.\n     *   Of the two items in the array, the first is the input feature(s) and\n     *   the second is the output target(s).\n     * @param args A `ModelFitDatasetArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fitDataset(dataset, args) {\n        return fitDataset(this, dataset, args);\n    }\n    /**\n     * Runs a single gradient update on a single batch of data.\n     *\n     * This method differs from `fit()` and `fitDataset()` in the following\n     * regards:\n     *   - It operates on exactly one batch of data.\n     *   - It returns only the loss and matric values, instead of\n     *     returning the batch-by-batch loss and metric values.\n     *   - It doesn't support fine-grained options such as verbosity and\n     *     callbacks.\n     *\n     * @param x Input data. It could be one of the following:\n     *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n     *     multiple inputs).\n     *   - An Object mapping input names to corresponding `tf.Tensor` (if the\n     *     model has named inputs).\n     * @param y Target darta. It could be either a `tf.Tensor` a multiple\n     *   `tf.Tensor`s. It should be consistent with `x`.\n     * @returns Training loss or losses (in case the model has\n     *   multiple outputs), along with metrics (if any), as numbers.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async trainOnBatch(x, y) {\n        // TODO(cais): Support sampleWeight and classWeight.\n        // TODO(cais): Support Dataset objects.\n        const standardizeOut = await this.standardizeUserData(x, y);\n        const inputs = standardizeOut[0];\n        const targets = standardizeOut[1];\n        const trainFunction = this.makeTrainFunction();\n        const losses = trainFunction(inputs.concat(targets));\n        const lossValues = [];\n        for (const loss of losses) {\n            const v = await loss.data();\n            lossValues.push(v[0]);\n        }\n        tfc.dispose(losses);\n        return singletonOrArray(lossValues);\n    }\n    /**\n     * Extract weight values of the model.\n     *\n     * @param config: An instance of `io.SaveConfig`, which specifies\n     * model-saving options such as whether only trainable weights are to be\n     * saved.\n     * @returns A `NamedTensorMap` mapping original weight names (i.e.,\n     *   non-uniqueified weight names) to their values.\n     */\n    getNamedWeights(config) {\n        const namedWeights = [];\n        const trainableOnly = config != null && config.trainableOnly;\n        const weights = trainableOnly ? this.trainableWeights : this.weights;\n        const weightValues = this.getWeights(trainableOnly);\n        for (let i = 0; i < weights.length; ++i) {\n            if (trainableOnly && !weights[i].trainable) {\n                // Optionally skip non-trainable weights.\n                continue;\n            }\n            namedWeights.push({ name: weights[i].originalName, tensor: weightValues[i] });\n        }\n        return namedWeights;\n    }\n    /**\n     * Setter used for force stopping of LayersModel.fit() (i.e., training).\n     *\n     * Example:\n     *\n     * ```js\n     * const input = tf.input({shape: [10]});\n     * const output = tf.layers.dense({units: 1}).apply(input);\n     * const model = tf.model({inputs: [input], outputs: [output]});\n     * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n     * const xs = tf.ones([8, 10]);\n     * const ys = tf.zeros([8, 1]);\n     *\n     * const history = await model.fit(xs, ys, {\n     *   epochs: 10,\n     *   callbacks: {\n     *     onEpochEnd: async (epoch, logs) => {\n     *       if (epoch === 2) {\n     *         model.stopTraining = true;\n     *       }\n     *     }\n     *   }\n     * });\n     *\n     * // There should be only 3 values in the loss array, instead of 10\n     * values,\n     * // due to the stopping after 3 epochs.\n     * console.log(history.history.loss);\n     * ```\n     */\n    set stopTraining(stop) {\n        this.stopTraining_ = stop;\n    }\n    get stopTraining() {\n        return this.stopTraining_;\n    }\n    get optimizer() {\n        return this.optimizer_;\n    }\n    set optimizer(optimizer) {\n        if (this.optimizer_ !== optimizer) {\n            this.optimizer_ = optimizer;\n            this.isOptimizerOwned = false;\n        }\n    }\n    dispose() {\n        const result = super.dispose();\n        if (result.refCountAfterDispose === 0 && this.optimizer != null &&\n            this.isOptimizerOwned) {\n            const numTensorsBeforeOptmizerDisposal = tfc.memory().numTensors;\n            this.optimizer_.dispose();\n            result.numDisposedVariables +=\n                numTensorsBeforeOptmizerDisposal - tfc.memory().numTensors;\n        }\n        return result;\n    }\n    getLossIdentifiers() {\n        let lossNames;\n        if (typeof this.loss === 'string') {\n            lossNames = toSnakeCase(this.loss);\n        }\n        else if (Array.isArray(this.loss)) {\n            for (const loss of this.loss) {\n                if (typeof loss !== 'string') {\n                    throw new Error('Serialization of non-string loss is not supported.');\n                }\n            }\n            lossNames = this.loss.map(name => toSnakeCase(name));\n        }\n        else {\n            const outputNames = Object.keys(this.loss);\n            lossNames = {};\n            const losses = this.loss;\n            for (const outputName of outputNames) {\n                if (typeof losses[outputName] === 'string') {\n                    lossNames[outputName] =\n                        toSnakeCase(losses[outputName]);\n                }\n                else {\n                    throw new Error('Serialization of non-string loss is not supported.');\n                }\n            }\n        }\n        return lossNames;\n    }\n    getMetricIdentifiers() {\n        if (typeof this.metrics === 'string' ||\n            typeof this.metrics === 'function') {\n            return [toSnakeCase(Metrics.getLossOrMetricName(this.metrics))];\n        }\n        else if (Array.isArray(this.metrics)) {\n            return this.metrics.map(metric => toSnakeCase(Metrics.getLossOrMetricName(metric)));\n        }\n        else {\n            const metricsIdentifiers = {};\n            for (const key in this.metrics) {\n                metricsIdentifiers[key] =\n                    toSnakeCase(Metrics.getLossOrMetricName(this.metrics[key]));\n            }\n            return metricsIdentifiers;\n        }\n    }\n    getTrainingConfig() {\n        return {\n            loss: this.getLossIdentifiers(),\n            metrics: this.getMetricIdentifiers(),\n            optimizer_config: {\n                class_name: this.optimizer.getClassName(),\n                config: this.optimizer.getConfig()\n            }\n        };\n        // TODO(cais): Add weight_metrics when they are supported.\n        // TODO(cais): Add sample_weight_mode when it's supported.\n        // TODO(cais): Add loss_weights when it's supported.\n    }\n    loadTrainingConfig(trainingConfig) {\n        if (trainingConfig.weighted_metrics != null) {\n            throw new Error('Loading weight_metrics is not supported yet.');\n        }\n        if (trainingConfig.loss_weights != null) {\n            throw new Error('Loading loss_weights is not supported yet.');\n        }\n        if (trainingConfig.sample_weight_mode != null) {\n            throw new Error('Loading sample_weight_mode is not supported yet.');\n        }\n        const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config);\n        const optimizer = deserialize(tsConfig);\n        let loss;\n        if (typeof trainingConfig.loss === 'string') {\n            loss = toCamelCase(trainingConfig.loss);\n        }\n        else if (Array.isArray(trainingConfig.loss)) {\n            loss = trainingConfig.loss.map(lossEntry => toCamelCase(lossEntry));\n        }\n        else if (trainingConfig.loss != null) {\n            loss = {};\n            for (const key in trainingConfig.loss) {\n                loss[key] = toCamelCase(trainingConfig.loss[key]);\n            }\n        }\n        let metrics;\n        if (Array.isArray(trainingConfig.metrics)) {\n            metrics = trainingConfig.metrics.map(metric => toCamelCase(metric));\n        }\n        else if (trainingConfig.metrics != null) {\n            metrics = {};\n            for (const key in trainingConfig.metrics) {\n                metrics[key] = toCamelCase(trainingConfig.metrics[key]);\n            }\n        }\n        this.compile({ loss, metrics, optimizer });\n    }\n    /**\n     * Save the configuration and/or weights of the LayersModel.\n     *\n     * An `IOHandler` is an object that has a `save` method of the proper\n     * signature defined. The `save` method manages the storing or\n     * transmission of serialized data (\"artifacts\") that represent the\n     * model's topology and weights onto or via a specific medium, such as\n     * file downloads, local storage, IndexedDB in the web browser and HTTP\n     * requests to a server. TensorFlow.js provides `IOHandler`\n     * implementations for a number of frequently used saving mediums, such as\n     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n     * for more details.\n     *\n     * This method also allows you to refer to certain types of `IOHandler`s\n     * as URL-like string shortcuts, such as 'localstorage://' and\n     * 'indexeddb://'.\n     *\n     * Example 1: Save `model`'s topology and weights to browser [local\n     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n     * then load it back.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * console.log('Prediction from original model:');\n     * model.predict(tf.ones([1, 3])).print();\n     *\n     * const saveResults = await model.save('localstorage://my-model-1');\n     *\n     * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * loadedModel.predict(tf.ones([1, 3])).print();\n     * ```\n     *\n     * Example 2. Saving `model`'s topology and weights to browser\n     * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n     * then load it back.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * console.log('Prediction from original model:');\n     * model.predict(tf.ones([1, 3])).print();\n     *\n     * const saveResults = await model.save('indexeddb://my-model-1');\n     *\n     * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * loadedModel.predict(tf.ones([1, 3])).print();\n     * ```\n     *\n     * Example 3. Saving `model`'s topology and weights as two files\n     * (`my-model-1.json` and `my-model-1.weights.bin`) downloaded from\n     * browser.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * const saveResults = await model.save('downloads://my-model-1');\n     * ```\n     *\n     * Example 4. Send  `model`'s topology and weights to an HTTP server.\n     * See the documentation of `tf.io.http` for more details\n     * including specifying request parameters and implementation of the\n     * server.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * const saveResults = await model.save('http://my-server/model/upload');\n     * ```\n     *\n     * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n     * scheme-based string shortcut for `IOHandler`.\n     * @param config Options for saving the model.\n     * @returns A `Promise` of `SaveResult`, which summarizes the result of\n     * the saving, such as byte sizes of the saved artifacts for the model's\n     *   topology and weight values.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async save(handlerOrURL, config) {\n        if (typeof handlerOrURL === 'string') {\n            const handlers = io.getSaveHandlers(handlerOrURL);\n            if (handlers.length === 0) {\n                throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n            }\n            else if (handlers.length > 1) {\n                throw new ValueError(`Found more than one (${handlers.length}) save handlers for ` +\n                    `URL '${handlerOrURL}'`);\n            }\n            handlerOrURL = handlers[0];\n        }\n        if (handlerOrURL.save == null) {\n            throw new ValueError('LayersModel.save() cannot proceed because the IOHandler ' +\n                'provided does not have the `save` attribute defined.');\n        }\n        const weightDataAndSpecs = await io.encodeWeights(this.getNamedWeights(config));\n        const returnString = false;\n        const unusedArg = null;\n        const modelConfig = this.toJSON(unusedArg, returnString);\n        const modelArtifacts = {\n            modelTopology: modelConfig,\n            format: LAYERS_MODEL_FORMAT_NAME,\n            generatedBy: `TensorFlow.js tfjs-layers v${version}`,\n            convertedBy: null,\n        };\n        const includeOptimizer = config == null ? false : config.includeOptimizer;\n        if (includeOptimizer && this.optimizer != null) {\n            modelArtifacts.trainingConfig = this.getTrainingConfig();\n            const weightType = 'optimizer';\n            const { data: optimizerWeightData, specs: optimizerWeightSpecs } = await io.encodeWeights(await this.optimizer.getWeights(), weightType);\n            weightDataAndSpecs.specs.push(...optimizerWeightSpecs);\n            weightDataAndSpecs.data = io.concatenateArrayBuffers([weightDataAndSpecs.data, optimizerWeightData]);\n        }\n        if (this.userDefinedMetadata != null) {\n            // Check serialized size of user-defined metadata.\n            const checkSize = true;\n            checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize);\n            modelArtifacts.userDefinedMetadata = this.userDefinedMetadata;\n        }\n        modelArtifacts.weightData = weightDataAndSpecs.data;\n        modelArtifacts.weightSpecs = weightDataAndSpecs.specs;\n        return handlerOrURL.save(modelArtifacts);\n    }\n    /**\n     * Set user-defined metadata.\n     *\n     * The set metadata will be serialized together with the topology\n     * and weights of the model during `save()` calls.\n     *\n     * @param setUserDefinedMetadata\n     */\n    setUserDefinedMetadata(userDefinedMetadata) {\n        checkUserDefinedMetadata(userDefinedMetadata, this.name);\n        this.userDefinedMetadata = userDefinedMetadata;\n    }\n    /**\n     * Get user-defined metadata.\n     *\n     * The metadata is supplied via one of the two routes:\n     *   1. By calling `setUserDefinedMetadata()`.\n     *   2. Loaded during model loading (if the model is constructed\n     *      via `tf.loadLayersModel()`.)\n     *\n     * If no user-defined metadata is available from either of the\n     * two routes, this function will return `undefined`.\n     */\n    getUserDefinedMetadata() {\n        return this.userDefinedMetadata;\n    }\n}\n// The class name is 'Model' rather than 'LayersModel' for backwards\n// compatibility since this class name shows up in the serialization format.\n/** @nocollapse */\nLayersModel.className = 'Model';\nserialization.registerClass(LayersModel);\n/**\n * A `tf.Functional` is an alias to `tf.LayersModel`.\n *\n * See also:\n *   `tf.LayersModel`, `tf.Sequential`, `tf.loadLayersModel`.\n */\n/** @doc {heading: 'Models', subheading: 'Classes'} */\nexport class Functional extends LayersModel {\n}\nFunctional.className = 'Functional';\nserialization.registerClass(Functional);\n//# sourceMappingURL=training.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source keras/models.py */\nimport { dispose, io, serialization, util } from '@tensorflow/tfjs-core';\nimport { getUid } from './backend/state';\nimport { Input } from './engine/input_layer';\nimport { getSourceInputs, Node } from './engine/topology';\nimport { LayersModel } from './engine/training';\nimport { NotImplementedError, RuntimeError, ValueError } from './errors';\nimport { deserialize } from './layers/serialization';\nimport * as generic_utils from './utils/generic_utils';\nimport { convertPythonicToTs } from './utils/serialization_utils';\nimport { getExactlyOneShape } from './utils/types_utils';\n/**\n * Parses a JSON model configuration file and returns a model instance.\n *\n * ```js\n * // This example shows how to serialize a model using `toJSON()` and\n * // deserialize it as another model using `tf.models.modelFromJSON()`.\n * // Note: this example serializes and deserializes only the topology\n * // of the model; the weights of the loaded model will be different\n * // from those of the the original model, due to random weight\n * // initialization.\n * // To load the topology and weights of a model, use `tf.loadLayersModel()`.\n * const model1 = tf.sequential();\n * model1.add(tf.layers.repeatVector({inputShape: [2], n: 4}));\n * // Serialize `model1` as a JSON object.\n * const model1JSON = model1.toJSON(null, false);\n * model1.summary();\n *\n * const model2 = await tf.models.modelFromJSON(model1JSON);\n * model2.summary();\n * ```\n *\n *  @param modelAndWeightsConfig JSON object or string encoding a model and\n *       weights configuration. It can also be only the topology JSON of the\n *       model, in which case the weights will not be loaded.\n *  @param custom_objects Optional dictionary mapping names\n *       (strings) to custom classes or functions to be\n *       considered during deserialization.\n * @returns A TensorFlow.js Layers `tf.LayersModel` instance (uncompiled).\n */\nexport async function modelFromJSON(modelAndWeightsConfig, customObjects) {\n    if (!('modelTopology' in modelAndWeightsConfig)) {\n        modelAndWeightsConfig = { modelTopology: modelAndWeightsConfig };\n    }\n    modelAndWeightsConfig = modelAndWeightsConfig;\n    let modelTopology = modelAndWeightsConfig.modelTopology;\n    if (modelTopology['model_config'] != null) {\n        // If the model-topology JSON contains a 'model_config' field, then it is\n        // a full model JSON (e.g., from `keras.Model.save()`), which contains\n        // not only the model's architecture in its 'model_config' field, but\n        // additional information such as the model's optimizer. We use only the\n        // 'model_config' field currently.\n        modelTopology = modelTopology['model_config'];\n    }\n    const tsConfig = convertPythonicToTs(modelTopology);\n    const model = deserialize(tsConfig, customObjects);\n    if (modelAndWeightsConfig.weightsManifest != null) {\n        // Load the weight values keyed by the original tensor names in the model\n        // file that was loaded.  These should match the keys of the weight\n        // manifest.\n        const weightValues = await io.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model.weights.map(weight => weight.originalName));\n        // Map the weights to the unique tensor names generated during model loading\n        const uniqueWeightValues = {};\n        for (const weight of model.weights) {\n            uniqueWeightValues[weight.originalName] =\n                weightValues[weight.originalName];\n        }\n        model.loadWeights(uniqueWeightValues);\n        // Dispose temporary weight values.\n        dispose(weightValues);\n    }\n    return model;\n}\n/**\n * Load a model, including its topology and optionally weights.  See the\n * Tutorial named \"How to import a Keras Model\" for usage examples.\n *\n * Example 1: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 2. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Load a model from user-selected files from HTML\n * [file input\n * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).\n *\n * ```js\n * // Note: this code snippet will not work without the HTML elements in the\n * //   page\n * const jsonUpload = document.getElementById('json-upload');\n * const weightsUpload = document.getElementById('weights-upload');\n *\n * const model = await tf.loadLayersModel(\n *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));\n * ```\n *\n * Example 4. Load a model from an HTTP server.\n *\n * ```js\n * const model = await\n *     tf.loadLayersModel('https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');\n * model.summary();\n * ```\n *\n * @param pathOrIOHandler Can be either of the two formats\n *   1. A string path to the `ModelAndWeightsConfig` JSON describing\n *      the model in the canonical TensorFlow.js format. This path will be\n *      interpreted as a relative HTTP path, to which `fetch` will be used to\n *      request the model topology and weight manifest JSON.\n *      The content of the JSON file is assumed to be a JSON object with the\n *      following fields and values:\n *      - 'modelTopology': A JSON object that can be either of:\n *        1. a model architecture JSON consistent with the format of the return\n *            value of `keras.Model.to_json()`\n *        2. a full model JSON in the format of `keras.models.save_model()`.\n *      - 'weightsManifest': A TensorFlow.js weights manifest.\n *      See the Python converter function `save_model()` for more details.\n *      It is also assumed that model weights can be accessed from relative\n *      paths described by the `paths` fields in weights manifest.\n *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`\n *      method.\n * @param options Optional configuration arguments for the model loading,\n *   including:\n *   - `strict`: Require that the provided weights exactly match those required\n *     by the layers.  Default true.  Passing false means that both extra\n *     weights and missing weights will be silently ignored.\n *   - `onProgress`: A progress callback of the form:\n *     `(fraction: number) => void`. This callback can be used to monitor the\n *     model-loading process.\n * @returns A `Promise` of `tf.LayersModel`, with the topology and weights\n *     loaded.\n */\nexport async function loadLayersModelInternal(pathOrIOHandler, options) {\n    if (options == null) {\n        options = {};\n    }\n    if (typeof pathOrIOHandler === 'string') {\n        const handlers = io.getLoadHandlers(pathOrIOHandler, options);\n        if (handlers.length === 0) {\n            // For backward compatibility: if no load handler can be found,\n            // assume it is a relative http path.\n            // TODO(cais): Reformat the args into a single `LoadOptions` once the core\n            // is refactored.\n            handlers.push(io.browserHTTPRequest(pathOrIOHandler, options));\n        }\n        else if (handlers.length > 1) {\n            throw new ValueError(`Found more than one (${handlers.length}) load handlers for ` +\n                `URL '${pathOrIOHandler}'`);\n        }\n        pathOrIOHandler = handlers[0];\n    }\n    return loadLayersModelFromIOHandler(pathOrIOHandler, undefined, options);\n}\n/**\n * Load a model and optionally its weights, using an IOHandler object.\n *\n * @param handler The instance of `IOHandler` to be used during the model\n *   loading.\n * @param customObjects Any optional custom objects to be used during model\n *   loading.\n * @param strict Whether the weight loading will be done in strict mode.\n *   Default: `true`.\n */\nexport async function loadLayersModelFromIOHandler(handler, customObjects, options) {\n    if (options == null) {\n        options = {};\n    }\n    if (handler.load == null) {\n        throw new ValueError('Cannot proceed with model loading because the IOHandler provided ' +\n            'does not have the `load` method implemented.');\n    }\n    const artifacts = await handler.load();\n    let modelTopology = artifacts.modelTopology;\n    if (modelTopology['model_config'] != null) {\n        modelTopology = modelTopology['model_config'];\n    }\n    const strict = options.strict == null ? true : options.strict;\n    // If weights are provided and the weight-loading mode is strict, use\n    // fast weight initialization. This skips costly initializers such as\n    // 'orthogonal' and saves unnecessary computation in cases where\n    // the initialized weight values will immediately be overwritten by\n    // loaded weight values.\n    const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict;\n    const model = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit);\n    const trainingConfig = artifacts.trainingConfig;\n    if (trainingConfig != null) {\n        model.loadTrainingConfig(trainingConfig);\n    }\n    if (artifacts.userDefinedMetadata != null) {\n        model.setUserDefinedMetadata(artifacts.userDefinedMetadata);\n    }\n    // If weightData is present, load the weights into the model.\n    if (artifacts.weightData != null) {\n        // Loading weights requires weightSpecs.\n        if (artifacts.weightSpecs == null) {\n            throw new ValueError('LayersModel artifacts contains weight data, but not weight specs. ' +\n                'Therefore loading of weights cannot proceed.');\n        }\n        const { modelWeights, optimizerWeights } = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs);\n        model.loadWeights(modelWeights, strict);\n        if (model.optimizer != null && optimizerWeights.length > 0) {\n            await model.optimizer.setWeights(optimizerWeights);\n        }\n        // Dispose temporary weight values.\n        dispose(modelWeights);\n        dispose(optimizerWeights.map(w => w.tensor));\n    }\n    return model;\n}\nfunction decodeModelAndOptimizerWeights(buffer, specs) {\n    const name2Tensor = io.decodeWeights(buffer, specs);\n    const modelWeights = {};\n    const optimizerWeights = [];\n    specs.forEach(spec => {\n        if (spec.group === 'optimizer') {\n            optimizerWeights.push({ name: spec.name, tensor: name2Tensor[spec.name] });\n        }\n        else {\n            modelWeights[spec.name] = name2Tensor[spec.name];\n        }\n    });\n    return { modelWeights, optimizerWeights };\n}\n/**\n * A model with a stack of layers, feeding linearly from one to the next.\n *\n * `tf.sequential` is a factory function that creates an instance of\n * `tf.Sequential`.\n *\n * ```js\n *  // Define a model for linear regression.\n *  const model = tf.sequential();\n *  model.add(tf.layers.dense({units: 1, inputShape: [1]}));\n *\n *  // Prepare the model for training: Specify the loss and the optimizer.\n *  model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n *\n *  // Generate some synthetic data for training.\n *  const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);\n *  const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);\n *\n *  // Train the model using the data then do inference on a data point the\n *  // model hasn't seen:\n *  await model.fit(xs, ys);\n *  model.predict(tf.tensor2d([5], [1, 1])).print();\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class Sequential extends LayersModel {\n    constructor(args) {\n        super({ inputs: [], outputs: [] });\n        args = args || {};\n        this.trainable = true;\n        this.built = false;\n        // Set model name.\n        this.name = (args.name != null) ? args.name : getUid('sequential_');\n        // Add to the model any layers passed to the constructor.\n        if (args.layers != null) {\n            for (const layer of args.layers) {\n                this.add(layer);\n            }\n        }\n    }\n    // Helper function to Sequential.add  Throws if the new output shape will be\n    // invalid.\n    checkShape(layer) {\n        const shape = layer.inboundNodes[0].outputTensors[0].shape;\n        if (shape.some(x => x < 0)) {\n            throw new ValueError('Negative dimension size caused by adding layer ' +\n                `${layer.name} with input shape [` +\n                `${layer.inboundNodes[0].inputTensors[0].shape}]`);\n        }\n    }\n    /**\n     * Adds a layer instance on top of the layer stack.\n     *\n     * ```js\n     *  const model = tf.sequential();\n     *  model.add(tf.layers.dense({units: 8, inputShape: [1]}));\n     *  model.add(tf.layers.dense({units: 4, activation: 'relu6'}));\n     *  model.add(tf.layers.dense({units: 1, activation: 'relu6'}));\n     *  // Note that the untrained model is random at this point.\n     *  model.predict(tf.randomNormal([10, 1])).print();\n     * ```\n     * @param layer Layer instance.\n     *\n     * @exception ValueError In case the `layer` argument does not know its\n     * input shape.\n     * @exception ValueError In case the `layer` argument has multiple output\n     *   tensors, or is already connected somewhere else (forbidden in\n     *   `Sequential` models).\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    add(layer) {\n        const isLayerModelInstance = layer instanceof Sequential || layer instanceof LayersModel;\n        let modelLayer;\n        if (isLayerModelInstance) {\n            modelLayer = layer;\n            if (modelLayer.outputs.length !== 1) {\n                throw new ValueError('All layers in a Sequential model ' +\n                    'should have a single output tensor. ' +\n                    'For multi-output layers, ' +\n                    'use the functional API.');\n            }\n            if (modelLayer.inputs.length !== 1) {\n                throw new ValueError('All layers in a Sequential model ' +\n                    'should have a single input tensor. ' +\n                    'For multi-input layers, ' +\n                    'use the functional API.');\n            }\n        }\n        if (this.outputs.length === 0) {\n            // first layer in model: check that it is an input layer\n            if (layer.inboundNodes.length === 0) {\n                // create an input layer\n                if (layer.batchInputShape == null) {\n                    throw new ValueError('The first layer in a Sequential model must ' +\n                        'get an `inputShape` or `batchInputShape` argument.');\n                }\n                // Instantiate the input layer.\n                const x = Input({\n                    batchShape: layer.batchInputShape,\n                    dtype: layer.dtype,\n                    name: layer.name + '_input'\n                });\n                // This will build the current layer and create the node connecting\n                // the current layer to the input layer we just created.\n                layer.apply(x);\n            }\n            if (isLayerModelInstance) {\n                this.outputs = modelLayer.outputs;\n                this.inputs = modelLayer.inputs;\n            }\n            else {\n                if (layer.inboundNodes.length !== 1) {\n                    throw new ValueError('A layer added to a Sequential model must not already be ' +\n                        `connected somewhere else. LayersModel received layer ${layer.name} ` +\n                        `which has ${layer.inboundNodes.length} pre-existing inbound ` +\n                        'connections.');\n                }\n                if (layer.inboundNodes[0].outputTensors.length !== 1) {\n                    throw new ValueError('All layers in a Sequential model ' +\n                        'should have a single output tensor. ' +\n                        'For multi-output layers, ' +\n                        'use the functional API.');\n                }\n                this.checkShape(layer);\n                this.outputs = [layer.inboundNodes[0].outputTensors[0]];\n                this.inputs = getSourceInputs(this.outputs[0]);\n            }\n            this.inboundNodes = [];\n            // We create an input node, which we will keep updated\n            // as we add more layers.\n            // (This call has side effects.)\n            // tslint:disable-next-line:no-unused-expression\n            new Node({\n                outboundLayer: this,\n                inboundLayers: [],\n                nodeIndices: [],\n                tensorIndices: [],\n                inputTensors: this.inputs,\n                outputTensors: this.outputs,\n                // no model-level masking for now\n                inputMasks: generic_utils.pyListRepeat(null, this.inputs.length),\n                outputMasks: [null],\n                inputShapes: this.inputs.map(x => x.shape),\n                outputShapes: this.outputs[0].shape\n            });\n        }\n        else {\n            const outputTensor = layer.apply(this.outputs[0]);\n            if (Array.isArray(outputTensor)) {\n                throw new TypeError('All layers in a Sequential model ' +\n                    'should have a single output tensor. ' +\n                    'For multi-output layers, ' +\n                    'use the functional API.');\n            }\n            this.checkShape(layer);\n            this.outputs = [outputTensor];\n            // update self.inbound_nodes\n            this.inboundNodes[0].outputTensors = this.outputs;\n            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n        }\n        this.layers.push(layer);\n        this.built = false;\n    }\n    /**\n     * Removes the last layer in the model.\n     *\n     * @exception TypeError if there are no layers in the model.\n     */\n    pop() {\n        if (this.layers.length === 0) {\n            throw new TypeError('There are no layers in the model.');\n        }\n        this.layers.pop();\n        if (this.layers.length === 0) {\n            this.outputs = [];\n            this.inboundNodes = [];\n            this.outboundNodes = [];\n        }\n        else {\n            const lastLayerIndex = this.layers.length - 1;\n            this.layers[lastLayerIndex].outboundNodes = [];\n            this.outputs = [this.layers[lastLayerIndex].output];\n            // update self.inbound_nodes\n            this.inboundNodes[0].outputTensors = this.outputs;\n            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n        }\n    }\n    call(inputs, kwargs) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.call(inputs, kwargs);\n    }\n    build(inputShape) {\n        // Call `getExactlyOneShape` without using its return value,\n        // to verify that exactly one input shape is provided.\n        getExactlyOneShape(inputShape);\n        if (this.inputs.length === 0 || this.outputs.length === 0) {\n            throw new TypeError('Sequential model cannot be built: model is empty.' +\n                ' Add some layers first.');\n        }\n        // actually create the model\n        this.model = new LayersModel({\n            inputs: this.inputs,\n            outputs: this.outputs[0],\n            name: this.name + '_model'\n        });\n        this.model.trainable = this.trainable;\n        // mirror model attributes\n        this.supportsMasking = this.model.supportsMasking;\n        // TODO(michaelterry): Add caches\n        this.inputLayers = this.model.inputLayers;\n        this.inputLayersNodeIndices = this.model.inputLayersNodeIndices;\n        this.inputLayersTensorIndices = this.model.inputLayersTensorIndices;\n        this.outputLayers = this.model.outputLayers;\n        this.outputLayersNodeIndices = this.model.outputLayersNodeIndices;\n        this.outputLayersTensorIndices = this.model.outputLayersTensorIndices;\n        this.nodesByDepth = this.model.nodesByDepth;\n        this.containerNodes = this.model.containerNodes;\n        this.outputNames = this.model.outputNames;\n        this.inputNames = this.model.inputNames;\n        // TODO(michaelterry): Add feedInputNames, feedInputs, if needed.\n        // TODO(michaelterry): Add callbackModel if needed.\n        this.built = true;\n    }\n    countParams() {\n        if (!this.built) {\n            this.build();\n        }\n        return super.countParams();\n    }\n    /**\n     * Print a text summary of the Sequential model's layers.\n     *\n     * The summary includes\n     * - Name and type of all layers that comprise the model.\n     * - Output shape(s) of the layers\n     * - Number of weight parameters of each layer\n     * - The total number of trainable and non-trainable parameters of the\n     * model.\n     *\n     * ```js\n     * const model = tf.sequential();\n     * model.add(\n     *     tf.layers.dense({units: 100, inputShape: [10], activation: 'relu'}));\n     * model.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));\n     *\n     * model.summary();\n     * ```\n     *\n     * @param lineLength Custom line length, in number of characters.\n     * @param positions Custom widths of each of the columns, as either\n     *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n     *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n     *   right-most (i.e., ending) position of a column.\n     * @param printFn Custom print function. Can be used to replace the default\n     *   `console.log`. For example, you can use `x => {}` to mute the printed\n     *   messages in the console.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    summary(lineLength, positions, printFn = console.log) {\n        if (!this.built) {\n            this.build();\n        }\n        super.summary(lineLength, positions, printFn);\n    }\n    /**\n     * Sets the weights of the model.\n     *\n     * @param weights Should be a list of Tensors with shapes and types matching\n     *   the output of `model.getWeights()`.\n     */\n    setWeights(weights) {\n        if (this.model == null) {\n            this.build();\n        }\n        this.model.setWeights(weights);\n    }\n    /**\n     * Returns the loss value & metrics values for the model in test mode.\n     *\n     * Loss and metrics are specified during `compile()`, which needs to happen\n     * before calls to `evaluate()`.\n     *\n     * Computation is done in batches.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const result = model.evaluate(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *   batchSize: 4,\n     * });\n     * result.print();\n     * ```\n     *\n     * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple inputs.\n     * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple outputs.\n     * @param args A `ModelEvaluateConfig`, containing optional fields.\n     *\n     * @return `Scalar` test loss (if the model has a single output and no\n     *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n     *   and/or metrics). The attribute `model.metricsNames`\n     *   will give you the display labels for the scalar outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    evaluate(x, y, args = {}) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before being used.');\n        }\n        return this.model.evaluate(x, y, args);\n    }\n    // TODO(cais): Add code snippet below once real dataset objects are\n    //   available.\n    /**\n     * Evaluate model using a dataset object.\n     *\n     * Note: Unlike `evaluate()`, this method is asynchronous (`async`);\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for evaluation. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs. Of the two items in the array, the\n     *   first is the input feature(s) and the second is the output target(s).\n     * @param args A configuration object for the dataset-based evaluation.\n     * @returns Loss and metric values as an Array of `Scalar` objects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async evaluateDataset(dataset, args) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before being used.');\n        }\n        return this.model.evaluateDataset(dataset, args);\n    }\n    /**\n     * Generates output predictions for the input samples.\n     *\n     * Computation is done in batches.\n     *\n     * Note: the \"step\" mode of predict() is currently not supported.\n     *   This is because the TensorFow.js core backend is imperative only.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predict(tf.ones([2, 10])).print();\n     * ```\n     *\n     * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n     *   the model has multiple inputs.\n     * @param conifg A `ModelPredictConfig` object containing optional fields.\n     *\n     * @return `tf.Tensor`(s) of predictions.\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and the model's expectations, or in case a stateful model receives a\n     *   number of samples that is not a multiple of the batch size.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(x, args = {}) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.predict(x, args);\n    }\n    /**\n     * Returns predictions for a single batch of samples.\n     *\n     * @param x: Input samples, as a Tensor, or list of Tensors (if the model\n     *   has multiple inputs).\n     * @return Tensor(s) of predictions\n     */\n    predictOnBatch(x) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.predictOnBatch(x);\n    }\n    /**\n     * See `LayersModel.compile`.\n     *\n     * @param args\n     */\n    compile(args) {\n        this.build();\n        this.model.compile(args);\n        this.optimizer_ = this.model.optimizer;\n        // tslint:disable-next-line:no-any\n        this.isOptimizerOwned = this.model.isOptimizerOwned;\n        this.loss = this.model.loss;\n        this.metrics = this.model.metrics;\n        // TODO(cais): Add this.lossWeights, this.sampleWeightMode,\n        //   this.weightedMetrics, this.targets.\n        this.metricsTensors = this.model.metricsTensors;\n        this.metricsNames = this.model.metricsNames;\n        // TODO(cais): Add sampleWeights.\n    }\n    get optimizer() {\n        return this.model == null ? undefined : this.model.optimizer;\n    }\n    set optimizer(optimizer) {\n        this.model.optimizer = optimizer;\n    }\n    /**\n     * Trains the model for a fixed number of epochs (iterations on a dataset).\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const history = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *   batchSize: 4,\n     *   epochs: 3\n     * });\n     * console.log(history.history.loss[0]);\n     * ```\n     *\n     * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n     * model has multiple inputs. If all inputs in the model are named, you can\n     * also pass a dictionary mapping input names to `tf.Tensor`s.\n     * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n     * the model has multiple outputs. If all outputs in the model are named, you\n     *  can also pass a dictionary mapping output names to `tf.Tensor`s.\n     * @param args  A `ModelFitConfig`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and what the model expects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fit(x, y, args = {}) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before ' +\n                'being used.');\n        }\n        return this.model.fit(x, y, args);\n    }\n    /**\n     * Trains the model using a dataset object.\n     *\n     * ```js\n     * const xArray = [\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     * ];\n     * const yArray = [1, 1, 1, 1];\n     * // Create a dataset from the JavaScript array.\n     * const xDataset = tf.data.array(xArray);\n     * const yDataset = tf.data.array(yArray);\n     * // Zip combines the `x` and `y` Datasets into a single Dataset, the\n     * // iterator of which will return an object containing of two tensors,\n     * // corresponding to `x` and `y`.  The call to `batch(4)` will bundle\n     * // four such samples into a single object, with the same keys now pointing\n     * // to tensors that hold 4 examples, organized along the batch dimension.\n     * // The call to `shuffle(4)` causes each iteration through the dataset to\n     * // happen in a different order.  The size of the shuffle window is 4.\n     * const xyDataset = tf.data.zip({xs: xDataset, ys: yDataset})\n     *     .batch(4)\n     *     .shuffle(4);\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [9]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const history = await model.fitDataset(xyDataset, {\n     *   epochs: 4,\n     *   callbacks: {onEpochEnd: (epoch, logs) => console.log(logs.loss)}\n     * });\n     * ```\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected to\n     *   generate a dataset iterator object, the `next()` method of which is\n     *   expected to produce data batches for evaluation. The return value of the\n     *   `next()` call ought to contain a boolean `done` field and a `value`\n     *   field.\n     *\n     *   The `value` field is expected to be an object of with fields\n     *   `xs` and `ys`, which point to the feature tensor and the target tensor,\n     *   respectively. This case is for models with exactly one input and one\n     *   output (e.g.. a sequential model). For example:\n     *   ```js\n     *   {value: {xs: xsTensor, ys: ysTensor}, done: false}\n     *   ```\n     *\n     *   If the model has multiple inputs, the `xs` field of `value` should\n     *   be an object mapping input names to their respective feature tensors.\n     *   For example:\n     *   ```js\n     *   {\n     *     value: {\n     *       xs: {\n     *         input_1: xsTensor1,\n     *         input_2: xsTensor2\n     *       },\n     *       ys: ysTensor\n     *     },\n     *     done: false\n     *   }\n     *   ```\n     *   If the model has multiple outputs, the `ys` field of `value` should\n     *   be an object mapping output names to their respective target tensors.\n     *   For example:\n     *   ```js\n     *   {\n     *     value: {\n     *       xs: xsTensor,\n     *       ys: {\n     *         output_1: ysTensor1,\n     *         output_2: ysTensor2\n     *       },\n     *     },\n     *     done: false\n     *   }\n     *   ```\n     * @param args A `ModelFitDatasetArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async fitDataset(dataset, args) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before ' +\n                'being used.');\n        }\n        return this.model.fitDataset(dataset, args);\n    }\n    /**\n     * Runs a single gradient update on a single batch of data.\n     *\n     * This method differs from `fit()` and `fitDataset()` in the following\n     * regards:\n     *   - It operates on exactly one batch of data.\n     *   - It returns only the loss and matric values, instead of\n     *     returning the batch-by-batch loss and metric values.\n     *   - It doesn't support fine-grained options such as verbosity and\n     *     callbacks.\n     *\n     * @param x Input data. It could be one of the following:\n     *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n     *     multiple inputs).\n     *   - An Object mapping input names to corresponding `tf.Tensor` (if the\n     *     model has named inputs).\n     * @param y Target darta. It could be either a `tf.Tensor` a multiple\n     *   `tf.Tensor`s. It should be consistent with `x`.\n     * @returns Training loss or losses (in case the model has\n     *   multiple outputs), along with metrics (if any), as numbers.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async trainOnBatch(x, y) {\n        return this.model.trainOnBatch(x, y);\n    }\n    /* See parent class for JsDoc */\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n        let configArray;\n        let extraModelConfig = {};\n        if (config instanceof Array) {\n            if (!(config[0].className != null) ||\n                config[0]['className'] === 'Merge') {\n                throw new ValueError('Legacy serialization format not supported yet.');\n            }\n            configArray = config;\n        }\n        else {\n            util.assert(config['layers'] != null, () => `When the config data for a Sequential model is not an Array, ` +\n                `it must be an Object that contains the 'layers' field.`);\n            configArray = config['layers'];\n            delete config['layers'];\n            extraModelConfig = config;\n        }\n        const model = new cls(extraModelConfig);\n        if (!(model instanceof Sequential)) {\n            throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model}`);\n        }\n        for (const conf of configArray) {\n            const customObjects = undefined;\n            const layer = deserialize(conf, customObjects, fastWeightInit);\n            if (fastWeightInit) {\n                layer.setFastWeightInitDuringBuild(true);\n            }\n            model.add(layer);\n        }\n        return model;\n    }\n    /**\n     * Setter used for force stopping of LayersModel.fit() (i.e., training).\n     *\n     * Example:\n     *\n     * ```js\n     * const model = tf.sequential();\n     * model.add(tf.layers.dense({units: 1, inputShape: [10]}));\n     * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n     * const xs = tf.ones([8, 10]);\n     * const ys = tf.zeros([8, 1]);\n     *\n     * const history = await model.fit(xs, ys, {\n     *   epochs: 10,\n     *   callbacks: {\n     *     onEpochEnd: async (epoch, logs) => {\n     *       if (epoch === 2) {\n     *         model.stopTraining = true;\n     *       }\n     *     }\n     *   }\n     * });\n     *\n     * // There should be only 3 values in the loss array, instead of 10 values,\n     * // due to the stopping after 3 epochs.\n     * console.log(history.history.loss);\n     * ```\n     */\n    set stopTraining(stop) {\n        // TODO(cais): When refactoring to remove the composition pattern happens,\n        // remove this method overriding.\n        if (this.model == null) {\n            throw new ValueError('Cannot set the stopTraining property of a sequential model before ' +\n                'it is compiled.');\n        }\n        this.model.stopTraining = stop;\n    }\n    get stopTraining() {\n        if (this.model == null) {\n            throw new ValueError('Cannot get the stopTraining property of a sequential model before ' +\n                'it is compiled.');\n        }\n        return this.model.stopTraining;\n    }\n    // TODO(cais): Override get trainableWeights() here\n    // tslint:disable-next-line:no-any\n    getConfig() {\n        // NOTE(cais): We override the return type of getConfig() to `any` here,\n        //   because the `Sequential` class is a special case among `Container`\n        //   subtypes in that its getConfig() method returns an Array (not a\n        //   dict).\n        const layers = [];\n        for (const layer of this.layers) {\n            const dict = {};\n            dict['className'] = layer.getClassName();\n            dict['config'] = layer.getConfig();\n            layers.push(dict);\n        }\n        return { name: this.name, layers };\n    }\n}\n/** @nocollapse */\nSequential.className = 'Sequential';\nserialization.registerClass(Sequential);\n//# sourceMappingURL=models.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { CallbackConstructorRegistry } from './base_callbacks';\nimport { Input, } from './engine/input_layer';\nimport { LayersModel } from './engine/training';\nimport { loadLayersModelInternal, Sequential } from './models';\n// TODO(cais): Add doc string to all the public static functions in this\n//   class; include exectuable JavaScript code snippets where applicable\n//   (b/74074458).\n// LayersModel and related factory methods.\n/**\n * A model is a data structure that consists of `Layers` and defines inputs\n * and outputs.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.model` is more generic, supporting an arbitrary graph (without\n * cycles) of layers. `tf.sequential` is less generic and supports only a linear\n * stack of layers.\n *\n * When creating a `tf.LayersModel`, specify its input(s) and output(s). Layers\n * are used to wire input(s) to output(s).\n *\n * For example, the following code snippet defines a model consisting of\n * two `dense` layers, with 10 and 4 units, respectively.\n *\n * ```js\n * // Define input, which has a size of 5 (not including batch dimension).\n * const input = tf.input({shape: [5]});\n *\n * // First dense layer uses relu activation.\n * const denseLayer1 = tf.layers.dense({units: 10, activation: 'relu'});\n * // Second dense layer uses softmax activation.\n * const denseLayer2 = tf.layers.dense({units: 4, activation: 'softmax'});\n *\n * // Obtain the output symbolic tensor by applying the layers on the input.\n * const output = denseLayer2.apply(denseLayer1.apply(input));\n *\n * // Create the model based on the inputs.\n * const model = tf.model({inputs: input, outputs: output});\n *\n * // The model can be used for training, evaluation and prediction.\n * // For example, the following line runs prediction with the model on\n * // some fake data.\n * model.predict(tf.ones([2, 5])).print();\n * ```\n * See also:\n *   `tf.sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function model(args) {\n    return new LayersModel(args);\n}\n/**\n * Creates a `tf.Sequential` model.  A sequential model is any model where the\n * outputs of one layer are the inputs to the next layer, i.e. the model\n * topology is a simple 'stack' of layers, with no branching or skipping.\n *\n * This means that the first layer passed to a `tf.Sequential` model should have\n * a defined input shape. What that means is that it should have received an\n * `inputShape` or `batchInputShape` argument, or for some type of layers\n * (recurrent, Dense...) an `inputDim` argument.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.sequential` is less generic, supporting only a linear stack of layers.\n * `tf.model` is more generic and supports an arbitrary graph (without\n * cycles) of layers.\n *\n * Examples:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have an input shape defined.\n * model.add(tf.layers.dense({units: 32, inputShape: [50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output, which equals\n * // `[null, 4]`. The 1st dimension is the undetermined batch dimension; the\n * // 2nd is the output size of the model's last layer.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * It is also possible to specify a batch size (with potentially undetermined\n * batch dimension, denoted by \"null\") for the first layer using the\n * `batchInputShape` key. The following example is equivalent to the above:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have a defined input shape\n * model.add(tf.layers.dense({units: 32, batchInputShape: [null, 50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * You can also use an `Array` of already-constructed `Layer`s to create\n * a `tf.Sequential` model:\n *\n * ```js\n * const model = tf.sequential({\n *   layers: [tf.layers.dense({units: 32, inputShape: [50]}),\n *            tf.layers.dense({units: 4})]\n * });\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function sequential(config) {\n    return new Sequential(config);\n}\n/**\n * Load a model composed of Layer objects, including its topology and optionally\n * weights. See the Tutorial named \"How to import a Keras Model\" for usage\n * examples.\n *\n * This method is applicable to:\n *\n * 1. Models created with the `tf.layers.*`, `tf.sequential`, and\n * `tf.model` APIs of TensorFlow.js and later saved with the\n * `tf.LayersModel.save` method.\n * 2. Models converted from Keras or TensorFlow tf.keras using the\n * [tensorflowjs_converter](https://github.com/tensorflow/tfjs/tree/master/tfjs-converter).\n *\n * This mode is *not* applicable to TensorFlow `SavedModel`s or their converted\n * forms. For those models, use `tf.loadGraphModel`.\n *\n * Example 1. Load a model from an HTTP server.\n *\n * ```js\n * const model = await tf.loadLayersModel(\n *     'https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');\n * model.summary();\n * ```\n *\n * Example 2: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 4. Load a model from user-selected files from HTML\n * [file input\n * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).\n *\n * ```js\n * // Note: this code snippet will not work without the HTML elements in the\n * //   page\n * const jsonUpload = document.getElementById('json-upload');\n * const weightsUpload = document.getElementById('weights-upload');\n *\n * const model = await tf.loadLayersModel(\n *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));\n * ```\n *\n * @param pathOrIOHandler Can be either of the two formats\n *   1. A string path to the `ModelAndWeightsConfig` JSON describing\n *      the model in the canonical TensorFlow.js format. For file://\n *      (tfjs-node-only), http:// and https:// schemas, the path can be\n *      either absolute or relative.\n *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`\n *      method.\n * @param options Optional configuration arguments for the model loading,\n *   including:\n *   - `strict`: Require that the provided weights exactly match those required\n *     by the layers.  Default true.  Passing false means that both extra\n *     weights and missing weights will be silently ignored.\n *   - `onProgress`: A function of the signature `(fraction: number) => void',\n *     that can be used as the progress callback for the model loading.\n * @returns A `Promise` of `tf.LayersModel`, with the topology and weights\n *     loaded.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport function loadLayersModel(pathOrIOHandler, options) {\n    if (options == null) {\n        options = {};\n    }\n    return loadLayersModelInternal(pathOrIOHandler, options);\n}\n/**\n * Used to instantiate an input to a model as a `tf.SymbolicTensor`.\n *\n * Users should call the `input` factory function for\n * consistency with other generator functions.\n *\n * Example:\n *\n * ```js\n * // Defines a simple logistic regression model with 32 dimensional input\n * // and 3 dimensional output.\n * const x = tf.input({shape: [32]});\n * const y = tf.layers.dense({units: 3, activation: 'softmax'}).apply(x);\n * const model = tf.model({inputs: x, outputs: y});\n * model.predict(tf.ones([2, 32])).print();\n * ```\n *\n * Note: `input` is only necessary when using `model`. When using\n * `sequential`, specify `inputShape` for the first layer or use `inputLayer`\n * as the first layer.\n *\n * @doc {heading: 'Models', subheading: 'Inputs'}\n */\nexport function input(config) {\n    return Input(config);\n}\nexport function registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n    CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);\n}\n//# sourceMappingURL=exports.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// Layer activation functions\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { deserializeKerasObject } from './utils/generic_utils';\n/**\n * Base class for Activations.\n *\n * Special note: due to cross-language compatibility reasons, the\n * static readonly className field in this family of classes must be set to\n * the initialLowerCamelCase name of the activation.\n */\nexport class Activation extends serialization.Serializable {\n    getConfig() {\n        return {};\n    }\n}\n/**\n * Exponential linear unit (ELU).\n * Reference: https://arxiv.org/abs/1511.07289\n */\nexport class Elu extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x: Input.\n     * @param alpha: Scaling factor the negative section.\n     * @return Output of the ELU activation.\n     */\n    apply(x, alpha = 1) {\n        return K.elu(x, alpha);\n    }\n}\n/** @nocollapse */\nElu.className = 'elu';\nserialization.registerClass(Elu);\n/**\n * Scaled Exponential Linear Unit. (Klambauer et al., 2017).\n * Reference: Self-Normalizing Neural Networks, https://arxiv.org/abs/1706.02515\n * Notes:\n *   - To be used together with the initialization \"lecunNormal\".\n *   - To be used together with the dropout variant \"AlphaDropout\".\n */\nexport class Selu extends Activation {\n    apply(x) {\n        return tfc.selu(x);\n    }\n}\n/** @nocollapse */\nSelu.className = 'selu';\nserialization.registerClass(Selu);\n/**\n *  Rectified linear unit\n */\nexport class Relu extends Activation {\n    apply(x) {\n        return tfc.relu(x);\n    }\n}\n/** @nocollapse */\nRelu.className = 'relu';\nserialization.registerClass(Relu);\n/**\n * Rectified linear unit activation maxing out at 6.0.\n */\nexport class Relu6 extends Activation {\n    apply(x) {\n        return tidy(() => tfc.minimum(6.0, tfc.relu(x)));\n    }\n}\n/** @nocollapse */\nRelu6.className = 'relu6';\nserialization.registerClass(Relu6);\n//* Linear activation (no-op) */\nexport class Linear extends Activation {\n    apply(x) {\n        return x;\n    }\n}\n/** @nocollapse */\nLinear.className = 'linear';\nserialization.registerClass(Linear);\n/**\n * Sigmoid activation function.\n */\nexport class Sigmoid extends Activation {\n    apply(x) {\n        return tfc.sigmoid(x);\n    }\n}\n/** @nocollapse */\nSigmoid.className = 'sigmoid';\nserialization.registerClass(Sigmoid);\n/**\n * Segment-wise linear approximation of sigmoid.\n */\nexport class HardSigmoid extends Activation {\n    apply(x) {\n        return K.hardSigmoid(x);\n    }\n}\n/** @nocollapse */\nHardSigmoid.className = 'hardSigmoid';\nserialization.registerClass(HardSigmoid);\n/**\n * Softplus activation function.\n */\nexport class Softplus extends Activation {\n    apply(x) {\n        return tfc.softplus(x);\n    }\n}\n/** @nocollapse */\nSoftplus.className = 'softplus';\nserialization.registerClass(Softplus);\n/**\n * Softsign activation function.\n */\nexport class Softsign extends Activation {\n    apply(x) {\n        return K.softsign(x);\n    }\n}\n/** @nocollapse */\nSoftsign.className = 'softsign';\nserialization.registerClass(Softsign);\n/**\n * Hyperbolic tangent function.\n */\nexport class Tanh extends Activation {\n    apply(x) {\n        return tfc.tanh(x);\n    }\n}\n/** @nocollapse */\nTanh.className = 'tanh';\nserialization.registerClass(Tanh);\n/**\n * Softmax activation function\n */\nexport class Softmax extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x Tensor.\n     * @param axis Integer, axis along which the softmax normalization is applied.\n     * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n     * an error.\n     *\n     * @returns a Tensor of the same shape as x\n     *\n     * @throws ValueError: In case `dim(x) < 2`.\n     */\n    apply(x, axis = (-1)) {\n        return tfc.softmax(x, axis);\n    }\n}\n/** @nocollapse */\nSoftmax.className = 'softmax';\nserialization.registerClass(Softmax);\n/**\n * Log softmax activation function\n */\nexport class LogSoftmax extends Activation {\n    /**\n     * Calculate the activation function of log softmax:\n     * log( exp(x_i) / sum(exp(x)) )\n     *\n     * @param x Tensor.\n     * @param axis Integer, axis along which the softmax normalization is applied.\n     * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n     * an error.\n     *\n     * @returns a Tensor of the same shape as x\n     *\n     * @throws ValueError: In case `dim(x) < 2`.\n     */\n    apply(x, axis = (-1)) {\n        return tfc.logSoftmax(x, axis);\n    }\n}\n/** @nocollapse */\nLogSoftmax.className = 'logSoftmax';\nserialization.registerClass(LogSoftmax);\n/**\n * Swish activation function\n */\nexport class Swish extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x Tensor.\n     * @param alpha Scaling factor for the sigmoid function.\n     * @returns a Tensor of the same shape as x\n     */\n    apply(x, alpha = 1) {\n        return tidy(() => tfc.sigmoid(x.mul(alpha)).mul(x));\n    }\n}\n/** @nocollapse */\nSwish.className = 'swish';\nserialization.registerClass(Swish);\nexport function serializeActivation(activation) {\n    return activation.getClassName();\n}\nexport function deserializeActivation(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'activation');\n}\nexport function getActivation(identifier) {\n    if (identifier == null) {\n        const config = {};\n        config['className'] = 'linear';\n        config['config'] = {};\n        return deserializeActivation(config);\n    }\n    if (typeof identifier === 'string') {\n        const config = {};\n        config['className'] = identifier;\n        config['config'] = {};\n        return deserializeActivation(config);\n    }\n    else if (identifier instanceof Activation) {\n        return identifier;\n    }\n    else {\n        return deserializeActivation(identifier);\n    }\n}\n//# sourceMappingURL=activations.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* original source: keras/regularizers.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { abs, add, serialization, sum, tidy, zeros } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\nfunction assertObjectArgs(args) {\n    if (args != null && typeof args !== 'object') {\n        throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an ` +\n            `object, but received: ${args}`);\n    }\n}\n/**\n * Regularizer base class.\n */\nexport class Regularizer extends serialization.Serializable {\n}\nexport class L1L2 extends Regularizer {\n    constructor(args) {\n        super();\n        assertObjectArgs(args);\n        this.l1 = args == null || args.l1 == null ? 0.01 : args.l1;\n        this.l2 = args == null || args.l2 == null ? 0.01 : args.l2;\n        this.hasL1 = this.l1 !== 0;\n        this.hasL2 = this.l2 !== 0;\n    }\n    /**\n     * Porting note: Renamed from __call__.\n     * @param x Variable of which to calculate the regularization score.\n     */\n    apply(x) {\n        return tidy(() => {\n            let regularization = zeros([1]);\n            if (this.hasL1) {\n                regularization = add(regularization, sum(tfc.mul(this.l1, abs(x))));\n            }\n            if (this.hasL2) {\n                regularization =\n                    add(regularization, sum(tfc.mul(this.l2, K.square(x))));\n            }\n            return regularization.asScalar();\n        });\n    }\n    getConfig() {\n        return { 'l1': this.l1, 'l2': this.l2 };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls({ l1: config['l1'], l2: config['l2'] });\n    }\n}\n/** @nocollapse */\nL1L2.className = 'L1L2';\nserialization.registerClass(L1L2);\nexport function l1(args) {\n    assertObjectArgs(args);\n    return new L1L2({ l1: args != null ? args.l1 : null, l2: 0 });\n}\nexport function l2(args) {\n    assertObjectArgs(args);\n    return new L1L2({ l2: args != null ? args.l2 : null, l1: 0 });\n}\n// Maps the JavaScript-like identifier keys to the corresponding keras symbols.\nexport const REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'l1l2': 'L1L2'\n};\nexport function serializeRegularizer(constraint) {\n    return serializeKerasObject(constraint);\n}\nexport function deserializeRegularizer(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'regularizer');\n}\nexport function getRegularizer(identifier) {\n    if (identifier == null) {\n        return null;\n    }\n    if (typeof identifier === 'string') {\n        const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        const config = { className, config: {} };\n        return deserializeRegularizer(config);\n    }\n    else if (identifier instanceof Regularizer) {\n        return identifier;\n    }\n    else {\n        return deserializeRegularizer(identifier);\n    }\n}\n//# sourceMappingURL=regularizers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n *  Advanced activation layers.\n */\nimport { clipByValue, elu, leakyRelu, prelu, relu, serialization } from '@tensorflow/tfjs-core';\nimport { Softmax as softmaxActivation } from '../activations';\nimport { cast } from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class ReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.supportsMasking = true;\n        if (args != null) {\n            this.maxValue = args.maxValue;\n        }\n    }\n    call(inputs, kwargs) {\n        inputs = getExactlyOneTensor(inputs);\n        let output = relu(inputs);\n        if (this.maxValue != null) {\n            output = clipByValue(output, 0, this.maxValue);\n        }\n        return output;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { maxValue: this.maxValue };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nReLU.className = 'ReLU';\nserialization.registerClass(ReLU);\nexport class LeakyReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA = 0.3;\n        if (args == null) {\n            args = {};\n        }\n        this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return leakyRelu(x, this.alpha);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { alpha: this.alpha };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nLeakyReLU.className = 'LeakyReLU';\nserialization.registerClass(LeakyReLU);\nexport class PReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA_INITIALIZER = 'zeros';\n        if (args == null) {\n            args = {};\n        }\n        this.supportsMasking = true;\n        this.alphaInitializer =\n            getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER);\n        this.alphaRegularizer = getRegularizer(args.alphaRegularizer);\n        this.alphaConstraint = getConstraint(args.alphaConstraint);\n        if (args.sharedAxes == null) {\n            this.sharedAxes = null;\n        }\n        else if (Array.isArray(args.sharedAxes)) {\n            this.sharedAxes = args.sharedAxes;\n        }\n        else if (typeof args.sharedAxes === 'number') {\n            this.sharedAxes = [args.sharedAxes];\n        }\n        else {\n            throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, ` +\n                `but got ${args.sharedAxes}`);\n        }\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const paramShape = inputShape.slice(1);\n        if (this.sharedAxes != null) {\n            for (const i of this.sharedAxes) {\n                paramShape[i - 1] = 1;\n            }\n        }\n        this.alpha = this.addWeight('alpha', paramShape, 'float32', this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);\n        // Set input spec.\n        const axes = {};\n        if (this.sharedAxes != null) {\n            for (let i = 1; i < inputShape.length; ++i) {\n                axes[i] = inputShape[i];\n            }\n        }\n        this.inputSpec = [new InputSpec({\n                ndim: inputShape.length,\n                axes,\n            })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        inputs = getExactlyOneTensor(inputs);\n        return prelu(inputs, this.alpha.read());\n    }\n    getConfig() {\n        const config = {\n            alphaInitializer: serializeInitializer(this.alphaInitializer),\n            alphaRegularizer: serializeRegularizer(this.alphaRegularizer),\n            alphaConstraint: serializeConstraint(this.alphaConstraint),\n            sharedAxes: this.sharedAxes\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nPReLU.className = 'PReLU';\nserialization.registerClass(PReLU);\nexport class ELU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) {\n            throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ` +\n                `ELU layer yet.`);\n        }\n        this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return elu(x);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { alpha: this.alpha };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nELU.className = 'ELU';\nserialization.registerClass(ELU);\nexport class ThresholdedReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_THETA = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return x.mul(cast(x.greater(this.theta), 'float32'));\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { theta: this.theta };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nThresholdedReLU.className = 'ThresholdedReLU';\nserialization.registerClass(ThresholdedReLU);\nexport class Softmax extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_AXIS = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        this.softmax = new softmaxActivation().apply;\n        this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return this.softmax(x, this.axis);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { axis: this.axis };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nSoftmax.className = 'Softmax';\nserialization.registerClass(Softmax);\n//# sourceMappingURL=advanced_activations.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { ValueError } from '../errors';\nimport { pyListRepeat } from './generic_utils';\nimport { isInteger, max } from './math_utils';\n/**\n * Transforms a single number of array of numbers into an array of numbers.\n * @param value\n * @param n: The size of the tuple to be returned.\n * @param name: Name of the parameter, used for generating error messages.\n * @returns An array of numbers.\n */\nexport function normalizeArray(value, n, name) {\n    if (typeof value === 'number') {\n        return pyListRepeat(value, n);\n    }\n    else {\n        if (value.length !== n) {\n            throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers.` +\n                ` Received: ${value.length} elements.`);\n        }\n        for (let i = 0; i < n; ++i) {\n            const singleValue = value[i];\n            if (!isInteger(singleValue)) {\n                throw new ValueError(`The ${name} argument must be an integer or tuple of ${n}` +\n                    ` integers. Received: ${JSON.stringify(value)} including a` +\n                    ` non-integer number ${singleValue}`);\n            }\n        }\n        return value;\n    }\n}\n/**\n * Determines output length of a convolution given input length.\n * @param inputLength\n * @param filterSize\n * @param padding\n * @param stride\n * @param dilation: dilation rate.\n */\nexport function convOutputLength(inputLength, filterSize, padding, stride, dilation = 1) {\n    if (inputLength == null) {\n        return inputLength;\n    }\n    const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1);\n    let outputLength;\n    if (padding === 'same') {\n        outputLength = inputLength;\n    }\n    else { // VALID\n        outputLength = inputLength - dilatedFilterSize + 1;\n    }\n    return Math.floor((outputLength + stride - 1) / stride);\n}\nexport function deconvLength(dimSize, strideSize, kernelSize, padding) {\n    if (dimSize == null) {\n        return null;\n    }\n    if (padding === 'valid') {\n        dimSize = dimSize * strideSize + max([kernelSize - strideSize, 0]);\n    }\n    else if (padding === 'same') {\n        dimSize = dimSize * strideSize;\n    }\n    else {\n        throw new ValueError(`Unsupport padding mode: ${padding}.`);\n    }\n    return dimSize;\n}\n//# sourceMappingURL=conv_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Convolutional Layers\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { convOutputLength, deconvLength, normalizeArray } from '../utils/conv_utils';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Transpose and cast the input before the conv2d.\n * @param x Input image tensor.\n * @param dataFormat\n */\nexport function preprocessConv2DInput(x, dataFormat) {\n    // TODO(cais): Cast type to float32 if not.\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        if (dataFormat === 'channelsFirst') {\n            return tfc.transpose(x, [0, 2, 3, 1]); // NCHW -> NHWC.\n        }\n        else {\n            return x;\n        }\n    });\n}\n/**\n * Transpose and cast the input before the conv3d.\n * @param x Input image tensor.\n * @param dataFormat\n */\nexport function preprocessConv3DInput(x, dataFormat) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        if (dataFormat === 'channelsFirst') {\n            return tfc.transpose(x, [0, 2, 3, 4, 1]); // NCDHW -> NDHWC.\n        }\n        else {\n            return x;\n        }\n    });\n}\n/**\n * 1D-convolution with bias added.\n *\n * Porting Note: This function does not exist in the Python Keras backend.\n *   It is exactly the same as `conv2d`, except the added `bias`.\n *\n * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.\n * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.\n * @param bias Bias, rank-3, of shape `[outDepth]`.\n * @param strides\n * @param padding Padding mode.\n * @param dataFormat Data format.\n * @param dilationRate\n * @returns The result of the 1D convolution.\n * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.\n */\nexport function conv1dWithBias(x, kernel, bias, strides = 1, padding = 'valid', dataFormat, dilationRate = 1) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        // Check the ranks of x, kernel and bias.\n        if (x.shape.length !== 3) {\n            throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ` +\n                `${x.shape.length} instead.`);\n        }\n        if (kernel.shape.length !== 3) {\n            throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ` +\n                `${kernel.shape.length} instead`);\n        }\n        if (bias != null && bias.shape.length !== 1) {\n            throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ` +\n                `${kernel.shape.length} instead`);\n        }\n        // TODO(cais): Support CAUSAL padding mode.\n        if (dataFormat === 'channelsFirst') {\n            x = tfc.transpose(x, [0, 2, 1]); // NCW -> NWC.\n        }\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv1dWithBias is not ' +\n                'implemented yet.');\n        }\n        let y = tfc.conv1d(x, kernel, strides, padding === 'same' ? 'same' : 'valid', 'NWC', dilationRate);\n        if (bias != null) {\n            y = K.biasAdd(y, bias);\n        }\n        return y;\n    });\n}\n/**\n * 1D-convolution.\n *\n * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.\n * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.s\n * @param strides\n * @param padding Padding mode.\n * @param dataFormat Data format.\n * @param dilationRate\n * @returns The result of the 1D convolution.\n * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.\n */\nexport function conv1d(x, kernel, strides = 1, padding = 'valid', dataFormat, dilationRate = 1) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv1dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 2D Convolution\n * @param x\n * @param kernel kernel of the convolution.\n * @param strides strides array.\n * @param padding padding mode. Default to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param dilationRate dilation rate array.\n * @returns Result of the 2D pooling.\n */\nexport function conv2d(x, kernel, strides = [1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv2dWithBiasActivation(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 2D Convolution with an added bias and optional activation.\n * Note: This function does not exist in the Python Keras Backend. This function\n * is exactly the same as `conv2d`, except the added `bias`.\n */\nexport function conv2dWithBiasActivation(x, kernel, bias, strides = [1, 1], padding = 'valid', dataFormat, dilationRate, activation = null) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        if (x.rank !== 3 && x.rank !== 4) {\n            throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, ` +\n                `but received ${x.rank}.`);\n        }\n        if (kernel.rank !== 3 && kernel.rank !== 4) {\n            throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, ` +\n                `but received ${x.rank}.`);\n        }\n        let y = preprocessConv2DInput(x, dataFormat);\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv1dWithBias is not ' +\n                'implemented yet.');\n        }\n        y = tfc.fused.conv2d({\n            x: y,\n            filter: kernel,\n            strides: strides,\n            pad: padding === 'same' ? 'same' : 'valid',\n            dilations: dilationRate,\n            dataFormat: 'NHWC',\n            bias,\n            activation\n        });\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]);\n        }\n        return y;\n    });\n}\n/**\n * 3D Convolution.\n * @param x\n * @param kernel kernel of the convolution.\n * @param strides strides array.\n * @param padding padding mode. Default to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param dilationRate dilation rate array.\n * @returns Result of the 3D convolution.\n */\nexport function conv3d(x, kernel, strides = [1, 1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv3dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 3D Convolution with an added bias.\n * Note: This function does not exist in the Python Keras Backend. This function\n * is exactly the same as `conv3d`, except the added `bias`.\n */\nexport function conv3dWithBias(x, kernel, bias, strides = [1, 1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        if (x.rank !== 4 && x.rank !== 5) {\n            throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ` +\n                `${x.rank}.`);\n        }\n        if (kernel.rank !== 4 && kernel.rank !== 5) {\n            throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ` +\n                `${x.rank}.`);\n        }\n        let y = preprocessConv3DInput(x, dataFormat);\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv3dWithBias is not ' +\n                'implemented yet.');\n        }\n        y = tfc.conv3d(y, kernel, strides, padding === 'same' ? 'same' : 'valid', 'NDHWC', dilationRate);\n        if (bias != null) {\n            y = K.biasAdd(y, bias);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 4, 1, 2, 3]);\n        }\n        return y;\n    });\n}\n/**\n * Abstract convolution layer.\n */\nexport class BaseConv extends Layer {\n    constructor(rank, args) {\n        super(args);\n        this.bias = null;\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        BaseConv.verifyArgs(args);\n        this.rank = rank;\n        generic_utils.assertPositiveInteger(this.rank, 'rank');\n        if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) {\n            throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is ` +\n                `not implemented yet.`);\n        }\n        this.kernelSize = normalizeArray(args.kernelSize, rank, 'kernelSize');\n        this.strides = normalizeArray(args.strides == null ? 1 : args.strides, rank, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        checkPaddingMode(this.padding);\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        this.activation = getActivation(args.activation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.dilationRate = normalizeArray(args.dilationRate == null ? 1 : args.dilationRate, rank, 'dilationRate');\n        if (this.rank === 1 &&\n            (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) {\n            throw new ValueError(`dilationRate must be a number or an array of a single number ` +\n                `for 1D convolution, but received ` +\n                `${JSON.stringify(this.dilationRate)}`);\n        }\n        else if (this.rank === 2) {\n            if (typeof this.dilationRate === 'number') {\n                this.dilationRate = [this.dilationRate, this.dilationRate];\n            }\n            else if (this.dilationRate.length !== 2) {\n                throw new ValueError(`dilationRate must be a number or array of two numbers for 2D ` +\n                    `convolution, but received ${JSON.stringify(this.dilationRate)}`);\n            }\n        }\n        else if (this.rank === 3) {\n            if (typeof this.dilationRate === 'number') {\n                this.dilationRate =\n                    [this.dilationRate, this.dilationRate, this.dilationRate];\n            }\n            else if (this.dilationRate.length !== 3) {\n                throw new ValueError(`dilationRate must be a number or array of three numbers for 3D ` +\n                    `convolution, but received ${JSON.stringify(this.dilationRate)}`);\n            }\n        }\n    }\n    static verifyArgs(args) {\n        // Check config.kernelSize type and shape.\n        generic_utils.assert('kernelSize' in args, `required key 'kernelSize' not in config`);\n        if (typeof args.kernelSize !== 'number' &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 3)) {\n            throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with ` +\n                `length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n    getConfig() {\n        const config = {\n            kernelSize: this.kernelSize,\n            strides: this.strides,\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n            dilationRate: this.dilationRate,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            biasConstraint: serializeConstraint(this.biasConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/**\n * Abstract nD convolution layer.  Ancestor of convolution layers which reduce\n * across channels, i.e., Conv1D and Conv2D, but not DepthwiseConv2D.\n */\nexport class Conv extends BaseConv {\n    constructor(rank, args) {\n        super(rank, args);\n        this.kernel = null;\n        Conv.verifyArgs(args);\n        this.filters = args.filters;\n        generic_utils.assertPositiveInteger(this.filters, 'filters');\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError(`The channel dimension of the input should be defined. ` +\n                `Found ${inputShape[channelAxis]}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const kernelShape = this.kernelSize.concat([inputDim, this.filters]);\n        this.kernel = this.addWeight('kernel', kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        this.inputSpec = [{ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } }];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let outputs;\n            const biasValue = this.bias == null ? null : this.bias.read();\n            const fusedActivationName = generic_utils.mapActivationToFusedKernel(this.activation.getClassName());\n            if (fusedActivationName != null && this.rank === 2) {\n                outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate, fusedActivationName);\n            }\n            else {\n                if (this.rank === 1) {\n                    outputs = conv1dWithBias(inputs, this.kernel.read(), biasValue, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);\n                }\n                else if (this.rank === 2) {\n                    // TODO(cais): Move up to constructor.\n                    outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n                }\n                else if (this.rank === 3) {\n                    outputs = conv3dWithBias(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n                }\n                else {\n                    throw new NotImplementedError('convolutions greater than 3D are not implemented yet.');\n                }\n                if (this.activation != null) {\n                    outputs = this.activation.apply(outputs);\n                }\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const newSpace = [];\n        const space = (this.dataFormat === 'channelsLast') ?\n            inputShape.slice(1, inputShape.length - 1) :\n            inputShape.slice(2);\n        for (let i = 0; i < space.length; ++i) {\n            const newDim = convOutputLength(space[i], this.kernelSize[i], this.padding, this.strides[i], typeof this.dilationRate === 'number' ? this.dilationRate :\n                this.dilationRate[i]);\n            newSpace.push(newDim);\n        }\n        let outputShape = [inputShape[0]];\n        if (this.dataFormat === 'channelsLast') {\n            outputShape = outputShape.concat(newSpace);\n            outputShape.push(this.filters);\n        }\n        else {\n            outputShape.push(this.filters);\n            outputShape = outputShape.concat(newSpace);\n        }\n        return outputShape;\n    }\n    getConfig() {\n        const config = {\n            filters: this.filters,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    static verifyArgs(args) {\n        // Check config.filters type, shape, and value.\n        if (!('filters' in args) || typeof args.filters !== 'number' ||\n            args.filters < 1) {\n            throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 ` +\n                `but got ${JSON.stringify(args.filters)}`);\n        }\n    }\n}\nexport class Conv2D extends Conv {\n    constructor(args) {\n        super(2, args);\n        Conv2D.verifyArgs(args);\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if ((typeof args.kernelSize !== 'number') &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 2)) {\n            throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with ` +\n                `length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n}\n/** @nocollapse */\nConv2D.className = 'Conv2D';\nserialization.registerClass(Conv2D);\nexport class Conv3D extends Conv {\n    constructor(args) {\n        super(3, args);\n        Conv3D.verifyArgs(args);\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if (typeof args.kernelSize !== 'number') {\n            if (!(Array.isArray(args.kernelSize) &&\n                (args.kernelSize.length === 1 || args.kernelSize.length === 3))) {\n                throw new ValueError(`Conv3D expects config.kernelSize to be number or` +\n                    ` [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`);\n            }\n        }\n    }\n}\n/** @nocollapse */\nConv3D.className = 'Conv3D';\nserialization.registerClass(Conv3D);\nexport class Conv2DTranspose extends Conv2D {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n        if (this.padding !== 'same' && this.padding !== 'valid') {\n            throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' ` +\n                `and 'valid', but received padding mode ${this.padding}`);\n        }\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length !== 4) {\n            throw new ValueError('Input should have rank 4; Received input shape: ' +\n                JSON.stringify(inputShape));\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError('The channel dimension of the inputs should be defined. ' +\n                'Found `None`.');\n        }\n        const inputDim = inputShape[channelAxis];\n        const kernelShape = this.kernelSize.concat([this.filters, inputDim]);\n        this.kernel = this.addWeight('kernel', kernelShape, 'float32', this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], 'float32', this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        // Set input spec.\n        this.inputSpec =\n            [new InputSpec({ ndim: 4, axes: { [channelAxis]: inputDim } })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            let input = getExactlyOneTensor(inputs);\n            if (input.shape.length !== 4) {\n                throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but ` +\n                    `received a tensor of rank-${input.shape.length}`);\n            }\n            const inputShape = input.shape;\n            const batchSize = inputShape[0];\n            let hAxis;\n            let wAxis;\n            if (this.dataFormat === 'channelsFirst') {\n                hAxis = 2;\n                wAxis = 3;\n            }\n            else {\n                hAxis = 1;\n                wAxis = 2;\n            }\n            const height = inputShape[hAxis];\n            const width = inputShape[wAxis];\n            const kernelH = this.kernelSize[0];\n            const kernelW = this.kernelSize[1];\n            const strideH = this.strides[0];\n            const strideW = this.strides[1];\n            // Infer the dynamic output shape.\n            const outHeight = deconvLength(height, strideH, kernelH, this.padding);\n            const outWidth = deconvLength(width, strideW, kernelW, this.padding);\n            // Porting Note: We don't branch based on `this.dataFormat` here,\n            // because\n            //   the tjfs-core function `conv2dTranspose` called below always\n            //   assumes channelsLast.\n            const outputShape = [batchSize, outHeight, outWidth, this.filters];\n            if (this.dataFormat !== 'channelsLast') {\n                input = tfc.transpose(input, [0, 2, 3, 1]);\n            }\n            let outputs = tfc.conv2dTranspose(input, this.kernel.read(), outputShape, this.strides, this.padding);\n            if (this.dataFormat !== 'channelsLast') {\n                outputs = tfc.transpose(outputs, [0, 3, 1, 2]);\n            }\n            if (this.bias != null) {\n                outputs =\n                    K.biasAdd(outputs, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                outputs = this.activation.apply(outputs);\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        let channelAxis;\n        let heightAxis;\n        let widthAxis;\n        if (this.dataFormat === 'channelsFirst') {\n            channelAxis = 1;\n            heightAxis = 2;\n            widthAxis = 3;\n        }\n        else {\n            channelAxis = 3;\n            heightAxis = 1;\n            widthAxis = 2;\n        }\n        const kernelH = this.kernelSize[0];\n        const kernelW = this.kernelSize[1];\n        const strideH = this.strides[0];\n        const strideW = this.strides[1];\n        outputShape[channelAxis] = this.filters;\n        outputShape[heightAxis] =\n            deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);\n        outputShape[widthAxis] =\n            deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);\n        return outputShape;\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['dilationRate'];\n        return config;\n    }\n}\n/** @nocollapse */\nConv2DTranspose.className = 'Conv2DTranspose';\nserialization.registerClass(Conv2DTranspose);\nexport class SeparableConv extends Conv {\n    constructor(rank, config) {\n        super(rank, config);\n        this.DEFAULT_DEPTHWISE_INITIALIZER = 'glorotUniform';\n        this.DEFAULT_POINTWISE_INITIALIZER = 'glorotUniform';\n        this.depthwiseKernel = null;\n        this.pointwiseKernel = null;\n        if (config.filters == null) {\n            throw new ValueError('The `filters` configuration field is required by SeparableConv, ' +\n                'but is unspecified.');\n        }\n        if (config.kernelInitializer != null || config.kernelRegularizer != null ||\n            config.kernelConstraint != null) {\n            throw new ValueError('Fields kernelInitializer, kernelRegularizer and kernelConstraint ' +\n                'are invalid for SeparableConv2D. Use depthwiseInitializer, ' +\n                'depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, ' +\n                'pointwiseRegularizer and pointwiseConstraint instead.');\n        }\n        if (config.padding != null && config.padding !== 'same' &&\n            config.padding !== 'valid') {\n            throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: ` +\n                `'same' and 'valid', but received ${JSON.stringify(config.padding)}`);\n        }\n        this.depthMultiplier =\n            config.depthMultiplier == null ? 1 : config.depthMultiplier;\n        this.depthwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER);\n        this.depthwiseRegularizer = getRegularizer(config.depthwiseRegularizer);\n        this.depthwiseConstraint = getConstraint(config.depthwiseConstraint);\n        this.pointwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER);\n        this.pointwiseRegularizer = getRegularizer(config.pointwiseRegularizer);\n        this.pointwiseConstraint = getConstraint(config.pointwiseConstraint);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < this.rank + 2) {\n            throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ` +\n                `${this.rank + 2}, but received input shape: ` +\n                `${JSON.stringify(inputShape)}`);\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n            throw new ValueError(`The channel dimension of the inputs should be defined, ` +\n                `but found ${JSON.stringify(inputShape[channelAxis])}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]);\n        const pointwiseKernelShape = [];\n        for (let i = 0; i < this.rank; ++i) {\n            pointwiseKernelShape.push(1);\n        }\n        pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);\n        const trainable = true;\n        this.depthwiseKernel = this.addWeight('depthwise_kernel', depthwiseKernelShape, 'float32', this.depthwiseInitializer, this.depthwiseRegularizer, trainable, this.depthwiseConstraint);\n        this.pointwiseKernel = this.addWeight('pointwise_kernel', pointwiseKernelShape, 'float32', this.pointwiseInitializer, this.pointwiseRegularizer, trainable, this.pointwiseConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], 'float32', this.biasInitializer, this.biasRegularizer, trainable, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.inputSpec =\n            [new InputSpec({ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let output;\n            if (this.rank === 1) {\n                throw new NotImplementedError('1D separable convolution is not implemented yet.');\n            }\n            else if (this.rank === 2) {\n                if (this.dataFormat === 'channelsFirst') {\n                    inputs = tfc.transpose(inputs, [0, 2, 3, 1]); // NCHW -> NHWC.\n                }\n                output = tfc.separableConv2d(inputs, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, 'NHWC');\n            }\n            if (this.useBias) {\n                output = K.biasAdd(output, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                output = this.activation.apply(output);\n            }\n            if (this.dataFormat === 'channelsFirst') {\n                output = tfc.transpose(output, [0, 3, 1, 2]); // NHWC -> NCHW.\n            }\n            return output;\n        });\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        delete config['kernelInitializer'];\n        delete config['kernelRegularizer'];\n        delete config['kernelConstraint'];\n        config['depthwiseInitializer'] =\n            serializeInitializer(this.depthwiseInitializer);\n        config['pointwiseInitializer'] =\n            serializeInitializer(this.pointwiseInitializer);\n        config['depthwiseRegularizer'] =\n            serializeRegularizer(this.depthwiseRegularizer);\n        config['pointwiseRegularizer'] =\n            serializeRegularizer(this.pointwiseRegularizer);\n        config['depthwiseConstraint'] =\n            serializeConstraint(this.depthwiseConstraint);\n        config['pointwiseConstraint'] =\n            serializeConstraint(this.pointwiseConstraint);\n        return config;\n    }\n}\n/** @nocollapse */\nSeparableConv.className = 'SeparableConv';\nexport class SeparableConv2D extends SeparableConv {\n    constructor(args) {\n        super(2, args);\n    }\n}\n/** @nocollapse */\nSeparableConv2D.className = 'SeparableConv2D';\nserialization.registerClass(SeparableConv2D);\nexport class Conv1D extends Conv {\n    constructor(args) {\n        super(1, args);\n        Conv1D.verifyArgs(args);\n        this.inputSpec = [{ ndim: 3 }];\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        delete config['dataFormat'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if (typeof args.kernelSize !== 'number' &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 1)) {\n            throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with ` +\n                `length 1, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n}\n/** @nocollapse */\nConv1D.className = 'Conv1D';\nserialization.registerClass(Conv1D);\nexport class Cropping2D extends Layer {\n    constructor(args) {\n        super(args);\n        if (typeof args.cropping === 'number') {\n            this.cropping =\n                [[args.cropping, args.cropping], [args.cropping, args.cropping]];\n        }\n        else if (typeof args.cropping[0] === 'number') {\n            this.cropping = [\n                [args.cropping[0], args.cropping[0]],\n                [args.cropping[1], args.cropping[1]]\n            ];\n        }\n        else {\n            this.cropping = args.cropping;\n        }\n        this.dataFormat =\n            args.dataFormat === undefined ? 'channelsLast' : args.dataFormat;\n        this.inputSpec = [{ ndim: 4 }];\n    }\n    computeOutputShape(inputShape) {\n        if (this.dataFormat === 'channelsFirst') {\n            return [\n                inputShape[0], inputShape[1],\n                inputShape[2] - this.cropping[0][0] - this.cropping[0][1],\n                inputShape[3] - this.cropping[1][0] - this.cropping[1][1]\n            ];\n        }\n        else {\n            return [\n                inputShape[0],\n                inputShape[1] - this.cropping[0][0] - this.cropping[0][1],\n                inputShape[2] - this.cropping[1][0] - this.cropping[1][1], inputShape[3]\n            ];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                const hSliced = K.sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);\n                return K.sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);\n            }\n            else {\n                const hSliced = K.sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);\n                return K.sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);\n            }\n        });\n    }\n    getConfig() {\n        const config = { cropping: this.cropping, dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nCropping2D.className = 'Cropping2D';\nserialization.registerClass(Cropping2D);\nexport class UpSampling2D extends Layer {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_SIZE = [2, 2];\n        this.inputSpec = [{ ndim: 4 }];\n        this.size = args.size == null ? this.DEFAULT_SIZE : args.size;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n    }\n    computeOutputShape(inputShape) {\n        if (this.dataFormat === 'channelsFirst') {\n            const height = inputShape[2] == null ? null : this.size[0] * inputShape[2];\n            const width = inputShape[3] == null ? null : this.size[1] * inputShape[3];\n            return [inputShape[0], inputShape[1], height, width];\n        }\n        else {\n            const height = inputShape[1] == null ? null : this.size[0] * inputShape[1];\n            const width = inputShape[2] == null ? null : this.size[1] * inputShape[2];\n            return [inputShape[0], height, width, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            let input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            if (this.dataFormat === 'channelsFirst') {\n                input = tfc.transpose(input, [0, 2, 3, 1]);\n                const height = this.size[0] * inputShape[2];\n                const width = this.size[1] * inputShape[3];\n                const resized = input.resizeNearestNeighbor([height, width]);\n                return tfc.transpose(resized, [0, 3, 1, 2]);\n            }\n            else {\n                const height = this.size[0] * inputShape[1];\n                const width = this.size[1] * inputShape[2];\n                return input.resizeNearestNeighbor([height, width]);\n            }\n        });\n    }\n    getConfig() {\n        const config = { size: this.size, dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nUpSampling2D.className = 'UpSampling2D';\nserialization.registerClass(UpSampling2D);\n//# sourceMappingURL=convolutional.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Depthwise Convolutional Layers\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { convOutputLength } from '../utils/conv_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { BaseConv, preprocessConv2DInput } from './convolutional';\n/**\n * 2D convolution with separable filters.\n * @param x Input tensor.\n * @param depthwiseKernel Convolution kernel for depthwise convolution.\n * @param strides Strides (Array of two integers).\n * @param padding Padding model.\n * @param dataFormat Data format.\n * @param dilationRate Array of two integers, dilation rates for the separable\n *   convolution.\n * @returns Output tensor.\n * @throws ValueError If depthwiseKernel is not a 4D array.\n */\nexport function depthwiseConv2d(x, depthwiseKernel, strides = [1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        let y = preprocessConv2DInput(x, dataFormat);\n        if (x.rank !== 4) {\n            throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ` +\n                `${x.rank}-D`);\n        }\n        if (depthwiseKernel.rank !== 4) {\n            throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ` +\n                `${depthwiseKernel.rank}-D`);\n        }\n        y = tfc.depthwiseConv2d(y, depthwiseKernel, strides, padding === 'same' ? 'same' : 'valid', 'NHWC', dilationRate);\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]);\n        }\n        return y;\n    });\n}\nexport class DepthwiseConv2D extends BaseConv {\n    constructor(args) {\n        super(2, args);\n        this.depthwiseKernel = null;\n        this.depthMultiplier =\n            args.depthMultiplier == null ? 1 : args.depthMultiplier;\n        this.depthwiseInitializer = getInitializer(args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.depthwiseConstraint = getConstraint(args.depthwiseConstraint);\n        this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < 4) {\n            throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. ` +\n                `Received input shape: ${JSON.stringify(inputShape)}.`);\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : 3;\n        if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n            throw new ValueError('The channel dimension of the inputs to DepthwiseConv2D should ' +\n                `be defined, but is not (${inputShape[channelAxis]}).`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const depthwiseKernelShape = [\n            this.kernelSize[0], this.kernelSize[1], inputDim, this.depthMultiplier\n        ];\n        this.depthwiseKernel = this.addWeight('depthwise_kernel', depthwiseKernelShape, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [inputDim * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let outputs = depthwiseConv2d(inputs, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);\n            // TODO(cais): Add support for dilation.\n            if (this.useBias) {\n                outputs = K.biasAdd(outputs, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                outputs = this.activation.apply(outputs);\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const rows = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        const cols = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        const outFilters = this.dataFormat === 'channelsFirst' ?\n            inputShape[1] * this.depthMultiplier :\n            inputShape[3] * this.depthMultiplier;\n        const outRows = convOutputLength(rows, this.kernelSize[0], this.padding, this.strides[0]);\n        const outCols = convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], outFilters, outRows, outCols];\n        }\n        else {\n            // In this case, assume 'channelsLast'.\n            return [inputShape[0], outRows, outCols, outFilters];\n        }\n    }\n    getConfig() {\n        const config = super.getConfig();\n        config['depthMultiplier'] = this.depthMultiplier;\n        config['depthwiseInitializer'] =\n            serializeInitializer(this.depthwiseInitializer);\n        config['depthwiseRegularizer'] =\n            serializeRegularizer(this.depthwiseRegularizer);\n        config['depthwiseConstraint'] =\n            serializeConstraint(this.depthwiseRegularizer);\n        return config;\n    }\n}\n/** @nocollapse */\nDepthwiseConv2D.className = 'DepthwiseConv2D';\nserialization.registerClass(DepthwiseConv2D);\n//# sourceMappingURL=convolutional_depthwise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Recurrent Neural Network Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, SymbolicTensor } from '../engine/topology';\nimport { Layer } from '../engine/topology';\nimport { AttributeError, NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, Initializer, Ones, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor, isArrayOfShapes } from '../utils/types_utils';\nimport { batchGetValue, batchSetValue } from '../variables';\nimport { deserialize } from './serialization';\n/**\n * Standardize `apply()` args to a single list of tensor inputs.\n *\n * When running a model loaded from file, the input tensors `initialState` and\n * `constants` are passed to `RNN.apply()` as part of `inputs` instead of the\n * dedicated kwargs fields. `inputs` consists of\n * `[inputs, initialState0, initialState1, ..., constant0, constant1]` in this\n * case.\n * This method makes sure that arguments are\n * separated and that `initialState` and `constants` are `Array`s of tensors\n * (or None).\n *\n * @param inputs Tensor or `Array` of  tensors.\n * @param initialState Tensor or `Array` of tensors or `null`/`undefined`.\n * @param constants Tensor or `Array` of tensors or `null`/`undefined`.\n * @returns An object consisting of\n *   inputs: A tensor.\n *   initialState: `Array` of tensors or `null`.\n *   constants: `Array` of tensors or `null`.\n * @throws ValueError, if `inputs` is an `Array` but either `initialState` or\n *   `constants` is provided.\n */\nexport function standardizeArgs(inputs, initialState, constants, numConstants) {\n    if (Array.isArray(inputs)) {\n        if (initialState != null || constants != null) {\n            throw new ValueError('When inputs is an array, neither initialState or constants ' +\n                'should be provided');\n        }\n        if (numConstants != null) {\n            constants = inputs.slice(inputs.length - numConstants, inputs.length);\n            inputs = inputs.slice(0, inputs.length - numConstants);\n        }\n        if (inputs.length > 1) {\n            initialState = inputs.slice(1, inputs.length);\n        }\n        inputs = inputs[0];\n    }\n    function toListOrNull(x) {\n        if (x == null || Array.isArray(x)) {\n            return x;\n        }\n        else {\n            return [x];\n        }\n    }\n    initialState = toListOrNull(initialState);\n    constants = toListOrNull(constants);\n    return { inputs, initialState, constants };\n}\n/**\n * Iterates over the time dimension of a tensor.\n *\n * @param stepFunction RNN step function.\n *   Parameters:\n *     inputs: tensor with shape `[samples, ...]` (no time dimension),\n *       representing input for the batch of samples at a certain time step.\n *     states: an Array of tensors.\n *   Returns:\n *     outputs: tensor with shape `[samples, outputDim]` (no time dimension).\n *     newStates: list of tensors, same length and shapes as `states`. The first\n *       state in the list must be the output tensor at the previous timestep.\n * @param inputs Tensor of temporal data of shape `[samples, time, ...]` (at\n *   least 3D).\n * @param initialStates Tensor with shape `[samples, outputDim]` (no time\n *   dimension), containing the initial values of the states used in the step\n *   function.\n * @param goBackwards If `true`, do the iteration over the time dimension in\n *   reverse order and return the reversed sequence.\n * @param mask Binary tensor with shape `[sample, time, 1]`, with a zero for\n *   every element that is masked.\n * @param constants An Array of constant values passed at each step.\n * @param unroll Whether to unroll the RNN or to use a symbolic loop. *Not*\n *   applicable to this imperative deeplearn.js backend. Its value is ignored.\n * @param needPerStepOutputs Whether the per-step outputs are to be\n *   concatenated into a single tensor and returned (as the second return\n *   value). Default: `false`. This arg is included so that the relatively\n *   expensive concatenation of the stepwise outputs can be omitted unless\n *   the stepwise outputs need to be kept (e.g., for an LSTM layer of which\n *   `returnSequence` is `true`.)\n * @returns An Array: `[lastOutput, outputs, newStates]`.\n *   lastOutput: the lastest output of the RNN, of shape `[samples, ...]`.\n *   outputs: tensor with shape `[samples, time, ...]` where each entry\n *     `output[s, t]` is the output of the step function at time `t` for sample\n *     `s`. This return value is provided if and only if the\n *     `needPerStepOutputs` is set as `true`. If it is set as `false`, this\n *     return value will be `undefined`.\n *   newStates: Array of tensors, latest states returned by the step function,\n *      of shape `(samples, ...)`.\n * @throws ValueError If input dimension is less than 3.\n *\n * TODO(nielsene): This needs to be tidy-ed.\n */\nexport function rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, constants, unroll = false, needPerStepOutputs = false) {\n    return tfc.tidy(() => {\n        const ndim = inputs.shape.length;\n        if (ndim < 3) {\n            throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);\n        }\n        // Transpose to time-major, i.e., from [batch, time, ...] to [time, batch,\n        // ...].\n        const axes = [1, 0].concat(math_utils.range(2, ndim));\n        inputs = tfc.transpose(inputs, axes);\n        if (constants != null) {\n            throw new NotImplementedError('The rnn() functoin of the deeplearn.js backend does not support ' +\n                'constants yet.');\n        }\n        // Porting Note: the unroll option is ignored by the imperative backend.\n        if (unroll) {\n            console.warn('Backend rnn(): the unroll = true option is not applicable to the ' +\n                'imperative deeplearn.js backend.');\n        }\n        if (mask != null) {\n            mask = mask.asType('bool').asType('float32');\n            if (mask.rank === ndim - 1) {\n                mask = tfc.expandDims(mask, -1);\n            }\n            mask = tfc.transpose(mask, axes);\n        }\n        if (goBackwards) {\n            inputs = tfc.reverse(inputs, 0);\n            if (mask != null) {\n                mask = tfc.reverse(mask, 0);\n            }\n        }\n        // Porting Note: PyKeras with TensorFlow backend uses a symbolic loop\n        //   (tf.while_loop). But for the imperative deeplearn.js backend, we just\n        //   use the usual TypeScript control flow to iterate over the time steps in\n        //   the inputs.\n        // Porting Note: PyKeras patches a \"_use_learning_phase\" attribute to\n        // outputs.\n        //   This is not idiomatic in TypeScript. The info regarding whether we are\n        //   in a learning (i.e., training) phase for RNN is passed in a different\n        //   way.\n        const perStepOutputs = [];\n        let lastOutput;\n        let states = initialStates;\n        const timeSteps = inputs.shape[0];\n        const perStepInputs = tfc.unstack(inputs);\n        let perStepMasks;\n        if (mask != null) {\n            perStepMasks = tfc.unstack(mask);\n        }\n        for (let t = 0; t < timeSteps; ++t) {\n            const currentInput = perStepInputs[t];\n            const stepOutputs = tfc.tidy(() => stepFunction(currentInput, states));\n            if (mask == null) {\n                lastOutput = stepOutputs[0];\n                states = stepOutputs[1];\n            }\n            else {\n                const maskedOutputs = tfc.tidy(() => {\n                    const stepMask = perStepMasks[t];\n                    const negStepMask = tfc.onesLike(stepMask).sub(stepMask);\n                    // TODO(cais): Would tfc.where() be better for performance?\n                    const output = stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask));\n                    const newStates = states.map((state, i) => {\n                        return stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask));\n                    });\n                    return { output, newStates };\n                });\n                lastOutput = maskedOutputs.output;\n                states = maskedOutputs.newStates;\n            }\n            if (needPerStepOutputs) {\n                perStepOutputs.push(lastOutput);\n            }\n        }\n        let outputs;\n        if (needPerStepOutputs) {\n            const axis = 1;\n            outputs = tfc.stack(perStepOutputs, axis);\n        }\n        return [lastOutput, outputs, states];\n    });\n}\nexport class RNN extends Layer {\n    constructor(args) {\n        super(args);\n        let cell;\n        if (args.cell == null) {\n            throw new ValueError('cell property is missing for the constructor of RNN.');\n        }\n        else if (Array.isArray(args.cell)) {\n            cell = new StackedRNNCells({ cells: args.cell });\n        }\n        else {\n            cell = args.cell;\n        }\n        if (cell.stateSize == null) {\n            throw new ValueError('The RNN cell should have an attribute `stateSize` (tuple of ' +\n                'integers, one integer per RNN state).');\n        }\n        this.cell = cell;\n        this.returnSequences =\n            args.returnSequences == null ? false : args.returnSequences;\n        this.returnState = args.returnState == null ? false : args.returnState;\n        this.goBackwards = args.goBackwards == null ? false : args.goBackwards;\n        this._stateful = args.stateful == null ? false : args.stateful;\n        this.unroll = args.unroll == null ? false : args.unroll;\n        this.supportsMasking = true;\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n        this.stateSpec = null;\n        this.states_ = null;\n        // TODO(cais): Add constantsSpec and numConstants.\n        this.numConstants = null;\n        // TODO(cais): Look into the use of initial_state in the kwargs of the\n        //   constructor.\n        this.keptStates = [];\n    }\n    // Porting Note: This is the equivalent of `RNN.states` property getter in\n    //   PyKeras.\n    getStates() {\n        if (this.states_ == null) {\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            return math_utils.range(0, numStates).map(x => null);\n        }\n        else {\n            return this.states_;\n        }\n    }\n    // Porting Note: This is the equivalent of the `RNN.states` property setter in\n    //   PyKeras.\n    setStates(states) {\n        this.states_ = states;\n    }\n    computeOutputShape(inputShape) {\n        if (isArrayOfShapes(inputShape)) {\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        // TODO(cais): Remove the casting once stacked RNN cells become supported.\n        let stateSize = this.cell.stateSize;\n        if (!Array.isArray(stateSize)) {\n            stateSize = [stateSize];\n        }\n        const outputDim = stateSize[0];\n        let outputShape;\n        if (this.returnSequences) {\n            outputShape = [inputShape[0], inputShape[1], outputDim];\n        }\n        else {\n            outputShape = [inputShape[0], outputDim];\n        }\n        if (this.returnState) {\n            const stateShape = [];\n            for (const dim of stateSize) {\n                stateShape.push([inputShape[0], dim]);\n            }\n            return [outputShape].concat(stateShape);\n        }\n        else {\n            return outputShape;\n        }\n    }\n    computeMask(inputs, mask) {\n        return tfc.tidy(() => {\n            if (Array.isArray(mask)) {\n                mask = mask[0];\n            }\n            const outputMask = this.returnSequences ? mask : null;\n            if (this.returnState) {\n                const stateMask = this.states.map(s => null);\n                return [outputMask].concat(stateMask);\n            }\n            else {\n                return outputMask;\n            }\n        });\n    }\n    /**\n     * Get the current state tensors of the RNN.\n     *\n     * If the state hasn't been set, return an array of `null`s of the correct\n     * length.\n     */\n    get states() {\n        if (this.states_ == null) {\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            const output = [];\n            for (let i = 0; i < numStates; ++i) {\n                output.push(null);\n            }\n            return output;\n        }\n        else {\n            return this.states_;\n        }\n    }\n    set states(s) {\n        this.states_ = s;\n    }\n    build(inputShape) {\n        // Note inputShape will be an Array of Shapes of initial states and\n        // constants if these are passed in apply().\n        const constantShape = null;\n        if (this.numConstants != null) {\n            throw new NotImplementedError('Constants support is not implemented in RNN yet.');\n        }\n        if (isArrayOfShapes(inputShape)) {\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        const batchSize = this.stateful ? inputShape[0] : null;\n        const inputDim = inputShape.slice(2);\n        this.inputSpec[0] = new InputSpec({ shape: [batchSize, null, ...inputDim] });\n        // Allow cell (if RNNCell Layer) to build before we set or validate\n        // stateSpec.\n        const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        if (constantShape != null) {\n            throw new NotImplementedError('Constants support is not implemented in RNN yet.');\n        }\n        else {\n            this.cell.build(stepInputShape);\n        }\n        // Set or validate stateSpec.\n        let stateSize;\n        if (Array.isArray(this.cell.stateSize)) {\n            stateSize = this.cell.stateSize;\n        }\n        else {\n            stateSize = [this.cell.stateSize];\n        }\n        if (this.stateSpec != null) {\n            if (!util.arraysEqual(this.stateSpec.map(spec => spec.shape[spec.shape.length - 1]), stateSize)) {\n                throw new ValueError(`An initialState was passed that is not compatible with ` +\n                    `cell.stateSize. Received stateSpec=${this.stateSpec}; ` +\n                    `However cell.stateSize is ${this.cell.stateSize}`);\n            }\n        }\n        else {\n            this.stateSpec =\n                stateSize.map(dim => new InputSpec({ shape: [null, dim] }));\n        }\n        if (this.stateful) {\n            this.resetStates();\n        }\n    }\n    /**\n     * Reset the state tensors of the RNN.\n     *\n     * If the `states` argument is `undefined` or `null`, will set the\n     * state tensor(s) of the RNN to all-zero tensors of the appropriate\n     * shape(s).\n     *\n     * If `states` is provided, will set the state tensors of the RNN to its\n     * value.\n     *\n     * @param states Optional externally-provided initial states.\n     * @param training Whether this call is done during training. For stateful\n     *   RNNs, this affects whether the old states are kept or discarded. In\n     *   particular, if `training` is `true`, the old states will be kept so\n     *   that subsequent backpropgataion through time (BPTT) may work properly.\n     *   Else, the old states will be discarded.\n     */\n    resetStates(states, training = false) {\n        tidy(() => {\n            if (!this.stateful) {\n                throw new AttributeError('Cannot call resetStates() on an RNN Layer that is not stateful.');\n            }\n            const batchSize = this.inputSpec[0].shape[0];\n            if (batchSize == null) {\n                throw new ValueError('If an RNN is stateful, it needs to know its batch size. Specify ' +\n                    'the batch size of your input tensors: \\n' +\n                    '- If using a Sequential model, specify the batch size by ' +\n                    'passing a `batchInputShape` option to your first layer.\\n' +\n                    '- If using the functional API, specify the batch size by ' +\n                    'passing a `batchShape` option to your Input layer.');\n            }\n            // Initialize state if null.\n            if (this.states_ == null) {\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ =\n                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n                }\n                else {\n                    this.states_ = [tfc.zeros([batchSize, this.cell.stateSize])];\n                }\n            }\n            else if (states == null) {\n                // Dispose old state tensors.\n                tfc.dispose(this.states_);\n                // For stateful RNNs, fully dispose kept old states.\n                if (this.keptStates != null) {\n                    tfc.dispose(this.keptStates);\n                    this.keptStates = [];\n                }\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ =\n                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n                }\n                else {\n                    this.states_[0] = tfc.zeros([batchSize, this.cell.stateSize]);\n                }\n            }\n            else {\n                if (!Array.isArray(states)) {\n                    states = [states];\n                }\n                if (states.length !== this.states_.length) {\n                    throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), ` +\n                        `but it received ${states.length} state value(s). Input ` +\n                        `received: ${states}`);\n                }\n                if (training === true) {\n                    // Store old state tensors for complete disposal later, i.e., during\n                    // the next no-arg call to this method. We do not dispose the old\n                    // states immediately because that BPTT (among other things) require\n                    // them.\n                    this.keptStates.push(this.states_.slice());\n                }\n                else {\n                    tfc.dispose(this.states_);\n                }\n                for (let index = 0; index < this.states_.length; ++index) {\n                    const value = states[index];\n                    const dim = Array.isArray(this.cell.stateSize) ?\n                        this.cell.stateSize[index] :\n                        this.cell.stateSize;\n                    const expectedShape = [batchSize, dim];\n                    if (!util.arraysEqual(value.shape, expectedShape)) {\n                        throw new ValueError(`State ${index} is incompatible with layer ${this.name}: ` +\n                            `expected shape=${expectedShape}, received shape=${value.shape}`);\n                    }\n                    this.states_[index] = value;\n                }\n            }\n            this.states_ = this.states_.map(state => tfc.keep(state.clone()));\n        });\n    }\n    apply(inputs, kwargs) {\n        // TODO(cais): Figure out whether initialState is in kwargs or inputs.\n        let initialState = kwargs == null ? null : kwargs['initialState'];\n        let constants = kwargs == null ? null : kwargs['constants'];\n        if (kwargs == null) {\n            kwargs = {};\n        }\n        const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n        inputs = standardized.inputs;\n        initialState = standardized.initialState;\n        constants = standardized.constants;\n        // If any of `initial_state` or `constants` are specified and are\n        // `tf.SymbolicTensor`s, then add them to the inputs and temporarily modify\n        // the input_spec to include them.\n        let additionalInputs = [];\n        let additionalSpecs = [];\n        if (initialState != null) {\n            kwargs['initialState'] = initialState;\n            additionalInputs = additionalInputs.concat(initialState);\n            this.stateSpec = [];\n            for (const state of initialState) {\n                this.stateSpec.push(new InputSpec({ shape: state.shape }));\n            }\n            // TODO(cais): Use the following instead.\n            // this.stateSpec = initialState.map(state => new InputSpec({shape:\n            // state.shape}));\n            additionalSpecs = additionalSpecs.concat(this.stateSpec);\n        }\n        if (constants != null) {\n            kwargs['constants'] = constants;\n            additionalInputs = additionalInputs.concat(constants);\n            // TODO(cais): Add this.constantsSpec.\n            this.numConstants = constants.length;\n        }\n        const isTensor = additionalInputs[0] instanceof SymbolicTensor;\n        if (isTensor) {\n            // Compute full input spec, including state and constants.\n            const fullInput = [inputs].concat(additionalInputs);\n            const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n            // Perform the call with temporarily replaced inputSpec.\n            const originalInputSpec = this.inputSpec;\n            this.inputSpec = fullInputSpec;\n            const output = super.apply(fullInput, kwargs);\n            this.inputSpec = originalInputSpec;\n            return output;\n        }\n        else {\n            return super.apply(inputs, kwargs);\n        }\n    }\n    // tslint:disable-next-line:no-any\n    call(inputs, kwargs) {\n        // Input shape: `[samples, time (padded with zeros), input_dim]`.\n        // Note that the .build() method of subclasses **must** define\n        // this.inputSpec and this.stateSpec owith complete input shapes.\n        return tidy(() => {\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            let initialState = kwargs == null ? null : kwargs['initialState'];\n            inputs = getExactlyOneTensor(inputs);\n            if (initialState == null) {\n                if (this.stateful) {\n                    initialState = this.states_;\n                }\n                else {\n                    initialState = this.getInitialState(inputs);\n                }\n            }\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            if (initialState.length !== numStates) {\n                throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ` +\n                    `${initialState.length} initial state(s).`);\n            }\n            if (this.unroll) {\n                console.warn('Ignoring unroll = true for RNN layer, due to imperative backend.');\n            }\n            const cellCallKwargs = { training };\n            // TODO(cais): Add support for constants.\n            const step = (inputs, states) => {\n                // `inputs` and `states` are concatenated to form a single `Array` of\n                // `tf.Tensor`s as the input to `cell.call()`.\n                const outputs = this.cell.call([inputs].concat(states), cellCallKwargs);\n                // Marshall the return value into output and new states.\n                return [outputs[0], outputs.slice(1)];\n            };\n            // TODO(cais): Add support for constants.\n            const rnnOutputs = rnn(step, inputs, initialState, this.goBackwards, mask, null, this.unroll, this.returnSequences);\n            const lastOutput = rnnOutputs[0];\n            const outputs = rnnOutputs[1];\n            const states = rnnOutputs[2];\n            if (this.stateful) {\n                this.resetStates(states, training);\n            }\n            const output = this.returnSequences ? outputs : lastOutput;\n            // TODO(cais): Porperty set learning phase flag.\n            if (this.returnState) {\n                return [output].concat(states);\n            }\n            else {\n                return output;\n            }\n        });\n    }\n    getInitialState(inputs) {\n        return tidy(() => {\n            // Build an all-zero tensor of shape [samples, outputDim].\n            // [Samples, timeSteps, inputDim].\n            let initialState = tfc.zeros(inputs.shape);\n            // [Samples].\n            initialState = tfc.sum(initialState, [1, 2]);\n            initialState = K.expandDims(initialState); // [Samples, 1].\n            if (Array.isArray(this.cell.stateSize)) {\n                return this.cell.stateSize.map(dim => dim > 1 ? K.tile(initialState, [1, dim]) : initialState);\n            }\n            else {\n                return this.cell.stateSize > 1 ?\n                    [K.tile(initialState, [1, this.cell.stateSize])] :\n                    [initialState];\n            }\n        });\n    }\n    get trainableWeights() {\n        if (!this.trainable) {\n            return [];\n        }\n        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n        return this.cell.trainableWeights;\n    }\n    get nonTrainableWeights() {\n        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n        if (!this.trainable) {\n            return this.cell.weights;\n        }\n        return this.cell.nonTrainableWeights;\n    }\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.cell != null) {\n            this.cell.setFastWeightInitDuringBuild(value);\n        }\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            returnSequences: this.returnSequences,\n            returnState: this.returnState,\n            goBackwards: this.goBackwards,\n            stateful: this.stateful,\n            unroll: this.unroll,\n        };\n        if (this.numConstants != null) {\n            config['numConstants'] = this.numConstants;\n        }\n        const cellConfig = this.cell.getConfig();\n        if (this.getClassName() === RNN.className) {\n            config['cell'] = {\n                'className': this.cell.getClassName(),\n                'config': cellConfig,\n            };\n        }\n        // this order is necessary, to prevent cell name from replacing layer name\n        return Object.assign({}, cellConfig, baseConfig, config);\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const cellConfig = config['cell'];\n        const cell = deserialize(cellConfig, customObjects);\n        return new cls(Object.assign(config, { cell }));\n    }\n}\n/** @nocollapse */\nRNN.className = 'RNN';\nserialization.registerClass(RNN);\n// Porting Note: This is a common parent class for RNN cells. There is no\n// equivalent of this in PyKeras. Having a common parent class forgoes the\n//  need for `has_attr(cell, ...)` checks or its TypeScript equivalent.\n/**\n * An RNNCell layer.\n *\n * @doc {heading: 'Layers', subheading: 'Classes'}\n */\nexport class RNNCell extends Layer {\n}\nexport class SimpleRNNCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        this.units = args.units;\n        assertPositiveInteger(this.units, `units`);\n        this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.stateSize = this.units;\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        // TODO(cais): Use regularizer.\n        this.kernel = this.addWeight('kernel', [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.built = true;\n    }\n    // Porting Note: PyKeras' equivalent of this method takes two tensor inputs:\n    //   `inputs` and `states`. Here, the two tensors are combined into an\n    //   `Tensor[]` Array as the first input argument.\n    //   Similarly, PyKeras' equivalent of this method returns two values:\n    //    `output` and `[output]`. Here the two are combined into one length-2\n    //    `Tensor[]`, consisting of `output` repeated.\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (inputs.length !== 2) {\n                throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);\n            }\n            let prevOutput = inputs[1];\n            inputs = inputs[0];\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(prevOutput),\n                    rate: this.recurrentDropout,\n                    training\n                });\n            }\n            let h;\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            if (dpMask != null) {\n                h = K.dot(tfc.mul(inputs, dpMask), this.kernel.read());\n            }\n            else {\n                h = K.dot(inputs, this.kernel.read());\n            }\n            if (this.bias != null) {\n                h = K.biasAdd(h, this.bias.read());\n            }\n            if (recDpMask != null) {\n                prevOutput = tfc.mul(prevOutput, recDpMask);\n            }\n            let output = tfc.add(h, K.dot(prevOutput, this.recurrentKernel.read()));\n            if (this.activation != null) {\n                output = this.activation.apply(output);\n            }\n            // TODO(cais): Properly set learning phase on output tensor?\n            return [output, output];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nSimpleRNNCell.className = 'SimpleRNNCell';\nserialization.registerClass(SimpleRNNCell);\nexport class SimpleRNN extends RNN {\n    constructor(args) {\n        args.cell = new SimpleRNNCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nSimpleRNN.className = 'SimpleRNN';\nserialization.registerClass(SimpleRNN);\nexport class GRUCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        if (args.resetAfter) {\n            throw new ValueError(`GRUCell does not support reset_after parameter set to true.`);\n        }\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :\n            args.activation);\n        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.implementation = args.implementation;\n        this.stateSize = this.units;\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const inputDim = inputShape[inputShape.length - 1];\n        this.kernel = this.addWeight('kernel', [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n        //   of the weights and bias in the call() method, at execution time.\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (inputs.length !== 2) {\n                throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            let hTMinus1 = inputs[1]; // Previous memory state.\n            inputs = inputs[0];\n            // Note: For superior performance, TensorFlow.js always uses\n            // implementation 2, regardless of the actual value of\n            // config.implementation.\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training,\n                    count: 3\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: 3\n                });\n            }\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            let z;\n            let r;\n            let hh;\n            if (0 < this.dropout && this.dropout < 1) {\n                inputs = tfc.mul(inputs, dpMask[0]);\n            }\n            let matrixX = K.dot(inputs, this.kernel.read());\n            if (this.useBias) {\n                matrixX = K.biasAdd(matrixX, this.bias.read());\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n            }\n            const recurrentKernelValue = this.recurrentKernel.read();\n            const [rk1, rk2] = tfc.split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1);\n            const matrixInner = K.dot(hTMinus1, rk1);\n            const [xZ, xR, xH] = tfc.split(matrixX, 3, matrixX.rank - 1);\n            const [recurrentZ, recurrentR] = tfc.split(matrixInner, 2, matrixInner.rank - 1);\n            z = this.recurrentActivation.apply(tfc.add(xZ, recurrentZ));\n            r = this.recurrentActivation.apply(tfc.add(xR, recurrentR));\n            const recurrentH = K.dot(tfc.mul(r, hTMinus1), rk2);\n            hh = this.activation.apply(tfc.add(xH, recurrentH));\n            const h = tfc.add(tfc.mul(z, hTMinus1), tfc.mul(tfc.add(1, tfc.neg(z)), hh));\n            // TODO(cais): Add use_learning_phase flag properly.\n            return [h, h];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            recurrentActivation: serializeActivation(this.recurrentActivation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n            implementation: this.implementation,\n            resetAfter: false\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nGRUCell.className = 'GRUCell';\nserialization.registerClass(GRUCell);\nexport class GRU extends RNN {\n    constructor(args) {\n        if (args.implementation === 0) {\n            console.warn('`implementation=0` has been deprecated, and now defaults to ' +\n                '`implementation=1`. Please update your layer call.');\n        }\n        args.cell = new GRUCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        if (config['implmentation'] === 0) {\n            config['implementation'] = 1;\n        }\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nGRU.className = 'GRU';\nserialization.registerClass(GRU);\nexport class LSTMCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :\n            args.activation);\n        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.unitForgetBias = args.unitForgetBias;\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.implementation = args.implementation;\n        this.stateSize = [this.units, this.units];\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        var _a;\n        inputShape = getExactlyOneShape(inputShape);\n        const inputDim = inputShape[inputShape.length - 1];\n        this.kernel = this.addWeight('kernel', [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        let biasInitializer;\n        if (this.useBias) {\n            if (this.unitForgetBias) {\n                const capturedBiasInit = this.biasInitializer;\n                const capturedUnits = this.units;\n                biasInitializer = new (_a = class CustomInit extends Initializer {\n                        apply(shape, dtype) {\n                            // TODO(cais): More informative variable names?\n                            const bI = capturedBiasInit.apply([capturedUnits]);\n                            const bF = (new Ones()).apply([capturedUnits]);\n                            const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);\n                            return K.concatAlongFirstAxis(K.concatAlongFirstAxis(bI, bF), bCAndH);\n                        }\n                    },\n                    /** @nocollapse */\n                    _a.className = 'CustomInit',\n                    _a)();\n            }\n            else {\n                biasInitializer = this.biasInitializer;\n            }\n            this.bias = this.addWeight('bias', [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n        //   of the weights and bias in the call() method, at execution time.\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            inputs = inputs;\n            if (inputs.length !== 3) {\n                throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            let hTMinus1 = inputs[1]; // Previous memory state.\n            const cTMinus1 = inputs[2]; // Previous carry state.\n            inputs = inputs[0];\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training,\n                    count: 4\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: 4\n                });\n            }\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            // Note: For superior performance, TensorFlow.js always uses\n            // implementation 2 regardless of the actual value of\n            // config.implementation.\n            let i;\n            let f;\n            let c;\n            let o;\n            if (0 < this.dropout && this.dropout < 1) {\n                inputs = tfc.mul(inputs, dpMask[0]);\n            }\n            let z = K.dot(inputs, this.kernel.read());\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n            }\n            z = tfc.add(z, K.dot(hTMinus1, this.recurrentKernel.read()));\n            if (this.useBias) {\n                z = K.biasAdd(z, this.bias.read());\n            }\n            const [z0, z1, z2, z3] = tfc.split(z, 4, z.rank - 1);\n            i = this.recurrentActivation.apply(z0);\n            f = this.recurrentActivation.apply(z1);\n            c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(z2)));\n            o = this.recurrentActivation.apply(z3);\n            const h = tfc.mul(o, this.activation.apply(c));\n            // TODO(cais): Add use_learning_phase flag properly.\n            return [h, h, c];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            recurrentActivation: serializeActivation(this.recurrentActivation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            unitForgetBias: this.unitForgetBias,\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n            implementation: this.implementation,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nLSTMCell.className = 'LSTMCell';\nserialization.registerClass(LSTMCell);\nexport class LSTM extends RNN {\n    constructor(args) {\n        if (args.implementation === 0) {\n            console.warn('`implementation=0` has been deprecated, and now defaults to ' +\n                '`implementation=1`. Please update your layer call.');\n        }\n        args.cell = new LSTMCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        if (config['implmentation'] === 0) {\n            config['implementation'] = 1;\n        }\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nLSTM.className = 'LSTM';\nserialization.registerClass(LSTM);\nexport class StackedRNNCells extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.cells = args.cells;\n    }\n    get stateSize() {\n        // States are a flat list in reverse order of the cell stack.\n        // This allows perserving the requirement `stack.statesize[0] ===\n        // outputDim`. E.g., states of a 2-layer LSTM would be `[h2, c2, h1, c1]`,\n        // assuming one LSTM has states `[h, c]`.\n        const stateSize = [];\n        for (const cell of this.cells.slice().reverse()) {\n            if (Array.isArray(cell.stateSize)) {\n                stateSize.push(...cell.stateSize);\n            }\n            else {\n                stateSize.push(cell.stateSize);\n            }\n        }\n        return stateSize;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            let states = inputs.slice(1);\n            // Recover per-cell states.\n            const nestedStates = [];\n            for (const cell of this.cells.slice().reverse()) {\n                if (Array.isArray(cell.stateSize)) {\n                    nestedStates.push(states.splice(0, cell.stateSize.length));\n                }\n                else {\n                    nestedStates.push(states.splice(0, 1));\n                }\n            }\n            nestedStates.reverse();\n            // Call the cells in order and store the returned states.\n            const newNestedStates = [];\n            let callInputs;\n            for (let i = 0; i < this.cells.length; ++i) {\n                const cell = this.cells[i];\n                states = nestedStates[i];\n                // TODO(cais): Take care of constants.\n                if (i === 0) {\n                    callInputs = [inputs[0]].concat(states);\n                }\n                else {\n                    callInputs = [callInputs[0]].concat(states);\n                }\n                callInputs = cell.call(callInputs, kwargs);\n                newNestedStates.push(callInputs.slice(1));\n            }\n            // Format the new states as a flat list in reverse cell order.\n            states = [];\n            for (const cellStates of newNestedStates.slice().reverse()) {\n                states.push(...cellStates);\n            }\n            return [callInputs[0]].concat(states);\n        });\n    }\n    build(inputShape) {\n        if (isArrayOfShapes(inputShape)) {\n            // TODO(cais): Take care of input constants.\n            // const constantShape = inputShape.slice(1);\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        let outputDim;\n        this.cells.forEach((cell, i) => {\n            nameScope(`RNNCell_${i}`, () => {\n                // TODO(cais): Take care of input constants.\n                cell.build(inputShape);\n                if (Array.isArray(cell.stateSize)) {\n                    outputDim = cell.stateSize[0];\n                }\n                else {\n                    outputDim = cell.stateSize;\n                }\n                inputShape = [inputShape[0], outputDim];\n            });\n        });\n        this.built = true;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const getCellConfig = (cell) => {\n            return {\n                'className': cell.getClassName(),\n                'config': cell.getConfig(),\n            };\n        };\n        const cellConfigs = this.cells.map(getCellConfig);\n        const config = { 'cells': cellConfigs };\n        return Object.assign({}, baseConfig, config);\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const cells = [];\n        for (const cellConfig of config['cells']) {\n            cells.push(deserialize(cellConfig, customObjects));\n        }\n        return new cls({ cells });\n    }\n    get trainableWeights() {\n        if (!this.trainable) {\n            return [];\n        }\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.trainableWeights);\n        }\n        return weights;\n    }\n    get nonTrainableWeights() {\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.nonTrainableWeights);\n        }\n        if (!this.trainable) {\n            const trainableWeights = [];\n            for (const cell of this.cells) {\n                trainableWeights.push(...cell.trainableWeights);\n            }\n            return trainableWeights.concat(weights);\n        }\n        return weights;\n    }\n    /**\n     * Retrieve the weights of a the model.\n     *\n     * @returns A flat `Array` of `tf.Tensor`s.\n     */\n    getWeights() {\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.weights);\n        }\n        return batchGetValue(weights);\n    }\n    /**\n     * Set the weights of the model.\n     *\n     * @param weights An `Array` of `tf.Tensor`s with shapes and types matching\n     *     the output of `getWeights()`.\n     */\n    setWeights(weights) {\n        const tuples = [];\n        for (const cell of this.cells) {\n            const numParams = cell.weights.length;\n            const inputWeights = weights.splice(numParams);\n            for (let i = 0; i < cell.weights.length; ++i) {\n                tuples.push([cell.weights[i], inputWeights[i]]);\n            }\n        }\n        batchSetValue(tuples);\n    }\n}\n/** @nocollapse */\nStackedRNNCells.className = 'StackedRNNCells';\nserialization.registerClass(StackedRNNCells);\nexport function generateDropoutMask(args) {\n    const { ones, rate, training = false, count = 1 } = args;\n    const droppedInputs = () => K.dropout(ones(), rate);\n    const createMask = () => K.inTrainPhase(droppedInputs, ones, training);\n    // just in case count is provided with null or undefined\n    if (!count || count <= 1) {\n        return tfc.keep(createMask().clone());\n    }\n    const masks = Array(count).fill(undefined).map(createMask);\n    return masks.map(m => tfc.keep(m.clone()));\n}\n//# sourceMappingURL=recurrent.js.map", "/**\n * @license\n * Copyright 2020 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nvar __rest = (this && this.__rest) || function (s, e) {\n    var t = {};\n    for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\n        t[p] = s[p];\n    if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\n        for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\n            if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\n                t[p[i]] = s[p[i]];\n        }\n    return t;\n};\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode } from '../common';\nimport { InputSpec } from '../engine/topology';\nimport { AttributeError, NotImplementedError, ValueError } from '../errors';\nimport { Initializer } from '../initializers';\nimport { convOutputLength, normalizeArray } from '../utils/conv_utils';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport { getExactlyOneShape } from '../utils/types_utils';\nimport { generateDropoutMask, LSTMCell, RNN, RNNCell } from './recurrent';\nclass ConvRNN2DCell extends RNNCell {\n}\n/**\n * Base class for convolutional-recurrent layers.\n */\nclass ConvRNN2D extends RNN {\n    constructor(args) {\n        if (args.unroll) {\n            throw new NotImplementedError('Unrolling is not possible with convolutional RNNs.');\n        }\n        if (Array.isArray(args.cell)) {\n            throw new NotImplementedError('It is not possible at the moment to stack convolutional cells.');\n        }\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 5 })];\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            if (kwargs && kwargs['constants']) {\n                throw new ValueError('ConvRNN2D cell does not support constants');\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    computeOutputShape(inputShape) {\n        let outShape = this.computeSingleOutputShape(inputShape);\n        if (!this.returnSequences) {\n            outShape = [outShape[0], ...outShape.slice(2)];\n        }\n        if (this.returnState) {\n            outShape =\n                [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])];\n        }\n        return outShape;\n    }\n    getInitialState(inputs) {\n        return tfc.tidy(() => {\n            const { stateSize } = this.cell;\n            const inputShape = inputs.shape;\n            const outputShape = this.computeSingleOutputShape(inputShape);\n            const stateShape = [outputShape[0], ...outputShape.slice(2)];\n            const initialState = tfc.zeros(stateShape);\n            if (Array.isArray(stateSize)) {\n                return Array(stateSize.length).fill(initialState);\n            }\n            return [initialState];\n        });\n    }\n    resetStates(states, training = false) {\n        tfc.tidy(() => {\n            if (!this.stateful) {\n                throw new AttributeError('Cannot call resetStates() on an RNN Layer that is not stateful.');\n            }\n            const inputShape = this.inputSpec[0].shape;\n            const outputShape = this.computeSingleOutputShape(inputShape);\n            const stateShape = [outputShape[0], ...outputShape.slice(2)];\n            const batchSize = inputShape[0];\n            if (batchSize == null) {\n                throw new ValueError('If an RNN is stateful, it needs to know its batch size. Specify ' +\n                    'the batch size of your input tensors: \\n' +\n                    '- If using a Sequential model, specify the batch size by ' +\n                    'passing a `batchInputShape` option to your first layer.\\n' +\n                    '- If using the functional API, specify the batch size by ' +\n                    'passing a `batchShape` option to your Input layer.');\n            }\n            // Initialize state if null.\n            if (this.getStates() == null) {\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));\n                }\n                else {\n                    this.states_ = [tfc.zeros(stateShape)];\n                }\n            }\n            else if (states == null) {\n                // Dispose old state tensors.\n                tfc.dispose(this.states_);\n                // For stateful RNNs, fully dispose kept old states.\n                if (this.keptStates != null) {\n                    tfc.dispose(this.keptStates);\n                    this.keptStates = [];\n                }\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));\n                }\n                else {\n                    this.states_[0] = tfc.zeros(stateShape);\n                }\n            }\n            else {\n                if (!Array.isArray(states)) {\n                    states = [states];\n                }\n                if (states.length !== this.states_.length) {\n                    throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), ` +\n                        `but it received ${states.length} state value(s). Input ` +\n                        `received: ${states}`);\n                }\n                if (training) {\n                    // Store old state tensors for complete disposal later, i.e., during\n                    // the next no-arg call to this method. We do not dispose the old\n                    // states immediately because that BPTT (among other things) require\n                    // them.\n                    this.keptStates.push(this.states_.slice());\n                }\n                else {\n                    tfc.dispose(this.states_);\n                }\n                for (let index = 0; index < this.states_.length; ++index) {\n                    const value = states[index];\n                    const expectedShape = stateShape;\n                    if (!util.arraysEqual(value.shape, expectedShape)) {\n                        throw new ValueError(`State ${index} is incompatible with layer ${this.name}: ` +\n                            `expected shape=${expectedShape}, received shape=${value.shape}`);\n                    }\n                    this.states_[index] = value;\n                }\n            }\n            this.states_ = this.states_.map(state => tfc.keep(state.clone()));\n        });\n    }\n    computeSingleOutputShape(inputShape) {\n        const { dataFormat, filters, kernelSize, padding, strides, dilationRate } = this.cell;\n        const isChannelsFirst = dataFormat === 'channelsFirst';\n        const h = inputShape[isChannelsFirst ? 3 : 2];\n        const w = inputShape[isChannelsFirst ? 4 : 3];\n        const hOut = convOutputLength(h, kernelSize[0], padding, strides[0], dilationRate[0]);\n        const wOut = convOutputLength(w, kernelSize[1], padding, strides[1], dilationRate[1]);\n        const outShape = [\n            ...inputShape.slice(0, 2),\n            ...(isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters])\n        ];\n        return outShape;\n    }\n}\n/** @nocollapse */\nConvRNN2D.className = 'ConvRNN2D';\nexport class ConvLSTM2DCell extends LSTMCell {\n    constructor(args) {\n        const { filters, kernelSize, strides, padding, dataFormat, dilationRate, } = args;\n        super(Object.assign({}, args, { units: filters }));\n        this.filters = filters;\n        assertPositiveInteger(this.filters, 'filters');\n        this.kernelSize = normalizeArray(kernelSize, 2, 'kernelSize');\n        this.kernelSize.forEach(size => assertPositiveInteger(size, 'kernelSize'));\n        this.strides = normalizeArray(strides || 1, 2, 'strides');\n        this.strides.forEach(stride => assertPositiveInteger(stride, 'strides'));\n        this.padding = padding || 'valid';\n        checkPaddingMode(this.padding);\n        this.dataFormat = dataFormat || 'channelsLast';\n        checkDataFormat(this.dataFormat);\n        this.dilationRate = normalizeArray(dilationRate || 1, 2, 'dilationRate');\n        this.dilationRate.forEach(rate => assertPositiveInteger(rate, 'dilationRate'));\n    }\n    build(inputShape) {\n        var _a;\n        inputShape = getExactlyOneShape(inputShape);\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError(`The channel dimension of the input should be defined. ` +\n                `Found ${inputShape[channelAxis]}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const numOfKernels = 4;\n        const kernelShape = this.kernelSize.concat([inputDim, this.filters * numOfKernels]);\n        this.kernel = this.addWeight('kernel', kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        const recurrentKernelShape = this.kernelSize.concat([this.filters, this.filters * numOfKernels]);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', recurrentKernelShape, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            let biasInitializer;\n            if (this.unitForgetBias) {\n                const init = this.biasInitializer;\n                const filters = this.filters;\n                biasInitializer = new (_a = class CustomInit extends Initializer {\n                        apply(shape, dtype) {\n                            const biasI = init.apply([filters]);\n                            const biasF = tfc.ones([filters]);\n                            const biasCAndO = init.apply([filters * 2]);\n                            return K.concatenate([biasI, biasF, biasCAndO]);\n                        }\n                    },\n                    /** @nocollapse */\n                    _a.className = 'CustomInit',\n                    _a)();\n            }\n            else {\n                biasInitializer = this.biasInitializer;\n            }\n            this.bias = this.addWeight('bias', [this.filters * numOfKernels], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            if (inputs.length !== 3) {\n                throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            const training = kwargs['training'] || false;\n            const x = inputs[0]; // Current input\n            const hTMinus1 = inputs[1]; // Previous memory state.\n            const cTMinus1 = inputs[2]; // Previous carry state.\n            const numOfKernels = 4;\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(x),\n                    rate: this.dropout,\n                    training,\n                    count: numOfKernels\n                });\n            }\n            const dropoutMask = this.dropoutMask;\n            const applyDropout = (x, mask, index) => {\n                if (!mask || !mask[index]) {\n                    return x;\n                }\n                return tfc.mul(mask[index], x);\n            };\n            let xI = applyDropout(x, dropoutMask, 0);\n            let xF = applyDropout(x, dropoutMask, 1);\n            let xC = applyDropout(x, dropoutMask, 2);\n            let xO = applyDropout(x, dropoutMask, 3);\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: numOfKernels\n                });\n            }\n            const recDropoutMask = this.recurrentDropoutMask;\n            let hI = applyDropout(hTMinus1, recDropoutMask, 0);\n            let hF = applyDropout(hTMinus1, recDropoutMask, 1);\n            let hC = applyDropout(hTMinus1, recDropoutMask, 2);\n            let hO = applyDropout(hTMinus1, recDropoutMask, 3);\n            const kernelChannelAxis = 3;\n            const [kernelI, kernelF, kernelC, kernelO] = tfc.split(this.kernel.read(), numOfKernels, kernelChannelAxis);\n            const [biasI, biasF, biasC, biasO] = this.useBias ?\n                tfc.split(this.bias.read(), numOfKernels) :\n                [null, null, null, null];\n            xI = this.inputConv(xI, kernelI, biasI, this.padding);\n            xF = this.inputConv(xF, kernelF, biasF, this.padding);\n            xC = this.inputConv(xC, kernelC, biasC, this.padding);\n            xO = this.inputConv(xO, kernelO, biasO, this.padding);\n            const [recKernelI, recKernelF, recKernelC, recKernelO] = tfc.split(this.recurrentKernel.read(), numOfKernels, kernelChannelAxis);\n            hI = this.recurrentConv(hI, recKernelI);\n            hF = this.recurrentConv(hF, recKernelF);\n            hC = this.recurrentConv(hC, recKernelC);\n            hO = this.recurrentConv(hO, recKernelO);\n            const i = this.recurrentActivation.apply(tfc.add(xI, hI));\n            const f = this.recurrentActivation.apply(tfc.add(xF, hF));\n            const c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(tfc.add(xC, hC))));\n            const h = tfc.mul(this.recurrentActivation.apply(tfc.add(xO, hO)), this.activation.apply(c));\n            return [h, h, c];\n        });\n    }\n    getConfig() {\n        const _a = super.getConfig(), { 'units': _ } = _a, baseConfig = __rest(_a, ['units']);\n        const config = {\n            filters: this.filters,\n            kernelSize: this.kernelSize,\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n            dilationRate: this.dilationRate,\n            strides: this.strides,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n    inputConv(x, w, b, padding) {\n        const out = tfc.conv2d(x, w, this.strides, (padding || 'valid'), this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC', this.dilationRate);\n        if (b) {\n            return K.biasAdd(out, b, this.dataFormat);\n        }\n        return out;\n    }\n    recurrentConv(x, w) {\n        const strides = 1;\n        return tfc.conv2d(x, w, strides, 'same', this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC');\n    }\n}\n/** @nocollapse */\nConvLSTM2DCell.className = 'ConvLSTM2DCell';\ntfc.serialization.registerClass(ConvLSTM2DCell);\nexport class ConvLSTM2D extends ConvRNN2D {\n    constructor(args) {\n        const cell = new ConvLSTM2DCell(args);\n        super(Object.assign({}, args, { cell }));\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nConvLSTM2D.className = 'ConvLSTM2D';\ntfc.serialization.registerClass(ConvLSTM2D);\n//# sourceMappingURL=convolutional_recurrent.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Basic Layers.\n */\nimport { any, notEqual, serialization, tidy, transpose, util } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport * as K from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { assertPositiveInteger, mapActivationToFusedKernel } from '../utils/generic_utils';\nimport { arrayProd, range } from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class Dropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.rate = Math.max(Math.min(args.rate, 1), 0);\n        // So that the scalar doesn't get tidied up between executions.\n        this.noiseShape = args.noiseShape;\n        this.seed = args.seed;\n        this.supportsMasking = true;\n    }\n    getNoiseShape(input) {\n        if (this.noiseShape == null) {\n            return this.noiseShape;\n        }\n        const inputShape = input.shape;\n        const noiseShape = [];\n        for (let i = 0; i < this.noiseShape.length; ++i) {\n            noiseShape.push(this.noiseShape[i] == null ? inputShape[i] : this.noiseShape[i]);\n        }\n        return noiseShape;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            if (0 < this.rate && this.rate < 1) {\n                const training = kwargs['training'] == null ? false : kwargs['training'];\n                const noiseShape = this.getNoiseShape(input);\n                const output = K.inTrainPhase(() => K.dropout(input, this.rate, noiseShape, this.seed), () => input, training);\n                return output;\n            }\n            return inputs;\n        });\n    }\n    getConfig() {\n        const config = {\n            rate: this.rate,\n            noiseShape: this.noiseShape,\n            seed: this.seed,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    dispose() {\n        return super.dispose();\n    }\n}\n/** @nocollapse */\nDropout.className = 'Dropout';\nserialization.registerClass(Dropout);\nexport class SpatialDropout1D extends Dropout {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [{ ndim: 3 }];\n    }\n    getNoiseShape(input) {\n        const inputShape = input.shape;\n        return [inputShape[0], 1, inputShape[2]];\n    }\n}\n/** @nocollapse */\nSpatialDropout1D.className = 'SpatialDropout1D';\nserialization.registerClass(SpatialDropout1D);\nexport class Dense extends Layer {\n    constructor(args) {\n        super(args);\n        // Default activation: Linear (none).\n        this.activation = null;\n        this.useBias = true;\n        this.kernel = null;\n        this.bias = null;\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        if (args.batchInputShape == null && args.inputShape == null &&\n            args.inputDim != null) {\n            // This logic is copied from Layer's constructor, since we can't\n            // do exactly what the Python constructor does for Dense().\n            let batchSize = null;\n            if (args.batchSize != null) {\n                batchSize = args.batchSize;\n            }\n            this.batchInputShape = [batchSize, args.inputDim];\n        }\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation);\n        if (args.useBias != null) {\n            this.useBias = args.useBias;\n        }\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.supportsMasking = true;\n        this.inputSpec = [{ minNDim: 2 }];\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const inputLastDim = inputShape[inputShape.length - 1];\n        if (this.kernel == null) {\n            this.kernel = this.addWeight('kernel', [inputLastDim, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n            if (this.useBias) {\n                this.bias = this.addWeight('bias', [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n            }\n        }\n        this.inputSpec = [{ minNDim: 2, axes: { [-1]: inputLastDim } }];\n        this.built = true;\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        outputShape[outputShape.length - 1] = this.units;\n        return outputShape;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Dense layer accepts only a single input.\n            const input = getExactlyOneTensor(inputs);\n            const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());\n            let output;\n            if (fusedActivationName != null) {\n                output = K.dot(input, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null);\n            }\n            else {\n                output = K.dot(input, this.kernel.read());\n                if (this.bias != null) {\n                    output = K.biasAdd(output, this.bias.read());\n                }\n                if (this.activation != null) {\n                    output = this.activation.apply(output);\n                }\n            }\n            return output;\n        });\n    }\n    getConfig() {\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nDense.className = 'Dense';\nserialization.registerClass(Dense);\nexport class Flatten extends Layer {\n    constructor(args) {\n        args = args || {};\n        super(args);\n        this.inputSpec = [{ minNDim: 3 }];\n        this.dataFormat = args.dataFormat;\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        for (const dim of inputShape.slice(1)) {\n            if (dim == null) {\n                throw new ValueError(`The shape of the input to \"Flatten\" is not fully defined ` +\n                    `(got ${inputShape.slice(1)}). Make sure to pass a complete ` +\n                    `\"input_shape\" or \"batch_input_shape\" argument to the first ` +\n                    `layer in your model.`);\n            }\n        }\n        return [inputShape[0], arrayProd(inputShape, 1)];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            let input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsFirst' && input.rank > 1) {\n                const permutation = [0];\n                for (let i = 2; i < input.rank; ++i) {\n                    permutation.push(i);\n                }\n                permutation.push(1);\n                input = input.transpose(permutation);\n            }\n            return K.batchFlatten(input);\n        });\n    }\n    getConfig() {\n        const config = {};\n        if (this.dataFormat != null) {\n            config['dataFormat'] = this.dataFormat;\n        }\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nFlatten.className = 'Flatten';\nserialization.registerClass(Flatten);\nexport class Activation extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.activation = getActivation(args.activation);\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            return this.activation.apply(input);\n        });\n    }\n    getConfig() {\n        const config = { activation: serializeActivation(this.activation) };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nActivation.className = 'Activation';\nserialization.registerClass(Activation);\nexport class RepeatVector extends Layer {\n    constructor(args) {\n        super(args);\n        this.n = args.n;\n        this.inputSpec = [{ ndim: 2 }];\n    }\n    computeOutputShape(inputShape) {\n        return [inputShape[0], this.n, inputShape[1]];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            return K.repeat(inputs, this.n);\n        });\n    }\n    getConfig() {\n        const config = {\n            n: this.n,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nRepeatVector.className = 'RepeatVector';\nserialization.registerClass(RepeatVector);\nexport class Reshape extends Layer {\n    constructor(args) {\n        super(args);\n        this.targetShape = args.targetShape;\n        // Make sure that all unknown dimensions are represented as `null`.\n        for (let i = 0; i < this.targetShape.length; ++i) {\n            if (this.isUnknown(this.targetShape[i])) {\n                this.targetShape[i] = null;\n            }\n        }\n    }\n    isUnknown(dim) {\n        return dim < 0 || dim == null;\n    }\n    /**\n     * Finds and replaces a missing dimension in output shape.\n     *\n     * This is a near direct port of the internal Numpy function\n     * `_fix_unknown_dimension` in `numpy/core/src/multiarray/shape.c`.\n     *\n     * @param inputShape: Original shape of array begin reshape.\n     * @param outputShape: Target shape of the array, with at most a single\n     * `null` or negative number, which indicates an underdetermined dimension\n     * that should be derived from `inputShape` and the known dimensions of\n     *   `outputShape`.\n     * @returns: The output shape with `null` replaced with its computed value.\n     * @throws: ValueError: If `inputShape` and `outputShape` do not match.\n     */\n    fixUnknownDimension(inputShape, outputShape) {\n        const errorMsg = 'Total size of new array must be unchanged.';\n        const finalShape = outputShape.slice();\n        let known = 1;\n        let unknown = null;\n        for (let i = 0; i < finalShape.length; ++i) {\n            const dim = finalShape[i];\n            if (this.isUnknown(dim)) {\n                if (unknown === null) {\n                    unknown = i;\n                }\n                else {\n                    throw new ValueError('Can only specifiy one unknown dimension.');\n                }\n            }\n            else {\n                known *= dim;\n            }\n        }\n        const originalSize = arrayProd(inputShape);\n        if (unknown !== null) {\n            if (known === 0 || originalSize % known !== 0) {\n                throw new ValueError(errorMsg);\n            }\n            finalShape[unknown] = originalSize / known;\n        }\n        else if (originalSize !== known) {\n            throw new ValueError(errorMsg);\n        }\n        return finalShape;\n    }\n    computeOutputShape(inputShape) {\n        let anyUnknownDims = false;\n        for (let i = 0; i < inputShape.length; ++i) {\n            if (this.isUnknown(inputShape[i])) {\n                anyUnknownDims = true;\n                break;\n            }\n        }\n        if (anyUnknownDims) {\n            return inputShape.slice(0, 1).concat(this.targetShape);\n        }\n        else {\n            return inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            const outputShape = inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n            return input.reshape(outputShape);\n        });\n    }\n    getConfig() {\n        const config = {\n            targetShape: this.targetShape,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nReshape.className = 'Reshape';\nserialization.registerClass(Reshape);\nexport class Permute extends Layer {\n    constructor(args) {\n        super(args);\n        if (args.dims == null) {\n            throw new Error('Required configuration field `dims` is missing during Permute ' +\n                'constructor call.');\n        }\n        if (!Array.isArray(args.dims)) {\n            throw new Error('Permute constructor requires `dims` to be an Array, but received ' +\n                `${args.dims} instead.`);\n        }\n        // Check the validity of the permutation indices.\n        const expectedSortedIndices = range(1, args.dims.length + 1);\n        if (!util.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) {\n            throw new Error('Invalid permutation `dims`: ' + JSON.stringify(args.dims) +\n                ' `dims` must contain consecutive integers starting from 1.');\n        }\n        this.dims = args.dims;\n        this.dimsIncludingBatch = [0].concat(this.dims);\n        this.inputSpec = [new InputSpec({ ndim: this.dims.length + 1 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        this.dims.forEach((dim, i) => {\n            outputShape[i + 1] = inputShape[dim];\n        });\n        return outputShape;\n    }\n    call(inputs, kwargs) {\n        return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch);\n    }\n    getConfig() {\n        const config = {\n            dims: this.dims,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nPermute.className = 'Permute';\nserialization.registerClass(Permute);\nexport class Masking extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.supportsMasking = true;\n        if (args != null) {\n            this.maskValue = args.maskValue == null ? 0 : args.maskValue;\n        }\n        else {\n            this.maskValue = 0;\n        }\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { maskValue: this.maskValue };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    computeMask(inputs, mask) {\n        const input = getExactlyOneTensor(inputs);\n        const axis = -1;\n        return any(notEqual(input, this.maskValue), axis);\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const axis = -1;\n            const keepDims = true;\n            const booleanMask = any(notEqual(input, this.maskValue), axis, keepDims);\n            const output = input.mul(booleanMask.asType(input.dtype));\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMasking.className = 'Masking';\nserialization.registerClass(Masking);\n//# sourceMappingURL=core.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Embedding Layer.\n *\n * Original source: keras/constraints.py\n */\nimport { notEqual, serialization, tidy, zerosLike } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class Embedding extends Layer {\n    constructor(args) {\n        super(args);\n        this.embeddings = null;\n        this.DEFAULT_EMBEDDINGS_INITIALIZER = 'randomUniform';\n        if (args.batchInputShape == null && args.inputShape == null) {\n            // Porting Note: This logic is copied from Layer's constructor, since we\n            // can't do exactly what the Python constructor does for Embedding().\n            // Specifically, the super constructor can not be called after the\n            // mutation of the `config` argument.\n            let batchSize = null;\n            if (args.batchSize != null) {\n                batchSize = args.batchSize;\n            }\n            if (args.inputLength == null) {\n                // Fix super-constructor to what it would have done if\n                // 'config.inputShape' were (None, )\n                this.batchInputShape = [batchSize, null];\n            }\n            else {\n                // Fix super-constructor to what it would have done if\n                // 'config.inputShape' were (config.inputLength, )\n                this.batchInputShape =\n                    [batchSize].concat(generic_utils.toList(args.inputLength));\n            }\n        }\n        this.inputDim = args.inputDim;\n        generic_utils.assertPositiveInteger(this.inputDim, 'inputDim');\n        this.outputDim = args.outputDim;\n        generic_utils.assertPositiveInteger(this.outputDim, 'outputDim');\n        this.embeddingsInitializer = getInitializer(args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER);\n        this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.embeddingsConstraint = getConstraint(args.embeddingsConstraint);\n        this.maskZero = args.maskZero;\n        this.supportsMasking = args.maskZero;\n        this.inputLength = args.inputLength;\n    }\n    build(inputShape) {\n        this.embeddings = this.addWeight('embeddings', [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint);\n        this.built = true;\n    }\n    // Override warnOnIncompatibleInputShape because an embedding layer allows\n    // the input to have varying ranks.\n    warnOnIncompatibleInputShape(inputShape) { }\n    computeMask(inputs, mask) {\n        return tidy(() => {\n            if (!this.maskZero) {\n                return null;\n            }\n            else {\n                inputs = getExactlyOneTensor(inputs);\n                return notEqual(inputs, zerosLike(inputs));\n            }\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (this.inputLength == null) {\n            return [...inputShape, this.outputDim];\n        }\n        // inputLength can be an array if input is 3D or higher.\n        const inLens = generic_utils.toList(this.inputLength);\n        if (inLens.length !== inputShape.length - 1) {\n            throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received ` +\n                `input shape has shape ${inputShape}`);\n        }\n        else {\n            let i = 0;\n            for (let k = 0; k < inLens.length; ++k) {\n                const s1 = inLens[k];\n                const s2 = inputShape[k + 1];\n                if ((s1 != null) && (s2 != null) && (s1 !== s2)) {\n                    throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received ` +\n                        `input shape has shape ${inputShape}`);\n                }\n                else if (s1 == null) {\n                    inLens[i] = s2;\n                }\n                i++;\n            }\n        }\n        return [inputShape[0], ...inLens, this.outputDim];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Embedding layer accepts only a single input.\n            let input = getExactlyOneTensor(inputs);\n            if (input.dtype !== 'int32') {\n                input = K.cast(input, 'int32');\n            }\n            const output = K.gather(this.embeddings.read(), input.as1D());\n            return output.reshape(getExactlyOneShape(this.computeOutputShape(input.shape)));\n        });\n    }\n    getConfig() {\n        const config = {\n            inputDim: this.inputDim,\n            outputDim: this.outputDim,\n            embeddingsInitializer: serializeInitializer(this.embeddingsInitializer),\n            embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            embeddingsConstraint: serializeConstraint(this.embeddingsConstraint),\n            maskZero: this.maskZero,\n            inputLength: this.inputLength\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nEmbedding.className = 'Embedding';\nserialization.registerClass(Embedding);\n//# sourceMappingURL=embeddings.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Merge Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { l2Normalize } from '../losses';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as mathUtils from '../utils/math_utils';\nimport { getExactlyOneShape } from '../utils/types_utils';\n/**\n * Generic Merge layer for element-wise merge functions.\n *\n * Used to implement `Sum`, `Average`, `Concatenate`, etc.\n */\nexport class Merge extends Layer {\n    constructor(args) {\n        super(args || {});\n        this.supportsMasking = true;\n    }\n    /**\n     * Logic for merging multiple tensors, to be overridden by subclasses.\n     * @param inputs\n     */\n    mergeFunction(inputs) {\n        throw new NotImplementedError();\n    }\n    /**\n     * Computes the shape of the result of an elementwise operation.\n     *\n     * @param shape1: Shape of the first tensor.\n     * @param shape2: Shape of the second tensor.\n     * @returns Expected output shape when an elementwise operation is carried\n     *   out on 2 tensors with shapes `shape1` and `shape2`.\n     * @throws ValueError: If `shape1` and `shape2` are not compatible for\n     *   element-wise operations.\n     */\n    computeElementwiseOpOutputShape(shape1, shape2) {\n        if (shape1 == null || shape2 == null) {\n            return null;\n        }\n        else if (shape1.length < shape2.length) {\n            return this.computeElementwiseOpOutputShape(shape2, shape1);\n        }\n        else if (shape2.length === 0) {\n            return shape1;\n        }\n        const outputShape = shape1.slice(0, shape1.length - shape2.length);\n        for (let k = 0; k < shape2.length; ++k) {\n            const i = shape1[shape1.length - shape2.length + k];\n            const j = shape2[k];\n            if (i == null || j == null || i < 0 || j < 0) {\n                outputShape.push(null);\n            }\n            else if (i === 1) {\n                outputShape.push(j);\n            }\n            else if (j === 1) {\n                outputShape.push(i);\n            }\n            else {\n                if (i !== j) {\n                    throw new ValueError('Operands could not be broadcast together with shapes ' +\n                        JSON.stringify(shape1) + ' ' + JSON.stringify(shape2));\n                }\n                outputShape.push(i);\n            }\n        }\n        return outputShape;\n    }\n    build(inputShape) {\n        // Used purely for shape validation.\n        if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) {\n            // Make sure that inputShape is an Array of shape.\n            inputShape = [getExactlyOneShape(inputShape)];\n        }\n        inputShape = inputShape;\n        if (inputShape.length < 2) {\n            throw new ValueError('A merge layer should be called on an Array of at least 2 inputs.' +\n                ` Got ${inputShape.length} input(s).`);\n        }\n        // Make sure that there is at most one unique batch size among the input\n        // shapes.\n        let batchSizes = [];\n        for (const shape of inputShape) {\n            if (shape != null && shape[0] !== null) {\n                batchSizes.push(shape[0]);\n            }\n        }\n        batchSizes = generic_utils.unique(batchSizes);\n        if (batchSizes.length > 1) {\n            throw new ValueError(`Can not merge tensors with different batch sizes. ` +\n                `Got tensors with shapes: ${JSON.stringify(inputShape)}.`);\n        }\n        let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1);\n        for (let i = 1; i < inputShape.length; ++i) {\n            const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n            outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n        }\n        // If the inputs have different ranks, we have to reshape them to make them\n        // broadcastable.\n        const allRanks = inputShape.map(shape => shape.length);\n        if (inputShape.indexOf(null) === -1 &&\n            generic_utils.unique(allRanks).length === 1) {\n            this.reshapeRequired = false;\n        }\n        else {\n            this.reshapeRequired = true;\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (this.reshapeRequired) {\n                const reshapedInputs = [];\n                const inputDims = inputs.map(input => input.rank);\n                if (inputDims.indexOf(null) === -1) {\n                    // If ranks of all inputs are available, we simply expand each of them\n                    // at axis=1 until all of them have the same rank.\n                    const maxNDim = mathUtils.max(inputDims);\n                    for (let x of inputs) {\n                        const xNDim = x.rank;\n                        for (let k = 0; k < maxNDim - xNDim; ++k) {\n                            x = K.expandDims(x, 1);\n                        }\n                        reshapedInputs.push(x);\n                    }\n                    return this.mergeFunction(reshapedInputs);\n                }\n                else {\n                    // Transpose all inputs so that batch size is the last dimension.\n                    // [batchSize, dim1, dim2, ...] -> [dim1, dim2, ..., batchSize]\n                    let transposed = false;\n                    for (const x of inputs) {\n                        const xNDim = x.rank;\n                        if (xNDim == null) {\n                            const xShape = x.shape;\n                            const batchSize = xShape[0];\n                            const newShape = xShape.slice(1).concat([batchSize]);\n                            let xTransposed = x.reshape([batchSize].concat(mathUtils.arrayProd(xShape.slice(1))));\n                            xTransposed = tfc.transpose(xTransposed, [1, 0]);\n                            xTransposed = xTransposed.reshape(newShape);\n                            reshapedInputs.push(xTransposed);\n                            transposed = true;\n                        }\n                        else if (xNDim > 1) {\n                            const dims = mathUtils.range(1, xNDim).concat([0]);\n                            reshapedInputs.push(tfc.transpose(x, dims));\n                            transposed = true;\n                        }\n                        else {\n                            // We don't transpose inputs if they are 1D vectors or scalars.\n                            reshapedInputs.push(x);\n                        }\n                    }\n                    let y = this.mergeFunction(reshapedInputs);\n                    const yNDim = y.rank;\n                    if (transposed) {\n                        // If inputs have been transposed, we have to transpose the output\n                        // too.\n                        if (yNDim == null) {\n                            const yShape = y.shape;\n                            const yNDim = yShape.length;\n                            const batchSize = yShape[yNDim - 1];\n                            const newShape = [batchSize].concat(yShape.slice(0, yShape.length - 1));\n                            y = tfc.transpose(y.reshape([-1, batchSize]), [1, 0])\n                                .reshape(newShape);\n                        }\n                        else if (yNDim > 1) {\n                            const dims = [yNDim - 1].concat(mathUtils.range(0, yNDim - 1));\n                            y = tfc.transpose(y, dims);\n                        }\n                    }\n                    return y;\n                }\n            }\n            else {\n                return this.mergeFunction(inputs);\n            }\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = inputShape;\n        let outputShape;\n        if (inputShape[0] == null) {\n            outputShape = null;\n        }\n        else {\n            outputShape = inputShape[0].slice(1);\n        }\n        for (let i = 1; i < inputShape.length; ++i) {\n            const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n            outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n        }\n        let batchSizes = [];\n        for (const shape of inputShape) {\n            if (shape != null && shape[0] !== null) {\n                batchSizes.push(shape[0]);\n            }\n        }\n        batchSizes = generic_utils.unique(batchSizes);\n        if (batchSizes.length === 1) {\n            outputShape = batchSizes.concat(outputShape);\n        }\n        else {\n            outputShape = [null].concat(outputShape);\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        return tfc.tidy(() => {\n            if (mask == null) {\n                return null;\n            }\n            if (!Array.isArray(mask)) {\n                throw new ValueError('`mask` should be an Array');\n            }\n            if (!Array.isArray(inputs)) {\n                throw new ValueError('`inputs` should be an Array');\n            }\n            if (mask.length !== inputs.length) {\n                throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same ` +\n                    `length, but have different lengths ` +\n                    `(${inputs.length} vs ${mask.length})`);\n            }\n            if (mask.every(m => m == null)) {\n                return null;\n            }\n            mask = mask.map(m => m == null ? m : tfc.expandDims(m, 0));\n            let output = mask[0];\n            for (let i = 1; i < mask.length - 1; ++i) {\n                output = tfc.logicalAnd(output, mask[i]);\n            }\n            return output;\n        });\n    }\n}\nexport class Add extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.add(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nAdd.className = 'Add';\nserialization.registerClass(Add);\n/**\n * Calculate the element-wise sum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Add` layer, by using no input argument\n *    or a single configuration argument. The resultant `Add` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const addLayer = tf.layers.add();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = addLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.add([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.add([input1, input2]).print();\n * // Gives [[11, 22], [33, 44]].\n *\n */\nexport function add(config) {\n    if (Array.isArray(config)) {\n        const layer = new Add({});\n        return layer.apply(config);\n    }\n    else {\n        return new Add(config);\n    }\n}\nexport class Multiply extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.mul(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMultiply.className = 'Multiply';\nserialization.registerClass(Multiply);\n/**\n * Calculate the element-wise product of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Multiply` layer, by using no input argument\n *    or a single configuration argument. The resultant `Multiply` layer can\n *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const multiplyLayer = tf.layers.multiply();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = multiplyLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.multiply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.multiply([input1, input2]).print();\n * // Gives [[10, 40], [90, 160]].\n *\n */\nexport function multiply(config) {\n    if (Array.isArray(config)) {\n        const layer = new Multiply({});\n        return layer.apply(config);\n    }\n    else {\n        return new Multiply(config);\n    }\n}\nexport class Average extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.add(output, inputs[i]);\n            }\n            return tfc.mul(1 / inputs.length, output);\n        });\n    }\n}\n/** @nocollapse */\nAverage.className = 'Average';\nserialization.registerClass(Average);\n/**\n * Calculate the element-wise arithmetic mean of inputs, which all have the same\n * shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Average` layer, by using no input argument\n *    or a single configuration argument. The resultant `Average` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const averageLayer = tf.layers.average();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = averageLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.average([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.average([input1, input2]).print();\n * // Gives [[5.5, 11], [16.5, 22]].\n *\n */\nexport function average(config) {\n    if (Array.isArray(config)) {\n        const layer = new Average({});\n        return layer.apply(config);\n    }\n    else {\n        return new Average(config);\n    }\n}\nexport class Maximum extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0];\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.maximum(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMaximum.className = 'Maximum';\nserialization.registerClass(Maximum);\n/**\n * Calculate the element-wise maximum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Maximum` layer, by using no input argument\n *    or a single configuration argument. The resultant `Maximum` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const maximumLayer = tf.layers.maximum();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = maximumLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.maximum([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);\n * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);\n * tf.layers.maximum([input1, input2]).print();\n * // Gives [[10, 20], [30, 40]].\n *\n */\nexport function maximum(config) {\n    if (Array.isArray(config)) {\n        const layer = new Maximum({});\n        return layer.apply(config);\n    }\n    else {\n        return new Maximum(config);\n    }\n}\nexport class Minimum extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0];\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.minimum(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMinimum.className = 'Minimum';\nserialization.registerClass(Minimum);\n/**\n * Calculate the element-wise minimum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Minimum` layer, by using no input argument\n *    or a single configuration argument. The resultant `Minimum` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const minimumLayer = tf.layers.minimum();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = minimumLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.minimum([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);\n * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);\n * tf.layers.minimum([input1, input2]).print();\n * // Gives [[1, 2], [3, 4]].\n *\n */\nexport function minimum(config) {\n    if (Array.isArray(config)) {\n        const layer = new Minimum({});\n        return layer.apply(config);\n    }\n    else {\n        return new Minimum(config);\n    }\n}\nexport class Concatenate extends Merge {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_AXIS = -1;\n        if (args == null) {\n            args = {};\n        }\n        this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n        this.supportsMasking = true;\n        this.reshapeRequired = false;\n    }\n    build(inputShape) {\n        // Used purely for shape validation.]\n        if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) ||\n            inputShape.length === 1) {\n            throw new ValueError('A `Concatenate` layer should be called on a list of at least 2 ' +\n                'inputs');\n        }\n        inputShape = inputShape;\n        let allNoneShape = true;\n        for (const shape of inputShape) {\n            if (shape != null) {\n                allNoneShape = false;\n                break;\n            }\n        }\n        if (allNoneShape) {\n            return;\n        }\n        const shapeSet = [];\n        for (let i = 0; i < inputShape.length; ++i) {\n            const shapeWithoutConcatAxis = inputShape[i].slice();\n            shapeWithoutConcatAxis.splice(this.axis, 1);\n            let exists = false;\n            for (const shape of shapeSet) {\n                if (util.arraysEqual(shape, shapeWithoutConcatAxis)) {\n                    exists = true;\n                    break;\n                }\n            }\n            if (!exists) {\n                shapeSet.push(shapeWithoutConcatAxis);\n            }\n        }\n        if (shapeSet.length > 1) {\n            throw new ValueError('A `Concatenate` layer requires inputs with matching shapes ' +\n                'except for the concat axis. Got input shapes: ' +\n                JSON.stringify(inputShape));\n        }\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            return K.concatenate(inputs, this.axis);\n        });\n    }\n    computeOutputShape(inputShape) {\n        if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) {\n            throw new ValueError('A `Concatenate` layer should be called on a list of inputs.');\n        }\n        const inputShapes = inputShape;\n        const outputShape = inputShapes[0].slice();\n        const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis;\n        // Porting Note: the line above is because TypeScript doesn't support\n        //   negative indices.\n        for (const shape of inputShapes.slice(1)) {\n            if (outputShape[axis] == null || shape[axis] == null) {\n                outputShape[axis] = null;\n                break;\n            }\n            outputShape[axis] += shape[axis];\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        if (mask == null) {\n            return null;\n        }\n        if (!Array.isArray(mask)) {\n            throw new ValueError('`mask` should be an array for Concatenate');\n        }\n        if (!Array.isArray(inputs)) {\n            throw new ValueError('`inputs` should be an array for Concatenate');\n        }\n        if (mask.length !== inputs.length) {\n            throw new ValueError(`Mismatch in the length of mask (${mask.length}) ` +\n                `and the legnth of inputs (${inputs.length})`);\n        }\n        return tfc.tidy(() => {\n            let allNullMasks = true;\n            mask.forEach(m => {\n                if (m != null) {\n                    allNullMasks = false;\n                    return;\n                }\n            });\n            if (allNullMasks) {\n                return null;\n            }\n            const outputMasks = [];\n            for (let i = 0; i < inputs.length; ++i) {\n                if (mask[i] == null) {\n                    // Input is unmasked. Append all 1's to masks.\n                    outputMasks.push(tfc.onesLike(inputs[i]).asType('bool'));\n                }\n                else if (mask[i].rank < inputs[i].rank) {\n                    // Mask is smaller than the input, expand it.\n                    outputMasks.push(tfc.expandDims(mask[i], -1));\n                }\n                else {\n                    outputMasks.push(mask[i]);\n                }\n            }\n            const concatenatedMasks = tfc.concat(outputMasks, this.axis);\n            return tfc.all(concatenatedMasks, -1, false);\n        });\n    }\n    getConfig() {\n        const config = {\n            'axis': this.axis,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nConcatenate.className = 'Concatenate';\nserialization.registerClass(Concatenate);\n/**\n * Concatenate an `Array` of inputs.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Concatenate` layer, by using no input argument\n *    or a single configuration argument. The resultant `Concatenate` layer can\n *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const concatLayer = tf.layers.concatenate();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 3]});\n * const input2 = tf.input({shape: [2, 4]});\n * const output = concatLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 7], with the first dimension as the undetermined batch\n * // dimension and the last dimension as the result of concatenating the\n * // last dimensions of the two inputs.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 3]});\n * const input2 = tf.input({shape: [2, 4]});\n * const output = tf.layers.concatenate([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension and the last dimension as the result of concatenating the\n * // last dimensions of the two inputs.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);\n * const input2 = tf.tensor2d([[10, 20], [30, 40]], [2, 2]);\n * tf.layers.concatenate([input1, input2]).print();\n * // Gives [[1, 2, 10, 20], [3, 4, 30, 40]].\n *\n */\nexport function concatenate(config) {\n    if (Array.isArray(config)) {\n        const layer = new Concatenate({});\n        return layer.apply(config);\n    }\n    else {\n        return new Concatenate(config);\n    }\n}\n/**\n * Interpretable potentially negative axis index.\n *\n * For example, given axis = -1, and dim = 3, this function will return 2.\n *\n * @param axis The axis index, may be a positive, zero or negative integer.\n * @param dim Total number of dimensions, a positive integer.\n * @returns A non-negative axis index equivalent to the input `axis`.\n */\nfunction interpretAxis(axis, dim) {\n    while (axis < 0) {\n        axis += dim;\n    }\n    return axis;\n}\nfunction batchDot(x, y, axes) {\n    if (x.shape.length > 3 || y.shape.length > 3) {\n        throw new NotImplementedError('batchDot is not implemented for tensors of 4D or higher rank yet');\n    }\n    tfc.util.assert(x.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, ` +\n        `but got ${x.shape.length}`);\n    tfc.util.assert(x.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, ` +\n        `but got ${y.shape.length}`);\n    if (typeof axes === 'number') {\n        axes = [axes, axes];\n    }\n    if (x.dtype === 'complex64' || y.dtype === 'complex64') {\n        throw new NotImplementedError('batchDot is not implemented for complex64-type Tensors yet.');\n    }\n    const xNDim = x.shape.length;\n    const yNDim = y.shape.length;\n    if (axes == null) {\n        // Behave like batchMatmul by default.\n        axes = [xNDim - 1, yNDim - 2];\n    }\n    const axesArray = axes;\n    return tfc.tidy(() => {\n        let diff;\n        if (xNDim > yNDim) {\n            diff = xNDim - yNDim;\n            const diffShape = [];\n            for (let i = 0; i < diff; ++i) {\n                diffShape.push(1);\n            }\n            y = y.reshape(y.shape.concat(diffShape));\n        }\n        else if (yNDim > xNDim) {\n            diff = yNDim - xNDim;\n            const diffShape = [];\n            for (let i = 0; i < diff; ++i) {\n                diffShape.push(1);\n            }\n            x = x.reshape(x.shape.concat(diffShape));\n        }\n        else {\n            diff = 0;\n        }\n        let out;\n        if (x.shape.length === 2 && y.shape.length === 2) {\n            if (axesArray[0] === axesArray[1]) {\n                out = x.mul(y).sum(axesArray[0]);\n            }\n            else {\n                out = x.transpose([1, 0]).mul(y).sum(axesArray[1]);\n            }\n        }\n        else {\n            const adjX = axesArray[0] !== x.shape.length - 1;\n            const adjY = axesArray[1] === y.shape.length - 1;\n            out = x.matMul(y, adjX, adjY);\n        }\n        if (diff > 0) {\n            let idx;\n            if (xNDim > yNDim) {\n                idx = xNDim + yNDim - 3;\n            }\n            else {\n                idx = xNDim - 1;\n            }\n            const squeezeAxes = [];\n            for (let i = idx; i < idx + diff; ++i) {\n                squeezeAxes.push(i);\n            }\n            out = out.squeeze(squeezeAxes);\n        }\n        if (out.shape.length === 1) {\n            out = out.expandDims(1);\n        }\n        return out;\n    });\n}\nexport class Dot extends Merge {\n    constructor(args) {\n        super(args);\n        this.axes = args.axes;\n        this.normalize = args.normalize == null ? false : args.normalize;\n        this.supportsMasking = true;\n        this.reshapeRequired = false;\n    }\n    build(inputShape) {\n        tfc.util.assert(Array.isArray(inputShape) && inputShape.length === 2 &&\n            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');\n        const shape1 = inputShape[0];\n        const shape2 = inputShape[1];\n        if (shape1.length > 3 || shape2.length > 3) {\n            throw new NotImplementedError('Dot layer does not support tensors of 4D or higher rank yet.');\n        }\n        const axes = this.interpretAxes(shape1, shape2);\n        if (shape1[axes[0]] !== shape2[axes[1]]) {\n            throw new ValueError(`Dimension incompatibility: ` +\n                `${shape1[axes[0]]} !== ${shape2[axes[1]]}`);\n        }\n    }\n    mergeFunction(inputs) {\n        if (inputs.length !== 2) {\n            throw new ValueError('A `Dot` layer must be called on exactly 2 inputs, ' +\n                `but received ${inputs.length} input(s).`);\n        }\n        let x1 = inputs[0];\n        let x2 = inputs[1];\n        let axes;\n        if (!Array.isArray(this.axes)) {\n            axes = [\n                interpretAxis(this.axes, x1.shape.length),\n                interpretAxis(this.axes, x2.shape.length)\n            ];\n        }\n        else {\n            axes = this.axes.map((axis, i) => interpretAxis(axis, inputs[i].shape.length));\n        }\n        if (this.normalize) {\n            x1 = l2Normalize(x1, axes[0]);\n            x2 = l2Normalize(x2, axes[1]);\n        }\n        return batchDot(x1, x2, axes);\n    }\n    interpretAxes(shape1, shape2) {\n        let axes;\n        if (!Array.isArray(this.axes)) {\n            // `this.axes` is a single integer.\n            axes = [\n                interpretAxis(this.axes, shape1.length),\n                interpretAxis(this.axes, shape2.length)\n            ];\n        }\n        else {\n            // `this.axes` is an Array of integers.\n            axes = this.axes;\n        }\n        return axes;\n    }\n    computeOutputShape(inputShape) {\n        tfc.util.assert(Array.isArray(inputShape) && inputShape.length === 2 &&\n            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');\n        const shape1 = inputShape[0].slice();\n        const shape2 = inputShape[1].slice();\n        if (shape1.length > 3 || shape2.length > 3) {\n            throw new NotImplementedError('Dot layer does not support tensors of 4D or higher rank yet.');\n        }\n        const axes = this.interpretAxes(shape1, shape2);\n        shape1.splice(axes[0], 1);\n        shape2.splice(axes[1], 1);\n        shape2.splice(0, 1);\n        const outputShape = shape1.concat(shape2);\n        if (outputShape.length === 1) {\n            outputShape.push(1);\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        return null;\n    }\n    getConfig() {\n        const config = {\n            'axes': this.axes,\n            'normalize': this.normalize\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nDot.className = 'Dot';\nserialization.registerClass(Dot);\n// TODO(cais): Add functional interfaces for the merge layers.\n//# sourceMappingURL=merge.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Noise Layers.\n */\nimport { greaterEqual, randomUniform, serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { Layer } from '../engine/topology';\nimport { getExactlyOneTensor } from '../utils/types_utils';\nexport class GaussianNoise extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.stddev = args.stddev;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { stddev: this.stddev };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const noised = () => K.randomNormal(input.shape, 0, this.stddev).add(input);\n            const output = K.inTrainPhase(noised, () => input, kwargs['training'] || false);\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nGaussianNoise.className = 'GaussianNoise';\nserialization.registerClass(GaussianNoise);\nexport class GaussianDropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.rate = args.rate;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { rate: this.rate };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            if (this.rate > 0 && this.rate < 1) {\n                const noised = () => {\n                    const stddev = Math.sqrt(this.rate / (1 - this.rate));\n                    return input.mul(K.randomNormal(input.shape, 1, stddev));\n                };\n                return K.inTrainPhase(noised, () => input, kwargs['training'] || false);\n            }\n            return input;\n        });\n    }\n}\n/** @nocollapse */\nGaussianDropout.className = 'GaussianDropout';\nserialization.registerClass(GaussianDropout);\n/**\n * Applies Alpha Dropout to the input.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs\n * to their original values, in order to ensure the self-normalizing property\n * even after this dropout.\n * Alpha Dropout fits well to Scaled Exponential Linear Units\n * by randomly setting activations to the negative saturation value.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the\n *     shape for randomly generated keep/drop flags.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n */\nexport class AlphaDropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.rate = args.rate;\n        this.noiseShape = args.noiseShape;\n    }\n    _getNoiseShape(inputs) {\n        return this.noiseShape || getExactlyOneTensor(inputs).shape;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { rate: this.rate };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.rate < 1 && this.rate > 0) {\n                const noiseShape = this._getNoiseShape(inputs);\n                const droppedInputs = () => {\n                    const input = getExactlyOneTensor(inputs);\n                    const alpha = 1.6732632423543772848170429916717;\n                    const scale = 1.0507009873554804934193349852946;\n                    const alphaP = -alpha * scale;\n                    let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate);\n                    keptIdx = K.cast(keptIdx, 'float32'); // get default dtype.\n                    // Get affine transformation params.\n                    const a = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5;\n                    const b = -a * alphaP * this.rate;\n                    // Apply mask.\n                    const x = input.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP));\n                    return x.mul(a).add(b);\n                };\n                return K.inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs['training'] || false);\n            }\n            return inputs;\n        });\n    }\n}\n/** @nocollapse */\nAlphaDropout.className = 'AlphaDropout';\nserialization.registerClass(AlphaDropout);\n//# sourceMappingURL=noise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Normalization layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { moments, serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Applies batch normalization on x given mean, var, beta and gamma.\n *\n * I.e. returns:\n *   `output = (x - mean) / (sqrt(var) + epsilon) * gamma + beta`\n *\n * @param x Input tensor.\n * @param mean Mean of batch.\n * @param variance Variance of batch.\n * @param beta Tensor with which to center the input.\n * @param gamma Tensor by which to scale the input.\n * @param epsilon Fuzz factor.\n * @returns The result of the batch normalization.\n */\nexport function batchNormalization(x, mean, variance, beta, gamma, epsilon = 1e-3) {\n    let out;\n    if (x.rank === 2) {\n        out = tfc.batchNorm2d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else if (x.rank === 3) {\n        // TODO(cais): Check rank; give proper error message.\n        out = tfc.batchNorm3d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else if (x.rank === 4) {\n        out = tfc.batchNorm4d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else {\n        throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} ` +\n            `yet`);\n    }\n    return out;\n}\n/**\n * Non-broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    return tidy(() => {\n        const meanAndVariance = tfc.moments(x, reductionAxes);\n        const mean = meanAndVariance.mean;\n        const variance = meanAndVariance.variance;\n        const normed = batchNormalization(x, mean, variance, beta, gamma, epsilon);\n        return [normed, mean, variance];\n    });\n}\n/**\n * Broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    return tidy(() => {\n        const meanAndVariance = tfc.moments(x, reductionAxes);\n        const mean = meanAndVariance.mean;\n        const variance = meanAndVariance.variance;\n        const targetShape = [];\n        for (const axis of math_utils.range(0, x.rank)) {\n            if (reductionAxes.indexOf(axis) !== -1) {\n                targetShape.push(1);\n            }\n            else {\n                targetShape.push(x.shape[axis]);\n            }\n        }\n        const broadcastMean = mean.reshape(targetShape);\n        const broadcastVariance = variance.reshape(targetShape);\n        const broadcastGamma = gamma == null ? null : gamma.reshape(targetShape);\n        const broadcastBeta = beta == null ? null : beta.reshape(targetShape);\n        const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon);\n        return [normed, mean, variance];\n    });\n}\n/**\n * Batch normalization for use in training (not inference).\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nexport function normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    if (util.arraysEqual(reductionAxes.slice().sort(), math_utils.range(0, x.rank - 1))) {\n        return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);\n    }\n    else {\n        return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);\n    }\n}\nexport class BatchNormalization extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.supportsMasking = true;\n        this.axis = args.axis == null ? -1 : args.axis;\n        this.momentum = args.momentum == null ? 0.99 : args.momentum;\n        this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n        this.center = args.center == null ? true : args.center;\n        this.scale = args.scale == null ? true : args.scale;\n        this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n        this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n        this.movingMeanInitializer =\n            getInitializer(args.movingMeanInitializer || 'zeros');\n        this.movingVarianceInitializer =\n            getInitializer(args.movingVarianceInitializer || 'ones');\n        this.betaConstraint = getConstraint(args.betaConstraint);\n        this.gammaConstraint = getConstraint(args.gammaConstraint);\n        this.betaRegularizer = getRegularizer(args.betaRegularizer);\n        this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const axis = this.axis >= 0 ? this.axis : (this.axis + inputShape.length);\n        const dim = inputShape[axis];\n        if (dim == null) {\n            throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but ` +\n                `the layer received an input with shape ` +\n                `${JSON.stringify(inputShape)}.`);\n        }\n        this.inputSpec =\n            [new InputSpec({ ndim: inputShape.length, axes: { [axis]: dim } })];\n        const shape = [dim];\n        if (this.scale) {\n            this.gamma = this.addWeight('gamma', shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint);\n        }\n        if (this.center) {\n            this.beta = this.addWeight('beta', shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint);\n        }\n        this.movingMean = this.addWeight('moving_mean', shape, null, this.movingMeanInitializer, null, false);\n        this.movingVariance = this.addWeight('moving_variance', shape, null, this.movingVarianceInitializer, null, false);\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            const input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            const ndim = inputShape.length;\n            const reductionAxes = math_utils.range(0, ndim);\n            const axis = this.axis >= 0 ? this.axis : (this.axis + ndim);\n            reductionAxes.splice(axis, 1);\n            const broadcastShape = generic_utils.pyListRepeat(1, ndim);\n            broadcastShape[axis] = inputShape[axis];\n            const sortedReductionAxes = reductionAxes.slice();\n            sortedReductionAxes.sort();\n            const needsBroadcasting = !util.arraysEqual(sortedReductionAxes, math_utils.range(0, ndim).slice(0, ndim - 1));\n            const normalizeInference = () => {\n                if (needsBroadcasting) {\n                    const broadcastMovingMean = this.movingMean.read().reshape(broadcastShape);\n                    const broadcastMovingVariance = this.movingVariance.read().reshape(broadcastShape);\n                    const broadcastBeta = this.center ? this.beta.read().reshape(broadcastShape) : null;\n                    const broadcastGamma = this.scale ? this.gamma.read().reshape(broadcastShape) : null;\n                    return batchNormalization(input, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon);\n                }\n                else {\n                    return batchNormalization(input, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);\n                }\n            };\n            if (!training) {\n                return normalizeInference();\n            }\n            const [normedTraining, mean, variance] = normalizeBatchInTraining(input, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon);\n            const doMovingAverage = (variable, value, momentum) => {\n                tfc.tidy(() => {\n                    const decay = 1 - momentum;\n                    const origValue = variable.read();\n                    const updateDelta = origValue.sub(value).mul(decay);\n                    variable.write(origValue.sub(updateDelta));\n                });\n            };\n            // Perform updates to moving mean and moving variance for training.\n            // Porting Note: In PyKeras, these updates to `movingMean` and\n            //   `movingAverage` are done as a deferred Graph, added to the `Layer`'s\n            //   `update`s using the `add_update()` method. Here we do it imperatively\n            //   and encapsulate the updates in a function that is invoked\n            //   immediately.\n            const updateMovingMeanAndVariance = () => {\n                doMovingAverage(this.movingMean, mean, this.momentum);\n                doMovingAverage(this.movingVariance, variance, this.momentum);\n            };\n            updateMovingMeanAndVariance();\n            return normedTraining;\n        });\n    }\n    getConfig() {\n        const config = {\n            axis: this.axis,\n            momentum: this.momentum,\n            epsilon: this.epsilon,\n            center: this.center,\n            scale: this.scale,\n            betaInitializer: serializeInitializer(this.betaInitializer),\n            gammaInitializer: serializeInitializer(this.gammaInitializer),\n            movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),\n            movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer),\n            betaRegularizer: serializeRegularizer(this.betaRegularizer),\n            gammaRegularizer: serializeRegularizer(this.gammaRegularizer),\n            betaConstraint: serializeConstraint(this.betaConstraint),\n            gammaConstraint: serializeConstraint(this.gammaConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nBatchNormalization.className = 'BatchNormalization';\nserialization.registerClass(BatchNormalization);\nexport class LayerNormalization extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.axis = args.axis == null ? -1 : args.axis;\n        if (typeof this.axis === 'number') {\n            if (!Number.isInteger(this.axis)) {\n                throw new Error(`Expected axis to be an integer, but received ${this.axis}`);\n            }\n        }\n        else if (Array.isArray(this.axis)) {\n            for (const axis of this.axis) {\n                if (!Number.isInteger(axis)) {\n                    throw new Error(`Expected axis to be an array of integers, ` +\n                        `but received ${JSON.stringify(this.axis)}`);\n                }\n            }\n        }\n        else {\n            throw new Error(`Expected axis to be an integer or an array of integers, ` +\n                `but received ${JSON.stringify(this.axis)}`);\n        }\n        this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n        this.center = args.center == null ? true : args.center;\n        this.scale = args.scale == null ? true : args.scale;\n        this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n        this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n        this.betaRegularizer = getRegularizer(args.betaRegularizer);\n        this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n        this.supportsMasking = true;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const nDims = inputShape.length;\n        // Convert axis to array and resolve negatives.\n        if (typeof this.axis === 'number') {\n            this.axis = [this.axis];\n        }\n        for (let i = 0; i < this.axis.length; ++i) {\n            if (this.axis[i] < 0) {\n                this.axis[i] += nDims;\n            }\n        }\n        // Further validate axes.\n        for (const axis of this.axis) {\n            if (axis < 0 || axis >= nDims) {\n                throw new Error(`Invalid axis: ${axis}`);\n            }\n        }\n        if (this.axis.length !== generic_utils.unique(this.axis).length) {\n            throw new Error(`Found duplicate axes in: ${this.axis}`);\n        }\n        const paramShape = this.axis.map(axis => inputShape[axis]);\n        const trainable = true;\n        if (this.scale) {\n            this.gamma = this.addWeight('gamma', paramShape, 'float32', this.gammaInitializer, this.gammaRegularizer, trainable);\n        }\n        else {\n            this.gamma = null;\n        }\n        if (this.center) {\n            this.beta = this.addWeight('beta', paramShape, 'float32', this.betaInitializer, this.betaRegularizer, trainable);\n        }\n        else {\n            this.beta = null;\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        const input = getExactlyOneTensor(inputs);\n        const inputShape = input.shape;\n        const nDims = inputShape.length;\n        return tidy(() => {\n            const keepDims = true;\n            let { mean, variance } = moments(input, this.axis, keepDims);\n            const broadcastShape = generic_utils.pyListRepeat(1, nDims);\n            for (const dim of this.axis) {\n                broadcastShape[dim] = inputShape[dim];\n            }\n            const broadcast = (v) => {\n                if (v != null && v.shape.length !== nDims &&\n                    this.axis !== [nDims - 1]) {\n                    return v.reshape(broadcastShape);\n                }\n                else {\n                    return v;\n                }\n            };\n            let scale = broadcast(this.gamma.read());\n            let offset = broadcast(this.beta.read());\n            // TODO(https://github.com/tensorflow/tfjs/issues/2120): The tiling below\n            // is a workaround for the limitation of core's batchNormalization?d don't\n            // support broadcasting in their gradients. In addition, the tiling is\n            // necessary to ensure correctness on the browser CPU backend regardless\n            // of forward or backward computation. Remove this workaround once the\n            // limitation is addressed. See .\n            const momentsTiling = [];\n            const scaleOffsetTiling = [];\n            for (let i = 0; i < nDims; ++i) {\n                if (this.axis.indexOf(i) !== -1) {\n                    momentsTiling.push(inputShape[i]);\n                    scaleOffsetTiling.push(1);\n                }\n                else {\n                    momentsTiling.push(1);\n                    scaleOffsetTiling.push(inputShape[i]);\n                }\n            }\n            mean = mean.tile(momentsTiling);\n            variance = variance.tile(momentsTiling);\n            scale = scale.tile(scaleOffsetTiling);\n            offset = offset.tile(scaleOffsetTiling);\n            return batchNormalization(input, mean, variance, offset, scale, this.epsilon);\n        });\n    }\n    getConfig() {\n        const config = {\n            axis: this.axis,\n            epsilon: this.epsilon,\n            center: this.center,\n            scale: this.scale,\n            betaInitializer: serializeInitializer(this.betaInitializer),\n            gammaInitializer: serializeInitializer(this.gammaInitializer),\n            betaRegularizer: serializeRegularizer(this.betaRegularizer),\n            gammaRegularizer: serializeRegularizer(this.gammaRegularizer)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nLayerNormalization.className = 'LayerNormalization';\nserialization.registerClass(LayerNormalization);\n//# sourceMappingURL=normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Padding Layers.\n */\n// Porting Note: In Python Keras, the padding layers are in convolutional.py,\n//   but we decided to put them in a separate file (padding.ts) for clarity.\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Pads the middle dimension of a 3D tensor.\n *\n * @param x Input `tf.Tensor` to be padded.\n * @param padding `Array` of 2 integers, how many zeros to add at the start and\n *   end of the middle dimension (i.e., dimension 1).\n * @return A padded 3D `tf.Tensor`.\n */\nexport function temporalPadding(x, padding) {\n    return tidy(() => {\n        if (x.rank !== 3) {\n            throw new ValueError(`temporalPadding expects input tensor to be 3-D, but received a ` +\n                `${x.rank}-D tensor.`);\n        }\n        if (padding == null) {\n            padding = [1, 1];\n        }\n        if (padding.length !== 2) {\n            throw new ValueError(`temporalPadding expects input padding pattern to be a length-2 ` +\n                `array, but received a length-${padding.length} array.`);\n        }\n        const pattern = [[0, 0], padding, [0, 0]];\n        return tfc.pad(x, pattern);\n    });\n}\n/**\n * Pads the 2nd and 3rd dimensions of a 4D tensor.\n *\n * @param x Input `tf.Tensor` to be padded.\n * @param padding `Array` of two `Array`s, each of which is an `Array` of two\n *   integers. The amount of padding at the beginning and end of the 2nd and 3rd\n *   dimensions, respectively.\n * @param dataFormat 'channelsLast' (default) or 'channelsFirst'.\n * @return Padded 4D `tf.Tensor`.\n */\nexport function spatial2dPadding(x, padding, dataFormat) {\n    return tidy(() => {\n        if (x.rank !== 4) {\n            throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ` +\n                `${x.rank}-D tensor.`);\n        }\n        if (padding == null) {\n            padding = [[1, 1], [1, 1]];\n        }\n        if (padding.length !== 2 || padding[0].length !== 2 ||\n            padding[1].length !== 2) {\n            throw new ValueError('spatial2dPadding expects `padding` to be an Array of two Arrays, ' +\n                'each of which is an Array of two integers.');\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (dataFormat !== 'channelsLast' && dataFormat !== 'channelsFirst') {\n            throw new ValueError(`Unknown data format: ${dataFormat}. ` +\n                `Supported data formats are 'channelsLast' and 'channelsFirst.`);\n        }\n        let pattern;\n        if (dataFormat === 'channelsFirst') {\n            pattern = [[0, 0], [0, 0], padding[0], padding[1]];\n        }\n        else {\n            pattern = [[0, 0], padding[0], padding[1], [0, 0]];\n        }\n        return tfc.pad(x, pattern);\n    });\n}\nexport class ZeroPadding2D extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.dataFormat =\n            args.dataFormat == null ? imageDataFormat() : args.dataFormat;\n        // TODO(cais): Maybe refactor the following logic surrounding `padding`\n        //   into a helper method.\n        if (args.padding == null) {\n            this.padding = [[1, 1], [1, 1]];\n        }\n        else if (typeof args.padding === 'number') {\n            this.padding =\n                [[args.padding, args.padding], [args.padding, args.padding]];\n        }\n        else {\n            args.padding = args.padding;\n            if (args.padding.length !== 2) {\n                throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but ` +\n                    `received a length-${args.padding.length} array.`);\n            }\n            let heightPadding;\n            let widthPadding;\n            if (typeof args.padding[0] === 'number') {\n                heightPadding = [args.padding[0], args.padding[0]];\n                widthPadding = [args.padding[1], args.padding[1]];\n            }\n            else {\n                args.padding = args.padding;\n                if (args.padding[0].length !== 2) {\n                    throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, ` +\n                        `but received a length-${args.padding[0].length} array.`);\n                }\n                heightPadding = args.padding[0];\n                if (args.padding[1].length !== 2) {\n                    throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, ` +\n                        `but received a length-${args.padding[1].length} array.`);\n                }\n                widthPadding = args.padding[1];\n            }\n            this.padding = [heightPadding, widthPadding];\n        }\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let rows;\n        let cols;\n        if (this.dataFormat === 'channelsFirst') {\n            if (inputShape[2] != null && inputShape[2] >= 0) {\n                rows = inputShape[2] + this.padding[0][0] + this.padding[0][1];\n            }\n            else {\n                rows = null;\n            }\n            if (inputShape[3] != null && inputShape[3] >= 0) {\n                cols = inputShape[3] + this.padding[1][0] + this.padding[1][1];\n            }\n            else {\n                cols = null;\n            }\n            return [inputShape[0], inputShape[1], rows, cols];\n        }\n        else {\n            if (inputShape[1] != null && inputShape[1] >= 0) {\n                rows = inputShape[1] + this.padding[0][0] + this.padding[0][1];\n            }\n            else {\n                rows = null;\n            }\n            if (inputShape[2] != null && inputShape[2] >= 0) {\n                cols = inputShape[2] + this.padding[1][0] + this.padding[1][1];\n            }\n            else {\n                cols = null;\n            }\n            return [inputShape[0], rows, cols, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => spatial2dPadding(getExactlyOneTensor(inputs), this.padding, this.dataFormat));\n    }\n    getConfig() {\n        const config = {\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nZeroPadding2D.className = 'ZeroPadding2D';\nserialization.registerClass(ZeroPadding2D);\n//# sourceMappingURL=padding.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Pooling Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode, checkPoolMode } from '../common';\nimport { InputSpec } from '../engine/topology';\nimport { Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { convOutputLength } from '../utils/conv_utils';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { preprocessConv2DInput, preprocessConv3DInput } from './convolutional';\n/**\n * 2D pooling.\n * @param x\n * @param poolSize\n * @param stridesdes strides. Defaults to [1, 1].\n * @param padding padding. Defaults to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param poolMode Mode of pooling. Defaults to 'max'.\n * @returns Result of the 2D pooling.\n */\nexport function pool2d(x, poolSize, strides, padding, dataFormat, poolMode) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        checkPoolMode(poolMode);\n        checkPaddingMode(padding);\n        if (strides == null) {\n            strides = [1, 1];\n        }\n        if (padding == null) {\n            padding = 'valid';\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (poolMode == null) {\n            poolMode = 'max';\n        }\n        // TODO(cais): Remove the preprocessing step once deeplearn.js supports\n        // dataFormat as an input argument.\n        x = preprocessConv2DInput(x, dataFormat); // x is NHWC after preprocessing.\n        let y;\n        const paddingString = (padding === 'same') ? 'same' : 'valid';\n        if (poolMode === 'max') {\n            // TODO(cais): Rank check?\n            y = tfc.maxPool(x, poolSize, strides, paddingString);\n        }\n        else { // 'avg'\n            // TODO(cais): Check the dtype and rank of x and give clear error message\n            //   if those are incorrect.\n            y = tfc.avgPool(\n            // TODO(cais): Rank check?\n            x, poolSize, strides, paddingString);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]); // NHWC -> NCHW.\n        }\n        return y;\n    });\n}\n/**\n * 3D pooling.\n * @param x\n * @param poolSize. Default to [1, 1, 1].\n * @param strides strides. Defaults to [1, 1, 1].\n * @param padding padding. Defaults to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param poolMode Mode of pooling. Defaults to 'max'.\n * @returns Result of the 3D pooling.\n */\nexport function pool3d(x, poolSize, strides, padding, dataFormat, poolMode) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        checkPoolMode(poolMode);\n        checkPaddingMode(padding);\n        if (strides == null) {\n            strides = [1, 1, 1];\n        }\n        if (padding == null) {\n            padding = 'valid';\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (poolMode == null) {\n            poolMode = 'max';\n        }\n        // x is NDHWC after preprocessing.\n        x = preprocessConv3DInput(x, dataFormat);\n        let y;\n        const paddingString = (padding === 'same') ? 'same' : 'valid';\n        if (poolMode === 'max') {\n            y = tfc.maxPool3d(x, poolSize, strides, paddingString);\n        }\n        else { // 'avg'\n            y = tfc.avgPool3d(x, poolSize, strides, paddingString);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 4, 1, 2, 3]); // NDHWC -> NCDHW.\n        }\n        return y;\n    });\n}\n/**\n * Abstract class for different pooling 1D layers.\n */\nexport class Pooling1D extends Layer {\n    /**\n     *\n     * @param args Parameters for the Pooling layer.\n     *\n     * config.poolSize defaults to 2.\n     */\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = 2;\n        }\n        super(args);\n        if (typeof args.poolSize === 'number') {\n            this.poolSize = [args.poolSize];\n        }\n        else if (Array.isArray(args.poolSize) &&\n            args.poolSize.length === 1 &&\n            typeof args.poolSize[0] === 'number') {\n            this.poolSize = args.poolSize;\n        }\n        else {\n            throw new ValueError(`poolSize for 1D convolutional layer must be a number or an ` +\n                `Array of a single number, but received ` +\n                `${JSON.stringify(args.poolSize)}`);\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else {\n            if (typeof args.strides === 'number') {\n                this.strides = [args.strides];\n            }\n            else if (Array.isArray(args.strides) &&\n                args.strides.length === 1 &&\n                typeof args.strides[0] === 'number') {\n                this.strides = args.strides;\n            }\n            else {\n                throw new ValueError(`strides for 1D convolutional layer must be a number or an ` +\n                    `Array of a single number, but received ` +\n                    `${JSON.stringify(args.strides)}`);\n            }\n        }\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const length = convOutputLength(inputShape[1], this.poolSize[0], this.padding, this.strides[0]);\n        return [inputShape[0], length, inputShape[2]];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Add dummy last dimension.\n            inputs = K.expandDims(getExactlyOneTensor(inputs), 2);\n            const output = this.poolingFunction(getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, 'channelsLast');\n            // Remove dummy last dimension.\n            return tfc.squeeze(output, [2]);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling1D extends Pooling1D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling1D.className = 'MaxPooling1D';\nserialization.registerClass(MaxPooling1D);\nexport class AveragePooling1D extends Pooling1D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling1D.className = 'AveragePooling1D';\nserialization.registerClass(AveragePooling1D);\n/**\n * Abstract class for different pooling 2D layers.\n */\nexport class Pooling2D extends Layer {\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = [2, 2];\n        }\n        super(args);\n        this.poolSize = Array.isArray(args.poolSize) ?\n            args.poolSize :\n            [args.poolSize, args.poolSize];\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else if (Array.isArray(args.strides)) {\n            if (args.strides.length !== 2) {\n                throw new ValueError(`If the strides property of a 2D pooling layer is an Array, ` +\n                    `it is expected to have a length of 2, but received length ` +\n                    `${args.strides.length}.`);\n            }\n            this.strides = args.strides;\n        }\n        else {\n            // `config.strides` is a number.\n            this.strides = [args.strides, args.strides];\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let rows = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        let cols = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        rows =\n            convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]);\n        cols =\n            convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], inputShape[1], rows, cols];\n        }\n        else {\n            return [inputShape[0], rows, cols, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n            dataFormat: this.dataFormat\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling2D extends Pooling2D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling2D.className = 'MaxPooling2D';\nserialization.registerClass(MaxPooling2D);\nexport class AveragePooling2D extends Pooling2D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling2D.className = 'AveragePooling2D';\nserialization.registerClass(AveragePooling2D);\n/**\n * Abstract class for different pooling 3D layers.\n */\nexport class Pooling3D extends Layer {\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = [2, 2, 2];\n        }\n        super(args);\n        this.poolSize = Array.isArray(args.poolSize) ?\n            args.poolSize :\n            [args.poolSize, args.poolSize, args.poolSize];\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else if (Array.isArray(args.strides)) {\n            if (args.strides.length !== 3) {\n                throw new ValueError(`If the strides property of a 3D pooling layer is an Array, ` +\n                    `it is expected to have a length of 3, but received length ` +\n                    `${args.strides.length}.`);\n            }\n            this.strides = args.strides;\n        }\n        else {\n            // `config.strides` is a number.\n            this.strides = [args.strides, args.strides, args.strides];\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 5 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let depths = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        let rows = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        let cols = this.dataFormat === 'channelsFirst' ? inputShape[4] : inputShape[3];\n        depths = convOutputLength(depths, this.poolSize[0], this.padding, this.strides[0]);\n        rows =\n            convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]);\n        cols =\n            convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], inputShape[1], depths, rows, cols];\n        }\n        else {\n            return [inputShape[0], depths, rows, cols, inputShape[4]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n            dataFormat: this.dataFormat\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling3D extends Pooling3D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool3d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling3D.className = 'MaxPooling3D';\nserialization.registerClass(MaxPooling3D);\nexport class AveragePooling3D extends Pooling3D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool3d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling3D.className = 'AveragePooling3D';\nserialization.registerClass(AveragePooling3D);\n/**\n * Abstract class for different global pooling 1D layers.\n */\nexport class GlobalPooling1D extends Layer {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n    }\n    computeOutputShape(inputShape) {\n        return [inputShape[0], inputShape[2]];\n    }\n    call(inputs, kwargs) {\n        throw new NotImplementedError();\n    }\n}\nexport class GlobalAveragePooling1D extends GlobalPooling1D {\n    constructor(args) {\n        super(args || {});\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            return tfc.mean(input, 1);\n        });\n    }\n}\n/** @nocollapse */\nGlobalAveragePooling1D.className = 'GlobalAveragePooling1D';\nserialization.registerClass(GlobalAveragePooling1D);\nexport class GlobalMaxPooling1D extends GlobalPooling1D {\n    constructor(args) {\n        super(args || {});\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            return tfc.max(input, 1);\n        });\n    }\n}\n/** @nocollapse */\nGlobalMaxPooling1D.className = 'GlobalMaxPooling1D';\nserialization.registerClass(GlobalMaxPooling1D);\n/**\n * Abstract class for different global pooling 2D layers.\n */\nexport class GlobalPooling2D extends Layer {\n    constructor(args) {\n        super(args);\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = inputShape;\n        if (this.dataFormat === 'channelsLast') {\n            return [inputShape[0], inputShape[3]];\n        }\n        else {\n            return [inputShape[0], inputShape[1]];\n        }\n    }\n    call(inputs, kwargs) {\n        throw new NotImplementedError();\n    }\n    getConfig() {\n        const config = { dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class GlobalAveragePooling2D extends GlobalPooling2D {\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                return tfc.mean(input, [1, 2]);\n            }\n            else {\n                return tfc.mean(input, [2, 3]);\n            }\n        });\n    }\n}\n/** @nocollapse */\nGlobalAveragePooling2D.className = 'GlobalAveragePooling2D';\nserialization.registerClass(GlobalAveragePooling2D);\nexport class GlobalMaxPooling2D extends GlobalPooling2D {\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                return tfc.max(input, [1, 2]);\n            }\n            else {\n                return tfc.max(input, [2, 3]);\n            }\n        });\n    }\n}\n/** @nocollapse */\nGlobalMaxPooling2D.className = 'GlobalMaxPooling2D';\nserialization.registerClass(GlobalMaxPooling2D);\n//# sourceMappingURL=pooling.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Layers that augment the functionality of a base layer.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { InputSpec, Layer, SymbolicTensor } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { VALID_BIDIRECTIONAL_MERGE_MODES } from '../keras_format/common';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { rnn, standardizeArgs } from './recurrent';\nimport { deserialize } from './serialization';\n/**\n * Abstract wrapper base class.\n *\n * Wrappers take another layer and augment it in various ways.\n * Do not use this class as a layer, it is only an abstract base class.\n * Two usable wrappers are the `TimeDistributed` and `Bidirectional` wrappers.\n */\nexport class Wrapper extends Layer {\n    constructor(args) {\n        // Porting Note: In PyKeras, `self.layer` is set prior to the calling\n        //   `super()`. But we can't do that here due to TypeScript's restriction.\n        //   See: https://github.com/Microsoft/TypeScript/issues/8277\n        //   As a result, we have to add checks in `get trainable()` and\n        //   `set trainable()` below in order to prevent using `this.layer` when\n        //   its value is `undefined`. The super constructor does use the getter\n        //   and the setter of `this.layer`.\n        super(args);\n        this.layer = args.layer;\n    }\n    build(inputShape) {\n        this.built = true;\n    }\n    // TODO(cais): Implement activityRegularizer getter.\n    get trainable() {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        if (this.layer != null) {\n            return this.layer.trainable;\n        }\n        else {\n            return false;\n        }\n    }\n    set trainable(value) {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        if (this.layer != null) {\n            this.layer.trainable = value;\n        }\n    }\n    get trainableWeights() {\n        return this.layer.trainableWeights;\n    }\n    // TODO(cais): Implement setter for trainableWeights.\n    get nonTrainableWeights() {\n        return this.layer.nonTrainableWeights;\n    }\n    // TODO(cais): Implement setter for nonTrainableWeights.\n    get updates() {\n        // tslint:disable-next-line:no-any\n        return this.layer._updates;\n    }\n    // TODO(cais): Implement getUpdatesFor().\n    get losses() {\n        return this.layer.losses;\n    }\n    // TODO(cais): Implement getLossesFor().\n    getWeights() {\n        return this.layer.getWeights();\n    }\n    setWeights(weights) {\n        this.layer.setWeights(weights);\n    }\n    getConfig() {\n        const config = {\n            'layer': {\n                'className': this.layer.getClassName(),\n                'config': this.layer.getConfig(),\n            }\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.layer != null) {\n            this.layer.setFastWeightInitDuringBuild(value);\n        }\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const layerConfig = config['layer'];\n        const layer = deserialize(layerConfig, customObjects);\n        delete config['layer'];\n        const newConfig = { layer };\n        Object.assign(newConfig, config);\n        return new cls(newConfig);\n    }\n}\nexport class TimeDistributed extends Wrapper {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < 3) {\n            throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received ` +\n                `input shape ${JSON.stringify(inputShape)}`);\n        }\n        this.inputSpec = [{ shape: inputShape }];\n        const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        if (!this.layer.built) {\n            this.layer.build(childInputShape);\n            this.layer.built = true;\n        }\n        super.build(inputShape);\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        const childOutputShape = this.layer.computeOutputShape(childInputShape);\n        const timesteps = inputShape[1];\n        return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1));\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            // TODO(cais): Add 'training' and 'useLearningPhase' to kwargs.\n            inputs = getExactlyOneTensor(inputs);\n            // Porting Note: In tfjs-layers, `inputs` are always concrete tensor\n            // values. Hence the inputs can't have an undetermined first (batch)\n            // dimension, which is why we always use the K.rnn approach here.\n            const step = (inputs, states) => {\n                // TODO(cais): Add useLearningPhase.\n                // NOTE(cais): `layer.call` may return a length-1 array of Tensor in\n                //   some cases (e.g., `layer` is a `Sequential` instance), which is\n                //   why `getExactlyOneTensor` is used below.\n                const output = getExactlyOneTensor(this.layer.call(inputs, kwargs));\n                return [output, []];\n            };\n            const rnnOutputs = rnn(step, inputs, [], false /* goBackwards */, null /* mask */, null /* constants */, false /* unroll */, true /* needPerStepOutputs */);\n            const y = rnnOutputs[1];\n            // TODO(cais): Add activity regularization.\n            // TODO(cais): Add useLearningPhase.\n            return y;\n        });\n    }\n}\n/** @nocollapse */\nTimeDistributed.className = 'TimeDistributed';\nserialization.registerClass(TimeDistributed);\nexport function checkBidirectionalMergeMode(value) {\n    generic_utils.checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, 'BidirectionalMergeMode', value);\n}\nconst DEFAULT_BIDIRECTIONAL_MERGE_MODE = 'concat';\nexport class Bidirectional extends Wrapper {\n    constructor(args) {\n        super(args);\n        // Note: When creating `this.forwardLayer`, the original Layer object\n        //   (`config.layer`) ought to be cloned. This is why we call\n        //   `getConfig()` followed by `deserialize()`. Without this cloning,\n        //   the layer names saved during serialization will incorrectly contain\n        //   the 'forward_' prefix. In Python Keras, this is done using\n        //   `copy.copy` (shallow copy), which does not have a simple equivalent\n        //   in JavaScript. JavaScript's `Object.assign()` does not copy\n        //   methods.\n        const layerConfig = args.layer.getConfig();\n        const forwDict = {};\n        forwDict['className'] = args.layer.getClassName();\n        forwDict['config'] = layerConfig;\n        this.forwardLayer = deserialize(forwDict);\n        layerConfig['goBackwards'] =\n            layerConfig['goBackwards'] === true ? false : true;\n        const backDict = {};\n        backDict['className'] = args.layer.getClassName();\n        backDict['config'] = layerConfig;\n        this.backwardLayer = deserialize(backDict);\n        this.forwardLayer.name = 'forward_' + this.forwardLayer.name;\n        this.backwardLayer.name = 'backward_' + this.backwardLayer.name;\n        this.mergeMode = args.mergeMode === undefined ?\n            DEFAULT_BIDIRECTIONAL_MERGE_MODE :\n            args.mergeMode;\n        checkBidirectionalMergeMode(this.mergeMode);\n        if (args.weights) {\n            throw new NotImplementedError('weights support is not implemented for Bidirectional layer yet.');\n        }\n        this._stateful = args.layer.stateful;\n        this.returnSequences = args.layer.returnSequences;\n        this.returnState = args.layer.returnState;\n        this.supportsMasking = true;\n        this._trainable = true;\n        this.inputSpec = args.layer.inputSpec;\n        this.numConstants = null;\n    }\n    get trainable() {\n        return this._trainable;\n    }\n    set trainable(value) {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        this._trainable = value;\n        if (this.forwardLayer != null) {\n            this.forwardLayer.trainable = value;\n        }\n        if (this.backwardLayer != null) {\n            this.backwardLayer.trainable = value;\n        }\n    }\n    getWeights() {\n        return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights());\n    }\n    setWeights(weights) {\n        const numWeights = weights.length;\n        const numeightsOver2 = Math.floor(numWeights / 2);\n        this.forwardLayer.setWeights(weights.slice(0, numeightsOver2));\n        this.backwardLayer.setWeights(weights.slice(numeightsOver2));\n    }\n    computeOutputShape(inputShape) {\n        let layerShapes = this.forwardLayer.computeOutputShape(inputShape);\n        if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) {\n            layerShapes = [layerShapes];\n        }\n        layerShapes = layerShapes;\n        let outputShape;\n        let outputShapes;\n        let stateShape;\n        if (this.returnState) {\n            stateShape = layerShapes.slice(1);\n            outputShape = layerShapes[0];\n        }\n        else {\n            outputShape = layerShapes[0];\n        }\n        outputShape = outputShape;\n        if (this.mergeMode === 'concat') {\n            outputShape[outputShape.length - 1] *= 2;\n            outputShapes = [outputShape];\n        }\n        else if (this.mergeMode == null) {\n            outputShapes = [outputShape, outputShape.slice()];\n        }\n        else {\n            outputShapes = [outputShape];\n        }\n        if (this.returnState) {\n            if (this.mergeMode == null) {\n                return outputShapes.concat(stateShape).concat(stateShape.slice());\n            }\n            return [outputShape].concat(stateShape).concat(stateShape.slice());\n        }\n        return generic_utils.singletonOrArray(outputShapes);\n    }\n    apply(inputs, kwargs) {\n        let initialState = kwargs == null ? null : kwargs['initialState'];\n        let constants = kwargs == null ? null : kwargs['constants'];\n        if (kwargs == null) {\n            kwargs = {};\n        }\n        const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n        inputs = standardized.inputs;\n        initialState = standardized.initialState;\n        constants = standardized.constants;\n        if (Array.isArray(inputs)) {\n            initialState = inputs.slice(1);\n            inputs = inputs[0];\n        }\n        if ((initialState == null || initialState.length === 0) &&\n            constants == null) {\n            return super.apply(inputs, kwargs);\n        }\n        const additionalInputs = [];\n        const additionalSpecs = [];\n        if (initialState != null) {\n            const numStates = initialState.length;\n            if (numStates % 2 > 0) {\n                throw new ValueError('When passing `initialState` to a Bidrectional RNN, ' +\n                    'the state should be an Array containing the states of ' +\n                    'the underlying RNNs.');\n            }\n            kwargs['initialState'] = initialState;\n            additionalInputs.push(...initialState);\n            const stateSpecs = initialState\n                .map(state => new InputSpec({ shape: state.shape }));\n            this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2);\n            this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2);\n            additionalSpecs.push(...stateSpecs);\n        }\n        if (constants != null) {\n            throw new NotImplementedError('Support for constants in Bidirectional layers is not ' +\n                'implemented yet.');\n        }\n        const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor;\n        for (const tensor of additionalInputs) {\n            if (tensor instanceof SymbolicTensor !== isSymbolicTensor) {\n                throw new ValueError('The initial state of a Bidirectional layer cannot be ' +\n                    'specified as a mix of symbolic and non-symbolic tensors');\n            }\n        }\n        if (isSymbolicTensor) {\n            // Compute the full input and specs, including the states.\n            const fullInput = [inputs].concat(additionalInputs);\n            const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n            // Perform the call temporarily and replace inputSpec.\n            // Note: with initial states symbolic calls and non-symbolic calls to\n            // this method differ in how the initial states are passed. For\n            // symbolic calls, the initial states are passed in the first arg, as\n            // an Array of SymbolicTensors; for non-symbolic calls, they are\n            // passed in the second arg as a part of the kwargs. Hence the need to\n            // temporarily modify inputSpec here.\n            // TODO(cais): Make refactoring so that this hacky code below is no\n            // longer needed.\n            const originalInputSpec = this.inputSpec;\n            this.inputSpec = fullInputSpec;\n            const output = super.apply(fullInput, kwargs);\n            this.inputSpec = originalInputSpec;\n            return output;\n        }\n        else {\n            return super.apply(inputs, kwargs);\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const initialState = kwargs['initialState'];\n            let y;\n            let yRev;\n            if (initialState == null) {\n                y = this.forwardLayer.call(inputs, kwargs);\n                yRev = this.backwardLayer.call(inputs, kwargs);\n            }\n            else {\n                const forwardState = initialState.slice(0, initialState.length / 2);\n                const backwardState = initialState.slice(initialState.length / 2);\n                y = this.forwardLayer.call(inputs, Object.assign(kwargs, { initialState: forwardState }));\n                yRev = this.backwardLayer.call(inputs, Object.assign(kwargs, { initialState: backwardState }));\n            }\n            let states;\n            if (this.returnState) {\n                if (Array.isArray(y)) {\n                    states = y.slice(1).concat(yRev.slice(1));\n                }\n                else {\n                }\n                y = y[0];\n                yRev = yRev[0];\n            }\n            if (this.returnSequences) {\n                yRev = tfc.reverse(yRev, 1);\n            }\n            let output;\n            if (this.mergeMode === 'concat') {\n                output = K.concatenate([y, yRev]);\n            }\n            else if (this.mergeMode === 'sum') {\n                output = tfc.add(y, yRev);\n            }\n            else if (this.mergeMode === 'ave') {\n                output = tfc.mul(.5, tfc.add(y, yRev));\n            }\n            else if (this.mergeMode === 'mul') {\n                output = tfc.mul(y, yRev);\n            }\n            else if (this.mergeMode == null) {\n                output = [y, yRev];\n            }\n            // TODO(cais): Properly set learning phase.\n            if (this.returnState) {\n                if (this.mergeMode == null) {\n                    return output.concat(states);\n                }\n                return [output].concat(states);\n            }\n            return output;\n        });\n    }\n    resetStates(states) {\n        this.forwardLayer.resetStates();\n        this.backwardLayer.resetStates();\n    }\n    build(inputShape) {\n        nameScope(this.forwardLayer.name, () => {\n            this.forwardLayer.build(inputShape);\n        });\n        nameScope(this.backwardLayer.name, () => {\n            this.backwardLayer.build(inputShape);\n        });\n        this.built = true;\n    }\n    computeMask(inputs, mask) {\n        if (Array.isArray(mask)) {\n            mask = mask[0];\n        }\n        let outputMask;\n        if (this.returnSequences) {\n            if (this.mergeMode == null) {\n                outputMask = [mask, mask];\n            }\n            else {\n                outputMask = mask;\n            }\n        }\n        else {\n            if (this.mergeMode == null) {\n                outputMask = [null, null];\n            }\n            else {\n                outputMask = null;\n            }\n        }\n        if (this.returnState) {\n            const states = this.forwardLayer.states;\n            const stateMask = states.map(state => null);\n            if (Array.isArray(outputMask)) {\n                return outputMask.concat(stateMask).concat(stateMask);\n            }\n            else {\n                return [outputMask].concat(stateMask).concat(stateMask);\n            }\n        }\n        else {\n            return outputMask;\n        }\n    }\n    get trainableWeights() {\n        return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights);\n    }\n    get nonTrainableWeights() {\n        return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights);\n    }\n    // TODO(cais): Implement constraints().\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.forwardLayer != null) {\n            this.forwardLayer.setFastWeightInitDuringBuild(value);\n        }\n        if (this.backwardLayer != null) {\n            this.backwardLayer.setFastWeightInitDuringBuild(value);\n        }\n    }\n    getConfig() {\n        const config = {\n            'mergeMode': this.mergeMode,\n        };\n        // TODO(cais): Add logic for `numConstants` once the property is added.\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        const rnnLayer = deserialize(config['layer']);\n        delete config['layer'];\n        // TODO(cais): Add logic for `numConstants` once the property is added.\n        if (config['numConstants'] != null) {\n            throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants ` +\n                `present is not supported yet.`);\n        }\n        // tslint:disable-next-line:no-any\n        const newConfig = config;\n        newConfig['layer'] = rnnLayer;\n        return new cls(newConfig);\n    }\n}\n/** @nocollapse */\nBidirectional.className = 'Bidirectional';\nserialization.registerClass(Bidirectional);\n//# sourceMappingURL=wrappers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { InputLayer } from './engine/input_layer';\nimport { Layer } from './engine/topology';\nimport { input } from './exports';\nimport { ELU, LeakyReLU, PReLU, ReLU, Softmax, ThresholdedReLU } from './layers/advanced_activations';\nimport { Conv1D, Conv2D, Conv2DTranspose, Conv3D, Cropping2D, SeparableConv2D, UpSampling2D } from './layers/convolutional';\nimport { DepthwiseConv2D } from './layers/convolutional_depthwise';\nimport { ConvLSTM2D, ConvLSTM2DCell } from './layers/convolutional_recurrent';\nimport { Activation, Dense, Dropout, Flatten, Masking, Permute, RepeatVector, Reshape, SpatialDropout1D } from './layers/core';\nimport { Embedding } from './layers/embeddings';\nimport { Add, Average, Concatenate, Dot, Maximum, Minimum, Multiply } from './layers/merge';\nimport { AlphaDropout, GaussianDropout, GaussianNoise } from './layers/noise';\nimport { BatchNormalization, LayerNormalization } from './layers/normalization';\nimport { ZeroPadding2D } from './layers/padding';\nimport { AveragePooling1D, AveragePooling2D, AveragePooling3D, GlobalAveragePooling1D, GlobalAveragePooling2D, GlobalMaxPooling1D, GlobalMaxPooling2D, MaxPooling1D, MaxPooling2D, MaxPooling3D } from './layers/pooling';\nimport { GRU, GRUCell, LSTM, LSTMCell, RNN, RNNCell, SimpleRNN, SimpleRNNCell, StackedRNNCells } from './layers/recurrent';\nimport { Bidirectional, TimeDistributed } from './layers/wrappers';\n// TODO(cais): Add doc string to all the public static functions in this\n//   class; include exectuable JavaScript code snippets where applicable\n//   (b/74074458).\n// Input Layer.\n/**\n * An input layer is an entry point into a `tf.LayersModel`.\n *\n * `InputLayer` is generated automatically for `tf.Sequential`` models by\n * specifying the `inputshape` or `batchInputShape` for the first layer.  It\n * should not be specified explicitly. However, it can be useful sometimes,\n * e.g., when constructing a sequential model from a subset of another\n * sequential model's layers. Like the code snippet below shows.\n *\n * ```js\n * // Define a model which simply adds two inputs.\n * const model1 = tf.sequential();\n * model1.add(tf.layers.dense({inputShape: [4], units: 3, activation: 'relu'}));\n * model1.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));\n * model1.summary();\n * model1.predict(tf.zeros([1, 4])).print();\n *\n * // Construct another model, reusing the second layer of `model1` while\n * // not using the first layer of `model1`. Note that you cannot add the second\n * // layer of `model` directly as the first layer of the new sequential model,\n * // because doing so will lead to an error related to the fact that the layer\n * // is not an input layer. Instead, you need to create an `inputLayer` and add\n * // it to the new sequential model before adding the reused layer.\n * const model2 = tf.sequential();\n * // Use an inputShape that matches the input shape of `model1`'s second\n * // layer.\n * model2.add(tf.layers.inputLayer({inputShape: [3]}));\n * model2.add(model1.layers[1]);\n * model2.summary();\n * model2.predict(tf.zeros([1, 3])).print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Inputs', namespace: 'layers'}\n */\nexport function inputLayer(args) {\n    return new InputLayer(args);\n}\n// Advanced Activation Layers.\n/**\n * Exponetial Linear Unit (ELU).\n *\n * It follows:\n * `f(x) =  alpha * (exp(x) - 1.) for x < 0`,\n * `f(x) = x for x >= 0`.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * References:\n *   - [Fast and Accurate Deep Network Learning by Exponential Linear Units\n * (ELUs)](https://arxiv.org/abs/1511.07289v1)\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function elu(args) {\n    return new ELU(args);\n}\n/**\n * Rectified Linear Unit activation function.\n *\n * Input shape:\n *   Arbitrary. Use the config field `inputShape` (Array of integers, does\n *   not include the sample axis) when using this layer as the first layer\n *   in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function reLU(args) {\n    return new ReLU(args);\n}\n/**\n * Leaky version of a rectified linear unit.\n *\n * It allows a small gradient when the unit is not active:\n * `f(x) = alpha * x for x < 0.`\n * `f(x) = x for x >= 0.`\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function leakyReLU(args) {\n    return new LeakyReLU(args);\n}\n/**\n * Parameterized version of a leaky rectified linear unit.\n *\n * It follows\n * `f(x) = alpha * x for x < 0.`\n * `f(x) = x for x >= 0.`\n * wherein `alpha` is a trainable weight.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function prelu(args) {\n    return new PReLU(args);\n}\n/**\n * Softmax activation layer.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function softmax(args) {\n    return new Softmax(args);\n}\n/**\n * Thresholded Rectified Linear Unit.\n *\n * It follows:\n * `f(x) = x for x > theta`,\n * `f(x) = 0 otherwise`.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * References:\n *   - [Zero-Bias Autoencoders and the Benefits of Co-Adapting\n * Features](http://arxiv.org/abs/1402.3337)\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function thresholdedReLU(args) {\n    return new ThresholdedReLU(args);\n}\n// Convolutional Layers.\n/**\n * 1D convolution layer (e.g., temporal convolution).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input over a single spatial (or temporal) dimension\n * to produce a tensor of outputs.\n *\n * If `use_bias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model, provide an\n * `inputShape` argument `Array` or `null`.\n *\n * For example, `inputShape` would be:\n * - `[10, 128]` for sequences of 10 vectors of 128-dimensional vectors\n * - `[null, 128]` for variable-length sequences of 128-dimensional vectors.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional',  namespace: 'layers'}\n */\nexport function conv1d(args) {\n    return new Conv1D(args);\n}\n/**\n * 2D convolution layer (e.g. spatial convolution over images).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input to produce a tensor of outputs.\n *\n * If `useBias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model,\n * provide the keyword argument `inputShape`\n * (Array of integers, does not include the sample axis),\n * e.g. `inputShape=[128, 128, 3]` for 128x128 RGB pictures\n * in `dataFormat='channelsLast'`.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv2d(args) {\n    return new Conv2D(args);\n}\n/**\n * Transposed convolutional layer (sometimes called Deconvolution).\n *\n * The need for transposed convolutions generally arises\n * from the desire to use a transformation going in the opposite direction of\n * a normal convolution, i.e., from something that has the shape of the output\n * of some convolution to something that has the shape of its input while\n * maintaining a connectivity pattern that is compatible with said\n * convolution.\n *\n * When using this layer as the first layer in a model, provide the\n * configuration `inputShape` (`Array` of integers, does not include the\n * sample axis), e.g., `inputShape: [128, 128, 3]` for 128x128 RGB pictures in\n * `dataFormat: 'channelsLast'`.\n *\n * Input shape:\n *   4D tensor with shape:\n *   `[batch, channels, rows, cols]` if `dataFormat` is `'channelsFirst'`.\n *   or 4D tensor with shape\n *   `[batch, rows, cols, channels]` if `dataFormat` is `'channelsLast`.\n *\n * Output shape:\n *   4D tensor with shape:\n *   `[batch, filters, newRows, newCols]` if `dataFormat` is\n * `'channelsFirst'`. or 4D tensor with shape:\n *   `[batch, newRows, newCols, filters]` if `dataFormat` is `'channelsLast'`.\n *\n * References:\n *   - [A guide to convolution arithmetic for deep\n * learning](https://arxiv.org/abs/1603.07285v1)\n *   - [Deconvolutional\n * Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv2dTranspose(args) {\n    return new Conv2DTranspose(args);\n}\n/**\n * 3D convolution layer (e.g. spatial convolution over volumes).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input to produce a tensor of outputs.\n *\n * If `useBias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model,\n * provide the keyword argument `inputShape`\n * (Array of integers, does not include the sample axis),\n * e.g. `inputShape=[128, 128, 128, 1]` for 128x128x128 grayscale volumes\n * in `dataFormat='channelsLast'`.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv3d(args) {\n    return new Conv3D(args);\n}\n/**\n * Depthwise separable 2D convolution.\n *\n * Separable convolution consists of first performing\n * a depthwise spatial convolution\n * (which acts on each input channel separately)\n * followed by a pointwise convolution which mixes together the resulting\n * output channels. The `depthMultiplier` argument controls how many\n * output channels are generated per input channel in the depthwise step.\n *\n * Intuitively, separable convolutions can be understood as\n * a way to factorize a convolution kernel into two smaller kernels,\n * or as an extreme version of an Inception block.\n *\n * Input shape:\n *   4D tensor with shape:\n *     `[batch, channels, rows, cols]` if data_format='channelsFirst'\n *   or 4D tensor with shape:\n *     `[batch, rows, cols, channels]` if data_format='channelsLast'.\n *\n * Output shape:\n *   4D tensor with shape:\n *     `[batch, filters, newRows, newCols]` if data_format='channelsFirst'\n *   or 4D tensor with shape:\n *     `[batch, newRows, newCols, filters]` if data_format='channelsLast'.\n *     `rows` and `cols` values might have changed due to padding.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function separableConv2d(args) {\n    return new SeparableConv2D(args);\n}\n/**\n * Cropping layer for 2D input (e.g., image).\n *\n * This layer can crop an input\n * at the top, bottom, left and right side of an image tensor.\n *\n * Input shape:\n *   4D tensor with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, rows, cols, channels]`\n *   - If `data_format` is `\"channels_first\"`:\n *     `[batch, channels, rows, cols]`.\n *\n * Output shape:\n *   4D with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, croppedRows, croppedCols, channels]`\n *    - If `dataFormat` is `\"channelsFirst\"`:\n *     `[batch, channels, croppedRows, croppedCols]`.\n *\n * Examples\n * ```js\n *\n * const model = tf.sequential();\n * model.add(tf.layers.cropping2D({cropping:[[2, 2], [2, 2]],\n *                                inputShape: [128, 128, 3]}));\n * //now output shape is [batch, 124, 124, 3]\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function cropping2D(args) {\n    return new Cropping2D(args);\n}\n/**\n * Upsampling layer for 2D inputs.\n *\n * Repeats the rows and columns of the data\n * by size[0] and size[1] respectively.\n *\n *\n * Input shape:\n *    4D tensor with shape:\n *     - If `dataFormat` is `\"channelsLast\"`:\n *         `[batch, rows, cols, channels]`\n *     - If `dataFormat` is `\"channelsFirst\"`:\n *        `[batch, channels, rows, cols]`\n *\n * Output shape:\n *     4D tensor with shape:\n *     - If `dataFormat` is `\"channelsLast\"`:\n *        `[batch, upsampledRows, upsampledCols, channels]`\n *     - If `dataFormat` is `\"channelsFirst\"`:\n *         `[batch, channels, upsampledRows, upsampledCols]`\n *\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function upSampling2d(args) {\n    return new UpSampling2D(args);\n}\n// Convolutional(depthwise) Layers.\n/**\n * Depthwise separable 2D convolution.\n *\n * Depthwise Separable convolutions consists in performing just the first step\n * in a depthwise spatial convolution (which acts on each input channel\n * separately). The `depthMultplier` argument controls how many output channels\n * are generated per input channel in the depthwise step.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function depthwiseConv2d(args) {\n    return new DepthwiseConv2D(args);\n}\n// Basic Layers.\n/**\n * Applies an activation function to an output.\n *\n * This layer applies element-wise activation function.  Other layers, notably\n * `dense` can also apply activation functions.  Use this isolated activation\n * function to extract the values before and after the\n * activation. For instance:\n *\n * ```js\n * const input = tf.input({shape: [5]});\n * const denseLayer = tf.layers.dense({units: 1});\n * const activationLayer = tf.layers.activation({activation: 'relu6'});\n *\n * // Obtain the output symbolic tensors by applying the layers in order.\n * const denseOutput = denseLayer.apply(input);\n * const activationOutput = activationLayer.apply(denseOutput);\n *\n * // Create the model based on the inputs.\n * const model = tf.model({\n *     inputs: input,\n *     outputs: [denseOutput, activationOutput]\n * });\n *\n * // Collect both outputs and print separately.\n * const [denseOut, activationOut] = model.predict(tf.randomNormal([6, 5]));\n * denseOut.print();\n * activationOut.print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function activation(args) {\n    return new Activation(args);\n}\n/**\n * Creates a dense (fully connected) layer.\n *\n * This layer implements the operation:\n *   `output = activation(dot(input, kernel) + bias)`\n *\n * `activation` is the element-wise activation function\n *   passed as the `activation` argument.\n *\n * `kernel` is a weights matrix created by the layer.\n *\n * `bias` is a bias vector created by the layer (only applicable if `useBias`\n * is `true`).\n *\n * **Input shape:**\n *\n *   nD `tf.Tensor` with shape: `(batchSize, ..., inputDim)`.\n *\n *   The most common situation would be\n *   a 2D input with shape `(batchSize, inputDim)`.\n *\n * **Output shape:**\n *\n *   nD tensor with shape: `(batchSize, ..., units)`.\n *\n *   For instance, for a 2D input with shape `(batchSize, inputDim)`,\n *   the output would have shape `(batchSize, units)`.\n *\n * Note: if the input to the layer has a rank greater than 2, then it is\n * flattened prior to the initial dot product with the kernel.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function dense(args) {\n    return new Dense(args);\n}\n/**\n * Applies\n * [dropout](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf) to\n * the input.\n *\n * Dropout consists in randomly setting a fraction `rate` of input units to 0 at\n * each update during training time, which helps prevent overfitting.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function dropout(args) {\n    return new Dropout(args);\n}\n/**\n * Spatial 1D version of Dropout.\n *\n * This Layer type performs the same function as the Dropout layer, but it drops\n * entire 1D feature maps instead of individual elements. For example, if an\n * input example consists of 3 timesteps and the feature map for each timestep\n * has a size of 4, a `spatialDropout1d` layer may zero out the feature maps\n * of the 1st timesteps and 2nd timesteps completely while sparing all feature\n * elements of the 3rd timestep.\n *\n * If adjacent frames (timesteps) are strongly correlated (as is normally the\n * case in early convolution layers), regular dropout will not regularize the\n * activation and will otherwise just result in merely an effective learning\n * rate decrease. In this case, `spatialDropout1d` will help promote\n * independence among feature maps and should be used instead.\n *\n * **Arguments:**\n *   rate: A floating-point number >=0 and <=1. Fraction of the input elements\n *     to drop.\n *\n * **Input shape:**\n *   3D tensor with shape `(samples, timesteps, channels)`.\n *\n * **Output shape:**\n *   Same as the input shape.\n *\n * References:\n *   - [Efficient Object Localization Using Convolutional\n *      Networks](https://arxiv.org/abs/1411.4280)\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function spatialDropout1d(args) {\n    return new SpatialDropout1D(args);\n}\n/**\n * Flattens the input. Does not affect the batch size.\n *\n * A `Flatten` layer flattens each batch in its inputs to 1D (making the output\n * 2D).\n *\n * For example:\n *\n * ```js\n * const input = tf.input({shape: [4, 3]});\n * const flattenLayer = tf.layers.flatten();\n * // Inspect the inferred output shape of the flatten layer, which\n * // equals `[null, 12]`. The 2nd dimension is 4 * 3, i.e., the result of the\n * // flattening. (The 1st dimension is the undermined batch size.)\n * console.log(JSON.stringify(flattenLayer.apply(input).shape));\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function flatten(args) {\n    return new Flatten(args);\n}\n/**\n * Repeats the input n times in a new dimension.\n *\n * ```js\n *  const model = tf.sequential();\n *  model.add(tf.layers.repeatVector({n: 4, inputShape: [2]}));\n *  const x = tf.tensor2d([[10, 20]]);\n *  // Use the model to do inference on a data point the model hasn't see\n *  model.predict(x).print();\n *  // output shape is now [batch, 2, 4]\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function repeatVector(args) {\n    return new RepeatVector(args);\n}\n/**\n * Reshapes an input to a certain shape.\n *\n * ```js\n * const input = tf.input({shape: [4, 3]});\n * const reshapeLayer = tf.layers.reshape({targetShape: [2, 6]});\n * // Inspect the inferred output shape of the Reshape layer, which\n * // equals `[null, 2, 6]`. (The 1st dimension is the undermined batch size.)\n * console.log(JSON.stringify(reshapeLayer.apply(input).shape));\n * ```\n *\n * Input shape:\n *   Arbitrary, although all dimensions in the input shape must be fixed.\n *   Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n *\n * Output shape:\n *   [batchSize, targetShape[0], targetShape[1], ...,\n *    targetShape[targetShape.length - 1]].\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function reshape(args) {\n    return new Reshape(args);\n}\n/**\n * Permutes the dimensions of the input according to a given pattern.\n *\n * Useful for, e.g., connecting RNNs and convnets together.\n *\n * Example:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.permute({\n *   dims: [2, 1],\n *   inputShape: [10, 64]\n * }));\n * console.log(model.outputShape);\n * // Now model's output shape is [null, 64, 10], where null is the\n * // unpermuted sample (batch) dimension.\n * ```\n *\n * Input shape:\n *   Arbitrary. Use the configuration field `inputShape` when using this\n *   layer as the first layer in a model.\n *\n * Output shape:\n *   Same rank as the input shape, but with the dimensions re-ordered (i.e.,\n *   permuted) according to the `dims` configuration of this layer.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function permute(args) {\n    return new Permute(args);\n}\n/**\n * Maps positive integers (indices) into dense vectors of fixed size.\n * eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]\n *\n * **Input shape:** 2D tensor with shape: `[batchSize, sequenceLength]`.\n *\n * **Output shape:** 3D tensor with shape: `[batchSize, sequenceLength,\n * outputDim]`.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function embedding(args) {\n    return new Embedding(args);\n}\n// Merge Layers.\n/**\n * Layer that performs element-wise addition on an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape, and returns a\n * single tensor (also of the same shape). The inputs are specified as an\n * `Array` when the `apply` method of the `Add` layer instance is called. For\n * example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const addLayer = tf.layers.add();\n * const sum = addLayer.apply([input1, input2]);\n * console.log(JSON.stringify(sum.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function add(args) {\n    return new Add(args);\n}\n/**\n * Layer that performs element-wise averaging on an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape, and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const averageLayer = tf.layers.average();\n * const average = averageLayer.apply([input1, input2]);\n * console.log(JSON.stringify(average.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function average(args) {\n    return new Average(args);\n}\n/**\n * Layer that concatenates an `Array` of inputs.\n *\n * It takes a list of tensors, all of the same shape except for the\n * concatenation axis, and returns a single tensor, the concatenation\n * of all inputs. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 3]});\n * const concatLayer = tf.layers.concatenate();\n * const output = concatLayer.apply([input1, input2]);\n * console.log(JSON.stringify(output.shape));\n * // You get [null, 2, 5], with the first dimension as the undetermined batch\n * // dimension. The last dimension (5) is the result of concatenating the\n * // last dimensions of the inputs (2 and 3).\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function concatenate(args) {\n    return new Concatenate(args);\n}\n/**\n * Layer that computes the element-wise maximum an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const maxLayer = tf.layers.maximum();\n * const max = maxLayer.apply([input1, input2]);\n * console.log(JSON.stringify(max.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function maximum(args) {\n    return new Maximum(args);\n}\n/**\n * Layer that computes the element-wise minimum of an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const minLayer = tf.layers.minimum();\n * const min = minLayer.apply([input1, input2]);\n * console.log(JSON.stringify(min.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function minimum(args) {\n    return new Minimum(args);\n}\n/**\n * Layer that multiplies (element-wise) an `Array` of inputs.\n *\n * It takes as input an Array of tensors, all of the same\n * shape, and returns a single tensor (also of the same shape).\n * For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const input3 = tf.input({shape: [2, 2]});\n * const multiplyLayer = tf.layers.multiply();\n * const product = multiplyLayer.apply([input1, input2, input3]);\n * console.log(product.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function multiply(args) {\n    return new Multiply(args);\n}\n/**\n * Layer that computes a dot product between samples in two tensors.\n *\n * E.g., if applied to a list of two tensors `a` and `b` both of shape\n * `[batchSize, n]`, the output will be a tensor of shape `[batchSize, 1]`,\n * where each entry at index `[i, 0]` will be the dot product between\n * `a[i, :]` and `b[i, :]`.\n *\n * Example:\n *\n * ```js\n * const dotLayer = tf.layers.dot({axes: -1});\n * const x1 = tf.tensor2d([[10, 20], [30, 40]]);\n * const x2 = tf.tensor2d([[-1, -2], [-3, -4]]);\n *\n * // Invoke the layer's apply() method in eager (imperative) mode.\n * const y = dotLayer.apply([x1, x2]);\n * y.print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function dot(args) {\n    return new Dot(args);\n}\n// Normalization Layers.\n/**\n * Batch normalization layer (Ioffe and Szegedy, 2014).\n *\n * Normalize the activations of the previous layer at each batch,\n * i.e. applies a transformation that maintains the mean activation\n * close to 0 and the activation standard deviation close to 1.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape` (Array of integers, does\n *   not include the sample axis) when calling the constructor of this class,\n *   if this layer is used as a first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Batch Normalization: Accelerating Deep Network Training by Reducing\n * Internal Covariate Shift](https://arxiv.org/abs/1502.03167)\n *\n * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}\n */\nexport function batchNormalization(args) {\n    return new BatchNormalization(args);\n}\n/**\n * Layer-normalization layer (Ba et al., 2016).\n *\n * Normalizes the activations of the previous layer for each given example in a\n * batch independently, instead of across a batch like in `batchNormalization`.\n * In other words, this layer applies a transformation that maintanis the mean\n * activation within each example close to0 and activation variance close to 1.\n *\n * Input shape:\n *   Arbitrary. Use the argument `inputShape` when using this layer as the first\n *   layer in a model.\n *\n * Output shape:\n *   Same as input.\n *\n * References:\n *   - [Layer Normalization](https://arxiv.org/abs/1607.06450)\n *\n * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}\n */\nexport function layerNormalization(args) {\n    return new LayerNormalization(args);\n}\n// Padding Layers.\n/**\n * Zero-padding layer for 2D input (e.g., image).\n *\n * This layer can add rows and columns of zeros\n * at the top, bottom, left and right side of an image tensor.\n *\n * Input shape:\n *   4D tensor with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, rows, cols, channels]`\n *   - If `data_format` is `\"channels_first\"`:\n *     `[batch, channels, rows, cols]`.\n *\n * Output shape:\n *   4D with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, paddedRows, paddedCols, channels]`\n *    - If `dataFormat` is `\"channelsFirst\"`:\n *     `[batch, channels, paddedRows, paddedCols]`.\n *\n * @doc {heading: 'Layers', subheading: 'Padding', namespace: 'layers'}\n */\nexport function zeroPadding2d(args) {\n    return new ZeroPadding2D(args);\n}\n// Pooling Layers.\n/**\n * Average pooling operation for spatial data.\n *\n * Input shape: `[batchSize, inLength, channels]`\n *\n * Output shape: `[batchSize, pooledLength, channels]`\n *\n * `tf.avgPool1d` is an alias.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling1d(args) {\n    return new AveragePooling1D(args);\n}\nexport function avgPool1d(args) {\n    return averagePooling1d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling1d(args) {\n    return averagePooling1d(args);\n}\n/**\n * Average pooling operation for spatial data.\n *\n * Input shape:\n *  - If `dataFormat === CHANNEL_LAST`:\n *      4D tensor with shape:\n *      `[batchSize, rows, cols, channels]`\n *  - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *      `[batchSize, channels, rows, cols]`\n *\n * Output shape\n *  - If `dataFormat === CHANNEL_LAST`:\n *      4D tensor with shape:\n *      `[batchSize, pooleRows, pooledCols, channels]`\n *  - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *      `[batchSize, channels, pooleRows, pooledCols]`\n *\n * `tf.avgPool2d` is an alias.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling2d(args) {\n    return new AveragePooling2D(args);\n}\nexport function avgPool2d(args) {\n    return averagePooling2d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling2d(args) {\n    return averagePooling2d(args);\n}\n/**\n * Average pooling operation for 3D data.\n *\n * Input shape\n *   - If `dataFormat === channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, depths, rows, cols, channels]`\n *   - If `dataFormat === channelsFirst`:\n *      4D tensor with shape:\n *       `[batchSize, channels, depths, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`\n *   - If `dataFormat=channelsFirst`:\n *       5D tensor with shape:\n *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling3d(args) {\n    return new AveragePooling3D(args);\n}\nexport function avgPool3d(args) {\n    return averagePooling3d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling3d(args) {\n    return averagePooling3d(args);\n}\n/**\n * Global average pooling operation for temporal data.\n *\n * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.\n *\n * Output Shape:2D tensor with shape: `[batchSize, features]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalAveragePooling1d(args) {\n    return new GlobalAveragePooling1D(args);\n}\n/**\n * Global average pooling operation for spatial data.\n *\n * Input shape:\n *   - If `dataFormat` is `CHANNEL_LAST`:\n *       4D tensor with shape: `[batchSize, rows, cols, channels]`.\n *   - If `dataFormat` is `CHANNEL_FIRST`:\n *       4D tensor with shape: `[batchSize, channels, rows, cols]`.\n *\n * Output shape:\n *   2D tensor with shape: `[batchSize, channels]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalAveragePooling2d(args) {\n    return new GlobalAveragePooling2D(args);\n}\n/**\n * Global max pooling operation for temporal data.\n *\n * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.\n *\n * Output Shape:2D tensor with shape: `[batchSize, features]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalMaxPooling1d(args) {\n    return new GlobalMaxPooling1D(args);\n}\n/**\n * Global max pooling operation for spatial data.\n *\n * Input shape:\n *   - If `dataFormat` is `CHANNEL_LAST`:\n *       4D tensor with shape: `[batchSize, rows, cols, channels]`.\n *   - If `dataFormat` is `CHANNEL_FIRST`:\n *       4D tensor with shape: `[batchSize, channels, rows, cols]`.\n *\n * Output shape:\n *   2D tensor with shape: `[batchSize, channels]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalMaxPooling2d(args) {\n    return new GlobalMaxPooling2D(args);\n}\n/**\n * Max pooling operation for temporal data.\n *\n * Input shape:  `[batchSize, inLength, channels]`\n *\n * Output shape: `[batchSize, pooledLength, channels]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling1d(args) {\n    return new MaxPooling1D(args);\n}\n/**\n * Max pooling operation for spatial data.\n *\n * Input shape\n *   - If `dataFormat === CHANNEL_LAST`:\n *       4D tensor with shape:\n *       `[batchSize, rows, cols, channels]`\n *   - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *       `[batchSize, channels, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=CHANNEL_LAST`:\n *       4D tensor with shape:\n *       `[batchSize, pooleRows, pooledCols, channels]`\n *   - If `dataFormat=CHANNEL_FIRST`:\n *       4D tensor with shape:\n *       `[batchSize, channels, pooleRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling2d(args) {\n    return new MaxPooling2D(args);\n}\n/**\n * Max pooling operation for 3D data.\n *\n * Input shape\n *   - If `dataFormat === channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, depths, rows, cols, channels]`\n *   - If `dataFormat === channelsFirst`:\n *      5D tensor with shape:\n *       `[batchSize, channels, depths, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`\n *   - If `dataFormat=channelsFirst`:\n *       5D tensor with shape:\n *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling3d(args) {\n    return new MaxPooling3D(args);\n}\n// Recurrent Layers.\n/**\n * Gated Recurrent Unit - Cho et al. 2014.\n *\n * This is an `RNN` layer consisting of one `GRUCell`. However, unlike\n * the underlying `GRUCell`, the `apply` method of `SimpleRNN` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const rnn = tf.layers.gru({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `GRUCell`'s number of units.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function gru(args) {\n    return new GRU(args);\n}\n/**\n * Cell class for `GRU`.\n *\n * `GRUCell` is distinct from the `RNN` subclass `GRU` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `GRU` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.gruCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `GRUCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.gruCell({units: 4}),\n *   tf.layers.gruCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `gruCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `GRUCell`, use the\n * `tf.layers.gru`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function gruCell(args) {\n    return new GRUCell(args);\n}\n/**\n * Long-Short Term Memory layer - Hochreiter 1997.\n *\n * This is an `RNN` layer consisting of one `LSTMCell`. However, unlike\n * the underlying `LSTMCell`, the `apply` method of `LSTM` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const lstm = tf.layers.lstm({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = lstm.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `LSTMCell`'s number of units.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function lstm(args) {\n    return new LSTM(args);\n}\n/**\n * Cell class for `LSTM`.\n *\n * `LSTMCell` is distinct from the `RNN` subclass `LSTM` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `LSTM` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.lstmCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `LSTMCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.lstmCell({units: 4}),\n *   tf.layers.lstmCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `lstmCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `LSTMCell`, use the\n * `tf.layers.lstm`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function lstmCell(args) {\n    return new LSTMCell(args);\n}\n/**\n * Fully-connected RNN where the output is to be fed back to input.\n *\n * This is an `RNN` layer consisting of one `SimpleRNNCell`. However, unlike\n * the underlying `SimpleRNNCell`, the `apply` method of `SimpleRNN` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const rnn = tf.layers.simpleRNN({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `SimpleRNNCell`'s number of units.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function simpleRNN(args) {\n    return new SimpleRNN(args);\n}\n/**\n * Cell class for `SimpleRNN`.\n *\n * `SimpleRNNCell` is distinct from the `RNN` subclass `SimpleRNN` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `SimpleRNN` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.simpleRNNCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `SimpleRNNCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.simpleRNNCell({units: 4}),\n *   tf.layers.simpleRNNCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `SimpleRNNCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `SimpleRNNCell`, use the\n * `tf.layers.simpleRNN`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function simpleRNNCell(args) {\n    return new SimpleRNNCell(args);\n}\n/**\n * Convolutional LSTM layer - Xingjian Shi 2015.\n *\n * This is an `ConvRNN2D` layer consisting of one `ConvLSTM2DCell`. However,\n * unlike the underlying `ConvLSTM2DCell`, the `apply` method of `ConvLSTM2D`\n * operates on a sequence of inputs. The shape of the input (not including the\n * first, batch dimension) needs to be 4-D, with the first dimension being time\n * steps. For example:\n *\n * ```js\n * const filters = 3;\n * const kernelSize = 3;\n *\n * const batchSize = 4;\n * const sequenceLength = 2;\n * const size = 5;\n * const channels = 3;\n *\n * const inputShape = [batchSize, sequenceLength, size, size, channels];\n * const input = tf.ones(inputShape);\n *\n * const layer = tf.layers.convLstm2d({filters, kernelSize});\n *\n * const output = layer.apply(input);\n * ```\n */\n/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */\nexport function convLstm2d(args) {\n    return new ConvLSTM2D(args);\n}\n/**\n * Cell class for `ConvLSTM2D`.\n *\n * `ConvLSTM2DCell` is distinct from the `ConvRNN2D` subclass `ConvLSTM2D` in\n * that its `call` method takes the input data of only a single time step and\n * returns the cell's output at the time step, while `ConvLSTM2D` takes the\n * input data over a number of time steps. For example:\n *\n * ```js\n * const filters = 3;\n * const kernelSize = 3;\n *\n * const sequenceLength = 1;\n * const size = 5;\n * const channels = 3;\n *\n * const inputShape = [sequenceLength, size, size, channels];\n * const input = tf.ones(inputShape);\n *\n * const cell = tf.layers.convLstm2dCell({filters, kernelSize});\n *\n * cell.build(input.shape);\n *\n * const outputSize = size - kernelSize + 1;\n * const outShape = [sequenceLength, outputSize, outputSize, filters];\n *\n * const initialH = tf.zeros(outShape);\n * const initialC = tf.zeros(outShape);\n *\n * const [o, h, c] = cell.call([input, initialH, initialC], {});\n * ```\n */\n/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */\nexport function convLstm2dCell(args) {\n    return new ConvLSTM2DCell(args);\n}\n/**\n * Base class for recurrent layers.\n *\n * Input shape:\n *   3D tensor with shape `[batchSize, timeSteps, inputDim]`.\n *\n * Output shape:\n *   - if `returnState`, an Array of tensors (i.e., `tf.Tensor`s). The first\n *     tensor is the output. The remaining tensors are the states at the\n *     last time step, each with shape `[batchSize, units]`.\n *   - if `returnSequences`, the output will have shape\n *     `[batchSize, timeSteps, units]`.\n *   - else, the output will have shape `[batchSize, units]`.\n *\n * Masking:\n *   This layer supports masking for input data with a variable number\n *   of timesteps. To introduce masks to your data,\n *   use an embedding layer with the `mask_zero` parameter\n *   set to `True`.\n *\n * Notes on using statefulness in RNNs:\n *   You can set RNN layers to be 'stateful', which means that the states\n *   computed for the samples in one batch will be reused as initial states\n *   for the samples in the next batch. This assumes a one-to-one mapping\n *   between samples in different successive batches.\n *\n *   To enable statefulness:\n *     - specify `stateful: true` in the layer constructor.\n *     - specify a fixed batch size for your model, by passing\n *       if sequential model:\n *         `batchInputShape=[...]` to the first layer in your model.\n *       else for functional model with 1 or more Input layers:\n *         `batchShape=[...]` to all the first layers in your model.\n *       This is the expected shape of your inputs *including the batch size*.\n *       It should be a tuple of integers, e.g. `(32, 10, 100)`.\n *     - specify `shuffle=False` when calling fit().\n *\n *   To reset the states of your model, call `.resetStates()` on either\n *   a specific layer, or on your entire model.\n *\n * Note on specifying the initial state of RNNs\n *   You can specify the initial state of RNN layers symbolically by\n *   calling them with the option `initialState`. The value of\n *   `initialState` should be a tensor or list of tensors representing\n *   the initial state of the RNN layer.\n *\n *   You can specify the initial state of RNN layers numerically by\n *   calling `resetStates` with the keyword argument `states`. The value of\n *   `states` should be a numpy array or list of numpy arrays representing\n *   the initial state of the RNN layer.\n *\n * Note on passing external constants to RNNs\n *   You can pass \"external\" constants to the cell using the `constants`\n *   keyword argument of `RNN.call` method. This requires that the `cell.call`\n *   method accepts the same keyword argument `constants`. Such constants\n *   can be used to conditon the cell transformation on additional static inputs\n *   (not changing over time), a.k.a an attention mechanism.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function rnn(args) {\n    return new RNN(args);\n}\n/**\n * Wrapper allowing a stack of RNN cells to behave as a single cell.\n *\n * Used to implement efficient stacked RNNs.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function stackedRNNCells(args) {\n    return new StackedRNNCells(args);\n}\n// Wrapper Layers.\n/** @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'} */\nexport function bidirectional(args) {\n    return new Bidirectional(args);\n}\n/**\n * This wrapper applies a layer to every temporal slice of an input.\n *\n * The input should be at least 3D,  and the dimension of the index `1` will be\n * considered to be the temporal dimension.\n *\n * Consider a batch of 32 samples, where each sample is a sequence of 10 vectors\n * of 16 dimensions. The batch input shape of the layer is then `[32,  10,\n * 16]`, and the `inputShape`, not including the sample dimension, is\n * `[10, 16]`.\n *\n * You can then use `TimeDistributed` to apply a `Dense` layer to each of the 10\n * timesteps, independently:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.timeDistributed({\n *   layer: tf.layers.dense({units: 8}),\n *   inputShape: [10, 16],\n * }));\n *\n * // Now model.outputShape = [null, 10, 8].\n * // The output will then have shape `[32, 10, 8]`.\n *\n * // In subsequent layers, there is no need for `inputShape`:\n * model.add(tf.layers.timeDistributed({layer: tf.layers.dense({units: 32})}));\n * console.log(JSON.stringify(model.outputs[0].shape));\n * // Now model.outputShape = [null, 10, 32].\n * ```\n *\n * The output will then have shape `[32, 10, 32]`.\n *\n * `TimeDistributed` can be used with arbitrary layers, not just `Dense`, for\n * instance a `Conv2D` layer.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.timeDistributed({\n *   layer: tf.layers.conv2d({filters: 64, kernelSize: [3, 3]}),\n *   inputShape: [10, 299, 299, 3],\n * }));\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'}\n */\nexport function timeDistributed(args) {\n    return new TimeDistributed(args);\n}\n// Aliases for pooling.\nexport const globalMaxPool1d = globalMaxPooling1d;\nexport const globalMaxPool2d = globalMaxPooling2d;\nexport const maxPool1d = maxPooling1d;\nexport const maxPool2d = maxPooling2d;\nexport { Layer, RNN, RNNCell, input /* alias for tf.input */ };\n/**\n * Apply additive zero-centered Gaussian noise.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * This is useful to mitigate overfitting\n * (you could see it as a form of random data augmentation).\n * Gaussian Noise (GS) is a natural choice as corruption process\n * for real valued inputs.\n *\n * # Arguments\n *     stddev: float, standard deviation of the noise distribution.\n *\n * # Input shape\n *         Arbitrary. Use the keyword argument `input_shape`\n *         (tuple of integers, does not include the samples axis)\n *         when using this layer as the first layer in a model.\n *\n * # Output shape\n *         Same shape as input.\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function gaussianNoise(args) {\n    return new GaussianNoise(args);\n}\n/**\n * Apply multiplicative 1-centered Gaussian noise.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](\n *      http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function gaussianDropout(args) {\n    return new GaussianDropout(args);\n}\n/**\n * Applies Alpha Dropout to the input.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs\n * to their original values, in order to ensure the self-normalizing property\n * even after this dropout.\n * Alpha Dropout fits well to Scaled Exponential Linear Units\n * by randomly setting activations to the negative saturation value.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the\n *     shape for randomly generated keep/drop flags.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function alphaDropout(args) {\n    return new AlphaDropout(args);\n}\n/**\n * Masks a sequence by using a mask value to skip timesteps.\n *\n * If all features for a given sample timestep are equal to `mask_value`,\n * then the sample timestep will be masked (skipped) in all downstream layers\n * (as long as they support masking).\n *\n * If any downstream layer does not support masking yet receives such\n * an input mask, an exception will be raised.\n *\n * Arguments:\n *   - `maskValue`: Either None or mask value to skip.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * @doc {heading: 'Layers', subheading: 'Mask', namespace: 'layers'}\n */\nexport function masking(args) {\n    return new Masking(args);\n}\n//# sourceMappingURL=exports_layers.js.map", "import * as losses from './losses';\nimport * as metrics from './metrics';\n/**\n * Binary accuracy metric function.\n *\n * `yTrue` and `yPred` can have 0-1 values. Example:\n * ```js\n * const x = tf.tensor2d([[1, 1, 1, 1], [0, 0, 0, 0]], [2, 4]);\n * const y = tf.tensor2d([[1, 0, 1, 0], [0, 0, 0, 1]], [2, 4]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * `yTrue` and `yPred` can also have floating-number values between 0 and 1, in\n * which case the values will be thresholded at 0.5 to yield 0-1 values (i.e.,\n * a value >= 0.5 and <= 1.0 is interpreted as 1.\n * )\n * Example:\n * ```js\n * const x = tf.tensor1d([1, 1, 1, 1, 0, 0, 0, 0]);\n * const y = tf.tensor1d([0.2, 0.4, 0.6, 0.8, 0.2, 0.3, 0.4, 0.7]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryAccuracy(yTrue, yPred) {\n    return metrics.binaryAccuracy(yTrue, yPred);\n}\n/**\n * Binary crossentropy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0], [1], [1], [1]]);\n * const y = tf.tensor2d([[0], [0], [0.5], [1]]);\n * const crossentropy = tf.metrics.binaryCrossentropy(x, y);\n * crossentropy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction, probabilities for the `1` case.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryCrossentropy(yTrue, yPred) {\n    return metrics.binaryCrossentropy(yTrue, yPred);\n}\n/**\n * Sparse categorical accuracy metric function.\n *\n * Example:\n * ```js\n *\n * const yTrue = tf.tensor1d([1, 1, 2, 2, 0]);\n * const yPred = tf.tensor2d(\n *      [[0, 1, 0], [1, 0, 0], [0, 0.4, 0.6], [0, 0.6, 0.4], [0.7, 0.3, 0]]);\n * const crossentropy = tf.metrics.sparseCategoricalAccuracy(yTrue, yPred);\n * crossentropy.print();\n * ```\n *\n * @param yTrue True labels: indices.\n * @param yPred Predicted probabilities or logits.\n * @returns Accuracy tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function sparseCategoricalAccuracy(yTrue, yPred) {\n    return metrics.sparseCategoricalAccuracy(yTrue, yPred);\n}\n/**\n * Categorical accuracy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0, 0, 0, 1], [0, 0, 0, 1]]);\n * const y = tf.tensor2d([[0.1, 0.8, 0.05, 0.05], [0.1, 0.05, 0.05, 0.8]]);\n * const accuracy = tf.metrics.categoricalAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth: one-hot encoding of categories.\n * @param yPred Binary Tensor of prediction: probabilities or logits for the\n *   same categories as in `yTrue`.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalAccuracy(yTrue, yPred) {\n    return metrics.categoricalAccuracy(yTrue, yPred);\n}\n/**\n * Categorical crossentropy between an output tensor and a target tensor.\n *\n * @param target A tensor of the same shape as `output`.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalCrossentropy(yTrue, yPred) {\n    return metrics.categoricalCrossentropy(yTrue, yPred);\n}\n/**\n * Computes the precision of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const precision = tf.metrics.precision(x, y);\n * precision.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to be contain only 0-1 values.\n * @param yPred The predicted values. Expected to be contain only 0-1 values.\n * @return Precision Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function precision(yTrue, yPred) {\n    return metrics.precision(yTrue, yPred);\n}\n/**\n * Computes the recall of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const recall = tf.metrics.recall(x, y);\n * recall.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to be contain only 0-1 values.\n * @param yPred The predicted values. Expected to be contain only 0-1 values.\n * @return Recall Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function recall(yTrue, yPred) {\n    return metrics.recall(yTrue, yPred);\n}\n/**\n * Loss or metric function: Cosine proximity.\n *\n * Mathematically, cosine proximity is defined as:\n *   `-sum(l2Normalize(yTrue) * l2Normalize(yPred))`,\n * wherein `l2Normalize()` normalizes the L2 norm of the input to 1 and `*`\n * represents element-wise multiplication.\n *\n * ```js\n * const yTrue = tf.tensor2d([[1, 0], [1, 0]]);\n * const yPred = tf.tensor2d([[1 / Math.sqrt(2), 1 / Math.sqrt(2)], [0, 1]]);\n * const proximity = tf.metrics.cosineProximity(yTrue, yPred);\n * proximity.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Cosine proximity Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function cosineProximity(yTrue, yPred) {\n    return losses.cosineProximity(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean absolute error.\n *\n * Mathematically, mean absolute error is defined as:\n *   `mean(abs(yPred - yTrue))`,\n * wherein the `mean` is applied over feature dimensions.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [0, 0], [2, 3]]);\n * const yPred = tf.tensor2d([[0, 1], [0, 1], [-2, -3]]);\n * const mse = tf.metrics.meanAbsoluteError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsoluteError(yTrue, yPred) {\n    return losses.meanAbsoluteError(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean absolute percentage error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [10, 20]]);\n * const yPred = tf.tensor2d([[0, 1], [11, 24]]);\n * const mse = tf.metrics.meanAbsolutePercentageError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MAPE`, `tf.metrics.mape`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute percentage error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsolutePercentageError(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\nexport function MAPE(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\nexport function mape(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean squared error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [3, 4]]);\n * const yPred = tf.tensor2d([[0, 1], [-3, -4]]);\n * const mse = tf.metrics.meanSquaredError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MSE`, `tf.metrics.mse`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean squared error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanSquaredError(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\nexport function MSE(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\nexport function mse(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\n//# sourceMappingURL=exports_metrics.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport { modelFromJSON } from './models';\n//# sourceMappingURL=exports_models.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as regularizers from './regularizers';\n// tslint:disable-next-line:max-line-length\nimport { L1L2 } from './regularizers';\n/**\n * Regularizer for L1 and L2 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l1 * abs(x)) + sum(l2 * x^2)\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l1l2(config) {\n    return new L1L2(config);\n}\n/**\n * Regularizer for L1 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l1 * abs(x))\n * @param args l1 config.\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l1(config) {\n    return regularizers.l1(config);\n}\n/**\n * Regularizer for L2 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l2 * x^2)\n * @param args l2 config.\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l2(config) {\n    return regularizers.l2(config);\n}\n//# sourceMappingURL=exports_regularizers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/callbacks.py */\nimport { BaseCallback } from './base_callbacks';\nimport { LayersModel } from './engine/training';\nimport { NotImplementedError } from './errors';\nimport { resolveScalarsInLogs } from './logs';\nexport class Callback extends BaseCallback {\n    constructor() {\n        super(...arguments);\n        /** Instance of `keras.models.Model`. Reference of the model being trained. */\n        this.model = null;\n    }\n    setModel(model) {\n        if (!(model instanceof LayersModel)) {\n            throw new Error('model must be a LayersModel, not some other Container');\n        }\n        this.model = model;\n    }\n}\nfunction less(currVal, prevVal) {\n    return currVal < prevVal;\n}\nfunction greater(currVal, prevVal) {\n    return currVal > prevVal;\n}\n/**\n * A Callback that stops training when a monitored quantity has stopped\n * improving.\n */\nexport class EarlyStopping extends Callback {\n    constructor(args) {\n        super();\n        if (args == null) {\n            args = {};\n        }\n        if (args.restoreBestWeights) {\n            throw new NotImplementedError('restoreBestWeights = True is not implemented in EarlyStopping yet.');\n        }\n        this.monitor = args.monitor || 'val_loss';\n        this.minDelta = Math.abs(args.minDelta || 0);\n        this.patience = args.patience || 0;\n        this.verbose = args.verbose || 0;\n        this.mode = args.mode || 'auto';\n        this.baseline = args.baseline;\n        if (['auto', 'min', 'max'].indexOf(this.mode) === -1) {\n            console.warn(`EarlyStopping mode '${this.mode}' is invalid. ` +\n                `Falling back to mode 'auto'.`);\n            this.mode = 'auto';\n        }\n        if (this.mode === 'min') {\n            this.monitorFunc = less;\n        }\n        else if (this.mode === 'max') {\n            this.monitorFunc = greater;\n        }\n        else {\n            // For mode === 'auto'.\n            if (this.monitor.indexOf('acc') !== -1) {\n                this.monitorFunc = greater;\n            }\n            else {\n                this.monitorFunc = less;\n            }\n        }\n        if (this.monitorFunc === less) {\n            this.minDelta *= -1;\n        }\n    }\n    async onTrainBegin(logs) {\n        this.wait = 0;\n        this.stoppedEpoch = 0;\n        if (this.baseline != null) {\n            this.best = this.baseline;\n        }\n        else {\n            this.best = this.monitorFunc === less ? Infinity : -Infinity;\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        await resolveScalarsInLogs(logs);\n        const current = this.getMonitorValue(logs);\n        if (current == null) {\n            return;\n        }\n        if (this.monitorFunc(current - this.minDelta, this.best)) {\n            this.best = current;\n            this.wait = 0;\n            // TODO(cais): Logic for restoreBestWeights.\n        }\n        else {\n            this.wait++;\n            if (this.wait >= this.patience) {\n                this.stoppedEpoch = epoch;\n                this.model.stopTraining = true;\n            }\n            // TODO(cais): Logic for restoreBestWeights.\n        }\n    }\n    async onTrainEnd(logs) {\n        if (this.stoppedEpoch > 0 && this.verbose) {\n            console.log(`Epoch ${this.stoppedEpoch}: early stopping.`);\n        }\n    }\n    getMonitorValue(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        const monitorValue = logs[this.monitor];\n        if (monitorValue == null) {\n            console.warn(`Metric for EarlyStopping ${this.monitor} is not available. ` +\n                `Available metrics are: ${Object.keys(logs)}`);\n        }\n        return monitorValue;\n    }\n}\n/**\n * Factory function for a Callback that stops training when a monitored\n * quantity has stopped improving.\n *\n * Early stopping is a type of regularization, and protects model against\n * overfitting.\n *\n * The following example based on fake data illustrates how this callback\n * can be used during `tf.LayersModel.fit()`:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense({\n *   units: 3,\n *   activation: 'softmax',\n *   kernelInitializer: 'ones',\n *   inputShape: [2]\n * }));\n * const xs = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const ys = tf.tensor2d([[1, 0, 0], [0, 1, 0]], [2, 3]);\n * const xsVal = tf.tensor2d([4, 3, 2, 1], [2, 2]);\n * const ysVal = tf.tensor2d([[0, 0, 1], [0, 1, 0]], [2, 3]);\n * model.compile(\n *     {loss: 'categoricalCrossentropy', optimizer: 'sgd', metrics: ['acc']});\n *\n * // Without the EarlyStopping callback, the val_acc value would be:\n * //   0.5, 0.5, 0.5, 0.5, ...\n * // With val_acc being monitored, training should stop after the 2nd epoch.\n * const history = await model.fit(xs, ys, {\n *   epochs: 10,\n *   validationData: [xsVal, ysVal],\n *   callbacks: tf.callbacks.earlyStopping({monitor: 'val_acc'})\n * });\n *\n * // Expect to see a length-2 array.\n * console.log(history.history.val_acc);\n * ```\n *\n * @doc {\n *   heading: 'Callbacks',\n *   namespace: 'callbacks'\n * }\n */\nexport function earlyStopping(args) {\n    return new EarlyStopping(args);\n}\nexport const callbacks = { earlyStopping };\n//# sourceMappingURL=callbacks.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// This file lists all exports of TensorFlow.js Layers\nimport * as constraints from './exports_constraints';\nimport * as initializers from './exports_initializers';\nimport * as layers from './exports_layers';\nimport * as metrics from './exports_metrics';\nimport * as models from './exports_models';\nimport * as regularizers from './exports_regularizers';\nexport { CallbackList, CustomCallback, History } from './base_callbacks';\nexport { Callback, callbacks, EarlyStopping } from './callbacks';\nexport { InputSpec, SymbolicTensor } from './engine/topology';\nexport { LayersModel } from './engine/training';\nexport { input, loadLayersModel, model, registerCallbackConstructor, sequential } from './exports';\nexport { RNN } from './layers/recurrent';\nexport { Sequential } from './models';\nexport { LayerVariable } from './variables';\nexport { version as version_layers } from './version';\nexport { constraints, initializers, layers, metrics, models, regularizers };\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/** DataType enum. */\nexport var DataType;\n(function (DataType) {\n    DataType[DataType[\"DT_INVALID\"] = 0] = \"DT_INVALID\";\n    DataType[DataType[\"DT_FLOAT\"] = 1] = \"DT_FLOAT\";\n    DataType[DataType[\"DT_DOUBLE\"] = 2] = \"DT_DOUBLE\";\n    DataType[DataType[\"DT_INT32\"] = 3] = \"DT_INT32\";\n    DataType[DataType[\"DT_UINT8\"] = 4] = \"DT_UINT8\";\n    DataType[DataType[\"DT_INT16\"] = 5] = \"DT_INT16\";\n    DataType[DataType[\"DT_INT8\"] = 6] = \"DT_INT8\";\n    DataType[DataType[\"DT_STRING\"] = 7] = \"DT_STRING\";\n    DataType[DataType[\"DT_COMPLEX64\"] = 8] = \"DT_COMPLEX64\";\n    DataType[DataType[\"DT_INT64\"] = 9] = \"DT_INT64\";\n    DataType[DataType[\"DT_BOOL\"] = 10] = \"DT_BOOL\";\n    DataType[DataType[\"DT_QINT8\"] = 11] = \"DT_QINT8\";\n    DataType[DataType[\"DT_QUINT8\"] = 12] = \"DT_QUINT8\";\n    DataType[DataType[\"DT_QINT32\"] = 13] = \"DT_QINT32\";\n    DataType[DataType[\"DT_BFLOAT16\"] = 14] = \"DT_BFLOAT16\";\n    DataType[DataType[\"DT_FLOAT_REF\"] = 101] = \"DT_FLOAT_REF\";\n    DataType[DataType[\"DT_DOUBLE_REF\"] = 102] = \"DT_DOUBLE_REF\";\n    DataType[DataType[\"DT_INT32_REF\"] = 103] = \"DT_INT32_REF\";\n    DataType[DataType[\"DT_UINT8_REF\"] = 104] = \"DT_UINT8_REF\";\n    DataType[DataType[\"DT_INT16_REF\"] = 105] = \"DT_INT16_REF\";\n    DataType[DataType[\"DT_INT8_REF\"] = 106] = \"DT_INT8_REF\";\n    DataType[DataType[\"DT_STRING_REF\"] = 107] = \"DT_STRING_REF\";\n    DataType[DataType[\"DT_COMPLEX64_REF\"] = 108] = \"DT_COMPLEX64_REF\";\n    DataType[DataType[\"DT_INT64_REF\"] = 109] = \"DT_INT64_REF\";\n    DataType[DataType[\"DT_BOOL_REF\"] = 110] = \"DT_BOOL_REF\";\n    DataType[DataType[\"DT_QINT8_REF\"] = 111] = \"DT_QINT8_REF\";\n    DataType[DataType[\"DT_QUINT8_REF\"] = 112] = \"DT_QUINT8_REF\";\n    DataType[DataType[\"DT_QINT32_REF\"] = 113] = \"DT_QINT32_REF\";\n    DataType[DataType[\"DT_BFLOAT16_REF\"] = 114] = \"DT_BFLOAT16_REF\";\n})(DataType || (DataType = {}));\nexport var SaverDef;\n(function (SaverDef) {\n    /** CheckpointFormatVersion enum. */\n    let CheckpointFormatVersion;\n    (function (CheckpointFormatVersion) {\n        CheckpointFormatVersion[CheckpointFormatVersion[\"LEGACY\"] = 0] = \"LEGACY\";\n        CheckpointFormatVersion[CheckpointFormatVersion[\"V1\"] = 1] = \"V1\";\n        CheckpointFormatVersion[CheckpointFormatVersion[\"V2\"] = 2] = \"V2\";\n    })(CheckpointFormatVersion = SaverDef.CheckpointFormatVersion || (SaverDef.CheckpointFormatVersion = {}));\n})(SaverDef || (SaverDef = {}));\n//# sourceMappingURL=compiled_api.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst CUSTOM_OPS = {};\n/**\n * Register an Op for graph model executor. This allow you to register\n * TensorFlow custom op or override existing op.\n *\n * Here is an example of registering a new MatMul Op.\n * ```js\n * const customMatmul = (node) =>\n *    tf.matMul(\n *        node.inputs[0], node.inputs[1],\n *        node.attrs['transpose_a'], node.attrs['transpose_b']);\n *\n * tf.registerOp('MatMul', customMatmul);\n * ```\n * The inputs and attrs of the node object is based on the TensorFlow op\n * registry.\n *\n * @param name The Tensorflow Op name.\n * @param opFunc An op function which is called with the current graph node\n * during execution and needs to return a tensor or a list of tensors. The node\n * has the following attributes:\n *    - attr: A map from attribute name to its value\n *    - inputs: A list of input tensors\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function registerOp(name, opFunc) {\n    const opMapper = {\n        tfOpName: name,\n        category: 'custom',\n        inputs: [],\n        attrs: [],\n        customExecutor: opFunc\n    };\n    CUSTOM_OPS[name] = opMapper;\n}\n/**\n * Retrieve the OpMapper object for the registered op.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function getRegisteredOp(name) {\n    return CUSTOM_OPS[name];\n}\n/**\n * Deregister the Op for graph model executor.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function deregisterOp(name) {\n    delete CUSTOM_OPS[name];\n}\n//# sourceMappingURL=register.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { clone, util } from '@tensorflow/tfjs-core';\nexport function getParamValue(paramName, node, tensorMap, context) {\n    const inputParam = node.inputParams[paramName];\n    if (inputParam && inputParam.inputIndexStart !== undefined) {\n        const start = inputParam.inputIndexStart;\n        const end = inputParam.inputIndexEnd === 0 ?\n            undefined :\n            (inputParam.inputIndexEnd === undefined ? start + 1 :\n                inputParam.inputIndexEnd);\n        if (inputParam.type === 'tensor') {\n            return getTensor(node.inputNames[inputParam.inputIndexStart], tensorMap, context);\n        }\n        if (inputParam.type === 'tensors') {\n            const inputs = node.inputNames.slice(start, end);\n            return inputs.map(name => getTensor(name, tensorMap, context));\n        }\n        const tensor = getTensor(node.inputNames.slice(start)[0], tensorMap, context);\n        const data = tensor.dataSync();\n        return inputParam.type === 'number' ?\n            data[0] :\n            util.toNestedArray(tensor.shape, data);\n    }\n    const attrParam = node.attrParams[paramName];\n    return attrParam && attrParam.value;\n}\n/**\n * Retrieve the tensor based on input name by extracting the node name and\n * output index information.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n */\nexport function getTensor(name, tensorsMap, context) {\n    const [nodeName, index] = parseNodeName(name);\n    const contextId = context.currentContextIds.find(contextId => {\n        return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId)];\n    });\n    return contextId !== undefined ?\n        tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] :\n        undefined;\n}\n/**\n * Retrieve the tensors based on input name for current context.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n */\nexport function getTensorsForCurrentContenxt(name, tensorsMap, context) {\n    return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];\n}\n/**\n * Returns the node name and index from the Node input name.\n * @param inputName The input name of the node, in format of\n * node_name:output_index, i.e. MatMul:0, if the output_index is not set, it is\n * default to 0.\n */\nexport function getNodeNameAndIndex(inputName, context) {\n    const [nodeName, index] = parseNodeName(inputName);\n    return [\n        getNodeNameWithContextId(nodeName, context && context.currentContextId),\n        index\n    ];\n}\nfunction getNodeNameWithContextId(name, contextId) {\n    return !!contextId ? `${name}-${contextId}` : name;\n}\nexport function parseNodeName(name) {\n    const parts = name.split(':');\n    if (parts.length === 1) {\n        return [name, 0];\n    }\n    const nodeName = parts[0];\n    return [nodeName, Number(parts[parts.length - 1])];\n}\nexport function split(arr, size) {\n    const res = [];\n    for (let i = 0; i < arr.length; i += size) {\n        res.push(arr.slice(i, i + size));\n    }\n    return res;\n}\nexport function getPadding(node, tensorMap, context) {\n    let pad = getParamValue('pad', node, tensorMap, context);\n    if (pad === 'explicit') {\n        // This is 1d array, we need to convert it to 2d array\n        pad = getParamValue('explicitPaddings', node, tensorMap, context);\n        const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];\n        for (let i = 0; i < 4; i++) {\n            explicitPadding[i][0] = pad[i * 2];\n            explicitPadding[i][1] = pad[i * 2 + 1];\n        }\n        return explicitPadding;\n    }\n    return pad;\n}\n/**\n *  Reuse the tensor if it is marked as keep, otherwise clone the tensor to\n *  avoid disposal. This is important for TensorArray and TensorList ops, since\n *  internally they use a tensor as the id for TensorArray and TensorList, and\n * to simplify lookup, they also use Tensor.id as the key to the internal map.\n * These id tensors have been marked as kept in the backend, we need avoid clone\n * them in order to create new Tensor.id.\n * @param tensor\n */\nexport function cloneTensor(tensor) {\n    return tensor.kept ? tensor : clone(tensor);\n}\n//# sourceMappingURL=utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Add',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AddV2',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AddN',\n        'category': 'arithmetic',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'BiasAdd',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sub',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'RealDiv',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Div',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'DivNoNan',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'FloorDiv',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Mul',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Maximum',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Minimum',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Pow',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'SquaredDifference',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Mod',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'FloorMod',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    }\n];\n//# sourceMappingURL=arithmetic.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Abs',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Acos',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Asin',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atan',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atan2',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'y', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Ceil',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ClipByValue',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'clip_value_min', 'name': 'clipValueMin', 'type': 'number' },\n            { 'tfName': 'clip_value_max', 'name': 'clipValueMax', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Complex',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'real', 'type': 'tensor' },\n            { 'start': 1, 'name': 'imag', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ComplexAbs',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Cos',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Cosh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Elu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Exp',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Floor',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Log',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Imag',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'outputType',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Neg',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Real',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'outputType',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Prelu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'alpha', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Relu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Relu6',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'clipValueMin',\n                'name': 'clipValueMin',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'clipValueMax',\n                'name': 'clipValueMax',\n                'type': 'number',\n                'defaultValue': 6\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Selu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sigmoid',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sin',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sinh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sqrt',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Rsqrt',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Square',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Tan',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Tanh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sign',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Round',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Expm1',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Log1p',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Reciprocal',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Softplus',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Asinh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Acosh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atanh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Erf',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Prod',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axes', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'keep_dims',\n                'name': 'keepDims',\n                'type': 'bool',\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LeakyRelu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'alpha',\n                'name': 'alpha',\n                'type': 'number',\n                'defaultValue': 0.2\n            },\n            {\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=basic_math.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'LoopCond',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'name': 'pred', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Switch',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'data', 'type': 'tensor' },\n            { 'start': 1, 'name': 'pred', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Merge',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Enter',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'frame_name', 'name': 'frameName', 'type': 'string' },\n            { 'tfName': 'is_constant', 'name': 'isConstant', 'type': 'bool' }\n        ]\n    },\n    {\n        'tfOpName': 'Exit',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'NextIteration',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'size', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },\n            { 'tfName': 'dynamic_size', 'name': 'dynamicSize', 'type': 'bool' },\n            { 'tfName': 'clear_after_read', 'name': 'clearAfterRead', 'type': 'bool' },\n            {\n                'tfName': 'identical_element_shapes',\n                'name': 'identicalElementShapes',\n                'type': 'bool'\n            },\n            { 'tfName': 'tensor_array_name', 'name': 'name', 'type': 'string' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayWriteV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayReadV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{\n                'tfName': 'dtype',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    },\n    {\n        'tfOpName': 'TensorArrayGatherV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayScatterV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorArrayConcatV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }, {\n                'tfName': 'element_shape_except0',\n                'name': 'elementShapeExcept0',\n                'type': 'shape',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArraySplitV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 2, 'name': 'lengths', 'type': 'number[]' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorArraySizeV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'flowIn', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayCloseV3',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'StatelessIf',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'cond', 'type': 'tensor' },\n            { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }\n        ],\n        'attrs': [\n            { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },\n            { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'If',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'cond', 'type': 'tensor' },\n            { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }\n        ],\n        'attrs': [\n            { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },\n            { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'StatelessWhile',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },\n            { 'tfName': 'body', 'name': 'body', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'While',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },\n            { 'tfName': 'body', 'name': 'body', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListScatter',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' }\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListScatterV2',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 3, 'name': 'numElements', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListGather',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListGetItem',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListSetItem',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListReserve',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 1, 'name': 'numElements', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListFromTensor',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' }\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListStack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' },\n            { 'tfName': 'num_elements', 'name': 'numElements', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListSplit',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 2, 'name': 'lengths', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListConcat',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListPopBack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListPushBack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n];\n//# sourceMappingURL=control.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'AvgPool',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPool',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPoolWithArgmax',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' }, {\n                'tfName': 'include_batch_in_index',\n                'name': 'includeBatchInIndex',\n                'type': 'bool'\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AvgPool3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPool3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Conv1D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'stride', 'name': 'stride', 'type': 'number' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NWC'\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'dilation',\n                'name': 'dilation',\n                'type': 'number',\n                'defaultValue': 1\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Conv2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            { 'tfName': 'useCudnnOnGpu', 'name': 'useCudnnOnGpu', 'type': 'bool' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': '_FusedConv2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'use_cudnn_on_gpu',\n                'name': 'useCudnnOnGpu',\n                'type': 'bool',\n                'defaultValue': true\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'dilations',\n                'name': 'dilations',\n                'type': 'number[]',\n                'defaultValue': [1, 1, 1, 1]\n            },\n            {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.0001\n            },\n        ]\n    },\n    {\n        'tfOpName': 'Conv2DBackpropInput',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 2, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 0, 'name': 'outputShape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n        ]\n    },\n    {\n        'tfOpName': 'DepthwiseConv2d',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'input', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'DepthwiseConv2dNative',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'input', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'FusedDepthwiseConv2dNative',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'dilations',\n                'name': 'dilations',\n                'type': 'number[]',\n                'defaultValue': [1, 1, 1, 1]\n            },\n            {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Conv3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ],\n    },\n    {\n        'tfOpName': 'Dilation2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'rates', 'name': 'dilations', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }\n        ]\n    }\n];\n//# sourceMappingURL=convolution.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Fill',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n            { 'start': 1, 'name': 'value', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'LinSpace',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'start', 'type': 'number' },\n            { 'start': 1, 'name': 'stop', 'type': 'number' },\n            { 'start': 2, 'name': 'num', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'OneHot',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'depth', 'type': 'number' },\n            { 'start': 2, 'name': 'onValue', 'type': 'number', 'defaultValue': 1 },\n            { 'start': 3, 'name': 'offValue', 'type': 'number', 'defaultValue': 0 },\n        ],\n        'attrs': [\n            {\n                'tfName': 'axis',\n                'name': 'axis',\n                'type': 'number',\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Ones',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'OnesLike',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'RandomUniform',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'minval',\n                'name': 'minval',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'maxval',\n                'name': 'maxval',\n                'type': 'number',\n                'defaultValue': 1\n            },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'seed2',\n                'name': 'seed2',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Range',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'start', 'type': 'number' },\n            { 'start': 1, 'name': 'stop', 'type': 'number' },\n            { 'start': 2, 'name': 'step', 'type': 'number', 'defaultValue': 0 },\n        ],\n        'attrs': [{ 'tfName': 'Tidx', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TruncatedNormal',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'means',\n                'name': 'mean',\n                'type': 'number',\n                'defaultValue': 0.0\n            },\n            {\n                'tfName': 'stddev',\n                'name': 'stdDev',\n                'type': 'number',\n                'defaultValue': 1.0\n            },\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number' }, {\n                'tfName': 'seed2',\n                'name': 'seed2',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Zeros',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'ZerosLike',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'Multinomial',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'logits', 'type': 'tensor' },\n            { 'start': 1, 'name': 'numSamples', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number' },\n            { 'tfName': 'seed2', 'name': 'seed2', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'output_dtype', 'name': 'output_dtype', 'type': 'dtype' }\n        ]\n    }\n];\n//# sourceMappingURL=creation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'NonMaxSuppressionV2',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV3',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV4',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'T_threshold',\n                'name': 'threshold',\n                'type': 'dtype',\n                'notSupported': true\n            },\n            {\n                'tfName': 'pad_to_max_output_size',\n                'name': 'padToMaxOutputSize',\n                'type': 'bool'\n            }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV5',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' },\n            { 'start': 5, 'name': 'softNmsSigma', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Where',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ListDiff',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'y', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=dynamic.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'TopKV2',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'k', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'sorted', 'name': 'sorted', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Unique',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n    },\n    {\n        'tfOpName': 'UniqueV2',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' },\n        ],\n    },\n];\n//# sourceMappingURL=evaluation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'PlaceholderWithDefault',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'default', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'Placeholder',\n        'category': 'graph',\n        'attrs': [\n            { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    { 'tfOpName': 'Const', 'category': 'graph' }, {\n        'tfOpName': 'Identity',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'IdentityN',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Snapshot',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Rank',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Size',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Shape',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'ShapeN',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Print',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'data', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'message', 'name': 'message', 'type': 'string' }, {\n                'tfName': 'first_n',\n                'name': 'firstN',\n                'type': 'number',\n                'notSupported': true\n            },\n            {\n                'tfName': 'summarize',\n                'name': 'summarize',\n                'type': 'number',\n                'defaultValue': 3\n            }\n        ]\n    },\n    { 'tfOpName': 'NoOp', 'category': 'graph', 'inputs': [] }, {\n        'tfOpName': 'StopGradient',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'FakeQuantWithMinMaxVars',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'min', 'name': 'min', 'type': 'number' },\n            { 'tfName': 'max', 'name': 'max', 'type': 'number' }\n        ]\n    }\n];\n//# sourceMappingURL=graph.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'ResizeBilinear',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'images', 'type': 'tensor' },\n            { 'start': 1, 'name': 'size', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ResizeNearestNeighbor',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'images', 'type': 'tensor' },\n            { 'start': 1, 'name': 'size', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'CropAndResize',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'image', 'type': 'tensor' },\n            { 'start': 1, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 2, 'name': 'boxInd', 'type': 'tensor' },\n            { 'start': 3, 'name': 'cropSize', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'method', 'name': 'method', 'type': 'string' }, {\n                'tfName': 'extrapolation_value',\n                'name': 'extrapolationValue',\n                'type': 'number'\n            }\n        ]\n    }\n];\n//# sourceMappingURL=image.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Equal',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'NotEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Greater',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'GreaterEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Less',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LessEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalAnd',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalNot',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalOr',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Select',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n            { 'start': 1, 'name': 'a', 'type': 'tensor' },\n            { 'start': 2, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'SelectV2',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n            { 'start': 1, 'name': 'a', 'type': 'tensor' },\n            { 'start': 2, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=logical.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': '_FusedMatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' }, {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.0001\n            },\n            {\n                'tfName': 'transpose_a',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'transpose_b',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'transpose_a',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'transpose_b',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'BatchMatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'adj_x',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'adj_y',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'BatchMatMulV2',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'adj_x',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'adj_y',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Transpose',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'perm', 'type': 'number[]' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=matrices.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'FusedBatchNorm',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'FusedBatchNormV2',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'FusedBatchNormV3',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'LRN',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'depth_radius',\n                'name': 'radius',\n                'type': 'number',\n                'defaultValue': 5\n            },\n            { 'tfName': 'bias', 'name': 'bias', 'type': 'number', 'defaultValue': 1.0 },\n            {\n                'tfName': 'alpha',\n                'name': 'alpha',\n                'type': 'number',\n                'defaultValue': 1.0\n            },\n            {\n                'tfName': 'beta',\n                'name': 'beta',\n                'type': 'number',\n                'defaultValue': 0.5\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Softmax',\n        'category': 'normalization',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'LogSoftmax',\n        'category': 'normalization',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'SparseToDense',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },\n            { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'defaultValue': true,\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Max',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Mean',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Min',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Sum',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'All',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Any',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'ArgMax',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'ArgMin',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Prod',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Cumsum',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'exclusive', 'name': 'exclusive', 'type': 'bool' },\n            { 'tfName': 'reverse', 'name': 'reverse', 'type': 'bool' }\n        ]\n    }\n];\n//# sourceMappingURL=reduction.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'ConcatV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'end': -1, 'name': 'tensors', 'type': 'tensors' },\n            { 'start': -1, 'name': 'axis', 'type': 'number' }\n        ],\n        'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]\n    },\n    {\n        'tfOpName': 'Concat',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 1, 'end': 0, 'name': 'tensors', 'type': 'tensors' },\n            { 'start': 0, 'name': 'axis', 'type': 'number' }\n        ],\n        'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]\n    },\n    {\n        'tfOpName': 'GatherV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'Gather',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Reverse',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'dims', 'type': 'bool', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ReverseV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Slice',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'begin', 'type': 'number[]' },\n            { 'start': 2, 'name': 'size', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'StridedSlice',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'begin', 'type': 'number[]' },\n            { 'start': 2, 'name': 'end', 'type': 'number[]' },\n            { 'start': 3, 'name': 'strides', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'begin_mask',\n                'name': 'beginMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'end_mask',\n                'name': 'endMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'new_axis_mask',\n                'name': 'newAxisMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'ellipsis_mask',\n                'name': 'ellipsisMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'shrink_axis_mask',\n                'name': 'shrinkAxisMask',\n                'type': 'number',\n                'defaultValue': 0\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Pack',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'Unpack',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'num',\n                'name': 'num',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Tile',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'reps', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Split',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'axis', 'type': 'number', 'defaultValue': 0 },\n            { 'start': 1, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'num_split',\n                'name': 'numOrSizeSplits',\n                'type': 'number',\n                'defaultValue': 1\n            }]\n    },\n    {\n        'tfOpName': 'SplitV',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'numOrSizeSplits', 'type': 'number[]' },\n            { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'ScatterNd',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'values', 'type': 'tensor' },\n            { 'start': 2, 'name': 'shape', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'GatherNd',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'SparseToDense',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },\n            { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'defaultValue': false,\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=slice_join.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'FFT',\n        'category': 'spectral',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'IFFT',\n        'category': 'spectral',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'RFFT',\n        'category': 'spectral',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' }, {\n                'start': 1,\n                'name': 'fft_length',\n                'type': 'number',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'IRFFT',\n        'category': 'spectral',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' }, {\n                'start': 1,\n                'name': 'fft_length',\n                'type': 'number',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=spectral.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Cast',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'SrcT',\n                'name': 'sdtype',\n                'type': 'dtype',\n                'notSupported': true\n            },\n            { 'tfName': 'DstT', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'ExpandDims',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Pad',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' },\n        ],\n        'attrs': [{\n                'tfName': 'constant_value',\n                'name': 'constantValue',\n                'type': 'number',\n                'defaultValue': 0\n            }]\n    },\n    {\n        'tfOpName': 'PadV2',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' }, {\n                'start': 2,\n                'name': 'constantValue',\n                'type': 'number',\n                'defaultValue': 0\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Reshape',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'shape', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Squeeze',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'axis',\n                'tfDeprecatedName': 'squeeze_dims',\n                'name': 'axis',\n                'type': 'number[]'\n            }]\n    },\n    {\n        'tfOpName': 'SpaceToBatchND',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'paddings', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'BatchToSpaceND',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'crops', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'DepthToSpace',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'block_size', 'name': 'blockSize', 'type': 'number' },\n            { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string' }\n        ]\n    },\n    {\n        'tfOpName': 'BroadcastTo',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': []\n    }\n];\n//# sourceMappingURL=transformation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport * as tensorflow from '../data/compiled_api';\nimport { getRegisteredOp } from './custom_op/register';\nimport { getNodeNameAndIndex } from './executors/utils';\nimport * as arithmetic from './op_list/arithmetic';\nimport * as basicMath from './op_list/basic_math';\nimport * as control from './op_list/control';\nimport * as convolution from './op_list/convolution';\nimport * as creation from './op_list/creation';\nimport * as dynamic from './op_list/dynamic';\nimport * as evaluation from './op_list/evaluation';\nimport * as graph from './op_list/graph';\nimport * as image from './op_list/image';\nimport * as logical from './op_list/logical';\nimport * as matrices from './op_list/matrices';\nimport * as normalization from './op_list/normalization';\nimport * as reduction from './op_list/reduction';\nimport * as sliceJoin from './op_list/slice_join';\nimport * as spectral from './op_list/spectral';\nimport * as transformation from './op_list/transformation';\nexport class OperationMapper {\n    // Singleton instance for the mapper\n    static get Instance() {\n        return this._instance || (this._instance = new this());\n    }\n    // Loads the op mapping from the JSON file.\n    constructor() {\n        const ops = [\n            arithmetic, basicMath, control, convolution, creation, dynamic,\n            evaluation, logical, image, graph, matrices, normalization, reduction,\n            sliceJoin, spectral, transformation\n        ];\n        const mappersJson = [].concat(...ops.map(op => op.json));\n        this.opMappers = mappersJson.reduce((map, mapper) => {\n            map[mapper.tfOpName] = mapper;\n            return map;\n        }, {});\n    }\n    // Converts the model inference graph from Tensorflow GraphDef to local\n    // representation for TensorFlow.js API\n    transformGraph(graph, signature = {}) {\n        const tfNodes = graph.node;\n        const placeholders = [];\n        const weights = [];\n        const initNodes = [];\n        const nodes = tfNodes.reduce((map, node) => {\n            map[node.name] = this.mapNode(node);\n            if (node.op.startsWith('Placeholder')) {\n                placeholders.push(map[node.name]);\n            }\n            else if (node.op === 'Const') {\n                weights.push(map[node.name]);\n            }\n            else if (node.input == null || node.input.length === 0) {\n                initNodes.push(map[node.name]);\n            }\n            return map;\n        }, {});\n        let inputs = [];\n        const outputs = [];\n        let inputNodeNameToKey = {};\n        let outputNodeNameToKey = {};\n        if (signature != null) {\n            inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);\n            outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);\n        }\n        const allNodes = Object.keys(nodes);\n        allNodes.forEach(key => {\n            const node = nodes[key];\n            node.inputNames.forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                node.inputs.push(nodes[nodeName]);\n                nodes[nodeName].children.push(node);\n            });\n        });\n        // if signature has not outputs set, add any node that does not have\n        // outputs.\n        if (Object.keys(outputNodeNameToKey).length === 0) {\n            allNodes.forEach(key => {\n                const node = nodes[key];\n                if (node.children.length === 0) {\n                    outputs.push(node);\n                }\n            });\n        }\n        else {\n            Object.keys(outputNodeNameToKey).forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                const node = nodes[nodeName];\n                if (node != null) {\n                    node.signatureKey = outputNodeNameToKey[name];\n                    outputs.push(node);\n                }\n            });\n        }\n        if (Object.keys(inputNodeNameToKey).length > 0) {\n            Object.keys(inputNodeNameToKey).forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                const node = nodes[nodeName];\n                if (node) {\n                    node.signatureKey = inputNodeNameToKey[name];\n                    inputs.push(node);\n                }\n            });\n        }\n        else {\n            inputs = placeholders;\n        }\n        let functions = {};\n        if (graph.library != null && graph.library.function != null) {\n            functions = graph.library.function.reduce((functions, func) => {\n                functions[func.signature.name] = this.mapFunction(func);\n                return functions;\n            }, {});\n        }\n        const result = { nodes, inputs, outputs, weights, placeholders, signature, functions };\n        if (initNodes.length > 0) {\n            result.initNodes = initNodes;\n        }\n        return result;\n    }\n    mapSignatureEntries(entries) {\n        return Object.keys(entries || {})\n            .reduce((prev, curr) => {\n            prev[entries[curr].name] = curr;\n            return prev;\n        }, {});\n    }\n    mapNode(node) {\n        // Unsupported ops will cause an error at run-time (not parse time), since\n        // they may not be used by the actual execution subgraph.\n        const mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {};\n        if (node.attr == null) {\n            node.attr = {};\n        }\n        const newNode = {\n            name: node.name,\n            op: node.op,\n            category: mapper.category,\n            inputNames: (node.input ||\n                []).map(input => input.startsWith('^') ? input.substr(1) : input),\n            inputs: [],\n            children: [],\n            inputParams: {},\n            attrParams: {},\n            rawAttrs: node.attr\n        };\n        if (mapper.inputs != null) {\n            newNode.inputParams =\n                mapper.inputs.reduce((map, param) => {\n                    map[param.name] = {\n                        type: param.type,\n                        inputIndexStart: param.start,\n                        inputIndexEnd: param.end\n                    };\n                    return map;\n                }, {});\n        }\n        if (mapper.attrs != null) {\n            newNode.attrParams =\n                mapper.attrs.reduce((map, param) => {\n                    const type = param.type;\n                    let value = undefined;\n                    switch (param.type) {\n                        case 'string':\n                            value = getStringParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'string[]':\n                            value = getStringArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'number':\n                            value = getNumberParam(node.attr, param.tfName, (param.defaultValue || 0));\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'number[]':\n                            value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'bool':\n                            value = getBoolParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'bool[]':\n                            value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'shape':\n                            value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'shape[]':\n                            value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'dtype':\n                            value = getDtypeParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'dtype[]':\n                            value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'func':\n                            value = getFuncParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'tensor':\n                        case 'tensors':\n                            break;\n                        default:\n                            throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`);\n                    }\n                    map[param.name] = { value, type };\n                    return map;\n                }, {});\n        }\n        return newNode;\n    }\n    // map the TFunctionDef to TFJS graph object\n    mapFunction(functionDef) {\n        const tfNodes = functionDef.nodeDef;\n        const placeholders = [];\n        const weights = [];\n        let nodes = {};\n        if (tfNodes != null) {\n            nodes = tfNodes.reduce((map, node) => {\n                map[node.name] = this.mapNode(node);\n                if (node.op === 'Const') {\n                    weights.push(map[node.name]);\n                }\n                return map;\n            }, {});\n        }\n        const inputs = [];\n        const outputs = [];\n        functionDef.signature.inputArg.forEach(arg => {\n            const [nodeName,] = getNodeNameAndIndex(arg.name);\n            const node = {\n                name: nodeName,\n                op: 'Placeholder',\n                inputs: [],\n                inputNames: [],\n                category: 'graph',\n                inputParams: {},\n                attrParams: { dtype: { value: parseDtypeParam(arg.type), type: 'dtype' } },\n                children: []\n            };\n            node.signatureKey = arg.name;\n            inputs.push(node);\n            nodes[nodeName] = node;\n        });\n        const allNodes = Object.keys(nodes);\n        allNodes.forEach(key => {\n            const node = nodes[key];\n            node.inputNames.forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                node.inputs.push(nodes[nodeName]);\n                nodes[nodeName].children.push(node);\n            });\n        });\n        const returnNodeMap = functionDef.ret;\n        functionDef.signature.outputArg.forEach(output => {\n            const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);\n            const node = nodes[nodeName];\n            if (node != null) {\n                node.defaultOutput = index;\n                outputs.push(node);\n            }\n        });\n        const signature = this.mapArgsToSignature(functionDef);\n        return { nodes, inputs, outputs, weights, placeholders, signature };\n    }\n    mapArgsToSignature(functionDef) {\n        return {\n            methodName: functionDef.signature.name,\n            inputs: functionDef.signature.inputArg.reduce((map, arg) => {\n                map[arg.name] = this.mapArgToTensorInfo(arg);\n                return map;\n            }, {}),\n            outputs: functionDef.signature.outputArg.reduce((map, arg) => {\n                map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);\n                return map;\n            }, {}),\n        };\n    }\n    mapArgToTensorInfo(arg, nameMap) {\n        let name = arg.name;\n        if (nameMap != null) {\n            name = nameMap[name];\n        }\n        return { name, dtype: arg.type };\n    }\n}\nexport function decodeBase64(text) {\n    const global = env().global;\n    if (typeof global.atob !== 'undefined') {\n        return global.atob(text);\n    }\n    else if (typeof Buffer !== 'undefined') {\n        return new Buffer(text, 'base64').toString();\n    }\n    else {\n        throw new Error('Unable to decode base64 in this environment. ' +\n            'Missing built-in atob() or Buffer()');\n    }\n}\nexport function parseStringParam(s, keepCase) {\n    const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);\n    return keepCase ? value : value.toLowerCase();\n}\nexport function getStringParam(attrs, name, def, keepCase = false) {\n    const param = attrs[name];\n    if (param != null) {\n        return parseStringParam(param.s, keepCase);\n    }\n    return def;\n}\nexport function getBoolParam(attrs, name, def) {\n    const param = attrs[name];\n    return param ? param.b : def;\n}\nexport function getNumberParam(attrs, name, def) {\n    const param = attrs[name] || {};\n    const value = param['i'] != null ? param['i'] : (param['f'] != null ? param['f'] : def);\n    return (typeof value === 'number') ? value : parseInt(value, 10);\n}\nexport function parseDtypeParam(value) {\n    if (typeof (value) === 'string') {\n        // tslint:disable-next-line:no-any\n        value = tensorflow.DataType[value];\n    }\n    switch (value) {\n        case tensorflow.DataType.DT_FLOAT:\n            return 'float32';\n        case tensorflow.DataType.DT_INT32:\n        case tensorflow.DataType.DT_INT64:\n        case tensorflow.DataType.DT_INT8:\n        case tensorflow.DataType.DT_UINT8:\n            return 'int32';\n        case tensorflow.DataType.DT_BOOL:\n            return 'bool';\n        case tensorflow.DataType.DT_DOUBLE:\n            return 'float32';\n        case tensorflow.DataType.DT_STRING:\n            return 'string';\n        default:\n            // Unknown dtype error will happen at runtime (instead of parse time),\n            // since these nodes might not be used by the actual subgraph execution.\n            return null;\n    }\n}\nexport function getFuncParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.func) {\n        return param.func.name;\n    }\n    return def;\n}\nexport function getDtypeParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.type) {\n        return parseDtypeParam(param.type);\n    }\n    return def;\n}\nexport function getDtypeArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.type) {\n        return param.list.type.map(v => parseDtypeParam(v));\n    }\n    return def;\n}\nexport function parseTensorShapeParam(shape) {\n    if (shape.unknownRank) {\n        return undefined;\n    }\n    if (shape.dim != null) {\n        return shape.dim.map(dim => (typeof dim.size === 'number') ? dim.size : parseInt(dim.size, 10));\n    }\n    return [];\n}\nexport function getTensorShapeParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.shape) {\n        return parseTensorShapeParam(param.shape);\n    }\n    return def;\n}\nexport function getNumericArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param) {\n        return ((param.list.f && param.list.f.length ? param.list.f :\n            param.list.i) ||\n            [])\n            .map(v => (typeof v === 'number') ? v : parseInt(v, 10));\n    }\n    return def;\n}\nexport function getStringArrayParam(attrs, name, def, keepCase = false) {\n    const param = attrs[name];\n    if (param && param.list && param.list.s) {\n        return param.list.s.map((v) => {\n            return parseStringParam(v, keepCase);\n        });\n    }\n    return def;\n}\nexport function getTensorShapeArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.shape) {\n        return param.list.shape.map((v) => {\n            return parseTensorShapeParam(v);\n        });\n    }\n    return def;\n}\nexport function getBoolArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.b) {\n        return param.list.b;\n    }\n    return def;\n}\n//# sourceMappingURL=operation_mapper.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getTensor } from '../executors/utils';\nimport { getBoolArrayParam, getBoolParam, getDtypeArrayParam, getDtypeParam, getNumberParam, getNumericArrayParam, getStringArrayParam, getStringParam, getTensorShapeArrayParam, getTensorShapeParam } from '../operation_mapper';\n/**\n * Helper class for lookup inputs and params for nodes in the model graph.\n */\nexport class NodeValueImpl {\n    constructor(node, tensorMap, context) {\n        this.node = node;\n        this.tensorMap = tensorMap;\n        this.context = context;\n        this.inputs = [];\n        this.attrs = {};\n        this.inputs = node.inputNames.map(name => this.getInput(name));\n        if (node.rawAttrs != null) {\n            this.attrs = Object.keys(node.rawAttrs)\n                .reduce((attrs, key) => {\n                attrs[key] = this.getAttr(key);\n                return attrs;\n            }, {});\n        }\n    }\n    /**\n     * Return the value of the attribute or input param.\n     * @param name String: name of attribute or input param.\n     */\n    getInput(name) {\n        return getTensor(name, this.tensorMap, this.context);\n    }\n    /**\n     * Return the value of the attribute or input param.\n     * @param name String: name of attribute or input param.\n     */\n    getAttr(name, defaultValue) {\n        const value = this.node.rawAttrs[name];\n        if (value.tensor != null) {\n            return getTensor(name, this.tensorMap, this.context);\n        }\n        if (value.i != null || value.f != null) {\n            return getNumberParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.s != null) {\n            return getStringParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.b != null) {\n            return getBoolParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.shape != null) {\n            return getTensorShapeParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.type != null) {\n            return getDtypeParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.list != null) {\n            if (value.list.i != null || value.list.f != null) {\n                return getNumericArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.s != null) {\n                return getStringArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.shape != null) {\n                return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.b != null) {\n                return getBoolArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.type != null) {\n                return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n        }\n        return defaultValue;\n    }\n}\n//# sourceMappingURL=node_value_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This file exports ops used by the converters executors. By default it\n * re-exports all ops. In a custom build this is aliased to a file that will\n * only exports ops for a given model.json.\n */\nexport * from './ops';\n//# sourceMappingURL=ops_for_converter.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'BiasAdd':\n        case 'AddV2':\n        case 'Add': {\n            return [tfOps.add(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'AddN': {\n            return [tfOps.addN(getParamValue('tensors', node, tensorMap, context))];\n        }\n        case 'FloorMod':\n        case 'Mod':\n            return [tfOps.mod(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        case 'Mul':\n            return [tfOps.mul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        case 'RealDiv':\n        case 'Div': {\n            return [tfOps.div(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'DivNoNan': {\n            return [tfOps.divNoNan(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'FloorDiv': {\n            return [tfOps.floorDiv(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Sub': {\n            return [tfOps.sub(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Minimum': {\n            return [tfOps.minimum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Maximum': {\n            return [tfOps.maximum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Pow': {\n            return [tfOps.pow(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'SquaredDifference': {\n            return [tfOps.squaredDifference(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'arithmetic';\n//# sourceMappingURL=arithmetic_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Abs':\n        case 'ComplexAbs':\n            return [tfOps.abs(getParamValue('x', node, tensorMap, context))];\n        case 'Acos':\n            return [tfOps.acos(getParamValue('x', node, tensorMap, context))];\n        case 'Acosh':\n            return [tfOps.acosh(getParamValue('x', node, tensorMap, context))];\n        case 'Asin':\n            return [tfOps.asin(getParamValue('x', node, tensorMap, context))];\n        case 'Asinh':\n            return [tfOps.asinh(getParamValue('x', node, tensorMap, context))];\n        case 'Atan':\n            return [tfOps.atan(getParamValue('x', node, tensorMap, context))];\n        case 'Atan2':\n            return [tfOps.atan2(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context))];\n        case 'Atanh':\n            return [tfOps.atanh(getParamValue('x', node, tensorMap, context))];\n        case 'Ceil':\n            return [tfOps.ceil(getParamValue('x', node, tensorMap, context))];\n        case 'Complex':\n            return [tfOps.complex(getParamValue('real', node, tensorMap, context), getParamValue('imag', node, tensorMap, context))];\n        case 'Cos':\n            return [tfOps.cos(getParamValue('x', node, tensorMap, context))];\n        case 'Cosh':\n            return [tfOps.cosh(getParamValue('x', node, tensorMap, context))];\n        case 'Elu':\n            return [tfOps.elu(getParamValue('x', node, tensorMap, context))];\n        case 'Erf':\n            return [tfOps.erf(getParamValue('x', node, tensorMap, context))];\n        case 'Exp':\n            return [tfOps.exp(getParamValue('x', node, tensorMap, context))];\n        case 'Expm1': {\n            return [tfOps.expm1(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Floor':\n            return [tfOps.floor(getParamValue('x', node, tensorMap, context))];\n        case 'Log':\n            return [tfOps.log(getParamValue('x', node, tensorMap, context))];\n        case 'Log1p': {\n            return [tfOps.log1p(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Imag':\n            return [tfOps.imag(getParamValue('x', node, tensorMap, context))];\n        case 'Neg':\n            return [tfOps.neg(getParamValue('x', node, tensorMap, context))];\n        case 'Reciprocal': {\n            return [tfOps.reciprocal(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Real':\n            return [tfOps.real(getParamValue('x', node, tensorMap, context))];\n        case 'Relu':\n            return [tfOps.relu(getParamValue('x', node, tensorMap, context))];\n        case 'Round': {\n            return [tfOps.round(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Selu':\n            return [tfOps.selu(getParamValue('x', node, tensorMap, context))];\n        case 'Sigmoid':\n            return [tfOps.sigmoid(getParamValue('x', node, tensorMap, context))];\n        case 'Sin':\n            return [tfOps.sin(getParamValue('x', node, tensorMap, context))];\n        case 'Sign': {\n            return [tfOps.sign(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Sinh': {\n            return [tfOps.sinh(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Softplus': {\n            return [tfOps.softplus(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Sqrt': {\n            return [tfOps.sqrt(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Square': {\n            return [tfOps.square(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Tanh': {\n            return [tfOps.tanh(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Tan':\n            return [tfOps.tan(getParamValue('x', node, tensorMap, context))];\n        case 'Relu6':\n        case 'ClipByValue':\n            return [tfOps.clipByValue(getParamValue('x', node, tensorMap, context), getParamValue('clipValueMin', node, tensorMap, context), getParamValue('clipValueMax', node, tensorMap, context))];\n        case 'Rsqrt':\n            return [tfOps.rsqrt(getTensor(node.inputNames[0], tensorMap, context))];\n        case 'Prod':\n            return [tfOps.prod(getParamValue('x', node, tensorMap, context), getParamValue('axes', node, tensorMap, context))];\n        case 'LeakyRelu':\n            return [tfOps.leakyRelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n        case 'Prelu':\n            return [tfOps.prelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'basic_math';\n//# sourceMappingURL=basic_math_executor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This differs from util.assertShapesMatch in that it allows values of\n * negative one, an undefined size of a dimensinon, in a shape to match\n * anything.\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = '') {\n    util.assert(shapesEqualAllowUndefinedSize(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nexport function shapesEqualAllowUndefinedSize(n1, n2) {\n    if (n1.length !== n2.length) {\n        return false;\n    }\n    for (let i = 0; i < n1.length; i++) {\n        if (n1[i] !== -1 && n2[i] !== -1 && n1[i] !== n2[i]) {\n            return false;\n        }\n    }\n    return true;\n}\n//# sourceMappingURL=tensor_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize } from './tensor_utils';\n/**\n * The TensorArray object keeps an array of Tensors.  It\n * allows reading from the array and writing to the array.\n */\nexport class TensorArray {\n    constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) {\n        this.name = name;\n        this.dtype = dtype;\n        this.maxSize = maxSize;\n        this.elementShape = elementShape;\n        this.identicalElementShapes = identicalElementShapes;\n        this.dynamicSize = dynamicSize;\n        this.clearAfterRead = clearAfterRead;\n        this.tensors = [];\n        this.closed_ = false;\n        this.idTensor = scalar(0);\n        keep(this.idTensor);\n    }\n    get id() {\n        return this.idTensor.id;\n    }\n    get closed() {\n        return this.closed_;\n    }\n    /**\n     * Dispose the tensors and idTensor and mark the TensoryArray as closed.\n     */\n    clearAndClose(keepIds) {\n        this.tensors.forEach(tensor => {\n            if (keepIds == null || !keepIds.has(tensor.tensor.id)) {\n                tensor.tensor.dispose();\n            }\n        });\n        this.tensors = [];\n        this.closed_ = true;\n        this.idTensor.dispose();\n    }\n    size() {\n        return this.tensors.length;\n    }\n    /**\n     * Read the value at location index in the TensorArray.\n     * @param index Number the index to read from.\n     */\n    read(index) {\n        if (this.closed_) {\n            throw new Error(`TensorArray ${this.name} has already been closed.`);\n        }\n        if (index < 0 || index >= this.size()) {\n            throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);\n        }\n        const tensorWithState = this.tensors[index];\n        if (tensorWithState.cleared) {\n            throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read ` +\n                `(perhaps try setting clear_after_read = false?).`);\n        }\n        if (this.clearAfterRead) {\n            tensorWithState.cleared = true;\n        }\n        tensorWithState.read = true;\n        return tensorWithState.tensor;\n    }\n    /**\n     * Helper method to read multiple tensors from the specified indices.\n     */\n    readMany(indices) {\n        return indices.map(index => this.read(index));\n    }\n    /**\n     * Write value into the index of the TensorArray.\n     * @param index number the index to write to.\n     * @param tensor\n     */\n    write(index, tensor) {\n        if (this.closed_) {\n            throw new Error(`TensorArray ${this.name} has already been closed.`);\n        }\n        if (index < 0 || !this.dynamicSize && index >= this.maxSize) {\n            throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);\n        }\n        const t = this.tensors[index] || {};\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},\n          because the value dtype is ${tensor.dtype}, but TensorArray dtype is ${this.dtype}.`);\n        }\n        // Set the shape for the first time write to unknow shape tensor array\n        if (this.size() === 0 &&\n            (this.elementShape == null || this.elementShape.length === 0)) {\n            this.elementShape = tensor.shape;\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`);\n        if (t.read) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);\n        }\n        if (t.written) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);\n        }\n        t.tensor = tensor;\n        keep(tensor);\n        t.written = true;\n        this.tensors[index] = t;\n    }\n    /**\n     * Helper method to write multiple tensors to the specified indices.\n     */\n    writeMany(indices, tensors) {\n        if (indices.length !== tensors.length) {\n            throw new Error(`TensorArray ${this.name}: could not write multiple tensors,` +\n                `because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);\n        }\n        indices.forEach((i, index) => this.write(i, tensors[index]));\n    }\n    /**\n     * Return selected values in the TensorArray as a packed Tensor. All of\n     * selected values must have been written and their shapes must all match.\n     * @param [indices] number[] Optional. Taking values in [0, max_value). If the\n     *    TensorArray is not dynamic, max_value=size(). If not specified returns\n     *    all tensors in the original order.\n     * @param [dtype]\n     */\n    gather(indices, dtype) {\n        if (!!dtype && dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);\n        }\n        if (!indices) {\n            indices = [];\n            for (let i = 0; i < this.size(); i++) {\n                indices.push(i);\n            }\n        }\n        else {\n            indices = indices.slice(0, this.size());\n        }\n        if (indices.length === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        // Read all the PersistentTensors into a vector to keep track of\n        // their memory.\n        const tensors = this.readMany(indices);\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, 'TensorArray shape mismatch: ');\n        return stack(tensors, 0);\n    }\n    /**\n     * Return the values in the TensorArray as a concatenated Tensor.\n     */\n    concat(dtype) {\n        if (!!dtype && dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);\n        }\n        if (this.size() === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        const indices = [];\n        for (let i = 0; i < this.size(); i++) {\n            indices.push(i);\n        }\n        // Collect all the tensors from the tensors array.\n        const tensors = this.readMany(indices);\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);\n        return concat(tensors, 0);\n    }\n    /**\n     * Scatter the values of a Tensor in specific indices of a TensorArray.\n     * @param indices nummber[] values in [0, max_value). If the\n     *    TensorArray is not dynamic, max_value=size().\n     * @param tensor Tensor input tensor.\n     */\n    scatter(indices, tensor) {\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n        }\n        if (indices.length !== tensor.shape[0]) {\n            throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n        }\n        const maxIndex = Math.max(...indices);\n        if (!this.dynamicSize && maxIndex >= this.maxSize) {\n            throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);\n        }\n        this.writeMany(indices, unstack(tensor, 0));\n    }\n    /**\n     * Split the values of a Tensor into the TensorArray.\n     * @param length number[] with the lengths to use when splitting value along\n     *    its first dimension.\n     * @param tensor Tensor, the tensor to split.\n     */\n    split(length, tensor) {\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n        }\n        let totalLength = 0;\n        const cumulativeLengths = length.map(len => {\n            totalLength += len;\n            return totalLength;\n        });\n        if (totalLength !== tensor.shape[0]) {\n            throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n        }\n        if (!this.dynamicSize && length.length !== this.maxSize) {\n            throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), ` +\n                'and the TensorArray is not marked as dynamically resizeable');\n        }\n        const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n        const tensors = [];\n        tidy(() => {\n            tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n            for (let i = 0; i < length.length; ++i) {\n                const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n                const indices = [0, previousLength, 0];\n                const sizes = [1, length[i], elementPerRow];\n                tensors[i] = reshape(slice(tensor, indices, sizes), this.elementShape);\n            }\n            return tensors;\n        });\n        const indices = [];\n        for (let i = 0; i < length.length; i++) {\n            indices[i] = i;\n        }\n        this.writeMany(indices, tensors);\n    }\n}\n//# sourceMappingURL=tensor_array.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize } from './tensor_utils';\n/**\n * TensorList stores a container of `tf.Tensor` objects, which are accessible\n * via tensors field.\n *\n * In order to get a copy of the underlying list, use the copy method:\n * ```\n *    TensorList b = a.copy();\n *    b.tensors().pushBack(t);  // This does not modify a.tensors().\n * ```\n *\n * Note that this is not a deep copy: the memory locations of the underlying\n * tensors will still point to the same locations of the corresponding tensors\n * in the original.\n */\nexport class TensorList {\n    /**\n     *\n     * @param tensors list of tensors\n     * @param elementShape shape of each tensor\n     * @param elementDtype data type of each tensor\n     * @param maxNumElements The maximum allowed size of `tensors`. Defaults to -1\n     *   meaning that the size of `tensors` is unbounded.\n     */\n    constructor(tensors, elementShape, elementDtype, maxNumElements = -1) {\n        this.tensors = tensors;\n        this.elementShape = elementShape;\n        this.elementDtype = elementDtype;\n        if (tensors != null) {\n            tensors.forEach(tensor => {\n                if (elementDtype !== tensor.dtype) {\n                    throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor.dtype}`);\n                }\n                assertShapesMatchAllowUndefinedSize(elementShape, tensor.shape, 'TensorList shape mismatch: ');\n                keep(tensor);\n            });\n        }\n        this.idTensor = scalar(0);\n        this.maxNumElements = maxNumElements;\n        keep(this.idTensor);\n    }\n    get id() {\n        return this.idTensor.id;\n    }\n    /**\n     * Get a new TensorList containing a copy of the underlying tensor container.\n     */\n    copy() {\n        return new TensorList([...this.tensors], this.elementShape, this.elementDtype);\n    }\n    /**\n     * Dispose the tensors and idTensor and clear the tensor list.\n     */\n    clearAndClose(keepIds) {\n        this.tensors.forEach(tensor => {\n            if (keepIds == null || !keepIds.has(tensor.id)) {\n                tensor.dispose();\n            }\n        });\n        this.tensors.length = 0;\n        this.idTensor.dispose();\n    }\n    /**\n     * The size of the tensors in the tensor list.\n     */\n    size() {\n        return this.tensors.length;\n    }\n    /**\n     * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1)\n     * tf.Tensor.\n     * @param elementShape shape of each tensor\n     * @param elementDtype data type of each tensor\n     * @param numElements the number of elements to stack\n     */\n    stack(elementShape, elementDtype, numElements = -1) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (numElements !== -1 && this.tensors.length !== numElements) {\n            throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);\n        }\n        assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, 'TensorList shape mismatch: ');\n        return tidy(() => {\n            const reshapedTensors = this.tensors.map(tensor => reshape(tensor, elementShape));\n            return stack(reshapedTensors, 0);\n        });\n    }\n    /**\n     * Pop a tensor from the end of the list.\n     * @param elementShape shape of the tensor\n     * @param elementDtype data type of the tensor\n     */\n    popBack(elementShape, elementDtype) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (this.size() === 0) {\n            throw new Error('Trying to pop from an empty list.');\n        }\n        const tensor = this.tensors.pop();\n        assertShapesMatchAllowUndefinedSize(tensor.shape, elementShape, 'TensorList shape mismatch: ');\n        return reshape(tensor, elementShape);\n    }\n    /**\n     * Push a tensor to the end of the list.\n     * @param tensor Tensor to be pushed.\n     */\n    pushBack(tensor) {\n        if (tensor.dtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(tensor.shape, this.elementShape, 'TensorList shape mismatch: ');\n        if (this.maxNumElements === this.size()) {\n            throw new Error(`Trying to push element into a full list.`);\n        }\n        keep(tensor);\n        this.tensors.push(tensor);\n    }\n    /**\n     * Update the size of the list.\n     * @param size the new size of the list.\n     */\n    resize(size) {\n        if (size < 0) {\n            throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);\n        }\n        if (this.maxNumElements !== -1 && size > this.maxNumElements) {\n            throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);\n        }\n        this.tensors.length = size;\n    }\n    /**\n     * Retrieve the element at the provided index\n     * @param elementShape shape of the tensor\n     * @param elementDtype dtype of the tensor\n     * @param elementIndex index of the tensor\n     */\n    getItem(elementIndex, elementShape, elementDtype) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (elementIndex < 0 || elementIndex > this.tensors.length) {\n            throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);\n        }\n        if (this.tensors[elementIndex] == null) {\n            throw new Error(`element at index ${elementIndex} is null.`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, 'TensorList shape mismatch: ');\n        return this.tensors[elementIndex];\n    }\n    /**\n     * Set the tensor at the index\n     * @param elementIndex index of the tensor\n     * @param tensor the tensor to be inserted into the list\n     */\n    setItem(elementIndex, tensor) {\n        if (tensor.dtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n        }\n        if (elementIndex < 0 ||\n            this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {\n            throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, 'TensorList shape mismatch: ');\n        keep(tensor);\n        this.tensors[elementIndex] = tensor;\n    }\n    /**\n     * Return selected values in the TensorList as a stacked Tensor. All of\n     * selected values must have been written and their shapes must all match.\n     * @param indices indices of tensors to gather\n     * @param elementDtype output tensor dtype\n     * @param elementShape output tensor element shape\n     */\n    gather(indices, elementDtype, elementShape) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n        // When indices is greater than the size of the list, indices beyond the\n        // size of the list are ignored.\n        indices = indices.slice(0, this.size());\n        if (indices.length === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        return tidy(() => {\n            const tensors = indices.map(i => reshape(this.tensors[i], elementShape));\n            return stack(tensors, 0);\n        });\n    }\n    /**\n     * Return the values in the TensorList as a concatenated Tensor.\n     * @param elementDtype output tensor dtype\n     * @param elementShape output tensor element shape\n     */\n    concat(elementDtype, elementShape) {\n        if (!!elementDtype && elementDtype !== this.elementDtype) {\n            throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n        if (this.size() === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        return tidy(() => {\n            const tensors = this.tensors.map(t => reshape(t, elementShape));\n            return concat(tensors, 0);\n        });\n    }\n}\n/**\n * Creates a TensorList which, when stacked, has the value of tensor.\n * @param tensor from tensor\n * @param elementShape output tensor element shape\n */\nexport function fromTensor(tensor, elementShape, elementDtype) {\n    const dtype = tensor.dtype;\n    if (tensor.shape.length < 1) {\n        throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor.shape}`);\n    }\n    if (tensor.dtype !== elementDtype) {\n        throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${elementDtype}`);\n    }\n    const outputShape = tensor.shape.slice(1);\n    assertShapesMatchAllowUndefinedSize(outputShape, elementShape, 'TensorList shape mismatch: ');\n    const tensorList = unstack(tensor);\n    return new TensorList(tensorList, elementShape, dtype);\n}\n/**\n * Return a TensorList of the given size with empty elements.\n * @param elementShape the shape of the future elements of the list\n * @param elementDtype the desired type of elements in the list\n * @param numElements the number of elements to reserve\n */\nexport function reserve(elementShape, elementDtype, numElements) {\n    return new TensorList([], elementShape, elementDtype, numElements);\n}\n/**\n * Put tensors at specific indices of a stacked tensor into a TensorList.\n * @param indices list of indices on how to scatter the tensor.\n * @param tensor input tensor.\n * @param elementShape the shape of the future elements of the list\n * @param numElements the number of elements to scatter\n */\nexport function scatter(tensor, indices, elementShape, numElements) {\n    if (indices.length !== tensor.shape[0]) {\n        throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n    }\n    const maxIndex = Math.max(...indices);\n    if (numElements != null && numElements !== -1 && maxIndex >= numElements) {\n        throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);\n    }\n    const list = new TensorList([], elementShape, tensor.dtype, numElements);\n    const tensors = unstack(tensor, 0);\n    indices.forEach((value, index) => {\n        list.setItem(value, tensors[index]);\n    });\n    return list;\n}\n/**\n * Split the values of a Tensor into a TensorList.\n * @param length the lengths to use when splitting value along\n *    its first dimension.\n * @param tensor the tensor to split.\n * @param elementShape the shape of the future elements of the list\n */\nexport function split(tensor, length, elementShape) {\n    let totalLength = 0;\n    const cumulativeLengths = length.map(len => {\n        totalLength += len;\n        return totalLength;\n    });\n    if (totalLength !== tensor.shape[0]) {\n        throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n    }\n    const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n    const tensors = tidy(() => {\n        const tensors = [];\n        tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n        for (let i = 0; i < length.length; ++i) {\n            const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n            const indices = [0, previousLength, 0];\n            const sizes = [1, length[i], elementPerRow];\n            tensors[i] = reshape(slice(tensor, indices, sizes), elementShape);\n        }\n        tensor.dispose();\n        return tensors;\n    });\n    const list = new TensorList([], elementShape, tensor.dtype, length.length);\n    for (let i = 0; i < tensors.length; i++) {\n        list.setItem(i, tensors[i]);\n    }\n    return list;\n}\n//# sourceMappingURL=tensor_list.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { scalar } from '@tensorflow/tfjs-core';\nimport { TensorArray } from '../../executor/tensor_array';\nimport { fromTensor, reserve, scatter, split } from '../../executor/tensor_list';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = async (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'If':\n        case 'StatelessIf': {\n            const thenFunc = getParamValue('thenBranch', node, tensorMap, context);\n            const elseFunc = getParamValue('elseBranch', node, tensorMap, context);\n            const cond = getParamValue('cond', node, tensorMap, context);\n            const args = getParamValue('args', node, tensorMap, context);\n            const condValue = await cond.data();\n            if (condValue[0]) {\n                return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n            }\n            else {\n                return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n            }\n        }\n        case 'While':\n        case 'StatelessWhile': {\n            const bodyFunc = getParamValue('body', node, tensorMap, context);\n            const condFunc = getParamValue('cond', node, tensorMap, context);\n            const args = getParamValue('args', node, tensorMap, context);\n            // Calculate the condition of the loop\n            const condResult = (await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap));\n            const argIds = args.map(tensor => tensor.id);\n            let condValue = await condResult[0].data();\n            // Dispose the intermediate tensors for condition function\n            condResult.forEach(tensor => {\n                if (!tensor.kept && argIds.indexOf(tensor.id) === -1) {\n                    tensor.dispose();\n                }\n            });\n            let result = args;\n            while (condValue[0]) {\n                // Record the previous result for intermediate tensor tracking\n                const origResult = result;\n                // Execution the body of the loop\n                result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);\n                const resultIds = result.map(tensor => tensor.id);\n                // Dispose the intermediate tensor for body function that is not global\n                // kept, not input/output of the body function\n                origResult.forEach(tensor => {\n                    if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n                        resultIds.indexOf(tensor.id) === -1) {\n                        tensor.dispose();\n                    }\n                });\n                // Recalcuate the condition of the loop using the latest results.\n                const condResult = (await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap));\n                condValue = await condResult[0].data();\n                // Dispose the intermediate tensors for condition function\n                condResult.forEach(tensor => {\n                    if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n                        resultIds.indexOf(tensor.id) === -1) {\n                        tensor.dispose();\n                    }\n                });\n            }\n            return result;\n        }\n        case 'LoopCond': {\n            const pred = getParamValue('pred', node, tensorMap, context);\n            return [cloneTensor(pred)];\n        }\n        case 'Switch': {\n            const pred = getParamValue('pred', node, tensorMap, context);\n            let data = getParamValue('data', node, tensorMap, context);\n            if (!data.kept) {\n                data = cloneTensor(data);\n            }\n            // Outputs nodes :0 => false, :1 => true\n            return (await pred.data())[0] ? [undefined, data] : [data, undefined];\n        }\n        case 'Merge': {\n            const inputName = node.inputNames.find(name => getTensor(name, tensorMap, context) !== undefined);\n            if (inputName) {\n                const data = getTensor(inputName, tensorMap, context);\n                return [cloneTensor(data)];\n            }\n            return undefined;\n        }\n        case 'Enter': {\n            const frameId = getParamValue('frameName', node, tensorMap, context);\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.enterFrame(frameId);\n            return [cloneTensor(data)];\n        }\n        case 'Exit': {\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.exitFrame();\n            return [cloneTensor(data)];\n        }\n        case 'NextIteration': {\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.nextIteration();\n            return [cloneTensor(data)];\n        }\n        case 'TensorArrayV3': {\n            const size = getParamValue('size', node, tensorMap, context);\n            const dtype = getParamValue('dtype', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const dynamicSize = getParamValue('dynamicSize', node, tensorMap, context);\n            const clearAfterRead = getParamValue('clearAfterRead', node, tensorMap, context);\n            const identicalElementShapes = getParamValue('identicalElementShapes', node, tensorMap, context);\n            const name = getParamValue('name', node, tensorMap, context);\n            const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);\n            context.addTensorArray(tensorArray);\n            return [tensorArray.idTensor, scalar(1.0)];\n        }\n        case 'TensorArrayWriteV3': {\n            const id = getParamValue('tensorArrayId', node, tensorMap, context);\n            const index = getParamValue('index', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const writeTensorArray = context.getTensorArray(id.id);\n            writeTensorArray.write(index, writeTensor);\n            return [writeTensorArray.idTensor];\n        }\n        case 'TensorArrayReadV3': {\n            const readId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const readIndex = getParamValue('index', node, tensorMap, context);\n            const readTensorArray = context.getTensorArray(readId.id);\n            return [readTensorArray.read(readIndex)];\n        }\n        case 'TensorArrayGatherV3': {\n            const gatherId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const gatherIndices = getParamValue('indices', node, tensorMap, context);\n            const gatherDtype = getParamValue('dtype', node, tensorMap, context);\n            const gatherTensorArray = context.getTensorArray(gatherId.id);\n            return [gatherTensorArray.gather(gatherIndices, gatherDtype)];\n        }\n        case 'TensorArrayScatterV3': {\n            const scatterId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const scatterIndices = getParamValue('indices', node, tensorMap, context);\n            const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n            const scatterTensorArray = context.getTensorArray(scatterId.id);\n            scatterTensorArray.scatter(scatterIndices, scatterTensor);\n            return [scatterTensorArray.idTensor];\n        }\n        case 'TensorArrayConcatV3': {\n            const concatId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const concatTensorArray = context.getTensorArray(concatId.id);\n            const concatDtype = getParamValue('dtype', node, tensorMap, context);\n            return [concatTensorArray.concat(concatDtype)];\n        }\n        case 'TensorArraySplitV3': {\n            const splitId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const splitTensor = getParamValue('tensor', node, tensorMap, context);\n            const lengths = getParamValue('lengths', node, tensorMap, context);\n            const splitTensorArray = context.getTensorArray(splitId.id);\n            splitTensorArray.split(lengths, splitTensor);\n            return [splitTensorArray.idTensor];\n        }\n        case 'TensorArraySizeV3': {\n            const sizeId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const sizeTensorArray = context.getTensorArray(sizeId.id);\n            return [scalar(sizeTensorArray.size(), 'int32')];\n        }\n        case 'TensorArrayCloseV3': {\n            const closeId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const closeTensorArray = context.getTensorArray(closeId.id);\n            closeTensorArray.clearAndClose();\n            return [closeTensorArray.idTensor];\n        }\n        case 'TensorListSetItem': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const index = getParamValue('index', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            tensorList.setItem(index, writeTensor);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListGetItem': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const readIndex = getParamValue('index', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDType = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.getItem(readIndex, elementShape, elementDType)];\n        }\n        case 'TensorListScatterV2':\n        case 'TensorListScatter': {\n            const scatterIndices = getParamValue('indices', node, tensorMap, context);\n            const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListReserve': {\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = reserve(elementShape, elementDtype, numElements);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListGather': {\n            const gatherId = getParamValue('tensorListId', node, tensorMap, context);\n            const gatherIndices = getParamValue('indices', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(gatherId.id);\n            return [tensorList.gather(gatherIndices, elementDtype, elementShape)];\n        }\n        case 'TensorListStack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.stack(elementShape, elementDtype, numElements)];\n        }\n        case 'TensorListFromTensor': {\n            const tensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = fromTensor(tensor, elementShape, elementDtype);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListConcat': {\n            const concatId = getParamValue('tensorListId', node, tensorMap, context);\n            const tensorList = context.getTensorList(concatId.id);\n            const concatDtype = getParamValue('dtype', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            return [tensorList.concat(concatDtype, elementShape)];\n        }\n        case 'TensorListPushBack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            tensorList.pushBack(writeTensor);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListPopBack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDType = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.popBack(elementShape, elementDType)];\n        }\n        case 'TensorListSplit': {\n            const splitTensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const lengths = getParamValue('lengths', node, tensorMap, context);\n            const tensorList = split(splitTensor, lengths, elementShape);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'control';\n//# sourceMappingURL=control_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getPadding, getParamValue } from './utils';\nfunction fusedConvAndDepthWiseParams(node, tensorMap, context) {\n    const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n    const isBiasAdd = extraOp === 'biasadd';\n    const isPrelu = activationFunc === 'prelu';\n    const isBatchNorm = extraOp === 'fusedbatchnorm';\n    const numArgs = getParamValue('numArgs', node, tensorMap, context);\n    if (isBiasAdd) {\n        if (isPrelu && numArgs !== 2) {\n            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu ' +\n                'must have two extra arguments: bias and alpha.');\n        }\n        if (!isPrelu && numArgs !== 1) {\n            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd must have ' +\n                'one extra argument: bias.');\n        }\n    }\n    if (isBatchNorm) {\n        throw new Error('FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.');\n    }\n    const stride = getParamValue('strides', node, tensorMap, context);\n    const pad = getPadding(node, tensorMap, context);\n    const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n        .toUpperCase();\n    const dilations = getParamValue('dilations', node, tensorMap, context);\n    const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n    return {\n        stride,\n        pad,\n        dataFormat,\n        dilations,\n        biasArg,\n        preluArg,\n        activationFunc\n    };\n}\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Conv1D': {\n            const stride = getParamValue('stride', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilation = getParamValue('dilation', node, tensorMap, context);\n            return [tfOps.conv1d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), stride, pad, dataFormat, dilation)];\n        }\n        case 'Conv2D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            return [tfOps.conv2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n        }\n        case '_FusedConv2D': {\n            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n            return [tfOps.fused.conv2d({\n                    x: getParamValue('x', node, tensorMap, context),\n                    filter: getParamValue('filter', node, tensorMap, context),\n                    strides: [stride[1], stride[2]],\n                    pad: pad,\n                    dataFormat: dataFormat,\n                    dilations: [dilations[1], dilations[2]],\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        }\n        case 'FusedDepthwiseConv2dNative': {\n            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n            return [tfOps.fused.depthwiseConv2d({\n                    x: getParamValue('x', node, tensorMap, context),\n                    filter: getParamValue('filter', node, tensorMap, context),\n                    strides: [stride[1], stride[2]],\n                    pad: pad,\n                    dataFormat: dataFormat,\n                    dilations: [dilations[1], dilations[2]],\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        }\n        case 'Conv2DBackpropInput':\n        case 'Conv2dTranspose': {\n            const shape = getParamValue('outputShape', node, tensorMap, context);\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            return [tfOps.conv2dTranspose(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), shape, [stride[1], stride[2]], pad)];\n        }\n        case 'DepthwiseConv2dNative':\n        case 'DepthwiseConv2d': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            return [tfOps.depthwiseConv2d(getParamValue('input', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n        }\n        case 'Conv3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            return [tfOps.conv3d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2], stride[3]], pad, dataFormat, [dilations[1], dilations[2], dilations[3]])];\n        }\n        case 'AvgPool': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.avgPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n        }\n        case 'MaxPool': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.maxPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n        }\n        case 'MaxPoolWithArgmax': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            const includeBatchInIndex = getParamValue('includeBatchInIndex', node, tensorMap, context);\n            const { result, indexes } = tfOps.maxPoolWithArgmax(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad, includeBatchInIndex);\n            return [result, indexes];\n        }\n        case 'AvgPool3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.avgPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n        }\n        case 'MaxPool3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.maxPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n        }\n        case 'Dilation2D': {\n            const strides = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            // strides: [1, stride_height, stride_width, 1].\n            const strideHeight = strides[1];\n            const strideWidth = strides[2];\n            // dilations: [1, dilation_height, dilation_width, 1].\n            const dilationHeight = dilations[1];\n            const dilationWidth = dilations[2];\n            return [tfOps.dilation2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [strideHeight, strideWidth], pad, [dilationHeight, dilationWidth], 'NHWC' /* dataFormat */)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'convolution';\n//# sourceMappingURL=convolution_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Fill': {\n            const shape = getParamValue('shape', node, tensorMap, context);\n            const dtype = getParamValue('dtype', node, tensorMap, context);\n            const value = getParamValue('value', node, tensorMap, context);\n            return [tfOps.fill(shape, value, dtype)];\n        }\n        case 'LinSpace': {\n            const start = getParamValue('start', node, tensorMap, context);\n            const stop = getParamValue('stop', node, tensorMap, context);\n            const num = getParamValue('num', node, tensorMap, context);\n            return [tfOps.linspace(start, stop, num)];\n        }\n        case 'Multinomial': {\n            const logits = getParamValue('logits', node, tensorMap, context);\n            const numSamples = getParamValue('numSamples', node, tensorMap, context);\n            const seed = getParamValue('seed', node, tensorMap, context);\n            return [tfOps.multinomial(logits, numSamples, seed)];\n        }\n        case 'OneHot': {\n            const indices = getParamValue('indices', node, tensorMap, context);\n            const depth = getParamValue('depth', node, tensorMap, context);\n            const onValue = getParamValue('onValue', node, tensorMap, context);\n            const offValue = getParamValue('offValue', node, tensorMap, context);\n            return [tfOps.oneHot(indices, depth, onValue, offValue)];\n        }\n        case 'Ones': {\n            return [tfOps.ones(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'OnesLike': {\n            return [tfOps.onesLike(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'RandomUniform': {\n            return [tfOps.randomUniform(\n                // tslint:disable-next-line:no-any\n                getParamValue('shape', node, tensorMap, context), getParamValue('minval', node, tensorMap, context), getParamValue('maxval', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'Range': {\n            const start = getParamValue('start', node, tensorMap, context);\n            const stop = getParamValue('stop', node, tensorMap, context);\n            const step = getParamValue('step', node, tensorMap, context);\n            return [tfOps.range(start, stop, step, getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'TruncatedNormal': {\n            const shape = getParamValue('shape', node, tensorMap, context);\n            const mean = getParamValue('mean', node, tensorMap, context);\n            const stdDev = getParamValue('stdDev', node, tensorMap, context);\n            const seed = getParamValue('seed', node, tensorMap, context);\n            return [tfOps.truncatedNormal(shape, mean, stdDev, getParamValue('dtype', node, tensorMap, context), seed)];\n        }\n        case 'Zeros': {\n            return [tfOps.zeros(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'ZerosLike': {\n            return [tfOps.zerosLike(getParamValue('x', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'creation';\n//# sourceMappingURL=creation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nfunction nmsParams(node, tensorMap, context) {\n    const boxes = getParamValue('boxes', node, tensorMap, context);\n    const scores = getParamValue('scores', node, tensorMap, context);\n    const maxOutputSize = getParamValue('maxOutputSize', node, tensorMap, context);\n    const iouThreshold = getParamValue('iouThreshold', node, tensorMap, context);\n    const scoreThreshold = getParamValue('scoreThreshold', node, tensorMap, context);\n    const softNmsSigma = getParamValue('softNmsSigma', node, tensorMap, context);\n    return {\n        boxes,\n        scores,\n        maxOutputSize,\n        iouThreshold,\n        scoreThreshold,\n        softNmsSigma\n    };\n}\nexport const executeOp = async (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'NonMaxSuppressionV5': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node, tensorMap, context);\n            const result = await tfOps.image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n            return [result.selectedIndices, result.selectedScores];\n        }\n        case 'NonMaxSuppressionV4': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n            const padToMaxOutputSize = getParamValue('padToMaxOutputSize', node, tensorMap, context);\n            const result = await tfOps.image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n            return [result.selectedIndices, result.validOutputs];\n        }\n        case 'NonMaxSuppressionV3':\n        case 'NonMaxSuppressionV2': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n            return [await tfOps.image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];\n        }\n        case 'Where': {\n            const condition = tfOps.cast(getParamValue('condition', node, tensorMap, context), 'bool');\n            const result = [await tfOps.whereAsync(condition)];\n            condition.dispose();\n            return result;\n        }\n        case 'ListDiff': {\n            return tfOps.setdiff1dAsync(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context));\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'dynamic';\n//# sourceMappingURL=dynamic_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'TopKV2': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const k = getParamValue('k', node, tensorMap, context);\n            const sorted = getParamValue('sorted', node, tensorMap, context);\n            const result = tfOps.topk(x, k, sorted);\n            return [result.values, result.indices];\n        }\n        case 'Unique': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const result = tfOps.unique(x);\n            return [result.values, result.indices];\n        }\n        case 'UniqueV2': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const result = tfOps.unique(x, axis);\n            return [result.values, result.indices];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'evaluation';\n//# sourceMappingURL=evaluation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Const': {\n            return tensorMap[node.name];\n        }\n        case 'PlaceholderWithDefault':\n            const def = getParamValue('default', node, tensorMap, context);\n            return [getTensor(node.name, tensorMap, context) || def];\n        case 'Placeholder':\n            return [getTensor(node.name, tensorMap, context)];\n        case 'Identity':\n        case 'StopGradient':\n        case 'FakeQuantWithMinMaxVars': { // This op is currently ignored.\n            const data = getParamValue('x', node, tensorMap, context);\n            return [cloneTensor(data)];\n        }\n        case 'IdentityN':\n            return getParamValue('x', node, tensorMap, context)\n                .map((t) => cloneTensor(t));\n        case 'Snapshot':\n            const snapshot = getParamValue('x', node, tensorMap, context);\n            return [cloneTensor(snapshot)];\n        case 'Shape':\n            return [tfOps.tensor1d(getParamValue('x', node, tensorMap, context).shape, 'int32')];\n        case 'ShapeN':\n            return getParamValue('x', node, tensorMap, context)\n                .map((t) => tfOps.tensor1d(t.shape));\n        case 'Size':\n            return [tfOps.scalar(getParamValue('x', node, tensorMap, context).size, 'int32')];\n        case 'Rank':\n            return [tfOps.scalar(getParamValue('x', node, tensorMap, context).rank, 'int32')];\n        case 'NoOp':\n            return [tfOps.scalar(1)];\n        case 'Print':\n            const input = getParamValue('x', node, tensorMap, context);\n            const data = getParamValue('data', node, tensorMap, context);\n            const message = getParamValue('message', node, tensorMap, context);\n            const summarize = getParamValue('summarize', node, tensorMap, context);\n            console.warn('The graph has a tf.print() operation,' +\n                'usually used for debugging, which slows down performance.');\n            console.log(message);\n            for (let i = 0; i < data.length; i++) {\n                console.log(Array.prototype.slice.call(data[i].dataSync())\n                    .slice(0, summarize));\n            }\n            return [input];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'graph';\n//# sourceMappingURL=graph_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'ResizeBilinear': {\n            const images = getParamValue('images', node, tensorMap, context);\n            const size = getParamValue('size', node, tensorMap, context);\n            const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n            return [tfOps.image.resizeBilinear(images, [size[0], size[1]], alignCorners)];\n        }\n        case 'ResizeNearestNeighbor': {\n            const images = getParamValue('images', node, tensorMap, context);\n            const size = getParamValue('size', node, tensorMap, context);\n            const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n            return [tfOps.image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners)];\n        }\n        case 'CropAndResize': {\n            const image = getParamValue('image', node, tensorMap, context);\n            const boxes = getParamValue('boxes', node, tensorMap, context);\n            const boxInd = getParamValue('boxInd', node, tensorMap, context);\n            const cropSize = getParamValue('cropSize', node, tensorMap, context);\n            const method = getParamValue('method', node, tensorMap, context);\n            const extrapolationValue = getParamValue('extrapolationValue', node, tensorMap, context);\n            return [tfOps.image.cropAndResize(image, boxes, boxInd, cropSize, method, extrapolationValue)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'image';\n//# sourceMappingURL=image_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Equal': {\n            return [tfOps.equal(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'NotEqual': {\n            return [tfOps.notEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Greater': {\n            return [tfOps.greater(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'GreaterEqual': {\n            return [tfOps.greaterEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Less': {\n            return [tfOps.less(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LessEqual': {\n            return [tfOps.lessEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LogicalAnd': {\n            return [tfOps.logicalAnd(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LogicalNot': {\n            return [tfOps.logicalNot(getParamValue('a', node, tensorMap, context))];\n        }\n        case 'LogicalOr': {\n            return [tfOps.logicalOr(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Select':\n        case 'SelectV2': {\n            return [tfOps.where(getParamValue('condition', node, tensorMap, context), getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'logical';\n//# sourceMappingURL=logical_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'BatchMatMul':\n        case 'BatchMatMulV2':\n        case 'MatMul':\n            return [tfOps.matMul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context), getParamValue('transposeA', node, tensorMap, context), getParamValue('transposeB', node, tensorMap, context))];\n        case 'Transpose':\n            return [tfOps.transpose(getParamValue('x', node, tensorMap, context), getParamValue('perm', node, tensorMap, context))];\n        case '_FusedMatMul':\n            const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n            const isBiasAdd = extraOp === 'biasadd';\n            const isPrelu = activationFunc === 'prelu';\n            const numArgs = getParamValue('numArgs', node, tensorMap, context);\n            if (isBiasAdd) {\n                if (isPrelu && numArgs !== 2) {\n                    throw new Error('Fused MatMul with BiasAdd and Prelu must have two ' +\n                        'extra arguments: bias and alpha.');\n                }\n                if (!isPrelu && numArgs !== 1) {\n                    throw new Error('Fused MatMul with BiasAdd must have one extra argument: bias.');\n                }\n            }\n            const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n            return [tfOps.fused.matMul({\n                    a: getParamValue('a', node, tensorMap, context),\n                    b: getParamValue('b', node, tensorMap, context),\n                    transposeA: getParamValue('transposeA', node, tensorMap, context),\n                    transposeB: getParamValue('transposeB', node, tensorMap, context),\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'matrices';\n//# sourceMappingURL=matrices_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'FusedBatchNorm':\n        case 'FusedBatchNormV2': {\n            return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n        }\n        case 'FusedBatchNormV3': {\n            return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n        }\n        case 'LRN': {\n            return [tfOps.localResponseNormalization(getParamValue('x', node, tensorMap, context), getParamValue('radius', node, tensorMap, context), getParamValue('bias', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context), getParamValue('beta', node, tensorMap, context))];\n        }\n        case 'Softmax': {\n            return [tfOps.softmax(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'LogSoftmax': {\n            return [tfOps.logSoftmax(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'SparseToDense': {\n            return [tfOps.sparseToDense(getParamValue('sparseIndices', node, tensorMap, context), getParamValue('outputShape', node, tensorMap, context), getParamValue('sparseValues', node, tensorMap, context), getParamValue('defaultValue', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'normalization';\n//# sourceMappingURL=normalization_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Max': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.max(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Mean': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.mean(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Min': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.min(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Sum': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.sum(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'All': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.all(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Any': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.any(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'ArgMax': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.argMax(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'ArgMin': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.argMin(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Prod': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.prod(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Cumsum': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const exclusive = getParamValue('exclusive', node, tensorMap, context);\n            const reverse = getParamValue('reverse', node, tensorMap, context);\n            return [tfOps.cumsum(getParamValue('x', node, tensorMap, context), axis, exclusive, reverse)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'reduction';\n//# sourceMappingURL=reduction_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tidy, util } from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'ConcatV2':\n        case 'Concat': {\n            const n = getParamValue('n', node, tensorMap, context);\n            const axis = getParamValue('axis', node, tensorMap, context);\n            let inputs = getParamValue('tensors', node, tensorMap, context);\n            inputs = inputs.slice(0, n);\n            return [tfOps.concat(inputs, axis)];\n        }\n        case 'GatherV2':\n        case 'Gather': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const input = getParamValue('x', node, tensorMap, context);\n            const indices = getParamValue('indices', node, tensorMap, context);\n            return [tfOps.gather(input, tfOps.cast(indices, 'int32'), axis)];\n        }\n        case 'ReverseV2':\n        case 'Reverse': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const input = getParamValue('x', node, tensorMap, context);\n            return [tfOps.reverse(input, axis)];\n        }\n        case 'Slice': {\n            // tslint:disable-next-line:no-any\n            const begin = getParamValue('begin', node, tensorMap, context);\n            // tslint:disable-next-line:no-any\n            const size = getParamValue('size', node, tensorMap, context);\n            return [tfOps.slice(getParamValue('x', node, tensorMap, context), begin, size)];\n        }\n        case 'StridedSlice': {\n            const begin = getParamValue('begin', node, tensorMap, context);\n            const end = getParamValue('end', node, tensorMap, context);\n            const strides = getParamValue('strides', node, tensorMap, context);\n            const beginMask = getParamValue('beginMask', node, tensorMap, context);\n            const endMask = getParamValue('endMask', node, tensorMap, context);\n            const ellipsisMask = getParamValue('ellipsisMask', node, tensorMap, context);\n            const newAxisMask = getParamValue('newAxisMask', node, tensorMap, context);\n            const shrinkAxisMask = getParamValue('shrinkAxisMask', node, tensorMap, context);\n            const tensor = getParamValue('x', node, tensorMap, context);\n            return [tfOps.stridedSlice(tensor, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)];\n        }\n        case 'Pack': {\n            return tidy(() => {\n                const axis = getParamValue('axis', node, tensorMap, context);\n                const tensors = getParamValue('tensors', node, tensorMap, context);\n                // Reshape the tensors to the first tensor's shape if they don't\n                // match.\n                const shape = tensors[0].shape;\n                const squeezedShape = tfOps.squeeze(tensors[0]).shape;\n                const mapped = tensors.map(tensor => {\n                    const sameShape = util.arraysEqual(tensor.shape, shape);\n                    if (!sameShape &&\n                        !util.arraysEqual(tfOps.squeeze(tensor).shape, squeezedShape)) {\n                        throw new Error('the input tensors shape does not match');\n                    }\n                    return sameShape ? tensor : tfOps.reshape(tensor, shape);\n                });\n                return [tfOps.stack(mapped, axis)];\n            });\n        }\n        case 'Unpack': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const tensor = getParamValue('tensor', node, tensorMap, context);\n            return tfOps.unstack(tensor, axis);\n        }\n        case 'Tile': {\n            const reps = getParamValue('reps', node, tensorMap, context);\n            return [tfOps.tile(getParamValue('x', node, tensorMap, context), reps)];\n        }\n        case 'Split':\n        case 'SplitV': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const numOrSizeSplits = getParamValue('numOrSizeSplits', node, tensorMap, context);\n            const tensor = getParamValue('x', node, tensorMap, context);\n            return tfOps.split(tensor, numOrSizeSplits, axis);\n        }\n        case 'ScatterNd': {\n            const indices = getParamValue('indices', node, tensorMap, context);\n            const values = getParamValue('values', node, tensorMap, context);\n            const shape = getParamValue('shape', node, tensorMap, context);\n            return [tfOps.scatterND(indices, values, shape)];\n        }\n        case 'GatherNd': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const indices = getParamValue('indices', node, tensorMap, context);\n            return [tfOps.gatherND(x, indices)];\n        }\n        case 'SparseToDense': {\n            const indices = getParamValue('sparseIndices', node, tensorMap, context);\n            const shape = getParamValue('outputShape', node, tensorMap, context);\n            const sparseValues = getParamValue('sparseValues', node, tensorMap, context);\n            const defaultValue = getParamValue('defaultValue', node, tensorMap, context);\n            return [tfOps.sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ?\n                    defaultValue :\n                    tfOps.cast(defaultValue, sparseValues.dtype))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'slice_join';\n//# sourceMappingURL=slice_join_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'FFT': {\n            return [tfOps.fft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'IFFT': {\n            return [tfOps.ifft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'RFFT': {\n            return [tfOps.rfft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'IRFFT': {\n            return [tfOps.irfft(getParamValue('x', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'spectral';\n//# sourceMappingURL=spectral_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Cast': {\n            return [tfOps.cast(getParamValue('x', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'ExpandDims': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.expandDims(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Squeeze': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.squeeze(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Reshape': {\n            return [tfOps.reshape(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n        }\n        case 'PadV2':\n        case 'Pad': {\n            return [tfOps.pad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('constantValue', node, tensorMap, context))];\n        }\n        case 'SpaceToBatchND': {\n            const blockShape = getParamValue('blockShape', node, tensorMap, context);\n            const paddings = getParamValue('paddings', node, tensorMap, context);\n            return [tfOps.spaceToBatchND(getParamValue('x', node, tensorMap, context), blockShape, paddings)];\n        }\n        case 'BatchToSpaceND': {\n            const blockShape = getParamValue('blockShape', node, tensorMap, context);\n            const crops = getParamValue('crops', node, tensorMap, context);\n            return [tfOps.batchToSpaceND(getParamValue('x', node, tensorMap, context), blockShape, crops)];\n        }\n        case 'DepthToSpace': {\n            const blockSize = getParamValue('blockSize', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context).toUpperCase();\n            return [tfOps.depthToSpace(getParamValue('x', node, tensorMap, context), blockSize, dataFormat)];\n        }\n        case 'BroadcastTo': {\n            return [tfOps.broadcastTo(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'transformation';\n//# sourceMappingURL=transformation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { NodeValueImpl } from './custom_op/node_value_impl';\nimport { getRegisteredOp } from './custom_op/register';\nimport * as arithmetic from './executors/arithmetic_executor';\nimport * as basicMath from './executors/basic_math_executor';\nimport * as control from './executors/control_executor';\nimport * as convolution from './executors/convolution_executor';\nimport * as creation from './executors/creation_executor';\nimport * as dynamic from './executors/dynamic_executor';\nimport * as evaluation from './executors/evaluation_executor';\nimport * as graph from './executors/graph_executor';\nimport * as image from './executors/image_executor';\nimport * as logical from './executors/logical_executor';\nimport * as matrices from './executors/matrices_executor';\nimport * as normalization from './executors/normalization_executor';\nimport * as reduction from './executors/reduction_executor';\nimport * as sliceJoin from './executors/slice_join_executor';\nimport * as spectral from './executors/spectral_executor';\nimport * as transformation from './executors/transformation_executor';\n/**\n * Executes the op defined by the node object.\n * @param node\n * @param tensorMap contains tensors for executed nodes and weights\n */\nexport function executeOp(node, tensorMap, context) {\n    const value = ((node, tensorMap, context) => {\n        switch (node.category) {\n            case 'arithmetic':\n                return tfc.tidy(() => arithmetic.executeOp(node, tensorMap, context));\n            case 'basic_math':\n                return tfc.tidy(() => basicMath.executeOp(node, tensorMap, context));\n            case 'control':\n                return control.executeOp(node, tensorMap, context);\n            case 'convolution':\n                return tfc.tidy(() => convolution.executeOp(node, tensorMap, context));\n            case 'creation':\n                return tfc.tidy(() => creation.executeOp(node, tensorMap, context));\n            case 'dynamic':\n                return dynamic.executeOp(node, tensorMap, context);\n            case 'evaluation':\n                return tfc.tidy(() => evaluation.executeOp(node, tensorMap, context));\n            case 'image':\n                return tfc.tidy(() => image.executeOp(node, tensorMap, context));\n            case 'graph':\n                return tfc.tidy(() => graph.executeOp(node, tensorMap, context));\n            case 'logical':\n                return tfc.tidy(() => logical.executeOp(node, tensorMap, context));\n            case 'matrices':\n                return tfc.tidy(() => matrices.executeOp(node, tensorMap, context));\n            case 'normalization':\n                return tfc.tidy(() => normalization.executeOp(node, tensorMap, context));\n            case 'reduction':\n                return tfc.tidy(() => reduction.executeOp(node, tensorMap, context));\n            case 'slice_join':\n                return tfc.tidy(() => sliceJoin.executeOp(node, tensorMap, context));\n            case 'spectral':\n                return tfc.tidy(() => spectral.executeOp(node, tensorMap, context));\n            case 'transformation':\n                return tfc.tidy(() => transformation.executeOp(node, tensorMap, context));\n            case 'custom':\n                const opMapper = getRegisteredOp(node.op);\n                if (opMapper && opMapper.customExecutor) {\n                    return opMapper.customExecutor(new NodeValueImpl(node, tensorMap, context));\n                }\n                else {\n                    throw TypeError(`Custom op ${node.op} is not registered.`);\n                }\n            default:\n                throw TypeError(`Unknown op '${node.op}'. File an issue at ` +\n                    `https://github.com/tensorflow/tfjs/issues so we can add it` +\n                    `, or register a custom execution with tf.registerOp()`);\n        }\n    })(node, tensorMap, context);\n    if (value instanceof Promise) {\n        return value.then((data) => [].concat(data));\n    }\n    return [].concat(value);\n}\n//# sourceMappingURL=operation_executor.js.map", "/**\n * ExecutionContext captures the runtime environment of the node. It keeps\n * track of the current frame and iteration for the control flow ops.\n *\n * For example, typical Dynamic RNN model may contain loops, for which\n * TensorFlow will generate graphs with Enter/Exit nodes to control the\n * current execution frame, and NextIteration Nodes for iteration id increment.\n * For model with branch logic, TensorFLow will generate Switch/Merge ops.\n */\nexport class ExecutionContext {\n    constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}) {\n        this.weightMap = weightMap;\n        this.tensorArrayMap = tensorArrayMap;\n        this.tensorListMap = tensorListMap;\n        this.functionMap = functionMap;\n        this.rootContext = { id: 0, frameName: '', iterationId: 0 };\n        this.contexts = [this.rootContext];\n        this.lastId = 0;\n        this.generateCurrentContextIds();\n    }\n    newFrame(id, frameName) {\n        return { id, frameName, iterationId: 0 };\n    }\n    /**\n     * Set the current context\n     * @param contexts: ExecutionContextInfo[] the current path of execution\n     * frames\n     */\n    set currentContext(contexts) {\n        if (this.contexts !== contexts) {\n            this.contexts = contexts;\n            this.generateCurrentContextIds();\n        }\n    }\n    get currentContext() {\n        return this.contexts;\n    }\n    /**\n     * Returns the current context in string format.\n     */\n    get currentContextId() {\n        return this._currentContextIds[0];\n    }\n    /**\n     * Returns the current context and all parent contexts in string format.\n     * This allow access to the nodes in the current and parent frames.\n     */\n    get currentContextIds() {\n        return this._currentContextIds;\n    }\n    generateCurrentContextIds() {\n        const names = [];\n        for (let i = 0; i < this.contexts.length - 1; i++) {\n            const contexts = this.contexts.slice(0, this.contexts.length - i);\n            names.push(this.contextIdforContexts(contexts));\n        }\n        names.push('');\n        this._currentContextIds = names;\n    }\n    contextIdforContexts(contexts) {\n        return contexts ?\n            contexts\n                .map(context => (context.id === 0 && context.iterationId === 0) ?\n                '' :\n                `${context.frameName}-${context.iterationId}`)\n                .join('/') :\n            '';\n    }\n    /**\n     * Enter a new frame, a new context is pushed on the current context list.\n     * @param frameId new frame id\n     */\n    enterFrame(frameId) {\n        if (this.contexts) {\n            this.lastId++;\n            this.contexts = this.contexts.slice();\n            this.contexts.push(this.newFrame(this.lastId, frameId));\n            this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));\n        }\n    }\n    /**\n     * Exit the current frame, the last context is removed from the current\n     * context list.\n     */\n    exitFrame() {\n        if (this.contexts && this.contexts.length > 1) {\n            this.contexts = this.contexts.slice();\n            this.contexts.splice(-1);\n            this.currentContextIds.shift();\n        }\n        else {\n            throw new Error('Cannot exit frame, the context is empty');\n        }\n    }\n    /**\n     * Enter the next iteration of a loop, the iteration id of last context is\n     * increased.\n     */\n    nextIteration() {\n        if (this.contexts && this.contexts.length > 0) {\n            this.contexts = this.contexts.slice();\n            this.lastId++;\n            const context = Object.assign({}, this.contexts[this.contexts.length - 1]);\n            context.iterationId += 1;\n            context.id = this.lastId;\n            this.contexts.splice(-1, 1, context);\n            this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));\n        }\n        else {\n            throw new Error('Cannot increase frame iteration, the context is empty');\n        }\n    }\n    getWeight(name) {\n        return this.weightMap[name];\n    }\n    addTensorArray(tensorArray) {\n        this.tensorArrayMap[tensorArray.id] = tensorArray;\n    }\n    getTensorArray(id) {\n        return this.tensorArrayMap[id];\n    }\n    addTensorList(tensorList) {\n        this.tensorListMap[tensorList.id] = tensorList;\n    }\n    getTensorList(id) {\n        return this.tensorListMap[id];\n    }\n    dispose(keepIds) {\n        for (const key in this.tensorArrayMap) {\n            this.tensorArrayMap[key].clearAndClose(keepIds);\n        }\n        for (const key in this.tensorListMap) {\n            this.tensorListMap[key].clearAndClose(keepIds);\n        }\n    }\n}\n//# sourceMappingURL=execution_context.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { parseNodeName } from '../operations/executors/utils';\n/**\n * Given graph inputs and desired outputs, find the minimal set of nodes\n * to execute in order to compute the outputs. In addition return other useful\n * info such:\n * - Missing inputs needed to compute the output.\n * - Whether the subgraph contains dynamic ops (control flow, dynamic shape).\n * - Alternative inputs in order to avoid async (dynamic op) execution.\n */\nexport function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {\n    const usedNodes = new Set();\n    const missingInputs = [];\n    let dynamicNode = null;\n    let syncInputs = null;\n    // Start with the outputs, going backwards and find all the nodes that are\n    // needed to compute those outputs.\n    const seen = new Set();\n    const inputNodeNames = Object.keys(inputs).map(name => parseNodeName(name)[0]);\n    let initNodeNames = [];\n    if (initNodes != null) {\n        initNodeNames = initNodes.map(node => parseNodeName(node.name)[0]);\n    }\n    const frontier = [...outputs];\n    while (frontier.length > 0) {\n        const node = frontier.pop();\n        if (isControlFlow(node) || isDynamicShape(node)) {\n            if (dynamicNode == null) {\n                dynamicNode = node;\n                syncInputs = dynamicNode.children.map(child => child.name)\n                    .filter(name => usedNodes.has(name));\n            }\n        }\n        usedNodes.add(node.name);\n        // Weights are dead end since we already have their values.\n        if (weightMap[node.name] != null) {\n            continue;\n        }\n        // This node is a dead end since it's one of the user-provided inputs.\n        if (inputNodeNames.indexOf(node.name) !== -1) {\n            continue;\n        }\n        // This node is a dead end since it doesn't have any inputs.\n        if (initNodeNames.indexOf(node.name) !== -1) {\n            continue;\n        }\n        if (node.inputs.length === 0) {\n            missingInputs.push(node.name);\n            continue;\n        }\n        node.inputs.forEach(input => {\n            // Don't add to the frontier if it is already there.\n            if (seen.has(input.name)) {\n                return;\n            }\n            seen.add(input.name);\n            frontier.push(input);\n        });\n    }\n    return { inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs };\n}\n/**\n * Given the execution info, return a list of nodes in topological order that\n * need to be executed to compute the output.\n */\nexport function getNodesInTopologicalOrder(graph, weightMap, executionInfo) {\n    const { usedNodes, inputs } = executionInfo;\n    const frontier = [];\n    const inputNodes = Object.keys(inputs)\n        .map(name => parseNodeName(name)[0])\n        .map(name => graph.nodes[name]);\n    const initNodes = graph.initNodes;\n    inputNodes.forEach(input => {\n        if (usedNodes.has(input.name)) {\n            frontier.push(input);\n        }\n    });\n    graph.weights.forEach(weight => {\n        if (usedNodes.has(weight.name)) {\n            frontier.push(weight);\n        }\n    });\n    if (initNodes != null) {\n        initNodes.forEach(node => {\n            if (usedNodes.has(node.name)) {\n                frontier.push(node);\n            }\n        });\n    }\n    const seen = new Set();\n    const orderedNodes = [];\n    while (frontier.length > 0) {\n        const node = frontier.pop();\n        seen.add(node.name);\n        if (!weightMap[node.name]) {\n            orderedNodes.push(node);\n        }\n        node.children.forEach(child => {\n            if (!seen.has(child.name) && usedNodes.has(child.name) &&\n                child.inputs.every(input => seen.has(input.name))) {\n                frontier.push(child);\n            }\n        });\n    }\n    return orderedNodes;\n}\nconst CONTROL_FLOW_OPS = [\n    'Switch', 'Merge', 'Enter', 'Exit', 'NextIteration', 'StatelessIf',\n    'StatelessWhile', 'if', 'While'\n];\nconst DYNAMIC_SHAPE_OPS = [\n    'NonMaxSuppressionV2', 'NonMaxSuppressionV3', 'NonMaxSuppressionV5', 'Where'\n];\nexport function isControlFlow(node) {\n    return CONTROL_FLOW_OPS.indexOf(node.op) >= 0;\n}\nexport function isDynamicShape(node) {\n    return DYNAMIC_SHAPE_OPS.indexOf(node.op) >= 0;\n}\n//# sourceMappingURL=model_analysis.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tidy, util } from '@tensorflow/tfjs-core';\nimport { getNodeNameAndIndex, getParamValue, getTensor, getTensorsForCurrentContenxt, parseNodeName } from '../operations/executors/utils';\nimport { executeOp } from '../operations/operation_executor';\nimport { ExecutionContext } from './execution_context';\nimport { getExecutionSubgraph, getNodesInTopologicalOrder, isControlFlow } from './model_analysis';\nexport class GraphExecutor {\n    /**\n     *\n     * @param graph Graph the model or function graph to be executed.\n     * @param parent When building function exector you need to set the parent\n     * executor. Since the weights and function executor maps are set at parant\n     * level, that function executor can access the function maps and weight maps\n     * through the parent.\n     */\n    constructor(graph, parent) {\n        this.graph = graph;\n        this.parent = parent;\n        this.compiledMap = new Map();\n        this._weightMap = {};\n        this.SEPERATOR = ',';\n        this._functions = {};\n        this._functionExecutorMap = {};\n        this._outputs = graph.outputs;\n        this._inputs = graph.inputs;\n        this._initNodes = graph.initNodes;\n        this._signature = graph.signature;\n        this._functions = graph.functions;\n        // create sub-graph executors\n        if (graph.functions != null) {\n            Object.keys(graph.functions).forEach(name => {\n                this._functionExecutorMap[name] =\n                    new GraphExecutor(graph.functions[name], this);\n            });\n        }\n    }\n    get weightIds() {\n        return this.parent ? this.parent.weightIds : this._weightIds;\n    }\n    get functionExecutorMap() {\n        return this.parent ? this.parent.functionExecutorMap :\n            this._functionExecutorMap;\n    }\n    get weightMap() {\n        return this.parent ? this.parent.weightMap : this._weightMap;\n    }\n    set weightMap(weightMap) {\n        const weightIds = Object.keys(weightMap).map(key => weightMap[key].map(tensor => tensor.id));\n        this._weightIds = [].concat(...weightIds);\n        this._weightMap = weightMap;\n    }\n    get inputs() {\n        return this._inputs.map(node => {\n            return {\n                name: node.name,\n                shape: node.attrParams['shape'] ?\n                    node.attrParams['shape'].value :\n                    undefined,\n                dtype: node.attrParams['dtype'] ?\n                    node.attrParams['dtype'].value :\n                    undefined\n            };\n        });\n    }\n    get outputs() {\n        return this._outputs.map(node => {\n            return {\n                name: node.name,\n                shape: node.attrParams['shape'] ?\n                    node.attrParams['shape'].value :\n                    undefined,\n                dtype: node.attrParams['dtype'] ?\n                    node.attrParams['dtype'].value :\n                    undefined\n            };\n        });\n    }\n    get inputNodes() {\n        return this._inputs.map(node => node.signatureKey || node.name);\n    }\n    get outputNodes() {\n        return this._outputs.map((node) => {\n            const name = node.signatureKey || node.name;\n            return node.defaultOutput ? (`${name}:${node.defaultOutput}`) : name;\n        });\n    }\n    get functions() {\n        return Object.keys(this._functions).reduce((map, key) => {\n            map[key] = this._functions[key].signature;\n            return map;\n        }, {});\n    }\n    getCompilationKey(inputs, outputs) {\n        const sortedInputs = inputs.map(node => node.name).sort();\n        const sortedOutputs = outputs.map(node => node.name).sort();\n        return sortedInputs.join(this.SEPERATOR) + '--' +\n            sortedOutputs.join(this.SEPERATOR);\n    }\n    /**\n     * Compiles the inference graph and returns the minimal set of nodes that are\n     * required for execution, in the correct execution order.\n     */\n    compile(inputs, outputs) {\n        const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);\n        const { missingInputs, dynamicNode, syncInputs } = executionInfo;\n        if (dynamicNode != null) {\n            throw new Error(`This execution contains the node '${dynamicNode.name}', which has ` +\n                `the dynamic op '${dynamicNode.op}'. Please use ` +\n                `model.executeAsync() instead. Alternatively, to avoid the ` +\n                `dynamic ops, specify the inputs [${syncInputs}]`);\n        }\n        if (missingInputs.length > 0) {\n            const outNames = outputs.map(n => n.name);\n            const inNames = Object.keys(inputs);\n            throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs ` +\n                `[${inNames}]. Missing the following inputs: [${missingInputs}]`);\n        }\n        return getNodesInTopologicalOrder(this.graph, this.weightMap, executionInfo);\n    }\n    /**\n     * Executes the inference for given input tensors.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     */\n    execute(inputs, outputs) {\n        inputs = this.mapInputs(inputs);\n        const names = Object.keys(inputs).sort();\n        this.checkInputs(inputs);\n        this.checkInputShapeAndType(inputs);\n        outputs = this.mapOutputs(outputs);\n        this.checkOutputs(outputs);\n        const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n        const outputNodeNames = outputs.map(name => parseNodeName(name)[0]);\n        let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n        // If no outputs are specified, then use the default outputs of the model.\n        if (outputNodes.length === 0) {\n            outputNodes = this._outputs;\n        }\n        const compilationKey = this.getCompilationKey(inputNodes, outputNodes);\n        // Do nothing if the compiled graph cache contains the input.\n        let orderedNodes = this.compiledMap.get(compilationKey);\n        if (orderedNodes == null) {\n            orderedNodes = this.compile(inputs, outputNodes);\n            this.compiledMap.set(compilationKey, orderedNodes);\n        }\n        const tensorArrayMap = {};\n        const tensorListMap = {};\n        return tidy(() => {\n            const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n            const tensorsMap = Object.assign({}, this.weightMap);\n            Object.keys(inputs).forEach(name => {\n                const [nodeName, index] = parseNodeName(name);\n                const tensors = [];\n                tensors[index] = inputs[name];\n                tensorsMap[nodeName] = tensors;\n            });\n            const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n            const intermediateTensorConsumerCount = {};\n            for (let i = 0; i < orderedNodes.length; i++) {\n                const node = orderedNodes[i];\n                if (!tensorsMap[node.name]) {\n                    const tensors = executeOp(node, tensorsMap, context);\n                    if (tensors instanceof Promise) {\n                        throw new Error(`The execution of the op '${node.op}' returned a promise. ` +\n                            `Please use model.executeAsync() instead.`);\n                    }\n                    tensorsMap[node.name] = tensors;\n                    this.checkTensorForDisposal(node.name, node, tensorsMap, context, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount);\n                }\n            }\n            // dispose the context for the root executor\n            if (this.parent == null) {\n                context.dispose(tensorsToKeep);\n            }\n            return outputs.map(name => getTensor(name, tensorsMap, context));\n        });\n    }\n    getFrozenTensorIds(tensorMap) {\n        const ids = [].concat.apply([], Object.keys(tensorMap)\n            .map(key => tensorMap[key])\n            .map(tensors => tensors.map(tensor => tensor.id)));\n        return new Set(ids);\n    }\n    checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount) {\n        // Skip output nodes and any control flow nodes, since its dependency is\n        // tricky to track correctly.\n        if (node.category === 'control' || outputNames.indexOf(nodeName) !== -1) {\n            return;\n        }\n        tensorMap[nodeName].forEach(tensor => {\n            if (tensor != null) {\n                intermediateTensorConsumerCount[tensor.id] =\n                    (intermediateTensorConsumerCount[tensor.id] || 0) +\n                        node.children.length;\n            }\n        });\n        node.inputs.forEach(input => {\n            // Skip any control flow nodes, since its dependency is tricky to track\n            // correctly.\n            if (input.category !== 'control') {\n                const tensors = getTensorsForCurrentContenxt(input.name, tensorMap, context);\n                if (tensors != null) {\n                    tensors.forEach(tensor => {\n                        if (tensor && !tensorsToKeep.has(tensor.id)) {\n                            const count = intermediateTensorConsumerCount[tensor.id];\n                            if (count === 1) {\n                                tensor.dispose();\n                                delete intermediateTensorConsumerCount[tensor.id];\n                            }\n                            else if (count != null) {\n                                // only intermediate nodes has count set, inputs and weights are\n                                // not.\n                                intermediateTensorConsumerCount[tensor.id]--;\n                            }\n                        }\n                    });\n                }\n            }\n        });\n    }\n    /**\n     * Executes the inference for given input tensors in Async fashion.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     */\n    async executeAsync(inputs, outputs) {\n        return this._executeAsync(inputs, outputs);\n    }\n    /**\n     * Executes the inference for given input tensors in Async fashion.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     * @param isFunctionExecution Flag for executing a function.\n     * @param tensorArrayMap Optional, global TensorArray map by id. Used for\n     * function execution.\n     * @param tensorArrayMap Optinal global TensorList map by id. Used for\n     * function execution.\n     */\n    async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {\n        if (!isFunctionExecution) {\n            inputs = this.mapInputs(inputs);\n            this.checkInputs(inputs);\n            this.checkInputShapeAndType(inputs);\n            outputs = this.mapOutputs(outputs);\n            this.checkOutputs(outputs);\n        }\n        const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n        // Graph with control flow op requires runtime evaluation of the execution\n        // order, while without control flow the execution order is pre-determined\n        // in the compile method.\n        const tensorMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);\n        const results = outputs.map(name => getTensor(name, tensorMap, context));\n        // dispose all the intermediate tensors\n        const outputIds = results.map(t => t.id);\n        const inputIds = Object.keys(inputs).map(name => inputs[name].id);\n        const keepIds = new Set([...outputIds, ...inputIds, ...this.weightIds]);\n        Object.keys(tensorMap).forEach(key => {\n            const tensorArray = tensorMap[key];\n            tensorArray.forEach(tensor => {\n                if (tensor && !tensor.isDisposed && !keepIds.has(tensor.id)) {\n                    tensor.dispose();\n                }\n            });\n        });\n        // dispose the context for the root executor\n        if (this.parent == null) {\n            context.dispose(keepIds);\n        }\n        return results;\n    }\n    async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {\n        const mappedInputs = inputs.reduce((map, tensor, index) => {\n            map[this.inputs[index].name] = tensor;\n            return map;\n        }, {});\n        return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);\n    }\n    /**\n     * When there are control flow nodes in the graph, the graph execution use\n     * ExecutionContext to keep track of the frames and loop iterators.\n     * @param inputs placeholder tensors for the graph.\n     * @param context the execution context object for current execution.\n     * @param isFunctionExecution Flag for executing a function.\n     */\n    async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {\n        const names = Object.keys(inputs);\n        const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n        const outputNodeNames = outputNames.map(name => parseNodeName(name)[0]);\n        const outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n        const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes, this.weightMap);\n        const stack = [...inputNodes, ...this.graph.weights].map(node => {\n            return { node, contexts: context.currentContext };\n        });\n        const tensorsMap = Object.assign({}, this.weightMap);\n        Object.keys(inputs).forEach(name => {\n            const [nodeName, index] = parseNodeName(name);\n            const tensors = [];\n            tensors[index] = inputs[name];\n            tensorsMap[nodeName] = tensors;\n        });\n        const intermediateTensorConsumerCount = {};\n        const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n        const added = {};\n        while (stack.length > 0) {\n            const promises = this.processStack(inputNodes, stack, context, tensorsMap, added, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount, usedNodes);\n            await Promise.all(promises);\n        }\n        if (dynamicNode == null && !isFunctionExecution) {\n            console.warn(`This model execution did not contain any nodes with control flow ` +\n                `or dynamic output shapes. You can use model.execute() instead.`);\n        }\n        const missingOutputs = outputNodes\n            .filter(node => !isControlFlow(node) &&\n            !getTensor(node.name, tensorsMap, context))\n            .map(node => node.name);\n        if (missingOutputs.length > 0) {\n            let alternativeMsg = '';\n            if (dynamicNode != null) {\n                alternativeMsg =\n                    `Alternatively, to avoid the dynamic ops, use model.execute() ` +\n                        `and specify the inputs [${syncInputs}]`;\n            }\n            throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided ` +\n                `inputs [${names}]. Consider providing the following inputs: ` +\n                `[${missingInputs}]. ${alternativeMsg}`);\n        }\n        return tensorsMap;\n    }\n    processStack(inputNodes, stack, context, tensorMap, added, tensorsToKeep, outputNames, intermediateTensorConsumerCount, usedNodes) {\n        const promises = [];\n        while (stack.length > 0) {\n            const item = stack.pop();\n            context.currentContext = item.contexts;\n            let nodeName = '';\n            // The tensor of the Enter op with isConstant set should be set\n            // in the parent scope, so it will be available as constant for the\n            // whole loop.\n            if (item.node.op === 'Enter' &&\n                getParamValue('isConstant', item.node, tensorMap, context)) {\n                [nodeName] = getNodeNameAndIndex(item.node.name, context);\n            }\n            // only process nodes that are not provided as input nodes.\n            if (inputNodes.indexOf(item.node) === -1) {\n                const tensors = executeOp(item.node, tensorMap, context);\n                if (!nodeName) {\n                    [nodeName] = getNodeNameAndIndex(item.node.name, context);\n                }\n                const currentContext = context.currentContext;\n                if (tensors instanceof Promise) {\n                    promises.push(tensors.then(t => {\n                        tensorMap[nodeName] = t;\n                        context.currentContext = currentContext;\n                        this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n                        this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n                        return t;\n                    }));\n                }\n                else {\n                    tensorMap[nodeName] = tensors;\n                    this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n                    this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n                }\n            }\n            else {\n                this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n            }\n        }\n        return promises;\n    }\n    processChildNodes(node, stack, context, tensorMap, added, usedNodes) {\n        node.children.forEach((childNode) => {\n            const [nodeName,] = getNodeNameAndIndex(childNode.name, context);\n            if (added[nodeName] || !usedNodes.has(childNode.name)) {\n                return;\n            }\n            // Merge op can be pushed if any of its inputs has value.\n            if (childNode.op === 'Merge') {\n                if (childNode.inputNames.some(name => {\n                    return !!getTensor(name, tensorMap, context);\n                })) {\n                    added[nodeName] = true;\n                    stack.push({ contexts: context.currentContext, node: childNode });\n                }\n            }\n            else // Otherwise all inputs must to have value.\n             if (childNode.inputNames.every(name => {\n                return !!getTensor(name, tensorMap, context);\n            })) {\n                added[nodeName] = true;\n                stack.push({ contexts: context.currentContext, node: childNode });\n            }\n        });\n    }\n    /**\n     * Releases the memory used by the weight tensors.\n     */\n    dispose() {\n        Object.keys(this.weightMap)\n            .forEach(key => this.weightMap[key].forEach(tensor => tensor.dispose()));\n    }\n    checkInputShapeAndType(inputs) {\n        Object.keys(inputs).forEach(name => {\n            const input = inputs[name];\n            const [nodeName,] = parseNodeName(name);\n            const node = this.graph.nodes[nodeName];\n            if (node.attrParams['shape'] && node.attrParams['shape'].value) {\n                const shape = node.attrParams['shape'].value;\n                const match = shape.length === input.shape.length &&\n                    input.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim);\n                util.assert(match, () => `The shape of dict['${node.name}'] provided in ` +\n                    `model.execute(dict) must be [${shape}], but was ` +\n                    `[${input.shape}]`);\n            }\n            if (node.attrParams['dtype'] && node.attrParams['dtype'].value) {\n                util.assert(input.dtype === node.attrParams['dtype'].value, () => `The dtype of dict['${node.name}'] provided in ` +\n                    `model.execute(dict) must be ` +\n                    `${node.attrParams['dtype'].value}, but was ${input.dtype}`);\n            }\n        });\n    }\n    mapInputs(inputs) {\n        const result = {};\n        for (const inputName in inputs) {\n            if (this._signature != null && this._signature.inputs != null &&\n                this._signature.inputs[inputName] != null) {\n                const tensor = this._signature.inputs[inputName];\n                result[tensor.name] = inputs[inputName];\n            }\n            else {\n                result[inputName] = inputs[inputName];\n            }\n        }\n        return result;\n    }\n    checkInputs(inputs) {\n        const notInGraph = Object.keys(inputs).filter(name => {\n            const [nodeName] = parseNodeName(name);\n            return this.graph.nodes[nodeName] == null;\n        });\n        if (notInGraph.length > 0) {\n            throw new Error(`The dict provided in model.execute(dict) has ` +\n                `keys: [${notInGraph}] that are not part of graph`);\n        }\n    }\n    mapOutputs(outputs) {\n        return outputs.map(name => {\n            if (this._signature != null && this._signature.outputs != null &&\n                this._signature.outputs[name] != null) {\n                const tensor = this._signature.outputs[name];\n                return tensor.name;\n            }\n            return name;\n        }, {});\n    }\n    checkOutputs(outputs) {\n        outputs.forEach(name => {\n            const [normalizedName] = parseNodeName(name);\n            if (!this.graph.nodes[normalizedName]) {\n                throw new Error(`The output '${name}' is not found in the graph`);\n            }\n        });\n    }\n}\n//# sourceMappingURL=graph_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { io, Tensor } from '@tensorflow/tfjs-core';\nimport { OperationMapper } from '../operations/operation_mapper';\nimport { GraphExecutor } from './graph_executor';\nexport const TFHUB_SEARCH_PARAM = '?tfjs-format=file';\nexport const DEFAULT_MODEL_NAME = 'model.json';\n/**\n * A `tf.GraphModel` is a directed, acyclic graph built from a\n * SavedModel GraphDef and allows inference execution.\n *\n * A `tf.GraphModel` can only be created by loading from a model converted from\n * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using\n * the command line converter tool and loaded via `tf.loadGraphModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class GraphModel {\n    /**\n     * @param modelUrl url for the model, or an `io.IOHandler`.\n     * @param weightManifestUrl url for the weight file generated by\n     * scripts/convert.py script.\n     * @param requestOption options for Request, which allows to send credentials\n     * and custom headers.\n     * @param onProgress Optional, progress callback function, fired periodically\n     * before the load is completed.\n     */\n    constructor(modelUrl, loadOptions = {}) {\n        this.modelUrl = modelUrl;\n        this.loadOptions = loadOptions;\n        this.version = 'n/a';\n        if (loadOptions == null) {\n            this.loadOptions = {};\n        }\n    }\n    // Returns the version information for the tensorflow model GraphDef.\n    get modelVersion() {\n        return this.version;\n    }\n    get inputNodes() {\n        return this.executor.inputNodes;\n    }\n    get outputNodes() {\n        return this.executor.outputNodes;\n    }\n    get inputs() {\n        return this.executor.inputs;\n    }\n    get outputs() {\n        return this.executor.outputs;\n    }\n    get weights() {\n        return this.executor.weightMap;\n    }\n    findIOHandler() {\n        const path = this.modelUrl;\n        if (path.load != null) {\n            // Path is an IO Handler.\n            this.handler = path;\n        }\n        else if (this.loadOptions.requestInit != null) {\n            this.handler = io.browserHTTPRequest(path, this.loadOptions);\n        }\n        else {\n            const handlers = io.getLoadHandlers(path, this.loadOptions);\n            if (handlers.length === 0) {\n                // For backward compatibility: if no load handler can be found,\n                // assume it is a relative http path.\n                handlers.push(io.browserHTTPRequest(path, this.loadOptions));\n            }\n            else if (handlers.length > 1) {\n                throw new Error(`Found more than one (${handlers.length}) load handlers for ` +\n                    `URL '${[path]}'`);\n            }\n            this.handler = handlers[0];\n        }\n    }\n    /**\n     * Loads the model and weight files, construct the in memory weight map and\n     * compile the inference graph.\n     */\n    async load() {\n        this.findIOHandler();\n        if (this.handler.load == null) {\n            throw new Error('Cannot proceed with model loading because the IOHandler provided ' +\n                'does not have the `load` method implemented.');\n        }\n        const artifacts = await this.handler.load();\n        return this.loadSync(artifacts);\n    }\n    /**\n     * Synchronously construct the in memory weight map and\n     * compile the inference graph. Also initialize hashtable if any.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    loadSync(artifacts) {\n        this.artifacts = artifacts;\n        const graph = this.artifacts.modelTopology;\n        let signature = {};\n        if (this.artifacts.userDefinedMetadata != null) {\n            signature = // tslint:disable-next-line:no-any\n                this.artifacts.userDefinedMetadata.signature;\n        }\n        this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;\n        const weightMap = io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);\n        this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, signature));\n        this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);\n        if (artifacts.modelInitializer != null) {\n            const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer);\n            this.initializer = new GraphExecutor(initializer);\n            this.initializer.weightMap = this.executor.weightMap;\n            this.initializer.execute({}, []);\n        }\n        return true;\n    }\n    /**\n     * Save the configuration and/or weights of the GraphModel.\n     *\n     * An `IOHandler` is an object that has a `save` method of the proper\n     * signature defined. The `save` method manages the storing or\n     * transmission of serialized data (\"artifacts\") that represent the\n     * model's topology and weights onto or via a specific medium, such as\n     * file downloads, local storage, IndexedDB in the web browser and HTTP\n     * requests to a server. TensorFlow.js provides `IOHandler`\n     * implementations for a number of frequently used saving mediums, such as\n     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n     * for more details.\n     *\n     * This method also allows you to refer to certain types of `IOHandler`s\n     * as URL-like string shortcuts, such as 'localstorage://' and\n     * 'indexeddb://'.\n     *\n     * Example 1: Save `model`'s topology and weights to browser [local\n     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n     * then load it back.\n     *\n     * ```js\n     * const modelUrl =\n     *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n     * const model = await tf.loadGraphModel(modelUrl);\n     * const zeros = tf.zeros([1, 224, 224, 3]);\n     * model.predict(zeros).print();\n     *\n     * const saveResults = await model.save('localstorage://my-model-1');\n     *\n     * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * model.predict(zeros).print();\n     * ```\n     *\n     * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n     * scheme-based string shortcut for `IOHandler`.\n     * @param config Options for saving the model.\n     * @returns A `Promise` of `SaveResult`, which summarizes the result of\n     * the saving, such as byte sizes of the saved artifacts for the model's\n     *   topology and weight values.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async save(handlerOrURL, config) {\n        if (typeof handlerOrURL === 'string') {\n            const handlers = io.getSaveHandlers(handlerOrURL);\n            if (handlers.length === 0) {\n                throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n            }\n            else if (handlers.length > 1) {\n                throw new Error(`Found more than one (${handlers.length}) save handlers for ` +\n                    `URL '${handlerOrURL}'`);\n            }\n            handlerOrURL = handlers[0];\n        }\n        if (handlerOrURL.save == null) {\n            throw new Error('GraphModel.save() cannot proceed because the IOHandler ' +\n                'provided does not have the `save` attribute defined.');\n        }\n        return handlerOrURL.save(this.artifacts);\n    }\n    /**\n     * Execute the inference for the input tensors.\n     *\n     * @param input The input tensors, when there is single input for the model,\n     * inputs param should be a `tf.Tensor`. For models with mutliple inputs,\n     * inputs params should be in either `tf.Tensor`[] if the input order is\n     * fixed, or otherwise NamedTensorMap format.\n     *\n     * For model with multiple inputs, we recommend you use NamedTensorMap as the\n     * input type, if you use `tf.Tensor`[], the order of the array needs to\n     * follow the\n     * order of inputNodes array. @see {@link GraphModel.inputNodes}\n     *\n     * You can also feed any intermediate nodes using the NamedTensorMap as the\n     * input type. For example, given the graph\n     *    InputNode => Intermediate => OutputNode,\n     * you can execute the subgraph Intermediate => OutputNode by calling\n     *    model.execute('IntermediateNode' : tf.tensor(...));\n     *\n     * This is useful for models that uses tf.dynamic_rnn, where the intermediate\n     * state needs to be fed manually.\n     *\n     * For batch inference execution, the tensors for each input need to be\n     * concatenated together. For example with mobilenet, the required input shape\n     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].\n     * If we are provide a batched data of 100 images, the input tensor should be\n     * in the shape of [100, 244, 244, 3].\n     *\n     * @param config Prediction configuration for specifying the batch size and\n     * output node names. Currently the batch size option is ignored for graph\n     * model.\n     *\n     * @returns Inference result tensors. The output would be single `tf.Tensor`\n     * if model has single output node, otherwise Tensor[] or NamedTensorMap[]\n     * will be returned for model with multiple outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(inputs, config) {\n        return this.execute(inputs, this.outputNodes);\n    }\n    normalizeInputs(inputs) {\n        if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {\n            // The input is already a NamedTensorMap.\n            return inputs;\n        }\n        inputs = Array.isArray(inputs) ? inputs : [inputs];\n        if (inputs.length !== this.inputNodes.length) {\n            throw new Error('Input tensor count mismatch,' +\n                `the graph model has ${this.inputNodes.length} placeholders, ` +\n                `while there are ${inputs.length} input tensors.`);\n        }\n        return this.inputNodes.reduce((map, inputName, i) => {\n            map[inputName] = inputs[i];\n            return map;\n        }, {});\n    }\n    normalizeOutputs(outputs) {\n        outputs = outputs || this.outputNodes;\n        return !Array.isArray(outputs) ? [outputs] : outputs;\n    }\n    /**\n     * Executes inference for the model for given input tensors.\n     * @param inputs tensor, tensor array or tensor map of the inputs for the\n     * model, keyed by the input node names.\n     * @param outputs output node name from the Tensorflow model, if no\n     * outputs are specified, the default outputs of the model would be used.\n     * You can inspect intermediate nodes of the model by adding them to the\n     * outputs array.\n     *\n     * @returns A single tensor if provided with a single output or no outputs\n     * are provided and there is only one default output, otherwise return a\n     * tensor array. The order of the tensor array is the same as the outputs\n     * if provided, otherwise the order of outputNodes attribute of the model.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    execute(inputs, outputs) {\n        inputs = this.normalizeInputs(inputs);\n        outputs = this.normalizeOutputs(outputs);\n        const result = this.executor.execute(inputs, outputs);\n        return result.length > 1 ? result : result[0];\n    }\n    /**\n     * Executes inference for the model for given input tensors in async\n     * fashion, use this method when your model contains control flow ops.\n     * @param inputs tensor, tensor array or tensor map of the inputs for the\n     * model, keyed by the input node names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     *\n     * @returns A Promise of single tensor if provided with a single output or\n     * no outputs are provided and there is only one default output, otherwise\n     * return a tensor map.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async executeAsync(inputs, outputs) {\n        inputs = this.normalizeInputs(inputs);\n        outputs = this.normalizeOutputs(outputs);\n        const result = await this.executor.executeAsync(inputs, outputs);\n        return result.length > 1 ? result : result[0];\n    }\n    convertTensorMapToTensorsMap(map) {\n        return Object.keys(map).reduce((newMap, key) => {\n            newMap[key] = [map[key]];\n            return newMap;\n        }, {});\n    }\n    /**\n     * Releases the memory used by the weight tensors.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    dispose() {\n        this.executor.dispose();\n        if (this.initializer) {\n            this.initializer.dispose();\n        }\n    }\n}\n/**\n * Load a graph model given a URL to the model definition.\n *\n * Example of loading MobileNetV2 from a URL and making a prediction with a\n * zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n * const model = await tf.loadGraphModel(modelUrl);\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n *\n * Example of loading MobileNetV2 from a TF Hub URL and making a prediction with\n * a zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';\n * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n * @param modelUrl The url or an `io.IOHandler` that loads the model.\n * @param options Options for the HTTP request, which allows to send credentials\n *    and custom headers.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport async function loadGraphModel(modelUrl, options = {}) {\n    if (modelUrl == null) {\n        throw new Error('modelUrl in loadGraphModel() cannot be null. Please provide a url ' +\n            'or an IOHandler that loads the model');\n    }\n    if (options == null) {\n        options = {};\n    }\n    if (options.fromTFHub) {\n        if (modelUrl.load == null) {\n            if (!modelUrl.endsWith('/')) {\n                modelUrl = modelUrl + '/';\n            }\n            modelUrl = `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;\n        }\n    }\n    const model = new GraphModel(modelUrl, options);\n    await model.load();\n    return model;\n}\n//# sourceMappingURL=graph_model.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { GraphModel, loadGraphModel } from './executor/graph_model';\nexport { deregisterOp, registerOp } from './operations/custom_op/register';\nexport { version as version_converter } from './version';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\n/**\n * Apply a mapping function to a nested structure in a recursive manner.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapResult`.  The `DeepMapResult` either provides a\n *   replacement value for that node (i.e., replacing the subtree), or indicates\n *   that the node should be processed recursively.\n */\nexport function deepMap(input, mapFn) {\n    return deepMapInternal(input, mapFn);\n}\n/**\n * @param seen: A Map of known object mappings (i.e., memoized results of\n *   `mapFn()`)\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepMapInternal(input, mapFn, seen = new Map(), containedIn = new Set()) {\n    if (input == null) {\n        return null;\n    }\n    if (containedIn.has(input)) {\n        throw new Error('Circular references are not supported.');\n    }\n    if (seen.has(input)) {\n        return seen.get(input);\n    }\n    const result = mapFn(input);\n    if (result.recurse && result.value !== null) {\n        throw new Error('A deep map function may not return both a value and recurse=true.');\n    }\n    if (!result.recurse) {\n        seen.set(input, result.value);\n        return result.value;\n    }\n    else if (isIterable(input)) {\n        // tslint:disable-next-line:no-any\n        const mappedIterable = Array.isArray(input) ? [] : {};\n        containedIn.add(input);\n        for (const k in input) {\n            const child = input[k];\n            const childResult = deepMapInternal(child, mapFn, seen, containedIn);\n            mappedIterable[k] = childResult;\n        }\n        containedIn.delete(input);\n        return mappedIterable;\n    }\n    else {\n        throw new Error(`Can't recurse into non-iterable type: ${input}`);\n    }\n}\n// TODO(soergel, kangyizhang) Reconsider naming of deepZip() to avoid confusion\n// with zip()\n/**\n * Zip nested structures together in a recursive manner.\n *\n * This has the effect of transposing or pivoting data, e.g. converting it from\n * a row-major representation to a column-major representation.\n *\n * For example, `deepZip([{a: 1, b: 2}, {a: 3, b: 4}])` returns\n * `{a: [1, 3], b: [2, 4]}`.\n *\n * The inputs should all have the same nested structure (i.e., of arrays and\n * dicts).  The result is a single object with the same nested structure, where\n * the leaves are arrays collecting the values of the inputs at that location\n * (or, optionally, the result of a custom function applied to those arrays).\n *\n * @param inputs: An array of the objects to zip together.\n * @param zipFn: (optional) A function that expects an array of elements at a\n *   single node of the object tree, and returns a `DeepMapResult`.  The\n *   `DeepMapResult` either provides a result value for that node (i.e.,\n *   representing the subtree), or indicates that the node should be processed\n *   recursively.  The default zipFn recurses as far as possible and places\n *   arrays at the leaves.\n */\nexport function deepZip(inputs, zipFn = zipToList) {\n    return deepZipInternal(inputs, zipFn);\n}\n/**\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepZipInternal(inputs, zipFn, containedIn = new Set()) {\n    // The recursion follows the structure of input 0; it's assumed that all the\n    // other inputs have the same structure.\n    const input = inputs[0];\n    if (containedIn.has(input)) {\n        throw new Error('Circular references are not supported.');\n    }\n    const result = zipFn(inputs);\n    if (result.recurse && result.value !== null) {\n        throw new Error('A deep zip function may not return both a value and recurse=true.');\n    }\n    if (!result.recurse) {\n        return result.value;\n    }\n    else if (isIterable(input)) {\n        // tslint:disable-next-line:no-any\n        const mappedIterable = Array.isArray(input) ? [] : {};\n        containedIn.add(input);\n        for (const k in input) {\n            const children = inputs.map(x => x[k]);\n            const childResult = deepZipInternal(children, zipFn, containedIn);\n            mappedIterable[k] = childResult;\n        }\n        containedIn.delete(input);\n        return mappedIterable;\n    }\n    else {\n        throw new Error(`Can't recurse into non-iterable type: ${input}`);\n    }\n}\n// tslint:disable-next-line:no-any\nexport function zipToList(x) {\n    if (x === null) {\n        return null;\n    }\n    // TODO(soergel): validate array type?\n    if (isIterable(x[0])) {\n        return { value: null, recurse: true };\n    }\n    else {\n        return { value: x, recurse: false };\n    }\n}\n/**\n * Apply an async mapping function to a nested structure in a recursive manner.\n *\n * This first creates a nested structure of Promises, and then awaits all of\n * those, resulting in a single Promise for a resolved nested structure.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapAsyncResult`.  The `DeepMapAsyncResult` either provides\n *   a `Promise` for a replacement value for that node (i.e., replacing the\n *   subtree), or indicates that the node should be processed recursively.  Note\n *   that the decision whether or not to recurse must be made immediately; only\n *   the mapped value may be promised.\n */\nexport async function deepMapAndAwaitAll(input, mapFn) {\n    const seen = new Map();\n    // First do a normal deepMap, collecting Promises in 'seen' as a side effect.\n    deepMapInternal(input, mapFn, seen);\n    // Replace the Promises in 'seen' in place.\n    // Note TypeScript provides no async map iteration, and regular map iteration\n    // is broken too, so sadly we have to do Array.from() to make it work.\n    // (There's no advantage to Promise.all(), and that would be tricky anyway.)\n    for (const key of Array.from(seen.keys())) {\n        const value = seen.get(key);\n        if (value instanceof Promise) {\n            const mappedValue = await value;\n            seen.set(key, mappedValue);\n        }\n    }\n    // Normal deepMap again, this time filling in the resolved values.\n    // It's unfortunate that we have to do two passes.\n    // TODO(soergel): test performance and think harder about a fast solution.\n    const result = deepMapInternal(input, mapFn, seen);\n    return result;\n}\n/**\n * Determine whether the argument is iterable.\n *\n * @returns true if the argument is an array or any non-Tensor object.\n */\n// tslint:disable-next-line:no-any\nexport function isIterable(obj) {\n    return obj != null && (!ArrayBuffer.isView(obj)) &&\n        (Array.isArray(obj) ||\n            (typeof obj === 'object' && !(obj instanceof tf.Tensor)));\n}\n/**\n * Determine whether the argument can be converted to Tensor.\n *\n * Tensors, primitives, arrays, and TypedArrays all qualify; anything else does\n * not.\n *\n * @returns true if the argument can be converted to Tensor.\n */\n// tslint:disable-next-line:no-any\nexport function canTensorify(obj) {\n    return obj == null || isPrimitive(obj) || Array.isArray(obj) ||\n        (typeof obj === 'object' && (obj instanceof tf.Tensor)) ||\n        tf.util.isTypedArray(obj);\n}\n/**\n * Returns true if the given `value` is a primitive type. Otherwise returns\n * false. This is equivalant to node util.isPrimitive\n */\nfunction isPrimitive(value) {\n    return (value === null ||\n        (typeof value !== 'object' && typeof value !== 'function'));\n}\n//# sourceMappingURL=deep_map.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { deepMap, isIterable } from './deep_map';\nexport function deepClone(container) {\n    return deepMap(container, cloneIfTensor);\n}\n// tslint:disable-next-line: no-any\nfunction cloneIfTensor(item) {\n    if (item instanceof tf.Tensor) {\n        return ({ value: item.clone(), recurse: false });\n    }\n    else if (isIterable(item)) {\n        return { value: null, recurse: true };\n    }\n    else {\n        return { value: item, recurse: false };\n    }\n}\n//# sourceMappingURL=deep_clone.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * A ring buffer, providing O(1) FIFO, LIFO, and related operations.\n */\nexport class RingBuffer {\n    /**\n     * Constructs a `RingBuffer`.\n     * @param capacity The number of items that the buffer can accomodate.\n     */\n    constructor(capacity) {\n        this.capacity = capacity;\n        // Note we store the indices in the range 0 <= index < 2*capacity.\n        // This allows us to distinguish the full from the empty case.\n        // See https://www.snellman.net/blog/archive/2016-12-13-ring-buffers/\n        this.begin = 0; // inclusive\n        this.end = 0; // exclusive\n        if (capacity == null) {\n            throw new RangeError('Can\\'t create a ring buffer of unknown capacity.');\n        }\n        if (capacity < 1) {\n            throw new RangeError('Can\\'t create ring buffer of capacity < 1.');\n        }\n        this.data = new Array(capacity);\n        this.doubledCapacity = 2 * capacity;\n    }\n    /**\n     * Map any index into the range 0 <= index < 2*capacity.\n     */\n    wrap(index) {\n        // don't trust % on negative numbers\n        while (index < 0) {\n            index += this.doubledCapacity;\n        }\n        return index % this.doubledCapacity;\n    }\n    get(index) {\n        if (index < 0) {\n            throw new RangeError('Can\\'t get item at a negative index.');\n        }\n        return this.data[index % this.capacity];\n    }\n    set(index, value) {\n        if (index < 0) {\n            throw new RangeError('Can\\'t set item at a negative index.');\n        }\n        this.data[index % this.capacity] = value;\n    }\n    /**\n     * Returns the current number of items in the buffer.\n     */\n    length() {\n        let length = this.end - this.begin;\n        if (length < 0) {\n            length = this.doubledCapacity + length;\n        }\n        return length;\n    }\n    /**\n     * Reports whether the buffer is full.\n     * @returns true if the number of items in the buffer equals its capacity, and\n     *   false otherwise.\n     */\n    isFull() {\n        return this.length() === this.capacity;\n    }\n    /**\n     * Reports whether the buffer is empty.\n     * @returns true if the number of items in the buffer equals zero, and\n     *   false otherwise.\n     */\n    isEmpty() {\n        return this.length() === 0;\n    }\n    /**\n     * Adds an item to the end of the buffer.\n     */\n    push(value) {\n        if (this.isFull()) {\n            throw new RangeError('Ring buffer is full.');\n        }\n        this.set(this.end, value);\n        this.end = this.wrap(this.end + 1);\n    }\n    /**\n     * Adds many items to the end of the buffer, in order.\n     */\n    pushAll(values) {\n        for (const value of values) {\n            this.push(value);\n        }\n    }\n    /**\n     * Removes and returns the last item in the buffer.\n     */\n    pop() {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        this.end = this.wrap(this.end - 1);\n        const result = this.get(this.end);\n        this.set(this.end, undefined);\n        return result;\n    }\n    /**\n     * Adds an item to the beginning of the buffer.\n     */\n    unshift(value) {\n        if (this.isFull()) {\n            throw new RangeError('Ring buffer is full.');\n        }\n        this.begin = this.wrap(this.begin - 1);\n        this.set(this.begin, value);\n    }\n    /**\n     * Removes and returns the first item in the buffer.\n     */\n    shift() {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        const result = this.get(this.begin);\n        this.set(this.begin, undefined);\n        this.begin = this.wrap(this.begin + 1);\n        return result;\n    }\n    /**\n     * Removes and returns a specific item in the buffer, and moves the last item\n     * to the vacated slot.  This is useful for implementing a shuffling stream.\n     * Note that this operation necessarily scrambles the original order.\n     *\n     * @param relativeIndex: the index of the item to remove, relative to the\n     *   first item in the buffer (e.g., hiding the ring nature of the underlying\n     *   storage).\n     */\n    shuffleExcise(relativeIndex) {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        const index = this.wrap(this.begin + relativeIndex);\n        const result = this.get(index);\n        this.set(index, this.pop());\n        return result;\n    }\n}\n//# sourceMappingURL=ring_buffer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { RingBuffer } from './ring_buffer';\nexport class GrowingRingBuffer extends RingBuffer {\n    /**\n     * Constructs a `GrowingRingBuffer`.\n     */\n    constructor() {\n        super(GrowingRingBuffer.INITIAL_CAPACITY);\n    }\n    isFull() {\n        return false;\n    }\n    push(value) {\n        if (super.isFull()) {\n            this.expand();\n        }\n        super.push(value);\n    }\n    unshift(value) {\n        if (super.isFull()) {\n            this.expand();\n        }\n        super.unshift(value);\n    }\n    /**\n     * Doubles the capacity of the buffer.\n     */\n    expand() {\n        const newCapacity = this.capacity * 2;\n        const newData = new Array(newCapacity);\n        const len = this.length();\n        // Rotate the buffer to start at index 0 again, since we can't just\n        // allocate more space at the end.\n        for (let i = 0; i < len; i++) {\n            newData[i] = this.get(this.wrap(this.begin + i));\n        }\n        this.data = newData;\n        this.capacity = newCapacity;\n        this.doubledCapacity = 2 * this.capacity;\n        this.begin = 0;\n        this.end = len;\n    }\n}\nGrowingRingBuffer.INITIAL_CAPACITY = 32;\n//# sourceMappingURL=growing_ring_buffer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport * as seedrandom from 'seedrandom';\nimport { deepClone } from '../util/deep_clone';\nimport { deepMapAndAwaitAll, deepZip, zipToList } from '../util/deep_map';\nimport { GrowingRingBuffer } from '../util/growing_ring_buffer';\nimport { RingBuffer } from '../util/ring_buffer';\n// Here we implement a simple asynchronous iterator.\n// This lets us avoid using either third-party stream libraries or\n// recent TypeScript language support requiring polyfills.\n/**\n * Create a `LazyIterator` from an array of items.\n */\nexport function iteratorFromItems(items) {\n    return new ArrayIterator(items);\n}\n/**\n * Create a `LazyIterator` of incrementing integers.\n */\nexport function iteratorFromIncrementing(start) {\n    let i = start;\n    return iteratorFromFunction(() => ({ value: i++, done: false }));\n}\n/**\n * Create a `LazyIterator` from a function.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const iter = tf.data.iteratorFromFunction(func);\n * await iter.forEachAsync(e => console.log(e));\n * ```\n *\n * @param func A function that produces data on each call.\n */\nexport function iteratorFromFunction(func) {\n    return new FunctionCallIterator(func);\n}\n/**\n * Create a `LazyIterator` by concatenating underlying streams, which are\n * themselves provided as a stream.\n *\n * This can also be thought of as a \"stream flatten\" operation.\n *\n * @param baseIterators A stream of streams to be concatenated.\n * @param baseErrorHandler An optional function that can intercept `Error`s\n *   raised during a `next()` call on the base stream.  This function can decide\n *   whether the error should be propagated, whether the error should be\n *   ignored, or whether the base stream should be terminated.\n */\nexport function iteratorFromConcatenated(baseIterators, baseErrorHandler) {\n    return new ChainedIterator(baseIterators, baseErrorHandler);\n}\n/**\n * Create a `LazyIterator` by concatenating streams produced by calling a\n * stream-generating function a given number of times.\n *\n * Since a `LazyIterator` is read-once, it cannot be repeated, but this\n * function can be used to achieve a similar effect:\n *\n *   LazyIterator.ofConcatenatedFunction(() => new MyIterator(), 6);\n *\n * @param iteratorFunc: A function that produces a new stream on each call.\n * @param count: The number of times to call the function.\n * @param baseErrorHandler An optional function that can intercept `Error`s\n *   raised during a `next()` call on the base stream.  This function can decide\n *   whether the error should be propagated, whether the error should be\n *   ignored, or whether the base stream should be terminated.\n */\nexport function iteratorFromConcatenatedFunction(iteratorFunc, count, baseErrorHandler) {\n    return iteratorFromConcatenated(iteratorFromFunction(iteratorFunc).take(count), baseErrorHandler);\n}\n/**\n * Create a `LazyIterator` by zipping together an array, dict, or nested\n * structure of `LazyIterator`s (and perhaps additional constants).\n *\n * The underlying streams must provide elements in a consistent order such\n * that they correspond.\n *\n * Typically, the underlying streams should have the same number of\n * elements. If they do not, the behavior is determined by the\n * `mismatchMode` argument.\n *\n * The nested structure of the `iterators` argument determines the\n * structure of elements in the resulting iterator.\n *\n * @param iterators: An array or object containing LazyIterators at the\n * leaves.\n * @param mismatchMode: Determines what to do when one underlying iterator\n * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)\n * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`\n * causes the zipped iterator to terminate with the furst underlying\n * streams, so elements remaining on the longer streams are ignored.\n * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling\n * in nulls for the exhausted streams, until all streams are exhausted.\n */\nexport function iteratorFromZipped(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n    return new ZipIterator(iterators, mismatchMode);\n}\n/**\n * An asynchronous iterator, providing lazy access to a potentially\n * unbounded stream of elements.\n *\n * Iterator can be obtained from a dataset:\n * `const iter = await dataset.iterator();`\n */\nexport class LazyIterator {\n    /**\n     * Collect all remaining elements of a bounded stream into an array.\n     * Obviously this will succeed only for small streams that fit in memory.\n     * Useful for testing.\n     *\n     * @returns A Promise for an array of stream elements, which will resolve\n     *   when the stream is exhausted.\n     */\n    async toArray() {\n        const result = [];\n        let x = await this.next();\n        while (!x.done) {\n            result.push(x.value);\n            x = await this.next();\n        }\n        return result;\n    }\n    /**\n     * Collect all elements of this dataset into an array with prefetching 100\n     * elements. This is useful for testing, because the prefetch changes the\n     * order in which the Promises are resolved along the processing pipeline.\n     * This may help expose bugs where results are dependent on the order of\n     * Promise resolution rather than on the logical order of the stream (i.e.,\n     * due to hidden mutable state).\n     *\n     * @returns A Promise for an array of stream elements, which will resolve\n     *   when the stream is exhausted.\n     */\n    async toArrayForTest() {\n        const stream = this.prefetch(100);\n        const result = [];\n        let x = await stream.next();\n        while (!x.done) {\n            result.push(x.value);\n            x = await stream.next();\n        }\n        return result;\n    }\n    /**\n     * Draw items from the stream until it is exhausted.\n     *\n     * This can be useful when the stream has side effects but no output.  In\n     * that case, calling this function guarantees that the stream will be\n     * fully processed.\n     */\n    async resolveFully() {\n        let x = await this.next();\n        while (!x.done) {\n            x = await this.next();\n        }\n    }\n    /**\n     * Draw items from the stream until it is exhausted, or a predicate fails.\n     *\n     * This can be useful when the stream has side effects but no output.  In\n     * that case, calling this function guarantees that the stream will be\n     * fully processed.\n     */\n    async resolveWhile(predicate) {\n        let x = await this.next();\n        let shouldContinue = predicate(x.value);\n        while ((!x.done) && shouldContinue) {\n            x = await this.next();\n            shouldContinue = predicate(x.value);\n        }\n    }\n    /**\n     * Handles errors thrown on this stream using a provided handler function.\n     *\n     * @param handler A function that handles any `Error` thrown during a `next()`\n     *   call and returns true if the stream should continue (dropping the failed\n     *   call) or false if the stream should quietly terminate.  If the handler\n     *   itself throws (or rethrows) an `Error`, that will be propagated.\n     *\n     * @returns A `LazyIterator` of elements passed through from upstream,\n     *   possibly filtering or terminating on upstream `next()` calls that\n     *   throw an `Error`.\n     */\n    handleErrors(handler) {\n        return new ErrorHandlingLazyIterator(this, handler);\n    }\n    // TODO(soergel): Implement reduce() etc.\n    /**\n     * Filters this stream according to `predicate`.\n     *\n     * @param predicate A function mapping a stream element to a boolean or a\n     * `Promise` for one.\n     *\n     * @returns A `LazyIterator` of elements for which the predicate was true.\n     */\n    filter(predicate) {\n        return new FilterIterator(this, predicate);\n    }\n    /**\n     * Maps this stream through a 1-to-1 transform.\n     *\n     * @param transform A function mapping a stream element to a transformed\n     *   element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    map(transform) {\n        return new MapIterator(this, transform);\n    }\n    /**\n     * Maps this stream through an async 1-to-1 transform.\n     *\n     * @param transform A function mapping a stream element to a `Promise` for a\n     *   transformed stream element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    mapAsync(transform) {\n        return new AsyncMapIterator(this, transform);\n    }\n    /**\n     * Maps this stream through a 1-to-1 transform, forcing serial execution.\n     *\n     * @param transform A function mapping a stream element to a transformed\n     *   element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    serialMapAsync(transform) {\n        return new AsyncMapIterator(this, transform).serial();\n    }\n    /**\n     * Maps this stream through a 1-to-many transform.\n     *\n     * @param transform A function mapping a stream element to an array of\n     *   transformed elements.\n     *\n     * @returns A `DataStream` of transformed elements.\n     */\n    flatmap(transform) {\n        return new FlatmapIterator(this, transform);\n    }\n    /**\n     * Apply a function to every element of the stream.\n     *\n     * @param f A function to apply to each stream element.\n     */\n    async forEachAsync(f) {\n        return this.map(f).resolveFully();\n    }\n    /**\n     * Apply a function to every element of the stream, forcing serial execution.\n     *\n     * @param f A function to apply to each stream element.  Should return 'true'\n     *   to indicate that the stream should continue, or 'false' to cause it to\n     *   terminate.\n     */\n    async serialForEach(f) {\n        return this.serialMapAsync(f).resolveWhile(x => (x === true));\n    }\n    /**\n     * Groups elements into batches, represented as arrays of elements.\n     *\n     * We can think of the elements of this iterator as 'rows' (even if they are\n     * nested structures).  By the same token, consecutive values for a given\n     * key within the elements form a 'column'.  This matches the usual sense of\n     * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n     *\n     * Thus, \"Row-major\" means that the resulting batch is simply a collection of\n     * rows: `[row1, row2, row3, ...]`.  This is contrast to the column-major\n     * form, which is needed for vectorized computation.\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @returns A `LazyIterator` of batches of elements, represented as arrays\n     *   of the original element type.\n     */\n    rowMajorBatch(batchSize, smallLastBatch = true) {\n        return new RowMajorBatchIterator(this, batchSize, smallLastBatch);\n    }\n    /**\n     * Groups elements into batches, represented in column-major form.\n     *\n     * We can think of the elements of this iterator as 'rows' (even if they are\n     * nested structures).  By the same token, consecutive values for a given\n     * key within the elements form a 'column'.  This matches the usual sense of\n     * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n     *\n     * Thus, \"column-major\" means that the resulting batch is a (potentially\n     * nested) structure representing the columns.  Each column entry, then,\n     * contains a collection of the values found in that column for a range of\n     * input elements.  This representation allows for vectorized computation, in\n     * contrast to the row-major form.\n     *\n     * The inputs should all have the same nested structure (i.e., of arrays and\n     * dicts).  The result is a single object with the same nested structure,\n     * where the leaves are arrays collecting the values of the inputs at that\n     * location (or, optionally, the result of a custom function applied to those\n     * arrays).\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @param zipFn: (optional) A function that expects an array of elements at a\n     *   single node of the object tree, and returns a `DeepMapResult`.  The\n     *   `DeepMapResult` either provides a result value for that node (i.e.,\n     *   representing the subtree), or indicates that the node should be processed\n     *   recursively.  The default zipFn recurses as far as possible and places\n     *   arrays at the leaves.\n     * @returns A `LazyIterator` of batches of elements, represented as an object\n     *   with collections at the leaves.\n     */\n    columnMajorBatch(batchSize, smallLastBatch = true, \n    // tslint:disable-next-line:no-any\n    zipFn = zipToList) {\n        // First collect the desired number of input elements as a row-major batch.\n        const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch);\n        // Now 'rotate' or 'pivot' the data, collecting all values from each column\n        // in the batch (i.e., for each key within the elements) into an array.\n        return rowBatches.map(x => deepZip(x, zipFn));\n    }\n    /**\n     * Concatenate this `LazyIterator` with another.\n     *\n     * @param iterator A `LazyIterator` to be concatenated onto this one.\n     * @param baseErrorHandler An optional function that can intercept `Error`s\n     *   raised during a `next()` call on the base stream.  This function can\n     *   decide whether the error should be propagated, whether the error should\n     *   be ignored, or whether the base stream should be terminated.\n     * @returns A `LazyIterator`.\n     */\n    concatenate(iterator, baseErrorHandler) {\n        return new ChainedIterator(iteratorFromItems([this, iterator]), baseErrorHandler);\n    }\n    /**\n     * Limits this stream to return at most `count` items.\n     *\n     * @param count The maximum number of items to provide from the stream. If\n     * a negative or undefined value is given, the entire stream is returned\n     *   unaltered.\n     */\n    take(count) {\n        if (count < 0 || count == null) {\n            return this;\n        }\n        return new TakeIterator(this, count);\n    }\n    /**\n     * Skips the first `count` items in this stream.\n     *\n     * @param count The number of items to skip.  If a negative or undefined\n     * value is given, the entire stream is returned unaltered.\n     */\n    skip(count) {\n        if (count < 0 || count == null) {\n            return this;\n        }\n        return new SkipIterator(this, count);\n    }\n    /**\n     * Prefetch the first `bufferSize` items in this stream.\n     *\n     * Note this prefetches Promises, but makes no guarantees about when those\n     * Promises resolve.\n     *\n     * @param bufferSize: An integer specifying the number of elements to be\n     *   prefetched.\n     */\n    prefetch(bufferSize) {\n        return new PrefetchIterator(this, bufferSize);\n    }\n    // TODO(soergel): deep sharded shuffle, where supported\n    /**\n     * Randomly shuffles the elements of this stream.\n     *\n     * @param bufferSize: An integer specifying the number of elements from\n     * this stream from which the new stream will sample.\n     * @param seed: (Optional.) An integer specifying the random seed that\n     * will be used to create the distribution.\n     */\n    shuffle(windowSize, seed) {\n        return new ShuffleIterator(this, windowSize, seed);\n    }\n    /**\n     * Force an iterator to execute serially: each next() call will await the\n     * prior one, so that they cannot execute concurrently.\n     */\n    serial() {\n        return new SerialIterator(this);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on LazyIterator.  Unfortunately they can't be placed in separate files,\n// due to resulting trouble with circular imports.\n// ============================================================================\n// Iterators that just extend LazyIterator directly\n// ============================================================================\nclass ArrayIterator extends LazyIterator {\n    constructor(items) {\n        super();\n        this.items = items;\n        this.trav = 0;\n    }\n    summary() {\n        return `Array of ${this.items.length} items`;\n    }\n    async next() {\n        if (this.trav >= this.items.length) {\n            return { value: null, done: true };\n        }\n        const item = this.items[this.trav];\n        this.trav++;\n        return { value: deepClone(item), done: false };\n    }\n}\nclass FunctionCallIterator extends LazyIterator {\n    constructor(nextFn) {\n        super();\n        this.nextFn = nextFn;\n    }\n    summary() {\n        return `Function call`;\n    }\n    async next() {\n        try {\n            return this.nextFn();\n        }\n        catch (e) {\n            // Modify the error message but leave the stack trace intact\n            e.message =\n                `Error thrown while iterating through a dataset: ${e.message}`;\n            throw e;\n        }\n    }\n}\nclass SerialIterator extends LazyIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Serial`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        return this.upstream.next();\n    }\n}\nclass SkipIterator extends LazyIterator {\n    constructor(upstream, maxCount) {\n        super();\n        this.upstream = upstream;\n        this.maxCount = maxCount;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.count = 0;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Skip`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        // TODO(soergel): consider tradeoffs of reading in parallel, eg.\n        // collecting next() promises in an Array and then waiting for\n        // Promise.all() of those. Benefit: pseudo-parallel execution.  Drawback:\n        // maybe delayed GC.\n        while (this.count++ < this.maxCount) {\n            const skipped = await this.upstream.next();\n            // short-circuit if upstream is already empty\n            if (skipped.done) {\n                return skipped;\n            }\n            tf.dispose(skipped.value);\n        }\n        return this.upstream.next();\n    }\n}\nclass TakeIterator extends LazyIterator {\n    constructor(upstream, maxCount) {\n        super();\n        this.upstream = upstream;\n        this.maxCount = maxCount;\n        this.count = 0;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Take`;\n    }\n    async next() {\n        if (this.count++ >= this.maxCount) {\n            return { value: null, done: true };\n        }\n        return this.upstream.next();\n    }\n}\n// Note this batch just groups items into row-wise element arrays.\n// Rotating these to a column-wise representation happens only at the dataset\n// level.\nclass RowMajorBatchIterator extends LazyIterator {\n    constructor(upstream, batchSize, enableSmallLastBatch = true) {\n        super();\n        this.upstream = upstream;\n        this.batchSize = batchSize;\n        this.enableSmallLastBatch = enableSmallLastBatch;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> RowMajorBatch`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        const batch = [];\n        while (batch.length < this.batchSize) {\n            const item = await this.upstream.next();\n            if (item.done) {\n                if (this.enableSmallLastBatch && batch.length > 0) {\n                    return { value: batch, done: false };\n                }\n                return { value: null, done: true };\n            }\n            batch.push(item.value);\n        }\n        return { value: batch, done: false };\n    }\n}\nclass FilterIterator extends LazyIterator {\n    constructor(upstream, predicate) {\n        super();\n        this.upstream = upstream;\n        this.predicate = predicate;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Filter`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        while (true) {\n            const item = await this.upstream.next();\n            if (item.done || this.predicate(item.value)) {\n                return item;\n            }\n            tf.dispose(item.value);\n        }\n    }\n}\nclass MapIterator extends LazyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Map`;\n    }\n    async next() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return { value: null, done: true };\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // That's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying\n        // any intermediate Tensors.  Here we are concerned only about the\n        // inputs.\n        const mapped = this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mapped);\n        // TODO(soergel) faster intersection\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return { value: mapped, done: false };\n    }\n}\nclass ErrorHandlingLazyIterator extends LazyIterator {\n    constructor(upstream, handler) {\n        super();\n        this.upstream = upstream;\n        this.handler = handler;\n        this.count = 0;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> handleErrors`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        while (true) {\n            try {\n                return await this.upstream.next();\n            }\n            catch (e) {\n                if (!this.handler(e)) {\n                    return { value: null, done: true };\n                }\n                // If the handler returns true, loop and fetch the next upstream item.\n                // If the upstream iterator throws an endless stream of errors, and if\n                // the handler says to ignore them, then we loop forever here.  That is\n                // the correct behavior-- it's up to the handler to decide when to stop.\n            }\n        }\n    }\n}\nclass AsyncMapIterator extends LazyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> AsyncMap`;\n    }\n    async next() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return { value: null, done: true };\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // That's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying\n        // any intermediate Tensors.  Here we are concerned only about the\n        // inputs.\n        const mapped = await this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mapped);\n        // TODO(soergel) faster intersection\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return { value: mapped, done: false };\n    }\n}\n// Iterators that maintain a queue of pending items\n// ============================================================================\n/**\n * A base class for transforming streams that operate by maintaining an\n * output queue of elements that are ready to return via next().  This is\n * commonly required when the transformation is 1-to-many:  A call to next()\n * may trigger a call to the underlying stream, which will produce many\n * mapped elements of this stream-- of which we need to return only one, so\n * we have to queue the rest.\n */\nexport class OneToManyIterator extends LazyIterator {\n    constructor() {\n        super();\n        this.outputQueue = new GrowingRingBuffer();\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        // Fetch so that the queue contains at least one item if possible.\n        // If the upstream source is exhausted, AND there are no items left in\n        // the output queue, then this stream is also exhausted.\n        while (this.outputQueue.length() === 0) {\n            // TODO(soergel): consider parallel reads.\n            if (!await this.pump()) {\n                return { value: null, done: true };\n            }\n        }\n        return { value: this.outputQueue.shift(), done: false };\n    }\n}\nclass FlatmapIterator extends OneToManyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Flatmap`;\n    }\n    async pump() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return false;\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // that's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying any\n        // intermediate Tensors.  Here we are concerned only about the inputs.\n        const mappedArray = this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mappedArray);\n        this.outputQueue.pushAll(mappedArray);\n        // TODO(soergel) faster intersection, and deduplicate outputTensors\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return true;\n    }\n}\n/**\n * Provides a `LazyIterator` that concatenates a stream of underlying\n * streams.\n *\n * Doing this in a concurrency-safe way requires some trickery.  In\n * particular, we want this stream to return the elements from the\n * underlying streams in the correct order according to when next() was\n * called, even if the resulting Promises resolve in a different order.\n */\nexport class ChainedIterator extends LazyIterator {\n    constructor(iterators, baseErrorHandler) {\n        super();\n        this.baseErrorHandler = baseErrorHandler;\n        // Strict Promise execution order:\n        // a next() call may not even begin until the previous one completes.\n        this.lastRead = null;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.iterator = null;\n        this.moreIterators = iterators;\n    }\n    summary() {\n        const upstreamSummaries = 'TODO: fill in upstream of chained summaries';\n        return `${upstreamSummaries} -> Chained`;\n    }\n    async next() {\n        this.lastRead = this.readFromChain(this.lastRead);\n        return this.lastRead;\n    }\n    async readFromChain(lastRead) {\n        // Must await on the previous read since the previous read may have advanced\n        // the stream of streams, from which we need to read.\n        // This is unfortunate since we can't parallelize reads. Which means\n        // prefetching of chained streams is a no-op.\n        // One solution is to prefetch immediately upstream of this.\n        await lastRead;\n        if (this.iterator == null) {\n            const iteratorResult = await this.moreIterators.next();\n            if (iteratorResult.done) {\n                // No more streams to stream from.\n                return { value: null, done: true };\n            }\n            this.iterator = iteratorResult.value;\n            if (this.baseErrorHandler != null) {\n                this.iterator = this.iterator.handleErrors(this.baseErrorHandler);\n            }\n        }\n        const itemResult = await this.iterator.next();\n        if (itemResult.done) {\n            this.iterator = null;\n            return this.readFromChain(lastRead);\n        }\n        return itemResult;\n    }\n}\nexport var ZipMismatchMode;\n(function (ZipMismatchMode) {\n    ZipMismatchMode[ZipMismatchMode[\"FAIL\"] = 0] = \"FAIL\";\n    ZipMismatchMode[ZipMismatchMode[\"SHORTEST\"] = 1] = \"SHORTEST\";\n    ZipMismatchMode[ZipMismatchMode[\"LONGEST\"] = 2] = \"LONGEST\"; // use nulls for exhausted streams; use up the longest stream.\n})(ZipMismatchMode || (ZipMismatchMode = {}));\n/**\n * Provides a `LazyIterator` that zips together an array, dict, or nested\n * structure of `LazyIterator`s (and perhaps additional constants).\n *\n * The underlying streams must provide elements in a consistent order such\n * that they correspond.\n *\n * Typically, the underlying streams should have the same number of\n * elements. If they do not, the behavior is determined by the\n * `mismatchMode` argument.\n *\n * The nested structure of the `iterators` argument determines the\n * structure of elements in the resulting iterator.\n *\n * Doing this in a concurrency-safe way requires some trickery.  In\n * particular, we want this stream to return the elements from the\n * underlying streams in the correct order according to when next() was\n * called, even if the resulting Promises resolve in a different order.\n *\n * @param iterators: An array or object containing LazyIterators at the\n * leaves.\n * @param mismatchMode: Determines what to do when one underlying iterator\n * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)\n * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`\n * causes the zipped iterator to terminate with the furst underlying\n * streams, so elements remaining on the longer streams are ignored.\n * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling\n * in nulls for the exhausted streams, until all streams are exhausted.\n */\nclass ZipIterator extends LazyIterator {\n    constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n        super();\n        this.iterators = iterators;\n        this.mismatchMode = mismatchMode;\n        this.count = 0;\n        this.currentPromise = null;\n    }\n    summary() {\n        const upstreamSummaries = 'TODO: fill in upstream of zip summaries';\n        return `{${upstreamSummaries}} -> Zip`;\n    }\n    async nextState(afterState) {\n        // This chaining ensures that the underlying next() are not even called\n        // before the previous ones have resolved.\n        await afterState;\n        // Collect underlying iterator \"done\" signals as a side effect in\n        // getNext()\n        let numIterators = 0;\n        let iteratorsDone = 0;\n        function getNext(container) {\n            if (container instanceof LazyIterator) {\n                const result = container.next();\n                return {\n                    value: result.then(x => {\n                        numIterators++;\n                        if (x.done) {\n                            iteratorsDone++;\n                        }\n                        return x.value;\n                    }),\n                    recurse: false\n                };\n            }\n            else {\n                return { value: null, recurse: true };\n            }\n        }\n        const mapped = await deepMapAndAwaitAll(this.iterators, getNext);\n        if (numIterators === iteratorsDone) {\n            // The streams have all ended.\n            return { value: null, done: true };\n        }\n        if (iteratorsDone > 0) {\n            switch (this.mismatchMode) {\n                case ZipMismatchMode.FAIL:\n                    throw new Error('Zipped streams should have the same length. ' +\n                        `Mismatched at element ${this.count}.`);\n                case ZipMismatchMode.SHORTEST:\n                    return { value: null, done: true };\n                case ZipMismatchMode.LONGEST:\n                default:\n                // Continue.  The exhausted streams already produced value: null.\n            }\n        }\n        this.count++;\n        return { value: mapped, done: false };\n    }\n    async next() {\n        this.currentPromise = this.nextState(this.currentPromise);\n        return this.currentPromise;\n    }\n}\n// Iterators that maintain a ring buffer of pending promises\n// ============================================================================\n/**\n * A stream that prefetches a given number of items from an upstream source,\n * returning them in FIFO order.\n *\n * Note this prefetches Promises, but makes no guarantees about when those\n * Promises resolve.\n */\nexport class PrefetchIterator extends LazyIterator {\n    constructor(upstream, bufferSize) {\n        super();\n        this.upstream = upstream;\n        this.bufferSize = bufferSize;\n        this.buffer = new RingBuffer(bufferSize);\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Prefetch`;\n    }\n    /**\n     * Refill the prefetch buffer.  Returns only after the buffer is full, or\n     * the upstream source is exhausted.\n     */\n    refill() {\n        while (!this.buffer.isFull()) {\n            const v = this.upstream.next();\n            this.buffer.push(v);\n        }\n    }\n    next() {\n        this.refill();\n        // This shift will never throw an error because the buffer is always\n        // full after a refill. If the stream is exhausted, the buffer will be\n        // full of Promises that will resolve to the end-of-stream signal.\n        return this.buffer.shift();\n    }\n}\n/**\n * A stream that performs a sliding-window random shuffle on an upstream\n * source. This is like a `PrefetchIterator` except that the items are\n * returned in randomized order.  Mixing naturally improves as the buffer\n * size increases.\n */\nexport class ShuffleIterator extends PrefetchIterator {\n    constructor(upstream, windowSize, seed) {\n        super(upstream, windowSize);\n        this.upstream = upstream;\n        this.windowSize = windowSize;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.upstreamExhausted = false;\n        this.random = seedrandom.alea(seed || tf.util.now().toString());\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    randomInt(max) {\n        return Math.floor(this.random() * max);\n    }\n    chooseIndex() {\n        return this.randomInt(this.buffer.length());\n    }\n    async serialNext() {\n        // TODO(soergel): consider performance\n        if (!this.upstreamExhausted) {\n            this.refill();\n        }\n        while (!this.buffer.isEmpty()) {\n            const chosenIndex = this.chooseIndex();\n            const result = await this.buffer.shuffleExcise(chosenIndex);\n            if (result.done) {\n                this.upstreamExhausted = true;\n            }\n            else {\n                this.refill();\n                return result;\n            }\n        }\n        return { value: null, done: true };\n    }\n}\n//# sourceMappingURL=lazy_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport * as seedrandom from 'seedrandom';\nimport { iteratorFromConcatenated, iteratorFromFunction, iteratorFromItems, iteratorFromZipped, ZipMismatchMode } from './iterators/lazy_iterator';\nimport { canTensorify, deepMapAndAwaitAll, isIterable } from './util/deep_map';\n// TODO(soergel): consider vectorized operations within the pipeline.\n/**\n * Represents a potentially large list of independent data elements (typically\n * 'samples' or 'examples').\n *\n * A 'data example' may be a primitive, an array, a map from string keys to\n * values, or any nested structure of these.\n *\n * A `Dataset` represents an ordered collection of elements, together with a\n * chain of transformations to be performed on those elements. Each\n * transformation is a method of `Dataset` that returns another `Dataset`, so\n * these may be chained, e.g.\n * `const processedDataset = rawDataset.filter(...).map(...).batch(...)`.\n *\n * Data loading and transformation is done in a lazy, streaming fashion.  The\n * dataset may be iterated over multiple times; each iteration starts the data\n * loading anew and recapitulates the transformations.\n *\n * A `Dataset` is typically processed as a stream of unbatched examples --i.e.,\n * its transformations are applied one example at a time. Batching produces a\n * new `Dataset` where each element is a batch. Batching should usually come\n * last in a pipeline, because data transformations are easier to express on a\n * per-example basis than on a per-batch basis.\n *\n * The following code examples are calling `await dataset.forEachAsync(...)` to\n * iterate once over the entire dataset in order to print out the data.\n *\n * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}\n */\nexport class Dataset {\n    constructor() {\n        this.size = null;\n    }\n    // TODO(soergel): Make Datasets report whether repeated iterator() calls\n    // produce the same result (e.g., reading from a file) or different results\n    // (e.g., from the webcam).  Currently we don't make this distinction but it\n    // could be important for the user to know.\n    // abstract isDeterministic(): boolean;\n    /**\n     * Groups elements into batches.\n     *\n     * It is assumed that each of the incoming dataset elements has the same\n     * structure-- i.e. the same set of keys at each location in an object\n     * hierarchy.  For each key, the resulting `Dataset` provides a batched\n     * element collecting all of the incoming values for that key.\n     *\n     *  * Incoming primitives are grouped into a 1-D Tensor.\n     *  * Incoming Tensors are grouped into a new Tensor where the 0'th axis is\n     *    the batch dimension.\n     *  * Incoming arrays are converted to Tensor and then batched.\n     *  * A nested array is interpreted as an n-D Tensor, so the batched result\n     *    has n+1 dimensions.\n     *  * An array that cannot be converted to Tensor produces an error.\n     *\n     * If an array should not be batched as a unit, it should first be converted\n     * to an object with integer keys.\n     *\n     * Here are a few examples:\n     *\n     * Batch a dataset of numbers:\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8]).batch(4);\n     * await a.forEachAsync(e => e.print());\n     * ```\n     *\n     * Batch a dataset of arrays:\n     * ```js\n     * const b = tf.data.array([[1], [2], [3], [4], [5], [6], [7], [8]]).batch(4);\n     * await b.forEachAsync(e => e.print());\n     * ```\n     *\n     * Batch a dataset of objects:\n     * ```js\n     * const c = tf.data.array([{a: 1, b: 11}, {a: 2, b: 12}, {a: 3, b: 13},\n     *   {a: 4, b: 14}, {a: 5, b: 15}, {a: 6, b: 16}, {a: 7, b: 17},\n     *   {a: 8, b: 18}]).batch(4);\n     * await c.forEachAsync(e => {\n     *   console.log('{');\n     *   for(var key in e) {\n     *     console.log(key+':');\n     *     e[key].print();\n     *   }\n     *   console.log('}');\n     * })\n     * ```\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @returns A `Dataset`, from which a stream of batches can be obtained.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    batch(batchSize, smallLastBatch = true) {\n        const base = this;\n        tf.util.assert(batchSize > 0, () => `batchSize needs to be positive, but it is\n      ${batchSize}`);\n        let size;\n        if (this.size === Infinity || this.size == null) {\n            // If the size of this dataset is infinity or null, the new size keeps the\n            // same.\n            size = this.size;\n        }\n        else if (smallLastBatch) {\n            // If the size of this dataset is known and include small last batch, the\n            // new size is full batch count plus last batch.\n            size = Math.ceil(this.size / batchSize);\n        }\n        else {\n            // If the size of this dataset is known and not include small last batch,\n            // the new size is full batch count.\n            size = Math.floor(this.size / batchSize);\n        }\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator())\n                .columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat);\n        }, size);\n    }\n    /**\n     * Concatenates this `Dataset` with another.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]);\n     * const b = tf.data.array([4, 5, 6]);\n     * const c = a.concatenate(b);\n     * await c.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param dataset A `Dataset` to be concatenated onto this one.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    concatenate(dataset) {\n        const base = this;\n        let size;\n        if (this.size === Infinity || dataset.size === Infinity) {\n            // If the size of any of these two dataset is infinity, new size is\n            // infinity.\n            size = Infinity;\n        }\n        else if (this.size != null && dataset.size != null) {\n            // If the size of both datasets are known and not infinity, new size is\n            // sum the size of these two datasets.\n            size = this.size + dataset.size;\n        }\n        else {\n            // If neither of these two datasets has infinite size and any of these two\n            // datasets' size is null, the new size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).concatenate(await dataset.iterator()), size);\n    }\n    /**\n     * Filters this dataset according to `predicate`.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n     *   .filter(x => x%2 === 0);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param predicate A function mapping a dataset element to a boolean or a\n     * `Promise` for one.\n     *\n     * @returns A `Dataset` of elements for which the predicate was true.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    filter(predicate) {\n        const base = this;\n        let size;\n        if (this.size === Infinity) {\n            // If the size of this dataset is infinity, new size is infinity\n            size = Infinity;\n        }\n        else {\n            // If this dataset has limited elements, new size is null because it might\n            // exhausted randomly.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).filter(x => tf.tidy(() => predicate(x)));\n        }, size);\n    }\n    /**\n     * Apply a function to every element of the dataset.\n     *\n     * After the function is applied to a dataset element, any Tensors contained\n     * within that element are disposed.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param f A function to apply to each dataset element.\n     * @returns A `Promise` that resolves after all elements have been processed.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async forEachAsync(f) {\n        return (await this.iterator()).forEachAsync(f);\n    }\n    /**\n     * Maps this dataset through a 1-to-1 transform.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]).map(x => x*x);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param transform A function mapping a dataset element to a transformed\n     *   dataset element.\n     *\n     * @returns A `Dataset` of transformed elements.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    map(transform) {\n        const base = this;\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).map(x => tf.tidy(() => transform(x)));\n        }, this.size);\n    }\n    /**\n     * Maps this dataset through an async 1-to-1 transform.\n     *\n     * ```js\n     * const a =\n     *  tf.data.array([1, 2, 3]).mapAsync(x => new Promise(function(resolve){\n     *    setTimeout(() => {\n     *      resolve(x * x);\n     *    }, Math.random()*1000 + 500);\n     *  }));\n     * console.log(await a.toArray());\n     * ```\n     *\n     * @param transform A function mapping a dataset element to a `Promise` for a\n     *   transformed dataset element.  This transform is responsible for disposing\n     *   any intermediate `Tensor`s, i.e. by wrapping its computation in\n     *   `tf.tidy()`; that cannot be automated here (as it is in the synchronous\n     *   `map()` case).\n     *\n     * @returns A `Dataset` of transformed elements.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    mapAsync(transform) {\n        const base = this;\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).mapAsync(transform);\n        }, this.size);\n    }\n    /**\n     *  Creates a `Dataset` that prefetches elements from this dataset.\n     *\n     * @param bufferSize: An integer specifying the number of elements to be\n     *   prefetched.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    prefetch(bufferSize) {\n        if (bufferSize == null) {\n            throw new RangeError('`Dataset.prefetch()` requires bufferSize to be specified.');\n        }\n        const base = this;\n        return datasetFromIteratorFn(async () => (await base.iterator()).prefetch(bufferSize), this.size);\n    }\n    /**\n     * Repeats this dataset `count` times.\n     *\n     * NOTE: If this dataset is a function of global state (e.g. a random number\n     * generator), then different repetitions may produce different elements.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]).repeat(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: (Optional) An integer, representing the number of times\n     *   the dataset should be repeated. The default behavior (if `count` is\n     *   `undefined` or negative) is for the dataset be repeated indefinitely.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    repeat(count) {\n        const base = this;\n        let size;\n        if (this.size != null && count > 0) {\n            // If this dataset has size and count is positive, new size is current\n            // size multiply count. This also covers the case that current size is\n            // infinity.\n            size = this.size * count;\n        }\n        else if (count === 0) {\n            // If count is 0, new size is 0.\n            size = 0;\n        }\n        else if (this.size != null && (count === undefined || count < 0)) {\n            // If this dataset has size and count is undefined or negative, the\n            // dataset will be repeated indefinitely and new size is infinity.\n            size = Infinity;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => {\n            const iteratorIterator = iteratorFromFunction(async () => ({ value: await base.iterator(), done: false }));\n            return iteratorFromConcatenated(iteratorIterator.take(count));\n        }, size);\n    }\n    /**\n     * Creates a `Dataset` that skips `count` initial elements from this dataset.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).skip(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: The number of elements of this dataset that should be skipped\n     *   to form the new dataset.  If `count` is greater than the size of this\n     *   dataset, the new dataset will contain no elements.  If `count`\n     *   is `undefined` or negative, skips the entire dataset.\n     *\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    skip(count) {\n        const base = this;\n        let size;\n        if (this.size != null && count >= 0 && this.size >= count) {\n            // If the size of this dataset is greater than count, the new dataset's\n            // size is current size minus skipped size.This also covers the case that\n            // current size is infinity.\n            size = this.size - count;\n        }\n        else if (this.size != null &&\n            (this.size < count || count === undefined || count < 0)) {\n            // If the size of this dataset is smaller than count, or count is\n            // undefined or negative, skips the entire dataset and the new size is 0.\n            size = 0;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).skip(count), size);\n    }\n    /**\n     * Pseudorandomly shuffles the elements of this dataset. This is done in a\n     * streaming manner, by sampling from a given number of prefetched elements.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).shuffle(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param bufferSize: An integer specifying the number of elements from this\n     *   dataset from which the new dataset will sample.\n     * @param seed: (Optional) An integer specifying the random seed that will\n     *   be used to create the distribution.\n     * @param reshuffleEachIteration: (Optional) A boolean, which if true\n     *   indicates that the dataset should be pseudorandomly reshuffled each time\n     *   it is iterated over. If false, elements will be returned in the same\n     *   shuffled order on each iteration. (Defaults to `true`.)\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    shuffle(bufferSize, seed, reshuffleEachIteration = true) {\n        if (bufferSize == null || bufferSize < 0) {\n            if (this.size == null) {\n                throw new RangeError('`Dataset.shuffle()` requires bufferSize to be specified.');\n            }\n            else {\n                throw new RangeError('`Dataset.shuffle()` requires bufferSize to be specified.  ' +\n                    'If your data fits in main memory (for regular JS objects), ' +\n                    'and/or GPU memory (for `tf.Tensor`s), consider setting ' +\n                    `bufferSize to the dataset size (${this.size} elements)`);\n            }\n        }\n        const base = this;\n        const random = seedrandom.alea(seed || tf.util.now().toString());\n        return datasetFromIteratorFn(async () => {\n            let seed2 = random.int32();\n            if (reshuffleEachIteration) {\n                seed2 += random.int32();\n            }\n            return (await base.iterator()).shuffle(bufferSize, seed2.toString());\n        }, this.size);\n    }\n    /**\n     * Creates a `Dataset` with at most `count` initial elements from this\n     * dataset.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).take(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: The number of elements of this dataset that should be taken\n     *   to form the new dataset.  If `count` is `undefined` or negative, or if\n     *   `count` is greater than the size of this dataset, the new dataset will\n     *   contain all elements of this dataset.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    take(count) {\n        const base = this;\n        let size;\n        if (this.size != null && this.size > count) {\n            // If the size of this dataset is greater than count, the new dataset's\n            // size is count.\n            size = count;\n        }\n        else if (this.size != null && this.size <= count) {\n            // If the size of this dataset is equal or smaller than count, the new\n            // dataset's size is the size of this dataset.\n            size = this.size;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).take(count), size);\n    }\n    /**\n     * Collect all elements of this dataset into an array.\n     *\n     * Obviously this will succeed only for small datasets that fit in memory.\n     * Useful for testing and generally should be avoided if possible.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]);\n     * console.log(await a.toArray());\n     * ```\n     *\n     * @returns A Promise for an array of elements, which will resolve\n     *   when a new stream has been obtained and fully consumed.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async toArray() {\n        if (this.size === Infinity) {\n            throw new Error('Can not convert infinite data stream to array.');\n        }\n        return (await this.iterator()).toArray();\n    }\n    /**\n     * Collect all elements of this dataset into an array with prefetching 100\n     * elements. This is useful for testing, because the prefetch changes the\n     * order in which the Promises are resolved along the processing pipeline.\n     * This may help expose bugs where results are dependent on the order of\n     * Promise resolution rather than on the logical order of the stream (i.e.,\n     * due to hidden mutable state).\n     *\n     * @returns A Promise for an array of elements, which will resolve\n     *   when a new stream has been obtained and fully consumed.\n     */\n    async toArrayForTest() {\n        if (this.size === Infinity) {\n            throw new Error('Can not convert infinite data stream to array.');\n        }\n        return (await this.iterator()).toArrayForTest();\n    }\n}\n// TODO(soergel): deep sharded shuffle, where supported\nDataset.MAX_BUFFER_SIZE = 10000;\n/**\n * Create a `Dataset` defined by a provided iterator() function.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const iter = tf.data.iteratorFromFunction(func);\n * const ds = tf.data.datasetFromIteratorFn(iter);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n */\nexport function datasetFromIteratorFn(iteratorFn, size = null) {\n    return new class extends Dataset {\n        constructor() {\n            super(...arguments);\n            this.size = size;\n        }\n        /*\n         * Provide a new stream of elements.  Note this will also start new streams\n         * from any underlying `Dataset`s.\n         */\n        async iterator() {\n            return iteratorFn();\n        }\n    }();\n}\n/**\n * Create a `Dataset` from an array of elements.\n *\n * Create a Dataset from an array of objects:\n * ```js\n * const a = tf.data.array([{'item': 1}, {'item': 2}, {'item': 3}]);\n * await a.forEachAsync(e => console.log(e));\n * ```\n *\n * Create a Dataset from an array of numbers:\n * ```js\n * const a = tf.data.array([4, 5, 6]);\n * await a.forEachAsync(e => console.log(e));\n * ```\n * @param items An array of elements that will be parsed as items in a dataset.\n *\n * @doc {heading: 'Data', subheading: 'Creation', namespace: 'data'}\n */\nexport function array(items) {\n    return datasetFromIteratorFn(async () => iteratorFromItems(items), items.length);\n}\n/**\n * Create a `Dataset` by zipping together an array, dict, or nested\n * structure of `Dataset`s (and perhaps additional constants).\n * The underlying datasets must provide elements in a consistent order such that\n * they correspond.\n *\n * The number of elements in the resulting dataset is the same as the size of\n * the smallest dataset in datasets.\n *\n * The nested structure of the `datasets` argument determines the\n * structure of elements in the resulting iterator.\n *\n * Note this means that, given an array of two datasets that produce dict\n * elements, the result is a dataset that produces elements that are arrays\n * of two dicts:\n *\n * Zip an array of datasets:\n * ```js\n * console.log('Zip two datasets of objects:');\n * const ds1 = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);\n * const ds2 = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);\n * const ds3 = tf.data.zip([ds1, ds2]);\n * await ds3.forEachAsync(e => console.log(JSON.stringify(e)));\n *\n * // If the goal is to merge the dicts in order to produce elements like\n * // {a: ..., b: ...}, this requires a second step such as:\n * console.log('Merge the objects:');\n * const ds4 = ds3.map(x => {return {a: x[0].a, b: x[1].b}});\n * await ds4.forEachAsync(e => console.log(e));\n * ```\n *\n * Zip a dict of datasets:\n * ```js\n * const a = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);\n * const b = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);\n * const c = tf.data.zip({c: a, d: b});\n * await c.forEachAsync(e => console.log(JSON.stringify(e)));\n * ```\n *\n * @doc {heading: 'Data', subheading: 'Operations', namespace: 'data'}\n */\nexport function zip(datasets) {\n    // manually type-check the argument for JS users\n    if (!isIterable(datasets)) {\n        throw new Error('The argument to zip() must be an object or array.');\n    }\n    let size;\n    if (Array.isArray(datasets)) {\n        for (let i = 0; i < datasets.length; i++) {\n            size = size == null ? datasets[i].size :\n                Math.min(size, datasets[i].size);\n        }\n    }\n    else if (datasets instanceof Object) {\n        for (const ds in datasets) {\n            size = size == null ? datasets[ds].size :\n                Math.min(size, datasets[ds].size);\n        }\n    }\n    return datasetFromIteratorFn(async () => {\n        const streams = await deepMapAndAwaitAll(datasets, d => {\n            if (d instanceof Dataset) {\n                return { value: d.iterator(), recurse: false };\n            }\n            else if (isIterable(d)) {\n                return { value: null, recurse: true };\n            }\n            else {\n                throw new Error('Leaves of the structure passed to zip() must be Datasets, ' +\n                    'not primitives.');\n            }\n        });\n        return iteratorFromZipped(streams, ZipMismatchMode.SHORTEST);\n    }, size);\n}\n/**\n * A zip function for use with deepZip, passed via the columnMajorBatch call.\n *\n * Accepts an array of identically-structured nested elements and either batches\n * them (if they are primitives, numeric arrays, or Tensors) or requests\n * recursion (if not).\n */\n// tslint:disable-next-line:no-any\nfunction deepBatchConcat(rows) {\n    if (rows === null) {\n        return null;\n    }\n    // use the first item to decide whether to recurse or batch here.\n    const exampleRow = rows[0];\n    if (canTensorify(exampleRow)) {\n        // rows is an array of primitives, Tensors, or arrays.  Batch them.\n        const value = batchConcat(rows);\n        return { value, recurse: false };\n    }\n    // the example row is an object, so recurse into it.\n    return { value: null, recurse: true };\n}\n/**\n * Assembles a list of same-shaped numbers, number arrays, or Tensors\n * into a single new Tensor where axis 0 is the batch dimension.\n */\nfunction batchConcat(arrays) {\n    if (arrays.length === 0) {\n        // We can't return an empty Tensor because we don't know the element shape.\n        throw new Error('Can\\'t make a batch of zero elements.');\n    }\n    if (arrays[0] instanceof tf.Tensor) {\n        // Input is an array of Tensors\n        return tf.stack(arrays);\n    }\n    else {\n        // Input is a possibly-nested array of numbers.\n        return tf.tensor(arrays);\n    }\n}\n//# sourceMappingURL=dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { Dataset } from '../dataset';\n/**\n * Represents a potentially large collection of text lines.\n *\n * The results are not batched.\n */\nexport class TextLineDataset extends Dataset {\n    /**\n     * Create a `TextLineDataset`.\n     *\n     * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n     */\n    constructor(input) {\n        super();\n        this.input = input;\n    }\n    async iterator() {\n        const inputIterator = await this.input.iterator();\n        const utf8Iterator = inputIterator.decodeUTF8();\n        const lineIterator = utf8Iterator.split('\\n').map(line => {\n            // Windows/DOS format text file has extra line breaker at the end of line.\n            if (line.endsWith('\\r')) {\n                line = line.slice(0, -1);\n            }\n            return line;\n        });\n        return lineIterator;\n    }\n}\n//# sourceMappingURL=text_line_dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { Dataset } from '../dataset';\nimport { TextLineDataset } from './text_line_dataset';\nconst CODE_QUOTE = '\"';\nconst STATE_OUT = Symbol('out');\nconst STATE_FIELD = Symbol('field');\nconst STATE_QUOTE = Symbol('quote');\nconst STATE_QUOTE_AFTER_QUOTE = Symbol('quoteafterquote');\nconst STATE_WITHIN_QUOTE_IN_QUOTE = Symbol('quoteinquote');\n/**\n * Represents a potentially large collection of delimited text records.\n *\n * The produced `TensorContainer`s each contain one key-value pair for\n * every column of the table.  When a field is empty in the incoming data, the\n * resulting value is `undefined`, or throw error if it is required.  Values\n * that can be parsed as numbers are emitted as type `number`, other values\n * are parsed as `string`.\n *\n * The results are not batched.\n *\n * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}\n */\nexport class CSVDataset extends Dataset {\n    /**\n     * Create a `CSVDataset`.\n     *\n     * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n     * @param csvConfig (Optional) A CSVConfig object that contains configurations\n     *     of reading and decoding from CSV file(s).\n     *\n     *     hasHeader: (Optional) A boolean value that indicates whether the first\n     *     row of provided CSV file is a header line with column names, and should\n     *     not be included in the data. Defaults to `true`.\n     *\n     *     columnNames: (Optional) A list of strings that corresponds to\n     *     the CSV column names, in order. If provided, it ignores the column\n     *     names inferred from the header row. If not provided, infers the column\n     *     names from the first row of the records. If hasHeader is false and\n     *     columnNames is not provided, this method throws an error.\n     *\n     *     columnConfigs: (Optional) A dictionary whose key is column names, value\n     *     is an object stating if this column is required, column's data type,\n     *     default value, and if this column is label. If provided, keys must\n     *     correspond to names provided in columnNames or inferred from the file\n     *     header lines. If isLabel is true any column, returns an array of two\n     *     items: the first item is a dict of features key/value pairs, the second\n     *     item is a dict of labels key/value pairs. If no feature is marked as\n     *     label, returns a dict of features only.\n     *\n     *     configuredColumnsOnly (Optional) If true, only columns provided in\n     *     columnConfigs will be parsed and provided during iteration.\n     *\n     *     delimiter (Optional) The string used to parse each line of the input\n     *     file. Defaults to `,`.\n     */\n    constructor(input, csvConfig) {\n        super();\n        this.input = input;\n        this.hasHeader = true;\n        this.fullColumnNames = null;\n        this.columnNamesValidated = false;\n        this.columnConfigs = null;\n        this.configuredColumnsOnly = false;\n        this.delimiter = ',';\n        this.delimWhitespace = false;\n        this.base = new TextLineDataset(input);\n        if (!csvConfig) {\n            csvConfig = {};\n        }\n        this.hasHeader = csvConfig.hasHeader === false ? false : true;\n        this.fullColumnNames = csvConfig.columnNames;\n        this.columnConfigs = csvConfig.columnConfigs;\n        this.configuredColumnsOnly = csvConfig.configuredColumnsOnly;\n        if (csvConfig.delimWhitespace) {\n            util.assert(csvConfig.delimiter == null, () => 'Delimiter should not be provided when delimWhitespace is true.');\n            this.delimWhitespace = true;\n            this.delimiter = ' ';\n        }\n        else {\n            this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : ',';\n        }\n    }\n    /**\n     * Returns column names of the csv dataset. If `configuredColumnsOnly` is\n     * true, return column names in `columnConfigs`. If `configuredColumnsOnly` is\n     * false and `columnNames` is provided, `columnNames`. If\n     * `configuredColumnsOnly` is false and `columnNames` is not provided, return\n     * all column names parsed from the csv file. For example usage please go to\n     * `tf.data.csv`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async columnNames() {\n        if (!this.columnNamesValidated) {\n            await this.setColumnNames();\n        }\n        return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) :\n            this.fullColumnNames;\n    }\n    /* 1) If `columnNames` is provided as string[], use this string[] as output\n     * keys in corresponding order. The length must match the number of inferred\n     * columns if `hasHeader` is true .\n     * 2) If `columnNames` is not provided, parse header line as `columnNames` if\n     * hasHeader is true. If `hasHeader` is false, throw an error.\n     * 3) If `columnConfigs` is provided, all the keys in `columnConfigs` must\n     * exist in parsed `columnNames`.\n     */\n    async setColumnNames() {\n        const columnNamesFromFile = await this.maybeReadHeaderLine();\n        if (!this.fullColumnNames && !columnNamesFromFile) {\n            // Throw an error if columnNames is not provided and no header line.\n            throw new Error('Column names must be provided if there is no header line.');\n        }\n        else if (this.fullColumnNames && columnNamesFromFile) {\n            // Check provided columnNames match header line.\n            util.assert(columnNamesFromFile.length === this.fullColumnNames.length, () => 'The length of provided columnNames (' +\n                this.fullColumnNames.length.toString() +\n                ') does not match the length of the header line read from ' +\n                'file (' + columnNamesFromFile.length.toString() + ').');\n        }\n        if (!this.fullColumnNames) {\n            this.fullColumnNames = columnNamesFromFile;\n        }\n        // Check if there are duplicate column names.\n        const counts = this.fullColumnNames.reduce((countAcc, name) => {\n            countAcc[name] = (countAcc[name] + 1) || 1;\n            return countAcc;\n        }, {});\n        const duplicateNames = Object.keys(counts).filter((name) => (counts[name] > 1));\n        util.assert(duplicateNames.length === 0, () => 'Duplicate column names found: ' + duplicateNames.toString());\n        // Check if keys in columnConfigs match columnNames.\n        if (this.columnConfigs) {\n            for (const key of Object.keys(this.columnConfigs)) {\n                const index = this.fullColumnNames.indexOf(key);\n                if (index === -1) {\n                    throw new Error('The key \"' + key +\n                        '\" provided in columnConfigs does not match any of the column ' +\n                        'names (' + this.fullColumnNames.toString() + ').');\n                }\n            }\n        }\n        this.columnNamesValidated = true;\n    }\n    async maybeReadHeaderLine() {\n        if (this.hasHeader) {\n            const iter = await this.base.iterator();\n            const firstElement = await iter.next();\n            if (firstElement.done) {\n                throw new Error('No data was found for CSV parsing.');\n            }\n            const firstLine = firstElement.value;\n            const headers = this.parseRow(firstLine, false);\n            return headers;\n        }\n        else {\n            return null;\n        }\n    }\n    async iterator() {\n        if (!this.columnNamesValidated) {\n            await this.setColumnNames();\n        }\n        let lines = await this.base.iterator();\n        if (this.hasHeader) {\n            // We previously read the first line to get the columnNames.\n            // Now that we're providing data, skip it.\n            lines = lines.skip(1);\n        }\n        return lines.map(x => this.makeDataElement(x));\n    }\n    makeDataElement(line) {\n        const values = this.parseRow(line);\n        const features = {};\n        const labels = {};\n        for (let i = 0; i < this.fullColumnNames.length; i++) {\n            const key = this.fullColumnNames[i];\n            const config = this.columnConfigs ? this.columnConfigs[key] : null;\n            if (this.configuredColumnsOnly && !config) {\n                // This column is not selected.\n                continue;\n            }\n            else {\n                const value = values[i];\n                let parsedValue = null;\n                if (value === '') {\n                    // If default value is provided, use it. If default value is not\n                    // provided, set as undefined.\n                    if (config && config.default !== undefined) {\n                        parsedValue = config.default;\n                    }\n                    else if (config && (config.required || config.isLabel)) {\n                        throw new Error(`Required column ${key} is empty in this line: ${line}`);\n                    }\n                    else {\n                        parsedValue = undefined;\n                    }\n                }\n                else {\n                    // A value is present, so parse it based on type\n                    const valueAsNum = Number(value);\n                    if (isNaN(valueAsNum)) {\n                        // The value is a string and this column is declared as boolean\n                        // in config, parse it as boolean.\n                        if (config && config.dtype === 'bool') {\n                            parsedValue = this.getBoolean(value);\n                        }\n                        else {\n                            // Set value as string\n                            parsedValue = value;\n                        }\n                    }\n                    else if (!config || !config.dtype) {\n                        // If this value is a number and no type config is provided, return\n                        // it as number.\n                        parsedValue = valueAsNum;\n                    }\n                    else {\n                        // If this value is a number and data type is provided, parse it\n                        // according to provided data type.\n                        switch (config.dtype) {\n                            case 'float32':\n                                parsedValue = valueAsNum;\n                                break;\n                            case 'int32':\n                                parsedValue = Math.floor(valueAsNum);\n                                break;\n                            case 'bool':\n                                parsedValue = this.getBoolean(value);\n                                break;\n                            default:\n                                parsedValue = valueAsNum;\n                        }\n                    }\n                }\n                // Check if this column is label.\n                (config && config.isLabel) ? labels[key] = parsedValue :\n                    features[key] = parsedValue;\n            }\n        }\n        // If label exists, return an object of features and labels as {xs:features,\n        // ys:labels}, otherwise return features only.\n        if (Object.keys(labels).length === 0) {\n            return features;\n        }\n        else {\n            return { xs: features, ys: labels };\n        }\n    }\n    getBoolean(value) {\n        if (value === '1' || value.toLowerCase() === 'true') {\n            return 1;\n        }\n        else {\n            return 0;\n        }\n    }\n    // adapted from https://beta.observablehq.com/@mbostock/streaming-csv\n    parseRow(line, validateElementCount = true) {\n        const result = [];\n        let readOffset = 0;\n        const readLength = line.length;\n        let currentState = STATE_OUT;\n        // Goes through the line to parse quote.\n        for (let i = 0; i < readLength; i++) {\n            switch (currentState) {\n                // Before enter a new field\n                case STATE_OUT:\n                    switch (line.charAt(i)) {\n                        // Enter a quoted field\n                        case CODE_QUOTE:\n                            readOffset = i + 1;\n                            currentState = STATE_QUOTE;\n                            break;\n                        // Read an empty field\n                        case this.delimiter:\n                            readOffset = i + 1;\n                            // If delimiter is white space and configured to collapse\n                            // multiple white spaces, ignore this white space.\n                            if (this.delimiter === ' ' && this.delimWhitespace) {\n                                break;\n                            }\n                            result.push('');\n                            currentState = STATE_OUT;\n                            break;\n                        // Enter an unquoted field\n                        default:\n                            currentState = STATE_FIELD;\n                            readOffset = i;\n                            break;\n                    }\n                    break;\n                // In an unquoted field\n                case STATE_FIELD:\n                    switch (line.charAt(i)) {\n                        // Exit an unquoted field, add it to result\n                        case this.delimiter:\n                            result.push(line.substring(readOffset, i));\n                            currentState = STATE_OUT;\n                            readOffset = i + 1;\n                            break;\n                        default:\n                    }\n                    break;\n                // In a quoted field\n                case STATE_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Read a quote after a quote\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE_AFTER_QUOTE;\n                            break;\n                        default:\n                    }\n                    break;\n                // This state means it's right after a second quote in a field\n                case STATE_QUOTE_AFTER_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Finished a quoted field\n                        case this.delimiter:\n                            result.push(line.substring(readOffset, i - 1));\n                            currentState = STATE_OUT;\n                            readOffset = i + 1;\n                            break;\n                        // Finished a quoted part in a quoted field\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE;\n                            break;\n                        // In a quoted part in a quoted field\n                        default:\n                            currentState = STATE_WITHIN_QUOTE_IN_QUOTE;\n                            break;\n                    }\n                    break;\n                case STATE_WITHIN_QUOTE_IN_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Exit a quoted part in a quoted field\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE;\n                            break;\n                        default:\n                    }\n                    break;\n                default:\n            }\n        }\n        // Adds last item based on if it is quoted.\n        if (currentState === STATE_QUOTE_AFTER_QUOTE) {\n            result.push(line.substring(readOffset, readLength - 1));\n        }\n        else {\n            result.push(line.substring(readOffset));\n        }\n        // Check if each row has the same number of elements as column names.\n        if (validateElementCount && result.length !== this.fullColumnNames.length) {\n            throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);\n        }\n        return result;\n    }\n}\n// TODO(soergel): add more basic datasets for parity with tf.data\n// tf.data.FixedLengthRecordDataset()\n// tf.data.TFRecordDataset()\n//# sourceMappingURL=csv_dataset.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env, tensor, util } from '@tensorflow/tfjs-core';\nimport { LazyIterator } from './lazy_iterator';\n/**\n * Provide a stream of tensors from microphone audio stream. The tensors are\n * representing audio data as frequency-domain spectrogram generated with\n * browser's native FFT. Tensors representing time-domain waveform is available\n * based on configuration. Only works in browser environment.\n */\nexport class MicrophoneIterator extends LazyIterator {\n    constructor(microphoneConfig) {\n        super();\n        this.microphoneConfig = microphoneConfig;\n        this.isClosed = false;\n        this.fftSize = microphoneConfig.fftSize || 1024;\n        const fftSizeLog2 = Math.log2(this.fftSize);\n        if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 ||\n            !Number.isInteger(fftSizeLog2)) {\n            throw new Error(`Invalid fftSize: it must be a power of 2 between ` +\n                `2 to 4 and 2 to 14, but got ${this.fftSize}`);\n        }\n        this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43;\n        this.sampleRateHz = microphoneConfig.sampleRateHz;\n        this.columnTruncateLength =\n            microphoneConfig.columnTruncateLength || this.fftSize;\n        this.audioTrackConstraints = microphoneConfig.audioTrackConstraints;\n        this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0;\n        this.includeSpectrogram =\n            microphoneConfig.includeSpectrogram === false ? false : true;\n        this.includeWaveform =\n            microphoneConfig.includeWaveform === true ? true : false;\n        if (!this.includeSpectrogram && !this.includeWaveform) {\n            throw new Error('Both includeSpectrogram and includeWaveform are false. ' +\n                'At least one type of data should be returned.');\n        }\n    }\n    summary() {\n        return `microphone`;\n    }\n    // Construct a MicrophoneIterator and start the audio stream.\n    static async create(microphoneConfig = {}) {\n        if (env().get('IS_NODE')) {\n            throw new Error('microphone API is only supported in browser environment.');\n        }\n        const microphoneIterator = new MicrophoneIterator(microphoneConfig);\n        // Call async function start() to initialize the audio stream.\n        await microphoneIterator.start();\n        return microphoneIterator;\n    }\n    // Start the audio stream and FFT.\n    async start() {\n        try {\n            this.stream = await navigator.mediaDevices.getUserMedia({\n                audio: this.audioTrackConstraints == null ? true :\n                    this.audioTrackConstraints,\n                video: false\n            });\n        }\n        catch (e) {\n            throw new Error(`Error thrown while initializing video stream: ${e.message}`);\n        }\n        if (!this.stream) {\n            throw new Error('Could not obtain audio from microphone.');\n        }\n        const ctxConstructor = \n        // tslint:disable-next-line:no-any\n        window.AudioContext || window.webkitAudioContext;\n        this.audioContext = new ctxConstructor();\n        if (!this.sampleRateHz) {\n            // If sample rate is not provided, use the available sample rate on\n            // device.\n            this.sampleRateHz = this.audioContext.sampleRate;\n        }\n        else if (this.audioContext.sampleRate !== this.sampleRateHz) {\n            throw new Error(`Mismatch in sampling rate: ` +\n                `Expected: ${this.sampleRateHz}; ` +\n                `Actual: ${this.audioContext.sampleRate}`);\n        }\n        const streamSource = this.audioContext.createMediaStreamSource(this.stream);\n        this.analyser = this.audioContext.createAnalyser();\n        this.analyser.fftSize = this.fftSize * 2;\n        this.analyser.smoothingTimeConstant = this.smoothingTimeConstant;\n        streamSource.connect(this.analyser);\n        this.freqData = new Float32Array(this.fftSize);\n        this.timeData = new Float32Array(this.fftSize);\n        return;\n    }\n    async next() {\n        if (this.isClosed) {\n            return { value: null, done: true };\n        }\n        let spectrogramTensor;\n        let waveformTensor;\n        const audioDataQueue = await this.getAudioData();\n        if (this.includeSpectrogram) {\n            const freqData = this.flattenQueue(audioDataQueue.freqDataQueue);\n            spectrogramTensor = this.getTensorFromAudioDataArray(freqData, [this.numFrames, this.columnTruncateLength, 1]);\n        }\n        if (this.includeWaveform) {\n            const timeData = this.flattenQueue(audioDataQueue.timeDataQueue);\n            waveformTensor = this.getTensorFromAudioDataArray(timeData, [this.numFrames * this.fftSize, 1]);\n        }\n        return {\n            value: { 'spectrogram': spectrogramTensor, 'waveform': waveformTensor },\n            done: false\n        };\n    }\n    // Capture one result from the audio stream, and extract the value from\n    // iterator.next() result.\n    async capture() {\n        return (await this.next()).value;\n    }\n    async getAudioData() {\n        const freqDataQueue = [];\n        const timeDataQueue = [];\n        let currentFrames = 0;\n        return new Promise(resolve => {\n            const intervalID = setInterval(() => {\n                if (this.includeSpectrogram) {\n                    this.analyser.getFloatFrequencyData(this.freqData);\n                    // If the audio stream is initializing, return empty queue.\n                    if (this.freqData[0] === -Infinity) {\n                        resolve({ freqDataQueue, timeDataQueue });\n                    }\n                    freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength));\n                }\n                if (this.includeWaveform) {\n                    this.analyser.getFloatTimeDomainData(this.timeData);\n                    timeDataQueue.push(this.timeData.slice());\n                }\n                // Clean interval and return when all frames have been collected\n                if (++currentFrames === this.numFrames) {\n                    clearInterval(intervalID);\n                    resolve({ freqDataQueue, timeDataQueue });\n                }\n            }, this.fftSize / this.sampleRateHz * 1e3);\n        });\n    }\n    // Stop the audio stream and pause the iterator.\n    stop() {\n        if (!this.isClosed) {\n            this.isClosed = true;\n            this.analyser.disconnect();\n            this.audioContext.close();\n            if (this.stream != null && this.stream.getTracks().length > 0) {\n                this.stream.getTracks()[0].stop();\n            }\n        }\n    }\n    // Override toArray() function to prevent collecting.\n    toArray() {\n        throw new Error('Can not convert infinite audio stream to array.');\n    }\n    // Return audio sampling rate in Hz\n    getSampleRate() {\n        return this.sampleRateHz;\n    }\n    flattenQueue(queue) {\n        const frameSize = queue[0].length;\n        const freqData = new Float32Array(queue.length * frameSize);\n        queue.forEach((data, i) => freqData.set(data, i * frameSize));\n        return freqData;\n    }\n    getTensorFromAudioDataArray(freqData, shape) {\n        const vals = new Float32Array(util.sizeFromShape(shape));\n        // If the data is less than the output shape, the rest is padded with zeros.\n        vals.set(freqData, vals.length - freqData.length);\n        return tensor(vals, shape);\n    }\n}\n//# sourceMappingURL=microphone_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { browser, env, image, tensor1d, tensor2d, tidy, util } from '@tensorflow/tfjs-core';\nimport { LazyIterator } from './lazy_iterator';\n/**\n * Provide a stream of image tensors from webcam video stream. Only works in\n * browser environment.\n */\nexport class WebcamIterator extends LazyIterator {\n    constructor(webcamVideoElement, webcamConfig) {\n        super();\n        this.webcamVideoElement = webcamVideoElement;\n        this.webcamConfig = webcamConfig;\n        this.isClosed = true;\n        this.resize = false;\n        if (this.needToResize()) {\n            this.resize = true;\n            this.cropSize =\n                [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth];\n            this.cropBoxInd = tensor1d([0], 'int32');\n            if (this.webcamConfig.centerCrop) {\n                // Calculate the box based on resizing shape.\n                const widthCroppingRatio = this.webcamConfig.resizeWidth * 1.0 / this.webcamVideoElement.width;\n                const heightCroppingRatio = this.webcamConfig.resizeHeight * 1.0 /\n                    this.webcamVideoElement.height;\n                const widthCropStart = (1 - widthCroppingRatio) / 2;\n                const heightCropStart = (1 - heightCroppingRatio) / 2;\n                const widthCropEnd = widthCropStart + widthCroppingRatio;\n                const heightCropEnd = heightCroppingRatio + heightCropStart;\n                this.cropBox = tensor2d([heightCropStart, widthCropStart, heightCropEnd, widthCropEnd], [1, 4]);\n            }\n            else {\n                this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]);\n            }\n        }\n    }\n    summary() {\n        return `webcam`;\n    }\n    // Construct a WebcamIterator and start it's video stream.\n    static async create(webcamVideoElement, webcamConfig = {}) {\n        if (env().get('IS_NODE')) {\n            throw new Error('tf.data.webcam is only supported in browser environment.');\n        }\n        if (!webcamVideoElement) {\n            // If webcam video element is not provided, create a hidden video element\n            // with provided width and height.\n            webcamVideoElement = document.createElement('video');\n            if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) {\n                throw new Error('Please provide webcam video element, or resizeWidth and ' +\n                    'resizeHeight to create a hidden video element.');\n            }\n            webcamVideoElement.width = webcamConfig.resizeWidth;\n            webcamVideoElement.height = webcamConfig.resizeHeight;\n        }\n        const webcamIterator = new WebcamIterator(webcamVideoElement, webcamConfig);\n        // Call async function to initialize the video stream.\n        await webcamIterator.start();\n        return webcamIterator;\n    }\n    // Async function to start video stream.\n    async start() {\n        if (this.webcamConfig.facingMode) {\n            util.assert((this.webcamConfig.facingMode === 'user') ||\n                (this.webcamConfig.facingMode === 'environment'), () => `Invalid webcam facing mode: ${this.webcamConfig.facingMode}. ` +\n                `Please provide 'user' or 'environment'`);\n        }\n        try {\n            this.stream = await navigator.mediaDevices.getUserMedia({\n                video: {\n                    deviceId: this.webcamConfig.deviceId,\n                    facingMode: this.webcamConfig.facingMode ?\n                        this.webcamConfig.facingMode :\n                        'user',\n                    width: this.webcamVideoElement.width,\n                    height: this.webcamVideoElement.height\n                }\n            });\n        }\n        catch (e) {\n            // Modify the error message but leave the stack trace intact\n            e.message = `Error thrown while initializing video stream: ${e.message}`;\n            throw e;\n        }\n        if (!this.stream) {\n            throw new Error('Could not obtain video from webcam.');\n        }\n        // Older browsers may not have srcObject\n        try {\n            this.webcamVideoElement.srcObject = this.stream;\n        }\n        catch (error) {\n            console.log(error);\n            this.webcamVideoElement.src = window.URL.createObjectURL(this.stream);\n        }\n        // Start the webcam video stream\n        this.webcamVideoElement.play();\n        this.isClosed = false;\n        return new Promise(resolve => {\n            // Add event listener to make sure the webcam has been fully initialized.\n            this.webcamVideoElement.onloadedmetadata = () => {\n                resolve();\n            };\n        });\n    }\n    async next() {\n        if (this.isClosed) {\n            return { value: null, done: true };\n        }\n        let img;\n        try {\n            img = browser.fromPixels(this.webcamVideoElement);\n        }\n        catch (e) {\n            throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`);\n        }\n        if (this.resize) {\n            try {\n                return { value: this.cropAndResizeFrame(img), done: false };\n            }\n            catch (e) {\n                throw new Error(`Error thrown cropping the video: ${e.message}`);\n            }\n            finally {\n                img.dispose();\n            }\n        }\n        else {\n            return { value: img, done: false };\n        }\n    }\n    needToResize() {\n        // If resizeWidth and resizeHeight are provided, and different from the\n        // width and height of original HTMLVideoElement, then resizing and cropping\n        // is required.\n        if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight &&\n            (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth ||\n                this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) {\n            return true;\n        }\n        return false;\n    }\n    // Cropping and resizing each frame based on config\n    cropAndResizeFrame(img) {\n        return tidy(() => {\n            const expandedImage = img.toFloat().expandDims(0);\n            let resizedImage;\n            resizedImage = image.cropAndResize(expandedImage, this.cropBox, this.cropBoxInd, this.cropSize, 'bilinear');\n            // Extract image from batch cropping.\n            const shape = resizedImage.shape;\n            return resizedImage.reshape(shape.slice(1));\n        });\n    }\n    // Capture one frame from the video stream, and extract the value from\n    // iterator.next() result.\n    async capture() {\n        return (await this.next()).value;\n    }\n    // Stop the video stream and pause webcam iterator.\n    stop() {\n        const tracks = this.stream.getTracks();\n        tracks.forEach(track => track.stop());\n        try {\n            this.webcamVideoElement.srcObject = null;\n        }\n        catch (error) {\n            console.log(error);\n            this.webcamVideoElement.src = null;\n        }\n        this.isClosed = true;\n    }\n    // Override toArray() function to prevent collecting.\n    toArray() {\n        throw new Error('Can not convert infinite video stream to array.');\n    }\n}\n//# sourceMappingURL=webcam_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * Represents a data source readable as a stream of binary data chunks.\n *\n * Because `Dataset`s can be read repeatedly (via `Dataset.iterator()`), this\n * provides a means to repeatedly create streams from the underlying data\n * sources.\n */\nexport class DataSource {\n}\n// TODO(soergel): consider convenience factory functions here\n// in combination with chainable source->dataset above, e.g.:\n// tf.data.url(...).asCsvDataset().shuffle().batch()\n//# sourceMappingURL=datasource.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nexport class StringIterator extends LazyIterator {\n    /**\n     * Splits a string stream on a given separator.\n     *\n     * It is assumed that the incoming chunk boundaries have no semantic meaning,\n     * so conceptually the incoming stream is treated simply as the concatenation\n     * of its elements.\n     *\n     * The outgoing stream provides chunks corresponding to the results of the\n     * standard string split() operation (even if such a chunk spanned incoming\n     * chunks).  The separators are not included.\n     *\n     * A typical usage is to split a text file (represented as a stream with\n     * arbitrary chunk boundaries) into lines.\n     *\n     * @param upstream A readable stream of strings that can be treated as\n     *   concatenated.\n     * @param separator A character to split on.\n     */\n    split(separator) {\n        return new SplitIterator(this, separator);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on StringIterator.  Unfortunately they can't be placed in separate files, due\n// to resulting trouble with circular imports.\n// ============================================================================\n// We wanted multiple inheritance, e.g.\n//   class SplitIterator extends QueueIterator<string>, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\nclass SplitIterator extends StringIterator {\n    constructor(upstream, separator) {\n        super();\n        this.upstream = upstream;\n        this.impl = new SplitIteratorImpl(upstream, separator);\n    }\n    summary() {\n        return this.impl.summary();\n    }\n    async next() {\n        return this.impl.next();\n    }\n}\nclass SplitIteratorImpl extends OneToManyIterator {\n    constructor(upstream, separator) {\n        super();\n        this.upstream = upstream;\n        this.separator = separator;\n        // A partial string at the end of an upstream chunk\n        this.carryover = '';\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Split('${this.separator}')`;\n    }\n    async pump() {\n        const chunkResult = await this.upstream.next();\n        if (chunkResult.done) {\n            if (this.carryover === '') {\n                return false;\n            }\n            // Pretend that the pump succeeded in order to emit the small last batch.\n            // The next pump() call will actually fail.\n            this.outputQueue.push(this.carryover);\n            this.carryover = '';\n            return true;\n        }\n        const lines = chunkResult.value.split(this.separator);\n        // Note the behavior: \" ab \".split(' ') === ['', 'ab', '']\n        // Thus the carryover may be '' if the separator falls on a chunk\n        // boundary; this produces the correct result.\n        lines[0] = this.carryover + lines[0];\n        for (const line of lines.slice(0, -1)) {\n            this.outputQueue.push(line);\n        }\n        this.carryover = lines[lines.length - 1];\n        return true;\n    }\n}\n//# sourceMappingURL=string_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nimport { StringIterator } from './string_iterator';\nexport class ByteChunkIterator extends LazyIterator {\n    /**\n     * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n     *\n     * The byte arrays producetd from the ByteChunkIterator on which this is\n     * called will be interpreted as concatenated.  No assumptions are made about\n     * the boundaries of the incoming chunks, so a multi-byte UTF8 encoding of a\n     * character may span the boundary between chunks.  This naturally happens,\n     * for instance, when reading fixed-size byte arrays from a file.\n     */\n    decodeUTF8() {\n        return new Utf8Iterator(this);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on ByteChunkIterator.  Unfortunately they can't be placed in separate files,\n// due to resulting trouble with circular imports.\n// ============================================================================\n// We wanted multiple inheritance, e.g.\n//   class Utf8Iterator extends QueueIterator<string>, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\nclass Utf8Iterator extends StringIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        this.impl = new Utf8IteratorImpl(upstream);\n    }\n    summary() {\n        return this.impl.summary();\n    }\n    async next() {\n        return this.impl.next();\n    }\n}\n/**\n * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n *\n * This is tricky because the incoming byte array boundaries may disrupt a\n * multi-byte UTF8 character. Thus any incomplete character data at the end of\n * a chunk must be carried over and prepended to the next chunk before\n * decoding. Luckily with native decoder, TextDecoder in browser and\n * string_decoder in node, byte array boundaries are handled automatically.\n *\n * In the context of an input pipeline for machine learning, UTF8 decoding is\n * needed to parse text files containing training examples or prediction\n * requests (e.g., formatted as CSV or JSON). We cannot use the built-in\n * decoding provided by FileReader.readAsText() because here we are in a\n * streaming context, which FileReader does not support.\n *\n * @param upstream A `LazyIterator` of `Uint8Arrays` containing UTF8-encoded\n *   text, which should be interpreted as concatenated.  No assumptions are\n *   made about the boundaries of the incoming chunks, so a multi-byte UTF8\n *   encoding of a character may span the boundary between chunks.  This\n *   naturally happens, for instance, when reading fixed-size byte arrays from a\n *   file.\n */\nclass Utf8IteratorImpl extends OneToManyIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        if (env().get('IS_BROWSER')) {\n            this.decoder = new TextDecoder('utf-8');\n        }\n        else {\n            // tslint:disable-next-line:no-require-imports\n            const { StringDecoder } = require('string_decoder');\n            this.decoder = new StringDecoder('utf8');\n        }\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Utf8`;\n    }\n    async pump() {\n        const chunkResult = await this.upstream.next();\n        let chunk;\n        if (chunkResult.done) {\n            return false;\n        }\n        else {\n            chunk = chunkResult.value;\n        }\n        let text;\n        if (env().get('IS_BROWSER')) {\n            text = this.decoder.decode(chunk, { stream: true });\n        }\n        else {\n            text = this.decoder.write(Buffer.from(chunk.buffer));\n        }\n        this.outputQueue.push(text);\n        return true;\n    }\n}\n//# sourceMappingURL=byte_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n// inspired by https://github.com/maxogden/filereader-stream\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { ByteChunkIterator } from './byte_chunk_iterator';\n/**\n * Provide a stream of chunks from a File, Blob, or Uint8Array.\n * @param file The source File, Blob or Uint8Array.\n * @param options Optional settings controlling file reading.\n * @returns a lazy Iterator of Uint8Arrays containing sequential chunks of the\n *   input File, Blob or Uint8Array.\n */\nexport class FileChunkIterator extends ByteChunkIterator {\n    constructor(file, options = {}) {\n        super();\n        this.file = file;\n        this.options = options;\n        util.assert((file instanceof Uint8Array) ||\n            (env().get('IS_BROWSER') ?\n                (file instanceof File || file instanceof Blob) :\n                false), () => 'FileChunkIterator only supports File, Blob and Uint8Array ' +\n            'right now.');\n        this.offset = options.offset || 0;\n        // default 1MB chunk has tolerable perf on large files\n        this.chunkSize = options.chunkSize || 1024 * 1024;\n    }\n    summary() {\n        return `FileChunks ${this.file}`;\n    }\n    async next() {\n        if (this.offset >= ((this.file instanceof Uint8Array) ?\n            this.file.byteLength :\n            this.file.size)) {\n            return { value: null, done: true };\n        }\n        const chunk = new Promise((resolve, reject) => {\n            const end = this.offset + this.chunkSize;\n            if (this.file instanceof Uint8Array) {\n                // Note if end > this.uint8Array.byteLength, we just get a small last\n                // chunk.\n                resolve(new Uint8Array(this.file.slice(this.offset, end)));\n            }\n            else {\n                // This branch assumes that this.file type is File or Blob, which\n                // means it is in the browser environment.\n                // TODO(soergel): is this a performance issue?\n                const fileReader = new FileReader();\n                fileReader.onload = (event) => {\n                    let data = fileReader.result;\n                    // Not sure we can trust the return type of\n                    // FileReader.readAsArrayBuffer See e.g.\n                    // https://github.com/node-file-api/FileReader/issues/2\n                    if (data instanceof ArrayBuffer) {\n                        data = new Uint8Array(data);\n                    }\n                    if (!(data instanceof Uint8Array)) {\n                        return reject(new TypeError('FileReader returned unknown type.'));\n                    }\n                    resolve(data);\n                };\n                fileReader.onabort = (event) => {\n                    return reject(new Error('Aborted'));\n                };\n                fileReader.onerror = (event) => {\n                    return reject(new Error(event.type));\n                };\n                // TODO(soergel): better handle onabort, onerror\n                // Note if end > this.file.size, we just get a small last chunk.\n                const slice = this.file.slice(this.offset, end);\n                // We can't use readAsText here (even if we know the file is text)\n                // because the slice boundary may fall within a multi-byte character.\n                fileReader.readAsArrayBuffer(slice);\n            }\n            this.offset = end;\n        });\n        return { value: (await chunk), done: false };\n    }\n}\n//# sourceMappingURL=file_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { FileChunkIterator } from './file_chunk_iterator';\n/**\n * Provide a stream of chunks from a URL.\n *\n * Note this class first downloads the entire file into memory before providing\n * the first element from the stream.  This is because the Fetch API does not\n * yet reliably provide a reader stream for the response body.\n */\nexport async function urlChunkIterator(url, options = {}) {\n    let urlString;\n    let requestInit;\n    if ((typeof url) === 'string') {\n        urlString = url;\n    }\n    else {\n        urlString = url.url;\n        requestInit = getRequestInitFromRequest(url);\n    }\n    const response = await util.fetch(urlString, requestInit);\n    if (response.ok) {\n        const uint8Array = new Uint8Array(await response.arrayBuffer());\n        return new FileChunkIterator(uint8Array, options);\n    }\n    else {\n        throw new Error(response.statusText);\n    }\n}\n// Generate RequestInit from Request to match tf.util.fetch signature.\nconst getRequestInitFromRequest = (request) => {\n    const init = {\n        method: request.method,\n        headers: request.headers,\n        body: request.body,\n        mode: request.mode,\n        credentials: request.credentials,\n        cache: request.cache,\n        redirect: request.redirect,\n        referrer: request.referrer,\n        integrity: request.integrity,\n    };\n    return init;\n};\n//# sourceMappingURL=url_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n// Skip tslint any type check cause this method is aiming to check type of\n// input.\n// tslint:disable-next-line:no-any\nexport function isLocalPath(source) {\n    return (typeof source === 'string') && source.substr(0, 7) === 'file://';\n}\n//# sourceMappingURL=source_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { DataSource } from '../datasource';\nimport { FileChunkIterator } from '../iterators/file_chunk_iterator';\nimport { isLocalPath } from '../util/source_util';\n/**\n * Represents a file, blob, or Uint8Array readable as a stream of binary data\n * chunks.\n */\nexport class FileDataSource extends DataSource {\n    /**\n     * Create a `FileDataSource`.\n     *\n     * @param input Local file path, or `File`/`Blob`/`Uint8Array` object to\n     *     read. Local file only works in node environment.\n     * @param options Options passed to the underlying `FileChunkIterator`s,\n     *   such as {chunksize: 1024}.\n     */\n    constructor(input, options = {}) {\n        super();\n        this.input = input;\n        this.options = options;\n    }\n    async iterator() {\n        if (isLocalPath(this.input) && env().get('IS_NODE')) {\n            // tslint:disable-next-line:no-require-imports\n            const fs = require('fs');\n            this.input = fs.readFileSync(this.input.substr(7));\n        }\n        // TODO(kangyizhang): Add LocalFileChunkIterator to split local streaming\n        // with file in browser.\n        return new FileChunkIterator(this.input, this.options);\n    }\n}\n//# sourceMappingURL=file_data_source.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { DataSource } from '../datasource';\nimport { urlChunkIterator } from '../iterators/url_chunk_iterator';\nimport { isLocalPath } from '../util/source_util';\nimport { FileDataSource } from './file_data_source';\n/*\n * Represents a URL readable as a stream of binary data chunks.\n */\nexport class URLDataSource extends DataSource {\n    /**\n     * Create a `URLDataSource`.\n     *\n     * @param url A source URL string, or a `Request` object.\n     * @param options Options passed to the underlying `FileChunkIterator`s,\n     *   such as {chunksize: 1024}.\n     */\n    constructor(url, fileOptions = {}) {\n        super();\n        this.url = url;\n        this.fileOptions = fileOptions;\n    }\n    // TODO(soergel): provide appropriate caching options.  Currently this\n    // will download the URL anew for each call to iterator().  Since we have\n    // to treat the downloaded file as a blob/buffer anyway, we may as well retain\n    // it-- but that raises GC issues.  Also we may want a persistent disk cache.\n    async iterator() {\n        if (isLocalPath(this.url)) {\n            return (new FileDataSource(this.url, this.fileOptions))\n                .iterator();\n        }\n        else {\n            return urlChunkIterator(this.url, this.fileOptions);\n        }\n    }\n}\n//# sourceMappingURL=url_data_source.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { datasetFromIteratorFn } from './dataset';\nimport { CSVDataset } from './datasets/csv_dataset';\nimport { iteratorFromFunction } from './iterators/lazy_iterator';\nimport { MicrophoneIterator } from './iterators/microphone_iterator';\nimport { WebcamIterator } from './iterators/webcam_iterator';\nimport { URLDataSource } from './sources/url_data_source';\n/**\n * Create a `CSVDataset` by reading and decoding CSV file(s) from provided URL\n * or local path if it's in Node environment.\n *\n * Note: If isLabel in columnConfigs is `true` for at least one column, the\n * element in returned `CSVDataset` will be an object of\n * `{xs:features, ys:labels}`: xs is a dict of features key/value pairs, ys\n * is a dict of labels key/value pairs. If no column is marked as label,\n * returns a dict of features only.\n *\n * ```js\n * const csvUrl =\n * 'https://storage.googleapis.com/tfjs-examples/multivariate-linear-regression/data/boston-housing-train.csv';\n *\n * async function run() {\n *   // We want to predict the column \"medv\", which represents a median value of\n *   // a home (in $1000s), so we mark it as a label.\n *   const csvDataset = tf.data.csv(\n *     csvUrl, {\n *       columnConfigs: {\n *         medv: {\n *           isLabel: true\n *         }\n *       }\n *     });\n *\n *   // Number of features is the number of column names minus one for the label\n *   // column.\n *   const numOfFeatures = (await csvDataset.columnNames()).length - 1;\n *\n *   // Prepare the Dataset for training.\n *   const flattenedDataset =\n *     csvDataset\n *     .map(({xs, ys}) =>\n *       {\n *         // Convert xs(features) and ys(labels) from object form (keyed by\n *         // column name) to array form.\n *         return {xs:Object.values(xs), ys:Object.values(ys)};\n *       })\n *     .batch(10);\n *\n *   // Define the model.\n *   const model = tf.sequential();\n *   model.add(tf.layers.dense({\n *     inputShape: [numOfFeatures],\n *     units: 1\n *   }));\n *   model.compile({\n *     optimizer: tf.train.sgd(0.000001),\n *     loss: 'meanSquaredError'\n *   });\n *\n *   // Fit the model using the prepared Dataset\n *   return model.fitDataset(flattenedDataset, {\n *     epochs: 10,\n *     callbacks: {\n *       onEpochEnd: async (epoch, logs) => {\n *         console.log(epoch + ':' + logs.loss);\n *       }\n *     }\n *   });\n * }\n *\n * await run();\n * ```\n *\n * @param source URL or local path to get CSV file. If it's a local path, it\n * must have prefix `file://` and it only works in node environment.\n * @param csvConfig (Optional) A CSVConfig object that contains configurations\n *     of reading and decoding from CSV file(s).\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   configParamIndices: [1]\n *  }\n */\nexport function csv(source, csvConfig = {}) {\n    return new CSVDataset(new URLDataSource(source), csvConfig);\n}\n/**\n * Create a `Dataset` that produces each element by calling a provided function.\n *\n * Note that repeated iterations over this `Dataset` may produce different\n * results, because the function will be called anew for each element of each\n * iteration.\n *\n * Also, beware that the sequence of calls to this function may be out of order\n * in time with respect to the logical order of the Dataset. This is due to the\n * asynchronous lazy nature of stream processing, and depends on downstream\n * transformations (e.g. .shuffle()). If the provided function is pure, this is\n * no problem, but if it is a closure over a mutable state (e.g., a traversal\n * pointer), then the order of the produced elements may be scrambled.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const ds = tf.data.func(func);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * @param f A function that produces one data element on each call.\n */\nexport function func(f) {\n    const iter = iteratorFromFunction(f);\n    return datasetFromIteratorFn(async () => iter);\n}\n/**\n * Create a `Dataset` that produces each element from provided JavaScript\n * generator, which is a function*\n * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions),\n * or a function that returns an\n * iterator\n * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions).\n *\n * The returned iterator should have `.next()` function that returns element in\n * format of `{value: TensorContainer, done:boolean}`.\n *\n * Example of creating a dataset from an iterator factory:\n * ```js\n * function makeIterator() {\n *   const numElements = 10;\n *   let index = 0;\n *\n *   const iterator = {\n *     next: () => {\n *       let result;\n *       if (index < numElements) {\n *         result = {value: index, done: false};\n *         index++;\n *         return result;\n *       }\n *       return {value: index, done: true};\n *     }\n *   };\n *   return iterator;\n * }\n * const ds = tf.data.generator(makeIterator);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * Example of creating a dataset from a generator:\n * ```js\n * function* dataGenerator() {\n *   const numElements = 10;\n *   let index = 0;\n *   while (index < numElements) {\n *     const x = index;\n *     index++;\n *     yield x;\n *   }\n * }\n *\n * const ds = tf.data.generator(dataGenerator);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * @param generator A Javascript generator function that returns a JavaScript\n *     iterator.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   configParamIndices: [1]\n *  }\n */\nexport function generator(generator) {\n    return datasetFromIteratorFn(async () => {\n        const gen = await generator();\n        return iteratorFromFunction(() => gen.next());\n    });\n}\n/**\n * Create an iterator that generate `Tensor`s from webcam video stream. This API\n * only works in Browser environment when the device has webcam.\n *\n * Note: this code snippet only works when the device has a webcam. It will\n * request permission to open the webcam when running.\n * ```js\n * const videoElement = document.createElement('video');\n * videoElement.width = 100;\n * videoElement.height = 100;\n * const cam = await tf.data.webcam(videoElement);\n * const img = await cam.capture();\n * img.print();\n * cam.stop();\n * ```\n *\n * @param webcamVideoElement A `HTMLVideoElement` used to play video from\n *     webcam. If this element is not provided, a hidden `HTMLVideoElement` will\n *     be created. In that case, `resizeWidth` and `resizeHeight` must be\n *     provided to set the generated tensor shape.\n * @param webcamConfig A `WebcamConfig` object that contains configurations of\n *     reading and manipulating data from webcam video stream.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   ignoreCI: true\n *  }\n */\nexport async function webcam(webcamVideoElement, webcamConfig) {\n    return WebcamIterator.create(webcamVideoElement, webcamConfig);\n}\n/**\n * Create an iterator that generate frequency-domain spectrogram `Tensor`s from\n * microphone audio stream with browser's native FFT. This API only works in\n * browser environment when the device has microphone.\n *\n * Note: this code snippet only works when the device has a microphone. It will\n * request permission to open the microphone when running.\n * ```js\n * const mic = await tf.data.microphone({\n *   fftSize: 1024,\n *   columnTruncateLength: 232,\n *   numFramesPerSpectrogram: 43,\n *   sampleRateHz:44100,\n *   includeSpectrogram: true,\n *   includeWaveform: true\n * });\n * const audioData = await mic.capture();\n * const spectrogramTensor = audioData.spectrogram;\n * spectrogramTensor.print();\n * const waveformTensor = audioData.waveform;\n * waveformTensor.print();\n * mic.stop();\n * ```\n *\n * @param microphoneConfig A `MicrophoneConfig` object that contains\n *     configurations of reading audio data from microphone.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   ignoreCI: true\n *  }\n */\nexport async function microphone(microphoneConfig) {\n    return MicrophoneIterator.create(microphoneConfig);\n}\n//# sourceMappingURL=readers.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { array, Dataset, zip } from './dataset';\nexport { CSVDataset } from './datasets/csv_dataset';\nexport { TextLineDataset } from './datasets/text_line_dataset';\nexport { csv, func, generator, microphone, webcam } from './readers';\nexport { FileDataSource } from './sources/file_data_source';\nexport { URLDataSource } from './sources/url_data_source';\nexport { version as version_data } from './version';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function assertNotComplex(tensor, opName) {\n    if (!Array.isArray(tensor)) {\n        tensor = [tensor];\n    }\n    tensor.forEach(t => {\n        if (t != null) {\n            util.assert(t.dtype !== 'complex64', () => `${opName} does not support complex64 tensors in the CPU backend.`);\n        }\n    });\n}\n//# sourceMappingURL=cpu_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { backend_util, DataStorage, engine, env, kernel_impls, KernelBackend, max, slice_util, TensorBuffer, upcastType, util } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV3Impl = kernel_impls.nonMaxSuppressionV3Impl;\nconst split = kernel_impls.split;\nconst tile = kernel_impls.tile;\nconst topkImpl = kernel_impls.topkImpl;\nconst whereImpl = kernel_impls.whereImpl;\nimport * as seedrandom from 'seedrandom';\nimport { assertNotComplex } from './cpu_util';\nfunction mapActivation(backend, x, activation, preluActivationWeights) {\n    if (activation === 'linear') {\n        return backend.linear(x);\n    }\n    else if (activation === 'relu') {\n        return backend.relu(x);\n    }\n    else if (activation === 'elu') {\n        return tf.elu(x);\n    }\n    else if (activation === 'relu6') {\n        return backend.relu6(x);\n    }\n    else if (activation === 'prelu') {\n        return backend.prelu(x, preluActivationWeights);\n    }\n    throw new Error(`Activation ${activation} has not been implemented for the CPU backend.`);\n}\nexport class MathBackendCPU extends KernelBackend {\n    constructor() {\n        super();\n        this.blockSize = 48;\n        this.firstUse = true;\n        this.data = new DataStorage(this, engine());\n    }\n    write(values, shape, dtype) {\n        if (this.firstUse) {\n            this.firstUse = false;\n            if (env().get('IS_NODE')) {\n                backend_util.warn('\\n============================\\n' +\n                    'Hi there \uD83D\uDC4B. Looks like you are running TensorFlow.js in ' +\n                    'Node.js. To speed things up dramatically, install our node ' +\n                    'backend, which binds to TensorFlow C++, by running ' +\n                    'npm i @tensorflow/tfjs-node, ' +\n                    'or npm i @tensorflow/tfjs-node-gpu if you have CUDA. ' +\n                    'Then call require(\\'@tensorflow/tfjs-node\\'); (-gpu ' +\n                    'suffix for CUDA) at the start of your program. ' +\n                    'Visit https://github.com/tensorflow/tfjs-node for more details.' +\n                    '\\n============================');\n            }\n        }\n        const dataId = {};\n        this.data.set(dataId, { values, dtype, refCount: 1 });\n        return dataId;\n    }\n    /**\n     * Create a data bucket in cpu backend.\n     * @param shape Shape of the `TensorInfo`.\n     * @param dtype DType of the `TensorInfo`.\n     * @param values The value of the `TensorInfo` stored as a flattened array.\n     */\n    makeTensorInfo(shape, dtype, values) {\n        const outId = this.write(values, shape, dtype);\n        return { dataId: outId, shape, dtype };\n    }\n    /** Increase refCount of a `TensorData`. */\n    incRef(dataId) {\n        const tensorData = this.data.get(dataId);\n        tensorData.refCount++;\n    }\n    /** Decrease refCount of a `TensorData`. */\n    decRef(dataId) {\n        if (this.data.has(dataId)) {\n            const tensorData = this.data.get(dataId);\n            tensorData.refCount--;\n        }\n    }\n    move(dataId, values, shape, dtype) {\n        this.data.set(dataId, { values, dtype, refCount: 1 });\n    }\n    numDataIds() {\n        return this.data.numDataIds();\n    }\n    async read(dataId) {\n        return this.readSync(dataId);\n    }\n    readSync(dataId) {\n        const { dtype, complexTensorInfos } = this.data.get(dataId);\n        if (dtype === 'complex64') {\n            const realValues = this.readSync(complexTensorInfos.real.dataId);\n            const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n            return backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        return this.data.get(dataId).values;\n    }\n    bufferSync(t) {\n        const data = this.readSync(t.dataId);\n        let decodedData = data;\n        if (t.dtype === 'string') {\n            try {\n                // Decode the bytes into string.\n                decodedData = data.map(d => util.decodeString(d));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode encoded string bytes into utf-8');\n            }\n        }\n        return tf.buffer(t.shape, t.dtype, decodedData);\n    }\n    makeOutput(values, shape, dtype) {\n        const dataId = this.write(values, shape, dtype);\n        return engine().makeTensorFromDataId(dataId, shape, dtype, this);\n    }\n    disposeData(dataId) {\n        if (this.data.has(dataId)) {\n            const { complexTensorInfos } = this.data.get(dataId);\n            if (complexTensorInfos != null) {\n                this.disposeData(complexTensorInfos.real.dataId);\n                this.disposeData(complexTensorInfos.imag.dataId);\n            }\n            this.data.delete(dataId);\n        }\n    }\n    disposeIntermediateTensorInfo(tensorInfo) {\n        const dataId = tensorInfo.dataId;\n        if (this.data.has(dataId)) {\n            const tensorData = this.data.get(dataId);\n            tensorData.refCount--;\n            if (tensorData.refCount < 1) {\n                this.disposeData(dataId);\n            }\n        }\n    }\n    async time(f) {\n        const start = util.now();\n        f();\n        const kernelMs = util.now() - start;\n        return { kernelMs };\n    }\n    memory() {\n        return {\n            // Unreliable due to automatic gc. The numbers above are cumulative.\n            unreliable: true,\n            reasons: ['The reported memory is an upper bound. Due to automatic garbage ' +\n                    'collection, the true allocated memory may be less.']\n        };\n    }\n    stridedSlice(x, begin, end, strides) {\n        assertNotComplex(x, 'stridedSlice');\n        const outShape = slice_util.computeOutShape(begin, end, strides);\n        if (outShape.some(axis => axis === 0)) {\n            return tf.tensor([], outShape);\n        }\n        const buffer = tf.buffer(outShape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < buffer.size; i++) {\n            const loc = buffer.indexToLoc(i);\n            const newLoc = new Array(loc.length);\n            for (let j = 0; j < newLoc.length; j++) {\n                newLoc[j] = loc[j] * strides[j] + begin[j];\n            }\n            buffer.set(xBuf.get(...newLoc), ...loc);\n        }\n        return buffer.toTensor();\n    }\n    diag(x) {\n        const xVals = this.readSync(x.dataId);\n        const buffer = tf.buffer([x.size, x.size], x.dtype);\n        const vals = buffer.values;\n        for (let i = 0; i < xVals.length; i++) {\n            vals[i * x.size + i] = xVals[i];\n        }\n        return buffer.toTensor();\n    }\n    unstack(x, axis) {\n        const num = x.shape[axis];\n        const outShape = new Array(x.rank - 1);\n        let outIndex = 0;\n        for (let i = 0; i < x.rank; i++) {\n            if (i !== axis) {\n                outShape[outIndex++] = x.shape[i];\n            }\n        }\n        const begin = new Array(x.rank).fill(0);\n        const size = x.shape.slice();\n        size[axis] = 1;\n        const res = new Array(num);\n        for (let i = 0; i < res.length; i++) {\n            begin[axis] = i;\n            res[i] = tf.slice(x, begin, size).reshape(outShape);\n        }\n        return res;\n    }\n    reverse(x, axis) {\n        assertNotComplex(x, 'reverse');\n        const buffer = tf.buffer(x.shape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < buffer.size; i++) {\n            const outLoc = buffer.indexToLoc(i);\n            const inLoc = outLoc.slice();\n            axis.forEach(ax => inLoc[ax] = x.shape[ax] - 1 - inLoc[ax]);\n            buffer.set(xBuf.get(...inLoc), ...outLoc);\n        }\n        return buffer.toTensor();\n    }\n    neg(x) {\n        assertNotComplex(x, 'neg');\n        // TODO(lina128): Use mul directly once neg is modularized.\n        return tf.mul(tf.scalar(-1), x);\n    }\n    addN(tensors) {\n        assertNotComplex(tensors, 'addN');\n        const vals = tensors.map(t => this.readSync(t.dataId));\n        const result = tf.buffer(tensors[0].shape, tensors[0].dtype);\n        const resultVals = result.values;\n        for (let i = 0; i < tensors.length; i++) {\n            const currVals = vals[i];\n            for (let j = 0; j < resultVals.length; j++) {\n                resultVals[j] += currVals[j];\n            }\n        }\n        return result.toTensor();\n    }\n    softmax(logits, dim) {\n        const axes = util.parseAxisParam([dim], logits.shape);\n        // TODO(annxingyuan): Call maxImpl rather than op as part of softmax kernel\n        // modularization.\n        const maxLogit = max(logits, axes);\n        const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n        // TODO(lina128): Use sub directly once softmax is modularized.\n        const a = tf.sub(logits, maxLogit.reshape(expandedShape));\n        const b = tf.exp(a);\n        const sumExp = this.sum(b, axes).reshape(expandedShape);\n        // TODO(annxingyuan): Call divImpl rather than op as part of softmax\n        // kernel modularization.\n        return tf.div(b, sumExp);\n    }\n    pow(a, b) {\n        assertNotComplex([a, b], 'pow');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aValue, bValue) => Math.pow(aValue, bValue));\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        assertNotComplex([a, b], 'matMul');\n        const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n        const leftDim = transposeA ? a.shape[2] : a.shape[1];\n        const rightDim = transposeB ? b.shape[1] : b.shape[2];\n        const batchDim = a.shape[0];\n        const aValues = this.readSync(a.dataId);\n        const bValues = this.readSync(b.dataId);\n        const [aBatch, aOuterStep, aInnerStep] = transposeA ?\n            [a.strides[0], 1, a.strides[1]] :\n            [a.strides[0], a.strides[1], 1];\n        const [bInnerStep, bOuterStep, bBatch] = transposeB ?\n            [1, b.strides[1], b.strides[0]] :\n            [b.strides[1], 1, b.strides[0]];\n        const size = leftDim * rightDim;\n        const result = tf.buffer([batchDim, leftDim, rightDim], a.dtype);\n        const resVals = result.values;\n        const blockSize = this.blockSize;\n        for (let b = 0; b < batchDim; b++) {\n            for (let i0 = 0; i0 < leftDim; i0 += blockSize) {\n                for (let j0 = 0; j0 < rightDim; j0 += blockSize) {\n                    for (let k0 = 0; k0 < sharedDim; k0 += blockSize) {\n                        // for when blockSize doesn't evenly divide the input\n                        const iBlock = Math.min(i0 + blockSize, leftDim);\n                        const jBlock = Math.min(j0 + blockSize, rightDim);\n                        const kBlock = Math.min(k0 + blockSize, sharedDim);\n                        for (let i = i0; i < iBlock; i++) {\n                            for (let j = j0; j < jBlock; j++) {\n                                let sum = 0.0;\n                                for (let k = k0; k < kBlock; k++) {\n                                    sum += aValues[b * aBatch + i * aOuterStep + k * aInnerStep] *\n                                        bValues[k * bInnerStep + j * bOuterStep + b * bBatch];\n                                }\n                                resVals[b * size + (i * rightDim + j)] += sum;\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return result.toTensor();\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        let result = this.batchMatMul(a, b, transposeA, transposeB);\n        if (bias) {\n            // TODO(lina128): Use add directly once fusedBatchMatMul is modularized.\n            result = tf.add(result, bias);\n        }\n        if (activation) {\n            result =\n                mapActivation(this, result, activation, preluActivationWeights);\n        }\n        return result;\n    }\n    floorDiv(a, b) {\n        assertNotComplex([a, b], 'floorDiv');\n        const op = (a, b) => Math.floor(a / b);\n        const outputDtype = 'int32';\n        return this.broadcastedBinaryOp(a, b, outputDtype, op);\n    }\n    sum(x, axes) {\n        assertNotComplex(x, 'sum');\n        backend_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(outShape, resultDtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let sum = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                sum += aVals[offset + j];\n            }\n            vals[i] = sum;\n        }\n        return result;\n    }\n    prod(x, axes) {\n        assertNotComplex(x, 'sum');\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(outShape, resultDtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let prod = 1;\n            for (let j = 0; j < reduceSize; ++j) {\n                prod *= aVals[offset + j];\n            }\n            vals[i] = prod;\n        }\n        return result;\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        assertNotComplex(x, 'unsortedSegmentSum');\n        const res = [];\n        // Reshape the segment id's so that they can be broadcast with\n        // x. The new shape should be [segmentIds.shape, 1, ..., 1]\n        const numIters = x.rank - segmentIds.rank;\n        for (let i = 0; i < numIters; ++i) {\n            segmentIds = segmentIds.expandDims(i + 1);\n        }\n        for (let i = 0; i < numSegments; ++i) {\n            const segmentId = tf.scalar(i, 'int32');\n            const mask = tf.equal(segmentId, segmentIds).asType('float32');\n            const sum = mask.mul(x).sum(0);\n            res.push(sum);\n        }\n        return tf.stack(res);\n    }\n    argMin(x, axis) {\n        assertNotComplex(x, 'argMin');\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('argMin', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, 'int32');\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let min = aVals[offset];\n            let minIndex = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value < min) {\n                    min = value;\n                    minIndex = j;\n                }\n            }\n            vals[i] = minIndex;\n        }\n        return result;\n    }\n    argMax(x, axis) {\n        assertNotComplex(x, 'argMax');\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('argMax', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, 'int32');\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let max = aVals[offset];\n            let maxIndex = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value > max) {\n                    max = value;\n                    maxIndex = j;\n                }\n            }\n            vals[i] = maxIndex;\n        }\n        return result;\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        assertNotComplex(x, 'cumsum');\n        if (axis !== x.rank - 1) {\n            throw new Error(`backend.cumsum in CPU expects an inner-most axis=${x.rank - 1} ` +\n                `but got axis=${axis}`);\n        }\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(x.shape, resultDtype);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        const finalDim = x.shape[x.rank - 1];\n        const indexAdjuster = reverse ?\n            (i, j) => i + finalDim - j - 1 :\n            (i, j) => i + j;\n        for (let i = 0; i < aVals.length; i += finalDim) {\n            for (let j = 0; j < finalDim; j++) {\n                const idx = indexAdjuster(i, j);\n                if (j === 0) {\n                    vals[idx] = exclusive ? 0 : aVals[idx];\n                }\n                else {\n                    const prevIdx = indexAdjuster(i, j - 1);\n                    vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :\n                        aVals[idx] + vals[prevIdx];\n                }\n            }\n        }\n        return result;\n    }\n    equal(a, b) {\n        assertNotComplex([a, b], 'equal');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal === bVal) ? 1 : 0;\n        });\n    }\n    notEqual(a, b) {\n        assertNotComplex([a, b], 'notEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal !== bVal) ? 1 : 0;\n        });\n    }\n    less(a, b) {\n        assertNotComplex([a, b], 'less');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal < bVal) ? 1 : 0;\n        });\n    }\n    lessEqual(a, b) {\n        assertNotComplex([a, b], 'lessEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal <= bVal) ? 1 : 0;\n        });\n    }\n    greater(a, b) {\n        assertNotComplex([a, b], 'greater');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal > bVal) ? 1 : 0;\n        });\n    }\n    greaterEqual(a, b) {\n        assertNotComplex([a, b], 'greaterEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal >= bVal) ? 1 : 0;\n        });\n    }\n    logicalAnd(a, b) {\n        assertNotComplex([a, b], 'logicalAnd');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return aVal && bVal;\n        });\n    }\n    logicalOr(a, b) {\n        assertNotComplex([a, b], 'logicalOr');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return aVal || bVal;\n        });\n    }\n    select(condition, a, b) {\n        assertNotComplex([condition, a, b], 'select');\n        const values = this.readSync(condition.dataId);\n        const aValues = this.readSync(a.dataId);\n        const bValues = this.readSync(b.dataId);\n        const result = tf.zeros(a.shape, upcastType(a.dtype, b.dtype));\n        const newValues = this.readSync(result.dataId);\n        let index = 0;\n        const offset = condition.rank === 0 || condition.rank > 1 || a.rank === 1 ?\n            1 :\n            util.sizeFromShape(a.shape.slice(1));\n        for (let i = 0; i < values.length; i++) {\n            for (let j = 0; j < offset; j++) {\n                if (values[i] === 1) {\n                    newValues[index++] = aValues[i];\n                }\n                else {\n                    newValues[index++] = bValues[i];\n                }\n            }\n        }\n        return result;\n    }\n    where(condition) {\n        assertNotComplex([condition], 'where');\n        const condVals = this.readSync(condition.dataId);\n        return whereImpl(condition.shape, condVals);\n    }\n    topk(x, k, sorted) {\n        assertNotComplex(x, 'topk');\n        const xVals = this.readSync(x.dataId);\n        return topkImpl(xVals, x.shape, x.dtype, k, sorted);\n    }\n    min(x, axes) {\n        assertNotComplex(x, 'min');\n        backend_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let min = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value < min) {\n                    min = value;\n                }\n            }\n            vals[i] = min;\n        }\n        return result;\n    }\n    minimum(a, b) {\n        assertNotComplex([a, b], 'minimum');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => Math.min(aVal, bVal));\n    }\n    mod(a, b) {\n        assertNotComplex([a, b], 'mod');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n            const rem = aVal % bVal;\n            if ((aVal < 0 && bVal < 0) || (aVal >= 0 && bVal >= 0)) {\n                return rem;\n            }\n            else {\n                return (rem + bVal) % bVal;\n            }\n        });\n    }\n    maximum(a, b) {\n        assertNotComplex([a, b], 'maximum');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => Math.max(aVal, bVal));\n    }\n    all(x, axes) {\n        assertNotComplex(x, 'all');\n        backend_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let all = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                all = all && value;\n            }\n            vals[i] = all;\n        }\n        return result;\n    }\n    any(x, axes) {\n        assertNotComplex(x, 'any');\n        backend_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let anyVal = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                anyVal = anyVal || value;\n            }\n            vals[i] = anyVal;\n        }\n        return result;\n    }\n    squaredDifference(a, b) {\n        assertNotComplex([a, b], 'squaredDifference');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n            const diff = aVal - bVal;\n            return diff * diff;\n        });\n    }\n    linear(x) {\n        return x;\n    }\n    relu(x) {\n        assertNotComplex(x, 'relu');\n        const res = tf.zeros(x.shape, x.dtype);\n        const resVals = this.readSync(res.dataId);\n        const inVals = this.readSync(x.dataId);\n        for (let i = 0; i < inVals.length; ++i) {\n            resVals[i] = Math.max(0, inVals[i]);\n        }\n        return res;\n    }\n    relu6(x) {\n        assertNotComplex(x, 'relu');\n        const res = tf.zeros(x.shape, x.dtype);\n        const resVals = this.readSync(res.dataId);\n        const inVals = this.readSync(x.dataId);\n        for (let i = 0; i < inVals.length; ++i) {\n            resVals[i] = Math.min(Math.max(0, inVals[i]), 6);\n        }\n        return res;\n    }\n    prelu(x, a) {\n        assertNotComplex([x, a], 'prelu');\n        return this.broadcastedBinaryOp(x, a, x.dtype, (xValue, aValue) => xValue < 0 ? aValue * xValue : xValue);\n    }\n    eluDer(dy, y) {\n        assertNotComplex([dy, y], 'eluDer');\n        const resultValues = new Float32Array(y.size);\n        const values = this.readSync(y.dataId);\n        const dyValues = this.readSync(dy.dataId);\n        for (let i = 0; i < values.length; ++i) {\n            const v = values[i];\n            if (v >= 1) {\n                resultValues[i] = dyValues[i];\n            }\n            else {\n                resultValues[i] = dyValues[i] * (v + 1);\n            }\n        }\n        return this.makeOutput(resultValues, y.shape, 'float32');\n    }\n    atan2(a, b) {\n        assertNotComplex([a, b], 'atan2');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aValue, bValue) => Math.atan2(aValue, bValue));\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        let result = this.conv2d(input, filter, convInfo);\n        if (bias) {\n            // TODO(lina128): Use add directly once fusedConv2d is modularized.\n            result = tf.add(result, bias);\n        }\n        if (activation) {\n            result =\n                mapActivation(this, result, activation, preluActivationWeights);\n        }\n        return result;\n    }\n    conv2d(x, filter, convInfo) {\n        assertNotComplex([x, filter], 'conv2d');\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const padLeft = convInfo.padInfo.left;\n        const padTop = convInfo.padInfo.top;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const y = tf.buffer(convInfo.outShape, x.dtype);\n        const xBatchStride = x.strides[0];\n        const xRowStride = isChannelsLast ? x.strides[1] : x.strides[2];\n        const xColStride = isChannelsLast ? x.strides[2] : 1;\n        const xChannelStride = isChannelsLast ? 1 : x.strides[1];\n        const yBatchStride = y.strides[0];\n        const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];\n        const yColStride = isChannelsLast ? y.strides[2] : 1;\n        const yChannelStride = isChannelsLast ? 1 : y.strides[1];\n        const xVals = this.readSync(x.dataId);\n        const wVals = this.readSync(filter.dataId);\n        const yVals = y.values;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n            const xOffset1 = b * xBatchStride;\n            const yOffset1 = b * yBatchStride;\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const yOffset2 = yOffset1 + yR * yRowStride;\n                const xRCorner = yR * convInfo.strideHeight - padTop;\n                for (let wR = 0; wR < filterHeight; wR++) {\n                    const xR = xRCorner + wR * dilationHeight;\n                    if (xR < 0 || xR >= convInfo.inHeight) {\n                        continue;\n                    }\n                    const wOffset1 = wR * filter.strides[0];\n                    const xOffset2 = xOffset1 + xR * xRowStride;\n                    for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                        const yOffset3 = yOffset2 + yC * yColStride;\n                        const xCCorner = yC * convInfo.strideWidth - padLeft;\n                        for (let wC = 0; wC < filterWidth; wC++) {\n                            const xC = xCCorner + wC * dilationWidth;\n                            if (xC < 0 || xC >= convInfo.inWidth) {\n                                continue;\n                            }\n                            const wOffset2 = wOffset1 + wC * filter.strides[1];\n                            const xOffset3 = xOffset2 + xC * xColStride;\n                            let wOffset3 = wOffset2;\n                            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                const xVal = xVals[xOffset3 + d1 * xChannelStride];\n                                for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                                    yVals[yOffset3 + d2 * yChannelStride] +=\n                                        xVal * wVals[wOffset3 + d2];\n                                }\n                                wOffset3 += convInfo.outChannels;\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return y.toTensor();\n    }\n    conv3d(x, filter, convInfo) {\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const padFront = convInfo.padInfo.front;\n        const padLeft = convInfo.padInfo.left;\n        const padTop = convInfo.padInfo.top;\n        const y = tf.buffer(convInfo.outShape, x.dtype);\n        const xVals = this.readSync(x.dataId);\n        const wVals = this.readSync(filter.dataId);\n        const yVals = y.values;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n            const xOffset1 = b * x.strides[0];\n            const yOffset1 = b * y.strides[0];\n            for (let yF = 0; yF < convInfo.outDepth; ++yF) {\n                const yOffset2 = yOffset1 + yF * y.strides[1];\n                const xFCorner = yF * convInfo.strideDepth - padFront;\n                for (let wF = 0; wF < filterDepth; wF++) {\n                    const xF = xFCorner + wF * dilationDepth;\n                    if (xF < 0 || xF >= convInfo.inDepth) {\n                        continue;\n                    }\n                    const wOffset1 = wF * filter.strides[0];\n                    const xOffset2 = xOffset1 + xF * x.strides[1];\n                    for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                        const yOffset3 = yOffset2 + yR * y.strides[2];\n                        const xRCorner = yR * convInfo.strideHeight - padTop;\n                        for (let wR = 0; wR < filterHeight; wR++) {\n                            const xR = xRCorner + wR * dilationHeight;\n                            if (xR < 0 || xR >= convInfo.inHeight) {\n                                continue;\n                            }\n                            const wOffset2 = wOffset1 + wR * filter.strides[1];\n                            const xOffset3 = xOffset2 + xR * x.strides[2];\n                            for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                                const yOffset4 = yOffset3 + yC * convInfo.outChannels;\n                                const xCCorner = yC * convInfo.strideWidth - padLeft;\n                                for (let wC = 0; wC < filterWidth; wC++) {\n                                    const xC = xCCorner + wC * dilationWidth;\n                                    if (xC < 0 || xC >= convInfo.inWidth) {\n                                        continue;\n                                    }\n                                    const wOffset3 = wOffset2 + wC * filter.strides[2];\n                                    const xOffset4 = xOffset3 + xC * convInfo.inChannels;\n                                    let wOffset4 = wOffset3;\n                                    for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                        const xVal = xVals[xOffset4 + d1];\n                                        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                                            yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];\n                                        }\n                                        wOffset4 += convInfo.outChannels;\n                                    }\n                                }\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return y.toTensor();\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        assertNotComplex([dy, filter], 'conv2dDerInput');\n        const dx = tf.buffer(convInfo.inShape, 'float32');\n        const dxValues = dx.values;\n        const dyValues = this.readSync(dy.dataId);\n        const fltValues = this.readSync(filter.dataId);\n        const [fltS0, fltS1, fltS2] = filter.strides;\n        const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth, dataFormat } = convInfo;\n        const topPad = filterHeight - 1 - convInfo.padInfo.top;\n        const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const xBatchStride = dx.strides[0];\n        const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];\n        const xColStride = isChannelsLast ? dx.strides[2] : 1;\n        const xChannelStride = isChannelsLast ? 1 : dx.strides[1];\n        const yBatchStride = dy.strides[0];\n        const yRowStride = isChannelsLast ? dy.strides[1] : dy.strides[2];\n        const yColStride = isChannelsLast ? dy.strides[2] : 1;\n        const yChannelStride = isChannelsLast ? 1 : dy.strides[1];\n        for (let b = 0; b < batchSize; ++b) {\n            for (let d1 = 0; d1 < inChannels; ++d1) {\n                for (let xR = 0; xR < inHeight; ++xR) {\n                    const xRCorner = xR - topPad;\n                    const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                    const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                    for (let xC = 0; xC < inWidth; ++xC) {\n                        const xCCorner = xC - leftPad;\n                        const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                        const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                        let dotProd = 0;\n                        for (let yR = xRMin; yR < yRMax; ++yR) {\n                            const wR = yR * strideHeight - xRCorner;\n                            for (let yC = xCMin; yC < yCMax; ++yC) {\n                                const wC = yC * strideWidth - xCCorner;\n                                const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;\n                                const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n                                for (let d2 = 0; d2 < outChannels; ++d2) {\n                                    const pixel = dyValues[dyOffset + yChannelStride * d2];\n                                    const weight = fltValues[fltOffset + d2];\n                                    dotProd += pixel * weight;\n                                }\n                            }\n                        }\n                        const dxOffset = xBatchStride * b + xRowStride * xR +\n                            xColStride * xC + xChannelStride * d1;\n                        dxValues[dxOffset] = dotProd;\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        const dx = tf.buffer(convInfo.inShape, 'float32');\n        const dxValues = dx.values;\n        const [dxS0, dxS1, dxS2, dxS3] = dx.strides;\n        const dyValues = this.readSync(dy.dataId);\n        const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n        const fltValues = this.readSync(filter.dataId);\n        const [fltS0, fltS1, fltS2, fltS3] = filter.strides;\n        const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo;\n        const frontPad = filterDepth - 1 - convInfo.padInfo.front;\n        const topPad = filterHeight - 1 - convInfo.padInfo.top;\n        const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let d1 = 0; d1 < inChannels; ++d1) {\n                // Frames of depth\n                for (let xF = 0; xF < inDepth; ++xF) {\n                    const xFCorner = xF - frontPad;\n                    const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));\n                    const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);\n                    // Rows as per standard 2d matrix notation\n                    for (let xR = 0; xR < inHeight; ++xR) {\n                        const xRCorner = xR - topPad;\n                        const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                        const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                        // Columns as per standard 2d matrix notation\n                        for (let xC = 0; xC < inWidth; ++xC) {\n                            const xCCorner = xC - leftPad;\n                            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                            const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                            let dotProd = 0;\n                            for (let yF = xFMin; yF < yFMax; ++yF) {\n                                const wF = yF * strideDepth - xFCorner;\n                                for (let yR = xRMin; yR < yRMax; ++yR) {\n                                    const wR = yR * strideHeight - xRCorner;\n                                    for (let yC = xCMin; yC < yCMax; ++yC) {\n                                        const wC = yC * strideWidth - xCCorner;\n                                        const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;\n                                        const fltOffset = fltS0 * (filterDepth - 1 - wF) +\n                                            fltS1 * (filterHeight - 1 - wR) +\n                                            fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;\n                                        for (let d2 = 0; d2 < outChannels; ++d2) {\n                                            const pixel = dyValues[dyOffset + d2];\n                                            const weight = fltValues[fltOffset + d2];\n                                            dotProd += pixel * weight;\n                                        }\n                                    }\n                                }\n                            }\n                            dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =\n                                dotProd;\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    conv2dDerFilter(x, dy, convInfo) {\n        assertNotComplex([x, dy], 'conv2dDerFilter');\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const dW = tf.buffer(convInfo.filterShape, 'float32');\n        const leftPad = convInfo.padInfo.left;\n        const topPad = convInfo.padInfo.top;\n        const xBuf = this.bufferSync(x);\n        const dyBuf = this.bufferSync(dy);\n        for (let wR = 0; wR < filterHeight; ++wR) {\n            const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n            const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n            for (let wC = 0; wC < filterWidth; ++wC) {\n                const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                    for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                        // Need to convolve.\n                        let dotProd = 0;\n                        for (let b = 0; b < convInfo.batchSize; ++b) {\n                            for (let yR = yRMin; yR < yRMax; ++yR) {\n                                const xR = wR + yR * strideHeight - topPad;\n                                for (let yC = yCMin; yC < yCMax; ++yC) {\n                                    const xC = wC + yC * strideWidth - leftPad;\n                                    if (isChannelsLast) {\n                                        dotProd +=\n                                            xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n                                    }\n                                    else {\n                                        dotProd +=\n                                            xBuf.get(b, d1, xR, xC) * dyBuf.get(b, d2, yR, yC);\n                                    }\n                                }\n                            }\n                        }\n                        dW.set(dotProd, wR, wC, d1, d2);\n                    }\n                }\n            }\n        }\n        return dW.toTensor();\n    }\n    conv3dDerFilter(x, dy, convInfo) {\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dw = tf.buffer(convInfo.filterShape, 'float32');\n        const dwValues = dw.values;\n        const [dwS0, dwS1, dwS2, dwS3] = dw.strides;\n        const dyValues = this.readSync(dy.dataId);\n        const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n        const xValues = this.readSync(x.dataId);\n        const [xS0, xS1, xS2, xS3] = x.strides;\n        const frontPad = convInfo.padInfo.front;\n        const leftPad = convInfo.padInfo.left;\n        const topPad = convInfo.padInfo.top;\n        for (let wF = 0; wF < filterDepth; ++wF) {\n            const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));\n            const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);\n            const wOffset1 = wF * dwS0;\n            for (let wR = 0; wR < filterHeight; ++wR) {\n                const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n                const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n                const wOffset2 = wR * dwS1 + wOffset1;\n                for (let wC = 0; wC < filterWidth; ++wC) {\n                    const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                    const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                    const wOffset3 = wC * dwS2 + wOffset2;\n                    for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                        const wOffset4 = d1 * dwS3 + wOffset3;\n                        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                            let dotProd = 0;\n                            for (let b = 0; b < convInfo.batchSize; ++b) {\n                                const xOffset1 = b * xS0;\n                                const yOffset1 = b * dyS0;\n                                for (let yF = yFMin; yF < yFMax; ++yF) {\n                                    const xF = wF + yF * strideDepth - frontPad;\n                                    const xOffset2 = xF * xS1 + xOffset1;\n                                    const yOffset2 = yF * dyS1 + yOffset1;\n                                    for (let yR = yRMin; yR < yRMax; ++yR) {\n                                        const xR = wR + yR * strideHeight - topPad;\n                                        const xOffset3 = xR * xS2 + xOffset2;\n                                        const yOffset3 = yR * dyS2 + yOffset2;\n                                        for (let yC = yCMin; yC < yCMax; ++yC) {\n                                            const xC = wC + yC * strideWidth - leftPad;\n                                            const xOffset4 = xC * xS3 + xOffset3;\n                                            const yOffset4 = yC * dyS3 + yOffset3;\n                                            dotProd +=\n                                                xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];\n                                        }\n                                    }\n                                }\n                            }\n                            dwValues[wOffset4 + d2] = dotProd;\n                        }\n                    }\n                }\n            }\n        }\n        return dw.toTensor();\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        let result = this.depthwiseConv2D(input, filter, convInfo);\n        if (bias) {\n            // TODO(lina128): Use add directly once fusedDepthwiseConv2D is\n            // modularized.\n            result = tf.add(result, bias);\n        }\n        if (activation) {\n            result =\n                mapActivation(this, result, activation, preluActivationWeights);\n        }\n        return result;\n    }\n    depthwiseConv2D(x, filter, convInfo) {\n        assertNotComplex([x, filter], 'depthwiseConv2D');\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const padLeft = convInfo.padInfo.left;\n        const padTop = convInfo.padInfo.top;\n        const chMul = convInfo.outChannels / convInfo.inChannels;\n        const y = tf.buffer(convInfo.outShape, x.dtype);\n        const xVals = this.readSync(x.dataId);\n        const wVals = this.readSync(filter.dataId);\n        const yVals = y.values;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n            const xOffset1 = b * x.strides[0];\n            const yOffset1 = b * y.strides[0];\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const yOffset2 = yOffset1 + yR * y.strides[1];\n                const xRCorner = yR * convInfo.strideHeight - padLeft;\n                for (let wR = 0; wR < filterHeight; ++wR) {\n                    const xR = xRCorner + wR * dilationHeight;\n                    if (xR < 0 || xR >= convInfo.inHeight) {\n                        continue;\n                    }\n                    const wOffset1 = wR * filter.strides[0];\n                    const xOffset2 = xOffset1 + xR * x.strides[1];\n                    for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                        const yOffset3 = yOffset2 + yC * y.strides[2];\n                        const xCCorner = yC * convInfo.strideWidth - padTop;\n                        for (let wC = 0; wC < filterWidth; ++wC) {\n                            const xC = xCCorner + wC * dilationWidth;\n                            if (xC < 0 || xC >= convInfo.inWidth) {\n                                continue;\n                            }\n                            const wOffset2 = wOffset1 + wC * filter.strides[1];\n                            const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n                            let yOffset4 = yOffset3;\n                            let wOffset3 = wOffset2;\n                            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                const xVal = xVals[xOffset3 + d1];\n                                for (let q = 0; q < chMul; ++q) {\n                                    yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];\n                                }\n                                yOffset4 += chMul;\n                                wOffset3 += chMul;\n                            }\n                        }\n                    }\n                }\n            }\n        }\n        return y.toTensor();\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        assertNotComplex([dy, filter], 'depthwiseConv2DDerInput');\n        const dx = tf.buffer(convInfo.inShape, 'float32');\n        const dxValues = dx.values;\n        const [dxS0, dxS1, dxS2] = dx.strides;\n        const dyValues = this.readSync(dy.dataId);\n        const [dyS0, dyS1, dyS2] = dy.strides;\n        const fltValues = this.readSync(filter.dataId);\n        const [fltS0, fltS1, fltS2] = filter.strides;\n        const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n        const topPad = filterHeight - 1 - convInfo.padInfo.top;\n        const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n        const chMul = outChannels / inChannels;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let d1 = 0; d1 < inChannels; ++d1) {\n                for (let xR = 0; xR < inHeight; ++xR) {\n                    const xRCorner = xR - topPad;\n                    const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                    const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                    for (let xC = 0; xC < inWidth; ++xC) {\n                        const xCCorner = xC - leftPad;\n                        const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                        const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                        let dotProd = 0;\n                        for (let yR = xRMin; yR < yRMax; ++yR) {\n                            const wR = yR * strideHeight - xRCorner;\n                            for (let yC = xCMin; yC < yCMax; ++yC) {\n                                const wC = yC * strideWidth - xCCorner;\n                                const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n                                const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n                                for (let dm = 0; dm < chMul; ++dm) {\n                                    const d2 = d1 * chMul + dm;\n                                    const pixel = dyValues[dyOffset + d2];\n                                    const weight = fltValues[fltOffset + dm];\n                                    dotProd += pixel * weight;\n                                }\n                            }\n                        }\n                        dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    depthwiseConv2DDerFilter(x, dy, convInfo) {\n        assertNotComplex([x, dy], 'depthwiseConv2DDerFilter');\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dW = tf.buffer(convInfo.filterShape, 'float32');\n        const leftPad = convInfo.padInfo.left;\n        const topPad = convInfo.padInfo.top;\n        const chMul = convInfo.outChannels / convInfo.inChannels;\n        const xBuf = this.bufferSync(x);\n        const dyBuf = this.bufferSync(dy);\n        for (let wR = 0; wR < filterHeight; ++wR) {\n            const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n            const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n            for (let wC = 0; wC < filterWidth; ++wC) {\n                const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                    const d1 = Math.trunc(d2 / chMul);\n                    const dm = d2 % chMul;\n                    let dotProd = 0;\n                    for (let b = 0; b < convInfo.batchSize; ++b) {\n                        for (let yR = yRMin; yR < yRMax; ++yR) {\n                            const xR = wR + yR * strideHeight - topPad;\n                            for (let yC = yCMin; yC < yCMax; ++yC) {\n                                const xC = wC + yC * strideWidth - leftPad;\n                                dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n                            }\n                        }\n                    }\n                    dW.set(dotProd, wR, wC, d1, dm);\n                }\n            }\n        }\n        return dW.toTensor();\n    }\n    tile(x, reps) {\n        assertNotComplex(x, 'tile');\n        return tile(this.bufferSync(x), reps);\n    }\n    gather(x, indices, axis) {\n        assertNotComplex([x, indices], 'gather');\n        const newShape = x.shape.slice();\n        const indicesValues = this.readSync(indices.dataId);\n        newShape[axis] = indicesValues.length;\n        const result = tf.buffer(newShape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < result.size; ++i) {\n            const newLoc = result.indexToLoc(i);\n            const originalLoc = newLoc.slice();\n            originalLoc[axis] = indicesValues[newLoc[axis]];\n            const originalIndex = xBuf.locToIndex(originalLoc);\n            result.values[i] = xBuf.values[originalIndex];\n        }\n        return result.toTensor();\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        assertNotComplex([x], 'batchToSpaceND');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n        const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n        const reshapedPermuted = backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n        const sliceBeginCoords = backend_util.getSliceBeginCoords(crops, blockShape.length);\n        const sliceSize = backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n        return tf.transpose(x.reshape(reshaped), permuted)\n            .reshape(reshapedPermuted)\n            .slice(sliceBeginCoords, sliceSize);\n    }\n    pool3d(x, convInfo, poolType) {\n        assertNotComplex(x, 'pool3d');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n            Number.POSITIVE_INFINITY);\n        const xValues = this.readSync(x.dataId);\n        const output = tf.buffer(convInfo.outShape, x.dtype);\n        const outputVals = output.values;\n        const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] *\n            convInfo.outShape[3] * convInfo.outShape[4];\n        const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];\n        const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];\n        const outputColStrides = convInfo.outShape[4];\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            const outputBatchOffset = batch * outputBatchStrides;\n            const inputBatchOffset = batch * x.strides[0];\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n                    const xDepthCorner = yDepth * strideDepth - padFront;\n                    let xDepthMin = xDepthCorner;\n                    while (xDepthMin < 0) {\n                        xDepthMin += dilationDepth;\n                    }\n                    const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n                    const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides;\n                    for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n                        const xRowCorner = yRow * strideHeight - padTop;\n                        let xRowMin = xRowCorner;\n                        while (xRowMin < 0) {\n                            xRowMin += dilationHeight;\n                        }\n                        const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n                        const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;\n                        for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n                            const xColCorner = yCol * strideWidth - padLeft;\n                            let xColMin = xColCorner;\n                            while (xColMin < 0) {\n                                xColMin += dilationWidth;\n                            }\n                            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n                            // Shader code begins\n                            const outputColOffset = outputRowOffset + yCol * outputColStrides;\n                            let minMaxValue = initialValue;\n                            let avgValue = 0;\n                            let count = 0;\n                            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n                                const xDepthOffset = inputBatchOffset + xDepth * x.strides[1];\n                                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                                    const xRowOffset = xDepthOffset + xRow * x.strides[2];\n                                    for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                                        const xColOffset = xRowOffset + xCol * x.strides[3];\n                                        const pixel = xValues[xColOffset + channel];\n                                        if ((poolType === 'max' && pixel > minMaxValue)) {\n                                            minMaxValue = pixel;\n                                        }\n                                        else if (poolType === 'avg') {\n                                            avgValue += pixel;\n                                            count++;\n                                        }\n                                        if (isNaN(minMaxValue)) {\n                                            break;\n                                        }\n                                    }\n                                    if (isNaN(minMaxValue)) {\n                                        break;\n                                    }\n                                }\n                                if (isNaN(minMaxValue)) {\n                                    break;\n                                }\n                            }\n                            const outputOffset = outputColOffset + channel;\n                            outputVals[outputOffset] =\n                                poolType === 'avg' ? avgValue / count : minMaxValue;\n                        }\n                    }\n                }\n            }\n        }\n        return output.toTensor();\n    }\n    avgPool3d(x, convInfo) {\n        assertNotComplex(x, 'avgPool3d');\n        return this.pool3d(x, convInfo, 'avg').toFloat();\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        assertNotComplex([dy, x], 'avgPool3dBackprop');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const dx = tf.buffer(x.shape, 'float32');\n        const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n        const dyBuf = this.bufferSync(dy);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n                    for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n                        for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n                            // Shader code begins.\n                            const dyDepthCorner = dxDepth - padFront;\n                            const dyRowCorner = dxRow - padTop;\n                            const dyColCorner = dxCol - padLeft;\n                            let dotProd = 0;\n                            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n                                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n                                    Math.floor(dyDepth) !== dyDepth) {\n                                    continue;\n                                }\n                                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                                    const dyRow = (dyRowCorner + wRow) / strideHeight;\n                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n                                        Math.floor(dyRow) !== dyRow) {\n                                        continue;\n                                    }\n                                    for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                                        const dyCol = (dyColCorner + wCol) / strideWidth;\n                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n                                            Math.floor(dyCol) !== dyCol) {\n                                            continue;\n                                        }\n                                        const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        dotProd += pixel;\n                                    }\n                                }\n                            }\n                            dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    maxPool3d(x, convInfo) {\n        assertNotComplex(x, 'maxPool3d');\n        return this.pool3d(x, convInfo, 'max').toFloat();\n    }\n    maxPool3dPositions(x, convInfo) {\n        const maxPositions = tf.buffer(convInfo.outShape, 'int32');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const xBuf = this.bufferSync(x);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n                    const xDepthCorner = yDepth * strideDepth - padFront;\n                    let xDepthMin = xDepthCorner;\n                    while (xDepthMin < 0) {\n                        xDepthMin += dilationDepth;\n                    }\n                    const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n                    for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n                        const xRowCorner = yRow * strideHeight - padTop;\n                        let xRowMin = xRowCorner;\n                        while (xRowMin < 0) {\n                            xRowMin += dilationHeight;\n                        }\n                        const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n                        for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n                            const xColCorner = yCol * strideWidth - padLeft;\n                            let xColMin = xColCorner;\n                            while (xColMin < 0) {\n                                xColMin += dilationWidth;\n                            }\n                            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n                            // Shader code begins\n                            let maxValue = Number.NEGATIVE_INFINITY;\n                            let maxPosition = -1;\n                            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n                                const wDepth = xDepth - xDepthCorner;\n                                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                                    const wRow = xRow - xRowCorner;\n                                    for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                                        const wCol = xCol - xColCorner;\n                                        const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);\n                                        if (pixel >= maxValue) {\n                                            maxValue = pixel;\n                                            maxPosition = wDepth * effectiveFilterHeight *\n                                                effectiveFilterWidth +\n                                                wRow * effectiveFilterHeight + wCol;\n                                        }\n                                    }\n                                }\n                            }\n                            maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return maxPositions.toTensor();\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        assertNotComplex([x, y], 'maxPool3dBackprop');\n        const maxPositions = this.maxPool3dPositions(x, convInfo);\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const dx = tf.buffer(x.shape, 'float32');\n        const maxPosBuf = this.bufferSync(maxPositions);\n        const dyBuf = this.bufferSync(dy);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n                    for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n                        for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n                            // Shader code begins\n                            const dyDepthCorner = dxDepth - padFront;\n                            const dyRowCorner = dxRow - padTop;\n                            const dyColCorner = dxCol - padLeft;\n                            let dotProd = 0;\n                            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n                                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n                                    Math.floor(dyDepth) !== dyDepth) {\n                                    continue;\n                                }\n                                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                                    const dyRow = (dyRowCorner + wRow) / strideHeight;\n                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n                                        Math.floor(dyRow) !== dyRow) {\n                                        continue;\n                                    }\n                                    for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                                        const dyCol = (dyColCorner + wCol) / strideWidth;\n                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n                                            Math.floor(dyCol) !== dyCol) {\n                                            continue;\n                                        }\n                                        const maxPos = effectiveFilterDepth *\n                                            effectiveFilterHeight * effectiveFilterWidth -\n                                            1 -\n                                            maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth +\n                                            wRow * effectiveFilterWidth + wCol;\n                                        const mask = maxPos === curPos ? 1 : 0;\n                                        if (mask === 0) {\n                                            continue;\n                                        }\n                                        const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        dotProd += pixel * mask;\n                                    }\n                                }\n                            }\n                            dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        assertNotComplex(x, 'resizeBilinear');\n        const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n        const xValues = this.readSync(x.dataId);\n        const result = new Float32Array(util.sizeFromShape([batch, newHeight, newWidth, numChannels]));\n        const effectiveInputSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutputSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        let outputIdx = 0;\n        const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n        const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n        for (let b = 0; b < batch; b++) {\n            for (let r = 0; r < newHeight; r++) {\n                const sourceFracRow = effectiveRowSizeRatio * r;\n                const sourceRowFloor = Math.floor(sourceFracRow);\n                const rowFrac = sourceFracRow - sourceRowFloor;\n                const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));\n                const topRowOffset = b * x.strides[0] + sourceRowFloor * x.strides[1];\n                const botRowOffset = b * x.strides[0] + sourceRowCeil * x.strides[1];\n                for (let c = 0; c < newWidth; c++) {\n                    const sourceFracCol = effectiveColSizeRatio * c;\n                    const sourceColFloor = Math.floor(sourceFracCol);\n                    const colFrac = sourceFracCol - sourceColFloor;\n                    const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));\n                    const topLeftOffest = topRowOffset + sourceColFloor * x.strides[2];\n                    const botLeftOffset = botRowOffset + sourceColFloor * x.strides[2];\n                    const topRightOffset = topRowOffset + sourceColCeil * x.strides[2];\n                    const botRightOffest = botRowOffset + sourceColCeil * x.strides[2];\n                    for (let d = 0; d < numChannels; d++) {\n                        // Begin shader.\n                        // Compute the fractional index of the source.\n                        const topLeft = xValues[topLeftOffest + d];\n                        const bottomLeft = xValues[botLeftOffset + d];\n                        const topRight = xValues[topRightOffset + d];\n                        const bottomRight = xValues[botRightOffest + d];\n                        const top = topLeft + (topRight - topLeft) * colFrac;\n                        const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;\n                        const newValue = top + (bottom - top) * rowFrac;\n                        result[outputIdx++] = newValue;\n                    }\n                }\n            }\n        }\n        return tf.tensor(result, [batch, newHeight, newWidth, numChannels]);\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        assertNotComplex([dy, x], 'resizeBilinearBackprop');\n        const [batch, xHeight, xWidth, depth] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        const output = new Float32Array(batch * xHeight * xWidth * depth);\n        // In the backwards pass, we want to find the pixels that were generated\n        // for each pixel in the input image the forward pass and add the\n        // corresponding coefficient from dy to the gradient (with some\n        // interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/3039375c86a5bbc9610c7725dcaa95d635f87ba2/tensorflow/core/kernels/resize_bilinear_op.cc#L275\n        const dyValues = this.readSync(dy.dataId);\n        let offset = 0;\n        for (let b = 0; b < batch; b++) {\n            const bOffset = b * x.strides[0];\n            for (let r = 0; r < yHeight; r++) {\n                const dxR = r * heightScale;\n                const topDxRIndex = Math.floor(dxR);\n                const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);\n                const topDxROffset = bOffset + topDxRIndex * x.strides[1];\n                const bottomDxROffset = bOffset + bottomDxRIndex * x.strides[1];\n                const dxRLerp = dxR - topDxRIndex;\n                const inverseDxRLerp = 1.0 - dxRLerp;\n                for (let c = 0; c < yWidth; c++) {\n                    const dxC = c * widthScale;\n                    const leftDxCIndex = Math.floor(dxC);\n                    const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);\n                    const dxCLerp = dxC - leftDxCIndex;\n                    const inverseDxCLerp = 1.0 - dxCLerp;\n                    const topLeftRCOffset = topDxROffset + leftDxCIndex * x.strides[2];\n                    const topRightRCOffset = topDxROffset + rightDxCIndex * x.strides[2];\n                    const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * x.strides[2];\n                    const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * x.strides[2];\n                    const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;\n                    const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;\n                    const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;\n                    const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;\n                    for (let d = 0; d < depth; d++) {\n                        const dyVal = dyValues[offset++];\n                        output[topLeftRCOffset + d] +=\n                            dyVal * inverseDxRLerpTimesInverseDxCLerp;\n                        output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;\n                        output[bottomLeftRCOffset + d] +=\n                            dyVal * dxRLerpTimesInverseDxCLerp;\n                        output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(output, [batch, xWidth, xHeight, depth], x.dtype);\n    }\n    resizeNearestNeighbor(x, newHeight, newWidth, alignCorners) {\n        assertNotComplex(x, 'resizeNearestNeighbor');\n        const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n        const xValues = this.readSync(x.dataId);\n        const output = new Float32Array(batch * newHeight * newWidth * numChannels);\n        const effectiveInputSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutputSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n        const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n        let outputOffset = 0;\n        for (let b = 0; b < batch; b++) {\n            const batchOffset = b * x.strides[0];\n            for (let r = 0; r < newHeight; r++) {\n                const sourceFracRow = effectiveRowSizeRatio * r;\n                const sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) :\n                    Math.floor(sourceFracRow));\n                const rowOffset = batchOffset + sourceNearestRow * x.strides[1];\n                for (let c = 0; c < newWidth; c++) {\n                    const sourceFracCol = effectiveColSizeRatio * c;\n                    const sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) :\n                        Math.floor(sourceFracCol));\n                    const colOffset = rowOffset + sourceNearestCol * x.strides[2];\n                    for (let d = 0; d < numChannels; d++) {\n                        // Begin shader.\n                        // Compute the fractional index of the source.\n                        const newVal = xValues[colOffset + d];\n                        output[outputOffset++] = newVal;\n                    }\n                }\n            }\n        }\n        return tf.tensor(output, [batch, newHeight, newWidth, numChannels], x.dtype);\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        assertNotComplex([dy, x], 'resizeNearestNeighborBackprop');\n        const [batch, xHeight, xWidth, depth] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        const output = new Float32Array(batch * xHeight * xWidth * depth);\n        const dyValues = this.readSync(dy.dataId);\n        // In the backwards pass, we want to find the pixels that were generated\n        // for each pixel in the input image the forward pass\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        // Loop over the output space.\n        for (let b = 0; b < batch; b++) {\n            const batchOffset = b * x.strides[0];\n            for (let r = 0; r < xHeight; r++) {\n                const rowOffset = batchOffset + r * x.strides[1];\n                // Compute bounds for where in dy we will look\n                const startRLerp = Math.floor(r * invHeightScale);\n                const startDyR = Math.floor(startRLerp - (winHeight / 2));\n                for (let c = 0; c < xWidth; c++) {\n                    const colOffset = rowOffset + c * x.strides[2];\n                    // Compute bounds for where in dy we will look\n                    const startCLerp = Math.floor(c * invWidthScale);\n                    const startDyC = Math.floor(startCLerp - (winWidth / 2));\n                    for (let d = 0; d < depth; d++) {\n                        let accum = 0;\n                        // loop over dy\n                        for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {\n                            const dyR = dyRIndex + startDyR;\n                            // Guard against the window exceeding the bounds of dy\n                            if (dyR < 0 || dyR >= yHeight) {\n                                continue;\n                            }\n                            const dyROffset = batchOffset + dyR * dy.strides[1];\n                            const sourceFracRow = dyR * heightScale;\n                            const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) :\n                                Math.floor(sourceFracRow));\n                            if (r !== sourceNearestRow) {\n                                continue;\n                            }\n                            for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {\n                                const dyC = dyCIndex + startDyC;\n                                // Guard against the window exceeding the bounds of dy\n                                if (dyC < 0 || dyC >= yWidth) {\n                                    continue;\n                                }\n                                const dyCOffset = dyROffset + dyC * dy.strides[2];\n                                const sourceFracCol = dyC * widthScale;\n                                const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) :\n                                    Math.floor(sourceFracCol));\n                                if (c === sourceNearestCol) {\n                                    accum += dyValues[dyCOffset + d];\n                                }\n                            }\n                        }\n                        output[colOffset + d] = accum;\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(output, x.shape, x.dtype);\n    }\n    localResponseNormalization4D(x, depthRadius, bias, alpha, beta) {\n        assertNotComplex(x, 'localResponseNormalization4D');\n        const channels = x.shape[3];\n        const maxD = channels - 1;\n        const xValues = this.readSync(x.dataId);\n        const size = x.size;\n        const result = new Float32Array(size);\n        function sumAcrossChannels(offset) {\n            const currentChannel = offset % channels;\n            let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n            const endSumOffset = offset - currentChannel +\n                Math.min(currentChannel + depthRadius, maxD);\n            let sum = 0.0;\n            for (; beginSumOffset <= endSumOffset; beginSumOffset++) {\n                const z = xValues[beginSumOffset];\n                sum += z * z;\n            }\n            return sum;\n        }\n        for (let offset = 0; offset < size; offset++) {\n            const sum = sumAcrossChannels(offset);\n            const val = xValues[offset] * Math.pow(bias + alpha * sum, -beta);\n            result[offset] = val;\n        }\n        return tf.tensor4d(result, x.shape);\n    }\n    LRNGrad(dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {\n        assertNotComplex(dy, 'LRNGrad');\n        const channels = dy.shape[3];\n        const dyValues = this.readSync(dy.dataId);\n        const inputImageValues = this.readSync(inputImage.dataId);\n        const outputImageValues = this.readSync(outputImage.dataId);\n        const result = new Float32Array(dy.size);\n        const size = dy.size;\n        for (let offset = 0; offset < size; offset++) {\n            const currentChannel = offset % channels;\n            const depthBegin = (offset - currentChannel) + Math.max(0, currentChannel - depthRadius);\n            const depthEnd = (offset - currentChannel) +\n                Math.min(channels, currentChannel + depthRadius + 1);\n            let norm = 0;\n            for (let k = depthBegin; k < depthEnd; k++) {\n                norm += Math.pow(inputImageValues[k], 2);\n            }\n            norm = alpha * norm + bias;\n            for (let k = depthBegin; k < depthEnd; k++) {\n                let dyi = -2 * alpha * beta * inputImageValues[k] *\n                    outputImageValues[offset] / norm;\n                if (offset === k) {\n                    dyi += Math.pow(norm, -beta);\n                }\n                dyi *= dyValues[offset];\n                result[k] += dyi;\n            }\n        }\n        return tf.tensor4d(result, dy.shape);\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        assertNotComplex(logits, 'multinomial');\n        const probabilities = normalized ? logits : tf.softmax(logits);\n        const batchSize = probabilities.shape[0];\n        const numEvents = probabilities.shape[1];\n        const res = tf.zeros([batchSize, numSamples], 'int32');\n        const resVals = this.readSync(res.dataId);\n        const probVals = this.readSync(probabilities.dataId);\n        for (let b = 0; b < batchSize; ++b) {\n            const offset = b * numEvents;\n            // The cdf won't include the last event. It will be implicit if no other\n            // event happened.\n            const cdf = new Float32Array(numEvents - 1);\n            cdf[0] = probVals[offset];\n            for (let event = 1; event < cdf.length; ++event) {\n                cdf[event] = cdf[event - 1] + probVals[offset + event];\n            }\n            const random = seedrandom.alea(seed.toString());\n            const outOffset = b * numSamples;\n            for (let sampleId = 0; sampleId < numSamples; ++sampleId) {\n                const r = random();\n                // Assume last event happened by default.\n                resVals[outOffset + sampleId] = cdf.length;\n                for (let event = 0; event < cdf.length; event++) {\n                    if (r < cdf[event]) {\n                        resVals[outOffset + sampleId] = event;\n                        break;\n                    }\n                }\n            }\n        }\n        return res;\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        assertNotComplex(indices, 'oneHot');\n        const res = new Float32Array(indices.size * depth);\n        res.fill(offValue);\n        const indicesVal = this.readSync(indices.dataId);\n        for (let event = 0; event < indices.size; ++event) {\n            if (indicesVal[event] >= 0 && indicesVal[event] < depth) {\n                res[event * depth + indicesVal[event]] = onValue;\n            }\n        }\n        return tf.tensor2d(res, [indices.size, depth], 'int32');\n    }\n    nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n        assertNotComplex(boxes, 'nonMaxSuppression');\n        const boxesVals = this.readSync(boxes.dataId);\n        const scoresVals = this.readSync(scores.dataId);\n        return nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        util.assert(dataFormat === 'NHWC', () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`);\n        util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n        const batchSize = x.shape[0];\n        const inputHeight = x.shape[1];\n        const inputWidth = x.shape[2];\n        const inputDepth = x.shape[3];\n        const outputHeight = inputHeight * blockSize;\n        const outputWidth = inputWidth * blockSize;\n        const outputDepth = inputDepth / (blockSize * blockSize);\n        const xValues = this.readSync(x.dataId);\n        const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);\n        let outputIdx = 0;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let h = 0; h < outputHeight; ++h) {\n                const inH = Math.floor(h / blockSize);\n                const offsetH = (h % blockSize);\n                for (let w = 0; w < outputWidth; ++w) {\n                    const inW = Math.floor(w / blockSize);\n                    const offsetW = (w % blockSize);\n                    const offsetD = (offsetH * blockSize + offsetW) * outputDepth;\n                    for (let d = 0; d < outputDepth; ++d) {\n                        const inD = d + offsetD;\n                        const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));\n                        result[outputIdx++] = xValues[inputIdx];\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(result, [batchSize, outputHeight, outputWidth, outputDepth]);\n    }\n    broadcastedBinaryOp(a, b, dtype, op) {\n        const newShape = backend_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const result = tf.buffer(newShape, dtype);\n        const aVals = this.readSync(a.dataId);\n        const bVals = this.readSync(b.dataId);\n        const aBroadcastDims = backend_util.getBroadcastDims(a.shape, newShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(b.shape, newShape);\n        const resVals = result.values;\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < resVals.length; ++i) {\n                resVals[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n            }\n        }\n        else {\n            const aBuf = this.bufferSync(a);\n            const bBuf = this.bufferSync(b);\n            for (let i = 0; i < resVals.length; ++i) {\n                const loc = result.indexToLoc(i);\n                const aLoc = loc.slice(-a.rank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = aBuf.locToIndex(aLoc);\n                const bLoc = loc.slice(-b.rank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = bBuf.locToIndex(bLoc);\n                resVals[i] = op(aVals[aIndex], bVals[bIndex]);\n            }\n        }\n        return result.toTensor();\n    }\n    split(x, sizeSplits, axis) {\n        return split(x, sizeSplits, axis);\n    }\n    dispose() { }\n    floatPrecision() {\n        return 32;\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return super.epsilon();\n    }\n    cropAndResize(images, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        const [batch, imageHeight, imageWidth, numChannels] = images.shape;\n        const numBoxes = boxes.shape[0];\n        const [cropHeight, cropWidth] = cropSize;\n        const output = tf.buffer([numBoxes, cropHeight, cropWidth, numChannels], 'float32');\n        const boxVals = this.readSync(boxes.dataId);\n        const boxIndVals = this.readSync(boxIndex.dataId);\n        const imageVals = this.readSync(images.dataId);\n        const inStride = images.strides; // to calculate flat indexes into image\n        const outStride = output.strides; // to calculate flat indexes into output\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc\n        for (let b = 0; b < numBoxes; b++) {\n            const startInd = b * 4;\n            const y1 = boxVals[startInd];\n            const x1 = boxVals[startInd + 1];\n            const y2 = boxVals[startInd + 2];\n            const x2 = boxVals[startInd + 3];\n            const bInd = boxIndVals[b];\n            if (bInd >= batch) {\n                continue;\n            }\n            const heightScale = (cropHeight > 1) ?\n                (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) :\n                0;\n            const widthScale = (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;\n            for (let y = 0; y < cropHeight; y++) {\n                const yInd = (cropHeight > 1) ?\n                    y1 * (imageHeight - 1) + y * (heightScale) :\n                    0.5 * (y1 + y2) * (imageHeight - 1);\n                if (yInd < 0 || yInd > imageHeight - 1) {\n                    for (let x = 0; x < cropWidth; x++) {\n                        for (let c = 0; c < numChannels; c++) {\n                            const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[ind] = extrapolationValue;\n                        }\n                    }\n                    continue;\n                }\n                if (method === 'bilinear') {\n                    const topInd = Math.floor(yInd);\n                    const bottomInd = Math.ceil(yInd);\n                    const yLerp = yInd - topInd;\n                    for (let x = 0; x < cropWidth; x++) {\n                        const xInd = (cropWidth > 1) ?\n                            x1 * (imageWidth - 1) + x * widthScale :\n                            0.5 * (x1 + x2) * (imageWidth - 1);\n                        if (xInd < 0 || xInd > imageWidth - 1) {\n                            for (let c = 0; c < numChannels; c++) {\n                                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                                output.values[ind] = extrapolationValue;\n                            }\n                            continue;\n                        }\n                        const leftInd = Math.floor(xInd);\n                        const rightInd = Math.ceil(xInd);\n                        const xLerp = xInd - leftInd;\n                        for (let c = 0; c < numChannels; c++) {\n                            let ind = c + leftInd * inStride[2] + topInd * inStride[1] +\n                                bInd * inStride[0];\n                            const topLeft = imageVals[ind];\n                            ind = c + rightInd * inStride[2] + topInd * inStride[1] +\n                                bInd * inStride[0];\n                            const topRight = imageVals[ind];\n                            ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +\n                                bInd * inStride[0];\n                            const bottomLeft = imageVals[ind];\n                            ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +\n                                bInd * inStride[0];\n                            const bottomRight = imageVals[ind];\n                            const top = topLeft + (topRight - topLeft) * xLerp;\n                            const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;\n                            ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[ind] = top + ((bottom - top) * yLerp);\n                        }\n                    }\n                }\n                else { // method == \"nearest\"\n                    for (let x = 0; x < cropWidth; ++x) {\n                        const xInd = (cropWidth > 1) ?\n                            x1 * (imageWidth - 1) + x * widthScale :\n                            0.5 * (x1 + x2) * (imageWidth - 1);\n                        if (xInd < 0 || xInd > imageWidth - 1) {\n                            for (let c = 0; c < numChannels; c++) {\n                                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                                output.values[ind] = extrapolationValue;\n                            }\n                            continue;\n                        }\n                        const closestX = Math.round(xInd);\n                        const closestY = Math.round(yInd);\n                        for (let c = 0; c < numChannels; c++) {\n                            const inInd = c + closestX * inStride[2] +\n                                closestY * inStride[1] + bInd * inStride[0];\n                            const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[outInd] = imageVals[inInd];\n                        }\n                    }\n                }\n            }\n        }\n        return output.toTensor();\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n        const sumDupeIndices = false;\n        return this.scatter(sparseIndices, sparseValues, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n    }\n    gatherND(x, indices) {\n        const indicesShape = indices.shape;\n        const sliceRank = indicesShape[indicesShape.length - 1];\n        const [resultShape, numSlices, sliceSize, strides] = backend_util.prepareAndValidate(x, indices);\n        if (numSlices === 0) {\n            return tf.tensor([], resultShape, x.dtype);\n        }\n        const buffer = new TensorBuffer([numSlices, sliceSize], x.dtype);\n        const indicesData = this.readSync(indices.dataId);\n        const xData = this.readSync(x.dataId);\n        for (let i = 0; i < numSlices; i++) {\n            const index = [];\n            let flattenIndex = 0;\n            for (let j = 0; j < sliceRank; j++) {\n                const dim = indicesData[i * sliceRank + j];\n                flattenIndex += dim * strides[j];\n                index.push(dim);\n            }\n            if (flattenIndex < 0 || flattenIndex >= x.size / sliceSize) {\n                throw new Error(`Invalid indices: ${index} does not index into ${x.shape}`);\n            }\n            for (let k = 0; k < sliceSize; k++) {\n                buffer.values[i * sliceSize + k] = xData[flattenIndex * sliceSize + k];\n            }\n        }\n        return buffer.toTensor().reshape(resultShape);\n    }\n    scatterND(indices, updates, shape) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(updates, indices, shape);\n        const defaultValue = tf.scalar(0);\n        const sumDupeIndices = true;\n        return this.scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n    }\n    fill(shape, value, dtype) {\n        dtype = dtype || util.inferDtype(value);\n        const values = util.getArrayFromDType(dtype, util.sizeFromShape(shape));\n        values.fill(value);\n        return engine().makeTensor(values, shape, dtype, this);\n    }\n    onesLike(x) {\n        if (x.dtype === 'string') {\n            throw new Error('onesLike is not supported for string tensors');\n        }\n        else {\n            return this.fill(x.shape, 1, x.dtype);\n        }\n    }\n    zerosLike(x) {\n        const values = util.getArrayFromDType(x.dtype, util.sizeFromShape(x.shape));\n        return this.makeOutput(values, x.shape, x.dtype);\n    }\n    linspace(start, stop, num) {\n        return backend_util.linspaceImpl(start, stop, num);\n    }\n    scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {\n        const flattenShape = [outputSize / sliceSize, sliceSize];\n        const indicesData = this.readSync(indices.dataId);\n        const updatesData = this.readSync(updates.dataId);\n        if (outputSize === 0) {\n            return tf.tensor([], shape, updates.dtype);\n        }\n        const buffer = new TensorBuffer(flattenShape, updates.dtype);\n        buffer.values.fill(this.readSync(defaultValue.dataId)[0]);\n        for (let i = 0; i < numUpdates; i++) {\n            const index = [];\n            let flattenIndex = 0;\n            for (let j = 0; j < sliceRank; j++) {\n                const dim = indicesData[i * sliceRank + j];\n                index.push(dim);\n                flattenIndex += dim * strides[j];\n            }\n            if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {\n                throw new Error(`Invalid indices: ${index} does not index into ${shape}`);\n            }\n            for (let k = 0; k < sliceSize; k++) {\n                if (sumDupeIndices) {\n                    buffer.values[flattenIndex * sliceSize + k] +=\n                        updatesData[i * sliceSize + k];\n                }\n                else {\n                    buffer.values[flattenIndex * sliceSize + k] = updates.rank === 0 ?\n                        updatesData[0] :\n                        updatesData[i * sliceSize + k];\n                }\n            }\n        }\n        return buffer.toTensor().reshape(shape);\n    }\n}\n//# sourceMappingURL=backend_cpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs, util } from '@tensorflow/tfjs-core';\nexport function simpleAbsImpl(vals) {\n    const resultValues = new Float32Array(vals.length);\n    for (let i = 0; i < vals.length; ++i) {\n        resultValues[i] = Math.abs(vals[i]);\n    }\n    return resultValues;\n}\nexport const absKernelFunc = (args) => {\n    const { x } = args.inputs;\n    const cpuBackend = args.backend;\n    let resultValues = new Float32Array(util.sizeFromShape(x.shape));\n    if (x.dtype !== 'complex64') {\n        const values = cpuBackend.data.get(x.dataId).values;\n        resultValues = simpleAbsImpl(values);\n    }\n    else {\n        const complexVals = cpuBackend.data.get(x.dataId);\n        const real = complexVals.complexTensorInfos.real;\n        const imag = complexVals.complexTensorInfos.imag;\n        const realVals = cpuBackend.data.get(real.dataId).values;\n        const imagVals = cpuBackend.data.get(imag.dataId).values;\n        for (let i = 0; i < realVals.length; i++) {\n            const real = realVals[i];\n            const imag = imagVals[i];\n            resultValues[i] = Math.hypot(real, imag);\n        }\n    }\n    return cpuBackend.makeOutput(resultValues, x.shape, 'float32');\n};\nexport const absConfig = {\n    kernelName: Abs,\n    backendName: 'cpu',\n    kernelFunc: absKernelFunc,\n};\n//# sourceMappingURL=Abs.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for binary ops. Supports broadcast.\n */\nexport function createSimpleBinaryKernelImpl(op) {\n    return (aShape, bShape, aVals, bVals, dtype) => {\n        const newShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const resultRank = newShape.length;\n        const resultStrides = util.computeStrides(newShape);\n        const resultSize = util.sizeFromShape(newShape);\n        const result = util.getTypedArrayFromDType(dtype, resultSize);\n        const aRank = aShape.length;\n        const bRank = bShape.length;\n        const aStrides = util.computeStrides(aShape);\n        const bStrides = util.computeStrides(bShape);\n        const aBroadcastDims = backend_util.getBroadcastDims(aShape, newShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(bShape, newShape);\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < result.length; ++i) {\n                result[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n            }\n        }\n        else {\n            for (let i = 0; i < result.length; ++i) {\n                const loc = util.indexToLoc(i, resultRank, resultStrides);\n                const aLoc = loc.slice(-aRank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n                const bLoc = loc.slice(-bRank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n                result[i] = op(aVals[aIndex], bVals[bIndex]);\n            }\n        }\n        return [result, newShape];\n    };\n}\n//# sourceMappingURL=binary_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Complex } from '@tensorflow/tfjs-core';\nexport function complex(args) {\n    const { inputs, backend } = args;\n    const { real, imag } = inputs;\n    const realVals = backend.data.get(real.dataId).values;\n    const imagVals = backend.data.get(imag.dataId).values;\n    const complexInfo = backend.makeTensorInfo(real.shape, 'complex64');\n    const complex = backend.data.get(complexInfo.dataId);\n    // The complex tensor owns the underlying real and imag tensorInfos, only the\n    // complex tensor tracks refCount, when complexData is disposed the\n    // underlying tensorData will be disposed.\n    complex.complexTensorInfos = {\n        real: backend.makeTensorInfo(real.shape, 'float32', realVals),\n        imag: backend.makeTensorInfo(imag.shape, 'float32', imagVals)\n    };\n    return complexInfo;\n}\nexport const complexConfig = {\n    kernelName: Complex,\n    backendName: 'cpu',\n    kernelFunc: complex\n};\n//# sourceMappingURL=Complex.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '@tensorflow/tfjs-core';\nexport function identity(args) {\n    const { inputs, backend } = args;\n    const { x } = inputs;\n    backend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nexport const identityConfig = {\n    kernelName: Identity,\n    backendName: 'cpu',\n    kernelFunc: identity\n};\n//# sourceMappingURL=Identity.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Real } from '@tensorflow/tfjs-core';\nexport function real(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const real = backend.data.get(input.dataId).complexTensorInfos.real;\n    const realVal = backend.data.get(real.dataId).values;\n    // When complex tensor is disposed, its underlying parts will be disposed too.\n    // Make new tensor out of the real value of the complex. This makes sure the\n    // value is still accessible even if complex tensor is disposed.\n    return backend.makeTensorInfo(real.shape, real.dtype, realVal);\n}\nexport const realConfig = {\n    kernelName: Real,\n    backendName: 'cpu',\n    kernelFunc: real\n};\n//# sourceMappingURL=Real.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { Cast, util } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { complex } from './Complex';\nimport { identity } from './Identity';\nimport { real } from './Real';\nexport function cast(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { dtype } = attrs;\n    // Casting to complex64.\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return identity({ inputs: { x }, backend });\n        }\n        // TODO(lina128): Import kernel function once zeros is modularized.\n        const zerosTensor = tf.zeros(x.shape);\n        const floatX = cast({ inputs: { x }, backend, attrs: { dtype: 'float32' } });\n        const result = complex({ inputs: { real: floatX, imag: zerosTensor }, backend });\n        zerosTensor.dispose();\n        backend.disposeIntermediateTensorInfo(floatX);\n        return result;\n    }\n    // Casting from complex64\n    if (x.dtype === 'complex64') {\n        const realPart = real({ inputs: { input: x }, backend });\n        const result = cast({ inputs: { x: realPart }, backend, attrs: { dtype } });\n        backend.disposeIntermediateTensorInfo(realPart);\n        return result;\n    }\n    if (!util.hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        const result = identity({ inputs: { x }, backend });\n        return { dataId: result.dataId, shape: result.shape, dtype };\n    }\n    if (dtype === 'int32') {\n        const values = backend.data.get(x.dataId).values;\n        const resultValues = Int32Array.from(values);\n        return backend.makeTensorInfo(x.shape, 'int32', resultValues);\n    }\n    if (dtype === 'bool') {\n        // This is essentially the result of notEqual(x, 0). We avoid using\n        // kernel notEqual to avoid circular dependency, i.e. binary_utils ->\n        // cast -> notEqual -> binary_utils.\n        const xVals = backend.data.get(x.dataId).values;\n        const zero = util.toTypedArray([0], x.dtype);\n        const [resultData, resultShape] = createSimpleBinaryKernelImpl((a, b) => (a !== b) ? 1 : 0)(x.shape, [], xVals, zero, 'bool');\n        return backend.makeTensorInfo(resultShape, 'bool', resultData);\n    }\n    throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nexport const castConfig = {\n    kernelName: Cast,\n    backendName: 'cpu',\n    kernelFunc: cast\n};\n//# sourceMappingURL=Cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { cast } from '../kernels/Cast';\nimport { complex } from '../kernels/Complex';\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param name Kernel name.\n * @param binaryKernelImpl A `SimpleBinaryKernelImpl` for the kernel.\n * @param binaryKernelComplexImpl Optional. If exists, represents a\n *     `ComplexBinaryKernelImpl` for the kernel, will be used when input dtype\n *     is `complex64`.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc(name, simpleImpl, complexImpl, dtype) {\n    if (complexImpl == null) {\n        return ({ inputs, backend }) => {\n            const { a, b } = inputs;\n            const cpuBackend = backend;\n            assertNotComplex([a, b], name);\n            const aVals = cpuBackend.data.get(a.dataId).values;\n            const bVals = cpuBackend.data.get(b.dataId).values;\n            const $dtype = dtype || a.dtype;\n            const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n            return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n        };\n    }\n    return ({ inputs, backend }) => {\n        const { a, b } = inputs;\n        const cpuBackend = backend;\n        if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n            const $aComplex = cast({ inputs: { x: a }, backend: cpuBackend, attrs: { dtype: 'complex64' } });\n            const $aComplexVals = cpuBackend.data.get($aComplex.dataId);\n            const aReal = $aComplexVals.complexTensorInfos.real;\n            const aImag = $aComplexVals.complexTensorInfos.imag;\n            const aRealVals = cpuBackend.data.get(aReal.dataId).values;\n            const aImagVals = cpuBackend.data.get(aImag.dataId).values;\n            const $bComplex = cast({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: 'complex64' } });\n            const $bComplexVals = cpuBackend.data.get($bComplex.dataId);\n            const bReal = $bComplexVals.complexTensorInfos.real;\n            const bImag = $bComplexVals.complexTensorInfos.imag;\n            const bRealVals = cpuBackend.data.get(bReal.dataId).values;\n            const bImagVals = cpuBackend.data.get(bImag.dataId).values;\n            const [resultRealData, resultImagData, resultShape] = complexImpl(a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);\n            const resultReal = cpuBackend.makeTensorInfo(resultShape, 'float32', resultRealData);\n            const resultImag = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImagData);\n            const result = complex({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend });\n            cpuBackend.disposeIntermediateTensorInfo($aComplex);\n            cpuBackend.disposeIntermediateTensorInfo($bComplex);\n            cpuBackend.disposeIntermediateTensorInfo(resultReal);\n            cpuBackend.disposeIntermediateTensorInfo(resultImag);\n            return result;\n        }\n        else {\n            const aVals = cpuBackend.data.get(a.dataId).values;\n            const bVals = cpuBackend.data.get(b.dataId).values;\n            const $dtype = dtype || a.dtype;\n            const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n            return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n        }\n    };\n}\n/**\n * Template that creates the complex type implementation for binary ops.\n * Supports broadcast.\n */\nexport function createComplexBinaryKernelImpl(op) {\n    return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) => {\n        const resultShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const resultSize = util.sizeFromShape(resultShape);\n        const resultRank = resultShape.length;\n        const resultStrides = util.computeStrides(resultShape);\n        const resultRealVals = util.getTypedArrayFromDType('float32', resultSize);\n        const resultImagVals = util.getTypedArrayFromDType('float32', resultSize);\n        const aBroadcastDims = backend_util.getBroadcastDims(aShape, resultShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(bShape, resultShape);\n        const aVals = backend_util.mergeRealAndImagArrays(aRealVals, aImagVals);\n        const bVals = backend_util.mergeRealAndImagArrays(bRealVals, bImagVals);\n        const aRank = aShape.length;\n        const aStrides = util.computeStrides(aShape);\n        const bRank = bShape.length;\n        const bStrides = util.computeStrides(bShape);\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < resultRealVals.length; i++) {\n                const aIdx = i % aVals.length;\n                const bIdx = i % bVals.length;\n                const result = op(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);\n                resultRealVals[i] = result.real;\n                resultImagVals[i] = result.imag;\n            }\n        }\n        else {\n            for (let i = 0; i < resultRealVals.length; i++) {\n                const loc = util.indexToLoc(i, resultRank, resultStrides);\n                const aLoc = loc.slice(-aRank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n                const bLoc = loc.slice(-bRank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n                const opResult = op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);\n                resultRealVals[i] = opResult.real;\n                resultImagVals[i] = opResult.imag;\n            }\n        }\n        return [resultRealVals, resultImagVals, resultShape];\n    };\n}\n//# sourceMappingURL=kernel_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const addImpl = createSimpleBinaryKernelImpl(((a, b) => a + b));\nexport const addComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal + bReal, imag: aImag + bImag };\n}));\nexport const add = binaryKernelFunc(Add, addImpl, addComplexImpl);\nexport const addConfig = {\n    kernelName: Add,\n    backendName: 'cpu',\n    kernelFunc: add\n};\n//# sourceMappingURL=Add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for unary op.\n */\nexport function createSimpleUnaryImpl(op) {\n    return (values, dtype, attrs) => {\n        const newValues = util.getTypedArrayFromDType(dtype, values.length);\n        for (let i = 0; i < values.length; ++i) {\n            newValues[i] = op(values[i], attrs);\n        }\n        return newValues;\n    };\n}\n//# sourceMappingURL=unary_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param name Kernel name.\n * @param op A `SimpleUnaryOperation` for the kernel.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the input. This is mainly used in certain\n *     kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFunc(name, op, dtype) {\n    return ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        assertNotComplex(x, name);\n        if (x.dtype === 'string' || dtype === 'string') {\n            throw new Error('unaryKernelFunc does not support string input/output');\n        }\n        const cpuBackend = backend;\n        const values = cpuBackend.data.get(x.dataId).values;\n        const xSize = util.sizeFromShape(x.shape);\n        const $dtype = dtype || x.dtype;\n        const newValues = util.getArrayFromDType($dtype, xSize);\n        for (let i = 0; i < xSize; ++i) {\n            newValues[i] = op(values[i], attrs);\n        }\n        return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n    };\n}\n/**\n * Template that creates a `KernelFunc` for unary ops from the given\n * `SimpleUnaryImpl`..\n * @param name Kernel name.\n * @param unaryImpl A `SimpleUnaryImpl` that implements the op.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the input. This is mainly used in certain\n *     kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFuncFromImpl(name, unaryImpl, dtype) {\n    return ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        assertNotComplex(x, name);\n        if (x.dtype === 'string' || dtype === 'string') {\n            throw new Error('unaryKernelFunc does not support string input/output');\n        }\n        const cpuBackend = backend;\n        const values = cpuBackend.data.get(x.dataId).values;\n        const $dtype = dtype || x.dtype;\n        const newValues = unaryImpl(values, $dtype, attrs);\n        return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n    };\n}\n//# sourceMappingURL=unary_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));\nexport const ceilKernelFunc = unaryKernelFuncFromImpl(Ceil, ceilImpl);\nexport const ceilConfig = {\n    kernelName: Ceil,\n    backendName: 'cpu',\n    kernelFunc: ceilKernelFunc,\n};\n//# sourceMappingURL=Ceil.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));\nexport const expKernelFunc = unaryKernelFuncFromImpl(Exp, expImpl);\nexport const expConfig = {\n    kernelName: Exp,\n    backendName: 'cpu',\n    kernelFunc: expKernelFunc,\n};\n//# sourceMappingURL=Exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));\nexport const expm1KernelFunc = unaryKernelFuncFromImpl(Expm1, expm1Impl);\nexport const expm1Config = {\n    kernelName: Expm1,\n    backendName: 'cpu',\n    kernelFunc: expm1KernelFunc,\n};\n//# sourceMappingURL=Expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));\nexport const floorKernelFunc = unaryKernelFuncFromImpl(Floor, floorImpl);\nexport const floorConfig = {\n    kernelName: Floor,\n    backendName: 'cpu',\n    kernelFunc: floorKernelFunc,\n};\n//# sourceMappingURL=Floor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));\nexport const logKernelFunc = unaryKernelFuncFromImpl(Log, logImpl);\nexport const logConfig = {\n    kernelName: Log,\n    backendName: 'cpu',\n    kernelFunc: logKernelFunc,\n};\n//# sourceMappingURL=Log.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function maxImpl(aVals, reduceSize, outShape, dtype) {\n    const vals = util.getTypedArrayFromDType(dtype, util.sizeFromShape(outShape));\n    for (let i = 0; i < vals.length; ++i) {\n        const offset = i * reduceSize;\n        let max = aVals[offset];\n        for (let j = 0; j < reduceSize; ++j) {\n            const value = aVals[offset + j];\n            if (value > max) {\n                max = value;\n            }\n        }\n        vals[i] = max;\n    }\n    return vals;\n}\n//# sourceMappingURL=Max_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const multiplyImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue * bValue));\nexport const multiplyComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return {\n        real: aReal * bReal - aImag * bImag,\n        imag: aReal * bImag + aImag * bReal\n    };\n}));\nexport const multiply = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);\nexport const multiplyConfig = {\n    kernelName: Multiply,\n    backendName: 'cpu',\n    kernelFunc: multiply\n};\n//# sourceMappingURL=Multiply.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));\nexport const rsqrtKernelFunc = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);\nexport const rsqrtConfig = {\n    kernelName: Rsqrt,\n    backendName: 'cpu',\n    kernelFunc: rsqrtKernelFunc,\n};\n//# sourceMappingURL=Rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Slice, slice_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function sliceImpl(vals, begin, size, shape, dtype) {\n    const isContinous = slice_util.isSliceContinous(shape, begin, size);\n    const length = util.sizeFromShape(size);\n    const xStrides = util.computeStrides(shape);\n    if (isContinous) {\n        const flatOffset = slice_util.computeFlatOffset(begin, xStrides);\n        return vals.subarray(flatOffset, flatOffset + length);\n    }\n    const outVals = util.getTypedArrayFromDType(dtype, length);\n    for (let i = 0; i < length; ++i) {\n        const rank = size.length;\n        const strides = util.computeStrides(size);\n        const loc = util.indexToLoc(i, rank, strides);\n        const xLoc = loc.map((idx, j) => idx + begin[j]);\n        const xIndex = util.locToIndex(xLoc, shape.length, xStrides);\n        outVals[i] = vals[xIndex];\n    }\n    return outVals;\n}\nexport function slice(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { begin, size } = attrs;\n    assertNotComplex(x, 'slice');\n    const [$begin, $size] = slice_util.parseSliceParams(x, begin, size);\n    slice_util.assertParamsValid(x, $begin, $size);\n    const vals = backend.data.get(x.dataId).values;\n    const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);\n    return backend.makeTensorInfo($size, x.dtype, outVals);\n}\nexport const sliceConfig = {\n    kernelName: Slice,\n    backendName: 'cpu',\n    kernelFunc: slice\n};\n//# sourceMappingURL=Slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const subImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue - bValue));\nexport const subComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal - bReal, imag: aImag - bImag };\n}));\nexport const sub = binaryKernelFunc(Sub, subImpl, subComplexImpl);\nexport const subConfig = {\n    kernelName: Sub,\n    backendName: 'cpu',\n    kernelFunc: sub\n};\n//# sourceMappingURL=Sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function transposeImpl(xVals, xShape, dtype, perm, newShape) {\n    const xRank = xShape.length;\n    const xSize = util.sizeFromShape(xShape);\n    const xStrides = util.computeStrides(xShape);\n    const newStrides = util.computeStrides(newShape);\n    const result = util.getTypedArrayFromDType(dtype, util.sizeFromShape(newShape));\n    for (let i = 0; i < xSize; ++i) {\n        const loc = util.indexToLoc(i, xRank, xStrides);\n        // Permute location.\n        const newLoc = new Array(loc.length);\n        for (let i = 0; i < newLoc.length; i++) {\n            newLoc[i] = loc[perm[i]];\n        }\n        const newIndex = util.locToIndex(newLoc, xRank, newStrides);\n        result[newIndex] = xVals[i];\n    }\n    return result;\n}\n//# sourceMappingURL=Transpose_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer, util } from '@tensorflow/tfjs-core';\nexport function uniqueImpl(values, axis, shape, dtype) {\n    // Normalize and validate axis.\n    const $axis = util.parseAxisParam(axis, shape)[0];\n    // Calculate the new shape that is suitable for extracting data along the\n    // given axis.\n    //\n    // The rank is 3.\n    // The size of the 1st dimension is the size of all the axes < the given axis.\n    // The size of the 2nd dimension is the same as the size of the given axis.\n    // The size of the 3rd dimension is the size of all the axes > the given axis.\n    //\n    // For example, for a 4D tensor with shape=[2, 3, 5, 4] and axis=2, the\n    // newShape would be: [2*3, 5, 4].\n    //\n    // Note that this is not the final output shape. This will be the shape for an\n    // intermediate TensorBuffer (see inputBuffer below) to allow us to extract\n    // values along the given axis. To demonstrate how it works, consider the\n    // following example:\n    //\n    // Input: a 3D tensor, with shape [1, 2, 3]\n    // [\n    //   [\n    //      [1,2,3],\n    //      [4,5,6]\n    //   ]\n    // ]\n    // Axis: 2 (the last axis).\n    // Along axis 2, we expect to extract 3 tensors: [1,4], [2,5], [3,6].\n    //\n    // For this example, newShape would be: [2, 3, 1], where 2 is calculated from\n    // 1*2. The re-shaped data would look like:\n    //\n    // [\n    //   [\n    //     [1], [2], [3]\n    //   ],\n    //   [\n    //     [4], [5], [6]\n    //   ]\n    // ]\n    //\n    // Then, we can construct a 3-level nested loop by the following dimension\n    // order to extract the values along the axis (dimension1):\n    // i: dimension1       // 0,1,2 (newShape[1])\n    //   m: dimension0     // 0,1   (newShape[0])\n    //     n: dimension2   // 0     (newShape[2])\n    //\n    //                       m, i, n\n    //                      ---------\n    // Iteration 0: data at [0, 0, 0] => \"1\"\n    // Iteration 1: data at [1, 0, 0] => \"4\"\n    // We got [1,4].\n    // Iteration 2: data at [0, 1, 0] => \"2\"\n    // Iteration 3: data at [1, 1, 0] => \"5\"\n    // We got [2,5].\n    // Iteration 4: data at [0, 2, 0] => \"3\"\n    // Iteration 5: data at [1, 2, 0] => \"6\"\n    // We got [3,6].\n    const newShape = [1, shape[0], 1];\n    for (let i = 0; i < $axis; i++) {\n        newShape[0] *= shape[i];\n    }\n    newShape[1] = shape[$axis];\n    for (let i = $axis + 1; i < shape.length; i++) {\n        newShape[2] *= shape[i];\n    }\n    // A map from unique elements (their string representations) to their values\n    // in \"indices\" (below).\n    const uniqueElements = {};\n    // The indices of each unique element in the original tensor along the given\n    // axis. It is 1D and has the same size as the given axis.\n    const indices = new Int32Array(shape[$axis]);\n    // Create a buffer so we can easily extract value at a given location.\n    const inputBuffer = new TensorBuffer(newShape, dtype, values);\n    // The indices along the given axis that have unique elements. This is a\n    // de-duped version of \"indices\" above.\n    const uniqueIndices = [];\n    const is1DTensor = newShape[0] === 1 && newShape[2] === 1;\n    for (let i = 0; i < shape[$axis]; i++) {\n        // Extract values along the axis.\n        let element;\n        if (is1DTensor) {\n            // Fast path for 1D tensor input.\n            element = values[i].toString();\n        }\n        else {\n            const axisValues = [];\n            for (let m = 0; m < newShape[0]; m++) {\n                for (let n = 0; n < newShape[2]; n++) {\n                    axisValues.push(inputBuffer.get(m, i, n));\n                }\n            }\n            element = axisValues.join(',');\n        }\n        // Dedup and update various indices.\n        if (uniqueElements[element] !== undefined) {\n            indices[i] = uniqueElements[element];\n        }\n        else {\n            const uniqueIndex = Object.keys(uniqueElements).length;\n            uniqueElements[element] = uniqueIndex;\n            indices[i] = uniqueIndex;\n            uniqueIndices.push(i);\n        }\n    }\n    // Now we know where each of the unique elements are located along the axis\n    // (uniqueIndices). Extract them from input buffer and store them in the\n    // output buffer.\n    const outputTmpShape = newShape.slice();\n    outputTmpShape[1] = Object.keys(uniqueElements).length;\n    const outputBuffer = new TensorBuffer(outputTmpShape, dtype);\n    uniqueIndices.forEach((uniqueElementIndex, i) => {\n        for (let m = 0; m < newShape[0]; m++) {\n            for (let n = 0; n < newShape[2]; n++) {\n                outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);\n            }\n        }\n    });\n    // The output shape can be calculated from the input shape with the size of\n    // the given axis replaced by the number of unique elements along that axis.\n    const outputShape = shape.slice();\n    outputShape[$axis] = outputTmpShape[1];\n    return {\n        outputValues: outputBuffer.values,\n        outputShape,\n        indices,\n    };\n}\n//# sourceMappingURL=Unique_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Shared kernel impls for use in other backends.\nexport { simpleAbsImpl } from './kernels/Abs';\nexport { addImpl } from './kernels/Add';\nexport { ceilImpl } from './kernels/Ceil';\nexport { expImpl } from './kernels/Exp';\nexport { expm1Impl } from './kernels/Expm1';\nexport { floorImpl } from './kernels/Floor';\nexport { logImpl } from './kernels/Log';\nexport { maxImpl } from './kernels/Max_impl';\nexport { multiplyImpl } from './kernels/Multiply';\nexport { rsqrtImpl } from './kernels/Rsqrt';\nexport { sliceImpl } from './kernels/Slice';\nexport { subImpl } from './kernels/Sub';\nexport { transposeImpl } from './kernels/Transpose_impl';\nexport { uniqueImpl } from './kernels/Unique_impl';\n//# sourceMappingURL=shared.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/*\n * base.ts contains all the exports from tfjs-backend-cpu\n * without auto-kernel registration\n */\nimport { registerBackend } from '@tensorflow/tfjs-core';\nimport { MathBackendCPU } from './backend_cpu';\nimport * as shared from './shared';\nexport { MathBackendCPU } from './backend_cpu';\nexport { version as version_cpu } from './version';\nexport { shared };\n// Side effects for default initialization of MathBackendCPU\nregisterBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acos } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const acosKernelFunc = unaryKernelFunc(Acos, (xi) => Math.acos(xi));\nexport const acosConfig = {\n    kernelName: Acos,\n    backendName: 'cpu',\n    kernelFunc: acosKernelFunc,\n};\n//# sourceMappingURL=Acos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acosh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const acoshKernelFunc = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi));\nexport const acoshConfig = {\n    kernelName: Acosh,\n    backendName: 'cpu',\n    kernelFunc: acoshKernelFunc,\n};\n//# sourceMappingURL=Acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asin } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const asinKernelFunc = unaryKernelFunc(Asin, (xi) => Math.asin(xi));\nexport const asinConfig = {\n    kernelName: Asin,\n    backendName: 'cpu',\n    kernelFunc: asinKernelFunc,\n};\n//# sourceMappingURL=Asin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asinh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const asinhKernelFunc = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi));\nexport const asinhConfig = {\n    kernelName: Asinh,\n    backendName: 'cpu',\n    kernelFunc: asinhKernelFunc,\n};\n//# sourceMappingURL=Asinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const atanKernelFunc = unaryKernelFunc(Atan, (xi) => Math.atan(xi));\nexport const atanConfig = {\n    kernelName: Atan,\n    backendName: 'cpu',\n    kernelFunc: atanKernelFunc,\n};\n//# sourceMappingURL=Atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atanh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const atanhKernelFunc = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi));\nexport const atanhConfig = {\n    kernelName: Atanh,\n    backendName: 'cpu',\n    kernelFunc: atanhKernelFunc,\n};\n//# sourceMappingURL=Atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from '@tensorflow/tfjs-core';\nexport function pool(xValues, xShape, dtype, strides, convInfo, poolType) {\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n        Number.POSITIVE_INFINITY);\n    const output = buffer(convInfo.outShape, dtype);\n    const outputVals = output.values;\n    const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];\n    const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];\n    const outputColStrides = convInfo.outShape[3];\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        const outputBatchOffset = b * outputBatchStrides;\n        const inputBatchOffset = b * strides[0];\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const xRCorner = yR * strideHeight - padTop;\n                const xRMin = Math.max(0, xRCorner);\n                const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n                const outputRowOffset = outputBatchOffset + yR * outputRowStrides;\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const xCCorner = yC * strideWidth - padLeft;\n                    const xCMin = Math.max(0, xCCorner);\n                    const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n                    let minMaxValue = initialValue;\n                    let avgValue = 0;\n                    let count = 0;\n                    for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n                        const xROffset = inputBatchOffset + xR * strides[1];\n                        for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                            const xCOffset = xROffset + xC * strides[2];\n                            const pixel = xValues[xCOffset + d];\n                            if ((poolType === 'max' && pixel > minMaxValue)) {\n                                minMaxValue = pixel;\n                            }\n                            else if (poolType === 'avg') {\n                                avgValue += pixel;\n                                count++;\n                            }\n                        }\n                        if (isNaN(minMaxValue)) {\n                            break;\n                        }\n                    }\n                    const outputOffset = outputRowOffset + yC * outputColStrides + d;\n                    outputVals[outputOffset] =\n                        poolType === 'avg' ? avgValue / count : minMaxValue;\n                }\n            }\n        }\n    }\n    return output;\n}\nexport function maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) {\n    const maxPositions = buffer(convInfo.outShape, 'int32');\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const xBuf = buffer(xShape, dtype, xValues);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const xRCorner = yR * strideHeight - padTop;\n                let xRMin = xRCorner;\n                while (xRMin < 0) {\n                    xRMin += dilationHeight;\n                }\n                // const xRMin = Math.max(0, xRCorner);\n                const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const xCCorner = yC * strideWidth - padLeft;\n                    let xCMin = xCCorner;\n                    while (xCMin < 0) {\n                        xCMin += dilationWidth;\n                    }\n                    const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n                    let maxValue = Number.NEGATIVE_INFINITY;\n                    let maxPosition = -1;\n                    for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n                        const wR = xR - xRCorner;\n                        for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                            const wC = xC - xCCorner;\n                            const pixel = xBuf.get(b, xR, xC, d);\n                            if (pixel > maxValue) {\n                                maxValue = pixel;\n                                if (flattenPositions) {\n                                    maxPosition = includeBatchInIndex ?\n                                        ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) *\n                                            convInfo.inChannels +\n                                            d :\n                                        (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;\n                                }\n                                else {\n                                    maxPosition = wR * effectiveFilterWidth + wC;\n                                }\n                            }\n                        }\n                    }\n                    maxPositions.set(maxPosition, b, yR, yC, d);\n                }\n            }\n        }\n    }\n    return maxPositions;\n}\n//# sourceMappingURL=pool_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool, backend_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { pool } from '../utils/pool_utils';\nimport { identity } from './Identity';\nexport function avgPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'avgPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    let res;\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        res = identity({ inputs: { x }, backend });\n    }\n    else {\n        const xValues = backend.data.get(x.dataId).values;\n        const strides = util.computeStrides(x.shape);\n        const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'avg');\n        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);\n    }\n    return res;\n}\nexport const avgPoolConfig = {\n    kernelName: AvgPool,\n    backendName: 'cpu',\n    kernelFunc: avgPool\n};\n//# sourceMappingURL=AvgPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPoolBackprop, backend_util, buffer } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function avgPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input } = inputs;\n    const x = input;\n    assertNotComplex([dy, input], 'avgPoolBackprop');\n    const { filterSize, strides, pad } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = buffer(x.shape, 'float32');\n    const avgMultiplier = 1 / (filterHeight * filterWidth);\n    const dyData = backend.data.get(dy.dataId).values;\n    const dyBuf = buffer(dy.shape, 'float32', dyData);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n                for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n                    // Shader code begins.\n                    const dyRCorner = dxR - padTop;\n                    const dyCCorner = dxC - padLeft;\n                    let dotProd = 0;\n                    for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n                        const dyR = (dyRCorner + wR) / strideHeight;\n                        if (dyR < 0 || dyR >= convInfo.outHeight ||\n                            Math.floor(dyR) !== dyR) {\n                            continue;\n                        }\n                        for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                            const dyC = (dyCCorner + wC) / strideWidth;\n                            if (dyC < 0 || dyC >= convInfo.outWidth ||\n                                Math.floor(dyC) !== dyC) {\n                                continue;\n                            }\n                            const pixel = dyBuf.get(b, dyR, dyC, d);\n                            dotProd += pixel;\n                        }\n                    }\n                    dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const avgPoolBackpropConfig = {\n    kernelName: AvgPoolBackprop,\n    backendName: 'cpu',\n    kernelFunc: avgPoolBackprop\n};\n//# sourceMappingURL=AvgPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedBatchNorm, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function batchNormKernelFunc(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, scale, offset, mean, variance } = inputs;\n    util.assert(mean.shape.length === variance.shape.length, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert(offset == null || mean.shape.length === offset.shape.length, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert(scale == null || mean.shape.length === scale.shape.length, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    assertNotComplex([x, mean, variance, scale, offset], 'batchNorm');\n    let { varianceEpsilon } = attrs;\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const xVals = backend.data.get(x.dataId).values;\n    const mVals = backend.data.get(mean.dataId).values;\n    const varVals = backend.data.get(variance.dataId).values;\n    const sVals = scale ? backend.data.get(scale.dataId).values :\n        new Float32Array([1]);\n    const offVals = offset ?\n        backend.data.get(offset.dataId).values :\n        new Float32Array([0]);\n    const outVals = new Float32Array(xVals.length);\n    const offValsLength = offVals.length;\n    const sValsLength = sVals.length;\n    const varValsLength = varVals.length;\n    const mValsLength = mVals.length;\n    let offi = 0;\n    let mi = 0;\n    let si = 0;\n    let vi = 0;\n    for (let i = 0; i < xVals.length; ++i) {\n        outVals[i] = offVals[offi++] +\n            (xVals[i] - mVals[mi++]) * sVals[si++] /\n                Math.sqrt(varVals[vi++] + varianceEpsilon);\n        if (offi >= offValsLength) {\n            offi = 0;\n        }\n        if (mi >= mValsLength) {\n            mi = 0;\n        }\n        if (si >= sValsLength) {\n            si = 0;\n        }\n        if (vi >= varValsLength) {\n            vi = 0;\n        }\n    }\n    return backend.makeTensorInfo(x.shape, x.dtype, outVals);\n}\nexport const batchNormConfig = {\n    kernelName: FusedBatchNorm,\n    backendName: 'cpu',\n    kernelFunc: batchNormKernelFunc,\n};\n//# sourceMappingURL=BatchNorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ClipByValue } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const clipKernelFunc = unaryKernelFunc(ClipByValue, (xi, attrs) => {\n    const clipAttrs = attrs;\n    if (xi > clipAttrs.clipValueMax) {\n        return clipAttrs.clipValueMax;\n    }\n    return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;\n});\nexport const clipConfig = {\n    kernelName: ClipByValue,\n    backendName: 'cpu',\n    kernelFunc: clipKernelFunc,\n};\n//# sourceMappingURL=Clip.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Imag } from '@tensorflow/tfjs-core';\nexport function imag(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const imag = backend.data.get(input.dataId).complexTensorInfos.imag;\n    const imagVal = backend.data.get(imag.dataId).values;\n    // When complex tensor is disposed, its underlying parts will be disposed too.\n    // Make new tensor out of the imag value of the complex. This makes sure the\n    // value is still accessible even if complex tensor is disposed.\n    return backend.makeTensorInfo(imag.shape, imag.dtype, imagVal);\n}\nexport const imagConfig = {\n    kernelName: Imag,\n    backendName: 'cpu',\n    kernelFunc: imag\n};\n//# sourceMappingURL=Imag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape, util } from '@tensorflow/tfjs-core';\nexport function reshape(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { shape } = attrs;\n    const xSize = util.sizeFromShape(x.shape);\n    const $shape = util.inferFromImplicitShape(shape, xSize);\n    const $xSize = util.sizeFromShape($shape);\n    util.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n        `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n        `shape must have the same number of elements.`);\n    backend.incRef(x.dataId);\n    const xData = backend.data.get(x.dataId);\n    if (xData.complexTensorInfos != null) {\n        const real = xData.complexTensorInfos.real;\n        const imag = xData.complexTensorInfos.imag;\n        real.shape = $shape;\n        imag.shape = $shape;\n    }\n    return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nexport const reshapeConfig = {\n    kernelName: Reshape,\n    backendName: 'cpu',\n    kernelFunc: reshape\n};\n//# sourceMappingURL=Reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Concat, util } from '@tensorflow/tfjs-core';\nimport { complex } from './Complex';\nimport { imag } from './Imag';\nimport { real } from './Real';\nimport { reshape } from './Reshape';\nexport function concat(args) {\n    const { inputs, backend, attrs } = args;\n    const { axis } = attrs;\n    const $axis = util.parseAxisParam(axis, inputs[0].shape)[0];\n    let outShape = backend_util.computeOutShape(inputs.map(t => t.shape), $axis);\n    if (util.sizeFromShape(outShape) === 0) {\n        return backend.makeTensorInfo(outShape, inputs[0].dtype, []);\n    }\n    // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n    const $inputs = inputs.filter(t => util.sizeFromShape(t.shape) > 0);\n    if ($inputs.length === 1) {\n        return $inputs[0];\n    }\n    const shapes = $inputs.map(t => t.shape);\n    backend_util.assertParamsConsistent(shapes, $axis);\n    if ($inputs[0].dtype === 'complex64') {\n        const reals = $inputs.map((t) => real({ inputs: { input: t }, backend }));\n        const imags = $inputs.map((t) => imag({ inputs: { input: t }, backend }));\n        const realConcated = concat({ inputs: reals, backend, attrs: { axis } });\n        const imagConcated = concat({ inputs: imags, backend, attrs: { axis } });\n        const result = complex({ inputs: { real: realConcated, imag: imagConcated }, backend });\n        reals.forEach(r => backend.disposeIntermediateTensorInfo(r));\n        imags.forEach(i => backend.disposeIntermediateTensorInfo(i));\n        backend.disposeIntermediateTensorInfo(realConcated);\n        backend.disposeIntermediateTensorInfo(imagConcated);\n        return result;\n    }\n    // Any concat of n-dimensional tensors across any axis can be reduced to\n    // a concatenation of two-dimensional tensors across the axis 1 by first\n    // partitioning the axes of the original tensors into those less than the\n    // axis to be concatenated and the rest. Then reshape the tensors\n    // into a two-dimensional tensor by collapsing these two sets of axes and\n    // concatenate the resulting matrices across the axis 1, finally reshaping\n    // the result to have the proper shape.\n    const inputs2D = $inputs.map(t => {\n        const innerSize = util.sizeFromShape(t.shape.slice($axis));\n        const shape = [-1, innerSize];\n        return reshape({ inputs: { x: t }, backend, attrs: { shape } });\n    });\n    // Concats 2d tensors along axis=1.\n    outShape =\n        backend_util.computeOutShape(inputs2D.map(t => t.shape), 1 /* axis */);\n    const outVals = util.getTypedArrayFromDType($inputs[0].dtype, util.sizeFromShape(outShape));\n    if (inputs2D[0].shape[0] === 1) {\n        // Use built-in TypedArray.set() method for speed.\n        let offset = 0;\n        inputs2D.forEach(t => {\n            const val = backend.data.get(t.dataId).values;\n            const size = util.sizeFromShape(t.shape);\n            outVals.set(val, offset);\n            offset += size;\n        });\n    }\n    else {\n        let colOffset = 0;\n        inputs2D.forEach(t => {\n            const tVals = backend.data.get(t.dataId).values;\n            let tIdx = 0;\n            for (let row = 0; row < t.shape[0]; ++row) {\n                const resIdx = row * outShape[1] + colOffset;\n                for (let col = 0; col < t.shape[1]; ++col) {\n                    outVals[resIdx + col] = tVals[tIdx++];\n                }\n            }\n            colOffset += t.shape[1];\n        });\n    }\n    const finalOutShape = backend_util.computeOutShape($inputs.map(t => t.shape), $axis);\n    const outInfo = backend.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);\n    inputs2D.forEach(t => backend.disposeIntermediateTensorInfo(t));\n    return outInfo;\n}\nexport const concatConfig = {\n    kernelName: Concat,\n    backendName: 'cpu',\n    kernelFunc: concat\n};\n//# sourceMappingURL=Concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const cosKernelFunc = unaryKernelFunc(Cos, (xi) => Math.cos(xi));\nexport const cosConfig = {\n    kernelName: Cos,\n    backendName: 'cpu',\n    kernelFunc: cosKernelFunc,\n};\n//# sourceMappingURL=Cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cosh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const coshKernelFunc = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi));\nexport const coshConfig = {\n    kernelName: Cosh,\n    backendName: 'cpu',\n    kernelFunc: coshKernelFunc,\n};\n//# sourceMappingURL=Cosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2D, util } from '@tensorflow/tfjs-core';\nexport const dilation2dConfig = {\n    kernelName: Dilation2D,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const xVals = cpuBackend.data.get(x.dataId).values;\n        const xRank = x.shape.length;\n        const filterVals = cpuBackend.data.get(filter.dataId).values;\n        const filterRank = filter.shape.length;\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        const outSize = util.sizeFromShape(outShape);\n        const outRank = outShape.length;\n        const outputVals = util.getArrayFromDType(x.dtype, outSize);\n        // Upsampling the input by fill in `dilation size - 1` values between each\n        // input value.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const xIndex = util.locToIndex([b, hIn, wIn, d], xRank, util.computeStrides(x.shape));\n                                        const filterIndex = util.locToIndex([h, w, d], filterRank, util.computeStrides(filter.shape));\n                                        const val = xVals[xIndex] + filterVals[filterIndex];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        const outputIndex = util.locToIndex([b, hOut, wOut, d], outRank, util.computeStrides(outShape));\n                        outputVals[outputIndex] = curVal;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(outputVals, x.dtype), outShape, x.dtype);\n        return { dataId, shape: outShape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2DBackpropFilter, util } from '@tensorflow/tfjs-core';\nexport const dilation2dBackpropFilterConfig = {\n    kernelName: Dilation2DBackpropFilter,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter, dy } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const $x = util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n        const $filter = util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        util.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy ` +\n            `must have the same rank as output ${outShape.length}, but got ` +\n            `${dy.rank}`);\n        const $dy = util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n        // The computed filter gradients has the same dimensions as the filter:\n        // [filterHeight, filterWidth, depth]\n        const gradients = util.makeZerosNestedTypedArray(filter.shape, filter.dtype);\n        // In the case of multiple argmax branches, we only back-propagate along the\n        // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n        // similarly to the max-pooling backward routines.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        let hMax = 0;\n                        let wMax = 0;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                            hMax = h;\n                                            wMax = w;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);\n        return { dataId, shape: filter.shape, dtype: filter.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2DBackpropFilter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2DBackpropInput, util } from '@tensorflow/tfjs-core';\nexport const dilation2dBackpropInputConfig = {\n    kernelName: Dilation2DBackpropInput,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter, dy } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const $x = util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n        const $filter = util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        util.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy ` +\n            `must have the same rank as output ${outShape.length}, but got ` +\n            `${dy.rank}`);\n        const $dy = util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n        // The computed gradients has the same dimensions as the input:\n        // [batch, inputHeight, inputCols, inChannel]\n        const gradients = util.makeZerosNestedTypedArray(x.shape, x.dtype);\n        // In the case of multiple argmax branches, we only back-propagate along the\n        // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n        // similarly to the max-pooling backward routines.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        let hInMax = (hBeg < 0) ? 0 : hBeg;\n                        let wInMax = (wBeg < 0) ? 0 : wBeg;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                            hInMax = hIn;\n                                            wInMax = wIn;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(gradients, x.dtype), x.shape, x.dtype);\n        return { dataId, shape: x.shape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2DBackpropInput.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const divImpl = createSimpleBinaryKernelImpl((a, b) => a / b);\nexport const div = binaryKernelFunc(Div, divImpl);\nexport const divConfig = {\n    kernelName: Div,\n    backendName: 'cpu',\n    kernelFunc: div\n};\n//# sourceMappingURL=Div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Elu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const eluKernelFunc = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : (Math.exp(xi) - 1));\nexport const eluConfig = {\n    kernelName: Elu,\n    backendName: 'cpu',\n    kernelFunc: eluKernelFunc,\n};\n//# sourceMappingURL=Elu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Erf } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nconst p = backend_util.ERF_P;\nconst a1 = backend_util.ERF_A1;\nconst a2 = backend_util.ERF_A2;\nconst a3 = backend_util.ERF_A3;\nconst a4 = backend_util.ERF_A4;\nconst a5 = backend_util.ERF_A5;\nexport const erfKernelFunc = unaryKernelFunc(Erf, (xi) => {\n    const sign = Math.sign(xi);\n    const v = Math.abs(xi);\n    const t = 1.0 / (1.0 + p * v);\n    return sign *\n        (1.0 -\n            (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t *\n                Math.exp(-v * v));\n});\nexport const erfConfig = {\n    kernelName: Erf,\n    backendName: 'cpu',\n    kernelFunc: erfKernelFunc,\n};\n//# sourceMappingURL=Erf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { add } from '../kernels/Add';\nimport { complex } from '../kernels/Complex';\nimport { concat } from '../kernels/Concat';\nimport { divConfig } from '../kernels/Div';\nimport { identity } from '../kernels/Identity';\nimport { imag } from '../kernels/Imag';\nimport { multiply } from '../kernels/Multiply';\nimport { real } from '../kernels/Real';\nimport { slice } from '../kernels/Slice';\nimport { sub } from '../kernels/Sub';\n/**\n * Calculate FFT of inner most elements of batch tensor.\n */\nexport function fftBatch(input, inverse, cpuBackend) {\n    const inputShape = input.shape;\n    const batch = inputShape[0];\n    const innerDim = inputShape[1];\n    const inputVals = cpuBackend.data.get(input.dataId);\n    const real2D = inputVals.complexTensorInfos.real;\n    const imag2D = inputVals.complexTensorInfos.imag;\n    // Collects real and imaginary values separately.\n    const resultShape = [batch, innerDim];\n    const resultSize = util.sizeFromShape(resultShape);\n    const resultReal = util.getTypedArrayFromDType('float32', resultSize);\n    const resultImag = util.getTypedArrayFromDType('float32', resultSize);\n    for (let b = 0; b < batch; b++) {\n        // TODO: Support slice ops for complex type.\n        const r = slice({\n            inputs: { x: real2D },\n            backend: cpuBackend,\n            attrs: { begin: [b, 0], size: [1, innerDim] }\n        });\n        const i = slice({\n            inputs: { x: imag2D },\n            backend: cpuBackend,\n            attrs: { begin: [b, 0], size: [1, innerDim] }\n        });\n        const input = complex({ inputs: { real: r, imag: i }, backend: cpuBackend });\n        // Run FFT by batch element.\n        const { real, imag } = fftImpl(input, inverse, cpuBackend);\n        const res = backend_util.mergeRealAndImagArrays(real, imag);\n        for (let d = 0; d < innerDim; d++) {\n            const c = backend_util.getComplexWithIndex(res, d);\n            resultReal[b * innerDim + d] = c.real;\n            resultImag[b * innerDim + d] = c.imag;\n        }\n        cpuBackend.disposeIntermediateTensorInfo(r);\n        cpuBackend.disposeIntermediateTensorInfo(i);\n        cpuBackend.disposeIntermediateTensorInfo(input);\n    }\n    const $realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultReal);\n    const $imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImag);\n    const result = complex({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend });\n    cpuBackend.disposeIntermediateTensorInfo($realInfo);\n    cpuBackend.disposeIntermediateTensorInfo($imagInfo);\n    return result;\n}\nexport function fftImpl(input, inverse, cpuBackend) {\n    const inputSize = util.sizeFromShape(input.shape);\n    const inputVals = cpuBackend.data.get(input.dataId);\n    const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values;\n    const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;\n    if (isExponentOf2(inputSize)) {\n        const result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);\n        const resultShape = [input.shape[0], input.shape[1]];\n        if (inverse) {\n            const realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.real);\n            const imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.imag);\n            const sizeInfo = cpuBackend.makeTensorInfo([], 'float32', util.createScalarValue(inputSize, 'float32'));\n            const sizeInfoCopy = identity({ inputs: { x: sizeInfo }, backend: cpuBackend });\n            const divRealInfo = divConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend });\n            const divImagInfo = divConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend });\n            const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values;\n            const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values;\n            cpuBackend.disposeIntermediateTensorInfo(realInfo);\n            cpuBackend.disposeIntermediateTensorInfo(imagInfo);\n            cpuBackend.disposeIntermediateTensorInfo(sizeInfo);\n            cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);\n            cpuBackend.disposeIntermediateTensorInfo(divRealInfo);\n            cpuBackend.disposeIntermediateTensorInfo(divImagInfo);\n            return { real: divRealVals, imag: divImagVals };\n        }\n        return result;\n    }\n    else {\n        const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n        const rawOutput = fourierTransformByMatmul(data, inputSize, inverse);\n        return backend_util.splitRealAndImagArrays(rawOutput);\n    }\n}\nfunction isExponentOf2(size) {\n    return (size & size - 1) === 0;\n}\n// FFT using Cooley-Tukey algorithm on radix 2 dimensional input.\nfunction fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {\n    if (size === 1) {\n        return { real: realVals, imag: imagVals };\n    }\n    const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n    const half = size / 2;\n    const evenComplex = backend_util.complexWithEvenIndex(data);\n    const evenRealVals = evenComplex.real;\n    const evenImagVals = evenComplex.imag;\n    const evenShape = [evenRealVals.length];\n    const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenRealVals);\n    const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenImagVals);\n    const evenTensorInfo = complex({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend });\n    const oddComplex = backend_util.complexWithOddIndex(data);\n    const oddRealVals = oddComplex.real;\n    const oddImagVals = oddComplex.imag;\n    const oddShape = [oddRealVals.length];\n    const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddRealVals);\n    const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddImagVals);\n    const oddTensorInfo = complex({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend });\n    // Recursive call for half part of original input.\n    const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);\n    const $evenRealVals = $evenComplex.real;\n    const $evenImagVals = $evenComplex.imag;\n    const $evenShape = [$evenRealVals.length];\n    const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenRealVals);\n    const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenImagVals);\n    const $evenTensorInfo = complex({\n        inputs: { real: $evenRealInfo, imag: $evenImagInfo },\n        backend: cpuBackend\n    });\n    const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);\n    const $oddRealVals = $oddComplex.real;\n    const $oddImagVals = $oddComplex.imag;\n    const $oddShape = [$oddRealVals.length];\n    const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddRealVals);\n    const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddImagVals);\n    const $oddTensorInfo = complex({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend });\n    const e = backend_util.exponents(size, inverse);\n    const eShape = [e.real.length];\n    const eRealInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.real);\n    const eImagInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.imag);\n    const complexInfo = complex({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend });\n    const exponentInfo = multiply({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend });\n    const addPart = add({\n        inputs: { a: $evenTensorInfo, b: exponentInfo },\n        backend: cpuBackend\n    });\n    const subPart = sub({\n        inputs: { a: $evenTensorInfo, b: exponentInfo },\n        backend: cpuBackend\n    });\n    const addPartReal = real({ inputs: { input: addPart }, backend: cpuBackend });\n    const subPartReal = real({ inputs: { input: subPart }, backend: cpuBackend });\n    const addPartImag = imag({ inputs: { input: addPart }, backend: cpuBackend });\n    const subPartImag = imag({ inputs: { input: subPart }, backend: cpuBackend });\n    const $real = concat({\n        inputs: [addPartReal, subPartReal],\n        backend: cpuBackend,\n        attrs: { axis: 0 }\n    });\n    const $imag = concat({\n        inputs: [addPartImag, subPartImag],\n        backend: cpuBackend,\n        attrs: { axis: 0 }\n    });\n    const $realVals = cpuBackend.data.get($real.dataId).values;\n    const $imagVals = cpuBackend.data.get($imag.dataId).values;\n    cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo(eRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(eImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(complexInfo);\n    cpuBackend.disposeIntermediateTensorInfo(exponentInfo);\n    cpuBackend.disposeIntermediateTensorInfo(addPart);\n    cpuBackend.disposeIntermediateTensorInfo(subPart);\n    cpuBackend.disposeIntermediateTensorInfo(addPartReal);\n    cpuBackend.disposeIntermediateTensorInfo(addPartImag);\n    cpuBackend.disposeIntermediateTensorInfo(subPartReal);\n    cpuBackend.disposeIntermediateTensorInfo(subPartImag);\n    cpuBackend.disposeIntermediateTensorInfo($real);\n    cpuBackend.disposeIntermediateTensorInfo($imag);\n    return { real: $realVals, imag: $imagVals };\n}\n// Calculate fourier transform by multplying sinusoid matrix.\nfunction fourierTransformByMatmul(data, size, inverse) {\n    const ret = new Float32Array(size * 2);\n    // TODO: Use matmul instead once it supports complex64 type.\n    for (let r = 0; r < size; r++) {\n        let real = 0.0;\n        let imag = 0.0;\n        for (let c = 0; c < size; c++) {\n            const e = backend_util.exponent(r * c, size, inverse);\n            const term = backend_util.getComplexWithIndex(data, c);\n            real += term.real * e.real - term.imag * e.imag;\n            imag += term.real * e.imag + term.imag * e.real;\n        }\n        if (inverse) {\n            real /= size;\n            imag /= size;\n        }\n        backend_util.assignToTypedArray(ret, real, imag, r);\n    }\n    return ret;\n}\n//# sourceMappingURL=fft_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FFT, util } from '@tensorflow/tfjs-core';\nimport { fftBatch } from '../utils/fft_utils';\nimport { reshape } from './Reshape';\nexport function fft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputSize = util.sizeFromShape(input.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({\n        inputs: { x: input },\n        backend,\n        attrs: { shape: [batch, innerDimensionSize] }\n    });\n    const result = fftBatch(input2D, false, backend);\n    const resultReshaped = reshape({ inputs: { x: result }, backend, attrs: { shape: input.shape } });\n    backend.disposeIntermediateTensorInfo(input2D);\n    backend.disposeIntermediateTensorInfo(result);\n    return resultReshaped;\n}\nexport const fftConfig = {\n    kernelName: FFT,\n    backendName: 'cpu',\n    kernelFunc: fft\n};\n//# sourceMappingURL=FFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FlipLeftRight, util } from '@tensorflow/tfjs-core';\nexport const flipLeftRightConfig = {\n    kernelName: FlipLeftRight,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const cpuBackend = backend;\n        const output = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(image.shape));\n        const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n        const imageVals = cpuBackend.data.get(image.dataId).values;\n        for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n            for (let row = 0; row < imageHeight; row++) {\n                const rowOffset = row * (imageWidth * numChannels);\n                for (let col = 0; col < imageWidth; col++) {\n                    const colOffset = col * numChannels;\n                    for (let channel = 0; channel < numChannels; channel++) {\n                        const coords = [batch, row, col, channel];\n                        const x = coords[2];\n                        const coordX = Math.round(imageWidth - x);\n                        const outIdx = batchOffset + rowOffset + colOffset + channel;\n                        let outputValue = imageVals[outIdx];\n                        // If the coordinate position falls within the image boundaries...\n                        if (coordX >= 0 && coordX < imageWidth) {\n                            // set the output to the image value at the coordinate position.\n                            const rotatedColOffset = coordX * numChannels;\n                            const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel;\n                            outputValue = imageVals[imageIdx];\n                        }\n                        output[outIdx] = outputValue;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(output, image.shape, image.dtype);\n        return { dataId, shape: image.shape, dtype: image.dtype };\n    }\n};\n//# sourceMappingURL=FlipLeftRight.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IFFT, util } from '@tensorflow/tfjs-core';\nimport { fftBatch } from '../utils/fft_utils';\nimport { reshape } from './Reshape';\nexport function ifft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputSize = util.sizeFromShape(input.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({\n        inputs: { x: input },\n        backend,\n        attrs: { shape: [batch, innerDimensionSize] }\n    });\n    const result = fftBatch(input2D, true, backend);\n    const resultReshaped = reshape({ inputs: { x: result }, backend, attrs: { shape: input.shape } });\n    backend.disposeIntermediateTensorInfo(input2D);\n    backend.disposeIntermediateTensorInfo(result);\n    return resultReshaped;\n}\nexport const ifftConfig = {\n    kernelName: IFFT,\n    backendName: 'cpu',\n    kernelFunc: ifft\n};\n//# sourceMappingURL=IFFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsFinite } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isFiniteKernelFunc = unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, 'bool');\nexport const isFiniteConfig = {\n    kernelName: IsFinite,\n    backendName: 'cpu',\n    kernelFunc: isFiniteKernelFunc,\n};\n//# sourceMappingURL=IsFinite.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsInf } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isInfKernelFunc = unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, 'bool');\nexport const isInfConfig = {\n    kernelName: IsInf,\n    backendName: 'cpu',\n    kernelFunc: isInfKernelFunc,\n};\n//# sourceMappingURL=IsInf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsNan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isNaNKernelFunc = unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, 'bool');\nexport const isNaNConfig = {\n    kernelName: IsNan,\n    backendName: 'cpu',\n    kernelFunc: isNaNKernelFunc,\n};\n//# sourceMappingURL=IsNaN.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log1p } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const log1pKernelFunc = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi));\nexport const log1pConfig = {\n    kernelName: Log1p,\n    backendName: 'cpu',\n    kernelFunc: log1pKernelFunc,\n};\n//# sourceMappingURL=Log1p.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LogicalNot } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const logicalNotKernelFunc = unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, 'bool');\nexport const logicalNotConfig = {\n    kernelName: LogicalNot,\n    backendName: 'cpu',\n    kernelFunc: logicalNotKernelFunc,\n};\n//# sourceMappingURL=LogicalNot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '@tensorflow/tfjs-core';\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxImpl } from './Max_impl';\nimport { transposeImpl } from './Transpose_impl';\nexport const maxConfig = {\n    kernelName: Max,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { reductionIndices, keepDims } = attrs;\n        const cpuBackend = backend;\n        let xShape = x.shape;\n        const xRank = xShape.length;\n        const origAxes = util.parseAxisParam(reductionIndices, xShape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        let xVals = cpuBackend.data.get(x.dataId).values;\n        if (permutedAxes != null) {\n            const newShape = new Array(xRank);\n            for (let i = 0; i < newShape.length; i++) {\n                newShape[i] = xShape[permutedAxes[i]];\n            }\n            xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n            xShape = newShape;\n        }\n        assertNotComplex(x, 'max');\n        backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n        const [maxOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(xShape, axes);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);\n        const dataId = cpuBackend.write(result, maxOutShape, x.dtype);\n        let outShape = maxOutShape;\n        if (keepDims) {\n            // reshape\n            const newShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n            outShape = newShape;\n        }\n        return { dataId, shape: outShape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPool, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { pool } from '../utils/pool_utils';\nimport { identity } from './Identity';\nexport function maxPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'maxPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    let res;\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        res = identity({ inputs: { x }, backend });\n    }\n    else {\n        const xValues = backend.data.get(x.dataId).values;\n        const strides = util.computeStrides(x.shape);\n        const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'max');\n        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);\n    }\n    return res;\n}\nexport const maxPoolConfig = {\n    kernelName: MaxPool,\n    backendName: 'cpu',\n    kernelFunc: maxPool\n};\n//# sourceMappingURL=MaxPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, buffer, MaxPoolBackprop } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxPoolPositions } from '../utils/pool_utils';\nexport function maxPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input, output } = inputs;\n    const x = input;\n    assertNotComplex([input, output], 'maxPoolBackprop');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n    const xValues = backend.data.get(x.dataId).values;\n    const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = buffer(x.shape, 'float32');\n    const dyData = backend.data.get(dy.dataId).values;\n    const dyBuf = buffer(dy.shape, 'float32', dyData);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n                for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n                    // Shader code begins.\n                    const dyRCorner = dxR - padTop;\n                    const dyCCorner = dxC - padLeft;\n                    let dotProd = 0;\n                    for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n                        const dyR = (dyRCorner + wR) / strideHeight;\n                        if (dyR < 0 || dyR >= convInfo.outHeight ||\n                            Math.floor(dyR) !== dyR) {\n                            continue;\n                        }\n                        for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                            const dyC = (dyCCorner + wC) / strideWidth;\n                            if (dyC < 0 || dyC >= convInfo.outWidth ||\n                                Math.floor(dyC) !== dyC) {\n                                continue;\n                            }\n                            const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 -\n                                maxPosBuf.get(b, dyR, dyC, d);\n                            const curPos = wR * effectiveFilterWidth + wC;\n                            const mask = maxPos === curPos ? 1 : 0;\n                            if (mask === 0) {\n                                continue;\n                            }\n                            const pixel = dyBuf.get(b, dyR, dyC, d);\n                            dotProd += pixel * mask;\n                        }\n                    }\n                    dx.set(dotProd, b, dxR, dxC, d);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const maxPoolBackpropConfig = {\n    kernelName: MaxPoolBackprop,\n    backendName: 'cpu',\n    kernelFunc: maxPoolBackprop\n};\n//# sourceMappingURL=MaxPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { maxPoolPositions, pool } from '../utils/pool_utils';\nexport function maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) {\n    const strides = util.computeStrides(xShape);\n    const maxPools = pool(xValues, xShape, dtype, strides, convInfo, 'max');\n    const maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex);\n    return [maxPools.values, maxPositions.values];\n}\n//# sourceMappingURL=MaxPoolWithArgmax_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPoolWithArgmax } from '@tensorflow/tfjs-core';\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxPoolWithArgmaxImpl } from './MaxPoolWithArgmax_impl';\nexport const maxPoolWithArgmaxConfig = {\n    kernelName: MaxPoolWithArgmax,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { filterSize, strides, pad, includeBatchInIndex } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(x, 'MaxPoolWithArgmax');\n        const values = cpuBackend.data.get(x.dataId).values;\n        const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad);\n        const [pooled, indexes] = maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo);\n        const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype);\n        const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype);\n        return [\n            { dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype },\n            { dataId: indexesDataId, shape: convInfo.outShape, dtype: 'int32' }\n        ];\n    }\n};\n//# sourceMappingURL=MaxPoolWithArgmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NonMaxSuppressionV4 } from '@tensorflow/tfjs-core';\nimport { kernel_impls } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\nimport { assertNotComplex } from '../cpu_util';\nexport const nonMaxSuppressionV4Config = {\n    kernelName: NonMaxSuppressionV4,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(boxes, 'NonMaxSuppressionPadded');\n        const boxesVals = cpuBackend.data.get(boxes.dataId).values;\n        const scoresVals = cpuBackend.data.get(scores.dataId).values;\n        const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n        return [selectedIndices, validOutputs];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV4.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NonMaxSuppressionV5 } from '@tensorflow/tfjs-core';\nimport { kernel_impls } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nimport { assertNotComplex } from '../cpu_util';\nexport const nonMaxSuppressionV5Config = {\n    kernelName: NonMaxSuppressionV5,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(boxes, 'NonMaxSuppressionWithScore');\n        const boxesVals = cpuBackend.data.get(boxes.dataId).values;\n        const scoresVals = cpuBackend.data.get(scores.dataId).values;\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        const softNmsSigmaVal = softNmsSigma;\n        const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n        return [selectedIndices, selectedScores];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV5.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NotEqual } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const notEqualImpl = createSimpleBinaryKernelImpl(((a, b) => (a !== b) ? 1 : 0));\nexport const notEqual = binaryKernelFunc(NotEqual, notEqualImpl, null /* complexOp */, 'bool');\nexport const notEqualConfig = {\n    kernelName: NotEqual,\n    backendName: 'cpu',\n    kernelFunc: notEqual\n};\n//# sourceMappingURL=NotEqual.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { PadV2, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function padV2(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { paddings, constantValue } = attrs;\n    assertNotComplex(x, 'pad');\n    const outShape = paddings.map((p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);\n    const start = paddings.map(p => p[0]);\n    const xVals = backend.data.get(x.dataId).values;\n    const xSize = util.sizeFromShape(x.shape);\n    const xRank = x.shape.length;\n    const xStrides = util.computeStrides(x.shape);\n    const resultSize = util.sizeFromShape(outShape);\n    const resultRank = outShape.length;\n    const resultStrides = util.computeStrides(outShape);\n    const resVals = util.getTypedArrayFromDType(x.dtype, resultSize);\n    if (constantValue !== 0) {\n        resVals.fill(constantValue);\n    }\n    for (let i = 0; i < xSize; i++) {\n        const coords = util.indexToLoc(i, xRank, xStrides);\n        const outCoords = coords.map((c, i) => c + start[i]);\n        const outIndex = util.locToIndex(outCoords, resultRank, resultStrides);\n        resVals[outIndex] = xVals[i];\n    }\n    const outId = backend.write(resVals, outShape, x.dtype);\n    return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nexport const padV2Config = {\n    kernelName: PadV2,\n    backendName: 'cpu',\n    kernelFunc: padV2\n};\n//# sourceMappingURL=PadV2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reciprocal } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const reciprocalKernelFunc = unaryKernelFunc(Reciprocal, (xi) => 1 / xi);\nexport const reciprocalConfig = {\n    kernelName: Reciprocal,\n    backendName: 'cpu',\n    kernelFunc: reciprocalKernelFunc,\n};\n//# sourceMappingURL=Reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, RotateWithOffset, util } from '@tensorflow/tfjs-core';\nexport const rotateWithOffsetConfig = {\n    kernelName: RotateWithOffset,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const { radians, fillValue, center } = attrs;\n        const cpuBackend = backend;\n        const output = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(image.shape));\n        const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n        const [centerX, centerY] = backend_util.getImageCenter(center, imageHeight, imageWidth);\n        const fullOpacityValue = 255;\n        const sinFactor = Math.sin(radians);\n        const cosFactor = Math.cos(radians);\n        const imageVals = cpuBackend.data.get(image.dataId).values;\n        for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n            for (let row = 0; row < imageHeight; row++) {\n                const rowOffset = row * (imageWidth * numChannels);\n                for (let col = 0; col < imageWidth; col++) {\n                    const colOffset = col * numChannels;\n                    for (let channel = 0; channel < numChannels; channel++) {\n                        const coords = [batch, row, col, channel];\n                        const x = coords[2];\n                        const y = coords[1];\n                        // coordX/coordY are the result of rotating and translating x/y.\n                        let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;\n                        let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;\n                        coordX = Math.round(coordX + centerX);\n                        coordY = Math.round(coordY + centerY);\n                        let outputValue = fillValue;\n                        if (typeof fillValue !== 'number') {\n                            if (channel === 3) {\n                                outputValue = fullOpacityValue;\n                            }\n                            else {\n                                outputValue = fillValue[channel];\n                            }\n                        }\n                        // If the coordinate position falls within the image boundaries...\n                        if (coordX >= 0 && coordX < imageWidth && coordY >= 0 &&\n                            coordY < imageHeight) {\n                            // set the output to the image value at the coordinate position.\n                            const rotatedRowOffset = coordY * (imageWidth * numChannels);\n                            const rotatedColOffset = coordX * numChannels;\n                            const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;\n                            outputValue = imageVals[imageIdx];\n                        }\n                        const outIdx = batchOffset + rowOffset + colOffset + channel;\n                        output[outIdx] = outputValue;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(output, image.shape, image.dtype);\n        return { dataId, shape: image.shape, dtype: image.dtype };\n    }\n};\n//# sourceMappingURL=RotateWithOffset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Round } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const roundKernelFunc = unaryKernelFunc(Round, (xi) => {\n    // The algorithm is based on banker's rounding.\n    const base = Math.floor(xi);\n    if (xi - base < 0.5) {\n        return Math.floor(xi);\n    }\n    else if (xi - base > 0.5) {\n        return Math.ceil(xi);\n    }\n    else {\n        if (base % 2.0 === 0.0) {\n            return base;\n        }\n        else {\n            return base + 1.0;\n        }\n    }\n});\nexport const roundConfig = {\n    kernelName: Round,\n    backendName: 'cpu',\n    kernelFunc: roundKernelFunc,\n};\n//# sourceMappingURL=Round.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Selu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nconst scaleAlpha = backend_util.SELU_SCALEALPHA;\nconst scale = backend_util.SELU_SCALE;\nexport const seluKernelFunc = unaryKernelFunc(Selu, (xi) => {\n    if (xi >= 0) {\n        return scale * xi;\n    }\n    else {\n        return scaleAlpha * (Math.exp(xi) - 1);\n    }\n});\nexport const seluConfig = {\n    kernelName: Selu,\n    backendName: 'cpu',\n    kernelFunc: seluKernelFunc,\n};\n//# sourceMappingURL=Selu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sigmoid } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sigmoidKernelFunc = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));\nexport const sigmoidConfig = {\n    kernelName: Sigmoid,\n    backendName: 'cpu',\n    kernelFunc: sigmoidKernelFunc,\n};\n//# sourceMappingURL=Sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sign } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const signKernelFunc = unaryKernelFunc(Sign, (xi) => {\n    if (xi < 0) {\n        return -1;\n    }\n    else if (xi > 0) {\n        return 1;\n    }\n    else {\n        return 0;\n    }\n});\nexport const signConfig = {\n    kernelName: Sign,\n    backendName: 'cpu',\n    kernelFunc: signKernelFunc,\n};\n//# sourceMappingURL=Sign.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sinKernelFunc = unaryKernelFunc(Sin, (xi) => Math.sin(xi));\nexport const sinConfig = {\n    kernelName: Sin,\n    backendName: 'cpu',\n    kernelFunc: sinKernelFunc,\n};\n//# sourceMappingURL=Sin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sinh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sinhKernelFunc = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi));\nexport const sinhConfig = {\n    kernelName: Sinh,\n    backendName: 'cpu',\n    kernelFunc: sinhKernelFunc,\n};\n//# sourceMappingURL=Sinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softplus } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\n// mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n// epsilon is the difference between 1.0 and the next representable float.\n// For a single precision 32 bit float this should be 2^-23, see:\n// https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\nconst epsilon = 1.1920928955078125e-7;\nconst threshold = Math.log(epsilon) + 2.0;\nexport const softplusKernelFunc = unaryKernelFunc(Softplus, (xi) => {\n    // Value above which exp(x) may overflow, but softplus(x) == x\n    // is within machine epsilon.\n    const tooLarge = xi > -threshold;\n    // Value below which exp(x) may underflow, but softplus(x) == exp(x)\n    // is within machine epsilon.\n    const tooSmall = xi < threshold;\n    const expX = Math.exp(xi);\n    let result;\n    if (tooSmall) {\n        result = expX;\n    }\n    else if (tooLarge) {\n        result = xi;\n    }\n    else {\n        result = Math.log(1.0 + expX);\n    }\n    return result;\n});\nexport const softplusConfig = {\n    kernelName: Softplus,\n    backendName: 'cpu',\n    kernelFunc: softplusKernelFunc,\n};\n//# sourceMappingURL=Softplus.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { transposeImpl } from './Transpose_impl';\nexport function transpose(args) {\n    const { inputs, attrs, backend } = args;\n    const { x } = inputs;\n    const { perm } = attrs;\n    assertNotComplex(x, 'transpose');\n    const xRank = x.shape.length;\n    const newShape = new Array(xRank);\n    for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = x.shape[perm[i]];\n    }\n    const values = backend.data.get(x.dataId).values;\n    const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);\n    const dataId = backend.write(result, newShape, x.dtype);\n    return { dataId, shape: newShape, dtype: x.dtype };\n}\nexport const transposeConfig = {\n    kernelName: Transpose,\n    backendName: 'cpu',\n    kernelFunc: transpose\n};\n//# sourceMappingURL=Transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, SpaceToBatchND, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { padV2Config } from './PadV2';\nimport { reshape } from './Reshape';\nimport { transpose } from './Transpose';\nexport function spaceToBatchND(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { blockShape, paddings } = attrs;\n    assertNotComplex([x], 'spaceToBatchND');\n    const prod = util.sizeFromShape(blockShape);\n    const completePaddings = [[0, 0]];\n    completePaddings.push(...paddings);\n    for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n        completePaddings.push([0, 0]);\n    }\n    const paddedX = padV2Config.kernelFunc({\n        inputs: { x },\n        backend,\n        attrs: { paddings: completePaddings, constantValue: 0 }\n    });\n    const reshapedPaddedShape = backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n    const permutedReshapedPaddedPermutation = backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n    const flattenShape = backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n    const reshapeInputs = { x: paddedX };\n    const reshapeAttrs = { shape: reshapedPaddedShape };\n    const paddedXReshaped = reshape({ inputs: reshapeInputs, backend, attrs: reshapeAttrs });\n    const transposeInputs = { x: paddedXReshaped };\n    const transposeAttrs = { perm: permutedReshapedPaddedPermutation };\n    const paddedXT = transpose({ inputs: transposeInputs, backend, attrs: transposeAttrs });\n    const resultReshapeInputs = { x: paddedXT };\n    const resultReshapeAttrs = { shape: flattenShape };\n    const result = reshape({ inputs: resultReshapeInputs, backend, attrs: resultReshapeAttrs });\n    backend.disposeIntermediateTensorInfo(paddedX);\n    backend.disposeIntermediateTensorInfo(paddedXReshaped);\n    backend.disposeIntermediateTensorInfo(paddedXT);\n    return result;\n}\nexport const spaceToBatchNDConfig = {\n    kernelName: SpaceToBatchND,\n    backendName: 'cpu',\n    kernelFunc: spaceToBatchND\n};\n//# sourceMappingURL=SpaceToBatchND.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sqrt } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sqrtKernelFunc = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));\nexport const sqrtConfig = {\n    kernelName: Sqrt,\n    backendName: 'cpu',\n    kernelFunc: sqrtKernelFunc,\n};\n//# sourceMappingURL=Sqrt.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport const squareConfig = {\n    kernelName: Square,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend }) => {\n        const { x } = inputs;\n        const cpuBackend = backend;\n        assertNotComplex(x, 'square');\n        const values = cpuBackend.data.get(x.dataId).values;\n        const newValues = new Float32Array(values.length);\n        for (let i = 0; i < values.length; ++i) {\n            const value = values[i];\n            newValues[i] = value * value;\n        }\n        const dataId = cpuBackend.write(newValues, x.shape, x.dtype);\n        return { dataId, shape: x.shape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const squaredDifferenceImpl = createSimpleBinaryKernelImpl(((a, b) => {\n    const diff = a - b;\n    return diff * diff;\n}));\nexport const squaredDifference = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);\nexport const squaredDifferenceConfig = {\n    kernelName: SquaredDifference,\n    backendName: 'cpu',\n    kernelFunc: squaredDifference\n};\n//# sourceMappingURL=SquaredDifference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Step } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const stepKernelFunc = unaryKernelFunc(Step, (xi, attrs) => {\n    const stepAttrs = attrs;\n    if (isNaN(xi)) {\n        return NaN;\n    }\n    else {\n        return xi > 0 ? 1 : stepAttrs.alpha;\n    }\n});\nexport const stepConfig = {\n    kernelName: Step,\n    backendName: 'cpu',\n    kernelFunc: stepKernelFunc,\n};\n//# sourceMappingURL=Step.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const tanKernelFunc = unaryKernelFunc(Tan, (xi) => Math.tan(xi));\nexport const tanConfig = {\n    kernelName: Tan,\n    backendName: 'cpu',\n    kernelFunc: tanKernelFunc,\n};\n//# sourceMappingURL=Tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tanh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const tanhKernelFunc = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi));\nexport const tanhConfig = {\n    kernelName: Tanh,\n    backendName: 'cpu',\n    kernelFunc: tanhKernelFunc,\n};\n//# sourceMappingURL=Tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unique } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { uniqueImpl } from './Unique_impl';\nexport function unique(args) {\n    const { inputs, attrs, backend } = args;\n    const { axis } = attrs;\n    const { x } = inputs;\n    assertNotComplex(x, 'unique');\n    const values = backend.data.get(x.dataId).values;\n    const { outputValues, outputShape, indices } = uniqueImpl(values, axis, x.shape, x.dtype);\n    return [\n        backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n        backend.makeTensorInfo([indices.length], 'int32', indices),\n    ];\n}\nexport const uniqueConfig = {\n    kernelName: Unique,\n    backendName: 'cpu',\n    kernelFunc: unique,\n};\n//# sourceMappingURL=Unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// We explicitly import the modular kernels so they get registered in the\n// global registry when we compile the library. A modular build would replace\n// the contents of this file and import only the kernels that are needed.\nimport { registerKernel } from '@tensorflow/tfjs-core';\nimport { absConfig } from './kernels/Abs';\nimport { acosConfig } from './kernels/Acos';\nimport { acoshConfig } from './kernels/Acosh';\nimport { addConfig } from './kernels/Add';\nimport { asinConfig } from './kernels/Asin';\nimport { asinhConfig } from './kernels/Asinh';\nimport { atanConfig } from './kernels/Atan';\nimport { atanhConfig } from './kernels/Atanh';\nimport { avgPoolConfig } from './kernels/AvgPool';\nimport { avgPoolBackpropConfig } from './kernels/AvgPoolBackprop';\nimport { batchNormConfig } from './kernels/BatchNorm';\nimport { castConfig } from './kernels/Cast';\nimport { ceilConfig } from './kernels/Ceil';\nimport { clipConfig } from './kernels/Clip';\nimport { complexConfig } from './kernels/Complex';\nimport { concatConfig } from './kernels/Concat';\nimport { cosConfig } from './kernels/Cos';\nimport { coshConfig } from './kernels/Cosh';\nimport { dilation2dConfig } from './kernels/Dilation2D';\nimport { dilation2dBackpropFilterConfig } from './kernels/Dilation2DBackpropFilter';\nimport { dilation2dBackpropInputConfig } from './kernels/Dilation2DBackpropInput';\nimport { divConfig } from './kernels/Div';\nimport { eluConfig } from './kernels/Elu';\nimport { erfConfig } from './kernels/Erf';\nimport { expConfig } from './kernels/Exp';\nimport { expm1Config } from './kernels/Expm1';\nimport { fftConfig } from './kernels/FFT';\nimport { flipLeftRightConfig } from './kernels/FlipLeftRight';\nimport { floorConfig } from './kernels/Floor';\nimport { identityConfig } from './kernels/Identity';\nimport { ifftConfig } from './kernels/IFFT';\nimport { imagConfig } from './kernels/Imag';\nimport { isFiniteConfig } from './kernels/IsFinite';\nimport { isInfConfig } from './kernels/IsInf';\nimport { isNaNConfig } from './kernels/IsNaN';\nimport { logConfig } from './kernels/Log';\nimport { log1pConfig } from './kernels/Log1p';\nimport { logicalNotConfig } from './kernels/LogicalNot';\nimport { maxConfig } from './kernels/Max';\nimport { maxPoolConfig } from './kernels/MaxPool';\nimport { maxPoolBackpropConfig } from './kernels/MaxPoolBackprop';\nimport { maxPoolWithArgmaxConfig } from './kernels/MaxPoolWithArgmax';\nimport { multiplyConfig } from './kernels/Multiply';\nimport { nonMaxSuppressionV4Config } from './kernels/NonMaxSuppressionV4';\nimport { nonMaxSuppressionV5Config } from './kernels/NonMaxSuppressionV5';\nimport { notEqualConfig } from './kernels/NotEqual';\nimport { padV2Config } from './kernels/PadV2';\nimport { realConfig } from './kernels/Real';\nimport { reciprocalConfig } from './kernels/Reciprocal';\nimport { reshapeConfig } from './kernels/Reshape';\nimport { rotateWithOffsetConfig } from './kernels/RotateWithOffset';\nimport { roundConfig } from './kernels/Round';\nimport { rsqrtConfig } from './kernels/Rsqrt';\nimport { seluConfig } from './kernels/Selu';\nimport { sigmoidConfig } from './kernels/Sigmoid';\nimport { signConfig } from './kernels/Sign';\nimport { sinConfig } from './kernels/Sin';\nimport { sinhConfig } from './kernels/Sinh';\nimport { sliceConfig } from './kernels/Slice';\nimport { softplusConfig } from './kernels/Softplus';\nimport { spaceToBatchNDConfig } from './kernels/SpaceToBatchND';\nimport { sqrtConfig } from './kernels/Sqrt';\nimport { squareConfig } from './kernels/Square';\nimport { squaredDifferenceConfig } from './kernels/SquaredDifference';\nimport { stepConfig } from './kernels/Step';\nimport { subConfig } from './kernels/Sub';\nimport { tanConfig } from './kernels/Tan';\nimport { tanhConfig } from './kernels/Tanh';\nimport { transposeConfig } from './kernels/Transpose';\nimport { uniqueConfig } from './kernels/Unique';\n// List all kernel configs here\nconst kernelConfigs = [\n    absConfig,\n    acosConfig,\n    acoshConfig,\n    addConfig,\n    asinConfig,\n    asinhConfig,\n    atanConfig,\n    atanhConfig,\n    avgPoolConfig,\n    avgPoolBackpropConfig,\n    batchNormConfig,\n    castConfig,\n    ceilConfig,\n    clipConfig,\n    complexConfig,\n    concatConfig,\n    cosConfig,\n    coshConfig,\n    dilation2dConfig,\n    dilation2dBackpropInputConfig,\n    dilation2dBackpropFilterConfig,\n    divConfig,\n    eluConfig,\n    erfConfig,\n    expConfig,\n    expm1Config,\n    fftConfig,\n    flipLeftRightConfig,\n    floorConfig,\n    identityConfig,\n    ifftConfig,\n    imagConfig,\n    isFiniteConfig,\n    isInfConfig,\n    isNaNConfig,\n    logConfig,\n    log1pConfig,\n    logicalNotConfig,\n    maxPoolConfig,\n    maxPoolBackpropConfig,\n    maxPoolWithArgmaxConfig,\n    maxConfig,\n    multiplyConfig,\n    nonMaxSuppressionV4Config,\n    nonMaxSuppressionV5Config,\n    notEqualConfig,\n    padV2Config,\n    realConfig,\n    reciprocalConfig,\n    reshapeConfig,\n    rotateWithOffsetConfig,\n    roundConfig,\n    rsqrtConfig,\n    seluConfig,\n    sigmoidConfig,\n    signConfig,\n    sinConfig,\n    sinhConfig,\n    sliceConfig,\n    softplusConfig,\n    spaceToBatchNDConfig,\n    sqrtConfig,\n    squareConfig,\n    squaredDifferenceConfig,\n    stepConfig,\n    subConfig,\n    tanConfig,\n    tanhConfig,\n    transposeConfig,\n    uniqueConfig,\n];\nfor (const kernelConfig of kernelConfigs) {\n    registerKernel(kernelConfig);\n}\n//# sourceMappingURL=register_all_kernels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// All exports from this package should be in base.\nexport * from './base';\nimport './register_all_kernels';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst contexts = {};\nconst WEBGL_ATTRIBUTES = {\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: true\n};\nexport function setWebGLContext(webGLVersion, gl) {\n    contexts[webGLVersion] = gl;\n}\nexport function getWebGLContext(webGLVersion) {\n    if (!(webGLVersion in contexts)) {\n        const newCtx = getWebGLRenderingContext(webGLVersion);\n        if (newCtx !== null) {\n            contexts[webGLVersion] = newCtx;\n        }\n        else {\n            console.log('Could not get context for WebGL version', webGLVersion);\n            return null;\n        }\n    }\n    const gl = contexts[webGLVersion];\n    if (gl.isContextLost()) {\n        delete contexts[webGLVersion];\n        return getWebGLContext(webGLVersion);\n    }\n    gl.disable(gl.DEPTH_TEST);\n    gl.disable(gl.STENCIL_TEST);\n    gl.disable(gl.BLEND);\n    gl.disable(gl.DITHER);\n    gl.disable(gl.POLYGON_OFFSET_FILL);\n    gl.disable(gl.SAMPLE_COVERAGE);\n    gl.enable(gl.SCISSOR_TEST);\n    gl.enable(gl.CULL_FACE);\n    gl.cullFace(gl.BACK);\n    return contexts[webGLVersion];\n}\nfunction createCanvas(webGLVersion) {\n    if (typeof OffscreenCanvas !== 'undefined' && webGLVersion === 2) {\n        return new OffscreenCanvas(300, 150);\n    }\n    else if (typeof document !== 'undefined') {\n        return document.createElement('canvas');\n    }\n    else {\n        throw new Error('Cannot create a canvas in this context');\n    }\n}\nfunction getWebGLRenderingContext(webGLVersion) {\n    if (webGLVersion !== 1 && webGLVersion !== 2) {\n        throw new Error('Cannot get WebGL rendering context, WebGL is disabled.');\n    }\n    const canvas = createCanvas(webGLVersion);\n    canvas.addEventListener('webglcontextlost', (ev) => {\n        ev.preventDefault();\n        delete contexts[webGLVersion];\n    }, false);\n    if (webGLVersion === 1) {\n        return (canvas.getContext('webgl', WEBGL_ATTRIBUTES) ||\n            canvas.getContext('experimental-webgl', WEBGL_ATTRIBUTES));\n    }\n    return canvas.getContext('webgl2', WEBGL_ATTRIBUTES);\n}\n//# sourceMappingURL=canvas_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nexport var PackingScheme;\n(function (PackingScheme) {\n    /**\n     * All values in a single texel are densely packed without any constraints.\n     *\n     * This is how the shader encodes a tensor with shape = [2, 3, 4]\n     * (indices are [batch, row, col]).\n     *\n     * 000|001   010|011   020|021\n     * -------   -------   -------\n     * 002|003   012|013   022|023\n     *\n     * 100|101   110|111   120|121\n     * -------   -------   -------\n     * 102|103   112|113   122|123\n     *\n     */\n    PackingScheme[PackingScheme[\"DENSE\"] = 0] = \"DENSE\";\n    /**\n     * Single texels contain only values from the same batch, and from adjacent\n     * rows and columns.\n     *\n     * This is how the shader encodes a tensor with shape = [2, 3, 5]\n     * (indices are [batch, row, col]).\n     *\n     * 000|001   002|003   004|xxx   020|021   022|023   024|xxx\n     * -------   -------   -------   -------   -------   -------\n     * 010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n     *\n     * 100|101   102|103   104|xxx   120|121   122|123   124|xxx\n     * -------   -------   -------   -------   -------   -------\n     * 110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n     *\n     */\n    PackingScheme[PackingScheme[\"SHARED_BATCH\"] = 1] = \"SHARED_BATCH\";\n})(PackingScheme || (PackingScheme = {}));\nexport var TextureUsage;\n(function (TextureUsage) {\n    TextureUsage[TextureUsage[\"RENDER\"] = 0] = \"RENDER\";\n    TextureUsage[TextureUsage[\"UPLOAD\"] = 1] = \"UPLOAD\";\n    TextureUsage[TextureUsage[\"PIXELS\"] = 2] = \"PIXELS\";\n    TextureUsage[TextureUsage[\"DOWNLOAD\"] = 3] = \"DOWNLOAD\";\n})(TextureUsage || (TextureUsage = {}));\nexport var PhysicalTextureType;\n(function (PhysicalTextureType) {\n    PhysicalTextureType[PhysicalTextureType[\"UNPACKED_FLOAT16\"] = 0] = \"UNPACKED_FLOAT16\";\n    PhysicalTextureType[PhysicalTextureType[\"UNPACKED_FLOAT32\"] = 1] = \"UNPACKED_FLOAT32\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_4X1_UNSIGNED_BYTE\"] = 2] = \"PACKED_4X1_UNSIGNED_BYTE\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_2X2_FLOAT32\"] = 3] = \"PACKED_2X2_FLOAT32\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_2X2_FLOAT16\"] = 4] = \"PACKED_2X2_FLOAT16\";\n})(PhysicalTextureType || (PhysicalTextureType = {}));\nexport function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {\n    return [columns, rows];\n}\nexport function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {\n    return matrixSize * channelsPerTexture;\n}\nexport function getColorMatrixTextureShapeWidthHeight(rows, columns) {\n    return [columns * 4, rows];\n}\n/**\n * Get shape for densely packed RGBA texture.\n */\nexport function getDenseTexShape(shape) {\n    const size = util.sizeFromShape(shape);\n    const texelsNeeded = Math.ceil(size / 4);\n    return util.sizeToSquarishShape(texelsNeeded);\n}\nexport function getMatrixSizeFromUnpackedArraySize(unpackedSize, channelsPerTexture) {\n    if (unpackedSize % channelsPerTexture !== 0) {\n        throw new Error(`unpackedSize (${unpackedSize}) must be a multiple of ` +\n            `${channelsPerTexture}`);\n    }\n    return unpackedSize / channelsPerTexture;\n}\nexport function decodeMatrixFromUnpackedColorRGBAArray(unpackedArray, matrix, channels) {\n    const requiredSize = unpackedArray.length * channels / 4;\n    if (matrix.length < requiredSize) {\n        throw new Error(`matrix length (${matrix.length}) must be >= ${requiredSize}`);\n    }\n    let dst = 0;\n    for (let src = 0; src < unpackedArray.length; src += 4) {\n        for (let c = 0; c < channels; c++) {\n            matrix[dst++] = unpackedArray[src + c];\n        }\n    }\n}\nexport function getPackedMatrixTextureShapeWidthHeight(rows, columns) {\n    return [\n        Math.max(1, Math.ceil(columns / 2)), Math.max(1, Math.ceil(rows / 2))\n    ];\n}\nexport function getPackedRGBAArraySizeFromMatrixShape(rows, columns) {\n    const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return w * h * 4;\n}\nexport function getTextureConfig(\n// tslint:disable-next-line:no-any\ngl, textureHalfFloatExtension) {\n    // tslint:disable-next-line:no-any\n    const glany = gl;\n    let internalFormatFloat;\n    let internalFormatHalfFloat;\n    let internalFormatPackedHalfFloat;\n    let internalFormatPackedFloat;\n    let textureFormatFloat;\n    let downloadTextureFormat;\n    let downloadUnpackNumChannels;\n    let defaultNumChannels;\n    let textureTypeHalfFloat;\n    let textureTypeFloat;\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        internalFormatFloat = glany.R32F;\n        internalFormatHalfFloat = glany.R16F;\n        internalFormatPackedHalfFloat = glany.RGBA16F;\n        internalFormatPackedFloat = glany.RGBA32F;\n        textureFormatFloat = glany.RED;\n        downloadUnpackNumChannels = 4;\n        defaultNumChannels = 1;\n        textureTypeHalfFloat = glany.HALF_FLOAT;\n        textureTypeFloat = glany.FLOAT;\n    }\n    else {\n        internalFormatFloat = gl.RGBA;\n        internalFormatHalfFloat = gl.RGBA;\n        internalFormatPackedHalfFloat = gl.RGBA;\n        internalFormatPackedFloat = glany.RGBA;\n        textureFormatFloat = gl.RGBA;\n        downloadUnpackNumChannels = 4;\n        defaultNumChannels = 4;\n        textureTypeHalfFloat = textureHalfFloatExtension != null ?\n            textureHalfFloatExtension.HALF_FLOAT_OES :\n            null;\n        textureTypeFloat = gl.FLOAT;\n    }\n    downloadTextureFormat = gl.RGBA;\n    return {\n        internalFormatFloat,\n        internalFormatHalfFloat,\n        internalFormatPackedHalfFloat,\n        internalFormatPackedFloat,\n        textureFormatFloat,\n        downloadTextureFormat,\n        downloadUnpackNumChannels,\n        defaultNumChannels,\n        textureTypeHalfFloat,\n        textureTypeFloat\n    };\n}\n//# sourceMappingURL=tex_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { getWebGLContext } from './canvas_util';\nimport { getTextureConfig } from './tex_util';\nexport function callAndCheck(gl, func) {\n    const returnValue = func();\n    if (env().getBool('DEBUG')) {\n        checkWebGLError(gl);\n    }\n    return returnValue;\n}\nfunction checkWebGLError(gl) {\n    const error = gl.getError();\n    if (error !== gl.NO_ERROR) {\n        throw new Error('WebGL Error: ' + getWebGLErrorMessage(gl, error));\n    }\n}\n// https://en.wikipedia.org/wiki/Half-precision_floating-point_format\nconst MIN_FLOAT16 = 5.96e-8;\nconst MAX_FLOAT16 = 65504;\nexport function canBeRepresented(num) {\n    if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED') || num === 0 ||\n        (MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16)) {\n        return true;\n    }\n    return false;\n}\nexport function getWebGLErrorMessage(gl, status) {\n    switch (status) {\n        case gl.NO_ERROR:\n            return 'NO_ERROR';\n        case gl.INVALID_ENUM:\n            return 'INVALID_ENUM';\n        case gl.INVALID_VALUE:\n            return 'INVALID_VALUE';\n        case gl.INVALID_OPERATION:\n            return 'INVALID_OPERATION';\n        case gl.INVALID_FRAMEBUFFER_OPERATION:\n            return 'INVALID_FRAMEBUFFER_OPERATION';\n        case gl.OUT_OF_MEMORY:\n            return 'OUT_OF_MEMORY';\n        case gl.CONTEXT_LOST_WEBGL:\n            return 'CONTEXT_LOST_WEBGL';\n        default:\n            return `Unknown error code ${status}`;\n    }\n}\nexport function getExtensionOrThrow(gl, extensionName) {\n    return throwIfNull(gl, () => gl.getExtension(extensionName), 'Extension \"' + extensionName + '\" not supported on this browser.');\n}\nexport function createVertexShader(gl, vertexShaderSource) {\n    const vertexShader = throwIfNull(gl, () => gl.createShader(gl.VERTEX_SHADER), 'Unable to create vertex WebGLShader.');\n    callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));\n    callAndCheck(gl, () => gl.compileShader(vertexShader));\n    if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {\n        console.log(gl.getShaderInfoLog(vertexShader));\n        throw new Error('Failed to compile vertex shader.');\n    }\n    return vertexShader;\n}\nexport function createFragmentShader(gl, fragmentShaderSource) {\n    const fragmentShader = throwIfNull(gl, () => gl.createShader(gl.FRAGMENT_SHADER), 'Unable to create fragment WebGLShader.');\n    callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));\n    callAndCheck(gl, () => gl.compileShader(fragmentShader));\n    if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {\n        logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));\n        throw new Error('Failed to compile fragment shader.');\n    }\n    return fragmentShader;\n}\nconst lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;\nfunction logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {\n    const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);\n    if (lineNumberRegexResult == null) {\n        console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);\n        console.log(shaderSource);\n        return;\n    }\n    const lineNumber = +lineNumberRegexResult[1];\n    const shaderLines = shaderSource.split('\\n');\n    const pad = shaderLines.length.toString().length + 2;\n    const linesWithLineNumbers = shaderLines.map((line, lineNumber) => util.rightPad((lineNumber + 1).toString(), pad) + line);\n    let maxLineLength = 0;\n    for (let i = 0; i < linesWithLineNumbers.length; i++) {\n        maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);\n    }\n    const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);\n    const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);\n    const afterErrorLines = linesWithLineNumbers.slice(lineNumber);\n    console.log(beforeErrorLines.join('\\n'));\n    console.log(shaderInfoLog.split('\\n')[0]);\n    console.log(`%c ${util.rightPad(errorLine[0], maxLineLength)}`, 'border:1px solid red; background-color:#e3d2d2; color:#a61717');\n    console.log(afterErrorLines.join('\\n'));\n}\nexport function createProgram(gl) {\n    return throwIfNull(gl, () => gl.createProgram(), 'Unable to create WebGLProgram.');\n}\nexport function linkProgram(gl, program) {\n    callAndCheck(gl, () => gl.linkProgram(program));\n    if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {\n        console.log(gl.getProgramInfoLog(program));\n        throw new Error('Failed to link vertex and fragment shaders.');\n    }\n}\nexport function validateProgram(gl, program) {\n    callAndCheck(gl, () => gl.validateProgram(program));\n    if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {\n        console.log(gl.getProgramInfoLog(program));\n        throw new Error('Shader program validation failed.');\n    }\n}\nexport function createStaticVertexBuffer(gl, data) {\n    const buffer = throwIfNull(gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));\n    return buffer;\n}\nexport function createStaticIndexBuffer(gl, data) {\n    const buffer = throwIfNull(gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n    callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));\n    return buffer;\n}\nexport function getNumChannels() {\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        return 1;\n    }\n    return 4;\n}\nexport function createTexture(gl) {\n    return throwIfNull(gl, () => gl.createTexture(), 'Unable to create WebGLTexture.');\n}\nexport function validateTextureSize(width, height) {\n    const maxTextureSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n    if ((width <= 0) || (height <= 0)) {\n        const requested = `[${width}x${height}]`;\n        throw new Error('Requested texture size ' + requested + ' is invalid.');\n    }\n    if ((width > maxTextureSize) || (height > maxTextureSize)) {\n        const requested = `[${width}x${height}]`;\n        const max = `[${maxTextureSize}x${maxTextureSize}]`;\n        throw new Error('Requested texture size ' + requested +\n            ' greater than WebGL maximum on this browser / GPU ' + max + '.');\n    }\n}\nexport function createFramebuffer(gl) {\n    return throwIfNull(gl, () => gl.createFramebuffer(), 'Unable to create WebGLFramebuffer.');\n}\nexport function bindVertexBufferToProgramAttribute(gl, program, attribute, buffer, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {\n    const loc = gl.getAttribLocation(program, attribute);\n    if (loc === -1) {\n        // The GPU compiler decided to strip out this attribute because it's unused,\n        // thus no need to bind.\n        return false;\n    }\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes));\n    callAndCheck(gl, () => gl.enableVertexAttribArray(loc));\n    return true;\n}\nexport function bindTextureUnit(gl, texture, textureUnit) {\n    validateTextureUnit(gl, textureUnit);\n    callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n}\nexport function unbindTextureUnit(gl, textureUnit) {\n    validateTextureUnit(gl, textureUnit);\n    callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function getProgramUniformLocationOrThrow(gl, program, uniformName) {\n    return throwIfNull(gl, () => gl.getUniformLocation(program, uniformName), 'uniform \"' + uniformName + '\" not present in program.');\n}\nexport function getProgramUniformLocation(gl, program, uniformName) {\n    return gl.getUniformLocation(program, uniformName);\n}\nexport function bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) {\n    callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));\n    callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));\n}\nexport function bindCanvasToFramebuffer(gl) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n    callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));\n    callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));\n}\nexport function bindColorTextureToFramebuffer(gl, texture, framebuffer) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));\n}\nexport function unbindColorTextureFromFramebuffer(gl, framebuffer) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));\n}\nexport function validateFramebuffer(gl) {\n    const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);\n    if (status !== gl.FRAMEBUFFER_COMPLETE) {\n        throw new Error('Error binding framebuffer: ' + getFramebufferErrorMessage(gl, status));\n    }\n}\nexport function getFramebufferErrorMessage(gl, status) {\n    switch (status) {\n        case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:\n            return 'FRAMEBUFFER_INCOMPLETE_ATTACHMENT';\n        case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:\n            return 'FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT';\n        case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:\n            return 'FRAMEBUFFER_INCOMPLETE_DIMENSIONS';\n        case gl.FRAMEBUFFER_UNSUPPORTED:\n            return 'FRAMEBUFFER_UNSUPPORTED';\n        default:\n            return `unknown error ${status}`;\n    }\n}\nfunction throwIfNull(gl, returnTOrNull, failureMessage) {\n    const tOrNull = callAndCheck(gl, () => returnTOrNull());\n    if (tOrNull == null) {\n        throw new Error(failureMessage);\n    }\n    return tOrNull;\n}\nfunction validateTextureUnit(gl, textureUnit) {\n    const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;\n    const glTextureUnit = textureUnit + gl.TEXTURE0;\n    if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {\n        const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;\n        throw new Error(`textureUnit must be in ${textureUnitRange}.`);\n    }\n}\nexport function getBatchDim(shape, dimsToSkip = 2) {\n    return util.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));\n}\nexport function getRowsCols(shape) {\n    if (shape.length === 0) {\n        throw Error('Cannot get rows and columns of an empty shape array.');\n    }\n    return [\n        shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]\n    ];\n}\nexport function getShapeAs3D(shape) {\n    let shapeAs3D = [1, 1, 1];\n    const isScalar = shape.length === 0 || (shape.length === 1 && shape[0] === 1);\n    if (!isScalar) {\n        shapeAs3D =\n            [getBatchDim(shape), ...getRowsCols(shape)];\n    }\n    return shapeAs3D;\n}\nexport function getTextureShapeFromLogicalShape(logShape, isPacked = false) {\n    let maxTexSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n    if (isPacked) {\n        maxTexSize = maxTexSize * 2;\n        // This logic ensures we accurately count the number of packed texels needed\n        // to accommodate the tensor. We can only pack values in the same texel if\n        // they are from adjacent pairs of rows/cols within the same batch. So if a\n        // tensor has 3 rows, we pretend it has 4 rows in order to account for the\n        // fact that the texels containing the third row are half empty.\n        logShape = logShape.map((d, i) => i >= logShape.length - 2 ?\n            util.nearestLargerEven(logShape[i]) :\n            logShape[i]);\n        // Packed texture height is at least 2 (the channel height of a single\n        // texel).\n        if (logShape.length === 1) {\n            logShape = [2, logShape[0]];\n        }\n    }\n    // If logical shape is 2, we don't squeeze, since we want to match physical.\n    if (logShape.length !== 2) {\n        const squeezeResult = util.squeezeShape(logShape);\n        logShape = squeezeResult.newShape;\n    }\n    let size = util.sizeFromShape(logShape);\n    if (logShape.length <= 1 && size <= maxTexSize) {\n        return [1, size];\n    }\n    else if (logShape.length === 2 && logShape[0] <= maxTexSize &&\n        logShape[1] <= maxTexSize) {\n        return logShape;\n    }\n    else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize &&\n        logShape[2] <= maxTexSize) {\n        return [logShape[0] * logShape[1], logShape[2]];\n    }\n    else if (logShape.length === 3 && logShape[0] <= maxTexSize &&\n        logShape[1] * logShape[2] <= maxTexSize) {\n        return [logShape[0], logShape[1] * logShape[2]];\n    }\n    else if (logShape.length === 4 &&\n        logShape[0] * logShape[1] * logShape[2] <= maxTexSize &&\n        logShape[3] <= maxTexSize) {\n        return [logShape[0] * logShape[1] * logShape[2], logShape[3]];\n    }\n    else if (logShape.length === 4 && logShape[0] <= maxTexSize &&\n        logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {\n        return [logShape[0], logShape[1] * logShape[2] * logShape[3]];\n    }\n    else {\n        if (isPacked) {\n            // For packed textures size equals the number of channels required to\n            // accommodate the texture data. However in order to squarify such that\n            // inner dimensions stay even, we rewrite size to equal the number of\n            // texels. Then in the return statement we rehydrate the squarified\n            // dimensions to channel units.\n            const batchDim = getBatchDim(logShape);\n            let rows = 2, cols = 2;\n            if (logShape.length) {\n                [rows, cols] = getRowsCols(logShape);\n            }\n            size = batchDim * (rows / 2) * (cols / 2);\n            return util.sizeToSquarishShape(size).map(d => d * 2);\n        }\n        return util.sizeToSquarishShape(size);\n    }\n}\nfunction isEven(n) {\n    return n % 2 === 0;\n}\n/**\n * This determines whether reshaping a packed texture requires rearranging\n * the data within the texture, assuming 2x2 packing.\n */\nexport function isReshapeFree(shape1, shape2) {\n    shape1 = shape1.slice(-2);\n    shape2 = shape2.slice(-2);\n    if (util.arraysEqual(shape1, shape2)) {\n        return true;\n    }\n    if (!shape1.length || !shape2.length) { // One of the shapes is a scalar.\n        return true;\n    }\n    if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 ||\n        shape2[1] === 0) {\n        return true;\n    }\n    if (shape1.length !== shape2.length) { // One of the shapes is a vector.\n        const shape1Cols = shape1.slice(-1)[0];\n        const shape2Cols = shape2.slice(-1)[0];\n        if (shape1Cols === shape2Cols) {\n            return true;\n        }\n        if (isEven(shape1Cols) && isEven(shape2Cols) &&\n            (shape1[0] === 1 || shape2[0] === 1)) {\n            return true;\n        }\n    }\n    return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);\n}\n// We cache webgl params because the environment gets reset between\n// unit tests and we don't want to constantly query the WebGLContext for\n// MAX_TEXTURE_SIZE.\nlet MAX_TEXTURE_SIZE;\nlet MAX_TEXTURES_IN_SHADER;\nexport function getWebGLMaxTextureSize(webGLVersion) {\n    if (MAX_TEXTURE_SIZE == null) {\n        const gl = getWebGLContext(webGLVersion);\n        MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n    }\n    return MAX_TEXTURE_SIZE;\n}\nexport function resetMaxTextureSize() {\n    MAX_TEXTURE_SIZE = null;\n}\nexport function resetMaxTexturesInShader() {\n    MAX_TEXTURES_IN_SHADER = null;\n}\nexport function getMaxTexturesInShader(webGLVersion) {\n    if (MAX_TEXTURES_IN_SHADER == null) {\n        const gl = getWebGLContext(webGLVersion);\n        MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);\n    }\n    // We cap at 16 to avoid spurious runtime \"memory exhausted\" error.\n    return Math.min(16, MAX_TEXTURES_IN_SHADER);\n}\nexport function getWebGLDisjointQueryTimerVersion(webGLVersion) {\n    if (webGLVersion === 0) {\n        return 0;\n    }\n    let queryTimerVersion;\n    const gl = getWebGLContext(webGLVersion);\n    if (hasExtension(gl, 'EXT_disjoint_timer_query_webgl2') &&\n        webGLVersion === 2) {\n        queryTimerVersion = 2;\n    }\n    else if (hasExtension(gl, 'EXT_disjoint_timer_query')) {\n        queryTimerVersion = 1;\n    }\n    else {\n        queryTimerVersion = 0;\n    }\n    return queryTimerVersion;\n}\nexport function hasExtension(gl, extensionName) {\n    const ext = gl.getExtension(extensionName);\n    return ext != null;\n}\nexport function isWebGLVersionEnabled(webGLVersion) {\n    try {\n        const gl = getWebGLContext(webGLVersion);\n        if (gl != null) {\n            return true;\n        }\n    }\n    catch (e) {\n        console.log('Error when getting WebGL context: ', e);\n        return false;\n    }\n    return false;\n}\nexport function isCapableOfRenderingToFloatTexture(webGLVersion) {\n    if (webGLVersion === 0) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    if (webGLVersion === 1) {\n        if (!hasExtension(gl, 'OES_texture_float')) {\n            return false;\n        }\n    }\n    else {\n        if (!hasExtension(gl, 'EXT_color_buffer_float')) {\n            return false;\n        }\n    }\n    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n    return isFrameBufferComplete;\n}\n/**\n * Check if we can download values from a float/half-float texture.\n *\n * Note that for performance reasons we use binding a texture to a framebuffer\n * as a proxy for ability to download float values later using readPixels. The\n * texture params of this texture will not match those in readPixels exactly\n * but if we are unable to bind some kind of float texture to the frameBuffer\n * then we definitely will not be able to read float values from it.\n */\nexport function isDownloadFloatTextureEnabled(webGLVersion) {\n    if (webGLVersion === 0) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    if (webGLVersion === 1) {\n        if (!hasExtension(gl, 'OES_texture_float')) {\n            return false;\n        }\n        if (!hasExtension(gl, 'WEBGL_color_buffer_float')) {\n            return false;\n        }\n    }\n    else {\n        if (hasExtension(gl, 'EXT_color_buffer_float')) {\n            return createFloatTextureAndBindToFramebuffer(gl);\n        }\n        const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n        if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {\n            const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n            return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);\n        }\n        return false;\n    }\n    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n    return isFrameBufferComplete;\n}\nfunction createFloatTextureAndBindToFramebuffer(gl) {\n    const texConfig = getTextureConfig(gl);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    const width = 1;\n    const height = 1;\n    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);\n    const frameBuffer = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    gl.deleteTexture(texture);\n    gl.deleteFramebuffer(frameBuffer);\n    return isFrameBufferComplete;\n}\nfunction createHalfFloatTextureAndBindToFramebuffer(\n// tslint:disable-next-line:no-any\ngl, textureHalfFloatExtension) {\n    const texConfig = getTextureConfig(gl, textureHalfFloatExtension);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    const width = 1;\n    const height = 1;\n    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);\n    const frameBuffer = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    gl.deleteTexture(texture);\n    gl.deleteFramebuffer(frameBuffer);\n    return isFrameBufferComplete;\n}\nexport function isWebGLFenceEnabled(webGLVersion) {\n    if (webGLVersion !== 2) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    // tslint:disable-next-line:no-any\n    const isEnabled = gl.fenceSync != null;\n    return isEnabled;\n}\nexport function assertNotComplex(tensor, opName) {\n    if (!Array.isArray(tensor)) {\n        tensor = [tensor];\n    }\n    tensor.forEach(t => {\n        if (t != null) {\n            util.assert(t.dtype !== 'complex64', () => `${opName} does not support complex64 tensors ` +\n                'in the WebGL backend.');\n        }\n    });\n}\n//# sourceMappingURL=webgl_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { device_util, env } from '@tensorflow/tfjs-core';\nimport { getMaxTexturesInShader, getWebGLDisjointQueryTimerVersion, getWebGLMaxTextureSize, isCapableOfRenderingToFloatTexture, isDownloadFloatTextureEnabled, isWebGLFenceEnabled, isWebGLVersionEnabled } from './webgl_util';\nconst ENV = env();\n/**\n * This file contains WebGL-specific flag registrations.\n */\n/**\n * True if WebGL is supported.\n */\nENV.registerFlag('HAS_WEBGL', () => ENV.getNumber('WEBGL_VERSION') > 0);\n/** 0: No WebGL, 1: WebGL 1.0, 2: WebGL 2.0. */\nENV.registerFlag('WEBGL_VERSION', () => {\n    if (isWebGLVersionEnabled(2)) {\n        return 2;\n    }\n    else if (isWebGLVersionEnabled(1)) {\n        return 1;\n    }\n    return 0;\n});\n/** Whether to check for numerical representation problems. */\nENV.registerFlag('WEBGL_CHECK_NUMERICAL_PROBLEMS', () => false);\nENV.registerFlag('WEBGL_BUFFER_SUPPORTED', () => ENV.get('WEBGL_VERSION') === 2);\n/** Whether the WebGL backend will sometimes forward ops to the CPU. */\nENV.registerFlag('WEBGL_CPU_FORWARD', () => true);\n/** Whether the WebGL backend will always use f16 textures for rendering. */\nENV.registerFlag('WEBGL_FORCE_F16_TEXTURES', () => false);\n/** Whether to turn all packing related flags on. */\nENV.registerFlag('WEBGL_PACK', () => ENV.getBool('HAS_WEBGL'));\n/** Whether we will pack the batchnormalization op. */\nENV.registerFlag('WEBGL_PACK_NORMALIZATION', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack the clip op. */\nENV.registerFlag('WEBGL_PACK_CLIP', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack the depthwise conv op. */\n// TODO: https://github.com/tensorflow/tfjs/issues/1679\nENV.registerFlag('WEBGL_PACK_DEPTHWISECONV', () => false);\n/** Whether we will pack binary ops. */\nENV.registerFlag('WEBGL_PACK_BINARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack unary ops. */\nENV.registerFlag('WEBGL_PACK_UNARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack array ops. */\nENV.registerFlag('WEBGL_PACK_ARRAY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack image ops. */\nENV.registerFlag('WEBGL_PACK_IMAGE_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack reduce ops. */\nENV.registerFlag('WEBGL_PACK_REDUCE', () => ENV.getBool('WEBGL_PACK'));\n/** Whether packed WebGL kernels lazily unpack their outputs. */\nENV.registerFlag('WEBGL_LAZILY_UNPACK', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will use the im2col algorithm to speed up convolutions. */\nENV.registerFlag('WEBGL_CONV_IM2COL', () => ENV.getBool('WEBGL_PACK'));\n/** The maximum texture dimension. */\nENV.registerFlag('WEBGL_MAX_TEXTURE_SIZE', () => getWebGLMaxTextureSize(ENV.getNumber('WEBGL_VERSION')));\n/** The maximum texture dimension. */\nENV.registerFlag('WEBGL_MAX_TEXTURES_IN_SHADER', () => getMaxTexturesInShader(ENV.getNumber('WEBGL_VERSION')));\n/**\n * The disjoint_query_timer extension version.\n * 0: disabled, 1: EXT_disjoint_timer_query, 2:\n * EXT_disjoint_timer_query_webgl2.\n * In Firefox with WebGL 2.0,\n * EXT_disjoint_timer_query_webgl2 is not available, so we must use the\n * WebGL 1.0 extension.\n */\nENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', () => {\n    const webGLVersion = ENV.getNumber('WEBGL_VERSION');\n    if (webGLVersion === 0) {\n        return 0;\n    }\n    return getWebGLDisjointQueryTimerVersion(webGLVersion);\n});\n/**\n * Whether the timer object from the disjoint_query_timer extension gives\n * timing information that is reliable.\n */\nENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', () => ENV.getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&\n    !device_util.isMobile());\n/**\n * Whether the device is physically capable of rendering to float32 textures.\n */\nENV.registerFlag('WEBGL_RENDER_FLOAT32_CAPABLE', () => isCapableOfRenderingToFloatTexture(ENV.getNumber('WEBGL_VERSION')));\n/**\n * Whether rendering to float32 textures is enabled. If disabled, renders to\n * float16 textures.\n */\nENV.registerFlag('WEBGL_RENDER_FLOAT32_ENABLED', () => {\n    return ENV.getBool('WEBGL_FORCE_F16_TEXTURES') ?\n        false :\n        ENV.getBool('WEBGL_RENDER_FLOAT32_CAPABLE');\n});\n/**\n * Whether downloading float textures is enabled (16 or 32 bit). If disabled,\n * uses IEEE 754 encoding of the float32 values to 4 uint8 when downloading.\n */\nENV.registerFlag('WEBGL_DOWNLOAD_FLOAT_ENABLED', () => isDownloadFloatTextureEnabled(ENV.getNumber('WEBGL_VERSION')));\n/** Whether the fence API is available. */\nENV.registerFlag('WEBGL_FENCE_API_ENABLED', () => isWebGLFenceEnabled(ENV.getNumber('WEBGL_VERSION')));\n/**\n * Tensors with size <= than this will be uploaded as uniforms, not textures.\n */\nENV.registerFlag('WEBGL_SIZE_UPLOAD_UNIFORM', () => {\n    // Use uniform uploads only when 32bit floats are supported. In\n    // 16bit\n    // environments there are problems with comparing a 16bit texture value\n    // with a 32bit uniform value.\n    const useUniforms = ENV.getBool('WEBGL_RENDER_FLOAT32_ENABLED');\n    return useUniforms ? 4 : 0;\n});\n/**\n * If the total number of bytes allocated on the GPU is greater than this\n * number, we will aggressively delete textures upon disposal with\n * gl.deleteMatrixTexture, rather than making them available for reuse.\n *\n * Default value -1 indicates that we will never aggressively delete textures.\n */\nENV.registerFlag('WEBGL_DELETE_TEXTURE_THRESHOLD', () => {\n    return -1;\n}, threshold => {\n    if (threshold < 0 && threshold !== -1) {\n        throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never ` +\n            `delete) or at least 0, but got ${threshold}.`);\n    }\n});\n//# sourceMappingURL=flags_webgl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Import shared functionality from tfjs-backend-cpu without triggering\n// side effects.\n// tslint:disable-next-line: no-imports-from-dist\nimport * as shared from '@tensorflow/tfjs-backend-cpu/dist/shared';\nconst { simpleAbsImpl: simpleAbsImplCPU, addImpl: addImplCPU, ceilImpl: ceilImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, multiplyImpl: multiplyImplCPU, rsqrtImpl: rsqrtImplCPU, sliceImpl: sliceImplCPU, subImpl: subImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU, } = shared;\nexport { simpleAbsImplCPU, addImplCPU, ceilImplCPU, expImplCPU, expm1ImplCPU, logImplCPU, multiplyImplCPU, sliceImplCPU, subImplCPU, floorImplCPU, maxImplCPU, rsqrtImplCPU, transposeImplCPU, uniqueImplCPU, };\n//# sourceMappingURL=shared.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AddNProgram {\n    constructor(outputShape, shapes) {\n        this.outputShape = [];\n        this.outputShape = outputShape;\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const snippets = [];\n        // Get target elements from every input tensor.\n        this.variableNames.forEach(variable => {\n            snippets.push(`float v${variable} = get${variable}AtOutCoords();`);\n        });\n        // Calculate the sum of all elements.\n        const operation = this.variableNames\n            .map(variable => {\n            return `v${variable}`;\n        })\n            .join(' + ');\n        this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        float result = ${operation};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=addn_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AddNPackedProgram {\n    constructor(outputShape, shapes) {\n        this.outputShape = [];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const snippets = [];\n        // Get target elements from every input tensor.\n        this.variableNames.forEach(variable => {\n            snippets.push(`vec4 v${variable} = get${variable}AtOutCoords();`);\n        });\n        // Calculate the sum of all elements.\n        const operation = this.variableNames\n            .map(variable => {\n            return `v${variable}`;\n        })\n            .join(' + ');\n        this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        vec4 result = ${operation};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=addn_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ArgMinMaxProgram {\n    constructor(reduceInfo, op, firstPass) {\n        this.variableNames = ['A'];\n        const { windowSize, batchSize, outSize } = reduceInfo;\n        if (!firstPass) {\n            this.variableNames.push('bestIndicesA');\n        }\n        this.outputShape = [batchSize, outSize];\n        const compOp = (op === 'max') ? '>' : '<';\n        const indexSnippet = firstPass ?\n            'inOffset + i;' :\n            'round(getBestIndicesA(batch, inOffset + i));';\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          int inIdx = ${indexSnippet};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${compOp} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=argminmax_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport function getVecChannels(name, rank) {\n    return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank).map(d => `${name}.${d}`);\n}\nexport function getChannels(name, rank) {\n    if (rank === 1) {\n        return [name];\n    }\n    return getVecChannels(name, rank);\n}\nexport function getSourceCoords(rank, dims) {\n    if (rank === 1) {\n        return 'rc';\n    }\n    let coords = '';\n    for (let i = 0; i < rank; i++) {\n        coords += dims[i];\n        if (i < rank - 1) {\n            coords += ',';\n        }\n    }\n    return coords;\n}\n//# sourceMappingURL=packing_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nexport function getGlslDifferences() {\n    let version;\n    let attribute;\n    let varyingVs;\n    let varyingFs;\n    let texture2D;\n    let output;\n    let defineOutput;\n    let defineSpecialNaN;\n    let defineSpecialInf;\n    let defineRound;\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        version = '#version 300 es';\n        attribute = 'in';\n        varyingVs = 'out';\n        varyingFs = 'in';\n        texture2D = 'texture';\n        output = 'outputColor';\n        defineOutput = 'out vec4 outputColor;';\n        // Use custom isnan definition to work across differences between\n        // implementations on various platforms. While this should happen in ANGLE\n        // we still see differences between android and windows (on chrome) when\n        // using isnan directly.\n        defineSpecialNaN = `\n      bool isnan_custom(float val) {\n        return (val > 0.0 || val < 0.0) ? false : val != 0.0;\n      }\n\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan_custom(val.x),\n          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n      }\n\n      #define isnan(value) isnan_custom(value)\n    `;\n        // In webgl 2 we do not need to specify a custom isinf so there is no\n        // need for a special INFINITY constant.\n        defineSpecialInf = ``;\n        defineRound = `\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n    }\n    else {\n        version = '';\n        attribute = 'attribute';\n        varyingVs = 'varying';\n        varyingFs = 'varying';\n        texture2D = 'texture2D';\n        output = 'gl_FragColor';\n        defineOutput = '';\n        // WebGL1 has no built in isnan so we define one here.\n        defineSpecialNaN = `\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    `;\n        defineSpecialInf = `\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    `;\n        defineRound = `\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n    }\n    return {\n        version,\n        attribute,\n        varyingVs,\n        varyingFs,\n        texture2D,\n        output,\n        defineOutput,\n        defineSpecialNaN,\n        defineSpecialInf,\n        defineRound\n    };\n}\n//# sourceMappingURL=glsl_version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Produces GLSL code that derives logical coordinates from a flat\n * index. The code performs integer division with each stride and decrements\n * the index until the index equals the final dimension coordinate.\n */\nexport function getLogicalCoordinatesFromFlatIndex(coords, shape, index = 'index') {\n    const strides = util.computeStrides(shape);\n    return strides\n        .map((stride, i) => {\n        const line1 = `int ${coords[i]} = ${index} / ${stride}`;\n        const line2 = i === strides.length - 1 ?\n            `int ${coords[i + 1]} = ${index} - ${coords[i]} * ${stride}` :\n            `index -= ${coords[i]} * ${stride}`;\n        return `${line1}; ${line2};`;\n    })\n        .join('');\n}\nfunction buildVec(x) {\n    if (x.length === 1) {\n        return `${x[0]}`;\n    }\n    return `vec${x.length}(${x.join(',')})`;\n}\n/**\n * Produces GLSL code that computes the dot product of the input x and y\n * vectors. Handles splitting inputs into increments of vec4s when necessary.\n */\nexport function dotify(x, y) {\n    if (x.length !== y.length) {\n        throw new Error(`Vectors to be dotted must be of the same length -` +\n            `got ${x.length} and ${y.length}`);\n    }\n    const slices = [];\n    const nearestVec4 = Math.floor(x.length / 4);\n    const nearestVec4Remainder = x.length % 4;\n    for (let i = 0; i < nearestVec4; i++) {\n        const xSlice = x.slice(i * 4, i * 4 + 4);\n        const ySlice = y.slice(i * 4, i * 4 + 4);\n        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n    }\n    if (nearestVec4Remainder !== 0) {\n        let xSlice = x.slice(nearestVec4 * 4);\n        let ySlice = y.slice(nearestVec4 * 4);\n        if (xSlice.length === 1) {\n            xSlice = xSlice.map(d => `float(${d})`);\n            ySlice = ySlice.map(d => `float(${d})`);\n        }\n        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n    }\n    return slices.map((d, i) => `dot(${d})`).join('+');\n}\n/**\n * Produces GLSL that computes the flat index from 3D coordinates.\n */\nexport function getFlatIndexFrom3D(shape) {\n    const strides = util.computeStrides(shape).map(d => d.toString());\n    return `\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;\n  }\n`;\n}\nexport const ENCODE_FLOAT_SNIPPET = `\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`;\n//# sourceMappingURL=shader_compiler_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nconst { getBroadcastDims } = backend_util;\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nexport function makeShader(inputsInfo, outputShape, userCode, usesPackedTextures) {\n    const prefixSnippets = [];\n    inputsInfo.forEach(x => {\n        const size = util.sizeFromShape(x.shapeInfo.logicalShape);\n        // Snippet when we decided to upload the values as uniform.\n        if (x.shapeInfo.isUniform) {\n            prefixSnippets.push(`uniform float ${x.name}${size > 1 ? `[${size}]` : ''};`);\n        }\n        else {\n            prefixSnippets.push(`uniform sampler2D ${x.name};`);\n            prefixSnippets.push(`uniform int offset${x.name};`);\n        }\n    });\n    const inputPrefixSnippet = prefixSnippets.join('\\n');\n    const inputSamplingSnippet = inputsInfo\n        .map(x => getInputSamplingSnippet(x, outputShape, usesPackedTextures))\n        .join('\\n');\n    const outTexShape = outputShape.texShape;\n    const glsl = getGlslDifferences();\n    const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);\n    let outputSamplingSnippet;\n    let floatTextureSetOutputSnippet;\n    let shaderPrefix = getShaderPrefix(glsl);\n    if (outputShape.isPacked) {\n        outputSamplingSnippet =\n            getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n        floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);\n    }\n    else {\n        outputSamplingSnippet =\n            getOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n        floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);\n    }\n    if (usesPackedTextures) {\n        shaderPrefix += SHADER_PACKED_PREFIX;\n    }\n    const source = [\n        shaderPrefix, floatTextureSampleSnippet, floatTextureSetOutputSnippet,\n        inputPrefixSnippet, outputSamplingSnippet, inputSamplingSnippet, userCode\n    ].join('\\n');\n    return source;\n}\nfunction getSamplerFromInInfo(inInfo) {\n    const shape = inInfo.shapeInfo.logicalShape;\n    switch (shape.length) {\n        case 0:\n            return getSamplerScalar(inInfo);\n        case 1:\n            return getSampler1D(inInfo);\n        case 2:\n            return getSampler2D(inInfo);\n        case 3:\n            return getSampler3D(inInfo);\n        case 4:\n            return getSampler4D(inInfo);\n        case 5:\n            return getSampler5D(inInfo);\n        case 6:\n            return getSampler6D(inInfo);\n        default:\n            throw new Error(`${shape.length}-D input sampling` +\n                ` is not yet supported`);\n    }\n}\nfunction getPackedSamplerFromInInfo(inInfo) {\n    const shape = inInfo.shapeInfo.logicalShape;\n    switch (shape.length) {\n        case 0:\n            return getPackedSamplerScalar(inInfo);\n        case 1:\n            return getPackedSampler1D(inInfo);\n        case 2:\n            return getPackedSampler2D(inInfo);\n        case 3:\n            return getPackedSampler3D(inInfo);\n        default:\n            return getPackedSamplerND(inInfo);\n    }\n}\nfunction getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false) {\n    let res = '';\n    if (usesPackedTextures) {\n        res += getPackedSamplerFromInInfo(inInfo);\n    }\n    else {\n        res += getSamplerFromInInfo(inInfo);\n    }\n    const inShape = inInfo.shapeInfo.logicalShape;\n    const outShape = outShapeInfo.logicalShape;\n    if (inShape.length <= outShape.length) {\n        if (usesPackedTextures) {\n            res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);\n        }\n        else {\n            res += getSamplerAtOutputCoords(inInfo, outShapeInfo);\n        }\n    }\n    return res;\n}\nfunction getPackedOutputSamplingSnippet(outShape, outTexShape) {\n    switch (outShape.length) {\n        case 0:\n            return getOutputScalarCoords();\n        case 1:\n            return getOutputPacked1DCoords(outShape, outTexShape);\n        case 2:\n            return getOutputPacked2DCoords(outShape, outTexShape);\n        case 3:\n            return getOutputPacked3DCoords(outShape, outTexShape);\n        default:\n            return getOutputPackedNDCoords(outShape, outTexShape);\n    }\n}\nfunction getOutputSamplingSnippet(outShape, outTexShape) {\n    switch (outShape.length) {\n        case 0:\n            return getOutputScalarCoords();\n        case 1:\n            return getOutput1DCoords(outShape, outTexShape);\n        case 2:\n            return getOutput2DCoords(outShape, outTexShape);\n        case 3:\n            return getOutput3DCoords(outShape, outTexShape);\n        case 4:\n            return getOutput4DCoords(outShape, outTexShape);\n        case 5:\n            return getOutput5DCoords(outShape, outTexShape);\n        case 6:\n            return getOutput6DCoords(outShape, outTexShape);\n        default:\n            throw new Error(`${outShape.length}-D output sampling is not yet supported`);\n    }\n}\nfunction getFloatTextureSampleSnippet(glsl) {\n    return `\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${glsl.texture2D}(textureSampler, uv).r;\n    }\n  `;\n}\nfunction getFloatTextureSetRSnippet(glsl) {\n    return `\n    void setOutput(float val) {\n      ${glsl.output} = vec4(val, 0, 0, 0);\n    }\n  `;\n}\nfunction getFloatTextureSetRGBASnippet(glsl) {\n    return `\n    void setOutput(vec4 val) {\n      ${glsl.output} = val;\n    }\n  `;\n}\nfunction getShaderPrefix(glsl) {\n    const SHADER_PREFIX = `${glsl.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${glsl.varyingFs} vec2 resultUV;\n    ${glsl.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${glsl.defineSpecialNaN}\n    ${glsl.defineSpecialInf}\n    ${glsl.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${SAMPLE_1D_SNIPPET}\n    ${SAMPLE_2D_SNIPPET}\n    ${SAMPLE_3D_SNIPPET}\n  `;\n    return SHADER_PREFIX;\n}\nconst SAMPLE_1D_SNIPPET = `\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SAMPLE_2D_SNIPPET = `\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SAMPLE_3D_SNIPPET = `\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SHADER_PACKED_PREFIX = `\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;\nfunction getOutputScalarCoords() {\n    return `\n    int getOutputCoords() {\n      return 0;\n    }\n  `;\n}\nfunction getOutputPacked1DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (packedTexShape[0] === 1) {\n        return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);\n      }\n    `;\n    }\n    if (packedTexShape[1] === 1) {\n        return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);\n      }\n    `;\n    }\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);\n    }\n  `;\n}\nfunction getOutput1DCoords(shape, texShape) {\n    if (texShape[0] === 1) {\n        return `\n      int getOutputCoords() {\n        return int(resultUV.x * ${texShape[1]}.0);\n      }\n    `;\n    }\n    if (texShape[1] === 1) {\n        return `\n      int getOutputCoords() {\n        return int(resultUV.y * ${texShape[0]}.0);\n      }\n    `;\n    }\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      return resTexRC.x * ${texShape[1]} + resTexRC.y;\n    }\n  `;\n}\nfunction getOutputPacked3DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texelsInLogicalRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;\n}\nfunction getOutput3DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\nfunction getOutputPackedNDCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);\n    let texelsInBatchN = texelsInBatch;\n    let batches = ``;\n    let coords = 'b, r, c';\n    for (let b = 2; b < shape.length - 1; b++) {\n        texelsInBatchN *= shape[shape.length - b - 1];\n        batches = `\n      int b${b} = index / ${texelsInBatchN};\n      index -= b${b} * ${texelsInBatchN};\n    ` + batches;\n        coords = `b${b}, ` + coords;\n    }\n    return `\n    ivec${shape.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      ${batches}\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec${shape.length}(${coords});\n    }\n  `;\n}\nfunction getOutput4DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2'], shape);\n    return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec4(r, c, d, d2);\n    }\n  `;\n}\nfunction getOutput5DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3'], shape);\n    return `\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},\n                             ${texShape[1]}));\n\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `;\n}\nfunction getOutput6DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3', 'd4'], shape);\n    return `\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `;\n}\nfunction getOutputPacked2DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (util.arraysEqual(shape, texShape)) {\n        return `\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      }\n    `;\n    }\n    // texels needed to accommodate a logical row\n    const texelsInLogicalRow = Math.ceil(shape[1] / 2);\n    /**\n     * getOutputCoords\n     *\n     * resTexRC: The rows and columns of the texels. If you move over one\n     * texel to the right in the packed texture, you are moving over one column\n     * (not two).\n     *\n     * index: The texel index\n     */\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getOutput2DCoords(shape, texShape) {\n    if (util.arraysEqual(shape, texShape)) {\n        return `\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));\n      }\n    `;\n    }\n    if (shape[1] === 1) {\n        return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;\n    }\n    if (shape[0] === 1) {\n        return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;\n    }\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      int r = index / ${shape[1]};\n      int c = index - r * ${shape[1]};\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getFlatOffsetUniformName(texName) {\n    return `offset${texName}`;\n}\nfunction getPackedSamplerScalar(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}() {\n      return ${glsl.texture2D}(${texName}, halfCR);\n    }\n  `;\n}\nfunction getSamplerScalar(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    if (inputInfo.shapeInfo.isUniform) {\n        return `float ${funcName}() {return ${texName};}`;\n    }\n    const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;\n    if (texNumR === 1 && texNumC === 1) {\n        return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n    }\n    const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}() {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler1D(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${packedTexShape[0]}, ${packedTexShape[1]}, index);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler1D(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int index) {\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texShape = inputInfo.shapeInfo.texShape;\n    const tNumR = texShape[0];\n    const tNumC = texShape[1];\n    if (tNumC === 1 && tNumR === 1) {\n        return `\n      float ${funcName}(int index) {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    if (tNumC === 1) {\n        return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (tNumR === 1) {\n        return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n    float ${funcName}(int index) {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler2D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const glsl = getGlslDifferences();\n    if (texShape != null && util.arraysEqual(shape, texShape)) {\n        return `\n      vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n\n        return ${glsl.texture2D}(${texName}, uv);\n      }\n    `;\n    }\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const valuesPerRow = Math.ceil(shape[1] / 2);\n    return `\n    vec4 ${funcName}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler2D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    if (texShape != null && util.arraysEqual(shape, texShape)) {\n        const texNumR = texShape[0];\n        const texNumC = texShape[1];\n        return `\n    float ${funcName}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['row', 'col'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const offset = getFlatOffsetUniformName(texName);\n    if (texNumC === 1) {\n        // index is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    if (texNumR === 1) {\n        // index is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    return `\n  float ${funcName}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${shape[1]} + col + ${offset};\n    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n    return sampleTexture(${texName}, uv);\n  }\n`;\n}\nfunction getPackedSampler3D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (shape[0] === 1) {\n        const squeezedShape = shape.slice(1);\n        const keptDims = [1, 2];\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['b', 'row', 'col'];\n        return `\n        ${getPackedSamplerFromInInfo(newInputInfo)}\n        vec4 ${funcName}(int b, int row, int col) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n    }\n    const texNumR = packedTexShape[0];\n    const texNumC = packedTexShape[1];\n    const valuesPerRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler3D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride0 = shape[1] * shape[2];\n    const stride1 = shape[2];\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['row', 'col', 'depth'];\n        return `\n        ${getSamplerFromInInfo(newInputInfo)}\n        float ${funcName}(int row, int col, int depth) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${stride0}, ${stride1}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n        float ${funcName}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${texNumC}.0, ${texNumR}.0);\n          return sampleTexture(${texName}, uv);\n        }\n      `;\n    }\n    if (texNumC === stride1 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n      float ${funcName}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};\n        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n        return sampleTexture(${texName}, uv);\n      }\n  `;\n}\nfunction getPackedSamplerND(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const rank = shape.length;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texNumR = packedTexShape[0];\n    const texNumC = packedTexShape[1];\n    const valuesPerRow = Math.ceil(shape[rank - 1] / 2);\n    let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);\n    let params = `int b, int row, int col`;\n    let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;\n    for (let b = 2; b < rank - 1; b++) {\n        params = `int b${b}, ` + params;\n        texelsInBatch *= shape[rank - b - 1];\n        index = `b${b} * ${texelsInBatch} + ` + index;\n    }\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(${params}) {\n      int index = ${index};\n      int texR = index / ${texNumC};\n      int texC = index - texR * ${texNumC};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler4D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride2 = shape[3];\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${stride1}, ${stride2}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride2 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} +\n          depth * ${stride2} + depth2;\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler5D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride3 = shape[4];\n    const stride2 = shape[3] * stride3;\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2', 'depth3'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          depth3;\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${stride1}, ${stride2}, ${stride3}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride3 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3]},\n               ${shape[2] * shape[3]}, ${shape[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler6D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    const stride4 = shape[5];\n    const stride3 = shape[4] * stride4;\n    const stride2 = shape[3] * stride3;\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${stride4}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride4 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},\n               ${shape[2] * shape[3] * shape[4]},\n               ${shape[3] * shape[4]},\n               ${shape[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getUniformSampler(inputInfo) {\n    const texName = inputInfo.name;\n    const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n    if (inSize < 2) {\n        return `return ${texName};`;\n    }\n    return `\n    for (int i = 0; i < ${inSize}; i++) {\n      if (i == index) {\n        return ${texName}[i];\n      }\n    }\n  `;\n}\nfunction getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n    const texName = inputInfo.name;\n    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n    const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n    const inRank = inputInfo.shapeInfo.logicalShape.length;\n    const outRank = outShapeInfo.logicalShape.length;\n    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n    const type = getCoordsDataType(outRank);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet;\n    const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n    if (inRank === 0) {\n        coordsSnippet = '';\n    }\n    else if (outRank < 2 && broadcastDims.length >= 1) {\n        coordsSnippet = 'coords = 0;';\n    }\n    else {\n        coordsSnippet =\n            broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n                .join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n        unpackedCoordsSnippet = 'coords';\n    }\n    else {\n        unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n            .map((s, i) => `coords.${fields[i + rankDiff]}`)\n            .join(', ');\n    }\n    let output = `return outputValue;`;\n    const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n    const isInputScalar = inSize === 1;\n    const outSize = util.sizeFromShape(outShapeInfo.logicalShape);\n    const isOutputScalar = outSize === 1;\n    if (inRank === 1 && !isInputScalar && !isOutputScalar) {\n        output = `\n      return vec4(outputValue.xy, outputValue.xy);\n    `;\n    }\n    else if (isInputScalar && !isOutputScalar) {\n        if (outRank === 1) {\n            output = `\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `;\n        }\n        else {\n            output = `\n        return vec4(outputValue.x);\n      `;\n        }\n    }\n    else if (broadcastDims.length) {\n        const rows = inRank - 2;\n        const cols = inRank - 1;\n        if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {\n            output = `return vec4(outputValue.x);`;\n        }\n        else if (broadcastDims.indexOf(rows) > -1) {\n            output = `return vec4(outputValue.x, outputValue.y, ` +\n                `outputValue.x, outputValue.y);`;\n        }\n        else if (broadcastDims.indexOf(cols) > -1) {\n            output = `return vec4(outputValue.xx, outputValue.zz);`;\n        }\n    }\n    return `\n    vec4 ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});\n      ${output}\n    }\n  `;\n}\nfunction getSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n    const texName = inputInfo.name;\n    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n    const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n    const outTexShape = outShapeInfo.texShape;\n    const inTexShape = inputInfo.shapeInfo.texShape;\n    const inRank = inputInfo.shapeInfo.logicalShape.length;\n    const outRank = outShapeInfo.logicalShape.length;\n    if (!inputInfo.shapeInfo.isUniform && inRank === outRank &&\n        inputInfo.shapeInfo.flatOffset == null &&\n        util.arraysEqual(inTexShape, outTexShape)) {\n        return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, resultUV);\n      }\n    `;\n    }\n    const type = getCoordsDataType(outRank);\n    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet;\n    const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n    if (inRank === 0) {\n        coordsSnippet = '';\n    }\n    else if (outRank < 2 && broadcastDims.length >= 1) {\n        coordsSnippet = 'coords = 0;';\n    }\n    else {\n        coordsSnippet =\n            broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n                .join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n        unpackedCoordsSnippet = 'coords';\n    }\n    else {\n        unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n            .map((s, i) => `coords.${fields[i + rankDiff]}`)\n            .join(', ');\n    }\n    return `\n    float ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      return get${texFuncSnippet}(${unpackedCoordsSnippet});\n    }\n  `;\n}\nexport function getCoordsDataType(rank) {\n    if (rank <= 1) {\n        return 'int';\n    }\n    else if (rank === 2) {\n        return 'ivec2';\n    }\n    else if (rank === 3) {\n        return 'ivec3';\n    }\n    else if (rank === 4) {\n        return 'ivec4';\n    }\n    else if (rank === 5) {\n        return 'ivec5';\n    }\n    else if (rank === 6) {\n        return 'ivec6';\n    }\n    else {\n        throw Error(`GPU for rank ${rank} is not yet supported`);\n    }\n}\n/** Returns a new input info (a copy) that has a squeezed logical shape. */\nfunction squeezeInputInfo(inInfo, squeezedShape) {\n    // Deep copy.\n    const newInputInfo = JSON.parse(JSON.stringify(inInfo));\n    newInputInfo.shapeInfo.logicalShape = squeezedShape;\n    return newInputInfo;\n}\nfunction getSqueezedParams(params, keptDims) {\n    return keptDims.map(d => params[d]).join(', ');\n}\n//# sourceMappingURL=shader_compiler.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ArgMinMaxPackedProgram {\n    constructor(shape, windowSize, op, firstPass) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        util.assert(shape.length > 2, () => `Packed arg${op.charAt(0).toUpperCase() +\n            op.slice(1)} supports only inputs with rank above 2.`);\n        const inSize = shape[shape.length - 1];\n        const outSize = Math.ceil(inSize / windowSize);\n        this.outputShape = shape.slice(0, -1);\n        if (outSize > 1) {\n            this.outputShape.push(outSize);\n        }\n        if (!firstPass) {\n            this.variableNames.push('bestIndicesA');\n        }\n        const outShape = this.outputShape;\n        const rank = outShape.length;\n        const dtype = getCoordsDataType(rank);\n        const coords = getChannels('coords', rank);\n        let sourceLocSetup;\n        let sourceRank;\n        if (outSize === 1) {\n            sourceRank = rank + 1;\n            const sourceLocDType = getCoordsDataType(sourceRank);\n            sourceLocSetup = `\n        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 1]};\n        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 2]};\n        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 1]};\n        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 2]};`;\n        }\n        else {\n            sourceRank = rank;\n            sourceLocSetup = `\n        ${dtype} sourceLocR = coords;\n        ++${coords[rank - 1]};\n        ${dtype} sourceLocG = coords;\n        ++${coords[rank - 2]};\n        ${dtype} sourceLocA = coords;\n        --${coords[rank - 1]};\n        ${dtype} sourceLocB = coords;\n        --${coords[rank - 2]};`;\n        }\n        const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, sourceRank);\n        const inChannel = '.' + channels[sourceRank - 1]; // e.g. \".b\" for rank 3.\n        const intChannels = channels.map(x => 'int ' + x);\n        const srcRCoords = getChannels('sourceLocR', sourceRank - 1).concat('inIdx.r');\n        const srcGCoords = getChannels('sourceLocG', sourceRank - 1).concat('inIdx.g');\n        const srcBCoords = getChannels('sourceLocB', sourceRank - 1).concat('inIdx.b');\n        const srcACoords = getChannels('sourceLocA', sourceRank - 1).concat('inIdx.a');\n        const compOp = (op === 'max') ? 'greaterThan' : 'lessThan';\n        const fetchCandidateIdx = firstPass ? '' : `\n          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),\n                             getBestIndicesAChannel(${srcGCoords.join()}),\n                             getBestIndicesAChannel(${srcBCoords.join()}),\n                             getBestIndicesAChannel(${srcACoords.join()})));`;\n        const fetchValue = `vec4(\n            getAChannel(${srcRCoords.join()}),\n            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,\n            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;\n        const getBestIndicesAChannelSnippet = firstPass ? '' : `\n      float getBestIndicesAChannel(${intChannels.join()}) {\n        return getChannel(getBestIndicesA(${channels.join()}),\n                                          vec2(${channels.slice(-2).join()}));\n      }`;\n        this.userCode = `\n      float getAChannel(${intChannels.join()}) {\n        return getChannel(getA(${channels.join()}),\n                               vec2(${channels.slice(-2).join()}));\n      }\n      ${getBestIndicesAChannelSnippet}\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        bool hasNextCol = ${coords[rank - 1]} < ${outShape[rank - 1] - 1};\n        bool hasNextRow = ${coords[rank - 2]} < ${outShape[rank - 2] - 1};\n        ${sourceLocSetup}\n        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},\n          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${fetchValue};\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          inIdx = srcIdx;\n          ${fetchCandidateIdx}\n          vec4 candidate = ${fetchValue};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=argminmax_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AvgPool2DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const avgMultiplier = 1 / (filterHeight * filterWidth);\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth};\n            wC+= ${dilationWidth}) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class AvgPool3DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy'];\n        this.outputShape = convInfo.inShape;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=avg_pool_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\n// (Ar + Ai)(Br + Bi) =\n// ArBr + ArBi + AiBr + AiBi = ArBr - AB + ArBi + AiBr\n// Yr = ArBr - AB\n// Yi = ArBi + AiBr\nexport const COMPLEX_MULTIPLY = {\n    REAL: 'return areal * breal - aimag * bimag;',\n    IMAG: 'return areal * bimag + aimag * breal;'\n};\nexport class BinaryOpComplexProgram {\n    constructor(op, aShape, bShape) {\n        this.variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        this.userCode = `\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${op}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_complex_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nconst CHECK_NAN_SNIPPET = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nexport const ADD = 'return a + b;';\nexport const SUB = 'return a - b;';\nexport const MUL = 'return a * b;';\n// We use native integer division to deal with floating point imprecision. Since\n// we implement floor division and glsl implements truncated division, we\n// correct for this by subtracting 1 from result when the result is negative and\n// there is a remainder.\nexport const INT_DIV = `\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`;\nexport const POW = `\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nif (b == 0.0) {\n  return 1.0;\n}\nreturn (round(mod(b, 2.0)) != 1) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`;\nexport const SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport const EQUAL = `return float(a == b);`;\nexport const NOT_EQUAL = `return float(a != b);`;\nexport const LESS = `return float(a < b);`;\nexport const LESS_EQUAL = `return float(a <= b);`;\nexport const GREATER = `return float(a > b);`;\nexport const GREATER_EQUAL = `return float(a >= b);`;\nexport const LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;\nexport const LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;\nexport const MAX = CHECK_NAN_SNIPPET + `\n  return max(a, b);\n`;\nexport const MIN = CHECK_NAN_SNIPPET + `\n  return min(a, b);\n`;\nexport const MOD = `if (b == 0.0) return NAN;\n  return mod(a, b);`;\nexport const ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;\nexport const PRELU = `return (a < 0.) ? b * a : a;`;\nexport class BinaryOpProgram {\n    constructor(op, aShape, bShape) {\n        this.variableNames = ['A', 'B'];\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        this.userCode = `\n      float binaryOperation(float a, float b) {\n        ${op}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nconst CHECK_NAN_SNIPPET = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\nexport const INT_DIV = `\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n`;\nexport const POW = `\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const PRELU = `\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;\nexport const ELU_DER = `\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`;\nexport const EQUAL = `\n  return vec4(equal(a, b));\n`;\nexport const NOT_EQUAL = `\n  return vec4(notEqual(a, b));\n`;\nexport const LESS = `\n  return vec4(lessThan(a, b));\n`;\nexport const LESS_EQUAL = `\n  return vec4(lessThanEqual(a, b));\n`;\nexport const GREATER = `\n  return vec4(greaterThan(a, b));\n`;\nexport const GREATER_EQUAL = `\n  return vec4(greaterThanEqual(a, b));\n`;\nexport const LOGICAL_AND = `\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`;\nexport const LOGICAL_OR = `\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`;\nexport const MAX = `\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const MIN = `\n  vec4 result = vec4(min(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const MOD = `\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport class BinaryOpPackedProgram {\n    constructor(op, aShape, bShape, checkOutOfBounds = false) {\n        this.variableNames = ['A', 'B'];\n        this.supportsBroadcasting = true;\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const rank = this.outputShape.length;\n        let checkOutOfBoundsString = '';\n        if (checkOutOfBounds) {\n            if (rank === 0 || util.sizeFromShape(this.outputShape) === 1) {\n                checkOutOfBoundsString = `\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;\n            }\n            else {\n                const dtype = getCoordsDataType(rank);\n                checkOutOfBoundsString = `\n          ${dtype} coords = getOutputCoords();\n        `;\n                if (rank === 1) {\n                    checkOutOfBoundsString += `\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;\n                }\n                else {\n                    const channels = getChannels('coords', rank);\n                    checkOutOfBoundsString += `\n            bool nextRowOutOfBounds =\n              (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};\n            bool nextColOutOfBounds =\n              (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `;\n                }\n            }\n        }\n        this.userCode = `\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${op}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${checkOutOfBoundsString}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ClipProgram {\n    constructor(aShape) {\n        this.variableNames = ['A'];\n        this.outputShape = aShape;\n        this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    `;\n    }\n    getCustomSetupFunc(min, max) {\n        return (gpgpu, webGLProgram) => {\n            if (this.minLoc == null) {\n                this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n                this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n            }\n            gpgpu.gl.uniform1f(this.minLoc, min);\n            gpgpu.gl.uniform1f(this.maxLoc, max);\n        };\n    }\n}\n//# sourceMappingURL=clip_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ClipPackedProgram {\n    constructor(aShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = aShape;\n        this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    `;\n    }\n    getCustomSetupFunc(min, max) {\n        return (gpgpu, webGLProgram) => {\n            if (this.minLoc == null) {\n                this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n                this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n            }\n            gpgpu.gl.uniform1f(this.minLoc, min);\n            gpgpu.gl.uniform1f(this.maxLoc, max);\n        };\n    }\n}\n//# sourceMappingURL=clip_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ComplexAbsProgram {\n    constructor(shape) {\n        this.variableNames = ['real', 'imag'];\n        this.outputShape = shape;\n        this.userCode = `\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `;\n    }\n}\n//# sourceMappingURL=complex_abs_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class ConcatProgram {\n    // Concats 2d tensors along axis=1. See comments in MathBackendWebGL.concat().\n    constructor(shapes) {\n        this.outputShape = [];\n        this.outputShape = backend_util.computeOutShape(shapes, 1 /* axis */);\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const offsets = new Array(shapes.length - 1);\n        offsets[0] = shapes[0][1];\n        for (let i = 1; i < offsets.length; i++) {\n            offsets[i] = offsets[i - 1] + shapes[i][1];\n        }\n        const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];\n        for (let i = 1; i < offsets.length; i++) {\n            const shift = offsets[i - 1];\n            snippets.push(`else if (yC < ${offsets[i]}) ` +\n                `setOutput(getT${i}(yR, yC-${shift}));`);\n        }\n        const lastIndex = offsets.length;\n        const lastShift = offsets[offsets.length - 1];\n        snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${snippets.join('\\n        ')}\n      }\n    `;\n    }\n}\n//# sourceMappingURL=concat_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ConcatPackedProgram {\n    constructor(shapes, axis) {\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = [];\n        this.outputShape = backend_util.computeOutShape(shapes, axis);\n        const shape = this.outputShape;\n        const rank = shape.length;\n        const dtype = getCoordsDataType(rank);\n        const coords = getChannels('coords', rank);\n        const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank);\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const offsets = new Array(shapes.length - 1);\n        offsets[0] = shapes[0][axis];\n        for (let i = 1; i < offsets.length; i++) {\n            offsets[i] = offsets[i - 1] + shapes[i][axis];\n        }\n        const channel = channels[axis];\n        const lastChannels = channels.slice(-2);\n        const allChannels = channels.join();\n        let getValueSnippet = `if (${channel} < ${offsets[0]}) {\n        return getChannel(\n            getT0(${allChannels}), vec2(${lastChannels.join()}));\n        }`;\n        for (let i = 1; i < offsets.length; i++) {\n            const shift = offsets[i - 1];\n            // Note: the >= comparison below may seem unnecessary given the check\n            // above but is needed to workaround branch execution issues on some\n            // devices. It makes all the conditions exclusive without relying on\n            // execution order.\n            getValueSnippet += `\n        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i - 1]}) {\n          return getChannel(\n            getT${i}(${shiftedChannels(channels, channel, shift)}),\n            vec2(${shiftedChannels(lastChannels, channel, shift)}));\n        }`;\n        }\n        const lastIndex = offsets.length;\n        const shift = offsets[offsets.length - 1];\n        getValueSnippet += `\n        return getChannel(\n          getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),\n          vec2(${shiftedChannels(lastChannels, channel, shift)}));`;\n        this.userCode = `\n      float getValue(${channels.map(x => 'int ' + x)}) {\n        ${getValueSnippet}\n      }\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${coords}), 0., 0., 0.);\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} + 1;\n        if (${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.g = getValue(${coords});\n        }\n\n        ${coords[rank - 2]} = ${coords[rank - 2]} + 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]}) {\n          result.a = getValue(${coords});\n        }\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} - 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]} &&\n            ${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.b = getValue(${coords});\n        }\n        setOutput(result);\n      }\n    `;\n    }\n}\n/**\n * Return an expression for coordinates into a vector where a given channel\n * will be offset by [shift].\n *\n * @param channels the channels to consider\n * @param channel the channel we want shifted\n * @param shift  the amount to subtract from the channel.\n *\n * @returns a string of the form 'x, y-[shift], z' where any one channel can\n * have the shift applied.\n */\nfunction shiftedChannels(channels, channel, shift) {\n    const channelIdx = channels.indexOf(channel);\n    const res = channels.map((c, idx) => {\n        if (idx === channelIdx) {\n            return `${c} - ${shift}`;\n        }\n        else {\n            return c;\n        }\n    });\n    return res.join();\n}\n//# sourceMappingURL=concat_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Conv2DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              if (${isChannelsLast}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv2DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        const rowDim = isChannelsLast ? 1 : 2;\n        const colDim = isChannelsLast ? 2 : 3;\n        const channelDim = isChannelsLast ? 3 : 1;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${channelDim}];\n\n        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n\n              if (${isChannelsLast}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv3DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.userCode = `\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {\n            int xF = wF + yF * ${strideDepth} - ${padFront};\n\n            if (xF < 0 || xF >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n              int xR = wR + yR * ${strideHeight} - ${padTop};\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n                int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv3DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padFront = filterDepth - 1 - convInfo.padInfo.front;\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;\n\n          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${filterDepth} - 1 - wF;\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${filterHeight} - 1 - wR;\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${filterWidth} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthwiseConv2DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${channelMul} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class DepthwiseConv2DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${channelMul}; dm++) {\n              int d2 = d1 * ${channelMul} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_backprop_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Conv2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivationWeights = false) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n        const inputDepthVec4Remainder = convInfo.inChannels % 4;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const rowDim = isChannelsLast ? 1 : 2;\n        const colDim = isChannelsLast ? 2 : 3;\n        const channelDim = isChannelsLast ? 3 : 1;\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivationWeights) {\n                activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivationWeights) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${channelDim}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${convInfo.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${inputDepthVec4Remainder === 1}) {\n\n              if (${isChannelsLast}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              }\n\n            } else if (${inputDepthVec4Remainder === 2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${inputDepthVec4Remainder === 3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\nexport class Conv3DProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n        const inputDepthVec4Remainder = convInfo.inChannels % 4;\n        this.userCode = `\n      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          int xF = xFCorner + wF * ${dilationDepth};\n\n          if (xF < 0 || xF >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            int xR = xRCorner + wR * ${dilationHeight};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${inputDepthVec4Remainder === 1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);\n              } else if (${inputDepthVec4Remainder === 2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${inputDepthVec4Remainder === 3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthwiseConv2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const xNumRows = convInfo.inHeight;\n        const xNumCols = convInfo.inWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${xNumRows}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${xNumCols}) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport class DepthwiseConvPacked2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['x', 'W'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = convInfo.outShape;\n        const xNumRows = convInfo.inHeight;\n        const xNumCols = convInfo.inWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const texelsAcross = filterWidth;\n        let mainLoop = `int xR; int xC; int xCOffset;`;\n        for (let r = 0; r < filterHeight; r++) {\n            for (let c = 0; c < filterWidth; c++) {\n                mainLoop += `\n          vec4 xTexelR${r}C${c * 2} = vec4(0.);\n          vec4 wR${r}C${c} = vec4(0.);\n          vec4 xR${r}C${c} = vec4(0.);`;\n            }\n        }\n        /**\n         * This vectorized implementation works by gathering the values needed for\n         * each output channel's dot product into vec4's and then multiplying them\n         * all together (this happens in the final double for-loop below). Most of\n         * the main loop consists of constructing these vec4's with the minimum\n         * number of texture2D calls, which means making use of all four returned\n         * values from a texture2D call at once.\n         */\n        for (let r = 0; r < filterHeight; r++) {\n            for (let texelC = 0; texelC < texelsAcross; texelC++) {\n                const c = texelC * 2;\n                mainLoop += `\n          xR = xRCorner + ${r * dilationHeight};\n          xC = xCCorner + ${c * dilationWidth};\n        `;\n                if (strideWidth === 1) {\n                    if (c < filterWidth) {\n                        // If padding is odd, the outer texels have to be composed.\n                        if (padLeft % 2 === 1) {\n                            // TODO: Ensure vec4 previous does not result in redundant sample,\n                            // and avoid setting xTexelRC's that exceed the boundary in the\n                            // first place rather than resetting them to vec4(0)).\n                            // To compute xCOffset:\n                            // - If padding is odd, we must add 1 to ensure we ask for an\n                            // even-numbered row.\n                            // - We subtract 2 to access the previous texel.\n                            mainLoop += `\n                xCOffset = xC + 1;\n                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= ${xNumCols}) {\n                    xTexelR${r}C${c}.zw = vec2(0.);\n                  }\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xCOffset = xC + 1 - 2;\n                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  vec4 previous = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= ${xNumCols}) {\n                    previous.zw = vec2(0.);\n                  }\n\n                  xR${r}C${c} = vec4(previous.zw, xTexelR${r}C${c}.xy);\n                } else {\n                  xR${r}C${c} = vec4(0, 0, xTexelR${r}C${c}.xy);\n                }\n              `;\n                        }\n                        else {\n                            // Padding is even, so xRC corresponds to a single texel.\n                            mainLoop += `\n                if(xR >= 0 && xR < ${xNumRows} && xC >= 0 && xC < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xR${r}C${c} = xTexelR${r}C${c};\n              `;\n                        }\n                        if (c + 1 < filterWidth) {\n                            // If dilation is even, the second entry should match the first\n                            // (either both are composed or both are single samples). But if\n                            // dilation is odd, then the second entry should be the opposite\n                            // of the first (if the first is composed, the second is a single\n                            // sample, and vice versa.)\n                            const nextTexelOffset = padLeft % 2 === 0 ?\n                                util.nearestLargerEven(dilationWidth) :\n                                dilationWidth;\n                            if ((dilationWidth % 2 === 0 && padLeft % 2 === 1) ||\n                                (dilationWidth % 2 !== 0 && padLeft % 2 !== 1)) {\n                                mainLoop += `\n                  xCOffset = xC + ${padLeft % 2} + ${nextTexelOffset};\n\n                  if(xR >= 0 && xR < ${xNumRows} &&\n                    xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                  }\n                `;\n                                // If dilation > 1 then the xRC's will not be able to share any\n                                // values, so each xRC will require two unique calls to getX.\n                                if (dilationWidth > 1) {\n                                    mainLoop += `\n                    xCOffset -= 2;\n                    if(xR >= 0 && xR < ${xNumRows} &&\n                      xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                      xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n                    } else {\n                      xTexelR${r}C${c} = vec4(0.);\n                    }\n                  `;\n                                }\n                                mainLoop += `\n                  xR${r}C${c + 1} = vec4(\n                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.xy);\n                `;\n                            }\n                            else {\n                                mainLoop += `\n                  xCOffset = xC + ${nextTexelOffset};\n\n                  if(xR >= 0 && xR < ${xNumRows} &&\n                    xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                  }\n\n                  xR${r}C${c + 1} = xTexelR${r}C${c + 2};\n                `;\n                            }\n                        }\n                    }\n                }\n                else { // stride > 1\n                    if (c < filterWidth) {\n                        mainLoop += `\n              if(xR >= 0 && xR < ${xNumRows}) {\n            `;\n                        // Depending on whether padLeft is even or odd, we want either the\n                        // xy or zw channels from X texels for xR${r}C${c}. If padLeft is\n                        // even, xR${r}C${c + 1} is simply the zw channels of texels we've\n                        // already sampled. But if padLeft is odd, xR${r}C{$c + 1}.zw will\n                        // need to come from the xy channels of a new texel, hence the `vec4\n                        // final` initialized below.\n                        if (padLeft % 2 === 1) {\n                            mainLoop += `\n                xCOffset = xC + 1 - ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < ${xNumCols}) {\n                  xTexelR${r}C${c + 2} = getX(batch, xR, xC + 1, d1);\n                } else {\n                  xTexelR${r}C${c + 2} = vec4(0.);\n                }\n\n                xR${r}C${c} = vec4(\n                  xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n              `;\n                            if (c + 1 < filterWidth) {\n                                mainLoop += `\n                  vec4 final = vec4(0.);\n                  xCOffset = xC + 1 + ${strideWidth};\n                  if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xR${r}C${c + 1} = vec4(xTexelR${r}C${c + 2}.xy, final.xy);\n                `;\n                            }\n                        }\n                        else {\n                            mainLoop += `\n                if(xC >= 0 && xC < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xCOffset = xC + ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${r}C${c + 2} = vec4(0.);\n                }\n\n                xR${r}C${c} = vec4(\n                  xTexelR${r}C${c}.xy, xTexelR${r}C${c + 2}.xy);\n              `;\n                            if (c + 1 < filterWidth) {\n                                mainLoop += `\n                  xR${r}C${c + 1} = vec4(\n                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n                `;\n                            }\n                        }\n                        mainLoop += `}`;\n                    }\n                }\n                if (c < filterWidth) {\n                    mainLoop += `\n            vec4 wTexelR${r}C${c} = getW(${r}, ${c}, d1, q);\n            wR${r}C${c} = vec4(wTexelR${r}C${c}.xz, wTexelR${r}C${c}.xz);\n          `;\n                    if (c + 1 < filterWidth) {\n                        mainLoop += `\n              vec4 wTexelR${r}C${c + 1} = getW(${r}, ${c + 1}, d1, q);\n              wR${r}C${c + 1} =\n                vec4(wTexelR${r}C${c + 1}.xz, wTexelR${r}C${c + 1}.xz);`;\n                    }\n                }\n            }\n        }\n        for (let r = 0; r < filterHeight; r++) {\n            for (let c = 0; c < filterWidth; c++) {\n                mainLoop += `dotProd += xR${r}C${c} * wR${r}C${c};`;\n            }\n        }\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 dotProd = vec4(0.);\n\n        ${mainLoop}\n\n        vec4 result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_packed_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class CropAndResizeProgram {\n    constructor(imageShape, boxShape, cropSize, method, extrapolationValue) {\n        this.variableNames = ['Image', 'Boxes', 'BoxInd'];\n        this.outputShape = [];\n        const [batch, imageHeight, imageWidth, depth] = imageShape;\n        const [numBoxes,] = boxShape;\n        const [cropHeight, cropWidth] = cropSize;\n        this.outputShape = [numBoxes, cropHeight, cropWidth, depth];\n        const methodId = method === 'bilinear' ? 1 : 0;\n        const [inputHeightFloat, inputWidthFloat] = [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];\n        const [heightRatio, heightScale, inY] = cropHeight > 1 ?\n            [\n                `${(imageHeight - 1) / (cropHeight - 1)}`,\n                '(y2-y1) * height_ratio',\n                `y1*${inputHeightFloat} + float(y)*(height_scale)`,\n            ] :\n            [\n                '0.0',\n                '0.0',\n                `0.5 * (y1+y2) * ${inputHeightFloat}`,\n            ];\n        const [widthRatio, widthScale, inX] = cropWidth > 1 ?\n            [\n                `${(imageWidth - 1) / (cropWidth - 1)}`,\n                '(x2-x1) * width_ratio',\n                `x1*${inputWidthFloat} + float(x)*(width_scale)`,\n            ] :\n            [\n                '0.0',\n                '0.0',\n                `0.5 * (x1+x2) * ${inputWidthFloat}`,\n            ];\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc\n        this.userCode = `\n      const float height_ratio = float(${heightRatio});\n      const float width_ratio = float(${widthRatio});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${batch}) {\n          return;\n        }\n\n        float height_scale = ${heightScale};\n        float width_scale = ${widthScale};\n\n        float in_y = ${inY};\n        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n        float in_x = ${inX};\n        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${methodId} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=crop_and_resize_gpu.js.map", "import { getCoordsDataType } from './shader_compiler';\nexport class CumSumProgram {\n    constructor(shape, exclusive, reverse) {\n        this.variableNames = ['x'];\n        this.outputShape = shape;\n        const rank = shape.length;\n        const val = exclusive ? '0.0' : `getX(${getCoords(rank, 'coords')})`;\n        const length = shape[shape.length - 1];\n        let condition = '';\n        let idxString = '';\n        // When exclusive is set, the cumsum op becomes roll op that copies the\n        // value from the previous index based on the direction specified by the\n        // reverse flag.\n        if (exclusive) {\n            condition = reverse ? `end != ${length - 1}` : 'end != 0';\n            idxString = reverse ? 'end + 1' : 'end - 1';\n        }\n        else {\n            condition = reverse ? `end + pow2 < ${length}` : 'end >= pow2';\n            idxString = (reverse ? 'end + pow2' : 'end - pow2');\n        }\n        this.userCode = `\n      uniform float index;\n      void main() {\n        ${getCoordsDataType(rank)} coords = getOutputCoords();\n        int end = ${getFinalCoord(rank, 'coords')};\n        float val = ${val};\n        int pow2 = int(pow(2.0, index));\n        if (${condition}) {\n          int idx = ${idxString};\n          ${getFinalCoord(rank, 'coords')} = idx;\n          val += getX(${getCoords(rank, 'coords')});\n        }\n        setOutput(val);\n      }\n    `;\n    }\n    getCustomSetupFunc(index) {\n        return (gpgpu, webGLProgram) => {\n            if (this.index == null) {\n                this.index = gpgpu.getUniformLocation(webGLProgram, 'index');\n            }\n            gpgpu.gl.uniform1f(this.index, index);\n        };\n    }\n}\nfunction getCoords(rank, name) {\n    if (rank === 1) {\n        return `${name}`;\n    }\n    else if (rank === 2) {\n        return `${name}.x, ${name}.y`;\n    }\n    else if (rank === 3) {\n        return `${name}.x, ${name}.y, ${name}.z`;\n    }\n    else if (rank === 4) {\n        return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n    }\n    else {\n        throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n    }\n}\nfunction getFinalCoord(rank, name) {\n    if (rank === 1) {\n        return `${name}`;\n    }\n    else if (rank === 2) {\n        return `${name}.y`;\n    }\n    else if (rank === 3) {\n        return `${name}.z`;\n    }\n    else if (rank === 4) {\n        return `${name}.w`;\n    }\n    else {\n        throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n    }\n}\n//# sourceMappingURL=cumsum_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nimport { getDenseTexShape, PackingScheme } from './tex_util';\nexport class DecodeMatrixProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        this.outPackingScheme = PackingScheme.DENSE;\n        const texShape = getDenseTexShape(outputShape);\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=decode_matrix_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nimport { getDenseTexShape, PackingScheme } from './tex_util';\nexport class DecodeMatrixPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outPackingScheme = PackingScheme.DENSE;\n        const texShape = getDenseTexShape(outputShape);\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=decode_matrix_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthToSpaceProgram {\n    constructor(outputShape, blockSize, dataFormat) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        this.outputShape = outputShape;\n        this.blockSize = blockSize;\n        this.dataFormat = dataFormat;\n        this.userCode = `\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${blockSize};\n      int offset_h = imod(h, ${blockSize});\n      int in_w = w / ${blockSize};\n      int offset_w = imod(w, ${blockSize});\n      int offset_d = (offset_h * ${blockSize} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `;\n    }\n    getHeightCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[1]`;\n        }\n        else {\n            return `coords[2]`;\n        }\n    }\n    getWidthCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[2]`;\n        }\n        else {\n            return `coords[3]`;\n        }\n    }\n    getDepthCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[3]`;\n        }\n        else {\n            return `coords[1]`;\n        }\n    }\n    getOutputDepthSize() {\n        if (this.dataFormat === 'NHWC') {\n            return this.outputShape[3];\n        }\n        else {\n            return this.outputShape[1];\n        }\n    }\n    getInputSamplingString() {\n        if (this.dataFormat === 'NHWC') {\n            return `getX(b, in_h, in_w, in_d)`;\n        }\n        else {\n            return `getX(b, in_d, in_h, in_w)`;\n        }\n    }\n}\n//# sourceMappingURL=depth_to_space_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DiagProgram {\n    constructor(size) {\n        this.variableNames = ['X'];\n        this.outputShape = [size, size];\n        this.userCode = `\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=diag_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport { ENCODE_FLOAT_SNIPPET } from './shader_compiler_util';\nimport { TextureUsage } from './tex_util';\nexport class EncodeFloatProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.outTexUsage = TextureUsage.DOWNLOAD;\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_float_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport { ENCODE_FLOAT_SNIPPET } from './shader_compiler_util';\nimport { TextureUsage } from './tex_util';\nexport class EncodeFloatPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = false;\n        this.outTexUsage = TextureUsage.DOWNLOAD;\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_float_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nexport class EncodeMatrixProgram {\n    constructor(outputShape, texShape, inputIsUnsignedByte = false) {\n        this.variableNames = ['A'];\n        const glsl = getGlslDifferences();\n        const [height, width] = texShape;\n        this.outputShape = outputShape;\n        let output = `result`;\n        if (inputIsUnsignedByte) {\n            output = `floor(result * 255. + 0.5)`;\n        }\n        this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / ${width};\n        int c = imod(flatIndex, ${width});\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n        vec4 values = ${glsl.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${glsl.output} = vec4(${output}, 0., 0., 0.);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_matrix_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\n/*\nThis is how the shader encodes a tensor with shape = [2, 3, 5]\n(indices are [batch, row, col]).\n\n000|001   002|003   004|xxx   020|021   022|023   024|xxx\n-------   -------   -------   -------   -------   -------\n010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\n100|101   102|103   104|xxx   120|121   122|123   124|xxx\n-------   -------   -------   -------   -------   -------\n110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\nSingle texels contain only values from the same batch, and from adjacent rows\nand columns.\n */\nexport class EncodeMatrixPackedProgram {\n    constructor(outputShape, texShape, inputIsUnsignedByte = false) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        const glsl = getGlslDifferences();\n        const [height, width] = texShape;\n        this.outputShape = outputShape;\n        let mainLoop = '';\n        let output = 'result';\n        if (inputIsUnsignedByte) {\n            output = 'floor(result * 255. + 0.5)';\n        }\n        for (let row = 0; row <= 1; row++) {\n            for (let col = 0; col <= 1; col++) {\n                const channel = row * 2 + col;\n                mainLoop += `\n          localCoords = coords;\n          if(localCoords[2] + ${col} < ${outputShape[2]}) {\n            localCoords[2] += ${col};\n            if(localCoords[1] + ${row} < ${outputShape[1]}) {\n              localCoords[1] += ${row};\n\n              flatIndex = getFlatIndex(localCoords);\n              offset = imod(flatIndex, 4);\n\n              flatIndex = idiv(flatIndex, 4, 1.);\n\n              r = flatIndex / ${width};\n              c = imod(flatIndex, ${width});\n              uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n              values = ${glsl.texture2D}(A, uv);\n\n              if(offset == 0) {\n                result[${channel}] = values[0];\n              } else if(offset == 1) {\n                result[${channel}] = values[1];\n              } else if(offset == 2) {\n                result[${channel}] = values[2];\n              } else {\n                result[${channel}] = values[3];\n              }\n            }\n          }\n        `;\n            }\n        }\n        this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        vec4 result = vec4(0.);\n        int flatIndex, r, c, offset;\n        ivec3 localCoords;\n        vec2 uv;\n        vec4 values;\n\n        ${mainLoop}\n\n        ${glsl.output} = ${output};\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_matrix_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const COMPLEX_FFT = {\n    REAL: 'return real * expR - imag * expI;',\n    IMAG: 'return real * expI + imag * expR;'\n};\nexport class FFTProgram {\n    constructor(op, inputShape, inverse) {\n        this.variableNames = ['real', 'imag'];\n        const innerDim = inputShape[1];\n        this.outputShape = inputShape;\n        const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;\n        const resultDenominator = inverse ? `${innerDim}.0` : '1.0';\n        this.userCode = `\n      const float exponentMultiplier = ${exponentMultiplierSnippet};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${op}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${innerDim});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${innerDim}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=fft_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FillProgram {\n    constructor(shape, value) {\n        this.outputShape = [];\n        this.variableNames = ['x'];\n        this.outputShape = shape;\n        this.userCode = `\n      uniform float value;\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    `;\n    }\n    getCustomSetupFunc(value) {\n        return (gpgpu, webGLProgram) => {\n            if (this.valueLoc == null) {\n                this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'value');\n            }\n            gpgpu.gl.uniform1f(this.valueLoc, value);\n        };\n    }\n}\n//# sourceMappingURL=fill_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class GatherProgram {\n    constructor(aShape, indicesLength, axis) {\n        this.variableNames = ['A', 'indices'];\n        const outputShape = aShape.slice();\n        outputShape[axis] = indicesLength;\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const sourceCoords = getSourceCoords(aShape, axis);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n    }\n}\nfunction getSourceCoords(aShape, axis) {\n    const rank = aShape.length;\n    if (rank > 4) {\n        throw Error(`Gather for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n        return `int(getIndices(resRC))`;\n    }\n    const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n    const sourceCoords = [];\n    for (let i = 0; i < aShape.length; i++) {\n        if (i === axis) {\n            sourceCoords.push(`int(getIndices(${currentCoords[i]}))`);\n        }\n        else {\n            sourceCoords.push(`${currentCoords[i]}`);\n        }\n    }\n    return sourceCoords.join();\n}\n//# sourceMappingURL=gather_gpu.js.map", "import { getCoordsDataType } from './shader_compiler';\nexport class GatherNDProgram {\n    constructor(sliceDim, strides, shape) {\n        this.sliceDim = sliceDim;\n        this.strides = strides;\n        this.variableNames = ['x', 'indices'];\n        this.outputShape = shape;\n        const stridesType = getCoordsDataType(strides.length);\n        const dtype = getCoordsDataType(shape.length);\n        const strideString = this.sliceDim > 1 ? 'strides[j]' : 'strides';\n        this.userCode = `\n        ${stridesType} strides = ${stridesType}(${this.strides});\n         void main() {\n          ${dtype} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${strideString};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `;\n    }\n}\n//# sourceMappingURL=gather_nd_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\nexport function createVertexShader(gl) {\n    const glsl = getGlslDifferences();\n    const vertexShaderSource = `${glsl.version}\n    precision highp float;\n    ${glsl.attribute} vec3 clipSpacePos;\n    ${glsl.attribute} vec2 uv;\n    ${glsl.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;\n    return webgl_util.createVertexShader(gl, vertexShaderSource);\n}\nexport function createVertexBuffer(gl) {\n    // [x y z u v] * [upper-left, lower-left, upper-right, lower-right]\n    const vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);\n    return webgl_util.createStaticVertexBuffer(gl, vertexArray);\n}\nexport function createIndexBuffer(gl) {\n    // OpenGL (and WebGL) have \"CCW == front\" winding\n    const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);\n    return webgl_util.createStaticIndexBuffer(gl, triangleVertexIndices);\n}\nfunction createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {\n    webgl_util.validateTextureSize(width, height);\n    const texture = webgl_util.createTexture(gl);\n    const tex2d = gl.TEXTURE_2D;\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(tex2d, texture));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));\n    webgl_util.callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null));\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n    return texture;\n}\nexport function getInternalFormatForFloat32MatrixTexture(textureConfig) {\n    return textureConfig.internalFormatFloat;\n}\nexport function createFloat32MatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT);\n}\nexport function getInternalFormatForFloat16MatrixTexture(textureConfig) {\n    return textureConfig.internalFormatHalfFloat;\n}\nexport function createFloat16MatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);\n}\nexport function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) {\n    return textureConfig.downloadTextureFormat;\n}\nexport function createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE);\n}\nexport function getInternalFormatForPackedMatrixTexture(textureConfig) {\n    return textureConfig.internalFormatPackedFloat;\n}\nexport function createPackedMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT);\n}\nexport function getInternalFormatForFloat16PackedMatrixTexture(textureConfig) {\n    return textureConfig.internalFormatPackedHalfFloat;\n}\nexport function createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat);\n}\nexport function bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {\n    const posOffset = 0; // x is the first buffer element\n    const uvOffset = 3 * 4; // uv comes after [x y z]\n    const stride = (3 * 4) + (2 * 4); // xyz + uv, each entry is 4-byte float.\n    webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));\n    const success = webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'clipSpacePos', vertexBuffer, 3, stride, posOffset);\n    return success &&\n        webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'uv', vertexBuffer, 2, stride, uvOffset);\n}\nexport function uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) {\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n    let dataForUpload, texelDataType, internalFormat;\n    if (data instanceof Uint8Array) {\n        dataForUpload = new Uint8Array(width * height * 4);\n        texelDataType = gl.UNSIGNED_BYTE;\n        internalFormat = gl.RGBA;\n    }\n    else {\n        dataForUpload = new Float32Array(width * height * 4);\n        texelDataType = gl.FLOAT;\n        internalFormat = textureConfig.internalFormatPackedFloat;\n    }\n    dataForUpload.set(data);\n    webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload));\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function uploadPixelDataToTexture(gl, texture, pixels) {\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n    if (pixels.data instanceof Uint8Array) {\n        webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));\n    }\n    else {\n        webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n    }\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function createBufferFromOutputTexture(gl2, rows, columns, textureConfig) {\n    // Create and bind the buffer.\n    const buffer = gl2.createBuffer();\n    webgl_util.callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer));\n    // Initialize the buffer to the size of the texture in bytes.\n    const bytesPerFloat = 4;\n    const valuesPerTexel = 4;\n    const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;\n    webgl_util.callAndCheck(gl2, () => gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));\n    // Enqueue a command on the GPU command queue to copy of texture into the\n    // buffer.\n    webgl_util.callAndCheck(gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));\n    webgl_util.callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));\n    return buffer;\n}\nexport function downloadFloat32MatrixFromBuffer(gl, buffer, size) {\n    const gl2 = gl;\n    const downloadTarget = new Float32Array(size);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n    return downloadTarget;\n}\nexport function downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {\n    const [w, h] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    const numChannels = 4;\n    const downloadTarget = new Uint8Array(tex_util.getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));\n    webgl_util.callAndCheck(gl, () => gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget));\n    // By wrapping the buffer in a Float32Array, we use native browser IEEE 754\n    // decoding of the 4 bytes that back each 32 bit float.\n    return new Float32Array(downloadTarget.buffer);\n}\nexport function downloadPackedMatrixFromBuffer(gl, buffer, batch, rows, cols, physicalRows, physicalCols, textureConfig) {\n    const gl2 = gl;\n    const downloadTarget = new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n    return downloadTarget;\n}\nexport function downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {\n    const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);\n    webgl_util.callAndCheck(gl, () => gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));\n    return packedRGBA;\n}\n//# sourceMappingURL=gpgpu_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { getWebGLContext, setWebGLContext } from './canvas_util';\nimport * as gpgpu_util from './gpgpu_util';\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\nexport class GPGPUContext {\n    constructor(gl) {\n        this.outputTexture = null;\n        this.program = null;\n        this.disposed = false;\n        this.vertexAttrsAreBound = false;\n        this.itemsToPoll = [];\n        const glVersion = env().getNumber('WEBGL_VERSION');\n        if (gl != null) {\n            this.gl = gl;\n            setWebGLContext(glVersion, gl);\n        }\n        else {\n            this.gl = getWebGLContext(glVersion);\n        }\n        // WebGL 2.0 enables texture floats without an extension.\n        let COLOR_BUFFER_FLOAT = 'WEBGL_color_buffer_float';\n        const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n        if (env().getNumber('WEBGL_VERSION') === 1) {\n            const TEXTURE_FLOAT = 'OES_texture_float';\n            const TEXTURE_HALF_FLOAT = 'OES_texture_half_float';\n            this.textureFloatExtension =\n                webgl_util.getExtensionOrThrow(this.gl, TEXTURE_FLOAT);\n            if (webgl_util.hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {\n                this.textureHalfFloatExtension =\n                    webgl_util.getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);\n            }\n            else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n                throw new Error('GL context does not support half float textures, yet the ' +\n                    'environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n            }\n            this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);\n            if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n                this.colorBufferHalfFloatExtension =\n                    webgl_util.getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);\n            }\n            else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n                throw new Error('GL context does not support color renderable half floats, yet ' +\n                    'the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n            }\n        }\n        else {\n            COLOR_BUFFER_FLOAT = 'EXT_color_buffer_float';\n            if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {\n                this.colorBufferFloatExtension =\n                    this.gl.getExtension(COLOR_BUFFER_FLOAT);\n            }\n            else if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n                this.colorBufferHalfFloatExtension =\n                    this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n            }\n            else {\n                throw new Error('GL context does not support color renderable floats');\n            }\n        }\n        this.vertexBuffer = gpgpu_util.createVertexBuffer(this.gl);\n        this.indexBuffer = gpgpu_util.createIndexBuffer(this.gl);\n        this.framebuffer = webgl_util.createFramebuffer(this.gl);\n        this.textureConfig =\n            tex_util.getTextureConfig(this.gl, this.textureHalfFloatExtension);\n    }\n    get debug() {\n        return env().getBool('DEBUG');\n    }\n    dispose() {\n        if (this.disposed) {\n            return;\n        }\n        if (this.program != null) {\n            console.warn('Disposing a GPGPUContext that still has a bound WebGLProgram.' +\n                ' This is probably a resource leak, delete the program with ' +\n                'GPGPUContext.deleteProgram before disposing.');\n        }\n        if (this.outputTexture != null) {\n            console.warn('Disposing a GPGPUContext that still has a bound output matrix ' +\n                'texture.  This is probably a resource leak, delete the output ' +\n                'matrix texture with GPGPUContext.deleteMatrixTexture before ' +\n                'disposing.');\n        }\n        const gl = this.gl;\n        webgl_util.callAndCheck(gl, () => gl.finish());\n        webgl_util.callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));\n        webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));\n        this.disposed = true;\n    }\n    createFloat32MatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createFloat16MatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createUnsignedBytesMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    uploadPixelDataToTexture(texture, pixels) {\n        this.throwIfDisposed();\n        gpgpu_util.uploadPixelDataToTexture(this.gl, texture, pixels);\n    }\n    uploadDenseMatrixToTexture(texture, width, height, data) {\n        this.throwIfDisposed();\n        gpgpu_util.uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig);\n    }\n    createFloat16PackedMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createPackedMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    deleteMatrixTexture(texture) {\n        this.throwIfDisposed();\n        if (this.outputTexture === texture) {\n            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n            this.outputTexture = null;\n        }\n        webgl_util.callAndCheck(this.gl, () => this.gl.deleteTexture(texture));\n    }\n    downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) {\n        return this.downloadMatrixDriver(texture, () => gpgpu_util.downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig));\n    }\n    downloadPackedMatrixFromBuffer(buffer, batch, rows, columns, physicalRows, physicalCols) {\n        return gpgpu_util.downloadPackedMatrixFromBuffer(this.gl, buffer, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);\n    }\n    downloadFloat32MatrixFromBuffer(buffer, size) {\n        return gpgpu_util.downloadFloat32MatrixFromBuffer(this.gl, buffer, size);\n    }\n    createBufferFromTexture(texture, rows, columns) {\n        this.bindTextureToFrameBuffer(texture);\n        const result = gpgpu_util.createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig);\n        this.unbindTextureToFrameBuffer();\n        return result;\n    }\n    createAndWaitForFence() {\n        const fenceContext = this.createFence(this.gl);\n        return this.pollFence(fenceContext);\n    }\n    createFence(gl) {\n        let query;\n        let isFencePassed;\n        if (env().getBool('WEBGL_FENCE_API_ENABLED')) {\n            const gl2 = gl;\n            const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);\n            gl.flush();\n            isFencePassed = () => {\n                const status = gl2.clientWaitSync(sync, 0, 0);\n                return status === gl2.ALREADY_SIGNALED ||\n                    status === gl2.CONDITION_SATISFIED;\n            };\n            query = sync;\n        }\n        else if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n            query = this.beginQuery();\n            this.endQuery();\n            isFencePassed = () => this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n        }\n        else {\n            // If we have no way to fence, return true immediately. This will fire in\n            // WebGL 1.0 when there is no disjoint query timer. In this case, because\n            // the fence passes immediately, we'll immediately ask for a download of\n            // the texture, which will cause the UI thread to hang.\n            isFencePassed = () => true;\n        }\n        return { query, isFencePassed };\n    }\n    downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {\n        return this.downloadMatrixDriver(texture, () => gpgpu_util.downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));\n    }\n    createProgram(fragmentShaderSource) {\n        this.throwIfDisposed();\n        const gl = this.gl;\n        const fragmentShader = webgl_util.createFragmentShader(gl, fragmentShaderSource);\n        const vertexShader = gpgpu_util.createVertexShader(gl);\n        const program = webgl_util.createProgram(gl);\n        webgl_util.callAndCheck(gl, () => gl.attachShader(program, vertexShader));\n        webgl_util.callAndCheck(gl, () => gl.attachShader(program, fragmentShader));\n        webgl_util.linkProgram(gl, program);\n        if (this.debug) {\n            webgl_util.validateProgram(gl, program);\n        }\n        if (!this.vertexAttrsAreBound) {\n            this.setProgram(program);\n            this.vertexAttrsAreBound = gpgpu_util.bindVertexProgramAttributeStreams(gl, this.program, this.vertexBuffer);\n        }\n        return program;\n    }\n    deleteProgram(program) {\n        this.throwIfDisposed();\n        if (program === this.program) {\n            this.program = null;\n        }\n        if (program != null) {\n            webgl_util.callAndCheck(this.gl, () => this.gl.deleteProgram(program));\n        }\n    }\n    setProgram(program) {\n        this.throwIfDisposed();\n        this.program = program;\n        if ((this.program != null) && this.debug) {\n            webgl_util.validateProgram(this.gl, this.program);\n        }\n        webgl_util.callAndCheck(this.gl, () => this.gl.useProgram(program));\n    }\n    getUniformLocation(program, uniformName, shouldThrow = true) {\n        this.throwIfDisposed();\n        if (shouldThrow) {\n            return webgl_util.getProgramUniformLocationOrThrow(this.gl, program, uniformName);\n        }\n        else {\n            return webgl_util.getProgramUniformLocation(this.gl, program, uniformName);\n        }\n    }\n    getAttributeLocation(program, attribute) {\n        this.throwIfDisposed();\n        return webgl_util.callAndCheck(this.gl, () => this.gl.getAttribLocation(program, attribute));\n    }\n    getUniformLocationNoThrow(program, uniformName) {\n        this.throwIfDisposed();\n        return this.gl.getUniformLocation(program, uniformName);\n    }\n    setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) {\n        this.throwIfDisposed();\n        this.throwIfNoProgram();\n        webgl_util.bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit);\n    }\n    setOutputMatrixTexture(outputMatrixTexture, rows, columns) {\n        this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);\n    }\n    setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) {\n        this.throwIfDisposed();\n        const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n        this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);\n    }\n    setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n        this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);\n    }\n    setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n        throw new Error('setOutputPackedMatrixWriteRegion not implemented.');\n    }\n    debugValidate() {\n        if (this.program != null) {\n            webgl_util.validateProgram(this.gl, this.program);\n        }\n        webgl_util.validateFramebuffer(this.gl);\n    }\n    executeProgram() {\n        this.throwIfDisposed();\n        this.throwIfNoProgram();\n        const gl = this.gl;\n        if (this.debug) {\n            this.debugValidate();\n        }\n        webgl_util.callAndCheck(gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));\n    }\n    blockUntilAllProgramsCompleted() {\n        this.throwIfDisposed();\n        webgl_util.callAndCheck(this.gl, () => this.gl.finish());\n    }\n    getQueryTimerExtension() {\n        if (this.disjointQueryTimerExtension == null) {\n            this.disjointQueryTimerExtension =\n                webgl_util.getExtensionOrThrow(this.gl, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2 ?\n                    'EXT_disjoint_timer_query_webgl2' :\n                    'EXT_disjoint_timer_query');\n        }\n        return this.disjointQueryTimerExtension;\n    }\n    getQueryTimerExtensionWebGL2() {\n        return this.getQueryTimerExtension();\n    }\n    getQueryTimerExtensionWebGL1() {\n        return this.getQueryTimerExtension();\n    }\n    beginQuery() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            const query = gl2.createQuery();\n            gl2.beginQuery(ext.TIME_ELAPSED_EXT, query);\n            return query;\n        }\n        const ext = this.getQueryTimerExtensionWebGL1();\n        const query = ext.createQueryEXT();\n        ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);\n        return query;\n    }\n    endQuery() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            gl2.endQuery(ext.TIME_ELAPSED_EXT);\n            return;\n        }\n        const ext = this.getQueryTimerExtensionWebGL1();\n        ext.endQueryEXT(ext.TIME_ELAPSED_EXT);\n    }\n    async waitForQueryAndGetTime(query) {\n        await util.repeatedTry(() => this.disposed || // while testing contexts are created / disposed\n            // in rapid succession, so without this check we\n            // may poll for the query timer indefinitely\n            this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')));\n        return this.getQueryTime(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n    }\n    getQueryTime(query, queryTimerVersion) {\n        if (queryTimerVersion === 0) {\n            return null;\n        }\n        if (queryTimerVersion === 2) {\n            const gl2 = this.gl;\n            const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);\n            // Return milliseconds.\n            return timeElapsedNanos / 1000000;\n        }\n        else {\n            const ext = this.getQueryTimerExtensionWebGL1();\n            const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);\n            // Return milliseconds.\n            return timeElapsedNanos / 1000000;\n        }\n    }\n    isQueryAvailable(query, queryTimerVersion) {\n        if (queryTimerVersion === 0) {\n            return true;\n        }\n        if (queryTimerVersion === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);\n            if (this.disjoint == null) {\n                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n            }\n            return available && !this.disjoint;\n        }\n        else {\n            const ext = this.getQueryTimerExtensionWebGL1();\n            const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);\n            if (this.disjoint == null) {\n                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n            }\n            return available && !this.disjoint;\n        }\n    }\n    pollFence(fenceContext) {\n        return new Promise(resolve => {\n            this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());\n        });\n    }\n    pollItems() {\n        // Find the last query that has finished.\n        const index = linearSearchLastTrue(this.itemsToPoll.map(x => x.isDoneFn));\n        for (let i = 0; i <= index; ++i) {\n            const { resolveFn } = this.itemsToPoll[i];\n            resolveFn();\n        }\n        this.itemsToPoll = this.itemsToPoll.slice(index + 1);\n    }\n    addItemToPoll(isDoneFn, resolveFn) {\n        this.itemsToPoll.push({ isDoneFn, resolveFn });\n        if (this.itemsToPoll.length > 1) {\n            // We already have a running loop that polls.\n            return;\n        }\n        // Start a new loop that polls.\n        util.repeatedTry(() => {\n            this.pollItems();\n            // End the loop if no more items to poll.\n            return this.itemsToPoll.length === 0;\n        });\n    }\n    bindTextureToFrameBuffer(texture) {\n        this.throwIfDisposed();\n        webgl_util.bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);\n        if (this.debug) {\n            webgl_util.validateFramebuffer(this.gl);\n        }\n    }\n    unbindTextureToFrameBuffer() {\n        if (this.outputTexture != null) {\n            webgl_util.bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);\n            if (this.debug) {\n                webgl_util.validateFramebuffer(this.gl);\n            }\n        }\n        else {\n            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n        }\n    }\n    downloadMatrixDriver(texture, downloadAndDecode) {\n        this.bindTextureToFrameBuffer(texture);\n        const result = downloadAndDecode();\n        this.unbindTextureToFrameBuffer();\n        return result;\n    }\n    setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) {\n        this.throwIfDisposed();\n        const gl = this.gl;\n        webgl_util.bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);\n        if (this.debug) {\n            webgl_util.validateFramebuffer(gl);\n        }\n        this.outputTexture = outputMatrixTextureMaybePacked;\n        webgl_util.callAndCheck(gl, () => gl.viewport(0, 0, width, height));\n        webgl_util.callAndCheck(gl, () => gl.scissor(0, 0, width, height));\n    }\n    setOutputMatrixWriteRegionDriver(x, y, width, height) {\n        this.throwIfDisposed();\n        webgl_util.callAndCheck(this.gl, () => this.gl.scissor(x, y, width, height));\n    }\n    throwIfDisposed() {\n        if (this.disposed) {\n            throw new Error('Attempted to use disposed GPGPUContext.');\n        }\n    }\n    throwIfNoProgram() {\n        if (this.program == null) {\n            throw new Error('No GPU program is currently set.');\n        }\n    }\n}\n/**\n * Finds the index of the last true element using linear search.\n * Note: We can't do binary search because Chrome expects us to explicitly\n * test all fences before download:\n * https://github.com/tensorflow/tfjs/issues/1145\n */\nexport function linearSearchLastTrue(arr) {\n    let i = 0;\n    for (; i < arr.length; ++i) {\n        const isDone = arr[i]();\n        if (!isDone) {\n            break;\n        }\n    }\n    return i - 1;\n}\n//# sourceMappingURL=gpgpu_context.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport * as shader_compiler from './shader_compiler';\nexport function compileProgram(gpgpu, program, inputs, output) {\n    const userCode = program.userCode;\n    const inputInfos = inputs.map((input, i) => {\n        const shapeInfo = {\n            logicalShape: input.shape,\n            texShape: input.isUniform ? null : input.texData.texShape,\n            isUniform: input.isUniform,\n            isPacked: input.isUniform ? false : input.texData.isPacked,\n            flatOffset: null\n        };\n        if (input.texData != null && input.texData.slice != null &&\n            input.texData.slice.flatOffset > 0) {\n            shapeInfo.flatOffset = input.texData.slice.flatOffset;\n        }\n        return { name: program.variableNames[i], shapeInfo };\n    });\n    const inShapeInfos = inputInfos.map(x => x.shapeInfo);\n    const outShapeInfo = {\n        logicalShape: output.shape,\n        texShape: output.texData.texShape,\n        isUniform: false,\n        isPacked: output.texData.isPacked,\n        flatOffset: null\n    };\n    const source = shader_compiler.makeShader(inputInfos, outShapeInfo, userCode, program.packedInputs);\n    const webGLProgram = gpgpu.createProgram(source);\n    // Add special uniforms (NAN, INFINITY)\n    let infLoc = null;\n    const nanLoc = gpgpu.getUniformLocation(webGLProgram, 'NAN', false);\n    if (env().getNumber('WEBGL_VERSION') === 1) {\n        infLoc = gpgpu.getUniformLocation(webGLProgram, 'INFINITY', false);\n    }\n    // Add user-defined uniforms\n    const uniformLocations = {};\n    for (let i = 0; i < program.variableNames.length; i++) {\n        const varName = program.variableNames[i];\n        const shouldThrow = false;\n        uniformLocations[varName] =\n            gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);\n        uniformLocations[`offset${varName}`] =\n            gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);\n    }\n    return {\n        program,\n        source,\n        webGLProgram,\n        uniformLocations,\n        inShapeInfos,\n        outShapeInfo,\n        infLoc,\n        nanLoc,\n    };\n}\nfunction validateBinaryAndProgram(shapeInfos, inputs) {\n    if (shapeInfos.length !== inputs.length) {\n        throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but ` +\n            `was executed with ${inputs.length} inputs`);\n    }\n    shapeInfos.forEach((s, i) => {\n        const shapeA = s.logicalShape;\n        const input = inputs[i];\n        const shapeB = input.shape;\n        if (!util.arraysEqual(shapeA, shapeB)) {\n            throw Error(`Binary was compiled with different shapes than ` +\n                `the current args. Shapes ${shapeA} and ${shapeB} must match`);\n        }\n        // The input is uploaded as uniform.\n        if (s.isUniform && input.isUniform) {\n            return;\n        }\n        const texShapeA = s.texShape;\n        const texShapeB = input.isUniform ? null : input.texData.texShape;\n        if (!util.arraysEqual(texShapeA, texShapeB)) {\n            throw Error(`Binary was compiled with different texture shapes than the` +\n                ` current args. Shape ${texShapeA} and ${texShapeB} must match`);\n        }\n    });\n}\nexport function runProgram(gpgpu, binary, inputs, output, customSetup) {\n    validateBinaryAndProgram(binary.inShapeInfos, inputs);\n    validateBinaryAndProgram([binary.outShapeInfo], [output]);\n    const outTex = output.texData.texture;\n    const outTexShape = output.texData.texShape;\n    if (output.texData.isPacked) {\n        gpgpu.setOutputPackedMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n    }\n    else {\n        gpgpu.setOutputMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n    }\n    gpgpu.setProgram(binary.webGLProgram);\n    // Set special uniforms (NAN, INFINITY)\n    if (env().getNumber('WEBGL_VERSION') === 1) {\n        if (binary.infLoc !== null) {\n            gpgpu.gl.uniform1f(binary.infLoc, Infinity);\n        }\n    }\n    if (binary.nanLoc !== null) {\n        gpgpu.gl.uniform1f(binary.nanLoc, NaN);\n    }\n    // Set user-defined inputs\n    inputs.forEach((input, i) => {\n        const varName = binary.program.variableNames[i];\n        const varLoc = binary.uniformLocations[varName];\n        const varOffsetLoc = binary.uniformLocations[`offset${varName}`];\n        if (varLoc == null) {\n            // The compiler inferred that this variable is not used in this shader.\n            return;\n        }\n        if (input.isUniform) {\n            // Upload the values of the tensor as uniform.\n            if (util.sizeFromShape(input.shape) < 2) {\n                gpgpu.gl.uniform1f(varLoc, input.uniformValues[0]);\n            }\n            else {\n                let vals = input.uniformValues;\n                if (!(vals instanceof Float32Array)) {\n                    vals = new Float32Array(vals);\n                }\n                gpgpu.gl.uniform1fv(varLoc, vals);\n            }\n            return;\n        }\n        // If the input was sliced, upload the flat offset index.\n        if (input.texData.slice != null && varOffsetLoc != null) {\n            gpgpu.gl.uniform1i(varOffsetLoc, input.texData.slice.flatOffset);\n        }\n        gpgpu.setInputMatrixTexture(input.texData.texture, varLoc, i);\n    });\n    if (customSetup != null) {\n        customSetup(gpgpu, binary.webGLProgram);\n    }\n    gpgpu.executeProgram();\n}\nexport function makeShaderKey(program, inputs, output) {\n    let keyInputs = '';\n    inputs.concat(output).forEach(x => {\n        const hasOffset = x.texData != null && x.texData.slice != null &&\n            x.texData.slice.flatOffset > 0;\n        const texShape = x.isUniform ? 'uniform' : x.texData.texShape;\n        keyInputs += `${x.shape}_${texShape}_${hasOffset}`;\n    });\n    const keyUserCode = program.userCode;\n    let key = program.constructor.name;\n    // Fast string concat. See https://jsperf.com/string-concatenation/14.\n    key += '_' + keyInputs + '_' + keyUserCode;\n    return key;\n}\n//# sourceMappingURL=gpgpu_math.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nexport class Im2ColPackedProgram {\n    constructor(outputShape, inputShape, convInfo) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        const { filterWidth, inChannels, strideWidth, strideHeight, padInfo, outWidth, dilationWidth, dilationHeight, dataFormat } = convInfo;\n        const { left, top } = padInfo;\n        const itemsPerBlockRow = inChannels * filterWidth;\n        const glsl = getGlslDifferences();\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const rowDim = isChannelsLast ? 0 : 1;\n        const colDim = isChannelsLast ? 1 : 2;\n        let unrolled = ``;\n        for (let row = 0; row <= 1; row++) {\n            for (let col = 0; col <= 1; col++) {\n                unrolled += `\n          blockIndex = rc.y + ${col};\n          pos = rc.x + ${row};\n\n          if(blockIndex < ${outputShape[1]} && pos < ${outputShape[0]}) {\n            offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${top};\n            d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});\n\n            if(d0 < ${inputShape[rowDim]} && d0 >= 0) {\n\n              offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${strideWidth}. - ${left}.);\n              d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${itemsPerBlockRow}.) / ${inChannels}.));\n\n              if(d1 < ${inputShape[colDim]} && d1 >= 0) {\n\n                ch = int(mod(float(pos), ${inChannels}.));\n\n                if (${isChannelsLast}) {\n                  innerDims = vec2(d1, ch);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;\n            }\n        }\n        this.userCode = `\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${unrolled}\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=im2col_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNProgram {\n    constructor(xShape, radius, bias, alpha, beta) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        const rad = radius;\n        const maxD = xShape[3] - 1;\n        this.outputShape = xShape;\n        // optimize pow(bias + alpha * sum, -beta)\n        // src: https://github.com/tensorflow/tensorflow/..\n        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n        // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n        let powOperator;\n        const basis = `float(${bias}) + float(${alpha}) * sum`;\n        if (beta === 0.5) {\n            powOperator = `inversesqrt(${basis})`;\n        }\n        else if (beta === 1.0) {\n            powOperator = `1.0/(${basis})`;\n        }\n        else {\n            powOperator = `exp(log(${basis}) * float(-${beta}));`;\n        }\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${rad}; j <= ${rad}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${maxD}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${powOperator};\n        setOutput(val);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNGradProgram {\n    constructor(inputShape, depthRadius, bias, alpha, beta) {\n        this.variableNames = ['inputImage', 'outputImage', 'dy'];\n        this.outputShape = [];\n        this.outputShape = inputShape;\n        this.depth = inputShape[3];\n        this.depthRadius = depthRadius;\n        this.bias = bias;\n        this.alpha = alpha;\n        this.beta = beta;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${depthRadius})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${depthRadius} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${alpha}) * norm + float(${bias});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${alpha})\n                * float(${beta})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${beta});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_grad_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNPackedProgram {\n    constructor(xShape, radius, bias, alpha, beta) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const rad = radius;\n        const maxD = xShape[3] - 1;\n        this.outputShape = xShape;\n        // optimize pow(bias + alpha * sum, -beta)\n        // src: https://github.com/tensorflow/tensorflow/..\n        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n        // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n        let powOperator;\n        const basis = `float(${bias}) + float(${alpha}) * sum`;\n        if (beta === 0.5) {\n            powOperator = `inversesqrt(${basis})`;\n        }\n        else if (beta === 1.0) {\n            powOperator = `1.0/(${basis})`;\n        }\n        else {\n            powOperator = `exp(log(${basis}) * float(-${beta}));`;\n        }\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${rad};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${rad}; j <= ${rad}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${powOperator};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MaxPool2DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'maxPos'];\n        this.outputShape = convInfo.inShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n          wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${effectiveFilterWidth} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class MaxPool3DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'maxPos'];\n        this.outputShape = convInfo.inShape;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n           wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${lastIndex} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                  wR * ${effectiveFilterWidth} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=max_pool_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MatMulPackedProgram {\n    constructor(aShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['matrixA', 'matrixB'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        const sharedDim = transposeA ? aShape[1] : aShape[2];\n        const sharedDimensionPacked = Math.ceil(sharedDim / 2);\n        const aSample = transposeA ? 'i * 2, rc.y' : 'rc.y, i * 2';\n        const bSample = transposeB ? 'rc.z, i * 2' : 'i * 2, rc.z';\n        const aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];\n        const bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const float sharedDimension = ${sharedDimensionPacked}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${sharedDimensionPacked}; i++) {\n          vec4 a = getMatrixA(rc.x, ${aSample});\n          vec4 b = getMatrixB(rc.x, ${bSample});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${aSwizzle[0]} * ${bSwizzle[0]});\n          result += (${aSwizzle[1]} * ${bSwizzle[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${addBiasSnippet}\n\n        ${applyActivationSnippet}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=mulmat_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MultinomialProgram {\n    constructor(batchSize, numOutcomes, numSamples) {\n        this.variableNames = ['probs'];\n        this.outputShape = [batchSize, numSamples];\n        this.userCode = `\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < ${numOutcomes - 1}; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(${numOutcomes - 1}));\n      }\n    `;\n    }\n    getCustomSetupFunc(seed) {\n        return (gpgpu, webGLProgram) => {\n            if (this.seedLoc == null) {\n                this.seedLoc = gpgpu.getUniformLocation(webGLProgram, 'seed');\n            }\n            gpgpu.gl.uniform1f(this.seedLoc, seed);\n        };\n    }\n}\n//# sourceMappingURL=multinomial_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class OneHotProgram {\n    constructor(numIndices, depth, onValue, offValue) {\n        this.variableNames = ['indices'];\n        this.outputShape = [numIndices, depth];\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${offValue}), float(${onValue}),\n                      float(index == coords.y)));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=onehot_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class PackProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        // Only input / output 3D tensors.\n        this.outputShape = outputShape;\n        const rank = outputShape.length;\n        if (rank === 0) {\n            this.userCode = `\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;\n        }\n        else {\n            const channels = getChannels('rc', rank);\n            const dtype = getCoordsDataType(rank);\n            const outOfBoundsCondition = getOutOfBoundsCondition(rank, outputShape, channels);\n            const setup = getSetup(rank, outputShape[outputShape.length - 1], outputShape[outputShape.length - 2], channels);\n            const output = getOutput(outputShape, channels);\n            this.userCode = `\n        void main() {\n          ${dtype} rc = getOutputCoords();\n\n          if(${outOfBoundsCondition}) {\n            setOutput(vec4(0));\n          } else {\n            ${setup}\n\n            setOutput(vec4(${output}));\n          }\n        }\n      `;\n        }\n    }\n}\nfunction getSourceCoordsArr(rank, dims) {\n    const coords = [];\n    for (let row = 0; row <= 1; row++) {\n        for (let col = 0; col <= 1; col++) {\n            let coord = `${row === 0 ? 'r' : 'rp1'}, ${col === 0 ? 'c' : 'cp1'}`;\n            for (let d = 2; d < rank; d++) {\n                coord = `${dims[dims.length - 1 - d]},` + coord;\n            }\n            coords.push(coord);\n        }\n    }\n    return coords;\n}\nfunction getOutOfBoundsCondition(rank, shape, dims) {\n    if (rank === 1) {\n        return `rc > ${shape[0]}`;\n    }\n    let cond = '';\n    for (let i = rank - 2; i < rank; i++) {\n        cond += `${dims[i]} >= ${shape[i]}`;\n        if (i < rank - 1) {\n            cond += '||';\n        }\n    }\n    return cond;\n}\nfunction getSetup(rank, cols, rows, dims) {\n    if (rank === 1) {\n        return '';\n    }\n    const innerDims = dims.slice(-2);\n    return `\n    int r = ${innerDims[0]};\n    int c = ${innerDims[1]};\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= ${cols};\n    bool rEdge = rp1 >= ${rows};\n  `;\n}\nfunction getOutput(shape, dims) {\n    const rank = shape.length;\n    const sourceCoords = getSourceCoordsArr(rank, dims);\n    if (rank === 1) {\n        return `getA(rc),\n            rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),\n            0, 0`;\n    }\n    return `getA(${sourceCoords[0]}),\n          cEdge ? 0. : getA(${sourceCoords[1]}),\n          rEdge ? 0. : getA(${sourceCoords[2]}),\n          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;\n}\n//# sourceMappingURL=pack_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class PadProgram {\n    constructor(xShape, paddings, constantValue) {\n        this.variableNames = ['x'];\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const type = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const unpackedCoords = ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);\n        if (rank === 1) {\n            this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float(${constantValue}));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `;\n            return;\n        }\n        this.userCode = `\n      ${type} start = ${type}(${start});\n      ${type} end = ${type}(${end});\n\n      void main() {\n        ${type} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float(${constantValue}));\n        } else {\n          ${type} coords = outC - start;\n          setOutput(getX(${unpackedCoords}));\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pad_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class PadPackedProgram {\n    constructor(xShape, paddings, constantValue) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const dtype = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const coords = getChannels('rc', rank);\n        const source = getChannels('source', rank);\n        const cLimit = `${coords[rank - 1]} < ${this.outputShape[rank - 1]}`;\n        const innerDims = rank === 1 ? 'source' : `vec2(${source.slice(-2).join()})`;\n        const componentSetup = [\n            `${dtype} rc = outputLoc;`, `${coords[rank - 1]} += 1;\n       if(${cLimit}) {\n      `,\n            rank === 1 ? '' : `}\n       rc = outputLoc;\n       ${coords[rank - 2]} += 1;\n       if(${coords[rank - 2]} < ${this.outputShape[rank - 2]}) {`,\n            rank === 1 ? '' : `  ${coords[rank - 1]} += 1;\n         if(${cLimit}) {`\n        ];\n        const paddingArea = rank === 1 ?\n            'rc < start || rc >= end' :\n            'any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))';\n        let mainLoop = '';\n        for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {\n            mainLoop += `\n        ${componentSetup[i]}\n        if (${paddingArea}) {\n          result[${i}] = float(${constantValue});\n        } else {\n          ${dtype} source = rc - start;\n          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n        }\n        mainLoop += (rank === 1 ? `} ` : `}}`);\n        this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pad_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Pool2DProgram {\n    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n        this.variableNames = ['x'];\n        if (poolType === 'avg' && computePositions) {\n            throw new Error('Cannot compute positions for average pool.');\n        }\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.outputShape = convInfo.outShape;\n        const isAvgPool = poolType === 'avg';\n        const batchFlattenPositionStr = `((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n        const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n        let initializationValue = '0.0';\n        if (!isAvgPool) {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n        }\n        if (computePositions) {\n            const compareOp = '>=';\n            this.userCode = `\n        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n        const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${compareOp} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${flattenPositions ? (includeBatchInIndex ? batchFlattenPositionStr :\n                flattenPositionStr) :\n                `wR * ${effectiveFilterWidth} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n            return;\n        }\n        const compareOp = 'max';\n        let returnValue = `${poolType}(${poolType}(${poolType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (poolType === 'avg') {\n            returnValue = `avgValue / count`;\n        }\n        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n        const filterWidthVec4Remainder = filterWidth % 4;\n        const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)\n            );\n\n            ${updateSnippet}\n          }\n\n          int xC = xCCorner + ${filterWidthNearestVec4};\n          if (${filterWidthVec4Remainder === 1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              initializationValue\n            );\n\n            ${updateSnippet}\n          }\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\nexport class Pool3DProgram {\n    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n        this.variableNames = ['x'];\n        if (poolType === 'avg' && computePositions) {\n            throw new Error('Cannot compute positions for average pool.');\n        }\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.outputShape = convInfo.outShape;\n        const isAvgPool = poolType === 'avg';\n        let initializationValue = '0.0';\n        if (!isAvgPool) {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n        }\n        if (computePositions) {\n            const compareOp = '>=';\n            this.userCode = `\n        const ivec3 strides =\n            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${effectiveFilterDepth};\n              wD += ${dilationDepth}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${effectiveFilterHeight};\n                wR += ${dilationHeight}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${effectiveFilterWidth};\n                  wC += ${dilationWidth}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${compareOp} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${flattenPositions ?\n                (includeBatchInIndex ?\n                    `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` :\n                    `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch`) :\n                `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                      wR * ${effectiveFilterWidth} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n            return;\n        }\n        const compareOp = 'max';\n        let returnValue = `${poolType}(${poolType}(${poolType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (poolType === 'avg') {\n            returnValue = `avgValue / count`;\n        }\n        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n        const filterWidthVec4Remainder = filterWidth % 4;\n        const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        this.userCode = `\n      const ivec3 strides =\n        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)\n              );\n\n              ${updateSnippet}\n            }\n\n            int xC = xCCorner + ${filterWidthNearestVec4};\n            if (${filterWidthVec4Remainder === 1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                initializationValue\n              );\n\n              ${updateSnippet}\n            }\n          }\n          setOutput(${returnValue});\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pool_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ReduceProgram {\n    constructor(reduceInfo, reduceType) {\n        this.variableNames = ['x'];\n        const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n        this.outputShape = [batchSize, outSize];\n        let initializationValue = '0.0';\n        let compareOp = ``;\n        if (reduceType === 'prod') {\n            initializationValue = '1.0';\n        }\n        else if (reduceType === 'min') {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '1.0 / 1e-20';\n            compareOp = `min`;\n        }\n        else if (reduceType === 'max') {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n            compareOp = `max`;\n        }\n        let returnValue = `${reduceType}(${reduceType}(${reduceType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (reduceType === 'sum') {\n            returnValue = `sumValue`;\n        }\n        else if (reduceType === 'prod') {\n            returnValue = `prodValue`;\n        }\n        else if (reduceType === 'all') {\n            returnValue = `allValue`;\n        }\n        else if (reduceType === 'any') {\n            returnValue = `anyValue`;\n        }\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        let updateSnippet = `\n      if (${reduceType === 'sum'}) {\n        sumValue += dot(values, ones);\n      } else if (${reduceType === 'prod'}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        let vecType = `vec4`;\n        if (reduceType === 'all') {\n            initializationValue = '1.0';\n            updateSnippet = `\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `;\n            vecType = `bvec4`;\n        }\n        else if (reduceType === 'any') {\n            initializationValue = '0.0';\n            updateSnippet = `\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `;\n            vecType = `bvec4`;\n        }\n        let checkOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n        }\n        this.userCode = `\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        vec4 minMaxValue = vec4(${initializationValue});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\n//# sourceMappingURL=reduce_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as shader_util from './shader_compiler_util';\nexport class ReshapePackedProgram {\n    constructor(outputShape, inputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        let mainLoop = ``;\n        for (let i = 0; i < 4; i++) {\n            let thisRC = `thisRC = rc;`;\n            if (i % 2 === 1) {\n                thisRC += `thisRC.z += 1;`;\n            }\n            if (i > 1) {\n                thisRC += `thisRC.y += 1;`;\n            }\n            mainLoop += `\n        ${thisRC}\n        ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ''}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${i}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${i > 0 ? '}' : ''}\n      `;\n        }\n        this.userCode = `\n      ${getReshapedInputCoords(inputShape)}\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${outputShape[1]};\n        int cols = ${outputShape[2]};\n\n        ${mainLoop}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\nfunction getReshapedInputCoords(shape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n    return `\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\n//# sourceMappingURL=reshape_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearBackpropProgram {\n    constructor(dy, x, alignCorners) {\n        this.variableNames = ['dy'];\n        this.outputShape = [];\n        this.outputShape = x.shape;\n        const [, xHeight, xWidth,] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        // In the backwards pass, we want to find the pixels that were generated for\n        // each pixel in the input image the forward pass and add the corresponding\n        // coefficient from dy to the gradient (with some interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearPackedProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        this.userCode = `\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,\n                                     ${oldWidth}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${depth - 1};\n        bool hasNextRow = coords.z < ${newWidth - 1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeNearestNeigborBackpropProgram {\n    constructor(dy, x, alignCorners) {\n        this.variableNames = ['dy'];\n        this.outputShape = [];\n        this.outputShape = x.shape;\n        const [, xHeight, xWidth,] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        // In the backwards pass, we want to find the pixels that were generated for\n        // each pixel in the input image the forward pass and add the corresponding\n        // coefficient from dy to the gradient (with some interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${effectiveXSize[0]}) *\n                (float(dyR) / float(${effectiveYSize[0]}));\n\n            float sourceFracCol =\n                float(${effectiveXSize[1]}) *\n                  (float(dyC) / float(${effectiveYSize[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${xHeight}) - 1),\n                ${alignCorners} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${xWidth}) - 1),\n                ${alignCorners} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_nearest_neighbor_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeNearestNeighborProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        // When align corners is false, we rounds the value with floor.\n        const roundBase = alignCorners ? '0.5' : '0.0';\n        this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_nearest_neighbor_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class ReverseProgram {\n    constructor(xShape, axis) {\n        this.variableNames = ['x'];\n        const rank = xShape.length;\n        if (rank > 4) {\n            throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n        }\n        this.outputShape = xShape;\n        if (rank === 1) {\n            this.userCode = `\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${xShape[0]} - coord - 1));\n        }\n      `;\n            return;\n        }\n        const getInCoord = (i) => {\n            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n                return `${xShape[i]} - coords[${i}] - 1`;\n            }\n            return `coords[${i}]`;\n        };\n        const inCoords = xShape.map((_, i) => getInCoord(i)).join(',');\n        const type = getCoordsDataType(rank);\n        this.userCode = `\n      void main() {\n        ${type} coords = getOutputCoords();\n        setOutput(getX(${inCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=reverse_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ReversePackedProgram {\n    constructor(xShape, axis) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const rank = xShape.length;\n        if (rank > 4) {\n            throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n        }\n        this.outputShape = xShape;\n        const channels = getChannels('rc', rank);\n        const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;\n        const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;\n        const type = getCoordsDataType(rank);\n        if (rank === 1) {\n            this.userCode = `\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${xShape[0]} - rc - 1),\n            ${xShape[0]} - rc - 1);\n          if(${nextColumn}){\n              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),\n                ${xShape[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `;\n        }\n        else {\n            this.userCode = `\n        void main() {\n          ${type} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${getR(channels.slice())};\n          if(${nextColumn}){\n            result.g = ${getG(channels.slice())};\n          }\n          if(${nextRow}) {\n            result.b = ${getB(channels.slice())};\n            if(${nextColumn}) {\n              result.a = ${getA(channels.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `;\n        }\n        function getR(channels) {\n            return getChannel(channels);\n        }\n        function getG(channels) {\n            channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getB(channels) {\n            channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getA(channels) {\n            channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n            channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getChannel(channels) {\n            const inCoordsArray = xShape.map((_, i) => getInCoord(i, channels));\n            const inCoords = inCoordsArray.join(',');\n            const innerDims = inCoordsArray.slice(-2).join(',');\n            return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;\n        }\n        function getInCoord(i, channels1) {\n            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n                return `${xShape[i]} - ${channels1[i]} - 1`;\n            }\n            else {\n                return `${channels1[i]}`;\n            }\n        }\n    }\n}\n//# sourceMappingURL=reverse_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class ScatterProgram {\n    constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true) {\n        this.variableNames = ['updates', 'indices', 'defaultValue'];\n        this.outputShape = shape;\n        const stridesType = getCoordsDataType(strides.length);\n        const dtype = getCoordsDataType(shape.length);\n        let indicesString = '';\n        if (indicesRank === 1) {\n            indicesString = 'i';\n        }\n        else if (indicesRank === 2) {\n            indicesString = 'i, j';\n        }\n        const indicesSnippet = `getIndices(${indicesString})`;\n        let updatesString = '';\n        if (updatesRank === 1) {\n            updatesString = 'i';\n        }\n        else if (updatesRank === 2) {\n            updatesString = 'i, coords[1]';\n        }\n        const updatesSnippet = `getUpdates(${updatesString})`;\n        const strideString = sliceDim > 1 ? 'strides[j]' : 'strides';\n        this.userCode = `\n        ${stridesType} strides = ${stridesType}(${strides});\n\n        void main() {\n          ${dtype} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${updateSize}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${sliceDim}; j++) {\n              int index = round(${indicesSnippet});\n              flattenedIndex += index * ${strideString};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${updatesSnippet};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `;\n    }\n}\n//# sourceMappingURL=scatter_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class SegmentOpProgram {\n    constructor(segOpInfo, segOpType) {\n        this.variableNames = ['x', 'segmentIds'];\n        const windowSize = segOpInfo.windowSize;\n        const batchSize = segOpInfo.batchSize;\n        const inSize = segOpInfo.inSize;\n        const numSegments = segOpInfo.numSegments;\n        const outSize = numSegments * Math.ceil(inSize / windowSize);\n        this.outputShape = [batchSize, outSize];\n        const initializationValue = '0.0';\n        const returnValue = `sumValue`;\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        const updateSnippet = `\n        sumValue += dot(values, segFilter);\n    `;\n        let checkValueOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkValueOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n        }\n        let checkSegmentIdOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkSegmentIdOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return -1.0;\n        }\n      `;\n        }\n        this.userCode = `\n      const float initializationValue = ${initializationValue};\n\n      float getValue(int batch, int inIdx) {\n        ${checkValueOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${checkSegmentIdOutOfBounds}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${numSegments})) * float(${windowSize}));\n        int currentSeg = int(mod(float(outIdx), float(${numSegments})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\n//# sourceMappingURL=segment_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class SelectProgram {\n    constructor(cRank, shape, rank) {\n        this.variableNames = ['c', 'a', 'b'];\n        this.outputShape = shape;\n        let cCoords;\n        let abCoords;\n        if (rank > 4) {\n            throw Error(`Where for rank ${rank} is not yet supported`);\n        }\n        if (rank === 1) {\n            abCoords = `resRC`;\n            cCoords = `resRC`;\n        }\n        else {\n            const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n            const cCoordVars = [];\n            const abCoordVars = [];\n            for (let i = 0; i < shape.length; i++) {\n                abCoordVars.push(`${currentCoords[i]}`);\n                if (i < cRank) {\n                    cCoordVars.push(`${currentCoords[i]}`);\n                }\n            }\n            cCoords = cCoordVars.join();\n            abCoords = abCoordVars.join();\n        }\n        const dtype = getCoordsDataType(rank);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        float cVal = getC(${cCoords});\n        if (cVal >= 1.0) {\n          setOutput(getA(${abCoords}));\n        } else {\n          setOutput(getB(${abCoords}));\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=select_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class SliceProgram {\n    constructor(destSize) {\n        this.variableNames = ['source'];\n        this.outputShape = destSize;\n        this.rank = destSize.length;\n        const dtype = getCoordsDataType(this.rank);\n        const uniformPart = `uniform int start[${this.rank}];`;\n        const sourceCoords = getCoords(this.rank);\n        let body;\n        const coordSum = destSize.map((_, i) => {\n            return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`;\n        });\n        body = `\n        ${dtype} sourceLoc;\n        ${dtype} coords = getOutputCoords();\n        ${coordSum.join('\\n')}\n      `;\n        this.userCode = `\n      ${uniformPart}\n      void main() {\n        ${body}\n        setOutput(getSource(${sourceCoords}));\n      }\n    `;\n    }\n    getCustomSetupFunc(start) {\n        if (start.length !== this.rank) {\n            throw Error(`The rank (${this.rank}) of the program must match the ` +\n                `length of start (${start.length})`);\n        }\n        return (gpgpu, webGLProgram) => {\n            if (this.startLoc == null) {\n                this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n                if (this.startLoc == null) {\n                    // This means the compiler has optimized and realized it doesn't need\n                    // the uniform.\n                    return;\n                }\n            }\n            gpgpu.gl.uniform1iv(this.startLoc, start);\n        };\n    }\n}\nconst coords = ['x', 'y', 'z', 'w', 'u', 'v'];\nfunction getCoords(rank) {\n    if (rank === 1) {\n        return 'sourceLoc';\n    }\n    else if (rank <= 6) {\n        return coords.slice(0, rank).map(x => 'sourceLoc.' + x).join(',');\n    }\n    else {\n        throw Error(`Slicing for rank ${rank} is not yet supported`);\n    }\n}\n//# sourceMappingURL=slice_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class SlicePackedProgram {\n    constructor(destSize) {\n        this.variableNames = ['source'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = destSize;\n        this.rank = destSize.length;\n        const dtype = getCoordsDataType(this.rank);\n        const coords = getChannels('coords', this.rank);\n        const sourceLoc = getChannels('sourceLoc', this.rank);\n        const innerDims = this.rank === 1 ? 'sourceLoc' : `vec2(${sourceLoc.slice(-2).join()})`;\n        const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;\n        const upperRow = `\n      result.x = ${getChannel};\n      if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n        ++${sourceLoc[this.rank - 1]};\n        result.y = ${getChannel};\n        --${sourceLoc[this.rank - 1]};\n      }\n    `;\n        const lowerRow = this.rank === 1 ? '' : `\n      --${coords[this.rank - 1]};\n      if (++${coords[this.rank - 2]} < ${destSize[this.rank - 2]}) {\n        ++${sourceLoc[this.rank - 2]};\n        result.z = ${getChannel};\n        if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n          ++${sourceLoc[this.rank - 1]};\n          result.w = ${getChannel};\n        }\n      }\n    `;\n        const sourceLocSetup = this.rank <= 4 ?\n            `sourceLoc = coords +\n            ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` :\n            destSize.map((_, i) => `${sourceLoc[i]} = ${coords[i]} + start[${i}];`)\n                .join('\\n');\n        this.userCode = `\n      uniform int start[${this.rank}];\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        ${dtype} sourceLoc;\n        ${sourceLocSetup}\n        vec4 result = vec4(0.);\n        ${upperRow}\n        ${lowerRow}\n        setOutput(result);\n      }\n    `;\n    }\n    getCustomSetupFunc(start) {\n        if (start.length !== this.rank) {\n            throw Error(`The rank (${this.rank}) of the program must match the ` +\n                `length of start (${start.length})`);\n        }\n        return (gpgpu, webGLProgram) => {\n            if (this.startLoc == null) {\n                this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n                if (this.startLoc == null) {\n                    // This means the compiler has optimized and realized it doesn't need\n                    // the uniform.\n                    return;\n                }\n            }\n            gpgpu.gl.uniform1iv(this.startLoc, start);\n        };\n    }\n}\n//# sourceMappingURL=slice_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class StridedSliceProgram {\n    constructor(begin, strides, size) {\n        this.variableNames = ['x'];\n        this.outputShape = size;\n        const rank = size.length;\n        const inputDtype = getCoordsDataType(size.length);\n        const dtype = getCoordsDataType(size.length);\n        let newCoords = '';\n        if (rank === 1) {\n            newCoords = 'coords * strides + begin';\n        }\n        else {\n            let outputAxis = 0;\n            newCoords =\n                size.map((_, i) => {\n                    outputAxis++;\n                    return size.length === 1 ?\n                        `coords * strides[${i}] + begin[${i}]` :\n                        `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;\n                })\n                    .join(',');\n        }\n        this.userCode = `\n      ${inputDtype} begin = ${inputDtype}(${begin});\n      ${inputDtype} strides = ${inputDtype}(${strides});\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        setOutput(getX(${newCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=strided_slice_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { getInternalFormatForFloat16MatrixTexture, getInternalFormatForFloat16PackedMatrixTexture, getInternalFormatForFloat32MatrixTexture, getInternalFormatForPackedMatrixTexture, getInternalFormatForUnsignedBytesMatrixTexture } from './gpgpu_util';\nimport { getPackedMatrixTextureShapeWidthHeight, getUnpackedMatrixTextureShapeWidthHeight, PhysicalTextureType, TextureUsage } from './tex_util';\nexport class TextureManager {\n    constructor(gpgpu) {\n        this.gpgpu = gpgpu;\n        this.numUsedTextures = 0;\n        this.numFreeTextures = 0;\n        this._numBytesAllocated = 0;\n        this._numBytesFree = 0; // How many bytes that have been allocated\n        // are available for reuse.\n        this.freeTextures = {};\n        this.logEnabled = false;\n        this.usedTextures = {};\n    }\n    acquireTexture(shapeRC, usage, isPacked) {\n        const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);\n        const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);\n        if (!(shapeKey in this.freeTextures)) {\n            this.freeTextures[shapeKey] = [];\n        }\n        if (!(shapeKey in this.usedTextures)) {\n            this.usedTextures[shapeKey] = [];\n        }\n        const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n        if (this.freeTextures[shapeKey].length > 0) {\n            this.numFreeTextures--;\n            this.numUsedTextures++;\n            this._numBytesFree -= texBytes;\n            this.log();\n            const newTexture = this.freeTextures[shapeKey].shift();\n            this.usedTextures[shapeKey].push(newTexture);\n            return newTexture;\n        }\n        let newTexture;\n        if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {\n            newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {\n            newTexture =\n                this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {\n            newTexture =\n                this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {\n            newTexture =\n                this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {\n            newTexture =\n                this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        this.usedTextures[shapeKey].push(newTexture);\n        this.numUsedTextures++;\n        this._numBytesAllocated += texBytes;\n        this.log();\n        return newTexture;\n    }\n    releaseTexture(texture, shape, logicalTexType, isPacked) {\n        if (this.freeTextures == null) {\n            // Already disposed.\n            return;\n        }\n        const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);\n        const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);\n        if (!(shapeKey in this.freeTextures)) {\n            this.freeTextures[shapeKey] = [];\n        }\n        const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n        const deleteTexThreshold = env().get('WEBGL_DELETE_TEXTURE_THRESHOLD');\n        if (deleteTexThreshold !== -1 &&\n            this._numBytesAllocated > deleteTexThreshold) {\n            this.gpgpu.deleteMatrixTexture(texture);\n            this._numBytesAllocated -= texBytes;\n        }\n        else {\n            this.freeTextures[shapeKey].push(texture);\n            this.numFreeTextures++;\n            this._numBytesFree += texBytes;\n        }\n        this.numUsedTextures--;\n        const texList = this.usedTextures[shapeKey];\n        const texIndex = texList.indexOf(texture);\n        if (texIndex < 0) {\n            throw new Error('Cannot release a texture that was never provided by this ' +\n                'texture manager');\n        }\n        texList.splice(texIndex, 1);\n        this.log();\n    }\n    log() {\n        if (!this.logEnabled) {\n            return;\n        }\n        const total = this.numFreeTextures + this.numUsedTextures;\n        console.log('Free/Used', `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`);\n        const freeRatio = this._numBytesFree / this._numBytesAllocated;\n        console.log(`Bytes allocated: ${this._numBytesAllocated}`);\n        console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`);\n    }\n    get numBytesAllocated() {\n        return this._numBytesAllocated;\n    }\n    get numBytesFree() {\n        return this._numBytesFree;\n    }\n    getNumUsedTextures() {\n        return this.numUsedTextures;\n    }\n    getNumFreeTextures() {\n        return this.numFreeTextures;\n    }\n    dispose() {\n        if (this.freeTextures == null) {\n            // Already disposed.\n            return;\n        }\n        for (const texShape in this.freeTextures) {\n            this.freeTextures[texShape].forEach(tex => {\n                this.gpgpu.deleteMatrixTexture(tex);\n            });\n        }\n        for (const texShape in this.usedTextures) {\n            this.usedTextures[texShape].forEach(tex => {\n                this.gpgpu.deleteMatrixTexture(tex);\n            });\n        }\n        this.freeTextures = null;\n        this.usedTextures = null;\n        this.numUsedTextures = 0;\n        this.numFreeTextures = 0;\n        this._numBytesAllocated = 0;\n        this._numBytesFree = 0;\n    }\n}\nfunction numBytesForInternalFormat(gl, internalFormat) {\n    // tslint:disable-next-line:no-any\n    const glany = gl;\n    if (internalFormat === glany.R32F) {\n        return 4;\n    }\n    else if (internalFormat === glany.R16F) {\n        return 2;\n    }\n    else if (internalFormat === glany.RGBA32F) {\n        return 16;\n    }\n    else if (internalFormat === gl.RGBA) {\n        return 16;\n    }\n    else if (internalFormat === glany.RGBA16F) {\n        return 8;\n    }\n    throw new Error(`Unknown internal format ${internalFormat}`);\n}\nexport function computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) {\n    // It is not possible to infer packed status from the texture type because\n    // depending on the textureConfig, different  texture types may resolve to the\n    // same internal format (e.g. in WebGL1, the internal format for\n    // UNPACKED_FLOAT16 textures is gl.RGBA). Therefore we pass in `isPacked`\n    // explicitly.\n    const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig);\n    let numElements;\n    if (isPacked) {\n        const [packedWidth, packedHeight] = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n        numElements = packedWidth * packedHeight;\n    }\n    else {\n        const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n        numElements = width * height;\n    }\n    const bytesPerElement = numBytesForInternalFormat(gl, internalFormat);\n    return numElements * bytesPerElement;\n}\nfunction internalFormatForPhysicalTexType(physicalTexType, textureConfig) {\n    switch (physicalTexType) {\n        case PhysicalTextureType.PACKED_2X2_FLOAT32:\n            return getInternalFormatForPackedMatrixTexture(textureConfig);\n        case PhysicalTextureType.PACKED_2X2_FLOAT16:\n            return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);\n        case PhysicalTextureType.UNPACKED_FLOAT32:\n            return getInternalFormatForFloat32MatrixTexture(textureConfig);\n        case PhysicalTextureType.UNPACKED_FLOAT16:\n            return getInternalFormatForFloat16MatrixTexture(textureConfig);\n        case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:\n            return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);\n        default:\n            throw new Error(`Unknown physical texture type ${physicalTexType}`);\n    }\n}\nfunction getPhysicalTextureForRendering(isPacked) {\n    if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED')) {\n        if (isPacked) {\n            return PhysicalTextureType.PACKED_2X2_FLOAT32;\n        }\n        return PhysicalTextureType.UNPACKED_FLOAT32;\n    }\n    if (isPacked) {\n        return PhysicalTextureType.PACKED_2X2_FLOAT16;\n    }\n    return PhysicalTextureType.UNPACKED_FLOAT16;\n}\nfunction getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {\n    if (logicalTexType === TextureUsage.UPLOAD) {\n        return PhysicalTextureType.PACKED_2X2_FLOAT32;\n    }\n    else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {\n        return getPhysicalTextureForRendering(isPacked);\n    }\n    else if (logicalTexType === TextureUsage.DOWNLOAD ||\n        logicalTexType === TextureUsage.PIXELS) {\n        return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;\n    }\n    throw new Error(`Unknown logical texture type ${logicalTexType}`);\n}\nfunction getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {\n    return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;\n}\n//# sourceMappingURL=texture_manager.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class TileProgram {\n    constructor(aShape, reps) {\n        this.variableNames = ['A'];\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[i] * reps[i];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const sourceCoords = getSourceCoords(aShape);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n    }\n}\nfunction getSourceCoords(aShape) {\n    const rank = aShape.length;\n    if (rank > 5) {\n        throw Error(`Tile for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n        return `imod(resRC, ${aShape[0]})`;\n    }\n    const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u'];\n    const sourceCoords = [];\n    for (let i = 0; i < aShape.length; i++) {\n        sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);\n    }\n    return sourceCoords.join();\n}\n//# sourceMappingURL=tile_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class UnaryOpProgram {\n    constructor(aShape, opSnippet) {\n        this.variableNames = ['A'];\n        this.outputShape = aShape;\n        this.userCode = `\n      float unaryOperation(float x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n    }\n}\nconst CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;\nexport const LINEAR = `return x;`;\nexport const ABS = `return abs(x);`;\nexport const RELU = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\nexport const RELU6 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`;\nexport const ELU = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\nexport const SELU = `\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${backend_util.SELU_SCALEALPHA};\n  float scale = ${backend_util.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`;\nexport function STEP(alpha = 0.0) {\n    return CHECK_NAN_SNIPPET + `\n    return x > 0.0 ? 1.0 : float(${alpha});\n  `;\n}\nexport const NEG = `return -x;`;\nexport const CEIL = `return ceil(x);`;\nexport const FLOOR = `return floor(x);`;\nexport const SIGN = `\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n`;\nexport const IS_NAN = `return float(isnan(x));`;\nexport const IS_INF = `return float(isinf(x));`;\nexport const IS_FINITE = `return float(!isnan(x) && !isinf(x));`;\nexport const ROUND = `\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`;\nexport const EXP = `return exp(x);`;\nexport const EXPM1 = `return exp(x) - 1.0;`;\nexport const LOG = `if (x < 0.0) return NAN;\n  return log(x);`;\nexport const LOG1P = `return log(1.0 + x);`;\nexport const SQRT = `return sqrt(x);`;\nexport const RSQRT = `return inversesqrt(x);`;\nexport const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n/**\n * mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n *\n * epsilon is the difference between 1.0 and the next representable\n * float. For a single precision 32 bit float this should be 2^-23, see:\n * https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\n *\n * too_large = (x > -threshold) is value above which exp(x) may overflow\n * but softplus(x) == x is within machine epsilon\n *\n * too_small = (x < threshold) is value below which exp(x) may underflow,\n * but softplus(x) == exp(x) is within machine epsilon.\n */\nexport const SOFTPLUS = `\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`;\nexport const ASIN = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`;\nexport const ACOS = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`;\nexport const ATAN = CHECK_NAN_SNIPPET + `\n  return atan(x);\n`;\nexport const SINH = `\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`;\nexport const COSH = `\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`;\nexport const TANH = `\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`;\nexport const ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`;\nexport const ACOSH = CHECK_NAN_SNIPPET + `\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));`;\nexport const ATANH = CHECK_NAN_SNIPPET + `\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;\nexport const ERF = `\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = ${backend_util.ERF_P};\n  float a1 = ${backend_util.ERF_A1};\n  float a2 = ${backend_util.ERF_A2};\n  float a3 = ${backend_util.ERF_A3};\n  float a4 = ${backend_util.ERF_A4};\n  float a5 = ${backend_util.ERF_A5};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`;\nexport const RECIPROCAL = `return 1.0 / x;`;\nexport const LOGICAL_NOT = `return float(!(x >= 1.0));`;\nexport const TO_INT = `return float(int(x));`;\nexport const CLONE = 'return x;';\n//# sourceMappingURL=unaryop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const LINEAR = `return x;`;\nexport const LOG = `\n  vec4 result = log(x);\n  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\n  result.r = isNaN.r == 1.0 ? NAN : result.r;\n  result.g = isNaN.g == 1.0 ? NAN : result.g;\n  result.b = isNaN.b == 1.0 ? NAN : result.b;\n  result.a = isNaN.a == 1.0 ? NAN : result.a;\n\n  return result;\n`;\nexport const RELU = `\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nexport const RELU6 = `\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nexport const ELU = `\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`;\nexport class UnaryOpPackedProgram {\n    constructor(aShape, opSnippet) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = aShape;\n        this.userCode = `\n      vec4 unaryOperation(vec4 x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=unaryop_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels, getSourceCoords } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class UnpackProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = false;\n        this.outputShape = outputShape;\n        const rank = outputShape.length;\n        const channels = getChannels('rc', rank);\n        const dtype = getCoordsDataType(rank);\n        const sourceCoords = getSourceCoords(rank, channels);\n        const innerDims = channels.slice(-2);\n        const coords = rank <= 1 ? 'rc' : `vec2(${innerDims.join(',')})`;\n        this.userCode = `\n      void main() {\n        ${dtype} rc = getOutputCoords();\n        vec4 packedInput = getA(${sourceCoords});\n\n        setOutput(getChannel(packedInput, ${coords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=unpack_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Import webgl flags.\nimport './flags_webgl';\nimport * as tf from '@tensorflow/tfjs-core';\nimport { complex, div, engine, env, imag, max, range, real, reshape, scalar, softmax, tensor, tidy, transpose } from '@tensorflow/tfjs-core';\nimport { backend_util, buffer, kernel_impls, slice_util, util } from '@tensorflow/tfjs-core';\nimport { DataStorage, KernelBackend, upcastType } from '@tensorflow/tfjs-core';\nimport { addImplCPU, ceilImplCPU, expImplCPU, expm1ImplCPU, floorImplCPU, logImplCPU, multiplyImplCPU, rsqrtImplCPU, simpleAbsImplCPU, sliceImplCPU, subImplCPU } from './kernel_utils/shared';\nconst { segment_util } = backend_util;\nconst split = kernel_impls.split;\nconst tile = kernel_impls.tile;\nconst topkImpl = kernel_impls.topkImpl;\nconst whereImpl = kernel_impls.whereImpl;\nimport { AddNProgram } from './addn_gpu';\nimport { AddNPackedProgram } from './addn_packed_gpu';\nimport { ArgMinMaxProgram } from './argminmax_gpu';\nimport { ArgMinMaxPackedProgram } from './argminmax_packed_gpu';\nimport { AvgPool3DBackpropProgram } from './avg_pool_backprop_gpu';\nimport * as binaryop_complex_gpu from './binaryop_complex_gpu';\nimport { BinaryOpComplexProgram } from './binaryop_complex_gpu';\nimport * as binaryop_gpu from './binaryop_gpu';\nimport { BinaryOpProgram } from './binaryop_gpu';\nimport * as binaryop_packed_gpu from './binaryop_packed_gpu';\nimport { BinaryOpPackedProgram } from './binaryop_packed_gpu';\nimport { getWebGLContext } from './canvas_util';\nimport { ClipProgram } from './clip_gpu';\nimport { ClipPackedProgram } from './clip_packed_gpu';\nimport { ComplexAbsProgram } from './complex_abs_gpu';\nimport { ConcatProgram } from './concat_gpu';\nimport { ConcatPackedProgram } from './concat_packed_gpu';\nimport { Conv2DDerFilterProgram, Conv2DDerInputProgram, Conv3DDerFilterProgram, Conv3DDerInputProgram } from './conv_backprop_gpu';\nimport { DepthwiseConv2DDerFilterProgram, DepthwiseConv2DDerInputProgram } from './conv_backprop_gpu_depthwise';\nimport { Conv2DProgram, Conv3DProgram } from './conv_gpu';\nimport { DepthwiseConv2DProgram } from './conv_gpu_depthwise';\nimport { DepthwiseConvPacked2DProgram } from './conv_packed_gpu_depthwise';\nimport { CropAndResizeProgram } from './crop_and_resize_gpu';\nimport { CumSumProgram } from './cumsum_gpu';\nimport { DecodeMatrixProgram } from './decode_matrix_gpu';\nimport { DecodeMatrixPackedProgram } from './decode_matrix_packed_gpu';\nimport { DepthToSpaceProgram } from './depth_to_space_gpu';\nimport { DiagProgram } from './diag_gpu';\nimport { EncodeFloatProgram } from './encode_float_gpu';\nimport { EncodeFloatPackedProgram } from './encode_float_packed_gpu';\nimport { EncodeMatrixProgram } from './encode_matrix_gpu';\nimport { EncodeMatrixPackedProgram } from './encode_matrix_packed_gpu';\nimport * as fft_gpu from './fft_gpu';\nimport { FFTProgram } from './fft_gpu';\nimport { FillProgram } from './fill_gpu';\nimport { GatherProgram } from './gather_gpu';\nimport { GatherNDProgram } from './gather_nd_gpu';\nimport { GPGPUContext } from './gpgpu_context';\nimport * as gpgpu_math from './gpgpu_math';\nimport { Im2ColPackedProgram } from './im2col_packed_gpu';\nimport { LRNProgram } from './lrn_gpu';\nimport { LRNGradProgram } from './lrn_grad_gpu';\nimport { LRNPackedProgram } from './lrn_packed_gpu';\nimport { MaxPool3DBackpropProgram } from './max_pool_backprop_gpu';\nimport { MatMulPackedProgram } from './mulmat_packed_gpu';\nimport { MultinomialProgram } from './multinomial_gpu';\nimport { OneHotProgram } from './onehot_gpu';\nimport { PackProgram } from './pack_gpu';\nimport { PadProgram } from './pad_gpu';\nimport { PadPackedProgram } from './pad_packed_gpu';\nimport { Pool3DProgram } from './pool_gpu';\nimport { ReduceProgram } from './reduce_gpu';\nimport { ReshapePackedProgram } from './reshape_packed_gpu';\nimport { ResizeBilinearBackpropProgram } from './resize_bilinear_backprop_gpu';\nimport { ResizeBilinearProgram } from './resize_bilinear_gpu';\nimport { ResizeBilinearPackedProgram } from './resize_bilinear_packed_gpu';\nimport { ResizeNearestNeigborBackpropProgram } from './resize_nearest_neighbor_backprop_gpu';\nimport { ResizeNearestNeighborProgram } from './resize_nearest_neighbor_gpu';\nimport { ReverseProgram } from './reverse_gpu';\nimport { ReversePackedProgram } from './reverse_packed_gpu';\nimport { ScatterProgram } from './scatter_gpu';\nimport { SegmentOpProgram } from './segment_gpu';\nimport { SelectProgram } from './select_gpu';\nimport { SliceProgram } from './slice_gpu';\nimport { SlicePackedProgram } from './slice_packed_gpu';\nimport { StridedSliceProgram } from './strided_slice_gpu';\nimport * as tex_util from './tex_util';\nimport { TextureUsage } from './tex_util';\nimport { TextureManager } from './texture_manager';\nimport { TileProgram } from './tile_gpu';\nimport * as unary_op from './unaryop_gpu';\nimport { UnaryOpProgram } from './unaryop_gpu';\nimport * as unary_packed_op from './unaryop_packed_gpu';\nimport { UnaryOpPackedProgram } from './unaryop_packed_gpu';\nimport { UnpackProgram } from './unpack_gpu';\nimport * as webgl_util from './webgl_util';\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\nconst binaryCaches = {};\nexport function getBinaryCache(webGLVersion) {\n    if (webGLVersion in binaryCaches) {\n        return binaryCaches[webGLVersion];\n    }\n    binaryCaches[webGLVersion] = {};\n    return binaryCaches[webGLVersion];\n}\nfunction mapActivationToShaderProgram(activation, packed = false) {\n    if (activation === 'linear') {\n        if (packed) {\n            return unary_packed_op.LINEAR;\n        }\n        return unary_op.LINEAR;\n    }\n    else if (activation === 'relu') {\n        if (packed) {\n            return unary_packed_op.RELU;\n        }\n        return unary_op.RELU;\n    }\n    else if (activation === 'elu') {\n        if (packed) {\n            return unary_packed_op.ELU;\n        }\n        return unary_op.ELU;\n    }\n    else if (activation === 'relu6') {\n        if (packed) {\n            return unary_packed_op.RELU6;\n        }\n        return unary_op.RELU6;\n    }\n    else if (activation === 'prelu') {\n        if (packed) {\n            return binaryop_packed_gpu.PRELU;\n        }\n        return binaryop_gpu.PRELU;\n    }\n    throw new Error(`Activation ${activation} has not been implemented for the WebGL backend.`);\n}\n// Empirically determined constant used to determine size threshold for handing\n// off execution to the CPU.\nconst CPU_HANDOFF_SIZE_THRESHOLD = 128;\n// Empirically determined constant used to decide the number of MB on GPU\n// before we warn about high memory use. The MB are this constant * screen area\n// * dpi / 1024 / 1024.\nconst BEFORE_PAGING_CONSTANT = 600;\nfunction numMBBeforeWarning() {\n    if (env().global.screen == null) {\n        return 1024; // 1 GB.\n    }\n    return (env().global.screen.height * env().global.screen.width *\n        window.devicePixelRatio) *\n        BEFORE_PAGING_CONSTANT / 1024 / 1024;\n}\n// Empirically determined minimal shared dimension in matmul before we forward\n// to a.mul(b).sum() in order to take advantage of GPU parallelism. See\n// https://github.com/tensorflow/tfjs-core/pull/1379 for benchmarks.\nexport const MATMUL_SHARED_DIM_THRESHOLD = 1000;\nexport class MathBackendWebGL extends KernelBackend {\n    constructor(gpgpu) {\n        super();\n        // Maps data ids that have a pending read operation, to list of subscribers.\n        this.pendingRead = new WeakMap();\n        // List of data ids that are scheduled for disposal, but are waiting on a\n        // pending read operation.\n        this.pendingDisposal = new WeakSet();\n        // Used to count the number of 'shallow' sliced tensors that point to the\n        // same data id.\n        this.dataRefCount = new WeakMap();\n        this.numBytesInGPU = 0;\n        // Accumulated time spent (including blocking) in uploading data to webgl.\n        this.uploadWaitMs = 0;\n        // Accumulated time spent (including blocking in downloading data from webgl.\n        this.downloadWaitMs = 0;\n        this.warnedAboutMemory = false;\n        this.warnedAboutCPUBackend = false;\n        this.pendingDeletes = 0;\n        this.disposed = false;\n        if (!env().getBool('HAS_WEBGL')) {\n            throw new Error('WebGL is not supported on this device');\n        }\n        if (gpgpu == null) {\n            const gl = getWebGLContext(env().getNumber('WEBGL_VERSION'));\n            this.binaryCache = getBinaryCache(env().getNumber('WEBGL_VERSION'));\n            this.gpgpu = new GPGPUContext(gl);\n            this.canvas = gl.canvas;\n            this.gpgpuCreatedLocally = true;\n        }\n        else {\n            this.gpgpu = gpgpu;\n            this.binaryCache = {};\n            this.gpgpuCreatedLocally = false;\n            this.canvas = gpgpu.gl.canvas;\n        }\n        this.textureManager = new TextureManager(this.gpgpu);\n        this.numMBBeforeWarning = numMBBeforeWarning();\n        this.texData = new DataStorage(this, engine());\n    }\n    numDataIds() {\n        return this.texData.numDataIds() +\n            (this.cpuBackend ? this.cpuBackend.numDataIds() : 0) -\n            this.pendingDeletes;\n    }\n    write(values, shape, dtype) {\n        if (env().getBool('WEBGL_CHECK_NUMERICAL_PROBLEMS') ||\n            env().getBool('DEBUG')) {\n            this.checkNumericalProblems(values);\n        }\n        if (dtype === 'complex64' && values != null) {\n            throw new Error(`Cannot write to a complex64 dtype. ` +\n                `Please use tf.complex(real, imag).`);\n        }\n        const dataId = {};\n        this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });\n        return dataId;\n    }\n    /** Increase refCount of a `TextureData`. */\n    incRef(dataId) {\n        const texData = this.texData.get(dataId);\n        texData.refCount++;\n    }\n    /** Decrease refCount of a `TextureData`. */\n    decRef(dataId) {\n        if (this.texData.has(dataId)) {\n            const texData = this.texData.get(dataId);\n            texData.refCount--;\n        }\n    }\n    move(dataId, values, shape, dtype) {\n        if (env().getBool('DEBUG')) {\n            this.checkNumericalProblems(values);\n        }\n        if (dtype === 'complex64') {\n            throw new Error(`Cannot write to a complex64 dtype. ` +\n                `Please use tf.complex(real, imag).`);\n        }\n        this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });\n    }\n    disposeIntermediateTensorInfo(tensorInfo) {\n        const dataId = tensorInfo.dataId;\n        if (this.texData.has(dataId)) {\n            const textureData = this.texData.get(dataId);\n            textureData.refCount--;\n            if (textureData.refCount < 1) {\n                this.disposeData(dataId);\n            }\n        }\n    }\n    readSync(dataId) {\n        const texData = this.texData.get(dataId);\n        const { values, dtype, complexTensors, slice, shape, isPacked } = texData;\n        // The presence of `slice` indicates this tensor is a shallow slice of a\n        // different tensor, and is using that original tensor's texture. Run\n        // `clone` in order to copy that texture and read from it.\n        if (slice != null) {\n            let program;\n            if (isPacked) {\n                program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n            }\n            else {\n                program = new UnaryOpProgram(shape, unary_op.CLONE);\n            }\n            const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n            const data = this.readSync(res.dataId);\n            this.disposeIntermediateTensorInfo(res);\n            return data;\n        }\n        if (values != null) {\n            return this.convertAndCacheOnCPU(dataId);\n        }\n        if (dtype === 'string') {\n            return values;\n        }\n        const shouldTimeProgram = this.activeTimers != null;\n        let start;\n        if (shouldTimeProgram) {\n            start = util.now();\n        }\n        let result;\n        if (dtype === 'complex64') {\n            const realValues = complexTensors.real.dataSync();\n            const imagValues = complexTensors.imag.dataSync();\n            result = backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        else {\n            result = this.getValuesFromTexture(dataId);\n        }\n        if (shouldTimeProgram) {\n            this.downloadWaitMs += util.now() - start;\n        }\n        return this.convertAndCacheOnCPU(dataId, result);\n    }\n    async read(dataId) {\n        if (this.pendingRead.has(dataId)) {\n            const subscribers = this.pendingRead.get(dataId);\n            return new Promise(resolve => subscribers.push(resolve));\n        }\n        const texData = this.texData.get(dataId);\n        const { values, shape, slice, dtype, complexTensors, isPacked } = texData;\n        // The presence of `slice` indicates this tensor is a shallow slice of a\n        // different tensor, and is using that original tensor's texture. Run\n        // `clone` in order to copy that texture and read from it.\n        if (slice != null) {\n            let program;\n            if (isPacked) {\n                program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n            }\n            else {\n                program = new UnaryOpProgram(shape, unary_op.CLONE);\n            }\n            const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n            const data = this.read(res.dataId);\n            this.disposeIntermediateTensorInfo(res);\n            return data;\n        }\n        if (values != null) {\n            return this.convertAndCacheOnCPU(dataId);\n        }\n        if (!env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED') &&\n            env().getNumber('WEBGL_VERSION') === 2) {\n            throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and ` +\n                `WEBGL_VERSION=2 not yet supported.`);\n        }\n        let buffer = null;\n        let tmpDownloadTarget;\n        if (dtype !== 'complex64' && env().get('WEBGL_BUFFER_SUPPORTED')) {\n            // Possibly copy the texture into a buffer before inserting a fence.\n            tmpDownloadTarget = this.decode(dataId);\n            const tmpData = this.texData.get(tmpDownloadTarget.dataId);\n            buffer = this.gpgpu.createBufferFromTexture(tmpData.texture, ...tex_util.getDenseTexShape(shape));\n        }\n        this.pendingRead.set(dataId, []);\n        if (dtype !== 'complex64') {\n            // Create a fence and wait for it to resolve.\n            await this.gpgpu.createAndWaitForFence();\n        }\n        // Download the values from the GPU.\n        let vals;\n        if (dtype === 'complex64') {\n            const ps = await Promise.all([complexTensors.real.data(), complexTensors.imag.data()]);\n            const realValues = ps[0];\n            const imagValues = ps[1];\n            vals = backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        else if (buffer == null) {\n            vals = this.getValuesFromTexture(dataId);\n        }\n        else {\n            const size = util.sizeFromShape(shape);\n            vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer, size);\n        }\n        if (tmpDownloadTarget != null) {\n            this.disposeIntermediateTensorInfo(tmpDownloadTarget);\n        }\n        const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);\n        const subscribers = this.pendingRead.get(dataId);\n        this.pendingRead.delete(dataId);\n        // Notify all pending reads.\n        subscribers.forEach(resolve => resolve(dTypeVals));\n        if (this.pendingDisposal.has(dataId)) {\n            this.pendingDisposal.delete(dataId);\n            this.disposeData(dataId);\n            this.pendingDeletes--;\n        }\n        return dTypeVals;\n    }\n    checkNumericalProblems(values) {\n        if (values == null) {\n            return;\n        }\n        for (let i = 0; i < values.length; i++) {\n            const num = values[i];\n            if (!webgl_util.canBeRepresented(num)) {\n                if (env().getBool('WEBGL_RENDER_FLOAT32_CAPABLE')) {\n                    throw Error(`The value ${num} cannot be represented with your ` +\n                        `current settings. Consider enabling float32 rendering: ` +\n                        `'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);\n                }\n                throw Error(`The value ${num} cannot be represented on this device.`);\n            }\n        }\n    }\n    getValuesFromTexture(dataId) {\n        const { shape, dtype, isPacked } = this.texData.get(dataId);\n        const size = util.sizeFromShape(shape);\n        if (env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {\n            const tmpTarget = this.decode(dataId);\n            const tmpData = this.texData.get(tmpTarget.dataId);\n            const vals = this.gpgpu\n                .downloadMatrixFromPackedTexture(tmpData.texture, ...tex_util.getDenseTexShape(shape))\n                .subarray(0, size);\n            this.disposeIntermediateTensorInfo(tmpTarget);\n            return vals;\n        }\n        const shouldUsePackedProgram = env().getBool('WEBGL_PACK') && isPacked === true;\n        const outputShape = shouldUsePackedProgram ? webgl_util.getShapeAs3D(shape) : shape;\n        const program = shouldUsePackedProgram ?\n            new EncodeFloatPackedProgram(outputShape) :\n            new EncodeFloatProgram(outputShape);\n        const output = this.runWebGLProgram(program, [{ shape: outputShape, dtype, dataId }], 'float32');\n        const tmpData = this.texData.get(output.dataId);\n        const vals = this.gpgpu\n            .downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture, tmpData.texShape[0], tmpData.texShape[1])\n            .subarray(0, size);\n        this.disposeIntermediateTensorInfo(output);\n        return vals;\n    }\n    async time(f) {\n        const oldActiveTimers = this.activeTimers;\n        const newActiveTimers = [];\n        let outerMostTime = false;\n        if (this.programTimersStack == null) {\n            this.programTimersStack = newActiveTimers;\n            outerMostTime = true;\n        }\n        else {\n            this.activeTimers.push(newActiveTimers);\n        }\n        this.activeTimers = newActiveTimers;\n        f();\n        // needing to split these up because util.flatten only accepts certain types\n        const flattenedActiveTimerQueries = util.flatten(this.activeTimers.map((d) => d.query))\n            .filter(d => d != null);\n        const flattenedActiveTimerNames = util.flatten(this.activeTimers.map((d) => d.name))\n            .filter(d => d != null);\n        this.activeTimers = oldActiveTimers;\n        if (outerMostTime) {\n            this.programTimersStack = null;\n        }\n        const res = {\n            uploadWaitMs: this.uploadWaitMs,\n            downloadWaitMs: this.downloadWaitMs,\n            kernelMs: null,\n            wallMs: null // will be filled by the engine\n        };\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n            res['kernelMs'] = util.sum(kernelMs);\n            res['getExtraProfileInfo'] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d }))\n                .map(d => `${d.name}: ${d.ms}`)\n                .join(', ');\n        }\n        else {\n            res['kernelMs'] = {\n                error: 'WebGL query timers are not supported in this environment.'\n            };\n        }\n        this.uploadWaitMs = 0;\n        this.downloadWaitMs = 0;\n        return res;\n    }\n    memory() {\n        return {\n            unreliable: false,\n            numBytesInGPU: this.numBytesInGPU,\n            numBytesInGPUAllocated: this.textureManager.numBytesAllocated,\n            numBytesInGPUFree: this.textureManager.numBytesFree\n        };\n    }\n    startTimer() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            return this.gpgpu.beginQuery();\n        }\n        return { startMs: util.now(), endMs: null };\n    }\n    endTimer(query) {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            this.gpgpu.endQuery();\n            return query;\n        }\n        query.endMs = util.now();\n        return query;\n    }\n    async getQueryTime(query) {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            return this.gpgpu.waitForQueryAndGetTime(query);\n        }\n        const timerQuery = query;\n        return timerQuery.endMs - timerQuery.startMs;\n    }\n    disposeData(dataId) {\n        if (this.pendingDisposal.has(dataId)) {\n            return;\n        }\n        if (this.pendingRead.has(dataId)) {\n            this.pendingDisposal.add(dataId);\n            this.pendingDeletes++;\n            return;\n        }\n        // No-op if already disposed.\n        if (!this.texData.has(dataId)) {\n            return;\n        }\n        this.releaseGPUData(dataId);\n        const { complexTensors } = this.texData.get(dataId);\n        if (complexTensors != null) {\n            complexTensors.real.dispose();\n            complexTensors.imag.dispose();\n        }\n        this.texData.delete(dataId);\n    }\n    releaseGPUData(dataId) {\n        const { texture, dtype, texShape, usage, isPacked, slice } = this.texData.get(dataId);\n        const key = slice && slice.origDataId || dataId;\n        const refCount = this.dataRefCount.get(key);\n        if (refCount > 1) {\n            this.dataRefCount.set(key, refCount - 1);\n        }\n        else {\n            this.dataRefCount.delete(key);\n            if (texture != null) {\n                this.numBytesInGPU -= this.computeBytes(texShape, dtype);\n                this.textureManager.releaseTexture(texture, texShape, usage, isPacked);\n            }\n        }\n        const texData = this.texData.get(dataId);\n        texData.texture = null;\n        texData.texShape = null;\n        texData.isPacked = false;\n        texData.slice = null;\n    }\n    getTexture(dataId) {\n        this.uploadToGPU(dataId);\n        return this.texData.get(dataId).texture;\n    }\n    /**\n     * Returns internal information for the specific data bucket. Used in unit\n     * tests.\n     */\n    getDataInfo(dataId) {\n        return this.texData.get(dataId);\n    }\n    getCPUBackend() {\n        if (!env().getBool('WEBGL_CPU_FORWARD')) {\n            return null;\n        }\n        if (this.cpuBackend == null) {\n            this.cpuBackend = engine().findBackend('cpu');\n        }\n        return this.cpuBackend;\n    }\n    /*\n    Tests whether all the inputs to an op are small and on the CPU. This heuristic\n    determines when it would be faster to execute a kernel on the CPU. WebGL\n    kernels opt into running this check and forwarding when appropriate.\n    TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more\n    sustainable strategy for optimizing backend execution of ops.\n     */\n    shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) {\n        const cpuBackend = this.getCPUBackend();\n        if (!this.warnedAboutCPUBackend && cpuBackend == null) {\n            console.warn('Your application contains ops that are small enough to be ' +\n                'executed on the CPU backend, however the CPU backend cannot ' +\n                'be found. Consider importing the CPU backend ' +\n                '(@tensorflow/tfjs-backend-cpu) for better performance.');\n            this.warnedAboutCPUBackend = true;\n        }\n        return cpuBackend != null &&\n            inputs.every(input => this.texData.get(input.dataId).texture == null &&\n                util.sizeFromShape(input.shape) < sizeThreshold);\n    }\n    getGPGPUContext() {\n        return this.gpgpu;\n    }\n    complex(real, imag) {\n        const result = this.makeOutput(real.shape, 'complex64');\n        const resultData = this.texData.get(result.dataId);\n        // The backend owns the reference to the underlying real and imaginary\n        // clones. These will explicitly get disposed when the complex tensor is\n        // disposed.\n        resultData.complexTensors = {\n            real: engine().keep(real.clone()),\n            imag: engine().keep(imag.clone())\n        };\n        return result;\n    }\n    real(input) {\n        const resultData = this.texData.get(input.dataId);\n        return resultData.complexTensors.real.clone();\n    }\n    imag(input) {\n        const resultData = this.texData.get(input.dataId);\n        return resultData.complexTensors.imag.clone();\n    }\n    slice(x, begin, size) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = sliceImplCPU(this.texData.get(x.dataId).values, begin, size, x.shape, x.dtype);\n            return this.makeOutput(size, x.dtype, outValues);\n        }\n        // Short-circuit computation if the slice is zero-sized.\n        if (util.sizeFromShape(size) === 0) {\n            return tensor([], size, x.dtype);\n        }\n        const { isPacked } = this.texData.get(x.dataId);\n        const isContinous = slice_util.isSliceContinous(x.shape, begin, size);\n        if (isPacked || !isContinous) {\n            const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n                new SlicePackedProgram(size) :\n                new SliceProgram(size);\n            const customSetup = program.getCustomSetupFunc(begin);\n            return this.compileAndRun(program, [x], null, customSetup);\n        }\n        this.uploadToGPU(x.dataId);\n        return this.shallowSlice(x, begin, size);\n    }\n    shallowSlice(x, begin, size) {\n        const xTexData = this.texData.get(x.dataId);\n        const t = this.makeOutput(size, x.dtype);\n        const newTexData = this.texData.get(t.dataId);\n        // Copy texture data from the original tensor.\n        Object.assign(newTexData, xTexData);\n        newTexData.shape = size;\n        newTexData.dtype = x.dtype;\n        let flatOffset = slice_util.computeFlatOffset(begin, x.strides);\n        if (xTexData.slice) {\n            // We are slicing an already sliced tensor, so we have to accumulate\n            // the offset.\n            flatOffset += xTexData.slice.flatOffset;\n        }\n        newTexData.slice = {\n            flatOffset,\n            // Point to the original dataId, which is used to do ref counting.\n            origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId\n        };\n        // Increase the ref count for that data bucket.\n        const refCount = this.dataRefCount.get(newTexData.slice.origDataId) || 1;\n        this.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);\n        return t;\n    }\n    stridedSlice(x, begin, end, strides) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.stridedSlice(x, begin, end, strides));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const outShape = slice_util.computeOutShape(begin, end, strides);\n        if (outShape.some(axis => axis === 0)) {\n            return tensor([], outShape);\n        }\n        const program = new StridedSliceProgram(begin, strides, outShape);\n        return this.compileAndRun(program, [x]);\n    }\n    reverse(x, axis) {\n        const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n            new ReversePackedProgram(x.shape, axis) :\n            new ReverseProgram(x.shape, axis);\n        return this.compileAndRun(program, [x]);\n    }\n    concat(tensors, axis) {\n        if (tensors[0].dtype === 'complex64') {\n            const reals = tensors.map((t) => real(t));\n            const imags = tensors.map((t) => imag(t));\n            return complex(this.concat(reals, axis), this.concat(imags, axis));\n        }\n        if (tensors.length === 1) {\n            return tensors[0];\n        }\n        if (tensors.length > env().getNumber('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n            const midIndex = Math.floor(tensors.length / 2);\n            const leftSide = this.concat(tensors.slice(0, midIndex), axis);\n            const rightSide = this.concat(tensors.slice(midIndex), axis);\n            return this.concat([leftSide, rightSide], axis);\n        }\n        if (env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') && tensors[0].rank > 1) {\n            const program = new ConcatPackedProgram(tensors.map(t => t.shape), axis);\n            return this.compileAndRun(program, tensors);\n        }\n        // Any concat of n-dimensional tensors across any axis can be reduced to\n        // a concatenation of two-dimensional tensors across the axis 1 by first\n        // partitioning the axes of the original tensors into those less than the\n        // axis to be concatenated and the rest. Then reshape the tensors\n        // into a two-dimensional tensor by collapsing these two sets of axes and\n        // concatenate the resulting matrices across the axis 1, finally reshaping\n        // the result to have the proper shape.\n        const outShape = backend_util.computeOutShape(tensors.map(t => t.shape), axis);\n        const tensors2D = tensors.map(t => t.as2D(-1, util.sizeFromShape(t.shape.slice(axis))));\n        const program = new ConcatProgram(tensors2D.map(t => t.shape));\n        const res = this.compileAndRun(program, tensors2D);\n        return res.reshape(outShape);\n    }\n    neg(x) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.neg(x));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.NEG, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.NEG);\n        return this.compileAndRun(program, [x]);\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n        const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n        const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n        const [batch, ,] = a.shape;\n        // Since the matrices are vectors, it is faster to call mul().sum()\n        // because sum() is O(sqrt(N)) due to divide-and-conquer.\n        if ((outerShapeA === 1 || outerShapeB === 1) &&\n            sharedDim > MATMUL_SHARED_DIM_THRESHOLD) {\n            if (transposeA) {\n                a = transpose(a, [0, 2, 1]);\n            }\n            if (transposeB) {\n                b = transpose(b, [0, 2, 1]);\n            }\n            const a3D = outerShapeB === 1 ? a : a.as3D(batch, sharedDim, 1);\n            const axis = outerShapeB === 1 ? 2 : 1;\n            const b3D = outerShapeB === 1 ? b.as3D(batch, 1, sharedDim) : b;\n            return this.multiply(a3D, b3D).sum(axis, true /* keepDims */);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        const program = new MatMulPackedProgram(a.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n        const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n        const [batch, ,] = a.shape;\n        const dtype = upcastType(a.dtype, b.dtype);\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;\n        const program = new MatMulPackedProgram(a.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [a, b];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (preluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        return this.compileAndRun(program, inputs, dtype);\n    }\n    multiply(a, b) {\n        if (a.dtype === 'complex64') {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const realProgram = new BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.REAL, a.shape, b.shape);\n            const imagProgram = new BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);\n            const inputs = [\n                this.makeComplexComponentTensorInfo(a, aData.complexTensors.real),\n                this.makeComplexComponentTensorInfo(a, aData.complexTensors.imag),\n                this.makeComplexComponentTensorInfo(b, bData.complexTensors.real),\n                this.makeComplexComponentTensorInfo(b, bData.complexTensors.imag)\n            ];\n            const real = this.compileAndRun(realProgram, inputs);\n            const imag = this.compileAndRun(imagProgram, inputs);\n            const complex = this.complex(real, imag);\n            real.dispose();\n            imag.dispose();\n            return complex;\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        if (this.shouldExecuteOnCPU([a, b])) {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const [outValues, outShape] = multiplyImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n            return this.makeOutput(outShape, dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_gpu.MUL, a.dtype);\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.MUL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], a.dtype);\n    }\n    localResponseNormalization4D(x, radius, bias, alpha, beta) {\n        const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n            new LRNPackedProgram(x.shape, radius, bias, alpha, beta) :\n            new LRNProgram(x.shape, radius, bias, alpha, beta);\n        return this.compileAndRun(program, [x]);\n    }\n    LRNGrad(dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {\n        const program = new LRNGradProgram(inputImage.shape, depthRadius, bias, alpha, beta);\n        return this.compileAndRun(program, [inputImage, outputImage, dy]);\n    }\n    tile(x, reps) {\n        if (x.dtype === 'string') {\n            const data = this.readSync(x.dataId);\n            const decodedData = data.map(d => util.decodeString(d));\n            const buf = buffer(x.shape, x.dtype, decodedData);\n            return tile(buf, reps);\n        }\n        const program = new TileProgram(x.shape, reps);\n        return this.compileAndRun(program, [x]);\n    }\n    pad(x, paddings, constantValue) {\n        const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n            new PadPackedProgram(x.shape, paddings, constantValue) :\n            new PadProgram(x.shape, paddings, constantValue);\n        return this.compileAndRun(program, [x]);\n    }\n    gather(x, indices, axis) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x, indices], () => this.cpuBackend.gather(x, indices, axis));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = new GatherProgram(x.shape, indices.size, axis);\n        return this.compileAndRun(program, [x, indices]);\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        util.assert(x.rank <= 4, () => 'batchToSpaceND for rank > 4 with a WebGL backend not ' +\n            'implemented yet');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n        const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n        const reshapedPermuted = backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n        const sliceBeginCoords = backend_util.getSliceBeginCoords(crops, blockShape.length);\n        const sliceSize = backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n        return transpose(x.reshape(reshaped), permuted)\n            .reshape(reshapedPermuted)\n            .slice(sliceBeginCoords, sliceSize);\n    }\n    spaceToBatchND(x, blockShape, paddings) {\n        util.assert(x.rank <= 4, () => 'spaceToBatchND for rank > 4 with a WebGL backend not ' +\n            'implemented yet');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const completePaddings = [[0, 0]];\n        completePaddings.push(...paddings);\n        for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n            completePaddings.push([0, 0]);\n        }\n        const paddedX = x.pad(completePaddings);\n        const reshapedPaddedShape = backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n        const permutedReshapedPaddedPermutation = backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n        const flattenShape = backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n        const paddedXT = transpose(paddedX.reshape(reshapedPaddedShape), permutedReshapedPaddedPermutation);\n        return reshape(paddedXT, flattenShape);\n    }\n    reduce(x, reduceType, dtype) {\n        const batchSize = x.shape[0];\n        const inSize = x.shape[1];\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const outSize = Math.ceil(inSize / windowSize);\n        const reduceInfo = { windowSize, inSize, batchSize, outSize };\n        const program = new ReduceProgram(reduceInfo, reduceType);\n        const output = this.compileAndRun(program, [x], dtype);\n        // No need to run another GPGPU program.\n        if (output.shape[1] === 1) {\n            return output;\n        }\n        return this.reduce(output, reduceType, dtype);\n    }\n    argReduce(x, reduceType, bestIndicesA = null) {\n        let batchSize = x.shape[0];\n        let inSize = x.shape[1];\n        if (bestIndicesA != null) {\n            batchSize = bestIndicesA.shape[0];\n            inSize = bestIndicesA.shape[1];\n        }\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const reduceInfo = {\n            windowSize,\n            inSize,\n            batchSize,\n            outSize: Math.ceil(inSize / windowSize)\n        };\n        const program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);\n        const inputs = [x];\n        if (bestIndicesA != null) {\n            inputs.push(bestIndicesA);\n        }\n        const output = this.compileAndRun(program, inputs, 'int32');\n        // No need to run another GPGPU program.\n        if (output.shape[1] === 1) {\n            return output;\n        }\n        return this.argReduce(x, reduceType, output);\n    }\n    argReducePacked(x, reduceType, bestIndicesA = null) {\n        const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;\n        const inSize = inShape[inShape.length - 1];\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null);\n        const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];\n        const output = this.compileAndRun(program, inputs, 'int32');\n        if (output.rank === x.rank) {\n            return this.argReducePacked(x, reduceType, output);\n        }\n        return output;\n    }\n    sum(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        return this.reduce(a2D, 'sum', outputDType).reshape(outShape);\n    }\n    prod(x, axes) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.prod(x, axes));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        return this.reduce(a2D, 'prod', outputDType).reshape(outShape);\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        let axis = 0;\n        const permutation = backend_util.getAxesPermutation([axis], x.rank);\n        let permutedX = x;\n        if (permutation != null) {\n            permutedX = transpose(x, permutation);\n            axis = backend_util.getInnerMostAxes(1, x.rank)[0];\n        }\n        const outShape = segment_util.computeOutShape(permutedX.shape, axis, numSegments);\n        const inSize = util.sizeFromShape([permutedX.shape[axis]]);\n        const a2D = permutedX.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        let result = this.segOpCompute(a2D, 'unsortedSegmentSum', segmentIds, outputDType, numSegments)\n            .reshape(outShape);\n        if (permutation != null) {\n            result =\n                transpose(result, backend_util.getUndoAxesPermutation(permutation));\n        }\n        return result;\n    }\n    segOpCompute(x, segOpType, segmentIds, dtype, numSegments) {\n        const batchSize = x.shape[0];\n        const inSize = x.shape[1];\n        const windowSize = segment_util.segOpComputeOptimalWindowSize(inSize, numSegments);\n        const segOpInfo = { windowSize, inSize, batchSize, numSegments };\n        const program = new SegmentOpProgram(segOpInfo, segOpType);\n        const output = this.compileAndRun(program, [x, segmentIds], dtype);\n        // No need to run another GPGPU program.\n        if (output.shape[1] === numSegments) {\n            return output;\n        }\n        segmentIds = range(0, numSegments).tile([inSize / windowSize]);\n        return this.segOpCompute(output, segOpType, segmentIds, dtype, numSegments);\n    }\n    argMinMaxReduce(x, axis, reduceType) {\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('arg' + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.rank);\n        if (!env().getBool('WEBGL_PACK_REDUCE') || x.rank <= 2) {\n            const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n            const inSize = util.sizeFromShape(reduceShape);\n            const a2D = x.as2D(-1, inSize);\n            return this.argReduce(a2D, reduceType).reshape(outShape);\n        }\n        return this.argReducePacked(x, reduceType);\n    }\n    argMin(x, axis) {\n        return this.argMinMaxReduce(x, axis, 'min');\n    }\n    argMax(x, axis) {\n        return this.argMinMaxReduce(x, axis, 'max');\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        if (axis !== x.rank - 1) {\n            throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.rank - 1} ` +\n                `but got axis=${axis}`);\n        }\n        const size = x.shape[axis];\n        let result = x;\n        // Use cumsum parallel algorithm, ref:\n        // https://developer.nvidia.com/gpugems/gpugems3/part-vi-gpu-computing/chapter-39-parallel-prefix-sum-scan-cuda\n        for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {\n            const program = new CumSumProgram(x.shape, false, reverse);\n            const customSetup = program.getCustomSetupFunc(i);\n            const prevResult = result;\n            result = this.compileAndRun(program, [result], result.dtype, customSetup);\n            prevResult.dispose();\n        }\n        // For exclusive cumsum, shift the end result in the direction of sum and\n        // add 0 to the front index.\n        if (exclusive) {\n            const program = new CumSumProgram(x.shape, exclusive, reverse);\n            const prevResult = result;\n            result = this.compileAndRun(program, [result]);\n            prevResult.dispose();\n        }\n        return result;\n    }\n    equal(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    notEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.NOT_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.NOT_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    less(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.less(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LESS, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LESS, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    lessEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LESS_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LESS_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    greater(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.greater(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.GREATER, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.GREATER, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    greaterEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.GREATER_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.GREATER_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    logicalNot(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.LOGICAL_NOT);\n        return this.compileAndRun(program, [x]);\n    }\n    logicalAnd(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LOGICAL_AND, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LOGICAL_AND, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    logicalOr(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LOGICAL_OR, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LOGICAL_OR, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    select(condition, a, b) {\n        const program = new SelectProgram(condition.rank, a.shape, a.rank);\n        return this.compileAndRun(program, [condition, a, b], upcastType(a.dtype, b.dtype));\n    }\n    where(condition) {\n        backend_util.warn('tf.where() in webgl locks the UI thread. ' +\n            'Call tf.whereAsync() instead');\n        const condVals = condition.dataSync();\n        return whereImpl(condition.shape, condVals);\n    }\n    topk(x, k, sorted) {\n        const xVals = x.dataSync();\n        return topkImpl(xVals, x.shape, x.dtype, k, sorted);\n    }\n    min(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'min', a2D.dtype).reshape(outShape);\n    }\n    minimum(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.minimum(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MIN, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MIN, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    mod(a, b) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MOD, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MOD, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    maximum(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.maximum(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MAX, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MAX, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    all(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'all', a2D.dtype).reshape(outShape);\n    }\n    any(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'any', a2D.dtype).reshape(outShape);\n    }\n    floorDiv(a, b) {\n        const op = binaryop_gpu.INT_DIV;\n        const outputDtype = 'int32';\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.INT_DIV, outputDtype);\n        }\n        const program = new BinaryOpProgram(op, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], outputDtype);\n    }\n    add(a, b) {\n        if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n            return this.complexSeparableBinaryOp(a, b, binaryop_gpu.ADD);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        if (this.shouldExecuteOnCPU([a, b])) {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const [outValues, outShape] = addImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n            return this.makeOutput(outShape, dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_gpu.ADD, dtype);\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.ADD, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    packedUnaryOp(x, op, dtype) {\n        const program = new UnaryOpPackedProgram(x.shape, op);\n        return this.compileAndRun(program, [x], dtype);\n    }\n    packedBinaryOp(a, b, op, dtype, checkOutOfBounds = false) {\n        const program = new BinaryOpPackedProgram(op, a.shape, b.shape, checkOutOfBounds);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    /**\n     * Computes a complex binary operation that can be decomposed into a simple\n     * binary operation on both the real and imagary parts.\n     */\n    complexSeparableBinaryOp(a, b, op) {\n        const aData = this.texData.get(a.dataId);\n        const bData = this.texData.get(b.dataId);\n        const [real, imag] = [\n            [aData.complexTensors.real, bData.complexTensors.real],\n            [aData.complexTensors.imag, bData.complexTensors.imag]\n        ].map(complexParts => {\n            const [aPart, bPart] = complexParts;\n            const aHandle = this.makeComplexComponentTensorInfo(a, aPart);\n            const bHandle = this.makeComplexComponentTensorInfo(b, bPart);\n            const program = new BinaryOpProgram(op, a.shape, b.shape);\n            return this.compileAndRun(program, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));\n        });\n        const complex = this.complex(real, imag);\n        real.dispose();\n        imag.dispose();\n        return complex;\n    }\n    // Returns a TensorInfo with the complex shape and the dataId of the\n    // underlying part. We need to do this because a reshaped complex tensor is\n    // not reflected in its parts.\n    makeComplexComponentTensorInfo(complexTensor, complexPart) {\n        return {\n            dataId: complexPart.dataId,\n            dtype: complexPart.dtype,\n            shape: complexTensor.shape\n        };\n    }\n    addN(tensors) {\n        if (tensors.length === 1) {\n            return tensors[0];\n        }\n        // Limit the number of uploaded textures for optimization.\n        if (tensors.length > env().get('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n            const midIndex = Math.floor(tensors.length / 2);\n            const leftSide = this.addN(tensors.slice(0, midIndex));\n            const rightSide = this.addN(tensors.slice(midIndex));\n            return this.addN([leftSide, rightSide]);\n        }\n        const dtype = tensors.map(t => t.dtype).reduce((d1, d2) => upcastType(d1, d2));\n        const shapes = tensors.map(t => t.shape);\n        // We can make sure shapes are identical in op level.\n        const usePackedOp = env().getBool('WEBGL_PACK');\n        const program = usePackedOp ?\n            new AddNPackedProgram(tensors[0].shape, shapes) :\n            new AddNProgram(tensors[0].shape, shapes);\n        return this.compileAndRun(program, tensors, dtype);\n    }\n    subtract(a, b) {\n        if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n            return this.complexSeparableBinaryOp(a, b, binaryop_gpu.SUB);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        if (this.shouldExecuteOnCPU([a, b])) {\n            const aData = this.texData.get(a.dataId);\n            const bData = this.texData.get(b.dataId);\n            const [outValues, outShape] = subImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n            return this.makeOutput(outShape, dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_gpu.SUB, a.dtype);\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.SUB, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    pow(a, b) {\n        const usePackedOp = env().getBool('WEBGL_PACK_BINARY_OPERATIONS');\n        const program = usePackedOp ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.POW, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.POW, a.shape, b.shape);\n        const dtype = upcastType(a.dtype, b.dtype);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    ceil(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = ceilImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.CEIL, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.CEIL);\n        return this.compileAndRun(program, [x]);\n    }\n    floor(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = floorImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.FLOOR, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.FLOOR);\n        return this.compileAndRun(program, [x]);\n    }\n    sign(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SIGN);\n        return this.compileAndRun(program, [x]);\n    }\n    isNaN(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_NAN);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    isInf(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_INF);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    isFinite(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_FINITE);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    round(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ROUND);\n        return this.compileAndRun(program, [x]);\n    }\n    exp(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = expImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.EXP, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.EXP);\n        return this.compileAndRun(program, [x]);\n    }\n    expm1(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = expm1ImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.EXPM1, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.EXPM1);\n        return this.compileAndRun(program, [x]);\n    }\n    softmax(logits, dim) {\n        const axes = util.parseAxisParam([dim], logits.shape);\n        // TODO(annxingyuan): Call maxImpl rather than op as part of softmax kernel\n        // modularization.\n        const maxLogit = max(logits, axes);\n        const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n        const a = this.subtract(logits, maxLogit.reshape(expandedShape));\n        const b = this.exp(a);\n        const sumExp = this.sum(b, axes).reshape(expandedShape);\n        // TODO(annxingyuan): Call divImpl rather than op as part of softmax kernel\n        // modularization.\n        return div(b, sumExp);\n    }\n    log(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = logImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_packed_op.LOG, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.LOG);\n        return this.compileAndRun(program, [x]);\n    }\n    log1p(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.LOG1P);\n        return this.compileAndRun(program, [x]);\n    }\n    sqrt(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SQRT);\n        return this.compileAndRun(program, [x]);\n    }\n    rsqrt(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = rsqrtImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.RSQRT);\n        return this.compileAndRun(program, [x]);\n    }\n    reciprocal(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.RECIPROCAL);\n        return this.compileAndRun(program, [x]);\n    }\n    relu(x) {\n        let program;\n        if (env().getBool('WEBGL_PACK')) {\n            program = new UnaryOpPackedProgram(x.shape, unary_packed_op.RELU);\n        }\n        else {\n            program = new UnaryOpProgram(x.shape, unary_op.RELU);\n        }\n        return this.compileAndRun(program, [x]);\n    }\n    relu6(x) {\n        let program;\n        if (env().getBool('WEBGL_PACK')) {\n            program = new UnaryOpPackedProgram(x.shape, unary_packed_op.RELU6);\n        }\n        else {\n            program = new UnaryOpProgram(x.shape, unary_op.RELU6);\n        }\n        return this.compileAndRun(program, [x]);\n    }\n    prelu(x, alpha) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.PRELU, x.shape, alpha.shape) :\n            new BinaryOpProgram(binaryop_gpu.PRELU, x.shape, alpha.shape);\n        return this.compileAndRun(program, [x, alpha]);\n    }\n    elu(x) {\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_packed_op.ELU, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.ELU);\n        return this.compileAndRun(program, [x]);\n    }\n    eluDer(dy, y) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.ELU_DER, dy.shape, y.shape) :\n            new BinaryOpProgram(binaryop_gpu.ELU_DER, dy.shape, y.shape);\n        return this.compileAndRun(program, [dy, y]);\n    }\n    selu(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SELU);\n        return this.compileAndRun(program, [x]);\n    }\n    int(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.TO_INT);\n        return this.compileAndRun(program, [x], 'int32');\n    }\n    clip(x, min, max) {\n        let program;\n        if (env().getBool('WEBGL_PACK_CLIP')) {\n            program = new ClipPackedProgram(x.shape);\n        }\n        else {\n            program = new ClipProgram(x.shape);\n        }\n        const customSetup = program.getCustomSetupFunc(min, max);\n        return this.compileAndRun(program, [x], null, customSetup);\n    }\n    abs(x) {\n        // TODO: handle cases when x is complex.\n        if (this.shouldExecuteOnCPU([x]) && x.dtype !== 'complex64') {\n            const outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.ABS, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.ABS);\n        return this.compileAndRun(program, [x]);\n    }\n    complexAbs(x) {\n        const xData = this.texData.get(x.dataId);\n        const program = new ComplexAbsProgram(x.shape);\n        const inputs = [\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.real),\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.imag),\n        ];\n        return this.compileAndRun(program, inputs);\n    }\n    sigmoid(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SIGMOID);\n        return this.compileAndRun(program, [x]);\n    }\n    softplus(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SOFTPLUS);\n        return this.compileAndRun(program, [x]);\n    }\n    asin(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ASIN);\n        return this.compileAndRun(program, [x]);\n    }\n    acos(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ACOS);\n        return this.compileAndRun(program, [x]);\n    }\n    atan(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ATAN);\n        return this.compileAndRun(program, [x]);\n    }\n    sinh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SINH);\n        return this.compileAndRun(program, [x]);\n    }\n    cosh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.COSH);\n        return this.compileAndRun(program, [x]);\n    }\n    tanh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.TANH);\n        return this.compileAndRun(program, [x]);\n    }\n    asinh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ASINH);\n        return this.compileAndRun(program, [x]);\n    }\n    acosh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ACOSH);\n        return this.compileAndRun(program, [x]);\n    }\n    atanh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ATANH);\n        return this.compileAndRun(program, [x]);\n    }\n    erf(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ERF);\n        return this.compileAndRun(program, [x]);\n    }\n    step(x, alpha) {\n        const program = new UnaryOpProgram(x.shape, unary_op.STEP(alpha));\n        return this.compileAndRun(program, [x]);\n    }\n    conv2dByMatMul(x, filter, convInfo, bias, activation, preluActivationWeights) {\n        // Reshapes conv2D input to 2D tensors, uses matMul and then reshape the\n        // result from 2D to 4D.\n        const xShape = x.shape;\n        const xTexData = this.texData.get(x.dataId);\n        const sharedMatMulDim = convInfo.inChannels;\n        const outerShapeX = xShape[0] * xShape[1] * xShape[2];\n        const outerShapeFilter = convInfo.outChannels;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const transposeA = false;\n        const transposeB = false;\n        // TODO: Once reduction ops are packed, batchMatMul will always be packed\n        // and we can remove this condition.\n        const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) &&\n            sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;\n        const reshapeWillBeExpensive = xShape[2] % 2 !== 0 && !!xTexData.isPacked;\n        if (batchMatMulWillBeUnpacked || !env().getBool('WEBGL_LAZILY_UNPACK') ||\n            !env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ||\n            !reshapeWillBeExpensive) {\n            const targetShape = isChannelsLast ? xShape[0] * xShape[1] * xShape[2] :\n                xShape[0] * xShape[2] * xShape[3];\n            const xReshaped = reshape(x, [1, targetShape, convInfo.inChannels]);\n            const filterReshaped = reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);\n            const result = this.fusedBatchMatMul({\n                a: xReshaped,\n                b: filterReshaped,\n                transposeA,\n                transposeB,\n                bias,\n                activation,\n                preluActivationWeights\n            });\n            return reshape(result, convInfo.outShape);\n        }\n        // Following optimization is specific to packed |x| with odd row count\n        // (For example, in channelLast mode, 'row count' refers to x.shape[2]):\n        // we avoid expensive packed 2x2 reshape by padding row count to next,\n        // even number. When x.shape[2] is odd, the result of packed batchMatMul is\n        // the same (has the same texture layout and and values in the texture) as\n        // it is for even x.shape[2] + 1. We make the odd-rows tensor to look like\n        // even-rows tensor before the operation and, after the batchMatMul,\n        // fix the even-rows result to have odd number of rows.\n        const targetShape = isChannelsLast ?\n            xShape[0] * xShape[1] * (xShape[2] + 1) :\n            xShape[0] * xShape[2] * (xShape[3] + 1);\n        const xReshaped = {\n            dataId: x.dataId,\n            shape: [1, targetShape, convInfo.inChannels],\n            dtype: x.dtype\n        };\n        // xTexData.shape gets referenced from GPGPUBinary.inShapeInfos.\n        // Decrementing row count, after batchMatMul->...->compileProgram leads to\n        // invalid row count within the reference in GPGPUBinary.inShapeInfos.\n        // Alternative fix would be to provide a copy to GPGPUBinary.inShapeInfos\n        // in compileProgram method, but that would affect compilation of all\n        // programs - instead, provide a copy here, with even row count, before\n        // calling batchMatMul->...->compileProgram and after that, the original\n        // xTexData.shape is restored.\n        const originalXTexDataShape = xTexData.shape;\n        xTexData.shape = xTexData.shape.slice();\n        xTexData.shape[xTexData.shape.length - 2]++;\n        util.assert(webgl_util.isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);\n        const filterReshaped = reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);\n        const pointwiseConv = this.fusedBatchMatMul({\n            a: xReshaped,\n            b: filterReshaped,\n            transposeA,\n            transposeB,\n            bias,\n            activation,\n            preluActivationWeights\n        });\n        const pointwiseConvTexData = this.texData.get(pointwiseConv.dataId);\n        util.assert(pointwiseConvTexData.isPacked, () => 'batchMatMul result is expected to be packed');\n        // Restore the input shape to original.\n        xTexData.shape = originalXTexDataShape;\n        // Set the output shape - there is no need for expensive reshape as data\n        // layout is already correct.\n        pointwiseConvTexData.shape = convInfo.outShape;\n        return engine().makeTensorFromDataId(pointwiseConv.dataId, convInfo.outShape, pointwiseConv.dtype);\n    }\n    conv2dWithIm2Row(x, filter, convInfo, bias, activation, preluActivationWeights) {\n        // Rearranges conv2d input so each block to be convolved over forms the\n        // column of a new matrix with shape [filterWidth * filterHeight *\n        // inChannels, outHeight * outWidth]. The filter is also rearranged so each\n        // output channel forms a row of a new matrix with shape [outChannels,\n        // filterWidth * filterHeight * inChannels]. The convolution is then\n        // computed by multiplying these matrices and reshaping the result.\n        const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo;\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const sharedDim = filterWidth * filterHeight * inChannels;\n        const numCols = outHeight * outWidth;\n        const x2ColShape = [sharedDim, numCols];\n        const transposeA = true;\n        const transposeB = false;\n        const xSqueezed = x.squeeze([0]);\n        const w2Row = filter.reshape([1, sharedDim, -1]);\n        const im2ColProgram = new Im2ColPackedProgram(x2ColShape, xSqueezed.shape, convInfo);\n        const im2Col = this.compileAndRun(im2ColProgram, [xSqueezed]).reshape([\n            1, x2ColShape[0], x2ColShape[1]\n        ]);\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;\n        const matmulProgram = new MatMulPackedProgram(im2Col.shape, [1, numCols, convInfo.outChannels], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [im2Col, w2Row];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (hasPreluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        const product = this.compileAndRun(matmulProgram, inputs);\n        if (isChannelsLast) {\n            return product.reshape([1, outHeight, outWidth, convInfo.outChannels]);\n        }\n        else {\n            return product.reshape([1, convInfo.outChannels, outHeight, outWidth]);\n        }\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n            (convInfo.padInfo.type === 'SAME' ||\n                convInfo.padInfo.type === 'VALID')) {\n            return this.conv2dByMatMul(input, filter, convInfo, bias, activation, preluActivationWeights);\n        }\n        if (env().getBool('WEBGL_CONV_IM2COL') && input.shape[0] === 1) {\n            return this.conv2dWithIm2Row(input, filter, convInfo, bias, activation, preluActivationWeights);\n        }\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, false) : null;\n        const program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [input, filter];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (preluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        return this.compileAndRun(program, inputs);\n    }\n    conv2d(x, filter, convInfo) {\n        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n            (convInfo.padInfo.type === 'SAME' ||\n                convInfo.padInfo.type === 'VALID')) {\n            return this.conv2dByMatMul(x, filter, convInfo);\n        }\n        if (env().getBool('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {\n            return this.conv2dWithIm2Row(x, filter, convInfo);\n        }\n        const program = new Conv2DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        const program = new Conv2DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    conv2dDerFilter(x, dy, convInfo) {\n        const program = new Conv2DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        const shouldPackDepthwiseConv = env().getBool('WEBGL_PACK_DEPTHWISECONV') &&\n            convInfo.strideWidth <= 2 &&\n            convInfo.outChannels / convInfo.inChannels === 1;\n        const fusedActivation = activation ?\n            mapActivationToShaderProgram(activation, shouldPackDepthwiseConv) :\n            null;\n        const inputs = [input, filter];\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        if (hasBias) {\n            inputs.push(bias);\n        }\n        if (hasPreluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        let program;\n        if (shouldPackDepthwiseConv) {\n            program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n            return this.compileAndRun(program, inputs);\n        }\n        program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n        return this.compileAndRun(program, inputs);\n    }\n    depthwiseConv2D(x, filter, convInfo) {\n        let program;\n        if (env().getBool('WEBGL_PACK_DEPTHWISECONV') &&\n            convInfo.strideWidth <= 2 &&\n            convInfo.outChannels / convInfo.inChannels === 1) {\n            program = new DepthwiseConvPacked2DProgram(convInfo);\n            return this.compileAndRun(program, [x, filter]);\n        }\n        program = new DepthwiseConv2DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        const program = new DepthwiseConv2DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    depthwiseConv2DDerFilter(x, dy, convInfo) {\n        const program = new DepthwiseConv2DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    conv3d(x, filter, convInfo) {\n        const program = new Conv3DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        const program = new Conv3DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    conv3dDerFilter(x, dy, convInfo) {\n        const program = new Conv3DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    cast(x, dtype) {\n        return backend_util.castTensor(x, dtype, this);\n    }\n    unstack(x, axis) {\n        const num = x.shape[axis];\n        const outShape = new Array(x.rank - 1);\n        let outIndex = 0;\n        for (let i = 0; i < x.rank; i++) {\n            if (i !== axis) {\n                outShape[outIndex++] = x.shape[i];\n            }\n        }\n        const begin = new Array(x.rank).fill(0);\n        const size = x.shape.slice();\n        size[axis] = 1;\n        const res = new Array(num);\n        for (let i = 0; i < res.length; i++) {\n            begin[axis] = i;\n            res[i] = this.slice(x, begin, size).reshape(outShape);\n        }\n        return res;\n    }\n    avgPool3d(x, convInfo) {\n        const program = new Pool3DProgram(convInfo, 'avg', false);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        const avgPool3dBackpropProgram = new AvgPool3DBackpropProgram(convInfo);\n        return this.compileAndRun(avgPool3dBackpropProgram, [dy], x.dtype);\n    }\n    maxPool3d(x, convInfo) {\n        const program = new Pool3DProgram(convInfo, 'max', false);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        const getPositions = true;\n        const maxPool3dPositionsProgram = new Pool3DProgram(convInfo, 'max', getPositions);\n        const maxPool3dPositions = this.compileAndRun(maxPool3dPositionsProgram, [x]);\n        const maxPool3dBackPropProgram = new MaxPool3DBackpropProgram(convInfo);\n        const result = this.compileAndRun(maxPool3dBackPropProgram, [dy, maxPool3dPositions], x.dtype);\n        maxPool3dPositions.dispose();\n        return result;\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        const program = env().getBool('WEBGL_PACK_IMAGE_OPERATIONS') ?\n            new ResizeBilinearPackedProgram(x.shape, newHeight, newWidth, alignCorners) :\n            new ResizeBilinearProgram(x.shape, newHeight, newWidth, alignCorners);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        const program = new ResizeBilinearBackpropProgram(dy, x, alignCorners);\n        return this.compileAndRun(program, [dy]);\n    }\n    resizeNearestNeighbor(x, newHeight, newWidth, alignCorners) {\n        const program = new ResizeNearestNeighborProgram(x.shape, newHeight, newWidth, alignCorners);\n        return this.compileAndRun(program, [x]);\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        const program = new ResizeNearestNeigborBackpropProgram(dy, x, alignCorners);\n        return this.compileAndRun(program, [dy]);\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        const probs = normalized ? logits : softmax(logits);\n        const batchSize = probs.shape[0];\n        const numOutcomes = probs.shape[1];\n        const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);\n        const customSetup = program.getCustomSetupFunc(seed);\n        return this.compileAndRun(program, [probs], 'int32', customSetup);\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        const program = new OneHotProgram(indices.size, depth, onValue, offValue);\n        return this.compileAndRun(program, [indices]);\n    }\n    diag(x) {\n        const program = new DiagProgram(x.size);\n        return this.compileAndRun(program, [x]);\n    }\n    cropAndResize(image, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        const program = new CropAndResizeProgram(image.shape, boxes.shape, cropSize, method, extrapolationValue);\n        return this.compileAndRun(program, [image, boxes, boxIndex], 'float32');\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n        const batchSize = x.shape[0];\n        const inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];\n        const inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];\n        const inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];\n        const outputHeight = inputHeight * blockSize;\n        const outputWidth = inputWidth * blockSize;\n        const outputDepth = inputDepth / (blockSize * blockSize);\n        const outputShape = (dataFormat === 'NHWC') ?\n            [batchSize, outputHeight, outputWidth, outputDepth] :\n            [batchSize, outputDepth, outputHeight, outputWidth];\n        const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);\n        return this.compileAndRun(program, [x]);\n    }\n    split(x, sizeSplits, axis) {\n        return split(x, sizeSplits, axis);\n    }\n    scatterND(indices, updates, shape) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(updates, indices, shape);\n        const flattenShape = [outputSize / sliceSize, sliceSize];\n        const flattenIndices = indices.reshape([numUpdates, sliceRank]);\n        const flattenX = updates.reshape([numUpdates, sliceSize]);\n        if (outputSize === 0) {\n            return backend_util.reshapeTensor(tensor([]), shape);\n        }\n        const defaultValue = scalar(0);\n        const program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.rank, flattenX.rank, strides, flattenShape);\n        const res = this.compileAndRun(program, [flattenX, flattenIndices, defaultValue]);\n        return res.reshape(shape);\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        const { sliceRank, numUpdates, strides, outputSize } = backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n        const sumDupeIndices = false;\n        const program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.rank, sparseValues.rank, strides, [outputSize, 1], sumDupeIndices);\n        const res = this.compileAndRun(program, [sparseValues, sparseIndices, defaultValue]);\n        return res.reshape(outputShape);\n    }\n    fft(x) {\n        const inverse = false;\n        return this.fftImpl(x, inverse);\n    }\n    ifft(x) {\n        const inverse = true;\n        return this.fftImpl(x, inverse);\n    }\n    fftImpl(x, inverse) {\n        const xData = this.texData.get(x.dataId);\n        const realProgram = new FFTProgram(fft_gpu.COMPLEX_FFT.REAL, x.shape, inverse);\n        const imagProgram = new FFTProgram(fft_gpu.COMPLEX_FFT.IMAG, x.shape, inverse);\n        const inputs = [\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.real),\n            this.makeComplexComponentTensorInfo(x, xData.complexTensors.imag),\n        ];\n        const real = this.compileAndRun(realProgram, inputs);\n        const imag = this.compileAndRun(imagProgram, inputs);\n        const complex = this.complex(real, imag).as2D(x.shape[0], x.shape[1]);\n        real.dispose();\n        imag.dispose();\n        return complex;\n    }\n    gatherND(x, indices) {\n        const indicesShape = indices.shape;\n        const sliceRank = indicesShape[indicesShape.length - 1];\n        const [resultShape, numSlices, sliceSize, strides] = backend_util.prepareAndValidate(x, indices);\n        const flattenIndices = indices.reshape([numSlices, sliceRank]);\n        const flattenX = x.reshape([x.size / sliceSize, sliceSize]);\n        const program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);\n        const res = this.compileAndRun(program, [flattenX, flattenIndices]);\n        return res.reshape(resultShape);\n    }\n    fill(shape, value, dtype) {\n        dtype = dtype || util.inferDtype(value);\n        if (dtype === 'string') {\n            // String type should be handled in CPU memory.\n            const values = util.getArrayFromDType(dtype, util.sizeFromShape(shape));\n            values.fill(value);\n            return engine().makeTensor(values, shape, dtype, this);\n        }\n        else {\n            const program = new FillProgram(shape, value);\n            const customSetup = program.getCustomSetupFunc(value);\n            return this.compileAndRun(program, [], dtype, customSetup);\n        }\n    }\n    onesLike(x) {\n        if (x.dtype === 'string') {\n            throw new Error('onesLike is not supported under string dtype');\n        }\n        else {\n            // TODO(cais, smilkov): Add WebGL shader for onesLike:\n            //   https://github.com/tensorflow/tfjs/issues/1293\n            return this.fill(x.shape, 1, x.dtype);\n        }\n    }\n    zerosLike(x) {\n        return this.fill(x.shape, x.dtype === 'string' ? '' : 0, x.dtype);\n    }\n    linspace(start, stop, num) {\n        // TODO: Use CPU implementation due to the precision problem in Safari.\n        return backend_util.linspaceImpl(start, stop, num);\n    }\n    makeTensorInfo(shape, dtype, values) {\n        const dataId = this.write(values, shape, dtype);\n        this.texData.get(dataId).usage = null;\n        return { dataId, shape, dtype };\n    }\n    makeOutput(shape, dtype, values) {\n        const { dataId } = this.makeTensorInfo(shape, dtype, values);\n        return engine().makeTensorFromDataId(dataId, shape, dtype, this);\n    }\n    unpackTensor(input) {\n        const program = new UnpackProgram(input.shape);\n        return this.runWebGLProgram(program, [input], input.dtype);\n    }\n    packTensor(input) {\n        const program = new PackProgram(input.shape);\n        const preventEagerUnpackingOutput = true;\n        return this.runWebGLProgram(program, [input], input.dtype, null /* customSetup */, preventEagerUnpackingOutput);\n    }\n    packedReshape(input, afterShape) {\n        const input3DShape = [\n            webgl_util.getBatchDim(input.shape),\n            ...webgl_util.getRowsCols(input.shape)\n        ];\n        const input3D = {\n            dtype: input.dtype,\n            shape: input3DShape,\n            dataId: input.dataId\n        };\n        const afterShapeAs3D = [\n            webgl_util.getBatchDim(afterShape), ...webgl_util.getRowsCols(afterShape)\n        ];\n        const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n        const preventEagerUnpackingOfOutput = true;\n        const output = this.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n        return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n    }\n    decode(dataId) {\n        const texData = this.texData.get(dataId);\n        const { isPacked, shape, dtype } = texData;\n        const shapeAs3D = webgl_util.getShapeAs3D(shape);\n        let program;\n        if (isPacked) {\n            program = new DecodeMatrixPackedProgram(shapeAs3D);\n        }\n        else {\n            program = new DecodeMatrixProgram(shapeAs3D);\n        }\n        const preventEagerUnpackingOfOutput = true;\n        const out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype, dataId }], dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n        return { dtype, shape, dataId: out.dataId };\n    }\n    runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {\n        const output = this.makeTensorInfo(program.outputShape, outputDtype);\n        const outData = this.texData.get(output.dataId);\n        if (program.packedOutput) {\n            outData.isPacked = true;\n        }\n        if (program.outPackingScheme === tex_util.PackingScheme.DENSE) {\n            const texelShape = tex_util.getDenseTexShape(program.outputShape);\n            // For a densely packed output, we explicitly set texShape\n            // so it doesn't get assigned later according to our typical packing\n            // scheme wherein a single texel can only contain values from adjacent\n            // rows/cols.\n            outData.texShape = texelShape.map(d => d * 2);\n        }\n        if (program.outTexUsage != null) {\n            outData.usage = program.outTexUsage;\n        }\n        if (util.sizeFromShape(output.shape) === 0) {\n            // Short-circuit the computation since the result is empty (has 0 in its\n            // shape).\n            outData.values =\n                util.getTypedArrayFromDType(output.dtype, 0);\n            return output;\n        }\n        const dataToDispose = [];\n        const inputsData = inputs.map(input => {\n            if (input.dtype === 'complex64') {\n                throw new Error(`GPGPUProgram does not support complex64 input. For complex64 ` +\n                    `dtypes, please separate the program into real and imaginary ` +\n                    `parts.`);\n            }\n            let texData = this.texData.get(input.dataId);\n            if (texData.texture == null) {\n                if (!program.packedInputs &&\n                    util.sizeFromShape(input.shape) <=\n                        env().getNumber('WEBGL_SIZE_UPLOAD_UNIFORM')) {\n                    // Upload small tensors that live on the CPU as uniforms, not as\n                    // textures. Do this only when the environment supports 32bit floats\n                    // due to problems when comparing 16bit floats with 32bit floats.\n                    // TODO(https://github.com/tensorflow/tfjs/issues/821): Make it\n                    // possible for packed shaders to sample from uniforms.\n                    return {\n                        shape: input.shape,\n                        texData: null,\n                        isUniform: true,\n                        uniformValues: texData.values\n                    };\n                }\n                // This ensures that if a packed program's inputs have not yet been\n                // uploaded to the GPU, they get uploaded as packed right off the bat.\n                if (program.packedInputs) {\n                    texData.isPacked = true;\n                    texData.shape = input.shape;\n                }\n            }\n            else if (!!texData.isPacked !== !!program.packedInputs) {\n                input = texData.isPacked ? this.unpackTensor(input) :\n                    this.packTensor(input);\n                dataToDispose.push(input);\n                texData = this.texData.get(input.dataId);\n            }\n            else if (texData.isPacked &&\n                !webgl_util.isReshapeFree(texData.shape, input.shape)) {\n                // This is a special case where a texture exists for a tensor\n                // but the shapes are incompatible (due to packing constraints) because\n                // the tensor did not have a chance to go through the packed reshape\n                // shader. This only happens when we reshape the *same* tensor to form\n                // *distinct* inputs to an op, e.g. dotting a vector with itself. This\n                // case will disappear once packed uploading is the default.\n                const savedInput = input;\n                const targetShape = input.shape;\n                input.shape = texData.shape;\n                input = this.packedReshape(input, targetShape);\n                dataToDispose.push(input);\n                texData = this.texData.get(input.dataId);\n                savedInput.shape = targetShape;\n            }\n            this.uploadToGPU(input.dataId);\n            return { shape: input.shape, texData, isUniform: false };\n        });\n        this.uploadToGPU(output.dataId);\n        const outputData = { shape: output.shape, texData: outData, isUniform: false };\n        const key = gpgpu_math.makeShaderKey(program, inputsData, outputData);\n        const binary = this.getAndSaveBinary(key, () => {\n            return gpgpu_math.compileProgram(this.gpgpu, program, inputsData, outputData);\n        });\n        const shouldTimeProgram = this.activeTimers != null;\n        let query;\n        if (shouldTimeProgram) {\n            query = this.startTimer();\n        }\n        gpgpu_math.runProgram(this.gpgpu, binary, inputsData, outputData, customSetup);\n        dataToDispose.forEach(info => this.disposeIntermediateTensorInfo(info));\n        if (shouldTimeProgram) {\n            query = this.endTimer(query);\n            this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });\n        }\n        if (!env().getBool('WEBGL_LAZILY_UNPACK') && outData.isPacked &&\n            preventEagerUnpackingOfOutput === false) {\n            const unpacked = this.unpackTensor(output);\n            this.disposeIntermediateTensorInfo(output);\n            return unpacked;\n        }\n        return output;\n    }\n    compileAndRun(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {\n        outputDtype = outputDtype || inputs[0].dtype;\n        const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput);\n        return engine().makeTensorFromDataId(outInfo.dataId, outInfo.shape, outInfo.dtype);\n    }\n    getAndSaveBinary(key, getBinary) {\n        if (!(key in this.binaryCache)) {\n            this.binaryCache[key] = getBinary();\n        }\n        return this.binaryCache[key];\n    }\n    getTextureManager() {\n        return this.textureManager;\n    }\n    dispose() {\n        if (this.disposed) {\n            return;\n        }\n        // Avoid disposing the compiled webgl programs during unit testing because\n        // it slows down test execution.\n        if (!env().getBool('IS_TEST')) {\n            const allKeys = Object.keys(this.binaryCache);\n            allKeys.forEach(key => {\n                this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);\n                delete this.binaryCache[key];\n            });\n        }\n        this.textureManager.dispose();\n        if (this.canvas != null &&\n            (typeof (HTMLCanvasElement) !== 'undefined' &&\n                this.canvas instanceof HTMLCanvasElement)) {\n            this.canvas.remove();\n        }\n        else {\n            this.canvas = null;\n        }\n        if (this.gpgpuCreatedLocally) {\n            this.gpgpu.program = null;\n            this.gpgpu.dispose();\n        }\n        this.disposed = true;\n    }\n    floatPrecision() {\n        if (this.floatPrecisionValue == null) {\n            this.floatPrecisionValue = tidy(() => {\n                if (!env().get('WEBGL_RENDER_FLOAT32_ENABLED')) {\n                    // Momentarily switching DEBUG flag to false so we don't throw an\n                    // error trying to upload a small value.\n                    const debugFlag = env().getBool('DEBUG');\n                    env().set('DEBUG', false);\n                    const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];\n                    env().set('DEBUG', debugFlag);\n                    if (underflowCheckValue > 0) {\n                        return 32;\n                    }\n                }\n                return 16;\n            });\n        }\n        return this.floatPrecisionValue;\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n    }\n    uploadToGPU(dataId) {\n        const texData = this.texData.get(dataId);\n        const { shape, dtype, values, texture, usage, isPacked } = texData;\n        if (texture != null) {\n            // Array is already on GPU. No-op.\n            return;\n        }\n        const shouldTimeProgram = this.activeTimers != null;\n        let start;\n        if (shouldTimeProgram) {\n            start = util.now();\n        }\n        let texShape = texData.texShape;\n        if (texShape == null) {\n            texShape = webgl_util.getTextureShapeFromLogicalShape(shape, isPacked);\n            texData.texShape = texShape;\n        }\n        if (values != null) {\n            const shapeAs3D = webgl_util.getShapeAs3D(shape);\n            let program;\n            let width = texShape[1], height = texShape[0];\n            const isByteArray = values instanceof Uint8Array;\n            if (isPacked) {\n                [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]);\n                program = new EncodeMatrixPackedProgram(shapeAs3D, [height, width], isByteArray);\n            }\n            else {\n                program =\n                    new EncodeMatrixProgram(shapeAs3D, [height, width], isByteArray);\n            }\n            const tempDenseInputHandle = this.makeTensorInfo([height, width], dtype);\n            if (isByteArray) {\n                this.texData.get(tempDenseInputHandle.dataId).usage =\n                    TextureUsage.PIXELS;\n            }\n            else {\n                this.texData.get(tempDenseInputHandle.dataId).usage =\n                    TextureUsage.UPLOAD;\n            }\n            this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);\n            // We want the output to remain packed regardless of the value of\n            // WEBGL_PACK.\n            const preventEagerUnpacking = true;\n            const encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, null, preventEagerUnpacking);\n            // Have the original texture assume the identity of the encoded output.\n            const outputTexData = this.texData.get(encodedOutputTarget.dataId);\n            texData.texture = outputTexData.texture;\n            texData.texShape = outputTexData.texShape;\n            texData.isPacked = outputTexData.isPacked;\n            texData.usage = outputTexData.usage;\n            this.disposeIntermediateTensorInfo(tempDenseInputHandle);\n            this.texData.delete(encodedOutputTarget.dataId);\n            // Once uploaded, don't store the values on cpu.\n            texData.values = null;\n            if (shouldTimeProgram) {\n                this.uploadWaitMs += util.now() - start;\n            }\n        }\n        else {\n            const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);\n            texData.texture = newTexture;\n        }\n    }\n    convertAndCacheOnCPU(dataId, float32Values) {\n        const texData = this.texData.get(dataId);\n        const { dtype } = texData;\n        this.releaseGPUData(dataId);\n        if (float32Values != null) {\n            texData.values = float32ToTypedArray(float32Values, dtype);\n        }\n        return texData.values;\n    }\n    acquireTexture(texShape, texType, dtype, isPacked) {\n        this.numBytesInGPU += this.computeBytes(texShape, dtype);\n        if (!this.warnedAboutMemory &&\n            this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {\n            const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);\n            this.warnedAboutMemory = true;\n            console.warn(`High memory usage in GPU: ${mb} MB, ` +\n                `most likely due to a memory leak`);\n        }\n        return this.textureManager.acquireTexture(texShape, texType, isPacked);\n    }\n    computeBytes(shape, dtype) {\n        return shape[0] * shape[1] * util.bytesPerElement(dtype);\n    }\n    tryRunOnCpuOrThrow(inputs, fn) {\n        if (this.shouldExecuteOnCPU(inputs)) {\n            try {\n                return fn();\n            }\n            catch (e) {\n                if (env().getBool('IS_TEST')) {\n                    throw new Error('CPU forwarding failed');\n                }\n            }\n        }\n        return null;\n    }\n}\nfunction float32ToTypedArray(a, dtype) {\n    if (dtype === 'float32' || dtype === 'complex64') {\n        return a;\n    }\n    else if (dtype === 'int32' || dtype === 'bool') {\n        const result = (dtype === 'int32') ? new Int32Array(a.length) :\n            new Uint8Array(a.length);\n        for (let i = 0; i < result.length; ++i) {\n            result[i] = Math.round(a[i]);\n        }\n        return result;\n    }\n    else {\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n}\n//# sourceMappingURL=backend_webgl.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport * as gpgpu_util from './gpgpu_util';\nimport * as webgl_util from './webgl_util';\nexport { MathBackendWebGL } from './backend_webgl';\nexport { setWebGLContext } from './canvas_util';\nexport { GPGPUContext } from './gpgpu_context';\n// WebGL specific utils.\nexport { gpgpu_util, webgl_util };\n/**\n * Enforce use of half precision textures if available on the platform.\n *\n * @doc {heading: 'Environment', namespace: 'webgl'}\n */\nexport function forceHalfFloat() {\n    env().set('WEBGL_FORCE_F16_TEXTURES', true);\n}\n//# sourceMappingURL=webgl.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// base.ts is the webgl backend without auto kernel registration.\nimport { device_util, registerBackend } from '@tensorflow/tfjs-core';\nimport { MathBackendWebGL } from './backend_webgl';\nexport { version as version_webgl } from './version';\nif (device_util.isBrowser()) {\n    registerBackend('webgl', () => new MathBackendWebGL(), 2 /* priority */);\n}\n// Export webgl utilities\nexport * from './webgl';\n// Export forceHalfFlost under webgl namespace for the union bundle.\nimport { forceHalfFloat } from './webgl';\nexport const webgl = { forceHalfFloat };\n//# sourceMappingURL=base.js.map", "import { env } from '@tensorflow/tfjs-core';\nimport { BinaryOpProgram } from '../binaryop_gpu';\nimport { BinaryOpPackedProgram } from '../binaryop_packed_gpu';\nimport { UnaryOpProgram } from '../unaryop_gpu';\nexport const CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;\nexport const CHECK_NAN_SNIPPET_BINARY = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nexport const CHECK_NAN_SNIPPET_BINARY_PACKED = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param opSnippets Op snippet to create `UnaryOpProgram`.\n */\nexport function unaryKernelFunc(opSnippet) {\n    return ({ inputs, backend }) => {\n        const { x } = inputs;\n        const webglBackend = backend;\n        const program = new UnaryOpProgram(x.shape, opSnippet);\n        return webglBackend.runWebGLProgram(program, [x], x.dtype);\n    };\n}\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param opSnippet Op snippet to create `BinaryOpProgram`.\n * @param packedOpSnippet Op snippet to create `BinaryOpPackedProgram`.\n * @param checkOutOfBoundsForPackedProgram Whether to set checkOutOfBounds=true\n *     when creating BinaryOpPackedProgram.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc(opSnippet, packedOpSnippet, checkOutOfBoundsForPackedProgram, dtype) {\n    // TODO(jingjin): handle complex64.\n    return ({ inputs, backend }) => {\n        const { a, b } = inputs;\n        const webglBackend = backend;\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(packedOpSnippet, a.shape, b.shape, !!checkOutOfBoundsForPackedProgram) :\n            new BinaryOpProgram(opSnippet, a.shape, b.shape);\n        const $dtype = dtype || a.dtype;\n        const output = webglBackend.runWebGLProgram(program, [a, b], $dtype);\n        return output;\n    };\n}\n//# sourceMappingURL=kernel_funcs_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan2 } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc, CHECK_NAN_SNIPPET_BINARY, CHECK_NAN_SNIPPET_BINARY_PACKED } from '../kernel_utils/kernel_funcs_utils';\nconst ATAN2 = CHECK_NAN_SNIPPET_BINARY + `\n  return atan(a, b);\n`;\nconst ATAN2_PACKED = `\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET_BINARY_PACKED + `\n  return result;\n`;\nexport const atan2KernelFunc = binaryKernelFunc(ATAN2, ATAN2_PACKED);\nexport const atan2Config = {\n    kernelName: Atan2,\n    backendName: 'webgl',\n    kernelFunc: atan2KernelFunc,\n};\n//# sourceMappingURL=Atan2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '@tensorflow/tfjs-core';\nexport function identity(args) {\n    const { inputs, backend } = args;\n    const { x } = inputs;\n    backend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nexport const identityConfig = {\n    kernelName: Identity,\n    backendName: 'webgl',\n    kernelFunc: identity\n};\n//# sourceMappingURL=Identity.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool, backend_util, util } from '@tensorflow/tfjs-core';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nimport { identity } from './Identity';\nexport function avgPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'avgPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        return identity({ inputs: { x }, backend });\n    }\n    const avgPoolProgram = new Pool2DProgram(convInfo, 'avg', false);\n    return backend.runWebGLProgram(avgPoolProgram, [x], 'float32');\n}\nexport const avgPoolConfig = {\n    kernelName: AvgPool,\n    backendName: 'webgl',\n    kernelFunc: avgPool\n};\n//# sourceMappingURL=AvgPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPoolBackprop, backend_util } from '@tensorflow/tfjs-core';\nimport { AvgPool2DBackpropProgram } from '../avg_pool_backprop_gpu';\nimport { assertNotComplex } from '../webgl_util';\nexport function avgPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input } = inputs;\n    const x = input;\n    assertNotComplex([dy, input], 'avgPoolBackprop');\n    const { filterSize, strides, pad } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);\n    const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);\n    return backend.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);\n}\nexport const avgPoolBackpropConfig = {\n    kernelName: AvgPoolBackprop,\n    backendName: 'webgl',\n    kernelFunc: avgPoolBackprop\n};\n//# sourceMappingURL=AvgPoolBackprop.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class BatchNormProgram {\n    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n        this.outputShape = [];\n        this.variableNames = ['x', 'mean', 'variance'];\n        backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n        backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n        let offsetSnippet = '0.0';\n        if (offsetShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n            this.variableNames.push('offset');\n            offsetSnippet = 'getOffsetAtOutCoords()';\n        }\n        let scaleSnippet = '1.0';\n        if (scaleShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n            this.variableNames.push('scale');\n            scaleSnippet = 'getScaleAtOutCoords()';\n        }\n        this.outputShape = xShape;\n        this.userCode = `\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${offsetSnippet};\n        float scale = ${scaleSnippet};\n        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=batchnorm_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class BatchNormPackedProgram {\n    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.variableNames = ['x', 'mean', 'variance'];\n        backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n        backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n        let offsetSnippet = 'vec4(0.0)';\n        if (offsetShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n            this.variableNames.push('offset');\n            offsetSnippet = 'getOffsetAtOutCoords()';\n        }\n        let scaleSnippet = 'vec4(1.0)';\n        if (scaleShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n            this.variableNames.push('scale');\n            scaleSnippet = 'getScaleAtOutCoords()';\n        }\n        this.outputShape = xShape;\n        this.userCode = `\n      void main() {\n        vec4 offset = ${offsetSnippet};\n        vec4 scale = ${scaleSnippet};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=batchnorm_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, FusedBatchNorm, util } from '@tensorflow/tfjs-core';\nimport { BatchNormProgram } from '../batchnorm_gpu';\nimport { BatchNormPackedProgram } from '../batchnorm_packed_gpu';\nexport const batchNormKernelFunc = ({ inputs, backend, attrs }) => {\n    const { x, mean, variance, offset, scale } = inputs;\n    util.assert(mean.shape.length === variance.shape.length, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert(offset == null || mean.shape.length === offset.shape.length, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert(scale == null || mean.shape.length === scale.shape.length, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    let { varianceEpsilon } = attrs;\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const finalInputs = [x, mean, variance];\n    let offsetShape = null;\n    if (offset != null) {\n        offsetShape = offset.shape;\n        finalInputs.push(offset);\n    }\n    let scaleShape = null;\n    if (scale != null) {\n        scaleShape = scale.shape;\n        finalInputs.push(scale);\n    }\n    const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n        new BatchNormPackedProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) :\n        new BatchNormProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);\n    const output = backend.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);\n    return output;\n};\nexport const batchNormConfig = {\n    kernelName: FusedBatchNorm,\n    backendName: 'webgl',\n    kernelFunc: batchNormKernelFunc,\n};\n//# sourceMappingURL=BatchNorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '@tensorflow/tfjs-core';\nimport { CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst COS = CHECK_NAN_SNIPPET_UNARY + `\n  return cos(x);\n`;\nexport const cosKernelFunc = unaryKernelFunc(COS);\nexport const cosConfig = {\n    kernelName: Cos,\n    backendName: 'webgl',\n    kernelFunc: cosKernelFunc,\n};\n//# sourceMappingURL=Cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\n// Without the equality check div produces 0.9999 for a = b, which when\n// floored can cause errors.\nconst DIV = `\nif (a == b) {\n  return 1.0;\n};\nreturn a / b;`;\n// We do the same as in ./binaryop_gpu, with vec4 and ivec4.\n// On Linux, the vectorized implementation produces NaNs when a and b are 0.\nconst DIV_PACKED = `\n  // vec4 one = vec4(equal(a, b));\n  // return one + (vec4(1.0) - one) * a / b;\n  vec4 result = a / b;\n  if(a.x == b.x) {\n    result.x = 1.;\n  }\n  if(a.y == b.y) {\n    result.y = 1.;\n  }\n  if(a.z == b.z) {\n    result.z = 1.;\n  }\n  if(a.w == b.w) {\n    result.w = 1.;\n  }\n\n  return result;\n`;\nexport const divKernelFunc = binaryKernelFunc(DIV, DIV_PACKED, true /* checkOutOfBoundsForPackedProgram */);\nexport const divConfig = {\n    kernelName: Div,\n    backendName: 'webgl',\n    kernelFunc: divKernelFunc,\n};\n//# sourceMappingURL=Div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FlipLeftRightProgram {\n    constructor(imageShape) {\n        this.variableNames = ['Image'];\n        this.outputShape = [];\n        const imageWidth = imageShape[2];\n        this.outputShape = imageShape;\n        this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${imageWidth} - x;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${imageWidth}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n    }\n}\n//# sourceMappingURL=flip_left_right_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FlipLeftRight } from '@tensorflow/tfjs-core';\nimport { FlipLeftRightProgram } from '../flip_left_right_gpu';\nexport const flipLeftRightConfig = {\n    kernelName: FlipLeftRight,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend }) => {\n        const { image } = inputs;\n        const webglBackend = backend;\n        const program = new FlipLeftRightProgram(image.shape);\n        const output = webglBackend.runWebGLProgram(program, [image], image.dtype);\n        return output;\n    }\n};\n//# sourceMappingURL=FlipLeftRight.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from '../../glsl_version';\nexport class FromPixelsProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        const glsl = getGlslDifferences();\n        const [height, width,] = outputShape;\n        this.outputShape = outputShape;\n        this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);\n\n        vec4 values = ${glsl.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=from_pixels_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from '../../glsl_version';\nexport class FromPixelsPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        const glsl = getGlslDifferences();\n        const [height, width,] = outputShape;\n        this.outputShape = outputShape;\n        this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(${width}.0, ${height}.0);\n            vec4 values = ${glsl.texture2D}(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=from_pixels_packed_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { FromPixels } from '@tensorflow/tfjs-core';\nimport { TextureUsage } from '../tex_util';\nimport { FromPixelsProgram } from './FromPixels_utils/from_pixels_gpu';\nimport { FromPixelsPackedProgram } from './FromPixels_utils/from_pixels_packed_gpu';\nexport const fromPixelsConfig = {\n    kernelName: FromPixels,\n    backendName: 'webgl',\n    kernelFunc: fromPixels,\n};\nlet fromPixels2DContext;\nfunction fromPixels(args) {\n    const { inputs, backend, attrs } = args;\n    let { pixels } = inputs;\n    const { numChannels } = attrs;\n    const isVideo = typeof (HTMLVideoElement) !== 'undefined' &&\n        pixels instanceof HTMLVideoElement;\n    const isImage = typeof (HTMLImageElement) !== 'undefined' &&\n        pixels instanceof HTMLImageElement;\n    const [width, height] = isVideo ?\n        [\n            pixels.videoWidth,\n            pixels.videoHeight\n        ] :\n        [pixels.width, pixels.height];\n    const texShape = [height, width];\n    const outShape = [height, width, numChannels];\n    if (isImage || isVideo) {\n        if (fromPixels2DContext == null) {\n            fromPixels2DContext = document.createElement('canvas').getContext('2d');\n        }\n        fromPixels2DContext.canvas.width = width;\n        fromPixels2DContext.canvas.height = height;\n        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n        pixels = fromPixels2DContext.canvas;\n    }\n    const tempPixelHandle = backend.makeTensorInfo(texShape, 'int32');\n    // This is a byte texture with pixels.\n    backend.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;\n    backend.gpgpu.uploadPixelDataToTexture(backend.getTexture(tempPixelHandle.dataId), pixels);\n    const program = env().getBool('WEBGL_PACK') ?\n        new FromPixelsPackedProgram(outShape) :\n        new FromPixelsProgram(outShape);\n    const res = backend.runWebGLProgram(program, [tempPixelHandle], 'int32');\n    backend.disposeData(tempPixelHandle.dataId);\n    return res;\n}\n//# sourceMappingURL=FromPixels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { ReduceProgram } from '../reduce_gpu';\n// Returns an array of configuration objects that describe each stage of the\n// reduction.\nfunction getReductionStages(inShape) {\n    const stages = [];\n    while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {\n        const outSize = stages.length ? stages[stages.length - 1].outSize : inShape[1];\n        const windowSize = backend_util.computeOptimalWindowSize(outSize);\n        stages.push({\n            inSize: outSize,\n            windowSize,\n            outSize: Math.ceil(outSize / windowSize)\n        });\n    }\n    return stages;\n}\nexport function reduce(x, dtype, reductionType, backend) {\n    const reductionStages = getReductionStages(x.shape);\n    let result = x;\n    for (let i = 0; i < reductionStages.length; i++) {\n        const { inSize, windowSize, outSize } = reductionStages[i];\n        const program = new ReduceProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, reductionType);\n        const previousResult = result;\n        result = backend.runWebGLProgram(program, [result], dtype);\n        if (previousResult.dataId !== x.dataId) {\n            backend.disposeData(previousResult.dataId);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=reduce.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ReshapePackedProgram } from '../reshape_packed_gpu';\nimport { getBatchDim, getRowsCols } from '../webgl_util';\nexport function packedReshape(input, afterShape, backend) {\n    const input3DShape = [getBatchDim(input.shape),\n        ...getRowsCols(input.shape)];\n    const input3D = {\n        dtype: input.dtype,\n        shape: input3DShape,\n        dataId: input.dataId\n    };\n    const afterShapeAs3D = [getBatchDim(afterShape),\n        ...getRowsCols(afterShape)];\n    const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n    const preventEagerUnpackingOfOutput = true;\n    const output = backend.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n    return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n}\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape, util } from '@tensorflow/tfjs-core';\nimport { packedReshape } from '../kernel_utils/reshape';\nimport { isReshapeFree } from '../webgl_util';\nexport function reshape(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { shape } = attrs;\n    const webglBackend = backend;\n    const xSize = util.sizeFromShape(x.shape);\n    const $shape = util.inferFromImplicitShape(shape, xSize);\n    const $xSize = util.sizeFromShape($shape);\n    util.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n        `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n        `shape must have the same number of elements.`);\n    const xTexData = webglBackend.texData.get(x.dataId);\n    if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) &&\n        !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {\n        return packedReshape(x, $shape, webglBackend);\n    }\n    webglBackend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nexport const reshapeConfig = {\n    kernelName: Reshape,\n    backendName: 'webgl',\n    kernelFunc: reshape\n};\n//# sourceMappingURL=Reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { reduce } from '../kernel_utils/reduce';\nimport { reshape } from '../kernels/Reshape';\nexport function maxImpl(x, reduceShape, outShape, backend) {\n    const inSize = util.sizeFromShape(reduceShape);\n    const xSize = util.sizeFromShape(x.shape);\n    const batchSize = xSize / inSize;\n    const reshapedInput = reshape({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend });\n    const reduced = reduce(reshapedInput, x.dtype, 'max', backend);\n    const reshapedOutput = reshape({ inputs: { x: reduced }, attrs: { shape: outShape }, backend });\n    backend.disposeIntermediateTensorInfo(reshapedInput);\n    backend.disposeIntermediateTensorInfo(reduced);\n    return reshapedOutput;\n}\n//# sourceMappingURL=Max_impl.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class TransposeProgram {\n    constructor(aShape, newDim) {\n        this.variableNames = ['A'];\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[newDim[i]];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const switched = getSwitchedCoords(newDim);\n        this.userCode = `\n    void main() {\n      ${dtype} resRC = getOutputCoords();\n      setOutput(getA(${switched}));\n    }\n    `;\n    }\n}\nfunction getSwitchedCoords(newDim) {\n    const rank = newDim.length;\n    if (rank > 6) {\n        throw Error(`Transpose for rank ${rank} is not yet supported`);\n    }\n    const originalOrder = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u', 'resRC.v'];\n    const switchedCoords = new Array(rank);\n    for (let i = 0; i < newDim.length; i++) {\n        switchedCoords[newDim[i]] = originalOrder[i];\n    }\n    return switchedCoords.join();\n}\n//# sourceMappingURL=transpose_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getVecChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class TransposePackedProgram {\n    constructor(aShape, newDim) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[newDim[i]];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        if (this.rank > 6) {\n            throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);\n        }\n        const dtype = getCoordsDataType(this.rank);\n        const outputOrder = getVecChannels('rc', this.rank);\n        const switchedOrder = new Array(this.rank);\n        for (let i = 0; i < newDim.length; i++) {\n            switchedOrder[newDim[i]] = outputOrder[i];\n        }\n        const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;\n        const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;\n        const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;\n        this.userCode = `\n    void main() {\n      ${dtype} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${getc};\n      if(${nextColumn}) {\n        result[1] = ${getc};\n      }\n      --${outputOrder[this.rank - 1]};\n      if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {\n        result[2] = ${getc};\n        if(${nextColumn}) {\n          result[3] = ${getc};\n        }\n      }\n      setOutput(result);\n    }\n    `;\n    }\n}\n//# sourceMappingURL=transpose_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { transposeImplCPU } from '../kernel_utils/shared';\nimport { TransposeProgram } from '../transpose_gpu';\nimport { TransposePackedProgram } from '../transpose_packed_gpu';\nexport function transposeImpl(x, perm, backend) {\n    const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n        new TransposePackedProgram(x.shape, perm) :\n        new TransposeProgram(x.shape, perm);\n    return backend.runWebGLProgram(program, [x], x.dtype);\n}\nexport { transposeImplCPU };\n//# sourceMappingURL=Transpose_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '@tensorflow/tfjs-core';\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { maxImplCPU } from '../kernel_utils/shared';\nimport { maxImpl } from './Max_impl';\nimport { transposeImpl, transposeImplCPU } from './Transpose_impl';\nexport const maxConfig = {\n    kernelName: Max,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { reductionIndices, keepDims } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        const maxInputIsTransposed = permutedAxes != null;\n        const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);\n        let maxInput = x;\n        if (maxInputIsTransposed) {\n            if (shouldExecuteOnCPU) {\n                const xTexData = webglBackend.texData.get(maxInput.dataId);\n                const values = xTexData.values;\n                const newShape = new Array(xRank);\n                for (let i = 0; i < newShape.length; i++) {\n                    newShape[i] = x.shape[permutedAxes[i]];\n                }\n                const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n                maxInput = webglBackend.makeTensorInfo(newShape, x.dtype);\n                const maxInputData = webglBackend.texData.get(maxInput.dataId);\n                maxInputData.values = maxInputValues;\n            }\n            else {\n                maxInput = transposeImpl(x, permutedAxes, webglBackend);\n            }\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n        }\n        backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n        const [maxOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(maxInput.shape, axes);\n        let outShape = maxOutShape;\n        if (keepDims) {\n            // rather than reshape at the end, set the target shape here.\n            outShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n        }\n        let out;\n        if (shouldExecuteOnCPU) {\n            const xTexData = webglBackend.texData.get(maxInput.dataId);\n            const values = xTexData.values;\n            const outValues = maxImplCPU(values, util.sizeFromShape(reduceShape), outShape, x.dtype);\n            out = webglBackend.makeTensorInfo(outShape, x.dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n        }\n        else {\n            out = maxImpl(maxInput, reduceShape, outShape, webglBackend);\n        }\n        if (maxInputIsTransposed) {\n            webglBackend.disposeIntermediateTensorInfo(maxInput);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPool, util } from '@tensorflow/tfjs-core';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nimport { identity } from './Identity';\nexport function maxPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'maxPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        return identity({ inputs: { x }, backend });\n    }\n    const maxPoolProgram = new Pool2DProgram(convInfo, 'max', false);\n    return backend.runWebGLProgram(maxPoolProgram, [x], x.dtype);\n}\nexport const maxPoolConfig = {\n    kernelName: MaxPool,\n    backendName: 'webgl',\n    kernelFunc: maxPool\n};\n//# sourceMappingURL=MaxPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPoolBackprop } from '@tensorflow/tfjs-core';\nimport { MaxPool2DBackpropProgram } from '../max_pool_backprop_gpu';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nexport function maxPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input, output } = inputs;\n    const x = input;\n    assertNotComplex([input, output], 'maxPoolBackprop');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n    const getPositions = true;\n    const maxPoolPositionsProgram = new Pool2DProgram(convInfo, 'max', getPositions);\n    const maxPoolPositions = backend.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);\n    const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);\n    const result = backend.runWebGLProgram(maxPoolBackPropProgram, [dy, maxPoolPositions], x.dtype);\n    backend.disposeIntermediateTensorInfo(maxPoolPositions);\n    return result;\n}\nexport const maxPoolBackpropConfig = {\n    kernelName: MaxPoolBackprop,\n    backendName: 'webgl',\n    kernelFunc: maxPoolBackprop\n};\n//# sourceMappingURL=MaxPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Pool2DProgram } from '../pool_gpu';\nexport function maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, backend) {\n    let program = new Pool2DProgram(convInfo, 'max', false);\n    const poolOutput = backend.runWebGLProgram(program, [x], 'float32');\n    program = new Pool2DProgram(convInfo, 'max', true, true, includeBatchInIndex);\n    const indexOutput = backend.runWebGLProgram(program, [x], 'float32');\n    return [poolOutput, indexOutput];\n}\n//# sourceMappingURL=MaxPoolWithArgmax_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPoolWithArgmax } from '@tensorflow/tfjs-core';\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { maxPoolWithArgmaxImpl } from './MaxPoolWithArgmax_impl';\nexport const maxPoolWithArgmaxConfig = {\n    kernelName: MaxPoolWithArgmax,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { filterSize, strides, pad, includeBatchInIndex } = attrs;\n        const webglBackend = backend;\n        util.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);\n        const dilations = [1, 1];\n        util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n            `Got strides ${strides} and dilations '${dilations}'`);\n        const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad);\n        const [result, indexes] = maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, webglBackend);\n        return [result, indexes];\n    }\n};\n//# sourceMappingURL=MaxPoolWithArgmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls, NonMaxSuppressionV3 } from '@tensorflow/tfjs-core';\nexport const nonMaxSuppressionV3Config = {\n    kernelName: NonMaxSuppressionV3,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        return kernel_impls.nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal);\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV3.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls, NonMaxSuppressionV4 } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\nexport const nonMaxSuppressionV4Config = {\n    kernelName: NonMaxSuppressionV4,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n        return [selectedIndices, validOutputs];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV4.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls } from '@tensorflow/tfjs-core';\nimport { NonMaxSuppressionV5 } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nexport const nonMaxSuppressionV5Config = {\n    kernelName: NonMaxSuppressionV5,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        const softNmsSigmaVal = softNmsSigma;\n        const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n        return [selectedIndices, selectedScores];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV5.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class RotateProgram {\n    constructor(imageShape, radians, fillValue, center) {\n        this.variableNames = ['Image'];\n        this.outputShape = [];\n        const imageHeight = imageShape[1];\n        const imageWidth = imageShape[2];\n        const sinFactor = Math.sin(radians).toFixed(3);\n        const cosFactor = Math.cos(radians).toFixed(3);\n        this.outputShape = imageShape;\n        const [centerX, centerY] = backend_util.getImageCenter(center, imageHeight, imageWidth);\n        const centerXString = centerX.toFixed(3);\n        const centerYString = centerY.toFixed(3);\n        let fillSnippet = '';\n        if (typeof fillValue === 'number') {\n            fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;\n        }\n        else {\n            fillSnippet = `\n        vec3 fill = vec3(${fillValue.join(',')});\n        float outputValue = fill[coords[3]];`;\n        }\n        this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - ${centerXString}) * ${cosFactor} - (float(y) - ${centerYString}) * ${sinFactor};\n          float coordYFloat = (float(x) - ${centerXString}) * ${sinFactor} + (float(y) - ${centerYString}) * ${cosFactor};\n          int coordX = int(round(coordXFloat + ${centerXString}));\n          int coordY = int(round(coordYFloat + ${centerYString}));\n          ${fillSnippet}\n          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n    }\n}\n//# sourceMappingURL=rotate_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { RotateWithOffset } from '@tensorflow/tfjs-core';\nimport { RotateProgram } from '../rotate_gpu';\nexport const rotateWithOffsetConfig = {\n    kernelName: RotateWithOffset,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const { radians, fillValue, center } = attrs;\n        const webglBackend = backend;\n        const program = new RotateProgram(image.shape, radians, fillValue, center);\n        const output = webglBackend.runWebGLProgram(program, [image], image.dtype);\n        return output;\n    }\n};\n//# sourceMappingURL=RotateWithOffset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '@tensorflow/tfjs-core';\nimport { CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SIN = CHECK_NAN_SNIPPET_UNARY + `\n  return sin(x);\n`;\nexport const sinKernelFunc = unaryKernelFunc(SIN);\nexport const sinConfig = {\n    kernelName: Sin,\n    backendName: 'webgl',\n    kernelFunc: sinKernelFunc,\n};\n//# sourceMappingURL=Sin.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SQUARE = `return x * x;`;\nexport const squareKernelFunc = unaryKernelFunc(SQUARE);\nexport const squareConfig = {\n    kernelName: Square,\n    backendName: 'webgl',\n    kernelFunc: squareKernelFunc,\n};\n//# sourceMappingURL=Square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport const squaredDifferenceKernelFunc = binaryKernelFunc(SQUARED_DIFFERENCE, SQUARED_DIFFERENCE);\nexport const squaredDifferenceConfig = {\n    kernelName: SquaredDifference,\n    backendName: 'webgl',\n    kernelFunc: squaredDifferenceKernelFunc,\n};\n//# sourceMappingURL=SquaredDifference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst TAN = `return tan(x);`;\nexport const tanKernelFunc = unaryKernelFunc(TAN);\nexport const tanConfig = {\n    kernelName: Tan,\n    backendName: 'webgl',\n    kernelFunc: tanKernelFunc,\n};\n//# sourceMappingURL=Tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '@tensorflow/tfjs-core';\nimport { transposeImpl } from './Transpose_impl';\nimport { transposeImplCPU as cpuTranspose } from './Transpose_impl';\nexport const transposeConfig = {\n    kernelName: Transpose,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { perm } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const newShape = new Array(xRank);\n        for (let i = 0; i < newShape.length; i++) {\n            newShape[i] = x.shape[perm[i]];\n        }\n        let out;\n        if (webglBackend.shouldExecuteOnCPU([x])) {\n            const xTexData = webglBackend.texData.get(x.dataId);\n            const values = xTexData.values;\n            const outValues = cpuTranspose(values, x.shape, x.dtype, perm, newShape);\n            out = webglBackend.makeTensorInfo(newShape, x.dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n        }\n        else {\n            out = transposeImpl(x, perm, webglBackend);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unique } from '@tensorflow/tfjs-core';\nimport { uniqueImplCPU } from '../kernel_utils/shared';\nimport { assertNotComplex } from '../webgl_util';\nexport function unique(args) {\n    const { inputs, attrs, backend } = args;\n    const { axis } = attrs;\n    const { x } = inputs;\n    assertNotComplex(x, 'unique');\n    // For now, always forward calculation to the CPU backend.\n    console.warn('WARNING: ', 'UI might be locked temporarily as data is being downloaded');\n    const values = backend.readSync(x.dataId);\n    const { outputValues, outputShape, indices } = uniqueImplCPU(values, axis, x.shape, x.dtype);\n    return [\n        backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n        backend.makeTensorInfo([indices.length], 'int32', indices),\n    ];\n}\nexport const uniqueConfig = {\n    kernelName: Unique,\n    backendName: 'webgl',\n    kernelFunc: unique,\n};\n//# sourceMappingURL=Unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { registerKernel } from '@tensorflow/tfjs-core';\nimport { atan2Config } from './kernels/Atan2';\nimport { avgPoolConfig } from './kernels/AvgPool';\nimport { avgPoolBackpropConfig } from './kernels/AvgPoolBackprop';\nimport { batchNormConfig } from './kernels/BatchNorm';\nimport { cosConfig } from './kernels/Cos';\nimport { divConfig } from './kernels/Div';\nimport { flipLeftRightConfig } from './kernels/FlipLeftRight';\nimport { fromPixelsConfig } from './kernels/FromPixels';\nimport { identityConfig } from './kernels/Identity';\nimport { maxConfig } from './kernels/Max';\nimport { maxPoolConfig } from './kernels/MaxPool';\nimport { maxPoolBackpropConfig } from './kernels/MaxPoolBackprop';\nimport { maxPoolWithArgmaxConfig } from './kernels/MaxPoolWithArgmax';\nimport { nonMaxSuppressionV3Config } from './kernels/NonMaxSuppressionV3';\nimport { nonMaxSuppressionV4Config } from './kernels/NonMaxSuppressionV4';\nimport { nonMaxSuppressionV5Config } from './kernels/NonMaxSuppressionV5';\nimport { reshapeConfig } from './kernels/Reshape';\nimport { rotateWithOffsetConfig } from './kernels/RotateWithOffset';\nimport { sinConfig } from './kernels/Sin';\nimport { squareConfig } from './kernels/Square';\nimport { squaredDifferenceConfig } from './kernels/SquaredDifference';\nimport { tanConfig } from './kernels/Tan';\nimport { transposeConfig } from './kernels/Transpose';\nimport { uniqueConfig } from './kernels/Unique';\n// List all kernel configs here\nconst kernelConfigs = [\n    atan2Config,\n    avgPoolConfig,\n    avgPoolBackpropConfig,\n    batchNormConfig,\n    cosConfig,\n    divConfig,\n    flipLeftRightConfig,\n    fromPixelsConfig,\n    identityConfig,\n    maxConfig,\n    maxPoolConfig,\n    maxPoolBackpropConfig,\n    maxPoolWithArgmaxConfig,\n    nonMaxSuppressionV3Config,\n    nonMaxSuppressionV4Config,\n    nonMaxSuppressionV5Config,\n    reshapeConfig,\n    rotateWithOffsetConfig,\n    sinConfig,\n    squareConfig,\n    squaredDifferenceConfig,\n    tanConfig,\n    transposeConfig,\n    uniqueConfig,\n];\nfor (const kernelConfig of kernelConfigs) {\n    registerKernel(kernelConfig);\n}\n//# sourceMappingURL=register_all_kernels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// All exports from this package should be in base.\nexport * from './base';\nimport './register_all_kernels';\n//# sourceMappingURL=index.js.map", "/** @license See the LICENSE file. */\n\n// This code is auto-generated, do not modify this file!\nconst version = '2.6.0';\nexport {version};\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport * from '@tensorflow/tfjs-core';\nexport * from '@tensorflow/tfjs-layers';\nexport * from '@tensorflow/tfjs-converter';\n\n// Export data api as tf.data\nimport * as data from '@tensorflow/tfjs-data';\nexport {data};\n\n// Import and register backends.\nimport '@tensorflow/tfjs-backend-cpu';\nimport '@tensorflow/tfjs-backend-webgl';\n\n// Import versions of all sub-packages.\nimport {version_core} from '@tensorflow/tfjs-core';\nimport {version_cpu} from '@tensorflow/tfjs-backend-cpu';\nimport {version_webgl} from '@tensorflow/tfjs-backend-webgl';\nimport {version_data} from '@tensorflow/tfjs-data';\nimport {version_layers} from '@tensorflow/tfjs-layers';\nimport {version_converter} from '@tensorflow/tfjs-converter';\nimport {version as version_union} from './version';\n\nexport const version = {\n  'tfjs-core': version_core,\n  'tfjs-backend-cpu': version_cpu,\n  'tfjs-backend-webgl': version_webgl,\n  'tfjs-data': version_data,\n  'tfjs-layers': version_layers,\n  'tfjs-converter': version_converter,\n  'tfjs': version_union\n};\n", "export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32')\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32')\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32')\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0, 'float32')\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}"],
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   "names": []
 }
diff --git a/dist/face-api.nobundle.cjs b/dist/face-api.nobundle.cjs
index bf1d70d..8fb7874 100644
--- a/dist/face-api.nobundle.cjs
+++ b/dist/face-api.nobundle.cjs
@@ -440,9 +440,9 @@ const tf2 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
 function normalize(x, meanRgb) {
   return tf2.tidy(() => {
     const [r, g, b] = meanRgb;
-    const avg_r = tf2.fill([...x.shape.slice(0, 3), 1], r);
-    const avg_g = tf2.fill([...x.shape.slice(0, 3), 1], g);
-    const avg_b = tf2.fill([...x.shape.slice(0, 3), 1], b);
+    const avg_r = tf2.fill([...x.shape.slice(0, 3), 1], r, "float32");
+    const avg_g = tf2.fill([...x.shape.slice(0, 3), 1], g, "float32");
+    const avg_b = tf2.fill([...x.shape.slice(0, 3), 1], b, "float32");
     const avg_rgb = tf2.concat([avg_r, avg_g, avg_b], 3);
     return tf2.sub(x, avg_rgb);
   });
@@ -462,7 +462,7 @@ function padToSquare(imgTensor, isCenterImage = false) {
     const createPaddingTensor = (paddingAmount2) => {
       const paddingTensorShape = imgTensor.shape.slice();
       paddingTensorShape[paddingAxis] = paddingAmount2;
-      return tf3.fill(paddingTensorShape, 0);
+      return tf3.fill(paddingTensorShape, 0, "float32");
     };
     const paddingTensorAppend = createPaddingTensor(paddingAmount);
     const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
@@ -1716,7 +1716,7 @@ class FaceFeatureExtractor extends NeuralNetwork {
       throw new Error("FaceFeatureExtractor - load model before inference");
     }
     return tf15.tidy(() => {
-      const batchTensor = input.toBatchTensor(112, true);
+      const batchTensor = tf15.cast(input.toBatchTensor(112, true), "float32");
       const meanRgb = [122.782, 117.001, 104.298];
       const normalized = normalize(batchTensor, meanRgb).div(tf15.scalar(255));
       let out = denseBlock4(normalized, params.dense0, true);
@@ -2153,7 +2153,7 @@ class TinyXception extends NeuralNetwork {
       throw new Error("TinyXception - load model before inference");
     }
     return tf19.tidy(() => {
-      const batchTensor = input.toBatchTensor(112, true);
+      const batchTensor = tf19.cast(input.toBatchTensor(112, true), "float32");
       const meanRgb = [122.782, 117.001, 104.298];
       const normalized = normalize(batchTensor, meanRgb).div(tf19.scalar(256));
       let out = tf19.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));
@@ -2325,17 +2325,14 @@ class FaceLandmark68NetBase extends FaceProcessor {
     });
     const batchSize = inputDimensions.length;
     return tf21.tidy(() => {
-      const createInterleavedTensor = (fillX, fillY) => tf21.stack([
-        tf21.fill([68], fillX),
-        tf21.fill([68], fillY)
-      ], 1).as2D(1, 136).as1D();
+      const createInterleavedTensor = (fillX, fillY) => tf21.stack([tf21.fill([68], fillX, "float32"), tf21.fill([68], fillY, "float32")], 1).as2D(1, 136).as1D();
       const getPadding = (batchIdx, cond) => {
         const {width, height} = inputDimensions[batchIdx];
         return cond(width, height) ? Math.abs(width - height) / 2 : 0;
       };
       const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h);
       const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w);
-      const landmarkTensors = output.mul(tf21.fill([batchSize, 136], inputSize)).sub(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
+      const landmarkTensors = output.mul(tf21.fill([batchSize, 136], inputSize, "float32")).sub(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(tf21.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
       return landmarkTensors;
     });
   }
@@ -2430,7 +2427,7 @@ class TinyFaceFeatureExtractor extends NeuralNetwork {
       throw new Error("TinyFaceFeatureExtractor - load model before inference");
     }
     return tf22.tidy(() => {
-      const batchTensor = input.toBatchTensor(112, true);
+      const batchTensor = tf22.cast(input.toBatchTensor(112, true), "float32");
       const meanRgb = [122.782, 117.001, 104.298];
       const normalized = normalize(batchTensor, meanRgb).div(tf22.scalar(255));
       let out = denseBlock3(normalized, params.dense0, true);
@@ -4292,7 +4289,7 @@ function resizeResults(results, dimensions) {
 }
 
 // package.json
-var version = "0.7.4";
+var version = "0.8.1";
 
 // src/index.ts
 __export(exports, {
diff --git a/dist/face-api.nobundle.cjs.map b/dist/face-api.nobundle.cjs.map
index 911cf15..c40396d 100644
--- a/dist/face-api.nobundle.cjs.map
+++ b/dist/face-api.nobundle.cjs.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["src/env/isNodejs.ts", "src/draw/drawContour.ts", "src/classes/Dimensions.ts", "src/utils/index.ts", "src/classes/Point.ts", "src/classes/Box.ts", "src/classes/BoundingBox.ts", "src/classes/ObjectDetection.ts", "src/classes/FaceDetection.ts", "src/ops/iou.ts", "src/ops/minBbox.ts", "src/ops/nonMaxSuppression.ts", "src/ops/normalize.ts", "src/ops/padToSquare.ts", "src/ops/shuffleArray.ts", "src/ops/index.ts", "src/classes/Rect.ts", "src/classes/FaceLandmarks.ts", "src/classes/FaceLandmarks5.ts", "src/classes/FaceLandmarks68.ts", "src/classes/FaceMatch.ts", "src/classes/LabeledBox.ts", "src/classes/LabeledFaceDescriptors.ts", "src/classes/PredictedBox.ts", "src/factories/WithFaceDetection.ts", "src/env/createBrowserEnv.ts", "src/env/createFileSystem.ts", "src/env/createNodejsEnv.ts", "src/env/isBrowser.ts", "src/env/index.ts", "src/dom/resolveInput.ts", "src/dom/getContext2dOrThrow.ts", "src/draw/DrawTextField.ts", "src/draw/DrawBox.ts", "src/draw/drawDetections.ts", "src/dom/isMediaLoaded.ts", "src/dom/awaitMediaLoaded.ts", "src/dom/bufferToImage.ts", "src/dom/getMediaDimensions.ts", "src/dom/createCanvas.ts", "src/dom/imageTensorToCanvas.ts", "src/dom/isMediaElement.ts", "src/dom/imageToSquare.ts", "src/dom/NetInput.ts", "src/dom/toNetInput.ts", "src/dom/extractFaces.ts", "src/dom/extractFaceTensors.ts", "src/dom/fetchOrThrow.ts", "src/dom/fetchImage.ts", "src/dom/fetchJson.ts", "src/dom/fetchNetWeights.ts", "src/common/getModelUris.ts", "src/dom/loadWeightMap.ts", "src/dom/matchDimensions.ts", "src/NeuralNetwork.ts", "src/common/depthwiseSeparableConv.ts", "src/faceFeatureExtractor/denseBlock.ts", "src/common/convLayer.ts", "src/common/disposeUnusedWeightTensors.ts", "src/common/extractConvParamsFactory.ts", "src/common/extractFCParamsFactory.ts", "src/common/types.ts", "src/common/extractSeparableConvParamsFactory.ts", "src/common/extractWeightEntryFactory.ts", "src/common/extractWeightsFactory.ts", "src/faceFeatureExtractor/extractorsFactory.ts", "src/faceFeatureExtractor/extractParams.ts", "src/common/loadConvParamsFactory.ts", "src/faceFeatureExtractor/loadParamsFactory.ts", "src/faceFeatureExtractor/extractParamsFromWeigthMap.ts", "src/faceFeatureExtractor/FaceFeatureExtractor.ts", "src/common/fullyConnectedLayer.ts", "src/faceProcessor/extractParams.ts", "src/faceProcessor/extractParamsFromWeigthMap.ts", "src/faceProcessor/util.ts", "src/faceProcessor/FaceProcessor.ts", "src/faceExpressionNet/FaceExpressions.ts", "src/faceExpressionNet/FaceExpressionNet.ts", "src/factories/WithFaceExpressions.ts", "src/draw/drawFaceExpressions.ts", "src/factories/WithFaceLandmarks.ts", "src/draw/DrawFaceLandmarks.ts", "src/draw/index.ts", "src/xception/extractParams.ts", "src/xception/extractParamsFromWeigthMap.ts", "src/xception/TinyXception.ts", "src/ageGenderNet/extractParams.ts", "src/ageGenderNet/extractParamsFromWeigthMap.ts", "src/ageGenderNet/types.ts", "src/ageGenderNet/AgeGenderNet.ts", "src/faceLandmarkNet/FaceLandmark68NetBase.ts", "src/faceLandmarkNet/FaceLandmark68Net.ts", "src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "src/faceFeatureExtractor/extractParamsTiny.ts", "src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "src/faceLandmarkNet/index.ts", "src/faceRecognitionNet/scaleLayer.ts", "src/faceRecognitionNet/convLayer.ts", "src/faceRecognitionNet/extractParams.ts", "src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "src/faceRecognitionNet/residualLayer.ts", "src/faceRecognitionNet/FaceRecognitionNet.ts", "src/faceRecognitionNet/index.ts", "src/factories/WithFaceDescriptor.ts", "src/factories/WithAge.ts", "src/factories/WithGender.ts", "src/ssdMobilenetv1/extractParams.ts", "src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "src/ssdMobilenetv1/pointwiseConvLayer.ts", "src/ssdMobilenetv1/mobileNetV1.ts", "src/ssdMobilenetv1/nonMaxSuppression.ts", "src/ssdMobilenetv1/outputLayer.ts", "src/ssdMobilenetv1/boxPredictionLayer.ts", "src/ssdMobilenetv1/predictionLayer.ts", "src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "src/ssdMobilenetv1/SsdMobilenetv1.ts", "src/ssdMobilenetv1/index.ts", "src/tinyYolov2/const.ts", "src/tinyYolov2/config.ts", "src/tinyYolov2/leaky.ts", "src/tinyYolov2/convWithBatchNorm.ts", "src/tinyYolov2/depthwiseSeparableConv.ts", "src/tinyYolov2/extractParams.ts", "src/tinyYolov2/extractParamsFromWeigthMap.ts", "src/tinyYolov2/TinyYolov2Options.ts", "src/tinyYolov2/TinyYolov2Base.ts", "src/tinyYolov2/TinyYolov2.ts", "src/tinyYolov2/index.ts", "src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "src/globalApi/ComposableTask.ts", "src/globalApi/extractFacesAndComputeResults.ts", "src/tinyFaceDetector/const.ts", "src/tinyFaceDetector/TinyFaceDetector.ts", "src/globalApi/nets.ts", "src/globalApi/PredictFaceExpressionsTask.ts", "src/globalApi/PredictAgeAndGenderTask.ts", "src/globalApi/ComputeFaceDescriptorsTasks.ts", "src/globalApi/DetectFaceLandmarksTasks.ts", "src/globalApi/DetectFacesTasks.ts", "src/globalApi/detectFaces.ts", "src/globalApi/allFaces.ts", "src/euclideanDistance.ts", "src/globalApi/FaceMatcher.ts", "src/tinyFaceDetector/index.ts", "src/resizeResults.ts", "src/index.ts"],
-  "sourcesContent": ["export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r)\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g)\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b)\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0)\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = input.toBatchTensor(112, true)\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = input.toBatchTensor(112, true)\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([\n          tf.fill([68], fillX),\n          tf.fill([68], fillY)\n        ], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = input.toBatchTensor(112, true)\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n"],
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+  "sourcesContent": ["export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32')\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32')\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32')\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0, 'float32')\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n"],
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   "names": []
 }
diff --git a/dist/face-api.nobundle.js b/dist/face-api.nobundle.js
index 5085db2..c61921c 100644
--- a/dist/face-api.nobundle.js
+++ b/dist/face-api.nobundle.js
@@ -447,9 +447,9 @@ import {
 function normalize(x, meanRgb) {
   return tidy(() => {
     const [r, g, b] = meanRgb;
-    const avg_r = fill([...x.shape.slice(0, 3), 1], r);
-    const avg_g = fill([...x.shape.slice(0, 3), 1], g);
-    const avg_b = fill([...x.shape.slice(0, 3), 1], b);
+    const avg_r = fill([...x.shape.slice(0, 3), 1], r, "float32");
+    const avg_g = fill([...x.shape.slice(0, 3), 1], g, "float32");
+    const avg_b = fill([...x.shape.slice(0, 3), 1], b, "float32");
     const avg_rgb = concat([avg_r, avg_g, avg_b], 3);
     return sub(x, avg_rgb);
   });
@@ -474,7 +474,7 @@ function padToSquare(imgTensor, isCenterImage = false) {
     const createPaddingTensor = (paddingAmount2) => {
       const paddingTensorShape = imgTensor.shape.slice();
       paddingTensorShape[paddingAxis] = paddingAmount2;
-      return fill2(paddingTensorShape, 0);
+      return fill2(paddingTensorShape, 0, "float32");
     };
     const paddingTensorAppend = createPaddingTensor(paddingAmount);
     const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
@@ -1762,6 +1762,7 @@ function extractParamsFromWeigthMap(weightMap) {
 // src/faceFeatureExtractor/FaceFeatureExtractor.ts
 import {
   avgPool,
+  cast as cast3,
   scalar,
   tidy as tidy9
 } from "@tensorflow/tfjs/dist/tf.es2017.js";
@@ -1775,7 +1776,7 @@ class FaceFeatureExtractor extends NeuralNetwork {
       throw new Error("FaceFeatureExtractor - load model before inference");
     }
     return tidy9(() => {
-      const batchTensor = input.toBatchTensor(112, true);
+      const batchTensor = cast3(input.toBatchTensor(112, true), "float32");
       const meanRgb = [122.782, 117.001, 104.298];
       const normalized = normalize(batchTensor, meanRgb).div(scalar(255));
       let out = denseBlock4(normalized, params.dense0, true);
@@ -2194,6 +2195,7 @@ function extractParamsFromWeigthMap5(weightMap, numMainBlocks) {
 // src/xception/TinyXception.ts
 import {
   add as add5,
+  cast as cast4,
   conv2d as conv2d3,
   maxPool,
   relu as relu3,
@@ -2229,7 +2231,7 @@ class TinyXception extends NeuralNetwork {
       throw new Error("TinyXception - load model before inference");
     }
     return tidy13(() => {
-      const batchTensor = input.toBatchTensor(112, true);
+      const batchTensor = cast4(input.toBatchTensor(112, true), "float32");
       const meanRgb = [122.782, 117.001, 104.298];
       const normalized = normalize(batchTensor, meanRgb).div(scalar2(256));
       let out = relu3(conv(normalized, params.entry_flow.conv_in, [2, 2]));
@@ -2411,17 +2413,14 @@ class FaceLandmark68NetBase extends FaceProcessor {
     });
     const batchSize = inputDimensions.length;
     return tidy15(() => {
-      const createInterleavedTensor = (fillX, fillY) => stack2([
-        fill3([68], fillX),
-        fill3([68], fillY)
-      ], 1).as2D(1, 136).as1D();
+      const createInterleavedTensor = (fillX, fillY) => stack2([fill3([68], fillX, "float32"), fill3([68], fillY, "float32")], 1).as2D(1, 136).as1D();
       const getPadding = (batchIdx, cond) => {
         const {width, height} = inputDimensions[batchIdx];
         return cond(width, height) ? Math.abs(width - height) / 2 : 0;
       };
       const getPaddingX = (batchIdx) => getPadding(batchIdx, (w, h) => w < h);
       const getPaddingY = (batchIdx) => getPadding(batchIdx, (w, h) => h < w);
-      const landmarkTensors = output.mul(fill3([batchSize, 136], inputSize)).sub(stack2(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(stack2(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
+      const landmarkTensors = output.mul(fill3([batchSize, 136], inputSize, "float32")).sub(stack2(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx))))).div(stack2(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
       return landmarkTensors;
     });
   }
@@ -2507,6 +2506,7 @@ function extractParamsTiny(weights) {
 // src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts
 import {
   avgPool as avgPool3,
+  cast as cast5,
   scalar as scalar3,
   tidy as tidy16
 } from "@tensorflow/tfjs/dist/tf.es2017.js";
@@ -2520,7 +2520,7 @@ class TinyFaceFeatureExtractor extends NeuralNetwork {
       throw new Error("TinyFaceFeatureExtractor - load model before inference");
     }
     return tidy16(() => {
-      const batchTensor = input.toBatchTensor(112, true);
+      const batchTensor = cast5(input.toBatchTensor(112, true), "float32");
       const meanRgb = [122.782, 117.001, 104.298];
       const normalized = normalize(batchTensor, meanRgb).div(scalar3(255));
       let out = denseBlock3(normalized, params.dense0, true);
@@ -2804,7 +2804,7 @@ function residualDown(x, params) {
 
 // src/faceRecognitionNet/FaceRecognitionNet.ts
 import {
-  cast as cast3,
+  cast as cast6,
   matMul as matMul2,
   maxPool as maxPool2,
   scalar as scalar4,
@@ -2821,7 +2821,7 @@ class FaceRecognitionNet extends NeuralNetwork {
       throw new Error("FaceRecognitionNet - load model before inference");
     }
     return tidy18(() => {
-      const batchTensor = cast3(input.toBatchTensor(150, true), "float32");
+      const batchTensor = cast6(input.toBatchTensor(150, true), "float32");
       const meanRgb = [122.782, 117.001, 104.298];
       const normalized = normalize(batchTensor, meanRgb).div(scalar4(256));
       let out = convDown(normalized, params.conv32_down);
@@ -3445,7 +3445,7 @@ class SsdMobilenetv1Options {
 
 // src/ssdMobilenetv1/SsdMobilenetv1.ts
 import {
-  cast as cast4,
+  cast as cast7,
   mul as mul3,
   scalar as scalar6,
   sub as sub3,
@@ -3461,7 +3461,7 @@ class SsdMobilenetv1 extends NeuralNetwork {
       throw new Error("SsdMobilenetv1 - load model before inference");
     }
     return tidy24(() => {
-      const batchTensor = cast4(input.toBatchTensor(512, false), "float32");
+      const batchTensor = cast7(input.toBatchTensor(512, false), "float32");
       const x = sub3(mul3(batchTensor, scalar6(0.007843137718737125)), scalar6(1));
       const features = mobileNetV1(x, params.mobilenetv1);
       const {
@@ -3791,7 +3791,7 @@ class TinyYolov2Options {
 
 // src/tinyYolov2/TinyYolov2Base.ts
 import {
-  cast as cast5,
+  cast as cast8,
   maxPool as maxPool3,
   scalar as scalar8,
   softmax as softmax3,
@@ -3853,7 +3853,7 @@ class TinyYolov2Base extends NeuralNetwork {
       throw new Error("TinyYolov2 - load model before inference");
     }
     return tidy28(() => {
-      let batchTensor = cast5(input.toBatchTensor(inputSize, false), "float32");
+      let batchTensor = cast8(input.toBatchTensor(inputSize, false), "float32");
       batchTensor = this.config.meanRgb ? normalize(batchTensor, this.config.meanRgb) : batchTensor;
       batchTensor = batchTensor.div(scalar8(256));
       return this.config.withSeparableConvs ? this.runMobilenet(batchTensor, params) : this.runTinyYolov2(batchTensor, params);
@@ -4481,7 +4481,7 @@ function resizeResults(results, dimensions) {
 }
 
 // package.json
-var version = "0.7.4";
+var version = "0.8.1";
 
 // src/index.ts
 import * as tf42 from "@tensorflow/tfjs/dist/tf.es2017.js";
diff --git a/dist/face-api.nobundle.js.map b/dist/face-api.nobundle.js.map
index cc9661b..a36da59 100644
--- a/dist/face-api.nobundle.js.map
+++ b/dist/face-api.nobundle.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["src/env/isNodejs.ts", "src/draw/drawContour.ts", "src/classes/Dimensions.ts", "src/utils/index.ts", "src/classes/Point.ts", "src/classes/Box.ts", "src/classes/BoundingBox.ts", "src/classes/ObjectDetection.ts", "src/classes/FaceDetection.ts", "src/ops/iou.ts", "src/ops/minBbox.ts", "src/ops/nonMaxSuppression.ts", "src/ops/normalize.ts", "src/ops/padToSquare.ts", "src/ops/shuffleArray.ts", "src/ops/index.ts", "src/classes/Rect.ts", "src/classes/FaceLandmarks.ts", "src/classes/FaceLandmarks5.ts", "src/classes/FaceLandmarks68.ts", "src/classes/FaceMatch.ts", "src/classes/LabeledBox.ts", "src/classes/LabeledFaceDescriptors.ts", "src/classes/PredictedBox.ts", "src/factories/WithFaceDetection.ts", "src/env/createBrowserEnv.ts", "src/env/createFileSystem.ts", "src/env/createNodejsEnv.ts", "src/env/isBrowser.ts", "src/env/index.ts", "src/dom/resolveInput.ts", "src/dom/getContext2dOrThrow.ts", "src/draw/DrawTextField.ts", "src/draw/DrawBox.ts", "src/draw/drawDetections.ts", "src/dom/isMediaLoaded.ts", "src/dom/awaitMediaLoaded.ts", "src/dom/bufferToImage.ts", "src/dom/getMediaDimensions.ts", "src/dom/createCanvas.ts", "src/dom/imageTensorToCanvas.ts", "src/dom/isMediaElement.ts", "src/dom/imageToSquare.ts", "src/dom/NetInput.ts", "src/dom/toNetInput.ts", "src/dom/extractFaces.ts", "src/dom/extractFaceTensors.ts", "src/dom/fetchOrThrow.ts", "src/dom/fetchImage.ts", "src/dom/fetchJson.ts", "src/dom/fetchNetWeights.ts", "src/common/getModelUris.ts", "src/dom/loadWeightMap.ts", "src/dom/matchDimensions.ts", "src/NeuralNetwork.ts", "src/common/depthwiseSeparableConv.ts", "src/faceFeatureExtractor/denseBlock.ts", "src/common/convLayer.ts", "src/common/disposeUnusedWeightTensors.ts", "src/common/extractConvParamsFactory.ts", "src/common/extractFCParamsFactory.ts", "src/common/types.ts", "src/common/extractSeparableConvParamsFactory.ts", "src/common/extractWeightEntryFactory.ts", "src/common/extractWeightsFactory.ts", "src/faceFeatureExtractor/extractorsFactory.ts", "src/faceFeatureExtractor/extractParams.ts", "src/common/loadConvParamsFactory.ts", "src/faceFeatureExtractor/loadParamsFactory.ts", "src/faceFeatureExtractor/extractParamsFromWeigthMap.ts", "src/faceFeatureExtractor/FaceFeatureExtractor.ts", "src/common/fullyConnectedLayer.ts", "src/faceProcessor/extractParams.ts", "src/faceProcessor/extractParamsFromWeigthMap.ts", "src/faceProcessor/util.ts", "src/faceProcessor/FaceProcessor.ts", "src/faceExpressionNet/FaceExpressions.ts", "src/faceExpressionNet/FaceExpressionNet.ts", "src/factories/WithFaceExpressions.ts", "src/draw/drawFaceExpressions.ts", "src/factories/WithFaceLandmarks.ts", "src/draw/DrawFaceLandmarks.ts", "src/draw/index.ts", "src/xception/extractParams.ts", "src/xception/extractParamsFromWeigthMap.ts", "src/xception/TinyXception.ts", "src/ageGenderNet/extractParams.ts", "src/ageGenderNet/extractParamsFromWeigthMap.ts", "src/ageGenderNet/types.ts", "src/ageGenderNet/AgeGenderNet.ts", "src/faceLandmarkNet/FaceLandmark68NetBase.ts", "src/faceLandmarkNet/FaceLandmark68Net.ts", "src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "src/faceFeatureExtractor/extractParamsTiny.ts", "src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "src/faceLandmarkNet/index.ts", "src/faceRecognitionNet/scaleLayer.ts", "src/faceRecognitionNet/convLayer.ts", "src/faceRecognitionNet/extractParams.ts", "src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "src/faceRecognitionNet/residualLayer.ts", "src/faceRecognitionNet/FaceRecognitionNet.ts", "src/faceRecognitionNet/index.ts", "src/factories/WithFaceDescriptor.ts", "src/factories/WithAge.ts", "src/factories/WithGender.ts", "src/ssdMobilenetv1/extractParams.ts", "src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "src/ssdMobilenetv1/pointwiseConvLayer.ts", "src/ssdMobilenetv1/mobileNetV1.ts", "src/ssdMobilenetv1/nonMaxSuppression.ts", "src/ssdMobilenetv1/outputLayer.ts", "src/ssdMobilenetv1/boxPredictionLayer.ts", "src/ssdMobilenetv1/predictionLayer.ts", "src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "src/ssdMobilenetv1/SsdMobilenetv1.ts", "src/ssdMobilenetv1/index.ts", "src/tinyYolov2/const.ts", "src/tinyYolov2/config.ts", "src/tinyYolov2/leaky.ts", "src/tinyYolov2/convWithBatchNorm.ts", "src/tinyYolov2/depthwiseSeparableConv.ts", "src/tinyYolov2/extractParams.ts", "src/tinyYolov2/extractParamsFromWeigthMap.ts", "src/tinyYolov2/TinyYolov2Options.ts", "src/tinyYolov2/TinyYolov2Base.ts", "src/tinyYolov2/TinyYolov2.ts", "src/tinyYolov2/index.ts", "src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "src/globalApi/ComposableTask.ts", "src/globalApi/extractFacesAndComputeResults.ts", "src/tinyFaceDetector/const.ts", "src/tinyFaceDetector/TinyFaceDetector.ts", "src/globalApi/nets.ts", "src/globalApi/PredictFaceExpressionsTask.ts", "src/globalApi/PredictAgeAndGenderTask.ts", "src/globalApi/ComputeFaceDescriptorsTasks.ts", "src/globalApi/DetectFaceLandmarksTasks.ts", "src/globalApi/DetectFacesTasks.ts", "src/globalApi/detectFaces.ts", "src/globalApi/allFaces.ts", "src/euclideanDistance.ts", "src/globalApi/FaceMatcher.ts", "src/tinyFaceDetector/index.ts", "src/resizeResults.ts", "src/index.ts"],
-  "sourcesContent": ["export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r)\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g)\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b)\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0)\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = input.toBatchTensor(112, true)\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = input.toBatchTensor(112, true)\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([\n          tf.fill([68], fillX),\n          tf.fill([68], fillY)\n        ], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = input.toBatchTensor(112, true)\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n"],
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+  "sourcesContent": ["export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32')\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32')\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32')\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0, 'float32')\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n"],
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   "names": []
 }
diff --git a/src/faceFeatureExtractor/FaceFeatureExtractor.ts b/src/faceFeatureExtractor/FaceFeatureExtractor.ts
index 45b5f6b..21f6d04 100644
--- a/src/faceFeatureExtractor/FaceFeatureExtractor.ts
+++ b/src/faceFeatureExtractor/FaceFeatureExtractor.ts
@@ -23,7 +23,7 @@ export class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorPara
     }
 
     return tf.tidy(() => {
-      const batchTensor = input.toBatchTensor(112, true)
+      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
       const meanRgb = [122.782, 117.001, 104.298]
       const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D
 
diff --git a/src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts b/src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts
index 409a3a8..9b47314 100644
--- a/src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts
+++ b/src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts
@@ -23,7 +23,7 @@ export class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtra
     }
 
     return tf.tidy(() => {
-      const batchTensor = input.toBatchTensor(112, true)
+      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
       const meanRgb = [122.782, 117.001, 104.298]
       const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D
 
diff --git a/src/faceLandmarkNet/FaceLandmark68NetBase.ts b/src/faceLandmarkNet/FaceLandmark68NetBase.ts
index 767ab16..b264a6d 100644
--- a/src/faceLandmarkNet/FaceLandmark68NetBase.ts
+++ b/src/faceLandmarkNet/FaceLandmark68NetBase.ts
@@ -26,10 +26,7 @@ export abstract class FaceLandmark68NetBase<
 
     return tf.tidy(() => {
       const createInterleavedTensor = (fillX: number, fillY: number) =>
-        tf.stack([
-          tf.fill([68], fillX),
-          tf.fill([68], fillY)
-        ], 1).as2D(1, 136).as1D()
+        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()
 
       const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {
         const { width, height } = inputDimensions[batchIdx]
@@ -39,7 +36,7 @@ export abstract class FaceLandmark68NetBase<
       const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)
 
       const landmarkTensors = output
-        .mul(tf.fill([batchSize, 136], inputSize))
+        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))
         .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>
           createInterleavedTensor(
             getPaddingX(batchIdx),
diff --git a/src/ops/normalize.ts b/src/ops/normalize.ts
index 1d7d817..36a4be0 100644
--- a/src/ops/normalize.ts
+++ b/src/ops/normalize.ts
@@ -3,9 +3,9 @@ import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
 export function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {
   return tf.tidy(() => {
     const [r, g, b] = meanRgb
-    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r)
-    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g)
-    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b)
+    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32')
+    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32')
+    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32')
     const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)
 
     return tf.sub(x, avg_rgb)
diff --git a/src/ops/padToSquare.ts b/src/ops/padToSquare.ts
index aa1e1f6..40cae19 100644
--- a/src/ops/padToSquare.ts
+++ b/src/ops/padToSquare.ts
@@ -26,7 +26,7 @@ export function padToSquare(
     const createPaddingTensor = (paddingAmount: number): tf.Tensor => {
       const paddingTensorShape = imgTensor.shape.slice()
       paddingTensorShape[paddingAxis] = paddingAmount
-      return tf.fill(paddingTensorShape, 0)
+      return tf.fill(paddingTensorShape, 0, 'float32')
     }
 
     const paddingTensorAppend = createPaddingTensor(paddingAmount)
diff --git a/src/xception/TinyXception.ts b/src/xception/TinyXception.ts
index 669b078..33280b1 100644
--- a/src/xception/TinyXception.ts
+++ b/src/xception/TinyXception.ts
@@ -48,7 +48,7 @@ export class TinyXception extends NeuralNetwork<TinyXceptionParams> {
     }
 
     return tf.tidy(() => {
-      const batchTensor = input.toBatchTensor(112, true)
+      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
       const meanRgb = [122.782, 117.001, 104.298]
       const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D